{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "\n",
    "pd.set_option('display.max_rows', 16)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (16.0, 9.0)\n",
    "import seaborn as sns\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (16.0, 9.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "# Information Coefficient Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "ret_df = pd.read_pickle('./data/factor_exposure/all_exposure.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>secID</th>\n",
       "      <th>ret_date</th>\n",
       "      <th>tradeDate</th>\n",
       "      <th>ret</th>\n",
       "      <th>rf</th>\n",
       "      <th>exret</th>\n",
       "      <th>ym</th>\n",
       "      <th>mktcap</th>\n",
       "      <th>size</th>\n",
       "      <th>rev</th>\n",
       "      <th>beta</th>\n",
       "      <th>bm</th>\n",
       "      <th>illiq</th>\n",
       "      <th>illiq_12m</th>\n",
       "      <th>mom_date</th>\n",
       "      <th>mom</th>\n",
       "      <th>vol</th>\n",
       "      <th>ivol</th>\n",
       "      <th>vol_clip</th>\n",
       "      <th>ivol_clip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.XSHE</td>\n",
       "      <td>2007-07</td>\n",
       "      <td>2007-06-29</td>\n",
       "      <td>0.316497</td>\n",
       "      <td>0.002481</td>\n",
       "      <td>0.314016</td>\n",
       "      <td>2007-06</td>\n",
       "      <td>4.266117e+10</td>\n",
       "      <td>24.476555</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4614</td>\n",
       "      <td>0.123739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000001.XSHE</td>\n",
       "      <td>2007-08</td>\n",
       "      <td>2007-07-31</td>\n",
       "      <td>0.048855</td>\n",
       "      <td>0.002404</td>\n",
       "      <td>0.046451</td>\n",
       "      <td>2007-07</td>\n",
       "      <td>5.616330e+10</td>\n",
       "      <td>24.751529</td>\n",
       "      <td>0.314016</td>\n",
       "      <td>0.6423</td>\n",
       "      <td>0.093992</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.042521</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.042521</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000001.XSHE</td>\n",
       "      <td>2007-09</td>\n",
       "      <td>2007-08-31</td>\n",
       "      <td>0.052105</td>\n",
       "      <td>0.002621</td>\n",
       "      <td>0.049484</td>\n",
       "      <td>2007-08</td>\n",
       "      <td>5.890714e+10</td>\n",
       "      <td>24.799228</td>\n",
       "      <td>0.046451</td>\n",
       "      <td>0.7722</td>\n",
       "      <td>0.097085</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.033926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.033926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000001.XSHE</td>\n",
       "      <td>2007-10</td>\n",
       "      <td>2007-09-28</td>\n",
       "      <td>0.201851</td>\n",
       "      <td>0.003095</td>\n",
       "      <td>0.198756</td>\n",
       "      <td>2007-09</td>\n",
       "      <td>6.197651e+10</td>\n",
       "      <td>24.850021</td>\n",
       "      <td>0.049484</td>\n",
       "      <td>0.7596</td>\n",
       "      <td>0.092276</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.023872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.023872</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000001.XSHE</td>\n",
       "      <td>2007-11</td>\n",
       "      <td>2007-10-31</td>\n",
       "      <td>-0.249116</td>\n",
       "      <td>0.003780</td>\n",
       "      <td>-0.252896</td>\n",
       "      <td>2007-10</td>\n",
       "      <td>7.448652e+10</td>\n",
       "      <td>25.033884</td>\n",
       "      <td>0.198756</td>\n",
       "      <td>0.7988</td>\n",
       "      <td>0.083411</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035921</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000001.XSHE</td>\n",
       "      <td>2007-12</td>\n",
       "      <td>2007-11-30</td>\n",
       "      <td>0.069845</td>\n",
       "      <td>0.003011</td>\n",
       "      <td>0.066834</td>\n",
       "      <td>2007-11</td>\n",
       "      <td>5.593078e+10</td>\n",
       "      <td>24.747381</td>\n",
       "      <td>-0.252896</td>\n",
       "      <td>0.9560</td>\n",
       "      <td>0.111084</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.033184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.033184</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>000001.XSHE</td>\n",
       "      <td>2008-01</td>\n",
       "      <td>2007-12-28</td>\n",
       "      <td>-0.137306</td>\n",
       "      <td>0.002949</td>\n",
       "      <td>-0.140255</td>\n",
       "      <td>2007-12</td>\n",
       "      <td>6.574629e+10</td>\n",
       "      <td>24.909069</td>\n",
       "      <td>0.066834</td>\n",
       "      <td>0.9468</td>\n",
       "      <td>0.094476</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.027254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.027254</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>000001.XSHE</td>\n",
       "      <td>2008-02</td>\n",
       "      <td>2008-01-31</td>\n",
       "      <td>-0.004504</td>\n",
       "      <td>0.002946</td>\n",
       "      <td>-0.007450</td>\n",
       "      <td>2008-01</td>\n",
       "      <td>5.850212e+10</td>\n",
       "      <td>24.792329</td>\n",
       "      <td>-0.140255</td>\n",
       "      <td>0.9654</td>\n",
       "      <td>0.109513</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.037722</td>\n",
       "      <td>0.013266</td>\n",
       "      <td>0.037722</td>\n",
       "      <td>0.013266</td>\n",
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       "      <td>2021-10-29</td>\n",
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       "      <td>0.001963</td>\n",
       "      <td>-0.042588</td>\n",
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       "      <td>2021-09</td>\n",
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       "      <th>504875</th>\n",
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       "      <td>900957.XSHG</td>\n",
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       "    <tr>\n",
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       "      <td>900957.XSHG</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>504880 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              secID ret_date  tradeDate       ret        rf     exret  \\\n",
       "0       000001.XSHE  2007-07 2007-06-29  0.316497  0.002481  0.314016   \n",
       "1       000001.XSHE  2007-08 2007-07-31  0.048855  0.002404  0.046451   \n",
       "2       000001.XSHE  2007-09 2007-08-31  0.052105  0.002621  0.049484   \n",
       "3       000001.XSHE  2007-10 2007-09-28  0.201851  0.003095  0.198756   \n",
       "4       000001.XSHE  2007-11 2007-10-31 -0.249116  0.003780 -0.252896   \n",
       "5       000001.XSHE  2007-12 2007-11-30  0.069845  0.003011  0.066834   \n",
       "6       000001.XSHE  2008-01 2007-12-28 -0.137306  0.002949 -0.140255   \n",
       "7       000001.XSHE  2008-02 2008-01-31 -0.004504  0.002946 -0.007450   \n",
       "...             ...      ...        ...       ...       ...       ...   \n",
       "504872  900957.XSHG  2021-09 2021-08-31  0.089869  0.002033  0.087837   \n",
       "504873  900957.XSHG  2021-10 2021-09-30 -0.040480  0.001998 -0.042478   \n",
       "504874  900957.XSHG  2021-11 2021-10-29 -0.040625  0.001963 -0.042588   \n",
       "504875  900957.XSHG  2021-12 2021-11-30  0.035831  0.002026  0.033805   \n",
       "504876  900957.XSHG  2022-01 2021-12-31 -0.022013  0.002014 -0.024027   \n",
       "504877  900957.XSHG  2022-02 2022-01-28 -0.011254  0.001921 -0.013175   \n",
       "504878  900957.XSHG  2022-03 2022-02-28 -0.034146  0.001919 -0.036066   \n",
       "504879  900957.XSHG      NaT 2022-03-14       NaN       NaN       NaN   \n",
       "\n",
       "             ym        mktcap       size       rev    beta        bm  \\\n",
       "0       2007-06  4.266117e+10  24.476555       NaN  0.4614  0.123739   \n",
       "1       2007-07  5.616330e+10  24.751529  0.314016  0.6423  0.093992   \n",
       "2       2007-08  5.890714e+10  24.799228  0.046451  0.7722  0.097085   \n",
       "3       2007-09  6.197651e+10  24.850021  0.049484  0.7596  0.092276   \n",
       "4       2007-10  7.448652e+10  25.033884  0.198756  0.7988  0.083411   \n",
       "5       2007-11  5.593078e+10  24.747381 -0.252896  0.9560  0.111084   \n",
       "6       2007-12  6.574629e+10  24.909069  0.066834  0.9468  0.094476   \n",
       "7       2008-01  5.850212e+10  24.792329 -0.140255  0.9654  0.109513   \n",
       "...         ...           ...        ...       ...     ...       ...   \n",
       "504872  2021-08  1.116880e+08  18.531220 -0.060402     NaN       NaN   \n",
       "504873  2021-09  1.218080e+08  18.617957  0.087837     NaN       NaN   \n",
       "504874  2021-10  1.168400e+08  18.576316 -0.042478     NaN       NaN   \n",
       "504875  2021-11  1.120560e+08  18.534509 -0.042588     NaN       NaN   \n",
       "504876  2021-12  1.161040e+08  18.569997  0.033805     NaN       NaN   \n",
       "504877  2022-01  1.135280e+08  18.547560 -0.024027     NaN       NaN   \n",
       "504878  2022-02  1.122400e+08  18.536150 -0.013175     NaN       NaN   \n",
       "504879  2022-03  1.083760e+08  18.501117 -0.036066     NaN       NaN   \n",
       "\n",
       "           illiq  illiq_12m mom_date       mom       vol      ivol  vol_clip  \\\n",
       "0            NaN        NaN      NaT       NaN       NaN       NaN       NaN   \n",
       "1       0.000040        NaN  2007-06       NaN  0.042521       NaN  0.042521   \n",
       "2       0.000020        NaN  2007-07       NaN  0.033926       NaN  0.033926   \n",
       "3       0.000025        NaN  2007-08       NaN  0.023872       NaN  0.023872   \n",
       "4       0.000030        NaN  2007-09       NaN  0.035921       NaN  0.035921   \n",
       "5       0.000048        NaN  2007-10       NaN  0.033184       NaN  0.033184   \n",
       "6       0.000025        NaN  2007-11       NaN  0.027254       NaN  0.027254   \n",
       "7       0.000039        NaN  2007-12       NaN  0.037722  0.013266  0.037722   \n",
       "...          ...        ...      ...       ...       ...       ...       ...   \n",
       "504872  0.067321   0.071830  2021-07  0.183971  0.012268  0.008334  0.012268   \n",
       "504873  0.045022   0.070197  2021-08  0.186047  0.022351  0.016555  0.022351   \n",
       "504874  0.058457   0.067646  2021-09  0.285164  0.011663  0.007700  0.011663   \n",
       "504875  0.070056   0.062884  2021-10  0.216730  0.009639  0.007046  0.009639   \n",
       "504876  0.078037   0.059672  2021-11  0.211045  0.010961  0.008692  0.010961   \n",
       "504877  0.044515   0.058502  2021-12 -0.059172  0.010559  0.008409  0.010559   \n",
       "504878  0.057218   0.060208  2022-01 -0.157182  0.006517  0.004195  0.006517   \n",
       "504879       NaN   0.062442  2022-02 -0.117647       NaN       NaN       NaN   \n",
       "\n",
       "        ivol_clip  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "5             NaN  \n",
       "6             NaN  \n",
       "7        0.013266  \n",
       "...           ...  \n",
       "504872   0.008334  \n",
       "504873   0.016555  \n",
       "504874   0.007700  \n",
       "504875   0.007046  \n",
       "504876   0.008692  \n",
       "504877   0.008409  \n",
       "504878   0.004195  \n",
       "504879        NaN  \n",
       "\n",
       "[504880 rows x 20 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ret_date</th>\n",
       "      <th>level_1</th>\n",
       "      <th>exret</th>\n",
       "      <th>illiq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-04</td>\n",
       "      <td>exret</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.114849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-04</td>\n",
       "      <td>illiq</td>\n",
       "      <td>-0.114849</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-05</td>\n",
       "      <td>exret</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-05</td>\n",
       "      <td>illiq</td>\n",
       "      <td>0.033204</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-06</td>\n",
       "      <td>exret</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.060844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2007-06</td>\n",
       "      <td>illiq</td>\n",
       "      <td>-0.060844</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2007-07</td>\n",
       "      <td>exret</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.201833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007-07</td>\n",
       "      <td>illiq</td>\n",
       "      <td>0.201833</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>2021-12</td>\n",
       "      <td>exret</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.195465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>2021-12</td>\n",
       "      <td>illiq</td>\n",
       "      <td>0.195465</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>2022-01</td>\n",
       "      <td>exret</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.249257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>2022-01</td>\n",
       "      <td>illiq</td>\n",
       "      <td>0.249257</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>exret</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.037933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>illiq</td>\n",
       "      <td>-0.037933</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>2022-03</td>\n",
       "      <td>exret</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.081608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2022-03</td>\n",
       "      <td>illiq</td>\n",
       "      <td>0.081608</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ret_date level_1     exret     illiq\n",
       "0    2007-04   exret  1.000000 -0.114849\n",
       "1    2007-04   illiq -0.114849  1.000000\n",
       "2    2007-05   exret  1.000000  0.033204\n",
       "3    2007-05   illiq  0.033204  1.000000\n",
       "4    2007-06   exret  1.000000 -0.060844\n",
       "5    2007-06   illiq -0.060844  1.000000\n",
       "6    2007-07   exret  1.000000  0.201833\n",
       "7    2007-07   illiq  0.201833  1.000000\n",
       "..       ...     ...       ...       ...\n",
       "352  2021-12   exret  1.000000  0.195465\n",
       "353  2021-12   illiq  0.195465  1.000000\n",
       "354  2022-01   exret  1.000000  0.249257\n",
       "355  2022-01   illiq  0.249257  1.000000\n",
       "356  2022-02   exret  1.000000 -0.037933\n",
       "357  2022-02   illiq -0.037933  1.000000\n",
       "358  2022-03   exret  1.000000  0.081608\n",
       "359  2022-03   illiq  0.081608  1.000000\n",
       "\n",
       "[360 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ic_illiq = ret_df.groupby('ret_date')[['exret','illiq']].corr(method='spearman')\n",
    "ic_illiq.reset_index(inplace=True)\n",
    "ic_illiq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "ic_illiq = ic_illiq[ic_illiq['level_1'] != 'illiq'].drop(['exret','level_1'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ret_date</th>\n",
       "      <th>illiq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-04</td>\n",
       "      <td>-0.114849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-05</td>\n",
       "      <td>0.033204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-06</td>\n",
       "      <td>-0.060844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2007-07</td>\n",
       "      <td>0.201833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2007-08</td>\n",
       "      <td>-0.191447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2007-09</td>\n",
       "      <td>-0.016126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2007-10</td>\n",
       "      <td>0.002307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2007-11</td>\n",
       "      <td>0.396168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>2021-08</td>\n",
       "      <td>0.062304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>2021-09</td>\n",
       "      <td>0.083059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>2021-10</td>\n",
       "      <td>-0.046243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>2021-11</td>\n",
       "      <td>0.241082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>2021-12</td>\n",
       "      <td>0.195465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>2022-01</td>\n",
       "      <td>0.249257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>-0.037933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>2022-03</td>\n",
       "      <td>0.081608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ret_date     illiq\n",
       "0    2007-04 -0.114849\n",
       "2    2007-05  0.033204\n",
       "4    2007-06 -0.060844\n",
       "6    2007-07  0.201833\n",
       "8    2007-08 -0.191447\n",
       "10   2007-09 -0.016126\n",
       "12   2007-10  0.002307\n",
       "14   2007-11  0.396168\n",
       "..       ...       ...\n",
       "344  2021-08  0.062304\n",
       "346  2021-09  0.083059\n",
       "348  2021-10 -0.046243\n",
       "350  2021-11  0.241082\n",
       "352  2021-12  0.195465\n",
       "354  2022-01  0.249257\n",
       "356  2022-02 -0.037933\n",
       "358  2022-03  0.081608\n",
       "\n",
       "[180 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ic_illiq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "ic_illiq.set_index('ret_date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ic_illiq.index = ic_illiq.index.to_timestamp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, ax = plt.subplots()\n",
    "# plt.bar(ic_illiq.index, ic_illiq['illiq'])\n",
    "# plt.gca().xaxis.set_major_locator(dates.MonthLocator())\n",
    "# plt.gca().xaxis.set_major_formatter(dates.DateFormatter(\"%b\\n%Y\"))\n",
    "# # fig.autofmt_xdate()\n",
    "# plt.show()\n",
    "ax = ic_illiq.plot.bar(rot=45)\n",
    "positions = [i if not i%4 else 0 for i in ax.get_xticks()]\n",
    "positions.append(ax.get_xticks()[-1])\n",
    "positions\n",
    "ax.set_xticks(positions)\n",
    "ax.set_xticklabels([t if not i%4 else \"\" for i,t in enumerate(ax.get_xticklabels())])\n",
    "# ax.xaxis.set_major_locator(ticker.MultipleLocator(20))\n",
    "# ax.xaxis.set_minor_locator(ticker.MultipleLocator(2.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ic_size = ret_df.groupby('ret_date')[['exret','size']].corr(method='spearman')\n",
    "ic_size.reset_index(inplace=True)\n",
    "ic_size = ic_size[ic_size['level_1'] != 'size'].drop(['exret','level_1'],axis=1)\n",
    "ic_size.set_index('ret_date',inplace=True)\n",
    "ax = ic_size.plot.bar(rot=45)\n",
    "positions = [i if not i%4 else 0 for i in ax.get_xticks()]\n",
    "positions.append(ax.get_xticks()[-1])\n",
    "positions\n",
    "ax.set_xticks(positions)\n",
    "ax.set_xticklabels([t if not i%4 else \"\" for i,t in enumerate(ax.get_xticklabels())])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ic_rev = ret_df.groupby('ret_date')[['exret','rev']].corr(method='spearman')\n",
    "ic_rev.reset_index(inplace=True)\n",
    "ic_rev = ic_rev[ic_rev['level_1'] != 'rev'].drop(['exret','level_1'],axis=1)\n",
    "ic_rev.set_index('ret_date',inplace=True)\n",
    "ax = ic_rev.plot.bar(rot=45)\n",
    "positions = [i if not i%4 else 0 for i in ax.get_xticks()]\n",
    "positions.append(ax.get_xticks()[-1])\n",
    "positions\n",
    "ax.set_xticks(positions)\n",
    "ax.set_xticklabels([t if not i%4 else \"\" for i,t in enumerate(ax.get_xticklabels())])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "cols = ['beta','size','bm','mom','rev','illiq','ivol']\n",
    "ic_df = pd.DataFrame()\n",
    "for col in cols:\n",
    "    temp = ret_df.groupby('ret_date')[['exret',col]].corr(method='spearman')\n",
    "    temp.reset_index(inplace=True)\n",
    "    ic_df[col] = temp[temp['level_1'] != col].drop(['exret','level_1','ret_date'],axis=1)[col].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "ic_df.index = np.sort(ret_df['ret_date'].unique().dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f460e67acd0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f460e449fd0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f460e3d7690>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f460e40c790>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f460e3838d0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f460e493f10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f4610fd7b10>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6hNRdzk8d1a/5AlnGetq8NnMAFzLL2Hn25g+X/eN8NhoC7FoxFm9HM57deZ5TyVJLpP8qPfHdUFwtR6ZU4WCmj46mBjllte2W83p8r4i0EmRKdZMF9QShrqhSeFoJe89l3dDfVaUWOZtafEsY0iT+myTlV7L4x1Ae2hRKtVyFjYkur+2NZvKaAHaFZ9609/avZzNY/Wcstw2y5cO7B2NvZoCHrTFH4zuvXB2Ny0dXS4P7fBz46VQqYanFnRe4B6JUqfn2RDJe9qYNXTdGOJqz/wlfnK2MeGxLGLvCM7tZyo5xSymq7/0dR3BSEe/fPZiZXnZoaAitnwTc5mlLRnENsVJPyDZxJqUYmVLdoKheDwRB4GFfJwCWT3Smn6XhVfsvP4idrf6rUKn5OyqH0JRi8svbv9C7kVzMq+CpHefw7G3KL0tGc/Q5P44/78drMwdgqq/NmsMXmf11MMkFXau8h6YU42hhgK2pXovH3TmkN6P6mfPxoXhKbuLCSqIosi8yG19XSxzMDdiwyJu+FgYs3RJGfO6tOUcoVGo++DtOah/SBHp6ehQVFfWouUGlFskvl2Ggo4WpvjY2pnrUKFSU1rTdqyqKIkVFRejptfxct5fg5EI0NQR8nJqOwJjn7YCHrTFP74zkznXBnLx4/SNjkvIruefbEOZ+d4r7N5wmr7y29ZMkJHoIZdUK3vojhulfnCQstYRXZnhw+NkJ7Foxlh8f9sFET5vnfjvPtM9PEHSTpUTtP5/NS7svMN7Nki8XDEWrvr7JJA9rzqaWUNaOOe1aRFHkSFwe41wtef2OgfTppc8Luy7ckjnyf0fnklZUzUo/16ucOnam+vy2fAyj+1nw2t5oLnXx+vBGcMu0p/k1LIMfg1N5xLeuKmx7mDLABg0hikPRuQzqbXqdJLx1OJFQgK6WBqOdLTh9HVON5nk7oK+tyUwvu0b7HMwNcLIwICixkMW+/Tp8jZ1nM3htb3TD3wY6mjhaGDLUwZQ37hiEvk7z3vgbSWm1nMd+DkNfR4v1i0Y0yNXP0pAl451ZMt6Z4wn5PPfreWatDeK9OZ7MGda+56Ap1GqR0NRipg6wafVYQRBYfZcnM74K5ONDCXxw9+BOX787iMwoJb24micmuQJ11Y1/emQkd38TzMObzrJ75Vh699LvZim7jlqFise3RXAkLh9bEz0WjHS46kX3X8fe3p7MzEwKCgq6W5QGKmoVlNUosTLWJb5IA1GEkopaijLBxkQXQRCora1tVQnV09PD3r7z88SVBCcVMcTeFGO9xqk2AKYG2vz15Hj2nMvi88MXWbQplFH9zHnhNo9GLck6i0ot8kPQJT795yIGOposm+jMzyFpzPgykDXzh15XY6uERFdQKVMyf/0pLuZVcN/Ivjw7tT+WV+R++3tYM7G/FQdjcvn4YDyrtkUQ8tIkDHV79vJerlTzyaF4NgSm4O1oxvcPjkBX69/11uQB1nwTkMzJiwXMGtK7Q9e4mFdJZkkNK/1cMdTV4oO7B/PgD6F8dTSRF27z6Kqv0u2Iosg3x5NwtTZi2sDGazU9bU0+nz+U2748yVM7Itm1Yiw6WjePn7Jn38ltJDythNf2RDPO1ZJXZrT/5rMw0sXHyZxDMXk8O839Okh4a3HiYj6jnC1aDKnuCrQ1Nbh7ePOLqHFuluyJyEKhUne4yvDv4Zm4WRvx2h0DSSuqIrWwmqSCSraHZuBl34sFI/t2VPwuQ6lSs2pbBDmltWxfOho706aVJH93a/5+cjxPbj/HMzvPE5JUxNt3DcJAp+OPeWJ+JaXVimYLKV2Lu60xD4525OdTqTw+yZU+N6FCty8yGx0tjYacaIA+vfTZvHgk8747xZ3rglkw0oH7Rva9Kb/flZTXKljyUxhnU4sZ72ZJYGIhSfmVuNkYd7doPQZtbW369eu4MayrKa6SM+/j44xytmDjQ4MatgcnFbJw4xlemeHB0gkuBAQEMGzYsBsqW3mtgguZpazyd23xOE0NgXtH2DNriB07QjNYeyyJe74NYfNiH/zcrbtElksFlTz/23ki0kuZOtCG9+Z4Ym2sx9wR9qz65RwPbQplpZ8Lz07t3+DFkbg5KK2WU1gpv+X78arVIk/viCQxv5JNDzf/bGhoCMwYbIetqR53fxPCzrMZPDKu58xZ15JRXM3j289xPqOUB0c78urMAY3Wk0MdzDAz0OZYfH6HFdUjcXWhw5MH1P1u492smDvCnu9PXmLGYDs8+9wajqlj8fnE51awZt6QZiNJbU31+OgeL5ZtCWfN4Yu8dPvNo6jf9LNzblkty7eGY2uqx7r7h3X4hXObpy0JeRU3pVv8RpJRXE1yQRV+PcASPc7Viiq5inPpHas0m5RfSWRGKXO97ZnY34pFY5x4Y9ZAflrsg6u1UY9pTfLuX3Uh7e/O8WzV42Brqse2x0bx5CRXfo/IZM7XIZR3oshKaH1+6mjn5vNTr2XJ+H6IwPabMIxUqVLz54VspgywbuQRGmBnwi+PjcLL3pR1x5MY/9ExHtl8lqNxN2d7q3K5yP0bThORVsIX84fy/pw6D/jJmyx07L/G18eTqJIrefG2q42qvq6W+Llbse5YUre1JDhzqRi1yFX9U1tCV0uTh8Y6cfIFP3qb6rExsGsqbIckFTLjq0CSC6r4Yv5Q1j84AmvjOu+yq7Uxe1f5cp+PA98EJHP3tyGs2BrOfetPMf3zk4x87whD3v6H5349T3hacY8K+f6vU1aj4LN/EvD98BhT1pxg2ZYw0ouubzHB7uTjQwkcicvj9ZkD2mTAGd7XDG9HM34ISumxPTQPROUw46tALhVU8u3C4bwz27NJp4emhoC/uzXHE/I7/H49Fp/P4D6m2Jj8G1ny2syBmBvq8MLvF3pcW6+OIIoi644nYW+m36pCP32QLQtG9uX7k8mEdFF7x9JqOcFJhXx3Ipn//Xb+utSN6bCiKgjC64IgJAmCcFYQBKdr9j0qCEKiIAjRgiCMqN9mLghCgCAIyYIgfNM5seuoVahYtiWMapmSjQ9508ug40V8pg+q854cirl5K2N1BUqVml3hmVQ0o9ycuFgX/jbRvfsV1TEuFmgIEJTYsZC8XRGZaGoIjaoUC4LAPG97ItJLu71Y09G4PDaHpPLouH7M83Zo0zlamho8O82dTQ/7kJhfwTv7Y1s9p7nF2OmUYuxM9bA3a7vn0N7MgMke1uw4m37TVSMMSS6isFLOnUOarlztZd+LTQ/7EPiCP6v8XYnKKuPRn8K4/cuT3X6vtIes0hreP1NDUn4lGxZ5c9fQPjiYG+BsaUhgB58nietPRnE1W06lMXeEQ5Ne75du96BSpmTdsaRukK7Oq6unrcFwx17tOs9AR4uFox0JSirsdI59XE45y7aE42huyOFnJjB7WJ9Goez6Opp8eI8XX943lPIaBYn5lajV4GhhgL+7NZMHWHMwOod7vj3F9C9Osiko5ZboR9jV7IvMwu+T46zaFsGmoBQuZJZel8V/Ra2Cr44mMu6jY6w9lsREdyuenuJGYGIhU9ac4MMD8VR2sPWIXKlmU1AKL/5+gQNROVTLe0YLk13hmXx3Ipn7R/XlobFObT5v6QRnskpr+LuHdbKQK9W8uS+aFb9E4GxlxN9Pjuf2wY3Tuq5k0gBrSqsVHWofWVQpIyK9hEkeVyv4pgbavHOXJ7E55Sz6IZRvApI4famox4x7ezl9qZhz6aUsm+jSpsjC1+8YQD9LQ575NbLDtURqFSpe+P084z46xtDVh1m48QwfHohnV0Qmn/3T9ZWVOxQTKAiCIzAHcAdmAB8B8+v36QMvAcOAfsAGYDTwOPCHKIprBEHYJQjC7aIoHujsF3CyNGSlvyv9Oxmq1ruXPl72phyKyWWFn0tnxbpp+SEohQ8OxPNARl/end04x/DExQLszfRxvqa4UXdgqq+Nl30vApMK2x2yrVKL7InIYoKbJdYmjfO4Zg/rw0cHE/gtPIOXbx/QVSK3m72R2Vga6fByB8I0/N2tWT7RhW8Ckpk+yJYpTeQuAHz2TwK/h2eyY+loHC3+HVdRFAlNKcbXxaLdOYsLRztyJC6fQzG5HQ7b6Q72RWZjrKeFXyuGGHszA56b5s6Tk904FJPLm/tiuGtdEJ/MHcKMVl6+naG0Wk5kRinnM8o4n1nKQDsTnp/e9nu/rEbBD4GX2BScikolsmXJ6KuK3kzob8WOs+nIlKqr8oW6C1EUKatRkF8ho7BChpuNMVbGuq2f2ANIyq/krT9imDOsD/e0s25Cc6w5fBFBgKenujW538PWhHtH2PPzqTTcfW/873QquQgfJ/MO3TvzvB344shFtp5O481Zg1o/oQmySmt4+MdQDHW1+HGxT5Nz+5XcNbQPdzXTTq3qLiV/XshmW2hdRdKPDsbz8FgnVvi5dMoofquQWljFy7ujsDTSJTK9lL8u1BWs0NfWZGJ/Kz6fP7RLajwcisnlxV0XKK1WMHWgDc9M6c/A3iYALBjZl48PJvDdiWR+D89k0RhHtDQFZAo1cpUamUKNib4W0wbaMsDO+Kr3mCiKHIrJ48MDcaQWVWOoo8nOsAz0tDWYWN8Ob/IAG0yaybW+niSWqPjkcBRjXSwadTxojSkDbHC2NGT9yWRmedn1iHoDBRUyVv0SQWhqMUvG9eOF2zzalCM53s0KLQ2Bo/H5eDdTnK05jicUIIp1v8e13OZpy7NT+7PnXBYfH6xTrDQ1BAbYGfP05P7NrpV6It8EJGFppMvcNr5jDHS0+Oq+Ycz5JpiXdl/guwdGtPseeX1vNL+FZzJjsC0LRzni2ccEz96mbAy6xDcByaQWVuHUhTpCR5PX/IEDoiiqBEE4AHx9xb5RQKgoipVAlCAIloIgGACTgMfqj9kL+AGdUlT1tDX58r6uy8GZPsiWTw4lkFNW02we4M1KTlkNS34K44lJblfl3l1JZkk1XxxJRE9bg+2hGTzi2w9nq39zQORKNSFJhU1aqLuL8W6WfH08ifJaRbteKMFJheSW1/L6HQOb3G9trIe/uxW7I7L43zT3TuUwnb5UxC9n0nl/jmezBUaaQq5UE5CQzwxPuw5f/6kpbhyLz+el3VH842iG+TWtgzYGXmLtsSQ0BFi2JZw9K30bFhd51SIFFTJGttCWpjkmulnhYK7P1tNpN42iWqtQcSgmlxmDbducf62tqcEdXr0Z4WjGyl8iWPlLBEvG9ePF2z06nDfdFL+GZfDN8SRS68PcBAFsTfQ4Fp9PX3MD5vm07G2vlCn5MSiFDYGXKK9VMmOwLRN6lTWqzDrezZLNIamEp5Ywtok+mDeCshoFb+6LJiythPwK2VVeeUMdTVZNcuXRcf26VZG+HIHQ3Dy4OyKT1/ZGUy2va+XVu5c+Y1za/xxdSXhaMXsjs1g2waXF99OzU93543w2uxPlzO3UFZunSqZk+dZw8stlyFVq5Eo1CpWa/AoZs4d1rI+2lbEuMwbb8Xt4Jv+b7t7u3PqyagUPbwqlWqbitxVjOl3wzFBXi/k+fZnv05eY7DJ+CExhfeAltoems8LPlcW+Tte9TkNPRalS8/TOSLQ0BHYsHU3vXvrklNUQllpCaEoxW06nYf5XbEM6QUcJSSrkiW3n8LAz5qfFngxxuNpTb2Oix2fzhvDgGEdW749hzeGLDfu0NQV0tTSpliv54kgizpaGzPSyY6aXHQqlyDt/xRKaUoybtRGbF/swztWSs6klHIzO4VBMHodi8jA31GHjQ94M79u1Rb5aIrOkmrXnaundy4BvFg5v93tEQ0NgyXhnXtkTxalLRW0Ow79enM8oZfnWcEqq5Xy1YBh3tmM9YKqvjY+TOcfi8nmxncWPjsblYWOii2cfkyb3PznZjScnu1FcJScyo4SItFL+js7h6Z2RHH52wk2hA5xLLyEwsZCXbvdo11zk2ceU56e588GBeH4Ly2x1/XAlO8+m81t4Jk9McuW5axxED41xYsPJFH4ISuGd2Z5t/szW6Kiiag0UAoiiqBQEQVMQBE1RFFVX7qunALC5Zns+0LS21I1cVlQf+zmMca5WDHXoxbC+va6Kb78eVMuV/B6eiV9/a/paGFyXa3x5JJGY7HKe+zUSV+txjYoQiKLIm/tiEATYuXQMC51KHwgAACAASURBVNaf5pNDCXz7wIiGY8LTSqiSq3pUpcRxrpasPZbEqeSihvDttrArIhMTPa2GJPummOvtwJG4fE4mFjDJo2MWtnPpJTyy+SzVchUD7IxZ6ddykZErOZtaTEWtslPWPV2tumpvd64L4vW90ay7f1jD4nrvuSze/SuOGYNtuXeEPY/+FMZLuy/wxfyhCIJAQnFdCfdRzu2zZELdy/L+kY58dDCexLyKm6I4z7H4fCplymY9LC1hZ6rPzqVjePevWDYGpXAhs4yXZ3hQWqMgt6yWnLJa8spq8XIwZeEox3Z9dpVMyer9sdib6fPibR4McTBlcB9TDHS0WLTpDK/vi2ZQH5MmK5aLosi20HQ+PZRASbWCKQNseGaqG4N6mxIQENDo+NHOFmhrCpxILOiUolqrUHEwOheZUsUIRzOcLY3a1C4sIbeCZVvCyCqt4TZPO3qb6mFlrIu1iR6m+tpsOZXGxwcT2Hk2g1dnDGDqQJsbbjQ7HJvHK3uiMNXXZp63PXOG2Td4eavlSt7YF8Pv4ZmM7GfO+3M8Wb41gpW/hLNv1bgOz+9ypZqXd0dhZ6LH45NankNsTfVY7NuP7wKSic0ub/A+dSWHYnIJTCzEz90KEz1ttDU10NESMNDRYq53x73Hi8Y4si8ym73nsrl/VNsL2cmUKpZuCSO1qIqfHhmJh23XfudBvU1ZM38oj01w5pNDCXx0MJ7NISms9HPFwVwfLQ0NtDQFtDU1sDDUucrA2xRH4/J4dU80vQy0cbYypJ+lIf0sjfCwNWZQb5MeYwhujrXHkojMKGXtgmENBgE7U31mDanLkzPQ1eT7E5cY72rZanhnc0RnlbF0SzhOlgZseWQUpgbNG3mHOvRi14qxVMiU6GhqoKOp0TDfFFbKOBSTy99ROXx9PIm19WHxFoY6vDvbk/t8HBoMwWNcLBjjYsGbswYRkV7Cc7+d5/4Np1m7YDhTr7OXTaFSs+VUGl8eTUShho0P+XTYc3/38D6sOZzA+pOXrpuiWqtQEZ5WwqnkIs6kFCGKMMLRrOGfhZEuu8IzeXlPFFZGuuxaMbZDXTUmD7Dm3b/iyCiuxsG8bfOnTKni5MUC7hzaulPF3FCHSR42TPKwYa63PdO/OMmre6L54SHvHv8cfnk0ETMDbR4c3b41BcBj4505Fp/PO3/FMqG/VavtB6HumXx9XwzjXC15ekr/RvutTfSYPaw3v4Vn8MzU/o0cIx1F6EihAEEQ/gcoRVH8vP7vbMCh3sM6FxgniuJT9ftOAQuB/fXbSwRBmA4sFEVxUROfvRRYCmBjYzNix44dHfxqHeNAioLQXCXp5WpU9T+NuZ7AvP46jO7dsl6vFkXyqkQyK9VkVKjJrFCTW61moLkmM521MdNrbBmLKlDyU6ycwhoRQ21YNVSPgRZda6XNqVTzSlANo+w0iSlSYaIj8MZofXS1/n0Iw/OUrD0nY767Drf302Zfkpw9SQpeG6WHq1mdPL8myDmUqmDdZAP068+trKzEyKj7Ku8p1SKrjlbj20eLRQPbFupWrRB56ng14/tosWhQ8+co1SLPBFTT30yTJ4a131iRUaHmw9AaDLUFeukK5FSp+XSiAbqabZv8fomTEZChZN3ktp/THH8my/k9UcFyL11G99YiqkDJFxEy+ptp8Ky3HtoaAn8ky9mdqGChhw5TnbT5JryS+DKBL/0NOjRhl8tFnj1ezUQHLR5sZWyUapH8apG8ajVyFfQ20sDOUECrjb2Qu4K152pJKlXzuZ8+Gp14QYVkK9kcLUN+RaqWAOhqgkINn07Ub3IuaI4TGQp+jJHz6ig93MyunhvKZSJvhtSgpQFvjdXHUPtfuVVqkW3xco6mKxlgrsFcdx2cTf89v7ln94MzNdQoYbVv+y3KFXKRY+kKjqYrKL8i/cVQG1xMNXE102CAuSYuvTQa/cZnc5VsjJKhpyXw+FDdRt/1MtGFSrbFycmuEhlkocFIWy20NEBbQ6j/L7iaaTbMUV2FQi3ya4Kcw2lKHIw10NWEpFI1mgIMsdJkhI0mf6UoyKkUucNFm9ku2mhqCORVqVl9ugYzXYFXR+t3SK79yXJ2JSp4arguw6xbty9XKUSeD6jCzVyLZ0d0vaH18/BaMirUfDqxc8/KtYiiyBshdT1OV4/Va9O8I1eJbIySEZqrYpmXLmNaeU93BQnFKn5NkJNc1nQ+5kMDdfDv27RiVVCt5s2QGkx1BWwMNMitUlNQIzasN9zNNLinvw79m7n/oXvfu0klKt4PrWW0nRZLvZqe15VqkffP1JJbpWa1rz6W+u3zCuZVqXnvTA3aGgKvjdZr13zZEuUykbA8JbVKET8HbQy0W76/ymUin0fUklqm5sGBOkxqZkw7gyiKRBao2BkvJ7e6bk6b3VeFm03nxvfy+/xdX33sjTv3+1XKRXKr1ORUqcmpEkkqVXGpVI1SBA0BnEw00BAgpezfdbOFnkBRrcgAcw1WDtXDWKdj80RulZqXAmt4YIAOUxzb9vtHFyr5NEzG08N1GdqG+fJKDqUq2B4vZ6mXLmOvw1wiV4nIqqswNu7c+F4qU7H6VC33umlzh0vHFMK8KjWvB9cw0EKTp4brtjjfVilE3gqpQamGt331MWlmPLMq1LwaXMMcV23ucm2fXP7+/uGiKHpfu72jo5ADDAAQBEELEOu9qZf3XWnCsaLOq3p5ewl13tX8pj5YFMX1wHoAb29v0c/Pr4MidozLl6tVqIjNKedceil/nM9mfVQpXoMHNZt/FpFewrIt4RRUyID6h9fSEBc7fY4nFxGYrWbhKEeW+zljbaxHYaWMd/6MZV9kNs5Whrwxuz/rjiXyWXgVr84YwGJfpy6z5qz6JQJ9HTnrHvUnPqeCBzed4e/CXg2es0qZkpc+O8EAOxPeW+SLlqYGPmOUBH4SwKE8fR6dPQZBEPgw8iQj+5lw+5TRDZ8dEBDAjR6ja/FNP0tKYVWb5dgRmo5CHcUTs0YyrJWQnvnVsfx0KhUvn7Htsg6lFFbx/HenMNbX47flY8gpq2Xe96fI1e/XpsIIoijy2pnjTHQ3Z/pknzZftznGjVeT/P0ptidWMWyIB98ei8Td1oQdy0Y3hExPmCBSviWMnQkFzPYbQXLAaca52+DvP6KVT2+eY8XnOBqXz1ePjmvU1+14Qj6bg1NJKawis6Saawv7aWsKOFsa4W5rzMT+Vl2W59cUZTUKog4f4YHRTkzybzocvK34AQuLqojJLsfGRA+7eq9gblktfp8GEK+242W/tuc9f7Y2CHcbXZbMHt/knNDHo4T5359iT7YR6x/0RkOj7pl+YlsEx9OrWTbBmRdv82jk0Wzu2Y0Rk/jkUAKDRoxpcz5oelE1359MZldEJrUKNf7uViwZ74ytqR4RaSVEpJcQnlbC7sRKQIGlkS5TBlgzdaANY1wsWHssiW8jkxnWtxffPTCixSgWP2CZSs3W02l8fvgiMTGNC0I4WxqydcmoLut3m1pYxePbI4jOqubhsU68PMMDXS1NkvIr+DUsk90RmUREybE00mXLo0MZ53a1F6NP/0Ie+jGUXVnGrH9wRJu8y5dJKaxi/5GTzBxsxzPzhrf5vBMZ//DrRQX6fQczqh1Vu1ujrFpB7OHDPDy2889KU6w0TOfl3VEY9xvSYl5aQYWMLafT2Ho6jeIqFS/d7sHyiTemvoQfsFQUScyvpFquQqlSo1CJKNVqNgamsDW+kKljhjW6D+RKNfO+P4WmpoKdK8c3eNgVKjUZxdWcvFjAuuPJvH+mFn93K56b5t5kC42OvnerZEqq5MqGCsjtpVKm5I0vA7Ez1ee7peNbTLfpP6SaGV8Fsj1Vj51LR7c5fSW/opY3vj2FppY2O5eP7fIWNHe28/gp/koe33aOn2PzMbJ24H/T3btsbXYxr4K3/oghJLkIZytDNs0bgL+7NSdOnOj0umqIj5wDHx4jstaCB2YNaff5BRUyPjwQT0BCPkVXFN3R0dTA3daYRwZbMMbZAm8ns4aUplqFiuisMsLSSjiXXoK7jTFPTnbrdPun7+MCyFAZ4Oc3smFbabWcDYGX6lJfvK/u/X18XzR62hksn+Pf7vD88WqR+O9C+C2piqV3jrmqZ21HqJYrCUstITi5kJCkIqKzy3Ax1WTDEh/6dSKPc8vms/QyUPHWA5Mw6kTP3HLjS7z3dxzlZv2bjSZTq0WWbgmjRFbDzmVjWu0+caQwlJOZZXzw0PguSY/o6Lc7AjwjCMIb1BVTChQE4RPgMBAArBcEwQhwAQpEUawQBOEQdXPEZ9QVYlrfWeGvJ3ramgzva8bwvmYsGOnAoh9CeXL7OfS0NRqFgf4Tk8uTO85hbazHx/d6McDWBDcbo4YBSiuqYu2xJH46lcq20DRmefXmn9g8quVKnprsxkp/F3S1NJnkYc0zOyNZ/WcscTnlvDvHs9N5WFGZZfwVlcOTk92wNNJlnJsuz07pz2eHL+LtaMaDY5xY889F8ipq+faB4Q0TiqGuFk9PceO1vdEcicvHy96U+NyKHtl7ydfVkmPx+fwYnMLdw+xbDBGCut6pLlaGDHVovTLlXG8HNgalsPdcVpv7kmWX1vDAxjOoRZGtS0bjYG6Ag7kBPk5mfH8imQUj+7ZaSCAhr4LMkhoeb6UfYVvR0tTgs7lDmPFVIMu2hNPX3IDNj/hctdDQ0BBYM38od60L5rGfwyitFRnVgfzUK3lgtCN7I7P543z2VT1pt5xK5c0/Yuhjps8Q+17MHtobJ0tDnCwN0dfW5GJeBQm5FcTnVnA2tZg/zmeTXyG7boXODkXnIlepuWto1+TTOloYXlWYCsDB3ICZg+345Uw6qya5timn+kJmKVFZZay+q/mCGiMczXh15gDe3h/LdyeTmTOsD49sDuNiXgXvzfFsd6jxeDdLPjmUQHB9PnprnLlUxJKfwpCp1Nw9rA+Pjut3Vai3i5URc+srVpdVKwi4mM/h2Dz+vJDDjrMZaGoIqNQiC0Y68Nadg9o052lrarDYtx8LRvaluEqOXKluyJXMKK7mhd8vMPe7U2x5dGSrYZjNIYoi2WW1BCTk88Hf8WhqCHz/4IirUgxcrY15ZcYA/jfdnfC0EtysjbBoYmEzzs2S12cO4K39sXx2OIH/TW/bPCqKIq/sjkJXS4M3Z7VPKZzsqM2JXA0+PpTA78vHdNni+lBsLgqVyEyv65N7ftfQ3rz/dxw/n0prUlG9mFfBxsBL7D2XjVylZsoAa5aMd25XC62uQBCEJos4DnXoxT3fhrDyl3D2rPLF5Yr77+OD8URmlPLNwuFXhYFra2rgbGWEs5UR83wc+Ckkje9OJHPH2iBmDenNh3cPbmToa43Sajl/ReWQmFdJckElSfmV5JTVoqUhsHeVb4d6SK7eH0NmSTU7lo5pdf7qa2HAe3M8eWpHJF8eTWyUz9YU5bUKHtp0lsJKGdseG90j+qQa6Gix/sERvL4vmm8CkimslPHRPV6dep7UapEfQ1L56GA8BjqavH3nIO4f1bdL6xqYGeowz9uebaHpPD/NvU2hnZdl23E2gw8PxFGrUDNrSG88bI1xsTbExcqIPr30m1U89bQ18XYyb3fho9aY5GHNllNpVMmUaGoI/HwqlXXHkiivravUG5xUxAf1z4goihyNz2ecq2WHlCRNDYGP7/Fi5ldBvPlHDF/f33bj4GVEUSQgoYBNwSmcvlSEQiWirSkwzMGMR3z7seNMCrd/eZKXbx/Ag6Md22W4hLp1/dH4fJ6f1r9TSirAI+P68WdUDm/vj2Wcq2WT76/vTiZzJC6fN2cNbFVJBXhsgjP3bzjDnnNZV637OkqHvqEoirmCIPwIJABVwCzgU8BcFEW5IAivAecANXBf/WnrgN2CIKwEDouieLDT0t8gDHS02LTYh4UbzrB8awSbH/ZpyN+6vOAe3MeUHx72adL64mhhyKdzh7DK35W1RxPZFZHJCEczPrh7MK7W/77ojHS1+P6BEXxxNJGvjiaSVFDJR/d4daqi8ceH4jEz0Oax8f8qWav8XYlIL2H1n7FoaAhsDknh/pF9G3kX5/s4sCkohY8OxvNovZLWk/JTL3PnkN7sjsjk7f2xfHAgnqkDbbh3uD3j3SwbTaiphVWEpZXwwm1ts4q62xrjZW/Kr2EZbfJyF1bKeGDjGcprFGxfevWLdqW/K4t/PMu+yKyGhXtzHImta5M0qYUc2vbibGXE6rs8+SEwhe+v6Ct4JSZ62nz3wAhmfx0MwMh+nXvhjHA0w8PWmC2n0rjPxwFRrOsN992JZCZ7WLP2/mFNFk0ZYPdvjplKLfLsr5F8dDAeIz2tJvMxymoUvLI7irjccj6Y0z4PUqVMydrjibhaG+Flf30bgC+d4Mwf57PZdia9Td6fX06no6+t2arC+PBYJ8LTSvj0UAKbglKpVajY9LBPh55Xz96mmBloczKxoNXrHo/PZ/nWcOzN9Pn50VH0acWDaWqg3VBpVaZUcfpSMQEJdb3u7h7efo+5nrZmI6+pZx9THMwNeGhTKPO+P8XPj4xqc55mTHYZJy8Wci69hMiMUvLrI2RGOJrx1YJhzX4/bU2NVhWlh8Y6EZ9bwdfHk0nIrcDd1hg3a2PcbIxwsTJqclG1KyKLU5eKeG+OZ6sVbK9FV1Pg6Sn9eXl3FEfi8rssx+6vCzk4mOsz5Do9KwY6Wtw7wp6tp9MoqBjY4NUvqpTx0cF4fg3LRE9bg3k+9o0K/vUEjPW0+eEhH2Z/Hcyjm8+yZ6UvZoY6HInNY2NQCovGOLZYGdxAR4sVfi7cP6ovGwMv8fXxJCpqFWxY5N0mRUYURXZFZPH+33EUV8kx1NHExdqIMc4WOFsZsjkkjVf3RrN7xVg027FA/ikklV/DMlnl79Lm98JdQ/sQlFjIuuNJjHGxaDFfMja7nMe3RZBeXM0PD/u0yZB8o9DS1OD9OYOxMNRl3fEkBvU2bVfLmCvJK6/l+d/O17XWGWDNh/d4ddpr1xyPjnNmy+k0Xtp9ob4atwa62proamlgZqCDvZk+DuYGDcpOfG45r+6JJjythNHO5rw3Z/BVhpbuYrKHdX1HijiOxxeQVVqDv7sVL97uwdG4fD77J4HYnHK+XTgctQiZJTWs6oSR383GmCcmufLZ4YvcOSS3wTiZXVrDnnNZ7D+fjZGuFhP7WzHR3QrP3qZoaAjIlWr+OJ/NhpOXSMirwNZEj0d8+zHW1RIfJ7OGtY6nVi77cox4848YDsXk8vG9Xtibtb1+wZdHEzHR0+rwPXglmhoCn9zrxcyvAnlrfyxrF/xboDa3rJYPD8SxNzKbmV52PNzG641xtsCzjwkbAi8x39uh3Yr4tXQoR/VG4e3tLYaFhXW3GA2UVMm5b/1pMkqq+fmRkRyJy291wd0UtQoVuloaLSo9f0fl8Pxv56mWqxjtbM6iMU5MHWjTLotbSFIh9288w2szB7BkvPNV+0qr5cz8Kois0hosjXQ5+txETPUbW0gPRueyfGs4Zgba6GhpcPrlyVfJ3RNCf6Hu5RyTXc7v4Znsi8yipFqBlbEud9e3hris7H/2TwJfH08i5KXJbbYwbjmdxut7o/nziXEtWqHLahQsWH+aS4WVbHl0VKOKqqIoMvOrIGqVKg4/M7HFRcJd9YrivlW+bZKxqzkcm8eGf86x48npnZ5ktp5O47W90excOpptoensi8xm4ai+vH3noDaHBClUalZsDedofD5r5g1hzrB/lZoLmaWs2hZBTmltXYhteS2P+Pbjf9Pd22RRfWnXBXaGZfDrsjGNxux68OAPZ0jIrSDwRf8WvYdlNQpGv3+Uu4b25sN7vFr93CqZktlfB1MlU7JpsU+rBWVaenaf2H6O05eKCH1lcrPz1P7z2TyzM7K+IufIJi2x3UlyQSUPbDxDpUzJ5sU+jHBsfmxFUWRjYN1CSC2Ck4UBw/qaMdShF0MdeuHZx7Rdi/rmkCvVrP4zhlPJRaQWVTc0stcQYFhfM6YNtGH6IFucLA0pqpQxec0JXK2M+HXZmHY/hwEBAYwbP4Fpn59ES1PgwFMTOv0dSqrk+Lx3hCXjna9rdM2lgkomfXaC56b2Z6W/K9tC0/nkYDzVchWPjOvHiokumHVRoY7rRXhaMQs2nGGYQy8+useLu74OxsFcn9+Xj22Xp2d7aF0o9NwR9nx8b50nr7ln92JeBa/tiSY0tZjhfXvx9p2eePa5ujjT3nNZPL0zkndne/JAG4qwyJQq3vojhu2hGUzysOb7B0e0ax1SLVdyx9ogSqsVPO7vyn0jHa5aK4miyC9n0ln9Zyy99LX58r5hna6Qfb24HAJ54mIBvy8f26gKcWscisnlpV0XqFGoeP2Ogdw/sm+T82tXrqte3h3F9tD0Fo8xN9ShTy994nLKMdbT4rWZA7l7eM/p7iBXqhnxzmEqZEo8+5jwyu0Drir2F5JcyJPbz1ElU+HTz5yTFws488rkThVCVajU3LkumMJKGf+b7s4fkdkEJxciijDSyRyZUsWFrDJEse73G+1sTkRaKbnltbjbGLN0gjOzhvRuMnouICCAiRMnsvNsBu/8GYsgCHyzcDgT2mBYjs4q4461QTwzpT9PTWm6TVlH+OpoImsOX2TDIm8m9LdkU1Aqa48lolSLLJvgzCp/13bNW/sis3hqRyQbFnm32UgqCEKTOaqSotpO8itqmffdKdKL63Lq7h/Vl9XtWHC3h+IqOTvPZrD1dBpZpTXYmOgy39sBA12t+kqiNeSWyyiukuHvbs3SCc4NVhlRFJn9TQj55bUcf96vyRvsQmYpj/0cxtt3DuI2z6YtvKIocu93pwhPK2Getz0f33t1rkNPUVSvRK5Ucyw+n10RmRyPz0epFvGyN+XeEfZ8f+ISzlaGbHl0VJs/r6xagc/7R1jg48DbdzVdcrtaruTBH0K5kFnKxoea92T9dSGHVdsi+Pr+4cz0avo3zy+vZeT7R3l+Wn8en9R1E1F76aqxrZQpGfXeEZRqEZlSzQu3ubNioku7X4K1ChWLfzxLaGox3ywczrSBNmw5nca7f8ZhaaTD2vuH42FrzIcH4tlyOg1nK0M+mzukxTzkI7F5LPk5jOUTXW5YWHtQYiEP/HCGj+4ZzHyf5sNifgqpi9bY//g4BrfRe1UjVyEItOmF0tL4/hqWwQu/X+Dg0+ObVHi3nUnn1b1R+Dias/Fh727pNdgWMkuqefCHUHLLall91yDmDOvTaK6uVah4dU80uyIyud3Tlndne94QpVuuVJNaVEViXiVxOeUEXMwnOqscAHcbY/R1NInJLuPvJ8d3qGr25fG9POd8OncI93Yyz/uy0tSa0a4ruGzQsTbRJTqrnDHOFqy+a9BNUUH8MpcXawY6mmgIAn8+Ma5D/QXXHL7IV0cTeXKyG89O7d/o2S2vVfDN8WQ2Bl7CSE+Ll27zYF4zngxRFFm48QxRWWUce86vxTz0vPJaVmwNJyK9lFX+Ljw71b1Dxo6k/Epe2RNFaEox5oY6POLrxINjnBAEeHlXFH9F5TCxvxWfzRty3byLXcVlIz/AX0+Oa1NlXlEUee+vODYGpTC4jylf3De0RU9lV6+rRFGs6yurrOstW6tQUVwlJ6Okmozimvr/VuNoYcCzU927rFprV3I4Ng+ZUsUMT7sm7+u88lqe2HaO0NRiBvcxZf8T4zp9zeisMu76OhiVWsTBXJ97httzz3D7hurDRZUygpIKOZFQ0JBnvHSCMxP7W7W4vrlyfDOKq1m8+Sw1chVHn5vY6rt72ZYwQpKLCHpxUpPOpY6iUKmZtTaIoio5RrpapBRWMXWgDa/PHNihavUKlRq/TwLo00ufX5ePadM5kqLahWSV1vD4tgimDbRl+UTn6251UqlFjsfn89OpVAIT6zr8GOtpYWuih62pHvramhxPyEcU60JtVvg5k1xQxbIt4a0uhkVRbFX+8LRi5n1/mvUPjmDyNc2Te6KieiWFlTL2RWazKzyT2Jy6ReCX9w1tdwuSJ7af4+TFAv543LdR7qFMqWLJT2EEJxXy9f3DWyzHr1KLTF1zAj1tTf56clyTv/2O0HRe2h3VrJJwo+jKsX17fwxbT6fx8b1eV3lD20ulTMkDG88Qm13OKGdzAhML8Xe3Ys28oVd5WIISC3nh9/Pklteyws+FJye7NfJeFlXKmP7FSSyNdNn3uO8N68spiiJ3rA2iRqHiyDMTm11MTv/iJPramux7vPMv3KZoaXxzymoY88ExXpnhwdIJ/4Yoi6LItyeS+fhgAv7uVnyzcERD392eSkGFjCU/h3E+o5S+5gas8HPh7uF90NXSJL+ilmVbwjmXXsrTU9x4cpJbpyMIOkNmSTWHY/M4FJNLaEoxT0/pz5OTO2asujy+oihy57pgiqvkHHt+Yqfu8wc2niGzpJrjz/td9/fePzG5LN0Sjo2JLq/NHMgdXnY9xsPTHj4/fJEvjyay7v5h3NHBvF5RFHlx1wV+Dcvk/TmD6V1zCT8/P/LLa9kUnMovp9OokCmZ523Pi7d5tGpoSS6o5PYvApnpZcfn84c2eUx4WgkrtoZTKVPy6dwhLYYrt5WzqcV8czyJ4wkFGOlqYaKnRV5Fncdq6Xjnbn322kNkRilzvwthgpsVGxZ5tyi3Wi3yxh/RbD2dzkNjHHl15sBWa1T09HVVT0WpUrM5JJWBvU26rC1PcFIhmhoCI53Mu+z+vHZ8TyUXsWDDaZ6b2p8nWpjv43LKuf3LQJ6a7MYzUxu3h+ksUZllzPkmmL7mBrwxayB+7p1LPfshKIV3/ozlt+Vti1aTFNVbhKJKGbramo0SqHPKathwMoVtoWnIlGqMdLWwMtbln6cndIm3t6RK3mSo1c00ocZklxGZUcp8b4d2/ybn0kuYv/40SpWamV69WennwgA7E5QqNau2RXAoJq/NHovL3qofF/vg38REsOSns8TnVhD4gn+3hDbFzwAAIABJREFULsy6cmwVKjUlVfJ259k1RWl1XQh+Yn5liwuc8loF7+yP5bfwTDxsjVkzb2hDrqIoiizbEk5AQgF/POF7ww0C+89n88T2c6x/cATTmuj/eza1mLnfneLje7za1Yy7PbQ2vlPXnMDWVK8h+qBGruLFXRf443w2dw7pzadzh7S64OopqNUiR+LyWHc8iQuZZdia6HH/qL5sD02ntFrBmnlDOtzv8XrRlhSRlrhyfC978d+5axAPjnFq9pySKjmLN59lsa9TI2NeYaWMke8dYaWfK89Pb70wTmcRRZHAxEKGO5p1umBId1NYKeu0p1ChUvPYz2GcvFjAooE6yAxt2BWehVKt5nZPO5ZPdGlz5AXUpcGsPZbEtiWjrgqjrJYr2XAyhXXHE7Ez1Wf9ohFdPj/GZJfxbUAyF/Mq+ODuwS2G5fdUNgen8Nb+WF6+3YNlzdQbUKtFXt0bxfbQDJZNdOal2zza9DzfTOsqifbT1Pgu3xLOiYsFBPzPr9mQ5RVbwwlKLKzzprZSNLSj5JbVYm6o0yXv9kqZkmlrTgCw7/FxrXYRaE5RvTlWGRINWBjpNvnStvs/e+cdV1X9//Hnh62AqKi4hURxi0ruFHeu0tQyLRt+bdqysmnz17ZtSysttbTSXGmapuXeOHPvPTAVBBE4vz/ekAqXcS8XLlzfz8fDB3LOued8Lp97z/m81+sdVIyXetZh6TPtGdounEBfL0Z0r+O0lOTCXg+UG+pWDGJgs2oO/U0aVS3FkuHtpEnyP8fp+vFi7h23mkcnrWfuluO80rNOrtPqekVWokKQH5/9uYuMjqKEpJQ0kYWQIhk9yApvTw+nGKkAJYv78MuDLVkwrC0PtK2epZezhJ837/VryDd3RXE6PombP1vCqD93kpySyi9rDzFv63Ge7FzTJVHrrvXKU6V0Mb76e4/N/RNX7CfQz4seDV1nPN1Qoywr98aSeCmFg7EX6PPFMmZuPMLTXSL4uH9kkTFSQRStO9ctz/SHW/H9vU2pWro4H/yxAw9j+OXBFoXOSAVJ33bWPaB1jTI0rBzEuGX7Mt1zruTH1QeIOfgvT/28gZV7Tl+1b87mY6RaFNhn0hhDm5pli7yRCjglndXb04PPBjSmXqUgxm1JYsq6w/SNqsyfT0bz2cDGdhmpIKKKVUsX58Xpm7mYnEJqqsXPaw7SbuQiPpy/g051QpgxNH+ceHUrBjFqQGPmPdG2SBqpIOJo3eqX592521m1NzbT/pRUiYL/uOogD7ernmsjVbk2eb5bbVJSLd79fbvN/eOW7mXO5mMMviEs34xUgPJBfk57tgf4ejF6UBSxF5J4YMJaLian5PwiGxT9J4ByFcEBvjzZOSJXUvCKfZQr4cdz3WrzUHQ441fs49ul+4iNT+LJTjW5u1XuWtcA+Hh58GB0dV6avoWhP6znrT71/6vxW7LrFBeTU52m0OmuBPh65XoB26F2CPMeL8VLM7Ywct4O5m09zp6T8TQNLZ1JZKyg8PL0YMgN1/HS9C2s2Rd7lZx/bHwSszcdY0CzqrkWaMsP2tQsw7dL9zLqz11MXLmf5FSLb++2nQVQVEg3ftrULMvmw2epXKpYrmrM3IG7WoYy7KcNLN11OlN/T5C0ufHL99OkWinOXEji/glrmfZQq/9qKmdtOEL1sv5EFKEaUXfD39eLcfc05YNf/uLRW25wuB8qiCPktZvrcvfY1Tw/dTP/HD3H1qPnaFilJKMGNC4QYbmijDGGt/s0YOuRJdw2ejkRIYE0qVaKqNBSNKpSik/+3MnUdYd5rEMNHu9YQ41UJVuqBhfn3tZhfPnXbga1qHaVUNf0mMO8MnMrneuEOK1lYUFRr1IQ7/eL5OEf1vHir5v/E4SzBzVUFcVOgop7M7R9DQa3vo5tx845JKN/R7NqJCSl8O7c7Ww5cpbPBjambsUg5m89TqCfV57bwihXU8rfh09vb0SXuiGMmLYZgPdvbegUJVdH6dekCh/+sYMHJqwjvJw/pf19KFnchxPnEklKSWVAs7z3H8sLzcKC8fH0YNTCXYSXC2DMoKg8NSgvbOS3GFBho3uDCrzx2z98t3yfTUN13tbjHD2byGs316NmSAC9PlvKveNWM/WhliQlp7JqXyyPttcFt6sp7e9Dx2reeTJS04mOKEe3+uWZsu4QlUoW4+P+kfRsULHI1Iq6mhJ+3kwc0pxf1hxizf5YZsQcYeLKywq7wzo5XmOuXHs83K46v6w9yGuztv7X+3rR9hM8+dMGmoWV5pPbG+WLcGt+071BBbYfC+eTP3dRq0KJ/9pd5hY1VBXFQYr5eGarKJsdHh6G+9tWp3G1Ugz9YR29P1/Gyz3rsGDbCaIjyjm18bdymR4NKtKqehniLib/p9znKor5ePLhbZH8uOoAZ+IvseN4HGfikzhzIYnoiLJ56p/srPENbF6Vsxcu8Vqvem6Rgnkt4+vlye1Nq/L5ol0cjL2Q6fM/buk+qpQuRvta5fD0MIweFJXWO3wtHWqFYFnQ04Wp6Er+8FbvBnSpW54udcvb1X5CESqVLPZfm5CUVIudJ86zZt8ZygT4ZNlNQVFsEejnzVOdI3h26iZmbTxKpVLFeHDCOmqGBDLmrqgi/f18vGNNth8/zxu/baVGuYBcteJJR1ceiuJCrg8tzexHb+DxyTG88KtE+jrWLrqplUWBUv4+habmOjqiXCZlvdRUi8IStHq5Z11XD0FxIgObV+WLv3YzYeV+nuta+7/tmw+fZdW+WF7sXvu/LIPrQ0vzbt8GPD45htX7zlCrfCDh5TTt190IKu5ttwq+YhtPD0Ot8iVcqtavFG36RVXh++X7eeO3f0hMTiGkhC/f3du00LaAyy0eHoYPbo2kzxfLePiHdcwY2jrXGVoatlEUFxMc4Mt39zTlqc41aVA5iHa11FC9lvHwMJpeqeQLFYKK0aVuCJNXHyTx0mVhi++W7aOYtyf9oq5WmO7VqBKPdahBSqpFjyz6PiuKoijOwdPDMKJHHY6dS8Tb04Pxg5vlqJZbVPD39WLMoCg8PQwPT1yXa3ElNVQVpRDg4WEY2r4GM4a2LvKeM0VRCi+DWoTy74VLzIg5AkjLs+kbjtCnSSWbDeQf71iDcfdcz5A2rhEeUxRFuZZoUT2YzwY05uf7W7i8RMnZVCldnJF9G7L16Dnemr0tV69RQ1VRFEVRrhGahZWmVvnA/1rVTFp9kKTkVO7Kor+qMYboiHL4ehXd+ihFUZSiRPcGFf5TXHc3OtYJ4d5WYYxbto8/th7P8Xg1VBVFURTlGsEYw6AWoWw9eo4Ve2IZv3w/N9QoQw1tO6MoiqIUAM90jaBepRI8/csGjvybkO2xaqgqiqIoyjVEr0YVKeHnxROTYzh2LpG7W4a6ekiKoijKNYKvlyef3t6YS8mpPD4phuSU1CyPVUNVURRFUa4hivt4cWtUFY6dS6RacHHaRaiAm6IoilJwhJXx543e9Vm1L5ZP/tyV5XFqqCqKoijKNcagFqH4eHkwuHUYHh6qMq0oiqIULL0aVaJvk8p8+ufOLI/RPqqKoiiKco1RNbg4y59tT+lC0lNYURRFufZ49aa6HD+XyL4s9mtEVVEURVGuQYIDfLVnr6IoiuIy/H29GD+4WZb7jWVZBTgc+zDGnAT2u3ocdhIEnHX1IAqQMsApVw+igNC5dW90ft0bnV/35lqaX51b90bn173R+bVNNcuyymbcWKgN1aKIMWa0ZVn3uXocBYUxZo1lWVGuHkdBoHPr3uj8ujc6v+7NtTS/Orfujc6ve6Pzax+a+ut8Zrp6AEq+oXPr3uj8ujc6v+6Nzq/7onPr3uj8ujd5ml81VJ2MZVn6hXNTdG7dG51f90bn173R+XVfdG7dG51f9yav86uGqpJXRrt6AEq+oXPr3uj8ujc6v+6Lzq17o/Pr3uj82oHWqCqKoiiKoiiKoiiFCo2oKoqiKIqiKIqiKIUKNVQVRVEURVEURVGUQoXDhqoxZoQxZpcxZrUxJvSK7UHGmB+NMdvS9tVN2z7EGHPQGBOT9s8z78NXFEVRFEVRFEVR3A2HDFVjTDWgNxABvAa8c8XuJGC8ZVm10va9krY9GHjBsqzItH8pDo9aURRFURRFURRFcVscjai2A+akGZtzgBbpOyzLSrAsa3bar/uAUmn/Lw2ccvB6iqIoiqIoiqIoyjWCl4OvK0ea0WlZVrIxxtMY42kjStoPmJv2f2/gXWPMe8BsYLiVg+RwmTJlrNDQUAeHqBQE8fHx+Pv7u3oYSj6gc+ve6Py6Nzq/7ovOrXuj8+ve6PzaZu3atacsyyqbcbujhmpGA9NkPMAY0wjoBTRL2zTMsizLGFMMmIIYsT/ZeN19wH0AISEhjBw50sEhKgVBXFwcAQEBrh6Gkg/o3Lo3Or/ujc6v++KKufVMTsAzJYEk39IFet1rEf3uujc6v7Zp167dflvbHTVUjwK1AYwxXoB1ZTTVGFMGGAfcbllWAmkHpP1MMMbMBOrYOrFlWaNJa4YbFRVlRUdHOzhEpSBYtGgROkfuic6te6Pz697o/Lov+T63lgWndsCh1Wn/1sCJreDhDY9vgsCQ/Lu2ot9dN0fn1z4crVGdD9yYptzbDVhsjHnPGNM5zXCdBDxnWdaW9BcYYyqk/fQA2gMxeRu6oiiKoiiK4hRi98Cid+DTJvBZU5j+MGz5FQLKQdP7IOUi/DPD1aNUFOUawqGIqmVZx4wxY4HtQDzQExiJCCb1Q8SV3jTGvJn2kmjgBWNMNJCCGLrT8zRyRVEURVEUxXFSLsH68bBhEhxcCRgIbQ0tH4FqrSA4HDw8JMq6+0/YOh2aDim48e1fDjvnwQ3DwDew4K6rKM7mUiKM700YFaBtWzCZqiYVGzia+otlWaOAUVdsuvWK//9o4yVDHb2WoiiKoiiK4mT+egf+fg/K1oIOL0ODWyGocubjjIE6N8Pi9yHuJARk0jxxPgdWwoRb4NIFieT2Gwfl6+f/dRUlP1j5JRxYRjWA38vBjW/lyli9dOkShw4dIjExMd+HWBD4+flRuXJlvL29c3W8w4aqoiiKoiiKUkRJPAsrR0PtnnDr+JwXzXVuFqN22yyIuid/x3Z0A0zsB4EVoOPLMHs4jOkAXd+BJndrNEopWsSfEidPjc4cTPClysovZHsujNVDhw4RGBhIaGgopoh/7i3L4vTp0xw6dIiwsLBcvcbRGlVFURRFUVzNnr/E4FAUe1n9DVw8Czc8lTvDL6QelL5O0n/zk5PbYXxv8CsBg6aLgfzAEghtBbMehymDIfFc/o5BUZzJorcgKR46/x+7qw+G5g/Dyi/g92clrT4bEhMTCQ4OLvJGKoAxhuDgYLuiw2qoKoqiKEpR5NwR+P4m+HGA1BoqSm65lAArPofqHaBiZO5ek57+u/dvuBCbP+M6sw++7wXGU4zUklVke0BZGDgF2o8QgaexXSE1JdtTKUqh4MQ2WDMWou6FshHyPeryBrQYKunAuTBW3cFITcfe96KGqqIoiqIURQ6skJ/7l8C8F107FqVosX4CxJ+EG56073V1eoGVAtt+c/6Yzh2B726C5AQYNA2Cq1+938MD2jwFN30KxzfD/qXOH4OiOJs/RoBPAEQ/e3mbMdD5/y4bqyu+cN34CjlqqF6JZcGC1+HwOlePRFEURVGy5+Aq8CoGTe+XxU6MLR1DRclAyiVY+jFUaQ7VWtr32goNoWQ12DrNuWM6vRu+7SKR2jumQEjdrI+te4ss/Df94twxKIqz2bVAVKvbPAX+Za7el26sVm0Ba77JMapaWHjnnXfYsmVLzgc6CTVUr2T/Mlg8EpaPyvlYRVEURXElB1dApSbQ5U0IawMzH1NHq5Izm36GswclmmpvSmF6+u+eRZBwxjnjObIevuksNXx3zZDPdHb4FIeIblIrm5zknDE4StIFaTuiKBlJTYF5I8Sx0+x+28cYAw1vh9O7isy9+5lnnqFu3WwcSU7GPQxVZ3khVo+Rn7v/1NoHRVEUpfCSFA9HN0LVZuDpBX3HQUAITL5D2ocoii1SU2HJhxBSH2p0cuwcdXpBajJsn5P38ez5C8b1AO/icO88qNQ4d6+r3w8S/4XdC/I+BntJ+Bc2TIZJA+HdMHi/prTSUZQrWT8BTmyBTq+Bl2/Wx9W5GTx9YeOkghubHezZs4cWLVrQsGFDevbsyd13382sWbOYO3cukZGRREZG/tdqZufOnbRp04YGDRrw2muvOeX6Rbs9TeI5mPagSKs37J+3c50/Bv/MhNLVIXa3ePgqRzlnnIpyLRF/SrztKZfASpV6JitVbsSlqkGpMAgsX7jaC6wcDXv/gps/g2IlXT0aRcmZw+vku1WlmfzuHwz9J8A3XeDnuzDVhrl2fIrrsCy5n+35C8I7SmqhR1pcYtssOLUD+n7r+D24UmMoUVkimpEDHB/nlmkwdQgEh8MdU6FEhdy/tno7KFZa0n8jujo+Bns4tAYWvil/29RkCKwIje6U4Mb3N8v3L7xjwYxFKdwkXYCFb0h6fZ2bsz+2WEn5DG+eItkxntn0F53zLBzb5Nyxlq8PXd/OcvfUqVPp3bs3w4cPJy4ujqFDhwLQpUsXunTpwqxZs5gyZQoAw4YNY+zYsYSFhdG8eXMGDBhAeHh4noZXtA1VnwAxMOeNkDQQvxKOn2vd93LjuWU0fNMJdv6hhqqi2MvuhTD1Pog/kf1xXsWgdBjUvFF65LmSi+fhz/+TNg1nD8Idv8qiX1EKMwfTIjiVr7+8rUJDEZqZ+j+anDoMVd6H8A6Fyymk5I3Es1Q6NAsOBYpar4fn5X2WJYq8i96GA8tk25IPIKiKRCAb3Cq9HEtfJ1FRR0lP/109Rloj+QXZf47tv8PPd4ujZcAkKFbKvtd7ekPdXrBhkmQX+PjbPwZ7OH8cfrgNPLygxcNQ+yao2FgcAHEnYMIt8EN/WUPWuyV/x6IUftaOhbjj0G9c7u6/DftL3feuBRBxY74Pzx66d+9Onz598PX15f77r05hTkpK4pVXXuG330Rcbfny5fTp0weAs2fPsnfv3mvcUPXwgG7vwZj28Nc7IvfsCCnJIh1dvb0Yp5WawK750O45545XUdyVlGTpE7b4fShTE279HgJDwHhImwHjIe0QzuyDM3shdi8cXiuLqLq9oUID1419/QQxUtu/CH+PhHHdRXEysLzrxuQoJ3dI9LpcLVePRMlvDq6CMhFQvPTV2xv0A09vPGc+AxP7SO1qp9egYiPXjFNxLss/o8auMbBrjBh31dtLixn/srD0I1HCDawI3UZCvT6y8N04WcSTlnwg57jp06sNXEeo2wtWfAY75ooBbC/rvoMSleDOX6Xm1BHq9YU130oKcv2+jp0jN6Smwq/3i0F836LM99eAcnDXLPixP/xyrxjvUffk33iKIhfjxBArVkoCS+7sPLuUIN+30BtyL1YW3hGKB0v6b3aGajaRz/yidu3arFixgnfeeYcOHTpQo0aN//Z98MEHDBw4kJCQEAA8PT2JiYlx6vWLtqEKkoLS+E5RPGw8SHoUZSQ1BZZ9Ig9sW0X6O+bA+SPQfaT8Ht5RPJIXYjMvAtyJpAuiNNagv/QoUxRHOHtYGrAfWA6N7oCu72bt3S5zhWftQiy8HwExP7jOUE1Jll6CVZpDm6fFu/9Df+nRN2jG5R5+RYWf75IWDw8sKXpjV3JPaqpEVGv3tL2/bi9WHfenrf8eceKOjhajpfv79keulMJDaips+JF/g+pSsuMwcajvWiApgwCBFaDre7IW8vaTbQ36yb+4E7B5qpQ2NchjqRRApSgxiLdMs99QTb4o5SGRAxw3UkFSmktUEnGo/DRUl38KexZCz4+zdgIWKynpyz/fBbMel0ydVo/m35iKCqd3w+qvYf1EcQiDPG9vfCv39chFjbXfSTS177e5f42nt9yj137neJZCPnH69GmCg4N5/fXXCQsLIzQ0FIAjR47w66+/snTp5TZRUVFRzJkzh65du3Lu3DmKFSv2X/2qo7iHmFKHl2VhPGd4ZmEly4JZT8D8V2BCH/nSZGTVGKm3qNFFfg/vCFhSd+CuXEqESQOk995vT7h6NEpR5EIsrPgSvmwlNRO3fC01nrlNwSpeWuoyNv3kOuXGbbPg3wPQUmouCGsjHv74U2Ksxu5xzbgcIXYPnNgqAiNTBkuNsOKenN4p81y1eZaHWB7eojT5aIw4Ybb8Css/K8BBKk7n4Ar49wBHK3QWw6z3l/Dkdrh/Mdw6Xua62X2XjdQrCSgHzR+QLDQvn7yPxcMD6twkxnLsXvteu28JXLoANTrnfQz1bhFj/UJs3s6VFYfXwoLXJNW38V3ZH+tTHPr/IGnVf7wE+5fnz5gKit+fg+lDxaFrLwdWwMR+8GkTWDUaanSEe+dKND92N4xpB78+COeOOj6+laMlol+YuJQomQ3VWkNoa/te26A/pFyU2u9CxMSJE6lduzYNGjTgf//733+G57hx49i/fz9RUVFERkZy+vRpRo0axciRI4mMjKRDhw5cvHgxz9d3D0PVvwy0e1E8dP/MvLzdsqTR7rrvoElaGsaP/UWxLZ2TO6QwPupuUU4ESZEqVkpufu5I8kX46U7xEF7XTv5mO+e7elSKM7EsWDdeooO2nDOOkpoqdai/3CvR0N+fkVTf+/8Wr729RN4BF07DThc9bJaPEnGniG6Xt1VtBnfNlDSvMe3FE1wU+pttmy0/24+QaNvCN107HiX/OLBCfqYLKWWHXwlJa6/SrPAt6hT72PAjePtzsuwVDgoPD8lIqXOTbQM1P2n+kFzz57tlXZFbdv4BXn6SGplX6vWF1Evwz4y8nysjiefgl8EQUB5u+iR36aqe3uKwLVkFpj8smWvOJjlJ0p33/u38c6ezdbpkG60fDzOGyrM/N1iWqEqP7QpHYqDtcHh8s0QXqzaXaP8j66DV47D5FzFk570Ix+3sy7l2HMx5Wj572TmULQsWvgUbf7Lv/I6y7ns4fxSin7H/tZUai7DYhsnOH5ejWBaPPjCYf7ZuYfPmzYwYMYJx48bRo0cPnn/+eY4dO0ZMTAwxMTEEBwcTFhbGggULiImJYfXq1QQEBOR5CA4bqsaYEcaYXcaY1caY0Az7BhtjdhpjNhtjmqRtK22MWWSM2W2M+Txvw7ZB1L1Qri7Mff7yjWHx+7DsU7h+CPT4EG6bIB/oX+657CFa8y14eF/tKfPwlLqPXfNz/+UsKqRcgp/vkQbEPT6CAZPlizH7Ke0F5i5cPC9KijOGyjyPjoZtv+X9vPuXwycNYXwvceI0uUdSTAfPg+Dqjp2zentpqRHzQ97HZy8HVsKh1bLYylivVTESBv8hNYDTH4Lvb3KuwZ8fbJ8j98A2T8liYMkHcg9T3I+Dq0TxNNgOkYoaneHYxrxFMBTXcSlB0mzr3EyqZwEbpFlRqhrc/DkcjRFjI7fsnCtGal7SftOp0BCCa4j6r7OZ/RT8ux/6fG1fyrxvANw0SiKHf/6ffdc8vpWKh+dI1Dn+9OXtliVK37OHwwe1JOjyfS8x+p1N/Gn47Un527Z9Vhwkvz+Ts8M28ZwEQea/IhHoR9dDu+czqzn7lYBOr8LDq6BmZ1jxBXzREr5oJbWdZw9nf50DK+C3pyRq6eEN0x7KuqXkii/gr7dlTZTf64zki2KkV23pmBPGGImq7l8imV6uxLIkS+HkdlEJP3fEZUNxyFA1xlQDegMRwGvAO1fsKwY8CzQCbgfSc42GAjMsy6oOhBhjnKsn7ukF3d4V1c6lH0k675+vQ4PbpGbOGAnD9/hQUnrnPi8Rk5gfRL0uoNzV5wvvJMqlx50sA51Xzh9zPLqTkgxT/gfbf5M6lqh7pLdTt/dE4Gbpx84dq1LwHN0IX7WVmqX2L8Kj68SInDRAHh6OpPCAPARmPQ4W0OcbSTfr9q7ImucFTy/5ju6YKzVUBcnyUeBXEhoNtL2/TDjcM0fuGUdi4PMWIraUm5Tai+clfbiguBArKp+10iLDN74D5erA1PvVMHFHDq6UCKk9giQ100pbds7LnzHZwrIkMpMfUaVrje2z4eK5vLficza1e4izb9VoMaRz4vRuCRikfx7zijGSBr1viXMW05Yl7QlnDxcRqrbPQrUW9p/nurYQNViikukZELm59i/3UnPnlyLq99518F649Jn9rJmky64dJyUqt02EkLrw0yA4tNb+8WXHnOGSedjrC4h+FloMlfldmI1g6cntkoG0bTZ0fkPUbn1ziKaVDpPjntwu4l/exSRl+sO6ElCx9Qw9exgm3ykR6/4ToOs7oo+x4ovMx+5fLlmVEd0ke3D6w7n7jDrKuu9F76btcMfFotLrvQsqApyR1FSIPyllRP/uByzpsBJ/CpJdE8xyNKLaDphjWVYKMAe48lvcDFhlWVacZVmbgDLGmOJAeyA9L3caEO3gtbMmtLWkgSz+QDxhEd0kBcPjirfZeFDal+4rWbxfPAvX/y/zuaq3l5+FJSJhWfDXu5JuuXas/a9PTZXI0NZp0Pn/pI4lnertRXl1yQf215oohQPLEufM1x3F837XLKlLKxUK9/wOTe4WT9+E3hB30v7zb/oFTm6Dzq/JosCZKWaRA6Uf5KafnXfOnIjdK/WpUfdmX1Pr4SHHPLxKlPj+fB2+6Xy1pzsjZw/Bl61lYVFQXsgdc0XtNz2F2ac49B0rdWBTh2TtbVaKHvGnpUa1SlP7XleujmgxFKShuvcvWUgveqvgrumuxPwo8+eMdFln0/FVEaqc8UjOdf3pn78anZx3/Xp9AUvqsB0hNVWMyd+fh48aSBbS6q+lpU+bpxwfV6fXxKCa9lDunDW7F8DJf9hVfTDcMUUMvppd5JnuX1bEnJ7aIcZd7R4w8BfZ/kM/OLXL8XFeyT8zJSW37TNiCBsja8ZGd8Lf78HSTy4fm3gW9i+TIMeY9lI3P2i6aD7YY6jAsremAAAgAElEQVT5l4GmQ+B/8yUtuPUT8nz+vPnlkhaQrL/Jd8hzrf8PEuVu2F+eewteE2M5nfPHJS24ZFWp5e4/ESo3Ff2GHflwD0yPplZpBtdFO36eUtUkIrtxcsGXHCXFi4F69pC0YSoVBmVryTrSGJdFVR01VMsBpwAsy0oGPI0xnhn3pXESCMmw/QSQP70fOr8uXpnQG2ShZqtxbqfXRDhpzyJJlbMlSBEYAuUbFI461aQLUhO48A3w9JEHlr3ETJQPfrsXoOUjmfd3eVM+mHNykd7hSi4liEexsKdhFjQrvxTnzHVtJR03tNXlfd5+8oC7+XNJGRwdLZH53JKcBIvelO9D7RwaVztCuVqyyCnIWtAVX0jbnKb35XwsSOrSrd9Dv+/kRv5dD3kQZuTcEfiup0Q4LyXI97YgRI22zxbFzytbkJSrJV7qfYthfG+Y+Zj0nP7rPVj5FX4JNsavFH4OrZKf2Qgp2cQYMQ72LLKvnjAvbExzPq0a7dLUsSLP+eNixDS49WrHe2HByyetX6QH/HRX9mVEO+ZKSUWpUOddv0w4VIiUnqqOPEP+ehu+7SJ9YUPqyLPy6V2S8puXNj5XpgBnF41MZ9koCCjP4UpdRdSz5VAJtgxZAPf8Jg7nYiUvHx8YIuJ/GHFC2/Nct8WFWJg1TJ71rR+/vN0YWUPU6SURyu97iUH/dlWpRf3jJQipB/f9BWF5dKQEV5fe6vctktrgSbeLoZ94Vp5hR9ZJr9pytS+PrcdH4nD+9QHJGktJvtwm6NbxoqDr4w8Df0qLQt/p/PremIlw7rAY+HltvdPwNkm3PbL+v01Wfq+NEs7AqZ0y9uBw0R4pVlJ+9/SWEq3Es9JmKI/Y+16MI2/eGPM0kGxZ1odpvx8BqliWlWKM6Qe0tizrsbR9y4GBSDS1tWVZZ4wxXYCBlmUNsnHu+4D7AEJCQppMmjTJ7vF5XTpHspe/LESzwDP5AhHbP+VY+Y7EBttoWQOE7RlP1QNTWdJ6Aile+dxMOgt8Lp6m/qY3CIjbw57rBmGsFK7bO4Hlzb/mol/uWsp4XTpHs5UPEe9fhZjIN+RhYoPKB6cRvnssm+o9z+kyuRDpAOLi4pxSLJ1bIrZ9SoVjEuX+N6geRyt05GTZlqR6+hbYGAobnsnxNF9xP+cDq7OxwctZzi9A4LmdRMa8wPnAcDY0fB0rm4dw+txWPDyHmju/ZGP9EcQGR+XHW/jvGmuafEBcoIP1rrnE61IcLZYP5mTZFmyr/XjOL8hAyTMbqb/pDS76lmZDw9e56FcGAJ+LsUTGvIBP0hk2NngVv8Rj1PnnAw5U6c2e6nfbPFfQv1uBVM6WrJftNU2qGLuWR2bHm0dKEq2W3smx8tHsrPng1Tsti7C9Eyh7cileyRfwTEnAM1UUli/4lGF18y9tnlMpvFy3+zsqH5rBktY/ZHvfs3VvDj61ivqb32BDg1c5UzoyX8fpkZJEy2V3cT4wnKCzWzlWvgM7Ih7K12sWJL6JJwnd9yN7wwaS5Bucr9eqfHA64bu/ZdX1n3HBv3KBP3dzS/rn63DFruys+UCm/Z7JCbRaegeHK/Vgd7hz+4xWODKPiB2fsfu6uzhY9ZZcv86kptBi+b3EBYSxpe5wUrycUDebgRo7vqTikd9Z3+gtzgXVtnmMf9w+rl/zGHvC7mRr8I12za88118koVgF1jd6I8f1askzm/BLPMG5EjW4ULzyf2uG2ls/oOzJJaxt8j7xAWGZXmdSLxGxfRSB53cR71+NuICw//4l+ZR2em9Uk3qJ0H2TqXpgCslexfBOjmdv6O3sD82c/l72xBLqbn2PPWED8UqOp+rBafxT6wmOl4++6jjvpHNExjyPX+JJjlS8Ea/kOLwvncUn6V+8L50nLiCMA1X7cL5EjUzXyArfxJM0XjecRL+yrG/0To5/h5y+v7JGuZeEYuXZXO95vMqGExISQlBQEMbZ/WctC5+kM/gmxZLs6UeiXwXb60IrFf/4A1jGM+0z49g4LMvi7NmzHD9+nLi4q43edu3arbUsK9Mi01FD9Q6gtmVZLxhjvID9lmVVStvXGnjQsqyBab/vQupVf03bvtMYcyfQ0LKsbHMqoqKirDVr1tg9PqexbymM6yYemTo3Ffz1D62V9OSkOPHsRaS1y/ikkaRi2IqM2mLGIxKtemCJeAuzIuUSfHmDhP8fXpkroYNFixYRHR2du3HklZgfYdoDUg/jX0ZUbc/sBd8gkaiv0UmaKxfVPoGrvxal1gb9pY1Ayaq5e92C12HxSFHerdAw5+M3/iTpoC0flQyELFi0aBHRrZrJ561kNbj39/xr0p1wBkZGiMe427v5cw2AY5slfWnrNPk+OFpje2AlTOwrHsdBM8RbO66HpMzcOfVytGvWEyLY1v/Hy/WjIKm4i96SmlefAHhsA/hnsdi1LKlXSjwLQxZmbi2xY56kfg2cIi0AciI5SdLvJg+U+tuoex37Gyiu4duukJIkUZZssHlvToqHd8Lg+sHSxzA/2Tpd0n7v/BW2/y73t6GrHRdeK2zMfFzKcKq2kFKL9K4B+cEXreV7P0Ra5hXoc9de5r4g9f93TElr9XcF236TNc2gGZL940wsS4Qyt06Xz9x10bl73a750rowP9d5F8/D5y3lM3L/3+AbmPmYaQ9J6vITW1i0aqP987tzPvx4m6Rqth0OtXpkjgaf2in6LFem//sGidpsyarSISP6OalLLUwcWgMzHpUo6i1jss4q+Plu2DpDSomiBkOPD2wfd/6YZBmd2ilrSf8ykkLtFwS7/pSSwOuiofUwqQfObt0Td0KiynEn4O5ZuVqD5er7u3uhvB9juNT3ew55VCExMZtMBcuSGtJLFySjwctX1sLZZQRYFiTEynPBx18E+rJ7r0nx0qWheHDu2xDawM/Pj8qVK2fqr2qMsWmoOnpnnQ88YYx5CegGLDbGvAf8ASwCRhtjAoDqwEnLss4bY+YCNwHvI0JMox28dsFRpSn4lpAbWX4bqqmpUnd0cKX8O7BSfi9ZFe7847KBWfo6SXHZPDV3huqBlVLg3fKR7I1UkPB+95GyKN44qXAtYE9uh9+Gicpbp9flht/qCdi/VOTTN0xKq901YnyE3iC1t+Ed8s+4ciZbpomKXXB1SeNd+SXU7SX11Nk1xT5/TMQa6vXJnZEKkj52YAUs+0Q+47V7Zn3s6q9Far3P1/n7dyxWCmp1l56qnV+Xm6yzSDgjNbbrJ4g6paeP/F3zIgRVtRncNUMedmO7iYrh2YNSM3RlSmaXt6QP37QHZIFSKlQeaFMGS+pR7ZukJmjpR1k7DbbNks85wLKPpfb4Srb/JsZublOuvHygVnfOlogg6O+R0HBAwbe1UBwjOUlS36IGO/Z6H3/5nOyYm/+G6qafwb8chLWVtMD1E0QBtZ8DGgv2YllSH1u2FgTmQ5VR/ClRQi1bS4RcFr0FHUY4/zogPaqPb5I0/qJA+xFiCM18HB5afrVRtnMe+ASKce9sjJE02+NbJe3z/r8hqHLOr9v4sxgoee3pmh2+gXDLV7K2mvWEGFtXPk/PHxMHcpO7pb+4I9ToKMb2vBfEQVS6uqz7Gt4OyQmicbJqNHgXlzVUjU6iInx4jajf7/1L1hCthznlLTuVylHw0LKcj+v2vggolayS/f0tsDw8mHa+jOuaxHPiXF7+maj9V2oixnt4x8zHJpyRNcDZw+Icye0aLDdUbyeOqUkD8J7Qi7Cu74iezpVjSIqXudsyTRw0CbEiEBnWRr5rnj6SRt3k3quN+0sJ4jRa9qmsiTq8BM2H5bzGS02F0W3lfQ9dLWWWBYBDxQ6WZR0DxgLbgdeB4UA1oLRlWUnAi8B64CcgPddnFNDRGLMbOGFZ1u95HHv+4+ktXr9dC/K3di7uBHzaCD5rKtHPbbMlR7zDyzBkUWYDs25vWazkJHyUkizGXYlKolyXG6q1ksX0lQXsribpgtS9eBcXgyndc+3hIYuuW0bDM/vg7tlyQ/ELEgNrYh9pUVTY2bcUpt4nRuMDS+DxjdD8QYmUjWkH39+ctXjP3+9JdKXdC/Zd88a3oGJj8eJmUe/rmXxBhMmqt7e/cbUjRA6UG6Czej0mXxTv/sgIqd9NTREF8Ce3Q5dc1AvlRMVGcPdvkJoMZ/ZLq6cra4NBDMB+34la8k+DRHH8yxvg4GpRVLxtvDgOVo2xXV+UkiwiEcE1xKHw13tXz1dqqkSrwjvYZ9wbw96wgVJTs+47h96+4gKObRSvub1CSldSo4vUzOVnnX/Cv/I9rtdHPPoB5aDFQ7BlKhzdkH/XTWfbb3LffD8CvmojBvLBVc4TFVv9tczDrd+LyMzi9+W7nR9smCQtOOrmPp3VpXj7wU2fSnbJgiucb5YlrVSqR2fOCnEWvgEimpOcJPfbnGqxk+LFUVjn5vx31lVrCdHPiwNn/YSr960aI8+R5g/afm1uqdUNhq6RZ45fCVHq/6g+fNJYdBkiB6b1ME2LTjZKy6p5YAk8d0hasuXX3BQE/sEwdJWsBXN6Hhpj2zDzKyH1uY9vgu4fiFNqYl8xSK/s9XrxPEzoK7Wkt//gmDJ0TgRXlzmp0UnWMDMfE4fGb0/JOuKtKnKf2zhZ1mm3T4Kndsq64sFlskb57UkYe6M4cA6tEUfJyAhxll84LfewG57MXSDCw0PWTmcPSjDFEc4eho8bSoQ8l8KeDlflW5Y1yrKscMuyGlqWdcCyrFsty5qUtm+qZVk1LMuKsCxrfdq2BMuyulqWVd2yrMzFC4WV8I5w7tDVamLOZt6LMnk9PpKbzPA9MGAS3DDMdjpg3d7yc2sOMturvoLjm+HGt3OWCU/HGIjoLt41JxRNc/44/P6cLBIcZfbTojh7y+jM/bjS8fYTIyH6GUm/ePaAKAH++bo8iAorx7fCj7eL0tvtk8RDFVRZbgbDtojn88AK8exlNFZP7xZhqcZ32Z9O5+ULt34ni8ifBtlUJKx8aIZ46Nrb0R8vL1RvJ4JAMRPzfq4T22BMB0lBq99XvOsPLoFm9zvusbZFSF14YLH8C2tj+5jSYdD7C1mgj+8t3vUhCyBygOxv+4w4GxbbSFPa8IM8CDu8JC2lvHzF+ZTuODuyHuKOyXfWTv4t2UAcU4vfFw9rYeb0bkmttvU3upY4uFJ+VsmdhoBN0tVWneUQssU/M+Uz3aDf5W0tH5HMiQWv5d9104mZKOIfHV4Gb3/53HzTCd6vJS288sKlBIlM1bwRykaI86tsLXE2ZnQ2pabKwvLne7JXCs+KlGQxbGp2ybo0oDBStbkI1a0afbk1y/Et4hir4aS2NFlRpobcbw+vFXHI7Ng+By7FQ/1b83dM6dyQlko6+2l5RoEYy2u+kYwiZ6TFe3hKNtaQhXDXTMnIqny9PANv+gQCstA28fF3biaTq/ALco6x7e0nJRJD10hm1JH1ouY/41FxTP94u2zrO/Zyl5D8wK+EKBy3HiZO5alDJJujWCn5PA34WYS/+n4j5YHp7z24uigw9/pSUpy/aAFfd5ASuogbZd9jG8VJYw9hbaBmV7mnOtL6btFb4sSKmQifNhYBseSkbF9SCOXjChnpNRY/3yXRjBPbnBtd3fOXeENaPyF9TcvUyNmzUaoaVIqS9N+sOHtY6h1rdM4+tdMWtbrJImN3HhWPY/fAt50lNfWbTjBpoHxh7CHmB4iZIBLx4R1y/zpvP7h5lEQNp96X98VJfnD2kNTG+BSXep6MBpRfkHg+b/8RTu/KbKwufFNSO9oOd+z6JavCLV/LAmLKYFgzVvqv7pwPexdT5eA0qXOpZFtszOl4eEp0Ysfv0hPPESwLVn8j6Snnj8Dtk6HX585NyclIYHn53mZHre7idGhyD9y3UAzcdIKri2d77Vj49+Dl7ZcSYOFb8l2v3VOcNB1eEtXW9Ob2238T0ThHWj0YI83Y447L36ywsukXiYrtWyyRsSu92gWBZcliMu6kZLEc3yIpc65o+XNghXxvs3LY5YbSYaLouDMfDdVNP0mZSsUryhb8gmSxtWu+49/v3BB3UtLeGtwmC7l758Dw3dD3W/nMT3sox4VRtmz4USIR6aU3PsVF8fZinPQpT/9c7FoAo9vIwnLLVJj/sv3X2jhZvp+FrXdqbujwkjhdZzwiNXPpnzdntqXJito9oVVaDXHG6OWVbPxJMs6qtcr6GGfi4Slpv74BUn+YdEE+TwlnpBzFmRgjRsWAyaJ2W6GBc89/reDlI9kgj66HZg/ImvTjBnIP6/2ltAnKbzw805SQ/4L7F8Mz+6X0qP2LULNz1vWixkDk7WJst3kaen4CT22XoM910Y4riHd6TTJKRkXBHy/nvuXhye1ioF4/BB5cLg7XeS/AFy0l2yIL1FDNiaDK4pHwLQEL/w8+bwajrof5r0jKbnZYljyUs/KkJl+U6EipUHmg2kO9WyQNLKv0rbnPSypJ13ftry2s0ly8NXlJ/z26Eb7pIvn+d82Cdi+KUf5ZM6ldyY2M+uG1krZQrXXuU5evxLuYGHl+JcX7ldN8FSTxp8VITYqTusbshJOqt89srB7dIL3Omj+YtxqsGh2ltmr7HEkT+uVeSZn+rgeeKYn2pxTnldaPywJ32kP2R/TjT4sz5Ldhkmb14DLxHBYWWj0KPT+yLaTRJs3Z8Pd7l7etGiPGdsdXLn+Ho+4Vx8Hc56SVwPY58l4djRKHtpYawiUf5vz3tiw48Y/00Vv4lkTzd8yV73r8Kfl+HdssC/QNk6T+5fhWx8YFsoib8Yg4UULqygPaL0jaJ6SmOn5eezh/TB7Gb1aEkeHwSaQ8VMe0k0yEgmzlZVmSmVLFzrY0tqjRWUoOnJE1k5FzR2HvYolSZXz2NB0CgRVh/qv597fb9LM8+9IzFkCeZ/X6SJrj8U1SE+4IqalSu1Yh8mrjplwt0XfYt1hS9L6/GSbcIgJot3wtRsj68ZJ6l1t2zpdUv6otJHpb1PANkPvdqR0i9rfzD3EY5kfNsC3aj5B7229P2s6Iiz8l67P6fQu25U9geTEUTm6DOU/D8s/FoWNvuymlYCleWkqmHl4pug69vpCynYKkYqQ4HOwVbvMPFqO2yV3yDM0rZWvK87jmjaJ18lF9yZzMKcK64DXJcGnzlJzjjl8kIowl6dVZkI8ydW5E5O3y79wRqX35Z6Ys1rb/DvfMznqRuPQjMWhLhUmhdekMct9LPxbjY+AU+4uS6/QSY3TzVGibQVxlzVhJC273YuZr5gZPL0nP2TlXUo/s/VLsXSzKfr4lpIavbE2pJY26Rwr613wjnuIeH2btKT60VtIk/ctcXZdqL4Hlxcj79kYxYu6a6XrhmD2LYOr9klZ7xxQon31rEuCysfrj7WKsFisl/1o9lvfx3PAkNL0/rUfWOXEuXDzHun/20CQnAS5n4+MvD4Bvb5R+bT0+zN3rdi+UHmoJsdITuNmDhbPfYFaUrCJCGqu/kTktHiwpueEdrxZJ8vCUEoHR0RKpObFV0pLyQvsXJeNh9RjJ7LiSS4my+N4xV+4H/x5I22GQwtscKPaB1D8FVbK9P+mC3CtO7Ujr3VZDfpaoCIvelvfXephEfj29RXBq+sOSZdE4U3cz53IpQcZ27qgsetN78XkXFyfask/EWI9ybpuNLNk6XdK8q7fL+7lqdpG0+D2LnB8R2DIVsKB+v8z7vItJecbMx+T91O3l3GuDRDwqNr7cZ/FKanUXg/WvdyVbxN77247f5Znd55vMRnjkQHn2rflW1DNvfFscS16+4jDbPEWMpiF/5tyb88AKmHyHGMC3T7LdD74oEN5RxHyWfAhWqjxrCgpPL4left5c7pWD51+dErrlV1GHLai03yup3l6CE+kaGunRfqXwE1xdUsuvdcrVknTj6OdgyQew8iup3e/58dVOwnQOrRFhyOjnZV2fTs3OEt1d8w28art9mRqq9lCioniEmw6R6ODEvvDDrZLrnTH0vnacGKnhnURV7ZvOYpSkp1+c3i2tKer2zl1LiYwEVRLP+pYMhuqu+fIwrNE586LTHmp1E+XfgyvsE9L5Zyb8MliixHf+evUC1b+MtB5p/gBMfwR+vV8UDTu+erUhemiNGKnFSkm9aV7S3EC8UL2/lPTtaQ/KAio1WR5SqSng5Sd/r/wWEUhOkqj80k9kQT7wJ/tSUqu3l1qFSQMk7aLT687xjoF4v30DgMvzdf7wIuec216qNocWD8tCunbP7Os/kpOkDnnZJ5LOOPDnopvidMOT0nLpr3flXpP4r9TYZaRCA4mkLx8lv0d0zdt1qzSV+9TSj0VN9uJ5MUp3zJNa9UsXwKvYZbn+Gp1FHCfuuBhx54/ITw9PkfgPKCdqr0lxoog85X/iIMrobLIsmDFUjKU6N4tAw8afpTUAQPEymVtcNBwgrbb+eEnqcvOrbs+yJJp7eC3cNjGzMVf7Jrl3/f6cRNbK1rR9nv3LRH0zMCRv40m6IFoGIfWcs7Cu2kIciTvnXv3eki+mRW2bOX4/3PiTiHiUCbe9P/IOccjMGS5ihc5sJ3Z0Y84KuV3fk+f39IdFqMQeJ+iyTyGoijiKM2KMONbCO4gj4Mp7s2+gtJSbMljqzLJT1D+2CSbeKveAO6ZKC6yiTJc30zLLTuZ/fWpGAkOkLnPyHfDXO1crM2+cDOXq5s5RnB9EPy8OibjjUNvOOkFFKSyUCZfyqrbD5Zk5/WFxSKZr6YA8T+e/Is/0FjaMUS+fNCExNVSdy3VtxQv20yCYfKd4PdMf7FumibJWeCeJgsXuEcNrXHcxNEJbS3qQp0/eoiH1bpGH/Ylt4t04thl+ulu8xH2/zVtft+rtZXzbZufeUN39p/w9KjYWgyGrSHPp62DQNIkILx8lUZO+38qC5eBqSZkqXlpShktWcfw9XEndXnDqBVj4RprH38aYOr0uHvfcejbPHZVFdv1+Of+tT+2SRcrRGKlT7PJmrvrUZiK8Awz4SdJ+mw6x//VFhfYvSp3Z9KHS4sCWQX5qZ9rfdEPe/qaFhcDyMqfLPpUoTL2+WRvd0c9JRMovyLGsiYy0ew7GtJeyhri0tPySVSVKVLOL3AMyZn0EVc65/UOPD8Qh9dc70D5DGvmyTyTK1OGly5EWy5IFbewecTxkvId4eMg5v2wtxmqvzxx/z9mx+H1JIW0/wnbE0cNDIv9ftISp/8scrUlNkfEtHyWtg9o8LQ9iR8VKlnwohnzvr5zTr9PTWyKzO/+4nNKd3uYrIVZKLdo9Z/95T+2Ue1yXN7O5tpfoB4xuB3NfdO4cxvwgz616fbI+xj8Yur0nPTeXj5Jyg9xwaC0cWCbP7KzmwKd41umA9fqkObBfFcPElpPl9G4Yf4s4vgdNE6dPUad4aSmfipmQfau1/KJ2T3GOLPlAnGxVm8n95dBqKatwFZ5e4sC7lJC/PXgVpSAoFSp20IQ+4pz29pdoKUg50L7FUopoq/QpB4pQblwhpHZPKU7evUAWY6kpkoI4dQhUbiqyz57eogw4eJ4sRCf0kYjn7j/Fu5eXaGGdmwEjKSznjkp01zdQDBkHPgxX4Rso9R3bZ+eulujsIflwlq0lEeacauY8vWWx0PMTSZca017UyMb3lrTHu39znpGaTtvh8PAqKUh/YAk8tEKKzPv/KPL/kwfCdz1zbp+QmiJpDqOul/6YOYlk7JoPX90A/+6H2yZI3U5eDKrr2or8fwH1sHIJ3sVkcXP+KPz+/OXtliXzM/9VEdj594BEvPL6Ny0stHpcFqmpyZkNuyvxDYB75ogjzBlUaiIqncHhkuHw0ApRBOw+UsRPHP2sNewvxu7f74lTJ51dC8TDWufmq/v2GSOL86rNs76HlKstNX8xEyRi6Wz+mSlR+vr9sk9VLFFBDK6jGyRTIp3Es/DDbWIENblHBE3mvywpiI4o7cbulWh3/X6Z2x/lhRpd5Pv1RStRhFw1RtLMqzSTNHBHlKA3/QyYnFupVGgoKe4xE+Sz4AySk0TEKaJbzs+fur0l9Xfhm1cL/FmWPMv2LJLIZsKZy8+/5Z+CbxA0vtOx8Rkjz7ykOFjw6tX7LEs+y9/3ku/+oGnZ6xYUNWp0FMGpnFKe84uub0sk/Nf7JGMkXYyuXtZ1cQWCh2fuOzIoSmHHx1/Eu0LqwU93yto+NVWe9SWryfPQAdSNk1ca3yke6D9ekgfMrgXS83DApKsXzkGV4d65MLGf5GJXiJTmvXkhsLxEOjb9DDvmyALpnjmSMuQManWTyPDJbbbrfdJJThL5/eSLYpzbc+NtcpcY8pPvFKOv9HUSSc2qpi2vlI3IvK1MDfG0rh0r0tlftRXFyFrdZcF8pVf78Dr5mxyNgeodZN/yUbLwsuVJP7wWJqc13x4wOf/elztSuYkYbks+gPL1pbXB1uli8BtPKeTvPtJ5n/fCgH+wOCGS4uS7kB3OduR0ey/nYxw976HVor79wBJ5b7/cK06tmz93rDar7XCpz581TFoDOauG79gmqR+v1ETmIaex1eouD9+ln8j9IKiy1JHH7pY00PQUz13zYc6z4kwM7yROgarNcpe6P/d58PASpUVnUqOzRHutVImANrhNyjP2LYVx3USNNLsU1YxYljyLwtrkzgHb9hlxCsx8XLIm8rpg3zlP1Hht1UdlxBjpkfhZU/lcXhctDoejMXKOK/H2l/v26V2i9JsXJ3C52qIcuvwzefaVqytZBSu/FHHE4sGS7m7rOaU4jm+gCBiN7Qq/Pyspt9VaO/8eqijXOn5BUrIwrhv82F80HI5vknpxB8tJ1FB1Bq0ek4fb0o/TajOn2q67KV5aJKWXfCRGjTO8i3V7SYTWeEgrDmfW59XsCjwhAlLZGarzX4ZDq6SfVE5tOmxRtbm07Fg1RvpcusLw8PSStMv6/STtb9VoqdEFEcOq2lzma31af75+46ROKTVZWorMeETee8VGl895erfUGvmn1drltU7tWh6LvKwAACAASURBVCT6WYlC/f6MRL2vayuKcflZn+hq6uUQjSpq+PjL92VMe8m6iE+Tsu8/0XHjxMdf6t1/7C+Rdf+yshj18Rfjq8FtYgjmlti9kpa57jt50Pb/IfdR5C5vSKuCqUPEWWcM3DntahGs8I6iQr1qtKRB7/oDMOKAqdZS/lXvkPnvsXO+ZLV0fNX598WAstKz29PnaoO8Wku5jy3/DBrfnTtRsmOb5TkUu0cM0NyQ3kLs2xslgt31HYfexn/E/CC10dVz2cYsMERS0X69T4zEsrXlmVcxUu7lCWekzdu5w5J2HVQZmj+ctzGC/H02/SzO3aR4uHBKrt3jI/ncukNmSGGkavPLjk+43F5IURTn4h8sz8CxXaWUKaRenrIX1FB1Fh1fFe9oaKvs5dd9/LNP6bOXOr3FwGv+4OV8cGdRooLUm26fLcaBDcqeWApbPxcvcV4W2EGVodOrOR+X3xQrKcqi7V8UD/uBFXBwpdRyJcSKId3uBWnCDBLJufU7UWGddAfct0gWgOePSxozlniX1Eh1DC9fyU44tFrqpp0pvKIUHCF1RQV11uPiVBv4S84R45yI6Cr1rfuWyII//pREay+clt6Jt03I/p6YkixiQmu+lUwY4yHnbD/CvhYaPv6ifjimgyhC3j7Jdt2wlw+0HCpRysNrJNVz/1JY+51E1NLTSpveJ72yky+KBkFwODS3LTKRZ2zVzBojqdVTBsvfJzuxrsRzkoWy8iu5d940Soyt3FK1uTgIV34l6biOtuiIPyVjbf6gffV+DW8TMbHACgWnBu9XQr4LU/4nWSHN7pcotKq+5j/Rz0mp1ol/0kqnFEXJF0pUkDLAmY+Jcy4PXRjUUHUWxshDr6DxD5a+TvlFrW7w5/9JP8GMi7dTu4jY/ilUihIhInfCy1cWMFWayu+WJTVbtrzd/mVkUfztjSIm1X+iKELHn5Q05qzUL5XcUbKqe9VrXas0uVsULktWE1EwZ3DDk5nrSC/Ewvheoo7d91uoc1Pm1+3+U9KGz+wVIyX6WWl342jUskJDeGSNRPRyioj5FBfDJKyN/J6cJM6wNd/Aii9gxedSZ+lfVlKIB07Jf0XyjNTpJXVFy0ZlbahuniL143HHZW47vORYP98OL0s/4BmPSG8+RwzG9N6pDXOR9psRZ4iR2Uu9W6RGtqDn9VrHy0f6Nv67X52eipLflKom9fZ5RMWUlOyJ6CY/t8+5evvp3fDTICzjJWl97v7ANSb7BWjFSEljO7AMPm0Cx7dIvW7lJgU3RkUpzBgjBmHk7fl7neKlYdAMSV/9+W5peZPOhVjptzu+t6Ty3zoeHt8s48pram2pUMfSNr18JE243zh4fKOkJ+5fJjXzEd0da1+WVzy9JEtm/xKpy8/I3yOlzjiwPAxZIGJmjhipIOnOPT6SXroxExw7R8xEme+C7vucF9z9mVlYCQy57IBWFKXQo4aqkj3l6kgEZPts+f3UThEbGRUFsbvZWudJFSRIp35fqVdOiBUhlhqdXD0iRbk2KVZS+jhXaym1o2u/E4N11PUSfbvhKXhgqURbC1NriKDK0PFlGLYV+n0nzi9X0XiQ9FpdnmEMSz5MU0W+FYb8KcJTeSW8g5TOrHfAUD20RkSwHImmKoqiKIUaNVSV7DFGlC33/CUe9FHXwz8zpGbqsY2cKd0o53NcS3R8FZ7cDo0GunokinJt4xsg/ZzDO8DMR6XfaalqcP/f0hqsoGoSHcG7mAjlORqldAZ+JcRY3TJNBONA1I3nvyLCGL2/dF67EWOkPvfIehFmyi2WBX+8LI3kG/Z3zlgURVGUQoNDhqox5jpjzFpjzC5jTCZlIGPMLcaYTcaYrcaYd67Yvt8YE5P2zwnyeUqBUKs7pFyE7b9LxPCxjaJ0qQJBmTHGPiEWRVHyD+9iouDb/CFReB38hwg7Kbmj2QPyc+WXogL8xwjpkdr7K+f3xKx/qyh7x0zM/Wt2/C7pydHPXha4UxRFUdwGR3OeXgTeAKYDy4wxv1qWtfWK/WeAtsBZYLkxpq5lWVuAOMuyIvM0YqXgqdZKVDorNnbfliCKorgnXr5w41uuHkXRpGQVEf5ZNRpSkkQp9ZYx+ZMu7R8s4n0bJklmSk41nCnJ0r88OFzEnBRFURS3w9HU37bAbMuyUoBZab//h2VZCy3Lik3bfxAoZYwpDsTnabSKazBG6i3VSFUURbm2aDFUFHVr94Q+3+RvTW+jO6XGf8ecnI9d/70IMHV8VdqEKYqiKG6Ho4aqv2VZiWn/PwHYzHU0xpQCGgJrgeJAqDFmvTFmsTFG5VAVRVEUpTBTMRIejRFxp/w2CKu3h8CKsG589sddPA8L34KqLaQ0RVEURXFLjGVZ2R9gzP+A/2XYXM2yrApp++8HKlmW9ZKN104C/rAs65u0341lWZYxph3wqWVZ9Wy85j7gPoCQkJAmkyZNcuBtKQVFXFwcAQEBrh6Gkg/o3Lo3Or/uTVGd37A9E6h6YAormo/hol8Zm8eE7v2R0P2TWNv4Xc6XiCjgEbqeojq3Su7Q+XVvdH5t065du7WWZUVl3J6joWoLY8wOoIFlWYnGmBHAGcuyRmU45kmgjmVZg7M4x1Ggclp6sE2ioqKsNWvW2D0+peBYtGgR0dHRrh6Gkg/o3Lo3Or/uTZGd39g98EkjaD8C2jyVef/5Y7K/ZhfpPXsNUmTnVskVOr/ujc6vbYwxNg1VR1N//wC6G2M8gZ7AfGNMdWPMlLSLdQa6Aw9cMYBSxhi/tP83BI5mZ6QqiqIoinKNUfo6CL1BeqracqQvfBNSLkGHlwt+bIqiKEqB4qih+grwNLATmGFZ1jYgAKiZtv9ToBqwOq0VzbNApbTf1wOfkzmdWFEURVGUa51Gd8CZvbB/2eVtiWfhzzdg/XhoOgRKh7lufIqiKEqB4JB8n2VZJ4HmGbZtAOqn/T+ropH6jlxPURRFUZRrhNo3wW9PiVFasRGsHgNLPoSEM1C3t/RNVRRFUdyefNSZVxRFURRFsROf4lC/D2yYDLsXQtwxCO8EHUZAhYauHp2iKIpSQKihqiiKoihK4aLJPdKmplQo9BsL1Vq6ekSKoihKAaOGqqIoiqIohYuKkfD0LihWCoxx9WgURVEUF6CGqqIoiqIohY/ipV09AkVRFMWFONRHtaAwxpwE9rt6HHYSBJx19SAKkDLAKVcPooDQuXVvdH7dG51f9+Zaml+dW/dG59e90fm1TTXLsspm3FioDdWiiDFmtGVZ97l6HAWFMWaNrQa97ojOrXuj8+ve6Py6N9fS/Orcujc6v+6Nzq99ONpHVcmama4egJJv6Ny6Nzq/7o3Or3uj8+u+6Ny6Nzq/7k2e5lcNVSdjWZZ+4dwUnVv3RufXvdH5dW90ft0XnVv3RufXvcnr/KqhquSV0a4egJJv6Ny6Nzq/7o3Or/uic+ve6Py6Nzq/dqA1qoqiKIqiKIqiKEqhQiOqiqIoiqIoiqIoSqFCDVVFURRFURRFURSlUKGGqqIoiqIoiqIoilKoUENVURRFURRFURRFKVSooaooiqIoiqIoiqIUKrxcPYDsKFOmjBUaGurqYSjZEB8fj7+/v6uHoeQDOrfujc6ve6Pz677o3Lo3Or/ujc6vbdauXXvKsqyyGbcXakM1NDSUNWvWuHoYSjYsWrSI6OhoVw9DyQd0bt0bnd+iT0pqCiPXjKRXeC8iSkdctU/n133RuXVvdH7dG51f2xhj9tvarqm/iqIoilIEWXpkKRP+mcC4LeNcPRRFURRFyZHj8cdJtVJzfbzDhqoxZoQxZpcxZrUxJvSK7UHGmB+NMdvS9tVN2z7EGHPQGBOT9s/T0WsriqIoyrXOrzt/BWDhwYVcTLno4tEoiqIoStasOLqCTr90YtiiYbl+ZjlkqBpjqgG9gQjgNeCdK3YnAeMty6qVtu+VtO3BwAuWZUWm/Utx5NqKoiiKcq1zKuEUiw4uok5wHeIvxbP08FJXD0lRFEVRbHI+6Twjlo6glF8pFhxYwEPzHyIuKS7H1zkaUW0HzEkzNucALdJ3WJaVYFnW7LRf9wGl0v5fGjjl4PUURVEURUlj1u5ZJFvJvN7qdYJ8g5i3f56rh6QoiqIoNnl71ducvHCSUe1H8dYNb7Hu+DoGzxtMbGJstq8zlmXZfTFjzHDgkmVZH6b9fhiomjFKaox5DThvWdZ7xpgPgU6AAWYDwy0bFzfG3AfcBxASEtJk0qRJdo9PKTji4uIICAhw9TCUfEDn1r3R+S26WJbF/x35P/w9/RlWfhg/nP6BdfHreLPym/h4+AA6v+7K3ot7mXxyMv3L9ifUN9TVw1HyAf3uujfX4vxuuLCBr09+zY1BN9K9ZHcANl/YzLenvqWUZykeDnmYPp36rLUsKyrjax1V/c1oYJqMBxhjGgG9gGZpm4ZZlmUZY4oBU4B+wE+ZTmxZo4HRAFFRUZYqYxVuVL3MfdG5dW90fosu60+s58SBE7zW9DWia0Tjc9iH5fOX4xnuSXTVaEDn112ZsWgGh1MO88WpL/i0/ac0rdDU1UO6pkm1UrmYcpFiXsWcdk797ro319r8nk44zcszXqZ26dq82e1NvD29AYgmmpbHWzJ0wVA+P/N5lq93NPX3KFAGwBjjBVhXRlONMWWAccDtlmUlkHZA2s8EYCZQx8FrK4qiKMo1y9SdUynuVZwuoV0AuL7C9ZT0Lcm8fYUr/fdiykXeXvU2x+OPu3oobsG/if+y8OBCoopHUTGgIg/Of5BFBxe5eljXNM/+/Szdp3bXz7ii2MCyLF5d/ipxSXG82fqykZpO45DGjL1xLNll9zpqqM4HbkxT7u0GLDbGvGeM6ZxmuE4CnrMsa0v6C4wxFdJ+egDtgRgHr60oiqIo1yRxSXHM3TeXrmFdKe5dHABvD286VO3AooOLSExOdPEIL7Pq6Com/jOR6bunu3ooLiU5Ndkp55m9dzbJqcl0COrA2C5jqVmqJk8sfILZe2bn/OI8ciz+GLP3zM52QXmtseTwEubsm8PJhJMM/3s4l1IvuXpIilKomLF7BgsPLuTRxo8SXirc5jERpSOYdcusLM/hkKFqWdYxYCywHXgdGA5UQwST+iHiSm9e0YqmJPCCMWYzsB44AFyzT649/+5x2oNLUVzJ7n9388uOX3TxoigFxO/7fichOYHeNXpftb1zaGcuJF8oVOq/a4+vBcRgLUgSkhP4etPXxF+KL9Dr2mLxocW0+KEFe87uyfO5pu+eTq3StajsU5mSfiX5usvXRJaL5P/ZO/O4mrP/jz9ve0qULRKVVJRIyL5vNcJXyL4zw9gNMxjGOvadMca+jt1YkwghUohEad8XJe37/fz+aPTTtN97CzOej8c8ZuZzzuecc+/tfu55v8/7/Xr/dO8nTvsWyqSSGQkZCUy6MYkf7/3I87fPK2yeL4ms3CzWuK2hoUZDVnZYydPYp2x7su1TL+srX/lsSM5KZu3jtVjWsWRUk1El9i0pdF7iOqqCIOwUBMFQEITmgiCECoIwVBCEk4Ig/CkIgtpHZWhaCILwXhCE6YIgmP3df15RQkr/BW6F3GLAxQGsd1//qZfyla9IRUhSCBMcJ7D84XIcghw+9XK+8pX/BBf8LmBY3RDzmuYFrrfRbkN15eo4hjh+opUV5oOh+iz2WaXWeb3gd4FtT7dx5NWRSpuzKARB4Pfnv5ORm8Ff/n9JNdabhDe8in/FgEYD8q+pKaqxu+duOtfvzMpHK6WeoyjSc9KZ7jyd6NRoVBVUK9Qg/pI45H2I0ORQFrZZyEDDgQwzHsbhV4e5GXLzUy/tK1+RmOjUaHLFsqke+jj6MSnZKUxvMR15OXmJx5HYUP038Dr+NclZyZU2X2BiIIsfLEZRTpEzvmcISgyqtLk/FbFpsSy4u4A3CW8+9VK+IkNi02L51ulbBEHAWNOYtY/Xliox/pWvfEU6/BL8eBH3gv8Z/g+RqKCGoYKcAj0b9vxswn/Tc9J5Gf8Sw+qGZImzeB5bOSdxgiDkG1N/vv6T9Jz0Spm3KDxiPHgR9wI1RTWuBl6VagN40f8iCnIKfGPwTYHrKgoqbOm6hbZ127LMdRn3I+5Lu+x8csQ5LHBZgNdbL9Z1WseARgNwDHbkfcZ7mc3xJRKREsHeF3vp1bAXHXQ6ADC/9Xya1WzGkgdLCEkK+cQr/MqXTsD7AALeB8jMaCwL4cnh2Jy3YanrUpmM5xblhqqCKs1rNZdqnP+soRqREsGIqyPY9lQ2oRpiQcxvnr/hGulaZHtqdiqzb89GWV6ZozZHUZJXYuuTrTKZ+3Nm7eO1OAQ78P2t73mb9vZTL+crMiApK4nvbn5HQkYCu3vuZm2ntSRnJ7PWbe2nXtpXvvKv5rzfeRTkFLBtZFtkex+9PqTnpMvUWJEUr7de5IhzmNxsMnIiOR5HV07475OYJwQkBjCg0QASMhO46P/psoz2v9yPlooWi6wWEZsWi3uMu0TjZIuzuRJ4hS71u6CpolmoXVFekS1dt9BYszFz78zFO967iFHKhyAIrHFbw52wO/zU5id6NOzBEOMhZImz/vM5x+sfr0ckEjG/1fz8a0rySmzqsgkFOQXm3JnzSR0kX/myCU8OZ8jlIQy8OJB2f7ZjrMNY1j1ex+WAy2TnVlwe9Ok3p8kWZ3Mp4BLXg69LPd6jqEdY1rEsJKBUXv6zhuoR7yPkCDk4BjvKJAHeI9qD3c93863Tt2x9srXAmIIg5HvZNnTegGkNUyaYTcA5zDk/NOrfiEu4C04hTvRv1J/EzERmOs/8+vD+wsnIyWDGrRkEJwazrfs2TGuaYqhpyLfm3+IQ7MDt0Nufeolf+cq/kszcTK4EXqGbbrcijRWAVnVaoaWi9Vmo/z6JeYIIEZ3qd8K0hmmlGaqnfU9TVbEqi9suxryWOYe9D1fqqcQHfN/58iDiAaOajKJ3w96oK6pzOeCyRGM9iHjAu4x3BcJ+/4m6kjq/9fgNTWVNpt2cRlhymKRLB2Cf1z5OvznNBLMJjGgyAgAjTSMsaltw5s0ZxIJYqvG/VFzCXXAOc2aK+RTqqtct0FZXvS5rO63FP8GfRfcW/edPnr8iGbs8dyEnkmNJ2yX8z/B/iAUxZ9+cZdH9RWx+srlC5szMzeSC3wW66nbFvKY5Kx6uIDo1WuLxYlJjCEoMom3dtlKv7T9pqCZkJHDe7zy6VXV5n/meR5GPpB7zvP95qipWZVDjQex/uZ/x18cTlRIFwEHvgziFODHXcm5+zbMxpmOorVqbTR6b/pVCNOk56fzq9iv61fT5pd0vrOu0Du94bxbfXyz1D1xadpqMVvmV8pAjzmH+3fk8i33Gmk5rCjyAJjabiJGmESsfrSQpK6nIe1OyUipzuV/5yr+K60HXeZ/5niFGQ4rtoyCnkKf+G36HLHFWJa6uME9inmCiZUJVpaq00W6D11uvCn92x6fH4xTqRH/D/qgqqDLedDzhKeHcDK38vMEDLw9QRaEKQ42HoqKgQm+93jiFOEn0Hlz0v4iWihYd63cssV+tKrXY3Ws3uUIuU29OLXc6RmxaLOfenGOm80y2P9vONwbfMKvlrAJ9hhgNISQppNIcD58TH8ot6WnoMbbp2CL7dNDpwFzLuTiHOWNzwYYj3kcq9BTsK/8ufN/5cjXwKiObjGSo8VAWWi3kqM1RHo54SO+GvbkUcKlC8v0dgx15n/meUU1GsabTGnLEOVLt192i3QC+GqqSctLnJBm5GWzuupmqSlW5FiSdtHtSVhI3Q25iY2DD8vbLWd95Pf7v/Rl8eTC/ef7Gtqfb6KPXhzFNx+Tfo6qgynSL6XjFeeEYXDniF6/iX1VaHuHeF3uJSIlgSdslKMkr0a1BN+a1modTiBM7nu2QaMzU7FSWuS6j7Ym27Hm+519p4H+OvE17yz6vfQz4awB3wu+w2Gpxfv3GDyjKKbKiwwreZbxjk8em/OtJWUkcfHmQvuf6Yn3eukgj9itf+ZxxCHIg8L30iq3SIAgCJ3xOYFDNoNQf/g/hv6/SX1XS6gqTnZvN87fPsaxjCUCbum3IEXJ4Gvu0Que94H+BHHEOQ42GAtBNtxsNNRpy4OWBSv29CE8O53rwdYYYDaGacjUA+hn0Iz0nHecw53KNlZCRwJ3wO/Qz6IeiXOkhdAbVDNjZfSfRqdFMvzW9VMM4MTORXZ67GHp5KD3O9GDZw2X4vPNhbNOxrGy/EjlRwW1ib73eVFeu/p8UVdrntY+w5DAWWS0qMZxxnNk4ztmew7yWORs8NjDg4gBuhtz8umf5AglLDivVyS4IAnue78E5tHzf7aLY+nQr6krqTDCbUOC6gpwCdkZ2JGUlVUjk2imfU+hX06eNdhsaaDRgYZuFPI5+zGHvwxKN5xblhqayJo01G0u9tv+coZqWncYJnxN0rd8VEy0TejXshXOos1QhqdcCr5GZm5lfLsBa35rT/U6jo67D7ue70dfQZ0X7FYXEL/o36o+RphFbn24lK7divd+Poh5hf8Werqe6Yn/Fnu1Pt+MR7VEo7DlHnCO19y/wfSAHvQ/Sv1F/Wmu3zr8+pukYBhsNZp/XvnKrE3pEe2B3yY4L/hdoVrMZOz13svLRykor8+OX4MfVwKufhUhJZSAWxLxMe8lM55n0OtuLbU+3UbtKbTZ33Yy9iX2R95jWMGWs6VjO+53nL/+/WPd4Hb3O9GLzk81oq2nzPvM9Z9+creRXIhlZuVlsdN/I4vuLv6hSUmJB/K+t5ecZ60l4cnilzun7zpcFLguY7DSZ+PT4Sp37Y56/fc6r+FeMMBlR6Hfkn7Sq04oaKjV4mlaxRmFJeMd7k5GbkW+oWtS2QEFOoUJP4XLFuZx9c5bW2q0xqG4AgLycPGNNx/Iq/hXu0ZLlh0rCYe/DyInkGN10dP41yzqW1FOrx5WA4usFFsWH2qn9G/Uv8z0tardgXee8KKYFLguKfYalZKXwrdO3/PHiD1QUVJjVchbn+p/D0c6RH1r/UKQxpiyvzEDDgdwOvV0puhPZ4mypQhBlhWesJ3tf7KWfQT/a1WtXan9DTUN+7/k7u3vuRllemTl35jDv7ryvxuoXRHpOOvZX7BlzfUyJDp+jr46y03Mns27PYp/XPok/Y/dod+5H3Gdys8n5Dq6PsdK2oq5aXZmre3vHe/Mi7gX2xvb5vy8DDQfSs0FPtj/bzuv41+UaTxAEHkU+wqquVSFHlyT85wzVC/4XeJ/5ngnN8rwV1vrWpOWk4RLuIvGY5/3OY6xpTFOtpvnXGmg04JjNMRZbLea3nr/lF2b/GHk5eeZZziMiJYI/ff6UeP7SEAtiNnlsQkddh+kW01GRV+HAywOMdxxP+xPtaf9ne1ofa03zI82xOGpBuz/bSeyxEQSBlY9WUkWhCnMt5xZoE4lELLJaRNu6bVn+cDk7n+0s9YQtMzeTDe4bmOA4ATmRHIf6HuKYzTEmmk3kzJszFSpaEJESwT6vfQy6NIhBlwbx072fGHplKF5vvSpkvs+JXZ672PN2Dy/evmCs6Viu/O8KB/sepFfDXiXeN7X5VPQ09FjyYAknfU7SvUF3TvU7xTGbY7St25Zjr45VuFNGWsKSwxjtMJrDrw5zKeCSxBEAn4Jpt6ZhddyKoZeH8ovrL5z0OYlnrOdn/56XRlp2GlOcpjDr9qxKzTfc/3I/qgqqJGYm8qPLj5/MaXH89XGqKlYtVkTpY+Tl5Omt15uX6S8/WR3RD9oLLeu0BPIiiMxrmldoPVXXSFciUiIYajy0wPX+jfqjpaLFQe+DFTb3x8Snx3PB/wK2BrbUUauTf11OJMc3Bt/wMOphuQy8i/4XaaLVBGMt43Kto0eDHiy2Wszd8LuserSq0OY5MzeTWbdn4fvOlx3dd3DE+giTmk3CSNOoVGfIYKPB5Ag5XPC/UK41ScJy1+VYn7cu92ZZliRnJfOjy49oq2mz2Gpxue7tqNORM7ZnmGg2EacQJzxiPCpolV+RNbdCb5GclYxfgh9LXZcWaYB6xnqy5ckWuul2w0bfhm1Pt7HkwZJyH/gIgsDWJ1upU6UOw02GF9lHXk6e/o364xrpKlPnzWnf06gqqBZwholEIn5p9wtaylr8dO+ncu2zg5KCiE2PxaqulUzW958yVHPEORzxPkKLWi2wqG0BQOs6rampWlPiOpA+73x4/e41/2tcuFyAkrwSw0yGUU+9XrH3t9dpT4d6HdjzYg+JmYkSraE0rgReweedDzMtZjLFfAqHrQ9zb9g9tnbdymCjwdga2DLMZBgTzSbyfYvv0VHXYe3jtRKdHl4OvIxHjAdzLOdQQ7VGoXZFOUU2dd1Ed93u7Hmxh77n+vLHiz8KbKjEgpjX8a858PIAQy4P4cirIww1HspZ27NY1LZAJBIx23I2i6wWcTfsLpNuTCIhI0Gq9+hjQpNCGeswlr7n+rLt6TbUFNRYZLWIrd22kp6TziiHUWx/uv1fm3cSmxbLEe8jWFSxwGmIE3Ms59BQo2GZ7lVRUGFz183MsJiBg50DazqtoWmNPAfOeNPxvE1/y9XAqxW5fKm4FXIL+8v2hCWHsa3bNoYYDeHAywNfRG2852+f8yDiAW3rtqW6cnWcQ51Z7baa0Q6jsbtkR2Dipw1flYZbobdIz0nnTcKbSlMcDUsOwzHYEXtje35u+zNu0W7s8txVIXMlZSUV64WPSY3hZshN/tf4f0U6PIvCWt+abCGbO2F3ZLjKsvMk5gkG1QzQUtHKv2ZV14rX715X2O/cad/T1FCpQQ/dHgWuK8srM7LJSO5H3Mf3nW+FzP0xJ3xOkJWbxTizcYXabBvZIhbEZU43coty4/W71ww0HCjRWoYaD2Vys8mc8zvH7y9+z7/+QW/gcfRjVnZcSef6ncs1bkONhrSt25azb85WqOPIO96biwEXyRHn8NO9nz5JRJMgCKx8uJKYtBjWdV6HupJ6ucdQkFPg5H7UKAAAIABJREFUu+bfoaWixd4XeytglV+pCC75X0JHXYc5lnNwDHbkwMsDBdoTMhL44e4PaKtps6rjKtZ2Wsu05tO4GHCRKU5TyvWsuxV6ixdxL5jWYhoqCirF9htgOAABgUsBlyR+XR+TmJnItcBrfGPwDVWVqhZoq65SnVUdVxGYGMiJ1yfKPOYH3Z+vhqoEOAY7EpkaWSD2W15Onr56fXEJdynydC8zN7PE2mQX/C6gJKdEP4N+Eq9rbqu5pGansv3pdonHKI6MnAx2PNuBaQ1T+ur3zb9eVakqPRr24Mc2P7LQaiHzWs1jZsuZfNf8OxZbLSYyNbJcxdLTc9K5GniVje4baV6rOYMaDyq2r4aSBpu6buKM7Rksa1uy49kOrM9Zs/3pdn50+ZFup7sx9MpQtjzZgqKcIr/3/J2f2/5caJM23GQ4W7puwSfeh9EOo2XiYUrLTmPW7VkEJAYwq+UsHAY5cNTmKMNNhtOjQQ/O9z9P/0b92eu1l2FXh1XKxqey2fN8DzniHGyr25YpJ+qfNNZszBTzKWiraRe43q5eO4w1jTnsffizU4zMzs1mvft6Zt+ZTQONBpzud5ruDbrzU5ufaFazGT8/+Pmzr3t8wOsAGkoabOyykT96/4GLvQtOg51Y33k9SVlJjLw6krthdz/1MiXicsBldNR1aF6rOTue7SjxpDAtO00mJ5+HvQ8jL5JndNPRDDQcmJ+2IOv8oKiUKHqd6cVP934q0lg9/eY0uUIuw0yGlXnM5rWaoymvyfUg6UsMlJdccS7PYp/lh/1+oI12G8SCuEKU7qNSonCJcGFQ40FFhqvaG9ujqqAqcb5VWUnNTs2PJDGoZlCoXb+aPmY1zLgSWHL4b444hz3P9/Ct07fUU6tXqHZqeZhhMYP+jfrzm+dvXPC7gFgQs8x1GbfDbrOwzUKJ9y5DjYcSlRrFg8gHEq+tJARBYKP7RjSVNdnUZROBiYFsebKlQuYqicuBl3EIdmBai2lS1YNUUVBhrOlYHkY9/E9EZX3pRKdG8yjqEbaNbBlvOh5rPWu2Pd2WbwuIBTEL7y/M0+XougkNJQ1EIhFTW0xlTac1PH/7nJHXRnI9+Do+73xKDB3OEeew7ek29Kvplxrir1tVl9barbnof1EmYeQX/S+SkZvBMOOif1/a1WtHi1otuBpU9gMGtyg3dNR10K2qK/X64D9kqAqCwMGXBzGoZkAX3S4F2qz1rckWZ3Mr5Fah+zZ5bOKc3znm3pmLzzufAm0fygX0aNCjyHjysmKkacQIkxGcfnOapzGyzSs69voY0anRzGs1r8yx4m3qtqFHgx7s89pHbFpssf3Eghj/DH+WPlhKt9Pd+OneT1RRrMKydsvKNJeJlgk7euzghM0JmtRowl6vvbhFudG+XntWd1zNrSG3ONf/XH5B7aLo0bAHe3vvJT49nsk3pMsjEwSBFY9WEPA+gPWd1zOp2STqV61foE9Vpaqs7LCSnd138i7jHcOvDsc/wV/iOT83QpNCOe93HjsjO2op1pLp2CKRiHFm4whIDOBe+D2Zji0tyx4u4+iro4wwGcER6yP5n7uSvBKbu25GSU6JObfnfLaK0wHvA3AOc2ZEkxH5Dh2RSIS2mjbW+tac6ncK3aq6zHCewd4Xe7+oPKmY1BgeRT2in0E/5reeT1x6XCHP9gf8Evzoeban1BL+celxXPC7QP9G/aldpTYAP7X5iaY1mrL4/mJCk0KlGv9jdnruJC0njWtB1/jjxR8F2rJyszj75ixd6ncp14++nEiOlmotuR95v8JOMIvDN8GXlOyUQoaqeS1zVORVKiRX9MybMwiCwGCjwUW2V1Ouhl1jOxyCHPBL8JP5/PC3k9N5FslZyUw0m1hsv36N+uHzzqfYdYQnhzPBcQI7PXfSR68PZ/qfkWp/IRKJWNZ+GR3qdWD5w+XMcp7FxYCLTGsxLb/sjCR01e1KTdWaFSaqdDvsNh4xHkxrMY3eer0Z1WQUJ3xOVGqN4NCkUFY/Wk2rOq1K/EzLir2xPRpKGvzh9Ufpnb/ySbkSeAUBgf4G/RGJRCzvsBwjTSMWuCwgNCmUfV77eBDxIP934WP6GfRjf5/9JGclM//ufIZcHoLVCSu6n+7OWIexHI87zj6vfTiFOOH7zpczb84QnBTMrJazUJBTKHVtAw0HEpocKrU4nVgQc/rNaSxqW5SYWmBjYINfgh9vEt6UOmauOBf3aHeZqP1+4D9jqLpGuuKb4Ms403GFjKhmNZtRX71+ofDfmyE3+dPnTwYaDkRDSYMZzjOIS4/Lb3cOdSYpKylfREkaZljMoJ5aPZY9XCazfLJ3Ge/Y77WfrvW7FhA1KgvzLOeRI84p9pTXO96bfhf6sS1mG47BjvRq2IsDfQ5wbdA1DDUNyzVXs1rN2NNrD3ft7+I81Jk1ndYU2CCWRss6LdnZI0/l8Lub30msLHvK9xRXA6/yfYvvaV+vfYl9u+h24YztGVTkVdj2dJtE832O7PTciaK8It+af1sh4/fR64O2mnal5YuVBc9YTy4FXGKi2UQWWi1ESV6pQLu2mjbru6wnKCmo2DyVT82BlwdQVVBlhEnRG09tNW0OWx/GWt+a7c+2M99lPpli2UvcVwTXgq4hIGDbyJbmtZpjrW/NYe/DhSIoolOjmXpzKslZydwIviHV53T89XGyxdmMMx2Xf01ZXpnNXTcjJycns9x433e+XA64zDjTcXxj8A07PXfiFOKU3349+HqeQ6xJ0TlLJdGySktyxDkyUaIsDx9OTP9pqCrJK9Gidov8sgWyIluczXm/83Su37nENJtxpuOorlKdCY4TZH6ilZSVxBSnKbjHuLO642qa1WpWbF9rfWsURApcDixYUzU7N5u//P9i8OXB+CX4sbbTWtZ1XoeGkobU61OUU2Rz180YaRpxJ/wOI5uM5Dvz76Qe066xHS7hLjLPPc7OzWbzk80YVDPIdz7MtpyNYXVDljxYItNUn5LWsMBlAQpyCqzptAZ5OXmpx1RTVGNUk1HcCbvzr4zG+rcgCAIX/S/SsnZLdDXyHISqCqps7bYVOZEcU5ymsMtzFzb6NsWWC7OobYGjnSNnbc+yqcsmZrWclX/o4p3uzban25h7Zy6DLw/mV7dfaV6rOd11u5dpfT0b9ERNUY0LftLliD+KfERIUgj2xkULZH6gj14f5EXyXAssPWXhVfwrkrOTvxqq5UUsiNnntY/aqrWLDKERiURY61vjFu2Wb4iGJ4ez9MFSzGqYsbTtUrZ33877jPfMvj07v4bReb/z6KjryCQOu4piFX5umxdeuM9rn9TjQV4IZ3pOOnMs55T7Xl0NXUY1HcXFgIt4x3kXaHsY+ZAJ1yeQK85lTI0x3B56m5UdVtJau7VUCl9aKloS329Zx5Kt3bbi/96faTenlfvk68XbF6xzX0fn+p2ZbD65TPfUVK3JhGYTuBN+p0LC2Sobn3c+OAQ5MKrJKGpVke1p6gcU5RQZ03QMT2Ke8OLtiwqZozyIBTHr3ddTS7UWU8ynFNuvbd22zLSYiWOwY4WHD5aXqJQorgVew66xHZoqmsX2U1VQZW2ntcy1nMuN4BsciSt7aP+n5HLgZcxrmefnSc9uORtBEAo4iJKzkpl2axop2SmMbDKSmLQY/N5LdnKWnJXMSZ+T9GzYE71qegXadNR1WNtpLX4Jfhx6eUjSl5TPtqfbUFdSZ1KzSSxvvxzzWuYsureIV/GvEASB46+PY1DNgHZ1S1cZ/Se6Sro0qNpAYv0FSXkS84T66vULhf5DXs6SX4KfTBWU74ffJz4jvsT6sgB11OpwxPoI6orqTLwxEddIV5nM/y7jHZMcJ+Ed782mLptKFbzSUtGig04HLgdcZqP7Rqbfms4357+h9fHWLHmwBBMtE871PydVuG9RVFGswp5ee9jQeQMLWi8oVTCpLEwwm4BeNT0WuCyQqQLw6TenCUkKYV6refknTMryyqzttJbEzESWuS6rcIfh/pf78Y73Zln7ZUX+LUvKiCYjUFNUk9k+7yuyxyvOi+CkYAYYDihwvX7V+mzovIGo1CgaajTkl3a/lPg9UlFQwVjLmN56vZnUbBIrO6zksPVhftX9lYfDH3Kq3yk2dN7ArJazWNVhVZm/k1UUq9BXry83Qm5IJZh30vckWipapYpkaqlo0a5eOxyCHEpN2/rgiGxTt43E6/on/3pDVRAE1ritwSPGg8nmkwudlnzARt8GsSDGMdgx35MGsKHLBhTlFWlaoymrO67m+dvnLHddTkRKBI+iHjHAcIBM5JcBOtXvhI2+DXu99kodThqSFMJp39MMajwoX6q/vExpNgUtFS3Wua/L/1G4HnSdabemoVNVh6M2R2mt3rrMAh8VTQedDmzovAGvOC9m3p5Z5qLI7zLeMffOXOpUqcOvHX8t1+c5sslIaqvWZsuTLZ/lSVt52PZ0GxpKGkWKgMgSu8Z2VFWqyiHvQzIdNzIlEpdwl3Llv14NvIpXnBezWs4q9e94gtkEejXsxaYnm6SSoJc1h1/lGc4f12kuDpFIxHiz8Uwxn4JXuhdhyWEVvTyp8H3ni1+CH7YG/7/5r6dejzGmY7gSeAXvOG+yc7OZc3sOQe+D2NJ1C+NNxwNIHCJ45s0ZUrJTmNis6FC/jjodsahtwd1w6fJ93aPduRdxL78UgbK8Mtu6baO6SnVmOM/gZujNMpekKQqRSERf/b4FHLCyIjU7lbl35hZSyxcEgScxTwqdpn6gjXbe5sU9Rnbhv44hjlRTrkZ7nZKjYCAvv+uozVEaVG3A97e+53qwdDm8sWmxjL8+nsDEQHZ030HPhj3LdJ9dYzvi0uM46XuSqNQoTLRMmNRsEpu7bmZ/7/0lngxLg6aKJn31+8psz1JFsQqbu2wmLSeN+S7zZZIbnpiZyO7nu7Gqa0UnnU4F2oy1jJnVchbOYc4yL9HxManZqRx5dYTuut1L3cSXl2rK1bA3tscx2FEq3YM3CW+4Hnwdtyg3/BL8iEuP+6JKqX3OXPS/iIq8Cr0b9i7U1q5eO45aH+VAnwNS7X3VldRpWqMpffX7MqnZpEJO0dIYaDiQ9Jx0bgTfkGj+iJQI7obfxa6xXbF20cfY6NsQmRrJ87fPS+z3KPIRRppGBYT0pOVfbagKgsBGj42c9D3JONNxJR5vG2oa0lizMQ5BDmx/th2vOC+WtV9WIEext15vprWYxuXAy3x/83tEiBjYSDI1vuJY0HoBaopqLH+4XCrBmW1Pt6Eor8i0FtMkHkNdSZ2ZFjN5FvsMxxBHjr8+zgKXBZjXNOdQ30NlDs2tTHo27MmK9itwi3Jj/t3Sfzhzxbn86PIjCRkJbOm6pdy5QKoKqkxtMZXnb59zO0z2RZgrC49oD+5H3Gdis4kyCTUriSqKVRhmPIybITdlluuXmp3K5BuT+f7W9wy7Moz7EfdLNSTTstPY+nQrpjVMy1T2QyQSsa7TOr4x+IZtT7ex5vGaSi2VUhQJGQmce5N3+lJXvW6Z7xtqPBQRogrLL5MVlwIuoSCnQF+9vgWuTzSbiJaKFuvd1/PzgzxF3hUdVtCuXjvqqNXBSNNIIkM1MzeTo6+O0q5uO0xrmBbbr3299ryKfyVxCKIgCGz22FyoFEFN1Zrs7L6T5Kxk5t2ZV+aSNMVhrWeNWBAXCCeWBU4hTjiFODHLeVYBwabAxEDeZ74v1lBtWqMpaopquEfJxlDNyMngduhtejboWWbht5qqNTnQ9wDmNc1ZcHcBp3xOSTR3XHocYx3GEp0aze6eu+mo07HM93Zr0A0Xexcej3zMuf7n2NR1E9MtptOrYS+ZhJhWJoaahixpu4QnMU/Y+Wyn1OPtfbGXpMwk5reaX6SDZnTT0bTRbsPax2tL1NCQhrNvzublGhfjrJKWMU3HoCyvzH6v/QWuByYGsuLhCva93YdDkEOh9AJBEPCI9uC7m99hd8mO+XfnM+nGJAZdGkS3091oebQlk29M/mRlqT7GIciBZa7LWPlwJWvc1rDefT2bn2zGO9679Js/IZm5mTgEO9CjYY9iFZ7Na5lTU7VmJa+sIM1rNUdPQ09ih83x18eRQ67UsN8PdG/QHRV5lRKrNmTkZPAs9plMw37hX2ColmSI7Hi2gyOvjjDcZDhzLeeW6pW20bfh+dvnHPI+hL2xPb31CntTvjP/jj56fQhIDKBdvXbl2hyWhRqqNZjfaj6ebz0l3kQefHkQpxAnxpuNl/rLNNBwICZaJix9sJS1j9fSTbcbe3rtqXBjRhoGGA5gYZuF3A67zWq31cUaLIIgsN59PY+iHvFz259pUqOJRPMNNByInoYe255u+yI9mh/CKGur1i62fpesGdFkBApyCjIJoxUEgeUPlxOeEs605tNIykpi6s2pTHCcgGesZ7H3HfQ+SGxaLD+2+bHMJwyK8or82vFXxpmO40+fP/NyPct4cl8RnPA5QUZuRgEl87JQu0ptmldpzgX/C5+k5ENZyBHncC3oGp10OlFdpXqBNnUldaZbTOdp7FOuBV1jVstZBQy6jjodeRbzjJSslHLNeSngEnHpcaVuUNvXa4+AwKOoR+Ua/wNOIU68jH+ZV9f6H6UIjLWMWddpHQCDGg+SymtvqGmIYXVDmav/OgQ55Kkw127OApcFnH1zFvj//NRWdVoVeZ+CnAKWdSx5HC2bnMYHEQ9Iy0mjj16fct2noaTBnl576FK/C6vcVnErtLCQYmnsfbGX6NRo/uj9R7k1ICDvdFNWJ5ufGttGttg1tmP/y/1SKYsHJgZywucEAw0HFivuIieSY1m7ZWSLsytEBTg7N5sjr47Qqk4rzGuZy3x8yNvn2RnZcTXwKhEpETyJecKMWzMY8NcALgVcIjgzmAUuC+h6qiuL7y/GNcKVO2F3GOMwhvGO43kd/5qZFjM5a3uWA30OsLHLRhZbLWac2Tjco90lSn+SJSlZKSxzXcb14OvcDL3JlcArnHtzjiPeR5h+a7pM8vsrijthd0jOSi5VffdTIxKJGGg4kKexTwlODC7XvSlZKZz3O08f/T4Faj2XhJqiGl11u3Ij+AbZ4qJLMz6LfUaWOEtmZWk+8EU/JQVBYMBfA5h8YzLHXh0rEMa25/ke9nrtxa6xHT+1+alMoVPW+tZAngrv/Nbzi+wjEolY2WEl9sb2zLSYKZsX8g/6N+pP27pt2fp0a7lKrgiCwPan29n8ZDN99fLCCaRFXk6eBa0XkJWbhV1jOzZ13VRijafPhRFNRjCp2STOvjlbrHDPQe+DnPA5wTjTcVIJYinIKTC75WwCEwNlVtuqMrkUcAnPt5581+I7VBVUK2XOmqo16d+oPxcDLkqtSnrB/wIOQQ5Maz6NqS2mcnngZRZZLSIoMYjRDqOZcWsGL+NeFrgnKiWKgy8P0levb35N5bIiJ5JjXqt5/NDqB5xCnPjO6TuSs5Kleg2SkJqdyonXJ+iu212i8P5OVTuRmJlY6TmMZcUtKi9ktbgNw/8M/0cHnQ5MMJtQSJGzo05HcoQc3KLKLtyTlZvFAa8DmNUwyw9RLY6mNZqioaQhUZ5jtjib7c+2Y1jdsEBI88d0a9CNy/+7zGzL2eUe/59Y61vzNPapzArEx6XH4Rblho2+Dbt77qaDTp6a7GHvw3jEeFBbtXYhtfSPaaPdhuCkYJms53rwdbRUtCQyFFUUVNjcLU9gaI3bmnKdQsWmxXL2zVn6G/aXqmTJv4mFVgtpotWERfcXEZESUa57w5PDWfFwBYMvDUZZXpnpFtNL7K+rocs403FcCbzCs9hn0iy7ENeCrhGbFltu5195GWc6DkRgf8WecdfH4fnWk6nNp+Jo58gKnRXs772fvvp9cQ515tub3zLDeQYxaTEsbLOQ63bXmWw+GWMtY1prt6aPXh+GmQxjruVc1nVex/O3z5l269MZq5cCLpGWk8b+3vu5a3+XB8Mf4DbSjX299xGXHvdZR/Jc9L9I7Sq1sdKWrbFVEdg2skVeJM8F//KJKp33O09qdiqjm44u133fGHxDQmYCDyMfFtnuFuWGgkihWEelpEhsqIpEoiUikchfJBK5i0QivX+0TRSJRH4ikeilSCSy/PualkgkuiMSiQJEItFv0i07jyxxFj0a9iA2LZZ17uuwOW/DgL8GMO/OPHZ67sTWwJal7ZaW2Wupo67D1m5b2dVjF8ryysX2U1VQ5ee2P2Nas/jQMGkQiUQsbbeU7NxsfvMs21slFsSsdlvNXq+9DDYazNpOayWqgVkUrbVbc9f+Lr+0+6VM0tmfCzMsZmCtZ82WJ1sKnShcDrjMlidbsNazlkhs6p90b9Ad81rm7PLcJVNvoVgQE5QYVCGnXoIgcODlAX5+8DMWtS0kLiovKcNNhpOZmymVce+X4McatzVY1bXKd8woyisy3GQ41wZdY6bFTJ7GPmX41eF85/RdfvmnLU/zPPHSfPZjTceyrtM6PN960u10N/qe68uoa6OYfXs2qx6t4o8Xf+RvpGJSY2ReO/b46+MkZSVJHJ5mqJx32nbS9+Rnk2/7MZcDL6OhpEHn+p2LbFeQU+D3nr8zx3JOIUdki9otUFdU515E2csg7X+5n/CUcGZYzCjVsSkvJ0/bum1xjXQt93t3/s15QpJCmGM5p8Qwz4YaDWXyvP0QNu0Y7Cj1WAA3gm+QK+Rio2+DqoIq27ttp3fD3mz02IhTiBOWdSxLfP/a1csThpK2TFJ6Tjp3w+/Ss0FPid8nRTlFlrRdQkxaTJl/ayFPZVssiGXiDP63oCyvzKYumxAEgR/u/FCm513g+0AW3VtEvwv9+Mv/LwYaDuSM7ZkypRVNajaJ2lVqs8ZNdikYYkHMwZcHMdI0KlcotyRoq2kzpukYaqjUYLHVYm4MvsG0FtOooVoDOZEcbeq2YXn75dyxv8OWrlvY2GUjVwddZUSTESU6lPvo9WFNpzU8i33GDOcZBfYjOeIcXMJdmHtnLisfriz2ZEwaxIKYP33+xLymeaE9civtVrSt25YDLw98lqXe4tLjcI10pX+j/l9ECH7tKrXpUr8LF/wulLlaSI44h+Ovj2NZx7LE9Jai6FCvAxpKGlwLKlr991HUI8xrmctct0aip7tIJGoI/A8wBmyAdYD9322qwE+ABaAP7AXaAtOBS4IgbBaJROdEIpG1IAjlduVnZ2cTHh5ORkbext26ijXWxtbkiHPIzM0kIycj7/TP3I7qytXx9SmfBHg96pGQmkACFSt/rqKiQv369VFULNqY1K2qyxDjIZz0OcmkZpNooNGg2LGyxdksfbCUK4FXGG86vsiNm7RIU8ftUyEnkmNlx5XEpMWw+P5ialepTcs6LXkY+ZClD5bSRrsNqzqukkn4lUgkYnbL2UxwnMCJ1ydkltty9NVRNnpsRIQIHXUdDKob0KhaI5rWbEqvBpLnM2WLs1n9aDXn/M7RV68vKzuslJljo6wYaxnTvFZzTvueZlSTUeX+m03LTmP+3flUUazC2k5rC70XVRSrMNl8MiOajOCU7ykOex9m7PWxmNc050XcC6aYT5FatMTGwIZ66vVwCnEiPiOeuPQ4QpJC8IjxKHRSrCCnQId6HVjTaQ1VlapKPGd4cjjr3ddzO+w2HXU6ShyeJhKJsDe2Z7XbarzivCoszE0SUrNTcQ51pp9BvzIJPfwTRTlF2tVrl5+rXNrfVnBiMHtf7MVaz7pMojyQF/57I+QGAe8DSi3JlS3OxiPaA6cQJ64FXcOyjmUhoZiKooFGA0xrmOIQ5MBY07FSj+cQ5EBjzcb5r1lRXpH1ndej9lCNC/4XaF235NNNI00jxpuNz6trXt2AkU1GSrQOl3AX0nPSyx32+09a1G7BYKPBHH99HNtGtphomZTYPzYtljO+Z+hv2F9mBe3/Lehq6DK/9XyWui7FPdq9xBDAq4FXWXhvISoKKoxoMoKxTceWOQwR8p7vP7T6gQUuC7jgf6HYGrrlwSXchYDEANZ0WiPzPVRRzLGcU6qzVFleucwiXR+w1rcmV8hl8f3FzHCewYLWC3AIcuCS/yVi02PRUNIgKSuJ+Ix4NnTOEwyVFY+iHhGcFMyvHX8tsn26xXRGXcurifu5OXquBl4lV8j97MN+P8bexB7nMGduhNygn0G/UvvfCr1FZGokP7b5sdxzKcor0luvN1cDr5KWnVbAIL0VeovX715XSGlDSd213QAHQRByRSKRA7DrozYr4LEgCCmAl0gkqikSiaoA3YEPdT/+AroC5TZUw8PDqVq1Knp6esU+SMSCGBGiSnnQSIIgCMTHxxMeHo6+vn6x/SaaTeTsm7PsebGH1R1XF9knR5zDvDvzuB12m1ktZzHRbOJn+7o/BR+UNEc5jGLm7ZksabuEX1x/Qb+6Plu7bZVoE1wcrbVb07l+Z/a/3E833W4Sqy1/ICs3i8Peh2lWsxmd6nci8H0gAYkBPIx8SLY4m6Y1mrK07dJyn+wnZSUx7848HkU9YnKzyUy3mP7JcqWGGg9l8f3FuEe7l1vOfO3jtQQmBrKn154Sc7HVFNWYYDaB4SbDOffmHAdfHqSuWl2ZFHCHvI1ui9otCl3PyMkgKjWKyJRIIlIiCE4K5k+fPxl7fSy7e+wu16bsw3gHXx5k/8v9yInkmN1ydrlDd/6JbSNbtj7dykmfk5+VoXoz5CbpOelSCQl11OmIU4gTfu/9MNI0KrafIAiserQKFXkVFrRZUObxP5wMuka6FmuoPot9xnm/89wOu01iZiKqCqp00unE7JazK/U5ba1vzUaPjYQmhZbo9CyNiJQIPN96MqvlrALX5eXkWdZ+GbaNbMsUSj+75WxCEkNY776eBlUb0Kl++Y12x2BHaqjUKFa4qTzMbjkb51BnVjxcwVHroyU6AL+eppaMtb41693XcyngUrGGqiAI7H+5H0NNQ/b13iexQmhfvb6c9DnJ9qfb6dWwl9QO9QMvD1AYI8hoAAAgAElEQVRPrZ7Uzo/PgX4G/RAEgcX3F2N3yQ45kRwddTqyyHARnet35pTvKda5r2P2ndls7rq5xCjC8vCnz59oqWgV+x42r9WcTjqdOOR9iGHGw4oVLKpscsW5nPc7j3lNc/SrFb8v/9xoW7ctDTUactr3dJkM1aOvjqJbVZcu9btINJ+Nvg1n35zlbvjdPIeIOJddnrvY67UXsxpmDDMZJtG4JSHp7rQ2EAcgCEIOIC8SieT/2fY3b4E6/7geC0hUmCojI4MaNWqU+CMvJ5L7rI01kUhEjRo18k+Fi6NWlVoMNR7KlcArxSZL73mxh9tht/mpzU9Majbps37dn4rqKtX5rcdvyCHHD3d/oKpSVXb32C3VqVZx/NDqB5TklBh1bZTEQisfuBp4lbfpb5luMZ2pzaeyocsGzvc/z+ORj1nfeT2xabEMvzqc1Y9Wk5SVVKYxo1KiGHNtDB7RHqxov4KZLWd+UkGP3g17U025GqfflC9n5XrQdS74X2Cy+eR8g6E0VBVUGdV0FNftrvPXgL8qvKySioIK+tX06aDTgaHGQ1nQegG/9fiNiOQIRjuMJvB9YJnGSc9J53LAZQZeHMhvz3+jm243Lg28xMRmE6V2tKgpqmFrYMv14Ou8y3gn1VjS8j7jPa6Rruzz2sder73UV69Pi1qFHQBlpUO9vOLqpan/Xgm8glu0G7NaziqX+Fw99XroaejhGlV0nmpYUhjjr4/nZshNOul0Ymu3rbjYu7Cp66b8IvKVxYdN440QyUoZfOBDPvM/VZgh73e3tXbrMoXhyonkWNNpTZ4ehMt8/BLKV/M2LTuNe+H3ZKaSW025Gj+0+gGvOC/O+Z0rtt+H01TbRrZfT1OLQUVBhT56fXAKcSo2vNMrzgu/BD+GGQ+TqoyFSCRiodVCErMSyxW6XRTPYp/xLPYZY0zHVHp0UUVh28iWLV23MMdyDjfsbrCrxy56NOyBorwio5qOYknbJbiEuzDTeaZMUpYiUiK4G1Z6yZPvW3xPYmYix14fk3pOWXE58DKBiYGMajrqUy+lXMiJ5BhiNIRnsc/wfVdyBOnzt895/vY5o5qMkvi5aVnHkjpV6nAt8BqJmYl87/w9e732MqjxIA5ZH5JpWZoPiCTJERGJRPOBHEEQtvz9/5GA7t8nrEOAjoIgzPq77SEwErj89/UEkUjUBxgpCEKhwn8ikWgKMAWgTp06lidPnizQXq1aNQwNSw6z+lLw9/cnMbFkIZmk3CSWRSyjRZUWjKlZ8O3yz/Bne8x2Wqu1ZnRN6U5WJCUlJQV19c/DI1YaQZlBXH1/FTtNO+oqyVat+WPe5bzj99jficmOYajWUDpU7VDuMcSCmNWRq1GSU2KBdtHF2dPF6Vx9fxWXZBfU5dSx07LDUq3404WEnAS2xWwjLTeNSbUnYaRS/CkTVN5ne/7dee4m32Vl/ZVoyJeuJp0r5LIqchWqcqrM056HvOjzzyX5mLCsMHbH7CaXXL6t9S0GKoVP3sWCGL8MP9xT3fFM8yRTyKSuYl3sNO0wVi1aDbO8fPh8o7Ki+DXqV2yr29K7WmGl84rGMdER12RX3uX+v6FcU6EmA6oPoIWa5IYqwJrINajJqTFTu2jhu9TcVFZFrqKmQk3maM8pt9Pm7LuzuKa4sk53HYqigpvbk/EncUtxY5nOMqopVH7qxD+/vxuiNiCHHPPqzpN4zDWRa1CWU2au9lxZLJGEnAQ2Rm9EAQV+qPsDVeXL5jx8kvqEQ3GHmFVnFoYqstkPCILAztidhGWG8bPOz0U+i869O4dLsgtL6i2hpuKnK0/xuf/uBmYEsiVmC6NqjMJKvfCp6on4EzxJfcKq+nnPcWk5FX8K1xRXfqz7I/WUJEvn2BO7h6DMIJbrLEdZTjani5JSmZ/vo5RHnIg/gaGyId/W/laq134x4SLOSc4s01mGpoJmiX3/iP0D/wx/lukso4p82Z3GYkFMYGYgz9Oe8yLtBdlCNpoKmmgpaKEpn/dv8yrmaCmU3WDKEmexMnIl1eSrMU97XoUf+Mj6803NTWVJxBLaqLVhWI3iTzQPvD2AT4YPK3VWSvU5/5XwF7eTbqOloEVCTgKDtQbTQb2D1O9bt27dngiCUFiJSRCEcv8DjAJW//3fCkDER20dgeMf/b8/UBW4CTT++9poYGNp81haWgr/5NWrV4WufamU9bVsct8kmB82FwISAvKvJWYmCr3O9BKsz1kLKVkpFbXEUrl9+/Ynm/tzJjkzWfjW6VvB7JCZsNF9o5CTm1Ou+2+F3BLMDpkJ1wKvldr3ZdxLwf6yvWB2yExY5rpMyMzJLNQnJjVG+Ob8N0Lb420Fr7deZVpDZX22Qe+DBLNDZsIfz/8oU/9L/pcEs0NmgnOIcwWvrOIISwoTvjn/jWB51FLY9mSbsP3pdmGt21rhlwe/CD/c+UHocbqHYHbITLA6biUsfbBUeBz1WMgV58p0DR9/vhOuTxB6n+ld7r9TaQlNDBWaHWomjLk2RjjgdUB4FPlIeJ/xXmbjb/bYLLQ43EJIzkwusv2XB78IzQ83F3zifSQa/27YXcHskJnwMPJhgevRKdGCxRELYYXrConGlQX//P7+8fwPweyQmRCTGiPReH7v/ASzQ2bC8VfHZbC6/+fl25dCq6OthFFXRwkZORllumfmrZlCt1PdZP6dCHwfKFgcsRAW3F1QqC02NVawPGopLLm/RKZzSsLn/rsrFosFm3M2wvjr4wu1pWSlCK2PtZbp+5iQniC0P9FeGH99vCAWi8t9v3+Cv2B2yEzY9WyXzNYkDZX9+V7yvySYHzYX7C/bC6GJoRKNkZ6dLnT4s4Mw5/acMvX3ifcRzA6ZCdufbi9z/+Wuy4UuJ7sIZofMhJZHWgrTb04XfnnwizDlxhSh3/l+guVRS8HskJkw1Wlquda+5/keweyQmeAR7VGu+ySlIj7fn+//LLQ+1rrY37rw5HDB/LC5sMljk9RzvY5/LZgdMhO6neomPIt5JvV4HwA8hCJsQUlj/m4Cff8O97UB7olEog0ikag38BiwEIlE6iKRqDnwVhCEZMAR+JCh/L+/x/giCQ4OplUr2covl8Q4s3Eoyyvz+/PfgTznwoqHK3ib9pZ1ndahpqhWaWv5StlQV1JnZ/edDDcZziHvQ8y7O69cNVYPvjyIjroOvRr2KrWvaQ1Tjtsczy/HM95xPDGpMfntcelxTLoxibdpb9ndczdmNc0kek0VhV41PazqWnHmzZlS1RvFgpi9Xnsx0jSii65kORafA/Wr1ueo9VGa1mjKXq+97PPaxwX/C7iEu/D63WuMtYzZ0HkDd4beYXn75bTWbl2hIdrDTIYRmRqJS7hLhc1RFKd8TyEvkmdDlw2MNxuPVV0rmQq3ddLpVGyZmqcxTznnd47RTUcXW7OxNFrVaYWCnAKuEQXDfw95H0IsiBlvNl6icSuC7g26A3l1AosjLTuNmc4zi6y7ei3oGnIiuSLri0uDaU1TVnZciedbTy4HXC61f0pWCvcj7tNbr7fMvxP61fSZ2Gwi14KuMejSILY82YJHtAfZ4mwOvDxAjjiHyeaTSx/oP45IJMK2kS3u0e6FStVcC7pGek46dkZ2Mpuvukp1pltMxz3andtht8t9/2+ev6Eir1JpdcQ/Nz6ECIcmhzLkyhCuBF4p9xgOQQ4kZiaW+T001jKmV8NeHHt1jPcZ70vsm5qdysQbE7kSeIVW2q3Y0HkDLsNc2NFjB8vaL2NPrz1c/t9l3Ee6M8x4GI+jH5e5UkJcehz7vfbTXbe7TPLdPxXDjIflpQkFFv0MPfH6BCJEjDAZIfVcJlom7O65m9O2p4vU55A1Ej3lBUGIBg4CvsBKYAHQENASBCEL+Bl4BpwGpv19206gp0gkCgBiBUGQbQXyfzFaKlqMMBnB9eDr+Cf4czHgIo7Bjnxv8T3NajX71Mv7SjEoyCmwyGoR8yzncSv0VpnLQzyLfYbnW0/GNB1T5rIL8nLyzGo5i81dN+OX4If9FXuexjwlISOByTcmE50aza4euyrloSIJ9sb2RKVG8SDyQYn9bobcJCgxiMnmkz9pbq0s0FTR5HDfw7iPdMdztCePRjzCeagzV/53hV09dtFXv2+l1SzuptuNOlXqsOzhMo54H6mUguzpOelc8L9A9wbdy1SOQhKa125eZJmaF29fsPj+Yuqq1WVq86kSj19FsQoWtS0K1FN9l/GOs2/O8o3BNyXWE61sDKoZ0KBqA5xDnYvtczXoKrfDbjPfZX6B8jGCIOAQ5ICVtlW58njLSp+GfWio0bBMz8jbYbfJEmcVmScrC6aYT+HH1j+ipazFEe8jjHccT5eTXTjle4r+jb4q/ZaVD8qp/3Q+nH9zHsPqhpjXlK142xCjIehX02fzk81k55a97MrDyIfcCLnBxGYT0VQpOVz130z3Bt05Z3sOY01jFt5byKJ7i8pcW1gQBP70+RPD6oblqqE5rfk00nPSOfDyQIn9/vT5k8TMRA70OcDGLhvpq9+3yAMakUhEF90uZOZm4hHjUaY1/P78dzJzM2VSs/pTYlrTFNMappzyOVWo7JdrpCtn3pyht15vtNUkkgcqREedjhXyW1AUEu/0BEHYKQiCoSAIzQVBCBUEYaggCCf/bjsvCEJjQRCMBUF49ve1dEEQrAVBaCQIwneyegGfitTUVGxtbTExMWHFihXcuXMHOzs7unbtiomJCbt27aJfv34YGRmxZMkSqecbZzoOVQVVVrmt4le3X2mt3Zrxpp+Pt/4rxTPGdAyNqjVi/8v9ZaobeMDrANWVq0tU17RXw16csDmBupI6Ex0nMuLqCMKSw9jRfQettCsvCqC8dNXtSi3VWpzyPVVsH0EQ2Ou1Fz0NPXo1KP2k+UtAJBKhoqDyyUXQFOQU2NF9B401G7PBYwPW56w57H24Qg3W60HXScpKqhCVwA98KFNzL+IegiCQmp3KGrc1jLo2iixxFus6r5NaVKt9vfb4JvgSl56nFXjs1TEyczNlpiotK0QiEd0bdMct2o3krOQi+5x9cxbD6obY6Nuw/dl2lj1cRrY4G684L8JTwrExsKmwtfVu2JvH0Y9LFfW6EXyDOlXqVJhKtaJcntDMvj77uDfsHlu6bqGXXi+aaDXh2+ayL73wb6Weej3aaLfhcsDl/N8933e+vIx/yWCjwTJ/5inIKTDPch4hSSFlFufLzs3mV7df0a2q+1lFP3wq6qrXZX+f/UxtPpWrQVcZenkonrGepd73/O1zXr97zXCT4eX6XA01DbFtZMux18cITQotsk9adhqHvQ/TUadjmaLBWtVphbK8Mg8iSnZ6AwQmBnL2zdl8J8eXjr2xPQGJAflGulgQs89rH1NvTkVHXYfZLb9MY1z6auKfkHWP1+HzzkemY5pomZSpvlBgYCC3bt2iVq1atG7dmgYNGvDw4UPevHlDUFAQLVq0wNfXl/r169OgQQN++eUXFBQkf7urq1RnZJOR7PXai4aSBr92/PWLKEj8lTxVtgnNJrD4/mLuRdyjc/3OxfYNeB/AnfA7TGs+TeINtKGmISe+OcGie4t4FPWI7d22l1jP7nNAUU6RQY0H8ceLP4hMiSyyvum9iHv4vPNhVYdVX//2K4AmNZqwr/c+nsQ8Yffz3Wz02MiBlwfoqNMRBTkF5EXyyIvkUZBTwEjTiL76fUssPF8SknrgJeFDmZrD3oc59voYsWmxDDMZxkyLmTIpjdCuXju2Pd3Gw8iHdNXtykmfk/Rs2FPq8lQVQfcG3TnkfYgHEQ/oq1/wRNI73ptX8a9Y2GYhw02Go1tVlz0v9hCZEom2mjZKckr0aNCjwtbWR68Pe732civ0FkOMhhTZJykriQeRDxhuMrxSIirUldTp2bBnuetYfiWP/o368/ODn/F864lFbQvOvjmLkpxSmcpoSELn+p2x0rZi9/Pd9DPoV2oawZFXRwhOCua3Hr/JrDzLl46CnALTWkzDqq4VP937idEOo+mg04HvzL8rFJGVI87BNdKV3Z67UVdUl+hzndVyFjdDbrLBfQM7euwo1H7K9xTvM9/zXfOynW+pKKjQSrsV9yPu8yMl7+W3PNmCioIKU1tIHlXzOdFXvy8bPTZy2vc0JlomLL6/mNtht7HWs2ZZ+2UVXumgoviyY+c+IY0bN6ZevXooKirSvn17qlevjqWlJerq6ujr66OtrY2hoSEqKirUrl27VHXfsjDWdCxt67ZlTac1Mju+/0rlYK1vjbaadqkhLoe8D6EiryL1KZOGkgY7uu/grv1d2uu0l2qsyuKDl/3sm7OF2gRBYM+LPeio61TYqc5X8rCsY8m+3vs43PcwTWo0wSPag/sR93EOdcYx2JEL/hdY6rqUnmd6stF9I2FJYeWe40XcC4k88JLwoUzNpiebqKpUlaM2R1lktUhm9fuaaDVBU1kT10hXTvqcJDk7mcnNPs88RvOa5mipaBUZ/nvuzTmU5ZXp16gfIpGI6RbTWdnh/9i777AojjcO4N+9wnEc3EkRkKLYsSt2RWOLXbHEWKLRxJLEml9i7zHGXhNjbLEbe9fElliixq6xIIL03rnjetn5/XFCROodBwc4n+fhSdwy+x7D7u3szL7zPR4kPMCp16fQ0atjiUzplaWOYx34iH0KHP57KuQUdKwOfWr0KbE4KMv5sNqHEPKEOP36NFR6Fc6HnceHPsWf7zQ/DMNgesvpkGlk2P50e4HbJigSsPXpVnTx7mLWPL4VXXO35jgVcApf+32NwJRAjPpjFMZfGo+HiQ8RlhGGdQ/Xofux7pj05yTEymMxs+VMsxpCrnau+KLJF7gWcw1/x+R8RUOpU2L3i91o59EOTSo3KXKZHTw7IEIWgejM/L+b7ifcx7XoaxjXaFyJTKliDUKeEAG1AnAl8gqGnRuGGzE3MLPlTIuMHLKmct2jWpSez9KQNaxFIPjviRyfz89zm+KQCCTY3r3giy9VNvE5fIyuPxor76/Ek6Qneb4rmqhIxLmwcxhSZ4hF3pVhGKZcJdpyF7mjo1dHHHh5APY29hjhOyL7Hc17CffwNPkpFrRZUGHmuCvr/Nz8sMVtS67lhBA8THyIQ68O4cDLA9gbuBftPdujhVsLcBkuOAwn+6eNRxvUkOTuWTwYdNDsJ/CmchO5YVyjcbDn2+PT+p+Cz7Xs30/W57wddxu3Ym/B39Mf9ZzrWfQYlsLlcNHZuzMuRFyA1qDNnutQqVPifNh59PDpAbHNf9OyDKg1AFVEVbD0ztISTzTDMAy6+3THjmc7kKpKhbPQOcd6PavHgZcH0NytOeo71y/RWCjLsOPb4cNqH+JixEXUd66PTF0mBte2XBKlvPg6+aJ/zf74Leg3DPUdmu87xavurwIhBDNbzSzReMozEV+EsY3GYrjvcBwNPopdz3dhzIUxAAAuw0UHrw4YUGsAOnp2LNZ1dVS9UTgZchIr769Emyptsss6GnwUaeq0IvemZvH39AdgnEM7r+sWIQQbHm6Am50bRtYrX/OmFubjOh9jX+A+KHQK7Oi+o0y/8lVUtEfVTMHBwYiOjoZWq8Xff/+NjIyCs5ZR1KDagyARSPLsVVXr1Zh5YyYYMPi0fq7phd8bc1rNQXO35lj/cD36nOyDEyEnoGf12PZ0G1yFrgioFWDtEN97DMOghXsLrPlgDS5+dBFfNvkSr9JeYcOjDVj7cC1WP1iNlfdXYvm95Rh+bjjuxd/LsX+qKhWXIi6hf83+pfaUd5rfNIxtNNbijdQs7TzaIU2dhnRNepntTc3SpWoXKHQK3E+4n73sj/A/oNQr8xxy27pKa5wdeBatqrQq8dh6+PQAS1j8GfVnrnVXoq4gThH3Xl8fy6OAmgGQ6+RY82ANqomrlfhQfwCY0mwKeBweNjzckOf627G3cTnyMsY3Hg9Pe88Sj6e8s+PbYXSD0bgw+AIWtFmAWS1n4cqQK/ipy0/oWrVrsa+rfC4fM1vORKQsEvte7gNgTLa36/kutK7SGs1cm5lUXjVxNXg7eONm7M081z9IfICnKU8xofGEUktYWFp8JD7Y03MPjvU/ViEaqQBtqJqtU6dO+Oqrr9C4cWMMHToUPj4+1g6JKuPs+HYY4TsCV6OvIjQjNHu5ntVjxo0ZeJz0GMs7LC9TmUJLm4e9BzZ324ydPXbC3c4di24vQr+T/XAv4V72NE1U2eFq54qJTSfiypAruPfJPdwZcQe3h9/GzWE3cXbAWXjYe+DLK1/iSuR/s5GdCDkBHavDUN+hVozcstpWaQvAOFzOz83PytEUrHWV1hDyhDmG/2YlUTJleF1JqF2pNnzEPrgUcSnHckII9r7Yi6oOVdHJu5N1gqPM0sK9BTxEHlDpVRhUe1CpJI5zE7lhdIPRuBR5KVcyIK1Bi+X3lqOqQ1WMaTCmxGOpSGx5tvi47scYWX+kxTO+dvDqgE5enbD1361IUibhePBxpKpT8WVj83Kv+nv64178PWgMmlzr9rzYA0eBY3Zm6orGz82v1DLylgbaUDWDj48PLl26hHPnziEoKAgLFixAp06dcOyY8d06e3t7REREZG///PlzuLhUnD8aynzDfYdDyBNm96oSQrD0zlJci76G2a1mo4dPDytHWDa0dG+J/b33Y32n9eBxeHC1cy3xIWOU+TgMB0KeECK+CA42DpAIJPCR+GB3z92o51wP317/Nrt3/EjwEbSu0jrPIcHllZvIDYvbLsbCtgutHUqhBFwB/D39cTX6KljC4mXqyxLLxGoqhmHQw6cH7ifez86iDABPkp/gWcozjKo/qtxPS/W+4TAcDKw9EEKesFQbBp81+AwuQhd8c+0bjDg/AoPPDEa/k/3Q43gPRMgiMKf1nOyh71TZMLPlTOhYHVbeW4mdz3eihVsLs3sF/T39oTao8TDhYY7lYRlhuB5zHcN9h1e43tSKil7xKaoUOdo6YlDtQfg97Hfj3KZPfsbxkOMY32g8RtQr/kTMFQnDMOhWrRtOBZzC+YHny3UygPeVRCDB9g+3o22Vtlh0exGmX5+OBEUChtct2fcdrWFwncHlpvHd2bszklXJeJHyAsdD3iRRKoX3hYsie/hv5H/Df/e+2AuJQFJhe0AqunGNxuH8wPOl2stjx7fDoraLUENSA2KBGF72XvB18kU7j3aY33p+9nuMVNnhLfbGmAZjcCnyEpJVycWa47qle0vYcGxyzaG9N3AvBFxBhRrRU9GV62RKFFUefVr/UxwOOoxJf05CcHowBtUehCnNplg7rDKLy+HS6WjKMTu+HX7q8hPm3pyLCxEX4C5yxwfeH1g7rPdaR6+O4DJcnA8/j3Nh59C9WvcSy8RqqlqVaqG6pDouRl7EUN+hiJZF48+oPzGu0Tj6sKqc4nF4qGxXudSP28m7Ex0qXs6MazQO58POw9PBEy3dW5pdjpAnRAv3FrgV9998qimqFJwNPYuAWgEVJtPv+4A2VCmqlHnYe6B3jd44E3oGnbw6YUGbBVYfckdRJYnP5WNFhxXwdfJFbcfa4HHoV481SQQStHBvgYNBB8ESFh/V+cjaIWXLGv679d+tSFGlYP/L/eByuCWedZiiKOuz49vhaP+jsOHYFPu+yN/TH6vur0KsPBae9p44FHQIOlaHUfVHWShaqjSUy6G/lpjqxdoqwmegzDfNbxqmNJuCVR+sojft1HuBy+FibKOx6OjV0dqhUDAO/2UJixqSGiZn1SxpPar1AAHBiZATOPn6JHpX722VHjmKokqf2EZskfdHs6epibkJlV6Fw68O4wPvD1BdUr3YZVOlp9w1VG1tbZGamlquG3qEEKSmpsLWlr7I/b5ytXPFhMYTIOQJrR0KRVHvoa5Vu4LP4WOY77AyN6KjlmMt1JTUxC9PfoFKr6JT0lAUZTIfsQ887T1xM/Ymzrw+gwxNBs30XA6Vu64cLy8vxMTEIDk52dqhFIutrS28vN7faUgoiqIo63EXuePSR5fgbOts7VDy1N2nO3759xe0qdIGdZ3qWjsciqLKGYZh4O/pjzOhZxAqDUUjl0bwcy3b04dRuZW7hiqfz0f16rTbnqIoiqKKoyzPtdenRh/sf7kfExpPsHYoFEWVU/6e/jj86jCiM6Mx1W9qmRs9QhWu3DVUKYqiKIqq2KqJq+H28NvWDoOiqHKslXsr8Dl8uNq5olvVbtYOhzIDbahSFEVRFEVRFFWh2PHtMKf1HHjZe9HEleUUrTWKoiiKoiiKoiqcIXWGWDsEqhiYspw9l2GYZACR1o7DRBIAUmsHUYpcAKRYO4hSQuu2YqP1W7HR+q3Y3qf6pXVbsdH6rdho/eatGiEk1zxkZbqhWh4xDLONEPLeZH9gGOYBIaSFteMoDbRuKzZavxUbrd+K7X2qX1q3FRut34qN1q9pyt08quXAWWsHQJUYWrcVG63fio3Wb8VG67fionVbsdH6rdiKVb+0oWphhBB6wlVQtG4rNlq/FRut34qN1m/FReu2YqP1W7EVt35pQ5Uqrm3WDoAqMbRuKzZavxUbrd+Ki9ZtxUbrt2Kj9WsC+o4qRVEURVEURVEUVabQHlWKoiiKoiiKoiiqTKENVYqiKIqiKIqiKKpMoQ1ViqIoiqIoiqIoqkyhDVWKoiiKoiiKoiiqTKENVYqiKIqiKIqiKKpM4Vm6QIZhFgAYDSAdwBBCSMSb5RIAWwA0A5AJYAwh5EVBZbm4uBAfHx9Lh0hZkEKhgEgksnYYVAmgdVux0fqt2Gj9Vly0bis2Wr8VG63fvD18+DCFEFL53eUWbagyDFMNwEAAdQH0BrASwNA3q7UA9hFChjMM0w/AYgBDCirPx8cHDx48sGSIlIVdu3YNnTp1snYYVAmgdVux0fqt2Gj9Vly0bis2Wr+WpU9PB0cgAMfOztqhAKD1mx+GYSLzWm7pob+dAfxBCDEA+ANA26wVhBAVIeT3N/+MAOBo4WNTFEVRFBduTNAAACAASURBVEVR5ZA+JQXS06ehef0ahGWtHQ5VARBCEDFsGOIXLrJ2KJSZLD301xVACgAQQvQMw3AZhuG+abi+bQiAixY+NkVRFEVRFFUOJa1ZC+mpUwAAjkQCYdMmsGvWDLYNG4Hv4QF+FXdwhEIrR0mVJ9qwMOgio6BPSIRBLgfX3t7aIVEmYgghliuMYWYA0BNC1r/5dxwA77cbqgzDNAOwB0BrQogqjzImAJgAAG5ubs0PHTpksfgoy5PL5bCnJ36FROu2Ynuf6pcbGwu2UiWQ9+i9oPepft83FbFuGaUSlWfNhrppE2jr1Qc/NBQ2oaHgJSTk2I4ViWBwcoSuVm1kDv3YStGWrIpYv9Yi/OsviI8cBQBIx4yGuk0bK0dE6zc/nTt3fkgIafHuckv3qMYDqAcADMPwAJB3GqkuAHYDGJ5XIxXGHbYB2AYALVq0IO+O49bpdIiJiYFarbZw6GWPra0tvLy8wOfzrR1KvuhY+4qL1m3F9r7UL9HrEdy2HWwbNEDVXTvBMIy1QyoV70v9vo8qYt2mHTiARJ0O9WbPhrBBg+zlhowMqIODoU9IgC4uHrqEeKifPYf66lU0XrwIfDc3K0ZdMipi/VpL9KHD0FStCrAsPF6Hours2dYOidaviSzdUL0C4H8MwyyEMZnS3wzDrAZwGcBfAA4BmFNYtt+CxMTEwMHBAT4+PhX6hoMQgtTUVMTExKB69erWDoeiKKpcUgcGgs3MhPLOHcivX4cDvUGgqDKFEIKMo8dgW79+jkYqAHArVYKoVascy1TPniNiyBCoHj0Cv1ev0gyVKkeIVgvFvXuQBPQHVyxB6o4d0KemgufsbO3QKBNYNJkSISQBwC4ArwB8D2AmgGoAnGB8L7UtgGUMwzx581PJ1GOo1Wo4OztX6EYqADAMA2dn5/ei55iiKKqkKO8bM8fzqlRB0uo1IHq9lSOiKOpt6ufPoQkKQqWPC5wIIputb10wQiGUDx+VcGRUeab6918QpRKidu0g6dsHMBggu3DB2mFRJrJ01l8QQjYRQmoRQpoQQqIIIR8TQg4RQg4SQkSEkKZv/WSYc4yK3kjN8r58ToqiqJKifPAANj4+cJ83F9rQUGQcO2btkCiKekvGkaNghEKI+/Yt0vYMnw9h48ZQPaINVSp/8tu3AQ4HotatIahdG4I6dSA7d97aYVEmsnhDlaIoiioa+a1bkJ49a+0wSoT85i2k/roTGcePI/PKFSju3YP6VTCI4d0k8CWHGAxQPnwIu5YtYN+1K4QtmiP5p00wyOWlFgNFUfkzyBWQnj8Pca9eJmVktWvuB3VQEAxyRQlGR5Vnitu3IWzcGFyxGAAg7tsXqsePoY2JsXJklCks/Y4qRVEUVQSEZRE/fwH08fFgFUo4Dhtq7ZAsRnrmDOJmzQbyyCov7tcPnqtXlUocmpAQsDIZ7Fq2BMMwcJs1CxFDPkbqjh1w/frrUomBoqj8yX4/D6JUwrGIw36zCJv5ASwL9dN/IWrXrlgxsGo1DDIZ+K6uxSqHKjsMUinUz57D5csvs5eJe/dG8rp1kJ3/HS5fTLBidJQpaI8qRVGUFSjv3YM+Ph58Dw8kLFmCzCtXrB2SRWReuYK4OXNh16oVav9zG7X++hPVT55A1d27IBk8CLKzZ6EJDS2VWLLeT7VrYcx4L2zUCOK+fZG2azd08fGlEgNFUfnLOHoMgtq1YdukiUn7CZs1BTgcKB89LnYMyes3ILxff7AK2jtbUSju3AVYFqL2/z3EsPHyhNDPD7Jz56wYGWUq2lA1Q0REBLp06YLevXvD19cXP/zwA0aOHAlfX1+MGTMGALBkyRI0bdoULVu2xO3btwEAu3fvxpgxY9CmTRvUq1cPu3btQpcuXVCzZk1s3brVip+IoqjSJj15ChwHB/gcPwbbRg0R+823UD54YO2wikVx+zZi//cNbBs2gNfPP4Pn6Ai+hwds69WDqE0buH77LRhbW6T+urNU4lHevw++hwf4Hh7Zy1z/9zVACJI3bCyVGCiKyps6KAjqZ89QacgQk3NycO3tIahTB6pHD4sdh+L+PRikUmScPl3ssqjiS9u7D7I//ihWGYrbt8ERiSBs3DjHcnHfPtCEhED9KrhY5VOlp1wP/U1Ytgyal0EWLVNQzxfuc+cWut2tW7cQHh4OoVAIV1dXXLp0CXv37kWjRo1w/fp13LhxA48ePUJsbCx69OiBFy+MM/I8ffoU9+7dwx9//IFRo0YhPDwcGo0G7dq1wxdffGHRz0JRVNnEKhSQXb4MSZ8+4Dk6wnvLFkSO+ATREyeh2v59sK1Tx9ohmkz56DGiJ02GTY0aqLp1K7j2olzb8JycUOmjj5B++DAqT5kMfpUqJRYPIQTKBw9g36FDjuV8T084jf4Uqdt3wPHTUbmmw6AoqnRkHDkKxsYGkv79zNrfzs8P0lOnQPR6MDzzbmdZlQqaN42W9P0H4DhsGBgO7cOxFsWdu0hctgyMrS2ETZrkeMhoUjm3bsGudWswfH6O5eKePZH4wzLIzp2Dbd1vLBEyVcLo2WimOnXqwMPDA46OjpBIJPD39weHw0H16tXx4MEDdO/eHRwOB97e3hCJRIh/M8ysffv24PF4qF69Oho0aABHR0e4u7tDqVRa+RNRFFVaZJcvgyiVkAwIAADwHB1Rdcd2cAQCRI+fAF1cnJUjzB8hBKxSCX1aGnSxsdCEhkJ+4waiv/gCfFdXVP11B7iV8p95zPmzMQDLIm33nhKNUxsWBkNaGuxatsgdw4QJ4EqM8+pZivrlS5qkiaKKiFWpID17Fg49ehR4vSiI0M8PrFIJ9atXZsehDgwEDAY49OoJbVgYFLf/MbssqnhYjQYJixcbG6eEIHH1arPK0UZFQRcTk+e7yzwnJ4jatYPs/HmQPHIoUGVPue5RLUrPZ0kRCAQ5/s1/66mNXC6Hra1tofvx33nSQ70/CCEwZGSA5+ho7VAoK5CeOg2+tzeEfn7Zy/ienvDesR2Rn4xE9KTJqH7kcK6nwdamjYlFzJQp0Lx8mWsdr0oVVN21EzwXlwLL4Ht6QtK3D9KPHoXzl1+U2Dnw7vupb+M6OEDcrx8yjhyBQSoFVyIp1rEMcgUihg6DuE8feCxfVqyyKOp9ILt4EWxmJioN+cjsMuz8mgEAVI8emz0yQvXvUwCA28yZUN5/gPR9+2Dv397smCjzpW7dBm1EBLx/3QHV4ydI2bQJimHDIWrdKte2hBCk/LwZfHc3VPoo59+Q4s3rdvkl2ZL07YO4WbOhevwk+2+IKrtoj2oJ6NmzJy5fvgyWZREXFweFQoEqJTjEjSpfCMsiYeFChLRrj6SNG0H0emuHRJUiXVwclHfvQhIQkOu9LNu6deGxYjk0L18iZft2K0WYN9WTJ4gYOhS6mBhUnjYVbvPno8oPS+Gxdg28ft6E6ieOF3mYltPYsSBKJdJ/+63E4lU+eABe5crgV6uW53pJQACIVgvZhYvFPpbq4QNjWefOQZ+SUuzyKKqik505A763N+xatjS7DL6HB3hVqkBZjPdUVU+fGt9jr1IFjkOHQn79OrQREWaXR5lHExqKlO3bIe7XD/bt28N53FjwPTyQ+MMPed4jJa9bh5RNmxA/f0GunAeKW7fBq1IFNtV98jyWfdduYAQCpB84UAKfpPzQJyeDsKy1wygUbaiWAC6Xi65du6JZs2YICAjA1q1bTU4UQFVMxGBA/PwFyDh6DLaNGiH1ly2IHDMGuoSEYpWri4tDyrbtyDh+Aop796CLjy/V+SqpopOeOQsQkj3s910O3bpB3Ls3Un7ZAnVw2Uj4IPv9d0R+OhocOzv4HD4El6++gtPIT1Bp8GBI+vSBQ9euJvWM2tapA/vOnZG+bz/YEnjtgRAC5f37sGvZIt9rr23DBrCpVRPSU6eKfTzFnbsAjwei0yH9yJFil0dRZU3m1atIWPqDReYt1SUlQXHnLiT9+hb73siuWTOoHj02exin6um/sG1iTLhTaejHAJ+PtBJ8gEblRlgW8YsWgWNnB7fZswAAHFtbuM6aBU1wMNIPH86xfdqePUjdvgOVhg2FuHcvJK1enf1gl+j1UNy5A1H7dvn+bXHtRXAe+zlk589Deu58yX64Mkr14gVCunRF4tKl1g6lUOV66K+1+Pj44MFb2TlT3nqCfu5N2usWLVpgzpw5OfbLyggMAA0bNsS1a9ey/51QzIYKVfYRgwHxc+dBevo0XCZPRuXJkyA9cwbxi79D+ICB8Fi5AvYffGByubr4eESO+hS62Ngcyxk+HzY+PhB17ACHzp0hbNrU7IQTlGUQQiA9fRp2LVrAxssr3+3cFsyH4s4dxM+dB59DB61Wb4QQpG7diuQNGyH084PXz5ssNlTXefx4RI4YgYxjx+D06acWKTOLLiYG+sTEAntrGIaBJCAAyWvXQRsRARsfH7OPp7h7B3Z+fsan9AcPwmXcODA2NmaXR1FlhT49PTv5DAConz+H9/Zt4Do4mF1m5h9/ACwLcd++xY5P2NwPst9/hz4uDnxPT5P21ScnQx8XD+Eo4/WH7+oKcc+ekB4/gcpTp+WZEI6yPOmJE1A9eIgqS78Hz9k5e7lD9w9h17YNkn/8CeLevcFzdIT03HkkLl8Bh+7d4b5ggXGuboaD5LXrAAMLUZvWYDMzYV/I3LouEydC8c8dJCxeDGGTxrDx9i7pj1lmEK0W8XPmAjod0n87CHHv3nm+IlNW0B7VcoSwLKSnT0Ofnm7tUCgTEYMBcXPmGBupU6eg8uRJAABJ//6ofuwYeG5uiP7iSyStXWvSk2F9cjKixnwGg1QKn0MHUfPSRXj/ugPuixfDafSn4FV2QdrefYgcOQoh7f0RO3MmMv/8s8wmETBkZkL17Hm5GI5iDvXTp9CGh+fbm5qF5+gI9wXzoX7+HGm7d5dOcO9gVSrEz56N5A0bIe7XD1V377Lo+6R2fs1g16IFUnftBtFqLVYuUPD7qW+T9O8PcDiQnjlj9rH06enQvAyCXZvWcPp0FAzJKZBdLP5wYoqyNtmFCwjr0xeyixfhMnkyPNevg+rFC0R99jkMGRlmlys9ew629etDUKNGsWO0e/Oev/LRI5P3VT01vp8qbPLfFCZOo0aCVSgsMtKiLDHI5ZBdvoz4hYvwuktXhPbpi/QjR8AWcO0tjfsEfWoqElevgV2LFpAMHpxjHcMwcJ87F6xcjuSNGyG/dQtxc+bArmVLeKxeBYbLBcPjwWPlCoj79UPyhg2IX7AAYBjYtW1b4HEZHg+ea1YDDIPY6dNBdLqS/JhlSsqWLdAEB8Nz3VrwPT0Rv2AhWI3G2mHlizZUy5HUrVsRN2s2Epf+YO1QKgzCsha/Sc51DL0ecbNmQ3bmLCp//TUqT5yYY72gRnX4HD6ESkM+Qur2Hci8dLlI5erT0xH1+VjokpPhvW0bhE2bwqZqVdi3bw/HYUPhOn06qu7ciTr/3IbnhvWw7/QBFDf+RsykyYgaPQaasLCS+LhmM8gViBo9BhFDhuB1p85I+H4pFPfuVaghzNLTp8EIBHDo2bPQbR169oTDh92Q/ONP0ISFl0J0/9FGRyNi+AhIz5yFy9Qp8Fi1EpwS6CF0njAe+vj4YjUU86K8fx/cSpVgU7Nmgdvx3dwgatsW0lOnzX44orx/HyAEojZtIGrfHjbVqyNt774y+zCIogqjjYhAzNRpiP36f+B7eKD6sWOoPHkSxL16weunH6EJDkbkmM+gT0szuWxNeDjUz59D3M+8KWneJahTBxyRyLyG6r9PAR4PtvXrZy8TNm4M2yaNkb5/f4V4YKr45x9EjhyF4DZtETtlKmTnz0NQvx44AgESFi7C665dkbJ9OwyZmQCMrxGlHz2KmGlfI7hNW4T1DzBmRi4BrFZrbCQplXD/bnGeQ3UFtWvD8ZMRyDh8BDFTpkJQvTq8ft4EzluJSRkeDx4rlkMS0B+akNewrVevSA9V+Z6eqPL9Eqj/fYrkH3+y6Gcrq1QvXiBl6zZIAgIg7t0b7osXQxsejpQtW6wdWr5oQ7WcUNy5g+SfNoHr4gLZ+fNQPXtu7ZDKHYNMhsTlyxH95VcI/2gIQj7ohKDGTfCqdRvIb9woseOm7d8P2blzqPzNN3D5Mu+5cjm2tnBftAg2tWoied26Qp/uGWQyRI8dB21kJLw3/1xg5jquvT3EPXvCY+VK1L51E+7ffQd1UBDCAwYg+ccfy8STNKLVInbqVKhfvULlaVMhbNIYGceOIerT0Qjp+AFStm6zdogme7ehwmq1kJ7/HQ7duoFrb1/o/gzDwH3hQjBCIeLnzSu1Brv8+nWED/4Iurg4eG/5BZUnTiyxd+xFHTrAtkljJCz5HplXr5q8f8aJk8h86xWKLMoHD4zvpxZhPkTJgABjgqv7DwrdNi/KO3fB2NlB2LAhGA4HjiM/gfrZM6iePDGrPIoylfLxY8RMmYq0AwegS0wyqwyi00F24SIiP/sMoT17QX7tGlynfwufQwdhW/e/eZ0dOnWC1+bN0IaHI2r0aOiTk006juzceYBhIO7dy6w438VwuRA2bQrVQ/N6VG3r1AHnnVkanEaOhDYiAopbt7KXGeQKqF+9yvWKTVlGCEH8/AXQxsTA+fPPUW3fXtS58w+8N22Cz/FjqLrzV9jWroPktevwulNnhPbshddduiJhwUKonjyBQ+fOMGRkIHzoMKRs327R7yDjg/bPIf/rL7jNmA5BAQ8VK0+eDK6jI3iVKsF7+3ZwxeJc2zBcLqosW4bK06bCZeqUIsch7tkTlYYMQeqOHdnZgkubPjW1VJJqEq0W8XPngefkBLe5xlcT7Tv4QxLQH6nbd0D9qmzkxHhXuXxhjRDyXiQnyrrR1SUlIXb6DNj4+KDqrl0IHzAASatXo+qe3e/F78FSUrZsRdrefRD4+oLn4gJBnTrgubhAfv06Yv/3Daod/A22deoUXpAJWI0Gab/uhF3r1nCZML7AbRkeD67ffIuYiRORcewYHIcPz7tMhQLRX3wJdUgIvDf9BFGbNkWOh+Fy4Tj0Yzh07YLEFSuRsvkXSM+fR5XFiyEqZKhMSSEsi7h586G4fRtVfvgBlQYPAmD8nPIbN5D+20Ekr18PSf9+4Fsge7Y+NRWZf/0FwNgYBMMAYGDbqKHF6j9u7jzIfv8dgtq1YetbF4K6vmAVCrBSKSQDBhS5HF7lynCfOwdxs2Yjbc9eOH/+mcmxGKRSEJYFt1KlAq8XhGWR8vNmpGzeDIGvL7x+3Fji7+0wDAPvLVsQPX4CYqZMhcfKFZD06VOkfXVJSYifPx9gWTiOGgXXGdPBsbGBLiEBuuhoOI38pEjlOHTrBo6dHaSnT+c5DUJhFHfvwq558+x3UisNGIDkDRuRvm8f7JrRqQ+okqV8/BjRY8eBGAzIvHwZiUt/gLBpUzh0725MKMPlgmi1IDqd8b9aLdg3/zUu00EbHo6MkydgSE4B38MDlb/+GpUGDwKvcuU8j2nv3x7e27Yh+quvEDl6DKqfOJ6rsZcXQgik587CrnVr8N3cLPY7EPo1Q8qmn2GQyfJsxOQZi8EA9bNnEPfP3bMr7tEDiatWIWHxd+BWqgRdTAwMUikAgOvoiFrXr5XICBPA2IDjmtj4z4/62TPoYmNRZdkyVBo0MMc6hmEgatcOonbtoHrxAmm798Agk8Jx+DDjyJCaNcEwDPTp6UhYtBjJa9dBceNveKxckZ3dnej10EZFQxP6GoKatSCoUb1IcWnCwhD95VfQJyTAY+2aQq/5XLE4+2+soDl3GS4XLl99VaQY3uY2dw6Ujx4hdtYs1Dh9GjwnJ5PLMBXR65F59SoyDh+B4tYtSAIC4LFieYkeM2XLVmhevYLX5s05pmRznT0b8ht/I37BAvgc/A0Ml1uicZjK4g1VhmEWABgNIB3AEEJIxFvrxgKYDUADYDQhxOSc4ra2tkhNTYWzs3OFbqQRQpCamgpbgQBx304HK5ej2q6d4Lu5wmXSJCQuXQr59etw6NTJ2qGWC4aMDKQfOgRxnz7G9xLe4jhiOMKHDEHMVxPhc/SIRS9S0pMnoU9OhseqlUXa3r5zJwhbNEfyz5sh6d8fHFHOZA6EZRE7YyZU//4Lz/XrzUq+BAA8Fxd4rlkNycABSFiyBFGfj30zdKbgdydLQtKatZCdPZt9c5SFIxJB3KsXbOvVQ2jPXpBdvAjntxKSmYOwLKLHT8hzKBMjFKLmubMmJ+R4l/rlS0hPnIBdq1YAh4PMy1eQcfQYAIDn6gpRO9MeCIj794fs0mUkrV4NjkgEx6Ef57utLj4ein/uQBMSkv2jT0wEAHDEYth4e8OmWlXwq1aFKDYWCTf+hj4tDYbUVOji46GLiYFkwAC4L1oIjlBo/i/BBDxHR1TdvQsxX36FuOkzwMoVBX7GLLKzZ40JWfr3Q/q+fVA9eWJ8h+6xsSdTWMTkEByhEA49eyLzwgWw8+eBY2dX5Nh1SUnQhobmuAnkiESoNHgw0vbtg2tCAvju7kUuLwshBNITJyFq19YiD2eoikn15Amix40Ht7ILqu3dCzYzE5mXLxuvFyuL9p0DAOBwYP/BB3AcNhQif/8i3aiKWreC18aNiB4/HukHD8H5szGF7qN+/hy6yCi4jC/4oa2p7Jo3BwiB6skT2HfsWKR9tGFhYBUKCBs3ybWOsbFB5UmTkbZvH7iOjrBt2BB8L08QrRYpP22C/Oo1iHt0t+hnAIwPtqM+/RTOoWFIlcng9NlnRRoVkh/Z+d/B8Plw6Na1wO2EDRrAc/WqPNfxHB3huXEDpKdOI/H77xEWMAD2HTpAExYGbVhY9qtTjK0tPNevg0PnzgUeS3H7NmKmfQ3GxgbV9u6BsGnTIn0Wc66jRcURCuG5bi0ihnyM5B9/RJXFi0vsWLqEBIjOnMXrhYugT0oCz90donbtID11CuLevYr895sXVqmE/Pp1ZF69Cq6DGLb168O2YQMIatSAJiQEKdu2Qdy/Hxy65KwjnqMj3ObORdyMGUg/cMDiyQ2Ly6INVYZhqgEYCKAugN4AVgIY+madEMZGajMA1QFsB1D0rqA3vLy8EBMTg2QLPXEqy2xtbWH7+x/IuH8fVZYvh6B2bQCA49CPkb5vH5LWrIG9vz/N5FoEafsPgCiVcM7jC5Lv7g7vzZsROXIUYiZPQdXduyzytJTodEjdth3CJk1gV8ReT4Zh4DZ9OiKGDUfqrt3ZSZeyJG/YaBwqM2+eRb4o7du3R41TpxA9cSLiZs8BGMaYYKaUpO7ajbSdO+E4YgScv5iQ5zY2Pj4Q+Poi8+KlYjdUZefOQR0YCPfFi4yNfEIAQqBPz0DkqFFI+GEZvDf/XKxjJG/8ERyxGF6bfgJXLAYhBPqkZGheBYHn7m7y00qGYeC5dg1ipk5FwqJFYJXKXDeFhBBkHDuGxOUrQJRKMAIBBDVrQtSmDQR1agNcLnRR0dBGRUH1/AVkFy/B3mCATCIB19kZPCcn2DZsCJdJkyAZkHt+15LGtbeH947tiJk2zfgZFYoCe48JIZCeOgVh06bwXLUKDh9+iPi58xA+cBBsqlcHx94etr6+RT6+ZEAApCdOIPPKFZP+/pV37wEA7FrnPL8dR36CtD17kH7wEFz/93WRy8siv34d8fPmQdy7NzzXrTV5f6riUz19iqhx48F1dka1PXuMPZRubhDUqgWXr74ynutPngBcLhgbGzB8Pjg2Nsb/z/rh88HY2IArFufoYSkq+w7+ELVri9Tt2+H48ZBcD1bfJT171thw6m7ZRp6wcWOAy4Xy0aMi3+j/l0gpd0MVAByHDYXjsKE5lhGDARmHj0B68mSJNFST162DJuQ1dLVrI2n1Gihu3UaVFcvBd3U1uSzCspBduABRhw5F7mXOD8MwqDRwAOxaNEf8woVQPn4MQa1aELVrB0GtWrDx9kLiylWImTwFVZZ8h0rvJEUCjPdDafsPIGnNGghq1IDXL7/Axqt4D4UtybZuXdh36gT5teslNmpTfusWYqZMhUilgqBjB+N9SMeOICyL8IGDEL9oMWqcPWtStmlWqYT8xg3I/rgA+fXrIGo1uI6OIBpN9jyxjI0NmDe90e5z5+ZZjrhvH0jPnkHS+g0Q+Xcocu94qSCEWOwHwBgAP7z5fx6AqLfWdQJw4K1/vwZgV1B5zZs3J+8DVqcj8Uu+J/FLviep+/aTzJs3iTY2lsiuXiWBdX1J7Lx5ufaRXrhIAuv6krQjR6wQ8X+uXr1q1eMXhUEuJ0GtWpOoryYWuJ3099+Nv++ZswjLssU+bvqJkySwri+R/fWXyftGT5lKgpr5EV1ycvayjDNnSWBdXxI3f4FF4nubQakkEZ+OJoH16pOMM2cJISVft7I//yKBdX1J9JSphNXrC9w2+ZdfSGBdX6KNjzf7eAaVigR37kzCBg4irMGQa33Kjh3G+rpypcBy8to3i/LxYxJY15ckb9lqdpz5HlejIdFTp5HAur4kadOm7L8BXWoqiZo4iQTW9SURo8cQ1atXhf4+Wa2WXC3kc1oDq9GQ6Glfk8C6viR1z558t1M+fWa8/h08lL1MEx1NwgZ/RALr+pLICRNMO67BQEI6dyGRn31u0n6x8+aRoJat8vx9R02cRF61bkMMKpVpseh05HXvPiSwri8JbNCQaBMSTdo/S2Hnry4tjYQOGEjSj58wq/yK7O3rbln09+7dJKhFSxLS7UOijYuzaixFveaxOh151d6fRE+eXCJxhA3+iIQPH0FYjaZI28ctWGg8dwu4nuclcfVqEli/gcX/RuS3bpHAur4kfsn35Opff5G0Q4fJyyZNyau27UjmtWsml6e4f58E1vXN/j4vaQa5nER+Ptb4t/DLL9nfT6zBQDLOnSMhH3YngXV9SdSEL4g+M7NUYjJV2pEjJLCuL1EHB1u87Iyz50hgw0YktH8AuZHHfbvi0SMS6FuPxH+3pMhle0L6dwAAIABJREFUyv+5Q161aUsC6/qSV+3ak/jvviPyu3cJq9cT1mAg6tAwknHmLElYsZJEfj6WZN68WWB52pgYEtSyFXnZtBlJ2bmLsDqdyZ+zOAA8IHm1LfNaaO4PgJkA/vfWv2MBcN/8/8cANr617h8A1Qsq731pqGY1SF82bmK8OXnrJ7R/QJ43OizLkvChw0iwfwdiUCisELVReWiopuzcRQLr+hLl48eFbpu0aZPxQrt1W7GOyer15HXPXiQ0YIBZjUp1WBgJrN8g+6Kl/Pdf8rJRYxLxycgifxGbyqBQkIhRnxobq+fOlXjdRn3xJQnp0pUY1OpCt1WHhRXaeClM8rZtJLCuL5H/cyfP9axWS0L79iPBnTvneU4ZFAoSOX48Ce3Xn+jS0vIsI2L0GPKqXfsSOydZnY7EzppNAuv6koSVq0jmtWvkVXt/8rJhI+MXiwk3XWX13GX1ehI5YQIJ8mue7+85fsn35GWjxkQvleZYbtBoSMqOHUT5778mHzdp40YS6FvPpIchId0+JFGTJuW5Tv7PHePDg5GjSOK69UR6/jxRv35d6Jd/2sGDJLCuL0n5dScJ9K1HkjZuNOlzZCmsflN+3Wn8nmnYiCgePChwW21Cosk39OURq9WSuMWLjQ/QJk8p1oMxSzCo1ST9xEmSvG0bSVy7jsQtXkxivvmWPG/WjIR06Uq0sbFWjS9L1BdfkqBWrYleJst3m8ybN0lgXV8ivXCxRGJIWLEy++85dOBAEjt3Lkndv5+ow8Ly3D60fwCJ/HysycdRh4QYz89du4oZ8X/06ekkuOMH5HWv3sSgVGafu+qQEBLaP8B4vV+1yqRzMP67JeRlk6bEIJdbLM7CsBoNiZk+w9jg/m4Jybx+nYQOGJh9L5t57ZrFH7JbkjYuLvvaa0mpe/Yavws+GUn0Mlm+1+b4H34ggXV9ieL+/ULLTPvtNxLYoCF53bsPkf/zT6EPp4tKGxtLoiZ8QQLr+pKwQYOJ6sULi5RbFPk1VBliwRT6DMPMAKAnhKx/8+84AN6EEAPDMEMA+BNCpr1Z9w+ATwghYe+UMQHABABwc3NrfujQIYvFV1ZJtm2HzatXSF65AhyFAtyERPASE8GRSqFq3w5sPu9M8l+HwmnNGsj79YOiT+9SjtpILpfDvggZTK1Gp4PL/AXQu7sjoyhD8AiBeOdOCO8/QOr8edB7eZl1WMHDh6i0fQcyxo+Dpnlzs8pw+O0ghDdvIv3raZD8uhPg8ZA6exZIMSZaL5RGA8dNP4P/+jUSPxkBxt+/ZI6j16Pyt9OhbtMamfkkjXqX0/dLQWxtkT5jusmHYzIz4bJgIXR1aiPjnemB3sZ//RpOa9ZC0b075G+9d8ioVKj088/gh4YZh9FWrYr0r6cBbw0R5wcFwWnDRsiGDIGqaxeTYywyloXD4cOwu27MVK3z8IDs889M/lsty+cuNy4Ozt8vhfLDD3PUAwBAp0Pl2XOgrecL6bhxljtmUhJcFi5C5sABUPboUej2nJRUVJ4/H7KPh0DVJY/6JgT2p05D8OwpuAmJYN5MdUH4fMgDAqDM470xRqWC88JFMLi7I/2b/6HS5s3gRUQiZdkPAJ9v0ucpsH5ZFs6LFoMIhWDUajAqFdLmzM79XUMI7P78E/bHT0Dv5YnMwYOhM2FIdXnCyOWotG07bIKDoW7SBILAQBAOB4p+/aDs3AmwQoIRh/0HYHfzJgCAcDggIjuwQjtoJRIoRo8G6+Jc6jHlhRcVDedlyyDv0weKfn3z3Ea8ew8ET54gefUqk/+Wi0SjgeDZc/Cjo8GLjgI/KhocuRyEz0fq3DkwvPWuN6NWo/L/voGiVy8o8kimVBin5SsAgwFp8+cVP25CIPn1VwgePUbarJnQV6uW89zV6eBw7Bjsrt+Aqk0byEaNLPxv0WCAy+w50NWuDWkhSRwtjmVhf/IkRJevAAD0Ls5Q9OsPdcsWQDHety0tzt8tgUEiQcbX04pfGCEQnTkD+z8uQN2kCaTjxgJ8fv7XZo0GLku+B+FxkTp/ft7nicEAhyNHYHf9BjQNG0I69nMQS+eTIASCR4/gcPgIOHI5lF27Qt6/X8mct2/p3LnzQ0JI7uQSebVezf0BMBI5h/7GvrXOH7mH/joUVN770KOqz8ggLxs2IvHfLzVr/+jJk0lQMz+zh4cVBWswEPmtW4TVanOtK6u9MlnSDh029qLdvl3kffTp6cY6+eEHs47JsiwJDRhAXvfsVaynXLrkZPKymR8JrFefBDXzI6qgV2aXZQqDXE7CP/mEvKhXv0hP9syhuHfPOMz28uUi75P088/G4b8JCSYfL37J9ySwfgOifv260G1j584lgQ0aEtUr4+9bn55Owj4aQgIbNCTSP/4g0osXSaBvPRL11cTs3jGWZUn4x0NJ8AeditRDXFwsy5LkrdtI4vr1Zh+vrJ+7MTNmkJdNmhJtYs5rm/Si8bWHzBs3LH7MsIGDSPjwEUXaNv3YcRJY1zf776QgBo2GqF6+JBmnTpHICROMw5YPH861XeL69cbRH0+fEkIIyfzb2AuVcfq0aR+EFFy/WcMMM06fJurXr0mQX3MSNnAQMSiV2duwer3xvKnrSyI/H0tCOnfJHrqnDgkxOZ6yTBUUREK6dCUvGzXO/l1roqNJ5Pjxxt6gAQOJ8smTUo0pq+4Tlq8gBqUyR09UWTx3o6dMzXcUhEGlIkF+zUnsnLmlFg/LskQdFkZetW5DwgYNznH/Ir971/j9Y+bvMXX/fuO5HxiY5/r04ydIwqpVRSor48yZ7OGyWd6tX5ZlSfLmzdmvyhgKGVUlv327RHuviyL9+AmSdvBQiY0AKykJy1eQlw0bWWRUVNb1M27+/BwjaYpybU5cszbXOl1amvEVrawedgv1ouZHn5FB4uYvMMazbn2JHouQ0hv66w7gIQAugP4ADgFYDaA7ABsAgQDsATQB8E9h5b0PDdWsYV7KZ8/N2l8dFkZeNm5Cgjt0zHdIY3FlDZ1N2rQp17qy+IWZhdXpSEi3D0nYR0NMHm4SPe1r8qpNW7MusrK/jO9epp84afK+70revJkE+tYr9L1JS9PLZORph47klb9/roaCJSRu2EAC6zcocKjYu9Shocbhv3v3mXQsdWgYCWzQkMQtXlyk7XVpaeRVq9YkfMQnRJeSQkIDBpCXDRsR2Z9/Zm+Tus94oxK3cBFhWTa7zvNqfJRVZfncJYQQTWQkCWzQMNc7O1FfTSTB/h1K5Es6ccMGElivPtGnpxe6bcyMGeRV23YmX1tYrdbYAPKtR6R//JG9XBsfT142bkJivp3+37YGA3ndsxcJ+2iISccgpOD6jZ4y1fj+7JuHHLI//yKBvvVIzLfTCcuyxKBUZr/3nLBiJWENBmJQq0nK9u0kqHkLElivPomdN48kbfyRxC1YSKK+mkjChnxMQvv2I/Jbt0yO1Zqkly6Rl838SHCHjtkPCLKwLEukFy6S4A4djddhC50zLMsSdWhovn87epmMBH/Qibzu3SfPB1Fl8dxVBweTQN96uW6wWY2GJG380eQHxpaSlc8j6cefspelbN9OAuv6El1qqlll6tLSSGDDRiRh2bJc65RPnpDA+g1yPHDKjzY2lgS1aEnChw0vUkMmdfdu44OjceNzPFR6V9z8+SSomZ/J78dT/w1RN+e94LepX7/OHgL97nle2PkbO3fum1e/viMx33xLIsaMIaH9+pOg5i3Iy4aNSPrJ4t9bmiJ66jQS1LJViQ4jZ1k234aqRfvhCSEJAHYBeAXgexjfWa0GwIkQogUwH8BjAEcA5D/+7j2ScfIkBLVrwbZBfbP2F1SvDp+Dv4EjEiHqs8+QtG49iE5nsfjUwcFIXr8e4PGQtmcvDHK5xcouabI/LkAXHQ2XLyaYnMGt0sABMKSnI/PaNZP2I4QgdctW8D09IelbtPkgC+L85Zeode0aHLoWnF7e0rgODsj4YgJYuQKxX/8vOwW9pShu34awUSNwTRjGLKhRA4LatSG7eMGkYyWtWwuOQIDKkycXaXueoyNcZ0yH6uFDhPXpC21EBLx++QUObw3vdBr5CZzHj0fG4cNI+eUXJG/8EfyqVVFp4MACSqZMYVO1KioNGoT0o0ehjYkFYJwDV37jBsT9+5XIXG/2HToCLFvoxO+EECjv3IWoTWuTry0Mnw+vjRshbNYMsTNmQn7zFgAgef0GgJAcWYIZDgeOn3wC9bNnUP37r+kfKA+6xCRk/vknJIMGgSMQAAAcunRG5WnTIDt3DskbNiJy9Jjs7OJus2aC4XDAEQjgPG4cal6+BMcRIyA9dRopv/yCzD//hC4uDlyxGKxajdj/fQNdbKxFYi1pin/+QeyUqRDUqgWfo0chbNQox3qGYSDu0R01fj8PQd26iJ81G7r4+GIfN3XbdoT17oPE75eC6PW51ieuXAl9UhI8li/LrqOyTlC7NsR9+iBt/37oU1JA9HpknDiJ0F69kbJ5M0Tt2xun7Cpl4h7dIQnoj5QtW6B69gwAoPr3Kfje3mZPRcdzdIRDp06Qnj2X437LIFcgdvoM8NxcwXFwQNquXQWWk7DkexCDAR4rVxRp9gan0aNRZen3UNy8iajx4/O8HyM6HTIvXYZ9165FmtuWysmuRQswtraQ/32zWOWk/3YQDJ8Pl8mTTP6OcJs1C4JatSA9cxaq589A1Brwvb0h7t0b1Q7sRyUT5mK3BOfPPwMrkyHj+PESKd8gkyFi2LD8N8ir9VpWfip6j6o61JggJmXHr8Uuy6BQkNh584wvQH/8MdFERRW7TFajIaEBA4xZ527cyDU8hZCy+WSXEGNPRGjffuR1nz5mJQFhdToS7N+BRH35lUn7ZZw+/SYb6UGTj1nWXL16lUjPnzc+FTRzaHpe9BkZJLBe/RxPuIsq6adNxoQ3RezlzRpi/O7fbWFYg4GEj/iEBDXzI/K7d/PehmVJzIwZ2YnPMs6cMekY1lZWz923aePjyctGjUns7DmEkP96FEoiKyMhxuGur1q1JrEzZxW4Xda1O+2Q+T3oeqnU2FvftJkxMYZvPZK4Zk3u7TLlJMiveY6e1qLIr36zhtBrIiJyLGdZNjvj8ssmTQsdxWFQKnMlhtKEh5Og5i1I2EdDCh2eaG2swUBCBww0JnQrQs+TOiyMBDXzM2aWzeM1mKJSh4WRl40ak5AuXY1Dqb/4MkdPReb16/kO/ctSVs/drCSAkePHk9c9e2UnZMm8ccOqSXT0UikJ7tSZvO7ZixiUShLc8QMS8823xSozK2u97M//svrHzppNAuvVJ4oHD4zZgevVJ5romDz3z8rKm1fixsLqV3r+PAls0JCEDhiYq/ysv5+346JMEzl+PHndvYfZ+xvkb67ZM2bkub6snr8FCf/kExLSuYvFMwGzLEuiJ08mgQ0alk6PKmUa6alTAIcDcT7JB0zBsbODx9Kl8Fy/DtqwcIQPGAjlo0fFKjN508/QBAWhytLvYd+hA+w7dULart0wyBXFjrekZV68CE1ICFzGjzdrwmyGx4NkQADkN25AX8Q5e9MPHUbcrNkQNm8OyaBBJh+zLBL37g2n0aORvn8/pGfOWKRMxZ27AMtC1L6d6fH07AEQgszLlwvd1iCTIW7efPCqVIHT6NEmHYfhcFB1+zbUvHgBonx6ARiGgcfSpbDv3BnCpk0h7m2dhGYVGd/dHY7Dh0N6+jQ0YWHIOHkKtg0bZs8pbWkMlwuRvz/kN2+CvEl+lBfl3TsAAFGb1mYfiysWo+qO7eC5VkbCd0vAlUjgPCH3XMJcexEkgwZBdvFika9F+SF6PTKOHIWoXTvYVKuWYx3DMPBY9gOcxoxBtX17Cx3FwREKc/UC2fj4oMqyH6B+9gxJK1YUK9aSJjt3DpqXL1H566+L1PMkqF4d7t99B9WjR0j+8SezjklYFgkLF4GxtYXPoYNwW7gA8hs3EDFqFHSJSTDIZIhfsBCC2rXgMqVoI0DKEkH16pAEBEBx428wPB68Nv0En2NHYd+hQ6nPzfw2rlgMj2U/QBsejrg5c6FPTISwSeNilWnfwR9cJyfjfRwA6fnzkJ46BZcvv4Rd8+ZwHDUK4HCQtmdPrn0JIUhauw68ypXhNGqkyccW9+4N7182QxcTg4iPPoLin3+y18nO/w6OWAyRf3vzP9x7zt6/A7SRkdBGR5u1v/TsObAKBRyLmCiyPHD+/HPo4uIgu3jRouWm792LzMtX4PrNN/luQxuqVkIMBkjPnIHIv71ZkznnR9yrF2qcOgmukxPiZsw0u1GpfPQYqTt2QPLR4Owhjy6TJsIglSL9t98sFm9JYBUKJK5cBYGvL8R9zB9+Kxk4EDAYID17rsDtCCFI2bIVCYsXw75jR1TdsR2ct7LBlneu07+FXYsWiF+4COqgoGKXp7h9GxyRKNcwu6IQ1KoFm1o1kXmh4IslYVnEzZ4DXVwcPNeuAceMrHgcOzvwKlcucBvGxgbev2xGtd8OlMhQVApwnjAejK0t4mbMhCYoCJKBJTvsyb5jBxhSU6F+EZjvNoo7d8GrUgX8qlWLdSyeiwuq/roTtvXrw23unHyHwjt9MgLQ6ZB++Eixjie/8Tf0CQmoNGxonus5dnZwmz3LrHMzi7h7dzh99hnSfztY6LXTWliNBskbNsK2fn2ITciYL+nXF5WGfITU7dsh//tvk4+bcewYlPfvw23mDGMjZcQIeG3+GdqISEQMG4a4mbOgT0lBlWXLy+13iPu8uai6ayeqnzoJh27drNpAfZuobVs4jhqFzAvGV0eEjYvXUGX4fEj69YX86lWoXrxAwuLvIGzaFC4TvwIA8N3cIOnTBxnHj8OQkZFjX/nVa1A9fgyXSZPM+m4CAPsOHVD96BFwXZwRNXYcUnfuAqvRIPPPP+HQrVu5/fspC0QdjLMdKG6aPvyXEIL0336DoH49CJs2tXRoVmPfqRNsqldH2q87s3ISFZvq33+RuHoN7Lt0gdNnY/LdjjZUrUR5967xhqEExprzPT3hsXIFdPHxSFqZ/1NtQggUd+5C/eoViMGQvZxVKBA3axb4Hh5wmz0ne7mwUSOIOnZA2q5dYBVlt1c1Zes26BMS4L5wQZHe+8iPoEYNCJs0gfTkiXxPTMKySFqxEskbNkDcvx+8Nv1k9hdPWcXw+fDcsB5csRgxU6eBVauLVZ7i1i3YtW4NxsxU5+IePaF88KDA3qXUHb8a37GbOQN2fn7mhlpk5vTaU0XDc3aG06ejoH7xAuDzS7znWuTvDzAM5Deu57me1WqhvHMHolatLHITbuPlieonjkPSv3/+2/j4QNSxA9IPHyrW++Lphw6C5+oKh86dzS6jKFy/+R+EzZsjfuFCaEJCspcTvR6akBBkXrsGVqUq0RgKkn7gN+ji4uA6Y7rJ567b3LkQ1K6NuJmzoEtMLPJ+uqQkJK1eA7tWrSAZPDh7uUOnTvDZvw/Q6yG/dg3O48dB2KihSTGVJRyRCKK2bcvkgzvXb7+BTY0aYPh8COrVK3Z5kgEDQHQ6RH06GmBZeKxZneOew+nzz0CUyhwPmIjBgOT162BTrRoqDS7eyCsbHx/4HDoMh27dkLRqFSKHjwArl0Pcq1exyn3f2fj4gO/lZdZ7qqpHj6AJDobj8OFl5iGNJTAcDpw+GwN1YCCUd+8VuzxDRgZi/vc/8F1d4bF8WYG/K3p3ZSUZp06B4+AA+xJKkmPn5wfnsZ8j4+ixPBMCEZZFwnffIWrMGIQHDEBwm7aIGj8BKVu2IH7BAuhiYuCxcgW49qIc+1WeOBGG9HSkl9H5bTXh4UjdtQuSgACLNFAkgwZBE/Ia6ucvcq0jej3i585D2p49cBw1Ch4rVpjd+CrreC4uxocfUVFIP2B+j7o2Kgq6mBizhv1myRr+K8tn+K/in3+MDw569zIOv6L+z95dh0d1pQ8c/96ZuLu7EEMCCe5SaLHqlrpuu91ubaW22/52u9Z2K3TrpbZ1qrhTSNEEIpAEiBB3d8/M+f0xkELjyUQI5/M8fZ4yd+bOmZy5c4+85z2XPPt77kFtbY3l4sUY2NoO6XsZ2NtjMmECDQe6njGr2bARTU0N1ld337EcCna3346mrJza3b2HvXelNT+fhoOHsLnhhiH/nVIMDXF/9VVUZmbkP/wIRX/7G1lr1pAaOZXMVavJf+C3ZN+4hpaMjCEtR1c0NTWUv/ce5nPnYj5zZr9frzI1xf21tR2Jo2q3b6f+4CGaTp6kJTOr0+zZeSX//BeipQXXvz/XqVFmEhqKzzdf4/znp3HoYZ9naXBUJiZ4vveubkBZD0mqTEJCMA4ORtvQgMvf/orRL/ayNgkKwnzOHCo//wztuQGmms1baEk/i+PvH9PLdai2MMf9v6/h+NhjNJ85g9rGZlBLEiTdMgjzuXNoiI7u98Bg1ZdfobK0xHoQ0XyjlfXVV6O2t6fiow8HdR4hhC4Ev6xcNwlibd3j82VHdQRo6uup270Hq+XLhzSjn8PDD2M8bhxFzzxLe1VVx+NCo6HomWepXv81dnfdhduLL2B11VW0FxdR9tp/qd2+A/tf34tZRESnc5qGh2M+ezYVH36EtrFxyMo+EEIISv6ly5Lo9Kc/6uWcVsuvQjE2pmbDDxc9rqlvIO/BB3VrUh55GOc/Pz3mZ9XMZ87EfO5cytetQ1NT0+3zGmNjddmnL5ilP+98NlXzWQPvqBoFBGDk50f1t9/RlHzqotnutuJiCv74J4x8fXH9xz/G1Ijm5UxtZYXvxg24/uPvw/J+FnPn0pSYeNHvJugGpyo++ACTiRMxG0AnZzDMZ8/G0N29Y01cf1V/8y0oCjY3/krPJeuaobMT7q+8QmtBAbVbt6EyMsZ2zY24vfgC7q++QntFBVk3/IrqDQP7PANVvm4d2traQd0jjP39cf37czSdOEHBH/5I3n33kb3mJjKXLydtxkyyblxDxYcfdqxxq9u7l7rdu3F46CGMfHy6PKehqyt2d9whQzaHmJGnJxbz5+vtfC7/9384P/MM1qtWdXnc/p67dQNMW7agbW2l7I3XMQkLw3LpUr2VQVEUHB74Dd5ffI7Hm2+M2QHz4WQxZw6isZHG+IQ+v6a9vJza3buxue5aVGZmQ1i6kaEyNsbutltpOHCQ5rS0AZ+n8qOPqd+/H+fH/9SnEPyBx0VKA1a3axeiuRnra64e0vdRGRnh9p8XyfrVjRT/7TncX1sLGg2FTz1N7datODz0EA6/exBFUbC+WlcWTXU1LRkZPcbWO/zuQXJuuVUXzuLrM6SfoT/q9u6l4dAhnJ9+qte1hX2ltrTE8oorqNm6Dacnn0RlbExbSSl5v32AltQ0XJ57Dts1N+rlvS4FTn/8A1nXXkfFBx/g9MfODb328nLyH3kUTWUlajtb7O+666LjDYcPY+Dm2m1jrS8URcH+nrsp+uvfyL7hBgzd3bFcuhTLK5ZQ+uJ/EM3NeLzxOipz895PJl0yDF1dh+29LObPo/ytt2g4dOiiBmjtjh205eXh/NSTwz4IopxLvFex7n3ay8r6/BsnNBrqo6Ko/vZbLBYuxNDFZYhL+jPzGdMZd/QoKjPTTgN5phGRFD7+OEVPP01jTAwu//dsR+NOaLVoKirQ1NbqQjX19LduKyyk6rPPsb76akyCggZ1LutVqzCfM+dcOevQ1tWiqa2lLT+fur0/UvrSy5S+9DImoaG0lZZiHByMfQ/rsKRLk9mUyZhNmdz98ZkzMQ4OpuKjj9HU1dFeWITbP/85JAPbw7HM5XJhNn0GGBjQcPjQRTPUoq2N6u+/x8jLq9OAe/V330FbGzZrethq5RJnc9NNlK97n8qP/4fb8//u9+vbCgooXbsWyyuu6HPEm+yojoCaDRsx8vYeloXWJsHBOD70EGVr11KzcRP1+/dTt3s3jn/4Aw7339fp+Wobmy5nUi9kNmUKZjNnUPHhhyhPPzVURe8XbVMTJc8/j3FgILa33qrXc9tcdy21W7dSv28fRv7+5P3mATQ1NXi+8zYW8+bp9b1GO5PgYKxWrqTy08+wve02DJ2dO44JISj8y1/QNjRgOmUKZWtfw2LefIz9fHXH29tpiI7BctnSQTc8bW64AYvFi6nft4/a3bup/Pzzjj3r3F9bi7Gf36DOL13eTMaPR21nR/2Bgx0dVaHVUrFuHcaBgVgM8RrP7livWkXFu+9Ru317r5mslfp6yt9/n+qv1tNWWIiBi0tHopfh9MvlI+cZOjvh9fFHlL/9DuVvv01jfDyGTk60FRfTXlLSsT+l6ZQpuDz7DCZ6WFNY9t/XAXB85OFBnwt0+2l2FYru8Nvf0pqfT93uPdTt2oW2oQHXd96WM12XofMDq4VPPEnZK69iPmvmoCKKpOGhtjDHbMoU6g8e6hiUb0pKouiZZ2lJTQXAYslinJ98EiNPT0R7O1Vff4P5rJkdbZ6xyMDWVre/+TffYHPD9ZiEhfVrv96aLVugvR2nJ/s+2Cs7qsOsrbCQxthYHB97bNhG5O3vvYf6ffsoelqXGMn56af6vV3HLzk+9BA5t92O45NPkb9zF1YrV2KxYL5eQ5mFVoumpqZPa9Iq3n9fN1L56SeDSqDUFbPp0zFwdaX87XdoKy5GMTHG+7NPMQ0L0+v7XCocH32E2p07KX/zTVz/8Y+Ox6vXr6fhpwM4/+UvWF25jMyVqyh8+il8vvwSRa2mOTkZbV0dFrP1kzbfwNYWm+uvx+b669HU1VEfFQUoWF15pV7OL12+FJUK8zmzaThwEKHRoKjV1O/fT0v6WdxeemnEwvyN/f0xCQujZvOWbn/DhUZDyb/+heM331LW3o7Z9Ok4PfUklosW6f23cbAUtRrHhx/CbGokpa+8ihAC00mTMHR1wcDFFdHeRsV768i6/gZsb7oJx0cf6XU9U3eakpKp2bwZu3vuxtDNTc+fpDMjDw/s77kb+3vuRgghlyFcxqyuuorSV17GwbNuAAAgAElEQVSlvaQExz/oZ1mSNPTM586h7JVXac3JoerLr6j87DMM7O1xf+01WnNzKX/3XTJXrMTu3nsw9vOnvagIl7/8eaSLPeTs7rqT6h9+IOfW20BRMPTyxDggEJOgIOzuuhO1lVWXrxNCULN5C6aRERh5uPf5/UbXXesyYODqis/33100EzXUFAMD3F58gbwHf4fdHXdg283WBP1hFhGBz3ffcuqttzBISKBuzx5UFhZYXrkM58cfH1BjQghBa3Y2jTExNETH0BgTg6aqCuvrr8Plz3/uNpSz/uBBKj74EKsVK7rd83IwFLUa62uupuKddzEODMDzvfeGpaEzWhl5eGB7801Uff4FdnffjbGfHy2ZmZS8+B/M58zB9tZbUFQqnP/vWQr/+CcqPvoIh/vuo/7wYVAUzGbM0HuZ1JaW3a4RkqSBsJg3n9rNW2hOSsJk0iTK330PQ09PrK4a2YEQ69WrKHn+BVoyMjD29+90vHbrVqq+/IrmmTMJ+/PTQ7bnrD6Zz5iB77ddb71jc+21lL3+BlVffUXtjh04/fEPWF9/fb86ftqWFgqfegoDJyccfvMbfRW7z2Qn9fKmGBri8tzfaM3KxnT85TnAfSmymDuXsldeJfOaaxFNTdjctAanP/6xYxsx69WrKH35FSreeRcAAxcXLBYsGMESDw8jT0/8d2yn6WQiLenptJw9S0t6OvX79tFeVYnrX//a5euak0/RmpmJy13P9ev9ZEd1mCmKMiIzcUY+Pvhv36bXc5qGhVF/441EvP46DTEx1G7dRs2GjSiKqs9JT4RGQ2NcHHU7d1G3bx/txcXAuQt+3jxU5uZUffUVTbFxuL388kVp+9srKyl5/gVqt2zByM8Ppyef0Ovnu5D9XXehMjXD9qY13Y4WXU4cHniAmu9/oGztWtxfeYXCPz2OysQE13//q2O2yWr5cup27ab89TewXLCAhiNHMQkNHfKsrZKkD+azZ4FKRf2Bg2gbG2lOSsLl78+N+Kyk1fLllLz4H2q2bMHpsccuOiba2ih78y2MQ0Mouf22S6KT2hu1tTUuzz6Dza9uoPgf/6TomWdpKy7B8aHf9fkcZa/9l9aMDDw/+ED+fksjwnLBAlgw0qWQ+sM4KAgjb29Qq3H9x987LYszdHHB/eWXsL1pDWWvv4H11VeP+P1huBi6uOjyHSz7OSlY4V/+Qs2GjTg+8kiX7byazZtRDA37HfV2efxFpSGlGBhgMXs2FrNno7a2pvKTT7C58Vc9bhrfGBdH7bZt1O7Zg6asHMXYGIt5czH/7W8xnzEdQy+vjlFoyyuXUfjEk2TffDNOjz2K3d13U7t1KyXPv4CmoQGHBx/E/oHfDGm2RLW1dZdrei9XBnZ22N17D+Wvv0H+Y7+n+fRp3N94HUMnp47nKIqCy9/+SmZsLAWPP0HL2bPY33PPCJZakvrOwNYW04kTqT9wgMa4OAycnLAegn2v+10uR0fMZ82idstWHB955KIw5OofNtCWl4fHu++QO4JlHAomwcF4f/4ZRU89Tfmbb2ISGorlot7XCjfGxlL5v/9hc9MaLOboZ9mBJEljn6Io+G7aiGJo2OO+wGaRkXh/+skwlmx0sr/rLmq+/4Hq9etx+O3F+RBEWxu127ZhsXBhvwcLx/Z+GtKwc3jod6gd7Cn+xz8RWm2Xz6n84gtybr2N6h82YDZ5Cu6vvsK4I4fxeOMNbNfciJG390WhUubTpuG3cQOWixZR+vIrnF24iMInn8LI2xvf77/D8ZGHZUr/EWB/552oHRyo37cP6xuux+qKKzo9x8DODpe//pWWlBRob5dJJKRLisX8eTQnJ9MYE4PdPXePmt8Z69WraCsooCnh560TtC0tlL/zDqbh4XrdfmM0URQFl+f+hklYGIVPPEFLZlaPz9c2NFD49J8x9PDA+fHHh6mUkiSNFSoTkx47qdLPjAMDMZ87l8ovvkTb0nLRsfrDh9FUVg5o/3HZUZX0Sm1hgfPjj9OcmEjNhg2djjdER1Py7+exmD+fcYcP4fH6f3X7yfaylYjaxgb3/76G6z//gcrMDOdnnsH7yy8wGTduqD6K1AuVuTkuzz6L+by5uJxL1NUVq2VLsVq1CpW1NaY9pPGXpNHGfK4uq7faxgbbG0fPNlSWixejmJpSs3lLx2PVX39De3Exjo89OqbXRKpMTPB443UUQ0PyH3oITX19t88teekl2vLzcXv+33K7KkmSpCFmf/ddaMrLqd269aLHazdvRm1jg8Xcuf0+p+yoSnpntWoVphERlL7yKpqamo7HW3NzKXj0MYx8fXB75eV+NxwURcHmhhvw37kDu9tulaNco4DVsqV4rVvXa126Pf9v/LdtHTUzUpLUFyahIZhOmYLDww+Nqg3cVebmWC5ZQu3OnWhbW9E2NlL+3nuYTZ+O+RAkKxttDN3cdJk3c3IofOqpLqN36g8eonr919jdfTdmkZEjUEpJkqTLi9nMmRgHBVH5v/8hhABAU1dH3Y/7sFp+FcoA2oCyoyrpnaIouDzzFzTV1ZS98SYAmvp68h58EADPt99GbWExkkWUhpliYICBg8NIF0OS+kVRqfD58gvs9Lw3sz5Yr16FtqaGhgMHqPziCzQVFTg++uhIF2vYmE+fhvOTT1C/90fd1mGFhTQeP071xo2UvfUWRX/+M0YB/jg++shIF1WSJOmyoCgKdnffRUv6WRoOHQKgbvduREsL1qv7H/YLekympCiKH/AtYA18LIT41y+OXwc8B6iBLUKIJ/X13tLoYxISgu1NN1H15ZfYXKfbXqA1KxuvDz/AyMtrpIsnSZJ0STOfORO1gwNVX35F06lTmM+fh9llFlpve/vtNCUnU/7mm5S/+ebPBxQFQ09P3P/zH73u7S1JkiT1zHr5cspeXUvlxx9jMXcuNZs2Y+TtjcmkSQM6nz6z/j4D/AvYBBxRFGWDEOL0BcergPlADXBUUZQwIcQpPb6/NMo4PvIwtTt2kHPnXWjr6nB+9pnLIixNkiRpqCkGBlivWE7lJ58C4PjI5TdzqCgKrn//O6aTJqEYGmLk4YGhuzsGrq5ymYEkSdIIUIyMsL3tNspefZW6/ftpPHYMh4cfGnDuBH2G/s4HtgshNMDWc//uIITYL4SoPHc8D5CbKY5xahsbHP/we7R1ddisWYPtLbeMdJEkSZLGDKtVulAqy6VLR2R/7tFAZWKC3a23YnvjjZjPmoWRt7fspEqSJI0g2zU3opiaUvj4EwADDvsFUM4vdh0sRVGKhRAu5/7/N4CbEOKvXTzPFjgOTBBCNHVx/H7gfgBnZ+eI9evX66V80tCor6/Hoqf1pkJgmJVF27lNk6VLR691K13SZP2OAUJgcvQorWFhaK2tLzok63fsknU7tsn6Hdsul/q1XP81ZlFRtPr5UfVE79uDLVy4ME4I0Snz3YBCfxVF+TXw6188/Mseb3dzvO8Az3fVSQUQQqwD1gFERkaKBQsWDKSI0jCJioqi1zpa2Pum7NLo06e6lS5Zsn7HiG5+X2X9jl2ybsc2Wb9j2+VSv63+/mRGR+N9/31MGsTnHVBHVQjxAfDBhY8pipKmKIqJEKIZcAJKf/k6RVH+CDQIIT4cyPtKkiRJkiRJkiRJo5eRpyfjDh9CGeTWbvpco7oHWKEoihpYBexVFMVfUZTvARRFWQqsAB7Q43tKkiRJkiRJkiRJo4jK3HzASZTO0+caVUdgC7rZ1I+EEP9UFGUS8LkQYoKiKKnoZnDrzr1kvRDihV7OWQbk6KWAw8caXWbjy4UDUD7ShRgmsm7HNlm/Y5us37HtcqpfWbdjm6zfsU3Wb9e8hRCOv3xQbx1VSUdRlHVCiPtHuhzDRVGU2K4WP49Fsm7HNlm/Y5us37HtcqpfWbdjm6zfsU3Wb//oM/RX0tky0gWQhoys27FN1u/YJut3bJP1O3bJuh3bZP2ObYOqX9lR1TMhhLzgxihZt2ObrN+xTdbv2Cbrd+ySdTu2yfod2wZbv7KjKg3WupEugDRkZN2ObbJ+xzZZv2OXrNuxTdbv2Cbrtx/kGlVJkiRJkiRJkiRpVJEzqpIkSZIkSZIkSdKoIjuqkiRJkiRJkiRJ0qgiO6qSJEmSJEmSJEnSqCI7qpIkSZIkSZIkSdKoIjuqkiRJkiRJkiRJ0qhiMNIF6ImDg4Pw8fEZ6WJIPWhoaMDc3HykiyENAVm3Y5us37FN1u/YJet2bJP1O7bJ+u1aXFxcuRDC8ZePj+qOqo+PD7GxsSNdDKkHUVFRLFiwYKSLIQ0BWbdjm6zfsU3W79ilz7qtLGzg5L48Zl7rj4m5oV7OKQ2OvHbHNlm/XVMUJaerx2XoryRJkiRJ0mUodnsWpw8Vsv2dRNpbNSNdHEmSpIvIjqokSZIkSdJlprmhjcwT5Th5W1KUUcOuD06h1WhHuliSJA0hoRXknKpgz0enKMqoGeni9GpUh/5KkiRJkiRJ+pd+vARNu5YFtwVTnFHDgfVpRH2ZysLbglEUZaSLJ0mSHrU2tZMSXURSVAHVJY0AFKZXc9P/TcfYdPR2B0dvySRJkiRJkqQhceZIEQ6eFjh6WuLoaUljbSux27MxszRixjX+I108SZL0QAhBzOZMEvfl09aiwdnXiivuCcXCzoSNr8Rz+Nt0Ft0RMtLF7JbsqEqSJEmSJF1GyvPrKMutY+6acR2PTVvlS2NdK3E7dTlNTC2NqCpppPrcfyqVws1/m46hkXqkii1JUj+VZtcRtyMH30kORFzlg7OPVcexycu8id+Zg99kR3wmOIxgKbsn16hKkiRJkiRdRs4cKUJloDBumnPHY4qiMP/mIPzCHYnbmcOhb9M5G1tCe6sGWxcz6iqbKcupHcFSS5eDwvRqyvPrRroYY0ZKdBFqQxWL7wq9qJMKMG2FL/bu5uz/PIXmhrYRKmHP5IyqJEmSJEm0t2mI+jyV8Cu8cPCwGOniSENE06YlLaYEv0mOnbakUakUlt0/noqCeixsjDGxMERRFJrqWvno8UMUZ9XiFmg7QiWXxrrG2la2vHkSlUrhV09HYuNkNtJFuqRp2rSkHy/BL9yxy3WoakMVi+8M5bsXYjn4TRpX3B02AqXsmZxRlSRJkiSJrJPlpMYUc3TD2ZEuijSEshLLaW5oI2SWa5fHVSoFR09LTC2NOpIqmVoaYeVgQmmWnFHtTVN9a0eyGql/4nZmo2nToiiw870k2uSWSYOSlVhOS2M7wTNdun2Oo5clEVd5kxZTQuaJsmEsXd/Ijqok6YEQgtamdmrLm6gtbxrp4kiSdBnQtGs5fbiQpvpWvZwv/XgJALmnKinLlaF3Y9WZI0VY2BrjEWLXr9c5+1pTLDuqPWqsbeW7F2L5/j9xaNrlVj/9UVfZTPKBAkJmurD03jAqChuI+iIFIcRIF+2SlRJdhLmNMR7BPV/rEct9cPC0IOqLFJrq9HM/0RfZUR1mFQX1fPm3aDLiS0e6KNIgleXW8c2/j/PRE4d496Eo3v/9AT575iifPXuU4szRvzfVcCrJqiV2exZarbzhSJI+VJc28v1/4tj/WQpxO3IGfb7mhjZykisImeWKkYm6I6GONLbUVzWTd7qCoBkuqFT924LG2ceKhuoW6qtahqh0l7a2Vg3b30mktqKZ5oY28s5UjnSRLinHt2UBELnCF68we6av8iUtpoTknwpGuGSXpsbaVnJPVRI0vfdrXa1WseSuUJob2knYnTtMJdTRagUHvkrt9vgl3VHVaLRkJJRSkn1pjPBVFTew6bUEqoobObohQzbaL2Gadi17/3ea+uoWfCc6EL7Ei1nXBbDojmDMrYw49G06QtYvoGsAb383kZjNWRz6Ok2OjkrSIKXGFPPNv45TW96EjbMZOckVgz5nRnwpWo1g/Hx3xi/wICOhVIYvjkEp0cUIAcEzuw777Ymzny4RS0m2HIj9Ja1WsPej05Rk17Ls1+MxNjPgbKyckOirquIGUo4WM2GeB5Z2JgBEXOmDz0QHDn2TTlHGpfed02i0I9rOTztWjNCKHsN+L2TvboHPBHtSoouGNRog91QFST0MRgy4o6ooyrOKopxVFOW4oig+vzh2r6Io6YqiJCuKEnHuMTtFUaIURclQFOXtgb7vL96HfZ+mcPpwoT5ON6SqSxvZuDYBFIUZ1/hRU9YkZ1UvYfG7cqgsbGDRbcEsvC2Ymdf6M3mpFyGz3Jh+tT8lWbWkx5aMdDFHhYNfp9Fc10ZAhBNJPxWQsGd4R+skaaxobW7nx/+dZu/Hp3HwtGDNM9OYsMCjY/uQwUg7VoKNsxmOXpZMWuSJ2kBF/C45qzraaLWCpkoxoAE/IQQpR4pwC7QZUJIaRw9LVAYKJTL8t5MjP5wl80QZc24IJCDCCd9wRzJPltHeJtdY9sWxrVmoDVVMudK74zFFpbDkrhAs7E3YtS6JhppLYyZfaAWnDhbw8eOH+OD3B9i4Np6jGzLIPFE2bJ9BCEHK0SKcfa2wdTHv8+tC57jRVNdGdlL5EJbuYqcPFWJqZdTt8QF1VBVF8QauBYKAvwMvXnDMFHgKmAzcDLx17tBDwGYhhD/grCjKVQN57wupVAqu/tYUpVcP9lRDqra8iU1rE9C2C65+NJwpS72xdTEjbmeOnF26BFUWNRC7I5vASCd8Jnbedyp4hguOXpYc3ZBB+wgmAijNqSX5QMGIfscyEkpJO1ZCxHIflt4bRkCkE0d/yCDtePGIlWm0EEIQ9WUq299JpLW5faSLM+Q0bVrK8upGbQr8wdC0a9n2duKQDj62t2r47sU4UmKKiVzhwzW/n4ylnQk+E+wBBjWrWlfZTGF6NeOmOaMoCmZWRoTOdiM1ppi6ymZ9fQRJD+J35ZC5W7Dt7cR+NXq1WkHyTwXUlDURMrv/s6mgyxDq4GEpO6q/kLg/n5N785i40INJiz0BCIxwoq1ZQ+4pGf7bm7K8Os7GlhK+2BOzX3RYjM0Mueo3E2hpbGf/5ykjVMKfCSE4uiGDoxsyqCis73S8qriBjWsTiPoiFXt3C4JnuNDapOHEnlx2vJvE/548TMoGLT+8HMe+z84QvzuHrJNlem8DlOfVU1HQQPCMvs2mnucVZo+5jTGnDxXptTzdaahpITupgpAeZn0Huj3NQmCHEEKjKMoOfu6MAkwHjgkh6oEkRVEcFEUxAxYB9517zkZgAbBjgO/fwTXAmpzkCprqWzG16L5HPlLqq5rZ9FoCbS0arv79ZOzddSn/pyzz5sdPzpCTXDFqN9mVOhNaQdTnKRgaqZlz47gun6OoFGbfEMDGVxM4sTePyOU+w1rGuspmojdlkBajm9FVGyiEzHIb1jKAbn3ET1+mdmSUU1QKi+8MobGmlR8/OYO5lTHuQZfvNgfxu3I4dUAX7rLl9ROsfGgSxmaGvbzq0iCEoDSnrmM/vPK8eqqLG9FqBU7ellz/RAQq9SW98uQiifvzyU4sp6a0Eb/Jjh2ZUvXpzJEiqooauOqBCfiFO3Y8buVgiq2LGdlJ5R2N5P46H/0ROPXnPTXDr/Dk1IECTuzNZW43v3XS8GptaufEnlyMrCA/pYr1fz/G/FuCCIhw6vY1Wo2WtOMlxO3IobqkESdvS/wnd//83jj7WnHmcCFajXZMXcP9JYSgsrCBrJNlHNuShc9EB2b/KrDjuHuwLSbmhpyNK73oepU6i9mcibGZAeFLuv79cvCwYNpqP458f5bcUxV4hdkPcwl/djautCPSJH5XDo5elgRNd8F/iiMpR4uI3Z6DgZGKhbcHEzLLteNe0N6qoSyvnpKsGk7F67KqZyeW01SnG7i1djRl2X3jcfSy1Es5U47q9kkOiHTu/ckXUKkUQma5Ersjm7rK5o4w7KGScrQIoRU9tlGVgcy2KIryBNAmhFh77t8FgNe5juuNwGwhxKPnjh0FbgG2nXu8SlGUZcAtQog7uzj3/cD9AM7OzhHr16/vsSwNpYLsfQLPuQpW7oNvHNQXC0xswMBk8OfSagQZuwTtTeCzQMHU/udzCq0gfavA0Ax8l4zMj73QCmrzwMIV1EYD+7z19fVYWFw+++1VnhUUxQrcpinY+vX8N8s9pKWhGAJWKBiaXvxcTaugqRLMHEGl1k+jVtMmKD8jqEgFBNgHQWMZtNRCwHKl39/pwdStEIK8w4L6QvBbpmBi/fN7a1oFWXsFbU3gu+TiY5eLukJB7gGBlRdYeyrkHxUYW4P3AgUD4+H5ewzFtatpE9TkQNVZQfO5QBcDUzCxBRMbUBQoOwVOExUcQ8dGvbc3C9K3CRQVaFrAe6GChbN+P9uF9wufxUqnjnDxCS2VaRB0rYLaUHesP/WbsVOLoga/Ky6+FxVEa6nJg3Grh+97KXWv7JSgNEngMqcJCysz8qMFzZVg7Q2uEQpqIwWtRqBpBU0rNFVA+WlBa73u+nMMU7D0YFADKdXZgoJogf8yBRPbof1OaFoFRfGCpnLwmKVgajey30Ftu6CuAOqLBPXF0H4u2MDcGbzmKqgMLi5f4XEtNTkQdE3nYz25nNpVjWWCrB9Fr/cErUaQsUP3O+t/pYLSz0Rg+qBpE5zdJjAwBa95CrW5uuuhuern51h5gsuUzm2+C11Yv5pWQWM5FB4XaFrAOVzBLnBw16hWI0jbLDB3As/Z/e9ftNbr7jeO4xWcxg/d31mIc/c1c/BdpGLhwoVxQojIXz5voDOqv+zdKj0cO3+8p9f8/GIh1gHrACIjI8WCBQt6LEh7m4b3DxzA3sST2QsCenxub2rLm/hs/VGCZ7qw4M7QQZ0LIO90JWdqT7D012EEdjGqYa/kc/DrNMa5h+MWaDPo9+uv9OMl7D56Cs8QW1Y+HN7vDIAAUVFR9FZHY0V9VQtfbozGI9iK1XeH9/pDUh3ayFfPxaAud2HB7SGArsGZEl3M0W1naaprw8LWmMlLvQmd7YqBkXrAZSvJqmXbO4k01bYSONWZGdf4YWVvSmVhA1//6xgUObPg7v59pwdTt2nHijmdf5qZ1/ozZZl3p+O1EU18/2IcpcdU3PLX6YP67JeaquIGvtsYi4OnKdf9KQJDIzU5kyvY8V4SZTGmrH40HHNr4yEvx0DqtzC9isPfZ9BQ1YyVoynWDqZYOZpi5WBKcWYNGTHFtDVrsHe3YPpyd/wnO2Jq+XOkixCCXeuSyUoqZ/E1U7Fz6/vamdFq/+cpoCnihicj2bT2BOoqGxasmaDX9zhzpIjTjWdYevfELiNwCtyq2JiSgI9DWMdsWV/rt6KwnlPrjzF3TSATF1w8o1EZ1MBXf4/BssWb6cv89PJZpIFpbWrn081H8JlgjblHFQsWLECzQkv8zhyOb8umoUhBCF1j+kIOnhZMvc0X34kOemnc15Q18nl0NJ4O4wib6z7o83Wn8Gw1ez/SJSw0MTcgd7+GJfeEDmo2uCtarSDrZBktDe2EzHbt9r6uadeyaW0CRRk1GJsb4DPeDs8Q3X/dzTrluVSy+bUTeNuF4T+l7+Ueze2q5oY2KvLrqa9qpq5KlwG6oaoZe3cLIpf79PlermnXkn68hOMnszC10nLNvTMxNO75tT72Zex4NwlHdSDj53vo4+P0y8Fv0mhvyeeaxyJx9rHqeLyyqIHMhDIcvSzxHt/7bG9X9du8uo0fPzlNdnwFphoHFt0Rgon5wCKsMk+UcaYlibmru75f9EVzRgJVhY3Me3DWgPoGfZGXUsnphhMsWBPKuGn6D/0tAkIAFEUxAIQQQnPBsQv/Mo5A2QWPVwFOgF4W8xgYqnH2tqLo7ODXqaYd04U/nY0vY+6adoxMBvrn0ck8WYaBkQrfLtYxAoTMdiV2exZxO7NxCwwf1Hv1lxCCuF05GJmoyTtTRey2LKatkg2R7gghOLA+FaERLLg1qE+jXTZOZkxY6MHJH/OYsMADRVE48FUqRRk1OPtaMev6AE4fKuTg12nE7cgm/Aovwua69ft7p9UKor5MQa1WuP7JCFx8rTuO2bmZM2WZN7Hbswma4YJnP/fNG4iG6hYOrE/Dxc+K8Cu8unyOlb0pS+8NY+PaBBKj8pmytHNndixqaWxj+ztJqA1VLP/tRAzP3dS9x9uz8ncT2fZ2IhtfTeDqx8KxsB3akJv+qK9q5sgPGaQfL8HCzhjPEDtqypvIO1NJQ7RuzzW1gYqACCfGz3fH2deqy2tEURTm3RxEQVo1+z47w3WPRwzZTbAnQgjyU6owszbC3m3gMxdleXWcPlzIpIWeOHhYEjrHlYTduXoNmdJqBfG7cnDwtOi2EeTib42xmQHZSRX9bsinHytBUSkERHQeTLVzNccv3JGkqHwmX+GFkeng7onSwCVG5dPS2M7Ulb6cztJN4ajVKqau8MV7vD1njhRhYKTG2MwAE3NDjM0MsLAxxsXfWq+h6FYOpphYGFKSVTskHVWNRkvstmzidmRjaW/CdX+agpWDKdvfSWTne8nMuMaPKcu8B/2ZNG1aUmOKSdiT25GIrKWxnclLu75nHf5Wl3l24e3BBM907dPvlnugDaaWhqTHlvarozoa1ZY3cWJvHmcOF9Le9nNGWGNzA8ysjMlOqiAjoYzFd4bg4mfd7XlaGts4dbCQxH15NNS0Yu9uzsLbQ3rtpAL4TnLAfZwNMZuzCJzqPKxLZcpy60jan8/4ue4XdVJB9ztp5zq4QVcTC0OWPziRxH35HPnhLF//8xgrfjcJB4/+3Z+EEJw5UoSplRFeoQNv74XOcWfX+8nkn6kcslDrM4cKMTYzwG9yz6HxA73r7AV+ryjK/wHLgYOKorwE7AGigHWKolgA/kCZEKJOUZRdwGrgFXSJmNYN8L07cQ2w4cSeXNpaNR0Nv/4SQpAaU4yZtRGNNa1kxJcRMmtgCQdAN3OWdbIcrzD7bkeYDI3UTFrsSfTGTMpy6/QWm94XuacrqcivZ9EdwRSmV3N8ezYuftYjGvvfFSHEkKz36q+EPblknSxn5nX+WDv2PVq1eWsAACAASURBVFvi1OU+pB4tZvvbiTTUtGJsZqBbuzDTFUWlEDzDlYK0KmK3Z3Pk+7PE7cjGP8KJcZHOuAXa9GkEPDW6mPK8epbeG3ZRJ/W8iKu8SY8tIerLVG5+dtqQzl5q2rTsej8ZTZuWxXeG9ngzdw+yxSvMnvidOYTNcRvUTaelqZ2805Wo1MqoXQ+k1Qp2f3iK2rImrj6XCOdCHsF2rH4knK1vnmTrm4nc+OfIfq8BK0yvpqq4AZ+JDnqZldW0aTnxYy6x27MRWohc4cOUZd4X/c62tWqoK2/GzNqoTyPAZlZGzL0pkD0fnubk3rxuG4ZDpaasiagvUshPqQIFgqe7MHWVL1b2pv06jxCCQ9+kY2JmSOQKHwDC5roTvzuXUwcKmHGNv17Km5lQRnVJI8vuG9/tb6FarcIz1I6cpHKEVvR55kwIQdrxEjyDbTslMTlvyjJvMhPKSI8tGdIZtL5orG0lI74Ut0CbjnwPY0l397vWpnZO7M3FZ4I9Tt5WnM66+LiTtxVO3ladXjcUFEXB2ceKYj0mVBJCUFfRTFluHfG7cynNriV4litzbwzsGLi95g+T2fdpCtEbM6kqbmThrcGoDQcQ1tjczqkDhZz4MZfGmlYcvSxZdt94MuJLOfLDWcxtjRg39eLZnZSjRST9VED4Ek9CZ/c934NKrcJ/ihMpR4pobR785MdIKMutI2FPLmfjSlEUGDfdhXGRzljam2BuY9zRwcxLqWTfp2f44aU4Ji3xYvoq3462RmNtKwVpVeSnVJF+vIS2Fg0ewbYsujMEzxC7PrfxFEVh9q8C+ebfxzm+PZs5NwT2/iI9EFrBT1+lYmJhyIxrhm5CR1EUJi32xDXAmm1vJbL/8xRueDKi179Pe6uG/NQqspMqyEkqp76qhSnLvAa1htx3ogMmFoacPlw4JP2CpvpWMk6UMX6uOwaGPbdJB3TVCCGKFUX5GEgFGoBVwMuAnRCiVVGUZ4AEQAvcdO5lbwI/KIryILBHCLFzIO/dFdcAa+J3CUqzagecnKU0p47qkkYW3BrEib15pBwtGlRHtTS3jobqFnwn9TztPn6+B/E7c4jbmcOV948f8Pv1V/zOHCxsjRk3zYWASGfKcuvY89FpbvzL1CFfPN1X6bEl7PvkDB4hdoyf545nqN2wz74IITj6QwYJe3Lxn+JIeD+TlRib6X7Yor5MJWyuOzOu9uvUmHcfZ4v7OFuKM2tI3J9PWkwxpw8WYm5tRECkM0EzXHD07HoQo61FQ/SmDJx9rQiI7HrE1sBQzYJbgtj02glit2frrQH9S0IIos7NGC/9dRg2zr136Gdc48c3/zpO/K5cZl7b93IJIagqaiQ7uZzc5AqKztbo9itT4JrfT8Z93OhL0nRsSya5pyqZf0tQt6H+rgE2LLozhJ3vJZN8oJCJC/se3iSEYM/Hp6ivbIEvU3H1t8Z/shO+4Q797oSdP9/m109QmF6N32RHZl8fgJVD5/MYGqn7HcIbGOnM2dhSYjZn4jPRvl/p8wdKq9Fy8sd8jm3JRFErzF0TSH1VC4n78kmLLWHCAg8ir/TBxKJvAyaZCWUUplcz/+ZxHde0lYMpPhMcOH24kKkrfAfUkL6QEIK4ndnYOJv1OursM8GBs7GllObWdRrxb23WZcy0dzMndI57R6e0OKOGuopmpq/y7fa8Tt6WWNgak3emasQ6qpp2LYn784ndlkVrsy54y32cDRMWeuA70WFYkvrUljd1+f0fDK1WUJpdS3ZiOdlJ5dSUNTHjGn8mLvS4qHGaGJVPS4NuNnU0cPa1IudUBa1N7QOeZW9uaCNhdy6lObWU5dXR0qDLempsbsCy+8Z3ShBlYKjmintCsXUx49iWLGrLm1jxu0kY9+P9a8oa2fZWIlXFjXgE27LkrlA8gm1RFAWfifY01rby4//OYGZljMe5tmRpTi1RX6TiHmTbr3vUeQERTiT/VEBOUsVFycouBQfWp5EUlY+hiW5SZdIij24jfTyD7bj52ekc/uEsJ/bkkp1YjmeIHQVpVVQWNgBgZKLGN1y373x3bZreOHpaEjrLtWN2sy/tjF/KSiznyPdnsXUxY/GdIb0Okp86VEhJVi1L7g4dlllcJ28rZlzjx75PU8g8UdZtlIxGo2X/ZylkxJXS3qbF0FiNZ4gdU1faEzS9f9l+f0ltqCJohgtJ+/NprG3tdiBzoFKji9G2C0Ln9D7wM+DhHSHEm+g6n+fdeMGxH4AffvH8JmDQW9J0xcXPGhQoyqgecEc1Naa4I3StuaGN6I2Z1JQ19mv27EJZJ8pQVAo+43vuqBqbGjB+gQfxu3KoKm4YlgZbcWYNhenVzPlVIGoDFWrgyvsn8M3zx9n1fjLX/nEKaoORzeaXc6qCvR+fxsbZjJKsGrITy7G0NyFsrhshs9z0ftF0RaPREvVZCinRxYyf787cNeMG1FEOm+tOYKRzrzd0Fz9rXPysaWvRkJ1YTtrxEpKi8jm5L48r7gntNMoLkLA7h8aaVq68f0KPo24ewXYEz3AhYXcugVOdh2Q24uSPeaQcKSJyhU+Xa7K74uhpSeBUZxL36VL7m9t0PwvY1qqhILWKnOQKcpIqOrbNsHe3IHypF54hdkR9nsLe/53mpmen96sBM9TK8+uI35VL8AwXxs/rubHvF+6IR7Atx7ZkEjjVqc/ZzEuya6mvbOlozGYmlHLo23QOfZuOlaMpts5m2DiZYe1kio2zGdr2nhPpZZ0spzC9Wrd2ceHAssl2R1EU5t8SxFfPxbDv0zNc+6ehDQEuy6tj/2cplOXW4TPRgfk3j+tocE1Y4MHxrVkk/pjHmUOFBE51xiPYDvcgm27/9u1tGg5/fxZ7d/NON9oJC9zJTiznbHzpoBsLuacqKc/TRb709vfxCrMDBbKTyjt1VA9/m87ZuFLOxsLxbdn4T3Fi4kIP0o6VYGCowreHKARFUXAPsiUnuaJfs7X6IIQgO6mCw9+lU1PahFeYPVNX+FCYXk3ST/nsfC8ZC1tjxs93J3yx16AHBrqTeUK3Nm75gxO7XcrTH421rURvzCA7SZf1U1EpuAVYY2ppxKFv0ilMr2bR7cEYmxnS2qybTfU+N5s6Gjj7WIGAkpxaPIMHFl54dEMGZw4X4uBpiX+4I47eVjh6WWLvbt7tDIuiKExd4YuNkxl7Pz7N5tcSWPVIeJ8iOQrSqtj5XjJCCFY/Gt5pGYyBoZqrHpjAhlfi2fFuEtf9aQpm1kbseC8JU0tDlv06bEADIq4BNphZG3E2rvSS6qjmJFeQFJVP6Fw3Zl3r36cOmpGpAQtvDSZgshP7Pj/DmcOFuAbaEDTdBfdxtjh6WehlUGnaaj/SY3Uz4Mt/O7HPr6spa+LQN2lkJ1Vg5WBCTlIF374Qy1UPTOh2Ccj5a9U9yIZx04av/oKm69prMZsyux2MS9iVS2p0MaFz3AiY4oRboI1efwNDZ7lxcm8eqdHFeo18EkJw+rBuj9e+tEVHT0tuEEzMDbF3M6fwbM2AXq/R6BZ1+0x0wNjMkKDprsRsyiTlaDHTVw9smj/zZDlugdZ9Gp2ftMiTpP35HFifxupHwoe8IRC3Mwdjc4OLGlg2zmYsuj2EXe8nc+SHs0O+HUFNWRNW9iZdftais9XsfDcJOzdzrvn9ZAyM1GSeKOPUwQKiN2ZybEsW4+e5E7nc56JELfrU1qph1/vJ5CRVMHWlL1NX+AwqBLk/o86GxmoCpzoTONWZ5oY2drybxI8fn8HIxOCihfH1VS0k7MklIMIJV//u14ScN+uGALKTKoj6IoXr/hSh1+9ZdpJuhNJ/iiPTVvRv1H/6aj8y4ks5vi2LBbcGdzqen1pFwu5cCtKq0LRpMTBW4xlsS8RV3niPt79ohHfJ3aH88FIch75OY/Fdg0+Ipg9CK4j6IhVjMwNm9yFUSVEU5q4Zx9f/OEb0pkwWdvE36UpGXCkqtcKkRR4YmxkybaUv1SWNZJ4o00WMlDZSkFZFe6tufZGJLWgWaLsclNJqBdEbM7BxNuu1Yz1Q5tbGzF0zjr0fnyZpf/6At1bpTW1FExteicfQSM2y+8bjP+Xi7WMs7UxYdEcIk5Z4Erstm7TjJZw6WAjoEtF4BNli7WiKoYkBhsZqjEwNyE2uoK6imasfC+/UgPAMtsPG2YykqPxBdVSFEMTtyMbCzphxfTiPqYURLr7W5CRVMP2CfANZJ8s4fbiIKVd6EzLTlaSf8kk5UkT68RJQdDM+vYUkegTZkhpdTEVhQ7/XTA1UW4uGneuSyD1Via2LGSsfmtSxRtfFz5rwK7zITiwnKSqf6I2ZVBY1sOSu0CFZKnL6kO77EL0xA+/x9oMaVNG0a9n5XhKluXX4hTviM9Eer1B7TMwNEUJwYm8e0Rsy+CbvOMvuG0/emUrdbGo/f1eHktO5gZCSrIF1VLUaLZknygiIcGLpr/sfSRY41RkDYzU71yWx6bUErn50co9trVMHCzjwVRrWTqYsf3AiNk5dT0CYmBuy8qFJfP9iLFveOIm1oylNtW1c9/iUAbc1VCqFgClOnDpYOKgZ6OHU2txO1Jcp2LqYMe/Gcf3u/HiG2nHHP2ehFQL1EEQ7mFsbE3GVN9EbM8lPqcSjl+9ge6uG+F05xO/KRaVWmHVdABMXeVCSXcuudcl892Ici+8IuWgWX2gFpbl1HN+aRVuLhvk39y03ib6o1CpmXO3PjveSSIku7hRyXlFYz/HtWfhPcWLhbX1rI/SXnZs5Ln7WnD5cSPgVnnr7/MWZtVQVNbDw9r6Ve/RfMX3k6m9DakzxgPb2yj1VSXN9G0HnNsa1sNUlC0mJLmLaSt9+N+irSxqpKmogbG7f4ufNrIyYfUMAUV+kknyggAkLhi6bWUVhPdmJ5Uxd6dtp8XpAhBNFGR4k7svH3t2iX2sx+iM7qZxtbyVi727OtJV++IY7dFwA5fl1bH0rEQs7E1Y9HN4xihcY6UxgpDNVxQ2c2JtH0k8FnDlSxOSlXkxa7KnXtR/VpY38+L8zFGfVMP+WoCFrqPeFiblugf2mtQnsXJfM6kd+zhAdszkDrVb0ORzJ1MKImdf5s/+zFLISy/W2lrOysIHdH57C3sOCxXeG9vt6sXY0JWyuO8kHCghf4tURyiOELolMzKZMzG2MCZvrhs94hx5HDV38rIm4yofY7dn4TnLsMlxSq9GiaRd9St6gDx1hQ3eF9Dms1M7VXJeIa18e4+e697p+XQhBRnwZniF2F4182zibXZR1WQhBQ3Ur2Unl/PRlKvG7crpsAKdGF1NV3MiV948f0rDKcdOcSY0p5vj2LEJmueq9ESeEbpBACLj+iYgeQzft3SxYdt94tBotpTl15KdUkZ9aSWJUfpezz76THLpsICkqhfHz3Dn0bTqlObUDngUrOltNUUYNc9eM63Njz3uCPTGbMmmoaQF0swH7P0/BwdOCaSt9URuomHvjOKav9iPtWAln40oJX9z7SPn5SKWC1Kph66imxhSTe6qSmdf6M2mJZ6e/gUqlW4/uF+5I7PZsYjZnYutsRuRy/Xbo6qtayD1VgaOXJWW5daQfLxnUAMT5pDxL7w3rNMOmKAqTr/DC1d+aXe8n8/1LcagNVHiPt+80Sz6STMwNz0U7dV6nqmnX0lDd0uO1VpBeTXN9G/497P/aG9+JDix/YCI73k1i49p4Vj86uVOklVaj5cj3GZzcl4dXqB1L7xvfa6SNpZ0JKx+exA8vx+tmtu8IHvRMdkCkM4n788lKLB90lMVwOLY5i/rKFq7705QBz9ApKgV115t76MWkxZ6cOljIga/TWfOXqd1GAWratHz/UhzlefUETnVm1nUBWNjqIrfcAmy48c9T2bkuiV3vJ1Oa7YV7sC1ZJ8vJPllGQ00rigIzrvUflmjHX/INd8DJx4rjW7MYN825I9JAq9Gy7xPd5MW8m4Z2Uil0jhv7Pj1DfmrVgAal6qta0Gq1GJsZYmSsRlEpnD5UgKGxusf9ny80djqqgdYkHyigoqCh30mJUqOLMbEw1IVOnRM8y5XdH5zSVU4/M6VmnSwH6HV96oVC57iRmVDGkR/O4hlq1+2I32Al7M7FwEjFxG46w7OuC6CquJGoz1N0M3v93Cy4N0IIjm3JwsLWGE27YMd7STh4WjB9lR82zmZs/u8JjEzUrH40vMvwXlsXcxbeFkz4Ek9iNulmV5Oi8pm6wpfx89wHNUtYmlNL/K5cMhNKURmoWPbrzutkRoKxqQGrzt04t711kmv+MAWAlOhiJi/x6te6qeAZLue2M8jCd5LDoEfImuvb2Pb2SQyM1LostgPs/EUu9+HM0SKiN2Vy5f3jaW1q58dPz5CZUEZApG7EsK+DEZErfMhJrmD/Fyk4+1l1JBUSQnRcY/XVLQREODFhvke3GWr1oaGmhaMbMnAPsu3TrNiFpq70Je1YMQfWp3Hd41N6LGNpdh11lc29rmFTFEUXKjnPnROHUondno1fuONF4TftbRqObc3Eyduy13WRg6UoCtNX+/HdC7Ek7s8ncrmPXs+fcrSIvNOVzLtpXJ+vE5Va1RGGH7ncB027luaGNtqaNbQ2t9PWrKGtRYNrQPdRDMEzXYjenEnSTwUsvmNgjdzYHTmYWhoSOrvvuRJ8JjgQsylTF6YrBPs/T6G1ScPVvw+9qCFnZGLA+HnufR6Es7QzwdrRlPzUqiGb+f6l04cKsXe3YPJSr16vz4irvKkqaSBmcxbWTmZ6vW+lRBchBCz9dRi73k/m2JZMAiKcBrQ85syRc0l5rvDqMQzUxc+aNX+Zxt5PTpN7qnLUrE29kLOvFbmnKi5KAKVp07L1rZMUpldz63Mzur3mMuJ1OyIMNkmL93h7VvxuItvfTmTjq/GsfGgSdZXNFGXUUHS2hpKsGloa25m4yIPZ1wf0edDNwcOSqx+bTGVhPSGzBj9g7+JrhYWtManRRYyb5jwqEkR2pyS7lsT9eYyf545rwPBvm9hXBoZq5q0Zx7a3EzmxN5eIK326fF7czmxdsslutok0tzHmmj9M4dA36STsySVhTy4Gxmq8Qu3wneSAz3iHPg8w65uiKMy81p9NaxNI/kk3kA9wYm8epTl1LL03bMiXwQVEOHFsSya71iWz6pHwfg2YleXW8d0Lsbr8Iej2UTcyNaC1WaMbmO5jm25kFyLqkau/7oIq7Oc2NS2NbWQnlhMY6XzRiK3vJAeMzQxIOVrU77JknSzDwdOiXwlMFEVh4e3BqNQq9n1ypqNiB+J8g7w8vx4hfj5PbUUT6cdKCJvj3u2FpzZQcdUDE3Dxt2bvR6fJTiwfcDm6kpNcQVluHVNX+nLz/01j8Z0htDa1s+3tRL76RwxCwOpHw3tN6GTrYs6Vv5nA9U9EYOtizoH1aez/PAXRz7+b0ApyT1WwcW0C3z4fS96ZSiYv9eb2f84cFZ3U80wtjVj9aDhGpgZseeMEUV+kYGJuSMRV/dvWRaVWEbnCh/K8+o4BlYFqqG5h49p4GqpbWf7AhEEl4TKzMiJ8sScZ8aWkxhTz3YuxZJ0sZ/YNASy9N6xfM+ZqtYold4XS1qzRfSeEoCy3jo2v6malDYzUhMxyI+tkOd//J45vn4/l9OFC2ls1vZ+8nw5/m057m4YFt/Q/bMjY1ICZ1/pTnFnTsXVWd87G68J++zM45hKhYGxmwI+fnEGr+Xm7gVMHCqmvbGHGtf7D0qBy9rHCZ6IDJ/bm0tLYprfz1le1cOjbs7gF2gwqKkJtoMLc2hgbZzOcvK1wD7LtWCbSHd0SEhfSj5XQXN//z1SWW0fe6UomLfbsV5Zue3dzLGyNyU4spzoTshPLmXmt/6C24DnPPdiWwrSqi74rQ6Ust46y3DpC57j16TuoKAqLbgvB1d+aHz850+VM30Cc3+rBfZwNNk5mTF/tR215M2cOF/b7XKU5tfz0ZSoewbbM7EPmUBMLQ1Y8OJG7X5w9qmZTz3P2saKpro26Cl2uAK1WsOejU+SnVKHVio7w+V/SagWZCaX4THAY8C4NF/IMsWPlw5Ooq2rhs2eOsvHVBGI2ZVJX2Yz/FCeWPziRuTeO63dkiLOPlV46qXAuymK+O3lnqji4Pq3f7ZTBEFrR5/bk+eQ8ZlZGzBhA4qjh5jPRAb/Jjhzflk1NWVOn4xUF9cTtzGHcdOceB6/UBirm3xLE6kfCWfG7idz78hyu+s0Egme4jlgn9TyPIFs8Q+2I25FDS1M7VcUNHNuim2joLoGmPhkaq7n2j1MwNjNg02sJfe5jCSE4+E0aRud2uph1fQBTrvQmcKozQdOdmbKs72tex0xH1dLOBAs7Y4r6uU41I74MTbu2UziGgaFuNjEjoYyWpvY+n6+xtpWizJoBhVVa2Jowb00gRRk1nNyb1+/Xn5caU8yO95L4+p/H+OyZoxz8Oo38lEoSduUCMGlJzyPihkZq3f5NnhbsXJdMXkrlgMtyISEEx7dmYeVgQtAMF1RqFcEzXbnluRksvC0YtwAbVj8S3q8QCxc/a675w2QiV/hw5kjR/7N31+FRXekDx793ZuLuRIgRNwIR3N1pCy11/dW7ZdulDqXuu/VSpWWhWHF39wgJEQgB4oEYcZ/c3x+BLJQkJJOJEM7neXgKM3fOPdOTzNxzz3vel93/bdkkv6SgkhObLrB43hE2fBND4cUyBt7pwcMfDmTAHb20UtpD20ws9Zn6Yn293ZzUEsInu2mUgc4rzA4zGwNObLpw3Y2M1ii8VM6qzyIpyqtk0rNBzdZNa6k+Y5zRN9Zh58IEKstqmPZiMMGjb76a0hhLByMG3NGL1FP5rPtPNCs+OkFBdhnD7vPmnjfDGH6fN498PIhh93qhrq3/cv7rkwjUWrwIT0vI52xEDiHjXTXKTAjg098eW1dTDq9Oprqy8c8hWZY5F5mDk49lqwqEq/Qkht3r3VB+AOrLYERsScHJx0LjJCmaCJ/iRlV5LTG7NP/cu5Ysy+z78zR1tXWMeNCnQxMAXRU4zBF1bR0xu1v/nqJ3pKGjp2z1BFuSJFwCrUlPLOBitIyjt0WrMkc3x8nbgupKNblppVpprznxB7NQ6ihalbxEqVN/k9XITJdNP8Q2JFtri6yzhRTnVuB7ZRuMS4AV9h5mnNicQk0rbmyVF1ezZcEpDEx1GNuKpDySJLVbHoa2snO7sk81pbj+923Jac5F5zJohgduQfWZr2trbvx/lH22kIqSGq3WFXX0suCOl/oQNtmNSc8G8fjnQ7jv7X6MeMBHK8mvtKHvOBeCR/fk1L5M9i5p/U11Tch1Mhu/i2HVJxEt+nk9uSON/MxSht7r3aWSETZnyN2eKBRSfZ37a65n6upkdi9KRM9QxeCZLduG19PPEtdA65uWS+loA6b3qs+SvS2V3YsSUenWT6w7amXe1NqAO17ui5GZHhu+Pkl64s3nBGcjLpGdXMSA6b3wG+RAnzHO9J/Wi2H3ejPqYb9WJartNhNVqI83z04ubNXF95ljF+vvlLveGC7sM9AedU0dyRHNr2ZcKyU2D2Rw661ZyJxXvx649bbm2PrzDSm9W6OipJpDK5Pp4W7KiAd8sHIwIv5gFuu+PEnc/ky8+tm1aOWrPtw0GDNbAzb/cIrsc5olqrpWalw+OaklhIx3vW71WqlU4DfYgWmz+2hUS1aSJPpNcSdsshunj1xkz6LGJ6u1NWrOnrjE+q+iWfTmYY5vuICJlQGjH/XjwQ8G0mds1y9ob9HDiGmz+xA2yRW/IZrd7VUoFYRO1HxVNSe1mNWfR1JTpeaOl/rQsw1Fpa+la6Bi6D1euPW25u43wjTO4H1V0AgnnHwsyE4uoveonjzwbn8Chjo2XCTq6qsIGObErLn1K/v5mWUkHmp9BEVjaqvV7PvzDOZ2hoSMa92q97UkhcTQe7woL6omYlNKo8fkptWH/XqEtP4zp1dfW3r1teX4xgvkZ5VycmcalaU17VbCqCk2PU3o1ceGmF3pVJa1fVU16fglUk7l02+ae7tto7gZK0djPMPsiNqaSk5qy1f4SgoqSY7MwU/D2sKuAVb1CbMkGPWwr9Ym6VdLPmWc0c6Ny6bUVKk5e/wivfratOrGC9RHnkx6tjfqajWbvott1U3mxiQezkZXX9kQAi9JEv2n9aK8qJpTezJa1EZZURXbf42jorSGiU8HtTiLd1dn5WSMUkfBpQvFHF17noRD2YRMcCF4tDOBw52oLK3hXGTODa9LjspBpaNoSIylLbYupoRPdsM1qPNCNZsjSRID7/IgZIILCYey2dXEdYo2xe7JIC2+gJzUEvYvPdPssYU55ZzYlIJ7H5suW4u8McYW+vSb6k5afAHJ1/y8xe6uD48dco/XLf87Z+NsgkeoLZFbU7l4vpghd3t2+GKKsYU+d7zcFzMbAzZ9F9tstGV1ZS2HV53DxtkEnzaU+byqW01U7T3MKS+upjjvxhCAxhTnVZB1thDvfo3vGbB1McHC3qhV4b/nY3IxtdbHylGzjdeSJDH8fh909JTs/D2B8uJqamvULZ58H1x5lurKWkY84IvfYAcmPdebxz8fwoSnA+k9qmershjrG+sw9cVgjMx02fhtjEYT56tkWebEphRMrPQbklZpW/hkN8KnuHH66MWG8GlZlhtCrn5/9RDbf42n8FIFYRNdefD9AUz/Zx+8+/Xo9HI8rWHlaEz4FPc2ZdPzCrfDVINV1fTEAtb+OxqVjpK75oRovVyCZ5gdE58JarJWW2tIColJzwbx0EcDGTzDs8mLfkmS8O7fA/teZpzYdKFVKyVNidicQnFeJcPu825zung7N1P8BtlzcmdaozeMkiNzUCgkjW+ODZ3lha6+ip0LEzi5M51efWw6JdQwbLIb1VVqTl5Z3dVUeXE1B1Yk0cPdlKCRHbOfsilDZ3lhYKrLzoUJLQ4tv7qqrOleUCcfC+x7meHYT9JqTWxDU12sHI3IPHNZa202Jjkyh+pK+IyeSQAAIABJREFUNf4tqK/XGEsHI8Y9GcDl7DJWfxap8cpqdUUt566UFLk2RNXB0xxnfyuitqXeEKouyzIFWWXEH8hk5+8J/Petw/z+6iEyzxQy/H5vjW7EdlVKpQJbZxMSDmYRtS0V/6GODdcXTj4W9Zmv92Ve95r6sN9cXAKsOiyZXVdy9UZH+BQ3zhy9yM7f4jWO4lGr6xpdsb7q8sUyjqw9h2ugFaETXTl95CIJTYSslxdXs+3nOJQqBUPvad/kPO0hcIQTNs4mHFxxlqqKWgpzyjm27jyuQdZdagtXW/Sb4o5CIeESaNXqfBfaYmiqy/R/9sXK0YgtC05x5mjjc6OoramUFVZpXNLx77r28lErXU1ukXW2qEXLylf3fXmFNz7okiThO8Cew6uTW1TjtLqylozEy/VJfdqwJG9oqsuw+7zZ9nMcC185CIBCKaGrr0LPSEXoRFd8+t94lyItPp+k45cIneSKpcP/+qqjp2zIjthaRmZ6TJvdh5UfnWDHwnhmvBqq0aQuLb6AnJRiht/v3a6TwrBJbkiSxLH15ykvqaa8qIr8zDKUOgp69bHBd6A9jl4WnRIK2JUolArCJrqy649ELsTcPANwcV4FCYeyiN6ehkUPQ6a8ENxszdOuQqWrbNEeP0mS6H9HL9Z8HkXs7vQmEzO0RG5aCVHb0/AZ0KOhaHxbDZrhSfrpy+xcGM89b4U37Nmtz/abg5OvRatXn64yNNVl6D1ebP81HkmCftM0K8nVVlaOxniE2BKzJ4Peo3tqdBe8sqyGXX8kUFtVx8iHfNu1NmtL6BvpMOohX9Z/fZKja88z+O7mQ9CqymtIOJiFR4itxpNMla6SO+eEsHfvXo1e3xxHbwsSDmShrqlrt5qlCQezMLczbFMiF2c/Kyb/ozdbF5xi1ScRTHq+NzY9WzdJPBtxidqauoaw32v1n+bOig9PEL0jjeBRzqSfLiAtPp+0hALKi6oBMDDRwb6XOQHDnHDytuhWk9SrbN1MyT5XhEeoLUNneTVc90iSROBwRw4svz7z9cVzhZQXV2s17PdWFDapPgP3kTXnKMqtwMHTHCsnY6wcjbFswdan2ho16/4TTUl+JZOe633Dz1aduo6dCxPQ0VUy/AEfDEx0uXi+iP3LkrB1McHa6X/HF+VWsOHrk5QVVjHuyYBb4nv97xQKieH3e/PXxxEcXXuOy9llKFQKjXJDdFXmdobc+3Y/9EwVpKSkUFnZ9q0NmvKfZkRFiS5FNRc5GZmLnoEOVxM816nrUNhUM+BxawqrsyhMvPHmiL6+Pk5OTujotOyapVtNVC17GKFnqCL7XCG+TSw3y3UyeZmlZJ0tJG5/JvYeZs1mg/TqZ8fRtec4suYcE54KbHaCkxZfgLq2Drfgtu+J8AixRc8wmMJL5VRX1lJdUZ9xMie1hF2/J1KUU0H4FLeGX8KaKjV7/zyDRQ9DQttwkd0YE0t9ht/vw5YFp4jYnNLq2rL1q6kXMLbUw2dA28MAbiZ0oiuSAo6uPY+dmynD7vPGM8zultlz0VG8wu04sTmlyQzAdWqZ5MgcEg5lNexJcO9tw4gHfTSeFHVlDh7muAZaEbUtDf8hjhq9R3VtHbv+SMTARKdFNVNbStdAxehH/VjzRRSHVp5lxIO+QP2kuDivkpAJrm1q3yPUlkspxegb63RKGv6rwie7cS4yh+htaQy8y6PFr5NlmaRjFzm0KpnK0hqG3OPVqe/jWj39LAkc5kjM7nRcg6yarfkXfyCLmio1fcZor7i6Njl5WxC7O4NLKUU4eGrnJsy18rNKuXi+iIF3erT5ArOnjyV3zglh47cxrPk8inFPBuDSiiyziYezsXQwwtblxgnm1VC86G1pRG5NBRn0DFX09LWkp58lDh7mmNkadJuL5KYEDHFEV09JyATXG24Kefe358ja6zNfJ0flotRR4BKo3bDfW1HfcS7oG+kQtz+TU3szUdfWr6xKCglje5maAepGV51lWWb/0iQuni/GwESHNV9EMe7/Aq4LpY7cmkpOagnj/i+gIUR0zGP+LP/gOFt/jGPmG2HoGajITSthw7cx1KnrmPbPPlrJNdFZbF1MCRju1BCSP+IBn1ty0t0ccztDLly4gImJCa6urp36+SLLMiUFlVSW1qBnoMLE2gCFQqIop5xqUzWWDkaNLkrJskx+fj4ZGRm4ubUsm3m3unKXFBL2HuY3JFSqrqjl9NGLpCcWkJ1cSFV5/b4VEyt9wqc0P+kyMtNj4F0eHFx5luMbLzQ7SbsQk4u+kQ72vbTzy97T1/KG0jhqdR37/qwvK1GUW8HIh3xQ6Sg5tuE8JfmV3NGGulfNcQ+2wWdADyK3pOASYNWqD7T0hAIuXSiuD4PsoBDbkPGuBAxzEpPTZiiUCkInuLJ7USIpsXm4BllTeKmc7OQispMLORstk1gVh7GFHmGT3PAdaK/VUMKuqP/0Xix7/zhRW1NbNVG6KnJLCvmZpUx8Nkjrk3kHD3P6jnUhalsqrkHWuPW24VxUTkM9ybaQJKnFCSfak0UPIzzD7Ti1N4PgMc4tSr1fkF3G/qVnyEwqxM7NlCkvBHe51asBd3mQfvoyu/5IZNa8fo1+Ll1NvOTk03VX3xw8zZEkyDh9uV0mqomHslEoJXwGaCe0zcrRmBmvhrLxuxg2fRfL4JmeWNobUnq5ipKCSkoLKqmqUOMVbnfdzbr8rFIuXShm8EzPJi8GB0zvRW11HTbOJjj7WWLratrpK/gdzdzOsMlrKD0DFd79enD6SDaD7vRAz1DF+agcnP0stVr3/FbmN9gBv8EO1KnrKMypID+zlEspxcTsSmfjtzFMfr73DZPVuCs15EMnuhIw1LH+Z/v7WIbd64X/EEdy00qI2JSCZ5jddWGvhqa6jHsigLX/iWbPokT8hzmyZcEp9AxUTP9nCJb2XePGXlv0n+rOhZhcLOwM8W1FWa9bSWVlZadPUqH+msHEUh+VjpLSy5WoL5VjYKJDVUUtRuZ6TV7rS5KElZUVubm5LT5Xt/u0sO9lRkpsHuXF1dSp64jdnUH8gUyqK9WY2RrQq48NDp7mOHhZtPiiO2ikE/mZpURsTsHSwajRNNd5GaWkxObh3sem1WnQW0OpVDDiAR/MbQ05suYcpQWVhE50JXZXOv5DHHBox7pXg+/2IuPMZXb+nsA9b4W3KLV8TbWa4xvr66Y2tcrdXsQk9ea8+9kRsSXlShkXGkppGJjoYGQLQ6f1pqef5W1zAWblaIx3eA9i92YQNNKpVXtl8zJKiNySWn/R206ZJsOnuJGWkM+exaexczMjOTIHJx/Nw367orCJbpw9kcPBlWcZ86hfk1EsV/e9R25JQUdPybD7vPEf7NAlw/p1dJWMfsSPVZ9FcmB5EqMf8bvhmKTjlygvqmbUQ76d0MOW0TPUwcbZhIwzlwmfot221TV1nDl6EbfeNlrNdGtkrscdL/dl289xHFiedN1zBqa6SMC5qBysexoTOtEV9942JB6unzA3l3XY1NqASc8Gaa2f3VHgMEfi99dPrHq4m1JWVN1t9gxqk0KpwNLeCEv7+uvL/LIMMo8WsvHbGCY9F9Qwsc86e5mDK87iEmhF+GQ3JIXU8LO9d8kZivMqSTmVh76JDkNn3bjX1MHTnP7T3Tmy+hznTuZiaW/ElBd6ayUnRFega6Di3nn9UOkoOn0i1566ynuTJAlDU12UKonivEpK8itRqhQY3uTzu7X973ZX8lf3tWxZcIqcK2nTe4XYEjzaWeMEIZJUX8ahMKecXX8kYmZj0LDnQpZl4vdncnBlMnqGqoaCvO1JkiT6jnPB1NqAnQsT2PBNDIZmugxo57pXegYqRj3sx7r/RHNk9bkbPghrqtSkJxSQm1FCQWYZ+ZmlFOVVgEyHrqYKLadQKhh0pwfHNpzH1tkEe09z7HuZYW5nyL59+7SemfFWED7FjbMRlzix8UJDiO3NqNX1Ib96xjoMubv9klEoVQpGP+rHyg8jWP/1yfqwXy2H+nc2cztDwie7cWz9eVQ69Tfm/j75rKuT2bv4NImHs/EMs2PwTM92L3zeVnZupoRMcCFiUwpGZnoEj/nfPlxZljm5Mw0rRyOtZdFuL04+FpzckU5NVeOhiZo6fzKXyrIajZMoNUdXX8XEZ4NIjy9ApafExFIPI3M9VDpK6tR1JB2/RMTmFLb+GIeVoxGlhVW4BVl32dIwtworR2McPM2J259B6WVrlCoFroFdo1xMV2bmIuHn78eO3xIaVlarymvZ+lMcpjYG193A09VXMenZIPYtTSJqWyoAk5/v3eTNyz5jnCnIKqOipJoxj/l3q5ucgFit7wR6hjpY9FBQerkKQzNdrd8s7nYjautigq6BirzMUgKGO9J7ZM9m96C2lFJHwfgnA1n58Qk2/3CKma+HotJRsOe/9bXDnP0sGfWIX4deLHmE2GJsoce+pWfoN9Vdo1IGreXkbUHvkT2J2Z2OW5A1siyTeeYyp49d5FxkDjVVaiQJzGwNsXYyxqtfD2xdTG7LCc+twr2PTUP5BaF+pSRgqGND+GlL9jpGb0slL72UCU8FtntpBCsHYwbc0YuDK88iKSSt7InvakInuqJW19WX5JFgxP3/m6yqa+rYsTCec1G5hE5yrV9Z6CJ3mG8mdKIrhZfKidqWSuyedHwHORA8qieXL5ZTkFXGqEd8u/x7cfS2IGpbGtnJhTi3Ys/nzcQfzMLESh8nH+2HFEN9NJJrI5EOV+t5e4XbcTYih4jNKVSV1Wpc/ku4XsAwR7b/Ek/c/kyc/a26fAm4ruJq5N7Vyaq6po7amjqmPx14w7WeQqlg+P3eWDsZo66ta/Z6S5KkRiM6BKEtVLpKjevF37Ttdmm1EylVCu55KwxdfZXW7xQZmuoy6dkgVn0WxcZvY6gqq6WssIoBd/aiz2jnTgk56+Fuxj1vhnfoOftPdyctIZ8dC+OpVcsklEejo6/EI8QW7349sHMzbVGmVUHoqkImuJJ4OJt9S88w7omAZldW8jNLObEpBY9Q2w6b8AeNcCIz6TIqXeUtXyOuKeGT3UCuL/UjAcPv96G2po6tP54iLaGAQTM8OiSCRZuUSgXjngggbGIZ0TvTiN+fSdy+TPSNVBiZ6zW6raSrse9ljkIpkXHmstYmqvmZpWSeuUy/qW6dFrqtUCrw7tcDzzA7inLKu0wyrludex8bDM10KS+qxqOvuCHaGp6h9aUTt/8aj1wnM+HpwCb3ktZnWnbq4B4KQvvrdhNVAFOrtq+gNsXayYQxj/qxZcEpTKz0ueNffW/pTGmaUOkqGf2oH+u/OomeKQyf5YdbsE2L9qwKwq3A0FSXgXd5cGBZEovnHSV8shsBwx2vq11bVlTFyR1pxO3PRM9Q1aH15ySFxMRnuvf+OEmSCJ9SnxUwYnMKdTIUXSrn4vkiRjzog18jZUNuFZYORox6yJd+U9yJ3Z1OwqEswie73BLbI3T0lNi5mWqtnqosy+xfloSekQr/oY5aabMtFApJTFK1SKlUEDTCiahtabhqWOv5dlZfAUJFZVlNm5PmCUJbpaSk8I9//AOVSoWenh5eXl5s3LgRfX19Fi1axIoVKzAwMGD27NkAeHh4cPLkSYyNjTU+Z7ecqLY392Ab7n4zDFNrg9s2YY+tiylP/Hsoe/fubbIOrSDcygKGOuLgac7BlWc5uPIs8QezGHK3JxY9jIjenkr8wSzqauvwDLcjbJKb2M/WDq5OVmVZJnJLKgqlxNgnArpNQhZji/qs8ppkmO5MTt4WRGxOobKsps2RS0nHL5F1tpDh93t32+iA213fsS4EDHW8ba+X2urv1R8EAeDAiiTy0ku12qZ1T+Ob5tnYtm0bMTExlJSU8O233xIZGcmGDRuYP38+r732Gv/85z+ZPXs2MTExeHt7t2mSCmKiqrHWFg8XBOHWczUrYkpsHgdXnmX9VycbMiB79+9B3/EumNu2z74MoZ4kSfSb6o6ptQHmtoY4eLZfZnOhZZx8LDixKYXz0bn4tSH5UVV5DYdWJWPnZnpLr5ALzZMUUofk0BAEof15enri4+PDl19+ya5duwgODkatVuPg4IC/vz+ZmZmUlZWxefNmpk6d2ubziYmqIAhCMyRJwq23DT39LDm1J5Py4ioChztpJUmb0DKSJImJTBfSw90M+15m7F+ehJWjMXZummXUP7bhApUl1Ux5vneXLCskCILQVbVnhYHm6Ov/r5zRc889x+uvv37d82PHjmX37t1s3bqVpUuXtvl8Gm2IkSTJXZKkSEmSkiVJerOR5++UJOmUJEkJkiR9cs3jqZIknbzy57m2dFwQBKEjqXSU9BnrzKAZnmKSKtzWFEoFE54OxMhMl00/xFKcX9HqNnLTSojbm0HAUEdsnEWEkiAIwq1k0KBBbNq0idraWgDy8/MBmDp1Kps2bWpYZW0rTTM3vAV8AHgDUyVJ+nuu68vAMCAQGCFJkv+Vx0tlWQ6+8uc7Dc8tCIIgCEInMjDRZdKzvVHX1LH5+1iqK2pb/Fq5Tmbf0jPoG+vQb5p7O/ZSEARBaA9hYWFMmzaNkJAQgoKCWLJkCQBDhgxhw4YNTJgwQSvn0TT0dxjwrCzLakmSNl75d8LVJ2VZ3nP175IkpQMWkiQZAmVt6awgCIIgCF2DpYMR458MYMM3MWz7JZ5JzwaiUN78/nfikWwuXShm1CO+Yu+iIAjCLcLV1ZWIiIiGf8+ZM4c5c+Zcd4xKpSIzM1Nr59R0RdVIluXKK3/PARpN+ypJkgXQG4gEDAFXSZKiJUk6IElSiIbnFgRBEAShC+jpa8nQWV6kxedz8K/kmx5fXlzNkdXnsPcww7ufyBgvCIIgNE2SZbn5AyTpCeCJvz3sIsuy/ZXnnwIcZVme18hrlwE7ZFn+9cq/JVmWZUmSRgDfyLIc0MhrngSeBLCzswtZtmyZBm9L6CilpaVtTj0tdE1ibLs3Mb7dW0eP78XoOvLPgH2IhKVn44mRZFkmbb9M2SVwHyehbyYSKGlC/O52b2J8u7e2jK+ZmRkeHrdWObPGJCcnU1RUdN1jI0aMiJRlOfTvx9409FeW5V+AX659TJKkJEmS9K+sqtpSv6rK3455GSi7Okm90pZ85b97JEmykiRJKcuy+m/n+wn4CSA0NFQePnz4zboodKK9e/cixqh7EmPbvYnx7d46enzrhsps/j6W9OgC+g/vjYOnxQ3HxOxKJyH7LENneRE43KnD+tbdiN/d7k2Mb/fWlvFNTEzExOTWTz6nr69Pnz59WnSspqG/O4BJkiQpgSnATkmSekmStApAkqSxwCTg6asvkCTJQpIk/St/7w1k/32SKgiCIAjCrUehkBjzuD+mNgZs/SmOkoLK657PTSvh8OpkXIOsCRjm2Em9FARBuLXdLBK2q2tt/zWdqM4H5gBngfWyLJ8GjIGrRX2+AVyAE1dK0bwGOF75dzTwPTeGEwuCIAiCcIvSM1Ax8ZlA1DV1bFlwiprq+nvR1ZW1bP81HgMTXUY95IskiZBfQRCE1tLX1yc/P/+WnazKskx+fv51tVhvRqOsv7Is5wL9//ZYDPXlaJBl2buJlwZqcj5BEARBELo+ix5GjHnMn00/xLLnv6cZ85gfB1acpTCnnOmz+6BvLLL8CoIgaMLJyYmMjAxyc3M7uysa09fXx8mp5Vs/NC1PIwiCIAiCcAPXIGv6TXXn2Lrz1FSpSYnNI3SiK47eN+5bFQRBEFpGR0cHNze3zu5GhxITVUEQBEEQtCpkvAt56aWci8qhh7spYZNcO7tLgiAIwi1GTFQFQRAEQdAqSZIY9bAvVo5G+A60R6HUNCWGIAiCcLsSE1VBEARBELROR09J2KTbK0xNEARB0B6pK2eOkiQpF0jt7H60khlQdNOjug9rIK+zO9FBxNh2b2J8uzcxvt3b7TS+Ymy7NzG+3ZsY38a5yLJs8/cHu/RE9VYkSdJPsiw/2dn96CiSJEXIshza2f3oCGJsuzcxvt2bGN/u7XYaXzG23ZsY3+5NjG/riE0j2rehszsgtBsxtt2bGN/uTYxv9ybGt/sSY9u9ifHt3to0vmKiqmWyLItfuG5KjG33Jsa3exPj272J8e2+xNh2b2J8u7e2jq+YqApt9VNnd0BoN2Jsuzcxvt2bGN/uS4xt9ybGt3sT49sKYo+qIAiCIAiCIAiC0KWIFVVBEARBEARBEAShSxETVUEQBEEQBEEQBKFLERNVQRAEQRAEQRAEoUsRE1VBEARBEARBEAShS9F4oipJ0lxJkpIlSTohSZLrNY+bSZK0VJKk01ee87/y+P9JkpQuSdLJK3+Ube++IAiCIAiCIAiC0N1olPVXkiQXYA0QBkwEHpBl+Z4rzxkAI2RZ3ixJ0hTgIVmWZ0qS9BqQJcvyopaex9raWnZ1dW11/4SOU1ZWhpGRUWd3Q2gHYmy7NzG+3ZsY3+5LjG33Jsa3exPj27jIyMg8WZZt/v64SsP2RgBbZFlWS5K0Bfju6hOyLFcAm6/8MwWwuPJ3SyC2NSdxdXUlIiJCwy4KHWHv3r0MHz68s7shtAMxtt2bGN/urTuM7+XsTMzseqBQiACsa3WHsRWaJsa3exPj2zhJklIbe1zT0F9bIA9AluVaQNlEKO9MYNuVv+sAn0qSFC9J0meSJEkanlsQBEEQhG7s4rmz/Db7KRa/NpuMhLjO7o4gCILQCTQN/Z0D1Mqy/J8r/84CesqyrL7mmD7AH0A/WZYrJEmSZFmWr4QGrwJ+l2V5RSNtPwk8CWBnZxeybNkyTd6X0EFKS0sxNjbu7G60O7mujoqCPAysbLhd7rHcLmN7uxLj273d6uObefwgF6OOoWtkTHVpCRYePjgNGIqusWlnd63T3epjKzRPjG/3Jsa3cSNGjIiUZTn0749rGvqbDfgCSJKkAuS/TVKtgd+Be6+EAiNfmRFfmbRuAPwaa1iW5Z+AnwBCQ0NlsTzetd0OIQyyLLPthy9J3LeLic+/jO+Q4Z3dpQ5xO4zt7UyMb/d2q4/vf7euwdHbj7veeIcT61dxYt0qEtMv0G/aTMKmzUCp0vTy5dZ3q4+t0Dwxvt2bGN/W0TT0dycw/kq470TgwJVw3rFXJq7LgNdlWY6/+gJJkuyv/FcBjAROtq3rgtAxjq9dSfy+XegbGbP7j58pLy7q7C4JgiB0W6WXC8i5cA63PqHo6OkzcOb9PPLvH3ALDuHQisWs+eQdqivKO7ubgiAIQhPKCi/z/RP3kXTsUJva0WiiKsvyRWAhcAZ4D3gFcKE+YdJMYADw4TWlaMyBNyVJigOigTRgXZt6LnQYWZZJPLiXmuqqzu5Khztz5AAHly3CZ9Aw7p7/MdXl5ez94+fO7pYgCEK3lXIyEgC34JCGx8xs7Zj60huMfeofpMXFsOLdNygrvNxZXRQEQRCaEb9vFxUlxZxYv6pN7WhcR1WW5W9lWfaQZbm3LMtpsizfLcvyMlmWl8qybCTLcvA1fwplWX5eluWAK8e/fDUUWOj6MhPj2fzN50RtXt/ZXelQWUmn2fLdv3Hw9mPc0y9i4+xK+PSZJB7cy4VokY1aELqT0oJ81nzyDjt/+Z7ivJzO7k6XUVFSzG+zn+LClcljR7gQHYGxhSU2Lm43PBc4cizT58wlPyOdZfNe4fLFrA7rlyAIgnBzsiwTt2cHSpWKi8lJXDqfrHFbGk9UhdtHRmJ9xsX4vTu5Xe4vFOVcYt3n72NsacW0f72JSlcXgH533I2lY092/PKdCD0ThG4iO/kMi9/4J2nxsZzavZ1f//EkO376lqKcS53dtU539vgRLmdncnDZog75/FfX1pISG41bn9AmE9e59w1j5twPqCwvY+ncOVw8d7bd+6WpitISjqxaSnp8LHV16pu/QBAE4RaXeTqey9mZDL3/UVS6esTs3KJxW2KiKtxUxul4kCQuZ2eSeSahs7vT7irLSlnzyTuoa2q449W3MTQ1a3hOpaPDuKf/QUl+HgeX/fe611WUFHN01TJWvv8WeemNloMSBKGLSdi/m+XzX0Op0uG+97/g8a9/JnDUOOL37eS32U+ybcFXlBTkdXY3O03y8cNIkoKcC+dIjY1u9/NlJSVSXVGOW58bkj9ex8HLh3vf/RQdPX1WvPM6+Rlp7d43TexZ+COHVyxhxbtvsOCph9j+0zekxEShrq3t7K4JgiC0i7g9O9A1MCBw5Dh8Bg0l8eBeqsrLNGpLTFSFZtWp1WQlncZ/6Eh09A2I27Ojs7vUrsqLClnx7htczs5iykuvY+XY84ZjHLx8CR47iehtG8lKSuTyxSx2/voDPz37KIdWLCb77BmWzp1DSkxUJ7yD24O6tlYktRLapK5Ozb7Fv9WH93v6cP+H/8bG2RVTaxtGP/4Mj3/zC73HTCTx4F4Wvzab7LNnOrvLHa6qvIzUUzEEj5uEsYUlx9eubPdzXjgZiUKpwjkg+KbHWjo4MevdT5Dr6oja0vW2pqTERpN4cC9h02Yw5aXXcQ7ozelD+1n14Tx+evYR8jPSO7uLgiAIWlVVXs6ZowfxGTgMHX19eo+ZSG1VFQn7d2vUnpio3uIqSkv46blH+XPuvzi2ZgV56alaDc/KSTlPTWUFrsEheA8YQtKRg9025LU4L5dl81/jclYm01+Zi0tg0xdKQ+59CBNLa9Z8/A6/zX6KuN3b8Bk0lIc//45HvvgeMxtbVn88n5gdmzvwHdweZFlm/Rcf8MsLT5CTcr6zu9NpTqxfxdFVos50a6hra8hKSuT4ur9Y9varRGxYTe+xk7jrzfeui5wAMLG0ZuSjT/Hgx1+ho6/P8ndeI/HQPq33SZZlko4dovBittbbbqvzkcepU9fiM2goIZPvID3hFFlJp9v1nBeiI3Dy9UPP0LBFx5tYWuM1YDCJB/d1qe+mmuoqdv7yHRb2jgyccR9e/QYx+cVXePbnJUz711vIdXVs+uYzamtqOrvmbuYkAAAgAElEQVSrgiAIWnPm8H5qq6oIGDkGgB69PLFz9yRmxxaN5ie3byGybiLt1ElK8nLR0dXj4LJFHFy2CDNbOzzC+jNgxv0t/rJvSubp+gpDTj7+mFjZELdnO2eOHiRwxFhtdL/LuJydycr336KqrIy73ngHJ9+AZo/XNTBk3NMvsvOX7+g9diLB4yZjbGHZ8Pysdz9l41efsvOX7+vj9B94DIVCqbX+yrLc5P6t7u70wb2cjzqBSkeXtZ++x/0f/hsjc4vO7laHqq2p4ejq5dRWVxM4atxt9/6bIssy+5csJD89FZWuHipdXVR6eihVOhRkppGVdIbaK9nLLRycGPPkCwSNGtdsm1ZOztz3/hes/+JDNn/9GQUZaQyceT+Sou33eevUanb+8h2ndm9HqVIRNvUuwqfPREdPv81ta8PZ40cwtrDE3sMba2dXjq1ZwfF1K5k+Z267nK84L5e8tBSGPfBYq17Xe8wEEvbv5vSh/QSNHt8ufWutY6uXU3TpIjPnftiQ4wBApauLR1h/kCTWffYeh5b/t9XvVxAEoas6tWc7Vk7O9Ojl1fBY77ET2L7gazIT43Hya/76+u/EiuotLu1UDLoGhjz8+Xc89cMfjPm/57FyciZy8/o2p4SG+kRKZnY9MLa0wsHLB0sHJ+J2d6/w39y0FJa9/Sq1VVXcPe/Dm05Sr3IJCubxr39m8KyHrpukQv1EdvqcufSZMIXITetY9/kH1FRWaqW/sbu28es/nqAo56JW2ruVlBcVsvuPn7H38mHWu59SUVrMus/ev+1KJ6WdOkl1RTl16lpO7drW2d3pMs5FHidiw2qKci5RkJVBVlIi5yOPk3hgDxUlJQSOGsuUl17nmZ8W89h/Ftx0knqVoakZM+e+T8CIMRxdvZwNX37c5t/nmspK1n3+Pqd2byd0yp149R/M0dXL+f3lZzl7/HCnJ66rqarkwslIPMIHICkU6Oob0Gf8ZM5FHCMvLaVdztlQluYm+1P/zt7TB2tn1zYl7NCmvLQUTqxfhf+wUTgHBDV6jEdoP3qPmUDEhtWknhJl5QVBuPXlpaVwMTmJwJFjr1tM8RkwFD1DI05qEGUoVlRvcWlxMTj5BaBQKjG2tCJo9HiCRo9n1Udvk3BgN4Pu1vzOvyzLZJ5OwL1vGACSJBEwYkz9ikVmeqP7NzVVUVLMyW2bCBw17oZJn7bU1alJPnGU4pxLlJcUU15USEVxEZmnE9DR12fGvI+wctLee1IolYx85Cksejiw5/efWfHeGzckZ2qtvLQUdi9cgLqmhm0/fMXMuR9oZWXnVrF74Y/UVJQz7ql/YOXkzMTnX2b9Fx+yfcHXTHzhX7fNKnPSsUPoGRph6+pOzM4thE+fiUKpvRX7W1FtTQ37Fv2CpWNPHvr0G5Qq7X69KVU6jL3yc7dv8W+oaz5h+py5Gv3+lRcVsvrjd8i5cI7RTzxH7zETAAgaNZ5dCxew/osPcQnqw/hnZmNsaaXV99FSKSejqK2uwjN8YMNjfcZPIWLDGo6vX8XE51/W+jnPR0dgamOLZSu/WyRJImj0eHb/toCL587So5en1vvWUnJdHTt+/g5dQyOG3mSldNiDj5MWf4qt3/+Hhz79BgMT0w7q5e2rpqqS8qIizGztOrsrqGtrqa2uQs/QqLO7IghacWrPDhRKFb5DRlz3uI6+Pn7DRhKzfQvlRYUYmpm3uM3b5wq3nalrazi1ezullws67JxFOZcovJTd6F5K/6EjKcnLJT0hTuP2CzIzqCgpxtHXv+Exv6EjkRQK4vfu1LjdvysrvMyKd9/g8Mol/PnWy+2WMffE+tVs+PdH7Fv8G1Gb1pIaG01JQT49/QOZ9c6nWp2kXqvP+ClMffkN8lJTWDr3XxrvRautrmbT15+hZ2jE4HsfJj3hFCe3b9Jyb7uu5BNHOXPkAP3vnIWVkzMAnuEDGXzvw5w+tO+22a+prq3l3Imj9AoJp++k6ZQW5JMccbSzu9Xpojavo/BSNiMeekLrk9SrJEkidPIdjHzkSc5HneDYmhWtbuPyxSyWzp1DfkYa0+a82TBJBXDyC+DBj79ixMP/R+aZBHb++oM2u98qZ48fRt/E9LoIEwMTU4JGj+P0oX1aL91TW1ND2qmTuAU3XZamOX5DRqDS0yO2k1dVT+3eTlZSIsMffPymNyV19PSZ9MK/KC8qYsfP33b6KvrtYPuP37D4tRdR13bu3uCq8nL+fPNlFr3ywm0XESR0T7U1NSQc2INHaL9GP/t6j55YHwXWyqSsYqKqBXnpqSx582W2//g1+5cs7LDzpsXFAOAc0PuG53qF9UfXwJCE/bs0bv/a/alXGZlb4N43nPh9u7SSXr+0IJ8V77xO4aVsRj32DHW1tSyb90rDe9OWssLLHF+7Ave+YTy/cDkvLl7DUz/8wUOffM20f73V7ndXPcL6M3PeB1SWlrJ0nmZ1//b/uZC89FTGPzOb8GkzcAsOYf+fv98WBe8ry0rZ9ev32Di7EjbtruueC582A7+hIzm8cgmxu7Z1+gVIW0VsWH1D6aNrpcfHUllWimf/wbj3DcXUxpaT29rnhsXR1cvZt/g3aqq0E7beXsoKL3N09XLcQ8JxDQ5p9/MFj5uM7+DhHFq5hAtXwlVbIi8thaVz51BVXsbd8z6kV0i/G45RKJX0nTiNftPv5lzEUTJPd3xJMHVtDecij+MR2u+GlfqQSXcgSQoiNq7W6jkzE+OpqapsddjvVXqGRvgMHMrpQ/tbVQZBm5PDssLL7P9zIT39AvEbOrJFr7Fz92DQPQ9w9thhrd4AFm6Un5HO6cP7qSwrJTs5qdP6oa6tYf0XH5CbeoHi3Bxitouki0LzZFnmzJEDFOfldnZXmnQu4hiVJcUEjGw8h42VU096+gUSu3Nrq2pKi4lqG8h1dURtXsfi12dTWpBPT/8gko4c6LCyGWlxMRiZWzSsLl1LR1cP7wGDSTp6SOO9VBmJcRiamWPew+G6xwNGjKG8qJCUmJZfoDWmOC+X5e+8RklBPne9/g7B4yZx3/tfYGxpxaoP3ybhwJ42tX+twyuWUFtdzbAHn0DP0KhTQkQdvHy5973PUOnqsuKd11t1gXshOoLoLRvoM2EKbn3qVxzGPPUCSqWKbT982e0Lye9fspCywkLGPv0iSpXOdc9JksSYJ1/A0cePHT99w7ePzmLle29ydNUyMk7H31IT15rKSo6sWsqxtSuaLF2RdOwQOvoGuAb1QaFQEjR6AunxsVqvI1mcl8PhlUuI2LCa/742m4udeGF3MweW/oG6pobhDz7eIeer/5l7HpueLmz++rMW7RfPz0hjxXtvolQqmfXuZ9h7ejd7fMjEaRhZWLJ/ycIOX2lLOxVDdUX5dWG/V5lYWeM3dARxu3dQVnhZa+e8cPIESh0dnP0b39PZEr1HT6CmqpLEA3tbdHxGYhw/Pv0Qsbu2anzOa0Vv3UhVeTmjnni2Vd8xYVPupKd/ELsX/nhbZzJvb8fWLEelq4skKUiN7Zx9wXJdHVu//5K0uBjGPfMiLkF9OLZ2JVXlXSdjtdD1nNq9jY1ffsLmbz7vspEXcXu2Y2Jlg0tQ0xUzeo+dSHHuJRL2t/z6XkxUb0KWZTZ/8zlrP3uP/UsWcmrPdjLPJJKfkc5fH85jzx8/4xIYzMOffcuox55GXVvLqd3bm2yvprKS1R+9TVpcbJv7lRYXQ0//oCa/EP2GjqSmqpKzxw9rdI6M0/E4+fjf0L5bcAiGZuacakNSpaKciyyf/xoVxcXMePO9hvAyUxtbZr37KY4+fmz59guOrlrW5l/K3LQUTu3eTvC4yVg6OLaprbaydHDi3vc+x9zegTUfv8PeRT9TXVnR7GvKCi+z9YcvsXZ2Zeh9jzY8bmJpzYhHniTzdALRWzZotZ/q2lqqigu12qam0uJiObVrGyGTpze590ylo8OMN99n6r/eJGjUOCpKijm0YjHL336V3196VqsX1H93LvI4UVs2aOXLI+nYIaorKpCQOLb2xrDSOnX9Pmv3vmENmUQDR45FqaOj9TDwmO2bQYbxz/6T2qoq/pz7L478tZQ6tXZvilSUFLcp9O1ichLxe3fSd+JULOw77vdbR0+fKS+/UV8u6d8fNfse8jPTWfHuGygUCmbO+7BFn0M6+voMnHEfWUmJ7RLaLcsycXt3NroV4ezxw+gaGODcRImusKkzqKurY9VHb2vtd+t8dCQ9/QLR0dc847FdL09sXXsRu/PmZRBqa2rY/tO39WG3P33LvsW/IdfVaXzu2upqYndtxb1vWKvzN0gKBROefwl9YxNWfTiPy9mZGvdDaNzl7ExOH9pP8NhJ2PXyIK2TEljtW/wbpw/tY/C9D+M/bBSD73mQypJiojav65T+CF1fdvIZdv+2AGMLSzJPx3fJrT5JRw+SEhuN//BRzVa48AgbgKOPH9t//JozRw60qG0xUb2JopxLJB7cy8XkJKI2r2P7gq9ZNm8Ov7/8DFlJiYz5v+eZ/sq8hpXNnv5BxO7c0uQKV+SmtVw4GcmxNW3bT5efnkp5USHOgTeG/V7l6OOPma0d8RoU2S3OzaEkLxfHRjLgKlUq/IaO5HzUcarLSlvddnlxEcvmv0Z1eRkz536Ag5fPdc/rGxlz1xvv4DdkBIdWLOZ81IlWn+MqWZbZu+gX9AwN6X/XLI3b0SZjC0tmzf+YwFFjidy0joUvPUPyicY/eGRZZtuCr6gqL2PSC/+6rswB1N+McA8J5+DSRRRkZWitj7sXLiB+2ULKizp3slqnVrN74QLM7HowcOZ9zR6r0tXFM2wAIx55koc+/YZnf/mTiS/8i9LLBaz99N12CV/Nz0hn45efsOf3Hzm88s82txe/dyfmdvb0nTSN0wf33RDWnZEYT0VxEV79BzU8ZmhqhveAIcTv2621u/I11VXE7t5Or9B++A8bxUOffYPPoGEcXrmEpfPmcOl8cpsn5jXVVRz5ayk/PfMIy+a9olHfZVlm9x8/YWhmTv87O/7326KHAxOef5mcC+fY9esPjf4/KcjKZOV7bwJcmaQ6tbj9gBFjsHBw4uCff2j9BkFu6gW2/fAli9+YTUpMVMPjV5POufcNR6Wj0+hrLR0cmf7KXC5nZbJ8/qsU5+a0qS+XLpzjclYGbm0M276aVCk3LYXss2eaPfb42hVczspg+itz6T12EhEbVtffcNDwc+LMkQNUFBfRZ/wUjV5vYmnNXW++i1xXx18fzKWkIE+jdoTGHVuzEqWODqGT78AlsA/ZyWdaFSKuDREbVhO5aS19xk8hfNoMAHp4eOERNoCIjaupKCnu0P4IXV95cREb/v0xRhaWPPDxV1g6OHFgye9a2XqnLacP72fjV5/i4OlD2JQ7mz1WqVJx52vzcfDyYdPXn3H68P6bti8mqjeRcyEZgDtefZt/LFrF41/9zB2vvs3Ix57moU+/IWj0+OtWHIPHTaI4N4fzURE3tFVeXMSJDavQ0TcgLS62TROLq3s4XQKaXmKXJAm/oSNJi4tpdVx7xtX9qdckUrpW0KhxKBQKkjf+RWlBfqvajtuzg9L8PO56413s3D0aPUap0mHs0y9iZG7RprCsC9ERpJ06yYCZ92FgbKJxO9qma2DImP97nnvf+wx9I2PWff4+az97n7z0VFJiozm2ZgXrv/iQn597jAvREQx74DGsnV1vaEeSJMY++QIqPT22fv+fNq0IXHXx3Flid21DVqtbfMervZzavZ38jDSG3f9Yq2tLGpiY4jt4OJP+MYeL55PZ/M0XWvn/c5W6tobN33yOjp4ePoOGcXTV0mZLQlWWlja7LaDw0kXSE07hP2wUYVPuRKlScXztX9cdk3TsECo9vRsu6IPHTaKmsoKEA62/KdWY04f2UVlS3HDRrW9kzMTnX2by7NcovJjN4tdn8/tLz3Dkr6WtXv2RZZnkE0f5/aVnObxyCT39A8lLS2HtZ+9SW13d5OvSE04Rv28XySeOkh4fS07KeWJ2bCE76TSD732ozTWjNdUrJJz+d80ifu9OVr77BnsX/Uzcnh1cPHeW3LQUVr77OnVqNXfP+7DVK20KpZIh9z5EQVYGcXu1WxYsI7H+M97Q1IzVH80nctPa+kzvifFUlBTjGT6g2de7BYcw4833KC8uYum8ORqHnqtra9i24CsMzcxvyBSpCd/Bw9DRNyB2Z9PfG/mZ6RxfuxKfQcNw7xvGqMeeZsTD/0dyxFGWz3/9hqSI6tqaZiewsiwTvXUjlg5OjSY3bCkrx57c9ca7VJaWsOqDed1m4nI++gQbvvyk07aoFF66SMKB3QSNHo+RuQUugb2R6+ralGyytc4cOcC+xb/h1X8wwx9+4rrrxkH3PEB1ZSXH1/3VTAvC7aauTs2mrz6lvLiQqS+9gZG5BUMfeJTL2Zla267QVokH97L5689x8PLlrjfeQdfg5t/DugaG3Pn6Ozh4+bL5m89vOlkV5Wlu4tKFcyiUSqx6uqBQKjHvYY95D/smj+8V0g9jC0tObtuIR+j1iTKOr11BTWUVM956n1UfziV251aGP/SERv1KjYvBvIc9pja2zR7nN3QUR/5aSuLBvfSbPrPF7WcmxqNrYIi1s0ujz1vYO3LHa/NZ/ck7LJ33CjPefLdFYXeyLBO/bxcO3n708PBq9lilSoX/8NGcWLeKkoI8TCytW9x/qA9f3fvfX7Gwd6T3mImtem1HcfDy5YGPviRq8zoOr/yTc9eEdJj3sMfRx4+e/kEENrE5HeoTXA29/1G2//g1GYlx9GzDHi+5ro5dv36PkZk5aiQSD+7VeIWgrarKyzm0YjGOPv543OSiuTkeYf0Z/uAT7F30M/uWLNTaPsZDyxeTk3KOaXPm4t43FLmujv1LFqKjp0/wuEkNx9VWVxO1ZT3H1ixHz8iYR//9Q6OT7vh9u0CS8Bs2CiNzCwJHjSNmx2YG3DULUxtb5Lo6ko8fxj049IbX23t4Y+fuycltmwgeO6lNe7CvXnRb93Shp3/gdc95DxiMc2Bvko4c4PSh/RxeuYTDK5dg5+6B7+AR+A4Z3mym07y0FPYtWUjKyUisnJyZOfdDnAOCSDy0j83ffM7Grz5l6kuvX5fAp6aqkr2Lfmly4mHn7kHAsNEav19tGDDjXtS1taTGRhOzfQu1Nf+bcBuYmNZPUhvJJdASHmEDcPDy5fDKP/EdNLxNobHXykyMw9TGlgc+/oot3/6bvYt+ITf1AgqVCpWOLm7BN09q5Ojjxz1vf8yqD+exbP5r3PXa/Jt+rv/d0dXLyU05z7Q5c7VSnkXXwBDfQcNIOLCHIfc9jJG5xXXPy7LMzl++Q6Wn1/D9K0kSfSdOw8yuB5u++oxFr7yAoakZVWWlVJaXUVtVhUpXj2lz3sI1qM8N58w+e4ZL588y8rGn25z/wM7dg+lz5rLqo7dZ8/E7zJj7Prr6Bm1qszOVFuSz5ZsvqCwrJXjMhDZ9P2nq+NoVKJTKhtUeey9fVHp6pMZG33Cd1h4qSorZ+cv32Ht6M+G5l24IjbTu6YLf4OGc3LqRkInTtHrubQu+5nJ2JlNfer1VJUGEzndo+eL6vcxPv9iwqOPeN5yefoEcWfknfkNGdGppo/h9u9j2w1c4+fpzx6tvt+q7SVffgDtfn8+aj99h89efN3uscv78+W3savv56aef5j/55JOd2oeIjWtQ6enRZ9zkFh2vUCioqawkbs92fAcPa/jiLc7NYct3/8Zv2EhCJk4jLzWF5BNH6TthaqvrH9ap1ez69Xt6hfajV0h4s8fqGxuTFneS7KTTBI9r+QXs/iULsXF2bTZzobldDwrUUJCUQNyeHTgH9L5pDdSLyUkcX7eS/nfNanI19VpmNnZEbVmPvrHJdWUSWuLktk0kHtjD+Gdna3yR2BEUCgWO3n74Dh6Oub0DIZOmM+LhJwmfNgOvfoOwc/e46bhZOjgSvXUDarUaj7D+Gvfl1O5txO7cyugnnqOkuoas2Cj8hoxAvxNWo4/89SepMVFMfekNTNpYS9Le05uKkmKit6zH0My8zXUW0+Ji2fHLdwSNHk/YlDuRJAW9QvuTk3qeqM3rMLO1w8bFjaSjh1j3+fucPXYIBy9fclMvICmUNySMkevq2LbgK3r08qLP+PrPGqueLkRv2UBtdRXufcPIPJNA9NYN9L9rVqOr6wqVirjd23HyDWxRFuuUlBRcXW9sJ/N0PMfXrmTQrAfp4X7j/ycdXT169PIkYPhoAkeOxdjCkty0FOL37SR6y3py01PRMzTCzKa+D5fOJ3Ny+yZ2/baAo6uWUV5UxND7H2Hs0y9iceWmn42zKwYmpkRtWktJXh69QsKRJInc1Aus+vBtUk5GEjrlTsY/90/8h43Ce+AQPML649YnlPCpM9A3Nr7p+21PkqTAJTCY3qMnED59Br6Dh9PTPxBb114Me+CxNn3+SJKEhYMj0Vs2oKOv32SUy981Nb5wJWT69x9x9g/CZ+BQvPsPBgmitqwn58I53EPCWpy11sjcAo+wASQdOcDJHVvoFdqvxRfEF5OT2Pr9f/AfOrIhFFIbTG1sid25hfh9u7B0cLpuT3Dc3h1Eb9nAyEefpqff9d8plg5OuAaHUHQpGwMTM6ydXXH08cc9OJTivFySjhzAf/hodHT1rnvd/iULKc7LYcJzL6FsIly6Ncxs7bDu6Urk5nVkJyfhM3Aoimvq9TY3tl2JLMts/PJjCi9dRKFQotTVwb1PWIf2oTg3h20LviJo1Hh8Bg4F6iMVMk8ncOnCuYbP2/a094+fyTp7mjtem9/kd5mNizvRW9dTU1mJwtJGK+ObffYMu377gZK8XM5FHKVXSD/0jETN1paqq1Oz7O3XiNm+mdqaGszserQ6sqsxLfn9PXviCLt/W0DQqPEMmHFvw+OSJGHt7Fq/p1mS2hTB0RZxe3awbcFXOPsHccer8zS6gapU6eDVfxCZpxOI2rSO7fFJ2fPnz//p78eJ0N9myLLMpfPJ2Lq6t+p1gaPGoVAqidnxv5Tjh1YsRpIUDJhRv88uaPQEKktLSDp2qNX9unguieqKCpybCfu9lt/QURRkZXCphSVRyouLKMhMb3R/6t8Z2fRg1jufoqOnx/J3Xr9pWZn4fTtR6erhPWBIi/pi3sMe54Ag4vZsb1XYZnlRIUf++hPngN64921+Mt9VmNna0WfcZFwCg1t94a2jp49X/7Zlea4oLeHA0kU4+vjjO3g4lh6+ACQe2qtRe21RnJtD5Ka1+A0Z0eZJJdR/uI94+Enc+4ax+7cFXIi+MTS/pSpKS9jy3RdY2Dsy/MH/RUQoVSqmzH4N54DebPvhKxa/PpuNX36MnoEBM956n7vf/gjvgUOJWL/qhj19afGxFOfm4D/8fyuDptY2BAwfTdye7ZQW5HP26CGUOjq49238Qs974BD0TUw5uOyPNoUMRm/diL6RMb6Dh9/0WBMra0Kn3MmDH39VvxVizATSTp1k1Qdz+eUfT/Dzc4+x5I1/cnzdXxhbWDDikad4/Kuf6Dtx2g21TvuMm8zAmfcTv28n+xb/RvS2jSx58yUqSoq56413GfbAY1j0cMDO3QPngN54hg/Ef9iom0aVdDSFQomlgxNe/QbRb/rMVu1JbYqTjz+9Qvvx/+zdZ1yUV9rA4f8zjaE3AQVEikoRAcVesbckmmrUxFTT1mTT2667Jtk3iZueTWLipvdiTNPYCKLYFRVBQVBEBZTeYZj2vB9QV6TOUCXn+pJf5ilz8Ey7T7nvvb+sbpes8iVnc6kuK734GS8pFIy5cRHXPPosds4uDJ4yw6L7ufTuw/zlKzDW1nJs1/ZWXWPQ17L+vTewd3Uj5rYlFv8NzfHw82fh/72OvbMLP//7eTa+/za11dVUl5Wy7YuP8QkZxOBJ0xq91isgiHlPLGPu439jxn1/JebWuxh1/c119U7Ly/jjstq2VaUlpO/ewaCYqa1a9tZa/YePYtqSpZxOPtRsgsbuLGnT72QlHWDiLXcSMHQYGXt2tuv2i9bY+8sPSJLUYCCk3+AoSnKzO7zcx7nj6RyO28jQWVfj0cgA4wUuXr0ZPHkGyXEb2yWRoSzLJHz9KbZOzlz/7PN1S/SXPd5hdep7ohOJe8k9dhRdZTlbPv2AD+5bzM+vvED6nh3NblNpK5PRwKaVb9E7aACT7ri3wfG6FUwxHFj3S5eUq8lNT2PjB2/Tb3AU8576R5uCd43WluueXs7A0eOaPEcEqs2oLCmiprwMr4Agi65zcHVjwIgxpMTHYqjVUXA6i6MJW4iaeRVOvTwA8AuPwKV3H6uKk59OrgsGL1+W15Tg0eNQqTUcaWVN1cbqpzbHzduHBc+/grOHJ2te+ie56amNnmfQ15K2YxsDRo6xaD9Z+OQZlOXntTpTcmneOb7955MYa2uJWXx3l5Si6QqDJkzBoKshY98uq67f8e3n1FZVMuX88jWNoxO+oeGkbt/a6enQE775DAmJsTcvbrd7KpRK5vz1STz6BbD2rX+3qqTI5WRZJva/71JdVsqcBx9vMIqo0miY98QyvINDqSwuYto9S7llxVsXRz0nLLoDJImtX3xU77oj8bHY2Nk3mA0fMa8uu+q+X38kfe9O/COjm/wxrNbYMPmOe8k/eYIvn3mEvMzjFv995YUFZOzdSfjk6RZ/+Xj0C2Dy7fdy78rPmPPXJ3H39cMrMIiZDzzC/au+5MZlLzJ01tXNzraNuv5mhsy8msR1PxP38fv4DYrgtlfewT9yqMV/S08zfsFt6GuqSbGwWHpjslPr9uZdvkplwMgx3L/qS6tmvRzde+HRL4DcY0dadf6O776kOOcMM+59CK19+8+Ie/oHsvDFNxgx9waOxMfy+ZNLWff2K+h1OqYtWYqksOznj1dgf0Zfv4BjO7eRtmPrxccPx27AbDISNb39Z+bCJ03DNyycnT98dcWVLynOzWHrlx/jHzmUyGsYtQcAACAASURBVOmzGThyLFWlJeQc67y6wOWFBSTHbSZ80jQc3etvHbrwmdyR2X/NZhOxH63E3tmF0TcsavH8UdfNR6FQkrvPukoNlzp1+CBnjiYz6rqb8Y8cyvzlK5CB7/75VJO/0YT69v/2E04eXtz55ipue+UdoufMI+9EBr+9/hIr71nEurdfIX3PjnZP1JiTloquqpKR185vMqHduJsXIyOz47uma653BJPRwKYP3sbBzZ1rHn2mweoSa6i1Wq7665NNHheBajPyT54AwDOg5SWql4uaMYfaqipSt29l+zefYWNnx4hL9ohKCgURU2eRk3aUwtNZFt37dEoSHv6Bze4Fu5SNnT1Bw0eRtmNbq2pK5qQdQalW42XBTJaDmzvzl6/A1tmF2I9WNpo04cS+3dRWVxEeY9l+sgHDR6N1cCQ5bmOL5+ZlHuebZY9TU1HBDcv+D49+ARY915XMJySsLsvz1tYNSFwqL/M4SbEbiJoxp96/Wei4GEpysy++FzrD2YxjpO3YyrCrr704sNNeNFpbrnnsWQDWvf2KxZnzjmz9g/Td2xlz0y1NLl1Xa7Xc9M8XuXflZ0RMmVlvP5JTLw9GzLuB9D07Lg681FZXkbFnJyFjJzT40Hf27E3Y+Ekc3LCWyqLCetl+GxM6diLzn6tLWvLtP560+LWQtLmuJE3U9Dktn9wElUZDyJgJXPf0cuY+/ncGTZzS6r2HdTPfSxh13Xwm33kf1z71T7Gv6jx3Xz+cPLza5b2Yc75Gtmsf75ZPtoBPSBi5GcdafF9lp6aQuO5nIqfN7tBBCJVazfiFtzP/uRUolEpOpyQxYt6NuPtaltTqghHzbqTPgGBiP3qPiuJCTEYDSbHr8Y+K7pDSZ5IkMfGWu6gpL2Pfr1dOoh2zycT6d19DpVYz476/IklSXUkttcaqVWTWuDCjCDIj5jbMz9HLzx87ZxdOdWCgmhK3mbzMDCbeelerBucd3NyJmDaL4oxUKoqsz/osm80kfPMZTh5eREydCdStMljw/L/ROjryw7/+Tur2+G6VOba7yU1PJffYUaLnzEOhVNaVB1x0B0ve+4Tr//YCwWMmcOrwQX57/SXeW7KIX19/kaKcxmufWyrr8AEUSmWz+7mdPDwZOusajiZssWpQ2lp7f1lNUfZppt71QLuuIGmOCFSbkZd5AiQJTyuCHZ+QQfTq24+d58urDL/mhgZZZwdNnIJSpSKpmeyElzPU6shNT8UvvOmyNI0ZNGEyuvNLO1sqc5CdepQ+A4KbHMlpitbBgZhb76YgK5PDsQ2DypT4WJw8POkb1rqZ4AtUGg1h4ydxfN+uZpe9nTyUyHfLn0al0dTVYg0Oteh5rnSSQmFVlue6BEorsXNyZsyN9Ud9B4wai0KpInV764szt8WFckL2Lq4Mb8c9a5dy9vRi2j1LOZtxjF2rv2n1dcW5OcR9/D6+YeEMv6b5FOwKhbLJvefDrr4OJw8vtny2CrPJxLGdCRgN+nrLfi81Yt5NyMgolKoml/1eqk//YG596U36DAxhw3tvEPvhe60aoDLoazn8x0aCho1o1R7XjiIpFIydfytDZlxl8axXT+fpH0D+qZNtvk9TNbLbyickDGNtLQVZmU2eY9DXsmHlmzh7ejHhljuaPK9d2xUcyuIV/+HqR55m1HXzrb6PQqlk1tLHMBmNbFz5Ful7dlJVUtyh+xx7Bw0gdFwMiWt/7pJlftbY89P3nDueztQlf8Hh/J5Mja0d/lFDydi9o1OW/x7dFkfajq2MvHZ+o9sDJEnCLzyS0ylJjbZHluU2ZSmuLi8j4ZvP8A0LJ2TsxFZfN3RWXfLCttTFTt+zk/yTJxh706J6v+OcPeu2arl5+/L7f17lg/sW88fH73M241inr5rq7vav/QmtvQPhk+p/LysUSvwjhjD9nge574MvuHHZi4THTOXU4UPEfvhuuzx3VtIBvAeGtji4MWLejdg7u7Dm5eUUWDjhVXjmFBtWvknC15+SnXakVeXPirLPsGfNdwSPHt9ifpz21CN/BSR881mrl4k2Jz/rBG59fKzaJCxJElEz5lBVWoKDq9vFD59L2Tk5M2DkWFITtjTYV2gyGsk8sK/BF1POsVRMRqPFG6j9I4cyePJ0Etf+xPfPP9vkaF1e5nHys060etnv5QaOGotfeAQ7vv28XlBZUVTIqeRDhE2YYtWPz8GTp2MyGklNaDxgSomP5acVz+HSx5sFL7xqcRmIniJs/GSQZVK3xzd7nizLFJw6yf7f1vDDv/7O2ePHmLDojgZL8GwdHAkYMoy0nQmdUlogY88OctNTGTv/1g7NdBkyZgKDYqay5+fvOXOk5c8Ko8HAurf+jVKtZvbSx5staN0StcaGibfeSeHpLA7HbiAlfjNuPn3pHdR4tlQ3bx+iZ88lYurMVi+RtHN24Ya/vcCwq68jafPvfPfcM1SVljR7zbEd2+qVpBG6H49+gZSczbF6HzpAeWE+5QX5rcpBYCnv84ODzS3vzEo6QFneOSbffm+nZrNVa+v28V++N9pSrr29ibn1Lk4dPkjsf9/FpXcfAiLbVv+1JeMWnF/m9+3nHfo87eHciQx2/fgNoeNiGuSiGDhyLJUlxeS2UOO2rYpzs/njo5X4hoUz6vqmByb6RQyhuqy0wb5NWZbZuPItPrjvNk4k7mnyerPZxKGN69i06j+kJmyp9xm7/dvPqa2uYsodlmWCdvbsjYt/fw7HbrBqSanJaGTHd5/j7utHyLiGAbK9iysL//Ua8578B33DI0mJ28TXf3+MTx65t9X7y7uzkwf3k9/MQFlrlJzLJWPvLiKnz272M0qhVOIXHsGUO+9n5LU3kX00pc17gKtKSyjIymzVShOtvQM3/uNFFEol3z/3DOdakYemqrSEzave4fMnHiR913b2r/2J7/75FCuX1C1lTk3Y0ujrTjab2fzf/6C20TLp9s5NctvjAtWCUyfZ+/MPxH74TpsLpOefzMTTwv2plwodPwkPP38m3npXk/u9IqfNora6irRd/6sjlJV0gM+ffJCfVjzHRw8tYdMHb1N67ixQt+xXoVRZHEhKCgXT732I2UsfIz8rk8+feogTiXuBug/l0ylJrP6/ZXz5zMOobbStTnbU4Hkkicl33IdeV8OOb/+3dv7otjiQZQZNnGLVfXv5+dNnQDDJcZvqjfwZ9XriPvmAjSvfxC88kvn/fLnFzMM92YWSNke3/tHoCGnJ2Rx+f+c13r/3Vj5/8kG2fvkxVSXFjLr+ZsKaqGEYOi6GqpJizqQkd3Tz2b/uZ1z7+DAoxrrXiSUm33Evrr29+f2d11pMPpTw1SfkZ51g5gMPN9jrZI0BI8bQd1AECd98ytmMY4THTG32x0zM4ruZcud9Fj2HQqlk4i13ctXDT1GQdZIvn21836osy6Tv3s62rz/F3devS8pHCK3j6R8Ismzx6PmlclKbr5HdFo5uvXD29CInrelA9eSBfWhs7ejXSJmXK0XE1FkEREWjr6kmanrHz/w79fIkevbcTl/mZylZltny6SrsnJyZ3MjnVWD0SJQqFRl7Oi4gMur1rH3r3yg1GmY/2Pyg4oUB/1OHD9Z7/NCmdRzZGgvAz/9+gc3/fafB4FB+Vibf/P1x/vh4ZV0tyUu+V2M/fJfkuE0MnXVNoxnaW+IZMRRdZUWLA86NObI1lpKzuYxbcFuTf7tSpSIoegRXP/wU9636gun3PYRCqWLzqv+0aRCsq9VWV/Pr6y+x5dMGiWMtkrjuF5RKJVGtrPYBdfvJlWp1vSSq1rjwWmztlgh3n77c/NwKNLZ2/PDC35r87DXoa9nz0/d89Nd7SInfTNTMOSx592Me+PBrrnr4aYKGjeRU8iF+f+c1Pn7kPo4mbKm30uDwHxvISTvKxMV3Nyj51dF6XB3Vo+dn3ErO5pK6Pd7qwKi6vIyKogKLEyldSqO1ZfEr7zR7jk/IINx8+nJ483p8QwYR/8VHZCbuxcWrD7OXPkZuRhrJcZtI2RJLyLiJ5J3IoM+AYKtr6YWOn4RX0EDWvrWCn//9POGTplN4+iTnTmRg5+zC+IW3EzltVptqM7n7+tUlRPn9FwZPmYFXYH+ObI3FNywcF6/eVt938OQZbPrgbXLT0/AJDqXwdBbr3n6FwjOnGDp7LhMW3Y5S1fbSAFe6sAlT2LzqP5w7kU6f/sEXHy8vyOf7559FX1ND4NDh9Bschd/gqBb3gQZGD0dja0vq9nj6RXRcKvTC01mcTU9j4q13tWnGsrU0WlvmPPQEX//9cTa+/zZzH/9bo8HiicQ9HFj/K0NmXU1QdPvU3JMkiUm338MXTz50ccl2RwkePR6X3t788sq/+PafTzHzgYcvDkRVlhTzx0crOb5vF54BQcxe+tifJvnYlehCBvqCU5l4Dwyx6h7ZqUewsbNvskZ2W/mEDCIr6QCyLDd4LcmyzMmD+/GPGNLmmc2uJEkSMx94hOS4TURYmCHZWiPm3Uhy3Ca2fvERHhOarqvdlY7v20VueirTlixtdPWHjZ0d/SKHkr57JxNvuatDAvxtX31CQVYm8578R4u11x3de+Hm7cuplCSGna+xmpueRvxnHxI4dDhXP/IMO3/4in2/reHMkWRmP/g47j592bn6axLX/YytoxNzHnqCgaPHkX8yk1OHD3Lq8EGS4zbj4OZ+scqDpRz6+OLhH8iB339l8OQZrf5MNuhr2fXD1/QZGNLqpZk2dvYMnjQd197efLf8adJ2bWPwpO75+mpJ+p7tGPW15KanUltdbVHSzguqy8s4Eh9L6PhJFk162Dk5Ezx6PEe3xTF+wW1W79/MSjqArZOzRdVG6pZ0r+CHF/7G6heXMe+JZfgNiqAo5wzZqUfITk3hzJHDVJeVEjRsFBMW3VFvT33w6HEEjx6HbDZz+shhtn31CevfeY2DG34jZvESnDw82PbVJ/iFR1odU7XFlftN0Qiz2UTa9ngChw6nsqSYXT9+Q8jYiVZ9IbYlkZIlJEkictostny6ik8fewClWsP4hbczdPZcVGo1oeMnMfLa+exf+xOHN6/HUKtj9PlaYNZy8/Zh4QuvsvXLjzi0cR0uvfswbclSwiZMRqXRtMvfNfqGhaRujyfu4/eZcOudlJzNZcS8m9p0z+Ax49ny2X9J/mMjBVmZbP3iIzR2dlz3zHMERHXs0qsrSfDocWz55AOObI27GKhWl5ex+v+WYdDpuPm5FRYlmVJrbBgwYiwZe3cw5e772yXLW2MOx21EqVJ1aNB2Oa/A/kxYdDvxn3/I3p9/IHrOvHrvgYriQjasfAsP/0AmLLqzXZ/bw8+fsTffir66qsNHKL0Cglj04uv8+tqLrH1zBYVnTlFYVMKnn63EZDAwfuHtDLvqWotrOv+ZGAwGsrOz0XXxjMP4B5/EpNGSmtp85k5nZ+dGz3EZFEl0wABMJnOHDAj5hIRxdFscpedyce1TP8FQwamTVJYUE9CKvdbdnZ2zCyOvbdt3miVs7OwZfeNC4j5+H02//kDjK2C6isloJOHrz3Dz9iW8idI/ULf8NzNxL+fOD7q3p+P7dnNww28MnT231YGa3+AoUrZsxmgwoK+p5rc3X8bR3Z1Zf3kMlUbDhEV3EBAVzfp33+CbZY9j5+RMZUkxg6fMYMLCOy6WkusdNIDeQQMYee1NGHQ6zGazVYES1P0uHDrrGjaufJPTyUmtHiDe/9saKkuKmfPQkxYPOPqEDMLd14/Dm9dfsYHqkfg/UGlsMOprOX0kiQHDR1t8j6TNv2PU1xI9Z57F10ZNn8PRbXEcTYgnavpsi6+XzWayDh/EP2KIxYM4ju69mL/8ZVb/3zJ+WvEcaq0tuvMrxRxc3eg7KIKIKTPxC296xZSkqKsHfsuLb3A0YQsJ33zGt/94Agc3d8xGU1229C4YyO5RgeqZI8lUlhQTc9sS1DZaflrxHEe2/mHViGfehUDVwhqq1ggbP5mkzevp0z+Y8Qtva/Cj1cHVjZhb72LE3BvI2LOD4NFtC1ShLkHRlDvvZ9R1N2Pr5NTuP1hs7OyYeMudrH/3dda/8/r5Op/NZyxtiUZrS8jYCST/sZEjW2MJiIpmxv0Pd/oyhO7Oxs6e/iNGc2zHVmIW343ZaGDNS8upKCzg+r+/YFUm5NBxMRzZGktm4j6Cm6l3ZS2Dvpaj2+LoP2JMq7NZt5ehs67hdEoS27/9nD0//0DAkGEMGD4K/8hofv/Pq5j0eq7665MWJxdrjZHzGmaj7Cj2Lq7c+I8X+eOj99j947dA3Y+T6fc+1CEZS3ua7OxsHB0d8ff379JZ5+JcR2RZbnEffkVFBY6O9RP4mYxG8rOUmNQasrOzCQho/6zoPsF1S4qz0440CFQzD+wDEAOLVoqYMpOD638je2c8JyMj8Qkd1Kn7fJuTsmUTJWdzmPvEsmYHvIKGjUShVHFs9/Z2DVTLCwvYuPJNPAOCGL/w9lZf1y9iCIc2riUn7Qh7f1lNTXkZC154tV4t876DIlj8yn/Y8skHFGafZs5fn2xQ2ulS1q54u1TImAkkfP0pB9b/0qpAde8vq9n5/VcMHDkW3zDL959fmDSJ++QD8jKPN5nVvrUubD3qrM/K0rxz5KQdYfQNC0lc9xNZhxItDlSNej0HN6wlYMgwevW1fMVJ7/4D8QwIImnTOiKnzbL4b8/PyqSmvMzqTOj2Lq7c9M+XiP90FZJSiW9oOL6h4Th7elnUFkmhYNDEKQwYOYZ9v/7I/rU/MX7hbbj07mNVu9rK6kBVkqRlwG1ACXCjLMtZlxy7C3gaqAVuk2U5UZIkN2AN0BfYKMvyA21peGNSE+LR2NoRGD0ClVpD7/4D2b3mWwZNnGzxktD8kydw9vSq92HVUbQODtzx+soWz7NzciZymuWjNM3pyCAvdPwkkmI3kHvsKIMmTm2XL9QhM6/m1OGDRM+ey5BZ14hlik0ImzCZtB1bydizg5Qtm8jPOsG8J5ZZnSSrb/hg7F3dOBy7HpVGg66yAl1lJbqqCuycXYiaNrtNy7gy9uyktqqKiCkzrb6HtSSFgmse+xunkw9xfN9uju/fTfquBJAkkGVmPvAIbt6+nd6ujqBSq5l+70N4B4dyLO0Y19/7F5FZt5V0Ol2XB6kAKo0NNeVljS6tbYlBp0OSJDw8PcnMalvSj6a4+fiidXQiJ+1og5mZkwf34xU4QAwuWkmpUjH5jntZs+J51ry8HIVSSe/+wfiFRxA0dAS9+zeekK2j6Wuq2fnD1/iEDGpxJlNr70C/iCgy9uxg4i13ttv7ae/P32M0GCweVOwbFo6kULBh5ZtUFhUy/b6HGt3ypbV3YNbSx9qlra2h0miImDqL3T9+Q8nZnAaDPhfIssy2rz5h/29rCB49nllLH7X6OcMmTGbb15+StPl3pt/7kMXXm4wGTicnkb5nJyf278bDP5Drnl7eKcv8j277AySJ8EnTKDiV2eT2g2bvkRBHTXkZw65qPqt/Uy4kUd30/tvkpB6xeMAgK+kAQJv279s6OLbb61SjtWXsTbcw+oYFnbIdqylWvXokSeoHXAsEA7OBFcD888dsqQtShwABwH+BUcBS4FdZll+XJOlHSZJmybK8vu1/Qh1DrY70PTsIHj3u4tLEsTcu4seX/knKls0WB3j5WSfalEhJqHvTTrnzPn56eblVyyAa4+Hnz5J3Pm6Xe/Vk/SKisHd1Y+PKNzEZjcz6y6OtKm3SFIVCScjYiSSu/YnTKUkNjhedOc2Uu+63+kfH4dgNuPTuQ99BlpUuai9KlYqAIcMIGDKMKXffz9n0Y2Ts24XWzr5TlyJ3BkmSGDxpOkWSRgSpFurqIBVAbWNDtSxjMhgs3qqh19UgKSQ07TDj0xRJkvAJDiX3ssy/NRXlnM04xsg2lIcR6pKsRN3xF4K8PDh9JIkzKYfZs+Z7dv/4LfOXv9zsTF9H2b/2J6rLSpn3xLJWvUcGjhrHxpVvkpd5nN4W1GtviizLnEjcS0BUdJMBXVNs7Ozp0z+Y3PRUwidN71bLXqOmz2bvzz9wcMNaJt9xb4PjZpOJTR+8zZGtfxA1Yw6Tbr+nTQGFjZ09oWMnkrpj6/nary3nKjGbTZw8mMixnds4kbgXfU01GltbvIPDyDqUyJZPVzH17rbPS5mMRhRKZaOvL9ls5sjWOPzCI3Hq5YF/ZDTH9+2mODe7yZUnJxL3kJm4j6qyUqpLS6gqK6WyuAjPgKA2/Q4JGTOBbV98zKFN6ywPVA8fwMM/sNsN5HVlkArWz6hOAtbLsmySJGk9cGnxoJHAXlmWK4FkSZJ6SZJkB0wGlpw/52cgBmi3QPXE/j0YdDWEjvvfvo1+kUPxHhjK7p++Z9DEqa3+Uq+trqL03FnCY5reZyG0jqd/IPe+3/1T6vc0CoWSsPGT2Pfrj8QsXtIuwdbYGxcREBmNjZ0dWgdHbBwcsLGzY/s3n7Pv1x9RqtXELL7b4h/zRTlnyEk7wviFt3eLQEChUOITEoZPSFhXN0UQ6snLy+PfK1YgGQ149+vHo489jr+/PykpKZw8eZINGzbwxBNPNHm9QVeD2kaLJHXsAIVPcBgn9u+hqrTk4o+urEOJyLKZwKHDOvS5/wwUajX9IqIuLgmtqazg8ycfJP7zj1j0f6916gBUVWkJ+3/7iYGjxrV6KW/d8l8l6bu3t0ugmn/yBJXFRQQNsy7ZXcTUmWgdHJh8Z8NgsCvZu7gSMmY8KfGxjJ1/S73A0aCvZe0bL5N5YB+jb1jI6BsWtMv3Z+S02STHbeLotrhmS5VVFBWSHLeJ5C2bqCwqROvgyICRYxg4cix+4ZGoNBq2ff0p+35ZTS8//zZNVsiyzHfPPY2tg2PdYMhlr+/stCOUF+Qxdv4twP8y5mYdOtBooFpVWsKvr72EWmuDo7sHds4u+PTug52zCxFTLV+yeym1jZZBMVM5uOE3KkuKW52QSV9TTe6xVKKvutbq5+6prA1UPYFCAFmWjZIkKSVJUsqybLr02HkFgNdlj+cD1qd/bcTRhC04uPei7yUjGJIkMeamRaz+199Jjtt48U2nq6wkdfsW0nYmMGTmVYRclpzoQg2mztifKggdZcyNiwgaNgqf87UN20qt1Ta6V2b8wtsxGQwc+P0XVGo14xbcVu+D/sKIa2VxERFTZjT4kkn+YyMKpYrwmKmX31oQhEt4eXnx6muv8sTDD2M2GusdGzx4MIMHNz0TYDaZMNTWdkr5rguDPLnHUhkwcgwAmQf3Y+vkTO/AtgcmQn22Do6MX3Ab6995jdQdW5ssNdYRdq3+GpPRwLgFi1t9ja2DI37hkaTv3s64BYvbPGNzInEPSBIBQ6wbBBk0cUqXZDNtjaHnyxJt//ZznHp5UpR9msIzpynKOY1Rr2fKnfcTNWNOuz2fV2B/egcNIGnzeqJmXNUgaMs5lsq+X1eTmbgPWTbTL2IIk2+7h8DoEQ2W+I67+VaKzpwi7pP3cfP2bTaRT3POZqRxNj0NqJu9H37N9fWOH4n/A42t7cU9qc6eXrh5+5KVlEj0nLkN7pe0eT1mk5GF/3q3Q/IzRE6bReK6n0mO28jo6xe06prTR5Ixm0z4R1i3P7UnszZQvbxAo9TMsQvHm7vmfw9K0j3APVD3pRwfH99iYwzVVZw8lIhX1HC2bttW75gsyzj08SXhuy85U1BE0bEjlGSmI5tMKFRqNn7wH7IrqlFdUuc0L2k/AMfP5nGqrOXn/zOrrKxsVR8JXSfjbJ5V11nUt/0G0Csskr2/rOZMTi7ew8dgrNVRlJZCfspB9OVlABzYGke/STMvJtswm4wc/mMjTv0C2XvwkFXtFKwj3ruWcXZ2pqKiokvbcOrUKRYvXsyUCRMw6vVUVFQgyzIVFRVs27aNVatW8cUXX3Do0CHuv/9+NBoNw4YNw93dnaceq9u3ZDx/vk6n67D+N5uMSEoVu2M3klOjRzabydi/B+d+gQ2+owXLNfbelWWw8/Ai9pNVnNMZUXRA8rfL6UqKORK7AY9BUSSlpUNaequvVXj5UJZ0gB9XvoPHoMg2tSM1Pg57rz7sPXCw5ZOvAJf3r0MfXw5tXAeA2s4erVsv3AYOwtk/iFIb+3Z/H9v0DeRc/EbWfvMVjufzM8iyTH7yAbJ3xqPS2uIVNYxeYRHYOLmQU6MnZ3vjtXGdhozC5mQma/79PKE33IKNk4vF7cnashGFSo2jT18SvvmMglojdh5eAJgMelJ3bsU1KJgdu3dfvEbVy4tTKUnExW5GcUmOGrPJRPLvv+DU15/D6RmQnmFxe1rDqa8/+9b9gs7Vq8HgfGPv39Pb6tp5PK+AzML6x/7srA1UzwKhAJIkqQD5/GzqhWOXFq/yoG5W9cLjJdTNruY3dmNZllcBqwCGDRsmx8TEtNiYA+t/q0t6sui2RjN19ffqxffPPUPG2tXY2NkTMWUmgyfX7UP48pmHUeXnEHPrXRfP/z0lEQdXN6bNbr9Rqp4qPj6e1vSRcOWxtG/liRPZ+P7bHNkai41BR3baEYy1tfiEhBF1x72U5Z1j+7ef42Cj4ZrH/oaNnR1pO7dxUFfDlAW34t+GBAKC5cR71zKpqakXs+hu+XQV+acy2/X+nv0CmXT7Pc2e4+DggEKhQKlWIZvNODo6IkkSjo6O2NnZoVKpcHR0ZNmyZbzxxhtMnTqVWbNm4e3tjVKqW2Xk5OqGQqFAq9UyZEjHvecKEmIxVpUTExNDzrFUDtTqGDP76os1fAXrNfXeHdDbg++WP41deRGjrr+5w9sR+9FKVCo1Nzz0GHbOlgUg8sSJ/JCdRX7iLubcervV2d4rigtJXJnHuAW3MbKHfJ5d3r8jhkRRfDYHd18/bB0cm76wnRhGj+KDvdtRFJ4lZuEtGPV6Yj98l+wdW+g/fBSz/vKoRXVCoyMi+OrZRzi7dRMLXnjVorI9el0Nhz95l9BxE5l4y518/uSDnNsRx60vKoUFpQAAIABJREFUv4Vaq+XotjgOGQxMvfmWevuzs1yd+PFwIgG93OplGU/bsZWD1VVMeehxAod0XJksXwdbfnnlBXzsbRg4sn7Fi8bevx+u+RL/iCgmT+meM/tdydqNDLHATEmSlNQlU0qQJOkVSZKmA3uBIZIkOUiSFAkUyLJcAWwErjl//bXn79EuUhPi8PAPbDKddN+wwUy6/V5mPvAI977/GVPvuh+vgCC8AoIIj5nGwfW/UXI25+L5+SdFIiVBsJSkUDD9vgcJHT+J7KMphIyZwC0vv8XNz/2bkDETGHntTcx84BGyU1P4bvlTVJYUk/zHBpw9vegX3rYRdUH4M1Eo6wJV02XLfy9ITU1l1KhRSJLE1Kl1S+r1uhpUNjYoOmn/ok/IIPJOnsCg03Hy4L66Gn1iMKpD+YaGM2DEGPb+sprKkuIOfS6T0cCxndsIGj7K4iAVzidbvOsB9Loatn31idXtyEysK3nU2rqpVyI7Zxd8QwZ1SpAKdfsswyZOJn33DvKzMvnuuac5svUPRt+wkGsefdaiIBXApXcfrn70GYpzs/lpxXMWvTbTd23HoKshfNI0bB2dmPWXRyk5l8uWz/8LwJGtsTh79cbnsooGvqHhqDQ2ZB1KrPf4wQ1rcfHqQ0Bkx5bIChw6DJfefUj4+lMMtc3X3i45l0tZ3jmry9L0dFbNqMqyfE6SpE+AY0AVcDXwKuAmy7JekqS/AwcBM3BhWO8dYI0kSQ8Am2VZ3tDm1gPFudmcO5HBxFvubPa8obMa3xQ+7uZbSd+dwNYvP2beE8sw1OoozslmwMi21fwUhD8jhULJrL88yvR7Hmw0edmgiVOwd3bh19df4qtnH6GyuIhxNy8W2WeFK0pLM58dTXl+6bxRX9vo8Xr7ymQZva4GY22tVQGFtXxCwpB/MnP2+DEyD+zDJzgMrX3Hl3v7sxu/6HZOJO5lx3dfMuO++iVGKouLKM7Nxq8dBgZPHkxEV1lB2ATr98O6+/Yl+qpr2ffLagZPmm5VArvMA3tx9vTC3dfP6nYIDUVOnc3B9b/VrTrU2HDN43+zuC7ppfzCI5m99DE2vv82Xzz1EHMeeqJVr8PkLZtx9fbFJzjs4n2GX3M9+35ZjbuPH6ePJDPmhoUN9tKqNBr6hoVz8lAiF16heZnHyU1PrUv62MG/ORQKJdOWPMgPLzzLju++IGbxkibPvVCWRgSqjbO6p2RZfkeW5f6yLEfKsnxaluWbZFn+9vyxNbIsD5BlOViW5YPnH6uRZXmWLMtBsizf115/QGrCFiRJQcjYiVZdb+/iyshr53Ni/x5OHT5EwaksZNmMZ4BIpCQI1pAkqdkM2/5R0cxf/jJmk6musLRIoiQIFlG0EKgGBweze+dOyvLziN20EX11XR4Ga5dXWsN7YAhIEsd2JlBw6qTViW4Ey7j29mbIzKtIid9MflYmZrOJzAP7+PmVf7HqL3fwwwt/4+i2uDY/z9GEOGydnOk3uG2z5KOvuxlHdw9iP3oPs8nU8gWXMNTqOJ2cRGD0iG6RMb4ncfftS+DQ4Th7erHwX6+2KUi9IGTsRBa9+DpaB0d++Nff2bX6G8zmpvu8KOcMuceOEh4ztV7/jr1pEV6B/Yn//L8gy01WNfCPiqbkbA6leeeAutlUlY1Np/3m8AuPIHLabBJ//5WcY6lNnpeVdABnTy9cent3SruuNFf0NIYsyxxNiMdvcCQObu5W32fo7Lk4e/Vmy2erOHeiLhlAYwWfBUFoH16B/bnl5TeZv3xFp2QhFYSeRFIoUCiVGGr1DY6ZzWb++cxTPPjgg0yYMhWtrR22Ts64+/ii7IQEOxfY2Nnj4edP8pZNAG2q4yxYZtR1N6O1d2DdW//mw6V389OK5zibkcawq67FJySM2A/fozg3p+UbNUFXVUlm4l5Cxk5okOnVUmqtlkm3L6HwdBYHN/xm0bWnkpMwGvQEDbWuLI3QvGse+xt3vvFBk9vqrNGrbz8Wvfg6YeNi2PnDV/z44j+pLitt9NyULZvrBrMvy8isVKmZ/eATqGxs6DsoAmdPr0av9z+/vDcr6QDV5WWk7dzKoAmTO3Vlx4RFt+PUy4ONK9/E0MjAYvqeHZxKOoB/1DAx2NKEKzpQNeprGTByDJHTZrXpPiq1mom33ElR9ml2r/kOraMTju4e7dRKQRAa4+jWq91K5wjCn4G/vz/79+9n+fLl3HfPEoz6WrKysnBwcCAmJobVq1dTXVZK2IAB7EzYxsFDh5g6fXqbgwlr+YSE1SV96uUhlmZ2Iq2DA+NuvvV8Ap6+XP3oM9zz3idMWHQHcx56EqVGw9q3VmDUNxzoaI303dsxGY2EjW97fW6A/sNHEzBkGDu+/4qK4sKWLzgvM3EPGls7fMMGtXyyYDGlStUhS2Q1Wltm/uVRpt/7ELlpR/lm2RNUFNXvd5PRyNFtcQQOHXGxFvOl3Lx9WLziba56+Kkmn8e1jzfOnl5kJSWSHLcJk8FA1Iyr2v3vaY7G1o7p9zxEydkcdn7/1cXHZVlm94/f8tvrL+EZEMSYG1pXxubP6IoOVNU2WmJuvYsBI8a0+V79h4+mb9hgasrL8AoIEiMbgiAIQrel0thg1Osxm80XHzOZjFSVlmBjb4/azv7iEuGucmFfWeCQ4eI7tZNFTpvNQ5/+wPXPPs/AkWNRni/R4ejei5n3P0xBViZbv/zYqnsf3bYFV29fvAL7t0tbJUli8h33IZtMxH/+Uauukc1mTiTuxT8q+uLfJlw5JEli8OTp3PiPF6kuL+X755+pN0iReXAf1WWlDJ48rcl7uPbxaXY7gyRJ+EdGczo5iaRNv+MXHtGus8Ot1S8iisFTZpC49mfOZhzDbDTy+39eZcf3XxI6Loab/vFSp+YPuNJc0YFqe5IkiZjbliBJCnoHiYLkgiAIQvelsrEBqDcrVlVSUjeD2YatMO3Jb3AUrt6+Te4hEzqWWqtt9PGg6BFEz5nLoY1rydi3y6J7luXnkZN2hLDxk9p18MHFqzcjrr2R9F0JpO9uvCbnpc5lZlBdVtqjs/3+GXgPDOH6Z1+guqyU75/7X7CaErcJe1c3AqLatrfdPyoaQ62OiqIComY2nlS1M0y85S4c3NzZ8N4bpP/yHWk7tjLu5sXMWvpYszk9BBGo1uPpH8iiF19n+DXXd3VTBEEQBKFJas2FQLVu35PRYKCmvAxbJydU548BLF26lOXLl3dFE7FzcubON96vS6wkdCvjF96OV2B/Nq18i/LCRsvaNyp1ezwAoeNi2r1NI+beSO+gAWxe9U6LS4AzE/ciSYp6NTKFK1NdsPo81WWl/PD8s5w7kcHJg4kMmjC5zatC/AYNRqFU4eTh2aWDGjZ2dky/ZynFudnUFBdwzWPPMvLam8RKk1YQgeplvAL7Y2Nn39XNEARBEIQmKVQqFEoFxtq6QLWyuAgAB9fuMZsqdG9KlZqr/voUZrOJtW+uoKq0pMVr6hJYbsE3NLzJBDZta5OKWUsfx2g0sOHdN5AvWdZ+uROJe/EODsXW0and2yF0Pu+BoVz3zPNUlpTwzbInkGUz4ZOaXvbbWhpbOyYsuoPJd9yHQtG1WyH8o6K5+pGnCbluUbtsWfyzEIGqIAiCILSCLMtd3YSL6spA2WDQ12LQ6dBVVmDn4tqqxEnd6e8Quo5L7z7MuO+v5GWe4OOH72HvL6sxGgxNnp+XeZyS3GxCx8d0WJvcvH2YdNsSTqckcWD9r42eU16YT8Gpk2LZbw/jExzK9c8+j1Ktpu+gCFz7+LTLfaPnzO02r5WBo8ZhK5K1WqRrUgEKgiAIwhVEq9VSVFSEu7t7t1mupdLYUFNRRkVxIQqlEvtWJOSQZZmioiK0TexfFP5cBo4ah0e/AOK/+IiErz8lOW4jMYvvJnBow9qkRxPiUKrVDBw1rkPbNHjyDDIP7Cfh60/xC4/Eo1/AxWPV5WXs/OFrAAK7SfAhtB+f4FDueGNlve0Lwp+bCFQFQRAEoQW+vr5kZ2dTUFDQ1U25SK/ToasoB+pKkhTV/K9On06nazIY1Wq1+Pr6dkobhe7PtY8P1z75D7IOJbLl8w/5+d8v4BMyiH4RUXgF9scroD9aB0fSdmwjaOiIDq9DKUkS0+99kM8e/wu//+dVFr34BjUV5exf+xOH/9iAsbaW8EnTcfMWr+GeyNGtV1c3QehGRKAqCIIgCC1Qq9UEBAS0fGInys/K5IuXl+Hi1YfbX3+vXpmO+Ph4hgwZ0oWtE640/lHRLA6PJGnTOpI2r6+buTy/TNzW0YmainJCx0/qlLbYOTkz8/6HWfPycr5Z9gSFZ04hy2ZCx8UwYu4Noi6vIPxJiEBVEARBEK5A7r5++EcOJXr2XFFLUmgXSpWKobPnMnT2XPQ11eSfzCTv5HHOncjAbDQSMKTzsuwGDBnG0NlzObx5PRFTZzDsqus6JImTIAjdlwhUBUEQBOEKpFSpuP7Z57u6GUIPpbG1wzcsHN+w8C5rQ8ziuxm/4DZRa1IQ/qRE1l9BEARBEASh26nLbi2CVEH4s5K6c5p6SZIKgFNd3Q4LOQNlXd2ITtQLaL4yd88h+rZnE/3bs4n+7dn+TP0r+rZnE/3bs4n+bVw/WZYb1O7p1oHqlUiSpFWyLN/T1e3oLJIk7ZdleVhXt6MziL7t2UT/9myif3u2P1P/ir7t2UT/9myify0jlv62v9+6ugFChxF927OJ/u3ZRP/2bKJ/ey7Rtz2b6N+erU39KwLVdibLsnjD9VCib3s20b89m+jfnk30b88l+rZnE/3bs7W1f0WgKrTVqq5ugNBhRN/2bKJ/ezbRvz2X6NueTfRvzyb61wJij6ogCIIgCIIgCILQrYgZVUEQBEEQBEEQBKFbEYGqIAiCIAiCIAiC0K2IQFUQBEEQBEEQBEHoVkSgKgiCIAiCIAiCIHQrIlAVBEEQBEEQBEEQuhVVVzegOb169ZL9/f27uhlCM6qqqrC3t+/qZggdQPRtzyb6t2cT/dtzib7t2UT/9myifxuXmJhYKMuyx+WPd+tA1d/fn/3793d1M4RmxMfHExMT09XNEDqA6NuerSv6t2xjFmpPO+yGeHbq8/4ZifdvzyX6tmcT/duzif5tnCRJpxp7XCz9FQRBEDpcbWYZFVvOUB53uqubIgiCIHRTRd+mUbYpq6ubIXQTIlAVBEEQOpQsy5T+ngmAsaAGQ2FNF7dIEARB6G6MRTXUHCqgat85ZFnu6uYI3YAIVAVBEIQOVXO4EEN2JU5T/QDQpRZ1cYsEQRCE7qb6YD4A5goDhrNVXdwaoTvo1ntUG2MwGMjOzkan03V1U9pMq9Xi6+uLWq3u6qYIgiB0CNlortub2tsex8l+VCcXokstxnG8b1c3TRAEQegmZFmm+mA+Ki87jHnV1GaUoPF26OpmCV3sigtUs7OzcXR0xN/fH0mSuro5VpNlmaKiIrKzswkICOjq5giCIHSIyt1nMRXrcL0zHEkhYRvqTsW2M5irDSjsxCCdIAiCAPozFRiLdLjeMIDK7Tno0ktwnNi3q5sldLErbumvTqfD3d39ig5SASRJwt3dvUfMDAuCIDTGXGOkIu40Nv1dsBngAoA21A3MoEsv6eLWCYIgCN1F9YF8UCmwDe+FzUBXarPKMetNXd0soYtdcYEqcMUHqRf0lL9DEAShMRXxZzDXGHGeFXDx807T1xGFg5qa1OIubp0gCILQHchGMzWHC7Ad5I5Cq0I7wBVMMrWZZV3dNKGLXZGBalfLy8vjiSeesOiaXr16dVBrBEEQuh9jqY6KHTnYRXmi8fnfPiNJIaENdkN3rBjZZO7CFgqCIAjdge5YMeZqI3ZD62ps2/g7I6kV1IqVN396IlC1gpeXF6+88kpXN0MQBKHbKt+QBYDT9H4NjtmGuiHrTNRmlXdyqwRBEITupupAPgoHNdr+rgBIagU2gc5ii4ggAlVrZGVloVQqmTdv3sXHFi1aRGxsLHv27GHkyJFERUWxbNmyLmylIAhC16hOyqf6UAGOE3xRuWobHLcZ4ApKCV0nLf81ldciG8ReJ0EQhO7GXG1Al1aMXZQnkvJ/W+JsBrhiLKzBWCxyufyZXXFZfy9V+tsJ9LntW2dJ422Py9VBLZ4XGRlJSkoKer0elUrFnj17+PTTTxkyZAi//vorAQEBXH311Wzbto0JEya0axsFQRC6K2NhDSVrjqPp54TTFL9Gz1HYKLEJckGXWoQ8J6BD9+vLZpm8tw+i8XHA/fZBIjeAIAhCN1J9uBBMMnZDPOs9rh3oShmgyyjBYWSfrmlcD2AsraXkx3QcxvlgG+zW1c2xmNUzqpIkLZMk6bgkSfskSfK/7NhdkiRlSJKUIklS9PnH3CRJipck6YQkSe+1rdldT6FQMHHiRLZt28aePXsYPnw4VVVVmM1mAgMDkSSJWbNmsX379q5uqiAIQqeQjWaKvk4FhYTbgmAkZdNfMbahbhiLdBgLajq0Tcb8asyVBnTHSqg+VNChzyUIgiBYpvpAHiovO9Te9vUeV3nYonSxEct/28BYrKNg1WFqM0op+/0ksix3dZMsZtWMqiRJ/YBrgWBgNrACmH/+mC3wNDAECAD+C4wClgK/yrL8uiRJP0qSNEuW5fVtaXxrZj470jXXXMP69euxt7dn7ty5SJJ0Rb4IBEEQ2kPpukwMuVW4Lw5D5dJwye+ltKFu8MsJdKnFqD3tOqxNtVl1WSNVHraU/XYC7QAXlA6aDns+QRAEoXWMhTXoT1fgPMu/wWoXSZLQDnSlOqkA2WRuduBTaMhYVEPBf5Mx60w4jPOhcnsOtRmlaAe6dnXTLGJtr08C1suybALWA6MvOTYS2CvLcqUsy8lAL0mS7IDJwG/nz/kZiLHyubuNadOmsXv3bhISEpg1axbOzs5otVoyMzMB2LhxI2PGjOniVgqCIHS8mpRCqnadrVteFObe4vkqFy3qPvbUpBZ1aLtqs8pROGpwvyUUc62J0l9PdOjzCYIgCK1TdTAfJLCN8mz0uM0AV+RaE/rTFVY/h/5MBYa89t0m2N0ZCmso+OAwst6Ex5LBOM/0R+GkoWJbdlc3zWLW7lH1BAoBZFk2SpKklCRJeT5wvXjsvALA67LH84HeVj53t2FnZ4enpycGgwFnZ2cAPvzwQxYsWEBtbS2zZ88mJiamaxspCFc4Q2EN5ioDNv2curopQhOMxTqKV6ej7uuI80z/Vl+nDXWjYssZTFUGlPbqDmmbPqscmwAn1F72OE32o3zzKWoiC7Ed1L4lw6oO5mPT1xFVL9t2vW9nk01mjAU1GM5WoT9bheFsJcZiHW43DMQmwLmrmycIAmDIq8JYpEPhoEZpr0bhoEFho+zqZllENpqp3n8OmyAXVM42jZ6j7e8CCtCll1j1+SObZQo/ScFca8JldgD2Y7x7fJ4CQ341Bf9NBrNMryURaPrULal2GONN+YYs9LmVaLwdWrhL9yFZs1RVkqQnAKMsy2+c//9coK8syyZJkm4Exsmy/Nfzx3YBi6ibTR0ny3KJJEkzgEWyLC9u5N73APcAeHl5RX/77bf1jjs7O9O/f3+L29xdHT9+nLKyK7egcWVlJQ4OV84LXmi9Lu9bGVyyJNwy6r5UTk4xI19Z38Pd2uX9a1MGsgR6C8cDVDXQJ1GBSgdnxpgxWrCK16YM+u5SkjfYTIVP+2+bUNWA/1YlBaFmyvrJYIa+uxQo9XB6nBlzO8XGyloI2KKk2l0md3j3qA3b4vtXBvt8sCmTUOlAXVP3X5UOJLnuPWdWyOgdQFMFFX1kCsI7b2uLqhq8ExUUBpupbnyy5U+ryz+bhQ7VVP9KJrDPk3A+LWFb2jDYMivlTn+ftoXTGQnPIwpyo01UezR9ns9uBZIZssdY/tmqKQe/nUr0djKaaolKT5n8we332W+Njnz/KvTgt0MBMuSMMGO45GkUBvCPV1DpJZMf0f1eI5MmTUqUZXnY5Y9bO6N6FggFkCRJBcjnZ1MvHLt0qNqDulnVC4+XUDe7mt/YjWVZXgWsAhg2bJh8+Yxkamoqjo6OVja7+9FqtQwZMqSrm2G1+Ph4MWtsBdksIxvMHToCaqrQoz9TgTbUzaoRxK7sW2NRDcU/pKPPKkfd2x7DuSpG9ouqG10V2sWl/SsbzZx9aS9I0PvRaBR2rfsWr80qo+iLVGSTGffbQ+nX37K9L7JZ5tzRffQ+bmKgX18cRnsjqdpvH1L1oXyKOcagqdFofOq+sfUDK8h/9xDhZd643TCwXZ/HrkhiTFA0mr5d/x3V0vu35kgRRRuPggKUTjYoXW1QuWhRutig9rJD3cceVS87JKVE0depqE6WETZhJJKic2YjyjZlUVF1Bp+jGrymDEHlfmXPVLcn8b3bs13ev6YKPRUJ2VTvz8NcbUTlrsV+dh9sApwxVRswVxgwV+mpSStGcbqC0HtGo9B071Fd2SRz7rX9KHxVDL8hqtnfKOWm05THnmL88LEWr7yp2J5DGZn4PTSSmpRCWH8S50QtbgtCumyVVke+f4u/O0a1oQDPpVH4NzJrWqo7gWLXWQbeNrzJWezuxtpfBLHATEmSlNQlU0qQJOkVSZKmA3uBIZIkOUiSFAkUyLJcAWwErjl//bXn7yEIfyrGslrK/zjNuVf3k7t8J+Vxp5HN7T+ypcsoIe+tAxR9fpTKhJx2v39Hkc0ylbtyyXvzAIZzVbjeNBCP+yJAAbWZpV3dvB6rOqkAc5UBc6WB0t9Ptuqaqn3nKPhvMgpbFZ5/ibpYqN0SkkKi152D0PR1pGzdSc69tp/qQ/nt9p6ozSpHslGi7v2/bJIaX0ccJvhSvT8PXUb7ZJPUHS9F0qqQtCoq4s+0yz07kmwwU7ouE5WXHT7Pj6XP0yPwvDcSt/nBOM/wxy7KE7WX/cWahtoQN8wVBgy5lZ3TPrNM9YF81D4OgETR12nIhu4xUy1cOcx6E6YKfVc3o81K1mRQuT0Hm0Bnet0djtdjw3Cc4IumryO2wW7YD/PCcWJfnCb5gUlGf7L7r9KrTsrHVKzDabJfiwPp2oGuIEOtFZ/XtZllKN21qFxscBzng+d9kaCQKPggiaoDedY2v1vSpZdQfTAfxxjfJpf2Ooz1AVmmckduJ7fOelbNqMqyfE6SpE+AY0AVcDXwKuAmy7JekqS/AwcBM3Dz+cveAdZIkvQAsFmW5Q1tbn0PYsivRuVh2+PXzvdUZp2RmpRCqg8VoD9VjtLFBpWHHWpPW1QedbMS1Qfz69Ksy2AT5Iy6tz3lm06hP1OB203BKGzbXtZYNsuUx56iYssZVB52aHwdKfv9JEpXLXaD23dPXnsyleupOpBHdWIexoIabAa64nr9gIsjfmofR2ozu/+Xb3swVxswltai9rRr19nFpshy3eCAytMWbYg7lduysRviiTao8dlr2SRTti6Typ252AxwwX1BSKtnYBuj9rLH485wdBkllP1+kuJvj6FOyMH1hoEX99ZYS59VjsbPsV4ReQDnqX7ojhRR8kM6nn8d2qb9sbIsU3u8FG2QMyovOyq2nMGQX92hmYzbqiIhG1Oxjl53h7fqNaYNdgMJalKL0fh2/GyxPqsMU2ktzjP9kTRKij4/Sum6TFzn9ZxtP0LHkGUZ/clyqhLzqEkuQDaYsRvqhdM0vxYzkXdHsslM7YlS7Ef0afH1bxPgBCoJXUZp3Xu2m5LNMhVbzqDuY1+X/b0Fah8HFHYqqvaewza8V6u/F2WzTO3JMmwH/S+5n6avI14PDaHwkyOU/paJbah7u/z26mpmvYmSn4+j8rCtG7BogspNi+3gXlTtOYvT5L4otN3/b7e6hbIsv0Nd8HnBTZccWwOsuez8GmCWtc/XkxmLdeS9dQCbfk44XxV4RW1ybi/GEh1V+/NwHO9zRbxxoO4LUXekiOqD+dQcKwajjNJdi120F6YKPcaCanRpxXB+dkjprMFxUl/so71QudsiyzJVO3MpXXeSvP8cxP2W0Db1valcT/G3adRmlmEX7YXL3CAkCQo+TKH4u2MonTXY+HWfhESySabmaBHViXno0ovBDJp+TrjN98M2yqPeoI020JmK7TmY9aZuv6SpLQznqij4KBlzhQGUEure9mi8HVD7OKAd4NIhyx/1ZyowZFfiMjcIu2gvalIKKV2TgdfDQ5HU9f+tzTVGir5OpTajFIdxPjjPCmgQBFpLO8AVmwddqE4qoGztCcrWnsBjSYTV9zPXGDHkVeE0uF+DY5JaiduCEPLfO0TJjxm43xpq9SChqViHqbQWm4m+2A7uRWVCDhVbs3G7sX2WFbc3Y2ktFVvOYBvu3upZcKW9Go2fE7pjxThPa/jv2d6qDuQjaZRow9xRaJQ4TPChclsONgFO2EWKDatCQ6ayWqr251GVmIepWIekUWIb4YFCo6Ryz1mqk/JxGOWN46S+HZa4rSPosyuR9WZsAltOJCSpldgEOLfbSpGOUpNciLGgBreFIa363JUUEk4z/Cn96ThFX6Xivii0VcGq4VwVco0Rm8sGXRVaFS5zg8h/+yAVW8/gPDPA6r+luyiPPYWpWIfHPRFI6ub/bRzH+1JzuJCqfXW/ubu7KyMiuIwsyz1i5vFCIiuls83/t/fe4ZFf9b3/60zvVXUl7Wr7StuLu73uBRfANgYC2MZgCDWFJL8kN8kvt+Q+9yaBQJIL5NIxEIwxjmMbF9zLuqy399U2aVe9Tu8z5/7xHWmL2mjUZ8/refZZlSlH8/mW82nvD547lxB6sYXuf92N/ZIqXDcvQu+c/7P+pJQkTwYx1ThGdECllMR2dhN4+gQymYVsbt5cNGK7uhn4dRM6hxHHZdVauVyt47xjU2ZzZPoT5OIZTLXO8/q7hBA4rqrBWOuk7xeH6f7OXty31WPwmJHZHDItkZkcOpsB69qyMY/55OkQfY8cQiazeD+yAvuWyqHf+R9opOc7e+j76SEqvrR+zvR6hV5JvPU1AAAgAElEQVRsIfzaGfQuE86tddg2V2AsHzkLZV7iJvx6K6mWEJblc2sGWC6VhUxuUllFgFRrmN4fHQCDDu+9y7X5cm0R4gd7ib7fCXqB85oanDcsnFJnPbKtHWHWY9tUic6kx3v3Mnp/eIDQy2fOU/DN9Cfo/clBMr1xvPcux37J1Au3C53AvrGCTGeU8Jtt5OKZMaPdkbfbMVTaRsz+Jk+HQIKpfuTgjKnGgfvWeoLPniL6XgeOyxcUtebEca0k3bxMm89qv6SKyLsdczaDE3z2JFKC+/YlE3qeZZWP0AvNZEMp9K7puzflUlni+3uxri0bOs7dt9aTagkz8JtjGKsdczpbrZg5pJSkWkJE3m7X+g9z2r3CdePC844fx9YaQi+eJrKtjej7nbhuqMOxtXZe7CMHW14KcVRBC/gFnz1FNphEPwM9iDKjZXz1bvN5LRajPj4nCb1yGkOFFeuawqu8HJdVQ04S+M8TBTurg1VYI6kFmxY4sG0oJ7KtHccVC2bks5ouUm0RIm+2Yb+0qqDjxFTnxLTYRWRbG44rq+f8fNp556haLBb6+vrw+/3z4iIzGlJK+vr6sFgsCL3AccUCbOvLCb18msg7HcT29uC6oQ77ZdVzMsOY7o4ReOoE5jGU2kDrYws8cRxh1mPfUonjygVDjlI2kmLgieMkDvVhWuxGGHVE3u7AcU3tvIh4Rnd0YSizUvm1zaMKjAi9blTnaxDzIheVX91I/y+PEHzm5IiPcVy1APedS0Y85tOdUXp/fBCd1UD559ZirDz/ZqG3G/E/tIae7+yh9ycHqfji+kk7VZNFZiXRHZ1YVvnwP9A4rkCLqd6d71MNzilHNdkcpP+XR5FSUjmJEtLkqSC9PzmIzmag/OG15wUTpJRk+xOEXj5N+LVWYrt78Ny1BMvqyV8Ds6EU8f29OK6oHhL2siz3YttUQfiNVqzryzFV288GQjKSss+uGbUseKqwNPoJv95Koql/1AxaNpwi8PQJ9D4LVX+yZdgxlGoOgU6MKWzkuLqGxLEBAs+cwlzvLmijdSHJ4wH0btPQWBrH1hoi73YQeaMNzweXTvj1ppPkyQDxfb24blqIwTcxJ3rQUU0c7Z+WIMUgicN9yGQW26azdhd6Hb5PrKL7X3bR94vDVHx5w4xVVsisJPJ2O7E93fg/2TDhz00x9chMjtjubiJvt5PuiCIsBhxX1eC4vHrEQKzBY8F33wqcW2sIPt9M8LlmMgNJPB9cOmPiYMWSPBHEUGlD7ygsOGTOCw4mjgewb64c59HFMVhSG9/bQ/xAL7lYBr3bRNWfXjJuNi9xuI9MVwzfx1ZO+LN3XLEABASePEHfzw/j/9TYzmryZBC9T+tPHQnXLfXE9vcSevk03nuWT2gtcwWZlQz8RkuYuD9QeJLHeU0tfY8cIr6/F9soM2znCnPPAxqH2tpaWltb6enpme2lTBqLxUJtbe3Q9zqbEc9dS7FfXk3wt6cIPtdM8HctWJZ7sa4pw9rom3UHA7QNYu+PD5AdSFLRqUPekRvxYpGNpAg+14xpoRODz0LknQ4ib7djWeXDssxD6JUz5JIZ3HcsxnFVDZnuGF3f2kVkWxvuW+pn/g+bAJlAgtSpIK6bF03JjU7vNFH28FrSHVEQIAy6oX/h189oje8GndazdY5zkumL0/PDAwijTnNwRtlEGcusWmb1+/vp/ekh/Pc3FHzjmw6SJwLkImnsWyoL+vx0Zj2mWifJE3NDUEnmJOE3Wgn9rhm920wulCbwxDF8n5p4CWmiaYC+nx1C7zFT/vDaYZFdIQQGvxXfR1div7Rq6CZtXuHF++Flk9o4R97rACm1DcA5uO9YQuLoAAO/acK5tZb+x5rQu0yUf371jGSzTHVOdHYj8UOjO6rx/b0gIduXIH6gF9u686NmyVNBrbdpDIdG6AS+j67UhMd+eYTKr2wYVu48FjInSZ4IYFl1Vlnb4LFg21BO9P1OnDfUzep5di4yKwk8dRK9x4zz2trxn3ABxiobereZ+OHpdVRju7rRu83DsiAGtxnfx1bR+6MDRLd34rx6+kvWkqdDBP7juHZdBqLbO+ZNxU8pE3jqBNHtnRgqbXjuXoZtY0VBgQtjpR3/A40En28m8norMpXFe++KKWtfmGpkJkeqJTSh881YZUfnMJI4NjAtjmpkm9bakA2lECYd1kY/hio7oeebibzXMeZ5KaUk9MoZ9H4L1nXjZDlGQat8EQSePD6mszpSf+qFGHwWHJdVE3mnHcfVNfOyUiOyrY10exTfJ1dNqNfWssqH3mchur1TOapTjdFoZPHi0r5RGMttlH16NcmWEPF9PcQP9pE40s+ADsxLPNg2lGtlLeaZN18ulaX3pwfJRdK4bl1E6IUWQq+eGbFvKfh8s1aKeu9yjJV23LcnibzTQXR7B4nD/RgX2Cn/2NkMoLHKjnWNn8i2dpxX18wJp3w0Ynu0QIltQ3EX25EQOjE0QuNc3Hcu0aL6r7ciDLqhzzobTtHzowPITI6K3183rsNirnfj+9hK+h87Stc3d+G9Z/mYF/HJMF55fmx3N8JiwLKqcMEH8xIP4TdaySWzszrYPBtJ0f9YE8mmAaxry/Deu5zoex0En2smtrP7vLLrMV8nmCS2r5fg86cwVtgo++yacZ0ac72biq9uJPJOO6EXW+j72SEq/3BTcX9IDqLvdWBZ6RvKBg6itxvx3LWE/keP0v/vRzAtdOJ/oHHGnC6hE1hW+Ygf7EVmcyOWJsX29mCotEFWEn7tzHnl8TKTI9UaLqicV+804b1vBX0/Pkjg2VN4P1S4YE+6M0oulhnKYgzivK5uKOMzV4Ju0e0dpDuj+D7ZMCFnfBAhBJYGH7FdXcjMyMHJyZINp0g0DeC8rm7EAJZlhRdTnVMr1b5qwbRVVeXiGYIvNBN9rwOd04Tvkw3EdnYR3dWN6+b6OevYXAykWsNE3+8cs8poLIQQuG+rR2fSE3qxBZnOadm9GRCumyipM2FkurD+1EGETmBZ7iXRNIDMySnNGIffaCX47CnMS9y471iMpcE/FCBINg0Qfu0M9kurRg0aJJoGSLdF8N67fFLnkOPyai2z+h/HCTx1YsRs6FB/6jifnfOGOqI7uwi90Iz//sai1zQSU/35X0gukSH0YguWBt+Eyqgh32ZzSSWhF1pI98Yxls2NlrCRmHtnpmII8yIXnruWUvXnl1DxlQ04t9aRHUgw8PgxOv7uPfofPULi2MC0jDcZCZmT9P/yCOm2CL7fW4Xr+oWEFuQIv3aGdGf0vMcmm4PEdnThuKZmyBHVu8y4b62n+i8upfwL66j40oZhZarOGxYik1kib89d6WwptdEJpkWuGen3FELg+eBSbFsqCb98mtCrp8nFM/T+8AC5cIqyh1YXXLJoW1dO5Vc2oneb6PvZIfp/3UQukZnS9YZeO0PXN3aO+rq5ZDafAStcvQ80pWRyWk/SbCBzkvjBXrr+eTfJkwE8dy/D94lV6CwGHNfUYlrsJvDUCTJ98ZGfLyWp9gihl0/T9X920/G/thP87UlMC52Uf35dwU6g0AucV9fgvq2edEeUVEd0/CeNgKNTkIukcVw5sjNnXV+O/ZIqbFsqKf/c2hnPDFobfchEluSp4fbOBJKkWkLY1pfjvLaWdHuU5LGz2fZUWwQyEvMo/anD3mulD8fVNUTf6SB+pL/gNSbz/akXzvc1VtiwNPqJvN1BLlnc+SXTuSEdg8mSDacI/q4F81I31jXFB6csq3zIVG7aFLhje7pBgm3j6BF+++XVZHriJE9M/RpyqSzht9ro/MYOzRm+cgFVf7IZ29oybJsryYVSc16oZiRkTpINpUi1hkl3x2Z7OUUjpSTw9El0dqNWzVRkoEIIgevGhbjvWEx8fy99Pz88J8cfJU8EQBTenzqIebmHXDQ9VAkwFUTebSf47Cms68ooe3gttvXnZ7Fdt9aTi6RHHXsi01lCzzej95jHPL8LxXFZNfYrqonu6CITSAz7/VB/6jifnd5hwrm1lvjBPk3XYIrIBBJ0fn0HgecKG/dWDPEDvch0Duf1dUWdC/bNlaCD2Pud07C6qWPeZVQvRoQQmGqdmGqduG5dROpMmNjOLmJ7tXEoercJ/wOrR8zGTRVSSoLPnCRxuB/PB5dibdQ2O72rJJ6ggf7Hm6j40gaETmglZk8eR+8247phuEy2MOox14988TAtcGj9aW9ppRhzsj+3PUqmO4bn7pkblSB0QosaZiWhF1qIbu8kG0pR9unVE1byNVbZqfjSBkKvnCb86hmSJwJ471sxJX2H6e4YoRdbIKvN6XLdONz+8UN92siACd6sTItcoBdaqeWKmetTleks0V3dRN5qI9MTx1Bupeyh1ecpNAudwPexFXR9axf9jzVpynvnRIyTLSECT50g3RYBoZW2um6rx9rgw1BhK+omY11XTuDpk8R2d2OqnniViadFYCizDssGDv1NQuC9d/b6dszLvdqohcN9wxzB+L58RcO6cvQeM8EXWwi/fmbouEg1a5uU0YSURsJ9Wz3xQ31EtrVhLTDTnzgewFBhRe8a3gPluq6O7oN9BJ46ifeeZRMSrMiGknT+0y5NKfyaGmwbKorO+EgpGXi8CZnO4fnQskllIS1LNS2BxJH+aTkHY7u6MdaOLZZkW1dO8Lcnib7bPuy4KJZcIkPknXYib7WRi2YwLXbheWjNefdUa4MPnd1AbGdXwcfHbJCNpkmdCpI8GSTVGiYbSJKNpLRhgXn8n149p/+G0Yjv1ca/ee9dPiV7A+c1tQiTnsCTx+n54X58962YM2KDoDlbxmr7hKvLBtW8E8cGpmRfGN3VReDJE1hW+fB9dOTeUvMiF5ZVPsKvt+K4vPq8MlQpJQNPaCX0/gcbpyx77by2luh7nSPqAZztTx2/NcZxdQ2Rd9oJPndKu3dPslIjF8/Q++ODZPsTRF5vxbLCOy26DrE9Pej9ljF1GMZC7zJjWekjurML1y2L5qyo0txclWJUhBCYF7rw3r2cBX91Gb5PrkJmJMEXmqfk9aWUxPb3EtvTTfxwn3aza48Qfr2VyNua83huBiZnAs8Hl5BujRDZ1gZoSpzpzhieu5YUVaLpunEhMpGZs1nV2O5u0IsZn0sqdALvR1ZgXVtGNpDE97GVRQsLCYMO9y31lH9xPcKgo/dHB8gMDI9KTgQpJYGnTiCMOk2l981WcrH0sMfFdnej95g1x3MC6Exan2piktmcwDMn6fnevnErEXKxNMEXW+j439sJ/MdxhEmP7+MrqfyjTSOOETJ4LHg/tIxUS4jw62cAzeHo/9VRer67l1w4hefDy6j+L5dR8aUNuK6rw1hpL/qmqLcbsaz0Et/TPebfkjgeIPxGK/FDfaR7YlpZ7JkwlqDAceWCOSsmojPpsSz1ED/cPyyzGNvXg7HWgaHMijDocF5dQ/JEkNSZMADJ5hCGcuuEssDCoMO2tozkieCIx+2FyEyO1KngsNEHg5jqnFoJ8M4uen94gGx0/NccJPTiaWQ6ixBCq6D539sJvXx6Qq8xSPS9ThJHB/B8oH7SPVjCqMe81EP8yHCbTJZUR5R0RxT7prFL54VRh21LFfFDfWSDyUm9p0znCP6uWft8X2jBVOvUqn1+f/2wDb4w6LBtqNDetwg7nP++WaLvdxI/2k+mLz7m+VvI55yNpgk8c5Kuf95Fx9+9S9/PDxN9vxNh0GFe7sV5bR2eDy/F/0Ajxio7A483ac7rPCKXyhJ89hTGGge2Key9dFxWje9jK0l3ROn61i5Cr51BZmc/uyrTOZKnQ5iXTNzB0btMGKvsJKcg+x/b38vAr5swL3WPq7TrunkRMpEh/GbreT+PbGsntrsb100LsTZMXbuRwWPBtrGC6Pud5x3Pg/2pI6n9joTOrMd140JSp0Ikjk7uM5OZHH0/P0SmN47/wUYMfgsDjzeRS2Yn9boXkg2lSJ4IYFtfPinH2n5JFblIWhulOEeZe+kqRcEIow7b2nIyvXFCL7SQ6ohiqp64auW5JI4O0P+LwyP+zrrGj/v24Zkb67pyLHt6CP2uBVONU6uZX+nFUmT/o6nGgWWVj8hbbTiuWjArvbij9VjKnCS2txvLytkRthJ6ge/3VpGLpEbM4kwU80IXZQ+vpfMf3ifyVhueu0ZXKZVSQlaOeqOK7+sleTyA50NLMS1yaTPK3jpfGCsbTpE8NnoP2rjrXeIm/PoZcolMURH12N4eIm9pAZXE4f4xe3T7Hj1KsmkAyyofzq01mjL1ODcE64ZyrEf6Cb10mlwsQ3R7BzIrcV5fh/P6uilXKrVtrKD/cL+mhjxCdimXyND3i8PI+DnlpzrN4cjpJbbNc1tEwdLoJ/Efx8l0x4baBDJ9cdKtkfOuRfbLqgi9cobw62fwfaJBG2PUOPHrj3VNGeHXWzXBoHE2w6nTWv/YWFk99231GCqsDDxxjO5v76Es7yiMRborSnRHJ46ranDfsZjkiQCRN9sIvajpAXjvWTauMzf0Wj0xgr89iXm5B/sVxY3fuRBLg4/EkX4yPfEpFR+J7e4CncC6fvy+f8dlVUTebCWyvbPoua65VJa+Rw6RPB7AusaP8/qF42afbJsrtU33nm6cVxUv5hTdqWWohjBogmkGr4VcMouMZ8jF0+TiGYRJT9ln1ow6Y1tKycCvm0g0DWBe7MJ10yLMS93aOLQRrtUGn4Wuf93NwBPHJzU/eKYJv3aGbCilzd6c4uCabUMF5sVuBp46Qej5ZuJ7evDcs2xW544nT4e09oWlEyv7HcS8wkNkW/ukZo8nmgbof/QIpoUu/A+sHlfR11Tj0GZJv9WO48oF6B0mEicCBJ89iaXRj3OECrvJ4ry2ltiurvP0AArtTz0X+6VVhN9qI/RCM5aV3qLOCy1zfIzkiSDe+1ZgbfCjsxro+b/7CD53Cu+HR67CK2bkZmxvj9YmMUkhJMtKHzqXiej2TqyrZzb5Uigqo1oCOC6rRph0RN5oHf/B4xDZ1obOZaLya5up+MoGyh5ei//+BvyfasD38ZFvEEIIPB9eBjpBzw/2I3NSk32fxA3QdeNCcrEMkXc6JvPnTJhsOMXAb47R/t/fJTGCwmzyRIBcOD0lPRbFInRiSpzUQQweM7b1mkrpWJmk8Munaf8f747Yw5dLZgj89iTGGgf2y6oxLTh7wzo3+xDb0zNuD9pYaH2qWsZsomT6Eww8cQzTQid6j5nwGOdLsiVEsmkA1231Wnn1Ek9hg8mFwPuhpeidRiJvtWFe5qXqa5tx31o/LeM0rA0+hFmvZflHIPJOOzKeoezhtZR/aT3ej67AeV0dlpVeelfJWQkCTYTB8sT4obPHXCxf9mtdd/amqjMbcFxRrQnPHerTBI5GaS8YC2OtQ1O2PdA77mMTQ/1jY2c87JsqKf/8OmQ6R/d39hI/1Dfm44PPNSPM+qG+I8syL2UPraHyjzdhqnMy8PgxEk0FRP1z0P9YExh0+D6yYso295aVmk0Sh6cuAi+zktjuHk2JsoARTwa/FcsKL9HtnUVlv3KJDL0/OjDU9uD/VGNBJZKmBQ6MNQ5iO7om/J7nkjg6oKl8f2Ed3nuXa2PbfBYtQywleq8Z8zIv9ks10Zj+Xx7R5jWPQGxHF4kj/bjvWEz559bhunEh5nr3qAFFY5Ud9231JA71TfrvmCky/QnCb7Rh3VBe1HldCHq3mbL7G/Hf30Aulqbnu3sJPt88Le9VCEP9qQVmBS/EstwLWS2zWAwym2PgN00Yy62UfXp1wdVxrpsXIdNZwq+3khlI0P/vh/Oq9VN3DTqXkfQABv/mCYlQ6XW4blhIuiNKYoxrtMzk6P72Hrr+dTeh186Q6T2rSRF66TSxXVrmeDDQaa5347hyAdF3O4Zmbp9LbH8vHf/zPUKvnC54rQCxvd0YayY/U1roBfbNlSSaBsgEJlehMl0oR7UE0NmM2C+pIra3Z1IHWro7RvJYAMfl1RgrbJhqnViWebCuLsO6ZmzhG4PbrGU4chLX9XWT7vMw1Tkxr/BqUfN324nt7SFxbIBUW4RseOpLlmQmR/j1M3R+fQfRnV3ojDr6Hjk0TCQqtqsbYdHPy/6esXBsrUWmctq4khHI9CeGSqL6fnqQyDvnl2WHXjqtlbZ+6OxcOtdNC7Ub1jkOYWzP5C6upoX5PtWTExtTI7OS/kePAOD7+CocV9eQagmRHEWYKfzKaXQ2w7CxLYWgsxkp+9w6yr+wjrIHGqe150kY9VjXlOVFFc7fyOaSGSJvtg2NgzIvdGHfVIn7lnr8n2ggVDczImyTQe82Y6x1kDh8duMQ39ujCZld0HvkuGoB6HUM/McxgIKFlM5FCIF1jZ/EsYFxRZCSxwMYa50FjQQwL3RR+ZUNGCqs9P3sEOF8Vv9CEicCJI7047q+bpjDZqy0U/ZgI8YKG32/OEyqPTLme3pPCtJnwnjvXjalw+wNHjPGajvxI2M73IUiMzn6f3WEXDhVsGI2aKJKuXBqXMf/QnKxND0/2E/qdBjf762a8AgP++ZKTcSsbezPfzRkNkfyRBDLSi/mejf2S6rw3L6EsgdXU/mHm6j4wnrKHlyN774VeO5cgu9jK8n0xgk+PXzGdmYgQeCZk5iXuCd0rXJcVYN5iZvA0ydHFX+bSwSfO4UQTGhOZLFYV5dR+bXN2DZWEH7tzITvNaMhc3JC5Z/JE/nxWkX24prrXWAQ54nMTYTY3h6ywRSu2xZPaOyJscKGbWMFkXc66PvpQWRG4n+gcVr1RlzX1SETGaLvaaJAQ/2p3omNbrNtqMBQZiX00ulRy/HDb7ZpLSZZSej5Zjq/voOuf95F/+NNhF8+jW1zJc4LtDlct9ZjKLMy8JuzJcC5RIb+x47S/4vD5BJZwq+eKXhvm+6JkW6NTNnUCfuWSpAQ2zk3A1fKUS0RHFfXAHKorLEYIm+3g0Fgv7S4GXn2S6s0deLr64pew7m4b1mEzEgCT56g/5dH6P3hAbr/dbcWfXqppageqVwiQzaUJDOQ0EoIe2LE9vXQ+U87CT7XjHmJm8o/3kT5lzYgzHqtdzPv/OdSWeIHe7GtLR+3BGa+Yaq2a4GBbe0jqh9qGwVB5R9uwrLKR+A/TxB45iQyJ0l3Rolsa8N+SdV5pVLGSruWqX27nWw4RborSrotMqlstM6kx1TnHFF1NNkcJLytbUS14dDLLaROh/Heo80dtV9ShbAaRsyqps6ESRwdwHFNbdFjcIxl1mmL/F+IbVMFMpklfkGGK/JOB7lYZkRBq/mEtcFP6kz47DHUGcM2Qnmo3mHCfkkluWgGncOI3l/cfFnrmjLISBJHRs9a5pIZrc93AgIZereZit9fh7XRT/CZk0N9zIPInCT47Cn0HjOOK0cuK9VZDJQ9tBqdRU/vTw6OGphMng7hOyGwbawYNl92KrCs8mmBnpNBYnt7CL18mv7HjtLzw/0TUpXNJbVxZ/F9vbhvXzwk0lfQGlb60HvMRCdQdZONpOj5/n5N1OVTDUV9NrYN5aAXRW/qUi1hZCpbsL6AZZnWYxp9v1Mr98sjc5pIFhK8E8yYC53A+9GVoNOy7jI7N4NW2Wia8FttxPf34ryuDsMUBlzGQmcxDAV4Ar89NenJCqn2CF3f2kn7f32b7n/bS/j1M6S7oqPuYURGG8MzWv97IQijHvNid1Eq1VJKIm+0Yqi0YVk5cR0M140LISdJd8Xw/d4qjOXTO5/UVOfEvMxD+M02ZDqraQcUkYkWeoHzhrpRs6qZQJLwK6exrPZT+UebqPrzS3DfsRhh1BHb0YV5uUcTzrug+kpn0uP9yHKygSTB505h6Yeub+3SWghuXEjlH2xEZnMFZ1Vje3pAMGXXdoNfE1WMvt9Z0LGeDaXo/MYOeh85NDQGaTqZ23VfioIxeLUBytHtnbhuXDihCBhoUebYzi5s6yuKHkMxqE48VZhqnSz4m8vJxfL9OlHt//iBPkIvnSbTG9eGdRfoNIbfaCX43CkY4ZwyVNoo+8ya85Qsyx5aQ8+/7aX3xweo+MJ6Ekf7kakcto1Tv/GbCzi31tD7gwPEdnefF6xINgeJ7+/FddNCjOU2/Pc3EnzmpKaC258gF0ujsxhw3Vo//DVvWkRsXw/h184gTHrt4lpAD9pYmJd6CL9yeqhPVUpJ+PVWQi80g9Syu86rFuC4cgE6m5HEiQDhV89g21yJbb3mJOvMehyXV2ujlXpi591IQ6+cRli1UtL5gHmxG73bRGxX99CNK5fKEnmzFXN+7uR8xtLgI/Rii9YXGUiCAOsoQmbOa2qJvteBud5VdOuBaZELncOojVAa5VhNngpBTo6qmDwawqjH94kG+h87SvC5ZmRGDgUS4vt6tBmDHx37mqZ3myl7aA3d3z17bRq83mcCSRIHewm/2UbGDJ4Pjd5zPhksDT7Cr56h53v7zq7LZSIbSRPd3onnziXjvkY2mqb3JwdJt4XxfmTFhLKpkJ8DeHk1oeebSXfHxq3SyMXS9HxvH9mBJGUPri5atVhnM2Jd7Se2pxv37YsnrGCaODYAOiZ07LhuXkjyZEBrXcifz9F3O7ReuHuWjztDeyQMHjPeDy+j/9GjhF8/M6JC/0SRUpI4OkCySSttNvitGMosGHzWgu/T2XCK+MFe4gf6tGxmDow1Dpxbi+8JLgZh1OO6rZ6BXx0lvrenqACrzEki29oIPt+MzmbEcU0NyWMBgs81E3yuGb3Pgv3SKpzX1p53vbIEgKzEMsGxNBdiWe4l+OwpssHkhKoqkscCpDtjWgCkiOuowW/Fe/cy0IsZqz5zXldL7w8OEHj2lNb6UeRnZ1tfQfiVM4ReasHS6D8vABR89iRSgucO7fpm8FpwXlOL85paspEUOqthVOXcwRLgyLZ2atAh/ILyL6wfCu7bt1QR3d6J85raMc9nKSXxPd2Yl7intFLGfkkV/b88QvL4+FMVAk+fIH0UtZsAAB/GSURBVDOQIBfL0HuoD73fguPSamxbKgtq3ZgoylEtIZxba4nv6SHyXgeu64ZnNbPRNDqbYcQLT3RHFzKd08rn5hDCoEPvMqF3nXWeLY1+DBVWQi+0kBlI4r+/YVznOvTqGa1JvtGvNcrrBegEQicQFgOW5d5hA6hN1Xb89zfS++MD9D5yCKEXmlrtDGXKZhrzUg/GBXbCb7Ziy28aZU6bW6d3m3BsrQW0DaLng0vR+y0EnzkJEjz3LBvxAmUss2LbWEnkvQ50VgPm5V70zsnN4zQvcRN+mSFVv/7Hmkgc6sO6tgzHFdWE32on9NJpwm+24bi8mtjubgx+6zD5eseVCwi/2UrkrTa8d2tjWFJtERKH+3HdvGhOjkYaCaETWDdUEHmzjWwkhd5hIvpuB7no/M+mAhir7eg9ZuKH+sj0xLUb9CjHkMFnwf/JRgzlxZdbC53QHJHd3ZryrnF4Vj15PAAGHeYJKldDXhDtoysZ0AlCL7Yg8+0SwReaMVbbCxLHMFbZ8d/fQO+PDtL3s0OYl3mIH+zTxh8BhgorXStzLJ6mY9hU58T38ZXa9dlvxeC3oDPp6f3xAeKH+7Qswxgb3EwgSe8P95MZSOD/VOOEMqnnYt9SSejFFqLvdgw7vy8k/KY2Xqrss2snPdbGvrmS+L5e4of7sK2dWOAt0TSAqc41oeuL0OvwfXyVVmL46BGMCyH43iksK73YLileAde6vhzroT7tOMxKXDfUFT2iItUWIfjsSW2+rUEHmXMqc4R2Hvs+0YCxbORzU+YkoReatSoXCYYyK85r67CuKcO4oHh19MlgW19O5C3N0bSu8Y94LRiNbChJ/6+bSB4LYGn04713+dA9MhNMkjjST3x/L6Hnm8mFU7jvXDL0N9r6tP3JZPca5uVe4BSJY4EJBYLCb7Sic5omVVpqv6S4yrxiMS/1YKx1DFVYFNvbK/TajN3+Xx0dqqADTT0/vk8L2I/kSBaS4HHdqs0+78oM0PjwpvN0K5w3LiS6q4vQy6fx3bdi1NdIt0bI9CVwjrDHnwzW1X50NgPR9zvHdFTj+ePWdcui/PzZXiLvdhB87hTBF5u1IGCR0yhGY37sxBQFYVrgwLzMQ2RbG86ra4YivblEhuDzzUTf7cBx1YLzLoiQj/q9045psWtUZcG5hBAC1/ULMfit9D/WRPd39lL26dWjRtRDL7UQeuk01g3l+O5bOcwhHQvLMg+++1bQ/+hRgKLVaucDQgic19bS/8ujQ0Ipsd3dpNsi+D62cpgYkPOqGgxlVlKngti3jH5Tct24kNiebnLhNPbbJy9CZV6o9d5E3+8i+MxJMgNJ3HcuwXHVAm180xKPNlLp1TPapkcnqHhwuBiE3mnCvqlSmyF28yL0DhPhV04jzPrzRjDNB+wbK4i83kp8fy+2zZWE32jFvMxTlCM11xBCYGnwEX2vA3LgGCezMpaSc6FY15RpY12aAsNeT0pJ4tiAlrUtsgVA6AXe+1aAXhB++TSJo/1kB5J4P7u84OuLZZkX773LGfh1E8mTwbOzeVf7MZbbOPLaa0WtraD1CzGiQ21p8JN48nyV5gvJxdL0/NtecvEM5Z9ZW3TmA7TNoW1tmXYO31o/aql+LpYm8nY71jVlUzJ71bzci95lIraja0KOajaSIt0ewXXTxJWKDT4L3nuW0//LI9S16cCkw3vv8kk5cNqs5BUIg27oOPR9bOWESjUzgSSh3zUT292NzmrA88Gl2C+rQiazZPoSZHrjpHvjRN9tp+e7eyj79JphVR4ym2Pg8WPEdndrPX7X1GCoLG6+9FQidAL37Yvp/f5+wtvaR0wAjMSgWq5M5/DcvQz7pVXn/S0GtxnHZdXYL60i+NtTWsuWBPdd2t7M2i8w1TqKbj0ZxFhlQ+cwkmjqL9hRTbVFSB4P4Lqtfsrmnc4EQghc19XR9/PD6L3moqoMBrGuL8fwymlt37i6DKQk8PQJ9F4zzmtri35dnUlP+efXcfC114btpwxuM44rFhB5qw3ntbWj7mcHxyNa10ytQq8w6LBtqiTyTvtQwPtCcsksgSePY6i04dxaqz1nfQW29RWku6L0/ugA4TfbptxRnT9HoaIgnNfWkgunh1RA40f76frmLqLvdWBa5CKyTcs2nUvicB/ZgeSofVFzFdu6cso/vxaZytL97T0M/OdxkqeCQ/XyUkqCv2sm9NJpbJsqtEHVE3BSh95nQ4VW4mXWz/lxHpPFuqYcvVdTxBUZCD7fjKnOOerICOtKH+7bFo+5uTbky5uE1VD0yKJzEUYd5oUuTd01laP882u1wMw5GwHTAgf+TzZo6tVfGD4TcRDHNTWQlfnZv1HiB/u0kUgTLJ2fbYxVdoxVdmK7u4m+10kuksZ10/zPpg5ibfBDDm18yQxI6JuXuBFWw4jqv9G328l0xbCumdyxLHQC7z3LsV9WRbo1gnm5Z8I3ePvmSiq+vIHqv7yUii/nZ/NOcz/YWFgbhqs0X0hsdzfZQJKyh1ZPykkdxH7lAmQyO6zn91zC29qRyeyUjccQOoEtr5QZeqml4L7c5PEASIouO7atL8e2pRJdVuD94NIpUX/XmfX4ProS3ydXke1P0P0vuzW18HE0IGQ2R+jV03R+fQexfT04t9ZS9WeXaLOZ9Tp0NiOmOie2jRW4b15E+Rc3IMwGer637zzleK1P+ZA2Z/OWRXg/shxj1exkUEfCstQzVOpeyOzZ+IFeen96EL3LTMVXN2pTGUb5W4QQuO9YjOOaGiJvtxN46gS5RAZziEn1p577+rZ15cT392rjbgog/GYrwqTHcdn8aH05F0ujH2OtY9L3CKHTsqqZrhjxA71E3ukg0xXDc+fSCWXVJ4rzujqEUU/oxZYRfy+zkti+HqyrfNOyR7FfUglZycATx0cU/gq92EI2kMR797JhQQxjpR3b5kqSxwbITHK+9YXMr92YYlzMyzwYq+2E32jVhC52d2OosFH+xfXaaIPfHCP88ml0VgPOqzXHNLKtHb3HXHT51WxiXuii4ksbCD57kuj7XUTf6UDnMmFbU4bMSaLvdmiqincvm1Qm1Lm1VsvYFVkWNV8QeoHj6hqCT5+kKqbTSpLub5h0Ftlz51JcNy2ashEtjqsWoHOZ8NyxZMxS4vE27cZyG5YGP9F3O0h3RBEm/dB5Md+wbawg+Nwp0vny2JkSc5oJzEvcCLMec71rWnpgLkTodVgbfMQP9SEzuaGbcrI5SOC3p7A0+LTRIZN9H5022stc7y56XuJc6kE+V6XZNYqoXnSXpvw9VceneaEL24Zywq+3YltfPiyTm0tkiGxrw7LaP+k54+fiuHIBqZYQoZe1zIuh0oZtbRnW9eWjXncSTQPobAaMBYzCGQ3v3cs4amynZooUPwexrS3HvMhN/+NNBP7zBPGDfTivr9POvQscrVRHlIHHm0i3RfLz1ZeMm8Eyllmp+OJ6en9ykL5HDuK9ezmWRr/Wp9wa1oI2RQo5Tjfu2xfT9c2dhF46PeosTNCUcvt/dQRTrZOyz6wpqLxbCC1ri4DIG22k26MIKaYkiAPgumUR8UN9DDzWRMUfbBzzHpwJJIjv68FxZc28C9aCdj2t+PKGKQlyWNfls6q/ayEbTmFe4cXSOL39tnq71sccfvk0qdbwMM2X5IkAucj0jUc0Vtpx37mE4G9P0vPdvfgfaBw6r1NtEU0087KqUa/d9s2VhF85Q2xXF67rCw8KDgoJjkZp77ovQoQQOLfWkumJE9vbg/OGOir/YCPmhZq4iPee5VjXaKqT0R2dpDqiJE8GcVxRXVS2cS5g8Fnwf6qRBX9zGb6Pr8RU6ySyvUNzUi+vnrSTOkipO6mD2C+pQmczYO8R2ty6KRh6LvRiSh0M6+oy/B9fNel+V9BEpHKxDInD/UMCTPMR64ZyECDjmWHy+PMdYdBR/rm1ePK9xDOBdU0ZMpHV5hmiKR32/eIwBq9Zq86YohYAITR13qmcjTybnKvSfCGpDk35275pajda7juXoDPrGXji+DAFysi2dmQiOyViQeeid5oo//w6qv/yUjx3LUFnNRB6+TRd39w54kiToZLxZZ5JHTtCryPpZloyjnqXibKHVuP58FLS7RF6v7+f7n/eRWR7B7lUFpnJEXqphe7/s5tsIInvk6vwf6qx4DJL7TNbi3mZl4HfHKPrmzs1Beb7G+eskwpaQNN+WTXR7R2jZs+ju7rof/QIpkUuyj5bmJM6iBAC9wcW47i2llRLCCkkpilq29BZDHg/soJMb5zQOHNhI29pY+ccV8+v1pdzmarzQsuqLiLTG0dmcnjuWjIjWX7nNTXobAaCvzs/qyqlJLZbG484OMt6Wt7/6hrKHlpDJpCg+9u7SZ4MIrOSgSeOoXMYcd82+ogog9+KeYmb2I6uCU3lSDQNjDmxZP6FTBTjYl1XjjuUwrzcM6znVOgEvo+vovenBxn4zTGMCxwIo27GG9+nA53ZgG1DBbYNFeQSGdKdUUyLilf/vFjRmfQ4rqoh8Eoz7tvqZ3s504653o1poZN0R1QrBZ6nGNxmLA1+ZDo7obEp84WpVBQvBMtyL8KkJ36gD/NSD32/OIxMZPF/du28zDbMFJZGv6bSfLh/mPMR29ml9VcVIBg1EfQOE+47ljDw6yai2ztxXK5lu3OJDOG32rA0+EYt/5/0e7vMOK6qwXFVDdlQku7v7CXw9EkqvrrxPIc03RElF04XXfY7UwghcFy+APvmSmJ7erSS1CeOayq1DiOZnjjW9eWaoF4RwUed2UDZg40MPHGcxJE+yh9eMy+qP1w3LiS2q5v+Xx3FtqEc4wIHpmo7OpuR6PudDDxxDPMSN/4HVxdVOSSEwH1bPXqbgVNNJ6mbouoj0LQ2HFcuIPJ2O5bV/hHvD7l4huj2TmzryofNqL5Ysa4tw7LXj3mJe8ZaKnQWA87r6gg+e4q+fz9MLpYhG0iSDSaR6Ry2LZXTPh7RssJLxZc30PfIIXp+sB/LSq+mVfKJVePe+2ybKxn4dROp5lDBglbR7Z3oxriWqLttCSL0YsyGb2HQaWq2PzxAqiWE/dKqeZtFGg2dxTAvbn5zFef1dezJnWThRXLD8n1iFblIekbKSqcT/6caZnsJJYMw6rA0+Igf6gW9INUS0mYCVk1d+WgpYqyyofdqKs3nOqoymyO2pxvrKt+0nGe2TRXEdnURfO4U1kYfepeZyDsdyPjMqV/rXWbcty+m/9+PENvRdf6Yr2NalnWqhUamC2HUY7+kCtuWSlItISJvt5PpjuG/v3HSgmVCr8N33wpkTs4bcUK9w4Tnw8s0ddPfni1T1HvMZANJzCu8lN3fMKkeRk3QsI5+eWIqlnwertvqSTQNMPDrJir/aNN5GV+ZlYRePY1MZYfU/RVaYqfsgcYZf1/HFdXEdnWRag6h95gxVtuxrNLmRk92vF+hGMttVHx5A/2/PELicD+Wld5Rx8Kdi3VtGYGnThDd0VWQo5oNpUgc6cNxzejHnXJUL1J0Jj1ln15N+LUz807hVDH9CJ0gN799tglh8FigBJzy+bLpmy9YV/uJ7+3RFNOvrpmxTcJ8RgiBtcFPZHsnuVR2KLuUODqg9VdtLn6cynjv6717OZ3f2kXgqRN471tJ5M1WLCu9M5qNt64tw1TvIvi7ZqzryoYcgsSxAQyVtimdfTgTCCG0HuppCPzOt+uVfWMF9o0VefXmKKn2COn2CPpGf1EzdWcSnUmP974V9PzbXgLPnMT3kRXkYmmi73cSebuDbDCJdbV/Xkx+KHWEUU/lH22e7WWgsxjwP7ia+L4ezMu9BVUn6kx6rGvLiO/rIffBpeMqV0d3dkJu7HFGylG9iNFZDbg/MHq9uUKhUFzMWFb6EBY9xmo77g/Uz/Zy5g2WRj+Rt9tJHhsYUuCM7exC5zBiWTl9GUVDmRXXjQsJvdBMLqWVzc10v7YQAs+dS+j+9h5Cr57B84HF5FJZkqeCOK5QQeFSQO8woV9hmvNl3BdiXuTCeW0t4ddakcksiSP9yHQO8xI3ng8txbJqesWCFPMPoRt5FNlY2LdUEtvRRXx/z5ijC2VOEn2/SyutHmW+MihHVaFQKBSKEdGZ9VT+8Wb0duNFI6Y2FZgXuxAWA/FD/VhXl5GNpokf6cdxxfQrpzu31hDf202yaQDzcs+UiMFNFFOtU5tJ+FYbjkurSPfEISvnnWOjKD1cNy0icXSA+KE+bBsqcFy1QGVRFVOKaZELQ5mV6I6uMR3V5IkA2f4E7lvGniutHFWFQqFQKEbBMM9KNecCQq/DssqrZWxykviebsjKaSv7vfC9vfeuoP9XR3HfUj/t7zca7lvrie/vJfDbUxg8ZjDoMC+eeadZoTgXYdBR/oV1kJUlp02imBsIIbBtqST0fDOZ3jiGUbKl0e2d6GyGcefeqhCxQqFQKBSKKcXa4CcXTZM6HRqanTqVc0zHwlTnpOpPt8zqjFm9y4Tz+joSh/qI7tTK2yYjtKNQTBU6s0E5qYppxb6pAgREd3aN+PtsJKVl9TeNr2KsHFWFQqFQKBRTimWlF/SC0CtnpmV26nzAeXUNeq8ZmczOG7VfhUKhmCx6lxnLCi+xXV3DZlsDxHZqVTaFzE8uylEVQiwRQuwUQhwXQvzVCL+/RwixXwhxSAjx9+f8vEUIsSf/78vFvLdCoVAoFIq5jc5iwLzETbJpYFpmp84HhFGH566lCKMOa6MSqlEoFBcPts2VZIMpwq+fQWbPOqtSSqLvd2Kqd2GsGH8+bbEZ1b8G/iewEvigEOLCQUMDwLXAWuB6IcTq/M8jUsoN+X/fLvK9FQqFQqFQzHGsjdq8TWvD9MxOnQ9YG/0s+G9XYvCPrmqpUCgUpYa10Y95hZfQCy10/csuEscGAEieDJLpjY85kuZcihVTuhb4kpQyK4R4Jv/9ocFfSilfHfxaCHEG8AohbEC0yPdTKBQKhUIxj7Cu9hN+vfWin9U93+aFKhQKxWQRBh1lD60mcbCPwLOn6P3hASwNPmRWIizavNWCXkfK4bXD4z5JiE4pZVX+698HFkgp/3aEx3mB99Eyq3Y0Z7YNiAB/JKXcOcJzPg98HqCysnLzo48+OuH1KWaOSCSCw6GkzUsRZdvSRtm3tFH2LV2UbUsbZd/S5mK0r8iCu0XgOyHQZQWBhTl6G8/3P6+//vqdUsotFz533IyqEOJh4OELfnyhdztauPC7wP+SUsaBuBCiUkophRDXAz8F1lz4BCnl94DvAWzZskVed9114y1RMYu89tprKBuVJsq2pY2yb2mj7Fu6KNuWNsq+pc3FbN9sKEV0RyfVl1Shd5oKes64jqqU8gfAD879mRCiSQhhkVImgAqg+8LnCSH+BIhKKX94zmvJ/P+vCiH8Qgi9lDJb0EoVCoVCoVAoFAqFQjHv0LtMuG5YOKHnFNuj+iJwhxDiSeAu4AEhxFLgH6SU9wohbgHuAG4dfEK+DDgupUwIIdYDHeM5qTt37uwVQrQUucbZwg0EZ3sRM0gZ0Dvbi5ghlG1LG2Xf0kbZt7S5mOyrbFvaKPuWNsq+I7NopB8W26NaDjyNlk39kZTy7/LO58+llGuFEEfRnOBw/imPAs8AvwQyQAz4qpRy14TffI4jhPielPLzs72OmUIIsWOkmvJSRNm2tFH2LW2UfUubi8m+yraljbJvaaPsOzGKyqhKKXuAyy/42V400SSklCtHeeraYt5vnvH0bC9AMW0o25Y2yr6ljbJvaaPsW7oo25Y2yr6lzaTsW+wcVcUoSCnVCVeiKNuWNsq+pY2yb2mj7Fu6KNuWNsq+pc1k7ascVcVk+d5sL0AxbSjbljbKvqWNsm/pomxb2ij7ljbKvhOgqB5VhUKhUCgUCoVCoVAopguVUVUoFAqFQqFQKBQKxZxCOaoKhUKhUCgUCoVCoZhTKEdVobhIEUKI2V6DYvpRdi5tlH1LEyGEY7bXoFAoJo66Jk8tylFVjIoQwi2EWC+EKGqMkWLuIoRwS9WgXrIIITxCiGuEEEZl59JDCOETQvy1sm/pIYTwCiG+CfyFEMIy2+tRTC1qX1XaqL3V1KMcVcWICCEeBPYAfw58XQixbJaXpJgihBBe4KgQ4o/z36vrQAmRt+sO4C+Af8v/TEV4SwQhxFeB1wEHkFPnb+kghPhT4DlgM2CXUiaUfUsHta8qbdTeanpQH6JiGEIIPbAO+IiU8hNAK/AZIURd/vdq0zu/WQGcAR4UQlillDll09JACHENsBG4Rkp5B3CTEKJaRXhLAyHEOuB24C+llH8hpcxKKXOzvS7F5BFCfBG4HrhbSrkVuFIIUafsWxrk91VrUfuqUmYVam815ShHVQFoGyAhxH8RQqyQUmaBK4AF+V+/m//+RgC16Z1fCCHqhRDXnvOjMuA+4E3gG4MPm/GFKaaEvH2vB5BSvonmxHQIIS4BtgELVGR3/nKBffcBh4BovnzwH4QQDwkhnLO7SkUx5G17Xf7b70sp75BSduS/fxmon5WFKaaEc+2b31fdAFTmf632VfMcIUSDEOJrQohF+R85gI+h9lZTiqqRv4gRQggppRRCbAW+DbwE/DchxKvAXwI/FkKcAe4EOoEaIYRLShmavVUriuD7QLcQYiC/0X1NShkVQvw1sEsI0SClPDx4PMzyWhUTZ9C+vVLK/UC7EKIaeDT/71vAc0KIR6SUrbO5UEVRDNq3X0q5F3gG+Hu0DdCjaBuj+rx9T8ziOhUT59xz90A+oCTR9mZL818jhNCpzOq8ZNC+ASnlHrRr8beEEO2ofdW8JZ8l/RrwIPA+0CCEeFFK+Vj+92pvNYWoKPvFzWCkZyXwlpTyj9Ec1L8F9gP/HfgqYAT+BfiAupjODwbLTYQQm9AyqIeBWwDyTqpBShlE62H8Rv7n6kI6TxjFvreCZsd8Vma1lPKvgC8CWwDbLC1XMUFGO3/zG55Xgf8K3CCl/Abwh8ASwD5Ly1VMgFFsextA3hnVSynTwC7g4+f8XDEPGMW+N+fP3UeA76Jdk9W+ah5yjtMZAu6XUn4WrfphvRDCnA8qqb3VFKIc1YsQIcTtQoifAf+fEMKEdkMUQojFUspm4BG0MqSfAF+RUv6JlPIdIJDP1CjmKOfY9s+EEEa0gMPn0EpRyoQQN+QfKgGklP8IlAshPpJ/vipTmcNccO4O2vdhztr3xvzjhJQyASClPADoAf8sLVtRIOPY1w/cBCClfFZKGc5/fRSwAJ7ZWbWiEAo9d4FBp3Qn0CWEMKrr8tyngHP3ZgAp5TeBP1D7qvnFOfb9UyGEHa2aZf/grwGXlDJJ3q9Se6upQzmqFxlCiMVoZWNPogkm/RmwAWgGLgeQUv4lWnToZillXAhRLYR4Gmg7p39GMce4wLYbgP8fWCel3AEcRGvy35ovM8rmb6YA/wA8BCryN5e5wL7rOWvfnZy17zV5+0ohhEEIsUUI8Qha9P74bK1dMT4F2vcqIYQr/3i9EGJTfvPkRNl3zjLBc3fQUXUDt0kp0+q6PLcp0L5XDp67UsqkEKJC7avmBxfYdyPavnnZOefqcbQSboOUMqP2VlOLclQvAvIbmq8JIbagqc49LqX8DfA3QAyoQ4viLs6XqwD8M/lSQrSG/+eklL8/w0tXjMMYtv1rIAjcKYSwSyl70UaWGDhbIprO///rvEKsYo5RpH1vyT+9AvhH4KCU8nYpZc8s/AmKMZjM+Zv/+k+AvVLK26SU7bPwJyhGYZK2Jf/Yn8z8yhWFMBn75nuRr0btq+Ys49g3AtyVz6wCbAWO5p1UvdpbTS3KUS1xhBBXAtvRTqS70aI79+YjtyeAfeTLQNEurl/Of70ErUkc4Ekp5XdmbtWKQhjHtifRSsccnHVc9gBH0CL3i2dhyYoJMAn7bhVCLM07Lh+QUv79zK9eMR6TPH+X58vMHpJSfn3mV68Yi0nadsng60gpvz+jC1cUxCTtuzSfiVP7qjlKgfZ1cjawlAaeFEL8HrBfCHHFLCy7ZFGO6sXBd6SUHwYeB95Dc0j/Jf+7nfnvA2i9qWEhxJPAdcBuUEIOc5yxbLsLaAOq8lG+JJpNd6FFBBVzn2LtGwIY7FNVzFmKtW8AQEqZmvklKwqkWNuGZ2Oxigkz2Wuz2lfNbQqx72Bv8QeBZ4GPAp/O9x4rpgjlqJY+O4Bf5b8OA6vRlASvFUJskVIGgC7gmrw4x58CX5ZSbpRSNs3KihWFMp5tg2jy95dLbYYbUsqDUsqfqDLQeYGyb2mj7Fu6KNuWNsq+pU2h9r0sL5L0LvB5KeXdUsrts7LiEkY5qiWOlDIlpYzkvz4OVKFF9P4R+Nt8iUIj0DnYCC6lbJu9FSsKpUDbrkab46afvZUqikHZt7RR9i1dlG1LG2Xf0qZA+64BeqTGX+f7VxXTgGG2F6CYOYQQKwBPPnP6HSFEEG3o9EbgM1LKzKwuUFE0Bdg2O6sLVEwKZd/SRtm3dFG2LW2UfUub8ew7q4u7SFCO6sWFEfi1EKIB+FfgVSnlX83ymhRTg7JtaaPsW9oo+5YuyraljbJvaaPsO8sIqUb7XDQIIT4J/AxNzex7UsofzfKSFFOEsm1po+xb2ij7li7KtqWNsm9po+w7+yhH9SJCCHEXWl39P+VV6BQlgrJtaaPsW9oo+5YuyraljbJvaaPsO/soR/UiQgghpDJ4SaJsW9oo+5Y2yr6li7JtaaPsW9oo+84+ylFVKBQKhUKhUCgUCsWcQo2nUSgUCoVCoVAoFArFnEI5qgqFQqFQKBQKhUKhmFMoR1WhUCgUCoVCoVAoFHMK5agqFAqFQqFQKBQKhWJOoRxVhUKhUCgUCoVCoVDMKZSjqlAoFAqFQqFQKBSKOYVyVBUKhUKhUCgUCoVCMaf4f5d7OKaPyLyyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x648 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ic_df[cols].rolling(3).mean().plot(subplots=True,grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "information ratio = IC.mean()/IC.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beta    -0.069800\n",
       "size    -0.226358\n",
       "bm       0.316642\n",
       "mom     -0.077611\n",
       "rev     -0.477104\n",
       "illiq    0.530235\n",
       "ivol    -0.714804\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Information ratio\n",
    "ic_df.apply(lambda x: x.mean()/x.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "# Fund performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Fund return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "START = '2007-01-01'\n",
    "END = '2022-03-31'\n",
    "# Security Id\n",
    "fund_info = DataAPI.SecIDGet(assetClass=\"F\",pandas=\"1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>secID</th>\n",
       "      <th>ticker</th>\n",
       "      <th>secShortName</th>\n",
       "      <th>cnSpell</th>\n",
       "      <th>exchangeCD</th>\n",
       "      <th>assetClass</th>\n",
       "      <th>listStatusCD</th>\n",
       "      <th>listDate</th>\n",
       "      <th>transCurrCD</th>\n",
       "      <th>ISIN</th>\n",
       "      <th>partyID</th>\n",
       "      <th>delistDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.OFCN</td>\n",
       "      <td>000001</td>\n",
       "      <td>华夏成长</td>\n",
       "      <td>HXCZ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26812.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000003.OFCN</td>\n",
       "      <td>000003</td>\n",
       "      <td>中海可转债债券-A</td>\n",
       "      <td>ZHKZZZQ-A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16550.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000004.OFCN</td>\n",
       "      <td>000004</td>\n",
       "      <td>中海可转债债券-C</td>\n",
       "      <td>ZHKZZZQ-C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16550.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000005.OFCN</td>\n",
       "      <td>000005</td>\n",
       "      <td>嘉实增强信用定开债券</td>\n",
       "      <td>JSZQXYDKZQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26817.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000006.OFCN</td>\n",
       "      <td>000006</td>\n",
       "      <td>西部利得量化成长混合-A</td>\n",
       "      <td>XBLDLHCZHH-A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40283.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000007.OFCN</td>\n",
       "      <td>000007</td>\n",
       "      <td>鹏华国有企业债债券</td>\n",
       "      <td>PHGYQYZZQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26816.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>000008.OFCN</td>\n",
       "      <td>000008</td>\n",
       "      <td>嘉实中证500ETF联接-A</td>\n",
       "      <td>JSZZ500ETFLJ-A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26817.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>000009.OFCN</td>\n",
       "      <td>000009</td>\n",
       "      <td>易方达天天理财货币-A</td>\n",
       "      <td>YFDTTLCHB-A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26932.0</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21154</th>\n",
       "      <td>970133.OFCN</td>\n",
       "      <td>970133</td>\n",
       "      <td>东证融汇添添益中短债债券-C</td>\n",
       "      <td>DZRHTTYZDZZQ-C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76920.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21155</th>\n",
       "      <td>970134.OFCN</td>\n",
       "      <td>970134</td>\n",
       "      <td>海通安润90天滚动持有中短债债券-A</td>\n",
       "      <td>HTAR90TGDCYZDZZQ-A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70083.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21156</th>\n",
       "      <td>970135.OFCN</td>\n",
       "      <td>970135</td>\n",
       "      <td>海通安润90天滚动持有中短债债券-C</td>\n",
       "      <td>HTAR90TGDCYZDZZQ-C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70083.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21157</th>\n",
       "      <td>970136.OFCN</td>\n",
       "      <td>970136</td>\n",
       "      <td>银河水星季季增利三个月滚动持有债券-A</td>\n",
       "      <td>YHSXJJZLSGYGDCYZQ-A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89520.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21158</th>\n",
       "      <td>970137.OFCN</td>\n",
       "      <td>970137</td>\n",
       "      <td>银河水星季季增利三个月滚动持有债券-C</td>\n",
       "      <td>YHSXJJZLSGYGDCYZQ-C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89520.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21159</th>\n",
       "      <td>970138.OFCN</td>\n",
       "      <td>970138</td>\n",
       "      <td>银河安益9个月持有期混合(FOF)-A</td>\n",
       "      <td>YHAY9GYCYQHH(FOF)-A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89520.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21160</th>\n",
       "      <td>970139.OFCN</td>\n",
       "      <td>970139</td>\n",
       "      <td>银河安益9个月持有期混合(FOF)-C</td>\n",
       "      <td>YHAY9GYCYQHH(FOF)-C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89520.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21161</th>\n",
       "      <td>980003.OFCN</td>\n",
       "      <td>980003</td>\n",
       "      <td>太平洋证券六个月滚动持有债券</td>\n",
       "      <td>TPYZQLGYGDCYZQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>UN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27776.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21162 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             secID  ticker         secShortName              cnSpell  \\\n",
       "0      000001.OFCN  000001                 华夏成长                 HXCZ   \n",
       "1      000003.OFCN  000003            中海可转债债券-A            ZHKZZZQ-A   \n",
       "2      000004.OFCN  000004            中海可转债债券-C            ZHKZZZQ-C   \n",
       "3      000005.OFCN  000005           嘉实增强信用定开债券           JSZQXYDKZQ   \n",
       "4      000006.OFCN  000006         西部利得量化成长混合-A         XBLDLHCZHH-A   \n",
       "5      000007.OFCN  000007            鹏华国有企业债债券            PHGYQYZZQ   \n",
       "6      000008.OFCN  000008       嘉实中证500ETF联接-A       JSZZ500ETFLJ-A   \n",
       "7      000009.OFCN  000009          易方达天天理财货币-A          YFDTTLCHB-A   \n",
       "...            ...     ...                  ...                  ...   \n",
       "21154  970133.OFCN  970133       东证融汇添添益中短债债券-C       DZRHTTYZDZZQ-C   \n",
       "21155  970134.OFCN  970134   海通安润90天滚动持有中短债债券-A   HTAR90TGDCYZDZZQ-A   \n",
       "21156  970135.OFCN  970135   海通安润90天滚动持有中短债债券-C   HTAR90TGDCYZDZZQ-C   \n",
       "21157  970136.OFCN  970136  银河水星季季增利三个月滚动持有债券-A  YHSXJJZLSGYGDCYZQ-A   \n",
       "21158  970137.OFCN  970137  银河水星季季增利三个月滚动持有债券-C  YHSXJJZLSGYGDCYZQ-C   \n",
       "21159  970138.OFCN  970138  银河安益9个月持有期混合(FOF)-A  YHAY9GYCYQHH(FOF)-A   \n",
       "21160  970139.OFCN  970139  银河安益9个月持有期混合(FOF)-C  YHAY9GYCYQHH(FOF)-C   \n",
       "21161  980003.OFCN  980003       太平洋证券六个月滚动持有债券       TPYZQLGYGDCYZQ   \n",
       "\n",
       "      exchangeCD assetClass listStatusCD listDate transCurrCD ISIN  partyID  \\\n",
       "0            NaN          F           UN      NaN         CNY  NaN  26812.0   \n",
       "1            NaN          F           UN      NaN         CNY  NaN  16550.0   \n",
       "2            NaN          F           UN      NaN         CNY  NaN  16550.0   \n",
       "3            NaN          F           UN      NaN         CNY  NaN  26817.0   \n",
       "4            NaN          F           UN      NaN         CNY  NaN  40283.0   \n",
       "5            NaN          F           UN      NaN         CNY  NaN  26816.0   \n",
       "6            NaN          F           UN      NaN         CNY  NaN  26817.0   \n",
       "7            NaN          F           UN      NaN         CNY  NaN  26932.0   \n",
       "...          ...        ...          ...      ...         ...  ...      ...   \n",
       "21154        NaN          F           UN      NaN         CNY  NaN  76920.0   \n",
       "21155        NaN          F           UN      NaN         CNY  NaN  70083.0   \n",
       "21156        NaN          F           UN      NaN         CNY  NaN  70083.0   \n",
       "21157        NaN          F           UN      NaN         CNY  NaN  89520.0   \n",
       "21158        NaN          F           UN      NaN         CNY  NaN  89520.0   \n",
       "21159        NaN          F           UN      NaN         CNY  NaN  89520.0   \n",
       "21160        NaN          F           UN      NaN         CNY  NaN  89520.0   \n",
       "21161        NaN          F           UN      NaN         CNY  NaN  27776.0   \n",
       "\n",
       "      delistDate  \n",
       "0            NaN  \n",
       "1            NaN  \n",
       "2            NaN  \n",
       "3            NaN  \n",
       "4            NaN  \n",
       "5            NaN  \n",
       "6            NaN  \n",
       "7            NaN  \n",
       "...          ...  \n",
       "21154        NaN  \n",
       "21155        NaN  \n",
       "21156        NaN  \n",
       "21157        NaN  \n",
       "21158        NaN  \n",
       "21159        NaN  \n",
       "21160        NaN  \n",
       "21161        NaN  \n",
       "\n",
       "[21162 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fund_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "fund_id = ['110022','009550','001938','009341',\n",
    "           '000751','004997','009863','001714',\n",
    "           '001410','004851','005827','110011',\n",
    "           '260108','163402','320007','163417',\n",
    "           '001511','161728','161005','161131']\n",
    "# fund_id = [id_+'.OFCN' for id_ in fund_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>secShortName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>000751</td>\n",
       "      <td>嘉实新兴产业股票</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>001410</td>\n",
       "      <td>信达澳银新能源产业股票</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>001511</td>\n",
       "      <td>兴全新视野灵活配置定开混合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>001714</td>\n",
       "      <td>工银瑞信文体产业股票-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>001938</td>\n",
       "      <td>中欧时代先锋股票-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4930</th>\n",
       "      <td>004851</td>\n",
       "      <td>广发医疗保健股票-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5069</th>\n",
       "      <td>004997</td>\n",
       "      <td>广发高端制造股票-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5904</th>\n",
       "      <td>005827</td>\n",
       "      <td>易方达蓝筹精选混合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17463</th>\n",
       "      <td>163402</td>\n",
       "      <td>兴全趋势投资混合(LOF)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17464</th>\n",
       "      <td>163402</td>\n",
       "      <td>兴全趋势投资混合(LOF)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17478</th>\n",
       "      <td>163417</td>\n",
       "      <td>兴全合宜灵活配置混合-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17479</th>\n",
       "      <td>163417</td>\n",
       "      <td>兴全合宜灵活配置混合(LOF)-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17480</th>\n",
       "      <td>163417</td>\n",
       "      <td>兴全合宜灵活配置混合-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17481</th>\n",
       "      <td>163417</td>\n",
       "      <td>兴全合宜灵活配置混合(LOF)-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18243</th>\n",
       "      <td>260108</td>\n",
       "      <td>景顺长城新兴成长混合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18330</th>\n",
       "      <td>320007</td>\n",
       "      <td>诺安成长混合</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ticker       secShortName\n",
       "716    000751           嘉实新兴产业股票\n",
       "1363   001410        信达澳银新能源产业股票\n",
       "1457   001511      兴全新视野灵活配置定开混合\n",
       "1677   001714       工银瑞信文体产业股票-A\n",
       "1911   001938         中欧时代先锋股票-A\n",
       "4930   004851         广发医疗保健股票-A\n",
       "5069   004997         广发高端制造股票-A\n",
       "5904   005827          易方达蓝筹精选混合\n",
       "...       ...                ...\n",
       "17463  163402      兴全趋势投资混合(LOF)\n",
       "17464  163402      兴全趋势投资混合(LOF)\n",
       "17478  163417       兴全合宜灵活配置混合-A\n",
       "17479  163417  兴全合宜灵活配置混合(LOF)-A\n",
       "17480  163417       兴全合宜灵活配置混合-A\n",
       "17481  163417  兴全合宜灵活配置混合(LOF)-A\n",
       "18243  260108         景顺长城新兴成长混合\n",
       "18330  320007             诺安成长混合\n",
       "\n",
       "[27 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fund_name = fund_info.loc[fund_info['ticker'].isin(fund_id),['ticker','secShortName']]\n",
    "fund_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>secShortName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>000751</td>\n",
       "      <td>嘉实新兴产业股票</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>001410</td>\n",
       "      <td>信达澳银新能源产业股票</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>001511</td>\n",
       "      <td>兴全新视野灵活配置定开混合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>001714</td>\n",
       "      <td>工银瑞信文体产业股票-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>001938</td>\n",
       "      <td>中欧时代先锋股票-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4930</th>\n",
       "      <td>004851</td>\n",
       "      <td>广发医疗保健股票-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5069</th>\n",
       "      <td>004997</td>\n",
       "      <td>广发高端制造股票-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5904</th>\n",
       "      <td>005827</td>\n",
       "      <td>易方达蓝筹精选混合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15407</th>\n",
       "      <td>110022</td>\n",
       "      <td>易方达消费行业股票</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17007</th>\n",
       "      <td>161005</td>\n",
       "      <td>富国天惠精选成长混合(LOF)-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17102</th>\n",
       "      <td>161131</td>\n",
       "      <td>易方达科润混合(LOF)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17239</th>\n",
       "      <td>161728</td>\n",
       "      <td>招商瑞智优选灵活配置混合(LOF)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17463</th>\n",
       "      <td>163402</td>\n",
       "      <td>兴全趋势投资混合(LOF)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17478</th>\n",
       "      <td>163417</td>\n",
       "      <td>兴全合宜灵活配置混合-A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18243</th>\n",
       "      <td>260108</td>\n",
       "      <td>景顺长城新兴成长混合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18330</th>\n",
       "      <td>320007</td>\n",
       "      <td>诺安成长混合</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ticker       secShortName\n",
       "716    000751           嘉实新兴产业股票\n",
       "1363   001410        信达澳银新能源产业股票\n",
       "1457   001511      兴全新视野灵活配置定开混合\n",
       "1677   001714       工银瑞信文体产业股票-A\n",
       "1911   001938         中欧时代先锋股票-A\n",
       "4930   004851         广发医疗保健股票-A\n",
       "5069   004997         广发高端制造股票-A\n",
       "5904   005827          易方达蓝筹精选混合\n",
       "...       ...                ...\n",
       "15407  110022          易方达消费行业股票\n",
       "17007  161005  富国天惠精选成长混合(LOF)-A\n",
       "17102  161131       易方达科润混合(LOF)\n",
       "17239  161728  招商瑞智优选灵活配置混合(LOF)\n",
       "17463  163402      兴全趋势投资混合(LOF)\n",
       "17478  163417       兴全合宜灵活配置混合-A\n",
       "18243  260108         景顺长城新兴成长混合\n",
       "18330  320007             诺安成长混合\n",
       "\n",
       "[20 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fund_name = fund_name.drop_duplicates('ticker')\n",
    "fund_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TradingDate</th>\n",
       "      <th>TradingMonth</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>NAV</th>\n",
       "      <th>ReturnNAV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-12-28</td>\n",
       "      <td>2001-12</td>\n",
       "      <td>000001</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002-01-31</td>\n",
       "      <td>2002-01</td>\n",
       "      <td>000001</td>\n",
       "      <td>1.0020</td>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002-02-28</td>\n",
       "      <td>2002-02</td>\n",
       "      <td>000001</td>\n",
       "      <td>1.0130</td>\n",
       "      <td>0.0110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002-03-31</td>\n",
       "      <td>2002-03</td>\n",
       "      <td>000001</td>\n",
       "      <td>1.0230</td>\n",
       "      <td>0.0099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002-04-30</td>\n",
       "      <td>2002-04</td>\n",
       "      <td>000001</td>\n",
       "      <td>1.0460</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2002-05-31</td>\n",
       "      <td>2002-05</td>\n",
       "      <td>000001</td>\n",
       "      <td>1.0270</td>\n",
       "      <td>-0.0182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2002-06-30</td>\n",
       "      <td>2002-06</td>\n",
       "      <td>000001</td>\n",
       "      <td>1.0730</td>\n",
       "      <td>0.0448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2002-07-31</td>\n",
       "      <td>2002-07</td>\n",
       "      <td>000001</td>\n",
       "      <td>1.0450</td>\n",
       "      <td>-0.0261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774381</th>\n",
       "      <td>2021-07-30</td>\n",
       "      <td>2021-07</td>\n",
       "      <td>980003</td>\n",
       "      <td>1.4812</td>\n",
       "      <td>0.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774382</th>\n",
       "      <td>2021-08-31</td>\n",
       "      <td>2021-08</td>\n",
       "      <td>980003</td>\n",
       "      <td>1.4900</td>\n",
       "      <td>0.0059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774383</th>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>2021-09</td>\n",
       "      <td>980003</td>\n",
       "      <td>1.4935</td>\n",
       "      <td>0.0023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774384</th>\n",
       "      <td>2021-10-29</td>\n",
       "      <td>2021-10</td>\n",
       "      <td>980003</td>\n",
       "      <td>1.4991</td>\n",
       "      <td>0.0037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774385</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>980003</td>\n",
       "      <td>1.5044</td>\n",
       "      <td>0.0035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774386</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>2021-12</td>\n",
       "      <td>980003</td>\n",
       "      <td>1.5095</td>\n",
       "      <td>0.0034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774387</th>\n",
       "      <td>2022-01-28</td>\n",
       "      <td>2022-01</td>\n",
       "      <td>980003</td>\n",
       "      <td>1.5165</td>\n",
       "      <td>0.0046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774388</th>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>2022-02</td>\n",
       "      <td>980003</td>\n",
       "      <td>1.5208</td>\n",
       "      <td>0.0028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>774389 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       TradingDate TradingMonth  Symbol     NAV  ReturnNAV\n",
       "0       2001-12-28      2001-12  000001  1.0000     0.0000\n",
       "1       2002-01-31      2002-01  000001  1.0020     0.0020\n",
       "2       2002-02-28      2002-02  000001  1.0130     0.0110\n",
       "3       2002-03-31      2002-03  000001  1.0230     0.0099\n",
       "4       2002-04-30      2002-04  000001  1.0460     0.0225\n",
       "5       2002-05-31      2002-05  000001  1.0270    -0.0182\n",
       "6       2002-06-30      2002-06  000001  1.0730     0.0448\n",
       "7       2002-07-31      2002-07  000001  1.0450    -0.0261\n",
       "...            ...          ...     ...     ...        ...\n",
       "774381  2021-07-30      2021-07  980003  1.4812     0.0052\n",
       "774382  2021-08-31      2021-08  980003  1.4900     0.0059\n",
       "774383  2021-09-30      2021-09  980003  1.4935     0.0023\n",
       "774384  2021-10-29      2021-10  980003  1.4991     0.0037\n",
       "774385  2021-11-30      2021-11  980003  1.5044     0.0035\n",
       "774386  2021-12-31      2021-12  980003  1.5095     0.0034\n",
       "774387  2022-01-28      2022-01  980003  1.5165     0.0046\n",
       "774388  2022-02-28      2022-02  980003  1.5208     0.0028\n",
       "\n",
       "[774389 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fund_df = pd.read_csv('./data/Fund_NAV_Month.csv',dtype={'Symbol':str})\n",
    "fund_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "top_fund_df = fund_df[fund_df['Symbol'].isin(fund_id)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_fund_df['Symbol'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "top_fund_df.rename(columns={'Symbol':'ticker','TradingMonth':'ret_date','ReturnNAV':'ret'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "top_fund_df = pd.merge(top_fund_df, fund_name, on='ticker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "top_fund_df = top_fund_df[['ret_date','ticker','secShortName','ret']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "top_fund_df['ret_date'] = pd.to_datetime(top_fund_df['ret_date']).dt.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ret_date</th>\n",
       "      <th>ticker</th>\n",
       "      <th>secShortName</th>\n",
       "      <th>ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-09</td>\n",
       "      <td>000751</td>\n",
       "      <td>嘉实新兴产业股票</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-10</td>\n",
       "      <td>000751</td>\n",
       "      <td>嘉实新兴产业股票</td>\n",
       "      <td>0.0090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11</td>\n",
       "      <td>000751</td>\n",
       "      <td>嘉实新兴产业股票</td>\n",
       "      <td>0.0445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>000751</td>\n",
       "      <td>嘉实新兴产业股票</td>\n",
       "      <td>-0.0369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>000751</td>\n",
       "      <td>嘉实新兴产业股票</td>\n",
       "      <td>0.1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-02</td>\n",
       "      <td>000751</td>\n",
       "      <td>嘉实新兴产业股票</td>\n",
       "      <td>0.0504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-03</td>\n",
       "      <td>000751</td>\n",
       "      <td>嘉实新兴产业股票</td>\n",
       "      <td>0.1634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-04</td>\n",
       "      <td>000751</td>\n",
       "      <td>嘉实新兴产业股票</td>\n",
       "      <td>0.1434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1785</th>\n",
       "      <td>2021-07</td>\n",
       "      <td>320007</td>\n",
       "      <td>诺安成长混合</td>\n",
       "      <td>0.2288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1786</th>\n",
       "      <td>2021-08</td>\n",
       "      <td>320007</td>\n",
       "      <td>诺安成长混合</td>\n",
       "      <td>-0.1790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1787</th>\n",
       "      <td>2021-09</td>\n",
       "      <td>320007</td>\n",
       "      <td>诺安成长混合</td>\n",
       "      <td>-0.0445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1788</th>\n",
       "      <td>2021-10</td>\n",
       "      <td>320007</td>\n",
       "      <td>诺安成长混合</td>\n",
       "      <td>0.0194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1789</th>\n",
       "      <td>2021-11</td>\n",
       "      <td>320007</td>\n",
       "      <td>诺安成长混合</td>\n",
       "      <td>0.0405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790</th>\n",
       "      <td>2021-12</td>\n",
       "      <td>320007</td>\n",
       "      <td>诺安成长混合</td>\n",
       "      <td>-0.0307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1791</th>\n",
       "      <td>2022-01</td>\n",
       "      <td>320007</td>\n",
       "      <td>诺安成长混合</td>\n",
       "      <td>-0.1629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>320007</td>\n",
       "      <td>诺安成长混合</td>\n",
       "      <td>0.0496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1793 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ret_date  ticker secShortName     ret\n",
       "0     2014-09  000751     嘉实新兴产业股票  0.0000\n",
       "1     2014-10  000751     嘉实新兴产业股票  0.0090\n",
       "2     2014-11  000751     嘉实新兴产业股票  0.0445\n",
       "3     2014-12  000751     嘉实新兴产业股票 -0.0369\n",
       "4     2015-01  000751     嘉实新兴产业股票  0.1100\n",
       "5     2015-02  000751     嘉实新兴产业股票  0.0504\n",
       "6     2015-03  000751     嘉实新兴产业股票  0.1634\n",
       "7     2015-04  000751     嘉实新兴产业股票  0.1434\n",
       "...       ...     ...          ...     ...\n",
       "1785  2021-07  320007       诺安成长混合  0.2288\n",
       "1786  2021-08  320007       诺安成长混合 -0.1790\n",
       "1787  2021-09  320007       诺安成长混合 -0.0445\n",
       "1788  2021-10  320007       诺安成长混合  0.0194\n",
       "1789  2021-11  320007       诺安成长混合  0.0405\n",
       "1790  2021-12  320007       诺安成长混合 -0.0307\n",
       "1791  2022-01  320007       诺安成长混合 -0.1629\n",
       "1792  2022-02  320007       诺安成长混合  0.0496\n",
       "\n",
       "[1793 rows x 4 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_fund_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Factor return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# factor_ret = pd.read_pickle('./data/factors/factors_all.pkl')\n",
    "factor_ret = pd.read_pickle('./data/factors/factors_all_long_only.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rf</th>\n",
       "      <th>exmktret</th>\n",
       "      <th>small_only</th>\n",
       "      <th>high_only</th>\n",
       "      <th>rev_long</th>\n",
       "      <th>illiq_long</th>\n",
       "      <th>ivol_long</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ret_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-02</th>\n",
       "      <td>0.002946</td>\n",
       "      <td>0.024010</td>\n",
       "      <td>0.106350</td>\n",
       "      <td>0.060789</td>\n",
       "      <td>0.059612</td>\n",
       "      <td>0.048717</td>\n",
       "      <td>0.060142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-03</th>\n",
       "      <td>0.002746</td>\n",
       "      <td>-0.195480</td>\n",
       "      <td>-0.185605</td>\n",
       "      <td>-0.204875</td>\n",
       "      <td>-0.177674</td>\n",
       "      <td>-0.184564</td>\n",
       "      <td>-0.157342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04</th>\n",
       "      <td>0.002862</td>\n",
       "      <td>0.022519</td>\n",
       "      <td>-0.073984</td>\n",
       "      <td>-0.008138</td>\n",
       "      <td>0.026001</td>\n",
       "      <td>-0.039478</td>\n",
       "      <td>-0.021866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05</th>\n",
       "      <td>0.002953</td>\n",
       "      <td>-0.080798</td>\n",
       "      <td>-0.020726</td>\n",
       "      <td>-0.056888</td>\n",
       "      <td>-0.038600</td>\n",
       "      <td>-0.027371</td>\n",
       "      <td>-0.050738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-06</th>\n",
       "      <td>0.003113</td>\n",
       "      <td>-0.236014</td>\n",
       "      <td>-0.258596</td>\n",
       "      <td>-0.240880</td>\n",
       "      <td>-0.249542</td>\n",
       "      <td>-0.207909</td>\n",
       "      <td>-0.222131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07</th>\n",
       "      <td>0.002806</td>\n",
       "      <td>0.014972</td>\n",
       "      <td>0.110389</td>\n",
       "      <td>0.072120</td>\n",
       "      <td>0.084431</td>\n",
       "      <td>0.084488</td>\n",
       "      <td>0.068393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08</th>\n",
       "      <td>0.002681</td>\n",
       "      <td>-0.170063</td>\n",
       "      <td>-0.234635</td>\n",
       "      <td>-0.187086</td>\n",
       "      <td>-0.205410</td>\n",
       "      <td>-0.189043</td>\n",
       "      <td>-0.163784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09</th>\n",
       "      <td>0.002889</td>\n",
       "      <td>-0.067913</td>\n",
       "      <td>-0.088250</td>\n",
       "      <td>-0.068698</td>\n",
       "      <td>-0.063282</td>\n",
       "      <td>-0.080665</td>\n",
       "      <td>-0.086935</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07</th>\n",
       "      <td>0.001942</td>\n",
       "      <td>-0.064776</td>\n",
       "      <td>-0.001619</td>\n",
       "      <td>-0.044277</td>\n",
       "      <td>-0.064251</td>\n",
       "      <td>-0.020196</td>\n",
       "      <td>-0.040926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08</th>\n",
       "      <td>0.001941</td>\n",
       "      <td>0.014199</td>\n",
       "      <td>0.057072</td>\n",
       "      <td>0.068252</td>\n",
       "      <td>0.019832</td>\n",
       "      <td>0.066361</td>\n",
       "      <td>0.054721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09</th>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.002272</td>\n",
       "      <td>-0.024647</td>\n",
       "      <td>0.013658</td>\n",
       "      <td>0.009320</td>\n",
       "      <td>0.009460</td>\n",
       "      <td>0.032838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10</th>\n",
       "      <td>0.001998</td>\n",
       "      <td>0.001777</td>\n",
       "      <td>-0.025143</td>\n",
       "      <td>-0.041059</td>\n",
       "      <td>0.008093</td>\n",
       "      <td>-0.021662</td>\n",
       "      <td>-0.015415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11</th>\n",
       "      <td>0.001963</td>\n",
       "      <td>-0.006047</td>\n",
       "      <td>0.130358</td>\n",
       "      <td>0.043855</td>\n",
       "      <td>0.070874</td>\n",
       "      <td>0.100720</td>\n",
       "      <td>0.050708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12</th>\n",
       "      <td>0.002026</td>\n",
       "      <td>0.018185</td>\n",
       "      <td>0.037671</td>\n",
       "      <td>0.050223</td>\n",
       "      <td>0.045295</td>\n",
       "      <td>0.083863</td>\n",
       "      <td>0.065554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01</th>\n",
       "      <td>0.002014</td>\n",
       "      <td>-0.085436</td>\n",
       "      <td>-0.086267</td>\n",
       "      <td>-0.045570</td>\n",
       "      <td>-0.110829</td>\n",
       "      <td>-0.062290</td>\n",
       "      <td>-0.045721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02</th>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.010633</td>\n",
       "      <td>0.049548</td>\n",
       "      <td>0.034011</td>\n",
       "      <td>0.050488</td>\n",
       "      <td>0.039255</td>\n",
       "      <td>0.035960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                rf  exmktret  small_only  high_only  rev_long  illiq_long  \\\n",
       "ret_date                                                                    \n",
       "2008-02   0.002946  0.024010    0.106350   0.060789  0.059612    0.048717   \n",
       "2008-03   0.002746 -0.195480   -0.185605  -0.204875 -0.177674   -0.184564   \n",
       "2008-04   0.002862  0.022519   -0.073984  -0.008138  0.026001   -0.039478   \n",
       "2008-05   0.002953 -0.080798   -0.020726  -0.056888 -0.038600   -0.027371   \n",
       "2008-06   0.003113 -0.236014   -0.258596  -0.240880 -0.249542   -0.207909   \n",
       "2008-07   0.002806  0.014972    0.110389   0.072120  0.084431    0.084488   \n",
       "2008-08   0.002681 -0.170063   -0.234635  -0.187086 -0.205410   -0.189043   \n",
       "2008-09   0.002889 -0.067913   -0.088250  -0.068698 -0.063282   -0.080665   \n",
       "...            ...       ...         ...        ...       ...         ...   \n",
       "2021-07   0.001942 -0.064776   -0.001619  -0.044277 -0.064251   -0.020196   \n",
       "2021-08   0.001941  0.014199    0.057072   0.068252  0.019832    0.066361   \n",
       "2021-09   0.002033  0.002272   -0.024647   0.013658  0.009320    0.009460   \n",
       "2021-10   0.001998  0.001777   -0.025143  -0.041059  0.008093   -0.021662   \n",
       "2021-11   0.001963 -0.006047    0.130358   0.043855  0.070874    0.100720   \n",
       "2021-12   0.002026  0.018185    0.037671   0.050223  0.045295    0.083863   \n",
       "2022-01   0.002014 -0.085436   -0.086267  -0.045570 -0.110829   -0.062290   \n",
       "2022-02   0.001921  0.010633    0.049548   0.034011  0.050488    0.039255   \n",
       "\n",
       "          ivol_long  \n",
       "ret_date             \n",
       "2008-02    0.060142  \n",
       "2008-03   -0.157342  \n",
       "2008-04   -0.021866  \n",
       "2008-05   -0.050738  \n",
       "2008-06   -0.222131  \n",
       "2008-07    0.068393  \n",
       "2008-08   -0.163784  \n",
       "2008-09   -0.086935  \n",
       "...             ...  \n",
       "2021-07   -0.040926  \n",
       "2021-08    0.054721  \n",
       "2021-09    0.032838  \n",
       "2021-10   -0.015415  \n",
       "2021-11    0.050708  \n",
       "2021-12    0.065554  \n",
       "2022-01   -0.045721  \n",
       "2022-02    0.035960  \n",
       "\n",
       "[169 rows x 7 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor_ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "reg_df = pd.merge(factor_ret.reset_index(), top_fund_df, on='ret_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "reg_df['exret'] = reg_df['ret']-reg_df['rf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ret_date</th>\n",
       "      <th>rf</th>\n",
       "      <th>exmktret</th>\n",
       "      <th>small_only</th>\n",
       "      <th>high_only</th>\n",
       "      <th>rev_long</th>\n",
       "      <th>illiq_long</th>\n",
       "      <th>ivol_long</th>\n",
       "      <th>ticker</th>\n",
       "      <th>secShortName</th>\n",
       "      <th>ret</th>\n",
       "      <th>exret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-02</td>\n",
       "      <td>0.002946</td>\n",
       "      <td>0.024010</td>\n",
       "      <td>0.106350</td>\n",
       "      <td>0.060789</td>\n",
       "      <td>0.059612</td>\n",
       "      <td>0.048717</td>\n",
       "      <td>0.060142</td>\n",
       "      <td>161005</td>\n",
       "      <td>富国天惠精选成长混合(LOF)-A</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.001554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-02</td>\n",
       "      <td>0.002946</td>\n",
       "      <td>0.024010</td>\n",
       "      <td>0.106350</td>\n",
       "      <td>0.060789</td>\n",
       "      <td>0.059612</td>\n",
       "      <td>0.048717</td>\n",
       "      <td>0.060142</td>\n",
       "      <td>163402</td>\n",
       "      <td>兴全趋势投资混合(LOF)</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>-0.000346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-02</td>\n",
       "      <td>0.002946</td>\n",
       "      <td>0.024010</td>\n",
       "      <td>0.106350</td>\n",
       "      <td>0.060789</td>\n",
       "      <td>0.059612</td>\n",
       "      <td>0.048717</td>\n",
       "      <td>0.060142</td>\n",
       "      <td>260108</td>\n",
       "      <td>景顺长城新兴成长混合</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.000154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-03</td>\n",
       "      <td>0.002746</td>\n",
       "      <td>-0.195480</td>\n",
       "      <td>-0.185605</td>\n",
       "      <td>-0.204875</td>\n",
       "      <td>-0.177674</td>\n",
       "      <td>-0.184564</td>\n",
       "      <td>-0.157342</td>\n",
       "      <td>161005</td>\n",
       "      <td>富国天惠精选成长混合(LOF)-A</td>\n",
       "      <td>-0.2830</td>\n",
       "      <td>-0.285746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-03</td>\n",
       "      <td>0.002746</td>\n",
       "      <td>-0.195480</td>\n",
       "      <td>-0.185605</td>\n",
       "      <td>-0.204875</td>\n",
       "      <td>-0.177674</td>\n",
       "      <td>-0.184564</td>\n",
       "      <td>-0.157342</td>\n",
       "      <td>163402</td>\n",
       "      <td>兴全趋势投资混合(LOF)</td>\n",
       "      <td>-0.1038</td>\n",
       "      <td>-0.106546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2008-03</td>\n",
       "      <td>0.002746</td>\n",
       "      <td>-0.195480</td>\n",
       "      <td>-0.185605</td>\n",
       "      <td>-0.204875</td>\n",
       "      <td>-0.177674</td>\n",
       "      <td>-0.184564</td>\n",
       "      <td>-0.157342</td>\n",
       "      <td>260108</td>\n",
       "      <td>景顺长城新兴成长混合</td>\n",
       "      <td>-0.1344</td>\n",
       "      <td>-0.137146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008-04</td>\n",
       "      <td>0.002862</td>\n",
       "      <td>0.022519</td>\n",
       "      <td>-0.073984</td>\n",
       "      <td>-0.008138</td>\n",
       "      <td>0.026001</td>\n",
       "      <td>-0.039478</td>\n",
       "      <td>-0.021866</td>\n",
       "      <td>161005</td>\n",
       "      <td>富国天惠精选成长混合(LOF)-A</td>\n",
       "      <td>0.0289</td>\n",
       "      <td>0.026038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008-04</td>\n",
       "      <td>0.002862</td>\n",
       "      <td>0.022519</td>\n",
       "      <td>-0.073984</td>\n",
       "      <td>-0.008138</td>\n",
       "      <td>0.026001</td>\n",
       "      <td>-0.039478</td>\n",
       "      <td>-0.021866</td>\n",
       "      <td>163402</td>\n",
       "      <td>兴全趋势投资混合(LOF)</td>\n",
       "      <td>0.0463</td>\n",
       "      <td>0.043438</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1711</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.010633</td>\n",
       "      <td>0.049548</td>\n",
       "      <td>0.034011</td>\n",
       "      <td>0.050488</td>\n",
       "      <td>0.039255</td>\n",
       "      <td>0.035960</td>\n",
       "      <td>110022</td>\n",
       "      <td>易方达消费行业股票</td>\n",
       "      <td>-0.0211</td>\n",
       "      <td>-0.023021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.010633</td>\n",
       "      <td>0.049548</td>\n",
       "      <td>0.034011</td>\n",
       "      <td>0.050488</td>\n",
       "      <td>0.039255</td>\n",
       "      <td>0.035960</td>\n",
       "      <td>161005</td>\n",
       "      <td>富国天惠精选成长混合(LOF)-A</td>\n",
       "      <td>-0.0209</td>\n",
       "      <td>-0.022821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.010633</td>\n",
       "      <td>0.049548</td>\n",
       "      <td>0.034011</td>\n",
       "      <td>0.050488</td>\n",
       "      <td>0.039255</td>\n",
       "      <td>0.035960</td>\n",
       "      <td>161131</td>\n",
       "      <td>易方达科润混合(LOF)</td>\n",
       "      <td>-0.0047</td>\n",
       "      <td>-0.006621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1714</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.010633</td>\n",
       "      <td>0.049548</td>\n",
       "      <td>0.034011</td>\n",
       "      <td>0.050488</td>\n",
       "      <td>0.039255</td>\n",
       "      <td>0.035960</td>\n",
       "      <td>161728</td>\n",
       "      <td>招商瑞智优选灵活配置混合(LOF)</td>\n",
       "      <td>0.0292</td>\n",
       "      <td>0.027279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1715</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.010633</td>\n",
       "      <td>0.049548</td>\n",
       "      <td>0.034011</td>\n",
       "      <td>0.050488</td>\n",
       "      <td>0.039255</td>\n",
       "      <td>0.035960</td>\n",
       "      <td>163402</td>\n",
       "      <td>兴全趋势投资混合(LOF)</td>\n",
       "      <td>0.0181</td>\n",
       "      <td>0.016179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.010633</td>\n",
       "      <td>0.049548</td>\n",
       "      <td>0.034011</td>\n",
       "      <td>0.050488</td>\n",
       "      <td>0.039255</td>\n",
       "      <td>0.035960</td>\n",
       "      <td>163417</td>\n",
       "      <td>兴全合宜灵活配置混合-A</td>\n",
       "      <td>0.0080</td>\n",
       "      <td>0.006079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1717</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.010633</td>\n",
       "      <td>0.049548</td>\n",
       "      <td>0.034011</td>\n",
       "      <td>0.050488</td>\n",
       "      <td>0.039255</td>\n",
       "      <td>0.035960</td>\n",
       "      <td>260108</td>\n",
       "      <td>景顺长城新兴成长混合</td>\n",
       "      <td>-0.0111</td>\n",
       "      <td>-0.013021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.010633</td>\n",
       "      <td>0.049548</td>\n",
       "      <td>0.034011</td>\n",
       "      <td>0.050488</td>\n",
       "      <td>0.039255</td>\n",
       "      <td>0.035960</td>\n",
       "      <td>320007</td>\n",
       "      <td>诺安成长混合</td>\n",
       "      <td>0.0496</td>\n",
       "      <td>0.047679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1719 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ret_date        rf  exmktret  small_only  high_only  rev_long  \\\n",
       "0     2008-02  0.002946  0.024010    0.106350   0.060789  0.059612   \n",
       "1     2008-02  0.002946  0.024010    0.106350   0.060789  0.059612   \n",
       "2     2008-02  0.002946  0.024010    0.106350   0.060789  0.059612   \n",
       "3     2008-03  0.002746 -0.195480   -0.185605  -0.204875 -0.177674   \n",
       "4     2008-03  0.002746 -0.195480   -0.185605  -0.204875 -0.177674   \n",
       "5     2008-03  0.002746 -0.195480   -0.185605  -0.204875 -0.177674   \n",
       "6     2008-04  0.002862  0.022519   -0.073984  -0.008138  0.026001   \n",
       "7     2008-04  0.002862  0.022519   -0.073984  -0.008138  0.026001   \n",
       "...       ...       ...       ...         ...        ...       ...   \n",
       "1711  2022-02  0.001921  0.010633    0.049548   0.034011  0.050488   \n",
       "1712  2022-02  0.001921  0.010633    0.049548   0.034011  0.050488   \n",
       "1713  2022-02  0.001921  0.010633    0.049548   0.034011  0.050488   \n",
       "1714  2022-02  0.001921  0.010633    0.049548   0.034011  0.050488   \n",
       "1715  2022-02  0.001921  0.010633    0.049548   0.034011  0.050488   \n",
       "1716  2022-02  0.001921  0.010633    0.049548   0.034011  0.050488   \n",
       "1717  2022-02  0.001921  0.010633    0.049548   0.034011  0.050488   \n",
       "1718  2022-02  0.001921  0.010633    0.049548   0.034011  0.050488   \n",
       "\n",
       "      illiq_long  ivol_long  ticker       secShortName     ret     exret  \n",
       "0       0.048717   0.060142  161005  富国天惠精选成长混合(LOF)-A  0.0045  0.001554  \n",
       "1       0.048717   0.060142  163402      兴全趋势投资混合(LOF)  0.0026 -0.000346  \n",
       "2       0.048717   0.060142  260108         景顺长城新兴成长混合  0.0031  0.000154  \n",
       "3      -0.184564  -0.157342  161005  富国天惠精选成长混合(LOF)-A -0.2830 -0.285746  \n",
       "4      -0.184564  -0.157342  163402      兴全趋势投资混合(LOF) -0.1038 -0.106546  \n",
       "5      -0.184564  -0.157342  260108         景顺长城新兴成长混合 -0.1344 -0.137146  \n",
       "6      -0.039478  -0.021866  161005  富国天惠精选成长混合(LOF)-A  0.0289  0.026038  \n",
       "7      -0.039478  -0.021866  163402      兴全趋势投资混合(LOF)  0.0463  0.043438  \n",
       "...          ...        ...     ...                ...     ...       ...  \n",
       "1711    0.039255   0.035960  110022          易方达消费行业股票 -0.0211 -0.023021  \n",
       "1712    0.039255   0.035960  161005  富国天惠精选成长混合(LOF)-A -0.0209 -0.022821  \n",
       "1713    0.039255   0.035960  161131       易方达科润混合(LOF) -0.0047 -0.006621  \n",
       "1714    0.039255   0.035960  161728  招商瑞智优选灵活配置混合(LOF)  0.0292  0.027279  \n",
       "1715    0.039255   0.035960  163402      兴全趋势投资混合(LOF)  0.0181  0.016179  \n",
       "1716    0.039255   0.035960  163417       兴全合宜灵活配置混合-A  0.0080  0.006079  \n",
       "1717    0.039255   0.035960  260108         景顺长城新兴成长混合 -0.0111 -0.013021  \n",
       "1718    0.039255   0.035960  320007             诺安成长混合  0.0496  0.047679  \n",
       "\n",
       "[1719 rows x 12 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# results = {}\n",
    "# for fund in fund_id:\n",
    "#     df_ = reg_df[reg_df['ticker'] == fund].copy()\n",
    "#     X = df_[['exmktret','SMB','HML','rev','illiq','ivol']]\n",
    "#     y = df_['exret']\n",
    "#     X = sm.add_constant(X)\n",
    "#     reg = sm.OLS(y, X).fit().get_robustcov_results(cov_type='HAC', maxlags=4)\n",
    "#     results[fund] = pd.DataFrame([reg.params,reg.tvalues],columns=['alpha','exmktret','SMB','HML','rev','illiq','ivol'],\n",
    "#                                   index=['coefs','tvalues'])\n",
    "results = {}\n",
    "for fund in fund_id:\n",
    "    df_ = reg_df[reg_df['ticker'] == fund].copy()\n",
    "    X = df_[['exmktret','small_only','high_only','rev_long','illiq_long','ivol_long']]\n",
    "    y = df_['exret']\n",
    "    X = sm.add_constant(X)\n",
    "    reg = sm.OLS(y, X).fit().get_robustcov_results(cov_type='HAC', maxlags=4)\n",
    "    results[fund] = pd.DataFrame([reg.params,reg.tvalues],columns=['alpha','exmktret','small_only','high_only','rev_long','illiq_long','ivol_long'],\n",
    "                                  index=['coefs','tvalues'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['易方达消费行业股票']\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>exmktret</th>\n",
       "      <th>small_only</th>\n",
       "      <th>high_only</th>\n",
       "      <th>rev_long</th>\n",
       "      <th>illiq_long</th>\n",
       "      <th>ivol_long</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>coefs</th>\n",
       "      <td>0.004232</td>\n",
       "      <td>0.882684</td>\n",
       "      <td>-0.331372</td>\n",
       "      <td>-0.929381</td>\n",
       "      <td>0.463787</td>\n",
       "      <td>0.303213</td>\n",
       "      <td>0.350980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tvalues</th>\n",
       "      <td>0.972255</td>\n",
       "      <td>4.925441</td>\n",
       "      <td>-1.291826</td>\n",
       "      <td>-2.308913</td>\n",
       "      <td>2.156502</td>\n",
       "      <td>1.157880</td>\n",
       "      <td>0.896414</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            alpha  exmktret  small_only  high_only  rev_long  illiq_long  \\\n",
       "coefs    0.004232  0.882684   -0.331372  -0.929381  0.463787    0.303213   \n",
       "tvalues  0.972255  4.925441   -1.291826  -2.308913  2.156502    1.157880   \n",
       "\n",
       "         ivol_long  \n",
       "coefs     0.350980  \n",
       "tvalues   0.896414  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['汇添富开放视野中国优势六个月持有期股票-A']\n"
     ]
    },
    {
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       "      <td>-0.589578</td>\n",
       "      <td>-0.040776</td>\n",
       "      <td>0.726693</td>\n",
       "      <td>0.391443</td>\n",
       "      <td>-0.969102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tvalues</th>\n",
       "      <td>0.711968</td>\n",
       "      <td>2.983790</td>\n",
       "      <td>-2.475614</td>\n",
       "      <td>-0.090614</td>\n",
       "      <td>2.055199</td>\n",
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      "text/plain": [
       "            alpha  exmktret  small_only  high_only  rev_long  illiq_long  \\\n",
       "coefs    0.004956  0.834559   -0.589578  -0.040776  0.726693    0.391443   \n",
       "tvalues  0.711968  2.983790   -2.475614  -0.090614  2.055199    0.712029   \n",
       "\n",
       "         ivol_long  \n",
       "coefs    -0.969102  \n",
       "tvalues  -2.197731  "
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['中欧时代先锋股票-A']\n"
     ]
    },
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       "      <td>0.523567</td>\n",
       "      <td>-1.239861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tvalues</th>\n",
       "      <td>2.411238</td>\n",
       "      <td>7.729249</td>\n",
       "      <td>-0.348268</td>\n",
       "      <td>-0.636044</td>\n",
       "      <td>2.623610</td>\n",
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      "text/plain": [
       "            alpha  exmktret  small_only  high_only  rev_long  illiq_long  \\\n",
       "coefs    0.009180  1.135847   -0.106902  -0.178939  0.538569    0.523567   \n",
       "tvalues  2.411238  7.729249   -0.348268  -0.636044  2.623610    1.215374   \n",
       "\n",
       "         ivol_long  \n",
       "coefs    -1.239861  \n",
       "tvalues  -2.656295  "
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     "output_type": "stream",
     "text": [
      "['易方达均衡成长股票']\n"
     ]
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       "      <td>1.470982</td>\n",
       "      <td>-0.232457</td>\n",
       "      <td>0.003018</td>\n",
       "      <td>0.058447</td>\n",
       "      <td>1.284621</td>\n",
       "      <td>-2.143836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tvalues</th>\n",
       "      <td>1.983351</td>\n",
       "      <td>5.567900</td>\n",
       "      <td>-0.811607</td>\n",
       "      <td>0.007163</td>\n",
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       "      <td>3.064515</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            alpha  exmktret  small_only  high_only  rev_long  illiq_long  \\\n",
       "coefs    0.010737  1.470982   -0.232457   0.003018  0.058447    1.284621   \n",
       "tvalues  1.983351  5.567900   -0.811607   0.007163  0.172085    3.064515   \n",
       "\n",
       "         ivol_long  \n",
       "coefs    -2.143836  \n",
       "tvalues  -5.541030  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['嘉实新兴产业股票']\n"
     ]
    },
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       "      <td>0.469708</td>\n",
       "      <td>0.465698</td>\n",
       "      <td>-0.797507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tvalues</th>\n",
       "      <td>2.987700</td>\n",
       "      <td>8.821298</td>\n",
       "      <td>1.614396</td>\n",
       "      <td>-2.302584</td>\n",
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      ],
      "text/plain": [
       "            alpha  exmktret  small_only  high_only  rev_long  illiq_long  \\\n",
       "coefs    0.018069  1.403525    0.399663  -1.364502  0.469708    0.465698   \n",
       "tvalues  2.987700  8.821298    1.614396  -2.302584  2.075983    1.979754   \n",
       "\n",
       "         ivol_long  \n",
       "coefs    -0.797507  \n",
       "tvalues  -1.324924  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['广发高端制造股票-A']\n"
     ]
    },
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       "\n",
       "         ivol_long  \n",
       "coefs    -0.603646  \n",
       "tvalues  -2.601715  "
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      "['诺安成长混合']\n"
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       "            alpha  exmktret  small_only  high_only  rev_long  illiq_long  \\\n",
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      "['兴全合宜灵活配置混合-A']\n"
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       "           alpha  exmktret  small_only  high_only  rev_long  illiq_long  \\\n",
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      "['兴全新视野灵活配置定开混合']\n"
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       "            alpha  exmktret  small_only  high_only  rev_long  illiq_long  \\\n",
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      "['招商瑞智优选灵活配置混合(LOF)']\n"
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      "['富国天惠精选成长混合(LOF)-A']\n"
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       "            alpha  exmktret  small_only  high_only  rev_long  illiq_long  \\\n",
       "coefs    0.003022  1.196394    0.325799  -0.791604  0.170369    0.359012   \n",
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     "text": [
      "['易方达科润混合(LOF)']\n"
     ]
    },
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       "            alpha  exmktret  small_only  high_only  rev_long  illiq_long  \\\n",
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   ],
   "source": [
    "for fund in fund_id[0:10]:\n",
    "    print(fund_name.loc[fund_name['ticker']==fund,'secShortName'].values)\n",
    "    display(results[fund])\n",
    "print('#################'*5)\n",
    "for fund in fund_id[10:20]:\n",
    "    print(fund_name.loc[fund_name['ticker']==fund,'secShortName'].values)\n",
    "    display(results[fund])"
   ]
  },
  {
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   "outputs": [],
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