{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: import all the necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "# activate plot theme\n",
    "import qeds\n",
    "\n",
    "qeds.themes.mpl_style();\n",
    "plotly_template = qeds.themes.plotly_template()\n",
    "colors = qeds.themes.COLOR_CYCLE\n",
    "\n",
    "from sklearn import (linear_model, metrics, model_selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('Airfares.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Explore the numerical predictors and outcome (FARE) by creating a correlation table, heat map, and examining some scatterplots between FARE and those predictors. What seems to be the best single predictor of FARE?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUPON</th>\n",
       "      <th>NEW</th>\n",
       "      <th>HI</th>\n",
       "      <th>S_INCOME</th>\n",
       "      <th>E_INCOME</th>\n",
       "      <th>S_POP</th>\n",
       "      <th>E_POP</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>PAX</th>\n",
       "      <th>FARE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COUPON</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020223</td>\n",
       "      <td>-0.347252</td>\n",
       "      <td>-0.088403</td>\n",
       "      <td>0.046889</td>\n",
       "      <td>-0.107763</td>\n",
       "      <td>0.094970</td>\n",
       "      <td>0.746805</td>\n",
       "      <td>-0.336974</td>\n",
       "      <td>0.496537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW</th>\n",
       "      <td>0.020223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054147</td>\n",
       "      <td>0.026597</td>\n",
       "      <td>0.113377</td>\n",
       "      <td>-0.016672</td>\n",
       "      <td>0.058568</td>\n",
       "      <td>0.080965</td>\n",
       "      <td>0.010495</td>\n",
       "      <td>0.091730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HI</th>\n",
       "      <td>-0.347252</td>\n",
       "      <td>0.054147</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027382</td>\n",
       "      <td>0.082393</td>\n",
       "      <td>-0.172495</td>\n",
       "      <td>-0.062456</td>\n",
       "      <td>-0.312375</td>\n",
       "      <td>-0.168961</td>\n",
       "      <td>0.025195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S_INCOME</th>\n",
       "      <td>-0.088403</td>\n",
       "      <td>0.026597</td>\n",
       "      <td>-0.027382</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.138864</td>\n",
       "      <td>0.517187</td>\n",
       "      <td>-0.272280</td>\n",
       "      <td>0.028153</td>\n",
       "      <td>0.138197</td>\n",
       "      <td>0.209135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E_INCOME</th>\n",
       "      <td>0.046889</td>\n",
       "      <td>0.113377</td>\n",
       "      <td>0.082393</td>\n",
       "      <td>-0.138864</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.144059</td>\n",
       "      <td>0.458418</td>\n",
       "      <td>0.176531</td>\n",
       "      <td>0.259961</td>\n",
       "      <td>0.326092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S_POP</th>\n",
       "      <td>-0.107763</td>\n",
       "      <td>-0.016672</td>\n",
       "      <td>-0.172495</td>\n",
       "      <td>0.517187</td>\n",
       "      <td>-0.144059</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.280143</td>\n",
       "      <td>0.018437</td>\n",
       "      <td>0.284611</td>\n",
       "      <td>0.145097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E_POP</th>\n",
       "      <td>0.094970</td>\n",
       "      <td>0.058568</td>\n",
       "      <td>-0.062456</td>\n",
       "      <td>-0.272280</td>\n",
       "      <td>0.458418</td>\n",
       "      <td>-0.280143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.115640</td>\n",
       "      <td>0.314698</td>\n",
       "      <td>0.285043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DISTANCE</th>\n",
       "      <td>0.746805</td>\n",
       "      <td>0.080965</td>\n",
       "      <td>-0.312375</td>\n",
       "      <td>0.028153</td>\n",
       "      <td>0.176531</td>\n",
       "      <td>0.018437</td>\n",
       "      <td>0.115640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102482</td>\n",
       "      <td>0.670016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAX</th>\n",
       "      <td>-0.336974</td>\n",
       "      <td>0.010495</td>\n",
       "      <td>-0.168961</td>\n",
       "      <td>0.138197</td>\n",
       "      <td>0.259961</td>\n",
       "      <td>0.284611</td>\n",
       "      <td>0.314698</td>\n",
       "      <td>-0.102482</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.090705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FARE</th>\n",
       "      <td>0.496537</td>\n",
       "      <td>0.091730</td>\n",
       "      <td>0.025195</td>\n",
       "      <td>0.209135</td>\n",
       "      <td>0.326092</td>\n",
       "      <td>0.145097</td>\n",
       "      <td>0.285043</td>\n",
       "      <td>0.670016</td>\n",
       "      <td>-0.090705</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            COUPON       NEW        HI  S_INCOME  E_INCOME     S_POP  \\\n",
       "COUPON    1.000000  0.020223 -0.347252 -0.088403  0.046889 -0.107763   \n",
       "NEW       0.020223  1.000000  0.054147  0.026597  0.113377 -0.016672   \n",
       "HI       -0.347252  0.054147  1.000000 -0.027382  0.082393 -0.172495   \n",
       "S_INCOME -0.088403  0.026597 -0.027382  1.000000 -0.138864  0.517187   \n",
       "E_INCOME  0.046889  0.113377  0.082393 -0.138864  1.000000 -0.144059   \n",
       "S_POP    -0.107763 -0.016672 -0.172495  0.517187 -0.144059  1.000000   \n",
       "E_POP     0.094970  0.058568 -0.062456 -0.272280  0.458418 -0.280143   \n",
       "DISTANCE  0.746805  0.080965 -0.312375  0.028153  0.176531  0.018437   \n",
       "PAX      -0.336974  0.010495 -0.168961  0.138197  0.259961  0.284611   \n",
       "FARE      0.496537  0.091730  0.025195  0.209135  0.326092  0.145097   \n",
       "\n",
       "             E_POP  DISTANCE       PAX      FARE  \n",
       "COUPON    0.094970  0.746805 -0.336974  0.496537  \n",
       "NEW       0.058568  0.080965  0.010495  0.091730  \n",
       "HI       -0.062456 -0.312375 -0.168961  0.025195  \n",
       "S_INCOME -0.272280  0.028153  0.138197  0.209135  \n",
       "E_INCOME  0.458418  0.176531  0.259961  0.326092  \n",
       "S_POP    -0.280143  0.018437  0.284611  0.145097  \n",
       "E_POP     1.000000  0.115640  0.314698  0.285043  \n",
       "DISTANCE  0.115640  1.000000 -0.102482  0.670016  \n",
       "PAX       0.314698 -0.102482  1.000000 -0.090705  \n",
       "FARE      0.285043  0.670016 -0.090705  1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_data = df.corr()  \n",
    "correlation_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Otbkp53Ia//1q6upIzSvEy9Gu2XxeDracy8nXWHYuOw93exsML10I0vIKcbWzVjfSbMzNCHZ3ITm78bfS8pT4ujjgaG0JgJO1JX4uDiRnNb8/tYSamhoyL5ynQ4RmF58OEVGkndXeJTDt3Bl8g0I1jofAiI6olIUo83LVy/Zt3kBJcRH9x9x5Q2U5uG0zgVHRWqPm+mRvaY61uRnJV+4rtXVcyC3A27FtNTh0paammvTz5wjpGK2xPCQqmpQzp7TmuXDmFAGhYSgUpo3pO3aiuLCAgtyLDeutrsHYRDPyZqJQkHo2mdqaxuhtdVUVM595nLefmsGPH79HekrrRrGvycgQEw83Ks+e11hcefY8pt6eN7QKy87R1FVUUp7UdFu6zJiG+4tP4TR1MqZ+2iO9bY27XcN15MBV15GjFzKJ8HK74fU0XEfcm0Tlpg/uQbaymLgj2ve9tqq6poZTF1LoEaF5v9E9IoLEZO33G5e9+vWXjHruaR57/x02HdjfksUUf0M6fUygUqlISEjg/vvvx9zcvMn3VlZW7N69m+LiYiZOnNjk+8DAQKKjo9m6datOujoqFApKShrGSWzduhUPDw969erVJN2ECRNYsWIFu3btYty4cerlY8aMYevWrSxbtozJkyffdnlEyyorUVFfV4eljY3GcksbG1JOan8SWFJchI29w1XpbdXf2WnplnRwSzyqwkIie/Rp8l1b4mDV0PAsvKq7T2FpGU7W1+/6seiFadhamGNkaMDsrftZceC4+rvUPCUfLd/E2Zw8zBUm3NUzmq8emcgj384no0C/T12tzBpuPksrqjSWl1ZUYm1u1mw+SzNTSi9qjhkprazCyNAQC1MTSiqqSErPxtzUhKkDuwMNT5uPXshgU+JpdZ5dp86jMDbi8eF9qKuvx8jQkB0nznLwXBqtqfnjwZaSpONa85QUKbGxd2ySHhqOB3tnF3LSU9m6YgnTX30bwxsYE5GXncWF0yeY/OTzt1iTltO4r1RqLC+pqMTmGvtKe1aqUlFXV4e1jZ3GcitbO1THE7XmKVYqsbuqa5m1bcN+oSpS4ujiSkjHaPZu2UTHbj3wDuhA+vmz7N2ykdraGkpVKmzs7XHx8GDyjH/g4eNLZUU529at4cu3/80/3/8vzm43/rCppRhaWGBgaEjtVefM2tIyTK0sr78CA7CIiaLsyDG4IkpbqyqhcNU6qjKyMDAywiI6EqcHp5D76+9UXWjd88LNUl9HSq6+jpTjZH39bbLw+QfV15E5W/ez8mDjuadrgDcDI4J49LsFui10K1CqVNTW1WFvo9kTw8HGlgNJSVrzmJua8dQ9k+gYGISRkRE7Dh/ijVnfUPXIo8T26t0axRZ/Azpt2GVlZVFfX4/3FX2Hr5aR0fDUv7k03t7exMXF3VY56urqOHToEAkJCdxxxx3q323uNx0dHbGwsCA9XXNwtImJCffffz/fffcdI0eOxNa29btSiZtngGaYvaGr0LVC71d9d6l7jLYcJxP2s2nxAsZPfwJbLWOU9GloVDAvjh2o/vzyvFVA094+Ddvn+l2Anv55MeYKBeFerjw2rDdZhcVsONrwVDUpPZuk9Gx12uNp2fz4+GQm9OjIl2u333ZdbkaktzujujRO+jB/h/YusjcylXJ9M9vl8jb0cbKnX1gH1iYkkVFQhIOVBcM7hTIgPJCtSQ1PacO93Ojo68HSvUfJLS7Bzc6a4Z3CUJaWczglQ+v6W9LVxwP19dc8HJpupsZtUlNdzZ/ff8Xwe+7F3tnlhn4/YftmrGztCOqovStba+ro68HYLo1P2OdtPwBo6RFnYHADR0j71uTfub7+mmfJq3eaxm3WsHzYnXehKlLyxduvQX09VrZ2dO03gM2rlmNw6QGAX1AIfleMS/YLDuGTV15ie9waJjz4yO1UR7dusYukWWAHjG1tm3TDrMkvoCa/8aFRVXomRna2WPfuTn4ba9gNjQrmhTED1Z9f+f3SdURL2ubOl1d65pclmCtMCPdyY8bQXmQpi9lw9DQ25mb83/ghvLN4PSVXPVhpT64+juqvcX61s7bm3hEj1Z/D/PxRqkqYt26NNOwADP/aXSRbi04bdrqY8a2+vv6W32URFxfHxo0bqbnU7WPQoEE3FWnT9ruDBg1i2bJlzJ8/n8cee+ya+detW3dDjdLuRvX0rZUdWNcsrKwxMDSkpFgzYlSmUjWJWlxmZWNL6VXpS1UNA+Qtr3oSdzJhPyt/+Z6x02a0yRkxd546z4lLk3wAmFya9MfBykJjhj87S3MKSsqvu75spQqA8xfzcbCyYNrA7uqG3dXq6us5lXkRLwe726jBrTmddZGMDY3/hkZGDceWpZmC4ismKrAwVTSJzFyptKISK1NTjWWWpgpq6+rU46gGRgZxPC1L3UDLLS7BxNiIMV0i2HaiYdbLoR2D2X06Rd3wzS0uwdbCnN6hAa3asGvueChVFWNlo/0hlZWtHSVFV6UvbjgerGxsURUpyc3MYNkv37Psl++BS+f9+nrenvEA9z37EoFXdP2sqanh8K7tdOk3SGMSKn05mZFDer5S/dnoUoPDytxUY1+xMlW065vNa7G0tsbQ0JDiIqXG8pLiIqxs7bTmsbGzQ6UlPTRG7hQKUybPeJK7H34MVVERNvZ27N4Uj6mZOZbW1lrXa2hohHdAB/Kys26rTrpSV1ZGfV0dRldF54wsLRrGxF2HZZdOVKamU5N7/W7XVemZWESG3XJZW8rOU+dJSm+8jiiMtV9H7C3NKbyp60gB9pbmPDigOxuOnsbfxQEna0v+O/UOddrL92Dxrz/BQ9/8QdoVx2pbY2dtjZGhIQVXnS8LVcU4NHN+1SYiIIDVO1v3Yaj4a9Npw87DwwMDAwPS0tK0dnkE8PRs6KeemppKeHjTqZXT09Px8GgctG5ubk5padMTamlpKZaWmiffvn37MmXKFExMTHBwcNC4kfD09CQtTfuTsfz8fMrKyjR+9zJDQ0MefPBB3n33XY1umtrExsYSGxt7zTQAaUPvuG4acfOMjI1x8/Ej5cQxwrp0Vy8/f+IYoTFdtebxDAhk89KF1FRXqceHpJw4jpWtnUZE7sSBvaya/QNjHnyU0C7dWrYit6i8qrpJN8h8VSldO3hzKrNhHIzC2IiOvh7MWr/zptZtYGCgvsA3J8DVkbM5rTuODBrGe1TVaHYTUpVXEuDqSFZhQ6PEyNAQHyd7NjbTMAVILygixEMzCuV/aR11lx5amRgZqv//sqsjwsZGRk0e+NddNxqie8bGxnj4+nM2KVFjNsqzSccI76x9H/YOCGLD4j+orq7C5NLxcDbpGNZ29tg5OVNXW8sTb3+gkWf/5njOJSUy6cnnsXPU7Lp88tABykpUxFwx5b0+VdXUNpnkR1VeQaCrE5mXjh1jQ0N8nO1Zf+SkPorY4oyNTfDyD+B04lE69WiMEpw+dpSobj215vENCmHVH3OprqpSj788fewINvYOOFwVuTUyNlZ32zy0eyfhMV2a7bJbX19PVuoFPHx9dVG121dbR3VmNqYd/DXGyF39WRtDayvMgjtQuGLtDf2Uws2V2htoLLa28qpqyqu0XEcCGq8jJkZGRPl68N2GXTe1bsMrriOnMi/y0Dd/aHz/yOAeWJuZ8r8129Tn7rbKxNiYEF8/9iUdZ3C3xvuN/UnHGdhF+/2GNmfSUnFq5oHK346BvO5AF3S6Fa2trYmJiWH16tWUlzd9klNSUkJMTAzW1tYsWbKkyffJyckcOXKEgQMHqpd5enqSfNVA1NraWs6fP69uJF5maWmJh4cHzs7OTZ4ODxgwgMzMTHbv3t3kdxcvXoyxsXGTVypc1rVrV8LCwvjtt9+0ft9eGJiboQgMQBEYAIYGmLi6oAgMwNi16Tiy9qr70FiO7t7B4R1byMvKZMOCuZQUKYnpPxiALUsX8vtnje/bCu/eCxOFKatm/0huRjqnDh1gd9wqug+NVT89TNq/hxU/f8fA8ffgHRRCSZGSkiIl5aXNv+eqrfhzzxHu7duFfmEB+Ls48PL4IZRXVRN/xZiwV+4cyit3Nr6T7s7uHekV7Iengy2eDraMigljUu8YjWjdgwO60a2DD+72NgS6OfGvOwbTwdWRFVdNY60v+5Iv0DskgBAPF5xtrBjXLZKqmhqOpTVGBsZ1i2Rct8ZueQln07A2N2VYdCiO1pZ08vMk2s+TPadT1GnOZOXS2d+bcC837CzM8XdxZEBEEGeyctU9Fs5k5dI7xJ9ANydsLcwI8XChR7Cf+qaoNfUaNpLDO7dxcNtmcjMzWPvHHFTKQroOHAJA/OL5zP7kPXX6qB69MVGYsuzn78jJSCPp4H52rF1Br2EjMTAwwMjYGFdPb40/S2sbjIxNcPX0xtRMc1zawW2bCAiNaHLz35bsPp1C37AAwjxdcbG14s4eHdWTQ1w2oUdHJvTQnITGzc4aNztrTI2NMVeY4GZnjbNN49hVI0MDdRpjQ0OszExxs7NWj1nSpwEjx7J/2xb2bI4nJyOdpXN+pqiwkN5DhgOwav48vn3vLXX6zr37ojA15Y/vviIrLZWj+/ewccUyBo4coz5PXszK5MD2reRmZ3Hh7BnmfPkp2empjJ7U+P62uMULOXn0MPkXc8hIOc+CH74hM+0CvS79blug2r0Py05RWHTuiLGTI7axQzGytqL00isNbIYMwGlq055AljEdqa+qovx40/c3WvXsilloEMYO9hg7O2EzZADmYcGU7DvY4vXRhT/3HmFK3870Cw3Az7mZ68j4Ibwyfoj6853do+gZ5KtxHbnniutIRXUNKbkFGn8lFZWUVVWTkltATV1dq9fzZk0ePoI1O3ewYttWUjIz+ez3eeQplYwfMAiAbxcv4umPG+831uzcwfo9u0nJzORCdha/r1vL4k0buWtI03fCCnGrdD7H6hNPPMG//vUvnn/+ee6//378/Pyor68nMTGRRYsW8csvv/D000/z4Ycf8vnnnzNmzBisra05efIkP//8M+Hh4YwdO1a9vvHjx/P555/j6+tLp06dqKysZNWqVZSUlNxQdOyyvn37snPnTj7//HOUSiUxMTFUVVWxadMmVq9ezYwZM3Byan7M1LRp03jppZfaRHeiW2UWGozXlx+rPztOn4rj9KkUr1lPznv/1WPJdCe8aw/KS0rYtWYlJcVKnD08ueepF9TRt5KiIpS5jTfYZuYWTH72Jdb/MYdf3n8LMwsLug+NpfvQxn3r0LbN1NXVEr9oHvGL5qmX+wSFct+Lr7Re5W7BHzsTMDUx5rlRA7A2NyUpPYeXfluuMUW7q61mNykjQwNmDO2Fm50NtXV1ZBYW8X38Lo1Gm5WZKS+OHYiDlSWllZWcycrjmV+WcjKj9Rsv2uw+dR4TI0NiY8IxVxiTUVDE79sParyXzNZCc4InZVk583ckMCw6lC4B3pRUVBB3+IT6HXYA20+co74eBkYEYm1hRnllFaezctlyrHGWybjDJxgQEcTImHAszBSUlFdy+Hw625LOtnzFrxLZvRdlpSVsW72MkiIlLh5e3PfsS+rImqpISUFuY/3MLCyY+sLLrJ73K9/PfB1zS0t6Dx9Fr+Gjbvq3C3Ivcv5kEnfNeEpn9WkJO06ew8SooTutmcKEjHwlc7buu+a+AvCPEf00Pod6ulJYWsZnq7YAYG1mppHG0dqSboE+nL+Yzy+b97ZMZW5QTK8+lJWo2LBsMcXKQty9fHj0pVdxcL60XygLyctp3C/MLSx57OXXWfLrj3z2+v9hbmnJwFFjGTCq8VpdX1fH1rUrufhzJkZGxgSGR/DMm+9qNOrLy0pZ9OMsiouUmFtY4Onrz1Ov/wffDk1fmaEv5cdPorQwx6Z/H4ysLKm+mEfevEXqd9gZWVth7NB0xlTLmI6UJSZRX13T5DuMjLAbPhgjayvqa2ourXMhFWfa0Iyg1zB/5yFMjY15dlR/rM1NOZGew0u/rdC4jrhcdR0xNDDgsaG9cbWzvnQdKeaH+N1t5uGfLgzt3oOikhJ+XbWC/KIiAjw9+eTZF3C/dC+Zr1SSkat5Tfx11Uqy8/MwNDTEx9WNVx96RMbXXWIgY+x0wqC8vFznY8QLCgrULyjPz8/X+oLykydPsmDBAk6cOEFlZSWurq7079+fu+66S+MF5YB6ZsqMjAxMTU0JDAxk6tSp+Pv7q9O88sor+Pr68vjjjzdbrtraWlavXs2GDRvIzMzEyMiIwMBAJkyYoPUF5XPnztWYMOXDDz9kx44dPPTQQ7c1a6d0xWywc+bb+i5Cm/Hr1gP6LkKbMTSy7dzk6VsH17b1egB9OqGHiGdb1aND8xOU/d1Er1qt7yK0GQ8YaB/L+Hf055C2Nw5eX5p7B2Nbkzn54Vb7LY/5P7fab7W2FmnYiWuThl0Dadg1koZdI2nYNZKGXSNp2DWShl0jadg1koZdI2nYNWo3DbsprTczrscfP7Xab7U2GakohBBCCCGEEO2czsfYCSGEEEIIIcQNk1kxdUK2ohBCCCGEEEK0cxKxE0IIIYQQQuiPzIqpExKxE0IIIYQQQoh2TiJ2QgghhBBCCP0xkIidLkjETgghhBBCCCHaOYnYCSGEEEIIIfTGwFBiTbogW1EIIYQQQggh2jlp2AkhhBBCCCFEOyddMYUQQgghhBD6I5On6IRE7IQQQgghhBCinZOInRBCCCGEEEJ/ZPIUnZCtKIQQQgghhBDtnETshBBCCCGEEPojY+x0QiJ2QgghhBBCCNHOScROCCGEEEIIoT8SsdMJidgJIYQQQgghRDsnETshhBBCCCGE3hjIrJg6IVtRCCGEEEIIIdo5idjpwc6Zb+u7CG1Cn9ff1HcR2gyLD9/TdxHajMrqWn0Xoc1wsLLQdxHaDDdbK30Xoc0oq6rWdxHajIMjR+q7CG3G/SVl+i5Cm7HLVM4Xlw3TdwFulIyx0wmJ2AkhhBBCCCFEOycROyGEEEIIIYT+GErEThckYieEEEIIIYQQ7ZxE7IQQQgghhBD6YyCxJl2QrSiEEEIIIYQQ7Zw07IQQQgghhBCinZOumEIIIYQQQgj9kclTdEIidkIIIYQQQgjRzknETgghhBBCCKE/8oJynZCInRBCCCGEEEK0cxKxE0IIIYQQQuiNgbzuQCdkKwohhBBCCCFEOycROyGEEEIIIYT+yKyYOiEROyGEEEIIIYRo5yRiJ4QQQgghhNAfmRVTJyRiJ4QQQgghhBDtnETshBBCCCGEEPpjKLEmXZCtKIQQQgghhBDtnETshBBCCCGEEPojY+x0QiJ2QgghhBBCCNHOScROCCGEEEIIoTcG8h47nZCG3V/EwS0b2bthDSVFRTh7eDD07vvwDgppNv3FjDTWz/+NrJRzmFlYEtN/EH1G3YHBpVD4qUMHSNi2iZy0VGqrq3F096DPyLEERXdurSq1KLPoSOyn3IVZSBDGzk5kv/sJqrUb9F2s27J30wZ2rFtFiVKJi6cnI6dMxS84tNn02emprJ77K+nnz2JuaUW3gUMYOPZO9T5w/mQSP3/0TpN8z7z7Mc7unk2WH92zi0Xff0VwxxgeeO4l3VXsFhzYEs/u9asvHQ+eDL/nfnyuczys+2M2mSnnMLe0IqbfIPqNHq/eFicT9nNw2yZy0i5QU12Nk7sHfUfdQfAVx8Oc/75L6umTTdbt5O7J4299oPtK3qLt69eyceUyipWFuHl5M3HqI3QIC9eatrqqigU/ziI95RzZGekEBIfyzJua+0RRYQHLfvuVtJRz5GZl0a3fAO7/xzOtUZWbVl9fz951Kzm2axsV5WW4+foz6K57cdSyP18pPfkU25cuJD87E0tbO7oMHkHHvgPV3+dnZbBn7QoupqdSnJ9Hj9ix9Bw5TmMdP7/9MqqC/Cbr9guP4o7HWn977d20nu1rL58vvBh173XOF2mprJr3C+nnGs8Xg8ZNUB8j504m8fOHM5vke/a9T9Tni9qaGrauXs6hndtQFRbi5O7O8LunEBzVqUXqeKN2xcexdc0KVEVKXD29GHffNPxDwppNn5WWyrI5P5F2LhkLKyt6DBrG0DsmqrcFwKFdO9iyZjl52VmYmpsTFBHFmMlTsbazAyA7PY0NSxeSkXKegtyLDB1/F8Mn3NPSVb1ph7ZuZF/8WkqKlDi5ezL47nvxDmz+XJqbkcaGBXPJvtBwbxHdbxC9R45Tb5uSIiWbF88nJy2Fwos5RPTozaipj7ZWdW7Kzg3r2LJmBSplIa6e3txx/zQCQrWfKwGy0i6wdPZPpJ5t2C96Dh7GsPF3aewXOzesZeeGdRTk5mLv6MSQOybQtd9A9fd7Nm/g4PatZGekU19fh6evP7F3Tb7m/ijElaRh9xeQdGAv8QvnMWLKVLwCg0nYupEFX/2XR998H1sHxybpK8vLmf/5x3gHBjPt5bfIz8li9ewfMVGY0mPYSABST5/ELyScAeMmYmZpxfF9u1g86wvue+GVazYY2wtDc3Oqzl1AtS4e13/rtxGiC4n7drPmjzmMvf8hfIJC2Ld5A7999iFPv/Mxdo5OTdJXlJcx+5P38Q0O5fHX3yEvO4slP81CoTClT+xojbRPz/wIcysr9WdLa5sm6yu4mEPcot/xvcaNYWs5vn8P6xfMJfbeB/EJDObAlo388eXHPP7WB9g6NN0WleXlzPvfh/gEhfDwK2+Tn5PNyl+/R2FqSs9howC4cOYkfqHhDLzjLswtrTi2dyeLvv0fD7z4mrrBePfjz1JbU6Neb01NDd//5xXCu3RvnYrfgIRdO1g8+yfueXgGAaFh7Fi/jm8/mMmr//0CByfnJunr6uowUZjQb/gokg4fpLy0tEmamuoaLG1sGDZuAjs3rm+NatyygxvXkbB5PcPufQh7Fzf2xa1k6TefMfW1d1CYmWnNU5Sfy/LvviCiRx9GPDCdzHNn2Lzod8ytrAnq1AVoaADbODjRoWNndq9ZpnU9k198jfq6OvXn0uIi/vjkHYJiuuq8nteTuHc3q3+fw9gHHsI3KJR9m9Yz59MPeObdT5o9X/z6yXv4hYTyxBvvkpedyeKfZqEwNaVv7BiNtM+883Gz54v4JQs5vHs746c9irO7J2eOHeX3Lz9lxmtv4+Hr33IVvobDe3axYt6v3Dn1EfyCQ9m9cT0/ffIeL77/GfZO2rfFDx/NJCAkjGfefp/crEwW/PANClNTBowcC0DK6ZPM/+5LRk95gMgu3VEVKVk6+yf+mPUFM15+A4DqqkrsnZyJ7NKDuMXzW7XON+rEgb1sXPQ7wyY/gFeHYA5t28ifX3/KI6+/h00z9xYLv/wYr8AQHvi/NynIyWLNnJ8wUSjoPrTh3qK2phpzKyt6DB/NkR1bW7tKN+zwnp0sn/sLE6ZNxz84jF3xcfz48Xu89OFn2Gs5V1aUlfH9BzPxDwnj2f980LBffP8VClNTBo5qeMizKz6O1fPncfcjj+MTGETq2TP8+dMszC2tiOjccB44e+I40T37MD44BBOFKdvWreL7j97hhXc/wdnNvVW3gbi21atXs2TJEgoLC/Hx8eHRRx8lIiKi2fQJCQn8/vvvpKamYmxsTHh4OA899BCentd+sHiz2v0Yu88++4yxY8eyYMECjeWJiYmMHTuWoqIicnJyGDt2rNa/gwcPkpaWxtixY0lKStJYx2uvvca4ceMoKirSWD5t2jTmzp3b4nW7Ufvi1xHVqy+d+g3Eyd2D4ZMfwMrGjkNbN2pNf3zfLqqrKhkzbQbOnl6Edu5GzxGj2Be/jvr6egCGTbqfXrFj8PDvgIOLK/3G3Imbjx+njyS0ZtVaTNme/eR//wslW3ZAXb2+i3PbdsWtIaZPf7oOGIyLhydj7puGla0d+zbHa01/dM9OqquqmDj9CVy9vIno2p1+o8ayc/0a9T5wmaWNDda2duo/w6umJK6tqWHRd18xdMI9ODi7tFgdb9Te+LV07N2Pzv0G4eTuSeyUqVjZ2nGwmePh2L6dVFdVMm7aY7h4ehPWuRu9Roxm7xXHw4hJD9Andiyel46H/mMn4O7rz6nDB9XrMbe0wsrWTv2XlnyK6spKovsMaJV634jNq1fQY8Ageg8ZjpunN3c99Ci29vbs2LBOa3pTMzMmTX+CPkOHY6flRg7A0cWFu6ZNp8fAwVhecUPf1tTX13No60a6Dh1JUKcuOHl4Mvy+h6mqrODUwb3N5kvcuRVLGzsG3nUvDm7uRPbuT1j3XiRsbmzEuvn602/83YR27YGJiULreiysrLG0sVX/pSQlojAzUzcOW9PO9auJ6dOfbgOGNJwv7n8Ia1t79m3S3mvhyO7L54t/XDpf9KDfyLHsjLu588Xh3dvpN3IcIdGdcXBxpcfgYQR3jGHnutUtWt9r2b5uFV37DqDHoKG4enoxfurDWNvZs2eT9ocUh3btoLqyikkznsLNy4eobj0ZNPoOtq9bpd4WF5JPY+vgSP/YMTg4u+AbGEyfYbGknj2jXo93QCBjpkwlpndfTBSmrVLXm3VgUxyRvfoQ3Xcgju4eDJ30AJY2dhzatklr+qT9u6muqmLU1Edx9vAiJKYbPYaP4sDGOPW2sXV0Zug99xPVqx9mlpatWZ2bsnXtSrr1G0jPQcNw9fTizgcfwcbOjt3NPLxK2LWdqspKpjz+FO7ePnTs3pNBY8azbW3jfnFw51Z6DBpCTO++OLq4EtOrLz0GDWPzqmXq9dz3j+foO3wknn4BuHh4MvGhGZiZmXPq6KHWqLZ+GRi23t9t2r59Oz/88AP33HMPn3/+OWFhYbz11ltcvHhRa/rs7GzeeecdIiIi+N///sc777xDZWUlb7/99m2X5WrtvmEHoFAoWLx4cZMG2NXefvtt5syZo/HXsWNHvL29cXBwIDExUZ22urqakydP4uTkxLFjx9TLMzMzyc/Pp2PHji1Wn5tRW1NDdmoK/uGRGsv9wyNJP5esNU/GuWS8A0MwUSiuSB9FSZGSovy8Zn+rqrICMwsL3RRc6ExNTQ2ZF84TGBGlsTwwoiNpyae15klNPoNvsOY+EBjZEZWyEGVerkbaWf/5Nx8+/w9++fhdzp043mRd8UsWYufkTEyf/jqoze2prakhKzWFgKuOh4CwSNKvuKm6Uvq5ZHyuOh46RFzaFvm5WvMAVFZc+3g4tGMLHSKjtUbN9aGmppq082cJ7dhJY3lIx2jOa+lC+ldTnJ9HWXERPiGNXamMFQo8OwSTdf5ss/myUs7he1X3K9/QCC6mXqC2tqaZXNdWX1/P8T07CO3as9Vv6mtqashMOU9QpOY1LDAyitSz2s8XaWebni+CIqNRKQspvOp88e3br/HBc0/w80fvNDlf1FTXYGxiorHMxMSEC2dO3U6VbllNTQ0ZKecIjorWWB4c2ZGUZsp0Ifk0/iGhGtsiOCqa4sLGbeEXFIpKWUjSoQPU19dTqirmyJ5dhEbHtFxldOzyvYVf2FX3FmERZDRzb5F5LhmvDsGa9xZhkde9t2hramqqyTivZb+Iir7GfnEK/5AwjeM5OKoTxYUFFOQ23OzXVNc0efBjolCQdjZZo7fHlWpraqiursLcou0+NPs7WrZsGUOGDGHEiBF4e3vz2GOPYW9vz9q1a7WmP3v2LLW1tUydOhUPDw8CAgK4++67ycrKum7b5Wb9JRp2UVFRuLq6Mn/+tbszWFtbY29vr/FncukiExUVxdGjR9VpT506hY2NDQMHDtRYfvToURQKBaGh+u9yBlBWoqK+rg5LG83ucZY2NpQWa99ZSoqLtKS3VX+nzcEt8agKC4ns0UcHpRa6VKZSUVdXh9Wlf8PLrGxtUTVzwigpLmqa/tJnVZHyUn47xj7wMJOffI4pTz6Hk5s7v37yHimnTqjzJB87SuL+PYyb+rAOa3Tr1MeDtWbdLG1sm923S4qK1Pt/Y/qG46O0me13YPMGVIUFRPXsq/X7/JwsUk+fJOaKcVj6VlrcsJ9Y29ppLLe2tUOlVOqlTK2pVNXwb2lxVVdiC2sb9XfalBUXac1TV1dLRUnJLZUl9VQSxfl5RDaz/7SkMlUxdXV1WvZ5W0qa2d9VRUqt5xdoGDMFDfvRuKkPM+Wp57n3qedxcnPnl4/f5fwV54ugyI7s3rCW3KxM6urqSD5+lKSE/epzTmsrvbQtmtbNrtkyad0WNnYN3106jnyDgrn3H8/yx7df8srD9/L2k9Opp55JM57SdRVaTHPnUgsb22bvLUqLtZ1LbdXftRell6+pV50rra5xrlQplVjbatb98ufL+1JIVDT7tm4i9Wwy9fX1pJ1LZt+WjdTW1lCqUmld79pFf2BqaqbuqvmXZmDQen+3obq6muTkZGJiNB/UxMTEcOLECa15AgMDMTIyYv369dTW1lJWVsbGjRsJCgrC9qr95nb9JcbYGRgY8OCDD/Luu+8ybtw43N1vvh9yVFQU33//PdXV1ZiYmJCYmEhkZCRRUVH88MMP6nSJiYmEhoaiUGjvbqMvBmjuqA2h/2vtvFd9d6mrgLYcJxP2s2nxAsZPfwJbLeMvRBth0HQfuPb5q5l94FImZ3cPnN091F/7BAZTmJfLjrjV+IWEUaoqZslPs7j7sacwt2xbTxMNrq54fX2TY0QzQ5Pkl1fUJOmJhP3EL57PhEef1DoeCeDQ9i1Y2doRpOdJIbRpsh3q+Uu+P+jkgT1sWtDYZX7cY08DTfeN+uvtGw2ZNPNQr3X5jTq2ezuuPn44e/ncUn5d0HaMXPOaoWW7XbkebecLZV4eO9etUk/8MPreB1n26w988do/MTAwwMHFlc59B5Cg57FWWs8X1/i3bfrd5f2h4T85Geksn/sLQ+6YSEhUNMXKQlYvmMviX75n8mPtp3EHNN0lrntduTr55f1Ed0VqLU2KXF9/nUPk6vOE5vJhd96FqkjJV/95DerrsbK1o0u/AWxZtRwDw6Zxlu3rVrNn0wYee+UN6S2lY+vWrSMuLu666UaMGEFsbKzGsuLihgdCdpcmQrrMzs6OI0eOaF2Pq6srM2fO5IMPPmDWrFnU19cTEBDAW2+9datVaNZfomEH0LVrV8LCwvjtt9/417/+pTXNyy+/3GR80K+//oqlpSUdO3akqqqKkydPqqN3gwYNIiwsjKysLAoLC7G3tycxMZHRo0drXf+N7igOQeEExXS7+UpqYWFljYGhYZNoRJlK1SQqd5mVlidupapigCZP204m7GflL98zdtqMv8yMmH81FtbWGBoaqp+cX1ZaXNzkyfJlVja2lBRrpi+5tA80lwfAKyCQxH27AbiYkY6qSMmvn7yn/v7yRfzN6ffz1MyPNG70WkPj8aDUWF6qKm7+eLC1bRKZK1MfD5p5TiTsZ/nPs7jjocc0ZsS8Um1NDUf3bCem7yAMjYxusSa6Z2nTsJ8UFxVqLFcVN33S/FcQENkJN98A9efammqgIXJgbe+gXl5eomoSkbuShY0tZVedL8tVKgwNjW5pjFCZqphziYcZdNe9N51XFyysbbSfL1TFWNlq3w7WtnZazy/Q9JpxJa+ADurzRUNaG+575kWqq6soLynB2s6e9Yv+0DoZRWuwvLQtro7OaevRcJm1lmje5evv5Wj45pVL8Q4IZODohkkz3H18UZia8e27bxB71+RmHwi1JZfPpVffK5SpirGw1r5tLLXcW1w+lzaXpy2yvHRN1fbvfHWPh8us7ewoviqadzkCfnlfMlGYMmnGk9z18GOoioqwsbdjz6Z4TM3MsbS21si7fd1q1v35B9Nfeg2fDkE6qVeb14qvO4iNjW3SYLtZ13r4c7XCwkK++OILBg8eTP/+/SkvL2fevHl8+OGHvPvuu03aJrfjL9Owg4ZJTV566SXOnNE+luaf//wnvr6+GsvMzc0BcHd3x9nZmcTEREJCQjh16hTPPPMMZmZmBAYGkpiYiL+/P4WFhc2Or7vRHWX+7sM3V7FrMDI2xs3Hj5QTxwi7Yva98yeOEdrMbGueAYFsXrqQmuoqjC/19045cRwrWzuNiNyJA3tZNfsHxjz4KKFddNMQFbpnbGyMh68/Z48nEtmtp3r52aTEZmdk9AkMYv2i+VRXV6n7/J89noi1nT1217jJyk69oL6wefoH8NR/PtT4Pn7pQipKSxlz/0PY62EiFSNjY9x9/DifdIzwLj3Uy8+fOE5oM11ZvAIC2bhkgcbxcC7pWMO2cGzcFkkH9rLi1+8YN+0xjWPtaqcOH6SspIRObWjSFABjYxO8/Ttw6ugRYno2dqk+dfQI0T166bFkLUNhZqYx02V9fT0WNraknkrC7dIMjDXV1WSePUPfO+5qdj3ufgGcTTyssSz1VBIuPr4YGd38JTRp3y6MjI0J7qyf2VKNjY3x8PMn+arzRfLxRCKa2a+9OwSxftEfGueL5KSG88W1GmVZaReadGcDMDFRYGLvQG1NDccP7tMoR2syNjbG0y+A08eO0rF74zFw5lgiUd16aM3jGxjMmgXzqK6qUo8lO3PsKDb2jduiqqqyyY3a5c9XzTXTZjXeWxwn9Ip9NeXkcYI7aT+XegQEsnXZVfcWJ5veW7R1xsYmePo37BfRPXqrl58+dpSOzeyrvoEhrJ4/96r94gg29g5NJhUzMjbGzrFh7PXhPTsJj+misb9sXbOSuMULeOSlV+U1B22QjU3DA6HCQs2HpEqlskkU77LVq1djZmbGQw89pF724osv8tBDD3HixIlrzqZ5s/4SY+wuCw4OplevXvz6669av3dycsLDw0Pj78qDKSoqisTERE6ePImtra26S2dkZCSJiYkkJiZiZmZGUFDbenrSfWgsR3fv4PCOLeRlZbJhwVxKipTE9B8MwJalC/n9s8Yb8PDuvTBRmLJq9o/kZqRz6tABdsetovvQWPUTiKT9e1jx83cMHH8P3kEhlBQpKSlSUl56a2NK2hoDczMUgQEoAgPA0AATVxcUgQEYu+rnyfHt6j1iFId2buPAts1czMxg9e+zUSkL6T5wCADr/5zPLx+/q07fsUcfTBQKlvw0i5z0NI4f3Mf2NSvpM3yUeh/YtX4tSQn7yc/JIicjnfV/zufEoQP0GDIcAIWpGa5e3hp/5haWKMzMcfXyxthYP8+NegwdyZHd2zm0Ywt5WRnELfgNVVEhnfs3bItNSxcw99P31ekjuvfGRGHKil+/52JGGicT9rMrbiU9rjgeju/fzbKfvmXwnZPwuc7xkLB9M/6h4Xpp2F7PoNHj2Lt1M7s2bSA7I43Fv/5IUWEhfYeOAGDFH7/x1cw3NPJkpaeRnnKeUpWKysoK0lPOk55yXiPN5WUV5eWUlZaQnnKerPS0VqvXjTAwMCBmwBAOxq8j+UgCeZkZbJj3CyampoRc8RAgbu5PxM39Sf05qs8ASpSFbF0yn4LsLI7t3k7Svl10HjRcnaa2pobc9FRy01OpqammtLiI3PRUlLmaM6TV19dzfPd2gjt3b/b1Cq2hz/DRHNqxlQNbNzWcL+Y1nC+6DRoKwPpFf2i8wzK656XzxY+XzhcH9rF99Qr6jLjyfLGGpIT95GVnkZORxvpFf3Ai4QA9h4xQryftbDLHD+yj4GIOKadPMvvTD6ivr6ffqLGtuwGu0C92DAe3b2Hvlo3qLpTFygJ6Dh4GwNqFv/P9B/9Rp+/Uqy8mpgoW/vAN2empJO7fy+ZVy+kXO0a9LcJjunI84QC7N64n/1Jdl8/9BU8/f/UrFBomvUoh80IKNdVVqIqUZF5IIS8nu9W3QXO6Dh7BsT07OLJzK/lZmWxcOI+SIiWd+g0CYOuyRcz//Ip7i249MVEoWDPnR3Iz0zl96AB716+m65ARGtGNnLQL5KRdoKqinPLSUnLSLpCXldHq9buWASPHcmDbFvZujicnI51lc36muLCQnpeuf2sWzGPWe2+p08f07ovC1JQF339FVloqifv3sGnlMvqPbNwvcrMyObhjK7nZWaSePcPcrz4lOz2Vkfc0Ru83r1rOmgXzuOfRf+Ds5k6xspBiZSHlZU1fNfOX005mxTQxMSEwMJDDhw9rLD98+DBhYdob4pWV13rYo9unPX+piB3A1KlTefLJJzl48OD1E1+lY8eOfP311xw4cIDIyMaZoC6Pv1OpVISHh+vthrU54V17UF5Swq41KykpVuLs4ck9T72gfkJWUlSkcYNhZm7B5GdfYv0fc/jl/bcws7Cg+9BYug9tjDYe2raZurpa4hfNI37RPPVyn6BQ7nvxldarXAsxCw3G68uP1Z8dp0/FcfpUitesJ+e9/+qxZLcmqnsvykpK2Lpyqfoluw889y919K2kSEnBxRx1ejMLCx785yusmvsrs/7zb8wsLek9YhS9R4xSp6mtrSFu4e8UFxZgolDg4uHFA8+9RHDHtj2zW0S3npSXlrBjzXJKipQ4e3gx+al/qrs/lRQpKczTPB7ue+7/WPv7bH56703MLSzoOXQkPS69dwng4LZN1NXVsn7hXNYvbBy35RMcytQXX1N/Lsy9SMqpJCZMf7IVanrzOvfuS2mJivVLFlGkLMTd24fHX/63+olycWFhkxvL7z6YScEVMx9+9PILAHwxf2mTZZcdO7gfBydn3vrq+5aqyi3pMiSWmupqNv/5O5Vlpbj5BjD+iec1GlmqwgKNPLaOztzx2DNsW7qQxB1bsbS1ZcCEyRqvKSgtUvL7x40v5y7Ky+XYrm14BgZz19ON78lMTz6FMvciIx6Y3oK1vL6oHr0oK1WxRX2+8OaB5/9PHXFSaTlfTPvnq6yc+wvfvv0aZpaW9Bkxij4jGocl1NbUsG7BvKvOF/8i5IqZIGuqq4hfupDCixdRmJkS3DGGux79B+YW+pv2vlPP3pSVqNi0YgnFykLcvLx5+MVX1NuiWFlI/hXbwtzCgkf/9TrL5vzEF2++grmFJf1HjqH/Fe/z69pvIJXl5eyKX8eqP+ZgZm5Bh7AIRk2+X52muLCA/73eOGwk/2IOezfHExAazuOvvtXyFb8BYV17UFFawu61KygtLsLJ3ZO7/tF4b1FarNS4tzA1t+Cep19iw4LfmPPBW5hZWNJtSCzdhmj2ZJr9/psan88mHsbGwZHH32k7195OPftQqlIRv3zxpf3Ch0deelX9vs+m+4UlM15+nSW//sjnb/wf5haWDBg1Vv1uQ2h4L+jWtSvJzcrEyMiYDuERPPXGuxoRvV3x66itrWHuV59qlKdrv4Htb3zmX9j48eP59NNPCQoKIjw8nLVr11JQUMDIkQ33DbNnz+b06dO8+27DA/WuXbuyfPly/vjjDwYMGEBZWRm//fYbTk5OBAYG6rRsBuXl5e2kY4B2n332GcXFxbz5ZuOJYtasWWzYsIGqqirmzp1LRUUF06dP5+2338bfX/MlqBYWFpiaNkxPe/HiRR555BHMzc2ZPn06w4c3PJkpLy9nypQpKBQKJk2axMSJE2+rzLrsitme9Xn9zesn+ps49OF710/0N1FZXavvIrQZLrZta1IafTqb036mS29pzjayX1ymaEPjWPUtr6RM30VoM1zkGFEbFtW2epk1J/f1d6+fSEecZ752/UTXcfkF5QUFBfj6+jJ9+nR1UOizzz7j2LFj/PRTY++Pbdu2sXjxYjIzM1EoFISEhDBt2jR8fHQ7iVbbCj3pyOTJk9m0qekLNK9s/F321FNPMWJEQ1cRFxcX3NzcyM7OJiqq8Z1g5ubmBAYGcurUqTbz/johhBBCCCFE6xs9enSzkyk+//zzTZb179+f/v1b/n2/7b5hp23j2dnZsXDhQvVnW1tbVq5ceUPru/LVBlf65JNPbq2AQgghhBBCiOa1x3ditEF/qclThBBCCCGEEOLvqN1H7IQQQgghhBDtmA7f5fZ3JltRCCGEEEIIIdo5idgJIYQQQggh9EfG2OmEROyEEEIIIYQQop2Thp0QQgghhBBCtHPSFVMIIYQQQgihP9IVUyckYieEEEIIIYQQ7ZxE7IQQQgghhBB6YyCvO9AJ2YpCCCGEEEII0c5JxE4IIYQQQgihPzLGTickYieEEEIIIYQQ7ZxE7IQQQgghhBD6YygRO12QiJ0QQgghhBBCtHMSsRNCCCGEEELoj4HEmnRBtqIQQgghhBBCtHMSsRNCCCGEEELoj4yx0wmJ2AkhhBBCCCFEOycROyGEEEIIIYT+yHvsdEIidkIIIYQQQgjRzknETg9+3XpA30VoEyw+fE/fRWgzYv7vVX0Xoc1Y96psi8tszE31XYQ242Rmrr6L0GaUVlbpuwhtxsbjZ/VdhDYj2M1J30VoM7r4e+m7COImGcismDohW1EIIYQQQggh2jmJ2AkhhBBCCCH0R2bF1AmJ2AkhhBBCCCFEOycNOyGEEEIIIYRo56QrphBCCCGEEEJ/5HUHOiEROyGEEEIIIYRo5yRiJ4QQQgghhNAfQ4k16YJsRSGEEEIIIYRo5yRiJ4QQQgghhNAfGWOnExKxE0IIIYQQQoh2TiJ2QgghhBBCCL0xkBeU64RE7IQQQgghhBCinZOInRBCCCGEEEJ/DCTWpAuyFYUQQgghhBCinZOInRBCCCGEEEJ/ZFZMnZCInRBCCCGEEEK0cxKxE0IIIYQQQuiPzIqpExKxE0IIIYQQQoh2TiJ2QgghhBBCCP2RWTF1QraiEEIIIYQQQrRz0rATQgghhBBCiHZOumL+hUwb2J0xXSKwNjPlREYO/1u9lZTcgmbTR/t68OjQXng72mNmYkxOkYrVCUks2HVInSa2Uygvjx/aJO/wd76lqqa2RepxM/Zu2sCOdasoUSpx8fRk5JSp+AWHNps+Oz2V1XN/Jf38Wcwtreg2cAgDx96JwaVpds+fTOLnj95pku+Zdz/G2d2zyfKje3ax6PuvCO4YwwPPvaS7irUis+hI7KfchVlIEMbOTmS/+wmqtRv0XazbUl9fz764lRzfvZ3K8jJcffwZMPFeHN09rpkvI/kUO5YvoiA7E0sbOzoPHkFknwHq74/v3s7J/bspyMmkvq4eZy9veoy8A4+AIHWa2f95BVVhfpN1+4ZFMnbGM7qr5A3aFR/H1jUrUBUpcfX0Ytx90/APCWs2fVZaKsvm/ETauWQsrKzoMWgYQ++YqD5GAA7t2sGWNcvJy87C1NycoIgoxkyeirWdHQB7N8dzcOc2cjLSqK+vx8PXnxETJuEf0vyxqU8jY8LoHeKPuULBhdwCFu0+RLZS1Wx6G3MzxnePwtvRHmcbK/afvcC87Qc10jw9sj9B7s5N8mYVFvP+0rZxfPUOCSDazxNTE2OyCouJP3qSfFXpNfN4OdoxKDIYJ2tLSioq2Zd8gSMpGRppFMZG9A3rQIiHK2YmJqjKK9h+IplTmRcB6BHkR5C7Mw5WltTW1ZFZWMT2pGTyrvPbre3+fl0Y2SkMKzNTTmVe5Ou4HVzIK2w2fZSPOw8N7I6Xgx2mJsZcLFax7vBJFu89qk7j62TP/f27EujqhLu9DXO3H2DuVftOW/R3PEYObolnz4Y1lBQV4ezhydC778MnKKTZ9Bcz0oibP4eslHOYWVgR038QfUfdoT53njy0n0PbNpOddoHa6mqc3D3oPXIcwdGd1es4ums7q+b80GTd//ryR4xNFLqvZBtiIJOn6IQ07C757LPPKC4u5s0339RYfubMGV544QV+/PFHAKZPn86nn35KUFCQttXozZQ+nbmnVyc+WLaRtPxCpg7oxidT7+CBL+dSXlWtNU95VTVL9h7lXE4+FdXVRPm488KYQVRUV7N8/zGNdPd98ZtG3rbQqEvct5s1f8xh7P0P4RMUwr7NG/jtsw95+p2PsXN0apK+oryM2Z+8j29wKI+//g552Vks+WkWCoUpfWJHa6R9euZHmFtZqT9bWts0WV/BxRziFv2O7zUaku2Bobk5VecuoFoXj+u/22fj9GoJm+I4vGUDQ6ZMw97Fjf1xq1g+6zPuf2UmCjMzrXmK8/NY+cOXhHXvw7D7HyHrXDJb/5yHmZUVgdFdgIaGX1BMV9z9AzFWKDi8JZ4V333O5H++jp2zKwD3vPAqdXV16vWWFRex4NN3CezUteUrfpXDe3axYt6v3Dn1EfyCQ9m9cT0/ffIeL77/GfZO2o+RHz6aSUBIGM+8/T65WZks+OEbFKamDBg5FoCU0yeZ/92XjJ7yAJFduqMqUrJ09k/8MesLZrz8BgBnTyYR3aM3fkEhmJiasn3dKn78+F2ee+cjnN3cW3UbXM/QqGAGRQYxb9sBLhaVEBsTxpOx/Xjnz/VU1tRozWNsZEhpRRUbjp6iT4i/1jQ/bdyNkVFjpxhjQyNeuXMoh86nt0g9blb3QF+6BfqwNiGJgpJSeoUEcE/vzvy4cRfVzZzfbS3MmNgzhmOpmaw+eAwvRzuGdgylvLKa01kNjTZDAwPu7tWZiupqVuw/iqq8EmtzU2rr6tXr8Xay53BKOtmFxQD0DevAPb078/Om3VRUa9/mre3untFM6N6R/67aQnqBkvv6duG9KaOZ/t2Ca15Tlx84RsrFAiqqa4jwcuOZkf2orK5hVUISAKYmxuQoVew8eZ4HB3RrzSrdsr/jMZJ0YA8bFs5jxJSpeAcGc3DrRhZ89Qkz3nwfW4em587K8nL++PwjvANDmPby2xTkZLFq9g8oFKb0GDYSgNTTp/ANCWPAuImYWVpxfN8uFs/6nPteeFWjwWiiUPDEzE801v9Xb9QJ3ZGumH8Rd/WM5vcdB9l24iznLxbw/tJ4LBQmDI0KbjbP6axcNh07Q0puAdlKFRuOnmb/2VQ6+lwd1ainoKRM468t2BW3hpg+/ek6YDAuHp6MuW8aVrZ27NscrzX90T07qa6qYuL0J3D18iaia3f6jRrLzvVrqK+v10hraWODta2d+s/QUPNQqa2pYdF3XzF0wj04OLu0WB1bQ9me/eR//wslW3ZAXf31M7Rx9fX1HNkaT5chsQRGd8HR3ZOh9z5EdWUFpxP2Npvv2K6tWNrYMWDiFBxc3Yno1Y/Qbr05tLnxyfHwB6bTsd9gnL18sHdxY+Dd96EwNePCiePqNOZW1lja2Kr/LpxIRGFqRmCnLi1ab222r1tF174D6DFoKK6eXoyf+jDWdvbs2bRea/pDu3ZQXVnFpBlP4eblQ1S3ngwafQfb161SHyMXkk9j6+BI/9gxODi74BsYTJ9hsaSePaNez71PPEOfYbF4+vnj4u7BhGmPYmpuxumjh1uj2jdlQEQg8UdPceRCJlnKYuZu24+piTFdOng3m6egpIzFe4+wL/kCpZVVWtOUVVWjKq9U/3VwdURhbMyeMyktVJOb06WDD3vPpHA66yJ5qlLWJhxHYWxEuKdbs3mi/bworahkY+IpCkrKOHohk+NpWXQL9FGnifTxwMLUhKV7j5BRUERxeQUZBUVkK4vVaf7cfYhjqVnkqUrJU5Wy+uBxzE0VeDrYtWSVb8qd3aNYuPswO0+d50JuIZ+s3Iy5woRBEYHN5knOzmNr0lku5BWSU6Ri0/EzHDyfTqR348OM01m5/LhpD1uSkpttFLU1f8djZF/8Ojr26ktMv0E4uXsyYvJUrGzsSNi6SWv6Y/t2UV1VydhpM3Dx9CK0czd6jhjN3vh16nPn8En30zt2LB7+HXBwcaXfmDtx8/Hn9JGrIrYGBljZ2mn8/S0YGLTe31+YNOz+AtztbXC0tmT/2TT1sqqaWo5cyCTC+8afjge6ORHp7caRC1d3qzFm/nNTWfTCNN6/dwyBbk2fVrW2mpoaMi+cJzAiSmN5YERH0pJPa82TmnwG3+AQTBSNT74CIzuiUhaizMvVSDvrP//mw+f/wS8fv8u5K27aL4tfshA7J2di+vTXQW2ELhXn51GmKsY7JEK9zFihwCMgiKzz55rNl51yDp+QcI1lPqHh5KalUFur/QasrraGmupqzCwstH5fX19P0p6dhHTtgYnC9BZqc+tqamrISDlHcFS0xvLgyI6knDmlNc+F5NP4h4RqHCPBUdEUFxZSeOkY8QsKRaUsJOnQAerr6ylVFXNkzy5Co2OaLUttTcN2Mre01EHNdMfR2hJbC3NOZlxUL6uureNsdh7+Lo46/a1eIf4kpWejLC3X6Xpvha2FOVZmpqRcbOyqX1NXR1qeEg8H22bzedjbkpKr2c34/MV8XO1sMLx0sxTk7kxGQRFDO4bwjxH9eGhwT3qHBKi/10ZhbIShgQEV1dojYa3Nzc4aBytLEq6IHFXV1HIsLYswT9cbXk8HV0fCPF05mprZEsVsFX/HY6S2poas1BT8wzXvL/zDI0k/d0ZrnoxzyXgHat5fBIRHUVJUSFF+XrO/VVVZjpmF5nmxpqqKr159ni9ffpaFX/+X7NSUW6+M+NuRrph/AQ5WDTeVhaWakbTC0jKcrK20ZdGw6IVp2FqYY2RowOyt+1lxoLEhk5qn5KPlmzibk4e5woS7ekbz1SMTeeTb+WQUFOm2IjehTKWirq4OKxvNmxArW1vOJh3TmqekuAhbewfN9Jfyq4qU2Du7YGVrx9gHHsbTP4DamhqO7N7Br5+8x8P/+jd+l8YlJR87SuL+PTz51nstUDNxu8pUDZEBC2trjeUW1jaUFCmbzVeqKsIrWHPsmbm1DXV1dVSUlGCp5anpnjXLMTE1xT8yusl3AGmnkiguyCO8Z7+bq4QOlKqKmzlG7FAdT9SaR1Wk1HKM2DV8p1Q2ROiCgrn3H8/yx7dfUl1dRV1tLUGRHZk046lmy7Luz/mYmpoR3rn1u6Nei415Q2NbVV6hsVxVXomthbnOfsfZxoogd2d+iN+ls3XeDkvThpvP0spKjeVllVVYmTf/AMLSTMGFXM3oS1llFUaGhpgrTCitrMLWwhwfJ3tOpOeweM9hbC3MGdoxBIWxEVuOa78pHhwVQo5SRaYerylXsre8fE3VbGAUlpbjZH39hxO/PXWf+po6b8dB1hw60SLlbA1/x2OkrERFfV0dljaaQzAsbWxJOdn0QS9AaXER1vb2V6VvyF9SrMTOqelYwgNb4lEVFhLVo496mYObG6OnTsfVy4fKigr2b1rPnI/fYfq/38HBtflo+l+CocSadEEadldISEjg7rvv1lh25ViZ61m3bh1xcXHXTWdQb0m9m9/NFk9taFQwL44dqP788rxVAFzVmxADDIDrd617+ufFmCsUhHu58tiw3mQVFrPhaMMT/aT0bJLSs9Vpj6dl8+Pjk5nQoyNfrt1+y3XQmaueAtfX118nyn7Vl5c22uXBzc7uHjhfMcGGT2AwhXm57IhbjV9IGKWqYpb8NIu7H3sKc8vrN5pFyzt1cC9bFs5Vfx7z6OUGxlX7Bo3/zs1p8vXlg0pLviNbN3Js1zbGP/E8CjPtNzjH92zHxccPZ8/muyy1tCZ1rq+/5nZo+t3lbdDwn5yMdJbP/YUhd0wkJCqaYmUhqxfMZfEv3zP5saaNux1xa9i7OZ5H/+91zMy1RzZbS9cAbyb1aZyo4LsNO4HmzpK665bcO8SforJyjqdlXz9xCwjzcmN4dONY4MV7Djf8z9VVNKDpheQmGRhAWWU1cYeTqAdyilSYKUwYFBmstWE3KCIIL0c7ft9+QIdb/OYMigjkmZGNvS/eWLgWoEkXfQMMbmjz/PO3FZgrTAj1dOGRQT3IUarYeEx7o7at+bseI9ppu1+4xrmzSfpmlgMnE/azafF8xk//B7ZXzAngFRCE1xWTcXl1COKnd/7NgS0bGD7pgZuugfj7kYbdFSIjI3nyySc1ll24cIH33ruxyExsbCyxsbHXT/fBj7dUvst2njrPiYwc9WcTIyOgIXKXW1yiXm5naU5ByfW7NFye2er8xXwcrCyYNrC7umF3tbr6ek5lXsRLz2MhLKytMTQ0bBKBKS0ubhKhuMzKxpaSYs30JZeiO83lAfAKCCRx324ALmakoypS8usnjfvE5Yv/m9Pv56mZH2k0DEXL84+IxvWfjYPzay+NWylTFWN9RfSpXFWMuVXTSXAus7S2pay4WGNZeYkKQ0NDzK7qQnhk60b2rF3G2BnP4OqrfWKAMlUx548dYcDEe2+6TrpgaW2DoaEhqquOkZLiomb3d2tbO63pL38HsHnlUrwDAhk4ehwA7j6+KEzN+PbdN4i9a7LGxEU74tawbvF8HnnxVXw6ND82qbUkpmaRkts4Btf40sQNNuZmGt2/rM1NKS6vbJL/VhgZGtA90Iddp1Oou81G061Kzs4lq7AxGmZ06cm4pZkpqorGelooFM2OhwIorajC0kxzEgcLUwW1dXXqCUVKK6qoq6/TuOXPV5WiMDbCXGGiMfHIoMhgQj1dWbDzIEVl+ut+t+fMBU5m/qn+rLjimnrlTJ12lmZNesZok1PUcE1NyS3A3tKc+/p1aTcNu7/rMXIlCytrDAwNKS3WjCCXqoqbRPEus7SxVZ8rr0x/+bsrnUzYz4pfvmPstBkaM2JqY2hoiLuvPwUXc66Z7i9BInY6IQ27KygUCjw8NG/KS0vb1vTL0DDz1tXdIPNVpXTt4K2eTlphbERHXw9mrd95U+s2MDBAYWx0zTQBro6czWm+z3hrMDY2xsPXn7PHE4ns1lO9/GxSIuFdumvN4xMYxPpF86mursLk0gxTZ48nYm1nr7WbxGXZqRfUN7We/gE89Z8PNb6PX7qQitJSxtz/EPbtfCKV9khhZqYx02V9fT0W1jaknUrC1ccPgJrqajLPJdNn3MRm1+PmF8C5xMMay1JPncDZ2w8jo8ZT5aEtG9i3dgVjZjyt8ZqDq53ctwsjY2OCYvQz852xsTGefgGcPnaUjt17qZefOZZIVLceWvP4BgazZsE8qquq1GNFzhw7io29PfaXjpGqqsomkwld/nzlPdm2tatYv2QBD7/4Spt5zUFlTQ2VKs3xkkVl5YR4uJB6aRp7YyNDOrg6sWy/9u6qN6ujrweWZqbsOZ2ik/XdiuqaWpQ1mg2nkopKfJ0d1JOaGBka4uVox9ZmuksCZBYWNZme3s/ZgRxlsfqGPKNASZiXZpcxBysLqmpqNRp1gy816ubvPKj3CbnKq6qbzHRZUFJKjL8Xp7MaxpaaGBkR4e3OT5v23NS6DQwM1A9f24O/6zFyJSNjY9x9/Dh/4hhhV9xPpJw4Rkgz53PPgEA2L11ATXWVegbL8yeOYWVrrxGRSzqwl1Wzv2fMgzM01t2c+vp6Lmak4eKlv14fon2Rht1fxJ97jnB//66k5hWSnq/kgf5dKa+qJj6xcSKRV+5seB/d+0sbnsbd2b0j2cpi9ck62teDSb1jWH7FyfrBAd1ISs8hvUCJpamCCT060sHVkc9Wb2m9yjWj94hRLP7hGzwDAvEJDGb/lnhUykK6DxwCwPo/55Nx/iwPvfQaAB179GHz8iUs+WkWA8fcSV5OFtvXrGTQuAnq7me71q/FzskJV08vampqObJ7BycOHWDyk88BoDA1w/WqE6y5hSV1tXVNlrcXBuZmmHheeqBhaICJqwuKwADqVCpqcnKvnbkNMjAwIHrAUA5sWIO9qxt2zq4c2LAaE1NTgjs3Nmg2zPsZgGH3PQxAZO8BHN2xme1LFxDRuz9Z55M5uX8Xwx+Yrs6TsCmOPWuWMey+R7BzdlU/0TU2McH0im6G9fX1HN+7g6CYbs2+XqE19Isdw4LvvsQ7IBC/oBD2bN5AsbKAnoOHAbB24e+knUtWv6agU6++bFi2iIU/fMOQOyaQm5XF5lXLGXrnXepjJDymK3/+/B27N64nOCoalbKQFfNm4+nnr36FwpbVK4j78w8mP/40zm4eqJRKoGESG/NmJprRl63HkxkeHUpOkYrcohKGdwqlsqaGg1dMRnV//4axgXO3HVAv87w0yYiZwph66vF0sKW2rq7Ju716hwRwOvPidd8P19oOnk2lZ7A/BSWlFJaU0TPYn+raWpIyGrvCjercMAHRmoSGcUVHUtKJ8fdmUGQwR1LS8XSwI9LHg1UHGsc1Hz7fkGZIVAgJ59OwNTejT2gAh883bs+hHUMI93Jn2b4jVFbXqMf8VdXUUl2r/1fpACzdl8jkPp1Jz1eSXqBkSp/OVFRVs/l4sjrNP8cOAuCTlZsBGNc1gmylivR8JdDwXruJPaJZdbBxXJaxoSE+Tg1jsRRGRthbWhDg4kh5dTVZhZo9BtqKv+Mx0n1oLCt++Q4PvwC8OgSRsG0zqiIlnfsPBmDz0oVkppzjvudfBiCiey92rF7Gytk/0GfkHRRczGJ33Cr6jW58T+7x/XtY+ct3DJ44GZ+gEHWPIyNjY/XQju2rluLp3wF7FzeqKsrZv3k9F9PTiJ3yYOtvhNb2F5+tsrVIw+4v4o+dCZiaGPPcqAFYm5uSlJ7DS78t13gK6WqrOZmEkaEBM4b2ws3ORv2S2O/jd7Hiiou0lZkpL44diIOVJaWVlZzJyuOZX5ZqzJClL1Hde1FWUsLWlUvVL19+4Ll/qaNvJUVKje4LZhYWPPjPV1g191dm/effmFla0nvEKHqPGKVOU1tbQ9zC3ykuLMBEocDFw4sHnnuJ4I7Nz/jX3pmFBuP15cfqz47Tp+I4fSrFa9aT895/9ViyW9d58AhqqqvY+ufvDS8o9/Xnjsef02hkqQoLNPLYODox9tGn2bFsIYk7t2Jpa0v/Oyer32EHkLhjC3W1tcTN+V4jb2i3Xgy99yH154zkUxTlXmT4fY+0UA1vTKeevSkrUbFpxRKKlYW4eXnz8IuvqKNvxcpC8q84RswtLHj0X6+zbM5PfPHmK5hbWNJ/5Bj6x45Rp+nabyCV5eXsil/Hqj/mYGZuQYewCEZNvl+dZvfGOGpra5n39f80ytOl7wAmzdDs7q5v8YmnMTE24u5enbC49PLlb9bt0JiK/vJkGlf6v/FDNT5H+XiQryrl7UXr1MscrS0Jcndm9pbmX7OhL/uSL2BsZMTQjqGYXXpB+aJdCRrvsLM213woUVRWweI9hxgcGUwnPy9KLr364PI77ABUFZUs2p3AoMhgHhzYg9KKKhJTM9l96rw6TYx/w0OwSX00XwGy8+Q5dp1qfuba1rRozxFMTYx5ckQfrMxMOZl5kVfnr9a4prrYaI6zNjQw5JFBPXC1taa2ro4sZTG/bN7L6kvvsANwtLbgm+l3qT97ONgyunM4Ry9k8q95K1u+Yrfg73iMhHftSXlJCTvXrKCkWImzhxeTnnpRHX0rKVKizG3c783MLZjy7L+I+2MOv7z/JmYWFvQYOpLuQxuH5xzatom6ulriF80jftE89XKfoFDuf/FVACrKylgz7xdKi4swNTfH1duX+//5Kh7+HVqp5qK9MygvL9d/h+Y2oDVfUH67Y+z+Kv4xrNf1E/1NxPzfq/ouQpux7lXZFpf5ONrpuwhtxsYrIiV/d96OzY8J/rvZePysvovQZgS3gVcRtRVd/L30XYQ2Y1Iv7bM2tzXK3+a32m/ZPTC51X6rtUnE7pLnn39e6/KgoCBWrmx8inbl/wshhBBCCCFEWyANOyGEEEIIIYT+GMismLogW1EIIYQQQggh2jmJ2AkhhBBCCCH0R2bF1AmJ2AkhhBBCCCFEOycNOyGEEEIIIYRo56QrphBCCCGEEEJ/DKUrpi5IxE4IIYQQQggh2jmJ2AkhhBBCCCH0R153oBOyFYUQQgghhBCinZOInRBCCCGEEEJvDGSMnU5IxE4IIYQQQggh2jmJ2AkhhBBCCCH0R15QrhMSsRNCCCGEEEKIdk4idkIIIYQQQgj9MZRYky7IVhRCCCGEEEKIdk4idkIIIYQQQgj9aWdj7FavXs2SJUsoLCzEx8eHRx99lIiIiGbT19fXs2LFCtauXUtOTg7W1tYMHjyYadOm6bRc0rATQgghhBBCiBuwfft2fvjhB5544gnCw8NZs2YNb731Fl9//TUuLi5a8/z000/s37+fhx56CD8/P0pLSyksLNR52aRhJ4QQQgghhNCfdhSxW7ZsGUOGDGHEiBEAPPbYYxw8eJC1a9fy4IMPNkmfnp7OqlWr+PLLL/H29m7RsknDTgghhBBCCCGuo7q6muTkZO68806N5TExMZw4cUJrnr179+Lm5sbBgwd5++23qa+vJzIykoceegg7Ozudlk8adkIIIYQQQgi9MWjFWTHXrVtHXFzcddONGDGC2NhYjWXFxcXU1dU1aZDZ2dlx5MgRrevJzs7m4sWLbN++neeeew4DAwN+/vlnZs6cyccff4yhDusuDTs9GBoZpO8itAmV1bX6LkKbse7VV/VdhDYj9r339F2ENuOHx/6h7yK0GW/4Oeu7CG3GY4fO6rsIbcZ9vWP0XYQ2w9vRVt9FaDMcrCz0XQTRhsXGxjZpsN0sg5voOlpfX091dTUvvPACnp6eALzwwgs8/vjjnDlzhpCQkNsqy5XkdQdCCCGEEEIIcR02NjYYGho2mfhEqVQ2263S3t4eIyMjdaMOwMPDAyMjI3Jzc3VaPmnYCSGEEEIIIfTHwKD1/m6DiYkJgYGBHD58WGP54cOHCQsL05onLCyM2tpasrKy1Muys7Opra1tdhbNWyUNOyGEEEIIIYS4AePHj2fjxo3ExcWRlpbG999/T0FBASNHjgRg9uzZvPbaa+r0nTp1okOHDnz++eecPXuWs2fP8vnnnxMSEkJgYKBOyyZj7IQQQgghhBD6Y9h+XnfQr18/iouLWbhwIQUFBfj6+vLmm2+qo28FBQVkZ2er0xsaGvLGG2/w/fff88orr6BQKOjUqROPPPKITidOAWnYCSGEEEIIIcQNGz16NKNHj9b63fPPP99kmYODAy+//HJLF0sadkIIIYQQQgg9MpDRYbogW1EIIYQQQggh2jmJ2AkhhBBCCCH0px2NsWvLJGInhBBCCCGEEO2cROyEEEIIIYQQemNwm++XEw0kYieEEEIIIYQQ7ZxE7IQQQgghhBD6I7Ni6oRsRSGEEEIIIYRo5yRiJ4QQQgghhNAfmRVTJyRiJ4QQQgghhBDtnETshBBCCCGEEPojs2LqhETshBBCCCGEEKKdk4adEEIIIYQQQrRz0hVTCCGEEEIIoT+GEmvSBdmKQgghhBBCCNHOScROCCGEEEIIoT8yeYpOSMOuneof3oEYfy/MFCZkFhSx9lASecWl18zj42TPsOgQnG2sUJVXsvv0eRLOpWuk6RboQ5cAb2wtzSmvrOZ05kU2Jp6murYWAAOgf0QgUT7uWJmZUlJRybHULLYmnaW+vr6lqtusA1vi2b1+NSVFRTh7eDL8nvvxCQppNv3FjDTW/TGbzJRzmFtaEdNvEP1Gj8fg0gnlZMJ+Dm7bRE7aBWqqq3Fy96DvqDsIju6sXsec/75L6umTTdbt5O7J4299oPtK3qD6+nr2xa3k+O7tVJaX4erjz4CJ9+Lo7nHNfBnJp9ixfBEF2ZlY2tjRefAIIvsMUH9/fPd2Tu7fTUFOJvV19Th7edNj5B14BASp08z+zyuoCvObrNs3LJKxM57RXSVbmFl0JPZT7sIsJAhjZyey3/0E1doN+i5WixjWMYQeQX6YK0xIzStk2b6j5BSpmk1vbW7KmC6ReDrY4mRtRcL5NBbuOtRs+k5+ntzbrysn0rP5ZfPelqjCbVuydTO/b4gjv6gIf3cPnrl7Ep2CgrWmzcrP465/v9Jk+X+fepaeEZEAbDmUwLLtWzmTlkpldTX+7h5MjR1Fv+hOLVkNnbqnVyeGdQzB0lTBmexcfty4h7R8ZbPpewT6Mjw6BH8XRxTGRqTlK1m89wgHzqap07x9TyyR3u5N8qblFfLc7GUtUIubo4/rCMC+jXEc3LqRooI8zC2tCI7uzJAJk1GYmbVofW/GlnVrWL9iCUWFhXh4+3DPtOkEhUdoTVtdVcW8778h9dxZsjLSCQwJ48X/vNfsupNPJPHfN1/FzdOLNz/7qqWqoDNxK5ez/M9FKAvy8fL146HH/0FYZJTWtMePHGbV0sUknzpFWVkpbu4ejL5zAoNHjFSnKczPZ/YPszifnExWZgb9Bw/lqX/+q7WqI/6ipGHXDvUK8adHkB8rDxwjX1VKv7AO3NevK9/G7aCqplZrHjsLcyb37cyRlAyW7UvEx8me2JgwyiqrOZmRA0CEtztDokJYffAYqXmF2FtaMKZrBMZGhqw6eByA3qH+dO3gw4r9iVwsUuFia824blHU1NWx48S5VtsGAMf372H9grnE3vsgPoHBHNiykT++/JjH3/oAWwenJukry8uZ978P8QkK4eFX3iY/J5uVv36PwtSUnsNGAXDhzEn8QsMZeMddmFtacWzvThZ9+z8eePE19YX+7sefpbamRr3empoavv/PK4R36d46FW9GwqY4Dm/ZwJAp07B3cWN/3CqWz/qM+1+Z2eyNQnF+Hit/+JKw7n0Ydv8jZJ1LZuuf8zCzsiIwugvQ0PALiumKu38gxgoFh7fEs+K7z5n8z9exc3YF4J4XXqWurk693rLiIhZ8+i6Bnbq2fMV1yNDcnKpzF1Cti8f13y/puzgtZmBEIP3DA1mwK4Hc4hKGRoXw6NDefLx8I5VX7NtXMjY0pLSyis3HztAjyO+a63ewsmBU5wjO5eS1QOl1I/7Afv63cAH/nHIvHTsEsWTbZv759RfMfeNt3Bwcm8336dPPEujprf5sY2mp/v/DZ07TJSSUGePGY2Nhyfp9e3j1u2/48vl/NttgbEvGd4tiXNdIvlq3nYyCIu7p1Yk37hrB0z8vpqJa+34R4e3GsbQs/tiZQElFJf3COvCvcYN5c+E6Tly6tny8YhPGhkbqPCZGhnz64Hh2nk5pjWpdk76uI8f27WLjkvmMfuARfAJDKMy7yKo5P1JTU83YqY+26jZozv6d21nwyw/cO/1xAsPC2RK3hi/fe5u3PvsaB2fnJunr6uowMVEwcORojiUcpLy0+YfNpSUl/PLlZ4RGRaMsaPpQsK3ZuXUzv8z6hulPPUNoRCRxq1bw7r9f4bPvf8LZxbVJ+lMnkvDx8+eOuydh7+DA4YMH+O7zzzBRKOg3aAgA1dXV2NjYMv6eycSvXd3aVWpzDOQF5TpxQw27oqIi5s2bx8GDBykoKMDKygofHx/uuusuYmJirpn3lVdewdfXl8cffxyAzz77jE2bNnH//fczadIkdbrExEReffVV5s6di62trXr5rl27WL16NWfPnqWmpgY3Nze6d+/OuHHjsLOzA6CqqoolS5awdetWcnJyMDU1JTQ0lMmTJxMS0vjULT4+ns8//xx3d3e+//57jXIeOHCAt99+GzMzMxYtWqRRJm2++eYbvL29tX7X0roH+rLr1Hl1g2zF/kSeHzuISG93Es6na83TuYM3JeWVxB1uiDTlq0rxcLClZ7Cfej1ejnZkFChJTM0CoKisgqMXMgn1bDxpeTnacSbzImeyctVpzmRexNPBlta2N34tHXv3o3O/QQDETpnK2aSjHNy6kcF3TmqS/ti+nVRXVTJu2mOYKBS4eHqTl5XB3vh19Bg6EgMDA0ZMekAjT/+xE0g+doRThw+qL8jmllYaaRL37qS6spLoK6Jcra2+vp4jW+PpMiRW3SAbeu9D/PTGi5xO2Etkb+1lO7ZrK5Y2dgyYOAUAB1d3ci6c59DmDer1DH9gukaegXffx/ljh7lw4ri6YWduZa2R5sTeHShMzQjs1EWn9WxpZXv2U7ZnPwCur/5Tz6VpOX1DO7D52BmOXTrWF+xK4M27R9LJ35O9Zy5ozVNYWs6K/YkAdPRtPgpsaGDAvf26Enf4BB3cnLA0Vei+AjqwYOMGRvXqzbi+/QF4YdK97D1+nKXbtvLE+AnN5rOxtMLRVvv57rl7Jmt8fnjMOHYdS2T7kcPtomE3pnM4S/cdZc+lfeDLddv5+Ykp9AvrwIajp7Tm+fmqaOyi3Yfp4u9F90AfdcOupKJKI02/0ABMTYzZdOxMC9Ti5ujrOpJ+9gye/h3o2LMvAHZOznTs2ZeTh/a3cI1vXPzK5fQeOIR+w0YAMOWRx0g6lMDW9Wu4874Hm6Q3NTPjvsf+AUDGhZRrNuzmfPMFPQcOhvp6EvbsapkK6NCqJYsZOGw4Q0eOBuCRfzzN4QMHWL9qJfc9PL1J+gmT79X4PGLMOI4fOczeHdvVDTsXNzce/sdTAOzZsa2FayD+Lm5o8pT333+fM2fO8Mwzz/Ddd9/x+uuv06VLF1Sq5rvtXItCoWDx4sUUFRVdM92cOXP48MMP8ff35/XXX+ebb77h0Ucf5eLFi6xduxZoeOLxxhtvsHbtWiZNmsSsWbN45513sLOz4+WXX2bfvn1Nfru0tJTExESN5Rs2bMBZyxMogK+//po5c+Zo/Hl4XLt7W0uxszTH2txU40l4TV0dqXmFeDnaNZvPy8GWczmaT8XOZefhbm+D4aXuI2l5hbjaWasbaTbmZgS7u5Cc3fhbaXlKfF0ccLRueErtZG2Jn4sDyVmt+2S+tqaGrNQUAsIjNZYHhEWSflb7zUL6uWR8AkMwUTTeaHaI6IhKWYgyP7fZ36qsqMDMwqLZ7w/t2EKHyGhsr/GUv6UV5+dRpirGO6Sxi4yxQoFHQBBZ55uPpGannMMnJFxjmU9oOLlpKdTWan9CX1dbQ011dbPbpL6+nqQ9Ownp2gMThekt1Ea0JAcrC2wszDiddVG9rKa2jnM5efg6O9z2+mNjwigsKePgubTrJ9aT6poaTqVeoHuY5r7fPSycY+fOXjPva999y+iXXuDxjz9gc8LB6/5WWWUF1tc4f7QVrrZW2FtZcDglU72sqqaWpPRsQjxcbmpd5gqTJo25Kw3tGMyh8xnkq649fKCl6fM64h0YTE5aKunnkgEoKsjj9NFDBEZG306VdKamuprUc8mEX9WNOCw6hrOnmg5FuBlb1q2hWKlk9MR7bms9raW6uppzZ04T3VmzB0p05y6cOpF0w+spKyvD8qqHoOIKBoat9/cXdt2IXUlJCcePH2fmzJlERzeccFxcXAgOvvWnj1FRUeTn5zN//nwee+wxrWlOnz7NokWLeOSRRxg/frx6uYuLC9HR0ZSUlACwYsUKkpKS+PTTTwkMDFSnefbZZ1GpVHzxxRf8+OOPmF3qimZoaMigQYOIj48nKqqhb3RRURH79+9nwoQJLF++vElZbG1tNaKI+mRl1nCjXHrVRbO0ohJr8+b75VuamVJ6sUAzT2UVRoaGWJg2XIST0rMxNzVh6sCGLoVGhoYcvZDBpsTT6jy7Tp1HYWzE48P7UFdfj5GhITtOnG31m7iyEhX1dXVYWmv+u1ja2HL+5HGteUqKirCxd7gqvQ0ApUVF2Ds1vXk5sHkDqsICoi49Vb1afk4WqadPcvcTz91CLXSnTFUMgIW15kXDwtqGkiJls/lKVUV4BYdpLDO3tqGuro6KkhIsbe2a5NmzZjkmpqb4N3MDknYqieKCPMJ79ru5SohWYW3ecA4pKa/UWF5SUYmNhfltrTvI3ZloX0/+t3rLba2npSlLSqitq8Ph0vF/mb2NDfknT2jNY25qylMT7yYqoANGRkbsOHqEN378jn8/+DAjevTUmmfxls1cLCwktkcvnddB1+wsGxodRWXlGsuVZeU4Wllqy6JVbKdQHK0t2ZaUrPV7d3sbIr3d+WDZxlsvrI7o8zoS0a0X5SUlzPnkHaiHurpaonr2YfCEyU3y60OJqpi6ujqsL/WMuszGzo6TiUdueb0ZF1JYtegPXn7vYwyNjK6foQ1QFRdRV1eHrb29xnJbe3uUhxJuaB0H9+7h2OFDzPz085YoohBq123YmZubY25uzt69ewkPD0ehuP1uNQYGBjz44IO8++67jBs3Dnf3poOqt2zZgpmZGWPGjNG6DisrK3W66OhodaPuShMmTOD//u//OHz4MD17Nl54hw0bxosvvshjjz2GhYUFmzdvJiwsDDc3t9uum65FerszqkvjU+X5O7SfRAxuYDaherRPbnJ5zhMfJ3v6hXVgbUISGQVFOFhZMLxTKAPCA9l66SId7uVGR18Plu49Sm5xCW521gzvFIaytJzDKRk3Wbvb16Te9fUYcI1t0TT55RU1SXoiYT/xi+cz4dEnsXNsOtYC4ND2LVjZ2hEU1emGy6wLpw7uZcvCuerPYx596tL/adajnuvvG02+vrxRtOQ7snUjx3ZtY/wTz6Mw094IOL5nOy4+fjh76qerstAU4+/FhB6NjfBfNu0BtJ0PDK44IG6ehamCSb078/uOA5RXVd/yevSqvr7Z48XOypopQ4erP4f5+lFUUsK8Deu0Nuw2Jxzk6yV/8vYjj+LmqL9ofnP6hQbw2LDe6s/vLW2YJKjpXmFwwxNj9QzyZWr/bny6egu5zUTjhkUFU9DGIrr6uI5cOH2C7WuWM/LeaXj6d6DgYg7rF8xl68olDBw38RZrontXb4fbmSSturqaHz77mLumPoyTa9u737oerfvJDdx7nTx+jM8/fI+HnniSoJDQFirdX4CMsdOJ6zbsjIyMePbZZ/nqq6+Ii4sjICCAsLAw+vbtqzF+7WZ17dqVsLAwfvvtN/71r6azAGVmZuLm5oax8bWLmJmZqY68Xc3HxweAjAzNBoevry++vr5s376dESNGEB8fz8SJE6mt1T7xyPTpmv2nLS0t+fXXX5ukW7duHXFxcdcsL4CBuz8e4TfW3eJ01kUyNjR2WTUyatjxLc0UFJdXqJdbmCoorahskv+y0opKrEw1u8VZmiqoratT34QNjAzieFqWuoGWW1yCibERY7pEsO1Ew6yXQzsGs/t0Cknp2eo0thbm9A4NaNWGnYWVNQaGhpQUKzWWl6qK1U9Pr2Zla0vpVd1/L0e6rs5zImE/y3+exR0PPdZkJrPLamtqOLpnOzF9B7X6k0f/iGhc/+mvURZoqI/1FU+Ty1XFmFtp3x4Alta2lBUXaywrL1FhaGiImaXmU/ojWzeyZ+0yxs54Bldff7QpUxVz/tgRBky8V+v3ovUlpWWTmleo/mx86SWw1uZmFJU1nkOszBSornEOuR43O2tsLMx4dGhjY+HyTc/7943l05WbyS0uueX165KdlRVGhoYUXLXvF6pUTaJ41xLu58/q3TubLN+ccJCZv/7M69MebrMzYu4/m8qZ7MaugyaXzmF2FuYaXSRtLcxQXrGfNKdnkC/PjOzPF2u3acyIeSVjQ0MGRgQSf/Q0dXqYRflq+ryObFn+JxHdehLTdyAALp7eVFdWsuq3n+g/erzeo1lW1jYYGhpSrCzUWK4qKsLmqijejSoqLCArPY3ZX3/O7K8bIlf19fXU19fzxD3jefrVNwnvdO15G/TB2sYWQ0NDlAWavZ6KlMomUbyrnTiWyPtvvMakBx5kxJhxLVlMIYAbnDylT58+dOvWjePHj3Py5EkSEhJYtmwZDzzwAPfcc+t9pKdNm8ZLL73EmTNN+7LfzFOh60ckmn4/bNgwNmzYgJ+fH3l5efTu3Zvt27drzf/uu++qI4TQ0J1Tm9jYWGJjY69b3k9Wbb1umsuqamqpqinTWKYqryTA1ZGswoaLiZGhIT5O9mxsZnA7QHpBUZNxEv6X1nH5AmtiZNjkYtvw79C4/YyNjJo81K+rr7/Ws80WYWRsjLuPH+eTjhHepYd6+fkTxwntrH0mRq+AQDYuWUBNdRXGJg2R53NJx7C2s8fOsXF8ZdKBvaz49TvGTXuMsGvMdHnq8EHKSkropIdJUxRmZhozXdbX12NhbUPaqSRcffyAhjESmeeS6XONp79ufgGcSzyssSz11Amcvf0wMmo8PRzasoF9a1cwZsbTGq85uNrJfbswMjYmKKbbrVVM6FxlTQ2VKs3xksVlFQS5O5N+aRp7Y0ND/F0cWZ2gvfvZjUjLV/LflZs0lo3oFIaFwoSl+45SUKLf8VRXMjE2JsTHl/0nkxjcpfF8sf9kEgNibnzCnzPpaTjZaHbj23hwP+/M/oV/P/gQgzq33cmDKqpryFZqjpMvLCkj2teDs5fGcJsYGRHm6cqcbQeuua7ewX48FduPr9ZtV0+8ok33QB+szc3YeOx0s2lakz6vI9VVVU3uJQwMDWkaM9UPYxMTfAICSTp6mC69G7uQnjh6mM49b61rsb2DI298+qXGsq1xazhx5DCP/+tVHJ1vbixnazExMSEgKJgjhw7Sq3/j9f7ooYP06NP8kIOkxKO8/8Zr3HP/VEbf2XaisG2WvMdOJ254BKFCoSAmJoYpU6bw8ccfM2zYMP744w+qq2+9y01wcDC9evXSGv3y9PQkOzv7uuv38PAgNTVV63eXl2vr6tm/f39SUlKYPXs2/fv3x9S0+UkeXF1d8fDwUP/pu8vmvuQL9A4JIMTDBWcbK8Z1i6SqpoZjaVnqNOO6RTKuW+OA8ISzaVibmzIsumH8Qyc/T6L9PNlzxXTTZ7Jy6ezvTbiXG3YW5vi7ODIgIogzWbnqhvaZrFx6h/gT6OaErYUZIR4u9Aj241Rm40QMraXH0JEc2b2dQzu2kJeVQdyC31AVFdK5f8OMU5uWLmDup++r00d0742JwpQVv37PxYw0TibsZ1fcSnoMjVU3/o/v382yn75l8J2T8AkKoaRISUmRkvLSppGGhO2b8Q8Nx74NXIwMDAyIHjCUgxvXcfZoAvlZGWz84xdMTE0J7tx4w7Jh3s9smPez+nNk7wGUFBWyfekCCnKyOL5nOyf37yJm0DB1moRNcexetYTBkx/EztmV0uIiSouLqCzXfOBQX1/P8b07CIrp1qbew3QzDMzNUAQGoAgMAEMDTFxdUAQGYOyqfWKl9mrHybMMiggi0tsdVztr7ukTQ1VNLYfPN0bdJ/XuzKTemlEGd3sb3O1tMDUxxkJhgru9DS62DeM6q2tqyVGqNP4qqqqprK4hR6mitq5t3LBeNmnIMNbs3sWKHdtJycrifwvnk1dUxJ39Gm7cvl22hGf+9191+jW7d7F+315SsrK4kJ3N7xviWLJ1MxMHDVanid+/j7d//oknxk+gU2Aw+UVF5BcVUXyN2QHbklUJSdzZPYoegb54O9rxVGxfKqpr2H6icUKZp2P78XRs481snxB/nh01gHnbD5KUnoOdhTl2FuZYmTUdtjGsYwiJqZnkFLWNyC3o7zoS1DGGhO2bOb5/N4V5FzmXlMjWFX8SFNVJ79G6y4aOvYPdWzaxI349WelpLPj5B4oKC+g/vOFdbEvnzebTt/6tkSczLZW08+coKVZRUVFB2vlzpF2awMvI2BhPH1+NP2sbW4xNTPD08cXM/PbG+LakMRMmsmXDejauXUN66gV+/vZrCvLzGT56LADzfv6Rt19ufEXO8SOHee/frzJs1Bj6DhpCYUEBhQUFFCmVGus9fzaZ82eTKS8ro0Sl4vzZZNIuNP9wRIjrueX32Pn4+FBbW0t1dTUmJia3XICpU6fy5JNPcvCg5uxiAwYMYOXKlaxevVpj8pTLSkpKsLKyYsCAAcyZM4fk5OQm4+yWLFmCjY2N1lcyWFhY0KdPHzZt2sRDDz10y+XXh92nzmNiZEhsTDjmCmMyCor4fftBjXfY2V41CYKyrJz5OxIYFh1KlwBvSioqiDt8Qv2qA4DtJ85RX9/wjitrCzPKK6s4nZXLliumpI47fIIBEUGMjAnHwkxBSXklh8+nsy3p2jPJtYSIbj0pLy1hx5rllBQpcfbwYvJT/1SPYygpUlKY19jgNDO34L7n/o+1v8/mp/fexNzCgp5DR9JjaOMLQw9u20RdXS3rF85l/RVj2HyCQ5n64mvqz4W5F0k5lcSE6U+2Qk1vTOfBI6iprmLrn783vKDc1587Hn9Oo5GlKtTsSmLj6MTYR59mx7KFJO7ciqWtLf3vnKx+1QFA4o4t1NXWEjdH8xUhod16MfTexmMnI/kURbkXGX7fIy1Uw5ZnFhqM15cfqz87Tp+K4/SpFK9ZT857/71GzvZly/FkTIyMGN+9I+amJqTlFfLDxl0a77Czs2x6k/X8mEEan8O93SkoKeODpe3vJe5Du3ajuLSE2WtXk19cRIC7B588+Yx6PFx+kZKMXM1ZDmevXU12QT6GhoZ4u7jy6gPTNMbXLdu+ldq6Wj5ftIDPFy1QL48JCuarF9r+exGX7U9EYWzEo0N6Ymmm4ExWHv/5M07jHXZONppdtIdHh2BsZMjDg3vw8ODGh0jH0rJ4c+E69WdXWysifdz5bNWWFq/HzdDXdaTfqDswALasWIyqsABzK2uCO8Yw8I67W6fiN6Bbn36UqlSsWbyQosICPHx8eerVN9SRtaLCQvJysjXyfPXef8jPbdxe77z0HADf/bmi1crdEvoMGERJcTGL/5hHYWEB3r5+vDrzPZxdG175U1hQQE5m44yymzesp7KygpWLF7Fy8SL1cmcXV76ZM0/9+V9PPq7xOwf37m6S5m/jLz5bZWsxKC8vv+Zj1OLiYj788EOGDh2Kn58f5ubmJCcn89133+Hn58fMmTOv+QPa3mNXXFzMm2++qU4za9YsNmzYQFVVlcZ77H799VeWLl3K2LFj6d27N05OTuTk5LB+/Xo8PDyYMmUK1dXVvPbaa1y8eJFp06YRHh5OSUkJK1euZMuWLbz88sv06NFwsYmPj+e7775Tv6euqqqKiooKbC71i7/6+8vvsfv666+xvmq2QSsrq1tu0N5MV8y/Ml+na/dN/ztRXjUT3d9Z7Hvv6bsIbcYPl94JJeBfnnK+uOyxQ63/IK2tGt8l4vqJ/ia8HdvG7N1tgYNV23/FSGsJdm8fPU5Kdu27fiIdserd/DCb9u6GZsUMCQlhxYoVZGVlUV1djaOjIwMGDNB4wfjtmDx5Mps2bWqyfNq0aQQGBrJ69Wo2bNhAbW0trq6u9OjRg1GjRgENfZ9nzpzJkiVLmD9/vvoF5WFhYbz//vuEhjY/A5FCobihWT6ffLJpVGbmzJl06tTpxisphBBCCCGEaMJAZsXUietG7ITuScSugUTsGknErpFE7BpJxK6RROwaScSukUTsGknErpFE7Bq1l4hd6Z79rfZblj3/uhO8SYdWIYQQQgghhGjnbnnyFIDjx4/z1ltvNfv95bFqQgghhBBCCKGVvO5AJ26rYRcYGMjnn3+uq7IIIYQQQgghhLgFt9WwMzU1xcPDQ1dlEUIIIYQQQvzdGMroMF2QrSiEEEIIIYQQ7dxtReyEEEIIIYQQ4rbIGDudkIidEEIIIYQQQrRzErETQgghhBBC6I+8oFwnJGInhBBCCCGEEO2cROyEEEIIIYQQemNgILEmXZCtKIQQQgghhBDtnETshBBCCCGEEPojs2LqhETshBBCCCGEEKKdk4idEEIIIYQQQn9kVkydkIidEEIIIYQQQrRzErETQgghhBBC6I/MiqkTshWFEEIIIYQQop2Thp0QQgghhBBCtHPSFVMIIYQQQgihPzJ5ik5IxE4IIYQQQggh2jmJ2OlBB1dHfRehTXCwstB3EdoMG3NTfRehzfjhsX/ouwhtxqPffaPvIrQZH86Q/eKyHwfF6LsIbcZpS1t9F6HNcLW10ncR2gx7SzN9F0HcJAN5QblOSMROCCGEEEIIIdo5idgJIYQQQggh9MdQYk26IFtRCCGEEEIIIdo5idgJIYQQQggh9EfG2OmEROyEEEIIIYQQop2TiJ0QQgghhBBCfyRipxMSsRNCCCGEEEKIdk4idkIIIYQQQgj9kVkxdUK2ohBCCCGEEEK0cxKxE0IIIYQQQuiNgYyx0wmJ2AkhhBBCCCFEOycROyGEEEIIIYT+GErEThckYieEEEIIIYQQ7Zw07IQQQgghhBCinZOumEIIIYQQQgj9MZBYky7IVhRCCCGEEEKIdk4idkIIIYQQQgj9kclTdEIidkIIIYQQQgjRzknETgghhBBCCKE/8oJynZCGnRBCCCGEEELcoNWrV7NkyRIKCwvx8fHh0UcfJSIi4rr5MjMzee6556ivr2fRokU6L5c07NqhfZs3sCtuNSqlEhcPT2InP4BvcGiz6XPSU1nz+2wyzp/F3NKKLgMGM2DMnRhoeTpy4cwpfv34HZzcPHjyPx+ql//y0TtcOH2iSXpnD0+e/M9HuqmYjmxfv5aNK5dRrCzEzcubiVMfoUNYuNa01VVVLPhxFukp58jOSCcgOJRn3nxHI01RYQHLfvuVtJRz5GZl0a3fAO7/xzOtUZWbsis+jq1rVqAqUuLq6cW4+6bhHxLWbPqstFSWzfmJtHPJWFhZ0WPQMIbeMVFjvzi0awdb1iwnLzsLU3NzgiKiGDN5KtZ2dgDs3RzPwZ3byMlIo76+Hg9ff0ZMmIR/SPP7oz4N6xhCjyA/zBUmpOYVsmzfUXKKVM2mtzY3ZUyXSDwdbHGytiLhfBoLdx1qNn0nP0/u7deVE+nZ/LJ5b0tUodWYRUdiP+UuzEKCMHZ2IvvdT1Ct3aDvYrWIYdEN+4XFpf1i6d7r7xdju16xX5xLY8F19ov7+nclKT2bXza1zf3iz/VxzFu1knylEn8vL56f+iCdQps/f1yWmpXFtFdfpr6+ns2/ztH4rrqmhl+WLmHt9m3kFRbiYGvLvWPGMil2ZEtVQyfi16xkzZI/KSoswNPHl/umP05IRKTWtFVVVfz6zRdcOJtMZnoaQWHhvPrex03S7dq6mTVLFpGdkYG5hQUR0Z2Y/PCj2Nk7tHR1bsvKpUtYNP8PCgry8fXz4/GnniUqOlpr2iOHEliyaCGnT5ygtLQED08v7rzrbkaMHqORrrq6mt/nzGbj+jgK8vOws7fnrklTGH/X3a1RpVv256JF/Pbbb+Tn5REQEMDzL75ITEyM1rTnzp3j4w8/5Pz585SUlODk7Mzw4cN5dMYMTExMAEg4eJCvv/6aCxcuUFlRgZubG3eMH8/9DzzQmtVqO9rRrJjbt2/nhx9+4IknniA8PJw1a9bw1ltv8fXXX+Pi4tJsvurqaj766CMiIiI4duxYi5RNGnbtzLF9u1k3/zdG3zcNn8AQ9m+JZ+7nH/Hkfz7CztGpSfqK8jLmfPoBvsGhPPrvmeRnZ7Hs5+9QKEzpPWK0Rtry0lKW/vQtAWERFBcWanw36R/PUVtbo/5cW1PNN2++TETXHi1T0VuUsGsHi2f/xD0PzyAgNIwd69fx/+zddXiTV/vA8W/dnUKFttQFSinuWqDIsDF0Y8ydDcZ+k3d75/JuMIZtYwpsuLsVdyulheJanErqkjT9/VFICUmhQNuk2/25Lq6LnJznyTnpSfKc5z7y0zef88H4SbjWctfJr1arsbC0oF23niQnHCQ/N1cnj0qpws7Rka59BrBz4/rqqMYDS9izi+WzptN/xHPUCwlj98b1/D7uK97+egIutfS3i1+//ZyA0HBGffo1N69eYd6vP2JpZUWHHo8BcP7kceZOm0yvoU/RoElzsjMVLJnxO3N+nsSL7/0XgDPHk4lq0Zp6waFYWFmxfe1KfvvuS9764lvcPTyr9T24n471g2gfEcS8XfHczMohJjKUF2Ja892yjRSqVHqPMTc1JbewiM1HTtEiuN49z+9qb0vPxvU5ez21Ckpf/UxtbCg6e4HstXHU+fAdQxenytxuF/N3xnMjK4euDUN5oWtrvlt6n3ZRUPF20auJcbeLDbt3MWHmDN555jmiwkJZtH49o7/5mjnjvsdDz/fHbUqVio8mT6RRWDiHjiXrPP/R5IncSEvjvedfxMfTg/TMTAqLiqqyKo9sz/atzPr1Z0a8/DohEfXZuHol4z79kK+n/kItd90LthK1GgtLS2J69eHwwf3k5ebo5DmZfJRpE75j6DPP06RFazIVGcz4eQo/j/+W9774pjqq9VC2bNrIT5Mn8vrot2kQ2ZAVS5fw4btj+XXGX9Su46GTP/nIEfwDAhg0dBiubm4c2LePH8Z/h4WlJZ27dtPk+/qzT7h54wZvjv0/vOvWRZGRTmFhYTXW7MFtWL+e8ePG8e577xHVqBELFyzgrVGjmLdgAR4euu+FhYUFvXr3JiQ0FAcHB06dPMlXX36JSqVi1JtvAmBja8vgwYMJDArC2tqaxMOH+fqrr7C2tmbgE8bdyf23W7p0KV26dKF79+4AvPTSSxw8eJA1a9bw9NNPl3vc9OnTqVevHg0aNDBsx27ChAls2rRJJz00NJRx48bd89jnnnuOXr16MWDAAADef/99jhw5wpgxY+jUqZMmX1xcHNOmTdMKS5aUlLB+/Xo2bNjAxYsXAfD09KR9+/b06NEDW1tbAHJycpg3bx67d+8mLS0NOzs7GjZsyNChQ/Hx8dGcb/bs2cyZM4eoqCi++EI7KrNq1Sp+/vlnfH19mTp1qqZMEydO1FuvRYsWYWlpec+6V4XdG9bQqHU7mrTvDEDPYU9z+shhDmyJI+bxITr5k/bsQllUSP9nX8bC0pI63j7cvHqZ3RvW0KpbT63ozLLpv9CodTtKSiD54D6t89ja22s9TtyzE2VhIdFtO1Z+JR/B5lXLadGhE627lP6IDHzmBY4dPsSODWvpM1T3LpiVtTWDn38FgCsXz+vt2LnVrs3Akc8DkLB3VxWW/uFtX7uSpm070KJTDAD9RjzLiaQE9mxaT49Bw3TyH9q1A2VhEYNffB0LS0s86vpy48pltq9dSfvY3piYmHDh9EmcXN1oH1t6t9XVvTZtusay7K8/NOcZ9op25HLAyBc4Gr+fk4kJRtexaxsWyOYjpzhy8SoA83bF8/ETPWjk783eUxf0HpORm8/y/UkANPTzKvfcpiYmDGvXlHUJxwj0qIWdVfV/N1S2vD37yduzH4A6H4w1cGmqTrvw0naRdKtdzN0Zz8eDehDt782ee7SLZbfaReR92sXwdk1Ze+gYQR61sLU2znYxZ9UqerXvQL8uXQAY+8yz7Ek8zOIN63l1qO73x21TZ88iyNeX6PAInY7d3sTD7E9KYtEPk3B2dATAS0/HyNisXbaYtl260ql7aVRxxEuvkhR/gE2rVzLo6Wd18ltZW/PMrREcKefP6e3YnT5xDFe3WsT2Lb0OcvfwoGvvvvz1y49VWJNHt3j+XLrG9qTnY30AeO2t0RzYt5eVy5by7Isv6+Qf+tQIrceP9evP4UPx7Ni2VdOxO7h/H4cOHmD67Hk43Rr54eFpXL8V+syeNYvejz1Gv/79AXjn//6PPbt3s2jhQl57/XWd/D4+PlrXn56enhw8eJCEhARNWnh4OOHhZVFxb29vNm/eTMKhQ//Kjp1JDVkVU6lUcvr0afrfagu3RUdHc+yY7si22/bv38/+/fv54Ycf2LWr6q4lKxz3bNSoETNnztT69/HHHz/Ui1paWvL333+jVCrvme/777/nl19+oWnTpnzxxRdMnjyZJ598ksTERM2bkpOTwzvvvMOBAwd48cUXmTZtGh9++CEFBQW8/fbbnDx5UuucLi4uHD16lOvXr2ulx8XF4e6uG9GxsrLSqffMmTMN0qlTqVRcuXCOwPoNtdID60eScuaU3mNSzp7CLzgMizvKG1S/IdmKDBSpNzVp+zZvICcrk/a9++s7jY6D2zYTFBmFk6vbQ9SkaqhUSlLOnSGsYSOt9NCGUZw7edwwhaoGKpWKy+fPEhKpPTwmpEFDzp86ofeYC6dP4h+q3S5CIqPIysgg41a7qBccRrYig+RDBygpKSE3O4vDe3YRFqV/6AlAsUqFSqnExs6uEmpWeVztbXG0tebk1RuaNFWxmrPXU/Fzf/ShULHR4WTk5HHwbMojn0tUH027uKLdLs5dT8Wv9qO3ix7R4aTnGne7UKpUnDh3lhYNtX9XWkQ2JOmu38877YyPZ8eheMY8/Yze57fu3094YCBzVq/isddeYeDoNxk//U/yCgoqtfyVSaVUcv70KSIbNdZKbxDdmFPHy79gu5/g8AgUGekc2reHkpISsrMy2bN9C1FNmj1iiauOUqnk1MmTNGmmXcYmzZqR/ACRhry8XBwcHDSPd23fRkhYGIvmz2P4wP48M2wIP078gfy8vEore2VTKpUcP36cFi1baqW3aNmSxMTECp0jJSWFPbt307hx43LznDh+nMTERKLvkUcYXlZWFmq1GudbNyZuc3Z2RqFQ6D0mPT2dKVOmMGbMGE1QqqpUeCimubk5Li4ulfKibdu2JT4+nlWrVtGvXz+9ebZv386WLVt4//33ad26tSa9Tp06NGvWjJyc0rtiM2fO5ObNm0ybNg03t9JORu3atfnwww8ZPXo0EydOZMqUKZrIlIODA2FhYcTFxTF8+HAAzp07x6VLl4iNjSU+Pl6rHCYmJpVW70eVl5NNiVqN3a27n7fZOTqRk3xU7zE5mQocXdx08gPkZGXi4l6b65cusnX5Yp7/4FNMTe/f10+9dpULJ48x5LXRD1mTqpGblY1arcbByVkr3cHJmZNJFfvyrYlys0u/ZOxv/V1vs3dyJvtokt5jsjMVON01t8Pe0bn0OYUCV/fa+AWHMOzVN5nz02SUyiLUxcUEN2jI4Bd1707etnbhXKysrIlo3PTRKlXJHGysAMjJ1x7uk1NQiKOtzSOdO9jTnSg/b35YteWRziOqn6ZdFGi3i+yCQpwesV2EeLoTVc+bCSu3PNJ5qpoiK4titRpXJ+3vD1cnJ/Yf0f/9kZqRwde//sI3Y8ZgZ6P/fbpy4waJJ05gaW7B16PHkJOby/jp00uPHT2m0utRGbJvXbA5Omv/5js5u3D0cPlzKO8nOCyCV8e+x0/jv0VZVEhxcTENGjXmxbeMNxKelZmJurgYF1ft3wlnF1cyDh6o0Dn27NpJwsGDfD/1J03a1atXOJqUhKWFJR999gU5OTn8OHECaWmpfPTZF/c4m+EoFAqKi4txveu9cHV1Zd/ee8+Zfe7ZZzlx/DhFRUX069+fV197TSdP7549ycjIoLi4mOdfeIHHBw6s1PLXGNW4KubatWtZt27dffN1796d2NhYvc/pW6eiPOPHj6dHjx6EhVX9+gMGmWNnY2PDkCFDmDVrFjExMdjfNcwPYOvWrXh7e2t16u5kb2+PWq1m+/btdOzYUdOpu83U1JT+/fszfvx4zp8/j7+/v+a5rl278vPPPzN06FBMTU3ZsGEDbdu2xaacHyhjY8JdjamkhLuTtPLrPFei+Z9KqWThL1PoNmgYLhUcJhO/fTP2Ts4ENyw/cmNIuu8P/4pldHW+ZEpK7vnFo/vcrXZxK/n65Uss+/tPuvR9nNDIKLIUGaya9zeL/vyFIS/pdu52rFvN3s1xvPDuR1jbVO0dqfuJ9q/LgBZlEcw/N+0BoOSOtl/KpPTz85BsrSwZ3Loxs3ccIL/o3iMQhOFF+9fl8ZZl7eKP2+3irjZgUgntYlCbxszeXnPaxd3fByX3+P74ZOoUBnTtSoPgkHLPpy4pwQT47I1R2N+6Qz32mWd48+uvSFMocLvrbrcx0fte3OtH9j4uX7zA37/8RN/BQ4mMboIiI515f/7Gnz9O4qXRxj5/Vc/vRAV+T48mJfK/zz/llVFvEXbH4mUl6tJ28d5HH2N369rvtbfG8MHYMWSkp+t0JI3Jg3xGbvvqq6/Izcvj1MmTTJ40iZkzZjDyGe0o97RffyU/P58jSUlMmTwZLy8vevbqVc4ZRWWIjY0tt8N2P46OjpiampJx11oUCoVCJ4p3W2JiIkeOHGHOnDmaNLVaTd++fXnllVceuiz6VLhjFx8fzxN3jfnt1asXI0eOfKgXjo2NZfny5SxcuFDvOa5cuULdunXveY7MzExycnLKzXd7fPOlS5e0OnaNGzdGpVJx+PBhGjRowJYtW/jPf/7D4cOHdc5RUFCgU+969erx3Xe6q15V9A5A7dBIwpo0v2++u9naO2BiakpOVqZWem52lk605jZ7J2dyMu/Kn5VV+pyjE9mZCm5euczSP39h6Z+/ALcuckpK+PTFpxj+5jsE3TH0U6VSkbBrO03adcLMzOyB61CV7BwdMDU1JStT+8OWnaXAwUn/+/NPYOdQ+iWTnanQSs/Jyiy3XTg4OevNf/s5gM0rluATEETHXqXzKzx9/bC0suanL/9L7MAhWov17Fi3mrWL5vLc2x/gGxhUORV7BMkp17iYWtYOzG9Foh1srMnMKxsKZm9tSXbBw0/a93B2wNHWmhdiym5A3f6h/3r4Y3y/YjM3s3Tn3AjDqM524WRrzYtdddvFN08+xvjlxtMunB0dMTM1Je2uIUQZWVm4lvP9ceDoEQ4dS+b3RQuB0t8MdUkJbYYP5Z1nn6NflxhqOTvj7uqq6dQB1PP2BuB6WqpRduwcbl2wZWaka6VnZSp0ongPYsXCeQSEhNJrQOm1hK9/AFbW1nz53lgGPjkSNz3TQAzN0ckJUzMzMtLTtNIVGRm43GclzyOJh/no3Xd46tnneayf9vQOVzc33NzdNZ06AB8/PwBuXL9ulB07Z2dnzMzMSEvTfi8yMjJwdbv3dJQ6txZWCQgIQK1W8+UXX/DkU09hbl52+e1963MRFBREWloav/7yy7+zY1eBEWPGwMLCgqCgIBISEmjbtq0mPSEhodxg1JQpU7Qe79mzh/nz5/P999/rBKYeVYU7dg0aNOC1u0LIdo8wj8bMzIynnnqKCRMm0Lt3b53n776Dei/l3TG5fY67nzczM6NLly7ExcWRk5ODo6Mj9evX19uxs7Ky0llA5fZStXer6B2AJfsfbiUcc3NzvPz8OZOcpLUa5ZnkI0Q01j9W3ycgmA2L5qBUFmFhYanJ7+DsgnMtd9TFxbzyqfaqXPs3x3E2OYnBr43G2U37B+f4oQPk5WQT3a7jQ9WhKpmbW+DjH8iJxMNEt2yjST+ReJioFq0MWLKqZW5ujne9AE4eSaRh87J6njqSRGQz/auW+gWFsHreLJRFRZp5dqeOJOLo4oLLrdVDi4oKdYbm3n5858dz25qVrF88j2ffft9otjkoVKkozNZe0TArr4BgT3cupSmA0ot6/9purIrXP4y5IlLSFIxfob2wVPdG4dhaWrBkXyLpObqL8QjDKa9dhOhrFwcfrV2MW67dLmIbhWNjhO3CwtycUP8A9iUl0aVl2ffHvqQkOjXXfwNy1rfaNza3HTjA9KVL+OOLL3G/ddHfMDSUjXv3kFdQgK21NVC6NQKAh54Vio2BuYUF9YKCOZJwiOZt22vSjyQcolmrNvc48t6KCu/xXaozisA4WFhYEBwSQvyB/bTv1FmTHn9gP207dCz3uKTDCXz07js8+cyzDHhikM7z9RtEsn3LZvLz8rC51em/nFI6B7WOntUljYGFhQVhYWHs27uXmJgYTfrevXvp3LnzPY7UplarKS4uRq1Wl5unpKTkvutPCMPr168f33//PcHBwURERLBmzRrS09Pp0aN00aUZM2Zw8uRJvvzySwD8bt28uO3UqVOYmprqpFeGCnfsLC0t8fIqf/Wvh9G2bVuWLFnC7NmziYjQ3mfM29ublJR7Tzh3cnLCzs5Os2Lm3S5dugSgt9wxMTG88cYbXL9+XeuDejcTE5NKr/ejaNW1B4t//wnveoH4BoVwYOtGshUZNO1YuppZ3KK5XD53lqfHfgBAZIvWbFmxmKV/TKN9736kXbvGjjXL6fjYAExMTDAzN6eOt4/Wa9g5OGJmbqGTDnBw2yYCwurjaqSrm3Xq1Ye/pk7ENyiYgNAwdm5YR2ZGBm1jSpekXT7nLy6ePsXrH32mOebqpRSKVSpys7MpLCzg0vlzANStVxblvZ1WkJ+Piakpl86fw8zcHM+6uu+RIbSL7c28aZPxCQiiXnAoezZvIEuRTsvOXQFYM382KWdPa7YpaNSqLRuWLmD+rz/Spe8Abl69yuaVy4jpP1BzIyQiuikL/5jG7o3rCYmMIluRwfJZM/Cu56/ZQmHLquWsWziHIS+/gbuHF9m37vqbW1pqfrSNxY7jZ+jcIISbmTnczM6hS2QIRapiEs5d1uQZ3Lp00vq8XWVzbT1dSue0WlmYU1JSgqeLI8XqEm5kZqNUFXNdob3fWUGREjMTE530msbExhoL71vffaYmWNSpjWVQAOrsbFTXb9774Bpk+7EzdIkM4UZWzq1tMEIoVBVz6I52MaRNabuYu7OsXXjdahfWt9qFl4sjqvu0C1NT42wXQ3v14tOpU4gIDKRhaChL4uJIzUinf0zp98ePc2aTfOYMUz78CIBAH1+t44+dPYupiYlWerc2bflj8WK++PlHnn/8CbLzcpkwYzqdW7TQmc9nTGL7DmDahO8ICAkhOLw+m9euQpGeRucepRGU+TP+4Oypk1rbFFy+eAGVSkV2dhYFBQVcOHsGAL+AQACim7fgjykT2bh6JZGNm6BIT2fWbz9TLzBI7xYKxmLAoCF89+XnhIZHUL9BJKuWLyUtLY1effoB8McvP3Pi2DH+N6H05vfhQ/F89N7/8Vi//nSO6Ub6rQiXqZkpzrcinp1iujJr5gzGf/MVTz7zLLk5Ofw0eSLtOnTE2UjWM9Bn2PDhfPzf/xJRvz5RUVEsXrSI1Js3GfD44wBMnTKFo0eP8uNPpfMJV69ahaWVFUFBQViYm5N87Bg/Tp1K586dNYvvzZs7Fy9vb83F/aH4eGb9/bfMsasB2rVrR1ZWFvPnzyc9PR0/Pz8+/vhjzR526enpXLt2zSBlM/g+diNHjuTDDz/UmWfXoUMHvv32W3bt2qU3tJmTk4O9vT3t27dn06ZNDBs2TCucqVarWbJkCb6+vlrDMG/z8vIiODiY48eP88EHH1R+xapIg+atyMvNYduqpeRkKqjtVZfhb76jiaxlZypIv1m24qe1rS0jxrzHqlnT+eXzj7Cxs6N1t5606tbzgV87/eYNzh1PZuA9Fs8wtMat25Kbk836xQvIVGTg6ePLy+99qOmIZmVkkHpd+8M27ZvPSb9jhdBv3yud2D9p7hKdtNuOHNyPay13PpnyS1VV5YE0atmavJxsNi1frNmY/dm339dE37IUGaTdKGsXNra2vPB/H7F05u9M+vh9bGztaN+jt2ZrA4Cm7TpSmJ/Prri1rJwzE2sbWwLD69NzyJOaPLs3rqO4uJhZU3/QKk+Tth0Y/KLuJHFD2nL0NBZmZvRr3hAbKwtSUjP4deMurb3KnO1059mO7t1J63GEjyfpOXl8s+SfuVn3bdZhIdSdXBaZcXt+BG7PjyBr9XqufzXegCWrXFuOnsbC3Iz+t9rFxZsZ/BpXgXbxmHa7qH+rXXy9uOa1i66tWpOZnc2fS5aQpsggwMeH7999D89bQwRTFQou3bWS9P3YWlsz+T8fMn76nzzz4Qc42tnRvmmze26fYAxatutATnYWy+fPQZGeQV0/P97+7+fUql0HAEVGOjeuXdE6ZvxnH5F6o2xl1Y/eKv3um7l8LQDtunQjPz+fuFXLmfPHr9jY2RIeGcWQkc9VU60eTsfOXcjOzGTOXzNIT0vDz9+fL/73nSaylp6WxtUrZTdANqxdQ2FBAQvnzmHh3LK5RHU8PJg5r3TYro2tLd98/wM/TpzAqJdewN7BgdZt2/HsS69Ub+UeUNdu3cjMzOTP338nNTWVwMBAJkyciOetrRpSU1O5fCuYAKUjw2b8+ScpKSmUlJTg4enJwCeeYOiwsvavVquZMnkyV69cwczMjLp16/La669rOovCuPXq1Yte5QyZHT363osLxsTE3DOo9ChM8vPz7zsOYMKECaSnpzNmjPbFrampKU73ufOmbx87Pz8/Xn65bA+UTz/9lMTERExNTTX72JWUlDBu3Dj27NnDE088QePGjXF2diYlJYWVK1fSpk0bYmJiyM7OZuzYsZiZmTFy5Ej8/f1JS0tjwYIFJCUl8cUXXxASUjrBe/bs2ezcuVOzT11BQQEqlUrTqbz7+bi4OH7++Wd+/fVXnXo5Ojo+9Byzhx2K+U/jam9cER1DKqghCyxUh50nzxu6CEbjhWnGvc9VdfrlxVcNXQSj8UGDyh++U1OdtDPe6F91q+OkuxDdv5WLnbWhi2A0rMqZPmRsCm9U30gQq9rGORy8MlQ4YpeQkMCIEdqbT7q5uTF9+vRHLsTIkSMZNWqU1t5wJiYmjB07lnXr1rFhwwYWLVqEiYmJZoPy21E8BwcHxo0bx7x585g2bRrp6enY2trSsGFDxo8fr7VB5N2sre//wS8sLNSpN8C0adOMaoimEEIIIYQQNVIN2aDc2FUoYicql0TsSknEroxE7MpIxK6MROzKSMSujETsykjEroxE7MpIxK5MjYnYpaZW22tZ1ap1/0w1lMHn2AkhhBBCCCH+vUxMasZ2B8bukTp2W7Zs0cxHu5u7uzs//ih3m4UQQgghhBCiqj1Sx6558+aahUl0TmwuwUAhhBBCCCHEfdSg7Q6M2SP1vmxtbbE1sr2qhBBCCCGEEOLfRsJqQgghhBBCCMORVTErhcxUFEIIIYQQQogaTiJ2QgghhBBCCMORVTErhbyLQgghhBBCCFHDScROCCGEEEIIYTgyx65SSMROCCGEEEIIIWo4idgJIYQQQgghDMZE9rGrFBKxE0IIIYQQQogaTiJ2QgghhBBCCMMxlVhTZZB3UQghhBBCCCFqOOnYCSGEEEIIIUQNJ0MxhRBCCCGEEIYji6dUConYCSGEEEIIIUQNJxE7IYQQQgghhOFIxK5SSMROCCGEEEIIIWo4idgJIYQQQgghDEe2O6gU0rEzgGNXbhi6CEbBw8ne0EUwGsev3DR0EYzGf+u5G7oIRuN/L75q6CIYjRd/+dHQRTAaz/YdZOgiGI2+TesbughGw9fNydBFMBoJF64aughGo0WQr6GLIKqRdOyEEEIIIYQQBqOWOXaVQuKeQgghhBBCCFHDScROCCGEEEIIYTDqEkOX4J9BInZCCCGEEEIIUcNJxE4IIYQQQghhMOoSCdlVBonYCSGEEEIIIUQNJxE7IYQQQgghhMGUSMSuUkjETgghhBBCCCFqOInYCSGEEEIIIQxGAnaVQyJ2QgghhBBCCFHDScROCCGEEEIIYTCyKmblkIidEEIIIYQQQtRw0rETQgghhBBCiBpOhmIKIYQQQgghDEa2O6gcErETQgghhBBCiBpOInZCCCGEEEIIg5GIXeWQiJ0QQgghhBBC1HASsRNCCCGEEEIYjFoCdpVCInZCCCGEEEIIUcNJxE4IIYQQQghhMDLHrnJIxE4IIYQQQgghajiJ2P2DdKofTJNAH2wsLLiUrmDlwaPczMopN7+9tRWxjcLxdHHEzd6Owxcus2RfolYed0d7OjcIxtPFCVd7WzYfOcXmo6equioVVlJSwt61KziyaxsF+Xl4+PnTaeAw3Dy973ncpdMn2L5kPmnXrmDn5EyTzt1p2Laj5vm0q5fZs2Y5Ny5dJCstlRaxj9GyRx+tc/zx6Xtkp6fpnLteRCR9XxpVKfWrDD2iw2kd6o+NpSUXbqazYPchrimyy83vaGNNv+aR+Li54O5oz/4zF5i1/aBWnjd6tCfY013n2KsZWXy9ZEOl1+FRLd66mdkb1pGWmYm/pxejnhhMo+AQvXmvpqUy8MP3ddLHv/4mLes3AGDLoXiWbt/KqZSLFCqV+Ht6MSK2J+2iGlVlNSpV16hQWgTXw9bSgoupGSzZm8j1zPLbhYONFY81bYC3qxO1HOyJP5vCvF2Hys3fqJ43w9s3JfnSNf7ctLcqqlBtrKMa4DJ0INahwZi71+Lal+PIXmN87bwyDG4dTbeoUOysrDh19Sa/xO0iJU1Rbv6WwX50bxSOf21XLM3NSElVsHDPYfafuaiVr3fj+nRvFIa7oz3ZBYXsP32BmVv3U6BUVXGN7u/glo3s3bCanMxM3L28iHliOD7BoeXmv3E5hfVz/+Lq+bNY29oR3b4TbXr2xcTEBICcTAUbF87h2sULZNy4RoMWbeg98gWd8+zfuJ74bZvISk/Fxs6e4KjGdOo/CEtr6yqr64NaungRc2fPIi0tDX9/f14f9RYNGzXSm/dQfDwL583l2LFkcnNy8K5bl4GDBtOz92Na+eLWr2POrFlcSrmIrZ0dTZo245XX38DNza0aavTwtqxdxbpli8nMyMDLx5fBz7xAcER9vXmVRUX8PW0qF8+e5erlFILCwhn72ddaeU4cSWL8xx/oHPvpxB/xrOtTJXUwZmokYlcZjLJjl5mZyaxZszh48CDp6enY29vj6+vLwIEDiY6Ovuex77//PkeOHAHA3Nyc2rVr06VLFx5//HHMzMwAUKvVrFq1iri4OC5duoSZmRnBwcH079+fpk2bas6VlJTEBx+UfegcHR0JCgpi5MiR+Pv7V0HNH17bsABah/qzZF8iqdk5dIwI5umOzZm0eitFqmK9x5ibmpJXWMT2Y2doGuirN4+FuRmK3HySL12nS6T+C2FDOrhxLfGb19N12DO41PZg37oVLPlxAiP+80W5P46ZaTdZNm0S9Vu0oftTz3Pl7Ck2L5iNjb0DwY2aAKVfyo6utQhs2Jjdq5fqPc+Qt/9DiVqteZyblcmccV8QHN1Ub35DiIkMoVODYGZtO8CNzBxio8N5LbYdXyxcT6FK/wWVuZkpuQVFbEg8QZtQ/e389427MTMrC/ibm5rxfv8YDp27VCX1eBRxB/bzw/x5jB06jIaBwSzetpmxUyfx938/xcO1/AuJ7994kyDvsh9XRzs7zf8TTp2kSWgYL/bph6OtHev37eGDaT8yefTYcjuMxqRj/SDaRwQxf2c8N7Jy6NowlBe6tua7pRvLbxempe1i85FTtAiud8/zu9rb0qtJfc5eT62C0lc/Uxsbis5eIHttHHU+fMfQxaky/Zs3pG+zBkxas50r6ZkMatWITwbF8tpviyhQKvUeU9/Hk6QLV5i9/QDZBYW0jwji3X5d+Gjuao5dvg5Au/AARnRoxtR1O0i+dA0PZwde694OCzMzpq7bUZ1V1JF8YC9x82fRfegI6gaFEL91I/OmjOeFj7/GSc/3Q2F+PnMnfodPUAgj3/uEtOtXWTXjNywsrWjRtQcAKqUSG3sHWnXvRcKOLXpf9+i+3WxeMo8eTz6LT1AIitSbrP7rd1RKJb1GPFeVVa6wTXFxTP5hAqPHvkNkwyiWLl7E/40dw4y/Z1PHw0Mn/9GkRPwDAxkyfDhubrXYv28v4779H5aWlsR06w5AUuJhvvr8M1557XXatu9Aeno6P4z/ji8//ZjvJ02p7ipW2P6d25n7x68Mf+EVgsIj2LJ2NZO+/IRPfpiKm3ttnfxqtRoLS0s69ehFUvwB8vNyyz33Jz9Mxc7eQfPYwdGxSuog/h2MsmP39ddfU1hYyKhRo/D09EShUHDkyBGys8u/m3ynmJgYRowYQVFREfv37+eXX37B1NSUgQMHAjBu3Dji4+MZMWIEjRs3prCwkM2bN/P555/z0ksv0bNnT63zTZ06FQcHB27evMkvv/zCxx9/zE8//YTdHRd6htYqpB7bj50h+dI1ABbvO8y7fWNo6OfFgTMpeo9R5OWz+lAyUPrjrM+V9EyupGcC0D4isApK/vBKSko4tHUjTWN6aDpk3YY/yy8fjuHEwb1Etumg97iknVuxc3Sm48BhALh6eHLtwjniN6/XnMfDzx8Pv9JOzYENq/Wex/aOL2KAo3t2YGltrTmHMehQP4i4xBMcvnAFgL+37efLYb1pEujDrhPn9B6TnpPHor2HgdKoiz55RdoXeU0DfLA0N2fPqfOVV/hKMm/jBnq2ak2ftu0BGDN4GHuPHmXJtq280m9Aucc52tnj5uSk97m3Bg3Revxs7z7sOpLE9sMJNaJj1y48kM1HTpF08SoAc3fG8/GgHkT7e7Pn1AW9x2Tk5rNsfxIAkX5e5Z7b1MSE4e2asvbQMYI8amFrbVn5FahmeXv2k7dnPwB1Phhr4NJUnd5N6rN4byJ7Tp4HYNKabUx/bRjtIwJYf/iE3mN+37RH6/H8XYdoGuBDi2A/TccuzKsOJ6/eYGvyaQBuZuWwJfkUrYINf4N0X9xaIlu1pVG7jgB0G/IUZ48mcWjrRjr2H6ST/+i+XSiLCuk98kUsLC1x965L2rUr7ItbS/OYWExMTHCu5U63wU8CcPzQfr2ve+nMKbz8A4ls2QYA51ruNGjZhhOHDlRNRR/CgnlziO3Zi959+gLw5pi32bd3D8uWLObFV17Vyf/k0yO1HvftP4BD8QfZtmWLpmN39MgR3N1r88SQoQB4enkxYOATTJzwfdVW5hFtWLGU1p260K5raT2GPv8SRw8dZOu6NQx48mmd/FbW1jz50msAXLpw/p4dOwcnJxwc9f/W/JvIHLvKYXRz7HJycjh69ChPP/00UVFR1K5dm5CQEAYMGED79u0rdA4rKytcXFyoU6cOvXv3Jioqij17Sn98tm/fzvbt23nzzTfp2bMnHh4e+Pn5MXLkSHr37s2vv/5Kaqr2XWYnJydcXFwICQnh2WefJSMjgxMn9P/IGYKLnQ0ONtacvuPuuKpYzYWb6fi4uRiwZFUrKy2VvKxMfEMjNGnmlpZ4B4Zw9dyZco+7ev4sfmERWml+YfW5cfECxcUPNyyopKSEo3t2ENa0JRaWVg91jsrm5mCHk60Nxy/f0KQpi9WcuZaKf+3KHfLSKtSf5EvXUOTmV+p5H5VSpeLExQs0D9f+ezcPj+DI2fLbCMB/pv1Er3fG8PJ337A5/uA98wLkFRbgYGv7SOWtDq72tjjaWnPySlm7UBWrOXc9Fb/aro98/h7R4aTn5nHwrP4bSsI41XFywNXeloTzlzVpRapijqZcJ8yrzgOdy8bSgpyCIs3jY5evU6+2GyG3hm/XcrCjWaAfB88Zto0Uq1Rcu3ge/4gGWun+EQ24dPa03mMunz2NT1AoFpaWd+SPJCdTQWZaxSPUPkEh3Ei5yOVbr5OZnsbpxEME1o96iJpUPqVSyYkTJ2jWvLlWerPmzTl6JKnC58nNzcXeoewmaGTDhqSlpbJrx3ZKSkpQKBRsioujZatWlVb2yqZSKrl45jQRUdojxiIaRXPmxLFHPv9X/zeGsc+N4PtP/sPxpMT7HyDEPRhdxM7GxgYbGxv27t1LREQElpaPfrfX0tKSnJzSuWZbt27Fy8uLVnq+RAYMGMDy5cvZtWsXffr00Xn+9rkAVOUMVzIEe+vSjkRuQaFWek5BIY42xjNWv7LlZpdGEm0dtIct2Do4kpOZUe5xeVmZ2IaE6xyjVhdTkJODnZPzA5fl4olkstJSadCy7QMfW1UcbUrbRXZ+gVZ6dn4hTrY2lfY67o72BHu682vcrko7Z2VR5ORQrFbjetfQFhdHR9KO6/9BtrGy4vXHnyAyIBAzMzN2JB7mv79N48Onn6V7i5Z6j1m0ZTM3MjKIbWG8Fye3OdxqFzl3fV9kFzx6uwjxdCeqnjcTVm55pPOI6udsV/q3v/vmTGZePq72Fb9h0SM6HDcHW010DmDH8bM42FjxxdBemGCCuZkpm4+eYuZW/dGs6pKXk02JWo3dXd8Pdo6OnD+eqfeYnKxMHF1c78rvpHnOuZbu3GN9Ipq1JD83h7/HfwUloFYX06BFazoN0I0SGkKmQoG6uBgXV+26uri4cjCtYlHFXTt3EH/gAFN+nqZJq98gko8++YwvPv2EwsJCiouLadqsOe9/+FGllr8y5WRnoVarcbzr2sDRyZljisMPfV4nFxeGv/gq9YKCUalU7Nm6iQmffsjbn35FSP0G9z/BP4wE7CqH0XXszMzMePPNN5kyZQrr1q0jICCA8PBw2rZtS2ho+ZOZ9VGr1Rw6dIj4+Hj69i0dSnD58mV8fPRPSnVzc8PW1pZLl/TPE8rKymLu3LnY2NgQEmK44VYN/bx4rEnZh37W9tIvWZ0PhYnJP2oq6vEDe9g072/N4z4vvQGgmbB+W0lJCSZop+m4+5jb75TJfY4rx5Hd26njWw/3uvrnKlaHpgE+DG7TWPN42oadAOW0gcprGa1D/cnMy+doyrVKO2eVKynRaTe3Ods7MDSmm+ZxuF89MnNymLVhrd6O3eb4g0xdvJBPn3sBDyOc/B/tX5fHW5ZFAf64NXTu7mEvJpg80i+rrZUlg9o0Zvb2A+QX6Z+PJYxH+/BAXu7WRvP4y0XrAf3fDBVtFS1D6vF0h+aMX7FZa+Gu+nU9eKJVI37ZsIuTV2/i6eLIc51bMrRNY+bsjH+EWlSOu38vSj8b9/otuOu5W5+bB/n1uHjyODtXL6f70BF4+QeSceM6cfNnsX3FEtr3KX+IeHXTeW9KE+8rKfEwX3zyMaNGjyH8jgVGzp87x+QfvmfEyGdo1qIlaWmp/Dx1CuO//R8ffPRx5Ra+kulcazzi+Ty86+LhXVfzODA0jLQbN1i/fMm/smMnKofRdewA2rRpQ7NmzTh69CjHjx8nPj6epUuX8tRTTzFo0P3vZq1bt46NGzdqomqdOnViyJAh9zmqzN0f3ueffx6AgoICvLy8eO+993B2dtY5bu3ataxbt+6+51d7+FEnrGGFy3O345evc+mOVcrMTEtH1NrbWJF1R3TG3spS5658TRbQoBEefgGax8Wq0ovH3KxMHO64g5qfk60TxbuTraMTeVnad2Pzs7MxNTXD+iHmTeZlZ3E2KYFOt+bsGUrSxaucvxmneWx+a3ETRxtrrbvwDjZWZOVXTrswMzWheZAvu06eR22Et9uc7e0xMzUlPStLKz0jO1snincvEfX8WbV7p0765viDfD79Dz4a+azRroiZnHKNi6llEWzzW98XDjbWZObd8X1hbUn2I3xfeDg74GRrzYtdW2vSbn+XfvPkY4xfvvmeq/SK6rXv9EVOXi0bjmtxa3ExFzsb0rLL5gM52dqQWYEh1i1D6vFWzw5MXL1VZ0XMYe2asP3YWeKSTgJwMTUDawtzXu3elnm7Dhnsu8PW3gETU1Ny7vo9yMvO1oni3Wbv6ETuXflzs0u/X+weYJ7U1uWLiGjagka3VmOu7e2DsqiQ1X/9SdtefTG99fcwFCdnZ0zNzEi/a+VnRUY6rq73HrKdePgw740dwzPPv0Df/tqd1Fl/zSQsIoIhw0vnIAYGBWFtbcOoV1/m+RdfpnadBxv2Wx3sHRwxNTUlU6E9Eig7U4GjnmvBR+EfEsr+Hdsq9Zw1hTFeQ9RERtmxg9Ihj9HR0URHRzN06FAmTZrEnDlz6N+/PxYWFvc8tm3btgwdOhQLCwtcXV01q2ECeHt7k5Kif1x/WloaeXl5eHlpLwzw5Zdf4uDggJOTE7b3mEMTGxtLbGzsfev21bJN981zL0WqYtJz8rTSsvMLCKpTS7PQibmpKb7uLqw/fPyRXsuYWFpba610WVJSgq2jExdPJGsWOlEplVw5c4q2fQeWex7PegGcSUrQSrt4Ipnavn6YmT34RyJ53y7MzM0Jadz8/pmrUKFKRWG29hDhzLx8Qr1qay7szc1MCaxTi6X7Kz5H4l4a+nlhZ22lWWzB2FiYmxPq68f+48l0blK2Wun+48l0iK74IjenLqVQ666Lto0H9/PFjD/58Oln6NTYeBbMuZu+dpGVV0CIp7vmBpG5qSn+td1YdfDoQ79OSpqCccu1v9tiG4VjY2nBkn2JpOeUv3iAqH4FSiXXFNqR1fScPKL8vDl9rXSumIWZGRF16zBj6757nqt1qD+jerRn0ppt7NbzXWBlbq5z0VasrsDIiipmZm6Oh289zh87QniTsu/vc8eOEFbO6sbeAUFsXjIflbIIc4vSqRnnjx3F3skZJ7daFX5tVVEhJqbayxyUPjaOi1sLCwtCQ0M5sH8/HTt30aQf2L+f9h06lnvc4YRDvDd2LCOfe44nBuveUC8sKMDUVLvTenuFZWNdPMPcwgLfwCCOHU6gaeuy6RbJhxNo3LL1PY58cCnnzuLk8uhzncW/l9F27O7m6+tLcXExSqXyvh07Ozs7nc7ZbR06dODbb79l9+7dOvPsFi1ahLm5OW3atNFKr1OnDk7lrI5nLHafPE/7iEBuZuWQlpNLh4ggilTFJN5aDRFgQIvSKOHivWWTcz2cSyc1W5mbU1JSgoezA8XqEs2ddTNTE9wd7YHSiz97ays8nB30di6rm4mJCdEdurB//Wpc63ji7F6H/etXYWFlRWiTFpp86/7+HYDuT5YuIR3ZpgOHt29m6+K5RLbuwJVzp0net4vYEWX7DBWrVKRfK33vVColuVmZ3Lx0EQsra5zvWNq4pKSEo7u3E9K4uVHtPXTb1qOn6RYVxvXMbG5m5tCtURiFKhUH71gp9cn2pRcwf28rmzfh7Vra3q0tzSmhBG9XJ4rVap3971qHBnDyyg2tO/zGZnCXrnw+/XfC/fxpGBjE0u1bSc3MpH+70lVTf1q6mGPnzzHprbcBWL17F+ZmZoT4+GJiYsLOpMMs3rqZV/o/rjln3P59fDb9D15/fCCNgkJIyyy9oWJhbq61LYKx2n7sDF0iQ7iRlcPNrBxiIkMoVBVz6FzZwhlDbg3rnXvHUDkvl9IohrVF6feFl4sjKnUJNzKzUaqKuX5X+ygoUmJqaqKTXtOY2Fhj4X3rN8XUBIs6tbEMCkCdnY3q+k3DFq4SrTx4lIEto7icruBKRhYDWzaiQKlkW/JZTZ5RPUsXMZu0ujSq0DYsgDd7dmDGln0kX7qmmaunKi7WLKCy/8xF+jRtwJlrqZy8egNPZ0eGtW3CgbMXDX6XvnlMLCv+nIZnvQDqBoZwaNsmcjIVRLfvDMCWJfO5cv4cw0a/C0BE81bsWLWMlTN+o02PPqTfuMbudStp26uf1mif6ymlq8sW5edjYmLC9ZQLmJmZU8urdKXhoMho9m1ci6efv2Yo5rbliwmKbGTwaN1tTwweyleff0pYeASRDRuyfOkSUlNT6dO/PwC//PQjx48la7YpOBQfz/vvvE3f/gOI6dadtLTSaJ+ZqSnOLqULubVq05Zx//uaZUsW06x5C9LSUpkycSIhoaF6t1AwFl0f68cfk76nXnAwQWERbF23hsyMdDp0K93iYvHfMzh/+iRjPvlSc8yVlIsUq1TkZGdRWFBAyrnSz5GPf+nIo7iVy3Bzr42Xjx/FKiV7tm0hYd8eXn5Hdx9VISrK6Dp2WVlZ/O9//yMmJoZ69ephY2PD6dOnWbRoEVFRUfeMmFVE27Zt2blzJxMnTkShUBAdHU1RURGbNm1i1apVvPjii9SqVfG7bsZix/GzWJiZ0btJfawtLbicpmDm1n1ae9jpWxjh1e7ttB6HedchIzdPs/iBg7W1Vh43BzuaBfly7kYaf242/KbDTbrEolIq2bxwNoV5uXj4BdDvldFanazsjHStY5zc3On70ii2LZlP0o6t2Dk50WHAEK1tCnIzFcz+7nPN48zUmxzZtQ3voBAGvlG2j9Wl0ydQ3LxB96eer8JaPry4pJNYmJvxRKtG2N7aoPzHtTu09ipzsdP9TL3bL0brcaSvF2nZuXy6YK0mzc3BjmBPd2ZsMXw7uJeYps3Iys1hxppVpGVlEuDpxbjXRmnmw6VlKrh8U/vifMaaVVxLT8PU1BSf2nX44KmRWvPrlm7fSrG6mIkL5jFxwTxNenRwCFPGGP8+Z1uOnsbC3Iz+zRtiY2XBxZsZ/Bq3S6td3L5Av9PoxzppPa7v40l6Th5fL/5nbtZ9m3VYCHUnf6d57Pb8CNyeH0HW6vVc/2q8AUtWuZbsS8TS3IwXYlpjb23Jqas3+XTBOq097Nwd7LWO6R4VhrmZKc91aclzXco+I0cuXuWjeaVbxSzYnUAJMLRtY9zs7cjOL2D/mRTN/HBDimjagvycHHatXkFOlgJ3L28GvT5GE33LycxEcbNsyKq1jS1D3nyH9XNm8ufXn2Bta0vzmFiax2iP1vnjy/9qPT6dmICTay1evdVe2vTsAyawbflishXp2Ng7EBTZiA73GG1S3TrHxJCVlclfM/4kPS0N/4AA/jduPB4epdsjpaWlcfly2c2gtatXUVBQwLw5s5k3Z7YmvY6HB/MWLQGgR69e5OflsWThQn6cPAk7e3uiGzfmpVdfr97KPaBmbdqRm53F6oXzycxIx8vXjzc++Bi32qU3ejMz0rl5TXue+eQvPyXtjrbz+dg3Afhl0QqgdBG+hTP/RJGehoWlJV4+vrzxwcdENjGevXCrk7FGbGsak/z8fKN6J5VKJXPmzOHQoUNcvXoVpVKJm5sbzZs3Z/DgwTg4ONzz+Pfffx8/Pz9efvnlcvMUFxezatUqNmzYwJUrVzAzMyMoKIgBAwbo3aD877//rtSI3aMOxfyn8HCyv3+mf4njV/45d/0f1X/rVWxVuX+D/10qf3XXf5sXf/nR0EUwGv/X1zhWTjQGfZvWv3+mf4nukca/h2Z1OXmt4ltP/NO1CDLcom4P4nK6/pVoq8LtUUn/REYXsbOwsGDEiBGMGDHioY7/+uuv75vHzMyMPn36lLulwW2RkZGsWLHiocohhBBCCCGEuD9DD8v+pzC6DcqFEEIIIYQQQjwYo4vY3cvRo0f55JNPyn1+wYIF1VcYIYQQQgghxCOTgF3lqFEdu6CgICZOnGjoYgghhBBCCCGEUalRHTsrK6tytzEQQgghhBBC1DyyKmblkDl2QgghhBBCCFHD1aiInRBCCCGEEOKfRVbFrBwSsRNCCCGEEEKIGk4idkIIIYQQQgiDkTl2lUMidkIIIYQQQghRw0nETgghhBBCCGEwEq+rHBKxE0IIIYQQQogaTiJ2QgghhBBCCIORVTErh0TshBBCCCGEEKKGk4idEEIIIYQQQlTQqlWrWLx4MRkZGfj6+vLCCy9Qv359vXmTkpJYtmwZJ0+eJDc3Fy8vL/r06UPXrl0rvVzSsRNCCCGEEEIYTE3a7mD79u38+uuvvPLKK0RERLB69Wo++eQTpk6dSu3atXXyHzt2DD8/PwYMGICrqyvx8fFMmTIFCwsLOnbsWKllk46dEEIIIYQQQlTA0qVL6dKlC927dwfgpZde4uDBg6xZs4ann35aJ/+gQYO0Hvfs2ZPExER27dpV6R07mWMnhBBCCCGEMBh1SUm1/XsUSqWS06dPEx0drZUeHR3NsWPHKnye/Px87O3tH6ks+kjETgghhBBCCPGvsHbtWtatW3fffN27dyc2NlYrLSsrC7VajbOzs1a6s7Mzhw8frtDr79u3j8OHD/Ptt99WuMwVJR07IYQQQgghhMFU5xS72NhYnQ7bgzIxMXmo45KTkxk3bhwvvvgiISEhj1QGfaRjJ4QQQgghhBD34ejoiKmpKRkZGVrpCoVCJ4p3t6NHj/Lpp58yfPhwevbsWSXlk46dAbQI9DF0EYxCXpHS0EUwGrmFRYYugtF46dAZQxfBaPzWKfr+mf4lnu076P6Z/iW+XTbf0EUwGmdjJhi6CEbD0kIu6W4L8ahl6CKIB1RTVsW0sLAgKCiIhIQE2rZtq0lPSEigdevW5R535MgRPvvsM4YOHUrfvn2rrHyyeIoQQgghhBBCVEC/fv3YuHEj69atIyUlhV9++YX09HR69OgBwIwZM/jPf/6jyZ+UlMQnn3xCbGwsHTt2JCMjg4yMDDIzMyu9bHJ7RwghhBBCCGEwj7paZXVq164dWVlZzJ8/n/T0dPz8/Pj44481e9ilp6dz7do1Tf64uDgKCwtZsmQJS5Ys0aTXrl2b33//vVLLJh07IYQQQgghhKigXr160atXL73PjR49Wufx3WlVRTp2QgghhBBCCIOpKXPsjJ3MsRNCCCGEEEKIGk4idkIIIYQQQgiDUUvArlJIxE4IIYQQQgghajiJ2AkhhBBCCCEMpgQJ2VUGidgJIYQQQgghRA0nHTshhBBCCCGEqOFkKKYQQgghhBDCYGS7g8ohETshhBBCCCGEqOEkYieEEEIIIYQwGLVE7CqFROyEEEIIIYQQooaTiJ0QQgghhBDCYCRgVzkkYieEEEIIIYQQNZxE7IQQQgghhBAGI6tiVg6J2AkhhBBCCCFEDScROyGEEEIIIYTByKqYlUMidkIIIYQQQghRw0nETgghhBBCCGEwMseuckjHrgbauWEtm1ctJ0uRgYe3D/2eGklAWES5+a9cvMDiGb9z8cxpbO3tadW5K936D8TExESTZ8f6NezYsJb0mzdxqVWLmL4DaNauo+b5fVs3M/eXqTrn/t+fs7GwtKzU+j2IvZvWs33NSnIUCmp716XnsBHUCwkrN/+1lIusnPUnl86ewcbOnmYdu9CpzwDNe3H2eDJ//O9znePe/Goc7p7eABSrVGxdtYxDO7eRnZFBLU9Puj0xlJDIRlVSxwfROjSAqHreWFmYczUji7jE46Rl597zmLpuznRqEEItBztyCgrZd/oCh89f1spjaW5G2/BAQr3qYG1hQXZ+AduPnebElRsAtAiuR7CnO672dhSr1VzJyGR78mlS7/Pa1W1Qq0Z0bRiKnZUlp67d5LeNe0hJU5Sbv0WQH92iQvGv7YaluRkpaQoW7T3MgTMpmjyfDoqlgY+nzrEpqRm8NWNpFdTi0Sxcv45ZK1eQplDgX7cuo0c8TaOw8Psed/HqVUZ+8B4lJSVsnj5T6zmlSsWfSxazZvs2UjMycHVyYljvxxgc26OqqlGpBreOpltUKHZWVpy6epNf4nbds120DPaje6Nw/Gu7lraLVAUL9xxm/5mLWvl6N65P90ZhuDvak11QyP7TF5i5dT8FSlUV16jqWEc1wGXoQKxDgzF3r8W1L8eRvWaDoYtVqbavX8PGFUtLf2Pr+vD4iOcIDNf/G6ssKmLebz9z6fxZrl2+REBIGKM+/kIrz+F9u9mxYR2Xzp9DpSzCw9uHbv0HEtm0eXVU55EsWriA2X/9TVpaKv4BAbw5egyNoqP15j139izjv/uWc+fOkZuTQ61atYjp1o3nXngRCwsLAFJTU5n8ww+cOHGcSykpxPbowYcff1KNNXp4SxcvYu7sWaSlpeHv78/ro96iYaNGevMeio9n4by5HDuWTG5ODt516zJw0GB69n5MK9+SRQtZsmgh165epU4dD558+mm69+hZDbUR/1TSsathDu3eyZK//uTxkc8TEBrOzrh1/PLtV7z77QRcarnr5C/Iy2PaN58TEBbO6M+/4cbVK8ydNgUrKys69uoDwM64daycO4tBz7+MX1AwF8+cYv5vP2NrZ0/9xk0157K0suKD76dond+QnbqkvbtZNXsmjz31DH7BYezbtJ6Z33/DqC/H4exWSyd/QX4e08d9Rb3QMF7575ekXrvCot9/xtLKiraxvbXyjvriO2zs7TWP7RwcNf+PWzyfhN3b6TfyBdw9vTl1JJHZk7/nxf98ipeff9VV+D6aB/nRLMiXNfHJpOfk0io0gEGtG/Pbxl0oVcV6j3GytebxltEcuXiFVQePUNfNmZiGYeQXKjl5tbTTZmpiwhOtGlOgVLJ8fyLZ+YU42FhRrC67u+ZTy4WE85e4lpEFQNvwQAa1bswfm3YbzUVsv2aR9GnagClrt3M5PZNBrRrx34HdeeOPReWWsb6PB0dSrjJnZzw5BYW0Cw/k//p05uP5azl2+ToA3y3fhLmpmeYYCzNTvn+6HztPnq+Oaj2QDbt3MWHmDN555jmiwkJZtH49o7/5mjnjvsejlu5n5jalSsVHkyfSKCycQ8eSdZ7/aPJEbqSl8d7zL+Lj6UF6ZiaFRUVVWZVK0795Q/o2a8CkNdu5cqtdfDIoltd+W0SBUqn3mPo+niRduMLs7QfILiikfUQQ7/brwkdzV2vaRbvwAEZ0aMbUdTtIvnQND2cHXuveDgszM6au21GdVaxUpjY2FJ29QPbaOOp8+I6hi1Pp4nftYNGM3xn07IsEhIWzY/1afvrmcz4YPwlXPb+xarUaC0sL2nXrSXLCQfJzdW9mnU4+SkiDSHoPHoatvQMHdmzjt/H/Y9R/Py+3w2gM4jas54fx4xn77rtERTVi8cKFvP3Wm8yaNx8PDw+d/BYWFvTo1YuQkFDsHRw4feok33z1FcWqYl4bNQoo7Qg7OTvz1NNPs2zJkuqu0kPbFBfH5B8mMHrsO0Q2jGLp4kX839gxzPh7NnX0vBdHkxLxDwxkyPDhuLnVYv++vYz79n9YWloS0607AMuWLGbaTz/yzrvvER5Rn2PHkhn3zTc4ODjQum276q6iwaklYFcpjLJjN2HCBDZt2qSTHhoayrhx4+557HPPPceNG6UXpJaWlnh4eNC7d2969Ci7c1xUVMTixYvZunUr169fx8rKirCwMIYMGUJoaKgmX1xcHBMnTtQ8dnFxISIigpEjR+r9UqsOW9esoFm7jrTq3BWAAU8/x/HDh9gZt57eQ4br5D+4aztFhYUMffl1LC2t8PTx5cblS2xZs5IOPR/DxMSEgzu20rJTFxq3bguAW+06XDxzhk0rlmp17MAER2eX6qhmhexcv4roNu1p1qELAL2ffIZTSYns27SBbk8M1cl/ePdOlEVFPP78q1hYWlKnrg83rlxm57rVtOneSyuCaefoqNWZu1PC7u2069GH0KjGALTo3JUzyUfYuXYVT7z0ehXUtGKaBPqy99R5TYdsTfxRXuvRnghvDw5fuKz3mKh6dcktKGRj0gkA0nPy8HRxolmQr+Y8DXy9sLWyYM6OA5rJzVn5BVrnWbj7kNbjVQePMqpXR7xdnTlzPbVS6/mwejeOYMm+RPacugDA5LXb+eOVobQLD2RD4gm9x/yxea/W4wW7E2jiX5fmQb6aC/icAu0OTLuwAKwszNl05FQV1OLRzFm1il7tO9CvS+lnZuwzz7In8TCLN6zn1aHDyj1u6uxZBPn6Eh0eodOx25t4mP1JSSz6YRLOjqWfGS/32lVXiUrWu0l9Fu9NZM+tjvikNduY/tow2kcEsP6w/nbx+6Y9Wo/n7zpE0wAfWgT7adpFmFcdTl69wdbk0wDczMphS/IpWgUb7uZPZcjbs5+8PfsBqPPBWAOXpvJtXrWcFh060bpLNwAGPvMCxw4fYseGtfQZ+pROfitrawY//woAVy6e19uxe3zk81qPewwczNFDB0g8sNeoO3ZzZ8+mZ+/e9O3XH4Ax77zDnj27WbJoIa+8pvtbV9fHh7o+PprHnp6exB+MJyEhoSzNy4sxY0vbzeaNutd5xmrBvDnE9uxF7z59AXhzzNvs27uHZUsW8+Irr+rkf/LpkVqP+/YfwKH4g2zbskXTsVu/dg29H+tDl66lbc3L25sTx44xe9bf/8qOnagcRtmxA2jUqBFjxozRSjM3r1hxhwwZQs+ePcnPz2fjxo38+OOP2Nvb065dO5RKJf/973+5evUqzzzzDBEREWRnZ7Ny5Uree+893n//fZo3LxseYWVlxa+//kpJSQmXLl1i6tSpfPHFF0ycOBEzM7N7lKLyqVRKLp07q4m03RYaGcX5U/ovQC6cOkFAWDiWllZl+Rs2Ys3CuaTfvIFb7TqolCrMLbQjbxaWllw8c5pilQqzW++7sqiIz0e9jFqtxtuvHrFPDKFuvYBKrmXFqFQqrpw/pxNpC2oQycUzJ/Uek3LmFH4hoVpRxuAGUWxcsoCM1Ju43nEx+tOn/0GlUlHby5uOj/UnILx+2WsrVZjfGlZym4WFBRfK+RtUBydbG+ytrTh/I12TplKrSUlV4OXqVG7HzsvFifM307TSzt1Io76PJ6YmJqhLSgj2dOdyeiYxDUMJ8nAnX6nkxOUb7Dl5rtxVrCzNzTA1MSk34lHd6jjZ42JvS8L5K5q0IlUxyZeuEepVu9yOnT42lhY6nbk7xTQM4dC5y/cdAlvdlCoVJ86dZXhv7c9Mi8iGJJ3U/5kB2Bkfz45D8cz46hs279ur8/zW/fsJDwxkzupVrN6+DStLS1pFNeKVIUOxtbau9HpUpjpODrja25Jwx9DjIlUxR1OuE+ZVp9yOnT53t4tjl6/ToX4QIZ7unLx6k1oOdjQL9OPguZR7nEUYkkqlJOXcGTo/1lcrPbRhFOdOHq/U1yrMz8fWzv7+GQ1EqVRy4vhxhg1/Uiu9eYsWJCUmVugcl1JS2LtnN23b1exOilKp5MSJEwy+6+ZXs+bNOXokqcLnyc3Nxf2O6wylUonlXaOeLK2sOJ6cjEqlqvA17z+FzLGrHEa7Kqa5uTkuLi5a/xwcHCp0rI2NDS4uLnh5efHUU0/h5eXFnj2ld1iXL19OcnIyH330ER07dqR27doEBgby5ptv0qRJEyZNmkRBQVk0wsTEBBcXF1xdXWnYsCFDhw7lwoULXL16tUrqfS+52dmo1WocHJ210u2dnMnOVOg9JkuhwMHJSSvt9uPbx4Q2jGLf1k1cPHOakpISUs6eZu+WjRQXq8jNzgagtpcXQ158lWfHvMtTr7+FuYUlkz/9kJvXqv99AMjLzkKtVmPnqF03O0cncjIz9R6TnanA/q789rfei5xb74WDkzN9RjzL0NdHM+z10dTy8OTP777k3IljmmOCGzRk94Y13Lx6BbVazemjiSTH7y/3b1Ad7KxKfxxyCwu10vMKi7CzttJ3SOlx1pbk3tVJySsswszUFBvL0s6rk60NoV61MTUxZdGeBHYeO0ujet60jwgq97ydI0O5rsjmSrr+v0V1c7azBSAzL18rXZGXj4udTYXPE9soDDcHO7bdisLczdPFkQY+nsQlld9RMhRFVhbFajWud30fuDo5kVZO203NyODrX3/hk1dfw85G//t05cYNEk+c4NSFC3w9egxjRz7DnsOH+fynHyu7CpXO+dbfXpGr3S4y8/I1z1VEj+hw3BxsNdE5gB3HzzJr+wG+GNqLBWOe4deXh3AhNZ2ZW/dXTuFFpcvNuvUb6+Ssle7g5Ey2QlFpr7Nt3WoU6Wk0a9eh0s5Z2RQKBcXFxbi4umqlu7q6kp6WVs5RpV587lk6tm3DoMcH0DAqipdffa0qi1rlMhUK1HreCxcXV9LT0ss5StuunTuIP3CAx/qW3TRo1rwFa1at5PixZEpKSjh+7BirVixHpVKRWYntTfy7/CtuB1haWqJSlc6h2bJlC1FRUQQF6V6UDhgwgHfffZeEhARatmxZ7rkAiov1z1mqDneMGCxVUsLdSXcdcXd2rfSu/QeSnalg0qf/gZIS7J2cadquA5tXLsPEtLTvXy84lHrBZcNU64WEMu79d9i+bjUDnn7uUarzSEzufjNKSri7vncdcFf2Eq3zuHt64e7ppXneNygERWoqO9euxD+0dIGJXsOeZun0X5n0n7GYmJjgWrsOjdt2IH7H1kevUAWF1/WgW1TZIjGL9iSU/ufuG14m3PkHfygmJpBXqGRdQjIlwPXMbKwtLejUIIQtR3WHG3aqH0xdN2dmbz+gU5zq0i4sgJe6ttY8/mpJ6eIOum+PSYXvErYM9mNE+2Z8v2oLN8uJxnWNDCE9J4+DZ403KnP3Z6akpET3c3TLJ1OnMKBrVxoEh5R7PvWt75/P3hiFvW1pB3rsM8/w5tdfkaZQ4ObsXFlFf2TtwwN5uVsbzeMvF60HdNtFeWn6tAypx9MdmjN+xWZuZuVo0uvX9eCJVo34ZcMuTl69iaeLI891bsnQNo2ZszP+EWohqprJ3b8hJej54X04CXt3s2zWDEaOeltrlIix0v2+4L7vxedffUVebh6nTp1i6uRJ/D1zBiNGPlOFpawed7eLktLE+0pKPMwXn3zMqNFjCI8oG/0z4plnSE9P47WXXqQEcHVxIbZHT+bM+htTM6ONuwgjZ7Qdu/j4eJ544gmttF69ejFy5MgKn6O4uJjNmzdz/vx5zRy7K1euEBkZqTe/r68vAJcv6x+2lpqayuLFi6lVqxZeXl46z69du5Z169bdt1x+kdFENm9V0Wpo2Dk4YGpqStZdd9dzsjKxv+sO422OzrrRvJys0ijK7cidpaUVQ158jSeefYnszEwcXZzZvSkOK2sb7MqJkpqamuETEEiqgSJ2tg6OmJqaaiJtt+VmZ2HvpH9unIOTs27+rNLFPu6O/N2pbkAgSft2ax7bOToyfNTbKJVF5Ofk4ODswvoFc/QuXlNVTl+7ydWMsmiY2a0OuJ21FdkFZVE7W0tLcgvLHzaYW1CEnbX2UBBbK0uK1Wryi5SaPOoStdaFblp2LpbmZthYWmjyAXRqEEKYdx3m7TyoEx2rTvvPXOTUtZuaxxa3hk0729poDZF0srVGkVegc/zdWgb7MapHeyat2aa1IuadzE1N6Vg/iLjEk0a50aqzoyNmpqak3XUnOCMrC9dy2v+Bo0c4dCyZ3xctBEo7geqSEtoMH8o7zz5Hvy4x1HJ2xt3VVdOpA6jnXbqC7PW0VKPq2O07fVEzdxTK2oWL3d3twobM3Pu335Yh9XirZwcmrt6qsyLmsHZN2H7srCZ6ezE1A2sLc17t3pZ5uw4ZZRv5t7NzvP0bm6GVnp2lO/LlYSTs3c1fU3/gyVffNPoVMZ2dnTEzM9OJzmVkpON6V+TqbnXqlK5B4B8QgFpdzDdffsmwJ5+qsUMLnZydMTUzIz1d+71QVOC9SDx8mPfGjuGZ51+gb/8BWs9ZWVnz7gcf8vb/vUd6ejpubm6sXL4MW1tbnMq5pvsnk6GYlcNoP2UNGjTgtde0w/d2dnYVOvavv/5izpw5KJVKzM3NGTBgALGxsZrny7s7re/5goICnnjiCUpKSigsLCQwMJD3339fs3TvnWJjY7VepzwbH3JRBXNzC+r6B3AyKZFGLcqiESePJBLZTH+E0S84lJVz/kZZVKSZW3byyGEcXVx17haamZvj7OYGlK6+GRHdBFNT/XeNSkpKuHrxAl5+fg9Vl0dlbm6OVz1/Th9NosEddT99NIn6TfT/YPoEBrN+wRyUyiIsbs0pPJ2chIOzyz07ZVdTLujtOFtYWGLh4kqxSsXRg/u0ylHVlKpiFCrtC8+cgkL83F25pijtrJqZmlLXzZmteqJqt13JyCTYU7vu9dxdua7I0lx4Xk5XEF5Xe7EgV3tbilTFWp26zrc6dXN3HiQ9J++R6veoCpQqrimytdIycvKI8vPSLOZiYWZGuHcdZm47cM9ztQ6px+ux7Ziydrtm4RV9mgf54mBjzcYjxjcME8DC3JxQ/wD2JSXRpWXZjaV9SUl0aq7/MzPr2++0Hm87cIDpS5fwxxdf4u5SekHTMDSUjXv3kFdQoJlTd/HWUHWParzZUREFSiXXFNrzPtNz8ojy8+b0tbJ2EVG3DjO27rvnuVqH+ms6+7v1rIBqZW6u03krVpfoRoOE0TA3t8DHP5ATiYeJblkW2T2ReJioFg9+M/ZO8bt3MuvHSQx/dRTRLVvf/wADs7CwIDQsjH379tI5JkaTvn/vPjp27lTh85SoSyguLkatVldFMauFhYUFoaGhHNi/n46du2jSD+zfT/sOHcs97nDCId4bO5aRzz3HE4OHlJvP3Nyc2rVLr8c2xW2gVZs25V57CXE/Rtuxs7S01BsVq4h+/frRtWtXrKyscHV11eqoeXl5cfHiRb3H3U739Czbk8rKyoqJEydiamqKs7Mz1gZeDKBDj8eY/dNkfAOD8A8JY9fG9WRmZGhW8Fo5dxYpZ0/xygefANC4dVvWL17AnGlT6NpvIDevXWHj8qV0H/CE5n25cfUKF0+fwi84hLzcHLauXsG1SxcZ9nLZqlfrFs3HLzgEdw9PCvLy2L5+NVdSLvD4sy9U+3twW5tuvVj461Tq+gfiGxzK/s1xZCsyaNap9Edo/YI5XDp3hmf/70MAolq2YfOyRSz+7Wc6Ptaf1GtX2b5qOZ36lu1jt2v9apxruVPbqy7FxSoO79rBsfgDDH1ttOZ1U86cJisjHU9fP7IUGWxaupCSkhLa9XxMt5DV6OCZi7QM8Sc9J5eMnDxahvijLC4m+fI1TZ6ejUuHgayOPwrA4fOXiPb3oVODEA6fv4S3qzMNfL1YeeCI5piEc6V5ukSGEn8uBScba9qEBZBwxyIQMQ1DiajrydJ9hylUqjRz/opUxSgNOGz5Tivjk3m8RUMup2dyJSOTgS2jKFCq2H7sjCbPG7Glk/wnr90OQJtbF+8zt+4n+dJ1nG1L512p1MU6C6h0bRhK0sUrXM/MwVgN7dWLT6dOISIwkIahoSyJiyM1I53+MaWr7P44ZzbJZ84w5cOPAAj08dU6/tjZs5iamGild2vTlj8WL+aLn3/k+cefIDsvlwkzptO5RQud+XzGaOXBowxsGcXldAVXMrIY2LIRBUol25LPavKM6tkegEmrtwHQNiyAN3t2YMaWfSRfuqaZj6cqLmsX+89cpE/TBpy5lsrJqzfwdHZkWNsmHDh7sUZH60xsrLHwvvXbbGqCRZ3aWAYFoM7ORnX95r0PrgE69erDX1Mn4hsUTEBoGDs3rCMzI4O2MaUrGS6f8xcXT5/i9Y8+0xxz9VIKxarSOemFhQVcOn8OgLr1SldAPbhrO39NnUi/4U8TFB5BlqI0Imhmbo6dfcXWDjCEIcOG8dnHHxMRUZ+GUVEsWbyI1NSb9BvwOAA/TZ1C8tGjTP7xJwDWrF6NlaUlAUFBWFiYczz5GD/9OJWOnTtrLRJy8mTpokS5ubmYmppw8uQJLMwt8A8wzGJsFfHE4KF89fmnhIVHENmwIcuXLiE1NZU+/UtXDP3lpx85fiyZ7yeVbgl1KD6e9995m779BxDTrTtptyKfZqamOLuUri6ecvEix5KPElG/AdnZWcyfO5dzZ8/y3of/NUwlDUxtsMkb/yxG27F7FA4ODuV2Cjt06MDMmTM5ffq0zjy7xYsX4+joSPQdm2+amJg8dAezKkS3akNeTjYbli4iS5GBZ11fXnjnA1zdS++MZysySL1+XZPfxtaOl977iMXTf2PCR+9iY2dHx56P0eGOTkiJWs3WNSu48ccVzMzMCYqoz6iPv9SK6OXn5bLgt5/JylRgY2uLt58/r3/0GX6BwdVX+btEtmhFXm42W1YsITtTQR1vH54a/a4m+padqSD9Rtl7YW1ry8ixH7Di7z/56dP/YG1nR5vuPWnTvZcmT7FKxdp5s8jKSMfC0pLaXnV56q3/IzSqrE2olEXELZlPxo0bWFpbEdIwmoEvvIqNbcUiylVl3+kLmJuZEdMwDOtbG5Qv2BWvtYedg432jYnMvAIW7TlE5wYhNKpXl5xbWx/cOVwtu6CQBbvj6dQghKc7tiC3oIiki1fYfeKcJk+0f+kS14PbNNE6/87jZ9l14izGYOn+JCzNzXihS0vsrC05dTWVzxau09rDrpaj9t+wW1Qo5mamPNu5Bc92bqFJP5JylY/nr9U8ruNkTwNfTyas3FLl9XgUXVu1JjM7mz+XLCFNkUGAjw/fv/senre+P1IVCi7d8f1REbbW1kz+z4eMn/4nz3z4AY52drRv2uye2ycYkyX7EkvbRUxr7K0tOXX1Jp8uWKe1oqu7g/bqhd2jwjA3M+W5Li15rktZpP7Ixat8NG81ULo1RgkwtG1j3OztyM4vYP+ZFGZtv3eE2NhZh4VQd3JZJNft+RG4PT+CrNXruf7VeAOWrHI0bt2W3Jxs1i9eQKYiA08fX15+70PN72FWRgap169pHTPtm89JTy3r1H77XumK3pPmlu7TtnPDOtTFxSye+QeLZ/6hyRcUXl9nM3NjEtO1G5mZmUz/8w/SUlMJCAxk3IQfNDe/01JTtaaumJmZMXPGdC6lpFBSUoKHhwePD3yCIUO1tx8a+aT2Sps7tm/Hw9OTxcuWV32lHlLnmBiysjL5a8afpKel4R8QwP/GjcfD49Z7kZam9V6sXb2KgoIC5s2Zzbw5szXpdTw8mLeotF2o1Wrmz51DysWLmJub06hxE6b8/ItWcEGIB2WSn59vdF3kCRMmkJ6errPdgampKU73uQP83HPP0atXLwYMGKD3eaVSyX/+8x9u3LjByJEjiYiIICcnhxUrVrBlyxbee+89WrQovYCLi4tj2rRpLFiwoHIqdsvDDsX8p8krMo6l8I3B+ZsVW1nr32DPaeNdeKS6/dYp+v6Z/iWe3XjQ0EUwGt8um2/oIhiNs5MnGLoIRqNpQF1DF8FoFN1xw+7fzsXe9v6ZjMDWY9V3E7hDuPFGhx+V0UbsEhISGDFihFaam5sb06dPf6TzWlhY8Pnnn7N48WLmzp2r2aA8PDycr7/+mrCwsPufRAghhBBCCCGMiFFG7P7pJGJXSiJ2ZSRiV0YidmUkYldGInZlJGJXRiJ2ZSRiV0YidmVqSsRuS/KZ+2eqJB0jAqvttaqbLLsjhBBCCCGEEDWc0Q7F1GfLli1MnTpV73Pu7u78+OOP1VwiIYQQQgghxKOoyasFG5Ma1bFr3rw5ISEhep+rqRtfCiGEEEIIIcSjqlG9IVtbW2xta8ZYYSGEEEIIIcT9lUjErlLIHDshhBBCCCGEqOFqVMROCCGEEEII8c+iloBdpZCInRBCCCGEEELUcBKxE0IIIYQQQhiMzLGrHBKxE0IIIYQQQogaTjp2QgghhBBCCFHDyVBMIYQQQgghhMHIUMzKIRE7IYQQQgghhKjhJGInhBBCCCGEMBi1ROwqhUTshBBCCCGEEKKGk4idEEIIIYQQwmAkYFc5JGInhBBCCCGEEDWcROyEEEIIIYQQBqNGQnaVQSJ2QgghhBBCCFHDScROCCGEEEIIYTCyj13lkIidEEIIIYQQQtRwErEzgKiVqwxdBKNwsEcPQxfBaGw8esbQRTAaw1tHG7oIRuOknZOhi2A0+jatb+giGI2zMRMMXQSjEfDGaEMXwWhMfuMtQxfBaLx0M8XQRTAez48wdAkqRCJ2lUMidkIIIYQQQghRw0nETgghhBBCCGEwagnYVQqJ2AkhhBBCCCFEDScROyGEEEIIIYTByBy7yiEROyGEEEIIIYSo4aRjJ4QQQgghhBA1nAzFFEIIIYQQQhiMDMWsHBKxE0IIIYQQQogaTiJ2QgghhBBCCINRS8SuUkjETgghhBBCCCFqOInYCSGEEEIIIQxGAnaVQyJ2QgghhBBCCFHDScROCCGEEEIIYTAyx65ySMROCCGEEEIIIWo4idgJIYQQQgghDKYEidhVBonYCSGEEEIIIUQNJxE7IYQQQgghhMGUyBy7SiEROyGEEEIIIYSo4SRiJ4QQQgghhDAYdQ0L2K1atYrFixeTkZGBr68vL7zwAvXr1y83//nz5/n55585deoU9vb2xMbGMmTIEExMTCq1XBKxE0IIIYQQQogK2L59O7/++iuDBg1i4sSJhIeH88knn3Djxg29+fPy8vjoo49wdnbm+++/58UXX2TJkiUsXbq00ssmEbt/CLtm0Ti0boGZgz3KG6ko1sZRdPGS3ryOHdvi2LGt3ueufDcJdW4eVvV8cR85TOf5a1N+QZWaXqllfxS74taxdfVysjMV1PGuS5/hI/EPDS83/9WUiyyd+TspZ09ja29Pi05dien7uNYdk0O7drBl9TJSr13FysaG4PqR9B4yAgdnZwCuXUphw5L5XD5/jvSbN4jpN5BuAwZVdVUf2pPtmtCjUTj21lacuHKDqet2cCE1o9z8kb6ePNOxOXVdnbGyMOdGVjZrE46zaG+iJo9fLReebN+UoDq18HRx5O/tB/h7+8HqqE6FHNgSx+71q8jJzMTdy5tug57ENzi03Pw3Lqewds4Mrpw/i42dPdHtOtGuVz9Nuzgev5+D2zZxPeUCKqWSWp5etO3Zl5Coxlrn2bdxHQe3biQzPRUbO3tCohrTZcAQLK2tq7S+DyJu9QpWL15IZkY63r5+DH/+ZULrN9Cbt6ioiOk/TuLCmdNcuZRCcHgEH3z1nU6+XVs3s3rxAq5dvoyNrS31oxox5NkXcHZxrerqPJCDWzayd8PqW+3Ci5gnhuNzn3axfu5fXD1/FmtbO6Lbd6JNz76adpGTqWDjwjlcu3iBjBvXaNCiDb1HvqBznv0b1xO/bRNZt9pFcFRjOvUfZFTtYvv6NWxcsZQsRQYedX14fMRzBIZH6M2rLCpi3m8/c+n8Wa5dvkRASBijPv5CK8/hfbvZsWEdl86fQ6UswsPbh279BxLZtHl1VKdaWEc1wGXoQKxDgzF3r8W1L8eRvWaDoYtVLTrWD6JJoA/WFhZcTlew6mAyN7Nyys1vb21F90ZheLo44mpvR+KFyyzdl1SNJX50tg0isGvcEDNbW1TpGWRu343y6rV7HxPVALsGEZg5OqAuKCT/+Emyd+8DwKlLB2zDdb9/1Eol16f9WSV1MGY1aY7d0qVL6dKlC927dwfgpZde4uDBg6xZs4ann35aJ/+WLVsoLCxk9OjRWFlZ4efnx6VLl1i6dCn9+vWr1KiddOz+AWzqh+EcG4Ni1XoKL17Cvlljaj05iOtTf6M4M0snf/auveQcOKSV5jawLyUlJahz87TSr039FXV+gebx3c8bUsKeXSyfNZ3+I56jXkgYuzeu5/dxX/H21xNwqVVLJ39Bfh6/fvs5AaHhjPr0a25evcK8X3/E0sqKDj0eA+D8yePMnTaZXkOfokGT5mRnKlgy43fm/DyJF9/7LwDKokJcarnToEkL1i2aW611flBPtIxiQPOGjF+5hUvpCoa3bcJXQ3vx/LR55Bcp9R6TX6Rk2YEjnL+RToFSRf26Hozq0Y5CpYqV8ckAWFmYc12Rzc7j53i6Q7PqrNJ9Hd2/h/Xz/iZ22NP4BoVwYMtG5kz+jpc/+QYnV912UZifz6wf/odvcCjPvv8padevsWL6L1haWdGya08ALpw6Tr2wCDr2HYiNnT1H9u5kwU8/8NTb/9F0GI/s28XGxXPp9dRz+AaFkpF6g5Uzf0OlUvLYCN2LfUPYs30rs379mREvv05IRH02rl7JuE8/5Oupv1DLvbZO/hK1GgtLS2J69eHwwf3k5epeuJ1MPsq0Cd8x9JnnadKiNZmKDGb8PIWfx3/Le198Ux3VqpDkA3uJmz+L7kNHUDcohPitG5k3ZTwvfPw1Tq5uOvkL8/OZO/E7fIJCGPneJ6Rdv8qqGb9hYWlFi649AFApldjYO9Cqey8SdmzR+7pH9+1m85J59HjyWXyCQlCk3mT1X7+jUirpNeK5qqxyhcXv2sGiGb8z6NkXCQgLZ8f6tfz0zed8MH4SrrXcdfKr1WosLC1o160nyQkHyc/N1clzOvkoIQ0i6T14GLb2DhzYsY3fxv+PUf/9vNwOY01jamND0dkLZK+No86H7xi6ONWmTVgArUL9WbovkbTsXDpEBDGiYzMmr95GkapY7zHmpqbkFRax49hZmgT6VHOJH511UACO7VqTuXUHyqvXsI2MwPWxHtycPR91jm77B3Bo2xLrer5k7dyLKi0dE0tLzOxsNc9nbd+l6eTd5vZ4X4quXK3SuohHo1QqOX36NP3799dKj46O5tixY3qPOX78OPXr18fKykor/99//83169fx8PCotPI9UMduwoQJbNq0CQAzMzPs7e3x9fWlTZs2dO/eHXPz0tO9//77+Pn58fLLLwOQmZnJrFmzOHjwIOnp6ZrjBg4ciJeXF88///w9X3fo0KEMG1YaPXrjjTe4ePEiP/74I97e3nrL9+STTzJ48GBNelJSEh988AF///03Tk5OmvRdu3axatUqzpw5g0qlwsPDg+bNm9OnTx+cnZ2Ji4tj4sSJesu0aNEiLC0tH+TtqzIOrZqTm5BEbvxhABRrNmAV5I9d02iyNm7VyV9SpKTkjot6M0cHLH3rkr5kpU5edW4e6rz8qiv8I9i+diVN23agRacYAPqNeJYTSQns2bSeHoN0o42Hdu1AWVjE4Bdfx8LSEo+6vty4cpnta1fSPrY3JiYmXDh9EidXN9rH9gbA1b02bbrGsuyvPzTn8QkIwicgCIBNK5ZUQ00fXv/mkczfncDOE+cAGLdiM3PfHEGn+kGsPqT/C+j0tVROX0vVPL6emU2bMH8a+HhqOnYnr97k5NWbAAxpE13FtXgwe+PW0LB1Oxq36wRA7NARnElO5ODWjXTuP1gn/5F9O1EWFdJn5EtYWFpS29uH1KuX2Ru3lhYxPTAxMaH74Ke0jmn/2ABOHznMiYSDmo7dpTOn8PYPpGHL0mi4cy13GrZsy/FD+6u4xhW3dtli2nbpSqfupR2TES+9SlL8ATatXsmgp5/VyW9lbc0zr44CIOX8Ob0du9MnjuHqVovYvgMAcPfwoGvvvvz1y49VWJMHty9uLZGt2tKoXUcAug15irNHkzi0dSMd++tG3I/u24WyqJDeI1/EwtISd++6pF27wr64tTSPicXExATnWu50G/wkQLl/50tnTuHlH0hkyzZAabto0LINJw4dqJqKPoTNq5bTokMnWnfpBsDAZ17g2OFD7Niwlj5Dn9LJb2VtzeDnXwHgysXzejt2j4/U/l3vMXAwRw8dIPHA3n9Mxy5vz37y9pT+3et8MNbApak+LUP82HHsLMcuXQdgyb5E3unbhUg/Lw6eSdF7jCIvnzW3fnMifCrvIra62DVqSP7xE+QnHwcga9surHx9sIuMIHu37mffzNkJu8gGpM5diCpDoUlXpaZp/n/3tZiFRx3MnRxRbNhcdRURAKxdu5Z169bdN1/37t2JjY3VSsvKykKtVuN8axTXbc7Ozhw+fFjveTIyMqh1V8Dh9vEKhcJwHTuARo0aMWbMGNRqNZmZmSQmJjJr1iw2b97MF198gbWeoSVff/01hYWFjBo1Ck9PTxQKBUeOHCE7O5tatWoxc+ZMTd7Vq1cTFxfH999/r0m7fc6TJ0+SmZlJ586d2bBhAyNHjtR5LUtLSxYtWkRsbKxWJ+5uM2fOZNGiRTz22GMMGzYMd3d3rl69yoYNG1izZg1Dhw4FwMrKil9//VXv6xgFM1MsvDzI3rVXK7nwzDmsfLzLOUibXeOo0iECySd0nqv94khMzMxQ3kwle9suCs9frJRiPyqVSsXl82fp0PMxrfSQBg05f0q3HgAXTp/EPzQMizv+diGRUaxbNI+M1Ju4utemXnAYaxfMIfnQAcIbNSEvJ5vDe3YRFmVcnZeK8HB2wNXejvhzZUNyi1TFHEm5Srh3nXI7dncLrONGuHcd/t5uPBei5SlWqbh68Twtu/XUSg8Ib8ClM6f0HnPp7Gl8g0K12kVg/YZsXb4IRdpNXGrpRrIACgsKsLYtu/vqExRC0p6dXDp7mroBQWSmp3Iy8RBBDaIqoWaPTqVUcv70KXr2e1wrvUF0Y04dr1hb0Cc4PIIFf03n0L49NGrWgpzsLPZs30JUE+OJ5BarVFy7eF4TabvNP6IBl86e1nvM5bOn8bmrXfhHRLJt+WIy01Jx1hPJ0scnKISje3dx+expvAOCyExP43TiIQLrG0m7UClJOXeGzo/11UoPbRjFuZPHK/W1CvPzsbWzr9RziurlYmeDg401Z66X3fxTFau5cDMdHzfncjt2NZqpKRa1a5F7SPuivfDiJSw86ug9xNq/HsVZWVj5+uDSOxZMTCi6cpXsnXu0RkHdybZ+GMq0dJTXrld6FWqC6hyKGRsbq9Nhe1APOnyyshdJKc8Dd+zMzc1xcXEBwM3NjYCAAKKjo3nrrbdYtGgRw4cP18qfk5PD0aNH+fzzz4mKKv0hq127NiEhIZo8t88HYGNjg6mpqVbabevXr6dDhw60bNmSb775hqeeegozMzOtPJGRkaSlpTF37lxeeuklvXU4efIkCxYs4LnnnqNfv36a9Nq1axMVFUVOTtldaRMTE71lMRamtraYmJpSfNcQyeLcPKzs7e5/AhOwjY4k7/ARKC4bQlGcnUPGyrUUXb6KiZkZtlENqPX0UG5On03RBcN/cedml94xsXfU7rzbOzmTfVT/uP3sTAVOd835sXd0Ln1OocDVvTZ+wSEMe/VN5vw0GaWyCHVxMcENGjL4xderpB5VyeXWkI+MXO2Ia0ZuPrUc7t82/np9OE62NpiZmjBrx8EKdwQNKS8nmxK1GjsH7XZh5+jEueNH9R6Tk5mJ413tws7REYDczEy9HbsDmzeQnZFOZMuyuar1m7UiPyeHmeO+gBJQq4uJbNmGzgOGPGq1KkX2rbuMjs7a32dOzi4cPXyonKPuLzgsglfHvsdP479FWVRIcXExDRo15sW3jCeCoWkXt/6ut9k5OnL+eKbeY3Ky9LULJ81zFe3YRTRrSX5uDn+P/0rTLhq0aE0nI5mXm5uVjVqtxsHJWSvdwcmZk0mJ+g96CNvWrUaRnkazdh0q7Zyi+tlblw4lyy0o1ErPLSjCwcZK3yE1nqmNNSampqjztX9L1fn5mNnqv4Fu5uSAmYM91sGBZG7cQkkJOLZpiUuvWNIWLtXJb2JpgXVQgN7onzAujo6OmJqakpGhvVaBQqHQieLd5uLiojc/UO4xD6tS5tj5+fnRuHFjdu3apdOxs7GxwcbGhr179xIREfHQka6CggK2b9/OV199RUBAAFZWVuzbt49WrVpp5TMxMeHpp5/myy+/pE+fPnh6euqca8uWLVhbW9O7d2+9r2VvXwPvKD7knQ7roEDMnZw0wzhvU6Wlo0orWySl6NIVzJydcGjdnDQj6NjdpnMHpKTknndFdJ+79b7dSr5++RLL/v6TLn0fJzQyiixFBqvm/c2iP39hyEvG3bnrVD+IUT3aax7/d/4aQPcumAkmFWouY/9ajo2lBWHetXmuUwuuK7LZeER/1MvY6G0X3ONumW722yfSyXosfj9xi+Yy4IXXcHYrG1px4eQxtq9eRo9hI/H2DyT9xnXWz/ubrSsW07HP4zrnMZS735uS+70393H54gX+/uUn+g4eSmR0ExQZ6cz78zf+/HESL402rnlHd9ez9LNxr7rrbxgP8m5dPHmcnauX033oCLz8A8m4cZ24+bPYvmIJ7fsMeIAzVS2dNlCC3vb/MBL27mbZrBmMHPU2rnrmcgrjFennxWNNypZwn3VrkSyd35DqCUYY1INcZpmYmGBibo4ibjPFitKbR4q4zdR+cjAWddxRXr+pld8mNBgTExPyT9SM39iqoK4hi6dYWFgQFBREQkICbduW3dxNSEigdevWeo8JCwtj+vTpFBUVafpBCQkJuLq6UqeO/qjvw6q0xVN8fHxISEjQSTczM+PNN99kypQprFu3joCAAMLDw2nbti2hoeWvRna3HTt2UKtWLQIDAwHo2LEjGzZs0OnYATRt2pTw8HD++usv/u///k/n+StXruDh4aGZE3gvBQUFPPHEE1pp9erV47vvdFeGq+iY3TYl5nT0qNgwyftR5+VRolZjdld0zszOttwJvXeya9KIwouXUN1MvW/eoktXsG1Q/oqT1cnOofSOSXamQis9JytTJ4p3m4OTs978t58D2LxiCT4BQXTs1QcAT18/LK2s+enL/xI7cIjWhbyx2XPqAsevLNQ8trwVzXa1tyU1u6wtONtZk1GBRXCuZ2YDcP5mOi52Ngxv18ToO3a29g6YmJqSk6XQSs/NztKJ1txm7+REbqZ21CYvu3TRobuPORa/n2V//EzfZ17SWRFzy7KF1G/Wkui2HQGo7e2DsrCQlX/9Tvte/TC9a3RBdXO4dZcxM0N7VdusTIVOFO9BrFg4j4CQUHoNKP2e9PUPwMrami/fG8vAJ0fi5l6xyFZVKmsXd/+ds8tvF45O5N6VP1fTLsof5n+3rcsXEdG0BY3ubBdFhaz+60/a9upr8HZh5+iAqakpWZnad5OzsxQ43GM6Q0Ul7N3NX1N/4MlX3/xHrYj5b3Hi8nUupyk0j81MS3fJsrexIuuOIYV2Vpbk3BXF+6dQ5xeUXmfZ2nDnkmOmNjYU5+n/LS3OzaOkWK3p1AEUKzIpKVZjZm+v07GzjQij4Mw5Sgr/me/hP02/fv34/vvvCQ4OJiIigjVr1pCenk6PHqXD/WfMmMHJkyf58ssvAejQoQNz5szhhx9+YPDgwVy+fJmFCxcydOjQSh+iWamrYpZXuDZt2tCsWTOOHj3K8ePHiY+PZ+nSpTz11FMMGlSx4Sjr16+nU6dOmsedO3dm4cKFpKWl4eamu6LZyJEjeeeddzh1SvdC9EHG8VpZWeksoGJhYaE3b0XH7KZ+80OFX/++itUor1zDKtBfa47c3Y/1MXWwxzokkIzlayr0UpYedSiuQGexOpibm+NdL4CTRxJp2Lysc3/qSBKRzVroPcYvKITV82ahLCrSzJs5dSQRRxcXXG4NqyoqKsTUVHt7x9uPjf1mUn6RUmely/ScXKL962oWOrEwM6O+jye/b9rzQOc2MTHBwsAXoBVhZm6Op289ziUfIaJJWTs4d+woYY2b6j2mbkAQGxfPQ6UswtyitF2cTT6Cg7MLzm5lnZLkA3tZPn0afUa+RHgT3QtUZVGRTtsxMTVFExU2MHMLC+oFBXMk4RDN25ZFdo8kHKJZqzYPfd6iwnt8Zoyk7mbm5nj41uP8sSNaf7tzx44QFq2/XXgHBLF5yXytdnH+2FHsnZxxeoAbPKqiwlvtoIxRtQtzC3z8AzmReJjolmXt4ETiYaJa6N44fRDxu3cy68dJDH91FNEt9d/JFsatSFVMeo525yU7v4DAOm5cSS/ttJibmuLn7sr6w5U7J9NoqNUob6Ri5VuXgjPnNMlWvt5aj+9UdPUaJmammDk6UJxVepPUzNEBEzNTirO1F6GyqO2OhXstsnbsrro61AA1JWIH0K5dO7Kyspg/fz7p6en4+fnx8ccfU7t26YiE9PR0rl0r2wrDzs6Ozz//nJ9//pnRo0djb29P//79taaDVZZK69hdvHjxnqu6WFpaEh0dTXR0NEOHDmXSpEnMmTOH/v37l9tRui0lJYVjx45x4sQJ/vrrL026Wq0mLi5OawXM20JCQmjVqhXTp09nyBDtOS7e3t4kJyejVCrv+9omJiZ4eXndM4+hZe/eh+uAxyi6fIWii5exaxqNmYM9ube2NHDs0gFLb09SZ2ovzW8X3ZCSoiLyj+rOnbJv2RSVIhPVjVQwM8O2YX1swkNInbe4WupUEe1iezNv2mR8AoKoFxzKns0byFKk07JzVwDWzJ9NytnTmm0KGrVqy4alC5j/64906TuAm1evsnnlMmL6D9TclIiIbsrCP6axe+N6QiKjyFZksHzWDLzr+Wu2UFCpVNy4XLogiUpZRHamgisXzmNpbU2tOsa12teSfUkMadOYS2kKLqUrGNqmMQVFSjYfLVswYuxjpTdMxq0oXYmrT9P6XFNkc+nWXdpIX08ebxHFyoNlc9TMTU3xrVUa5bE0M8PFzpaA2m7kK5VczdDdYqM6tYjpwbI/f8bLPxCfwGAObttEdmYGjdt3AWDTknlcOXeWJ8e8D0D95q3ZtnIpy6f/QtuefUm/fo1d61bQvnd/Tbs4un83y/6YRszAofgGh5JzK/JrZm6Oza3FIIIbRrM3bg2efv6aIXdbly8kOLKRwaMyt8X2HcC0Cd8REBJCcHh9Nq9dhSI9jc49egEwf8YfnD11UmubgssXL6BSqcjOzqKgoIALZ88A4BdQOnoiunkL/pgykY2rVxLZuAmK9HRm/fYz9QKD9G6hYCjNY2JZ8ec0POsFUDcwhEPbNpGTqSC6fWcAtiyZz5Xz5xg2+l0AIpq3YseqZayc8RttevQh/cY1dq9bSdte2nsOXU+5AEBRfj4mJiZcT7mAmZk5tbxKR2UERUazb+NarXaxbfligoyoXXTq1Ye/pk7ENyiYgNAwdm5YR2ZGBm1jSvdoWj7nLy6ePsXrH32mOebqpRSKVSpys7MpLCzg0vnSC9y69fwBOLhrO39NnUi/4U8TFB5BlqI0Imhmbo6dvUM117BqmNhYY+F96/rA1ASLOrWxDApAnZ2N6q6IzD/JnpMXaB8RSGpWLmk5ubSPCKRIpSLpwhVNnv4tGgKw5I79Tz2cS//uVubmlJSU4OHsQLG65J773xmL3IREnLt2ouj6DZRXr2PbIBxTWzvyjpRePzm0aoZF7dqkL1sFQFHKZZQ3buLUpQNZ20s7bI7tWlF07TrKG3dF6+qHo1IoKLos2xzUJL169aJXr156nxs9erROWr169fjmm6rfAqhSOnYXLlwgPj5ebwerPL6+vhQXF1eoc7VhwwZCQ0N54403tNJ37dpFXFwcgwYN0hstHDFiBK+99hoHD2pvnNyhQwdWrFjBqlWr9PaWc3JyatQ8u/yjx1HY2uDYvg1m9nYob6SSOmuBZg87Mwd7zF11h1rZRTckLymZEqVK96RmZjh364yZgz0lKtWtc86n4NTZqq5OhTVq2Zq8nGw2LV+s2VT32bff10TfshQZpN0oW13KxtaWF/7vI5bO/J1JH7+Pja0d7Xv01mxtANC0XUcK8/PZFbeWlXNmYm1jS2B4fXoOeVKTJysjnR8+Khvim3bjOns3xxEQFsHLH3xS9RV/AAv2HMbKwpzXurfB3tqK41du8MHcVVqRvdqO2m3d1MSU5zq1oI6TA8VqNVcVWfy5eS+rbm11AODmYMuPzw/UPPZydaJX4wgSL1zh/2atqPqK3UP9W4tV7Fi9jJxMBe5edRny+ljNMNqcTAUZqTc0+a1tbBn+1rusmT2D37/6GBtbW1rG9KBFTNkKige3bUKtLmb9/L9ZP/9vTbpvSBgj3v4PAO169sUE2LJ8EdkZ6djYOxDSMJqOfbWHchtSy3YdyMnOYvn8OSjSM6jr58fb//2cWrVLx/grMtK5ce2K1jHjP/uI1Btl79dHb70GwMzlawFo16Ub+fn5xK1azpw/fsXGzpbwyCiGjDSOPdpui2jagvycHHatXkFOlgJ3L28GvT5GE33LycxEcVO7XQx58x3Wz5nJn19/grWtLc1jYmkeoz0q448v/6v1+HRiAk6utXj1q/EAtOnZB0xg2/LFZCtK20VQZCM69B2IsWjcui25OdmsX7yATEUGnj6+vPzeh5r5cFkZGaRe196Iedo3n5OeWnaB+u17YwCYNLd0C5idG9ahLi5m8cw/WDyzbLuYoPD6OpuZ11TWYSHUnVw2LcPt+RG4PT+CrNXruX7r7/9PtPP4WSzMTOnZJAIbSwsupWXy19b9WnvYOdnqrpD+cve2Wo9Dveuge/XmEQAAZDJJREFUyM3jh5W62zIZm4LTZ8mytsa+aWPM7GxRpaWTsXKNJvpmamuLmZP2sO70lWtxbN8GtwGPUaIqpijlElk7tEfLmFhYYB0cSM7++Gqri7GqSRuUGzOT/Pz8Cr+TEyZMID09XbPdQVZWFocPH2b+/Pl4eXlptju4cx+7rKws/ve//xETE0O9evWwsbHh9OnTTJs2jXr16vH5559rvcbixYtZtWoVv//+O1AaHXnmmWd44okn6NOnj1be9PR0nnnmGT777DOioqKYMGECWVlZfPzxx5o8P//8Mxs2bKCoqEhrH7vp06ezZMkSHnvsMVq3bk2tWrW4fv0669evx8vLi6FDhxIXF8fPP/+sd7sDR0dHnRU5K6pSh2LWYAd79Lh/pn+JnzY+2NDIf7LhrWve1hJVJcTT8PPTjMWxK//OJcD18XTWPy/w3yjgDd074/9Ws954y9BFMBov3TSeRd4MzeX5EYYuQoX8sWXf/TNVkmc7/nPn+z5wxC4hIYERI0ZgamqKnZ0dfn5+DB06lNjYWL2RNxsbG0JDQ1m+fDlXr15FqVTi5uZGhw4dKhTh27dvH5mZmbRpozsHxNXVlfDwcNavX6/ZSuFuQ4YM0WyqfqeRI0cSFBTEqlWr2LBhA8XFxdSpU4cWLVrQs2fZHliFhYWMGKH7oZg2bZrRD9EUQgghhBDC2KklYFcpHihiJyqHROxKScSujETsykjEroxE7MpIxK6MROzKSMSujETsykjErkxNidj9trn6InbPd5KInRBCCCGEEEJUOpljVzlM759FCCGEEEIIIYQxk4idEEIIIYQQwmAkYlc5JGInhBBCCCGEEDWcROyEEEIIIYQQBqOWiF2lkIidEEIIIYQQQtRw0rETQgghhBBCiBpOhmIKIYQQQgghDEZGYlYOidgJIYQQQgghRA0nETshhBBCCCGEwcjiKZVDInZCCCGEEEIIUcNJxE4IIYQQQghhMCVIxK4ySMROCCGEEEIIIWo4idgJIYQQQgghDKZE5thVConYCSGEEEIIIUQNJxE7IYQQQgghhMGoJWBXKSRiJ4QQQgghhBA1nETshBBCCCGEEAYjc+wqh0TshBBCCCGEEKKGk4idEEIIIYQQwmDUErGrFNKxM4CnTBwMXQSj8GROnqGLYDRCPGoZughGw8fNydBFMBp1nOwNXQSj4SvtQsPSQn66b5v8xluGLoLRGD75B0MXwWj8+uZoQxfBaPyfoQsgqpX8OgghhBBCCCEMRubYVQ6ZYyeEEEIIIYQQNZx07IQQQgghhBCihpOhmEIIIYQQQgiDkZGYlUMidkIIIYQQQghRw0nETgghhBBCCGEwst1B5ZCInRBCCCGEEELUcBKxE0IIIYQQQhiMbHdQOSRiJ4QQQgghhBA1nETshBBCCCGEEAYjAbvKIRE7IYQQQgghhKjhJGInhBBCCCGEMBg1ErKrDBKxE0IIIYQQQogaTiJ2QgghhBBCCIORVTErh0TshBBCCCGEEKKGk4idEEIIIYQQwmDUErGrFBKxE0IIIYQQQogaTiJ2QgghhBBCCIORgF3lkIidEEIIIYQQQtRwErETQgghhBBCGIysilk5JGInhBBCCCGEEDWcROz+QZ7u0IzeTerjYG3FscvXmbh6G+dvppebP8rPi+e7tMTHzQVrC3OuZ2azKj6Z+bsT9Obv3CCYjx7vxu6T5/lgzqoqqsWjO7R1I/vi1pCTqaCWpzednxiGT1BouflvXk5hw7y/uXbhLNa2dkS160TrHn0wMTEBICdTweZFc7mecp6MG9ep36I1PUe8UF3VeWQ9osNpHeqPjaUlF26ms2D3Ia4pssvN72hjTb/mkfi4ueDuaM/+MxeYtf2gVp43erQn2NNd59irGVl8vWRDpdfhUW1Zu5r1yxeTmZGBl48vg0Y+T3BEfb15lUVFzPrlRy6ePcPVy5cICg3n7c++Kvfcp48lM/7jD/DwrsvHE6ZUVRUqzYoli1kwdw7p6Wn41avHy6+/SWRUlN68hw/Fs3jBfE4eO0Zubg5e3nXpP/AJuvfqrZVPqVQye+YMNq5fR3paKs4uLgwcPJR+A5+ojio9tKWLFzF39izS0tLw9/fn9VFv0bBRI715D8XHs3DeXI4dSyY3JwfvunUZOGgwPXs/ppUvbv065syaxaWUi9ja2dGkaTNeef0N3NzcqqFGD2/RwgXM/utv0tJS8Q8I4M3RY2gUHa0377mzZxn/3becO3eO3JwcatWqRUy3bjz3wotYWFgAkJqayuQffuDEieNcSkkhtkcPPvz4k2qsUeXrWD+IJoE+WFtYcDldwaqDydzMyik3v721Fd0bheHp4oirvR2JFy6zdF9SNZa4+lhHNcBl6ECsQ4Mxd6/FtS/Hkb3G+H4LHlSHiCAaB9TF2tKCy2mZrDl07785gF8tF7o2CqO2oz3Z+YXsOnGOg2dTNM+bmpjQNiyAhvW8cbSxIjU7l42JJzlzPVWTx7eWC61C/fF0ccTRxppl+5I4fOFyldVT/HP8Yzt2EyZMYNOmTQCYmZlRq1YtWrVqxfDhw7G2tgZg2rRprF69mldffZXu3btrHT9z5kw2b97MlClTsLOz06SPHz+e8+fP8/3332t+wIzBkDbRDGrViP8t28jFVAVPd2jGd0/1YcSUWeQXKfUek1+kZPHeRM7dSKdAqaSBjydjenekUKli2YEjWnk9nR15uWtrDl+4Uh3VeWjHDuxl44LZdB3yFHUDQzi0bSMLp37Pcx99haOr7oVVYX4+8yd/R92gUJ5692PSr19l9czfsbC0pHlMDwCKVUps7O1p0a0Xh3dsre4qPZKYyBA6NQhm1rYD3MjMITY6nNdi2/HFwvUUqlR6jzE3MyW3oIgNiSdoE+qvN8/vG3djZlYW8Dc3NeP9/jEcOnepSurxKPbv3M68P39l2PMvExQewZZ1q5n81ad8MmEqru66nVO1Wo2FhSUde/TiSPxB8nNzyz13bk4Of06eQFhkFIr0tKqsRqXYsmkjP02eyOuj36ZBZENWLF3Ch++O5dcZf1G7jodO/uQjR/APCGDQ0GG4urlxYN8+fhj/HRaWlnTu2k2T7+vPPuHmjRu8Ofb/8K5bF0VGOoWFhdVYswe3KS6OyT9MYPTYd4hsGMXSxYv4v7FjmPH3bOp46L4XR5MS8Q8MZMjw4bi51WL/vr2M+/Z/WFpaEtOt9PcjKfEwX33+Ga+89jpt23cgPT2dH8Z/x5effsz3k4y30x+3YT0/jB/P2HffJSqqEYsXLuTtt95k1rz5eOh5LywsLOjRqxchIaHYOzhw+tRJvvnqK4pVxbw2ahRQeoPEydmZp55+mmVLllR3lSpdm7AAWoX6s3RfImnZuXSICGJEx2ZMXr2NIlWx3mPMTU3JKyxix7GzNAn0qeYSVy9TGxuKzl4ge20cdT58x9DFqRStQ/1pGVKPZfuTSMvOpX1EIE+2b8rUtdvL/Zs729owtF0TEs5dZuneRHxqudCzcQS5hUUcv3wdgE4Ngmno58XKA0e5mZ1DYJ1aDGoTzZ+b9mhuulqam3EzM5vE85fp17xhtdXZkGS7g8rxjx6K2ahRI2bOnMmvv/7Kk08+yerVq/njjz+A0jvMW7du5fHHH2f9+vU6xw4dOhQHBwemTZumSdu5cyc7duxgzJgxRtWpAxjYIorZO+PZduws52+m8/XSOGwtLYiJDCn3mJNXb7L56GnO30znmiKbuKST7D9zkUhfT618ZqamfPR4N37ftIerGZlVXZVHcmDTOhq0akNU2464eXoRM/gp7BydObRtk978yft3oywqoueIF3D3qktodDNadOvJgY3rNOO9ndzciRn0JJGt2mF9Rye/JuhQP4i4xBMcvnCFq4os/t62HysL83teZKTn5LFo72H+v737DmvqfPsA/g0hIey9l0zZ4t4KOOvAvap1VKutWqtWW6u1djhf21pnW2t/FvceuBDQOnGigoNRBAQRRFYIYWW9f6DBEFBaISeQ+3NdXpc55znhPicJ5D73M26mPIGworLWNqWVIgjKKuT/3KzNwdXWxvV/0hvpTP676BPH0SWoF7r36QdbB0eMmzoDxiamuBh5utb2Ojwexs+YiR59+sP0LVWWHVs2oFNQCFw9664Iq5MjB/ahT/8BGDA4FE4tWmDW3HkwMzPHyePHam0/7oOJmDxtOnz9A2BrZ4/BQ4eha/ceuHKp+gZH7K2buBt7G8vXrEXb9u1hY2sLLx9ftGrdRkVn9d8c3L8X/QcMxKDQIXBu0QKfzf8c5ubmOH70SK3tJ0yajGnTZ8A/oBXs7O0xZNhw9OjZE5cuXJC3efjgASwtrTBq7DjY2tnB188Pw0eOwqNHj1R0Vv/Nvj17MGDQIAwZOgwtXFwwf+FCmFtY4OjhQ7W2d3B0xMBBg+Hh6QlbW1t079ETffv1x7179+RtbO3sMH/BAgwcNBhGRsYqOpPG08nTGVcSUpHw9Dly+SU4ejMeXG1t+Dvb1XlMUWkZztxNwL30rDpvsDYXpddvIX/rdpRcuAJIm8cX9I4ezriamIrErOd4UVyC4zfvg6utDT+nul/ztm6OEJRVIOJeAvIEQtxNe4q49Cx0ee0maYCzHa4mpuGfnBcoEpYhNjUTKdkv0Mmzuk1KTh7OP/gHCVnPIUPzuJ5ENZp1YqetrQ1TU1NYWloiKCgIQUFBuH79OgAgJiYGVlZWGD16NDIzM/HkyROFYzkcDubNm4fLly/j2rVrKCwsxJYtW/D+++/DxaX2KgZTbE2MYG6oj9uPq0v9lWIJ4p88g6+D8t3WurjbWMDP0VapKjctpCNyiopxNi6pwWJuDBKxGDkZ6Wjh7aew3cXbF1mpKbUe8yw1BQ5unuBwua+190MJvwj8/Lxaj2kqzA31Yayni8SsXPk2kUSKxzl5cLFq2G5hnVu64NHTHBQJyxr0ed+VWCRCRmoKfFoFKmz3btUaj5MS3+m5L0ScRnFREQaOGP1Oz6MqIpEI/yQno2379grb27Zvj0cPHtRxlLLSUiEMDQ3lj2MuX4KnlxcOH9iP8SOHYcr7Y7Fl/S8oKy1tsNgbmkgkQlJSEtp36KCwvX2HDnj4oP5d5YRCIQxeuxb+AQHIz89DzJXLkMlkKCoqwvnoaHTq3LnBYm9oIpEISYmJ6Nixk8L2Dh074n58fL2e42lmJm5cv4bWbWrvutnUmerrwlCXp9BVTiyR4smLAjiamzAXGGk0Ji9f89TXX3OpFBl5b37NHcxNFI4BgMfP82BragStl8M72FpaEEulCm1EEimcLEwb7gSaIJlMprJ/zVmz7YpZGy6XC/HL7meRkZEICgoCj8dD586dERkZiY8+Uhw35eLignHjxmHz5s1wcXGBra0thg8fzkTob2RmoAcAKCxR/CJVKCyDheHbK0wH5k2CsZ4u2Fos7Lh4CydiH8r3tXN1RJCvBz76fX/DBt0ISksEkEml0DdUvDusZ2QMYWLtd8yFxXwYmpopbNN/eXdZWMyHiYVyV72mwkhXBwAgKCtX2C4oq4Cxnm6D/RxLIwN42Frij+iYBnvOhlIiKIZUKoWhiYnCdiMTEyTej/vPz5v1JB0nD+7FopVrocVmv2OUqlHM50MqkcDUTPH9bmJqhsLY2/V6jusxV3EvNhY/b/5Vvi07+xke3r8PLoeLpd8vR0lJCbasX4f8/Dws/X55g55DQ+EXFdV6LUxNzRCbX79rEXP1Cu7cvo1Nv1X36vD188fSb7/H8u++RUVFBSQSCdq174Cvvl7aoPE3pKKiIkhquRZmZma4ffPmG4+dPvVDJCclobKyEqFDh+LjmbMaM1TGGPCqfpcKyxW7FwvLK2H48vcsaV5eveYl5Yq9VkrKK2Gky3vjcWnPFbvlC8srwdbSgp4OFyXlFXj8PA8dPZzxJLcA+SVCuFqZw9veWj6un5B3oTGJXXJyMi5evIhWrVohJycHjx49woIFCwAAISEhWLNmDSZPnqzUxXLEiBGIjo5GfHw8tmzZArYafInr7e+J+YOC5I+/2nMSAGot1tenhD9n+xHocjnwcbDB9N6dkV1UjKj4ZBjp8vDl0F5YfjgSJeXqPV5GQc3fjTIZ/s3vy1d3c5ra79h2ro4Y07W6+9vvUVcB1P6+qGvrf9GlpQv4pWV4mJnTYM/Z0Fg13hTvcsdOJBLhj3VrMXLih7CoZVya+lP6gNTrzf7wfjzW/PAdPpkzF17ePtVHS2VgAVi0dBn0DQwAALPmzsfiBfNRWFCglDCoE6X3RdXGt7ofH4fl3y7DnHnz4f3aJDzpaWnY+MvPmDh5Ctp37IT8/Dz8tnkTfvq/NVi8dFnDBt/Aan6plMnw1vfFDytXolRYin/++QebN27Arh1hmDh5SiNGqRr+znYY3Lb6dX01eZTSr40m9jeC1M3PyRaDXnvN99aYMOwVFt7+vaqut8mrvztn7yZgUDs/fNK/GyCToUBYhnvpTxHYwuE/Rt88NPNCmso068Tuzp07GDVqFCQSCSQSCTp27IgZM2bgxIkTCAgIgKlpVdnb398fOjo6uH79Orp3767wHPfv30dubi7YbDYSEhJgb29f58+LiIjA2bNn3xoXCwaQ2bT4z+d1NSkNj54+lz/malclm2YGegqzNZnq66Kw5O1d414N1k3LLYCpvi4m9eyAqPhkuFiZwcJQHz9NHFId+8s/9NFLP8GULXuRmV/0n8+joekZGIKlpQVhseI4wFJBMfQMax/joW9kXGt7AHUeo67uZ2Qj/UW0/LH2y8lNjHR5Cl0kDXV1UFzWMIk6W4uFDu5OiElOV8uBzwaGRtDS0kJxUaHCdgGfD6MaVbz64hcWIPtpJsI2r0fY5vUAqruQfDJ6KD5dvAw+gerXJc3I2BhabDYKa0zyUlRYCFPTNydfD+LjsPTLhfjgw2kYPHSYwj4zc3OYW1rKkzoAcHR2BgDkPn+ulomdsYkJtNhsFChdiwKYvSXe+Lg4LFowH1OmfYQhwxR7cOzeuQNePj4YO34CAMDN3R08ni7mzPwY06Z/DCtr64Y9kQZgYmICNpuNgnzFa1FYj2th/fLGhourK6RSCVavWIH3J3wAbe2m/dUiKes5sl7728bWqvpdaqCrg+LXekDov6zAkKYv+Vkufs+v/i7w6u+nAY+r+JrzuBCW1z72HABKyitgwOMqbNPjcSGRSuXjLEsrRTgQc7eqisflQFBegV7+nigUqm/3ddJ0NO3fvm/h5+eHWbNmQVtbG2ZmZtDW1oZEIsG5c+dQUFCAIUOqExaZTIbIyEiFxE4oFGL9+vUYOnQoTExMsG3bNgQGBsLCwqLWn9e/f3/079//rXG9t+bPdzqvskoRyioVk5F8gRDtXB2R9KxqPBWHzYa/sx1+j/p33eO0WCx5opj0LBdTtuxV2D81pCMMeTr45fQlZBcWv8NZNDy2tjZsnFogPeEhvNpUj51JT3wIz8B2tR5j5+qOi8cOQCyqhDaHK29vYGwCY/PaX2d1VSEWo0KgONMlv7QMLe2skJFXldhos7XgZm2BY7caZsrtAGc76PN0cD05vUGer6FpczhwcnXHo/h7aNulm3x7Qvw9tOn038Y9mZqZ45ufNypsu3j2NBLi7uHjLxbD3NLqnWJuLBwOBx6enrhz+xZ6BIfIt9+5fQvdegbVedz9uHtY+uVCTJjyIYaPUh5P6Ovnj8sX/kZZaSl09aq6hWdlVo33rW12SXXA4XDQsmVL3L51C0EhveTbb9+6hR5vuBZx9+5i0YIFmDx1KkaNGau0v6K8HFpair06Xs0eq67jOjgcDlp6eeHmzRsI6d1bvv3WjZsICgmu9/PIpDJIJBJIa4wdaooqxRIU1BjaICgrh5u1OZ4VVP3t1dbSgrOlGSLj3m2sLlEPlWIJKsXKr7mrtQWevfyuw9bSgpOFGaLi655v4Gl+EVraK97AcbW2QHZhsdLNT4lUCkF5BbRYLHg7WOORGvd6UQV1vDncFDXrxI7L5cLOTnH2ojt37kAgECgtV/DixQt8//33eP78Oaxf3lXdunUr9PT0MH78eLDZbFy7dg0bN27Ed999p9LzqI9DN+IwoXs7ZOQVIjO/CB/0aIeyShGi7yfL23w1tOoLzKpj5wAAwzr4I7uwWF51a+Vsh9FdWuP4yy/95SKx0jp4JeUVYGtpvXF9PCa1C+mHU2FbYdvCFQ6uHrh3+W+U8IsQ2L3qC8rFYweR/SQVYz/7EgDg074TYk4fw+kd29D5vVAUPs/BjchT6DJgiELXpOeZVZPrVJaXgcVi4XnmE7C1tWFhW3cFVx1cfJiCvq288JwvwAt+CfoGeqFCLEbsaxPtTOhRlfTuulQ9tsjerKpayeNqQwYZ7M2MIZFKlda/69LSFcnPcpEvqHtJAKb1HjwE2zeug4u7J9y8vHEpMgL8wgL06Fu1nMXR3WFI++cfzP+2ejzYs8wMSMRilBQLUF5ejsy0VACAo4sr2NrasHdyVvgZhkbG0OZwlLarm+Gjx2Ltih/Q0tsHvn7+OBV+DPn5+RgYOhQA8L+tvyEpIQFr1lVVIuPu3sHSRV9g8NBhCOndV17V0WJrwcSkqsdDcO8+2L0jDD+tXokJUz6EsKQEv25cj+49g2Biqr6TAYwaMw4rf/gOXt4+8A8IQPixo8jLy0PosKqK5NZftyAx4ZF8mYK7d+7gq4WfY8iw4ejdtx/yX14LtpaW/Dw7d+2GH9eswvGjR9C+Q0fk5+dh0/r18GzZUm2TXAAY+/77+H7ZMvj4+CKgVSscPXIYeXkvMHT4CADAr5s34dHDh9i4pWps5ZnTp6HD5cLV3R0cjjYSHyXg1y2bERQSAu5rE1ElJ1d9ARYKhdDSYiE5OQkcbQ5cXF1Vf5Lv6HryE/TwcUNesRD5JVVT31eKxbj/2mRjwzpWTUt/9Eb1pDM2JlWT6+hoa0Mmk8HGxBASqeyta6E1NSxdHjj2L79vabHAsbYC190VUoEA4ucvmA3uP7rxzxN093ZDnkCIfIEQ3b2rXvMHGdWv+ZD2/gAg/94U+zgT7d2d0LeVF+6kZsLRwhSBLexx+Hr1mG57M2MY6vKQU1QMI10eevq4gwUWrialydtw2Gz5/AkssGCsx4O1sSHKKkUKFURCamrWiV1tIiMj0bZtW7i7uytsd3Z2hr29PaKiojBhwgTcuHEDly5dwtq1a+UJ4Ny5czFnzhycPXtWad07pu27ehc62tr4bEAPGOrqIOHpcyzcGa4wxbKVsaHCMVosFmb07gJrE0NIpFI8KyzGH9HXEH67/jPkqRvvdh1RLizBtTPhEBbzYWFrj5Ez58urb8LiIhS9qJ4lUkdXD6M/XYio/TuxY/W34Onpo32v/mjfS7HyGrZKcXzM4/v3YGRmjo+X/9T4J/UOou8ng6PNxqjOgdB7uUD5logrCmvYmerrKR335dDeCo/9neyQLxDiu4MR8m3mhvrwsLVE2IUbjXcCDaB91+4QCgQ4ffgA+IUFsHNyxuzF38gra/zCQuQ9V7xTumnl98h/7X2yfOFcAMDvh8JVFndjCArpBQGfj707w1CQnw9nFxcsX7NWnnQU5Ocj+1n1IrhREWdQUV6OQ/v24tC+6uq9tY0NduyvmgpfV08Pq3/+BVvWr8OcGR/BwNAQXbp1x4czPlHtyf1LIb17o7iYj51h21GQnw8XV1es+fEn2NhULfeSn5+PrKzqaxFx+hTKy8uxf+8e7N+7R77d2sYG+w9XrdP23sCBKCstxdFDh7Bl4wboGxigdZs2mDFztmpP7l/q3acv+Hw+/tr+P+Tn5cHVzQ0/rvsFtrYvr0VensK1YLPZ2BH2F55mZlYlKzY2GDFyFMaOG6fwvJMnTFB4fOXyZdjY2uLI8ab3ObqamAoOWwsD2vpAl8vB03w+dl68pbCembGe8qQaH/frpvC4pb01ioSl+OVk01oT9W14Xp5w2LhW/th82kSYT5uI4tOReL5Svf9O1iUmKQ0cNhvvtfaGLpeDrAI+dl26XeM1V5yIrKi0DHsvx6JvoBfauTlBUF6OiLsJ8jXsgKpqb7CfB0z1dVEpluCf7Bc4ejMeFaLqv8t2ZsaYFFTd8yjIzwNBfh64l56F8AbqcaNuaFmHhsEqKytrlldy3bp1KC4uxrJl1V/ICwsLMWXKFMydOxdBQUFKx+zatQvnzp3DunXr8Omnn2LAgAEYV+MP1alTpxAWFoZNmzbByuq/dbl6166YzcWEbuq9zpUqqfvC76o0vL3f2xtpiBaW6jc+jSk62sxPXKUuuByNuydbp40RV5kOQW2M3/gL0yGojb2fzWM6BLXxxeAgpkOol8X7z6jsZ60c857KfpaqNdu/DvPmKX+oTU1NcezYsTqPmTBhAia8vMO4c+fOWtsMHDgQAwcObJAYCSGEEEII0XQ0xq5hNOsFygkhhBBCCCFEEzTbih0hhBBCCCFE/VHBrmFQxY4QQgghhBBCmjiq2BFCCCGEEEIYo65rfTY1VLEjhBBCCCGEkCaOEjtCCCGEEEIIaeKoKyYhhBBCCCGEMbTcQcOgih0hhBBCCCGENHFUsSOEEEIIIYQwhiZPaRhUsSOEEEIIIYSQJo4qdoQQQgghhBDGSKlg1yAosSOEEEIIIYSQBiQSifC///0PFy9eRGVlJVq1aoVPPvkEFhYWdR5z9uxZnD9/HhkZGZDJZHB1dcX48ePh6+tbr59JXTEJIYQQQgghjJHJZCr7pyp//PEHYmJisHDhQqxevRqlpaX4/vvvIZFI6jzm/v376N69O5YvX44ff/wR9vb2WLZsGZ49e1avn0mJHSGEEEIIIYQ0EKFQiKioKEyZMgWtW7eGu7s75s+fj/T0dMTFxdV53IIFCzBo0CC4ubnBwcEBM2fOhK6uLmJjY+v1cymxI4QQQgghhDCmuVXsUlJSIBaL0bp1a/k2S0tLODg4ICEhod7PIxaLIRKJYGBgUK/2lNgRQgghhBBCSAMpLCyElpYWjIyMFLabmpqisLCw3s+zc+dO8Hg8dOzYsV7tafIUQgghhBBCCGOkKhz7FhERgbNnz761Xb9+/dC/f3+FbTt37sSBAwfeeNzKlSvr3CeTycBiseoVZ3h4OCIiIrB8+XLo6enV6xhK7AghhBBCCCEaoX///koJW32FhoYiKCjojW0sLS0hlUohlUpRXFwMY2Nj+b6ioqJ6zXAZHh6OXbt2YdmyZfD09Kx3fJTYEUIIIYQQQhjTVJaxMzY2VkjU6uLu7g5tbW3cvXtXngjm5eXh6dOn8Pb2fuOxx44dw+7du7Fs2bJ6L3PwCiV2hBBCCCGEENJA9PX10adPH2zfvh0mJiYwNDTEn3/+iRYtWqBVq1bydkuWLIGnpycmTZoEADhy5Ah27tyJ+fPnw97eXj4ej8vlQl9f/60/lxI7Bhzq1YbpENRCjE79ZvjRBG1dHJgOQW2YGdSvH7kmMNXnMR2C2rj3JJvpENSGp03di9tqmhkvMpkOQW388dk8pkNQG+PWr2M6BPUxOIjpCOpFlevLqcq0adPAZrPxf//3f6ioqECrVq0wb948sNlseZucnByFBctPnToFsViM//u//1N4rpCQEMyb9/bPOCV2hBBCCCGEENKAuFwuZsyYgRkzZtTZ5s8//3zj43+LljsghBBCCCGEkCaOKnaEEEIIIYQQxqhyuYPmjCp2hBBCCCGEENLEUcWOEEIIIYQQwpjmOHkKE6hiRwghhBBCCCFNHFXsCCGEEEIIIYyRUsGuQVDFjhBCCCGEEEKaOKrYEUIIIYQQQhhDY+waBlXsCCGEEEIIIaSJo4odIYQQQgghhDFUsWsYVLEjhBBCCCGEkCaOKnaEEEIIIYQQxkipYtcgqGJHCCGEEEIIIU0cVewIIYQQQgghjKGCXcOgih0hhBBCCCGENHFUsSOEEEIIIYQwRgYq2TUEqtgRQgghhBBCSBNHiR0hhBBCCCGENHHUFZMQQgghhBDCGFruoGFQxY4QQgghhBBCmjiq2DVTh8+fw56zZ5BfVAQXe3t8NvZ9BHq2rLVtdt4LjPhyodL2n+fORyf/gMYO9Z1cjYrAhdPhEBQVwtreEUMmTIarl0+d7bMzn+Bo2J/IeJwCPQMDdArpgz5DR4LFYr32nGdwNSoCBS9ewNTcAr2GDEe77kHy/df/jkLs5YvIyXoKmUwKe2cX9B85Fi4tvRvzVN8q9kI0rkedRgmfD0s7e/QeNR5OHrW/5gCQm5WJs/t2IDs9FTw9A7TuEYxuA4bIr0Xi3Vu4e+lv5GQ+gUQkgoWtHbq8FwrPVm3kzxEfcxknd/yh9NxfbNwGbQ634U/yPzp74jiOHzqIooJ8ODi3wJSPZ8Lbz7/Wtg/j7uHk0cNISUpCaakQNrZ2GDhsOEL6vSdvU5ifj7A/fkNaSgqyn2WhR0hvzF7whapO550cOngQO3fuRH5eHlxdXTHv88/RunXrWtumpqZi7Zo1SEtLQ0lJCSwsLdG3b198NH06OBwOAOBObCw2b96MJ0+eoKK8HDY2NhgydCgmfPCBKk/rP7kQcQpnjx8Bv7AQdo5OGDPlI3j4+NbaVlRZiV2/b0ZGaiqyszLh7uWNBd+vUmiT9OA+flq2WOnY79Zvga2DY6OcQ0M5duQw9u3Zjfz8fLi4uGD2nLkICAyste3dO3dwaP8+JCQ8grCkBPYODhg5egwGDBqs0O7o4UM4evgQcrKzYW1tgwmTJqHfewNUcDbvRs/PB/ptAsDW04O4oBD8y9cgys558zGt/KDv5wO2kSGk5RUoS0yG4NpNAIBxr57Q81b+XSwVifD89+2Ncg7voqePO9q4OoDH5SArn48zdx/hRXHJG49xtjBFn0AvWBkZQFBWgZikNMSmZsr3a7FY6OblioAW9jDS1UGeQIhz8cl4/DxP3sbJwhSdW7rA1tQIRro8HL95H3FPshrtPBsLr5UfTMeNBK+lB7QtLZCz4kcIzkQxHZbak1HFrkFQYtcMRd+8gV/27cGC8R+glYcnjvx9Dp//8jN2/7ASNubmdR7387zP4eFY/eXDSN9AFeH+Z/euX8XxXdsxfPI0uHh6Iyb6LLatXYmFa9bB1MJSqX15aSm2rv4BLi298dn3q/Ei+xn2b90Ero4OggaEAgBios/i1L7dGDX1Yzi5eyDj8T849Odv0NU3gG+bdgCAxwkP0apTVwz1bAkOVweXIk5i6/8tx/wVP8LSxlal1+CVR7evI+rAbvQbNxGO7p6IvXgO+zf9iOnLVsHYzEKpfUVZGfau/z84urfE5EXfoeB5Nk6G/QEuVwcd+1QlMBnJSXBu6Y2eoSPA0zfAw5sxOPzbeoyfv1ghYeRwufjkhx8Vnl+dkrqrF//G9t+2YNrsOfDy9cPZk+FY8fVXWLf1T1haWSu1T0p4BKcWLhgyagxMzcxwL/Y2fl+/DhwuF92DewEARCIRjIyMMXT0WESfOaXqU/rPoiIj8dOPP+LLRYvQKjAQhw4exNw5c7D/4EHY2NgotedwOBg4aBA8W7aEoaEh/klOxsoVKyAWizHns88AALp6ehgzZgzc3N3B4/EQHxeHVStXgsfjYeSoUao+xXq7dfUy9v3vD4z/6BO4e/vgQsRpbFjxLb79ZTPMLa2U2kulUnC4XAS/NxD379xGWamwzuf+9pfN0DcwlD82NDJqlHNoKOejo7Hxl3WYt2Ah/ANa4diRw/hiwXyE7doD61reFw/vx8PFzQ1jx4+HubkFbt28gR//bw24XC569+0HADh+9Ah+/3ULFn65CN4+vkhIeIQfV6+GoaEhunTrrupTrDeeuyuMuncB/+IViLJzoOfvA7PB7+HFngOQltT+mht26wReCycUX70BcX4BWFwu2Pp68v3Fl2PkSd4r5iOGoPJZdqOey3/RpaULOnm2wPFb95EvEKKHjxsm9GiHzRGXUSmW1HqMiZ4uxnVvi3tpWTh2Ix6OFqYY0MYHwopKJGY9BwAE+3kgwNkOJ28/xAtBCdysLTC6a2tsP38dOUUCAABXm40XfAHi07MwtIN631R+Ey1dXVSmPoEgIhrWXyvfNCekMTXZxG7dunU4f/680vb169fD1dUVx44dw/bt2zFixAhMnDhRoc39+/exeHH1XVVDQ0O4uLhg/Pjx8PGprvbs2bMHe/fuVfoZJiYm2LlzZwOeTcPaF3kWA7p0xZCeQQCA+eM/wPUHD3D0wnl8MqLuL1rGBgYwNzZRTZAN4OKZE2jfPQidgvsAAIZNmoqk+Lu4di4SA8aMV2p/J+YyKisqMO7j2eBwdWDr6ITcZ09x6cxJ9HxvMFgsFmKvXkTH4F5o3aUbAMDcyhqZqY/x98lj8sRu/My5Cs87Ysp0PIy9haT4u4wldjejIxDQuRtadw8GAPQbOxGpD+/jzsXzCB42Wqn9g5sxEFVWYPDk6eBwubCyd0BezjPciI5Ah979wWKx0HfMBIVjug8ahpT7cUiOi1WsBLJYMFDj983JI4cR1Kcver83EAAwdeanuHf7NiJPnsD4D6cptR8+9n2Fx/0GheJh3D3cuHJZnthZ2djgw5mzAQDXr1xq5DNoOHt278agwYMxdNgwAMDCL77A9WvXcPjQIcyaPVupvaOjIxxfu9lja2uL2NhY3Lt3T77N29sb3t7V1Wp7e3v8/fffuHf3rlondlEnjqFLcC9071OViIybNgMP78bi4tkzGD5hklJ7HR4PE2bMAgA8fZL+xsTO0NgYhkbGjRN4Izi4fy/6DxiIQaFDAACfzf8cN29cx/GjRzD9k5lK7SdMmqzweMiw4bh7JxaXLlyQJ3aREWcwaHAoevXpCwCws7dHUkIC9uzepdaJnX5gAMoSk1D2KBEAUHwpBjpOjtD394Hg2i2l9mwTY+j7+yFv3yGIC4vk28V5+fL/yypFkFWK5I85NtbQNjZCUdTfjXci/1FHD2dcTUyVJ2THb97H56Eh8HOyw53XKnCva+vmCEFZBSLuJQAA8gRC2JsZo0tLF/nzBDjb4WpiGv7JeQEAiE3NhKu1OTp5uuDYzXgAQEpOHlJyqip4Q5rw1Pel12+h9HrVe8V68QKGo2k6pE33JVcrTTaxA4DAwEDMnz9fYZvRyzujkZGRGDFiBM6dO4fx48eDzWYrHb9582YYGhqCz+dj//79+P777/Hbb7/BxMRE3sbe3h6rVil2t9HSUt+hiSKxGElP0vF+v/4K2zv4+uJ+Ssobj128eSMqRCI4WltjTJ9+CGnXvjFDfSdisQhZaanyStsrnv6tkP5PUq3HPElJgktLb3C4Oq+1D0TEoX0oeJELcytriEVicGpUmzhcLjIfp0AiFoOtrfyRkYjFEIkqoavHTIVTIhYjOyMdHfsodnFy8fHD09R/aj0mKzUFju4tweFWn6urjz8uhR8GPz8PJrVUPAGgsqIMPD19hW3iykpsWjwPMqkU1o5O6DF4BGycWrzbSTUQkUiE1H+SEVrjhkarNm2RlPCo3s9TWloK8zquSVMhEomQmJiI8TW6SHbs1Anx8fH1eo7MzExcv3YN3Xv0qLNNUmIi4uPj8dFHH71TvI1JLBIh43EK+oYOU9juE9gaj5MS3vn5V34xHyKRCHaOjhgwYgy81LhLu0gkQlJSEsaMU7yh0b5DBzx8cL/ezyMUCmH5WqVTJBKBy1X8XcrV0UHio0cQi8XQruV3KeO0tMCxsoDwbpzC5oqMp+DYKFf3AYDn0gKS4mLoODnCdFB/gMVC5bNsCK5eh7SsvNZj9Hy9IMovgCjneYOfwrsw0deFoS4Pqa91jxRLpcjIK4CjuUmdiZ2DuYnCMQDw+HkeWrWwhxaLBalMBraWFsRSqUIbkUQKJwvThj8RQjSY+mYo9aCtrQ1TU1OFf2w2G4mJiRAIBHj//feho6OD2NjYWo83NjaGqakpWrRogTFjxkAoFCI5OVmhDZvNVvoZxsbqeye2SCCARCqFaY27xWZGxijg82s9RleHh9mjx+CHj2fip7nz0c7bB9/8tgUR12JUEfJ/IhQIIJVKlSpFBsYmEBQV1XqMoKgIhjVeu1ePBfyqY1r6t8LNi+eR8TgFMpkMmakpuHnhHCQSMYQCQa3Pe+bgXujo8OQVPVUrLRFAJpVCv0Z3L30jYwiLa3/NhcX8WtpXPS4pLqr1mNsXoiEoLIR/x67ybWY2Nhg4cRpGfTIXQ6bOBFubix1rl6Pg+ZvHo6iKoJgPqVQKY1PFLw/GpqYoKiio13PE3riOB/fuoveAgY0RosoUFRVBIpHAzMxMYbuZmRny8/LqOKrK1A8/RLcuXTBi2DC0CgzEzFmzlNoMGjAAXTt3xqSJEzFy5EiMGDmyQeNvSCWCYkilUhjV+P1hZGyC4jp+f9SHsakpxk+fiY8XfoVPvlgMazt7rPvuayQ/fPBuATciflERpBIJTGu8L0xNzVCQX7/PSMzVK7hz+zYGDxki39a+Q0ecOXUSiQmPIJPJkJiQgFMnwiEWi8F/h2vcmLR0eWBpaUFaVqawXVpWBraeXq3HsI0NwTY0AM/DDfxzF1AU9Te0TUxgOrB/re1ZXA547q4ofZjY4PG/KwNe1U3PkvJKhe0l5ZXyfXUdJ6xxjLC8EmwtLejpVCX3j5/noaOHM8wNqm4MulqZw9ve+o3PSzSLTCZT2b/mTA1vmb27yMhIdO/eHdra2ggKCkJkZCQ6dOhQZ/vy8nJER0cDQK2VvabotblAALwclMqqva2JoSHef21iCO8WLigSlGB3xGn079ylEaN8d0qn9IbzBKAwSQoAeWePV9v7DBsJAb8Im75fAshkMDA2QdvuPXHh5HGwaqnUXo44hevnozDjq2/Aq+MPv+oovejK2xRa12xfx3YAiXdu4fzhfRg6bSaMzavH7Dm4esDB1aP6sZsH/lz+NW5fiELfMeozeUbN1x0ymfK2WiQ+fID1a1Ziyiez4NHSq5GiUy2lz0A9rsXKlSshLC3FP8nJ2LhhA3aEhWHylCkKbX7/4w+UlZXhwf372LRxI+zs7DBgoHonw3X9PvivbOwdYGPvIH/s1tIL+bm5iAw/Ck9fv3d89sZV83Mvq9r4Vvfj47D822WYM28+vF+beGbilCkoKMjHrBnTIQNgZmqK/u8NwN7du6DFVu97yv/mex+LxQJLWxtF0X9DUlR1I60o+m9YTRgDjrUlRM9fKLTXbekBFouFsqTae1Ookp+TLQa1rX7N9l6u/SY4C4DsLZ+OmntfvXVefYk+ezcBg9r54ZP+3QCZDAXCMtxLf4rAFg4ghDScJp3Y3blzB6NeG8Ph4+ODRYsW4cqVK1ixYgUAICQkBAcPHkRhYSFMa9y1nzatanxNRUUFZDIZ3N3d0apVK4U2T58+VfgZANChQwcsXKg8IDYiIgJnz559a9xBXj7o3bZxqjsmhoZga2kpVecKBcUw+xdjPnxdXXHq6uWGDq/B6BsaQktLS15pe6WkmA/DOsZ7GZoo340veXmdDF5eGw5XB2Omz8LID2dAwOfDyNQE189HQ4enC31DQ4VjL0ecQsShvZi2cAmc3DzAFD0DQ7C0tJSqc0JBsVJV7hV9I2OU1NL+1b7XJd65hfDtv2Pw5OkKM2LWRktLC7bOLijIVY8uRoZGxtDS0lKqzvGLipSqeDUlPLiPVd8swZgPJqHfoNA3tm0KTExMwGazkZ+fr7C9sLAQZm+YVAmAfAINV1dXSKVSrFi+HBM++EChO529vT0AwN3dHfn5+fhj61a1TewMDI2gpaUFflGhwnYBvwhGr3XFbwguni1xS43HYRqbmECLzUZBgeL7oqiwQKm6W1N8XBwWLZiPKdM+wpBhwxX26ejw8OXir/H5F4tQUFAAc3NznAw/Dj09PRir6ZhcaVk5ZFIp2Hq6EL22XUtXF5LS0lqPkQhLIZNI5UkdAEiK+JBJpGAbGCgldno+Xih/nAZZRUVjnMK/kvwsF7/nV8et/TLhNuBxUfxaN1J9HlepIve6kvIKGPAUu93q8biQSKUoezm2sLRShAMxd6uqeFwOBOUV6OXviUJh7deVaJ7mXklTlSad2Pn5+WHWa12CdHR0cOnSJZibm8PDo+qLto2NDTw8PHDu3DmMrNE1aMWKFdDX10dqairCwsIwb948pX7/NjY2WLZsmcI2XV3dWuPp378/+vevvfvF64S379br/P4LjrY2Wjq3wM1HDxHSvrpKeevRQwT9i2Tyn8wMWKjpH18A0NbmwN7FFckP4tGqY3VVMflBPALad6r1GGf3lji1bxdElZXysWX/PIiDkakZzGrMgsfW1obJyy+7965fhU/rtgpjKy+ePoGzh/dj6sLFjC9zwNbWhq1TC6QlPIB32+rXPD3hAVq2rn2cpL2rO/4+uh9iUaV8Bsu0hAcwMDZVqMg9un0DJ8O2YtCk6QrPXReZTIbcrExYqcnU7hwOB64enoi7G4vOPXrKt8ffjUXHrnVP4PDofjxWfbMEoydMxMBhI1QRaqPjcDjw8vLCzRs30Lt3b/n2GzduICQkpN7PI5VKIZFIIK0xXuZ1MpkMIpGozv1M0+Zw4OTmjoS4e2j3cqIkAHgUdw9tOjVsL4XMtFQYm745QWISh8NBy5YtcfvWLQSF9JJvv33rFnq8nICrNnH37mLRggWYPHUqRo0ZW2c7bW1tWFlV/X49Hx2Fzl27qu84dakUotw86Dg5oPxxmnyzjpO9wuPXVWbngMXWAtvIEJLiqu76bCNDsNhakAgUlwjgWFmCY2mB4ivXGu8c/oVKsQSVYsXESlBWDldrCzwrrLrRx9bSgpOFGaLiax+7DgBP84vQ0l5xDKKrtQWyC4uVFp2WSKUQlFdAi8WCt4M1HmWqR7d9QpqLJp3Ycblc2NnZKWyLiopCVlYWhrzW118mk4HP5ysldtbW1jA2Noa9vT0qKyuxcuVKbNy4Ub4+E1D1R6nmz1B3Y/v2w/fbtsLHxRUB7h44euFv5BUVYWjPqhkTfz18EI9SU7Fx4ZcAgNNXr0CbzYankzNYWixcvXcPh8+fw8yRyrMpqpOe7w3G3l83wsnVHS08vXDtXCSKCwvRqVfVLGyn9+9GxuN/8PHibwEArbt0Q9TRg9i/dRN6DRmJvJxnOH/iGPoMHyXvkvUi+xkyHv8DJ3dPlAlLcOnMCeQ8zcDYGdUzBv598jgiDu7FuE/mwNLGFsUv7/pzuFzo1phYRFU69O6P8O2/w66FKxzcPHDn0t8Q8IvQpkfVF/a/jx7As/RUjJ+3CADg26Ezrpw6hhNhf6Dre0NQkJuNa2dPovvAYfJr8fDWdZzY/jtCRoyFk0dLlLysjrK1taH7cimMyyePwt7FDaZWNqgsL8OtvyOR+zQT/ccpzyrIlEHDR2Dj2jXw8PRCS19fRJ46iYL8fPQdWLXm1u7/bUNKchKWrV4LoGodu1XffI2+gwajW3AvFL6s9mlpacH4tWpO2uOqyYjKSkvBYmkh7XEKtLU5cHR2Vu0J/gvvjx+PZd98Ax9fX7Rq1QpHDh9G3osXGD6iKnndvGkTHj58iC2//goAOH3qFLg6OnB3dwdHWxuPEhKwZfNmhISEyCfG2L9vH+zs7eH88rzv3rmD3bt2qfUYOwDoM3go/rfhZ7Tw8IC7lw8unj0DfmEBevat6pZ+ZFcY0lOSMf/bFfJjnmVmQCIWo0RQjIrycmSmpQIAHF1cAQDRJ4/D3NIKdo7OkIhFuH7pAu7dvI6PF36l+hP8F0aNGYeVP3wHL28f+AcEIPzYUeTl5SH05eypW3/dgsSER/h5wyYAVa/xVws/x5Bhw9G7bz95FZitpQWTl5XwzIwMJDx6CB9fPwgExTiwbx/SUlOx6OtvmDnJehLei4dJn2BUPs+FKPs59Py8oaWnj9IHVZPqGHZuD46VFQqOVy1zUpmZBVHuCxj36oniy1UJm1H3zqjMeQ5Rbo1qna83xEVFqMxSv2UOXrnxzxN093ZDnkCIfIEQ3b3dUCkW40HGM3mbIe2r1gA9fqtqcp3Yx5lo7+6Evq28cCc1E44WpghsYY/D16snobE3M4ahLg85RcUw0uWhp487WGDhalJ1wsxhs2FmUDWkgQUWjPV4sDY2RFmlSKGCqO5Yujxw7F9+b9RigWNtBa67K6QCAcQ1KrikWs2bAOS/adKJXU1PnjxBUlISfvjhB4VulxUVFfjyyy/x4MED+PnVPs4hODgY+/btw6lTpzB06FAVRdw4enfoCH5JCf46GY58Ph+u9vb48bP5sLWoqsTkFxUh60WuwjF/nTyBnPw8aGlpwcnaBounTFX78XWBnbpCKBAg+vhhFBcVwsbBCVMXLobZy9kLi4sKkf9al0BdPX1MX7QUR/7ahvXffAldPX30HDAYPd+rXlRXKpXi4pkTeJH9DGy2Ntx8fDH7mxUKFb2Y6AhIJGLs2vSzQjztugcpJICq5NOuE8pKSnD1dDhKiotgaeeAMbM/l1ffSvhFKHrtNefp6mHcZ1/g7N4d2L5qGXh6eujY+z106F1dcb576TykUgmiD+5G9MHd8u1OHl6Y8HnVciHlpaU4vXs7hMV86OjqwtrRGRMWLIadi5uKzvztuvYMRklxMQ7v3Y3CwgI4OrfA4h9WwtK66g5zYUEBnj+r/tLyd1QkKirKceLwQZw4fFC+3dLKGlt2VF+HL2Z9rPBzYm9cU2qjbvr07Qs+n4/tf/6JvLw8uLm5Yd369bC1rVqmIy8vD1lPn8rbs9lshG3fjszMTMhkMtjY2mLkqFEY9371DIpSqRSbNm5E9rNnYLPZcHBwwKzZs+XJorpq37U7hIJinD50APzCAtg5OePTxctg/rK6xC8swIscxWrCxhXfIf+1z9EPC6rW8tt6+AQAQCwW49CO7SgqyAeHy4WdoxM+XbwM/o3U9b6hhPTujeJiPnaGbUdBfj5cXF2x5sefYPNy+Zb8/HxkZVUvFB1x+hTKy8uxf+8e7N+7R77d2sYG+w8fBVD1vjiwby8yMzKgra2NwDZtsem3rfL3mroqT0lFMY8Hg3ZtwNbXgzi/AIUnz8irb1p6emAbK3ZxLzgZAaMeXWE+fDBkYgkqM5+i+Mp1hTYsDgc8DzeU3LqjsnP5L2KS0sBhs/Fea2/ocjnIKuBj16XbCmvYGesp9loqKi3D3sux6BvohXZuThCUlyPiboJ8qQMA0NbSQrCfB0z1dVEpluCf7Bc4ejMeFSKxvI2dmTEmBVX3DAny80CQnwfupWch/Fb9Z2hlGs/LEw4b18ofm0+bCPNpE1F8OhLPV/7EYGREE7DKysqaZIq8bt06FBcXK3ST/OOPP/DgwQOsX79eqf2yZctgbGyM+fPny9ex27Vrl8IMlydOnMD+/fuxbds28Hg87NmzB5cuXVJa7gCA0ni9f6Mxu2I2JTE66r0AuioVlNA4g1dat2haFfLG5GxhwnQIauPeE/Wtcqiap43F2xtpin2HmI5Abfxh7cR0CGpj3Pp1TIegNhyjjzMdQr2MXr9LZT/rwGcT3t6oiVLTju7/nkgkwoULF9C1a9da93fr1g1Xr16FUFj3orJ9+vSBRCJBeHi4fFtWVhYmTpyo9E8ikdT5PIQQQgghhBCiSk22YteUUcWuClXsqlHFrhpV7KpRxa4aVeyqUcXuNVSxk6OKXTWq2FVrKhW7kb/sVNnPOjRXfZZjamjNpmJHCCGEEEIIIZqKEjtCCCGEEEIIaeKa1ayYhBBCCCGEkKaFFihvGFSxI4QQQgghhJAmjip2hBBCCCGEEMbIQBW7hkAVO0IIIYQQQghp4qhiRwghhBBCCGGMlAp2DYIqdoQQQgghhBDSxFHFjhBCCCGEEMIYmhWzYVDFjhBCCCGEEEKaOKrYEUIIIYQQQhhDFbuGQRU7QgghhBBCCGniqGJHCCGEEEIIYYyUKnYNgip2hBBCCCGEENLEUcWOEEIIIYQQwhgq2DUMqtgRQgghhBBCSBNHFTtCCCGEEEIIY2iMXcOgih0hhBBCCCGENHGU2BFCCCGEEEJIE0ddMQkhhBBCCCGMoQXKGwarrKyMrqQGioiIQP/+/ZkOQy3QtahG16IaXYtqdC2q0bWoRteiGl2LanQtqtG1IKpGXTE11NmzZ5kOQW3QtahG16IaXYtqdC2q0bWoRteiGl2LanQtqtG1IKpGiR0hhBBCCCGENHGU2BFCCCGEEEJIE0eJHSGEEEIIIYQ0cZTYEUIIIYQQQkgTR4kdIYQQQgghhDRxlNgRQgghhBBCSBNHiR0hhBBCCCGENHGU2BFCCCGEEEJIE0eJnYbq168f0yGoDboW1ehaVKNrUY2uRTW6FtXoWlSja1GNrkU1uhZE1VhlZWUypoMghBBCCCGEEPLfUcWOEEIIIYQQQpo4SuwIIYQQQgghpImjxI4QQgghhBBCmjhK7AghhBBCCCGkiaPEjhCi0bKysiCT1T2HlFgsRlxcnAojIuqitLQUd+/exe3bt8Hn85kOhxBCCHkjmhWzmTt27Fi92g0dOrRR41AXe/bsQUBAAFq2bAkOh8N0OIxKSUmpVzt3d/dGjoRZQ4YMQVhYGExMTAAAU6ZMwZo1a2BlZQUAKCwsxOTJk3H8+HEGo1QdkUj0xs+GVCpFbm4ubGxsVBiV6qWlpeHbb79FUVERZDIZ9PT0sGjRIgQGBjIdGiNkMhmys7MhFothb28PNpvNdEiMoM9HNYlE8tb3wYsXL2BpaamiiNSbVCqFlhbVU0jj0mY6ANK4Tpw4Uec+FouFwsJCiMVijUnsoqKisG/fPnC5XHh5eSEgIAABAQHw8PDQuC8q8+fPB4vFemO1isViNfuEpub5C4VCSKXSN7ZpzkaOHKmQ6H7xxRf48ssvYW5uDgDg8/mYMWNGs39fhIWFwdLSEosWLQKHw8G+ffvw22+/4bfffmM6NJV7/vw5li9fjoyMDACAhYUFvvrqq2Z/06c29PmoNn/+fMyfPx/Ozs617j979iy2b9+Offv2qTgy1Zs0aRI2btwIIyMjAMCPP/6IadOmyd8nmnaDkDCHErtm7s8//6x1e05ODnbu3IkrV66ga9euKo6KOdu3b8ezZ88QHx+P+/fv49SpU9i1axd4PB58fX3h7++PgIAAjfjCsm3bNqZDaDJYLBbTIahMzSQ2PT0dIpHojW2ao5SUFCxbtgweHh4AgDlz5uCDDz5AWVkZdHV1GY5Otf766y+IRCLMmzcPXC4XR44cwZYtW/Dzzz8zHZrK0eejmpOTE+bPn4/3338fw4cPl/+ezM/Px4YNG5CQkIBJkyYxHKVqFBYWKtwQvHnzJiZMmKDQRlPeF4RZlNhpmOLiYuzfvx9nzpyBt7c3fvzxR/kXF01hZ2cHOzs79O/fHwCQmZmJBw8eID4+HgcOHEBYWBjdVSPkDTQh0S0uLlboQmZkZAQdHR3w+XyNS+wePXqEBQsWwN/fHwDg6emJqVOnoqKiAjo6OgxHp3404fMBAJ9//jk6deqEX3/9FTdu3MDcuXORmJiIrVu3okWLFtiwYYNGdEmtL015XxBmUWKnISoqKnDs2DEcOXIEVlZWWLx4Mdq1a8d0WIwrKipCeno60tLSkJaWhoqKCo1JdKdNm/bGPzQymUwjumKyWCyUlJQodMUtKSmBQCCQ/59opprvi1fbXr03AMDQ0FDVYalcYWEhHBwc5I8tLCzA5XJRVFQEa2trBiMjTOvatSt8fX3x888/Y9asWdDS0sLEiRMRGhpKiQwhDKDErpmTSqWIiorCnj17wGazMWPGDAQHB2vsL9zi4mI8ePAA9+/fR1xcHLKzs+Hh4QE/Pz9Mnz4dPj4+4PF4TIepEq93o5LJZPjqq6+wYMECWFhYMBiV6slkMsyaNUvh8eeff67wWJM+LywWS6PO901ef18Aiu8NTbnxAVS9J2pO+vC28bnNFX0+lCUmJiItLQ2WlpZ48eIF+Hw+JBIJtLU15ysmvS+IuqBZMZu5mTNnIjc3F4MHD8agQYPA5XJrbacJd52BqhkQLSwsEBwcDD8/P3h5eWlMIvc2o0eP1siuM/fv369Xu1fd0Jq70NBQhc9EeXm50mekoqKi2Sc09L6oVvM9AdT+vjhw4IAqw2IEfT6qlZaW4vfff8fly5cxbtw4jBgxAvHx8diwYQP09fUxf/58uLi4MB2mSoSGhsLBwUFe4c/IyICtra18BlWJRIKsrCyNeF8QZlFi18yFhobK/1/b3SRNuusMAEuWLEFSUhKMjIzg7+8v/0fdiTQ3sSOKzp07V692vXr1auRIiLqg90Q1uhbVpkyZAiMjI8ybNw8tWrSQb3894RszZgzGjBnDXJAqsnfv3nq1GzduXCNHQjQdJXbNHN11ViYWi5GUlCSfGTMpKQnGxsbyJM/Pz08jkxtK7AhRlp+fj5iYGGRlZYHFYsHOzg5dunSRT29PiKbauXMnxo0bV2eXyxs3bmDz5s3YsWOHiiMjRHNRYkc0nkgkQmJiIm7fvo2zZ8+irKxMYyqYr9PUxG706NH1aqcJ3cyIojNnzmDbtm0QiUTQ09MDUFWN4HA4mDZtGt577z2GI1StyspK3Lt3D0+fPgWLxYK9vT0CAwPr7OLfHPH5fJw5cwahoaHy98QrQqEQJ06cwIABA+TrmWk6Pp8PY2NjpsNgXEVFBcLDwzFq1CimQyHNnOaMbNVwdNdZ2euVuwcPHiAxMREikUhjEpsffvhB4XFlZSU2bdqkNH350qVLVRmWypWXl8PS0hIhISEa89q/yaefflqvdhs3bmzkSJgVGxuL3377DYMGDcLw4cPlvyvz8/Nx+PBh/P7777C2tkabNm0YjlQ1bt26hQ0bNoDP5ytsNzY2xpw5c9C+fXuGIlOt8PBw5ObmKiV1AKCvr49nz54hPDxcaQ0zTZObm4vo6GhER0fjf//7H9PhqASfz0dycjLYbDZatWoFNpsNsViMU6dO4dChQ5BIJJTYkUZHiZ0GqOuu819//aVxd50TExPlXTATEhJQWVkJS0tL+Pv7o1evXvD391dYu6o5qzlhTlBQEDOBMOybb75BdHQ0jhw5Am9vb/Tu3Rtdu3aVD3rXNE+ePIGlpSW6du2qceu1ve7IkSMYNmwYJk+erLDd3Nwc06dPB5fLxeHDhzUisUtOTsbKlSvRrl07DB8+HI6OjgCqJog4cuQIVq1ahdWrV8PT05PhSBvfzZs3MX369Dr39+nTB1u3btXIxE4sFuP69euIjIxEXFwc7O3tNWKsIQAkJCTgu+++Q2lpKVgsFjw8PPDZZ59h5cqVkEgkGDNmDHr37s10mEQDUFfMZi42Nhbff/99nXedT58+jW+++UYjvpwAVZPJmJqaIiAgAP7+/ggICKAqDQFQtRTG33//jejoaOTl5aFHjx7o27cv3NzcmA5NpU6dOoXo6GhkZWWhW7du6NOnD7y9vZkOS+XGjBmDH3/8UZ7E1JSZmYkFCxZg//79Ko5M9b7//nuYmJhgzpw5te5fv349+Hw+vvnmGxVHpnqjRo3Cli1b6rwBmJubi9mzZ2tU1+2MjAxERkbiwoUL4HK5yM/Px9KlSzVqrdwlS5bA2NgYY8aMQVRUFMLDw2FjY4OxY8dq9BJTRPWoYtfM0V1nRVu2bFFYaJeQV4yMjDBkyBAMGTIEycnJCAsLw/z587F7924YGBgwHZ7KDBw4EAMHDkR6ejqioqKwfPlyGBkZoU+fPggODoapqSnTIaqERCJ549gxLpcLiUSiwoiYk5iYiBUrVtS5f/DgwViyZIkKI2IOm81GXl5enYldfn6+0pp/zVVkZCQiIyORmZmJzp0744svvoCfnx+GDx8OKysrpsNTqbS0NKxatQrOzs6YMGECTpw4gYkTJ6Jbt25Mh0Y0jGb89tFgKSkpb+wK0atXL6SkpKgwImbl5uYqfBkrLS1V2F9ZWYmoqChVh0XURHl5OSIjI/HHH38gOTkZQUFBSmMONUWLFi3w0Ucf4a+//sL777+Pa9eu4cMPP4RQKGQ6NJWwt7fH3bt369x/584d2NvbqzAi5lRUVLzx5oaBgQEqKytVGBFz3NzccO3atTr3x8TEaEyVf/PmzQgMDMTOnTsxd+5cBAQEaExSW1NJSYl8khgejwcdHR24uroyHBXRRJr5CdQgdNdZ0XfffQeBQCB/PHnyZOTk5MgfC4VCbNq0iYnQCIMePnyIdevW4YMPPkBkZCR69+6NsLAwzJs3T2PH2r3y6NEjXL9+HWlpafDy8tKY6/HqPXDz5k2lfTdu3MCOHTs0ZsyMtbU1EhIS6tyfkJCgMRWagQMH4vjx4wgPD1f42ymRSHD8+HH5rJiaYMCAATh9+jS+/PJLhIeHK02so2lKSkogEAhQXFwMoOrGsUAgUPhHSGOjrpjN3Ku7zv379691vybddQaqFmQn5HUff/wxhEIhgoKC8PPPP9c5pkqTvJrR7ty5c5BIJAgJCcHGjRtha2vLdGgqM2jQICQkJGD58uWws7ODo6MjWCwWMjIykJ2djc6dO2PQoEFMh6kSPXr0wPbt2+Ho6AgXFxeFfampqfjrr7/Qr18/hqJTrS5dumDEiBHYtm0bdu3aJf9MZGdno6KiAsOGDUPXrl0ZjlI1ZsyYgQ8//BAxMTGIiorCX3/9hVatWkEmk2nUDeNXZs2aJf+/TCbD559/rvCYxWJp5FJKRLUosWvmXt11NjMzQ4cOHRT2vbrr/P777zMUHSHMe/bsGTgcDs6ePYuzZ8/W2U5TJkNYunQpHj16hHbt2uGTTz5B27ZtNXLgP4vFwhdffIHOnTvj4sWLyMrKAgA4ODhg/Pjx6N69O8MRqs6IESNw7949zJ07F4GBgXBwcJAnuXFxcfDy8sKIESOYDlNlJk6ciE6dOuHChQvIzs6GTCaDn58fevbsqREzg76Ow+GgZ8+e6NmzJ3JychAVFYX09HQsXLgQbdu2Rbdu3TTis/KmMaiEqBLNitnMyWQyrF27FleuXKnzrvOXX36pMV/cQkNDsWPHDpiYmABQXpS7sLAQkydPprtqGuTcuXP1aqcp03a/mjnW0NDwjb8Xmvs6dkSRWCzG8ePHFZJce3t79OzZE6GhoRrTRZdUKy8vx/bt23H9+nVIJBK0atUK06dPh6GhIWJjYxEZGYnbt2/j6NGjTIeqFkpLS2td/5CQhkQVu2aO7jorS09Pl08EIJPJkJGRgZKSEgCQ940nmkNTErb6GjduHNMhqAWpVIpjx47h+vXrEIvFCAwMxNixY984Zrk509bWxogRIzSqMleb+o6TqrlOaHO0Z88enDt3DkFBQeBwOLh06RJ+/fVXLFq0CO3bt0f79u1RVFTEdJiMe/jwISIjIxETE4ODBw8yHQ5p5qhiRzRKaGgoWCzWG8faUT94zVRZWYl79+7h6dOnYLFYsLe3R2BgoMZ+kdd0Bw8exK5duxAQEAAdHR3cuXMHISEhmD17NtOhMeLKlSsKSW5d47abu1d/Q95GE/6GfPTRR/jggw/Qo0cPAFUL2X/xxRc4fPgw2Gw2w9Exq6ioCOfPn0dkZCRyc3MREBCAbt26acyES4Q5VLFr5uius6Jt27YxHQJRQ7du3cKGDRuUZnUzNjbGnDlz0L59e4YiY1Z6erq8ym9nZ6c0cUZzdu7cOUyfPh0DBw4EAMTGxmLFihWYNWuWxnRdfyUiIgJbtmyBnZ0dOBwOrl27hufPn2PSpElMh6ZybxpLdefOHYSHh2tMUpOXlwdfX1/5Y09PT7DZbBQUFNS5zl9zJpPJEBsbi7NnzyI2Nhbu7u7Izs7GTz/9BHd3d6bDIxqCErtm7vDhw/K7zkZGRjh27BiKi4s19q6zrq4u0yEQNZOcnIyVK1eiXbt2GD58uHxWzIyMDBw5cgSrVq3C6tWrNWpShJSUFKxfvx4ZGRny6jaLxYKTkxM+++wzjfiSkpubq5DQt2nTBjKZDAUFBTA3N2cwMtU7efIkRo8ejQkTJgAAoqOjsXXrVo1M7Pz9/ZW2paSkICwsDA8ePED//v0xduxYBiJTPalUCm1txa+RbDZbI2fE3LVrF86dOwcOh4Pg4GBMnToVNjY2GDp0qMbeSCfMoMSumaO7zorGjx9P3WiIgn379iE4OBhz5sxR2O7j4wMfHx+sX78e+/btwzfffMNQhKr19OlTLF68GHZ2dpg3bx6cnJwgk8mQmZmJo0ePYsmSJfjpp5/g4ODAdKiNSiwWKyxOz2KxwOFwIBKJGIyKGTk5OQpdyIKDg7F582YUFhbC1NSUwciYlZOTg507d+Lq1avo3LkztmzZolFLgshkMvz0008KE+dUVlZi06ZNCp+dpUuXMhGeSh08eBCjRo3CuHHjNKZiS9QTJXbNHN11VkTdaEhNiYmJb3xfDB48GEuWLFFhRMzau3cv/P398fXXXyvcBHFzc0PPnj2xfPly7N27FwsXLmQwStUICwtT+IIqEomwd+9ehZntZsyYwURoKlVZWQkejyd/zGazweFwUFFRwWBUzCkuLsa+fftw5swZ+Pj4YO3atfDw8GA6LJULCQlR2hYUFKT6QNTAlClTEBUVhcjISHTv3h0hISFwc3NjOiyigSixa+borrMi6kZDaqqoqJDPklobAwMDVFZWqjAiZsXHx2Pp0qW1VrZZLBZGjRqlEWs2+fr6IicnR2Gbt7c3Xrx4IX+sSb0ezpw5o9CVXSKRIDIyEkZGRvJtQ4cOZSAy1Tpw4ACOHDkCKysrfP3112jbti3TITFm7ty5TIegNoYOHYqhQ4ciMTERkZGR+Oqrr2BhYQGZTIaioiI4OTkxHSLREJTYaQC661w7Te9GQ6pYW1sjISGhzsH+CQkJsLKyUnFUzCkpKYGZmVmd+y0sLCAUClUYETNWrVrFdAhqw9LSEtHR0QrbTExMcPHiRfljFoulEYndrl27wOVyYW5ujtOnT+P06dO1ttOE7oekWlpaGpydneHl5QUvLy9Mnz4dFy9eRFRUFL7++mu4ubmha9euGDlyJNOhkmaOErtmju46K6NuNOR1PXr0wPbt2+Ho6Kg062Nqair++usv9OvXj6HoVM/CwgLp6emwsLCodX9qaqpGduN+m9GjR2PDhg2wsbFhOpQG9+effzIdgtoIDg7WuL+Z5O3mzp2LsLAwmJiYAADWrFmDTz/9FP369cOTJ08QGRmJ48ePU2JHGh0lds0c3XVWRN1oSE0jRozAvXv3MHfuXAQGBsLBwQEsFgsZGRmIi4uDl5eXRi3K3KlTJ/zvf/+Dm5ub0sQYhYWFCAsLQ6dOnRiKjjQFs2fPxrJly5rllPfz5s1jOgSihmqujfvw4UN5F35nZ2d89NFHmDJlChOhEQ1DiR3RKNSNhtTE4XCwfPlyHD9+HBcvXsSDBw8AAPb29pg4cSJCQ0MVZn1r7saOHYvbt29jxowZCAoKks9+mZGRgUuXLsHMzAxjxoxhOEqiznJzczVyynsAuH//PiIjI/H5558zHQpRMzWXhiCkMdC7rJn74Ycfat2up6cHBwcH9OvXT951QBNQNxpSG21tbYwYMUKjKnN10dfXx9q1a7Fjxw5cuXIFJSUl8u1BQUGYOHHiGyebIUTTFBYWIjo6GlFRUXj+/Dl8fHyYDomoGIvFou8WRC1QYtfMGRoa1rpdKBQiKioKR48exZo1a+Ds7KziyJhB3WhITXw+H2fOnEFoaKjChEJA1efkxIkTGDBggMLsf82dgYEBZs6ciU8++QR8Ph8AYGxsTF9cCHlJKpXi1q1biIqKQmxsLKRSKcaOHYv+/ftr9Np+mqrmmn61recHUG8g0vgosWvm3jQdsVQqxaZNmxAWFqYxiy8TUlN4eDhyc3OVkjqgqkr17NkzhIeHY8KECQxEp3qlpaW4fv06OnXqBD09PYWKvlAoxI0bN9ClSxeFdc0I0RTPnj1DZGQkzp8/Dx0dHQQHB+PDDz/EzJkz0bVrV0rqNFTNNf00dT0/wjxK7DSYlpYWBg8ejG+//ZbpUAhhzM2bNzF9+vQ69/fp0wdbt27VmMQuIiIC9+/fr3XxYX19fVy+fBlFRUUYPnw4A9ExTyKRoLKyUmFNN6I5Zs2aheDgYHz55Zfw9fVlOhyiJmhNP6IutJgOgDBLR0cHFRUVTIdBCGNycnLeOEW9tbU1nj9/rsKImHX58mUMHDiwzv2DBg3CpUuXVBgRM+Li4nD58mWFbQcPHsSoUaMwduxYLFu2TD7+EACWLVtGy0BoAAcHB8TGxuLGjRtIS0tjOhxCCFFAiZ2Gu3v3Luzs7JgOgxDGsNls5OXl1bk/Pz8fWlqa86vy2bNncHJyqnO/o6Oj0tqYzdGhQ4eQn58vf5ycnIydO3ciODgYU6ZMQVpaGg4cOCDf7+vr22xnT124cKFCEhsWFgaBQCB/zOfzFaZynzlzZrOdlGvjxo1YsmQJysrK8NVXX+HTTz/FkSNHAGjemrCEEPVDXTGbuZiYmFq3l5aWIiUlBVFRUdSFgGg0Nzc3XLt2Dd7e3rXuj4mJgZubm4qjYo5MJgOfz4eVlVWt+/l8PqRSqYqjUr309HRMmjRJ/vjKlSvw8vLCp59+CqBqIfddu3bhww8/ZCpElUlKSoJYLJY/PnXqFPr16yefnEsqlaKgoEC+v7mPL/L09ISnpyemTZuGy5cvIyoqClKpFBs2bEBQUBA6d+4MMzMzpsMkhGggSuyaudWrV9e6XVdXF/b29vjss8/QvXt3FUdFiPoYOHAg1qxZAwsLCwwcOBBsNhtA1ViqkydP4sSJE1i4cCHDUaqOs7Mz7t69Cw8Pj1r337lz540VveZCKBTC2NhY/jghIQHt2rWTP/bw8FCo6BHNsHfvXgwbNgw8Hg86Ojro3bs3evfujadPnyIqKgr79u3D1q1bcfz4caZDJYRoIErsmrnw8HCmQyBErXXp0gUjRozAtm3bsGvXLtja2gIAsrOzUVFRgWHDhqFr164MR6k6vXv3xh9//AEnJyd06tRJYd+1a9dw8OBBfPTRRwxFpzqmpqbIycmBpaUlRCIRUlNTFSbQKSsra7ZdL0nd9u3bh/fee09pVlgHBwdMmTIFEydOxM2bNxmKjhCi6SixI4RovIkTJ6JTp064cOECsrOzIZPJ4Ofnh549e8LT05Pp8FSqX79+ePDgAVauXAl7e3s4ODgAADIzM5GdnY1u3bqhX79+DEfZ+Nq2bYvt27dj0qRJuHnzJnR0dBQWnk5PT5ffBGjuaPHlajKZ7I372Ww2OnfurKJoCCFEESV2GkAikSA8PBwXL15EVlYWWCwWbG1tERwcjMGDB8u7nhGiyV6NmyHA559/jg4dOsh/ZwBVFYnx48drTNft8ePHY9WqVVi6dCl4PB7mzZunUKGLiopCYGAgcwGq0NsWXxaJREyGp3KU5BJC1BWrrKzszbefSJMmEomwdOlSJCQkICAgAE5OTpDJZMjMzER8fDx8fX3x3XffUZciorFen93vTV5NFEE0i1AoBI/HU7oBJhAIwOPxNOJ35y+//FKvdpowEVdoaCgsLCzeOlPutm3bVBQRIYRUo4pdM3fo0CHk5OTg559/VprZLyUlBStWrMDhw4cxduxYhiIkhFnjx4+v1x14mgxBM+nr69e6XZMSfU1I2P6N3r170wL1hBC1RIldM3fp0iVMnTq11una3d3dMWXKFOzdu5cSO6KxVqxYUee+O3fuIDw8XKO6K48ePfqtbVgsFvbv36+CaAhRPwMGDGi26/QRQpo2SuyauefPn8PLy6vO/d7e3sjNzVVhRISoF39/f6VtKSkpCAsLw4MHD9C/f3+NuvExY8aMOvdlZGTg5MmTkEgkKoyIEPVB4+sIIeqMErtmjsfjQSAQwNLSstb9AoFAPgCeEE2Xk5ODnTt34urVq+jcuTO2bNmiMTMfvtKrVy+lbfn5+di9ezfOnz+PNm3aKCzcTYgmedusmIQQwiRK7Jo5Hx8fnDx5EnPmzKl1/8mTJ+Ht7a3iqAhRL8XFxdi3bx/OnDkDHx8frF27ts4FujVJaWkpDh06hBMnTsDZ2RnLly+Hn58f02ERwpht27YpLFz/ikQiQWVlJY29I4QwimbFbOaSkpKwaNEitG/fHiNGjFBYk+rIkSO4desWVq9ejZYtWzIcKSHMOHDgAI4cOQIrKytMmjQJbdu2ZTokxonFYpw6dQr79++HkZERPvjgA41apJ2QusTFxaG4uFhh2Y+DBw9i7969kEgkCAwMxMKFC2FgYMBglIQQTUWJnQa4ceMGNm7cqDStu4GBAWbPnk2LqRKNFhoaCi6XC39//zdOYb506VIVRsWcCxcuYNeuXRCJRBg7diz69u2rUZPHEPImS5cuRdu2bTF06FAAQHJyMhYsWIA+ffrA0dERR44cQVBQED788ENmAyWEaCTqiqkBOnbsiMDAQNy5cwfPnj0DANjZ2aF169bg8XgMR0cIs4KDg2lChNf8/PPP4HK56N69OzIyMupcj+tNk6wQ0lylp6crjDG9cuUKvLy88OmnnwIALCwssGvXLkrsCCGMoMSumbt9+zZ+/fVXbNiwQakyJxQKMXXqVMyaNQtt2rRhKEJCmDVv3jymQ1Arvr6+YLFYeP78eZ1tKBEmmkooFCqMsUtISEC7du3kjz08PJCfn89EaIQQQoldc3fq1CkMHz681kV29fX1MWLECJw4cYISO0IIAGDVqlVMh0CI2jI1NUVOTg4sLS0hEomQmpqKCRMmyPeXlZWBw+EwGCEhRJNRYtfMpaenY+rUqXXuDwgIwIEDB1QYESHq5ffff69XO+p6SAhp27Yttm/fjkmTJuHmzZvQ0dGBj4+PfH96errGLZFCCFEflNg1c3w+/40TQrBYLKVJVQjRJE+ePHlrG03qenjs2LF6tXs1eQQhmmT8+PFYtWoVli5dCh6Ph3nz5ilU6KKiohAYGMhcgIQQjUaJXTNnYWGBtLQ02NnZ1bo/PT0d5ubmKo6KEPWxcuVKpkNQKydOnHhrGxaLRYkd0UjGxsZYvXo1hEIheDye0oyxixYtoknJCCGMocSumWvXrh12796Ndu3aQUdHR2FfeXm5fB8hpH5Gjx6NDRs2wMbGhulQGsWff/7JdAiEqL3axq0DgKGhoYojIYSQapTYNXOjR4/G1atXMWPGDAwaNEhhgfJTp05BJpNh1KhRDEdJCGmqZs+ejWXLlsHS0pLpUAhpdDQmlxCiziixa+ZMTEywdu1abNmyBTt37oRMVrUePYvFQuvWrfHJJ5/A1NSU4SgJIU1Vbm4uJBIJ02EQohI0JpcQos4osdMAVlZW+Pbbb1FSUqKwQLmBgQHDkRFCCCFNB43JJYSoM0rsNIiBgQE8PT2ZDoMQQghpsioqKnDkyBHExMQgJycHLBYL1tbW6NatG4YOHao0np0QQlSFEjtCCCGEkHqQSCRYsmQJHj9+jLZt26Jdu3aQyWTIzMzEvn37EBsbi1WrVinNlkkIIapAiR0hhNRCIpGgsrISurq6TIdCCFETERERyM7Oxi+//AJnZ2eFfU+ePMHixYsRERGBgQMHMhQhIUST1b1yNSGEaIC4uDhcvnxZYdvBgwcxatQojB07FsuWLUNJSYl837Jly2jtR0I0VExMDEaPHq2U1AGAs7MzRo0ahZiYGAYiI4QQSuwIIRru0KFDyM/Plz9OTk7Gzp07ERwcjClTpiAtLQ0HDhyQ7/f19QWHw2EiVJVYuHChQiIbFhYGgUAgf8zn8zFlyhT545kzZ8LExESVIRLCmIyMDAQEBNS5v1WrVvWaOZMQQhoDJXaEEI2Wnp4OPz8/+eMrV67Ay8sLn376KYYOHYrp06fj5s2bDEaoWklJSRCLxfLHp06dglAolD+WSqUoKCiQPw4KCgKPx1NpjIQwpaSkBMbGxnXuNzY2Vvi8EEKIKlFiRwjRaEKhUOGLWkJCAtq2bSt/7OHhoVDRI4RoLqlU+saJUVgsFqRSqQojIoSQajR5CiFEo5mamiInJweWlpYQiURITU3FhAkT5PvLysqadddLQkj9yWQy/PTTT3X+ThCJRCqOiBBCqlFiRwjRaG3btsX27dsxadIk3Lx5Ezo6OvDx8ZHvT09Ph62tLYMRqhaLxQKLxWI6DELUUkhIyFvbBAcHqyASQghRRokdIUSjjR8/HqtWrcLSpUvB4/Ewb948hbvxUVFRCAwMZC5AFatZkaisrMSmTZvkiy5TRYJosrlz5zIdAiGE1IlVVlYmYzoIQghhmlAoBI/HUxo/IxAIwOPxNKY75i+//FKvdvQFlxBCCFEvlNgRQgghhBBCSBNHs2ISQgghhBBCSBNHiR0hhBBCCCGENHGU2BFCCCGEEEJIE0eJHSGEEEIIIYQ0cZTYEUIIIYQQQkgT9/+zjIGXudwDDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,ax=plt.subplots(figsize=(15,10))\n",
    "colormap=sns.diverging_palette(220,10,as_cmap=True)\n",
    "sns.heatmap(df.corr(),annot=True,cmap=colormap)\n",
    "plt.savefig(\"correlation.jpeg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown in the above heat map, the air fare is highly positively with the distance and coupon. We then can plot its relationship by using the scatter plots as in the below graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S_CODE</th>\n",
       "      <th>S_CITY</th>\n",
       "      <th>E_CODE</th>\n",
       "      <th>E_CITY</th>\n",
       "      <th>COUPON</th>\n",
       "      <th>NEW</th>\n",
       "      <th>VACATION</th>\n",
       "      <th>SW</th>\n",
       "      <th>HI</th>\n",
       "      <th>S_INCOME</th>\n",
       "      <th>E_INCOME</th>\n",
       "      <th>S_POP</th>\n",
       "      <th>E_POP</th>\n",
       "      <th>SLOT</th>\n",
       "      <th>GATE</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>PAX</th>\n",
       "      <th>FARE</th>\n",
       "      <th>log_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>*</td>\n",
       "      <td>Dallas/Fort Worth   TX</td>\n",
       "      <td>*</td>\n",
       "      <td>Amarillo            TX</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5291.99</td>\n",
       "      <td>28637.0</td>\n",
       "      <td>21112.0</td>\n",
       "      <td>3036732</td>\n",
       "      <td>205711</td>\n",
       "      <td>Free</td>\n",
       "      <td>Free</td>\n",
       "      <td>312</td>\n",
       "      <td>7864</td>\n",
       "      <td>64.11</td>\n",
       "      <td>4.160600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>*</td>\n",
       "      <td>Atlanta             GA</td>\n",
       "      <td>*</td>\n",
       "      <td>Baltimore/Wash Intl MD</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>5419.16</td>\n",
       "      <td>26993.0</td>\n",
       "      <td>29838.0</td>\n",
       "      <td>3532657</td>\n",
       "      <td>7145897</td>\n",
       "      <td>Free</td>\n",
       "      <td>Free</td>\n",
       "      <td>576</td>\n",
       "      <td>8820</td>\n",
       "      <td>174.47</td>\n",
       "      <td>5.161753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>*</td>\n",
       "      <td>Boston              MA</td>\n",
       "      <td>*</td>\n",
       "      <td>Baltimore/Wash Intl MD</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>9185.28</td>\n",
       "      <td>30124.0</td>\n",
       "      <td>29838.0</td>\n",
       "      <td>5787293</td>\n",
       "      <td>7145897</td>\n",
       "      <td>Free</td>\n",
       "      <td>Free</td>\n",
       "      <td>364</td>\n",
       "      <td>6452</td>\n",
       "      <td>207.76</td>\n",
       "      <td>5.336384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ORD</td>\n",
       "      <td>Chicago             IL</td>\n",
       "      <td>*</td>\n",
       "      <td>Baltimore/Wash Intl MD</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2657.35</td>\n",
       "      <td>29260.0</td>\n",
       "      <td>29838.0</td>\n",
       "      <td>7830332</td>\n",
       "      <td>7145897</td>\n",
       "      <td>Controlled</td>\n",
       "      <td>Free</td>\n",
       "      <td>612</td>\n",
       "      <td>25144</td>\n",
       "      <td>85.47</td>\n",
       "      <td>4.448165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MDW</td>\n",
       "      <td>Chicago             IL</td>\n",
       "      <td>*</td>\n",
       "      <td>Baltimore/Wash Intl MD</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2657.35</td>\n",
       "      <td>29260.0</td>\n",
       "      <td>29838.0</td>\n",
       "      <td>7830332</td>\n",
       "      <td>7145897</td>\n",
       "      <td>Free</td>\n",
       "      <td>Free</td>\n",
       "      <td>612</td>\n",
       "      <td>25144</td>\n",
       "      <td>85.47</td>\n",
       "      <td>4.448165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  S_CODE                  S_CITY E_CODE                  E_CITY  COUPON  NEW  \\\n",
       "0      *  Dallas/Fort Worth   TX      *  Amarillo            TX    1.00    3   \n",
       "1      *  Atlanta             GA      *  Baltimore/Wash Intl MD    1.06    3   \n",
       "2      *  Boston              MA      *  Baltimore/Wash Intl MD    1.06    3   \n",
       "3    ORD  Chicago             IL      *  Baltimore/Wash Intl MD    1.06    3   \n",
       "4    MDW  Chicago             IL      *  Baltimore/Wash Intl MD    1.06    3   \n",
       "\n",
       "  VACATION   SW       HI  S_INCOME  E_INCOME    S_POP    E_POP        SLOT  \\\n",
       "0       No  Yes  5291.99   28637.0   21112.0  3036732   205711        Free   \n",
       "1       No   No  5419.16   26993.0   29838.0  3532657  7145897        Free   \n",
       "2       No   No  9185.28   30124.0   29838.0  5787293  7145897        Free   \n",
       "3       No  Yes  2657.35   29260.0   29838.0  7830332  7145897  Controlled   \n",
       "4       No  Yes  2657.35   29260.0   29838.0  7830332  7145897        Free   \n",
       "\n",
       "   GATE  DISTANCE    PAX    FARE  log_price  \n",
       "0  Free       312   7864   64.11   4.160600  \n",
       "1  Free       576   8820  174.47   5.161753  \n",
       "2  Free       364   6452  207.76   5.336384  \n",
       "3  Free       612  25144   85.47   4.448165  \n",
       "4  Free       612  25144   85.47   4.448165  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# notice the log here!\n",
    "y = np.log(df[\"FARE\"]) #ln(price)\n",
    "df[\"log_price\"] = y\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"DISTANCE\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    df.plot.scatter(x=var , y=\"FARE\",alpha=0.9, s=3, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "plt.savefig(\"scatter1.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"DISTANCE\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    df.plot.scatter(x=var , y=\"log_price\",alpha=0.9, s=3, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "plt.savefig(\"scatter1.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"COUPON\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    df.plot.scatter(x=var , y=\"log_price\",alpha=0.9, s=3, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "plt.savefig(\"scatter1.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S_CODE</th>\n",
       "      <th>S_CITY</th>\n",
       "      <th>E_CODE</th>\n",
       "      <th>E_CITY</th>\n",
       "      <th>COUPON</th>\n",
       "      <th>NEW</th>\n",
       "      <th>VACATION</th>\n",
       "      <th>SW</th>\n",
       "      <th>HI</th>\n",
       "      <th>S_INCOME</th>\n",
       "      <th>E_INCOME</th>\n",
       "      <th>S_POP</th>\n",
       "      <th>E_POP</th>\n",
       "      <th>SLOT</th>\n",
       "      <th>GATE</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>PAX</th>\n",
       "      <th>FARE</th>\n",
       "      <th>log_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>*</td>\n",
       "      <td>Dallas/Fort Worth   TX</td>\n",
       "      <td>*</td>\n",
       "      <td>Amarillo            TX</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5291.99</td>\n",
       "      <td>28637.0</td>\n",
       "      <td>21112.0</td>\n",
       "      <td>3036732</td>\n",
       "      <td>205711</td>\n",
       "      <td>Free</td>\n",
       "      <td>Free</td>\n",
       "      <td>312</td>\n",
       "      <td>7864</td>\n",
       "      <td>64.11</td>\n",
       "      <td>4.160600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>*</td>\n",
       "      <td>Atlanta             GA</td>\n",
       "      <td>*</td>\n",
       "      <td>Baltimore/Wash Intl MD</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>5419.16</td>\n",
       "      <td>26993.0</td>\n",
       "      <td>29838.0</td>\n",
       "      <td>3532657</td>\n",
       "      <td>7145897</td>\n",
       "      <td>Free</td>\n",
       "      <td>Free</td>\n",
       "      <td>576</td>\n",
       "      <td>8820</td>\n",
       "      <td>174.47</td>\n",
       "      <td>5.161753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>*</td>\n",
       "      <td>Boston              MA</td>\n",
       "      <td>*</td>\n",
       "      <td>Baltimore/Wash Intl MD</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>9185.28</td>\n",
       "      <td>30124.0</td>\n",
       "      <td>29838.0</td>\n",
       "      <td>5787293</td>\n",
       "      <td>7145897</td>\n",
       "      <td>Free</td>\n",
       "      <td>Free</td>\n",
       "      <td>364</td>\n",
       "      <td>6452</td>\n",
       "      <td>207.76</td>\n",
       "      <td>5.336384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ORD</td>\n",
       "      <td>Chicago             IL</td>\n",
       "      <td>*</td>\n",
       "      <td>Baltimore/Wash Intl MD</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2657.35</td>\n",
       "      <td>29260.0</td>\n",
       "      <td>29838.0</td>\n",
       "      <td>7830332</td>\n",
       "      <td>7145897</td>\n",
       "      <td>Controlled</td>\n",
       "      <td>Free</td>\n",
       "      <td>612</td>\n",
       "      <td>25144</td>\n",
       "      <td>85.47</td>\n",
       "      <td>4.448165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MDW</td>\n",
       "      <td>Chicago             IL</td>\n",
       "      <td>*</td>\n",
       "      <td>Baltimore/Wash Intl MD</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2657.35</td>\n",
       "      <td>29260.0</td>\n",
       "      <td>29838.0</td>\n",
       "      <td>7830332</td>\n",
       "      <td>7145897</td>\n",
       "      <td>Free</td>\n",
       "      <td>Free</td>\n",
       "      <td>612</td>\n",
       "      <td>25144</td>\n",
       "      <td>85.47</td>\n",
       "      <td>4.448165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>LGA</td>\n",
       "      <td>New York/Newark     NY</td>\n",
       "      <td>*</td>\n",
       "      <td>West Palm Beach     FL</td>\n",
       "      <td>1.08</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>2216.70</td>\n",
       "      <td>32991.0</td>\n",
       "      <td>37375.0</td>\n",
       "      <td>8621121</td>\n",
       "      <td>991717</td>\n",
       "      <td>Controlled</td>\n",
       "      <td>Free</td>\n",
       "      <td>1030</td>\n",
       "      <td>34324</td>\n",
       "      <td>129.63</td>\n",
       "      <td>4.864684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>EWR</td>\n",
       "      <td>New York/Newark     NY</td>\n",
       "      <td>*</td>\n",
       "      <td>West Palm Beach     FL</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>2216.70</td>\n",
       "      <td>32991.0</td>\n",
       "      <td>37375.0</td>\n",
       "      <td>8621121</td>\n",
       "      <td>991717</td>\n",
       "      <td>Free</td>\n",
       "      <td>Constrained</td>\n",
       "      <td>1030</td>\n",
       "      <td>34324</td>\n",
       "      <td>129.63</td>\n",
       "      <td>4.864684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>*</td>\n",
       "      <td>Philadelphia/Camden PA</td>\n",
       "      <td>*</td>\n",
       "      <td>West Palm Beach     FL</td>\n",
       "      <td>1.17</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>6797.80</td>\n",
       "      <td>27994.0</td>\n",
       "      <td>37375.0</td>\n",
       "      <td>4948339</td>\n",
       "      <td>991717</td>\n",
       "      <td>Free</td>\n",
       "      <td>Free</td>\n",
       "      <td>960</td>\n",
       "      <td>6016</td>\n",
       "      <td>124.87</td>\n",
       "      <td>4.827273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>IAD</td>\n",
       "      <td>Washington          DC</td>\n",
       "      <td>*</td>\n",
       "      <td>West Palm Beach     FL</td>\n",
       "      <td>1.28</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>5566.43</td>\n",
       "      <td>31981.0</td>\n",
       "      <td>37375.0</td>\n",
       "      <td>4549784</td>\n",
       "      <td>991717</td>\n",
       "      <td>Free</td>\n",
       "      <td>Free</td>\n",
       "      <td>858</td>\n",
       "      <td>4877</td>\n",
       "      <td>129.62</td>\n",
       "      <td>4.864607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>DCA</td>\n",
       "      <td>Washington          DC</td>\n",
       "      <td>*</td>\n",
       "      <td>West Palm Beach     FL</td>\n",
       "      <td>1.28</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>5566.43</td>\n",
       "      <td>31981.0</td>\n",
       "      <td>37375.0</td>\n",
       "      <td>4549784</td>\n",
       "      <td>991717</td>\n",
       "      <td>Controlled</td>\n",
       "      <td>Free</td>\n",
       "      <td>858</td>\n",
       "      <td>4877</td>\n",
       "      <td>129.62</td>\n",
       "      <td>4.864607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>638 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    S_CODE                  S_CITY E_CODE                  E_CITY  COUPON  \\\n",
       "0        *  Dallas/Fort Worth   TX      *  Amarillo            TX    1.00   \n",
       "1        *  Atlanta             GA      *  Baltimore/Wash Intl MD    1.06   \n",
       "2        *  Boston              MA      *  Baltimore/Wash Intl MD    1.06   \n",
       "3      ORD  Chicago             IL      *  Baltimore/Wash Intl MD    1.06   \n",
       "4      MDW  Chicago             IL      *  Baltimore/Wash Intl MD    1.06   \n",
       "..     ...                     ...    ...                     ...     ...   \n",
       "633    LGA  New York/Newark     NY      *  West Palm Beach     FL    1.08   \n",
       "634    EWR  New York/Newark     NY      *  West Palm Beach     FL    1.08   \n",
       "635      *  Philadelphia/Camden PA      *  West Palm Beach     FL    1.17   \n",
       "636    IAD  Washington          DC      *  West Palm Beach     FL    1.28   \n",
       "637    DCA  Washington          DC      *  West Palm Beach     FL    1.28   \n",
       "\n",
       "     NEW VACATION   SW       HI  S_INCOME  E_INCOME    S_POP    E_POP  \\\n",
       "0      3       No  Yes  5291.99   28637.0   21112.0  3036732   205711   \n",
       "1      3       No   No  5419.16   26993.0   29838.0  3532657  7145897   \n",
       "2      3       No   No  9185.28   30124.0   29838.0  5787293  7145897   \n",
       "3      3       No  Yes  2657.35   29260.0   29838.0  7830332  7145897   \n",
       "4      3       No  Yes  2657.35   29260.0   29838.0  7830332  7145897   \n",
       "..   ...      ...  ...      ...       ...       ...      ...      ...   \n",
       "633    3      Yes   No  2216.70   32991.0   37375.0  8621121   991717   \n",
       "634    0      Yes   No  2216.70   32991.0   37375.0  8621121   991717   \n",
       "635    3      Yes   No  6797.80   27994.0   37375.0  4948339   991717   \n",
       "636    3      Yes   No  5566.43   31981.0   37375.0  4549784   991717   \n",
       "637    3      Yes   No  5566.43   31981.0   37375.0  4549784   991717   \n",
       "\n",
       "           SLOT         GATE  DISTANCE    PAX    FARE  log_price  \n",
       "0          Free         Free       312   7864   64.11   4.160600  \n",
       "1          Free         Free       576   8820  174.47   5.161753  \n",
       "2          Free         Free       364   6452  207.76   5.336384  \n",
       "3    Controlled         Free       612  25144   85.47   4.448165  \n",
       "4          Free         Free       612  25144   85.47   4.448165  \n",
       "..          ...          ...       ...    ...     ...        ...  \n",
       "633  Controlled         Free      1030  34324  129.63   4.864684  \n",
       "634        Free  Constrained      1030  34324  129.63   4.864684  \n",
       "635        Free         Free       960   6016  124.87   4.827273  \n",
       "636        Free         Free       858   4877  129.62   4.864607  \n",
       "637  Controlled         Free       858   4877  129.62   4.864607  \n",
       "\n",
       "[638 rows x 19 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are VACATION, SW,SLOT, and GATE  that are counted as the categorical data. Therefore, if we need to use these information in our regression model, we need to convert it to be the dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.get_dummies(df,columns=['VACATION', 'SW','SLOT','GATE'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['S_CODE',\n",
       " 'S_CITY',\n",
       " 'E_CODE',\n",
       " 'E_CITY',\n",
       " 'COUPON',\n",
       " 'NEW',\n",
       " 'HI',\n",
       " 'S_INCOME',\n",
       " 'E_INCOME',\n",
       " 'S_POP',\n",
       " 'E_POP',\n",
       " 'DISTANCE',\n",
       " 'PAX',\n",
       " 'FARE',\n",
       " 'log_price',\n",
       " 'VACATION_Yes',\n",
       " 'SW_Yes',\n",
       " 'SLOT_Free',\n",
       " 'GATE_Free']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model 1: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['COUPON',\n",
    " 'DISTANCE',\n",
    " 'VACATION_Yes']]\n",
    "y=df[['log_price']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=5,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 5-fold CV on the train data: 0.35066051937292386\n",
      "RMSE on 5-fold CV on the test data: 0.3521068477995167\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += np.sqrt(np.mean(e_train*e_train))     # compute rmse for the estimation rmse test data\n",
    "    err_test += np.sqrt(np.mean(e_test*e_test))  \n",
    "rmse_train_5cv_model1 = err_train/5              #average the rmse\n",
    "rmse_test_5cv_model1 = err_test/5              #average the rmse\n",
    "print('RMSE on 5-fold CV on the train data: {}'.format(rmse_train_5cv_model1))\n",
    "print('RMSE on 5-fold CV on the test data: {}'.format(rmse_test_5cv_model1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alternative way to calculate the RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Model1:Linear) on 5-fold CV on the train data: 0.3507727349525686\n",
      "RMSE (Model1:Linear) on 5-fold CV on the test data: 0.35118351783147095\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += np.sqrt(metrics.mean_squared_error(y[train], reg.predict(X[train])))      \n",
    "    err_test += np.sqrt(metrics.mean_squared_error(y[test], reg.predict(X[test])))\n",
    "    \n",
    "rmse_train_5cv_model1 = err_train/5              #average the rmse\n",
    "rmse_test_5cv_model1 = err_test/5              #average the rmse\n",
    "print('RMSE (Model1:Linear) on 5-fold CV on the train data: {}'.format(rmse_train_5cv_model1))\n",
    "print('RMSE (Model1:Linear) on 5-fold CV on the test data: {}'.format(rmse_test_5cv_model1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model1:Linear) on 5-fold CV on the train data: 0.2899921832415116\n",
      "MAE (Model1:Linear) on 5-fold CV on the test data: 0.2918824010247195\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += metrics.mean_absolute_error(y[train], reg.predict(X[train]))      \n",
    "    err_test += metrics.mean_absolute_error(y[test], reg.predict(X[test]))\n",
    "mae_train_5cv_model1 = err_train/5              #average the rmse\n",
    "mae_test_5cv_model1 = err_test/5              #average the rmse\n",
    "print('MAE (Model1:Linear) on 5-fold CV on the train data: {}'.format(mae_train_5cv_model1))\n",
    "print('MAE (Model1:Linear) on 5-fold CV on the test data: {}'.format(mae_test_5cv_model1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Model1:Linear) on 5-fold CV on the train data: 0.3507727349525686\n",
      "RMSE (Model1:Linear) on 5-fold CV on the test data: 0.35118351783147095\n"
     ]
    }
   ],
   "source": [
    "# Summary of model1:\n",
    "print('RMSE (Model1:Linear) on 5-fold CV on the train data: {}'.format(rmse_train_5cv_model1))\n",
    "print('RMSE (Model1:Linear) on 5-fold CV on the test data: {}'.format(rmse_test_5cv_model1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model1:Linear) on 5-fold CV on the train data: 0.2899921832415116\n",
      "MAE (Model1:Linear) on 5-fold CV on the test data: 0.2918824010247195\n"
     ]
    }
   ],
   "source": [
    "print('MAE (Model1:Linear) on 5-fold CV on the train data: {}'.format(mae_train_5cv_model1))\n",
    "print('MAE (Model1:Linear) on 5-fold CV on the test data: {}'.format(mae_test_5cv_model1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now, we conduct the model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['COUPON',\n",
    " 'DISTANCE',\n",
    " 'VACATION_Yes','GATE_Free']]\n",
    "y=df[['log_price']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Model2:Linear) on 5-fold CV on the train data: 0.3276909739846823\n",
      "RMSE (Model2:Linear) on 5-fold CV on the test data: 0.3301257349907664\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += np.sqrt(metrics.mean_squared_error(y[train], reg.predict(X[train])))      \n",
    "    err_test += np.sqrt(metrics.mean_squared_error(y[test], reg.predict(X[test])))\n",
    "    \n",
    "rmse_train_5cv_model2 = err_train/5              #average the rmse\n",
    "rmse_test_5cv_model2 = err_test/5              #average the rmse\n",
    "print('RMSE (Model2:Linear) on 5-fold CV on the train data: {}'.format(rmse_train_5cv_model2))\n",
    "print('RMSE (Model2:Linear) on 5-fold CV on the test data: {}'.format(rmse_test_5cv_model2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model2:Linear) on 5-fold CV on the train data: 0.2743965939830425\n",
      "MAE (Model2:Linear) on 5-fold CV on the test data: 0.27710590652660716\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += metrics.mean_absolute_error(y[train], reg.predict(X[train]))      \n",
    "    err_test += metrics.mean_absolute_error(y[test], reg.predict(X[test]))\n",
    "mae_train_5cv_model2 = err_train/5              #average the rmse\n",
    "mae_test_5cv_model2 = err_test/5              #average the rmse\n",
    "print('MAE (Model2:Linear) on 5-fold CV on the train data: {}'.format(mae_train_5cv_model2))\n",
    "print('MAE (Model2:Linear) on 5-fold CV on the test data: {}'.format(mae_test_5cv_model2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model1:Linear) on 5-fold CV on the train data: 0.2899921832415116\n",
      "MAE (Model1:Linear) on 5-fold CV on the test data: 0.2918824010247195\n"
     ]
    }
   ],
   "source": [
    "print('MAE (Model1:Linear) on 5-fold CV on the train data: {}'.format(mae_train_5cv_model1))\n",
    "print('MAE (Model1:Linear) on 5-fold CV on the test data: {}'.format(mae_test_5cv_model1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model2:Linear) on 5-fold CV on the train data: 0.2743965939830425\n",
      "MAE (Model2:Linear) on 5-fold CV on the test data: 0.27710590652660716\n"
     ]
    }
   ],
   "source": [
    "print('MAE (Model2:Linear) on 5-fold CV on the train data: {}'.format(mae_train_5cv_model2))\n",
    "print('MAE (Model2:Linear) on 5-fold CV on the test data: {}'.format(mae_test_5cv_model2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Last, we conduct the model with all information\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['S_CODE',\n",
       " 'S_CITY',\n",
       " 'E_CODE',\n",
       " 'E_CITY',\n",
       " 'COUPON',\n",
       " 'NEW',\n",
       " 'HI',\n",
       " 'S_INCOME',\n",
       " 'E_INCOME',\n",
       " 'S_POP',\n",
       " 'E_POP',\n",
       " 'DISTANCE',\n",
       " 'PAX',\n",
       " 'FARE',\n",
       " 'log_price',\n",
       " 'VACATION_Yes',\n",
       " 'SW_Yes',\n",
       " 'SLOT_Free',\n",
       " 'GATE_Free']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['COUPON',\n",
    " 'NEW',\n",
    " 'HI',\n",
    " 'S_INCOME',\n",
    " 'E_INCOME',\n",
    " 'S_POP',\n",
    " 'E_POP',\n",
    " 'DISTANCE',\n",
    " 'PAX',\n",
    " 'FARE',\n",
    " 'VACATION_Yes',\n",
    " 'SW_Yes',\n",
    " 'SLOT_Free',\n",
    " 'GATE_Free']]\n",
    "y=df[['log_price']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Model3:Linear) on 5-fold CV on the train data: 0.10912336441067641\n",
      "RMSE (Model3:Linear) on 5-fold CV on the test data: 0.11455464358336528\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += np.sqrt(metrics.mean_squared_error(y[train], reg.predict(X[train])))      \n",
    "    err_test += np.sqrt(metrics.mean_squared_error(y[test], reg.predict(X[test])))\n",
    "    \n",
    "rmse_train_5cv_model3 = err_train/5              #average the rmse\n",
    "rmse_test_5cv_model3 = err_test/5              #average the rmse\n",
    "print('RMSE (Model3:Linear) on 5-fold CV on the train data: {}'.format(rmse_train_5cv_model3))\n",
    "print('RMSE (Model3:Linear) on 5-fold CV on the test data: {}'.format(rmse_test_5cv_model3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Based on the RMSE, the model with all information performs the best."
   ]
  }
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