DormAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the data from the CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dorm</th>\n",
       "      <th>URM</th>\n",
       "      <th>LGB</th>\n",
       "      <th>lessthaneightyk</th>\n",
       "      <th>fouryrgrad</th>\n",
       "      <th>sixyrgrad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Next</td>\n",
       "      <td>20.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>86.4</td>\n",
       "      <td>94.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baker</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>20.6</td>\n",
       "      <td>87.8</td>\n",
       "      <td>95.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Burton</td>\n",
       "      <td>20.8</td>\n",
       "      <td>10.2</td>\n",
       "      <td>18.6</td>\n",
       "      <td>86.3</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EC</td>\n",
       "      <td>19.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>24.9</td>\n",
       "      <td>75.3</td>\n",
       "      <td>89.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MacGregor</td>\n",
       "      <td>27.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>28.2</td>\n",
       "      <td>80.9</td>\n",
       "      <td>92.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Maseeh</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>23.3</td>\n",
       "      <td>87.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>McCormick</td>\n",
       "      <td>20.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>27.9</td>\n",
       "      <td>88.8</td>\n",
       "      <td>95.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>New</td>\n",
       "      <td>43.5</td>\n",
       "      <td>16.1</td>\n",
       "      <td>31.4</td>\n",
       "      <td>80.5</td>\n",
       "      <td>91.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Random</td>\n",
       "      <td>17.2</td>\n",
       "      <td>40.8</td>\n",
       "      <td>35.5</td>\n",
       "      <td>71.6</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Senior</td>\n",
       "      <td>36.7</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.9</td>\n",
       "      <td>59.7</td>\n",
       "      <td>78.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Simmons</td>\n",
       "      <td>30.9</td>\n",
       "      <td>8.7</td>\n",
       "      <td>27.4</td>\n",
       "      <td>83.3</td>\n",
       "      <td>93.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Dorm   URM   LGB  lessthaneightyk  fouryrgrad  sixyrgrad\n",
       "0        Next  20.5  11.0             23.2        86.4       94.3\n",
       "1       Baker  16.0   4.2             20.6        87.8       95.7\n",
       "2      Burton  20.8  10.2             18.6        86.3       94.0\n",
       "3          EC  19.2  38.0             24.9        75.3       89.4\n",
       "4   MacGregor  27.2   8.3             28.2        80.9       92.2\n",
       "5      Maseeh  21.4   6.4             23.3        87.2        NaN\n",
       "6   McCormick  20.2   8.1             27.9        88.8       95.8\n",
       "7         New  43.5  16.1             31.4        80.5       91.3\n",
       "8      Random  17.2  40.8             35.5        71.6       88.0\n",
       "9      Senior  36.7  40.0             30.9        59.7       78.1\n",
       "10    Simmons  30.9   8.7             27.4        83.3       93.5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('dormdata.csv')\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.84267544869\n",
      "5.27300251807\n"
     ]
    }
   ],
   "source": [
    "print data[\"fouryrgrad\"].std()\n",
    "print data[\"sixyrgrad\"].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 50)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g277V4HqDVs2WvQyHU9vLpxq2PtzAdvWTq0/0E89G/9/2/Jr7v8rMm4dcs05mbmLLHjoH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\nMwSTJEmSJElS7xmCSZIkSZIkqff+H64MwSS6BegxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a33e13f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.scatter(data.URM, data.LGB,s=1000)\n",
    "plt.ylabel('% LGB',fontsize=40)\n",
    "plt.xlabel('% URM',fontsize=40)\n",
    "plt.title('MIT Dorm Demographics',fontsize=40)\n",
    "plt.tick_params(axis='both', which='major', labelsize=20)\n",
    "plt.ylim([0,50])\n",
    "plt.xlim([0,50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run a multiple regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'statsmodels.formula.api' from '/home/mirthbottle/anaconda/lib/python2.7/site-packages/statsmodels/formula/api.pyc'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reload(smf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data6yr = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lm4 = smf.ols(formula='fouryrgrad ~ URM + LGB + lessthaneightyk', data=data, missing='drop').fit()\n",
    "lm6 = smf.ols(formula='sixyrgrad ~ URM + LGB + lessthaneightyk', data=data6yr, missing='drop').fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>fouryrgrad</td>    <th>  R-squared:         </th> <td>   0.880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.829</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   17.13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 04 Jul 2017</td> <th>  Prob (F-statistic):</th>  <td>0.00132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:07:49</td>     <th>  Log-Likelihood:    </th> <td> -27.393</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    11</td>      <th>  AIC:               </th> <td>   62.79</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     7</td>      <th>  BIC:               </th> <td>   64.38</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>   96.9863</td> <td>    6.788</td> <td>   14.287</td> <td> 0.000</td> <td>   80.934   113.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>URM</th>             <td>   -0.3188</td> <td>    0.151</td> <td>   -2.107</td> <td> 0.073</td> <td>   -0.677     0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LGB</th>             <td>   -0.5252</td> <td>    0.100</td> <td>   -5.232</td> <td> 0.001</td> <td>   -0.763    -0.288</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lessthaneightyk</th> <td>    0.0305</td> <td>    0.324</td> <td>    0.094</td> <td> 0.928</td> <td>   -0.736     0.797</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.264</td> <th>  Durbin-Watson:     </th> <td>   2.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.877</td> <th>  Jarque-Bera (JB):  </th> <td>   0.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.271</td> <th>  Prob(JB):          </th> <td>   0.872</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.446</td> <th>  Cond. No.          </th> <td>    257.</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             fouryrgrad   R-squared:                       0.880\n",
       "Model:                            OLS   Adj. R-squared:                  0.829\n",
       "Method:                 Least Squares   F-statistic:                     17.13\n",
       "Date:                Tue, 04 Jul 2017   Prob (F-statistic):            0.00132\n",
       "Time:                        15:07:49   Log-Likelihood:                -27.393\n",
       "No. Observations:                  11   AIC:                             62.79\n",
       "Df Residuals:                       7   BIC:                             64.38\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept          96.9863      6.788     14.287      0.000        80.934   113.038\n",
       "URM                -0.3188      0.151     -2.107      0.073        -0.677     0.039\n",
       "LGB                -0.5252      0.100     -5.232      0.001        -0.763    -0.288\n",
       "lessthaneightyk     0.0305      0.324      0.094      0.928        -0.736     0.797\n",
       "==============================================================================\n",
       "Omnibus:                        0.264   Durbin-Watson:                   2.136\n",
       "Prob(Omnibus):                  0.877   Jarque-Bera (JB):                0.275\n",
       "Skew:                          -0.271   Prob(JB):                        0.872\n",
       "Kurtosis:                       2.446   Cond. No.                         257.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(lm4.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>sixyrgrad</td>    <th>  R-squared:         </th> <td>   0.828</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.743</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   9.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 04 Jul 2017</td> <th>  Prob (F-statistic):</th>  <td>0.0103</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:07:50</td>     <th>  Log-Likelihood:    </th> <td> -21.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    10</td>      <th>  AIC:               </th> <td>   50.95</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     6</td>      <th>  BIC:               </th> <td>   52.16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>   99.5284</td> <td>    5.052</td> <td>   19.699</td> <td> 0.000</td> <td>   87.166   111.891</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>URM</th>             <td>   -0.2476</td> <td>    0.111</td> <td>   -2.231</td> <td> 0.067</td> <td>   -0.519     0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LGB</th>             <td>   -0.3079</td> <td>    0.075</td> <td>   -4.132</td> <td> 0.006</td> <td>   -0.490    -0.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lessthaneightyk</th> <td>    0.1360</td> <td>    0.237</td> <td>    0.573</td> <td> 0.587</td> <td>   -0.445     0.717</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.699</td> <th>  Durbin-Watson:     </th> <td>   2.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.705</td> <th>  Jarque-Bera (JB):  </th> <td>   0.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.287</td> <th>  Prob(JB):          </th> <td>   0.927</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.813</td> <th>  Cond. No.          </th> <td>    255.</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              sixyrgrad   R-squared:                       0.828\n",
       "Model:                            OLS   Adj. R-squared:                  0.743\n",
       "Method:                 Least Squares   F-statistic:                     9.653\n",
       "Date:                Tue, 04 Jul 2017   Prob (F-statistic):             0.0103\n",
       "Time:                        15:07:50   Log-Likelihood:                -21.477\n",
       "No. Observations:                  10   AIC:                             50.95\n",
       "Df Residuals:                       6   BIC:                             52.16\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept          99.5284      5.052     19.699      0.000        87.166   111.891\n",
       "URM                -0.2476      0.111     -2.231      0.067        -0.519     0.024\n",
       "LGB                -0.3079      0.075     -4.132      0.006        -0.490    -0.126\n",
       "lessthaneightyk     0.1360      0.237      0.573      0.587        -0.445     0.717\n",
       "==============================================================================\n",
       "Omnibus:                        0.699   Durbin-Watson:                   2.373\n",
       "Prob(Omnibus):                  0.705   Jarque-Bera (JB):                0.152\n",
       "Skew:                          -0.287   Prob(JB):                        0.927\n",
       "Kurtosis:                       2.813   Cond. No.                         255.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(lm6.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 94.22244227,  97.07675347,  93.76870783,  86.46096478,\n",
       "        94.07552983,  95.82918733,  88.07375545,  87.53594202,\n",
       "        82.32915778,  92.92755923])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm6.predict(data6yr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 86.4,  87.8,  86.3,  75.3,  80.9,  87.2,  88.8,  80.5,  71.6,\n",
       "        59.7,  83.3])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fouryrgrad.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "p4yr = pd.DataFrame(index=data.Dorm, data=lm4.predict(data), columns=[\"pred4yr\"])\n",
    "p6yr = pd.DataFrame(index=data6yr.Dorm, data=lm6.predict(data6yr), columns=[\"pred6yr\"])\n",
    "\n",
    "predicted = p4yr.join(p6yr).join(data.set_index(\"Dorm\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred4yr</th>\n",
       "      <th>pred6yr</th>\n",
       "      <th>URM</th>\n",
       "      <th>LGB</th>\n",
       "      <th>lessthaneightyk</th>\n",
       "      <th>fouryrgrad</th>\n",
       "      <th>sixyrgrad</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dorm</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>Next</th>\n",
       "      <td>85.381794</td>\n",
       "      <td>94.222442</td>\n",
       "      <td>20.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>86.4</td>\n",
       "      <td>94.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baker</th>\n",
       "      <td>90.308531</td>\n",
       "      <td>97.076753</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>20.6</td>\n",
       "      <td>87.8</td>\n",
       "      <td>95.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burton</th>\n",
       "      <td>85.565986</td>\n",
       "      <td>93.768708</td>\n",
       "      <td>20.8</td>\n",
       "      <td>10.2</td>\n",
       "      <td>18.6</td>\n",
       "      <td>86.3</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EC</th>\n",
       "      <td>71.666707</td>\n",
       "      <td>86.460965</td>\n",
       "      <td>19.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>24.9</td>\n",
       "      <td>75.3</td>\n",
       "      <td>89.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MacGregor</th>\n",
       "      <td>84.816731</td>\n",
       "      <td>94.075530</td>\n",
       "      <td>27.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>28.2</td>\n",
       "      <td>80.9</td>\n",
       "      <td>92.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maseeh</th>\n",
       "      <td>87.514035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>23.3</td>\n",
       "      <td>87.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>McCormick</th>\n",
       "      <td>87.144025</td>\n",
       "      <td>95.829187</td>\n",
       "      <td>20.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>27.9</td>\n",
       "      <td>88.8</td>\n",
       "      <td>95.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New</th>\n",
       "      <td>75.621562</td>\n",
       "      <td>88.073755</td>\n",
       "      <td>43.5</td>\n",
       "      <td>16.1</td>\n",
       "      <td>31.4</td>\n",
       "      <td>80.5</td>\n",
       "      <td>91.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random</th>\n",
       "      <td>71.157039</td>\n",
       "      <td>87.535942</td>\n",
       "      <td>17.2</td>\n",
       "      <td>40.8</td>\n",
       "      <td>35.5</td>\n",
       "      <td>71.6</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior</th>\n",
       "      <td>65.220819</td>\n",
       "      <td>82.329158</td>\n",
       "      <td>36.7</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.9</td>\n",
       "      <td>59.7</td>\n",
       "      <td>78.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simmons</th>\n",
       "      <td>83.402771</td>\n",
       "      <td>92.927559</td>\n",
       "      <td>30.9</td>\n",
       "      <td>8.7</td>\n",
       "      <td>27.4</td>\n",
       "      <td>83.3</td>\n",
       "      <td>93.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pred4yr    pred6yr   URM   LGB  lessthaneightyk  fouryrgrad  \\\n",
       "Dorm                                                                       \n",
       "Next       85.381794  94.222442  20.5  11.0             23.2        86.4   \n",
       "Baker      90.308531  97.076753  16.0   4.2             20.6        87.8   \n",
       "Burton     85.565986  93.768708  20.8  10.2             18.6        86.3   \n",
       "EC         71.666707  86.460965  19.2  38.0             24.9        75.3   \n",
       "MacGregor  84.816731  94.075530  27.2   8.3             28.2        80.9   \n",
       "Maseeh     87.514035        NaN  21.4   6.4             23.3        87.2   \n",
       "McCormick  87.144025  95.829187  20.2   8.1             27.9        88.8   \n",
       "New        75.621562  88.073755  43.5  16.1             31.4        80.5   \n",
       "Random     71.157039  87.535942  17.2  40.8             35.5        71.6   \n",
       "Senior     65.220819  82.329158  36.7  40.0             30.9        59.7   \n",
       "Simmons    83.402771  92.927559  30.9   8.7             27.4        83.3   \n",
       "\n",
       "           sixyrgrad  \n",
       "Dorm                  \n",
       "Next            94.3  \n",
       "Baker           95.7  \n",
       "Burton          94.0  \n",
       "EC              89.4  \n",
       "MacGregor       92.2  \n",
       "Maseeh           NaN  \n",
       "McCormick       95.8  \n",
       "New             91.3  \n",
       "Random          88.0  \n",
       "Senior          78.1  \n",
       "Simmons         93.5  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VUZLUMSml7xU9/Q4m97R6NCJuIN/e+Qw5UPFmYH3y7bZrAP8EvlhRz+MRcSLw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\nmgucCATw2bLy+wDLA1eklP5YVn428OW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gDPQqoLfUIkPiKW8WERFJrspKOP54mDcPzjrr\ns/u1tbUsWFBOefliKiv/TXZ2709bxzU27vi0ddyUKZPVOi7F1INZACXKIiIi7amsrOSII0ZTX7/V\n9bnz8gpZv76Cfv366TCTNKMCs8RT3iwiIpI827fDscfCFVfAxRe3Pk6HX6cX9WAWERER6YBQKITP\nl5eUuX2+PEKhEGVl11FdXRDbuZy4aHQG1dV9KCu7zqUIRdKfMeYiY8zfjTEfG2M+McasNcZMM8aY\nuHGHGGP2tPHrAa++BhERkZ6sthbGjIGLLmq7uAyQlZVFUVERgwYNoqioSMXlHkI7mNOcdmKIiIi0\nLdk7mFeseJQRI04iFNqIO4cIbsPvH0x19Wa9ItgF2sGcGYwx9wPnAu8BTwENwGhgIHCvtXZys7GH\nAFuB9cCTLUy30Vr7eBtrKW8WERFxWX09jB4NRx0Fv/89GGVfGSVVObP+M4KIiIhktMLCQhobPwA+\nxu0ezI2NO1i+fCXGjMOd4jJAX4wZy4IF5Vx+eeLtNkTSnTFmAk5xeTMwzFr7Yex+FvA4MMkY86S1\nNr6YvN5ae21qoxUREZF44TCccQYceqiKy9I2tcgQERGRjJaVlcWAAYOBNS7PvJaiosO5997H2bXr\nAldn3rVrMuXli12dUyQN/QCwwE1NxWUAa20EuBrnpMyfexSbiIiItCEahfPPh0AA7r5bxWVpmwrM\nIiIikvGKiyeSm3uPq3Pm5pZz/vkT2Lz5NWCYq3PDUCor/00kEnF5XpG08uXYtaX+NVti1+NiO5qb\n62OMudgYc2XsOjh5IYqIiEg8a+HnP4f33oMHHwS1UZb2qMAsIiIiGW/q1GKsfRrY5tKM27B2KSee\neALZ2QfhbusNgF5kZ/emqqrK5XlF0sqO2PVrLXzWP3bNavb7JqOBO4A5sesGY8xLxpivJiVKERER\n+Zyrr4a1a2HJEvD7vY5GMoEKzCIiIpLx8vPzKSmZTiAwDeeN/K6wBAIXM3PmZfj9fny+PDdC/AKf\nL49QKJSUuUXSxDKcNhglxpgDmm7Gdiw377Hc9FlD7P53Y/cOAI4HXgJGAhXGmNzkhy0iItJzzZsH\nixfDs89Cr15eRyOZQgVmERER6RZKS6+ioGA7Pt+8Ls3j882joOAdSkuvwu/3E43WuxTh50Wj9fi1\nJUS6t4eA54ABwP8ZY+40xswH1gPHAMHYuD0A1toPrLWzrbXrrbV1sV+rgTHAq0ARcGHKvwoREZEe\n4p57nALzCy/AQQd5HY1kEnVRERERkW4hJyeHioolDBkygtpaiEZn4Gye7CiLzzeP/PxbqKhYRXZ2\nNoWFhTQ2fgB8jLttMupobNxBYWGhi3OKpBdr7R5jzDigBPgxcD4QAlYAZwCPxYa+3848UWPMn4Dh\nwAjgD62NnT179qe/HzlyJCNHjkz8CxAREelBnnoKfv1rWLEC+vXzOhpJ1MqVK1m5cmXK1zXWdvU1\nUkkmY4zV35GIiEjHBYNBRo0az9tvf5tQ6E907L+nbyMQmEZBwXYqKpbQr1lWPXDgUbz++hzgRBej\nXM7AgVezadNfXZyzZzHGYK3VeeYZyhizN/AR8JG19uAOjD8deBJ4zlp7aitjlDeLiIgkYOVKOOss\nWLYMhg71OhpxU6pyZrXIEBERkW6lX79+vPTSqwQCvycr6xpycycBy4G6uJF1wHICgUn4/YOZMWMY\nmzat+VxxGaC4eCK5ufe4GmNubjnFxRNdnVMkw5wL5AAPdHD8UbHrluSEIyIi0jP94x9Ocfmhh1Rc\nlsRpB3Oa004MERGRzqmrgxNOgFNOgZKSWhYsKKe8fDGVlf8mO7s3Pl8e0Wg9jY07KCo6nOLiiUyZ\nMpn8/PwW56utraWgYACh0GtAXxci3IbfP5jq6s2trint0w7mzGCM2dda+3HcvSOAF2N/HGytfTd2\n/0hgfXzya4w5EViKU5A+xlr791bWUt4sIiLSCf/5D4wcCbffDhMmeB2NJEOqcmYVmNOcEmUREZGO\nC4Xg5JPhm990EmXTLJWKRCJUVVURCoXw+/0UFhaSldWx4yhmzZrN/PlraWhYSuf6OsezBAKnMWPG\ncObMKe3CPKICc2Ywxvwd2AVsxGlm/k3gNKAeGBc7xK9p7Arg68BfgW2x24cD3wcs8Btr7fVtrKW8\nWUREpIPefhuOPRZKS2HKFK+jkWRRgVkAJcoiIiIdFYnAmWdCbi7cfz/4fO7NHQ6HGTRoOFu2TCIa\nLUl4Hp9vLv3738emTWvIzs52L8AeSAXmzGCMmQmcAwwAcoFq4Bngt9ba7XFji4EJwCCgN5ANvIdT\ncL7NWvuXdtZS3iwiItIBO3bAccfB1Knwy196HY0kkwrMAihRFhER6Yg9e5ydF+++65yAnZPj/hrB\nYJAhQ0ZQW3sp0egMOreT2eLzzSM//xbWrVv1hT7P0nkqMEs85c0iIiLt+/hj+P73YdQouL7V94Kk\nu1CBWQAlyiIiIu2xFmbOhL//HV58EfLykrdWMBhk1KjxVFf3oaHhLjrWk3kbgcA0Cgq2U1GxRMVl\nl6jALPGUN4uIiLQtFILTToOiIrjzzs+3k5PuKVU5817JXkBEREQkma6/3iksL12a3OIyQL9+/di4\n8VUuu2wofv9gcnMnAcuBuriRdcByAoFJ+P2DmTFjGJs2rVFxWURERERcF4lEqKysZOPGjVRWVhKJ\nRFoYA+edB/n5XzyrRKSrtIM5zWknhoiISOvuvBNuuAFWr4Y+fVK7dm1tLQsWlFNevpjKyn+Tnd0b\nny+PaLSexsYdFBUdTnHxRKZMmUx+fn5qg+sBtINZ4ilvFhGRnqSmpoaFC8tZtGgxmze/Rnb2QZ/m\nouHw+5/molOnFnPAAflcdBEEg/D007D33l5HL6miFhkCKFEWERFpzcMPQ0kJvPyy85qflyKRCFVV\nVYRCIfx+P4WFhWRlZXkbVDenArPEU94sIiI9QTgcpqzsOubOvRljxrFr1wXAUKBXs1F1wFpyc+/B\n2qc54oil7NlzFMuX78U++3gTt3hDBWYBlCiLiIi05JlnokyaZPnjH7fy7W8bFXR7IBWYJZ7yZhER\n6e4+fx7IH4GCDjz1Ecbs5Gtfu4AVK+5Vy7YeRgVmAZQoi4iINGl6DfC2297gv/+9Hr//J+TkvNHi\na4BqSdH9qcAs8ZQ3i4hIIjLlTbRgMMiQISOorb2UaHQG0Jk0yOLzzSM//xbWrVulInMPogKzAEqU\nRUREmr8GaO1P2L27DIgAgWajPv8aYEnJdEpLryInJ8eboCXpVGCWeMqbRUSkozrTvzgdNi6Ew2EG\nDRrOli2TiEZLEp7H55tL//73sWnTGrKzs12MUNKVCswCKFEWEZGe7fOvAS4AvtyBp7YRCEyjoGA7\nFRVLtEOjm1KBWeIpbxYRkfYk0r84HTYuzJo1m/nz19HQ8DSd27kczxIIjGXGjGHMmVPqVniSxlRg\nFkCJsoiI9Fx6DVDaogKzxFPeLCIibUmsf7H3Gxdqamro27eIUGgjHYu5Pdvw+wdTXb05LXZnS3Kl\nKmfeK9kLiIiIiHRWOBxm1KjxseJyCZ3fqWGIRkuorf0Fo0aNp7GxMRlhioiIiEgGaNq4sGXLJBoa\nltLxQm1fGhqWsmXLJIYMGUEwGExmmC1auLAcY8bhTnEZoC/GjGXBgnKX5hNRgVlERETSUFnZdVRX\nF8R2LicuGi1h27avUFZ2nUuRiYiIiEgmcW/jwqWebFxYtGhxrJWHe3btmkx5+WJX55SeTS0y0pxe\n9RMRkZ4mGa8B7r33ILZv36LXALsRtciQeMqbRUSkJZncvzgSiZCXtz/h8DvAvi7OXEdOTh/q63eS\nlZXl4rySbtQiQ0RERHqkhQvLgdNx8zXAcHg0d9+9wKX5RERERCQT1NTUMHfuzTQ03EXXissAhoaG\nu7jppvnU1ta6EV67qqqqyM4+CKe4HAEqgY2xa6QLM/ciO7s3VVVVXQ9SBBWYRUREJM0sXLiYUGiO\nq3Na+xN+//s/ujqniIiIiKS3TO9f/O677xKJ7AaOAvYHRgPnxK77ASOBbUCo03P7fHmEQp1/TqQl\nKjCLiIhI2ohEIrz55jigj8szD2XHjv/y/vvvuzyviIiIiKSrTO1fHA6HmTVrNqNGjWX37mOAOcB2\nYCvODuatwDvAo8DbOLnzbCDc4TWi0Xr8fr/LkUtPpQKziIiIpI2ysg+x9lzA5/LMvYADuemm+S7P\nKyIiIiLpKBKJsHnza8Awl2ceSmXlv4lEutKionXBYJBBg4Yzf/5adu/ehFNEPhEnn22uF3AQzu7m\nfwNrgeFAsAOr1NHYuIPCwkL3ApceTQVmERERj0QiESorK9m4cSOVlZVJS1IzxZ//DHffvT95edOS\ntEIvFi9+Jklzi4iIiEg6+Xz/Yjclr39xMBhkyJARbNkyiYaGpXS8tUdfYCkwCRhB+0XmtRQVHa4D\n/sQ1KjAnyBhzmjHmBWPM28aYBmPMZmPMI8aY78WNO8QYs6eNXw949TWIiEjq1dTUcOONNzFw4FHk\n5e3PEUeM5phjzuGII0YTCOzHwIFHceONN6Xs4JB0sWwZzJwJixa9g7VvJWmV3bz99ps9vpAvIiIi\n0hOEQiF8vrykzJ2M/sXhcJhRo8ZTW3sp0WgJnT+U0AAlwKXAeKCx1ZG5ueUUF09MOFaReMZa63UM\nGccY8zvgcmAH8GTsWoRz5H02MMla+0Bs7CE4zXHWx8bG22itfbyNtaz+jkREMl84HKas7Drmzr0Z\nY8bFesEN5fOvutUBa8nNvQdrn6akZDqlpVeRk5PjTdAp8sorcOaZ8PTT8N3vRggE9qOx8V3c3W1S\nB/QhEDiQDRuWU1RU5OLc4gVjDNbarh4HL92I8mYREWmusrKSI44YTX39VtfnzssrZP36Cldzylmz\nZjN//joaGp6m88Xl5iwwFqc1SGkLn2/D7x9MdfVm8vPzu7COZIJU5cwqMHeSMeZgoBp4Hxhsra1p\n9tnxwApgi7W2KHavqcBcbq2dksB6SpRFRDJcMBhk1KjxVFf3oaHhj3TsVbdtBALTKCjYTkXFEvr1\n65fsMD2xfj2cdBLcfz+MHu3cGzDgO2zZciNOrzm3LAeuplevj/jLXx5m0KBBLs4tXlCBWeIpbxYR\nkeYikQh5efsTDr+D2xsXcnL6UF+/07UWEzU1NfTtW0QotJGOt8VoyzZgMLAZaF5EtgQCpzFjxnDm\nzGmp+CzdTapyZrXI6LxDcL5vrzYvLgNYa18GPsbpsi4iItKlPmoNDUvZsmUSQ4aMIBjsyGEdmeWt\nt+DUU+H22z8rLgNMnDgGuNPl1cqBiTotW0RERKSHyMrKYsCAwcAal2d2v3/xwoXlGDMOd4rL4PRk\nHouTA3/G55tHQcE7lJZe5dI6Ig4VmDvvLSAMDDPGHNj8A2PMCJz/LPZiC8/1McZcbIy5MnYdnIJY\nRUTEQ270UYtGS6itvZRRo8bT2Nh6H7VMU13t7FwuK4OJce3fZs6cgXNIyTaXVtsWm+8MnZYtIiIi\n0oMUF08kN/ceV+dMRv/iRYsWx1rouWkysDj2e4vPN5f8/FuoqFhCdna2y2tJT6cWGQkwxlwKzAVq\ncPoq1+D0YB4HrMTpwbwjNrapRUb8N9rExl5grX27jbX0qp+ISIZys49aIDCWGTOGdYtX2WprYcQI\n+PGP4YorWh7Tu3chNTWHAc/S9R50pwHDgWMZOPBqNm36axfmk3ShFhkST3mziIjEq62tpaBgAKHQ\nazi7ervK/f7FyWzlAX2AjQQCP+v2rfekZWqRkcastbcAZwJZwIXAr2N/DgL3NBWXYxqAa4HvAgfE\nfh0PvASMBCqMMbkpC15ERFKipqaGuXNvpqHhLrpWIAUwNDTcxU03zae2ttaN8DzzySdOW4xTToFf\n/7r1cTNnXoIxG4B5XVxxHvAOcJVOyxYRERHpYfLz8ykpmU4gMI0v7vvrLEsgcDEzZ17m6uF4VVVV\nZGcfhLvFZXAOE9+XnJzDmTFjGJs2rVFxWZJGBeYEGGN+hfOewUJgAJCHU0DeCjxgjPlt01hr7QfW\n2tnW2vXW2rrYr9XAGOBVnJ3PF6b8ixARkaRKRh81Y8ayYEG5S/Ol3u7dcOaZ8K1vwQ03gGmj7j5t\n2kXk5DQA83FeGursDwQ29twtwBLgPaxdypQpkxOKXUREREQyU2npVRQUbMfn69rGhWT1Lw6FQvh8\nea7O2WTvvffhpZeeZc6cUrXFkKRyryN5D2GMOR74LfCYtfbyZh+tN8ZMAN4EZhpj7rTWVrU2j7U2\naoz5E847uyOAP7Q2dvbs2Z/+fuTIkYwcObIrX4KIiKSA00dtjqtz7to1mfLyq7n88hJX502FaBQm\nTYJAAO66q+3iMji7TWbOnMHcuasIhe4DlgN30bFXG7cB04DtwCrgq7HTst3dbSKptXLlSlauXOl1\nGCIiIpJhcnJyqKhYwpAhI6ithWh0Bp17w9Di882L9S9e5Xqh1u/3E43Wuzpnk6ysRg4++OCkzC3S\nnHowd5Ix5kagBLjUWntbC58/BvwAmGitfaKduU7H6eH8nLX21FbGqJeciEiGSWYftZycPtTX73T1\n1OpksxZ+8hOorIRly8Dv79hz4XCYQYOGs3nzuezZ0wDcjHMa9mRgKM5rf03qgLU4J2UvBS4DrgKy\n8fnm0r//fWzatEY7N7oR9WCWeMqbRUSkLcFgkFGjxlNd3SfWxq5jGxcCgWlJ7V+snx0kmdSDOX3t\nHbse1MrnTffDHZjrqNh1S5ciEhGRtJLMPmrZ2b2pqqpyed7kmjUL/vlPePLJjheX4bPdJgceeDs+\nXy+gEjgSuBrnwJJC4Fuxa5/Y/SOBzUApkKXTskVEREQEgH79+rFx46tcdtlQ/P7B5OZOwnlLri5u\nZB2wnEBgEn7/4KT3L87KymLAgME4myXctJaiosNVXJaUUIG5817BeZfiYmNMn+YfGGNOAY4BQsBf\nY/eONOaLLwIbY07E2V5lgT8nO2gREUmdZPZR8/nyCIVCSZk7GW66CZ54Ap59FvZNoN7er18/1q1b\nRf/+9xEInA+chfOv2J1ABfBw7Lozdr8EyMfZbTKW/v3vY926VTrQRERERETIycnhf/93NtXVmykr\nO5KBA68mJ6cPeXmF9Or1LfLyCsnJ6cPAgVdTVnYk1dWbU9K/+IILfojPl+vqnDrgWlJJLTI6KVYs\nfg4YBXwCPAG8CwwETosNm26tvTU2fgXwdZyferfFPj8c+D5Ocfk31trr21hPr/qJiGSYyspKjjhi\nNPX1W12fOy+vkPXrKygqKnJ9brctWgRlZfDKK/DVr3ZtrnA4TFnZdcydezPGjGXXrsm01iYjEChn\nz56lzJx5GaWlV2nncjelFhkST3mziIgkIhKJUFVVRSgUwu/3U1hYmNJdv9bChReGWLRoHdYW0rHW\nHe3Zht8/mOrqzTqDpIdLVc6sAnMCjDE+4GfAOTiF5QBQC7wK3GKtXd5sbDEwARgE9AaygfdwCs63\nWWv/0s5aSpRFRDKM+qg57TAuuQRWroRDD3Vv3traWhYsKKe8fDGVlf8mO7s3Pl8e0Wg9jY07KCo6\nnOLiiUyZMlnJdDenArPEU94sIiKZ6JprnHNKTjjheu64YzUNDUvp3CGE8WzsgOvhzJlT6laYkqFU\nYBZAibKISKYaOPAoXn99DnCii7MuZ+DAq9m06a8uzum+FSvg7LOdthjf/W7y1vF6t4l4SwVmiae8\nWUREMs3NN8Pttztv/O2/v3PA9ZYtk4hGSxKeUwdcS3M65E9ERCSDFRdPJDf3HlfnzIQ+auvWOcXl\nRx5JbnEZnANRioqKGDRoEEVFRSoui4iIiEjGuPde57ySF16AL33pswOu8/Nvweebi9NVtTOsDrgW\nz2gHc5rTTgwRkcxUW1tLQcEAQqHX6Cl91N54A044Ae68E8aP9zoa6e60g1niKW8WEZFM8fTTcNFF\nzpt/3/zm5z8LBoOMGjWe6uo+NDTcRcd+lthGIDCNgoLtVFQs0QHX8intYBYREclg+fn5lJRMJxCY\nRud3H8SzBAIXM3PmZWlbXA4GYcwYuP56FZdFRERERFqzahVMnQpPPQVf/3qEyspKNm7cSGVlJZFI\nhH79+rFx46tcdtlQ/P7B5OZOApbjHGjdXB2wnEBgEn7/YGbMGMamTWtUXBZPaAdzmtNODBGRzBUO\n94w+ah98AMcdB9OmwYwZXkcjPYV2MEs85c0iIpLu/vUvOOmkPUyY8BirV89l8+bXyM4+6NNDq8Ph\n9z89tHrq1GIAHXAtXaJD/gRQoiwikumCwSBDhoygtvZSotEZdO5EaIvPN4/8/FtYt25VWu5G+Phj\npy3GySfDnDleRyM9iQrMEk95s4iIpLONG8McddRudu/+KVlZhl27LgCGAr2ajaoD1pKbew/WPk1J\nyXRKS68iJydHB1xLQlRgFkCJsohId9Bd+6iFQnDqqfCNb8Add4BRqU9SSAVmiae8WURE0tWrr1Zz\n7LGGvfZ6lHB4IlDQgafS/+cBSX/qwSwiItJNZEIftUjki/3f2h4P557rnHh9220qLouIiIiIZgDB\n6QAAIABJREFUtGTDhm0cc0w90Wgl4fCldKy4DNCXhoalbNkyiSFDRhAMBpMZpkiXaAdzmtNODBGR\n7qW2tjZt+qjV1NSwcGE5ixYtbrf/W/NYrHUOJtm+3TmcJCcnqWGKtEg7mCWe8mYREUk3NTVhCgr+\nQzgcwtqhCc+T7meySPpSiwwBlCiLiHRnXvVRC4fDlJVdx9y5N2PMuE71f8vOzuFXv4LVq6GiAvLy\nkh6uSItUYJZ4yptFRCSd7N4Nhx22hbfffoto9CQ6dxZLPEsgMJYZM4YxZ06pWyFKD6ACswBKlEVE\nxF2f7wf9Rzrb/+2MM1awbNn+vPwy6KBq8ZIKzBJPebOIiKSLaBQmTNjNsmXPs2fPd+l4W4y2bMPv\nH0x19eakv+ko3Yd6MIuIiIirgsEgQ4aMYMuWSTQ0LKWz/d8qK2/gxhvrWLiwWsVlEREREZEWWAs/\n+Qm8/vq75OQ8iTvFZYC+GDOWBQvKXZpPxD0qMIuIiPQA4XCYUaPGU1t7KdFoCZ1/Rc9g7Wiggh/9\naCyNjY1JiFJEREREJLNdeSVs2AB77TWFUOhHrs69a9dkyssXuzqniBtUYBYREekBysquo7q6gGh0\nRpfm2bOnmOrqPpSVXedSZCIiIiIi3cONNzqHYD/1VISqqleBYS6vMJTKyn8TiURcnleka9SDOc2p\nl5yIiHRVTU0NffsWEQptRP3fpLtQD2aJp7xZRES8tHAhXHutcxh2KFTJEUeMpr5+q+vr5OUVsn59\nBUVFRa7PLd2PejCLiIiIKxYuLMeYcaj/m4iIiIiI+x5/HH7zG3jhBejbF0KhED5fXlLW8vnyCIVC\nSZlbJFEqMIuIiHRzixYtZteuC1ydU/3fRERERERg+XLnUL+lS+Eb33Du+f1+otH6pKwXjdbj9/uT\nMrdIotQiI83pVT8REemKSCRCXt7+hMPvAPu6OHMdOTl9qK/fSVZWlovzinSMWmRIPOXNIiKSamvX\nwqmnwuLFcPzxn91XDi7pQi0yREREpMuqqqrIzj4IdxNbgF5kZ/emqqrK5XlFRERERNLf66/DuHGw\nYMHni8sAWVlZDBgwGFjj8qprKSo6XMVlSTsqMIuIiHRj6v8mIiIiIuKuYBDGjIEbboDTT295THHx\nRHJz73F13dzccoqLJ7o6p4gbVGAWERHpxtT/TURERETEPe+/D6NHQ0kJnH9+6+OmTi3G2qeBbS6t\nvA1rlzJlymSX5hNxjwrMIiIi3VhhYSGNjR8AH7s8cx2NjTsoLCx0eV4RERERkfRUVwennAJnnQWX\nXdb22Pz8fEpKphMITAO6ekaAJRC4mJkzLyM/P7+Lc4m4TwVmERGRbkz930REREREui4UgvHjYdgw\nuPbajj1TWnoVBQXb8fnmdWltn28eBQXvUFp6VZfmEUkWFZhFRES6OfV/ExERERFJXCQCZ58NBx8M\nt94KxnTsuZycHCoqlpCffws+31w6v5PZ4vPNJT//FioqlpCdnd3Z0EVSwljb1W36kkzGGKu/IxER\n6Yra2loKCgYQCr0G9HVhxm34/YOprt6sV/TEM8YYrLUd/PFOegLlzSIikgx79sCUKfDee7BkCeTk\ndH6OYDDIqFHjqa7uQ0PDXXQsJ99GIDCNgoLtVFQsoV+/fp1fWHq8VOXM2sEsIiLSzan/m4iIiIhI\n51kLv/wlvPkmLF6cWHEZoF+/fmzc+CqXXTYUv38wubmTgOVAXdzIOmA5gcAk/P7BzJgxjE2b1qi4\nLGmv2+1gNsbsDRwA5Fhrg17H01XaiSEiIm4Ih8MMGjScLVsmEY2WJDyPzzeX/v3vY9OmNXpFTzyl\nHcyf191y4EQobxYREbdddx08+CC8/DK4tbeitraWBQvKKS9fTGXlv8nO7o3Pl0c0Wk9j4w6Kig6n\nuHgiU6ZM1oYO6bJU5czdosBsjPkmMB04CTgkdttaa7Pixp0NDADetdYuTG2UiVGiLCIibgkGg3z7\n2zPYufM+IBfoTJ5h8fnmkZ9/C+vWrdIuCvGcCszdOwdOhPJmERFx0513wo03wurV8JWvJGeNSCRC\nVVUVoVAIv99PYWGhDtEWV6UqZ874/9UaY64BrsZp99HeNywXmANEjDHLrLXvJTs+ERGRdLFzZz+y\nsh6hT5+fs3NnMMH+byoui6QD5cAiIiLJ88gj8D//A6tWJa+4DJCVlUVRUVHyFhBJkYzuwWyMKQNK\nAR+wB/gbsLqNRx4GGmLjxyc9QBERkTSxeTOccgrcdpuPrVtvVv83kQymHFhERCR5nn8efvELePZZ\nGDDA62hEMkPGtsgwxgwC1uPs2NgAnG2tfcsYMx54Auf1QF8Lzz0JjAMettael8qYE6FX/UREpKu2\nb4fjjoNf/xouvviz++r/Jpmsp7bI6Ck5cCKUN4uISFf97W9w+unw5JNwzDFeRyPSdWqR0b6f4uzA\nrgXGWGs/6OBz64DTgcHJCkxERCRdfPghjBkDF174+eIyQH5+PpdfXsLll5eo/5tI5lAOLCIikgQb\nN8IPfgD33qviskhnZfJPjicAFijvRGIN0HSqdkeaToqIiGSs+no47TSnwHzFFW2PVf83kYyhHFhE\nRMRlW7fCySfD/PlOWzkR6ZxOF5iNMU3NF3d7fEBIQez6j04+90nsmudiLCIiImklHIYzz4RDD3VO\nvzY9rpGAiLuUA4uIiHRP774Lo0fDlVfCued6HY1IZkrkkL8qYCvwG3dD6bSm3nLRTj63X+z6sYux\niIiIpI1oFM4/H3Jz4e67VVwWcUkVyoFFRES6lZ07nZ3L558PP/uZ19GIZK5ECsy7Y9c1bgaSgKad\nI4d08rlvx67VLsYiIiKSFqyFn/8c3nsPHnwQ1EZZxDXKgUVERLqRhgYYOxaOPx6uvtrraEQyWyIF\n5ndi14ibgSRgDc7p2WM7+oAxJhv4IU7futVJiktERMQz11wDa9fCkiXg93sdjUi3ohxYRESkm2hs\nhB/+EL72NZg3T2/8iXRVIgXmV2PXQW4GkoDHYtdjjTFndPCZ3wFfif3+QfdDEhER8c78+fDoo/Ds\ns9Crl9fRiHQ7yoFFRES6gT17oLgY9toLFi50riLSNYn83+hPOLsmJhtj9nE5ns5YDGyIxfJnY8xP\njTE5LQ00xvQ3xvwZmI6zc2O5tfaV1IUqIiKSXPfe6+y+eOEFOOggr6MR6ZaUA4uIiGQ4a2H6dAgG\n4ZFHIDvb64hEugdjre38Q8bcCvwUWAWc7dVJ2saYAcDfgQNxkuY6nNcXD4v9eRXQF+jf9AiwDRjq\n8enfHWaMsYn8HYmISM/x1FMwbRqsWAGHHeZ1NCKpYYzBWpvSF1qVA6c35c0iItKe2bOdVnIrV8J+\n+7U3WiTzpSpn7nSB2RgzAidJ/RVwCvAJzqt6r+AcGrKrvTmstas6HWnr8QwAHgGObJq+pWGx61rg\nTGvtNrfWTzYlyiIi0paXX3b6xz3zDAwZ4nU0IqmT6gKzcuD0p7xZRETacsstcOut8MorcPDBXkcj\nkhrpXGDew+cTWEPLCW1rrLXW1TPtjTEG+AEwCTgOZzdHk09wdnHcAyzOtKxTibKIiLTmn/+Ek0+G\nhx6C73/f62hEUsuDArNy4DSnvFlERFpz//1wxRVOcbmw0OtoRFIn3QvMXWGttb4uztEmY0wesB/w\nibW2LplrJZsSZRERacmbb8LIkXD77fCDH3gdjUjqeVRg7grlwEmmvFlERFqybBlMnQrLl8O3vuV1\nNCKplaqcOZFdFGWuR+Eya209UO91HCIiIsmwbRucdBL87/+quCySQsqBRUREMswrr0BxMTz9tIrL\nIsmU0CF/6cAYc03stw9Za9/sxHMDgB8BWGuvTUZsbtJODBERaW7HDhgxAqZMgV/+0utoRLzjxSF/\n6aCn5MCJUN4sIiLNrV/vbMq4/34YPdrraES8kbYtMtJFsz54E6y1T3XiuTHAs6TgNUU3KFEWEZEm\nH38MJ57o/Lr+eq+jEfFWDy4w94gcOBHKm0VEpMlbb8HxxzsH+02c6HU0It5JVc68V7IXEBERka7b\nvRsmTIAjjoDrrvM6GhERERGR9FRd7excLitTcVkkVXpigblpx0bU0yhEREQ6KBqF886DAw6AO+4A\n0+P2bIqIC5QDi4hIt1dbC2PGwLRpcNFFXkcj0nMkcshfpjskdu1xJ2uLiEjmsdZJkD/+2DmcxNct\nX2wXkRRQDiwiIt3aJ5/AqafCKafAr3/tdTQiPUuXCszGmOOAScBwoC/Qi/Z3RVtrrZuF7Q41WjPG\nBIDvADNiz7zhYgwiIiJJccUV8NprsHw57L2319GICCgHFhERSTe7d8MZZ8C3vgU33KA3/kRSLaEk\n1xizD3AvML7plmsRtbxeKXBNSx8BT5rE/smxpEtBiYiIJNkNN8DSpbBqFeyzj9fRiIhyYBERkfQT\njcKkSU6+fNddKi6LeCHRXRSPAGNwktt64DXgezi7Iv4P2IXzGt5BsfEW+EdsbKJa+0dEIv/oWA3c\n3IVYREREkupPf3L6La9eDQce6HU0IhKjHFhERCSNWAs//SnU1MCyZZDVExvBiqSBTv9fzxgzFjgZ\nJ2F+FJhqrf3EGLMnNmSWtfap2NgjcXZdjAdygXOstVsSiLMKeDnu3vF8lszvaOf5PcAnwFZgObDM\nWrun7UdERES88fjjcM018PLLUFDgdTQiAsqBRURE0tGsWfDPf8JLL4Hf73U0Ij2XsbZD7ds+e8CY\nB4GzgRqgn7V2V+z+Hpxkd0JTct3smf8BZgGbgKHW2lCXA29jve7EGGM7+3ckIiKZq6ICzjsPnn8e\njjzS62hE0pcxBmttyl6CVQ6c/pQ3i4j0LDfd5Lz198or0Lu319GIpKdU5cztHUbSkuE4Se19TYl1\nnC8Eba29GlgPDAQuTmDNlqyK/Wpv54aIiEhGWLPGKS4vXqziskgaUg4sIiKSJhYtgj/8AV54QcVl\nkXSQSIH5S7Hrm3H3m7YLtPZSwv04ifcPE1jzC6y1I621J1hr/+rGfCIiIl56/XU4/XRYsABGjPA6\nGhFpgXJgERGRNPDEE3DVVc4bf1/9qtfRiAgkVmBu6tv8Qdz9T2LXg2jZ27FrUQJrioiIdFv//S+M\nGQM33gjjxnkdjYi0QjmwiIiIx1asgGnTnAP9Dj3U62hEpEkiBeaa2DUv7v57sethrTz35dj1gATW\nFBER6Zbefx9Gj4aZM2HSJK+jEZE2KAcWERHx0Lp1cPbZ8Mgj8J3veB2NiDSX1f6QL3gDJ1EeEHd/\nA87OjLHGmEtbOKH6jNi1BpcZY/JwTukeDvQFegG+dh6z1toT3Y5FRESkoz76CE4+Gc45B6ZP9zoa\nEWmHcmARERGPvPGG86bf3XfDyJFeRyMi8RIpMP8NOAEYFnd/CXAm8FXgbmPMDGttXSzxnQOMwOlR\n90oX4v0CY8ylwLXAvp15jM/65YmIiKTcrl1Oz+Wjj4ayMq+jEZEOUA4sIiLigWDQaSf329/C+PFe\nRyMiLTHWdi7HNMYci3NydQj4srW2LnY/B9jIZ7s6Ijg7Nb6Ek8waIAocY61d40rwxswBrqSFU7tb\n0PSFNo211tr2dnh4zhhjO/t3JCIi6S0SgTPOgH32gT//GfZKpGGVSA9njMFa25Ec0K31lAOnOeXN\nIiLdzwcfwHHHOX2XZ8zwOhqRzJOqnLnTP9Jaa1cD9+Ds1hjc7H4YZ/fGDpwENhvnNcK9+Cyx/oWL\nifXhwFWxP76Bs6Mktykc4AfAPsC3gJnAtthn9wC53TWxFhGR9LZnD0ydCo2NUF6u4rJIplAOLCIi\nklp1dU47uYkTVVwWSXed3sHc7oTGHAD8AjgROBhoANYCt1trN7i4zu3AT4Aw8HVr7dux+3twkusJ\n1tqnmo3fB1gMjAaetNae6VYsyaSdGCIi3Ye1UFICa9bAiy9CIOB1RCKZK9U7mNujHNh7yptFRLqP\nUAhOOQUOOwxuvx1M2vwbXySzpCpndr3AnCrGmI3AN4H7rbXnN7vfYnId+ywPeBNnV8lZ1trHUhhy\nQpQoi4h0H3PmOKdev/wyHHCA19GIZLZ0KzCnSk/JgROhvFlEpHuIRJxdy3vvDQ88AD69eyOSsLRt\nkZFG+sau/2zl873jb1hr64FFOK8rnv+FJ0RERJLkjjtg0SJ4/nkVl0WkS5QDi4hIt7VnD1x0kbOD\n+b77VFwWyRSZXGBuerG4Ou5+Q+y6XyvPbYxdj+jK4saY04wxLxhj3jbGNBhjNhtjHjHGfK+V8Ucb\nY54xxtTExm8wxkw3xmTy34GIiHTAQw85u5dffBG+8hWvoxGRDOdpDiwiIpIs1sLll8N//gOPPQY5\nOV5HJCIdldXVCWIF1ZNwXtU7AMi21p4YN6Y3kAOErLW1XV0zpq5pvbj7H+IcdDLgC0849o1dv5To\nwsaY3wGX4xzm8mTsWgScDpxpjJlkrX2g2fjxOL3vdgEPA7XAOGAecDRwdqKxiIhIenvuOZg+HSoq\noH9/r6MREbf0xBxYREQkmX77W3jhBaedXF6e19GISGckXGA2xhwGLASGN7+N0/st3hXADGCHMabA\nWhtJdN1mKoGhQEHc/f+L3RsFXNnCc8fGrg0tfNYuY8zBOCdyvwsMttbWNPvseGAFcC3wQOzevsDd\nQAQ43lr7r9j9q2NjJxpjzrLWPpJIPCIikr7++leYNAmWLIHBg72ORkTc0FNzYBERkWS66y64+25Y\nvRry872ORkQ6K6H2DLEdG2txEmvT7Fdrbot93htnp4cb1sXmPDLu/gux63eMMVOafxDbSfwjnB8A\n/pXguofgfN9ebV5cBrDWvgx8DBzU7PYPcb7uB5uKy7GxYeA3sa/hkgRjERGRNPXaazBhgtM77uij\nvY5GRNzQw3NgERGRpHjkEbj2Wmf3cp8+XkcjIonodIE5dgr1E0Aezq7c/wEOBc5q7Rlr7VacZBjc\nS64rYtfRcX2M7wU+iv3+bmPMq8aYB4wxrwKP89nXfHeC674FhIFhxpgDm39gjBmB8/rhi81un4CT\nzD/fwlyrcHaRHG2MiX/NUUREMtSWLXDKKXDzzXDyyV5HIyJuUA4sIiLivhdegF/8Ap59FoqKvI5G\nRBKVyA7mnwIHA3uAM6y1pdbat4DGdp5bjbPbYkgCa7bkOeC/OAn+qKab1toPgJ/gFHWb1js7dm3a\nYXK/tfbhRBa11n4I/Arne/B/xpi7jDHXGWMewSkiPx9bv8mhseubLcwVBbbitCpRZ04RkW7gnXdg\n9GiYNQvOOcfraETERT06BxYREXHb3/8OP/qRc6Df4Yd7HY2IdEUiPZjH4SSuT1lrl3XiuTdiV1f+\nm5S1NgR8rZXPHjbGvAPMxuk31/R1vgX8wVp7axfXvsUY81+c/nsXNvuoErjHWruj2b2mk7w/omVN\n9/fvSkwiIuK9Dz+EMWOguBguUfMjke6mx+fAIiIibtm4EcaPh3vugWOPbX+8iKS3RArMh8Wuz3Xy\nuaaTs/drc5RLrLWrgO/HWk8cCDRYa+vcmNsY8yvgf4H5OL313sX5vvwWeMAYc4S19go31hIRkczQ\n0ABjx8KJJzq7l0Wk2+nxObCIiIgbtm512sjNmwennup1NCLihkQKzE3J8Y42R31RU4/haAJrJsxa\n24hTAHaFMeZ4nELyY9bay5t9tN4YMwGnFcZMY8yd1toqPtuh3NoPFU33d7a25uzZsz/9/ciRIxk5\ncmRCsYuISHKEwzBxIgwYADfdBKatI79EJCErV65k5cqVXobQo3NgERERN7z3Hpx0ElxxBZx3ntfR\niIhbEikw1wJfwtkR0RlNr/J1NilPCmNM77hWFh01Fuf1yJXxH1hrdxlj1gA/wDnZuwr4D/Bd4BvE\nndptjPHhfF8iwJbWFmxeYBYRkfSyZw9MngxZWbBgAeyVyOkGItKu+P/IXlZWluoQenoOLCIi0iU7\ndzrt5H78Y/j5z72ORkTclMiPwW/Frkd38rlTcAqz/2pvYDIZYw40xtyAc7heIvaOXQ9q5fOm++HY\n9SWcg1VObmHs8UAA+Etsl4mIiGQQa51Tr6ur4eGHITu7/WdEJGP19BxYREQkYQ0NMG4cjBgB11zj\ndTQi4rZECszP4RRMJxpjCjrygDFmDHBM7I/PJLBml8WS6t/i7CqeiVPYTcQrOF//xcaYPnFrnILz\ndYaAv8ZuL8bZsXKOMea7zcbuDczB+YHjjgRjERERD82eDX/7Gzz1FOTmeh2NiCRZT8+BRUREEtLY\nCGedBYWFMH++2smJdEfGWtu5B4w5EKedwz44OzHGWmvfNcaMB54ArLXW12z8KOAhIB+nD9zXrLW7\nuxS0MYfitJzwA0Fgo7W2vpWxBwC/Bn4K5DXdBiLW2pwE1jY4P2CMAj7B+ZrfBQYCp8WGTW9+Snfs\ne/MosBvne1ELnB77Gh611p7Txnq2s39HIiKSfLfcArfeCqtXw5e+5HU0Ij2PMQZrbcp+RO3pOXAm\nUN4sIpJ+9uyB88932mM88YTe+BNJtVTlzJ0uMAMYYyYDC3F2334MPIhzcMlPY/emAH2Bk4BjcZLZ\nPcB4a+2yhIM15nTgRqAo7qOPgduAa6y10dhYA5QAs3AOZWn6ZoaBe4AbrLWbE4zDB/wMOAensBzA\nKRq/CtxirV3ewjNHxWI5CueHgkpgAfCHtjJhJcoiIunnvvtg1ix45RU45BCvoxHpmVJdYI6tOZke\nnAOnO+XNIiLpxVqYPh3+9S94/nkI6B0akZRL6wIzgDFmOk6im4WTULc6FGgELrHWLkxoMWe9C4E7\nY/O19I2xxHYDx3ZsPAEc12zsJ8BdwFxr7TuJxpFqSpRFRNLL00/DRRfBSy/BwIFeRyPSc3lRYI6t\nqxw4TSlvFhFJL2Vlzq7llSth//29jkakZ0r7AjOAMWYIcA1wKi33c7Y47SR+Y61N+GCTWK/jN/ms\nZ1wY2ITTcuJQ4IBm640DfgMMx0msPwTmA7daaz9MNAavKFEWEUkfq1bBmWfCsmUwbJjX0Yj0bF4V\nmGNrKwdOQ8qbRUTSxx/+4LSUW70aDj7Y62hEeq6MKDB/Ookx++McYFKI8yreJ0A1sMpa+4EL888C\n/gcneX4E+GlTomyM2QvntcR5OAl+LXBgbOx8YLa19uOuxuAVJcoiIunhX/+CMWPggQdg1CivoxER\nLwvMzWJQDtwBxpiLgKnAt3CK368DfwL+2FKia4w5ms+K5bnAWzitSf5grd3TxjrKm0VE0sD998MV\nVzjt5AoLvY5GpGfLqAJzshljKoDvA28Ag5t6zMWN+T1OvzlwEuuzrLWPpS7K5FCiLCLivbfeguOP\nd3ZinHmm19GICKRHgTnZukMObIy5HzgXeA94CmgARuOcY3KvtXZy3PjxwGJgF/AwTuF8HHAYTiuQ\ns9tYS3mziIjHnnkGpkyBigoYNMjraEREBeZmjDHVwJeBUmvtnFbGDAL+jZNYP2+tPTWFISaNEmUR\nEW9t2wbHHecc6nfhhV5HIyJNekiBOaNzYGPMBOAxYDMwrNnu6yzgceA04Exr7ZOx+/vGxu4LHN3U\nXsQYkwOsAL4HnGutfaSV9ZQ3i4h4aPVqmDDBObPke9/zOhoRgdTlzC31jGuTMeZvxpjJxpjcZATU\niqb+cm+2Mab5Z88lMRYREekhamqcthiXXKLiskhPpxw4IT/AKXzf1LwPtLU2AlyN0y7j583G/xDo\nDTzYvHe1tTaM0zLDAJekIG4REemkDRucN/0eeEDFZZGeqNMFZpxeaAuAd4wxtxtjvuNyTC3xx66h\n1gbEEs8m25IbjoiIdHeffAKnneb8+tWvvI5GRNKAcuDO+3LsurWFz7bErsfFdjQDnEBsJ3YL41fh\ntNc42hiT7WqUIiLSJZWVcOqpcOutMHq019GIiBcSKTBHcHYP9AKmAWuNMeuMMRfHXmtLBxGvAxAR\nkcy1e7fzet+gQfC733kdjYikCeXAnbcjdv1aC5/1j12zmv3+0Nj1Czu2Y/2nt8aNFxERj23fDied\nBKWl8MMfeh2NiHglkQJzX+BKoBInyTbAkcAdwHZjzJ+MMXohQkREMlI0Cj/+MfTqBXfeCaZbd3gV\nkU5QDtx5y3C+TyXGmKZ2H009mK9tNq7ps/1i149ama/p/v5uBikiIomprXWKyxddBBdf7HU0IuKl\nrPaHfJ619n3gd8DvjDEjgYuAM4C9gTygGCg2xmwC/gj82Vq706V4BxljOjJXh8ZZa1e5EJOIiHQT\n1jr9lj/8EJYtg6xO/1tSRLor5cAJeQj4MTAG+D9jzBKcdh+jcNpnBIGvAntSFI+IiLikvt5pJXfy\nyXDFFV5HIyJeM26ctBzbkXA+MBUYFLvdNPFuYDFwt7X2lQTn39NsPrdYa23alw50GraISOpceSUs\nX+782jddXngXkRal6kTsdmJQDtwOY4wPKMEpNH8dp8C8Amc3+GPAQKC/tfa/xpg1wHeBIc0P+Ws2\n12ux8QOttf9p4XNbWlr66Z9HjhzJyJEjXf+aRER6unAYxo2DPn1g4UK98SeSTlauXMnKlSs//XNZ\nWVlKcmZXCsyfm9CY4cDFwFk4uzngs8T4TeBu4F5r7Y4WHm9tzqbk2s1viLXW+lycLylUYBYRSY3f\n/95JkFetgt69vY5GRNqTDgXm5pQDd44xZm+clhcfWWsPjt27DzgPOM9a+3DceF9sfDawj7W2sYU5\nlTeLiCRZNArnnecUmR99VG/8iaS7VOXMrheYP53YmH2Ac3FeHxzS7CMLNAKPA3+w1v6tA3OtxP3d\nG1hrT3B7TrcpURYRSb6FC+Haa2H1aujb1+toRKQj0q3A3EQ5cMcYYyYDC4GbrbUzYveKgQXAPdba\n4rjx3wcqgJXW2u+3MqfyZhGRJGpqJ/fmm/DMM+D3ex2RiLSnOxSYAzjJ9cXAUL64+6Jp4ReBS6y1\nW5MSSIZToiwiklxPPAE/+xmsXAnf+IbX0YhIR6VxgVk5cDPGmH2ttR/H3TsC5+sHGGxOwtzSAAAg\nAElEQVStfbdpLLAZ2Bc41lr7j9j9vXHaagwHzrHWPtrKWsqbRUSS6De/geeeg5decg7EFpH0l7EF\nZmPMUJwdG2cD+zTdxjm843mcnQdnAMfEPrNADTDMWlvlajDdgBJlEZHkeeklOOccJ1H+zne8jkZE\nOiPdCszKgVtmjPk7sIv/Z+/Ow6uqrj6Of7chGIKiRu1AaIwY20qRaoulVkVUEGeqUhyqYsCKtg4E\nq7bSGmMtr0MFtK3WAYhjFXFAoQqCMtVWoa0DSFsBYyRYh0RFM3iTy3r/ODcaY4Z7b8695w6/z/Pk\nOck5Z++zCI9xsbLP2rAW+AjYBzgWqAeON7NV7e4fAzyE18P6AaAOOAH4OvCQmZ3axbOUN4uIJMiM\nGXDbbbByJey+e9DRiEi00qrA7JzbCTgTOAfYt/V05Pg/vNff7jCzN9qMGQxcDfwQL8G+18zG9ziY\nDKNEWUQkMVav9na+fughOPTQoKMRkVilQoFZOXD3nHOXAKcCewF9gBrgL8C1ZralkzEHAlOBA4E8\nYANe64zfd5UYK28WEUmMu+6CK6/0istFRUFHIyKxSIsCs3NuOF5CfTJe8gdeUm3AM8BtwGNm1tLF\nHH/GW+lRbWbFcQeToZQoi4j4b/16OOwwuP12OOGEoKMRkXgEWWBWDpyalDeLiPhv/nw47zx49ln4\n5jeDjkZEYpWsnDnm/T6dc7sD4/GS6r1bT0eOtUAlcJuZbYhyykfwkuvCWGMRERGJVXU1jB4N11+v\n4rKIRE85sIiIZJtly+Ccc+DJJ1VcFpGuxVxgBja3GdeaVK/EW6kxz8xCMc63NXLcLo5YREREovbu\nu3DkkTBlCpx1VtDRiEiaUQ4sIiJZ4x//gHHj4MEHYejQoKMRkVQXT4E5N3L8ALgH+JOZre9BDBuA\nih6MFxER6dbWrXDUUTB2LEyeHHQ0IpKGlAOLiEhW+M9/4LjjvHZyhx8edDQikg5i7sHsnPsb3kqN\nB8ysKSFRyafUS05EpOeamuDoo71X+265BVyg24KJiB+S3YNZOXDqU94sItJzb74JhxwC5eVQWhp0\nNCLSU2mxyZ8knhJlEZGeaWnxVi3n5cF990FOTtARiYgfgtzkT1KT8mYRkZ557z2vuHzOOXDJJUFH\nIyJ+SNlN/kRERNLFtm3wk594K5jnzlVxWURERESkIx995L3x98MfqrgsIrHztcDsnNsfuBAwM5vo\n59wiIiKxMINLL/V6yD39NPTuHXREIpKplAOLiEg6a2ryCsvf+Q5MmxZ0NCKSjnxtkeGcGwM8ipdc\na52YD/Sqn4hIfKZNgz//GZYvh4KCoKMREb+lUosM5cCpQXmziEjsWlpg3Djo1cvLnfXGn0hmUYsM\nERGRON12G9x5J6xapeKyiIiIiEhHzOC88+Djj+GJJ1RcFpH4qcAsIiIZZe5cuPpqWLEC+vcPOhoR\nERERkdR0+eWwdi0sWQLbbx90NCKSzjKmwOyc2w7YC9gFyItmjJmtSGhQIiKSVIsWwYUXej2X99or\n6GhERBJPObCIiMTjuutg4UJvUcYOOwQdjYiku7QvMDvnRgA/Bw4HYvmdm5EBf34REfH87W9wxhnw\n2GMwZEjQ0YiIJJZyYBERidcdd8Cf/uS1k9t116CjEZFMkNbJpXOuHLiy9csgYxERkeCsXevtfH33\n3XDQQUFHIyKSWMqBRUQkXg8/DOXl3kbYhYVBRyMimSJtC8zOudFAeZtT1cByoAb4JJCgREQk6V5/\nHY46CmbOhKOPDjoaEZHEUg4sIiLxWrIEzj/fayu3995BRyMimcTvAvMq4DCf5+zMBZHjNrzXA28y\nM0vSs0VEJAX8738wahT88pdw2mlBRyMiWUw5sIiIpLTnn4fTT/dWMO+/f9DRiEimcemajzrn3gZ2\nAx4wsx8HHU+iOOf0bwYRkQ588AGMGAEnnQRXXtnt7SKSYZxzmFnWtYfIlhw4HsqbRUQ69uqrcPjh\ncOedcNxxQUcjIsmUrJx5u0Q/IIF2ihyfCjQKERFJuoYGLzk+9FD49a+DjkZEJKmUA4uISNTeeMNr\nJ/e736m4LCKJk5AezM65kcAxgJnZJYl4BvA/4GtAKEHzi4hICmpuhh/9CPbcE2bMAJd16xdFJNU4\n574B7IVX/P0Q+I+ZbUzQ45QDi4hIVN55x2sn9/OfwxlnBB2NiGSyRK1gHgZMjnwkyl8jx28l8Bki\nIpJCtm2Ds8+G7baD2bO9o4hIEJxzvZxzlznnNgOvAk8A90aO/3XOveacOzcBj1YOLCIi3frwQxg9\n2tun5KKLgo5GRDJdOv/T/PeAAeOdc32CDkZERBLLDC6+GN58E+bOhdzcoCMSkUzlnDvKOVfnnHvP\nOVfYwfVd8Qq9/wd8FXAdfOwF3OqcW+icy/MxPOXAIiLSpcZGOOEEOOgguOqqoKMRkWzQ5SZ/zrl4\nt00aDhyOl/xWtL9oZlfHOe/nOOd+BVwNLAJONbMP/Zg3lWizEhERz1VXwfz5sGwZ7LRTd3eLSKZL\n5IYlzrkbgTLgFTP7dgfXlwOHtDm1BfgnXnuMnYD9gdbCtAFzzew0H+PL+Bw4HsqbRUS8dnInnww7\n7AD33qs3/kSyXbI2+euuwLwNLymOa+7I8QvjzSwnzjk/m9y54ZFPJwGnAe8BdwN/j3y+rbs5zGxF\nT+NINCXKIiLw+9/DzTfDqlXw5S8HHY2IpIIEF5j/DhwA/M7MLm937UfAg3g57hbgPDNb2MEcxwG3\nAAMi9x5hZst8iC0rcuB4KG8WkWzX2k7uvfe8hRl6409EUq3A7Gcg5lOBuX3x2xFbMdzMLCGbHPpJ\nibKIZLv77oNf/AJWroTi4qCjEZFUkeAC85tAf+AcM5vT7tqjwBjgY2A/M9vUxTx7Af8C+gL3mdlZ\nPsSWFTlwPJQ3i0g2M4OyMlizBhYvhvz8oCMSkVSQrAJztMnlx8DtwEdR3j8cOAwv2fWlHUYn2n+D\nEv4NExGR5Fm4EC65BJYuVXFZRJJq98ixo9YTB+DluHd2VVwGMLONzrlZwMXAQT7GpxxYREQ+57e/\nhWefheXLVVwWkeTrrsB8JzARb9XFOOAiM3usu0mdc1PxCsyY2Rd6MPskUfOKiEgKWLnSe8VvwQL4\n1reCjkZEssxWYFegXwfXdo0cV0c51wuR41d6GlSEcmAREfmcW2+Fykovf95556CjEZFs1GWB2czO\ndc7dD9yBtxP2w865+cAFZrYlGQF2EZuSaxGRDPXii97mJPffD8OGBR2NiGShzXiF5MEdXPsA+BIQ\n7bZJrfc1+xCXcmAREfmcP//ZW728YgV89atBRyMi2arbxDiyGcm+wA14m4aMAdY75y5wzul1PBER\n8dWGDXDMMXDLLTBqVNDRiEiWehqv7cTJzrn2CzL+FTl+P8q5DowcN/sRmIiISKsnn4TJk73jwIFB\nRyMi2SyqlRdm1hTZQXsY8BKwI3AT8Dfn3JAExiciIlmkpgaOPBIqKmDs2KCjEZEsdi/ewooi4Mp2\n1+7EKz5PcM7t09UkzrlBwAS8ns2LExCniIhkqeeeg7POgsceg333DToaEcl20b7aB4CZ/RNvY5Mr\ngE+A7wFrnHPXO+f6JCC+uDjnejnndulgxYmIiKSoujoYPRrOPRd+8pOgoxGRbGZmr/BZIXmqc+5G\n51x+5NojwKNAH2CZc+5U59zncmrn3HbOudOAZ4E8vPYYtyc6buXAIiLZ4eWX4cQT4d574cADu79f\nRCTRnJnFN9C5vfES70PwVmVUAT8zs6cim/z9BjAzy/Ep1q5i6Q2cAZyEt8q6oM3lWrzNVeYB95tZ\nKNHx+Mk5Z/H+HYmIpIv6ehg5Eg4+GK6/HtSASUS645zDzBL20yKyeOJJYDhervsR8BheXvkuXq77\n9ci1D4GXI8edgCGRY2t8V5jZtQmIMWNz4HgobxaRbLBxIwwfDtOnwymnBB1N8rW0tFBVVUVTUxN5\neXkUFxfTq5d+ryrSmUTnzJ8+p6dJmHNuEnAd3i7bBjyEl3T/jCQUmJ1zw4G7ga+1nurgttY/ZDVw\nlpmtTGRMflKiLCKZ7pNP4IQTYMAAuPNOFZdFJDrJSJadc32B24DTI6c6SspcB9daz7UAU83shgTE\nltE5cDyUN4tIpnvrLW9BxqWXwnnnBR1N8tTW1jJ7diVz5sxj48ZXyM3dnZycvoTD9YRC71BSMoTS\n0rFMnFhKQUFB9xOKZJG0KTADOOf6A7cCx/NZIutIcIHZOTcSWADk8llS/TGwMXLcAdgrcmwVAo41\ns6WJistPSpRFJJOFw3D66dDcDHPnghYfiEi0kpUsR551FHApMIKOC7nt1QOPAxVm9t8ExJPxOXA8\nlDeLSCZ7/31v5fKpp8LUqUFHkxyhUIiKimlMn34Tzh1PY+N4vK6t/drctRVYTZ8+d2H2BFOmXEx5\n+RX07t07mKBFUkxaFZg/ncy5ccDNwJcipxJWYHbO9cNLoneNnFoIXAs81z6zdM4dBFwOHBc59R5Q\nYmZbExGbn5Qoi0imMoPzz4fXXoOFCyEvL+iIRCSdJLPA3OaZRXjt4YYCXwF2xtvT5CPgA+A/eK0y\nVphZY4JiyIocOB7Km0UkU9XXexthDxsGN96YHW/8VVdXM3LkGGpq+tPQcDtQGMWozeTnT6KwcAtL\nlsynqKgo0WGKpLy0LDADOOd2wVvJ7ADM7C5fH/DZc34BTMNbMX2lmf02ijFXANdExvzSzK5PRGx+\nUqIsIplq6lRYvBieeQZ23DHoaEQk3QRRYE4F2ZIDx0N5s4hkolAIxoyBL38ZZs+G7bbrfky6q66u\nZujQ4dTVXUQ4XEZ0Lw+1MnJyZlBQcDNr1qxQkVmyXtoWmJPFObcSOAhYZWbDYxi3AjgY+KuZHZKo\n+PyiRFlEMtH06XDHHbByJey2W9DRJJ42IxHxXxYXmLMiB46H8mYRyTThMJxxBjQ0wMMPZ0c7uVAo\nxODBw9i06UzC4Slxz5OTM52BA+9h3boXyM3N9TFCkfSSrJw5nX/31bpr99wYx7Xe/w1/wxERkWhU\nVsLNN3urlzO5uFxbW8sNN9zIoEEH0rfvzuy33ygOOuhU9ttvFPn5OzFo0IHccMON1NXVBR2qiKQX\n5cAiIlnADC680NvY78EHs6O4DFBRMY2amsLIyuX4hcNl1NT0p6Jimk+RiUhX0nkFcxPexianmNm8\nGMb9CHgQCJlZynf81EoMEckk8+d7fZeffRa+kaElDm1GIpIcWbyCOSty4HgobxaRTHLlld4+Jc8+\nC/36dX9/JqitrWXAgBKamtYSXc/l7mwmL29famo2UlBQ4MN8IulHK5i7937kGGtDnQGR4wc+xiIi\nIt1Ytgx+8hN44onMLS5XV1czePAwZs5cTVPTWhob7waO4PPFZSJfH0Fj4900Nb3CzJmrGTx4GNXV\n1ckPWkTSjXJgEZEMd9NN3qrlJ5/MnuIywOzZlTh3PP4UlwEG4NxxzJpV6dN8ItKZdC4wr8fr9H56\ntAOccw74Md5rha8mKC4REWnnH/+AceNg7lz47neDjiYxWjcj2bTpTBoaFhB9YjyAhoYFbNp0JkOH\nDleRWUS6oxxYRCSD3X033Hij107uS18KOprkmjNnXuTtP/80Np5NZWXUL/yISJzSucD8ROS4v3Pu\nxijHXAd8J/L5fP9DEhGR9v7zHzj+eG9TvxEjgo4mMUKhECNHjonsdD2F2Ha6BnCEw1Ooq7uIkSPH\n0NzcnIgwRSQzKAcWEclQjz8Ol10GixbBHnsEHU1ytbS0sHHjK8D3fJ75ADZseJmWlhaf5xWRttK5\nwPwn4K3I55Odc6ucc2Occ33b3uScy3fOnRDZOfuSyOm3gNuTGKuISFZ6800YPRqmTYMxY4KOJnG0\nGYmIJJFyYBGRDLR8OZxzjtdObp99go4m+aqqqsjN3R3Y0eeZ+5GbuxtVVVU+zysibaXtJn8Azrnv\nA0uBthuVGPA/oB7oC3yFz5aSOaARONzMnk9iqHHTZiUikq7eew8OOQTOPRfKelZ3TWnajEQkGNm6\nyR9kRw4cD+XNIpKu/vUvb1HGn/8MRxwRdDTBWLt2LQcddCpbt671fe5+/b7FX//6IIMHD/Z9bpFU\np03+omBmfwcO5rNedA7vz9QfKIkct2tzbR3wg0xOrEVEUsFHH8HRR8NJJ2V2cRm0GYmIJJ9yYBGR\nzPHf/8Kxx8Kf/pS9xWWAvLw8wuH6hMwdDteTl5fX/Y0iEre0XsHcKrJxyXHASXgNe76K917FR3iv\nAr4APAwsTLdlDVqJISLppqnJS5L33htuvRVchq8vHDToQNavvwbw818ESxk06NesW/ecj3OKZJZs\nXsHcKpNz4HgobxaRdFNV1cLBBxvnnvsOp5/eSHFxMb169Qo6rEC0tLTQt+/OhEJv4W+bjK307t2f\n+voPsvZ7K9ktWTlzRhSYM5kSZRFJJy0tMG4c9O4N990HOTlBR5RYSoRFgqMCs7SnvFlE0kFtbS2z\nZ1dyxx2Lee21m8jNfZQ+fe4jHK4nFHqHkpIhlJaOZeLE0qxrl6aFGyL+U4sMERFJK2YwaRLU18Pd\nd2d+cRm0GYmIiIiIRCcUCjF16lUMGFDClVf+h9demwvsSXPzL9m6dS319a/T3PwW69dfQ3n5SxQW\n7sXUqVcRCoWCDj1pSkvH0qfPXb7O2adPJaWlY32dU0S+SAVmERHpMTO47DJ49VV45BFvBXM2aGpq\nIienb0LmzsnpS1NTU0LmFhEREZHkqa6uZvDgYcycuZqmprU0Nd0O7ARs3+7OfsARNDbeTVPTK8yc\nuZrBg4dRXV2d/KADMHFiKWZPAJt9mnEzZguYMOFsn+YTkc50+t6tc25Tgp5pZrZXguYWEZEAXHcd\nPPUULF8OfRNTb01J2oxEJPMoBxYRET9VV1czdOhw6uouIhwuw9t7NRoDaGhYwKZNMxg6dDhr1qyg\nqKgokaEGrqCggClTLmbmzEk0NCwg+u9VR4z8/HMpK5ucda1GRILQaQ9m59w2wIjuv+i2k7gOvv7c\nvWYW9YvTzrkr2wy8uqPz8Wo7XwzxjAfmdHNb2MxyI/fvAbzexb0PmNnpXTxPveREJKXdcQf83//B\nqlXQv3/Q0SSXejCLBCdR/eSUA6cv5c0ikmpCoRCDBw9j06YzCYenxD1PTs50Bg68h3XrXiA3N9fH\nCFOPvmci/gp8kz/nXBWfT5Lby8XbqbptkB8A9UBfYOc25w1vJ+tmADPbM+oAP0vyaZuUtz0fr1iS\n/DbP/TYwppPLw4HDgAVmNiZyf2uB+UXgsQ7GrDWzR7p4nhJlEUlZ8+bBxRd7K5dLSoKOJhjajEQk\nGAksMFehHDgtKW8WkVQzdepVzJy5hoaGJ+j5atzjKCv7HtdcU+5XeCkr/lXfAEZOzgwKCm7OilXf\nIt0JvMDc5SDnioC5wPeAF4AbgaVmVtfmngJgJDAZ+D7wPPAjM4upmU4kiYZ2qz7anI9XTKtIouGc\new4YBpxgZgsj51oLzJVmNiGOOZUoi0hKevppOOMMWLwYvv3toKMJzg033Eh5+Us0Nt7t25x9+pzJ\n1Vfvz89/Hv+qDZFMl6xkud0zlQOnMOXNIpJKamtrGTCghKamtUChDzNuJi9vX2pqNmZFy4fq6mpG\njhxDTU1/GhpuAwZEMWoz+fmTKCzcwpIl81VcFiGFC8zOuT54ifK3gOlmdmkUY64Hfg68Agwzs6h3\nLXLOHdr6uZkt7+h8vNrO11POucHAy3jd6PdozW5VYBaRTPT883D88d6GfgcfHHQ0waqrq6OwcC+a\nml4husS3O9n1jweReCW7wKwcOPUpbxaRVJKoRQgVFftz6aXZsQghFApRUTGN6dNvwrnjaGw8GzgA\nbzPEVluB1eTnV7Jt2wIuuWQy5eVXqC2GSEQqF5gvAW4AnjezA2MY93e8nwSXmtn0mB6aBpxzvwd+\nClS065PXWmBeDDwC7ArUAn8zs1eimFeJsoiklHXr4PDDYfZsOPbYoKNJDd7rj6t92ozkWMrKhmXF\n648iPRFAgVk5cIpT3iwiqcRro3YbMMTHWbOzjVpdXR2zZlVSWTmPDRteJjd3N3Jy+hIO19Pc/B4l\nJUMoLR3LhAlna4GGSDupXGB+AfguUGZmN8cw7kLgJmCNmX0vpoemOOdcHrAF2AHY08xq2lxrLTC3\n/0Y7YBkw3sze7GJuJcoikjKqquCQQ+Daa+HHPw46mtShzUhEki+AArNy4BSnvFlEUkVLSwt9+vyQ\nlpbHge18nFkbQbe0tFBVVUVTUxN5eXkUFxdn7fdCJBrJypnj+a9wr8jxrRjH/a/d+B6J9MADeCfG\n1w23B74MYGbVfsQCnIK3ocsTbYvLEQ3A1Xgb/G2KnBsCXAUcDixxzu1nZo0+xSIikhBvvw2jRsFl\nl6m43F7v3r1ZsmR+ZDMSerQZyZIlK1RcFklNyoFFRCQqjz/+Fi0tlfhbXAboR27ublRVVVGSpTts\n9+rVK2v/7CKpLJ6fdttHjl+LcVzr/dt3eVf0qvAKtkfGOG5Em7F+ORdvhfJt7S+Y2btmdpWZvWhm\nWyMfq4DReH38SoBzfIxFRMR3H34IRx3lFZYvvDDoaFJTUVERa9asYODAe8jPPw6vJX80NpOffxwD\nB96jna5FUptyYBER6db69XDuuV+hT5/EtDvLyelLU1PUv18UEUmKeFYwvwHsA5zlnJtpZt3uZO2c\n2w44q814v/Rkibcvy8Odc4OAA4E3gSejHWdmYefcncAwYDjw+87uveqqqz79fMSIEYwYMSLOaEVE\nYtfQ4G3od/DBUK62wF0qKipi7drnI5uR7Bv1ZiRlZdqMRKQ7y5YtY9myZUGGoBxYRES69MYbMHo0\nXH55HRUVf0nIM8LhevLy8hIyt4hIvOLpwfx/wOV4K3ZnA+ebWUsX9+cAtwA/iYy51symxh3xZ/Nu\ni8x3opk9HsO40XiFYDOzHB/iuAm4ALjKzH4T49gT8FpnPGVmx3Ryj3rJiUhgmpvhxBNh553h7rth\nO7/f8stg2oxEJLEC6MGsHDjFKW8WkSC98463V8n558MFF7TQt+/OhEJvATv6+BT1YBaR2KTyJn+7\nAuuBXSOn/gvcDDwDbIiszM3Ba/1wOF7x9Zt4qyXeBfYxs7oeBx5/cn0K8Geg3sx69JM+0stuC97/\nMT63uV+U41v/oXKLmV3QyT1KlEUkENu2wfjx8P778OijoMW18dNmJCL+C6DArBw4xSlvFpGgbN0K\nhx0GxxwDv4ksOxs06EDWr78GOMLHJy1l0KBfs27dcz7OKSKZLGU3+TOzWufcGLwVEP2ArwN/aL3u\nnGvpYF6H9z7wGD8S6x46KXJ804e5xgG7AI93Vlx2zu0PvNg+23XOHQFMxvsHwr0+xCIi4hszKCuD\nqipYtEjF5Z7SZiQi6U85sIiIdKSpCU44Ab7/fbj66s/Ol5aOpbz8Lhob/Ssw9+lTSWnpWN/mExHx\nS8wrmD8d6FwJ8Ce8FRrdWYL3GuHGOJ81BhjT7vTZeMXZZ4HudsJ2QF9gPz7bwftOM5sUTzxt4loJ\n/AA43sw6bLDknHsW2Bt4js92fBqC930z4Fdm9n9dPEMrMUQk6a6+Gh55BJYt89pjiIikmmSvYG7z\n3KzPgVOV8mYRSbaWFjj5ZMjPh/vu+3w7ubq6OgoL96Kp6RVggA9P20xe3r7U1GxUezURiVrKtsj4\nwgTOfRs4EW8Ho/7ADsDHeK0jXgAeNbOXe/iMcqAcL5n+9HTkGOsfwAENwHfN7D89iOmbwKt4if2e\nnWWzzrlSvO/PYGA3IBd4G6/g/Ecz+2s3z1GiLCJJ9Yc/wE03wcqV8JWvBB2NiEjHgiowt3l+VubA\nqUx5s4gk07ZtMGECvP02zJ8PvXt/8Z6pU69i5szVNDQsoGd7rBr5+cdSVjaMa67RrtsiEr20KTAn\nQ5vkuqc+xOuTd7WZveTDfAmnRFlEkun+++Gyy7zi8p57Bh2NiEjngi4wJ0M258DxUN4sIsliBpdc\nAs8/D4sXQ9++Hd8XCoUYPHgYmzadSTg8Je7n5eRMZ+DAe1i37gVy1btORGKgAnMbzrmdgLYvaTtg\nE97KjUnA091MsQ342MzeT0yEiaNEWUSS5S9/gdJSWLoUBg8OOhoRka5lSYE5a3PgeChvFpFkmTYN\nHngAli+HXXbp+t7q6mqGDh1OXd1FhMNlxLaS2cjJmUFBwc2sWbOCoqKinoQtIlkoZTf5C4KZfYi3\n8uJTzjnwfjK/Y2ZvBBGXiEimWLUKxo+Hxx9XcVlEJFUoBxYRST1/+hPMmuXlz90VlwGKiopYs2YF\nI0eOoaZmKQ0NtxFdT+bN5OdPorBwC0uWqLgsIqltu+5v6ZxzLsc5d6pzbrZz7m/OuX87576wiYlz\nbrBz7gfOuW/15Hnt7Bn56G7lhoiIdOGll7zNSe67Dw48MOhoRERSn3JgEZHs9OCD8JvfeG0xvvrV\n6McVFRWxdu3zTJ58AHl5+9Knz5nAUmBruzu3AkvJzz+TvLx9KSv7HuvWvaDisoikvLhbZDjnRgB3\n8flfvTnAzCyn3b2/Bq4CPgK+amaNcT00C+lVPxFJpI0bYfhwmDEDxo0LOhoRkegF1SJDOXDqUt4s\nIom0aBGcdRY8/TQMGRL/PHV1dcyaVUll5Tw2bHiZ3NzdyMnpSzhcT3Pze5SUDKG0dCwTJpxNQUGB\nf38AEclKKd2D2Tl3PPAI3gpoB7Tg7Zq9Mx0n11/C21HbAaeY2bwexp01lCiLSKJs2QKHHOJt6jdp\nUtDRiIjEJogCs3Lg1Ka8WUQS5W9/gxNOgMceg4MO8m/elpYWqqqqaGpqIi8vj+LiYnr1SotOpiKS\nJlK2wOyc2w3YCOyI1xNuCvAgMAp4lA6S68i45cDBwB1mdl4P4+4orhxgCN5qkn7AF2Joz8zu9jsO\nvylRFpFEqKuDQw+F006DK64IOhoRkdglu8CsHDj1KW8WkURYuxaOOALmzIFjjtMxqwYAACAASURB\nVAk6GhGR2KTyJn8X4iXWIWCUma2BTzcc6crfgEOA/eN4Zqecc0VAOXAK0CeGoQZkZHItItKV+no4\n7jg48kj45S+DjkZEJG0oBxYRyTKbNsFRR8HMmSoui4h0JZ4C89F4ienc1sQ6Sv+NHAfG8cwOOecO\nAp4AdsJ79VBERLoQCnkb+n3jG/C730H3dREREYlQDiwikkX+9z9vQcYVV3hv/WUqtekQET/E81Nj\nr8jx2RjHfRg59ovjmV/gnOuH1wNvZ2AbcA/wHPAnvOT/D8B/gD2A0XivDhpwH952rSIiWSUc9jYm\nycuDO+5QcVlEJEbKgUVEssQHH8Do0TB+PPz0p0FH47/a2lpmz65kzpx5bNz4Crm5u3+60WAo9M6n\nGw1OnFiqjQZFJCrbxTGmb+S4NcZxra/uNcXxzI6cB+yOlzCfYWZnm9ntba4vNbNbzOxyM9sPOBF4\nHzgVwMzu8ikOEZGUZwYXXABvvw0PPABalCAiEjPlwCIiWaChwWsnd9hh8KtfBR2Nv0KhEFOnXsWA\nASWUl7/E+vXXEAptob7+dbZuXUt9/es0N7/F+vXXUF7+EoWFezF16lWEQqGgQxeRFBdPgbk2cvxy\njOP2jhzfjeOZHTk6cvyHmT3Q3c1mNh84Fu81wlucc9/0KQ4RkZT361/D6tUwf763gllERGKmHFhE\nJMM1N8OPfgR77gnTp2fWG3/V1dUMHjyMmTNX09S0lsbGu4Ej+OILNv2AI2hsvJumpleYOXM1gwcP\no7q6OvlBi0jaiKfAvC5yHBHjuBPwVlqsjuOZHRkUme+xTq5/YQdtM3semIu3ksT3XbxFRFLRjBkw\nbx48+ST08+UFbRGRrKQcWEQkg23bBmefDTk5MHs2bBdPtSRFVVdXM3TocDZtOpOGhgVAYZQjB9DQ\nsIBNm85k6NDhKjKLSKfi+ZG5EG8FxAnOuUHRDHDOnQl8O/Ll43E8syM7R45vtjvfHDn2pWOtveeO\n9CkOEZGUdffdXoF58WLYffegoxERSWvKgUVEMpQZXHwxbN4MDz4IublBR+SfUCjEyJFjqKu7iHB4\nCrHvDesIh6dQV3cRI0eOobm5ufshIpJ14ikw3wm8A+QCC51z+3Z1s3PuHOA2vJUWm4AH43hmR1qb\nALXvZ/dR5NjZr+Qau7kuIpIRHn8cLrsMFi2CoqKgoxERSXvKgUVEMlRFBaxa5eXPffp0f386qaiY\nRk1NIeFwWY/mCYfLqKnpT0XFNJ8iE5FMEnOB2czqgVK8XauLgH84554Eftx6j3Ou3Dl3h3NuE15i\nnYeXDP/YzLb5EjnURI67tju/KXI8oJNx34gctcWViGSsZcvgnHNgwQLYZ5+goxERSX/KgUVEMtPN\nN8P998NTT8FOOwUdjb9qa2uZPv0mGhpuI/aVy+05Ghpu48YbZ1JXV+dHeCKSQeLqKmRmT+LtRP0R\nXpJ6JHAy3goNgCuBCcAeeD/FPgDGmNkLPQ24jZcjx/alk79HnnmMc26Pthecczvj9Z0z4HUfYxER\nSRn//CeMGwcPPABDhwYdjYhI5lAOLCKSWe67D264wWsn9+VYt3BNA7NnV+Lc8fj38soAnDuOWbMq\nfZpPRDJF3G3rzexhYDDwB+B9vIS2/cdHwK3Avma2uMfRft7yyDNGtDt/b+S4PbDCOXe+c+5I59z5\nwD+AL0Wud7YxiohI2vrvf+HYY+G22+Dww4OORkQk8ygHFhHJDAsXwiWXeO3kiouDjiYx5syZR2Pj\neF/nbGw8m8rKeb7OKSLpz5lZ93dFM5Fz3wKKgZ2Aj/Fe3/uXj68Dtn/e14A38FZiDDGzdW2u3Q2c\nwWerST43FKgG9jez9xMRm5+cc+bX35GIZLY334RDDoErr4QJE4KORkQksZxzmFlP3/f1Iw7lwClC\nebOIRGvlSjj5ZHjiCRg2LOhoEqOlpYW+fXcmFHoL2NHHmbfSu3d/6us/oFcvdV0SSXXJypl9+2kQ\nSW7XdXujf8970zl3GF5vu63tLk8EPsF7RbH9N/EfwKmZmliLSHZ67z048ki44AIVl0VEkkk5sIhI\nennxRa+4fN99mVtcBqiqqiI3d3dCIT+LywD9yM3djaqqKkpKSnyeW0TSVcwFZufc8Mina80s6s7u\nkd5vQwDMbEWsz+2ImS3v5Hwz8BPn3DXA4cCXgQZgtZn9zY9ni4ikio8+gqOPhh/+EH7+86CjERHJ\nTMqBRUTS32uvwTHHwC23wKhRQUeTWE1NTeTk9E3I3Dk5fWlqakrI3CKSnuJZwbwM77W7E4HHYxg3\nDHgSb+ftpLxHYWZvAHOS8SwRkSB88olXWN5/f5g2LehoREQy2jKUA4uIpK2aGhg9GioqYOzYoKNJ\nvLy8PMLh+oTMHQ7Xk5eXl5C5RSQ9xb3JXw8E3itPRCQTtLTA6adDQQHceis4/XQVEUll+iktIhKQ\nujqvuDxpEvzkJ0FHkxzFxcU0N7+Lt++sn7bS3PwexZm6M6KIxCWZBebWpFo7b4iI9JAZnHee1x7j\n3nshJyfoiEREpBPKgUVEAvTxx15bjGOOgcsvDzqa5OnVqxd77bUv8ILPM6+mpGSINvgTkc9JZoF5\nt8gxMe9oiIhkkcsvh7Vr4ZFHYPvtg45GRES6oBxYRCQgn3wCJ50E3/oWXHdd0NEkX2npWPr0ucvX\nOfv0qaS0NAt6jIhITJJSYHbObQ+Mj3xZ5dOc4R5+tPgRh4hIsl1/PSxc6H3ssEPQ0YiISGeUA4uI\nBCcchjPP9PLl227LznZyEyeWYvYEsNmnGTdjtoAJE872aT4RyRRdvtPgnBvPZ0lxe9c45yZ3M78D\n+gLfjBwNWBprkF3MLSKSVe680+u3vGoV7Lpr0NGIiGQm5cAiIunNDH76U6it9RZlZGs3h4KCAqZM\nuZiZMyfR0LCAnv0vxMjPP5eysskUFBT4FaKIZAhn1nk7OOdcOVDO53vGxdNHrnXMO8B3zGxLLEF2\nEtuyKGLYDu+1xK8DOZH7/0Wky72ZHdbTOBLNOWdd/R2JSPZ4+GG48EJYvhz23jvoaEREguWcw8wS\nUmxVDpyelDeLSKsrroCnn4ZnnoEddww6mmCFQiEGDx7Gpk1nEg5PiXuenJzpDBx4D+vWvUBubq6P\nEYpIIiUyZ24r2t/jdRRINMEZ8DHwOt6qjRv9SKwBzGxEtPc653YGfgb8CsgHTjGzjX7EISKSDEuW\nwPnnw6JFKi6LiCSRcmARkTRz443w6KOwcqWKywC9e/dmyZL5DB06nLo6CIfLiG0ls5GTM4OCgptZ\nsmSFissi0qEuVzB3OMC5bXhJ84lm9nhCokoQ59wxwBPAv4EDzKwh4JC6pZUYIvLCC3Dssd4K5uHD\ng45GRCQ1JGs1RpvnKQdOccqbRWTOHKio8IrLX/ta0NGklurqakaOHENNTX8aGm4DBkQxajP5+ZMo\nLNzCkiXzKSoqSnSYIuKzZOXM8W7yl5a938zsL8DjeP3wfhZwOCIi3Xr1VTjhBJg9W8VlEZEUoBxY\nRCRFPfaY1xpj0SIVlztSVFTE2rXPM3nyAeTl7UufPmfivWSztd2dW4Gl5OefSV7evpSVfY91615Q\ncVlEuhTzCuZ055w7F/gT8E8zGxp0PN3RSgyR7PXGG3DIITBtGpxxRtDRiIiklmSvYE536ZYDx0N5\ns0j2WrYMxo2DJ5+E73436GhSX11dHbNmVVJZOY8NG14mN3c3cnL6Eg7X09z8HiUlQygtHcuECWdr\nQz+RNJesnDkbC8xjgbnAh2a2S9DxdEeJskh2evttr7h8wQVw0UVBRyMiknpUYI5NuuXA8VDeLJKd\n/vEPOPpomDsXRowIOpr009LSQlVVFU1NTeTl5VFcXEyvXtFu1yUiqS7VNvn7lHMuBzgd7xXBNWb2\nahRjBgFDgW1mdm/MUfprYOSozvQikpI+/NBLkk87TcVlEZFUoRxYRCT1/PvfcNxxcMcdKi7Hq1ev\nXpSUlAQdhoikuXh+LXUscBewDa+PWzQ+AWYDzjn3rpktiuO5PRbZSfs8vA1aXg8iBhGRrjQ2ej2X\nf/ADuOqqoKMREZE2lAOLiKSQ6moYPRquvRbGjAk6GhGR7BbPJn+tP7qfM7MN0Qwws43AKrwVHyfF\n8cy4Oed6OeeKnHNnA88DxZFLjyUzDhGR7jQ3wymnQGEh3HwzuBR+8bulpYUNGzawdu1aNmzYQEtL\nS9AhiYgkmnJgEZEU8e67cOSRMHkyjB8fdDQiIhLPCubv4a1+eCbGcc8Aw4FhcTzzC5xz4R4MfwP4\nnR9xiIj4Yds2mDgRWlrgrrtgu3h+/ZdgtbW1zJ5dyZw589i48RVyc3f/dDOQUOidTzcDmTixVJuB\niEgmUg4sIpICPvrIayd38slQVhZ0NCIiAvGtYN4jcnwtxnEbI8fiOJ7ZERfnx0rgcDP70Kc4RER6\nxAymTIFNm2DePMhNse6YoVCIqVOvYsCAEsrLX2L9+msIhbZQX/86W7eupb7+dZqb32L9+msoL3+J\nwsK9mDr1KkKhUNChi4j4STmwiEjAmpq8dhhDh8I11wQdjYiItIpnBfP2keMnMY5rrTTkx/HMjqzA\nW0XSnU+AD4BXgafM7AWfni8i4ovf/haeeQaWL4d8v35C+qS6upqRI8dQU9Ofpqa1QGEnd/YDjqCx\n8QhgMzNnTuKhh4axZMl8ioqKkhewiEjiKAcWEQlQS4u3CfaXvgR//GNqt5MTEck28RSY64AvAf1j\nHNd6vy+rJsxshB/ziIgE6dZbobISVq6EXXYJOprPq66uZujQ4dTVXUQ4XIa3AC4aA2hoWMCmTTMY\nOnQ4a9asUJFZRDKBcmARkYCYwbnnQkMDPPgg5OQEHZGIiLQVT4uM1k1Njopx3OjIUTtXi4gADzzg\nrV5evBi++tWgo/m8UCjEyJFjIsXlKURfXG7lCIenUFd3ESNHjqG5uTkRYYqIJJNyYBGRAJjBpZfC\n+vXwyCPQu3fQEYmISHvxFJiX4FUaRjvnDolmgHPuULxk3IDFcTxTRCSjPPUUXHwxPPkkDBwYdDRf\nVFExjZqawsjK5fiFw2XU1PSnomKaT5GJiARGObCISACuuw4WLYKFC6Fv36CjERGRjjizaFq4tRng\n3FfxNivZHngfOMXMlnZx/0jgAaAArxfc183szbgjzjLOOYv170hEUttzz3mbk8yfDz/4QdDRfFFt\nbS0DBpR003M5FpvJy9uXmpqNFBQU+DCfiAg45zCzpHXgVA6c+pQ3i2Se22+Ha6+FVaugf6wNikRE\nJGk5c8w9mM3sLefc1cA0YBdgsXNuGfA4sB74GNgB2Ac4ARiBt9rDgGuUWItINmlpaaGqqoqmpiby\n8vLYurWYE0/sxT33pGZxGWD27EqcOx5/issAA3DuOGbNquTSS6f4NKeISHIpBxYRSa6HHoKKCm8j\nbBWXRURSW8wrmD8d6NzvgZ9FvuxqktYq+e/N7OI4nhOOdUyUzMzi2eQwqbQSQyT91NbWMnt2JXPm\nzGPjxlfIzd2dnJy+NDfvSmPjnyksvIWLL96FiRNLU3JF76BBB7J+/TXAET7OupRBg37NunXP+Tin\niGSzZK9gbvNc5cApSnmzSOZ4+mk44wxvr5JvfzvoaERE0leycua4C8wAzrlxQAXwjS5u+zdwpZnN\ni/MZ2+IZFwUzs5Tfe1aJskj6CIVCVFRMY/r0m3DueBobxwMHAP3a3NUIPEefPndh9gRTplxMefkV\n9E6R3UpaWlro23dnQqG3gB19nHkrvXv3p77+A3r1ysi6hogkWVAF5sizlQOnIOXNIpnh+efh+OO9\nDf0OPjjoaERE0ltaFJgBnHMO+C5wMDAAr5KyFdgMrDSzf/Rw/mV0vTpkJ2C/Nl9/hNcfrx7oCwzk\ns+qOAS9G4sPMDutJbMmgRFkkPVRXVzNy5BhqavrT0HA70bWX2Ex+/iQKC7ewZMl8ioqKEh1mtzZs\n2MB++42ivv513+fu27eYF19cQklJie9zi0j2CbLAHHm+cuAUo7xZJP2tWwdHHAGzZ8MxxwQdjYhI\n+kvZHsztRbK4NZEP35nZiM6uOee+DTwS+XIecIOZre7gvgOAnwM/wkvGzzazV/yPVkSyUXV1NUOH\nDqeu7iLC4TI+eyu6OwNoaFjApk0zGDp0OGvWrAi8yNzU1EROTmK2587J6UtTU1NC5hYRSTblwCIi\n/qqqgqOOghtvVHFZRCTdbBd0APFyzu0KPAEUAxeb2biOEmsAM1ttZqcAF+Gt5njCOZd6jU9FJO2E\nQiFGjhwTKS5PIfricitHODyFurqLGDlyDM3NzYkIM2p5eXmEw/UJmTscricvLy8hc4uIZAvlwCKS\nid5+G0aNgssvhx//OOhoREQkVmlbYAYuxHsdcYmZ/T6aAWb2B2AJ8LXIeBGRHqmomEZNTWFk5XL8\nwuEyamr6U1ExzafI4lNcXExz87t4b1r7aSvNze9RXFzs87wiIlkn7XJg59yxzrnFzrk3nXMNzrmN\nzrm5zrnvt7tvD+fcti4+7k927CKSeB98AKNHe5v6XXBB0NGIiEg8etyDOSjOuZeAwcBPzey2GMZN\nAm4FXjGzlN+PVr3kRFJXbW0tAwaU0NS0luh6LndnM3l5+1JTs5GCguAWmA0adCDr118DHOHjrEsZ\nNOjXrFv3nI9zikg2C7oHc1DSLQd2zl0HXAq8BzwWOZYAJwC5wJlmdn/k3j2A1/H6RT/WwXRrzeyR\nDs63Pkt5s0iaaWjw2mLstx/cdBO4rPupLiKSWIH3YHbObYp8ama2Vwfn4/W5+Xpgj8ixLsZx77cb\nLyISl9mzK3HuePwpLgMMwLnjmDWrkksvneLTnLErLR1LefldNDb6V2Du06eS0tKxvs0nIpIoyoH9\n45z7MnAJ8D9gXzOrbXPtUOBZ4Gqg/crkF83s6mTFKSLBaG6GceOgqAhmzlRxWUQknXW1yV9x5Nh+\nGUBx5Fy8P/79WlbQ+vy9YxxX0m68iEhc5syZR2PjNb7O2dh4NpWVvw60wDxxYilXXrkXsBnvLeye\n2ozZAiZMuMmHuUREEq44clQO3HN74LXke75tcRnAzJY75z4Cdk9iPCKSIrZtg9JS7/M5c2C7dG7e\nKSIiXRaYq+k4Ee7sfLJtAPYHJjjnpptZU3cDnHN5wAS8+DckOD4RyWAtLS1s3PgK8D2fZz6ADRte\npqWlhV69uvoRnTgFBQVMmXIxM2dOoqFhAT2rRRj5+edSVjY50LYfIiIxUA7sn9eAEPA959yu7VYw\nDwd2BDpqedHfOXcusCtQC/zNzF5JRsAiknhmMHkyvPEGLFoEublBRyQiIj3VafXCzIpjOR+Ah/GS\n6z2Bh51zPzazDzq72Tm3E3Af3g7aBjyUlChFJCNVVVWRm7s7odCOPs/cj9zc3aiqqqKkpKT72xOk\nvPwKHnpoGJs2zSAcjn81dU7ODAoL36K8/AofoxMRSRzlwP4xs/edc5cB04FXnXOP4RWMS4DjgUXA\neR0MHRX5aOWcc8uA8Wb2ZmKjFpFE+81vYMUKWLYM8vODjkZERPwQzPI4f8wEzsF79e4o4L/OuUpg\nKd7KjAYgHy+BPRw4G9gtMrYK0LvaIhK3pqYmcnL6JmTunJy+NDV1uyAtoXr37s2SJfMZOnQ4dXUQ\nDpcR20pmIydnBgUFN7NkyQpytTRFRMQvaZUDm9nNzrk3gNmRuFttAO4ys/fanGvA68n8GNDa83oI\ncBXen2WJc24/M2tMeOAikhB/+APccw+sXAk77xx0NCIi4heXzjstO+f2Bp7B22Ermj+Iw2sqeoSZ\nvZbI2Pyi3bBFUtOGDRvYb79R1Ne/7vvcffsW8+KLSwJdwdyqurqakSPHUFPTn4aG24iuJ/Nm8vMn\nUVi4hSVL5lNUVJToMEUkCyVrR+xUlE45cGQF82/xCuN/xNvw75vAtcCRwPVm9otu5sgBVuH1pZps\nZr/v5D7lzSIp7P774fLLvdXLe+4ZdDQiItkhWTlzWheYAZxzuwA3AGcAvbu4NQTcDVxuZu93cV9K\nUaIskppaWlrIzx9Kc/M/8fYv8stWevfuT339B4H1YG4vFApRUTGN6dNvwrnjaGw8GzgA6Nfmrq3A\navLzK9m2bQGXXDKZ8vIrtHJZRBImmwvMkB45sHPuUOBZ4GEz+1G7a32A/wJfAfY2s6pu5poI3NHR\nXG3usfLy8k+/HjFiBCNGjOjJH0FEfPKXv3ib+i1dCoMHBx2NiEjmWrZsGcuWLfv064qKChWYY+Gc\n2x04Fq/q0R/YAfgY2AK8ACxs9wpeWlCBWSQ1bd4MAwduobn5A2CQjzMvZdCgX7Nu3XM+zumPuro6\nZs2qpLJyHhs2vExu7m7k5PQlHK6nufk9SkqGUFo6lgkTztaGfiKScNleYG6Vyjmwc+4GYApwkZn9\nsYPrDwM/BMaa2aPdzHUCXuuMp8zsmE7uUd4skoJWrYITT4QnnoDvfz/oaEREskuycuZOl8c5565M\n1EPN7OoEzPkuUBn5EBFJmNpaGD0aRo3awLPP3klj492+zd2nTyWlpWN9m89PBQUFXHrpFC69dAot\nLS1UVVXR1NREXl4excXFKbPiWkSkJ5QD+2r7yHH3Tq63ng9FMdeBkeOmLu8SkZTy0ktw8slw330q\nLouIZLJOVzA757YRXU+3mJlZTiLmzURaiSGSWj76CEaOhBEj4PLL6ygs3IumpleIrjdxdzaTl7cv\nNTUbtQJYRKQbiVqNoRzYP865HwEP4vVdHmpmW9pcOxpYADQBA8zsfefc/sCL7ZNf59wRkXt7AweZ\n2d87eZ7yZpEUsmEDHHoozJgB48YFHY2ISHYKfAVzaxxRzGHd3Nf+urI+EUlLn3wCJ50EQ4bAtdeC\ncwVMmXIxM2dOoqFhAdH9yOyMkZ9/LmVlk1VcFhEJnnJgf8wDngZGAuudc4/iFZsH4bX1gM/3hp4O\n7O2cew5vU0KAIcDheN+/X3VWXBaR1LJlCxx5JFx5pYrLIiLZoKsVzId2M/Yi4EQgDCwGlgIbgHqg\nL1ACHIG3O/R2wKPA7wHMbLkPsWcFrcQQSQ3hMJx6KmzbBnPnQk5kDVooFGLw4GFs2nQm4fCUuOfP\nyZnOwIH3sG7dC9oYT0QkCglcwawc2EfOuRzgZ8CpeIXlfKAOeB642cyWtrm3FO97OxjYDcgF3gae\nA/5oZn/t5lnKm0VSQF2dt3L5tNPgiiuCjkZEJLslawVzXJv8OeduxksU/wOcYmavdHHvYOABYB+8\nJLIszli7imcP4MfAMLz31PsB3b2CaGa2l9+x+E2JskjwzODcc+H112HhQth++89fr66uZujQ4dTV\nXUQ4XEZsK5mNnJwZFBTczJo1KygqKvIzdBGRjBXEJn/KgVOb8maR4NXXw6hRcOCB8Lvfgcv6rVhF\nRIKVsgVm59xo4EngPWCwmb0TxZgvA68AuwJHm9niOGLtaN5ewPXAhXgrROCLlR3r7Hw69MFToiwS\nvF/8Ap55BpYuhR137Pie6upqRo4cQ01NfxoabiO6nsybyc+fRGHhFpYsma/isohIDJJdYFYOnPqU\nN4sEKxSC44+H/v1h9mwVl0VEUkGycubtur/lC87DS1jvjCaxBjCzt4E78RLc8+J4ZmfuAC7GW6nh\n8F6hIxLfu3j/AGjb/87w+rm9AVTH80Dn3Hjn3LZuPpo7GPcD59xfnHO1zrkG59xLzrmLnXPx/B2I\nSJLccAM8/jg8+WTnxWWAoqIi1q59nsmTDyAvb1/69DkT763pre3u3AosJT//TPLy9qWs7HusW/eC\nissiIqkvq3NgEZGuhMNw1lnQpw/ccYeKyyIi2SaeFczVQCFwupk9GMO4U4H7gc1m1uNKinPuEGA5\nXsL8V2C8mb3eZufvE83scefcDsAo4Argu8AzeK801sb53G8DYzq5PBw4DFhgZmPajBmDt8lJI95O\n2nXA8cA3gYfM7JQunqeVGCIBmTULfvMbWLUKBkSzIDmirq6OWbMqqaycx4YNL5Obuxs5OX0Jh+tp\nbn6PkpIhlJaOZcKEs7Whn4hInAJYwZzVOXA6UN4sEgwz+OlP4d//9hZl5OUFHZGIiLRK5RYZjUBv\nYKKZVcYw7mxgNvCJmfWJ6aEdzzcHGA98DOzRuvt0++S6zf3b4a0gGQ+sAg4zs209jaNdTM/h9cA7\nwcwWRs7tCGwEdgR+YGb/ipzvDTwLfB84zczmdjKnEmWRADzyCFxwASxbBl//evzztLS0UFVVRVNT\nE3l5eRQXF9OrVy/f4hQRyVYBFJiVA6c45c0iwfjVr+Cpp7yWcv36BR2NiIi0lcotMlpXPYyMcdyo\nduN76gd4SfR9rYl1VyKJ9Ll4xd6D8ZJs30Q2cvk+UAP8pc2lH+Htgv3n1uJyJJ4Q8Cu8VxfP9zMW\nEemZpUvhvPNgwYKeFZcBevXqRUlJCYMHD6akpETFZRGR9KUcWESknRkzYN48b+WyissiItkrngLz\nSryi6KnOuSOjGeCcGwWcgpcMr4rjmR35auS4rpPrX3gxx8xagLvw4j/dpzhaTeKzvnxtl04cFjm/\nqIMxK4AG4AfOuVyf4xGROKxeDaedBg89BN/5TtDRiIhIClEOLCLSxl13eQXmxYth992DjkZERIIU\nT4H5j3y2ach859yvnHM7d3Sjc24n59xUYH7kWQbcHG+w7WwfOb7V7nx95NhZY9PXIsd9fIoD51we\n8GMgDMxqd/kbkeN/248zszDwOtALGOhXPCISn/XrvZ2v77wTDj006GhERCTFKAcWEYl4/HG4/HJY\ntAi0V7WIiMT8rraZrXLOXQv8Eq8PXQXwK+fcv4ANeCty84ESYL/IPa29Pq4zs+f8CBz4AK/1RPtV\nGu8BRcDenYzbNXLczac4wFuZsjPwhJnVtLu2U+T4YSdjW893+A+U/2fvvuOkqu7/j78OzQWs2KJY\nEDUxRE3RaGxARJqiWLBGVDS2xIo1VuwRlSjWJIKoPxXFhggCAi4l8WtJu8MmIgAAIABJREFUNJaY\nWBARTFTAguCysJzfH3cxuC4Lu8zOvTPzej4e+xiYuXPmjavy4cO5nyMpP2bOhO7dYeBAOOCAtNNI\nkrLGGliSEuXl8Otfw5gx8EP/ykqSRAMazAAxxotDCJ8DV5LsomgB7FL9tbxlRfUi4NIY440NDVqL\nt0kK5HY1nn8d2BLoCZxTy/u6Vz+uqOHbECeR7Ez5Yw7XlJQnn3wCXbtC//5wzDFpp5EkZZU1sKRS\n9/e/w2GHwfDhsPPOaaeRJGVFg0+bijHeEEJ4DDgDOJBkx0RNHwBPALfFGKc39LNW4AVgD2CnGs+P\nAXoBPwghXBFjvHzZCyGEM4EDSJrBL+QiRAihA7Ab8CHwTC2XLCvi16nlteWf/3xFnzFgwIBvfty5\nc2c6d+5c35iSVuDLL6Fnz6RQPuustNNIklakvLyc8vLytGNYA0sqWW+/DfvtB3/8I+y9d9ppJElZ\nEr59Ht1qLBTChsCmwJrAV8BHMcZPc7J47Z/XDRgLzAc2ijEuqn5+LeDfwMbVl35CMue4PbAhyY6S\nCPSMMY7PQY5bgNOAATHGq2p5/X6Sw1SOijE+XOO1piQN6ObAmjHGxbW8P+bqeyTp2yoqkubyD38I\nt98OIaz8PZKkbAghEGNM/f/cpVoDZ5F1s9R4PvwQ9toLLrsMjj8+7TSSpFWVr5o5Zw3mfKtuzk4g\nmT93UYzxueVe+yXwNNCy+qllB7Isc22M8ZIcZFgD+AhYC9iqlvnLhBD6kRz8d2+MsV+N1/au/jWU\nxxhr/TtgC2WpcSxZAoccAi1bwgMPQNOmaSeSJNVHVhrM+ZaFGjirrJulxjFnTtJcPuEEOPfctNNI\nkurDBvNqCiFsA1wEdCHZybEQeAm4Ncb4dI4+oy9wL/BUjPHAFVyzFvAeSRN6zxjj36qfXwN4DtgV\nOCLGOGIF77dQlnJs6dJk58XHH8PIkdCiRdqJJEn1VaoN5pXJRw2cVdbNUu7Nnw9duiRf112XdhpJ\nUn0VTIM5hNAC+AXwQ2A9oEWM8cocZMu8EMJUYHdg/xjjmDqu6w2MIDnoZTgwj2QO3veBETHGI+p4\nr4WylEMxwjnnwAsvwPjx0Lp12okkSQ2RdoO5lGvgrLJulnJr0aJk5nL79sncZcfJSVLhyXyDOYRQ\nBlwGnAqsvfxrMcamNa4dSHIIyocxxi4Ni/qdz192oMqiGOPHuViznp+/HfBPYCbJeIw6/0GGEHYD\nLiY5ELAMeJdkdMatdb3XQlnKrWuvhYcegilTYL310k4jSWqotBrMpV4DZ5l1s5Q7S5bA4YdDkyYw\nfLjj5CSpUGW6wRxC2Jhk9lsHvj3XDSDWUlzvBvyFZA7cbjHGFxsW91trLq1e744Y4+mru15WWShL\nuXPXXXDDDTBtGmyySdppJEmrI40GszVwtlk3S7kRI5x4IsycCaNGwRprpJ1IktRQ+aqZm9T3DSGE\nADwJ/IiksJ4KnAys8JbAGOPzwIzqn+5b75S1W1T9uNqFuqTi98gjcNVVyVgMm8uSpPqyBpZUKi68\nEF5/HR5/3OayJGnV1LvBDBxJcjBdBK6JMXaKMf4ZeGUl75tAUozv1oDPrM1/qh+X5Gg9SUVq3Dg4\n/XR45hnYeuu000iSCpQ1sKSiN3AgPP00jBkDa66ZdhpJUqFoSIP58OrHv8UYL63H+16vftyuAZ9Z\nmxeqH7fP0XqSitDzz8PRRyc7MHbcMe00kqQCZg0sqajdfTfceWdyx9/666edRpJUSBrSYN6JZOfG\n8Hq+79Pqxw0a8Jm1uZtkN8hxIQT/blXSd7zxBhx4INx3H+yxR9ppJEkFzhpYUtF67DG47LKkudy2\nbdppJEmFpiEN5mXF8Qf1fF/Vanzmd8QYJwJ3AJsAT1cfuiJJALz/PvTsCTffnDxKkrSarIElFaUJ\nE+DUU2H0aNh227TTSJIKUbMGvGcBsC7Qqp7vW3as1rwGfOZ3hBA6AiOArYCewDshhMdIDlyZDXy9\nsjVijFNykUVStvz3v9C1K/zud3DkkWmnkSQVCWtgSUXnxRfhqKPg0Ufhpz9NO40kqVA1pME8k6S4\n/jFwfz3e17H68d8N+MzalJPcpkj145rAMdVfqyLSsF+/pAz7/HPo0QOOPRZ+85u000iSiog1sKSi\n8s9/wgEHwJAh0LHjyq+XJGlFGnKr3iSSuW9HhBDKVuUNIYRtgANICtqJDfjMFS693FfNn6/Kl6Qi\nsnAh7L8/dOoEl1ySdhpJUpGxBpZUND74INmUceONSf0sSdLqaMjuhSHAmSS3+90FHFfXxdVz4R6t\n/qwKkoNJcuGKHK0jqQgsXgyHHQbt2sEf/gDBPz5LknLLGlhSUfjkk2Sc3LnnwtFHp51GklQMQoxx\n5VfVfFMItwCnk+zGeBG4GWgD3F793FbA5kA34DfVrwFcGmO8dvVjl44QQmzI90gqJUuXQt++MH9+\ncgJ28+ZpJ5IkNbYQAjHGvP51ojVwtlk3Syv3xRfwy18mu5av8K+rJKno5atmbmiDuSnJ4SIH8r8Z\ncCu8vPrx3hhjv3p/WImzUJbqFiOccQb84x8wbhy0bJl2IklSPqTUYLYGzjDrZqluX3+djMXYYQe4\n9Vbv+JOkUpCvmrkhM5iJMVbFGA8GzgY+pe4Zb3OA0y2sJTWGK6+EadNg1Ciby5KkxmUNLKlQLV4M\nhx8ObdvC4ME2lyVJudWgHczfWiCENYDuwF5AO2Ad4CtgNjAZGBNjXLh6MUuXOzGkFbv11uRr6lTY\neOO000iS8imNHcw1Pt8aOGOsm6XaLV0Kxx0Hc+bAyJGOk5OkUpLpERlZFULouAqXzYsxvtHoYXLE\nQlmq3QMPwIUXJs3ldu3STiNJyre0G8xZUow1cENYN0vfFSP07w8vvQTjx0OrVmknkiTlU2YbzCGE\ny6p/+F6M8YHcR1rh57YDBlX/dEqM8eZarlnKyufhLQS2izHOzmnARmKhLH3X6NFwwgkwcSL86Edp\np5EkpSHfDWZr4Oyzbpa+6+qr4ZFHYPJkWG+9tNNIkvItyzOYBwCXA21zG2WlLiE5UGUf4MmVXFvX\nPLxWJL8GSQVo6lTo1y+5vc/msiQpjwZgDSypgNx5JwwblhyEbXNZktSYmjXgPZ+TzJh7P8dZViiE\nsA5wNMnOjNtjjDNW8pbngJm1PL8tsDtwZAjh7BjjVzkNKqlR/eMf0KdPMh5j113TTiNJKjHWwJIK\nxvDhcM01MGUKbLJJ2mkkScWuIQ3mD0mK67VznKUuvYEWwGLg1lW4/pYY41M1nwwhbATMAFoCBwH3\n5zCjpEb07rvQsyfcfjt07Zp2GklSCbIGllQQxo6FM8+ECROgffu000iSSkFDRmQ8TXKb3d45zlKX\nTtWPL8cYP2roIjHGT4CJ1T/dc7VTScqL2bOhWze44opkB7MkSSmwBpaUeX/9K/TtC088ATvskHYa\nSVKpaEiD+XaSWwQPCyHskeM8K/ITklsDJ+dgrRdI/nCwUw7WktTI5s2D7t3hpJPgxBPTTiNJKmHW\nwJIy7bXX4KCD4P77Yffd004jSSol9W4wV++eOILkJOoxIYTfhhBa5jzZty07TOWDHKy1bI1Nc7CW\npEa0YAHst18yGuOCC9JOI0kqZdbAkrLsvfeSmnnwYOjRI+00kqRSU+8ZzCGEodU/fA3YAxgM/D6E\n8AowG/h6JUvEGOMJ9fzYdaof567kukurH/9ZxzULqh/XrWcGSXm0aBEcfDD88IcwcCCEkHYiSVIp\nswaWlFX/+U8yTu7SS+Hww9NOI0kqRQ055O84klv1WO6xNUmhvarqW1wvITngpM5dIjHGa1ZhrTWr\nH5fWM4OkPKmqSmbHtWoFf/qTzWVJUiYchzWwpIz57LNknNzxx8Mpp6SdRpJUqhrSYIZkftuqPFeb\nuPJLvmMu0ArYuAHvrWmj5daUlDExwm9/C3PmwJgx0Kyh/5eSJCn3rIElZcaCBdCrF3TpAhddlHYa\nSVIpa0jrZqucp1i5WcDmwG45WGvZGrNysJakHLvkEnj5ZZg0CcrK0k4jSdI3rIElZUZlJfTpA9tu\nCzfd5B1/kqR01bvBHGPMxSEj9TUF2B3YO4SwVoxxfkMWCSGsDXQh2UEyJYf5JOXAoEHw+OMwZQqs\nvXbaaSRJ+h9rYElZsXQpHHsstGgBd98NTZqknUiSVOoK5beicdWPawPnrcY651avATB2tRJJyql7\n7oFbboHx42HDDdNOI0lSJlgDS/qWGOH00+Gjj2D4cMfJSZKyIcRYv3FwIYQyktOnv4wxLmyUVLV/\n7kvATkAVcFiM8Yl6vv9A4FGSOXl/izHukvuUuRdCiPX9HkmF5skn4dRT4bnnYLvt0k4jSSoEIQRi\njHm7KdwaOPusm1UKLrsMnn46qZvXWSftNJKkrMtXzbxKO5hDCOuGEK4LIbwDLABmA/NDCO+FEH4f\nQli/UVMmLiQ59boJMCKEcMOqfG4IoU0I4XpgRPV7l1avJSkDysvhpJNg1Ciby5KkbLEGlpQlt9wC\nDz8MY8faXJYkZctKdzCHELYFxgNbLHtquZeXvXk20C3G+K+cJ/x2louAq5f73EXV2Z4HpgOfVz+/\nLtAe+AXQHVhjudwXxxiva8ycueRODBWzv/0NevZMCuVf/jLtNJKkQtLYuzGsgQuPdbOK2f33w8UX\nw9SpsOWWaaeRJBWKfO1grrPBHEJoBvwN2IGkoK0r0JvAz2KMi3Oa8LuZzgOuBZpWP7WyKnJZ5irg\nwhjjTY2VrTFYKKtY/etfSVP5zjvhwAPTTiNJKjSNWSxbAxcm62YVq1Gj4MQTYdIk6NAh7TSSpEKS\nlREZh/C/wnoucBLQFmhR/Xgy8Gn1tR2AQxsn5v/EGG8A9gImVD8VVvIF8CywZykU1lIh+PBD6N4d\nrr3W5rIkKZOsgSVlwpQpcMIJ8NRTNpclSdm1sh3MD5MUzAuBn8cY36rlmu2Al4GWwOMxxkYvsJf7\n7B8CXYE9gE2BZfPo5gIfAX8Bnq0td6FwJ4aKzZw5sNde8OtfwznnpJ1GklSoGnkHszVwAbJuVrF5\n5ZVkU8ZDD0GXLmmnkSQVoqyMyHiHZI7b3THGk+u47o/AicD0GOM2OU9ZwiyUVUzmz4e994auXZPd\ny5IkNVQjN5itgQuQdfOKLVmyhBkzZlBRUUFZWRnt2rWjWbNmacdSHd55Bzp1gttug4MPTjuNJKlQ\nZWVExsbVj39dyXXLXt9o9eJIKlYVFdC7N+y0E1xzTdppJEmqkzWwCt7cuXO54Yab6NBhN1q3Xpef\n/KQre+xxBD/5SVdatVqHDh1244YbbmLevHlpR1UNs2ZBt25w5ZU2lyVJhWFlDeY1qx8/W8l1y06u\nbr16cSQVoyVL4MgjYYMN4PbbITT6351JkrRarIFVsCorK7n44gFsttk2XH75P3jrrauprPyIBQve\n58sv32DBgvdZvPg/vPXW1Vx++T9o23ZrLr54AJWVlWlHFzB3bjIW49RTk5FykiQVAu+LktSoYoST\nT4YFC5ITsJs2TTuRJElScZo5cyb77NOb2bM3paLiDZIzKWuzNtCFr7/uAszi5ptPZsSIXZkwYSRb\nbLFF/gLrW+bPh333hV694Pzz004jSdKqW9kOZklqsBiT4vif/4THH4c11kg7kSRJUnGaOXMmO+/c\nkenT+7Jw4dOsuLlc02YsXPg006f3ZeedOzJz5szGjKkVWLQoGYexww7w+9+nnUaSpPpZ1Qazp2VI\nqrfrr4dnnoHRo2HNNVd+vSRJGWMNrIJQWVnJPvv0Zt68M6iq6g/Udx5ZoKqqP/PmncE++/Rm8eLF\njRFTK1BVBUcfDWuvDXfd5Tg5SVLhCXWdtBxCWMqqF9bLfhtc2fUxxuhojlXkadgqVH/+M1x7LUyb\nBm1XdQONJEmrqDFPxLYGLkylXDdffPEAbr75ZRYuHEX9m8vLi7Rq1Yuzz96Fq6++PFfxVIcY4aST\n4P33k00Z3vEnScqlxqyZv/U59Wgw1xVm+UVWFjrGGJ3CuopKuVBW4Xr0UTjjDJg8GbbdNu00kqRi\nlMcGszVwgSjVunnu3Llsttk2K5m5XB+zKCvbgdmz36NNmzY5WE91ufBCmDQJJk6EtdZKO40kqdjk\nq8G8KiMyAisvmMMqXiepyD37LPzmNzBmjM1lSVJBswZWQRg6dBgh7E9umssAmxFCL4YMGZaj9bQi\nN9wATz2V1M02lyVJhazOHcxKX6nuxFBheuEF2H9/eOwx2GuvtNNIkopZvnZjqHCUat3cocNuvPXW\n1UCXHK46kQ4dLuXNN/+awzW1vKFD4cork3Fym22WdhpJUrHKxIgMpa9UC2UVnjffhC5dYMgQ2G+/\ntNNIkoqdDWbVVIp185IlS2jdel0qK/8D5HIL7Je0aLEpCxZ8TrNmjg7Ptccfh9NOg/Jy+P73004j\nSSpmWRqRIUl1mjEDevSAm26yuSxJkpQvM2bMoHnzDcltcxlgbZo334AZM2bkeF1NnAinnAJPP21z\nWZJUPPzraEmr5eOPoWtXOP98+NWv0k4jSZJUOioqKmjatHWjrN20aWsqKioaZe1S9dJLcMQRyYHY\nP/tZ2mkkScoddzBLarAvvkh2Lv/qV3D66WmnkSRJKi1lZWVUVS1olLWrqhZQVlbWKGuXorfeSs4q\nGTIEOnVKO40kSbnlDOaMK8VZcioMX38N3bvDj38MgwdDcAqmJCmPnMGsmkqxbnYGc2GYORP23BOu\nvhqOOSbtNJKkUuIMZkmZtXgxHHYYbL453HKLzWVJkqQ0NGvWjK233gF4Mccrv8Q22+xoczkHPvkk\nGSd39tk2lyVJxcsGs6R6WboUjj8+eRw2DJr4fxFJkqTU9OvXh5Yt783pmi1bDqNfvz45XbMUffkl\n9OwJhx6aNJglSSpWjsjIuFK81U/ZFSOcdRb8/e8wbhy0apV2IklSqXJEhmoq1bp53rx5tG27NRUV\nrwOb5WDFWZSV7cDs2e/Rpk2bHKxXmioqkubydtvBHXd4x58kKR2OyJCUOVdfDZMnw6hRNpclSZKy\noE2bNvTvfyatWp0MrG6DPdKq1Umcc85ZNpdXw5IlcPjhsPHGcNttNpclScXPHcwZV6o7MZQ9d9wB\ngwbBtGnwve+lnUaSVOrcwayaSrlurqysZPvtd2X69L5UVfVv8DpNmw6iffv7efPNF2nevHkOE5aO\nZePkPv4YRo6EFi3STiRJKmX5qpk9tUHSSj30EFx7LUydanNZkiQpa1q0aMGECSPZeeeOzJsHVVVn\nA/X5s2SkadM/0KbNYCZMmGJzuYFihHPPhbffhmeftbksSSodjsiQVKcxY5K5y2PHwlZbpZ1GkiRJ\ntdliiy14+eUptG9/P61a9QJmreI7Z9GqVS/at7+fl1+ewhZbbNGYMYvaddcljeWnn4bWrdNOI0lS\n/thglrRCf/kLHHssPPkkbL992mkkSZJUly222II33niBs876OWVlO9CyZV9gIvBljSu/BCbSqlVf\nysp24Oyzd+HNN1+0ubwa7roLhgyB8ePB8dWSpFLjDOaMK+VZcjUtWbKEGTNmUFFRQVlZGe3ataNZ\nM6e8NJbXXoOuXeG++6B797TTSJL0bc5gVk3Wzd82b948hgwZxrBhj/Luu6/RvPkGNG3amqqqBSxe\nPIdtttmRfv36cPzxx3mg32p65BE4+2yYMgW23jrtNJIk/U++amYbzBlX6oXy3LlzGTp0GPfc8yjv\nvfc6zZtv+E1hXFn5yTeF8Qkn9LMwzqH33oOOHZND/Q4/PO00kiR9lw1m1VTqdXNd3KjReMaNg2OO\nSUZj7Lhj2mkkSfo2G8wCSrdQrqys5IorrmXQoFsIYX++/vpY4OfA2std9SXwEi1b3kuMo+jf/0wu\nv/wiWniaxmr56CPYay84/3w4+eS000iSVDsbzKqpVOtmpef55+GAA5JxcnvskXYaSZK+ywazgNIs\nlGfOnMk++/Rm9uxNWbjwT0DbVXjXLFq1Opm2bT9iwoSRzo9roHnzoFMnOPJIuOiitNNIkrRiNphV\nUynWzUrPG29Aly4wbBj07Jl2GkmSameDWUDpFcozZ85k5507Mm/eGVRVnQ3U57+BSNOmf6BNm8Ge\ngN0ACxYkM5d32w1uvBGCf2SXJGWYDWbVVGp1s9IzfXoyTu6GG5KNGZIkZZUNZgGlVShXVlay/fa7\nMn16X6qq+jd4naZNB9G+/f28+eaLNG/ePIcJi1dlZXJ73/e+B0OHQpMmaSeSJKluNphVUynVzUrP\nf/8Le+4J/fvDb36TdhpJkuqWr5rZNpIy44orrmX27LbVO5cbrqrqbGbP3pQrrrg2R8mKW1VVcjBJ\nWRncfbfNZUmSJKk2n38OPXrAscfaXJYkaXnuYM64UtmJMXfuXDbbbBsqKt5g1WYur8wsysp2YPbs\n92jTpk0O1itOMSbF8b/+Bc88kzSZJUkqBO5gVk2lUjcrHQsXQrdusPPO8Ic/OE5OklQY3MFcAEII\nXUIIT4QQ/hNCqAghzA4hjA0h9Fzumi1DCEvr+HowzV9DVgwdOowQ9ic3zWWAzQihF0OGDMvResXp\nssvgxRdh5Eiby5IkSVJtFi+GQw+FrbaCQYNsLkuSVJM7mBsohDAQOBf4EHgGmANsCOwETIgxXlh9\n3ZbA+8CrwJO1LPVGjPHxOj6nJHZidOiwG2+9dTXQJYerTqRDh0t5882/5nDN4nHzzXDnnTB1Kmy0\nUdppJEmqH3cwq6ZSqZuVX0uXQt++MH8+PPYYeMSLJKmQ5KtmbtbYH1CMQggnkjSX7wFOjjEuqfF6\n01re9mqM8cp85Cs0S5Ys4b33Xgd2yfHKP+fdd19jyZIlNGvmv+rLu+++ZPfFtGk2lyVJkqTaxAhn\nngmzZsHYsTaXJUlaEUdk1FMIoQVwNfABtTSXAWKMVXkPVsBmzJhB8+YbAmvleOW1ad58A2bMmJHj\ndQvbU0/B+efDuHGwxRZpp5EkSZKy6Yor4C9/Sernli3TTiNJUna5rbP+upKMwhgExBDCfsCPgArg\nxRjj/63gfZuGEE4C1gfmAs/HGF/PR+Csq6iooGnT1o2ydtOmramoqGiUtQvR5MlwwgkwZgz88Idp\np5EkSZKy6dZb4cEHkzv+1lkn7TSSJGWbDeb6+zkQgUrgFWD76p8DhBDCFKBPjHFOjfd1rf5iuWvL\ngWNjjB82buRsKysro6pqQaOsXVW1gDJPrwPg739PDicZPhx+/vO000iSJEnZ9MADMHCgZ5VIkrSq\nHJFRfxsBATgPWArsQTLbYUdgHNAReGS56xcCV5Ic/rde9VcnYBLQGZgQQijpG67atWvH4sWfAvNz\nvPKXLF48h3bt2uV43cLz9tuw337wxz9Cl1yeoyhJkiQVkdGj4ZxzknFy/jFCkqRVY4O5/pb9M1sM\n7B9jfD7GuDDG+CZwMDAL6BRC2BUgxvhpjHFAjPHVGOOX1V/TgO7AC8A2wK9T+HVkRrNmzdh66x2A\nF3O88ktss82OJX/A36xZ0K0bXHMNHHRQ2mkkSZKkbJo6Ffr1g5EjoUOHtNNIklQ4Srvz1jCfVz++\nUnO0RYzx6xDCOOB4YBeSBnKtYoxVIYS7gV1Jdj3fuqJrBwwY8M2PO3fuTOfOnRuaPbP69evD5Zff\ny9df5257bcuWw+jXr0/O1itEc+ZA167w29/C8cennUaSpIYrLy+nvLw87RiSitSrr0KfPsnc5V13\nTTuNJEmFJcQYV36VvhFC6AcMAZ6JMe5Xy+sDgXOA38UYB65krQOAJ4GxMcZ9V3BNLIXv0bx582jb\ndmsqKl4HNsvBirMoK9uB2bPfo02bNjlYr/DMn5+Mw9h7b/j979NOI0lSboUQiDGGtHMoO0qlblbu\nvfsudOoEgwfDIYeknUaSpNzJV83siIz6m0hyqN+Kbpravvrx/VVYa7fqx+mrG6rQtWnThv79z6RV\nq5P535mJDRVp1eokzjnnrJJtLi9alIzD+MlP4Lrr0k4jSZIkZdPs2ck4uQEDbC5LktRQ7mBugBDC\nk8D+wDkxxpuXe74b8AzwGbBVjHF+COGnwKs1t1OEELoATwMtgD1ijP+3gs8qmZ0YlZWVbL/9rkyf\n3peqqv4NXqdp00G0b38/b775Is2bN89hwsKwZAkcfjg0aQLDh0PTpmknkiQp99zBrJpKqW5Wbsyb\nBx07Qt++cMEFaaeRJCn38lUz22BugBBCW+AvwObAJOAVoD3QG1gKHB5jfLL62ueAbYG/khwACLAj\nsDfJVt1LYowr3GNaaoXyzJkz2XnnjsybdwZVVWcD9flvINK06R9o02YwL788hS222KKxYmZWjHDi\niTBzJowaBWuskXYiSZIahw1m1VRqdbNWz1dfwT77JA3mgXUONpQkqXDZYM64EML6wGXAAcAmwJfA\nFOD3McaXl7uuH3AQyeiMDYDmwMckDefbY4x/WcnnlFyhPHPmTPbZpzezZ2/KwoV/ZNVmMs+iVauT\nadv2IyZMGFmSzWVIdl5MngwTJsCaa6adRpKkxmODWTWVYt2shlm0CA44ADbfHP78Zwj+n0SSVKRs\nMAso3UK5srKSK664lkGDbiGEXnz99XHAz4G1l7vqS+AlWrUaxtKlT3POOWdx+eUXleRYDEh2Xtx7\nL0yZAuuvn3YaSZIalw1m1VSqdbPqp6oKjjwyGSv3yCPQrFnaiSRJajw2mAVYKM+bN48hQ4YxbNij\nvPvuazRvvgFNm7amqmoBixfPYZttdqRfvz4cf/xxJXugH8Ddd8M118C0adC2bdppJElqfDaYVVOp\n181auRjhlFPg3Xdh9GgoK0s7kSRJjcsGswAL5eUtWbKEGTNmUFFRQVlZGe3ataOZWw547DE4/fRk\nNMa226adRpKk/LDBrJqsm7UyF18Mzz4LEyfCWmulnUaSpMZng1mAhbLqNmECHHUUjBsHP/1p2mkk\nScofG8yqybpZdRk0KJm3PHUqbLBB2mkkScqPfNXMbv+UCtSLLya4wpxLAAAgAElEQVTN5cces7ks\nSZIkrcg998DgwTaXJUlqLDaYpQL0z38mJ18PHQp77ZV2GkmSJCmbnnwSLroIysth883TTiNJUnGy\nwSwVmA8+gB494MYboVevtNNIkiRJ2VReDiedBGPHwg9+kHYaSZKKV5O0A0hadZ98Al27wrnnwtFH\np51GkiRJyqa//Q0OOwweeQR+9rO000iSVNxsMEsF4osvkp3LRx4JZ5yRdhpJkiQpm/71r+ROvz//\nGTp3TjuNJEnFL3jScrZ5GrYAvv4aevaE7beHW2+F0Ojnf0qSlG35OhFbhcO6WQAzZyZnlFx5JRx7\nbNppJElKV75qZhvMGWehrCVL4OCDYa214P77oYn3HUiSZINZ32HdrE8/TZrLJ58MZ5+ddhpJktKX\nr5rZVpWUYUuXwgknJE3mYcNsLkuSJEm1mT8f9t0X+vSxuSxJUr41SzuApNrFCOecA++9B+PHQ/Pm\naSeSJEmSsqeiAnr3hp12gquuSjuNJEmlxwazlFHXXAOTJsHkydCqVdppJEmSpOxZsiQ5BHujjeD2\n2z2rRJKkNNhgljLozjuTkRjTpsG666adRpIkScqeGOGkk5IDsR9+GJo2TTuRJEmlyQazlDHDhye7\nl6dOhe99L+00kiRJUvbECOefD2+9BRMmQIsWaSeSJKl02WCWMmTsWDjzzKRI3mqrtNNIkiRJ2XT9\n9UntPHkytG6ddhpJkkqbDWYpI/76VzjmGBg5EnbYIe00kiRJUjb96U/J17Rp0KZN2mkkSVKTtANI\ngtdeg4MOgvvvh912SzuNJElS7oQQ9gshjA8hfBhCWBhCeC+E8EgI4RcruH73EMKYEMLc6uv/EUI4\nM4Tgn13EiBFwxRUwfjxsumnaaSRJEkCIMaadQXUIIUS/R8Vt+nTo2BFuugkOPzztNJIkFYYQAjHG\nkHYO1S2EcD1wHjAHeLL6cRvgAKA50DfG+OBy1/cGHgW+Bh4G5gH7A9sBI2KMK6yWrJuL37PPwtFH\nJ83lH/847TSSJGVfvmpmG8wZZ6Fc3P7zH9hrLzj3XDjllLTTSJJUOGwwZ18IYWNgNvAJsEOMce5y\nr3UCngOmxxi3qX5uLeA9YC1g9xjjK9XPt6i+9hfAkTHGR1bwedbNReyFF2D//eHxx2HPPdNOI0lS\nYchXzextZlJKPvsMuneHfv1sLkuSpKK0JcmfN15YvrkMEGOcDMwHNlzu6UOBDYCHljWXq6+tBC4B\nAnBqY4dW9rz5JvTuDcOG2VyWJCmLbDBLKViwAHr1gn32gYsuSjuNJElSo3gHqAR2CSGsv/wLIYSO\nJDuVn13u6V8CERhXy1pTgIXA7iGE5o0TV1k0Ywb06AGDBsG++6adRpIk1aZZ2gGkUlNZCX36wLbb\nwo03QvDmXkmSVIRijJ+FEM4HBgH/DCE8CcwlmcG8P0kjefn7uH5Q/fh2LWtVhRDeBzoA7YF/N2Z2\nZcPHH0PXrnDBBXDUUWmnkSRJK2KDWcqjpUvhuOOgRQu4+25o4j0EkiSpiMUYB4cQPgCGAr9e7qV3\ngXtjjHOWe26d6scvVrDcsufXzW1KZdEXXyQ7l48+Gk47Le00kiSpLjaYpTyJEU4/HT76CJ55Bpr5\nX58kSSpy1TuYrwFuBm4H/gtsB/weeDCE8JMY44W5+rwBAwZ88+POnTvTuXPnXC2tPFq4MDnQb6+9\n4LLL0k4jSVLhKC8vp7y8PO+fGzxpOds8Dbt4XHYZjB4Nzz0Ha6+ddhpJkgpbvk7EVsOFEDoBzwGP\nxRgPrfFaS5JRGN8Dto0xzgghvAjsBOy8/CF/y73ndZIRGR1ijN8ZkWHdXBwWL4aDDoL11oN77/WO\nP0mSVke+amZ/u5by4JZb4OGHk53LNpclSVKJ6EVyaF95zRdijF8DL5L8eeSn1U8vaxp/v+b1IYSm\nwFbAEmB6I2RVBixdCscfn/x46FCby5IkFQp/y5Ya2f33w003wfjxsNFGaaeRJEnKmzWqHzdcwevL\nnq+sfpwEBKBHLdd2AloBf4kxLs5ZQmVGjHD22TBjBjzyCDRvnnYiSZK0qmwwS41o1Cg47zwYNw62\n3DLtNJIkSXk1laRhfFIIYdPlXwgh9AT2ACqAv1Y//SgwBzgihLDTcteuAVxNshv6zjzkVgquugom\nT07q51at0k4jSZLqwxnMGecsucI1ZQr06ZPMXf75z9NOI0lScXEGc/aFEAIwFtgH+Ap4guSQvw7A\nftWXnRljvG259/QGRgCLgOHAPOAAkrEZI2KMR9TxedbNBeq225KRctOmwcYbp51GkqTika+a2QZz\nxlkoF6ZXXoHu3eGhh6BLl7TTSJJUfGwwF4bq2cm/BY4gaSy3ImkavwAMjjFOrOU9uwEXA7sBZcC7\nwBDg1roKY+vmwvTgg3DBBTB1KrRrl3YaSZKKiw1mARbKheidd6BTp2QnxsEHp51GkqTiZINZNVk3\nF54xY5JD/SZMgO23TzuNJEnFJ181c7PG/gCplMyaBd26JTPkbC5LkiRJtZs2DY47Dp56yuayJEmF\nzkP+pByZOzcZi3HqqXDCCWmnkSRJkrLpH/+AQw6B//f/4Be/SDuNJElaXTaYpRz46ivYd1/o1QvO\nPz/tNJIkSVI2vftuUjffemty558kSSp8zmDOOGfJZd+iRUljecst4c9/huA0SEmSGp0zmFWTdXP2\nffQR7LknXHghnHRS2mkkSSp+HvInwEI566qq4IgjYOlSePhhaOZUc0mS8sIGs2qybs62efOSg7CP\nOgp+97u000iSVBo85E/KuBjhlFPgs89g9Giby5IkSVJtFixI7vjr3j3ZvSxJkoqLLTGpgS66KDmg\nZOJEWGONtNNIkiRJ2VNZCQcfDD/4Adxwg+PkJEkqRjaYpQa48UYYORKmTIG11ko7jSRJkpQ9VVVw\nzDHQqpVnlUiSVMxsMEv1NHQo3HYbTJsGG2yQdhpJkiQpe2KE006DTz6BMWMcJydJUjHzt3mpHh5/\nHC65BMrLYbPN0k4jSZIkZdOll8JLL8GkSVBWlnYaSZLUmGwwS6to0qTkUL+xY+H73087jSRJkpRN\nf/gDPPooTJ0Ka6+ddhpJktTYbDBLq+Cll+CII2DECPjZz9JOI0mSJGXTvffCzTcnzeUNN0w7jSRJ\nygcbzNJKvPUW7L8/3H03dOqUdhpJkiQpm556Ci68EJ57DrbYIu00kiQpX2wwS3WYORO6d4eBA+GA\nA9JOI0mSJGVTeTn8+tfJgX7bbZd2GkmSlE9N0g4gZdWnn0LXrtC/PxxzTNppJEmSpGz629/gsMNg\n+HDYeee000iSpHyzwSzV4ssvoUePpFA+66y000iSJEnZ9O9/Q69e8Kc/wd57p51GkiSlIcQY086g\nOoQQot+j/KqogJ49k1v77rgDQkg7kSRJqimEQIzR36X1Devm/PvwQ9hzTxgwAPr1SzuNJEmqKV81\nsw3mjLNQzq8lS+CQQ6BlS3jgAWjaNO1EkiSpNjaYVZN1c37NmQMdO8Lxx8O556adRpIk1SZfNbOH\n/EnVli5NDiaprIQRI2wuS5IkSbWZPz+54693b5vLkiTJBrMEQIxw3nnw9tvw7LPQokXaiSRJkqTs\nqaiAAw+En/0Mrr027TSSJCkLbDBLwHXXwfjxMHkytG6ddhpJkiQpe5YsgaOOgjZtPKtEkiT9jw1m\nlby77oIhQ2DatKRYliRJkvRtMcIpp8BXX8GoUY6TkyRJ/2ODWSXt4YfhqqtgyhTYZJO000iSJEnZ\ndMEF8MYbMGECrLFG2mkkSVKW2GBWyRo3Dk4/PSmSt9467TSSJElSNg0cCKNHJ5sy1lwz7TSSJClr\nbDCrJD3/PBx9NDz5JOy4Y9ppJEmSpGy6+264885knNz666edRpIkZZENZpWcN95ITr6+7z7YY4+0\n00iSJEnZ9NhjcNllyUHYbdumnUaSJGWVDWaVlOnToUcPuPlm6Nkz7TSSJElSNk2YAKeemoyV23bb\ntNNIkqQsa5J2AClf/vtf6NYNLroIjjwy7TSSJElSNr34Ihx1VLKD+ac/TTuNJEnKOhvMKgmff57s\nXD7mGPjNb9JOI0mSJGXTP/8JBxwAQ4fCXnulnUaSJBWCEGNMO4PqEEKIfo9Wz8KFyc7lnXZKRmOE\nkHYiSZK0ukIIxBj9XV3fsG5efR98kDSVr702ORBbkiQVtnzVzDaYM85CefUsXpwc6NemDdx7LzRx\nz74kSUXBBrNqsm5ePR9/nDSXTzsNzjgj7TSSJCkX8lUz225T0Vq6FI47LmkqDx1qc1mSJEmqzRdf\nJAdgH3mkzWVJklR/ttxWQwihSwjhiRDCf0IIFSGE2SGEsSGEHrVcu3sIYUwIYW4IYWEI4R8hhDND\nCH4PGkGMcOaZ8OGH8Mgj0Lx52okkSZKk7Pn662Tm8u67w4ABaaeRJEmFqFnaAQpVCGEgcC7wITAS\nmANsCOwEdAbGLndtb+BR4GvgYWAesD/wB2B34PA8Ri8JV1wB06ZBeTm0bJl2GkmSJCl7Fi+Gww+H\ntm1h8GDPKpEkSQ3jDOYGCCGcCPwRuAc4Oca4pMbrTWOMVdU/Xgt4D1gL2D3G+Er18y2A54BfAEfG\nGB9ZwWc5S66eBg+G226DqVNh443TTiNJkhqDM5hVk3Vz/SwbJzdnDowc6R1/kiQVI2cwZ1R1Y/hq\n4ANqaS4DLGsuVzsU2AB4aFlzufqaSuASIACnNmroEvLAA3DDDTB+vM1lSZIkqTYxQv/+MH06PPqo\nzWVJkrR6HJFRf11JRmEMAmIIYT/gR0AF8GKM8f9qXP9LIALjallrCrAQ2D2E0DzGuLjxYhe/0aPh\nnHNg4kRo1y7tNJIkSVI2XXMNTJoEkydDq1Zpp5EkSYXOBnP9/ZykYVwJvAJsX/1zgBBCmAL0iTHO\nqX7uB9WPb9dcKMZYFUJ4H+gAtAf+3ZjBi9nUqdCvH4waBT/6UdppJEmSpGy6804YNiypn9dbL+00\nkiSpGDgio/42IhlrcR6wFNiDZL7yjiS7lDsCy89TXqf68YsVrLfs+XVznrREvPoqHHJIMh5j113T\nTiNJkiRl0/Dhye7l8eNhk03STiNJkoqFDeb6W/bPbDGwf4zx+Rjjwhjjm8DBwCygUwjBVmcevPsu\n7Lsv3HEHdO2adhpJkiQpm8aOhTPPhGeegfbt004jSZKKiSMy6u/z6sdXYowfLv9CjPHrEMI44Hhg\nF+AF/rdDeR1qt+z5z1fwOgMGDPjmx507d6Zz5871Dl2MZs+Gbt1gwADo0yftNJIkqTGVl5dTXl6e\ndgypIP31r9C3L4wcCTvskHYaSZJUbEKMceVX6RshhH7AEOCZGON+tbw+EDgH+F2McWAI4X7gKOCo\nGOPDNa5tStKAbg6sWdshfyGE6Pfou+bNg44d4eij4cIL004jSZLyLYRAjDGknUPZYd1cu9deS+70\nu/de6NEj7TSSJCmf8lUzOyKj/iaSHOrXYQWvb1/9+H714ySSmc21lXOdgFbAX2prLqt2X32VjMXo\n2RMuuCDtNJIkSVI2TZ+e1M2DB9tcliRJjccGcz3FGGcCo4AtQghnLf9aCKEb0B34DBhb/fSjwBzg\niBDCTstduwZwNUmz+s48RC8KixbBwQfDj34EAwdCcN+SJEmS9B3/+U+yc/nii+Hww9NOI0mSipkj\nMhoghNAW+AuwOckO5VeA9kBvYClweIzxyeWu7w2MABYBw4F5wAHA94ERMcYj6vgsb/WrVlUFRx4J\nixfDiBHQzAnikiSVLEdkqCbr5v/57DPo1AkOOwwuuSTtNJIkKS35qpltMDdQCGF94DKSRvEmwJfA\nFOD3McaXa7l+N+BiYDegDHiXZJbzrXVVwhbKiRjhlFPg3Xdh9GgoK0s7kSRJSpMNZtVk3ZxYsCA5\nCHuXXWDQIO/4kySplNlgFmChvMxFF8Gzz8KkSbDWWmmnkSRJabPBrJqsm6GyEnr3ho03hqFDoYkD\nESVJKmn5qpkdMqDMu+kmeOIJmDrV5rIkSZJUm6VL4bjjoEULuPtum8uSJCl/bDAr0+65Jzn1eto0\n2GCDtNNIkiRJ2RMjnH46zJ4NY8d6VokkScovSw9l1pNPJqMxysth883TTiNJkiRl0+WXw/PPw3PP\nQcuWaaeRJEmlxgazMum55+Ckk+CZZ+AHP0g7jSRJkpRNt9wCDz+cjJNbZ52000iSpFJkg1mZ8/LL\ncNhhMGIE7LRT2mkkSZKkbLr//uS8kqlTYaON0k4jSZJKlQ1mZcq//gX77w9//jN07px2GkmSJCmb\nRo2C886DSZNgyy3TTiNJkkqZDWZlxsyZ0L07XHcdHHhg2mkkSZKkbJoyBU44AZ5+Gjp0SDuNJEkq\ndU3SDiABfPopdOsGZ54Jxx2XdhpJkiQpm155Bfr0gYcegl12STuNJEmSDWZlwPz5sO++cMgh0L9/\n2mkkSZKkbHrnHdhvP7jrLujSJe00kiRJiRBjTDuD6hBCiMX8PaqoSJrL3/8+3HknhJB2IkmSVAhC\nCMQYrRz0jWKvm2fNgr32gksuScZjSJIkrUy+amYbzBlXzIXykiVw6KHQogU8+CA0bZp2IkmSVChs\nMKumYq6b586Fjh3h2GPh/PPTTiNJkgpFvmpmD/lTKmKEk06ChQvh4YdtLkuSJEm1+eqrZCxGr142\nlyVJUjbZYFbexQjnnQdvvQUTJiQ7mCVJkiR926JFcNBBsMMO8Pvfp51GkiSpdjaYlXfXXw9jx8KU\nKdC6ddppJEmSpOypqoKjj4a1104O9fOsEkmSlFU2mJVXf/pT8jVtGrRpk3YaSZIkKXtihFNPhc8+\ng9GjHScnSZKyzQaz8mbECLjiCpg8GTbdNO00kiRJUjZddBG8+ipMnAhrrJF2GkmSpLrZYFZePPss\nnHYajB8P22yTdhpJkiQpm268EUaOhKlTYa210k4jSZK0cjaY1eheeAF+9St4/HH48Y/TTiNJkiRl\n09ChcNttyTi59ddPO40kSdKqscGsRvXmm3DAAXDPPbDnnmmnkSRJkrLpiSfgkkugvBw22yztNJIk\nSavOBrMazYwZ0KMHDBoE++2XdhpJkiQpmyZNgpNPhrFj4fvfTzuNJElS/TRJO4CK08cfQ9eucMEF\nyXgMSZIkSd/18stwxBHJgdg/+1naaSRJkurPBrNy7vPPoXt3OPro5GA/SZIkSd/11lvQqxfcfTd0\n6pR2GkmSpIYJMca0M6gOIYRYSN+jhQuTsRg//jEMHgwhpJ1IkiQVoxACMUYrDX2j0OrmmTOTM0qu\nvhqOOSbtNJIkqRjlq2a2wZxxhVQoL14MBx0E664L990HTdwfL0mSGokNZtVUSHXzp5/CXnvBKafA\nWWelnUaSJBWrfNXMtgCVE0uXwvHHQ4xwzz02lyVJkqTafPllcsffoYfaXJYkScWhWdoBVPhihLPP\nhhkzYNw4aN487USSJElS9lRUQO/e8ItfwJVXpp1GkiQpN2wwa7VddRVMngzl5dCqVdppJEmSpOxZ\nsgSOOAK+9z249VbPKpEkScXDBrNWy223wf33w9SpyexlSZIkSd+2dCmceCIsWgSPPOI4OUmSVFxs\nMKvBHnwQrr8epkxJdmJIkiRJ+rYY4bzz4O23Yfx4aNEi7USSJEm5ZYNZDTJmTDJ3eeJE2GqrtNNI\nkiRJ2XTddfDss8lIudat004jSZKUezaYVW/TpsGxx8JTT8H226edRpIkScqmu+6CIUOS+nm99dJO\nI0mS1DhsMKteXnsNDjkEHngAdtst7TSSJElSNj3ySHIY9pQpsMkmaaeRJElqPDaYtcreew969kxO\nve7WLe00kiRJUjaNGwenn56Mxth667TTSJIkNS4bzFolH32UNJUvuwwOOyztNJIkSVI2Pf889O0L\nTzwBO+6YdhpJkqTG1yTtAMq+zz6D7t3hhBPg5JPTTiNJkiRl0xtvwIEHwr33wh57pJ1GkiQpP2ww\nq04LFsB++yW7l3/3u7TTSJIkSdn0/vvQowfcfHMyVk6SJKlUhBhj2hlUhxBCTOt7VFkJBxyQHEoy\ndCiEkEoMSZKk7wghEGO0OtE30qyb//tf2HNP6N8ffvObVCJIkiR9R75qZhvMGZdWoVxVBb/6FVRU\nwKOPQjOndUuSpAyxwaya0qqbP/8cOneGQw6BSy/N+8dLkiStUL5qZtuG+o4Y4bTT4OOP4ZlnbC5L\nkiRJtVm4EHr1ShrMl1ySdhpJkqR02DrUd1x2Gbz0EkyaBGVlaaeRJEmSsmfxYjj0UNhqKxg0yHFy\nkiSpdNlg1rfcfDOMGAFTp8Laa6edRpIkScqepUvhuOOgSZPkrJImHp0uSZJKmA1mfeO++5LdF9Om\nwYYbpp1GkiRJyp4Y4cwzYdYsGDsWmjdPO5EkSVK6bDALgKeeggsuSMZibLFF2mkkSZKkbLriimRD\nRnk5tGyZdhpJkqT02WAWkyfDr38NY8bAD3+YdhpJkiQpmwYPhgcfTMbJrbNO2mkkSZKywQZzifv7\n35PDSYYPh513TjuNJEmSlE0PPAA33JA0lzfeOO00kiRJ2WGDuYS9/Tbstx/88Y+w995pp5EkSZKy\nafRoOOecZJxcu3Zpp5EkScoWG8wlatYs6NYNrrkGDjoo7TSSJElSNk2dCv36wdNPQ4cOaaeRJEnK\nniZpB1D+zZmTNJdPOw2OPz7tNJIkSVI2vfoqHHJIMnd5l13STiNJkpRNNphLzPz5sO++0Ls3nHtu\n2mkkSZKkbHrnnaRuvuMO2GeftNNIkiRlV4gxpp1BdQghxFx9jxYtSmYut2+fzF0OISfLSpIk5V0I\ngRij1Yy+kcu6efZs2HNPuOgiOPHEnCwpSZKUd/mqmW0wZ1yuCuWqKjjsMGjSBIYPh6ZNcxBOkiQp\nJTaYVVOu6uZ586BjR+jbFy64IAfBJEmSUpKvmtlD/kpAjHDyyfDll8nhJDaXJUmSpO/66qtkLMa+\n+9pcliRJWlU2mEvAhRfC66/DxImwxhppp5EkSZKyZ9EiOPhg+NGP4Prr004jSZJUOGwwF7mBA5Nd\ny1OmwJprpp1G0v9v777DJanKPI5/X4Y4gGQkCAxRVIIIKILOEERAJCyIiETTICYURFdRQJSVXUUF\nlCBRVCQJqDsEScMQRUmKgAozZFYGlDgMMHPf/eM9za3pW9Vd3bf7dvp9nqee27f7VNXpU6eq3q46\ndY6IiIh0n7lzo0uMxRbTWCUiIiIijdIF5j52+ulw8slw442wzDKdzo2IiIiISPdxh898Bp55BqZM\ngfn1C0lERESkIQqf+tTFF8MRR8DUqbDyyp3OjYiIiIhIdzr8cLjjDrj2Wlh44U7nRkRERKT36AJz\nH7r6avj0p+HKK2GddTqdGxERERGR7nTccXDJJXDDDbD44p3OjYiIiEhv0gXmPnPbbfDRj8JFF8FG\nG3U6NyIiIiIi3emss+DEE+Pi8rLLdjo3IiIiIr1LF5j7yH33wc47wxlnwMSJnc6NiIiIiEh3uvRS\n+PrXozu5VVbpdG5EREREepsuMPeJhx+G7baD730Pdtqp07kREREREelO110HkyfD5ZfDm9/c6dyI\niIiI9L75Op0BGb2nnoJtt4VDD4V99+10bkREREREutOf/gR77gkXXAAbb9zp3IiIiIj0B11g7nHP\nPQfbbw8f+QgcfHCncyMiIiIi0p3uvz+e9DvtNNhyy07nRkRERKR/mLt3Og9Sg5l50TZ6+eW4uLz+\n+jFAidkYZ05ERESkQ8wMd1f0I6+rFTc/8gi8971w9NGw//5jnDERERGRDhmrmFkXmLtcUaA8Zw7s\nthssthj84hcwn9qii4iIyADRBWapVhQ3z5wZF5cPPBC+9KUOZExERESkQ8YqZtZlySaY2UNmNlQw\nPVGVdrUaaYfM7NxG1z80BJ/4BLz2Gpx9ti4ui4iIiEj3MbP968TBQ2b2WiZ9y+Pm55+HHXaA3XfX\nxWURERGRdpm/0xnoUQ48C/wQqL4L8GLBPHcBl+a8f09DK/YYzO+BB+Cqq2DBBRuZW0RERERkzNwF\nHFXw2URgK+CygvlGHTfPng277gqbbALf+U4jc4qIiIhII3SBuXnPuvu3G0h/l7sfPdqVHnMMXHMN\nXH89jB8/2qWJiIiIiLSHu98N3J33mZndnF7+NOfjUcfNc+bAXnvB8svDT36isUpERERE2kkXmHvI\nySfDWWfBjTfCUkt1OjciIiIiIo0zs/WAzYDHyG/BPCruMHkyzJoF558P48a1eg0iIiIikqXee5u3\nkJntbWZfM7MvmNmWZlarPFcys8kp/WQzW7+RlZ13Xjzad9VVsOKKo8x5n5k6dWqns9D1VEb1qYzq\nUxmVo3KqT2VUn8pI+tyBRJdzp+eOyjeKuNkdDjsM7rsPLr5Y3cn1Oh0L+4+2af/RNu0/2qbSDF1g\nbt4KwDnAd4i+mK8F/mFmEwvSbwucnNKfDNxtZtea2Sr1VnTFFXDwwfF3jTVak/l+ooNffSqj+lRG\n9amMylE51acyqk9lJP3KzBYG9gbmAmcUJGs6bj72WLjySpgyBRZdtFW5lk7RsbD/aJv2H23T/qNt\nKs3QBebmnAlsQ1xkXhRYHzgFmABcVtXKYhZwNLAxsFSaJhEXpLcErjazRWqtbN994ZJLYP2G2jyL\niIiIiHSdPYElgcvd/fGqz0YdN592WlxgXnrpVmdbRERERIroAnMT3P3b7j7V3We6+2x3v9fdPwP8\nABhPZrTslOYod7/L3Z9P043AdsAfgLWAT9Za389/Dptv3r7vIyIiIiIyRiYT3WOcWv1BK+Lm3/8e\nVlqpHdkWERERkSKW3+2ZNMPM1gT+ATzj7suVSP8J4DTg1+6+R0EabSARERGRHO5unc6DlGdmbwXu\nAR4FJhT0v1w0r+JmERERkSaMRcw8f7tXMGBmpr9le3yrm14/nERERESkT9Qb3K8Wxc0iIiIiXUpd\nZLTWu9Pf6W1KLyIiIiLSc8xsIWAfYnC/M5tYhOJmERERkSE0XlUAACAASURBVC6lC8wNMrN1zWx8\nzvsTgB8TrTJ+nnl/IzMb0ZrCzLYBvpjS/6Jd+RURERER6QIfJgbtuyxncD9AcbOIiIhIr1IXGY3b\nEzjUzKYBDwMvAGsCOwILAVOA4zLpfwCsbWY3A4+l9zYAtiaC5G+4+61jlHcRERERkU6oDO730xpp\nFDeLiIiI9CC1YG7cdcDvgDWAvYAvAROBG4D93H1nd5+TSX8OcAewCTHq9UHECNizAAOOMbOhqumJ\n7ArNbLWcNNnp3HZ/6U4ys23M7BIze9LMZpvZ42Z2hZltn5N2czO7zMyeMbNZZna3mR1sZn1d12uU\n0Q6ZNANXj8xs/zrfecjMXsuZb2DqUaNlNIj1qMLMdjSz35vZo6lePGhmF5jZZgXpB6YeVZQtowGv\nR58ys1vN7AUze9HM/mhmB+a12kzpB7EelSqjQa5HvcbM1gW2IAb3u7xG0ry4eUMiZi6Km3We6lE6\nr/YXxQD9RzFL/1GM1ZvMbHczO8HMppnZc2kbnFNnnob3R4trA39I9eNZM7vOzHYsm0+1YG6Qu08D\npjWQ/izgrOr3zWwGsATwQyJgznqxYHF3AZfmvH9P2fz0GjP7H+DLxA+S3wBPA8sBGwNbAldk0u4C\nXAS8DJwP/AvYiSjjzYnW532nThlNYuQPuUGqR3cBRxV8NhHYCrgs++YA1qOGyygz36DUI8zsv4HD\niP3r0vR3LWBnYHcz29fdz82kH7R61HAZJYNWj35J3Jz+J3AucbN5W+Bkon/ZA6rSD2I9aqiMkoGq\nR73I3e+nRMOWvLjZzDYEdimYReepHqXzan9RDNB/FLP0H8VYPe0bxBNdLxJPeK1bK3Ez+6OZfR84\nhLiu9FNgQeAjwO/M7HPuflLdXLq7pg5MwAxgesm0qwFDwJmdzvcYl9Gn0vc+A5g/5/NxmdeLA0+l\nHWijzPsLAjcRA8p8uNPfqcNlNJD1qEbZ3ZzqxY6DXo8aLKOBq0fAG4E5wBPAMlWfTUrl8cAg16Mm\nymgQ69F/pO/8D2CpzPvzA79N9WLXAa9HjZbRwNUjTSPqjM5TPTjpvNpfk2KA/psUs/TfpBirt6d0\nLF0z83oIOKcgbcP7I3GDYQj4G/CGzPurEjcMZwGr1sunHlWQrmRmCwLfIfq5PtDn7XYEAHefm/l3\nD2BZ4FfufmcmzavE3R4jHrPsG02UkSRmth6wGfA487Z6Grh6VKRGGQ2i1YiWd39w92eyH7j79URf\n/Mtl3h7EetRoGQ2iXYk+ZI9z939X3kzH7m8S9eJzmfSDWI8aLSMZYDpP9TSdV/uLYoD+o5il/yjG\n6mHufr27P1gyeTP740FE/TjG3Z/PzPMI8BNivLmP1VuxusjorIXMbG/irsBLwJ+Bae4+VJB+JTOb\nDCwDPAPc4u5/GZusjrltiUDkB4Cnfl/eBswGbvORA7xsRewQV+Ysaxpxx2VzM1vA3Uf0udujGi2j\nikGqR0UOJOrL6Z5uzSWDWI+KFJVRxSDVo38ArwLvNLNlsj+ezGwicZf44kz6QaxHjZZRxSDVoxXS\n3xk5n01Pf99rZvOnYH8Q61GjZVQxSPVIhuk81bt0Xu0vigH6j2KW/qMYa3A0sz9ulf7mzXM5cRNi\na+BbtVasC8ydtQIxmEmFATPM7GMefT1X2zZNr6c3s6nA/u7+aPuy2RGbEjvFq8CdwHrpf4jvPQ34\nkLs/nd57c/r79+oFufvc1Of1W4nBGf/WzoyPoUbLqGKQ6tEIZrYwsDfxaMgZVR8PYj0aoU4ZVQxM\nPXL3f5vZV4ibOfea2aVEgLUW0ZfVlcCnM7MMXD1qoowqBqYeEY+XAaye89ka6e/86fXfGcB6RONl\nVDFI9UjQearX6bzaXxQD9CXFLP1HMdbgaGh/NLPxwMrAC+7+z5zl/SP9XafeitVFRuecCWxDXGRe\nFFgfOAWYAFxmZutn0s4CjiYGbVsqTZOAa4mB7q42s0XGKuNjZHnigvthRF8wWxB3vzcggpSJwAWZ\n9Eukv88VLK/y/pItz2nnNFpGg1iP8uxJ1IPL3f3xqs8GsR7lqVVGA1mP3P0EYHci8Pok8NX0/yPA\nz6pu5AxkPWqwjAaxHk0hjtmHmNlSlTfNbH6iLCoqnw1iPWq0jAaxHknQearH6bzaXxQD9B3FLP1H\nMdbgaHR/bNn+qwvMHeLu33b3qe4+091nu/u97v4Z4s7veOCoTNqZ7n6Uu9/l7s+n6UZgO+APxN3h\nT3bie7RRpW6+Buzk7re4+yx3/yuwGzFy5iQze1fHcth5DZXRgNajPJOJlt6ndjojXaywjAa1HqWW\nORcRNwfXJG4Mbkw8ZnaumR3bwex1hUbKaEDr0XnAFUTZ3Gtmp5jZj4jRubcgfoRD3DAcVA2V0YDW\nIwk6T/U4nVf7i2KAvqOYpf8oxpK20wXm7nNK+juxXkKPAdxOJ+5E1U3fY55Nf++sfvTC3V9muG+Y\nd6a/lbsqS5Cv8v6zBZ/3okbLKFef16N5mNlbiRFSHyP6Eqo2iPVoHiXKKFc/1yMzmwQcC1zq7oe5\n+0PpxuBdxIjMjwOHmtmENMvA1aMmyihXP9cjj/EVdgL+kxjZeb80/Q3YnBgEifQZDGA9aqKMipbT\nt/VIdJ7qBzqv9hfFAP1HMUv/UYw1UBrdH1u2/+oCc/eZmf4u2qb0vaLSN1NRJa6MfFp5LKOSfkS/\nMGY2juhraA7DHdj3g0bLqJZ+rUfV6g0INIj1qFq9MqqlX+vRB4kymVr9QbqZcxtxPt0ovT2I9ajR\nMqqlX+sR7j7X3b/n7hu6+3h3X9rddwceBtYGnnb3h1PyQaxHjZZRLX1bj0TnqT6g82p/UQzQhxSz\n9B/FWAOjof3R3WcRNwIXM7M35ixv7fR3RJ/O1XSBufu8O/0te/BtNH2vuIYIVN5a8Pl66W9lFNRr\nibto2+eknUR0O3KT99eotY2WUS39Wo9eZ2YLAfsQAwKdWZBsEOvR60qWUS39Wo8WSn+XK/i88v6r\n6e8g1qNGy6iWfq1HtewFLAicm3lvEOtRLXllVMsg1qO+p/NU39B5tb8oBhgsiln6j2Ks/tLM/nht\n+ps3zwfS32vqrtndNY3xBKwLjM95fwIxQuNc4KuZ9zcCLCf9NsDLKf1mnf5ebSinS9N3+2LV++9P\n7z8NLJ7eW5x4nONlYONM2oWAm1P6PTr9nTpcRgNZjzLfc1+iT6lLa6QZyHrUYBkNXD0C9kjl8gSw\nUtVnO6Tv/BKw1KDWoybKaODqUaVu5Lz3dqIlyExghWzaQatHTZTRQNajQZ50nuqPSefV/poUA/Tn\npJil/ybFWP0xEReJh4BzirZzo/sjcdNgiGilvGTm/QnAM8Sgj6vWy5ulmWQMmdmRwKHANOJxhBeI\nztZ3JDb6FGA3d5+T0l9HNEu/mehvDmADYGuiBes33P27Y/kdxoKZrQzcBKxC3FG5E1gD2IWo/Hu6\n+6WZ9LsAFwKvEJ3Y/wvYmXg04EJ3/8iYfoEx0EgZDWo9qjCzG4j+pXZy98tqpBu4elRRpowGsR6Z\nmRGDYrwPeBG4BPg/4umBHVOyg939x5l5BqoeNVpGg1iPAMzsViLYu4c497+FKJ+XiP3uxqr0A1WP\noLEyGtR6NMh0nuoPOq/2F8UA/UkxS/9RjNW70v61a/p3BWKwxenADem9p939sKr0De2PZvZ94EtE\ndxkXEa3a9wSWBj7n7ifXzWinr74P4kR0hv5L4N60oV8B/kkMyrZ3TvqPAb9NFeh54qDwEPEIwxad\n/j5tLqtlgOOJbh5mE3diLgI2KUj/buB/ibssLwF3A18g5+5bv0xly2jA69G6xAX3h8rUhQGtR6XK\naFDrETAu1YGbiX7PXyV+PP0G2Eb1qLEyGuB6dCjwx3Tufxl4ADiBqhZfA16PSpfRoNajQZ10nuqv\nSefV/poUA/TfpJil/ybFWL07AUcSLY+Lpgdz5ml4fyQGfvwDcQPiOaIR4w5l86kWzCIiIiIiIiIi\nIiLSFA3yJyIiIiIiIiIiIiJN0QVmEREREREREREREWmKLjCLiIiIiIiIiIiISFN0gVlERERERERE\nREREmqILzCIiIiIiIiIiIiLSFF1gFhEREREREREREZGm6AKziIiIiIiIiIiIiDRFF5hFRERERERE\nREREpCm6wCwywMxsKE3XdjovImPBzI7M1PuJnc5Pu2jfFhERaS2dW2XQKG4WkUbM3+kMiDTLzM4A\nPpb+dWANd394jNa9GnBA+nequ18/FuttE2/nws3sp8AnM28d5e5Ht3OdUp6ZvRnYBdgKWAtYBlgM\neB6YCfwZuBW4eKz2rzHS1nrfamZ2MLAk8Ky7H19ytp76jmWZ2ZHAkQUfv0bU3enALcDZ7n7XGOZt\nErBl+vcsd39krNYtIlKL4uaWafm51czWB/YBtgXeBLyBiMEeB24CrnL3K1q9Xmmc4ubeoLh5mOJm\nGUu6wCw9yczGA3sw74ngAOBbY5SFCcSB2tPUy4Fy25jZlsAn6NMTdi8zszWA7wG7Apbezm6npdL0\nZmJfOy7d1T/S3W8ay7wKAF8EVgMeAsoEyl71tx/lfbf5gaXTtCnwBTM7FfiMu49FWWzJ8LnhOkCB\nsoh0nOLm7mRmiwA/JBpizMe822elNL2TuDGw9JhnUF6nuLnnKG4eSXGztJ0uMEuv2oO4W1w58Bmw\nP2MXKEsdZrYwcFr69yXm3V7SQWa2A/BL4s6+A3OAaWl6DHgOWAJYAdgCeA+x/bYG3oF+5HRK6f3H\n3ce1MyNd5HzgvMz/CwArAzsR9RVgMjALOHRssyYi0jUUN3cZM1sUmAJMJLbLw8CvgXuIFoVLAOsC\n2xPnNekQxc09S3HzSIqbpa10gVl61QHp72vABcRjZauZ2Vbufl3HciVZ3wLWJAKvC4BDOpsdATCz\nzYFLieO/A5cAh7n7jBrzLAJ8HPgyEUBLZ1j9JAPnfnf/bc77J5jZ54AT0v+fM7MfufujY5g3EZFu\ncUD6q7i5e5zK8MXlY4Cj3X1OTrqvmpkuMHeI4uaeprh5JMXN0lYa5E96jpmtznBAdgXwg8zHH8ud\nqQ3ZGKP19CQzewdxQdmBLwAvdDZHAmBmbwAuZPjm4vHu/qFaQTKAu7/s7j8B3gac3OZsirSEu/8Y\n+FP6d37gAx3MjohIRyhu7j5mtj3wUWKb/Mjdjyi4uAyAuz8+ZpmT1ylulkGiuFlaQReYpRd9jOFA\n9WepI/q/pvd2M7PFGlmYmW1sZseb2V1m9rSZvWpmz5jZrWb2fTN7ZybtJDMbAiojzBpwVGbk2den\nqnVMTe/PLZGfumnNbBUz+6yZXWBm95vZC2b2ipn908yuM7OvpKBozJnZOOB04vjyG3e/tA3ruCWV\n0StmtlyJ9Mun7TpkZrfUSLeMmX3dzKaZ2ZNp+U+l/w9LjzPWW9d7zOw7ZnaNmT1uZrPN7EUzm25m\nvzKzD5ZYxogRm81smzT/dDN7OX22ar1lVfkcsGJ6fRvRsqI0d5/l7ocX5HlSJs9HpPfWMbMfmdm9\nZvZc+my/qvneYWbfMLPLzezh9N1mmdkjZnaJme1tZqXPVWa2j5ldbWYz03IeMLNTzOytJecvPVp2\nmbRmtriZfdTMTjezO8zs35ljzO3pGLNGjXU8lI4nq6W3JuQdb3LKtfRo2Gb2YTO7MFP+/zazu83s\nODNbs868edt9lTTvfanu/9vMbjKzg9LxYSzdkHm9Tq2Eo9l3K3WB4UFUDJhqI7dT4fYws83M7CQz\n+2sqs5fTNjnPzBTki0izFDd3X9x8WPr7AvDNdq/MFDcrbi5gipsr8yluDoqbZXTcXZOmnpmIg89D\nwBDwDLBAev8r6b25wCdLLms80Z/WUGbe6qny2Sppnkk5n+XNN6dqXddV0pbIV820KQ9l8vxPYIs6\n66os49oWbqP/TMt9DlgpvXdkZl1HtGAdB2SW9+US6b+aSX9AjWU+V6dcnwQ2q7Ges0pum8uAxWos\nJ1tek4AfFyxz1QbL7cnMcnZs8b45KbPsI4B9if67qvO8X2aeI0qW1x+BFeusfxHgyoLlDaW87F1V\nthPrlP2IzxtJS/RrNrvEd3yVGEwjbx0zqtLmLWOeci27bwPLAzfXyN8Q8ArwlQa2+w7AswXbYIho\nPbfAKOta6eMJ8F+ZtD9p176b8lTm3HBNzrzjif7wam2HIeB3eevWpEmTpqIJxc3Z81RXxM3Aqpl1\nnz1G9eCATN4VN5cvN8XNipuzaRQ3t2jfRXFz307qg1l6zdZEYObABe7+Wnr/F8QB0YiWGqfXWoiZ\nLQRMBTZJy5pN9El3E/Bv4A3AesSjIWsz3PLjHuA/0mffSfNWd5bfbgun9d5DfIf7iB8NCwOrEKMb\nbwwsB/zOzN7u7mMyIquZrU2cLB043N2faNOqziMe8VyCGHn7+3XSfyL9fYHYXvMws4OJUbydGJDw\nIiKAeAZYlhhgZWfgjcBVZrapu9+fs56Fibp0PdHS4cG0vOWIu8D7EgN9bAecA+xW4rt+hQg+ngTO\nJrb7/MSo4q+UmL/yHTdI+Qd41t2nlJ23Ce8BvkEMgnI6UZaziZG1/y+TbhGiP8ib0/QAMbDN0sDq\nRB+RKxP1+RIz28Ld5xas80JgW2IbvgCcQTzmtQARzO2T3ruqVV+yhPmABYl+yK8G/kz8gB0i9tXN\niXo1DjjRzB53999ULeNTRCB1GlGPZhKDb1S7o5GMWbRYu4E4vjlRv84kWrWNJ8pyD6L8jjWz+dz9\n2DqLfQdRXyEeCb2VqKObAJ8mBrzZFjgcOKqR/I7C2zKvax0HR7vv/gq4E9gL2JMo028S+2vW09l/\nzGxB4BrgXWmeB4lj1H3EvrEWsF/Kw45E34/b1vzGIiLDFDd3X9z8XqJ8nDjfYGa7EfHsRsRAcs8Q\nFwnPdfcLW7BOxc2Km6spbm6A4uYRFDdLvk5f4dakqZGJCIgrd6o2r/rsqsxn69RZzkmZtLcDK9dI\nuyXwhqr35rn7WCLfrWyJsQrwtjrL+AgRpMwFzqiRbtQtMaqWd31a5q2AZd5vaQvmtMzjM8ssvGOe\ntl8l3Uk5n29C3AmfSwRWuXWBOEm9ktLdXJBmi+q6UvX5IsRJsJKf9xaky5bXEPGDaFR3YIGDMsu8\nvBXboGr5k6ry/Djw5jrzbAwsX+Pz+YkfRJXl7luQbp9MmhnktFAB3k0E0Nm73e1uiTEf8P46y1gP\neCIt4x810s1I65pecnvU3LeJQLaSZiqweE6a9zHcmuYVYP0S2306sEZOuk0z+9nTjKI1BiWPJ8Db\n0zrLHCfase/WrD8p/Q8z6b8LzJeTZhzDLUXmApObLTtNmjQN1oTiZuiyuLnquL8H8OvM/3mt8K4H\nlmlBXVDc3Fh5KW5W3Jz9XHHzvGkVN2vKndQHs/QMi77R/oO4WzXD3W+uSnJO5nXhoCVmtgpx996J\nO+07eI3BM9x9qrs/33TGW8zdH3X3v9ZJcx7xo8KAPcei/yYzO5BolTGHOJB7m1d5Sub1J2uk+1Tm\ndV4LnSOIgOwF4INFdcGj5cKxRJm+y8w2y0lzU6264u4vE61CXkpv7Vsj36R1vQjs6e4v1klbz0qZ\n19NHuaxaKq1yJrv732oldPfb3f2pGp/PIfq7m5HeKiqvQzKv9/WclkfufgvxyOeYcfchd/99nTT3\nAF8nym0Ni9HK28rMliUebXWi5cse7j5iIE53v5poTWDEPnJYdZrsYtPy9nH3EfXL3f9IBJoGLEW0\nJGo5MxtnZqtajIR9NcMD89zq7tOK5mvDvlsmrysQP2Ad+LW7f83dh3LWPZc4jlXK9ZDqNCIi1RQ3\nhy6Mm1fIvP42sY1eJi7i70dc/DueiP+ciK0vM7PRPnmsuLkxipsVNwOKm/MobpYiusAsvWQv4m4Y\nwM9zPv81mYOYmRWNWL0nwwfPE2qdqHtc5YfEIsAG7VyRma0E/DfDo2H/uZ3rA3D3+4hHlQzY3cyW\nyMnXUsQjOQ7c6e53VH2+JPE4pwO/cvf/q15GlV9kXr+/yXy/CPwl5ftd9ZITJ9B/NrOuKstkXj9b\nK6GZbVEwIMY8g1LUyPPD3qJHCVPg8AeivEYEV2Y2gbjj7sDt7n5jjcWdTp3v3iHZAXTq1YlW2BFY\nKL3+mbvPrJH2JOJHpAE71ziuVvax6gsYWddmXpcaPKaEeQaLIh6Pewg4gQjIHbiXco/V1tTgvlvG\nh4lHQQGOq7PuOQz/0FjbGh+oSEQGj+LmxoxV3Lxk5vU6xCP8m7j75939l+7+K3c/hIhtKl3NbcIo\nL5Iobm6Y4mbFzRWKm5uguHkwqQ9m6SXZ1hW/qP7Q3WeZ2SXEnf8Vif63LstZznsyr3/X0hyOIYtR\nuvcBNgPWABYn+n3K8yain6N2OZnof28Gw6PBjoVTiZYdCxN3Rn9c9fl+REDgwE9z5t+CuNHmwFwz\n26XO+rLl+5a8BKlvqA8DuwAbEv23LcZwf4RZb6qzPph3NN+xNJoW6LWC1XmkwGtXYHei78GViLqc\ndwN0cTNbvKrVwKaZ19fUWpe7v2pmNwJ1RyRvJTNbDdifeOx0XeLH5cIFycvUidHK/uCo11Lk5VRm\nOxDb5a1Ef3N5bq2z3mwrp6XqZbKkonpaaRnyFeDH7v5qvQW1Yd+t572Z16uklhm1ZMvsLdTuG09E\nRHFzRhfFzZX4pnKeOjhd/J2Hu083s08zXOZfAP5nlOtW3Nw+iptbRHHz6xQ3z0txcw/QBWbpCWb2\nFuLg7sAt7v5gQdJziOARIrDOC5SzB7gRAV23M7MFiEEXKt8ze6KoPmlUDvBvaGN+PgzslNb92fRI\nTDPLWYZ5f8RUe8Tdq4P9i4jHCJcmHverDpQrg5TMIgYTqDYh8/ozaSqVXXJO9Ga2PtEiaK30Vr1t\nU2a7FD6G2qBnMq+XLEwV7iUe2czaBvg85QLoUnk2s5WB3xCDXED5upwNlLOPMD5QYrVl0rSMmX2R\n6COs0vLBq/6+njT9bdu+mrFi5vXfS6T/OxEoV+YtCpSfLni/Iju4TtEPhUZlB4syIrjdlBj5fGHi\nUdHrqHOhoE37bj0TMq8vaHDeVv3QEJE+pLh5WLfFzcwbwzxHjeO/u08xsyeIWGdFM3tzpRsFxc2F\nFDen5Omv4ubRU9xcRXGzFNEFZukVH8+8HtEKI+Na4iS9MrCTmS3t7v+qSlM5wM0tc3euC51EBMlO\nnHguI0aafpx41LEyWnAlsIHo7L7l0qN0J6S8XOjuV4xicesRI70WOZt560HlzvrZwKHA+mmU6j+m\nvL0rLdOB8/P6yiJG0359cQ3md55WL6ksriZGz3bgUeB/gfuJxx9nZ9ZxDDFKb5luipq6YJ/jiczr\nNWoldPd/A7/Nvpe+X1l185z6Evw9cUfZiSDrt8Towf8kyqvSr9bBwFbpdXVdXizzelaJvL1UP0lr\nmNnexGArnqYbiMF6HiKC/crxZ3mGWwq1vb90okVFRZnyyPZjuHhhquHtNZbud/ffVr33UzP7HtEi\naAXgCjPbsOhR3jbuu/UsQfEPp3oWrJ9ERAaY4uZhXRM3J5UuBxz4c14folXuYPii4JpApZ9exc35\nFDcrbm41xc0ZipulFl1glq6XBtqoBIYGnGRmJ5WYdQHibtyJVe9XOqQfZ2YLdmGwXHgATo8MfZwo\ni8eI0VYfLkg7Fo8M7USc5B2YaWaHF6SbmH2dSfeHNCBCRa2TRdFnpzLcL92niB8NAJMzafIGKYF5\nA4CPu/vPaqy/ns8DyxH5PBv4VNGPBjP7xijW06ybKqunTQNFNGgvhoPk3wO7FbV+N7N98t5Psttw\nfIn1Llo6h/XVC5aOTn/nADsVDVxiZq3qV62s7I/GMuWR/TGS94Oz67j7383sE0RLn2WB04jjVZ5O\n7bs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a31427a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(121)\n",
    "plt.scatter(data.fouryrgrad,lm4.predict(data),s=500)\n",
    "plt.plot(range(1,100),range(1,100))\n",
    "plt.ylabel('Predicted 4-year \\n Graduation Rate',fontsize=30)\n",
    "plt.xlabel('Actual 4-year Graduation Rate',fontsize=30)\n",
    "plt.tick_params(axis='both', which='major', labelsize=20)\n",
    "plt.ylim([55,95])\n",
    "plt.xlim([55,95])\n",
    "plt.subplot(122)\n",
    "plt.scatter(data6yr.sixyrgrad,lm6.predict(data6yr),s=500)\n",
    "plt.plot(range(1,110),range(1,110))\n",
    "plt.ylabel('Predicted 6-year \\n Graduation Rate',fontsize=30)\n",
    "plt.xlabel('Actual 6-year Graduation Rate',fontsize=30)\n",
    "plt.tick_params(axis='both', which='major', labelsize=20)\n",
    "plt.ylim([75,100])\n",
    "plt.xlim([75,100])\n",
    "plt.suptitle('Predicted vs. Actual Dorm Graduation Rates\\n\\n\\n',fontsize=30,y=1.05)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oLgEzACwR23fen7MjrV10jzs7MpbtO/d1qCIAAGif61zoLgEzACwBXz46lAePDmW0TtGI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\niIiIiIiIiIiImJb/D4bjKBzXc1HOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127caf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(121)\n",
    "plt.plot(data.fouryrgrad - lm4.predict(data),'o',markersize=20)\n",
    "plt.xticks(range(11),data.Dorm,rotation='vertical');\n",
    "plt.ylabel('Residuals',fontsize=30)\n",
    "plt.tick_params(axis='both', which='major', labelsize=20)\n",
    "plt.subplot(122)\n",
    "plt.plot(data.sixyrgrad - lm6.predict(data),'o',markersize=20)\n",
    "plt.xticks(range(11),data.Dorm,rotation='vertical');\n",
    "plt.tick_params(axis='both', which='major', labelsize=20)\n",
    "plt.suptitle('Residuals\\n\\n\\n',fontsize=30,y=1.05)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 94.22244227,  97.07675347,  93.76870783,  86.46096478,\n",
       "        94.07552983,  95.42980641,  95.82918733,  88.07375545,\n",
       "        87.53594202,  82.32915778,  92.92755923])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(lm6.predict(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}