1 - Hello World in Zipline backtester.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hello World in Zipline backtester\n",
    "## 1. Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The zipline extension is already loaded. To reload it, use:\n",
      "  %reload_ext zipline\n"
     ]
    }
   ],
   "source": [
    "# zipline's magic command for enabling support to run in jupyter notebook\n",
    "%load_ext zipline\n",
    "%matplotlib notebook\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use('ggplot')\n",
    "\n",
    "from zipline.api import order_percent, order_target_percent, order, record, symbol, set_benchmark, get_order, \\\n",
    "    schedule_function, date_rules, time_rules, get_open_orders\n",
    "from zipline.utils.calendars import get_calendar\n",
    "from zipline.finance.execution import MarketOrder\n",
    "from zipline import run_algorithm\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Define Zipline backtest functions\n",
    "Below is the simplest example on how to define a strategy and backtest with zipline  \n",
    "\n",
    "TODO: using the custom dataset ingested in the notebook `0 - Preparing custom dataset for Zipline`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def initialize(context):\n",
    "    set_benchmark(symbol('AAPL'))\n",
    "\n",
    "def handle_data(context, data):\n",
    "    order(symbol('AAPL'), 1)\n",
    "    record(AAPL=data.current(symbol('AAPL'), 'close'))\n",
    "    \n",
    "def analyze(context, perf):\n",
    "    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,8))\n",
    "    ax1.plot(perf.portfolio_value)\n",
    "    ax1.set_ylabel('portfolio $ value')\n",
    "    ax1.legend(loc=0)\n",
    "    \n",
    "    ax2.plot(perf.AAPL)\n",
    "    ax2.set_ylabel('Symbol price')\n",
    "    plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ocx3KGFwTEflKgME1EQVPs8w1p+ILZQyuiYh8xcw1EQWTfUCjLbhWVjyhX1+oVQyuiYhU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AAAAN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\niIiIiHTC5JqIiIiISCdMromIiIiIdPL/DmO2nnRmE3UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13082a438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>algo_volatility</th>\n",
       "      <th>algorithm_period_return</th>\n",
       "      <th>alpha</th>\n",
       "      <th>benchmark_period_return</th>\n",
       "      <th>benchmark_volatility</th>\n",
       "      <th>beta</th>\n",
       "      <th>capital_used</th>\n",
       "      <th>ending_cash</th>\n",
       "      <th>ending_exposure</th>\n",
       "      <th>...</th>\n",
       "      <th>short_exposure</th>\n",
       "      <th>short_value</th>\n",
       "      <th>shorts_count</th>\n",
       "      <th>sortino</th>\n",
       "      <th>starting_cash</th>\n",
       "      <th>starting_exposure</th>\n",
       "      <th>starting_value</th>\n",
       "      <th>trading_days</th>\n",
       "      <th>transactions</th>\n",
       "      <th>treasury_period_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03 21:00:00+00:00</th>\n",
       "      <td>111.940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.089010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04 21:00:00+00:00</th>\n",
       "      <td>102.500</td>\n",
       "      <td>5.865048e-08</td>\n",
       "      <td>-5.225000e-09</td>\n",
       "      <td>-6.754044e-07</td>\n",
       "      <td>-0.003028</td>\n",
       "      <td>1.947812</td>\n",
       "      <td>3.011096e-08</td>\n",
       "      <td>-102.552250</td>\n",
       "      <td>9.999897e+06</td>\n",
       "      <td>102.500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-11.224972</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2</td>\n",
       "      <td>[{'price': 102.55125, 'dt': 2000-01-04 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05 21:00:00+00:00</th>\n",
       "      <td>104.000</td>\n",
       "      <td>1.350778e-06</td>\n",
       "      <td>1.394750e-07</td>\n",
       "      <td>1.153407e-05</td>\n",
       "      <td>0.011807</td>\n",
       "      <td>1.382169</td>\n",
       "      <td>1.117246e-07</td>\n",
       "      <td>-104.053000</td>\n",
       "      <td>9.999793e+06</td>\n",
       "      <td>208.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>244.652536</td>\n",
       "      <td>9.999897e+06</td>\n",
       "      <td>102.500</td>\n",
       "      <td>102.500</td>\n",
       "      <td>3</td>\n",
       "      <td>[{'price': 104.052, 'dt': 2000-01-05 21:00:00+...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06 21:00:00+00:00</th>\n",
       "      <td>95.000</td>\n",
       "      <td>1.473590e-05</td>\n",
       "      <td>-1.665375e-06</td>\n",
       "      <td>-7.923378e-05</td>\n",
       "      <td>-0.075992</td>\n",
       "      <td>1.349523</td>\n",
       "      <td>6.048969e-06</td>\n",
       "      <td>-95.048500</td>\n",
       "      <td>9.999698e+06</td>\n",
       "      <td>285.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.323849</td>\n",
       "      <td>9.999793e+06</td>\n",
       "      <td>208.000</td>\n",
       "      <td>208.000</td>\n",
       "      <td>4</td>\n",
       "      <td>[{'price': 95.0475, 'dt': 2000-01-06 21:00:00+...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07 21:00:00+00:00</th>\n",
       "      <td>99.500</td>\n",
       "      <td>1.786630e-05</td>\n",
       "      <td>-3.204500e-07</td>\n",
       "      <td>-7.253909e-06</td>\n",
       "      <td>-0.032092</td>\n",
       "      <td>1.254860</td>\n",
       "      <td>8.875120e-06</td>\n",
       "      <td>-99.550750</td>\n",
       "      <td>9.999599e+06</td>\n",
       "      <td>398.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.260461</td>\n",
       "      <td>9.999698e+06</td>\n",
       "      <td>285.000</td>\n",
       "      <td>285.000</td>\n",
       "      <td>5</td>\n",
       "      <td>[{'price': 99.54975, 'dt': 2000-01-07 21:00:00...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10 21:00:00+00:00</th>\n",
       "      <td>97.750</td>\n",
       "      <td>1.651106e-05</td>\n",
       "      <td>-1.025437e-06</td>\n",
       "      <td>-2.875804e-05</td>\n",
       "      <td>-0.049046</td>\n",
       "      <td>1.125805</td>\n",
       "      <td>9.108744e-06</td>\n",
       "      <td>-97.799875</td>\n",
       "      <td>9.999501e+06</td>\n",
       "      <td>488.750</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.429697</td>\n",
       "      <td>9.999599e+06</td>\n",
       "      <td>398.000</td>\n",
       "      <td>398.000</td>\n",
       "      <td>6</td>\n",
       "      <td>[{'price': 97.798875, 'dt': 2000-01-10 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11 21:00:00+00:00</th>\n",
       "      <td>92.750</td>\n",
       "      <td>2.057337e-05</td>\n",
       "      <td>-3.530175e-06</td>\n",
       "      <td>-8.919835e-05</td>\n",
       "      <td>-0.097790</td>\n",
       "      <td>1.062626</td>\n",
       "      <td>1.187010e-05</td>\n",
       "      <td>-92.797375</td>\n",
       "      <td>9.999408e+06</td>\n",
       "      <td>556.500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-6.688599</td>\n",
       "      <td>9.999501e+06</td>\n",
       "      <td>488.750</td>\n",
       "      <td>488.750</td>\n",
       "      <td>7</td>\n",
       "      <td>[{'price': 92.796375, 'dt': 2000-01-11 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12 21:00:00+00:00</th>\n",
       "      <td>87.190</td>\n",
       "      <td>2.482292e-05</td>\n",
       "      <td>-6.870634e-06</td>\n",
       "      <td>-1.455015e-04</td>\n",
       "      <td>-0.151983</td>\n",
       "      <td>1.019136</td>\n",
       "      <td>1.513846e-05</td>\n",
       "      <td>-87.234595</td>\n",
       "      <td>9.999321e+06</td>\n",
       "      <td>610.330</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8.377574</td>\n",
       "      <td>9.999408e+06</td>\n",
       "      <td>556.500</td>\n",
       "      <td>556.500</td>\n",
       "      <td>8</td>\n",
       "      <td>[{'price': 87.233595, 'dt': 2000-01-12 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13 21:00:00+00:00</th>\n",
       "      <td>96.750</td>\n",
       "      <td>4.618992e-05</td>\n",
       "      <td>-1.835720e-07</td>\n",
       "      <td>2.724823e-05</td>\n",
       "      <td>-0.058734</td>\n",
       "      <td>1.171122</td>\n",
       "      <td>2.983483e-05</td>\n",
       "      <td>-96.799375</td>\n",
       "      <td>9.999224e+06</td>\n",
       "      <td>774.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.210994</td>\n",
       "      <td>9.999321e+06</td>\n",
       "      <td>610.330</td>\n",
       "      <td>610.330</td>\n",
       "      <td>9</td>\n",
       "      <td>[{'price': 96.798375, 'dt': 2000-01-13 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14 21:00:00+00:00</th>\n",
       "      <td>100.440</td>\n",
       "      <td>4.602533e-05</td>\n",
       "      <td>2.763306e-06</td>\n",
       "      <td>7.027879e-05</td>\n",
       "      <td>-0.023009</td>\n",
       "      <td>1.124341</td>\n",
       "      <td>3.127241e-05</td>\n",
       "      <td>-100.491220</td>\n",
       "      <td>9.999124e+06</td>\n",
       "      <td>903.960</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.013700</td>\n",
       "      <td>9.999224e+06</td>\n",
       "      <td>774.000</td>\n",
       "      <td>774.000</td>\n",
       "      <td>10</td>\n",
       "      <td>[{'price': 100.49022, 'dt': 2000-01-14 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18 21:00:00+00:00</th>\n",
       "      <td>103.940</td>\n",
       "      <td>4.577097e-05</td>\n",
       "      <td>5.908009e-06</td>\n",
       "      <td>1.098881e-04</td>\n",
       "      <td>0.011202</td>\n",
       "      <td>1.079792</td>\n",
       "      <td>3.249357e-05</td>\n",
       "      <td>-103.992970</td>\n",
       "      <td>9.999020e+06</td>\n",
       "      <td>1039.400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.143450</td>\n",
       "      <td>9.999124e+06</td>\n",
       "      <td>903.960</td>\n",
       "      <td>903.960</td>\n",
       "      <td>11</td>\n",
       "      <td>[{'price': 103.99197, 'dt': 2000-01-18 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19 21:00:00+00:00</th>\n",
       "      <td>106.560</td>\n",
       "      <td>4.466723e-05</td>\n",
       "      <td>8.522581e-06</td>\n",
       "      <td>1.377413e-04</td>\n",
       "      <td>0.036633</td>\n",
       "      <td>1.034484</td>\n",
       "      <td>3.308234e-05</td>\n",
       "      <td>-106.614280</td>\n",
       "      <td>9.998913e+06</td>\n",
       "      <td>1172.160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.484899</td>\n",
       "      <td>9.999020e+06</td>\n",
       "      <td>1039.400</td>\n",
       "      <td>1039.400</td>\n",
       "      <td>12</td>\n",
       "      <td>[{'price': 106.61328, 'dt': 2000-01-19 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-20 21:00:00+00:00</th>\n",
       "      <td>113.500</td>\n",
       "      <td>5.250339e-05</td>\n",
       "      <td>1.615081e-05</td>\n",
       "      <td>2.200544e-04</td>\n",
       "      <td>0.104148</td>\n",
       "      <td>1.025274</td>\n",
       "      <td>3.855032e-05</td>\n",
       "      <td>-113.557750</td>\n",
       "      <td>9.998800e+06</td>\n",
       "      <td>1362.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.448514</td>\n",
       "      <td>9.998913e+06</td>\n",
       "      <td>1172.160</td>\n",
       "      <td>1172.160</td>\n",
       "      <td>13</td>\n",
       "      <td>[{'price': 113.55675, 'dt': 2000-01-20 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-21 21:00:00+00:00</th>\n",
       "      <td>111.310</td>\n",
       "      <td>5.305637e-05</td>\n",
       "      <td>1.351714e-05</td>\n",
       "      <td>1.676762e-04</td>\n",
       "      <td>0.082652</td>\n",
       "      <td>0.992729</td>\n",
       "      <td>4.001248e-05</td>\n",
       "      <td>-111.366655</td>\n",
       "      <td>9.998688e+06</td>\n",
       "      <td>1447.030</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.814095</td>\n",
       "      <td>9.998800e+06</td>\n",
       "      <td>1362.000</td>\n",
       "      <td>1362.000</td>\n",
       "      <td>14</td>\n",
       "      <td>[{'price': 111.365655, 'dt': 2000-01-21 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-24 21:00:00+00:00</th>\n",
       "      <td>106.250</td>\n",
       "      <td>5.975998e-05</td>\n",
       "      <td>6.933728e-06</td>\n",
       "      <td>7.132182e-05</td>\n",
       "      <td>0.033606</td>\n",
       "      <td>0.980794</td>\n",
       "      <td>4.502545e-05</td>\n",
       "      <td>-106.304125</td>\n",
       "      <td>9.998582e+06</td>\n",
       "      <td>1487.500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.361904</td>\n",
       "      <td>9.998688e+06</td>\n",
       "      <td>1447.030</td>\n",
       "      <td>1447.030</td>\n",
       "      <td>15</td>\n",
       "      <td>[{'price': 106.303125, 'dt': 2000-01-24 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-25 21:00:00+00:00</th>\n",
       "      <td>112.250</td>\n",
       "      <td>6.575790e-05</td>\n",
       "      <td>1.532802e-05</td>\n",
       "      <td>1.500239e-04</td>\n",
       "      <td>0.092038</td>\n",
       "      <td>0.970218</td>\n",
       "      <td>4.991935e-05</td>\n",
       "      <td>-112.307125</td>\n",
       "      <td>9.998470e+06</td>\n",
       "      <td>1683.750</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.195911</td>\n",
       "      <td>9.998582e+06</td>\n",
       "      <td>1487.500</td>\n",
       "      <td>1487.500</td>\n",
       "      <td>16</td>\n",
       "      <td>[{'price': 112.306125, 'dt': 2000-01-25 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-26 21:00:00+00:00</th>\n",
       "      <td>110.190</td>\n",
       "      <td>6.555468e-05</td>\n",
       "      <td>1.223241e-05</td>\n",
       "      <td>1.073395e-04</td>\n",
       "      <td>0.071753</td>\n",
       "      <td>0.944663</td>\n",
       "      <td>5.110560e-05</td>\n",
       "      <td>-110.246095</td>\n",
       "      <td>9.998359e+06</td>\n",
       "      <td>1763.040</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.231489</td>\n",
       "      <td>9.998470e+06</td>\n",
       "      <td>1683.750</td>\n",
       "      <td>1683.750</td>\n",
       "      <td>17</td>\n",
       "      <td>[{'price': 110.24509499999999, 'dt': 2000-01-2...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-27 21:00:00+00:00</th>\n",
       "      <td>110.000</td>\n",
       "      <td>6.371386e-05</td>\n",
       "      <td>1.192281e-05</td>\n",
       "      <td>9.814651e-05</td>\n",
       "      <td>0.069936</td>\n",
       "      <td>0.916880</td>\n",
       "      <td>5.118600e-05</td>\n",
       "      <td>-110.056000</td>\n",
       "      <td>9.998249e+06</td>\n",
       "      <td>1870.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.952490</td>\n",
       "      <td>9.998359e+06</td>\n",
       "      <td>1763.040</td>\n",
       "      <td>1763.040</td>\n",
       "      <td>18</td>\n",
       "      <td>[{'price': 110.055, 'dt': 2000-01-27 21:00:00+...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-28 21:00:00+00:00</th>\n",
       "      <td>101.620</td>\n",
       "      <td>8.236370e-05</td>\n",
       "      <td>-2.328375e-06</td>\n",
       "      <td>-4.782259e-05</td>\n",
       "      <td>-0.011505</td>\n",
       "      <td>0.939124</td>\n",
       "      <td>6.434639e-05</td>\n",
       "      <td>-101.671810</td>\n",
       "      <td>9.998148e+06</td>\n",
       "      <td>1829.160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.502915</td>\n",
       "      <td>9.998249e+06</td>\n",
       "      <td>1870.000</td>\n",
       "      <td>1870.000</td>\n",
       "      <td>19</td>\n",
       "      <td>[{'price': 101.67081, 'dt': 2000-01-28 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-31 21:00:00+00:00</th>\n",
       "      <td>103.750</td>\n",
       "      <td>8.138461e-05</td>\n",
       "      <td>1.500337e-06</td>\n",
       "      <td>-1.473707e-05</td>\n",
       "      <td>0.009385</td>\n",
       "      <td>0.916853</td>\n",
       "      <td>6.514691e-05</td>\n",
       "      <td>-103.802875</td>\n",
       "      <td>9.998044e+06</td>\n",
       "      <td>1971.250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.315943</td>\n",
       "      <td>9.998148e+06</td>\n",
       "      <td>1829.160</td>\n",
       "      <td>1829.160</td>\n",
       "      <td>20</td>\n",
       "      <td>[{'price': 103.801875, 'dt': 2000-01-31 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-01 21:00:00+00:00</th>\n",
       "      <td>100.250</td>\n",
       "      <td>8.267901e-05</td>\n",
       "      <td>-5.154775e-06</td>\n",
       "      <td>-6.759800e-05</td>\n",
       "      <td>-0.024826</td>\n",
       "      <td>0.902270</td>\n",
       "      <td>6.747201e-05</td>\n",
       "      <td>-100.301125</td>\n",
       "      <td>9.997943e+06</td>\n",
       "      <td>2005.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.985135</td>\n",
       "      <td>9.998044e+06</td>\n",
       "      <td>1971.250</td>\n",
       "      <td>1971.250</td>\n",
       "      <td>21</td>\n",
       "      <td>[{'price': 100.300125, 'dt': 2000-02-01 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-02 21:00:00+00:00</th>\n",
       "      <td>98.810</td>\n",
       "      <td>8.117951e-05</td>\n",
       "      <td>-8.039816e-06</td>\n",
       "      <td>-8.650562e-05</td>\n",
       "      <td>-0.038753</td>\n",
       "      <td>0.881914</td>\n",
       "      <td>6.782795e-05</td>\n",
       "      <td>-98.860405</td>\n",
       "      <td>9.997845e+06</td>\n",
       "      <td>2075.010</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.482538</td>\n",
       "      <td>9.997943e+06</td>\n",
       "      <td>2005.000</td>\n",
       "      <td>2005.000</td>\n",
       "      <td>22</td>\n",
       "      <td>[{'price': 98.859405, 'dt': 2000-02-02 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-03 21:00:00+00:00</th>\n",
       "      <td>103.310</td>\n",
       "      <td>8.570314e-05</td>\n",
       "      <td>1.404919e-06</td>\n",
       "      <td>-1.480332e-05</td>\n",
       "      <td>0.004844</td>\n",
       "      <td>0.874805</td>\n",
       "      <td>7.221755e-05</td>\n",
       "      <td>-103.362655</td>\n",
       "      <td>9.997741e+06</td>\n",
       "      <td>2272.820</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.253437</td>\n",
       "      <td>9.997845e+06</td>\n",
       "      <td>2075.010</td>\n",
       "      <td>2075.010</td>\n",
       "      <td>23</td>\n",
       "      <td>[{'price': 103.361655, 'dt': 2000-02-03 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-04 21:00:00+00:00</th>\n",
       "      <td>108.000</td>\n",
       "      <td>9.016171e-05</td>\n",
       "      <td>1.171742e-05</td>\n",
       "      <td>5.578708e-05</td>\n",
       "      <td>0.050560</td>\n",
       "      <td>0.867287</td>\n",
       "      <td>7.655342e-05</td>\n",
       "      <td>-108.055000</td>\n",
       "      <td>9.997633e+06</td>\n",
       "      <td>2484.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.068816</td>\n",
       "      <td>9.997741e+06</td>\n",
       "      <td>2272.820</td>\n",
       "      <td>2272.820</td>\n",
       "      <td>24</td>\n",
       "      <td>[{'price': 108.054, 'dt': 2000-02-04 21:00:00+...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-07 21:00:00+00:00</th>\n",
       "      <td>114.060</td>\n",
       "      <td>9.804201e-05</td>\n",
       "      <td>2.564962e-05</td>\n",
       "      <td>1.412154e-04</td>\n",
       "      <td>0.109597</td>\n",
       "      <td>0.865362</td>\n",
       "      <td>8.322895e-05</td>\n",
       "      <td>-114.118030</td>\n",
       "      <td>9.997519e+06</td>\n",
       "      <td>2737.440</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.437081</td>\n",
       "      <td>9.997633e+06</td>\n",
       "      <td>2484.000</td>\n",
       "      <td>2484.000</td>\n",
       "      <td>25</td>\n",
       "      <td>[{'price': 114.11703, 'dt': 2000-02-07 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-08 21:00:00+00:00</th>\n",
       "      <td>114.870</td>\n",
       "      <td>9.610312e-05</td>\n",
       "      <td>2.758777e-05</td>\n",
       "      <td>1.488234e-04</td>\n",
       "      <td>0.117469</td>\n",
       "      <td>0.847891</td>\n",
       "      <td>8.324487e-05</td>\n",
       "      <td>-114.928435</td>\n",
       "      <td>9.997404e+06</td>\n",
       "      <td>2871.750</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.679673</td>\n",
       "      <td>9.997519e+06</td>\n",
       "      <td>2737.440</td>\n",
       "      <td>2737.440</td>\n",
       "      <td>26</td>\n",
       "      <td>[{'price': 114.927435, 'dt': 2000-02-08 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-09 21:00:00+00:00</th>\n",
       "      <td>112.620</td>\n",
       "      <td>9.642881e-05</td>\n",
       "      <td>2.195704e-05</td>\n",
       "      <td>1.040735e-04</td>\n",
       "      <td>0.095671</td>\n",
       "      <td>0.834971</td>\n",
       "      <td>8.479329e-05</td>\n",
       "      <td>-112.677310</td>\n",
       "      <td>9.997291e+06</td>\n",
       "      <td>2928.120</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.494209</td>\n",
       "      <td>9.997404e+06</td>\n",
       "      <td>2871.750</td>\n",
       "      <td>2871.750</td>\n",
       "      <td>27</td>\n",
       "      <td>[{'price': 112.67631, 'dt': 2000-02-09 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-10 21:00:00+00:00</th>\n",
       "      <td>113.500</td>\n",
       "      <td>9.472880e-05</td>\n",
       "      <td>2.423927e-05</td>\n",
       "      <td>1.149321e-04</td>\n",
       "      <td>0.104148</td>\n",
       "      <td>0.819412</td>\n",
       "      <td>8.484235e-05</td>\n",
       "      <td>-113.557750</td>\n",
       "      <td>9.997178e+06</td>\n",
       "      <td>3064.500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.787878</td>\n",
       "      <td>9.997291e+06</td>\n",
       "      <td>2928.120</td>\n",
       "      <td>2928.120</td>\n",
       "      <td>28</td>\n",
       "      <td>[{'price': 113.55675, 'dt': 2000-02-10 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-11 21:00:00+00:00</th>\n",
       "      <td>108.750</td>\n",
       "      <td>1.014054e-04</td>\n",
       "      <td>1.140873e-05</td>\n",
       "      <td>2.560160e-05</td>\n",
       "      <td>0.057826</td>\n",
       "      <td>0.816378</td>\n",
       "      <td>9.077516e-05</td>\n",
       "      <td>-108.805375</td>\n",
       "      <td>9.997069e+06</td>\n",
       "      <td>3045.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.456556</td>\n",
       "      <td>9.997178e+06</td>\n",
       "      <td>3064.500</td>\n",
       "      <td>3064.500</td>\n",
       "      <td>29</td>\n",
       "      <td>[{'price': 108.804375, 'dt': 2000-02-11 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-14 21:00:00+00:00</th>\n",
       "      <td>115.810</td>\n",
       "      <td>1.143662e-04</td>\n",
       "      <td>3.117084e-05</td>\n",
       "      <td>1.271648e-04</td>\n",
       "      <td>0.126552</td>\n",
       "      <td>0.821903</td>\n",
       "      <td>1.013435e-04</td>\n",
       "      <td>-115.868905</td>\n",
       "      <td>9.996953e+06</td>\n",
       "      <td>3358.490</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.912563</td>\n",
       "      <td>9.997069e+06</td>\n",
       "      <td>3045.000</td>\n",
       "      <td>3045.000</td>\n",
       "      <td>30</td>\n",
       "      <td>[{'price': 115.86790500000001, 'dt': 2000-02-1...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-16 21:00:00+00:00</th>\n",
       "      <td>171.100</td>\n",
       "      <td>4.046510e-02</td>\n",
       "      <td>5.564869e-01</td>\n",
       "      <td>1.087192e-02</td>\n",
       "      <td>50.801393</td>\n",
       "      <td>0.416391</td>\n",
       "      <td>4.717991e-02</td>\n",
       "      <td>-171.186550</td>\n",
       "      <td>9.237249e+06</td>\n",
       "      <td>6327620.200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.918705</td>\n",
       "      <td>9.237420e+06</td>\n",
       "      <td>6252747.480</td>\n",
       "      <td>6252747.480</td>\n",
       "      <td>4499</td>\n",
       "      <td>[{'price': 171.18555, 'dt': 2017-11-16 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 21:00:00+00:00</th>\n",
       "      <td>170.150</td>\n",
       "      <td>4.046445e-02</td>\n",
       "      <td>5.529736e-01</td>\n",
       "      <td>1.075638e-02</td>\n",
       "      <td>50.513775</td>\n",
       "      <td>0.416348</td>\n",
       "      <td>4.718438e-02</td>\n",
       "      <td>-170.236075</td>\n",
       "      <td>9.237079e+06</td>\n",
       "      <td>6292657.450</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.913898</td>\n",
       "      <td>9.237249e+06</td>\n",
       "      <td>6327620.200</td>\n",
       "      <td>6327620.200</td>\n",
       "      <td>4500</td>\n",
       "      <td>[{'price': 170.235075, 'dt': 2017-11-17 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-20 21:00:00+00:00</th>\n",
       "      <td>169.980</td>\n",
       "      <td>4.046013e-02</td>\n",
       "      <td>5.523449e-01</td>\n",
       "      <td>1.073387e-02</td>\n",
       "      <td>50.462307</td>\n",
       "      <td>0.416302</td>\n",
       "      <td>4.718467e-02</td>\n",
       "      <td>-170.065990</td>\n",
       "      <td>9.236909e+06</td>\n",
       "      <td>6286540.320</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.912978</td>\n",
       "      <td>9.237079e+06</td>\n",
       "      <td>6292657.450</td>\n",
       "      <td>6292657.450</td>\n",
       "      <td>4501</td>\n",
       "      <td>[{'price': 170.06499, 'dt': 2017-11-20 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-21 21:00:00+00:00</th>\n",
       "      <td>173.140</td>\n",
       "      <td>4.049379e-02</td>\n",
       "      <td>5.640319e-01</td>\n",
       "      <td>1.109221e-02</td>\n",
       "      <td>51.419013</td>\n",
       "      <td>0.416276</td>\n",
       "      <td>4.722172e-02</td>\n",
       "      <td>-173.227570</td>\n",
       "      <td>9.236736e+06</td>\n",
       "      <td>6403582.900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.928001</td>\n",
       "      <td>9.236909e+06</td>\n",
       "      <td>6286540.320</td>\n",
       "      <td>6286540.320</td>\n",
       "      <td>4502</td>\n",
       "      <td>[{'price': 173.22657, 'dt': 2017-11-21 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-22 21:00:00+00:00</th>\n",
       "      <td>174.960</td>\n",
       "      <td>4.050149e-02</td>\n",
       "      <td>5.707631e-01</td>\n",
       "      <td>1.129929e-02</td>\n",
       "      <td>51.970027</td>\n",
       "      <td>0.416235</td>\n",
       "      <td>4.723299e-02</td>\n",
       "      <td>-175.048480</td>\n",
       "      <td>9.236561e+06</td>\n",
       "      <td>6471070.560</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.936543</td>\n",
       "      <td>9.236736e+06</td>\n",
       "      <td>6403582.900</td>\n",
       "      <td>6403582.900</td>\n",
       "      <td>4503</td>\n",
       "      <td>[{'price': 175.04748, 'dt': 2017-11-22 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 18:00:00+00:00</th>\n",
       "      <td>174.970</td>\n",
       "      <td>4.049699e-02</td>\n",
       "      <td>5.708001e-01</td>\n",
       "      <td>1.129795e-02</td>\n",
       "      <td>51.973055</td>\n",
       "      <td>0.416189</td>\n",
       "      <td>4.723299e-02</td>\n",
       "      <td>-175.058485</td>\n",
       "      <td>9.236386e+06</td>\n",
       "      <td>6471615.390</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.936486</td>\n",
       "      <td>9.236561e+06</td>\n",
       "      <td>6471070.560</td>\n",
       "      <td>6471070.560</td>\n",
       "      <td>4504</td>\n",
       "      <td>[{'price': 175.05748499999999, 'dt': 2017-11-2...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-27 21:00:00+00:00</th>\n",
       "      <td>174.090</td>\n",
       "      <td>4.049577e-02</td>\n",
       "      <td>5.675452e-01</td>\n",
       "      <td>1.119163e-02</td>\n",
       "      <td>51.706630</td>\n",
       "      <td>0.416146</td>\n",
       "      <td>4.723680e-02</td>\n",
       "      <td>-174.178045</td>\n",
       "      <td>9.236211e+06</td>\n",
       "      <td>6439240.920</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.932077</td>\n",
       "      <td>9.236386e+06</td>\n",
       "      <td>6471615.390</td>\n",
       "      <td>6471615.390</td>\n",
       "      <td>4505</td>\n",
       "      <td>[{'price': 174.177045, 'dt': 2017-11-27 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-28 21:00:00+00:00</th>\n",
       "      <td>173.070</td>\n",
       "      <td>4.049562e-02</td>\n",
       "      <td>5.637724e-01</td>\n",
       "      <td>1.106846e-02</td>\n",
       "      <td>51.397820</td>\n",
       "      <td>0.416103</td>\n",
       "      <td>4.724179e-02</td>\n",
       "      <td>-173.157535</td>\n",
       "      <td>9.236038e+06</td>\n",
       "      <td>6401686.230</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.926947</td>\n",
       "      <td>9.236211e+06</td>\n",
       "      <td>6439240.920</td>\n",
       "      <td>6439240.920</td>\n",
       "      <td>4506</td>\n",
       "      <td>[{'price': 173.156535, 'dt': 2017-11-28 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-29 21:00:00+00:00</th>\n",
       "      <td>169.480</td>\n",
       "      <td>4.054209e-02</td>\n",
       "      <td>5.504934e-01</td>\n",
       "      <td>1.062931e-02</td>\n",
       "      <td>50.310929</td>\n",
       "      <td>0.416089</td>\n",
       "      <td>4.729543e-02</td>\n",
       "      <td>-169.565740</td>\n",
       "      <td>9.235869e+06</td>\n",
       "      <td>6269065.200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.907447</td>\n",
       "      <td>9.236038e+06</td>\n",
       "      <td>6401686.230</td>\n",
       "      <td>6401686.230</td>\n",
       "      <td>4507</td>\n",
       "      <td>[{'price': 169.56474, 'dt': 2017-11-29 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 21:00:00+00:00</th>\n",
       "      <td>171.850</td>\n",
       "      <td>4.055885e-02</td>\n",
       "      <td>5.592600e-01</td>\n",
       "      <td>1.089969e-02</td>\n",
       "      <td>51.028459</td>\n",
       "      <td>0.416054</td>\n",
       "      <td>4.731581e-02</td>\n",
       "      <td>-171.936925</td>\n",
       "      <td>9.235697e+06</td>\n",
       "      <td>6356903.350</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.918664</td>\n",
       "      <td>9.235869e+06</td>\n",
       "      <td>6269065.200</td>\n",
       "      <td>6269065.200</td>\n",
       "      <td>4508</td>\n",
       "      <td>[{'price': 171.935925, 'dt': 2017-11-30 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 21:00:00+00:00</th>\n",
       "      <td>171.050</td>\n",
       "      <td>4.055711e-02</td>\n",
       "      <td>5.563007e-01</td>\n",
       "      <td>1.080250e-02</td>\n",
       "      <td>50.786255</td>\n",
       "      <td>0.416010</td>\n",
       "      <td>4.731909e-02</td>\n",
       "      <td>-171.136525</td>\n",
       "      <td>9.235526e+06</td>\n",
       "      <td>6327481.600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.914645</td>\n",
       "      <td>9.235697e+06</td>\n",
       "      <td>6356903.350</td>\n",
       "      <td>6356903.350</td>\n",
       "      <td>4509</td>\n",
       "      <td>[{'price': 171.135525, 'dt': 2017-12-01 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-04 21:00:00+00:00</th>\n",
       "      <td>169.800</td>\n",
       "      <td>4.055912e-02</td>\n",
       "      <td>5.516767e-01</td>\n",
       "      <td>1.065112e-02</td>\n",
       "      <td>50.407811</td>\n",
       "      <td>0.415969</td>\n",
       "      <td>4.732645e-02</td>\n",
       "      <td>-169.885900</td>\n",
       "      <td>9.235356e+06</td>\n",
       "      <td>6281411.400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.908311</td>\n",
       "      <td>9.235526e+06</td>\n",
       "      <td>6327481.600</td>\n",
       "      <td>6327481.600</td>\n",
       "      <td>4510</td>\n",
       "      <td>[{'price': 169.88490000000002, 'dt': 2017-12-0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-05 21:00:00+00:00</th>\n",
       "      <td>169.640</td>\n",
       "      <td>4.055478e-02</td>\n",
       "      <td>5.510848e-01</td>\n",
       "      <td>1.062986e-02</td>\n",
       "      <td>50.359370</td>\n",
       "      <td>0.415923</td>\n",
       "      <td>4.732671e-02</td>\n",
       "      <td>-169.725820</td>\n",
       "      <td>9.235186e+06</td>\n",
       "      <td>6275662.160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.907443</td>\n",
       "      <td>9.235356e+06</td>\n",
       "      <td>6281411.400</td>\n",
       "      <td>6281411.400</td>\n",
       "      <td>4511</td>\n",
       "      <td>[{'price': 169.72482000000002, 'dt': 2017-12-0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-06 21:00:00+00:00</th>\n",
       "      <td>169.010</td>\n",
       "      <td>4.055205e-02</td>\n",
       "      <td>5.487542e-01</td>\n",
       "      <td>1.055272e-02</td>\n",
       "      <td>50.168635</td>\n",
       "      <td>0.415879</td>\n",
       "      <td>4.732890e-02</td>\n",
       "      <td>-169.095505</td>\n",
       "      <td>9.235017e+06</td>\n",
       "      <td>6252524.950</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.904264</td>\n",
       "      <td>9.235186e+06</td>\n",
       "      <td>6275662.160</td>\n",
       "      <td>6275662.160</td>\n",
       "      <td>4512</td>\n",
       "      <td>[{'price': 169.094505, 'dt': 2017-12-06 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-07 21:00:00+00:00</th>\n",
       "      <td>169.452</td>\n",
       "      <td>4.054819e-02</td>\n",
       "      <td>5.503894e-01</td>\n",
       "      <td>1.060230e-02</td>\n",
       "      <td>50.302452</td>\n",
       "      <td>0.415833</td>\n",
       "      <td>4.732930e-02</td>\n",
       "      <td>-169.537726</td>\n",
       "      <td>9.234847e+06</td>\n",
       "      <td>6269046.192</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.906275</td>\n",
       "      <td>9.235017e+06</td>\n",
       "      <td>6252524.950</td>\n",
       "      <td>6252524.950</td>\n",
       "      <td>4513</td>\n",
       "      <td>[{'price': 169.536726, 'dt': 2017-12-07 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-08 21:00:00+00:00</th>\n",
       "      <td>169.370</td>\n",
       "      <td>4.054376e-02</td>\n",
       "      <td>5.500860e-01</td>\n",
       "      <td>1.059027e-02</td>\n",
       "      <td>50.277626</td>\n",
       "      <td>0.415787</td>\n",
       "      <td>4.732942e-02</td>\n",
       "      <td>-169.455685</td>\n",
       "      <td>9.234678e+06</td>\n",
       "      <td>6266181.890</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.905782</td>\n",
       "      <td>9.234847e+06</td>\n",
       "      <td>6269046.192</td>\n",
       "      <td>6269046.192</td>\n",
       "      <td>4514</td>\n",
       "      <td>[{'price': 169.454685, 'dt': 2017-12-08 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11 21:00:00+00:00</th>\n",
       "      <td>172.670</td>\n",
       "      <td>4.058087e-02</td>\n",
       "      <td>5.622950e-01</td>\n",
       "      <td>1.096337e-02</td>\n",
       "      <td>51.276718</td>\n",
       "      <td>0.415763</td>\n",
       "      <td>4.737015e-02</td>\n",
       "      <td>-172.757335</td>\n",
       "      <td>9.234505e+06</td>\n",
       "      <td>6388444.660</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.921427</td>\n",
       "      <td>9.234678e+06</td>\n",
       "      <td>6266181.890</td>\n",
       "      <td>6266181.890</td>\n",
       "      <td>4515</td>\n",
       "      <td>[{'price': 172.756335, 'dt': 2017-12-11 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-12 21:00:00+00:00</th>\n",
       "      <td>171.700</td>\n",
       "      <td>4.058033e-02</td>\n",
       "      <td>5.587062e-01</td>\n",
       "      <td>1.084617e-02</td>\n",
       "      <td>50.983046</td>\n",
       "      <td>0.415720</td>\n",
       "      <td>4.737474e-02</td>\n",
       "      <td>-171.786850</td>\n",
       "      <td>9.234333e+06</td>\n",
       "      <td>6352728.300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.916561</td>\n",
       "      <td>9.234505e+06</td>\n",
       "      <td>6388444.660</td>\n",
       "      <td>6388444.660</td>\n",
       "      <td>4516</td>\n",
       "      <td>[{'price': 171.78585, 'dt': 2017-12-12 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-13 21:00:00+00:00</th>\n",
       "      <td>172.270</td>\n",
       "      <td>4.057691e-02</td>\n",
       "      <td>5.608151e-01</td>\n",
       "      <td>1.091024e-02</td>\n",
       "      <td>51.155616</td>\n",
       "      <td>0.415675</td>\n",
       "      <td>4.737552e-02</td>\n",
       "      <td>-172.357135</td>\n",
       "      <td>9.234161e+06</td>\n",
       "      <td>6373990.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.919163</td>\n",
       "      <td>9.234333e+06</td>\n",
       "      <td>6352728.300</td>\n",
       "      <td>6352728.300</td>\n",
       "      <td>4517</td>\n",
       "      <td>[{'price': 172.35613500000002, 'dt': 2017-12-1...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-14 21:00:00+00:00</th>\n",
       "      <td>172.220</td>\n",
       "      <td>4.057246e-02</td>\n",
       "      <td>5.606301e-01</td>\n",
       "      <td>1.090196e-02</td>\n",
       "      <td>51.140478</td>\n",
       "      <td>0.415629</td>\n",
       "      <td>4.737559e-02</td>\n",
       "      <td>-172.307110</td>\n",
       "      <td>9.233989e+06</td>\n",
       "      <td>6372312.220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.918824</td>\n",
       "      <td>9.234161e+06</td>\n",
       "      <td>6373990.000</td>\n",
       "      <td>6373990.000</td>\n",
       "      <td>4518</td>\n",
       "      <td>[{'price': 172.30611, 'dt': 2017-12-14 21:00:0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-15 21:00:00+00:00</th>\n",
       "      <td>173.870</td>\n",
       "      <td>4.057794e-02</td>\n",
       "      <td>5.667352e-01</td>\n",
       "      <td>1.108952e-02</td>\n",
       "      <td>51.640024</td>\n",
       "      <td>0.415587</td>\n",
       "      <td>4.738479e-02</td>\n",
       "      <td>-173.957935</td>\n",
       "      <td>9.233815e+06</td>\n",
       "      <td>6433537.740</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.926538</td>\n",
       "      <td>9.233989e+06</td>\n",
       "      <td>6372312.220</td>\n",
       "      <td>6372312.220</td>\n",
       "      <td>4519</td>\n",
       "      <td>[{'price': 173.95693500000002, 'dt': 2017-12-1...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-18 21:00:00+00:00</th>\n",
       "      <td>176.420</td>\n",
       "      <td>4.059752e-02</td>\n",
       "      <td>5.761707e-01</td>\n",
       "      <td>1.137692e-02</td>\n",
       "      <td>52.412050</td>\n",
       "      <td>0.415553</td>\n",
       "      <td>4.740770e-02</td>\n",
       "      <td>-176.509210</td>\n",
       "      <td>9.233638e+06</td>\n",
       "      <td>6528069.260</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.938465</td>\n",
       "      <td>9.233815e+06</td>\n",
       "      <td>6433537.740</td>\n",
       "      <td>6433537.740</td>\n",
       "      <td>4520</td>\n",
       "      <td>[{'price': 176.50821000000002, 'dt': 2017-12-1...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-19 21:00:00+00:00</th>\n",
       "      <td>174.540</td>\n",
       "      <td>4.060704e-02</td>\n",
       "      <td>5.692142e-01</td>\n",
       "      <td>1.115182e-02</td>\n",
       "      <td>51.842870</td>\n",
       "      <td>0.415517</td>\n",
       "      <td>4.742289e-02</td>\n",
       "      <td>-174.628270</td>\n",
       "      <td>9.233464e+06</td>\n",
       "      <td>6458678.160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.928899</td>\n",
       "      <td>9.233638e+06</td>\n",
       "      <td>6528069.260</td>\n",
       "      <td>6528069.260</td>\n",
       "      <td>4521</td>\n",
       "      <td>[{'price': 174.62726999999998, 'dt': 2017-12-1...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-20 21:00:00+00:00</th>\n",
       "      <td>174.350</td>\n",
       "      <td>4.060276e-02</td>\n",
       "      <td>5.685111e-01</td>\n",
       "      <td>1.112715e-02</td>\n",
       "      <td>51.785347</td>\n",
       "      <td>0.415471</td>\n",
       "      <td>4.742322e-02</td>\n",
       "      <td>-174.438175</td>\n",
       "      <td>9.233289e+06</td>\n",
       "      <td>6451821.750</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.927895</td>\n",
       "      <td>9.233464e+06</td>\n",
       "      <td>6458678.160</td>\n",
       "      <td>6458678.160</td>\n",
       "      <td>4522</td>\n",
       "      <td>[{'price': 174.437175, 'dt': 2017-12-20 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-21 21:00:00+00:00</th>\n",
       "      <td>175.010</td>\n",
       "      <td>4.059972e-02</td>\n",
       "      <td>5.709534e-01</td>\n",
       "      <td>1.120110e-02</td>\n",
       "      <td>51.985165</td>\n",
       "      <td>0.415426</td>\n",
       "      <td>4.742431e-02</td>\n",
       "      <td>-175.098505</td>\n",
       "      <td>9.233114e+06</td>\n",
       "      <td>6476420.060</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.930900</td>\n",
       "      <td>9.233289e+06</td>\n",
       "      <td>6451821.750</td>\n",
       "      <td>6451821.750</td>\n",
       "      <td>4523</td>\n",
       "      <td>[{'price': 175.09750499999998, 'dt': 2017-12-2...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-22 21:00:00+00:00</th>\n",
       "      <td>175.010</td>\n",
       "      <td>4.059524e-02</td>\n",
       "      <td>5.709534e-01</td>\n",
       "      <td>1.119862e-02</td>\n",
       "      <td>51.985165</td>\n",
       "      <td>0.415380</td>\n",
       "      <td>4.742433e-02</td>\n",
       "      <td>-175.098505</td>\n",
       "      <td>9.232939e+06</td>\n",
       "      <td>6476595.070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.930797</td>\n",
       "      <td>9.233114e+06</td>\n",
       "      <td>6476420.060</td>\n",
       "      <td>6476420.060</td>\n",
       "      <td>4524</td>\n",
       "      <td>[{'price': 175.09750499999998, 'dt': 2017-12-2...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26 21:00:00+00:00</th>\n",
       "      <td>170.570</td>\n",
       "      <td>4.066721e-02</td>\n",
       "      <td>5.545223e-01</td>\n",
       "      <td>1.065588e-02</td>\n",
       "      <td>50.640932</td>\n",
       "      <td>0.415382</td>\n",
       "      <td>4.750420e-02</td>\n",
       "      <td>-170.656285</td>\n",
       "      <td>9.232768e+06</td>\n",
       "      <td>6312454.560</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.906291</td>\n",
       "      <td>9.232939e+06</td>\n",
       "      <td>6476595.070</td>\n",
       "      <td>6476595.070</td>\n",
       "      <td>4525</td>\n",
       "      <td>[{'price': 170.655285, 'dt': 2017-12-26 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27 21:00:00+00:00</th>\n",
       "      <td>170.600</td>\n",
       "      <td>4.066272e-02</td>\n",
       "      <td>5.546333e-01</td>\n",
       "      <td>1.065704e-02</td>\n",
       "      <td>50.650015</td>\n",
       "      <td>0.415336</td>\n",
       "      <td>4.750420e-02</td>\n",
       "      <td>-170.686300</td>\n",
       "      <td>9.232598e+06</td>\n",
       "      <td>6313735.400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.906332</td>\n",
       "      <td>9.232768e+06</td>\n",
       "      <td>6312454.560</td>\n",
       "      <td>6312454.560</td>\n",
       "      <td>4526</td>\n",
       "      <td>[{'price': 170.68529999999998, 'dt': 2017-12-2...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28 21:00:00+00:00</th>\n",
       "      <td>171.080</td>\n",
       "      <td>4.065897e-02</td>\n",
       "      <td>5.564097e-01</td>\n",
       "      <td>1.071070e-02</td>\n",
       "      <td>50.795338</td>\n",
       "      <td>0.415290</td>\n",
       "      <td>4.750469e-02</td>\n",
       "      <td>-171.166540</td>\n",
       "      <td>9.232426e+06</td>\n",
       "      <td>6331670.800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.908503</td>\n",
       "      <td>9.232598e+06</td>\n",
       "      <td>6313735.400</td>\n",
       "      <td>6313735.400</td>\n",
       "      <td>4527</td>\n",
       "      <td>[{'price': 171.16554000000002, 'dt': 2017-12-2...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29 21:00:00+00:00</th>\n",
       "      <td>169.230</td>\n",
       "      <td>4.066834e-02</td>\n",
       "      <td>5.495629e-01</td>\n",
       "      <td>1.048737e-02</td>\n",
       "      <td>50.235241</td>\n",
       "      <td>0.415254</td>\n",
       "      <td>4.751996e-02</td>\n",
       "      <td>-169.315615</td>\n",
       "      <td>9.232257e+06</td>\n",
       "      <td>6263371.530</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.899046</td>\n",
       "      <td>9.232426e+06</td>\n",
       "      <td>6331670.800</td>\n",
       "      <td>6331670.800</td>\n",
       "      <td>4528</td>\n",
       "      <td>[{'price': 169.314615, 'dt': 2017-12-29 21:00:...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4528 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AAPL  algo_volatility  algorithm_period_return  \\\n",
       "2000-01-03 21:00:00+00:00  111.940              NaN             0.000000e+00   \n",
       "2000-01-04 21:00:00+00:00  102.500     5.865048e-08            -5.225000e-09   \n",
       "2000-01-05 21:00:00+00:00  104.000     1.350778e-06             1.394750e-07   \n",
       "2000-01-06 21:00:00+00:00   95.000     1.473590e-05            -1.665375e-06   \n",
       "2000-01-07 21:00:00+00:00   99.500     1.786630e-05            -3.204500e-07   \n",
       "2000-01-10 21:00:00+00:00   97.750     1.651106e-05            -1.025437e-06   \n",
       "2000-01-11 21:00:00+00:00   92.750     2.057337e-05            -3.530175e-06   \n",
       "2000-01-12 21:00:00+00:00   87.190     2.482292e-05            -6.870634e-06   \n",
       "2000-01-13 21:00:00+00:00   96.750     4.618992e-05            -1.835720e-07   \n",
       "2000-01-14 21:00:00+00:00  100.440     4.602533e-05             2.763306e-06   \n",
       "2000-01-18 21:00:00+00:00  103.940     4.577097e-05             5.908009e-06   \n",
       "2000-01-19 21:00:00+00:00  106.560     4.466723e-05             8.522581e-06   \n",
       "2000-01-20 21:00:00+00:00  113.500     5.250339e-05             1.615081e-05   \n",
       "2000-01-21 21:00:00+00:00  111.310     5.305637e-05             1.351714e-05   \n",
       "2000-01-24 21:00:00+00:00  106.250     5.975998e-05             6.933728e-06   \n",
       "2000-01-25 21:00:00+00:00  112.250     6.575790e-05             1.532802e-05   \n",
       "2000-01-26 21:00:00+00:00  110.190     6.555468e-05             1.223241e-05   \n",
       "2000-01-27 21:00:00+00:00  110.000     6.371386e-05             1.192281e-05   \n",
       "2000-01-28 21:00:00+00:00  101.620     8.236370e-05            -2.328375e-06   \n",
       "2000-01-31 21:00:00+00:00  103.750     8.138461e-05             1.500337e-06   \n",
       "2000-02-01 21:00:00+00:00  100.250     8.267901e-05            -5.154775e-06   \n",
       "2000-02-02 21:00:00+00:00   98.810     8.117951e-05            -8.039816e-06   \n",
       "2000-02-03 21:00:00+00:00  103.310     8.570314e-05             1.404919e-06   \n",
       "2000-02-04 21:00:00+00:00  108.000     9.016171e-05             1.171742e-05   \n",
       "2000-02-07 21:00:00+00:00  114.060     9.804201e-05             2.564962e-05   \n",
       "2000-02-08 21:00:00+00:00  114.870     9.610312e-05             2.758777e-05   \n",
       "2000-02-09 21:00:00+00:00  112.620     9.642881e-05             2.195704e-05   \n",
       "2000-02-10 21:00:00+00:00  113.500     9.472880e-05             2.423927e-05   \n",
       "2000-02-11 21:00:00+00:00  108.750     1.014054e-04             1.140873e-05   \n",
       "2000-02-14 21:00:00+00:00  115.810     1.143662e-04             3.117084e-05   \n",
       "...                            ...              ...                      ...   \n",
       "2017-11-16 21:00:00+00:00  171.100     4.046510e-02             5.564869e-01   \n",
       "2017-11-17 21:00:00+00:00  170.150     4.046445e-02             5.529736e-01   \n",
       "2017-11-20 21:00:00+00:00  169.980     4.046013e-02             5.523449e-01   \n",
       "2017-11-21 21:00:00+00:00  173.140     4.049379e-02             5.640319e-01   \n",
       "2017-11-22 21:00:00+00:00  174.960     4.050149e-02             5.707631e-01   \n",
       "2017-11-24 18:00:00+00:00  174.970     4.049699e-02             5.708001e-01   \n",
       "2017-11-27 21:00:00+00:00  174.090     4.049577e-02             5.675452e-01   \n",
       "2017-11-28 21:00:00+00:00  173.070     4.049562e-02             5.637724e-01   \n",
       "2017-11-29 21:00:00+00:00  169.480     4.054209e-02             5.504934e-01   \n",
       "2017-11-30 21:00:00+00:00  171.850     4.055885e-02             5.592600e-01   \n",
       "2017-12-01 21:00:00+00:00  171.050     4.055711e-02             5.563007e-01   \n",
       "2017-12-04 21:00:00+00:00  169.800     4.055912e-02             5.516767e-01   \n",
       "2017-12-05 21:00:00+00:00  169.640     4.055478e-02             5.510848e-01   \n",
       "2017-12-06 21:00:00+00:00  169.010     4.055205e-02             5.487542e-01   \n",
       "2017-12-07 21:00:00+00:00  169.452     4.054819e-02             5.503894e-01   \n",
       "2017-12-08 21:00:00+00:00  169.370     4.054376e-02             5.500860e-01   \n",
       "2017-12-11 21:00:00+00:00  172.670     4.058087e-02             5.622950e-01   \n",
       "2017-12-12 21:00:00+00:00  171.700     4.058033e-02             5.587062e-01   \n",
       "2017-12-13 21:00:00+00:00  172.270     4.057691e-02             5.608151e-01   \n",
       "2017-12-14 21:00:00+00:00  172.220     4.057246e-02             5.606301e-01   \n",
       "2017-12-15 21:00:00+00:00  173.870     4.057794e-02             5.667352e-01   \n",
       "2017-12-18 21:00:00+00:00  176.420     4.059752e-02             5.761707e-01   \n",
       "2017-12-19 21:00:00+00:00  174.540     4.060704e-02             5.692142e-01   \n",
       "2017-12-20 21:00:00+00:00  174.350     4.060276e-02             5.685111e-01   \n",
       "2017-12-21 21:00:00+00:00  175.010     4.059972e-02             5.709534e-01   \n",
       "2017-12-22 21:00:00+00:00  175.010     4.059524e-02             5.709534e-01   \n",
       "2017-12-26 21:00:00+00:00  170.570     4.066721e-02             5.545223e-01   \n",
       "2017-12-27 21:00:00+00:00  170.600     4.066272e-02             5.546333e-01   \n",
       "2017-12-28 21:00:00+00:00  171.080     4.065897e-02             5.564097e-01   \n",
       "2017-12-29 21:00:00+00:00  169.230     4.066834e-02             5.495629e-01   \n",
       "\n",
       "                                  alpha  benchmark_period_return  \\\n",
       "2000-01-03 21:00:00+00:00           NaN                 0.089010   \n",
       "2000-01-04 21:00:00+00:00 -6.754044e-07                -0.003028   \n",
       "2000-01-05 21:00:00+00:00  1.153407e-05                 0.011807   \n",
       "2000-01-06 21:00:00+00:00 -7.923378e-05                -0.075992   \n",
       "2000-01-07 21:00:00+00:00 -7.253909e-06                -0.032092   \n",
       "2000-01-10 21:00:00+00:00 -2.875804e-05                -0.049046   \n",
       "2000-01-11 21:00:00+00:00 -8.919835e-05                -0.097790   \n",
       "2000-01-12 21:00:00+00:00 -1.455015e-04                -0.151983   \n",
       "2000-01-13 21:00:00+00:00  2.724823e-05                -0.058734   \n",
       "2000-01-14 21:00:00+00:00  7.027879e-05                -0.023009   \n",
       "2000-01-18 21:00:00+00:00  1.098881e-04                 0.011202   \n",
       "2000-01-19 21:00:00+00:00  1.377413e-04                 0.036633   \n",
       "2000-01-20 21:00:00+00:00  2.200544e-04                 0.104148   \n",
       "2000-01-21 21:00:00+00:00  1.676762e-04                 0.082652   \n",
       "2000-01-24 21:00:00+00:00  7.132182e-05                 0.033606   \n",
       "2000-01-25 21:00:00+00:00  1.500239e-04                 0.092038   \n",
       "2000-01-26 21:00:00+00:00  1.073395e-04                 0.071753   \n",
       "2000-01-27 21:00:00+00:00  9.814651e-05                 0.069936   \n",
       "2000-01-28 21:00:00+00:00 -4.782259e-05                -0.011505   \n",
       "2000-01-31 21:00:00+00:00 -1.473707e-05                 0.009385   \n",
       "2000-02-01 21:00:00+00:00 -6.759800e-05                -0.024826   \n",
       "2000-02-02 21:00:00+00:00 -8.650562e-05                -0.038753   \n",
       "2000-02-03 21:00:00+00:00 -1.480332e-05                 0.004844   \n",
       "2000-02-04 21:00:00+00:00  5.578708e-05                 0.050560   \n",
       "2000-02-07 21:00:00+00:00  1.412154e-04                 0.109597   \n",
       "2000-02-08 21:00:00+00:00  1.488234e-04                 0.117469   \n",
       "2000-02-09 21:00:00+00:00  1.040735e-04                 0.095671   \n",
       "2000-02-10 21:00:00+00:00  1.149321e-04                 0.104148   \n",
       "2000-02-11 21:00:00+00:00  2.560160e-05                 0.057826   \n",
       "2000-02-14 21:00:00+00:00  1.271648e-04                 0.126552   \n",
       "...                                 ...                      ...   \n",
       "2017-11-16 21:00:00+00:00  1.087192e-02                50.801393   \n",
       "2017-11-17 21:00:00+00:00  1.075638e-02                50.513775   \n",
       "2017-11-20 21:00:00+00:00  1.073387e-02                50.462307   \n",
       "2017-11-21 21:00:00+00:00  1.109221e-02                51.419013   \n",
       "2017-11-22 21:00:00+00:00  1.129929e-02                51.970027   \n",
       "2017-11-24 18:00:00+00:00  1.129795e-02                51.973055   \n",
       "2017-11-27 21:00:00+00:00  1.119163e-02                51.706630   \n",
       "2017-11-28 21:00:00+00:00  1.106846e-02                51.397820   \n",
       "2017-11-29 21:00:00+00:00  1.062931e-02                50.310929   \n",
       "2017-11-30 21:00:00+00:00  1.089969e-02                51.028459   \n",
       "2017-12-01 21:00:00+00:00  1.080250e-02                50.786255   \n",
       "2017-12-04 21:00:00+00:00  1.065112e-02                50.407811   \n",
       "2017-12-05 21:00:00+00:00  1.062986e-02                50.359370   \n",
       "2017-12-06 21:00:00+00:00  1.055272e-02                50.168635   \n",
       "2017-12-07 21:00:00+00:00  1.060230e-02                50.302452   \n",
       "2017-12-08 21:00:00+00:00  1.059027e-02                50.277626   \n",
       "2017-12-11 21:00:00+00:00  1.096337e-02                51.276718   \n",
       "2017-12-12 21:00:00+00:00  1.084617e-02                50.983046   \n",
       "2017-12-13 21:00:00+00:00  1.091024e-02                51.155616   \n",
       "2017-12-14 21:00:00+00:00  1.090196e-02                51.140478   \n",
       "2017-12-15 21:00:00+00:00  1.108952e-02                51.640024   \n",
       "2017-12-18 21:00:00+00:00  1.137692e-02                52.412050   \n",
       "2017-12-19 21:00:00+00:00  1.115182e-02                51.842870   \n",
       "2017-12-20 21:00:00+00:00  1.112715e-02                51.785347   \n",
       "2017-12-21 21:00:00+00:00  1.120110e-02                51.985165   \n",
       "2017-12-22 21:00:00+00:00  1.119862e-02                51.985165   \n",
       "2017-12-26 21:00:00+00:00  1.065588e-02                50.640932   \n",
       "2017-12-27 21:00:00+00:00  1.065704e-02                50.650015   \n",
       "2017-12-28 21:00:00+00:00  1.071070e-02                50.795338   \n",
       "2017-12-29 21:00:00+00:00  1.048737e-02                50.235241   \n",
       "\n",
       "                           benchmark_volatility          beta  capital_used  \\\n",
       "2000-01-03 21:00:00+00:00                   NaN           NaN      0.000000   \n",
       "2000-01-04 21:00:00+00:00              1.947812  3.011096e-08   -102.552250   \n",
       "2000-01-05 21:00:00+00:00              1.382169  1.117246e-07   -104.053000   \n",
       "2000-01-06 21:00:00+00:00              1.349523  6.048969e-06    -95.048500   \n",
       "2000-01-07 21:00:00+00:00              1.254860  8.875120e-06    -99.550750   \n",
       "2000-01-10 21:00:00+00:00              1.125805  9.108744e-06    -97.799875   \n",
       "2000-01-11 21:00:00+00:00              1.062626  1.187010e-05    -92.797375   \n",
       "2000-01-12 21:00:00+00:00              1.019136  1.513846e-05    -87.234595   \n",
       "2000-01-13 21:00:00+00:00              1.171122  2.983483e-05    -96.799375   \n",
       "2000-01-14 21:00:00+00:00              1.124341  3.127241e-05   -100.491220   \n",
       "2000-01-18 21:00:00+00:00              1.079792  3.249357e-05   -103.992970   \n",
       "2000-01-19 21:00:00+00:00              1.034484  3.308234e-05   -106.614280   \n",
       "2000-01-20 21:00:00+00:00              1.025274  3.855032e-05   -113.557750   \n",
       "2000-01-21 21:00:00+00:00              0.992729  4.001248e-05   -111.366655   \n",
       "2000-01-24 21:00:00+00:00              0.980794  4.502545e-05   -106.304125   \n",
       "2000-01-25 21:00:00+00:00              0.970218  4.991935e-05   -112.307125   \n",
       "2000-01-26 21:00:00+00:00              0.944663  5.110560e-05   -110.246095   \n",
       "2000-01-27 21:00:00+00:00              0.916880  5.118600e-05   -110.056000   \n",
       "2000-01-28 21:00:00+00:00              0.939124  6.434639e-05   -101.671810   \n",
       "2000-01-31 21:00:00+00:00              0.916853  6.514691e-05   -103.802875   \n",
       "2000-02-01 21:00:00+00:00              0.902270  6.747201e-05   -100.301125   \n",
       "2000-02-02 21:00:00+00:00              0.881914  6.782795e-05    -98.860405   \n",
       "2000-02-03 21:00:00+00:00              0.874805  7.221755e-05   -103.362655   \n",
       "2000-02-04 21:00:00+00:00              0.867287  7.655342e-05   -108.055000   \n",
       "2000-02-07 21:00:00+00:00              0.865362  8.322895e-05   -114.118030   \n",
       "2000-02-08 21:00:00+00:00              0.847891  8.324487e-05   -114.928435   \n",
       "2000-02-09 21:00:00+00:00              0.834971  8.479329e-05   -112.677310   \n",
       "2000-02-10 21:00:00+00:00              0.819412  8.484235e-05   -113.557750   \n",
       "2000-02-11 21:00:00+00:00              0.816378  9.077516e-05   -108.805375   \n",
       "2000-02-14 21:00:00+00:00              0.821903  1.013435e-04   -115.868905   \n",
       "...                                         ...           ...           ...   \n",
       "2017-11-16 21:00:00+00:00              0.416391  4.717991e-02   -171.186550   \n",
       "2017-11-17 21:00:00+00:00              0.416348  4.718438e-02   -170.236075   \n",
       "2017-11-20 21:00:00+00:00              0.416302  4.718467e-02   -170.065990   \n",
       "2017-11-21 21:00:00+00:00              0.416276  4.722172e-02   -173.227570   \n",
       "2017-11-22 21:00:00+00:00              0.416235  4.723299e-02   -175.048480   \n",
       "2017-11-24 18:00:00+00:00              0.416189  4.723299e-02   -175.058485   \n",
       "2017-11-27 21:00:00+00:00              0.416146  4.723680e-02   -174.178045   \n",
       "2017-11-28 21:00:00+00:00              0.416103  4.724179e-02   -173.157535   \n",
       "2017-11-29 21:00:00+00:00              0.416089  4.729543e-02   -169.565740   \n",
       "2017-11-30 21:00:00+00:00              0.416054  4.731581e-02   -171.936925   \n",
       "2017-12-01 21:00:00+00:00              0.416010  4.731909e-02   -171.136525   \n",
       "2017-12-04 21:00:00+00:00              0.415969  4.732645e-02   -169.885900   \n",
       "2017-12-05 21:00:00+00:00              0.415923  4.732671e-02   -169.725820   \n",
       "2017-12-06 21:00:00+00:00              0.415879  4.732890e-02   -169.095505   \n",
       "2017-12-07 21:00:00+00:00              0.415833  4.732930e-02   -169.537726   \n",
       "2017-12-08 21:00:00+00:00              0.415787  4.732942e-02   -169.455685   \n",
       "2017-12-11 21:00:00+00:00              0.415763  4.737015e-02   -172.757335   \n",
       "2017-12-12 21:00:00+00:00              0.415720  4.737474e-02   -171.786850   \n",
       "2017-12-13 21:00:00+00:00              0.415675  4.737552e-02   -172.357135   \n",
       "2017-12-14 21:00:00+00:00              0.415629  4.737559e-02   -172.307110   \n",
       "2017-12-15 21:00:00+00:00              0.415587  4.738479e-02   -173.957935   \n",
       "2017-12-18 21:00:00+00:00              0.415553  4.740770e-02   -176.509210   \n",
       "2017-12-19 21:00:00+00:00              0.415517  4.742289e-02   -174.628270   \n",
       "2017-12-20 21:00:00+00:00              0.415471  4.742322e-02   -174.438175   \n",
       "2017-12-21 21:00:00+00:00              0.415426  4.742431e-02   -175.098505   \n",
       "2017-12-22 21:00:00+00:00              0.415380  4.742433e-02   -175.098505   \n",
       "2017-12-26 21:00:00+00:00              0.415382  4.750420e-02   -170.656285   \n",
       "2017-12-27 21:00:00+00:00              0.415336  4.750420e-02   -170.686300   \n",
       "2017-12-28 21:00:00+00:00              0.415290  4.750469e-02   -171.166540   \n",
       "2017-12-29 21:00:00+00:00              0.415254  4.751996e-02   -169.315615   \n",
       "\n",
       "                            ending_cash  ending_exposure  \\\n",
       "2000-01-03 21:00:00+00:00  1.000000e+07            0.000   \n",
       "2000-01-04 21:00:00+00:00  9.999897e+06          102.500   \n",
       "2000-01-05 21:00:00+00:00  9.999793e+06          208.000   \n",
       "2000-01-06 21:00:00+00:00  9.999698e+06          285.000   \n",
       "2000-01-07 21:00:00+00:00  9.999599e+06          398.000   \n",
       "2000-01-10 21:00:00+00:00  9.999501e+06          488.750   \n",
       "2000-01-11 21:00:00+00:00  9.999408e+06          556.500   \n",
       "2000-01-12 21:00:00+00:00  9.999321e+06          610.330   \n",
       "2000-01-13 21:00:00+00:00  9.999224e+06          774.000   \n",
       "2000-01-14 21:00:00+00:00  9.999124e+06          903.960   \n",
       "2000-01-18 21:00:00+00:00  9.999020e+06         1039.400   \n",
       "2000-01-19 21:00:00+00:00  9.998913e+06         1172.160   \n",
       "2000-01-20 21:00:00+00:00  9.998800e+06         1362.000   \n",
       "2000-01-21 21:00:00+00:00  9.998688e+06         1447.030   \n",
       "2000-01-24 21:00:00+00:00  9.998582e+06         1487.500   \n",
       "2000-01-25 21:00:00+00:00  9.998470e+06         1683.750   \n",
       "2000-01-26 21:00:00+00:00  9.998359e+06         1763.040   \n",
       "2000-01-27 21:00:00+00:00  9.998249e+06         1870.000   \n",
       "2000-01-28 21:00:00+00:00  9.998148e+06         1829.160   \n",
       "2000-01-31 21:00:00+00:00  9.998044e+06         1971.250   \n",
       "2000-02-01 21:00:00+00:00  9.997943e+06         2005.000   \n",
       "2000-02-02 21:00:00+00:00  9.997845e+06         2075.010   \n",
       "2000-02-03 21:00:00+00:00  9.997741e+06         2272.820   \n",
       "2000-02-04 21:00:00+00:00  9.997633e+06         2484.000   \n",
       "2000-02-07 21:00:00+00:00  9.997519e+06         2737.440   \n",
       "2000-02-08 21:00:00+00:00  9.997404e+06         2871.750   \n",
       "2000-02-09 21:00:00+00:00  9.997291e+06         2928.120   \n",
       "2000-02-10 21:00:00+00:00  9.997178e+06         3064.500   \n",
       "2000-02-11 21:00:00+00:00  9.997069e+06         3045.000   \n",
       "2000-02-14 21:00:00+00:00  9.996953e+06         3358.490   \n",
       "...                                 ...              ...   \n",
       "2017-11-16 21:00:00+00:00  9.237249e+06      6327620.200   \n",
       "2017-11-17 21:00:00+00:00  9.237079e+06      6292657.450   \n",
       "2017-11-20 21:00:00+00:00  9.236909e+06      6286540.320   \n",
       "2017-11-21 21:00:00+00:00  9.236736e+06      6403582.900   \n",
       "2017-11-22 21:00:00+00:00  9.236561e+06      6471070.560   \n",
       "2017-11-24 18:00:00+00:00  9.236386e+06      6471615.390   \n",
       "2017-11-27 21:00:00+00:00  9.236211e+06      6439240.920   \n",
       "2017-11-28 21:00:00+00:00  9.236038e+06      6401686.230   \n",
       "2017-11-29 21:00:00+00:00  9.235869e+06      6269065.200   \n",
       "2017-11-30 21:00:00+00:00  9.235697e+06      6356903.350   \n",
       "2017-12-01 21:00:00+00:00  9.235526e+06      6327481.600   \n",
       "2017-12-04 21:00:00+00:00  9.235356e+06      6281411.400   \n",
       "2017-12-05 21:00:00+00:00  9.235186e+06      6275662.160   \n",
       "2017-12-06 21:00:00+00:00  9.235017e+06      6252524.950   \n",
       "2017-12-07 21:00:00+00:00  9.234847e+06      6269046.192   \n",
       "2017-12-08 21:00:00+00:00  9.234678e+06      6266181.890   \n",
       "2017-12-11 21:00:00+00:00  9.234505e+06      6388444.660   \n",
       "2017-12-12 21:00:00+00:00  9.234333e+06      6352728.300   \n",
       "2017-12-13 21:00:00+00:00  9.234161e+06      6373990.000   \n",
       "2017-12-14 21:00:00+00:00  9.233989e+06      6372312.220   \n",
       "2017-12-15 21:00:00+00:00  9.233815e+06      6433537.740   \n",
       "2017-12-18 21:00:00+00:00  9.233638e+06      6528069.260   \n",
       "2017-12-19 21:00:00+00:00  9.233464e+06      6458678.160   \n",
       "2017-12-20 21:00:00+00:00  9.233289e+06      6451821.750   \n",
       "2017-12-21 21:00:00+00:00  9.233114e+06      6476420.060   \n",
       "2017-12-22 21:00:00+00:00  9.232939e+06      6476595.070   \n",
       "2017-12-26 21:00:00+00:00  9.232768e+06      6312454.560   \n",
       "2017-12-27 21:00:00+00:00  9.232598e+06      6313735.400   \n",
       "2017-12-28 21:00:00+00:00  9.232426e+06      6331670.800   \n",
       "2017-12-29 21:00:00+00:00  9.232257e+06      6263371.530   \n",
       "\n",
       "                                   ...            short_exposure  short_value  \\\n",
       "2000-01-03 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-04 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-05 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-06 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-07 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-10 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-11 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-12 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-13 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-14 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-18 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-19 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-20 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-21 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-24 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-25 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-26 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-27 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-28 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-01-31 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-02-01 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-02-02 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-02-03 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-02-04 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-02-07 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-02-08 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-02-09 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-02-10 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-02-11 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2000-02-14 21:00:00+00:00          ...                       0.0          0.0   \n",
       "...                                ...                       ...          ...   \n",
       "2017-11-16 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-11-17 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-11-20 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-11-21 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-11-22 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-11-24 18:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-11-27 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-11-28 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-11-29 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-11-30 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-01 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-04 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-05 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-06 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-07 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-08 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-11 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-12 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-13 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-14 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-15 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-18 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-19 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-20 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-21 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-22 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-26 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-27 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-28 21:00:00+00:00          ...                       0.0          0.0   \n",
       "2017-12-29 21:00:00+00:00          ...                       0.0          0.0   \n",
       "\n",
       "                           shorts_count     sortino  starting_cash  \\\n",
       "2000-01-03 21:00:00+00:00             0         NaN   1.000000e+07   \n",
       "2000-01-04 21:00:00+00:00             0  -11.224972   1.000000e+07   \n",
       "2000-01-05 21:00:00+00:00             0  244.652536   9.999897e+06   \n",
       "2000-01-06 21:00:00+00:00             0   -7.323849   9.999793e+06   \n",
       "2000-01-07 21:00:00+00:00             0   -1.260461   9.999698e+06   \n",
       "2000-01-10 21:00:00+00:00             0   -3.429697   9.999599e+06   \n",
       "2000-01-11 21:00:00+00:00             0   -6.688599   9.999501e+06   \n",
       "2000-01-12 21:00:00+00:00             0   -8.377574   9.999408e+06   \n",
       "2000-01-13 21:00:00+00:00             0   -0.210994   9.999321e+06   \n",
       "2000-01-14 21:00:00+00:00             0    3.013700   9.999224e+06   \n",
       "2000-01-18 21:00:00+00:00             0    6.143450   9.999124e+06   \n",
       "2000-01-19 21:00:00+00:00             0    8.484899   9.999020e+06   \n",
       "2000-01-20 21:00:00+00:00             0   15.448514   9.998913e+06   \n",
       "2000-01-21 21:00:00+00:00             0   10.814095   9.998800e+06   \n",
       "2000-01-24 21:00:00+00:00             0    3.361904   9.998688e+06   \n",
       "2000-01-25 21:00:00+00:00             0    7.195911   9.998582e+06   \n",
       "2000-01-26 21:00:00+00:00             0    5.231489   9.998470e+06   \n",
       "2000-01-27 21:00:00+00:00             0    4.952490   9.998359e+06   \n",
       "2000-01-28 21:00:00+00:00             0   -0.502915   9.998249e+06   \n",
       "2000-01-31 21:00:00+00:00             0    0.315943   9.998148e+06   \n",
       "2000-02-01 21:00:00+00:00             0   -0.985135   9.998044e+06   \n",
       "2000-02-02 21:00:00+00:00             0   -1.482538   9.997943e+06   \n",
       "2000-02-03 21:00:00+00:00             0    0.253437   9.997845e+06   \n",
       "2000-02-04 21:00:00+00:00             0    2.068816   9.997741e+06   \n",
       "2000-02-07 21:00:00+00:00             0    4.437081   9.997633e+06   \n",
       "2000-02-08 21:00:00+00:00             0    4.679673   9.997519e+06   \n",
       "2000-02-09 21:00:00+00:00             0    3.494209   9.997404e+06   \n",
       "2000-02-10 21:00:00+00:00             0    3.787878   9.997291e+06   \n",
       "2000-02-11 21:00:00+00:00             0    1.456556   9.997178e+06   \n",
       "2000-02-14 21:00:00+00:00             0    3.912563   9.997069e+06   \n",
       "...                                 ...         ...            ...   \n",
       "2017-11-16 21:00:00+00:00             0    0.918705   9.237420e+06   \n",
       "2017-11-17 21:00:00+00:00             0    0.913898   9.237249e+06   \n",
       "2017-11-20 21:00:00+00:00             0    0.912978   9.237079e+06   \n",
       "2017-11-21 21:00:00+00:00             0    0.928001   9.236909e+06   \n",
       "2017-11-22 21:00:00+00:00             0    0.936543   9.236736e+06   \n",
       "2017-11-24 18:00:00+00:00             0    0.936486   9.236561e+06   \n",
       "2017-11-27 21:00:00+00:00             0    0.932077   9.236386e+06   \n",
       "2017-11-28 21:00:00+00:00             0    0.926947   9.236211e+06   \n",
       "2017-11-29 21:00:00+00:00             0    0.907447   9.236038e+06   \n",
       "2017-11-30 21:00:00+00:00             0    0.918664   9.235869e+06   \n",
       "2017-12-01 21:00:00+00:00             0    0.914645   9.235697e+06   \n",
       "2017-12-04 21:00:00+00:00             0    0.908311   9.235526e+06   \n",
       "2017-12-05 21:00:00+00:00             0    0.907443   9.235356e+06   \n",
       "2017-12-06 21:00:00+00:00             0    0.904264   9.235186e+06   \n",
       "2017-12-07 21:00:00+00:00             0    0.906275   9.235017e+06   \n",
       "2017-12-08 21:00:00+00:00             0    0.905782   9.234847e+06   \n",
       "2017-12-11 21:00:00+00:00             0    0.921427   9.234678e+06   \n",
       "2017-12-12 21:00:00+00:00             0    0.916561   9.234505e+06   \n",
       "2017-12-13 21:00:00+00:00             0    0.919163   9.234333e+06   \n",
       "2017-12-14 21:00:00+00:00             0    0.918824   9.234161e+06   \n",
       "2017-12-15 21:00:00+00:00             0    0.926538   9.233989e+06   \n",
       "2017-12-18 21:00:00+00:00             0    0.938465   9.233815e+06   \n",
       "2017-12-19 21:00:00+00:00             0    0.928899   9.233638e+06   \n",
       "2017-12-20 21:00:00+00:00             0    0.927895   9.233464e+06   \n",
       "2017-12-21 21:00:00+00:00             0    0.930900   9.233289e+06   \n",
       "2017-12-22 21:00:00+00:00             0    0.930797   9.233114e+06   \n",
       "2017-12-26 21:00:00+00:00             0    0.906291   9.232939e+06   \n",
       "2017-12-27 21:00:00+00:00             0    0.906332   9.232768e+06   \n",
       "2017-12-28 21:00:00+00:00             0    0.908503   9.232598e+06   \n",
       "2017-12-29 21:00:00+00:00             0    0.899046   9.232426e+06   \n",
       "\n",
       "                           starting_exposure  starting_value  trading_days  \\\n",
       "2000-01-03 21:00:00+00:00              0.000           0.000             1   \n",
       "2000-01-04 21:00:00+00:00              0.000           0.000             2   \n",
       "2000-01-05 21:00:00+00:00            102.500         102.500             3   \n",
       "2000-01-06 21:00:00+00:00            208.000         208.000             4   \n",
       "2000-01-07 21:00:00+00:00            285.000         285.000             5   \n",
       "2000-01-10 21:00:00+00:00            398.000         398.000             6   \n",
       "2000-01-11 21:00:00+00:00            488.750         488.750             7   \n",
       "2000-01-12 21:00:00+00:00            556.500         556.500             8   \n",
       "2000-01-13 21:00:00+00:00            610.330         610.330             9   \n",
       "2000-01-14 21:00:00+00:00            774.000         774.000            10   \n",
       "2000-01-18 21:00:00+00:00            903.960         903.960            11   \n",
       "2000-01-19 21:00:00+00:00           1039.400        1039.400            12   \n",
       "2000-01-20 21:00:00+00:00           1172.160        1172.160            13   \n",
       "2000-01-21 21:00:00+00:00           1362.000        1362.000            14   \n",
       "2000-01-24 21:00:00+00:00           1447.030        1447.030            15   \n",
       "2000-01-25 21:00:00+00:00           1487.500        1487.500            16   \n",
       "2000-01-26 21:00:00+00:00           1683.750        1683.750            17   \n",
       "2000-01-27 21:00:00+00:00           1763.040        1763.040            18   \n",
       "2000-01-28 21:00:00+00:00           1870.000        1870.000            19   \n",
       "2000-01-31 21:00:00+00:00           1829.160        1829.160            20   \n",
       "2000-02-01 21:00:00+00:00           1971.250        1971.250            21   \n",
       "2000-02-02 21:00:00+00:00           2005.000        2005.000            22   \n",
       "2000-02-03 21:00:00+00:00           2075.010        2075.010            23   \n",
       "2000-02-04 21:00:00+00:00           2272.820        2272.820            24   \n",
       "2000-02-07 21:00:00+00:00           2484.000        2484.000            25   \n",
       "2000-02-08 21:00:00+00:00           2737.440        2737.440            26   \n",
       "2000-02-09 21:00:00+00:00           2871.750        2871.750            27   \n",
       "2000-02-10 21:00:00+00:00           2928.120        2928.120            28   \n",
       "2000-02-11 21:00:00+00:00           3064.500        3064.500            29   \n",
       "2000-02-14 21:00:00+00:00           3045.000        3045.000            30   \n",
       "...                                      ...             ...           ...   \n",
       "2017-11-16 21:00:00+00:00        6252747.480     6252747.480          4499   \n",
       "2017-11-17 21:00:00+00:00        6327620.200     6327620.200          4500   \n",
       "2017-11-20 21:00:00+00:00        6292657.450     6292657.450          4501   \n",
       "2017-11-21 21:00:00+00:00        6286540.320     6286540.320          4502   \n",
       "2017-11-22 21:00:00+00:00        6403582.900     6403582.900          4503   \n",
       "2017-11-24 18:00:00+00:00        6471070.560     6471070.560          4504   \n",
       "2017-11-27 21:00:00+00:00        6471615.390     6471615.390          4505   \n",
       "2017-11-28 21:00:00+00:00        6439240.920     6439240.920          4506   \n",
       "2017-11-29 21:00:00+00:00        6401686.230     6401686.230          4507   \n",
       "2017-11-30 21:00:00+00:00        6269065.200     6269065.200          4508   \n",
       "2017-12-01 21:00:00+00:00        6356903.350     6356903.350          4509   \n",
       "2017-12-04 21:00:00+00:00        6327481.600     6327481.600          4510   \n",
       "2017-12-05 21:00:00+00:00        6281411.400     6281411.400          4511   \n",
       "2017-12-06 21:00:00+00:00        6275662.160     6275662.160          4512   \n",
       "2017-12-07 21:00:00+00:00        6252524.950     6252524.950          4513   \n",
       "2017-12-08 21:00:00+00:00        6269046.192     6269046.192          4514   \n",
       "2017-12-11 21:00:00+00:00        6266181.890     6266181.890          4515   \n",
       "2017-12-12 21:00:00+00:00        6388444.660     6388444.660          4516   \n",
       "2017-12-13 21:00:00+00:00        6352728.300     6352728.300          4517   \n",
       "2017-12-14 21:00:00+00:00        6373990.000     6373990.000          4518   \n",
       "2017-12-15 21:00:00+00:00        6372312.220     6372312.220          4519   \n",
       "2017-12-18 21:00:00+00:00        6433537.740     6433537.740          4520   \n",
       "2017-12-19 21:00:00+00:00        6528069.260     6528069.260          4521   \n",
       "2017-12-20 21:00:00+00:00        6458678.160     6458678.160          4522   \n",
       "2017-12-21 21:00:00+00:00        6451821.750     6451821.750          4523   \n",
       "2017-12-22 21:00:00+00:00        6476420.060     6476420.060          4524   \n",
       "2017-12-26 21:00:00+00:00        6476595.070     6476595.070          4525   \n",
       "2017-12-27 21:00:00+00:00        6312454.560     6312454.560          4526   \n",
       "2017-12-28 21:00:00+00:00        6313735.400     6313735.400          4527   \n",
       "2017-12-29 21:00:00+00:00        6331670.800     6331670.800          4528   \n",
       "\n",
       "                                                                transactions  \\\n",
       "2000-01-03 21:00:00+00:00                                                 []   \n",
       "2000-01-04 21:00:00+00:00  [{'price': 102.55125, 'dt': 2000-01-04 21:00:0...   \n",
       "2000-01-05 21:00:00+00:00  [{'price': 104.052, 'dt': 2000-01-05 21:00:00+...   \n",
       "2000-01-06 21:00:00+00:00  [{'price': 95.0475, 'dt': 2000-01-06 21:00:00+...   \n",
       "2000-01-07 21:00:00+00:00  [{'price': 99.54975, 'dt': 2000-01-07 21:00:00...   \n",
       "2000-01-10 21:00:00+00:00  [{'price': 97.798875, 'dt': 2000-01-10 21:00:0...   \n",
       "2000-01-11 21:00:00+00:00  [{'price': 92.796375, 'dt': 2000-01-11 21:00:0...   \n",
       "2000-01-12 21:00:00+00:00  [{'price': 87.233595, 'dt': 2000-01-12 21:00:0...   \n",
       "2000-01-13 21:00:00+00:00  [{'price': 96.798375, 'dt': 2000-01-13 21:00:0...   \n",
       "2000-01-14 21:00:00+00:00  [{'price': 100.49022, 'dt': 2000-01-14 21:00:0...   \n",
       "2000-01-18 21:00:00+00:00  [{'price': 103.99197, 'dt': 2000-01-18 21:00:0...   \n",
       "2000-01-19 21:00:00+00:00  [{'price': 106.61328, 'dt': 2000-01-19 21:00:0...   \n",
       "2000-01-20 21:00:00+00:00  [{'price': 113.55675, 'dt': 2000-01-20 21:00:0...   \n",
       "2000-01-21 21:00:00+00:00  [{'price': 111.365655, 'dt': 2000-01-21 21:00:...   \n",
       "2000-01-24 21:00:00+00:00  [{'price': 106.303125, 'dt': 2000-01-24 21:00:...   \n",
       "2000-01-25 21:00:00+00:00  [{'price': 112.306125, 'dt': 2000-01-25 21:00:...   \n",
       "2000-01-26 21:00:00+00:00  [{'price': 110.24509499999999, 'dt': 2000-01-2...   \n",
       "2000-01-27 21:00:00+00:00  [{'price': 110.055, 'dt': 2000-01-27 21:00:00+...   \n",
       "2000-01-28 21:00:00+00:00  [{'price': 101.67081, 'dt': 2000-01-28 21:00:0...   \n",
       "2000-01-31 21:00:00+00:00  [{'price': 103.801875, 'dt': 2000-01-31 21:00:...   \n",
       "2000-02-01 21:00:00+00:00  [{'price': 100.300125, 'dt': 2000-02-01 21:00:...   \n",
       "2000-02-02 21:00:00+00:00  [{'price': 98.859405, 'dt': 2000-02-02 21:00:0...   \n",
       "2000-02-03 21:00:00+00:00  [{'price': 103.361655, 'dt': 2000-02-03 21:00:...   \n",
       "2000-02-04 21:00:00+00:00  [{'price': 108.054, 'dt': 2000-02-04 21:00:00+...   \n",
       "2000-02-07 21:00:00+00:00  [{'price': 114.11703, 'dt': 2000-02-07 21:00:0...   \n",
       "2000-02-08 21:00:00+00:00  [{'price': 114.927435, 'dt': 2000-02-08 21:00:...   \n",
       "2000-02-09 21:00:00+00:00  [{'price': 112.67631, 'dt': 2000-02-09 21:00:0...   \n",
       "2000-02-10 21:00:00+00:00  [{'price': 113.55675, 'dt': 2000-02-10 21:00:0...   \n",
       "2000-02-11 21:00:00+00:00  [{'price': 108.804375, 'dt': 2000-02-11 21:00:...   \n",
       "2000-02-14 21:00:00+00:00  [{'price': 115.86790500000001, 'dt': 2000-02-1...   \n",
       "...                                                                      ...   \n",
       "2017-11-16 21:00:00+00:00  [{'price': 171.18555, 'dt': 2017-11-16 21:00:0...   \n",
       "2017-11-17 21:00:00+00:00  [{'price': 170.235075, 'dt': 2017-11-17 21:00:...   \n",
       "2017-11-20 21:00:00+00:00  [{'price': 170.06499, 'dt': 2017-11-20 21:00:0...   \n",
       "2017-11-21 21:00:00+00:00  [{'price': 173.22657, 'dt': 2017-11-21 21:00:0...   \n",
       "2017-11-22 21:00:00+00:00  [{'price': 175.04748, 'dt': 2017-11-22 21:00:0...   \n",
       "2017-11-24 18:00:00+00:00  [{'price': 175.05748499999999, 'dt': 2017-11-2...   \n",
       "2017-11-27 21:00:00+00:00  [{'price': 174.177045, 'dt': 2017-11-27 21:00:...   \n",
       "2017-11-28 21:00:00+00:00  [{'price': 173.156535, 'dt': 2017-11-28 21:00:...   \n",
       "2017-11-29 21:00:00+00:00  [{'price': 169.56474, 'dt': 2017-11-29 21:00:0...   \n",
       "2017-11-30 21:00:00+00:00  [{'price': 171.935925, 'dt': 2017-11-30 21:00:...   \n",
       "2017-12-01 21:00:00+00:00  [{'price': 171.135525, 'dt': 2017-12-01 21:00:...   \n",
       "2017-12-04 21:00:00+00:00  [{'price': 169.88490000000002, 'dt': 2017-12-0...   \n",
       "2017-12-05 21:00:00+00:00  [{'price': 169.72482000000002, 'dt': 2017-12-0...   \n",
       "2017-12-06 21:00:00+00:00  [{'price': 169.094505, 'dt': 2017-12-06 21:00:...   \n",
       "2017-12-07 21:00:00+00:00  [{'price': 169.536726, 'dt': 2017-12-07 21:00:...   \n",
       "2017-12-08 21:00:00+00:00  [{'price': 169.454685, 'dt': 2017-12-08 21:00:...   \n",
       "2017-12-11 21:00:00+00:00  [{'price': 172.756335, 'dt': 2017-12-11 21:00:...   \n",
       "2017-12-12 21:00:00+00:00  [{'price': 171.78585, 'dt': 2017-12-12 21:00:0...   \n",
       "2017-12-13 21:00:00+00:00  [{'price': 172.35613500000002, 'dt': 2017-12-1...   \n",
       "2017-12-14 21:00:00+00:00  [{'price': 172.30611, 'dt': 2017-12-14 21:00:0...   \n",
       "2017-12-15 21:00:00+00:00  [{'price': 173.95693500000002, 'dt': 2017-12-1...   \n",
       "2017-12-18 21:00:00+00:00  [{'price': 176.50821000000002, 'dt': 2017-12-1...   \n",
       "2017-12-19 21:00:00+00:00  [{'price': 174.62726999999998, 'dt': 2017-12-1...   \n",
       "2017-12-20 21:00:00+00:00  [{'price': 174.437175, 'dt': 2017-12-20 21:00:...   \n",
       "2017-12-21 21:00:00+00:00  [{'price': 175.09750499999998, 'dt': 2017-12-2...   \n",
       "2017-12-22 21:00:00+00:00  [{'price': 175.09750499999998, 'dt': 2017-12-2...   \n",
       "2017-12-26 21:00:00+00:00  [{'price': 170.655285, 'dt': 2017-12-26 21:00:...   \n",
       "2017-12-27 21:00:00+00:00  [{'price': 170.68529999999998, 'dt': 2017-12-2...   \n",
       "2017-12-28 21:00:00+00:00  [{'price': 171.16554000000002, 'dt': 2017-12-2...   \n",
       "2017-12-29 21:00:00+00:00  [{'price': 169.314615, 'dt': 2017-12-29 21:00:...   \n",
       "\n",
       "                          treasury_period_return  \n",
       "2000-01-03 21:00:00+00:00                    0.0  \n",
       "2000-01-04 21:00:00+00:00                    0.0  \n",
       "2000-01-05 21:00:00+00:00                    0.0  \n",
       "2000-01-06 21:00:00+00:00                    0.0  \n",
       "2000-01-07 21:00:00+00:00                    0.0  \n",
       "2000-01-10 21:00:00+00:00                    0.0  \n",
       "2000-01-11 21:00:00+00:00                    0.0  \n",
       "2000-01-12 21:00:00+00:00                    0.0  \n",
       "2000-01-13 21:00:00+00:00                    0.0  \n",
       "2000-01-14 21:00:00+00:00                    0.0  \n",
       "2000-01-18 21:00:00+00:00                    0.0  \n",
       "2000-01-19 21:00:00+00:00                    0.0  \n",
       "2000-01-20 21:00:00+00:00                    0.0  \n",
       "2000-01-21 21:00:00+00:00                    0.0  \n",
       "2000-01-24 21:00:00+00:00                    0.0  \n",
       "2000-01-25 21:00:00+00:00                    0.0  \n",
       "2000-01-26 21:00:00+00:00                    0.0  \n",
       "2000-01-27 21:00:00+00:00                    0.0  \n",
       "2000-01-28 21:00:00+00:00                    0.0  \n",
       "2000-01-31 21:00:00+00:00                    0.0  \n",
       "2000-02-01 21:00:00+00:00                    0.0  \n",
       "2000-02-02 21:00:00+00:00                    0.0  \n",
       "2000-02-03 21:00:00+00:00                    0.0  \n",
       "2000-02-04 21:00:00+00:00                    0.0  \n",
       "2000-02-07 21:00:00+00:00                    0.0  \n",
       "2000-02-08 21:00:00+00:00                    0.0  \n",
       "2000-02-09 21:00:00+00:00                    0.0  \n",
       "2000-02-10 21:00:00+00:00                    0.0  \n",
       "2000-02-11 21:00:00+00:00                    0.0  \n",
       "2000-02-14 21:00:00+00:00                    0.0  \n",
       "...                                          ...  \n",
       "2017-11-16 21:00:00+00:00                    0.0  \n",
       "2017-11-17 21:00:00+00:00                    0.0  \n",
       "2017-11-20 21:00:00+00:00                    0.0  \n",
       "2017-11-21 21:00:00+00:00                    0.0  \n",
       "2017-11-22 21:00:00+00:00                    0.0  \n",
       "2017-11-24 18:00:00+00:00                    0.0  \n",
       "2017-11-27 21:00:00+00:00                    0.0  \n",
       "2017-11-28 21:00:00+00:00                    0.0  \n",
       "2017-11-29 21:00:00+00:00                    0.0  \n",
       "2017-11-30 21:00:00+00:00                    0.0  \n",
       "2017-12-01 21:00:00+00:00                    0.0  \n",
       "2017-12-04 21:00:00+00:00                    0.0  \n",
       "2017-12-05 21:00:00+00:00                    0.0  \n",
       "2017-12-06 21:00:00+00:00                    0.0  \n",
       "2017-12-07 21:00:00+00:00                    0.0  \n",
       "2017-12-08 21:00:00+00:00                    0.0  \n",
       "2017-12-11 21:00:00+00:00                    0.0  \n",
       "2017-12-12 21:00:00+00:00                    0.0  \n",
       "2017-12-13 21:00:00+00:00                    0.0  \n",
       "2017-12-14 21:00:00+00:00                    0.0  \n",
       "2017-12-15 21:00:00+00:00                    0.0  \n",
       "2017-12-18 21:00:00+00:00                    0.0  \n",
       "2017-12-19 21:00:00+00:00                    0.0  \n",
       "2017-12-20 21:00:00+00:00                    0.0  \n",
       "2017-12-21 21:00:00+00:00                    0.0  \n",
       "2017-12-22 21:00:00+00:00                    0.0  \n",
       "2017-12-26 21:00:00+00:00                    0.0  \n",
       "2017-12-27 21:00:00+00:00                    0.0  \n",
       "2017-12-28 21:00:00+00:00                    0.0  \n",
       "2017-12-29 21:00:00+00:00                    0.0  \n",
       "\n",
       "[4528 rows x 38 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# perform the backtest inline in jupyter notebook\n",
    "#%zipline --bundle fxcm --trading-calendar forex --start 2008-1-3 --end 2018-1-1 -o backtest.pickle --data-frequency daily\n",
    "%zipline --bundle quantopian-quandl --start 2000-1-1 --end 2018-1-1 -o backtest.pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mZdljwKmTMKx7xzkQ64efdrMOVD5P6DMyMpCRkQEAiI+Px6JFi9wel5eXh7y8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\n1G5VSZrjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131e97fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2001-01-02 21:00:00+00:00    0.000000e+00\n",
      "2001-01-03 21:00:00+00:00   -9.185000e-07\n",
      "2001-01-04 21:00:00+00:00    6.804706e-05\n",
      "2001-01-05 21:00:00+00:00   -1.389092e-04\n",
      "2001-01-08 21:00:00+00:00    5.607603e-05\n",
      "2001-01-09 21:00:00+00:00    2.510444e-04\n",
      "2001-01-10 21:00:00+00:00   -3.158537e-04\n",
      "2001-01-11 21:00:00+00:00    8.630696e-04\n",
      "2001-01-12 21:00:00+00:00   -5.675155e-04\n",
      "2001-01-16 21:00:00+00:00   -5.694379e-05\n",
      "2001-01-17 21:00:00+00:00   -2.798964e-04\n",
      "2001-01-18 21:00:00+00:00    1.879196e-03\n",
      "2001-01-19 21:00:00+00:00    8.883646e-04\n",
      "2001-01-22 21:00:00+00:00   -3.002679e-04\n",
      "2001-01-23 21:00:00+00:00    1.620075e-03\n",
      "2001-01-24 21:00:00+00:00   -1.120552e-06\n",
      "2001-01-25 21:00:00+00:00   -8.377726e-04\n",
      "2001-01-26 21:00:00+00:00   -6.071793e-04\n",
      "2001-01-29 21:00:00+00:00    3.610724e-03\n",
      "2001-01-30 21:00:00+00:00    1.061609e-04\n",
      "2001-01-31 21:00:00+00:00   -2.466408e-04\n",
      "2001-02-01 21:00:00+00:00   -9.951885e-04\n",
      "2001-02-02 21:00:00+00:00   -1.045906e-03\n",
      "2001-02-05 21:00:00+00:00   -9.433887e-04\n",
      "2001-02-06 21:00:00+00:00    2.131456e-03\n",
      "2001-02-07 21:00:00+00:00   -8.845952e-04\n",
      "2001-02-08 21:00:00+00:00   -1.132691e-06\n",
      "2001-02-09 21:00:00+00:00   -4.221139e-03\n",
      "2001-02-12 21:00:00+00:00    1.537907e-03\n",
      "2001-02-13 21:00:00+00:00   -1.594595e-03\n",
      "                                 ...     \n",
      "2017-11-16 21:00:00+00:00    1.383728e-02\n",
      "2017-11-17 21:00:00+00:00   -6.419041e-03\n",
      "2017-11-20 21:00:00+00:00   -1.156143e-03\n",
      "2017-11-21 21:00:00+00:00    2.151585e-02\n",
      "2017-11-22 21:00:00+00:00    1.213140e-02\n",
      "2017-11-24 18:00:00+00:00    6.584097e-05\n",
      "2017-11-27 21:00:00+00:00   -5.795436e-03\n",
      "2017-11-28 21:00:00+00:00   -6.756805e-03\n",
      "2017-11-29 21:00:00+00:00   -2.394379e-02\n",
      "2017-11-30 21:00:00+00:00    1.619514e-02\n",
      "2017-12-01 21:00:00+00:00   -5.379785e-03\n",
      "2017-12-04 21:00:00+00:00   -8.451635e-03\n",
      "2017-12-05 21:00:00+00:00   -1.091081e-03\n",
      "2017-12-06 21:00:00+00:00   -4.300904e-03\n",
      "2017-12-07 21:00:00+00:00    3.030557e-03\n",
      "2017-12-08 21:00:00+00:00   -5.605708e-04\n",
      "2017-12-11 21:00:00+00:00    2.257216e-02\n",
      "2017-12-12 21:00:00+00:00   -6.488617e-03\n",
      "2017-12-13 21:00:00+00:00    3.837892e-03\n",
      "2017-12-14 21:00:00+00:00   -3.354006e-04\n",
      "2017-12-15 21:00:00+00:00    1.107166e-02\n",
      "2017-12-18 21:00:00+00:00    1.692391e-02\n",
      "2017-12-19 21:00:00+00:00   -1.227001e-02\n",
      "2017-12-20 21:00:00+00:00   -1.255514e-03\n",
      "2017-12-21 21:00:00+00:00    4.366798e-03\n",
      "2017-12-22 21:00:00+00:00   -1.832011e-08\n",
      "2017-12-26 21:00:00+00:00   -2.925089e-02\n",
      "2017-12-27 21:00:00+00:00    2.035844e-04\n",
      "2017-12-28 21:00:00+00:00    3.257066e-03\n",
      "2017-12-29 21:00:00+00:00   -1.251300e-02\n",
      "Name: returns, Length: 4276, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# another way of performing a backtest\n",
    "start_date = pd.to_datetime('2001-01-01').tz_localize('US/Eastern')\n",
    "end_date = pd.to_datetime('2018-01-01').tz_localize('US/Eastern')\n",
    "perf = run_algorithm(initialize=initialize, handle_data=handle_data, analyze=analyze, data_frequency='daily', capital_base=10000, bundle='quantopian-quandl', start=start_date, end=end_date)\n",
    "print(perf.returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f40f748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the backtest result can also be loaded from disk and analyzed\n",
    "df = pd.read_pickle(\"backtest.pickle\")\n",
    "df.portfolio_value.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 4. Simple moving average strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def initialize(context):\n",
    "    context.aapl = symbol('AAPL')\n",
    "    set_benchmark(context.aapl)\n",
    "    schedule_function(ma_crossover_handling, date_rules.every_day(), time_rules.market_open(hours=1))\n",
    "    \n",
    "\n",
    "def analyze(context, perf):\n",
    "    f, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,8))\n",
    "    ax1.plot(perf.AAPL, label='price', c='black')\n",
    "    ax1.plot(perf.ma20, label='ma20', c='b')\n",
    "    ax1.plot(perf.ma50, label='ma50', c='r')\n",
    "    ax1.scatter(perf.index[perf.signal == 1], perf.AAPL[perf.signal == 1], label='ma50', c='g', marker='^')\n",
    "    ax1.scatter(perf.index[perf.signal == -1], perf.AAPL[perf.signal == -1], label='ma50', c='r', marker='v')\n",
    "    ax2.legend(loc=0)\n",
    "    ax1.set_ylabel('AAPL chart')\n",
    "    \n",
    "    ax2.plot(perf.portfolio_value)\n",
    "    ax2.set_ylabel('portfolio $ value')\n",
    "    ax2.legend(loc=0)\n",
    "    \n",
    "    plt.show()  \n",
    " \n",
    "  \n",
    "def handle_data(context,data):\n",
    "    record(leverage=context.account.leverage)\n",
    "    record(AAPL=data.current(symbol('AAPL'), 'close'))\n",
    "    \n",
    "    hist = data.history(context.aapl,'price', 50, '1d')\n",
    "    ma50 = hist.mean()\n",
    "    ma20 = hist[-20:].mean()\n",
    "    \n",
    "    record(ma20=ma20)\n",
    "    record(ma50=ma50)\n",
    "        \n",
    "def ma_crossover_handling(context,data):\n",
    "    hist = data.history(context.aapl,'price', 51, '1d')\n",
    "    ma50 = hist[-50:].mean()\n",
    "    ma20 = hist[-20:].mean()\n",
    "    \n",
    "    ma50_prev = hist[-51:-1].mean()\n",
    "    ma20_prev = hist[-21:-1].mean()\n",
    "    \n",
    "    open_orders = get_open_orders()\n",
    "    \n",
    "    if ma20 > ma50 and ma20_prev < ma50_prev:\n",
    "        if context.aapl not in open_orders:\n",
    "            order_target_percent(context.aapl, 1.0)\n",
    "            record(signal=1)\n",
    "    elif ma20 < ma50 and ma20_prev > ma50_prev:\n",
    "        if context.aapl not in open_orders:\n",
    "            order_target_percent(context.aapl, 0.0)\n",
    "            record(signal=-1)\n",
    "    else:\n",
    "        record(signal=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.5/site-packages/empyrical/stats.py:704: RuntimeWarning: invalid value encountered in true_divide\n",
      "  out=out,\n",
      "/anaconda/lib/python3.5/site-packages/empyrical/stats.py:790: RuntimeWarning: invalid value encountered in true_divide\n",
      "  np.divide(average_annual_return, annualized_downside_risk, out=out)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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CBigywABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgRcAJdEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgAG6DBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgR4BJ9AlgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIGKDLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6BFwAl0SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAboMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBHgEn0CWBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgYoMsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoEXACXRIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIABugwQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEeASfQJYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBigywABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgRcAJdEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgAG6DBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgR4BJ9AlgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIGKDLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6BFwAl0SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAboMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBHgEn0CWBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgYoMsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoEXACXRIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIABugwQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEeASfQJYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBigywABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgRcAJdEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgAG6DBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgR4BJ9AlgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIGKDLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6BFwAl0SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAboMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBHgEn0CWBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgYoMsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoEXACXRIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIABugwQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEeASfQJYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBigywABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgRcAJdEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgAG6DBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgR4BJ9AlgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIGKDLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6BFwAl0SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAboMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBHgEn0CWBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgYoMsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoEXACXRIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIABugwQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEeASfQJYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBigywABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgRcAJdEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgAG6DBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgR4BJ9AlgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIGKDLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6BFwAl0SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAboMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBHgEn0CWBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgYoMsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoEXACXRIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIABugwQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEeASfQJYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBigywABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgRcAJdEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgAG6DBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgR4BJ9AlgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIGKDLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6BFwAl0SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAboMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBHgEn0CWBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgYoMsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoEXACXRIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIABugwQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEeASfQJYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBigywABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgRcAJdEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgAG6DBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgR4BJ9AlgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIGKDLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6BFwAl0SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAboMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBHgEn0CWBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgYoMsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoEXACXRIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIABugwQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEeASfQJYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBigywABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOgRcAJdEggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgAG6DBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgR4BJ9AlgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIGKDLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6BFwAl0SCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAboMlFFg1113XWPYsGGbR8TmtVrtfRGR/c+EbK/1ev2i6dOnf3qg+95jjz3e0tXVdWi9Xv9oRKwTEaMj4qWIuC8ifjl37twfXHnllU8t7X477bTTSqNGjTqsVqu1z7vPqIh4LCJ+0dnZefYVV1zxj6Xdw+8JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBhaASfQh9bX3XMQaG9vry9u2cEM0Nvb2/eJiPMiYsUlbOPZiNijo6PjV4u7pr29/e0RcW1ErLuYa16s1+t7T58+/ZocuCxJgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMA8AQN0USidwEID9EfnnRb/WLbRgQ7QJ02atFW9Xr8lItoioqtWq13U1dV19bBhw/63Xq+vXa/XPxURO83Dm9XZ2bnhFVdc8fDCmDvvvPPYUaNG3RER681b//xarfbTiJhVq9W2rdfrX4mIMRHxSkRs1dHRcVfpGmJDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBIRMEBPpFHKHLhAe3v7CfV6/fYRI0bcftlllz2ZPYa9s7Pz7/MG2AN6hHt7e3t2GnyHee/57PTp07+3cAXt7e1nRcQR86757vTp0w9b+JqJEyeeWKvVvjbv50d1dHScseA1EydO/ECtVrs5IoZHxM0dHR0fHvhOXUmAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDMFDNCbqelehRRYxgF69mj2VSNiRkdHx2r9bay9vX1cRDw/73d3dnR0bLrgdQcddNCI55577umIyK67d4MNNtjwhBNO6Fr4XpMmTfpBvV4/OPt5W1vb5lOnTr29kJCKIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFByAQP0kjfY9iKWcYA+MyJGR8QdHR0d71ucY3t7ezYgzwbsf+no6Hj3gtdNnDjxY7Va7brsZ7Va7ehp06ad3t99Jk+evEVXV9fv51136rRp045pZt/q9frLETEqexR9RDzVzHu7FwECBAgQIECAAAECBAgQIECAAAECBAgQIJCcwBrzvsJ2dq1Wy2YhXgQILCBggC4OpRdYxgH6HyNikyWdQN97771Xfu21116YB3h5R0fHxAUxF3x8e1tb25ZTp069tT/sD3/4w8NXX3317D4rRcQtHR0dH2pmU+r1+tyIGNbMe7oXAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkLxAZ61Wy75i1osAgQUEDNDFofQCyzJAnzRp0oH1ev2HGU6tVjt02rRpP1gYauLEiWfUarUj513zb9OmTbthwWva29unR8Tu2c/mzJmz6lVXXdX7uPdFzNvb2++OiPdExNMdHR3Zf/nVtFe9Xn8tIkbU6/Xo6lrkCfJNW8eNyi0wbFjPf4PR2dlZ7o3aXWEEZK4wrahcIbJXuZbnumF5y5W/kovLXCXbnvum5S73FlSmAFmrTKsLs1GZK0wrKleI7FWu5UOy4ba2tmz20T2+qNVqI4dkETclkLCAAXrCzVP6wASWZTCA2+8AACAASURBVIDe3t4+rFarXVCv1/fNHn2e/b8j4mednZ3/qtVqa9dqtX0iYpd5FXyjo6PjqwtX097enp04f39EvNzR0TFmSdW2t7dfExE7ZNfMnDlzhf/+7/+ePZDd7bbbbm9c2nXTpk27fdiwYWtmg8+nn86eOO9FYHAC2R9Ta6zR8991PPXUU/5DjMHxuXoZBGRuGdC8pSkCstcURjcZoIC8DRDKZU0TkLmmUbrRIATkbhBYLl0uAVlbLj5vXgYBmVsGNG9pioDsNYXRTSJi9dVXj3n/McY/a7XaUucM0AhUTcAAvWodr+B+l2WA3svU3t6ePZY9+07yjfuh+7+1Wu2UhU+eL/Dev0bEBhHxZEdHx5pLop84ceLUWq02Kbtm7ty5q1155ZUzBtKq9vb2+tKu+/73vx8TJkxY2mV+T4AAAQIECBAgQIAAAQIECBAgQIAAAQIECFRLwAC9Wv222wEKGKAPEMpl6Qos6wB98uTJ63d1dZ0eEZ+IiP6+A+TVWq12Vb1eP7Kjo+OfCwu1t7c/FBFvi4jHOjo61l6SYHt7+8URkZ1qzx6R/aYrrrji8YGIG6APRMk1BAgQIECAAAECBAgQIECAAAECBAgQIECAQD8CBuhiQaAfAQN0sSi9wLIM0CdOnLh1rVb7eUSMi4h/1Gq1rw4bNuxXY8eOffaZZ555XVtb2861Wu2kiBgfEf/b1dX1scsvvzw7cd73am9vH/IT6B7hXvr4FmKDHg1ViDZUqgiZq1S7C7VZ2StUO0pfjLyVvsWF26DMFa4llShI7irR5kJsUtYK0YZKFSFzlWp3oTYre4VqR9LFeIR70u1TfAsEDNBbgGyJfAUGO0D/xCc+MWrMmDHZ6fG1IuKJ7PHtHR0d2f9ueO2+++7vamtruyMiVoiIP3Z0dGy24AWt+A70gcjW6/XsNPta2XegP/nkkwN5i2sINAhkf5ivuWbPtxA88cQTvgNdPoZcQOaGnNgCixGQPdFopYC8tVLbWpmAzMlBHgJyl4d6NdeUtWr2Pc9dy1ye+tVeW/aq3f9m7v51r3ud70BvJqh7lU7AAL10LbWhhQUGO0CfOHHiJ7NHs2f3qdVqx06bNu2UxalOmjTp/Hq9fsC8/w+p906dOvXu3mvb29unR8Tu2T/PmTNn1auuuur5xd2nvb09e997IuLpjo6ONZrZRQP0ZmpW817+MK9m3/PctczlqV/ttWWv2v1v9e7lrdXi1pM5GchDQO7yUK/mmrJWzb7nuWuZy1O/2mvLXrX738zdG6A3U9O9yihggF7GrtpTg8BgB+jt7e1HR8Sp827yiY6Ojl8ujnTSpEmH1Ov178/7/R4dHR1Te6+dOHHiibVa7WvZP7e1tW05derUW/u7z4c//OHhq6++ejZcHx0Rt3R0dHyomS00QG+mZjXv5Q/zavY9z13LXJ761V5b9qrd/1bvXt5aLW49mZOBPATkLg/1aq4pa9Xse567lrk89au9tuxVu//N3L0BejM13auMAgboZeyqPTUIDHaAPmnSpCPr9foZ2U3q9fpO06dPv2ZxpBMnTvxcrVY7O/t9rVabOG3atMt7r504ceLHarXadfN+d/S0adNO7+8+kydP3qKrq+v38647ddq0acc0s4UG6M3UrOa9/GFezb7nuWuZy1O/2mvLXrX73+rdy1urxa0nczKQh4Dc5aFezTVlrZp9z3PXMpenfrXXlr1q97+ZuzdAb6ame5VRwAC9jF21pwaBZRig716v17PHr2cD9NOnT5+enUjv97XgY9rb2to2nTp16p29F7a3t4+MiKciYlxE3NvR0fGu7JYL32jSpEk/qNfrB2c/b2tr23zq1Km3N7OFBujN1KzmvfxhXs2+57lrmctTv9pry161+9/q3ctbq8WtJ3MykIeA3OWhXs01Za2afc9z1zKXp36115a9ave/mbs3QG+mpnuVUcAAvYxdtacGgcEO0HfZZZdVRowY8c+IWCkiXoqIrTo6Ou5ZmHXixImfqNVq2en0toj45wYbbLD2CSec0LXgdQs+xj0ijuro6Og+2d77am9v3zJ7bHtEDI+Imzs6Oj7c7PYZoDdbtHr384d59Xqe945lLu8OVHd92atu7/PYubzloV7tNWWu2v3Pa/dyl5d89daVter1PO8dy1zeHaju+rJX3d43e+cG6M0Wdb+yCRigl62j9hPt7e0fjIi391LUarXVeh/JHhG/jYj/WpCpo6PjwoXZJk2a9LV6vX7ivJ/PrNVq50TEr+r1+nO1Wu119Xr9kxFx4LzBd/b49n2mTZt2ycL32XnnnceOGjXqjohYb97vflir1X5aq9VmdXZ2blur1bLHtY+JiFkR8YGOjo67mt1CA/Rmi1bvfv4wr17P896xzOXdgequL3vV7X0eO5e3PNSrvabMVbv/ee1e7vKSr966sla9nue9Y5nLuwPVXV/2qtv7Zu/cAL3Zou5XNgED9LJ11H5i4sSJF9ZqtU8NlKKjo6O//zuoTZo0aUq9Xj88+3rzJdxrTjYEnzZt2pmLu6a9vT0b5l8bEesu5poX6/X63kv6rvWB7qW/6wzQl0fPezMBf5jLQasFZK7V4tbrFZA9WWilgLy1Utta/qaTgbwE/LsuL/nqrStr1et53juWubw7UN31Za+6vW/2zg3Qmy3qfmUTMEAvW0ftp1kD9G7J9vb2Tev1+gG1Wi071f7meY91nxkRD2aPXG9raztv6tSpDyyNfZ999hk9a9asz9ZqtfZ5p+Oz70d/rF6vX9vV1fWdK6644h9Lu8ey/t4AfVnlvM9ASQbyEvBhMC9568qeDLRSQN5aqW0tA3QZyEvAv+vykq/eurJWvZ7nvWOZy7sD1V1f9qrb+2bv3AC92aLuVzYBA/SyddR+CCwkYIAuEssr4A/z5RX0/sEKyNxgxVzfLAHZa5ak+wxEQN4GouSaZgrIXDM13WugAnI3UCnXLa+ArC2voPcPVkDmBivm+mYJyF6zJN3HAF0GCCxZwABdQgiUXMAAveQNbsH2/GHeAmRLNAjInEDkJSB7eclXc115q2bf89y1zOWpX9215a66vW/1zmWt1eLWkzkZyEtA9vKSL9+6Bujl66kdNVfAAL25nu5GoHACBuiFa0lyBfnDPLmWJV+wzCXfwmQ3IHvJti7JwuUtybYlXbTMJd2+ZIuXu2Rbl1zhspZcy5IvWOaSb2GyG5C9ZFtXuMIN0AvXEgUVTMAAvWANUQ6BZgsYoDdbtHr384d59Xqe945lLu8OVHd92atu7/PYubzloV7tNWWu2v3Pa/dyl5d89daVter1PO8dy1zeHaju+rJX3d43e+cG6M0Wdb+yCRigl62j9kNgIQEDdJFYXgF/mC+voPcPVkDmBivm+mYJyF6zJN1nIALyNhAl1zRTQOaaqeleAxWQu4FKuW55BWRteQW9f7ACMjdYMdc3S0D2miXpPgboMkBgyQIG6BJCoOQCBuglb3ALtucP8xYgW6JBQOYEIi8B2ctLvprryls1+57nrmUuT/3qri131e19q3cua60Wt57MyUBeArKXl3z51jVAL19P7ai5AgbozfV0NwKFEzBAL1xLkivIH+bJtSz5gmUu+RYmuwHZS7Z1SRYub0m2LemiZS7p9iVbvNwl27rkCpe15FqWfMEyl3wLk92A7CXbusIVboBeuJYoqGACBugFa4hyCDRbwAC92aLVu58/zKvX87x3LHN5d6C668tedXufx87lLQ/1aq8pc9Xuf167l7u85Ku3rqxVr+d571jm8u5AddeXver2vtk7N0Bvtqj7lU3AAL1sHbUfAgsJGKCLxPIK+MN8eQW9f7ACMjdYMdc3S0D2miXpPgMRkLeBKLmmmQIy10xN9xqogNwNVMp1yysga8sr6P2DFZC5wYq5vlkCVcxeV1dXXH311XHPPffEDjvsEJtuummzOCt9HwP0Srff5gcgYIA+ACSXEEhZwAA95e4Vo/Yq/mFeDPnqViFz1e193juXvbw7UK315a1a/S7CbmWuCF2oXg1yV72e57VjWctLvrrrylx1e5/3zquYvZ/85Cfx5S9/uZt+2LBhceONN8bb3/72vFuR/PoG6Mm30AaGWMAAfYiB3Z5A3gIG6Hl3IP31q/iHefpdS3sHMpd2/1KuXvZS7l56tctbej1LvWKZS72DadYvd2n2LcWqZS3FrqVds8yl3b+Uq69i9tZee+3o7Ozsa1s2TP/CF76QchsLUbsBeiHaoIgCCxigF7g5SiPQDAED9GYoVvseVfzDvNodz3/3Mpd/D6pagexVtfP57Fve8nGv8qoyV+Xu57d3ucvPvmory1rVOp7/fmUu/x5UtYKBZm/OnDnx4IMPxr333tv9PzNmzIgpU6Ykx/bCCy/EBhts0FD3OuusE7fccktyeylawQboReuIeoomYIBetI6oh0CTBQzQmwxawdsN9A/zCtLY8hAJyNwQwbrtUgVkb6lELmiigLw1EdOtBiQgcwNiclGTBeSuyaBut1gBWROOVgvIXKvFrdcrMNDs3X333fHv//7vfXC1Wi0eeOCBWGmllZLCvP7662O//fZrqPmDH/xgTJ06Nal9FLFYA/QidkVNRRIwQC9SN9RCYAgEDNCHALVitxzoH+YVY7HdIRSQuSHEdeslCsiegLRSQN5aqW2tTEDm5CAPAbnLQ72aa8paNfue565lLk/9aq890OzNmjUr1ltvvejq6uoDu+aaa2LjjTdOCvC4446LH/3oRw01r7/++nHDDTcktY8iFmuAXsSuqKlIAgboReqGWggMgYAB+hCgVuyWA/3DvGIstjuEAjI3hLhuvUQB2ROQVgrIWyu1rZUJyJwc5CEgd3moV3NNWatm3/PctczlqV/ttQeTvW222SYeeuihPrAzzjgj9tprr6QAP/rRj3Y/gn7B18orr7zIz5LaVEGKNUAvSCOUUVgBA/TCtkZhBJojYIDeHMcq32Uwf5hX2cnemycgc82zdKfBCcje4LxcvXwC8rZ8ft49eAGZG7yZdyy/gNwtv6E7DExA1gbm5KrmCchc8yzdaXACg8newQcfHNmp895X9ij0k08+eXAL5nh19r3t73nPe/qt4L777ouxY8fmWF36Sxugp99DOxhaAQP0ofV1dwK5Cxig596C5AsYzB/myW/WBgohIHOFaEMli5C9SrY9t03LW270lV1Y5irb+lw3Lne58ldqcVmrVLsLsVmZK0QbKlnEYLL3ne98J775zW/2OW2xxRZx+eWXL5NbZ2dnvPbaazF8+PAYMWLEMt1jsG/Khv/ZfwTQ3yvbR/aY+mHDhsVGG20U48aNG+ztK3+9AXrlIwBgKQIG6CJCoOQCBuglb3ALtjeYP8xbUI4lKiAgcxVockG3KHsFbUxJy5K3kja2wNuSuQI3p8SlyV2Jm1uwrclawRpSgXJkrgJNLugWB5K9b3/7293fdZ4NmPfff/++nWRD5r/+9a9Rqw18LHTppZdG9uj3p59+uvs+I0eOjMmTJ8c3vvGN7uH1UL6+8pWvxMUXX7zUJSZMmBBXXnllrLPOOku91gXzBQzQpYHAkgUG/m9KkgQIJClggJ5k2wpV9ED+MC9UwYpJXkDmkm9hshuQvWRbl2Th8pZk25IuWuaSbl+yxctdsq1LrnBZS65lyRcsc8m3MNkNLC17jz76aGy55ZaL3d8f/vCHWGuttQa0/2eeeSY22WSTyE6fL/w6/PDD46ijjhrQfZb1ooW/w31J90nt8fTLatLM9xmgN1PTvcooYIBexq7aE4EFBAzQxWF5BZb2h/ny3t/7CSwsIHMykZeA7OUlX8115a2afc9z1zKXp35115a76va+1TuXtVaLW0/mZCAvgaVl76yzzoopU6YstrwLLrggtt9++wGVf9NNN8Xee++92Gsvuuii+OhHPzqgew32oieeeCI23XTTAb8tO3G/4Pe9D/iNFb7QAL3Czbf1AQkYoA+IyUUE0hUwQE+3d0WpfGl/mBelTnWUR0DmytPL1HYie6l1LO165S3t/qVYvcyl2LX0a5a79HuYyg5kLZVOladOmStPL1PbyZKy19XVFdn3nP/zn/9c7Lbe+MY3dj/u/A1veMNSt37JJZfEf/7nfy72uuyR8L/85S9j7bXXXuq9BnvBFVdcEZ/73OcG/LZRo0bF/fff37LvZx9wYQW+0AC9wM1RWiEEDNAL0QZFEBg6AQP0obOtyp19KKxKp4uzT5krTi+qVonsVa3j+e5X3vL1r+LqMlfFrue/Z7nLvwdVqUDWqtLp4uxT5orTi6pVsqTs3XLLLbHnnns2kOy4446LnMxed911IxtQjx8/fol8p59+epx99tlLvGbDDTeMq6++OlZYYYWmtuJLX/pS/PSnPx3UPX/1q1/FBhtsMKj3VPliA/Qqd9/eByJggD4QJdcQSFjAAD3h5hWkdB8KC9KICpUhcxVqdsG2KnsFa0jJy5G3kje4gNuTuQI2pQIlyV0FmlyQLcpaQRpRoTJkrkLNLthWl5S9ww47rPt0ee8rGyZfddVVsfvuu8c999zTsJONNtoopk6dGmPHjl3sDrMT4Nmgvfe1//77x1NPPRU///nPG96TDe3PPPPMpkpl3+OefZ977yt7lHw2UO/v+9h7r8keXT958uSm1lHmmxmgl7m79tYMAQP0Zii6B4ECCxigF7g5iZTmQ2EijSpRmTJXomYmthXZS6xhiZcrb4k3MMHyZS7BppWgZLkrQRMT2YKsJdKoEpUpcyVqZmJbWVz2Xnjhhdhkk03i1Vdf7dvRiSeeGNnQe8aMGbHbbrvFgw8+2LDbD33oQ3HppZdGrdb/mCgbvN9666197zn++ONjr732ih122GGRe2XfP559D3kzXtngPBugL/i64YYb4qGHHorPf/7zMXv27H6X+Y//+I846aSTmlFCJe5hgF6JNtvkcggYoC8HnrcSSEHAAD2FLhW7Rh8Ki92fMlYnc2Xsahp7kr00+lSWKuWtLJ1MZx8yl06vylSp3JWpm8Xei6wVuz9lrE7mytjVNPa0uOxdfPHF8ZWvfKVvEyNGjIg777yz7zHt2fei77rrrot8P3p2Qv1973tfv5t///vfH48//njf7374wx92D88feOCB7v/9yiuv9P3uiCOOiOyx6814ZSfNF7xX9qj5u+++O7K9/+Uvf4nzzjsvRo8eHdl3vmf/AUDvK9tHth+vgQkYoA/MyVXVFTBAr27v7bwiAgboFWn0EG7Th8IhxHXrfgVkTjDyEpC9vOSrua68VbPvee5a5vLUr+7aclfd3rd657LWanHryZwM5CWwuOxlA+277rqrr6zsn7OB94Kvhx9+OHbeeed47rnn+n78jW98Iz796U8vsp25c+fG2972toZHpl977bWRPfo9e51wwgkN9992223jkksuaQrL0UcfHT/+8Y+XuJfslzfeeGPss88+fdetuOKKcf/998ewYcOaUkfZb2KAXvYO29/yChigL6+g9xMouIABesEblEB5PhQm0KSSlShzJWtoQtuRvYSaVYJS5a0ETUxsCzKXWMNKUq7claSRCWxD1hJoUslKlLmSNTSh7fSXvfvuuy+22267hl1kJ9IX/ll2QfaY8+uuu67v2uykd3Z6fOFXdmJ98803b/jxn//855gwYUL3z7LvQT/kkEP6fr/KKqt0nw5f3OPgB0OcPSb+5ptv7nvLUUcdFYcffvgit3j66afjve99b8PPb7rpplh33XUHs1xlrzVAr2zrbXyAAgboA4RyGYFUBQzQU+1ccer2obA4vahKJTJXlU4Xb5+yV7yelLkieStzd4u5N5krZl/KXpXclb3DxdmfrBWnF1WpROaq0uni7bO/7C18GnzNNdeM2267LYYPH77IBr785S/HT37yk76ff+pTn4pTTjllkev+8Ic/dD/yvfe1wgordH/vee+AvL8B+29+85t461vfutxoW221VTzyyCN99/nud78bu+yyS7/33XTTTeOJJ57o+90555zT/X3vXksXMEBfupErqi1ggF7t/tt9BQQM0CvQ5CHeog+FQwzs9osIyJxQ5CUge3nJV3Ndeatm3/PctczlqV/dteWuur1v9c5lrdXi1pM5GchLYOHszZ49O7Ih8owZM/pKOuywwxq+D33BWk877bTIhsy9rx133LH7O8UXfl155ZWR3af3tc4668Qtt9zS98/1ej023njjyE6B976aMbzu7OzsfnR89gj53ld22n2TTTbplzx7/PyvfvWrvt8ddNBBcfzxx+fVnqTWNUBPql2KzUHAAD0HdEsSaKWAAXortcu5lg+F5exrkXclc0XuTrlrk71y97dou5O3onWk/PXIXPl7XMQdyl0Ru1LOmmStnH0t8q5krsjdKXdtC2cv+17y/fffv2HT2aA7G3j39zr//PPj61//et+vttxyy5g+ffoil5577rlx6qmn9v18m222icsuu6zhuv322y+uv/76vp9lj4c/6aSTlqsBjz/+eLz//e9vuMeCj45f+OZnnXVWTJkyZan7Wa6iSvpmA/SSNta2miZggN40SjciUEwBA/Ri9iWlqnwoTKlb5ahV5srRxxR3IXspdi3dmuUt3d6lWrnMpdq5tOuWu7T7l1L1spZSt8pRq8yVo48p7mLh7O27774NJ7Cz7y3PTo8v7nXFFVfE5z73ub5fv+Md74gbb7xxkcsXfiz8xIkT4zvf+U7DddngOhtg97623HLPGDfuR3HrraPijW+cGxttNCfe+945sckmr8U73zn/RPnCi91zzz3xt7/9rfs72++///6YNGlS3yUrrbRSPPDAA4v9bvVsgJ8N8ntfY8eO7b5X5uS1ZAEDdAkhsGQBA3QJIVByAQP0kje4BdvzobAFyJZoEJA5gchLQPbykq/muvJWzb7nuWuZy1O/umvLXXV73+qdy1qrxa0nczKQl8CC2ctOZmePNs8ee977ygbae+yxx2LLu/nmm2Ovvfbq+/1qq60Wd9999yLXH3HEETF16tS+nx9wwAGRDdUXfF100UVxzDHHzPvRx6OtbXp0dY3ud+2PfOTVOPfc52LcuHrD76+77rruE/TZI+FXWWWV+MxnPtPwnezrr79+3HDDDYvdz7/+9a/YbLPNGn7frO9iz6vHrVrXAL1V0tZJVcAAPdXOqZvAAAUM0AcI5bLFCvhQKBytFpC5Votbr1dA9mShlQLy1kpta2UCMicHeQjIXR7q1VxT1qrZ9zx3LXN56ld77QWzd9xxxzU8Mj07rf2nP/0pxowZs1ikv/zlL7H99tv3/T673z/+8Y9FTmxnj2PPhtu9ryOPPDK++MUvNtz3qquuis9+9uyIODoiPr3UxrzvfbPjJz95NlZaaf4Qvb29PX73u9/1vXfYsGEN/0FAVusFF1yw2Htng/f3vve98cwzz/Rd8/3vfz923nnnpdZT9QsM0KueAPtfmoAB+tKE/J5A4gIG6Ik3sADl+1BYgCZUrASZq1jDC7Rd2StQMypQirxVoMkF26LMFawhFSlH7irS6AJsU9YK0ISKlSBzFWt4gba7YPa23XbbuOmmm/qqmzx5csP3gfdXdn8ntrNHqI8fP77h8t122y1uu+22vp+dfPLJDY9Kz37xX//1pzj++A9HRP+nzvtbf7fdXomzz34+arWIrq6ueNOb3rRE3QMPPLDvO9v//vdhcf75Y+J3vxsZzz3XFtnB++w+c+feFy++mD2G/o6I+GMceuhH4qtfzYb6XksSMECXDwJLFjBAlxACJRcwQC95g1uwPR8KW4BsiQYBmROIvARkLy/5aq4rb9Xse567lrk89au7ttxVt/et3rmstVrcejInA3kJLJi97OT1go9fX9rj27OaZ8+eHW9729says+G8Ouuu27Dz7LvI7/vvvv6fnbuuefGrrvu2vfPs2ZFfPjDY+Pxx8cuQvG2t82NnXaaFf/zP8Pj2mtXXOT3U6Y8F5Mnz4qHH344tt566yVSnnTSSZGdhv/1r0fGpz89IV59dekjrba2WbH++sNj3XXnxIQJXbHzzrNis83m5NWywq5rgF7Y1iisIAJL/7dNQQpVBgECyyZggL5sbt41X8CHQmlotYDMtVrcer0CsicLrRSQt1ZqWysTkDk5yENA7vJQr+aaslbNvue5a5nLU7/aay+Yvbe//e3x0EMP9YH84Ac/iJ122mmpQO985zvjpZde6rvu8ssvjy222KLhfZtuumk88cQTfT+75JJLIjvxnr2efbYWhxwyPn7721GLrLX22vfGZZfNjLe8Za3u391///DYZZfV4sUX2/quXXHFrrj22mfi3nsv7/7O8yW9vv/9K+Keez4a5503Jjo7l32ctfvur8RXv/pirLFG11J9qnKBAXpVOm2fyyqw7P/GWdYVvY8AgZYKGKC3lLuUi/lQWMq2FnpTMlfo9pS6ONkrdXsLtzl5K1xLSl+QzJW+xYXcoNwVsi2lLErWStnWQm9K5grdnlIXt2D23vCGN0T2SPbe14JD7iUhbLXVVvHII4/0XfLDH/4wdthhh4a3rLPOOvHqq6/2/eznP/95bLLJJvG3vw2P/fcfH48+OryfJT4bEefH61+/Wtx8880xenTPo92vuWaFOPjgxkfEv/Odc2LrrY+M88/PvkO9v9daMWHCN2PmzD1i9uz5w/flae7YsV1xxRXPxAYbzF2e25TmvQbopWmljQyRgAH6EMG6LYGiCBigF6UT6dbhQ2G6vUu1cplLtXPp1y176fcwpR3IW0rdKketMleOPqa2C7lLrWPp1itr6fYu1cplLtXOpV/3gtlbeeWVG06SX3XVVfG+971vqZvceeed449//GPfdaeeemrsu+++ff88a9asyE63L/j69a9/HX/72wbxhS+sErNmLTzQnhkRm0fEvX1vWfg7048+elz8+MeN35U+YcL/jRkzpmTn1CPi4YjojIhsZHVojBjxrZgzZ2S/e3n/+2fH4YfPbahS8QAAIABJREFUjJEj6zFrVi3++tfhcdZZN8ecORtFxJsXu/8NN3yt++T7sGFLJarEBQbolWizTS6HgAH6cuB5K4EUBAzQU+hSsWv0obDY/SljdTJXxq6msSfZS6NPZalS3srSyXT2IXPp9KpMlcpdmbpZ7L3IWrH7U8bqZK6MXU1jT73Zq9frMXz48Ojqmv9I8uuvvz7e9a53LXUj++23X2TX9r6OPPLI+OIXv9j3z9mj27NHuC/4OvPMf8SRR67dz73/GRG7RcQfGn630UYbxbXXXtv3s+w703faafW4994Ri6kvO+2eDfVXiYj+91Cr1WPvvV+Jk09+IUYsdJs99tgjsiF/xJoRcVCstNIR8cor4xrW+tnPno5NN/Vd6L0oBuhL/T8VF1RcwAC94gGw/fILGKCXv8dDvUMfCoda2P0XFpA5mchLQPbykq/muvJWzb7nuWuZy1O/umvLXXV73+qdy1qrxa0nczKQl0Bv9l5++eUYM2ZMQxm/+93v4s1vXvwJ7N6Ls4H5ZZdd1vfePffcM4499thYddVVu3923333xXbbbbfAvUfE61//SvzrX42Pbd9kk9fiwQffEy++mJ0gb3yNHDkyHn744ajV5o+gHnxweHz846v1c4J9yZrDh9dj4sRX4jOfmRnrrJOdUl/09Y1vfCO+973v9f1i220/Epdc8uN48slanHLKuO7T6mec8UJebSvkugbohWyLogokYIBeoGYohcBQCBigD4Vqte7pQ2G1+l2E3cpcEbpQzRpkr5p9z2vX8paXfHXXlbnq9j7PnctdnvrVWlvWqtXvIuxW5orQhWrW0Ju9J598MtZcMzttPf/15z//OSZMmLBUmOyR7eeee27Dddmg+zOf+Uwcc8wxcdttt8Vuu2WnynteK6xwcLz66g8art9jj5fjlFNeiG23/UD84x//6HfNm266KdZdd92G311++Yrx+c/3DOoH8tprr5fjC1+YGWut1f/gvPceV199dXf9C76y0/jZd70ffPDB8YUvfMmj2xcCN0AfSAJdU2UBA/Qqd9/eKyFggF6JNg/pJn0oHFJeN+9HQObEIi8B2ctLvprryls1+57nrmUuT/3qri131e19q3cua60Wt57MyUBeAr3Ze/DBBxcZTmc/W3HFFZda2n/913/F8ccf3+91N998czz00EPxH//xH/N+X4sRI+6LOXPW67t+881nxxVXzIjscPn2228ff/nLX/q919FHHx2f+9znFvndTTeNipNPvj/uvTd7/Pw7ImL1Ra55/es7Y8qU52ObbWYvdT/ZBX//+9/jgx/84GKvHejj7Qe0WEkuMkAvSSNtY8gEDNCHjNaNCRRDwAC9GH1IuQofClPuXpq1y1yafStD1bJXhi6mswd5S6dXZalU5srSybT2IXdp9SvlamUt5e6lWbvMpdm3MlTdm70//elPsckmm/RtadiwYd0nwRd8ZPri9vuzn/0sDj300H5/feCBB8b6668fRxxxxLzf7xQRP2u49sILZ8S//VvPYLu9vT2yR8f398oeCf/73/8+xo4du8ivs8fG33LLLfN+/obYbLMfxezZH4nVV++KD3xgdvd3na+8cn3ALcu+C36DDTaIl156qd/3nHzyyZF997vXfAEDdGkgsGQBA3QJIVByAQP0kje4BdvzobAFyJZoEJA5gchLQPbykq/muvJWzb7nuWuZy1O/umvLXXV73+qdy1qrxa0nczKQl0Bv9n7961/HNtts01fGuHHj4m9/+9uAylr4Ee0LvukjH/lI90nuE088cd6PfxMRW/Vd8s53zolf/erpaGvr+dH+++8fv/zlLxe7bvZ961/84hcbfl+v12PDDTeM559/vu/nP/zhD2OHHXYYUP2Lu2jixIndA/v+Xtl/MPDVr351ue5ftjcboJeto/bTbAED9GaLuh+BggkYoBesIQmW40Nhgk1LvGSZS7yBCZcvewk3L8HS5S3BpiVesswl3sBEy5e7RBuXYNmylmDTEi9Z5hJvYMLl92bv2muvbRg4v/71r4877rhjQDt7+OGHY+utt+732je96U2x6667xtlnnx0R74yIexuu+853nouJE2f1/Swbjk+bNm2x644ZM6Z7qD1+/Pi+ax577LHYYostGt5z6623Rrb28ry+/vWvx/nnn9/vLbLvdD/nnHOW5/ale68BeulaakNNFjBAbzKo2xEomoABetE6kl49PhSm17PUK5a51DuYbv2yl27vUqxc3lLsWto1y1za/Uu1erlLtXPp1S1r6fUs9YplLvUOplt/b/amTp0ae+yxR99G1l133bjpppsGtLGXX3451ltv/neaL/ym7BT6jTfeGBEHR8QP+n79utd1xm23PRkjRsx/x3HHHRc/+tGPlrjuZz7zmTj22GP7rsmG/9mj4ntfq6yySvf3qA/k8fNLWig7CZ+diO/vtfnmm8eVV145IJ+qXGSAXpVO2+eyChigL6uc9xFIRMAAPZFGFbhMHwoL3JySliZzJW1sAtuSvQSaVKIS5a1EzUxkKzKXSKNKVqbclayhBd6OrBW4OSUtTeZK2tgEttWbvWxofcABB/RVvPHGG8c111wz4B1kA/RskL7k148j4v/ru2S33V6Jc86Z/9j17BdnnXVWTJkyZYm3WWGFFeK3v/1trLnmmt3XnXbaaQ2nwbPT8D/96U8HXPviLuzs7IyDDjqo4ZHyoyPihxHx/6+xRlz0pz8t9xpluoEBepm6aS9DIWCAPhSq7kmgQAIG6AVqRqKl+FCYaOMSLlvmEm5e4qXLXuINTKx8eUusYSUoV+ZK0MQEtyB3CTYt0ZJlLdHGJVy2zCXcvMRL783et7/97YbvFt9qq62W+Cj1hbedXf/II48sRePvEfGWvmtOPfX52HffVxrek313+QknnNDws+x7z7P65s6d2/fzfffdN0499dTuf957770bTst/9rOfjWOOOaYpncm+X/3FF1/s/j74wyZOjOw/Kdg0Ih6LiLbbbovaG9/YlHXKcBMD9DJ00R6GUsAAfSh13ZtAAQQM0AvQhMRL8KEw8QYmWL7MJdi0kpQseyVpZCLbkLdEGlWiMmWuRM1MaCtyl1CzEi9V1hJvYILly1yCTStJyb3ZO+mkkyJ7fHrva/vtt48LLrhgwLvMvhP8tttuW8L12aA5GzvPf91441PxjnfMH4pnv8keJX/EEUc0XJedJv/5z38el156acPPv/nNb8Zee+0VG220UcyYMaPvd9///vdj5513HnDtA7lw5h13xKhPfjLevMDFr6y7brxwzTVRHzNmILco/TUG6KVvsQ0up4AB+nICejuBogsYoBe9Q8Wvz4fC4veobBXKXNk6ms5+ZC+dXpWhUnkrQxfT2oPMpdWvslQrd2XpZPH3IWvF71HZKpS5snU0nf30Zu+oo46KM844o6/wbCB+zjnnDHgjhxxySPeQe+HX6NGj5z3afXJEzH+s+iqrdMU99zwRbW2N77j66qsj+47zBV/Z96ePHTs2PvjBD8bs2bMbfrfTTjstsm72ePe3vGX+SfcBb2IxFw6///6YMGlSDHvmmYYrXhs7Np6fPj3mbrjh8i5RivcboJeijTYxhAIG6EOI69YEiiBggF6ELqRdgw+FafcvxeplLsWulaNm2StHH1PZhbyl0qny1Clz5ellSjuRu5S6lXatspZ2/1KsXuZS7Fo5au7NXja0zk5u97722Wef7u8WH+grO72efY/6gq/sn7fbbru466674rjj1ow///n9fb/ebrtX4+KLn13k9tOmTWt4lHx2wT333BPjx49f5LvO+6tt3Lhx8de//jVqteaMqkbceWeM/9SnYtizjbU+FBG3fu1rse0hhwyUqPTXGaCXvsU2uJwCzfm30nIW4e0ECAydgAH60NlW5c4+FFal08XZp8wVpxdVq0T2qtbxfPcrb/n6V3F1mati1/Pfs9zl34OqVCBrVel0cfYpc8XpRdUq6c1eNjC/5JJL+rZ/6KGHxle/+tUBc5x99tlx+umnN1x/7bXXdj9evV6P2GKLNeLxx4f3/f64416Igw9+eZH733777bHLLrs0/Pyxxx6LrM7sO9APO+ywfk+6975hsN/dvkgBXV2RDc1XuO667v8Z8VA2Km98/SEidoyIA77yle56vHoEDNAlgcCSBQzQJYRAyQUM0Eve4BZsz4fCFiBbokFA5gQiLwHZy0u+muvKWzX7nueuZS5P/equLXfV7X2rdy5rrRa3nszJQB4CL7/8cvznf/5n93eX/+///m9DCUceeeQiJ8GXVON3v/vdOOWUUxouufPOO7uHqg89NCy22eZ1Db+74YanYv31G7//PLugXq93n1q///77u6+fPHlyTJkype+9nZ2d3UP55557rt9yDj/88MgeRz/oV1dXjPnOd2L0RRfFsKefXuzbfxsRn4iIlyJi3333jVNPPXXQS5X1DQboZe2sfTVLwAC9WZLuQ6CgAgboBW1MQmX5UJhQs0pSqsyVpJEJbkP2EmxawiXLW8LNS7R0mUu0cYmXLXeJNzCh8mUtoWaVpFSZK0kjE9vG+eefH1//+tf7rTr7+YEHHjjgHV166aWLDK4fffTRGDZsWFx44Upx7LGr9N1rjTU64847n4zFPWX9+eefj+x+Y8aMib322itGjBjRUEd/j4vPLnjrW98a2XeoT5gwYcB19144+vzzY9xiLHqveeiNb4yNHn88es/NZ4P+iy++eNBrlfUNBuhl7ax9NUvAAL1Zku5DoKACBugFbUxCZflQmFCzSlKqzJWkkQluQ/YSbFrCJctbws1LtHSZS7RxiZctd4k3MKHyZS2hZpWkVJkrSSMT28Zaa6212IrPOOOM7uH1QF/PPvtsbLbZZjF79uzut2y55ZYxffr0+Ne/2mK33VaLRx+d//j23Xd/Jc4++/mB3nqR6+6444745Cc/2fDziy66KLbZZpsYOXLkoO/b9vTTscbWW0fbS9m58kVfnWuuGa9MmhQXvuEN8fmjj+6+YIUVVoitt946LrzwwkGvV9Y3GKCXtbP21SwBA/RmSboPgYIKGKAXtDEJleVDYULNKkmpMleSRia4DdlLsGkJlyxvCTcv0dJlLtHGJV623CXewITKl7WEmlWSUmWuJI1MbBtLGqB/73vfW2RIvbTtZae/s8etjx8/Pk477bRYffX1Y7fdJsT//E/jCfLvfve52GWXWUu73WJ/nz3m/ZBDDolrrrmm+5rBPm5+4RuPO/LIGH3ZZQ0/nvOOd8SrH/tYvPrxj8ec97wnoq0tZsyY0f2o+8xt1VVXjdrijtAv887SfqMBetr9U/3QCxigD72xFQjkKmCAnit/KRb3obAUbUxqEzKXVLtKVazslaqdhd+MvBW+RaUrUOZK19IkNiR3SbSpFEXKWinamNQmZC6pdpWm2CUN0LNHk2ePKF/W14sv1mLSpAlxzz2NJ8I33PC1+MUvnonh8w+kL9MSXV1d8Yc//KH7Me/vete7lnmYPeLuu2O1HXaIWr3eV8cru+8ez5999jLVVeU3GaBXufv2PhABA/SBKLmGQMICBugJN68gpftQWJBGVKgMmatQswu2VdkrWENKXo68lbzBBdyezBWwKRUoSe4q0OSCbFHWCtKICpUhcxVqdoG2uqQB+uWXXx5bbLHFMlU7a1bEnntOiNtvH9Xw/re9bW5cccUzsfrqXct036a/qasrVttllxj5xz/23bpr9Oh46pZbomvNNZu+XNlvaIBe9g7b3/IKGKAvr6D3Eyi4gAF6wRuUQHk+FCbQpJKVKHMla2hC25G9hJpVglLlrQRNTGwLMpdYw0pSrtyVpJEJbEPWEmhSyUqUuZI1NIHtzJo1K97+9rcvttLrrrsuNtxww2XaybnnjolTT1254b1rrTU3rrzymVhrrYIMzyNixenTY9XDD2+o88VjjomZn/3sMu276m8yQK96Aux/aQIG6EsT8nsCiQsYoCfewAKU70NhAZpQsRJkrmINL9B2Za9AzahAKfJWgSYXbIsyV7CGVKQcuatIowuwTVkrQBMqVoLMVazhBdjuo48+GltuueViK/nNb34Tb33rW5ep0u22Wz3uu2/+956vsUZn98nzt761c5nuNxRvqs2cGWtss00Me/LJvtvPfctb4qkbb4wY1XhyfijWL+M9DdDL2FV7aqaAAXozNd2LQAEFDNAL2JTESvKhMLGGlaBcmStBExPdguwl2rhEy5a3RBuXcNkyl3DzEi5d7hJuXmKly1piDStBuTJXgiYmtoU77rgjPvnJT/Zb9fDhw+OBBx6IUcswSH7ssWGxxRava7jvtGnPxFZbvVYoobGnnBJjv/vdhppmXHhhzP63fytUnSkVY4CeUrfUmoeAAXoe6tYk0EIBA/QWYpd0KR8KS9rYAm9L5grcnJKXJnslb3DBtidvBWtIBcqRuQo0uYBblLsCNqWkJclaSRtb4G3JXIGbU9LS/vu//zsOOOCAfne3+ptXj7t+d9cy7fyCC0bH1742ru+948d3xl13PRnDhi3T7YbkTSPuvDNW2333qL02f6j/6kc+Es9efHFEzYhrWdEN0JdVzvuqIuDfLlXptH1WVsAAvbKtb9rGfShsGqUbDVBA5gYI5bKmC8he00ndcAkC8iYerRaQuVaLWy8TkDs5aJWArLVK2jq9AjInC60WuOiii+KYY47pd9kRG42Ie392b6w4fMVBl7XXXuPj5ptX6Htfe/sr8e1vPz/o+wzFG2qzZsXYs86K0eedF7Wu+d/FXh8+PJ76P/8nOpfwnfBDUU/Z7mmAXraO2k+zBQzQmy3qfgQKJmCAXrCGJFiOD4UJNi3xkmUu8QYmXL7sJdy8BEuXtwSblnjJMpd4AxMtX+4SbVyCZctagk1LvGSZS7yBCZZ/5plnxre+9a3+K98u4vgvHx8HvfugQe1s5sxavPvda8Zrr80fE5133rOx446vDuo+Q3HxyFtvjVW+9KUY/sgji9x+5sEHx4vHHTcUy1bqngbolWq3zS6DgAH6MqB5C4GUBAzQU+pWMWv1obCYfSlzVTJX5u4We2+yV+z+lK06eStbR4u/H5krfo/KWKHclbGrxdyTrBWzL2WuSubK3N1i7u2oo46KSy+9tP/i9opY9d2rxu173T6oU+i/+MUKcdBB4/vuOWJEPe6554kYO7aeK8LI3/8+Juy5Z9TmzFmkjtc23TRmXHZZ1EePzrXGMixugF6GLtrDUAoYoA+lrnsTKICAAXoBmpB4CT4UJt7ABMuXuQSbVpKSZa8kjUxkG/KWSKNKVKbMlaiZCW1F7hJqVuKlylriDUywfJlLsGmJl7zffvvF9ddf3/8uvhhRG1eL47Y4blCn0L/whVWio2OlvntuvfXs+OlPZ+QrNXt2rPHRj8bwhx9uqKM+cmS89PnPx8zPfjZi5Mh8ayzJ6gboJWmkbQyZgAH6kNG6MYFiCBigF6MPKVfhQ2HK3UuzdplLs29lqFr2ytDFdPYgb+n0qiyVylxZOpnWPuQurX6lXK2spdy9NGuXuTT7lnLV/77Dv8fdd93d/xaOj4haxKqjBn4KvbMzYuONXxczZgzru+eJJ74Q++//cq5MY849N1Y+9dSGGrJT58+feWbMXW+9XGsr2+IG6GXrqP00W8AAvdmi7kegYAIG6AVrSILl+FCYYNMSL1nmEm9gwuXLXsLNS7B0eUuwaYmXLHOJNzDR8uUu0cYlWLasJdi0xEuWucQbmGD562+0frz4zIuLVr5hREzs+XEtBn4K/YYbRsWnPjWh4X6/+92T8eY3d+am0/bPf8YaH/pQtM2a1VfDaxtvHM9cfXXEsPmD/twKLNnCBugla6jtNF3AAL3ppG5IoFgCBujF6keK1fhQmGLX0q5Z5tLuX8rVy17K3UuvdnlLr2epVyxzqXcwzfrlLs2+pVi1rKXYtbRrlrm0+5da9U89+1Rs/O6NG8vOJjvviIh/j4iV5/9qoKfQd9ttQtx226i+N66//py44Yanc6VZ9eCDY8VrrumroV6rxTO/+EXM2WijXOsq6+IG6GXtrH01S8AAvVmS7kOgoAIG6AVtTEJl+VCYULNKUqrMlaSRCW5D9hJsWsIly1vCzUu0dJlLtHGJly13iTcwofJlLaFmlaRUmStJIxPZxrGXHRsXHnnh/GrbIuKYiBi+6AYGcgr99ttHxC67rN7w5ilTnovJk+ef/G41zchf/zpW22OPhmVf3nvveOGb32x1KZVZzwC9Mq220WUUMEBfRjhvI5CKgAF6Kp0qbp0+FBa3N2WtTObK2tni70v2it+jMlUob2XqZhp7kbk0+lS2KuWubB0t7n5krbi9KWtlMlfWzhZvX7Pmzop3f/7dMevqBYbba0TEZxZf69JOoR922Cpx5ZUr9d3g9a/vjOzx7SNH5rT/116L1T/2sRjxP//TV0DXKqvEk7/+ddTHj8+pqPIva4Be/h7b4fIJGKAvn593Eyi8gAF64VtU+AJ9KCx8i0pXoMyVrqXJbEj2kmlVKQqVt1K0MalNyFxS7SpNsXJXmlYWfiOyVvgWla5AmStdSwu7ofP+fF6c+LUTI+5YoMQFvve8v8KXdAr92Wdrsemma8Zrr80fDX3tay/EIYe8nJvB6B/8IMaddFLD+s+fdlq8ss8+udVUhYUN0KvQZXtcHgED9OXR814CCQgYoCfQpIKX6ENhwRtUwvJkroRNTWRLspdIo0pSpryVpJEJbUPmEmpWiUqVuxI1s+BbkbWCN6iE5clcCZtawC1lp883+8lm8fz3no94bIECt4uIrZdc8OJOoZ9zzpg47bT5X5o+cmQ9/vjHJ2L8+HouAsMfeCBW23HHaHt5/gD/tfe8J57Jvgt92LBcaqrKogboVem0fS6rgAH6ssp5H4FEBAzQE2lUgcv0obDAzSlpaTJX0sYmsC3ZS6BJJSpR3krUzES2InOJNKpkZcpdyRpa4O3IWoGbU9LSZK6kjS3YtrpPn996YsRpETF7geL2ioj1llxsf6fQr79+VBxwwPjo7Jw/Ftp111fi3HOfb/nOa889F2O/9a0YfdFFUZs7t2H9p3/+85izySYtr6lqCxqgV63j9jtYAQP0wYq5nkBiAgboiTWsgOX6UFjAppS8JJkreYMLvD3ZK3BzSliavJWwqQXfkswVvEElLU/uStrYAm5L1grYlJKXJHMlb3ABttd3+vzJ5yO+vVBBX4yIcUsvcsFT6LfeOjL23ntCvPpq40joqquejve9b87Sb9bEK1b6yU9i5W98I9qeX3Rw//Kee8YLZ57ZxNXcanECBuiyQWDJAgboEkKg5AIG6CVvcAu250NhC5At0SAgcwKRl4Ds5SVfzXXlrZp9z3PXMpenfnXXlrvq9r7VO5e1VotbT+ZkYKgFfnr/T+NLt3wp2h5oi66fdM1fboWI4V8ZHrXakkc7XfWu6Kx3xlnbnBXvem2faG+fEC+91NZQ9iGHzIyvfe3Fod5Kw/1XuvjiWOUrX+l3zTnrrRfPXH551MePb2lNVV3MAL2qnbfvgQoYoA9UynUEEhUwQE+0cQUq24fCAjWjIqXIXEUaXcBtyl4Bm1LikuStxM0t6NZkrqCNKXlZclfyBhdoe7JWoGZUpBSZq0ijc9zmIy8+EtkQ/faO2+PWS2/tq+QNG7wh9v3Ovt3/PPPlmRFL+OrybMi+7cr7xoHtG8eMGY3fJz5p0isxZcrzsZQ5fFMFhj34YKy+/fbR9uqrDfetjxoVMw88MGYedljUx45t6pputngBA3TpILBkAQN0CSFQcgED9JI3uAXb86GwBciWaBCQOYHIS0D28pKv5rryVs2+57lrmctTv7pry111e9/qnctaq8WtJ3My0CqBQw89NH72s5/1LbfffvvFBRdc0P3PTzzxRHR1LXA6vZ+ijjlmXFx00eiG33z847PivPOei+HDW7WLiJgzJ1bbZZcYedddDYvO2nHHePHYY6Nz7bVbWIylMgEDdDkgsGQBA3QJIVByAQP0kje4BdvzobAFyJZoEJA5gchLQPbykq/muvJWzb7nuWuZy1O/umvLXXV73+qdy1qrxa0nczLQKoFtt902Hnjggb7lTj/99DjqqKO6/3kgA/QPfnCN+Pvf50/KP/CB2fHjH8+IFVZo1Q561hkzZUqsfNZZDYvO3G+/ePHkk1tbiNX6BAzQhYHAkgUM0CWEQMkFDNBL3uAWbM+HwhYgW6JBQOYEIi8B2ctLvprryls1+57nrmUuT/3qri131e19q3cua60Wt57MyUArBF599dVYb731ovP/sXce4E1VbRz/3+5BW4otonwyZMgWFJAtIBRlj6aAgIIMBdlDQGSrICJbBIuyV8NG2SCgoIAMFUGQjSK0zEJ3m/s9J5Cb3CZtk+bm3tzc9zzP93w295x3/N6/Nembc05WluBu69ataNmypfHnvBroCQleqFq1iCjUHTsSULlyhhzhCz58T51CROvW4CzyyChVCrd37gQfGChrLOTMTIAa6KQGIpA7AWqgk0KIgIcToAa6hxdYhvToQ6EMkMmFiABpjgShFAHSnlLktemX9KbNuiuZNWlOSfra9U26027t5c6ctCY3cfJHmiMNyEHg9OnTaNasmcjV33//jdKlSxtfy6uBvm1bAHr3LiSsDw424MyZm7Ie3c6lpCAyKgo+ly4JcfDe3ri9ZQsyqlaVAyP5yIEANdBJGkQgdwLUQCeFEAEPJ0ANdA8vsAzp0YdCGSCTCxEB0hwJQikCpD2lyGvTL+lNm3VXMmvSnJL0teubdKfd2sudOWlNbuLkjzTnvAauXbuGo0ePIiMjAy1atEBoaKjzRj3Mgl6vx+DBg4WsihUrhiNHjqBIkce7yvNqoE+cGIqvvy4grK9fPw1r1tyRlVLIF18gZMYMkc/EYcPwaOhQWeMgZ9YEqIFOqiACuROgBjophAh4OAFqoHt4gWVIjz4UygCZXIgIkOZIEEpO4LiyAAAgAElEQVQRIO0pRV6bfklv2qy7klmT5pSkr13fpDvt1l7uzElrchMnf6Q55zTwySef4KuvvgLP80ZD7Jjybdu2IZCO8xaBnTRpEhYuXCi8xnajL1myxO4GesuWETh50k9YP2xYIoYOfeRc8RxY7XPuHCKaN4dXaqqwKr1qVdzetAnw9XXAEk11BQFqoLuCKtn0JALUQPekalIuRMAGAWqgkyycJUAfCp0lSOsdJUCac5QYzZeKAGlPKpJkxx4CpDd7KNEcKQmQ5qSkSbbsJUC6s5cUzXOWAGnNWYK03lECpDlHiZnnnz17Fk2aNLEy8Nlnn6Fr1675N+yBKzt37oyDBw8KmQ0aNAijRo2yq4GeksKhXLkiyMw0t4DWrLmN+vXTXU7K6/ZthMyciaAVK8BlZgr+eC8vJOzahczy5V0eAznImwA10PNmRDO0TYAa6NquP2WvAQLUQNdAkV2cIn0odDFgMm9FgDRHolCKAGlPKfLa9Et602bdlcyaNKckfe36Jt1pt/ZyZ05ak5s4+SPN5V8DrFE+Z84cKwNlypTBDz/8AI6jloUJTrVq1RAfHy+wWrBgAdq0aWNXA/3wYT/odBHCWm9vHmfP3kRw8ONd/y4ZPI/gRYsQMn06vB5Z73RP6tYND6ZOdYlrMuo4AWqgO86MVmiLAP3XSFv1pmw1SIAa6BosusQp04dCiYGSuTwJkObyREQTXESAtOcisGTWJgHSGwlDbgKkObmJkz9GgHRHOpCLAGlNLtLkx0SANJc/LbAj2+vXr4/Lly/bNLBixQo0atQof8Y9bNWdO3dQpUoVUVYHDhwwHndvzx3os2YVwOefm++Vr1IlHdu333YppeBvvkHYuHE2fWSULYvb69eDL1TIpTGQcfsJUAPdflY0U5sEqIGuzbpT1hoiQA10DRXbRanSh0IXgSWzORIgzZE4lCJA2lOKvDb9kt60WXclsybNKUlfu75Jd9qtvdyZk9bkJk7+SHP508Dp06fB7vHOabz66qtYtWpV/ox72KqffvoJHTt2FLIKCAjA+fPn4evra1cDvUuXQti/P0BY37PnI0yalOgySl7//ovCDRvCKzlZ5IP398ejXr3wqH9/8KHmhr7LAiHDdhOgBrrdqGiiRglQA12jhae0tUOAGujaqbWrMqUPha4iS3ZzIkCaI20oRYC0pxR5bfolvWmz7kpmTZpTkr52fZPutFt7uTMnrclNnPyR5vKngWHDhmHNmjW5Lt67dy/KlSuXPwcetCo2NhYTJkwQMmK70bdv327X6S5ZWUCFCkXw6JGXsH7Bgrto1SrVNYR4HoV69EDA7t0i+8nR0Xj4wQfIKlrUNX7JqlMEqIHuFD5arAEC1EDXQJEpRW0ToAa6tusvRfb0oVAKimTDEQKkOUdo0VwpCZD2pKRJtvIiQHrLixA9l5oAaU5qomTPHgKkO3so0RwpCJDWpKBINhwhQJpzhNbjudeuXTMe356ZmSks7t27N9avX4+7d+8Kr3Xu3BnTp0933IGHrRg6dCjWrl0rZBUTE4OZM2fa1UD/808fREUVFhE5fvwmihQxuIRS0OrVKDh8uMh2UpcueDBtmkv8kVFpCFADXRqOZMVzCVAD3XNrS5kRASMBaqCTEJwlQB8KnSVI6x0lQJpzlBjNl4oAaU8qkmTHHgKkN3so0RwpCZDmpKRJtuwlQLqzlxTNc5YAac1ZgrTeUQKkOUeJAWPHjsW3334rLGRHkv/yyy9YsmQJZs2aJbzu7++Po0ePIiIiwnEnHrSiffv2OHLkiJAR4/fee+/Z1UBfsiQIY8YUFNYWL56Jw4fjXULH/4cfUKhnT3BpaYL9rIgIxO/fDz483CU+yag0BKiBLg1HsuK5BKiB7rm1pcyIgJEANdBJCM4SoA+FzhKk9Y4SIM05SozmS0WAtCcVSbJjDwHSmz2UaI6UBEhzUtIkW/YSIN3ZS4rmOUuAtOYsQVrvKAHSnKPEgEaNGhnv8DaNXr16YeLEiYiPj8crr7yC9PR04dnw4cMxZMgQx5140Ap2Vzy7M9402JcMdDqdXQ30fv0KYvPmIGFtdHQyZs++Lzkd35MnEdGhg6h5zpzcWbwYaVFRkvsjg9ISoAa6tDzJmucRoAa659WUMiICIgLUQCdBOEuAPhQ6S5DWO0qANOcoMZovFQHSnlQkyY49BEhv9lCiOVISIM1JSZNs2UuAdGcvKZrnLAHSmrMEab2jBEhzjhHLyspC6dKlRU3yDRs2GBvnbAwePBh6vV4wGhkZadx9zXaja3U0aNAAFy9eFNJfuHAhWrZsaVcDvUaNwrhxw0dY+9ln99G1a7KkKP2OHEF4z57wvndPZDepWzc8mDpVUl9kzDUEqIHuGq5k1XMIUAPdc2pJmRABmwSogU7CcJYAfSh0liCtd5QAac5RYjRfKgKkPalIkh17CJDe7KFEc6QkQJqTkibZspcA6c5eUjTPWQKkNWcJ0npHCZDmHCN29epV1KlTR7Tot99+E45p//PPPxGVbcfyjBkz0LFjR8ccedDsGjVq4MaNG0JGy5Ytw2uvvZZnA/3ff71Rs+bTIhI//BCPsmXNd8/nG1NqKgK3bEHw0qXwO3XKykxy+/a4P3Mm4GNu3ufbFy10OQFqoLscMTlQOQFqoKu8gBQ+EciLADXQ8yJEz/MiQB8K8yJEz6UmQJqTmijZs5cAac9eUjRPCgKkNykokg1HCJDmHKFFc6UiQLqTiiTZyYsAaS0vQvRcagKkOceI7tu3D926dRMWhYWFgTXNOc7cnoiJicGhQ4eEOSVLlsTBgweNDWMtjkqVKuGexe5utkOffQkhL+1t3BiI/v3Nd48XLGjAH3/chLMY/Q8eRMFBg+Adb/su9bRXXsGduDhqnqtIrNRAV1GxKFRFCFADXRHs5JQIyEeAGujysfZUT3m9MffUvCkv5QiQ5pRjr3XPpD2tK0De/Elv8vImb8jzj63EiAi4ggD9rnMFVbJpiwBpjXQhNwHSnGPEFy1ahPHjxwuLqlWrhu+++05kZPfu3ejevbvotSZNmoCt9fX1dcyhB8wuVaoUUlNThUwYL8YtL+19+GEYli4NFtY1aZKKpUvv5p8IzyN40SKETpoEzmCwaSezeHHcXr8ehmeeyb8fWik7AWqgy46cHKqMADXQVVYwCpcIOEqAGuiOEqP52Qnk9caciBEBqQmQ5qQmSvbsJUDas5cUzZOCAOlNCopkwxECpDlHaNFcqQiQ7qQiSXbyIkBay4sQPZeaAGnOMaKjR48GO4LcNDp06IA5c+aIjBgMBrz66qu4dOmS6PURI0YY70jX0uB5Hv/73/9EKe/Zswfly5fPs4HepEkkzp41f+Hgww8T8f77j/KHj+cROnYsCixebHN95rPPIrlbNyS9/Tb4sLD8+aBVihGgBrpi6MmxSghQA10lhaIwiUB+CVADPb/kaJ2JAH0oJC3ITYA0Jzdx8ke/70gDShCg33VKUNe2T9KctuuvVPakO6XIa88vaU17NVc6Yy1p7p9//gFrgF+8eNG4Q7xPnz4O489+PPsHH3yAQYMGWdlhR72/9dZbYA1k03j22Wdx9OhR43Hv7PVTp04hIyMDL730Enw89K7tlJQUlC5dWsSHHW9fokSJXBvoDx5wqFixCHje3PbZtOk2atRId7hmbEHgunUIt1GntFq1kNS7N1KbNKEj2/NF1j0WUQPdPepAUbgvAWqgu29tKDIiIAkBaqBLglHTRrT0oVDThXaj5ElzblQMjYVC2tNYwRVOl/SmcAE06J40p8Giu0HKpDs3KIJGQiCtaaTQbpSmljTHGt3r1q0T6G/atAk1atRwqBrVq1fHf//9J6xZuHAhWrZsadPG2rVrMXToUNGzLVu24OWXX8aUKVMwb9484zPWlJ85c6ZDcahl8t27d1G5cmVRuCdOnABreOamvR9+8EfXrk8J6/z8eJw9+x8CAhzP3OfCBUS0aAGvR+Ld64nDhuEROxHA2UvVHQ+JVkhMgBroEgMlcx5HgBroHldSSqhdu3aFvb29awKoyXEcezfH/md858Dz/NJ169aJL9OxA1lMTEwTg8HQleO4egDYZS6ZAG4B+B3AXgDL9Xp9jmfhtGrVKsjf378/x3E6AKUA+AO4DuD7rKysORs2bLhqRxj5mkIN9Hxho0UWBLT0oZAK7x4ESHPuUQctRkHa02LVlcuZ9KYce616Js1ptfLK5k26U5a/lryT1rRUbffIVUuaK1q0qAg62/m9detWuwvBGuesgW45TMeR2zLCdpk3bNgQFy5cEB737t0bw4YNQ6VKlZCZyf4s+3jkZsfuAN1w4r///ouaNdmft83j7NmzCA0NzbWB/tlnIZgzJ0RYVKNGGjZtumN/hllZ8P39d/jv34+QWbPAWbBmRu7NnImUmBj77dFMtyZADXS3Lg8F5wYEqIHuBkWgEKQloNPpzGf8ZDPtaAP9zTffDM/IyGCXvLTJI8pqer3+lK05Op2OnbezDUCZHGwk8jzfZd26dd9JS+KxNWqgu4Kqtmxq6UOhtirrvtmS5ty3Np4eGWnP0yvsXvmR3tyrHlqIhjSnhSq7X46kO/eriadGRFrz1Mq6b16epDnWsF66dCn27t2LOnXq4N133zU2adlIT09HyZIlrQpx9epVu49PZ7vEp0+fLtgICgrC6dOn4e/P9hfZHmy+5e5ydh/4559/js6dO4sWDBw4ECNHjnRfoeQzMvblAXYfvOW4cuUKfH19c2ygJyVxaNo0Elev+gjL3n//IT788GGeUXDJyQiZNg1Ba9fCKzHR5vzkjh1xf8aMPG3RBPUQoAa6empFkSpDgBroynAnry4kkK2Bfg3AXwCimEtHGug6nS7sye7yl5+EuxHAOoPBcBFAlpeX13MA2DuZDgBa2Wqgt27dOsTf3/9XAGWf+I/lOG4NgBSO4xrxPD8aQAEAyQDq5tSEdwYXNdCdoUdrGQFP+lBIFVUHAdKcOurkiVGS9jyxqu6bE+nNfWvjqZGR5jy1su6dF+nOvevjSdGR1jypmurIxZM0t3PnTrzzzjsCeNa4Zsejs8Ea5aypnn2sWbMG9evXz7NYbLd4rVq1RMe3d+nSBdOmTct17e+//4433nhDNKd///7C8e3A0wD+h3LlMrB373abtuLjvfDff96oWDEDarsqnX3BoFmzZkJe7K53Vovc/k43dGhBrF0bJGKxcuUdNGyYlnudUlPxVPfu8P/xxxznpVetijt6Pfggsf08BUAT3JoANdDdujwUnBsQoAa6GxSBQpCWgE6nm8jz/DFfX99jq1evvtWpU6cSWVlZl5kXBxvoywB0A8DeZcTo9fotOUTKNWzY0Hv//v3m84OeTIyOjp7EcdzYJz9+oNfrP7e0ER0dXYfjuAMA2FcDD+j1+obS0qAd6FLz1KI9T/pQqMX6qTFn0pwaq+YZMZP2PKOOasmC9KaWSnlOnKQ5z6mlmjIh3ampWuqOlbSm7vqpMXpP0tzzzz+PtDRzk5XdNc7uHGfjp59+QseOHa1K1LNnT0yaNCnP0u3atQs9evQQzdu+fTuqVKmS61rrO8C98dxzHXH9ekUArLFe7cn6m+jZMx3VqoXg/Hkf3L7thYQEb9y86YXTp33B8xxKlszEN9/cxQsvWP3pNs/4lZpw7NgxtG3bVnAfEhKCv/5ie8Rsb3TZvDkA/foVEoVbrVo6tmy5netV5VxSEsJ790bAAfbnadsjvVIl3F25EoaICKVwkF8XEaAGuovAklmPIUANdI8pJSWSE4H8NNB1Oh2769z4tTuO40bExcWZzxmyE3WfPn187927lwCA7WQ/W6FChUoTJ040ZF8eExOzgOf5d5+8Aaq5du3aY3a6sGsa7UC3CxNNyoWAJ30opEKrgwBpTh118sQoSXueWFX3zYn05r618dTISHOeWln3zot059718aToSGueVE115OJJmst+xzmrALuDmw2205zdPZ59sN3hixYtyrNY3bp1w759+4R5VatWxffff5/nOoPBgOLFi8NgeAbAxwBYM7lgnutymhASYkBc3B1UqZKRbxvOLkxOTga7x7xy5crw8/PL1dzBgwdFx9UXLlwYJ0+eNK7Jrr2rVzlERUUiMfHxsftsFChgwK5dCShePCtHP76nTqHgkCHwPX/eak5GmTJIr1sXafXqIbVpU6huC7+zxdLIemqga6TQlGa+CVADPd/oaKFaCOSzgc6OWWdfr3wQHBxcZMmSJamO5hsdHR3FcdxOto7juFFxcXGf2bLRsWPHWgaD4ecn86bExcV96Kiv3OZTA11Kmtq05UkfCrVZQfVlTZpTX808JWLSnqdUUh15kN7UUSdPipI050nVVE8upDv11ErtkZLW1F5B9cXvKZqLj49HtWqm3dyP68Aa6kePHjX+M7t3fNasWVYFql27NtatW5dr4a5fvw42j92xbhpffPEFOnXqlGfBT53yRYcOm5CayuaG5DnfngmlSmVg584EBAbaM1vaOZcuXULLli3x4MEDsPvcd+zYgfDw8BydZD9Wn32Z4PDhw8b5ltr755+baN8+HMeOie+Tnzv3Htq3T7FtPzMTwYsXI/Tjj8FlinflGwoUwN1Fi5Bux/H80hIia0oQoAa6EtTJp5oIUANdTdWiWPNFwNEGuk6nY18BfAAggN15rtfrdcyxTqfz9vb2fhaAd0BAwM28muqWx7d7eXnVXrt27S+2EmjYsKFPZGQk88cukTmo1+vZveqSDWqgS4ZSs4Y85UOhZguowsRJcyosmoeETNrzkEKqJA3Sm0oK5UFhkuY8qJgqSoV0p6JiqTxU0prKC6jC8D1Fc+w49V69eokqYNkcHzhwINavX29VofLly2PPnj25Vm7q1KmYO3euMCc0NBTHjx9HUA73aLNe7sGD/li9Ogjbtrmmy92v30OMGfNQdsWxI+9Z09w02H3uo0ePzjGOzZs3o1+/fsLzcuXKYe/evcafLbU3aVIixo8PFdlp3z4Zc+feF73GJSfDf88eBOzejYC9e+H1gP0pWjyywsNxd9kyZLz0kux8yKEyBKiBrgx38qoeAtRAV0+tKNJ8EnC0gd6xY8caBoPh8dcsgfF+fn6z0tPT2aU+b1ucFZTOmt08z3+ybt26/bZC0+l07GuYHdizjIyM8E2bNonfuVgs0ul0vwFgl/8k6PX6wvlM1eYyaqBLSVObtjzlQ6E2q6fOrElz6qybJ0RN2vOEKqonB9KbemrlKZGS5jylkurKg3SnrnqpOVrSmpqrp87YPUVzH3/8Mb766itREV566SVs3brV+Fr79u1x5MgRqyIVKVLE2AzPPh4+fGi8N71ixYpo3bo1EhLY7ZaPxzvvvIPJkyeLlrDN6X/84Yt16wKxeXMgbt/2zkMQpwBsB7ANAPvz7AwAdY1rqlRhd6FnIDIyCxERBhQubMDnn4fg7FlfwaaPD4/duxNQtqx896Fb3+cOeHt749q1aznmmv3ofMuj7y21V7lyhvGud9MoVizTeHR7SIh517/fsWMIf/ddeN+6laM/ds/5vfnzkVWqlDr/haSo80WAGuj5wkaLNESAGugaKrZWU3W0gR4TE/M2z/NLGC+O4ybyPP8mgDI58OM5jhtt63h2nU7Hdpy/AiBJr9cXyI2/Tqf7DkALNufRo0cB27dvT7OnXu3bt/9fXvPi4uKOeXt7F8nKyhK9ac1rHT0nAiYC7I05u2uJDXa0F7uHigYRcCUB0pwr6ZLt3AiQ9kgfchIgvclJm3wxAqQ50oESBEh3SlDXpk/SmjbrrmTWnqK5tm3bWjXIK1SoIOx2rl69unAfuiXvgIAAXL58WVSCAwcOoHfv3mBNdFtj//79eOGFF4RHZ8/6YMCAMPz5p7kBnHNN2Z9OBwIQ+3w8vxKqVi2Hp59+aNxNX69ePcHMuXPeiIqKQHq6uQ3yyivpWLnyHoKDzU1mV2pp8eLF+PBD6xs72ZHsJUuWtOn622+/xZgxY4Rn7FSADRs2GH82ae/uXSAiggfPm3NbufIuGjdmXyx4PHx/+QXhXbrAKzk5xxST+vbFQ7Yb3teeOriSFNmWm0BkZKTxyxwA/uU4Ls8+g9zxkT8ioDQBaqArXQHy73IC+WigD+F5nn19kQ129zk7yn2Hl5fXuMTExN8LFCgQynFcB57npwIIY5N4nm+7bt26zZbJ6HS6PwFUAHBLr9cXyS3R6OjotRzHxbA5mZmZERs3brxjDxidTpfnOz32LdKnnnrKHnM0hwgQASJABIgAESACRIAIEAEiQASIABEgAkSACBABDRDIyMhAWFgYUlLEd2WXKVMG58+fZydqgjXKc9pIkZycjMAnF4rv27cPr7/+unGNrVG/fn0cPHhQeJSVBbz4IvAn++tpDsPfPwlpaV8CYM3zH+2qiL+/v3FnPNsBbxpjxwIffyxeXro0sGoVUKOGXWadmsSa37/8Yn2zJ7sPfujQoTZtT5s2DSNHjhSevfHGG9i2je26N4+NG9kJAeafAwKA+/cBf9N16GfPAnXqPH7R1qhWDWCNc53x9lIa2iZADXRt15+yz4EANdBJGh5PwNEGenR09Eccx1meJ7QbwBt6vT7LEpZOp2NfZzzAvvgH4Kxer2fvzISGtk6nuwjgeQDX9Xp9sdxA63S6ZQC6sTlZWVnPbdiw4R97CkMNdHso0RwiQASIABEgAkSACBABIkAEiAARIAJEgAgQASJABCwJsCPEWdP7LGu0WoyiRYvin3/+MTai2Q70nAabw+ay0aRJE2HXuq35K1asQJcuXYRHq1cDb7IzP22MmjWBrl2Bf/6ZimnTrO8JDwkJQd26dUV3iluaadWqFbZs2SK8xDZflysHXL8udubjA0ycCLA+9eNNuNKPxMREhIeH2/wSAmO2ezf7s7P1mDBhAiay4J6MDh06YN06dluoeQwcCFhcMY/XXgOEa+nZce21agFXroiNMxBsYcuWwHPPSZ8wWVQrAWqgq7VyFLdLCVAD3aV4ybg7EHC0gR4TEzOc5/nPTbEbDIaX1q9ff9JWLjqdTg8gmj0zGAwvrl+//nfTPDl2oNMR7u6gMM+PwVOOJfP8SnlOhqQ5z6ml2jIh7amtYuqOl/Sm7vqpMXrSnBqrpv6YSXfqr6FaMiCtqaVSnhOnJ2lux44d6NGjh1Ac1vA9c+YMZs+ejalT2QGctgfbdV6+fHnjw1q1auHq1as2J7Jd7qdOnTLuZmeD7T5v2DACFy74CPMjIrLQtWsKOnRIQenSj/cw5XT0eYMGDVCtWjVjfDmNjRs3GmMyjSNHfNGlSziSktg+KPFo1y4FX375AJwLOiVs133Hjh1thlm6dGn8+KPtnfXsrvj58+cL66KjozH3SbfcpL3KlYHTp82mR458iMGDk4DkZBTq0AF+p9h98eaRGhWF+7GxgJ+f5/yLSJk4RYCOcHcKHy3WAAEX/GdBA9QoRVURcLSBHh0d/S7HcQueJJmg1+sfX/5sY0RHR/fiOC72yaNeer3+G9M0Oe5At6cQPM+z3exF2R3ot9i3D2kQAQcJsDfmRYo8voXg5s2bdAe6g/xouuMESHOOM6MV0hAg7UnDkazYR4D0Zh8nmiUdAdKcdCzJkv0ESHf2s6KZzhEgrTnHj1Y7TsCTNMea2y1atBAgsEb3xYsXwZq2P//8c45w2I5odjw5G2XLlkVSUpLNuV27dsVnn30mPNuwIRADBoSL5n7zzV28/jq7SdM8tm7divfee8/KJjv2nDWf+/Xrl2NsL7/8MjZv3gzOoit+5Yq30e+JE9YN5BUr7qBRozTHhZDHipkzZ2L69Ok2Zz399NM4ceKEzWfs/vMlS5YIzywZMu2lphZB9uvTN21KQI2qyQjv2xeB27eL7KZXq4Y7ej34J0fuS54oGVQlAaZBugNdlaWjoGUiQA10mUCTG+UIONpAj4mJac7z/PdPIj6p1+tfyin6mJiYZjzP73jyfLRerxe+lqnT6di5Oh3Ys4yMjPBNmzblcOEMu2pG9xuAKgBybdjnhyI10PNDjdZYEvCkD4VUWXUQIM2po06eGCVpzxOr6r45kd7ctzaeGhlpzlMr6955ke7cuz6eFB1pzZOqqY5cPElz586dQ+PGjUXg//rrL1SqVAmZmZk5FmTRokVgd3OzO9RZQzunwRrZpqPgmblGjQrj0iXz7vNKldKxY8dtqx3ghw8fZn8ztTK7atUqREREICoqKlexmOKznMT8z54dglmzCsBgMLdGXnwxHd9/bx2Ds2pkje8ffvjBphl2f/yFCxdsPmNfEli7dq3wrHfv3mDHurPBtLdsWRHj0fOmUSr0Fo71mYHQFcvgffOmyGbmc8/h9tatMERGOpsOrfcwAtRA97CCUjqSE6AGuuRIyaC7EXC0gd6+ffvi3t7epgtiftfr9S/mlJNls53juBFxcXHCVwqjo6MncRw39skbm9pr1679xZadhg0b+kRGRrLmejCAg3q9/lUpGVIDXUqa2rTlSR8KtVlB9WVNmlNfzTwlYtKep1RSHXmQ3tRRJ0+KkjTnSdVUTy6kO/XUSu2RktbUXkH1xe9JmmN3oZt2kpsqwY4Pt9zh7evrixIlSuDvv/8WisV2Vnfu3Bn//vsvarKLy20MdsQ7u+fbtBN8+fIgjBpVUDRz8eI7iIqy3v1tq7HP7LA72318fFCmTBnwPJ+jeEqVKgV2zDybm32sWhWEESPEcXz77V00aybeBe+MMg0Gg/FLCA8ePMjRzJUrV8DYZh+MPfvigWkMHDgQI590zJn2mjcvgpMngQCk4DOMxHteX8PPYM3QEBaG25s3I7NMGWdSobUeSoAa6B5aWEpLMgLUQJcMJRlyVwKONtBZHjqdjl3aUwxAol6vZ++mbL4bi46OHsBx3By2huO4N+Pi4labOERHR0dxHLfzybNRcXFx5rOKLGB17NixlsFgMJ6HxHHclLi4uA+lZEkNdClpatOWJ6VfDfkAACAASURBVH0o1GYF1Zc1aU59NfOUiEl7nlJJdeRBelNHnTwpStKcJ1VTPbmQ7tRTK7VHSlpTewXVF78naS4+Pt54p7jlaN26NbZs2SK8xBrs/v7+2L9/v/DaRx99hL59++K3335D8+bNReuff/554y5xdnQ7O96djZs3vdCwYWE8fGi+h7xy5XRs32575/ft27fx4ovifU2sIb9nzx6jPRYTa/7nNXr27Inx48ebjqo2Tre1E758+Qzs2pUAL+tr0vNyYfP5+fPn0ahRo1zXnj59GuzO+eyD3Um/a9cu4WXWPGdNdDYuX/ZFvXqR8EIWNqEtWuE7mz54X1/cWbkS6XXr5it+WuT5BKiB7vk1pgydI0ANdOf40WoVEMhPAz06OnoGx3FDWHocxzWNi4t7/M4s29DpdOwMnobsZW9v72Jr1qy5bpqi0+nYhTrxAMIAnNXr9RVtNeJjYmIW8Dz/Llvn5eVVc+3atcekxEoNdClpatOWJ30o1GYF1Zc1aU59NfOUiEl7nlJJdeRBelNHnTwpStKcJ1VTPbmQ7tRTK7VHSlpTewXVF78nae7hw4coV66cqAisWZ6WZt7RPHr0aOPO702bNgnz3n//fXz44YfGXd7dunUTXi9SpAiOHz9uVdQRI8KwahU7gNM8Vq++jQYN0m0KICsrC8WLFxftMmd+pk59fIMm+2fm256xcuVKNGxo/BOuMDZuDET//uLm9YIFd9GqlTS70FevXo3hw4cL/goWLIj798U3fLI75osVY3u4xKNTp0748ccfhRfZ8e3sGHc25s8PwaxPOMxDf3THUpvpp9WsicQxY5BRvbo9eGiORglQA12jhae07SZADXS7UdFEtRLITwNdp9Oxdy7nwE7CAf7w8/Ort3LlykRLBjExMV15nl/+5LXv9Xp9y+yMLI9xB/CBXq//3HKOTqerzY5tB8DOEjqg1+vF7+QkgE4NdAkgatyEJ30o1HgpVZM+aU41pfK4QEl7HldSt06I9ObW5fHI4EhzHllWt0+KdOf2JfKYAElrHlNK1SSiNs2lZqZi86XNaFuqLfy9/UWcMzIyjMez5zZ27NiBNWvWYMmSJcK0Ll26YNq0aYiLi8OQIcZ9SMZRsWJF0e5p9hrrxb/4YhHR7vMOHZIxZ464oZw9hipVquDOnTvCy3PmzEGHDh2MP0+cOBFff/21aAlrqi9fbvpzrfnRiBEjMHjwYNHcrCygSZNInD9vPkI9tx3xjopz2LBhRmam0bJlS2zbtg3saHfT2Llzp/GY9+yjTZs2+PXXX4WX2U5+dp86G6Nfu4SP/uqBMrC+Pz25Qwck9eqFjCpVHA2X5muQADXQNVh0StkhAtRAdwgXTVYDAZ1OVw9AaVOsHMdF8DxvalwfArDIMg+9Xm9+52fxQKfTjQAw7clL53ie/4zjuN85jgvleb49gL5s4zk75t1gMFRfv369+RKgJ4tat24d4u/vz97tPD6rCPia47g1HMelZGVlNeI4jh3XXgBACoA6er3+lNSMqYEuNVHt2VPbh0LtVcjzMibNeV5N1ZIRaU8tlfKMOElvnlFHNWVBmlNTtTwnVtKd59TS3TMhrbl7hTwvPrVpbuHvCzHpyCSMrzUefSr3sSoI2+mdyc41tzGeeuopnDp1Cl988QVmzZolzGDHtsfGxuKrr77Cxx9/LLzeoEEDsN3XlmPfPn906/aU6LVjx27i2WfNzWRbvgcMGIANGzYYH4WGhuLQoUMoVKiQ8WfWzB8zZoxoGWtIs5jWrVsnep0d4z5p0iQrF1u3BuC99x7bM42NG2+jZk3bu+IdUTLb8W55Z/wnY8Zg16xZyExKwnkACYAxzuz3zzMfUVFR+PPPPwV3s2fPRnR0NFLidqPYkF7whbhWvI8P7i5fjrQGDRwJkeZqnAA10DUuAEo/TwLUQM8TEU1QG4Ho6OglHMe9bW/cer0+x38PdDrdFAAj2UnuOdhjR7S31ev1xjvMbQ2dTsea+dsAlMlhSiLP813WrVtn+8IaexPJYR410J0ESMvZ1QJgx2+xcfPmTdE3ZQkPEXAFAdKcK6iSTXsIkPbsoURzpCJAepOKJNmxlwBpzl5SNE9KAqQ7KWmSrdwIkNZIH3ITUJPmUjJTUH1VddxPu49w/3Ace/MYAn0CRcjYEe7sKHdbo3379pg7dy4WLVpkvEvcNOrUqQO9Xm9snrMmummY5lvaGj48DKtXm49vf/nldGzZcjvPsrGYZsyYgVu3boE1wV9++WVhTfaj49mDv/76C8HBwWCxXb8u3LSJtm3b4ssvv7Tyx3ah16tXGNeuscNBH4/mzVMQG3svz9hym/DgyhX0qFsXbF8/+18FAJ0DA+GbwvZQAexrAxcBPPW//6FAZCT4gADwQUEwhIcj/eWX8fpXX+HIk/vdWXs/rlcv1ImPR6DFvfQm/4agINz/4guktm7tVMy0WHsEqIGuvZpTxo4RoAa6Y7xotgoISNlAZ+k+OWad7TavD+AZAOwinPM8z2/hOG6uXq9/kBeWbt26BaekpLzPcZzuye54dj/6dZ7ntxkMhtkbNmy4mpeN/D6nBnp+ydE6EwE1fSikqnkGAdKcZ9RRjVmQ9tRYNfXGTHpTb+3UGjlpTq2VU3fcpDt1109N0ZPW1FQtz4hVTZpju88nH5kMHjw4cBhXa5zVLvSqVasiIYHtibYebNe5Tqcz7pYeNGiQMKFChQrYvXu38Wh01kg3DXZXN7uz2zSOHfNF585PISXFS3ht7NgHeO+9JKfEkJqailq1aglxV69eHZs3bzbaXLBgASZPnizYr1+/vug4dUvHsbHBmDAhTHjJy4vH4cPxeO65LLvj4xITETJtGgK//x5eiYngUp27R53dPs+ONC0IgN1Oz45AtTX+Cq+J8K0zkFWypN2x0kQiYCJADXTSAhHInQA10EkhRMDDCVAD3cMLLEN6avpQKAMOciEDAdKcDJDJhU0CpD0ShpwESG9y0iZfjABpjnSgBAHSnRLUtemTtKbNuiuZtVo0Z7n73MTL1i50doz4tSc7nrNzPXHiBFijbc+ePXj7bfOhn+Hh4WDP2M5wthvcNEaNGgV29Dobv/7qiy5dnsKjR+bmOXv9559voVgx+xvUOdX65MmTmDlzJoKCgvDhhx+iWLFixqnZ72U3Nftt2Xn4kEP16k+LYnzvvUcYOzYxb4llZcH/wAGEjRwJnxs38p4v4YzF6I70eZ+jVTvnj5uXMCwypSIC1EBXUbEoVEUIUANdEezklAjIR4Aa6PKx9lRPavlQ6Kn8tZgXaU6LVXePnEl77lEHrURBetNKpd0nT9Kc+9RCS5GQ7rRUbWVzJa0py1+L3tWiOcvd56Y62dqF3rhxY5w7d86qlOXLlzc2ztlgz9k8yzFw4EBj8/z06dPCy9OnT0fnzp1x4sTjnefZm+edOiXhiy/yPNDTKVnt3bsXb731lmCDXU14/PjxHG2OGxeKb74pIDwPCTFAr7+DypUzxGt4Hv4//gg/9r+TJ+H7++/wSnJuJ31+El2AdzHEZx5++/MOChRw/osI+YmB1qifADXQ1V9DysC1BKiB7lq+ZJ0IKE6AGuiKl0D1AajlQ6HqQVMCAgHSHIlBKQKkPaXIa9Mv6U2bdVcya9KckvS165t0p93ay505aU1u4uRPDZqztfvcVLnsu9CbN2+O3377zaqwffv2xUcffWR8ned5tGrVCmzXd25j8eLFePbZ5oiOjsDDh+Kd502apCI29i782OWWLhynTp1CixYtBA++vr64fPkyOM52O+TKFW/jXeg8b34eEMDj00/uoUvl4/A9fRo+ly4hYO9e+J49a1fkbF84u7PzCoBi9eujyKuv4oOjR7Fq1y5EAYgEUKtJE7R9/XVwKSngkpLgc/YsAnfsAJfGDnE3jyx/fxwr0Bgr7jTHdryBSyiFqChg6dKbMBjYjeo0iIDjBKiB7jgzWqEtAtRA11a9KVsNEqAGugaLLnHKavhQKHHKZE5hAqQ5hQugYfekPQ0XX4HUSW8KQNe4S9KcxgWgUPqkO4XAa9AtaU2DRVc4ZTVoTth9foUHLgJg12Q//xhc9l3oHTp0wC+//GJFdfXq1WjQoIHwOmuet2zZMlf6W7ZsxahRzXDmjK9oXuPGqVi06C78/V1fvOvXrxvvR7ccf/31F0JCQnJ0PrS3D3y27cLLOI6SuIwSuGL8/zDYcZT7E6tpNWvir2bN8ObkyTgPIPPJ66yhHxkZidGjR2PZsmVCDN26dcPUqVOFn+/fv49ft27F6VGj0JxdwQPgW3C4F3UFW3Y9Pp7eNL78EmjfnhrorleT53qgBrrn1pYyk4YANdCl4UhWiIDbEqAGutuWRjWBqeFDoWpgUqB2ESDN2YWJJrmAAGnPBVDJZI4ESG8kDrkJkObkJk7+GAHSHelALgKkNblIkx8TAXfXnLD7/Px9YLFF3dip5k+a6Ja70Lt27YoffvjBqsDs2HN2/LnlYA3fuXPn5iiGBQv+wHvvVRI9b9TocfM8IEAeDSUlJaFs2bIiZ4cOHUKJEiXEAWRlwe/QIQTp9QjYvh1eKSn5CjCzRAk8evddJHftiqXLlxvvYzeN4sWL4/Dhw8Yfp0yZgnnz5gnP2rZtiy9ZJxzAxYsX0axZM6SIYmAt9KUAuoriCgsDzp8HDAZqoOerYLTISIAa6CQEIpA7AWqgk0KIgIcToAa6hxdYhvTc/UOhDAjIhcwESHMyAyd3AgHSHolBTgKkNzlpky9GgDRHOlCCAOlOCera9Ela02bdlcza3TUn7D5fwwN/WZAKAzDk8c+Wu9B79eqF7du3i5Cy486vXLkCHx8fK9Rbt27FV199ZXXse8GCBdG48TVs2GDe6V2iRCb27o2XrXnOgmXHzZcuXRqpqalC7AdmzkRlnofX/fvwSkyE1507CNizB97//eewlDJKlUJqixZIr1YNGVWrwlC4sGCD3Qu/fv164ef27dsLXzhgXzyw3HHO7pRfvny5ce7gwYOh1+stYvEGwHarvymKz8+Px4YNHNgJ9TdvUgPd4eLRAoEANdBJDEQgdwLUQCeFEAEPJ0ANdA8vsAzpufuHQhkQkAuZCZDmZAZO7gQCpD0Sg5wESG9y0iZfjABpjnSgBAHSnRLUtemTtKbNuiuZtTtrTnT3+QQblCxeM+1C/2DIB9iwYYNoMjtynB09ntNgTepjx47h66+/xs6dO+Hv74/Ro6fj00/7IjXV3Hb46KMH6Ns3SfZy1ahRAzdu3AA7gP4LANWdiOASSuJvv4qo0b0E+FdrI61+fcCbNbjFIzk5GU2bNjV+8cA0PvnkE3Tv3t3445IlSzBmzBjhWc2aNbFx40bjz0WLFs1mbRyAiaLX/P15fPPNPXTpUsj4OjXQnSgqLaUd6KQBIpAHAWqgk0SIgIcToAa6hxdYhvTc+UOhDOmTCwUIkOYUgE4ujQRIeyQEOQmQ3uSkTb7odxxpQCkC9LtOKfLa80ta017Nlc7YnTUn7D4HD0wBkJaN1kAAj/uvwi70v5f9jVWrVokmVqxYEbt27bILdWZmJtLS0vD1109j+vRQYY2vL49ff72FiAiDXXaknPR6s2Zodfo0JgGwbnXb9sR7eyPt1VeRUbkyfk8siXGLK+FvlMF1PL5/fPjwRAwZ8shqMfsywYgRI8DujM8+2JcLKlV6fKQ9+5LCgAEDhCnly5fHnj17jI1+1vA3D1Yg1oQ37+RnzfNvv72Lxo0zhGP1qYEupWK0Z4t2oGuv5pSxYwSoge4YL5pNBFRHgBroqiuZ2wXszh8K3Q4WBSQJAdKcJBjJSD4IkPbyAY2W5JsA6S3f6GhhPgmQ5vIJjpY5RYB05xQ+WuwAAdKaA7BoqiQE3FVzot3nLFN23fZtGykXBPAqgGoA24Xe+nxrLF3M7to2D8vjxe2BlpzMoWbNwrh3z9yubt8+GXPn3rdnubRzeB77a9fGm9ev22U3o2JFJOt0SGnXDoaICOMange6di2E/fvNF7eHhhrw88+3ULAgL7LLjr9nx+BnH0FBQTh79qxwDP7u3buF3ehsLtt1fvToUWzevBn9+vWzWD4VwEjhZy8vHitW3MWrr6bRF7/tqihNsocANdDtoURztEyAGuharj7lrgkC1EDXRJldmqS7fih0adJkXFECpDlF8WvaOWlP0+WXPXnSm+zINe+QNKd5CSgCgHSnCHZNOiWtKVP2jIwM7NixA0899RTYUdS27spWJjLXe3VXzYl2nzMMrCd+OQce7QFUebwLvc6ZOjgUd0g0sXPnzpg+fbrdMOfOLYCpU827z9nCPXviUb58pt02pJoY/PXXCJsoPv6c2c6KjETmCy/AEBZm/F/WM88g9fXXkVmhgk3XZ874oGlT8/3mbFKvXo8wcWKiaP6bb76JAwcOWNmoW7cu4uLihNd/+eUXdOjQQTTvhRdeQEJCAu7evQuA3TfPjgj4nJ2RJsxr1y4Z8+Y9/iKCu2pPqtqRHfkIUANdPtbkSZ0EqIGuzrpR1ETAbgLUQLcbFU3MgQC9MSdpyE2ANCc3cfJnIkDaIy3ISYD0Jidt8kV/bCUNKEWAftcpRV57fklr8tecHVkdHR0N1hBkgx1RPXXqVFSrVk3+YBTw6I6as9p9zrgsA3ApB0DDzCeEB/wUgNQ9qaKJgwYNwgcffGAX3Rs3vNCgQWGkpJibvo0bp2L5ctYUlnFkZSFo5UqEjRkDzmA+Np790+pKldBo2zabd5fnFmHfvuHYsiVQNGXx4juIinp8Nn5SUhLKli1r08TAgQMxcqR5J/mff/6JqKioHNy9AGATgHKi5xzHY9++BJQt+/iLCO6oPRkrTK4kJEANdAlhkimPJEANdI8sKyVFBMwEqIFOanCWAL0xd5YgrXeUAGnOUWI0XyoCpD2pSJIdewiQ3uyhRHOkJECak5Im2bKXAOnOXlI0z1kCpDVnCTq+/vTp02jWrJloIcdx6Nq1K0aNGoWCBdkZ4Z473FFzVrvPGf7FAK7aqAPbVG15YjjbfL5bPO/TTz/F22+/bVcR+/UriM2bg4S5rOm7bdttVKmSYdd6pyelpiJo/XoUWLAAPpesvzHAsvincWMsX77cYVcXLngbd6Gnp5tbKd7ePPr0ScIrr/yC7t2b5mhz6dKlaNKkifD8+vXrqFWrVrb5wQB6AMbb2sOtbPXv/xCjRz8UXndH7TkMlRa4BQFqoLtFGSgINyZADXQ3Lg6FRgSkIEANdCkoatsGvTHXdv2VyJ40pwR18skIkPZIB3ISIL3JSZt80e840oBSBOh3nVLkteeXtCZ/zbdt24bevXvbdBwREYFvvvkG1atXlz8wmTy6m+Zs7j5nLGIB/GsDCuvhvm7x+lEA28TzWA1ff91ykm24P/7oh06dHt8bbhpduiRh2rQHslTD58wZFHr7bfjcuGHTHzvIfQKAqlWr4vvvv89XTN9+G4yxY8NsrGVfEFgHYA6Ax6cxmAa70uDkyZMoVKiQ8BrbrV6hQgVkZrLd5CUADADQE4At2wBrno8a9RCcRRfH3bSXL6C0yC0IUAPdLcpAQbgxAWqgu3FxKDQiIAUBaqBLQVHbNuiNubbrr0T2pDklqJNPRoC0RzqQkwDpTU7a5It+x5EGlCJAv+uUIq89v6Q1+Wu+ePFifPTRRzk6DgkJgV6vR+XKlSUJ7t69e8bdw8xup06dEBhoPlLbYDAgOTkZBQoUkMSXPUbcTXNrzq3BsIPD4M15w4szH6Oe8WUGcMs6I+8u3vB6wTwv83gm+M28aOLWrVvx0ksv2cTB88CRI35YtCgYO3cGwGAwtxkKFjTgxx/jUaiQ+Qh1e5jmZw6XnIzIRo3g888/NpevBxADgEXy3HPPCVcOOOqL5TtgQEFs3GjeZW9tg31b4b0n3oAhQ4Zg+PDhVtOGDRuGNWu8ASwCEGAzlPLlMzBu3AM0aJBu9dzdtOcoS5rvPgSoge4+taBI3JMANdDdsy4UFRGQjAA10CVDqVlD9MZcs6VXLHHSnGLoNe+YtKd5CcgKgPQmK25yRl8SIg0oRIB+1ykEXoNuSWvyF33KlCmYN29ero6LFy+OQ4cOgR3t7sxg962/8cYb+OOPP4xmmjdvjthY1qwErl27Zjw2/uLFi8a7pefPny9qrjvjN7e17qa5K4lXwJrojJXlWPH+Cty/cd8qlT6r+sAv0E94/Y/tf+DA1wdE844ePYqiRYtarWX3nffpUwgnT5rXW0765JP76N492VXoRXZDpkxBiA0dZlSsiN+iolBz5kyYiAQFBeHvv//Od1xffDELM2awhvd4AKwBbj1efPEg5s71Bs9noXTp0lYT0tOB+fODMX16KHje9r8XTZqkYv78ewgOFtfSZMzdtJdvoLRQcQLUQFe8BBSAmxNw7t2LmydH4REBIgD2xpl9BbNoVlYWbt2y8ZVTgkQE8iBAb8xJInITIM3JTZz80R8iSANKEKDfdUpQ17ZP0py2669U9qQ7pchrzy9pTf6aDx482LjD3DRatGiBhIQEsKar5fj111/xzDPPOBXg+fPn0ahRI5ENdoT8iy++iAEDBmDDhg3CMxbXiBEjnPJnz2K1aK5mzZr491/xGe7sWHZ2PLvlmDt3LqZOnSp67fLly/DzEzfJMzKAJk0iceGCr01MNWqkYd26O/DxsYeic3N8jx1DRIcO4LKyBEMZ5csjcdw4pNWvj4uXLqFBgwYiJxcuXMjXFywSExONR8CnpaUBeAHAIABvAWD3l4tHgwapmDgxEWXLsmPazePff73RvXshnDljzY7dGR8VlYoePZJQr1666Mj27PbVoj3nqkur5SBADXQ5KJMPNROgBrqaq0exEwE7CFAD3Q5INCVXAvTGnAQiNwHSnNzEyZ+JAGmPtCAnAdKbnLTJFyNAmiMdKEGAdKcEdW36JK3JX/fOnTvj4MGDguMxY8agW7duKFeunCiYAwcO2NyJ60jEzMabb74pWhIdHY1Zs2ahYsWKePBAfNf2kSNH8L///c8RFw7PVYvm2JcMbt++LeQXGRmJ7du3W32pwdad9tkb78zIkiVBGDOmoBWvAgUM6Nw5GSNHPkRgoO2d0w5DzmUB9+ABIps2hY/FlwN4X18k7N6NzDJljCvv379v1IflyL6r3vvKFQStXp1naCfj4/FqXFy2eQXhxQ0Ez48Dn21HupcXj4YN01C3bhoePvTC9eve2LEjAElJ5mPzTcbatUvGiBEPUby4+YsAuQWkFu3lCZUmKE6AGuiKl4ACcHMC1EB38wJReETAWQLUQHeWIK2nN+akAbkJkObkJk7+TARIe6QFOQmQ3uSkTb4YAdIc6UAJAqQ7Jahr0ydpTf66N27cGOfOnRMcz5kzBx06dAA7tj0z07zzdseOHU7fg852urOd5ZaD7Yxeu3Yt2rVrZ5V87969MWHCBJdCUYvm2BcaHj58KLDYtGkTatSoYcUmPT0dr7zyCuLj443P3nnnHUyePFk079EjDnXqFMadO+bjy4sUyULfvo/QsWMyQkJc3zg3BRT2wQcIXrlSFF/i8OF4NGSI8Bo7zr5EiRIiPbIvD1SpUkWYE7hmDcKHDQPv7c3eLOWomU0GA9pb7HRnEyMArPPywg1DDLpyK2Hgc15vyzDbdT5kyCMMHfow1x3n2deqRXsu/ReQjEtCgBrokmAkIx5MgBroHlxcSo0IMALUQCcdOEuA3pg7S5DWO0qANOcoMZovFQHSnlQkyY49BEhv9lCiOVISIM1JSZNs2UuAdGcvKZrnLAHSmrMEHV/PdvayHb6mwZrZ9erVQ3l2hHZiovD6xo0bwY4Rd2Z8+eWX+PTTT61MsF3m//zDbi4Uj9q1a2PdunXOuMxzrVo0V7JkSbDmuGlkbyBbJsp2nK9cuRJslzq7V97XV3zU+Oefh2DWrBARm3XrbqN2bbP9PMFJMMHv8GFE6HQiS2m1a+PO2rUAa4RbjGrVqglfCmAvr1ixQnQdAJeSgqerVwd3/z5ya5TMAzAgW+z3AIQBMISHY8Oc0xg44mncvGn7bvTsaZcunYHZs++jatUMh4moRXsOJ0YLZCdADXTZkZNDlRGgBrrKCkbhEgFHCVAD3VFiND87AXpjTpqQmwBpTm7i5M9EgLRHWpCTAOlNTtrkixEgzZEOlCBAulOCujZ9ktbkrXtKSorVsez79+9HmTJl8PLLL+PmzZtCQKwh27BhQ6cCHDdunNWd3bkZZE3T7777zimfeS1Wg+bYDuzsR9nv27cPL7zA7vB2bNy65YW6dQsjJcW8y7pJk1QsXXrXMUPOzk5JQWF2dPvly4IlQ3AwEn74AVlFi1pZb9KkCc6ePSu8Pnv2bLDj/y1H8MKFCJ08GRxvvYOetbeHA5iTzXIMgLVs4xLHGe9cT+rTB4mJHBYsKICFCwsgNTXntkvz5imYPv0+wsLyt2NfDdpztsy0Xh4C1ECXhzN5US8BaqCrt3YUORGwiwA10O3CRJNyIUBvzEkechMgzclNnPyZCJD2SAtyEiC9yUmbfDECpDnSgRIESHdKUNemT9KavHW/evUq6tSpI3LKmpShoaGoW7curly5IjyLjY1F8+bNnQqwb9++2LJli9022C74PXv22D0/PxPVoLnU1FSUKlVKlN6hQ4eMx5o7Oj75JATz55t3n7M7vvfsScALL5iP63fUpqPzvW7eRNjYsQjctk209P7HHyO5Rw+b5mJiYsByNo3x48ejT58+ormP4uPxR926qJCcjPLZrEwEYOsygGEAPn+y+zz+2DHwgYHCyosXvTFlSijOnfNFoUIGFC6chYgIA/73vyy88UYKnn/evrvOc+KjBu05WluarwwBaqArw528qocANdDVUyuKlAjkiwA10POFjRZZEKA35iQHuQmQ5uQmTv5MBEh7pAU5CZDe5KRNvhgB0hzpQAkCpDslqGvTJ2lN3rofPXpUdPd4YGAg/v77b3Ach6ZNm+LM7JcEIwAAIABJREFUmTNCQKa70Z2JkN2t/ssvv9htgjWILZumdi90YKIaNPfgwQNUqFBBlNWvv/6KZ555xoFMgdRUoHr1p3Hvnvl48s6dkzB9+gOH7OR3MjtmPXjBAhSYPx9eyckiM+kvvYTbmzZZHd1umpT9yxf9+/fH6NGjBRvsfvjGjRvjxo0bxteqAWDEmgLoDMA/h6BnARhosfs8v7nlZ50atJefvGiN/ASogS4/c/KoLgLUQFdXvShaIuAwAWqgO4yMFmQjQG/MSRJyEyDNyU2c/JkIkPZIC3ISIL3JSZt8MQKkOdKBEgRId0pQ16ZP0po8dWdNctYQ37ZtG9juZtOwbFi3adMGrElrGlOnTkW3bt2MP7IjxRcuXIhvv/0WxYoVw6RJk1C2bFnjEdvPPfccChYsaDOR+vXr49KlS7kkyXZGDwbQAMApFC68ECdPHnApFDVoLj4+Huw4e8vxxx9/oFChQg6xWbcuEIMGhYvWHDhwC6VLO7eT2p4gfH/7DYV69oT3f/9ZTed9fZGwcycyczmSfsyYMViyZImw9s0338Tnn7O944/H0qVL8eGHH9oMpTCA+ByC1ANoFx6O7LvP7cnJ2Tlq0J6zOdJ6eQhQA10ezuRFvQSoga7e2lHkRMAuAtRAtwsTTcqFAL0xJ3nITYA0Jzdx8mciQNojLchJgPQmJ23yxQiQ5kgHShAg3SlBXZs+SWuur3tWVhaaNWsmuk/a5LVWrVpYv3698cdOnTrhxx9/FAJi95e/++67xp/ZMexsR7CtwZrn33zzDZgtNlizPSkpCQEBAahYsSIePXpkYxnbSf0+gPcAPCU89/Y+hL//Lgn/nLYPS4BLDZq7fv26wNOUMvsSRFBQkEME2rZ9CseOmWHWq5eGtWvvOGQjP5O9//kHEc2bw/uOtS9DUBDuz5iB1FatcjU9Y8YMfPHFF8IcpmH2BQ7TyPvLGbbNs/MQyo0fb7z7XO6hBu3JzYT85Y8ANdDzx41WaYcANdC1U2vKVKMEqIGu0cJLmDa9MZcQJpmyiwBpzi5MNMkFBEh7LoBKJnMkQHojcchNgDQnN3HyxwiQ7kgHchEgrbme9OnTp40NdFujXbt2mDdvnvHRO++8g507dwrTRowYgcGDB+P+/fto2LAhEhIScg02JCQEfn5+YMePZ2bavl87KuoD7NrFDtpmh2z72bTXuHEq5s+/h5AQ3iVw1KC5Cxcu4NVXXxXlz+6v9/HxsZvJ1aveqFPnadH82Ni7aN7cfAKB3cYcmZiRgYjWreH3+++iVTzHIblzZzwcMQKGwmyPeO6D7T5nu9BNo3r16ti8ebPo5/9s7G7Py+71sDB4Hz8uuvs8rzVSPVeD9qTKley4lgA10F3Ll6yrnwA10NVfQ8qACORKgBroJBBnCdAbc2cJ0npHCZDmHCVG86UiQNqTiiTZsYcA6c0eSjRHSgKkOSlpki17CZDu7CVF85wlQFpzlmDe67M3Ii1XTJs2DV26dDG+xO6Y3rhxo/DYdOc0a6SvWrUqb0e5zmgOYCiA1+yyU7ZsBhYvvosSJaQ/atwdNGcwGDBr1iwcPHjQ2CgfOHAgvL3N95Rn/9IDe3bt2jW72JkmzZpVAJ9/HiqsCQ/PwokTt+Bn+3sLDtnObTK77zz0k09EU9IrVTLuOs+sWNFuP1u3bsV777ETCh6PkiVL4qeffjL+c0pKivEKAcbR0XF33DikPDlZwdG1zs53B+05mwOtdw8C1EB3jzpQFO5LgBro7lsbiowISEKAGuiSYNS0EXpjrunyK5I8aU4R7OSUdsmRBmQmQL/rZAZO7mgnMGlAEQL0u04R7Jp0SlpzfdkHDBiADRs2WDniOA4nT55EZGSk8Vn2Rjnbkd68eXNER0c7GeRSAG85bKNgQQMWLryLevXSHV6b2wJ30Nzq1asxfPhwIUx2P32HDh2En48fP47WrVsLP7Oj29kR7vYOtvu8Q4cI/PefuSn/9ttJ+PTTB/aayNc872vXENmoEbxSzbvcM59/Hgnffw8+1NzMt8f44cOHodPphKlhYWE4c+aM8edff/0Vbdq0sWnmDXaNAIAdOTi5ceGCIrvPWTjuoD172NMc9ydADXT3rxFFqCwBaqAry5+8EwGXE6AGussRe7wDemPu8SV2uwRJc25XEs0ERNrTTKndIlHSm1uUQVNBkOY0VW63SZZ05zal8PhASGuuL3Ht2rVt7l5+5plnjI1I02B3nrO7zE2jffv2OHXqFC5duuREkHUBPN41nH0EBPBo0eIO1q+v9+Qe9E0AHjfzTcPbm8eiRXcRFZXmRAzipe6guaJFi4qCioqKwuLFi4XXfv75Z9EXF8LDw8F2pdszfv7ZD717h+PePXPznK3btCkBNWpk2GMif3N4HoW6dkXA/v3CenZs++0tW5Dx0ksO2zx37hwaN24sWnfx4kUEBAQYWX300Uc2ba4H0B7AfwCmAZhlMavh889j5Y8/OhyLVAvcQXtS5UJ2lCVADXRl+ZN39ydADXT3rxFFSAScIkANdKfw0WL6ZitpQAEC9GFQAejk0kiAtEdCkJMA6U1O2uSLfseRBpQiQL/rlCKvPb+kNdfW/NatW3gph+bl2LFjRUdkT506FXPnzs01oEmTJmHfvn3Yb9Ekff311413pLMdwgULFsSOHTuwdCnbdc4Ga4qLdwpHRmahe/ckvPVWMry976FCBXYnOhvFAWwBUEUUw9NPZ+Hw4VsICJCGldKau3v3LipXrixKht0df/nyZeE1xtd0tD57sUiRImC70vMaq1YFYfToMGRmilsH1aqlY+vW2+Bc0FHgHjxA4JYtCIqLg9+JE6IQk7p3x4Nsx7nnlYPp+aNHj/DCCy+Ipv/www/Go9uHDh2KtWvXWplitb1VoACeSkwESzUJACPNyLLb4xctXIimLVvaG4Lk85TWnuQJkUHFCFADXTH05FglBFzwnzuVZE5hEgGNEKAGukYK7cI06Y25C+GSaZsESHMkDKUIkPaUIq9Nv6Q3bdZdyaxJc0rS165v0p12ay935qQ11xLfvn07evXqZeWE7T7fvXs32M5m05g9ezbYneg5jWrVqmHz5s1ISEhA7969wXYId+rUCRMnTgQ7Dt40zEdvs/uuxbum+/Z9hBEjEuHv/3h2enq68W5r8whGvXpX8dNPT4nCmDLlvrHhLsVQWnN6vR6DBw8WpcLYfvfdd8JrO3fuBDtC3zRKlCiBQ4cO5Zp+bGwwJkwIs5pTrlwGli27i6JFpb1P3u+nnxC0ahUCd+wAl2Z9QkBWkSKI378ffEhIvsvGuMTHxwvrv/32WzRr1sx4tDvTWfbRv39/fFyoEEInTwbHs4PcHzfR9wJ45v33UfTDD/MdixQLldaeFDmQDfcgQA1096gDReG+BKiB7r61ociIgCQEqIEuCUZNG6E35pouvyLJk+YUwU5OaQc6aUBmAvS7Tmbg5I5O2SANKEKAftcpgl2TTklrri37zJkzMX36dMEJ4z158mRjE5I10S1HbGwsJkyYYDMgHx8fsGa8ebc4YDAYjP+Nyj5SUlJQunRZAOyo7DrC45CQDBw/fhvBwY8bm2zwPI9ixYoZbZnGjh27MHFiA/z885MuO4DnnsvETz/Fw4dtI3ZyKK25Pn364PvvvxdlwXZas539psG+qNCvXz/h5+zPLRdv2BAItvPckpfpedOmqZg37x4KFDAzdxKfcXmBmTMRaqErWzbvxsYitXlzp9yxawSOHDki2GDHtvft29d4tDv7AodpdOvWDW+++aZxZz+7f/3p6tXB3b9v3IXOMjeEhyP+2DHF7j43xam09pwqBi12KwLUQHerclAwbkiAGuhuWBQKiQhISYAa6FLS1KYtemOuzbormTVpTkn62vZN2tN2/eXOnvQmN3HyR5ojDShBgHSnBHVt+iStubburAnLmrGm0aNHD3z88cc2na5cuRIffPCBzWc9e/YEO77d3vHKK2vwzz9DRdMHD36IESMeWpkoU6YMkpPNu8u3bNmClJTa6NgxQjQ3NvYumjdPtTeEHOcpqbmkpCRUqVIFqaniPCIjI433zZtGXFwchgwZIvzM1rAvMGQf+/f7o0sX8W5905x+/R5i1KiH8BZfhe40P+8LF1D4tdfAZWbatJUVEYHE8eOR0p7dRO7cGDZsGNasWSMYYcfas1MSGI87d+4Ir69evRoNGjQQfg5euFDYhc7uYU8cNw5Jffo4F4wEq5XUngThkwk3IkANdDcqBoXilgSoge6WZaGgiIB0BKiBLh1LrVqiN+ZarbxyeZPmlGOvdc+kPa0rQN78SW/y8iZvoB3oJAJFCNDvOkWwa9Ipac21ZW/atCnOnDkjOPn000/x9ttv23S6YcMGDBgwwOpZUBDb3fwzIiLEDW1bRtip2Xv3+uOddwoiK8vcuX3uuVT88MM9BAZa74Rmu4bZveCmwZrHderURcuWETh1yk94vU6dNOj15qZpfskpqbmcGLMd/leuXBGOwl++fDlGjRolpFijRg1s2sTukxePnj3DsWNHoNXrMTHJmDnzfn4R5bqu0FtvIWAvOxTdPHhfX6Q2bYpknQ5pjRoBvr6S+J43bx6mTJki2Kpdu7bx7nN2pL3lqQXsyPtKlSoJ87iUFGEXurvsPmfBKak9SQpCRtyGADXQ3aYUFIibEqAGupsWhsIiAlIRoAa6VCS1a4femGu39kplTppTijz5Je2RBuQkQHqTkzb5oj+2kgaUIkC/65Qirz2/pDXX1TwrKwtsd3eaxf3U7P7tOnXMx6pbet+xYwfYTvPsg903PWvWrFwDZRuqN28ORGxsAZw9a9081etvo06ddJs2atasiX///Vd4tmzZMrz22mtYvz4QAwea72hnE3bsSEDlyhlOQVNSc+yoccuj2i0T+euvvxDy5L7wRYsWYfz48cLjunXrgn2xwHKwslaqVATJyeJj9L28eBw4EI/nn5f2znPm23/fPjzVrZsojuSOHfHgo4/AFyrkVF1sLWZH3bMj702jSJEi2LVrl3EHuuU4fvw42DPLwXahh02ahAfjx7vF7nN6Tye5PDRtkBromi4/JW8HAWqg2wGJphABNROgBrqaq+cesSv5odA9CFAUchMgzclNnPyZCJD2SAtyEiC9yUmbfNEfW0kDShGg33VKkdeeX9Ka62rOdjSzxqvl+O2333LcSX7w4EF07tzZKqBBgwbleLT7o0ccvv46GEuXBuP2bdtnhffv/xCjR1sf3W5yxI7evnjxouB34cKFaNmyJViDuEaNp3HnjtnuCy9k4Pvvb9vcyW4vSaU0x44cr1atGtgXG2wNtsuf3QfPxpdffgl2WoBpsDu/2a50y3HwoD86d7Y+vr1nz0eYNCnRXhz2z8vIQORrr8HXolZZhQsj/scfwRcoYL8dB2aePXsWTZo0Ea1gTfUWLVqIXrt8+TL8/MynFRgfpqYicPNmpLRtC/j7O+DVdVOV0p7rMiLLShGgBrpS5MmvWghQA10tlaI4iUA+CVADPZ/gaJlAgN6YkxjkJkCak5s4+TMRIO2RFuQkQHqTkzb5YgRIc6QDJQiQ7pSgrk2fpDXX1X337t3o3r274KBgwYI4ffq0cEx4ds/Hjh1DW9ZszDY++eQTkR3TY3YFdqtWEfj992yNS4v1DRqkYtmyu7me6B0VFYU///xTWDV79mxER0cbf545swCmTw8VRZRXQz4vokppbsmSJRgzZkyO4bHGcNWqVZ/kPRPTp08X5jZv3hyxsbGitePHh2LRInHjevbse2jfPgVe4k3peSGx63lwbCzCJkwQzb03YwZSOna0a31+JqWkpKB06dKipRMnThTtzg8LCxNdU5AfP3KtUUp7cuVHfuQjQA10+ViTJ3USoAa6OutGURMBuwlQA91uVDQxBwL0xpykITcB0pzcxMmfiQBpj7QgJwHSm5y0yRcjQJojHShBgHSnBHVt+iStua7u8+fPB2t+m0ZO92ibnrPmerNmzawCYo1b1sDNPnbuDMA779g+trtkyUz06vUIXbsmw8cn9xxbt24NdgS3aXz22Wfo2rWr8Ud2NHzr1pH480/zsfChoQYcP34LQUHW96nbQ1MpzbVp0wa//vprjiGuWLECjdj94YDx3m92/7dptGvXTvQze71evcK4fNkMd/jwRAwZ8sgeBA7P8bpzB4Xr1YNXonlne/qLL+L2d9+xNyoO23NkAdPtjRs3hCWlSpUSnVhQsmRJ/PTTT46YVGyuUtpTLGFy7DIC1EB3GVoy7CEEqIHuIYWkNIhATgSogU7acJYAvTF3liCtd5QAac5RYjRfKgKkPalIkh17CJDe7KFEc6QkQJqTkibZspcA6c5eUjTPWQKkNWcJ5rx+yJAhonuzu3TpgmnTpuW44NKlS6hfv77V802bNoE1MbOPLl0KYf/+ANHLdeumoXfvR3jttTS7+6odO3YUNUDZDuNevXoJds+f90HjxpHgefOfw6dMuY+33krOFzwlNHft2jXUrl0713jnzp2L9u3bG+dMmDBBtOO8U6dO+OKLL4T1ly55o379p0X2tm9PQJUqzt0Pn1OAYSNGIHjVKtHjhE2bkGFDF/kqSi6LRowYgVXZfFtOz+uLIVLH44w9JbTnTLy01n0JUAPdfWtDkbkHAWqgu0cdKAoi4DIC1EB3GVrNGKY35poptdskSppzm1JoLhDSnuZKrmjCpDdF8WvSOWlOk2VXPGnSneIl0EwApDXXlZrdI37y5EnBwfjx49GnT58cHd68eRMvv/yy1fPDhw+jePHiotcvX/ZGvXriBu6nn97H22873tR+++23sWfPHsH+qFGjMGDAAJG/t94qhL17zc16b28e0dEpGDToIYoXt32neE6JKqE51hyfOnWqEBI7Tp/tpLbceT9p0iT07NnTOGf06NFYtmyZMJ8xsrwTPTY2GBMmhAnPCxfOMu7Kd8VmcL+ff0bEkyP1TQ6T27XDfYsd8q5TMYy7zRs2bAiDwWDTzeuvv45vvvnGlSFIZlsJ7UkWPBlyKwLUQHerclAwbkiAGuhuWBQKiQhISYAa6FLS1KYtemOuzbormTVpTkn62vZN2tN2/eXOnvQmN3HyR5ojDShBgHSnBHVt+iStuabuPM+jXLlyePTIfKT3ypUrjY3InMaDBw9QoUIFq8cXLlxAYGCg6PXJk0OxYIH5/u2wsMfHqgcGOn6s+rvvvovv2FHgT8bgwYPBdh1bjv37/dGly1NWsfn48OjUKRmTJj2Av799LOXQXEJCAo4ePYqaNWsiMjISr732Gv766y8hQHZE/d27d7Ft2zbhNXZiwPDhw40/Dx06FGvXrhWesS8+sC9AmEbnzoVw8KD5CwUdOyZjxoz79gGwd1ZqKgrMn4+QefPApaUJqwxBQYjfvx+GokXtteT0vPfffx/sJARbI6+TFZx2LqEBObQnYbhkyo0JUAPdjYtDobkFAWqgu0UZKAgi4DoC1EB3HVutWKY35lqptPvkSZpzn1poLRLSntYqrmy+pDdl+WvRO2lOi1VXPmfSnfI10EoEpDXXVPq///5D9erVRcZZQ7doLk3PjIwMlChRwiqgf//9V/Qau5e8evWnce+et/A6u+984kTz/diOZMUa5nq9Xljy3nvvYezYsSITbPNxmzYROHHCz6bpNm2SMX++fQ1kV2uO7Zhu2rQp0tLSEBoaitmzZ6NHjx6iuNevX48NGzaAfanBNCx3mWdvGLMd+WxnPs8DCxYE4+OPzbvP2fqvv76LFi1SHcGe61z/vXsRNnYsfK5etZr3YOJEJFkcsS+Z01wMnTt3Do0bN7Y5Y+DAgRg5cqQcYTjtw9XaczpAMqAaAtRAV02pKFCFCFADXSHw5JYIyEWAGuhykfZcP/TG3HNr666ZkebctTKeHxdpz/Nr7E4Zkt7cqRraiIU0p406u1uWpDt3q4jnxkNac01tDx48iM6dOwvGg4ODwZqQHJf7n5RtNdizN9DXrQvEoEHhosAPHLiF0qUdO0rdZIA1hpcvXy7Yy35cuenBw4cc5swpgMWLg5GS4mUFbseOBFSunPcd4K7W3LBhw7BmzZocC/vss8/iyJEj+OyzzzDP4hj01q1b46uvvjKuY3fAb9++XbDBdqYPGDAEH30UhuXLg0W2/f15/PbbTYSEOL7731aQBb78EqGffmoz/rTatXGH7Yz3Nn95wjUKtrbau3dv0Y590wzLo+/liiW/flytvfzGRevUR4Aa6OqrGUUsLwFqoMvLm7wRAdkJUANdduQe55DemHtcSd0+IdKc25fIYwMk7Xlsad0yMdKbW5bFo4MizXl0ed02OdKd25bG4wIjrbmmpOxO6HHjxgnGX3zxRZvNx+zeszfQvb29ce3aNdG0li0jcPKkeSd4vXppWLv2Tr4TmTBhAmJjY4X1HTt2xIwZM3K0d/u2F+bPL4ClS4ORmmr+E/kbb6Rg0aJ7ecbhas3ltsufBdevXz+MGTMGCxYswOTJk4V469evLzTeu3Xrhn379gnPhg+fiBMnPsC+feZj200PR49ORP/+5qP68wSQywS/Q4fwVMeO4NhWd4vBe3sjqUcPPBw5EnxQkDMu8r329OnTaNasmdX6L7/8Em3bts23XTkXulp7cuZCvpQlQA10ZfmTd/cnQA10968RRUgEnCJADXSn8NFiAPTGnGQgNwHSnNzEyZ+JAGmPtCAnAdKbnLTJFyNAmiMdKEGAdKcEdW36JK25pu7sSOsVK1YIxjt06IA5c+bk6Sx78zcsLAxnzpwR1p044YtWrSJFdpw9Pnzq1KmYO3euYLNNmzaYP39+nrHOm1cAU6aEiuZ9/30CqlbNfRe6KzXH7j6vWrVqrrHv2rULFStWNN5xzu46N41KlSph586dxh9jYmJw6NAh4VmVKr/j998rW9kdMyYRffs+Qh4HC+TJ0vh+4+5dRDZtCu+bN0Xz02rUwINP/s/eeYBHUa1v/J1NTwiBUOSqiKIi4LUhehH40ywgUhQzoQkiCgh2bIgFAZUrCiIqIKiIYEkmIFVEEEUFURRQ8Sp2rKGFAKkk2fk/Z0k22WSTzGZnZ6e853l87jV7zld+33vnTvbLOedxFJ99tiY7oZw0YsQIrF+/3seF2O0v/vjACiOU2rNC/oxRPwJsoOvHkpbsSYANdHvWlVmRgJcAG+gUQ7AE+GIeLEGuD5QANRcoMc7XiwC1pxdJ2tFCgHrTQolz9CRAzelJk7a0EqDutJLivGAJUGvBEvS/XjTMt27d6v3wgQcewK233lqrs8oNdPHv4u70snHLLQ2wfHn5DuSTTirGli37EBlZq+lqJ4g7wqdPn+79XOwyfuWVV2o1KI50/89/TsDhw+XHuV98cSGWLTtYY0M5lJp75513II4ar260atXKs7NcHKW/YcMGiOPqy4b4Y4Vvv/3W85n4I4Ivvvii9KOTAfzhY1Ic2z5r1iH066fTveeqiuQRIxC7YYOPn6Pjx+PoXXeJv+artR5GTNi5cyeuuuoqH1ebNm3CGWecYYT7oH2EUntBB0cDliLABrqlysVgw0CADfQwQKdLEjCSABvoRtK2py++mNuzrmbOipozc3XsHRu1Z+/6mi076s1sFbF/PNSc/WtsxgypOzNWxZ4xUWuhqes555yDrKwsr3HRkPZ3/HVl75Ub6K1bt8b777/vmfbPPy506HACiovLv5YWO6DHjQvu+PAXX3wR4h7rstGlSxe8+eabmsCIo9wff9x3F3ptO+JDqbnJkydj/vz51cYuTga4/fbbPZ+Lu+Uvvvhin7li1/mpp56KXr164Ztvvin9TOxSn+GdFx/vxhtvHMRFF9V+37smiAASXn4ZSRWO/BfrCrp2RZY4xcAkzfOyXMSufbF7X4wLL7wQK1as8PzRgRVGKLVnhfwZo34E2EDXjyUt2ZOANf5fwZ7smRUJGEKADXRDMNvaCV/MbV1eUyZHzZmyLI4IitpzRJlNkyT1ZppSOCYQas4xpTZVotSdqcph62CoNf3LKxrnooFecXz88cdo2bJlrc4GDx6Mjz76yDvvmWee8RwnLsaTTyZi9uxE72exsW588cVeNGzoe192rU4qTVi0aBEmTpzo/aloKr/99tuazBQUAN26NcUff5RvgW/RohgffLAPMTH+TYRSc1deeSW+/vprv45Fk1c0yFu0aOH5XFVVz3HvBw4c8M4XR9eL3efdu3fHDz/8UPpzcQLARd45AwfmYebMbE18tEyK3LULTfr2hXTsmHd6SePG2L9+PdxNm2oxYeic4uJirFy5Ejk5OZBlGXFxcYb6D8ZZKLUXTFxcaz0CbKBbr2aM2FgCbKAby5veSMBwAmygG47cdg75Ym67kpo+IWrO9CWybYDUnm1La8rEqDdTlsXWQVFzti6vaZOj7kxbGtsFRq3pX9LPPvsMAwYM8BqOjo7Gjz/+iEgN56yLRvvIkSORl5fnacKLRqVY//ffLvTs2QRZWRFeu0OH5mL69MNBJ5Ceno67xDHhpePcc8/F2rVrNdtdtSoWN9+c7DP/4YcP4+abc/3aCJXmxPHrV1xxRbU+xc7pinmKicOGDfMc6V42Ro8ejUmTJkEwOHjwIADxRw8/+9gUu8+7di3UzKemiVJeHhr36oWon319HHz9dRR266aLDxopJxAq7ZGx8wiwge68mjPjwAiwgR4YL84mAcsRYAPdciUzXcB8MTddSWwfEDVn+xKbNkFqz7SlsWVg1Jsty2rqpKg5U5fHtsFRd7YtrekSo9b0L8nixYsxYcIEr+E2bdp47tvWOjIzMz3Hi4smblRUFHbujMLIkcnYu7e8eS5sbdy4D2edVazVbLXzRJN+7Nix3s/FPeEffPCBZruqClxzTSNs21a+5Twx0Y3Nm/ehUSN3FTuh0tx9992H119/3etPNLjEHzMcPXoUJSUlaNKkSZVYZsyYgZkzZ3p/3qFDB8ybN8+zM/34eADAE97Pk5N2S3t7AAAgAElEQVRLsGPH3qDunHft3YuYjz5CzKZNnv+M8DTqy0fOzTfjyMMPa+bPidoJhEp72iPgTLsQYAPdLpVkHqEiwAZ6qMjSLgmYhAAb6CYphIXD4Iu5hYtn0dCpOYsWzgZhU3s2KKKFUqDeLFQsm4RKzdmkkBZLg7qzWMEsHC61pn/xHnnkEbz88stew3379vU0ZesyVq6MxV13NURBge9X0d27F2DJkvI71utiu2zNe++9hxtuuMFronnz5ti6dWtAJnfsiEKfPr4N6h49CjxHnTdp4ttE11Nzrn/+QcTPP+OT11/HrlWr0FxVcSoAcah47Jln4rSUFBR26ICic88FoqOr5LR+/XqMGDHC+/P4+Hg8/fTTGDduXOnPvgJwrvfzYcNy8d//+u76j966FTGbN6OwY0ccu+QSv9wid+9GvKIg5sMPEfXdd9WyPXbeeTiwfLnfWAMqCCf7JaCn9ojY2QTYQHd2/Zl97QTYQK+dEWeQgKUJsIFu6fKZIni+mJuiDI4KgppzVLlNlSy1Z6py2D4Y6s32JTZdgtSc6UriiICoO0eU2RRJUmv6l6HyPebi6PC77747YEevvJKAhx9OqrLu1FOLkZ5+ACedVHV3d8BOAM+d6yLmsiF2au/cuTNgU7fd1gDLlsX7rBM70e+55yhGjMj17tr2as7txt5ffoF65AhcOTmQxD9Hj3ruAnc3agRxvLnr0CHPz1zin+xsRO3aBdf+/R4fEXv3IiIzU3Oc7nr1oNarB3f9+ig5+WQU9OiBvWecgbaDBqEiyZiYGBQWiiPa2wL41sd+RsYBXHLJ8bvKXfv2IenhhxG3erXn31VJQtaSJb5HrxcXo97s2UicNQtSSUmNsboTErB/3TqUnHaa5pw4MTACfN4FxouzqyfABjrVQQI1E2ADnQohAZsTYAPd5gU2ID2+mBsAmS58CFBzFES4CFB74SLvTL/UmzPrHs6sqblw0neub+rOubU3OnNqTX/i7du3xz///OM1PHfuXPTr1y8gR3v2RKBLl6YoLvb9Crpjx0LMn5+Fhg3VgOzVNHnbtm24+uqrvVMSExPx/fffB2z/r79cnpgLClxV1rZpU4Q3Z32PUz9f4dmFHfvtt8DevYA4/z3MIzM6Gm8eO4bfAPwJ4I/Sf/ZiElQ8iqbYi/OxE/0S1uOGLt8hIvuQp7EfsWcPXPn5PtEfO+ccHHrhBUT+/LPnn7g1axC9Y0etGRa1bYvsp59G0Xnn1TqXE+pOgM+7urPjSl8CbKBTESRQMwE20KkQErA5ATbQbV5gA9Lji7kBkOnChwA1R0GEiwC1Fy7yzvRLvTmz7uHMmpoLJ33n+qbunFt7ozOn1vQlLu7bbt26tY9Rcf+5uAc9kPHf/ybiuecSfZYMHZqLxx8/jKioQCzVPnfXrl3o2bOnd6K4d/2330Q7OfCxYUMMbr21IY4edSERR3AOvsG5+BodsBWDpbcQrR7fvW2FkYdYHEMMGsD3yHY9YncnJqKwc2cUdu3q+afklFP0MEsbtRDg844S0YsAG+h6kaQduxJgA92ulWVeJFBKgA10SiFYAnwxD5Yg1wdKgJoLlBjn60WA2tOLJO1oIUC9aaHEOXoSoOb0pElbWglQd1pJcV6wBKi1YAn6rv/qq6/Qu3dv7w8F359++gniWHCto7gY+M9/TkBmZoR3yeDBuXjqqcOQQvCNtIiva9euPuGJn8XFiZvEAxiq6tld7lqUhmObv8YJeXsCWFz3qUcB/Argr4gInNqtG07s2BFqXByifvgB4n7yqDrspq97NNWvVCMikDtyJAquugrHzj8fuv8lRCiCtplNPu9sVtAwpsMGehjh07UlCITgdcUSeTNIEnAMATbQHVPqkCXKF/OQoaXhaghQc5RGuAhQe+Ei70y/1Jsz6x7OrKm5cNJ3rm/qzrm1Nzpzak1f4uvWrcPIkSO9Rk866SR8/vnnATlZvz4GI0Y08lmzfv0+tG1bHJAdrZNzc3PRqlUrn+kij3//+9+1m1BVRPzxB2Lefx8Jr73maVrXdagxMRD3gIvGsuvAAagJCXAnJ3vuLFcTE4/fXX7iiShu3Rq7f/wRT86fj90AvgMQGxcHEfPpp59exb2Une25K13co+65T/3AAcR8/DGit21D5B/iwPbQj+JTT8Wh555DUbt2oXdGD9US4POO4tCLABvoepGkHbsSYAPdrpVlXiRQSoANdEohWAJ8MQ+WINcHSoCaC5QY5+tFgNrTiyTtaCFAvWmhxDl6EqDm9KRJW1oJUHdaSXFesASotWAJ+q5fsmQJ7r//fu8Pzz//fKxZsyYgJ8OGJWPjxtgKNo5hzZoDAdkIdHLHjh2xZ0/5jvFZs2YhtVcvz45ycYe3aJKL+8rdSUke0xF793qa0hH//ANXdrZmd4WIxsHu/XDiqAFA27bYf+wYiuPioNarB0RHa7bz4osvYsqUKd75F1xwAVavXq15vWeiqiL6iy8Qu2bN8Tz274f6++9w/ZOJaPjezX4sIhY4ry0KL7kE7hNOON7Yb9gQReIPD2Ji0KRbN0RkZXn9lyQno/iMM1B8+ukouuAC5F9zDdT4+MDi42zdCfB5pztSxxpkA92xpWfiGgmwga4RFKeRgFUJsIFu1cqZJ26+mJunFk6JhJpzSqXNlye1Z76a2Dki6s3O1TVnbtScOeti96ioO7tX2Dz5UWv61uKZZ57B008/7TV62WWXYdGiRZqd7NkTgU6dmkJVy796nj49G0OH5mm2EfBEVcX8fv1w/vbtOA1AAwBNoqMRV1QESfVtJAdqW42MxP/crbHTfS62ox3exGA0+ncT7NgRhchIIDMzE263O1CzGD9+PNLS0rzrBg0ahBkzZgRsp2yBSPOLL6KQkRGPlcujUT8nE52wGYk4ii9xIZ5+719ofXb1LCL/9z/PDnzRWM9LSUFJy5Z1joULQ0eAz7vQsXWaZTbQnVZx5hsoATbQAyXm8PmyLP8iEKiquigjI2NyoDhSUlIekCRplDChKErV84gCNcj5tRJgA71WRJxQCwG+mFMiRhOg5owmTn9lBKg9asFIAtSbkbTpSxCg5qiDcBCg7sJB3Zk+qTV96/7ggw/i1Vdf9RodPHiwT0O9Nm9Tp9bHvHn1vNPq13fjyy/3Ij4+uEZ2dX7FkebJN96ImM2bawtN8+dFZ56J3BEjcOyiizy7sJ9f0AjTptX3WT9/PjBqVN0b6H369MGOHTu8NidNmoTRo0drjrHixF27InH77Q2xe3eU3/XDhuXiv/89XCfbXGQuAnzemaseVo6GDXQrV4+xG0GADXQjKNvIhyzL4s8pVUmSXkhPT7890NRSUlKekiTp7tIGekSg6zk/cAJsoAfOjCt8CfDFnIowmgA1ZzRx+isjQO1RC0YSoN6MpE1fggA1Rx2EgwB1Fw7qzvRJrelbd9HErXhk+2233YYJEyZocpKfD7Rv3wzZ2S7v/JtuysHkyUc0rQ94UkEBGg0bhpgtWwJeWnlBScOGKOzeHfkDBqCwWzdAKv/qvKgI6NWrCb7/vrxBLU4+/9//gP37A9+BLnasn3XWWcjLK9+V/+abb6JLly4B5/HXXxG44oomPswrGjn55GK8//5+1KsXmj9gCDhgLgiKAJ93QeHj4goE2ECnHEigZgJsoFMhARFgAz0gXKaYzAa6Kcpg6SD4Ym7p8lkyeGrOkmWzRdDUni3KaJkkqDfLlMo2gVJztimlpRKh7ixVLksHS63pW74BAwbgs88+8xoV93TfeOONmpykpcVh/PiGPnM//ngvWrYs0bQ+0ElJ996LhDfeqHGZGhWFwo4dUXTOOVBjYhBx8KDYHQR306YoEXeBN2uG4hYtUNKihU/TvLLRjz6KweDBjXx+vGwZcMklgTfQf//9d1xyySU+trZv3w7R0ApkiD9YSE1tjO3b/d+93q1bAZ56Khsnnhj4EfOBxMG5xhHg88441nb3xAa63SvM/IIlwAZ6sAQdtl6HBvpMSZLuBFCkKEqMw/CFJV020MOC3VZO+WJuq3JaIhlqzhJlsmWQ1J4ty2rapKg305bGtoFRc7YtrakTo+5MXR5bBUetBV/Offv2ee45j4yMxMKFC3Hw4EGv0Tlz5qB///6anPTu3RhffVXezO3atQBvvJGlaW2gk+KWL0fDW27xWXYIwGMAdgPIBvDQ9Ok47+qroSYkBGq+ynxxx3jPnk3w7bflu9C7dgXeeivwBvr69esxYsQIr48GDRpg165dkCrseq8t4OJicYR8Q7z3XpzP1Ph4N269NQfXXpuPk08OzR8u1BYbPw8dAT7vQsfWaZbZQHdaxZlvoATYQA+UmMPnB9tAl2U5A8AAAFmKojR2OE5D0mcD3RDMtnbCF3Nbl9eUyVFzpiyLI4Ki9hxRZtMkSb2ZphSOCYSac0ypTZUodWeqctg6GGotuPKqquppkH/55Zd+DaWnp6NTp061Ovn11wh07uy7g3rhwoO44orCWtcGOiHil1/QpFcvuHJzvUvd8fEYfNJJSP/xR+/PgrlX3F9My5bF4bbbfHfYf/rpfpxySlFAKYg75sVd82XjggsuwOrVqwOysXBhPB56qIHPmubNi7Fu3X4kJfG49oBgWmgyn3cWKpbJQ2UD3eQFYnhhJ8AGethLYK0Agmmgy7J8IYBNAMSfRX6mKEpHa2VvzWjZQLdm3cwUNV/MzVQNZ8RCzTmjzmbMktozY1XsGxP1Zt/amjUzas6slbF3XNSdvetrpuyoteCq8f333+PSSy+t1sgHH3yAVuLC71rGSy8lYNKkJO+sxo1LsH37XkRE1LZS++eu/fsR/8YbSFi0CBF79/osPDR7NsZt2YK33nrL+/OBAwdi5syZ2h3UMrOwEGjXzveO9zvvzMG99wZ2x/u0adPw/PPPe7317t0bCxYs0Byn2H3eqVNT/PlnpHdN/fpuLF16AG3bFmu2w4nWI8DnnfVqZtaI2UA3a2UYl1kIsIFulkqYMA5Zljf6CasbAPEnjH8DKP9zzprjF+canQSgBQChOVWSpKnp6emPmjBt24XEBrrtSmp4QnwxNxy54x1Sc46XQNgAUHthQ+9Ix9SbI8se1qSpubDid6xz6s6xpTc8cWotOORLlizB/fffX62Rb775BsnJybU6GTw4GR99FOudl5qah2eeEQepBz9c//yD+o8/jrjVqyEVVd3tnZeaiuxnnsFLL70Eseu8bJx77rlYu3Zt8AFUsPDII/Xx8sv1vD9JTHTjxRcPoWtX7Tvt77jjDmRkiIM6jw9xx7y4a17rWLEiFuPG+dbk1VcP4vLLtceg1RfnmYsAn3fmqoeVo2ED3crVY+xGEGAD3QjKFvVRttu8UvhlmqnLOUBla/+JjIw878033zxgUTSWCpsNdEuVy5TB8sXclGWxdVDUnK3La+rkqD1Tl8d2wVFvtiup6ROi5kxfIlsGSN3ZsqymTIpaC64sd955JxRF8WtE3In+66+/QjCuaeTkSDjnnGY4dqz86+YXX8xCnz4FwQUHwLVvH5pceSUiMjP92io64wwcWLsWanw8PvnkE4hd52UjNjYWP/zwAyJ03Aa/a1ckevZs6hNLRISKyZMPY8SIPGi5xjw1NRWbN2/22njooYcwduxYTayOHQN69WqC3bvL72K/4IJjWLXqgCbfmpxwkmkJ8Hln2tJYLjA20C1XMgZsMAE20A0GbiV3pQ10PUPOkyRpuaqqExVF+V1Pw7RVPQE20KmOYAnwxTxYglwfKAFqLlBinK8XAWpPL5K0o4UA9aaFEufoSYCa05MmbWklQN1pJcV5wRKg1oIj2LlzZ0+T3N9o1qxZtXejl80vKQHGjm2INWvErY3HR2Skim++yUT9+nXZg1MhkpISNBo0CDFbtviNr7BDB2TPno2Sk8Thl8DBgwchdp1XHMuXL0fz5s0hctFrjB6djDVrynfbl9kdNiwXU6ceRlR5b9uvyy5duuDnn3/2fvbCCy/g6quvrjW87GwJ06fXx6JFCT5z587NQr9+wf+xQq0BcELYCfB5F/YS2CYANtBtU0omEiICbKCHCKwdzMqy3LViHqoqTl6XxLHu4s13OYDZteUpSZKqqmqBy+U62Lp1618nT57srm0NP9eXABvo+vJ0ojW+mDux6uHNmZoLL38ne6f2nFx943On3oxn7nSP1JzTFRCe/Km78HB3oldqre5Vz8rKwjnnnFOtgUaNGuHrr7+u9nNVBR56KAmvvurb0O3evQBLlmTVPbDSlYlPPonE2b5fQaqxscjv3x+511+PovPOq+KjXbt22FvpfnQx6ZZbbsHEiRODjkkYKChw4f77m6HCKexeuwMG5GH27Oxqd4Orquq5Uz4vL8+7ZunSpejQoUO1sf3yS4Tn2Pj09Djk5fmeBnD22UVYu3a/rnfN6wKJRkJCgM+7kGB1pFE20B1ZdiYdAAE20AOAxalA2a50SZKeT09Pv51MzE+ADXTz18jsEfLF3OwVsl981Jz9amqVjKg9q1TKHnFSb/aoo5WyoOasVC37xErd2aeWZs+EWqt7hd577z3ccMMN1Rpo2rQpduzYUe3nzz5bz7MjuuKoV8+NFSsOoHXr4roHBiB23TokjxzpY6PkX//C/jVr4D7hhGptDxkyBJs2baryudDJZ599hhNPPNH7mbjffcGCBRB53n777ahfv77nyPc5c+ZAkiSMHj0abdq08WuradNmmDwZ8Hd1+S23HMXEiUf9xnjkyJEqNrds2YIWLVr4nT9nTj1Mm5YIt7vqV/kul4rVqw/gvPOq3gsfFHwuNi0BPu9MWxrLBcYGuuVKxoANJsAGusHAre4uJSVlkshBkqTPFEV51+r5OCF+NtCdUOXQ5sgX89DypfWqBKg5qiJcBKi9cJF3pl/qzZl1D2fW1Fw46TvXN3Xn3NobnTm1Vnfi06ZNw/PPP1+tgRtvvBFT/HWIAbz1Vhzuvruhz9qoKBVLlhxE587H6hxUxC+/ID4tDfVeeAGS2OJeOtTISBxYuhRF7dvXaHvq1KmYN2+e3zmzZ8/Gtdde6/ns6NGjuPjiiyEa2mKIn8+YMQOXXnqp93h1cYe6+Fnl49Urau7FF7Nx551JKCjw/apdHOU+cmRulTh2796NHj16+PxcHOcufFUe8+YlYOrUpGrznTTpMEaPruqjzvC50PQE+LwzfYksEyAb6JYpFQMNEwE20MME3qpuU1JSZorYXS6Xu0GDBg/Mnz+ff95o8mKygW7yAlkgPL6YW6BINguRmrNZQS2UDrVnoWLZIFTqzQZFtFgK1JzFCmaTcKk7mxTSAmlQa3Uvkmgab9261WtA7N7evHkz9uzZA3F8+9tvv43TTz+9ioODB13o0KGpz3HikqTihRcOoX//wO/ijvzpJ8SuWoW4NWsQ9d13fhM6/OijyB01qtZkMzIycMcdd/id9/DDD+Pmm2/2fJaWlobx48f7zHvzzTcxePDgKmvHjRuHCRMmICIiwvNZZc2tXx+FG25IRklJ+dftgsfKlQfQrp3v16did7zgXDaSk5MhdsJXHq++Go8HH2xQ5ecRESquuqoAo0fn4IIL+NVsrYKw2QQ+72xW0DCmwwZ6GOHTtSUIsIFuiTKZJ8jSI9zFn35uVhSli3kiYyTVEWADndoIlgBfzIMlyPWBEqDmAiXG+XoRoPb0Ikk7WghQb1oocY6eBKg5PWnSllYC1J1WUpwXLAFqrW4Ei4qK0Lp1axQUlDe8Fy1ahI4dO+K7777zfJaQ4Hu3eZmntLQ4jB/vu/u8uh3XNUanqqj/+OOoN3dujdPyBg5E9owZ4ljMWpPdtWsXevbs6Xfe0KFDMX36dM9nopn+yiuv1GqvbEK3bt3QqVMnzxHvV155pffo+8zMTLjdbvhjIu5Df+65bB8fb731Fu6++27vz9q2bYv169f7zFm3LhYjRyZXie2mm3I8jfOTTnJrjpsT7UWAzzt71TOc2bCBHk769G0FArW/cVghC8ZoGAFZlsUbXyKAhYqi3GSYYzqqMwE20OuMjgtLCfDFnFIwmgA1ZzRx+isjQO1RC0YSoN6MpE1fggA1Rx2EgwB1Fw7qzvRJrdWt7l999RV69+7ts1jshBY7omsb48Y1wIoV8d5pl19egFdfzaptWZXPE+bNQ9LUqTWuy7vmGmTPmgVERmqyX1hYiDPPPBMlJSVV5nfo0AFLly71/FzceV723zUZrjTp008/hbBX1kAXH4u7yp9/Xnx1enwkJLjx1VeZiIsrXyx2si9evNj7A3Fk/Guvveb997//duHyy5siO9vl4/GRRw5jzBge116XWtlpDZ93dqpmeHNhAz28/Ond/ATYQDd/jUwVoSzL4gylVpIkLUlPT7/eVMExGL8E2ECnMIIlwBfzYAlyfaAEqLlAiXG+XgSoPb1I0o4WAtSbFkqcoycBak5PmrSllQB1p5UU5wVLgFqrG8GXX34ZjzzyiHfxGWecAXG8eG1D9KVPOeVEn2lPPZWNIUPyalvq83n05s1oNGgQJHfV3dQlTZqg4Morkd+vH45dcklAdsVkcce4uGu88mjSpAl27tzp+bH44wHxRwQ1DaEtsbvc3xC73N99912fBvrevS60b38C3O7yr93nz8/yHLkuxrfffosrrrjCx1zFXfG7d0d6dp7/9pvvHwvcc88R3HVXTsAcuMB+BPi8s19Nw5URG+jhIk+/ViHABrpVKmWSOFNTUxeoqnojgK8VRTnfJGExjBoIsIFOeQRLgC/mwRLk+kAJUHOBEuN8vQhQe3qRpB0tBKg3LZQ4R08C1JyeNGlLKwHqTispzguWALVWN4Jjx47FypUrvYsHDhyImTNn1mjszz8j0KlTUxQX+36t/PnnmQEdK+46cABNLr8cEfv2+fgTu83zhg3DsfbtgdL7xuuS3ZgxY7B69Wq/S8Xx9ImJiTjrrLOQm1v9ju4TTzwRTz75JIYNG1ZtCKqq+jTQxcTU1EbYvDnGu6Z373wsWHDI04gXzXPhv+J4/PHH0bfvSMycmYjFi+N97lEX8/r0yce8eYe0nF5fF1RcYzECfN5ZrGAmDpcNdBMXh6GZggAb6KYog3WCSE1N7aSq6scAVFVV/y8jI2OLdaJ3ZqRsoDuz7npmzRdzPWnSlhYC1JwWSpwTCgLUXiio0mZ1BKg3asNoAtSc0cTpTxCg7qgDowhQa3UjfdFFF+Hvv//2Ln7qqacwZMiQGo0NH56M99+P9Zlz5plF+PDD/dqDcLuRfN11iK202/3Ivfci5847tdupYeYdd9yBjIwMvzNEY71p06a4+OKLa/Q1ceJEjBw5EmJnfnXDXwP99dfjcd99DbxLIiNVbNu2F19//R6uv973QM+LL+6Mbt1WYO7cBjh61PfIdmGgRYtirF27H0lJqi5caMT6BPi8s34NzZIBG+hmqQTjMCsBNtDNWhkTx5WamjpPVdXRAPa4XK7L0tLSfjZxuI4PjQ10x0sgaAB8MQ8aIQ0ESICaCxAYp+tGgNrTDSUNaSBAvWmAxCm6EqDmdMVJYxoJUHcaQXFa0ASotcARisa5aKBXHB988AFatWpVrTGx+7xDh6ZQVd+vlMePP4q77z6qOYh6L7yA+k884TO/oFs3ZIl7wV1Vm8iaDVeYuGHDhirN6rKPn332WYij3Gv6YwHxxwSpqamIjIzE2WefjezsbL9h+GugHz4soV27E1BQUJ7LmDFH8eWXV+KLLzZ77Zxyyplo3nwnNm8uv0u+opNzzjnmuVe+WTP/R8jXhQvXWJ8An3fWr6FZMmAD3SyVYBxmJcAGulkrY+K4Ro8eHZWVlfWMJEnjAORKkvScJElprVu3/mby5Ml8ozNZ7dhAN1lBLBgOX8wtWDSLh0zNWbyAFg6f2rNw8SwYOvVmwaJZPGRqzuIFtGj41J1FC2fBsK2otWPHjmHjxo1ISkry7ISOCOK48rqUbNWqVbj55pu9S0Ucu3bt8pwcUd2YMSPRc8x4xSGOF581Kxtxcdp2SEdt24bG114LSVykXjpKTjgB+997D+7GjeuSit81xcXFuOyyy/Djjz9W+VzsKm/RogUmTZrkd+34du3waMeO3s/+88Yb+C4ry+9cdcIE5OTkoGL2eYMH447Z5yMtrXJjXNxhvh7A9wDi0bjx9ThwoHynepmD2FgVo0bl4I47cjRz1Q0cDZmegBWfd6aH6tAA2UB3aOGZtmYCbKBrRsWJgoAsy79UINEcQIQ4zr30Z0UADgHI10BLVRTldA3zOCVIAmygBwmQy3nsIjVgOAH+Mmg4cjosJUDtUQpGEqDejKRNX4IANUcdhIMAdRcO6s70aTWtiV3Lw4cP9zTQxRDHjd93330hLZ5oKAtOZQ1y0Tx+6aWXvD579OiBxWIHeDVD9LvF7vO//470zhg6NBfTpx/WFLdr3z4kLFiAhNdegytHNJKPD9XlwsG0NByr0LDWZFDDJNHYXrNmjeco9y1bym+hbNOmDdq1a4fXX3/dr5WdLhfOrfAHDb2Ki/Ge6v8PBPIiIxErlX7F7nZ7/jDg0IwZ2NxqGPr2baIhSt8pAwbkYcKEIwHdJx+wEy6wNAGrPe8sDdvmwbOBbvMCM72gCbCBHjRCZxmQZVnsMPf3xlimJS1/birmiga6aL5zhJgAG+ghBuwA83wxd0CRTZYiNWeygjgoHGrPQcU2QarUmwmK4LAQqDmHFdwk6VJ3JimEA8KwmtbETu+ePXt6KyOOCf/mm29Qv379kFRr6dKlmDx5MqKiojBt2jRcccUV6NOnD3bs2OH1d8899+Cuu+6q1v+HH8Zg6NBGPp+/++5+nHOO2E9T/XBlZSFx+nTEp6dDKiysMvHI3XcjZ/z4kORdZvSLL75A//79fXyceeaZfnenD42KwuKiIlT80nwkgIXVRCjutWxZ+pn4UtTdsCH2bdsGd2wchg5NxqZNvvfFV5dovXpuLFyYhY4dj4WUBY1bn4DVnnfWJ27fDFdVvqYAACAASURBVNhAt29tmZk+BNhA14ejY6yUNtD1yJcNdD0oarDBBroGSJxS8y+7LheaNWvmmZOZmQm3mzc1UDKhJcBfBkPLl9Zr+HKPzzvKw0ACfNYZCJuuPASoOQohHASou3BQd6ZPq2nt1VdfxYMPPuhTrPnz5+Oqq67SvYDi7m5xRHxubq7HduPGjfHxxx/jnHPOgdiVXjbeeust/N///V+1/kePbog1a+K8n//738ewbt2BGuOVDh1Ckz59EPnbb37nFV5yiWf3OUJ8fH1RURHErvP8/OoPzXzkkUfQqVMnXPzJJ0h67DFIFXacPwTg8Woy/RhA59LPVEnCkUceQe7o0Z6f5OVJGDBgPb755gwAHcT/G/u10qCBGy+9lIVLLmHzXPf/AdjQoNWedzYsgW1SYgPdNqVkIiEiwAZ6iMDa1eyAAQNa6JXbsmXL9uhli3aqJ8AGOtURLAG+mAdLkOsDJUDNBUqM8/UiQO3pRZJ2tBCg3rRQ4hw9CVBzetKkLa0EqDutpDgvWAJm1JpoTosjwvfv34+BAweieXNxE+LxIY5rr3x8+KBBgzBjxoxgUVRZv3btWtx0000+P584cSKeeOIJ788Ev++++w716tXz6/+TT6Jx3XWNUFRU/lXy449nY8SIvJq+kELyiBGI3bChyhxxbHv+1Vfj8BNPQE30vVNddwClBocMGYJNmzZVa178UUHLli0h5efjhPbtIWVne3ehzwUwrpqVaQBSS4/rLNt9/vM//3hOFPjrr7/w+ONlrXdxnHtvNGw4AB06XIrDhyORmRmBLl0KMX78UTRqxM0Koaq93eya8XlnN8ZOyYcNdKdUmnnWlQAb6HUlx3UkYBECbKBbpFAmDpMv5iYujk1Do+ZsWlgLpEXtWaBINgqRerNRMS2SCjVnkULZLEzqzmYFNXE6ZtOauON83LhxWLlypYeaaEyLHeZdu3b1/Ls4vl0c415xNG3aFNu3b4dUdp+2TrynTJmCF1980cdaUlISDh8uv7v87LPPxnvvvVfFo9igPnNmImbPrgdVLf8aOTZWxZdfZqJBg+pvckyYNw9JU6f62FSjo5Eny8gZOxYlp52mU4bazMyePRtPPvmk38nR0dGe49zFUfpiJLz4IupPnerdhS6q6HsAvK+ZRQCGle4+X9GyJcaMGYOCgoIqvoQO1q1bh1NPPVVb0JxFAn4ImO15xyJZlwAb6NatHSM3hgAb6MZwphcSCBsBNtDDht42jvlibptSWiYRas4ypbJdoNSe7Upq6oSoN1OXx5bBUXO2LKvpk6LuTF8i2wRoNq2JO8dvv/12H76iObtkyRK0b98erVu39jk+vWzia6+9hksvvVTXuohj4Xfu3FmjzeHDh3vuRq84/vnHhVtuaYjPPoupsnb48FxMm1begK88IXrrVjRKTYVUUuL9qKRRIxxYvRolp5yia35ajb3zzjsYNWqU3+miHu+//773s8q70L8E0L4WR7fHxODalSvRJyUFR48e9Tt7zpw5Ve5i1xo/55FAGQGzPe9YGesSYAPdurVj5MYQYAPdGM70QgJhI8AGetjQ28YxX8xtU0rLJELNWaZUtguU2rNdSU2dEPVm6vLYMjhqzpZlNX1S1J3pS2SbAM2kNXHfdrt27ZCVlRUwX7EzWTRyY2NjA17rb0FOTg7atm2LkgqNbH/zxO7sa6+91vvRvn0u9OnTGH/9dXxHdsXRvXsBFiw4hLg4/7vPo776ytM8d+XkeJeJu8Gz3ngDhV266JJXXYzs2LEDffr08btU/LzyLv2Ku9D/AXCiBqcxMTEoLCz0OzM5OdlzrDsHCQRLwEzPu2Bz4frwEmADPbz86d38BNhAN3+NGCEJBEWADfSg8HExAL6YUwZGE6DmjCZOf2UEqD1qwUgC1JuRtOlLEKDmqINwEKDuwkHdmT7NpDWx21vs+q7ruPXWW/HAAw/UdbnPug0bNuD666+v1daWLVvQokULz7zffovATTcl47vvonzWRUaqmDDhCMaMyYXL5d+kdPgwmvbogYjMTJ8JR++8E0fvvbfWOEI5ITMzExdeeKFfF3fddRfuuecen88q7kIXt5NX/VOCwKK96KKLsHz58sAWcTYJ+CFgpucdC2RtAmygW7t+jD70BNhADz1jeiCBsBJgAz2s+G3hnC/mtiijpZKg5ixVLlsFS+3ZqpymT4Z6M32JbBcgNWe7kloiIerOEmWyRZBm0prYQS6ORK/rELkMHDgQnTt3RseOHSHuRq/rGDJkCDZt2lTj8mbNmuGLL77A0aMuPP10Il57LQFFRb5fGZ98cjHmzDmECy8sqt5Wfj4a3Hsv4t9+22dOweWXI+vll4GIiLqmocs6sQv/tNNO87sbv7qj1SvuQu8NYG0QkYiazpw5MwgLXEoCxwmY6XnHmlibABvo1q4fow89ATbQQ8/Y1h5SU1N7ut3uyyRJOh9AYwCJ4v/HNSStKopyuoZ5nBIkATbQgwTI5XwxpwYMJ8BfBg1HToelBKg9SsFIAtSbkbTpi1+2UgPhIsBnXbjIO8+vmbSmKAruvPNObxFOOeUUNGnSBF9+KW7S9h2dOnXCHXfcgaFDh0Ic/e5viDvRReO1cWPxtZv28dVXX6F3b9H2rX5IkoTp02fi5JOH4YEHkvDbb1X3WZ90UjHeeecAGjcW+7D9jPx8JLz+Ouq98AIi9u3zmVB4ySU4uGQJoNOR9Nqz9z9T3D//zz/iQHbfsX79es9R95VHxV3ofwMQ5wKIg/nFQey/A/iX+Fl8PKbExeHAwYM1hjdx4kTccsstwabA9STA7+moAd0IsIGuG0oasikBNtBtWthQpzVw4MAObrd7IYBWFXyV6aniJUiVNSY+Ez8TDfTw/ulpqCGZxD4b6CYphIXDMNMXERbGyNADIEDNBQCLU3UlQO3pipPGaiFAvVEiRhOg5owmTn+CAHVHHRhFwExamz9/PiZPnuxNXewif+ONNzz3Xx87dgyJiYlISkry/CP+uxjiDvInn3yyWlyjR4/GpEmTAsI5atQovPPOO6VrzkN09KM499yuiIzMw8knJ6Kg4AAyM8VR7fWRm+t/L0zTpiV4/fWDaNu22Md39JYtiPz1V6gJCag/ZQoi9u6tEps7IQH7N25EycknBxR3KCf37dsX27dv93EhtPPjjz9We+98xV3oFReKdnl9AFEzZ2Jb584YM2YMxD3roq6HDx+uksZLL72EK6+8MpTp0bZDCJjpeecQ5LZNkw1025aWielEgA10nUA6yUxqauplqqquKb3+p7oGeUUkZQ31inPZQDdINGygGwTaxm74Ym7j4po0NWrOpIVxQFjUngOKbKIUqTcTFcMhoVBzDim0ydKk7kxWEBuHYyatiUa4aIiXDXEfumiq1zSKi4sxduzYCg1v39nnnnsu1q7VfoD4Dz/8gO7dewFoD6AfgNsBxGpWQHS0ipEjc3HbbUfRoEHFfTJAwoIFSHr00RptqS4XsmfPRv4112j2acRE3z8qOO7x1FNPxebNm6t1X3EXus8Xm2KHUKNGwO+/I/PIEc/R8D///LPnyP3Bgwdj586dPjY/+OADtGpVcR+SERnThx0JmOl5Z0e+TsqJDXQnVZu51oUAG+h1oebgNcOGDUsoKCj4DUCjUgzzXC7XwpKSklskSRIXPKklJSUto6KiEt1udwtJkrqoqip+fgKAHEmSxhUXF38s1i5btmyPg1Ealjob6Iahtq0jvpjbtrSmTYyaM21pbB8YtWf7EpsqQerNVOVwRDDUnCPKbLokqTvTlcS2AZlJaxMmTMDixYu9rK+77road5dXLMr333+PTz75BOvWrcOWLVu8H9WvXx//+9//II5cr24UFADffhuFTz6Jwcsv/42DB08DEBNwzdu0KcLLL2ehRYuSKmsjv/8eTXr1glTNcfNiQUHXrjh6770ouuCCgH2HesEjjzyCl8V97BXGZZddhkWLFtXo2t8udFWSIM2YAdx1FzIzM+F2lx9x72+n+y+//IKYmMDrEWomtG89AmZ63lmPHiOuSIANdOqBBGomwAY6FRIQAVmW7wDwjGiUA5ihKMp9woAsy88BEBf5VNlZLstynKqqMyVJGgOgSJKklPT09FUBOebkOhNgA73O6LiwlABfzCkFowlQc0YTp78yAtQetWAkAerNSNr0JQhQc9RBOAhQd+Gg7kyfZtKaOMp79erV3kLcdtttEE31QMaePXsgjn4vHy3x/PMbUVhYH//8E4HMzAjvfx444EJUlIq9eyNQVFT3r3pPPbUYvXvn4/bbc5CY6Lvr3BNHcTEa9++P6Eo7q8tiLOzQAUcmTEDRRRcFkqqhc+fOnYvHHnvMx6fY+f/QQw/VGEflXeiCjtqwIVx//gnEx1dpoD/++OOYM2eOj82//vrL0FzpzL4EzPS8sy9lZ2TGBroz6sws606g7m9VdffJlRYmIMuyuDxJnAGVL3aVK4qSI9KpqYFelm5KSsp8SZJuAnAIwNmKomRaGIVlQmcD3TKlMm2gfDE3bWlsGxg1Z9vSmj4xas/0JbJVgNSbrcppiWSoOUuUyXZBUne2K6lpEzKT1lJTU32OBBe7nkVTPZAhjnRv2fJ0lJQMBCCa7/8OZHm1c087rRh9+uQjL09CTo4LjRuX4MILi9Cu3TE0aVK+g9qfgYS5c5FUqflcNq+4eXPs/+ADqHFxusQZKiPLly/HLbeI/T/lY+bMmRg4UHCueVTchS52nx+dNAn1S++lr7wD/ffff0fnzp09x7qLceONN2LKlCm1ueDnJKCJgJmed5oC5iTTEmAD3bSlYWAmIcAGukkKYZUwZFn+A8CJAD5UFOXSsrgrNdAjS3eo+6TVr1+/xJiYmL8BxKuq+lBGRsY0q+Rt5TjZQLdy9cwRO1/MzVEHJ0VBzTmp2ubKldozVz3sHg31ZvcKmy8/as58NXFCRNSdE6psjhzNpLXLL7/cc9x62Zg1a5bYeKIJlOi3fvllNNavj8GCBftQVHS6pnXVT3IjOfkv9OmTjKuuykenTsdQwynwfs1EffUVEmfOROyGDX4/dyckIOvVV3HMZ8d8kGGHaPnHH3+MQYMG+VhfuXIlLrzwwlo9VtyF7m7YEPu/+ALNWrb0rKvcQBc/27Bhg+co/5YtW2L8+PFITEys1QcnkIAWAmZ63mmJl3PMS4ANdPPWhpGZgwAb6Oaog2WikGU5r/QCpYWKoojd5J6Rmpr6jKqq4nh3taCgIHHVqlViXpUhy7I4uv0qANsURfmPZRK3cKBsoFu4eCYJnS/mJimEg8Kg5hxUbJOlSu2ZrCA2D4d6s3mBTZgeNWfCojggJOrOAUU2SYpm0ppoxoqGatkQ92uLe7ZrGqoKLFoUj5kzE3HwYEQQVMUu8u8AbASwGpGRX2Dr1vfwr3/9K2CbET/9hKSpU/02zsUO7KxXXoHoxhefdRZKTjklYPvhWPDnn3/iP//x/TpS/LFDUlKSpnDELvSkKVNweNIk5N98M5o1a+ZZ56+BrskgJ5FAHQiY6XlXh/C5xEQE2EA3UTEYiikJsIFuyrKYNyhZlsXR7dEAXlQUZVxZpCkpKVMkSRIXBqkul6tVWlraz/6yqHCM+15FUQJ/ezcvGtNGxga6aUtjmcD4Ym6ZUtkmUGrONqW0XCLUnuVKZumAqTdLl8+SwVNzliyb5YOm7ixfQsskYCatnX766SgoKPCyq22Hs2ieP/54fcydW68W3gfQvDnQqlU9NGtWguTkfGzduhQ7d67FKae0xN69PyInZxOAo147Yufz3XffHXAdo7ZvR6MhQ+A6Wm6ropGcsWNxpJZ7wwN2atCCIUOGYNMmwQm45ppr8Pzzz2v3XFCAuBUrkH/11XDFxbGBrp0cZ+pIwEzPOx3ToqkwEGADPQzQ6dJSBNhAt1S5wh+sLMt/ARB/Xvm6oijDyyJKSUkZJ0mSeONUVVXtn5GRsdpftLIsrwFwJYBjiqLEhj8j+0fABrr9axzqDPliHmrCtF+ZADVHTYSLALUXLvLO9Eu9ObPu4cyamgsnfef6pu6cW3ujMzeL1vLz83HGGWf4pC+ODRfHeFc3Zs2qh6eeql8Dsh8B3ANgFWJjY7Bx40bEx8dj+PDh+Prrr6tdN27cOEycOBFSgGe2R23bhkbXXQdXTk4V2yXJyci5/Xbk3nSTZ/e5FUdhYSHEXeiRkZHo37+/5z/rMsyiubrEzjXWJkDtWbt+ZoqeDXQzVYOxmJGANd90zEjSITHJsizOgOoG4GNFUbqWpZ2amtpJVdWPRQNdkqQF6enpN1dGIsuyOA9J7ExPBsAd6AZphg10g0Db2A1fzG1cXJOmRs2ZtDAOCIvac0CRTZQi9WaiYjgkFGrOIYU2WZrUnckKYuNwjNLavHnzsGbNGpx//vk48cQTIRrmKSkpOKX0CPO//voLF198sQ/pXbt2oWHDhn7pL18eh1tuqfpZq1ZFaN36e6xceSOAL8TXbd71Q4cOxSeffII9e/bUWNFt27Z5YgxkRH/+OZJF8zw312eZaJznjh2L3Ouvh5qQEIhJ2841SnO2BcjE6kyA2qszOi6sRIANdEqCBGomwAY6FRIQgdTU1CdVVb0XQJaiKI3LFnfr1i2ySZMmfwMQPyuUJKlnenr6RxWMSykpKa9JkjS09K1/laIoVwfkXOPka665pmlERIT4beViSZIuAiD+aSSWq6q6KCMjY4RGU1Wm9e3bNz42NnYXgNNKP9yjKMqptdkT62JiYm6VJEkGcHrpPfJ/AFhTUlIye9myZTX/1lObgxo+ZwM9CHhc6iHAF3MKwWgC1JzRxOmvjAC1Ry0YSYB6M5I2ffGdjhoIFwE+68JF3nl+jdDasmXLcNttt1WBK5rn7777rucebdEs79mzp3eOiEs0usV/Vh7btkVj4MBGKCws/3pWklRMn34YQ4bkwV8zXmtlxa7qX375BRER2u9Tj966FcnDhsGVl+fjpqB7dxx68UU2zivBN0JzWuvNec4iQO05q96hzJYN9FDSpW07EGAD3Q5VNDCHlJSUKyRJere0CX6xoihflrlPTU2dqqrqg6WfuQGsVVX1G5fLFa+qam8A4gwroTmxS71venr6O6EIXZbl8j/LreQg2AZ6amrq06qqVrw8qtYGuizLIm+R65nV5HtEVdWh1R17HywjNtCDJcj1fDGnBowmQM0ZTZz+yghQe9SCkQSoNyNp05cgQM1RB+EgQN2Fg7ozfYZaa6qq4tJLL8Xu3bv9Ai67a1wc1z5o0CDvHLHzXDTVK4/ffotA376NkZXl2+B+9NHDGDWqfPf3fffdh9dffz3gorZo0QJbtmzRvC76o4+QfNNNVXaeF1x6KbIWLABiYjTbcsrEUGvOKRyZZ+AEqL3AmXGFfwJsoFMZJFAzATbQqZCACJTuNM8sPYZ9tqIod5YZGDFiRGxubu6nAM7zOVvK14PQ3CJFUW4IyHEAkys10H8H8D2AK4SJYBro11577QUul2sbgKLSfxIB1NhA79evX2JMTIw4a6tVqf8FkiS9BSBfkqTuqqo+AKAeAPHnvZ0URdkZQKqaprKBrgkTJ9VAgC/mlIfRBKg5o4nTXxkBao9aMJIA9WYkbfoSBKg56iAcBKi7cFB3ps9Qa23Tpk0YMmRItXDFTm+x4/udd97B2LFjvfPE3eeiqS6G2w2IXeerV8dixYo4HDzo2zwfMSIXjz122OdqcdG437FjB/r27evXt2jQHzp0qMpnnTp1Qnp6eo1icP3zD+JWrULcypWI3rGjytz8nj1xaO5cNs+roRhqzTnzf8nMWgsBak8LJc7RQoANdC2UOMfJBNhAd3L165j7gAEDTo6IiBAXHuUriiIa1N5x9dVXN4iMjJwjSdLA0t3mFT/OkyTp6TZt2kyZPHmy2KEekiHL8mRVVbdFRUVte/PNN/cOGjTo1JKSkl+Fs7o20GVZFr/VfAbgQkmSHlFVVVxC1aK2BnpKSsoUSZIeLk30PkVRnqqYdEpKSkdJkjYBiASwSVEUcb+8roMNdF1xOtIYX8wdWfawJk3NhRW/o51Te44uv+HJU2+GI3e8Q2rO8RIICwDqLizYHek0FFo7cOAAfvjhB89956NGjcKHH35YI9szzzx+8OCPP/7onXfhhRdixYqVWLw4Hs8+m4jMTP9HqvfoUYCFC7MQKb4d8jOGDRuGjRs3+nzSpUsXzJo1C507d0ZepWPXBw4ciJkzZ/o3lp+PpMmTEb9kCSTV/yGO+b16HW+eR0c7Uk9akg6F5rT45RwSoPaoAb0IsIGuF0nasSsBNtDtWtkw5zVo0KAT3W53D7fbfaLL5XK73e5fiouLNy5fvjzb6NB0aqCPBzADgDir61wAP9TWQB89enTUoUOH9gNIAvBd27Zt/+3vDwdSU1Pnqao6RnBxuVwXp6WliV3uug020HVD6VhDfDF3bOnDljg1Fzb0jndM7TleAoYCoN4MxU1n3IFODYSJAJ91YQLvQLd6a03sOL/55ptx5MiRoGhefnlPnHxyBhYuFIcP+h9t2hRh+fIDqFev2hsJsWHDBlx//fVeAwMGDMCMGTMQHR2NBx54AK+99pqPcXFX+4QJE6o4jNizBw1Hj0a0n2PlyyYX/t//4eCiRdx5Xkvl9dZcUELjYkcRoPYcVe6QJssGekjx0rgNCLCBboMiMoWaCQTbQB8wYECLiIiIbwEkqKraPSMj40NZln+rrYFeel/8OhGdJEkT0tPTn/QX6cCBAzu43W5x9L2YNy09PX2injVlA11Pms60xRdzZ9Y9nFlTc+Gk72zf1J6z62909tSb0cTpj5qjBsJBgLoLB3Vn+tRTa9u3b4csyygoKAgaZqdOn2Pz5ouqtdO5cyGeffYQmjWr/aDGt99+Gx988AG6d++O/v37e67mEEPcy96jRw8fH2L3udiFXnFEb96M5FGj4Dp82G88JcnJyBsyBDl33gk1Li7o3O1uQE/N2Z0V89OXALWnL08nW2MD3cnVZ+5aCLCBroUS51iaQLANdFmW1wDoDWCxoijDBQyNDXTv8e0ul+uStLS0rf5Alt4rL357iQfwkaIoXfUEzga6njSdaYsv5s6sezizpubCSd/Zvqk9Z9ff6OypN6OJ0x81Rw2EgwB1Fw7qzvSpp9auvvpqbNsW3OGAzZo1w7//vQgbNlxRpSBnnVWEvn3z0bdvAc44o1iXgg0dOtR7xHxkZCS2bNmCk046yWs7ZuNGJN90E6TCQh9/7rg4FPTti/z+/VHYuTOqPUNelyjtZURPzdmLDLMJNQFqL9SEnWOfDXTn1JqZ1o0AG+h148ZVFiIQTAM9JSVlkCRJbwI4BOAsRVHEkeyaGuiyLGcAuFbMLyoqaljT8fWyLH9VejT8fkVRmuqJlw10PWk60xZfzJ1Z93BmTc2Fk76zfVN7zq6/0dlTb0YTpz9qjhoIBwHqLhzUnelTL63l5+fjjDPOqBPE+Ph49O7dGykpKfjf/67AlCkNfexERal4+ulspKTk18l+TYuysrJw//33Y8+ePZ6j58UR72Uj5sMPkXzjjZAq7agvOuMMHHrpJRSX3t2ue1A2N6iX5myOiemFgAC1FwKoDjXJBrpDC8+0NRNgA10zKk60KoG6NtCHDBnSsKio6DsAJwAYoyjK/DIGWnagy7Isdpz/B0CuoijVX3Z1vCG/GsBVwn5OTk7s2rVrff8kuBr4AwYMOLm2uqSnp2+LiIhoVlJSgv37Pf1/DhIIiIB4MW/a9Pjfdezbtw9ud+1HywXkgJNJoBIBao6SCBcBai9c5J3pl3pzZt3DmTU1F076zvVN3Tm39kZnrpfWvvvuuyrHoVfMRfip/DvxkiVL0KFDB8TExEDs/t6+PQp9+iRDVX2/dn3uOdE8D/5YeC1sXfv2IXbVKsSuWIFoP7vpC3r1wuHnnoNar8avq7S4cuwcvTTnWIBMvM4EqL06o+PCSgSaNGmCiIgI8dO/JEmqtc9AgCTgNAJsoDut4jrnO3DgwItUVb3Y7XafKklSfUmSorS4cLvdakZGxo1a5gY7p64NdFmWXwIgYvxUUZROANSyWDQ20MW96W0B7FUUpVlNeaSkpKRJkpQq5hQXFzd+++23D2rJW5Zlb0zVzZ87dy4aNWqkxRznkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIAlCezcuRMjRozwbB4QR6jn5eXhrLPOwqRJk3DBBRdoymnp0qWeHeTVjcGDB+PNN8VBheVD3EHeqlUr7NkDrFoFPPMM8MsvvhZmzADGj9cUQnCTxB+8T5gACIfV/fH7ddcBCxfyuPbgSHM1CZAACdiJABvodqomc9GNABvouqF0lqGUlBRZkqQnALSsa+aKonj+vCnUoy4N9NTU1C6qqn4IoMTtdl+4dOnSryvGqbGB/nMpnz8URTmlpjxlWX4NwDAxp6SkpPmyZcv+1MKFDXQtlDiHBEiABEiABEiABEiABEiABEiABEjA7gQ6d+6MzZs3V0lT7Ap/6KGHPEecb9q0CcnJyWjfvj0kqerXotOmTcPEiRP9ourZsyfEXePDhw/3+fytt47hiSei8LXPN0flUyZNAh591AD6omE+ZgzwktgPUs3o1QtYuRKI0rT/xYCg6YIESIAESMAEBNhAN0ERGIL5CLCBbr6amD6ilJSUSZIkPVIaaF01pJq1gX7llVfG1KtXT9xJfpYkSTPS09PvqVwUjQ30kO9A5xHupv+fiy0C5NFQtiijpZKg5ixVLlsFS+3ZqpymT4Z6M32JbBcgNWe7kloiIerOEmWyRZCiGS52nWsdEyZMwB133FFl+p133om0tDSfn4srzc4880zMmTPHc0z7hRdeiNzcXM+cU099Dr/9dmu1bs8+uwhr1x4Mfb9aVVF/wgTEvyb2Z1Qd7gYNkDdkCHLuvhuIj9eKifNqIMDnG+URLgLUXrjI288vj3C3X02Zkb4E6tr81DcKWrMMgZSUCoEWqQAAIABJREFUlI6SJH1Sepy50I/4jWGNJEk73G73QZfLVaQ1mfT09EVa5wYzL9Ad6CkpKVMkSXoYwB+xsbFtFi9efPy3ogpDYwM95Hega+GiqqrYzX6SuAN97969WpZwDgn4EBAv5mVfRGRmZvIOdOoj5ASouZAjpoNqCFB7lIaRBKg3I2nTlyBAzVEH4SBA3YWDujN9iuPaRZNb64iKisK2bdvEKYD4448/sG/fPs/R7w8++KCPCXH8++jRo31+9sEHH2DWrFnIyhqHX365oVqXjRqVIC3tINq0KdYaVt3mieb5ww+jnjiWvcJQo6KQ37cv8vv3R2GXLkB0dN3sc5VfAny+URjhIkDthYu8/fyecMIJvAPdfmVlRjoSYANdR5hOMJWSkvKGJEmDShvoHxQXFw95++2395k590Ab6LIsFwKIliRpodvtfs9fbpIkPQegMYADqqreJuZIkrRPUZSNZfNlWc4AcK3496KioobLly/Pro6TLMtix/u5APYritJUT55soOtJ05m2+GLuzLqHM2tqLpz0ne2b2nN2/Y3Onnozmjj9UXPUQDgIUHfhoO5Mn3/++Sf+85//6J78okWLcNlll/nYVVVg+vREzJ6dWMVfw4YluPTSQlx+eQF69ChEfLyqe0yVDdafMgX1XnzRN8bISByaPx8FPXuG3L9THfD55tTKhz9vai/8NbBLBGyg26WSzCNUBNhADxVZm9qVZfl3ACcDyC4qKmpZU1PYLAjq0ECv6283mxRF6VaWd4Wd7GK3xyVpaWliR3qV0a1bt8gmTZqI5noCgI8URemqJzs20PWk6UxbfDF3Zt3DmTU1F076zvZN7Tm7/kZnT70ZTZz+qDlqIBwEqLtwUHemz6+//hpXXnml3+TPOuss7N69u05gPv74Y7Rs2dK7trAQmDIlCa++Kr7C8R0PPngEY8bkICKiTq4CXiRlZaH+9OlIWLzYZ60aEYFDc+ei4KqrArbJBdoJ8PmmnRVn6kuA2tOXp5OtsYHu5Oozdy0E2EDXQolzvARkWS4AEAXgbUVRUqyAJowN9CskSVonGEmSNCE9Pf1Jf7wGDhzYwe12f1o6b1p6evpEPbmyga4nTWfa4ou5M+sezqypuXDSd7Zvas/Z9Tc6e+rNaOL0R81RA+EgQN2Fg7ozfYpj1a+77roqyTdu3Biff/45XnrpJcyePRs5OTmaAUVGRuKnn36COO49P1/C0qVxmD8/AT//LL4W8x2PP56NESPyNNuu60TX338jdt06xK1di+itWyGVlPiYUl0uHHrhBRT061dXF1ynkQCfbxpBcZruBKg93ZE61iAb6I4tPRPXSIANdI2gOO04AVmWMwE0kSRpQXp6+s1W4BJoA11LThrvQBeXS4nj7ZMAfKcoytmlR9/7uEhNTZ2nquoY8UOXy3VxWlraNi0xaJ3DBrpWUpxXHQG+mFMbRhOg5owmTn9lBKg9asFIAtSbkbTpq/R3DTRr1swDIzMzE263m2BIIOQE+KwLOWI6KCWwdOlS3H777VV4PPbYY7jhhvJ7yvv3748vvviiyrzmzZt77kKvODp16oR58xTMn18PS5bE49ChqlvLJUnFk08extChoW2eS0eOIGnCBMSvWFFjzQ/NmoV8WaYuDCDA55sBkOnCLwFqj8LQiwAb6HqRpB27EmAD3a6VDVFesix/DKAjgFWKolwdIje6mg1XA10kUfEYdwD3KYryVMXkZFm+RBzbDiASgM8R8HpBYANdL5LOtcMXc+fWPlyZU3PhIk+/1B41YCQB6s1I2vQlCFBz1EE4CFB34aDuTJ9ih/mkSZN8ku/duzfmzp0LsZO8bOzatQtXXXUViouLvT9r1aoV3n//fc9zcsWKFZgzZw4aNWqEceOewh13tENmpv8z2aOjVTz9dDauvTY/tNCLi5E8fDhiN22q0c+Re+5Bzl13hTYWWvcS4PONYggXAWovXOTt55cNdPvVlBnpS4ANdH152t5aSkrKOEmSngdwKCEh4aRXX31VHOluqiHLcmcAZ5QFJUlSY1VVyxrXmwG8VDFgRVFeDTQBLTvQhc1+/folxsTEiD9tblXqY74kSW9JkpRfUlLSXZIkcVx7PQDit62OiqLsDDSW2uazgV4bIX5eGwG+mNdGiJ/rTYCa05so7WklQO1pJcV5ehCg3vSgSBuBEKDmAqHFuXoRoO70Ikk7tRGYPn06nn32We+0Xr164eWXX/a7TMwT88vG/PnzPU31iuPgQRf692+MX38tb75X/Lxt2yLMmnUIZ59d3oivLca6fp44bRoSnxdfxVUd7thYFHbrhrzrrkNh9+51dcF1dSDA51sdoHGJLgSoPV0w0ggANtApAxKomQAb6FRIQARkWY4D8CWAs1RV/W9GRsaDARkwYHJKSsqrkiRdr9WVoigB/+9AawNdxCDLsmjmvwPgzGpiOqKq6tCMjIzVWmMOZB4b6IHQ4lx/BPhiTl0YTYCaM5o4/ZURoPaoBSMJUG9G0qYvQYCaow7CQYC6Cwd1Z/qcMGECFi9e7E1++PDhmDZtml8Yqqpi0aJF2Lx5M6644grxvY3PPHHfeWpqI2zfLm7m8x3duxfgppty0bVrIaSAv00KvDYRf/2Fpp07Qzp2zGdx3oABKOjd29M8V+PEV3UcRhPg881o4vTH31upAb0JsIGuN1HasxsBA1717IaM+QwcOLCN2+3eAKCZJElPqKo6VVEU3zf5MGIyWwNdoBg2bFhCfn7+LZIkid/KRENd/Bb2h6qq77jd7meXLVu2J1TI2EAPFVnn2OUvhc6ptVkypebMUgnnxUHtOa/m4cyYegsnfWf6puacWfdwZ03dhbsCzvE/evRorFmzxpvwnXfeiXvvvTdgAKoKjBnTEGvW+Dal27Qpwpw5h9CqVeh3nFcMOunuu5Hw1lveH6lRUTiQkYGi9u0Dzo0L9CXA55u+PGlNOwFqTzsrzqyZABvoVAgJ1EyADXQqpAqBlJSU4RqwnCRJ0iOljeD9kiStAvANgMNut1vVsB4ZGRmvaZnHOcERYAM9OH5czd1K1IDxBPjLoPHM6fE4AWqPSjCSAPVmJG364jOOGggXAT7rwkXeeX5TUlLw6aefehOfMmUKbrzxxoBBzJ2bgMceS/JZd+KJxVi16gCaNXMHbC+YBRE//YSm3btDcpf7zRk5EkemTg3GLNfqRIDPN51A0kzABKi9gJFxQTUE2ECnNEigZgJsoFMhVQjIsizezDU1wQGUaUjr/DJ/qqIo/i+SYk10JcAGuq44HWmML+aOLHtYk6bmworf0c6pPUeX3/DkqTfDkTveITXneAmEBQB1FxbsjnTao0cP7N6925v7888/j2uuuSYgFp98Eo3BgxvB7S7/ujQpyY233z6As84ydue5CLzhmDGIW11+2587Lg77Pv0U7iZNAsqLk0NDgM+30HCl1doJUHu1M+IMbQTYQNfGibOcS4ANdOfWvtrMSxvooSYjGugRoXZC+wAb6FRBsAT4Yh4sQa4PlAA1FygxzteLALWnF0na0UKAetNCiXP0JEDN6UmTtrQSoO60kuK8YAmcf/752L9/v9fMG2+8ga5du2oyW1ICKEocpk5NQna2y7tGklQsXpyF7t0LNdnRa5KUl4fEadNQ75VXfEweve02HJ0wQS83tBMkAT7fggTI5XUmQO3VGR0XViLABjolQQI1E2ADnQqpQkCW5YVGYFEU5QYj/DjdBxvoTldA8PnzxTx4hrQQGAFqLjBenK0fAWpPP5a0VDsB6q12RpyhLwFqTl+etKaNAHWnjRNnaSNQXFyMvLw81K9f32eBqqpo0aIFSkQnvHS8++67OOecc2o0LO4737gxBk88UR/ffx9VZe499xzBXXflaAtOp1nRn36KBnffjcg9e3wsupOSsHfLFqgNGujkiWaCJcDnW7AEub6uBKi9upLjusoE2ECnJkigZgJsoFMhJGBzAmyg27zABqTHF3MDINOFDwFqjoIIFwFqL1zknemXenNm3cOZNTUXTvrO9U3dObf2eme+ceNG3HzzzSgsLMStt96Ke++91+vi8OHDaNu2rY/Lzz77DCeffHK1YYhe+913N4CixPudc9llBVi4MAuu8g3peqdUxV69WbNQ/6mn/PrJfuwx5N3AfSghL0IADvh8CwAWp+pKgNrTFaejjbGB7ujyM3kNBNhA1wCJU0jAygTYQLdy9cwRO1/MzVEHJ0VBzTmp2ubKldozVz3sHg31ZvcKmy8/as58NXFCRNSdE6oc+hyzs7PRsWNHiEZ52Vi3bh1atmyJt99+G5MnT0Zubq5PID/++CPi4/03x8XEV15JwMMPJ/kNXjTPn3vuEOrXV0OfXKmH6E8+QeOBA6v4U2NjceSBB5B7442AxK9xDSuIBkd8vmmAxCkhIUDthQSrI42yge7IsjPpAAjwzSsAWJxKAlYkwAa6Fatmrpj5Ym6uejghGmrOCVU2Z47UnjnrYteoqDe7Vta8eVFz5q2NnSOj7uxcXeNymzZtGp5//vkqDhMTE3H06NEqP4+NjcVPP/0EqZqG899/u9C9e1Pk5PhuL2/TpggPP3wEXbsae+c5VBWNrr0WMZ995pNL4UUXIXvmTJS0bGkcbHrSTIDPN82oOFFnAtSezkAdbI4NdAcXn6lrIsAGuiZMnEQC1iXABrp1a2eWyPlibpZKOCcOas45tTZbptSe2Spi73ioN3vX14zZUXNmrIr9Y6Lu7F/jUGf45ZdfIjU1FQUFBZpd9e3bF/Pmzat2/qhRDfHOO3E+n//3v9kYMiQPERGa3eg2MXrLFjSWZR97R2+7DUfFMfXhCEi3zOxtiM83e9fXzNlRe2aujrViYwPdWvVitMYTYAPdeOaW95iSknKFJEmxkiTlpaenb9CaUGpq6mWqqsYHuk6rfc7zT4ANdCojWAJ8MQ+WINcHSoCaC5QY5+tFgNrTiyTtaCFAvWmhxDl6EqDm9KRJW1oJUHdaSXGePwI///wz+vfvj0OHDmkC1LRpU0+zXdyRLnan+xvvvReDG25o5PPRoEG5mDGj/Hh4Tc50nNRIlhGzZYvXYvGJJ2Lf5s1AdLSOXmhKbwJ8vulNlPa0EqD2tJLivNoIsIFeGyF+7nQCbKA7XQEB5i/LclsAuwCokiTNTk9Pv0urCVmWZwG4Tax1uVxnpaWl/ax1LefVnQAb6HVnx5XHCfDFnEowmgA1ZzRx+isjQO1RC0YSoN6MpE1ffKejBsJFgM+6cJG3vt99+/Z5mue///57jcmIY9ovvfRSjBgxAoMGDUJERAQyMzPhdrurrDt2DOjWrSn27In0fpacXIJNm/YhOdm4+84rBhb92WdoPGCAT6zZTzyBvOuvt34RbZ4Bn282L7CJ06P2TFwci4XGBrrFCsZwDSfABrrhyK3tUJblRwA8KprgAM5RFOV/WjO69tprz3a5XN+UNt8fTk9Pf0LrWs6rOwE20OvOjiuPE+CLOZVgNAFqzmji9FdGgNqjFowkQL0ZSZu++E5HDYSLAJ914SJvbb85OTlISUnBN9+Ir5DKR1xcHPLz8z0/EI1y0TQfNWoUmjdvrun31pdfTsAjjyT52Hz22UNISTluMxwjeehQxH74odd1SbNm2Ct2o8fEhCMc+gyAAJ9vAcDiVF0JUHu64nS0MTbQHV1+Jq+BABvoGiBxSjkBWZbFke09APyqKMrpgbKRZfkXAC0AbFAUpWeg6zk/cAJsoAfOjCt8CfDFnIowmgA1ZzRx+isjQO1RC0YSoN6MpE1fggA1Rx2EgwB1Fw7q1vZZVFTkaYx/WKGpLDI699xzkZGRgb///hvbt29Hhw4d0KKF+Hrp+KhNa7m5Ejp0aIqsrPJLztu3P4blyw9ACtO3o5G7d6NpD/EVW/k4PHUqckeOtHYRHRJ9bZpzCAamGQYC1F4YoNvUJRvoNi0s09KNQJheEXWLn4YMJiDL8l8AmgFYrijKtYG6l2V5KYBrAPypKMopga7n/MAJsIEeODOu8CXAF3MqwmgC1JzRxOmvjAC1Ry0YSYB6M5I2fQkC1Bx1EA4C1F04qFvXp6qqGD9+PNLT032SOOWUU7By5Uo0adKk2uRq09rChfF46KEGPutF8/yii46FDVjSffch4fXXvf5LkpOx9/PPgbi4sMVEx9oJ1KY57ZY4kwQCI0DtBcaLs6snwAY61UECNRNgA50KCYiALMsFAKIkSVqQnp5+c0CLAaSmps5TVXU0gAJFUeIDXc/5gRNgAz1wZlzhS4Av5lSE0QSoOaOJ018ZAWqPWjCSAPVmJG36EgSoOeogHASou3BQt57Po0ePYsGCBViyZAn27t3rk0BycjJWrFiBli1b1phYTVorKQG6dGmK334rv/u8R48CLF6cFTZYrqwsnHDRRZAKxNdsx8fRO+7A0fvuC1tMdBwYAT7fAuPF2foRoPb0Y+l0S2ygO10BzL82Amyg10aIn/sQkGX5KIB4VVUXZ2RkjAgUT0pKyquSJA1nAz1QcnWfzwZ63dlx5XECfDGnEowmQM0ZTZz+yghQe9SCkQSoNyNp0xff6aiBcBHgsy5c5K3l9/rrr8eGDeLGQN8RGxsLRVHQrl27WhOqTmuFhcD99zdA5T0cinIAHTuGb/d5vdmzUf/JJ715qVFR2Lt1K9zNxKGPHFYgwOebFapkzxipPXvWNRxZsYEeDur0aSUCbKBbqVomiFWW5V8BiKPXtyqK0inQkGRZ3gKgA49wD5Rc3eezgV53dlx5nABfzKkEowlQc0YTp78yAtQetWAkAerNSNr0xXc6aiBcBPisCxd56/gV95n37dvXb8Bz5sxB//79NSXjT2v797tw443J+PLLaB8b//73Mbz7bvjuPkdBAU7o1AkRmZneuPIGDED2c89pypWTzEGAzzdz1MGJUVB7Tqx6aHJmAz00XGnVPgTYQLdPLQ3JRJbllQD6ACgGcJqiKOJOdE1DluWTAPwm+nEA1iuK0kvTQk4KigAb6EHh42I20KmBMBDgL4NhgE6XHgLUHoVgJAHqzUja9MVnHDUQLgJ81oWLvHX8jh071nO/eeXRqVMnpKWlQZK0fXVZWWtff+3CDTck4++/y49tFz4kScUbb2ShS5fCsECK+PVXNBw7FtHffOPjf//atSg699ywxESndSPA51vduHFV8ASoveAZ0sJxAmygUwkkUDMBbW+hpEgCpQRkWb4VwGwAqqqqaRkZGUO0wpFl+S1xDbpYK0nSPenp6c9oXct5dSfABnrd2XHlcQJ8MacSjCZAzRlNnP7KCFB71IKRBKg3I2nTF9/pqIFwEeCzLlzkreF33759aN++PUrEJeUVRoMGDbB8+XKceeaZmhOpqLVdu/aie/fGOHAgwmd9fLwb/8/edUBHVS7hb9MbJAESgoCgCEoVpfdeRDpsaAJKR+kgTUCKCtJVeDSRJi1BqnRQQBBC1QcKIvUhIKGk92T3nbnJ9k22t7sz53B8yf7//DPffG9z7507M998E4+2bVVzx40+wAoL/fbsQcjEifBITtbQllGnDp7v3GmFE1iFPRHg7zd7os1nqSPA3GM+WAsBTqBbC0nWI1YEOIEu1sjayK+OHTsW8vX1pSryEDpCLpevKFKkyNjVq1dn5XfkkCFDvOPj45fK5fLheWsSvL29X92yZUucjcxktWoIcAKd6WApAnxhbimCvN9UBJhzpiLG662FAHPPWkiyHmMQYL4ZgxKvsSYCzDlrosm6jEWAeWcsUu657siRI/jggw80nJ8xYwbatWuH0qVLmwSKOtemTEnCvHmFNPaXKpWNdeteoFIlaqhofyn0xRcotHy5zsEyPz88370bWVWr2t8oPtEiBPj7zSL4eLMFCDD3LACPt2ogwAl0JgQjUDACnEBnhpiMQGRk5DC5XP4fyp/nbb4vl8vXeHp6/pyZmXknOzs72cvLK8jDw+MViUTSXCKRDAZQhjpl5VWuj9mxYwcPdjIZefM2cALdPNx4lwoBvjBnNtgbAeacvRHn8xQIMPeYC/ZEgPlmT7T5LEKAOcc8cAQCzDtHoO46Z65duxaUMFcIVaPv2bPHLAcUXJPLgfLls3H7tqp1+1tvZWL9+hcoVkxmlm5LN/meOoWivXrpqMl67TXErViB7EqVLD2C9zsAAf5+cwDofKSAAHOPiWAtBDiBbi0kWY9YEeAEulgja2O/IiMjv5bL5dTOnZLoQmLcwJEC1yjxvmPHDtrHYicEOIFuJ6BFfAxfmIs4uE7qGnPOSQPjBmYx99wgyE7kIvPNiYLhJqYw59wk0E7mJvPOyQLiZObMnDkTa9asUVrVpUsXLFu2zCwrFVyLiQHq1tVUsXv3U9SqlW/jRLPOM2VTkT594HfihMaWVKkUCZ9/DnlgoCmqeK0TIcDfb04UDDczhbnnZgG3obucQLchuKxaFAhwAl0UYXSME3mV6F8C0OyLpd8cGvA0KTo6eoVjrHXfUzmB7r6xt5bnfGFuLSRZj7EIMOeMRYrXWRsB5p61EWV9BSHAfGN+2BsB5py9EefzCAHmHfOgIAQGDRqEgwcPKpeMHDkSkydPNgs0BdeGDQNWrVKpKFs2G6dPx0LioCegXjdvIrxZMw2fEidNQvKoUWb5yZucBwH+fnOeWLibJcw9d4u47fzlBLrtsGXN4kDAQZeP4gCPvQA6d+4c4uXlNUQikbQFUAeAvxouaQBiABzMyspas3v37njGzP4IcALd/piL7US+MBdbRJ3fH+ac88dIrBYy98QaWef0i/nmnHERs1XMOTFH13l9Y945b2ycwbI2bdrg2rVrSlPmz5+PPn36mGUacS0wMAIvvQQkUwlHnkyYkIixY9V+YZZ28zcFT5yIwM2blQpyQkPx5MIFwF/98Zn5+nmn4xDg7zfHYe/uJzP33J0B1vOfE+jWw5I1iRMBTqCLM64O86pjx46FAgICCqWmpibt3bs3yWGG8MFKBDiBzmSwFAG+MLcUQd5vKgLMOVMR4/XWQoC5Zy0kWY8xCDDfjEGJ11gTAeacNdFkXcYiwLwzFin3XFepUiUkJCQond+6dSsaN25sFhjEtT17IkAV6Arx8JAjJuYJXnrJMbPPPV68QPFatSBJT1falDRqFJImTTLLR97kXAjw95tzxcOdrGHuuVO0besrJ9Btiy9rd30EOIHu+jFkDxiBAhHgBDoTxFIE+MLcUgR5v6kIMOdMRYzXWwsB5p61kGQ9xiDAfDMGJV5jTQSYc9ZEk3UZiwDzzlik3G8dJc4pga4up0+fxiuvvGIWGMS1jh0jQMXdCmndOg3r1sWZpc8am4KWLkXhBQuUquTe3ngSEwNZ8eLWUM86HIwAf785OABufDxzz42Db2XXOYFuZUBZnegQ4AS66ELKDjECmghwAp0ZYSkCfGFuKYK831QEmHOmIsbrrYUAc89aSLIeYxBgvhmDEq+xJgLMOWuiybqMRYB5ZyxS7rfujz/+QOvWrZWOSyQS3L59G76+vmaBkZTkiYoVi0MuV23fuPE5WrTIMEufxZsyMlC8bl14xsYqVaV264b4r7+2WDUrcA4E+PvNOeLgjlYw99wx6rbxmRPotsGVtYoHAU6giyeW7AkjoBcBTqAzMSxFgC/MLUWQ95uKAHPOVMR4vbUQYO5ZC0nWYwwCzDdjUOI11kSAOWdNNFmXsQgw74xFyv3WHT58GAMGDFA6HhERgUuXLpkNxIkTfujTp4hyv6+vHNevP4aZ+Xiz7VBs9N+xA6GjR2voiT18GNlVqlismxU4BwL8/eYccXBHK5h77hh12/jMCXTb4MpaxYMAJ9DFE0v2hBHQiwAn0JkYliLAF+aWIsj7TUWAOWcqYrzeWggw96yFJOsxBgHmmzEo8RprIsCcsyaarMtYBJh3xiLlfutWr16NWbNmKR2vXbs2du3aZTYQCxYUxtKlQcr9tWplYPfu52brs2ijXI5ibdvC59o1pZqMevXwfMcOi9TyZudCgL/fnCse7mQNc8+dom1bXzmBblt8WbvrI8AJdNePIXvACBSIACfQmSDGIrB+/Xr8+OOPqFOnDsaNGwdPT09hK1+YG4sgr7MWAsw5ayHJekxFgLlnKmK83hIEmG+WoMd7zUGAOWcOarzHUgSYd5YiKM79OTk5Qvv2GzduKB3s2rUrvvnmG7MdjowsijNnVO3fP/wwCZ98kmS2Pks2+pw9i2Ldu2uoePHdd0hv08YStbzXyRDg7zcnC4gbmcPcc6Ng29hVTqDbGGBW7/IIcALd5UPIDjACBSPACXRmiDEInD59Gj169FAunT17NgYOHCj8zBfmxiDIa6yJAHPOmmiyLlMQYO6ZghavtRQB5pulCPJ+UxFgzpmKGK+3BgLMO2ugKD4dmzdvxsSJEzUcW758OTp37myWs9nZwBtvRCAtzUO5f92652jd2jHzz0MHDID/4cNKW7LLlkXsqVNA3kvqZjnJm5wOAf5+c7qQuI1BzD23CbXNHeUEus0h5gNcHAFOoLt4ANl8RsAQApxAN4QQf04I0Ow5mkGnLg8fPhR+5Atz4zly5MgRbNu2DRUqVMCYMWPg5+dn/GYrrUxLS4Ovr68QN1cV5pyrRs717WbuuX4MXckD5psrRUsctjLnxBFHV/OCeedqEbO9vcnJyWjYsCGePn2qPKxixYrC/aiiC5qpVly54o327cM0tl29+i+KFJGZqsqy9XI5/PfsQciIEZDI5UpdCXPmIEVt3rtlh/BuZ0GAv9+cJRLuZwdzz/1ibiuPOYFuK2RZr1gQ4AS6WCLJfjAC+SDACXSmhjEIlCxZUmfZtWvXEBoaygl0YwAEcPPmTbRs2RLUjpBkxIgRmDJlipG7rbNs7dq1+PLLL+Hv74+vvvoKTZs2tY5iO2vhm0E7A87HKRFg7jEZ7IkA882eaPNZhABzjnngCASYd45A3TnPvHPnDm7duoVTp05h3bp1GkZu3boVjRs3Ntvwr74KwvyHO2GVAAAgAElEQVT5hZX7y5fPxokTsWbrM2ejx/PnCJ4yBf7792tslxUujCcXL0IeGGiOWt7jxAjw95sTB0fkpjH3RB5gO7rHCXQ7gs1HuSQCnEB3ybCx0YyA8QhwAt14rNx1JSV8X331VWRT3zs1Wb16Nd59911+2GokMebNm6cxs69y5cqginR7SWxsLOrWrYuMjNw2hXQRfP78eXh5ednLBKudwzeDVoOSFZmIAHPPRMB4uUUIMN8sgo83m4EAc84M0HiLxQgw7yyGUBQKTp48KXQ9S09P1/GnefPm2LRpk0V+du1aFDExqvnngwalYNasBIt0mrLZ9+RJhIwaBc9nz3S2JY0ahaRJk0xRx2tdBAH+fnORQInQTOaeCIPqIJc4ge4g4PlYl0GAE+guEyo2lBEwDwFOoJuHmzvtun37tt63/fv164e5c+dyAt1IMrRr1w6///67cnWRIkVw9epVI3dbvmzfvn0YNmyYhqKdO3eiTp06wu+otXtCQoKQWJdInPvPv71uBuVyOS5duiRU7NMLDyyMgL24x0gzAoQA8415YG8EmHP2RpzP4+865oDiPqRRo0Z4/PixDiDUsv3YsWPCCCxzJSlJgipVIpCdrbrH+f77F2jWTDdZb+4ZBe3z+vtvhLVtC4melwNSu3RB/JIlgLe3LY5mnQ5GgP+uOjgAbnw8c8+Ng29l1zmBbmVAWZ3oEHDuJ+iig5sdYgTsjwAn0O2PuauduHfvXgwfPlzH7HLlygnt9fjC3HBEX7x4gWrVqoESsupCLyfYaw76ggULsHTpUo3zKa7Tpk3D9evXQS9EPHr0CPTwiroLFC6sanFo2EP7rrAX50aOHAl6yYBk4sSJGD16tH0d5dOcDgF7cc/pHGeDHIIA880hsLv1ocw5tw6/w5xn3jkMeqc5eMmSJVi4cKFee/r27Qvq5GWJREf7Y8yYUKUKHx/g+vUn8PPLHa1lawn58EME7NmjcYwsJATxX3yB9E6dbH0863cgAvz95kDw3fxo5p6bE8CK7nMC3YpgsipRIsAJdFGG1Xmd6tGjx9tyuTyILIyKijrlvJaKxzJOoIsnlrbyhKrMly1bpqPe29sbd+/eBVUFRERECJ//+++/kMlktjLFZfXu3r0bH330kY79v/76K8qUKWMXv95//30cPXpU46zy5cvjxIkTGDVqFH744QflZ9Qmcf369UJsnVHscTNIM+ubNWumdD8gIAB//vkniPcs7ouAPbjnvuiy59oIMN+YE/ZGgDlnb8T5PEKAeefePEhOTkbNmjWRlJSkA0RQUBDOnDmDYsWKmQ3Sn396oXPnYkhJ8VC71wE2b7bPfavXrVsIa9oUErUXqdObNUP84sWQhYeb7RdvdA0E+PvNNeIkRiuZe2KMqmN84gS6Y3DnU10HAU6gu06sRGGpVCq9AqAaAHl0dLTrDeZ1wShwAt0Fg2Znk+mt/59++knvqbdu3UJgYCAn0A3EZOzYsfRSkM6qbdHb0Kh+I7tElB5M6WuLSA+lqLW7djt5qvSg2Duj2ONmUF8lDL1sQC8dsLgvAvbgnvuiy55rI8B8Y07YGwHmnL0R5/MIAeade/OAXtr95JNP9ILw5Zdf4r333jMboNhYD7RvXwwPH2o+WqJx6i1b2ieBTnPPA9ReVJYFBeFJTAzkISFm+8UbXQcB/n5znViJzVLmntgi6jh/OIHuOOz5ZNdAgBPorhEn0ViZl0B/My+B7pylj6JBO9cRTqCLLKA2cKdGjRpCZbk+uXjxIkqWLMkJ9AJwp7btlLzWh2HXSV3xzahvbBA1TZXUQr5q1ap6z5k1a5bQMlG76qNevXrYsWOHzW0z5wB73AxSS/vjx49rmEet7d99911zTOY9IkHAHtwTCVTshhUQYL5ZAURWYRICzDmT4OLFVkKAeWclIF1QDXUua9q0KWislULefPNNdOvWTZh5TqOlzJW0NEAqLYYrV3w0VFA+fuNG4MkT2yfQPe/dQ3jjxpDkqFrFJ40YgaQpU8x1i/e5GAL8/eZiARORucw9EQXTwa5wAt3BAeDjnR4BTqA7fYjEZSAn0O0fT06g2x9zVzrx2bNnoIcY+QlVplesWJET6AUE9caNG2jRooXeFf5t/XF11VX4e/nblBY0q75Xr156z2jYsCFiYmKQlZWl8Tm15b906ZJN7TJXua1vBgmLSpUqITU1VcPECRMmgLoJsLgvArbmnvsiy57rQ4D5xrywNwLMOXsjzucRAsw79+XBlStX0L59ew0Atm/fDro/sUSoW/qIESHYvTtAQ02tWpn45Rcf+PraZ/RY8IQJCNy6VWmDLCAAsTExkBUpYol7vNeFEODvNxcKlshMZe6JLKAOdIcT6A4En492CQQ4ge4SYRKPkZxAt38sOYFuf8xd6cSCEq/kx65du1C3bl1OoBcQ1JUrV2LOnDn6V9QGPp39KYZUHWJTWhRkA91Y5Te3/u+//wbN/nY2sfXN4Pnz59GlSxcdtzt27IgVK1Y4Gxxsjx0RsDX37OgKH+UCCDDfXCBIIjOROSeygLqIO8w7FwmUDczcsGEDpk6dqtRctmxZnD59GhKJZY8iz571QffumnPTS5fOxoEDz1GlSnHhPOoOlt89kDVc9fznH4Q3aABJdrZSXfKwYUicPt0a6lmHiyDA328uEigRmsncE2FQHeQSJ9AdBDwf6zIIWHbV6jJusqHOggAn0O0fCU6g2x9zVzqxwOQvAJpZ16ZNG06gFxDU3r174+TJk/pXVARC+4XiQu8LNq1CHz9+PLZt22Yy9Y4cOYLKlSubvM/WG2x9M0gt7WkGurZQt4Vjx47Z2j3W78QI2Jp7Tuw6m+YABJhvDgDdzY9kzrk5ARzkPvPOQcA7wbFTpkzBRuqnniedO3fG8uXLLbZs+vTC+O67IKWeoCAZ9u59hooVZXa7bw2eMgWBar7J/fzw5OxZyMLDLfaPFbgOAvz95jqxEpulzD2xRdRx/nAC3XHY88mugQAn0F0jTqKxkhPo9g8lJ9Dtj7krnThy5Ejs3LkzX5O/+uorREZG2u1BhCthR7ampaWhSpUqSE9P1296SUAyWIIZdWfYtAq9Q4cOuHz5ssnwrVq1SqetoslKbLDB1jeDnTp1wsWLF3Us9/HxAVXle3l52cArVukKCNiae66AAdtoPwSYb/bDmk/KRYA5x0xwBALMO0eg7hxnUscn6vykkMmTJ4PuPy2VBg3Cce+e6nr9448TMWZMst2+4zweP0bx+vUhycxUupI8cCASZ8+21DXe72II8PebiwVMROYy90QUTAe7wgl0BweAj3d6BDiB7vQhEpeBnEC3fzw5gW5/zF3pxObNm+Ovv/7K12RqTT5o0CBOoOeDEFWeUwV6vlIIwHgg1Nd2VehyuVyYU5+UlKQ0g+JK8+sNCT3EGj58OG7fvo0iRYogLCzM0Ba7fG7Lm0HCiaruc3Jy9PpCYw3KlStnFz/5EOdDwJbccz5v2SJHI8B8c3QE3O985pz7xdwZPGbeOUMU7G8D3aNUqlQJiYmJysOpu1mrVq0sMubOHU80apTbpl0hhw/HokqVbLsl0AvPmIGgtWuV58t9fPDk118hK1HCIt94s+shwN9vrhczsVjM3BNLJB3vByfQHR8DtsC5EeAEunPHR3TWcQLd/iHlBLr9MXeVE6l6+vXXX9dIJBYrVgzPnj1TuhAYGIi9e/eCErIktp4l5yrYKeycNWsWVq9erTLbD4B2Mfp0QOJpuyr0x48fo2bNmhrQ7dixA927dzcIJyWKU1JShLhS9TXN/27btq3BfbZeYMubQWpb/8EHH+TrArWZbNGiha1dZP1OioAtueekLrNZDkSA+eZA8N30aOacmwbewW4z7xwcAAcd//DhQ9SuXVvj9JiYGJQqVcoii9auDcSMGcFKHeHhObh8+QlorLo9uOb1118Ia9cOErUOZCn9+iFh7lyL/OLNromAPTjnmsiw1bZGgLlna4TdRz8n0N0n1uypeQhwAt083HiXmQhwAt1M4CzYxgl0C8AT+dbff/8d7dq1U3opkUiEn/fv36/hube3t9AenFqVcwJdkxQ6Ffz0jEjVpTB38VgAwbapQr9//z6uX7+OgQMHKg2jlx6oqwAlgQvqLqCP3iVKlAA92PL09HQo+215Mzh9+nR89913+fq3cuVKUEt8FvdEwJbcc09E2euCEGC+MT/sjQBzzt6I83mEAPPOPXlw/Phx9OvXT+l8oUKFhPsWuue0RPr0KYITJ+it5VyJjEzFkiXxwv+2Ndf89uxByIQJ8EhNVZ4v9/JC7JkzyLHwxQBLMOG9jkPA1pxznGd8srMjwNxz9gi5jn2cQHedWLGljkHAsitXx9jMp7owApxAt3/wOIFuf8xd5cStW7diwoQJSnNfeeUVtGzZEmvWrNFxYcCAAVi7di0n0NWQefToEWrVqqWJ1QAAGwCodwen3HZpQALrVaFnZ2djyJAhOHz4sE6sqlevLrwE8cUXX2D58uUm05Eean311Vfo1q2byXuttcGWN4NNmzYV5pznJ0uWLEFkZKS1XGE9LoaALbnnYlCwuXZAgPlmB5D5CA0EmHNMCEcgwLxzBOqOP3Px4sVYtGiR0hDqmLVnzx6LDEtLk6By5QhkZKgeZa5c+QIdOuS2ALMZ1zIzUfizzzTatiscSendGwkLFljkF292XQRsxjnXhYQttxMCzD07Ae0Gx3AC3Q2CzC5ahAAn0C2CjzebigAn0E1FzPL1nEC3HEOxapg2bRrWrVundK99+/Z44403sHDhQr0u0xw7rkBXQUOt26mFu1KoEOJjAMsAxKlBKAVQOfdna81C3759O8aNG6c3TlKpFEuXLsX58+fRpUsXs+hLSfRdu3bpviCQp00uB+7d80R4uAyBgXKzzihok61uBvW1u/f19UVGRobSHHrxoH///lb3iRW6BgK24p5reM9W2hsB5pu9EefzmHPMAUcgwLxzBOqOPZPmntevXx9xcaqbIqpGn2thm/Njx3zRv39RpXOennJcvfovgoNz70dswrXsbBTp1w9+J0/qgJpZtSqeb98OebCqpbxjkefT7Y2ATThnbyf4PJdEgLnnkmFzSqM5ge6UYWGjnAgBTqA7UTCcyZTu3burem1Z0TCJRDIntxYT8ujoaMf2CLaiX86sihPozhwdx9pGyVVKsipk4sSJoNZ61OJan3ACXRMVmhV+9epV1S/fBED5anon4b7a2jYA6uX+bI0qdJlMJrRnv3nzpt44TZ06FR999JEw275atWqIj89taahPQkJCULFiRZw9e1bn4969e2OBnmqKS5e8MWlSCK5f94avrxytWqXjjTey8MorOShbNhuvv54Ff3/LuG2rm8GoqCiMHUs99XMlODhYwOiXX35R/o74P2zYMMsc4N0ui4CtuOeygLDhNkWA+WZTeFm5HgSYc0wLRyDAvHME6o49c/78+UJHK3Wh6nOqQrdEPvkkGOvXBypV1KmTgZ07nyt/tgXX/LdtQ+j48Tpmp/TsiYTPPoPFNz6WAMJ7HY6ALTjncKfYAJdAgLnnEmFyCSM5ge4SYWIjHYgAJ9AdCL4zHy2VSmWU5LaRjcQ7TqDbCFxttZxAtxPQLnYMJWEpcZqcnKy0fMOGDUKydfTo0Xq94QS6ChZqAU6twDWkL4ByAH4AoJZXF5LnlETPE0ur0I8dO5ZvhTRVjv/000+oUKGCcNrIkSOxc+dODTO//fZb0Fx7+lenTh2kpqaiRo0ayMzM1FgXFlYemzb9ArncA9nZALVM3LPHH1u2BEAuz//yITBQhmHDkjF8eAr8/c37M2Krm0FtPNq1awfi9cGDB5W+01gD9SS7i/1fm821EAFbcc9Cs3i7SBFgvok0sE7sFnPOiYMjYtOYdyIOrh7Xnjx5ggYNGiAtLU35Kb14TOPALJUGDcJx756XUs3kyYkYOVJ1P2t1rsnlCGvVCt7XryvPlPv6IuHzz5Haq5el7vB+ESBgdc6JABN2wT4IMPfsg7M7nMIJdHeIMvtoCQKcQLcEPRHvVUugW5sjlE3hBLoducMJdDuC7UJH3b9/X2irpy4XL17EtWvX8P777+v1hCqaY2NjhcpmqrwODAxEuXKUMXY/oRbpGtXZQQCoo7oHgKMAzqhhQu3bqY17nlhahU6zyc+dO6cBeps2bUCtyKl9e/PmzZWf7d27F8OHD9dYS7+jhLm6rFq1CrNnz877FfWiXwyABrr7mh3cl17KxrRpiejYMR0SE/+S2OJmkBLlb7/9tsBhhVC7duK9+ksGI0aMwJQpU8z2mze6NgK24J5rI8LW2xIB5pst0WXd+hBgzjEvHIEA884RqDvuzEmTJuH7779XGkDxpxd8y5cvb5FRDx96onbt4ho6Dh+ORZUq2RpnRURECD9bY/SYz+nTKNajh8aZL9asQXq7dhb5wpvFgwB/v4knlq7mCXPP1SLmvPZyAt15Y8OWOQcCJj7Sdg6j2QrbIyCVSu/ZsAJdcCA6OvoV23vCJ3ACnTmgD4H9+/djyJAhyo9CQ0OFpPiFCxfynZtNDyGoSpn2HTp0CFTtTEnXAQMo0epeQjP8jh8/rnK6FoB3836MAaAqaAZezstFq0FkbhX6pUuX0LFjRw2wlyxZgsjISL0BoPmD1KI8KytL+JxiduXKFYSFhems/9///od69doCOAKgttUCWrduBpYvj0NEBDU2MU5scTP48OFD1K6t6depU6dALw9s3rxZadjAgQPVXiYwzl5eJR4EbME98aDDnlgbAeabtRFlfYYQYM4ZQog/twUCzDtboOqcOm/duiW8zEsvXCskv7FQpnqwY4c/Ro8OVW4LCZEJ88896AXmPLE214q8/z78jtLb0bmS/coriD11ioatm2o+rxcpAtbmnEhhYrdsgABzzwaguqlKTqC7aeDZbaMR4AS60VDxQkbANRHgBLprxs3WVs+dOxfLli1THtOoUSNs27YNf/31l0YFs7odlFxfsWIFvvvuO+WvaWY6Jd6pHbi7CFUyV69eHc+ePVO53A1A1bwf/wQQpYZGCIAxmuiYW4U+ePBgHDhwQKmMKixofrmPj0++8FOCfeHChcLnffr0Ac0k1Cfp6UCDBkn499/XDYbSz0+O999PQcmSObh92wv37nni7l0v/O9/nnrbu7/2WhY2bnyBMmVUD9MKOsQWN4PUpn3QoEHKY2n++R9//IFPP/1Uo6Vkr169lHgZBIIXiA4BW3BPdCCxQ1ZDgPlmNShZkZEIMOeMBIqXWRUB5p1V4XRqZdr3Kn5+fjh9+jRKlChhsd0TJgRj61bV/PM2bdLw3XdxGnqtyTXPO3cQ3rgxJHLVSKp4at2eT7c2ix1kBS6JgDU555IAsNEOQ4C55zDoRXcwJ9BFF1J2yMoIcALdyoCyOkbA2RDgBLqzRcQ57OnRo4fwMEMhirbVjx8/Rs2aNfUa2bhxY1DFrrbs3r0btWpRCbZ7iL5KZowEUDTP/38AfKuFRam8buiVAFBLdz/A2Cp0mle/fPlyfPPNN0hJSdFQPH36dAwbNswg8Ddu3BC6B1StWlWoQtcWKhIZOjQUBw/663zm5SWHt7ccXl4QEuZvvPEIlSodR+fOVVGyZEmN9X/+6YVPPw3Gr7/qtn6XSORo3TodPXqkoXnzdBT0zoUtbgbpxYGvvvpKaS/NZoyKioL2yySdO3cW8GZxTwRswT33RJK9NgYB5psxKPEaayLAnLMmmqzLWASYd8Yi5drrfv/9d7TTam1uzdFI2vPPP/00AUOGaN4bWZNrhadPR5Dai+Oy4GA8uXAB8kBVEt+1I8bWWwMBa3LOGvawDvdBgLnnPrG2taecQLc1wqzf1RHgBLqrR5DtZwQMIMAJdKaIOgKUjKVKXPX27fT5t99+i3feeQdpaWl47bXXTAJt/PjxGDeOBoC7h2i3v4c/4DXZS5mYlifKkb1QNYtPBxVvQFJJAnltORb1WoSer/csELiff/4Z7733ns4aqv6nrgD0X1Pl+XMP/PGHF/7+2xt//+2FU6d8cf++l5aaxwBaYNGiQejZM9dGsqVv376gKvyQkBBs374dVapU0dhHRRqHDvlh2rRg/Puvp17TwsNz8PHHSejZM1VvB0Rb3AyS3TR/USE0G37atGnQnmdP8+TVuyyYii2vd20EbME910aErbclAsw3W6LLuvUhwJxjXjgCAeadI1C335kJCQnCi77UqUxd6F6BOmUVLlzYYmPo3mLgwCIaeg4deoqqVXPHVCnEWlyTJCSgeM2a8EhNVepOHj4cidOmWewLKxAXAtbinLhQYW/sgQBzzx4ou8cZnEB3jzizl+YjwAl087HjnYyASyDACXSXCJPdjKSZ5TTzWVsuXrwotNajxGipUlQubbzQXOldu3YZv8HFV37xxRcaFcqlq5dGp087Kb2S5ciwInIF5DJVuz99Lks8JJi9aDYGRBY8Q167clqh66OPPsLUqVNNRnP+/EL45psgyGQFXQIkAmgM4HfhxQp6wYKE5t0fPnxYeWaZMmWEn/Ul8f/5xxOdOhXLN4lOSho3Tsfq1XEoVEgTK1vcDL711luIjY1V2v6f//wHnTp1wurVqzFr1izl76nTwtatW03GlTeIAwFbcE8cyLAXtkCA+WYLVFlnQQgw55gfjkCAeecI1O13Js04P3nypM6BkyZNwqhRoyw2ZP9+P3z4YSiys1X3LsHBufPPPbXe1bUW1wJXrULw7NlK2+Wenog9exY5Wt23LHaOFbg8AtbinMsDwQ7YHQHmnt0hF+2BnEAXbWjZMSshwAl0KwEpdjWRkZEtZTJZS4lE8iqAwnK5PAHAXYlEcjw6Ovqo2P13Zf84ge7K0bOu7RkZGahUqRLSadi1mtDF0uXLl5W/adasGW7evGn04V5eXsIs6aCgIKP3KBZeunQJI0eOFOaJT5gwQacy3mSFdtgQGRmJM2fOKE+iB0P0gEhdqKX9o0ePDFpTr1497Nixo8B1kydPxqZNmzTW0Mz5c+fOgWagmyLR0f4YMya0wC2enjnIyWlF9ebCOoov2Ug+1ahRA//++6/Gfoof2ahPHj/2wLx5hfHjj35IT/fQu6Zy5Sx8//1zhIfLlJ9b+2aQbCbb1YXGEZQrV07AVt1+8pPGErC4JwLW5p57osheG4sA881YpHidtRBgzlkLSdZjCgLMO1PQcq212dnZeOWVV0BdztSF7h+oU1Z4eLhFDu3d64cRI0KRk6P56HLixESMHp2so9sqXMvORniDBvD6h+Zy5Upa+/aI0/MSukXO8WZRIGAVzokCCXbC3ggw9+yNuHjP4wS6eGPLnlkHAU6gWwdH0WqRSqXVAawHULUAJ695eHh8sH37dlUGTrSIuJ5jnEB3vZjZyuKYmBh07dpVR33r1q2xbt065e/37duH0aNHgxLu2kIX6Z999hk+/fRTZGWpWuZFR0ejfv36Jpv+7rvv4rfffhP2kW6ay05Vzc4s2knkNWvW6Mz7U/erIF9efvllobVhQUIzzikm6jJjxgwMHTpUZxu1Zt+1yx/Z2UDZsjkoWzYbZcrkwN9fDqreGDUqFOnp+f/pp/VTp/4PI0ZUFGamK4QS9T/++CNq1qypc2blypVx5MgRXRfkcng8egSPhASkPktHzE85iPkpG49vZyIQKaiAm6iMP1AK/+Blj38Q7JkMiVwG5ORAQn3gQ0Np6DqyZDLkvPQSciIiIAsNhSwkJPe/oaHIfvVV5BgxcuD48ePo16+f0sbAwEDQXHjiHL0cQHxXCLWkV6+yd2Yusm3WR4AfRFgfU9aYPwLMN2aHvRFgztkbcT5PcY2veOmTXmrUTrYySq6LQEpKCipUqKDjAM1Cp3skS4TuaUaNCtHpmtWnTwrmzUuw2Rgo/127EDpihIbpT3fvRlatWpa4w3tFigD/XRVpYF3ALeaeCwTJRUzkBLqLBIrNdBgCnEB3GPTOf3C3bt1qenh40MDYQADEFeqxq84Z9Z9TPDw8mm/fvv2C83vmXhZyAt294l2Qt9qznhVraQ40zYNWl+fPn+Po0aOg+eYKoUqC5cuXo3379mjatCn+/vtv5Wc09047OZ+aKsGzZ7lVx2FhlMTVtE7fvPWxY8cKlejOKvRSAVUtU6t7hRBOVNmvLr169QJVOGsLVY6rv3hArc8pkVuQ9OjRQ3ixQCF9+vQBtXVXF3qXISoqAF98URjx8forvfWdUaxYDpo0yUD58tl4++1M1KuXKTyMWrhwIZYsWaKxhVr8P35Mc9FVQtPPy/v44Nv58+GZmAiaF+gRHw/Pf/6Bz2+/wfPJE5uHMqtyZSROmYKMZs3yPYtmmk+fPl35ObVzpxcCSLRn2lN89cXO5o7wAU6BAD+IcIowuI0RzDe3CbXTOMqcc5pQuJUhzDvxhvvFixeoWlW31oK6OVFXJ3Nlxw5/jB2rmzzv1y8Fn3+uP3lOZ1nMNZkMYa1awVvt/iyzenU8o/sGCT8+NTeeYt5nMefEDA77ZlMEmHs2hdetlHMC3a3Czc6agQBfAZoBmjtskUqlNE3qT8qNqCXOqS/XXwDiAFAP4NfpHkXtc8qmVYqOjs5xB4xcxUdOoLtKpGxvZ8+ePfHLL79oHEQVA3v27EHhwoV1DKDqEKpyPnDgAMLCwrB27VrhQQj9XluXehKecstffFEIq1YFKdvteXrK0bJlOiZNSsLrr2cLZ12/fh0tW7bUOJeS8/pmtNseHeNOuH37NmhGtrpQAlx7BjjhpkjQqq+lFod3797V2H/v3j1QYj0/oQ4B1CJfIfQihFQqFX6kxPmWLQFYvjwIDx96GedE3qr+/VPwxRc0jUNXcnJyQPMM1RP3ilWvABgEoC+A0iadaLvFcokECXPmIPWDD/QeMnPmTI0qGJp9/p/ly+F55w5ub9qEa2vWoBgAmpCeVKgQBs+YIcw4zKpUCbKwMNsZzpqdDgF+EOF0IRG1Qcw3UYfXKZvsPnEAACAASURBVJ1jzjllWERvFPNOvCGml2u1O1QtWrRIuFc0V+7d80SzZuHIzNR8XDlgQDJmz04sMI9tCdck8fEImTwZ/lqdv158+y3S33nHXHd4n8gRsIRzIoeG3bMxAsw9GwPsRuo5ge5GwWZXzUKAE+hmwSb+TZGRkd3kcnl0XnKcHF4EYF50dPQLhfdSqbSIXC6fIpFIxikq1CUSSWRUVNQP4kfIdTzkBLrrxMqWlsbHxwsPN6jqWyFU6T1ixIgCk7dUaU3V6FSVS22vFW0XqeW1+uzuQYMGYdasWaDk+dy5hbB8eaF83SlaNAcVKmTDx+cWTp5cDuBeXuryKsqVK+3U1b8nT54UEssKCQkJ0UhuK34/ceJEbN68WQ0DSpC/hLfe6oArV2iGOM0upxcJniM6eg7q1SuU78Mg7XnqGzZsEF48+PtvL3z8cTAuXPA1mTq9eqVg7twEFJC3F+bSt23dGpInT0BJ87oAKD1d2eTT7LcheehQJNI8dh8fCGSkNvQ+Phg4aBAOHTokvPnVH8CAsmVRKSEBnnH0PljBkhMejqwqVZD59ttI7d0bsuLFDW3hz10YAX4Q4cLBc0HTmW8uGDQXN5k55+IBdFHzmXcuGjgjzL5//77OGK9bt27BX7v1mBG6FEsWLw7CokWaL3cPGpSMmTMLTp7TfnO55nP2LEJGjYLXo0calmZVqICnx4+TYhM84KXuhIC5nHMnjNhX2yDA3LMNru6olRPo7hh19tkUBDiBbgpabrRWKpVuBPAeJdAlEsn0qKioL/JzXyqVfgJgTl6yfXN0dLRq0KwbYeasrnIC3VkjYz+76MFG3759QdXT6nL16lUUKVLEoCH6Lsznzp2LZcuWKfdS5fjEid9i0qQQnD1rekI3VxHNXP8e587VR+nSwQbtsveC9evX45NP6OtOJdWqVcPBgwd1TKE58StWrMhr0jEDAM3YDsnX5DfeuIvnzxugaNFgUDt89Zbwr732Wt6LD1Rh3gvNmn2OW7dK4MGD/CvOvbzkKFpUhidPqJmISooXz8G4cUno0yc1/+qNzEwEbNuGwE2b4HnzJjxooLoFQq1L0j084C+XI1kuBz2WigfwDMBleOMWRuIh3kEcQpEDT+EfCc1GL44nmIgvURE3IPf1RUaLFvBISoIkLg5eDx4I89X1idzPD5L0dOEjWaFC+EUuR2JyMqjJe5AFvpDe5CFDkDRmDOBrLs8tMIC32hwBfhBhc4j5ADUEmG9MB3sjwJyzN+J8HiHAvBMvD27evIlmWmOUHtA1ugUJ586di2q8INy1ayq+/jreqA7q5nAtcMUKFP78c0jURnRRxKjD1YsNG4T7DxZGID8EzOEco8kIWAMB5p41UGQdhAAn0JkHjEDBCHACnRmiFwGpVErt29+gstCnT5+WOnHiRL4ZlCFDhnjHxcX9AwgdcG9ER0c7c4Gi20WcE+huF3INhy9evIgBAwYIVeTqUr9+fURHU5MJw6LvwlxzprQ3Spb8Cs+eDUNGhuV/VkqUSMCcOTlo3Todnpr5X8PG6llBldQxMTGoXLkyypYta5aOy5cvo0OHDjp727Vrp9EaXLFg0aIVWLyYUscfAihp5JlU370eL730ktA63dfXF+np6UL1f+7UjKMAahSoq2bNTCFBTnPNSWgOPbVBvH/fC4ULy0Cf6837ZmTA79Ah+B0+DL+ff4ZHYqKRNgM0FT07NBThFSrgzJ9/4n5SktAKnf6I/ArgVp6mff36ocNGejdLW4gzPVECE/EY1XU+9UEGluMjdJ9RCqlDh6g+z8lBoUWLUOirr4y21VoLsypWRNzXXyO7UiVrqWQ9ToIAP4hwkkC4iRnMNzcJtBO5yZxzomC4kSnMO/EG+9q1a2jTpo3SQRpLReOpzJWkJAkqV45QjgEjPd9//xzNmuXe2xgSU7lGlefFunfXUSsLCUH8ggVIb9fO0JH8uZsjYCrn3Bwudt+KCDD3rAimm6viBLqbE4DdN4iA5ZkOg0fwAldEQCqVUqt2KgHdFR0drXtHoeWUVCrdAaArFRZGR0cbLml1RVBc1GZOoLto4Kxg9r59+0Ct1jMyNB840MVRVFQUqLLZGNF3Yb5//34MGULJTB8AhwChtldXZs1KQLt2aVi2rBCio/2Rmmp8+7uIiBRUqHAQPXumo1On5saYqrMmNjYWVB3/8OFDoRJi69ataNiwobDO8949BGzdapTeMT//jO/U5pArNo166y1MXbYMOWqJ+cQXOZjS8hJ8n6RDDgnOoAGeoLjwv3Orq/P700upZnpII8OECePRvXtXPH0aiw4dCOeVAN7M19aIiBwsXRqHhg0zC67OkMngffUqvH/7LbeC+/FjeD5+DO/r101Kmj8FEJVn1TUAjRo1wqeffqoz017d4NDQUMQV0DKdUudfohWmYw7Oo46Or29WTUfDxlkoVy4br76aLfy3SBG5EMPgSZMgyckxKpbaizKLFsXe58/xV95bYCUAtK5cGT537sBDbeSB9j65tzcSZs5Eav/+MKokxizreJO9EeAHEfZG3L3PY765d/wd4T1zzhGo85nMO/FygF7W7tSpk9JBGvlFVenmypEjvvjgg6LK7T4+cvzxx78ICJAbpdJUrhXt2RO+v/yioTujQQPELV0K2UsvGXUmL3JvBEzlnHujxd5bEwHmnjXRdG9dnEB37/iz94YR4AS6YYzccoVUKqWKc4lEIlkZFRX1kSEQIiMjl8vl8uEAcqKjo2nYL4uTIMAJdCcJhJ3NWL16tTCTXFsqVqwImqFdsqSxVdH62y5eunQJHTt2BEDtyXXPobnmxYt/ij17PkTp0qUFMyi/ef++J27e9MZff3li8eIjyM6mJH6Y0LA7f3mASZN+wahRTU1GccaMGVi7dq1yX4sWLbBx40ZhPvnK+fMR9uwZekgk6OPpiTBJ/n8Sy2Vl4a6e02mCe+9p0yArUwbelGC/cg1Zp39DkRxqUK4rMkiQCR+kwxtn0FhIFl/B2yb7pdgQHCxDmzbpmDQpERERVPGej8hk8N+xQ6jW9jKjKiTH2xt/Z2XhMoDVAE7lzexQnObn5ydU+BMvLJGHAMLhiZ7Yhh9g8N0tvPxyNj74IAVD3jiOoovnwffCBYPH0x+3jI4dkdm6NTJr1sS/vr6o/tZbGvv++9//omhICDzv3hXi6nvqFAJ27IBETzt70pHSrx/SqELFgnmPBg3nBXZBgB9E2AVmPiQPAeYbU8HeCDDn7I04n0cIMO/Ey4Nff/0VUqlU6SCNB6MxYebK9OmF8d13qoFL9eplYMcOzU5qBek2hWveV64grH17DXXJw4cjcepUnnlubgDdcJ8pnHNDeNhlGyLA3LMhuG6mmhPobhZwdtdkBDiBbjJk7rFBKpVSJobmny+PiooaZchrqVT6DQBKtMujo6Ot0HTZ0In8ubEIcALdWKTEs+7333/Hu+++C7nWHLemTZti5cqVKFSokEnO6rswp4ru2rXfA/BfAOqzoKkKmFpqU2I9BUOHDgUlsbXl6dOnqF5dvV336wC2AXpaeNNeT89/cPmyB4rRoAgjJTk5GTVq1AD9VyHUHn3dunVo27atBj701g81aB8JgNL09AWoqJWn+n1qx6HdOLAugL2UeDez8plsyoYn5mEy5mA6MjVwzN/JgAAZxo/PbdP++uvZ0DtiMCsLnvfvw+vOHXjdvg3/vXvh81+KlfGSUacOUgYPRmaNGpAVK4YVq1aBZruTULL8Dz0V+cZr179yDYBBebh8jAVYirFGqaxWLRPbtj1HkYT7QkU9zSqnmekkV7dswa9r1wpN8P8AcCEiAnvVEv3Ej9dfJ/6phFr+lyql+VKH1x9/IHT0aKFaX59Qq8fUyEgkTZwIOSfSjYqbMy7iBxHOGBXx2sR8E29sndUz5pyzRkbcdjHvxBvfn3/+Ge+9R/eEuRIREWHRC7WNG4fh9m1VPcbEiYkYPVp1L2cISVO4FjpgAPwPH1aqzAkPx5OzZwE/P0PH8OeMgBIBUzjHsDEC1kSAuWdNNN1bFyfQ3Tv+7L1hBDiBbhgjt1zBCXTxhJ0T6OKJpbGeTJw4UaiwVhd6sPH555/Dy8vLWDUF3hRmZGTi1VfvAFBvrU61vfSzqg2eouJb+9ALFy6gc+fOWr8m23oAGAfoqcquVOkxDh6Uw1gXKFE+bdo0HX+pcn7v3r0F4kCvGIwH8GneHO8GaqspsU6/J83GN6QvGPZjaIG2OIQcFBwfL68H+OknH5Qrp79dOc0xD/rqK6Fq2pyW5hn16iGtUyekt2wJWQlqZq4pt27dQlJSEqpWrYr69esLrfGtKcSIXWoKf0A3DAvYgGepgQaPoQqVLVuew4emCqjJihUrlIl/+nW9evWwYwdNHcmV7OxslClTRmPPiRMnUL58ed0zMzJQeMECBK1Yka89GbVr4znp9+R3yQwGzQkX8IMIJwyKiE1ivok4uE7qGnPOSQMjcrOYd+IN8OHDhzFgwAClg2XLlsWZM2fMcviffzxRp05xjb3799NL11lG6zOWa143biC8RQsNvQnTpyNl2DCjz+KFjAAhYCznGC1GwNoIMPesjaj76uMEuvvGnj03DgFOoBuHk9ut4gS6eELOCXTxxNIYTzIzM/HWW28hPj5euXzQoEGYOXMmzWQwRoXOGn0X5jTPfMwYqulVl4UAPtb4Ddny448/6uik+ezDCnxA0QjAYgA1NfYOHpyMmTMTDfohk8nQpEkT3LlDSX7zhVqVx6h5FQFgS74T3zXPuYPSCMc/CIJxM/sGYC3WQfUASlNbCoBoVKu2HgcPkgVaIpOh0KJFKLR0qVHOZpcti6wqVZBTsiRySpQQ/gk/q81yN6SIKtCnTJmit8okLCwMY8aMwSeffGJIjcbnlCanJo1UO54GYLCPD44ULoVXy41DpUqRePQoCLdve+HePS9kZ+vy+ZNPEvHhh5pVKpMnT8amTZuU50RGRmLJkiUa55Yt+xqysqjvQG6fgYMHD6JatWr52u534ACCP/kEnrGxetfEf/YZUj/4wCTfebFzIMAPIpwjDu5iBfPNXSLtPH4y55wnFu5kCfNOvNHes2cPPvzwQ6WDFSpUAFWlmyNbtgTg449DlFtDQ3Pw++9PTHon1ViuhYwciYCdO5VnUSepJzExkAep2seb4wPvcT8EjOWc+yHDHtsaAeaerRF2H/2cQHefWLOn5iFgXjbFvLN4lwshoEigA9gtkUioH3OBIpfLxwCg4kG5XC5v6uHhYZBbUVFRlJtisTECnEC3McBOpv7IkSP4QCtxp68dtSlma1+YU6Kxfv1w/POPerX0fWrsLbRtVxeaf37u3Dmd49asWSMk9QsWqkC4qDMffcOG52jZMjfRefnyZSFB/+abbwoz2RUvCVAFcZ8+fQy6Sb75eHggXc9sa9rcHwCl628AaAKALNasi8g9Ig4huIia+A3VcQVv4VGZt3Hy/jvwxj2Uy5sXTmn0jWvW4OzJk0j4/ntMBaBqUEhzxf3RBFTFT1+fVNtO/xT/m/zNhL6KfsmLFwiZMgX+el5U0AYgJyIC8fPmIaNlS8DMFyq0dd68eRPbt28XqrqfPXsGT0+ab78YL7/8Mrp06aKx/I033sCuXbuwe8wYJB4+jK4AamvNUz8CoBWAeQCmqO0ODw/H+PHjQQlwDw8fnD7ti9GjQ/DsmarSu0iRHMTExCIgQPXSAtlw/vx5pSZKqI8cSc36c+WHH/wxenQ65HKKLP1ZWoidOweiTp06BfMnMxN+hw8jYMsW+J3S/HMmK1QIT48dQ45WG3iDhOQFDkeAH0Q4PARuZQDzza3C7RTOMuecIgxuZwTzTrwhj4qKwtixqpFL1KXq0KFDZjk8dGgofvzRX7m3Y8c0rFgRZ5IuY7jmee8ewhs1gkRGL8/mStK4cUgaT/3HWBgB0xAwhnOmaeTVjIBxCDD3jMOJVxlGgBPohjHiFe6NgMEkp3vD477eqyXQTQFBwSdjyi1pVrrpvaRNsYbXCghwAt29iEAJxm3baJZ4rtSuXVtIWFoi2hfmR496o1+/oloqaYK4bqV5QEAA/v77b53jaZY2tdY2LJRepeSkas56sWI5OHr0KRITb6J58+bIyZtBTknbHj2oBTzQt29f/PTTTwbVt2nTBkvnzkXnmjXxl9pDFNrYDsBwAPUBFMlHUxa8MB1zsBRjkIHceXkDByZj3LhYVK5MqXNNoUTu0aNHhcrsngC2an1OM8B/B0A11EkACLlragnm9zt2xPyhQ+H54AG86N+NG/A7cgQeSbRaV3KKFkX2q68iu1w5ZFWtirSuXSEvXNggLuYsyMrKwl9//YXQ0FCULFlSaPVOc+7T09MFdfRyA7XOf/vttyFJS0PxmjUhiY9Hvbwqf8WZowFQfXhTLy+c0vNiA80mHz16NKRSKX7/PQCdOoVpmDtpUiJGjcqtQpfL5ahYsaJgi0I2bNiARo1a4sABf6xfH4iLF7V6vgtt3u9hzRpfhIYa8+cM8Dl9GsXyuKc4J7N6dTyjypa8WezmYMp77I8AP4iwP+bufCLzzZ2j7xjfmXOOwd3dT2XeiZcB33//PSZNmqR0sEaNGgZHZem9Z8kBqlWLQHy8akDWokVx6NmTelIZL8ZwLXjiRASqjTuTBQTkVp8Xye+Oz/jzeaX7IWAM59wPFfbYHggw9+yBsnucwQl094gze2k+ApxANx87Ue80M4FuLCbEO0qg84BYYxGzYB0n0C0AzwW3dujQQajKVsj06dMNtEo37KT2hXm/fiE4ejQ3WZwrl4RW6zRfndqyL1u2TEMpzc3291dVE9CHI0aMMCGxPwTAKg2dRYvmoFy5+Th/flFe028Ic7mp4oHatjdqRC3gDYsi6b7u/fcx7egVAA1QBnJswEk0QUKBCv6H0uiB7TgnpIABsmnx4nihOp4St5To1Zbr168LbQ2p1SGlbR8XkJxX7KWW5v8FUAxAVcMuQe7picQZM5DavTvkIao2iEZstfqSb7/9Vpg/Ti850H/796ea/lwJXLUKhefMwWdyOWaonUyvHdwkXPz8kJCXfNdnGFW4U5v4/fuH4/hxdT4C9etnCP/8/Z9izpxeeRylKpOX8f7757BvXziePy/4T1Dx4jno2zcFrVqlo3LlbIMF+yFjxiAgOlrD1ITZs5EycKDVcWWFtkOAH0TYDlvWrIsA841ZYW8EmHP2RpzPIwSYd+Llwdq1azFjhupKvn79+ojWuh42xvvLl73RoYPmS7Hnz/+LkiVVVeLG6DHENY/Hj1G8fn1IMjOV6pKHDUPi9OnGqOc1jIAOAoY4x5AxArZCgLlnK2TdTy8n0N0v5uyxaQhwAt00vNxmdV4C3Zb+cgLdluiq6eYEup2AdpJjKIn84sULpTVUbduS2nVbIOoX5pcuxaJ27TDIZOp/PoagWLFdQkV5pUqVULkytXJXCVVdU0WyunTv3h1nz54t0KqiRYvi+XNKH5PsBtApn/V3AawDMB8PH94RHuLQwxzDEoAFC2Jw+3ZZnPhJghs3A/EODmALeiPEQPL8gEc79JVtxAvkVuIHBclw5MhTlCmTozxW22f64MGDBzh9+jR69aKkLkCvGnxk2FCjV8gKF0bcypXIaEIN551D4uLi4OPjg8BAmnCuEkUV+pX4eK1J98DBoCC8k6w5yzw/b959dyb27//UgLPPAMQDeDWvNb5p2ERE5KBFi3S0bJmOpk0z4KNbtA5JUhLC2raF1717SuUZDRrgeVSUaYfxaociwA8iHAq/2x3OfHO7kDvcYeacw0PglgYw78Qbdrr/o5dkFdKsWTNQVbqpsmRJEBYuVHXJKl8+CydOPDVVjcGXNQrPnImgNdTzK1fkPj54cu4cZMX1Deky+Xje4IYI8PebGwbdSVxm7jlJIERgBifQRRBEdsGmCHAC3abwsnJGwPEIcALd8TGwlwXUpppmTKvLyZMn8dprr1lkgvqF+fjxyVi8OEipLzAwB2vWHETt2hWFKnOqvC5btiyy1VpvHzx4ENWqVdOwoWHDhrh7lxLfuUJJ9z/++ENjzaxZs7BgwQIkC4lUSlRfAPBKAb7sxbVrr6Nu3Zp5e3KXNmjQAGfOnFHbFwpgDjVbB/LargNyjMFSLMQEeEJ/pcMDlMIvaIRd6IIf0A1yYUZ5rnz9dRy6ddNsMagvgf7w4UNcu3YN1DqepHRey3ayyFJJ7dwZSVOnIkfrZQVL9dpyP1WhF5o9G1SrT9X4CmlQpgzO3L+vcXTNmjVx8eJFvea8884lHDz4ttmmennFITs7BgDNPS84Gi+/nI0ZMxLxzju5renVxX/3boR+pHolQubnh3+vX4fejLvZ1vJGWyLADyJsiS7r1kaA+cacsDcCzDl7I87nEQLMO/HyYMmSJVi4cKHSwbZt2xr5IrMmJl26FMX586qRXYMGJWPWrESTgSuIa17Xr6NYhw7wSFPds6X07YuEefNMPoc3MAIKBPj7jbngKASYe45CXnzncgJdfDFlj6yLACfQrYsna2MEnA4BTqA7XUhsZpB6YpYOoZnTt2/fhq+FM5gVF+aUEy9dOgf//qtqfU0trufN02x1/tZbbyE2NlbpJ1UhUDWCQijJXr58eaSpPbyglu7qrd/Dw8Px66+/okmTJqCkc65URkDAJaSmqh6uaINZtep5XL1aVzk1nGw/deoUunbtmmcTJfIPAngJHsjBm/gdzfET3sFBtIDuzPR7KIPP8QkOow0e4GW9sevUKRXLl8frtPjOL4FO/tBseoVQa3aah07pX2pc2LJuXaQ8eYKUu3f1nkgt2ilJnlOqFLJffhnZlSohvU0b4WdXE0UV+uD4eHxXgPH16tUT2kH+8ssvwkO6S5dobIBKQkOLCnPLDxxQvdxhDBbU5r1//xRs2NAVv/56UuAF8C2AdwxunzgxEaNHa1bJS+LiUKJKFY29T3fvRlatWgb18QLnQIAfRDhHHNzFCuabu0TaefxkzjlPLNzJEuadeKP95Zdf4uuvv1Y62LFjR6ErmSmSlCRB5coRyMlRPZ7ctOk5mjfPMEWNsFYv12QyBH77LQrPmwdJhkon3VPFnj6NnJf13+OZfDhvcEsE+PvNLcPuFE4z95wiDKIwghPooggjO2FDBDiBbkNwWTUj4AwIcALdGaJgHxv27dunMe+cErjUPt1SoQvz4sUj8PHHwCIaOa4mR47ECvOh1YVaxtOsb4UsXboUUqlU+XNCQoLQ6l1dqEJ85MiRwvx2b29voXKhRYsWaNWqFf7880/l0jlzVuPRo144fDgRd+7QfO8APe5Ru25qox0IP7+i8PMrhtTUbGRmUiI+t4J9MFYLifEw0Fr9sg09MASrkQRVO0HtlRUrZuGHH54hOFiuoyS/BDpV51esWBGpqak6e8LCwvDbb78Jlfzvv/8+/jp2DE0BVAwMRKvISJTt1AmZ1asD3t6WhtVp9lMV+vHZs9GtAIsGDRoE6khAQths2rQJU6ZM0dixbVs00tKa4cYNb/z+exwOHaKOBtR9oYLGOmq3L5Wmol+/VFSokMtdwvro0aPKdT17rkJgYG8cO+aH+/e98rVs6tREfPSRZhI9rGVLeKvxP3HKFCSPGOE0eLMhBSPADyKYIfZEgPlmT7T5LEKAOcc8cAQCzDtHoG6fM2fPno1Vq1YpD6N7Prr3M0UOH/bDgAFFlFt8fOT4449/ERCge39lSK821/DPPwgdOxa+p0/rbE3t1g3xasl/Q7r5c0ZAHwL8/ca8cBQCzD1HIS++czmBLr6YskfWRYAT6NbFk7UZiUBkZGTjqKioU0Yu52UWIMAJdAvAc7GtVME9d+5cpdX169cXqnYtFw/MmROBlSs1Nb39dib27dNNQPfo0UOY862Q6dOnayT2//rrLzRv3lxD2Z07d4TEOVUWlypVCiVKlBA+156VTklUSqbmzjlfD6AfADJMz1DqfBwfia/xNUYXCMt0lMdnQqV6ObV1MoSH5UAm90B4uAxdu6aif//UfB/u5JdAJ4X9+/fHsWPHdGygFwsUiVyq0N+7dy9ycnLw7rvvIjg42PJQOqEGqkIPqFEDYQkJyMrHvsWLF4N4pRBKotMYgHtq88ZXrlyJDh06CEs020mWAUAV4BFYuHAIOncuCn9/zYOGDx8uYK0QGilw+PBh6uOA27e9cOyYL/bt88dvv+nybO7cePTtm6rsQFB42jQErVun1JXRsCGeb9/uhMizSfoQ4AcRzAt7IsB8syfafBYhwJxjHjgCAeadI1C3z5nTpk3DOrXr3vfeew9UlW6KTJkSjI0bA5VbGjTIQFTUc1NUKNeqc+3JjRso2rYtvLTGQtHirEqV8Cw6GvIQeiGbhREwHwH+fjMfO95pGQLMPcvw490qBDiBzmxgBApGgBPozBC7ISCVSl+Ry+X9JBIJZbzKREdH51/WZzerxH8QJ9DFH2OFhxMmTMDWrVuVDvfp0wfz58+3CABq2z52bCh27tTKOALYsuU5mjTRba334YcfYs+ePcpzP/roI0ydOlX5M7VU79Wrl/Ln0NBQYS64PqFkOc1QV8i4ceMwfvx4IVFK1eq5QknTHUYl0ZvjOI6iFTygv6JBFhyMtFatUHPHDtwAtYqnRD/NRL+LOqXl2HnuhNF4FpRApwdN9MBJW6hl/ZYtW4w+QywLqQpdOns2VDXgmp4dOXIElNRWF2oRqd7Kfd68eejbt6+wpHPnzrhw4YLGeuIgcVGfEK+2ayW5J0+eLHRFUIhcDnz7bSBmztR9kYFawc+enYCKFbPht28figwbpnFMwowZSO3ZE3KRvgQhFh6SH/wgQkzRdH5fmG/OHyOxWcicE1tEXcMf5p1rxMkcKz/++GONe5eBAweCqtJNkQYNwnHvnurRkL4OT8bqU+da0scfo5DafHaFjuQhQ5A4aRLg52esWl7HCOSLAH+/MTkchQBzz1HIi+9cTqCLL6bskXUR4AS6dGCk5AAAIABJREFUdfFkbVoISKXSIIlEEimXy/sDaJj3MfFOHh0drRqkzMjZDAFOoNsMWqdTrF2tXVDC0BjjKWE4fHioUHmrLh4ecixZEo/u3dP0qqGK8+++U0207tmzJxap9X6nRCUlLBVC7cz1VWPT59qJTXooQ/pff/11ZKjNsAPa5M2uzn8OuC/ScRVVUR63NOzOLlkSGc2bI6NBA2Q0aSK0Rx/2xhvYS28PqMlHQ4Zg6qefGgOdsEa7/Tzd4Dx48ED4jGbTN27cWEcXzYLXbk1u9IGuvDA9HRvGj8fU3bt1vKDK+6tXr8LTU/NPRr9+/XD8+HHlekXCOzExEVWqVBEq9xVCLd+1ux6oH7RgwQKddpMhISFCO33qjKAuK1cGYs4c3SQ6/f+CKtFnjHmIsk1rwyMhQWOf3MsLmXXqIK1dO6RSNb12Gbwrx09EtvODCBEF0wVcYb65QJBEZiJzTmQBdRF3mHcuEigzzBw1ahR++OEH5U56kfqTTz4xWtO9e55o0KC4xvrDh2NRpYrmfZixCpVcS02FrHRpeLx4odyaU6IE4pYuRWZDxWMpY7XyOkYgfwT4+43Z4SgEmHuOQl5853ICXXwxZY+siwAn0K2LJ2vLRUAilUpbSiSS/nK5vDMARfZNnW+cQLcTWziBbiegneCYWrVq4dGjR0pLaB5d+/btzbbswAE/DB6smkdHiry95fjPf+LQrl16vno122fnJpLXr6d267mi3eqPEpuU4NQn1LJ99erVyo+6deuGwYMHo23btnqWU6KzDk0Mp8Z8GD9+OKpVKye0WM/OluClZV+g8ZnFGvuSRo9G0sSJOrq+6NYNy8+d0/g9VY23bt3aaDxprntkZKRyvXobcmpBXq9ePWVCnRYVKVIEVGmtaF9v9EEiWXj37l2hLbu2aPNH8TlVh+/cuVO5fNiwYcLLFYcOHQK9aKEQHx8f/Pnnn/AvIGFNZ7/zzjtISkrSOH7z5s1o2pSm0KuEXiyZNi0Y69erWk2qf/7uu2nY1OZbhI4alW9k0hs3xovNm6ncWSTRE48b/CBCPLF0BU+Yb64QJXHZyJwTVzxdxRvmnatEynQ7hw4dih9//FG5ccyYMaCqdGNlxozCWLs2SLm8WLEcXLnyxOxLZCXXVqwAPvxQw4zYY8eQXZHuE1kYAeshwN9v1sOSNZmGAHPPNLx4df4IcAKd2cEIFIwAJ9CZIVZDoFu3bm94eHhQpfl7AF7KU6zNsXvU+dnDw2Pz9u3br1vtcFaULwKcQHcPcshkMpQpUwb0X4VQ6/Nq1aqZDUDXrkURE0NtzHOF8o9r175Akyb5J89pHSXDqRpYIeXLl8c333wjVI1TdXCdOnWQnq7SUVDV9dKlS0HVwQpp2bIlWrRoYbBKm24mbty4gcDA3CSnvrbamTVr4tmuXXqTmMuWLMFcrZZ/VAVNSW5jhZLklOClmeb0csMHH3wgtIdWCM3snjNnTq59fn5CC/GaNWsaq16U6xo1aoQ7d+5o+EZJcUqOa8uMGTOwdu1a5a8VnQ6Ie+ovZFBSXrs9uz7w4uLihMp1dendu7cG/xSfURJ9714/fPZZYTx6pDuN5NDBWDTcOAaBaiMVtM+M/+wzpH7wgSjj6MpO8YMIV46e69nOfHO9mLm6xcw5V4+ga9rPvHPNuBlj9fvvvy/c6yhk0qRJoKp0Y2Tt2kDMmKHZ1alr11R88028Mdv1rhG4FhYGvP46tfxSrklv1gwvvv/ebL28kRHIDwH+fmNuOAoB5p6jkBffuZxAF19M2SPrIsAJdOvi6XbaevfuHZqZmdmLqs0BKDI/2ryivllREolkc1RU1Bm3A8nBDnMC3cEBsNPx+pJ/58+fh7453MaYdPWqN9q2DdNYSkXkbdr8q5Gk16frwIEDQpW4tpQtW1ZoW75x40blR9SWmyq1S5curdcsqlxXbwNIiehy5cph27ZtBbpByfqffvpJWON17RqKdeoED7WkvdzTE08PHkS21lxthdKbN2+iWbNmyjMosXr48GFjoDN6Db3ssHfvXqGdO83sJr/cXbTnKBIe+/fvR/Xq1XWgoYp+9dEA1JWAEur169fH/fv3leuJP9RO0hhRf6mB1oeGhuLKlSs6bdwVutLSJFixIhDLlxdCerrqT1/fvimYNzce/nv2wH/nTviePg2JxsgBQObvj2fEwfLljTGN19gJAX4QYSeg+RgBAeYbE8HeCDDn7I04n8ffdeLmAL1sevLkSaWT+b34qo3C/v1+GDo0FHK56vrZy0uOH398hqpVs8wGTfiOO3MG6N5dQ8ez7du5dbvZqPLGghDgv6vMD0chwNxzFPLiO5cT6OKLKXtkXQQ4gW5dPN1Cm1Qq9ZRIJO3y5pq/C8Anz3GNFu3Uyh3A70+fPq114sQJ84ZYuQWitnWSE+i2xddZtOubqX3r1q0C21YXZPuUKcHYuFHVorpECeDePeDFC8MJ9JiYGHTt2tUoaKRSqc7safWNu3btAlWomyo9qAV7ly7w37ULfgcPwiM1VUNFwuzZSFFr861P/2czZ2LFmjUIK1YMK1etQt26dU01g9ebiMDp06fRg+aD50lQUJDQfl17/jl9/N133wkt2xVCLfEXLlyIBg0aaJxKLz5oV5bnZ9b//vc/obW+utBLHNRGviBZtKgQFi8upGa3DJcvP0FgoFz4nSQ1VUimh0yYoKEmq1w5PNu/H/JCqr0mQsbLrYwAP4iwMqCsrkAEmG9MEHsjwJyzN+J8HiHAvBMvD2i81jm1sVeff/45qCq9IImJ8UGvXkWRkaH5OHLJkjhERqZZBJaHRIKILl2AmBilnswqVfDs0CFAwo8/LQKXN+tFgL/fmBiOQoC55yjkxXcuJ9DFF1P2yLoI8BWkdfEUtTapVFo9b655bwDF8pzV5lCMXC7fJJFIlgGgzEFMdHR0fVED4+TOcQLdyQNkJfMuXryITp06KbVRS3BKqpsj1J66Xr1wPHigak09axYwYwbw77+GE+jUgptacRsSiUSCEydO4LXXXst3KVWR9+3b15Aq5eekiZoGDggMRGBKit59Kb17I2H+fKMeolCreboxoTnaLLZHgKryqZLll19+EQ6bN28e+vfvr7frAbXHpznoCqlYsSLee+89jY4FYWFhuHz5skbrfENedOjQQdijkHbt2mHNmjUFbnv40BN16oRrVNGMHJmEyZM1Z6oHT56MwE2bNHSltW2LONLP89ANhcYun/ODCLvAzIfkIcB8YyrYGwHmnL0R5/MIAeadeHnQvn17oVuTQmj0Fl3L5yfPn3ugceNwxMerxlrR2okTEzF6dLLFQPleuICinTtr6IlbvhxpWr+z+CBWwAjwtRxzwMEI8N9WBwdARMdzAl1EwWRXbIIAJ9BtAqt4lHbp0iXcy8uLZppTi3bFcFht3twF8L1MJvv+hx9++Ju8l0qlNIiZE+hOQAVOoDtBEOxgAs2eU3/bv0SJEqCkujly65YnmjQprrH1v/8FqlY1LoGekpKCChUqGDyaEpXUMrsguXTpEjp27JjvEi8vL2Ee+meffYaGcjn2AyioljejTh08p/bvnBA3GB9HLaCXFn799VdQG36aCf/kyRO9CXTtlysiIiLw5ptvarTap04I33zzjUmu0Px0mqOuEG9vbyGhXqRIkQL19O9fBMeO+antk+PYsVi89lqOal9aGop16QKfq1c1dCVOmoRkI+dFmuQMLzYZAX4QYTJkvMECBJhvFoDHW81CgDlnFmy8yUIEmHcWAujE26lLE3WLUsjXX38NqkrPT5YtC8LcuYU1Pu7TJwVffplglQLxIgMGwE9t7FZ2qVKIpZbuXqoXw50YTjbNBRHg7zcXDJpITGbuiSSQTuAGJ9CdIAhsglMjwAl0pw6P44zr3r27NG+ueWsAnnmWqPMlHkA0gE3R0dGntS3lBLrjYqd9MifQnScWtrRk+/btGDdunPKIypUr48iRI2YduXZtIGbMCFbujYjIwaNHnsJDDWMq0Glj+fLlkarVNl3bGLKP7CxIqA19kyZN8l2imE3+8+7daDJqFErlqCUr1XbJPTyQ1qEDEj7/HPLQULNw4U32Q8CYm0FKatNLGAqhlymo80Jysqp65auvvkJ3rRmIhrxISEjA22+/DUrkK2T27NkYaKDl/19/eaF16zBkZ6v+VPbrl4K5cxM0jvT85x8Ua9sWnnFxyt/LJRIkLFiAVKqO8fc3ZCJ/bkMEjOGeDY9n1W6GAPPNzQLuBO4y55wgCG5oAvNOvEGn+zS6X1MIvRytfn2u7fk77xTDf/+r6uzVpEk6Nm58YZX8ttfNmwhv1kzjyIRZs5AyaJB4A8CeORwB/n5zeAjc1gDmntuG3uqOcwLd6pCyQpEhwAl0kQXUWu6oJcDVOZIF4BAlzQHsjY6OzszvPE6gWysSluvhBLrlGLqCBnpYMWfOHKWpDRs2BCXVzZG+fYvgp59UlbQ9e6Zi69YAQZWxCXSaQ32PhqbnIy1btsSGDRsMmvf06VNUr14933V9+vTB/PnzETx+PAKpslxLMt96S2jZR8lzWXHNqnqDh/MChyFgzM2gMaMCqKVkeHi4yX589NFH2L17t3KfsS+kzJpVGKtXByn3lS6djbNnY3UqanxPnUKRPn0gkVGzFpXIAgOR3rKlwNkMmrvOsxpNjp2lG4zhnqVn8H5GQIEA8425YG8EmHP2RpzPIwSYd+LlQd26dfHgwQOlg+vWrUPr1lSDoSsPHniibl3N+7Ft256hUaN8HyuZBFzIiBEI2LVLuUcWEoIn589DHhhokh5ezAiYggB/v5mCFq+1JgLMPWui6d66OIHu3vFn7w0jwAl0wxi55QqtBHqqRCKZmZWVtW7Xrl3PjQGEE+jGoGSfNZxAtw/Ojj5l7ty5WLZsmdIManu+YsUKk83KzgYqVoxAaqpqLt2qVfEYMiRE0GVsAr1Lly44f/58vufv3bsXNWrUMGhfZmYmXnnllXzXLVy4EO+HhaFof5oyoRJq1R6/cCFyXn3V4Bm8wPkQMOZm8MWLF6hKcwXykUqVKoFGG5gjp06dQq9evTS2FitWDMHBwRg8eDD69u2rV+2ff3qhVSvNhP2pU09QrpxuZ4Sg5ctR+Isv8jUv7Z13EPef//C4AXMCaMEeY7hngXreyghoIMB8Y0LYGwHmnL0R5/MIAeadeHlAXZto3JJCtmzZord72NOnHkKnpthYRXNDIDQ0B7/99sTy6nO5HP47dyJkzBiNl1OTx41D4vjx4gWfPXMKBPj7zSnC4JZGMPfcMuw2cZoT6DaBlZWKCAFOoIsomNZ0RS0BrlCbBGBHXsv2k4bO4gS6IYTs9zkn0O2HtSNPmjhxIjZv3qw0oX///viigORcfrb+8Qe1odZMAF69GosqVXJ/Z2wCfejQofjxxx/1HtOoUSNs01Mtnp9NJUuWzBfan/fuRaPBg+Gp9uBGFhSEp8ePI6dUKUeGhM+2AAFjbgZzcnLw8ssv53vK8OHDMW3aNLOsIN1UUfPo0SO9+4m/xGNtkcuBGjWK48kT1cPB2bMTMHBgiq4euRxCpYxapbv2orR27RBHL8Lw3Eaz4mjOJmO4Z45e3sMI6EOA+ca8sDcCzDl7I87nEQLMO/HygLo0xcfTdL9c+eGHH4RraG3p2rUoYmJ8NX7dq1cKFi7UHHVkKlIez54heMoU+B84oLk1KEioPs8JVo0lM1U3r2cEjEGAv9+MQYnX2AIB5p4tUHVPnZxAd8+4s9fGI8AJdOOxcquVUqmU5prXz3NaruX8P3K5/HtPT8/vt2/ffl0fMJxAdx66cALdeWJhS0sGDRqEgwcPKo8YO3YsJkyYYPKRGzcGYMqU3GpzkjJlsnHu3DNEREQIPxubQJ8xYwbWrl2rcz61lqdK+bCwMKNta9WqFf7880+96+M//BDBVKWrJvE0S7p3b6P180LnQ8DYm8GKFSsiMTFRrwNbt25F48aNzXaORgPQDHV9Ur58eRw/fhyenqpEuWLd2LEhiIrKHXlA0qhRBrZty6d5C1XMREcjIDoaPufO6bR0p/2pXbognuzQc5bZzvHGfBEwlnsMISNgDQSYb9ZAkXWYggBzzhS0eK21EGDeWQtJ59Pz2muvIS0tTWnYvn37QFXp6nLrlheaNNEdqbR79zPUqmV++3bfn//P3nVAR1F14W/SE0JIAkGUIioKSpMqICCg0gSkZKmCoAhIEUFElCYIAoKgoFSVIi3Z0JGmKGBBOorSpAj+9CSEJKTvzn/uJFtmW2Y2m2Q3ufccDic779133/fuzs687917f0LoyJHwjrPxnD1xIm4OHQq9Rbkk90OQLfJ0BPj+5ukr6Ln2s+957tq5m+VMoLvbirA97oYAE+jutiJuZE+PHj0e0+l0rwqC8AqAymammRPqJwRBWJWZmbl+06ZNtw1tmEB3n4VkAt191iI/LenWrRt+//134xBTp07F66+/rnrIt98OhVZrIv+6dk3Bl18mqibQFyxYgJkzZ8rGb9CggayutFLjdu3aBapJnZaWZuziC2DR44/jtX//hZCZafw87bnnEE+R+Fw7Wim8btlO6ctg48aNcfXqVZtzOHXqFMLDw52e3+XLl0EHPuzJ/PnzQd87S9myJQBDh8rHXbcuFs2bO94g9Lp9GwHffSeldfdKSZGppfSTlIaSJf8RUOp7+W8Jj1AcEGB/Kw6r7F5zZJ9zr/UoLtaw3xXdla5YsaKMpN6zZw8oKt1c5s4Nxqefhsg+W7gwHi+/bHq3U4uQz7lziGjfHoLZ+6FRR79+wNdf42ZsLBPoaoHl9qoR4Pubasi4g4sQYN9zEZCsBkygsxMwAo4RYAKdPUQRAhqN5jkA/QEQWxBs1slAplOBVyo2u6pEiRJb7t+/T7v/dO2QVqs1RLIrGosbuRYBJtBdi6e7amvZsiXOnz9vNI+ivKkOuVpp3jwCFy8SPZ0t06Yl4PXX01QT6FFRURhtQfh16NABS5YsUWuS1P7u3btISkoCRTWc/vJLzE1LwyPp6TJdoo8PblPq9ipVnBqDO7kPAkpfBtu1a4c///zTpuFXrlyBTx5Tn3ft2hWHDh2yqb9JkybQarVW15KTBTzzzANISPAyXvPzE9GhQyr69UtB/foZDs93+P32G0r37SvbEBS9vRG7eTMyLSJ63GfFio4ltnyPUpOGhpoycxSd2fJMChsBpfe6wraTxy86CLDPFZ219KSZsN950moptzUrKwsPP/ywrMP+/ftBUekGofJGLVpE4MIF0/vlq6/ex8cf5y11u1QGadMm2di68HAkzpqFsIEDpc+VZk5TPmNuyQhYI8D3N/aKwkKAfa+wkC964zKBXvTWlGfkWgSYQHctnkVeW8eOHYMCAgKIRO8HoCWVNDObtIFMTwZQkgl093AHJtDdYx3y24qnn34ad+7cMQ5D9dBbtGihati7dwXUqPGgrM+OHXdQp45ONYH+008/4ZVXKHmFSSginiLjnRa9HiGTJiF4+XKbKpLffBOJTta8dtom7pgvCCh9GezVqxcOHDhgZUPJkiVx9uzZPNtm6yCIQakgCDhy5AgefFD+naHrlqUQzA2pXj0Ts2cnoHZtU+YES0P99+9HOJHoOjqbli2ZTz6JO3v2UCHRPM+LFdhHwNz3KAtBnz59cPDgQVC5ADowERYWxvAxAi5DQOm9zmUDsqJijwD7XLF3gUIBgP2uUGDP90Hv37+PJ554QjYOZUSjqHSDnD7tgxdflKdv37LlDurXt/8cnJvh3v/9h7LPPit7Tk5r1QoJc+cCDzyg+r01t/H4OiPgCAG+v7F/FBYC7HuFhXzRG5cJ9KK3pjwj1yLABLpr8SxW2rp27VrBx8ennyiKRKbL35yyo8/Jv5IFQfhap9Ot2bBhw9FiBZCbTJYJdDdZiHw0QxRFVK5cGRQFYBCqh16rVi1Vo/74oz/69i1t7BMQIOLs2Rvw9/dSvRHx999/o3Xr1rLxx44di5EjR6qyybxx8IIFCLFIC0/XRUHA/ddeQ+KkSUAeI46dNo47uhQBpS+Db775JrZu3Wo1Nm3cmZc0cNY42hhs1qwZbt26ZVPFpEmTMHjwYKtrxHt361YaR4742+z30ENZ+O232/A1BeNYtQueNw8hc+bIPo//+muktW3r7HS4nwIEzH1v+vTpmGB2KIcObMyxWBMFKrkJI2AXAaX3OoaQEXAVAuxzrkKS9ahBgP1ODVqe0zY+Ph41a9aUGXz8+HEpFaxBZswoiS++oNiKbClfPguHDt3OU7WtkAkTZAeq9UFBuHX4MMSwMLCveY7/FBVL2eeKykp63jzY9zxvzdzVYibQ3XVl2C53QYAJdHdZCQ+3o1u3bs8IgtBfEITuAAzhWea10mmGFwRBWJ2VlbVm48aNlzx8yh5jPhPoHrNUThuamJgoRUeai+XpfyXK58wpiXnzTBscDRumY9OmOKc2IiganqLizeX999/H8OHDlZhi1cbv559RundvCHq97FpGrVq4N3MmMmvXdkovd3JPBJS+DH7wwQdYuXKl1SRq1KiB3bt3u2RyJ0+exOeff46AgADcuHFDijo3SJ06dbB9+3ab49y/L2DSpBBs3BiEjAzrx63ly+PQurW8DIFMUVYWIl58Eb5mpRkyq1ZF3Lp10JttTLpkkqzEiIC57zVs2FC23tSISmWUKFGCEWMEXIKA0nudSwZjJYwApQ7zUn8okoFjBPKKAPtdXhF0z/70XFy/fn2ZcXSI2lD2htK3N2lSFlev+hjbvPlmMiZMSHR6Ql7x8SjboAG8zGqfJw8ciMQpUySd7GtOQ8sdnUSAfc5J4LhbnhFg38szhKwgBwEm0NkVGAHHCDCBzh7iUgQ0Go0fgE4AXgXQBoDpbSk7Kt0gXBvdpcjbV8YEegEBXYjDUK1nqsdsLs6QPL17h2P//gCjmiFDkjFxYqJTGxE6nQ6VKlWS2TRr1iyrtO5KYPO6c0ciEr3NUtRT1HnixIm4TzXuvL2VqOE2HoSA0pfBefPm2YwIbtq0KSj9uquF6jr27t3bqJZqrFOq+MDAQLtDxcd7ISoqEEuXBuP2bZOvtm+fimXL7jo0MXDzZoQNGyZrQ/UdKUVl+osvunp6rM9i45VeJG/fvi3DZe7cuejRowdjxQi4BAGl9zqXDMZKGAEml9gHCgkBvtcVEvAuGjY1NRWHDx/GP//8I2U8o4Oqzz77LOgdlP43lwsXLhifi48c8UPnzmVk13ftuoOaNZ1P317y009RklK154jo44Pbv/0GXfny0ifsay5adFajGAH2OcVQcUMXI8C+52JAi7E6JtCL8eLz1BUhwAS6Ipi4kTMIdOnSpayPjw8VQaYU74Zc0obU7qJWq2XWyxlgVfZhAl0lYB7Y/MSJE+jQoYPRcj8/P1y6dAlUo1mpUGB39erlkJhoqq+8dGk8XnopzemNiM6dOxujN/39/XH06FGEh4crNSm7nV6P8D59EGBR5zrxvfeQ/NZb6nRxa49BQOnL4KpVq0CZDSzlpZdewtKlS10+X0rpXrVqVVDZBINs27YNdevWzXWsb78NwrhxoWbfUxHHj99EWJhlshYzVTodIlq1gu+FC1b67/fvj3uUXtwBeZ+rUdzACgGD7+n1egQHB4M2jc2lUaNG2LBhAyPHCLgEAaX3OpcMxkoYASaX2AcKCQG+1xUS8C4Y9vvvv8fo0aNB6drNhUj0du3aYfbs2bLPr169Cm9vb/z9tw969y6N2FjTlk/lyln45Zc8pG9PS8MDDRrA28yWlG7dkDB/vtEG9jUXLDqrUIUA+5wquLixCxFg33MhmMVcFRPoxdwBePq5IqCcXclVFTdgBOwj0KNHj9qiKPYXRbEXgLJUtpgJ9ILxGCbQCwbnwhyFNjb69+9vNKFcuXI4duyYKpPOn/dBy5b01TTJsWM3Ua6c3mkC/fTp06C653fv3sU777yDrl27qrKJGtuqe57WqhXiKW23l4nsV62YO7g1AkpfBnfs2IE33njDai59+vTBJ598ki9zbNmypZTG2yBUJ9v8+2dv0IQEAXXrlkN6uunRa/r0BPTvn+LQTp+zZ1G6Tx9437xp1S61bVvc/eor5KmQZL6g5LlKDb5HG8APP/yw1UR8fX2lrAOU0p+FEcgrAkrvdXkdh/szAgYE2OfYFwoDAfa7wkA972PSYcJnnnkG169fV6SMMjNRVPqxY77o27c07t2Tv6uNGZOIUaOSFemy1SgwKgpho0fLLt3euxdZ1aoZP2Nfcxpe7ugkAuxzTgLH3fKMAPteniFkBTkIMIHOrsAIOEaACXT2kAJFQKPReAuC0F4UxX5arVZToIMX08GYQC/6C7927Vq8++67xonWqlULO3fuVDXxpUtLYMqUUsY+Dz2UhSNHslMXF9aDud+RIyjdrRsEnc5ol65cOdz5/nvo1Uayq0KDGxc2Akp9juqRU6YDSxk2bBioPnp+yMiRIxETE2NU3b17d1AqeSUyeHAYtm83pXuvUycD27fH5tpViI9H6LvvInDXLqu2sevXI6NZs1x1cANlCBh8b8+ePWjThirRWMt3332Hp59+WplCbsUIOEBA6b2OQWQEXIUA+5yrkGQ9ahBgv1ODlvu0vXnzJurVq6fYoBIlSuCbb/7FgAHhSEmRk+dPP52BmJg4BAY6yLzkYCTva9ekrGS+//xjbJXWvDni162T9WJfU7xc3NBFCLDPuQhIVqMaAfY91ZBxBzsIMIHOrsEIOEaACXT2kAJHoGPHjkH+/v6RMTExq/JjcEod7+3t3RBAQ0EQGgCgf6VpLFEUV8bExJhCde0YkGNjWwAvCoJQH0AVCoYFkAjgvCAIu0VRXKzVaq1DAm3ozNE3XBAEOjTwGAB/AP8B+E6n083fuHHjlfzAImfO/wNQnmpS37p1K7+GYb2FiMBnn30mS5/3/PPPg1JbK5Vbt7zQokVZWfr2jh1TsXhxdn3mAn/yS/dvAAAgAElEQVQwT09HkFaLknPmyOuee3khLjoaGY0bK50at/NQBJT6HJUqaGaDPCbynEj0/JBvvvkGEydONKquVq0a9u7dq2ioH37wx6uvSj9HRtm//xaqVDEdErGrSBQRtGYNQiZPhldamrFZepMmiNNqFY3PjXJHwOB78+fPBx2WsCUzZsxAv35UnYaFEcgbAkrvdXkbhXszAiYE2OfYGwoDAfa7wkA972MeP34cHTt2lCmiclyW6dxNDSohKOiyFXneuHE6VqyIR3CwE+R5ejqClyxB8Oefy55/acy45cuR3rq1zD72tbyvO2tQhwD7nDq8uLXrEGDfcx2WxV0TE+jF3QN4/rkhwAR6bgjxdZchEBkZ2UIQhFcBdAOIH9P6uEy5mSKNRmP3zUwJgd6tW7daXl5ev+YQ5o5MJDJ9kFarjXLUSKPREPm+A8DjdtoliqLYJyYmZnt+4MER6PmBauHpTMtKw5ZLW9D5sc7w96ZzGMD48eOxYsUKo1G9e/e2qkfnyOI33gjDjh2mqFhqGxUVi6ZNM6RuBfZgnpqKEmvWIHjRIpvpqhPHjEHyqFGFBz6PXGAIKPW5xMREPPnkk1Z2Ufp2SuOeH3L06FG8/PLLRtVk67lz5xAUFJTrcFlZQL16D8jqQQ4fnoT330/Kta+hQdC33yJ03DhZe45CVwxfrg0Nvjd06FAsWrTIZnuqg05ZCASBH6NzBZQbOERA6b2OYWQEXIUA+5yrkGQ9ahBgv1ODlvu03b59OwYPHmw0qFKlSvjll19An9MBbvOSRtSoRIkFuH9/uGwCrVqlYenSu05Fnvv/9BNKTZwIn8uXrUDJqlwZtw8cALxNNdYL9L3VfZaJLSlkBPj+VsgLUIyHZ98rxovv4qkzge5iQFldkUOAd/6K3JK614SIPKZ07YIg9AVQKcc68rt8q4FuQaBfBXAWgHQ0WQmBrtFomgL4OcdWItK3C4JwVKfTxQmCEOHl5dVVFEUqukt5yXSiKHaMiYmxmS+7U6dOJf39/Y8CeCJn/GWCIKwHkCoIQktRFN/PIeqpCO6zWq32pKtXkAl0VyNauPqW/LkEUw9NxeRGkzGo5iDJGKoBTbWgDUJRk1R7XIns3BmAgQPDZU27dUvB/PkJxs8K4sFcuH8fpXv3ht9R+rpYixRlu3691SaJkjlyG89DQKnPiaKIRx99FBkZ2Yc9DLJkyRJ06NAhXyaempqKqlWrgrJ6GITI1MYKMyN8+GEIli2jhCbZ8uCDOhw6dMty/8++7enpeKBJE9khk6xHHsHtH34AuC53ntfc4HuU2YA2ie3JY489BopS51TueYa8WCtQeq8r1iDx5F2KAPucS+FkZQoRYL9TCJSbNVu6dCmmTJlitIoOEG7YsEH6m+qj0zvnxo0bc64L8PO7hoyMB43tX3yRyPN4+PmpnJgoImTiRAQvX26zo650acRFRSHLxiFa9jWVWHPzPCPAPpdnCFmBkwiw7zkJHHezQoAJdHYKRsAxAkygs4e4HIE+ffqEZGZm9hBFkaLNDbmWLX0tTavV5h6u54R1Go1miiiKR3x9fY+sW7fuVs+ePSvrdDrp2LISAj0yMrIJgJGCIEzRarWnbZkQGRn5siAImwDQvC5qtVqKLreKfI+MjJwqCIIh1+9YrVY721wfjSUIwn4AFI2/X6vVtnBiyg67MIHuakQLT19qVirqr62PhPQEhPmH4UjvIwj0CZSiYSkq1iDTp09H//65VipAYqIgpW6/dct0cj88XIf9++8gPFxv1FcQD+YlZ8xAyS++sAkuked3lyzhuueF53oFPrIan2vQoAGuX78us3H9+vU2U7u7aiJUG/uvv/4yqhs3bhxGjBihSP3ff/ugdeuyFvbGolkz+SEAR8qCVq1C6Pt0/sokya+/jsSpUxXZwI3sI0C+l5aWJh3MoAMajsTX11dK5//aa69xNDo7lVMIqLnXOTUAd2IELBBgn2OXKAwE2O8KA/W8j/nhhx9i2bJlRkVdu3bFggULjH/TAVYqmUQHuQMDX0Bq6veyQffsuY3q1bNUGxKwbRvChwyx6id6eSGlb18kjh0LMTTUpl72NdVwc4c8IsA+l0cAubvTCLDvOQ0dd7RAgAl0dglGwDECTKCzh7gKAUGj0bShFO2iKHYCEJCj2NzHaCf6Z1EUV2dlZcVs3rzZFOLqKits6FFLoCs1RaPRxOSko6cU1/WioqKOm/cdNGiQ7927d+8AKAXgzFNPPVVjypQpJlYyp3H37t0Xi6Io5Ubz8vJqGBUVdUSpDUraMYGuBCXPaEPR5x8d+ggiRAgQMKnRJCkKvUmTJrhy5YpxEhQt8NJLL+U6qXHjSuHbb0vI2i1YcBddu6bKPsvvB3Pvy5dRtlUrCBZRxOmNGyPprbeQQTWuOVVyrutZlBqo8bm2bdvi1KlTsunv3r0bNWrUyDdIJkyYgOVmUTHPP/88Vq1apXi8F16IwJkzvsb2llkfclWk16PMyy/D77jsZwd3v/wSqZ0759qdG1gjQGQ5RVMRKf7ll19i2rRpxkY+Pj7Iovz7dqRdu3b49NNPUaoU/dyzMALKEVBzr1OulVsyAvYRYJ9j7ygMBNjvCgP1vI9pmeVs+PDheN/iACc9P/3333+YP/9xrFtX2jjo449n4qef7jj1Cle6a1f4Hzokm0BG3bq49/HHyKxZ0+HE2Nfyvu6sQR0C7HPq8OLWrkOAfc91WBZ3TUygF3cP4PnnhgAT6LkhxNcdItCtW7fqXl5eFGlOxWbL5TS29Ku/iDT38fFZt379+v8KGtL8ItC7d+8+TBRFKWRWFMXuMTExWvO5RUZGthYEYTd9JgjCuOjo6Fm25t6jR49Ger3+YE67GdHR0R+4EiMm0F2JZuHpMo8+N1hBUeiHex1GrSdrgdJKG2Tz5s2gqFxHcu6cD1q1kkfBtmyZhm+/jbfa6MjvB/PQ4cMRtIkSOmSL6OODuDVrkNGUqimwFEcE1Phcly5dcPjwYRlMv//+OypWrJhv0NF3jCJuDBIaGiqR+GS3ElmypASmTjWRrUFBevz5500EBirpnd3G5+xZRLRvDyE93dhJHxiI2K1bkfXUU8oVcUvs3LkTo0ePlkoBULQVpWY3z2owYMAA6W86mGFPqlWrhm3btiEoKF+S6/AqFVEE1NzriigEPK0CRoB9roAB5+EkBNjvPNMRqBzSiRMnjMZ//PHHePVV2vqRC50xrFfvAcTGmrKajRmTiFGjklVP3OfMGZR94QVZv8T33kPy8OHkSLnqY1/LFSJu4GIE2OdcDCirU4wA+55iqLhhLggwgc4uwgg4RoAJdPYQ1Qh06dKltLe3d2+KNgdQJ0eBpS8Z8p4e0Wq1jVQP4sIO+UWgazSa0QA+JVNFUewWExNjKAAmWW+evt3Ly6txVFTU77am1aJFC5+IiIh7AGjX/YBWq33OhdMn2/4HoDzV671165YrVbOuAkTAPPrcMCxFob9X8z3M7DZTZslvv/2Ghx9+2KF18+cHY9asEGMbIvAoSqBCBVNdZ8PF/Hww975wAWVbtoSgNyVnSB40CImTJxcgujyUuyGgxuc0Gg3I583l7NmzKFmyZL5N69q1a2jYsKFM/759+/D441TNI3e5fdtL2mjU600/nV99FY927dJy72zWInD9eoS9846sj+jnh9SXXpJSXGaQjZy9wSGm9NtYv3593L59226777//HqVLl8YXX3yB+Ph4XLx40SrrAXX+5ptvQOn9WRgBpQioudcp1cntGAFHCLDPsX8UBgLsd4WBet7HrFu3rmz/YMWKFXjxxRetFO/f74/evU3R59Tgl19u4ZFHrN8rc7Oq1LhxKPHtt8ZmuogI3KKDsgoLqbOv5YYwX3c1AuxzrkaU9SlFgH1PKVLcLjcEmEDPDSG+XtwRYAK9uHuAwvkTyVu2bNkOOXXN2wEw5J4196E0QRC2APhWFMXtOTXBD2m1WqopXmiSjwQ6zZXS1dOp+qeioqLOmE/SPMV7ZmZmmKOU9RqN5g8AtQDc0Wq18rDgPCLHBHoeAXSD7raizw1mhSSGIHFuoszKCxcuIDCXUNZevcJx4ICh0gIwYEAypk2T6zEozc8H89CRIxEUQ9UQskUfHIxbv/8OMSzMDZBnEwoLATU+16tXLxw4cEBm6v/+9798rUlN6SqJdL1586Zx3Dlz5oBsUSqRkaVx8KC/sfnLL6dg4UL1lU1KjR2LEmvW2Bw2s1o13F24EFlVqyo1q9i1IzK8efPmduddp04dbN9OjzRy2bt3LyiVaWKi6b5pLzKr2IHKE1aMgJp7nWKl3JARcIAA+xy7R2EgwH5XGKirH5Oeb+mgID1H//vvvxg6dKhMyZ49e1C9enUrxaNGhSI62pSBp3btDOzYEavaACEpCQ/UrQuvlBRj36SRI5E0dqxiXexriqHihi5CgH3ORUCyGtUIsO+phow72EGACXR2DUbAMQJMoLOHOERAo9HUy6lrTqxAeE5jy7rmB0RRXJWRkRGzdevWJGqj0WgonJSi0Iskgd6jR4/aer3+GADKU3ZKq9US+S0TjUZDEefPALiv1WqDHQGt0Whod14qWp2cnBywc+dOU05eBx27du1aITcXjo6OPuLt7V2Oouzu3KGS7CyehsDiPxdj6sGpUu1znAVwDQBlaH4QwL8AVphmRFG358+fdzjFzEygatWySE01pcH76qu7eOkl225HD+Zly2af66AoTaoT7Aqh2udlmjWDoDNFJyS/9RaSLWrruWIs1uFZCKjxuVdeeQVEZprLjRs38n3CgwcPxtatW43j9OzZE/PmzVM87sqVgRg3Tp7Gff/+WFSooPL7lZ6O8M6d4XfypM2x9WFhuHPgAMQyZRTbVpwafvfddxg4cKDdKdPBiD59qEqNtVj63gcffIARI0YUJ/h4rnlEQM29Lo9DcXdGQEKAfY4doTAQYL8rDNTtj0lEOWXVWb58OdLS0kAb5wEBAbh8+TLu3aPEeLbl9OnTCLM45JyWBtSqVRZJSab3ysmTEzFkiIkEVzr7oOXLEfKBqZqd6OWFO4cPQ1++vFIVfI9TjBQ3dBUCfH9zFZKsRy0C7HtqEeP29hCIiIiAt7dUhuWaIAi58gyMJCNQ3BBgAr24rbjC+Wo0mncBUIr2J3O6WPrKWVEUvxUEYY1Wq71qqbYoE+jt2rXzDw4O/gVAfZq3IAidoqOjt9nA4O8cmvOWVqs11Ie3uQKRkZFRgiB0p4tZWVllNm3aFKdkqTQajSFVvt3mixYtklLPsngmAimZKag4ryLiU+OBIwC+y5kH7VFQCWbiCU0B3HjiiSdw7tw5h5M9eBBoYpEXgs5WFDi/9vrrlPPYZGuJEsC//xaCIZ7pG2x1NgJjx47F7NmzZXDQxmB+y+eff463337bOEzVqlVBqeMdCW080osJtaWKGg89BJifR6FA8V9+ceIrQJudlMp99WrArCa60RaKIPryy/yGxCP1T506FZPtlIwIDg4GHcag/20JZRxYv3698dK4ceMwY8YMj8SBjWYEGAFGgBFgBBiBooNAeno66JAgPRO3b99elp1sypQp+PDDD1VNtkSJEkhKSrLK8LRpE9C1q0kVVQ767z9ABeed3Zme3WvUAE6fNinr0gXYKKuSp8pmbswIMAKMACPACDACqhBgAl0VXNy4uCDABHpxWWmV8zQjwM195I4oiuuJON+wYcNRRyqLMoHevXv3ZaIoSuFqoiiujImJ6W8LC41GcxHAowD+02q1lXLBaxWAvtRGp9NV3LhxI9Utz1WYQM8VIo9vMPfgXIzZMwZisgh8RicszKbUDEAJALtMn1Eq4v379zucN/E7Zof7pb2KU6cKGKozZyhcgU6MmAam9HyzZhWwITycpyNw/fp1VKpUie6d0lQo3eSXBUAWHz16FA0aNJDBR1k+ytg5iULk6qwc/6ZNSyJte/QAoqPlK0Blyymg3g5n63i54uKAlSuzyfJLl0xt6TQxnZyxsNfT194V9ms0GsSYlZEw1/nGG29g6dKldocZMmQIlixZYrxeUL7ninmzDkaAEWAEGAFGgBEougj07dsXq+lgJYAuXbpgw4YNEvm9ePFivPnmm6onTiVtjh8/btWve3dAqzV9/NxzwL59qtUD9P7aooW84/ffAy+84IQy7sIIMAKMACPACDACTiDABLoToHGXoo8AE+hFf42dmqEFgZ4iCMKYJ598cumUKVMU5ZYtqgR6ZGTk+4IgfJwD6pGAgICW33777X1bIGs0mnyPQOcU7k65t8d0oujz+mvq4276XYByHFDRAHOJyCHU75o+7NSpk4zQsTXZV14Jxd695vXP7+Pjj6XqCzbF1amhqL5d2Kuvwo8IvRwRAwJw58gR6As8DN5j3KFYGarW53788UcpDSUR6URUUymD/JbMzEwpkjw1NdU41MqVK9G6dWuroameZO3atSnDiHQtKCgIFI2ekhKAzp3Dcf68r6zPc8+lY9Wqu/Dzc24WXteuIaJpUwiUVzNHdBUqIHb3bojhhmoszukuar2aNWuGCxcu2JzWrl27pHWzJ9OmTZMd1ujatWuBHN4oamtQnOej9l5XnLHiubsGAfY51+DIWtQhwH6nDq+8tr548SKaNm0qU7Nx40bExcVh0KBBUlS6I6H1Cg8PR2xsdh1zIt7nz5+PyMhIWbc7d7xQv34EMjJM24qzZt1Dv36mZ2Olcyk1eDACzUojZT32GGIPHKC6E0pVSO3Y11TBxY1dgAD7nAtAZBVOIcC+5xRs3MkGApzCnd2CEXCMABPo7CE2ETAjwA3X6S1rnyAI34qiuEGr1SY7gq4oEuiRkZGDBUFYnDPvsz4+Ps3WrVuX/VZpQwqiBroS9xVFkaLZy1N05i3KGcziMQgs+XMJPjr0EcQsEZhpEX1uZxYU4Up1ee1Jdna8ckhIMG1GLFwYj5dfNhFtln3pwbxcuewqBDdv3nS6BrrXzZso8c03KLFqFbyS5IR90ogRSBo3zmPWhg3NXwRc5XP5ayWkjcSDZgdBhg0bBqqDbSm///47unXrJvv45MmToBeV69e98PLLZXD9uo/s+iuv3AdtQjorJT/9FCXnzpV1T+ncGQkFEJ3vrM0F3Y/qflLZC0P2AsvxKbuBo01m2kw2ZBWgvi+88ALoEEVuotfrpY3o3377DW3atMHrVM6CpVgi4Cn3umK5OEV00uxzRXRh3Xxa7HcFu0Dz5s3DnDlzZIOGhoYiJSUFGRkZdo0JDAzEp59+ipdeegk+Pj5SPfQ//vhDqpFOh0bN5cwZHwwbFoZz50yHQH18RJw4cRPh4epKKXnduoUHGjaEYJaZ7N6HH+L+G2+oBo59TTVk3CGPCLDP5RFA7u40Aux7TkPHHS0QoN95roHObsEI2EeACXT2DpsIdO/efbUoip0pUM6sgeFNKE0UxS0Avq1evfpuW1HpRY1A7969ey9RFCkHGrGOV3Q6XdPc0qxrNBqqTC0xJpmZmWGbN29OsOduGo3mDwC1ANzRarVlXemWTKC7Es2C05WalYr6a+sjIT0BuArArFS4PSu8y3vjjx//QFhwmF1DL13yRrNmD8iuHzp0CxUqZKe/tiV5fjDX61Fy9mwEL14MwcamTVb58rizbx/EIPPbTcFhzSO5HwJ59rkCmtLMmTOxYMEC42jPPPMMKMLHUiiF5nvvvSf7mEj1ihUrSp9duOCDzp1L4+5db2MbQRBx5MgtPPigosQv1jPOzETp7t3hf/iw7Frc2rVIp/yaxUx+/fVXnDt3Du3atcODDz4ozf7vv/+2mTGArlEZAIooJ7LbnlDWgwkTJhgvN2rUSEqRmptQWniqP2oQGqtzZ3rkUi9U45QOx9EhJz9nUxaoH5Z7uAgBT7nXuWi6rMYNEGCfc4NFKIYmsN8V3KLTwb9atWqBsh/lJqNGjcKYMWNw4MABnD17ViLOyzsoXp6cLCAmJhDR0UH44w/rNEkdOqRiyRKz1Gi5GZBzPXjePISYEf76gADcOnYMYmioQg2mZuxrqiHjDnlEgH0ujwByd6cRYN9zGjruaIEAE+jsEoyAYwSYQGcPsYuARqMJFgShuyiK/QBQtWVzfzGQ6VQXfa23t/fqqKgoY1GsokSgazSaTgBoR5zCA294eXk1i4qKovrmDiUyMnKqIAgTqZGXl1fjqKio3211aNGihU9ERASR61TN+oBWq3Ups8EEem4r5T7Xicw5c+YMWrZsiZhrMdnR5xCBnwHszcXOYACDgMmtJ2NQzUF2G9Omx8iRJoK9bFkdjh+/BcHBr0GeHsxFEaXGj0cJO1GZor8/4lesQHrz5u6zEGxJoSOQJ58rQOv37t2Lfv3oJzJbAgICpO+wJZFJZKllLe19+/bh8ccfN/Y9ccIXkZGlkZZmyg4xfXoC+vdPcXpGlPWhbKtW8LpnimTPevhh3KYi64GBTuv1tI5U43zkyJGS2SEhISAynVKT7ty5EwMHDrSaTseOHaWDEJS61BGBbq6XlDz11FP4nup1OhBK408HLSibh0Go3549e6QU/8eOHUP16tUVlSG4ceMGevbsKaWgp0j6r7/+Go8++qinLU+xttdT7nXFepGK2OTZ54rYgnrIdNjvCmahkpOT0adPHxw9ejTXAakdZdGh9OxK5MoVb/TsWRpXr8ozJhn60jvlrl138MADKg9+ZmbigUaN4G32XHS/d2/cmz1biVlWbdjXnIKNO+UBAfa5PIDHXfOEAPtenuDjzmYIMIHO7sAIOEZA2dMyo1jsEejZs2flrKysVwVB6AvAcnfWQKafFQRhlZeX11qdTncFIOYPh7RabZPCBJBs1+l0l8kGURRXxsTE9FdqT2Rk5POCIHwHwB9AnF6vf27Dhg1U2zxXiYyMbC0Iwm5qKAjCuOjo6Fm2OvXo0aORXq+XikELgjAjOjraOv9vrqPZb8AEeh7AK8CuVMO5f//+UiphSrGnH6ZHom9itgVrAPzjwBjaxyCvrgCE+YfhSO8jCPSxTY69/34prFpFZzWypW3bVHz9teNIAacfzEURIVOmIHjZMivjRUFAWtu2SHr7bWTVqFGASPNQnoCA0z5XwJO7e/cualj477Zt21C3bl2ZJX379gV9x82F6mvXrFlT9tmIEaHYuNGUieHZZ9MRHR2Xp1kFrVmD0LFjZTpSIiNBZRN0VarkSbendCaS+eef6SRStlDt8gEDBkCr1eLtt982fv7kk09KnxGBTZJbyQoivUmPQSijAGUWcCQ7duzAGzZSklKWAkr/f/XqVekgBpH7RIo7EssUrZUrVwb5Hx0OYPEMBDzlXucZaLKVShBgn1OCErdxNQLsd65G1LY+OixIh/tyk/bt22Px4sWGdK25NUd8vBc6dSqDy5dtk+chIXqsWhWPBg3sp4e3NYiQmIhS48YhaAslNzTJnV27kGnxjJyrkTkN2NeUIsXtXIUA+5yrkGQ9ahFg31OLGLe3hwAT6OwbjIBjBJhAZw9RjYBGo2maQ9dFUjCXmQIDkU7/UwidRxPokZGRTQRB2JMTGU7he89rtdpjSgHTaDSU1+w2gFIAzmi1WtqRtyoI1r1798WiKA4mvV5eXg2joqKOKB1DSTsm0JWgVPhtqD6yjHihb9nzAPYB2J+LfV1zCgBIaSIETGo0yW4Ueps2ZfDXX6aUex98kIhhw5IdDuDUg3lmJkpNmiTVOzcX0dsbKT17InnwYOgee6zwgWcL3BIBp3yukGZCGSPOnz9vHH3SpEkYPFi6pRulSZMmuHKFzpWZZPPmzWjQoIHssx07AvDGGyby09tbxMmT6mtJypTq9SjdtSv8j1j/tGQ+/jjS2rWT/kkblQqjkAoJaqeHpdrklBnAIBR19cknn2DFihUYP3688XOKDKd1oXToJLkR6FTDXKPRGPvT4SfKJOJIevfujf37c7upQ0o1/9VXXznU9dZbb1mljK9Xrx6ioqJAdUxZ3B8BT7rXuT+abKESBNjnlKDEbVyNAPudqxG11nfo0CGp9ExuQuVm1qxZIx3WUyKpqUCPHmVw7Jh1yvYHHtChW7cUKVtS+fL2y4HZGsfv8GGEjhgBn//9T3Y5o25dxG7bpsQ0m23Y15yGjjs6iQD7nJPAcbc8I8C+l2cIWUEOAkygsyswAo4RYAKdPcRpBPr37x9w//59ekuj/LUv5JDmBn1EFJN/ZQLYCWB1cnLytp07d6Y7PaCTHZ2JQNdoNE8D+AkAFd66LwhCm+jo6F/VmmCexh3AWK1WK8tFptFoGlPa9pz08Pu1Wm0LtWPk1p4J9NwQco/rVvXm6AhKOwCU/8CRUObzVvIG9qLQU1IEVKtWDjqd6dYfExOLxo0dRwuofjDPyEB4//4IsCCJRC8v3F24EGkdO7oH6GyF2yKg2ucKcSbvvvsu1q5da7SAonqWmWVdSEtLQ5UqVSgDiszKdevWoblF6QL6jtas+YAsjfuUKfcwcOD9PM3Q59w5RLRuDSEry66e1HbtkDB/PsQgUwR8ngZ1o850UOH69etGi+rXr48tW7Zg4cKFmD59uvHzVq1aSZvKSgn0v/76C23atDH29/b2lg5K2EuHSungKW0/+YQSoWh00mlPXnvtNezeLSW6kQn54JIlS+hQnpJhuE0hIuBJ97pChImHdiEC7HMuBJNVKUaA/U4xVE41zMzMRNu2baU65uayYMECdOrUCfPnzwdlwKlTpw4mTpwolbNRIno9MGRIGL77Tn4or2rVTEycmIhmzdLhYzso3aH64IULUXLGDAg0gJlQhrL4tWvzVNqLfU3JynIbVyLAPudKNFmXGgTY99SgxW0dIcAEOvsHI+AYASbQ2UNcgkDPnj0f0uv1fXPqpT+Zo9Qy2jpJFMUNgiCs0Wq18ly2LrEiW0lOhLwxL60gCGVEUTQQ10SCy0K6tFrtCvPhe/To8Zher/8NQFn6XBTFUd7e3j84MjEjI+P2pk2bKNpcJp06dVnLSKMAACAASURBVCrp7+9PRcgMeViXCoKwXhCEVJ1O11IQBErXTtWrUwE00Wq1J10IhaSKCXRXI5o/+qwIdBqGjm8kWIwXAfi84gP9X3oIDwjwekJOkOhFPXSiDp82/xQ9q/aUdf79dz9061bG+BlFt549exNBQVaJEWT91DyYUyq+0NGjEbiTzs2YhDZEEj7/HKnduuUPgKy1SCGgxucKe+IU7Tt69GijGWXLlsXx48eNJCpFPlMEtKUsX74crVu3tvp80CD5RmXFiln45ZfbTm1QmisP/uILhMyY4RCulM6dkfDFF0UuEr1atWpISkoyzr1kyZJSRPqcOXPw2WefGT/v0KGDdPhBKYFOZDllFzAXqkduL/qbSHzLrAPyBXmJnmIAkK0nsHx5X7Runf348N9//4FSthNp36tXLyl1fGRkJA4elCrAWAmVB5g5cyaefprOA7K4KwKedK9zVwzZLnUIsM+pw4tbK0dg3759oMwslJmncWM6I24S9jvlODrTkg7NTZ06Vdb1o48+Ah20y4vMnRuMTz+Vk+0PPqjD1q138NBDKmud5xji/8MPKP3qq1Zm6UuVQsInnyCtQ4e8mCwdHlT6HJengbgzI5CDAPscu0JhIcC+V1jIF71xmUAvemvKM3ItAkyguxZP1gagW7du9QVB6C8IArF3loU4DUzdda1WWzE/AIuMjFwhCIL1W5mdwbRarex7oNFoqJr0cjW2CYIwJTo6+kNbfTQaDZH5OwA8bkdnoiiKfWJiYrarGVNpWybQlSJVuO1sEug2TOo0uRMqPV3JobEU/UjkeeWQyrJ2CxcGY/p00yZIjRoZ2L07NteJK3kw97p9GyW++kpK2e5lRlSRcjEgAAlz5iC1S5dcx+IGjAAhoMTn3AUpIkyfe+45mTkUAUQkLQnVpB4yZIiVuYsWLZKigizl0CE/dO1qOuhC1xctikenTsqilh3h4nfoEAK3bEHArl3wvnXLZtPEceOQPGKEu8CbZzt0Oh0qVbK+Zx4+fFhKkb506VLjGD169JAIdaUbr/Hx8VZ17OnwBL2A2pJffvkFNIZteQ/ATKtLlSplwds7Flev/g2d7i4AiqRfjS1bJmDChAk4deqUXYzot+Cbb76xeVAjz8CyApcg4En3OpdMmJUUOgLsc4W+BEXSgO+//x79+9MrdPYz3MaNG2UHxtjvTMuelpWGLZe2oPNjneHv7Z9nf7hx44b0HHr/vilbUfXq1aWIcx8nwsNv3vTCkSN+2LAhCN9/L0/zTrXON22KRbVq9jMa2ZuQz4ULCJk8GQH7qD6ZXNIbNZKyIOnKl88zHuxreYaQFahEgH1OJWDc3GUIsO+5DMpir4gJ9GLvAgxALggwgc4ukm8IDBo0yPfu3bt0hJjIbEpG7Ws2mKjVau3nJc2DVe5GoNNU+vbtWyI1NXWYIAgUWkaEOhUR+08UxR16vf7zjRs3yovj5mH+ll2ZQHchmPmoSgmB/s8//yAoD+mVBw4Mw86dphR8/frdx4wZ93KdlcMHc1GElIbv008hpFtXaNAHBCAuOhqZ9erlOg43YAQMCHjSy+C9e/fw1FNPyRaPyFnDd5oI2dmzZdU7pLYUTdy9e3erRadM7x06lMHJk6Zak02bpiMqKs51DqLXw/fECYlMD/76ayu9d+fORapdotd1ZhSEpoSEBNBGsqV8++232LVrl5Sy3SAU1f3xxx8rJtApZWrlyvKDSlTfnFL22xIac9y4cQDocEVDAHRfpCi9ZgBKq4BDj7p1dyIubgSuXLnssB9lP1i5cqUK3dy0IBHwpHtdQeLCY+UfAuxz+YdtcdZctWpVJCcnGyEYPHgwJk2aZPyb/c7kHUv+XIKph6ZicqPJGFRzUJ7d5s0338TWrVtleqhMDZWrUSoZGcDixcFYty4IV6/azsnu5SVi9ep4PPecExX5MjJQtmVL+Pz7r5VJSW+9haQxYwAHJWuUzoPasa+pQYvbugIB9jlXoMg6nEGAfc8Z1LiPLQSYQGe/YAQcI8AEOntIgSDQq1evMllZWX1y6qXXoaDU/CLQC2RCHjQIE+juv1i2SBhLq7/88kt07tzZ6ckQKVe37gO4fdt0buWzz+5Co6HqAY7F0YN50LffIlQihKxF9PXF3UWLkNaOzs+wMALKEfCkl0FbEc579+4FpQ0nGTFihBSJZSkzZsxAv379bIKyaVMghg8PM14TBBGHD99yOl2mI+T9v/8epXOixgztRG9v3Js2TcoaIeZE0itfPfdqSanPGzVqZGUURW///fff2LRpk/Ha8OHDMX78eMUEOnV87LHHZDXNt2/fLtUYtSWUXnXJEh2AOQBK5RmogIA5SEt716iHUruvW7dOppcOd1BkIIt7IuBJ9zr3RJCtUosA+5xaxLh9bghcvXrVKmU7HSKkw4QGYb/LRiI1KxX119ZHQnoCwvzDcKT3EQT6yOuL54a3+fWjR4/i5ZdflnXp2bMnPv30U8VqLl70xogRYfjjD9PBTVudp0y5h4EDTVHuigcAELR8OUInTLDqklm1Ku7QM4qLyHMagH1NzcpwW1cgwD7nChRZhzMIsO85gxr3sYUAE+jsF4yAYwSYQGcPKXAENBpNTaqVHhMTY9r1LXAris+ATKC7/1rbSgNsbvWDDz4o1bn19TVP4qBuXocP+6FLF3la6AMHbuGxx4jMcSz2Hsx9T51CmU6dIFDYgplQvXMizZPefhtZNiI/cxuPrzMCnvYy+MQTT8hSZ27evNmYurRdu3b4888/rRZ18uTJGDTIduRRaipQp045JCV5Gft98EEihg0zRXe50ktKLFuGUh9aVyER/fyQ3rw5Ujt0QCpt0Po53lx1pU2u0kU1w9u0aWOljuqHJyYmYs+ePcZrY8eOxahRo1QR6FRj/M6dO0YdRGA3b97cajw6xNSs2T5cvtxb4dSovIb8nm3dkeqPtgKwX7q0c+dOHDt2TErtbhBKR0+fsbgnAp52r3NPFNkqNQiwz6lBq/i1pXTg7777Ls6cOYMyZcqgQoUK0r9HHnkEL774ojG7jjkylFFnzhw6GGYSqoEeExNj/ID9LhsKij7/6NBHECFCgIBJjSblKQp92rRpoJJABgkNDcWBAwdQurSyrDZbtgTgnXdCkZpqet609HpfXxGzZiWgR4/cD13b+sYIycko26QJvOOsMynFLV+O9NatXfpFY19zKZysTAEC7HMKQOIm+YIA+16+wFoslTKBXiyXnSetAgEm0FWAxU0ZAU9EgAl091+1f//9F88++6xdQ4kMofR8zsqdO15o2zYCN2+aos/Dw3X4889bEBT8Cth8ME9NRUTbtvC9cEFmVkqXLkgaNQq6xx5z1lzuxwh4XPRIvXr1cPPmTePKUaruVq1aQRRFELmekpJitarvvfce3nrrLburPWZMKaxbV8J4/cknM/HDDyai1tVuUnL6dJRcuNCu2szq1RH/zTfQVajg6qHzVR8dPiKy3FJq1qyJkJAQ/Prrr8ZLU6ZMkQ41KK2BTh2bNWuGS5cuGXUsWbIEHTp0QFYWcPasD44d85P+7d0bgIQE+xvU2Qqovvl0ALQOdHN+GgBlMigJP78yyMioC6CbxVT+B6A2gHj8/PPP0qGAl156ydiGDl5dvnwZVA+dxf0Q4I0v91uTom4R+1xRX2Hn5kfPKxRJTof7HGUtocw59Fvpl3Ogjn5zWrRogVu3bskGrlGjBnbv3m38jP1OHn1uACavUeh9+/bFjz/+aMR52LBh+OCDDxw6wY0bXqCD1d99Fyj9syVU67xevQzUr5+Bbt1SUbFi7geu7Q0aPHcuQiwi4umA5r2PP0ZKr17OOayDXuxrLoeUFeaCAPscu0hhIcC+V1jIF71xmUAvemvKM3ItAryb5lo8WRsj4HYIMIHudktiZRBFp1KUqi2hmueUnq9UKefS/RKJ07NnaRw86C9TP2ZMIkaNUhbNauvBPGTSJKvaycmDBiFx8mT3B5wtdHsEPO1lkDaP//nnHyOuCxculFJqXr9+3RiJbgn622+/LUV5WUpaWhqorMNff5VGZKQ8AvnPP2+idGmKOs4HEUWEvvMOgqKi7CrXlS6Nu199hYyGVL/bM4Q28F977TUrYwMCAqTDDebZASiCrk+fPqoIdCKrT548adQ/ZEg0rl7tgH37/JGS4pgwL1cuGa1bC9Drf8bq1UMBXAZFj/3www9S5N7ixYtBNdwpko/Ssb7++us4c6YDgE8s5nMRwCRs2zYRERGJaNToGdn106dPO/0b4hmr7LlWetq9znORZssNCLDPsS9YIqDX6/Hqq6/KiFhHKJmTtHTId/ny5VbNKXsWvb+w35mgMY8+N3xqGYVOv/8bNmyQnk+oBJCPj+165Ib+DRs2xLVr14yDfPHFF+jSpYvVepw44YtVq0rg0CE/XLliX2eVKpmYPfueRJx75XbmT8FXye/QIYT37Quv+6bU72kvvID4lSsV9HauCd/jnMONezmPAPuc89hxz7whwL6XN/y4twkBJtDZGxgBxwgwgc4ewggUcQSYQHf/Bf7ll1/Qo0cPm4YS8fPRRx85PYmPPgrB4sXBsv7PPpuOtWvjkMuejN1NL599+1DGImIgo1YtxG7dCuQhzbzTk+SORQ4BT3sZ7NixI44fP25ch5kzZ4KigiiNJtWltiVDhgzBxIkTZZcocp02o2nDdNKkqZg27R0ZCfvVV/Fo1y4t/9ZbFBGwbRuCtFr4//wzhMxMq7FEX18kjh+P+wMHQlEKi/yzVpFmrVYLOqxgSyiCLsOsBAUdfKCNZzUR6FRrlCK/syPGPwNgP6uAyQaK5BqKU6dGIDw8XPqYIuEpZS5FrxvG1+l0oBIfERERUhvKWrB69RoAFNX3os05hYbqkJBA16ndegB6yb5HH31UEV7cqGAR8LR7XcGiw6PlBwLsc/mBqmfrpFImAwYMUDUJOmxGB8jof4petxR/f39cvHjRmP2kuPudsfb57QTgLBXqBlALgD+kWuiHeh7CvE/mydKx23pONMc5OTkZVatWlUFPhwYp+t9cVqwIwoQJpSCKjrf++vS5jylTEhEYaL2eqpwDgPeVKwiZNg2BO3bIulKZrzs//ICsapRdJ3+kuPta/qDKWh0hwD7H/lFYCLDvFRbyRW9cJtCL3pryjFyLABPorsWTtTECbocAE+hutyRWBu3YsQNvvPGG1eeUcpfI9cqVKzs1iW3bAjBkSDY5Y5AHH9Rh9+47qqJYzR/Mb126hDJNm8L7xg2jTjEgAHd270ZWlSpO2cmdGAFLBDztZZA2kPft22echqHsAkVlmdejNp9n//79MX06pevOFopgf/7550GkKQltPtetG4uDB00HYAYOTJY2NwtChHv3EPDDDwj+/HP4XqQIZ7lk1Ksnpd5Mb9Agu2SDm6YI//rrrzFp0iRFkK1YsUKql66GQKd7N93Dgc8VkufxAAYgKOgHnD9/XlVq9aioKIwePRpAOQB/Asgm1u0LpZY/idat6+L114PRtGmGIhy4UcEh4Gn3uoJDhkfKLwTY5/ILWc/VSynZly5dajWB2rVr47nnnpMOdzlK625v5ufOnUNwcPYzTHH3u8V/LMZHcz4C9gMwZEN/EgCdn84Aqu2rhrO/EbNuEsqUc+TIEeNBO0ucT5w4IR26MwhhTM+S1I+EzjXMnl0Sn39e0qFzenmJePfdJIwYkeySR7kSixYh5JNPIJgdUDQYkKLRIOEzOmyYf1LcfS3/kGXN9hBgn2PfKCwE2PcKC/miNy4T6EVvTXlGrkWACXTX4snaGAG3Q4AJdLdbEiuDTKSI/BJFdtja0FIyo3/+8UH79mVk0at+fiI2boxFnTrWUaWOdJo/mCe9/z5Kzpwpa37vo49w30aKZCV2chtGwBYCnvYySFFC27ZtM06FaptTtLC91KbUkCKXKS23QSjbhHm9UPq8bdtfsWtXE2ObWrUysHNnbIE6jZCYiLBhwxBgVmPT0gBdWBgymjZF0jvvIOvxxwvUvtwGmzdvHig1uxKhaPWmTZuqItCJ0I6KygIQY3OIypWzULduBoKC/sLq1ZRNhGqVpuChhx6SNsbVCJERrVq1yunSCMCmHDJduZbAQL0UhUa/Bx07pros2ky5BdzSHAFPu9fx6nk+Auxznr+Grp5B165dcejQISu1n332GTQajfT5rFmzMH/+fFVD//bbb3j44YelPsXZ7yj6vObImkjdnGqN3+sA6Aye6VyyVZv9+/ejio1Dypbvj4888oh08Nog06aFYNEieRYywzWqcd6gQQaeeSYDbdqkokoV52ucmxscuHEjwkaMsOknGbVrI27tWoihoar8SG3j4uxrarHi9q5BgH3ONTiyFvUIsO+px4x72EaACXT2DEbAMQJMoLOHMAJFHAEm0N1/gYkkp+gPc2nWrJlU/5bq4aqV9HSgdesIXLjgK+s6a1YCXnklRa0606bXyZPQN28Or6Qko470Jk0QRzWTXVEoT7Vl3KGoIuBpL4NUy3zt2rXG5aBUqNOmTZNI8uz03tbSuXNnfPnll9KFw4cP26xZWafOKJw4MdfYmaKEqA56WFje02uq8h2dDiU/+QQlv/jCYTd9iRLS5mhm/fqq1OdnY3uRdbbGpEjyOnXqqCLQx4yZi3XrxgEw1av39hYxZkwSevVKQUREds36TZs2Yfjw4cZhq1Wrhr1796qaOmUnoHStqamGTfggAH0AdICXVxPo9SYblCquXTsDn3ySgBo16BAAS0Ej4Gn3uoLGh8dzPQLsc67H1JM1ZmVlgX6PTL8r2bMJCgqSapiXKlVK+jstLU0qSUPPK7aEfpso+pnqqRtk+/bt0m8qSXH2u2nbpmHR8EWAkz+z9C5Iz5KGki8GfKdOnYolS5YY8W7bti0o6w7J/v3+6N27tNVSDRmSjK5dU1CtWha8vV3ruUJSEso2bw7v27dlivWhoUgcMwYpr7xSIKW+irOvuXZFWZtSBNjnlCLF7VyNAPueqxEtvvqYQC++a88zV4YAE+jKcOJWjIDHIsAEuvsvHUWhzp1rIsnat2+PZcuWOW34li0BGDpUnrq9R48UfPppglOp+bxEEeW+/RagNMgWNZFvU+p2i1p7ThvOHRmBHAQ87WXQchOzW7duUqRW/fr1ccOs3IH5Ahs2Oql2aKdOnWQ11A3tfHwo7eY9ZGWZHtc0mhR89llCgfoKbYhThH1CVBSGnDqFsHhKQ25b9CVLSodqMmvXLlAb7Q32zjvvYP16qgWeu1CU1xNPPKGYQKfspE2bZuLatewIO4PMnXsXPXrII80oPfz48eONbRo2bCiR6mqFarTbIjCqV6+Bb775Ab/95oeJE+8iOVl5zXNBEDFnTgJ69rQRHafWQG6vCgFPu9epmhw3dksE2OfcclkKzShKz/7CCy9Yjb9w4UK8/PLLss/pWeD999/H6tWrrdpv3rwZb775puyZZ+XKlUbdxdXvzvxzBi92fBFiUt4OPlLJHyr9Yy50oOHAgQPGjwzZj+LivNCmTQRu3DAx5HQAc8aMe04dpFbqnCEffohgi/fX5Ndek7IT5XfUubmNxdXXlK4Tt3M9AuxzrseUNSpDgH1PGU7cKncEmEDPHSNuUbwRYAK9eK8/z74YIMAEuvsvMtXnNUQMkLWWqZ3VzmDChBAsX25K2ffkk5nYtu0OAgPVagK8//sPYW+9BT8bEScpnTsjISeCVr1m7sEI2EfA014GLdOEUx1tItApIsuetGjRAmvWrJHqZ1MdbXtSp85FnDghJ0O//joebdumFYgLxcXFYeDAgUbS1g/Axi5d0CojA35Hj8L71i0rOyjaKDY6GlnVqxeIjY4GMdUoz90USqleoUIFRQT61aveGDYsDMePEyLmEoM9e0JRvfpTsk8XLFiAmWblL6je/apVq3I3yqLF5MmT8dVXX1n1a9y4MWJistPIv/32KGi1/wBoDqA6fHw0yMpynM3E11fEzz/fRsWKrknjqnpixbSDp93riukyFalps88VqeXM82SI5P7ggw+Meug38Ndff4WPj49N3RSx3rx5c1y5csV4vXfv3pg9ezbo2eevv/4yfk6Hg3v0oCLfxTMCnTBq26ktEmMTla1TODD8k+G4sfMGNmzYIOtDBy0XLVokfUYHGf7++2/QQUxzoev163fGa6+F49Qp+bPJtGkJGDBAfRYyZYYDPmfPIqJ1awg60zNEWqtWiKfnHKFgtxz5Hqd01bidqxBgn3MVkqxHLQLse2oR4/b2EGACnX2DEXCMQME+zfJqMAKMQIEjwAR6gUOuesBRo0YhOjra2G/QoEEgksRZ6dChDE6cMG2cjBuXiBEjklWr84qLQ0SbNvC2EUGbUasW4tatK9CIAtUT4A4ei4CnvQwSoWn+nSUykw7GtGvXzu4aNGrUSIqMbtmyJS5fvmy33aBB07Bu3ftISvIytildWocff7yDMmVMqVLza7FpY5022M3FSNaKonTIptT48VY10nXh4YiLiUGWg0ME+WWzuV7avDevCepozNOnTyMsLCxXAn3TpkC8/34p2Zpk66V1rI+33+4HSutvLhQ9RhF9BqGas0SqqxXLVPCG/q1bt8by5culPy3HAnzQoMHrmDDhI6naxqVLPpgxIwQ3b8rzt3bvnoJ58wo2u4Ha+Re19p52rytq+BfH+bDPFcdVtz3ne/fu4cUXX8S1a9eMDTp06CBLC26rJ0U9v/LKK6CyIk8++SS0Wq3020lEOmVyMciECROkqHSS4uZ3//vf/9C1W1dc+58JWwkIOstm62eWEtn0gITjoZ6H8O7b72LLli1GLMuVKweK8qeDcvTOePXqVdnS+Ps/hD59/sTateFIS5Nv8TVrlo516+Lyj8cWRZTWaOB/8KDRJtHPD7d//BG6Rx4p8C9ccfO1AgeYB7RCgH2OnaKwEGDfKyzki964TKAXvTXlGbkWASbQXYsna2ME3A4BJtDdbkmsDHr99dexa9cu4+djxowBkerOCNU/r1btQWRkmG7v69fHolmzDNXqQiZNQnBOLT1DZ9HLC8lDh0rp+OBnGXmpegjuwAjYRMDTXgajoqIwevRo41xq1KiBwYMHY8SIEXZX+Omnn0b37t1lkV/UuEyZMoiNjTX2I6K1WbOvMGpUmExXu3apWLbsbv5tiOaM9uyzz+Lff/+VjV2lShXZJjnS0hA+YAACzFKJUoeshx5C7I4d0EdEFJqn0yGGP//8U9H4NE9/f3+7BDoFVr37biiioqj2uKXcB/AsgD+kKHYiGEiXQcaOHStlHDDIq6++io8//liRXeaNLl68KEX/WYqhbAB9vnjxYnz00UdWbbZu3Yp69epJn6ekCOjXLxwHD5pspBSvBw7cxiOPcBS66oVxsoOn3eucnCZ3cyME2OfcaDEK2RR6Rtm4caPMCvpdot+n3OTWrVu4dOkS6FkmMCfFlaW+fv3GoWvX0bh71wt37/ogM7MUfH2BWrViUa1aRr4/v+Q2h/y6Ts8c9AxoSXLjQQD9ANCjwP/MRn8CQHfQWTcIEDCp0SS0CmyF5557TqGJQ+HjMxtZWdbPJuXLZ2H79liULZt/By4DN29G2LBhMluTRo5E0tixCu13bTO+x7kWT9aWOwLsc7ljxC3yBwH2vfzBtThqZQK9OK46z1kNAkygq0GL2zICHogAE+juv2gajQa//fab0VCqp0ykujNy8qQvXnpJTladOXMDISHqau95X7uGsk2bQqAivwZ55BHEzZuH9AYNnDGN+zACihHwtJfBnTt3SmnODVK5cmV07twZn332md05V6tWDffv38d///1nbEMb0VTj2jyavU6dOti2bTtefz0Mu3fL6zB8/vldREbmX91qIvJr26hlHhoaKqUPNRchNRXhffvKIpDoelbFirg3bRrSn3++wNN40viWBwC8vb2lqDlL8fPzkzIBOPK9zz4LxuzZIVZ9y5W7j5s3aaP7mPEaZSCgDXSDUBQeEdgGIaJh3Lhxir8ThoaUuvWpp55CUlKSrO+AAQMwbdo06TOKTjMcwqIkvO1zqP229evj8bJl4XXvHoR79yDeTUTitVSkIBCX8CgOoDke7lwN7YeXha5SJYglSqi2jzuoQ8DT7nXqZset3REB9jl3XJWCtyk+Pl4iv81/Dx9//HHQ84yBEFdrVXaJkXUAiEwdCeAhuyqqVMlEr14peO21+0XqPO7BgwelUlyU6l4mDwCgcwnEcdNj33oAdO6uBgAqNe9rah3mH4bDvQ6jcf3GsgOVtsEcAOAbm5fCwnSIjo7DU09Z2KJ2YR2097p2DREdO8rK+WRVqIA7+/ZBdKZ2mAts43ucC0BkFaoQYJ9TBRc3diEC7HsuBLOYq2ICvZg7AE8/VwSYQM8VIm7ACHg2Akygu//6WdYMpHrKFJnqjKxYEYTx4021bmmDav/+O6pVlXr3XZRYu9bUj0JGzp/HzYAAqfYeCyOQnwh42ssgpQg31PkkXMLDw9GkSRNs377dCFOlSpWso5EsQCTiMz09HX379jVeMZDVsbFeaNkyAvHxprTbZcrocOTIrXzbfN6zZw+ImLUlRDYT6Wwuwv37KN2zJ/yOH7fqklGnDpLGjEE6RVQVYD3MmjVrgogCg9CBgD/++MPKPgPO9nwvOVlAw4YP4N49Uyp9UhIZmQKqLdq3b2dQDXWDlCpVCsePH0dAQID0UZ8+fbBv3z7j9fHjx2Po0KFOfY0sD12REnNC/scff5R8iALdZjikMBwPrytTBunNmiGtXTtk1K0L/YMUPsfiSgQ87V7nyrmzrsJBgH2ucHC3NeqFCxcwf/586Xdi5MiRKF++fIEZt27dOlDGK4OQX9CzzMMPUy5x9ULnbXv2PI9Dh+oDCFasYODAZEyZorBGuGKthdNQFEUpQwxF5suEzjX3B2B+Ji0TAP2zkdDGEIV+ZP4R7Nixw8FkiJU/A0CeocjHR0TPnikYNSoJ5crl0ztbJXdvfAAAIABJREFUWhoo8jxk5kx435G/Z8Z/9ZX03FBYwve4wkK++I7LPld8176wZ86+V9grUHTGZwK96KwlzyR/EGACPX9wZa2MgNsgwAS62yyFXUOonrB5mr9vvvkGRKo7I2+9FYoNG0y7MUTufP65upq23pcuoWyLFhDMozSHDwcWLMDNmzeZQHdmYbiPKgQ87WWQCNn27SnO175QKnGK7LInVN/y6NGjuHLlihQ1bS6nTp2SSPkdOwLwxhvhsmtbt95BvXq0C+t6mTFjBr744gubisnWB20Qql6xsSjTvj18zGqqmitIHjIEiRMmFAiJTpvZjzzyCDIzTfhQdo+vLUpTkH0PlX8IRw4fsRmBbivdOfVZsOAuunbNzgBAZHnHjh1lWFG90gY5GTvoGrUxyKxZs6Qass4IRZovWrRI1tWckL+4dSv0b74Jpclfldqgi4iArkIF6B56CLry5bP/z/lHte7FIFup7ZVqL57tPO1eVzxXqWjNmn3OPdaTIr+feeYZ3LhxQzKIymtQzWuhgA6Y0e/PTz/9ZASDnmGWLVvmFDiiCAwdGoatW+VZcpQoCwnR49Spm/ChdCkeLpS6nZ71ZFIWAJ2JLKluchSFPj5gPMaMMh1yIA10OO/ll19G1apVsWTJ87h69RmZ4s6dUzBmTFK+lWIREhJQYvlylFixAt5m5YYMRqS1aoX4VasK5BnPHqJ8j1Pna9w67wiwz+UdQ9bgHALse87hxr2sEWACnb2CEXCMABPo7CGMQBFHgAl091/g6tWrIyHBRHJrtVopelWt0AZWw4Zlcf26aRdq+vQE9O+fokpV2JAhCNy2zdhHDAiAcPkyUK4cE+iqkOTGziLgaS+DFG3UrFkzh9OlSK85c+bYbUNRxZTyndJ+PvbYY7L0n7SpXr8+RXVBikI/f96U63PSpHsYPJjygLpeqK7277//blPx7t27QbXeU1JSsH79eukeRlHPERER8LlwAWFvvAHf8+dt9o2NjkaGxSEB11sPKUX+E09QcVGTUH3wIUOGWA0XUSkCJw+etCLQ09P16NSpDP78Ux5t36ZNKr755q5MT8OGDXHN7ODA8uXL0bp1a6kN1TKlaEODLFy4UNoEd0YoFTylhCeh+Haqav6eRoPnHn4YPpcuIXDrVgiW6WOdGUhFH31AgBRxljR6NHSPPqqiZ/Fu6mn3uuK9WkVj9uxz7rGO//zzD1q0aCEzxvC7mt8W0u81ZWMxTzPuzG/StWve2LAhEKtXB+HaNdsMuL+/iIgIHcLDRZQr54uzZ4GrV+Uz3LgxFs88Y1YyKr8ByAf9//77r9XhR0rL7vO+DwQfdVtuelEPnajD7Kaz8U/0P6BsQI8++igiIyOlA9aUsWDPHn8MGFBaNpMuXVLwxRfqDk2rgcLr+nWUiYyEz5UrNrtlPv444jZsgL603C41Y7iiLd/jXIEi61CDAPucGrS4rSsRYN9zJZrFWxcT6MV7/Xn2uSOg7mk+d33cghFgBNwMASbQ3WxBLMyhCElKl2heg9DZDbTLl73RtCml8zPJTz/dxhNPKK9953/gAEr36iXTkTx8OIIXLJA+4wh09/anomKdp70M2qsVbliPkJAQUGYJ2vy0J5TGlQhrEkoBevHiRWNTItaJYCcZO7YU1qwx5QFt3z4Vy5bJiVxX+AGRvc8//7x1Hc8c5WvWrJE2/ykNORH8JFSbe9euXaAaoH5eXmh+7RpCPvsMPv/+KzMp/dlnERcd7QozHeqwRVBQND8RB5alKLwreOPcr+dQwq8EKBuA4X63dasfBg+WR/3Ttc2b76BBA3nk/4svvojTp0/bXLe6devi1q1bxmsG/FSDkJ6O2AMHsKB/fzQEQMU+SilQQscgYh95BM916QJ9qVLQh4RADAmBvkQJbFyehXu7T6IRfsejuITyuK5Ao+0mor8/Urp2RWa9esh86ilk0gGGQqqD6vQkCrCjp93rChAaHiqfEGCfyydgVaqlkh+dO3eW9TIvxaFSnarmVC5m1KhRxj7+/v6g6OngYGWp169f98K4caHYuze7RIltoeeCmfj113moXLmS8XAaHfatUSMTp0+bDgIOH56E999PUjUHd2p8/fp16XkpMVGeir5et3po/Epjp0ylTAQ9q/ZE5ZDKVv2TkgS0aFEWN2+aSvpQvXMq2VW6dD6lbBdFhPfpg4D9+23Oh8r0UOp2fc7zk1OTdlEnvse5CEhWoxgB9jnFUHFDFyPAvudiQIuxOibQi/Hi89QVIcAEuiKYuBEj4LkIMIHu3muXlpYmRZuaC9UgpLTDamXNmiCMHWuqf04RHydO3FJebjgjA2VbtpSRXUS03Dl4EA88+aRkDhPoaleF2zuDgKe9DNr6HpvPe+LEiVIE8gsvvGAXjhMnTqBsWcr1Cbz66qv44YcfjG2pNurYsWOlv6OjAzFqlKneZdmyOhw/ruJ7rmBB6GBP7969ceDAAbutidSvU6eONC9zobSi586dkz7q1asXZn/8MULmzUPJ+fNl7WI3b0ZGTnpzBSY51cRQC9zQuXTp0hJJ8HTjp3HnqrxmJx4BJi+ajCG1h8gI9N69Q/HTT3KSwF7NVjogQYcHDDJ16lRQyngSus+TnxiEosgpZa8jEVJT4XP6NHxPnYLvX39l/3/uHASzlPS5AXOZ6qMD+C7nYAbVvbWU+HgBbdpEGLOXBCAVj+AyOgV9j3eqbkLpiyfgZUEM5Dau4bro5YWsKlVwf+BApNDhLC95DXmleopqO0+71xXVdShO82Kfc4/VpvTplmU86Nn/559/zvc07pbPGBTVTIf8lMixY74YNChcRt7K+xGBS4cFN0kfL126FC+99JIsu8uIEcn44gsTWf/oo1n4+efbSoZ3yzaUbWYClaaxkL1796JatWoutfnOHS9MnRqCjRvlJVM+//wuIiOzS8rkhwStWIHQ8eOtVKc3a4bkQYOQTtkU3OT3ne9x+eEBrNMRAuxz7B+FhQD7XmEhX/TGZQK96K0pz8i1CDCB7lo8WRsj4HYIMIHudksiM4jSKFIKd3OxV1s4t5kMGxaKzZtNGyqdOqVi0SLlkalBy5cj1GIDKGHWLKT16ycjlCwjN3Ozi68zAmoR8MSXwfLly9ucZqVKlbBv3z5QhFLTpk1ttmncuDFiYmKM1z788ENZLVJK9U3pVUkuXfJGs2byTBMHD95CpUo6tTDbbb9jxw688cYbDvXRoQC6fy3IyU5hr/HHH3+MV3v0wANNmsDbLAI7/ZlnpFSfyk/4qJ/eypUr8cEHHxg7Pv3004jZEoPqfaoj/Zd0ucIngLABYTja5ygerZidgvzYsdto0CAComh6XB46NAnjx9uOlCOynCLwDfLOO+9g9OjRyMjIsDoUtX//flSpUsVkg14vkeX+Bw8aCXOff/6BoHcumkz09cXh555Dyx9+gGFLvWbNmjL7zAGgVLy9eoXj4kVTVCBdL1lSjx/23ELl9PPwvnwZ3tevy/9duyb9rcTOpKFDkWRjA179yhadHp54rys66BfPmbDPuce6U+YWyuBiKbYOV6WmpsLb2xt+fvJSIs7MhKKkKQsL/S4ZxDwDjj2d6enAxInyDDiWbStVykLJkhPx998zjZcMUfXmfrdpUxy6dpWn+e7T5z7eey8p/yKonQFLYZ8pU6ZIBwXMhTL0UKYZV0hKioBduwKwcWMgDhzwh04n38Jr3jwNa9fG59vjlO/Ro1LqdvPDe7py5RC3ahWyLN5hXTHfvOrge1xeEeT+ahFgn1OLGLd3FQLse65CkvUwgc4+wAg4RoAJdPYQRqCII8AEunsvMEV0W0Yh/vXXXwgLM0WYKpkBpUSsW/cB3L5tSuc3cybVJFZW/9z70iWU6doV3ndMUZkZdesidssWePn4MIGuZBG4jcsQ8MSXQVsEOqU0X7RokUSU3rhxw1jH3BIoijij+pYGWbZsGYhEN4g5wU7f9dq1H0BcnOm7PmdOAnr1UvZdz22RaKOeosrNa3lTSnPacKfyEgYZNGgQtm/fLh0McCSUGpayajy+cydKTZokaxq3fDnSc2qE52aXM9enT59uPHhA/Tt06IC6w+pi6vqpwEoLjQ8DwgABkxtPxuTWk6WLY8cmYfbsksaGRCZTVo/AQNGmOUSWR0VFGa8NHDgQtLEeFxeHWrVqGT+nBPxnR49GmZMnpUMFXrGx8IqLc7puuSgIyHr0UegqVYKuQgVkVa6MtPbtsePMGbz22mvGcStWrGi3pj01io/3Qt++4Th5Uk7SDBiQjGnT5KlpzQEg+0vOmIES69fnukwJc+ZkR6KzSAh44r2Ol86zEWCfc4/1W7169f/Zuw7oKMouetMTEkgCCV0pPwjSBCnSpShNpEgT6UW6CIIUERWxgIDSRBBBOihKbyJIERAEBKkiqDSlhCSk993/3AmzO7N9N5tks/neORzdna/e9zIz+7337sPEiRONFsNSICtWrNB9v2zZMnzwwQfSvWLOnDlGtO/27mbTpk2gU1sWHx8fiZmFpWbMCVlKBg8ujBMn/IyaFCigQefOSVL2c926qZgx42MsXLhQ1052JCvt7tatu6hVK1z1HsMO3t5aPP98Mrp3T0TLlinw0r/m2LvNHG3P96GdO8nzoheWiylc2Lj8iz0Ly8gA5s8PwqJFQUhMNM3eEhCgwU8/RTg1iFL1fL9/H+Ft28Lr7l3V0uk8T2nZ0p7t5FhbcY/LMajFRI8QEDYnTCG3EBC2l1vIu9+8woHufjoVO3IuAsKB7lw8xWgCAZdDQDjQXU4lqgVdv34djRo1Un3H2sMBdtaMvXDBG61bZ9I/y/Lzz/dQvrzlrFSPhAQEzZ+PoC+/hIciG4VjPNi0CanPPCMO+F3bhNxydXnxx6ChA511zJV02dHR0ahWrZqRvliL1PAesGXLFowcOVLXtmLFilIWuyxDhoRi584A3ednn83MPnKGzJo1C6RnVwqz30kxT8e+LHSqMwDIFmEwwKt9+6Jo06bwvn1b1yWjWDFE7N0LTViYLcPY3WbYsGHYvn27rt+rQ1/FxrIb8TDxITDdYDj6yccBoX6huD3uNvy9CqBs2XTcuuWta9i7dwJmzowxuw5D5gDWrSeW//zzj459oDeAOQDUd2u7t4b0cuWQVr06UmvUQPILL0jOc0P59ddf0blzZ93XrG8r0+ubmzE+3gODBhXGkSN6RwkDB06fvofAQNOBA/JYPmfPwu/gQfiQdv7SJXj/QwJ5tWgKFsT9w4eheVSuwP6du1ePvHivcy8N5L/dCJtzDZ0vXrwY06cbPogy18ZgNb4vREVFSYF3KUz/BhAeHo7Tp09L2eiOiiFTCmt3r1q1yuRw//7riR9/9MfSpUG4fl3/LJQb16mTiuXLo1RZ43zm8tkrC0un/P7779Ka+d5A4bvDpk1+GDnSfLDwM8+k4OuvoxAcbPm54ygOzuzH4Dy+I8nC4L3+/ftneYpPPw3CnDnmAxs8PbVgAGWPHtlD3e7x8CGK9O0L39OnVXuJHz4csSYo67O8YScNIO5xTgJSDGMzAsLmbIZKNHQyAsL2nAxoPh5OONDzsfLF1m1CQDjQbYJJNBII5F0EhAPdtXX3xx9/gIdXSrl9+7bd9Q/nzQvCJ5/oD1no+Dly5L5FOj+/AwcQMn68UVYB15LcogWiVq+WliVezF3bhtxxdXnR5pjRxcwu+W+GB+DMQJeFmd0qyu5HF1gzmzTvSjl69Ci6d++u+yokJAQXL17Ufd661R8jRugzm7y8tFJmdJEijtF9ywMzoKdFixa6w3p+z+z3jRs3SpncpGN3RORggoDvv0fo6NGqIZKffRZRa9ZkS+1Mw0PttiPbYk/4HmihBZYAuKNYShMALQEPeGBOqzmonjAWzz+v3u3OnRGoWTPNLASfffYZZs+erbvOurLMHvx6wQJsnzkTzL9/wU4A5RriadWqIa1GDclpnla1KrQF9Znx5oa8evUqmP2nFDrzrdEAk869fv2i0Gj0PxNmzXqIV16xj+XAIzFRCtAqaEDzn9i5Mx4qMhTthMStmufFe51bKSAfbkbYnGso/ZNPPsG8efNMLoY1w0kJbpgtzsYnT55EyZIlHdpEfHy8xIYiO+Q5CJ9byveNpCQPLF8eiK1bA3Dxorqkhzwps87HjYvDwIEJMGSVNxUYzHcasuwoHegsB8V53nmnkKpMinJjdeumYP36KLOsLw6BkA2dnn76adxTlKghgwCZBLIiZ874oGPHMCO6do7JrPO2bZPRr18C6tQx/07iyPxklPE7fBh+hw7Bf+dOeCapnfMpjRohct060gU4MnyO9BH3uByBWUyiQEDYnDCH3EJA2F5uIe9+8woHuvvpVOzIuQgIB7pz8RSjCQRcDgHhQHc5lagWdPbsWfCgTBZ/f3/89ddfdi+ahyynTulpdwcOjMf06eYpd33OnEFYp04mKYPTH3tMqk2c8aims3gxt1sdokMWEciLNsfD00mTJuHGjRsgdfcrr7yiQkGr1aJ06dJGyPCwmRSqSjHl+Pz7779BOnQK62HWqFEMSUl6Ss+PPnqIfv3sc3AaLkYZBMBrzBjbu3cvKleuLFGTk6LcEaHDlqUpAgMCULhPH/grsuk5Hmtjx7/xBrR2Mm9YWwtp5x88eKBrFtgvEAnlEjI/03nOhHrGHPDWOQjAo9LywVHNELJtP25c1+P75JNp+PHHCItBScuXL8eiqVNBX3wdAA1DQlDNwwPB0dHWlqq7nl6mDFLr15cyy+k0T69SBdoCBWzur2wYEUGHf02jZw6zGK1Jv36FsW+fv65ZjRqp2L1bj6W1/srrIaNHowDr3SvkwYYNSG1CpPK35MV7Xf7WWN7fvbA519Dh1KlTwWeGKfHw8MBPP/0E1kOng1sprJ3OrHSl3L9/H8HBwbp3BHM73LBhA8aNG6e77O3tLWWHM0gvNtYDK1cGYu3aAirmFcOxihfPwMqVkahWLd3kNHzX4bOXpUtkYUY2y4kYOtB5/bfffPD114HYtcsfycnGNOWkc//ss4euobRHq0hPT5f2FxZGB3eGFBzAfcvy448/qgIo7V083/FatQrHP//ondQeHlo0b54i0eW3bp1slRHG3jnJSBY8ebLRs1o5TnqpUniwezc0RdT16+2dK7vbi3tcdiMsxjdEQNicsIncQkDYXm4h737zCge6++lU7Mi5CAgHunPxFKMJBFwOAeFAdzmVqBZ0/PhxdOnSRfedYaapLauPjqYzrbgqW3DNmkjpoMWkpKdL9exIsasUrbc3EgYPRtyYMarsRvFibosWRBtnIuCuNmeqTrqy1riMoSm6d9JxK/sPHx6Kbdv0NO4VKqRJdTCzwOwqHXorHc6vvvqqrhb7/v370bdvX4fV/PXXX6NVq1ZSve/wVq2k2t9K0RQqhMQuXZDYuzfSK1d2eB65o8mM/xEG3Olkvb8BoAwAOaH/bF9gx2IgXY8tx5w2LQaDBz9yvptYne+JE0ieMAGlr12zee3JzZsjuW1bZBQvDk14OEhpL1Gbezjn9TwtLQ1ly5ZVrefAgQN44oknrK5x3z4/9OunPiTftSsCTz1lf7YbdU76fs8YPf09a7bf37cPeBQUYnVBbtrAXe91bqout9iWsDnXUOOYMWMkdhdzwvIbcXFx2Mf7pEK++OILdOjQQfqGztsBAwaAz2c6p1euXGmyVAzbMojtxRdfRKqiXFPz5s3BWuykan/ppTDcvm05q7h69VSJVr1ECctsN6NHj8b3iqApMm1xHlMOdHlrdOBv3hyAjz8uhLg4tSN93rxoqca6KwgD0xggeenSJTz55JNgIIRhwCQZg/h7zlGZNCkYq1cHqrqPHx+LsWPjHR3SYj+Jqr1PH/j+9pvZdhnh4YhcuxbpVatmyxqcOai4xzkTTTGWLQgIm7MFJdEmOxAQtpcdqObPMYUDPX/qXezadgScc0Jn+3yipUBAIJDDCAgHeg4Dbud0rGvcq1cvXS8eLrG+oT3y7bcBGDtWX0fQ31+DCxfuwlwyZ9Cnn6LQHFbh1UtK48aI+eADpFesaDS1eDG3RxuirTMQcFebs9WBzkymcqxxnaZ3Vu7cuVOVTfzTT37o00ft4Fy0KAodOyY7pIKEhAQjx6rS2costXbt2jk0Njv16dMHM2bMkPr7/vILinTvDg+N6UP4hH79pPsRPI2z0WxZAA+4GzZsiMREg4z8tx5lm5saROMJ7FoAnKKXXS3ly6dj9+4IBAUZ12L1iI5G0PLlCJo71+x+DMe77+UF7yVLJOd5dgvZA+iEkYWUwM8884zVaTMySN9fFP/+q3eovPxyAubMMV8D3tKgBVatQsjkyaomsW++ifgxY6yuxZ0buOu9zp11ltf3JmzONTRoWIucWefKLGbqiTTnhkKHrVxjnNnoI0bon1ksW7JkCeuTqIXvEnRiGzJcseTISy/1RJcuYThzRs9ipewdEqJBixbJUtZzmzbJNjF3b9myBSNHjtQNQ3atpUuXolOnTggKCpJqoJvaGzv88Ye3RF0eH69//hcpkoHjx++jQIHcr4duWKqFe0tO1r93BQYG4sqVK3aX4uLeExI8MHt2QXz5ZZBKgbVqpWLLlgc2YW+vdXvev48ir7wCn8uXzXZNadAA0fPmQfOImczeOXK6vbjH5TTiYj5hc8IGcgsBYXu5hbz7zSsc6O6nU7Ej5yIgHOjOxVOMJhBwOQSEA93lVKJa0J49e8BDNFmYLchagfZI376FsX+/nmq3VaskfP21acpgv337ULh/f3goqAbTnnwSEbt3AwY00vIaxIu5PdoQbZ2BgLvanKEDvUiRIjh37pxJyEjReueOvki3nMEtN+afcOvW4aoapcxC37cvwtyfskXVXL58Gc8995yqzbVr1xDwKBInKioK1atXt1m9L7/8MkgXKwv3fuLECd2hcuDixQiePt3seAm9eyOGDncHsrFN1pYlC/oEC8s/+A5wcJpRgzp1UrFy4i8os20ZNKGhSOjbF74nT8L311/hd+IEvC9fVt1Pzc1ANwiLc2wHMNvPD6f+/ttmLLPSsH79+rh165ZuCFIGsza7LTJvXhA++aSQrimDs3777R6Cgx1wYmRkIKxjR/ieOaMbT+vvj8jVq5HasKEty3HLNu56r3NLZbnJpoTNuYYiu3XrhmPHjukWM3bsWKnuOYPZLAl/M7z//vtSk9q1a0vOaKWYYrVh3XQ6r5VSpUoV7NixAwsXhuHTTwsaTVm/fopU57xevVS7Hbd8XyCjjaGTnOVqnn32WakcDK+bk61b/TFihEwLk9lqypRYjBiRPRnY9lgEmXl27dpltkvFihXB4GhbJS7OQyqXsnOnPw4coDNefTzH5+7evRH43/8ybB3StnZpafC5eBGhI0fC+/p1VR+Nvz9SGzdGcrNmSGnaFBnlyzv0LmbbQpzfStzjnI+pGNEyAsLmhIXkFgLC9nILefebVzjQ3U+nYkfORUA40J2LpxhNIOByCAgHusupRLUgwywNZguSitFWiYnxwFNPFUdamv52Pn9+NLp0MaY69L56VXJgKGl0tZ6eeLB5M9IM6ikq5xcv5rZqQ7RzFgLuanOGDnTSfxrSs8oYMtubWd+yzJo1y4gmlAeuQ4aoD5knTYrBqFEJdvudf/jhB6lGqSym2DBI4W54f2K2lakDf+7L0CHPmq6VKlXSzeF3+DACly6F34EDJp3Q8UOHInbqVLsPbvv37w/WIFVJeQDmGOgTQ4G5N4BUtRPBr8GXOD+zCv7X4UV4PrS9BitztBkGdRbAxUf/rgCQc9ReeuklLFiwwFl/LhbHadu2rSpIY86cOWBwgy1y754n6tUrhvR0/fNl+vQYDBxo2cFjbmyf8+cR1q6dUaZ+YrduiH3nHWgKq23ZljXm9Tbueq/L63px5/ULm3MN7Rremz/99FMpQ/zzzz+3ek//6quvpDamWG0YMEUdK8Uwa5rXzp8/D602TGIaSUjQtw8O1oC/I1q2TLH7PUI5Z8eOHXHq1CmTe+F7A68VKqQP0DJsaBgcHBqamYVuigkmJzVq+G5mOHezZs2wdu1aq0ti0voHHxTC2rWBSE01fyQ3d240unXLOn29R1wc/Pfuhc/Zs/D9/XfJee6hyJyXF8ySMpHr1yPdhlIvVjeZSw3EPS6XgM/H0wqby8fKz+WtC9vLZQW40fTCge5GyhRbyRYEhAM9W2AVgwoEXAcB4UB3HV0YroR1CJkNOF2RhVmzZk2QqtkWuXDBGxMnhuDsWT3toq+vFr//fheFCmmB9HT4nj4tOaf4z/fCBaNhY6ZORcKwYRanEy/mtmhDtHEmAu5qc4aH3XRYK//+lRj269dP5Vx/8803wZqpSiG7a7t2YTh/Xk29+vjjyejTJxXduiUiPNxyrVJ5PGa+TZumz8CuV68eNm/erJrv+PHj6NKli+o7UrOvXr3aQP1eWLfuLoYP/xQxMcx+pvM5GmPHvojx4/sYmYrXrVsosHYtgpYsgYeiPisbxr71FuIVVLC22FmbNm0k54BKXqW3wUzvg1OBg5kZfZJ4ZACd+sPjqbU4e6gaahwwGMvCIphT/yGAFAtt1q1bJ2Xh5YSwNuuhQ4d0U7399tsYPny4zVMPGRKKnTv19eBLlkzH4sXRqF3b/lronLTQ1KkS5b2haEJCEPPhh0gyyJK0eaF5tKG73uvyqDryxbKFzbmGmhs1aoTrisxfOsXr1q0rldhQUoIbrpaZ28yAvnnzJho0aGC0mTNnzqBo0aKq77t27YpffvlF993gwYMxZco0jBkTgq1bSc/y6NHnocWePRGoVi09yyAxC5vBbMpSNMpBrbGh/P67D9q1C1etY9KkWLz2Wu5moRP/Bw8emMWHz1wGPFqS9HRg6NBQ7Nmjf7aaaj9yZBzeektfgsVRpfj9+CNCxo6FV7RpdjJ53PQyZRC5YQMyHn/c0alcop+4x7mEGvLVIoTN5St1u9Rmhe0ZKqMjAAAgAElEQVS5lDry9GKEAz1Pq08sPgcQEA70HABZTCEQyE0EhAM9N9FXz80DF2ac07Fz6dIlXL161ehgiXS733//vcVFyzXyvvoqEBqN+jb+3HPJWLkiEoFLlqDg/PmqbHPDQZNefBHRX3xhNbtTvJi7jg3ll5W4q8199NFHuuwyX19fidLc8KBb1vH48eOxfv16ncp5EP3hh3TNqmX37lgMHlzZpGkwoGbGjIfo0cN69hIdq6SJl4UH7vPmzVONy/qszJ7+9ddfpe9Zs5UH+TwI37hx46O2LREc/D1iYoLNmivrqVapkoahQ+MREqLF/fue+O8/L/js2I3nlw6Ep0ZNVRq1eDGSX3zRZvM3OuB+BcATZrqn+QGf3gaSwvQNqq0DuvZCg5vAMWNfr9FAWj8/JLdqhci+ffFYt24mJwoNDQUpeknNb4m61uZN2tiQ9XFZJ1cWWw73lUMfOeKLHj0U2Eh61+KVVxIxeXIsQkPto3P3SEhA4X794Kdw5sjzaT08ELVyJVJatrRxd3m/mbve6/K+Ztx3B8LmXEO3NWrUQGRkpG4xLHnSpEkT6Vk6YcIEMMjWlISHh+Ps2bNSTfH33nvPqAlrcs+YMQN8hvMZvXv3btBhrpR589Zj3brOOHHCT/V9166JmDfPdrYVa0j+888/2LZtGw4fPgzSyGdk6J/tfJ/he40lGTAgFHv36p3MrMd++PB9FCliW2CgtfXZez0pKQkVKlSw2I3vbnzWmxOW35k8ORirVweabOLlpUWjRino3TsRL7ygr61u71rZ3vPOHRT64AMU2LLFave0SpUQuW4dNMWLW23r6g3EPc7VNeR+6xM25346zSs7EraXVzTl+usUDnTX15FYYe4iIBzouYu/mF0gkO0ICAd6tkNs0wT37t1Dy5YtEW0l+t8a9V9srAe6dAnDpUs+RvN6emqxbl0k2p34CAU/+8ziunhQ8mD7dmgDTR/gKDuLF3ObVCwaOREBd7U5ZmJ98cUX4KFyr169JGeqOZk5cybmz5+vu/zCCy9I9VENJdNBSofjayaHorNz06ZIqY6pJWEmOSnWZRk3bpxUp9RQ/vvvP+l70sQOHToUpHW/ePGiRC//4MH/4OFxGFqtOiPe0rze3loVRXgvrMEq9IUn9I5ZlppI7N0biS+9hLRatWCpICudDuXKlVNN6TncE14lvEwuQ3OuBzK+U2fQe4+oDc+i53Dky3TU/c/06lNr1kRymzZIrVcPqazl6u8PBhiUL1/epOODDg1inNPyzjvvYNmyZappWSOe9meL8LC/TZswXLhgrNMiRTLw9tuxEr2sXaXqMzJQYNUqFJoxA57x6mzCtIoVEcEyJl6m9WXLmvNSG3e91+UlHeS3tQqbcw2N81mRkqLnKmEwmhxcFRsbKwXZxsTEgBnlpHdXyqhRo7Bw4UKLG6EDvWHDhkbPcU/P2ihW7BfcuaP+HeHnp8WBA/dRpoyTa20/WiXfF1hzXRbuYfLkyRb3QKat1q3V2fRly6Zj9epIlC9vfZ3eFy7A79gxiapcExyM9AoVkF6+PDTM0HfgGUOK/aZNm6rWXK1aNVxQMHwxYM3Su92yZYF45x3jAEMGFrZvn4Tnn09G4cL2BaapFpSaCu+bN+G/bRuCvvgCnomJFjFm1nly69aIGz0a2tBQ1/jjyOIqxD0uiwCK7nYjIGzObshEBychIGzPSUCKYSAc6MIIBAKWERAOdGEhAgE3R0A40F1Dwcy0WLRokdXFkHrY0NnBTt5XrsB//37s3+2JEb8Nx12UUI31v/+lYebMGLS8vhIh48ebnUcTFITkF15AzNtvQ2tjvVnxYm5VbaKBkxEQNgcpG5xZ4bKYolSPiIhArVq1JMctMBTA6wCeNNIGs7a+/DIKjRqZd6KTUvzatWu6vsw+5wG8rXLzZjzaty+DyEh1Rput/ZXtxmE2ZuNNk115CJ7StCkSBg1Cat26Rm1u374tUeAqZfCqwfAv6G9yvM1TX8O/F/Tp6aWrX8GbI2ai+bpjqP4zK5erJe2JJ5AwZAgSWUfchNfYFL2rj4+P5ARhFnpOy9GjR9G9e3fVtMxKZJ1d1qi1RW7f9gLr0V65Yhy4xf6tWiVh6dJoS3ENJqfxvHsXwe+8gwCDsiXRc+ciyUwmvy3rzUttxL0uL2nLPdYqbC739UjHOR3oSmGW9v/+9z+jxdGJXqVKFSctuh48PA5Bq1U/D/39tViwIBrt2mUt49nSIt99913ItdvZzhTLjan+r74ail271FTnfKdZvjwKzzyjeKfJyIDP+fOZNb7PnoXPmTPwUbzTKMdmUJ4mLAya8HBkFCuGjKJFpZrfqfXrI61GDbPMXNRRz549dUOFhIRI1PizZ8+WAgn5TGWgHJ+xrDnuc+kSPCMi4PngAbwiI/Hf7w9xfn8swnEfRXEfwYiRxgoN1cDXXNwh18pa8d7eIEuL9N7BGvfy/8ufMzLgdecOPO/dg4f0TmhaGPBHxpy0mjWRWqOGzb8FnWSAOTKMuMflCMxiEgUCwuaEOeQWAsL2cgt595tXONDdT6diR85FQDjQnYunGE0g4HIICAd67qskPT0ddH4xC92aDG7TBjM7dJAOQbzu35cOXqQDoatXdV3/REXUwhkkIhCkaB49Og4jhsYg9NvVkjPCQ0GRyE5pTz6J5BYtkNK8OVKZ8epj2glibm3ixdya1sR1ZyMgbA7Yvn07hg0bpoO2bNmyoDNUKaZqkgONAJA+VF2rnP1mznwo0YIaikajkWhJldlwLDfBeqy2ymefBWH27EKq5kFBGmi1fyEhgYffpW0dCoAWizACw7HYYp/kpk2lmtnMntKGhEhtSRPbSVFHm3S2DAzggbahXLvmjWef1We3NcFhrK3yPh67tN+obVqFCojYt8/q/ZPZacxSU0qrVq1U9Ph2AOGUpob17Tmot7e3tKYWLVrYNEdaGsCyIXPmFERSkqdRn7ffjsHw4Qk2jaVqpNEgvE0b+Fy8qPs6/bHHcP/wYZj3KNg/jav2EPc6V9WM+65L2Fzu65YlnQxLeZCWnfTshsIAuUqVKiEhwYH7q9FopwE8rfq2ZMl0LFsWjRo10rIVmMWLF2P69OnSHOS+6lWjBqZ99RU0pUpZnPfffz3RsWM47txRs5Lw98/cudHo2DEZvocPI2TSJHjfuJHlPTAjO6lDB6Q99RQywsOlbHX+FwEBUlkdUrTLwsCGH3/8MfPjIwe+38GD8Dt8GL6nT8ODxc5dRBh8GDt5ssTmYx9ljItswI5liHucHWCJpk5BQNicU2AUgziAgLA9B0ATXUwiIBzowjAEApYREA50YSECATdHQDjQc1/BpEVWUvcWAdC+Vi3cO3MGdPk8APAYgCEA6tu43J1ohzuepdCh4X8IzoiC99Wr8HrAkdTy8KOPkNivn42jmm4mXsyzBJ/o7AACwuYAQ+d4wYIF8ccff6jQ/OGHHzBw4EDdd8WLF0fbtm3x9dcrARxGpjNdL6RzZ6Zw27b6LDNmRh84cABz5sxRtf3tt98kKi9bhGfEzzxTDHfv6g+469dPwTffRGL58i8xbdo0AMygIwW9ek3y+D4+WhQrloH4eE88fEgHrRYf4G1MwUdWl6D18UFKkyZSsNDZyEh8smEDmDvOsCNV4AG9wKRtZfYWgPHjg7F+fSAew03MxET0xAazc0WuWSMFIVmT9u3bS9nmSiFtf4cOHax1zdbrs2bNwty5c1VzBAQE4MSJEyhShE8l2+Tff73w7ruFsHu3OiOwePEMHD9+z974LGlSv/37UaRvX9UCHn74IRKt1Me1bcWu3Urc61xbP+64OmFzua9VlnFp3LixaiEMvGLAlylp164dfv/99ywunCVjTqrGeOaZFHz5ZTTCwrKppnh6OryvX4fXzZu4sGMHfv3mGzQAQI4YhvJqvb0RvXAhkl980eTePOLjpSDi5CPncWTVXdyN8kcSApCMzP96e2owrtYPKHL6YBaxsd49vXRpXPHxwa5//gGTxVkJvVRoKKqQNSAtTdqjl5UyXdZncX4LZq0n9uyJuEmToLHjWe/8leTciOIel3NYi5kyERA2JywhtxAQtpdbyLvfvMKB7n46FTtyLgLCge5cPMVoAgGXQ0A40LNJJVotvP/8UzrYYdZB+pNPgnXF4feIvlijkQ5UPNLSMHXcOCTv2IGXATDXr0A2Lclw2LhRoxBnpb6gLUsRL+a2oCTaOBMBYXOQamq2bt1aBytpwK9fv66C+ZtvvlHVN2Utzj179qBMmTLIyGAm23YA6jrrBQtqsG9fBEqXzsDOnTsxZAhDd9Ti6+uLv//+22TWtik979zpjyFDCqsu7dkTgerV06Tsb9LD68ULhw8fQFBQRdy86SUlGZcokSEd4NOvTebR5GQPXLzojXffDYbn2fN4GRvQBntQHRfsMrOzAP4oUQIvtmwJv6NH4f3PP1J/0rdqvX0QkRqMCISjIq7CD6bp7TWBgYidNk06gLZFevfuLQUkyBIUFARmFtJZnZvCLEbWQ1++fLlqGe+99x5effVVu5e2bZs/hg9X63zhwmh07pxk91hUelinTvA9dUrXl5S6948dgzaXcbN/M/b1yC/3uri4OLDkRIkSJXL9b8E+Dblf6/xic66suXPnzknBbrLw+U6nuimmFLYhIw1rhpPRilK1alU0atQIzZs3l9itxowZY3W7DRtexLFjeir40qXT8fPP97OF6MMzMhKBS5eiwLp1EnW5JckoXhz3TpyQKMplYe3yoEWLpPIeWc3i5rMkrWpVidXL66+/4JmcfTT1VpWQgw1I+57csiXiR4xAutNKAOTgBrIwlbjHZQE80dUhBITNOQSb6OQEBITtOQFEMYSEgHCgC0MQCFhGQDjQhYUIBNwcAeFAt6zguDgPkMa3Vi3T1IU8rDq0dCm0O3eiUkgIqpYoAa+HD+Fz4QK8b95UDa5lZiPp0ek4N6BRz2kzS+jdGzEff6zLtMzK/OLFPCvoib6OICBsDrh69SqaNWumgu8mM5x4n3kkS5Yswfvvv6/7zIw2OtUrV64MOqwAZlrPAAzqiZcrl44RI+KxenVbnDt33EhFJUuWlKjQbZVu3Yrg2DF97fOnn07F9u2ZjBh03JLmNDY2Vjfc999/j/r1rfNt7NnzA4YMWYWMjA7w9n4RxdL90R3f4i18hDBYPpS3de1m24WHI27gQMT37aujh7dlTDoyNm7cqGvarVs3o8xvW8bJjjak6u/VqxdYx1UWMhjItLr2ztmpUxGcPKnXe/Xqqdi9+4FD7LC+v/yCsK5dVUuImToVCYoyBvauLy+0d/d73d27dzF16lTs3btXcv6REeLbb79FKSu0zXlBd3l1je5uc3lBL0eOHEGPHj10Sy1cuDDOMyDXgjCA7s6dOxKdO9vLcuzYMfA5o5QZM2Zg165dunt9nTrtcPHidlX5jQkTYvH66/FOhYt1v4OWLEHgl1/C0w7K+ZRGjZBWvTq8bt+G140b8LWChaVF0yGf0rBhZo3vmjWl/0rMMxSNJrMmeUREZpksOtX57+ZN+P3yi5Qt70xJhQ+uo6wUqJdZ9Tzzn1+pUPR7MwDe4cHWf6elpsIzPl5au/SPUYb8LwAP5XfcXng40kuVQkbp0tAGB7s9Vbs5XYl7nDOtWIxlCwLC5mxBSbTJDgSE7WUHqvlzTOFAz596F7u2HQHhQLcdK9FSIJAnERAOdMtqGzMmBJs2BWDMmDiMHh2vTIBAxKVL+LtrV3SMiZHcUDklrFPHbImMYsWk2ns7f3sc8042w4vYjsmSM8y8pFavjriJE22iGrZ1P+LF3FakRDtnISBsDqCzvEEDkp3qhdncykzmTz75BPPmzdM1eOGFF8B6108//bSUlaaXVQD6mFBPFICvACwCoK8dytqsPHy3Rf780xvNm+vriLPP/PnR6NJFn4nMfXA/srD2NuuCWxM6BegckMXHpypKldqL+9eD0QHbJGd6W+yGP1KsDWXzdU1ICDwZlDB4MO7GxIBOZ3uEzmk6qdnPz89PwpEBDa4ihjbDLMivvqIN2C+7dvnj1VfVWejff/8A9eubzua3NkPhnj3hr3DuZxQujPu//AJtUJC1rnn2urvf60aOHIktW7ao9MMgkzfffDPP6iyvL9zdbS4v6IdMMYMGDdItVVVqxM4NMEildu3aql4sIxIWFoajR49KwXRHj3bBihWhujZeXlr8+us9FC9u3/PN0tJ8jx1D6MiRkkM6N4TPi7hx45DIciBkmdFqJfaXy5cvS/XmmbX/559/4saNG3jyySdRunRp9TK1WolVLGDLFviePJnpZI+IgIedGetpFSpgL1rh82sv4BCeRaJU8V0vlSqlYcOGSBQt6jzscwNvV55T3ONcWTvuuTZhc+6p17ywK2F7eUFLeWONwoGeN/QkVpl7CAgHeu5hL2YWCOQIAsKBbh7m7dv9MWxY5uE/q+m1qfo3Phj6B0ql/AMf1hr85hsEsmZuNghr0t3TakEX0+MAHgI4CiD2pZfQ4rPPdFSGTGSvU6cY7t/3ggc0GIc5GFD+J5Sr6gNN4cKqf6lPPYWMcuWcvlrxYu50SMWAVhAQNgfJAU5HuFJI6x4aqj8EnzJlClasWKFrQsctHaSkdlXTvRcCwPqpZc0gz4Nc1k0fDSAeLVu2xKpVdLpbFrLJjh4dgq1b9YUpihTJwMmT93TVLDgCqei5dlk+++wzdO/e3drwUpb6rVu3DNpxrtMAMp3SBRGL9tiB2jiN//ncRGnNBTyZcQ2ByKS6tVU0Pr5I7N0LiW+8gaLVqknd6Jiw14HOfqdOnZL+tWjRAk888YStS8iRdqtXr8akSZN0c9WsWVOi8ndE+Hxq0qQobtzQU++2apWEr7+OdmQ4+Jw5g/D27VV9Y957DwkOUMw7tIBc6OTu9zqWlYg2qAssB/rkAtxiSlGr1SVsgCwMY8eO1a2Ffyc//PCDw2sbMGCAxPJAMWQ9uXTJG+3bhyMlRX/s07VrIubN4y+PLAgdzhcuwG//fvjv3w/f334zOxid2xmPP46dly7hcmoq+EvFtqIomXXS0ypXRnrVqlL5FTq0L51KR8StNPgiFX+jPI6gMdp81QLPts3MNCcd/uDBg/HHH3/o1vTSSy9h27ZtOhr8GjVqgLXl+e9/rGNuSrRasA47M9RTTp3C2rfektbO+u3kC+gxYgSKlykDrY8PtIGBSKtVCxuOVcCYMfr3NHnY4sUzMGpUHHr2TISZUvdZUIboqkTA3Z+rQtuuh4CwOdfTSX5ZkbC9/KLp7N+ncKBnP8ZihryNgHCg5239idULBKwiIBzopiG6e9cTuxotRbvkzXgMtxCOTLphe4VZ4jw48b5922JXuuF/BLAewNC1a5EWFoYWivrGcudp06ZJhz6y/PKLL7p2DVONfejQfVSoYJ9zyN59iYOIrCAm+mYVAfFjEHj48KGUMaWU06dPo3jx4rqvWBN18+bNus8jRowAnerPP/88Ll26ZKCGSgC+Z/VUC+rhYfMo9OgRjk8/nWNRjaxfPmpUKE6f9lW14wHx5Mmkj9cLneXMhDN3nzM3UcWKFZGYmGjiMrPtOJ6ePlzZyAep6IPVaIrD8IQGqfDFXRTHPjyHeygGH2QevLP2eRmPm+g4NgxlBtSHtnBhuLvt7du3D/369dPBxR/Lv1lwfFj7W16+PBBTpwbrmnl4aHH8+H2ULp1hravJ64X794f/j3xaZkr644/j/pEjegpeh0Z13U7ubG8s28BMT0OpW7euUVa662rI/VbmzjaXV7S1fPlyqbSBLGRp+e677xxeflpaGnbv3g3WUie7i1zq5cYNL3TsGIaICH3pFx8fLQ4fvo/HH3fsHs1F+pw6hZBx4+Bz7ZrZNTNQOKlLF8SPHIn0J56Qnq1cG6nq+eT+B0AJCzvOKFIECYMHI8FEGZVbt7zQokU4EhP1/GDFimVg3777KFxYiz59+uCnn36yGU8G602cOBH16tUz24fsMj176t3+/v7+uHLlCrwVtdv/+ssLbdqo1xUUpMGECXHo1StBOM5t1kjWGop7XNbwE73tR0DYnP2YiR7OQUDYnnNwFKOIGujCBgQC1hAQDnRrCInrAoE8joBwoJtWIDMn/+3wNhr8/rVNGk545ABnLuRz3bujRI0aSKtTB2nMVPTwAOv+ef/1lzQWHeqshc7/RsfHo3mbNmA+nkwyTGcFa4GaOqiZOXMmevfurVvT5MnBWLVKT//35JNp2LcvwqY1O6uReDF3FpJiHFsREDYHJCUloUKFCirISGdepkwZ3Xe8Vxw4cED3+a233gIpkzt27ChlQBuLB2bM+B179lTAwYMBZtXh45OM8HBvBAVpERioRViYBpUrp6FgQS1Kl07Hf/95Yd68goiLUxe38PXV4uefjZ2nr776qooS/o033sC4ceNMzs/asFw7s+g7depkwWRIb/85gFq2mpWqHddao0YaXnstDs89p6eAd3fbu3jxohF9/iuvvCJRahctqqbitwXYhAQP1K5dTGULY8fGYfx4dRCFLWOxje+vvyKsc2dV8/hBgxA3YYJbUrm7s73RUdamTRsj1WeFrtpWOxLtzCPgzjaXV/Q+d+5czJo1S7dcsrTQqe5MoZP55ZeL4Pp1PUMIxx84MB7Tp8c6PFWBtWsRPGUKPCwwdCU3bYrY995DeiUG7mUK7Y6Z8qSvp9QFsJS8OF5eCKheHRmPPSbV7k4vXRoZZcsi5ZlngADz7ylr1hTAxIkhqn106JCEt946J5W/IYW7PcL1sbzMc889Z7LbggULwNryspAhaPv27brPZGRp3z4M586pgwoXLYpCx47J9ixFtM0iAuIel0UARXe7ERA2ZzdkooOTEBC25yQgxTAQGejCCAQClhEQDnRhIQIBN0dAONDNKzho3jwU+uQTqxbAnM2RAOSKwsxSGD2aVMfWhTWLn332WVVDUgvSOValShWjAVjPuGvXrtL3dPI//XQxREbqM0cmToyVarXnpIgX85xEW8xFBITNQaIOf+yxx1QGwYyqSooD6fbt24O1TmWRA3CYJcVsKVOyfv16qY56p07jH93ZWIe1YJYNz9tbi1mzHqJ7d33tc3nQ8ePHg/PKwtqv77POuIGQwnbgwIFm1+Lr64vUVGV9bb7G0tnKTL6aNu2hf/8EdOmSiKpV01Q083Jnd7e9qKgoVK9e3QirOnXqYOvWrTZhaNjorbeCsXKlPtCLVLXbtkWgVCkHarxqtQhr1w6+586ppskID0fcxIlIJPW/l/6Z6NCCXahTXrQ3lpc4fvy4xJBhGOSjhJbOpWHDhhmhzfvP1atX4eEhfobmhinmRZvLDZyyc87p06dj8eLFuim6dOmC+fPnZ3lKluvet88f331XAD/+6G803jPPpGDt2khLfmnza0hNRfC77yLQQnkXTWAg4keMQPxrrxndp2l3ZNlS7puTFShQQKpNbng/YFY9M+rNCf3jffsWxk8/qffZtu1q7N7d1yEsmzVrhrVr15rsaxgI2L9/f3z44Ye6tt9+G4CxY9XU7cw6/+STGIfWIjo5joC4xzmOnejpGALC5hzDTfTKOgLC9rKOoRghEwHhQBeWIBCwjIA4uRAWIhBwcwSEA928ggM2bkTomDGqBjEohFt4DJfxJE6hEnZhDi5AnTnAurasI2uL/Prrr+isyKYLCgqSKP8yMjLw+OOsfq6WJUuWgE4xHgwtXRqIadP01LhseeTIPZQr5zjtoi1rNmwjXswdQU30yQoCwuYy0StXrpzKYUyKVtbtlKVJkyb4+++/dZ95MP3iiy+CDmo5y8tQD3PmzMHNmzfBYJ1HswD45lE+mGNaK1MmHZ9/Ho1atViswljoLOe9TRYGCennz/yW2WLM/FLWLFWOxIN0OuRMZbRmtuP9tCU5QgC0AFBUchIkJXmCtOLVq6dJNKrNm+uzzU2t1d1tjzjT6ZlMT4uBXL58GYUKFbLbCC5c8Ebr1ursdX9/DYYNS8CIEfESi4E94r9jBwoPHWqyS1rVqoiZNg2pDchAkPclr9nbjRs3wFrCd+/elQKd6FSiPbGueXCw+n1l4cKF+Pjjj00qiX/nBQtmPXAn71tAzu8gr9lcziOU/TNOmDBB5ahlZvYHH3yQpYl5Hx40qDBu31ZnnMuDkkVm8+YHKFTIvvsx+3s+eIDQoUPhd/y40RozihdH0gsvIKVly8yscTMFvml3169fl7LDDYVsFYULF5a+puN8+PDhYLkRBnYx69tcoM69e55o0aIoHj7Us+F4eCRAq+X7Bv/9aTQXA8gmT54svSOtMggG4G80lr+RKfCVnUnzfusWecgy5dNPP0WPHj2k/+fjtEmTovjvPz32FSqkYc+eBwgIsB/vLBmC6CyCcIUN5DgC4rma45CLCR8hIGxPmIKzEBAOdGchKcZxVwSEA91dNSv2JRB4hIBwoJs3Ba8bN+B35AgySpaU/m07Uw6vjitr0IEHWvo6hbzIQ1/S4Jo6YDGczTCjkk7zX375RWr2xBNPICGB5PB6WblyJZ5++nlMmBCC3bvV1IXVq6dKhzE5LeLFPKcRF/MJm8u0gcqVKyMuTk+FvWXLFrB+sCx0pkdGRuo+b9iwAXSqv/baa9i0aZNJQ2I2OO9LPLDWCzN6ybNBWnXjwB5LFsls7g8/jJHo3c0JneWfKNg+WAeVVKlKMQw2MhxLrtVNBo/GjRtb/CPh9RUrViIgwB/x8R7w89OyqoZNkh9sj/T4dGQYClkL/ve//9mEk2GjF14Iw9mzaupatmFdWjKndOuWBE8147/FeYLmz0fBOXPgQSoWA2Ft3ejFi5Hcvr1Da3WlTnnN3ph1+aOiRr2MJWnZDx48qMoYZVmAdevWmYT70KFDFrPXXUlH7raWvGZz7oY/90MH8bZt23Rb4zN70qRJDm/14UMPtGxZFHfvmmbnKFw4Azt3PrCv7rlWC+8//oD/D7wNgBsAACAASURBVD8gcM0aeN25Y7S++GHDEDt5MqCoA25uE7LdnTx50qiElTI4kIGAzNCXhb+5GLSsfPdRzrFtmz+GD890vhvLfgCTAZyULjHbnY5z+Tl3+/ZtPEOnv0L27t0rsWsoxRRzCx38Tz75pNRsxYoCmDJFTSe/enUkWrSwHLDnsMJFR4sIiHucMJCcRkDYXE4jLuaTERC2J2zBWQgIB7qzkBTjuCsCwoHurpoV+xIIPEJAONDtM4U+fYzpADOzGFhn+B8AFwAkSg6oqlWr4e+/vaR6wH5+wFNPpRpRApO2mA4rWZ566ildLeDatWtLWVxKmTr1Jyxd2tTkIRjpkV95JdG+DTmhtXgxdwKIYgi7EBA2lwkX7xcPHuiDZmQHOa8xk5j10MlmIQsPhpldxTITa9asMYn5888/b9IBpm/8GObO3YrChR+XnM/x8Z746y9vsJ5qdLQnzp/3kRgymNE9YEAC2re3XttzxYoVmDJlim4KHlgrHfwXLlzA2LFjpcwvc8KSF3TcMTuNh9/KfSv78L7K+25goJ5S3B7jyw+2RwYAOZBLic33338PZtk5IqdP+6BPnyKIiTHtJa9XLwWrV0chKMj2bDyva9cQPH06/PftM1pSRlgY7p04YTbb0ZE95EafvGRvdJD36tXLLEz8u2vatKnuerdu3XDs2DGT7b/77juTmai5oYP8Nmdesjl31U3v3r1x4AB/V2QKn48jRoxwaLt8Ho8YEYpt24zrhZMJpG3bZEyeHIdSpWxkr0pJQdCiRSjw7bfwvnnT5Jq0/v54OHs2khQMW9YWr7S70qVL499//9V1+fLLLyUWCwoD7BikrJRq1apJv7vMyciRIdiypYDJy56eGfD2fh4FC56TaPJJ066Uhg0bgswaspCWnYFCSmHAzyuvvKL7yt/fX2IT8/b2lsptNW1aFDdu6LPPGzRIwcaNkRBVKqxZRfZcF/e47MFVjGoeAWFzwjpyCwFhe7mFvPvNKxzo7qdTsSPnIiAc6M7FU4wmEHA5BIQD3T6VnD3rg/btw6DVmrs9sqbrNVSs6AWt9nFcu6ZPayxXLh2ffvoQdeum6g5NPv/8c3z00Ue6RTRv3lzn2GJtdNZI10tdeHv/gvR04wySQYPiMW1abK4cxogXc/tsSLTOOgLC5jIxpKOZGVKykKGCNOeU+Ph4VT10fkenKFkuWGeUB9KOipJO1dExlP02b96MUaNG6b5i1hazt5hdP2vWLCkbnTXfLQkz6xlAQCEFLGnoDYUH4V999ZURlbQ9e8gPtmdYy1XG54svvkCHDh3sgUvVNjLSE7NnF8SaNQWg0Rg/Q999NwZDhqhZV2yZzO/QIRSaNg0+V66omkfPmYOkl1+2ZQiXbZNX7I2BKwy+Ye1yc8K/cVIjy1KvXj2Vk0zZb9GiRejYsaPL6sWdF5ZXbM6ddUDbP3XqlG6LpCnv06ePQ1vetCkAr72mrr1NuvYhQ+LRrl2yRXYYowlTU1F4wAD4Hzxodi0ZJUogavlypCnKydiycKXd8Rl+XEEH/84772Do0KFSSRo+6w2FDuu//vrL7DSJiR54/XV/7NqlxkHuUKJEOnbtikDRosYBXK+//joY0CNLp06dwN9uSlmwYIFEJS8LA/U2bNiOnTv9sWFDARw/7qdqv3HjAzRsmGoLLKJNNiAg7nHZAKoY0iICwuaEgeQWAsL2cgt595tXONDdT6diR85FQDjQnYunGE0g4HIICAe6/SpZvDgQ06era3naM0rx4hmoXTsVdeqk4vz5Jdi06W1W9ZOGYO1QHsRQWOv8zJkzj4bm4ctpAGraQNIuzpnzEK1a5R4NoHgxt0f7oq0zEBA2l4kiszmVh8bKLC1mb9FBpRRmcLMOMZ3Sc+fOdVgVrPNJHThL9u/fj759++qGK1GihETJyiw8QxYOc3MqD7UNnQ/sw1qmLVq0gEcWU77yg+2ZywxmrfpBgwZlWe1Xrnhj+vRCOHDAXzVWs2bJWLs2yrHx09NR5OWX4feoBAoHSatUCRH79yNXIssc24VRr7xib8uWLQOdXJaEFNSkoqakpKRITBFkyjAl7777LoYMGeIkFMUw9iCQV2zOnj3ltbZ8VjGDWRY6bPmMs1fu3PFE8+ZFERenf16HhGiwb999lChhOSjNaK6MDISOGoUABbW8YZuUBg0Q/cUX0ISH27tUVV3q7t27Y+PGjboxBg4cKNG2M0N85syZJsdmwHFAgHGWvdyYAWAffLAeAO8rw1lwSzVOkSIZGDw4AY0b896ULtWC5+sC2XrI2iNLqVKlwJIySlEHnXmiZs1NuHr1RSQkGL8nkY2MdPlZfBWxG1/RQY+AuMcJa8hpBITN5TTiYj4ZAWF7whachYBwoDsLSTGOuyIgHOjuqlmxL4HAIwSEA90xU5gwIQZr12bWtsu6kIKZNPCn0ahRRzRq1BkPHnhi69bjiIxkNt5uAGUe1enTz/bss8n47LOHKFbMzkOwrC9YNYJ4MXcyoGI4qwgIm8uEiBmfSlpzBt8wCIdCilNSnSp/QJOGlNgZMl9YBdyggZJa1d6+ptoz006ZbUp6dWZwsea2obDeqbLuu3ydjl06eCmdO3c2OuC2drhu6z7yg+2tXbsWEyZMMIIkq3V4DQdcvjwQU6fqg9ECAjS4dOkufI1LpdukHr/9+1FEEYjBTpHr1iHl2Wdt6u+KjfKCvUVGRkpZoTExMRYhpJPpvffek9rw75EsO+aENaDffpvBhUJyGoG8YHM5jUlOz1enTh3cUdQUZwBYy5Yt7V7GG2+E4Jtv1NTlS5ZE2VRaRTWZVovgt95C4KpVRmtIL1UKya1bS/9SGzVyOGBJaXdvvPEGPvvsM91cfJdhkA7p1c1lmvM9gsF3poSBOgw4ZAZ7pgQgNPQcoqMrmMU0MFAj0dqHhsbixIkPABwFQIaNh/jtt9/Ag2RZ1GxAzETXO9wNJ1i6NErK/BeSewiIe1zuYZ9fZxY2l181n/v7FraX+zpwlxUIB7q7aFLsI7sQEA707EJWjCsQcBEEhAPdMUVk1rv7GMA4ALUBMJOuHABjenXHZrDcq3r1VGzf/gA+eob47JjGpjHFi7lNMIlGTkRA2FwmmGqWCmD27Nno2bOndO3o0aNgFpcsISEhurqhpETPinPK2Q500j4b1h01NBc/Pz8pe5V1YOlcj46OVjWhw5dUqxRTFOTOWnN+sL2oqChJH3SMKuXll1/GnDlznPaXHBXlierVi6vG27z5AerVc5DaVqNBePPm8FGUPklu1gxRa9c6bc05PVBesDdmlpMxwpowsGXhwoVSs71792LAgAFmu3Tp0kXKNhWS8wjkBZvLeVRydsbKlSurAsW2bNmCunXr2rWIy5e98fzz4aqSU126JGL+/Id2jcPGgSx98u67qn6aAgUkqvbUxo0ddporB1TaHZ3ndKLLwrIuZLiQ329MbYBlX9hOKQwkZOb5jz/+KJW1UcqyZbvw3nvP49YtfW1y68CwTvxvKFcuDOHhJZCe7onk5AxcunQBwD0AzLx/xuQwhQppMHx4PF57LV5kn1sHOltbiHtctsIrBjeBgLA5YRa5hYCwvdxC3v3mFQ5099Op2JFzERAOdOfiKUYTCLgcAsKB7phKduzYIdXjk6VcuXKIjU1DZCQzEmoAeBwAM8tJwX4aLVsexunTdfDwYdZpj318tFKtvipV0h1bvJN7iRdzJwMqhrOKgLC5TIi6du0q1TWX5cMPP0T//v2lj7t27ZIcybKULVtWcqpTvvnmG9XhtDnAeRjdunVrFd171apVJeeXM+X+/fuoVauW2SG9vb1x4MABlC9f3uS++SVpXUn5Tjlx4oQuE5+fmb3GoAFnSH6xPeqEbAb//POPDjbSCtviKLUHZzp4Ll3SR4KNHx+LsWPVjg57xiuwZg1CFHS77Ht/716kV1WXP7FnzNxs6+r2dvPmTTRq1AgajZ4Jx9fXF6mpxkEQjRs3lu49lEw6ZWZ1mhZmtG/YsCE3oc+3c7u6zbm7Yvi39Pjjj6vKG5hyDlvCIS0N6NQpDGfP6uk8ChTQ4Nix+wgPt4+1ym/vXhQePBgeGXQeZ4rWxwdRq1YhpWlTp6lDaXesOc5SIrKQeYZ10S29e7AP28iSnJwsBds9fGgcMFChQgUcPHgQERFeGDiwMM6ccZD2xMruPT21aNYsBV27JqJ162T4q6uWOA07MZB9CIh7nH14idZZR0DYXNYxFCM4hoCwPcdwE72MERAOdGEVAgHLCAgHurAQgYCbIyAc6I4p2NABVaNGDZQuXVpyWpmSMmXK4Oefj+Kff3xw6pQPTp/2xalTvvjzT/tTyLPqYHBsx+Z7iRdzZyMqxrOGgLC5TIR69eolHQLLMnXqVAwbNkz6uH79eowfP153rWbNmti5c6f0edu2bSBFsjUZOXKk5IQn1TLpmYn78uXLJep4ZwoPulkP2ZzUr18f33//ve7yW2+9hZUrV6qaf/XVV2jbtq30HelaWbOUVOSPPfaYRP1Kx78zJD/Z3rp16/Dmm2/qYONzbvdulhRxnrz7biF89VWQbsAGDVLw3XfqzHe7ZktKQrG6deGlYChIbtoUUevWOSVL0q61OKGxK9sbSynwb2vWrFm6nfr7+0v3oLlz5xrtvlKlSvjpp5+k78eNG6dykLMf7wOyPPHEE1LQjJCcR8CVbS7n0cj5GWNjY40yqVlzm7W3rYlWCxw75osFCwri55/9VM3t+u2g1cLn3DkUWLsWgSYYPKK++ALJHTpYW45d15V2xyA4PvftEeU7APsRM7JemJJ3330XQ4awFjrAuIB9+/xx6JAfjh71xbVr9v8uM7fOd96JwdChLMUlxJUQEPc4V9JG/liLsLn8oWdX3KWwPVfUSt5ck3Cg5029iVXnHALCgZ5zWIuZBAK5goBwoNsGO7PxWJeX/yg8qOEBjCwNGzaUnErTpk0zOyAzRZlVopTKlXsiLq4vgKcAFEb58iEoV64AwsI02LYtFklJanrbRo2SsXZtlEtQt8v7EC/mttmQaOU8BITNZWI5cOBA/PDDDzpg6ewcM2aM9Hnx4sWYPn267hqd4HSIUpjN1q9fP6sKkTO67t27JznqmZFOJ2p2CB3oSgeacg4lPTu/p/OcTnSlmKK4TUlJAbPXvbycV1ojP9keaW9lRgNiXbx4cZw+fdqp6t+71w8DBhTRjenrq8WlS3cQEOD4NEGffYZCs2erBkjo1Qtx48ZBo6hb6/gMOdfTVe2Nzm0G2BjWPSdrAcssPPfcc0YgFS5cGOfPn5e+79SpE06ePKlrQ5YIZXYp9822LD0hJGcRcFWbyw4Url+/risrQGcr3+U9PHL3+IPlRurVq6fa7uXLl1GoUCGzENBxvmuXPxYsCML588bZ1OXLp2Pv3ggEBGgtw5iRkek0X7kSPn/8YbJt3BtvSPdSZ4vS7shsQcaZDEXWu3I+ZqTzecTyL7IoS9jwuz179mDQoEFGy2zXrp2kc5aFMSUpKcDdu17499/Mf0eO+OHAgQxERjJ93Hbnerduifj004fwzDrxmLOhzvfj5ad7XL5XtosAIGzORRSRD5chbC8fKj2btiwc6NkErBjWbRDI3V+QbgOj2IhAwHUREA5087ohVe3PP/+Ms2fPggdaixYtQseOHaUOzK5SZl3x8JeOKx7MmBNmfzILVBY6d+g0YrakLHRYVKlSRfrIQ+itW1mbj3UAYwFswY0b78DbnnJ9OWB64sU8B0AWU6gQEDaXCQezyJlNLsvo0aOlzGsKD5NZR1SWF154AV9++aX08dixYyp6VFPmxQNqOrB8fGw/MM6KmfLeGBERYXIIw3snnfnMvlfKoUOHQFrW7Jb8ZHt89tFuZGEwAindiYGzJDbWA1WrFodGo//JsWHDAzRp4mAddAAeiYko2qQJvO7eVS1T4++PhMGDET98OLR5xDHrqvZGtodz584ZmQHLSDA4h+Uljh8/rrpOxyQdlrSj6tWrIyoqSnedAT98h1IG0SxZsgTt27d3lqmJcWxEwFVtzsbl29yMttayZUvJJmVhmYFXXnlFenbSPsmUYEuwmc2T2tDwjz/+kNalFDqULQWCffhhQSxaVNDk6F5eWmzb9gA1a6ZZnj0tDaGjRiFgxw6z7RJ79MBDBic58RkgT2ZodwxKvnCBtcWNhY7xv/76S8XA8/bbb6uYdQwZVDiKI7Xk2e/OnTuoU4d09a0BsJQMS2gRT/kff5gVlRzs/fu3xauvlkXZsnrKexvULprkIAL55R6Xg5CKqawgIGxOmEhuISBsL7eQd795hQPd/XQqduRcBIQD3bl4itEEAi6HgHCgm1dJnz59dHSjbEUq4/fee0/qwNqdrOEpC7OueOBGqnZzsmbNGjRv3lx3mRnpPGSWhYfLPCySM67oDFPSFrMdHfmuJuLF3NU04v7rETaXqWM6nDZu3KhT+NChQ/HOO+9In5l9TqeULLzXzJs3T/po6Bg1ZTF16tTB1q1bc8yYzNHT0ilO2mel8yA6OhrVqlXTrY2ZZHT2ywwh2bno/GR7pjIhiTMziZ0p7duHqWrQjh4dh4kT47I0RcDmzZIzyJSklyyJyO++Q4aF53WWJndiZ1e0N2aFGrLpyFtmGZunnnpKcoSvWLFCxYLBNr/99psUlEMHulIOHz4s3buUJSnoyFQGKjoRVjGUBQRc0eayQ2Fff/016HS1JsrgWWttnXGdzAxkaJAlKCgIV65cMTv0mjUFMHGiaaaG0NAMzJ4dgzZt9OURTA5kxXmu9fBAwpAhiCVe2eA855oM7Y7BgKtWrTK5XAbM8TcXHeKyjBo1CpMnT9Z9/vzzz/HRRx/pPvP3F3+HOSpkBbD2G4zBQZcuXcqRdxFH9yH6GduaRqMRsAgEshWB/PJczVYQxeAOISBszyHYRCcTCAgHujALgYBlBIQDXViIQMDNERAOdPMK/vTTTzFnzhxdg7p16+oOayZNmgRmqMvSt29ffPzxx2jQoAGYKWJKFixYADraZeHBsLJOKA+USTkoi2GNUH5v7fAmN8xVvJjnBur5e05hc5n65wGz8kCYdNvMAKVMmTJFcmDJwoCgGTNmSB///PNPVTCPKWuiA54U8TklphzoZPYgfTup4w2FB+M8IKcYHpxn55rzk+2RJYU0ukphMANrWTtTPv64IBYu1GdP1q6dKmVMZlUCFy9GoVmz4KGorS2PmVq7Nh5s3gw4kd4/q+s11d8V7e3u3buoXbu2ye2SocDXN5NCmo72smXLQumcYMkJMk307t1b159Op2vXroEOTWUZnJIlS0p1jBlcSBXGx3uC2bQkxaDaPDy0ki+P/+/iaswO08i2MV3R5py9WQZ4NGrUCLRla1KgQAEwMKRixYrWmjrlOu+xfF7LYql0xp9/eqN163CkpqqPbMqWTcerr8ajR48k67TtGg1CR45EgILNRp47vXRpJHXvjsRu3ZBhUILKKZtVDGJod99++y1ef/11o2nkcjSG7zhkpfnkk0907Q2DCPn7i7/DHJX169dj/PjxUncG9Jmil1f+TnR0HtEv+xHID/e47EdRzGAPAsLm7EFLtHUmAsL2nIlm/h5LONDzt/7F7q0jIBzo1jESLQQCeRoB4UA3rz7DQyx/f3+QWpHZU3TYbObh+yNhLVDW5GXGODPHTYmhQ6pDhw6qerKkY1Zmw+zYsQPMKFWKcKDn6T83sXgnISB+DGYCyYzNZcuW6VDt2bOnRN1OeeONN/DNN9/orikZNExlFitVwwP7/fv352j9YUNKZ9Z7Zd1XS8LrdK5VrlzZSZZlfZj8ZntVq1bFw4cPdcBs2LABTZo0sQ7UoxZJSUlSrevHHnsM5cqVM9nv8GE/9Oypr4NOJ+nly3cRGGilXq8Nq/D87z8UnDsXBTZsgIdBPd2YqVORMGyYDaPkXhNXtLfTp0+D7y+GEhwcLGVfKoXZ6A8e6IMhWGP6xIkTKucTS9kwA51Zti1avAqA7BKPAyiDhg37488/Q/DggZdZJXh6atGgQSqmTo1F9epWaKpzT5V5ZmZXtDlHwaOjnM9E0rTTKU3nK2X58uWYOnWqzcOS3YTBIHx2ktKdgV3ZJWR+YQknWZ544gkcOHDAaLq0NKBzZzV7BxtNmhSLESPibQ4qCVy+HMEGWLDcxcOFC5HcunW2ZZwbbsjQ7hhUY+pZw0AbBtcZBiGzhNbSpUt1wxq+A5H2/f3338+S2hh8yPtZrVq1pIBq2aEuD0pWoDfffDNLc4jO2Y+AO93jsh8tMYMzEBA25wwUxRiOICBszxHURB9TCAgHurALgYBlBIQDXViIQMDNERAOdPMKZv1DQ5pRZk+ROpiZnqxXLgsP05gpwXrmPMBh7XQ64JXCwxxmlVPi4uJAx4Qyg2Ht2rVo1qyZrguv8ZCITnsKM0s5r6uJeDF3NY24/3qEzWXqmKwXCxcu1ClcmWHFA3glBftrr70GMmdQTN3b6DRnEA8dBC+//LLTs4ytWSXZOJR0zXRwtObhvYtJfrM9OpzoyJCFWf9KeuGYmBgwU5A0+vyegQ+y0HnVpk0bXL16VQo847ORdW0NJSnJA5UqFUdGhv5nx+bND1CvnuN10A3n8PrrLxTp2xfeinrHmuBg3DtxAtqCpmsHu4LpuaK9bd++HcNMBB7IgYRK3J577jmrgTCdO3fGjBmfY8yYEOzeHeAw7H5+WixZEoXnn09xeAzR0b3ojZWlkBgES0d0eHi4lH1+7949nboZ6EFmDd7LbBGWTmEwSHYI2a3kZzXHJ9vDNoPscK0WGDcuBN98U0C1BGadv/derM3L8rp5E+EtWsAzKUnXh87zqJUrkdq4sc3jOKOh4b2Ov4FKly5tNLRcD57PE7msFhtRH8qSNgMGDMDevXt1/ensHjt2rDOWqhuD88tOezJv0L4YaCHEtRFwxeeqayMmVpdVBITNZRVB0d9RBITtOYqc6GeIgHCgC5sQCFhGQDjQhYUIBNwcAeFAt6xgHrIxc0UWUiAzi4X1hFnDXBZTdMc8rCHlnyw8zGHtdAqp2pkNIQudC8zcIlWkUpi9xwMgUpmSGtAVRbyYu6JW3HtNwuYy9WtYZkKZgUX6dQb8yCIH+fAzHZvM+lSKnAWaW5YTHx8vZdSzRvKLL74oZdAzu9zVJL/ZHp3izCCXhc8wPstkYbAFA8YoDHggI4KsN0NH69NPPw1+Z0qeey4cly/76C5NmxaDwYMTnKp+35MnEaaoLczBYydNQvxrrzl1HmcO5or2tnjxYqPa5kWKFJFYK+icVIrSPkzhwsOYZcs24u23n8HZs5nU71mRkBANTp68hwIFss5ekJV15OW+rmhzjuDJ4NOWLVuqupIpisE+7777rup7MkqxxjUDY+nAZsb58ePHcf/+fZNTOyObOTExEQEBAUbPuS+++EL3W4GTM7CWAbZK+eSTgpg3Tx34U758OvbuvY8AW2NQtFoU6dEDfkePqsaOXL0aKS1aOAJ5lvqYsjsy55A+XxaydDHQj0JnOTO+lUImC/ndpmPHjjh16pTucnYEITNomrZz8eJFcL4aNWpkCQPROWcQcJd7XM6gJWZxBgLC5pyBohjDEQSE7TmCmuhjCgHhQBd2IRCwjIDrnVwKjQkEBAJORUA40C3DyYwq0vTJwsNg1kVnVt358+d139OR1aNHD9VgPKxZtGiR7jtmWcnZosxW/+6773TXWDtd+dmpSs7mwcSLeTYDLIY3QkDYXCYkzAZmLXBZ6CxYtWqV9JF07jxMloXOabkkBA99DTO7KlSogEOHDglrs4JAfrM9wyw+sqgwuIHC4DIGmSmFTlSZUp+MCKTrVsrt27dNBka88YY6m/KllxKxYIGeOt5Zhhk6eDACdu/WDZcRGor7P/8MbWios6Zw6jiuaG+GpSN4Lzl48KDkDDQUMl9s2rTJ6HtmAw8ePBh8x5oy5XFs2qQOHswKiPPnR6NLF31GbVbGyo99XdHmHNEDWRIMA3ZIvU2WFaVjnDThLE1hKLy/tW3bFrGxxhnd5mjVbVlnenq69CxmIC3HWblyJR5X1BefOXMm5s+frxuKAWUMWomM9MSePf7YtcsfBw/6q6by99fiu+9ILW5bCQMychRcuBAFDDLuE3r3RszMmbZsw+ltTNkdKdP524r64u8kBhIwAILCYAdDVq4Ab28c79kT5YKDUXvNGlxVlB9Z0bo1XjJTxz6xZ09kiMxxp+vUVQd0l3ucq+Ir1mWMgLA5YRW5hYCwvdxC3v3mFQ5099Op2JFzERAOdOfiKUYTCLgcAsKBblklX331lSpThRSPpGZv3Lgx/vnnH11nUvgx+1MpdJ7TiS6LnEWSlpYG0kWS+lYW1uVTZqS7nKFYWJB4Mc9L2nKPtQqby9Sj4f2J9yW57jkDdn799VedwuloZ+1WWUqVKqUyhooVK0pOMCGWEchvtmdYR5bMBmRcoZAdRZmNzu+UDnZT2cfnzp0Ds5UNZcWKApgyJUT3dcWKaTh4MMLp5uhz/jzC27RRjZvQqxdiPvnE6XM5Y0BXtDc6vncrghAYEGiuJrQhHTXZCbp06SK15z1oy5YAjBxpKniBDD98x7qJUaOaoWvX8mB2uUYDpKd7SP9lSfvUVA90714EERH6GulNmqRgw4ZIZ8CfL8dwRZuzVxG0T9qpLcIgWXMMT2Rx4T3PlJAthYeJpiQ1Fbh61RsPHnghPR0IDNQiKEgj/feHHzZi+nTWyU6WuvK5rAyEY5Y163zL0qXLcCQmzsEPP/hDozE+mvH01GLZsii0amW9dIHvr78i6PPP4b9vn9Gy00uWRMRPP+VaSQtzdpeamio50Hm/ULLSkBlFWU5E3tAYT0986uWFomlpeKDY5Y9eXmjp6anet0YDj4wMRM+Zg6SXX7bFXEQbN0DAHe5xbqCGfLUFYXP5St0utVlhey6ljjy9GOFAz9PqE4vPAQSEAz0HQBZTCARyEwHhOecAqwAAIABJREFUQLeMPun/SMsnCw9vSAtJR1VEhP5wn1TtTZs2VQ3G70jjLgud5qQiZFYos0OVQkeXoUMrN+3CnrnFi7k9aIm2zkBA2FwmiobOqTp16ujqnjNzjs5KWT777DN0795d99nwfpOVjDpn6DSvjJHfbI/OcmY/ysKs8gULFkgfDamG+R2zz/ft2yc5Onr16mUUlCFTJRvq+/RpH3TooKf/9vDQ4sqVu5LDydlimIXO8SO2bEGaC5ZJcUV7M7y3MGO2d+/eJtXEMjRTp06VAg9r1qwpsRdUq1ZNahsf74HGjYuqnN+FCmlQsuRL+OOPrbrxRowYgSlTppg1g61b/TFiRGHdddrOiRP3UKqUxtmmky/Gc0Wbswd4sly0atVKFaRqrj/f25Wllky1ozOblO+sya2UefPmSeWcKKxJfuuWF9auLYDDh/3wxx8+UnCHZaHD+zC8vN7HzZvf6JqqGap8UaTIVURGPm52qBkzHqJPn0SrEAUuW4bgd94x2y5y7VqkNGtmdZzsamCv3V27dg3PPvusyeVQUywIorwDnAFQ06A1ny4aspCcPAmtzdz32YWAGDenELDX1nJqXWIe90VA2Jz76tbVdyZsz9U1lHfWJxzoeUdXYqW5g4C1X365syoxq0BAIOA0BIQD3TKUPPylQ4CUi7Kw7l7fvn3Ba7KQJpL1XZVimAFTpkwZHDt2DJMnT9bRLLM9D5V37tzpNJ3m9EDixTynERfzCZvLtIFvv/0WY8eO1RlE9erVJVpYChkvrl69qrtGZ2eHDh10nw0d6DK7hrAuywjkN9tj2ZGPP/5YB0rz5s2xZs0a6TOzzU1RH8slTVhSgAFnSpk1axZeeeUVI5D5OK1UqQQyMvQ/PTZvfoB69VKdbpJe//6L8GbN4JmodzqlVa6MCGZV+2a9DrczF+yK9sY6v5GR+gxv2gPtwl75+OOCWLhQXcd50aIoXLnyPuiclKVKlSoSXbM5oe08/XRxxMbqs0vffz8GgwYl2Lsk0R6AK9qcrYrhuzoZDpS1ry313bp1Kxh4Zk3i4+PBetz6sihhKFduHooX74I7d7xw754nkpIMsputDaq4PnFiLEaPjpe+YcY7M98zZTbvtCZHCgjQ4O23Y9G/v3Xnue/hwyjSqxc8SN1gIFovL8S+8w4SbMzYt2NbdjW11+54DzJXc/wGgDIGs98CUNpw7x4emXsfMsSutYrGeRsBe20tb+9WrN4VEBA25wpayJ9rELaXP/WeHbsWDvTsQFWM6U4ICAe6O2lT7EUgYAIB4UC3bhaG9c4nTpwIZlwp5cCBA1ItQ6UcP35cOsiTpVChQrh48aJ0WHfv3j3d95MmTQLrhOZVES/meVVzeXfdwuYydcfDf2ZnyqLMIq9fvz5u3eKRcaYwi45ZebIYOtAZKMT61UIsI5DfbI81Z5X03MqALwZknD592ggw1rdmABnZWwzrB7MuMTOSTclzz4Xj8mXmDWbK1KkxGDYse5yggYsXI/gRFb08X1qFCoh7800kv/AC4OEaP4Fczd4YOFihQgWV+ky9/1i7jzBb99lniyIlRY9zy5bJWLUqCidOnACZDpRy5swZFC1a1OywY8eG4Ntv9XXUGzVKwbffChp3a3owdd3VbM6ePRjWD7fUlwwZn9hRukHP+NIIADPG1WVQ7FmnqbYNG6bgpZeSsGrVKJw7tx5ASxbKUDWl07xlyxS0a5ck/TcoyDpDh9ft2whr0wZe0dGqsTQhIWD5ioR+/aAxKOmS1b040t9eu2OwBAOTTclCAKMMLjDMIEDxncg+d0RL7tHHXltzj12LXeQmAsLmchP9/D23sL38rX9n7l440J2JphjLHRFwjdMjd0RW7Ekg4CIICAe6WhFe16+jwHoeXOll7MGDWHbhgu6LhiVL4th//6naXO7fH6WCgnTfJfbsiUspKWjRooWq3aZNm4wOhg8dOmR0IO0i5mHTMsSLuU0wiUZOREDYXCaYhvVZZZYLXmPJiAcP9BVAmSncpEkTnRYMHeikZWZNdSGWEchvtseyI8y8lEW2Ma1Wi6pVq5qlSS5fvjz+/vtvIzCff/55rFixwiTIEyYEY+3aQN21Nm2SsGyZ2unjNPtMT0d427bwuXTJaMjUGjUQO20aUuvVc9p0jg7kavZGnSrvI9zXlStXEKR4/7G2VybBDh8eih079O4sb28t9u+PQIUK6UhLS5No3pn1K4uSLtvU+Hv2+GPQID2Nu5eXFufO3UVIiHUHo7X15rfrrmZztuJ/5MgRvPzyy+C9SXm/IlPLgAEDcEnxt06GFt6HfHz0ATvW5qE91qo1C4mJnwOwlamCwbJk0eDvA7IteFubxux1Pz8tdu6MwJNP6hmxrA6WnIywzp3hqyjnwj7xQ4cibvx4aAvog06sjpXNDRyxOwZ0KctpyUtk6IEyHJC7NAzF0ors82zWqOsO74itue5uxMryAgLC5vKCltxzjcL23FOvubEr4UDPDdTFnHkJAeFAz0vaEmsVCDiAgHCgq0EL2LABoePGgZSG8MykY1yh0WCgQe1DZS8ehyV6e8ObGWsaDTwyMhA9Zw5utGiBWrVqqSZgvcTvvvtO95071B0WL+YO/OGJLllCQNhcJnwHDx6U6kzLUrx4cV1GMCnZlQ6oLVu2oK6ixvOMGTN0tazZn9nnzEIXYhmB/GZ7v/zyi67OL5Ehk8rRo0el7+g4tVfKlSsHOrpMyXffBeD110N1lwoXzsC5c/eyLRnc58wZhHXtCo/kZKPlaP38ELl+PVKfecbeLTq1vavZmyGzDh3nttoB/Zr79/th1qyCuHBB7YAcNCge778fq8NOTWMNic1g0aJFZrFNTPRA9erFkZys/+m6YEG0lNErxD4EXM3mbF29IVuUt7e3xNJCJyuzlXnfIrV7eHi4VEaC1+2RvXv9MGBAEYtdmCHetWsSnnnmBkaNagggxqC9P++iABjs0R/ARJuXYHdZAq0WIePGocA3+vrqnCypdWtEM1ju0W8cmxeQzQ0dsTuWC5kzZ47VlT0G4Kailcg+twqZWzdwxNbcGhCxuWxHQNhctkMsJjCDgLA9YRrOQkA40J2FpBjHXREQDnR31azYl0DgEQLCga42BY+kJBSrUwceDx9CvgEyP62qBYupBECu8qo8lEnx8gKdBZbk9ddfV9Hj5kXDFC/meVFreXvNwuYy9Wfo3AwJCZHKRFCYKUyngSzMVmdWpywJCQlSbWvWqGbWHh2iQqwj4M62Z4qB5VJkJOobsLJ0rlABm69dMwKrSuHCuBQVZRFELy8vXLt2Db4mao3fvOmFBg2KqfofOnQPFSpkWFeMgy18Tp1C8Ntvw/f8eaMRUmvWxIMdO3KVzt3V7G3Hjh0YOnSoDquyZctKjklrcumSNyZNCsHp08aZuyEhGhw5cg+hofrM4VWrVmHy5MmqYcl4wHemF0ixb0L69SuMffvooMyUTp0S8fnnD60tTVw3QMDVbM4WBTFYjEFjSmGpCJaMcIbcuOGFNm3CERtrWOf8BHr2jEb37nVQvHiG9I+3NgasjRw5Ujc1M91LliyJlJQUREVFITWVWemUCQA+tJqZ3qJFZnkDeypLFFi9GiGTJqm2n16+PCJ27oS2EJ34riWO2B3ZBvgexMDkbwwCBZS7qwngjOILkX3uWrrP6dU4Yms5vUYxn3shIGzOvfSZl3YjbC8vacu11yoc6K6tH7G63EdAONBzXwdiBQKBbEVAONCN4Q1csgT/Z+86oKOqmvD3UkglJKEqRYqANBHpIkVEOkjJgoAU6R2pgtJEigICIlWaSk02VAUpghQBKQrShF9BLAiEkEZ62f3PLNnytiRb3m62zJzD0bx379y533x5ebtzZybko48g5JaBVAAIBfDEhCc6Avgm957+lzKUYU6BKlNy8OBB1KpVy64+trdyfjG3N8KsXx8B5txTRH755Rd06tRJA09AQIAqOEklkCmwpSuu3irCWX4L3Jl7xiqw3FcqUVrnIIYpP9BfsSgvL9RTKAxK5erPoSzmsmUpJ1As9Ce3Xr2SePDAW3Pj00/j8dZbds4iVijgv38/QhYuhI9e2fnH27Yho3nzAqOfs/GNyl5/8MEHGjyoqgUFC/MSOhhBwcfERP3g49NZS5dSAFLsY2o/UbduXdEhIBpLBzCogkG5cuUMlty8OVAVpFdL0aI5uHz5obMl2hYYl8xd2Nk4Z47dVJ6d2kPoCv0tpL+JUsjQoWHYv19f13IA41GrVg2MGTNG1dKCeEn4zZw5Exs2bNAs/frrr4MOhZBQ0LdSpUqqYPpTqYqBA3cjJqY8fv7ZC/fv+4lMbto0CWvXpqBIETPbEWRnI2DvXlX2uZCVpdGlCAxE7P79yK5SRQpIJNdhC+8SExNV+OuW79c1sBWAI7kXOPtccte5nEJbuOZym2WDnQIB5pxTuMEjjWDueaTb7bJpDqDbBVZW6kYIcADdjZzJW2EEjCHAAXRDVIxloVM/vWMmKDQewBL6UgyAIiwMMRcuQJn7pV3Dhg3x77//Gp1ZpkwZUCBBsCSlxAlpzC/mTugUNzeJOffUwZRt3rp1a423CZe///5bVbpdvxz7+fPnod/33M1pYpftuTP3jP3tozxJcTjHOKxzAbwPYEtgIPqlpuaJvX47Ad3Bw4aJe2NXr56Fb755BH9tYrFd/KpSmpWFEi1awOfuXc0aGY0a4fHOnfZbMx/NjuYbHb6hwzbh4eGq0te0vq4sXrwYS5cu1Vxq164d1lM56DxkyJAwHDhgGMh8/vksfPBBElq3VgcSxUoGDx6M7777zkAzVc7o16+fwXXKEn7lFXEFg8OHY1CjhgU9owvM086zsKM5J8XOiSfEF7XotjOxVX9iooCXXiqFzEzdr0W2A+htoJrWnT9/vqo9yqVL2pznyZMn491339WMp8MhDx480PxMwXUKsj/9m06l3bsCoApWB/D331+qDo7kJ8KTJwjctg1BGzbA5949g+Fxa9YgXefAXX76HH3fVt61atUKv/32m1Gz3wJAHiPh7HNHe9b51rOVa863I7bI2RFgzjm7h9zXPuae+/rW0TvjALqjEef1XA0BDqC7msfYXkbAQgQ4gG4cMP0sdCok+rEJbFcDoCKRxr6UadOmDa5du2Z05pAhQzB79mwLPeZ8w/nF3Pl84u4WMeeeevj27dto1qyZyN13795FQkKCKvilK1evXlUFxVhsQ8Dduaf/t4/QKgwgOQ/Y5qvyMAE/QUDSzJkYfusWduzYYXIG9bKmntbGZOvWQEyZos0ipjEDBqRg3jz9XsK2+dHU7MCtWxE6hcoqayV2164C64XuSL5R9mafPn1UAXSS9957D2PHjhVhMXXqVGzevFlz7e2338Ynn3xi0hmnThXCW28VE90vVy4bEyY8UfUnzysueOTIEQwYQMFEseS15iuvlMBff2l7W8+YkYjhw01XAbIPi1xbqyM5JxVSa9aswUcffaRR16hRI+yU6OBLZGQAJkwI0+j29VXC27sc0tONH4719/dHenq6aGvbt28X/a2mbHnKmlfLsmXLIJPJcPLkSfTq1UtzvWjRorhy5Uq+MBW6cAFhgwbB+/Fjo2OTR4xA0vTp+eopyAG28m769OnYtGmT0S3MADDHxEHngtwzr10wCNjKtYKxmld1ZQSYc67sPde2nbnn2v5zJus5gO5M3mBbnBEBDqA7o1fYJkZAQgQ4gG4cTP1MPCpQSvkgxuR7AC2NZJ/TWPryl74ENia7d+9GgwYNJPRmwajiF/OCwd2TV2XOPfX+vXv3DJ4ht27dUvVYbdy4sYgiv//+OwIDAz2ZNpLs3d25ZywLnZoB/GUEvSIAKFebwt26FVhSlEpVn+r//e9/RjGn8sa6fbR1B6WlAR06FMetW76iuevWxaF9e3FQShKH6ivJyEDJJk3gff++5k56ixaI27rVLsvlp9SRfLtw4QK6dOmiMYn61FMWbWio9kCDflb4+PHjMWnSJKPboOrRbdqIfRkenoNTp2IQGpp/Oers7GzVQYvLly+L9NepUwfUi92YTJlSBFu3BmluNWiQgV27HlvUOzo/n7j7fUdyTiosp02bpimRTjp79OghqpRg7ToKBekqirNntXU42rRJQ40a07FkCdWeyl+oyhQFy0N0+o6TfadPn9ZMnjVrFoYOHYpdu3apysGrhSrJHD16NM9FvP77D8Vbt4Z3fLzRcWlt2yJ+7VrAR3uwJH+rHT/CVt7t27cPI0aMMGo4Pb2pXgBnnzver864oq1cc8Y9sU3OjQBzzrn9487WMffc2buO3RsH0B2LN6/meghwAN31fMYWMwIWIcABdNNw6Wbi/QegtImhFFgom5t5lzJ0qGiUflaM+maxYsVU/YvNKctokUMLYDC/mBcA6B6+JHPuKQGoT3Dt2rVFbPj1119VAfTXXntNdP2ff/4xKMfs4TSyavuewD39LPS6AH4xghbVT5mVe10/MEHVEQYOHAjqQ6wvFCiigJEpuXXLB+3bF0N6urZ8eEiIAocOPUK5cjlW+c2SSVQCucjMmaIpD8+fR05pU28Blmi3bKwj+bZw4UJ89tlnIgP1y6VTgJ0C7WqZN2+e0Sxxur9xYxBmzKBjFlr55JMEvP123iX+dcc/evQIo0ePVvU9Vwtl+NLhDGPvT99+649hw8SVNubOTcA775i/pmUecr/RjuScVOj17t1bUzmBdOqXTLdmHWpRPn58KPbuFR88W7UqDp07p6myxY8fP64KjtNBk5QU45UOjB34GD58OL755huNWRQ0p+oOa9euxZw5lCv9VJo0aYKoqCjT5isUKBoRAb9z5wzGZNapg+ShQ5HesSOg14rBGjzsPcdW3j18+BAvv/yyUTN/BlDHxEFne++L9TsfArZyzfl2xBY5OwLMOWf3kPvax9xzX986emccQHc04ryeqyHAAXRX8xjbywhYiAAH0E0Dpp+JV4YyPvWGU1tWVWlbvd7n6mFUvp3KuOsLlUmlL6vdQfjF3B286Fp7YM499deTJ0+M9jqnwHr79u01TvXz88OdO3dcy8lOaq0ncE//b98bAKjSiq5QTvKfRrLPlQHaXteUQZyUlISVK1eCDpOppXPnzli9mpqfmJbt2wMxaZK4lHudOpnYvTsWvuLkdMmZotp/3brwStSWjY9btQrpJsrOS26AjkJH8o388vPPFGrSCvVqpsxOtbz66qv480/y/FOhgF9HCtDpSWYm0LBhScTEaHs316yZiQMHYvMs224My8ePH+PFF18U3aLAZeXKlQ2Gp6YKaNSoBB4/1q5bqJASe/fG4sUXs+zpKrfR7UjOWQsalUg/c+aM6hAFtTEhXlL7ErXQM0e3moKl6yQlCRg0KBxnzmgzz0lHaKgC588/RFCQuILC0qVLsXjxYqPLqLPLdW/qZ8yrPxNQ/3SyXS1UgYFaXpiSwK+/Rug0ajKllawqVZC4cCEy69WDK5VekIJ3dOBAlwdqVJ4ACDJx0NlSbvB410dACq65Pgq8A0ciwJxzJNq8li4CzD3mg1QIcABdKiRZj7siwAF0d/Us74sRyEWAA+h5U0E3E687gF16wyn381IeX8ooFAqULVvWYJGtW7eiRYsWbsFDfjF3Cze61CaYc0/dlZmZiQoVKoh8R1lxFEDv1q2b5nqRIkVE/VZdytlOZqyncE/3b99bACL1/PAhAHWOdn5lcdevXy/KOKfWJdTCJC9RKoExY0Kxe7c4+3PhwgT06WP/bOLwvn3hf+yYxsTkwYOR9CHt2rEiNd/oUN/EiRMRHx+P8uXLo0yZMqqDOD4+Pti/fz9ycgwz/I8dO4aqVauqNk4lpWm8WqjPNPWb1pedOwMwdqy2bzTdp8MPDRpkWgUgBfIfPHigmZtXgPT77/3Qv39R0TrPPZeNgwcfISQk/9LxVhnoRpOk5pzU0CiVSlAGt7qMf//+/fHVV1+JlqHsblPZyPnZExPjhd69i+K338QndXx8lFizJh7t2hm2krh48aKq3YC+UPl2qtjwzDPPiG4tWrQI1PdcLXTgbd26dZgwYQIiI7VP20GDBoky0nWVeD18iBItWsArKUlzObtsWTw6eBBKnbYL+e3XWe5LwTt6tu3YsUO0JTr8/DdnnzuLm53CDim45hQbYSNcBgHmnMu4yu0MZe65nUsLbEMcQC8w6HlhF0GAA+gu4ig2kxGwFgEOoOeNnG4m3kEA2pzOp/M+BzDCRPa5WjNlZ1F5R12hDC7qL+oOwi/m7uBF19oDc+6pvyiQQAd06L9qOXLkCKjsMZW0VQt94KGWESy2I+Ap3NP920cdebU5kU97nlOuJxXn1u19rpt9ros0Bbp0e56XK1cOZ8+ezdcZycmCqof23bva3r2NG2cgOvpxvnNtHRC8dClCdDJKM+vWRaxOJrat+s2dLzXfjL2P5GdL9+7dsXz5clDWb6VKlUTDT5w4geeff150jR5H7doVw9Wr2nccW/3Wr18/US/oN954A5s2bQIFKI3J3LkhWL06WHSrQ4c0rF0b70pJufm5xi73peac1EbS37JOnTrlqfbq1asIDxeX8jfXjr59w3HsGNWX0kpQkALr1sWjefMMo2qo2kaNGjWQnKyqSaUROlxCh0z0ZcOGDZip0yaicePGiI6Ohj7Pp0yZgnHjxhldM2zYMAR8+63oXuyOHchs2tTcrTrVOCl4R4cP6BCCrrwO4AhnnzuVrwvaGCm4VtB74PVdCwHmnGv5y52sZe65kzcLdi8cQC9Y/Hl150eAA+jO7yO2kBGwCQEOoOcPn24m3kkAlJNGX+dTztXrZnwpQ1+ejR07VrPQW2+9hU8//TT/hV1kBL+Yu4ij3MhM5pzWmRTQosCWWihYSb1AKXNNLZRpevr0aTdiQMFtxZO4p/7b961Sic46kH8CYEruz/lln9Mw/exMailAPdJNBT91vXvwoL+qlLJaBEGJS5ceonhxhV1J4Hf8OIr26aNZQ+nnh/s3bwIOPvgmJd/S0tJUZc91D9yYAyKVyaaM2VdeecUg2/z69esI1ct21fcZrbFxYxzatDHM3DVnfRpD2bpkg65MmjQJ7777rlEeZWUB3bsXw88/iw8qLlkSj54908xd1iPHSck5ewA4Y8YMbNy40aTq4OBg3Lx506zni76SP/7wQfPmJUSXixXLwZYtcahVK+8WANTHfNcubZ0q+r35+uuvjVabonE0Xi1U2eHo0aPo0KEDLl++rLlOnNc9DKe+4ff99yjav7/IztTu3ZGwfLk9IHeITil499dff6meU7rSCsDBfA46O2SDvIjTICAF15xmM2yISyDAnHMJN7mlkcw9t3RrgWyKA+gFAjsv6kIIcADdhZzFpjIC1iDAAfT8UdPvB6ueYU7mHY2lMu6UEUF9RGvVqqXKmrI2MyZ/ax0/gl/MHY+5p6/InNMyoHbt2qqS7WqhfqkUIBs1apTmmvrLeU/njRT79yTuqf/2KRISsAIAdcJuCeADAPQBwdy/gffu3QOVbdcV6rWdlZWlKstN2Zs1a9ZE4cKFDVyUlgbUrl0KKSlemnvz5yegf3/7lnEXEhLwTI0aInseHTiArNrUuMVxIiXfbty4AcrctlaeffZZ/Pfff5rpvr6+qn7ougchYmO90LVrMdy5o60aUL58Nk6ejLG497munTExMWjZsqWq9LyuUGWD6dOng3DSl3v3vNG6dXEkJGjvVauWhe+/f2QtBB4xT0rOSQ0YPSuonL/u3zz9NTp37ozVq1dbtbR+5YKwsBzs3x+L554zbG2gvwD9btBhWQreU0Y5BchffPFFo3YcP34c1PdcLSVKlFBVqmrYsCH+/fdfzXX6vNC6dWuRDiE1FcVbtIDPvXua6zlhYXh04gQURcWtC6wCoYAmScE7evehthS6soAqhc2ahZShQwtoZ7yssyEgBdecbU9sj3MjwJxzbv+4s3XMPXf2rmP3xgF0x+LNq7keAhxAdz2fscWMgEUIcADdPLh0s9DVM8zJvNPVTl/8UZ9RdxN+MXc3jzr/fphzWh/17dsX1KNYLVQCloLq1AtULS+99JKqvzGL7Qh4GveM/e2z9G8gBcorVKiQZ+YzBT327t2LUqVKGThp1KhQ7Nmj7YX+6qsZiIy0fxn3Ek2bwufOHY09SZMmIXn8eNtJZIEGKflG+I4cOVK0OgXFKUBXpEgRVf9zypp97bXXcODAAXzxxRd5Wkq+ooMQJAoFsH17IObPDxEFrOneqlVxePNN67PP1UZQhi493/RFJpNh8eLFRt+v9u/3x9Ch4lLe5849RJky+QdELXCTWw2VknNSA/PDDz/g7bffzlOtteXbqWpBvXolERvrrdE/fHgyZszQ9hiXaj9XrlxBu3btNOroswEdRqEKEboVZejgLR0YUIvPzZsoMns2/E6dEpkSv2QJ0nr2lMq8AtEjFe/mz5+PlSufNh2h59kv06ah2MCBgJ9fgeyLF3U+BKTimvPtjC1yVgSYc87qGfe3i7nn/j521A45gO4opHkdV0WAA+iu6jm2mxEwEwEOoJsHlH4WurmZd+Zpd+1R/GLu2v5zReuZc1qvUcb5vHnzNBeqVKmiCjJRVqZa1P1VXdHXzmazp3HP1gosav/RIY5Hj/LO/KXSzMOHDzdw+YED/hgyRBsE9fVV4urVByhcmP4S20+KTJ6MoG3bNAvkFC2KmHPnYKrXuz0skZJvS5YsEbWPod7llAlrrJR+RkaGKth+8OBBk9uiqgGHDh3C7dvemDgxFBcuGAaoatfOxLffxsJIgrhVcOn3jlYrocz6NWvWwN9f3Ls6Jwd48cVSoqD+vHkJGDDAvhUMrNqck0ySknNSb0m/TLq+frlcblDC21wb9uwJwKhRYaLhJ07E4Pnns81VYfY4Y1U5qN1T9+7dRTrOnz+P0qVLw/uff1B48WIE7NwJQSl+7mU0bozHcjkguPbXNlLxjlpV0LPgzp07oJZ7O7jhAAAgAElEQVRZTZo0MdsvPNAzEJCKa56BFu9SCgSYc1KgyDqsQYC5Zw1qPMcYAhxAZ14wAnkj4NqfxNi7jAAjkC8CHEDPFyLNAN1MPEuzz81fxfVG8ou56/nM1S1mzmk9+Msvv6BTp04il1IQkr5AVgtllG7ZssXV3e4U9nsi96SowNKlSxdcuHAhTx/SwY8FC6jgrliSkwXUrFkKWVnajyXr18ehXTvbs5rzMsjn2jWUaNNGNIT6DCd+/DGUgdqMeHsSU0q+UUCcstDVMmjQIMyZM8ek+VQOmTJ+586di1u3bhmMa9GiBZYu3Y62bYvj4UNt1q56YGioAjt3xuKFF6QNQFKQlCpsUMa8rlCrHN3KG+p7Y8aEYtcurb9eey1d1dOaxTgCUnJOSoxTU1NV1VXov6bk119/RbFixaxatmPHYrh0qZBmboMGGdi92z6VLuiASsWKFUV29uzZE5GRkZprVJXjp59+gt/p0wjv3x9e6YbPO6WfH2IOH0bO889btWdnmuSsvHMmjNgWaRBgrkmDI2sxHwHmnPlY8UhpEWDuSYunJ2vjALone5/3bg4CHEA3ByUewwi4MAIcQDffebqZeIqwMMRcuODQTDTzLXXsSH4xdyzevBpU/W7VpZ6ph7KC6gd7qFB57OrVq4uCCtWqVcNvv/2mQaR9+/ZYt26dhyIk7bY9kXtSVGChwO3o0aPz/F3t0KGDybLhPXsWxY8/ajOce/dOwaJFidI614i28Lffhv8PP4juZFWqhISVK5FVq5bd15eSb1Sq/fr16xqbqdRx//79890DtZ+JiopSlUl/+PChZjxVvrh9eyw2bgw20PHmm6mYNSsJJUva59l85MgRVbUC3XLX9Dfh4sWLBhn1e/f6Y+RIbQWDQoWUuHHjAQIC7FvBIF9gnXSAlJyTcot79uzBqFGjTKoMDw8HlW+3Rn7+2RedOxcXTV2zJg6dOtnvkA793U5M1D7DgoKCkJKSorGB2rN8MmMGSjRpAm8j1TuoIkbCZ58h47XXrNmy081xVt45HVBskM0IMNdshpAVWIgAc85CwHi4ZAgw9ySD0uMVcQDd4ynAAOSDAAfQmSKMgJsjwAF0yxxMmXhF5sxB4qxZSBk61LLJbjqaX8zd1LFOvC3mnNg5ffr0UZViNiXdunXD559/7sQedR3TPJV7UlRguXnzJuhfWFgY6EM4lQdftGiRxvl5tRpYuzYIc+YU0YwtVSoHFy8+tHvVYt+ff0axLl0g6B3SUfr6ImnaNKQMG2ZX8krFNzpkpN9fmYLilpQ3pszfL7/8UpUVW69ePXTsOAKvv14OmZnaj4vPPZeN+fMT0aJFhl1xIeWnTp1SlWjWFeJULb2DDYmJAmrVKoWcHK2d27c/RrNm9rfR7iDYYQGpOCe1aVSh4ujRoybVUq9w6hlujYwcGYq9e7VVCp59Nhtnz8bAx8cabebNadasGW7fvm1y8MaNG9H9xg2ELF4sGqMICkLy8OGqzyDKYMPDK+at7nyjnJV3zocUW2QrAsw1WxHk+ZYiwJyzFDEeLxUCzD2pkGQ9HEBnDjACeSPAAXRmCCPg5ghwAN1CB6enI2DvXqR16QL4Gfb7tFCbWwznF3O3cKNLbYI5J3YXBcc//vhjkz7s3bu3KFDpUs52MmM9lXv2qMCya9cuUE9jtVSpUkVVMtyY/PGHD5o3LyG6JZfH4pVXMu3OkIDdu1FkyhR4GSkdHb98OdL0+hZLaZBUfPv333/RsGFDkWnU/oG+DLFW3nuvCLZsCdJMp8zuH3+MQenS4tLq1urPbx6VmH/11Vdx9+5d1dBQAONGjsTQ99836AfdqVMx/PKLtjz3qFFP8P77T/JbwiPvS8U5qcCjwx9UcWDgwIF5qqSA9Pbt2y1e9r//vNC4cUlkZ2u/9vjggySMHJlssS5LJlC/czqMYkx8fX3x24kTqPDGG/DSyUrPaNQI8V98AUXRopYs5RJjnY13LgEaG2kVAsw1q2DjSTYgwJyzATyeahMCzD2b4OPJOghwAJ3pwAjkjQAH0JkhjICbI8ABdDd3sAO2xy/mDgCZlxAhwJwTE4J6S1OPaVOSX69jppf5CHgy96SuwHLy5En06tVLA37RokVx5coVo85QKoGWLYvjf//z1dzv0iUVK1cmmO88G0Z637mDsDFjUOjyZZGW7HLlEHPqFOyVqioV33788UdQj2W1BAcHq6oBCIJ1H/X++ccbr75aQhR0HDAgBfPm2b+svq4DZs2ahaT16zEXQOXcG9llyiCjaVNk1a4NRbFiqn+rd5bDp5srIBFUxUDASy9lYv/+WBsY4b5TpeKcFAjRIQmqMkD81RV/f39R+X66R+0IqC2BpbJgQWGsWFFYMy0gQIELFx4iLMy+Jf6pBcE333xj1NxXXnkFh6tUQdCXX2ruKwUBjw4fRnb16pZu0SXGOxPvXAIwNtJqBJhrVkPHE61EgDlnJXA8zWYEmHs2Q8gKchHgADpTgRHIGwHrvlVhVBkBRsBlEOAAusu4ymkN5Rdzp3WN2xrGnBO7NjMzU9UHPS0tzajPqW/s+5SVyWIzAh7NPYkrsFy7dg1t2rTR+ISwpWxib29vo3764osgfPihtow7ZTz//PNDhIfbp8+2gRFZWSi8eDEKr1ghuhW/YgXSuna1mVvGFEjFt+joaIwbN06zRNWqVXHs2DGrbZ48uQi2bdNmn/v7K3H69EOUKuUgXwDwvnsXSR9+iMqHD5u9j0z44k9UwFJhAsZd74wiRewbJDXbMCcaKBXnpNgSZWhTpra+dO7cGRkZGTh06JDmFpV3f+GFFyxaNi1NQP36JRAfr33m9O2bgo8/tv9BkBkzZoDKtBuTZSNGYOy6dRCyszW3U2UyJCxbZtH+XGmwM/HOlXBjWy1HgLlmOWY8wzYEmHO24cezrUeAuWc9djxTjAAH0JkRjEDeCHAAnRnCCLg5AhxAd3MHO2B7/GLuAJB5CRECzDlDQlCWHvUENiYTJ07EhAkTmEUSIMDckwDEXBX3799X9dLWFcpAp0x0YxIXJ6Bu3VKintsLFiSgX79U6YzKT5NSiWIdO4oy0bOqVcOjI0cMyobnp8qc+1LxbcWKFViwYIFmyebNm2Pbtm3mmGAw5u+/vdG0qTj7fNCgZMyZk2SVPksmCampCNy2DQF79qDQpUuWTBWNVUDA/pmHUXeYe2bzWg0MAKk4Z4sN6rnr168HVRnQl02bNqFixYp499138c8//2DYsGEYOXKkxUtu3RqIKVOo+L9Wjh+PQeXK2sC1xUrNnPDZZ59h4cKFRkc/fuklhOtUu1D6+akqXeSULm2mdtcb5ky8cz302GJLEGCuWYIWj5UCAeacFCiyDmsQYO5ZgxrPMYYAB9CZF4xA3ghwAJ0Zwgi4OQIcQHdzBztge/xi7gCQeQkRAsw5Q0IsW7bMZJ/z6dOnY8SIEcwiCRBg7kkAYq4KyiClIJiuUA906oVuSkaMCMO+fQGa26+9lo4tW+KkM8oMTf7ffYfwwYNFIx9/9RUyWrUyY7ZlQ6TiGz0DKOioFirnvmTJEsuMyR09cWIR7Nghzj4/c+YhSpa0b/a51/37KNajB3zu3LHKbv1JZ+oOQ/l9MyXR5U5KpOKcFJhMnToVmzdvFqmqUaMG9u/fD+oTbotcu+aD/v2L4sEDbfZ5ixbp2LrVMc8T6tc+adIkgy28FxKCj5PEh1GejByJJx98YMt2nX6uM/HO6cFiA21CgLlmE3w82QoEmHNWgMZTJEGAuScJjKwEAAfQmQaMQN4IcACdGcIIuDkCHEB3cwc7YHv8Yu4AkHkJEQLMOUNCnD9/Hl1NlJGeO3cu3nnnHWaRBAgw9yQAUUcFlVx+8uSJ5gqVGm/cuLHJRfbu9cfIkeGa+1TG/erVBwgOdmApboUCxV97Db5//KGxI6N+fTzes0dacCTMBh48eDC+++47jX1Uzn3KlCkW23v/vhcaNiyJnBztR8QhQ5Ixe7Z9s8+9YmJQrHt3k8FzCt1TLm8UAPno0Xj29m14P3gAr8eP4RUbC69UwyoFjwOeRcb/zlHKtcU4uPMEZ3rGRURE4OzZsxq4g4KCVP3QS5QoYZMLoqICMG1aKNLTxV91bN78GC1bZtik29zJR44cwYABA1TDyYoGAAYCGKqnILt0aTz64Qcog7SHVsxdw5XGORPvXAk3ttVyBJhrlmPGM2xDgDlnG34823oEmHvWY8czxQhwAJ0ZwQjkjQAH0JkhjICbI8ABdDd3sAO2xy/mDgCZlxAhwJwzJARl81If9PT0dIObn3/+Obp168YskgAB5p4EIOqoaNKkiarvuVrWrl2Ljh07mlwkKUnAiy+WQlaW9iPKkiXx6NkzTVrD8tEWEBmJML22CLG7diGzYUNJ7ZCKbx06dMBlnZLQVM69X79+Ftu6dGkwFi8O0czz91fgp59iULy4/bLPveLiUDQiAr63bhnYmxMeji/i4rAOgLqg++HDh0FZyroipKXh8Pxb6L+xg+h6WocOSOvYETnPPAPFs88ip1QpwFubkWwxQG4wQSrOSQFFnTp1EBMTo1FFJd3btWtnteqMDGDWrCLYvNkwGF2nTib27Yt12HkK+n3s06EDKK+8N4BnTezKXtUtrAbRThOdiXd22iKrdRIEmGtO4ggPMoM550HOdrKtMveczCEubA4H0F3YeWy6QxDgALpDYOZFGIGCQ4AD6AWHvbuszC/m7uJJ19kHc864r2QyGc6cOSMOHAkCLl68iFIUGGKxGQHmns0QihR06tQJv/zyi+ba/Pnz0b9//zwX6dUrHCdP+mvGeHkpMWZMMsaPfwIbqzqbv7nMTJRo0gQ+//2nmZPesiXi9MpNm6/Q+Eip+Fa3bl08ePBAswiVc2/durVF5lHwkXqf37vno5nXp08KFi5MtEiPJYMp8F1UJjPod55dsSIS58xBxquv4vlq1ZCWpj1AsW/fPtB+9eXkiUJ4qfereAGGgXj1WEXhwsho0gTJY8ciq3ZtS0x1m7FScc5WQBITE1WHwnTl+PHjqFy5stWqp0wpgq1bDYPnL7+cifXr4+zehkDX8NiffoJf9+6omsdunowejSfTplm9X1ea6Cy8cyXM2FbrEGCuWYcbz7IeAeac9djxTNsQYO7Zhh/P1iLAAXRmAyOQNwIcQGeGMAJujgAH0N3cwQ7YHr+YOwBkXkKEAHPOOCGWLl2KxYsXi25Shm9UFBU2ZpECAeaeFChqdVBrAcoYVgv1BB4/fnyei2zbFojJk0MNxlAG6fLl8ahYMUdaI01oC9qwAUVmintoxxw6hOyaNSVb3xjfkpKSVOXYy5cvj4ZmZLxnZ2ejQoUKUCi0WeIHDx5ErVq1zLIzJUXA1q2BWLs2WNQvmiYfOPAItWtnmaXH4kE5OQgbNgwBOqXnSUd2+fKIjY6G4plnVCpr1qyJ+Ph4jXq5XI5XXnnFYLn//vPCwforMB3z8jVF6e2NJ+PHI/nddwHBsz4OO8szjg7W0AEbtXh7e+OPP/5AoUKF8vWfsQF//umtOgCiVIr9+c47yZg5MwlWqrXKFq9791CsUyf4PHxocn5K//5InDvXY1oMOAvvrHIoT3IpBJhrLuUutzCWOecWbnTJTTD3XNJtTmk0B9Cd0i1slBMh4FnfGDgR8GwKI+AoBDiA7iik3XcdfjF3X986686Yc8Y9Q71iqWesrixatAi9e1OBWBYpEGDuSYGiVgcFzLdv3665QAH1uRQ0ykMoDjxgQDiOHtVmoauHBwQo8OGHSejdO9XucU8hNRUlGjSAt07wNq1TJ8SvWSMZSPp8S01NRbNmzXDv3j3VGvPmzdP0UTa16P3791GvXj3RbSofXbx48TztTEgQsGlTENavD0ZCgmGf8Jo1M3HwYKz0OCuV8L18GUHr1yNQr6989rPP4vHu3cgpU0ZjO+2N9qiWzZs3o2XLlgZ7UyqBJlV98H3KK3get83yUUqfPkhcsMCjyro7yzMuMjISE3TaJFSsWBGnTp0yy2/GBk2fHoJNm4I1t/z9lVi4MAHduzu2/YOQkoJinTvD9+ZNAzNjwsMR0Ls30jt2RJaZB1ysBsTJJjoL75wMFjbHDggw1+wAKqvMEwHmHBOkoBBg7hUU8u63LgfQ3c+nvCNpEeAAurR4sjZGwOkQ4AC607nE5QziF3OXc5nLG8ycM+5C6n/eoEEDPH78WDUgKCgI58+fR2ioYbauy5OggDbA3JMWeOrFvWLFCo3Szp07Y/Xq1fkukpMDrFpF/bgLIzvb8OPKRx8lYuDAlHz12DogeOlShOhUfVAKAh5HRyOzUSNbVavm6/Ntz549GDFihEa3v78/Tp48idKlS5tc79KlS6K+8r6+vrhz545Ktyn5919vREQUxT//aMu1649dty4O7dunS7JPUiLExaHwypXw//Zb+Pz7r4FeRUgIYvfuRXaVKqJ7VGXj7t27mmt59cnu0KEYbl7OQWfsQ1scxGvlf0cZ3IP3gwcQ0o3vJa19eyRNnYqcihU9IhvdWZ5x+s+GNm3aYOPGjVbxjQ6D1K9fEqmpWs4PH56MGTOSrNJny6SQ6dMRvGmTSMWvAAYLAhb/8INNJeptsaug5zoL7woaB17f/ggw1+yPMa8gRoA5x4woKASYewWFvPutywF09/Mp70haBDiALi2erI0RcDoEOIDudC5xOYP4xdzlXObyBjPnTLvwyJEjmD59uqpc85w5c9CuXTuX97czbYC5J6031q5dq+KpWixtOfDrr74YPToMd+6IA72FCytw/vxDhIQopTVYT5uQkICSDRvCKzlZdCejQQOkDB6M9DZtAB/TQej8jNPnG1WUWLJkiWhaly5dsHLlSpOqqNz74MGDNffLlCmDc+fO5bl0v37GM/xpUuPGGZg48QkaN87Mz3yz73v99x+Kd+qkCmQbE6WvLx5v3YrMJk0Mbrdq1Qq//fab5jodyOjatatRPePGhSI6OlBzr3fvFCxalAgolfB68ABBX32F4JUrIeiUu1cPznnmGVV/9PT27ZFO/ePdtLS7szzjpkyZgq1bt2p8NWDAAFXFBWuEDtvMmxeimertrcTZszEoXdox7R6QkQG/EycQsG8fAvbsgUDlEHLlTz8/tC9SBH3Hjcu3moQ1e3eVOc7CO1fBi+20HgHmmvXY8UzrEGDOWYcbz7IdAeae7RiyhqcIcACdmcAI5I0AB9CZIYyAmyPAAXQ3d7ADtscv5g4AmZcQIcCcy5sQFDwXBEH1j0VaBJh70uK5c+dOjB07VqO0SpUq+OGHHyxaJDVVwIcfhmDLliDRvGnTkjB6tDiwbZFiMwcXXrAAhXWy6HWnZZcpg5ShQ5HyzjtW9TLW59uoUaOwa9cuA8soM71+/fqq6+nZ6dh7Zy8aBjbEzsid2LZtGx7oBKap5PnevXtN7u7qVV+0bWtY3v2NN9IxevQT1Ksncc/zzEwU694dhX75xahNSj8/JCxZgrQuXYze79ChA6gkvVo+/fRTvPXWW0bHrlkThI8+KqK5V65cNs6ciRHFwv0PHkTYiBEQMk0fEEhv2RIJn3wCxbPPmskS1xnmLM84ei7Q80EtVHmBDodZKllZQKNGJfHggbdmaufOaVi9Ot5SVRaPL3T2LAIjI+F/6BC8kgyz3RX+/nh05MjT6gYeLs7COw93g0dsn7nmEW52qk0y55zKHR5lDHPPo9xt181yAN2u8LJyN0CAv3l1AyfyFhiBvBDgADrzw1YE+MXcVgR5vqUIMOcsRYzHS4UAc08qJJ/qOX36NHr06KFRGhISIsomtmS1oUPDsH9/gGZKsWI5qix0Pz9LtFg+lkqPF2/bFj65fcmNaUh56y0kfvqpxcr1+da+fXtQSXZ9qV27Nr799ltVWfa1V9Zizvw5EE4LUCoMM/DzK5M/ZEgYDhzQ4hgenoPIyMeoXj3bYvvNmaBfBl89J7tCBaR17IjUHj3yDDB2794dP/30k2apvPrC37jhgzfeKCEy68SJh3j+eXEmcqEff0T4wIHwSjHdBkDp74/kgQORPGoUlG7UJsNZnnFDhw7F/v37Nb4aP348Jk2aZA6lRGP27vXHyJHhomvffvsIdepIfBBEdwWlEiFz5iD4iy/ytDdxxgykDB9u8Z7ccYKz8M4dseU9iRFgrjEjHI0Ac87RiPN6agSYe8wFqRDgALpUSLIed0WAA+ju6lneFyOQiwAH0JkKtiLAL+a2IsjzLUWAOWcpYjxeKgSYe1Ih+VTP7du30axZM5HSW7duITg42OKFrlzxRbt24szpVavi8Oab0vXpNmUUlXKn8t/0z/vhQ6PD4jZsQHrbthbtS59v1apVQ0JCglEdS5cuRadunfDSwpeQvNJ05n1ePcL/+ssbTZqUgFKp/Qg4dWoSxoyxTya/919/oUTLlqL+4znh4YjfuBGZ9eqZVSa9d+/eOHHihAaTGTNmYLiJoCRVzq5XT5yNPHt2IoYMMQyUe9++raosQKW3TfmUFlUUKYKUQYOQ+fLLyK5cGTmUlZ5Hf3mLCFAAg53lGdevXz8cPXpUg8C0adMwevRoixHp1SscJ0/6a+bVr5+BPXseW6zHkgmFP/kEhZcvNzlF6eWlOnjxZPJkwFubGW/JGu421ll452648n4MEWCuMSscjQBzztGI83pqBJh7zAWpEOAAulRIsh53RYAD6O7qWd4XI5CLAAfQmQq2IsAv5rYiyPMtRYA5ZyliPF4qBJh7UiH5VE9qaioqV64sUkrB0Oeff96qhSIiiuLsWW3K+auvZqiypx0mmZkI2L8fQevXo5BOWXFaP6dECcT8+COUQeJS83nZpsu3GzduoEaNGiaH0xcb76x+Bx+v+hj43nBYnTp1MGbMGLShvuwmZPbsEKxbpz28QL3kL1x4iMKF7dBLXqlEeN++8Ncp2U+Bxdj9+5H14otmu2zgwIE4dOiQZvzkyZPx7rvvmpw/aVIRbN+u9UHdupnYsyfWdMxbqYTP7dvwO3oUwatXw/vRozxtUwQEIOvll5EaEYH0jh2hDNT2XDd7UwU40FmecVSZgipUqOXDDz/E4MGDLULmv/+80KBBSdGBkJUr49GlS5pFeiwZHLxyJULmzzeYohQEZDZogLTOnZHeoQMUxQ3bJFiyjruNdRbeuRuuvB9DBJhrzApHI8CcczTivJ4aAeYec0EqBDiALhWSrMddEeAAurt6lvfFCOQiwAF0poKtCPCLua0I8nxLEWDOWYoYj5cKAeaeVEhq9VBQWDereseOHWjatKlVC+3aFYAxY8JEc3/4IQZVqtin/LhJI5VKBG7bhtApU0RDkj74AMkjR5q9N12+UTnrjh075jnXv7U/0u+lA9e1w+rVr4d5c+ehZs2aec5NSRFQt25JPHnipRk3ZEgyZs827N1s9gbyGKjChzJwdSR50CAkzZljkXrqjb1v3z7NHDokMHXqVJM69u/3x9Ch4pLeI0c+wQcfPMl3Xa+4OBRetEjlWyE7f04pgoKQFhEB2ldOpUr56neGAc7yjKNWAz///LMGko8//hh9+/a1CKKVK4Mxf36IZg4dCLl06QECtB0KLNKX3+DAL79E6AcfiIZR4JwyzakVgeKZZ/JT4bH3nYV3HusAD9o4c82DnO0kW2XOOYkjPNAM5p4HOt1OW+YAup2AZbVugwAH0N3GlbwRRsA4AhxAZ2bYigC/mNuKIM+3FAHmnKWI8XipEGDuSYWkVk+rVq1Efc+pFLluX3RLVkxPB+rWLYWEBG0QuHr1TOzd+xiBgXbIos7LOMqwfvtt+B8/rhlF5cljfvrJ7Cx0Xb59/vnnGDt2rEZXhQoVUL58efygk8GNZwFQcm281rCO4zpi7ZS1+cIYHR2AceO0hw8EQYnTp2Pw3HPi/uD5KjJjgM/NmyjWqRO8UlO12JQsiZgffoCySBEzNGiHUG/sqKgozQXqnT1r1iyTOlJTBVWZ+pgYcensJUvi0bOneZnJ3nfuIGThQgR8841Ztqqyj195BVlVqiCnTJmn/8qWRXbFilAWLmyWDkcNcpZnXOvWrXH9uvYkyLJlyyCTycyGgcr1v/Zacfz+u69mTq9eKVi8ONFsHZYMDJDLEWak8kHC4sVI7dXLElUeOdZZeOeR4HvYpplrHuZwJ9guc84JnOChJjD3PNTxdtg2B9DtACqrdCsEOIDuVu7kzRACXbt2LeHt7d0AQANBEOoDoH9F6Z5SqfwqOjp6gCVIRUREtBMEYWiuHqrHR7UdLyiVyi+io6O/M0dXp06dAv38/EYLgkDfDFGKCtUf/QfA/pycnOW7du36yxw91ozhALo1qPEcXQT4xZz54GgEmHOORpzXUyPA3JOeC2+//bYoCDxlyhSMGzfO6oXmzy+MlSvFQcny5dOxevUTvPhiltV6rZno++uvKN6+vWhq4vTpSBkxwix1unybMGEC6HCBWlq2bKnq9Z3fYYPCYwrj0qRLCPDJO+1Wv1d0y5bp2Lw5ziw7zR6UlobAyEhViWuvFHHf8cdff42M1183W5V6IGWbb968WTOPemcvWLAgTz1nzhRCr15FkZ2t/ajr66tUlftv2DDTbBt8r1xB4JYt8L1xAz6//w6vZMt6xSsLFUJqz55ImjbN4oMDZhtp4UBnecY1a9YMt2/f1li/Zs0adOrUyezdXL7siw4dxGXSd+2Ktci/5ixGh0ECo6IQtG4dBIVC/Ls+ezZShgwxR43Hj3EW3nm8IzwAAOaaBzjZybbInHMyh3iQOcw9D3K2nbfKAXQ7A8zqXR4BDqC7vAt5A/oIyGQykylIlgTQZ82a5XXjxo0vAAwyhbIgCOurVas27MMPPxR/o6IzQSaTUaPPAwDETUC1Y5KUSmWf6Ojob+3hTQ6g2wNVz9LJL+ae5W9n2C1zzhm84Jk2MPek9zv1rN62bZtGMZVppnLN1gplGDdv7o///hOXcvfxUWLChCcYPToZ3uLkY2uXMmteeGfK830AACAASURBVL9+8D96VDNWlYV+7pxZvbF1+UaZ+kd19AwaNAjvv/++qi96OqXeGxPa5zRg1quzMLQWnfU0Lv/7nw9ef704FArtR79Vq+Lw5psm9Jq1c+0gITUVQRs2qHrDe8fGGsxO6dMHiQsXWqj16fDZs2dj3bp1mrk9e/bEkiVL8tW1bVsgJk8OFY0LC8vBgQOxKFfOiqx7pRJeDx6g0MWLqoCq3/HjBgFVU0bllCqF+M8/V2WpF7Q4yzOuYcOG+PfffzVwfPnll3jjjTfMhueDD4rgyy+1ve6fey5bVVFBkOLbjawsBG7disDt21Ho2jWjNiVNmoTk8ePNttfTBzoL7zzdD56wf+aaJ3jZufbInHMuf3iSNcw9T/K2fffKAXT74svaXR8BKT5iuj4KvAO3QkAvgP43gJsAWtMmLQmgy2QySm9RN1m8pFQqF3p7e9/OycmpJAgCNb2sQzoFQVgQFRX1vjEQO3fuXNjPz+8igCq5668TBGEHFeAUBOE1pVI5DUAwAKpx2UQul1+W2hkcQJcaUc/Txy/mnufzgt4xc66gPeC56zP3pPc9BTs//fRTjWIKklGwzBaZPn0bNm0aDEAcICWdjRtnqDKrAwIcU9Ld9/JlFO/QQbSd9FatkPDJJ1CUKpXnNtV8S0tLQ3h4uChQvmrVKrz55puQ9ZDhzOkzxvVQSfehQJhfGC70vmCQhU5lrjdvDsScOSFIS9OWvQ8OVuDy5YeSYCQ8eYKiEREmA42ZdeogNjoa8Pe3yuV02ILK26uFMCFszJEPPwzBF1/Qa7ZWOndOw+rVOjXwzVFkZIzXvXsI+vJLBG3ZAq+k/PvIU5n35FGjVD3TqbS7Q0956NjvLM+42rVrI1bnsMWOHTvQtGlTs7xx8mQhDBwYLuL0xIlJmDDBsgoBxhYT0tIQNnAg/E+eNGlL8ogRSKJe6JJE683asssPchbeuTyQvIF8EWCu5QsRD5AYAeacxICyOrMRYO6ZDRUPzAcBDqAzRRiBvBHgADozxO0QkMlkHyqVygu+vr4Xtm/f/vCtt94qn5OT8ydt1NwAes+ePasoFApqzOcDgALgzeRyuaZxIpVk9/f3PwGgHoBsANXkcvkf+mBGRETMEQRhRu71KXK5fJHumIiIiFcEQSA9tM4JuVzeQmqHcABdakQ9Tx+/mHuezwt6x8y5gvaA567P3JPe99u3b8ekSZM0imvWrIlDhw7ZtBCV8F6x4jCALbkddsTqhgxJxuzZ+Qc1bTJCZ3J4377wP3ZMpE5RuLCqdHdq376AlzZ4rTtIzbcDBw6gg04Qnq5fvXoVoaGhePv9t/HDVz8YN7UugE6AAAEzG80UZaE/fOiFSZNCceyYYeC6d+8ULFokTa/oIhMnImgHnQ01lIzGjRG/ahUUJUpYDTWVtV+8eLFmftu2bbFhwwaz9OXkAO+8E46jR7UYBAYqcOPGA/hqW2ebpcvUICElBf5HjsDn9m14//MPvP/9F95//w2fe/dM6lUEBCC7Rg0QPinvvANFyZI22WDJZGd5xlWtWhXJOiXx9+zZg/r1qetW3rJvnz/Gjg1DVpb4a4wzZx7iueesqCygsxwdBgnv3x9+586ZNIKD5/l5yPh9Z+GdddbzLFdCgLnmSt5yD1uZc+7hR1fcBXPPFb3mnDZzAN05/cJWOQ8CHEB3Hl+wJXZCwJoAukwmo9QWVQNLLy+vxpGRkT/pm9ezZ89GCoXiLF0XBGFVVFTUKN0xQ4cO9Y2Pj6d+6UUA/Fa9evWaxkq99+jRY41SqRyWu1aDyMjIC1JCwQF0KdH0TF38Yu6Zfi/IXTPnChJ9z16buSe9/3/44QdQH3S1FC1aFFeuXLFpoRkzZmDjxo255w9nAqBCQNq67YUKKXHyZAzKlrUtoGaukdQru1jnzhCyDHuwU5A07ssvoQwWZ0LnvvehVKlS6N+/P77++mvNcg0aNMDu3buRlp2Glz5+CcmrTWTWUvvl0k+n6WahJyYKqv7Qf/5J5zPFUqlSFvbti0VoqI0Z+kolgpctQ4hOcFu9Unrz5kgePRqZjRvbnKVLvbE/+ugjzSZatGiBrVu3musaPHrkhZdeElcC2L07Fg0amN8L3ezFdAZSMD108mT4nTqV53Slvz+oHHjK8OE2Y2WOnc7yjHvuueeQnU1nkJ8KHaqhwzV5yZdfBmL69CJQKsVfYfTokYqlSxPM2b7JMUJSEor27o1Cly4ZjKES/Knduj2tHlC1qk3reOpkZ+Gdp+LvSftmrnmSt51jr8w55/CDJ1rB3PNEr9tnzxxAtw+urNV9EOAAuvv4kndiAgErAuiCTCajpnxUGPOmXC6vZgpcmUxG5eHpm5R7crm8LCW5q8dGRES0FgRBlWIlCMLUqKioT4zp0QvEmywHb62DOYBuLXI8T40Av5gzFxyNAHPO0Yjzevy8sx8Hbt68iddff120wJ07d+Dn52f1ohMnTgSVfNZKEwCUAV5Ic0mKoJolBvodPozQiRPhHRdnMC3lrbeQqFPGXpdvP/30E7p27SqaM3XqVIwZMwZrr6zFnJ/mPE20v507hOLwlXIT78top+lmoS9dGozFi0MM7OjbNwUzZyYhMND24HnIzJkIVh1i0IoiOBiPd+xAVh1VlyNJZNOmTZg+fbpGV+PGjRFNJeEtkDfeKI4bN7Qp5xMmPMHEiU8s0GB6aEZGBnx9fenAreEghQLBq1ej8MKFEHSCxca0pXXogISlS6EM0vb1lsRAPSXO8PeVAucUQNeVEydO4Pnnnze55a+/DsS0aYYtGzp1SsNnn8XDhscJqGx7eO/e8Dt/Xszn0FDEL1uGjJYtC6zkvj04UBA6nYF3BbFvXtPxCDDXHI+5p6/InPN0BhTc/pl7BYe9u63MAXR38yjvR2oEOIAuNaKsz+kQsDSA3q1bt4rU65w2IgjC2qioqOGmNiWTydY+7T6pkopyuVxVKp5Et3y7qSx2GteiRQuf4sWLUx3NQAAn5XJ5cylB5AC6lGh6pi5+MfdMvxfkrplzBYm+Z6/N3JPe//Hx8QaZpRcuXMCzz9I5RetkxIgR2Ldvn97kpQDe1VyjUt1XrkjT59tcK73i4hAyZw4C5XLRFKWXFx4dPozsauIzmVTCum7duqJS1oIggLL2y1Qog3rb6iEhIwGgRHpqFETnA8qJku1F61AW+o/dLqJ5k+cQG6vNyC9ZMgeLFyegZcsMc7diepxSicKLF6PwsmUGY+KXLEFaz562r6GjQb8FQJ06dfDtt99atAb1gF+7VlsBoH79DOzZ89giHfqDKQhMPKTy+1Rqv2XLlhg1ahReeOEFA72+v/6K4FWrUOjiRXg/eGBy3ayqVRG3aRNy9ILLNhmqN9kZnnHEeyrhrivnzp1DmTI6J0J0bqakCHj55ZJIThYfUhgwIAVz5iTa1k4+PR3hgwbB//hxkT05xYqpDoPo/85K6QtP0uUMvPMkvD15r8w1T/Z+weydOVcwuPOqqmqpqkpWJA8ePIBCoWBYGAGrEOAAulWw8SQPQoAD6B7kbE/dqqUB9IiIiI6CIHxDeCmVyvHR0dGG3xDmgtmjR4/xSqVyCf0oCEKHqKioA2qcZTIZpcd0p5+zsrLC9uzZY7K2oEwm+xXAiwAeyeVy6xtFGnEyB9A9lfnS7ZtfzKXDkjWZhwBzzjyceJT0CDD3pMfUWLbp8ePHUblyZasXo5Ln33//vd58en26T813NNfXr49Du3bpVq9j7cRCp04hfOBAeKWmalSkt2yJuM2bRSp//vlndO7cWXRt6NChmDVrlir7/KNzH0GpLW6UrzmUhd4xYTe+WfamaOyhQzGoWVNbLjtfRSYG+Fy7hsKffoqAw9R/Xit0QCBp5kykDKGa8tLKrl27VNn4aqlWrZoR3+e95vHjfujTp6hmkLe3UtUHPTjY+kz8yMhITJgwQbQwPT969+6NSZMmoXjx4kaN8nr0CL5Xr8L/u+8QuH07BKXYBkVoqKqke1qXLlCGhUkL5tPWVAX+ZWtsbCxq164t2tvly5dNYhYZGYAJE8RYTJqUhHffTYZgxbcZ1Ovc79gxBBw8qPqvl04vdjIqp0QJxMrlyMkjI15yx7i5QmfgnZtDzNvLRYC5xlRwNALMOUcjzuupEWDuMRekQoAD6FIhyXrcFQErPnK6KxS8L3dFwNIAeo8ePYYrlcrVuXjI5HK5yTqRMpksAoAqzUipVA6Pjo6mjHSVyGQy6pveEECKXC43bHypA7hMJqNUmg50KTk52f+7774zK0WoW7duxlM1dHRHRUVd8Pb2LpWTk4NHj6glOwsjYBkC9GJeosTTcx0xMTF8stUy+Hi0FQgw56wAjadIggBzTxIYDZRUqFAB6enaQDZl7VImsVpSUlJw5MgRVVln3eumrImIiMDp06eN3KYy7q9prnfrloaVK6nIj+Ml6NNPVZnauhInlyPz1Vc1lyjTnAKuunLv3j2k56Sj3tZ6iM+It8xwJeC1+jcoYrRZ0E2bZiAqykI9uqsqlfD96ScEr1ihCjbqi1IQkLhqFdK7dLHMVjNH79+/H4MHD9aMrlixognfm1ZI5xheeKEksrK0H30jI+PQrJn1fdAHDhyI7777zuiiwcHBGDt2LIYMGQJ/f3+ThhU6dgyhI0fCK9GQo8pChZDRqhVS+/ZFZosWZqKV/zBHPeOuX78O+kcl98uWpS5XWvnnn3/QoEED0bX//e9/KFy4sOgaVb2fNi0EW7ZQkS6tvP56OrZssa7nuf/OnQiZNg1eT4yX8KcDDHG7dyPbSCWB/NHlEaYQcBTv2AOMAHONOeBoBJhzjkac11MjwNxjLkiFAB389fZWVS+7JwhCvnEGqdZlPYyAqyDAAXRX8RTbaTUClgbQZTLZZAALcxdsJ5fLD5paPCIiop0gCKqsc6VSOSk6OvpT9ViZTHYdQHUAD+Vy+dO6OiYkIiIiUhCEHnQ7Ozu72O7du82qKymTyfJNnVm9ejWKFtVm3VgNJE9kBBgBRoARYAQYAUbACgToEJbuIT7KHlf3Race0jVq1MDt20+bfFPP6wEDBuS5SqNGjUAlnw1lFIAVmsshIXTwCzb1R7Ziu0+nUFYrZdnrlux++WWA+iw//YJC1ce9V69emiWqV6+uCjouObsEkw5Psij7XKXk97bAVnFQ98ABoF07K3eRlQX07w9s325cAaX/rlwJjBhh5QL5T6PDFh06qM6YqoTKfFMA1lJp1AjQpczs2cCsWZZqeTo+KysLxYoVQ1JSUp4K6EDI5s2b0bRpU9PjiPd0+ODaNdNjJk0CFi2yzlgHzqLf4UWLFqkOOFzL3Q8dIKDf1RdfpEJbT+XWrVsGpe4zMzNVveR15eOPKYBuuIGdO4Fu3azY2IoVgE41AwMNwcHA0aOAXnDfipV4CiPACDACjAAjwAgwAowAI+BqCHAA3dU8xvY6BAEOoDsEZl6kIBGwNIDeo0ePGUqlck6uza/L5XLDdJvcmzKZrCWAo/SjUqmcER0dPVe9V5lMRt8EVwTwj1wup46VJkUmk30NoC8NyMnJKbtr165/zcGMA+jmoMRjGAFGgBFgBBgBRqAgEahUqRLu3LmjMWH37t3okpuxvHHjRgwaNEhzr2bNmrh69Wqe5taqVUsToBMPfAbAf6JLVG38jTcKaPdffAEMGyZefO5c4IMPVNe++OILDNO5TwcDjp48irJLyyIuLc4yo+lI5VfHgLvaDPzq1Z/GZa0pc61afPTopwFyY0LlxakkvU5w2zKDzRtNWfrUX1wtFLi2pqLSxInAElXTpafSqhVw5Ih5NuiPogDxqzqVBPLSEhYWhv/++y/PTPRrp0/Dr39/VM49RGJUH/WcHzfOOoMdMCs+Ph70e0kVFPSla9euoFL8aqFy7bqVJijjhVo96EpGBlC+vPj8Cd2nyvj//gsUKmThppYvN40f/YLQIQfCWKcyhoUr8HBGgBFgBBgBRoARYAQYAUbAlRHgALore49ttxsCHEC3G7Ss2FkQsDSA7koZ6FzC3VlY5t52cGko9/avM+6OOeeMXvEMm5h79vFzq1atVJnValm+fDm1ulH92LFjR1AvcF1p0qSJKjO7e/fuRg1q2LAh/v77bxPGngXQSHNv0KAUzJ1rvFyzfXarozU7G8VatoTP779rLip9fPD4m2+Q/dJLoCpBc+aoz2wCLVq0QOdZnTHhxAR4C97wErT93POzVfG/tsjZsk80bPHiRPTpk5bfVKP3A776CkWmTjW4pwgPR8rgwUgdMMAuPbr1F7x48SI6deqkuRwYGKipVmDJxvbv98Pgwdo+2kFBCty8GQMfH0u0PB27cOFCLF26VDORKgdQpvknn3yCqKgoA4VUhv5lqj5gRCibnqoxPHnyBO0BLK1dG5V/+w1Cpri8PJXKT9i4ERlt21pusM4Mez3jpkyZosLAmFCA/MKFC3jmGTrgAuj7NCgoCH/88YdoalSUP8aNCzVQt359PDp0MKvTlZYz69cjZMYMA12Z9esjTSZDRuvWUJQsaROuPDlvBOzFO8adEdBHgLnGnHA0Asw5RyPO66kRYO4xF6RCgEu4S4Uk63FXBDiA7q6e5X1pELA0gO5KPdDNcbNSqaRs9tLUA/3hw4fmTOExjIAIAXoxL1XqaReCBw8ecA905ofdEWDO2R1iXsAEAsw9+1CjW7duopLr8+bN05Rpb9asmcmA6LFjx1C1alUDo2rXro3Y2FgTxlJ2t6YgEMqVy8aZMzHWZ2HbCInvxYso1rUrBIVCoym7QgXEyuX4ZMsWLKOs11yhwwTTFk3Djls7qLKR2SsrcrywY/x7iPvnWc2cEqUycPb0Y+TRgtuk/kKnT6Nor14QcnI0Y5T+/kj64AOk9uoFZUCA2bbZOpBKgbdp00ajhoKxpg9PmF7t0SMvvPSSuKPSoUMxqFlTnPlsjr09evQQ9WEfPnw4ZuQGaK9cuYJ+/fqJsuQp2E5zjMmCBQuwgkqL50q1atVwNDoaQVu3ImT+fNEUhb8/Hu/ciayXXjLHTKNj7PGMo4D4m2++madNEyZMwEQqAwDgxx9/RM+ePTXjqdUU4aYWon6bNsVx/bq4pPuVKw9QtKj298gcEII2bECRmTMNhiZNmYLksWNtKM9gzuo8Ro2APXjH6DICxhBgrjEvHI0Ac87RiPN6/LeVOSA1AiVLluQe6FKDyvrcCgEOoLuVO3kzxhCwNIAeERHRURCEb0iXUqkcHx0drf1mU2+BHj16jFcqlaqCkIIgdIiKilL1QyeRyWTRAFSpU1lZWWF79uxJMOUhmUz2KwBqDvhILpeXkNKTHECXEk3P1MUfCj3T7wW5a+ZcQaLv2Wsz9+zj/759+4KC4WqZNm0aRlN5cAB5BcMjIiLw2WefGRhVuXJlpKamaq5TVrL259oALovmHD8eg8qVLQ+USoVG4cWLUVgnY1n1juntjUtlymDaX3+BKolTuLx3796qHtKWyrZtgZg8WZytu3RpPHr0sDz73PvuXRTv0AFeCeLX1vgVK5DWtaulptk8njKTmzdvLtJz9+5dg37Z5izUpEkJ3L2rTTmfNy8BAwZoeWSODhpDpfZ1+7CvWbNGlCX/zjvv4DD1DsgV4jpx3piULl3a4PKlS5dQokQJBC9bhhA9PuQUK4bYb79FTtmy5porGif1M476wbdt2xY3b94UrUNZ5SkpKZpr5cuX1xw6+P7779G/f3/NPcLg/Pnzmp/PnCkEmayYSN/GjXFo0ybdvD3n5MDvxx8REBmJwL17DeYkTZuG5Nznj3kKeZStCEjNO1vt4fnuiwBzzX1966w7Y845q2fc3y7mnvv72FE75AC6o5DmdVwVAQ6gu6rn2G6zEbA0gN6tW7eK3t7e1L+cguJro6KihptaTCaTrQUwNPd+Rblc/qd6bERExBxBEFT1Ar28vBpHRkb+ZExPixYtfIoXL07fUgYBOCmXy8XfEpq9U+MDOYBuI4A8nfjLGejMA4ciwJxzKNy8mA4CzD370GHEiBHYt09bXnzMmDGYOnWqKsuaAmv6/Y/VVlA/9EOHDomMojlly5YVZWgPGTIE69at0xn3D4Aymp9Hj36CadMKqIw7WUGl3Lt0QaFLl4wCTLm3EQBeGzoUs2bNssgJ9+55oWPH4oiJ8dbMq149CwcPPoK39lK+Or3i4uB34gQKL1gAH70+1k/GjMETI+Xc81UqwYB///0XVLJfV27duoXg4GCLtY8ZE4pduwI183r0SMXSpSbPtxrVT1ytWLEiqLKTWg4cOKA6CKIWqrCwatUqzc/t2rXD+vXrRfquXr2qOjARF2fY654y0qlvOJRKhE6ciMDISNHc7IoVEbtjBxRGgu/5gSL1M472SfvVFcrGb9y4Mdq3p6L0Wjl16pQKu2+//RbDhg3T3KhUqRJOnjyp+pkq1/frVxSnTvlp7pcvn42TJ2Py5bOQmorglSsRuGMHvB88MAoFB8/zY4h97kvNO/tYyVrdAQHmmjt40bX2wJxzLX+5k7XMPXfyZsHuhQPoBYs/r+78CHAA3fl9xBbaiIClAXSKm8tkMip7TnUwb8rl8mqmTJDJZL8BeAHAPblcTqkgmnqbERERrQVBUH3rKwjC1KioqE+M6enZs2cjhUJBDTtp3IKoqKj3bdyyaDoH0KVE0zN18Yu5Z/q9IHfNnCtI9D17beaeffw/efJkbNu2TaN84MCB+Oijj3D//n3Uq1fP5KIvvvgivvvuO9H9tLQ0PP/886Jrp0+fVgUst27dmnudstbHasaEhipw8eJDBASYXxZdaiQos7tYp07wNhIwpbWoyU5knz6IWLjQrKWpzDVlns+ZE4LkZHGv9O3bH6NZs7z7RPteuQL/w4fhe+2a6p/3/ftG101r0wbxFPz1Mr8fu1kbMHMQlerXDU7TtF9//RXFiokzlM1Rt2FDEGbOLKIZWrVqFo4de2TOVM0YyjynDHRdoWB4eHi45tKOHTs05crpIlVMOH78uOY+cfjVV19VtcUxJlTuXdNjPSsLRfv2hd+pU+L3ex8fpL35JlL79EFmgwZmlyKX6hlHv7urV6/Ghg0bRHZRP3j6naVS+9T3PSYmRnOffufpdz86Ohrjxo3TXK9Ro4YqY//+fS8MHx6OixcLiXR+9FEiBg7UZrMbw0xISFC1HSikUwpefxwHzy2iuqSDpeKdpEaxMrdEgLnmlm516k0x55zaPW5tHHPPrd3r0M1xAN2hcPNiLogAB9Bd0GlssmUIWBFAp/LrlDYyglYylT2uF/heFRUVNUrXMplMRt/+0LdG9E3db3K5vIZugF09tkePHmuUSqUqDcPLy6tBZGTkBct2mPdoDqBLiaZn6uIXc8/0e0HumjlXkOh79trMPfv4f/bs2aIMcXWAUL8Xsv7qderUUWWr6srjx49BgXVdoZLXlBlcv3793MvUN11cUrpnz1TMnZuIwMCCC6J73buHwp99hoDdu+GlU4JevReFICA9IgJp3boho0kTmEq5pazzSZNCcfKkv4HDWrRIx9athlnN6oFejx6pekIH6FQEMOX1rGrVELtnD5RWZHtLxaQnT57ghRforKpWzp07hzJltBUGzF3r4kVfvPlmcc1wLy8lbt58gKAg8zlBhzV0+5lTqXLKiBcE7cfqCxcuoEuXLpp1fH198fvvv2vKzi9fvhyffGL0XK1qTqlSpUA66HlEIiQloVjXrvDVK5OuXoD8FL98ObKrV88XCimecVSavWnTpnj4kI59aIUw+Oabb0C/tyTjx49HVFSUZkDLli2xefNmbNmyBe+9957mOgXap007hBEjwhAbKy6bQIdfzp9/mKePvO7fR/iAASh07ZrR/Sv9/JD0wQdIGTQoX3x4gH0QkIJ39rGMtbobAsw1d/Oo8++HOef8PnJXC5l77upZx++LA+iOx5xXdC0EOIDuWv5ia61AwJoAes+ePasoFIobAOhbnIsAmsnlck0jSZlMFkDl1gFQ2lS2QqGovnPnzt/1zdMt4w5gilwuFzW2lMlkjXP1UEPGE3K5vIUVW8xzCgfQpUbU8/Txi7nn+bygd8ycK2gPeO76zD37+H7x4sXajFpAVdqZSq5/9dVXeP9904V3atWqhYMHD4qMMpYBTAHMzMxM0HitHADQTjSXSkFTye4GDTLts1EztQpPniBg1y6kzJqF0llZRmcp/f2RTeXtK1ZEdoUKyGjZEpmNGiE+XkC7dsXxzz/aXt5qBc8+m43dux+jTBlteXFd5YXOnkXYyJHw1skKNmVyZt26iFu/HooSJczclX2GUY9tKvOvKydOnDCoQmDO6mlpwAsvPIPsbO1H4F27YtGwofl8iIyMxIQJEzTLUXD/6NGjouWpLLuYi1CVKKdS5dQrvHPnzqL+4MZs37RpE1q3bq255X3vHop17GjSdzlFiyJ23z7k6GGlr1uKZxyVrKe2CfpCfc3nz5+vubx3716MHDlS87O/vz8uX76MLdu3YO6HczXXq1SZgdu3P0ROjvirCT8/JdaujcMbb5iupuBL2f8DBhgt2Z5ZqxbSZDJVpr7CiooF5nCKx5iHgBS8M28lHuXpCDDXPJ0Bjt8/c87xmPOKTxFg7jETpEKAA+hSIcl63BUBDqC7q2c9eF8ymexVAJranoIgFFMqlerA9WkAoiaEcrn8S2NwyWSyBQCm5t6jppWfKBSK215eXpUAUNqEKr0ir7LrnTt3Luzn50cB+Cq5er4QBGGHIAhpOTk5rwmCQN8aUxNHCs6/IpfLL0vtOg6gS42o5+njF3PP83lB75g5V9Ae8Nz1mXv28f2aNWtUJdvV0rx5c1VJd+qDThmppuSZZ57BxYv0GqUVCpZTJquu/P3338jIyFCVytZKUwjCCSiV4o87gqDE6NHJeO+9J9BJGrbPxvPR2q5JEyy9exfi3ZielNa6NeaXXIK5m6mokVion/esWYkIDTXMphbS0hC4dStC5s2DQE2m8lgo7QAAIABJREFUTYiCgvbVqiGtQ4en2bqFxKW0CwIk6nlfrlw5KBQKzfKHDh1CzZo1rTKndeviuH7dVzN35sxEDBuWd3lw3YX0D4O0atVKdRBEX6hKAlVLUAsFxCnYTpnp+pnbpUuXxj29vvNNmjQRZW+THgqih8ydC//vvoNg5OBFVqVKiD1wIM+KAVI84/R/n8k2+l09duwYQkJCNHuOj49XVYvQ9R39zl94eAFHN6kPHRSBt/c95OQEiSAsWzYb69bFo1bNTHg9fPj0IIdeGwH/gwcROno0vOhkhI7klCyJ+NWrkdmwoVUc4UnSIyAF76S3ijW6IwLMNXf0qnPviTnn3P5xZ+uYe+7sXcfujQPojsWbV3M9BDiA7no+Y4vzQSAiIuJLQRD6mwuUXC43+nswa9Ysr+vXr68TBGFgHro2VK9efeiHH36o/VZPb7BMJqNgPqVB6X6rqzsqSalU9omOjhbXKDV3A/mM4wC6REB6sBp+Mfdg5xfQ1plzBQQ8L8sn+e3EAQqSU+BMLXXr1gX1iab+54mJiSZXpdLXf/75p6g8NmWwdujQQTPHz88Pd+7cQU5OjirQqiuTJ/+Gzz+vgvR0w/7dS5bEo2dPceDN0u3TmtTPmfbQs2dPFCmi7a9tji7q7R0fG4vpAOhEpTnh6lQEYBEmYynGIxGhKFUqB4sWJaBlS3GWLpX9Dti/H36HD8Pv5El4pacbmKQoUgSp3bohq04dZNWsiexKlQAfw8x2c/ZizzF0MCJVp+T9vn37QByyRqZMKYKtW7XB2s6d07B6dbzZqsaOHYudO3dqxlNPb93DIeob3bp1A5WaV0uvXr1w9uxZ3L17V7QW8WbJkiXQz2ynQUeOHAH1FCc5d64Qvv46ED/95IdCCY/QR7EZ47MWobhS3MOd/JmwfLnJnuh5/X31vnsXgdu354vFe6dOYfWvv4rGXenXD+Vzg+epvXppMuEHDRokqiIRFhaG1JqpyDil5is9F+jMslZatkzH8uXxKB5zE2GjRsH3t9+QVbky4rZsUQXT/X78UfWPKioISvGBEara8HjzZuRUrJjvPniA4xDg9zrHYe3pKzHXPJ0Bjt8/c87xmPOKTxFg7jETpEKAA+hSIcl63BUBDqC7q2c9eF9SBdDVEPbo0aO9UqkcCoAaaxYDEAvgglKpXBsdHf2dOVD37ds3KC0tbZQgCLLc7Hj6jvQfpVJ5QKFQfLZr166/zNFjzRgOoFuDGs/RRYBfzJkPjkaAOedoxHk9NQLMPftwYc+ePRg1apRGOWXiUuBxypQp+S547do1UNBNLWfOnIFMRq9TTyU0NBTXr19X/T+V+qaS32qh8u+BgS9h/Pgw/PyzODxduXIWfvjhkU1Z6NOmTcPXX3+tWq5q1ar4/vvvNX2r890YoCrpnZ4b2H4GwNHBg1Hp/HkUunIl3+kJKILN6It2E8og7OUyyC5bFsqAAHg/fgy/o0cRvH49vBISTOpJf+01JCxb5hKlrSnbnLKZ1UJ9tSlD2xrZti0QkyeHaqbSAYSLFx+azQP9wPjMmTMxbNgwA1MWLFiAFStW5Gli/fr1sX37dgQEBKh40LBhQ8TG0seMp0LB9QULluCjj0KwaRMVrBJLYSThJJrhJYiD2X/NXgbfIdrfEd1ZeT3jAnbsQNjEiVB6extke+vqiMjOxi6dwPVULy/MpzkKBYScHMR/+inS3npLNeXq1ato27atCRz8AdCBgpKa+23apGH9+ngE/HgSYUOHwuvJE7PdnNGoEeLWrYMyPNzsOTzQMQjw31bH4MyrcECJOeB4BPj55njMecWnCDD3mAlSIcABdKmQZD3uigAH0N3Vs7wvRiAXAQ6gMxVsRYBfzG1FkOdbigBzzlLEeLxUCDD3pEJSrIcyaQcMGKC5WKZMGVWp5xs3buS74PHjx0Wl2SlITb2W1fLss8/iwoULqh8piJ2cnKy5R4F7ClLm5ACffx6MRYu05aVpkKX9r3WNNZbxvmvXLlUQ1Byhnu0VKlQQDVX39vZ69Ag+f/wBnz//hM/t2wiIjoa3TmDVHP15jUnp0weJCxYAFPR0AaFKBffv39dYShUN9Mv4m7uNP/7wQfPm4r7u5849NNk3Xl+vvi3r169Hu3btDJY/f/48unbtatKsatWqqaoX0AEQtXz66aeqbHS1+PqWQfXqN/Hrr+Ly5rpKK+AOLqEOiiBJczlVCMT1Lw+idCvqOiWWvJ5xVOq/ZL16EBISkNeXBA3oJLGO2lUARgCgXHBF2P/ZOw8wqarz/3/vLh1EUbAb0NgFEkuMvfzUKCpGYe8CKrFFElsSSzRWBHshFqJGjYr5K2XuglGjaKwxiRqxa1Bjj5qIKKL0MnP/zxlmdmeWmZ2Zndm599z5nOfJY5h7yvt+3u/ec2fee87poy9mzUq+zJEu5u/V/N2uXn4u6ZaMj309f/1MDXx5WvLIAZOML7YsbmzU/KuuCsWxA8XaXEv1mFtrKdrB+orWguVfi6OjuVqMejh8RnvhiEMUrCCBHoUo4kNHEiCB3pF06RsCISBAAj0EQbDcBB7MLQ+gheajOQuDFhGT0V7HBNJsXd3Q0NCuzj3P02677dbc1mzffdJJJl23qmy++eYyiWdTzJbomSt4zTbxe+65Z/KaWTC7557r6sMPW7YoP/jgJclzlttTPvnkE+2yyy5ZTc1W3mZlfTFl3rx5GjRoUFbVV155Reuas55blcWffaPn9rtRRy24VXXJNGXpxa+rS54JbbbXXjJsWN4tvkvvueNbmNXmmVuf33777Tr44IPbNbDRwaBB6+nrr1teHpg48WsNG1Z4O/9ly5Yldw0w57KnS77z2FeuXJnU4/wcuwCYowbMyx3mx6rMMnfuXO28884yL1es2viqSVL2sQS5nG6QJ0+N2VrqsavW+VdMnbpkH19Q6B7X89Zb1fuSS1bbGj2zc7NbwucZHzwo6VDzN+Y4+vaii7RojNm4q6W89tprOeJl+P9b0mbqrsX6la7Xyd3v1MZL3i8pronu3bXgrLO0yOwC4PDTRknwqli5kO6qaApDRZwAWot4gEPoHpoLYVBqxCS0VyOBroKbJNCrAJkhrCbAt0yrw4fxEChMgAR6YUbUaJsAD+YopNoE0Fy1iTNemgDa6xgttL2Nc9tj3nzzzfrxj3/cXKn1WdGDBw/WzJmrTtQxycfPPvusue6kSZN0wAEHNP/797/vqUsuyT6n/A9/mKchQ1Y/H7wQCZO0P/LII7OqXXDBBVnJ/bb6MAnh1tuQv/fee8ntvFuX009fS7FYD22nN3WRxqtRXiHzmq8v3X9/LTnsMJkt223d2trEMHO3guuvvz5rG/+iYaQqHn98Hz36aAvn0aMX6corvynYzbvvvqt99tknq95bb72V3E0hVzHHFphEeWbp169f8jNz3ECucsIJ1+iRR3aVWiXEV9VdrN69b9Yll+yV3L0gkZDicUdvv91Jm1x2tkYvvj2rywf3ulQ73HOMnPqWJHqhe1yhVeh/l7TqlZSWYjaQN6+C5Fp9nq511NFH6emnns5oZbZ4n6K+mqtHdaB20CsF+acrrNx006Sel++xh5btuqv8PPyL7pCKHU6gkO463AAGqBkCaK1mQh0aR9FcaEJRc4agvZoLeYc5TAK9w9DScUQIkECPSCBxAwL5CJBARxvlEuDBvFyCtC+VAJorlRj1K0UA7VWKZHY/H3zwQfNK8FwjDB8+XNOnT885+Lhx4/TTn/60+dpdd90lk6hOF7MKPN12r7320vvvt6xg3W677ZJnS5v/Xn755fL9tbX77uvp229bEor9+sX1/PNz1M0cx1xCaW2HaWpWn5tV6MUUc7b7gQce2Fy1U6dOyVXWTqtVtPff300nn5x9prO7zSu6/YC71eWD91X/ySfq9PHHzeedm1XA8Y020rL99tPikSO1YvDgYswJdR2ze4HZxSBdLr74Yp144onttvmWW3rq0ktbXqQwGrj//i/Vv3/bW4b/5S9/0XHHHdc8rkmGv/rqq3ntaH10gflxavLkydp6661XazN/vqNx49ZUU1N3JRK5vqKbs8IPM6eK67DDDtMtt2RufS59/d9l6rnrARqwMnsF9zs9v68VY3+ttY/cO7lCu5h7XL5V6NdJOiOHt/MkrZVn9Xm6+oWxC3Xn6XdmtH5VG2kdPaYDtI3ezsnQ7Jqw8NRT5Sxfrp63367EOuvo2/PO0xI39/nu7RYEDTucQDG663AjGKAmCKC1mghzqJxEc6EKR00Zg/ZqKtwd6iwJ9A7FS+cRIEACPQJBxAUItEWABDr6KJcAD+blEqR9qQTQXKnEqF8pAmivUiSz+/niiy+0/fbb5+y8W7du+vOf/6z9998/5/VTTz1V5557bvO1m266KZkMTxdzFrY5E9uU1iuVMzs0q73Nlu7Tp/fUr37VJ2usu+76Sj/60bKSnL/wwgt1552ZCUElE+KtP8vX6bPPPpu1irpPnz4ySfV0Mcc/33tvD112WW8tXNiS8O/VK6G//GXuasles3JYK1asOnu6c+eSfAl7ZZMsf/jhh5vN/OUvf6mzzz673Wa/9FJnHXZYv6z2G264Up73lQYMyJ9E//3vf5/1goQ5796ce99Wufrqq2WOITD6Ny9+mO3bW5cVK6Qf/7ivXnutS56uzLrvYZLmJq+31kq60Ue3PKPdLh2Vs48PNt5NXadMUP3mA7T++usn63z++edKmGXsrUquVejmBPrNJLXeq8Gczm5OX/dznH2e7nbJyiXaafJOmv+H+VIyv3+wttE1ekiHaFOZFwOyi1lhvnj48ORRA/H+/VddXLpUqq+PnLbbLWLLGjK3WhYwi81FaxYHz1LT0ZylgYuA2WgvAkEMiQsk0EMSCMwILQES6KENDYZBoDIESKBXhmMt98KDeS1HPxjf0Vww3BlVRa3OhFPpBBYvXqwtttgiZ8MRI0bo2muvTa5QzzznOl157bXX1plnnpncLr1Lly665pprZLbwTpdDDjlEt912W/KfQ4cO1csvv5zXwPPPP18nnXSyDj+8r158sSVZOWrUIl17beEtvDM7Pvroo/XUU09ljTVw4ECZM7FNWbFiRTJB2bdv3+Zt2RcsWKBnnnkmec65Oas9c2W9SaymV1m/8kpnnXfemnr99dUTqsWe1116lMLbwiTL77333mYDjznmmKyXKEq13Bxh7rrr6LnnumY1XX/9uJqavtSmm+ZOore2Y9SoUUntllvuuaeHzjlnrRzdfC3pKkm/NYrKup55VEH6gklUP7LHaJ3ySctq/cxGX/TsLz33oNYdZDZcz59AN9cyV6GbV0tOkXRHDgvNWvrZBVaf3/r6rbrkn5fI/4+vde/cRSN1sMbrWq2ZTL23lPnrrqkVt92tFTvtxHnm5YoqZO15rgtZQCJsDlqLcHBD6hqaC2lgasAstFcDQa6SiyTQqwSaYawlQALd2tBhOASKI0ACvThO1MpPgAdz1FFtAmiu2sQZL00A7XWMFnzfT668zbXa9ZFHHtGgQYP00ksvaezYsXrlldxnIZszo02y/aqrTEKxpbiu25xQb73Vd2tvzDbpDzzwgJ5/fjeNH599Fvp//vPf5ALXYstuu+2mjz/+OKt6emXw//73v+RW3+bs9/79+2vKlCl6++23ddZZZ2nePLPhtdS1a1ctW9ay6t1sM//oo3/RlVeuoZtu6iXfX/1r2rBhizVx4vxiTYxMvSuuuEK/+93vmv3JtYV5qc7Om+do5Mi++te/slfrmyT6ffd9qe98Z/Ukemt9mRcyTj755FKHzqpvJLDHHuvqv//t1Px5jx4JbbLJdL3zjtmmPveLHe+9917zixnphslE9fPjdcST39el/1igbRLZ27mbel/030G9X39MUz94QPv221edndy7FZhV6N123FG3ffONbpBkVqDnKmZ9+PttrT6f/6V+d+JR2vXtTvrh11+ov//fnP281Vdyf7qm7j/pJXXv1HI+fVlwaRwaAsytoQlF5A1Ba5EPcegcRHOhC0nNGIT2aibUHe4oCfQOR8wAlhMggW55ADEfAoUIkEAvRIjrhQjwYF6IENcrTQDNVZoo/RVLAO0VS6r0eubcZ7MCO7PssMMOevDBB7M+M0npgw46SN9+m706Nd+ImauRR48erSeffLJN40wi/g9/eEr77z9gtXrbb79cG2wQ10YbxXXQQUu1yy7Lc/a1fPlyffe73835QsDs2bNl7DAvBKTL4MGDk9uz53qBIF3HnOXuug/rzDOzt5dPXzfJ86uvni+zQ3utldZbp5vdCsx2/OWWr782SfR19Oab2Sv9DzhgqSZNWvWiQ2u9zpkzp/mjO+64I6nVcsrEib105ZW9s7qYMuUr7bXXsuQLGkZHp5122mpDmN0KMreDb94mfdmqFyzql3bXsdOH68J3n1F//Ser/SuDdtZOR7ygi3a/WCcOzH2WvHmx5fSTT9a3GS955PLzcEmTxo7VojFjsi/7vub/7j71vXa81l25auv5fGXWWtIhP5W+7OXool0u0phBrfoqBzBtQ0GAuTUUYagJI9BaTYQ5VE6iuVCFo6aMQXs1Fe4OdZYEeofipfMIECCBHoEg4gIE2iJAAh19lEuAB/NyCdK+VAJorlRi1K8UAbRXKZKr97PTTjvJrMzOLDfeeKOGDx++WmWzuvbSSy/VY489VtCgk046KXm2tClmS/SZM2cWbGNWsj/xxHX68ssN2qzb0LBYl1/+jXr29LPqvfvuu9pnn31ytt1rr72S27SXWvbY4yi9+uofs847N31svvkKXXbZN9pjj9zJ/FLHsbH+tGnTdMYZZzSbbnYsMAneShSTRD/yyHWytsuvq/P18stz1K9fy/ngCxcu1FZbbZU1pNnCf8stt2y3GZ9+Wq+99+6npUtbzrjfdddlybPYnYxv6ebvYe+9984a509/+pN+8IMfNH/WvE26WrTqyNExc2/XRTddutpZ4/eusZdOHdlN/zz3ZvXqmv1WxtKlS7Xjjjtq/vzCux3c0aOHhrz+uvyMNzvq33tPOuk8rTf7HwXZ/FFd9bNDlmlpypU+Xfto1pGzWIVekJxdFZhb7YqXzdaiNZujZ6ftaM7OuEXBarQXhSiGwwcS6OGIA1aElwAJ9PDGBssgUBECJNArgrGmO+HBvKbDH4jzaC4Q7AwqzkDvSBGYhLNJPKfLOuuso1mzZiW3Ms9X/vnPf+qyyy7LWs3duq5JrJoz0k059dRTdd999xXpxqmSJhasu/vuy3TvvV+pc8ZO12bl8UUXXVSwbfEVumi99f6tOXPMhtgt5eyzv9VJJy1Ul9WPQi++6wjUNOfKH3/88c2ebLzxxjLaqFQxSfSdd15Pixe3JLIvueQbHX/8ouYhXn/9dQ0ZMqT5347j6P33329Tv23Zt3y51NDQVy+91BJcx/H10ENf6nvfyz7v3PTT+siA2267TYccckhyiNarzzPHNQnpUY//RZc+cYD6KDsh/pj21/ldL1Wn7w/W97aP63vfW64dd1yud999UkcdddRq5ptXRk6XNFnS45IOMye0X3CBVpx0kpRIqP6jj9Rjxgz1mHiT6lfmf+FjgXrq79pYV+pTPaNFkllwvuGq4UzSn1XolVJ2ePrhuS48sYi6JWgt6hEOn39oLnwxqRWL0F6tRLrj/SSB3vGMGcFuAiTQ7Y4f1kOgIAES6AURUaEAAR7MkUi1CaC5ahNnvDQBtNdxWjBnlT/77LPNA5hk97nnnltwQHN+ukmgmnOwzUrc1iWzH5NIL21r759J+o2k1bdzzxznsMOWaMKE+erRY9Xq3mHDhrU7gVtfX6+HH35Yjz/+eHL7epOE7dfvHv33v41Zro0YsVi//W3hFcAFAUagwgsvvKAjjjii2ZNevXrpnXfeqahnp522lmbM6NHcp9nO/89//rL53+ecc47uueee5n+b7dPNNurtLRdc0Ft33dUrq/kxxyxK7niQqxx++OHJF07SxezQcNxxxyX/2bz6/AtfektSP0nbJLPRyYT0eTuM04rLfqhx/zxM3bRste7nqY+e0676h3bXX7W35mzm6/0PDpS0OFl3y7o6/b9EQjtntDR/CYnevbXkqKPU+bXX1PmNN1TX6oiGzIEe67S7pm6+pv45+F291fldJaZLWippkPmDWmVrurAKvb2qCm875tbwxiZqlqG1qEU0/P6gufDHKKoWor2oRrb6fpFArz5zRrSLAAl0u+KFtRAomQAJ9JKR0aAVAR7MkUS1CaC5ahNnvDQBtNdxWpgxY0bzWc6bbLKJ/vznP6tv375FD2jODzdJ0/333z+rzU033SSTXDTl/PPP16RJk3L2ue2228qcT756MauOB2rXXUfp0EN/rrfe6qwZM7pnrUY2bbbaaoVisa8Uj/8vub21SeyvKgPNVUlmJb1Zpm6S/M9Liue0Y+jQoTJnepti9PbBB+trjz2k5u5MOn/ASs2cOVe9e2dvHV80rIhVzLVl/ocffqguFVya/+STXTV69DpZ5P7xjzkaMCCefHHj//7v/xSPt8T02GOPTe6O0J7yxBNd9ZOfZI/1ne+s1COPzNWaa+aO+ZgxY/TQQw81D5d+cWTR8kXafuz2WvTpIsmcHLAyVeVgKZ3xNgnp50fM0h9+HtP4v4zLmUTP9OMZ7alRukj/ldk2/w1duO++Gvf003IyRGqsLOaHhI/1HZ3yHVcPjb5J6mwy5qli8vhmoX3P1TtiFXp7VBXuNsyt4Y5PlKxDa1GKph2+oDk74hRFK9FeFKMajE8k0IPhzqj2ECjme6893mApBCCwGgES6IiiXAI8mJdLkPalEkBzpRKjfqUIoL1Kkczdz0svvaQPPvhAe+yxhzbYoO3zx/NZ8ve//12jR4/W8uXL1b9/fz3xxBPqnjp/efz48br11ltzNh01apSmTJmS10Gzxfy9996bvP7aa501bNg6WWdTpxtuuunH+vBDs/rZfI3a1JxSnqNPs3LZJDsfkGTOZF/SXOfGG/+gvfc+RHV10oIF9Ro5sp/+85+WLrp18/XAA3O13XbpTGjHxsSG3ufOnavvf//7Waa+8sorWnfddStm/ooV0g47rKd58+qb+/z1r7/Vr361UCeeeGJy14B06datm4wOS9Ww0dXvf99LDz3UTfF4y9dwE/P775+rgQPzx/zCCy/UnXfe2WxDY2OjrrvuOjX8skHPNeVYCb+xpJ+uqm4S0uf+4Fzd/PrNGvTGMv1pSkJr+6uvRM+EOVd9dYV+rX/pFZ17zGbabdqN6rx0YVFJc9PPStXrOp2ui3fqp8UH/0aqK+1lEFahV0zaoeiIuTUUYagJI9BaTYQ5VE6iuVCFo6aMQXs1Fe4OdZYEeofipfMIECCBHoEg4gIE2iJAAh19lEuAB/NyCdK+VAJorlRi1K8UAbRXKZId28/HH3+c3PrcnAttkpnpcvXVV+uGG27IOfgll1wik4TMVwYNGqRHHnmk+fLjj3fVySf30aJFLedit8+rTyQNT61Mv0Fdux6lZcvy93nhhd/o5z9vOXu7fWNGq9WKFSs0YED2NvtPPfWUttxyy4o6ev75a2rSJLMkelXZfPMVmjDhEf34x+a075ZS7PED6RZvvdVJF164pp57zuxSsHq54or5+slPVm2Xnq9MnDhRV155ZfNl88LHxN9P1KCBg1pWnbdufHHLB93ru2tpfKn8530NeES6QNIBqtN3lKgowzlaN7kN/GU6X2/s9ZDq97tYTom/OCT8hOJ+XBP2mqCRW42sqH10FgwB5tZguNfiqGitFqMerM9oLlj+tTw62qvl6FfWdxLoleVJb9EjUOLX2egBwCMIRJ0ACfSoR7jj/ePBvOMZM0I2ATSHIoIigPaCIl+ZcU3y3CTRcxXP82TOYc9XzGriF198MevyBx/Ua9SodfTpp50qY2CBXgYPXq4HH/xSnaozXFV8qtQgW2+9tRZknLFtjgT44Q9/WKnuk/3MmtVZhx9uDhBvKQMH/kRvvvn/mj9Ya621kmef9+7du6ix33ijs1x3HS1YkPuliQMOWKq77ppXMMk8bdo0nXGG2VJ9Vdlmm220/VHba/IFk/PbkZFAT1b6VtL15vDyliZmofrukvZcp04/SaytNb5uOfe9GAfNSvX/p9HJpPlL2lGfaaPkmvfvD31Kux83o6Bf+cZwHCeZPB/QO/vFiWJsok74CDC3hi8mUbUIrUU1suH1C82FNzZRtwztRT3C1fOPBHr1WDOSnQRIoNsZN6yGQNEESKAXjYqKeQjwYI40qk0AzVWbOOOlCaA9u7Vgtm8327jnKm+88YbMKvN8pWvXrslV7SZxl1nefbdTcjv3zK29W/exxhoJbbxxXMuXS++/b85BL71suGFc9933ZbIfyuoEdtllF33yiVnNv6qY7cwPPPDA5n8vXLhQiUSi6MR2LsbmiO9dd11Xn3yS+QbDREm/aK4+duxYmfPIiykmeX7kkWvn1E6PHonkqvOzz/5WXXMvTM8a4umnn9ZRRx3V/FnPXj21bJNlWvlWG1v9ny8pU46zUicL5DF+LUl/lKOhKrzd+uPaT7dpjO7Xj7Vcqxzo0yeuQw5ZqoaGJfrBD5YXg4g6NUKAubVGAh0CN9FaCIJQYyaguRoLeIjcRXshCoblppBAtzyAmN/hBEigdzhiBoBAsARIoAfLPwqj82AehSja5QOasyteUbIW7dkdzUmTJun8803WMLt06dIlefa6SaB//fXXeZ18++231atXL82ZM0edO3fW2muvnUyoz5lTp8ce66bf//4effjhB6n2vrbbbqDOOedw7b33suZV459+Wq/HHuuqyy9fpMWLzWrcwmWbbaTbb5+rTTddUbhyjdYYMmSIXn/99WbvJ0yYoJEjV23vbeJuEtvm79e8QDF69Oh2U7rmmjV0/fVrZLQ3etlQ0lJtvPHGeuaZZ2RetihU/v73LjrhhLW1cGH2yvO+fePmbs3SAAAgAElEQVQaM2aRjjpqkdZaq3CiOj3OW2+9pf3337/QsNnXT5e0ZsZHf5aUvcnCav2ZHwd+ljw+vacG1HVV3O+sOX4/faF1NVf99La21j06Wu9r82TbddeNa999l2no0CXaY49l6ty+90dK84va1hFgbrUuZNYajNasDZ21hqM5a0NnveFoz/oQhsYBEuihCQWGhJQACfSQBgazIFApAiTQK0Wydvvhwbx2Yx+U52guKPKMi/bs1kDrba7T3qy77rp65ZVXdMABB2j27Nl5nbz//vuT56SnE7VbbbWVbrzxRg0cOFDz5s3T97//fcXjLSvEzYr3Qw89NGd/5503TnffbVZID8u6/r3vLdc118zXuusm5Pt1Wn/9dbXeetKcOZ8nV1BTchM48sgj9de//rX54gUXXKCTTjpJixcv1ve+973kf03p0aOHXn31VfXs2XKWeSlMP/mkPrkK3fczvyYfLelemSMCGhoa2uzu3//upClTeiTPUl++PPur9q67LtOkSfPUq1fxifP0YMY/o79FixYV745ZKG9y/+nyB0mfZvx7M0kfS8qx6UG3bt2SRxr06dNHCxY4+vjjen30UaekTwsXmv/VaeDA5dp99+Wqry/eJGrWJgHm1tqMexBeo7UgqNf2mGiutuMfpPdoL0j60RqbBHq04ok3lSdAAr3yTOkRAqEiQAI9VOGw0hgezK0Mm9VGozmrw2e18WjP6vDpT3/6k0455ZTVnNhiiy1ktsA+5phj9Pjjj+d1csCAAfroo4+yrg8ePFgzZ87UlClTdNZZZzVf6969u8y28Oa/ucrEiRN15ZVXSfqNpIskddPOO7+nyZN7qnv3VQlU9Fa83kxcTXzT5dRTT9W5556bfNnBrE7PLCZWe+21V/Gdt6o5atTaeuaZbhmfPq9NNmnUs88+m4xZ67JokaMHH+yWjO1LL3XJOe7uuy/TnXe2L3me7vDee++VeXFguTkroHUxw5pE9pKMCybvv2qh+Kpzz6+UlNnU7AhvpDg1+1x0U928KGBeGKBAoBIEuNdVgiJ9FEMArRVDiTqVJIDmKkmTvkohgPZKoUXdtgiQQEcfEGibAAl0FAKBiBMggR7xAFfBPR7MqwCZIbIIoDkEERQBtBcU+cqM+8gjj+iEE05YrbOddtpJZnX5r3/9a02ePLnkwUzi9Lzzzksm4dPlkEMO0W233Za3L3NGt1nNvqr0MWuj9dRT92jLLbdsboPeig+F2ZrfbNWeLqNGjdK1116rBx54ILkSPbOcffbZ+uUvf1l8561qXnfdp7r22p2zPt1773M1efJpq/U5c2Y3nX76WlqwYPXEeotWlmjixK+LOuu8kNGf/O8T7TtuXy15eYn0WUbtvSW9JemLjM/M5geDU/+eJ+nGVr2fKcnsVm/axVLJ9FQV8/di/m4oEKgEAe51laBIH8UQQGvFUKJOJQmguUrSpK9SCKC9UmhRty0CJNDRBwTaJkACHYVAIOIESKBHPMBVcI8H8ypAZogsAmgOQQRFAO0FRb4y4z711FM6+miz7Da77LfffvrjH/+oc845R/fcc0/Jg51xxhnJrdxXrlzZ3Pbmm2/Wj3/847x9zZgxQ6edlp1wNeewZ56fjd6KD8Xvfvc7XXHFFc0Ndl5/fT3e0KBrX3xR459/PqujA/v3lzd0aMHOF48apfiAAVn17rvvPp16qkm+vy+pf/O1fv3m6s47pR12aDmn/s03O2nIkH5KJPJ/pT7hhIUaO/bbim1zPvWdqTrzmTNV79TL+dqR/5Ev9ZKcLRzF74qv+ne6mBMEdk39wyTJp2W42kPSryWlTTfXH5a0WOq2bze9eceb6t4p9+4KBcFSAQKtCHCvQxLVIoDWqkWacdIE0BxaCIoA2guKfPTGJYEevZjiUWUJkECvLE96g0DoCJBAD11IrDOIB3PrQma9wWjO+hBa6wDaszZ0ScOfe+65nGdUDxs2TGZL9fHjx8ucW15uMedDv/baa+rVq1ferubPn68dd9xRS5cuTdbZbbfd5HleVn30VnwkzPnn5hz0dDGp3W/q6jTG9zXJzz5TfG2zELtTJ9U5eb7qJhJy4nF9PWGClowcmWWE2VnAnKEunSFpQtY1x/F19NGL9ZvffKvevX0dfnjfnFu29+sXl+su1siRi/Xd7+Y4YLx4t1er+dG3H8kk0f1WPpuKj1zziN579r2WNntK2i/1z6cktRwhL20q6ZhW3Zv3QxzJqXd00S4Xacwgc4g6BQLlE+BeVz5DeiiOAForjhO1KkcAzVWOJT2VRgDtlcaL2vkJkEBHHRBomwAJdBQCgYgTIIEe8QBXwT0ezKsAmSGyCKA5BBEUAbQXFPnKjPvyyy9raI6Vx2Zbd5M8f+utt7T//vsXHKxTp05Zq81bNzjooIN0xx13FOznL3/5i6677jqtvfbayfG/+93vcq8rSC13hXnz5mnQoEFZF1+TZE68/3uOJmZB9dZ5xjLp9kSfPvpi1iz5GWfYL1u2TJtttlmqVW9J/5K08Wq99O0b1957L9P06WYZd0vZbbdlOuGERdpvv6Xq3LmdjpbRzBwzcPfdd7f0sKOk9EL8/5daVJ++uoukg/IP1qdrH806char0MuIB01bCDC3ooZqEUBr1SLNOGkCaA4tBEUA7QVFPnrjkkCPXkzxqLIESKBXlie9QSB0BEighy4k1hnEg7l1IbPeYDRnfQitdQDtWRu6pOGzZ8/WAQccsJoTZgv2M880Bz5LF110UTL53bdvX22yySZ65ZVXsur36dNHDz30kPbdd1+ZhGquYrYTP+KII8qGhd5KQ/jDH/5Qn376aXOjOyWdJ+nzHN2Ya8fl6d53HH170UVaNKZlhfU333yjq666KjsBre0kTZU0sKChAwas1BNPfKFu3QpW7bAK5kx488JGc9lG6jyqs/wVvlZeuVJq2X1e9SPqVbdd7nPbE35CcT+uCXtN0Mitslfod5jxdBxpAtzrIh3eUDmH1kIVjpowBs3VRJhD6STaC2VYrDSKBLqVYcPoKhIggV5F2AwFgSAIkEAPgnq0xuTBPFrxtMEbNGdDlKJpI9qzO67vv/++9tprr9WcuOyyy3Tsscc2f75o0SKZVeZ33XWXLrnkkqz6N910kw4//HCNGTMmmUhvXbp06aLXX39da6yxRtmw0FtpCE888UQ9/LA5qHtVOaBTJz2WcS59Zm8/lXR7ju5zrT5fsmSJ9t57b3322Wc5WnTSfvs9pGef3V9LluROOJtGU6d+qT33XF6aQxWubfR8wQUXNPe6wTYbaPjlw/Xp65/qT2P/1DKaI/2y6ZeK18el7N3vm+s4jpNMng/onX1GfIVNprsaIcC9rkYCHQI30VoIglBjJqC5Ggt4iNxFeyEKhuWmkEC3PICY3+EESKB3OGIGgECwBEigB8s/CqPzYB6FKNrlA5qzK15Rshbt2R1NkwDdeeedV3Pi8ccf1zbbbLPa52+++aYOPPDA5s/N/zer003y8Mknn9To0aNXa1Ps9u3FkERvxVBqqXPjjTcmV4kXU8za8TdzVMy1+nzGjBk67bTTcnZrdi445ZRTNHduD5166lqaNavravVOPHGhLr7422LM6tA6999/v04++eTmMTbffHOZs+OvuOIKmV0T0uUHP/iBXnjhBX3++edKJBIdahOdQ8AQ4F6HDqpFAK1VizTjpAmgObQQFAG0FxT56I1LAj16McWjyhIggV5ZnvQGgdARIIEeupBYZxAP5taFzHqD0Zz1IbTWAbRnbeiShn/11VcaPHhwlhO9e/dObu1ukuK5yuTJkzV16lRtu+22Gjt2rLqnzsSOx+PJZLxJMqZLt27dkiugt9pqq4qAQm+lYXzmmWc0atSooht9LWmtVO0PJV0jaWWXLjr24Yc1IOOFivS2/q07NnowOxGky5df1ulHP+qnOXPqmz/70Y+W6JZbvg506/a0MX/72980cmTLluvmOALzksjBBx+s114zJ8avKuasdLMrAwn0oqVExTIJcK8rEyDNiyaA1opGRcUKEUBzFQJJNyUTQHslI6NBHgIk0JEGBNomQAIdhUAg4gRIoEc8wFVwjwfzKkBmiCwCaA5BBEUA7QVFvjLjmq3Zt9xyy6zOzHbsZlv29hTT7vLLL29u+tvf/lYjRoxoT1c526C30lCuXLkyecb9v//976IaPiLJ7C8Ql7SjpHQKebvtttPMmTNVX78qET58+HA9//zzq/VpVqabc9czy/vv1+vyy3tr+XJHRx65WAcdtFR53s0oysZKVKr/6CP1mDJFb375pXabas5sX1XMF/0vTjpJG956q1ZkrDR/YuRI/d+AAVq4cGHWDu6LR41SfABbtlciJvSRTYB7HYqoFgG0Vi3SjJMmgObQQlAE0F5Q5KM3Lgn06MUUjypLgAR6ZXnSGwRCR4AEeuhCYp1BPJhbFzLrDUZz1ofQWgfQnrWhSxpuVo1vvfXWWrx4cbMjuZKgxXpp+jNJdLN6d+jQocmz0StZ0FvpNL/55htNMcniN9/Ue++9J3PuvYl3f0nzJC3I6NKcBm5OuH9S0n6thnr00Uc1cODA5BbmZveBBQsyW0pm9fasWbOadyQo3dLqteg+dar6nHmm5tTVaf1WW7JfU1enX7f6bG6nTurrOC3J80RCTjyurydM0JKMFezV84CRok6Ae13UIxwe/9BaeGJRK5aguVqJdPj8RHvhi4mtFpFAtzVy2F0tAiTQq0WacSAQEAES6AGBj9CwPJhHKJiWuILmLAlUBM1Ee/YHdfz48br11luTjvzoRz/SnXfemXf79qC9RW/lR8D3fcVvvFEbX321zpF0bUaXm0l6V9JJkm5rNZTRyQknnKAPP/xQe+yxR9ZVs1X/LbfcktSPDcVZskTr7bSTnPnzkyvtX8kwuoekltdJpDUlma3tM38E8CUl+vTRF7NmyU8dYWCD39hoDwHudfbEynZL0ZrtEbTPfjRnX8yiYjHai0okg/eDBHrwMcCCcBMggR7u+GAdBMomQAK9bIQ13wEP5jUvgaoDQHNVR86AKQJoz34pmITqs88+qyVLlmifffZRp06dQusUeqtMaNIJ5Ofmz1d2KlzaRtL7kpa3GsqcC3777bfrgQce0EknmRT7qtK3b1+9+uqroX3pIh+xnrfeqt6XXKKY76vlFPTVa5sE+4utPvYdR99edJEWZZz3XpnI0AsEVhHgXocSqkUArVWLNOOkCaA5tBAUAbQXFPnojUsCPXoxxaPKEiCBXlme9AaB0BEggR66kFhnEA/m1oXMeoPRnPUhtNYBtGdt6Kw0HL1VLmwmgbzG+PHJFdivFtFtOlF+xRVXJLfpT5d9991X99xzTxE9hKtK+iWCxPz52k5SvlPiR0hqOSVdyW3cWX0erlhG0RrudVGMajh9QmvhjEuUrUJzUY5uuH1De+GOj03WkUC3KVrYGgQBEuhBUGdMCFSRAAn0KsKO6FA8mEc0sCF2C82FODgRNw3tRTzAIXMPvVUuIOkE8h3z52tMkd0+88wzuvDCC/XXv/61ucVpp52m3/zmN0X2EK5q6VXok3xfx+cx7XxJl2ZcY/V5uGIYVWu410U1suHzC62FLyZRtwjNRT3C4fUP7YU3NrZZRgLdtohhb7UJkECvNnHGg0CVCZBArzLwCA7Hg3kEgxpyl9BcyAMUYfPQXoSDG0LX0Ftlg2ISyJ3Gj9dukl4vousbbrhB48aN07x585pr33rrrTr00EOLaB2+KumXCFbOn68tJP0nh4l3Sjou9Tmrz8MXw6haxL0uqpENn19oLXwxibpFaC7qEQ6vf2gvvLGxzTIS6LZFDHurTYAEerWJMx4EqkyABHqVgUdwOB7MIxjUkLuE5kIeoAibh/YiHNwQuobeKhuUdAL5i/nzNUPSF5I27NFDvSZO1Hr9+yeT5X/729+aBz3mmGN09913Zxnx7LPPqn///pU1rIq9pVeh3+z7OjXHuM9I2jOdQOfs8ypGpraH4l5X2/GvpvdorZq0GcsQQHPoICgCaC8o8tEblwR69GKKR5UlQAK9sjzpDQKhI0ACPXQhsc4gHsytC5n1BqM560NorQNoz9rQWWk4eqt82NIJZMf31Xp78jPOOEPTpk1rHnSjjTbSZ5991vzv3r17a/bs2XIce78ip18iWDp/vjaTNKcV4v9K2iD1mTn7fM6sWfK7d698IOgRAhkEuNchh2oRQGvVIs04aQJoDi0ERQDtBUU+euOSQI9eTPGosgTs/XWgshzoDQKRJUACPbKhrZpjPJhXDTUDpQigOaQQFAG0FxT52hwXvVU+7ukEsjN/vkyC+IuMBPFVV12lG2+8Me+gu+22mzzPq7xRVe4x/RLBtb6vszPG7iXpW0nJHwDM6vOxY7XwxBOrbB3D1SIB7nW1GPVgfEZrwXCv5VHRXC1HP1jf0V6w/KM0Ogn0KEUTXzqCAAn0jqBKnxAIEQES6CEKhqWm8GBuaeAsNhvNWRw8y01He5YH0DLz0VvHBMwkkNccP17fjB2rRWPGNA8yadIknX/++XkHHTNmjMaOHdsxRlWx1/RLBAvnz9cPJb2dGvuXkq5P27HOOvr8hReU6NatipYxVK0S4F5Xq5Gvvt9orfrMa31ENFfrCgjOf7QXHPuojUwCPWoRxZ9KEyCBXmmi9AeBkBEggR6ygFhoDg/mFgbNcpPRnOUBtNh8tGdx8Cw0Hb11UNCWLlX3++/XksMPl7p2bR5k5syZ+ulPf5p30IkTJ2rYsGEdZFR1u02vQv/c92U2rV9L0tGSOhkzzBb1Eybo81GjlEgkqmsYo9UkAe51NRn2QJxGa4Fgr+lB0VxNhz9Q59FeoPgjNTgJ9EiFE2c6gAAJ9A6ASpcQCBMBEuhhioadtvBgbmfcbLYazdkcPbttR3t2x88269FbdSP28ssva+jQoXkHffrpp7XFFltU16gOGi1zK/vML/y+yZ+vs470n//o82+/JYHeQfzpNpsA9zoUUS0CaK1apBknTQDNoYWgCKC9oMhHb1wS6NGLKR5VlgAJ9MrypDcIhI4ACfTQhcQ6g3gwty5k1huM5qwPobUOoD1rQ2el4eitumH77LPPtPPOO+cctHv37nrnnXdUX19fXaM6cLT0KnTHN2nzVcV3HDkTJkinn67PP/+cBHoH8qfrFgLc61BDtQigtWqRZpw0ATSHFoIigPaCIh+9cUmgRy+meFRZAiTQK8uT3iAQOgIk0EMXEusM4sHcupBZbzCasz6E1jqA9qwNnZWGo7fqhm358uXadNNNcw6644476oEHHqiuQR08WutV6CaN7vfpo7pPP5V69CCB3sH86Z4EOhqoPgHm1eozr/UR0VytKyA4/9FecOyjNjIJ9KhFFH8qTYAEeqWJ0h8EQkaABHrIAmKhOTyYWxg0y01Gc5YH0GLz0Z7FwbPQdPRW/aANHjxYX3311WoDH3vssbrsssuqb1AHj5i5Ct2sPl8wdqx6jx2bHJUV6B0Mn+6bCXCvQwzVIoDWqkWacdIE0BxaCIoA2guKfPTGJYEevZjiUWUJkECvLE96g0DoCJBAD11IrDOIB3PrQma9wWjO+hBa6wDaszZ0VhqO3qoftgMOOECzZ89ebeAJEyZo5MiR1Teog0fMXIWe6NNHc198UetvtllyVBLoHQyf7psJcK9DDNUigNaqRZpx0gTQHFoIigDaC4p89MYlgR69mOJRZQmQQK8sT3qDQOgIkEAPXUisM4gHc+tCZr3BaM76EFrrANqzNnRWGo7eqh+2o48+Wk899dRqAz/66KMaOHBg9Q2qwohmFfqa48frm7FjteTnP9f666+fHJUEehXgM0SSAPc6hFAtAmitWqQZJ00AzaGFoAigvaDIR29cEujRiykeVZYACfTK8qQ3CISOAAn00IXEOoN4MLcuZNYbjOasD6G1DqA9a0NnpeHorfphO/PMMzV16tSsgbt06aJ33nlH5r+RLEuXqvv992vJ4Yerrnt3EuiRDHK4neJeF+74RMk6tBalaNrhC5qzI05RtBLtRTGqwfhEAj0Y7oxqDwES6PbECksh0C4CJNDbhY1GGQR4MEcO1SaA5qpNnPHSBNAeWqgmAfRWTdqrxrr22mt13XXXZQ08bNgwTZw4sfrGBDAimgsAOkOyAh0NVI0A97iqoWagFAE0hxSCIoD2giIfvXFJoEcvpnhUWQIk0CvLk94gEDoCJNBDFxLrDOLB3LqQWW8wmrM+hNY6gPasDZ2VhqO36oftzTff1JAhQ5RIJJKDH3roobr++uvVvXv36hsTwIhoLgDoDEkCHQ1UjQD3uKqhZiAS6GggYALc7wIOQISGJ4EeoWDiSocQIIHeIVjpFALhIUACPTyxsNUSHsxtjZy9dqM5e2Nnu+Voz/YI2mU/egsmXs8995zMmec77LCDhg4dKsepna/EaC4YzdX6qOiu1hVQPf/RWvVYM9IqAmgOJQRFAO0FRT5645JAj15M8aiyBGrn14LKcqM3CFhDgAS6NaEKraE8mIc2NJE1DM1FNrShdwzthT5EkTIQvUUqnFY4g+asCFPkjER3kQtpaB1Ca6ENTWQNQ3ORDW3oHUN7oQ+RNQaSQLcmVBgaEAES6AGBZ1gIVIsACfRqkY7uODyYRze2YfUMzYU1MtG3C+1FP8Zh8hC9hSkatWELmquNOIfNS3QXtohE1x60Ft3YhtUzNBfWyETfLrQX/RhXy0MS6NUizTi2EiCBbmvksBsCRRIggV4kKKrlJcCDOeKoNgE0V23ijJcmgPbQQjUJoLdq0mYsQwDNoYMgCKC7IKjX5phorTbjHqTXaC5I+rU9Ntqr7fhX0nsS6JWkSV9RJEACPYpRxScIZBAggY4cyiXAg3m5BGlfKgE0Vyox6leKANqrFEn6KYYAeiuGEnUqSQDNVZImfRVLAN0VS4p65RJAa+USpH2pBNBcqcSoXykCaK9SJOmHBDoagEDbBEigoxAIRJwACfSIB7gK7vFgXgXIDJFFAM0hiKAIoL2gyNfmuOitNuMepNdoLkj6tTs2uqvd2Ffbc7RWbeKMh+bQQFAE0F5Q5KM3Lgn06MUUjypLgAR6ZXnSGwRCR4AEeuhCYp1BPJhbFzLrDUZz1ofQWgfQnrWhs9Jw9GZl2Kw2Gs1ZHT5rjUd31obOOsPRmnUhs95gNGd9CK11AO1ZG7rQGU4CPXQhwaCQESCBHrKAYA4EKk2ABHqlidZefzyY117Mg/YYzQUdgdodH+3VbuyD8By9BUG9tsdEc7Ud/6C8R3dBka+9cdFa7cU8aI/RXNARqN3x0V7txr7SnpNArzRR+osaARLoUYso/kCgFQES6EiiXAI8mJdLkPalEkBzpRKjfqUIoL1KkaSfYgigt2IoUaeSBNBcJWnSV7EE0F2xpKhXLgG0Vi5B2pdKAM2VSoz6lSKA9ipFkn5IoKMBCLRNgAQ6CoFAxAmQQI94gKvgHg/mVYDMEFkE0ByCCIoA2guKfG2Oi95qM+5Beo3mgqRfu2Oju9qNfbU9R2vVJs54aA4NBEUA7QVFPnrjkkCPXkzxqLIESKBXlie9QSB0BEighy4k1hnEg7l1IbPeYDRnfQitdQDtWRs6Kw1Hb1aGzWqj0ZzV4bPWeHRnbeisMxytWRcy6w1Gc9aH0FoH0J61oQud4STQQxcSDAoZARLoIQsI5kCg0gRIoFeaaO31x4N57cU8aI/RXNARqN3x0V7txj4Iz9FbENRre0w0V9vxD8p7dBcU+dobF63VXsyD9hjNBR2B2h0f7dVu7CvtOQn0ShOlv6gRIIEetYjiDwRaESCBjiTKJcCDebkEaV8qATRXKjHqV4oA2qsUSfophgB6K4YSdSpJAM1VkiZ9FUsA3RVLinrlEkBr5RKkfakE0FypxKhfKQJor1Ik6YcEOhqAQNsESKCjEAhEnAAJ9IgHuAru8WBeBcgMkUUAzSGIoAigvaDI1+a46K024x6k12guSPq1Oza6q93YV9tztFZt4oyH5tBAUATQXlDkozcuCfToxRSPKkuABHpledIbBEJHgAR66EJinUE8mFsXMusNRnPWh9BaB9CetaGz0nD0ZmXYrDYazVkdPmuNR3fWhs46w9GadSGz3mA0Z30IrXUA7VkbutAZTgI9dCHBoJARIIEesoBgDgQqTYAEeqWJ1l5/PJjXXsyD9hjNBR2B2h0f7dVu7IPwHL0FQb22x0RztR3/oLxHd0GRr71x0VrtxTxoj9Fc0BGo3fHRXu3GvtKek0CvNFH6ixoBEuhRiyj+QKAVARLoSKJcAjyYl0uQ9qUSQHOlEqN+pQigvUqRpJ9iCKC3YihRp5IE0FwladJXsQTQXbGkqFcuAbRWLkHal0oAzZVKjPqVIoD2KkWSfkigowEItE2ABDoKgUDECZBAj3iAq+AeD+ZVgMwQWQTQHIIIigDaC4p8bY6L3moz7kF6jeaCpF+7Y6O72o19tT1Ha9UmznhoDg0ERQDtBUU+euOSQI9eTPGosgRIoFeWJ71BIHQESKCHLiTWGcSDuXUhs95gNGd9CK11AO1ZGzorDUdvVobNaqPRnNXhs9Z4dGdt6KwzHK1ZFzLrDUZz1ofQWgfQnrWhC53hJNBDFxIMChkBEughCwjmQKDSBEigV5po7fXHg3ntxTxoj9Fc0BGo3fHRXu3GPgjP0VsQ1Gt7TDRX2/EPynt0FxT52hsXrdVezIP2GM0FHYHaHR/t1W7sK+05CfRKE6W/qBEggR61iOIPBFoRIIGOJMolwIN5uQRpXyoBNFcqMepXigDaqxRJ+imGAHorhhJ1KkkAzVWSJn0VSwDdFUuKeuUSQGvlEqR9qQTQXKnEqF8pAmivUiTphwQ6GoBA2wRIoKMQCEScAAn0iAe4Cu7xYF4FyAyRRQDNIYigCKC9oMjX5rjorTbjHqTXaC5I+rU7Nrqr3dhX23O0Vm3ijIfm0EBQBNBeUOSjNy4J9OjFFE73sBAAACAASURBVI8qS4AEemV50hsEQkeABHroQmKdQTyYWxcy6w1Gc9aH0FoH0J61obPScPRmZdisNhrNWR0+a41Hd9aGzjrD0Zp1IbPeYDRnfQitdQDtWRu60BlOAj10IcGgkBEggR6ygGAOBCpNwPf95ZI6+76vRCJR6e7pr0YI1NfXJz2Nx+M14jFuBk0AzQUdgdodH+3VbuyD8By9BUG9tsdEc7Ud/6C8R3dBka+9cdFa7cU8aI/RXNARqN3x0V7txr6SnpuXMRwnmSJc4ThOl0r2TV8QiAIBEuhRiCI+QKANAr7vr5S0KvtJgQAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAKrCMQdx+kEDAhAIJsACXQUAYGIE/B9f5GkrpLM8vMvIu4u7nUAgXg8Xj9//vz1TddrrbXW5/X19SxD7wDOdNlCAM2hhqAIoL2gyNfmuOitNuMepNdoLkj6tTs2uqvd2Ffbc7RWbeKMh+bQQFAE0F5Q5CM57rqS6iQtcxynZyQ9xCkIlEGABHoZ8GgKAQhAoBYIDBs2bOP6+vpPjK/xeHyTGTNmfFoLfuNjcATQXHDsa31ktFfrCqiu/+iturwZTUJzqCAIAuguCOq1OSZaq824B+k1mguSfm2PjfZqO/54DwEIVI8ACfTqsWYkCEAAAlYS4MHcyrBZbTSaszp8VhuP9qwOn3XGozfrQma9wWjO+hBa6QC6szJsVhqN1qwMm9VGozmrw2e18WjP6vBhPAQgYBEBEugWBQtTIQABCARBgAfzIKjX9phorrbjH6T3aC9I+rU3NnqrvZgH7TGaCzoCtTk+uqvNuAfhNVoLgnptj4nmajv+QXqP9oKkz9gQgEAtESCBXkvRxlcIQAAC7SDAg3k7oNGkLAJorix8NC6DANorAx5NSyaA3kpGRoMyCaC5MgHSvF0E0F27sNGoHQTQWjug0aQsAmiuLHw0LoMA2isDHk0hAAEIlECABHoJsKgKAQhAoBYJ8GBei1EP1mc0Fyz/Wh4d7dVy9KvvO3qrPvNaHxHN1boCgvEf3QXDvRZHRWu1GPVgfUZzwfKv5dHRXi1HH98hAIFqEiCBXk3ajAUBCEDAQgI8mFsYNMtNRnOWB9Bi89GexcGz0HT0ZmHQLDcZzVkeQEvNR3eWBs5Cs9GahUGz3GQ0Z3kALTYf7VkcPEyHAASsIkAC3apwYSwEIACB6hPgwbz6zGt9RDRX6woIzn+0Fxz7WhwZvdVi1IP1Gc0Fy79WR0d3tRr56vuN1qrPvNZHRHO1roDg/Ed7wbFnZAhAoLYIkECvrXjjLQQgAIGSCfBgXjIyGpRJAM2VCZDm7SaA9tqNjobtIIDe2gGNJmURQHNl4aNxOwmgu3aCo1nJBNBaychoUCYBNFcmQJq3mwDaazc6GkIAAhAoiQAJ9JJwURkCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABKJKgAR6VCOLXxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUBIBEugl4aIyBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhElQAJ9KhGFr8gAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAkAiTQS8JFZQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQiCoBEuhRjSx+QQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBASQRIoJeEi8oQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIBBVAiTQoxpZ/IIABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgZIIkEAvCReVIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgqgRIoEc1svgFAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIlESCBXhIuKkMAAhBom8Dw4cN3qqurO1jSHpK2ldRP0gpJ/5X0D0l3eJ7392I5NjQ0DHEcZ4ykH6T6mitplu/7tzU1Nc1sqx/XdXs5jrODpJ1939851ceAVJuPPc9L//+83YwcOXJAPB7/sBh7fd+/u6mp6dhi6hZTp7GxcZTv+8dJGixpLUlzHMf5m+/7N3me91y+PoYMGdK1V69eJgZpn78jqa+kNSR9K+kd3/cfTyQSt82YMePTYmwJcx00F7zmGhoaJjmOc0yJOjnO87xJJbYJVXW0V5729tlnn07rrLPOIMdxdnYcx9zjzT3LzBv1JtD19fWbTp069aNCQT/88MPX6ty5c7p9+r63QardXz3P26dQHzZcj5reWjMfMWLENvF4/HjHcQ6UtImkbmbek/Se4zhP1tXVTS5GD8XEkvm1GEoSmgtec8yv0fg+Ue37HfNrbd7jXNc1z0z9i/N+Va1in7UK9cm8WojQqutRm1dt1BzzajTm1Wprj3m1uHsctSAAgWgSIIEezbjiFQQgEAAB13WfkbRnoaEdx/mj7/snep63PF/dsWPH1s2ePfs2SSfkq+M4zh+22Wabn40bNy6Rq47ruk9Jypc4CW0C3XXd7pKaJJkkeK6S8H1/fFNT07g8fm8u6d1CcZC0yHGcU2Kx2N1F1A1lFTRXmZc2ytVcO3+I2K2tF0FCKbgMo9Be+dpraGgY6zjOxfliXeyPuq7rmpec8r0QFYkEehT1lhF3p6Gh4VLHcc6W1CmfHnzfP72pqen6cu4N5d7rXNdlfm0VAJue6WzUHPNr/r94tJefDfNr4ZkiivNqOxJK3/Ts2XP9SZMmLS1MLHcN5tXiyaG5JKvANce8Go15tdr3O+bV4u911IQABKJHgAR69GKKRxCAQEAEXNd9T9J3U6vNPbNa2nGc/5iX233f39X3/TMlbWTM831/SlNT05H5THVd9wpJv0ldf8X3/avr6+vfj8fj3039yL69ueY4zhWxWOy8XP24rvu0pL1T1+ZJelHSbpJ6SWpPAv2Curq6+/PZnEgkvvY877Ny8Tc2Nk7xfX9kqp+nfN+/ob6+/r++7w/yfd/4ahib8jPP88xLBlkl9QO/We1v2s5yHOdjx3H+5/u+2QnA8D9E0lGp1X2+4ziHxmKxh8u1O4j2aC40mtuorq6uT1saiMfjfRzHMX+TdZL+7XneVkFoplJjor3ytdfY2Hix7/tjUzExP96+mtppJHmPKyGBnrniyqxYniXp0FS/UUmgR25+Tf8tNjY23u77/k9T/37J9/07Jb3h+/7C+vr6dVO7yAyXdJfneTeU8zfM/Fo8vSje4yzVHPNrBL5PVFt7zK+F73VRvMeNGDFiS0ldCjyP7+84znWmju/7tzc1NZmd3tpdmFeLR4fmQqM55tUIzKvVvt8xrxZ/r6MmBCAQPQIk0KMXUzyCAAQCIuC67p9Tq0Gme54Xb23GqFGj+q5cudIkds2Xe5P83jsWi5lV61nFPAwnEol/pVaimaT3Xp7nLUlXGjp0aI9u3br9VdJOklZK2sbzPJNcyCqu645xHGeBSSKnr2e8qdqeBHqHbzntuu7/SXoi5ciDko7IZJli+JIksy37/M6dO282efLkrzMdN6v3x40b55vfRfJJwXVds82x2Uq/s6RXPM8zW91bV9Bc+SGrhOaKscJ13ZMk3Wzq+r5/YVNT06XFtAtrHbRXfmQaGxsPTCQSA3zff+Grr7564+mnn16ZuSqk2AR6Y2PjWZI+rKure2Hq1KmfGMtc103f/6KSQI/c/Gri1NjYeLzv+3ek7guXNzU1XZBv7nJdt0tbO9cUUmQl7nXMry2UbXyms1FzhXSdut8xv4b8+0S1tcf8WvgvJ4rPcYW9lhoaGqY5jtOYqrtnKUer5fiuzffWYqCn6qC5JIjANVdMyPjeGv7f6YqJYyXvd8yrxRCnDgQgEFUCJNCjGln8ggAEQkmgoaHhUMdxTGLYJNAnxmKxX+T4Mm6SbObHQNXV1e06bdq051vXGTFixC6JRCJ5DrjjODfHYrFTinHYggS6WQk+xLwYEI/HN811RnlDQ8NIx3GmpPw92/O8a4rxPQfnRySZs2ZNWcPzvIXt6SfsbdBc2xFyXbcqmnNd1/y97mKSYyltfxx27ZRrH9ornWB7Eui5RolaAr0Ykrbp7bDDDluja9eu5j5gdq94yPO89K4Bxbhbcp1q3euMYa7rMr+mIuS6bmie6aKqOebX8H+fqLb2ct0gmV9LnjZMstmq762FPHRdd01Jn6d2IfvA87z0rmaFmua8zrzaLmxtNkJzfG+tvKqK6xHtFccpsxbzaunMaAEBCNhJgAS6nXHDaghAwFICo0eP7rl06dJ0ovZhz/PMduKZxXFd91NJG0p62/O8bfK56rru25LMNtCfeZ63SVsrrtN9hDmBnvpx7cvU1nuPeJ5nEumrFbMKT9JcSb0lPed5ntmWvuTiuq4nqcE0XLlyZd/77rvvq5I7saABmssfpGppbvjw4VvU1dX9O2XJ057n7WuBdMo2Ee2VjpAfIkpnlm5hm94aGxt/7vv+LSn7d/M8L/lSXEeUat3rMp41mF9XwQjVM10UNcf8mtRZ6L9PVFN7+e6hzK+lzy62zauFPGxsbDzR9/3k8V++71/c1NQ0rlCbfNeZV9tLru12aI7vrR2jrMK9or3CjFrXYF4tnRktIAABOwmQQLczblgNAQhYSsB13bUlpRO1D3qed1imK8OGDdvMnHWe/NXVcW6NxWI/z+eq67q3Skqf27aZ53kfFsIS5gR6q+1lz/U878o2fH9U0o9M7rtPnz49brvtNnO+edHFdd1+kt5Jrfz70vM88+9IFjSXP6zV0lxjY+Mlvu+brZnND3bHNzU13RVJsbVyCu2VHmV+iCidWbqFbXpzXdccV2K2f82ag4444oh1O3Xq1LtLly5f3Hvvvd+2n0hLy2rd68yIzK8t3MP2TBdFzTG/JvUW+u8T1dRevnsm82vps4lt82ohD13X/ZukPVK7QW0+Y8aMDwq1aeO7aOb27XxvbS9Iy787FHLbRs0xr9oxr1ZTe8yrhWhzHQIQiDoBEuhRjzD+QQACoSLguu4RkmYYoxzHuToWi52TaWDm1lG+75/e1NR0fT4HGhsbT/d9/7epvg6JxWJmK+o2S5kJ9Jclma3vNpa0TNKnjuP8zXGc26ZNm2aulVVc1z1V0sSUP0fEYrE/tfGwfoPjOOnt77fzPG92ocGHDBnStVevXhs6jrO/7/uGe3rbvgs8z7usUHtbr6O5/JHraM2lRjYrEM0PdAMkLV62bNn6DzzwwAJb9VSK3WivFFqr6vIDf+nM0i1s0ps5S3z27NnzzfEhkp7yPG+/hoaGUx3HOV3SphkUXjPz4rbbbnvXuHHjEu2l09H3OubX8D/TRU1zzK/2fJ+otvba+O4wyXGcY8z1+vr6TadOnfpRe+6ptXREik3zaqFYuq5r5lbzkrr5DfRvnuftVahNW9eZV8uh1+Z3M6t+KymgEas0x7xqz7xa6K+v0vc75tVCxLkOAQhEnQAJ9KhHGP8gAIHQEEj9gGS2aN05ZdROnue9lGlgqy0OXc/zmvI54Lqu2X7cbJNqVrX+vKmpyaxIb7OUmUDP27dZLb9gwYJfzpw50yTW21UaGhqudBwn+UJBIpH4wfTp01/M11FjY+NZvu8nzz53HOegWCxmVqSvVhoaGvZxHOepfP04jvNH3/dP9DxvebuMDnkjNNd2gDpCc61HdF13b0lPp7R6bywWOzrksqmIeWivfRhJoLePm216GzZsWP/6+vp08ma6pDpJ5kfjfOV+SY3tnas64l7H/Jp8CcKaZ7ooaI75dRUB7nftmyeYX0vjZpvOCnnX0NAw1nGci009892vqanpD4XatHWdebUcernbojm+t1ZeVcX1iPaK49S6FvNq+7jRCgIQsI8ACXT7YobFEICApQQaGhrOdBzn2pT5MzzPG57jx8BfS7o69fkQz/MeyeduQ0PDEMdxkqvOfd8/q6mpaUIhNO1MoL/iOM59iUTiacdx3k0kEkvr6+s38H3fbKF+gqReqXEne553VCEb8l1vbGy8yff9k831RCKxzfTp080Z7zmL67onSbrZXHQcpyEWi5kExGqljR/4TeJijOd5j7XXXhvaobm2o9QRmms9YkNDwx2O4xyf+vxHUddc2n+01747BD9EtJubVfPr8OHDt6+rq0vv3LJUUjdJ/3Yc56ylS5c+3bVrV1+SefnGPDNsnZrnr2tqajqjPYQ64l7H/GrXM10UNMf8uoqAbfNrtbXXxvcmVqCXMIHYprNCrrmu+15q97ElXbp0Wb/cI1KYVwsRL/06muN7a+mqqUwLtNc+jnxvbR83WkEAAvYRIIFuX8ywGAIQsJBAahXq45I6Sfpi5cqVg+67774vWrvS2Nh4oe/741Of7+d53pP53M0809T3/QubmpouLYSm1AS667pdli5d2unBBx9cnKvv4cOHb1FXV2f8+k7q+o89z3ugkB25rmcmGuPx+HfbOpeusbHxeN/37zD9OI4zOhaL3ZOrz9GjR/dcvnx5cjvcRCLRNWWnOXd+tImDpAs9z0v2E7WC5gpHtCM0lznqscce223RokVzJPWW9Nm22277nXK2YS7sUThqoL32x4EfIkpnZ6PeXNc1Z7Cas1jT5XNJgz3Pm5tJYNSoUX1XrlxptnHfUNKKeDy+2YwZMz4tlVJH3OuYX2XVM10UNMf8KnG/K/Xu11Kf+bV4djbqrC3vGhoadnMc5x+p741TY7HYqOJp5K7JvFouwez2aK4wz47QHPOqnfNqte93+cZjXi38d0sNCEAgGgRIoEcjjngBAQiEmMDw4cO3q6urMz+U95G01HGcA2Ox2DO5THZdN1Qr0IvB2tDQsKfjOGl/Hvc874B0uzFjxnT+5ptvtsrXz5prrvnObbfdtsJc74g3+dt42N/PcZyHJJmk+ljP89IvLRTjcujroLlwaK6hoWGk4zhTjGB837+qqanpN6EXT5kGor3itMcPEWUKLdXcVr0NHz58p7q6ullpCo7jnBGLxa7LRaWxsfEU3/d/Z645jvPLWCx2o/n/zK+V0VCpvaC54u5xHf1Mx/xqz/eJat/vmF9Lvatl17f1HteW142Njb/3ff9nqTp5d3hjXi1PO+1tjeaYV9urnXLbob3itMe8Wq7SaA8BCNhOgAS67RHEfghAINQEXNc1q5//nlo9Fvd9f3hTU5M5yzRnCdsZ6MXCdV33X5K2NS8IbLvttj3Tq2xHjhw5IB6Pf5ivn/r6+k2nTp2aPAe2I86SK/BjylW+75+9anF6Yru2towvlkMY6qG58GjOdV1zxMKQlC628zxvdhg00lE2oL3itccPEeWr0Ga9ua5rXixrPqYkkUgMnD59uplHVyuu624u6V1zwff9e5qamswOKmJ+LV9DpfaA5oq/x3X0Mx3zqz3fJ6p9v2N+LfXO1lLf5ntcPq+HDBnStVevXv9Lvchu/ruJ53nxXPWZV9uvnfa2RHPMq+3VTrnt0F7x2mNeLVdttIcABGwnQALd9ghiPwQgEFoCI0eO3DAej5uV55ut+t3bP7apqemPbRnc0NBwqOM4D6Z+KD+9qanp+nz1GxsbT/d9/7fmuuM4h8RiseR56G2VUrdwL9Rf+rrrup7Jgaf+vW56G9pSfohwXfdUSRNT/hwRi8X+1MbD+g2O4/widb1diclW2/mdH4vFLi/W37DWQ3OlJZU6UnOjRo1ab+XKlZ9Jqpf0kud5O4VVN5WwC+2Vpj1+iChPdbbrzWx/vnTp0gVm+jYkunTpsma+81hTP/6bc9JNedTzvIPM/2F+LU9DpbZGc+HRHPOrXd8nqn2/Y34t9e62qr7t97h8Xruua76fmu+p5vvyhFgsdla+usyr7dNOe1uhOebV9mqn3HZorzTtMa+WqzjaQwACthMggW57BLEfAhAIJYHUuaV/Ta3KNl/YT43FYjcVMnbYsGGb1dfXv5/6kn9rLBb7eRs/CNxqdnFNXd/M87y8K73TfXRgAj0myU2N05xAL+Rv5vXMM90lnet53pVt+P6opB9JWtmnT58e6W3gSxkvdX77v1NtbvM8L721XyndhKYumss+O7iYwHSk5lzXPUPSBGOH7/u/aGpqSr4cEsWC9krXHj9EtP8vISp6c13XzNkDDInOnTuvPXny5K9zURk6dGiPbt26LUpde8jzvENLpdeR97pctjC/rqIStme6qGiO+dW+7xPV1B7za6kzhBSVeTWX567rPiBpaOraYM/z3iid0OotmFfLo4jmSufXkZpjXrVvXq3m/Y55tfS/V1pAAALRIkACPVrxxBsIQCAEBFzXXVPSk5J2MOY4jvObWCx2VZGmOa7rfpra8v1tz/O2ydfOdd23JG0t6TPP8zYxebpCY3RgAv1NSdtJWrbtttv2SG/hXsiezOuHHXbYGl27dv3SLMaT9Ijneemtr7O6cV3XXJ8rqbek5zzP262UcdJ1Gxoa9nEc56nUv3/red6Z7eknDG3QXPg057ruq5K+J2lFp06dNpwyZYrRduQK2muf9vghon1/ClHSW2Nj492+7/8kRWInz/NeyvNj2CBJr6eutetlL+bX9unNtEJzCp3mmF+L0nOovk9U837H/FqUPporReke19pz13X7me/J5j01Sa96nrd9aXTy12ZebT9JNNc+dh2pOebVomISqnm1mvc75tWi9EElCEAgwgRIoEc4uLgGAQhUn0BqpdhfJO2eGv0yz/MuKMUS13VvlnSSaVNXV7frtGnTnm/dfsSIEbskEonnzOeO49wci8VOKWaMjkigNzY27u77vjnn3ZQnPM/bvxhbctXJONNyZTwe33TGjBnmZYKs0tDQMNJxnCmpD8/2PO+a9ozX2Nh4k+/7J6faHud53qT29BN0GzQXPs25rpuZ8Lrf87zDg9ZJR4yP9srTXq6YNDQ0THIc5xhzrb6+ftOpU6d+1J7Yua6bfqHqr57n7dOePsLWJmp6c133MEn3F3pWaGhoGOs4zsWp+X50LBa7pz2xYX4tnRqaSz5jhkpzzK/F6zhM3yeqfb9jfi1OJ1G7x7X2urGx8Re+799gPvd9v82j0Yojll2LebV0amiudGaZLTpCc8yrxcckTPNqte93zKvF64SaEIBA9AiQQI9eTPEIAhAIiEBqZbQ5v9xsLW7KDZ7n/apUc0aMGLFlIpGYnTo7+UVJe3metyTdj+u63SU9I8mcqbwykUhsO3369HeLGafUBHpjY+PhsVjM/MCfc3W767qbm6S5pO+kfpwY3tTUNKMYW3LVabU1mdlyb5jnefF03dR2b2aVnhlvfufOnTdrve1tY2PjKN/3H/Y875t8djQ2Njb6vj85xdjUM1vgz2uv3UG1Q3PJH8QC11yOL7DX+r6f3NGgXPuC0lahcdFex8SWBHpu5UVRb2PHjq2bPXv2a5IGSlqUSCR+OH369H9lEhgxYsQ2iUTiBUm9zM4rS5cuHfDggw8uLvT3yfzaHkLZbdBcODTH/BqN7xPVvt/lugMwv0b/Htc67q7rmu/RO5rvyytXrtzovvvu+6L82aGlB763lkYzivOqjZpjXo3GvFpt7TGvlna/ozYEIBAtAiTQoxVPvIEABAIk4LrudJPwTZlgtnD/VV1dXVvbqi+fNm1a+gzuLMtd171C0m9SH74i6apEIvF+XV3ddyWdIym5BZ3jOFfEYrHz8vxYbpLbe7S6dq2kdSR9JemsVtfMtumfZ36WWsX4nuM4Jin+Qjwe/7Surm6Z7/sbOI5zoKQTUj/sm0RhrKmpaUS5IWhsbJzi+/7IVD9mi/XrE4nEfx3HGeQ4zvmSDANTfuZ53m05vjw8bX4s8X3/T3V1deZFg3ccx/kmHo/3lLSV4zgNkg5OtfMdxzkuFovdXa7dQbRHc+HQXKu/mXpJn0jaQJJ5KWMDz/OWB6GPjhwT7ZWvPdd1TVLU3I8yy0/TO5g4jvNr3/czt/4324+aowFazxffl2T+l1nuSv3jHUlXtrrW5Hnewo7UR6X7jqLeDCPXdc0cbV5CM0eTzPd9/+r6+vrk0SLxeHwvcwSMpD4pniM9z5tWDlvm1+Lpobkkq8A1x/yaPBLK+u8T1b7fMb8WvtdF9R6X9tx13W0lpV9K+7Pneelz0AvDKaEG82rxsNBc8azaqlmu5phXozOvVvN+x7xamb9feoEABOwkQALdzrhhNQQgEEICGVvmFmvdx57nDchV2azU+Ne//nW74zjHt9HZHdtuu+2YfOeNu657rKR0EqWgTb7v79vU1GSSz82lBJ9uWbhw4ekzZ85cVnCgAhVSK+ybMpLcrVskHMe5JBaLJbe0bV1c1zU+7F2EHV9LOtXzPLMS3cpSQnzS/qG53Joxuzq0W3Ot/mYOkjTTfFbK8Qq2CRDtlX+/Gzly5IB4PP5hsbF3HGdcrvteY2Pjxb7vjy22n3K2hi92jErXi6Le0owaGxuH+75v5uo18nBb4TjOL2Kx2O/L5cr8WjxBNBcOzTG/FqXZ0D/bVft+x/xaWDdRvscZ7xsaGq50HMe8dG5e8m5samryClMpvQbzavHM0FzxrNqqWa7mmFeLioM182q17nfMq0XphkoQgEBECZBAj2hgcQsCEKg+gUp+Kcz4oelg3/fHSPqBpL6SzGrEWb7v39rU1JRM0uUrlUigNzY2mrf1d/V9/4eS+qdsMCu5v5X0ge/7f6uvr79z2rRpb1aauOu6R0oyLwF8T9JakuZI+puk33melzz/PVdxXXcjx3H2TyQS+zqOM1jSepL6STKrgA2/NxzHeaRTp06TW2//XmkfOro/NFdZwu3VXKsfIu6VZLSrRCKxy/Tp0/9ZWSvD0RvaKz8O/BBRPMMo6q3VBCU4kAAAIABJREFUfeM7vu+f5jjOIZI2kVRndrLwff9xx3Gu9zzvveJpFa7Z3nsd82ubbPP+2BqWZzobNcf8WvjvWRLaa4WJ+bWwbqI8r6aODPhY0sZmd5eFCxeuX4mXvAt87+Z7awHZobnCf5el1GjvsxzzalGUrZlXq3W/Y14tSjdUggAEIkqABHpEA4tbEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQGgES6KXxojYEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCESUAAn0iAYWtyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoDQCJNBL40VtCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCIKAES6BENLG5BAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBpBEigl8aL2hCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgEFECJNAjGljcggAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACB0giQQC+NF7UhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCCiBEigRzSwuAUBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAqURIIFeGi9qQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBARAmQQI9oYHELAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAARKI0ACvTRe1IYABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgYgSIIEe0cDiFgQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIlEaABHppvKgNAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIRJUACPaKBxS0IQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACECiNAAn00nhRGwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIkqABHpEA4tbEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQGgES6KXxojYEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCESUAAn0iAYWtyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoDQCJNBL40VtCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCIKAES6BENLG5BAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBpBEigl8aL2hCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgEFECJNAjGljcggAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACB0giQQC+NF7UhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCCiBEigRzSwuAUBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAqURIIFeGi9qQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBARAmQQI9oYHELAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAARKI0ACvTRe1IYABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgYgSIIEe0cDiFgQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIlEaABHppvKgNAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIRJUACPaKBxS0IQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACECiNAAn00nhRGwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIkqABHpEA4tbEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQGgES6KXxojYEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCESUAAn0iAYWtyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoDQCJNBL40VtCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCIKAES6BENLG5BAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBpBEigl8aL2hCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgEFECJNAjGljcggAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACB0giQQC+NF7UhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCCiBEigRzSwuAUBCEAAAhCAAAQgAAEIQAACEIAABCAAAQj8f/bOA1qKYuvCu3rISPShqJgx8USMiBgRE2bkXkBQEAOComRBJIgIgmQFA4qAKAiDos8IBhQTijmiD36eCoqiSJY0Xf+qhntn5k7sme6eDrvXcr3ndFWdc76zwXtnd1WTAAmQAAmQAAmQAAmQAAmYI0AD3RwvjiYBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEvApARroPm0syyKBVARatmy5TygUagygsRDiFADqn73VeCnljHnz5l1nN722bdseEolEVpqM81M4HD7E5BwOJwESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIIGsCdBAzxoVB5KAPwgUFxfLVJW43EBfGA6HL/RHF1gFCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZCAGwnQQHdjV5gTCdhIoIyB/jOAZQAuUCGdMtA7d+5cfsOGDUdlKlPX9TsBtNszrn04HJ6VaQ7vkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkECuBGig50qO80jAowSKi4uHSimXli9ffuns2bN/jz1O3SkDPRt0xcXFIQDK4N8fwCYA+4bD4X+ymcsxJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJJALARrouVDjHBLwEQG3GuitW7e+UEr5mkIthJg2d+7c632EnaWQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAm4kAANdBc2hSmRgJME8jHQr7vuukpbtmy5EUBLAP8GUBvAegBfAZi9du3aGW+//fauXOopLi5+Oub49nPC4fA7uazDOSRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQLQEa6NmS4jgS8CmBXA30Nm3aNNJ1/QUAB6dBs7RcuXKXqaPizeC7/PLLq1WsWHENgCoA/hcOhw9Tr2g3swbHkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIBZAjTQzRLjeBLwGYFcDPTi4uL6AD4BUAPARiHEZAAfSyl/kVLuDeByIcTNAMoB+KhWrVpnTpkyZWe26IqKijoJIZ5Q46WUw+bNmzc427kcRwIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAK5EqCBnis5ziMBnxDI0UB/H0BTAJ+XK1fugtmzZ/9ZFkdxcfFFAF4GoAkhOs+dO/exbJEVFxcvAnDOnvFHhMPh5dnO5TgSIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESyJUADfRcyXEeCfiEgFkDvaio6EwhxOI95R8XDoe/ToWiqKhojhCiNYAPwuHw6dkgKy4uPkgd2w5A/f2U9bxs1uYYEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEkhHgAY69UECASeQg4E+WgjRB8AP4XD46HT4WrdufauUchKAnWvXrq3y9ttv78qEu3Xr1gOklMPVOClll3nz5j2aaQ7vkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIAVBGigW0GRa5CAhwmYNdDLHK+edeW7du3ad/78+X9kmlBcXPw9AGXMb9+5c2fd559/fn2mObxPAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAlYQoIFuBUWuQQIeJpCDgf4tgAZmSw6FQoc+88wz6mj2lFdxcXFjAB/tGRAOh8Pq+HdeJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJOAIARrojmBmEBJwL4EcDPRlAI4C8KWmaddkW1mNGjV+mDJlys5044uKiiYJIW5VY6SUl82bN++lbNfnOBIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARLIlwAN9HwJcj4JeJxADgb6+wCaAlgRDofrW1V+586dy//999+/AdgbwB9r1649IJt3plsVn+uQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAA10aoAEAk7ArIEes0tcAtg/HA6vsQJh69atr5RSzldrCSEmzJ07t6cV63INEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEsiWAA30bElxHAn4lEAOBvoFQogFe3CMDIfDd1qBpri4+DkALfesdUI4HP7CinW5BgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlkS4AGerakOI4EfErArIGuMBQXF38EoDGAiBCi3dy5c+emwlNcXNxQCHHI3LlzX0wzpjYAdXx7BQBfh8Ph43yKm2WRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAm4mAANdBc3h6mRgB0EiouLzwBQ+u5yIcS/pJSj98RS7zd/PDZuOByeXjaPNm3aHK7r+scAlPGtrhellHOEEP9VprqUch9N006QUl4GoIkQYuzcuXP7pKqnqKjoFiHEZHVfStln3rx5Y+2onWuSAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQDoCNNCpDxIIGIGioqLpQoiO2ZYdDoeT/j3Rpk2bI3VdfxbAsVmsNSQcDt+TalxxcfESAKcq8x1APaveq55FXhxCAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAqUEaKBTDCQQMAJWGegKW3FxcUgI0VpK2QrAKQDqAAgB+AvAD0KI94QQ8+fMmfNZKsytWrU6QtO0H/fcfy0cDrcIWEtYLgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgEsI0EB3SSOYBgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQGEJ0EAvLH9GJwESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAEScAkBGuguaQTTIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESKCwBGuiF5c/oJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACLiFAA90ljWAaJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEAChSVAA72w/BmdBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEjAJQRooLukEUyDBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEigsARooBeWP6OTgO0EpJRbAFQEoAP4w/aADEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJOBmAvsA0ABsF0JUdXOizI0ECkGABnohqDMmCThIQEq5C0DIwZAMRQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk4H4CESFEOfenyQxJwFkCNNCd5c1oJOA4ASnlDgDlpZTQdbUJnRcJZE8gFNr97EUkEsl+EkeSQJ4EqLs8AXJ6TgSou5ywcVKOBKi3HMFxWl4EqLu88HFyHgSovTzgcWrWBKizrFFxoIUEqDsLYXIpUwSoPVO4ODgFAU3TIIRhEe4UQlQgKBIggXgCNNCpCBLwOQEp5SoABygD9Pfff/d5tSzPSgLqh6i6desaS65Zs4YPYFgJl2ulJEDdURyFIEDdFYJ6cGNSb8HtfSErp+4KST/Ysam9YPffqeqpM6dIM04sAeqOeigUAWqvUOT9F3fffffFnocxVgsh6vmvQlZEAvkRoIGeHz/OJgHXE6CB7voWuTZB/kDu2tb4OjHqztftdW1x1J1rW+PLxKg3X7bV9UVRd65vkW8TpPZ821pXFUaduaodgUmGugtMq11XKLXnupZ4NiEa6J5tHRN3iAANdIdAMwwJFIoADfRCkfd+XP5A7v0eerEC6s6LXfN+ztSd93vopQqoNy91yz+5Unf+6aXXKqH2vNYxb+ZLnXmzb17Pmrrzege9mz+1593euS1zGuhu6wjzcRsBGuhu6wjzIQGLCdBAtxhogJbjD+QBaraLSqXuXNSMAKVC3QWo2S4olXpzQRMCmAJ1F8Cmu6Rkas8ljfB5GtSZzxvs0vKoO5c2JgBpUXsBaLJDJdJAdwg0w3iWAA10z7aOiZNAdgRooGfHiaMSCfAHcqqiEASou0JQZ0zqjhpwkgD15iRtxiohQN1RC4UiQO0Vinyw4lJnweq3W6ql7tzSieDlQe0Fr+d2VUwD3S6yXNcvBGig+6WTrIMEUhCggU5p5EqAP5DnSo7z8iFA3eVDj3NzJUDd5UqO83IhQL3lQo1z8iVA3eVLkPNzJUDt5UqO88wQoM7M0OJYqwhQd1aR5DpmCVB7ZolxfCoCNNCpDRJIT4AGOhVCAj4nQAPd5w22sTz+QG4jXC6dkgB1R3EUggB1VwjqwY1JvQW394WsnLorJP1gx6b2gt1/p6qnzpwizTixBKg76qFQBKi9QpH3X1wa6P7rKSuylgANdGt5cjUScB0BGuiua4lnEuIP5J5pla8Spe581U7PFEPdeaZVvkiUevNFGz1XBHXnuZb5JmFqzzetdHUh1Jmr2+Pb5Kg737bW9YVRe65vkWcSpIHumVYx0QIRoIFeIPAMSwJOEaCB7hRp/8XhD+T+66kXKqLuvNAl/+VI3fmvp26uiHpzc3f8mxt159/eur0yas/tHfJHftSZP/rotSqoO691zD/5Unv+6WWhK6GBXugOML7bCdBAd3uHmB8J5EmABnqeAAM8nT+QB7j5BSyduisg/ACHpu4C3PwClE69FQA6Q4K6owgKRYDaKxT5YMWlzoLVb7dUS925pRPBy4PaC17P7aqYBrpdZLmuXwjQQPdLJ1kHCaQgQAOd0siVAH8gz5Uc5+VDgLrLhx7n5kqAusuVHOflQoB6y4Ua5+RLgLrLlyDn50qA2suVHOeZIUCdmaHFsVYRoO6sIsl1zBKg9swS4/hUBGigUxskkJ4ADXQqhAR8TsAqA11KCV3Xof6XVzAICCFQp04do9i1a9ey98Foe8GrzEd3aq76RVL9Ly8SMEOAX0CYocWx+RKg3vIlyPm5EKDucqHGOVYQoPasoMg1MhGgzjIR4n07CFB3dlDlmtkQoPayocQx2RCggZ4NJY4JMgF+wxzk7rP2QBDI10BXpvk///yD7du3GwY6r2ARCIVCRsGRSCRYhbPaghLIR3fqF8mKFSuicuXKhpnOiwSyIcAvILKhxDFWEaDerCLJdcwQoO7M0OJYKwlQe1bS5FqpCFBn1EYhCFB3haDOmIoAtUcdWEWABrpVJLmOXwnQQPdrZ1kXCewhkI+BrgzzjRs3GuapMqQqVKhg7Ozk7s7gyKtcuXJGsbt27QpO0ay04ARy0Z06HUP9s2PHDuOBH2XCV69enSZ6wbvpjQT4BYQ3+uSXLKk3v3TSW3VQd97ql5+ypfb81E331kKdubc3fs6MuvNzd91dG7Xn7v54KTsa6F7qFnMtBAEa6IWgzpgk4CCBfAz0LVu2YNu2bYYJVb58eQezZii3ECjp+86dO92SEvMIAIF8daf0qh7+qVSpEqpWrRoAYiwxXwL8AiJfgpxvhgD1ZoYWx1pFgLqziiTXMUuA2jNLjONzIUCd5UKNc/IlQN3lS5DzcyVA7eVKjvPKEqCBTk2QQHoCNNCpEBLwOYFcDXS1k/Pvv/82dp3vtddePqfE8lIRyNfIJFkSyIWAFbrbvHmzsRu9Vq1aPDUjlyYEbA6/gAhYwwtcLvVW4AYENDx1F9DGu6Bsas8FTQhACtRZAJrswhKpOxc2JSApUXsBabQDZdJAdwAyQ3iaAA10T7ePyZNAZgK5Gujq2HZloKvd58pE5xVMAlYYmcEkx6rzIWCF7pR5rnahKwO95J3q+eTEuf4mwC8g/N1ft1VHvbmtI8HIh7oLRp/dWCW158au+C8n6sx/PfVCRdSdF7rkzxypPX/2tRBV0UAvBHXG9BIBGuhe6hZzJYEcCORqoKt3Xq9fvx41atTg8e05cPfLFCuMTL+wYB3OEbBCd+oY9w0bNqBmzZooeae6cxUwktcI8AsIr3XM2/lSb97un1ezp+682jnv503teb+HXqiAOvNCl/yXI3Xnv556pSJqzyudcn+eNNDd3yNmWFgCNNALy5/RScB2Avka6DSfbG+RqwNYYWS6ukAm50oCVuiu5CEg/h3myha7Lil+AeG6lvg6IerN1+11bXHUnWtb4/vEqD3ft9gVBVJnrmhD4JKg7gLXctcUTO25phWeT4QGuudbyAJsJkAD3WbAXJ4ECk2ABnqhO+Dt+FYYmd4mwOwLQcAK3dFAL0TnvBuTX0B4t3dezJx682LXvJ8zdef9Hnq1AmrPq53zVt7Umbf65ZdsqTu/dNJ7dVB73uuZWzOmge7WzjAvtxCgge6WTjAPErCJAA10m8AGZFkrjMyAoGKZFhKwQnc00C1sSACW4hcQAWiyi0qk3lzUjAClQt0FqNkuK5Xac1lDfJoOdebTxrq8LOrO5Q3ycXrUno+b63BpNNAdBs5wniNAA91zLWPCJGCOAA10c7w4Op6AFUYmmZKAWQJW6I4GulnqwR7PLyCC3X+nq6fenCbOeIoAdUcdFIoAtVco8sGKS50Vrt/y0/chly+DOKEJxJH/LlwiBYhM3RUAOkMaBKg9CsEqAjTQrSLJdfxKgAa6XzvLukhgDwEa6JRCPgSsMDLzic+5wSRghe5ooAdTO7lWzS8gciXHebkQoN5yocY5+RKg7vIlyPm5EqD2ciXHeWYIUGdmaFk3Vv/oHcjHx5YuKIo6QbuwpXUBXL4SdefyBvk4PWrPx811uDQa6A4DZzjPEaCB7rmWMWESMEeABro5XhwdT8AKIzN2xWXLlmHy5Mn48MMP8eeff2Lnzp3G7QULFuDYY4+1BP+pp56KVatWobi4GBMmTIhb85dffkGTJk2Mz8aNG4c2bdpYEtMvi3zwwQcGN3WFw2E0bdq0IKVZoTsa6AVpnWeD8gsIz7bOk4lTb55sm+eTpu4830LPFkDtebZ1nkqcOitMu/RHRkHtQI+9RItWEC07QAj/f+VM3RVGd4zKHejUgHUEaKBbx5Ir+ZOA/3+a8WffWBUJZE2ABnrWqDgwCQErjMySZb/66iu0bNkS27ZtS4hEA90d8qOB7o4+MAvnCfDLL+eZBzki9Rbk7heuduqucOyDHpnaC7oCnKmfOnOGc9kokTF3AT98nRBcnHkBxDVdIbRQYRJzKCp15xBohkkgQO1RFFYRoIFuFUmu41cCNND92lnWRQJ7CNBApxTyIZDOQJ8zZw569eplLL9kyRIceOCBaUNdffXVWLx4MapVq4Y777wTjRo1QqVKlYw5hxxySOn/zydfNZc70HMnSAM9d3ac6W0C/ALC2/3zWvbUm9c65o98qTt/9NGLVVB7Xuya93KmzgrTs8jQ7sCqlUmDi1POhLixN4SmFSY5B6JSdw5AZoikBKg9CsMqAjTQrSLJdfxKgAa6XzvLukhgDwEa6JRCPgSsMtDVUe1HHnkkduzYgS5dumDQoEH5pJV2bjoD3bagPlmYBrpPGskyTBPgFxCmkXFCHgSotzzgcWrOBKi7nNFxYp4EqL08AXJ6VgSos6wwWT4o0u96YN2fKdcV7btCO6eF5XHdsiB155ZOBC8Pai94PberYhrodpHlun4hQAPdL51kHSSQggANdEojHwJWGei///47TjzxRCOV+++/H+3bt88nrbRzaaDnjpYGeu7sONPbBPgFhLf757XsqTevdcwf+VJ3/uijF6ug9rzYNe/lTJ0VpmeRbm2A7f+kDl65KrR7JkPUrJ13glLXIV9/AfLbzyCOaQRx4VVxu9vlrp1AqJyj716n7vJuKxfIkQC1lyM4TksgQAOdoiCB9ARooFMhJOBzAjTQfd5gm8uzykD/5Zdf0KRJEyPbcePGoU2bNrZlTgM9d7Q00HNnx5neJsAvILzdP69lT715rWP+yJe680cfvVgFtefFrnkvZ+rM+Z7JXbugd70qLrAoug5y3vQzBt1aAAAgAElEQVT4zy5vB+2ytnknqC99D3LK/aXriOu6Qzu9ufHv+gdvQc6dCmgatI63QTRqnHe8bBag7rKhxDF2EKD27KAazDVpoAez76w6ewI00LNnxZEk4EkCNNC90baxY8caxrK6Vq9ejQ0bNuCxxx7Dyy+/jFWrVqFChQo45phjcM011+DKK69MW5Qyqx9//HHjfeNqrUgkgrp16+L0009Hp06djHVSXQcccIBxS73bvHfv3sa7zadPn45PPvkEa9euNdYZP348iouLM4INh8P48MMPS+tKNaEkVuz9fGqw4h3o6qj5WbNm4aWXXsIPP/yATZs2oWbNmmjYsKHBv2XLllC/sFhxKcatWrUylspmd/6DDz6IkSNHGuMXLVpkHI1fcv3000949dVXDe7Lli3Dn3/uPk5v7733Nk4AUA8uNGvWLGXamQz0oqIiY+3TTjsN8+bNS7lOWT2nGrhx40bMmDEDb7zxBv7v//7P4FyrVi0cf/zxRq4XXnhhzjsIdu3ahfXr1xt9K1eunBWt4ho+JsAvIHzcXBeWRr25sCkBSIm6C0CTXVoitefSxvgsLerM+YbKTRug97o2LrA2erphZMul70Y/P7EpQl37552g/tRDkO+8Fl3nqIYI9RkOuW0r9DtuAP7ZsvtepcrQRk+DqFQl75iZFqDuMhHifbsIUHt2kQ3eujTQg9dzVmyOAA10c7w4mgQ8R4AGujdaFms4KoPy6quvxv/+97+kyV922WWYNGlSUlNQmdb9+vXD9u3bk84NhULo27cvbrvttqT3Yw109d5yZdbGXvXq1bPdQM+3hnwNdGXeqwcVli9fnlI8jRs3xhNPPGGYvfleUkqonNXDDpmMaRWrefPmhjn+73//GwsXLiwN//PPPxvzM11XXXWV0cNkprKTBvq7776Lrl274u+//06Z8rnnnotHHnkEVatWzVRWwn0a6KaRBXoCv4AIdPsdL556cxw5A0JtytOMByHVtWbNGui6Ti4k4AgBas8RzIEPQp05LwG5ZjX0QV3jAmsPzdt9zPr8mdHPjzsFodsG5Z2gPmV0vDEfKgdtwlPAqp+gj+oXt77o0A3amRfkHTPTAtRdJkK8bxcBas8ussFblwZ68HrOis0RoIFujhdHk4DnCNBA90bLYg10tfv2q6++Mt4Tfskll6B69er4/vvvMXnyZGOXrrpuvPFGDB06NK44tYv3uuuugzJkleF4880348wzz4QyzdUOcmW6r1u3zpgzYsQIdOzYMQFOiYGudqmrmOp/u3Tpgvr162Pbtm349ttvDXNfmbULFiwwdkyrS+3WVj90xV4HHXQQtm7dauyCVu9Ab9eunXH7jjvuMHYWl1z/+te/oP5RlxU15GOgb9myBeeffz7UTm51XXTRRcZOaPWFs6pZ7cZXDzio6+STT8Zzzz1n8M33Uv1Q/VW/BH388cfYb7/9ki753XffGfmpa9CgQUZvSq6VK1dCGc5nn302zjrrLBxxxBGGwa8MaqUbtdNb7aZXV8+ePdGnT5+EGE4Z6EuXLjVOMVAPadSpU8c4GaFBgwYGZ/Wlvtr5X7LD/eKLLzZOYzB70UA3SyzY4/kFRLD773T11JvTxBlPEaDuqINCEaD2CkU+WHGpM+f7Lf/vB+j39Y0GLl8BoYfmQV/4PGT4iejnDY5HqOc9eScYmTAE+PbzuHW0WwdAbt4EOSP+wX/UPwahfqPyjplpAeouEyHet4sAtWcX2eCtSwM9eD1nxeYI0EA3x4ujScBzBOw00KXaubJlk+eY5JVw1WoQFh3dHZtHrIGuPldmatmj2jdv3mwcHa5MVPXD8uuvv46jjz7aWEYZkeod48p8VOa5MnaPPfbYuFLVUfCXX365YWZXrlzZMGpr164dN6bEQFcfnnHGGZg9ezYqVqxorF/2mjNnjnHUu7rUMeQHHnhgSrTZvAPdqhryMdCHDRtm7HhWV/fu3Q2zP/ZSDyfcfvvtBl91pXoQwazG1MMK5513njGtrDEeu1Y6o109rKCOQC/7IEPJfJW76tfcuXNRpUoVfPrpp8bDGbGXEwa66rN6sENpQh0nr8xxpcfYq3z58pg5c6bxGgF1KR2qhwLMXDTQzdDiWH4BQQ04SYB6c5I2Y5UQoO6ohUIRoPYKRT5Ycakz5/stv/4U+gMxD/XXqI3QmOnQF70MOevRaEJH/huhvvflnWBkRB9g5Y9x64izLgIqV4ZcMD9hfW34oxD7JH8wPe9k9ixA3VlFkuuYJUDtmSXG8akI0ECnNkggPQEa6FQICficgK0GepJ3XvkcJ7RxMyGq1bC8zFgDXRmpardwsuvzzz/HpZdeatxSu82HDx9u/P///Oc/xnHY6howYABuvfXWpPOV8VtyfPvAgQNL55QMLjHQ1Q/jykw97LDDjFtOGOhW1ZCrga6OvT/hhBOM988fddRRxgMKyXaXK5NaHZWudnar94+r95Bbcam+KyO97NHsJWvHHvXetGlTqKPuzV4q50aNGiESiWDKlCnGCQexlxMGutpZrh5OqFSpkvEQh3o/e9lLGejqUicVKM2rB0fUCQpmLhroZmhxLL+AoAacJEC9OUmbsUoIUHfUQqEIUHuFIh+suNSZ8/3WP3oH8vGx0cD7H4TQ0EnQ310I+WTM726HHonQgDF5JxhRx8WvWR2/Tu06QL1DgK+WJqyv3XwHxMln5B033QLUna14uXgaAtQe5WEVARroVpHkOn4lQAPdr51lXSSwhwANdGul4ISBrnbkqmOrU13qmG51FPfhhx+OxYsXG8PUTumnn34aQgh8+eWXSU1JNW7Hjh2Ggbpx40bjqG919HrsVWKgKxName0lRqYTBrpVNeRqoKtjxUt2/afbBa543XnnnXjyyScNdJ999lnKXd9m1KdOHVA7zNX19ttvG0ewx14fffQR1PvL1TVmzBjjKP10l+rZ2rVroY6lV4Z5yaWOpFfH6vfo0QN9+8YcuQcYD02oo9XVpQx6ZdTHXkVFRcYR9pne1R77QIh6t3vs1blzZ7z88ss455xzDM0mu0p0N3jwYONEAPU6gJKj87NlSgM9W1IcpwjwCwjqwEkC1JuTtBmrhAB1Ry0UigC1VyjywYpLnTnf74Sd5vUbINRvJPQPF0E+MT6aUL1DERoyMe8EI72uBTZtyHodcX1PaKc1y3p8LgOpu1yocY4VBKg9KyhyDUWABjp1QALpCdBAp0JIwOcEaKBb22AnDHS1Kzf2KPWyFahjuNXx6cosV++2rlChgnE0uzqS++CDDzZM0HRXiQmq3jet5sReJXFLdrc7aaBbVUOuBvpTTz2Ffv36GTjUwwNqnVSXOgZdvUdcXbkcL55sXWU0q5glx8SX5FIytn///sax5upIfbUru0aNxJMQlGmuTGm1y1u9r149MJHq6tChA+67L/4oPScMdPWqAXV8u5lL7VZfsWKFmSmggW4KV+AH8wuIwEvAUQDUm6O4GWwPAeqOUigUAWqvUOSDFZc6c77f+ktzIF+IeSC6UWOEug2EvvQ9yCn3RxOqWw+hYQ/llaD6HVm/pRWwa1fW64gO3aCdeUHW43MZSN3lQo1zrCBA7VlBkWsoAjTQqQMSSE+ABjoVQgI+J0AD3doGO2GgK1NcmaSpLmV6lhxn/cUXX6BOnTrG+6GVwXjiiSfixRdfTFv0LbfcghdeeME4QrusKVlioJe8/9tJA92qGnI10BXTEkP5nXfeQf369VNyVDvE27dvb9x/6KGHcMUVV1gitFatWhnvky+741oZ4+p4eXUEuzqdQJ1SUPZS99q1a4evvvoqq1xat26N8eNjdgY4tANdcf3nn3+yyjF2UNmd7JkWoIGeiRDvxxLgFxDUg5MEqDcnaTNWCQHqjlooFAFqr1DkgxWXOnO+3/rcqZCvv1AaWJzWDNr1PSG/WAJ98u6T1YzrX/sidF/i769mMpY7tkO/dfdJadleon0XaOekPtkv23XSjaPurKDINXIhQO3lQo1zkhGggU5dkEB6AjTQqRAS8DkBWw10XQe2bPI5wTLlVa0GoWmW1xx75PXKlSuNXeWprnQG+kknnWS8Dz3dlY2Brna59+7d29Ej3EsM9HxrsMJAV0fjqyPyU112Geixu+DVQw4nn3yykYJ6H7s6FUBdqY74V++2Vzvn1XXRRRdBHdXeoEED4zh/9bCEOrFAXaeccgp+/fVX46j2CRMmxJXoxA70ww47DOp98+pVBHfddVdSxOXKlTM+VyZ4yXX00Ueb+nNHA90UrsAP5hcQgZeAowCoN0dxM9geAtQdpVAoAtReocgHKy515ny/9ekTId9/szSwaH4ZtLY3QX7zKfSJQ6MJ1ayN0OjpeSUo1/8FvW8nU2uINjdCO+9yU3PMDqbuzBLjeKsIUHtWkeQ6NNCpARJIT4AGOhVCAj4nYKeB7nN0jpYXa6C74Qj3QhjoQT/CXQlu/fr1xk5zdfR6yTH66vNbb70Vzz//PKpXrw516kDZEwo2bdqEhg0bQu1UV+9Jf/DBB1Pq98gjjzTei56Lga7mKJNdPaRQYtYnCzRs2DDj3eXqKrtzXD0gsWbNGpxxxhnGqwiSXelOPsj2DyYN9GxJcZwiwC8gqAMnCVBvTtJmrBIC1B21UCgC1F6hyAcrLnXmfL8jk4cDX3xUGlhcdjW0y6+GXPYV9LEDowntVQ2h8TFHveeQqvz1Z+hDupmaKYqug3bhVabmmB1M3ZklxvFWEaD2rCLJdWigUwMkkJ4ADXQqhAR8ToAGujcaHGugp9phXFJJ8+bNsWzZMqidvO+++67x8R133GG8+1rtMv7yyy+NXcfJLmWwHnfccdi4cSPOPvtszJo1K25YyRHu2Rjose8BV8eOH3jggSlhq3deq3dfq2vcuHHG7uiyl1U15LoDfenSpbjyyiuNtAYNGoQuXbqkrGfAgAGYMWOGcf+zzz4z3hlk1XX99ddjwYIFRg/V2spMVz1Tx55fffXVGDNmTEIodWx7ixYtjM+nTZuGCy5I/q635cuXG31XVy4GeqdOnbBw4UKo3eBvvhndbVA2oZJx6vOyBrp697qaqx4GUFpNdtoCDXSr1MR1siXALyCyJcVxVhCg3qygyDXMEqDuzBLjeKsIUHtWkeQ66QhQZ87rIzL6TuDHb0sDi7adoTW/FHL599BH9YsmVLEyQpOSPzidbdYJa2YxUVx5DbRLWmcxMvch1F3u7DgzPwLUXn78ODtKgAY61UAC6QnQQKdCSMDnBGige6PBsQa6Mj+VCZrsUruPL7nkEuNWx44dMWLE7neLqWPbu3btavx/dSy2OqY92aV2MavdzOoaOHBg6ZySsWYMdHXEeEmcTO8Mz8ZAt6qGXA10day42v29YcMGwyBWx6arX0rKXps3b8Zpp52GdevWQe3mXrRokaUiU++wLzHvZ86caexKV8ezq0s9tHD66acnxFNG+2WXXWZ8nu6d7IMHD8bUqVONcbkY6CXzlen99ddfY6+99krIRXFRx8Rv27bNuFfWQI89pn7kyJG49tprE9aggW6ppLhYFgT4BUQWkDjEMgLUm2UouZAJAtSdCVgcaikBas9SnFwsBQHqzDlpyD9+g/zha8inHwYikdLA4oae0Jo0g/xpOfR7e0UTCpVD6JHdrxpTl9y1E/LVZ4Fff4Zo2hyi4UkZk5dffwL9gXsyjosdIC5tA+2K9qbmmB1M3ZklxvFWEaD2rCLJdWigUwMkkJ4ADXQqhAR8ToAGujcaHGugq4wffvhhqCPNYy917LY6nvubb74xjF21S1m941pdapeyMnXV0djVqlXD/Pnzccwxx8TNV0amWlONqVy5MtRR8bVr144bY8ZAV7vOW7VqZcx/8sknoXbGp7qyMdCtqiFXA13lHnv0uHoHvNqJH3tJKdGjRw/MmzfP+Fg9wKAeZLDyUsbz8ccfD3Usu+q3MtDfeust1K1bF2qXfDJTX5nWape6yq9Zs2ZQxnvJO89LclM7x2+88UZE9nzJkYuB/uqrrxprqKtfv364/fbb40pXJxzcfPPNhjZLrrIGunpQQR3frt7DXrVqVUM7JacTlMyJNdCVTlXOSt9mLh7hboYWx/ILCGrASQLUm5O0GauEAHVHLRSKALVXKPLBikudOdNv+dsq6Pf2UF9AJATUbhsEcdwpkKt/gn737gfAS/8b9OjzEHseTtfnTYdcEDXURec7gHVrgY1/Q5xxPsR+iSfb6Uvehpw6LrpghYrAju1pixYXtYLWytrf1csGpO6c0R2jJBKg9qgKqwjQQLeKJNfxKwEa6H7tLOsigT0EaKB7QwqxBnqjRo0Mk/yaa64xdpsrQ/z777/H5MmTsWLFCqMgdcy3MntjrzfeeMN4b7YyUdXOYLWLWRmVoVAIn3zyiTH/zz//NKakMn7NGOhqJ7bKVRm+6v3b6lhzNb/E4FWGrzLq1ZWNga7GWVFDPga6qkmdAPDTTz8ZeV988cXGcfPqB8qff/7ZOBngww8/NO6pd3mrBxUUX6uvnj17GrvNq1SpYrzXXP2j+qmOlk91lRyNru6feeaZUP9er149o+evvPKKsd5BBx1kHN//119/5bQDXT3koNZetWqV0ecbbrjBYKTeyf7DDz/giSeeMLSrdvKrXfHqKmugq88+/fRTI74y0xW/K664AhdeeKGRn67rRn7qeHeVt9L+vffeC3UsvJmLBroZWhzLLyCoAScJUG9O0masEgLUHbVQKALUXqHIBysudeZMv/VnZ0C+9mzSYFq/URD1j4H841fod8W/Dk17aB5E+QqQW7dAv6MTsH33aWUJ117VoQ2ZCFEz/pV0+qKXIWc9Gh1evwGw/Lu0RYvzroDW5gZbwVB3tuLl4mkIUHuUh1UEaKBbRZLr+JUADXS/dpZ1kcAeAjTQvSGFWANdGbTKtFWGbbJLGZZqh3q5cuUSbiuTtH///oYxmexSZmXfvn1LjwQvO8aMga7mDh8+3DgyPNkVDofRtGlT41a2Broam28N+RjoJbmqhxfU+8JTXeqIcmWm16pVyxaBLV682HjfeeyldnUfe+yxKeMpo7ply5ZJDWs1SfVWHZ+ujkxXBnguO9DVOmpHePv27bF169aEXJS+hgwZYuyaV++6V1cyA119rkx09VCA2ome6ZowYYKRr5mLBroZWhzLLyCoAScJUG9O0masEgLUHbVQKALUXqHIBysudeZMvyM3xZ+SFxtVGzsDonotyHVrofeLN661ibMhqlSF/uZLkM9MSZ/sMY0gDjgEWP8X5Pp1wD9bgNW7H3AvvRo1hthnP8jXX4h+VqMWsOHv0n8XzS6G1i7eyLeaEnVnDVG5c/eJBuohC17ZEaD2suPEUZkJ0EDPzIgjgk2ABnqw+8/qA0CABro3mhxroCvDURmQjzzyCF577TXD7FRHWqvj2pVxqY71Tncps/qxxx6DMmHVWmpHr9oNrt6drXbxlj3aPXYtswa62u0+e/Zs40hztQNZHTteckR4rga6yiefGvI10FV8tdN61qxZeOmll7Bs2TKonek1a9Y0DGxlUqt/kh2lbpXaVM9OPvlk/P7778aS2b5rXR3lrh5oUGa76r3aGa52oV900UXGbnFVQzo+H3zwQalRHdu/snWphwseeOABvP/++8ZucfUqALUjv3Pnzsb7z8vqORUX9aCHemBCvW/+22+/Nd4rr7juvffeOOKII4xc1QMj9evXN42WBrppZIGewC8gAt1+x4un3hxHzoCA8d9X9fOgutTrfNTPGrxIwAkC1J4TlBmDOnNGAykN9Np1EBo11UhCblwPvXeHuIS0sU8C1WpAH3wLsGZ13smKJs0g2neBnP4A5MofIE49x9jVLt96qXRtceYF0Dp0yztWugWou/zwyq8/hf7KXGDFMqBiJYgOt0E75Yz8Fg3IbGovII12oEwa6A5AZghPE6CB7un2MXkSyEyABnpmRm4Yka3h6HSuse+idjo24wWXgBW6o4EeXP3kUjm/gMiFGufkSoB6y5Uc5+VDgLrLhx7n5kOA2suHHudmS4A6y5ZU7uPkrl3Qu6Z4mP+kpgh16W8sbhzT3j3+NDVNmetrVkMfPzj3BGJminMvhXZ157i19PA0yIXzSz8Tp50L7foelsRLtQh1lzteuep/0O/pDkgZXaRKVWhjZnAnehZYqb0sIHFIVgRooGeFiYMCTIAGeoCbz9KDQYAGujf6TAPdG31ils4QoIHuDGdGiRLgFxBUg5MEqDcnaTNWCQHqjlooFAFqr1DkgxWXOrO/31IZ4IO6Jg0kWnWEdlEr4546jlu/pShunDb8Eejh6cAXSyxJVFzaFtoV7eLW0ufPhHwlXPqZaHwWtJv6WBIv1SLUXe549edmQL76bMIC2u2DIRqenPvCAZlJ7QWk0Q6USQPdAcgM4WkCNNA93T4mTwKZCdBAz8zIDSNooLuhC8zBLQRooLulE8HJg19ABKfXbqiUenNDF4KXA3UXvJ67pWJqzy2d8Hce1Jn9/ZVfLoU+aVjSQFrveyGOPs64J3Ud+s1Xxo3Tbh8C/cFh6mb08ypVga1bEtc7+jiImrWBqtUg33wxaTzR+gZo518Rd0//z2zIF2dHPzvxNIS63mkrGOoud7yRUf2A5d8nLODE0fu5Z+2emdSee3rh9UxooHu9g8zfbgI00O0mzPVJoMAEaKAXuAFZhqeBniUoDgsEARrogWizq4rkFxCuaofvk6HefN9iVxZI3bmyLYFIitoLRJsLXiR1Zn8L9IXzIcPTkgbSHngGonKV0nuRLlcBkV3Rsf8+Afj28+i/V6oMrfvd0JWJWnKp47vHzoQoV874REoJ/fa2wLZ/EmKK626Hdvp5cZ/rr86DfO7J6GfHnYLQbYNsBUPd5YbXOKVA9XZXjEZKlqpWA9qY6RBaKLfFAzKL2gtIox0okwa6A5AZwtMEaKB7un1MngQyE6CBnpmRG0bQQHdDF3LPYcOGDfjtt99yWuDoo4/OaZ6fJ9FA93N33Vkbv4BwZ1/8mhX15tfOursu6s7d/fFzdtSen7vrntqoM/t7oc+cDLl4QWKg+g0Q6jcy7vNItzbA9kTju2SQaHYJtHY3Q5/zOOQb/wFCIYgO3aA1bR6/zvDewP/+mxBTu2UAxAlN4j7XFz4PGX4i+lmDExDqOdRWMNRdbnjlj99CH536dACt30iI+g1yWzwgs6i9gDTagTJpoDsAmSE8TYAGuqfbx+RJIDMBGuiZGblhBA10N3Qh9xzmzJmDXr165bTA6tWrc5rn50k00P3cXXfWxi8g3NkXv2ZFvfm1s+6ui7pzd3/8nB215+fuuqc26sz+XkTG3AX88HV8oEOPhNahG0S9Q+I+j/RsD2zelDIp7Z7JEPsdaNyXf/0BVKgEUa16wnh96njIJYsSPtdGToXYu07c5/pbL0HOnhL97KiGCPUZbisY6i43vPrLcyGffyrlZHFBS2jFnXJbPCCzqL2ANNqBMmmgOwCZITxNgAa6p9vH5EkgMwEa6JkZcURqAlYYmUHgSwPd2i5bobtdu3Zh/fr1qFmzJsrtOQbQ2iy5mp8I8AsIP3XT/bVQb+7vkR8zpO782FVv1ETteaNPXs+SOrO/g5H+NwLK7N5zaV36Q5zUNGngSN/rgPXrkid1TCOEeiV/l3rZCforYcj5M+M/PvxohPrfn7C2vvg1yJkPRT9PMc5KUtRdbjQjowcAP36TenKdutCGPwohaFukgkTt5aY9zkokQAOdqiCB9AT4XyIqhAR8ToAGus8bbHN5VhiZNqfI5X1IwArd0UD3oTBsLIlfQNgIl0snEKDeKIpCEKDuCkGdMRUBao86cIIAdWYvZanr0G8pinuvuTZgDMShRyYNHLnzJuDP35PeE53vgHbKGVklLD/7APrD8cfDi7Y3QWt+WcJ8/f03IadPjH5+cH2EBo7LKk6ug6g78+Tkuj+h979BveS+dLK4tA3kS3PiFtPufhDigIPNBwjIDGovII12oEwa6A5AZghPE6CB7un2MXkSyEyABnpmRhyRmoAVRib5koBZAlbojga6WerBHs8vIILdf6erp96cJs54igB1Rx0UigC1VyjywYpLndnbb7nxb+i9O8YbnKOnQdTcO2ngyKBbgDWrEu9VrARt7EyIihWzSlj+8Sv0u7qUiTsdombthPn6R+9APj42+nm9QxAa8kBWcXIdRN2ZJ6cvnA8ZnhadWLkKtLFPQh/YBVj3Z+nn4or20C5tYz5AQGZQewFptANl0kB3ADJDeJoADXRPt4/Jk0BmAjTQMzPiiNQErDAyyZcEzBKwQnc00M1SD/Z4fgER7P47XT315jRxxlMEqDvqoFAEqL1CkQ9WXOrM3n7Ln1ZAv7dnNIimQXv4WQgtlDRw5J7uwC8rE+6JxmdDu6m3qWQjk4cDX3xkzEn3bmz56fvQHxkVXbvuAQgNe9hULLODqTuzxIDIvb2An5aXThSnN4d2XXfos6dAvvVSdMGDDkdo0HjzAQIyg9oLSKMdKJMGugOQGcLTBGige7p9TJ4EMhOggZ6ZEUekJmCFkUm+JGCWgBW6o4Fulnqwx/MLiGD33+nqqTeniTOeIkDdUQeFIkDtFYp8sOJSZ/b2W37xEXRlZJdcNfdGaHTMLuIy4SMj+gArf0xISrtlAMQJTUwlKyMR4OulQOWqQP0GEKHkpr388mPok+6Nrv2vfRG67zFTscwOpu7MEZM7tkO/tThuktZzKESDEyCXfQV97MD4eyOnQuxdx1yQgIym9gLSaAfKpIHuAGSG8DQBGuiebh+TJ4HMBGigZ2bEEakJWGFkki8JmCVghe5ooJulHuzx/AIi2P13unrqzWnijKcIUHfUQaEIUHuFIh+suNSZvf3W334F8ulHokEOPRKhAWNSBo2MvhP48duE+9rdkyAOOMiWZOU3n0GfeHd07QwmvxVJUHfmKCY9kv/BZyAqVYF6UELvdS2wdXPpoqJtZ2jNLzUXJO/JBsUAACAASURBVCCjqb2ANNqBMmmgOwCZITxNgAa6p9vH5EkgMwEa6JkZcURqAlYYmeRLAmYJWKE7GuhmqQd7PL+ACHb/na6eenOaOOMpAtQddVAoAtReocgHKy51Zm+/9flPQb4yNxrkxNMQ6npnyqCR8YOB775IuK/dPw2iVvL3pudbQcIO5mo1EBo3M99l086n7szhlT98DX3MXdFJlSoj9OCc0n/XnxgP+eGi6P2jGiLUJ+bkA3PhfD2a2vN1ex0tjga6o7gZzIMEaKB7sGlMmQTMEMjXQK9RowZKzCwzcTnWHwSsMDL9QYJVOEnACt3t3LkTGzZsQM2aNVGuXDkn02csDxLgFxAebJqHU6bePNw8D6dO3Xm4eR5PndrzeAM9kj51Zm+j9CcmQH74VmkQ0fwyaG1vShk08uAw4KulCfe1SXMhKlayJVm5/Dvoo/pH165cFaEHZtsSq2RR6s4cXn3J25BTx0Un7XcgQvdMLv13+fkS6A+NiN7XNGhjn4TYq7q5QAEYTe0FoMkOlUgD3SHQDONZAjTQPds6Jk4C2RHI1UCPRCL4+++/Ub16dVSoUCG7YBzlOwJWGJm+g8KCbCdghe527NiBjRs3olatWgileE+e7YUwgGcI8AsIz7TKF4lSb75oo+eKoO481zLfJEzt+aaVri6EOrO3PZFxg4DvvywNIlp1hHZRq5RBIw+PBD77IP5+KATt4ecghD1fRcuV/4U+onc0ZoUKCE2eZysY6s4cXv3VZyGfmxGddEwjhHoNK/13uX079F7tgR07olrr1ANa03PNBQrAaGovAE12qEQa6A6BZhjPErDnpxbP4mDiJOA/Arka6FJKw0BX5vlee+3lPzCsKCsCVhiZWQXiIBKIIWCF7jZv3gxloisD3a4vadg0/xDgFxD+6aUXKqHevNAl/+VI3fmvp16piNrzSqe8nSd1Zm//IoNuAdasipqaN/SC1uSclEH1x8ZCfvxO/P29qiM0/inbEpW/rIR+T/fo+pqG0KPP2xZPLUzdmcOrz54C+dZLUR01bQ6tU0zPAEQmDwe++Ci68PFNELp1gLlAARgdRO3JXTt36+eX/0GcejbEsScGoNP2l0gD3X7GjOBtAjTQvd0/Zk8CGQnkaqCrhbds2YJt27YZu9B5jHtG1L4cYIWR6UswLMpWAvnqTh3frnafV6pUCVWrVrU1Vy7uDwJB/ALCH53zZhXUmzf75vWsqTuvd9C7+VN73u2dlzKnzuztVuS2NsC2f0qDaH1GQBx1bMqg+vSJkO+/GX9/n/0QGv6obYnK336BPvjWuPW1R+dDaCHbYlJ35tBG1PHsny8pnSQubg2t5TVxi+gfvAk5bWJ8H+9+EOKAg80F8/noIGpPX/Ac5Lzp0b+H7pkMsd+BPu+0/eXRQLefMSN4mwANdG/3j9mTQEYC+Rjouq4bJpQ6zr1ixYrGbnS1k5O7OTNi982AkndH79q1yzc1sRD3E8hFd+rUDPWP2nW+fft249h29fCP+sWSFwlkIhDELyAyMeF9+whQb/ax5cqpCVB3VEehCFB7hSIfrLjUmX39lv9shX5727gA2vBHIPbZP2VQ/amHIN95Lf7+wfURGhjz/muLU5Z//Ab9rpvj85wchqhQ0eJI0eWoO3NoIyP6ACt/LJ0k2neFdk6LuEXk5o3Qe3UApB79vNa/oA2dBFG5irmAPh4dRO1Fbro8rqPigpbQijv5uMvOlEYD3RnOjOJdAjTQvds7Zk4CWRHIx0BXAZSJ/s8//xiGlPr/vIJFoOTd0eohCl4k4BSBfHSnfpFUD/xUrlyZ5rlTDfNBnCB+AeGDtnm2BOrNs63zdOLUnafb5+nkqT1Pt88zyVNn9rUq6c7uDMa0/sxjkG++GJ9UmfddW52xXPcn9H7Xxy2rTZwNUcW+E8moO3NdjPS9Dli/rnSS1m0gRKPGCYvo0x+AfP+NuM9FmxugnXeFuYA+Hh007anj2/WureI7avNDOT6WT1xpNNCD0mnWmSsBGui5kuM8EvAIgXwN9JIy1c5OZaCr/+UVDALqpIE6deoYxa5du5a9D0bbC15lPrpTc9Uvkjwlo+Bt9FwCQfsCwnMN8lnC1JvPGuqRcqg7jzTKh2lSez5sqgtLos7sa4r87gvo4wdHA1SthtCEp9MG1OdNh1zwXNwYcdLp0Lr0sy1RuXE99N4d4tbXxs2EqFbDtphB0p3ctLG0p+L8KyBq1DLFVUYiuw3QmJ3l2qDxEAcdnrCOVBt47u0BrFkdvdeoMULdBpqK6efBZrQnd+2C/HixwV40PhuifHnPoZE/r4A+rGd83tSEJX2kgW4JRi7iYwI00H3cXJZGAoqAVQY6aQaPgJkfyINHhxXbRYC6s4ss101HgLqjPpwkQL05SZuxSghQd9RCoQhQe4UiH6y41Jl9/dbffxNyesw7qQ84GKG7H0wbUH/haciX5sSNEWdeAK1DN9sSlVs3Q+/eLm597f5pELX2ti1mkHQXd/z6oUdCu3O0qQfXk54QMPZJiOo1k/ZH/3AR5BPjo/eqVoPxQISmQX73OfRXnzUejhBF10HU3r3xI0iXGe3pj94P+cl7u/EcexJC3Yd4DpX+7kLIJyfF5/3vExDqMdRztbgtYRrobusI83EbARrobusI8yEBiwnQQLcYaICWM/MDeYCwsFSbCVB3NgPm8kkJUHcUhpMEqDcnaTNWCQHqjlooFAFqr1DkgxWXOrOv3/rLcyGffyoaIAsDLmEOAHFhS2hF9r2v2Ni13K04DoQ2YgpEnbq2wQmK7uSGv6H36RjPdtAEiIMOy5qt/L8foN/XNzq+XDlok+cZhniyS/61Fnr/G+Jj3j0JqFkL+h3XAzu2776XhR6zTtJDA7PVnvFO+Z7XxHMcORVib289dKDPegRy0SvxHTroMIQGTfBQ19yZKg10d/aFWbmHAA109/SCmZCALQRooNuCNRCLZvsDeSBgsEjHCFB3jqFmoBgC1B3l4CQB6s1J2oxVQoC6oxYKRYDaKxT5YMWlzuzrt/70w5Bvv1oaIJud5PrC+ZDhaXFJiSuvgXZJa9sSNY4I79Iy3igc9hBE3Xq2xQyK7pIdny3ad4V2Tous2eqLX4Oc+VB0/L/2Rei+x9LOj6h32q/7M6q99l2BSpUgp8bsTAegPTgHolLlrHPxw8BstSeXfwd9VP/4Pxc9h0I0OMFTGCIj7wBWLIvPuWZthEZP91QdbkyWBrobu8Kc3ESABrqbusFcSMAGAjTQbYAakCWz/YE8IDhYpkMEqDuHQDNM/JcImoa6dXfvTlmzZg10XSchErCNAP+esw0tF05DgLqjPApFgNorFPlgxaXO7Ot3ZNK9wJcflwYQl10N7fKr0wbU33oJcvaUuDGiXRdozS62LVEpJfSbr1TvMSyNoQ2ZCFHvUNti+k13Uu3q3rkTqFI17nh2+c1n0CfeHd/PJudAu6FX1mwjk0cAXyyJ6uiUM6F1jtmRnmQl/bGxkB+/E51z6tlAnboJrwfQeg2DOKZR1rn4YWC22tPfex1yRvwrF0SHbtDOvMAzGKQegX5bG2DHjvicQyFoDz9n6lUCninawURpoDsIm6E8SYAGuifbxqRJIHsCNNCzZ8WR8QSy/YGc3EjASgLUnZU0uVa2BKi7bElxnBUEqDcrKHINswSoO7PEON4qAtSeVSS5TjoC1Jl9+ogM6wn8vKI0QDbmm754AeTMyXFJiRt7Q1MGqI1XpGsrYNfO0gjaXWMhDjnCtoh+0p1csQz6w/cBG/6GOPVsiOt7lh6vrn/wFuS0Mkdl770PQiMfz4qt3Llz9zHi2/+J6qhTD2hNz007X3/zJchnYh7EOPRIiH33h1zydry2rmgH7dK2WeXil0HZak+fNx1ywXPxvFq0gnZV/JH8buYif/0Z+pBuSVPUJjwNUbWam9N3fW400F3fIiZYYAI00AvcAIYnAbsJ0EC3m7B/18/2B3L/EmBlhSBA3RWCOmNSd9SAkwSoNydpM1YJAeqOWigUAWqvUOSDFZc6s6/fkV7XAps2lAbQbh8C0fCktAGTGa7a7YMhGp5sX6IAIre3Bf7ZGs213yiI+sfYFtNPuouMHQgs+yrKrv/9EIcfbfy7vmA+5Lz4I/nV59qoqRC1M79LW37/JfRxg+L6oI19EqJ6zfQ6Wvou5JTR0THqOP6KlYCflsfPO/ZEhLrH75C3rekuWThb7ZU9QUKlL046HVqXfi6pJHMa+pJFCcf2l/58e89kiP0OzLwIR6QkQAOd4iCB9ARooFMhJOBzAjTQfd5gG8vL9gdyG1Pg0gEkQN0FsOkuKJm6c0ETApQC9RagZruoVOrORc0IWCrUXsAaXqByqTN7wMtdO6GrXd0xlzbkAYh6h6QNqJc1PpXZarOZrRKKqF3OmzeW5qb1GQ5xVEN74KiafPIaKOP4+85XxHES510Brc0Nxmd6eBrkwvkJHJUJq8zYTJc+dyrk6y9Ehx1yBEJ3jc00DfKbT6FPHBodV6PW7gck1FHzsVflqjB2ImtaxjX9MiBb7UUGdgV+Xx1f9kGHIzQo/j3ymbioUwSw/DvIDeuMY/5Flb2ABsdDVK6SaWre9/U5UyHfiNFP7IoNjld/EIFQOeMBHXHWhTzS3SRxGugmgXF44AjQQA9cy1lw0AjQQA9ax62rN9sfyK2LyJVIwD9fQrCX3iLAv++81S+vZ0u9eb2D3syfuvNm3/yQNbXnhy66vwbqzJ4eyb/+gN7/xrjFszkyWX76PvRHRsXPGzoJYv+D7El0z6qRvtcB69eVxtB6DIX49wm2xfSL7uTff0G/o1Mcp9h33etTx0MuWZTAUbTvAu2czO+1jwy+Ffjtl9L54rK20C5vl7EvxrHyI+/IOE4N0BzQV1aJODQoG+0lewDGSK9yFWgTZ2dtNBvvIB89AFj+fXx19Q6B1m8kRCV7TfSIiv3jN1mRFTf1gdb4rKzGctBuAjTQqQQSSE+ABjoVQgI+J0AD3ecNtrG8bH4gtzE8lw4oAeouoI0vcNnUXYEbELDw1FvAGu6Scqk7lzQigGlQewFsegFKps7sgS6Xfwd9VP/o4hUqQJsUzmi8JTtyWRs9HaJmbXsS3bNqRJn9f/1RGkPrNgii0Sm2xfSL7pIdsS7adYHWbLc5HpkwBPj28wSOolVHaBfFn1BQdpBcuwb6gM5xH2t3joY47KiMfUn37uuyk7Vb74I4/tSMa/plQCbtyU0boffpAOh60pK1cU9BVKueFY6EvwdiZokLr4JWdF1W6+QySOo69B7t4l7NkG4dcerZ0G7snUuowM6hgR7Y1rPwLAnQQM8SFIeRgFcJ0ED3aucKn3emH8gLnyEz8CMB6s6PXXV/TdSd+3vkpwypNz910zu1UHfe6ZXfMqX2/NZRd9ZDndnTF33pe5BT7o8uvs9+CA1/NGMwffECyJmT48Zpk8MQFSpmnJvPgLLHVWtd+0Oc2DSfJdPO9Yvu9EUvQ86K76tocwO083Yf6x65pzvwy8oEFuKS1tCuvCYtI33RK5CzHomO2as6tLEzILRQxr7I9X9B7xu/Mz7VpNh8My7sgwGZtKfPegRy0SspK832IQa1gP7hIsgnUhz5HioH7e4HINT76W245B+/Qb/r5uxXzvL1ANkv6P+RNND932NWmB8BGuj58eNsEnA9ARrorm+RaxPM9AO5axNnYp4mQN15un2eTZ6682zrPJk49ebJtnk+aerO8y30bAHUnmdb56nEqTN72qUvfB4y/ER08aMaItRneMZgyQw3bcoLGXeuZ1w4w4DI3bcBq38qHSVu7A3t1LPzXTblfL/oLpnZKq7qCK3F7t3lkT7XAerd12Uucd7l0NrEH/FfdkzkgXuArz+J9qTJOdBu6JVVT+T2bdC7tc5qrDj3UmhXx+90z2qiRwel057xTvs7rgfW/5WyOtGiCNpVHbKqXl8wH3LetNRjjz0R2u1DbPnzLT95D/qjMQ/xZMq4clVoE2fZkkum0F69TwPdq51j3k4RoIHuFGnGIYECEaCBXiDwPgjrl18GfdCKQJVA3QWq3a4plrpzTSsCkQj1Fog2u65I6s51LQlMQtReYFpd0EKpM3vw63OnQr7+Quni2R6PLLdthd6rA7Bzx+65WRrv+VYRGdYT+HlFNN9O3aE1bZ7vsinn+0V3kXGDgO+/jKtTXN4O2mVtYRyhfUsrIBJJ4CDOOB9ax9tS8pE7d+w+fnvHHh0AMPNQg2EEd2mZ8hjyuMANT0bo9sG29dptC6c10H9ZCV2dGpDuEhq0viMgjmiQsTQ9PA1y4fy047RuAyEaNc64ltkB+nMzIF991tQ0beyTENVrmpoT5ME00IPcfdaeDQEa6NlQ4hgS8DABGugebl6BU/fLL4MFxsjwJglQdyaBcbglBKg7SzBykSwJUG9ZguIwSwlQd5bi5GImCFB7JmBxaM4EqLOc0aWdqE8ZDbn03dIxZt53LL/4CPp/ZgFVq0Fr1wViP3uOeI4tIDLyDmDFsmi+194K7awL7YEDwC+6i6hj0svsVhYXt4bW8hrIzRuh90x+TLs4+QxoN9+RwFcqs33bVmDlf6FPvDt6X5m2456E2Cu7d2+riZEe7YEtmzL3cL8DEbon/rUBmSd5d0Q67emvhCHnz4wWV6cutJv7Qb+vT/yDEHvvA23wRIgqVdP/PTB1HOSSt6N/rs68APKrT+JPJVAxhk6CKF/BUqiR8UOA7z6Prrn/QcCvP6eNod0xMqsHAyxN1MOL0UD3cPOYuiMEaKA7gplBSKBwBGigF4691yP75ZdBr/chaPlTd0HruDvqpe7c0YegZEG9BaXT7qqTunNXP4KUDbUXpG4XrlbqzB72kVH9gOXfR42ztp2hNb/UnmAWrBoZPQD48Ztovu1uhtbsEgtWTr6EH3RnnBZwW9uEAsUFLaEVd4L87Rfog29NDuDYkxDqPiTunvxpBfSHRgDr1ibOOfxohPqbOI5bGeh33gT8+XvmHpavAO2ucUD5ckCd/Xx/hHc67UVG9QeWfxf9c9DsEmjtbob+8lzI55+KYymyOFI/Mn4w8N0X0fVadQRq1oacGv9edHHlNdAuye7I/cwNBYwTCHpdC2zeGI3duS/kgvnAT8tTLiE6dIN25gXZhOAYADTQKQMSSE+ABjoVQgI+J0AD3ecNtrE8P/wyaCMeLm0TAerOJrBcNi0B6o4CcZIA9eYkbcYqIUDdUQuFIkDtFYp8sOJSZ/b0O9L/RuCvP0oX17r2hzixqT3BLFg1wehrfQO086+wYOXkS/hBd/KP36DfdXNCgSXvFJc/fA19zF3JAdRvgFC/kaX35Mb10If1ANYnvi9dDRJXtId2aRtT/Yioo8h/WWlqjri0LbQr2pma47XBqbQnt2zefWKA1KN/btX7yRueBKlHdvfyv1Fz3ejLjb2hnXp2SgSRobcDq/5Xel906g5x2rnQ1QM2MSc+oEJFaCOnQlTL/oSBdNzlurXQ+90QN0Qb/ihQsRL0+/rG/d0UO8jMSRle67sd+dJAt4Mq1/QTARrofuomayGBJARooFMWuRLwwy+DudbOeYUjQN0Vjn2QI1N3Qe6+87VTb84zZ0T/HDPLXnqPAP/O817PvJgxdWZ913a/+7oIiOyKGnEDxkAceqT1wSxaMfLAPcDXn0SNvqs6QmvRyqLVE5fxg+7kqpXQhya+L1ucdSG0a2+F/vFiyMfGJGdY71CEhkwsvac/ej/kJ++l5K0NHAdxcH1T/Sh7qkDp5HqHAGtWA7t2Jl1PGa1in/1MxfLS4FTa05e+CzlldLQUtTN/wtMQFSoan8m//tjd73+2RMdU2QvamOkpj1+P9O4AbFwf/Xug+90Qx54I47SB4b3UVvHon7kbekFrco4lKI3XQEweHl2rclVoE2cZpwuoBwXk4tcAdWT82jWQb70UHXd8E4RuHWBJDkFYhAZ6ELrMGvMhQAM9H3qcSwIeIEAD3QNNcmmKfvhl0KVomVYaAtQd5VEIAtRdIagHNyb1FtzeF7Jy6q6Q9IMdm9oLdv+dqp46s560sZtYGWcxl3b/NIhae1sfzKIVI+ro8M+XlK4mrmgH7dLE48ktCueLd6DLFcugq3fHl7nUDmPt+h7QX3ga8qU5yZHVqYvQiCnGPbl9O/TubePfsR07q1oNaGNmQGiaKfyRSfcCX36cmF/jsyB//j9gzaqk64lLWkO7Mvm7200l4NLBKQ30J8ZDfrgomnXDkxG6fXBcFfpH70A+Pjb+z3avYRDHNEqo1ti13qVV/I72QRMgDjrMGBuZMAT4NvqOcnFxMbSW11pCTf/PLMgXn4mudVRDhPrEGOp77ujvvQ4548HouP0OROieyZbkEIRFaKAHocusMR8CNNDzoce5JOABAjTQPdAkl6bILyFc2hifp0Xd+bzBLi2PunNpY3yaFvXm08a6vCzqzuUN8nF61J6Pm+ui0qgz65shf14BfVjP6MJCg/bwsxChkPXBLFpRnzIacum7pauJi1tDa2mfieoH3cnvv4Q+blBCB8QpZ0Lr3Bdld/XHDaxWA6FxM42Pkh23HTtWND4L2k19THdanzoeckmMIbxnBfVwhFzxA/DNp8nXrPUvaCMfg9Dcq1fTMGImJNOecWpEn47Apg3RPwPtbobW7JKEUJG7ugB//Bod1/kOaKeckTBObvwbeu+OcZ9ro6dD1KxtfKaHp0EunB+9f0IThG6xZve3/vhYyI/eiebY/DJobW9KzPG/30G/v3/083LloE0O+7b3+egm2Vwa6FYT5Xp+I0AD3W8dZT0kUIYADXRKIlcCfvhlMNfaOa9wBKi7wrEPcmTqLsjdd7526s155ozII9ypgcIR4N95hWMfpMjUmfXdll9+DF3t/i25au6N0Ohp1geycMWyZqu4sCW0ok4WRohfyg+6Szgmu6TEE09DqOudiChDdsPfyRlWqIDQ5HnGPbn6J+h335aStejQDdqZF5juhT7rUchFLyfM07r0h/z+C8h3Xku5ptZ9CMSxJ5mO6YUJSQ30lT9CHxH/kII2YgpEnboJJUXUqQMx7y8X19wC7eyLEsYlHPEvBLSHnyt9kEZ//w3I6Q9E59U9AKFhD1uCMKLe1/7D16VrieLroV1wZWKOmzZA7xW/6z1V3ZYk5rNFaKD7rKEsx3ICNNAtR8oFScBdBGigu6sfXsrGD78Meok3c91NgLqjEgpBgLorBPXgxqTegtv7QlZO3RWSfrBjU3vB7r9T1VNn1pPW334V8ukYI+zQIxEakOJd2NaHz2lFfcaDkO+9HjXcUuxYzWnxJJP8oLtkx3kbpTY8GVqHbtD7XpcWl/bIfMNMlcu/gz4qZhdwmVnafY9B/Gtf0+j1+U9BvjI3YZ42dBLk50sgn38q5ZripNOhdelnOqYXJiTTXsKR53XrITTsoaTlRCYOjdu9L67qCK1Fq4Sx8rvPoY8fEv085tQB9aH8vx+g39c3el/Tdu/+Llc+b4yRQV13v+d+zyVu7A3t1LMTc5QSeo92wNboe921Pe9pzzuJACxAAz0ATWaJeRGggZ4XPk4mAfcToIHu/h65NUM//DLoVrbMKzUB6o7qKAQB6q4Q1IMbk3oLbu8LWTl1V0j6wY5N7QW7/05VT51ZTzrBuNyzI9n6SNatqD/9COTbr0QNt3NaQGvf1boAZVbyg+70dxdCPjkpkdExjaCddzn0B4el5adNnAVRZS/Ir5amHRt67D859UFf8BzkvOnxc0MhaJPmQi59D/KJ8anXDZWDcdx4teo5xXbzpGTaiwzvDfzvv1H9X3AltOLrk5ahPzYG8uPF0bEtWkG7Kv6odnVT/3BRPOMDDkbo7uj7xuXWLdC7Xx0XQ7t7EsQBB+WNL3J7W+CfraXraH1GQBx1bNJ1I2rn/cofo/W0vQla88vyziEIC9BAD0KXWWM+BGig50OPc0nAAwRooHugSS5N0Q+/DLoULdNKQ4C6ozwKQYC6KwT14Mak3oLb+0JWTt0Vkn6wY1N7we6/U9VTZ9aT1qdNhPzgzaghde6l0K7ubH0gC1fU5zwO+UbUqBVnnA+tY+pjxfMN7Qfd6W++CPnMY4kojmgA0eB4yBdmRe8dcDCw+qe4sdqoqRC16yDlTnYAokURtKs65IRbX/wa5Mwyu6j37KyWP3wNXR3zneYSPjVSy2ovsv6vxHeV9xoGcUyjpHT0px+GfPvV6J/vsy+Cds0tCWP1BfMh58W8uuGYRgj1in+oItK3E7D+r9K52s13QJyc+D51MwKQ2/6BflubeK3d+wjEvvsnr2fqOMglb0fraXYxtHZdzIQM7Fga6IFtPQvPkgAN9CxBcRgJeJUADXSvdq7wefvhl8HCU2QGZglQd2aJcbwVBKg7KyhyjWwJUG/ZkuI4KwlQd1bS5FpmCFB7ZmhxbK4EqLNcyaWel3jEcwdoLYqsD2Thigm75k9ogtAtAyyMEL+UH3SnvxKGnD8zkdGhRwI1agNfLCm9J5pdDLkousNf3VBHqYv9D4L+9iuQTz8Sv84++0McdhTU+7VFxYo59UFf+i7klNHxc/f0Vf7xG/S7bk6/br1DoA2eCCH8ZYGU1d6u996AnDYhyqJiZWgTnkp5lLr+3JOQr+5+f726xClnQusccxT7ns/18DTIhfOj4xqfDe2m3nHMI+MGAd9/GR1z2dXQLo/flW62+fL3X6EPjDfAtQfnQFSqnHQp/aVn4h/2aHA8Qj3vMRs2kONpoAey7SzaBAF//dfDROEcSgJBIUADPSidtr5OP/wyaD0Vrmg3AerObsJcPxkB6o66cJIA9eYkbcYqIUDdUQuFIkDtFYp8sOJSZ9b3OzLyDmDFsqgpds0t0M6+yPpAFq6ov/Ma5FMxu5UPro/QwHEWRohfyg+6S/WOcdQ7FNi6GVi3NqqBTj128925o/Qz7c7RhkmeYMQ3PBmh2wfnzV5+8yl09b7umEtc3Bpay2sgd+6EdagEHQAAIABJREFUfkvie7sTgh50OLQbe0Hsd2De+bhlgQQD/ZFRkEvfjaaX4eER/bVnIZ+dER1/7IkIdb87oTz9yUmQ7y6MaiDJSRT6rEchF70cHXPaudCu75EXKvnjN9BHxzz8UqkyQg/OSblmwoMWe++D0MjH88ohKJNpoAel06wzVwI00HMlx3meJdCyZct9QqFQYwCNhRCnAFD/7K0KklLOmDdv3nVWFFdcXFxDSnmpEOI8ACcCOARAFQDrAXwjpXxp165dU59//nn177ZdNNBtQ+v7hf3wy6Dvm+TDAqk7HzbVAyVRdx5oko9SpN581EwPlULdeahZPkuV2vNZQ11aDnVmfWMig28Ffvslaord1Ada47OsD2ThivLrT6E/EGO2VquB0Lgku6stiukH3elzpkK+8UIikWo1gE0b4j7X7n4Q+tiBcZ9re44J15+dAfnas1G9JNmpnAt2uWIZdPUwR8wlbugFrck5xieRXtcm5AmhAVKPD1e1GrTR0yDKV8glDdfNidXeb6tXY1ePdsDWLVH+HbpBO/OClHknHI1/2FEI3Vlmp796B/qj90N+8l503T0PL8QurP9nNuSLs6MfnXgaQl3vNM1MLv8O8tdfII5vDLnsa8jHxkTXqHsAQsMeTrmm/Pn/oA+LMe2FgDZpLkSF3E4+MJ28hyfQQPdw85i6IwRooDuCmUHcRKC4uFimyscqA72oqKiFEEKdcZPpv9RrhBDt5s6du8guRjTQ7SLr/3X98Mug/7vkvwqpO//11AsVUXde6JJ/cqTe/NNLL1VC3XmpW/7KldrzVz/dWg11Zn1nIn2vA9avK11Y6z4E4tiTrA9k4Ypy9c/Q7+4Wt6I2OWybieYH3ekzJ0MuXpC5C+UrQB2hrQ/qCqxdE9XFLQMgTmgCfeZDkItfK/1cnNMCWvuumdfNMEL+9gt09TBHzKUNHA9x8OHGJ2Uf9FCfqePI43Zj75mr9RkOcVTDvHNywwKx2vt18RuIjOofz+j+aRC1jL1iSS996XuQU+6P3kthUCe8yqFVR2gXxe/61xfOhwzHvCf93ycg1CP+1IBMzPSP3oF8fOzuYXtVgzjnYsiXYnacH9UQoT7DUy6T9J3pdz8IccDBmUIH/j4N9MBLgAAyEKCBTokEjkAZA/1nAOpMKuOxPKsM9NatW18jpVSPuapHHl+XUr6madqXkUhkfSgUqielbA+gzR74WwGcHg6Hv7CjGTTQ7aAajDX98MtgMDrlryqpO3/10yvVUHde6ZQ/8qTe/NFHr1VB3XmtY/7Jl9rzTy/dXAl1Zn13It1aA9u3lS6s9b8f4vCjrQ9k4Ypy21bot7WNW1Eb9jBE3QMsjBJdyg+60x8fC/nRO5n5HHokQgPGIDK0O7BqZel4cX1PaKc1gz5ldJxpLVoUQbuqQ+Z1M4yQW7dAV7vMI7t2j6xQAdq4p0vfqR4Zejuw6n/xPR8yEfrw3sCuPXP23NX2mP15J+WCBWK1t3rySOivRN9nro7fDw2ZmDZL+e3n0CcMiY6pXhOhsU8mzImM6gcs/z7a7/ZdoJ1zcdw4/e1XIZ+O2R1e/xiE+o0yRSkyrCfw84ronFAIiESicbM40SDStxOw/q/o31ld+kOc1NRUHkEcTAM9iF1nzWYI0EA3Q4tjfUGguLh4qJRyafny5ZfOnj3797Zt2x4SiUSMn/6sMtCLi4vbCCGaSSlHhMNhZdInXEVFRbcJIR7Yc2NROBw+1w7ANNDtoBqMNf3wy2AwOuWvKqk7f/XTK9VQd17plD/ypN780UevVUHdea1j/smX2vNPL91cCXVmbXdkJAK9S8u4RbWhkyD2P8jaQDasFumujrLeXLqy1vMeiAbH2xAJ8IPuIpNHAF8sycinZEe5sdN5+Xel40W7LtCaXYyEncpXdYTWIov3k2eMDMQeDy9aXgvt4uLSWQnGKwBtyguGmR93BLjamd6pO7SmzbOI6P4hsdpb1e1qyJX/jfYki4cX1Hh9RO9ooeXKI/Rw9Aj+khtlH1AQN/SE1qRZHCB9ySLIqeOjn2Vh4JclHLnp8rTQxQUtoRV3SjsmMuYu4IevoxzKaMX9XS1MhjTQC8OdUb1DgAa6d3rFTG0iYIeBnm2qxcXFSwGcrHaqlytXbt/Zs2f/me3cbMfRQM+WFMeVJeCHXwbZVe8RoO681zM/ZEzd+aGL3qmBevNOr/yUKXXnp256qxZqz1v98mq21Jm1nZNbNkHvoQ5OjF5ahiOhrc0g99USdkh3vA3aGefnvmCamX7QXWT8EOC7zzPyEXveqZ3KKI+o95SvUAd87r5E+67QzmmRcd1sB8g1q9QTCxD77B83JTL6TuDHb+M+Cz32H+PfEwzVtjdBa35ZtiFdPS5We79c2wJYt7Y0X+3WuyCOPzVt/vL3X6EP7BL/Z/yheQnviI/ceRPw5+9p15afL4H+0IjoWnXqIjRiStb85KaN0Htdk3a8aH0DtPOvSDsm4TUCTZtD69Q96zyCOpAGelA7z7qzJUADPVtSHOdbAoU00IuKikYLIfoouJqmNZ4zZ44y1C29aKBbijNQi/nhl8FANcwnxVJ3Pmmkx8qg7jzWMI+nS715vIEeTZ+682jjfJA2teeDJnqgBOrM2ibJtWugD+gct6h6/7WoVNnaQDasFpl0L/Dlx6Uri8vaQru8nQ2RfLIDvcwR3alAlbx3XH/0fshP3ovyvbg1tJbXJLyLXNzUB1rjs2zhHruo/OZT6BNj3rfdqDFC3QYaQyKThwNffBTN9Yp20C6NP+Lf9gRtChBnoLc5F9i8sTSS1msYxDGN0kaWmzbsPho/5tLGzICoUSvus0jP9sDmTdG1e98LcfRxcWPkd19AHz84+lmK4+BTJSSXfw9d6TDNlY2e9IXPQ4afiK5y+NEI9Y95z7tNvfD6sjTQvd5B5m83ARrodhPm+q4nUEgDvXXr1g9IKW9TkDRNO2nOnP9n77zDpKiyNv7e2zNIBgmCYlhzwjXn/LmrawAJ0wOomEFAFEmCCgIGBAOIoCQRURGYBsW86+rqrgnzmlfXFQMqWUUy0/d+TzVM9zTTM9NVXd11q+rt5/EPuu8595zf+05b3aerat4HbgPjAN1touHJxy8hwqO1SZ3SdyapEZ5a6LvwaG1Cp/SbCSqErwb6Lnyam9IxvWeKEsGugz5zV1/9/f+grHsSVzykhJzyJIQw/2tk9fgU6FeeT5Yu8ngWahB8F7+lH/BD6p7mGZ0UKULiBxTFxVAP3wf9xkspvme0g+zaA1XuP33tCIhDjnTXmBmyaRWHmnQ78Ml7QKMmSJx9vfcBiZVqxnjoRa+kaj2zA2T08rzXVIgN0gbonU4CNm1M/bkOvTPJoLpadPkWqN7pl9iXtzwAsfOuaSHx3p3S7iUvh42D2GOftDX6f/+Bsq5AUPHYoR7kfY9DyEhWKNQbL0E/XHGH08whcuREiDZ71JhPf/wu1MRbU2saNkZk/GNZ1RDmRRygh1l99p4NAfOPfLLpgmtIIAcCXg7Qo9HoRwCsn+5tqVOnTovZs2enfjKYQ0+VQzlAdwlkCNME4cNgCGXzfcv0ne8l9GUD9J0vZfNt0fSbb6XzdeH0na/l83Xx9J6v5fNN8fSZu1LpLz+Bsu4nXPGo3xCRCY+7u0mesqm/PQE9/+FU9gP+iMjA2/KyWxB8Fx/WG1j2Y818dt8LkeH3JtaoudOhX34muV6c+CfIS69FvG9p+hB3yFiIfQ7MC/ftk2qtgV9WAvUbQNStn3xZPT4V+pXnUrWefCbkxX0LUlO+N6nwntX7knbHABaDbQ85YgLErnvWWkL86iiweVMqbrvBu96yBarPdkP2WydDtG6Tllsv+RZq1LXp+xUVQXS8GPLMDrXWoZ6YBf1C1fuvVwSKLHXLeFn6e2dDNGhUaw1hXsABepjVZ+/ZEOAAPRtKXBNoAl4N0KPR6LkAnt0G99lYLGb7RjydOnVK/2lgBqXKysrejUQirePxOFasSN0TJ9CisjlXCFgH5DvttFMi1/Lly6GUciUvk5BATQToO/rDCwL0nRfUw7sn/RZe7b3snL7zkn6496b3wq1/obqnz9wlrT5chMSl0CseLVqheOwMdzfJUzb1zr8Qn1rpss077YziO6bnZbcg+G7LoEuAX1bVyMcaYBZdunVAGn9qNtTTc1Lrd/0Diobfi/Kr0gelRbc+ALHL7nnhnm3S+BOPQj03L7lcHH0yinrVfKnwbHN7va7Ce3rLZizpcEJaOUWjp0K0Sh9yZ6p3y8CLgV9XJ1+KXDcS8pCjkv+2LvNeft2F6bnveQSiabO056xbPpQPvbLqFlKiaMyDEM23fq9Y3aP8/tuhP3gr48uRHgMhjzs9K9y6vBzl1sA/Hk/1dOPdkNuuSJBVkhAuatmyJSKRxNUCfhRC1DpnCCEithxyAhygh9wAbB/wYoAejUatow3rcu3W9WfiSqmjFyxY8KFdPaLRaOonhtUET548Gc2bN7ebmutJgARIgARIgARIgARIgARIgARIgARIgARCQmD9Gy9jwzuvQ/26ChvfezPZdfFe+6H1RH+cgb7pi4+xfFCly3QX18GuT7wOIWVIVLTX5pLS06HXpe5xnSl6xz5D0fDcksRLGz9chBXD0s/ibjHsbqy8bVBa6M6znkdRi5oHp/Yqtb96zYJH8NtDqUuD1z3ieLS8daL9RAZHqN/X4Meu/+eI/c+9S1H+/TfJ2GaDb0OD0/6S/Hf50h/x8xXnp+Vus+A1yLr10p6L//YLfrrgzxkpNe7WA00uuqpGgj/3KUX5d6k6Ki/edeFbiVsHZPv4uWcnlP/4faqnASPR4Izzsg0P+zoO0MPuAPafkQAH6DRG6AkUeoAejUatn3VZZ54njkqEEKPKyspGOhGCA3Qn1BhDAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRQQWDjR+9hxY29MgLZ4ZAjsNOYab6AVb5yOX6+5Jy0Wnd59K+INGvhi/oLXeQP5x+Xdo/rTPvvdM/D2OGAtomXtFJYelVnlP/0Q3JppGVrxFcsTQvNNGgtdG9rX3gCv0wandy2zgGHoNU9MwtdRl73y+T3NnP/Admoca37Lht0OTZ/8XFyXeUfSlhPbv7mKyy75oJUHimx69NvW99jp+VWmzbiR+s+7Bke9U48Ay1uHFttLToex5LOJwNbNldZU1tspqQrRvXHxndeS77UuMvlaHJxn1pZcEGCAAfoNAIJZCDAATptEXoCHgzQpwLouQ28NUjvEIvFUteXsaEIL+FuAxaX2iYQhMuR2W6aAZ4ToO88lyCUBdB3oZTds6bpN8/Qh3pj+i7U8nvaPL3nKf7QbE6f5S51+UP3Qr/xUsZE4rBjUXTN8Nw3KUAGa8Bb3qsTEC9P7ha56R7IvfZ3fXe/+y5xyevtLr1eBZJ1Ge77YxB1dki+FP/bk1BlNVzS34qZ9lSVQavrAtSSsMrl/HfeDcW3TS50GXnZr8J7W376AUt7dEzbo2jKExDFdWrdt/zekdCfvJdcJztdjMi5pcl/q/9+hviYSpe8r9cAxZNSl8SvWGjdh728x/nWryuq7rn73igeMaHaWqq9/HvDxigaNg6iZeta+6i8ID7vQagXFyafEkedhKLeQ23lCNtiXsI9bIqzX7sEOEC3S4zrA0egkAP0aDR6B4CK/3NbP4k7KxaLbcgnVK31EgBtrHugL1u2LJ9bMXfACFgH5K1bbz1YXbp0Ke+BHjB9TW2HvjNVmWDXRd8FW1/TuqPfTFMkHPXQd+HQ2cQu6T0TVQleTfRZ7prGe7SvNok47nTIK/rnvkmBMsRv6AGsTH3/JXsNgTjyRNd397vv9Pp1UP261cylWQtExj6UtkavWws1sm/a/bPTFjRohMi9s13nbTeh/uR9qPtGpcKaNkPkroftpjFyfYX3Ni/+L5b1raShEJBTF2b14wX14D3Qb/8z2Z847jSIDhcl71luDdfVfbek+t+xBSJ3pnuh4sX4tV2BDeursqpTB/K+eRBb77Fd5aE/fR9qQiWN6jWAvOFOoOXOEEVFttmrf/4V+rEHUnG77gk5fLx17QQImbkG25sELKBVq1a8B3rANGU77hLgAN1dnszmQwKFGqCXlpYO0VqP2Ybogzp16pw+e/bsNflGxgF6vgkHN7/fPwwGV5lgd0bfBVtfU7uj70xVJph10W/B1NX0rug70xUKbn30XnC1Nakz+ix3NeJXR4HNmzImEqefC3lBzfcxzr0C9zLE77oR+OrTZEIRvRzyzA7ubbAtk999p39dBTX4spq57LYnIjdXPYNYL1kMNe1u4OfUpdyTiVq2RmS095f8119/ATW20hnUO9RFZFKZ6z7wImGF9zb95xMsH1hJQxs9qsenQL/yfJXyxTlRyI7dod59DXraXanXd94NkVvuz9hufNClwG+rM74mb54AsdueGV9TLz8DPXd66rU990PkxrsdI9VffgJ1903p8dZZ7KtXQJx6NkTXHln9uMBxAT4M5ADdh6Kx5IIS4AC9oLi5mYkECjFALykp6SOEqDjK+KKoqOiUOXPmrCwEDw7QC0E5mHv4/cNgMFUJflf0XfA1NrFD+s5EVYJbE/0WXG1N7oy+M1mdYNdG7wVbX1O6o89yUyJxRvF1le51vF06cW4pZIeLctukgNFqxnjoRa8kdxRntIPs2sP1CvzuO73sJ6hhme97n4R1wB8RGXhbRna6fAv0X5+Afq4MKN+SWtP2CET6jXSdt92E+sfvt54pX+khpz4ZiDORK7y38aP3sOLGSho2bIzI+MeyQqWeehz62blV10oJeedM6I/fhX5kUur1Gobb8Zt6Act/yrivuLA35GlnZ3xNzZ4C/WpqiG+dBS+vGJBV/ZkW1fajEHnjPRB77us4fxADOUAPoqrsyU0CHKC7SZO5fEkg3wP0aDTaHcAsANbf2zeRSOTkuXPnZj6qyANBDtDzADUkKf3+YTAkMgWuTfoucJL6oiH6zhcyBaZI+i0wUvqqEfrOV3IFqlh6L1ByGtsMfZabNPqbL6HuGFxtElFyGeRZ6fdZzm3H/EarhY9tHepWPA47DpGrb3R9U7/7Tv+wGOqWfjVzOfIERHrVfA/pxCB+7jTg0w+ARk0grxkOsed+rvO2m1CvXgk15PK0MHnv4xANGtpNZdz6Cu9teOd1rBx1Xaq+Zi0RGVvD/ekrdVLl7O9Kr4lLrgHWr4OOVbpk+4GHIjLg1ows4rdeB3z/TWZO+xyEyJCKC7KmL4mPGw588VHySXH+hZDndXHM27ofu7qmC7BpY8YcfnsvcwzCRiAH6DZgcWkoCXCAHkrZ2XRlAvkcoJeUlHQSQlhH7daNVpZsG55/W0gFOEAvJO1g7eX3D4PBUiM83dB34dHapE7pO5PUCH4t9FvwNTaxQ/rORFXCURO9Fw6dve6SPstNAfXWK9APWfcJzvwQ3a+GPOWs3DYpYLT619+gH610qend90YkcR9kdx9+912VS5xnwCNOOQuy+9VZgbOuZIB69Yw5w1tvXA91Tde02uUd0yFatMqqH5MXVXhv/WsvYdWYSj9waN0GkVsnZ1W6WvQq9IxxGdda90JHPA79zJzU60ccj0jvGzKuj985FPjv59XuK0dNgthl9yqvx60fOKxOXaBVXnU9xFEnZVV/dYtqGuaLdl0h21d/tY2cNvZpMAfoPhWOZReMAAfoBUPNjUwl4GSAHo1GLwUw0+pJCDGqrKysyrWJSkpKzhRCPAOgDoDlAE6JxWJfFpoDB+iFJh6c/fz+YTA4SoSrE/ouXHqb0i19Z4oS4aiDfguHzqZ1Sd+Zpkh46qH3wqO1l53SZ7nRr3LG9nbpRM/rIY/ObaiVW4X2ovWnH0BNqPQ1nRCwzqgVrdpA7HOgvWQ1rPa77/TnH0KNH1EjD3F2Z8hOl7jGrJCJEmcjX9UB0Dq5bU334y5kbbnuVeG9dS8/i9XjKnl9970QGX5vVun1J+9D3Tcq41rrUurW1QT0359Kvi6O/z/Iyyud7V4pMj5hFPDp+9XuK/50PmSXK9Je15s2QfWNpj3nhj5q2l3Q776Wua//Ow+yW8+s+IRlEQfoYVGafTolwAG6U3KM8y2BaDRqHfXvU9GAEKKF1vqubf9+A8CDlZuLxWIPb99sbQP0Ll26HKeUehlAfQDWjYAulVJ+XBO0TZs2LVm4cOGvboPlAN1touHJ5/cPg+FRKlid0nfB0tMv3dB3flEqGHXSb8HQ0W9d0Hd+Uyw49dJ7wdHS5E7os9zUUVPGQr9vfR2W+SH7jYRoe0RumxQwWv/8A9TNmc+aFpf3hzz+dFeq8avvrEubY93v0F9+Aj0v7SvQKlxEyaWQZ3VyhZcXSeLXdgM2rEtuLQePhtivrReluLpnhffWPj8fv9xf6fLo+xyIyJCxWe2lF38FNXpQ5rW77pm4V7h+7cXk66KG4XN8yhjg/Ter37dBI8i7ZkIUW+eYbX1kuoWAnBSD2GGHrOqvbpF6ajb0s/Myvmyd3W6d5c5HigAH6HQDCdRMgAN0OiR0BEpKSh4WQmT988lYLFbl76S2AXppaelIrXXNP+OsSv6yTMP6XAXiAD1XguGN9+uHwfAqFozO6btg6Oi3Lug7vynm73rpN3/r59fq6Tu/Kuf/uuk9/2vohw7os9xUio/qByxZXG0SOfgOiP0Ozm2TAkbrTRuh+pZm3rFZC0TGVrqvszXI27QJ+G0V0KwlRFFxIk5v3gR8+zXQaheIJjtmzOU331lnZFuX6teLXs1aDXFxX8iTz8x6vWkL40OuAFavSJYl+w6HOPRo08q0XU+F935/8jH8+mClM85ruE/59pvo5T9D3XRV5r2LioA/HgN8kBqKi3OikB27Z1yvZk6AftM6j6z6h7hyIOSxpyYX6Pdeh5p6Zyogw9+mbTDW3+6n70NZZ8RneuzXFpHBo52kDWwMB+iBlZaNuUSAA3SXQDKNfwhwgO4frViptwT89mHQW1rc3S0C9J1bJJnHDgH6zg4trs2VAP2WK0HGOyFA3zmhxhg3CNB7blBkjtoI0Ge1Ear+da0U1DVdAGtgnOkhBORdD1c7RHa+c34j4/0vBNb+nnGTyPSnk8/rFUuhxg0HVi4D2uwBOeh2QEagbusPrFgK1KsP2WsIxEGHV8nlN9/VeMZxNXLI3jdAHHF8fsXKY/b4yGuAH79L7iCuGABpXZ7c548K762ZOwO/PVrpnueHHoNI32FZdafXr4XqV8P9wJu1SLs/ueh0MeTZJRlzq8enQr/yXM377n8IItbf17aHenYe9FOzUzE2hv81bWS9p+nHHoB+8x9AvDx9aetdEbn1gaz4hGURB+hhUZp9OiXAAbpTcowjAZ8Q4BnoPhHKwDL99mHQQIQsyQEB+s4BNIbkTIC+yxkhE9ggQL/ZgMWlrhGg71xDyUQ2CdB7NoFxuSMC9JkjbIkg63LeasjlaQlEu27Qz8cSwyfxp/aQXa50voFHkfFbrwO+/ybj7nLqQggpE6+p2EPQLy5MrhPRywEhoMtmpGLrNYC88W6I1m3S8vnNd+rlZ6DnTreliBw0GmJ//17yPD52KPD15yl9L+gFefo5thiYuLjCe7/Ouh+/l81M9Xf0yZA9B2dVcuIe8b06AkpltV7UwE49MQv6hQXpeQ4/DvhwUfrfzK2Tk39Hasa4tKshiNPOgbywV1a1ZLPI6g//+XjrD2QqHvUbIjLh8WzCQ7OGA/TQSM1GHRLgAN0hOIaRgF8IcIDuF6XMq9NvHwbNI8iKnBCg75xQY0yuBOi7XAky3g4B+s0OLa51iwB95xZJ5rFLgN6zS4zrnRCgz5xQ2xqjv/gofcC0Q13IifOA337ZOkBvvpPz5B5Gxu+/Hfj32xkrkPfMgmi89bLs8R7tq6wRR55Y9Z7wrdtA3nA3RP0GyfV+8516eAL0G5Uusy0EYP2QQEhgnwOBX1YBy35M4yFH3Aex6x88VDK3reP33QJ88l4yiejYHfKcaG5JDYiu8N4v08dh7cLUQFiceAbkpf2yrjDe/yJg7Zqs1ovL+0Mef3rGteq5MuiFj6V7p+9wWJ6rnF+c2REyetnWvz3r/uuLv0pp07UH5Bntsqol20X6x++hRvZNr2vyguStGrLNE+R1HKAHWV325gYBDtDdoMgcJGAwAQ7QDRbH8NL89mHQcJwsL0sC9F2WoLjMVQL0nas4mawWAvQbLeIFAfrOC+rc0yJA79EHhSBAnzmnrF59Hnr2lFSC3fdCZHileyo7T+1ppJo7HfrlZzLWIIePh9h978RrmQboaNl66+Xbt38cchRk35sgZCTxit98F7+lH/BD6l73ossVkH86P9ll/PaBwLf/Teta3jkTYsfmnmqZy+Zq+j3Q7/wzmUL8pTNk50tySWlEbIX3Vk8ajXUvPJHq7/RzIC/I/izu+PDewNL0H01U12BN949XLz0FPa/SVRusv49h46DfeQ36xSdTKes3gBwzA6hbb+vl4zesS74m+42AaHukq3z172ugBlwUKE+7CggAB+huE2W+oBHgAD1oirIfEtiOAAfotIRTAn77MOi0T8aZRYC+M0uPsFRD34VFaTP6pN/M0CFsVdB3YVPcnH7pPXO0CHIl9JlzddW8B6FfSt0TXNi4BLTzXfMfqV58EjqWurR15R3lNcMh/nh04qmMA/Qayqs8gPWT73T5Fqi+XdLuCb395dnjN18N/PxD+rDx/hhEnR3yL1iedlCzJ0O/+kIyuzj1L5AX9cnTboVLW+G9VffcjPX/eD7V31kdIUu2nuGdzSM+5nrgf/9JX2r9gMT6kUjlqxHUq4/Ejynq1suYNuMAffS0hN/U8O1479cW8vL+UEOvSPfa6GkQ1t4uPqz7oavendIuUy+HjYfYY+sPaPjgAJ0eIIHaCHCAXhshvk4CPifAAbrPBfSwfD99GPQQE7d2mQB95zJQpsuKAH2XFSYucokA/eYSSKaraspFAAAgAElEQVSxRYC+s4WLi10kQO+5CJOpqiVAnzk3R5VLXJ/XBfL8C50nNCRSv/c61NQ7M1YjLu4LefKZ0Js3QV3t/8t5O0UuJzwOUb9hMjw+5Apg9Yq0dJHpqR9XON3Hyzi1YBb0X1P35hbHnArZY6CXJbmyd8V73srRQ7Ch0mX5xXldIc+/IOs94pNuAz56J2297DcSOPjwxNUK9L8XARs2wPrhgWjdptq86q8LoBfMSs8zYU7ilgfxu28CvvwkPbZJM+C31anniooh7y9LXt0h6wayWBgfdMnWW1Jse8hrR0Ac4u6Z7lmUYewSnoFurDQszBACHKAbIgTLIIF8EeAAPV9kg5+XX0IEX2MTO6TvTFQl+DXRd8HX2KQO6TeT1AhPLfRdeLQ2rVN6zzRFglkPfeZc1/hNVwHLf04mEFf0hzwu832One9S+Ej9zZdQdwzOuLFofwFku67Qy3+CuqmWy11bg761vwHxeOGbyOeOLVohcsf0tB3i/S8E1v6e9pzvB+jPx6CffDTV0yFHIXLtzfkkW5DcFe95K0b0w8b33kj9/Xa6BPLszlnXoB4aD/3WK2nrnZyhrZ6dB/3U7PQ8UxdCSInEfcjHXg9sWF99XbvsjsioSVnXbWdhfFQ/YEmlWxdc2g/yxDPspAj0Wg7QAy0vm3OBAAfoLkBkChIwmQAH6CarY3Zt/BLCbH2CWh19F1Rlze6LvjNbn6BVR78FTVF/9EPf+UOnIFZJ7wVRVfN6os+caZK4rLd1BrZSyQTyxnsg9tzXWUKDovSvq6EGX5qxInHKXyC794H+8lOou2+ssWprLSIS+pXUZbINatN5KYcfh0if9N7jfUqALZvTcvp+gP7K89CPT0n1tM9BiAwZ45ybIZEV73nLh16FTZ+8n6xKdO0JecZ5WVepZk6AfvPltPVy7AyIZi2zzmEt1B+9A2WdzV7pUdk7atEr0DPGV58zgx9tFVDD4vj4EcDnH6YY2fyRgVt1mJqHA3RTlWFdphDgAN0UJVgHCeSJAAfoeQIbgrT8EiIEIhvYIn1noCghKIm+C4HIBrVIvxkkRohKoe9CJLZhrdJ7hgkS0HLoM2fC6p+XQN2cfn/i7S/r7Syz91GJex9f1SFzIYceg0jfYVCLXoWeMa7GYsXl/YFfVqafxex9ezlXkOlKA5nuB+/7Afr2GrfZA5GRE3Pm53WCive8ZQMuxeYvP02WU3F7gmzry3gG+gPzIYrrZJsisU7H41DDegErlyX+Lc7sCBlN3Ytdb9kCdV03YHP6DzQqNhF5vHWEmjEeelHqLHvxp/Mhu6Tff91WswFbzAF6wARlO64T4ADddaRMSAJmEeAA3Sw9/FQNv4Twk1rBqZW+C46WfuqEvvOTWv6vlX7zv4Z+7IC+86NqwaiZ3guGjqZ3QZ85U0j/+22o+29PBTdqgsi4Spe7dpbWmKhMA+FEcXvsg8iwcch03+a04nffC/KGu6DfeBn6sQeq9hWJoNV9syGEwMqVK6GUNqb3KoWsXwv90dvQ334NcdBhEH/pnLi8duVHEAfo+qN3oSbdmmqzWUtExs4wV6csK6t4z1t6dVds+fbrZJS4ciDksadmmQVQD94D/fY/09Y7/dGEXrtm69nsjZpCHHtqVX9tdyZ4ctN69SFHToJo1iLruu0sVLGZ0C8+mWJ0zKmQPQbaSRHotRygB1peNucCAQ7QXYDIFCRgMgEO0E1Wx+za+CWE2foEtTr6LqjKmt0XfWe2PkGrjn4LmqL+6Ie+84dOQayS3guiqub1RJ8500T97Uno+TNTwQG5vHVFQ9UO0Js2Q+Suh6HmTod++ZmM8MTJZ0Jc1BtCRqDfex1q6p1V1zVuit3mvJR4funSpVCVLoXvTBFvo9TCx6CfK0sVccTxiPS+wduictxdf/UZ1F2VeqjXAJH75uSY1fvwive8n6/sgPKflyQLkn1uhDj8uKwLVC8uhI49lLbe6QC9tk3VC/Ohn3gkbZk46iSIC3tBNGxcW7jj16u8zx14KCIDKv2ownHmYARygB4MHdlF/ghwgJ4/tsxMAkYQ4ADdCBl8WQS/hPClbL4vmr7zvYS+bIC+86Vsvi2afvOtdL4unL7ztXy+Lp7e87V8vimePnMmlXpkEvRrLyaDxUl/hrzkGmfJDIxSC2ZB/3VB1cqEhJyyAGrqXcAHb2asXA4ZA7HPQYnX9BcfQY0bXnXdzrthtwe3ntkahAG6Xr0SauwQYPUKoGFjyEGjIdrsbqCy2Zekf1gMdUu/VIAQkFMXJq4a4OdHxXveTxefjfiqFclWZL+REG2PyLo1vX4d1PWXAZs2JmLEmR0go5dnHW9noV7yLdSoa1MhDRpB3vUwRHGxnTS21+r/fLz1LPvGTRP/iVZtbDGyvaHPAjhA95lgLLfgBPz9f4uC4+KGJOA/Ahyg+08zUyrmlxCmKBGuOui7cOltSrf0nSlKhKMO+i0cOpvWJX1nmiLhqYfeC4/WXnZKnzmjH7fOzP3qs2Sw6HwJ5F86O0tmYJT+7ReoKWOAr7+oUp28YzrU9LuBb77MWHnle8Hr7/4HdVv/KuusAfuuE7aeURuEAbrVh16/Fvjpe6DVrhCN8ndWcKHsolcug7qhR9p2cuJciLr1C1VCXvapeM9bUno69Lrfk3vIwXdA7HewrT31j99D//N5oPlOEGe0gyjK30A7edWHSBFkz0EQR5xgq1Yudp8AB+juM2XGYBHgAD1YerIbEqhCgAN0msIpAX4J4ZQc43IhQN/lQo+xTgnQd07JMc4JAfrNCTXG5EqAvsuVIOOdEqD3nJJjnB0CfvWZLt8CWANL657BHpwRGx90CfDbL6nhm83LP9vRyMu1Wmuoft2ADetTvV47Auqx+4HVKzOWVvky1pmGsFaQOOxY7Hr7/Yn4oAzQvdQpH3vrdWuhrrsgLbUc+1De7redjx4y5ax4z/vh/OMB631k20MOGwexxz6FKsPRPtaVDlC/AUTdeo7iGeQuAQ7Q3eXJbMEjwAF68DRlRySQRoADdBrCKQG/fgnhtF/GmUGAvjNDh7BVQd+FTXFv+6XfvOUf1t3pu7Aq733f9J73GoShAj/6TC/9Eeq+UcCKpcABf4S8dkTeL2Vc2Qt6w3qoa7um2UOOmgSxi78v2V2d3+Njrgf+95/ky9bZ9vrJR4EM9y237n8uL+6bXJuJlfWiOPFP2PXGMYl1HKCb+U6jVRzqqo7pPh85yfeXprfe81q1bIkl7Y8Nzd+wmQ7zf1UcoPtfQ3aQXwIcoOeXL7OTgOcEOED3XALfFuDHLyF8C5uFJwnQdzSDFwToOy+oh3dP+i282nvZOX3nJf1w703vhVv/QnXvR59Vuf/4pddCnvinvCGzzsJGeTlQVJQ4211/9zXUbQNS+1n3Bb8/VtAhft6azZBYzZoI/frfU69Y94n+9IP0lda9kfc5AKLzZWmXL0+cwd6rY5VhuzyzA9r0G5bIwQF6IdW0t1e8bxdg04bUdw5DxkLsc2DWSXQ8Dv3+G8B/Pwe0yjounwutv+G6Alj/ygtp21i3JhAtWuVza+YOGAEO0AMmKNtxnQAH6K4jZUISMIsAB+hm6eGnavz4JYSf+LLWzAToOzrDCwL0nRfUw7sn/RZe7b3snL7zkn6496b3wq1/obr3o8/iPdqn42nQCJF7Z+cFmV67BmrSbVvPwN7/EMg+N0J/8h70g/ek9mvZGpHR0/KyvwlJ1YsLoWMPpUqRMn0gHolAPrAAwno+wyPe/yJg7Zq0V2Sni9HmimsTz3GAboLKmWuID74M+HVV8kXZbwRE2yPTFltnqmP9uq2XFpeRtNfUc2XQCx8zt8FKlcl7HoFo3NQXtbJIMwhwgG6GDqzCXAIcoJurDSsjAVcIcIDuCsZQJvHjlxChFCpgTdN3ARPUJ+3Qdz4RKiBl0m8BEdJnbdB3PhMsQOXSewES0+BW/OazxBnNPc+vQrTyfbfdxK2emQv99OPJlCJ6GbBxA/Qzc1PbtD0CkX4j3dzWqFz60w+gJtTQX7MWiIytNGDfrvr4sN7Ash/TnpXd+6BN18sTz3GAbpTcacXEb74a+PmHlP97DoY8+uTkv/WaX7d64/tvgD32gbxywNZBeuMdoZWCGtgdWPu7uQ1WqkxOnAtRt74vamWRZhDgAN0MHViFuQQ4QDdXG1ZGAq4Q4ADdFYyhTOK3LyFCKVIAm6bvAiiqD1qi73wgUoBKpN8CJKaPWqHvfCRWwEql9wImqKHt+M1niTPCrTOat3vIyQsgiopdp1zlbHfr/t3HnAL9zr9SQ8Uz2kF27eH63qYk1KtXQA25ovpy9j4AkaF3Vvt6/I7BwDdfpr0e6TUEu7SLJp7jAN0UpavWsb12onsfyFP+klyonp4D/cycqoGHHgN5yllQE281t7nKlTXZEfLOmdVeRcEfTbDKQhPgAL3QxLmf3whwgO43xVgvCdgkwAG6TWBcniTgty8hKF0wCNB3wdDRb13Qd35TzN/10m/+1s+v1dN3flXO/3XTe/7X0A8dmOozvWnj1vsm77wrRPOdkij14v9CjR5YBa0cPh5i971dR55pgG6daYvvvk7uJS64CvL0c13f25SEibP+r+2aOPM+4+PIExDpNbTacuP33QJ88l7a65GBt2GX/9s6iOUA3RSlq9YRv3cE8NmHKa+XXAp5VqfkvzP+fVTXTqMmEEed6Hmz1j3Q6+2wAzb/51NsWfwVUFQE0a1n2g8DPC+SBfiCAAfovpCJRXpIgAN0D+FzaxIoBAEO0AtBOZh7mPolRDBps6sKAvQdveAFAfrOC+rh3ZN+C6/2XnZO33lJP9x703vh1r9Q3ZvoM/37GqjbBwCrlgPFdSCvGgJx6NEJJOqdf0FPv7sKHnHJNZAn/dlVbHrjBqhrulTNWVwH2LI5+bzsPwrioMNd3du0ZPHbBqT9aKByfaKWM/DV9Hug3/lnWktFN0/AzsduHaZygG6a2ql61NQ7od97PfmEOKcUsmPqChB2BujilLMgu1/tebMV73nWD0OWfv0VVHk5RJMdPa+LBfiPAAfo/tOMFReWAAfoheXN3Uig4AQ4QC848sBsaOKXEIGBy0aqJUDf0RxeEKDvvKAe3j3pt/Bq72Xn9J2X9MO9N70Xbv0L1b2JPlOxmdAvPplCUK8+5LDxEDvtDPVcGfTCx6rgEaefC3nBVa5i0999DWUNjmt5yDEzIJq3rG2Zr1+PW/e5/vSDjD2I7c5K3n6Remg89FuvpD1dNPYh7Nz2j4nnOEA31xrqkUnQr72YLLDy35lWcairOmZdvOh5PeTRJ2W9Pl8LTXzPy1evzJtfAhyg55cvs/ufAAfo/teQHZBAjQQ4QKdBnBLgAblTcozLhQB9lws9xjolQN85Jcc4JwToNyfUGJMrAfouV4KMd0qA3nNKjnF2CJjmM71hPdSQy4EN69Pb2PsAyCFjoWdNhH7jpaottmwNedsUV+9hrBa9Aj1jfM04rTPkJ5W5uq8d/Qq1Vj14D/Tb6WeRV+wtrhwIeeyp1ZaScYA+qQw777lXIoYD9EKpaH+f7X/MIo4/HfLy/olEeuUyqBt6ZJ1UjnsUolGTrNfna6Fp73n56pN580+AA/T8M+YO/ibAAbq/9WP1JFArAQ7Qa0XEBdUQ4AE5reEFAfrOC+rck76jBwpJgH4rJG3uVUGAvqMXvCJA73lFPlz7muYz9dJT0PNmZBTBuny0/vAt4OcfMr4uB9wKceChrgmonnwU+vlYzfna7IHIyImu7WlqIjVnGvQ/ns3MffBoiP3aVlt6xgH6g89g5513TsRwgG6q6oB6di70U4+nCjzsWESuvinxb/35h1DjR2Qu3vo7/PE7YM2vidfFyWdCXtzXiEZNe88zAgqLcESAA3RH2BgUIgIcoIdIbLYaTgIcoIdTdze65gG5GxSZwy4B+s4uMa53gwB95wZF5siWAP2WLSmuc5MAfecmTeayQ4Des0OLa50SMM1n8QmjgE/fd9SOOOokyKuudxSbKSj+wGjgw0U15zviBER6D3VtT1MTqacfh35mbsby5O1TE5fXr+6RaYBePONZtG7dOhHCAbqpqgPq5Weg505PFbj/IYgMuj3xb/Xq89Czp2T2RK8hwAGHQn/8LlBUBHHYcRDFxUY0atp7nhFQWIQjAhygO8LGoBAR4AA9RGKz1XAS4AA9nLq70TUPyN2gyBx2CdB3dolxvRsE6Ds3KDJHtgTot2xJcZ2bBOg7N2kylx0C9J4dWlzrlIBpPouPHgQs/spZO5EI5OjpEM1aOIvfLip+Uy9g+U815hLnRCE7dndlP5OTqJefhZ47LWOJ8v4YRJ0dqi1fL/4KytK14rHvQSgeeicH6CYLvq029ebL0DMnpCrdfS9Eht+b+LcqmwH996cye8K6nUKrXYzs0LT3PCMhsaisCHCAnhUmLgoxAQ7QQyw+Ww8HAQ7Qw6FzPrrkAXk+qDJnbQTou9oI8fV8EKDv8kGVOasjQL/RG14QoO+8oM49LQL0Hn1QCAKm+Sx+89XVXqK9Co9WbYDVK4Atm5MvibM6QZZcmjM6vewnqGG9as0jrxkO8ceja13n9wVq0avQM8ZVbaNBI0TunV1je1prqKljgfffBOrVh+w1BJG2R3KA7gNTWGeQq4m3pipt2gyRux5O/Ds+6Tbgo3cydiGnLoSQ0sgOTXvPMxISi8qKAAfoWWHiohAT4AA9xOKz9XAQ4AA9HDrno0sekOeDKnPWRoC+q40QX88HAfouH1SZszoC9Bu94QUB+s4L6tzTIkDv0QeFIGCaz+LXXw78sjLZuuh+NfT7bwA/fg80aAg02RGicVNgtz0hTjoTeuGj0K++kEJlXS76qJMS91zGvgdDCGdf36q/LoBeMCtdgrr1gHgc0AooKoY47jSIblcZOyh00z/6k/eh7htVNWWW94C3huiJHzvUqw9RvyHf39wUJ4+59Pf/g7q1f2oHISGnLICQEVT7Y5fDjkPk6hvzWFVuqU17z8utG0Z7SYADdC/pc28/EHB2BOaHzlgjCZBAggAH6DSCUwI8IHdKjnG5EKDvcqHHWKcE6Dun5BjnhAD95oQaY3IlQN/lSpDxTgnQe07JMc4OAdN8Fu/XDVi/LtmCHDwaYr+21baUOFN8eG/rC5yqa1q3gbzoaoj9q4+vLnH8jsHAN18mXxan/AWyex87aAO1Vn/zJZTFZPtH2yMR6TfCdq+m+c52AyEJ0Gt+gRp4SVq38q6ZEE2bI97/QmDt7+kkDjwUsvvVEC233t/exAe9Z6Iq/qyJA3R/6saqC0eAA/TCseZOJOAJAQ7QPcEeiE15QB4IGX3XBH3nO8kCUTB9FwgZfdME/eYbqQJVKH0XKDl91Qy95yu5fFusST5LXOq7V0dAqSRPOXw8xO5718hXTRm79Sz1TI+69SDvngWxQ92sNdK/roIafFnaetlvBETbI7POEbSFevlPUNY94bd7WGf6y4v72m7XJN/ZLj5EAVopqD6dt155YdtD3nQPsMc+Vf9Wh42H2KPmv1UT0NF7JqgQjBo4QA+GjuwifwQ4QM8fW2YmASMIcIBuhAy+LIIH5L6UzfdF03e+l9CXDdB3vpTNt0XTb76VzteF03e+ls/XxdN7vpbPN8Wb5DO9ZTNUn5I0dvL2KRA77VIjT712DfS8B6HffR2Il1dZKwffAbHfwVlrol59AXr25NR6677d4x6FKCrOOkfQFup1v0Ndd2GVtkS7rpDtL7Ddrkm+s118yALiQy4HVqduqyCty7MfeBhU39L0v9XR04w+87yiWHovZAbOY7scoOcRLlMHggAH6IGQkU2QQPUEOECnO5wS4AG5U3KMy4UAfZcLPcY6JUDfOSXHOCcE6Dcn1BiTKwH6LleCjHdKgN5zSo5xdgiY5DP9+29QA7qnD+Wss8eb7JhVS3rNr9BvvQL97Fxg44ZkjOw1BOLIE7PKYS2Kjx8BfP5hcr045hTIHoOyjg/iQq3iUL06VblUvnWPennKWbZbNsl3tosPWUB89CBg8Vepv4cLe0EcdmzVqzSMfwyiYWPj6dB7xkvkmwI5QPeNVCzUIwIcoHsEntuSQKEIcIBeKNLB24cH5MHT1A8d0Xd+UCl4NdJ3wdPU5I7oN5PVCW5t9F1wtTW9M3rPdIWCUZ9JPtMrlkLd2DMNrJxUZuvy61ZwfOxQ4OvPUwO/bj0h/++8rATT69duHeJXvmT1VddDHHVSVvFBXhS3zkBfl37Pa3ntzRCHHGW7bZN8Z7v4kAXEJ98BfPBW6u/p3FKIY0+Fuvnq9L/VyU9AFBUZT4feM14i3xTIAbpvpGKhHhHgAN0j8NyWBApFgAP0QpEO3j48IA+epn7oiL7zg0rBq5G+C56mJndEv5msTnBro++Cq63pndF7pisUjPpM8pleshhqVL8UWCEgpy6EEPa+go1PGQO8/2Zq4HdOKWTHi7ISTC16FXrGuNTaomLI8Y9C1K2fVXyQF8WHXgmsWp4+NB0xAWLXPW23bZLvbBcfsgD1+FToV55L/T2deAbEyWdBjbk+RWKHuohMKvMFGXrPFzL5okgO0H0hE4v0kIC9ozcPC+XWJEACzghwgO6MG6MAHpDTBV4QoO+8oM496Tt6oJAE6LdC0uZeFQToO3rBKwL0nlfkw7WvST7TX38OZZ09XvGoWw+RifNsC1Jl4HfSnyEvuabWPPrnH6AeuANYuiS19pCjELn25lpjw7AgPugS4Ldf0lqVDi/bbZLvwqBdLj2q52PQTz6aSnHw4ZB/ag81YVTquabNELnr4Vy2KVgsvVcw1IHfiAP0wEvMBnMkwAF6jgAZnh2BLl26tI3H47sJIXbUWhfNnz//kewiuSpXAhyg50owvPE8IA+v9l52Tt95ST+8e9N34dXei87pNy+oc0/6jh7wigC95xX5cO1rks/0px9ATRiZ81BOPVcGvfCxVJ4shuDq6TnQz8ypIr64uC/kyWeGyxTVdBvv1w1Yvy7tVTntKdtXCLASmOQ7ilszAfXmP6Bn3pta1GYPiHNLoafdlXpu590QueV+X6Ck93whky+K5ADdFzKxSA8JcIDuIfygb92pU6c9ioqKrtdadwPQpHK/sVgsUvnf3bp1a7Vly5b7hBBCa/3e/Pnz7ww6n0L1xwF6oUgHbx8ekAdPUz90RN/5QaXg1UjfBU9Tkzui30xWJ7i10XfB1db0zug90xUKRn0m+Uy//wbUlLEpsK3aIHLbZNug1WsvQj8yKRW3xz6IDKt0WfbtMuotm6Gs+3tv3pT+ipCQ98yCaJT2tZzteoISEO/dGSjfktZOZPrTjtozyXeOGghRkP7831DjK12FoUEjiI7doR97IEVhr/0RuaHSQN1gPvSeweL4rDQO0H0mGMstOAEO0AuOPBwblpaWdtNaTwXQAMD2PtPbD9AtKtFo9BUApwJYD6B1LBZbGw5a+e2SA/T88g1ydh6QB1ldc3uj78zVJsiV0XdBVte83ug38zQJQ0X0XRhUNrNHes9MXYJWlUk+U2+8BP3wfSnEtQy+q9NCf/wu1MRbUy83bY7IXTOrlU6vWAp1Y8+qr+93MCKD7wia5I77ifdoXyWWA3THOH0TqH/6HmpE37R6RfsLoJ9+PPVc2yMQ6Vfp6hEGd2fSe57BmFhaFgQ4QM8CEpeEmgAH6KGWPz/Nl5aWdtZal23LbnnsVwBvAdgbwH4AMg7QS0pKLhRCWDek0QC6xGKx+fmpMFxZOUAPl95udssDcjdpMle2BOi7bElxnZsE6Ds3aTJXbQTot9oI8fV8EKDv8kGVObMhQO9lQ4lrciVgks/Uy89Cz52Wamn/QxAZdLvtFvV3X0PdNiAVF4lAPrAAQsqMufTir6BGD6rymii9AvLP59veP6gBVS5z3/ZIRPqNcNSuSb5z1ECIgvSG9VDXdk3veO8DgP/9J/mcOPpkyJ6DfUGF3vOFTL4okgN0X8jEIj0kwAG6h/CDuHWHDh2aFhcXfwOgKQCltb513bp1Y1544YVN0Wh0IoCrqxugt2/fvtEOO+ywCkBEaz1t/vz5vYPIqNA9cYBeaOLB2Y8H5MHR0k+d0Hd+Uis4tdJ3wdHSD53Qb35QKXg10nfB09QvHdF7flHK33Wa5LMq9y7/49GIXDPcNmD9yyqo6y9Li5PjHoNo1DhjLv3Je1D33ZL+WvOdIEfcB1Gvvu39gxqQ4Hr7AOC3X4A6O0AOuh1iT+tcH/sPk3xnv/rwRcRv6Qf8sLjaxsUpZ0F2t762Nv9B75mvkV8q5ADdL0qxTq8IcIDuFfmA7ltSUnK9EGKMNSQXQowsKytLXm+qtgG6hSQajX4E4BAAi2Kx2AkBxVTQtjhALyjuQG3GA/JAyembZug730gVqELpu0DJaXwz9JvxEgWyQPoukLL6oil6zxcy+b5Ik3ymnngE+oXUBRXFMadA9qh6Znht0HV5OVTvTmnLRLuuEO26QYitX+fqX1dBlz0E/ftvEE2bQS96NW29nFQGsUPd2rYK3ev69zXA4i+BNntANN/Jcf8m+c5xEyEKVC8sgH5iVrUdi7M6QZZc6gsi9J4vZPJFkRyg+0ImFukhAQ7QPYQfxK2j0ah1tH4KgJUAdo3FYpsr+sxygG5d+r0EwPJYLNY6iIwK3RMH6IUmHpz9eEAeHC391Al95ye1glMrfRccLf3QCf3mB5WCVyN9FzxN/dIRvecXpfxdp0k+U49PhX7luSTQXM5qjfe/CFi7Jk0c0bUH5BntoDdu2Hom9dIfM4t34KGIDKh0D3V/S2xk9Sb5zkhAhhWlVy6DuqFHtVWJDhdBnltqWNWZy6H3fCGTL4rkAN0XMrFIDwlwgO4h/CBuHY1GfwZg/XxzYSwW61y5xywH6FMBWEczm2KxWL0gMip0TxygF5p4cPbjAXlwtPRTJ/Sdn9QKTq30XXC09EMn9JsfVApejfRd8DT1S0f0nl+U8nedJvlMPYAjnhgAACAASURBVHQv9Fv/SAIVfz4fsvQKR4Djw3oDy7YbkLfZA5GRE6EeGg/91ivVDwMdnvnuqNCQBpnku5BKYLvt+Jjr0+57XjmBuOAqyNPPtZ3TiwB6zwvqwdyTA/Rg6squ3CPAAbp7LJlp6yXYNwEoynQP8ywH6A8CuBzAhlgs1oBQcyfAAXruDMOagQfkYVXe277pO2/5h3V3+i6synvTN/3mDfew70rfhd0B3vVP73nHPkw7F8pnestm6Lf/Cag4xLGnA5vWAw0aQ0QiSdzxyWOAD95M/tu67Lpsf4EjOeLjhgNfWHc6TH+I7n2gH32gxpzijHaQXas/29ZRQQxKI1Ao3xG7ewTUy89Az52eMaG4oj/kcae7t1keM9F7eYQbstQcoIdMcLZrmwAH6LaRMaAmAtFodDmA5lrrx+fPn9+98tosB+gvADgLwJJYLLY7aedOgAP03BmGNQMPyMOqvLd903fe8g/r7vRdWJX3pm/6zRvuYd+Vvgu7A7zrn97zjn2Ydi6Uz+IPjAY+XJSOtmVryP63QLTcehfC+PgRwOcfJteI6GWQZ3Z0JIeKzYR+8cmqsUICWtWYU5x/IeR5XRzty6DsCBTKd9lVw1XZENC//QI1+LKMfz+y73CIQ4/OJo3na+g9zyUITAEcoAdGSjaSJwIcoOcJbFjTRqNR61PCoQA+jcVif6zMobYBes+ePYt/+eWXFQAaAXglFov9Kawc3eybA3Q3aYYrFw/Iw6W3Kd3Sd6YoEa466Ltw6e11t/Sb1wqEc3/6Lpy6m9A1vWeCCsGvoRA+0yuWQt3YMyNMcdxpkFcMSLy2/SWixUV9IE/9iyMR9OoVUJNuA35YbDs+l31tbxbSgEL4LqRo89p2dVd2kIPvgNjv4Lzu7VZyes8tkszDATo9QAI1E+AAnQ5xlUBJSck4IcR1sH7Lp9ThCxYs+Lhig9oG6NFo1PokMsWKFUIMLysrG+1qcSFNxgF6SIV3oW0ekLsAkSlsE6DvbCNjgAsE6DsXIDJF1gTot6xRcaGLBOg7F2EylS0C9J4tXFzskEAhfKZefhZ67rRqK5RTnkxcyj0+8hrgx++S68SVAyGPPdVhZ4kv16Am3wH8+21bOWTvoRBHnGArhovtESiE7+xVxNXZEFCvvQj9yKQqS+WICRC77plNCs/X0HueSxCYAjhAD4yUbCRPBDhAzxPYsKbt3LnzUVLKd6whOIC3165de/oLL7xg3Rfduj/6RABXW6/FYrHUDaIAdOnSpa1S6nUAjQFsAbBvLBb7Pqwc3eybA3Q3aYYrFw/Iw6W3Kd3Sd6YoEa466Ltw6e11t/Sb1wqEc3/6Lpy6m9A1vWeCCsGvoRA+i987AvgsdWn27anKIWMg9jkI8aFXAqusuxtufci+wyAOPSYnEdRfF0AvmFU1R5s9ACkznqHup7Npc4LjYXAhfOdhe4HdWq9bCzXwYiBentajHPMgRPOdfNE3vecLmXxRJAfovpCJRXpIgAN0D+EHdetoNDofQKdtQ/S3pJS95s2b92mmAXo0Gq0H4AoAt20bnltnn08pKyuzBu18uECAA3QXIIY0BQ/IQyq8x23Tdx4LENLt6buQCu9R2/SbR+BDvi19F3IDeNg+vech/BBtnW+f6Y0boPpfCJSnD9wqIxbnlEJ2vAjx/hcBa9ckX5IDb4M4IO0Oh7aV0Z9/CGXdW73yo04dyJvGQb/xEvSLC6vklLc8ALHzrrb3YkD2BPLtu+wr4Uq7BOLWrRE+ss7/Sj3khDkQ9RvYTeXJenrPE+yB3JQD9EDKyqZcJMABuoswmWorgQ4dOjQtLi5+E8AB24bo1tOfA7CG5Xtte+5pAK0BHAagDoAKL36wdu3aEyvOWifT3AlwgJ47w7Bm4AF5WJX3tm/6zlv+Yd2dvgur8t70Tb95wz3su9J3YXeAd/3Te96xD9PO+faZ/vciqPtructgmz0gu18NddcNQDyexC9vugfiD/vmJIdeuwZq0CVpecUl10Ce9Gfozz6Ess6O3+4hxz8G0dC6yCMf+SKQb9/lq27mBdTb/4R+8J4UCiEhpzwBYV3RwQcPes8HIvmkRA7QfSIUy/SMAAfonqEP9sbdunVrVV5ePgfAads6tS7pnulR2YP/AFAai8VWB5tOYbvjAL2wvIO0Gw/Ig6Smf3qh7/yjVZAqpe+CpKb5vdBv5msUxArpuyCq6o+e6D1/6OT3KvPhM716BfS//gbUbwh8/z/ot//pCJO89QGI1rmfCa4WzIL+64JEDeJP7SFKr4AQAnrzJqiro1Vqk1MX+mYY6AisAUH58J0BbYWiBL1pI9TNfYDVK7f2e/DhiFw3yje903u+kcr4QjlAN14iFugxAQ7QPRYg4NuLaDR6EYD+2840r67dL7TWYw8++ODHRo0apQLOpODtcYBecOSB2ZAH5IGR0leN0He+kiswxdJ3gZHSF43Qb76QKXBF0neBk9Q3DdF7vpHK14W67TP9+xqoW/oBv67KjUvDxpB3z4KIRHLLsy1aL1kMCAlh3fu80iPeswOg079Oi0y3LvzIRz4JuO27fNbK3FUJ6B+/h/77QqBuPYh2XSEaNPINJnrPN1IZXygH6MZLxAI9JsABuscChGX7aDRqXa79eCHELgCaaK3Xaa2XCSHejsVii8PCwYs+OUD3gnow9uQBeTB09FsX9J3fFAtGvfRdMHT0Sxf0m1+UClad9F2w9PRTN/Sen9Tyb61u+0xNvwf6HWdnnCcptmgF2WMQxF775x2smjcD+qWnUvvsvBsit9yf933DvoHbvgs7T/afPQF6L3tWXFkzAQ7Q6RASqJkAB+h0CAkEnAAH6AEXOI/t8YA8j3CZuloC9B3N4QUB+s4L6uHdk34Lr/Zedk7feUk/3HvTe+HWv1Ddu+kzvfi/UKMHOi+9fkOI87pAnHYORHGx8zw2IvWaX6CG9QE2rEtEiXbdINt3s5GBS50QcNN3TvZnTHgJ0Hvh1d7tzjlAd5so8wWNAAfoQVOU/ZDAdgQ4QKclnBLgAblTcozLhQB9lws9xjolQN85Jcc4JwToNyfUGJMrAfouV4KMd0qA3nNKjnF2CLjpM7XwMejnyuxsD/xhX+DX1RBHnwRxbqknl4JOXI76rZeBnXaGOOlM3v/cnoKOVrvpO0cFMCi0BOi90ErveuMcoLuOlAkDRoAD9IAJynZIYHsCHKDTE04J8IDcKTnG5UKAvsuFHmOdEqDvnJJjnBMC9JsTaozJlQB9lytBxjslQO85Jcc4OwTc9JmaOQH6zZez356XS8+eVcBWuum7gKFhO3kmQO/lGXCI0nOAHiKx2aojAhygO8LGIBLwDwEO0P2jlWmV8oDcNEXCUQ99Fw6dTeuSvjNNkWDXQ78FW19Tu6PvTFUm+HXRe8HX2IQO3fRZ/N4RwGcfZm5LSkCp9Nfa7IHIyIkmYGANBSbgpu8KXDq38zkBes/nAhpUPgfoBonBUowkwAG6kbL4t6jS0tKH3KheKaXnz59/hRu5wp6DA/SwO8B5/zwgd86Okc4J0HfO2THSOQH6zjk7RtonQL/ZZ8aI3AnQd7kzZAZnBOg9Z9wYZY+Amz6Lj7oWWPJt5gL2PwT48pP013b9AyIj7rNXMFcHgoCbvgsEEDZRMAL0XsFQB34jDtADLzEbzJEAB+g5AmR4OoFoNGr9FFe7wSUWi0XcyBP2HBygh90BzvvnAblzdox0ToC+c86Okc4J0HfO2THSPgH6zT4zRuROgL7LnSEzOCNA7znjxih7BCp8tvm/n2PFq38D9j4Q2LgBeu0aiKNPhtihbtYJ4wO6A7//lnG96HIF9LwZ6a9xgJ4126At5Ptb0BT1Tz/0nn+0Mr1SDtBNV4j1eU2AA3SvFQjY/tsG6Ha7qhi4V/aj5gDdLsbM6zlAd4djGLPwgDyMqnvfM33nvQZhrIC+C6Pq3vVMv3nHPsw703dhVt/b3uk9b/mHZXfLZ83Wr8Gy/hcD5eXpbbdsDTnwdojmLWvFocvLoXp3yryuYSPIWyZDDbgo/fV9DkRkyNhac3NB8Ajw/S14mvqlI3rPL0qZXycH6OZrxAq9JcABurf8A7d7aWnpJdk0pZSSAJoAOEQI0Q5ACwAbAdwihPjZylFWVjYrm1xcUzMBDtDpEKcEeEDulBzjciFA3+VCj7FOCdB3TskxzgkB+s0JNcbkSoC+y5Ug450SoPeckmOcHQKWz3Z48hGsfbYsc5g1RB98B9CoceLMdNGwccZ1evVKqCGXp71mncGu43HIP7WH2PcgqGl3Qb/7WnKN7DcCou2Rdsrl2oAQ4PtbQIT0YRv0ng9FM7RkDtANFYZlGUOAA3RjpAhvIWefffYODRs2vBXAIAA/SinPnDdv3hfhJeJu5xygu8szTNl4QB4mtc3plb4zR4swVULfhUlt73ul37zXIIwV0HdhVN2Mnuk9M3QIehWWz8r7dIbetKnmVoUEtAKOOAHyqushpHVuR+qhF/8XavTA1BORIsjJCyBE6utTveRbqEm3AauWQxx5IkTPQRCSdyAMuscy9cf3tzCqbkbP9J4ZOgShCg7Qg6Aie8gnAQ7Q80mXuW0RKCkpuUsIYX1S+WLjxo1HP/PMM+ttJeDijAQ4QKcxnBLgAblTcozLhQB9lws9xjolQN85Jcc4JwToNyfUGJMrAfouV4KMd0qA3nNKjnF2CFg+0yP6onzJt1mHyV5DIY48IW29/vfbUPffnnquWQtExj5UJad1qXds2QzUrZc2XM96cy4MBAG+vwVCRl82Qe/5UjYji+YA3UhZWJRBBDhAN0iMsJcSjUbrWWegb7u0e79YLDYp7Ezc6J8DdDcohjMHD8jDqbvXXdN3XisQzv3pu3Dq7lXX9JtX5MO9L30Xbv297J7e85J+ePa2fCYn3YpNH76dddPi+P+DvPy6xHqtNfDbL9BPzIJ+65VUjj33Q+TGu7POyYXhIsD3t3DpbVK39J5Javi7Fg7Q/a0fq88/AQ7Q88+YO9ggEI1GFwJoD+CtWCx2oo1QLq2GAAfotIZTAjwgd0qOcbkQoO9yocdYpwToO6fkGOeEAP3mhBpjciVA3+VKkPFOCdB7Tskxzg6BxAD9vluw6aN3sg8rKoI47RzoLz4CViwFNme4/PthxyJy9U3Z5+TKUBHg+1uo5DaqWXrPKDl8XQwH6L6Wj8UXgAAH6AWAzC2yJ1BaWjpFa90TwIpYLNYq+0iurI4AB+j0hlMCPCB3So5xuRCg73Khx1inBOg7p+QY54QA/eaEGmNyJUDf5UqQ8U4J0HtOyTHODgHLZ2LsEGz+6rMqYaJdV+hFr24dktt8iFP/AnlRH5tRXB4WAnx/C4vS5vVJ75mniV8r4gDdr8qx7kIR4AC9UKS5T1YEotHofACdAGyKxWLWJd35yJEAB+g5AgxxOA/IQyy+h63Tdx7CD/HW9F2IxfegdfrNA+jcEvQdTeAVAXrPK/Lh2tfymR55Dcp/WFylcTn0TqD1rtCvPAf91GxbYET7CyDbdbUVw8XhIcD3t/BobVqn9J5pivi3Hg7Q/asdKy8MAQ7QC8OZu2RBoEOHDk2Li4utTztNAPwQi8X2yCKMS2ohwAE6LeKUAA/InZJjXC4E6Ltc6DHWKQH6zik5xjkhQL85ocaYXAnQd7kSZLxTAvSeU3KMs0PA8pkacgXiK5dVCZMT50LUrZ94Xj16P/S//pZ1anFxX8iTz8x6PReGiwDf38Klt0nd0nsmqeHvWjhA97d+rD7/BDhAzz9j7pAFgWg02gzAHAB/BqCFEHPKysouyiKUS2ohwAE6LeKUAA/InZJjXC4E6Ltc6DHWKQH6zik5xjkhQL85ocaYXAnQd7kSZLxTAvSeU3KMs0PA8ln5tV2h161ND9tjH0SGjUs+pz/7EOreEVVTH3w45MXXQM2dBny4aOvr1n3V73gQolkLO6VwbYgI8P0tRGIb1iq9Z5ggPi6HA3Qfi8fSC0KAA/SCYA7PJiUlJRdn262Uskhr3RzAYQDOA9AQgOVJrZQ6YcGCBW9nm4vrqifAATrd4ZQAD8idkmNcLgTou1zoMdYpAfrOKTnGOSFAvzmhxphcCdB3uRJkvFMC9J5TcoyzQ0AIgfKe5wNKpYXJG+6C2Gv/5HNaa6jhfYBlP6atE5f3hzz+dOgtW6Cfmwe9dAnkqWdDHHionTK4NmQE+P4WMsENapfeM0gMn5fCAbrPBWT5eSfAAXreEYdrg2g0an1a0Q66TnpRCDGirKzsVgc5GJKBAAfotIVTAjwgd0qOcbkQoO9yocdYpwToO6fkGOeEAP3mhBpjciVA3+VKkPFOCdB7Tskxzg4BsWUzyvuUpIXI0dMgWraukka9/nfoWRNTz1tnmo97FKJBIztbci0JgO9vNIFXBOg9r8gHb18O0IOnKTtylwAH6O7yDH22bQN0pxz+B+CGWCw232kCxlUlwAE6XeGUAA/InZJjXC4E6Ltc6DHWKQH6zik5xjkhQL85ocaYXAnQd7kSZLxTAvSeU3KMs0NA/P4bygd0TwtJDMUbNamSxjrLXI25Hvje+goKEKefA3lBLzvbcS0JJAjw/Y1G8IoAvecV+eDtywF68DRlR+4S4ADdXZ6hzxaNRmdmC0FrvUVKuUZr/a2U8u158+a9m20s12VPgAP07FlxZToBHpDTEV4QoO+8oM496Tt6oJAE6LdC0uZeFQToO3rBKwL0nlfkw7WvWPEzym+8Kq1p+cB8iOI6GUHo39dAv/8GUK8+xBEnQBQXhwsYu3WFAN/fXMHIJA4I0HsOoDEkIwEO0GkMEqiZAAfodAgJBJwAB+gBFziP7fGAPI9wmbpaAvQdzeEFAfrOC+rh3ZN+C6/2XnZO33lJP9x703vh1r9Q3YsfvkH5LdeltosUQU5eAOve6HyQQL4I8P0tX2SZtzYC9F5thPh6tgQ4QM+WFNeFlQCPJMOqPPsODQEO0EMjteuN8oDcdaRMmAUB+i4LSFziOgH6znWkTFgDAfqN9vCCAH3nBXXuaRGg9+iDghD472eI33lDaqsGjRC5d3ZBtuYm4SXA97fwau915/Se1woEZ38O0IOjJTvJDwEO0PPDlVlJwBgCHKAbI4XvCuEBue8kC0TB9F0gZPRdE/Sd7yTzdcH0m6/l823x9J1vpfN94fSe7yX0RwMfv4v4xFtTtTbfCZExD/qjdlbpWwJ8f/OtdL4vnN7zvYTGNMABujFSsBBDCXCAbqgwLIsE3CLAAbpbJMOXhwfk4dPchI7pOxNUCF8N9F34NPeyY/rNS/rh3Zu+C6/2XndO73mtQEj2f+dfiE+/O9Vsmz0QGTkxJM2zTa8I8P3NK/Lcl96jB9wiwAG6WySZJ6gEOEAPqrLsiwS2EeAAnVZwSoAH5E7JMS4XAvRdLvQY65QAfeeUHOOcEKDfnFBjTK4E6LtcCTLeKQF6zyk5xtkhoP/1N6hH70+F7H0AIkPvtJOCa0nANgG+v9lGxgCXCNB7LoFkGnCAThOQQM0EOECnQ2wTKCkpudh2kIOA+fPnP+IgjCHbEeAAnZZwSoAH5E7JMS4XAvRdLvQY65QAfeeUHOOcEKDfnFBjTK4E6LtcCTLeKQF6zyk5xtkhoF9cCBV7KBVy0OGI9B9lJwXXkoBtAnx/s42MAS4RoPdcAsk0HKDTAyRQCwEO0GkR2wSi0agCoG0H2gvQsVisyF4IV2ciwAE6feGUAA/InZJjXC4E6Ltc6DHWKQH6zik5xjkhQL85ocaYXAnQd7kSZLxTAvSeU3KMs0NAPz0H6pk5qZAjT0Ck11A7KbiWBGwT4PubbWQMcIkAvecSSKbhAJ0eIIFaCHCATovYJrBtgG47zmaANUCP2Izh8gwEOECnLWoioLdshn54IvQn7wL7HAR5xQCIBg0TITwgp3e8IEDfeUGde9J39EAhCdBvhaTNvSoI0Hf0glcE6D2vyIdrX102A+rvTyWbFieeAXlpv3BBYLcFJ8D3t4Ij54bbCNB7tIJbBHgJd7dIMk9QCXCAHlRl89hXNBqdmcf0ydSxWOyyQuwT9D04QA+6wrn1p954Cfrh+1JfNLTrBtm+W+LfPCDPjS2jnRGg75xxY1RuBOi73Pgx2h4B+s0eL652hwB95w5HZrFPgN6zz4wR9gmoRyZBv/Zi6nPtGe0gu/awn4gRJGCDAN/fbMDiUlcJ0Huu4gx1Mg7QQy0/m8+CAAfoWUDiEhLwMwEO0P2sXv5rjw+9Eli1PG2jyPSnE//mAXn++XOHqgToO7rCCwL0nRfUw7sn/RZe7b3snL7zkn6496b3wq2/m93rb76E/uxDiAP/CLHPQWmp1bS7oN99LfmcOKcUsuNFbm7PXCRQhQDf32gKrwjQe16RD96+HKAHT1N25C4BDtDd5clsJGAcAQ7QjZPEqILiPdpXqYcDdKMkCl0x/CAYOsmNaJi+M0KG0BRBv4VGaqMape+MkiNUxdB7oZI7b83qxV9BjR0KxMsTe8jrx0Dsmxqiq3HDob/4KLm/6HQJ5Nmd81YPE5NAwodSonXr1gkYS5cuhVKKYEigIATovYJgDsUmHKCHQmY2mQMBDtBzgMdQEvADAQ7Q/aCSdzXGe3YAdPqHPDnuMYhGjflh0DtZQr0zPwiGWn7PmqfvPEMfyo3pt1DK7nnT9J3nEoS2AHovtNK72nj8gdHAh4tSOY84HpHeN0ArBf2vv0LPnpK2n7igF+Tp57haA5ORwPYE+P5GT3hFgN7zinzw9uUAPXiasiN3CXCA7i5PZiMB4whwgG6cJMYUpLWG6tMZKN/6K/6Khxw0GmL/thygG6NUuArhB8Fw6W1Kt/SdKUqEow76LRw6m9YlfWeaIuGph94Lj9b56lSvXwvV74Iq6eXwe6EeewBY/FWV18SVAyGPPTVfJTEvCSQI8P2NRvCKAL3nFfng7csBevA0ZUfuEuAA3V2ezEYCxhHgAN04SYwpSP++BmpA1fvCVfxanwfkxkgVqkLou1DJbUyz9J0xUoSiEPotFDIb1yR9Z5wkoSmI3guN1HlrVL3+d+hZE7PPX1wHcsyDEI2bZh/DlSTggADf3xxAY4grBOg9VzAyCQAO0GkDEqiZAAfodEhBCESj0TZSyhZKqUZCCJnNpmVlZf/KZh3X1EyAA3Q6pDoC+ruvoW4bUOVlcdo5kBf24q+paR1PCPCDoCfYQ78pfRd6CxQUAP1WUNzcbBsB+o5W8IoAvecV+WDsa12iXY25PuNZ5pk63OGQIxHv0B16972CAYBdGE2A729GyxPo4ui9QMtb0OY4QC8obm7mQwIcoPtQNL+UXFJScoKU8hqt9RkAmtusW8disSKbMVyegQAH6LRFdQT0B29BTb6j6sv7tUVk8GgO0GkdTwjwg6An2EO/KX0XegsUFAD9VlDc3GwbAfqOVvCKAL3nFflg7Kve/Af0zHtrb6bVLmjZ72bUPfQoLF26FEqp2mO4ggRyJMD3txwBMtwxAXrPMToGbkeAA3RaggRqJsABOh3iOoERI0bIzz777D4hRO9tyZ34zBqgR1wvLoQJOUAPoehZtqxeegp63oyqq5vsiMjdszhAz5Ijl7lLgB8E3eXJbNkRoO+y48RV7hCg39zhyCz2CNB39nhxtXsE6D33WIYtk96wHmpYL2DNr9W3XlQEcXYJIueWYufddk+s4wA9bE7xrl++v3nHPuw703thd4B7/XOA7h5LZgomASeDzWCSYFeuESgpKRknhLiuUsIvADQBsAsADcC6NHsjANanmxbb1lnPrwPwfkVcLBY73bWiQpyIA/QQi19L62reg9AvPV11VZ06iNw/nwN0WscTAvwg6An20G9K34XeAgUFQL8VFDc320aAvqMVvCJA73lF3v/7qthM6BefrL6RHVtAXnszxK5/4GdX/8vtyw74/uZL2QJRNL0XCBmNaIIDdCNkYBEGE+AA3WBx/Fhaly5dDlRKfbqt9hVSynbz5s17NxqNTgRwtTVAr3xm+bb1fQD0AiCFENObNm16zbRp07bkq/+OHTvuFIlEjgFwjBDiaADWf4lLzGutZ82fP/9SN/Y+7bTTipo3b36IEKJiH2vPgwAkzqyPRCJ7zp0791s39qopBwfo+Sbs3/xx6/LtH7yVsQE5dSEiRUVo3bp14nX+it+/Ovutcn4Q9JtiwaiXvguGjn7pgn7zi1LBqpO+C5aefuqG3vOTWubUqn9eAjXqGiAeTxV1yFGQZ3WEenEhRJMdIdp3g2i69W6B9Jk52oWpEvouTGqb1Su9Z5Yefq6GA3Q/q8faC0GAA/RCUA7RHpXOPrfOKG8fi8Wes9qvboBegaa0tPRErbW11jozfWosFrOG6nl5RKNRq7aMDzcH6CUlJSOEECOr24sD9LzIy6Q2CMRvGwB893XGCDmpDJF69TlAt8GTS90hwA+C7nBkFnsE6Dt7vLg6NwL0W278GO2MAH3njBujcidA7+XOMGwZtNZQ944EPv8w1XpREeTISRCtrAsbVn3QZ2FziRn90ndm6BDGKui9MKqen545QM8PV2YNDgEO0IOjpRGdRKPRdwEcCWBJLBbbegOqLAbo1prS0tJSrfXcbZd5PzUWi72ej6a2G6B/D+A/AM609nJzgF5aWjpSaz1iWw8bAfwbQEsAe1vPcYCeD3WZ0w6BeP+LgLVrMn8Bcc8jiDRtxgG6HaBc6woBfhB0BSOT2CRA39kExuU5EaDfcsLHYIcE6DuH4BiWMwF6L2eEoUug/70I6v7RaX2LsztDdrqkWhb0WehsYkTD9J0RMoSyCHovlLLnpWkO0POClUkDRIAD9ACJaUIr0Wh0+bbLoS+MxWKdK2oqLS29T2vd15pR77jjjnWru0R7NBr9H4A/AJgZi8WuzEdP0Wh0lNb63eLi4nfnzJmzrGvXrn+Ix+OLrb1cHqCfpZT6g9b6nVWrVn3y6quvlpeUlDwshEh86uMAPR/qMme2BPSmTVB9LTy/KgAAIABJREFUo9V/AXHHdER22pkD9GyBcp1rBPhB0DWUTGSDAH1nAxaX5kyAfssZIRM4IEDfOYDGEFcI0HuuYAxNEr15E9SIvsDKZamemzaDvHUyRN161X9+lZKfXUPjEnMa5fubOVqErRJ6L2yK569fDtDzx5aZg0GAA/Rg6GhMF9FodLM1G9ZaT5s/f37visJKSkruEkIMtGbUxcXFLR5//PFfMhVdWlr6mNb6AgBfxmKxAwvRWL4G6Jlq5wC9EIpyj2wIJO4pd3P1d0qwLo8X2e0P/BIiG5hc4yoBfhB0FSeTZUmAvssSFJe5QoB+cwUjk9gkQN/ZBMblrhGg91xDGYpE6tl50E/NTutVXDkQ8thTa+yfPguFPYxrkr4zTpLQFETvhUbqvDfKAXreEXMDnxPgAN3nAppWfjQata4H3UBrPWP+/Pk9K+orKSkZJoS4xRqgK6UOX7BgwceZai8tLX1Ia30pgN9jsViTQvTHAXohKHMP0wjozz6EurfiDgNVq5M33oPI3vtzgG6acCGohx8EQyCygS3SdwaKEuCS6LcAi2twa/SdweIEvDR6L+ACu9Se3rIZWPIt1NihQLw8lXWfgyCvvwNC1Pz1JX3mkhBMY4sAfWcLFxe7SIDecxFmyFNxgB5yA7D9WglwgF4rIi6wQyAajVr3E98XwIJYLFZaEVtaWtpNa239jFgD6BmLxWZkyhuNRq37np8AYEMsFmtgZ2+nazlAd0qOcX4moP71N+hH76+2BTnodkQOPJQDdD+L7NPa+UHQp8L5vGz6zucC+qx8+s1nggWkXPouIEL6sA16z4eiFbhk/esqqDuuB1avSN9ZCMhh4yF236vWiuizWhFxQR4I0Hd5gMqUWRGg97LCxEVZEOAAPQtIXBJqAhygh1p+95uPRqMLAHQE8FEsFju8YofOnTvvK6X8ctsA/a1YLHbS9rt36dLlaKXUom3Pf8VLuLujj9Z6CYA28Xgcy5ZVuo+YO+mZxacE1JOPQT9fVm318prhiBx2LAfoPtXXz2Xzg6Cf1fNv7fSdf7XzY+X0mx9V83/N9J3/NfRrB/SeX5UrXN1qzjTofzxbdcO2RyDSb2RWhdBnWWHiIpcJ0HcuA2W6rAnQe1mj4sJaCHCATouQQM0EOECnQ1wlUFJScp0QYhyALQCax2KxtRUbRKPR9wFYQ3VtXeK9uLj4xjlz5qy0Xi8pKTlZCPEwgD23vT5p/vz5/Vwtrppkfj4DvVOnTrvWxqisrOzdSCTS2hqgr1ix3S+6awvm64ElUP7gPdBvvVJtf5FeQ1F07CnYaaedEmuWL18OpVRgebAxcwhYHwTpO3P0CEsl9F1YlDajT/rNDB3CVgV9FzbFzemX3jNHCxMr0VqjfMgVwKrlVcqzPpPKo6uce5GxDfrMRHWDXxN9F3yNTe2Q3jNVGf/V1bJlS0QiEavwH4UQtc4Z/NchKyaB3AhwgJ4bP0ZvR6Bz584HSCk/t4bgQojSsrIy64z0xCMajZ4L4JltZ6FbT1nTOOtTUj0AFfc7tzy5Lh6Pt33iiSe+KwRgPw/Qo9GodUn8Gh+TJ09G8+bNa1vG10NGYPmQntj06QfVdt3suhFo8Od2IaPCdkmABEiABEiABEiABEiABEiABApFYMt3/8PSPl2qbCcbN8Euj7wAUVynUKVwHxIgARIgARIIMwEO0MOsPnuvlgAH6DSH6wSi0ah1XejdtNb/mD9//k2VNygtLR2ptb650nPWALiyDzcA6BaLxZ52vbBqEnKAXijS3MckAj9d3h7xZT9VW1LT3kPQ6LyoSSWzFhIgARIgARIgARIgARIgARIggQARWDN/Fn6bObFKR017X49G55UGqFO2QgIkQAIkQAJGE+AA3Wh5WJxXBDhA94p8iPeNRqN/BjAYwCkAKn5O/BuA56WUt82bN++LQuLx8wCdl3AvpFOCs5dWcZT36gTE46mmGjYG1q5J/ltGL0PxOVFeSjs4svumE16KzDdSBapQ+i5QchrfDP1mvESBLJC+C6SsvmiK3vOFTJ4VWX7nDdBffpLav8mOiFx7M+Qf9rVVE31mCxcXu0SAvnMJJNPYJkDv2UbGgGoI8BLutAYJ1EyAA3Q6xDMCI0aMkB9//HGLunX/n73zjpOiSN/4U9WLgARFRVER9fDuFO8M5xnOrKinnojizoIBE4IoCBJFgkQVkCBBRFBBQIQdguJPvDN7xjPniAkTipEgIEzV79OzuzPbO7M70z090z3dT//FTle94fs+zHTPO13VQN13330/VlvavaAxFXMDPRtQWuuvAOxp7oH+3XffZTOFYwJOQP+0FsrcZ6768cc2wMfm7gsVhzjlbBgdr8Due+wR/3vNmjXcAz3guvBLeuaNYIsWLag7vxQkJHFQdyEptE/SpN58UoiQhUHdhazgPkqX2vNRMXwYSqxvZ2C9+TxFxSGvuh7ib/+wHSl1ZhsZJ7hAgLpzASJNOCJA7TnCxklpCOy2227cA53KIIE6CLCBTnmEngAb6KGXQOgA6FXvQY0blMy73nYQhx0N/eJTFhZi/4Owx+hpkNs3YgM9dCrxLmHeCHrHPsyeqbswV7/wuVNvhWdOjwB1RxV4RYDa84q8//1qpaDMldG0SgQrh02GaNXadvDUmW1knOACAerOBYg04YgAtecIGyexgU4NkIBtAmyg20bGCUEjwAZ60CrKfDIRUP97GvrOiclhu+0Jsf9foZ/+d8rUZr2GovE/z2EDPRNUnneNAG8EXUNJQzYIUHc2YHFozgSot5wR0oADAtSdA2ic4goBas8VjIE0otevg+p7kSU3Oe5uiJ12sZ0vdWYbGSe4QIC6cwEiTTgiQO05wsZJaQjwCXTKggTqJsAGOhXiKoFIJPKgEGL++vXrH3j44Ye3uGo8T8bYQM8TWJr1LQH18BLoZfOS8bU5BKLlPtCP3J8Ss9iuPlouf44NdN9WM3iB8UYweDUthoyou2KoUnBipN6CU8tiyoS6K6ZqBStWai9Y9XQzG/3tl1A39LCYlDOWQtSrZ9sNdWYbGSe4QIC6cwEiTTgiQO05wsZJaQiwgU5ZkEDdBNhAp0JcJRCJRMy1tzSA9VrrpVLKBeXl5U+66sRlY2yguwyU5nxPQC2YYXnaXBx7KrDjTtD/tzht7Hs99Aob6L6vanAC5I1gcGpZTJlQd8VUreKPlXor/hoWYwbUXTFWLRgxU3vBqGM+stAfvgM1YXDSdMPtYUxd5MgVdeYIGyflSIC6yxEgpzsmQO05RseJNQiwgU5JkEDdBNhAp0JcJVCtgW5qy2ykm8fXWuuFhmEsWLx48TuuOnTBmJMGeiQSuRTAHNO9EGJkeXn5iGxCKS0tnSuEuMQcaxjGvosWLfo8m3m5jNFafwVgz1gshu+++y4XU5wbEAKxKSOBd15NZCPaXwAY9aCX3ZOaoZTY68GX2EAPSO2LIQ3eCBZDlYIXI3UXvJr6OSPqzc/VCW5s1F1wa+v3zKg9v1fIu/j0q89BzRyXDKB5Cxg3zXIUEHXmCBsn5UiAussRIKc7JkDtOUbHiTUIsIFOSZBA3QTYQKdCXCVQVlY2WGt9AYA21QxXNdLNl94GMC8Wi923bNmyb111nqWxSCRyLID9qoYLIXbRWt9S+fdzAO6sbioajc6taTqbBnokEmkMoLTG3CsAHGO+JoQYoLX+odr5N6LR6BtZppH1MDbQs0YVmoGx4T2Bb1Yn8hWXXQts+g16UZovK0pKsNcDL7KBHhp1eJ8obwS9r0EYI6Duwlh173Km3rxjH2bP1F2Yq+9t7tSet/z97F09tRL63pnJEFvvD2PQeEchU2eOsHFSjgSouxwBcrpjAtSeY3ScWIMAG+iUBAnUTYANdCokLwQikcghADoD6Ahgj2pOqprp5lLv5tLu8xs0aLBs/vz5G/MSSBqj1Z8Cz8ZnNBpN+X+STQO9+pPt2fix8yR7NvaqxrCBbodW8MdqraGu6QRs2ZRIVva/EXrtGuh7pqUAEPXro+Uy7oEefGX4J0PeCPqnFmGKhLoLU7W9z5V6874GYYyAugtj1f2RM7Xnjzr4MQr14CLoFQuToR18BIyeQx2FSp05wsZJORKg7nIEyOmOCVB7jtFxYg0CbKBTEiRQNwE20KmQfBMQpaWlJ0spL9RadwDQtJrDqma62cl7QGu94MADD/zPyJEjzeZ63g420POGloaLgIDeuAHqWnORiOQhb5oF/dlH0LMnpGQgGm6Plkv+yyfQi6C2QQmRN4JBqWRx5UHdFVe9ij1a6q3YK1ic8VN3xVm3IERN7QWhivnJQS2cCf3kyoRxceypkJdc48gZdeYIGyflSIC6yxEgpzsmQO05RseJNQiwgU5JkEDdBNhAp0IKRuDSSy9tsGHDhnZCiIsAnA6gXjXnVc30tUKI+2Kx2L1Lly59pWDBBdgRn0APcHEdpKZXfwo1+trkTCEgZywB3n0davqYVIuGgZYPvIjvvvsOSuX1ty0OsuGUIBLgjWAQq+r/nKg7/9coSBFSb0GqZvHkQt0VT62CFim1F7SKupePumM89CvPJgyKM0ohO1zsyAF15ggbJ+VIgLrLESCnOyZA7TlGx4k1CLCBTkmQQN0E2ECnQjwhEIlEdqpc3t18FPZoc0vwaoGYzXQdjUZLPAkuYE7ZQA9YQXNMR7/xItRtNyWt7LgzjFvmQL//JtSkYWmt7xl9Ct+v24DY5k3Ah+8ADRtC7Ncmx0g4nQTSE+CNIJXhBQHqzgvq4fVJvYW39l5mTt15ST/cvqm9cNe/ruxjE4YAH76dGCLKukCe2t4RMOrMETZOypEAdZcjQE53TIDac4yOE2sQYAOdkiCBugmwgU6FeE7A3CtcKXWB1vpCAAdUBmQ20A3PgwtAAGygB6CILqagHn8QetHspMXW+8MYNB76kw+gxg5M62n3u1dg7TaNbZNvAN5/Mz5GtL8Q8qyOLkZGUyRQQYA3glSCFwSoOy+oh9cn9Rbe2nuZOXXnJf1w+6b2wl3/urKPDe8JfLM6MUR06QN51EmOgFFnjrBxUo4EqLscAXK6YwLUnmN0nFiDABvolAQJ1E2ADXQqxHMCkUikFQDzSfTObKC7Xw420N1nWswW1aLZ0I8/mPyS4sgTIK/oB/3V51Aje6VNbbdb5+OHN15GbO7U5PmG20PeuhBCymLGwdh9SIA3gj4sSghCou5CUGQfpUi9+agYIQqFugtRsX2WKrXns4L4KJxY387A+l8TEclrR0IceKijCKkzR9g4KUcC1F2OADndMQFqzzE6TqxBgA10SoIE6ibABjoV4gmBc845Z8d69eqVATCfOj+mxhLupi75BLpLlWED3SWQATETmzYaeOvlRDbirE6Q7S+A/v5bqCFXps1yl+GT8cONA4FtWy3n5U2zIJq3CAgZpuEXArwR9EslwhUHdReuenudLfXmdQXC6Z+6C2fd/ZA1teeHKvgvBq0UVPcOgFaJ4OSwyRCtWjsKljpzhI2TciRA3eUIkNMdE6D2HKPjxBoE2ECnJEigbgJsoFMhBSMQiUS2E0K001pfBOAMAPUqnVfX4VohxGIhxPzFixcnu3wFizJ4jthAD15Nc8kodkMP4NsvEybEZb0hj24L/evPUP0vSWu6Xuv9sfWTD1LOyZ5DIQ4+IpdwOJcEUnUlJVq0qPhhxpo1a6BU8ks14iKBfBHgFxD5Iku76QhQb9SFFwSoOy+o06dJgNqjDtIR0OvXQfU1vxpKHnLc3RA77eIIGHXmCBsn5UiAussRIKc7JkDtOUbHiTUIsIFOSZBA3QTYQKdC8k6grKzspMr9zc8D0LTSYXXtbdJaPyClXKC1/k80Go3lPagQOWADPUTFzpCq1hqqRwTY+ntipBxwM8SfDoTe/BvUNZ1swRLndoY8M2JrDgeTQCYCvBHMRIjn80GAussHVdqsjQD1Rm14QYC684I6fZoEqD3qIB0B/dG7ULdcbzklZyyFqFf1nIU9btSZPV4c7Q4B6s4djrRinwC1Z58ZZ6QnwAY6lUECdRNgA50KyQuB88477yAppflz4vMB7FHppLrezEcKnxRCmE3zpdFodENeAqFRsIFOEZgEzOa5fvZR6HnTrV9SjJ8D0WxnaBWDuvJcW7BE5f7ptiZxMAlkIMAbQUrECwLUnRfUw+uTegtv7b3MnLrzkn64fVN74a5/bdmr+bdB//c/ydO77wVj1G2OYVFnjtFxYg4EqLsc4HFqTgSovZzwcXI1AmygUw4kUDcBNtCpEFcJlJWVXVf5tPmB1QxX19lbWuv5SqmFy5Yt+9ZV5zSWlgAb6BSGSUAtmQP9n+VWGPW2g5xeDiFl/PVY17PtwWq5L4zhU+zN4WgSyECAN4KUiBcEqDsvqIfXJ/UW3tp7mTl15yX9cPum9sJd/3TZ662/Q/W7BNi0MXFanHMR5L/KHMOizhyj48QcCFB3OcDj1JwIUHs54ePkagTYQKccSKBuAmygUyGuEohEIuaT5RpAdW19pbVeaBjGgsWLF7/jqkMay0iADfSMiAI/QMdiUL06Ar8nl26PJ13jV/6xmwcAn36YPY+SEsjpUQjDyH5OtZH6t43A75shdtzZ0XxOCiYB3ggGs65+z4q683uFghUf9RasehZLNtRdsVQqeHFSe8Graa4ZqZefhZ41PmlGCMib74TYublj09SZY3ScmAMB6i4HeJyaEwFqLyd8nFyNABvolAMJ1E2ADXQqxFUClQ100+Y6c2l2APOXLFnylKtOaMwWATbQbeEK5GC95muoYVel5nbAwTD6jk68rj/7GGrOrcC3X6aOFRLymqFQU0dZzslRMyB2b2mbm37zZajZE4AtmyCO/ydk5x62bXCClYD+YlXF/vatD4AQxfvxzhtBKtsLAtSdF9TD65N6C2/tvcycuvOSfrh9U3vhrn+67GPmPeXbryRP7X8QjH5jcgJFneWEj5MdEqDuHILjtJwJUHs5I6SBSgJsoFMKJFA3geL9hp2V9SWBSCTygNZ6wcaNG1c8/PDDW3wZZMiCYgM9ZAVPk65+40Wo225KOVNb41o9tRL63pmW8fKMUogOFyPW50Jgw/rEOXntSIgDD7UNOTaqN/DlZ0k7w6dCtNzHth1OqCCgli+AXlke/7c4pi3kpb2LFg1vBIu2dEUdOHVX1OUruuCpt6IrWSACpu4CUcaiTILaK8qy5S1ove5nqAGXAcpcvLDiEJf1hjy6bU4+qbOc8HGyQwLUnUNwnJYzAWovZ4Q0UEmADXRKgQTqJsAGOhVCAgEnwAZ6wAucRXrq4aXQy+5JGSkuuxby6JNTXteffQR1U//E6yV77QsMmQhtlCBmPsm+5uvklx3dBkIefmwWUSSHaK2hurW3zBFt20F26mrLDgdXENBbt0Jde75liX451lwCcdeiRMQbwaIsW9EHTd0VfQmLKgHqrajKFZhgqbvAlLLoEqH2iq5keQlYf/8t9Ev/hX7jf4C5clbVsV19yInzIBo0zMkvdZYTPk52SIC6cwiO03ImQO3ljJAGKgmwgU4pkEDdBNhAp0JIIOAE2EAPeIGzSE/NmQL9/OPWkW0OjS/JLkrqpVgwG9x66VzoJ1eiXsu9sfOgsfjR2A5KKdTcJ11cdDXkCadnEUVyiF6/DqrvRZY54h8nQV7ex5YdDq4goL/7BmpodyvPi3tCHndaUSLijWBRlq3og6buir6ERZUA9VZU5QpMsNRdYEpZdIlQe0VXMtcD1ut/hRrZG/j1pxTb4qiTILvkfh9InbleNhrMggB1lwUkDskLAWovL1hDaZQN9FCWnUnbIMAGug1YHEoCxUiADfRirJq7McfMp8k/+yhhVJzVEbL9hRmdpLsgj00ZCbzzatJWh4thLu9u59BffQ41spd1yqFHwbh6sB0zHFtJQL/3OtTk4RYe4vTzIM+7pCgZ8UawKMtW9EFTd0VfwqJKgHorqnIFJljqLjClLLpEqL2iK5nrAauVUejl89PalX1HQxxwcM4+qbOcEdKAAwLUnQNonOIKAWrPFYw0AoANdMqABOomwAY6FUICASfABnrAC5whvfhy6b0vADZtTIyUvYdD/OWwjGDSXZCr2ROhX3o6MVf8swNk6aUZbVUfoN99HepWa8MXrVrDGDbZlh0OriCg/vtv6PkzrDjaHAKjzyjorb9DR+dAv/MqRJtDIDp1gygp8TU63gj6ujyBDY66C2xpfZkY9ebLsgQ+KOou8CX2bYLUnm9LU5DA4vejg7sBP3yX6q/ZLpBjZ0NII+dYqLOcEdKAAwLUnQNonOIKAWrPFYw0wgY6NUACGQmwgZ4REQeQQHETYAO9uOuXa/T6l5+gBlgb3PLm2RC77JbRdNoG+r0zoZ9amZgrjv8nZOceGW1VH6Cefxx6zhTrnAYNIacughD8WLIF02ygL5sH/fAS67RGTSAnL4B+/gnouUnWxfBkOm8E7SqA490gQN25QZE2siVAvWVLiuPcJEDduUmTtuwQoPbs0AreWL3qPahxg9ImJs4ohexwsStJU2euYKQRmwSoO5vAONw1AtSeayhDb4hPoIdeAgSQgQA7FZQICQScABvoAS9whvTUwjugn3woOWq77SCnlUNImRFM2gb68gXQK8sTc8Vhx0B2vy6jreoD1MNLoJfNS5kjJ94D0bSZLVscDKjZE6Bf+m8qz5tnQ5vLJT7ziOWcHHsXxM7NfYuON4K+LU2gA6PuAl1e3yVHvfmuJKEIiLoLRZl9mSS158uyFCwoVeMH2NUdy1EzIHZv6Uos1JkrGGnEJgHqziYwDneNALXnGsrQG2IDPfQSIIAMBNhAp0RIIOAE2EAPeIHrSE+/9jzU7WOtI/76dxi9bsgKStoG+iPL40uCJ44DDobRd3RW9qoGqUWzoR9/MGWOvG4sxH5tbNniYCB28wDg0w9TeXYfBPXo/cAnH1jOieNOg7y4p2/R8UbQt6UJdGDUXaDL67vkqDfflSQUAVF3oSizL5Ok9nxZloIEFV++/fquwI/fp/gTp50DGbnctTioM9dQ0pANAtSdDVgc6ioBas9VnKE2xgZ6qMvP5LMgwAZ6FpA4hASKmQAb6MVcPeex67VroEb3sex9DvPp88ETIfbcOyvDaRvozz4Kfc+05Py994MxdFJW9qoGqZnjoF99LvVLlEt7Qx7T1pYtDgZi/S8Bfv05lecZpdBfrALee8N6bqddYIy727foeCPo29IEOjDqLtDl9V1y1JvvShKKgKi7UJTZl0lSe74sS0GC0mu+ghp2tcWX7DkU2G1PiBZ7uhoDdeYqThrLkgB1lyUoDnOdALXnOtLQGmQDPbSlZ+JZEmADPUtQHEYCxUqADfRirZzzuPW2rRX7zH3+scWIuLgn5HGnZW043QW5fu0FqNtvTtpo3gLGTbOytmkOjJmxrXovZY44swzy3Its2aprsHrmkfjy5WL3vSDKukA0auyabb8Y0r9vgeoRSR9Om0OB3zak6ACGATljaVbL+HuRJ28EvaBOn9QdNVBIAtRbIWnTVxUB6o5a8IoAtecVee/9qsdWQC++MxnIjjtBjp8DIdz/KpI6877eYYyAugtj1f2RM7XnjzoEIQo20INQReaQTwLuX7XmM1raJgESsE2ADXTbyIp+glp8F/RjD1jyEEecAHFFX1tfVqRtoH/4NtSEIUnbjZrAuPVeW8xig7sBa9ekzBHHnAJ5aS/or7+Afv1FiH3/BHHgobZsVw3Wn38MdWO/xFzRth1kp66ObPl5kv72K6gbrE91JOJt3ARo1BT47uuUFOTEeRBNd/RlarwR9GVZAh8UdRf4EvsqQerNV+UITTDUXWhK7btEqT3flaRgAcVuHQ68+3rynqzyfi8fAVBn+aBKm5kIUHeZCPF8vghQe/kiGz67bKCHr+bM2B4BNtDt8eJoEig6AmygF13JcgpYv/E/qNtutNrYbU/IoRMhGmxvy3baBvrqT6FGX5u0IyTkzGWAUtArFsL0L/Y7oOKJ7wYNU/zF98HrWQb8viU1loMOjze51ajewOZN8fPyyoEQfz/WVtzmYBWdA/3I8uS8PfeGMWIa9PpfoV98CmjcFOJv/4Co38C2bT9N0O+8CjVlZO0hGQYQi6WclzdMgdhrXz+lkoiFN4K+LEvgg6LuAl9iXyVIvfmqHKEJhroLTal9lyi157uSFCyg2LUXAhvXJ/yJbgMhD7d/b5dNwNRZNpQ4xm0C1J3bRGkvWwLUXrakOC4TATbQMxHi+bATYAM97Apg/oEnwAZ64EucSFCvXwc1tHvFst1VR0k9yMETHDVL0zbQf/weatAVFqhyyn3QD94H/diK5JcjZV0gT22fAl9v+g2qV6f0RdnnjxD7tbE+Pb/vn2AMnmC7iLER1wBff5Gc16gJ5IR7oMb0Sb6+0y6QZVcAZiM9D8sI2g7awQT13/9Az7/N9kzZewTEX/5me14hJvBGsBCU6aMmAeqOmigkAeqtkLTpq4oAdUcteEWA2vOKvLd+9eZNUNd0tN43jr7d9b3P+R7nbZ3D7p3vb2FXgHf5U3vesQ+aZzbQg1ZR5uM2ATbQ3SZKeyTgMwJsoPusIHkMJ2WPOQDiwqsgTzzDkde0DfQ0DXBRehn0kjlWH4cdDaP7oBS/es1XUMNqWXJ8p+bAT2tT5shbF9rav1z/8hPUgEtT7Vw1COr2saks2hwC2akbxO4tHXHycpJ6YCH0/y2yHYK4tDfkMW1tzyvEBN4IFoIyfdQkQN1RE4UkQL0VkjZ9VRGg7qgFrwhQe16R99av/vZLqBt6WIKQ06MQ9evnJTDqLC9YaTQDAeqOEvGKALXnFfng+WUDPXg1ZUbuEmAD3V2etEYCviPABrrvSpK3gNTMcdCvPpe0/9e/Q14zzPHT1Wkb6OYS7N3PjS/ZXudx4KEwrk1dWlyveg9qXGpjvS5bsvsgiMOOzpqbev5x6DlTUscfehTw+ovp7RglEEefDOyIPqeeAAAgAElEQVTQDOLw4yD2aJW1Py8HqrlToZ97zHYI4tzOkGdGbM8rxATeCBaCMn3UJEDdUROFJEC9FZI2fVURoO6oBa8IUHtekffWr37nNagpI5JBNG4KY/KCvAVFneUNLQ3XQYC6ozy8IkDteUU+eH7ZQA9eTZmRuwTYQHeXJ61lSaC0tPQ0KWV7rfU+AH4XQnwkhFiyePHil7M0wWFZEmADPUtQRT4svrf4wMuAX35KZCIuuQby2FMdZ1bbBXmsz4XAhuRedmkdtN4fxqDxKaf0my9DTR9tKyZxwumQF9Xy1HoaS2r2ROiXnrblwzJ4+0aQo2dANG3m3EaBZsYmDwfeez3prdUfgNWfZvQuTj4L8vxuGcd5MYA3gl5Qp0/qjhooJAHqrZC06auKAHVHLXhFgNrziry3ftUzj0DPm17tPqU1jGGT8xYUdZY3tDRcBwHqjvLwigC15xX54PllAz14NWVG7hJgA91dnqG0Nnz4cPnuu++eYiYvpVTl5eW1Pg7Zrl277Rs0aGCuN/yvWmDNa9asWbdZs2ZtDSXMPCTNBnoeoPrQpP5xLdSgLpbI5KgZOS1LXmsDfciVwPff1k1hj1YwRlb7wqRytHrhSei7bX5x0rwFjJtmZUVdKwXV72Jgw7rM4xs2AjZtTDvOXJZe/vPczDY8HhEzl0X89stEFKLd+fH96DMetSyxn3FeAQbwRrAAkOkihQB1R1EUkgD1Vkja9FVFgLqjFrwiQO15Rd5bv+qBe6H/b3EyiEOOhNFjSN6Cos7yhpaG6yBA3VEeXhGg9rwiHzy/bKAHr6bMyF0CbKC7yzOU1iKRyBEAzHWRNYCV0Wi0XW0gIpGI2TwvqwOUaePeaDR6cShh5iFpNtDzANWHJtXLz0DPuiUZWaMmkJPmQ0jpONpaG+hj+gJfrKrb7k7NYYy7K2WMevxB6EWzbcckZy6HMIyM8/QXn0CN6ZNxHLbbDnLs3RVP7W9L83udFnvCGH17Zjsej4j16gRs+i0Rhew3Bur2m4Hf0v8wIDFwvzaQV18P/cb/AGlAHH4sxHb52Y/QLiLeCNolxvFuEKDu3KBIG9kSoN6yJcVxbhKg7tykSVt2CFB7dmgFZ6y6+1boF55IJJTvFbCos+Bop5gyoe6KqVrBipXaC1Y9vcyGDXQv6dN3MRBgA70YquTzGEtLS68XQtxoNtCFEO3Ky8tXpgu5rKzseK31U5WNdlN7K4QQi7XW5qOiZwO4AoD5umnn+PLy8mqbOfscgo/DYwPdx8VxMTS1aDb04w8mLf717zB63ZCTh1ob6OOuA1a9X7ft7RvBmJL6JLRasRD6QfN3NPYOOeU+iO0bJSapF5+Cfv5xiJb7QLS/CKJ+RfNXrYxCL5+f2XibQ2D0GYXYhCHAh2+njm+yA+TEeY73j88cQO4j9KbfoMwGerVD3jgTav4M4IO36nbQoCEgZOIJfHHECZBd++UelAsWeCPoAkSasE2AurONjBNyIEC95QCPUx0ToO4co+PEHAlQezkCLNLpNe+z8r3CF3VWpEIp8rCpuyIvYBGHT+0VcfF8Fjob6D4rCMPxHQE20H1XkuILKBKJ/BvAaQA2rF27dqennnpqW7osIpFIFMB5lQ3y28rLy3tVHxeJRHoDMNd2Np9CnxONRs2GOo8cCbCBniPAIpkemzjU0jQV7S+EPKtjTtHXdkGu7hgP/cqzFtvijPOgH16afE1IyDuWpzSgUxr9WUYob5kLseNO8dH6y8+gRplvFxWHOKsTZPsL4v+utSFew48Zr+xwCdSDi6BXLEwbhbzxDohdd88ywsIP099+CWUu4V7tkLdF4/no/yy3HVB1xrYnuziBN4IuwqSprAlQd1mj4kAXCFBvLkCkCdsEqDvbyDjBJQLUnksgi8xMbHA3YO2a5D1btwGQhx+Xtyyos7yhpeE6CFB3lIdXBKg9r8gHzy8b6MGrKTNylwAb6O7yDKW1SCTyGYBWAJ6JRqMnpoNwxhln1G/cuPGPALYHsHHLli17rFixYn2NsSISiXwM4A8A3o9GoweGEqjLSbOB7jJQn5qLDe8JfLM6+QVFl76QR6X975h1BrU20B+5Hzp6d9LXcadBnFEKZX5JUu2Q06OJJ8OrXlZ3TYZ+8cmsY6gaaD5ZLXbdI/5nbOoo4O1XLDaM2SugN2+CuvZCIJb2NzyW8eKSayCPPRX6o3ehbrk+bTzismshjz7Zdqz5nmDmqd9+FfjiY2ujvHETGJPvRcpy/lkGJLr2hzzi+CxH528YbwTzx5aWaydA3VEdhSRAvRWSNn0lrqWkRIsWLeJ/rlmzBkopwiGBghDge15BMPvKiVYKqkfEslWWHDQeovX+eYuTOssbWhqugwB1R3l4RYDa84p88PyygR68mjIjdwmwge4uz1Bai0QiGwA0FEIsKC8vvyQdhI4dOx6llHq+8uny5dFotDTduNLS0klCiGsBrItGozuGEqjLSbOB7jJQn5qL9bkI2GDuhlBxyD6jINocklO0tV2Qm0uH6wW3Q696F+Ivf4fodAWweTNU34ss/uSEeyB2aGZ5LTZtNPDWy3XH1agJsNH6+xp5wxSIvfaNz4td0xHYvMliI95Af/NlqOmjs8pZDrgZ4k8HQm/bCnV9V+CXn1LmieNPh+x8dVb2CjUo/mXUrcOB999MddlyXxjDp0B//w3UkO6OQjKfzBdnXwBRUs/RfDcm8UbQDYq0YZcAdWeXGMfnQoB6y4Ue5zolQN05Jcd5uRKg9nIlWHzz9bqfofpZvxqS4+dANNs5b8lQZ3lDS8N1EKDuKA+vCFB7XpEPnl820INXU2bkLgE20N3lGQprZWVlyUdPzY641pdWNsY/FEK8mA6C1ro1AHO9LnN59reEEK/XMs78SfJR5lbGQoh51ccopfSSJUu6hAKyi0myge4iTJ+a0rEY1FUdzP+MiQjl8CkQLSsazk4POxfkeutWqKvNHRqShxx9O0SLPS2vxWrun27ua/7bRssYcUxb6FeeB7Ykm+TyunEQ+x0QHxfrenZKSmYDXS28A/rJh5LnGjZK7PFdc0L15r5+/02oxXcCX39hHXbAwTD6ZteQd8rY7rx4rJOGpZ9Wue+91hqq9wW15p6YXAsfcWF3yBPPtBuaa+Pt6M41pzQUegLUXeglUFAA1FtBcdNZJQHqjlLwigC15xV57/zqN1+Cmj4mGYBhQM5YAiGNvAVFneUNLQ3XQYC6ozy8IkDteUU+eH7ZQA9eTZmRuwTYQHeXZyisRSIRp+v9VXX3nOpOR6PR/N1xBbR6bKAHtLDV0tK//gzVv8Yv/CfeA9HU+vS3XRJ2L8hjZhN/W3L5dDl0EsTe+1ncxsw9u7/9MvnaH/4MfPqhZYzsOQxq3jRg3S+J12WfkRBtDo3/na6BLmc9ADXsauC7rxNzxAmnQz/979S0GzSEnLoodX/2F56Evntycvyee8MYMc0utryOr2sPeXH8PyE7V+yJnnYv+L/8zdwxPr7Evfj7sRCt/gB1Y7/UeP/yNxi9R+Q1j7qM29WdZ4HScaAIUHeBKqfvk6HefF+iQAZI3QWyrEWRFLVXFGVyNUg16xbol59J2tzvABjXjXPVR01j1Fle8dJ4LQSoO0rDKwLUnlfkg+eXDfTg1ZQZuUvAaSPT3ShoragJRCKR3wDUBzAvGo1eli6ZSCTyKgCz+7W1UaNGO8ydO3dzunGlpaWXCSHuArAlGo02LGowPgmeDXSfFCKPYegvP4Ma1TvpQQjI25dBGLn93sTuBXmsz4XAhuTS67LfGIj9D7JkHut/KfBrcrl0ccwp0M89lhxTvyHk5PlQZqP9h+8Sr8segyEOOapiyfWrrE+6m4PMJ+7VyGoMzNeGTISafqPFX9xgq9YwhlVrlFd60e+9DjV5eDKWxk1hTF6Qx8rZMx1/stxcbv7H79NOFFf0gzzyhPg5FZ0D/chyyzhzaXbZrlPiNa1iUObS/zVWAEDjppCT5qf8wMBetM5H29Wdc0+cSQJJAtQd1VBIAtRbIWnTVxUB6o5a8IoAtecVeW/8mtt9qX4XA1t/TwQgLugOeVJ+V7iizrypd9i9UndhV4B3+VN73rEPmmc20INWUebjNgE20N0mGkJ7kUhkNYCWAD6LRqPmUu2W49xzz921pKTkm4pHH/FaNBo9vDZMZWVl/bXW4wF8H41GW4QQp+sps4HuOlLfGUxp/DbZAcak+TnHafeCPGY2dy1N7yEQhxxpiSNmNr+3bU28JnsNh7r95sQXLKJtO8hOXREbcY1lSfWq5rD+7huooan7e5t7d+uHlyZ9mU3gifOgy++CfvxBSwzi8OMguw1I4aO/+hxqZC/L6/EfIpSU5MzSDQP6q89SfiQAc6/yPfeGOPgIiH+VQUgZd6Ve+i/07AnWvDt1hWzbzvKaWjgT+smVKeHJcXdD7LSLG2HbtmFXd7YdcAIJpCFA3VEWhSRAvRWSNn1VEaDuqAWvCFB7XpH3xq96/nHoOVOSzs3l22+5B6JJ07wGRJ3lFS+N10KAuqM0vCJA7XlFPnh+2UAPXk2ZkbsE2EB3l2corUUikfsBmJsSm0u0d4lGo3OrgygtLb1FCGGuE2yeHxmNRkfVBioSiZj7nl8E4MVoNHp0KIG6nDQb6C4D9aE59eKT0He5v/S43QvymPkE+FefJQiJLn0gjzop8bf+fQtUj4iFoBwzE9j8G/R//wPsvCtE27Mh6tdH7Kb+wGcfJW117gF5/D+h33kVasrI1CrsvKvlyWxxxPGQXftDr3ofytx3vdohjjsN8uKeKTb0+l+h+na2xjd+DkSznX1RdfV/i6EfuDcZy867Qt48O+2T4nrN11DDrrLmfVlvyKPbWl7TJvuVS6AfXmLNu0fqjx8KBcGu7goVF/0EmwB1F+z6+i076s1vFQlHPNRdOOrsxyypPT9WJX8xxSYNA95/M+ngoMNhXDMsfw4rLVNneUdMB2kIUHeUhVcEqD2vyAfPLxvowaspM3KXABvo7vIMpbXS0tILhRDm465mg3yrEOJGrfVKpVSJYRgXaq3NTXlNrZmPnf4xGo2aT6ynPSKRyBeVT7PfEY1Grw4lUJeTZgPdZaA+NKceuR86encysv0PgtFvTM6R2r0gj40bBKx6L+G35lJ9+pcfoQZYd3mQkxakfRohNnEo8MFbSVsdu0Ce0h7qyZXQC2dmzE1UNou1UhX7w6//NWmr2lLn1Q3Fx5r7uCuVeDndPu4ZnedpQGxMX+CLVck8Tj4L8vxuab3Fc+l9PrB5UzKXOpriNfdMF2d1gmx/QZ4yqdusXd15EiSdBo4AdRe4kvo6IerN1+UJbHDUXWBL6/vEqD3fl8i1AOP3ewO7ADp5PyW6DYA8/DjXfNRmiDrLO2I6SEOAuqMsvCJA7XlFPnh+2UAPXk2ZkbsE2EB3l2corZ144oklzZs3/1/lHudmE73mYerMfH1aNBq9tjZIkUjkZADmZshaax1ZsmTJslACdTlpNtBdBupDc2rJXOj/JP+7VD19nWuodi/IY1NHAW+/knArOlwCeUZyv3L99WqoEdYnv+XM5Wn3ao9NHwO8+VLS1jkXQf6rDCp6N/Qj5qIXdR/yljkQO1Y8Oa7+9zT0nRMrJphPbY++HaJevbQGYgMuBX5J7tEurxkGcVCtu05kCsO18/pn88uoGj8+6Dsa4oCDa/Wh7pkG/eyjFedLSiAn3APRqEna8Slc//p3GL1ucC1+O4bs6s6ObY4lgdoIUHfURiEJUG+FpE1fVQSoO2rBKwLUnlfkC+835YfdDRpCTpgXX2Es3wd1lm/CtJ+OAHVHXXhFgNrzinzw/LKBHryaMiN3CbCB7i7P0Frr0KFDS8MwHgZwYC0QzE12O0Sj0d9rgxSJRB4CcAaAjQ0aNGgxf/78jaEF6mLibKC7CNOnptTdt0K/8EQiOnHK2ZAdr8g5WrsX5GrWLdAvP5OM48wI5LnJJdH1x+9BjR+UjKtBQxjTFqeNszZbsdtuAt54se7c9twbxohpljH6s4+h13wFceiREA22r3V+ylPeF/eEPO60nFnmakA99TD0vbcnzTRsBDlpfp37s+vfNkA/FAV+/gHi5LMg9jug1jAsPzIwR+2wE4wJlt04ck0h6/l2dZe1YQ4kgToIUHeURyEJUG+FpE1fVQSoO2rBKwLUnlfkC+83NroPsPqT5P3gP06GvLzWZyhcDZA6cxUnjWVJgLrLEhSHuU6A2nMdaWgNsoEe2tIz8SwJsIGeJSgOy0ygW7du9X755ZdLtdbmfuj7Vs74GMCiaDSavktWOei88877u5Sy3FzqXWu9YsmSJb0ze+SIbAiwgZ4NpeIeEzP3BH/n1eQXFed2hjzTute4kwztXpCredOhn3kkGcdJ/4K84MrE3/rNl6DMJ8urjp2awxh3V9rQ1Nyp0M+ZC1JUHKJtO8hOXREb2Qv46vM60xEnnQl5QXcnKSPlKfrKJ98dGXNxUmzKCOCd15I8Kvd4d8uF+eMCNcy6a4a8ZS7Ejju55SJrO3Z1l7VhDiSBOghQd5RHIQlQb4WkTV9VBKg7asErAtSeV+QL61d/+yXUDebufclD9hkF0eaQggRCnRUEM53UIEDdURJeEaD2vCIfPL9soAevpszIXQJsoLvLk9ZIwHcE2ED3XUlcDyjll/6XXAN57Kk5+7F7QV5zGXBR44kD9fwT0HNuTca1174wbpiSNk513yzoJ/4vcU4cdxpE5x5Q13QEtmyuMzfR8QrIU8zf8dg/LMuem437Gj8CsGNRb/29Yhn6khLgwMNqXTa+Lpvxp8hfeRZ6/gzLMLf3Eozvmd7rfGBLtT3TPVq+3q7u7NSEY0mgNgLUHbVRSALUWyFp01cVAeqOWvCKALXnFfnC+lXLF0CvNJ+JqDx22Aly/F0Q0ihIINRZQTDTSQ0C1B0l4RUBas8r8sHzywZ68GrKjNwlwAa6uzxpjQR8R4ANdN+VxPWAYgMuA375MWFX9hwGcXDu+3bbvSBXK+6DfvC+ZH6HHgXj6sGJv9VjK6AX35k8/+e/wuh/Y1oeatk90A8vTZwz93U3G+Oq38UZ+ckeQyAOOTLjuHQDUr74OexoGN2rLTtvw2ps2mjgrZcrZjRvAVl2OXDwkRAiu49e/fuWiqc4fvze6tUoqVi+fftGNqLJPDRmLq//8XtJ5mdfANmuU/xvrTX0UyuhX30+vhS8OPsCCCkzG3Uwwq7uHLjgFBJIIUDdURSFJEC9FZI2fVURoO6oBa8IUHtekS+cX/NeQV3f1XLfIk5tD1nWpWBBUGcFQ01H1QhQd5SDVwSoPa/IB88vG+jBqykzcpdAdt/iu+uT1kiABApIgA30AsL2wFX8y4qrzgNi2xLe5eCJEPv+Medo7F6Qq0cfgC6vtiT7AQfD6Ds6EUfKXu2HHQPZ/bq0car/Wwz9wL3JcwcfAXlGKdTYgRnzkiOmQey5d8Zx6QaoJ/4P+r5ZyVN77g0ZudyeraY7Ak13hOp/Seq8Aw6Of5EkWu6T0aZ+40Uoc8/3mkebQ2D0GZVxvt0BavGd0I+tsDA3eg6N/63ffxNq0rDEOdGuE+TZF9h1kdV4u7rLyigHkUAGAtQdJVJIAtRbIWnTVxUB6o5a8IoAtecV+cL51V+vhhrR0+JQDp0MsXfrggVBnRUMNR1VI0DdUQ5eEaD2vCIfPL9soAevpszIXQJsoLvLk9ZIwHcE2ED3XUlcDUhv2QLV07rfubxpFkTzFjn7sXtBrp55BHre9KTfff8EY/CE+N86FoPqfzGwYX3ifF1Lradrxouj20LfNSljXnLaYogGDTOOSzdAv/oc1MxxjuZmPUlKyG4DIQ47us4p6ul/Qy+wLt1uThDnd4M8+ays3WU7UL3wJPTdk5PDm+0CY/zd8b9V+V3Qjz5gMSUnLYBo0jRb81mPs6u7rA1zIAnUQYC6ozwKSYB6KyRt+qoiQN1RC14RoPa8Ip9/v/FVqp57DPqeaVZnO+0COfaurFfeciNS6swNirRhlwB1Z5cYx7tFgNpziyTtsIFODZBA3QTYQKdCSCDgBNhAD3aB9bqfofpZn3SOL+/dZIecE7d7Qa5efhZ61vik3xYtIUdOi+97pz96B+qW5HLu5iB582yIXXZLG2dK87j1/hB/+Rv0AwvrzqvJDjAmzXecu/74PShzKfN8H3vuDWNEjS+aavhU/1kOvWROSiTxL6N2bu56hPqb1VDDazw5Mu4uiJ2aIzZhCPDh2xaf4ozzIDukeco+x8js6i5Hd5xOAnEC1B2FUEgC1FshadNXFQHqjlrwigC15xX5/PtVLz6V9gfO5g+f5WW98x9ANQ/UWUFx01klAeqOUvCKALXnFfng+WUDPXg1ZUbuEmAD3V2etEYCviPABrrvSuJqQPr7b6CGdLfYlLdFIbarn7Mfuxfk+p1XoaaMTPV78BEQzXaJ76GdOFruA2P41FpjVC8+CX1XtaehW+4DsdcfoF94ou68qj317gSA3vxbxT7rv//uZHr2cxo0hDFtcZ3j1YqF0A8usowRp7SH7JifvQS1ikH1ugDYsinhU7RtB9mpK2Lmjx8+escab7Un1LNPPPNIu7rLbJEjSCAzAeouMyOOcI8A9eYeS1rKngB1lz0rjnSXALXnLk8/WVN3ToT+39MpIYkufSCPOqmgoVJnBcVNZ5UEqDtKwSsC1J5X5IPnlw304NWUGblLgA10d3nSGgn4jgAb6L4riasB6dWfQo2+NmnTXB585nJXlsuze0GuV70PNS79nuY1kxZndYRsf2GtLPRrL0DdfnPy/PaNgd1bAp98UCc/ccTxkF3758RYv/Uy1Ir7gF9/sm/nlyznCAF5x/111ill2fRWf4Ax7Fb7MdmYoeZNh37mkeSMkhLIG2dBTRwKfP9NiiU5YylEvXo2PGQeald3mS1yBAlkJkDdZWbEEe4RoN7cY0lL2ROg7rJnxZHuEqD23OXpJ2uxsQPT3p/JW+ZA7LhzQUOlzgqKm84qCVB3lIJXBKg9r8gHzy8b6MGrKTNylwAb6O7ypDUS8B0BNtB9VxJXA9IfvQt1y/VJmw0bwZh6nys+7F6Q669XQ42wLgFeWyBy6CSIvferNU797utQtw63nYc4MwJ5bmfb89yaoO6eDP3Ck6nmDAOIxSyvy+nlEPUb1Oq6ZjNbnPQvyAuudCvUtHb0j99XrGgQ25Y4L044HfrFpy1PpledrGsZfqeB2tWdUz+cRwLVCVB31EMhCVBvhaRNX4nPbCnRokWL+J9r1qyBUopwSKAgBPieVxDMnjiJTRsNvPWy1ffue8EYdVvB46HOCo6cDrkNFDXgIQG+53kIP2Cu2UAPWEGZjusE2EB3HSkNkoC/CLCB7q96uB2NfvtVqKnVlk13cVltuxfk+ucfoQZeljnFZrtAmntri9o/gvSq96DGZdiL3GzAf7HK4k9c3BPyuNMyx5CnEere26GfejjVuvkExi8/Wl6XE+6B2KFZrZGo2ROgX/pv4ny+9hyvGYBaOBP6yWrL7dfBSl43DmK/A7KmqZWCLr+rIq+9/gDZpQ9E0x2tXPgFf9Y8OdA9AjXf72LmD14+/RAwG0z7HeDKqh7uRUtLxU7A7udrsefL+P1BgLrzRx3CGAW1F9yqx8wfPL/7uvV+rEtfyKNOLHjS1FnBkdMhG+jUgIcE+J7nIfyAuWYDPWAFZTquE2AD3XWkNEgC/iLABrq/6uF2NOrlZ6FnjU+abdESxugZrrixe0Gut2yG6lmW0bc48UzIC637ttecpFd/AjW6T522zAa0mjAEWPNVxThzWfQxt0PsukfGGPI1QEXnQD+yPNV8qz8Aqz+1vC7HzITYrfZYaz7RIc65CPJfmfnmmpv+5UeowVcCWzPvAy+7Xwdx2DFZu4wvj28+qVJ5pNOCXd1l7ZwDSaAOAjV1t+2+WdCPrah4azn+n5Cde5AfCbhGgO9zrqGkIRsEqDsbsDjUVQLUnqs4fWUsZm7z9MFbyWv7f5wEeXnd93D5SoA6yxdZ2q2LAHVHfXhFgNrzinzw/LKBHryaMiN3CbCB7i5PWiMB3xFgA913JXE1IPXso9D3TEva3OePMIZMdMWH3QtyrTVU93Mrntis45DXjoQ48NA6x+g1X0MNu6r2MU12gDFpPvRPa6HunQmsXQNxantPnz43g1UrFkI/uCg17j//FVj1nmUZdzl0MsTerWvNMXbLYOCjd5JfSHXqBtn2LFdqm8mIWnwX9GMPZBoG0akrZNt2lnH61eeh7l8ANNwe8qKrIFolc4yZ+9q/9oJlvDG7oklZddjVXcYgOYAEsiBQXXfffvE5tvXqBGxLbmUQXzVjp+ZZWOIQEshMgO9zmRlxhPsEqDv3mdJidgSovew4FeOo2PhBwMfvJe9XLrgS8qR/eZIKdeYJ9tA7pe5CLwHPAFB7nqEPnGM20ANXUibkMgE20F0GSnMk4DcCbKD7rSLuxqMeWwG9+M6k0T//FUb/G11x4uSCPNbnQmDD+tr9m03VSfMhSurVGWPG5eBb7w9jULUn713JOHcj6t9LoZfek2rooMOBVe8Dv21InJP9b4L4819qdRob09eyRL24tBfkMafkHmQWFvS6X6Cu7wr8vqXO0eL08yDPuyQxRq//FWrQFcl5e+8HY+ikxPnY8J7AN6stNms2Jp3oLouUOIQE6iRgaaC/8Qq2DbGuklHI/38sVfAJ8H0u+DX2Y4bUnR+rEo6YqL3g1jl284CKLW8qD9H5asjjT/ckYerME+yhd0rdhV4CngGg9jxDHzjHbKAHrqRMyGUCbKC7DJTmSMBvBNhA91tF3I1HPVQObT7tW3UcfASMnkNdceLkgjw2uFv8afDaDnH4cZDdBmSMT/+2Aar3BbXbOfIEyCv6ZbRT6AHqyYegF96R4lb8/Vho88uln9YmzslrhgF/+DP04w+ai0RDnHSmZT/w2NCrgO++To63uVx6rrmrZePJ+5cAACAASURBVPdAP7y0TjPiqBMhu/SNj9FffAI1b1rqUvW3RSvOv/wM9NypqWyu6Ad55AnJPLkHeq6l43wHBKq/333z9KOIjb/eYkWcfBbk+d0cWOYUEkgl4OTzlRxJIFcC1F2uBDnfKQFqzyk5/8/z8ge/NelQZ/7XSxAjpO6CWNXiyInaK446FUOUbKAXQ5UYo5cE2ED3kj59k0ABCLCBXgDIHrpQS++B/neyySlcbCw7uSCv+SVKTTSiRrO0NnR62zaoqzrUSlac1RGy/YUekk/vWj33OPTcKSknxT9Ohv5ileXpa9G1f8Uey599VDF+vzaQA2+GEBUfzbEBlwK//JSwJXuPgPjL3wqWs96wDmpQV2DLptp97n8QjH5joF95FuqO9CsCmHu9mz/yMMekO8QJp0NedHUyTzbQC1ZjOkoSsDTQV5QjVlPPrVrDGDaZyEjAFQJOPl9dcUwjoSZA3YW6/J4mT+15ij+vzmMjewFffZ7wIbr0gTzqpLz6rM04deYJ9tA7pe5CLwHPAFB7nqEPnGM20ANXUibkMgE20F0GSnMk4DcCbKD7rSLuxqMWzoR+cmXyS4vjT4fsnGxG5uLNyQV5bOJQ4IO3anUrpyyE2L5xVmHFup5d6zhxWW/Io9tmZaeQg9TLz0LPSm0kixPPgF79qXWJw/YXQj9wryU8OXQSxN77xV+LXdMR2JxsXsvrxkLs16aQ6UDdMw362Udr99miJYzRMxC7qX/yhwA1RovzLkm/rH3VuD1awRg5PTHLie4KCoXOAkmguu6+nn8H1KLZ1jylhJxyH0SDhoHMn0kVlgDf5wrLm94qCFB3VIJXBKg9r8jn32/shh7At18m70W7DYA8/Lj8O07jgTrzBHvonVJ3oZeAZwCoPc/QB84xG+iBKykTcpkAG+guA6W5VAKRSORUIcRJWmvz0cldADQBYG6S/IMQ4jUAT5SXlz9GdvkhwAZ6frj6xaq6ezL0C08mv7Q47VzIyGWuhOfkgjx2+83Aay+k929zefm6GuhywM0QfzrQlTzdNKLfehlq2ugUk+K0c6C//Ax4/81krY49NaU5XbWnuNYa6spzAK0T4+XwKRAt93Uz3Iy29Kr3ocZdV/s4c0/7ifOgri6tY0wjYNPGOn3JqYsgGm4fH+NEdxkT4QASyEDA0kC/bSzUyiUpM2Tf0RAHHEyWJJAzAb7P5YyQBhwQoO4cQOMUVwhQe65g9KWR2JArge+/Td6vXDUI4m9HexIrdeYJ9tA7pe5CLwHPAFB7nqEPnGM20ANXUibkMgE20F0GSnNJAmVlZedorc1HMVtnwWWV1nrgkiVLHshiLIfYIMAGug1YRTi0ZsNatDsf8uzzXcnEyQW5mjsV+rk0v4f5Y5v43udix52zjq3OBvotc2zZytppjgP1h29DTRiSYsVccl5//QXw+ovJc20OBd573Tq2eQvIG+8Aft8C1bPMck7eNAuieYscI7Q3Pd7Iv+FqYE1yL/aaFmSfUVCTb7BnuMbo6j+IcKK7nJxzMgnU+OHGVzdeB/3846n/jzteAXlK7StjECQJZEuA73PZkuI4NwlQd27SpC07BKg9O7SKa2xs0BXAj98ngpY9h0IcfIQnSVBnnmAPvVPqLvQS8AwAtecZ+sA5ZgM9cCVlQi4TYAPdZaA0V0EgEolMBHBtJY9sdaaFEJPLy8v7k6N7BNhAd4+lHy3FzMble28kQhORyyFPO8eVUJ1ckKvyu6AfrfE7mOYtYNw0y3ZMsdtuAt6o1nCuZkHecT+ElLZt5nuC/uwjKHM58xqH6HBxfHnD6qsFYPe9LEseVk2RQycDO+4E1f8SixU5aT5Ekx3ynUKKff3RO1CTbgBi24C99gXMJ+mrHy1aAmu+yiku0akrZNt2cRtOdJeTc04mgZoN9Ou6Qb9jLpBjPcQp7SE7diEvEsiZAN/nckZIAw4IUHcOoHGKKwSoPVcw+tJIbMClwC8/JWKTvYdD/OUwT2KlzjzBHnqn1F3oJeAZAGrPM/SBc8wGeuBKyoRcJpBtY9NltzQXZAKlpaUDhRBjAZhrD5sa2wrg31rr56SUn2utNwohGiml9hFCmOt7nQGgXiUTrbW+fsmSJambCAcZWh5zYwM9j3B9YDo2diDwyQeJSETnqyGPP92VyJxckKsHF0GvWGj1v/d+MIZOsh2TXrsmvgc3PnzbOrf1/jAG+fMtQn+9GmpEz5RcRccrgO+/sexXD3Mv5Wp7nFdNEmeUQhxzCtTQ7hY7csYSiHrb2eboxgS9YR2w9jtgn/2g+lwEbDR34XDvEEe3hbysd9ygE925FwkthZVAdd192T2S+kMRE8zf/gHjquvDioh5u0iA73MuwqSprAlQd1mj4kCXCVB7LgP1kblY387A+l8TEXm53Q115iNhhCgU6i5ExfZZqtSezwpSxOGwgV7ExWPoBSHABnpBMIfHSYcOHVoahvERgPqVzfMHDMPosWjRom9qo9ChQ4fdDcOYDuDcyjGbYrHYn5ctW5bbI43hwV5npmygB1sIsRHXAObS4JWHuKIf5JEnuJK0kwty9fiD0ItmW/0fcDCMvqn7gtsJUt07E/qplUDjppDdB0H8+S92phdsbLzpP7hbij/RuQfwwxroh5dmjmXX3SG7DYQa0yc51jAgb18GIbz/2FaLZkM//mDmPOyMaLkvjOFT4jOc6M6OK44lgXQELA30808B1v2SOszhj4FInARqEuD7HDXhBQHqzgvq9Mlru2BrINb7fOC3jYkk5YCbIP7kzX0a3+OCrTW/Zkfd+bUywY+L2gt+jQuVIRvohSJNP8VKwPtv4ouVHONOS6CsrGyE1trcDNdcjn1BeXm5dQ3iOriVlpbOFUJcbM7VWo9asmTJSGLOnQAb6Lkz9LOFfO475+SCXD3/BPScW63IDjsaRvdBOWPUv20A6m3n2VPY2SSg1/0C1c98G7Meokvf+P6A+v4F2ZiB6NQNelG1Ze+3bwRjyn1Zzc33IK1i0P9ZDv1QFNiyKTXXsi7Q5XfZC8P8gcC0coh69dhAt0eOo10iUPV+p2Pb8FX7f5hXIqmWGzeBMflelzzSTJgJOPl8DTMv5u4OAerOHY60Yp8AtWefWbHMiPUsA7ZsToQrB42HaL2/J+FTZ55gD71T6i70EvAMALXnGfrAOWYDPXAlZUIuE2AD3WWgYTcXiUReAHAkAHN93z2j0eiGbJlEIpHGAMwn1RsBeCkajf4j27kcVzsBNtCDrY5YnwuBDcnltGX/GyH+/FdXknZyQa7f+B/UbTda/IvjToO8OHVZc1eC9JkRvWUzlPlFUo1DXnU99M8/pD6dX1v8Nfca32kXGOPu9lW2+pefoO+fD/38E8lmY8t9IK8cCDXs6pRYzdURsOZr6K8+hzjkCOi5Uy1jzL3fxd6t2UD3VZXDE0zV+13spx/wTefat8GQ08sh6jcIDxhmmhcCTj5f8xIIjYaKAHUXqnL7Kllqz1flcDWY2FXnAdvMHfsqDjlkIsQ+f3TVR7bGqLNsSXGcmwSoOzdp0pYdAtSeHVocWxcBNtCpDxKomwAb6FSIqwQikcgaAM0BPBiNRs+xazwSiSwH0B7A2mg0upvd+RyfSoAN9GCrIp9fWji5INcfvgM1YbAFujjtXMjIZcEuRGV2WimoK1Pf+mTvEdC//pTSNM4ayu57wRh1W9bDCzlQf7EK+rnH41+eidM7ADvuDNUjYg1h510hb7oDQhqJ12PmUvdrzY+MikNc3BPS/LGFlGjRokX8tTVr1kApVch06CukBKp09/snH+C7XhfVSkGOnA6xR6uQUmLabhHg+5xbJGnHDgHqzg4tjnWTALXnJk1/2Yp1OwfQyWt1ecMUCPOHwB4c1JkH0OmS967UgGcE+J7nGfrAOWYDPXAlZUIuE2AD3WWgYTcXiUS2ACgRQswuLy/vbpdHJBK5A0BXAFuj0ai5jzqPHAmwgZ4jQB9P19u2QV3VwRKhHD0DokVLV6J2ckGuv/wMalRvi3/R/gLIszq5ElMxGIn1KAV+/91alwE3Aet/hZo5zlkK+/4JxuAJzuZ6MCtmLmNfbQ9pUXoZ5D/PtUQSmzkWePX5xGvipDMhL+jOLyE8qBddIqG7TS8/ix9GXFs7kkOOiq+yIEpKiI0EHBNw8vnq2BknkkAlAeqOUvCKALXnFfn8+k33w2Evf2hIneW33rSengB1R2V4RYDa84p88PyygR68mjIjdwmwge4uz9Bbc+EJ9PsBnM0n0N2TEhvo7rH0myW9cQPUtRdYwpLj50A029mVUJ1ckOsfv4cadIXFvzi3M+SZNZ5IdiVCfxqJ9bkI2LDOWpchE+NL7aspI5wFfcDBMPqOdjbXg1lq2T3QDy+t8Lzr7hXLOW5v7tKRPNRD5dY94VvvD2PQeDbQPagXXSYb6OuWzsOvd1u3F0jh0+ZQyF43QBjJFRXIkATsEHDy+WrHPseSQDoC1B114RUBas8r8vn1q7duhbr6POs9z5iZELvtkV/HtVinzjzBHnqn1F3oJeAZAGrPM/SBc8wGeuBKyoRcJsAGustAw24ulz3QO3fu3Gjz5s3mHuhml+XlaDR6VNh5upE/G+huUPSnDf3jWqhBXaxfWkxdBNFwe1cCdnJBrn/bCNX7fIt/0eFiyDNKXYmpGIzEBl4O/PyDtS4jpwO/bYAaN6j2FBo3TWm8JwYfchSMHtal8f3MQsdi0P97Ov7Uvfj7sRA7mzt7WA/99qtQU0cmX6zfAHLqfTBK6nEJdz8XN6CxVb3frR3ZB5tfeiZjluKU9pAdre+/GSdxAAlUEnDy+Up4JJArAeouV4Kc75QAteeUnL/n6c2boK7paL3nuXk2xC7e7MRHnflbL0GNjroLamX9nxe15/8aFUuEbKAXS6UYp1cE2ED3inxA/UYiEbMbMgyABjAnGo1aH0WtI+/S0tJZQghzvDl3TDQaHR5QTAVNiw30guIuqDP99WqoET2tX1rcsdyyz3QuATm5IE+3lF/oGujXdQF+WpvyZRI2/wY10rq8ffVB4rRzoB8xF+FIPcRRJ0J26ZtLOX03V6/7GarfJVZOo2fA2KMVG+i+q1bwAzLf73bbdVd8c/4pUNVXkNihGfDrz+n/X17eB/IfJwUfDjN0nYCTz1fXg6DB0BGg7kJXct8kTO35phSuBpLv1dDsBkud2SXG8W4QoO7coEgbTghQe06ocU46AmygUxckUDcBNtCpEFcJRCKRVgA+ABDfv1wIUb5169bey5cv/742R+eff/4usVjsVq21+diqqcnNhmH8edGiRV+6GlxIjbGBHtzC608/hLp5QDLB7erDuC3qWsJOL8hjXc1dGJKH7H8jxJ//6lpcfjcUM5ew/9H6licnzgO2bIYa3K3W8OW1I6HmTQN+sj69Hn8vPfEMyAuv8nvqtuOL9b8U+PWnxDzRtT9KjjqRDXTbJDkhVwLm+91Omzfgux6dLKZEp27Qi2alN19SD/K6sRD7/DFX95wfMgJOP19DhonpukyAunMZKM1lTYDayxpVUQ3U636B6nex9b5v4jyIpjt6kgd15gn20Dul7kIvAc8AUHueoQ+cYzbQA1dSJuQyATbQXQZKc0AkEhkCwNys13yS3Dx+B7BSa/28EOILrfVGIUQjAGaz/WgAZ1Y23E09aiHEsPLy8pvI0h0CbKC7w9GPVvR7b0BNviEZWpMdYEya71qoTi/I1f0LoB8qr4ijVWvIoZPMH9O4FpffDaVtoE9bBGzdBtX3olrDl4MnQr/0X+jHHkgZI/7ZAbL0Ur+nbju+2NRRwNuvJOaZeZaUXc4Gum2SnOCUgLkSAj5fBbnPH9Hkk/fw84yxSVM7NYe8eRZ0dC70a8+l/XELmu0COXQiRNNmTkPgvBAScPr5GkJUTNlFAtSdizBpyhYBas8WrqIZrH/+EWrgZZZ45a0LIRqZO/IV/qDOCs+cHgHqjirwigC15xX54PllAz14NWVG7hIIT0fDXW60loFAaWnpdCHE1ZVN9HhjvI4pCR0KIaaXl5f3ImD3CLCB7h5Lv1nSr70AdfvNybCat4BxUy1PSjoI3ukFudY63hTVG9ZBHHYsRP34ghShOdI20GcuB5SCuvq8WjnIMTPje4arcdeljJHdr4M47JjAMVTLF0CvrPyxhZldm0NQr98YNtADV2l/JqS/+6ZiFY+N64GGjVBv9z2x9dOPEsGKI0+AvKKfJXi18A7oJx+yvCbatoPs1NWfSTIqXxJw+vnqy2QYVNEQoO6KplSBC5TaC1xJ4wnpH76Dut56/SOnLYZo0NCThKkzT7CH3il1F3oJeAaA2vMMfeAcs4EeuJIyIZcJsIHuMlCaSxIoKysr01qb3b19s+DymdZ60JIlS9xbfzoLp2EYwgZ6cKusnnsceu6UZIKtWsMYNtm1hHlB7gxlzPwi6YfvLJON2Stg/rBAXXUeENuW1nB8mffGTSuWea+2BLw4ui3Epb0C+RS/fvV5qJnVnvht3BQlt96L3XffPc5ozZo1UEo5KwRnkUAGAio6B/qR5bWOEhddDXnC6Zbzetu2ipU/Pnon+XqLPWGMvp28SSBrAvx8zRoVB7pIgLpzESZN2SJA7dnCVTSD4z9EHNrdEq+csRSiXj1PcqDOPMEeeqfUXegl4BkAas8z9IFzzAZ64ErKhFwmwAa6y0BpLoWAKCsrOw3AyVrrQwE0B2Cu6bUBwFohxOsAnigvL38kw1PqROuQABvoDsEVwTT12APQi+9KRrr/QTD6jXEtcl6QO0MZMxvga9dYJpsNdPOIDbsKWPN1WsNVXzjpd1+HunMCsHEjxCntIEovhZCGs2B8PkuvXZOyL3zJLXOxe5u/xCNnA93nBSzy8GJdz64zAzlyOsQe5m4z1kO//QqUuf1A1dFsFxjj7y5yGgy/kAT4+VpI2vRVRYC6oxa8IkDteUU+v371N6uhhve0OJF33A8hZX4d12KdOvMEe+idUnehl4BnAKg9z9AHzjEb6IErKRNymQAb6C4DpTkS8BsBNtD9VhH34lEr7oN+8L6kwUOPgnH1YNcc8ILcGcq6GujqjvHQrzybani77WDctiTxut62Nb7ku9gu2Mvfx5/K730BsGljInfjmmHY4/T28b/ZQHemQc7KjkCdDfTGTSAnLUi78oP+8G2oCUOSTho3gTH53uycchQJgPtlUgTeEOB1nTfc6ZXveUHVgP7yM6hRvZPpCQlj1v2epcv3OM/Qh9oxdRfq8nuaPLXnKf5AOWcDPVDlZDJ5IMAGeh6g0iQJ+IkAG+h+qoa7sajFd0I/VvFks3mIY9pCXlrtS4wc3fGC3BnA2JArge+/tUyuegJdrYxCL5+faniHZjAm3OPMYZHPit0y2LIctmx/Ifbs1ieeFRvoRV5cn4dfZwP9kCNh9KjWJK+Wi/70w4q906uO7erDuI070Pi83L4Kj5+vvipHaIKh7kJTat8lSu35riSuBKQ//xjqxn5JWyX1YNy+1BXbToxQZ06ocU6uBKi7XAlyvlMC1J5TcpxXkwAb6NQECdRNgA10KoQEAk6ADfTgFljNnQL93OOJBMUp7SE7dnEtYV6QO0NZVwM9ZennKhctWsIYPcOZwyKflfJDkEOPQssx0+NZsYFe5MX1cfhaKagrz6k1QhG5DPK0c9OeT33iSiC+ZKngZbWPS+6r0Pj56qtyhCYY6i40pfZdotSe70riSkD6kw+gxg5M2qrfEMb0xa7YdmKEOnNCjXNyJUDd5UqQ850SoPackuO8mgTYQKcmSKBuAvymjwohgYATYAM9uAWOzbgJeP3FRIKi3fmQZ5/vWsK8IHeGMjakO/D9N5bJVU+g659/hBp4WarhPx0IY8DNzhwW+Sz1/BPQc25NZrHzrthr3sr432ygF3lxfRy+XvcLVL+La41QDp4Ase+f0p7Xa76GGnaV5ZycsRSiXj0fZ8zQ/ESAn69+qkZ4YqHuwlPrQmeqf/we+vUXIVq1hvjTgSnuqb1CV6Qw/vRH70CZK0lVHds3hjFlYWGcp/FCnXmGPtSOqbtQl9/T5Kk9T/EHyjkb6IEqJ5PJAwE20PMANegmI5FIq+o5RqPR1VV/1zyXC4vqdnOxE/a5bKAHVwGxiUOBD95KJCg6XgF5ytmuJcwLcmco1YqF0A8uSk6utj9yfM/vbhX7e1c/RORyyNNqfxrWWSTFMUt/9TnUyF6WYPdY9DiMJjuwgV4cJSzKKPXqT6BGV2wVkHJsVx9yyn0QJSVpT+uf1kJdZ13tIz5++0ZFyYJBF54AP18Lz5weuQ81NZAfAvEfh5o/KtuyOe5AXjUI4m9HW5zxPS8/7L22qt9/E2rSsGQYTXaAMSnNVlUFCpQ6KxBouuH7GzXgCwJ8z/NFGQIRBBvogSgjk8gjATbQ8wg3qKYjkYgCoCvz09FoNPENc41zuSCw2M3FUNjnsoEeXAXEzObP6k8SCYpLe0Me09a1hHlB7gyl3rgeatjVwPpfASEgr7wO4rDkF4k1f/iA+g0gJy+AqLedM4dFPktv2wZ1TUdg29ZEJs1vuh0NDj6cDfQir62fw9dvvQw1bXTaEMUBB0P2TX/OnKDXr4Pqe5H1y7MJ90Ds0MzPKTM2HxHg56uPihGiUKi7EBW7gKmqu2+FfuGJpMdDjoLRo9pTyWZTXUq0aNEiPoarCxWwOHl2pd95FWrKyKSXHXeGccucPHut3Tx15hn6UDum7kJdfk+Tp/Y8xR8o52ygB6qcTCYPBNhAzwPUoJus1iQ39WM2uo2qnGucywWFxW4uhsI+lw304CogNrgbsHZNIkF59WCIQ49yLWFekDtHaS4Prd9+FWKPvVKWgVbPPgp9z7QK42aDvedQiIMOd+4sADNjY/oCX6xKZLJDl2vRtMNF/JI1ALX1awrqv/+Gnj8jbXjy7Asg2nWqNXS9ZTNUzzLLeXnTLIjmFc0BHiSQiQA/XzMR4vl8EKDu8kGVNmNdU1e/qtq6qIoOtRdMneg3X4KaPiaZ3M67whh7p2fJUmeeoQ+1Y+ou1OX3NHlqz1P8gXLOBnqgyslk8kCADfQ8QA26yUgk8nm1J9ARjUb3rcq55rlcWFS3m4udsM9lAz24Coj1uRDYsD6RoOw3BmL/g1xLmBfkrqG0GNIqBv3so8CqDyD+9g+IQ47Mj6MisqrmTYd+5pFExNufeAZ2HjCaDfQiqmGxhaoeWAj9f9W2WqiWgNH/RuDPf601Ja0U1JXWLRfkyOkQe1h2uCk2JIy3gAT4+VpA2HSVvE7kU8BUg8sE0n0emi7YQHcZtE/N6deeh7p9bDK6XfeAceNMz6LlZ6tn6EPtmLoLdfk9TZ7a8xR/oJyzgR6ocjKZPBBgAz0PUGmSBPxEgA10P1XDvVjie2l3PxdQ5o4KFYccNhmiVWvXnPCC3DWUNJSBgHpqJfS9yS/cZNMd0eDwY7Bp0yZAV+0YQowk4B4B/cKTtRormbEEOsOWCrGrOgDbtiXff4dOgth7v6wC1F9/AZTUg9htj6zGc1DwCPDzNXg1LYaMqLtiqFJxxWh+nqkR11iDrrcdjBlLLK9Re8VV12yjVS8/Az3rluTw3feCMeq2bKe7Po46cx0pDWZBgLrLAhKH5IUAtZcXrKE0ygZ6KMvOpG0QYAPdBiwOJYFiJMAGejFWLXPMhVhCmBfkmevAEe4Q0J98ADV2oDvGaIUEciDQ4IjjELvyOqhqP05KZy7WqxOw6bfEKTlwLMQf22T0rBbNhn78wfg4cc5FkP+yLgWf0QAHBIIAP18DUcaiS4K6K7qSFTRg88e5eO156I/fA3bcqeJHua3+ANG4aa1xpN0OpXFTGJMXWOZQewUtZcGcqRefhL5rctJfy31hDJ9SMP81HVFnnqEPtWPqLtTl9zR5as9T/IFyzgZ6oMrJZPJAgA30PEClSRLwEwE20P1UDfdi0T//CDXwMuuXU7feC9GoiWtOeEHuGkoaykBAb9kC1aujZUUFQiOBQhNo+I+TsGO3fvhBmVJMru6RLo5Yv4uBdb8kTsk+IyHaHFpnyPqH76Cu75ocU1ICOWkBRMPtC50q/XlMgJ+vHhcgpO6pu5AWPsu01cvPQs8anzp6l90gjm4Lcdq5EPXrW86ruyZBv/iUdU5JPRi3L7Xeo3D7gCyrUFzD1HOPQc+dmgx67/1gDJ3kWRJ8j/MMfagdU3ehLr+nyVN7nuIPlHM20ANVTiaTBwJsoOcBKk2SgJ8IsIHup2q4F4v+ZjXU8J7WL6dmLocwDNec8ILcNZQ0lAWBtF/CZjGPQ0jADQIlI6Zi98OPjptas2ZN5gb6oCuAH79PuJY9hkAccmSdoahnHoGeN936vn3NMIiDDredgt64Hvo/ywANiNPOgWiyg20bnOAdAX6+esc+zJ6puzBXP3PusamjgLdfqX1giz0hB46DaFLxRLrevAmq/6XAlk0pc+TtyyBKSpKfkWygZy5AEY5IWYGg9f4wBqX5EUaBcuN7XIFA0431Wp7vb1SERwT4nucR+AC6ZQM9gEVlSq4SYAPdVZw0RgL+I8AGuv9q4kZEetX7UOOuS5qq3xDG9MVumOaXXa5SpLFsCehtW6GffwJi7bdo1KhRfNrGjRsRX1KUBwm4TeDnn6DNRsHmTRBt26Hk/K5o0aJF3EtWDfQbegDffpmISnQbAHn4cXVGme5HImbzW0Yuh96wDti6FaLZzlllGrtlMPDROxVj92gFOXQyRL16Wc3lIO8J8Asv72sQxgiouzBWPXPO+p1XoR4qB1a9n3GwOPIEyCv6xcelPH1cbbassSoWtZcRbVEOUE8+BL3wjmTsfzoQxoCbPcuFOvMMfagdU3ehLr+nyVN7nuIPlHM20ANVTiaTBwJsoOcBKk2SgJ8IsIHup2q4F4t++1WoqSOTBpvtAmP83e45AMALcldx0liWBKi7LEFxWM4E4j/O+H0LRP0Gtt/vYqP7AKs/ScQgLusNVJU9PQAAIABJREFUeXTbWmMyfamBlwO//Ggd06p1/Any+BKo27ZCnNoesqxLnbnp3zZC9T7fMkZ06gbZ9qycmdBAYQjwfa4wnOnFSoC6oyJqEtC/bYAa1BXYtDEVjvkE+bZt1tdL6kFOmBvfMipm/pC3lqa7HHsnxM67JuZSe8HUnnrsAejFdyWTO+BgGH1He5YsdeYZ+lA7pu5CXX5Pk6f2PMUfKOdsoAeqnEwmDwTYQM8D1KCbjEQinxYgRx2NRlsXwE/gXbCBHswSq/89DX3nxGRye7SCMdK6NHCumfOCPFeCnO+EAHXnhBrn5ErAru5qNg7EhVdBnnhGrWHo/2fvPKCkqLI+/n+vBhCRJIK4qIhxTWtWFnNCRVfS1IgEAVGy5BwkiuScRJEkqXsEUXSVNa1+mFizomJAESQqCEiceu871TPT1T3dM9PdVdVVXXXrHM+R7vdu+N3/VFf3rXpv528QQzrGvs8YUO6E0JPwhQcfMw+sev7T8PEOufd3iH5to9+qWBmheSeUN4uC5qeBQLJ6S0NI5MIHBEh3PihykinKLzZAzIjT8KxQEXziIuDrTyBmjo6yyq65EexfzSD0lViKOfjwGWC1ahufa7TEcZKVyYzh4rVVkLkLjWAvuRJK9+GOBU/nOMfQ+9ox6c7X5Xc0edKeo/g95Zwa6J4qJyVjAwFqoNsA1esmVVUV+rZnNuap61JvoFu3mXNEsI0bN66hKMq1AK5ljOkbj+r/hdZMlVIuys3NbWN1bjk5OQ9KKfVfu/8BoAqAnYyxd6WUs4LB4PtW+4u0Rw10O+k6Z1u8/Qrk0rlGAOdeCKX/OEsDogtyS3GSsQQJkO4SBEXDLCWQrO60yUOBbz4Px8By2oHf2bDYmMT6NyAXTkso5tJsyV2/QQyObcazRi3B781JyAcNcpZAsnpzNlry7hUCpDuvVNK6PMQbayFXzIs1GNEIFfMmQG54NymnfMB4sHP+Hp5D2ksKX8YM1pf+ly88Z8R72bVQug5xLH7SmWPofe2YdOfr8juaPGnPUfyeck4NdE+Vk5KxgQA10G2A6nWTBQ10u9O0rYGuqmqxzX+rG+iqquqPguUCaFAMMCGlHJmbmxuxFre1aKmBbi1Pt1gT/86FXLXYCOfSq6F0e9zS8OiC3FKcZCxBAqS7BEHRMEsJJKs7TX8i7/OPwjGU1rwWa1dArlmWUMzsvgfAG7Yodqzc+jPEiG6x75evAP7kvNDSunS4m0CyenN3NhRdphAg3WVKpdIXpwjMh/zPmhiHrIEK3rhV6HW58VOIKcOSCor3GAF28RXhOaS9pPBlzGDx4nLIl5Yb8V75TyidBjoWP+nMMfS+dky683X5HU2etOcofk85pwa6p8pJydhAgBroNkD1uskmTZoY67HZmOyqVat+scN8kQb6FgDfAqgf+oHA4ifQc3JylkspmxXk8ZaUcpqiKL9JKS+VUg4CULhMfYdgMBjn9n/zBKiBbp6hGy2I5xdBvvp8ODR27c3gj/a2NFS6ILcUJxlLkADpLkFQNMxSAsnqTswdB/nxeuMcfG8OeKOWxcYklj0F+dbLCcXM6t4K3q5nsWPl5k0QY/rEfZ/d1QQ82/KFdBKKmwYlTiBZvSVumUYSgeIJkO5IHUUJaLPHAJ9+EAOGdx0Kdpm+SBsghYCYOixq1ZXSSPKOA8CuqhceRtorjVhmvi9WPwf5SsC4FrrmRvD2fR1LhnTmGHpfOybd+br8jiZP2nMUv6ecUwPdU+WkZGwgQA10G6CSSXcTUFV1hJRyQ5kyZTYsX758Z7Nmzc7SNG1z6AcCCxvoqqreBuCNAhovAWgcDAa1QjoPPvjgKXl5eR8DOBPAvjJlypy9bNmyvVbTowa61USdtyelhHiyL7B5k/GDxS0NwFvE2V/XRLh0QW4CHk1NmQDpLmV0NNEEgWR1J56dAvn+W8Y5uH4jcPXhYiMo2nAvMdQ650MZNDE8RB76K9/XSRWh7z2LHzZCTNDvwYtzMA4+bj5Y1dDONHS4lECyenNpGhRWhhEg3WVYwdIQrjaiO7A19DXYOE6uDj5yNli5csbnUF4exIjHgB3bYqM69yLg+DHglx+Mz8Q23cGvvz38b9JeGorpgAuRuxDytVVG3eveAt6ulwOR5LsknTmG3teOSXe+Lr+jyZP2HMXvKefUQPdUOSkZGwhQA90GqGQyswjY2EB/BcA9API0TauzatWqrUXJZGdnN2OMFa571i8YDE6wmh410K0m6rw9+cn7EHOejAqEPdIb/LqbLQ2OLsgtxUnGEiRAuksQFA2zlECyuhNLZkO+86rxo3EpNzFpesN701eJxVz+RPBpy8EYg/xzL8TYfsCenaG57I6GoWVxxbThJdri3YeDXXJlYv5oVNoJJKu3tAdIDj1JgHTnybKmnFTohtzuDwKHDxk2atYC7zwI7LQzYuzKH7/N/zwqcrC23SHfexP47kvjM7HZo+C3/yv8b9JeymVy9USxcj7k68YWAOz628HbdHcsZtKZY+h97Zh05+vyO5o8ac9R/J5yTg10T5WTkrGBADXQbYBKJjOLgB0N9Pvvv79iuXLl9gAoC+DVYDCoN9JjDlVV9fd3A6gE4P1gMGisdWcRRmqgWwTSJWakpuXvfbv9VyOiGqeBj5gFlpVlaZR0QW4pTjKWIAHSXYKgaJilBJLVnVj5DOTrLxrNglJ+NNaGdgZ2xNxHV2wOfMJC4MQKEBMHR602Ar253rYHhL7sbkmHkgU+aTFYhZMs5UTGrCGQrN6s8UpW/E6AdOd3BUTnL/86ANGjRdSLfMw8sOo144KSQoPo0DjmPT4zAPH0RODzj4zPxEYtwe/NCf+btOdN7RXdnobddBd4qy6OJUs6cwy9rx2T7nxdfkeTJ+05it9TzqmB7qlyUjI2EKAGug1QyWRmEbCjgV5k+faBwWBwbHFUVFV9rWAP9ryqVaueOG/evONWEqQGupU0nbcl1r8OuXB6VCCsfV9wfVlfiw+6ILcYKJlLiADpLiFMNMhiAsnqTqxaDPnvXKNZcO1N4I/G35dcH6R1bw4cOmhErT8d/tUnxWbBe4+GfOc1yA3vxoxhDzwCufIZ4/XKVYEjh4GjR6LG0lPoFovEQnPJ6s1C12TKxwRIdz4ufpzU5c/fQzzR23iHc/DZz4MpSrGgxIJpkO8V7lAGFDZMxdOTID/6r/GZeHdT8Katw/8m7XlTezGr8dzaALy5tVuKJUOOdJYMLRprFQHSnVUkyU6yBEh7yRKj8cURoAY6aYMIlEyAGuikENsJqKp6J2PsVimlvpboKQAqAjgAYA9jTP/1+M1AIPC67YEU48CmBnpXADN0l4yxxoFA4IXi8svOzp7GGOtW8P7FwWBwo5UsqIFuJU1nbcnjxyCGdAL+0BctKDjOPBt88GQwzi0Pji7ILUdKBhMgQLpLABINsZxAsroTa1dArllmxHF5XShd4u9LLvOOQ3RqGhUza9sDcsHU4vOoVgP4fVfc99mN9SHfXWe8d9Z54E0egpg8NGo879AP7OobLGdFBs0TSFZv5j2SBSJA+wOTBqIJyP/9H8RT440XTzkVypNPl4hJ/r4LYlRP4K8DQJVq4IMngVU5GaVta0LnPG+qTyycDrne+BmH3XE/+AOPOJYs6cwx9L52TLrzdfkdTZ605yh+TzmnBrqnyknJ2ECAGug2QCWT+QRycnIaSSn1b+XnJMDkByllv9zcXGMTrQQmWTHEjgZ6dnb2WMZYfz0+IcQ1zz///P+KizUnJ6ePlDK09zlj7O5AIKA/kZ7Q0aRJk9NLGxgIBDYoilJT0zTs3h3ReC1tooff1/6zBvJbY5++TElVfvZBTKhKzxHgl1xlSwr6BXmNGjVCtnft2qVr2RY/ZJQIRBIg3ZEenCCQrO60V1dBBJ8Nh6rvN57Vc2Tc0OXePcjr0ybqvazJS5A3sD1w9HDS6bKzL4D86TvD93kXI2vAOOSN6gn9icLCQ2nTDfzG+knbpwn2E0hWb/ZHRB78QIB054cqJ56jFlwA8erzxmfJ3/+BrL6lbA8CQB49AvnrZrDa54CV0XcjA7TAsxCvrTJs/fNWZD1iPN1O2ku8Lpk0Mu+ZSZDvvxUOmd/dBIr6sGMpkM4cQ+9rx6Q7X5ff0eRJe47i95Tz6tWrQ8lfgWgbY6zUPoOnkqdkiEACBKiBngAkGpI8AVVVJwHoUTAzUZ1JxtiUQCBQ/BqoyYdS6gw7Gug5OTmzpJSddedCiAuff/75b4sLRFXVTgBm6+8zxrIDgYDxS0Yp0auqKktLcM6cOahWrVppw3z1/u+Th+HQGy9nfM7lLr0K1Z+cq+sm43OhBIgAESACmUTgwNoA9s0xntwrd8mVqDFuXtwUjv34LXZ2a2m8xxWcvuZ97OzxEI7/WOzlQbE4WPkTIQ8fCr9/wpV1UX3UTOwa2BFHvzDu16vSvjcqNnwwk7BSrESACBABIpAGAtofe7D90caQ+vYfBcdJ9zdD1Q6pfQ3/c9nT2L/0qbCt8v+8BacMmZiGTMiFkwR+HzcIh94xVsSpmNMWVVo7twe6kyzINxEgAkSACBABDxCgBroHikgpWE+Aui7WM/W9xezs7H6MMX3Pb725q2tM39P7VSnles75z1LKvxhjFYQQZzHG6gG4B0CZAnBSSjkwNzc3Yj05e5Ha0UDPzs6ezxgL3X6tado5q1at+qm4LHJych6WUs7X32eMtQoEAs8lmjE10BMlFT3OKw30GpMWoNzfL00NAs0iAkSACBCBlAkcfO0F7J0+Ojy/7PkX4dQpi+PaO/y/97BnWOFOLQCvWg21nnsNe+eMw8G1wag5vGJliAN/Rr2m1DgN2q7txcZavu7NOGXoJOwe2QtHPnwnPK5Sq46o3My5pVRThksTiQARIAJEwFYCe+dNwsE1yw0fnKPmzOUoUzuRheNiQzuwein2PTMl/Ea5y65FjTGh+8Pp8DCBPWP64fD6N43rjuaPonKLDh7OmFIjAkSACBABIuBpAtRA93R5KblUCVADPVVyNC8uAX1JcUVRNgEoV9A8X6MoSpcVK1b8VhyyJk2anKYoykwAjQvGHNY07YJVq1ZtTQdmOxro6XoCnZZwT00hefMnQ75nfNlPzYqzs9jldZH12BBbg6AloWzFS8aLIUC6I2k4QSBZ3YkP3ob2dMTTdbVqo8zIWXFDF++9AW2+0VjAGXVQZvgMyB3bkDekEyDzt8dg510Mpc1jyJsyDNizM/Qav7MhUK0GxIri96Vl196ErA79kPf0RMgP3g7HwO/JhpIdvXS8E2zJZyyBZPVGDImAFQRId1ZQ9IaN4/3bhT9nQp8/N9ZHVhvjRq9ksxTvvAZt0YzwNFbnfGQNmWx8HtG2UMkizYjxeTNGQX72oVHnxq2g3PeAY7HTOc4x9L52TLrzdfkdTZ605yh+TzmnJdw9VU5KxgYC1EC3AaqfTebk5AyXUj6uP33OGHsuEAi0TpRHdnb2QsbYQ/pcKeXI3NzcEYnONTPOjgZ6uvZATyRvKaV+I0ItfQ/0nTvzf5D3+6F/0ZfbfsksDMePQX6/EdD3vT3rXPBOA8EqVbE1B/2CvGbNmiEfO3bsoD3QbaVNxgsJkO5IC04QSFZ38pP3IeY8aYRavSaUMfGXcNf3hZW5C42xF10OpWC/dPH6i5AvrQAqVwVv1yu0p6w8ehTy8w/BKlcFu+BSyM83QMwcVSwWdv3t4G26QyyZDfnOq+Fx7JYG4C06OoGTfJZCIFm9EVAiYAUB0p0VFDPfhjywH6JXxLYi+s1aQ6eCnXl2ysmJDf8HOS9iAbnTzoAScVMZaS9ltK6eqE0bAXz1sXHd0bQ1+N1NHYuZdOYYel87Jt35uvyOJk/acxS/p5yfeuqptAe6pypKyVhNgBroVhP1uT1VVd8HcB2AA3rTNhgMHkwUiaqqJwHQn1SvAOCjYDD4z0TnmhlnRwNdVdWuAEK34TPGGgcCgReKizE7O3saY6zwlv+Lg8HgRjP5FJ1LDXQraTpvSwoBxnlaAqEL8rRgJidFCJDuSBJOEEhWd/KrjyH0H44LjyonQ5kQ0SSPSEIEn4VcZ1wGsLq3hJrliR5y+1aIxzsXO5zdcg94i04QwQWQ61aHx7G6t4K365moGxqXRgLJ6i2NoZErDxMg3bmruFJKfa8vsKystAYmv/oEYtpww2eZsuAzVoIpSspxyC8/hpge8ZlY9RQo458N2yPtpYzW1RO1yUOBbz43rjty2uWvnOPQQTpzCLzP3ZLufC4AB9Mn7TkI32OuqYHusYJSOpYToAa65Uj9bVBV1R0AqgN4KRgMNkqWhqqq+i+/+reu3cFg8NRk56cy3qYG+m0A3iiIZ2AwGNT3hI97qKr6GoD6APKqVq164rx58/Q94y07qIFuGUrfGaILct+V3BUJk+5cUQbfBZGs7uR3X0FMHGRwqlARytSlcbmJ+VMgP3gr/B6r3whcfThhxvL4cYgu2fr6PHHnsDsbgue0g3hpBeSLy4wxl9eF0iUixoQ90kC7CSSrN7vjscu+3L8X8v23gN93h5aIZmfUscsV2U2AgF90lwAKx4eID/8LuWSWfqc12IMdwOvpXx3Tc4i1KyHXRHxenfN3KAMinh5PIQx9lSwxfoAxs3wFKNONPdZJeylAzYAp2oRBwKavjOubB9uD33afY5GTzhxD72vHpDtfl9/R5El7juL3lHNqoHuqnJSMDQSogW4DVD+bVFX1KIAsxtjTgUAg6XVDVVV9CsCjAI4Hg0F9H3XbDzsa6Pfff3/FcuXK7QFQFsCrwWDwnniJqKqqv78bQCUA7weDwXpWJ0wNdKuJ+sceXZD7p9ZuypR056Zq+CeWZHUnN2+CGNPHAFS2LJRZuXGBxSxx2qQ1+D3JLXGq9XsY2KtfVsQerEEOeOOWEP9ZAxmYbwy48DIovYpf+t0/1XVfpsnqzX0ZlB6R+OgdyIXTgePH8gdnZYGPXwBWsXLpk2mELQT8oDtbwFlsVGoaRJ/WwMH9+ZZPKA8+YSHYCeUt9hTfnDbrCSBi32p2+7/Am+lfv1M/5K+bIUZ2NwwwDj53VXjVLNJe6mzdPFMb2w/48dtwiKxlZ/Cb73YsZNKZY+h97Zh05+vyO5o8ac9R/J5yTg10T5WTkrGBADXQbYDqZ5MWPIGur3F6f6Y/ga5rQFXVVwDojfM8TdPqrFq1St+LPOrIzs5uxhgrvD2/XzAYnGC1fqiBbjVR/9ijC3L/1NpNmZLu3FQN/8SSrO7k1p8hRhTuvpLPic9bo2/bEgMt5gmt5h3Ab703KbjauP7AD9/EncMatQS/Nwfi3XWQi2caY+qcD2XQxKT80OD0EEhWb+mJyjovMi8vv0H4l76jk3Gwtt3B691unSOylBQBr+suKRgODtZXZhC9W0dFwDv2B7vq+rREpfVtA+z7I+yLPdwT/J+3mvIt/9yb/zcfcYRumKlaLf/zkXPUrFkz9P87duyAEMKUP5rsDgLaE72Bn783tNT6MfAb7nQsONKZY+h97Zh05+vyO5o8ac9R/J5yTg10T5WTkrGBADXQbYDqZ5Nm9kBv1apVhSNHjuh7oOt7oW8IBoN108EylSfQVVVtA2CBHh9jbEQgEIjYSC4/alVVI5dxfxFAk2AwqBXm9OCDD56Sl5f3MYAzAewrU6bM2cuWLdtrdc7UQLeaqH/s0QW5f2rtpkxJd26qhn9iSVZ3cudvEEOiF9rhs3PByugLy0Qfmv6k+uZNpn5gFs9Mgvzwv3ELwtS24PUbQ2x4F3JexH14p50BZeQs/xQxgzJNVm8ZlFooVLl7B8Sg9jFhM/Vh8PpJ7/CUaem7Nl6v68614IsEJvfshBgY/cQ3u68ZeMPmtqcg9/0O0bdtlB8+cjbYaaeb8i2FgOiiAnnGTmS8/ziwcy8M2SXtmcLr6GR55HDo+kN++T9g/z7gpErg/2oGVud8aPqqA79uNq5vLLgZw0yypDMz9GhuqgRId6mSo3lmCZD2zBKk+YUEqIFOWiACJROgBjopxFICqqqOADBU/+1MbzAHg8FHEnWQnZ09jzGmj9fnjg4Gg8MSnZvMOFVVbwBwbuEcxtgpUsrCX5zXA3gm0l4wGFxY1H4iDXR9Tk5OznIpZbOC+foGqFOFEL8xxi5ljA0GcE7Bex2CweC8ZPJIdCw10BMlReOKEqALctKEEwRId05QJ5/J6k7+sQeif/Q+5nzqMrAK+j2A0Yc2/DFg2y/hF9kjvcGvuzkp6GL1c5CvBOLOYc07gt/aIPTjtpg+0hhT9RQo459Nyg8NTg+BZPWWnqis8yI3fw8xpneMwXQ1Ca3LxFuWvK67TKmW/G0LxLCu0eFeURdK50G2pyA/+xBCX8K98NCXj5+2PLzUupkAtMEdgV36vfD5R+RnHWnPDFnn5sod2yCmDgN+3xUdhKKANW4Fuf4NYPuvRs0f7QN+7U2OBUw6cwy9rx2T7nxdfkeTJ+05it9TzqmB7qlyUjI2EKAGug1Q/WxSVVX9aWp9I6zQ/uWMscDx48e7r169usi3LoOS/iS2pmlTpZQP6lMAHFEU5YIVK1YY38YshJqdnb2QMRa9xlwJ9oPBYMzfSaINdFVV9c3s9E1RGxTjQjDGRsV7gt2qlKmBbhVJ/9mhC3L/1dwNGZPu3FAF/8WQrO7kwf0QPVtGgeITFoBVyV+uNvLQ9Cdxd+8Iv8S7DAK7PLlFdsQ7r0Euif80OWvTDfz6OyA3fQ0xYaDhunwFKNMLd4nxX03dnHGyenNzLvFii7mZo2AQu+N+8AcSvrc209J2fbxe153rC1AQoPzlB4jRvaLDPbk6lHHzbU9BrFkKuXal4eeCS6H0iWiom4hAm/I4sPGzsAW9wcobqKF/k/ZMgHVwqlg6B/LtfyccAe84AOyqegmPt3og6cxqomQvEQKku0Qo0Rg7CJD27KDqT5vUQPdn3SnrxAlQAz1xVjQyQQKqqupPVo8qeJJcn3UMwCtSyvcYY79IKf9ijFUoWLpc/4alN5f1hruuR8kYGxoIBMYk6C7pYelsoBcGp6qqviafvuz7ZQCqANgJ4F0AM4PB4PtJJ5HEBGqgJwGLhkYRoAtyEoQTBEh3TlAnn8nqTh4/BtE5O/qcOWw62OlnxcAsut8s7zEC7OIrkoIuN34KMSX+wjys4IkvueUniFE9DLuMgz+1Ou6+7Ek5p8GWE0hWb5YHYLNB8d4bkAumxXhh198O3qa7zd7JfHEEvK67TKm8/H4jxPgBMeHySYvAKlW1NQ1t2nDgq0/CPlj9xuBq9JLuqQYgFs+EfHedYfumu8FbdQ79m7SXKlVn52mThgDffpFwELzLYLDLr0t4vNUDSWdWEyV7iRAg3SVCicbYQYC0ZwdVf9qkBro/605ZJ06AGuiJs6KRSRDIzs6eyRjTvzHry7GHGuMlTA/rkDE2MxAIdEvCFQ0thQA10EkiqRKgC/JUydE8MwRId2bo0dxUCaSiO63fw8DePWGXrG0P8Hq3xYSgdX8QOPRX+HXe90mw8y9OKtTQMqpDO8WdU/hEu9y1HWJwh6gxfGYQrFxoUSA6XEQgFb25KPxSQxGvrYbMXRA77sp/QukUsUpCqZZogJUEvK47K1nZaau4G6Lsbj5KKSF6tQIO7jc+t9r3A79G393M/CHWroRcs9QwdPEVUHrou7tRA908XWcsaCO6AVt/Ttg57zYM7NKrEh5v9UA6x1lNlOwlQoB0lwglGmMHAdKeHVT9aZMa6P6sO2WdOAFqoCfOikYmSSAnJydHSvkkgDoJTN0spRyQm5sbTGAsDUmCADXQk4BFQ6MI0AU5CcIJAqQ7J6iTz1R0p+n7yH72YRgeu/1f4M0ejYGpdWoK5B0Pv86HTAarfW5S0OM98V5ogHcfDnbJlZD790H0fij6PJ6GJxqTSoQGhwikordMQidyF0K+tio25Asvg9JLX6SKDicIeF13TjBNxWfMPuQFRtgt94C3iL5RSgqh74lmyUoics9OiIHRn1F8zDyw6jVTSSNmjvjgLcj5U4zXa54OZdRsX5zzLAHoQiNa37bAvt+N65fHhgJlykI8MwnYvy8mYt5zJNhFlzuWCZ3jHEPva8ekO1+X39HkSXuO4veUc2qge6qclIwNBKiBbgNUMhlFgOXk5NQHcJuUUl+vtDqAkwAcBLCbMfYpgDcDgYC+3ltJT6kT1hQJUAM9RXA0zfM/8FOJ3UmAvgi6sy5ejyoV3cU8bXfuhVD6j4tCpTc/RIdGUa/xETPB/nZm0ki1Pq2BP/fG/mDddwzY+ZdAHj0K0TV/v9nCgz8xF6zG35L2RRPsJZCK3uyNyFrrYuF0yPWvxxqtfS6UIZOtdUbWEibgdd0lDMLhgWLDu5DzJsRGUaUa+Phnw81y+ekHEAunhW7AYg92AL/hTlORy4/XQ8yN+IyqUBF8ynOWNOf1wOSmryEmRKwwUbYsQqugMEbfKUxVzpnJoRULuqjAcX03vvyDD5wAdvYFkPv3Qkx+HNj2S/Q1R58xYBdc4kzAPrg5zTGw5LhEAvTZSgJxigBpzyny3vNLDXTv1ZQyspYANdCt5UnWiIDrCFAD3XUlyZiA6II8Y0rlqUBJd54qZ8Ykk4ru5JcfQ0zPX542dJQtBz5jBRhXwi/Jo0cguuZE/8D85NNgp5yaNBvtyb7AT9/FzOODJoHVOQ+hH7s7NAakCI9hdW8Fa9MNTDFiStoxTbCcQCp6szyIJA3KI4cgF82E/Pl7sKtvAGvcMkrrkea0maOBzz+K9XBqLSij5yTpmYZbRSATdWdV7m6yI9a/DrlwetyQ+JApYLXPgRQaRP92wL4/8seVK4/QHunlTkg5lZiVIS66AkrPiM+wlC3nT5S/74YY0C76827SYrBKVaiBbpKtE9PjXr9E3JQnv/oYYlq0fvigiWB1znci3JBPOsc5ht7Xjkl3vi6/o8mT9hzF7ynn1ED3VDkpGRsIUAPKxqzdAAAgAElEQVTdBqheN6mq6k+hL8lSLsrNzY361qSqauiRquPHj+9/4YUXYtf18jocF+ZHDXQXFiVDQqIL8gwplMfCJN15rKAZkk4quou7ZHqRp8vlgf0QvVoWaSgsAqtUNWkyYt4EyA3vxszjw2eA1aodel3r9iBw2NhvXX+N1W8MrrZN2h9NsI9AKnqzL5rELIuVz0C+/mJ4MGuQA3bFdWBnnRdjoLibPVCpCpRJixNzSKMsJ5CJurMcggsMirdegVw2N24k7F/NwO9vDrl7B8Sg9tGfHQPGg53z95Qz0CYNAb79IupvmDeO/nxK2bj+24CmQXTJBjQtbKawoUraM0PWmblxb4iYtgzsRH0xwdBvQRDThgNf6wsKAqhYGXzCQkdv2COdOaMVv3sl3fldAc7lT9pzjr3XPFMD3WsVpXysJkANdKuJ+sCeqqr6o02SMTYrEAh0i0y5pPd8gMaVKVID3ZVlyYig6II8I8rkuSBJd54raUYklKrutH4PA3v3GA2Jh7qC36jvXJN/xP0BevoKsPInJs1FPL8I8tXnY+bxJ54Cq3Fa6PWi8YReLH8ilOkrkvZHE+wjkKre7IuoZMsyLw+ib2vg4IGYgezupuBNW0e9rumNv907Yo2WKQtldq5Tafjeb6bpzqsFE+tWQwYXxE/vzLOhDJ0K+d1XEBMHRY3hJpbHDm0n0qM5cPhQ2CbvPAjsirqWYtb0Pdb37DQ+Ex/uCf7PW+nJYEspp8eY3PIjxKiehjPOweeujlryX39KXa5dCfx1IHSzHqtZKz3BFeOFznGO4vetc9Kdb0vveOKkPcdL4JkAqIHumVJSIjYRoAa6TWC9bJYa6JlVXWqgZ1a93BQtXZC7qRr+iYV0559auynTVHWnzRkLfPKekcqlV0Pp9nj433LHVoihnaObIHNWgWVlJZ2+ePsVyKWxTy3y8QvAqlYL2dMe7wJs/zXGtvK08eRw0o5pguUEUtWb5YEkaDBmu4Ii8/jsXLAyZcOvat2aRTXqIofzOc+DZZVJ0DMNs5JApunOytzdZEusXQm5ZmmxIfFx8yG/+QJS3/884jDT8JY7tkEM7RRtL+Kzwyo+2oxRwBcbwubYXY3Bs9tSA90qwGm0Izd+CjFlmOGxYmUok5ekMYLkXdE5LnlmNMM8AdKdeYZkITUCpL3UuNGsWALUQCdVEIGSCVADnRSSNAFVVfVb18sxxp4LBAJRj5zQE+hJ47R9AjXQbUfsWQd0Qe7Z0ro6MdKdq8vj2eBS1Z344G3I+ZMNLooCPmkJWIWCJU63/AQxqkfU+8rc1SlxLK6JyacuBatQMWRTG9MH2Lwpxj410FNCbtukVPVmW0ClGBbPToF8/61iR0XexCHzjkN0alr82MlLwCpWdioVX/vNNN15tVhi9RLIV4LFpseadwQO7IN8KXrlENb6MfAb7kwJS8xnVeWToUxcmJKtkibF5HbJlVC6D6cGuuWk7TcoPnoH8umJhqPTzoAycpb9jk14oHOcCXg0NWUCpLuU0dFEkwRIeyYB0vQwAWqgkxiIQMkEqIFOCkmagKqqPwM4A8DXwWDwH5EGqIGeNE7bJ1AD3XbEnnVAF+SeLa2rEyPdubo8ng0uVd3Jw4cgerUC8o6H2bA23cCvvyP0b/nDNxDj+hvcTigPZcbKlDjK7b9C6E+YFzn4rCBY2XKhV7XBHYFdv8WOmbcmatnVlAKgSZYRSFVvlgWQhCF5/Fi+xo8cLnYWHzUbrObp+Zrf9ztE37bFj43YciCJMGioBQQySXcWpOtaE2LlfMjX1xQf30VXgFWuEnPTCstuA35Xk/A8+dN3kG+uBU4+BezeB8DKnRDXZmiv6imPA998brx/2bVQug6xnJHY8C7kvAmG3SonQ5mwkBrolpO236B462XIZU8Zjs69CEr/sfY7NuGBznEm4NHUlAmQ7lJGRxNNEiDtmQRI08MEqIFOYiACJROgBjopJGkC2dnZKxljqv4bGYBnACxUFGW7pmn63uh6c11/fTGAiDW/knaDYDC4JflZNKMoAWqgkyZSJUAX5KmSo3lmCJDuzNCjuakSMKM7bfYY4NMPwq7ZVdeDd8xvmstvPoeYPNQIq1IVKJP0S6TkD32vUdE1J2Yij2iOa4/eH9cwn/08WBlaNjt56vbMMKM3eyIq3qr85D0IfauCEg4+aBJYnfPyNf/rZoiR3YsdzYdMAat9TrrTIH8ANTFdogLx3GzI/75qRKOvyHDgT+PfShbwtzOAXzdHRczuaQreJH/xN3lwP8SAR4Gj+Te2sBvrgz/UNW6G4p1XIZfMjrbVqCX4vbGfJ2YRxbvRi09+DkrlKqhZs2bI/I4dOyCE/rMBHW4mIF5aAfniMiPEy6+D0mWwm0Omc5yrq+Pd4DLpms67VfBnZqQ9f9bdjqypgW4HVbLpJQLUQPdSNdOUi6qq+tpxrxU0yot6LdSU3kQ3c8hgMJj8BqFmPHp0LjXQPVrYNKRFF+RpgEwuYgiQ7kgUThAwozvx+hrIlfONsC+4FEqfJ0L/lp9vgJg5ynivWg0oY/V7D1M74jXII5dn10Z0B7ZGN110T5HLvKfmmWZZScCM3qyMIxFbYu44yI/XlziU9xoFduFl+Zrf+Fn+067FHLz3aLC/Ry1glUgYNMYCApmkOwvSda2JolsisJvuglz/BqDllRizPo63yl+FRLwcgHzhuajx8bbqCD193r8dsHePMbZceYRWjahazXJGUtPyb/SKWJVF/5tXLrqcGuiW07bXoFg+L3+Fg4KD3XAneOvH7HVq0jqd40wCpOkpESDdpYSNJllAgLRnAUQyESJADXQSAhEomQA10EkhKRFQVfVJABFrkqZkpqRJegNdsdyqDw1SA92HRbcoZbogtwgkmUmKAOkuKVw02CICZnQn3l0HuXimEUmd86EMyt83VGz4P8h54433TO4hWloDXbz9CuTSuTFU+Nj5YNWqW0SLzJglYEZvZn0nM18eOQTR+yHg2LESp/FOA8Gu/Ge+5j/8L+Qzk4odzzsPAruibjJh0FiLCGSK7ixK17Vmit6Uwu7Ngfz5e+DrT0uO+ap6UDoOCI3Rpg6LGR+5GkmhIbnvD4i+baLssrY9wOvdZhsfbVQPYMtPhv1TTgW/syFOa9IC/MQK9AS6beStNSyengT50X/DRtldTcCzo7VkrUfz1ugcZ54hWUieAOkueWY0wxoCpD1rOJIVaqCTBohAaQSogV4aIXq/WALZ2dn1ALRijF0JoCoAfW3Q2gVPph8E8IcZfMFgsI6Z+TQ3nwA10EkJqRKgC/JUydE8MwRId2bo0dxUCZjRXUzDsFZtKMNnhEIR770BuWCaEVbtc6EMmZxqmNC6NQMOH4qaH/nUoTx+HDJ3QdRTY/pgPnIW2GlnpOyXJlpLwIzerI2kZGvig7ch55eu18iGnHj9RciVxa+ywNp2B693ezrTIF8FBDJFd14vmDZjFPDFhnCarHEroHwFyGWxNz9FsYhY3UQb1RPY8mPU23xmIGYf9JhtRLLKgM8KgnFuG2bx7FTI99+Msc8qnITKLTvi4DU3699PbfNPhq0hoE0aAnz7haHTpq3B725qjXGbrNA5ziawZLZEAqQ7EohTBEh7TpH3nl96At17NaWMrCVADXRrefremqqq+oZmkjE2KxAIdPM9EBcAoAa6C4qQoSHQBXmGFi7DwybdZXgBMzR8M7qTn34Aoe+DXnhUrwllzLzQv2KeCD/3Iij9S95PuiSE2uCOwK7foobEW7ZX69Q0egndwZPAzsrfo5oO5wmY0Vs6oy/a6CvON3uwPfht9+VrftUiyH8/X2yYrFl78Nvzx9KRXgKZorv0Ukm/N23yUOCbz8OO2QPtwK6sl7/UeknH6XWgDMu/IUvr/zDwR8Sy7PqNUmOfAatWI8qCeHMt5PL8z6PQcebZUIZOtTVpufFTiCnDivWh9BsLnHeRrTGQ8ZIJhG5g+PUnYM9OoFIVoPZ5wPdfQf74HaS+Dcyvm4HdO6KMsNaPgd+g7+Tn3oPOce6tjZcjI915ubruzo205+76ZFJ01EDPpGpRrE4QoAa6E9Q97JMa6O4rLjXQ3VeTTImILsgzpVLeipN05616Zko2ZnQnv/4UQl9Ot/CofDKUiQtD/xLrXoAMPmu8d9HlUHqOTBmLNn0k8OX/oubHbaD3aAH8dSA8jvcZA3bBJSn7pYnWEjCjN2sjKd6a/OsARO/Wpe7LrFtgjVqC35uTr/mF0/L3cy7mYA1bgN/3QLrSID8RBDJBd34omDa2H/Djt+FUWcvO4DffDW3CQGDT18UjOPkUKOOehRQaRBcVyIveM50PmQJW+5yo+WLpXMi3XzF8XXcz+CO9bccsP98A8eLS6KXcC7zyJg+B3ZNtewzkoHgCYvVzkK8EkkLEuwwCu9zd22/QOS6pktJgiwiQ7iwCSWaSJkDaSxoZTSiGADXQSRpEoGQC1EAnhVhKQFXVt/NXDZercnNz89cvpcNRAtRAdxR/RjunC/KMLl/GBk+6y9jSZXTgZnQnv98IMT5/X9rQUb4ClOnLQ/8rXg5AvvCc8d7l10HpMjhlVnLLjxD60r0FB6t7C3i7XjH2NP1Jxj92h1/njw0F+8c1KfulidYSMKM3ayMp3pr4v/9ALoq4lM8qA+hPt+7cFjOJ3d0UvGnr0OvxbvKInMDqNwZX26YrDfITQSATdOeHghXdI7xwCwS5YxvE0jnA918DmhaLotwJUGYGIHdthxjcIeZ93mME2MVXRL2uTRwMfPel8ZkRcbOL3axDTzlv+hpCX5r+ty3G59FdTcBcvpe23WzSYT/E/9gxsHLlotzJH7+F0G/iSPLg/caCuXzlADrHJVlUGm4JAdKdJRjJSAoESHspQKMpcQlQA52EQQRKJkANdFIIEfA4AWqge7zANqZHF+Q2wiXTxRIg3ZE4nCBgRnfylx8hRhtNbShZUOauCqUhVi+BfCUYToldcyN4+76mUhRrV+bvcV7jtFDznFWvGWNPe7wLsP1Xw2/7fuDX3GDKL022joAZvVkXRcmWxJJZkO+8ZgzSnzz8Y1fcJ0rZLQ3AW3QMjY3Zm7lsOeDYUUOLN9YHf6hrutIgPxEEMkF3fiiYNrQzsGNrOFXeoR/Y1cb5WQoB7N8H/LAR4qnxUUj47OeBjZ9BzBwVg4o1aQ12VyMwroTf03o/lG+r4OCdBoJd+c+0YhYrnoZ84yU6B6SRuvx9F8SMUSGdsWtvBmvbHYyx/NULRvfKX6I9mYNz8ImLwSpWSmZW2sfSOS7tyMmhvn0G56hZM/9afMeOHRD6OZwOIpAGAqS9NED2iQtqoPuk0JRmygSogZ4yOpoYj4Cqqm/qr0spV6fyBHpOTk5HKaW+BqQMBoO3E2XzBKiBbp6hXy3QBblfK+9s3qQ7Z/n71bsZ3cntWyEe7xyFjs9ZBZaVBbFyPuTra8LvsetvB2/T3XbMmv4D+S8/GH7bdAO//g7b/ZKDxAiY0VtiHsyPitmnuVFLyM8/AjZvijEeuRKC1rctsO93Y8wZdaKbNVfWg9IpYsUG86GShQQJZILuEkwlo4fFrBDSdSjYZbErhMgDf0L0ahX92TJhIeR7b0CuXhKfwXkXgeurnJx4EuS61ZC5+duJFB585Gyw005PKz/x4jLIl1YYn0dX1gOnc4CtNRBPT4T86B2DecFNdOKtlyGXPVWib3bT3UCd84BffoT8eD1w5DBY41bgdza0NWYrjNM5zgqKZCNZAqS7ZInReKsIkPasIkl2qIFOGiACJROgBjopxFICZvdAz87OnsAY0zdm0xvoxu3zlkbpL2PUQPdXva3Mli7IraRJthIlQLpLlBSNs5KAGd3J33dDDGgXFQ6fvgKs/IkQz82G/O+rxo/YtzYAb57/pK6dhzZhELDpK8Nvs/bgt99np0uynQQBM3pLwk1KQ2VeHuSnH0DOi37ylT3aB6xMWYjZY2LtXnYtlK5DoD85Kzo3jVp+mt1YH/LddcacWrWhDKddnlIqjslJbtadydQyarqmN8UP/BmOmfcaBXbhZTE5SE2D6Ng4+rOlxwiI+ZOj5sdMvPRq8Bvrx/6tKlngMwOhm7vSeYjXX4Rc+YzxefT3f4D3Hp3OEHzlS1+6XbQv0uyuVRu6zsSQTsDhv4rlUfQmP12DYAyM84xgSOe4jCiT54Ik3XmupBmTEGkvY0rl+kCpge76ElGADhOgBrrDBfCae2qgu6+i1EB3X00yJSK6IM+USnkrTtKdt+qZKdmY0Z08sB+iV8voJsfERWCVq0I8OwXy/beMxkGa9n8uug81a/IQ+D3ZmVIOz8dpRm92wyluD3M+cAJw5jkQU4dF7akciueCS6H0eQLy4H6IntF/C+yR3pDPTDLCztKbeEEwhe6TtbuWRe27WXfpZuGkP63rA8DRw+EQ+IDxYOf8PW5IWrcHS2x4FpvHiRWAQ0UapQV/p+nOXbz3JuSCqYbbM86G8njEv9MdkMf9yR3bIIZ2ismS1b0V8gPjekQfwPuMAQ4fhF4jVusssPseyOhzM53jPC5ul6ZHunNpYXwQFmnPB0VOU4rUQE8TaHKTsQSogZ6xpXNn4GYb6Kqq6r+w6RuJ5gWDwbLuzDKzoqIGembVy03R0gW5m6rhn1hId/6ptZsyNaM7efQoRFc1Kh0+Zl5ob3Ixd1z+EqgFh/7jNG/YwvbU9X1z5f/+z/B7bw54o+jGpu1BkINiCZjRm51Y5R97IPo/HNcFn7QYrFKV0FPmUl+S+eWAMa72uVCGTIbctgViePT+5nzCQoi+baL/PkbNAatZy85UyHYcAm7Vnd+KpXVoBETskcuHTgU78+y4GLSBjwJ7dlqCiI+YCfa3My2xlYwR+dmHELOeMKZUqwFlrPFEejK2aGzpBMT6NyAXTit1ILvuZvBH9IX/vHPQOc47tcykTEh3mVQtb8VK2vNWPZ3MhhroTtIn35lAgBromVClDIrRbAM9Ozt7GWOsGYB9wWDw5AxK3bWhUgPdtaVxfWB0Qe76EnkyQNKdJ8vq+qTM6C60bLXeEIk4+PAZYLVqw6knwcXCaZDr3whHxO5oCP5A9DLzri+KhwM0ozc7scgfNkKMi7M/ebkTwGesBGP5Xx3lJ+9BzBlrhFLjb1CemAv5zecQk4car1eoCGXqUsQsWd15ENgVde1MhWzHIeBW3fmpWPoWCaJTk+jPi1GzwWrG35dcG90L+OUH04h4waoopg2lYEBu+hpiwkBjZvkToUw39kRPwSRNKYGAWDIL8p3XSmZ0Qnlw/UamKt76uYXOcfSn4QQB0p0T1MmnToC0RzqwigA10K0iSXa8SoAa6F6trEN5mWmgN2nS5HRFUb4AUBnAp8Fg8GqH0vCUW2qge6qcaU2GLsjTipucFRAg3ZEUnCBgVndal2zg2LFw6HzQRLA650N7si/w03fh19kDj4Dfcb/tKYrl8yDfXGv4vbE++EPRTwbbHgQ5KJaAWb3ZhVZ+9QnEtOGx5ovsWy43fgYx5XFjXKUqUCYthvjwv9HLtZ92BpSRs6BNHBy17Dtr1BL83hy70iC7xRBwq+78VDB5+BBEN/1ecePgY+eDVaseF0PRbUBCg7LKgF1zA3BqLcgXnisd35lnQxnq3JLpctsvEMMfi875qdVgnLZxKL14yY2Qh/6C6P5gqZNYTjvwO4vsk17qLPcPoHOc+2vkxQhJd16sambkRNrLjDplQpTUQM+EKlGMThKgBrqT9DPcd05OzrNFU5BS6ms0SgDfMsY+TCRFIUQZxpi+juN1AMrr86WUk3Nzc/smMp/GlEyAGuikkFQJ0AV5quRonhkCpDsz9GhuqgTM6k7r2QI4eCDsnuv7QW/8DPKVYFRIrFVn8JvuTjXMhOeJVYsg//18eDy79mbwR721VGvCMFw40Kze7EoppgFe6Oiya6F0HRJ2K3/6DkK/OaTwKFsOyqwgxH/WQAbmG68X7Lksls2FfOsV0qNdhUvQrlt1l2D4nhgm/9wL0ad1VC588hKwivr947GH3PozxMzRwO+7gGo1wG65B+z6O8EqVoL88n8Q00eWyoU1UMEbtyp1nF0D5N7fIfq1jc55ynNgJ1Wyy6Uv7cq84/kriPz8fcn516oNPmQKWFaW5zjROc5zJc2IhEh3GVEmTwZJ2vNkWR1JihrojmAnpxlEgBroGVQst4Va+LR5kbgKNaU30VM59Pn7AfwjGAxuScUAzYkmQA10UkSqBOiCPFVyNM8MAdKdGXo0N1UCZnWn6ftG/7En7F5/wjbek4GsXU/wuremGmbC88TalZBrlhrjL78OSpfBCc+ngfYSMKs3u6ITb70MueypGPPs1gbgzTuGX5fbf4V4vEvUOD53dUjz8tWIGzeuuRG8fV+It16BXDbXGH9GHSiPl75Hr115+tWuW3Xnp3rI3TsgBrWP/tvRt0c4Qb+HPP4hjx0F9P9OPAmMc+PvsOiNLADYVddD6g1UveFecPAhk8Fqn+sYZnn0KERXNTrnJ+aC1fibYzF50bF4dx3k4pmlpsb7jgE7/5JSx2XiADrHZWLVMj9m0l3m1zBTMyDtZWrl3Bc3NdDdVxOKyF0EqIHurnpkVDQFDXSrY34PQPdgMPix1Yb9ao8a6H6tvPm86YLcPEOykDwB0l3yzGiGeQJmdacN7QTs2GYEUr0msHtHTGC84wCwq+qZD7gUCzFPAl94GZReo2z3Sw4SI2BWb4l5SX6UWLsCcs2ymIns1nvBm3cIvx73idIeIyCemRi1EgO7/V/gzR6F/O5LCH0Z98KjTFnwmStpCefkS2Rqhlt1ZyqpDJssf9sCMSx6Ow2e4nLmctd2iMHG36WOgjVsAXb1DRALpwF7doLd2Qj8rsaOUpJSQnRuCuTlhePggyaB1TnP0bi85lwb0wfYvMlIS9/nXD8vr10BfPVJ6HV2VxPwbH3BQG8edI7zZl3dnhXpzu0V8m58pD3v1jbdmVEDPd3EyV+mEaAGeqZVzEXx5uTkRK0/J6XU9aQv664/ff4GYyzi0af4gQshJOf8iBDi9zJlyny1fPnynS5K0ROhUAPdE2V0JAm6IHcEu++dku58LwFHAJjVnTaqB7Dlp1Jj590eB7v06lLHmR0g3nkNcsksw8zZF0AZOMGsWZpvEQGzerMojBgzYuV8yNfXxLyuN2HYxVeEX4+3jzOULEAzGmT6YNaiE/gt90Du3wfR+6Eou/yJp8BqnGZXKmQ3DgG36s5PxZK//AAxupeRclYWlDmrUkIQb7/rdG0TkmzAmv73v39feFrRc0qy9mh8NAG55ScI/Tok4mDt+4JfcyOkEMDGT4Fy5cHOu8jT6Ogc5+nyujY50p1rS+P5wEh7ni9x2hKkBnraUJOjDCVADfQMLZxbwy5c1p0xNisQCHRza5x+iosa6H6qtrW50gW5tTzJWmIESHeJcaJR1hIwqzttXH/gh29KDUrfG51dcGmp48wOiNnLulZtKMNnmDVL8y0iYFZvFoURY0Y8OxXy/TejX699LvjACWCKEn5db8iIDo1KDuPsC8B7jwYrWw6hJ1B7tYx6Op13HQJ22bV2pUJ24xBwq+78VCz5/UaI8QOMlMtXgDJ9eUoIQn9XfdsAf+7Nn885+LhnwaqcnJI9Oydpj3cGtm8Nuyhs7trp00+2xdI5kG//20i5ysngY+dHnbf9wIPOcX6osvtyJN25ryZ+iYi055dK258nNdDtZ0weMpsANdAzu36ui15V1QUFQf0nGAzGrgHpuoi9HxA10L1fY7sypAtyu8iS3ZIIkO5IH04QMKs7bcrjwMbPSg2dDxgPds7fSx1ndoD87AOIWWMMM9VrQhkzz6xZmm8RAbN6syiMGDPazNHA5x8Zr593EXjPUWBlysSOfewB4Mjh+KFcdDl4p4FR+zpretPw+43h8axJa/B7mtqVCtmNQ8CtuvNTseTGTyGmDDNSrlwVysRFKSMQ61+HXDI7tPoDa5AD3rhlyrbsnCjG9oP88Vvj779gdQo7ffrFtjx6BKJP66jzMbvvAfCGLfyCIJwnneN8V3JXJEy6c0UZfBkEac+XZbclaWqg24KVjHqIADXQPVRMN6SSk5MTeupcX5r95JNPnjtv3rzjbojLzzFQA93P1TeXO12Qm+NHs1MjQLpLjRvNMkfArO60WU8An31YahB86FSwM88udZzZAXLjZxB6U7/wqFgZyuQlZs3SfIsImNWbRWHEmNHGDQB+iGhyN+8IfmuDuO60vm2Bfb/HvMeuuh6sXa+YprtYMhvynVfD41ndW8Hb9bQrFbIbh4BbdeenYsnPPoTQPy8Kj1NOhfLk06YQyIP7gbzjYFWqmbJj52QxfSTkl/8z/v4btwJvoNrp0je2xbvrIBfPNPJlDPzJZ8CqVfcNg8JE6Rznu5K7ImHSnSvK4MsgSHu+LLstSVMD3RasZNRDBKiB7qFiuiGVwiXcAXwSDAavcUNMfo+BGuh+V0Dq+dMFeersaGbqBEh3qbOjmakTMKs78fREyI/eKTmAipXBJyxMy5Kq+pN++hN/4aPcCVBmBlIHRDMtJWBWb5YGE2FMe7wLsP3X8Cvs0T7g194U113RsfogdtPdYC06gHFjuffCyeKNtZArIlZBqH0ulCGT7UqF7MYh4Fbd+alY4qN3IJ+eaKR82hlQRs7yPAIxfzLkB28beZ5/CUJbmjD6Ocps8bUxfYDNmwwzl14NpVvEDXRmHWTQfDrHZVCxPBQq6c5DxcywVEh7GVYwF4dLDXQXF4dCcwUB+sbiijJ4JwhVVQ8AOBHA4mAw2NY7mWVuJtRAz9zaOR05XZA7XQF/+ifd+bPuTmdtVndi0QzI//tP8WlcURf8vmZpefpcD0Ju/RliRGhRoPxDfyJt7mowzp1GTf5DWxVz1KxZM8Rix44d+spNruCi6csAF+6nrMfZfTjYJVfGjS3mifIGKlijlsU2xOQ3n0NMHmrYKq1hZLcAACAASURBVFsOfMZK0mQaK+9W3aURgeOuQkuuL5xuxOGTG0nk8nkQb66N4s/ubgretLXjNcnkAOSWnyBG9YhKgXcZDHb5dZmcVsqx0zkuZXQ00QQB0p0JeDTVFAHSnil8NDmCADXQSQ5EoGQC1EAnhVhKQFVV/fbncxhjiwKBwMOWGidjKRGgBnpK2GiSi3/gp+J4mwB9EfR2fd2anVndieXzIIs0BwpzdeLHbLl7B8Sg9tE/qs8MgJU7wa0l8FVcZvVmBywpJUTnpkBeXtg8HzQJrM55cd3JQ39B5i6A3LUd7Mb64NfdXGJYct8fEH3bRGvyyafBTjnVjnTIZhwCbtSd3wol3noFctlcI+3zLoLSb6znMcgXl0G8tCI6z7JlwactB8sq4/n87UpQLJ0D+fa/DfNVqoGPfSYtK93YlZMZu3SOM0OP5qZKgHSXKjmaZ5YAac8sQZpfSIAa6KQFIlAyAWqgk0IsJZCdnb2MMdYMwEfBYLCupcbJWEoEqIGeEjaaRA100oBDBOiLoEPgfe7WrO7E84sgX30+LkU+aRFYpappJSz374XoHf1kH5+0GKxSlbTGQc7iEzCrNzu4yqNHIbpG70nMn3gKrMZplrgLNeh7NAcO/RW2x7s9Dnbp1ZbYJyOlE3Cj7kqP2lsjxLrVkMEFRlIXXQGl5whvJRknG/nGSxArYvd65xMWuHrvdjcXRh47CtH7IeDI4XCY7L5m4A2buzlsW2Ojc5yteMl4MQRIdyQNpwiQ9pwi7z2/1ED3Xk0pI2sJUAPdWp6+t5adnX0PY+xlAPpalP8IBoMbfQ/FYQDUQHe4ABnsni7IM7h4GRw66S6Di5fBoZvVnf5knf6EXcxRrQaUsc+knUzcZmjH/mBXXZ/2WMhhLAGzerODqfxjD0T/6MWj+NSlYBUqWuZOG9sP+PFbo9mT3Rb8rsaW2SdDJRNwo+78VjOxdiXkmqVG2pdfB6XLYM9jYNt+Qd7wx2Ly5MOmgZ1ex/P525Gg/PFbCP2cWnjoW7U8+QxYtep2uMsIm3SOy4gyeS5I0p3nSpoxCZH2MqZUrg+UGuiuLxEF6DABaqA7XAAvuldVdRWARgA+L1OmzG3Lli3b68U8MyUnaqBnSqXcFyddkLuvJn6IiHTnhyq7L0ezuhOvrQ4tZ130YNfcCN6+ryMJa3qzYtsvhu/qNcFHzAIrQ8vlOlKQCKdm9WZH/HLrZogR3aObMXNXgXHFMndi8UzId9cZ/Z7rbwdvE+HTMk9kKB4BN+rOb5USq5dAvhI0/gYc/IxIJ3tde5U2f4vfR/eJcst7jQK78LLwa1LTgB++AU4+Bax6zXSGmHG+xIb/g5w3PuozXhkzL+PysDJgOsdZSZNsJUqAdJcoKRpnNQHSntVE/WuPGuj+rT1lnhgBaqAnxolGJUGgRYsWlY4dO/YcgPsAbAcw4vjx44EXXnhhXxJmaKhFBKiBbhFIH5qhC3IfFt0FKZPuXFAEH4ZgVnfirZchlz0VQ47ltAO/s6EjRGN+XAfA6IlfR2pR1KlZvdmRhPzuS4iJEU/CnngSlGlxVlUw4Vz8Zw1kYL5hoc75UAZNNGGRpiZDwI26SyZ+L4wVK5+BfP3FcCrMJzeRFGpve/smyNu2xci/fT/wa24I/Vtvnotx/YHNmwAlC+yhLuD1bvdC2W3JQax7ATL4rGH7/Eug9B1ji69MMUrnuEyplLfiJN15q56ZlA1pL5Oq5e5YqYHu7vpQdM4ToAa68zXwVASqqr5ZkJCuLX2d0Cz9+3DBa5sB7AZgbNRVfPYyGAzSN2YL1EENdAsg+tQEXZD7tPAOp026c7gAPnVvVndi/euQC6fH0OP9x4Gde6EjVEN7To8fkP80X+FR/kSE9rWuWNmRmMhpPgGzerODo/zkfYg5Txqmq9eE1U8zyq8/hZg6zPBRrjz4jBVgjL6S2lHTojbdqLt05O0mH2LJbMh3Xg2HxG5pAN6io5tCtCWWQu3t7PMwjn3zhZF/i47gtzQI/Vt++gHE7IgGcFYW+IDxYLXPtSWmTDcaczPGdTeDP9I709MyFT+d40zho8kpEiDdpQiOppkmQNozjZAMFBCgBjpJgQiUTIB+rSCFWEpAVVV97/PChnmkbV1r8V6P5z80NhgMWrdmpKVZZpYxaqBnVr3cFC1dkLupGv6JhXTnn1q7KVOzuhMb3oWcNyE6JUUBn74CrGw5x1KVmzdBjIleMtcvDRvHoCfg2KzeEnCR9BDx7jrIxTONebXPhTJkctJ2SpoQd5/1cfPBTvbvnr2WAi7FmBt1l8783eBLPDsF8v23wqGw+o3A1YfdEJqtMRRqb/fIXjjy4TtG/g2bg9/XLPRvkbsA8rXV0XGceQ6UoVNsjc0txuW2XyBeWApwBt6wBdjfziwxNG3uWODj9wyW9zQFb9LaLek4Eged4xzB7nunpDvfS8AxAKQ9x9B7zjE10D1XUkrIYgLUQLcYqN/NFTTQrcBADXQrKOp3Iki5FUAtTdOwc+dOi6ySGT8QoAtyP1TZfTmS7txXEz9EZFZ38vOPIGaOjkZlQwMylVqIZyZBfvhfYyrn4MOml/rjfCq+aE5iBMzqLTEvyY0S61ZDBhcYky68DEqvUckZKWV0aFWEbs2AI8ZiVLz7cLBLrrTUDxmLT8CNuvNbrcTccZAfrw+nze57INQs9fpRqL0/po7AX/95ycj/9n+BN3s09G9N30Liuy9jUPBxz4KdfIqnEcld2yFG9wIO/5WfZ83TwUfMAOPFP0+gPdkX+Ok7g2XzDuC33utpTqUlR+e40gjR+3YQIN3ZQZVsJkKAtJcIJRqTCAFqoCdCicb4mQA10P1cfRtyV1X1ZqvMBoPBiF97rbLqPzvUQPdfza3KmC7IrSJJdpIhQLpLhhaNtYqAWd3Jbz6HmDw0Khy3POkt/9gNMaQTcPyYEd/ldaF0GWQVPrKTJAGzekvSXULDxZplkGtXGGOvrAel04CE5iYzSNNXRND3OC44mPoweP1GyZigsSkScKPuUkwlY6dpM0YBX2ww9N+4FXgDNWPzSTTwQu3te3YaDjy/xMj/2pvBH+0NKTSIbs2Bo7E7vfHhM8Bq1U7UVcaNk0ePQoztB2zVd7szDj5oIlid84vNR+vbFtj3e/h93mUw2OXXZVz+VgZM5zgraZKtRAmQ7hIlReOsJkDas5qof+1RA92/tafMEyNADfTEONEoIpCxBKiBnrGlczxwuiB3vAS+DIB058uyO560Wd3JH7/N/wE84mBte4DXu83x3PQAxAvPQb4cMGJRssBnBcEU2i3HiQKZ1ZsdMYuV8yFfXxM2zerdDt62u+WuxMLpkOtfN/xcexP4o9HbDFjulAyGCLhRd34rjabfaPXN54b+H2gHfkdDz2Mo1N7+3EX4c8EMI9+Lr4DSYwTkti0Qw7vG5cAHTgA7+wJPMtJX5ZALpkYt61+YKPtXM/D7m8fNW2oaRKemgNR3z8s/+JApYLXP8SSnRJOic1yipGiclQRId1bSJFvJECDtJUOLxpZEgBropA8iUDIBaqCTQoiAxwlQA93jBbYxPbogtxEumS6WAOmOxOEEAbO6k79uhhgZ3Wzko2aD1TzdiXRifIaeQu/fLup1PuU5sJMquSI+vwVhVm928BKLZ0K+uy5smt12H/iD7S13Jd55FXLJ7GgtDp4EdtZ5lvsig9EE3Kg7v9VI02+0+vFb4++sZWfwm+/2PIZC7R1ctwZ7p0VsDVGw1YlY/zrkwulxOfBeo8AuvMyTjMTb/4ZcOid+bhUrg09aDMZif7KTf+yB6P9w9Hl08hKwipU9ySnRpOgclygpGmclAdKdlTTJVjIESHvJ0KKxJRGgBjrpgwiUTIAa6KQQIuBxAtRA93iBbUyPLshthEumiyVAuiNxOEHArO7k0SMQvVoBx47mh1+tBviYeWCcO5FOjM/QErFdo5cJ5k/MBavxN1fE57cgzOrNDl7iqfGQ//u/sGnWIAe8cUvLXcltv0AMfyzGLrv1XvDmHSz3RwYNAm7Und/qo+k3Wv1qLNXtppVK7KxFofYOf/Bf7BnV23BVrQaUsc9ALJkF+c5rcUPgXYeAXXatneE5Yltu3gQxfgCQl1e8/1q1wfs8EXOzW8yqN1llwGfnxm22O5KcQ07pHOcQeJ+7Jd35XAAOpk/acxC+x1xTA91jBaV0LCdADXTLkZJBIuAuAtRAd1c9MikauiDPpGp5J1bSnXdqmUmZWKE78cHbkLkLgDJlwR/q6qon5vRlYkXnplE/1PNBk8Dq0FO/TujUCr1ZHbc2bQTw1cdhs6xJa/B7mlrtBlIIiB4tgMN/RdvmHHziIt8/QWk58AiDbtSdnfm60bY2tBOwY1s4NN6hH9jVN7gxVEtjKtTe0Y2fY1ffiNVQyp0AZWYA2uAOwK7tcX2yR3qDX3ezpfE4bUwe+BNidE/gjz3RoShZgBbdUGf3PQDesEXUOP1mJ/2mp/BRvSaUMfOcTstx/3SOc7wEvgyAdOfLsrsiadKeK8rgiSCoge6JMlISNhKgBrqNcMl0PgFVVe9kjN0qpbwSwCkAKgI4AGAPY+wTAG8GAgFjM0QCZykBaqBbitNXxuiC3Ffldk2ypDvXlMJXgfhBd1rvh4D9+8J15T1GgF18ha/q7JZk3ag3bdwA4IeNYUSsRUfwWxrYgkybOgz4+tMY23zAeLBz/m6LTzJKe6C7QQOavpXGH7uN83DXoWCXXeOG0GyNofCcd3zrz9jRITvKFx81B0K/saCYgz3UFfzG+rbGl07joZuIpg0HNn4W5ZbVbwz8dQByfZGfRS64FEqfJ6LGinWrIYMLjNfOvwRK3zHpTMOVvtz42epKUBSUpQRId5biJGNJECDtJQGLhpZIgBroJBAiUDIBaqCTQmwjkJOT00hKqd8afU4CTn6QUvbLzc1dk8BYGpIEAWqgJwGLhkb/oMU5atasGXptx44dEEIQISJgOwH6Img7YnIQh4AfdKcN7Qzs2BrOnrXvC37NjaQHBwg4oTd5/DiwfQtQ7VSwCifFZK2N6AZs/dnQR7ue4HVvtYWOWLsCcs2yGNt+eRrXFqgJGHVCdwmE5ashmr7Vx4E/wzl7eX/vyMIWak/bvw+/PXhHVM1Zw+ZxzweFg9gDj4DfcX/UHHnoIOS6F4p9at3NopIb3o0N7/xLoGsBRw5BDO4YaqSHj8pVoUxcFDVHzJuASDus3u3gbbu7Oe20xEbnuLRgJidFCJDuSBJOESDtOUXee36pge69mlJG1hKgBrq1PMlaAQFVVScB6FHwz0R1JhljUwKBQB8CaR0BaqBbx9JvluiC3G8Vd0e+pDt31MFvUfhBd9rYfsCP34ZLy1p0Ar/lHr+V2hX5pltvUm/KjO0PbPsFqFAx1KhhZ54dxUIb+CiwZ2f4Nd5lENjldW3hJX/5AWJ0rxjbTH0YvH4jW3ySUXoC3Q0a0Lo+ABw9bPyd+WTVhcJzntQ0bG34T0BG3JT7tzOB37YUWx7WqCX4vTnR5yv9Ce6v9IXsPHBUORl86BSwSlVDycjtv0I83iUqMT5tGdiJ+Tc+hbZk6dsG+HOv8Xnusaf0U61quj9bU42T5nmLAOnOW/XMpGxIe5lULXfHSg10d9eHonOeQKKNTecjpQgyhkB2dnY/xthY/fsdAF1jxwG8KqVczzn/WUr5F2OsghDiLMZYPQD6r7dlChKUUsqBubm5EZt6ZUzqrgyUGuiuLEtGBEUX5BlRJs8FSbrzXEkzIiE/6E6bPhL48n/GD+6NW4E3UDOiPl4LMt16E2//G3LpHKP2V98A/WnvyEPr2QI4aDz1yHuPBvv7P2xDL14JQq5eEmWf3dEQ/IGI/ZFt8+5Pw+nWnT8pl5y11qERELGiEx86NeZmFi9yi9Terw/cBhzcn3Ca7J5s8CYPhcfLvDyIztnRTfiErblvIO85AuwiYzsVmXccoosarZMB44GzLwA+eQ9i1eKYJ+/56Llgp/7NfcmlOSI6x6UZOLkLESDdkRCcIkDac4q89/xSA917NaWMrCVADXRrefreWpMmTU5XFGUTgHIFzfM1iqJ0WbFixW/FwWnSpMlpiqLMBNC4YMxhTdMuWLVqlbHOqO/Jpg6AGuips/P7TLog97sCnMmfdOcMd7979YPuxDOTID/8b7jU7K4m4Nlt/F56R/JPt960Qe2B3TuicuXz1oAx46ug1qkJkJcXHsMHTwI76zxb+YhViyD//bzh46p6UDoOsNWnn42nW3d+Zh0v91DjV/87izj4qNlgNU/3PKqoBvojjYHtvxaf8wWXAt99aXxW3f4v8GaPhv8t/zoI0aO5J5ix624Gf6R3TC6avif8jm0GgzsbQl+5A5u+js27clXwCQujzueegJNCEnSOSwEaTTFNgHRnGiEZSJEAaS9FcDQthgA10EkURKBkAtRAJ4VYSiAnJ2e4lPJx/elzxthzgUCgdaIOsrOzFzLG9NvL9afQR+bm5o5IdC6NK54ANdBJHakSoAvyVMnRPDMESHdm6NHcVAn4QXdi2VOQb71sIKpeE3zgBLCKlVPFRvNSJJBuvRXdd1kPm499BqxajVAG+v7oonPTqGzS0dgTb70CuWyu4bfO+VAGTUyRKk0rjUC6dVdaPHa+ry9zjV83698qgTPPdkVzUR76C6L7g9F/Z2Png1WrbicKV9iO1N7W/h0gv/o4flzVaoBdfh3kGy+F32c33Ane+rHwv+UfuyH6R69Uod8QhqwsV+RaYhDHj0F+vxHY8hNwwaXgHfqGl2aPnKfNegL47MOE8mHX3Ajevm9CY70+yE/nOK/XMpPyI91lUrW8FStpz1v1dDIbaqA7SZ98ZwIBaqBnQpUyKEZVVd8HcB0AfQ3IWsFg8GCi4auqqm/spT+pXgHAR8Fg8J+JzqVxxROgBjqpI1UCdEGeKjmaZ4YA6c4MPZqbKgE/6E6sWQq5dmU0ooqVwfuNBatZK1V0NC8FAunUW2i/3B4tgEPRl+Ts0T7g194Uil4e2A/Rq2VUJqEnGqucnEJ2iU+Rn30IoTeKCo8q1aBMWJC4ARqZFIF06i6pwGwYLHIXQL62Ot/y+ZeAt+sFdvIpkEcOAYf+Ajs5/U1r+edeiD7R95bzyUt8cRNTpPa2TRoO8ebauFVn198BVK4K+Uow/H7RBrH8bQvEsK7R56unVoNxxQYl2WNSCgHGebHGY1bnKCEM1rwj+K0N7Ak0w6z66RyXYaXxdLikO0+X19XJkfZcXZ6MCo4a6BlVLgrWAQLUQHcAupddqqqqrw+p/yLxUjAYbJRsrqqq6r90NASwOxgMnprsfBofS4Aa6KSKVAnQBXmq5GieGQKkOzP0aG6qBPygO/GfNZCB+TGIWAMVvHGrVNHRvBQIpFNvcv8+iN7G/sGF4bKIZZHl7h0Q+jLvEQefsRLshPIpZJf4FLnlR4hRPY0JjIPPeR5MyZxGWOLZOj8ynbpzMlu593eI/g/nP31eeFQ+Gfpe2jL32dBWBez628Fad0vrk+lO/Z05WYtC31EN9CVPQax4Om5YTF/OfM9OyBeeM97/xzVQHhsa/rfcvAliTB/j/bJloczKdUOalsUg3nsDcsG00u3py7ePnA12ov78AR1+OcdRpd1FgHTnrnr4KRrSnp+qbW+u1EC3ly9Zz3wC1EDP/Bq6KgNVVY8CyGKMPR0IBDomG5yqqk8B0Dc5Ox4MBvV91OkwSYAa6CYB+ng6XZD7uPgOpk66cxC+j137QXdi/RuQC+P/IK88/aKPq5/+1NOpN/ndlxATB8cmGbFcuty6GWJEd2MMY+BPvWB7czHuk+8+WdI6/aoD0qk7J/Ir9BmzNUAxwfAug0PLhafr8MKT06myitTeb6+ugTZjVFxTfNIiyA3/BxnZYL/gUih9jJUq5DefQ0w2GuqoWBnK5CWphubKeXLbFojh0U/Zo9wJ0G94Y3c2BH75EXLHVrDLrgOrWMmVOTgRlF/OcU6wJZ/FEyDdkTqcIkDac4q89/xSA917NaWMrCVADXRrefremgVPoL8A4H56At06KVED3TqWfrNEF+R+q7g78iXduaMOfovCD7qTn30AMWtM3NJSAz29irdDb/KHjRAvB0L76bL6jYC9v4eSkt9+EbWfcDhTRQGfuhTshBNDe/KK8QMMCOVPhDJ9he1QQsvLd1GB48fCvnj/cWDnXmi7bz86sEN3buSoTXkc2PhZ6aGddR74oIm23yhSGIj85QeI0b2MuLKyoMxZVXqcHhgRqb3tn3yEvKGdY7OqVRvK8BkQ766DXDzTeD/iZp/QOa3oZ1n1mlDGzPMApegUxOKZkO+uAxgDq3cbWKNWtm+rkekQ/XKOy/Q6eS1+0p3XKpo5+ZD2MqdWbo+UGuhurxDF5zQBaqA7XQGP+TezB3qrVq0qHDlyRN8DXd8LfUMwGKzrMTyOpEMNdEewe8IpXZB7oowZlwTpLuNK5omA/aA7+d1XEBMHxdbrxJOgTFvmiTpmShJW600ePQrRr01ob+dkDt5lENjldSG//Bhi+ghjahr3ItcGdwB2bQ/7Zu16gde9JZk0aGyCBKzWXYJu0zpMHjoI0asVoGkJ+eU9R4JddHlCY80Oir1RpQKU6cvNms2I+VEN9C2/IK9T05i42R33gz/wCMRH70A+PdF4/7QzoIycFf63+OBtyPmTjfdPPwvKsOkZwSHZIKV+bjypEi3RniA4P5zjEkRBw9JIgHSXRtjkKooAaY8EYRWB/2fvzOOkKM43/lQNghcqikrEYLyVxCsqHr9oPBKNtwIDiBFPEAVFEQVRBEQRRQQEBEHFW9gBvDWaGO9bo0ZDDNHgLYI3oiwwVb9PLzC9w149Pd3TVV3P/MdO1Xt8n8e2dt+Zbg7QoyLJOGklwAF6WpVNqK9sNuv99c27p5r30LnpuVzuzKCldO7ceaoQwlvv7b0yl8sNDbqX6xomwAE63RGWAA/kYclxXzkE6Lty6HFvWAIu+K7ObbpXw2qzJTIjbgyLjvtCEIjab3reu1Cj6/lwRBO1id//CfLP50B5t0yeeq2/eo2BVYgWA2/Jjx0KzH3TX//rPZA5v9YwP3AkLmyKQNS+aypfEu/XGa56RbRYB6j+uf5ydtwF8tTzgC8+AXbaFWKt5rGVrf/1JtS4Wr/ebtgKmetujy2fSYHX9N7yc7vW+cCPPHcIxK57Q7/9GtTEWrd432QzZEbdXGhHPf0Y9N2T/fa23QmZQbWuXyY1zloqSsCFa1xFgTJZIAL0XSBMXBQDAXovBqiOhuQA3VHh2XZgAhygB0bFhUEIZLPZdgDe8/5U4a0XQlQtX76833333bewof0nnnhi63w+P05rfaK3BcDSTCaz44wZMz4JkpNrGifAATodEpYAD+RhyXFfOQTou3LocW9YAi74Tn+zCGrgGXURbfYLZK66KSw67gtBIGq/1RmAB61pk80gr54G/fxfG71lctBwYdapZ/8CfWfxBzjksAkQbbcKE457GiEQte+ShK2/+wbq5jHAgs8g/nAM5J9WfqM5P2UU8MaLfmm77g3Z6RSo6y4FFn/feMmtN4ccNhGiRc2vsZG/6tx6vPXmyFw9LfI8JgasM0A/4+g6ZcoJM1Y+UuK9f0KNucx/f/0NkBl7V+Hf6vH7oGdN99/nh25MlDyRmtJ0jUsEIJOGIkDfhcLGTREQoPcigMgQNQQ4QKcRSKBxAhyg0yGRE8hms5cC8D427n2T3Ht5DzZ8VGv9ohDiI631EiHEegC8Yfv+AI5cNXD3/KiFEEOqqqrqf0hn5NWmPyAH6OnXOK4OeSCPiyzjNkaAvqM/kiDggu/00p+hvG/9rflquSEy19+ZBHZnc0btN/XkQ9AzGhjEtWoNrFgOeN+s/dX2wD9qDRe9T64e/2egeQvoqlt8PXbeDZn+tb4BGqNSeln1yg92/PiDn2W3DpB9Lq3Ys6ljbM+o0FH7Lsnm8pNGAm+9XChBDhkHtGm78vbt1UsLPxennAv5uz9C//Ad9LN/AZqvDf2X2Q0O00WPvpAHHBZLa03dmjyWpIYErTNAv/YS4D/vFFWXmfZgzb/1/HlQIwf47zVvjsykWYV/qwfvgX5ohv/+b/dH5uxBhnTKMpIkkKZrXJIcmbs0AvRdaby4OjoC9F50LF2PxAG66w5g/00R4AC9KUJ8PxSBzp07TxRCnLNqiF4zGG8kUMGHQoiJVVVV54VKyk31EuAAncYIS4AH8rDkuK8cAvRdOfS4NywBF3yntYbqdVxdRM3WQmby7LDouC8Egaj9pubcAf2YP2DySvIG4+KIzhBSFiqs8cCgM4BvviqueuPWxT/bY19kzin9lvAhUNRsUQ/cA/1wrYGYV3+viyD3PiBsSO6rh0DUvksKcs1zzvt1L0ovjsxCbLNT8a2/hYQccztEyw2L1qq/PQA9s9YHRmq/u+f+yPSOZxirXvgb9G21ntW91XbIXFbrWd5JAa1A3jW9t+Klp6C9OwiseoluPSEPPabmX/rzj6GG9i2qSt50H4TMrLxeVN0C/dcH/L37HQJ5+vkV6IIpTCeQlmuc6ZxZXzEB+o6OSIoAvZcU+fTl5QA9fZqyo2gJcIAeLU9Gq0WgS5cuXbTWVwPYOgCY+VrrQbNmzcoFWMslJRDgAL0EWFxa/McqKdGmTZuany1YsABKKRIigdgJ8BfB2BEzQT0EXPFdvuex9eovb5wNsdZa9EaFCETtN3XbeOgXnixULw45GvLEXvV2o178O/T0cY12KvY7GPL0CypEA9BLfoQacnbxt4Jbbgg5fBJEyw0qVkfaE0Xtu6R4qVeeKRq+1tSx5/4rb//9wt/8snb4NTIXeb+KFr909VKoQWcW3/Vg9ZIdfoPMRfHcCE099Sj0PVP8YrZvj8zFo5LCWNG8a3ovv3w5tPdN8ndeh9hxF4hOp0A0W/n/IP31wpX61HrJIWMh2m1b8xN15yToZx/3r3cHHwXZzEAbeQAAIABJREFU/ayK9sNkZhJIyzXOTLqsqiEC9B29kRQBei8p8unLywF6+jRlR9ES4AA9Wp6MVpeA6NKli3cfvEO01nsA2BTA+gB+BLBICPEmgL9XVVU90cS31Mk2JAEO0EOC4zbwQE4TJEGAvkuCOnO64rv86MHAvHfrCC6vv4uDygr+ZxC13/LjhwPvvuEPlI7/M+RRXRrsqNFbvnvf/j74SMjuvStIBNBvvAA15ZqinGKf30OeeWFF60hzsqh9lxQrddO10K8/X5zeG65+s6hoKC66nAH5x3ruuuENYR+pgr7ff652IViZj7TQc99c+WGWLdpBHN4RolmzQmj1xH3QuVrP7m6/BzIXDE8KY0XzluI9vfgHqP5/rvv/qQuvhNhpV6hpY6Bffca/3h3RCbLjKRXth8nMJFCKz8zsgFXZSIC+s1G1dNRM76VDRxO64ADdBBVYg8kEOEA3WR3WRgIREOAAPQKIjobggdxR4RNum75LWABH07viOz3vX1DjhwHLqouUllfdBLHZLxxVv/JtR+23/IjzgY//5w+UVj33ubHO1MtPr/wmej13l/Fu/S479qgomJrby0++GnjTf661V4A8dwjErntXtJa0Jovad0lwqvn2+IBTgKU/N5lejpwKsenKOymt+dI/LYG65EzgpyV13gv7gSK98HOoIecU/psSx3SDPNa/1bx6eCb0A3f7+XbfB5k+lzbZRxoWlOI9vawaqk+2btu/3Q+Zsy9BfuKVwNuv+te7Jj4wlAZ+7CEYgVJ8FiwiV5FA0wTou6YZcUU8BOi9eLi6GJUDdBdVZ8+lEOAAvRRaXEsCFhLgAN1C0QwpmQdyQ4RwrAz6zjHBDWnXJd/pFcuhzu5URL727XENkSTVZUTtt/yAU4Hvvykwk+ddDrHLXk0y1P98beW3vpcvK1orTjgZ8sh6BlhNRixvgf7uG6ihfYqHmq1aQw6fCLHOuuUF5+5U3FlIPfkw9IypTau55dbIDB3f6Dr977drbgeORQuKr4cXjYTY4TdN51hjRX7yKOAfL/o/bd4CmUn+08nUnDugH5tVeF/sfQBkr4tKzmPjhlKueTUfpjnrBEDXfXRUZtqDyF93KfCfd3yOtZ6fbiMb1hwdgVJ8Fl1WRnKdAH3nugOS65/eS4592jJzgJ42RdlP1AQ4QI+aKOORgGEEOEA3TBCLyuGB3CKxUlQqfZciMS1qxTXf5c8/CViyuKCQHDASYsfSB0YWSWxUqVH6TSsFdXbHom+Sy8vGQmy18nnBTb30f+dCTRgB/Ox/E1f2uRRi932a2hrL++qFv0HfdkNRbJE9HfKw42PJ51LQKH2XBDedz0Ndehbw9cIm06/57e/GNuSHnQt89lFhiTipN+RBRzaZY80F+Z7H1tnjDXxXv9TMm6H/5v9b/N+hkKf2KzmPjRtK9V7+3K713mVATp4Ddc1A4MP/+noFuOOGjcxYc+kESvVZ6Rm4gwTqEqDv6IqkCNB7SZFPX14O0NOnKTuKlgAH6NHyZLQGCGSz2XWUUrsKITaVUrZUSi3OZDJfKaXezuVyTd+Dj2RDE+AAPTQ65zfyQO68BRIBQN8lgt35pK75Lj/ozKIhlOx7GcRuHZz3QaUAROk3vfh7qP4nF5UuR0+H2GiTwO3oT+ZD3TYe+ORDiA4HQJx6HkSztQLvj3JhzbdPx14O/PttP+we+yJzzuAo0zgZK0rfJQFQvfos9LTrAqWWQ8dDbLl1oLVq6mjo154rrBUHHwXZ/axAe2svanKAfueN0M/+xc9z0JGQJ/UuOY+NG0r1Xn0svb7liMlQk64CFnxawCDPuhhir9/ZiIU1R0ygVJ9FnJ7hHCVA3zkqvAFt03sGiJCSEjhAT4mQbCM2Ahygx4aWgbPZbAaA9xe9ngC8hxd6/17zlQfwGoBpixYtuuvpp59eQXLREuAAPVqeLkXjgdwltc3plb4zRwuXKnHNd/nh5wGffugPcs64AHLfg12SPNFeo/Sb/vRDKE/P1S8hIG+cDdGsWUk9eoNr75bJQtZ3XC8pVNmL1QtPQnsD/dWvzX6BzFU3lR3X9QBR+q7SLGs+WHFlf+DjD5pOvfNuyPQf0fS6VSvqPJu83TbIDBkXeL+3UP/wLdSFp9TZU/QN9FvHQr/0lP+f6mHHQ2ZPLymPrYtL9V6DA/S+Q6Dungx8+1UBhTxvKMQue9qKhnVHSKBUn0WYmqEcJkDfOSx+wq3TewkLkKL0HKCnSEy2EgsBDtBjwcqgnTp12lVKeQeAXVbRaMxretWadwGcksvl3iLB6AhwgB4dS9ci8UDumuJm9EvfmaGDa1W45rv8NYOA9+f6g5zuvSEPLv2Wxa75JKp+o/Sbnvsm1NihfmktN0Tm+jujKjWROPqjD6CuvMDP7X0o4IYZEGuvk0g9aUkape8qzaTmeeXXDylK6z1DvPY3x1e/KYeMhWgX7BEG3h79n3ehriu+w4G8djpEq/rv4qCX/gz9wD3Qn30ING9R40tdvRR465ViLFIic9P9hZ+t+Yx0cXRXyONOqjTKRPKV6r2GBuje4xz0IzOBn2o9cuKiqyF2+HUifTGpWQRK9ZlZ1bMaWwnQd7YqZ3/d9J79GprSAQfopijBOkwlwAG6qcpYXFfXrl33VUo9BmADALU99iMA735r3m+86wFoC6Dlqla9Ibq3drGU8vCZM2e+bDECo0rnAN0oOawqhgdyq+RKTbH0XWqktKoR13yXv+EK4J3XCxqJjj0gj+hslWY2Fxul39RLT0HfOtbH0XYrZIZNsBkP9LJqqL5da74Rv/olLxkNsc2OVveVdPFR+q7SveTHDQX+9aafdtM2kFfcCP1oFfRjs4EVy2veC3P7db1iBdSFJxcNZcXJfSAPPLzeNtUdE6GfeyIQAjnZuxvEysch5L0Pusz1exDZ0yAPOyFQHNsXleq9BgfoBx4O/fxfAVXr2jBkHES7bWxHxPojIFCqzyJIyRAkAPqOJkiKAL2XFPn05eUAPX2asqNoCXCAHi1P56Mde+yxLVu0aOF9pckbjnuvaiHEJK317blc7p01AXXt2vU3+Xz+FCFEHwAtVg3RP62urv71gw8+uNh5oBEA4AA9AoiOhuCB3FHhE26bvktYAEfTu+Y7Ne066FefLagtjugM2bGHo+pXvu0o/aYenwM96za/iRJvX1357oNlzA85p+g5x6JHX8gDDgu2mavqJRCl7yqJWH8yH+qKfkUpxUm9IQ9aedcM/c1X0G+/CqzfEmLP/UM9hmDN56Bjtw7I9L2s3jbzF50GfPd1IARy3N0Q6638vHj+6ouA//3Hv+42MqQPFNyiRaV6T825feUHI9Z8eR+iqcXQe1teNQVisy0sosFS4yJQqs/iqoNx3SJA37mlt0nd0nsmqWF3LRyg260fq4+fAAfo8TN2KkM2mx0EYKT3twwAnwH4Uy6X8+8R2gCNrl277qyU+guAX3p7hRCDq6qqrnEKXkzNcoAeE1gHwvJA7oDIBrZI3xkoigMlueY7deeN0M96x66VL3HQkZAn9XZAaTNajNJvavbt0H/xB02iw4GQPQeY0WgZVagp10C/8YLv0UOOhjyxVxkRuTVK31WSprplLPTL/rPD0XJDyFE3QzT3PnsdzUu9/BT0LbXu5NC8BeT4eyGaNStKUHN3hD7ZwEnlqFsgNtm0Zn3+8j7AF5/4nu45ALLDgYFj2bywVO95t8TXs2+DfurR4rYzzYD8iqKfyetuh9iwlc14WHtEBEr1WURpGcZxAvSd4wZIsH16L0H4KUvNAXrKBGU7kRPgAD1ypG4HzGaz3sPf9l41QP9dLpd7KSiRbDa7HwDvL2Xe8P2NXC7XIehermuYAAfodEdYAjyQhyXHfeUQoO/Koce9YQm45js16zbox+f4g5x9D4I8o39YfNxXIoEo/abunAT97OO+lin5MIR6eCb0A3f7ZHf4DTIXeZ/R5SssgSh9F7aGUvdpraH6nQj8/JPv8WO7Qx7TrdRQja7Xi7+H6n9y0Zr6nqWuP/8YamjfonXisOMB7xnoPy2p80x2OWwiRNt2NevzF58OfPtVYa8873KIXfaKtA9Tg4X1nv7sY6hhxbzX7FFOrIJosbaprbOuChII67MKlshUKSRA36VQVEtaovcsEcqCMjlAt0AklpgoAQ7QE8WfvuTZbNa7n91GAF7P5XL7lNphrQH8d7lcbuNS93N9XQIcoNMVYQnwQB6WHPeVQ4C+K4ce94Yl4Jrv1CNV0Pff5eNq5HbFYZlyX8MEovSbuula6NefLyQTR2YhTygeBNqohX7jRagpo/zSN22DzMipNrZiTM1R+q5STemlP0GdWzwsl1dOgdg8+lt25wf3AhYt8P9bOulsyIOOKGrVu1W8mnil/7OWGyJz/Z2Ff+e9b6cvqy78W14yGsK77bg3QD+vW9EHAeTFoyC2b18plInmCes9vXzZym/8a+/z9fW8hICcch+ElIn2x+RmEAjrMzOqZxW2EqDvbFXO/rrpPfs1NKUDDtBNUYJ1mEqAA3RTlbG0rmw2+yOAdQBMz+VyZ5baRufOnW8RQpwGYEkul1v5wDi+yiLAAXpZ+JzezAO50/In1jx9lxh6pxO75jv15MPQM2oNI3fcBZkBVzntgUo2H6Xf8mOHAnPfLJQvOp8GefgJlWwnllz63Tegxg/3Y7dqjcy1t8aSy5WgUfquUsz0wi+gLj2rKJ2cMBNibe/XzWhfatoY6Fef8f9b2v9QyNOKn72u/vYg9Myb/cTb7IjMJaML/85732Jf/H3h37L/CIidd4NWCqr3CUWDYDl0PMSWW0fbhKHRyvFe/pKewFdf1t9Zi3WQmTjT0K5ZVqUJlOOzStfKfOkhQN+lR0vbOqH3bFPM3Ho5QDdXG1ZmBgEO0M3QITVVZLPZ/wDYDsDduVyuR6mNZbPZOwD8GcB/c7ncyo/r81UWAQ7Qy8Ln9GYeyJ2WP7Hm6bvE0Dud2DXfqRefhJ4+3te83TbIDBnntAcq2XyUfsuPHADMn1coX/ToC3nAYZVsJ5Zc+t9vQ10/xI+9wUbIjPF+TeArLIEofRe2hlL36Q/egxp1sb9treaQk3IQIvo/Y9T5YNEvfonMFZOKSlb3ToX++8P+f2/7/B7yzAsL/17zW+zynMEQe+yLer9Jf/U0iNabl4rEyvXleC8/bijwL/9DQkUANtwYmetus5IJi46eQDk+i74aRnSFAH3nitLm9UnvmaeJrRVxgG6rcqy7UgSi/82zUpUzj5EEunTpMk1rfUbYAXitAXyob7AbCSXhojhAT1gAi9PzQG6xeBaXTt9ZLJ7FpbvmO/3my1A31nqeNG+PXVH3Rum3/GVnA19+Vqhf9h4Isef/VbSfOJLpee9CjR7sh16vJTLjaj0TPY6kKY8Zpe8qhUq/9QrUpFp3x9h4U2SuuSWW9Hr+PCjvAym1X7vvC7HrXjXPKhcbbYz8DVcA77xeWCGO7gZ5XPfCv/PDzwM+/dB//4wLIPc9GPq7r6Eu8m6y5r/kuLsh1nPjhmvleE/NmAb95EP1a96mLTIjJsfiBwa1j0A5PrOvW1ZsCgH6zhQl3KuD3nNP87g65gA9LrKMmxYCHKCnRUlD+ujUqdNeUsqXAQit9UmzZs2aEbS0zp07dxNC3ANAAdg/l8u9GnQv1zVMgAN0uiMsAR7Iw5LjvnII0Hfl0OPesARc852e+xbU2Mt9XPx2b1jrhNoXpd/q3DL6gisg2u8eqi6TNtX55vHa6yAzgbdqLkejKH1XTh2l7FXPPQF9x0R/y1bbIXPZ9aWECLxWL18OdV5XYMWKunsyGYiOp0A/9ziwwP/AijjtfMj9Dymsz18zEHj/34V/i1XPUddffAp1+TlFceXkORDNmgWuz+aF5XhPPfUI9D031d8+Hz9isy0ir70cn0VeDAM6Q4C+c0Zq4xql94yTxNqCOEC3VjoWXiECHKBXCLRLabp06TJMa+39VfZnAKfkcrlZTfXfuXPnjkKIO1c9P31ELpcb2tQevh+MAAfowThxVV0CPJDTFUkQoO+SoM6crvmuznCyxdrITKyiESpEICq/aa2hzulUNPCTl46B+NX2FeokvjT6w/9CXeXfGhtrNUfmxiZ/pYivoBREjsp3lUShHs1B3+f9irjq9Zs9kekX36+J+YlXAm838BluKQHlfc7bf8mBoyC2a1/4wZq3GxedT4M8/ATU+XZ78+bITHLHz+V4r84HvmrxF3sfANnrokpakrkMJlCOzwxui6UZToC+M1ygFJdH76VY3Aq3xgF6hYEznXUEOEC3TjI7Cu7cufO5QojRANYC8JwQ4g4hxEvNmzf/+M477/zp5JNPXnfZsmXt8vn8vkII75nnBwFYprW+eNasWRPs6NKOKjlAt0MnE6vkgdxEVdJfE32Xfo1N7NA13+lPP4TybjW8+iUE5JT7ILwBEV+xE4jKb7q6GqpvtqheedUUiM22iL2HuBPoT+ZDXdGvlkclMlPvjzttquNH5btKQlIzb4b+24P+pWq/QyBPPz+2EvTXC6HunATMfQvQusk88rrbITZsVViXnzwK+MeLfr2rbvHu+l0/yvGe/noR1CDvCXF1X+IPx0F2rf+9JsXjgtQRKMdnqYPBhipGgL6rGGomWoMAvUdLREWAA/SoSDJOWglwgJ5WZRPqK5vN5tdI7Xms6b8+AEHXeeF1Lpdz4353EejIAXoEEB0NwQO5o8In3DZ9l7AAjqZ3zXd60QKowb2K1JYTqyBarO2oAyrbdlR+q/e5ytffBdFyg8o2FEM2/fnHUEP7Fnv0pvv5IY8yWEfluzJKKHmrmjYG+tVnCvvE4SdAdi5+lnjJQQNs0D98B/3uP6AfuAv45qv6d6y7PmqeYy78P6mo6eOhX3zSr/ePx0F2OQP6Hy9CecP11a/NtkDmqikBKknHknK8p5WCOrcLsGxZHRii86mQh3dMByR2UTaBcnxWdnIGcJYAfees9Ik3Tu8lLkFqCuAAPTVSspGYCHCAHhNYV8Nms1nvvnYNDczr81t9a5vypTdAz7jKuNS+OUAvlRjXrybAAzm9kAQB+i4J6szpmu+84ZC6sEeR8HLMHRAbbEQzVIBAVH6rd8js3UkgY/8xWS/8HOrS3sUevXE2xFreza34CkMgKt+FyR12T37s5Su/Db7qVemBqbrtBugX/lZ/+b/5LTL9hhW9p+65CfqpR/x6Dzwc8uQ+UC88CX3beH9tu22RGTI2LBbr9pXrvbx3x5RPP6zTtzjjAsh9D7aOBwuOh0C5PounKkZNOwH6Lu0Km9sfvWeuNrZVxgG6bYqx3koTaGpQWel6mM9yAqsG6HF3wQF6CYQ5QC8BFpcWEeCBnIZIggB9lwR15nTNd7p6KVTfLsXX/JFTITZtQzNUgEBUftPv/xvqmoF+xS3WQWbizAp0EH+KmltpDzqz2KMTZkKsvU78yVOaISrfVRJPfng/4NP5hZTitH6Q+x9asRLUE/dB56bXm08c0w3y2O5F76k5t0M/Ntuvt8PvIXteCPXkQ9Azpvlrd9wFmQFXVayPpBOV6738lFHAG/6t8Vf3I/uPgNh5t6TbY35DCJTrM0PaYBmWEaDvLBMsReXSeykSM+FWOEBPWACmN54AB+jGS8QCSaA8Ahygl8fP5d08kLusfnK903fJsXc5s2u+q7klbu8Tip7xK4feALHlr1y2QcV6j8pv+p3XoW64wq+7VWtkrr21Yn3Emaje29OPuwdivfXjTJvq2FH5rpKQ8gNOBb7/ppBSnjcUYpc9K1aCfvcNqPHD680n+w2F+E1xLeqRKuj77/LX79YBmb6XoaGfV6yRhBOV6z11313Qj1bV6UIOnwixRbuEu2N6UwiU6zNT+mAddhGg7+zSK03V0ntpUjPZXjhAT5Y/s5tPgAN08zVihSRQFgEO0MvC5/RmHsidlj+x5um7xNA7ndhF3+W9b6BXL/UHU4Ouhdh2J6d9UKnmo/KbeuUZ6JvH+GW33QqZYRMq1UasefTi76H6n1yUg48ZKA95VL4rr4rGd+v5/4V66F5grbUgO54CNbQPkM/716nLrofYars4SyiKrb9eBDXojHrz1Tz/fL2WRe819E1zNes26MfnFNaKfQ+CPKN/xfpIOlG53lMv/h16+rg6bdSnQdK9Mn9yBMr1WXKVM7PNBOg7m9Wzu3Z6z279TKqeA3ST1GAtJhLgAN1EVVgTCURIgAP0CGE6FooHcscEN6Rd+s4QIRwrw0Xf5b1noP/wnT+YuuAKiPa7O6Z8Mu1G5Tf11KPQ90zxm9iuPTIDRyXTVMRZ9U8/QvUrvj22vHY6RKtNIs7kTriofBcXMb182crn3n/71coUrTcHvvqyKJ0cdQvEJpvGVUKduFprqHO7AdU/F7+32RbIXFXrv71V76rn/wp9e60Psfxqe2QuHQN1143Qz/ylEEMcdCTkSb0r1kfSicr1nv7gPahRFxe3ISTkTfdBCP5JK2l9Tclfrs9M6YN12EWAvrNLrzRVS++lSc1ke+EAPVn+zG4+Af62Yb5GrJAEyiLAAXpZ+JzezAO50/In1jx9lxh6pxO76Lv84F7AogUF3WWfwRC77+u0DyrVfFR+q3Nb6F33RubcIZVqI9Y8unoplHeXhFovefU0CG+oylcoAlH5LlTyAJv0vHehRg9udKWclINo3iJAtOiW5EcOAObPKwooDj4KsvtZdZKo156Hnnqt//Nf/BKZKyZBTRsD/eozhZ+LIzrVfMPelVe53tNLFkOdf1IdXJlpD7qCkH0GIFCuzwKk4BISqEOAvqMpkiJA7yVFPn15OUBPn6bsKFoCHKBHy5PRSMA4AhygGyeJNQXxQG6NVKkqlL5LlZzWNOOi7/LDzwM+/dAf6JzRH3Lfg6zRzOZCo/Kbyk2HfuI+X8N9D4Y84wKb0RRq1yuWQ53dqagXeeUUiM23SEV/STQRle/iql09Nht6zu0Nh197HWQmzIwrfYNx1YP3Qnu3lV/92nEXyHMGQ6y7Xp09+p03oG6o9cz0Vq2RufZW5CeMAP75mv/f6gknQx6ZrXgvSSWMwnv5nsfWKZ8D9KQUNTNvFD4zszNWZTIB+s5kddJdG72Xbn0r2R0H6JWkzVw2EuAA3UbVWDMJlECAA/QSYHFpEQEeyGmIJAjQd0lQZ04XfZf3bof7wXv+QOfkcyAP/BPNUAECUflN3TER+rknfA0PORryxF4V6CD+FDW3zu51XPG5ZPhEiC3axZ88pRmi8l1cePKTrgLeeqXh8NvuhMygWt/ujquQNeLqZdXQD9wN/dEHEHvsB3HwERAyU292Pe9fUKMv8d9bdz1kxt+LvPezef/y/1vtfhbkwUdVqIPk00ThPQ7Qk9fR9Aqi8JnpPbI+8wjQd+Zp4kpF9J4rSsffJwfo8TNmBrsJcIBut36sngSaJMABepOIuKABAjyQ0xpJEKDvkqDOnC76Ln/9EODfb/sDnezpkIcdTzNUgEBUfstPGQW88aKv4dFdIY+re5vjCrQUS4r8WccDShViyyHjINptE0suF4JG5bs4WNV8YOLCHsDi7xsMLwePgdh6+zjSRxZTf/wB1Ihad4HIZCAnz4EacT7wyXz/v9XTL4Dc7+DI8poeKArv5a8ZBLw/12e45/9B9h5oeuusr4IEovBZBctlqpQQoO9SIqSFbdB7FopmaMkcoBsqDMsyhgAH6MZIwUJIIB4CHKDHw9WFqDyQu6CyeT3Sd+Zp4kJFLvouP2kk8NbL/jDiuO6QR3dzQe7Ee4zCb/qLT6DGDwe+XuhrmLIPQeTP6QwsX1boz4YBauLmaqSAKHwXV3960QKowQ3fPUEc0w3y2O5xpY8srv7yc6jLehfFkzfOhhraB1i0wPeydwv4PfaNLK/pgaLwnv7vXKhrB61sVUjIgaMgtt3J9NZZXwUJROGzCpbLVCkhQN+lREgL26D3LBTN0JI5QDdUGJZlDAEO0I2RgoUkQaBjx45bZTKZ8wB499D7JYBqAB8AqFq6dOmkhx566Kdy6+rUqdOuQoi+QgjvwaJtAXhfpflEa/2IUmrinDlzPio3R2P7OUCPk266Y/NAnm59Te2OvjNVmXTX5aLv1M1joF95piCsOLwjxNFdoP/xEvDl5+kWPOHuhBBYb72Vz09esmQJvG/fBn6tvQ6w8Avol58GViwv2iZOOx9y/0MChzJ9Yf7crsDSnwtlyoHXQGy3s+llG1ufydc59coz0DePaZCd6HUx5N6/M5bt6sL0999CDTilqE55/Z1Ql/cBfvzB93L/ERA772Z8P1EVGJX39AfvQb/3T4idduXwPCpxUhQnKp+lCAlbqQAB+q4CkJmiXgL0Ho0RFQEO0KMiyThpJcABelqVZV9NEujSpcsxWuu7AGzQwOJ53mA9l8u932SwBhZks9nhAIZ4n5NvYMliIcRpVVVVs8PmaGofB+hNEeL7DRHggZzeSIIAfZcEdeZ00Xfqzhuhn/1LQXxx0JHQXy0A3v0HDWEjgXXWhRw+CaLVJjZWX2/N+fNPApYsLrwnB4yE2PE3qemv0o2YfJ1T99wE/dQjDSKp+bbxdu0rjazkfLq6GqpvtmifvOx6qCv7F/9s2ASItluVHN/WDSZ7z1amrLsuAfqMrkiCAH2XBHXm9AjQe/RBVAQ4QI+KJOOklQAH6GlVln01SqBTp057SClfALAOgB+FEFdrrZ/y/q217iaE6LkqwLzq6uq9HnzwQf+vdwHZZrNZ7x5zV69a/oUQYnQ+n695UGUmk9lfa30xgDYAvHtTHprL5Z4PGLqkZRygl4SLi2sR4IGcdkiCAH2XBHXmdNF3quoW6L8+4Iv/i18CX3xCM9hIoE1byDMvhNhqOxurb7DmvPdN3u+/LbwvL7gCov3uqeqxks2YfJ3LewPmjxr+zLK8ehpE680riStUrppnuZ/dEcjnC/tFt17QM6b68bznok+sgmi2VqgcNm4y2XvcTJ50AAAgAElEQVQ28mTN9ROgz+iMJAjQd0lQZ06PAL1HH0RFgAP0qEgyTloJcICeVmXZV6MEstnsswAOALACwIG5XO6l2huy2exFAK71fiaEGF5VVTWsFKQdO3bcMpPJeH8FagHg80wms/eMGTOK7oeazWa927m/CmALAP9s3779HsOHD/du7x7piwP0SHE6FYwHcqfkNqZZ+s4YKZwqxEXfqQfugX54hlM6p65ZISD+eBzEcSdBNPeOnOl65QeeDnzzVaEped5QiF32TFeTFezG1Otczbe2+3UrGjqvicV7jrhYy46B85p3ThB7/Q769Vqfk267FTLDJlRQ+eRTmeq95MmwgigJ0GdR0mSsoATou6CkuC5qAvRe1ETdjccBurvas/NgBDhAD8aJq1JEIJvNdgDwiteSEOKmqqqq3mu2N3ToUDl37tx3AXgPWvyuVatWm02dOrX4QZONMOnSpcsArfVob4nWuuesWbNurm95586dzxRCTFu17phZs2Y9HDVqDtCjJupOPB7I3dHapE7pO5PUcKcWF32n/jIbevbtDYu81XYQm3uf9eMrcgICWGcd7yZIwM8//wwEfAS6XlYNvD935bOUf/FLyJP7QGxv/m2tw/LLD+4FLFpQ2C77XAqx+z5hwzm/z9TrnJ73LtTowQ3rs/4GyIz1nrplxyt/SU/gqy/9YjfaGPjum8K/RYcDIXsOsKOZiKo01XsRtccwhhCgzwwRwrEy6DvHBDeoXXrPIDEsL4UDdMsFZPmxE+AAPXbETGAagS5duozUWl/i1aWU2nf27Nk1w/Q1X7Vvwa61PnzWrFlPBO0lm816X+nqumr9lrlc7rP69q76pnrN/VK11rfPmjXr1KA5gq7jAD0oKa5bkwAP5PREEgTouySoM6eLvlNPPQJ9z00Nii8Hj4HYenuaIwYC5fhNqzzww3fAhht7HwSNoTpzQuaHnA0s8I/QsvcgiD33N6dAyyopx3dxttrkh3m23BqZoePjLCHS2Pkr+gGfzG8wpujYA/KIzpHmND2Yqd4znRvrK40AfVYaL66OhgB9Fw1HRimdAL1XOjPuqJ8AB+h0Bgk0TiDdf3Wh+iRQD4Fat29fsmjRoo2efvpp7zbudV7ZbHY/ADXPLAdwRS6XGxoUaDab/SuAP3jrFy1atFZDOXr16rXWt99+6z0D3XvNz+Vy2wTNEXQdB+hBSXHdmgR4IKcnkiBA3yVBnTld9J168Uno6Q0MpVq1hrzmltQPaJNyvot+C8M6P+xc4LOPCltFzwGQHQ4ME4p7DH5WZn7SSOCtlxvWqP3uyFxwhTUa5kdfAsz7V4P1ynOHQOy6tzX9RFEor3lRUGSMpgjQZ00R4vtxEKDv4qDKmEEI0HtBKHFNEAIcoAehxDUuE+AA3WX1He09m80uAtAawNu5XG73hjB079691fLly1ffby+Xy+W6BEWWzWbvA3C8t37FihWt77vvvq/r23viiSe2XrFihVeP99JLly5d/6GHHvopaJ4g6zhAD0KJa+ojwAM5fZEEAfouCerM6aLv9BsvQE25pl7xxcFHQXY/i8aIiYCLfguDMj/ifODj/xW2itMvgNzv4DChuMfQAbrWGmrAKSvvqtDQa5e9kDnvcms0zE+8Enj71QbrlaNugdhkU2v6iaJQXvOioMgYTRGgz5oixPfjIEDfxUGVMYMQoPeCUOKaIAQ4QA9CiWtcJsABusvqO9j7qaeeuvaSJUt+XtX6I7lc7ujGMGSz2R8BrAfg5Vwu530jPdCr9m3itdadZs2aNae+jZ07d+4ohJi9+j0p5Y4zZ86cFygJAO8W8E2traqqei2TybTJ5/Pet+GbWs73SaBAwDuQb7bZZjX/XrhwoffIA9IhgdgJ0HexI2aCegi46Dv1zhvIj6v/5jqZAVdB7rwbvRITARf9Fgbliiv7Q8/3j8WZU8+DPOCwMKG4Z9UA3bRznV60ACsGndmoPmKPfdGs72XWaLhi2nXQLz9df73rrIdmE2Y4d3cPXvOssa/VhdJnVstnbfH0nbXSWV84vWe9hMY0sOmmmyKTyXj1fCaEaHLOYEzhLIQEKkSAA/QKgWYaMwhks1nv4/4LV1UzM5fLdWussmw2+yUAb4L4bi6X2yVoF127dt1XKfXSqvVvr7feevvedtttS2vvXzXM9+5XWPsv1Hvlcrk3gubJZrO6qbWTJ0/GJpts0tQyvk8CJEACJEACJOAQgep338TCgT3rdCxbbogt7n4cItPMIRps1UQCX150BpbNfbtQWqs+g7D+kW49O9pEXaKqacUXn2Lhpecg/+XnhZByg42g1vg2+joH/hGtB14dVdrY43x74zX48ZFcvXma/3p3bH7tzbHXwAQkQAIkQAIkQAIkQAIkUCIBDtBLBMblbhDgAN0NndnlKgLdunX7ZT6f/3jVP+/M5XI9GoOTzWa9tb8E8EEul9uuFJDZbPYhAKu/4f6q1npwdXV1zVC9RYsW+wkhRgLoAMB7BnrzVbEPyOVyzwfNwwF6UFJcRwIkQAIkQAIkUJvAsvffw5f9/lwHyrp/OAabXFD/N9NJkAQqSWDhoLNQ/Y7/udKNzhqAlsc2+tnXSpbHXGUQUEt/xhenHg21+PuiKGt3OAD65yWofucfhZ9vNvpmtGjf4FO3yqginq3f3TYRi3O31Rt8vSM7Y+M+g+JJzKgkQAIkQAIkQAIkQAIkEJ4AB+jh2XFniglwgJ5icdlaXQKV+ga6lzmbzW4M4C8A9m5Ei0cAVHt3Y/fWSCl3nzlzpv9VmyZE5C3c6fI4CfCWUHHSZeyGCNB39EYSBFz0nV7wGVZcWvc555lzh0Duvk8SMjiT00W/hRF3xZgh0HPfLGyVXc5A5vATwoTiHsNu4a7eegX5CSPq6CI79oDYegfkbxwJ/PwTxG/3R+acS6y65Xn+0RzU7Nvr9Zz88znIHHykc37kNc85yRNpmD5LBLvzSek75y2QGAB6LzH0qUvMW7inTlI2FDEBDtAjBspwZhOo1DPQV1NYle9cAGcA2LEWnY8A3NC+fftxc+fO9Ybof1r13la5XG71N+Qjgam1/hRAW+8Z6F9+6d2Rni8SCEbAO5C3adOmZvGCBQv4DPRg2LiqTAL0XZkAuT0UARd9p7/9Guri04p5tVgb8vo7IZq3CMWRm4IRcNFvwcgUr8rfcAXwzuuFH4qOp0Ae0SlMKO5ZNUA35Vynnn4U+u4pdXSRF14JsdOu0D/9CCz5EWi9uVXDc68h9dSj0PfU7c17T148CmL79s75kdc85yRPpGH6LBHsziel75y3QGIA6L3E0Kcu8eabb85noKdOVTYUJQEO0KOkyVhWEMhms18B8B4K/nYul2vwfoDdu3dvtXz58m9WNZXL5XJdymnQi1ddXd26efPmP9x7772FSXY2m30fwLYAfsjlchsBaPK55qXUwQF6KbS4tjYBHsjphyQI0HdJUGdOF32nf1oC1e/EYvG9b3uezdsLx/1fhIt+C8M0P2kk8NbLha3iuJMgj+4aJhT3mDZAf+Bu6IdnFuuy0caQI6dCrLX6yVZ2yqZefhr6luvrLV6Ovwdi3fXtbKyMqnnNKwMetwYmQJ8FRsWFERKg7yKEyVAlEaD3SsLFxY0Q4ACd9iCBxglwgE6HOEcgm80+C+AAAEsWLVq00dNPP72iPgjZbHY/AC+ueu+KXC4X+QNBV93mfZH3Ny0Af8/lcodGLQgH6FETdSceD+TuaG1Sp/SdSWq4U4uLvtP5PFTv4tthizP6Q+57kDvCJ9Spi34Lg1pNuQb6jRcKW8XR3SCP6x4mFPeYNkC/YyL0c08U6SKHjIVo532m2O6Xfvs1qIl1b0+PjTdF5ppb7G4uZPW85oUEx20lEaDPSsLFxRERoO8iAskwJROg90pGxg0NEOAAndYggcYJcIBOhzhHoEuXLiO11pd4jSul9p09e/Yr9UHIZrPeV7Cu9t7TWh8+a9as4r/yRECuc+fOZwohpq0KdVYul5saQdiiEBygR03UnXg8kLujtUmd0ncmqeFOLa76Lj9yADB/3kqh198A8qqbINZdzx3hE+rUVb+ViltNGwP96jOFbeKIzvCekc1XOAIm+a7O7flTdHcBPe9dqNGD64q0y17InHd5OPEs32WS9yxHyfIbIUCf0R5JEKDvkqDOnB4Beo8+iIoAB+hRkWSctBLgAD2tyrKvBglks9kOAGqG5kKIm6qqqnqvuXjo0KFy7ty57wLYGcB3rVq12mzq1KnLo8S66vnocwFsDeBbAO1yudyPUebwYnGAHjVRd+LxQO6O1iZ1St+ZpIY7tbjqO71oAVTVLUD1Usiju0Hs8Gt3RE+wU1f9Vipydes46Jf+XtgmDjsBMntaqWG4fhUBk3yXH3EB8PEHvrY9+kIecFgqtNKfzIe6ol+dXsQRnSA7npKKHkttwiTvlVo719tDgD6zR6s0VUrfpUlNu3qh9+zSy+RqOUA3WR3WZgIBDtBNUIE1VJxArdu4e7dvPzCXy71Uu4hsNnsRgGu9nwkhhldVVQ1b4/1TAUxv6H3v59lstk379u0XDh8+XK3ZYDabXQfAPQCO997TWp8+a9asmnhRvzhAj5qoO/F4IHdHa5M6pe9MUsOdWug7d7Q2oVP6LZgKao3bfItDj4Hs1jPYZq6qQ8Ak3+UHnAp8/02hRnne5RC77JUK1Wo+mDS4V51exJkXQu7z+1T0WGoTJnmv1Nq53h4C9Jk9WqWpUvouTWra1Qu9Z5deJlfLAbrJ6rA2EwhwgG6CCqyh4gQ6deq0h5TSe6iiN8j+UWs9MpPJPKW1Xkdr3Q3A6r96zKuurt7rwQcfXFy7yGw22+QAvUuXLgO01n0AzADwvFLq80wm01IptbcQ4mwANQ/5E0LcXFVVFdtfAzlAr7i9UpOQB/LUSGlVI/SdVXKlplj6LjVSWtEI/RZMJnX3ZOinHyssFgcdCXlSnRtHBQvGVcbc6lOrPFTvToD2P2OcluefezbTSxZDnX9SHcfJYRMg2m7lpBN5zXNS9oo3TZ9VHDkT8jba9ECCBHjNSxB+ylJzgJ4yQdlO5AQ4QI8cKQPaQqBLly7HaK3vArBBAzV7DwU9KpfLvb/m+yUM0Ec3wsP79vt1uVzOe0iejosbB+hxkU1/XB7I06+xiR3Sdyaqkv6a6Lv0a2xSh/RbMDXUjGnQTz5UWCwOOAyyR99gm7mq7gBXSrRp06bm5wsWLIBSdW6SVRFq+rtvoC7yPovsv+R1t0Ns2Koi+eNOolesgDq7Y3GaTDPIiVUQzZrFnd7I+LzmGSlL6oqiz1InqRUN0XdWyJTKIum9VMqaSFMcoCeCnUktIsABukVisdToCXTs2HErKWU/IcRRALYEsAzA+1rrXHV19cSHHnrop/qyBhmgd+zYcRspZXchxCGrvm2+GYBqAJ8CeBzALblcznsGeqwvDtBjxZvq4DyQp1peY5uj74yVJtWF0Xeplte45ui3YJKo3K3QT9xfWCz2OwTy9PODbeaqOgRM8Z3+6AOoKy/w6xMScspsCJlJjWr5Pllgmfdr36pX262QGTYhNf2V2ogp3iu1bq63iwB9ZpdeaamWvkuLkvb1Qe/Zp5mpFXOAbqoyrMsUAhygm6IE6yCBmAhwgB4TWAfC8kDugMgGtkjfGSiKAyXRdw6IbFCL9FswMdSc26Efm11YLDr8HrLnhcE2c1UdAqb4Tv/zNagJI/z6NtwYmetuS5Vi+QGnAN9/S++uImCK91JlMjZj7DWO0rhFgNc3t/Q2qVt6zyQ17K6FA3S79WP18RPgAD1+xsxAAokS4AA9UfxWJ+eB3Gr5rC2evrNWOqsLp++sls+64um3YJKpB+6GfnimP4Tc8/8gew8MtpmrjB0uqeeegL5jol9fu22RGTI2VYrlh5wNLPjM927HUyCP6JSqHktphte8UmhxbVgC9FlYctxXDgH6rhx63FsOAXqvHHrcW5sAB+j0Awk0ToADdDqEBFJOgAP0lAscY3s8kMcIl6EbJEDf0RxJEKDvkqDubk76LZj26uEZ0A/c4y/efV9k+gwOtpmr6hAwxXd1dN1lL2TOuzxViqmpo6Ffe67Qk7xkNMQ2O6aqx1KaMcV7pdTMtfYRoM/s0ywNFdN3aVDRzh7oPTt1M7FqDtBNVIU1mUSAA3ST1GAtJBADAQ7QY4DqSEgeyB0R2rA26TvDBHGkHPrOEaENaZN+CyaEemwW9Jw7/MUpHLQGIxHNKlN8p+6ZAv3Uo4WmxO/+CHnKudE0aUgUvfBzqGljgIWfQxx8FOTxfzaksmTKMMV7yXTPrJUiQJ9VijTz1CZA39EPSRGg95Iin768HKCnT1N2FC0BDtCj5cloJGAcAQ7QjZPEmoJ4ILdGqlQVSt+lSk5rmqHvrJEqFYXSb8FkVE/cD5271V/cfg9kLhgebDNX1SFgiu/U7ROgn/9roT5x6DGQ3XpSsRQTMMV7KUbM1gDQZ7RBEgTouySoM6dHgN6jD6IiwAF6VCQZJ60EOEBPq7LsiwRWEeAAnVYIS4AH8rDkuK8cAvRdOfS4NywB+i4sOe4LQ4B+C0ZNPfkw9Iyp/uIdd0FmwFXBNnNVHQKm+E7dMhb65acK9YnDT4DsfBoVSzEBU7yXYsRsjcMkeiAhAry+JQSeaTlApwciI8ABemQoGSilBDhAT6mwbIsEVhPgAJ1eCEuAvwyGJcd95RCg78qhx71hCdB3YclxXxgC9Fswaurpx6Dvnuwv3q49MgNHBdvMVXUImOK7NZ8PLo7MQp5wMhVLMQFTvJdixGyNA3R6ICECvL4lBJ5pOUCnByIjwAF6ZCgZKKUEOEBPqbBsiwRWE+AAnV4IS4C/DIYlx33lEKDvyqHHvWEJ0HdhyXFfGAL0WzBq6rknoO+Y6C/eegdkBl8XbDNX1SFgiu/yk68G/vFSoT5xzImQx55IxVJMwBTvpRgxW+MAnR5IiACvbwmBZ1oO0OmByAhwgB4ZSgZKKQEO0FMqLNsigdUEOECnF8IS4C+DYclxXzkE6Lty6HFvWAL0XVhy3BeGAP0WjJp68e/Q08f5i9tti8yQscE2c1UdAqb4Lj/xSuDtVwv1iRNOhjwyS8VSTMAU76UYMVvjAJ0eSIgAr28JgWdaDtDpgcgIcIAeGUoGSikBDtBTKizbIoHVBDhApxfCEuAvg2HJcV85BOi7cuhxb1gC9F1YctwXhgD9FoyaeuUZ6JvH+IvbboXMsAnBNnNVHQKm+C4/bijwrzcL9YnOp0EefgIVSzEBU7yXYsRsjQN0eiAhAry+JQSeaTlApwciI8ABemQoGSilBDhAT6mwbIsEVhPgAJ1eCEuAvwyGJcd95RCg78qhx71hCdB3YclxXxgC9FswavqNF6CmXOMvbtMWmRG1nokeLAxXrSJgiu/yYy4D3vtnQRfRrSfkocdQpxQTMMV7KUbM1jhApwcSIsDrW0LgmZYDdHogMgIcoEeGkoFSSoAD9JQKy7ZIYDUBDtDphbAE+MtgWHLcVw4B+q4cetwblgB9F5Yc94UhQL8Fo6bfehlq0kh/8aZtkBk5NdhmrqpDwBTf5a8ZBLw/t1CfOKk35EFHUrEUEzDFeylGzNY4QKcHEiLA61tC4JmWA3R6IDICHKBHhpKBUkqAA/SUCsu2SGA1AQ7Q6YWwBPjLYFhy3FcOAfquHHrcG5YAfReWHPeFIUC/BaOm33kd6oYr/MUbt0bmmluDbeaqOgRM8V1+5ABg/rxCfaJHX8gDDqNiKSZgivdSjJitcYBODyREgNe3hMAzLQfo9EBkBDhAjwwlA6WUAAfoKRWWbZHAagIcoNMLYQnwl8Gw5LivHAL0XTn0uDcsAfouLDnuC0OAfgtGTc99E2rsUH/xhq2Que72YJu5qg4BU3yXH3E+8PH/CvWJ086H3P8QKpZiAqZ4L8WI2RoH6PRAQgR4fUsIPNNygE4PREaAA/TIUDJQSglwgJ5SYdkWCawmwAE6vRCWAH8ZDEuO+8ohQN+VQ497wxKg78KS474wBOi3YNT0f96Buu5Sf/H6LZEZe3ewzVxVh4ApvssP7Qt8/nGhPtFzAGSHA6lYigmY4r0UI2ZrHKDTAwkR4PUtIfBMywE6PRAZAQ7QI0PJQCklwAF6SoVlWySwmgAH6PRCWAL8ZTAsOe4rhwB9Vw497g1LgL4LS477whCg34JR0+//G+qagf7iddZF5oYZwTZzVR0Cpvguf9nZwJefFeqTvQdB7Lk/FUsxAVO8l2LEbI0DdHogIQK8viUEnmk5QKcHIiPAAXpkKBkopQQ4QE+psGyLBFYT4ACdXghLgL8MhiXHfeUQoO/Koce9YQnQd2HJcV8YAvRbMGp6/n+hRl7oL27eHJlJs4Jt5qo6BEzxXX7QmcDXCwv1yb6XQezWgYqlmIAp3ksxYrbGATo9kBABXt8SAs+0HKDTA5ER4AA9MpQMlFICHKCnVFi2RQKrCXCATi+EJcBfBsOS475yCNB35dDj3rAE6Luw5LgvDAH6LRg1/fH/oLznZa9+ZTLITLkv2GauqkPAFN/lLzoN+O7rQn2y31CI3+xJxVJMwBTvpRgxW+MAnR5IiACvbwmBZ1oO0OmByAhwgB4ZSgZKKQEO0FMqLNsigdUEOECnF8IS4C+DYclxXzkE6Lty6HFvWAL0XVhy3BeGAP0WjJr+7GOoYX2LFsupD0AI/gobjGDxKlN8l+9/MrD4+0Jxsv8IiJ13C9MS91hCwBTvWYKLZYYkQJ+FBMdtZRGg78rCx81lEKD3yoDHrUUEOECnIUigcQL86wMdQgIpJ8ABesoFjrE9HshjhMvQDRKg72iOJAjQd0lQdzcn/RZMe73gM6ghZxctlpPnQDRrFiwAVxWzkxJt2rSp+dmCBQuglEqEUL7ficBPSwq55cWjILZvn0gtTFoZArzmVYaz61noM9cdkEz/9F0y3JkV/AY6TRAZAQ7QI0PJQCklwAF6SoVlWySwmgAH6PRCWAL8ZTAsOe4rhwB9Vw497g1LgL4LS477whCg34JR04sWQA3uVbRYTqyCaLF2sABcVczOlAF6nyywrLpQm7xkNMQ2O1KtFBPgNS/F4hrUGn1mkBgOlULfOSS2Ya3Se4YJYnE5HKBbLB5LrwgBDtArgplJSCA5AhygJ8fe9sw8kNuuoJ3103d26mZ71fSd7QraVT/9Fkwv/c1XUANPLx4Cj78HYt31gwXgKjMH6L07AvkV/gB9yFiIdttSrRQT4DUvxeIa1Bp9ZpAYDpVC3zkktmGt0nuGCWJxORygWyweS68IAQ7QK4KZSUggOQIcoCfH3vbMPJDbrqCd9dN3dupme9X0ne0K2lU//RZML/3Dt1AXnlI8BL7+ToiWGwYLwFXGDdC11lC9jiuua9gEiLZbUa0UE+A1L8XiGtQafWaQGA6VQt85JLZhrdJ7hglicTkcoFssHkuvCAEO0CuCmUlIIDkCHKAnx972zDyQ266gnfXTd3bqZnvV9J3tCtpVP/0WTC+9ZDHU+ScVD1tHT4fYaJNgAbjKvAH6iuVQZ3cqrmvEZIg2balWignwmpdicQ1qjT4zSAyHSqHvHBLbsFbpPcMEsbgcDtAtFo+lV4QAB+gVwcwkJJAcAQ7Qk2Nve2YeyG1X0M766Ts7dbO9avrOdgXtqp9+C6aXXvoT1Lndioeto26G2GSzYAG4yrwBevVSqL5diusaORVi0zZUK8UEeM1LsbgGtUafGSSGQ6XQdw6JbVir9J5hglhcDgfoFovH0itCgAP0imBmEhJIjgAH6Mmxtz0zD+S2K2hn/fSdnbrZXjV9Z7uCdtVPvwXTSy9fBnVO5+Jh61VTIDbbIlgArjJvgL7kR6jzuxfXde10iFa8q0Ca7cprXprVNac3+swcLVyqhL5zSW2zeqX3zNLD5mo4QLdZPdZeCQIcoFeCMnOQQIIEOEBPEL7lqXkgt1xAS8un7ywVzvKy6TvLBbSsfPotmGBaKaizji8etvJ52cHg1bPKBN/pH76DurBHsaZj7oDYYKPQfXGj+QRM8J75lFhhuQTos3IJcn8YAvRdGGrcEwUBei8KiozhEeAAnT4ggcYJcIBOh5BAyglwgJ5ygWNsjwfyGOEydIME6DuaIwkC9F0S1N3NSb8F1z7fpzOwbFlhg7xkNMQ2OwYPwJU+OynRps3KW6UvWLAASqmK09HffAU18PSivHL8PRDrrl/xWpiwcgR4zasca5cz0Wcuq59c7/Rdcuxdz0zvue6A6PrnAD06loyUTgIcoKdTV3ZFAgUCHKDTDGEJ8EAelhz3lUOAviuHHveGJUDfhSXHfWEI0G/BqeX7nwws/r6wQfYfAbHzbsEDcKXPzoQB+qIFUIN7FakiJ1ZBtFibSqWYAK95KRbXoNboM4PEcKgU+s4hsQ1rld4zTBCLy+EA3WLxWHpFCHCAXhHMTEICyRHgAD059rZn5oHcdgXtrJ++s1M326um72xX0K766bfgeuUHnQl8vdAfAve9DGK3DsEDcKVZA/QFn0INOad4gD55DkSzZlQqxQR4zUuxuAa1Rp8ZJIZDpdB3DoltWKv0nmGCWFwOB+gWi8fSK0KAA/SKYGYSEkiOAAfoybG3PTMP5LYraGf99J2dutleNX1nu4J21U+/BdcrP7Qv8PnHhQ2i5wDIDgcGD8CVZg3QP/sIati5xQP0qQ9ACP5ZIs1W5TUvzeqa0xt9Zo4WLlVC37mktlm90ntm6WFzNRyg26wea68EAf6mWgnKzEECCRLgAD1B+Jan5oHccgEtLZ++s1Q4y8um7ywX0LLy6bfgguVHDgDmz/MH6D36Qh5wWPAAXGnWAP2jD6CuvMBXJdMMmSlzqFLKCfCal3KBDWmPPjNECMfKoO8cE9ygduk9g8SwvBQO0C0XkOXHToAD9NgRMwEJJEuAA/Rk+ducnQdym9Wzt3b6zl7tbK6cvrNZPftqp9+Ca5a/7lLgP+/4A4zU8MYAACAASURBVPSuZ0L+4djgAbjSrAH6B+9BjbrYV6XF2shMrKJKKSfAa17KBTakPfrMECEcK4O+c0xwg9ql9wwSw/JSOEC3XECWHzsBDtBjR8wEJJAsAQ7Qk+Vvc3YeyG1Wz97a6Tt7tbO5cvrOZvXsq51+C65ZfsII4J+v+QP04/8MeVSX4AG40qwB+rx/QY2+xFdl3fWQGX8vVUo5AV7zUi6wIe3RZ4YI4VgZ9J1jghvULr1nkBiWl8IBuuUCsvzYCXCAHjtiJiCBZAlwgJ4sf5uz80Bus3r21k7f2audzZXTdzarZ1/t9FtwzdTU0dCvPecP0I/MQp5wcvAAXGnWAP3fb0NdP8RXpeWGyFx/J1VKOQFe81IusCHt0WeGCOFYGfSdY4Ib1C69Z5AYlpfCAbrlArL82AlwgB47YiYggWQJcICeLH+bs/NAbrN69tZO39mrnc2V03c2q2df7fRbcM3U7ROgn/+rP0A/9BjIbj2DB+BKswbo774BNX64r8pGmyAzejpVSjkBXvNSLrAh7dFnhgjhWBn0nWOCG9QuvWeQGJaXwgG65QKy/NgJcIAeO2ImIIFkCXCAnix/m7PzQG6zevbWTt/Zq53NldN3NqtnX+30W3DN1Ixp0E8+5A/QDzgMskff4AG40qwB+luvQE26ylel9ebIXD2NKqWcAK95KRfYkPboM0OEcKwM+s4xwQ1ql94zSAzLS+EA3XIBWX7sBDhAjx0xE5BAsgQ4QE+Wv83ZeSC3WT17a6fv7NXO5srpO5vVs692+i24ZmrOHdCPzfIH6B0OhOw5IHgArjRrgP7Gi1BTRvmqbN4WmSsnU6WUE+A1L+UCG9IefWaIEI6VQd85JrhB7dJ7BolheSkcoFsuIMuPnQAH6LEjZgISSJYAB+jJ8rc5Ow/kNqtnb+30nb3a2Vw5fWezevbVTr8F10w9UgV9/13+ht06INP3suABuNKoAbp65Rnom8f4qrTdCplhE6hSygnwmpdygQ1pjz4zRAjHyqDvHBPcoHbpPYPEsLwUDtAtF5Dlx06AA/TYETMBCSRLgAP0ZPnbnJ0HcpvVs7d2+s5e7WyunL6zWT37aqffgmum/vYA9Mxb/A077YrMhVcGD8CVZg3QX/w79PRxvirttkVmyFiqlHICvOalXGBD2qPPDBHCsTLoO8cEN6hdes8gMSwvhQN0ywVk+bET4AA9dsRMQALJEuAAPVn+Nmfngdxm9eytnb6zVzubK6fvbFbPvtrpt+CaqWcfh75zkr9h6x2QGXxd8ABcadYA/bknoO+YSD0d8yWveY4JnlC79FlC4B1PS985boAE26f3EoSfstQcoKdMULYTOQEO0CNHyoAkYBYBDtDN0sOmanggt0mt9NRK36VHS5s6oe9sUsv+Wum34Brylt/BWTW10gTfqacehb5nil/q9u2RubjWM9GbaoLvW0nABO9ZCY5Fl0SAPisJFxdHRIC+iwgkw5RMgN4rGRk3NECAA3RagwQaJ8ABOh1CAiknwAF6ygWOsT0eyGOEy9ANEqDvaI4kCNB3SVB3Nyf9Flx7/dYrUJOu8je03hyZq6cFD8CVBQIm+E797UHomTf7qvCW/E441ATvOQHa8SbpM8cNkFD79F1C4JkW9B5NEBUBDtCjIsk4aSXAAXpalWVfJLCKAAfotEJYAjyQhyXHfeUQoO/Koce9YQnQd2HJcV8YAvRbcGr6329DXT/E39ByQ2SuvzN4AK4sEDDBd+rxOdCzbvNV+c1vkek3jCqlnIAJ3ks5YrYHcJhEFyRCgNe3RLAzKa959ECEBDhAjxAmQ6WSAAfoqZSVTZGAT4ADdLohLAH+MhiWHPeVQ4C+K4ce94YlQN+FJcd9YQjQb8Gp6Q/egxp1sb+hxdrITKwKHoArCwRM8J16pAr6/rt8VXbrgEzfy6hSygmY4L2UI2Z7HCbRAwkR4PUtIfBMyw8N0QOREeAAPTKUDJRSAhygp1RYtkUCqwlwgE4vhCXAXwbDkuO+cgjQd+XQ496wBOi7sOS4LwwB+i04Nf3ph1DDzyvaIG+6H0LK4EG4soaACb5TD94D/dAMX5Hf7o/M2YOoUMoJmOC9lCNme4Zc4yiEewR4fXNPc1M6pvdMUcL+OjhAt19DdhAvAQ7Q4+XL6CSQOAEO0BOXwNoCeCC3VjqrC6fvrJbP2uLpO2uls7Jw+i24bHrRAqjBvYoH6BOrIFqsHTwIV5ozQL/vTuhHcwVFxN4HQPa6iAqlnACveSkX2JD26DNDhHCsDPrOMcENapfeM0gMy0vhAN1yAVl+7AQ4QI8dMROQQLIEOEBPlr/N2Xkgt1k9e2un7+zVzubK6Tub1bOvdvotuGb6h2+hLjyleIA+5g6IDTYKHoQrzRmg56ZDP3GfP0Df72DI0y+gQiknwGteygU2pD36zBAhHCuDvnNMcIPapfcMEsPyUjhAt1xAlh87AQ7QY0fMBCSQLAEO0JPlb3N2HshtVs/e2uk7e7WzuXL6zmb17Kudfguuma5eCtW3S/EAfeRUiE3bBA/CleYM0GdMg37yIX+A/rs/Qp5yLhVKOQFe81IusCHt0WeGCOFYGfSdY4Ib1C69Z5AYlpfCAbrlArL82AlwgB47YiYggWQJcICeLH+bs/NAbrN69tZO39mrnc2V03c2q2df7fRbcM20UlBnHV88QB82AaLtVsGDcKU5A/S7J0M//Zg/QD/oCMiTzqZCKSfAa17KBTakPfrMECEcK4O+c0xwg9ql9wwSw/JSOEC3XECWHzsBDtBjR8wEJJAsAQ7Qk+Vvc3YeyG1Wz97a6Tt7tbO5cvrOZvXsq51+K02zfJ8ssKy6sEleMhpimx1r/q0//gD6bw8CG24McWQWYp11Swvu0GoTfKfumAj93BP+AP3QYyC79XRIBTdbNcF7bpJ3q2v6zC29TemWvjNFCffqoPfc0zyujjlAj4ss46aFAAfoaVGSfZBAAwQ4QKc1whLggTwsOe4rhwB9Vw497g1LgL4LS477whCg30qjlu9/MrD4+8Im2X8ExM67QS/9CWrAaUD1zzXvif87FPLUfqUFd2i1Cb5Tt46FfumpAnVx2AmQ2dMcUsHNVk3wnpvk3eqaPnNLb1O6pe9MUcK9Oug99zSPq2MO0OMiy7hpIcABelqUZB8k0AABDtBpjbAEeCAPS477yiFA35VDj3vDEqDvwpLjvjAE6LfSqOUHnQl8vbCwSfa5FGL3faBefBJ6+viiYJlpD5YW3KHVJvhOTR0N/dpzBereXQPkCSc7pIKbrZrgPTfJu9U1feaW3qZ0S9+ZooR7ddB77mkeV8ccoMdFlnHTQoAD9LQoyT5IoAECHKDTGmEJ8EAelhz3lUOAviuHHveGJUDfhSXHfWEI0G+lUcsP7Qt8/rE/dD3zQsh9fg91/13Qj1QVBZPj7oZYr2VpCRxZbYLv8pNHAf940dfymG6Qx3Z3RAF32zTBe+7Sd6dz+swdrU3qlL4zSQ23aqH33NI7zm45QI+TLmOngQAH6GlQkT2QQCMEOECnPcIS4IE8LDnuK4cAfVcOPe4NS4C+C0uO+8IQoN9Ko5YfOQCYP88fuvboC3nAYVCPVEHff1dRMHnpGIhfbV9aAkdWm+C7/MQrgbdf9bU8/s+QR3VxRAF32zTBe+7Sd6dz+swdrU3qlL4zSQ23aqH33NI7zm45QI+TLmOngQAH6GlQkT2QQCMEOECnPcIS4IE8LDnuK4cAfVcOPe4NS4C+C0uO+8IQoN9Ko5Yfcxnw3j/9oWuXMyD/eBxUbjr0E/cVBRO9Lobc+3elJXBktQm+y48fBrz7D1/LzqdCHt7REQXcbdME77lL353O6TN3tDapU/rOJDXcqoXec0vvOLvlAD1OuoydBgIcoKdBRfZAAo0Q4ACd9ghLgAfysOS4rxwC9F059Lg3LAH6Liw57gtDgH4rjVqd52YffgJk59Ogbh0L/dJTRcFExx6QR3QuLYEjq03wXZ0PQ3Q9E/IPxzqigLttmuA9d+m70zl95o7WJnVK35mkhlu10Htu6R1ntxygx0mXsdNAgAP0NKjIHkiAA3R6IAYCPJDHAJUhmyRA3zWJiAtiIEDfxQCVIRskQL+VZg41azr04/43zcVev4M862Ks+W1mL6o44DDIHn1LS+DIahN8l792EPDfuQXiontvyIOPdEQBd9s0wXvu0nenc/rMHa1N6pS+M0kNt2qh99zSO85uOUCPky5jp4EAB+hpUJE9kEAjBPgNdNojLAEeyMOS475yCNB35dDj3rAE6Luw5LgvDAH6rTRq6qlHoO+5yd+0zY7IXDIa+Sv7Ax+9Xxxsp12RufDK0hI4stoE3zX0PHtHJHC2TRO85yx8hxqnzxwS26BW6TuDxHCsFHrPMcFjbJcD9BjhMnQqCHCAngoZ2QQJNEyAA3S6IywBHsjDkuO+cgjQd+XQ496wBOi7sOS4LwwB+q00avrtV6Em1hqKb7gxMtfdhvzA04FvvioOtslmyIy6ubQEjqw2wXf5ERcAH39QIC5OOx9y/0McUcDdNk3wnrv03emcPnNHa5M6pe9MUsOtWug9t/SOs1sO0OOky9hpIMABehpUZA8k0AgBDtBpj7AEeCAPS477yiFA35VDj3vDEqDvwpLjvjAE6LfSqOlP50MN71e0Sd44G6rficDyZcXBhIS8MQfRbK3Skjiwulzf6QWfQn+4xjf+S+Smb7m+aIc480LIfX5fYhQut41Aud6zrV/WmwwB+iwZ7q5npe9cd0By/dN7ybFPW2YO0NOmKPuJmgAH6FETZTwSMIwAB+iGCWJROTyQWyRWikql71IkpkWt0HcWiZWCUum30kTUP/0I1a970SY5ZBzUiPPrDSSvmgKx2RalJXFgdTm+q3Mb/Yh4yd6DIPbcP6JoDGMqgXK8Z2pPrMs8AvSZeZq4UBF954LKZvZI75mpi41VcYBuo2qsuZIEOECvJG3mIoEECHCAngD0lKTkgTwlQlrWBn1nmWApKZe+S4mQlrRBv5UmlNYa6rxuwNKfCxtFj77Qd0ysN5A8fzjEr/coLYkDq8P6TldXQ13YA6j2+UeFS/a5FGL3faIKxziGEgjrPUPbYVmGEqDPDBUm5WXRdykX2OD26D2DxbGsNA7QLROM5VacAAfoFUfOhCRQWQIcoFeWd5qy8UCeJjXt6YW+s0erNFVK36VJTfN7od9K1yg/7Fzgs48KG8WBf4J+9i/1BhInnQ150BGlJ0n5jrC+U689Dz312ljoeHcSEO22iSU2g5pDIKz3zOmAldhAgD6zQaX01UjfpU9TWzqi92xRyvw6OUA3XyNWmCwBDtCT5c/sJBA7AQ7QY0ec2gQ8kKdWWqMbo++Mlie1xdF3qZXWyMbot9Jlyd9wBfDO6/7GrbYDPqr/edzisBMgs6eVniTlO8L6Lj9pJPDWyz6d9VoCrVqHo/XzEuDrhSv37r4P5DmDIQT/JBEOpj27wnrPng5ZqQkE6DMTVHCvBvrOPc1N6ZjeM0UJ++vgAN1+DdlBvAT422q8fBmdBBInwAF64hJYWwAP5NZKZ3Xh9J3V8llbPH1nrXRWFk6/lS6bunsy9NOPBdv42/2QOfuSYGsdWlWq77xb5+unH4WeeTOQzxdIiVPOhfzdH0OT018vApb+BGzRjsPz0BTt2liq9+zqjtWaQoA+M0UJt+qg79zS26Ru6T2T1LC7Fg7Q7daP1cdPgAP0+BkzAwkkSoAD9ETxW52cB3Kr5bO2ePrOWumsLpy+s1o+64qn30qXTD02G3rO7cE2/nJrZC4fH2ytQ6tK8Z1e8iPU7TcAb9b65rnHqlkzyDF3QKy7vkPk2Gq5BErxXrm5uN9dAvSZu9on2Tl9lyR9t3PTe27rH2X3HKBHSZOx0kiAA/Q0qsqeSKAWAQ7QaYewBHggD0uO+8ohQN+VQ497wxKg78KS474wBOi30qnpLz6FuvycYBvXXgfyhhn8dvMatIL6Tn/8AZR32/ZvFtXhLQ4/AbIzb48fzIhctZpAUO+RGAmUQ4A+K4ce94YlQN+FJcd95RKg98olyP2rCXCATi+QQOMEOECnQ0gg5QQ4QE+5wDG2xwN5jHAZukEC9B3NkQQB+i4J6u7mpN/Caa//9SbUozngf+8BK1Y0GkRefxdEyw3CJUrpriC+0/k81CU9gW+/qkNBHHY8xAk9IJo1SykhthUXgSDeiys347pDgD5zR2uTOqXvTFLDrVroPbf0jrNbDtDjpMvYaSDAAXoaVGQPJNAIAQ7QaY+wBHggD0uO+8ohQN+VQ497wxKg78KS474wBOi3MNT8Pbq6Gvjg39Dv/XPloHeH30DfPbnoOd1y8HUQW+9QXqKU7Q7iO/3pfKjh/Yo7X78l5GnnQ+y6d8qIsJ1KEQjivUrVwjzpJUCfpVdbkzuj70xWJ9210Xvp1reS3XGAXknazGUjAQ7QbVSNNZNACQQ4QC8BFpcWEeCBnIZIggB9lwR15qTv6IFKEqDfoqedH9wLWLSgEFj0HADZ4cDoE1kcMYjv1EtPQd861u+y5YaQQ8ZBtNrE4s5ZetIEgngv6RqZ334C9Jn9GtrYAX1no2rpqJneS4eOJnTBAboJKrAGkwlwgG6yOqyNBCIgwAF6BBAdDcEDuaPCJ9w2fZewAI6mp+8cFT6htum36MHnx14OzH3LH6Af3RXyuJOiT2RxxCC+U7lboZ+43+9ytw7I9L3M4q5ZugkEgnjPhDpZg90E6DO79bO1evrOVuXsr5ves19DUzrgAN0UJViHqQQ4QDdVGdZFAhER4AA9IpAOhuGB3EHRDWiZvjNABAdLoO8cFD3Blum36OGr3HToJ+7zA++8GzL9R0SfyOKIQXyXv34I8O+3C10KfhDBYsXNKT2I98yplpXYSoA+s1U5u+um7+zWz+bq6T2b1TOrdg7QzdKD1ZhHgAN08zRhRSQQKQEO0CPF6VQwHsidktuYZuk7Y6RwqhD6zim5E2+WfoteAv3my1A3jvQDt1gbcvy9EJlM9MksjdiU77TWUBf2ABZ/X+hQ9h4Isef/WdoxyzaFQFPeM6VO1mE3AfrMbv1srZ6+s1U5++um9+zX0JQOOEA3RQnWYSoBDtBNVYZ1kUBEBDhAjwikg2F4IHdQdANapu8MEMHBEug7B0VPsGX6LXr4+ofvVg5/a73kkLEQ7baNPplFEfV330A/NAN64efwfvFvvvbaWOtX22Hprh2gf7lNUSfeWnXRqcUMr5wCsfkWFnXMUk0kwGueiaqkryb6LH2a2tARfWeDSumskd5Lp65JdMUBehLUmdMmAhyg26QWayWBEAQ4QA8BjVtqCPBATiMkQYC+S4I6c9J39EAlCdBv8dDOX9obWPi5H3yjjYG114knmYlRhYTY8lcQ+x4E7LIXhBDIXzMIeH9uvdWKo7tBHte98J5+9w2o8cP9td63+G+YASGlid2yJosI8JpnkVgWl0qfWSyexaXTdxaLZ3np9J7lAhpUPgfoBonBUowkwAG6kbKwKBKIjgAH6NGxdC0SD+SuKW5Gv/SdGTq4VgV955riyfZLv8XDX906Dvqlv8cT3LKo4vCOEMeeCNUn23DlQkAOvg7iV9vXrFEzpkE/+ZC/fpsdkblktGWds1wTCfCaZ6Iq6auJPkufpjZ0RN/ZoFI6a6T30qlrEl1xgJ4Edea0iQAH6DapxVpJIAQBDtBDQOOWGgI8kNMISRCg75Kgzpz0HT1QSQL0Wzy01QtPQt82Pp7gtkXNNIO8bAzU8H6NV/6r7SFXDcnVwNOB774prBfHdIM81v+Gum0IWK85BHjNM0eLNFdCn6VZXXN7o+/M1SbtldF7aVe4cv1xgF451sxkJwEO0O3UjVWTQGACHKAHRsWFaxDggZyWSIIAfZcEdeak7+iBShKg3+KhrVcshxo5APhkfjwJLIsqDjse+on7i6pea+sdsHz+vKKfiZP7QGzRDuqagUU/l8MmQrRtZ1nXLNdEArzmmahK+mqiz9KnqQ0d0Xc2qJTOGum9dOqaRFccoCdBnTltIsABuk1qsVYSCEGAA/QQ0LilhgAP5DRCEgTouySoMyd9Rw9UkgD9Fh9trfLA++8BP34fXxITI2tATRlVXNn6GwA//uD/bONNseWtD2BBn25Y8dnH/s/Xawmx617QLz3l/+wXv0TmikkmdsqaLCTAa56FollYMn1moWgpKJm+S4GIlrZA71kqnIFlc4BuoCgsySgCHKAbJQeLIYHoCXCAHj1TVyLyQO6K0mb1Sd+ZpYcr1dB3rihtRp/0mxk6pK0K9feHoe+d2mBbYuvtseXEe7H0zZex6LK+jbYvju4KedxJaUPEfhIiwGteQuAdS0ufOSa4Ie3Sd4YI4WAZ9J6DosfUMgfoMYFl2NQQ4AA9NVKyERKonwAH6HRGWAI8kIclx33lEKDvyqHHvWEJ0HdhyXFfGAL0Wxhq3NMUAf31QqhBZza4TOzWAVuOvLHm/U+Hng/9+vMNrpUDr4HYbuemUvJ9EghEgNe8QJi4qEwC9FmZALk9FAH6LhQ2boqAAL0XAUSGqCHAATqNQAKNE+AAnQ4hgZQT4AA95QLH2B4P5DHCZeiG/2gvJdq0aVPz/oIFC6CUIi0SiJ0Ar3exI2aCWgToN9ohLgL54ecBn35Yb3hxwGHYctDImve+mPsuVlzWG6heWndt8xaQ4++BaLZWXGUyrmMEeM1zTPCE2qXPEgLveFr6znEDJNg+vZcg/JSl5gA9ZYKyncgJcIAeOVIGJAGzCHCAbpYeNlXDA7lNaqWnVvouPVra1Al9Z5Na9tdKv9mvoakdqPvugn60qt7y5FFd0faci2re8z6gtuKxWdCzbqu7tv0eyFww3NQWWZeFBHjNs1A0C0umzywULQUl03cpENHSFug9S4UzsGwO0A0UhSUZRYADdKPkYDEkED0BDtCjZ+pKRB7IXVHarD7pO7P0cKUa+s4Vpc3ok34zQ4c0VqH/9x+oq1cOydd8ye5noe1JPWt+7A3Q88uWQV3RD/jik6Kl/9/e3YBLcpZ1wr+fPjNJkAhEIAQw8rlqgri6GxFYhfjKK0YIL8x09UzgjUZURPFFURB05Qpx5UMWFVZBiIuiQsj00zPBRAUVJYIuKuLHCllB8GMFFwhoMInMJHO63qsmffTk5Hx09fno6qpfX5eXZPqpquf53f9T1afv09XpaZdG75uKNvJY05wEnPPmBN+xw8pZxwrekOXKXUMK0cFpyF4Hi75LS9ZA3yVYu22NgAZ6a0ppIQTWF9BAl4xZBbwgn1XOdtsRkLvt6Nl2VgG5m1XOdrMIyNssaraZRqAcj2P8gssi/vmmuwxfevYL4wEX39EYX/mKlPJDfxHjV/3nO43tveiVkR72pdMczhgCUwk4503FZNA2BeRsm4A2n0lA7mZis9EOCMjeDiDaxSkBDXRBILC5gAa6hBBouYAGessLvIvL84J8F3HtekMBuROOeQjI3TzUu3tMeetu7fdi5eM3/bcof/+ddznU0g++Ih7w+Cec+veVBnr1v8dvvTLK3/nVO8Z/8ZdF7/kvjZS8TbAXterKMZzzulLp+a5Tzubr39Wjy11XKz//dcve/GvQlhlooLelktaxWwJ+M94tWfsl0BABDfSGFGIBp+EF+QIWrQVTlrsWFHEBlyB3C1i0BZ6yvC1w8RZg6uWf/kGMX/eyu8x030tfH/f/igtO/fvqBnpZlhEf/JMoP/e5SF/x1ZH271+AVZriIgk45y1StRZ3rnK2uLVb5JnL3SJXb7HnLnuLXb8mzV4DvUnVMJcmCmigN7Eq5kRgBwU00HcQs2O78oK8YwVvyHLlriGF6Ng05K5jBZ/zcuVtzgVo+eHLE8dj/H3PiDh5+51Wuu+nr477P/Thd2mgt5zD8hog4JzXgCJ0YApy1oEiN3CJctfAonRkSrLXkULvwTI10PcA2SEWWkADfaHLZ/IEthbQQN/ayIj1Bbwgl4x5CMjdPNQdU+5kYC8F5G0vtbt5rOXXXBHxgff/2+L37Yt9r78m7n//+2ugdzMSc121c95c+TtzcDnrTKkbtVC5a1Q5OjUZ2etUuXd1sRrou8pr5y0Q0EBvQREtYXaBAwcOPGhpaem5EfGkiDg3Ik5ExEcjYnj8+PHXXnfddf8y+97v2PLQoUNftry8/OyU0uMj4kERcUZEfDYiPhgR1544ceLnrr322pu3e5yNttdA3y3Z9u/XC/L217iJK5S7Jlal/XOSu/bXuEkrlLcmVaOdcxlf/+tRvuX1/7a4e58d+1/583HOOeec+rfVt3Bvp4BVNUnAOa9J1WjvXOSsvbVt8srkrsnVaffcZK/d9d3L1Wmg76W2Yy2igAb6IlbNnHdEYDAYXFyW5Zsj4h4b7PDDVWM95/yRWQ84GAxeWJblSyNiaZN9/H1EPCXn/GezHmez7TTQd0O1G/v0grwbdW7aKuWuaRXpxnzkrht1bsoq5a0plWjvPMpbb47xi7874ubqb3Yj0jc8NfYd+nYN9PaWvNErc85rdHlaMzk5a00pF2ohcrdQ5WrVZGWvVeWc62I00OfK7+ALIKCBvgBFMsWdFzh48OBX9nq934+Iu0XELSmll5dl+a7qv8uyPJxS+o7JUT984sSJC2b5hPhgMLikLMurJvu5rSzL1/Z6vXeWZfnplNLDyrL87oj4msnzn7z99tu/9G1ve9tNO71aDfSdFu3O/rwg706tm7RSuWtSNbozF7nrTq2bsFJ5a0IV2j+H8hMfi/L3fiviC+4b6fEXxdL+/Rro7S97I1fonNfIsrRuUnLWupIuxILkbiHK1MpJyl4ryzqXRWmgz4XdQRdIQAN9gYplqjsnUBTFuyPiayPiZEQ8Luf83tV7L4riBRHxyurfUkpXDIfDl9Q9elEUH4iIR0y2e3LO+dfW7qMoiqMRcWBynBcMh8NX1T3OVuM10LcS8vxGAl6Qy8Y8BORuHuqO1Orh9QAAIABJREFUKXcysJcC8raX2o61IiB3sjAvAdmbl3y3jitn3ap3U1Yrd02pRPfmIXvdq/lurVgDfbdk7bctAhrobamkdUwtUBTFoyLiDydN6zcMh8Nnr9348ssv791www1VA/y8iLjprLPOOvvKK6+8fdqDPOMZz7jHbbfddsc9EyP+JOf8H9fb9uDBg1/e6/X+fPLcsZzzwWmPMe04DfRppYxbK+AFuUzMQ0Du5qHumHInA3spIG97qe1YKwJyJwvzEpC9ecl367hy1q16N2W1cteUSnRvHrLXvZrv1oo10HdL1n7bIqCB3pZKWsfUAoPB4GVlWf5QtcF4PH700aNHTzXT1z6KonhRRLy8+veyLJ84Go1+c9qDXHLJJfc5efLkjZPxo5xzsd62l1566d2PHz9+y+S5X805XzztMaYdp4E+rZRxawW8IJeJeQjI3TzUHVPuZGAvBeRtL7Uda0VA7mRhXgKyNy/5bh1XzrpV76asVu6aUonuzUP2ulfz3VqxBvpuydpvWwQ00NtSSeuYWmDV7dtvvfHGG+91/fXXV7dxv8ujKIrHRMT/mDzxoznny6c+SEQURfGZiPiCaT+BnlL6ieFw+Pw6x5hmrAb6NErGrCfgBblczENA7uah7phyJwN7KSBve6ntWCsCcicL8xKQvXnJd+u4ctatejdltXLXlEp0bx6y172a79aKNdB3S9Z+2yKggd6WSlrH1AJFUVSfDL9PRPx5zvkrNtrw6U9/+lm33377P06ezznnwdQHiYh+v//SlNIPT7a5KOf8jrXbF0WRq6HVd7GPx+NHHj169C/rHGOasRro0ygZs56AF+RyMQ8BuZuHumPKnQzspYC87aW2Y60IyJ0szEtA9uYl363jylm36t2U1cpdUyrRvXnIXvdqvlsr1kDfLVn7bYuABnpbKmkdUwlcdtllZ9x6662fmwz+tZzzkzfbsCiK6vbqd4+IP8g5V59In/oxuT370Yh4YkScSCn9zHg8/u2lpaVPj8fjh0bEd0XE4yNiuSzL54xGozdMvfPJwAMHDnzhVtsMh8P3LS0tnbO8vBw33rhyV/mttvI8gYjqBfnZZ599iuJTn/pU9ZUHWAjsuoDc7TqxA6wjIHdisZcC8raX2o61IiB3sjAvAdmbl3y3jitn3ap3U1Yrd02pRPfmIXvdq/lurfi+971vLC0tVbv/eEppyz7Dbs3Dfgk0VUADvamVMa9dESiK4r5VL3Cy8yM558ObHagoik9GRNVB/EDO+ZF1J3XhhRfuO/vss7+5LMvqk+gPW2f7Y71e7xVHjhx5X919V+OLoii32u5nf/Zn4973vvdWwzxPgAABAgQIECBAgAABAgQIECBAgAABAgQIdEtAA71b9bbaKQU00KeEMqwdAocPHz53eXn5f09W88s552/ebGVFUVRjz42Ij+acH15Xod/vPzal9GMRcWFErPfzdnNK6aqyLF+Yc/5s3f1roNcVM54AAQIECBAgQIAAAQIECBAgQIAAAQIECBCYCGigiwKBdQQ00MWiUwJ7+Qn0oiiq7zZ/c0ScHhH/M6V0eVmW7z7rrLNu/vSnP31ur9c7lFJ6cUTcLSL+19LS0hOuvvrqf6hTELdwr6NlbF0Bt4SqK2b8TgjI3U4o2kddAbmrK2b8dgTkbTt6tp1VQO5mlbPddgVkb7uCtp9GQM6mUTJmpwXkbqdF7W9aAdmbVsq4rQTcwn0rIc93XUADvesJ6Nj69+o70C+55JL7nTx58qOT70//4BlnnPHVv/zLv3zrWu5+v//1KaXfmnw6/VjO+eBOl6Qsy49FxAOr70D/5CerO9J7EJhOoHpBfs4555wa/IlPfMJ3oE/HZtQ2BeRum4A2n0lA7mZis9GMAvI2I5zNtiUgd9vis/E2BGRvG3g2nVpAzqamMnAHBeRuBzHtqpaA7NXiMngTgfvd736+A11CCGwioIEuHp0TKIri0xFRfSn4n+ecv2IjgKc//eln3X777f84eT7nnAfTYhVF8b0R8erJ+GfknK/aaNuiKKoG+hMiYrx///77XHXVVf807XGmGaeBPo2SMesJeEEuF/MQkLt5qDum3MnAXgrI215qO9aKgNzJwrwEZG9e8t06rpx1q95NWa3cNaUS3ZuH7HWv5ru1Yg303ZK137YIaKC3pZLWMbVAURTvjoivjYhbb7zxxntdf/31J9fbuCiKx0TE/5g896M558unPchgMHh9WZbfWY0fj8fnHT169C832rbf778ipfTCydhHHz169A+nPc404zTQp1EyZj0BL8jlYh4CcjcPdceUOxnYSwF520ttx1oRkDtZmJeA7M1LvlvHlbNu1bspq5W7plSie/OQve7VfLdWrIG+W7L22xYBDfS2VNI6phYYDAYvK8vyh7ZqWBdF8aKIeHk1rizLJ45Go9+c9iD9fv9nUkrPqcb3er1HHjly5AMbbVsUxU9ExPdPnr8g5/z+aY8zzTgN9GmUjFlPwAtyuZiHgNzNQ90x5U4G9lJA3vZS27FWBOROFuYlIHvzku/WceWsW/VuymrlrimV6N48ZK97Nd+tFWug75as/bZFQAO9LZW0jqkFiqJ4VESc+pR3SukNw+Hw2Ws3vvzyy3s33HBD1fQ+LyJuOuuss86+8sorb5/2IP1+/wdSSq+aHOO7hsPh6zfatiiK90XEBVWf/uTJk/e95pprPjPtcaYZp4E+jZIx6wl4QS4X8xCQu3moO6bcycBeCsjbXmo71oqA3MnCvARkb17y3TqunHWr3k1Zrdw1pRLdm4fsda/mu7ViDfTdkrXftghooLelktZRS2DVbdyr27c/Luf83tU7KIriBRHxyurfUkpXDIfDl6x5/rKI+IWNnj948OCX9nq9G6rNI+LjEfHVOefq/9/pURTFsyLiDZN/fG/O+bG1FjLFYA30KZAMWVfAC3LBmIeA3M1D3THlTgb2UkDe9lLbsVYE5E4W5iUge/OS79Zx5axb9W7KauWuKZXo3jxkr3s1360Va6Dvlqz9tkVAA70tlbSOWgIHDx78yl6v9/sRcbeIuKUsy5ctLS29qyzLu5VleTgiqsZ29fjwiRMnLrj22mtvXn2Aoig2baBXY/v9/htTSs+cbHdjSunV4/H4PSmlm1NK506O8/TJ88tlWT5hNBpdX2shUwzWQJ8CyZB1BbwgF4x5CMjdPNQdU+5kYC8F5G0vtR1rRUDuZGFeArI3L/luHVfOulXvpqxW7ppSie7NQ/a6V/PdWrEG+m7J2m9bBDTQ21JJ66gtMBgMLi7L8s0RcY8NNv5wRDwp5/yRtc9P00C/6KKLTj/zzDN/MSIObTG5W6uGfc75qtqLmGIDDfQpkAxZV8ALcsGYh4DczUPdMeVOBvZSQN72UtuxVgTkThbmJSB785Lv1nHlrFv1bspq5a4plejePGSvezXfrRVroO+WrP22RUADvS2VtI6ZBA4cOPCgXq/3vSmlJ0XEF0bEbRHxkbIs84kTJ37muuuu+5f1djxNA31lu8Fg8HURcVlZlo+OiAdGxOkR8c8R8aGyLN85Ho+vPHbs2MdmWsAUG2mgT4FkiAa6DDRGwC+CjSlFpyYid50q99wXK29zL0EnJyB3nSx7IxYte40oQ+snIWetL3EjFyh3jSxLJyYle50o854sUgN9T5gdZIEFNNAXuHimTmAaAQ30aZSMWU/AC3K5mIeA3M1D3THlTgb2UkDe9lLbsVYE5E4W5iUge/OS79Zx5axb9W7KauWuKZXo3jxkr3s1360Va6Dvlqz9tkVAA70tlbQOAhsIaKCLxqwCXpDPKme77QjI3Xb0bDurgNzNKme7WQTkbRY122xXQO62K2j7WQVkb1Y529URkLM6WsbulIDc7ZSk/dQVkL26YsZvJKCBLhsENhfQQJcQAi0X0EBveYF3cXlekO8irl1vKCB3wjEPAbmbh3p3jylv3a39PFcud/PU7/axZa/b9d+r1cvZXkk7zmoBuZOHeQnI3rzk23dcDfT21dSKdlZAA31nPe2NQOMENNAbV5KFmZAX5AtTqlZNVO5aVc6FWYzcLUypWjFReWtFGRduEXK3cCVrzYRlrzWlbPRC5KzR5Wnt5OSutaVt/MJkr/ElWpgJaqAvTKlMdE4CGuhzgndYAnslUJblbRGxvyzLGI/He3VYx2mJwNLS0qmVLC8vt2RFlrEIAnK3CFVq3xzlrn01bfKK5K3J1Wnv3OSuvbVt+spkr+kVasf85KwddVy0VcjdolWsPfOVvfbUcp4rqf4YI6VTLcLbU0qnzXMujk2giQIa6E2sijkR2EGBsixPRsQdXVAPAgQIECBAgAABAgQIECBAgAABAgQIECBAgMAdAssppX0wCBC4s4AGukQQaLlAWZa3RsTpEVF9/PxTLV+u5e2gwPLy8tJNN910TrXLe93rXp9YWlryMfQd9LWr9QXkTjLmISB381Dv7jHlrbu1n+fK5W6e+t0+tux1u/57tXo52ytpx1ktIHfyMC8B2ZuXfCuPe3ZE9CLiRErp7q1coUUR2IaABvo28GxKgACBNgscOHDgC5eWlv6+WuPy8vK5x44d+1ib12ttzRCQu2bUoWuzkLuuVXy+65W3+fp39ehy19XKz3/dsjf/GnRhBnLWhSo3b41y17yadGVGsteVSlsnAQLzFtBAn3cFHJ8AAQINFfCCvKGFafm05K7lBW7o8uSuoYVp6bTkraWFbfiy5K7hBWrx9GSvxcVt0NLkrEHF6NBU5K5DxW7YUmWvYQUxHQIEWiuggd7a0loYAQIEtifgBfn2/Gw9m4DczeZmq+0JyN32/GxdT0De6nkZvTMCcrczjvZSX0D26pvZor6AnNU3s8X2BeRu+4b2MJuA7M3mZisCBAjUFdBArytmPAECBDoi4AV5RwrdsGXKXcMK0pHpyF1HCt2QZcpbQwrRsWnIXccK3qDlyl6DitHiqchZi4vb4KXJXYOL0/KpyV7LC2x5BAg0RkADvTGlMBECBAg0S8AL8mbVoyuzkbuuVLpZ65S7ZtWj7bORt7ZXuJnrk7tm1qULs5K9LlR5/muUs/nXoIszkLsuVr0Za5a9ZtTBLAgQaL+ABnr7a2yFBAgQmEnAC/KZ2Gy0TQG52yagzWcSkLuZ2Gw0o4C8zQhns20JyN22+Gy8DQHZ2waeTacWkLOpqQzcQQG520FMu6olIHu1uAwmQIDAzAIa6DPT2ZAAAQLtFvCCvN31berq5K6plWn3vOSu3fVt2urkrWkV6cZ85K4bdW7iKmWviVVp35zkrH01XYQVyd0iVKmdc5S9dtbVqggQaJ6ABnrzamJGBAgQaISAF+SNKEPnJiF3nSt5IxYsd40oQ2cmIW+dKXWjFip3jSpHpyYje50q99wWK2dzo+/0geWu0+Wf6+Jlb678Dk6AQIcENNA7VGxLJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGNBTTQpYMAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECESEBroYECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABDXQZIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECdwj4BLokECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABDXQZIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECdwj4BLokECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABDXQZIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECdwj4BLokECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABDXQZIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECdwj4BLokECBAYAcEDh48eEGv1/umiPiaiDg/Iu4bEbdHxD9ExO9HxBtzzr837aH6/f5FKaVnRcRXTfZ1Y0S8ryzLK0ej0ds3209RFGemlP5DRDyqLMtHTfbx4Mk2f5dzXvnfG+7m8OHDD15eXv6baeZbluUvjkajy6YZO82YwWBwSVmW3xoRXx4R94qIT6aU3lOW5Wtzzu/daB8XXXTR6WeeeWZVg5U1f1FE3CciPj8i/jkiPlSW5TvH4/GVx44d+9g0c2n6GLmbf+76/f6bUkrfUjMr35pzflPNbRozXO62l7sLL7xw373vfe9HppQelVKqzvHVOau6bixVRV5aWnrI1Vdf/bdbFfypT33qvfbv37+y/cp57/6T7X4353zhVvtYlOfblrm17ocOHTpveXn5mSmlJ0bEuRFxRnXti4iPpJR+p9frXTVNJqapp2vsNEoRMjf/zHXx+lqlU/a2lz3X2OnOcW3MWlEU1WunB00vMP1rrq326dq6ldAdz7ft/LaImXNtbcf7dXudPdfW6c5xRhEg0E4BDfR21tWqCBDYQ4GiKN4dEV+71SFTSr9UluV35Jxv22js5Zdf3rvhhhuujIhv22hMSum/n3feed95xRVXjNcbUxTFuyJio8ZJYxvoRVHcLSJGEVE1wdd7jMuy/NHRaHTFBut+eET81VZ1iIhbU0rPGQ6HvzjF2MYOkbud+cON7eZuxjchHrvZH4M0NnQRIXfbz12/3788pfSSjeo8bQO9KIrqj5w2+oOo1jTQ25i5VbVP/X7/x1JKPxgR+zbKRFmWzxuNRq/ezrlhu+e6oig6c42VuYgmZK5r19fq51v2tp8919jprhRtzNoMDaXP3v3udz/nTW960/Hp1O46yrV1ejmZO2U198y5tm6c2UV6v26vz3eurdOf64wkQKB9Ahro7aupFREgsMcCRVF8JCIeNvm0ea4+LZ1S+t/VBwnLsnxMWZY/EBEPrKZVluVbR6PR0zeaYlEUL4+IF02e/9OyLF+5tLT00eXl5YdN3mD/yuq5lNLLh8PhD2/QSL4+Ih4/ee4fI+KPI+KxEXFmRMzSQP+RXq/3KxvNeTwe/1PO+ePbZR8MBm8ty/LwZD/vKsvyNUtLS/9QluUjy7Ks1loZV4/vzDlXf2Rwp8fkzf3q0/7Vtu9LKf1dSun/lGVZ3Qmg8n9SRDxj8sm+MqX05OFw+Ovbnfe8tpe7xuTugb1e76zNcrC8vHxWSqn6uexFxIdzzl8yr9xs97hyt/3cDQaDl5RlefmkFtWbtn82udPIqXNcjQb66k9aVZ9Wfl9EPHmy3zY10Ft3jV35ORwMBj9XluW3T/77/WVZ/nxE/EVZlrcsLS2dPbmTzMGI+IWc82u28/PrGju9XhvPcwuauU5dX6sayV657fOda+x057o2Zu3QoUNfHBGnbfGa/AkppZ+qxpRl+XOj0ai649vMD9fW6elkrjGZc21twft1e32+c22d/lxnJAEC7RPQQG9fTa2IAIE9FiiK4lcnf616NOe8vPbwl1xyyX1OnjxZNXarX+qr5vfjh8Nh9an1Oz2qF8Hj8fiDk0+hVU3vx+WcP7cy6OKLL/68M84443cj4oKIOBkR5+Wcq8bCnR5FUTwrpXRz1UReeX7VX6jO0kDf9dtNF0Xxf0XEb08Wcl1EPG215cTw/RFR3Zb9pv379z/0qquu+qfVC68+vX/FFVeU1fshG0WgKIrqNsfVrfT3R8Sf5pyrW90v5EPutl+2ncjdNLMoiuK7IuJ11diyLF88Go1+bJrtmjhG7rZflcFg8MTxePzgsiz/6DOf+cxfXH/99SdXfxpk2gb6YDB4fkT8Ta/X+6Orr77676uZFUWxcv5rUwO9ddfYqlaDweCZZVm+cXJeeNloNPqRja5fRVGcttnda7ZK5U6c67p0jW3jeW4RM7dVrifnvNZcXyfrcb7b5vnONXaan5xTrxdambWtVt/v94+klAaTcV9b5yvW1vmd2++vW4Gvel7mTmHMPXPTlKxNv7u2+dq6VS138nzn2rqVtucJEGizgAZ6m6trbQQINEag3+8/OaVUNYarBvpPD4fD567zS3jVYKveCIxer/eYI0eO/MHaMYcOHXr0eDw+9T3gKaXXDYfD50yzyAVooFefBL+o+sOA5eXlh6z3HeX9fv9wSumtk/X+YM75v06z9nWc3xER1ffMVo/PzznfMst+FmEbudu8SkVR7EnuiqKofmYfXTXHJvn+u0XIz6xzlLv6crM00Nc7Shsb6NNoLlrmnvKUp3z+6aefXp0HqrtX/FrOeeXOAdMst/aYvTrXVRMriqIT11iZc32t/YO4QxvIXn1I19j6ZtUWi5a1rVZZFMU9I+ITk7uR/XXOeeXuZlttuu7zrq0zsW26kcy5tu58qqbbo+xN57R6lGtrfTNbECCwmAIa6ItZN7MmQGDBBC699NK7Hz9+fKVR++s55+p24qsfqSiKj0XEAyLiL3PO5220xKIo/jIiqltAfzznfO5mn7he2UeTG+iTRsKnJ7fce0fOuWqk3+VRfQIvIm6MiHtExHtzztVt6Ws/iqLI1XtC1YYnT568zzXXXPOZ2jtZkA3kbuNC7VXuDh48+O96vd6HJzO5Puf8dQsSn5mnKXf16bwBUd9s9RaLlrnBYPDssix/drKGx+acT/1h3G489upct+r1RieusTLn+robP6/T7FP2plG68xjX2Ppm1RaLlrWtVjkYDL6jLMtTXwNWluVLRqPRFVtts9Hzrq2zym2+ncy5tu5Osrbeq+xtbbR2hGtrfTNbECCwmAIa6ItZN7MmQGDBBIqi+IKIWGnUXpdzfsrqJRw4cOCh1XedV/+WUnrDcDh89kZLLIriDRGx8n1tD805/81WHE1uoK+5tewP5ZxfscnafyMivqHqfZ911lmfd+WVV1bfbz71oyiK+0bEhyaf+vt0zrn679Y+5G7j0u5V7gaDwX8py7K6NXP1Zt0zR6PRL7Q2cJOFyV39CnsDor7Z6i0WLXNFUVRfWVLd+vVO16GnPe1pZ+/bt+8ep5122qfe8pa3/PP2VO7Yeq/OdZNjdeYaK3Ourzvx8znLPmSvvpprbH2zyTl9oX5/3WqVRVG8JyK+ZnJHqIcfO3bsr7faZpPfSVffvt3vr7NCrtlu0c5vWy17ETPXxd9dne+2SvL6z7u2zuZmKwIEFk9AA33xambGBAgsoEBRFE+LiGPV1FNKrxwOhy9cvYzVt4wqy/J5o9Ho1RstczAYPK8sy5+c7OtJw+Gwug31po9tNtD/JCKqW959YUSciIiPpZTek1K68siRI9Vz23oURfE9EfHTk/U8bTgcvm2jHfb7/deklFZuf/+InPMNWx38oosuOv3MM898QErpCWVZVu4rt+v7kZzzS7fafpGfl7uNq7fbuZscubqzRPXm3IMj4l9OnDhxzrXXXnvzImdqmrnL3TRKdx7jDYj6Zqu3WKTMVd8lfsMNN9xUfYVIRLwr5/z1/X7/e1JKz4uIh6xa159X18bzzz//F6644orxrEK7fa7r6jVW5lxfZ/2Z3O52sldf0DW2vlm1xSJlbasVFkVRXV+rP1av3gN9T875cVtts9nzrq3b0dv0+rFQ75lskZGFylyXf3d1vpvt59m1dTY3WxEgsHgCGuiLVzMzJkBgwQQmb5ZXt2d91GTqF+Sc3796GWtu51rknEcbLbMoiur249UtUqtPtD57NBpVn0jf9LHNBvqG+64+LX/zzTd/79vf/vaqsT7To9/vvyKldOoPCsbj8VcdPXr0jzfa0WAweH5Zlqe++zyl9I3D4bD6RPpdHv1+/8KU0rs22k9K6ZfKsvyOnPNtM016ATaSu82LtBu5W3vEoigeHxHXT/L6luFw+P8uQHS2NUW5m43PGxCzuVVbLVrmDhw48KClpaW/naz4aET0IqJ6w3ijx69ExGDW69VunOu6fo2VOdfX2c9Y29tS9mbzc42t77ZoWdtqhf1+//KU0kuqcdXvgKPR6L9vtc1mz7u2bkdv/W1lzrV151M13R5lbzqntaNcW2dzsxUBAosnoIG+eDUzYwIEFkyg3+//QErpVZNpH8s5H1y7hKIoXhARr5z8+0U553dstMx+v39RSunUp87Lsnz+aDT6ia1IZmyg/2lK6ZrxeHx9SumvxuPx8aWlpfuXZVndQv3bIuLMyXGvyjk/Y6s5bPT8YDB4bVmW3109Px6Pzzt69Gj1He/rPoqi+K6IeF31ZEqpPxwOq+bDXR6bvLlfNS2elXP+rVnnuyjbyd3mldqN3K09Yr/ff2NK6ZmTf/8GubtDosvnu03O629KKX1L9fzS0tJDrr766pUGa61TTlEU5WSD3805X1hr4wUdvGjnuoMHD35lr9dbuXvL8Yg4IyI+nFJ6/vHjx68//fTTqxpWf3xTvW740sm1/qdGo9H3z1Ki3TjXdf0aK3Our7P8LO7ENrI3m6I3+eu7LVrWtlphURQfmdyF7HOnnXbaOdv9mhTX1q3E6z8vc66t9VOzM1vI3myOrq2zudmKAIHFE9BAX7yamTEBAgskMPkE6jsjYl9EfOrkyZOPvOaaaz61dgmDweDFZVn+6OTfvz7n/DsbLXP195mWZfni0Wj0Y1uR1G2gF0Vx2vHjx/ddd911/7Levg8ePPjver1eta4vmjz//+Scr91qHus9v7rJuLy8/LDNvo9uMBg8syzLN1b7SSldOhwO37zePi+99NK733bbbaduhTsej0+fzLP63vlLqzpExItzzqf208aH3G1d1d3I3eqjXnbZZWfceuutn4yIe0TEx88///wv2s5tmLde0fxHyN3sNfAGxGx2i5i5oiiq71+tvod15fGJiPjynPONqxUuueSS+5w8ebK6jfsDIuL25eXlhx47duxjdaV241zX5WuszG2dwN3IXNevr9X6ZW/r7G00wjW2nt0iZm2zFfb7/cemlH5/8vvj1cPh8JJ6IncdvRvnOdfWWLj3TDY55yxE5lxbF/PautfnO9fW7V4xbE+AwKILaKAvegXNnwCBxgpZVprtAAAgAElEQVQcPHjwEb1er3qT/KyIOJ5SeuJwOHz3ehNu2icyp0Ht9/tfm1JaWc87c87/98p2z3rWs/Z/9rOf/ZKN9nPPe97zQ1deeeXt1fO78Rf8m7zI//qU0q9FRNVUvzznvPJHC9MseSHGyF0zctfv9w+nlN5ahaYsyx8fjUYvWogAzThJuZsud96AmDFg62y2qJk7ePDgBb1e730rS0opff9wOPyp9WQGg8FzyrL8meq5lNL3DofD/1b9b9fYnctRnT3J3HTnud1+Xde162uVUdmbLnuusXXOaOuPXdSsbbbywWDw+rIsv3MyZsM7vbm2bj8/s+xB5qY7v7m2zpKuzbeRvemy59q689mzRwIEFktAA32x6mW2BAgsiEBRFNWnn39v8smx5bIsD45Go+p7TNd9NO070KdlLorigxFxfvUHAueff/7dVz5he/jw4QcvLy//zUb7WX2L4t34Drkt3kT58bIsf/COD6ePH7HZLeOndWjKOLlrTu6Koqi+ZuGiSTYekXO+oSk52el5yN30ufMGxM6kb5EzVxRF9cdl//pVJePx+MuOHj1aXUvv8iiK4uER8VfVE2VZvnk0GlV3UQnX2J3JUZ29yNz057ndfl3XpetrlVHZmz57rrF1zmrrXnNa9/vrRRdddPqZZ575fyZ/0F79/3NzzsvrSbm2bi8/s2y9yOe3jda7qJlzbV389+t2K3uurbOc3WxDgECbBDTQ21RNayFAoBEChw8ffsDy8nL1yfOH3vGed3nZaDT6pc0m1+/3n5xSum7yJvnzRqPRqzcaPxgMnleW5U9Wz6eUnjQcDk99H/pmj7q3cN9qfyvPF0WRI6I/+e+zV25BW+cNiKIovicifnqynqcNh8O3bfIi/TUppedOnp+pKbnmNn7/eTgcvmza9TZ5nNzVayrtZu4uueSS+508efLj1ddZR8T7c84XNDk725mb3NXLnTcgtpO2O7Zd9MxVt2g9fvz4zdUlvFrPaaedds+Nvot18kZY9T3p1eM3cs7fODGY+o/UdvNct14123iNlbl657ndzFyXrq/Od/XPd66xs19jF/08t9HKi6Kofk+tfl+tfm/+ieFw+PyNxvr9dfb8zLKlzLm2zpKbndhG9uplz7V1J1JnHwQILLKABvoiV8/cCRBonMDkO0t/d/Kp7OoX9e8ZDoev3WqiBw4ceOjS0tJHJ7/cv2E4HD57kzcC3lDdwXXy/ENzzht+0ntlH7vYQB9WH46ZHOdfG+hbrXf186u/0z0ifijn/IpN1v4bEfENEXHyrLPO+ryV28DXOd7k+9s/PNnmypzzyi396uymUWPl7s7fHTxNcXYzd0VRfH9E/EQ1j7IsnzsajU79gUjbHnJXP3fegNjeT0FbMlcURXXdfnClsX///i+46qqr/mk9mYsvvvjzzjjjjFsnz/1azvnJdQV381y33lzado2VuWZlrivX1+pnS/bqZ881tu4V4o7xbcnaeqsviuLaiLh48tyX55z/YjalO2/l2ro9RZmr77ebmXNtbcf7dbt1vnNtrf/zagsCBNoloIHernpaDQECcxQoiuKeEfE7EfEfqmmklF40HA5/fMoppaIoPja55ftf5pzP22i7oij+V0R8aUR8POd8btWj2+oYu9hA/0BEPCIiTpx//vmft3IL963ms/r5pzzlKZ9/+umnf7r6IF5EvCPnvHLb6zvtpiiK6vkbI+IeEfHenPNj6xxnZWy/378wpfSuyX//ZM75B2bZT1O2kbvm5a4oij+LiH8fEbfv27fvAW9961urfLfqIXez5c4bELP/GLQpc4PB4BfLsvzmicYFOef3rydTFMUjI+J/Tp6b6Q++XGNlrhJoS+a6cH2t6uV8FzOd71xj65/v2pS1tasviuK+1e/L1d+qRcSf5Zy/sr7Q+lu4ts4uKXOz2e1m5lxbp6pJo9+v283znWvrVPkwiACBFgtooLe4uJZGgMDeCUw+JfabEfGfJkd9ac75R+rMoCiK10XEd1Xb9Hq9xxw5cuQP1m5/6NChR4/H4/dW/55Set1wOHzONMfYjQb6YDD4T2VZVt/zXj1+O+f8hGnmst6YVd+5dXJ5efkhx44dq/6Y4E6Pfr9/OKX01sk//mDO+b/OcrzBYPDasiy/e7Ltt+ac3zTLfpqwjdw1L3drGl6/knN+ahOyspNzkLvt5W69WvT7/TellL6lem5paekhV1999d/OUrOiKFb+oOp3c84XzrKPJm7TtswVRfGUiPiVrV4v9Pv9y1NKL5lc8y8dDodvnqU+rrH11WTu1OvMRmWuC9fXKqmyt73sucZOf75rW9bWrnwwGDy3LMvXVP9eluWmX5E2vdq/jXRtra8mc/XNVm+xG5lzbZ2+Jk1+v263z3eurdPnxEgCBNonoIHevppaEQECeyww+WR09f3l1a3Fq8drcs7fV3cahw4d+uLxeHzD5HuT/zgiHpdz/tzKfoqiuFtEvDsiqu9TPjkej88/evToX01znLoN9MFg8NThcFi9ub/up9uLonh41TSPiC+avClxcDQaHZtmLuuNWXNLsupWewdyzssrYye3eas+oVcd76b9+/c/dO0tbweDwSVlWf56zvmzG81jMBgMyrK8amJcjatugf+Ps857ntvJ3ak3w+aeu7UZGAwGryrL8tRdDbY7v3nma6Njy93u1FUDfeO0tzFzl19+ee+GG27484j4soi4dTwef/XRo0c/uFrh0KFD543H4z+KiDOru68cP378wdddd92/zHJecI2tpyZzzchc166v1Xplb/vZW++n3TX2riptzNraVRZFUf0+/R+r35tPnjz5wGuuueZT9a4Gm492ba2nKXP1vHbrPRPX1na8X7fX5zvX1u3//NoDAQKLK6CBvri1M3MCBBoiUBTF0arhO5lOdQv37+v1epvdVv22I0eOrHwH951WURTFyyPiRZN//NOI+PHxePzRXq/3sIh4YUScuvVcSunlw+Hwhzf4xapqbn/NmudeFRH3jojPRMTz1zxX3Tb9E6v/bfIpxo+klKqm+B8tLy9/rNfrnSjL8v4ppSdGxLdN3tSvmoTD0Wh0aLvlGAwGby3L8vBkP9Ut1l89Ho//IaX0yJTSf46IyqB6fGfO+cp1fmm4vnqTpCzLt/V6veoPDT6UUvrs8vLy3SPiS1JK/Yj4psl2ZUrpW4fD4S9ud97z2l7umpG7NT83SxHx9xFx/4io/jDj/jnn2+aVkd04rtxtP3dFUVQN0ep8tPrx7St3MEkpvaAsy9W3/a9uO1p9LcDa68VXRET1f6sfvzD5jw9FxCvWPDfKOd+yG7nYzX22MXOVV1EU1XW6+kO06utJbirL8pVLS0unvl5keXn5cdXXwETEWRPbwznnI9txdo2dXk/mTlnNPXNdu75Ozgut+51ir893rrHTnevaep5bWX1RFOdHxMofpv1qznnle9CnA5pylGvrlFB3vO5p5flt0TLn2nrqKxcX/v26NXXc9fOda+v05zojCRBon4AGevtqakUECOyxwKpb5k575L/LOT94vcHVp9I++MEP/lxK6Zmb7OyN559//rM2+r7xoigui4iVJsqWcyrL8utGo1HVfP7XR401/ewtt9zyvLe//e0ntjzQFgMmn7AfrWpyr91inFL6L8Ph8NTtbNc+iqKo1vD4KebxTxHxPTnn6pPoC/uoUaOVNcrd+rmp7uwwc+7W/Nx8Y0S8vfq3Ol+xsEghlLvtn+8OHz784OXl5b+Ztu4ppSvWO+8NBoOXlGV5+bT72c6t4ac9xm6Ma2PmVpwGg8HBsiyr6/Xnb2B3e0rpucPh8PXbtXWNnV5Q5pqRua5dX6v1yt72s+caO925rs1ZqwT6/f4rUkrVH59Xf+w9GI1GeTqZeqNcW6f3krnprTYbud3MubZOVYfGv2+yehV7cb5zbZ0qNwYRINBSAQ30lhbWsggQ2DuBnfxlcNWb6t9UluWzIuKrIuI+EVF9GvF9ZVm+YTQanWrQbfTYiQb6YDCo/kr/MWVZfnVEPGgyh+qT3P8cEX9dluV7lpaWfv7IkSMf2GnpoiieHhHVHwH8+4i4V0R8MiLeExE/k3M+9f3v6z2KonhgSukJ4/H461JKXx4R94uI+0ZE9Qngyu8vUkrv2Ldv31Vrb/++02vYi/3J3c4qz5q7NW9CvCUiqvzGeDx+9NGjR/9wZ2c5/73J3fZr4A2IeoZtzNya88YXlWX5/6WUnhQR50ZEr7qTRVmW70wpvTrn/JF6YpuPnvVc16VrrMw1I3Ndu75W65W97WfPNXa6K0abszb5mpS/i4gvrO7wcsstt5yzE3/svcXv335/3SJ6Mjfdz+a0o2Z9PefaOpXwhg30la0Hg8Fc369bmcdene9cW6fKjUEECLRUQAO9pYW1LAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoJ6CBXs/LaAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoqYAGeksLa1kECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUE9AA72el9EECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0FIBDfSWFtayCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCegAZ6PS+jCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKClAhroLS2sZREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAPQEN9HpeRhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBASwU00FtaWMsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXoCGuj1vIwmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZYKaKC3tLCWRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL1BDTQ63kZTYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQItFdBAb2lhLYsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6glooNfzMpoAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWiqggd7SwloWAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNQT0ECv52U0AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLRUQAO9pYW1LAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoJ6CBXs/LaAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoqYAGeksLa1kECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUE9AA72el9EECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0FIBDfSWFtayCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCegAZ6PS+jCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKClAhroLS2sZREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAPQEN9HpeRhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBASwU00FtaWMsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXoCGuj1vIwmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZYKaKC3tLCWRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL1BDTQ63kZTYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQItFdBAb2lhLYsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6glooNfzMpoAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWiqggd7SwloWAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNQT0ECv52U0AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLRUQAO9pYW1LAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoJ6CBXs/LaAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoqYAGeksLa1kECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUE9AA72el9EECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0FIBDfSWFtayCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCegAZ6PS+jCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKClAhroLS2sZREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAPQEN9HpeRhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBASwU00FtaWMsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXoCGuj1vIwmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZYKaKC3tLCWRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL1BDTQ63kZTYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQItFdBAb2lhLYsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6glooNfzMpoAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWiqggd7SwloWAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNQT0ECv52U0AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLRUQAO9pYW1LAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoJ6CBXs/LaAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoqYAGeksLa1kECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUE9AA72el9EECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0FIBDfSWFtayCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCegAZ6PS+jCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKClAhroLS2sZREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAPQEN9HpeRhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBASwU00FtaWMsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXoCGuj1vIwmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZYKaKC3tLCWRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL1BDTQ63kZTYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQItFdBAb2lhLYsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6glooNfzMpoAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWiqggd7SwloWAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNQT0ECv52U0AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLRUQAO9pYW1LAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoJ6CBXs/LaAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoqYAGeksLa1kECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUE9AA72el9EECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0FIBDfSWFtayCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCegAZ6PS+jCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKClAhroLS2sZREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAPQEN9HpeRhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBASwU00FtaWMsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXoCGuj1vIwmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZYKaKC3tLCWRYAAAQIECBAgQIAAAQIECBAgQIAA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       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>algo_volatility</th>\n",
       "      <th>algorithm_period_return</th>\n",
       "      <th>alpha</th>\n",
       "      <th>benchmark_period_return</th>\n",
       "      <th>benchmark_volatility</th>\n",
       "      <th>beta</th>\n",
       "      <th>capital_used</th>\n",
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       "      <th>shorts_count</th>\n",
       "      <th>signal</th>\n",
       "      <th>sortino</th>\n",
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       "      <th>starting_exposure</th>\n",
       "      <th>starting_value</th>\n",
       "      <th>trading_days</th>\n",
       "      <th>transactions</th>\n",
       "      <th>treasury_period_return</th>\n",
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       "    <tr>\n",
       "      <th>2015-01-06 21:00:00+00:00</th>\n",
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       "      <td>-0.037325</td>\n",
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       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.000000e+07</td>\n",
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       "      <td>0.000000e+00</td>\n",
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       "      <th>2015-01-08 21:00:00+00:00</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-13 21:00:00+00:00</th>\n",
       "      <td>110.220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001449</td>\n",
       "      <td>0.341803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14 21:00:00+00:00</th>\n",
       "      <td>109.800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.005254</td>\n",
       "      <td>0.320369</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-15 21:00:00+00:00</th>\n",
       "      <td>106.820</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.032251</td>\n",
       "      <td>0.330520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>10</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16 21:00:00+00:00</th>\n",
       "      <td>105.990</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.039775</td>\n",
       "      <td>0.314364</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>11</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-20 21:00:00+00:00</th>\n",
       "      <td>108.720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.015039</td>\n",
       "      <td>0.328372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>12</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-21 21:00:00+00:00</th>\n",
       "      <td>109.550</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.007524</td>\n",
       "      <td>0.316715</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>13</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-22 21:00:00+00:00</th>\n",
       "      <td>112.400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018300</td>\n",
       "      <td>0.324274</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>14</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-23 21:00:00+00:00</th>\n",
       "      <td>112.980</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023554</td>\n",
       "      <td>0.312840</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>15</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-26 21:00:00+00:00</th>\n",
       "      <td>113.100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024641</td>\n",
       "      <td>0.302244</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>16</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-27 21:00:00+00:00</th>\n",
       "      <td>109.140</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.011234</td>\n",
       "      <td>0.324981</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>17</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-28 21:00:00+00:00</th>\n",
       "      <td>115.310</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044667</td>\n",
       "      <td>0.380646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>18</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-29 21:00:00+00:00</th>\n",
       "      <td>118.900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.077186</td>\n",
       "      <td>0.384136</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>19</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-30 21:00:00+00:00</th>\n",
       "      <td>117.160</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.061422</td>\n",
       "      <td>0.379818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>20</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-02 21:00:00+00:00</th>\n",
       "      <td>118.630</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074744</td>\n",
       "      <td>0.371598</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>21</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-03 21:00:00+00:00</th>\n",
       "      <td>118.650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074926</td>\n",
       "      <td>0.362840</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>22</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04 21:00:00+00:00</th>\n",
       "      <td>119.560</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083165</td>\n",
       "      <td>0.354761</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>23</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05 21:00:00+00:00</th>\n",
       "      <td>119.940</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090899</td>\n",
       "      <td>0.347140</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>24</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-06 21:00:00+00:00</th>\n",
       "      <td>118.930</td>\n",
       "      <td>0.001601</td>\n",
       "      <td>-0.000504</td>\n",
       "      <td>-0.005534</td>\n",
       "      <td>0.081715</td>\n",
       "      <td>0.342059</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>-9.920830e+06</td>\n",
       "      <td>7.916998e+04</td>\n",
       "      <td>9.915789e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.174902</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>25</td>\n",
       "      <td>[{'price': 118.98946500000001, 'dt': 2015-02-0...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-09 21:00:00+00:00</th>\n",
       "      <td>119.720</td>\n",
       "      <td>0.020639</td>\n",
       "      <td>0.006082</td>\n",
       "      <td>0.056876</td>\n",
       "      <td>0.088896</td>\n",
       "      <td>0.335303</td>\n",
       "      <td>0.002396</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.916998e+04</td>\n",
       "      <td>9.981655e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.583118</td>\n",
       "      <td>7.916998e+04</td>\n",
       "      <td>9.915789e+06</td>\n",
       "      <td>9.915789e+06</td>\n",
       "      <td>26</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-10 21:00:00+00:00</th>\n",
       "      <td>122.020</td>\n",
       "      <td>0.060972</td>\n",
       "      <td>0.025259</td>\n",
       "      <td>0.206590</td>\n",
       "      <td>0.109818</td>\n",
       "      <td>0.332281</td>\n",
       "      <td>0.027365</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.916998e+04</td>\n",
       "      <td>1.017342e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.387640</td>\n",
       "      <td>7.916998e+04</td>\n",
       "      <td>9.981655e+06</td>\n",
       "      <td>9.981655e+06</td>\n",
       "      <td>27</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-11 21:00:00+00:00</th>\n",
       "      <td>124.880</td>\n",
       "      <td>0.089811</td>\n",
       "      <td>0.049104</td>\n",
       "      <td>0.361154</td>\n",
       "      <td>0.135833</td>\n",
       "      <td>0.331204</td>\n",
       "      <td>0.061994</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.916998e+04</td>\n",
       "      <td>1.041187e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>288.046044</td>\n",
       "      <td>7.916998e+04</td>\n",
       "      <td>1.017342e+07</td>\n",
       "      <td>1.017342e+07</td>\n",
       "      <td>28</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-12 21:00:00+00:00</th>\n",
       "      <td>126.460</td>\n",
       "      <td>0.093791</td>\n",
       "      <td>0.062277</td>\n",
       "      <td>0.442536</td>\n",
       "      <td>0.150204</td>\n",
       "      <td>0.326065</td>\n",
       "      <td>0.068657</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.916998e+04</td>\n",
       "      <td>1.054360e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>356.459780</td>\n",
       "      <td>7.916998e+04</td>\n",
       "      <td>1.041187e+07</td>\n",
       "      <td>1.041187e+07</td>\n",
       "      <td>29</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-13 21:00:00+00:00</th>\n",
       "      <td>127.080</td>\n",
       "      <td>0.092508</td>\n",
       "      <td>0.067446</td>\n",
       "      <td>0.465876</td>\n",
       "      <td>0.155840</td>\n",
       "      <td>0.320394</td>\n",
       "      <td>0.068626</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.916998e+04</td>\n",
       "      <td>1.059530e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>378.444675</td>\n",
       "      <td>7.916998e+04</td>\n",
       "      <td>1.054360e+07</td>\n",
       "      <td>1.054360e+07</td>\n",
       "      <td>30</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-16 21:00:00+00:00</th>\n",
       "      <td>171.100</td>\n",
       "      <td>0.156395</td>\n",
       "      <td>0.195566</td>\n",
       "      <td>-0.015133</td>\n",
       "      <td>0.631637</td>\n",
       "      <td>0.231254</td>\n",
       "      <td>0.454198</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.204749e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.695655</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.190526e+07</td>\n",
       "      <td>1.190526e+07</td>\n",
       "      <td>726</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 21:00:00+00:00</th>\n",
       "      <td>170.150</td>\n",
       "      <td>0.156325</td>\n",
       "      <td>0.188877</td>\n",
       "      <td>-0.016202</td>\n",
       "      <td>0.622578</td>\n",
       "      <td>0.231124</td>\n",
       "      <td>0.454322</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.198060e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.676660</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.204749e+07</td>\n",
       "      <td>1.204749e+07</td>\n",
       "      <td>727</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-20 21:00:00+00:00</th>\n",
       "      <td>169.980</td>\n",
       "      <td>0.156220</td>\n",
       "      <td>0.187680</td>\n",
       "      <td>-0.016372</td>\n",
       "      <td>0.620957</td>\n",
       "      <td>0.230968</td>\n",
       "      <td>0.454328</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.196863e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.672914</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.198060e+07</td>\n",
       "      <td>1.198060e+07</td>\n",
       "      <td>728</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-21 21:00:00+00:00</th>\n",
       "      <td>173.140</td>\n",
       "      <td>0.156489</td>\n",
       "      <td>0.209930</td>\n",
       "      <td>-0.013032</td>\n",
       "      <td>0.651091</td>\n",
       "      <td>0.231047</td>\n",
       "      <td>0.455522</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.219113e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.733257</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.196863e+07</td>\n",
       "      <td>1.196863e+07</td>\n",
       "      <td>729</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-22 21:00:00+00:00</th>\n",
       "      <td>174.960</td>\n",
       "      <td>0.156498</td>\n",
       "      <td>0.222745</td>\n",
       "      <td>-0.011086</td>\n",
       "      <td>0.668447</td>\n",
       "      <td>0.230959</td>\n",
       "      <td>0.455891</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.231928e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.767108</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.219113e+07</td>\n",
       "      <td>1.219113e+07</td>\n",
       "      <td>730</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 18:00:00+00:00</th>\n",
       "      <td>174.970</td>\n",
       "      <td>0.156391</td>\n",
       "      <td>0.222815</td>\n",
       "      <td>-0.011060</td>\n",
       "      <td>0.668542</td>\n",
       "      <td>0.230801</td>\n",
       "      <td>0.455890</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.231999e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.766770</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.231928e+07</td>\n",
       "      <td>1.231928e+07</td>\n",
       "      <td>731</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-27 21:00:00+00:00</th>\n",
       "      <td>174.090</td>\n",
       "      <td>0.156316</td>\n",
       "      <td>0.216619</td>\n",
       "      <td>-0.012020</td>\n",
       "      <td>0.660150</td>\n",
       "      <td>0.230669</td>\n",
       "      <td>0.455993</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.225803e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.749540</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.231999e+07</td>\n",
       "      <td>1.231999e+07</td>\n",
       "      <td>732</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-28 21:00:00+00:00</th>\n",
       "      <td>173.070</td>\n",
       "      <td>0.156252</td>\n",
       "      <td>0.209437</td>\n",
       "      <td>-0.013142</td>\n",
       "      <td>0.650423</td>\n",
       "      <td>0.230544</td>\n",
       "      <td>0.456130</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.218620e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.729541</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.225803e+07</td>\n",
       "      <td>1.225803e+07</td>\n",
       "      <td>733</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-29 21:00:00+00:00</th>\n",
       "      <td>169.480</td>\n",
       "      <td>0.156639</td>\n",
       "      <td>0.184159</td>\n",
       "      <td>-0.017354</td>\n",
       "      <td>0.616189</td>\n",
       "      <td>0.230732</td>\n",
       "      <td>0.457712</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.193343e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.657150</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.218620e+07</td>\n",
       "      <td>1.218620e+07</td>\n",
       "      <td>734</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30 21:00:00+00:00</th>\n",
       "      <td>171.850</td>\n",
       "      <td>0.156741</td>\n",
       "      <td>0.200847</td>\n",
       "      <td>-0.014823</td>\n",
       "      <td>0.638789</td>\n",
       "      <td>0.230705</td>\n",
       "      <td>0.458373</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.210030e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.701914</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.193343e+07</td>\n",
       "      <td>1.193343e+07</td>\n",
       "      <td>735</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 21:00:00+00:00</th>\n",
       "      <td>171.050</td>\n",
       "      <td>0.156661</td>\n",
       "      <td>0.195214</td>\n",
       "      <td>-0.015695</td>\n",
       "      <td>0.631160</td>\n",
       "      <td>0.230570</td>\n",
       "      <td>0.458460</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.204397e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.686171</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.210030e+07</td>\n",
       "      <td>1.210030e+07</td>\n",
       "      <td>736</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-04 21:00:00+00:00</th>\n",
       "      <td>169.800</td>\n",
       "      <td>0.156619</td>\n",
       "      <td>0.186412</td>\n",
       "      <td>-0.017086</td>\n",
       "      <td>0.619240</td>\n",
       "      <td>0.230461</td>\n",
       "      <td>0.458666</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.195596e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.661583</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.204397e+07</td>\n",
       "      <td>1.204397e+07</td>\n",
       "      <td>737</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-05 21:00:00+00:00</th>\n",
       "      <td>169.640</td>\n",
       "      <td>0.156514</td>\n",
       "      <td>0.185286</td>\n",
       "      <td>-0.017240</td>\n",
       "      <td>0.617714</td>\n",
       "      <td>0.230307</td>\n",
       "      <td>0.458670</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.194469e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.658089</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.195596e+07</td>\n",
       "      <td>1.195596e+07</td>\n",
       "      <td>738</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-06 21:00:00+00:00</th>\n",
       "      <td>169.010</td>\n",
       "      <td>0.156426</td>\n",
       "      <td>0.180850</td>\n",
       "      <td>-0.017923</td>\n",
       "      <td>0.611707</td>\n",
       "      <td>0.230166</td>\n",
       "      <td>0.458727</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.190033e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.645547</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.194469e+07</td>\n",
       "      <td>1.194469e+07</td>\n",
       "      <td>739</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-07 21:00:00+00:00</th>\n",
       "      <td>169.452</td>\n",
       "      <td>0.156326</td>\n",
       "      <td>0.183962</td>\n",
       "      <td>-0.017413</td>\n",
       "      <td>0.615922</td>\n",
       "      <td>0.230012</td>\n",
       "      <td>0.458745</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.193145e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.653526</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.190033e+07</td>\n",
       "      <td>1.190033e+07</td>\n",
       "      <td>740</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-08 21:00:00+00:00</th>\n",
       "      <td>169.370</td>\n",
       "      <td>0.156221</td>\n",
       "      <td>0.183384</td>\n",
       "      <td>-0.017480</td>\n",
       "      <td>0.615140</td>\n",
       "      <td>0.229858</td>\n",
       "      <td>0.458747</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.192568e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.651527</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.193145e+07</td>\n",
       "      <td>1.193145e+07</td>\n",
       "      <td>741</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11 21:00:00+00:00</th>\n",
       "      <td>172.670</td>\n",
       "      <td>0.156523</td>\n",
       "      <td>0.206620</td>\n",
       "      <td>-0.014078</td>\n",
       "      <td>0.646609</td>\n",
       "      <td>0.229962</td>\n",
       "      <td>0.460042</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.215804e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.713697</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.192568e+07</td>\n",
       "      <td>1.192568e+07</td>\n",
       "      <td>742</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-12 21:00:00+00:00</th>\n",
       "      <td>171.700</td>\n",
       "      <td>0.156456</td>\n",
       "      <td>0.199790</td>\n",
       "      <td>-0.015126</td>\n",
       "      <td>0.637359</td>\n",
       "      <td>0.229837</td>\n",
       "      <td>0.460166</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.208974e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.694847</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.215804e+07</td>\n",
       "      <td>1.215804e+07</td>\n",
       "      <td>743</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-13 21:00:00+00:00</th>\n",
       "      <td>172.270</td>\n",
       "      <td>0.156360</td>\n",
       "      <td>0.203804</td>\n",
       "      <td>-0.014496</td>\n",
       "      <td>0.642794</td>\n",
       "      <td>0.229687</td>\n",
       "      <td>0.460197</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.212988e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.705027</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.208974e+07</td>\n",
       "      <td>1.208974e+07</td>\n",
       "      <td>744</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-14 21:00:00+00:00</th>\n",
       "      <td>172.220</td>\n",
       "      <td>0.156256</td>\n",
       "      <td>0.203452</td>\n",
       "      <td>-0.014530</td>\n",
       "      <td>0.642318</td>\n",
       "      <td>0.229534</td>\n",
       "      <td>0.460198</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.212635e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.703622</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.212988e+07</td>\n",
       "      <td>1.212988e+07</td>\n",
       "      <td>745</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-15 21:00:00+00:00</th>\n",
       "      <td>173.870</td>\n",
       "      <td>0.156245</td>\n",
       "      <td>0.215070</td>\n",
       "      <td>-0.012798</td>\n",
       "      <td>0.658052</td>\n",
       "      <td>0.229437</td>\n",
       "      <td>0.460497</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.224253e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.733836</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.212635e+07</td>\n",
       "      <td>1.212635e+07</td>\n",
       "      <td>746</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-18 21:00:00+00:00</th>\n",
       "      <td>176.420</td>\n",
       "      <td>0.156367</td>\n",
       "      <td>0.233025</td>\n",
       "      <td>-0.010219</td>\n",
       "      <td>0.682370</td>\n",
       "      <td>0.229425</td>\n",
       "      <td>0.461216</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.242209e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.780282</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.224253e+07</td>\n",
       "      <td>1.224253e+07</td>\n",
       "      <td>747</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-19 21:00:00+00:00</th>\n",
       "      <td>174.540</td>\n",
       "      <td>0.156394</td>\n",
       "      <td>0.219787</td>\n",
       "      <td>-0.012251</td>\n",
       "      <td>0.664442</td>\n",
       "      <td>0.229368</td>\n",
       "      <td>0.461640</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.228971e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.744400</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.242209e+07</td>\n",
       "      <td>1.242209e+07</td>\n",
       "      <td>748</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-20 21:00:00+00:00</th>\n",
       "      <td>174.350</td>\n",
       "      <td>0.156291</td>\n",
       "      <td>0.218450</td>\n",
       "      <td>-0.012436</td>\n",
       "      <td>0.662630</td>\n",
       "      <td>0.229217</td>\n",
       "      <td>0.461646</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.227633e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.740417</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.228971e+07</td>\n",
       "      <td>1.228971e+07</td>\n",
       "      <td>749</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-21 21:00:00+00:00</th>\n",
       "      <td>175.010</td>\n",
       "      <td>0.156200</td>\n",
       "      <td>0.223097</td>\n",
       "      <td>-0.011733</td>\n",
       "      <td>0.668924</td>\n",
       "      <td>0.229070</td>\n",
       "      <td>0.461687</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.232280e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.751993</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.227633e+07</td>\n",
       "      <td>1.227633e+07</td>\n",
       "      <td>750</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-22 21:00:00+00:00</th>\n",
       "      <td>175.010</td>\n",
       "      <td>0.156096</td>\n",
       "      <td>0.223097</td>\n",
       "      <td>-0.011717</td>\n",
       "      <td>0.668924</td>\n",
       "      <td>0.228918</td>\n",
       "      <td>0.461687</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.232280e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.751492</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.232280e+07</td>\n",
       "      <td>1.232280e+07</td>\n",
       "      <td>751</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26 21:00:00+00:00</th>\n",
       "      <td>170.570</td>\n",
       "      <td>0.156710</td>\n",
       "      <td>0.191834</td>\n",
       "      <td>-0.016778</td>\n",
       "      <td>0.626583</td>\n",
       "      <td>0.229266</td>\n",
       "      <td>0.463988</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.201017e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.663780</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.232280e+07</td>\n",
       "      <td>1.232280e+07</td>\n",
       "      <td>752</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27 21:00:00+00:00</th>\n",
       "      <td>170.600</td>\n",
       "      <td>0.156606</td>\n",
       "      <td>0.192045</td>\n",
       "      <td>-0.016723</td>\n",
       "      <td>0.626869</td>\n",
       "      <td>0.229114</td>\n",
       "      <td>0.463988</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.201229e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.663894</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.201017e+07</td>\n",
       "      <td>1.201017e+07</td>\n",
       "      <td>753</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28 21:00:00+00:00</th>\n",
       "      <td>171.080</td>\n",
       "      <td>0.156508</td>\n",
       "      <td>0.195425</td>\n",
       "      <td>-0.016194</td>\n",
       "      <td>0.631446</td>\n",
       "      <td>0.228965</td>\n",
       "      <td>0.464009</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.204608e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.672316</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.201229e+07</td>\n",
       "      <td>1.201229e+07</td>\n",
       "      <td>754</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29 21:00:00+00:00</th>\n",
       "      <td>169.230</td>\n",
       "      <td>0.156538</td>\n",
       "      <td>0.182399</td>\n",
       "      <td>-0.018214</td>\n",
       "      <td>0.613805</td>\n",
       "      <td>0.228911</td>\n",
       "      <td>0.464437</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.191582e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.636727</td>\n",
       "      <td>-9.183618e+04</td>\n",
       "      <td>1.204608e+07</td>\n",
       "      <td>1.204608e+07</td>\n",
       "      <td>755</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>755 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AAPL  algo_volatility  algorithm_period_return  \\\n",
       "2015-01-02 21:00:00+00:00  109.330              NaN                 0.000000   \n",
       "2015-01-05 21:00:00+00:00  106.250         0.000000                 0.000000   \n",
       "2015-01-06 21:00:00+00:00  106.260         0.000000                 0.000000   \n",
       "2015-01-07 21:00:00+00:00  107.750         0.000000                 0.000000   \n",
       "2015-01-08 21:00:00+00:00  111.890         0.000000                 0.000000   \n",
       "2015-01-09 21:00:00+00:00  112.010         0.000000                 0.000000   \n",
       "2015-01-12 21:00:00+00:00  109.250         0.000000                 0.000000   \n",
       "2015-01-13 21:00:00+00:00  110.220         0.000000                 0.000000   \n",
       "2015-01-14 21:00:00+00:00  109.800         0.000000                 0.000000   \n",
       "2015-01-15 21:00:00+00:00  106.820         0.000000                 0.000000   \n",
       "2015-01-16 21:00:00+00:00  105.990         0.000000                 0.000000   \n",
       "2015-01-20 21:00:00+00:00  108.720         0.000000                 0.000000   \n",
       "2015-01-21 21:00:00+00:00  109.550         0.000000                 0.000000   \n",
       "2015-01-22 21:00:00+00:00  112.400         0.000000                 0.000000   \n",
       "2015-01-23 21:00:00+00:00  112.980         0.000000                 0.000000   \n",
       "2015-01-26 21:00:00+00:00  113.100         0.000000                 0.000000   \n",
       "2015-01-27 21:00:00+00:00  109.140         0.000000                 0.000000   \n",
       "2015-01-28 21:00:00+00:00  115.310         0.000000                 0.000000   \n",
       "2015-01-29 21:00:00+00:00  118.900         0.000000                 0.000000   \n",
       "2015-01-30 21:00:00+00:00  117.160         0.000000                 0.000000   \n",
       "2015-02-02 21:00:00+00:00  118.630         0.000000                 0.000000   \n",
       "2015-02-03 21:00:00+00:00  118.650         0.000000                 0.000000   \n",
       "2015-02-04 21:00:00+00:00  119.560         0.000000                 0.000000   \n",
       "2015-02-05 21:00:00+00:00  119.940         0.000000                 0.000000   \n",
       "2015-02-06 21:00:00+00:00  118.930         0.001601                -0.000504   \n",
       "2015-02-09 21:00:00+00:00  119.720         0.020639                 0.006082   \n",
       "2015-02-10 21:00:00+00:00  122.020         0.060972                 0.025259   \n",
       "2015-02-11 21:00:00+00:00  124.880         0.089811                 0.049104   \n",
       "2015-02-12 21:00:00+00:00  126.460         0.093791                 0.062277   \n",
       "2015-02-13 21:00:00+00:00  127.080         0.092508                 0.067446   \n",
       "...                            ...              ...                      ...   \n",
       "2017-11-16 21:00:00+00:00  171.100         0.156395                 0.195566   \n",
       "2017-11-17 21:00:00+00:00  170.150         0.156325                 0.188877   \n",
       "2017-11-20 21:00:00+00:00  169.980         0.156220                 0.187680   \n",
       "2017-11-21 21:00:00+00:00  173.140         0.156489                 0.209930   \n",
       "2017-11-22 21:00:00+00:00  174.960         0.156498                 0.222745   \n",
       "2017-11-24 18:00:00+00:00  174.970         0.156391                 0.222815   \n",
       "2017-11-27 21:00:00+00:00  174.090         0.156316                 0.216619   \n",
       "2017-11-28 21:00:00+00:00  173.070         0.156252                 0.209437   \n",
       "2017-11-29 21:00:00+00:00  169.480         0.156639                 0.184159   \n",
       "2017-11-30 21:00:00+00:00  171.850         0.156741                 0.200847   \n",
       "2017-12-01 21:00:00+00:00  171.050         0.156661                 0.195214   \n",
       "2017-12-04 21:00:00+00:00  169.800         0.156619                 0.186412   \n",
       "2017-12-05 21:00:00+00:00  169.640         0.156514                 0.185286   \n",
       "2017-12-06 21:00:00+00:00  169.010         0.156426                 0.180850   \n",
       "2017-12-07 21:00:00+00:00  169.452         0.156326                 0.183962   \n",
       "2017-12-08 21:00:00+00:00  169.370         0.156221                 0.183384   \n",
       "2017-12-11 21:00:00+00:00  172.670         0.156523                 0.206620   \n",
       "2017-12-12 21:00:00+00:00  171.700         0.156456                 0.199790   \n",
       "2017-12-13 21:00:00+00:00  172.270         0.156360                 0.203804   \n",
       "2017-12-14 21:00:00+00:00  172.220         0.156256                 0.203452   \n",
       "2017-12-15 21:00:00+00:00  173.870         0.156245                 0.215070   \n",
       "2017-12-18 21:00:00+00:00  176.420         0.156367                 0.233025   \n",
       "2017-12-19 21:00:00+00:00  174.540         0.156394                 0.219787   \n",
       "2017-12-20 21:00:00+00:00  174.350         0.156291                 0.218450   \n",
       "2017-12-21 21:00:00+00:00  175.010         0.156200                 0.223097   \n",
       "2017-12-22 21:00:00+00:00  175.010         0.156096                 0.223097   \n",
       "2017-12-26 21:00:00+00:00  170.570         0.156710                 0.191834   \n",
       "2017-12-27 21:00:00+00:00  170.600         0.156606                 0.192045   \n",
       "2017-12-28 21:00:00+00:00  171.080         0.156508                 0.195425   \n",
       "2017-12-29 21:00:00+00:00  169.230         0.156538                 0.182399   \n",
       "\n",
       "                              alpha  benchmark_period_return  \\\n",
       "2015-01-02 21:00:00+00:00       NaN                -0.009517   \n",
       "2015-01-05 21:00:00+00:00  0.000000                -0.037420   \n",
       "2015-01-06 21:00:00+00:00  0.000000                -0.037325   \n",
       "2015-01-07 21:00:00+00:00  0.000000                -0.023831   \n",
       "2015-01-08 21:00:00+00:00  0.000000                 0.013684   \n",
       "2015-01-09 21:00:00+00:00  0.000000                 0.014772   \n",
       "2015-01-12 21:00:00+00:00  0.000000                -0.010242   \n",
       "2015-01-13 21:00:00+00:00  0.000000                -0.001449   \n",
       "2015-01-14 21:00:00+00:00  0.000000                -0.005254   \n",
       "2015-01-15 21:00:00+00:00  0.000000                -0.032251   \n",
       "2015-01-16 21:00:00+00:00  0.000000                -0.039775   \n",
       "2015-01-20 21:00:00+00:00  0.000000                -0.015039   \n",
       "2015-01-21 21:00:00+00:00  0.000000                -0.007524   \n",
       "2015-01-22 21:00:00+00:00  0.000000                 0.018300   \n",
       "2015-01-23 21:00:00+00:00  0.000000                 0.023554   \n",
       "2015-01-26 21:00:00+00:00  0.000000                 0.024641   \n",
       "2015-01-27 21:00:00+00:00  0.000000                -0.011234   \n",
       "2015-01-28 21:00:00+00:00  0.000000                 0.044667   \n",
       "2015-01-29 21:00:00+00:00  0.000000                 0.077186   \n",
       "2015-01-30 21:00:00+00:00  0.000000                 0.061422   \n",
       "2015-02-02 21:00:00+00:00  0.000000                 0.074744   \n",
       "2015-02-03 21:00:00+00:00  0.000000                 0.074926   \n",
       "2015-02-04 21:00:00+00:00  0.000000                 0.083165   \n",
       "2015-02-05 21:00:00+00:00  0.000000                 0.090899   \n",
       "2015-02-06 21:00:00+00:00 -0.005534                 0.081715   \n",
       "2015-02-09 21:00:00+00:00  0.056876                 0.088896   \n",
       "2015-02-10 21:00:00+00:00  0.206590                 0.109818   \n",
       "2015-02-11 21:00:00+00:00  0.361154                 0.135833   \n",
       "2015-02-12 21:00:00+00:00  0.442536                 0.150204   \n",
       "2015-02-13 21:00:00+00:00  0.465876                 0.155840   \n",
       "...                             ...                      ...   \n",
       "2017-11-16 21:00:00+00:00 -0.015133                 0.631637   \n",
       "2017-11-17 21:00:00+00:00 -0.016202                 0.622578   \n",
       "2017-11-20 21:00:00+00:00 -0.016372                 0.620957   \n",
       "2017-11-21 21:00:00+00:00 -0.013032                 0.651091   \n",
       "2017-11-22 21:00:00+00:00 -0.011086                 0.668447   \n",
       "2017-11-24 18:00:00+00:00 -0.011060                 0.668542   \n",
       "2017-11-27 21:00:00+00:00 -0.012020                 0.660150   \n",
       "2017-11-28 21:00:00+00:00 -0.013142                 0.650423   \n",
       "2017-11-29 21:00:00+00:00 -0.017354                 0.616189   \n",
       "2017-11-30 21:00:00+00:00 -0.014823                 0.638789   \n",
       "2017-12-01 21:00:00+00:00 -0.015695                 0.631160   \n",
       "2017-12-04 21:00:00+00:00 -0.017086                 0.619240   \n",
       "2017-12-05 21:00:00+00:00 -0.017240                 0.617714   \n",
       "2017-12-06 21:00:00+00:00 -0.017923                 0.611707   \n",
       "2017-12-07 21:00:00+00:00 -0.017413                 0.615922   \n",
       "2017-12-08 21:00:00+00:00 -0.017480                 0.615140   \n",
       "2017-12-11 21:00:00+00:00 -0.014078                 0.646609   \n",
       "2017-12-12 21:00:00+00:00 -0.015126                 0.637359   \n",
       "2017-12-13 21:00:00+00:00 -0.014496                 0.642794   \n",
       "2017-12-14 21:00:00+00:00 -0.014530                 0.642318   \n",
       "2017-12-15 21:00:00+00:00 -0.012798                 0.658052   \n",
       "2017-12-18 21:00:00+00:00 -0.010219                 0.682370   \n",
       "2017-12-19 21:00:00+00:00 -0.012251                 0.664442   \n",
       "2017-12-20 21:00:00+00:00 -0.012436                 0.662630   \n",
       "2017-12-21 21:00:00+00:00 -0.011733                 0.668924   \n",
       "2017-12-22 21:00:00+00:00 -0.011717                 0.668924   \n",
       "2017-12-26 21:00:00+00:00 -0.016778                 0.626583   \n",
       "2017-12-27 21:00:00+00:00 -0.016723                 0.626869   \n",
       "2017-12-28 21:00:00+00:00 -0.016194                 0.631446   \n",
       "2017-12-29 21:00:00+00:00 -0.018214                 0.613805   \n",
       "\n",
       "                           benchmark_volatility      beta  capital_used  \\\n",
       "2015-01-02 21:00:00+00:00                   NaN       NaN  0.000000e+00   \n",
       "2015-01-05 21:00:00+00:00              0.209389  0.000000  0.000000e+00   \n",
       "2015-01-06 21:00:00+00:00              0.228176  0.000000  0.000000e+00   \n",
       "2015-01-07 21:00:00+00:00              0.281259  0.000000  0.000000e+00   \n",
       "2015-01-08 21:00:00+00:00              0.397929  0.000000  0.000000e+00   \n",
       "2015-01-09 21:00:00+00:00              0.356131  0.000000  0.000000e+00   \n",
       "2015-01-12 21:00:00+00:00              0.364047  0.000000  0.000000e+00   \n",
       "2015-01-13 21:00:00+00:00              0.341803  0.000000  0.000000e+00   \n",
       "2015-01-14 21:00:00+00:00              0.320369  0.000000  0.000000e+00   \n",
       "2015-01-15 21:00:00+00:00              0.330520  0.000000  0.000000e+00   \n",
       "2015-01-16 21:00:00+00:00              0.314364  0.000000  0.000000e+00   \n",
       "2015-01-20 21:00:00+00:00              0.328372  0.000000  0.000000e+00   \n",
       "2015-01-21 21:00:00+00:00              0.316715  0.000000  0.000000e+00   \n",
       "2015-01-22 21:00:00+00:00              0.324274  0.000000  0.000000e+00   \n",
       "2015-01-23 21:00:00+00:00              0.312840  0.000000  0.000000e+00   \n",
       "2015-01-26 21:00:00+00:00              0.302244  0.000000  0.000000e+00   \n",
       "2015-01-27 21:00:00+00:00              0.324981  0.000000  0.000000e+00   \n",
       "2015-01-28 21:00:00+00:00              0.380646  0.000000  0.000000e+00   \n",
       "2015-01-29 21:00:00+00:00              0.384136  0.000000  0.000000e+00   \n",
       "2015-01-30 21:00:00+00:00              0.379818  0.000000  0.000000e+00   \n",
       "2015-02-02 21:00:00+00:00              0.371598  0.000000  0.000000e+00   \n",
       "2015-02-03 21:00:00+00:00              0.362840  0.000000  0.000000e+00   \n",
       "2015-02-04 21:00:00+00:00              0.354761  0.000000  0.000000e+00   \n",
       "2015-02-05 21:00:00+00:00              0.347140  0.000000  0.000000e+00   \n",
       "2015-02-06 21:00:00+00:00              0.342059  0.000533 -9.920830e+06   \n",
       "2015-02-09 21:00:00+00:00              0.335303  0.002396  0.000000e+00   \n",
       "2015-02-10 21:00:00+00:00              0.332281  0.027365  0.000000e+00   \n",
       "2015-02-11 21:00:00+00:00              0.331204  0.061994  0.000000e+00   \n",
       "2015-02-12 21:00:00+00:00              0.326065  0.068657  0.000000e+00   \n",
       "2015-02-13 21:00:00+00:00              0.320394  0.068626  0.000000e+00   \n",
       "...                                         ...       ...           ...   \n",
       "2017-11-16 21:00:00+00:00              0.231254  0.454198  0.000000e+00   \n",
       "2017-11-17 21:00:00+00:00              0.231124  0.454322  0.000000e+00   \n",
       "2017-11-20 21:00:00+00:00              0.230968  0.454328  0.000000e+00   \n",
       "2017-11-21 21:00:00+00:00              0.231047  0.455522  0.000000e+00   \n",
       "2017-11-22 21:00:00+00:00              0.230959  0.455891  0.000000e+00   \n",
       "2017-11-24 18:00:00+00:00              0.230801  0.455890  0.000000e+00   \n",
       "2017-11-27 21:00:00+00:00              0.230669  0.455993  0.000000e+00   \n",
       "2017-11-28 21:00:00+00:00              0.230544  0.456130  0.000000e+00   \n",
       "2017-11-29 21:00:00+00:00              0.230732  0.457712  0.000000e+00   \n",
       "2017-11-30 21:00:00+00:00              0.230705  0.458373  0.000000e+00   \n",
       "2017-12-01 21:00:00+00:00              0.230570  0.458460  0.000000e+00   \n",
       "2017-12-04 21:00:00+00:00              0.230461  0.458666  0.000000e+00   \n",
       "2017-12-05 21:00:00+00:00              0.230307  0.458670  0.000000e+00   \n",
       "2017-12-06 21:00:00+00:00              0.230166  0.458727  0.000000e+00   \n",
       "2017-12-07 21:00:00+00:00              0.230012  0.458745  0.000000e+00   \n",
       "2017-12-08 21:00:00+00:00              0.229858  0.458747  0.000000e+00   \n",
       "2017-12-11 21:00:00+00:00              0.229962  0.460042  0.000000e+00   \n",
       "2017-12-12 21:00:00+00:00              0.229837  0.460166  0.000000e+00   \n",
       "2017-12-13 21:00:00+00:00              0.229687  0.460197  0.000000e+00   \n",
       "2017-12-14 21:00:00+00:00              0.229534  0.460198  0.000000e+00   \n",
       "2017-12-15 21:00:00+00:00              0.229437  0.460497  0.000000e+00   \n",
       "2017-12-18 21:00:00+00:00              0.229425  0.461216  0.000000e+00   \n",
       "2017-12-19 21:00:00+00:00              0.229368  0.461640  0.000000e+00   \n",
       "2017-12-20 21:00:00+00:00              0.229217  0.461646  0.000000e+00   \n",
       "2017-12-21 21:00:00+00:00              0.229070  0.461687  0.000000e+00   \n",
       "2017-12-22 21:00:00+00:00              0.228918  0.461687  0.000000e+00   \n",
       "2017-12-26 21:00:00+00:00              0.229266  0.463988  0.000000e+00   \n",
       "2017-12-27 21:00:00+00:00              0.229114  0.463988  0.000000e+00   \n",
       "2017-12-28 21:00:00+00:00              0.228965  0.464009  0.000000e+00   \n",
       "2017-12-29 21:00:00+00:00              0.228911  0.464437  0.000000e+00   \n",
       "\n",
       "                            ending_cash  ending_exposure  \\\n",
       "2015-01-02 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-05 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-06 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-07 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-08 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-09 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-12 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-13 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-14 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-15 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-16 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-20 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-21 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-22 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-23 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-26 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-27 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-28 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-29 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-01-30 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-02-02 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-02-03 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-02-04 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-02-05 21:00:00+00:00  1.000000e+07     0.000000e+00   \n",
       "2015-02-06 21:00:00+00:00  7.916998e+04     9.915789e+06   \n",
       "2015-02-09 21:00:00+00:00  7.916998e+04     9.981655e+06   \n",
       "2015-02-10 21:00:00+00:00  7.916998e+04     1.017342e+07   \n",
       "2015-02-11 21:00:00+00:00  7.916998e+04     1.041187e+07   \n",
       "2015-02-12 21:00:00+00:00  7.916998e+04     1.054360e+07   \n",
       "2015-02-13 21:00:00+00:00  7.916998e+04     1.059530e+07   \n",
       "...                                 ...              ...   \n",
       "2017-11-16 21:00:00+00:00 -9.183618e+04     1.204749e+07   \n",
       "2017-11-17 21:00:00+00:00 -9.183618e+04     1.198060e+07   \n",
       "2017-11-20 21:00:00+00:00 -9.183618e+04     1.196863e+07   \n",
       "2017-11-21 21:00:00+00:00 -9.183618e+04     1.219113e+07   \n",
       "2017-11-22 21:00:00+00:00 -9.183618e+04     1.231928e+07   \n",
       "2017-11-24 18:00:00+00:00 -9.183618e+04     1.231999e+07   \n",
       "2017-11-27 21:00:00+00:00 -9.183618e+04     1.225803e+07   \n",
       "2017-11-28 21:00:00+00:00 -9.183618e+04     1.218620e+07   \n",
       "2017-11-29 21:00:00+00:00 -9.183618e+04     1.193343e+07   \n",
       "2017-11-30 21:00:00+00:00 -9.183618e+04     1.210030e+07   \n",
       "2017-12-01 21:00:00+00:00 -9.183618e+04     1.204397e+07   \n",
       "2017-12-04 21:00:00+00:00 -9.183618e+04     1.195596e+07   \n",
       "2017-12-05 21:00:00+00:00 -9.183618e+04     1.194469e+07   \n",
       "2017-12-06 21:00:00+00:00 -9.183618e+04     1.190033e+07   \n",
       "2017-12-07 21:00:00+00:00 -9.183618e+04     1.193145e+07   \n",
       "2017-12-08 21:00:00+00:00 -9.183618e+04     1.192568e+07   \n",
       "2017-12-11 21:00:00+00:00 -9.183618e+04     1.215804e+07   \n",
       "2017-12-12 21:00:00+00:00 -9.183618e+04     1.208974e+07   \n",
       "2017-12-13 21:00:00+00:00 -9.183618e+04     1.212988e+07   \n",
       "2017-12-14 21:00:00+00:00 -9.183618e+04     1.212635e+07   \n",
       "2017-12-15 21:00:00+00:00 -9.183618e+04     1.224253e+07   \n",
       "2017-12-18 21:00:00+00:00 -9.183618e+04     1.242209e+07   \n",
       "2017-12-19 21:00:00+00:00 -9.183618e+04     1.228971e+07   \n",
       "2017-12-20 21:00:00+00:00 -9.183618e+04     1.227633e+07   \n",
       "2017-12-21 21:00:00+00:00 -9.183618e+04     1.232280e+07   \n",
       "2017-12-22 21:00:00+00:00 -9.183618e+04     1.232280e+07   \n",
       "2017-12-26 21:00:00+00:00 -9.183618e+04     1.201017e+07   \n",
       "2017-12-27 21:00:00+00:00 -9.183618e+04     1.201229e+07   \n",
       "2017-12-28 21:00:00+00:00 -9.183618e+04     1.204608e+07   \n",
       "2017-12-29 21:00:00+00:00 -9.183618e+04     1.191582e+07   \n",
       "\n",
       "                                    ...            short_value  shorts_count  \\\n",
       "2015-01-02 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-05 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-06 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-07 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-08 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-09 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-12 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-13 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-14 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-15 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-16 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-20 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-21 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-22 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-23 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-26 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-27 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-28 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-29 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-01-30 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-02-02 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-02-03 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-02-04 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-02-05 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-02-06 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-02-09 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-02-10 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-02-11 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-02-12 21:00:00+00:00           ...                    0.0             0   \n",
       "2015-02-13 21:00:00+00:00           ...                    0.0             0   \n",
       "...                                 ...                    ...           ...   \n",
       "2017-11-16 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-11-17 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-11-20 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-11-21 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-11-22 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-11-24 18:00:00+00:00           ...                    0.0             0   \n",
       "2017-11-27 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-11-28 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-11-29 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-11-30 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-01 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-04 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-05 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-06 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-07 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-08 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-11 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-12 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-13 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-14 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-15 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-18 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-19 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-20 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-21 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-22 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-26 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-27 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-28 21:00:00+00:00           ...                    0.0             0   \n",
       "2017-12-29 21:00:00+00:00           ...                    0.0             0   \n",
       "\n",
       "                           signal     sortino  starting_cash  \\\n",
       "2015-01-02 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-05 21:00:00+00:00      -1         NaN   1.000000e+07   \n",
       "2015-01-06 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-07 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-08 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-09 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-12 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-13 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-14 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-15 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-16 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-20 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-21 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-22 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-23 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-26 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-27 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-28 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-29 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-01-30 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-02-02 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-02-03 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-02-04 21:00:00+00:00       0         NaN   1.000000e+07   \n",
       "2015-02-05 21:00:00+00:00       1         NaN   1.000000e+07   \n",
       "2015-02-06 21:00:00+00:00       0   -3.174902   1.000000e+07   \n",
       "2015-02-09 21:00:00+00:00       0   37.583118   7.916998e+04   \n",
       "2015-02-10 21:00:00+00:00       0  152.387640   7.916998e+04   \n",
       "2015-02-11 21:00:00+00:00       0  288.046044   7.916998e+04   \n",
       "2015-02-12 21:00:00+00:00       0  356.459780   7.916998e+04   \n",
       "2015-02-13 21:00:00+00:00       0  378.444675   7.916998e+04   \n",
       "...                           ...         ...            ...   \n",
       "2017-11-16 21:00:00+00:00       0    0.695655  -9.183618e+04   \n",
       "2017-11-17 21:00:00+00:00       0    0.676660  -9.183618e+04   \n",
       "2017-11-20 21:00:00+00:00       0    0.672914  -9.183618e+04   \n",
       "2017-11-21 21:00:00+00:00       0    0.733257  -9.183618e+04   \n",
       "2017-11-22 21:00:00+00:00       0    0.767108  -9.183618e+04   \n",
       "2017-11-24 18:00:00+00:00       0    0.766770  -9.183618e+04   \n",
       "2017-11-27 21:00:00+00:00       0    0.749540  -9.183618e+04   \n",
       "2017-11-28 21:00:00+00:00       0    0.729541  -9.183618e+04   \n",
       "2017-11-29 21:00:00+00:00       0    0.657150  -9.183618e+04   \n",
       "2017-11-30 21:00:00+00:00       0    0.701914  -9.183618e+04   \n",
       "2017-12-01 21:00:00+00:00       0    0.686171  -9.183618e+04   \n",
       "2017-12-04 21:00:00+00:00       0    0.661583  -9.183618e+04   \n",
       "2017-12-05 21:00:00+00:00       0    0.658089  -9.183618e+04   \n",
       "2017-12-06 21:00:00+00:00       0    0.645547  -9.183618e+04   \n",
       "2017-12-07 21:00:00+00:00       0    0.653526  -9.183618e+04   \n",
       "2017-12-08 21:00:00+00:00       0    0.651527  -9.183618e+04   \n",
       "2017-12-11 21:00:00+00:00       0    0.713697  -9.183618e+04   \n",
       "2017-12-12 21:00:00+00:00       0    0.694847  -9.183618e+04   \n",
       "2017-12-13 21:00:00+00:00       0    0.705027  -9.183618e+04   \n",
       "2017-12-14 21:00:00+00:00       0    0.703622  -9.183618e+04   \n",
       "2017-12-15 21:00:00+00:00       0    0.733836  -9.183618e+04   \n",
       "2017-12-18 21:00:00+00:00       0    0.780282  -9.183618e+04   \n",
       "2017-12-19 21:00:00+00:00       0    0.744400  -9.183618e+04   \n",
       "2017-12-20 21:00:00+00:00       0    0.740417  -9.183618e+04   \n",
       "2017-12-21 21:00:00+00:00       0    0.751993  -9.183618e+04   \n",
       "2017-12-22 21:00:00+00:00       0    0.751492  -9.183618e+04   \n",
       "2017-12-26 21:00:00+00:00       0    0.663780  -9.183618e+04   \n",
       "2017-12-27 21:00:00+00:00       0    0.663894  -9.183618e+04   \n",
       "2017-12-28 21:00:00+00:00       0    0.672316  -9.183618e+04   \n",
       "2017-12-29 21:00:00+00:00       0    0.636727  -9.183618e+04   \n",
       "\n",
       "                           starting_exposure  starting_value  trading_days  \\\n",
       "2015-01-02 21:00:00+00:00       0.000000e+00    0.000000e+00             1   \n",
       "2015-01-05 21:00:00+00:00       0.000000e+00    0.000000e+00             2   \n",
       "2015-01-06 21:00:00+00:00       0.000000e+00    0.000000e+00             3   \n",
       "2015-01-07 21:00:00+00:00       0.000000e+00    0.000000e+00             4   \n",
       "2015-01-08 21:00:00+00:00       0.000000e+00    0.000000e+00             5   \n",
       "2015-01-09 21:00:00+00:00       0.000000e+00    0.000000e+00             6   \n",
       "2015-01-12 21:00:00+00:00       0.000000e+00    0.000000e+00             7   \n",
       "2015-01-13 21:00:00+00:00       0.000000e+00    0.000000e+00             8   \n",
       "2015-01-14 21:00:00+00:00       0.000000e+00    0.000000e+00             9   \n",
       "2015-01-15 21:00:00+00:00       0.000000e+00    0.000000e+00            10   \n",
       "2015-01-16 21:00:00+00:00       0.000000e+00    0.000000e+00            11   \n",
       "2015-01-20 21:00:00+00:00       0.000000e+00    0.000000e+00            12   \n",
       "2015-01-21 21:00:00+00:00       0.000000e+00    0.000000e+00            13   \n",
       "2015-01-22 21:00:00+00:00       0.000000e+00    0.000000e+00            14   \n",
       "2015-01-23 21:00:00+00:00       0.000000e+00    0.000000e+00            15   \n",
       "2015-01-26 21:00:00+00:00       0.000000e+00    0.000000e+00            16   \n",
       "2015-01-27 21:00:00+00:00       0.000000e+00    0.000000e+00            17   \n",
       "2015-01-28 21:00:00+00:00       0.000000e+00    0.000000e+00            18   \n",
       "2015-01-29 21:00:00+00:00       0.000000e+00    0.000000e+00            19   \n",
       "2015-01-30 21:00:00+00:00       0.000000e+00    0.000000e+00            20   \n",
       "2015-02-02 21:00:00+00:00       0.000000e+00    0.000000e+00            21   \n",
       "2015-02-03 21:00:00+00:00       0.000000e+00    0.000000e+00            22   \n",
       "2015-02-04 21:00:00+00:00       0.000000e+00    0.000000e+00            23   \n",
       "2015-02-05 21:00:00+00:00       0.000000e+00    0.000000e+00            24   \n",
       "2015-02-06 21:00:00+00:00       0.000000e+00    0.000000e+00            25   \n",
       "2015-02-09 21:00:00+00:00       9.915789e+06    9.915789e+06            26   \n",
       "2015-02-10 21:00:00+00:00       9.981655e+06    9.981655e+06            27   \n",
       "2015-02-11 21:00:00+00:00       1.017342e+07    1.017342e+07            28   \n",
       "2015-02-12 21:00:00+00:00       1.041187e+07    1.041187e+07            29   \n",
       "2015-02-13 21:00:00+00:00       1.054360e+07    1.054360e+07            30   \n",
       "...                                      ...             ...           ...   \n",
       "2017-11-16 21:00:00+00:00       1.190526e+07    1.190526e+07           726   \n",
       "2017-11-17 21:00:00+00:00       1.204749e+07    1.204749e+07           727   \n",
       "2017-11-20 21:00:00+00:00       1.198060e+07    1.198060e+07           728   \n",
       "2017-11-21 21:00:00+00:00       1.196863e+07    1.196863e+07           729   \n",
       "2017-11-22 21:00:00+00:00       1.219113e+07    1.219113e+07           730   \n",
       "2017-11-24 18:00:00+00:00       1.231928e+07    1.231928e+07           731   \n",
       "2017-11-27 21:00:00+00:00       1.231999e+07    1.231999e+07           732   \n",
       "2017-11-28 21:00:00+00:00       1.225803e+07    1.225803e+07           733   \n",
       "2017-11-29 21:00:00+00:00       1.218620e+07    1.218620e+07           734   \n",
       "2017-11-30 21:00:00+00:00       1.193343e+07    1.193343e+07           735   \n",
       "2017-12-01 21:00:00+00:00       1.210030e+07    1.210030e+07           736   \n",
       "2017-12-04 21:00:00+00:00       1.204397e+07    1.204397e+07           737   \n",
       "2017-12-05 21:00:00+00:00       1.195596e+07    1.195596e+07           738   \n",
       "2017-12-06 21:00:00+00:00       1.194469e+07    1.194469e+07           739   \n",
       "2017-12-07 21:00:00+00:00       1.190033e+07    1.190033e+07           740   \n",
       "2017-12-08 21:00:00+00:00       1.193145e+07    1.193145e+07           741   \n",
       "2017-12-11 21:00:00+00:00       1.192568e+07    1.192568e+07           742   \n",
       "2017-12-12 21:00:00+00:00       1.215804e+07    1.215804e+07           743   \n",
       "2017-12-13 21:00:00+00:00       1.208974e+07    1.208974e+07           744   \n",
       "2017-12-14 21:00:00+00:00       1.212988e+07    1.212988e+07           745   \n",
       "2017-12-15 21:00:00+00:00       1.212635e+07    1.212635e+07           746   \n",
       "2017-12-18 21:00:00+00:00       1.224253e+07    1.224253e+07           747   \n",
       "2017-12-19 21:00:00+00:00       1.242209e+07    1.242209e+07           748   \n",
       "2017-12-20 21:00:00+00:00       1.228971e+07    1.228971e+07           749   \n",
       "2017-12-21 21:00:00+00:00       1.227633e+07    1.227633e+07           750   \n",
       "2017-12-22 21:00:00+00:00       1.232280e+07    1.232280e+07           751   \n",
       "2017-12-26 21:00:00+00:00       1.232280e+07    1.232280e+07           752   \n",
       "2017-12-27 21:00:00+00:00       1.201017e+07    1.201017e+07           753   \n",
       "2017-12-28 21:00:00+00:00       1.201229e+07    1.201229e+07           754   \n",
       "2017-12-29 21:00:00+00:00       1.204608e+07    1.204608e+07           755   \n",
       "\n",
       "                                                                transactions  \\\n",
       "2015-01-02 21:00:00+00:00                                                 []   \n",
       "2015-01-05 21:00:00+00:00                                                 []   \n",
       "2015-01-06 21:00:00+00:00                                                 []   \n",
       "2015-01-07 21:00:00+00:00                                                 []   \n",
       "2015-01-08 21:00:00+00:00                                                 []   \n",
       "2015-01-09 21:00:00+00:00                                                 []   \n",
       "2015-01-12 21:00:00+00:00                                                 []   \n",
       "2015-01-13 21:00:00+00:00                                                 []   \n",
       "2015-01-14 21:00:00+00:00                                                 []   \n",
       "2015-01-15 21:00:00+00:00                                                 []   \n",
       "2015-01-16 21:00:00+00:00                                                 []   \n",
       "2015-01-20 21:00:00+00:00                                                 []   \n",
       "2015-01-21 21:00:00+00:00                                                 []   \n",
       "2015-01-22 21:00:00+00:00                                                 []   \n",
       "2015-01-23 21:00:00+00:00                                                 []   \n",
       "2015-01-26 21:00:00+00:00                                                 []   \n",
       "2015-01-27 21:00:00+00:00                                                 []   \n",
       "2015-01-28 21:00:00+00:00                                                 []   \n",
       "2015-01-29 21:00:00+00:00                                                 []   \n",
       "2015-01-30 21:00:00+00:00                                                 []   \n",
       "2015-02-02 21:00:00+00:00                                                 []   \n",
       "2015-02-03 21:00:00+00:00                                                 []   \n",
       "2015-02-04 21:00:00+00:00                                                 []   \n",
       "2015-02-05 21:00:00+00:00                                                 []   \n",
       "2015-02-06 21:00:00+00:00  [{'price': 118.98946500000001, 'dt': 2015-02-0...   \n",
       "2015-02-09 21:00:00+00:00                                                 []   \n",
       "2015-02-10 21:00:00+00:00                                                 []   \n",
       "2015-02-11 21:00:00+00:00                                                 []   \n",
       "2015-02-12 21:00:00+00:00                                                 []   \n",
       "2015-02-13 21:00:00+00:00                                                 []   \n",
       "...                                                                      ...   \n",
       "2017-11-16 21:00:00+00:00                                                 []   \n",
       "2017-11-17 21:00:00+00:00                                                 []   \n",
       "2017-11-20 21:00:00+00:00                                                 []   \n",
       "2017-11-21 21:00:00+00:00                                                 []   \n",
       "2017-11-22 21:00:00+00:00                                                 []   \n",
       "2017-11-24 18:00:00+00:00                                                 []   \n",
       "2017-11-27 21:00:00+00:00                                                 []   \n",
       "2017-11-28 21:00:00+00:00                                                 []   \n",
       "2017-11-29 21:00:00+00:00                                                 []   \n",
       "2017-11-30 21:00:00+00:00                                                 []   \n",
       "2017-12-01 21:00:00+00:00                                                 []   \n",
       "2017-12-04 21:00:00+00:00                                                 []   \n",
       "2017-12-05 21:00:00+00:00                                                 []   \n",
       "2017-12-06 21:00:00+00:00                                                 []   \n",
       "2017-12-07 21:00:00+00:00                                                 []   \n",
       "2017-12-08 21:00:00+00:00                                                 []   \n",
       "2017-12-11 21:00:00+00:00                                                 []   \n",
       "2017-12-12 21:00:00+00:00                                                 []   \n",
       "2017-12-13 21:00:00+00:00                                                 []   \n",
       "2017-12-14 21:00:00+00:00                                                 []   \n",
       "2017-12-15 21:00:00+00:00                                                 []   \n",
       "2017-12-18 21:00:00+00:00                                                 []   \n",
       "2017-12-19 21:00:00+00:00                                                 []   \n",
       "2017-12-20 21:00:00+00:00                                                 []   \n",
       "2017-12-21 21:00:00+00:00                                                 []   \n",
       "2017-12-22 21:00:00+00:00                                                 []   \n",
       "2017-12-26 21:00:00+00:00                                                 []   \n",
       "2017-12-27 21:00:00+00:00                                                 []   \n",
       "2017-12-28 21:00:00+00:00                                                 []   \n",
       "2017-12-29 21:00:00+00:00                                                 []   \n",
       "\n",
       "                           treasury_period_return  \n",
       "2015-01-02 21:00:00+00:00                     0.0  \n",
       "2015-01-05 21:00:00+00:00                     0.0  \n",
       "2015-01-06 21:00:00+00:00                     0.0  \n",
       "2015-01-07 21:00:00+00:00                     0.0  \n",
       "2015-01-08 21:00:00+00:00                     0.0  \n",
       "2015-01-09 21:00:00+00:00                     0.0  \n",
       "2015-01-12 21:00:00+00:00                     0.0  \n",
       "2015-01-13 21:00:00+00:00                     0.0  \n",
       "2015-01-14 21:00:00+00:00                     0.0  \n",
       "2015-01-15 21:00:00+00:00                     0.0  \n",
       "2015-01-16 21:00:00+00:00                     0.0  \n",
       "2015-01-20 21:00:00+00:00                     0.0  \n",
       "2015-01-21 21:00:00+00:00                     0.0  \n",
       "2015-01-22 21:00:00+00:00                     0.0  \n",
       "2015-01-23 21:00:00+00:00                     0.0  \n",
       "2015-01-26 21:00:00+00:00                     0.0  \n",
       "2015-01-27 21:00:00+00:00                     0.0  \n",
       "2015-01-28 21:00:00+00:00                     0.0  \n",
       "2015-01-29 21:00:00+00:00                     0.0  \n",
       "2015-01-30 21:00:00+00:00                     0.0  \n",
       "2015-02-02 21:00:00+00:00                     0.0  \n",
       "2015-02-03 21:00:00+00:00                     0.0  \n",
       "2015-02-04 21:00:00+00:00                     0.0  \n",
       "2015-02-05 21:00:00+00:00                     0.0  \n",
       "2015-02-06 21:00:00+00:00                     0.0  \n",
       "2015-02-09 21:00:00+00:00                     0.0  \n",
       "2015-02-10 21:00:00+00:00                     0.0  \n",
       "2015-02-11 21:00:00+00:00                     0.0  \n",
       "2015-02-12 21:00:00+00:00                     0.0  \n",
       "2015-02-13 21:00:00+00:00                     0.0  \n",
       "...                                           ...  \n",
       "2017-11-16 21:00:00+00:00                     0.0  \n",
       "2017-11-17 21:00:00+00:00                     0.0  \n",
       "2017-11-20 21:00:00+00:00                     0.0  \n",
       "2017-11-21 21:00:00+00:00                     0.0  \n",
       "2017-11-22 21:00:00+00:00                     0.0  \n",
       "2017-11-24 18:00:00+00:00                     0.0  \n",
       "2017-11-27 21:00:00+00:00                     0.0  \n",
       "2017-11-28 21:00:00+00:00                     0.0  \n",
       "2017-11-29 21:00:00+00:00                     0.0  \n",
       "2017-11-30 21:00:00+00:00                     0.0  \n",
       "2017-12-01 21:00:00+00:00                     0.0  \n",
       "2017-12-04 21:00:00+00:00                     0.0  \n",
       "2017-12-05 21:00:00+00:00                     0.0  \n",
       "2017-12-06 21:00:00+00:00                     0.0  \n",
       "2017-12-07 21:00:00+00:00                     0.0  \n",
       "2017-12-08 21:00:00+00:00                     0.0  \n",
       "2017-12-11 21:00:00+00:00                     0.0  \n",
       "2017-12-12 21:00:00+00:00                     0.0  \n",
       "2017-12-13 21:00:00+00:00                     0.0  \n",
       "2017-12-14 21:00:00+00:00                     0.0  \n",
       "2017-12-15 21:00:00+00:00                     0.0  \n",
       "2017-12-18 21:00:00+00:00                     0.0  \n",
       "2017-12-19 21:00:00+00:00                     0.0  \n",
       "2017-12-20 21:00:00+00:00                     0.0  \n",
       "2017-12-21 21:00:00+00:00                     0.0  \n",
       "2017-12-22 21:00:00+00:00                     0.0  \n",
       "2017-12-26 21:00:00+00:00                     0.0  \n",
       "2017-12-27 21:00:00+00:00                     0.0  \n",
       "2017-12-28 21:00:00+00:00                     0.0  \n",
       "2017-12-29 21:00:00+00:00                     0.0  \n",
       "\n",
       "[755 rows x 42 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%zipline --bundle quantopian-quandl --start 2015-1-1 --end 2018-1-1 -o backtest.pickle"
   ]
  },
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