{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from mpl_toolkits import mplot3d\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "import scipy.cluster.hierarchy as shc\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.metrics import silhouette_samples\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "%matplotlib inline\n",
    "pd.set_option(\"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 23 - Silhouette score revisited and Principal Components Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the iris dataset, as in previous labs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dict = datasets.load_iris()\n",
    "\n",
    "iris = pd.DataFrame(iris_dict.data, columns = iris_dict.feature_names)\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale the columns of the iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "iris_scaled = scaler.fit_transform(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is code to compute the silhouette coefficient for each data point, with the number of clusters ranging from 2 to 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2 The average silhouette_score is : 0.630047128435471\n",
      "For n_clusters = 3 The average silhouette_score is : 0.5047687565398588\n",
      "For n_clusters = 4 The average silhouette_score is : 0.4450654880459854\n",
      "For n_clusters = 5 The average silhouette_score is : 0.3506774898464648\n",
      "For n_clusters = 6 The average silhouette_score is : 0.3524618716009234\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p/VzKDeHHqmm9gnJ+gZK82riNizbcqOeBTsfdwd/fTrUfP11Q2rK4x0CZF0bEYZTfiPtT9uFHAC+NiLWZeU0H8+p0n7rbeWJ7z1NVvB8YJ6azOkwiNIHfR8TbKOvgYEobVB8ab1xJU8MSCFKHqkaNDoVSljIzL6UUWQW4bTuTB+O5uHq/d0QcXsVxDKVINZS/94OhZMCKKA1+NRh2Q5uZGxh6ovZLSjH8IzPzSEpLx+/IzBuq7++s3ucNi+fOts/t313C0MXRue1PHSgt879rjGVsPRH5cGbeF3gi5eJnexzB0MXf/TLzwXRYOqR6CvXBqvNNlLqkfwTa/6rw4cAXMvNlmfkIShUTgEeNMemHAd/OzL/JzEcBJ1f9778dJT5a+0MvcCxARNyX8vQfhvaHu1TL1bHMfA3laReU6jJfjYi5Y4zSKuHwe0pyZSStuLuBU9r2jaMo6/AT1bpolTz5h8w8FHge23nTGhH7l8XJt2TmMyiNcN1KuXD/szHibM3vlFYJjCgN1q0ZY3at42+/avglw+cREQspN63vy8wXZuZhDO1brf1nIo+vibrZv5KhhMWxUcwHntzBuK12L6BqhLZ6ktga96fbWCrmHqI05ndZZr4mMx/fNu09Kdt8u88J2+GJETG3SjC3SjBtobQrMJLWcZHAC9u26SMp2/Tzo4zXfmP4bLirhNnRw4ZrJd8AHp+ZhwP/PML0RtvvDqTc8AOckJkPZOifiDrRKoXQ+mei7wD/R7n2fGnV76JxpnFXbBEx/PeoTq2/mlwTEc9p9WxdI4yiPWnfKpXyjOEDRfnbwg2Z+feZ+WRKUgxK4qvVeGVrvbT2t5Zt3afudp7o8Dw1koWUY22s1+rRRo6I1RHx3GhrnJS7n2N2pm0vzXgmEKTOnQT8JEor1pdGxO8pmXmAcydpnu+n3MQG8H8R8UuGbtKupDRUBfCN6r2X8oT3p4x8k/T66v0pwB+r4onXUxILr2wbrpUYeWa1rK35tBooA/h4RPwgIp5VFe/+96r/6VWrzz+LiJsoNz+PG2MZW8XvT4qIy6p5zB5j+LH8vO3zLyLiV8Brt2H8j1IuzFoXK58YdoNzDnBzVX3hJ5T2L4bPd7jTgY1RWtS+FPjPqv+1lCL62+JcSvIH4NPV+voR5ab4Rjp/ij2mzDyVUpcWykXj10YrQpqZFwHLKTdno90MnkvZJwP4XpQW8X9FqR99AeXp5E2UdQLw5igts/+YsZ+Ij+XPgCurqjE/ppS6WVh9N+L2ysx1DFVleTqlCPDPKcfg0WPMq3X8PSQiflhNf/iTxRWUutobo/zX+a8ppVLa45mM42uHZOadlDrZUJJWV1HW5aoOxv0dQ/v7yyPiymrc/SlVX8ZsLb9DfwPcEKWl+EspT8yh1Lf/HTt+TtgWe1HOpVdRqrtBuRG7c5Th30dpzHABcHm1X/yWUkXtswzdvN9NtV5bN4KvjIgrKKWnht9kXUZp3wLKMfwLhrZluw2U0hlQ9q8fRsQrquVo/XXeR6pjYaR/ZBnNRdX7oiqOHzBUnH5+9f4txvbrts+XVcfEvbYhhsnyfoaqs3wmIn4bEa3GX0fzW0rDhADvjohvMZS0bvcc4JooVXIuZahaWKuKAQytl+XAr6v1Moft3KfadHKeuofMvCgzY5zXaWPMdxnwaeCWiPhFRPyBoUT2rZQ2RSTtJEwgSJ07ldL+wW2UJ1srKRcEb6e0Hj/hqnqtR1JuXDdRnghtoLRs/rCqLjKZ+fUqvuspxQx/zQgX55n5GUrx+W9Skg0HUZ6QfZa7P8U8lXKx10eponG/avyNlAv2ayilII6grAeAl1ff/YxyEbIv5cbrg4z+JA1Kw2/fquKYS3nCNdYN+agy83+Av6OshzmU9fCX2zD+Jqq62pWPDRvkPykXc0spxSo3UBqre94Yk/0M5SZ/AWU93kbZj56wHaUDtlBujFuJpQMoF/ifAY7MzGvHGH2bZOZbKXXJoZS8+PpIxW2rYW/MUrd9tGltpdyAv5tyg7U/5cL3MuBtwC+rdXEs5QnaICUp8gLGbx9gND+hXHRupeznC6p+J1b7yWj+hvLE9CeU/fFelBbBfzvGOO+gVMXZREmGfJJyMdxuIyVB9cdqmH0oN7j/TFWSZZKOr4lwKuXG5JYqrs8ylBgYsXpLm7+g3LBfTrnBnkU53h+Xmf89AbF9lVKHfhbl+OoH/pdyfG3a0XPCNnoPJVm2iHLT80HGSJJkqbN/JOV8vp6yny6kHANvZOxqKCdR9rE7KfXdP0jVHkLb9H9NqY51NeV8fyNDf0fcPlxSSgRcSVlHDwb2ycybKSUcLqdcL/YxVPWgE+3JgZ9l5u2Z+SeGjqUE/t840/gKpc2KjZRj5gjKcVmr6lg9EvgPyvG6hrLtLhhjnAFKdY6fUM5vS6hKkg3z7Wo6Qan21k25qT+22qZQjr/PU47JAyjrpWsH9yno4Dw1Sf5EOa+sp5TOWElJtpwDHJGZvx9jXElTLLbx+lWSdmlR/j/+DODiqrizNKNFxO7Alsy8peqeQ7khORj4QWY+pM746haloTqAN4/zlFWSpGnPRhQlCYiIZ1Kezj2h6vXOGsORdiYPobRTcTGlBM3hlCeEA5T2QqRdSkQ8idH37a9WJbQkaUYygSBJxSGUhs9uBP45Mz9XczzSzuJqSoOIh1CK599EaYvl9Mz8fp2BSZNkOUP/NjHcr0fpL0kzglUYJEmSJEnSuGxEUZIkSZIkjcsEgiRJkiRJGpcJBEmSJEmSNC4TCJIkSZIkaVwmECRJkiRJ0rhMIEiSJEmSpHGZQJAkSZIkSeMygSBJkiRJksZlAkGSJEmSJI3LBIIkSZIkSRqXCQRJkiRJkjQuEwiSJEmSJGlcJhAkSZIkSdK4TCBIkiRJkqRxmUCQJEmSJEnjMoEgSZIkSZLGZQJBkiRJkiSNywSCJEmSJEkalwkESZIkSZI0LhMIkiRJkiRpXCYQZrCIOC0iPjHJ83h4RFzR1r0uIh4zVfPfGUTE0RFx7QRO72ER8duIuD0inj5R0x02j7u2084kIl4QEV+vOw6AiDg+Ir5TdxySJEnSVDGBsAurbjBbr2ZEbG7rfsFUxJCZ/5eZB07FvDoVEWdHxNuG9dspbphHim0EbwHel5nzM/OLUzTPnUJmfjIzH1d3HBMpIjIi7l13HJIkSdJ4TCDswqobzPmZOR/4A/CUtn6frDs+bbd9gMu2Z8SI6J7gWKbMdI59srhOJEmSNJVMIKg3Ij4eEbdFxGUR8aDWFxGxZ0R8PiI2RMTVEfE3o00kIp4YEZdX07kuIl5T9R+v+P5Y8z8oIi6KiE3Vd09t++6iiDiprftuxckj4j4R8T8RcVNEXBERz6n6nwy8AHhdVRLjyxFxDrA38OWq3+uqYY+MiO9V8/9ZRBw9xvKvi4g3VOvg5oj4aETMHmXYEZdrpNhGGPd3wL3aYp1Vbafzq2W9MiJe2jb8aRHxuYj4RETcChw/bHpjzfPQiPh5RNwSEZ9pX56IeHJE/LRahu9FxCGjLOsHI+Jdw/p9KSJeVX1+fUT8rtr+l0fEM9qGOz4ivhsR/xoRG4HTRtjOD42Ii6sYL46Ihw7bJo9p676rykxEzK7WycZqGS6OiN1HWYa9IuK/quNgY0S8b4Rh1lQlCbrb+t21j0bEvSPi21WcN0bEZ6r+/68a/GfV+n/ueOu3Wq6/i4ifA3dERHfVfV21Hq+IiEePtCySJEnSjjCBoKcCnwYWA+cD7wOIiAbwZeBnwCrg0cApEfH4UabzEeAvMnMBcF/gmzs4/55q/l8HVgCvAD4ZEeNWh4iIecD/AJ+qxn0e8IGIWJuZHwI+CbyzKonxlMx8EXcvofHOiFgFfBV4G7AEeA3w+YhYPsasXwA8HtgPOAA4dYTYRl2ukWIbPn5m7jcs1q3V+rsW2BN4FvD2iHhU22hPAz5HWcefHDa9seb5HOAYYF/gEKrkQ0Q8APhP4C+ApcB/AOdHxKwR1sm5wHMjIqpxdwMeV8UM8Dvg4cAi4M3AJyJij7bxjwCuAnYH/mnYulxC2UbvqeJ4N/DViFg6QhzD/Xk1z72qcV8GbB4+UER0AV8Bfg+soRwLnx4+XAfeStnmuwGrgfcCZOYjqu/vX63/z3S4fo8DnkTZpvsBLwcOr46/xwPrtiNGSZIkaUwmEPSdzLwgMweBc4D7V/0PB5Zn5lsysy8zrwI+TLkZH0k/sDYiFmbmzZn54x2c/5HAfOD0av7fpNzIHdfBNJ8MrMvMj2bmQGb+BPg88OwOYwJ4IXBBFVszM/8HuAR44hjjvC8zr8nMmyg3uyPFuiPLdQ8RsRfwMODvMnNLZv4UOAt4cdtg38/ML1bLcY+b5DG8JzOvr5bny8ChVf+Tgf/IzB9m5mBmfgzYWi3bcP8HJCVJACXB8f3MvB4gMz9bzaOZmZ8Bfgs8uG386zPzvdV2HB77k4DfZuY51ffnAr8G7pF4GUE/5eb83tUyXJqZt44w3IMpiZnXZuYd1TrenoYT+ylVT/bsYBqdrN/3VPvaZmAQmEU5/noyc11m/m47YpQkSZLGZAJBN7R9vhOYXRXD3gfYsypCvSkiNgFvpDwJHsmxlJvr31dFtR+yg/PfE7gmM5tt3/+e8gR4PPsARwyL/QXAyg5jak3j2cOmcRSwxxjjXDMs1j1HGGZHlmskewI3ZeZtY0zvGrbP8G0zv/q8D/DqYetmL0ZY3sxMyhP7VoLk+bSVgoiIF7cV1d9EKb2yrMPY96Qsa7tO1+U5wIXApyPi+oh4Z1U6ZLi9gN9n5kAH0xzL64AAfhSl2soJYwzbyfq9a71k5pXAKcBpwPqI+HREjLTvSZIkSTvEBIJGcw1wdWYubnstyMwRn8Bn5sWZ+TRKsfwvAuft4PyvB/aqqlK07A1cV32+A5jb9l17cuAa4NvDYp+fmX/ZCnekRRjWfQ1wzrBpzMvM08eIea9hsV6/Hcs1UmxjuR5YEhELRpleJ9Pc1nleA/zTsHUztyoBMJJzgWdFxD6UKgmfB6i6P0wpfr80MxcDv6TcaHcS2/WUm+12He0jmdmfmW/OzLXAQymlVtpLbbQv694xfmOFd1Tvo83vhsx8aWbuSama8IEY/Z8XOlm/d1svmfmpzDyKsj4S+Odx4pUkSZK2mQkEjeZHwG1V42xzIqIrIu4bEYcPHzAieiPiBRGxKDP7gVuB5j2muG1+SHnq/bqI6InSgOFTGKp//lPgmRExt7oRO7Ft3K8AB0TEi6pxeyLi8Ig4qPr+T5SGCNsN7/cJ4CkR8fhq2WdHaRBy9Rgx/3VErK7q5v898JntWK6RYhtVZl4DfA94RxXjIZR18YlOp7Gt86Tc9L8sIo6IYl5EPGlYEqM9xp8AN1KqVlyYmZuqr+ZRbnY3AETESyglEDp1AWU7P79qSPC5wFrK9oeyjzyvWs8PolSfoJrXIyPiflUbB7dSqhiMtM/+CPgjcHq1nLMj4mEjLOMGSuLihdX+cgKlbYLW/J7dtu/cXC13a37D1/82rd+IODAiHlW1kbCF0pbDjh5/kiRJ0j2YQNCIqjYJnkyp9341QzeAi0YZ5UXAuigt/b+MUmVgR+bfR7mxfkI17w8AL87MX1eD/CvQR7n5+hhtxeKr4vyPo7TXcD2lKP4/U+qJQ2nwcW1VPPyLVb93AKdW/V5T3Zg/jVJtYwPlqfBrGfuY+RSlobyrKI0Dvm07lmuk2MZzHKWBv+uBLwD/mJn/2+G42zzPzLwEeCmlwcubgSsZ9u8OI/gU8JjqvTWdy4EzgO9TtuP9gO92GnRmbqTso68GNlKqCTw5M2+sBnkT5Sb+ZkoDjZ9qG30lpWHJW4FfAd+mVGsYPo9Byva6N6XxymuB544S0ksp+8hG4GBKYqflcOCHEXE7pbHQv63aFYFS9eBj1fp/znas31nA6ZT96QZKKaA3AFSJve36y09JkiRpuChVlCXtiIhYB5y0jTfukiRJkjRtWAJBkiRJkiSNywSCJEmqVUT8Z0Ssj4hfjvJ9RMR7IuLKiPh5RBw21TFKkiQTCNKEyMw1Vl+QpO12NnDMGN8/Adi/ep0MfHAKYpIkScOYQJAkSbXKzP8H3DTGIE8DPp7FD4DFEbHH1EQnSZJ+nrXTAAAgAElEQVRaxvtv8+2ybNmyXLNmzWRMWpKkae3SSy+9MTOX1x3HNLOK8m84LddW/f44fMCIOJlSSoF58+Y98D73uc+UBChJ0nSyvdcjk5JAWLNmDZdccslkTFqSZq4rrijvBx5YbxzaIRHx+7pj2JVl5oeADwE86EEPSq9HJEm6p+29HpmUBIIkaRL8xV+U94suqjUMqQbXAXu1da+u+kmSpClkGwiSJGlndz7w4urfGI4EbsnMe1RfkCRJk8sSCJIkqVYRcS5wNLAsIq4F/hHoAcjMfwcuAJ4IXAncCbyknkglSZrZTCBIkqRaZeZx43yfwF9PUTiSJGkUVmGQJEmSJEnjsgSCJE0Xp55adwSSJEmawUwgSNJ08ZjH1B2BJEmSZjCrMEjSdPHTn5aXJEmSVANLIEjSdHHKKeX9ootqDUOSJEkzkyUQJEmSJEnSuEwgSJIkSZKkcZlAkCRJkiRJ4zKBIEmSJEmSxmUjipI0Xbz97XVHIEmSpBnMBIIkTRcPfWjdEUiSJGkGswqDJE0X3/teeUmSJEk1sASCJE0Xb3xjeb/oolrDkCRJ0sxkCQRJkiRJkjQuEwiSJEmSJGlcJhAkSZIkSdK4bANB2sXdPvgjbs5P1x2GJsDy/B0AGwZetcPTmh33YXnXyTs8HUmSJM0cJhCkXdwWfsGtzQvpYre6Q9EOGnjXUgC2Nn+0Q9Npcis0mhMRkiRJkmYQEwjSDNBgNt1hAmG6Gzy0bMMdOXFn9jPIbcyPP5uYoCRJkjRjmECQpGlizjc2ALD50cu3edzMJgNsgAiWxQksahwz0eFJkiRpF2cCQZKmiSXv+C0A121jAmEgN5GxmQXxKFZ0/S29sWoywpMkSdIuzgSCJO2iMvvpjw30xl7s2fVm5jbuX3dIkiRJmsZMIEjSLmgwb6MZd7CscSLLGifSiFl1hyRJkqRpzgSCJO1imtlHM+5gr8b7md/14LrDkSRJ0i6iUXcAkqSJkznAABtY0XiVyQNJkiRNKEsgSNI0sf79h4z5fWY//axnaeNFLGk8b4qikiRJ0kxhAkGSpon+A+eP+l1m0s96lnf9JcsaJxERUxiZJEmSZgITCJI0Tcz9yp8AuPPJu4/w7QBdsYDlXS+d2qAkSZI0Y5hAkKRpYrczfweMnEBIBmkwegkFSZIkaUfZiKIk7QKSrfTG6rrDkCRJ0i7MBIIkTXOZyQCbmM3aukORJEnSLswqDJI0jQ3m7QxyGwsaR7Ok6/l1hyNJkqRdmAkESZqmBthII+azunEGCxqP8J8XJEmSNKlMIEjSNPGnjz5gWJ8me3e/n9mxfy3xSJIkaWYxgSBJ08TAXnPu+pyZZAzQw4oaI5IkSdJMYiOKkjRNzD/veuafdz0Ag2xiNgfRYGHNUUmSJGmmsASCJE0Tiz60DoDbnr0HyRaWNk6w3QNJkiRNGUsgSNI0M8AG5jYOY0Hj4XWHIkmSpBnEBIIkTSNJk66Yx6qutxNhITJJkiRNHRMIkjSNJAMsajyd7lhWdyiSJEmaYUwgSNI008vqukOQJEnSDGT5V0maJq47dy1EP/s0jqo7FEmSJM1AJhAkaRpo5hYGlt3Bqq5/oidW1B2OJEmSZiATCJK0E8scpJ8NNGI2+5zzEObGDfCSuqOSJEnSTGQbCJK0E8ps0p/rGYgN7NZ4BvfuPp95H7+M+NjH6g5NkiRJM5QlECRpJ5LZZIANEMn8eDjLu/6S2bF/3WFJkiRJlkCQpqtrrrmGRz7ykaxdu5aDDz6Yf/u3f6s7JO2gwbyVgVjPgsYj2bf7XPbqfrfJA0mSJO00LIEgTVPd3d2cccYZHHbYYdx222088IEP5LGPfSxr166tOzRtp2ZsZo+uU1nceFrdoUiSJEn3YAkEaZraY489OOywwwBYsGABBx10ENddd13NUWnHNJgdB9cdhCRJkjQiSyBIu4B169bxk5/8hCOOOKLuULSdMgeICGaxZvSBLrhgyuKRJEmShjOBIE1zt99+O8ceeyxnnnkmCxcurDscbadBbmd23I+IMU7Lc+dOXUCSJEnSMFZhkKax/v5+jj32WF7wghfwzGc+s+5wtAMytrA4nj72QB/4QHlJkiRJNTCBIE1TmcmJJ57IQQcdxKte9aq6w9EOaOYWGixgYeMxYw943nnlJUmSJNXABII0TX33u9/lnHPO4Zvf/CaHHnoohx56KBdYR35aarKZuXF/GtFbdyiSJEnSqGwDQZqmjjrqKDKz7jA0AZI+utit7jAkSZKkMVkCQZJqNJi3E9HNbl3PrjsUSZIkaUyWQJCkmjRzK824nb0a72dOHFR3OJIkSdKYTCBIUg0ykwE2srLxWuZ3PbizkS66aFJjkiRJksZiFQZJmmIlebCeWbEfuzWeVXc4kiRJUkdMIEjSFGrmVvr5I7Mb92Gv7jOI2IaCYO96V3lJkiRJNTCBIElTIDPpz/U043Z273ota7o+Sm/stW0T+cpXykuSJEmqgW0gSNIka+adDHAL8xqHs0fXqfTGqrpDkiRJkraZCQRJmiTN3MIAN9MV81nVeAsLG08gIuoOS5IkSdouJhAkaQJlNhlkE0k/jZjP8saJ7NZ4Lt2xpO7QJEmSpB1iAkGSJshAbiJjC3PjAewWz2d+46E0onfiZjBnzsRNS5IkSdpGJhAkaYJk9LF31weY1zh8cmbw3/89OdOVJEmSOmACQZJ2UKvaQkQvc+MBdYcjSZIkTQr/xlGStlMzt9Cff2Qg1jOncTCrG2cQMYl52be+tbykXVBEHBMRV0TElRHx+hG+3zsivhURP4mIn0fEE+uIU5KkmcwSCJK0jZq5lUFuohHzWdp4CYsbT6U39pr8GX/jG+X9TW+a/HlJUygiuoD3A48FrgUujojzM/PytsFOBc7LzA9GxFrgAmDNlAcrSdIMZgJBkrZBM/sYiI2sbLyaxY1nTWwjidLM9WDgysy8CiAiPg08DWhPICSwsPq8CLh+SiOUJEkmECSpU5mDDLCBlY03sKTr2XWHI+1KVgHXtHVfCxwxbJjTgK9HxCuAecBjRppQRJwMnAyw9957T3igkiTNZLaBIEkdSvrpiT1MHkj1OA44OzNXA08EzomIe1zHZOaHMvNBmfmg5cuXT3mQkiTtyiyBIEkdSgZp0FVfAEuX1jdvaXJdB7Q3JLK66tfuROAYgMz8fkTMBpYB66ckQkmSZAJBkjrRzD4G2cTyxl/VF8TnP1/fvKXJdTGwf0TsS0kcPA94/rBh/gA8Gjg7Ig4CZgMbpjRKSZJmOBMIkjRMZpOkj2QrTfpI+okIdm+8jiVdz6o7PGmXk5kDEfFy4EKgC/jPzLwsIt4CXJKZ5wOvBj4cEa+kNKh4fGZmfVFLkjTzmECQNONkDrYlCLYCgxA9kAExCCTdsYJe1tAbezMr9mV27M+8xvA23abYG95Q3t/xjnrjkCZBZl5A+WvG9n7/0Pb5cuBhUx2XJEkaYgJB0i4ts58BNlL+Zj5IBiC66GF3euIAZrEPvbGGnlhBd+xODyvoYgkjtM1Wv+9/v+4IJEmSNIOZQJC0y8lsMsgtZPQR0cPieAbzGw8vSQJW0MViIqLuMCVJkqRpxQSCpGlrqCpCP0kfTfpKyYEI5sahLI7nsKBxFI2YXXeokiRJ0rRnAkHSTqe0i9ZKDvS1NWTYA9mAKN+Xqggr6GElPbEnPbEXvbGSuXEYPbFH3YshSZIk7VJMIEiqVeYAA9wMkUR2kdEE+mnEXLpZQU+spIdV9MZedMcKullGdyylm6U0mD+zqiKsXl13BJIkSZrBTCBImnKZgwyyiYwBIrpYEI9kYeNRdLOC7lhWkgNWO7inT3yi7ggkSZI0g5lAkDRlMgcZiA0QDebFkSyOpzK/8RAaMbfu0CRJkiSNwwSCpCmRmfTHn9gtns2Krr+iKxbWHdL0c8op5f3MM+uNQ5IkSTOSCQRJky6zn37+xLw4nJVdryHCU892+elP645AkiRJM5hX8ZImTTM3MxCbiOhhSTyf5V0nmzyQJEmSpimv5CVNuMx+BuJGGrGQFY2Xs1vjGXTForrDkiRJkrQDTCBImlDN7GOAG1ne+CuWNl5II2bVHZIkSZKkCWACQdIOyUySLTTZQrIZAlY2Xs+SrmfXHdqu54AD6o5AkiRJM5gJBEkdyWySbKbJFppsbWvLoEl3rGBuHMycWMvceCDzGg+qNdZd1oc+VHcEkiRJmsFMIEi6m8wmTTZXyYI+GtFDkgD0xmrmxv7MibX0xhp6Yy96WEUjeusNWpIkSdKkM4EgzVBDVQ9KqYJSoiCApDf2YnYcxJw4mFmxLz2xmh5WEtFVd9gz28knl3dLIkiSJKkGJhCkGaDJnfTzp7u6Ixskg3THcubF/ZgThzAr9qM39qGXVf7V4s7qN7+pOwJJkiTNYN4lSLu4hY1j6Ik97uoOeuiNvellbxoxp8bIJEmSJE0nJhCkXVxvrKI3VtUdhiRJkqRprlF3AJIkSZIkaednCQRJmi4OPbTuCCRJkjSDmUCQpOnizDPrjkCSJEkzmFUYJEmSJEnSuEwgSNJ08cIXlpckSZJUA6swSNJ0ce21dUcgSZKkGcwSCJIkSZIkaVwmECRJkiRJ0rhMIEiSJEmSpHHZBoIkTRcPeUjdEUiSJGkGM4EgSdPFO95RdwSSJEmawazCIEmSJEmSxmUCQZKmi2OPLS9JkiSpBlZhkKTpYuPGuiOQJEnSDGYJBEmSJEmSNC4TCJIkSZIkaVwmECRJkiRJ0rhsA0GSpotHP7ruCCRJkjSDmUCQpOniTW+qOwJJkiTNYCYQpF3crzbdzrf+dHPdYUgTaq95s3jK6hV1hyFJkjSjmECQdnHfu/EWzrziDyzq8XCf7v7tTacA8LdvPbPmSOq1tdnkwIXzTCBIkiRNMe8opBlgQU83K+fMqjsM7aAFA/0AM35bbtzaz73mz6k7DEmSpBnHf2GQJE0r/c0mRy1fXHcYkiRJM44JBEnStNHfbNLTaPColUvqDkWSJGnGMYEgSZo2Ng82WbtwHvO6u+oORZIkacaxDQRJmiYufsgj6g6hdv3NZPns3rrDkCRJmpFMIEjSNPGF57647hBq1zfY5OBFc+sOQ5IkaUayCoMkadrobgSHLllYdxiSJEkzkgkESZom3n7KSbz9lJPqDqNWgwl7zZ1ddxiSJEkzkgkESdK00cxkTpc/XZIkSXXwKkySNC1kJgA9jag5EkmSpJnJBIIkaVpIShsIESYQJEmS6mACQZI0LdwxMMiaebZ/IEmSVBf/xlGSponvHP24ukOo1e0Dg5ywalndYUiSJM1YJhAkaZq44OnPqTuEWvVEcOhuC+oOQ5IkacayCoMkTROztmxm1pbNdYdRm8FMVvsXjpIkSbWxBIIkTRP/+PpXAPDGM8+qOZKp18wEgt16/dmSJEmqiyUQJEk7vYFmsqCny39gkCRJqpEJBEnSTu/2gUHus3Be3WFIkiTNaCYQJEk7tczkzsFBnrDn0rpDkSRJmtGsTCpJ2mltHhjk5r4BHrF8Nx65cknd4UiSJM1oJhAkaZr4xjFPrTuEKbVxax9dEbzlkP144qqltn8gSZJUMxMIkjRNzKQEwmAmAwmfe/gh7Dl3Vt3hSJIkCdtAkKRpY+EtN7PwlpvrDmNK3NzXz0OXLTJ5IEmStBOxBIIkTROv/8fXAvDGM8+qOZLJN9hMjt17Rd1hSJIkqY0lECRJO5Utg03mdXdz5LJFdYciSZKkNiYQJEk7lc2Dgxy623x6Gv5ESZIk7Uy8OpMk7VQGm8miXmvYSZIk7WxMIEiSdir9mRy4cG7dYWiKRcQxEXFFRFwZEa8fZZjnRMTlEXFZRHxqqmOUJGmm8xGPJE0TFzz12XWHMCW6Ijhiqe0fzCQR0QW8H3gscC1wcUScn5mXtw2zP/AG4GGZeXNE2MqmJElTzASCJE0T33nU4+sOYdL1NZvM6Wqw7/w5dYeiqfVg4MrMvAogIj4NPA24vG2YlwLvz8ybATJz/ZRHKUnSDGcVBkmaJpatv4Fl62+oO4xJdXv/IIfutoCIqDsUTa1VwDVt3ddW/dodABwQEd+NiB9ExDEjTSgiTo6ISyLikg0bNkxSuJIkzUwmECRpmnjV20/lVW8/te4wJlVfMzl6993qDkM7p25gf+Bo4DjgwxGxePhAmfmhzHxQZj5o+fLlUxyiJEm7NhMIkqSdQjOTCHjEChMIM9B1wF5t3aurfu2uBc7PzP7MvBr4DSWhIEmSpogJBEnSTmFrs8nK2b0smdVTdyiaehcD+0fEvhHRCzwPOH/YMF+klD4gIpZRqjRcNZVBSpI005lAkCTtFAaayZJekwczUWYOAC8HLgR+BZyXmZdFxFsi4qnVYBcCGyPicuBbwGszc2M9EUuSNDP5LwySpJ1CXzPZfU5v3WGoJpl5AXDBsH7/0PY5gVdVL0mSVAMTCJI0TXzhOS+qO4RJtXWwyb38+0ZJkqSdlgkESZomLn7on9UdwqTZPDhITyP8BwZJkqSdmAkESZomVv1hHQDX7b2m1jgm2mAmN/cN8E+H7MeBC+fVHY4kSZJGMWojihFxW0TcWr1ua+u+LSJuncogJd3TCSecwIoVK7jvfe9bdyiaIn/97rfx1+9+W91hTLgtg03WzJvNMauW1R2KJEmSxjBqAiEzF2Tmwuq1oK17QWYunMogJd3T8ccfz9e+9rW6w5AmRHdE3SFIkiRpHB39jWNEHBURL6k+L4uIfSc3LEnjecQjHsGSJUvqDkPaYZlpAkGSJGkaGDeBEBH/CPwd8IaqVy/wickMSpI0czSB3q6O8tmSJEmqUSdXbM8AngrcAZCZ1wMLJjMoSdLMsXWwyd5zZ9cdhiRJksbRyb8w9GVmRkQCRIRNZEtSDT7zopPqDmFSNIEjltm0jiRJ0s6ukwTCeRHxH8DiiHgpcALw4ckNS5I03M8eeGTdIUyKLmDPOZZAkCRJ2tmNW4UhM98FfA74PHAA8A+Z+d7JDkzS2I477jge8pCHcMUVV7B69Wo+8pGP1B2SJtm+V17BvldeUXcYk6JhG4qSJEk7vU5KIAD8ApgDZPVZUs3OPffcukPQFHvp+/4FgDeeeVbNkUycwWbSBFbNnVV3KJIkSRpHJ//CcBLwI+CZwLOAH0TECZMdmCRp17exr59Hr1zC0lm9dYciSZKkcXRSAuG1wAMycyNARCwFvgf852QGJkna9WXCI1YsrjsMSZIkdaCTv3HcCNzW1n1b1U+SpB3SCJjV6OSnSJIkSXUbtQRCRLyq+ngl8MOI+BKlDYSnAT+fgtgkSbuwwUwGM7n3grl1hyJJkqQOjFWFYUH1/rvq1fKlyQtHkjSaj5/08rpDmFA3bu3jsXssZa95/oWjJEnSdDBqAiEz3zyVgUiSxvbr+x5adwgTKggeu3JJ3WFIkiSpQ+M2ohgRy4HXAQcDdz0mysxHTWJckqRh7vPLnwK7TiIhgB7bP5AkSZo2Orly+yTwa2Bf4M3AOuDiSYxJkjSCF5/1Pl581vvqDmNC9DWbAOy3YE7NkUiSJKlTnSQQlmbmR4D+zPx2Zp4AWPpAkrTdbtraz4n77ckec2bVHYokSZI6NG4VBqC/ev9jRDwJuB6w0qokabvc0jfA7rNn8aJ77VF3KJIkSdoGnSQQ3hYRi4BXA+8FFgKvnNSoJEm7pMFM7hwY5N0P3J/ZXV11hyNJkqRtMG4CITO/Un28BXjk5IYjSdpVNTO5YXMfL9x3JQ9auqjucCRJkrSNRk0gRMR7gRzt+8z8m0mJSJI0og+//LV1h7BD1m/p49Erd+NvD9y77lAkSZK0HcYqgXDJlEUhSRrX1fc+sO4QtttAM+mO4K3334+uRtQdjiRJkrbDqAmEzPzYVAYiSRrb/S/9AQA/e+CRNUey7W4fGOCQ3RbY7oEkSdI01kkjipKkncBzzzkLmJ4JhC2DTdYumld3GJIkSdoBjboDkCTt+vqbyeIec9aSJEnT2ZgJhIjoigj/slGStF3KPy9sZcXsXh66fHHd4UiSJGkHjJlAyMxB4LgpikWStAvZMtjkhs19PG6PpXzuEYew/8K5dYckSZKkHdBJedLvRsT7gM8Ad7R6ZuaPJy0qSdK0t6mvn786YDUn7LcnEf7zgiRJ0nTXSQLh0Or9LW39EnjUxIcjSRrN+191at0hdCwzAXjiqmUmDyRJknYR4yYQMvORUxGIJGls1+29pu4QOjaYyZyuLvaYM6vuUCRJkjRBxv0XhojYPSI+EhH/XXWvjYgTJz80SVK7w7/3bQ7/3rfrDqMjzYTeLkseSJIk7Uo6+RvHs4ELgT2r7t8Ap0xWQJKkkT3jvHN4xnnn1B1GRwYymd/t3zZKkiTtSjpJICzLzPOAJkBmDgCDkxqVJGla62s22WNOb91hSJIkaQJ1kkC4IyKWUhpOJCKOBG6Z1KgkSdPW5oFBNg82ecbq5XWHIkmSpAnUSfnSVwHnA/tFxHeB5cCzJzUqSdK0dOfAILf1D3D6offmMXssrTscSZIkTaBOEgiXAX8GHAgEcAWdlVyQJM0gmcmm/gHecf/9TB5IkiTtgjpJIHw/Mw+jJBIAiIgfA4dNWlSSpHt49xvfVncIY9qwtZ8jly7isSYPJEmSdkmjJhAiYiWwCpgTEQ+glD4AWAjMnYLYJEltblyxsu4QxvVXB6wmwr9vlCRJ2hWNVQLh8cDxwGrgDIYSCLcBb5zcsCRJwx31zQsB+M6jHl9zJPe0dbBJbyO4z8J5dYciSZKkSTJqAiEzPwZ8LCKOzczPT2FMkqQRPPH8zwI7XwLh1v4B7hgY5NX32ZuuhqUPJEmSdlWdNIa4OiIWRnFWRPw4Ih436ZFJknZ6G7b0MavR4MNHHMRx++5RdziSJEmaRJ0kEE7IzFuBxwFLgRcBp09qVJKknV4zk2Ymnz7qfjxgycK6w5EkSdIk6ySB0CqP+kTg45l5WVs/SdIMtWWwyaq5s1kyq6fuUCRJkjQFOkkgXBoRX6ckEC6MiAVAc3LDkiTt7PqaTVbNnVV3GJIkSZoiY/0LQ8uJwKHAVZl5Z0QsBV4yuWFJkoY7/c3/UncId8lMtgw2WdDdyc+IJEmSdgWdXPkdVb0f4n97S1J9bl20W90hMNhMbuzrh4RVc2fxzL2X1x2SJEmSpkgnCYTXtn2eDTwYuBR41KREJEka0aO/dj4A3zjmqVM+78Fmsn5rH10RPHblEp6zz+4csng+JpYlSZJmjnETCJn5lPbuiNgLOHPSIpIkjajOBMKtAwPcb/F83n3YAexmo4mSJEkzUieNKA53LXDQRAciSdo5ZSZ9g02WzeoxeSBJkjSDjVsCISLeC2TV2aA0qPjjyQxKklS/rYNNbu7rB4LVc2fz9NW2dyBJkjSTddIGwiVtnweAczPzu5MUjySpZpv6BtjabDK3q8Fxa1bypFXLOGDBXNs7kCRJmuE6aQPhY1MRiCSpfpnJ5sFBzjjsAB66fBE9je2p6SZJkqRd0agJhIj4BUNVF+72FZCZecikRSVJuoc3n/7eCZ9mM5OBZtKXyUCzyR0DTQ5eNI8/273+v4yUJEnSzmWsEghPnrIoJEnj2jp7TkfDDWbS3ywJgf7qc38z6QroiiAIkmQwkyBY3NvNilk9LJ/dy+6ze23rQJIkSSMaK4HQA+w+vL2DiHgYcMOkRiVJAkqVgsFMBhKe9KXzaGbyuSc/i4FmMpBNuiPoioAIMpOBTLoj2K23h93nzmb5rB52n9PLytm9LJ3Vy+Lebhb39JT33m7md3fZtoEkSZI6MlYC4UzgDSP0v7X67imTEpEk7YKarURAs9zkD2QyWH0ezKSrSgREAFnqj5VxYF53Fwt6unjc/32D7kZw04kvZcmsHlbM7mVJb0kG7Fa9L+rpZk5Xw6SAJEmSJtxYCYTdM/MXw3tm5i8iYs2kRSRpwt3eP8if6Ks7jF1Y0gQyobsRBNCI0t2Eu6oKzO/uYlFvF4uqEgBLertZOquXpb3dLOgpr/lVsmB+dzcLerqY191Fo5UMWDQPgLcdeu+6FlSSJEkz2FgJhMVjfNdZRVxJtTty2UJetv+qusPY5c3r7mK33lYSoNz8L+ju4v+3d+9xftT1vcdfn73kQrLhFkjWZAOkISDES2pCIZrDxYgQIGAJNwUCBLlU2tIIPYDW+gDlJmqPIAq1PKQeT8F6qSmiFNHUHi2XcEAUKYccaEsoCbsJ2WRJNnv7nj9+E1zW3exusvubnf29no/HPH4z85v5zntnNzO/fH7fmZlYW0NdTTXj7BUgSZKkgttZAWF1RHw0pfTX3WdGxMXAk8MbS9JQOXyvOg7fqy7vGJIkSZIKbmcFhCuB70XER/htwWAeMAb40HAHkyRJkiRJI0efBYSU0npgQUQcC8zJZv8gpfSTsiSTJL3VqlV5J5AkSVIF21kPBABSSj8FflqGLJIkSZIkaYSqyjuAJGmAbrutNEiSJEk5sIAgSUXxwAOlQRqFIuKEiHg+ItZExDU7We70iEgRMa+c+SRJkgUESZKUs4ioBr4MnAgcBpwTEYf1slwd8KfAY+VNKEmSwAKCJEnK3xHAmpTSiymlNuA+4NRelrsBuAVoLWc4SZJUYgFBkiTlbRrwcrfptdm8N0XE7wMNKaUflDOYJEn6rX6fwiBJGiHGj887gZSLiKgCvgBcMIBlLwEuAZgxY8bwBpMkqcJYQJCkovjhD/NOIA2XV4CGbtPTs3k71AFzgFURATAVWBkRS1JKq7s3lFK6G7gbYN68eWk4Q0uSVGm8hEGSJOXtCeDgiDgoIsYAZwMrd7yZUmpOKU1OKR2YUjoQeBT4neKBJEkaXhYQJKkobrihNEijTEqpA7gCeAh4DvhWSunZiLg+Ipbkm06SJO3gJQySVBSPPFJ6/Yu/yDeHNAxSSg8CD/aY96k+lj2mHJkkSdJb2QNBkiRJkiT1ywKCJEmSJEnql+3HcD8AAB2dSURBVAUESZIkSZLUL++BIElFse++eSeQJElSBbOAIElF8Z3v5J1AkiRJFcxLGCRJkiRJUr/sgSCNcq+++iq/+tWv8o6hITDrnnsAWHPRRcPS/sSJE1mwYMGwtC1JkqTis4AgjXIPPvggn//856mrq8s7inbTnc89B8BfvPTSkLfd2tpKQ0MDDzzwwJC3LUmSpNHBAoJUAcaPH8/++++fdwztpjFr1gAMy+9y3bp1LF26dMjblSRJ0ujhPRAkSVRXV/O+970v7xiSJEkawSwgSJLo7Oxkv/32yzuGJEmSRjAvYZCkgmgaP37Y2u7s7PQ+GZIkSdopCwiSVBBfmDt3WNrt6uoCoKrKTmmSJEnqmwUESapgbW1tNDU1cdxxx+UdRZIkSSOcBQRJKoiLn30WgK8dfviQtNfc3ExraytXX301Z5111pC0KUmSpNHLAoIkFcTM5uYhba+9vZ1bb72VY445ZkjblSRJ0ujkBa+SVMHq6+vzjiBJkqSCsIAgSRUqpcSECRPyjiFJkqSCsIAgSRWqo6ODvffeO+8YkiRJKgjvgSBJBfHKxIlD1lZrayt77703e+yxx5C1KUmSpNHNAoIkFcSX3/nOIWtr06ZNnHXWWUTEkLUpSZKk0c1LGCSpAlVXV3PkkUfmHUOSJEkFYgFBkgriY888w8eeeWa320kpkVJi+vTpQ5BKkiRJlcJLGCSpIKa1tOx2Gykl1q9fz+zZsy0gSJIkaVDsgSBJFSKlxLp165gzZw5f/epXqamxhixJkqSB89OjJFWIxsZG5syZwx133MGECRPyjiNJkqSCsQeCJFWA5uZm9txzT2677TaLB5IkSdol9kCQpIJ4cc89d2m99vZ2WltbufPOO5k8efIQp5IkSVKlsIAgSQXxtcMPH/Q6KSUaGxu59NJLmTNnzjCkkiRJUqXwEgZJGsWampo49NBDufDCC/OOIkmSpIKzgCBJBbHiqadY8dRTA15+27ZtVFVVcfPNN1NbWzuMySRJklQJvIRBkgpi8rZtg1p+06ZNnH/++TQ0NAxTIkmSJFUSeyBI0ig2bty4vCNIkiRplLCAIEmjVFdXF2PGjMk7hiRJkkYJCwiSNEpVV1ez11575R1DkiRJo4T3QJCkgvi3vfce1PJVVVXss88+w5RGkiRJlcYCgiQVxN++/e2DXmfGjBnDkESSJEmVyEsYJGkU6urqIqXEtGnT8o4iSZKkUcICgiQVxLWrV3Pt6tX9LpdSYvPmzUybNo3a2toyJJMkSVIl8BIGSSqIura2Pt9ra2tj8+bNdHR0kFKivr6eCy+8sIzpJEmSNNpZQJCkgmppaWHLli1UV1czfvx4jjvuOBYuXMjcuXOZMmVK3vEkSZI0ylhAkKQC6urqYvPmzVx99dXMnz+fmTNnEhF5x5IkSdIoZgFBkgpo48aNLFy4kLPPPjvvKJIkSaoQFhAkqSB+OXkyUOp90N7ezvnnn59zIkmSJFUSCwiSVBD3z54NQNNrr7FgwQLmzp2bcyJJkiRVEh/jKEkFklKiq6uLK664wnseSJIkqazsgSBJBfHpxx6jo7OTW44+moMPPjjvOJIkSaowFhAkqSDGdHaS2to4//zz7X0gSZKksvMSBkkqiLb2durq6liyZEneUSRJklSBLCBIUkEEMH3aNKqqPHRLkiSp/PwUKkkFkYDa2tq8Y0iSJKlCWUCQpALo6OjgF3vvTZWXL0iSJCknFhAkqQCam5t58Q//kJprrsk7iiRJkiqUBQRJKoDt27ezYMGCvGNIkiSpgllAkKQCqKmpYdFnPgPHHJN3FEmSJFUoCwiSNMJ1dHSQUmL8+PF5R5EkSVIFs4AgSSNYSonGxkbOPfdcxo0bl3ccSZIkVTALCJI0QnV2drJu3ToOOeQQLr744rzjSJIkqcJZQJAK7Ec/+hGHHHIIs2bN4uabb847jobQ1q1bWb9+Paeddhr33HMPe+yxR96RJEmSVOFq8g4gadd0dnbysY99jIcffpjp06czf/58lixZwmGHHZZ3NO2ilBJbtmzhjTfeYNKkSVx//fUsXryYiCgtcOaZ+QaUJElSRbOAIBXU448/zqxZs5g5cyYAZ599Nt///vctIBTUhg0baG9v54ADDuC6667j2GOPZcyYMW9d6I/+KJ9wkiRJEhYQpMJ65ZVXaGhoeHN6+vTpPPbYYzkm0q5qampiypQpfOITn2DevHm/7XHQ09atpVcvZ5AkSVIOLCBIUo5aWlqora3l9ttvf0tBqFeLF5deV60a9lySJElSTxYQpIKaNm0aL7/88pvTa9euZdq0aTkm0mBt3ryZ7du386Uvfan/4oEkSZKUM5/CIBXU/PnzeeGFF3jppZdoa2vjvvvuY8mSJXnH0gBt2LABgLvuuosjjjgi5zSSJElS/+yBIBVUTU0Nd9xxBx/84Afp7Ozkoosu4vDDD887lvqxdetWmpubmTFjBrfddtubN8GUJEmSRjoLCFKBLV68mMU7rovXiNbW1saGDRuoq6vjk5/8JCeffDI1NR6CJUmSVBx+epWkYZJSoqWlhZaWFsaNG8fy5cs577zzmDhx4q41eMEFQ5pPGkki4gTgfwDVwNdSSjf3eH8FcDHQATQCF6WU/qPsQSVJqmAWECRpiKWU2LhxI+3t7bztbW/jyiuv5Pjjj2eP3X38ogUEjVIRUQ18GfgAsBZ4IiJWppR+022xp4B5KaWtEXE5cCtwVvnTSpJUuSwgSNIQamlpYcuWLbzrXe/ikksuYf78+VRVDdH9apuaSq+TJw9Ne9LIcQSwJqX0IkBE3AecCrxZQEgp/bTb8o8C55Y1oSRJsoAgSUOhvb2dpqYm9tlnH2699VaOPfZYImJoN7J0ael11aqhbVfK3zTg5W7Ta4E/2Mnyy4EfDmsiSZL0OywgSNJu6OzspKmpiaqqKpYvX86yZct2/1IFSX2KiHOBecDRfbx/CXAJwIwZM8qYTJKk0c8CgiTtoqamJjo6Oli8eDGXX345U6dOzTuSVFSvAA3dpqdn894iIhYBnwCOTilt762hlNLdwN0A8+bNS0MfVZKkymUBQZJ2QXt7OxHBN7/5TWbPnp13HKnongAOjoiDKBUOzgY+3H2BiJgL3AWckFJ6rfwRJUnSEN3ZS5IqS2trK7NmzbJ4IA2BlFIHcAXwEPAc8K2U0rMRcX1ELMkW+xwwEfj7iHg6IlbmFFeSpIplDwRJGoTuj2g8+eSTy7vxyy8v7/akMkopPQg82GPep7qNLyp7KEmS9BYWECRpgFpbW3n99deZNWsWn/70pzn00EPLG+AsH3kvSZKk/FhAkKR+bNu2jebmZsaPH89VV13F0qVLqanJ4fD5cvaUu4aGnS8nSZIkDQMLCJLUh23btrFp0yYmTZrEihUrWLJkCRMmTMgv0HnnlV5XrcovgyRJkiqWBQRJ6iGlRFNTE7W1tVx77bWcdNJJjBs3Lu9YkiRJUq4sIEhSNx0dHbz22mu84x3v4KabbqK+vj7vSJIkSdKIYAFBkjIpJRobGznjjDP4+Mc/Tm1tbd6RJEmSpBGjKu8AkjRSbNiwgVmzZrFixQqLB5IkSVIP9kCQJGDLli1UV1dz4403MmbMmLzj9O7jH887gSRJkiqYBQRJFe+NN96gtbWVr3zlK8ycOTPvOH075ZS8E0iSJKmCeQmDpIrX0tLCddddx9y5c/OOsnPPP18aJEmSpBzYA0FSxauurh7ZPQ92uPTS0uuqVbnGkCRJUmWyB4KkitbW1kZE0NDQkHcUSZIkaUSzgCCpom3YsIGPfOQj7LnnnnlHkSRJkkY0L2GQVJFSSjQ2NjJlyhTOPffcvONIkiRJI549ECRVnK6uLtavX89BBx3Evffea+8DSZIkaQDsgSCpomzevJmWlhaOPvpobrjhBiZMmJB3pIH75CfzTiBJkqQKZgFB0qjX0dFBa2srW7ZsYerUqdx0000cddRRRETe0QZn0aK8E0iSJKmCWUCQVHgppTeLBNu3b6etrY2amhqqqqro6Ohg7NixNDQ0sGzZMs455xzGjh2bd+Rd8/TTpdd3vzvfHJIkSapIFhAkFcq2bdtoaWmhvb2d2tpaoNTDYMKECTQ0NDBz5kxmzZpFQ0MD9fX11NfXM2nSpOL1NujNlVeWXletyjWGJEmSKpMFBEmF0tzczDHHHMPcuXOZPn069fX1TJ06lbq6uryjSZIkSaOaBQRJhVJbW8t5553HO9/5zryjSJIkSRXFxzhKKpSUUt4RJEmSpIpkAUGSJEmSJPXLSxgkFc6OmydWnBtvzDuBJEmSKpgFBEmF0tXVxaRJk/KOkY8FC/JOIEmSpArmJQySCiGlxLp166ivr2fy5Ml5x8nHL35RGiRJkqQc2ANB0ojX3t5OY2MjCxcu5IYbbmDs2LF5R8rHddeVXletyjWGJEmSKpMFBEkjXlNTE+eccw4rVqygqsqOU5IkSVIe/CQuaUTr6uoiIjjvvPMsHkiSJEk58tO4pBGttbWV6dOnM2XKlLyjSJIkSRXNAoKkES2lVLn3PJAkSZJGEO+BIGnE2rp1K83NzZx44ol5RxkZ/uqv8k4gSZKkCmYBQdKIk1KisbGRsWPHcuONN3L88cfnHWlkePe7804gSZKkCmYBQdKIs2HDBg4++GC++MUvst9+++UdZ+T48Y9Lr4sW5ZtDkiRJFckCgqQRpbOzk46ODq655hqLBz195jOlVwsIkiRJyoEFBEkjwrZt22hubiYiWLp0KXPmzMk7kiRJkqRuLCBIytXrr7/O9u3bmTRpEpdddhmnnHIK+++/f96xJEmSJPVgAUFSbrZu3cqYMWP47Gc/y3vf+15qajwkSZIkSSNVVd4BJFWu5uZmlixZwtFHH23xQJIkSRrh/MQuqeza29tpampiwoQJnHDCCXnHKY677so7gSRJkiqYBQRJZdPV1UVjYyNVVVUsW7aMZcuWMWnSpLxjFcchh+SdQJIkSRXMAoKksujo6GD9+vV84AMf4Morr6S+vj7vSMXzj/9Yej3llHxzSJIkqSJZQJA07Nra2mhsbOTiiy/m8ssvJyLyjlRMn/986dUCgiRJknJgAUHSsGppaWHLli1ce+21nHHGGXnHkSRJkrSLLCBIGhYpJTZu3EhVVRW33347Rx11VN6RJEmSJO0GCwiShlR7ezsbNmwAYObMmdx0003MnDkz51SSJEmSdpcFBEm7LaXEli1b2Lp1K7W1tSxZsoTTTz+dt7/97d7vQJIkSRolLCBI2mVbt25l8+bNQKm3wYc//GGOO+446urqck42Sn3jG3knkCRJUgWzgCBpUNra2ti4cSMRwb777sull17KokWLOPDAA/OONvo1NOSdQJIkSRXMAoKkAXnjjTfYvHkz48aN48wzz+TEE0/k8MMP9xKFcrr//tLrWWflm0OSJEkVyQKCVAHa2trYsmXLLq3b3t5OW1sb++67L3/+53/OSSedxIQJE4Y4oQbkK18pvVpAkCRJUg4sIEij3OzZs5k7d+4ur19XV8fSpUtZuHAhNTUeMiRJkqRK5f8GpFFu4cKFLFy4MO8YkiRJkgquKu8AkiRJkiRp5LOAIEmSJEmS+uUlDJJUFN/+dt4JJEmSVMEsIEhSUUyenHcCSZIkVTAvYZCkovj610uDJEmSlAMLCJJUFBYQJEmSlCMLCJIkSZIkqV8WECRJkiRJUr8sIEiSJEmSpH5ZQJAkSZIkSf3yMY6SVBQPPph3AkmSJFUwCwiSVBR77JF3AkmSJFUwL2GQpKK4887SIEmSJOXAAoIkFcW3vlUaJEmSpBxYQJAkSbmLiBMi4vmIWBMR1/Ty/tiIuD97/7GIOLD8KSVJqmwWECRJUq4iohr4MnAicBhwTkQc1mOx5cDrKaVZwBeBW8qbUpIkWUCQJEl5OwJYk1J6MaXUBtwHnNpjmVOBe7PxbwPvj4goY0ZJkiqeBQRJkpS3acDL3abXZvN6XSal1AE0A/uWJZ0kSQKG6TGOTz75ZFNE/MdwtC2NYpOBpgK1O9zM3Zeh/9LVfV1eh+QdYDSLiEuAS7LJ7RHx6zzzVJCi/nssKvd3+bivy8d9XV679HlkWAoIKaX9hqNdaTSLiNUppXlFaXe4mbt8ipgZip077wwj0CtAQ7fp6dm83pZZGxE1wJ7Ahp4NpZTuBu6G4v6NFJH7urzc3+Xjvi4f93V57ernES9hkCRJeXsCODgiDoqIMcDZwMoey6wElmXjS4GfpJRSGTNKklTxhqUHgiRJ0kCllDoi4grgIaAauCel9GxEXA+sTimtBP4G+EZErAE2UioySJKkMrKAII0cdxes3eFm7vIpYmYw96iSUnoQeLDHvE91G28Fzhhks+7r8nFfl5f7u3zc1+Xjvi6vXdrfYe8/SZIkSZLUH++BIEmSJEmS+mUBQcpJROwTEQ9HxAvZ6959LPejiNgUEQ/0094JEfF8RKyJiGt6eX9sRNyfvf9YRBw4ND/J7hlA7hUR8ZuIeCYiHomIA/LI2VN/ubstd3pEpIjI/a7CA8kcEWdm+/vZiPhf5c7YmwH8jcyIiJ9GxFPZ38niPHL2yHRPRLzW1yMEo+RL2c/0TET8frkzjiZFPf4VUVGP2UVUxPNMkRX1HFlERTyvF9WwfB5JKTk4OOQwALcC12Tj1wC39LHc+4FTgAd20lY18P+AmcAY4JfAYT2W+SPgq9n42cD9I2AfDCT3scAe2fjlRcmdLVcH/Ax4FJg30jMDBwNPAXtn0/sXYV9Tuobv8mz8MODfR0Du/wb8PvDrPt5fDPwQCOBI4LG8Mxd1KOrxr4hDUY/ZRRyKeJ4p8lDUc2QRh6Ke14s6DMfnEXsgSPk5Fbg3G78XOK23hVJKjwBb+mnrCGBNSunFlFIbcF/Wfl/b+zbw/oiIXQk+hPrNnVL6aUppazb5KKXnw+dtIPsb4AbgFqC1nOH6MJDMHwW+nFJ6HSCl9FqZM/ZmILkTMCkb3xP4rzLm61VK6WeUnhTQl1OBv00ljwJ7RUR9edKNOkU9/hVRUY/ZRVTE80yRFfUcWUSFPK8X1XB8HrGAIOVnSkrp1Wx8HTBlN9qaBrzcbXptNq/XZVJKHUAzsO9ubHMoDCR3d8spVUnz1m/urAtYQ0rpB+UMthMD2dezgdkR8fOIeDQiTihbur4NJPengXMjYi2lu/j/cXmi7ZbB/u2rb0U9/hVRUY/ZRVTE80yRFfUcWUSj9bxeVIP+POJjHKVhFBE/Bqb28tYnuk+klFJE+EiUnYiIc4F5wNF5Z+lPRFQBXwAuyDnKYNVQ6qJ5DKVvDX8WEe9IKW3KNVX/zgG+nlL6fEQcBXwjIuaklLryDiZVqiIds4uowOeZIivqObKIPK+PYBYQpGGUUlrU13sRsT4i6lNKr2ZdhXanK9wrQEO36enZvN6WWRsRNZS6hG3YjW0OhYHkJiIWUSq6HJ1S2l6mbDvTX+46YA6wKuslPRVYGRFLUkqry5byrQayr9dSuvatHXgpIv4vpQ9LT5QnYq8Gkns5cAJASulfI2IcMJnd+zc13Ab0t68BKerxr4iKeswuoiKeZ4qsqOfIIhqt5/WiGvTnES9hkPKzEliWjS8Dvr8bbT0BHBwRB0XEGEo3CVu5k+0tBX6Ssrun5Kjf3BExF7gLWDKCrjfcae6UUnNKaXJK6cCU0oGUrgPO+0PdQP5G/oHSNytExGRK3TVfLGfIXgwk939SutkoEfF2YBzQWNaUg7cSOD+7+/GRQHO3S5o0OEU9/hVRUY/ZRVTE80yRFfUcWUSj9bxeVIP+PGIPBCk/NwPfiojlwH8AZwJkj2G6LKV0cTb9L8ChwMTsWrDlKaWHujeUUuqIiCuAhyjd3faelNKzEXE9sDqltBL4G0pdwNZQupnK2WX5KXdigLk/B0wE/j77luU/U0pLcgvNgHOPKAPM/BBwfET8BugErk4p5fot7QBzfxz464j4M0o3Xrog7/8cRsTfUfqgOTn7d/uXQC1ASumrlK7pXAysAbYCF+aTtPiKevwroqIes4uoiOeZIivqObKIinpeL6rh+DwS/i4kSZIkSVJ/vIRBkiRJkiT1ywKCJEmSJEnqlwUESZIkSZLULwsIkiRJkiSpXxYQJEmSJElSvywgaESLiH0j4ulsWBcRr2Tjm7LH6Azltt4WEd/Oxo+JiAey8U9HxFVDua1etn1aRBzWbfqCiHjbcG5zJ1nOiIjnIuKn2fTfRcQzEfFnEXF9RCzaybrzIuJLu7Ht63Z13V7a+vfsOc2SJEmShkBN3gGkncmer/tuKP1HHmhJKd0WEQcCDwzxtv4LWDqUbQ7CaZR+nh1FkQuAXwP/lUOW5cBHU0r/OyKmAvNTSrMGsmJKaTWweje2fR1w426sL0mSJGmY2ANBRVYdEX8dEc9GxD9FxHiAiPi9iPhRRDwZEf8SEYf2XDEiju7Ws+GpiKiLiAMj4td9bOuwiFgVES9GxJ90a2dFRPw6G67M5r2lnYi4Kit+9JotIhYAS4DPZXn+OzAP+GY2PT4i3hMR/5yt91BE1PfyM02JiO9FxC+zYUFfGbP550bE49k27oqI6oj4FPA+4G8i4nPAPwHTsmUWRsTXI2Jptv78iPhFtq3Hs33YvefGhIi4J3vvqYg4NZt/QUR8N9sPL0TErdn8m4Hx2ba+2eNnuyzLQ7c27sjG/yHbL89GxCW97JdB/T6y+Wdk++uXEfGzPv4mJEmSpIpiDwQV2cHAOSmlj0bEt4DTgf8J3A1cllJ6ISL+ALgTOK7HulcBH0sp/TwiJgKt/WzrUOBYoA54PiK+ArwTuBD4AyCAxyLin4HXd9LO72RLKR0XESuBB1JKOy6hOBG4KqW0OiJqgduBU1NKjRFxFvBZ4KIebX8J+OeU0ociohqYGBHv6SNjK3AW8N6UUntE3Al8JKV0fUQc123bX85y7egFsjx7HQPcD5yVUnoiIiYB23rk+QTwk5TSRRGxF/B4RPw4e+/dwFxge7Y/b08pXRMRV+zYVg/fAf4VuDqb3rEPAC5KKW3MCkhPRMR3sp4rA9HX38qngA+mlF7JskuSJEkVzwKCiuyllNLT2fiTwIFZMWAB8PcRsWO5sb2s+3PgC9k33d9NKa3ttnxvfpBS2g5sj4jXgCmUvqn/XkrpDYCI+C6wEFjZWwODyNbTIcAc4OFsvWrg1V6WOw44HyCl1Ak0R0RfGbuA91D6DzfAeOC1AWTpnunVlNIT2fY2Z+13X+Z4YEn89v4R44AZ2fgjKaXmbJ3fAAcAL/e1saxw8mJEHAm8QKmg8/Ps7T+JiA9l4w2UCkv9FhD6+X38HPh6Vpj6bn9tSZIkSZXAAoKKbHu38U5K/wmuAjb18S32m1JKN0fED4DFwM8j4oPsvBdCz23t7N9OB2+9PGhc9jqgbL0I4NmU0lGDXK+/Nu9NKV07hG32to3TU0rPv2Vm6Zv+wezPHe4DzgT+jVJRJEXEMcAi4KiU0taIWMVv9/cOg/59pJQuy3KeBDwZEe8ZRK8GSZIkaVTyHggaVbJvwl+KiDMAouRdPZeLiN9LKf0qpXQL8ASlb7QH61+A0yJij4iYAHwom7ce2D9KT5AYC5w8gGxbKF0eQS/TzwP7RcRR2Xq1EXF4L3keAS7PlqmOiD13kvERYGlE7J8tv09EHDCIn/15oD4i5mfr10VEzyLAQ8AfR/b1fkTMHUC77dklG735HnAqcA6lYgLAnsDrWfHgUODIXtYb9O8j+/t4LKX0KaCRUs8GSZIkqaJZQNBo9BFgeUT8EniW0n86e7oyu0neM0A78MPBbiSl9H+ArwOPA48BX0spPZVSageuz+Y/TOkb8/6y3QdcHaWbDf5e1u5XI+JpSpcsLAVuydZ7mlLX+57+FDg2In5F6ZKOw3aS8TfAJ4F/yvbBw8Dv3JhxJz97G6X7ENyeZXqY3/3m/wagFngmIp7Npvtzd7b8N3u+kVJ6HXgOOCCl9Hg2+0dATUQ8B9wMPNrLervy+/hcRPwqu/niL4BfDiC7JEmSNKpFSinvDJIkSZIkaYSzB4IkSZIkSeqXBQRJkiRJktQvCwiSJEmSJKlfFhAkSZIkSVK/LCBIkiRJkqR+WUCQJEmSJEn9soAgSZIkSZL6ZQFBkiRJkiT16/8Ddk9hHTQG1AMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in range(2,7):\n",
    "    # Create a subplot with 1 row and 2 columns\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "    fig.set_size_inches(18, 7)\n",
    "\n",
    "    # The 1st subplot is the silhouette plot\n",
    "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
    "    # lie within [-0.1, 1]\n",
    "    ax1.set_xlim([-1, 1])\n",
    "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    ax1.set_ylim([0, len(iris_scaled) + (k + 1) * 10])\n",
    "\n",
    "    # Initialize the clusterer with n_clusters value and a random generator\n",
    "    # seed of 10 for reproducibility.\n",
    "    clusterer = KMeans(n_clusters=k)\n",
    "    cluster_labels = clusterer.fit_predict(iris_scaled)\n",
    "\n",
    "    # The silhouette_score gives the average value for all the samples.\n",
    "    # This gives a perspective into the density and separation of the formed\n",
    "    # clusters\n",
    "    silhouette_avg = silhouette_score(iris_scaled, cluster_labels)\n",
    "    print(\"For n_clusters =\", k,\n",
    "          \"The average silhouette_score is :\", silhouette_avg)\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(iris_scaled, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(k):\n",
    "        # Aggregate the silhouette scores for samples belonging to\n",
    "        # cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = \\\n",
    "            sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        color = cm.nipy_spectral(float(i) / k)\n",
    "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                          0, ith_cluster_silhouette_values,\n",
    "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "        # Label the silhouette plots with their cluster numbers at the middle\n",
    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "        # Compute the new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "    ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
    "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "    ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "\n",
    "\n",
    "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
    "                  \"with n_clusters = %d\" % k),\n",
    "                 fontsize=14, fontweight='bold')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Principal Components Analysis\n",
    "\n",
    "Principal Components Analysis or PCA reduces the dimensions of the data set by successively finding the directions with the most variation and using these directions as the new coordinate system.\n",
    "\n",
    "The following code finds the first two principal components for the iris dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "pca.fit(iris)\n",
    "iris_rotated = pca.transform(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a new dataframe with the principal components and the species names."
   ]
  },
  {
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.684126</td>\n",
       "      <td>0.319397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.714142</td>\n",
       "      <td>-0.177001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.888991</td>\n",
       "      <td>-0.144949</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.745343</td>\n",
       "      <td>-0.318299</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.728717</td>\n",
       "      <td>0.326755</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-2.280860</td>\n",
       "      <td>0.741330</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-2.820538</td>\n",
       "      <td>-0.089461</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-2.626145</td>\n",
       "      <td>0.163385</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-2.886383</td>\n",
       "      <td>-0.578312</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-2.672756</td>\n",
       "      <td>-0.113774</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-2.506947</td>\n",
       "      <td>0.645069</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-2.612755</td>\n",
       "      <td>0.014730</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-2.786109</td>\n",
       "      <td>-0.235112</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-3.223804</td>\n",
       "      <td>-0.511395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-2.644750</td>\n",
       "      <td>1.178765</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-2.386039</td>\n",
       "      <td>1.338062</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-2.623528</td>\n",
       "      <td>0.810680</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-2.648297</td>\n",
       "      <td>0.311849</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-2.199820</td>\n",
       "      <td>0.872839</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-2.587986</td>\n",
       "      <td>0.513560</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-2.310256</td>\n",
       "      <td>0.391346</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-2.543705</td>\n",
       "      <td>0.432996</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-3.215939</td>\n",
       "      <td>0.133468</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-2.302733</td>\n",
       "      <td>0.098709</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-2.355754</td>\n",
       "      <td>-0.037282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-2.506669</td>\n",
       "      <td>-0.146017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-2.468820</td>\n",
       "      <td>0.130951</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-2.562320</td>\n",
       "      <td>0.367719</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-2.639535</td>\n",
       "      <td>0.312040</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-2.631989</td>\n",
       "      <td>-0.196961</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2.427818</td>\n",
       "      <td>0.378196</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>1.199001</td>\n",
       "      <td>-0.606092</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>3.499920</td>\n",
       "      <td>0.460674</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1.388766</td>\n",
       "      <td>-0.204399</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2.275431</td>\n",
       "      <td>0.334991</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2.614090</td>\n",
       "      <td>0.560901</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1.258508</td>\n",
       "      <td>-0.179705</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1.291132</td>\n",
       "      <td>-0.116669</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2.123609</td>\n",
       "      <td>-0.209729</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2.388003</td>\n",
       "      <td>0.464640</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2.841673</td>\n",
       "      <td>0.375269</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>3.230674</td>\n",
       "      <td>1.374165</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2.159438</td>\n",
       "      <td>-0.217278</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1.444161</td>\n",
       "      <td>-0.143413</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1.781295</td>\n",
       "      <td>-0.499902</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>3.076500</td>\n",
       "      <td>0.688086</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2.144243</td>\n",
       "      <td>0.140064</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1.905098</td>\n",
       "      <td>0.049301</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1.169326</td>\n",
       "      <td>-0.164990</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2.107611</td>\n",
       "      <td>0.372288</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2.314155</td>\n",
       "      <td>0.183651</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1.922268</td>\n",
       "      <td>0.409203</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1.415236</td>\n",
       "      <td>-0.574916</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2.563013</td>\n",
       "      <td>0.277863</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2.418746</td>\n",
       "      <td>0.304798</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.944110</td>\n",
       "      <td>0.187532</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1.527167</td>\n",
       "      <td>-0.375317</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1.764346</td>\n",
       "      <td>0.078859</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1.900942</td>\n",
       "      <td>0.116628</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>1.390189</td>\n",
       "      <td>-0.282661</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PC1       PC2  Species\n",
       "0   -2.684126  0.319397        0\n",
       "1   -2.714142 -0.177001        0\n",
       "2   -2.888991 -0.144949        0\n",
       "3   -2.745343 -0.318299        0\n",
       "4   -2.728717  0.326755        0\n",
       "5   -2.280860  0.741330        0\n",
       "6   -2.820538 -0.089461        0\n",
       "7   -2.626145  0.163385        0\n",
       "8   -2.886383 -0.578312        0\n",
       "9   -2.672756 -0.113774        0\n",
       "10  -2.506947  0.645069        0\n",
       "11  -2.612755  0.014730        0\n",
       "12  -2.786109 -0.235112        0\n",
       "13  -3.223804 -0.511395        0\n",
       "14  -2.644750  1.178765        0\n",
       "15  -2.386039  1.338062        0\n",
       "16  -2.623528  0.810680        0\n",
       "17  -2.648297  0.311849        0\n",
       "18  -2.199820  0.872839        0\n",
       "19  -2.587986  0.513560        0\n",
       "20  -2.310256  0.391346        0\n",
       "21  -2.543705  0.432996        0\n",
       "22  -3.215939  0.133468        0\n",
       "23  -2.302733  0.098709        0\n",
       "24  -2.355754 -0.037282        0\n",
       "25  -2.506669 -0.146017        0\n",
       "26  -2.468820  0.130951        0\n",
       "27  -2.562320  0.367719        0\n",
       "28  -2.639535  0.312040        0\n",
       "29  -2.631989 -0.196961        0\n",
       "..        ...       ...      ...\n",
       "120  2.427818  0.378196        2\n",
       "121  1.199001 -0.606092        2\n",
       "122  3.499920  0.460674        2\n",
       "123  1.388766 -0.204399        2\n",
       "124  2.275431  0.334991        2\n",
       "125  2.614090  0.560901        2\n",
       "126  1.258508 -0.179705        2\n",
       "127  1.291132 -0.116669        2\n",
       "128  2.123609 -0.209729        2\n",
       "129  2.388003  0.464640        2\n",
       "130  2.841673  0.375269        2\n",
       "131  3.230674  1.374165        2\n",
       "132  2.159438 -0.217278        2\n",
       "133  1.444161 -0.143413        2\n",
       "134  1.781295 -0.499902        2\n",
       "135  3.076500  0.688086        2\n",
       "136  2.144243  0.140064        2\n",
       "137  1.905098  0.049301        2\n",
       "138  1.169326 -0.164990        2\n",
       "139  2.107611  0.372288        2\n",
       "140  2.314155  0.183651        2\n",
       "141  1.922268  0.409203        2\n",
       "142  1.415236 -0.574916        2\n",
       "143  2.563013  0.277863        2\n",
       "144  2.418746  0.304798        2\n",
       "145  1.944110  0.187532        2\n",
       "146  1.527167 -0.375317        2\n",
       "147  1.764346  0.078859        2\n",
       "148  1.900942  0.116628        2\n",
       "149  1.390189 -0.282661        2\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_rotated_df = pd.DataFrame(iris_rotated, columns = [\"PC1\",\"PC2\"])\n",
    "iris_rotated_df[\"Species\"] = iris_dict.target\n",
    "iris_rotated_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display this dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Seaborn to plot a scatter plot where x is PC1 and y is PC2, with the points colored by the species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f243c094320>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 434.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x = \"PC1\", y = \"PC2\", data = iris_rotated_df, hue = \"Species\", \\\n",
    "           palette = [\"blue\",\"orange\",\"green\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare this scatter plot with all possible scatter plots from the original data. \n",
    "\n",
    "First create a new dataframe with the original iris data and the species names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   Species  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[\"Species\"] = iris_dict.target\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use pairplot() to plot the scatter plots.  hue will also work as a parameter here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/megan.owen/.local/lib/python3.4/site-packages/statsmodels/nonparametric/kde.py:488: RuntimeWarning: invalid value encountered in true_divide\n",
      "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
      "/home/megan.owen/.local/lib/python3.4/site-packages/statsmodels/nonparametric/kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  FAC1 = 2*(np.pi*bw/RANGE)**2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f243bd3d1d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/DJtmQ9f1TBoLabddsdvekcy82tu51+P1ULO2JsrlsbKwklXTVvU6E/Egzvtiu0La8Xg9bPhwA5PHTMZQBrqms2nPJqYcM6W7sZN22x1oUrnf/J/Ayb3dXdU0bRzwZETRMcBPlFKLU9i3lPBpkxeA8h5chUtcXZI4Y8oKBrxfgpBp+II+NtZuZGPtxqjyHwZ/GP63rqA8GIRAEAiar7uSnHIjpXgEISMxDGg7CAEf2J1QUAH9EI4PZeO+fv314bLVu1cDsOHyDYwoHNHvLgtZTBL2mMy82tu5N5WxqTLv5zgpnlMzHV/Qx/yt85m/dX5UefVR1WnqUWaQyv/x3UBbb09SSr2vlBqvlBoPnNnZxuoU9itl1DWZCZfKekzOZC5sJUGTkKv0GI9kGHDgHfjdRbD4ZPP3gXfMckEY6gyQfUucn9An0jjfis0KKSEHnxlk7FiTyoXrj4CXNU17SNO0JaGfXrZxIbBbKfVJCvuVMuqavBTl2XE5u3f/LS0wd1xFEkfIVXqMR2o7CP97NTTtMY+b9pjHbQfT1GNBSCEDZN8S5yf0iTTOt2KzQkrIwWcGGTvWpNJV+CHgRWAH0NdXIFcBT8QWapr2HeA7AGPGjOlr//pNXZO3RzdhMLMKg5nMSchtMsV2B5tIXVfLeKSAr+sGFKJpj1kuZAS5arspYYDsu8dxJYjdWpHG+VZsNnnEdrshB58ZZOxYk8qFq0MpdWtfT9Y0zQl8FXPnNgql1MPAw2AGrPe5h/3k0yYv7h7chAGGuxzomrgKC5lju+kgNh4pEPBzoH0/fiOAw26j7OvLac7Px1fgxtnWgHvbKnR7brvAZBK5bLv9xu6EkjFdD1pVE2DyHaCC0LIfw1VGg68p7mHEKvkMEFcmcX6JEbu1INYewTx2uKBlf0bEDAaMAB6vB3/Qj8PmoNxVnnOyH2K73WB3wrhpMP5qcJWCtxHeeMIsz2KCRhB/0G/+xvyt22ThmirWdb4t+gsQ3mpUSjUkef5UYJtSan8K+5RS6pq8nHNMz2modU0zJXFkx1UQAHPRurN5F3M3zqWutS6c1n3d7nWs2LLCdIG5aBFjXWUpjV8QhLRQUAFXPWG6shWNgAvvgTW3QNMejBMuZddFP2b2S11jYckFSzi25Fh2N+1m9ouzo8qdNiez/jYrqmxs6dicf+su9IJIe2zaYy5ar10Nh/dFl131BIw4MaWL15AcTqxdR9pwwAiws3Fn3P3h+NLjc27xKiTAVQaTb4enruuy1yseM8uzFH/Qz66m+OemsSVjcdgc6e5e2kjlne9qOuNcgdc6f3qTy/tqLNyEM4XD7X4OtQco60HDNUSJyyGuwoLQiafdE558wcwqOXfjXL52/NfCx7NfmkuDrymd3RSE1KDr5gLgpufhGyvCi1aAhjNqwotW6LT9F2fj8XrCD/eR5bWHa+PKGtqTfR8sCETb45y3zN95wwYlZrChvcHSriNt2OO1vj94vL0SqRCyGW9916IVzN9PXWeWZykyLqxJ2asspdTRfT1X07RC4MvAzanqT6r5rNl0+00mxhWguMDBgUOycBUEAL8RsJREsGm2qOO+SCQIQkai61A0Epr2Rrlo+grclmPBb/gty112V1yZjBOh14TsMUSMXZplqY8ZTEYOxx+0tn2/4U9pX4QhTA7GuAYSPDcFjECaepQZpGzhqmnad4FVSqmmzuNS4Gql1NKezlVKtQIZvd/fpeGa7I6rk7camgeyS4KQXnrQVIuM17PrdioLK6Mm4eqqauy6neVTltPsa2bNrjU5n+ZdyEIcLqj5AzgKwNuIM+CLGwuVhZU4dIdluTfgjWquuqoaXdOpa6mTZB1C8sTO1w6XddxrimMGQ5IesXYdOdc7bA6qq6qZPnY6xc7i8P3AoeeuO6QQQw7GuNp1u+W4yHX3+VT+9d9WSv136EAp1ahp2reBHheuQ4G6zoVrWWFyg6S0wEF9awdBQ2HTtYHsmiAMPiFNtQTxUbFxTTM/P5NF1YvCbi/VVdXMGj+Lb234VlTsRkleSbr/MkFIHYZhxhGu/UF4nLi/uZIl1YuYvTE6xrXcVc6SC5ZExQL+/Is/x2VzhR/8Q+NmxroZEvMqJE+i+fra1fD7y6LLCipSeumSvJKoud9qri/LL2PW+FlxdcryM3o/QxhMcjDG1Z3vthwXuS6HoymVmsRlmqbtAE5VnQ1qmmYDtiulTkrJBTqZMGGC2rq1N6GzqeGBDe/xm5d289iNZ6MnsRD96zv7WL75Y16980JGDM8fhB4KPZD2twfpst0BoWW/KQAe+7b+puehaCQer4eatTVRb9lnfn4m15x4DQEjgF23M3P9zLi38KumrZKMqdGk3W4hy2x3MEkwToxvb6RBxzKr8IG2A3zW8hkNHQ08suMRAGadNouji4/GrtvDi9YQGTxu0m67YredJJqvv/W8+b80gFmFPV4PP335p3G7Rj/5wk/CNmt1v0izXYvtZho9PHNkI/ta91nO9yunrmRU4ahEp6XddgeaVO64rgee1DTtoc7jmzvLsoK6pnbKivKSWrQClLrMndkDhztk4SoMGazkOCx3cnqIN/EFfZS7yrn9rNvDDyub9mxCobChCMbEbpxafio3nnIjXr+Xfca+nJRCEIYoiVzmDcMs+9pvTLe2zYvN+pPmoHccptxZgOEaQYOviX2t+8LjzVAG16+/PjwmqoqqKHQUopTCUAblrvKosSMxr0KPJJqvlQHd7dRb2Lahxcszdbfb7wv6qG+PTqBT314fZbNW94tHdjxCe6A9yiUeendtIYsI+ODo8+Dc74NuAyMIW/4rq2Nc/UE/Z486mxknz8Cm2QiqICvfWpnzsd+pfDK8A1M4+d87j/8G/C6F7aeVT5u8SbsJA5QUmLEZIokjDBWSkS0Ik0gXsDPeJN+Wz5wz5nDX5rvCbS08fyHz/zmfjbUbWXrh0rD746nlp/L9M77P3ZvvFikEYWiRyAWz4gQ4+F50+deWgT0fnp7ZrSxORUEF1VXVXHPiNTz+zuNcc+I1UWNy3qR5LN62mO2e7UB8vKAgxGGzmK/HTYPWg/BkjbUcjoVtG9euZpceTO4e0Um+Pf5eMG/SPPLt+T3WOdB2gBnrZ1BZWMmyLy/DF/T16tpCFuEshIk3wePfjHAVftQsz1IKHYVcecKV3PL8LVHPUYX27P2bkyFlo10pZSillimlvtH585BSKpiq9tPNp43epKVwoGvheuBw+0B1SRBSSjKyBWFCuoAlY8zjmPgoI+gLP4SE2rr1pVuZPnY6AMveXMa8SfOoLKzkxlNuDC9aQ3Ul5bswJGg7aC0p0rIvvvxPs0zphh5kcQzD4LazbuPuzXczfez0uLFx1+a7mHXaLIDww3uuxzwJPaDbYPrS6Pn6yz/tWrRCvByOhW03NH+S/D2iE8Mw4u4Fd22+C8MweqzTEewIH9ceru31tYUswtcKT10fI4dzvVmepXQEO7j1pVvjnqNC4yJX6fd2hqZpfwEeBtYrpfwxnx0DzAQ+Vko90t9rpYugodh/qJ0zjypN+pzikKuwSOIIQ4RkZAvCROoCWsRH+RJIexQ7iwHY7tnO4m2LWX7xIwQNQ6QQhKFJIhfMoN+63FEQPkwki+MzfNg0W3i8WNU5avhRLJ+ynMqiSkYVjpIdJ6F7/F544R6Ycl9XRtb2xu7lRSxs25dXmPw9InSOkeC+Yvh6rBOZVdhld/X62kIWYQSs7TWLpWFEDseaVNztvg18CXhP07R/aZr2nKZpL2qa9hHwEPDaUF60gplROGAoRgxPfsfVadcpyrNzsEUWrsLQICRbEEm3boghXcCSI7uSI7Tsh6a9ODWbZVvNvi6JKI/Xg1NpOGwOy7oihSBkPCGJhit/DzPXmr/HTQPd3rW7FaJkDPjbwofOtoaE4y00Fpt9zZZ1/IafH//jxzhtTlm0CtYYRng+RtOgMCaBTaDD2kZD8iKhcJAInB2tvbtHkNx9JVGdyPuFN+Dt9bWFLCLRnJrF4UQhGcFIKgsrcz6Eqt93PKXUPqXU7UqpY4FvAj8DbgVOUkp9WSm1pr/XSDd7G8yHjZHDepdkqaTAITuuwpDBne9myQVLwhNlr9wQQ/FQv7sIFp+M+60/s+T8hVFtLapexJpda7raPn8hblcF5a5yFlUviqubgVlSBSGakETDhjthxTTz9+Tb4d218NUH413pS48Jl7m3rWLJ+Yssx1toLK7ZtYZ7J90bVWfh+Qv5084/iYuwkJiY+ZhXHoq304JyuOrxhOEeVuEg7uKjen2PSOa+YlUn9n5RNayq7/cnYehTNMKUv4m01yseM8uzFHe+2/LZKNdtPmVyOINFOlKEP/HqHn70xx0suep0KoYlv+v687Xv4LTr/PGWSQPYOyFJ0p4ifCikt086q3Assanqr/w9xp5XaZh4PT7djtMIUFK7g6ajz8GnAjg1O25XBbrd3FUNGAE8Xg9+w49Dd0hW4S7SbrcwNGw3LSSSaJhyn5lFeNIcKKyA4ioY1vnmPCJLq+Eqo8HXZDneQmPRMAyCKkhQBbFpNpy6E6WpoZBRNe22m7N2azEfs+HOeDv99kZQwcRyOCnIKgzJ3Vdi65TkldDUET02YNCyCovtZhrNteYLmNNrurIKv74KzrnZnF+zlI5ABw3tDWEZQXe+mzx7t+uQtNvuQCNPhknwSX0bdl3rVVZhgOICJ5/UZ2/guJB96Jret53O2HgoVyn6liWUb1kSVa18zlvx7j6YLjHd6JIJQmaSKMbVVQq1W+HJa82yOW91LQgiNAd1SDje+jwWBcFiPra0U7/XDPVIRCgcJLKIxDabsJkkbNmqjtU5MiZylKAftiwxfyKZeGN6+jNI5NnzGF00Ot3dyChk4ZoEexpaqRiWvIZriBKXg62HO1BKoWlZ/xJEyDUi38ZrWrTcgrcR/8UL8Jw0jYAysGs65W+vxWHvevnT591dQcgUEslCeRujj+3RLz0jd1MNDAxlRI0Bq7EBomEpJEmsXXobre3U5jR3ZxPtuFqQzLwd9qAJ+nHYrD1oZP4XeoXNkcCGszsXhi9g6iCHdlzL8stw2nM7rltmiST4pL6tV4mZQpQUOOgIGBzy5nYGMCELiY2heu72qPgTv9/PrrFfYuaGb3HJ6mnM3PAtdo39Ev58MzN3SDO2Zm0NU56ZQs3aGnY17sJQRndXFYTMwkoW6orH4I0noo9dZeFTQrb/05d/yoeHPmTGuhlRYyBgBCzHxieHPpHxIiRHrF2+8UR8fOBVT0DH4a45/HcXmXO6kdimkpm3A0aAnY07mbFuBpesvoQZ62aws3FnVCZUmf+FXlM40jrGNTbpWBbhC/j4oPkDZq6fySWrL2Hm+pl80PwBvlDm7xwlZQtXTdMmaZr2N03Tdmqa9qGmaR9pmvZhqtpPF0opPqlv63ViJoDyTt3XumZvqrslCOklVuPv/bWwaQHcsA7mvIXnmHOZuzFao3Luxrl42uuBXmrGCkKmEikLNect0/53PAPjrzazDE+5zxwX3vrwKSHbt9Jonf3ibDxej+XYqD1cK+NFSI5Yu/zKIhh5ctfxTc/DsFHw+8sS67hakMy87fF6rOf+CF1umf+FXtPeYM6t1/wBvrfV/L3jGbM8S6lvr7ccS/Xt9T2cmd2k0lX4f4C5wGtAMIXtppWmNj8tHQFGDu/7wrW20cvnRw9PddcEYdAJu3cZPgqufJT2/GL8uoYjGKR8xx+xdyZ7S6g/pgLUtZjlZ486m8ljJlPsLKbZ18wjOx4RTT5h6BCbuGbYaDhU2xmHFVN36v3hf4b0khNptPqDfspd5cz/0nzKXGXYNBuGMuJE50XDUugVWky8atPe7nVcLfAFfZS7yrn9rNuj5u32QDt1LXU4bc6Ec7/f8IfrGIYR186mPZvMsdFZR1yHhSgCPtj7Mow5q0uLeO/LEPhOuns2YIiOqzWpXLg2K6XWpbC9jOCTTimcvrgKhzIQf9rY1kNNQch8Qu5ds1+czdmjzubKE67k1hf+nbrWunCa9uNf/S32l3+N/QdvU1lYGTXpVhZW8nHzx9zywi1hWY+H3nyIjbUbqSysZN6keeTbev+CSBAGnZCrfMjrIOS2FvBax2FFxCTFarTGjhE+s3gZAAAgAElEQVQDg9sm3sYd/3dHeGzNmzQPt8vNZcdexurdq8N1RcNSsMTKPq96wtyFDcWw2hLEZ3djU/n2fOacMYe7Nt8VZZsH2g4wY/0MKgsr+d2//a7HuX/ZRcv4wYQf8KO//yjczsLzFzL/n/PD94MlFyxhbOlYWbwKJg4XXHgPrLmly6anLzXLs5SQxn3sWHJkeVxvT/R7RtA07QxN084ANmqa9oCmaeeGyjrLhzShrMB9cRUenm8nz65T29hLV+FAh5nqWxAyiEj3rhknz+DWl26NdwebcD0A5dsej9MfmzdpHsveXBauf+tLtzJ97PTw8V2b78KQHSRhKBDrKt+0B566Dvzt1vqtIW1M6Faj9d5J99LU3hRetELX2KhrqWPmyTPDdUXDUkiIlX3GugHrNvPBP9JWpy81yxNgGEZ40QpdthnyBqhrreOBfz3A4urF3c79tS214UVrqCz2fiCuw0IUgY6uRSuYv9fcYpZnKTo68ybNixtLeo6nJ0rFjuuvYo4nRPxbARek4BppY09933dcNU2jvCgv+YXr4X3wl9mw86+QVwQTb4LzfwTdazYJwqAQcnEEsGk2a3cwm/nQ49j4c8YCKy5eTsAIYtNt3LbpNrZ7tkfVL3YWRx37DP/A/yGC0F8SyeDYHPD83WZsq6sUSo6C4UdEZWrVNZ2xpWP5yRd+gmEYLL94OXUtdTT7mvmvbf/F3DPnWo4tl92FQ3ew4fIN4kopdE8i+4x0A/Z74YV7umzV22gef2NFwmZ9hs/SNh161w7QxtqN3HXOXaycuhK/4cemxc/9LrvLsp24+4G8yBRCBBPYdBbbSEewg8XbFke51C/etpj5X5qf7q6llX4vXJVS1QCaph2jlIpKxqRp2jH9bT/d7D7YQnmRkzx74reQ3VFe5KS2KQlX4fZDsPIr5kA8+evQchD+sQhqX4Nrn5bFq5AaLATlIx+qjWCABu9BfEYAp27H7apAt5nThNPmpLqqmuljp+PQHeF/hybUNbvWYNcd1N20AWdbA+5tqxjNDBheicfriUrOAYTdJSOP0XU8LZ9Rkl9Ok79ZpBKEzCRSbqRqAkyaA4UVkF9ifv7ktTBuGlyyAA59iuEspMHowADzRyl0XUdHR0Pj0bcfZWPtRoCELsQKhcPmCOsdG8rA4/XIGBHisTtN+xt/ddei9I0nomWZ7E6MI79Aw8gT8Ol2nMNH4j7pm+iaZsa/WtwfQm7ukbZZXVVNaX4py6csD98HdF1nhGsEgOXc7w14LW088n5QXVWNrulRMa8gklA5i81hbdNZ7DYbkr+JpCy/LE5aKtdI5V//NBDrGvwH4MwUXmPQ+eBAC5UlffehrxiWx9ZPGnuu+PzdUP8BfHkejD7VLDviDNi8GJ6dC9P/29TKTBK/4QdFzvvCCxH0EPdkBAPsatzJ7JfmhuOOlpy/iLGlx6Pb7JTklTBr/CzmbpzL14/7evjfkTGuL+7ZxPyt881zL1rEWFcZOl3ukSFX41D9ZW+Y7mMhF5jbNt1OWX5ZXNsS7yRkFCG5kY33wdk3w5+/1zWmvvog7HoeTrkclk/FOPo8dk36d/77zYe55sRrwpmEQ67Bj7/zOLPGzwLM3ao1u9awuHoxczbOiY5xzXfjzjMf3iPjzWWMCHG4ymDy7ab7emQMdqQsU76bXWdexewXvxcx3y9k7CsPoW9ZYhkXW+wsZlH1ovDcXF1Vzazxs7jl+Vui5vWSvJLwdazm/qphVd3eD0Ltzlg3I8q+nTYns/42S2w+FymssLbpwoqezx2ilOaXWj5nlXbKCuYqmurMAtrnBjTtBOAkYAFwW8RHw4HblFIn9esCMUyYMEFt3bo1lU0mxDAUJ929gcnjKphx7uf61MaaNz7lf/+1l7fvnUJhXoL3BJ5d8OBE+PxX4KyYDGmv/x62/y9MWwgTv9Vzn5XB0jeWsvyt5QRVkG8e/03+c+J/kmfL+R3b5Ff9A8Rg2q4lLftNrb7YZBw3PQ9FI/G0fEbN+plxb8FXXbyC8qLReLweatbWUNdax+LqxSx4dUFc3dvPup05G+d0nTttFeWuciBecL7EUUxTu4d2I8BHhz5m2ZvL2O7ZnrDtyLZyiLTbLWSA7WYihgGH62D51PgxNfM5WHEJNO3BM3srNS9+j9vPuj3hmFnw6gJWXLyCgArg0B3YNBvvNbyHy+4KZ271eD2snLqSUYWjosZiZFsZNkbSbrs5a7c9zPVA4vn+ggcpXzLB8px9rfu475X7wp42pfml4UVrVBsxdhg791vtnpbkldDU0YQv6EPX9PCiNbLdu865i1teuKXba6UIsd1Mo2lveE4NE5prS45MX78GkM9aPmOmxRhdcfEKRheNTnRa2m13oEnFjus44FKgBPhKRPlh4NspaD9t1DV78fqDHNGPHddRnTI6H9e3clJlsXWlfyw2XYFPuSL+s/HXgOd9+Nv/g+OnQHFVt9d78PUH+e2O33LWqLPIt+Xzv+//L80dzcw/b768lcx1eoh78iVIve7rTL0eGeOaSMqjuxglXdPjHjDKi0ZTd2hv1MNIorYl3knIKHQdlLIeU0YgXO7T7d3K34TKFYojh5kPYHtjxkQIf2cMeORYjGxLxogAJBXjmnC+j3RDjDnHH/SzsXZj2K19+ZTlSdmh1dwPxN8POo/rWuoSxnn3dC0hS4mYU8OE5tosReRwrElFjOsaYI2maecqpWLV64Y0HxxoAejfwrXYXLh+eDDBwtXbBG89DcdUg6sk/nNNh3O+a7qirb0Vrnkq4bW2H9zOb3f8lvOOOI8ZJ81A0zQqCip4ZtcznFt5LpeNvazPf4cwRImMadW0bqU6nLrdMu7IqXfFuIY+TxSHFxuzmoxcR+x1m33NlvGzIv0hZBSGYY6pGzdA60EzrKN2qzmmdLsZj/X+WvI1naUXLqXcVZ5wzITGSmhnCg2WXrg07IUQqhtKgmMVayjyOEKYJGJcE873kQ/FMVJODlt0boPS/NKU2WHkrqyu6VRXVYcXyKF2vYHoRJdi8zlEaE6Nteksjve06/YEuUSy929OhlT+9ddomnZ1TFkzsLVzcTvkSMXCdXSxee5HnlbrCm89DYF2czc1EcNGwWnXwGuPwAcvwHEXxlVRSvGrrb+i2FnMVSdchdYZDzv16KlsP7idX279JReMuYDivAS7vkL2ERvTOm6aGRMSGSMSIdXhdlWw5PxFcTGublfn5xGxSpv2bGLh+QvDkjih2It1u00p5/C5TouXMTHEXveNfW9YxnVExk0JQlqxihf/6oPwz4fMmNd1d8Dk2zHcx3LQ62HeK/Mod5Uzb9K8KA3MUIzrkguWUJJXEhe3Om/SPBZvW4zH62FR9aLwjpRV3KDI4whhkohxdeeXs+T8hcyOmMOXnL8Q9/svmBUspJxi8w9UV1VHxbz21Q6tYrYXVS8CiNJ1jXxhIzafYxSNsLbpohHp7tmAITGu1vQ7xjXckKY9DJyAmZAJ4HLgI6AM+FApNScV1xlMv/8f/XE7z27/jIevm9Bz5W6Y/cQ2Jh1XzuKrTo//cPklpu/+15Z230jQD3+aBYUj4Ob/i8r0B7Bt/zZmrJ9BzedruHBM9MJ2z6E93LPlHmadNovvjv9uv/6WIUza/f4HPWbFKs4plOlUqV5nFYbot+Lz/zk/7k3gnafPxmjzhLMK65cuDMdHdUfkdXXdzgyr2KvMit8bLNJutyDxVnEkiiG85g/w5++Gd14939pAzYYbwrZ8avmpzDptFkcXH41dt6Ojo+s67nw3De0NlnGryy9eHna1jHzTbhU3mGHhIGm33Zy12yRiXGnZj7H5QRomXm9mFTYCuP/1KPo5Nye8P1jFVldXVXPnOXdiKKPPdpgoZnvl1JVR7cKgZRUW2800mmut8wncsK7HELqhyr7WfZax3qFcBwlIu+0ONKnccT0VmKSUCgJomvYb4O/AF4EdKbzOoLHrQEu/dltDjCp2sfugxY7r4f3wyctw2lU9N2JzwOnXwd9/CTv+AKddGfXx4+89ToG9gC9WfjHu1DHDx3DGiDP4/Tu/Z+ZJMyl0FPb1TxGGElZxTu+vhan3J0xmoNvslEcE/VtJbpS7yqlrqYuKdQrxw3HXUPk703vAqJqARwXwHdpruQhOdN1E8U0SyySkjVgZqUQxhG2dsh9X/h6GjcKngtS11nFq+anceMqN4Zc8Tt3JiMLonYJEcatBFcRpc8Y9oCeKGxRykCTs0ygaQYMK4Du0B6dmx63Z0bcsoXzLkui2zv5OwvuDL+ij3FUepSv5yI5HMJRBZVFln7ufyPat2hWbz1GC/gQ6rtmr/e4P+i3HhT/H9e5T+aqqFCiKOC4E3J0L2Y4UXmfQ6K8UTojRxfl85Gklbnf7g+cBBWPOTa6ho88D97Hw4s8g0PWVNrQ38Pwnz/PFI75IXgK916lHT6XF38LaD9f28a8QhhwhrclIYmKWuiPkvlWztoYpz0yhZm0Nuxp3mW/AdYepuxpBZWElzrYG89yqCeya+nNqNtzIlNWXULN+Jrsad2IEe04qEHIHi2tbYpmEdBByC/7dRbD4ZPO3EbAeW0E/XPAT2HAn/O4inAd3Ul1VzffP+D4LXl3ADRtuYMGrC2hob8BQRtTpiex+Z+POqLEnCFEkYZ/R8/E0ajbcwK72gxjnzo5uq4f7Q749nzlnzImy5TlnzCHfnt+vP0HmfKFH7HkJnmeyVzHDYbN+zgrlOshVUrlwXQC8oWnack3TVgCvAw9omlYIPJ/C6wwKBw6309Tmp6o0FQtXFy0dAQ4cjlm/737RDDIv/VxyDWk6nDkTmvfCtkfDxRs+3kBQBfniEfG7rSGOKT6GI4cdyVPvPxW/gBayk5DWZGiyt4hZ6o6G9oZwzBGYb/pmvzibhvYG3MEgS869NzypmvFRv8K9bZV5bvWdzN5yd/S5L82lwXuwx+uG4vei2pZYJiFdtB3simUF8/eGH8OVq6LH1hWPgSO/S9MVcG+8j9sm3BrWbgVzLPzHxv8wkzBFYGX39066l0d2PBI19gQhikT2+c2VYfu0no9vpeELs3p1fzAMIxyjHWrnrs13YRj9e6Eic77QMxpMXxptr9OXks2esTZszJs0L2pczJs0Dxu2NPcsvaTMVVgp9T+apj0HnNVZdKdSKrTHfVuC0wDQNK0E+B1wMqCAG9OdofidukMAHFXWf7faMW5z8fvevsOM7JTHwTDMhevo8eaCNFlGj4cRnzcldM6YAXYnaz9cS1VRFVXDEvv5a5rG+VXn89i7j/GW5y1OqTilP3+SMBTQdVM8/qbnu1zIYmKWuqM7yQ3d38HYdT9m1Xk/wFfgNmNaN/8Gfcp9cM6/43OP6VZap9tuazpjS8eyatqqTI7fE3KFRC73034VPbZcZXCoNqquXrsVW1tD0pIhIbv3BrzsbNzJf237r3BWYXGXFyxJZJ+Tb4Mp94GrNPF8rIK9uj/4jAT3BKN/dilzvtAjgXZ44Z6wTeNtNI+//rt092zAaA+2s3jb4ijX/MXbFjP/vPnp7lpaSXVOZR042NnucZqmHaeU+r8kzvs1sF4p9Q1N05xAQYr71Wve/ewwAEe5+9+VMe7CzjYPMfn4zreZ+7aDtwEqz+hdY5oGp1xpDtjtT+L5/FTePPgmlx3Xs9TNOZXn8NTOp3hq51OycM0RDGXQgIFPBycGbmWgJ+lokVByo/MNpz75Dso33W8mogEz8RPmbr4TzVrSJsk07rqC8mAQAkEgaDabvS9WhUwm5HIfmxRE16MTjwUDoNni6uYHAiy9cCkuuyscE+jxetDRMA59hq7r4QVDKG7V4/Ww4NUFIncjxMevxi4uE9ln60F48loAnLO3JpA6c+CJvD8YAfTDB0yXd5sDikZBRF4Cp805YFJlErMtdItuh5YDYZsGuqTHshSHzUFZfllUWVl+Wc67Cqfsf1zTtPuBK4G3gZDfiAK6XbhqmlYMnAfMBFBK+YC0v1Z+57NDjBiWR2Fe/7+ionw7ZUVO3vvsUFfh7hfN35Xje9/gEWdC2XHwj4VsHmaGFZ9WcVqPp7nsLs4efTbrPlrHD8/6oSRpynKMYIBdjTvj5G3Glh6fMElSJJaSG+cvwv3sf8J7z3a56rxwDxSONFPVr/wKNO2hZMr91pI2+Uk8mFhJjVz1hLl7nORusSCkDFdZvIxUjLQIwQDsfws2LTBlcTrdhY0TLuWgq5B5L/2/KImbPHse9/3zF3x33FWM/cd/o5//oyj7FrkbAUhuLgyFhMTWseeHF7Tufz1qIX2ziMOBNmY9f0uUHM7Y5+9DD83vVzwGI08OL15L8kpEqkxID0UV1vNwUXKhT0MRd57bcry583L7PpBKOZz3gVOVUr1KxKRp2njgYeAd4DTgNeA/lFKWwqeDlSL8wl+9RGmBkx/827iUtLdgw3u0+YJsmHOeWbDyq+bg++p/9a3BTzbDS7/gP0+/mH966/jV5F+FtVu7Y2fjTua/Op9ffOkXXHrMpX279tAk7ft1g53e3tPyGTVWsjIXr4jKHNwdUZIbaLif/U/zoSZEyRiYuRbQYMUl4bf+nmueoGb7r/smaZOMlEPukHa7hRyXZmjZD3+ZGy98/5VFXfYYKdVQNQEmzYHCCjylY6LkcMAcB/d84R6+87fvmGPi1P+g/Lk74ux7CMjd9ETabXfI222yc6HVrixElRn5pTR4PfhUAKdmR9cdXL3u2vg5+tT/oPzxq7uuFSE3kki2JgulysR2M42mvaY+duw83I1KwlBH5HCsSeUe+4eAg95nELYDZwDfV0r9U9O0XwM/BP5fqIKmad8BvgMwZswYy0ZSSZsvwIcHW/n6GakT+R3jLmDt9s/oCATJ0xTU/guOvbDnExM2eC6B4iPZ3PgO46u+mNSiFeC4kuNw57tZ9+G6XFu4poXBtt1IfEagz3GmIaLct5r2mjutkTTtITxPRjxc+QrcfZe0SSQ1Eki7I0ZOkU7bzSgCPjNm8P2YjOxT7+/6d6RUQ+3WsDub79YdluMg5OpV11qHr8Btad/iOtk3sspuk50LY93WQ0SU6UD5sK4MpXWH9lrP0QURuzkxciPd5T0Q+k9W2W6qMQLW8/CUn6enP4OAyOFYk8qFaxtmVuEXiFi8KqVmJz4FgFqgVin1z87jpzEXrmGUUg9j7soyYcKEAU+J+/6+wyjgqLLUhdoe5S4gYCh2H2jlRD4EfxuMOKHvDWo6bx73RVoaNnO2bXjSp+mazlmjzuJvn/yNpvYmSvLFxWcgGWzbjcSp2xPENfVx2CeKpQrJJ0R85mxr6DkWKlHsVk/XEQaFdNpuRpHIHsHcESuoMOMBLeo4NesxaCiDxdWLcee50R3DME64FF3sOyVkld0O4FyY8P7QFpG5umSMaduhcwYwxlXIMttNNbo9Qa6B7I5xtRpvuR7jmkq/oz8DPwNexnT3Df10i1JqH7BX07SQT+6FmG7DaSOUmOlzKVy4jinrStDE3s41+ogT+9XmP1wu7Epx4cev9+q8s0efTVAFeX7PkFMpEnqB21XBkvMXxUjWLMLt6mNMSHfyOjGflXz8CrPGz4rS+5s1fhYljmLzXCvtwQPvmOX9lPERhJRiZY/Tl8LTM7vstnCkGW8VI4/jdpXHjcGFkxeiazoLXl3A9euvZ8aL32XXRT/GcJVZX1/IXQZwLnTnl7Pk/IUx94eFYUmzrhjCLpfEEkdx9/O6IAwUoRjXWAmyLI5xLcsvsxxvsQmbco2UxbgCaJrmAsYopd7v5XnjMeVwnJguxzcopRqt6g6G3/+P/ridv7z5GQ9fd2bSLrg9ETQUN674F9efexR3tf8SPtoE31jRrza//to88tsaWfXhTnZctZyOksRyOJEopfjxP37MkcOP5JEpj/SrD0OItPv9pyNmxQgGaPAexGcEcOp23K6KpBIzJW6wmwyXEZ95bDbL2L5wfG1PsVs9ZdLMHdJutyDxVmF79HvBsxMis2mH7NZVBi37ojOyeusxnr2VhjNqTNmogI9AyRhmPH+zxAkOAllhtwM1F7bsx9j8IA0Tr8en23EaAdy1O9DHTDTdMi2yCqcib8IQQWw302iuhXeehRMuBmWYMpLvrYcTLw3HYGcbfYwpT7vtDjSpzCr8FeCXmIvPozsXoz9VSn21p3OVUm8AE1LVl/7yxt4mjq0oTNmiFcCma1S5Xby77xAcegUqPt+v9vZ3NLGrtY4ryyegPt7NyB2r2fOl7yd1rqZpnDX6LP6y+y8caDvAiIIR/eqLkLnoNnv/HihiHpoC+SV4COLXwYGBs6OJ9mB7V/KYzpgqX6L4qVB8bU+xW4litlKAYSjqW334AkGcdhtlhU50Pevn+kEjq77fkP0bBqhOWSb3MXDh3fDCvebiNWS3Nnv8A1TAh/7es5RHxIbX3bRB4gQznCFnw8FA/EuTnl5QBnzoW5ZQvmVJdPmct8B9tOUpifImeFWAvYf24rA5KHeVY0+B+2YWJCcbUIacjfaXoB8O7IBjzwfdBobPPB43Jd09GzAkptyaVM4C9wBnAU0QXowek8L2B4U2X4D39x3m2IqilLd9lLuQ+k8/hEOfwoj+LVz/0fA2ACe5T6C+6gzK31+Prf1QD2d1cfbos1EoNny8oV/9ELKYGHfewMv/zc7m3cxYfwOXrL6EGetnsrtpN7dtuo2atTXsatyFoUwlrFD8VCRR8bWh2K1IBiGO1TAU7+8/zGVLNzPp/o1ctnQz7+8/jGFIOFEqyKrvN2T/f5lr7rIunwpLToPHLgM0uHiBmUG4O7u1sHOHc5jl2Mj1uKVMIaNsuLuQihAhKablU2HJePP3/rfM8u7owxycaF7/uPlj856wbgY7G3cS6EUCQCsMZbCrcRc1a2uY8syUuPtLrpNRNjpY5A2DiTfB49+EByeYvyfeZJZnKQ7dIfcKC1K5cPUrpZpjyobcLLOjthlDwbEjUr9w/VxZAcd2vGse9HPH9e8Nb+F2DOOIvDL2H3setkAHFe+u7fnETkYXjuao4Ufx3EfP9asfQhbTdrBLGxDwTLg+rCcG5pu/uzbfxY2n3Ehdax2zX5xNQ7uZ2KPH+No0xbHWt/r49qNbqW30AlDb6OXbj26lvjW332Cmiqz6fkP2P/7qsC4rYP5ecwt462HyHd3brYWdq8IK5k2aFzU25k2aRyrDdoS+k1E2HDMH07THPG472FWnZV+XtmWozlPXmeXd0Yc52GpenzdpHsveXAaY94S5G+fi8Xr69OeGaGhvCGsYh9qNvL/kOhllo4OF3wtPXR9j59eb5VmKUkruFRakMh3X25qmXQPYNE0bC8zGTNQ0pHhjbxMAxw3AjuuYsgLG6O8T1POwJXDFSQa/EWBL03tMLB6Lpml4h1fSXHE8I3f8if2nfgNlS+5tzMSRE3l619PUHq6lalh2xggI/SDGnddvs1m6rRQ7i8P/Drmw6DY7Y0uPZ9XFK6zja3XdTE520/ODGsfqCwTDN/sQtY1efIHggF43V8iq7zdk/65Sa7d2R4HpGlw8JrHdWth5R7CDxdsWc/tZt4czRS7etpj5X5o/8H+T0CMZZcPJyOFESjFF1gn2IJnRhzk4dl5Ht3HbptvY7tkerpMKuQ5xkeyejLLRwcIIWNt5P3f3Mxm5V1iTyoXr94EfY0rhPAFswMwyPKR4s7aJkcPzGO5K/Vb8GHcB5fouPnMdR1U/YkBeP7SbtmAHpw7rWvzuP3Yyx7/yW9y7N1F//EVJtTNxlLlw3fDxBr51yrf63B8he4iKK7LbcZ9wKXpnfJ4jGLSUT2j2NYf/7UQz9V7tTnRXGeXopt+FrpvJFCJJQxyr026jqtQVddOvKnXhtNv61a5g0t33G/vdlbocNHr96f0uu0t8E3Kl9Daav4tGwKQ5UHykuZjVdTPmVRmAnritCDs3lIG9dT8er4c5G+eEu1FZWJmSuECh/1jZ8L+dOAJN0/i0sS1sq8DAzwV2J4ybZu76u0pNW3zjiWh3XpvDuk5ekTkXGwFTMqRoFNhjnmv6MAdH5k3Y17ovbnc15Mro8Xr6HJ/qtDmtpXpEdgcwbfTmL32Ob0wYg03XCBqKp7fuSfo+NiTR7TDl/vjkTFk8b9p1u9wrLEjZ9oZSqk0p9WOl1ESl1ITOf7enqv3B4vU9TRwzALutAEW6n5P0j3lbG9uvdv7e8DY2TefzRUeGy5pHjMM7bCQj3/wDJOlGUFFQwTHFx7D+4/X96o+QHcTFFa2fya6L7sQ44VIAyrc+yqLqeDexR3Y80iWlsHlpVyzW/rfM+MBEsVkD9Xd0E/9TVujkt9dPoKrUBZiLqt9ePyH8INrXdgWTRN9vqcsR9d39ePV23kv3d9lT/GDIlfKNJ8wM8BfeAxvuhIcnw8pLof5DWHeHaecBf4+xiKHx9fg7j7MwRoZkUfWibMsoPGSJteF/O3EEsy88nise2hJlqx/Xtw68/brKYPLtpt2tmGb+nny7WR6icGR8nSk/h+ZPYcUlZtzrikvgwNumnaaQcld53D1hcfVivAFvv+JT3flullywJDrU5IIluPPdKe3/UKUk386l46u4YcW/uOBXm7hhxb+4dHwVJflZvKApqoCjzoGVX4Elp5u/jzon6+VwYsfXoupFIofTX19pTdP+gvne2ZJksgr3hoFMEX7gUDtn3fcC151zFJeckvrU7sP2v8rJf72KH9lv4xc1k/vczvStPyVPd3DbMZdHlZd/8gpHv/EU733lAQ4fcXpSbW34eANPvv8kz172LEcNP6rPfRoCpH1bLNPT2ydMvX7Bg5Qf2g/eRgJ+P55jzsFvBHEYQZxtDbQbPpxtDbi3rUI/7Sp48lrz5JIxMOW+6OOQ3M0AcvBwB5ct3Ry367f6lklUDMvr865pT+0OEGm3W+id7Vp9v/Wtvqjv7qHrzuRnz74z2N9lND1JMpl/TOcuqh9WTI2vO+U+c6Ew8zlzcUPKKgMAACAASURBVNBNW5Hj67JjL2PGyTOwaTbybHmUu8pxJBniMYRIu+32dc6NtGFN07jioS1xtvqz6Sdzw4p/RZWl3H6TsVGrOt/9F6y6PP68mc9BSdcL71QQMAJ4vB78hh+H7sCpO7l67dX9lntKc1bhjLbduiavpU0+dfO5VJa4BquLg0vTXus5dgBsOlPweD2s3LGSrx3/NWyajaAK8qedf2LGKTNEDqef/DIFbWQEr4fiWwcgMRPAMM/rAPy15Vju8isKHb23r7r2ej5s28eVo8+L+6y+6kyq3l3HqDf/kPTCdeLIiTz5/pOs/2g9N592c6/7I2QPCeOK2urNN/mYE8aoOW+Z7mdLxsc3cs6/d/07FB8YeRwY+BilnuJ/dF3r08NlTsYV9QGr7zf2uytxOdL/XSYTPxhypWzaa103FP+aKP4qoq3I8bV692pW714NwIbLN2TjonVIE2nDnza2WdpqgdMWV5Zy+03GRq3qaNqgxQPadTujCkeFj+ta6lISn6prunghJMAfNCxtMhAccvlQkycHY1x9QR8r3l3BindXRJVffeLV6elQhtDvhatSalMqOpIJvLG3CZuu8bmywgFpv8jzBoecI6lvH8579UHOHNX7rz8kg3PqsM/FfaZsDvYfPYmq99aT3/AJ7e6ed1DdLjdjS8ay/mNZuOY6CeOK2iIyOY6bZj4U6TaMEy6l4YwafAXurh1Xb2NX3VB8YOTxAMvdQPdxlpnYbi4Q+901ef3p/y5DMayxb/A1zdxp1SPiVkOfxdYNxb/q9m5jEQ1loGs6j178KA0dDTyy4xG2e7ZL3N4QING4b/NFL1IHxH4T2ajNae60BnymvcbWUcr6vNjYuO5ivPuI0+akuqqa6WOnhxPKrNm1Ruw8hThsuqVN2m1ZrHOr25Oz6SxCxpI1WWzlvefNvU0cVVaA0z4AX4tSDDu4jbbi4wB4p75vb8b+r+FtKpzFjMortfz84Oe+QNDmYNT2p5Nuc+KoiXzQ9AG7m3b3qU9CduB2llhI2CzEvW2VWWHcNDOWavlUjE33s+uiH1Gz/ddMeeHb1Gz/tRkPu+dVs27JGLjiMfPhPfJ4EGKU+hPHmo52c4HY7+6Z1/ay7Noz0/tdWsmBfPVBeO52Mz41GOiKW900H654NL7uG0+Ydl00ImEsYii2dca6GVy//noWvLqA75/xfaqrqiVubwiQaNwfVVYw8PabSLKm43BXPPVzt5s2GFnHnhdfdsVj0fGAyWjE9oGSvBJmjZ/FglcXcMOGG1jw6gJmjZ9FSV5Jv9oVuhhRlBc3fy679kxGFA1SmEU6KKro2aazDBlL1vQ7xnWwGag4waChOPXeDXzh2HJunNR3qZpEOFvrOPOPX6Ru3AymvDOFS49z8IvzeheL0GH4+eLLt3Fu6Qlcd8QFCesd9ebTlO95lTevfZxAQc8PRU0dTfzgpR9w82k3893x3+1Vn4YQaff7z/QYV1r2Yzx7a/Qu6kdb0M+52XyDr2mmuH3THjzXPEHN9l8njoctroJX/wfGnBW9A3XJAvOzAWagsv+mIatw2u0WUmO7GZtV+HAdNNdC60HYvBhqt5oPRTesC9s7V/7etN8LfwJBH+QPBzRTQ/D1VXDOzV11Q3TGInpsNsvY8ZVTVzKiYMRgxu0NNmm33VTNuVbjHgYhq7B58ehdUc0Gv62OtrVx08y5VSmzjmHAlqVweg3oNjCCpp2e/Z2ueMBk4mf7QMJcCb2McU0zGW+7gYDBgZYOAkEDu01nRFEe9oHYdMkUmvbCu2vjswp/flpWx7j2YSyl3XYHmuzdY+8luw+20NoR5NiByijseQMAb/FYji6Gdzy9j4V5rWkX7YYvSgbHin3HTqbi4y2M3LGaT8/uWeamJK+EcaXjWP/Rem457RY0LevtPneJfQjKd0PrflPzT7ejjz6N8mAQAgEIBqG9oStLdUSMia/A3X087Pe2wpYlsCXm+lN+3sdu927B2Nc41p6u1Z92c43YBDc2DXRdZ3SxK/x9pv271HXTvh+ZYh5XTTAXqcNGmQ9HVzxqLmrdx5huwLoNln4pvp2JN8LXfmO+oAktfjtjEX1Y6x+H3IeFzCO0KPAHDRydiwIrW02L/Qb95g7/lPu6XgruXB+tJhBo75x/l0SfO/HGiDpJxM/2AdFgHVyG1tZTPzACsOEO8yeScVPS059BQMaSNf1euA52VuGB4o09A5yY6eDrGLqTjmFjOHoYbNhjEDQUtl68of1749s4NBsnFHW/Y9VRVEFj5WmMfOtP7DvtmwTzh/fY9sTRE3nsncfY2biTce5xSfdJGEKEXMP+92rzAeXc2XDK5fDUdeZxyBU46vi2rkx+NX8Ix5g42xq6j4c1gglis3qfhCYkQ/PtR7dS2+gNu+WNGzks5Tscg3mtbMbqe7z/8lNZ+fJHzP3yuMz6PkNxhEUj4IKfwJ+/Z9ptyB14z6vmvzfcaS4WrOy6fhes+mbXOS/+FFoOgN2J02YTTcohRCBg8N7+w8z6/Wth21127ZmcMHLY4O9oxc7ZJWPgylUw9QH4wwzrebpkDFz/557n30Txs/3MQyAarANPRtnoYGHrJt47S5GxZE0qLPyXwK+6+RkSvFHbRKHTxuji/AFpv8jzOt7hR6N0O0cXQ3sQPmpOPpZEKcVL9Ts4oehI8vSeH/7rxl2Eze9l5I7VSbV/5sgz0TWddR+tS7pPwhCj7WDXAxCYbmShRSqYO0pxx9d3HW+6H6YvhZIxuP/vVyw5996YeNhFXfGwr69KEI/SlX0yWepbfeEFEJjZE7/96FbqW1P/1nEwr5XNWH2PdzyzncvPPDLzvs9QHOHkO7oWrWD+/vP3zHHy5LXm8ebF5sI00q6nLzXHRuQ5k+8w2yyoEE3KIcaBlo7wggBM2531+9c40NIx+J2JnbOb9sCTNdDmSTxPN+2BDT+Gb66Mt9PIZ4dE8bMF/YsZFHsfeDLKRgcLTQ8/fwBdNp3FXisylqyRrMKdvLm3iaMrCtEHwE1WC/ooqn+LhiO/DMAxnRug79YbHFeaXBbCD9v2UdvuobrslKTqe4dX0lB5KiN3/JH9p15OMK/7neThzuGcVHYSz374LN8//fvYdMmUmnXEuobptujjkLRHouParfDCPTDzOXRgrMPFqktW4TM6dfacJeiXLoSL53e5Id+wznRtsznMRaut91POYMrQiORNakj0PYZkcDLq+9R1GHEi5BVZu05GjpPareZu6pT7YOTJZkbLp2ea5ZHnlB8PxWNA182xUjqWVdNWpUuTUugFGSU1ksid11HQdRw7TwO8v9bchY10J37hHvj677rqhOz+pudTmlVY13Sx9wEmo2x0sAi0mzbcnU1nGTKWrElZjKumaWOBXwAnAuFtS6XUMam6xkDR7g/y/r7DXHLK6AFpv6DxXXTDh7czo/CYIrBp8G59kK8cl5zr5EsNOwA4bVjyX2fd8V/GXbedkTtWUzfhuh7rTzpiEsveXMar+17l3Mpzk76OMESIdQ2LdecNSXskOoaw+yNFI9GBuPQAkUk9DMNcsCpl/u7FZBsZY2bXtR6lU7qLS7WKV0vkTiWSN6kh0fcYksEB8PkCeNr8Sf2/DDi6Dg6XtSta7Dip3Wq6Dd+wzrTrlgPRbVlINIgm5dAhWakRvz9oJscxFHZdY0RRHg7HIMnh+Nu6jq3m6ZIxZrKxJ6+NLhskvWCx94ElZ+VwWg7E23QWy+GAjCUrUmnly4HfAAGgGngU+H0K2x8w3vnsEAFDDVhipmGdiZlCUjgOGxxZBO82JP92bFP9dsbkV+B2Dkv6HG/xETSOOpmR259G97X2WP/0itMptBeyZveapK8hDCFiXcNi3XlD0h6JjnvjStYPqYVQ/M4VD21h8gMvce9f3uY33UinhOIpL1u6mUn3b+SypZt5f/9hDEPFtXXFQ1t4b/9hAgHrfojkTWoodTni5Bruv/xUnnltL/dffir3/uVt3j/Yyj1/fiup/5dBIZE8Tndu7z1J6vRTWkQYfMoLHHHzzW+uPZPygq5Fn98f5L0DLVz58CtMfuAlrnz4Fd470ILfn2JPgkTuvKXH9DBPP27u+HcXqjFAcjjCwOPOt7ZRd/7gvJhICzkohyNYkzI5HE3TXlNKnalp2g6l1CmRZSm5QCcDISmyYvNH3POXd3jw6tMpGwAdrOP+PoeSz/7BzvMeDJc9sA3ebdT453U9L0Qb/S2cv+UOLh1xFl8b1bud0IKmWk7atJDaiTP57Mxre6z/6DuPsqVuCy9d8RJFzoFZyKeJtGeCyQg5nO6yCtscUDjSzCQc+txVBt763ruS9UNqoa7JyxUPbYl6m/xvJ47g7q+cBBC3o3rwcAeXLd0c9/Z59S2T8AeNuLaqSv8/e3ceHmd1HX78e2fVjPZlJMv7bmMbY2MDNiYFTENoIBACgTQYAkmAQIvb/NIkTUOapCRpIU1JIAXsbMQBCgRCIU5KTSEmxizeF/CKd1m2dlnbSLO89/fHaMbSaEYajWY02/k8jx575l10pffMnbl67znXwfN3L2VsSeTlqFKw5M1gUh63MPzYbWjv4ZsvBXJaKwvtlBfYae/2UtPi5on1h9h+opXxpQ6+dc0c7v7NVmDo6zIq+r4+lAosPWIyDXyd9J32PtiSOiNcWiTDpTx24+lza1vd/HrjYW5cPBGzSeE3NC9sOc7nlk0NxebJli5uXv3ugH7lubuWMK7UGe3U8Qnvs4N/OOz7XKR+WhvQcTp6qkaSlsPJEmkduydbuljz9pEBMXrbxVMSH3/poqMONv504BJPy/5W4rW/lMdusiXyHnuPUsoEHFRK/S1wEsiIkc+umjOUOK2UJemuSmHj9tDd1qCpRbD+pKal26A0b/CBwIbm9zHQnFc0/FnXXSXjaRkzlzE7f0v9vE8Omeu6bOwy1p9Yz6tHX+XGmTcO+/uJNGcyDezkw9dVDd8ez5vCCJZaiJS/s25PPfdfPYeJ5fkD9h8sL9Vn6GHnAsmSNyPn8flZt6eedXsC02ifu2sJN69+t98+wZzXvo9TnqMV6fURFG394fAldYISsLSIGH1ev8GqDUdZteFov+dvWTI59P+o/YqRhMVJosXkkP20afA1s5O0HI5IPp+hI8boZ/vEaNbxeSIv8XTRXalpj0iZRA5c/w5wAiuBB4DlwOcSeP6k2VHTytSKgqSsX2p1N5DXcYLW6kv6PT+lT4Gmi8cNPnBd37SbEks+kxyVcbXh5OyrmLf+R1TtepHaCwa/JFOLpzK+YDzP7nuWG2bcIGu6iviMYKmF4ebv2CxmrpxTyQ2LJlDisNLq9vLi1hPYLGaU38i9XKA0EJ7j2ur2RrxGrW5v6JiMvi5JWlpEjD6r2RQxVvvGZrS8e0u6LPEUC4nZjGUxqcgxmknxN1wSr6JXwj4laK03a607gDZgpdb6U1rrd4c6LtXaur0cbuhkmmvgnZxEKKzfDEBXyex+z08pDvy7p2nwnBiv4WNjyx7mF02Ju+Kxu3gczdXzGbPrBczdZwbdVynF8onL2d+yn50NO+P6fkKMZKmFygL7gPzIJ1YsojLKNP5Sh5WVV8zkgbV7uHn1uzywdg8rr5hJqcM67HOJxAjPFd52tIn7wq7RfVfMZNvRJiALrkuSlhYRo8+Vb4sYq64+M7IqC+wRcwwzKn4lZjNWuSNyjJY7sngQJ/EqeiWyqvBiAgWaCnsfnwE+r7XemqjvkQzv1wQGcskqzFRUtwnDbMddNKXf86X2wNfepsGnxm0+c5Aufw8L4pgm3NfJ2VdR+qfdVO/8LTUXfXHQfZdUL+GFAy/wzL5nWFC5YETfV+SoESy1YLGYmF1VyPN3L8XnN7AMUXG2xe2NuKbdS/cuw1VoH9a5RGKYTIpZVYW8dO8yPD4/Sql+ucY1LW7ueWorz921hFuWTM7865KkpUXE6Gvt9nFPWH9yT7A/6a0abLWamV1ZwHN3LUluVeFkkpjNWO1ef9QYzcvL0iq7Eq+iVyIj/JfAvVrrDQBKqUsIDGTnJ/B7JNzO3oHr1CTdcS2q20RX8YyIJbunFMG+Ie64vtG4E5uycE7BhBG1o7toDM3jFlC5+yVOz78Bn6M06r55ljyWjVvGuqPr+Orir+KSv2iJoEiFQqK9cQyWLzgEk0lhNZvQWmPtnaLX0N4TsWDSUGuvWiymQQv+pFkxpqwRzBU2DE1Na1fEawREzFtOG6MU7yK1+vYBfh05fzV87WGr1ZxZhXCixbLEbMaR9cZFLkvknyr8wUErgNb6LQJL46S1PafacBXaKUxCGXFLTyvO1v10lp4TcfvkIjjQYuD1Ry7o4NcG/9e4g/lFU7CbRt6+2lkfw+TzUL392SH3XT5hOYY2eGbfMyP+viJLjNLyCZGWt9lX1843X9o1YLkbOJtP2Vesa68OtpSOGLng77e2tTviNbKmc06rLBeSE8L7gEP1nZkXq0ORWM4qwToQfWV8jA5FYlj0SmSUv6mUWqWUukwpdalS6jFgvVLqfKXU+Qn8Pgm191QbE8uS81fTwvrNKDRdUQauU4rAa8DhM5FfeNvPHKLJ28bisIrE8eourKRpwmIqP/g91s7GQfetyq/igjEX8MzeZ2jtbk3I9xcZrqsBnv3rs8URWo8HHnc1JPTbNHV6uHPNlgFTf29YNCH0+M41W2jqDFS/HMnaq5G+V99zi5EJ/n611vzwxvn9rtEPb0zryTijFu8itcL7gEdeP5h5sToUieWsEq0/TdTylmlJYlj0SuRU4fN6//122PMLAU2gynBa6fb6OdLQybULxibl/EV1mzBMVtzF0yJun9qnsvCssoF3h15r3I5VWZhfOGXAtnjVzrqSspqtVG/7L45/5L5B9/3EtE+w+fRm1uxZw8rzVyasDSJDjdLyCdGmQYUvnRKcFhWeTzmc6b4y5Sq5gr9fk1L82//s41vXzAlVwXzo1f385DNpnEMvy4XkhPA+YPuJVh56dT+/+fyF1Lf3ZEasDkViOat0+wweenX/gP70x5kco0ORGBa9EjZw1VpfnqhzjZYP6zvwa520O64ltRvoKpmJjjLNd3wBWE2wt8nPJ2f038fQBq81bufcwknkmRNXKa4nv5zGiRfi2vsHTi+8GU9B9CV2xhWMY/GYxTy992lum3MbJXklCWuHSFOD5fQlsRx93xwzpSKX+g9fOmWwqcA+n5+GTs+QhVPCl22J5dwidsHfb6vbi6swECdOm5myfBvfuXYuZpOi7owbq8WEz9B4fUbi8oyHk58aiSy/kBMi9QEXTi7BbDbhKrRTlm8LPDYpjjV1Yu0tJAZQ39GD129gNZtw5dto7faNTq78cGNbYjmrWEyK684bwznVRRhaU13i4LrzxmT/cjizroYFfw2OUnC3wI7/khjOQYmsKlwF/AAYq7X+K6XUHGCp1voXifoeibbvdDtAUgauts5TOM8c4PSMz0bdx2KCyYWws2Hg3Z1dbUdo8Jzh+qqlCW/bqZl/iev4Jqp2v8SJpXcPuu+1065l6+mt/HTHT7l/yf0Jb4tII8EckuB0nGC5+co5gQ9FwXL04dtHWLwrmGMWnK5390cmc98VM0NVE4NLTazdUQOcXXqipLd6YqTjr1kwfsDxsysLBgxeg9OMg8cOZ5qxGFpJniV07f52+Qx++sZBPnfxFO55elvo9/2jT59HntXE3zyzvd81mFVVGP8H/6FiORZJineRXsL7gGD/8dmfvduv/9h2rIn7nt3F+FIHz9x5EW1uX6iaeXCfR18/wLo99YmJ4WjiiW2J5axS7rCxaErFgBjN6uVwHOVw6dfg+VvPxvBNvwk8L3JKInNcnwT+FwjOuz0A/H0Cz59w+061YTObGFOUl/Bzl9T+GYCOivMG3W92Geys9+MLKwazrnE7FmXmvKLETRMO8jjLaB57Hq49azH3dAy677iCcSyfuJzn9z/PB40fJLwtIo0MlUPStxz9378f+Hc4A4EownPMzp9cHrHU/42LJ/LcXUv41jVzePT1AzT05qGGH3/j4okRj6/v6BnwvftOM9749ct56d5lyfmwmaMaOj08+voBbr14Cvc+vY0bFk3g6y/u6ndtvvLbnTR3ehObZ5yIfKgkxbtIL+F9wG0XT4nYf8yuLgk99vj0gCW47hkkDz+h4oltieWs0uT2RIzRJncWT5t1N50dtELg3+dvDTwvckoie60KrfXzgAGgtfYBMSWKKaWOKqV2K6V2KKW2JLBNg9p7uo3xZY6kfEgtqf0zXnsZPfnjB91vdim4fbCvz3quwWnC8wom4TAnZ0Hz09Mvx+x149r7hyH3/eT0T1JsL+aBdx/Ab0juX9aKJYckuHxCyYTAvwn44BOeY1bisEbMO23u9HDz6ne5+zdbWbenHp/fiHi82aQiHh/+x6GzP1Jg2ZZxpU5chXYZtCaQ128ErpWhQ3nKka6N02Ye8NyI8owTlQ+VhHgX6advHxCM1b5qWtyY+/QLJkXEfaLl4SdUvLEtsZw1osVotPe4rCA5rqJXIoszdSqlygkUYkIptQQ4M4zjL9daD17qNsH2nmpn/rjixJ/Y8FF86i3aXYtADf4heE7vcqrb6vzMcwU+vL3ffozTPS1cU3lh4tvWq6tkPG0VM6ja9Tvqzv0U2hx9uR2n1clNs25i9a7VrN61mnsW3JO0dok4DCffaRRzWIdaHzW43a81v717KT0+P1azibJ8G1fOqWTdnvrQvnd/ZDLVJQ7e+Mql+A3NC1uOYzYpTrZ0oZTql6PmN3TEvNWszv9JI8HrahgGFpPif//+I1hNipf/ZhlOm3nAtR1f6qDL0/8D/ojzjAeL5b6vAbMNTGbwumVBewEE8gcj5dhbzCaeu2sJrW4vShGxjwnPw3fYzFHXn46Z3wcdp8HvBbM18CX5qjktaoxm83uc5GmLXokcuP4/4BVgmlJqI+ACbkzg+ROqob2H5k4PE8sTn99aVL8Fi7d9yGnCAC4HlOfB1joft80LvABfa9yORZlYkIRpwn2dmn4Zs979GWUf/ommWVcOuu9FYy5id+NuHt/5OAsqF7B0bOJzb0UchpPvNIo5rOF5p+E5X323uwrsfO2qWfzj73b3y9cBWLenPmrO2WsfnOI7a/dx5ZxKnlixKDR174Utx3l8xaIBOa7BgioieYLX9eHX9vO5i6fw67eP8LmLp/CFX2+JeG3Hlzp4YsUiLKazA4GE5BlHi2VH+cDXwHWPwevfgY764efBiqxT7rBFzLF/+p0jrNpwlPGlDp6844J+fU6kPPw1n7+QuraeqH1gTPw+qHu/f17fZ38Ln3kGnv2s5KvmqGgxmtU5rnllgZzW8BzXvLJUt0yMMpXIdZ+UUhZgFqCA/Vpr7xCHBI87ArQQuFu7Smu9Otq+ixcv1lu2jHw28YaDDdz6i03cf/U5zB2b2LuuU977Zyo/fJ59lz2BNg+dP/uDLXC8Q7Hhs4Vorblq07eosBXx91M+mdB2DaA18954CL+zlL2f+umQu/f4evjee9+jy9vFmr9aw+TiycltX2Kl/E+RiYrdfjrqAgtxh/8V8ov/F5gONtx9R1qJtVdDew/XP7ZxwB2Jl+5dhqvQ3m/7qlsX8cDaPQP2/dXtF9Dc6aG6xBEatIZv/+jDgVzyK+dU8p1r56G1xmYxU2w3x1RVOAOkPG4h9tgNXtdvXTOHB9buCf0b7doG7xQ8cN08lEklv6pwV0Pk18DHfgDPrYj+2hHxSHnsxtPnnmzp4ubVA/ubb10zh7t/szX0+OGbFtDc5el3x6tvH6TRfOqxt6P2gTE5UwO/+quB8fqF1wKzuUbYT4uo0jp2o8Xoc3ctYVxpclbJSLkzNfDHrw2sKvzxh6B48JS8HJPy2E22RFYV/jTwqtb6A6XU/cD5Sqnvaa23xXD4JVrrk0qpSuA1pdQ+rfWf+5z7LuAugIkTJyakvftOBSoKT0h0RWHDT9nxV2mvWBDToBUCea4bT2nqOg0ajRPU9jTzMdeixLYrEqVomLyUie//N87Gg3RVzBh0d7vFzr0L7uXBTQ/yhXVf4MmPPcmEognJb2cGS0bs9jOcvI9o+3rd0HoCLDYMRznNZjMezNjMZspUfInwkdZHdRXY6fH5OdbUCZzNERsqp/WNr1wacbvVcnbq3hPrD6G17vemPc6WyAkluSee2A1e9+A1HeraBn3zaoNJ5fmJazyczenrK9prwFF69v9x5kwZ2qC5uxmP34PNbKMsrwyTksHEaIsnbn0+I7S0DcSWv2poHRrIBn37E2f7oJMtXSNfI9rvjd5nlyV3RhZITI+2WGM3J3Nc/V7Y/4fAV18f+35q2jNK5DU4UCJ/+m9prduVUpcAVwC/AB6P5UCt9cnef+uBl4ALw7av1lov1lovdrkSMx1m7+k2yvKtFOVFz+2MR1H9ZmzdjbRVLYn5mPMqAv++ddLHusbtmDGxsGhaQtsVTeOExfjNVlwfrI1p/+r8ar6y+Ct0ebu4/X9vZ0f9jiS3MLMlI3b7CeZ99BUt7yPavo0H4MfzMNb+Pw62HOCWP9zCx178GLf84RYOthzE0MbAcw3BajExvtQRerxwQglfu2oWn1n9Lpf+cD2HGzpD21vd3n77Qv98sWDOavh2n19z8+p3eWDtHr521Swctoy8o5q24ondvuu29v23r0i5gKOWm2WO8hpwt5z9fxw5U4Y2ONhyMCGvHTEyw41bn89gX107N616Z0DfFBQpZofKyw6+FgbbZ0jBfNa+SiYGnk8yienRF2vsWkwqYmxldY5rCl8LqSKvwcgSOXAN9uJXAz/TWv8BGPITgFIqXylVGPw/cCXwfgLbFdHeU21MSMKUivJjf8Qw2Wh3LYj5mClFUGqH9Se8vNawjXMKJlBgSfwSPZH4bU6axy2k/ODrmDydMR0zoXAC/7D4H9Bac/urt7N612rcPvfQB4rEC+byBTv0wfKdIu173WPw5oMAK5xILQAAIABJREFUNJ9/CyvXf5nazloAajtrWfnGSpq7m4fdLItJ8cMb54feXFdeMYOvvnB2GZRHXj8Y2v7E+kP99g3m67y49QRAKGe17/bHbjmf1W8eAgJ/af7qC7uy+6/NGSK4JuaLW0/w4A3zQ/+GX7vgtR31/GOTORDz4a+BjT8eUa5gc3czK99YmZDXjhhd9R09/Za26ds3wcD+KJirOqnc2W+f8Lzs4GthsH2GZHXCTWv6x+tNawLPJ5nEdPpy2kwD3hMfX7EIpy2L78Sl8LWQKvIajCyRc+lOKqVWAR8FHlRK2YltYFwFvKQC1XctwDNa61cT2K4BfH6DD+s7uGrumISeV/l7qDj6Cu2u82OeJgyB0vrnu+DPdScwrI1cMS72QW8iNExeiuv4JsoP/B8N866L6ZiJRRP59tJv8+QHT/Lo9kd5Zu8zrJizgmunXUulszLJLRYhfdfnGyrfKXxfgBduh5pAHo3HWRbqIINqO2vx+Ic/ddLt8fPQq/v51jVzKHFYGVOc129q0/YTrTz06n6evWsJCnDYzPzu3otDOY4leRa+c+087r/awGI2UeG08vzdS/H5DcwmxU/+7yDPb60Jna+mxY3Xl9t/hUwHwTUxv3/9fAzD4J8/MZeWTg+/uv0COnp81Lf38NQ7x/jGX53DFy6ZyrhSB1WjmX/sdQcKMX3sB4HpwX4voOFTPwebM+5cQY/fk7DXjhhdXr8RsW/6rzuX4PUb+A3Nq7tP8c+fmMu3PzE3lIMN8NK9y6JWDO67PmzcVYU9nbD554GCTCYzGH5451G49B/BmdyiNBLT6aut28/6vXU8c+cStNYopXh5Ww3XLhxHaYIzLtJGCl8LqSKvwcgSOXC9CbgK+HetdatSqhr46lAHaa0PA0OX302gI42deP2aiQnOqSo//ioWTxst45YP+9jzK2FDz24cKBYWj8404aDOkol0Fo+ncs9aGuZeO+QSPkFOq5N7F9zLgZYDvPLhK/xk2094dPujLK1eyienf5LLJ16OPUnr0Io+IuXyxbJvR12gkmovW1czY/PH9usox+aPxWYe/tRJm8XMhZNLmFqRj9mkMPeW7++7DEpDRw8mpRhb4hhwvK93EBq8h2oymUL7NbT38Pbh/ouOj3j5FJEwwTUxAWpb3Tz6xsHQsg0ArW4PB+o7eGDtHp67awlKqSGXTkoYiy0Q88+tOPtcAgoy2cy2hL12xOiymk0DlrZp6Ohhz6m2UA7rlXMq+dSi/gVg+sZ5NLHsMyiLDY78GbY/dfa5kolwxT/Hf84YSUynL4tJsbv2DDOri0LFwXbXnhkQo1klha+FVJHXYGQJm1egte7SWv9Oa32w9/EprfW6RJ0/kfaeDhRmmpjgwkyVB5+lx1FFZ9mcYR+7sEJjLdxNKZMotAz8IJ9UStEwaQnO5iPkN+wf9uEzS2fyDxf8A/96yb/y8SkfZ2/zXr76569y2XOXsWrnKrz+mIpLi9EWNnW4bNvTPHLZw4zNHwsEOshHlj9CWRzl5kvyLFyzYDx3PLmZ5T96k8+sfpf7rpjJlXMCd+ODU5tcEabNheec3bTqHfbVtYcGswmZgidGRYXTyn1XzOSBtXtC+ch/u3wG24428eAN8/nu7z9gX107R5s6uf6xjSx78E9c/9hG9te1YyRj6vdwptYPQ1leGY8sfyQhrx0xulz5tojTLoNTg6+cU8l9V8zk5tXvJj8+wyUpXmMhMZ2+gsvh9O1X77tiZnYvh5PC10KqyGswsoQuhzMaErGkyEOv7mP1nw/zq9svwGJOzNg9r+0wC1/+S+qm30zjlNim2/Z1rOcUXz/xEwrOfIJHPjK6d1wBzF435/3vd2iaeSXHLv3yiM5laIN9zft44/gbbKvfxgLXAh5Z/gileaUJam1cUl61ICnL4YxU2JIhhqOcZk/riCvY1ba6uWnVO0Mug/L96+cPuCMR7djn714auus6anfoUi8tfqh4Y3ewOPjaC7vYfqKV8aUOHrhuHnc8ubnfPsNaNmQ4ErTk04DTSvXHcCmP3VjitqG9h2++tCs0K6C6xMHT7xzh/MnllDislOXbuOPJzSNb1mYkkhSvMX3r3I3ptI7dnFwOB1L6WkiVOF6DKY/dZMvJ9SL2nW5nXIkjYYNWgOq9T2KYLLSOvTSu49/u2InSitN159HQ2YUrf3Tz9fxWBy1jF1D+4RucuPhLGNb47/qalIk55XOYUz6HTac28Yv3f8Gd6+7kl1f9kiJbUQJbLUYsbJqxCahwVIz4tOF5YxB5GZRvf2Lg0hDRjvX5z74mRjwFT4yKweJg+4nW0GNnWEXoYS8bMhzDmVo/nNMqU0JeO2J0eXx+1u2pD6UxvPGVS1m14ShsOArAc3ctGfmyNiORpHiN6VtLTKelnFwOB1L6WkgVeQ0OlN1/qohi76m2hK7fauluxnXoBc6MuQSfvWTYx2utebt9J+Ot49H+AjadHJ2KwuEaJl2E2eum7NCbCTvnhdUXct/C+/iw9UP+acM/5XwZ71wRzBvrK9KSEgDHmjqpbXWHpgJHOzaRf2gSyWcYOuqyDcNdWkSIZAlftiZ8+a1oSzpJfIpUycnlcITolXOfBM90eTl1pjuhA9eqA09j9nfTNOnjcR3/Yc8JGnwtLCyYyph8D5tqUnMnqaNsCu6CSir2/jGh551XMY+bZ93MmzVv8l/7/iuh5xbpqbLAzhOD5I0FH3/39x8MyGONdOwTo7lkihgxw9Dsr2tnzdtHeOyW84eMg+mV+ZKzLFIiPGd+48H6fjmvL249MSAHVuJTpFK5I3JedlbnuArRK+emCu851QbApAQNXM2edqr3/pL2ioX0FMRX0e3t9p2YMTErbxKnXR28cbSUM92K4rxRnvahFI2TLmLCB78nr+UY3aWTEnbqKyZewa7GXfxk209YPmE51QXVCTu3SD8Wi4nZVYWhJWwsZhOufBvfv35+aHrwd3//QWh6Xk2Lmy89tTWUxxp+bGWBHYsl5/7OlrGaOj3cuWYLNS1uWrp8/Or2CzCbFHaLiTVvH+GGRRP4wiVTaXV7efT1A3zv+nNHtmyIEHEKX7YGAn1TcCmvVreXtTtqeO6uJQASnyLlmrsDMRnsV/2G5oUtx/ncsqmMzcu5j/Uix+RchO8NDlzLEzNwHbPv11g9Zzg+9VNxHW9og3c6djE9bwJ5JjtzXZ28frSMrbV5LJ/qHvoECdY4YTHj9vwR175XObH07oSdVynFbXNu45tvfZNHtz/KDz7yg4SdW6Qni8U0YKkbV+96nceaOvstjQP981gjHSsyh8fnD+VgPb+1JrTm7ptfvaxf/mDQtz9hZHdREZHW+ubMB/um8P7pliWTE76EnhDx8PoNVm04GuhL+7hlyeSUtEeI0ZSTA9cSh5US58inVJg9bYzd83PaXIvojnPt1X3dR2n1t3N50QUAjCv0UJrn5b2T9pQMXH32Qlqr51K+fx01F34ebbYm7NwVjgr+ctJfsvbwWm6bexuzy2Yn7Nwi/fWtBGzpXdc1WMkzWGU4mMc6VNXgHKoqnBHCr4fDZg6tjXnTovHc+RdTsZhV6Lr3HRRIvqBIJ1azKWLfZDYpTrZ0SX8jUi5ajEodCJELcm7guudUGxMTdLe1eu8vsXjbaJh2Q9zneLt9J1ZlYaY9sDaVUjDX1cl7J4txexUO6+hXiWuYeBFltbsoOfoOLdP+IqHnvnrK1Wyo2cDDWx5m1ZWrEnpukb6COY/B6aN3f2Qy910xk3ue2kpNi7vfuq7h+wZzymZVFWIyqSG3i9EV7Xqs+fyFPLfpGFefNy60nEjwOgOs21Mv+YIi7ZQ7rBH7ptc+OMV31u6T/kakXFle5Bgty0vcjQYh0lVO/XnG6zc4UNfOxATkt1p6Wqne+0vOVF5Id+HkuM7h0z7e69jNDPtEbKazHc5cVydeQ7HjdGo+zLVVzqLHUYprX2KLNAE4rU6unno1b596m82nNw99gMgKfXMeAc6fXB5604XANOF7ntpKa7dvwL41LW7uXLOFpk5PxHOFbxejK9r1KMizcNvFU7j36W0DrvP918zlhS8t5fm7l8oAQKSVxi5PxL5p2YzK0GPpb0QqNbkjx2iTW2JSZL+cGrgebujE69dMSkCeSvWen2P2do7obuu2zn20G12c65ze7/kpJd3kW/28V5OaZXFQJhonXkDRia3Y2usSfvrLJ1xOsa2YVTvljutoMwxNQ3sPJ1u6aGjvwRildd/65jwClDisUddGDN+377ZI5wrfLkZXtOuhDR19vUG/wY1PvIPWWgatIq1Ei1mbxcRzdy1h1a2LcBXYpb8RKZOz67gKQY4NXPcmqKKwpbuJ6n2/om3MEnoKJsR9njfbtlJgcjLd3v8cJgVzKjrZVmvHm6L3xsaJFwJQse/VhJ/bZrbxsSkf473T77GjfkfCzy8iC07pvP6xjSx78E9c/9hG9te1j8rgNXytxMHWRgzft++2SOcK3y5GV6TrceWcSho7PRxu6Ix4rYJrZco1E+km2hqZhxs6uXn1uzywdg9fu2oWDpvErkgNa5QYtcofAUUOyKmB655TbVhMiuqSkd3JHPfBKky+HurjrCQM0OprZ3vXPs51TsekBl6GeZWduH0m3q9PzXRhj7OMtspZgYGrkfjR8+XjL6fAWsDqXasTfm4RWSqn2Iavlfji1hMD1moN5jqG7xueBznUdjG6Il2P+6+ew92/2cojrx/kwRvm99v22C3n88KW43LNRFqqLLAPWCPzhzfO55HXDwKBfvOrL+ySu1siZexWEz+8cf6AGLVbc+ojvchROVWcaeeJViaXO7GY4n9x2zpPMmbfGs5UL8OTPy7u82xo346BZoFjVsTt00vd2M0Gm2ryWFidmryFhkkXMX3zrymq2Upb7x3YRLFb7Fw5+Up+d/B3fND0AXPL5yb0/GKgVE6xDV8r0WYxU+qwRl27M3zfvtsinUuqfKZOpOsRjLWaFjf//r/7Q2tijit1kGcx8cW/mC7XTKQlq9XM7MoCnrtrCT5DYzYp7ntmO9tPtIb2qWlx4/UZKWylyGXdXoOHXt3fb63hh17dz08/uzDVTRMi6XLmzzN+Q7Pr5BmmugpGdJ4JO34MaOqnfTruc2it+VPbZsbbqqiwlkTcx2rWzK7oYvNJO/4UvT+2jpmL11aAa2/iizQBXDHxCvKt+azeKXddR0Oyp9iG58/6fEa/xwCuQjvjSp24Cu2DDlqC6ypG23eo7WJ0hV8Pm8XMlXMqWXXrIv7xrwLLXv3ircOYlKIs3y7XTGQMBbgK+88MkGnuIpVsFvOAmHQV2iQmRU7ImTuuB+vbcXv8TK+Mf+DqbNmL6/DvaJr0cbyOirjPs6vrILXeBq4tuXTQ/eZWdLKzroADTVbOcXnj/n7x0iYLjRMvYMyhN7G11+EprEro+R0WB1dMvIJXDr3C/ub9zCqLfPdZJEZwSmf4siWJmK4ZviTKlXMqWXnFTL7Up1y/LGmTO0ryLBGXa1j3fi0XTXPJdRZpy+v1s6++Y0DsgizhJNJDsd0csX8ttsvAVWS/nLnjuuN4YJrP9BHccZ247SH8FieNU64bUVv+0LqBQpOTeY5pg+43u6ITi0mzKVXVhYH6qZegUVTtejEp5//opI/isDj42e6fJeX84qy+Uzo3fv1yXrp3WcIGEOH5szcsmhAatIIsaZNrGjqjLyki11mks/qOnoix++1PzE14vylEPKL1rw3Sr4ockDMD123HWyiwWxhTHN8gsOj0O5TWvknjlGvxW+Mf/J7oOc0u90EW58/BrAb/61ieRTO91M2mk3Z0iupAeBylNI9biGvvHzH3tCf8/PnWfJZPWM66o+s43Ho44ecX/SVriu1wlruJtH/4dpHZvH4j4vU1m5RcZ5HWBltqRFITRDqQ5XBELsuZgevbh5qYPaYQpeJ4wzF8TN7yPTx55TRP+NiI2vGH1rewKgvn558T0/5zXR3Ud1o41pq6Wd2np1+G2ddN1e6XknL+j07+KDazTe66ZrDhLHcTaf/w7SLz9M1xjrakiCyDI9JRLLFrkcGqSBMSoyKX5cTA9URzFzUtbuaOLY7r+DEHnia/ZS91M1egzfHntZzyNPLn9q0scM7CaYrtzu+cii4Umk0nUzdd2F08lubq+YzZ+Vss7jMJP3+RrYjLJlzGH4/8keNtxxN+fpF84UuibDvaNGBJiSdWLKLUYY24v+SNZbbwNYLXvH1kwPWXZXBEOooldh9fsYjKAnuKWypEgCvfFjFGXdKvihyQE8WZ3jncBMDcsUXDPtbaVc+EHT+ivXw+bZUjWxLmv5pexaIsXFKwIOZjCu1+Jpd0s6nGzk3zOkb0/Ufi5OyrKF2/m+odz3Ji6d0JP/9Vk6/ijeNv8LPdP+OBZQ8k/PwiucKXRFFK8Z1X3u9Xrv+R1w/w/evnh6bayZI22SM8Z3nVhqMAPHfXEvy9S4rYZRkckYYGi12fobGYFJUFdqxWmSUg0sOZHj9rd9Twq9svwGxS+A3NC1uOM+YvpuOy5cTHepHDciLC3zzQQInDOmBqRSwmbf0BJn83p2d9DuKZZtxre+d+NnW+z2WFiygwO4d17FxXJ2sPVlDXYaaqIDW5Yd1FY2gav5jK3S9Rf87V9JSMT+j5i+3FXD7hcl7+8GVumnkT57rOTej5RfIF82cBTrZ0sW5PPev21Pfb59uf8EfcX2S2SDnLqzYc5baLpzCxPD9FrRJiaIPF7iSJXZGGPD4/qzYcDf2RJei2i6ekpkFCjKKsnyrs9vh5Y189iyaVDju/tezY/+A6+gqNk6/Fk18ddxva/B2srn+BCksJSwvOG/bx81ydAGyqSe2H/BNzrsYwW5n854dJRrWo66ZfR4m9hO++8118hi/h5xejR3JYc4tcb5GpJHZFppGYFbks6weubx5owO3xc9HU8mEdZ+usZdq736CraBoNUz4Z9/f3GF7+49RTtPu7uL50OZYhKglHUubwMa6wh/VHHSmrLgzgyyuiZs41FNXuxLVnbcLP77A4+OvZf83+lv08vffphJ9fjB7JYc0tcr1FppLYFZlGYlbksqyfKvzC1hqK8izMqY49v9Xk7WTW+rtRfg8nz/0bMMX3a2r1tfPj00+zv/sonyy5nDHW4Q2e+7p4/Bl+u7eS3XU25o9J3VpdDZMuovTULiZu/E+6KmbQWTU7oedfVLWIBa4F/GTbT1hctZi5FXMTen4xOiSHNbfI9RaZSmJXZBqJWZHLsnrgerihg9f31nH9wnGYY3xBK38PM/98H/ktezl+3lfwOMeEtnm1jyPdJ6nzNdPqa0MDVmUh35RHgTmfQrOTPGWn1d/O7q6DvNb2Hl7DyydLLmeec/qIfpaFY9p59VAZL+zJ59wqz0jSbUdGmTi0aAVz33yY6eu+zb5rH6aneGziTq8Un5/3eb77zndZ+aeVPP3xpxmTP2boA0XakRzW3CLXW2QqiV2RaSRmRa7K6oHrI68fxGxSfHROVUz7mz1tzFp/N8V171F7zhfocC2ky9/Nps732di+g73dR/Dp2IojmVDMzJvE5YUXUGEtGcmPAYDFBH85pYWX9rt450QeF0/sHvE54+W35XPwws8z6+3Hmf3yl9l/zYN0l01O2PkLbAWsPH8l/7bp37jj1TtYfeVqJhROSNj5hRBCCCGEEJklaweur+2p47931HL9wnGUOIee91986i2mvfMNbF2n+XDu3fy5qIyNp55iW9defNpPqbmIRc5zmGirxmUtocDkRKHwaT9u3UO30UOX0Y1He3GY8qi2luOIca3WWF00ro1NtUWs3lLElFIv1YWpqTAMgbVd9y+7l1lvP8GcF++lZuld1M/5BJgSUxxgQuEEvrLoK/x424/5zNrP8I8X/iNXT70ak8r6tGwhhBBCCCFEmKwbuGqt+cPuU3zl+Z1MLndy/cJxUfc1eTsoPfkmlQd+Q0vzNl4urGT9+EvY5H6dni4P+SYHC52zOdcxnbFWV8SqxDasOEnsADVqexWsmHeaR7eM5zt/KmPlklbmVnpH5XtH4i6q5oPLvsKU7c8y6a2fUrXrd9Sdez0tUy7BW+Aa8fmnlkzl/iX3s2rXKv7prX/iqb1P8dnZn+WyCZdRbC9OwE8ghBBCCCGEyARZMXBt6fTwzKbjnGx1s+VoMwfqOphakc/Xr5qN1WyioH4rW2pepMvXhs/vxutrw9vdRLPRQa3ZzDG7jTPjA3ma+b7TzHVMZY5jKpNs1Wl3h6/c6eOuhbWs2TWG7/ypnKmlHqaX+Shz+rl0spsKpzGq7fHmFXFgyZ2UnNrN2IOvM2njfzJp43/icZbTVTENT0Elvrxi/DYn2mSlZfLFeIpiz1mtdFbyzYu+yXun3uPlD1/m/o33Y1ZmphRPYXrJdMod5RTbiymyFWEz27hxxo3DXvZICCGEEEIIkd6UTuX6KnFQSjUAx2LYtQJoTHJzUkF+rvg0aq2vSuL5hzSM2I1XusaGtGt4+rYr5XELoxK74TLh2qSLdG3TvlTH7hBxm46/t8FkWnsh89ocbG/K+135rDso+ZmjS3nsJlvGDVxjpZTaorVenOp2JJr8XCKadP0dSruGJ13bNZrS9XeQju2SNsUnE9rYV6a1FzKvzZnWXsjMNo+U/My5Lb3mwQohhBBCCCGEEGFk4CqEEEIIIYQQIq1l88B1daobkCTyc4lo0vV3KO0annRt12hK199BOrZL2hSfTGhjX5nWXsi8NmdaeyEz2zxS8jPnsKzNcRVCCCGEEEIIkR2y+Y6rEEIIIYQQQogsIANXIYQQQgghhBBpTQauQgghhBBCCCHSmgxchRBCCCGEEEKkNRm4CiGEEEIIIYRIazJwFUIIIYQQQgiR1mTgKoQQQgghhBAircnAVQghhBBCCCFEWpOBqxBCCCGEEEKItCYDVyGEEEIIIYQQaU0GrkIIIYQQQggh0poMXIUQQgghhBBCpDUZuAohhBBCCCGESGsycBVCCCGEEEIIkdZk4CqEEEIIIYQQIq1l3MD1qquu0oB8yddwv1JOYle+4vhKCxK78hXHV8pJ3MpXnF8pJ7ErX3F+Zb2kD1yVUmal1Hal1NoI225XSjUopXb0fn1xqPM1NjYmp6FCJJnErshUErsiE0ncikwlsStEZJZR+B5/B+wFiqJsf05r/bej0A4hhBBCCCGEEBkoqXdclVLjgauBnyfz+wghhBBCCCGEyF7Jnir8Y+BrgDHIPjcopXYppV5QSk1IcnuEEEIIIYQQQmSYpA1clVLXAPVa662D7PZ7YLLWej7wGvDrKOe6Sym1RSm1paGhIQmtFSI5JHZFppLYFZlI4lZkKoldIYaWzDuuy4BrlVJHgWeB5Uqpp/ruoLVu0lr39D78ObAo0om01qu11ou11otdLlcSmyxEYuVy7Hr9XvY378dreFPdFBGHXI5dkbkkbkWmktgVYmhJK86ktf4G8A0ApdRlwD9orVf03UcpVa21PtX78FoCRZxykmFomjo9eHx+bBYz5fk2TCaV6mYJEZc2Txsr/riCI2eOsLByIT+/8ufYzLZUN0ukkPRxIptIPItUkvgTuWo0qgr3o5T6F2CL1voVYKVS6lrABzQDt492e9KBYWj217Vz55ot1LS4GV/q4Ge3LWZWVaF0RCIj/ceW/+B423EuGXcJb518i+f2P8etc25NdbNEikgfJ7KJxLNIJYk/kcuSvo4rgNZ6vdb6mt7//3PvoBWt9Te01nO11udprS/XWu8bjfakm6ZOT6gDAqhpcXPnmi00dXpS3DIhhu9052lePvQyl46/lM/P+zyzy2bz6w9+jaEHq9Emspn0cSKbSDyLVJL4E7lsVAauYnAenz/UAQXVtLjx+PwpapEQ8Xvl0Cv4DB9XTr4SgI+M+wh1XXXsatiV4paJVJE+TmQTiWeRShJ/IpfJwDUN2Cxmxpc6+j03vtSBzWJOUYuEiI/WmrWH1zKjZAaVzkoAFlQuwGKy8Prx11PcOpEq0seJbCLxLFJJ4k/kMhm4poHyfBs/u21xqCMK5iuU50sxG5FZjrcf58iZI1w45sLQcw6Lg6nFU9l0elMKWyZSSfo4kU0knkUqSfyJXDbqxZnEQCaTYlZVIS/du0wqxImM9nbt2wCc6zq33/OzSmfxhyN/oMPTQYGtIBVNEykkfZzIJhLPIpUk/kQuk4FrmjCZFK5Ce6qbIcSIvH3ybVwOV2iacNDMspn8/vDv2dmwk2XjlqWodSKVpI8T2UTiWaSSxJ/IVTJVWAiREF7Dy6bTm5hbMXfAtilFUwDY07RntJslhBBCCCGygAxchRAJsfvYm3T5uljW3obye/ttc1qdVDorZeAqhBBCCCHiIgNXIcTIeTp597WvYtKaj+98mQnvPDFgl0lFk/ig6YMUNE4IIYQQQmQ6GbimgGFoGtp7ONnSRUN7D4ahU90kIUbmncfYYbQzyVqMe9IyKt9/hbzWE/12mVg4kVOdp+jwdKSokSIVpL8TmUZiVqQ7iVGRq6Q40ygzDM3+unbuXLOFmhZ3qIz5rKpCqQgnMpPXjf/dx9hVVciFxZOprbgA17F3qdz9Esc/sjK029iCsQAcOXNkQNVhkZ2kvxOZRmJWpDuJUZHL5I7rKGvq9IQ6G4CaFjd3rtlCU6cnxS0TIk77/4cPfe10Ks1051h89kJax8yj7PAGMPyh3arzqwE4fOZwqloqRpn0dyLTSMyKdCcxKnKZDFxHmcfnD3U2QTUtbjw+f5QjhEhzu3/LzqJyAKb3Dk5bxp6L1d1CQd3ZYkwuhwuLycKhM4dS0kwx+qS/E5lGYlakO4lRkctk4DrKbBYz40sd/Z4bX+rAZjGnqEVCjICnCw69zo6SKootTiqsRQCcqZyNVoqimm2hXc0mM1XOKo60HklVa8Uok/5OZBqJWZHuJEZFLpOB6ygrz7fxs9sWhzqdYG5Ceb4txS0TIg5H3gSiVOCnAAAgAElEQVRfDztMPqY5q1EqkF/jtzroKh5PYe3OfrtX51fLHdccIv2dyDQSsyLdSYyKXCbFmZLAMDRNnR48Pj82i5nyfFsoYd5kUsyqKuSle5dF3C5ERjm8njPWPE742rnQOb/fpvbyaVQeeQvl96DNgTfU6vxqttVvo8ffg91sT0WLxSgK7++UUphVIEdL+j2RLsLfs2e4CuQ9WqQtk0kxw1XA83cvxec3sJhNVBbYJUZFTpCBa4LFUu3NZFK4CuVDu8gCR/7M3sppQDuTHZX9NnWUTmTMIR+O5qN0uWYCUF1QjaENjrUdY2bpzBQ0WIw2k0lRnm+TKpgiLUmFVpFpDENzsKFDYlbkJJkqnGBS7U3kjM4mqN/D3iIXAJPCBq5dxeMAcDaenRo8Nj+wJI5UFs4t0i+KdCWxKTKNxKzIZXLHNcGk2pvIGcc2ArDXaqbCX0SBpX+xiJ78cvyWPJyNH4aeq8qvChx65tjotVOknPSLIl1JbIpMIzErclnS77gqpcxKqe1KqbURttmVUs8ppT5USr2nlJqc7PYkm1R7Eznj6Aaw5PGBt4WJDtfA7cpEV1E1zsaDoafsZjsl9hJOdpwcxYaKVJN+UaQriU2RaSRmRS4bjanCfwfsjbLtC0CL1no68DDw4Ci0J6kSUe3NMDQN7T2cbOmiob0Hw9DJaq4Q8Tu6kY7KWRzvbhwwTTioq3gczqbDYJz9S3CFo4IT7SdGq5VilEXqv6QKpkhX0WLTbELeg0Vakv5U5LKkThVWSo0Hrga+D/y/CLtcB3yn9/8vAD9VSimtdca+S4y0arAUihAZoacdGvayb85V0NUyyMB1POYjb5HXVkt3yQQAXE4Xh1slxzUbDdZ/STV1kY7C37OtFhMd3T6u/elGeQ8WactuMfHAdfNw2sx0efzYLVKyRuSGZEf6j4GvAUaU7eOAEwBaax9wBihPcpuSLlg1eFypE1fh8EqUS9K9yAi120Eb7HXkAwMLMwV1FQeKMfXNc610VFLfVY/HLzGdbQbrv0bSLwqRTH1jU6G47Zeb5D1YpK2mTg+3/XITdzy5mZtXv8sdT27mtl9ukhgVOSFpA1el1DVAvdZ6awLOdZdSaotSaktDQ0MCWpe+JOk+u2Rt7NZsAWAvHkqtBRRb8yPu5i4cg0aR13K2GJPL6UKjqe2oHZWmivjEE7vSf4lUG2mfKzEsUiXW2JUYFbksmXdclwHXKqWOAs8Cy5VST4XtcxKYAKCUsgDFQFP4ibTWq7XWi7XWi12uCEVgsogk3WeXrI3dmi1QNI4P3KeYmBf959JmCz35ZThaz+a0unoLOdV01CS9mSJ+8cSu9F8i1Uba50oMi1SJNXYlRkUuS9rAVWv9Da31eK31ZOAzwBta6xVhu70CfK73/zf27pOx+a2JIEn3Iu1pDTWbcZdP52hXXdRpwkHd+S7srWcHqS5n78C1XQau2Ub6L5HpJIZFupMYFbls1NdxVUr9C7BFa/0K8AvgN0qpD4FmAgPcrOTzGdR39OD1G1jNJioL7FgiJNOPtLiTEEl35gR01nO49AqMliOMd1QMunt3QSWFx98DbYAyUWwrxmqyDjpwPdzQwTdfep/9de0sn13J/VefQ4lT3pTTXd/+yzAM/Bq01jR1egb0Y4YReF76OZFOYn0PjvU9XYhEM5kU0yvyee6uJfgMjcWkqCyQugEiNww5cFVKLQY+AowF3MD7wGta65ZYv4nWej2wvvf//9zn+W7g08NqcQby+Qz21bXzpae2hqoUPrFiEbOrCqMOXl2F9hS0VIgY9Oa3HnYWQguMsw9eT627wIXZ14OtsxFPQSVKKVxOV9SpwseaOrn+sbfxG5r544v57+0nOdniZs0XLsRqlg+G6c5kUpTn2watji7V00U6G+o9eLjv6UIkks9nsL++Q+JP5KSoEa6UukMptQ34BuAA9gP1wCXA/ymlfq2Umjg6zcxs9R09oQ4GAkn0X3pqK/UdPSlumRBxqNkCZhuHlA+zMuGyFw+6e3dBYCpxv+nCDhfH244P2NfnN7jnqW34Dc0D183jvuUzuPMjU3nncBO/eefYgP1FehqqOrpUTxeZTN7TRSpJ/IlcNtgdVyewTGvtjrRRKbUAmAEM/PQp+vH6jYgV4Hz+aKsECZHGajZD+XQOddVRZSvBogYvCNFdEMhpzWs9Qfv484HAwPXtlrfRWqPU2Ttsa945xp5TbXz5L2cypjgPgL+Y6WLDhw389E8fcvMFE8i3j3qGgximoapeSlVMkcnkPV2kksSfyGVR77hqrf8z2qC1d/sOrfXryWlWdrGaTRErwFlk2qPIND4PnNoJFTM51HWasXlDL7vszSvGb7aTd+Zk6DmX00WXr4uWnrMZB+3dXn7y+kHmjy/mgsml/c7x6UUTaO708NL2k4j0N1TVS6mKKTKZvKeLVJL4E7kslhzXKcB9wOS++2utr01eszKP1+unvqOnX6K81Rr4EFZZYOeJFYsG5CNUFpzNocnWQiWGNmjubsbj92Az2yjLK8OkTFGfF2mu7n3w99BTMZ2TJ7azoGjq0McoRXeBi7xIS+K011CWVwbAr98+yhm3l5sXT+h3FxZgRmUBk8qdPLv5OCuWTErczyOSIlj1sm8O65rPX4hGU3fGjV/D01+8CK9f4/b46PYajCnOw+Pz09DekzX9X6pIvzsy4e/HJXkWGjo9oUJMFU4rT95xASea3ThtZro8fiaUOfq9p+eyWOJMYjF+lQV2fnfvUjw+jd/QmE0Km0VR5pD4yzY+w0ejuxGv34vVbKXCUYHFlNuzzmL56f+bQPXf3wMyDyECr9fPvvoO7ukzMH18xSJmVxZgtZqxWEzMrirk+buX4vMbWMIqEGZroRJDGxxsOcjKN1ZS21nL2PyxPLL8EaaVTONQ66EBz88onSFvXOnu5FYAjuaXYaAZ2zvoHEp3QQX5ZyIviTPfNZ9ur59fbjzKwoklTHUVDDheKcVlMyv59TtH2Xe6jdljikb+s4ikCa/M6rCZqWvr4d9e2s3nLp7C11/cFerrfvTp88izmvjrn72bVf1fqki/OzKR3o8fX7GIR18/wLo99YwvdfDkHRfg9Rl86+X3z8bsrYslXokef33jLJZ9RHSGYVDX5hnwmbPEbiWJq1yKUeYzfBxoOcCX//Tl0Ovk4csfZmbpzJwevMYS4d1a60e01n/SWr8Z/Ep6yzJIfUdPqAOBQK7BPWGJ8haLibElDiaW5zO2xNGv8lu2Fipp7m4OvTEB1HbWsvKNlTS6GyM+39zdnMrmiljUbAFHGUcIxOZYe2wDV4+zDFtHAxiBHMaK3iV0gpWFX9lZS3Onh4/Pq456jqXTylHAH3efHsEPIEZLsDLruFInfgPuXLOFGxZNCA1aIdDXfeW3O2nu9GZd/5cq0u+OTKT343ue2soNiyaEHp9odnPnb/q/59/5G4lZiB5/feMsln1EdA2dnoifORsk/rJKo7sxNGiFwOvky3/6Mo3uxhS3LLViGbj+RCn1baXUUqXU+cGvpLcsg/gMHTlR3tAxHZ+thUo8fk/oBRdU21mL1++N+LzHL51u2qvZHMpvVSjG2EuHPgbocZZjMnzYOgMdrt1sp8ReQk17DVprfvXWESaUOZg7Nvqd1GKHldnVhbz6/qmE/Chi9AT7uBKHNWJf57SZBzyX6f1fqkTtdw3pd2MR7f24xGENPXbazFn5np0I0eKvb5zFso+IbqSfOUVmiPZZ2Wt4U9Si9BDLwPVc4E7g34Af9X79ezIblWksJhU5UT7GaUPZWqjEZrYxNn9sv+fG5o/FarZGfN5mto1m88RwuVug+RC4ZnK46xQuWzHWGKer9DgDd2bt7WfvllY4KqjpqOG9I83sPd3Ox+aOGZDbGu6CyWUcqOvgWFNn/D+HGHXBPq7V7Y3Y13V5/AOey/T+L1Wi9rsm6XdjEe39uNV99sNil8efle/ZiRAt/vrGWSz7iOhG+plTZIZon5WtJmuUI3JDLAPXTwNTtdaXaq0v7/1anuyGZZLKAjuPr1gU6kiC+QaxFmoIFjLpe/zPbltMeX5md+JleWU8svyR0AsvmMdS4aiI+HxZjPmSIkV681upmMWHXadizm+FswNXW1v/gevJ9pP85p1jFNgtXDK9YsjznDe+BIANB3N7qkymCfZxL249wYM3zO/X1/3o0+dRlm/Nuv4vVaTfHZlI78ePr1jEi1tPhB5PKAvktErMDhQt/vrGWSz7iOhc+baInzldEn9ZpcJRwcOXP9zvdfLw5Q+HUq1yldJ68KkFSqn/Bu7SWtePTpMGt3jxYr1ly5ZUN2MAj8dHQ6cnVFXYlW/DYjGHKhNaLSYsJoXbE7lqsFQVTnpFwZT/MtM1dmO2/kFY/6/4PvMMF2z6Jz5asZBPV18S06HK8LHo91+ndtEt1F5wOwC/O/g7/njkj3Tu+x4fnTOW25ZOHvI8WmtWPrudCyaX8fiKRSP4YTJGyuMWEhO74X2k2aQwNJgVWC0mfIbG6zOyqv9LlTTpd1N+AeON2/BVAiqcNprc3n7FFSFQ3yJSwcVclwVVhdM+dru7fTS5z/an5Q4beXm5W7AnW4WqChterKaYqgqnPHaTLZYoLwH2KaU2A6FqQ7IczlmGoTnU1DVg6Ycen9HvuR/eOJ+HXt1PQ0fPgKqZwUIm2cakTBH/OhTteZHGTm6Bkomc8Hfh0/5h3XHVJgseRwn2sDuuhjbwm1u5bNaCmM6jlGLu2GI2HmoMLQMg0p/X62d/Q2fUyusisaTfjZ/PZ7C/vmPA8nWzqwqzfiWARIklziQW4+f1+jnYJP1pLrCYLIzJH5PqZqSVWP689W3geuAHnM1x/VEyG5VpIlUhPNZnIBt87qsv7OJLl02Tqpki82gdqChcEchvBRhrLx/WKXqcZdjbzhZWqsgLfGgZ73IzscwZ83nOHVdMm9vHB7VnhvX9RerEUnldiHRQ39ETGrRCIFa/FBar2boSgMgM0p+KXBbLHdfjwCmtdTeAUsoBVCW1VRkmUhXCaFUHg5UJpQKhyCgtR8DdDK5ZHOoduFbHWFE4qMdZRnHTodDjzs7A8dPHDq9CXrDy8IaDjczvzXkV6U2qYIpM4fUbkWPVf3YZ+2xdCUBkBulPRS6L5Y7rbwGjz2N/73OiV6QqhNGqDgYrE0oFQpFRanpzbSpmcrjzNOXWQvKGWQHSk1+GrasZ5Qvcldj+oQOtFWXFHcM6T4nTxqQyJxs/lAJNmUKqYIpMYTWbIseq+ezHpWxdCUBkBulPRS6LZeBq0VqH5r/0/j9rS5cZhqahvYeTLV00tPdghP0Fq+/2+vZumjt7MAyDVbf2r/A2qdw5oDLhD2+czxPrD2VsBUJDGzS6G6ntqKXR3YihjaEPEtmhZgtY8qBkEh92naLaPvzqjz3OwNRie0cdfkPz3odeLLqEM966YZ9r7rhiNh9tptsrdzjSwWD9ps9nYLWYeGIElddFdNIvj1zf+LVbFavCYvWJsFjN1pUA0p3EekBlgX1AfxoeoyI7SMwPFMtU4Qal1LVa61cAlFLXAVl5q2OogguRtgcLLrkKbTzzxYswm1SoKibAS/cu61dV+KefXZiRVTMNbXCw5SAr31hJbWdtqHz9jNIZ6VQJUCTLiXehYgaGUhx113Fp2bxhnyI0cG07xaa2Ktrcmkm2Mhq7Tw1x5EDnjCnkj7tPsfvkGS6YLEsopNJg/aZhaPbVtfOlp7biKrDzwHXzmFyRj91iwpVvk0IiIyT98shFit9f3XEB//7p81AEZk/lWfv/Lk0mxayqwtD7eya+p2caifX+7FYTD1w3D6fNTJfHj92ae7+DbCcxH1ksP/mXgH9SSh1XSh0Hvg7cldxmpcZQBRcibQ8WXFq3p57P/vw9bBYzrkI7JpMKVQoeV+qksjCPsvzA/4PbM0lzd3PoxQNQ21nLyjdW0tzdnOKWiaTrPgOnd0PVPGq7m+kxvMMuzAR913I9xcb93dgtUJVfRmPP8AeuM6sKAdh8VOIv1QbrN/sWutl+opU7ntzMrb94D0AGrQkg/fLIRYrfO361mTNuLzevfpc7ntzM7b/aPKDwTd/390x8T880Eutn1Xf0cMevNnPHk5tDMXpHhBgVmU1iPrIh77hqrQ8BS5RSBb2Ph5eQlkGGKrgQbXsuFFzy+D2hF09QbWctHr9UUcx6x98FbUDVuaGKwtVxLBTvzSvEMFmwtp3m3Q97mDXORKm9nL1nNuM1PFhNsU+zK3JYGVfiYMuRZrhs2E0RCTRYvxm1iIhfpjslgvTLIzfU+3rwscRsakmsnyXFmXKDxHxkUe+4KqVWKHX2XrTWuqPvoFUpNU0pdUmyGziahiq4EG17LhRcspltjM0f2++5sfljsQ2zQI/IQEffApMVXLM43BVYhzWeHFeUiR5nGd11tXT1aOZNMFFsDZynuWf4ea4zqwrZcqxlQB66GF2D9ZuxFLoR8ZN+eeSGel8PPpaYTS2J9bOkOFNukJiPbLCeuBzYrpT6pVLqb5RSNymlblNK/YtS6k3gISDqp02lVJ5SapNSaqdS6gOl1Hcj7HO7UqpBKbWj9+uLI/+R4jdUwYVI2zO94FKsyvLKeGT5I6EXUXCufVkcd95Ehjm2EVwzwWLnUNdpii35FFjy4jpVj7MMS+sp7BaYWmWiyBaYchxPnuusMYW0dfs4WJ+1k0AywmD9phQRSS7pl0cuUvw+vmIRL249EXosMZt6EutnVRbYeVyK3WU9ifnIlNbR71YopczAcmAZUA24gb3A/2itjw96YqUUkK+17lBKWYG3gL/TWr/bZ5/bgcVa67+NtcGLFy/WW7ZsiXX3YTMMTVOnJ2rBhb7bnXYTXT0GPkNjMSnyrCY6evw4enO3vH5jyHNkUlEHQxs0dzfj8XuwmW2U5ZVlUoJ4yn/ByY7dpOhph3+bBPNuhPNv5a+3P4jP8PO1aTfGdbqJO1/AcXQnN5X/gs9cbKXd28rq/d/llmlf5tLq64Z1rrq2bv7+uR1875PzWLFkUlztyQApj1sYOnYj9WmGoanv6MHnNzCbFCYTGAbYLCa8fo3WOqP6v3SVxv1yyi9qrH1uePwWWs00uT2h9/Zyh43mbi9ev4HVbKKywI7FMvTvOFPf69PVKMZ6yi/SULHb3e0bEKN5ebHUWxWZJI6YT3nsJtugUa619gOv9X4Niw6MiIO3Qqy9X2k/py9YcGGo7T6fEaqWGaxE+Ngt5/PmvnoWTynjqy/sirkycd/t6cykTFQ4KlLdDDGaTrwH2g9j5qG15khXHReVzIr7dPUmF+fSybmubsBKgaUIs7LQ1HP6/7P35nFylHXi//upvue+ZzI5yAKThFMgEQ/Wrwmi6woaXeSSoARQQ3Qj0Z+7+l1WZBd1XReSzU8hiiJgAgoixBVdLgNiOCQcgiBkyJ1Mkrnv7umuquf7R3X39FHVXdPTM93TU+8X85r0U089Xc18+lPPU1XP+5lwW02VPmrKPLy4v6+UB64zgtS8aZYfb1u1lBf3dtPWUs0/P2CeHx0mjpOXJ09i/EYiGm92DnNtNHY/dHIT//iBRfHXsTuwS5orMw5eZ/K5vlhxYt0gFFJp7xlJisnbVi2lrb7cGbyWGE7MpzOll2WFEC4hxCtAJ/CYlPJ5k2oXCiFeFUL8UggxfyqPJ58k2jLBmBi/dutLrDxrXnzQGivPZiZO3O7gUFTs2wHCBY1L6AwPMKKFaM1lfmuUv4SaADij2lhRSwiFKk8d3aGJD1yFECxqruRPe2e3Ya8YMcuP1255kXNPnhMftMbKnfznUEx0Do/FBwQAFy6dn/T6UF+QNVtezGpwdc71DlNFTzCcFpPXbnmRnqATWw6lz5QOXKWUmpTyDGAecLYQInXxx/8BFkopT8e4q3uXWTtCiM8JIXYKIXZ2dXVN5SHbJqLpplY3Kc1tb9nMxKVqI57tFGPsToh9T0P9ieAJsHsSRuEYz/W3ANCijy8FXZ3jWq4Ai5srOdwf5MhAMHtlhwkxmdi1yo96lvzo4DBZJptzU42tNQFPTmZs51zvMFHsxq5jFXaYzUzLRBgpZT+wHfhwSnmPlDJ22fLHwFKL/X8kpVwmpVzW2Ng4tQdrEytbphDmtrdsZuJStRHPdooxdm0z2guHX4S5ZwGwN2oUzvWO62hE8EyfIRmoGO2Ml1d5anMfuLYY67nu3NeX0/4O1kwmdq3yo5IlPzo4TJbJ5txUY2t/MJKTGds51ztMFLux61iFHWYzWR+GF0L4gAuBhYn1pZT/lmW/RiAipewXQgSADwLfTakzR0oZ67F+DEP8VFCsZAqqqtM5PBaXMzSUedi8amnaHC5N09hy9bvoGhpDl5LRsMZx9WXU+N109AcByQ9XLeXzCfvZshHrOox2gRoGtxfKGkFRkiZuK0JBQUFRlGISdDjMVHb/3li/de4y4+XoEcpdfqrcZTk193qnly5ZRVh4kweu3jqG1QFC2ih+18TaXlhfjt+jsHNfLx99R2v2HRymhZhNeE3KvMCAR0kr/+EVS/G4oHMoREQ1F9rNCCxy9ISaSBRxKF4URSGkhopNulTSNFX4uHP1OznYG6TM60IRwjSWG8o8dPQHLYVNMVtx6hzXol55IA8xnPNbp0hoanw19I/1F6N0rODUB7zctmpp2hzX+kARx1Y+KGB8FgpVV+kOdhPRInhcHhoCDbiV2T2P2c6n3wYMAC8CmSd1JDMHuCtqJlaA+6SUvxFC/BuwU0r5a2CdEOJjgAr0AldO5ODzjZVM4cSGct7qHE46cd25+p1UBdzcufpsXALcLoUtz+7lh0/viy+T85//+xZdw2PcvfrspP0/dHITW695F25F2Ouk6Tp0vgE/vwz6D0DNArj0XvTGJbQP7Gbd79fRMdJBa3krN55zI/e8cQ9fOPMLtNW2OYneIXfaHwV/DTS0AfD2yBHm+OowhOET581uD24FRgO1VATHB67V0SVxekJHmVt+/ITadCmCE5sq+NM+Z55rMeF2KyxpruS+z7+HSPSRyrCq8Ylbn6Gxwse/rzyVhQ1lDIxG0HXJ/p5RvnDPyzNXYmORo2k62XbHSpc67X3tSfn8pnNuYuNLG+kOdrPp3E1OTp8GNE0Siuj867a/JJ3v7/vcuwmpOrqEqoCLXV0jaYPZRGGToggWN1fy4NpzZoZVOA8xnPNbm8T+hhUb2PzKZrYf2h5fBsSJ/3Gqo/1PRYAuwesu0rjKFwWMz0Kh6iq7+naxfvv6pO/FotpFs3rwauevPU9KeYmU8j+llDfHfrLtJKV8VUp5ppTydCnlqbE7tFLKb0QHrUgpvy6lPEVK+Q4p5Qop5ZuT/DyTwkqmYCYaOdgb5FO3P895tzzFrs5hLrv9OX749L749q/+8lXWLD+BQ31B9veOJu3/6BudXP7j5xHCMBlmPZGNdo1/WcH4/fPL6A12xRM9QMdIBzfsuIGVbStZ9/t19IaczrxDjugavP04tJ4J0Y7C3tGjtE5ifutfu7zMqwwx6qtOuuNa7THazEXQBMY817eODjEUiuR8bA75J9aBv/zHzxNWdVbfaeTWlw/2s/rOF7jiJ3+i3Ofm2q0v0TsSmdkSG4sczaj9OZa9od60fH79juu56rSr6BjpcHL6NNETDKed76/86QtI4Nybn+K8W55iMKil1TETNsVsxXNry+yd6wtJHmI4V8xif/329axsWxl/7cT/OD3BcLz/GYvJT93+fGnLmQoYn4WiO9gdH7TC+PeiO9idZc/Sxs7A9RkhxGlTfiRFgJVMwWwifJnXFS+zkjfUBDxpdZPazSJ3iKOGx7+sMfoPENbVeEDH6BjpoNpbTcdIB2GthJOYw9TS8TKM9sA84zHh3vAQ/epIzvNbx1TY0+thYU2IEV8tFQknm2qv0WYuS+IALGquRJfw8oH+nPZ3mDpikiaXIkxzYKy8zOtK2zajJDYWORrVfg4Oa2HLfB77t5PTpx474htFMLlzejGShxjOlWyxH3vtxL/BrJQzFTA+C0VEi5h+LyL67L5IbzlwFUK8JoR4Ffhb4CUhxFvRZWti5SWHlUzBbCL8aFiLl1nJG/qDkbS6Se1mkTvEcXuNxyISqVmAV3HTWp48r6+1vJWB8ACt5a14XSU+38Fh6mh/1LjT2mqImfbExEw53nHd3etBk4KFNSFG/TX41BG8kREAAq4KPIo3Z0FTW1MlioCdzuPCRUdM0qTp0jQHxspHw1rathklsbHI0bjt52Cvy2uZz2P/dnL61GNHfKNLJndOL0byEMO5ki32Y6+d+DeYlXKmAsZnofC4PKbfC4/iKdARFQeZsuwFwEeBvwdOBD4UfR0rLzliMoVYQojNtYqJRhLL59cF+GG0bPOTu/neJ09P2v69T57O5id3M682wHH1ZWn7b161lKYKn70DK2s0nuWPfWmjz/bXBRrZdO6meGDH5rhua9/GpnM3UTeJxzodZjm7HoHGJeAzrL17okvhtPrqc2ruzW7j5LKwOsSIrwaA8uhdVyEE1d76nAeuAa+L4+rLnXmuRUgsd/5y5wFuvfyspBx46+Vn8cudB7j5ondQV+5Jy7tFLbFJxSJHU2bfalvnr0vL5zedcxN3vHZHfI6fk9Onnpj4JjEeb1u1lGMDo/HXPreY3Dm9GMlDDOeKWexvWLGBbe3b4q+d+B/HKkZLWs5UwPgsFA2BBjas2JD2vWgINBT4yAqLkDLzowVCiJ9JKa/IVjZdLFu2TO7cuXPK2s9mFVY1HXfUICilNMp0icel4FYEoYiGx6Xg9QhC4XFDpq7LtP0TDYQ2DiyrVVhgXG0TQIO/Abfb/KrMLLWUFfxS5FTHbt7o2wf//Q5YuhpOvRCA77x9H786uoMfnLI2JznTt/9QQ8egwlfefZDaocN88NXNPPHOf+Zgy9kAPLj/dsL6GN848yc5HfJdz+5j+5udvPbNv8M7ke9V8VPwuJ1BLz0AACAASURBVIXJxW4kotE5PIamS1yKQAiQEjxuQUSVCAHuqFwjopWQVThQD8GeCRkwzazCqqaiSQ1NargVdzxfp1pYi9C6WvA/YK5xGwqp9ATDqLrErQjqA16GIlpSvyD1nN5Y7qU/pM4MEZMVmgrDR0GLgMsDFS3gmnzfwE6s2rEK61Kfrr5Lwf9w2WLXLEb9/hLvx01RfBYzY+oYvaFeVF3Frbip89fhc2e8QFbw2J1q7PzFT0l8EbUEm663WgrEZAqpuN0KrTXjj2YYBuLhJANxokk41YqpKCJp/xwODCqa04uFQp23hva+Xax7ctw8tmn5BtpqF6GkfKkdS5lDVt4wrnJz3Dnxoj2jR3M2CusS3ur2clrjEAAjfuOOa/JarnW8NfByzod8UksV//uXo7x2uJ+lxzlX5YsFXZe83T2SZmpva6ygvWs4rXxGmYRTSczRORowFaEkXU23ytdtNW3sGdiTZGF1rKv5Qdcle/tGs8Zm4jndakWCGRXPug5db+bd2mpmDDaL1dTYB2x9F2Zj30VVdd7uyWy1LjmmKD6LmYgWYffAbtP873HN3seFM81x/boQYgg4XQgxGP0ZAjoxlsiZ1ZgZiBNNwtNpxewNdsUHrRA18D25nt5gum3NsZQ5ZOX1B6FhEVS2xIt2jx7JeX7r4UE3oxGFhTUhAMLuMiIuX4qgqZ6gNsKIOpTTeyxuMR5p/tPevpz2d5gaMpnazcpnlEk4E3kyYGbK16kWVse6mh+sYjZTbOayT9ExRdZWM2NwLrHq9F3GMVvpwsxqXVI4VuFZHfOJWA5cpZTfkVJWAt+TUlZFfyqllPVSyq9P4zEWJVYG4phJeDqtmFZ24bCuptV1LGUOGenbZxiFE+62DqlBusIDzMnRKLynz7gaPq8yelIVghFfDZWjx+J1YmbhXOe5Vgc8zK0J8Ke9PTnt7zA1WJrao7bh1PIZZRLORJ4MmFb5WrXK+Y51ddJYxWym2Mxln6JjiqytVsbgicaq03cZJ2KRP2e01Tobs9AqbJXnVZO+/Wwi0x3Xs4QQZwH3x/6d+DONx1iUWBmIYybh6bRiWtmFvSaPzziWMoeMvP6Q8Xvh38aLxo3CuYmZ9vZ58Cg6jeXjHYxRXw0VCU8EVHkmN3AFWNJSyQv7+tBKeUmAGYalqT1qG04tn1Em4UzkyYBpla/dVjnfsa5OGquYzRSbuexTdEyRtdXKGDzRWHX6LuN4LPLnjLZaZ2MWWoWt8vxsezQ+lUxRfnP05wfA88CPgNuj//7B1B/a1KPrkq6hMQ73jdI1NIauy6SyzsEQnYMh9veM0NEfRFXHr2aZGYg3XPyOuEk4b1ZMXYfhY9B/0Pitp19Rqws0smn5uHlsxbwV/PhDtxPWNbqHj9Ib7KVjuIPuYDf1/vo0S9nGFRtRUOge7aY3FK07fAR98EjSe8bECLG2dFnCV/dmK288ZDwmnDCfOmYUzvWO694+N3MqwiRO9Rrx1yTNca32GoPinlBua7kCLJlTxfCYyptHB3Nuw2HyxHLosYEguq7H7esQNQZfsQyfR7Dl6nfx0yvfyZnza2amSTiVxFwtXHDpPUbHat4yuPx+uOIhkEa91FwaE+al5lYzq+TGFRvxuXzc/qHb2bZyGz8//+fc+oFb2fzBzY51NQ/Ul3v50RXJMfujK5ZmjE2rFQmKKp7N+hJpMZvd2qprKt3DR+gYPGj0E7T0uz+J8a0IJc0YbGYIjmgRjgwf4eDgQY4MHyGiJd9JdQyr4zRV+PhhSoz+8IoZbrXORlkjrPqVkUuvfNj4vepXJW8V3rhiY1r+n40xn4jlsF1KuQJACPEr4Cwp5WvR16cC35yWo5tCzGQKd191NmOqbilcSp387nMr/PvKUynzuhgNa9SWe9m86iwURcmPUdCm4ENxuWmrXcTWD9+JDvSO9XPNo5+NT+a+6Zyb2PjSRrqD3YYUoaaNu/7+LlRdRUrJ9174HtsPbU+v++4baPvjD1CWfx29cQntA7sdGUgp07vXeEx46VVJxbtHj+ARLhq9VRNuUkrY1+/h9Kbkuasjvlq86ijeyAhhTzl+VwC/q4zuscndcQX4095eTmmtzlLbYSqI5dUNj73FZ977N9z1zF7WrjgxKU963IKvP/Aaj77RaXS4Vi1lTo2fmsAMtLDGMMvVn7ofLr7b2H7fp+Pl+qoHaVe0eC5dMW8Fa85YkyTgSMytVd4qbjvvNhShIIRA0zUue/iyeN0bz7mR+9+6ny+c+YXC/j8oESIRDU/Kud3jVohENHw+8y6ToggWN1fy4NpzitMqbBafqx4ENZRedvXjoJmbsHVNzSqCNJMxbf7gZrZ+ZCth3dwqHNEitPe3Z5TQuBU3i2oXcdff30VEj+BRZs2KCGlomo43JUa9bgVN00tYzqRBeBQe/sp4vF68xSgvUTmTLnW8Li/Xv/t6Au4AQTWI1+Wd9TeN7Py1F8cGrQBSyr8AJ03dIU0PZjKF/T2jGYVLiZPfe0bCfPqOP7H6zhe45EfPsfrOF7jypy+gKAqNlb78nLAmMBldcblpqJiDAnxp+3VJk7mv33E9V512VVyKMBAeoKW8Bb/bzzWPXsP2Q9vN6z53I71nXQ4/v8wQQDkykNImZhNeeE5S8Z7Ro7T4cltqo3PExWhEobUyWRoRW8s16a6rp57uSdxxbajw0Vjp4097nZgsFLG8euHS+fzzA69y4dL5fPGel9Py5IVL5wNGjv38lhfRdIqnk58LZrn6novAHRgftEbLewf2J+XSlW0r0wQcsdzaG+rl6keuZuW2lXz0oY/ydv/bXPv4tUl1b9hxAyvbVjr5OE90j4ZZ/dMXkmJ29U9foHs081y62IoEc2vL8tcHyBdm8dm3J71syyeMxTRq5htP3aQMCOyIIM1kTGseWwMCWitaaQg0pJ1L7Epo3IqblvIW5lfOp6W8ZVYOWiH3GJ3RDB+F+1Ylx+t9q4zyEqU31Mvax9ey9om1rH5kNWufWMvax9fO+jxvpyf6qhDix0KI5dGf24FXp/rAphozmUKZ15VVuBSb/D4tMoYcJqNbiZqqvdXxf8ekCFbShKS6ZXXQf8BaAOXIQEqH1x+EhsVpyy7tHjnCHF9tTk3ujYqZ5lYmx8moyZI4ld7aSc1xBVjSXMmf9vaSbX1qh6khlhdrAp6k34kk5tTY6xklsTHDKlcrrrTysK88KZdWe6stc2tqjraqGyt38vHkUXVpLr6ZyXPnzeLTU5a3/kWiCDIXGZMjoZkYJRmj2dBV83gt4Rhxvhfm2Bm4rgZeB74U/XkjWjajMZMpjIa1rMKl2OT3aZEx5DAZ3UrUNBAeiP87JkWwkiYk1R3thZoF1gIoRwZSGvTuhSOvJNmEAUa1MY6M9eUuZur3oAhJS3lyp2UkOhBOFDRVe+roGTs6qUHn4jmV9IyE2dM9knMbDrkTy4v9wUjS70QSc2rs9YyS2Jhhlat1La3cOzaSlEsHwgOWuTU1R1vVjZU7+XjyuBVhLr4ppjuoE8UsPiOjeetfJIogc5ExORKaiVGSMZoNxW0eryUcI873wpysA1cpZUhKuUFK+YnozwYpZWg6Dm4qMZMpHFdfllb2vU+eHhcubV41Pvl9WmQMZY22ZAmJpIqaYvNW73jtjjQpQp2/Lk2akFT33TdQ99JWuPReo10bggWHGcpff238TnlMeN/oMSQy5zVc9/a5aS4P43ElD0bD7kB0LddEQVMdEX2MoUjua7Ge1GLMw3UeFy4Msbz4wIsH+e6Fp8d/J+bJzauW8sCLB+Ovi05ikwtmufrin8HLW+Fj308qr6s+LimXbmvfliadieXW1BxtVvfGc25kW/s2Jx/niYYyL7elCMVuW7WUhrIZHKNm8Vl7fF76F5uWb6AuML6PWb8iW2w64qWJUZIxmo2KFiOnpubYipbM+81g6vx1pt+L2Z7nhdXdDSHEfVLKi4UQr2G4EJOQUp4+1QdnxrJly+TOnTvz0pauS3pGwkkyBV2XdA6PoWo6HpeCSxGEIhp+j3FHQEqJqks0XcbLIpo+dTIGXTfmp6jmsgQzVDVCd6ibiK7iUdz43QFG1dG4FAFNozfYRViq+F1+dCEIR2UHUkrGtDHciosGJYBHD8ffU5c6vaFewpq5YKHIKfilyHzGbt750QoYG4ILNiQV/8+x5/m/b93Fvy+6grk53HX97LZGTqgZ5ZJTOtO2feiVHzBYMZ/fn/01AHYPvs5DB37M107/AcdXnZLTx5BScu3Wl/jAkiZuueSMnNooMgoetzCx2I3lVV3XUXWJIkCXhqhLCOKvhQAQNFX4ZrZQJDFHC2HYWRUF/HUwcgw01XhkWCggdfAEUH3VdI/1ENEieFwe6v31DIQH0HUdHcM67FE8qLqKJjXcihsFBUVRqPHV0D/WT1gLowglXl6E+bjgsWs3blP7ApUeFz3BMKoucSuC+oAXv3+G3+XQVGM+oBYBl8fo8EtplOmqceeqogXcmZeXSe1fNPgbcKfsk9pXSIxZq75DWA3TE+pB1VXcips6fx1DkaFC9TeKPnZDIbX0YjQbagiGuxLitRHc/kIf1ZQSUkP0hfri34tafy3+zJ+54LE71WSK8i9Ff18wHQdSCGIyhRi6LmnvGk6yCt/+6WW0NVbQ3jUcN2X+8wOvxrenmoan4CDT5hxmQpc6uwf3WNp/dTVCe/8u1j355QQr4C0cX3MC7QN70qx+i6rbcEcHyopQnCugpcjAIeh4Cc76TNqm3aNHcKHQHJUpTajZkKA/5GJOipgpxoivhopg8h1XgO6xoxxPbgNXIQSLWyp53rnjWjBS8yoYptY3O4e5dsuL8dz53QtP565n9rL+g4tZ3FxZXDIbu1iZ3xuXQNebyeUrb4Unvole0cLu8/4l2cx67iZOqDmB3f2Gub0h0MB1Z13H9TuuN83jTh7OH6krDHz+fQu54Ix5SbF626qlLGmqwOOZoY+063p6PK56ECIj8ItVCZbWn0HzqeAy7xpm61/ESIxRM8tw6j661Nk7uDepzoYVG9j8yub4igfOKgbjhMMq7T0jaTG6uLEcr7dEB69qBDrfhPuuSI7XplOyXmyZqYTVMHtM+uUnVp+It4TXr82GZQaQUsYsKecBXinl/sSf6Tm86cXMNPzZu3fSOTyWZMpM3J5oGi4GzIx+ibZJwwr45RQr4JfpDvWaW/1C3eZv5FA6/PV/jN8L3pu2affoUVp8tbjFxDtshweNE2hzubmUY9QXXcs1+tRHbOA6mbVcAU5qqeRwf5DD/cHslR2mhc7hsXgnC4zcGbMOf/bunfSMzFCpkJX5ffhoevm2tXDOdfSedXm6mfX36+gOdsdz91WnXRUftCbWme02yakg9bz/yWUL0mL12iI7z08YK6vwL1ItrVdktLRm61/kuo9ZnfXb17OybaXt95lNdI2ETWO0a6bmUTsMHx0ftIKteJ3p9IR6TPvlPaGeAh9ZYbFz6WoB8EMhxB4hxP1CiH8UQpTEM3ipWJmCI5qe0ZAZMw0XA9mMfmFpZSnTTMsjs9xeNit4YxvULITquWmb3h7pYE6O8yliA9em8ojp9hFfDV41iDdiiJQ8io8yd+XkzcJzjHmuLzh3XYsGKwtmLKfOWKuwlU1Yi5iXB2oJl9VZ5NpIvDyTadghv6Se912KKD1j60Sswpp5vobcjMF29sm2woGd95lNOFbhKI5VeFZiR850g5TyXOAU4Gngq8CLU31ghcDKFOxxKRkNmTHTcDGQzejnFVaWMpdpuWeW28tKnqFjcOA5OO49aZuCWpjDoZ6c5rYCHBx043Xp1PjMk+xIbEmcxMeFPXV0j01u4Lqgtoxyr4vn9szuq5LFhJUFM5ZTZ6xV2Mom7PKYlwf78I72WuRaT7w8k2nYIb+knvc1XZaesXUiVmGX9WOXuRiD7eyTbYUDO+8zm3CswlEcq/CsxFLOFK8gxPXAOUAF8DLwR+DphEeJrfbzA38AfBhzaX8ppbwhpY4PuBtYCvQAl0gp92Vqd7KCm0QJQ8DrQtUlEdWQK9UGPJZzXA/0jdI5NEZjpY8DPaNseqKdruExNq9aSmOFF0VRMsuZzCRLUh+XJXiiSUiLJEmYdE01REq6iqK4UYQLRQgUKQlpY3gVN3WBRpTonJTU+SQr5q3gq+/8KmB8CepdZRwOdXFo5AgBd4CgGmRexVxqfLUcGT2S9ix9W/WJDEQGCWth/G4/uq4T1jPLEhLFDHb3mQYKntGLUs608w74zXrDfFq7MGnTG0MHuOTl/+DaBR/hnTWLJtz0vz9ZS29Qsu6dh0231wx38KE/38bvl/0TB+a8C4DfHLyb3rFOvrVs64TfL5H/evQtuobG+MM/rZhUO0VAweMW7MVuquCmNuChLxhB03U0XRLRJHu7R+K5c8bNcY3lcF03rvLrmtHJ1zXofhOq5oK3whAxAahj0Lsb/Y1t9J61ilD1XBSgTHjo04IcGj5MfaCeKm8Vruij+JrUuPqRqy3nuNb56wipIdyKG6/iRQppS3xTILFewf+gduP2cP8oY6ohEgt4FYbHNA71BinzuhgNaxzfVE7A7ZpaEeNkMOtfwHiZy2vUCQ8ZAjEpwVtpbP/F5fE5g/rlD9BbXktYjxhx4q1BCfbE29UD9XSMHo2LwXSpG4POitakeEqKN8XLUGSINY+tSZvTHYtbv9vPwNgAh4YOxfslrRWt/PeL/12oOa4F/+Nmit1wWOXI8BgRdVx+53EL5lT4SnuO60inkXulFpXguaG8qaTnuPaEetCkhi51FKHgEi7q/fWZ5rgWPHanGjsR/g+ACjwMPAU8K6W0M9ljDDhXSjkshPAAfxRC/E5K+VxCnauBPinliUKIS4HvApdM7CPYJ1HC0Fjh458+vJiv/vLVtEHqg2vPSTMNj4Y1/r/7/5wkZaor9/DNX7/Oo290xvc37YCZCTwSxQgVTfCBbxpzoBIEH3rDYtr725MkHjeecyP3vHEPV5x8BRtf2kh3sJtNyzfQVrsIxeVGEQpttW1sPX8ruq7TE+rhmkevie+/ccUG/C4/Nz13U7zsO+/7Dtfv+FfOajyLn374p2i6hkdxU++vZ09UmJBNFhL/qAkDZ7v7OBSQ9kcN+VfNcWmbdo8a16ZyXcP10KCbhdWjlttH/NG1XEeT77i+PfgautRQcphXG+OU1irufnY/h/uDzK0JZN/BYVKkCm4+dHIT6z6wiE1P7DIV2lUF3AyMRrjs7OPwzQSjcCyHb/82vOvz8OsvJgtC9jwNbefB77+VtF1fcgHtKSKmW95/C4oiknJwLK+vOWMNv7jgFwyGBxEIbv/Q7WhSw+fyEVJDrPrtqvF2lt/CK8de4cyWM5MuOJqJb7LJcWYzui4ZCKqsic4Z/Pz7FvLRM+bxr9v+Eo/lf/zAIi7f8nxSX6FoLrZYCcLcftjyCaNs8fnw/n9KFttcsgV8VXD+zeApQ1dctAuVdQkxtmn5Btoe/xbKm7+BmgWoVz/BYHgw7QJ3k9YU70ibxdvGFRv5zvu+gy51gmoQt+Lm6MhRrn7kajpGOrjypCv5yAkfSfpObFyxkW++95t8TftaoS96Fx26DoNBNU3O1Fzmy77zjEWDka50OVN5PVCaA1cdnb6xvrTvW2207zRbsfOo8FkYgqY/AR8EXhNC/NHGflJKORx96Yn+pN7eXQncFf33L4EPCCGm7EyQKGFYs/yE+KAVxkVMfcEIjZU+5taW0VjpQ1EEncNj8ZNarO6aLS/y1tFhHn2jM2l/U8lINjHCOdeND1pj239+WVSklDwx+4YdN7CybSXX77ieq067KipXWk9vsCv+djGjnyY1rtt+XdL+121fz6Hhw0llX3/661x12lXc+dc7Wf2/qwl4ArRUzGEgMjhhWUiiZMERjBQ56hjseRLmLoutTZLEZIzCoxFBb9BFk4WYCSDi8hN2+dPMwppU6Q9P7jHfU1qNuVHP7nYeF54OUgU3Fy6dz5otL1oK7f56ZIiPfn8Hq+98gU/f8afilzPFcvgZl40PWmFcEHLm5UZ5ynYzEVPvWC/XbTfP6+u3ryeoBvnso5/lIw9+hPMfPJ+PPfQxdvfv5trHr03a58tPfpn3L3h/mrzDjvjGycPjpJ7fP7lsQdLrC5fOTxPhFJVQzEoQ1rdnvOyMy9LFNr9YBT3tsPUiuPN8ekN96dKwJ9fTe9bl8X16hJpVFmMWb9dtv46+sT5WP7KatU+sZe3ja5PmtX580cdN+irXEdbDtFa00hBocAatCfQEzeVMPcEiicmpYLjLQs7UlXm/GUxfqM/0+9YXyn2t+1IgayYQQpwKXA58BuNu6GHg93YaF0K4hBCvAJ3AY1LK51OqzAUOAkgpVWAASLu9I4T4nBBipxBiZ1dX7kGaKGGwEi2ZSUJicqbUumVeV1qZqWQkmxghUGs66dxKpBQTd8TEBR0jHYRNJmtbTewOuANpZUltxUROCScWu7KQXPYpZfIVu1PC/h0QCcLcpaabd48cmbRRONPAFSEY9ddQMTr+/6XKE10SZ5KCpnm1Aar8bp7Z7Vixc2UisZsquInlV6s8WxPwJL0uejlTLIdb5GoUl+l2MxFTwB3ImNfN8rbVPrrUcxbflGoenmjOTT2/p8qZJtJXKAhWgjBP2fhrq7hNqGMlDQuXjcv5rCSOibIYu6KlxIGoS7gshWWzCbux68iZojhyplmJnUtY/wFUApuAk6SUK6SU37DTuJRSk1KeAcwDzo4OgieMlPJHUsplUspljY2NuTQBJEsYrERLZpKQmJwpte5oWEsrM5WMZBMjBPtMJ51biZRi4o6YuKC1vBWvyWRtq4ndQTWYVpbUVkzklCBMsCsLyWWfUiZfsTsltD9mzNFrOd1089ujRyZhFDa+B80WRuEYI74aKkaPxV9Xe43rVpMduCpCcNKcKp55u4ds8/gdzJlI7KYKbmL51SrP9gcjSa+LXs4Uy+EWuRpdM91uJmIKqsGMed0sb1vtowglZ/FNqebhiebc1PN7qpxpIn2FgmAlCIskTNOwituEOlbSMO/o+J15K4ljoizGrmhJl+OrMWhSsxSWzSbsxq4jZ4riyJlmJXYeFb5ASvmfUspnpJQ5Xf6SUvYD24EPp2w6DMwHEEK4gWoMSdOUUF/u5fZPLzPmWT25m+998vT4lz82b6W+PP1k3lThY/OqpUl1b7v8LJoqvbb2p6zRmHMS+9LVLIDa4405JjULYMdGY3H6xO2X3ktdoJFNyzfEAzc2F2pb+zZuOucm7njtjvg8lLpAepJrCDSwYUXy/htXbGBeeWtSWVJbUQEIQJ2/jk3nbqK1vJU7XruDm865KWm/xLoxctnHoUC0PwrNp4HHn7ZpskbhQ4NuXEJS57cxcA2Or+Va5a1DoNAVMhc6TYRTWqs4Ohhif4/1PFuH/JCYWwEeePEgm1ct5YEXD/LdC5Pz7G3R8thry7xZTMRy+Cv3GiKzxFx98c/g5a1Gecr2upe2siklB9f56ti4/BbTvL5hxQbq/fVpebu1wpjzl1h2y/JbeOrAU2l1U3NsYk62qjObST2//3LnAW5LeP3AiweTXhddzJr1Ly691+hjxMpeudeI08Q6l2yB6uOSYzUlLjct30DdS1vj+9RLd1q8xWI2hlm8bVixgW3t25JeJw5wH9r1kGm7DYGGqfl/NsOpD3jTYvK2VUupDxRJTE4FFY3pMXzxz4zyEqXWX2v6vZjtc1yzWoVzbliIRiAipewXQgSAR4HvSil/k1DnC8BpUso1UTnTP0gpL87Ubj6twn6vQjgiiWg6bpdCU4UPt4UoRFV1OofHUDUdlyLwuRUURSRZiSdsFdY1wyqsq+AtBy1sWIVdHqhoAZd73CosNRShoCCMH6EQ0kJ4FQ91wg2aSq8CYSRexUudBCUSJOKrpFsLouoqbsVFg/DgAnqlSlhX8SoeFJfXaMtEgGBlCDaORUFRFNv7OFbhIrIK9+6BTWfC2Z+Dkz6WtnmyRuH/eLqGQwMKX3n3wYz1Fh1+hjP2/Y57/+5OxryVAPxk17c4seo0Prfkhoz7ZqOjP8hX7v8z3/7EaXzqXQuy71CcFDxuIT9WYU2XuBRBQ5mXwbCWJMAruOTGLD8rinkdhDE/PGZnjRkuPWXGHax4uQ5CQXf76EUnJFUUoeAXbqok9KMRRiIQGP8JGgINuBU3qq7SPdpNRI/gUlyUucso95TTE+ohokdwC2ursJllGHCswhlIPL+7XQp1fg89wTCqLnEXa8wmYtq/CBvz/3TVuCtV0ZjyugWQMHwsXqZXNNEbGRqPkxSrMGWNhPQwfaG+aJ/CTa2/Fr87+eJnqsW6ylNFT6gnvk9DoAGX4kqqU+2tjse3R/HEvwsFouB/3GyxGwqpSTFaH/Di95f4nTg1lB7T7vQL76VESA1l/b6lUPDYnWqmMsrnAHcJIVwYd3bvk1L+Rgjxb8BOKeWvgZ8APxNCvA30ApdO4fEAoCiCxkpfmgUzmynQ7VZonYydVFEMe2sMXYfutwyJgoVVmKaTUVxuGsqa062BK2+FV38Op1+K/vxm2v/2C6x77sZxG+B7bqTtd/+CZ7iTOamGwZoFNETbT+ucpR52VPQUP2wbhsrUfRyKkLefMH5bzW/Ng1G4uTyUtd5QwOhUV40coSs6cK3xNtAZPJTT+yYyp9pPXbmXZ/f0zOSB64whllsTqS/3TijPFgQrK2tqflQUY0CQyS781H/CWw8bry+6G/7wPZS3HqbBpM1MGVIRCgPhAdsm4Fi+zZSfnZxsTeL5faJ9g6IgtX+hjkHnm+kG1u7d8Kurxlc2UENJca9cei8NqXGf0K6qq+wZ2JNmOV1UuyhpkGnWB5hTMSftsFPrtJS3TPJ/xOxA1yV7+0ZnVoxOFjVkHtNNS0p28Gr3+zbbmLJLrlLKV6WUZ0opT5dSniql/Ldo+Teig1aklCEp5UVSyhOllGdLKfdMY+LPMAAAIABJREFU1fGkkmrBnHZTYKIJ0MIqTExaY2YN3LYW3vOPsG2tYa6MDlohagN89gZ6/89XzA2Dqe1PAMdQWSLs/YOx/lnVXNPNkzEKhzXoHM5sFI4xHB0YVw6PCwhqvA10hg5Nem6qiM9z7XbmuRaIgudZO1hZWc3yYza78BmXjb++/9PJryeQc3PNs05+njwzImazMdxpbmBd8M7x13177Md9lO5gt6nltDvoSPCmk5KI0YkyC63CzvfNHMshuxDif0hfviaOlDL9+cIZRKoFE6bZFJhoArQy/qnh9LqJ26Mmy6w2wFTDYGr7E2C2GSpLEikNo7CFlAkmZxQ+MuRGImgqyz4lfsRfi46gamRcxlTraySkBRmK9FHlndw8vFNaq9jxdjftncMsaq6cVFsOE6fgedYOVvnVLD9mswsHajO/tplzc82zTn6ePDMiZrNhaWBN+AyJKxsk1skQoxEt4th/i4CSiNGJMgutws73zZxMd1z/C7g5w8+MJtWCCdNsCkw0AVoZ/6ILeltaA6Mmy6w2wFTDYGr7E2C2GSpLku52GO2BltMsq0zGKHzIzlI4UXTFzai/Nnng6jVkC8fy8LjwKXOqAGc910JR8DxrB6v8apYfs9mFg32ZX9vMubnmWSc/T54ZEbPZsDSwJnyGxJUNEutkiFGPy+PYf4uAkojRiTILrcLO980cy7+4lPKp6TyQ6SZmwUydIzBtpsCyRvRVD9I7sJ9wWS3e1b+hbqADRdfQFRe9NfMJo+ENdlMXqEe59N7kOVgXbwFfFfqnfgljvfz0735CSAszFBlCIGgpayaoBjn6pZdokC7cWtjYL3EOV1mjPSlJAjFjYOxxtBXzVvDVs79KWAvTHezOKGoqsJzJIcb+Pxq/m08x3RwzCi+tPjGn5g8PuhFIGm3ccQUY8tcnDVxrvMa8p87QYdqqre8K26Gpyk9TpY9ndnfzmfcunFRbDhOn4HnWDjEra+oc17IEW6WuQ7AX1Ahc8RAMHYFP3gm/vDJ9jiuMv1aD6GufpzdQSUjqKGj4g71UeisNEY0WweNKF9HU+ev4yd/9hLBmiPB0qSeJlhJJyrGKl80f3Myax9YkzXGN7efk4+zMiJhNJfU8XtEIn7ofBg6MS8OqFxgCyCsfNl43LEFf9St6Bw4Q9pXjHRuhrvo4lDJrS2tDoIFbz7uVjmFjPfigGmRh1UJjHdbhjnhM6VKnO9gdj+96fz0D4YEJCcOcWLVmRsboZLGK6RK2CjcEGrj/gvsZVofRdA2X4qLCXUGZtyz7ziVM1ksVQog24DvAyUB8BrSU8vgpPK4pR1EEi5sreXDtOQUxBeoC2hWNdS//Z5JQ6YSX72X3sitY9+jVyXKNxiUo1zxuSBd0DR75F/SRY7Sf/11+sOtePnXyp7hhxw3xfW465yY2vrSR7mA3G5bfwqI3HsZ9/s1QdwJ4K6A8+mW3IyVJ/P8mFNpq29h6/lZ03TixXPPINaYCETsiJ4cCsP8Z4xHGylbTzXtHjyKRtPpyvePqoi6g4nHZm1c6HKjjuK5XjUeYhaDaW4eCKy+CJoCT51Tx3J5edF2WrriiSCl0nrWFohg575rHzS/g6bph4R46kizQu2QrXPUoaGPRq/4uWHYlvG+98f3SNfTXHqB96aWse+SqeA685f23cEw5xnXbr7MUbuhSZzA8mCblaJEtaZ17sxx77wX3ElKTTfFOPrbHjIjZRMzkYqt+ZQxSH/5KQrxugSf+LS4P01f9inahJvdBVmygTeooFg/jKUJB1VVueu6m+IXrNWes4ZpHk/sAbsXN2sfXJtVJjOXNH9xMWAtbxqITq5mZcTGaL9Jiemuhj2hKUXWVQyOH0s4Dx7uPd+RMWfgpcBugAiuAu4EtU3lQ00XMgjm3tozGSt+0fulNJRrP3kD3+9az7tkb0uUa4X7D7icUuPtj8NbD9P6fr7Bux7+wsm1lfNAa2+f6Hddz1WlGZ2n9k1+m+7R/gK0Xwc8+bsiyFWViUpIEYsZARVH40vYvWYpAHFFIESIl7Hsamk81lu0wYdJG4QG3rceEYwz76/GqQfzRBeoV4aLaW5+XR4UBTm6tYiAY4Y0jg3lpz2FiFDLP2iZmZa2Zb/xOvHA32mWIbFIFer+43KhXu9BYwuynHzJy7I/Pg86/wtZP0vvOT7PuyS8n5cDesd74oDVWlircsCvlsMqxutRprWg18nS0o+/kY/vMiJiNYXYe79trxGdSvK5KkoX1DhxgXUocrtu+nt6g9fk/NYZWtq1Mi9N1v19Hx3BHxjqHhg5ljEUnVrMzo2I0Hwx3mcT05SUtZ+oL9ZmeB/pCfVn2LG3sDFwDUsonMNZ83S+l/CZw/tQeVuljJdGIuFyZ5RpaJP7FjUmZqr3VpvtUe6uT2gXsSZ/yJBBxRCFFSP9+GDpq+ZgwTM4orOlwZNhN8wQGrkMBY4BcNZz4uHA9naH8DFxPaTW+B848V4ecUMPZRTapuTQqbwor7rQcGHAHsgo37Eo5JpJjnXxcopidx63iNUEWFvaVm8dDBtlNagxZ9T0C7kDGOlbfAafv4GDJLJQzqbpq+j1QS/gz28HOwHVMCKEA7UKILwohPgFUTPFxlTxWEg2PpmWWa7g88QnqMSnTQHjAdJ+B6B2sWLuAPelTngQijiikCNn/jPG7+VTLKpMxCh8bcaHq9ozCMYb8xpzWqoQEXetrpDN4OC/L2NSVe2mt8fPsHmfg6pADbm92kU1qLo3Km7y6mpYDg2owq3DDrpRjIjnWycclitl53CpeE2Rh3rER83jI8AhiagxZ9T2CajBjHavvgNN3cLBkFsqZ3Irb9Hswmx8TBnsD1y8BZcA6YClwBfCZqTyo2UBMchQLytgc14anN7DpPTcmlyfINahoMaQfNQuo+8PNbHrPjWxr38aN5yTvc9M5N3HHa3cYz8Qvv4WGpzekS0diUpJYMjCTkkz0MyQca7btDgVg3x/BV5l+AkhgMkbhwxMwCscY9VejCyVF0NRIWA8xEM7PYPPkOVU8v7cHVdPz0p7DLKKsEWqPh5W3WufK1Fz6yr1w0d3UvXA3m5bfkpQD63x1bFyxMalsw4oNNAQa4m/ZEGhgw4oNGevAxHKsk49LFLPzeO3fGALHxLKLf2bEZfR1XfVxbEqJsU3LN1AXsD7/p8bQtvZtaXG66dxNtFa0Zqwzr3Ke03dwmBgVjfG+LzAe0yUsZ6r115qeB2r9tVn2LG2E3TsaQogqQEoph6b2kDKzbNkyuXPnzkIeQnZsmnp1TaU32EVYV/EqHuqkQImMEvFX062PoeoqbsVFg7/euNIea9MVveoeCaJ7AvS6XOhSokkNTWq4FTcKCmPaGG7FTYMrgGdsyPxYJmgVTvsMWcx/RWQGLPgEkKKI3Y3vgMoWOPd6081BLcy7dqznY83vYmXzuyfc/IN/LeeeVyu58f17CbjtDxI//NJ/01tzIk8u+yoA+4be5IH9P+Qrp21kcfUZEz6OVJ7b08N/P9HOA9e+l6XHzaikX/C4hSKJ3UKgqTB81Pjt9hmPpUkNhMu40q8o4zkzMZcKAS4vaGF0l5ceNEJSxSVceISHSl8lfaG+aI53U+evZSgynJQn42ZWPYJHSTcPx5hIjp3mfFzw2C2JuLVzjo7HacToH1S0gNRh+JgRs4rb6OCPdCfV0SGhD+Kmzt+AEurN+F6qribFZY2vJimW6/31KIqSVGcGWoWd2C1G1JAxpzUxpt3+7PvNYEJqKOn7VeuvxZ/5Mxc8dqcaO1bhZRiCpsro6wHgKinli1N8bDMTM8OfmalX11G63qQhpZ7a0Eb7wO40i9gipRz3XRektakoCnUmBr5Eq3BGG19MSpIjMVFTrtsdppHBDujfByd+wLLKpI3CA26qfeqEBq0Aw/66pDmutT7jKmpn8FBeBq6ntFYhgKfbu2bawNWhUGgqHPsL3HfFeN696C6IBOGhNeb53SyXSp3ehPxsZlndsGIDm1/ZzPZD25MMqi3lLVkPcyI51snHMww7/Qldh643U6zCDxqd/KQl9KLLNUWtwlx6L0rTyTRUzLH9XrrU2d2/O2sst9Wkx65Z3Dl9BwfbqGPQ+WZyPr74Z9B0knFRsQQJq2H2DOxJ+36dWH0iXptT+koRO5ev7gDWSikXSikXAl/AGMg6mGHX1GtRrzvUY26TRLVs08zAl2gVdmx8DsD4/NaW0yyrTNooPOia0GPCMYZja7lKY8Bb6anFJdx5MwtX+j0c31jO0+3d2Ss7OIBxByvWSQLj92j3+KA1VpbFxG7HxLp++3pWtq2Mv3ZytgNgrz9hahXek1523xVJVmFb7aTUsRvLqfZrB4dJM9yZno/vu8IoL1F6LMYDPaHZ7euwM3DVpJRPx15IKf+IsTSOgxl2Tb0W9SK6Zm6TTH00KKFNKwNfolXYsfE5sP8ZwzZZ+zeWVSZjFJYSOobcNJVNPNaGAg249TBlUc27IhRqvY0cDR7Isqd9Tp9XwysH+hkI2hdHOcxiEgzucbLZhU2wa2KN5evYaydnO9jqT+RoFbbVTkodu7E8262nDlOAYxUGnO8X2Bu4PiWE+KEQYrkQ4v1CiFuBJ4UQZwkhzprqA5xx2DX1WtTzKC5zm6Sup9WNtWll4Eu0Cjs2Pgf2/9F4rEaxtgXvHjlCc45G4Z6gQkhVaCqf+MBwMPpIWPXw+B3WOl8TR0b3TbgtK06fV40mJc+87dwNcLBBgsE9Tja7sAl2TayxfB177eRsB1v9iRytwrbaSaljN5Znu/XUYQpwrMKA8/0CewPXdwCLgBuAbwInAWcCNwP/NWVHNlOxY+rVdUPuccnW5HqXbKVBCZjbJBn/0upLLqB79cN0oNEd7KbGV5Nm4Eu0ChfSxhcTjHQMd9Ad7EaXjtW1IIz0QNdbGddvBXhr5DDzcn1MeGDiRuEYg2VNANQOHYyX1ftb6A4dIayN5XQ8qZzYVEHA4+IPzuPCDjH0qMCm/2BUZJOQnxIM7sxbBpffDw2L4aK70/O7cJm3gT0T64YVG9jWvi3+etO5m6jx1Ux57nTyc5FT1mjMV738frjyYeP3qgeT+xNljfCZh+ELL8AXdxq/m05L64fon7qf7ooGOq55hO4rfoVu1k6WvovdWM5lbqoTiw4ZqWiCS1JM2ZdsMcpLlHp/fZqBfuOKjdTn2EcrFbIO26WUK6bjQEoGRTFkBtc8bm7mSxQgVDTB+TdD3QnGVaP//TrukWMsWnkrd33gViKKgkfXacCNu2ouXPM4uq7THuln3WOfjU/W3nTuJk6oOYGt5281DHyKF0VR+N77v1dQk69uIo3KKIpymDoOxNZvtZ7fOqQGOTLWy3tql+T0FrGlcJpzuOM65ilnzF1GTeLA1deCRHIseID5FW05HVMibkXh1LlV/GFXF1JKhCh5+Z5DJrLJaFxuY73jqx8zBqS/WGXUW3w+fPrXxpMLLh+MDcHtKyyFNopQaKttG8/PLi/Vniru+vCdRHQVT9TE+o33foOvaV/D6/JS46tJkuBMRe508vMMQQ3Bw19Jjq9EpA7BXhNpzSnxfojuCdA+1sO6P3wz+W8tEu5eZOu7YB7LVZ4q7vzwnXHraUOgAY8reb3hbDix6JAdxci3599sPAofGTVe27r/NjNRFAWfy8f1776egDtAUA3ic/lQJrDyRylixyrcDHwbaJVS/r0Q4mTgPVLKn0z50c1UMpl6EwUI/Qdg60XGieb8mw3b3yVbcG/9JC2Jz/LXLDBOJhXN9Aa7WffodUmTtdf9fh1bz99adAY+M2lUsR5rybP/GWN5jvoTLau8Hf07zfPn9rc5NOim3KNR7tEmvrMQDJQ1UZ00cDW+Qx2j+/MycAU4bW4NL+zby57uEU5orMhLmw4zFCsZTTTXAsbgVYjxQSsYefrYa0Y9gC2fyNwG5obUlpjNNUqDe3x7d7B7ynOnk59nAHZi1Ewidt8VsPp3UD0PwOg32Plb21hlwCyW56TE8kRxYtEhK8NH4Z6Lkue51iyAK38LNfMLd1xTSHewm2sfvzZpnmtreSt3/f1dtozzpYqdYfudwCNA7EHrXcB1U3VAJY+VAMFTZvw7UJtRkGAlYipGkcdMOtaSZ98foXHJ+BrAJrSPHAYmN3BtKg+T643MwbIm41Hh6NrStd5GBApHgvtya9CE0+cZApynd1lbYB1mCZMU6aGG7bcxQaYjdzr5eQZgJ77MJGL9B4zyKMX+ty7243MoAmahnCmiRcxlrfrsFkzaGbg2SCnvA3QAKaUK5HBLxQGwFiBERo1/B/syChKsREzFKPKYScda0oQGjPUos8xv3TVymIDio95TOeG3kBIODrhzmt8aY7CsCa86Sll0GRCX4qbW10jHyL6c20ylucpPS7XfmefqMGmRHm6v/TYmyHTkTic/zwDsxJeZRKxmQdJFymL/Wxf78TkUAbNQzuRxecxlrcrEHsUvNewMXEeEEPWABBBCvBsYyLwLCCHmCyG2CyHeEEK8LoT4kkmd5UKIASHEK9Gfb0z4E8w0rAQItccb/96xEVbeailISJUjFFq+lImZdKwlzYHnjXlQGea3Auwa6WCevz6nuZ/9IYWRiELLJAauAwEjxmtSHhfuyKNZGOC0udU8u7uHUMS5/jarsSPSy1bPbhsTZDpyp5OfZwB24itRIharc/HPjPIoxf63LvbjcygCbMR5qdEQaMib/KyUsHOp4svAr4EThBA7gEbgkzb2U4GvSClfEkJUAi8KIR6TUr6RUu9pKeUFEzrqAqJLnd5Qb1xMEBcf6boxH8VCahBHUYxHNlf/zniUR3GD24euuOm95lHCuopX8VD32e0okWBaW2ZyhDp/Heg63cFj0f3d1AUaUVzmf17Lz5BnrI7VkS1MM/t3GHHWuMiyipSS9pHDvLPGuk4mDg7kLmaKETML1wwfoqPpDMAQNL09+BfC2hhely/nthM5c34Nj71xjGf39LBicekaCWc92XKyDRlNvI2KJmMula4ad7IqWsbrRdvQdZ1eBcJIvGO9ybku2o6KoBvNkDK5PDQEGkyXNjDLnTW+mrzmbSc/zwCsYlTqMNBh9CFcHqNOrE8Ri0+hGFIxNYzi9tJWfUL631oCI8cs49+srwAkldX4augf689YJ1tcObHokBW3B5qWjOdhxQ0VjUZ5ieJW3BxffXyS/KzWXzvrl8OxYxV+SQjxfmAxIIC3pJRZe6dSyiPAkei/h4QQfwXmAqkD1xmDpfmu+gSUrjet7ZRJjeiQUlf/5J20uyTrdvyLLaNeqhxB11Ta+3ax7sn14/sv30Bb7aK0wet02/vMRA4O08z+HdDQBm6/ZZWjY30Ma6Gc57cejBuFc7/jOuatIOSpoHZwf7ys0d+KRKdjdC8LK3OzHadySms1fo/CY28ccwaupUo2Y3CMTDKaWBvbvw3v+jz8+ovmbSkKenmjdV6VQOcbqK/ex66zLmH9k1+O19mwYgOLahdZDl5juXOq8raTn2cAqTGqqcbUj1SLcPOphkwMTONfufReGhLjP8t3xCzmNn9wM2EtHC9bMW8Fa85Yw/rt65Pi0uvysuaxNROKVScWHTKihqHzTRN79smTnppRrITVMHsG9iR9vzas2MCJ1SfiLdHPbIesZzwhxEVAQEr5OvBx4BdCiLMm8iZCiIUYa78+b7L5PUKIPwshfieEyDwJr8BYme96gxbmv1ETAYyJJbB3rD8+aE1qNzrXL+txBbvig9b4/k+uN47L7mew+V4OM4zwKHS8bCyNkIFdkxQzHRwwjMIV3sk9fttf3kLdwN746+aAYcU8MNI+qXYT8boVTp9Xw+NvHEPXZd7adSgirGysZjk5WxtnXDY+aLVoK2NejbbTvezT8UFrrM767evpDmafb+3kbYc4Vhbh4aPjdezEf5Y6ZjF3aOhQUtnKtpXxTnWszrrfr+PQ0CEnVh3yy/Axi7g/VtjjmkJ6Qj1p36/129fTE+op8JEVFjuXav81esf0b4EPAD8BbrP7BkKICuAB4Dop5WDK5peA46SU7wD+f+AhizY+J4TYKYTY2dVVOBuopfnOynZmZpY0sQSGfeWTMuqFddX6uOx+BsfeNyUUPHYPvWA8VtN8asZqMaPw3BwXtj404KZ5EkbhGP3lLdQMHUCJWvOqPHX4XWUcGN41uYZTWHZcLZ1DY7x6OOt0/VlLwWN3MuTD9htrI4vpHbLk1Wg7EZcrZ0Okk7ftM6Pj1g42LMK24j9LHbOYC7gDSWXV3mrTuAy4A2llTqxmp+RjdzLMQquwatG3V0v4M9vBzsA1dgvlfOB2KeXDgK171EIID8agdauU8lep26WUg1LK4ei/fwt4hBBpt3yklD+SUi6TUi5rbJyc9GIyWJrvrGxnZrfyTSyB3rGRSRn1vIrb+rjsfgbH3jclFDx29+8w5jo1nZyx2q6RDho8VZTlMI9USuNR4ck8Jhyjr3wOLqlRM3QIACEEjf65HBjO3x1XgDPn16IIeOyNo9krz1IKHruTIR+231gbWUzvkCWvRtvxaFrOhkgnb9tnRsetHWxYhG3Ff5Y6ZjEXVINJZQPhAdO4DKrBtDInVrNT8rE7GWahVdht0bef7XNc7QxcDwshfghcAvxWCOGzs58w1KQ/Af4qpbzFok5LtB5CiLOj7RbtPXBL811gAmbJska49J6kunVljWw651s5G/XqAo1sWp5sHtu0fINxXHY/g2PvK032/RHqjgdvWcZqb40cyvlua19IYTSi5GXg2l9uLGRfNzj+uHCTfy6HR/agyfxdZazwu1nSUsUjr5fuY0azmkw2Vl03Hi/rP2j81vXx/RK3aRFDBFLeAB/fnDG/Z8yr0WNp2Hk3G5bfkpMh0snbsxhNhYFD0LvX+F3eBJdsSY7HS7Yk21Xt2Iiz1DGLuXmV85LKtrVvS7Oebjp3E/Mq5zmx6pBfKpotrMIWjoISoN5fb2oVrs+xr1YqCCkzz/ESQpQBHwZek1K2CyHmAKdJKR/Nst/fAk8DrxFdAxb4v8ACACnlZiHEF4FrMQzEQeDLUspnMrW7bNkyuXPnzqwfbErQdfTePfQO7CfsK8c7NkJd9XEodccb2+1YhTUVet6GgQPgKTPWb61egF7ZQq9UCeu5GfV0TaU32FVUVuEiY5IPsU6eaY/dSBD+YwEsuQCWXWVZbVQb4z07vswFTWfz8Zb3TPht/nzUy01P1fG5Mw9zYl1oMkeMkDoff+5btB93Hn869WoA/tr/Ir89tIUbzryDueXHT6r9RB59/Sg/fWYf/3vd+1jSUpW3dvNMweMWCpx3c8XMKgzWQhqzbR/7Pjz/Qzjvm+AtN2yuFvk9Y15NtQpLFY9ibRU2/TgzL28XPHZnZNwmYiZiumQL+Gug+63xPkTt8cYFSjMjdqY+SZY602UVLkKc2C021AgMHjIuKAphPOrl8kDVvJI2C4fVMD2hnrhVuN5fn03MVPDYnWrsWIVHgV8lvI7bgrPs90ey/A+UUn4f+H72wywSRrtQtnyChsTn7GsWGKr6imZ7V36Gj8I9FyU/q1+zAGX172ionpfzoSkuNw0Vc+zVdex9s4ODz4MWhpbTM1bbNXwIHclxgdwMu4eiS+G0VOS+FE4MKRQGUgRNTf65ABwYbs/rwPVdx9dz17P72PZKB0s+XLQDV4dcMTMGDx8zF9Jc87jxOnXbr78If/dt2HrheJ63ertMeTV6LG4g11UHnbw9CzETMf1iFZx/M2y9aLxeYj8kRiZjts06VjGXWmanjoPDpBg+Cnd/LK3vzJW/hZr5hTuuKcbr9jLHZt9+tjCjLoEVnHwIP+yIFRwc8sGep0BxQXPm+a2vDxvxuLAst0duDuTJKByjv7yFusG9CGm0V+trwqv42TOU35W0qgMeTp9Xw69f6XDswrOFTDncaltMzjSRPO/gkA+s+guesvQyJz4dSplZKGdyMMcZuE6EfAg/7IgVHBzywd4/QMPi9E5OCm8MHaDGXU6tpyK3t+n30Fo5ltO+ZvRUzsOrhqgeNkzHilBoLVtI++Cf8/YeMd57Qj2H+4O8dKAv7207FCGZcrjVtpicaRavm+dQIKz6C5HR9DInPh1KmVkoZ3Iwxxm4TgQ7woNsVLRYTDDP9QEyBwcTQgPQ8VLWx4TBuOO6IMfHhCMaHOh3MzevA1fjsZ/GvvElcOaVn0DH6D6GIv15ex+Ady6sw+dWeOiVw3lt16FIyZTDzbZ97Pvwyr0Tz/MODvnArL9wyRZjTutk+iEODjMNp+/sEMW5VBFF1yU9I2HCqobX7aK+3IuipEzRVaLLilzzuPFYjicAugaDhzMLmRJxuaHpFOO5fF01rhZVtBjlk/sA9uRQDrOD/c8aIpk5mQeuo9oYe0ePckHT2Tm9zaFBN5oUeR24DvvrGXMHaOxrp33BeQDMKzPmtr49+Bpn1r8vb+/l97hYdlwt217p4P9+5CTKvE5KzBe2cup0oyjQuARW/854DNPlMfJvLFcm5nchQLjgoxsgUG+dX53cW/IULJat+guKazxOrWJOU415gYlxPtl+hkPRUJT5dSpxe6BpScp3obGkxUwO5jhZDCMBvHVsiM/evZNDfUHm1Qa4/dPLWNxcaT54rWg2OitWdspMnRZdN2yAE90v8wfI7VgcSpe9T4HLa3TSM/BWVMyU6/zWPX3GSSOfA1eEoLdyPo19b8WLmgMLcAsP7QOv5nXgCnDeyc3s2N3Dtlc6uOzsBdl3cMjKhHLq9B4YdL1pnSvNZDWZ8is4ubfEKWgsZ+ovZBIvmdmIL/4ZNJ/qDF5LgKLNr1OJGoHON9NjuukUZ/A6y3DOrEDPSDieAAAO9QX57N076RnJIDsY7TK3U452ZX6zXPeb7jYdZjZ7noSmk4zBawZeGdwDwN8Echu47u1z43fr1AXyK0joqZhHzdAhPJERwFiIe07ZcewaeCWv7wOwuLmS4+rLuPvZfWSW3NoUAAAgAElEQVRbHszBHjnl1Okgl1yZaR8n95Y8BY3lXOPLzEZ83xVGucOMp2jz61TixLRDFGfgCoRVLZ4AYhzqCxJWM1hSczUM58NMPB1tOsxc+g8ad4Fal2at+tLA27T4aqn2lOf0Vnv6PLRWjJHvi7w9VfMRSBr6346XzS9v4+DI2wyGe/P6XkIIzjupmb8eGeLF/Y6kKR/klFOng1xyZS4mYif3lgwFjeVc48tZvaCkKdr8OpU4VmGHKM7AFfC6XcyrDSSVzasN4HW7rHfK1TCcDzPxdLTpMHNpf9T4Pe+dGavpUuelwd20lbXm9DaaDvvzLGaK0VMxHx2Flp7X42UnVJ6CRPJq73N5f7+/PbGBMq+Ln+7Yl/e2ZyM55dTpIJdcmYuJ2Mm9JUNBYznX+HJWLyhpija/TiWOVdghijNwBerLvdz+6WXxRBCbL1BfnuHkkKthOMf9dE2le/gIHYMH6R4+gq5FrzLpuiEQuWSrYxl0MGh/FCrnQPW8jNXeHj3CoDrKovK5Ob1Nx5CbsKZMycBVdfvoq5xLS/df4mWN/laqPLW81PNU3t/P73Fx3knN/Pa1I+w6NpT39mcbOeXU6SCX/FvWCKsehMvvhysfNn6vetDaRHzJViMn63paU7rU6Q520zHcQXewG12m13EoLgoay7n2M2waWC37FQ5FTdHm16lkllqFnXNGOs6lCkBRBIubK3lw7Tn2DW2phmG7Nskc9tM1lfa+Xax7cj0dIx20lreyafkG2mraUGLihoomOP9mqDsBvBVQ7pgtZyWRoDG/9cQPGlbUDLw0YDyG25bjwHVPn5E+5lblf+AK0Fn9Nyw+vAO3GkR1BxBCsLj6TF7sfpLBcB9V3tq8vt8Fp8/5f+ydeZhU1bW3331q6BFomm7AFnAKaoxGNMSJRGyHgJKIJjFGMIqaGK5GA+ZK1Hg13A/NjUYZYhBNVEDRGGNQE1TigJoYo0GDgigiURBakKZpoIfqGs7+/jh1ajzVXV1UU9N6n6eepk6dYRe1zj577bX2b/Hc2m3MfWE9v5l0bFbPXWpk1Kfum4Zl1m8HfbDsJ/ECObHnu+x58LdBywZYNh3aPksSaTK1yfqd67n6xauj/fip8xg5cCSGkr46X8m5LbvLrWe7p9Kq3+ou7/kYl9sSYkpUz44RZko5rhh4KIYIOOU1ObfJXKAUlA+AyY9b/9basusexjmFjDwznCndb56AYSjq+5Wx/8BK6vuVpdcB2AqUNcOtv+k6ir08rqVze+ThAtDU3sTVL02npTNGuGHzSlhyHjx4DijEaS1VNqywBtk9pAkDvLnrQwZ6qqn39s/oUut3eChzmdRX9s26qc8GHIShQwxpeT+y7Qs1X8bE5B+fPZv16/Ur9zDuC0N4+p1PWbdVoq57S0Z96r6gt/12TwI5hmH1uQ+eY/XBm1c6iui0+FoiAxAI9+MvXk2LL7trtoXskzNb7tgOD51r2dXCCdbfh85NT/zL5baybmoPsv4mOKPdjiuEvCdv+9e+om0rLD4bfvNluGu09Xfx2UUtziTPDGfEuykA/GYwYrg2Te1N+HWKxeoiDFK6vLsUyvr3WL81qEO8tvM9Dq8ahspwxnJds5cRA3xZF2ayae43gpByxaULDyofyoiqkbzY9DgBM/t2PuGoBiq8Ln7x9HuiMCxYpCOQk8Y+/pDfuR8PSX8tpKAPxb9SjitE7EbIR0pQcEyeGc6I41oAeA03DVXxAjoNVQ14VYrF6iIMUpoEOmHdMhhxQo+CBf/etYFdwQ6O6X9IRpfqCCg2tro5YIAvo+PTIeTysqPfcPbf/u+47cfVn0arv5kXmh7P+jWry91869hhvPTBdv66dlvWzy8UIOkI5KSxj9flde7HeyhZJZQwfSj+lXJcIWI3Qj5SgoJj8sxwRhzXAqC2op55p8yOGLC9FqW2IkPhBqE4+fB58LfDgV/tcdeXdqzGrVx8od8BGV1q/Q4PGsVBNX3nuAJ8OvAwandvpComNe6A6sM4pP+RPLXxfj7a817WrznuC0MZUVvJzD+/S4dfog8lTzoCOWnsU1tey7xT58X346fOo7a8dl99E6HQyFScKQ26HVcIQr5RguJM8sxwRhVaOtzo0aP1ypUrc92MfY4ZCtLSuR2/GcRruKmtqLcEFEzTWu/SG6GR0iTnC0D63Hb/cJElzHTeYjBSy+JrrZnwr5up8VQz/aBzMrrUw+9U8+T7Vfz85I8od/ddH9KvYztn/nserx31A9YdOD6yvSPYxsP/mYMv2MH44ZM4qN/n8SgvIR2i0l1NQ+VBuPcicvD+1t3M/PNappx0ID8/+wvZ+CqZknO7hdLtdyOk08+msY+pTVp8LfhDfrwuL7XltcUsspFz2y0Ku+3DZ3zKcYUgtpuPhILWmtYUgmPFSAbPjJzbbl9T3L94EWG43NRV7+fwQVhoRCht2rbD+0/D4Wd167QCrO9o4hNfM42Dul8H2x1vb/VywABfnzqtAHsq6thTMYjhW1fGOa6V7mq+e9BVLN/ye57ceF/ScZXufpw5bDJn7H8ehup9bbvDh/Zn/JFDWfiPjzn50DpOPVzusZImnX42jX0MZVBXUZfFhglFTx8+41OOKwQhH7EFx0oIeWYkI46rIBQDbz8MZgBGju9x16VbX8OlDI4d8LmMLrWnS/HRTg+nH9SW0fG9QimaBh7G5z59I1IWx6baM4BvHfhDdvt3sjvQQlAHMTDoCLXxXutKHv94ARt2r+Hyw3+eUfT1gi+P4L1Pd/Pfj73DMz/+KkP6p1GGQhAEQRAEQegTijZHSRBKBtOENxdZNSNrhne7a0eoi6e2/ZNj+h9Cf3dlRpdbva0MjWJkbUdGx/eWzYO+gEsHOeDT1x0/7+8dyLCqQziw+jBGVI/k8AHHcM6I73PK0HNY1fJ3Hv/47oyu63UbXNU4kg5/kMsXr8QXCO3N1xAEQRAEQRD2AnFcBaHQWb8cWjbAYWf2uOuftr7K7mAHX6s7JuPLvbGljGpPiBEDujI+R2/Y0W84beW1HLL55bSPUUrxpbqxHDvoZF5oepyV21dkdO39B1ZwxSmf4+3Nu/jp4+9IiRxBEARBEIQc0WeOq1JquFJqhVJqrVLqXaXUjx32UUqpeUqpD5VS7yilju2r9vQJpglt26D1E+uvaea6RUKpoTX87Q5rDVQPasJtwU7u3fQsh1cN43MJEuvpEgjBm01lfL6+vc/qtyahFBvrv8h+zaup7NzRq0NPHvIN9qs4gCUbZtMW2JXR5b98YC3njx7Ok6ua+L9n3hfntZiQPlzId8RGBcFC7gWBvo24BoGfaK2PAE4ArlRKHZGwz5nAyPDrciCznL5cYJrw2Vr43ekw50jr72dr5UYS9i0b/wGb/wVHnNNj7da7Pv4zOwNtnLffVzK+3Ntby/AFDY6sb8/4HJmwsX4UCt2rqCuAy3Dztf3PpzPYzuMf35Px9SeOauCMI4Zwzyv/Yf5LGzI+j5BHSB8u5Dtio4JgIfeCEKbPHFet9ada67fC/94DvAfsn7DbRGCxtvgnUKOUKgyJu47t8PsLoHWT9b51k/U+pt6kIPQpWsNzN0NFLYw8o9tdV+x4hyVNL3HaoKM5qDLzumcvflRBtTfIoftofatNW8Ugtg04mMM/fhZl9q62al35fnypbiyvbnuaD3evzuj6SimmnHQgX/lcHbcvX8eif3yc0XmEPEL6cCHfERsVBAu5F4Qw+2SNq1LqQOAYIFFdZX/gk5j3m0l2blFKXa6UWqmUWrl9e54YadAfvYFsWjdZ2wUhTJ/a7rtLYcu/4JgLwZ1a8faD9i1c994DHFQxhPP26z6duDt2dhq82VTGl4buwZWD1fHrGsZQ5dvBgU2v9frYEwd/jX6eGh7ZMBdTZyayZCjF1LGHMPqAgdz81Lssfu3jjM5TKORlv5tNpA8vSorKbsVGS4qist1sI/eCEKbPh59KqWrgcWCa1np3JufQWt+rtR6ttR5dX1+f3QZmitsLNSPit9WMsLYLQpg+s13fbvjrjTDwQDjktJS7fdC+he+/Mxev4eZHB34DbwZlYWyWfVCJ1nDc/nsyPsfesHXg59hdUc+RG54A3bv0II9RxtihE/mk/UNe2frnjNvgMhRXnzaS0QcM5KYn3+W3r/wn43PlO3nZ72YT6cOLkqKyW7HRkqKobDfbyL0ghOlTx1Up5cFyWpdorf/ksMsWILZ+x7Dwtvynsh6++0j0RqoZYb2vlM5G2Ac89z+w51M48UdguBx3eb/tEy59ew4A1x78LQZ6qjO+XKvP4Nn1lRw9pI36ykDG59krlMF7w05m0O6POXjL33t9+KH9j2ZE1UiWfvw79gRaM26Gx2Xw49NHcsLBtdzy9HvMfX69CDYVItKHC/mO2KggWMi9IITJPPzSA0opBdwHvKe1vjPFbk8BP1JK/R44Htiltf60r9qUVQzDqpv5/eetVAW317qBDKkwJPQx7/0Z3lwIX/gm1B/uuMu7ezZy+epf41Furj34mwwuq9mrSz7wVj+CpuL0g3bu1Xn2lo31X2Tkp//kS+89xMahxxNyl6V9rFKKU/f7Jos//BVLP76Xi0bOyLgdbsPgR40j8bo2MPv5D2hq7WTWuUfiyUUOtZAZ0ocL+Y7YqCBYyL0ghOkzxxUYA3wPWK2UWhXedgMwAkBrvQB4GjgL+BDoAC7pw/ZkH8OwypAIwr5i21r40+VQf5i1ttWBf7V+wFXvLqDC5WXGwd+izjuAzoDinW1eVm/zsqPDRXtAUVNuMrQ6xFFD/BxW58frHLhl2QeV/OOTCsYdvIPBVTmKttoog1UHjufUNfcx+r3FvH7UD3p1+KDyoRxbdzJ/3/Y0x9aN5ciBx2fcFJdhrXmtqy7j0ZWfsLm1g7sv/BL9yz0Zn1PYx0gfLuQ7YqOCYCH3gkAfOq5a678D3VZ61FZ+3ZV91QZBKCq2fwAPTgRPBZxyA7iS13Ys3/4W17//AHXeAfzkoHMJ+Gu4b3UlL35UgT9kUOYyGVgRoMJtsr7dxeuby1n6XjVlLpNR+/n5coOPI4f4GVhh8lm7i7+sq2T5h1UcWd9G44GZp9dmk+YBB7Ku4SQ+//GzbB10JBsbTuzV8ScNHs/Hbe/zwAe/4KZj7mOAd1DGbVFKcd7o4QzuX8Zv//YR3/j137nrgmM5atiAjM8pCIIgCIIgJNOXEVdBELLFx6/CYxeBGYKv3QKV8c5WSJv8dtOzzN+4jM9V7sc3as5l0co6XvukHEPBMUP3cOzQPRxU44tTBO4KKja0VrCuuZK126t4fXN8SrFCM2ZYKxNG7sDodhpq37L6gDOo2/MJJ/97Li+6y9ky+Ji0j/UYXiYM+x5LNsxm3rs/5ZqjZlPl7rdX7Rl76GCG9C/nrhc/5Nz5r3LVqSP54diDKfekCGMLgiAIgiAIvUIVmqjI6NGj9cqVK3PdDKHwyLnblZHtdrXB3+6Af8yD6qFw6o0wYFjcLv/p+JRZ63/Pv3at53PeL9Cx5Zusa66i3G1yfMMuvjJ8FwPKey4BozVs3lPGJ7vKaAu46OcNcdigDmorelc3dV/hDXQw9t1F9O/Yxlufn8y7B38DVPrrXT7e8z5PbLqPhsoD+eHhP2dwxbCeD+qBPb4A97/6Ef/8TwvDayu46tSRTBzVQJk7Ywc253YL0u8KGZFz2xW7FTJEbFcoVHJuu32NOK5CqZDzmzlt2w0F4NO34b2n4K3F0LnTKnlz3OXgrQLAbwZ4c9cGHt3yD1a0vIXSXkLNZ7Kn+Thqy4OcNHwXX27YQ4W7d2VjCg1PsJMvf/gEw3asZWf1MNYe/HU+GfplfGmKUX205z2e3vwQpjY5reHbfHXoBOrK99vrdq3ZsouHXt/Ixh0dDKrycuZRQznt80MYNayGgVW9ku/Pud2C9LtCRuTcdsVuhQwR2xUKlZzbbl8jjqtQKuT8Zna03Y2vwaol0LXHeu1pguYPwQxY0cMRJ+A77Fwu+nAdLWYTfu3Hp/fQaWwHFUSHygi0jka3juXwGoNRQ/dwRF1HXqX19jlaM2zHGo745BVqOrYC0FE2kF3VDXR5++N3V/CfYWPZWnek4+G7/Tt5aeuTrN/9DqAZ6B3M4Ir9ObzmWCYM/95eNEuzpmk3z6/dxtubW+kKWpMIg/uVMXRAOYP7lVFT6aXMbXDY0H5cdOKBTqfJi19S+l0hA3Juu2K3QoaI7QqFSs5tt68pOMdVKbUd2JjGrnVAcx83JxfI98qMZq31+D48f4/0wnYzJV9tQ9rVO2LblXO7hX1iu4kUwm+TL+Rrm97Pte32YLf5+P/WHYXWXii8NtvtzXm/K2PdbpHvnJqc225fU3COa7oopVZqrUfnuh3ZRr6XkIp8/T+UdvWOfG3XviRf/w/ysV3SpswohDbGUmjthcJrc6G1FwqzzXuLfOfSRir3CoIgCIIgCIIgCHmNOK6CIAiCIAiCIAhCXlPMjuu9uW5AHyHfS0hFvv4fSrt6R762a1+Sr/8H+dguaVNmFEIbYym09kLhtbnQ2guF2ea9Rb5zCVO0a1wFQRAEQRAEQRCE4qCYI66CIAiCIAiCIAhCESCOqyAIgiAIgiAIgpDXiOMqCIIgCIIgCIIg5DXiuAqCIAiCIAiCIAh5jTiugiAIgiAIgiAIQl4jjqsgCIIgCIIgCIKQ14jjKgiCIAiCIAiCIOQ14rgKgiAIgiAIgiAIeY04roIgCIIgCIIgCEJeI46rIAiCIAiCIAiCkNeI4yoIgiAIgiAIgiDkNeK4CoIgCIIgCIIgCHmNOK6CIAiCIAiCIAhCXiOOqyAIgiAIgiAIgpDXiOMqCIIgCIIgCIIg5DUF57iOHz9eA/KSV29fOUdsV14ZvPICsV15ZfDKOWK38srwlXPEduWV4avoKTjHtbm5OddNEISMENsVChWxXaEQEbsVChWxXUFwpuAcV0EQBEEQBEEQBKG0EMdVEARBEARBEARByGty7rgqpQ5TSq2Kee1WSk3LdbsEQRAEQRAEQRCE/MCd6wZordcBowCUUi5gC7A0p40SBEEQBEEQBEEQ8oacR1wTOA3YoLXemOuGCIIgCNnjzY07+dfHLbluhiAIgiAIBUrOI64JfBd4JNeNELKHqU1afC34Q368Li+15bUYKt/mS4RUyO8nZAOtNd+6+x8AfHjLmbhdYkOCIESRZ40gJCP3RTJ58+2VUl7gbOAxh88uV0qtVEqt3L59+75vnJARpjZZv3M9k5dNZtzj45i8bDLrd67H1Gaum7bPKGTbld+vtMmm7W5v64r8e+tu3942TRBSUsh9bqkizxoLsV0hFrkvnMkbxxU4E3hLa70t8QOt9b1a69Fa69H19fU5aJqQCS2+Fq5+8Wqa2psAaGpv4uoXr6bFVzrpgoVsu/L7lTbZtN0tOzsd/y0I2aaQ+9xSRZ41FmK7QixyXziTT47rBUiacFHhD/kjN5xNU3sT/pA/Ry0SeoP8fkK22NIadVY3i+MqCEIM8qwRhGTkvnAmL9a4KqWqgDOAH+a6LUL28Lq8NA5rZOLIiQzwDmCXfxdPrn8Sr8ub66YJaeB1eWmoaojrOBuqGiK/Xz6vvcjntpUizXuiqcI72ru62VMQhEInnf43dh9DGd0+awShFJExtDN54bhqrduBQbluh5BdBngHMHXUVKavmE5TexMNVQ3MbpzNAO+AXDdNSIPa8lrmnTovkqrSUNXAvFPnUVteG1l7kfjZyIEjc+4g5nPbSpW2rmDk37s6AzlsiSAIfUk6/W/iPo3DGpndODturGA/awShVJExtDMyihP6jB2+HZEbDqwUh+krprPDtyPHLRPSwVAGIweOZMmEJSz/1nKWTFgSGXzk89qLfG5bqbLHF8TrMuhX7mZ3Z7DnAwRBKEjS6X8T91mxeQULVi1g0ZmLkp41glCqyBjambyIuArFhZ0CFAgFHPPzA6ZEXAoFQxnUVdQlbU9n7UWu0nVlXUj+sacrSKXXRbnHkIirIBQxqfpfX9BHU1sTXpfXcZ8Vm1dwnb6OhuqGfdlcQchbZAztjExnCVklVr7bb/ppqIp/CDVUNeAxPDlqnZAt7PWvsSSuf82VjHtPbRP2PXt8QSq8LirL3Oz2lfZDVxCKmVT970e7Poo8C0I6JH20IPSAx/A4j6FVaY+hxXEVskpsCtCiNYu485Q7IzeenZ/vFMETCgt7/Wvsbxu7JimX6bo9tU3Y9+zxBajwuqjyutnVIY6rIBQrTv3vrDGzWPD2AsB6Ftz+xu3MbZwrfbQgdEO5uzxpDH3nKXdS7i7Pcctyi6QKC1klNgVo6YalAMw/fT5ew4vH5aGuog63IWZX6MSuf3VKBc5lum5PbRP2PXt8QSo8kiosCMVOYv8LcO3L1/JO8zuRfVZsXsGNJ9wofbQgdENHsINH33+U+afPx6VchHSIRWsWMXXUVGqoyXXzcoZ4EEJWSSyhsnTDUl7f+jpLJiyJi7RKuZLCJ9X6V+i5lI5QWuzxBRhQ4aHM7aLD78t1cwRB2EcYymBQeXzRiIaqBgwj9fNDEARrHNXa1crHuz+OlMNp7Wot+XGUOK5CVumuhIqNlCspftKxg75C7Cv/2OMLMrR/OR6XQac/lOvmCILQRzj1v7MbZwNWpFXSggUhPWrKahzL4dSUlW60FcRxFTKgu2hpOmmaqdY/JkZlhcIi0S4OqTkkJ6lgYl/5R1tXkAqvG5eh6BDHVRAKlp6ypZz63+krprPozEVcp6+TDCtBSJPWrlbHcjilPpYRx1XoFelEs7pLIQUpV1KM5FOUU+wr/+j0hyj3GBhK4QuE0FqjlMp1swRB6AXp9POp+l9Tm1LqRhB6gYxlnJEpL6FXZEMtVsqVFB+5VBFOROwrvwiGTIKmxusyKHMbaKAr2PdlkQRByC7p9PPS/wpCdjCU4XgvlXq2Qml/e6HXZGMGSMqVFB/5NDMo9pVf+MJOqtdtUOZ2AUi6sCAUIOn089L/CkJ2MDCYOWZm3L00c8xMjBJ33SRVWOgV2VCLlXIlxUc+qQiLfeUXvoDlpHpdBh639Rt0+IPUVkkERhAKiXT6eel/BSE7GIbBw2sfZsZxMyKqwg+vfZibTrop103LKeK4CmljahM03Pu1e9m0exML3l7AoPJBXHvctfhDfpo7m5MeUKmEHHpaByvkP3G/reFlwRkLmPrc1MjapwVnLAANTW1NWR+89CQQIvaVP9iOq8dlUB52XEVZWBAKDye1+FT9fG/733RK5EkZPaGUqC2v5YYTbiBoBjG1ydCqodxwwg0ln72QF46rUqoG+B1wJKCBS7XWr+W2VUIsTqIMd59+N/6Qn+8v/76jUEM+CfYI2SXVb/vI1x/BF/RR7i5ne8d2Jj83Oeu/vdhVYeELRFOFvZIqLAgFS2I0NVv9fDp9uvT7QqkRMkO0+FqSyuEMKh+E4Spdm8+Xbz4XeFZrfThwNPBejttTcJjapLmzmaa2Jpo7m63oaBZp8bXwm3//hhnHzeCBcQ8w47gZ7PDt4McrfpxSqCGfBHuE7JLqt7WVI01t9vjbx9lsRzMtvpa07FfsqrDoCkZThcvsiGtAHFdBKHT8IX9W+uJ0+nTp94VSo7mz2bEcTnNnc45blltyHnFVSg0ATgamAGit/UBpaz33kn0xE2maJpOOmMTNr94clyLUnVBDPgn2CNmlp9+2p8+dbHbWmFnMeWsOzZ3N3dqv2FVhYUdcPW6Dco+kCgtCoZLYby8evzgrfXE6fbr0+0KpETSDjjYfNIM5alF+kHPHFTgI2A48oJQ6GngT+LHWuj23zSocUs1EplOkuKc1I/bnQR2MOK32NTbv2dytUEM+CfYI2aWn37anz2Mj+LbowINrH+TSoy5l2opp3dqv2FVh0WWLM4mqsCAUNIljjZaulrT64p7GGV6Xl8ZhjUwcOTHyPHhy/ZNJok9O15I0YaFYcRtux/vCbeSD65Y78uGOdwPHAndrrY8B2oHrYndQSl2ulFqplFq5ffv2XLQxr8l0JtKePZ28bDLjHh/H5GWTWb9zfSRNM/bz7Z3bk66x4O0FzGmck1L2XmTxi9d2e/pta8pqmN04O+7z2Y2zqSmrAaIR/NveuI1Lll/CbW/cxqQjJjG0cijQvf2KXe0bsmW7vphUYW+MqrAg9AXF2ufmA4ljjftX359UriOxL+5pnAHW82LqqKlxz4Opo6ZGnhdQGqVBxHaFWMpcZY73RZmrLNdNyyn54LZvBjZrrV8Pv/8jCY6r1vpe4F6A0aNH633bvPwn0whUT5Ha2M+bO5uTrtHc2cyQqiEpZe9FFr94bben37a1q5UFqxbERVQXrFrATSfdRF1FHSZmUgT/5ldvZv7p84Hu7Vfsat+QLduNpAq7VGSNq13bVRCyTbH2uflA4ljjneZ3eHjtwyw6cxGmNh374nQywlq7Wh3X8sXuUwqlQcR2hVi6Ql2O98XC8Qtz27Ack3PHVWu9VSn1iVLqMK31OuA0YG2u21VIOEnU27OeTik6YD1MOgOdaa9TvH/1/dw+9nZ2de2iwl1BZ7CTYf2GUVNW063DIGVJCovelBvo7rf1h/zUlNVwYP8DcSkXA8sHUlNWE7fG1cn22gJtaUVQxa4KB19MqrAdce0ScSZByDt66v+dxhpXHnMlgysHp3xOpMoIM01LnM8f8qd8HsRm3dSW13LlMVc6jnMEoRgJ6ZDjfRHSpf38zLnjGuYqYIlSygv8B7gkx+0pKFJFoIAkAZwFZyyIKAHOOG5Gr9Yp+kN+Zv1zVtxDQygesinyVemu5PzDz+eK56+InOvOU+6k0l0JpM4SqK+oZ8mEJRJBLSK6wtHV2FRhEWcShPwi3f7f6/Jy4wk3Riawe8rscurrG4c10uJriVQlmH/a/B6zxiTTRig1PIbH8b7wGJ4ctir35IXjqrVeBYzOdTsKGacIVHNnc1KKzuY9m3mj6Q3mnz4ft3Go8MAAACAASURBVOHmt1/7Lb/6169YsXkFDVUNzG2cG5kJrSmricyuXnrUpfzs7z/LSABKKAz2RuQrEV/QxzUvXRN3rmteuoZF4xcByTP3jcMaufbL12JqM1JsWwYkxUFsxNVtGBgquu5VEIT8IJ3+v8XXwtTnpiYNpLt7RtSW17LgjAVs3rM54ux+buDnmPLMlMh5Fry9gF989Rdc/7frJZoqCGEUijvH3klLV0vk3qktq0Whct20nJI1x1UpNRr4KtAAdAJrgOe01juzdQ2hdzil6OxXtR/jDx4fFwmb0ziHG064gYAZ4PY3bo84sfNOncchNYewZMKSHtOKhcInm+UGAmbA8VwBHYi8t2fuB1cOxtQm3//r9+OKbB868NCSV88rBuw1rna0tcztotMva1wFIZ/oy5I0idlacxrnUFdRF3cut+HuNpK7L8r+CUI+YWLSGeqMu3du+cotmJT283OvR4VKqUuwUn0/wiplsw4oB74C/FQptQb4H631pr29lmDhtA7F1FaUNBAK4HF5cOEiZIZ4cuKTLFyzkKUblgJQ7i7nyheujJtVnbZiGgvHL+T7y7+fcra1mWRxJilBUlykSukylEFTW1NSalbQDMbZ3KDyQezy78If8uM23I7ncis3TW1NGMpg9srZrNi8gicmPhGZSIF4AQKNlpSwAseOuHpc1u/ndRsScRWEfUw6JWl6esZnIgTpVPrs7lV385PRP2Fn104GeAcwsHwgc9+cy4rNKyLHTfn8FCYdMYmgGbTGNMqVtYwgQSgEtNZJmY4/+/vPRJwpC+eoBMZorTudPlRKjQJGAuK4ZgGnWccFZyzAF/QxbcW0yLZZY2Yx5605NHc2c+cpdwLw+tbX0Vo7FzTWzoWO7ZnU7gSghOLAKX136qipXPzMxUkz3KY2+WDnBxHFOztK+syGZ1j43kKmfH4KsxtnRz63zzXl2SmR/WeOmckO3w5cyuVoe1vbt3LRsxfJzHqB4wuGcBsKQ1npTV63EXFmBUHoe9KJVqbzjLfLnCX2+7Fla5KuHS59ZqvIN1Q1cPvY2/EYnrjUYPt58E7zO5x7yLmMP3h83PPi3q/dK1lfQkmRSrQstpRUKbLXjqvW+jc9fL5qb68hRHFah7J5z+ZIKgFAXUUdvpCP/zfm//HR7o+45+17uO7467hCXYHW2nHG1KVc3c6kijBC8ZP4GxvKiDitED/DHTSDjjLt80+fz8L3FrLwvYV8YdAXWDh+IUEziNtwRwYh9v43v3ozM46bQUiHHG2vpasl6boys154dAXMSJowQJk4roKwT0ln/Wo6z/jWrlb+vfXf3DfuvogOwcubXmZw5eCUfbNT6bNdXbvixiyxz4NpK6Zx8ZEXJ2XhbNq9SbK+hJLCUIajzZf6uDtr314pdZBS6k6l1J+UUk/Zr2ydX7BwWmNS4a6IbPti3Re56tirmPXPWUx8ciK3vXEbk46YhNaai5+5GMCxiHdnsJNZY2Z1W0jcFoBqqG6grqKu5G+eYiT2N+6uREEg5LyG1aVcAJx7yLkMHzCcKc9O4aylZ7HDt8Nx/yGVQ1i0ZhF3nnJnkk3ev/r+pOsKhUdXMBTnuHpcRmTdqyAIfU+6a1N7esYrrRg1ZBSXLb+MCUsncNnyyxg1ZBRKpxaLcXqOxI5ZYttTW2aNN5yycBa8vYA5jXO6HaMIQrHhNF4vcW2mrKoKPwHcB/wZSnzlcB/itMakM9gZ2XbpUZcmzW7e/OrN3DfuPpram/iw9UMeW/dYUhHviSMncv/q+7nxhBs5aMBBlLvLJaJa4nS3nimogo6f2fXFEmfMK9wVjvsPKh/E1FFTqXRXsmj8IgI6gFu5+cXrv+Cd5neSrisUHr6AidcVH3GVcjiCsO/IZG2qE37T76gW392aO6eoUeyYJbY9+1Xvx/JvLUehkj5v7mymrqJOsr6EkuLhtQ8njdevP/76XDcrp2TTcfVpraWwZx/jtA7x4AEHc+/X7mXT7k3Ultc6zmTu9u8GrFnLG0+4MW497MwxM/n1W78GrAeT1nqffy8h/+huzZOpTce1Tk988ASQPGPuC/q45Su3RIQGbHU8l3IxuGpw3HVNbTJ99HTOO+y8iMLksH7DZGa9QPEFHCKuIs4kCPuMbGlUBE1nLYygDqY8xsDg9rG3s6trV6Q/H1I5hLmNcyN1XG2tDrdy49eWuN/80+fHVT+Y3TibgeUDRWleKBmUUvzw6B/S4rOWTXkNLz88+ocoVdoh12z2AHOVUjcDfwW67I1a67eyeI2SJ3YdimlaKoGXLr80Tma+cVhjnDpfQ1UDn7Z/ClizlkOqhrBkwhJ8QR8f7foo4rRedexVcQIKIohT2nS35slQBocOPJRFZy4iYAbwGJaq8OCjBnPBERckzZi3B9qpclfFlTuocFXgdjl3QYnlE+adKnNihYovEIooCoMVcW3rSj3QFQQhu2RLo8Lj8jhGSj2GJ+UxbpeboBmM689/8dVfcPCAgyPtKXeXs71jO5OfmxzX5z901kP4Qj48hoe6ijpxWoWSwqM8KFTcvTP7lNl4VOr7rRTIpkdyFPAD4P+AO8KvX2Xx/EIYex2KYRiRGUuIlra59rhr43LiZ42Zxf2r7488DGrKaiLrWAZXDqa5s9kxxfjqF6+OzPQIpUnimiewJj+a2ppo7WplcOVghvcbztCqoXhcnsi+9ZX1zG6cHbFDpRTXvHwNV7xwBZcsv4QrXriCa16+xlp71dZEc2dzRCkvlZCI2GJhkpgq7BFxJkHY52RDo6Kuoi6uX7cjoeWu8qR+3MY0zYh6MFj9+fV/u56gGYzTU3Dq85VSkeeLOK1CqeE3/Ux/KUEE86Xp+M3S1vvIZk9wHnCw1rq0/0f3IakEF7TWkciWRjOsehi3j709aZY1dha2M9ApUvNCt/SmALzbcMdFZAFH+/q07dOkkjeZFrkX8pOuYAhPrKqwy6BTHFdBKDgS+3WP8hDUQc7/y/kpnwl+M0V/HjP4lj5fEJLJJDW/FMhmxHUNkLqYl5B1bMGFWBqqGti4e2MksjXnzTl82PphRDQHLAfEjpq1+FqoLa+lwlPheC4RxBFsMo6EanArN1M+PyVus1PJmxZfS0q7FlssTDoDobiIq1XHVfT7BKEQcRtuhlYNZXi/4bhdbi5bflnSM6G1qzUyxjCUQeOwxrhzJPbntoBT4j6yTEkoZdyG2/G+cKvSzj7IZq9QA7yvlFou5XD2DbbgQmzazpzGOSx4ewEQXxrnrD+dxeRlk1m/cz0bd29k8rLJjHt8XGRbTVlN0rlEal6IpTez4kEzyAc7P+DiZy7mrKVnMeXZKZx5yJkR57W7kjdOdi22WLj4AiZed1RMwiupwoJQFDg9E+oq6tjWvi0yxrj4mYuZOmpqxHl16s89ypNUEu3OU+4s+bV8QmlT7ip3Ts13l+e4Zbklm277zVk8V0kTCAVo7mwmaAYpc5WhlCJgBvCiqDXBMAyorMcw4gUXDGWg0Uw9eioL3l6Qct3qjSfc6FiI/JCaQ6wUoFAgsl5RZjwFG6/LS+OwRiaOnMgA7wBcykVdRR0hM8TW9q1x4hnNnc0sWLUgTsZ9waoFXH/89Xzn8O/gNtw8vPZhx5I32RISEfKDLoeIa1fQRGtd8uqIgpBLTG0JPEb6WW8NRucOCPrB7YXKejCMlMcYymDK56cwauioSD/f39s/UrUAwuvyVkxn0ZmLuE5f59ifB3SAe96+J+55cc/b93D98dfT1NYkzwDBwjShY3u39llM+EI+ntnwDPNPn49LuQjpEE988ASTjpiU66bllGw6rpuAT7XWPgClVAUwJIvnLwkCoQDrW9czfcV06irqmHbsNG589cbo+pETbmbk33+Dccr1MPgIDMOgtrw2ae3hrDGz8Lq9jhGyCndF0jZ/yM+G1g1prV8USpMBnv5MHTU1rgTOrDGzmPPWHJo7m5ndOJtDBx6K23CjtWbSEZPiVKpnjpmJqU3OWnpWZOZw456NrNi8IvK+psxabWALiQiFT1fQjFMVtp3YrqBJuceVq2YJQknjqFlwymxGPn8Lxvt/gZoR8N1HYPAREefA6ZjZjbNZsGpBpB+/92v3Oo47TG3SUN3g1BRMbbJi84q4aggAV4y6gvP+cp6MRwTLaf1sLfz+Amjd5GifxciY4WPiykLNHDMTSny+N5u/9mNA7MKlUHhbjyilPlZKrVZKrVJKrcximwqO5s7miGNw6VGXRpxWCEdH/zmTlmMnWzdvx3bAee3hja/eSH1FvWN+fGewM2mboQxRchW6ZYdvR8Q2IWpnlx51aWRWvbmzObJ/YrT/5ldvRqMj76evmM6Pv/RjHhj3ADOOm8GCVQto7Wrd919M6FO6gmZcHdey8L87/ZIuLAi5wlGz4KXp1vgCLOcgZpyR6pjpK6YzceTEyPtNuzf1er1qqjWuHpcn2jYZj5Q2HdujTis42mcx4jSOCg+jSpZsRlzdsYrCWmu/Uqo3aiqNWuvmnncrTuz0m1gVsQHeAY4zl776wzCrB2MErf9uf8hPXUVdXJrN/avvRynFzDEz46Jet3zlFmrLayO12OyZTANDVP2KkKRUsB7SrYJmkObO5rh0cTv9N5BC4W6Ad0Dk37aCsEY77rvbvzvu/U7fTi5Zfklk23Wh6/buCwt5R1cwvo6rrTDsC4rjKgi5IqVmQWWMlkDrJgh0Qusn4PbiJ+R4zEH9D+KBcQ+wy7+L5z5+jjtPuZNrXrombtxhdBMnMTCSxiozx8zEF/TFt03GI6VL0B91Wm1aN1nbi5SQdr7fYsVWS5FsOq7blVJna62fAlBKTQRK1hHtDbHpN/NPnx9xKnf5dzkW+/6ovYn2M29hpKcCAyh3lyelFM8aMwu3stYRxjq0i99dzM+Pu54lx8zAX1aFt6udWtNFS3jGM/FaouRauPSmfA1EBZViU4Fj0389YYW7RBvZ5d8V+bddiN5reB33/bT9U8dj7fdib8WFaWoCIR3nuJa5rfRgibgKQu5I1Ud7Yx2BmhHQ/AEsOQ9qRuC9ZJnjMVvatnDFC1dExh51FXWRknydwU4qXBW4XamHm4ZhJI1VHl77cCSSG2mbPB9KF5fXssdY57VmhLW9SHEpl+P95lKlvcQmm6nCU4EblFKblFKbgJ8Cl6d5rAb+qpR6UymV7jEFT2xZms86PqOuoo5FaxZF1PXuX30/s8bMilMUmzlmJs99/Byf6QBNpo/mzmZCZigppfjGV28E4MpjruS2N27jkuWXcNsbt3HlqP+iZtl/U/fgN2n43TjqHvwmxkPnUqsRJdcio7fla2LT1O39Y9N/68rrmNM4J85GZo2Zxf2r748oWtvrUms1zBtzS7wa3il38uT6J6PvG2fHvRd7Kz78IWv1iNcVoyocdmKlJI4g5A7DMJLGF7PGzMJwhRVLa0bAxPnw8i+t962bqH3meuY1zk06xq5kYI89tnVsi9Rp9Zt+7nnnHkwz9f1eW16bNFaZOmqqPB+EKIbLsseaEdZ72z6N4nXi3Ibb8R61s+BKlax9e631BuAEpVR1+H1bLw7/itZ6i1JqMPCcUup9rfUr9odhZ/ZygBEjRmSryTnFKRo2c8xMfv3Wr3n0/UeZf/p8vIaXMlcZC8cvZEvbFnb5d/H0hqc565Cz4lJqUokhBMxAsjprIGAJL8TSugkj0MlI05UUiTU0Jb8QfG/Ipe32tqh7IBRIaUcAhstFubs8MpOu0TRUNTDjuBns7tpNubs8GskNBfAanrhZ9/7KzU2j/5vrDpuEt6udGqOam068ietMZ6VJIbdkw3a7ws6pJ2aNq73etVNK4gh9QDGOF/oCX9DHnLfmxEU557w1h9u/+n8wbY210x+nwOao7IhZUYPbFe3X6yvrueFvN8Spwze1N9HP249rX742SZgvFU5K8jVlNdx00k1cFyqd54PYbjcEOuCFn8O4W6FiIHTutN5/+4Fct6zP6Ap1Od6jvzz5l7luWk7Z615AKXWhUtHeRGvdFuu0KqUOUUp9pbtzaK23hP9+BiwFjkv4/F6t9Wit9ej6+vq9bXJe4BQNu/nVm7n0qEtZumEpVzx/BRWeCgZX1OEBFr+7GIApR04hEApEIlvdiiGgIuqsDdUNVnkbw4jOWNnUjAA0xs7/ULfi1rhIbLEvfO9rcmm7XpfX0S5SpVt5UhS79oSLXbf4Wpj63FSueOEKLll+CXPenMN/dv2Hfp5++E0/s1fOjkRzWwyYvea+uFn321b9BkJdEftyP3sddQEfDcEQdaGQNUki5A3ZsN2u8DpWjyvZce0Sx1XoA4pxvJAVTBPatlnrVdu24UXR3NnMtBXTuGT5JUxbMY3mzma8ygU1w61yI22fxZ2i+eRrmPvm3Ei/bmsixNJQ1cDmPZuTxjYm3WdYJI5V3IY7fuxS5E4riO12j7Ls8dELYeEE62/bZxRzZMWlXI73qKQK7z2DgH8rpe5XSl2plPqOUuoipdT/KqVeBm4DtqU6WClVpZTqZ/8b+BqwJgvtymtSRcMGeAdE02K8NfDZWmr+cTdTj/4ht71xGxOfnMisf87iqmOv4ot1XwRgwdsLkop3zxwz0/nHray3JMQT0y3+eAks+wmcehMMG219VuQL34ud2vLaXqV/1+FidoIdzT7lTuqwOslYm/1i3Re56tirmPXPWUx8ciK3vXEbk46YFEkHM5WLSUdMikv9mnTEJMxQ0LrYsNFw/A/hgTNhzpHwu9Mtqftu0smEwsNOB45f4yoRV0HYp9ilRH53eqS/re3czbyT/l/88+GEm6m1+2CnsYKnMq5fn/vm3KSxx5zGOZHUYRu7HI4gZIzLC+ctjrfH8xYX/RrXxPvrzlPuxFXE6dHpsNepwlrruUqpu4BTgTHAF4FO4D3ge1rrTd0dj1XrdWm4EL0beFhr/ezetivfsaNhiYuuG6obWDJhiZUW027Jf7ee9UumhxX6IDqDOeO4GZEZmPZAOzOOm0FtWS39y/oz98253HT89ckXNgyoPxwueQZCAev1j7nRdKCnfmSlYjx6odUxKGU99Iq4Tlax4pR+1V26lRvNoW89yqJTf0PA5cITClG3cjHuk64E4m320qMudZRpXzT+AWj9BNPtdv583H0wZRlU1sEL/5ssbT/l6ZIoLF4q2BFXWeMqCH2IaVrZUUG/c//pUErEWPJtRp7/EEtOvQu/4cZrBqn912KM4y+PqAhTfzh8//nIeTVmXL9u111dOH4hGo3X5cVQhmMUVoSVhL0i5Ic1f4JJj1nrWs0Q/HsJHF+8sjghHeKet++JSxW+5+17uN5pbF9CZGWNq9Y6BDwXfvX22P8AR2ejHYWEHQ1LVHwdWjU06liE5b/9lbXdRmdnjpnJ3LfmRtaZLB6/mCuP/iG15XXJFzZN2P5+fBHns++ytm1eaW2rGBjd/vQMaLyh6Is8Fyt2+lVaVNbj/uJ3GLr43PgC35VWylKszaYq1WSGAjDni5j/9Yrz5x07rDQf277at0UnTVo3QetGeOK/SqKweCnQFUyOuMoaV0HIInY0NfaZnth/BruSS4lUD8YA6hZ/M3rcdx6E1++F1+Y5nie0+5Okfn3F5hXMOO5ahvUbbjVHm45jGxFWEvYKZcDI0+Hh8+LHrkWcQh7SIVZsXhGZILKZcdyMHLUoPyjeXzzPiY2GLf/WcpZMWJJcpsRtyX97O1oc1x42VA3lxhNu5Ndv/TritDZUNbBfZT0j3/w9hpN6rFMR56d+BGOmWe9rRkC//ayo64v/C+uWlUSRZwFrcDL4CGuGfdoa62/MoCXWZhuqG5zXz4bt17tri/Pnu7ZYbxLtDizb69xZMoXFS4Hu1rj6xHEVhL3H6Zme1H+qZG2LsT+1Mqtij/vD92DEcSnP406hg+BW0RhIWmMbQegt2rTGDIlj1yJOQU95v5W4qrD0JDkkSTgpsWMPrzGpfWsJ8064OX4tythfMbizjcFlNZG0HHuNyuBHL8Z4bZ4VsbUFGXZ/Crs2g7/DuYizHWU9fwksvdx6oMVGwmSta2lgGFA9xBLnqB6SFPG0bXYobuaNvSPBJu+g1tcOQO0rdySvnzrp/1H7yh3Rk7VugqqwAIU9e/rqnOhnYnMFj6OqsEscV0HIGuHMrDjs/tMWY1IKvr0QJj9mLdWY/BjUHZZ6LJB4njB15XXMbpwdr4PQOJu6hOyuHsc2gtBbzCBUD4bzH7Js+PyHrPdmMNct6zPqKlLcb+lm0RUppe225zvhCJjx9TsZaZosGb8QvxnCG/JTu/x/MN7/CyMP/zpLxt2N3+XGu3UNtc/+D8bmldHCzJ+thRW3WkI49vpVpyLONQdYETblSlISpGaEFf0VhDCG1ox869Hk9VHh2XoDGKnd8eWVtDt+pqxmBJTXwJVvWCnCL/5vdLJEbK4osFOFY9e4lknEVRCyRzgzK+mZbgbhd2dZ2y99FjSWAGMkLXgxHDbByqqKPa5zZ/z7mH7Y7fZw6ICRLBq/kIAZxGO4qSuvw+329P33FEobdzmc9nN48oqoDU+cb20vUtyGm0MHHsqiMxcRMAN4DE9EcbuUydq3V0qVAd8CDow9r9b6f7N1jXzA1CYtvpa0xG7iD3QQT4DoNpfXWnAe6IwXVwhHwIxQkLq2rVZaxKKJkYeU8f5fqNv6jiVqY6fr2JFTbVqpPuNujaZYvDrHimzZ7+11LP33t65lmtb7xPUylSLNXpD0JNqRuHu69m24MA4/K3591KTHoKPZmg2tGoyx5FvUJQ6mJtwBS84L2+hD4NtpPXgq66ITJjUj4MKl1kDLFgkRsaaCxClV2GUoDCXiTIKQFWz139hn9vlLYPnPos5swAd/vjohLfgiuOhJ2LY6/rhAu9WHBzow6w6nxXDhb2uKPA/cbg9Dq/fL3fcVShNtRp1WsP4+eQVcUtxaroYycBtuTG3iNtySvUB2I65PAruAN4GuLJ43bzC1yfqd65NEB3pcv5FKPMFdDg+dGz979MLPrQF8rChCKAjb1ljrT8652zm9p3WjNZt6/hKorLVElU68Ipr6Yx+zeaUV2Rp3Kww5EryV8U5B7DrHNJ0dIU9JR7Qjdvfe2HcoYE22TLgDPJVWxH73FktYqXUT/GCFs53WHWqtn3V5oWtPdI3VYRPgoqfAcIOnAvZsjb83RKypIIlGXKO/m1KKMrdLxJkEIRs4PbNNMz6S6vI498eaaB8OVr/86GRo3YR5+NdZf/rPuPrpH/RuvCMIfYEZdLbhIk4VztjnKHKy+c2Haa3P11rfprW+w35l8fw5p8XXEjEgsFRSr37xalqcRJBiSRRPqB4Me5qsGaRxt1o1Le3ZozHTkkUR2rZaTmvrJiuNJ1FkoWaE5UiMuxUCHdY61vZt0X07d1qOgb02oPFGqKqz1r040cM6R6FAcBLtWHGrZXvhIvSxdVNT2nf7tuT9dQj+OMWKni6cAP52+NMPotfyVDrbqV042wzAczdF91+3DBafbQ26dCgNsRGhEHBa4wqWQJOkCgtCH6FU/DO/ss65P275MNqHd+6MKrYCLcdO5uqXpvd+vCMIfYEyUowpind8mrHP0QuUUj9TSr2rlHpHKbVKKXV8Fs/9tFKqJlvns8lmxPUfSqmjtNars3jOvMIf8juW+PCHehCRiRVPGDYaTr0pPlX37Lui6/tsYYRYUYRQIHq8U6rvOQus6K2dCmSfc/UfrL/rn4eTr4XHLoqP7j5+WXJ0VygeEkU7ho221jo/cKZjJDOlfe/eAveeFr+/1vHn9u+Jfx/sTLbTs++yPptzZOpyOLbNpxIbEQoKp1RhsNa5SsRVELKAU2bN+UvgtJuijuhhE6xSN/YEuL1UY9k10fPEZmZByjJ8PY53BKEv8JQn2/B3HrS2FykZ+xxpopQ6Efg6cKzWukspVQdkTVxEa31Wts4Vy157Kkqp1Uqpd4CvAG8ppdaFPXd7e9HgdXmdS3z0VFjbFk8AK6LqJOk9Zlq8MEKsKILLEz3eTvWdcAdcvQq+ea8VFTUD8dHbp34EX5luRXVHXxx1Wu1rporuCsVDrN2Bs+3F/PYp7dteexq7f+K592yNn+F3V1gTJuNutd6PuxVev8eKptrnciqH4/Ymnzv2M6GgiNZxjc/u8LqNSDRWEIS9wCmz5tHJln6A3f+OugBWP27pENj9cdmAeCHGhGyuVGX4ehzvCEJfEPBFbfhHK62/qx+3thcpGfsc6bMf0Ky17gLQWjdrrZuUUh8rpW4L+3FvKKU+B6CUqldKPa6U+lf4NSa8vVop9YDt9ymlvhXe/nHYGUYpdWH4XKuUUvcopVzh10Kl1JrwsdPTaXQ2QmxfB74BnAl8Dvha+L29vWioLa9l3qnz4kt8pFNY2xZPqBmRNKsJRMuCTJxvRVQTBZGqh1ozS/ZDpe0zqBpsCSq5K6y1gPePg+U3WNFc23nds9WKwqK6l72XaFZxEmt3YNlYN5FMR/s+4ebkEjZBf/K5W7fA2BmWDS6cYM30H/UtWPWI9X75DVa0198ef67Ycji2zSeeWwTCChanNa5gOa4ScRWELOBUDqd6sJWFZffHy2+AkadbmTALJ1jaAn+7PX5cseqRuPe1by1h3imzez/eEYS+QCnLhh8+D+4abf0deXrqJW9FQMY+R/r8FRiulPpAKTVfKTU25rNdWuujgLuAcJ1C5gKztdZfxhLj/V14+//Y+2utvwi8GHsRpdTngfOBMVrrUUAImAyMAvbXWh8ZvtYD6TR6r1OFtdYbww17UGv9vYTGPgh8z/HAAiS2sHavVIUTxRNs6fpho62IU1U91B4MwS745u8sgZrqoVGV384d0L/BUg42g1YEtnqotd1Om4BoFGvcrdaDqnOnta1lg7NcvlN0VygeEu1OKWc7CP/2SfaNovYv/22VV7I5bIJ1nt1bLLv9wQpLCRtg4VnJxewv/jOcMRPMEPx7SbS4vX3tAcMssaZEETARmRo3/wAAIABJREFUCCsKugIhFJaScCxel6xxFYSs4PZa/fKoC6zJ6M6d1t8/XJQ8Npj0WPS4j16B02dGxxWGG6rrI/2u4fYysmJQ78c7gtAXaO2crTjl6dy2qw/J2OdIE611m1LqS8BXgUbgUaXUdeGPH4n5Ozv879OBI1R0sqC/Uqo6vP27MeeNqakFwGnAl4B/hY+tAD4D/gwcrJT6NbAMy5HukWyucf1C7BullCvc0KLCLqzd+wPDgkd2uZnY2qoHnQxf/n70QWPn7g/+AjSvS60Km6rweFV9dN0swMu/tNa8hNUC4xSMJZpV3Nh2B2mVOoqzb9OEU66Hre9E10mNneG8RrZ1k7MtdrTAbxujNr36cesz+9h+Dc4OaWy7hYLFFzTxug1Uwqy4xyURV0HIChWDrH45du3f955MoSIcTs+3+2N/ByyakFK93YDMxjuCkG202b1NFykZ+xxporUOAS8BLymlVgMX2x/F7mY3BzhBax2Xn534fHdAAYu01tcnfaDU0cA4YCrwHeDSnk62146rUup64AagQim1O6aRfuDevT1/0WFHwc66LeoAnHhVnJpfJFo15WlnddXvP28N6lMVHq+ohSf/Kyp60/aZFbG1I1h2zdhvL5RoVinR21JHThFb22Yh3h4Nl7Mt2mUWYm36+MvF7kqErkAoSZgJrFThDr84roKw1zhlXplB5/64rJ+ljeHyWH3zvWNTjy8EIZ+wVYUTbVoyADJGKXUYYGqt14c3jQI2Akdhpfb+X/jva+HP/wpcBdwePn6U1noV8BxwJTAtvH1gQtT1BeBJpdRsrfVnSqlaoB/QDvi11o8rpdYBD6XT7mykCv8C+IVS6hdO3rTggGHEq7IaLmtNyrhbo6k+r86xHj7jbrX+7aS86lR4/LuPWDeyLbpgb6uoFSdB6H0kM3b/1k+SVYrHTLNm7Q2PNRHyxynxKsLBzuj+9oCq9qBsfBOhAOgKmknCTGClCrcEZF29IPSIaVoCTKkmG50yrwLtVn/cucNyUAMdVmRWqWj/m9ifg+hdCPmLUqltWsiUauDX4ZI1QeBD4HIsjaKBYYHdLuCC8P5XA78Jb3cDr2BFSmeFt6/BWr86E/iTfRGt9Vql1I3AX5VSBhDAcnQ7gQfC2wDS8iGzmSr8mFLq2IRtu4CNWutuKwSH04pXAlu01l/PYpvyF1dMtNRww2k/t5R+Y1N5wVqrGlsuJ3Y9aqoIGsj6QCH7xNqsU1mnc++Fib+xJk46d1oqwqMuiB5fM8I6h1AydIVThRMRcSZBSAOnUjeJ5eucMq+CPiu5b9lP4vtnI2bIlypjS/QuhHxEuSDkT7Zpuza80Gu01m8CJyVuD6f+3q61/mnC/s1YEdjE87QRTTGO3X5gzL8fBR51aEai39gj2fRm5gP/xEoP/m34348B65RSX+vh2B8D72WxLflNKGjl5X9nsXXztW2LOq0QLVejVLSMSOONMPkx+N4T1o26Z5s1Y9qx3XJMa4ZbkTHDiEbJYrcJpYlpWvbV+on1NxSMf2+a6e+Pth4UNSOcS+ssvdwaMNkqlmOvtZQqIToZ4y7bp19fyC1dwdSpwiLOJAg94FTqJrF8nZMKe78Gqz9O7J/NUPfHid6FkK9o09mmi3yNq5BMNiOuTcBlWut3AZRSRwD/C8zAChk7qkUppYYBE4BbgGuc9ikqQkHYtsZak1I92KrHWj3YOWVn1+ZoxLX2YEu1tXpwcnQ2cQZWEMB5tv47D8LLt8G6Zcm2k7i/LcYUK/rx7YXwjXnWpIiTzdYdaqkEA7z8f1bE9cQrrAjsCz+3jhdKhq6A6ei4lonjKgg9k0qAMTGd111ujSXsFEodcj7ODETf91bzQBByiRno2aaFrBAbKc1HstlDHWo7rWDlNAOHa63/08Nxc7Cc2+KZNkmMWsVGtdq2Rh2BzSthyXmw48O4wt9AtFyNLfkd6LT+PWZacnQ2cQZWEMB5tv4P34um7ybaTuL+oy5IFv344xTot5+1ptXJZg235dS6vVa5hUcvjNYNbPtM0tBKjO7WuPoCJlprh6MEQQCiJcxiqRlhiSvZY4w9TVYt9yXnWX1td2MK5Yofl0h2llAoKFdqmxZKimz2Uu8qpe5WSo0Nv+YDa5VSZVgLcZNQSn0d+CycZ50SpdTlSqmVSqmV27fnuYNmR61+dzrMOdL6+9naqPMacpg1evmX0bRhiArbvBqu+du6yZpFBUu8SQQVCoac2m6q2fqKgfHvbdtJ3D+Vrfla4fFLrdTfRJu1HyKShlbwZMN2u0sVtj4vnvlKIT8oqPFCTyiX1a/G9qPfXmhNAtpjjLbP0h9TgPO4RMgLisp2s41SyffC2XeJOFMJks1U4SnAFYTlkIFXgf/GclobUxwzBjhbKXUWUI5VzPYhrfWFsTtpre8lXFpn9OjR+T1Fn2pNii0x7/IkCyK0fQblNXDO3dB/f9ixPirGBPFlRTp3iqBCAZFT200lvtEZo1J+2ASr42/9JPq5vX8qW2vfbtnmCz+30tNqDoDmD6y12N8I16mWNLSCJxu260uRKmw7rr5AiHKPzJgL2aOgxgs9YRhWvxpbcSDYCX+7I7qtstZ5TBH0xR/3+j1w2k3W51L6Ji8pKtvNNlon3wuv3wNn/jLXLRP2MVkbRWqtO7XWd2itzw2/fqW17tBam2HFKadjrtdaDwvnU38XeDHRaS04elqTUjXEWmcYO2t03mL426+sNB+XB1xl8eVszr4LPBXWv1+dkxzpkkiW4IRT1PM7D0YFk+w1rA+cac3CP/NTyxbt/Vc9Ev8+MRPATnVv326txW68Id4OJQ2t5OkKhvB247iKsrAgdENlvdWvLr8hKnpXcwAc/8PoNt/u5EjUOQusEnixx518Lbz26+i5JVNLKCRcXsuGE21aKhWUHFmLuCqlxgA/Bw6IPa/W+uBsXaMg6Eli3tdiiePEzhq9cru1nvCjV6x9nGaVvj47Gr3yVMBlz1vS4BLJElLhFPWsGGRFRc/8pRVpfeDMqK2uW2b9nfQYdDRbtrfmT3DJM9ZsJ1jOrZ0JAJZt14ywriF2KCTQ3RpXsCKygiCkwKkPDwUSFN03WpOMsWOG134DZ8+FKU9btbMNN7x+L/z7oei5JVNLKCRCfms8MukxMFyWQva/l8Dxl+e6ZUIGKKXGA3MBF/A7rfX/pXtsNlOF7wOmA29iFaDtFVrrl4CXstie3GBHuRLrrtmRqKAf2rfFH9O+DarCx1UPtWZYE4+vEqdAyAA76hmL/b71E0ulOnbA8+ocaw3rwglR2+vXYJ0nFLQitNtWx6sU92sAVza7EqFYSKUqHIm4+iXiKgi9QrniJ8ZfnZNcU/u7j1iTlFXhe8804ejz4b0nnMclgpDvGG44bDw8fF7Uhs9ZEF+bWCgIlFIu4DfAGcBm4F9KqafCor49ks1ffJfW+pksnq8w6Wltn6ciuZzNxPkw8ECoGixrA4V9R3e2OG1Nsu253DDkSCsCGwpYae3VQ8VpFVLSFQzhcTuXwwHwBcVxFYSUOJU0O3+JtczDzpDZvNLKyrIzY5zGDDKuEAodl8cas8SWffJUWNuFPqMrGDqxeY//jqBp7uc2jE/r+nl/UuZ2vbaXpz0O+NCuOqOU+j0wEdjnjusKpdTtWDVbu+yNWuu3sniNwsApymVjhpLL2Tx5BVzybHrHC0K2SGWLlz1vrUt1wuWGAcNSnM+0xMlkYCSE8QVTRFwjqcLiuApCSpzEHh+dDBc9FZ/50nhDNDPGxqk/lnGFUKiYIXjs4uRleJc9n7s2FTldwdCJH2xre+q/HnqzbvPOToYNrDjw7gu/9NShQ6rP3kvndX/gk5j3m4Hj0z04m46rfdHRMds0cGoWr1H4hFKIN+36xErVHHyEDPaFfUMqWwxlINjhFBn47iNizyWOP2DidVrj6raUhMVxFYRuSCX2aLi7j55KfywUG9kcrwhp0bzHf4fttAJs3tnJfz30Zt2jl594x/4DK07KVbuy5rhqrVOVvCleMokwpRJvCnRC124oq7bSHyRaJaRLppHOnoTEenPunspACSVHMGQS0rqHcjgiziQIKemuj+6uX5X+WCg2lHK+F6SOa58RNM39bKfVZvPOToKmud9ennoLEJvWNyy8LS2y5hkppYYope5TSj0Tfn+EUuqybJ0/77BnNO0i4OkW9HYqUXLOAijrD8t+AnOPluLgQvpkaofgbIuxgh29OXdPZaCEkqMraNmJk+NaJuJMgtAz5bXJ5fO+86C1vTukPxaKDZc3uTzfeYulHE4f4jaMT4cNrIjbNmxgBW7D+HQvT/0vYKRS6iCllBerHOpT6R6czZDeQmA50BB+/wEwLYvnzy9SzWh2bO/+OFsk4ZJn4NLllqKrDsEfp/T+XIKQqR1CvGDHtDXW39hUst6c244MxCLlFkqa7hxXe5vUcRWEbmjfFi2fN2WZ9ffl25IrEyQi/bFQbAS7rNKRsffCK7db24U+oa6f9yd3X/ilZtt5HTawgrsv/FJzXT/vT/bmvFrrIPAjLJ/xPeAPWut30z0+m2tc67TWf1BKXW83TClVvKOSvZnRNAxLSKGzFf70AzjnbpkdFTJjb2fWuxMC6825eyoDJZQcXWHFYI87OZWr3CPiTILQI6GApR5sKwjbjLul++OkPxaKDTPkfC+MvzU37SkBytyu1w4dUn32o5efmG1VYbTWTwNPZ3JsNh3XdqXUICxBJpRSJwC7snj+/CKd9YHdkShPvzfnEkqXvbXDbJ1byi0ICXSF1696HVOFLXGm9i5xXAUhJS6Pcx/cUwkQ6Y+FYsPldr4XpI5rn1Lmdr2WSyEmJ7L5i1+DlaN8iFLqVaAe+HYWz58f2GI1pgmTH4fWj6M1pQYe3LsZTTvaZZoyOypkRm9n1hPFlioGQecO58FNb88tZZyEGOxUYSfH1WUovC6DDn9wXzdLEAqH6qEw6THYtSk6zhgwwtreE9IfC8XE3twLQlGRTVXht5RSY4HDAAWs01oHsnX+vCBWYr56MJwxyxJUih3UZ4LMjgqZ0hvbcSqR8J0HrTVT65Yll0wQuxT2gkiqsIPjCla6cLs4roLQPaGu+HHG+Q/lukWCkBvkXhDIgjiTUuqb9gs4G8txPRT4Rnhb8RArVjNmGiy9PHuCSvbsaM1w6684B0K6pGs7TmJLf/gejLog+j7RhsUuhQyJiDO5nW2mwuuiQ1KFBSE1bVvh0Qvj++xHL7S2C0IpIfeCECYbEddvdPOZBv6UhWvkB7FiNRUDRVBJKCxSiS1VDIx/LzYsZAF7jev/Z+/O45yq7sf/v87NMsnMMAszwzIOCCKooKgFLUhbGa1KXUqtra1bXVqV+mmp1I/2236w6q/208WPYmlFqlalLq3WDetuFWuLS0VEUBARFcEBIbPAzJBMlnt+f2QSMjM3k8wkmWzv5+ORB5Pk3psT8j4n9+Sc874Om/V19krsNjq6ZMRViLhCAes2O1RYk9mESEjqguiWcsdVa31hOgqSF2KT1XhbJaGSyC/xki15W3velxgWaZDMVOG9ch1XIeIbbHImIQqN1AXRTeb9DUQkWU3VWFh5M8xd0vNiyJJQSeSy2PiFfWtc1/xl332JYZEm/SVngvCIa6eMuAoRX/mocBvdu82WhDSi2EhdKBhKqTuVUjuVUu8MZv+s55FWSrmAl4ESwuV5SGt9TXZLFUfvZDUON3z3HxDq/tsMwZ5PJYmNyE1WyZbcNXDaIvjKb/bdj806LHEsBilyjdb+Rlx3e2WalxBx2eww8lC48OnwlEibI3yirgzo+EzaaVE8bHYYMQUueArMYPgyOOWjwo+LfHM38Afgz4PZORc+8S7gOK11h1LKAfxbKfW01vq1bBfMklWKeatsrbHZWYXIFVbxG7kvcSzSKJqcKc4aV5fdRlOXbyiLJET+sdmhsmHffWmnRTEyTfBslLgfasGumXTsvBEzOBrDvp3yEVdgL3k1lUNqrV9WSo0b7P5pzSpsdUu0vw7r6L7r6L7pVMs1pKyytaaSYViIbJA4FmkUGXEtsdssn3c5bXIdVyEGStppUYwk7odesGsmOzc8zt0nz2TxEeO4++Tw/WDXzGwWKyeyCiulbMCbwIHALVrr13s9fwlwCcDYsWMHX9JMiZetVbKzFr2cj91YEsciRqqx6+3uuDrjXA7HZZfkTCL98qrNHQxppwtWwcduKiTuh17Hzht58LzaXpdQrOWCp26kaswx2SpWTmQV1lqHgCOUUlXAo0qpQ7XW78Q8fxtwG8D06dNzbzQ2XrZWyc5a9HI+dmNJHIsYqcauzx9C0c9UYYeNrqBJMGRij7MOVoiByqs2dzCknS5YBR+7qZC4H3pmcLTljwVmcHR2ChSW1rMFpdQpSqmrlFI/j9wGsr/Wug1YAcxJZ7kyzipbq2RnFflG4likkTcQwmk3UCp+xxWgU0ZdhUietNOiGEncDz3Dvj36/x1RNTb8eBalLTmTUmopUAo0AncA3wD+k8R+dUBAa92mlHIDJwC/SVe5hoRVtlbJ8ifyjcSxSCNvIERJnGnCACWO8HN7/UEq3XItPiGSIu20KEYS90OvfMQVnHnP49HpwuFLEHkoH3FFKodVSv0FmA3UKqW2Addorf+U7P7pzCp8jNZ6qlJqrdb6OqXUjcDTSew3GljWvc7VAB7UWj+RxnINDatsrULkG4ljkSZev9lvx9XVnbSps0tGXIUYEGmnRTGSuB9a9pJXGXHIV7ngqXRnFT4rpWKlsnMv3u5/9yql6oFmwp3Sfmmt1wJHprEcQgghsswXCOGMk1EYwN09VVgyCwshhBA5yF7yajYTMVlJZ8f1ie7kSjcAqwlnFL4jjccXQgiRJxJNFXZ1TxWWEVchhBBCJCOdHdffaq27gIeVUk8ALkCuLi+EEEXI6w/FvRQOQImMuAohhBBiANK5qjk651lr3aW13h37mBBCiOLhDYRw9nOZm0hW4Y6uAui4ettgxa/gbxfCuoeyXRohhBCiIKU84qqUGgXsB7iVUkcCkWsfVBDOMiyEEKLIeAMhhpXE/4px2SNZhfN8qnDzZrj3DGjbAu4qePcRaP0YvvTf2S6ZEEIIUVDSMVX4JOACoAG4KebxPcDP0nB8IYQQecbrD1FbFv/i8NHruObziGvHTrjna+DbDXN+DbUHwb9vghW/hENOg7qDsl1CIYQQomCkPFVYa71Ma90IXKC1boy5zdVaP5KGMgohhMgz3gRZhV3RNa55OuLq74T7vwXtn8Hx14SvMWjY4OhLwF4S7rwKIYQQIkopNUYptUIptV4p9a5S6kcD2T+da1xXKqX+pJR6urtgk5VS303j8YUQQuQJX4KswjZD4bQZdOZjciat4e8/gqa34EtXQu2kfc+5KuGQr8L65eEpw0IIIYSICAJXaK0nAzOA/1JKTU5253R2XO8CngXqu++/D1yexuMLIYTIE+ER1/6/YlwOg735eDmc1ctg3d/giHNg7Iy+z088CVDw9gNDXjQhhBAiHfwh/8ztHdtf2dq+9aPtHdtf8Yf8M1M9ptZ6u9Z6dfff7cAGwrmSkpLOjmut1vpBwOwuTBDIwzMSIYQQqQiETIIh3e+IK4SnC+fdiOuOd+Cpq6D+czD1TOttykfA6Knw9v3h0VkhhBAij/hD/pkftH3w+AXPXDDz5EdOHnfBMxfM/KDtg8fT0XmNUEqNA44EXk92n3R2XDuVUjWA7i7MDGB3Go8vhBAiD/gC4d8sC27E1TTh8R+Cww1f+DGoft7f+GPDU4U/e2fIiieEEEKkQ7O3+cYFKxbUNnU2AdDU2cSCFQtqm73NN6bj+EqpcuBh4HKt9Z5k90tHVuGIHwOPAxOUUiuBOuAbaTy+EEKIPODt7rgmGnEtsefZiOvqZdC0Gr54RfjSN/1pOCr87/vPwKjDMl82IYQQIk2COjg60mmNaOpsIqiDo1M9tlLKQbjTet9AE/mmreOqtV6tlDoWOIjwtVw3aq0D6Tq+EEKI/ODzmwD9ZhUGKHHY8udyOF3t8MJ14U7o+NmJt3dXh5M2bXw6nMApgZ17d7JlzxZ27d1FwAxQ5ihjUvUkxlaMTb3sQgghxADYlX17fVn9uNjOa31ZPXZl357KcZVSCvgTsEFrfVOi7fuUK5UX71UQF3AZ8AXC04X/pZRaqrX2pes1hBBC5L7IKKorwYir22Gwx5cnHdf/3A7eVjju56BUcvs0HAVr7oe9LVA6vM/TITPEIx88wj3r7+Gj3R9ZHmJKzRR++vmfcnjd4amUXgghhEhajbvmikWNix6PTBeuL6tnUeMiT4275ooUDz0LOA9Yp5Ra0/3Yz7TWTyWzczqnCv8ZaAd+333/bOAe4Jv97aSUGtO970jCHd7btNa/S2O5hBBCDKH27s6o29n/iKvLbmN7Vx78thn0w2u3hBMy1U5Mfr/Rh8Oa++Djf8Pkr/Z4yhf0Mf/F+by6/VUOqDyAbx/0bfYr349qVzV2w4436GVjy0ae2/Ic5z11Htcdcx2nTzw9zW9MCCGE6Mtpc756YNWBX717zt03BnVwtF3Zt9e4a65w2pyvpnJcrfW/Cc/MHZR0dlwP7b4mT8QKpdT6JPaLXM9ntVJqGPCmUup5rXUy+wohhMgxHV3hVSKlCTquJQ4be/15kJxp8wvQ6YHPXzaw/Womgt0FH73co+NqapMFLy3gte2vcd7k85jdMBtlMYq7f8X+fLHhi9y65laueeUayp3lnLD/Cam+GyGEECIhp8356ujy0cdkuxyx0plVeHV3JmEAlFKfB1Yl2inV6/kIIYTILftGXPv/bdTlMNibD8mZ3v4rlFTCfp8b2H42B4ycEu64xnhw44P8+9N/c/bBZ9M4ptGy0xrhtrv54ZE/ZHzleK5eeTVb27cO5h0IIYQQeS+dHddpwCtKqY+VUh8DrwJHKaXWKaXWJnOAwVzPZ6iYpmZXexeftu5lV3sXpqkH9LwQInmp1iepj9nV0Z1wye1IMFXYYcMXMAnl8ufjbYONT8H4L4IxiElKow4Dz0Zo/wwAj9fDTW/exJSaKRw39rikDuGwOZh3+Dy01lz7yrVouTZsRiXTfkgbI7IpGDRpavOypbmTpjYvwaCZ7SIJMSTSOVV4Tio793c9H6XUJcAlAGPHDn2GRdPUbPysnYv/vIptrV4aqt3c/p3pHDRyGIahEj4vile2YzcfpVqfpD6mRyqx29E94ppoqrCrO+twpz9IhcsxiFIOgQ2PQ8gPBzQObv9R3UmVPv4XHPYN/vzun+kKdnHOIef0O9LaW627ltMPPJ373ruP57c8z4njThxceQpcqm1uMu2HtDEiE5KN3WDQ5L3P2pl375vR+Ft67jQOHjkMe4KEeELku7RFuNZ6S3+3/vZNdD0frfVtWuvpWuvpdXV16Spy0po7/dEvKIBtrV4u/vMqmjv9ST0vile2YzcfpVqfpD6mRyqx29EVRJH4Oq4uZ/j5vV05vM51zV+gYr/wpW0GY/gB4CyDj16m1dfKXzf+laNHH82oslEDPtTsMbMZM2wMi95cRMCUq81ZSbXNTab9kDZGZEKysbuzoyvaaYVw/M279012dnQNVVGFyJqs/zST6vV8hoI/GIo2EBHbWr34g6GknhdCJC/V+iT1MfvafUFKnbaEI4qxI645qW0rfPIKHDA7+Uvg9GbYYOSh8NE/eeyDx/AGvZw8/uRBHcpm2Dj9wNPZ1rGNJzY/MbjyiH4l035IGyOyKRAyLeMvGJLpwqLwZb3jyr7r+RynlFrTfRvct3qGOO02GqrdPR5rqHbj7D7pSvS8ECJ5qdYnqY/Z1+4LJrwUDoTXuEIOj7iuezD872CnCUeMmopu/Zi/vfcXJlZNpGFYw6APdXjd4YyrGMcf1/5RRl0zIJn2Q9oYkU0Om2EZf3ZbLpzSC5FZWY9yrfW/tdZKaz1Va31E9y2pi9AOlZoyJ7d/Z3q0oYisZ6kpcyb1PCReSC+JHkQxi41/mwG3n9d/feqPVX3843nTME1T6tYQ6egKJEzMBOGswpCjI65ah7MJj5gMwwY+rbeHUYfxhquErZ3bOXbMsSkdSinF3APn8mnHpzz+weOplUv0Yfl9ft50bAbR7+dqtyPhNtLOiEwZUV7C0nOn9Yi/pedOY0R5SZZLJkTmpTM5U8EyDMVBI4fx6GWz8AdDOO02asqcPZIwlNgNfjH3UEqd4esSxq7tSrSQXhI9iGJmFf93X3gU//fNw1HQpz4lo3d9DIZM5t27ml0dXVK3hsBub4DSBJfCgX0jrp1dOdhx3f42eN6HGf+V+rGqx/FkRRVuDKaPnJ7y4abWTmV8xXjueOcO5h44F/tgsh2LuHq3Hw674v89vJbn1u+Mfj+Xl9ii22jAYVd89Q8r5TtcZJxpmgxz27n7wqMxFJg6HH+maZID41FCZJREeD9iR4FavV0EQyZBUxMImZimJhAI8WnrXra27sXttFFTHh4R8odMfv30hmiihkQL6SXRgyhEvWcRBIOm5f1tbXvZsdtHXfevxdtavfz2mfeiI6z+kMkD/9nCjj2+pEYzmjv9/PrpDfi71/v4Qya3rPiAebMnSN0aIs0dfoa5EnemSrs7rpHrvuaUtQ+C4YBxX0j5UAFt8o9SF8f6Ajhtyc0c6I9SipMPOJlt7dt47uPnUj6e2Ke508+7n7YycWQ5oypdTBxZzoamNs6YNgbY9/28Y3dXtI2pKXPy22fe6/Md3ub1y0wqkXa7Ov007/HidhjYDIXbYdC8x8su+V4TRUB+po0jdhSorryEq+YcxJUPre0xIuQLmMy7903L539zxtTuX78SL6SXRA+i0PQeRT1x8gjmHz+pz6yDxS+8Hx3F+M0ZU/m/ZzcCcP4x47nw7jei+/7guImc+cdXkxrNME2T848Zz08e7lkfK7o7UlK3Mq+l08+Y4aUJt4usg2335dhazVAwvL61YTqUDEv5cK/4gmJeAAAgAElEQVS2vccepTmlrQVn+2f4h41M+ZhHjjiS+rJ6bl93O3PGz8FQ8jt0OjhsmnF1FXz7ttei7cet506jpmzf6VJdeQkuh8Evnljfo43Z1e7nra1t0W22t/m4NKbNk1FYkQ7DXAYtex18q1eMDnNJGyAKn3RcezFNTXOnH38wFD3pvvrUydFO6ZFjqpg3ewI2Q0VPwmOfh/CJ8U8eXstD82byaetelIK/XjIDrTWGUrR5Azz85tboQvpIoofYzqskehD5rPcsgjOmjWHxC+9z9amTqXI7aPMGWPzC+5wxbQzPrd8ZrTNXnzoZgJc3fsZdFxyFzVDYbQZn3/5an9GMRy+bRd2w8ChtIBBiZ0cXQVNjNxTLXvmoT32864KjgL51K7bOWy0DiDWQbYuVaWpa9/qTui5rZDrxnlwbcf3oJejcBUddnJbDPbtrNaWGg2O8XrZ9uobmg09K+ZiGMjj5gJO5Y90dvLztZWaPmZ16QYtUMGiys6OLQMjEbih+36ut+v0L73PNaVN44JIZtHkDVLod/P7FTT22WfbKR8ybPYFL73kTgPnHT4x2WsG63RJiMNp9ZtwYrXAn3l+IfCYd1xixo0Q3fvPw6BdOldsR7bT+90kH8ZOH11o+H6uuvIRd7V18/77V0V/EbvjGVH799Hvs6uji1nOnUVsaPrGrdjtYeu60PqNR1e7EJ35C5KLeswjqK139joJC+MSuyu2gptzJ6EpXdMT1oXkz+52REAiEeG9nB9+PqT+9Rz+2tXrxBc0+iZ4Gsr5c1qInp80bwNRQ4U789eK0Gzhsij25NuK6+h4oqYCGo1I+lN8M8KJnDUdWHohyfkZF09tp6bgCHD3qaJZ/sJzb1t7GsQ3HJrz8kOirdw6KF378Jcu2yjDgW7e9RkO1mwcumWG5TVX3d3pDtZvxtWUyk0pkhFJYxp9Uf1EMirrj2nv0xGbAY6u3ctcFR+G0G9x1wVEsfmETbd4AJ04ewZUnHcxef4i7LjgKt8PgxMkjOGPaGGrKndFtIyfK84+fGO20QvgL68qHwiNKS1/ajKe9iwqXnVJnCJuB5WjUL0+fKr/MirwRW5+UUtH6UeV2UFnq5PcvbupRH5a98hELTw2PYphao5RiVKULu6F6rBdr7vRbzkgA2NLcid1Q0U5r5NiR0dvI6EdDtZvhpQ4evWxWj1FSq/Xli57fyLVfPRStdY9R1Xhr0WUEpaeWzvDa/WRGXAHKnHb2eHNoxLVjF7z3JBx8MthS//FwZesGOkI+jqqcRHvtDoY1rQlnLE7DWabdsDNn3Bzu2XAPb+x4g6NHH53yMYvNzo6uHt+/LoeNZa981Gc0NXbE1TBUtNMA+9qcBy6ZwcqfNOK029BomUklMkJr4saoEIWuaDuu8TKZnnr4ftGRnsgo6aqPWvjBcRN7PH7rOZ/jh8dN7DOi+ttnNrKro4uxNaWWv7aOGFYSHbWN7PfHc6exq90fPcmOuOY0+WVW5Ife9enaUw/mh8dPijsKeuSYKs4/Zjxn3/5adI34f//tbcttl760md+cMbVHnVlyzue47u/v8tz6nXFHZCOjqpHj2Q3Vp4PZe2Q4Ui6r9bSyFj05zR3hBCEVSc4YKXXacmuN69v3gxmAiXPScrhndr5Juc3F5GFjaa+ZwPCmt3G278BfMTotx//Cfl/g8c2Pc/u626XjOggK3WP0auVPGi1Hs3yBUHTEdem506grL+nRHmxr9RIyNWNryoBwm3j7d6b3maGR7GW9hIjHMKxHXA1Z4iqKQNGGeXOnPzq6+uIVx/Lni47GYTMsR0m/Pq2By3o97unwW25705mHc/Wpk9nV3mV5gejyEnv0l7IHLpnB1adO5ncvvM/84yf22VZ+mRXplqnrBfcejZw1cYTlKOi82RMAmDd7QvRLd97sCZZrxCPbvrW1jZc3fsYDl8zgn1fO5q+XzODJtz/lufU7o69tVddGVLiidWzZKx8R7H7vse85sr48IrZckbJEshD33jbyOlJPe9q+2wdAdWlyHVe305Y7a1xNE968O3zt1qoxKR/OF/LzUvNaPld5IHZlY0/tgQBUNK1J+dgRDpuDE8edyGvbX2PdrnVpO26xMDU96rwGy9FUR3c9j1wVwOo72xazZCD2Mnorf9LIo5fNkmUFIi1M0zpGu/OBClHQirbjqtCc0j26etyN/+Q7d/6HoKmts/9aPF7qtFlu2+YN8Isn1uN2GNz4zcN7XCD6hm9Mhe5fd3/xxHq+ddtr/OKJ9Zx/zHgmjSzveTFz+WVWpFlkVPT0JSuZ9ZsVnL5kJRs/a09L57X3aKTdpizrx6gKFwCjKlz9rhGPHTE9cfIITj2igW/d9hrH3vAS377tNb500EiOHFMFEB2Rja0/t547jfte/Shaxy6cNZ4f3P9Wn/dcU+bk9u9Mj+5bU+aMO6rae1upp9Y+adkLwIhhrqS2L3XaaffmyIjrhseh5UM4+NS0HO7fre+y1+ziqMpJAPiGjcTvqqBy66q0HD9i9pjZlDnKuGXNLWk9bjEI9fp+D4aszwNCMb2Cba1eDqgr69Pm1PVqC4zuWR77VZdSN6xEOq0iLeKdq4bkckuiCBTtVOGuoNlnFPWT5r2Wa1Jshurz+F5/yHLbEcNK+L9vHs5ef4jyEjt3XXAUe/0hKtx2duz24XLY+cnDq/r8UvbgpTN59LJZkq1UZEwm12j2zoxtMwzL+lFb7uSfV87GHlOn2rwB67pU4eLFK47FaTeil6aIlDt2DetbW9tY9spHPHDJjGhW4boyJ6O+dCDnzBzP5p0d/PaZjdH157HvOXZUJLI2N966tN7bSj21tqV5LzVlTpz25H4XLXXa2NV9TeusMkPw8g1Q0QD7z0rLIZ/auYpKeykHlzeEH1CK3SMOoXrrqvDrGekZrXfb3Zx6wKk8sPEB/rXtX3yx4YtpOW4xsNt6tlV2m3UbEAzpHvfthopmPg+ZmodWfcKoLx1InbNoT6vEELFbnJP2HvEXolAV7Yir1S9Wi1/YxK3nfK7Hr6hLzvkcr2zaxZJejzdUu/qMqP7mjKm0+wJorbnyobWc9oeVXHj3G9gNxY8feJuzbn8dT0eX5S9lWuvoL7M1ZU6aO/1y0XKRVplco9l7NNLrD/YZBY2sM92/pgyliD6/9KXN3PCNviOm1z/xLsfd+E/8QevrIMeuYZ1//CRGDnOxf00Z+1WX4nTaqRtWgk3BhXe/Ee20Rvb1BkLR+gVE696oCle/o6oygpLYJy2dA/ohZJjLQUunP4MlStJb98Jn78DhZ6WlQ9ke9PLP5nUcVTkJW8w1VnePPAS7v5PyHe+m/Bqxjh97PKPKRnHDGzcQMHNkBDsPjCgvYem506J13mbQ5/v+1nOn8dCqT6L3f3PGVLTWnLDoZY678Z+csOhl/vivj2W9uxgSLofBrTExG4lRl6NoT+lFESnanwYdtr4jQrs6uujoCnL1qZOpKXMyssLF71/YxINvbuPMaQ3cfeHROG0KmxG+3fDsRsusbsPLSnjk+8cQCJnRbMWREYWd3Wtf42UalEtuiEzJ5PWCrUYub3yub/345elTAVCoHlkRTa35v28eTn2lC7fTTpXLzrVfPZSFp5iWMx4aqt2MrnTx8pWzsdsMRpSXYLcY4Yv3njfv7ODCu9/oU79kVDU1Wms27eyITuNORlWpg7a9AXa2+3jrkzZOOGTk0P9/t++Af1wLI6bA+C+l5ZAveNbg10E+X31Qj8d3103CVDaqPnmdjvqpaXktCGcYPnPSmSx+azHL3l3G9w77XtqOXcjsdoODRw7jwUtnEgyZmCY8+fanPUZTV27ayTemj+W4Q0ZF27Kr5hzS4ziy3l0MFV/A5M2PPNx/8Qx0d0b+F9dvZ/iU9CR8EyKXFd3PM5HkNE6b6vOLVSQr8C+eWE9ZiZ1AyOSVD5sBeOXDZgIhk4bq0vCIjt3gwlk916peOGs8bqeNERUuRlS4oqMyVe59o1FWo0uxIzrxpnM258KIhMhrmV6jGTsaOarCxYITDupRPxaccFD0tZwO1aP+XPnQWrTWlLls1A0rweGwUV/lZmxNGSOHuXqMiESyeo4c5mJsTRn1VW7LTmu893zDN6ay+IVNgHX9klHVwfukZS9tewMcUFee9D6Ra19+/97VXHrPmzy3/rNMFc9aKAiPzgN/B8z8QVouUwPw1M43GOGs5AD3qB6Pmw4XHTXjqdzyelpeJ9bhdYczfeR0bllzCxuaN6T9+IXKMBQOm4HNULidBqce0RDNf3Hh3W8wbXwtNzz7XrQtm3/8JErsSta7i6wY5jKYNr6Ws28P5304+/bXmDa+lmGuojulF0WoqEZce49mXvrFcfz1khmYpsZhM3A6FH84+8joKAsQd+TF6w/x22d6jij99pmN/OHsI6Gs5+v2HsVxO208ctkxBIJmn+PKJTdEpgz1aGKJ3eAXcw+l1Gljrz9ESUzn0uc3k64/vUdE+hth7a33ewb4wf1v9Zk6LPUrPf61yQPAIaMqkt6n2h1ua9/c0grAivd2MufQUf3tkj5aw9NXwYcrYOYP05JJGGBX125eb9vIySOOQll0hNtGTWHsO8spadtGV1VDWl4TQCnFdyZ/h82vbOZHK37E/afcT627Nm3HL0S9zwtevOJYXtrwWY/RrI3bd3PNaVNYeMrkaPtjGEpmZoisaN0bYkfb3h55HdY37abSZafCnXh/IfJZ1juuSqk7gVOBnVrrQzP5Wr1HM//4r4958p3PeiSnMd2a5k4/23d7+/0yctrDCUVir73a31ShyChOIpmczilEsnGYquZOP9+58z994jhS1wZaf+x2g/qqfd/IkZkTyZw0xr7nXe1dfRIBSf1KD601f33jExqq3dRXJZdRGOhziaENO/aku2jxvbIYVv0JpnwdJp2UtsM++tkrmGhmVR9i+XxL/eGMeedxaj5YQdP089L2ugDlznJ+eOQP+c0bv+HS5y/ljyf8UTqv/eh9XhAyNQ+8uY0b/7Epuk1DtZsHL53J2JrSHvsORVsqRG92Q3HdExv6fL8+cMmMLJZKiKGRC/MK7gbSc6X3BBKNZg7kciGZmnYpl9wQhSBRXUslzlO5rI/Ur8xZ+UEz73y6hzlTRlmOMsZTN6wEt8OGzVA0HjSCD3d1ovUQJKRbdRc8/3MY90WYdkHaDhvSJn/b/m8ml49hZEm15TYBdxXtNQcw/IMXw6O+aTauchw/OPIHbNmzhbOePItVO9J7+Z1C0rutembddsvEN70vdSNEtrid1smZ3M5cOKUXIrOyPuKqtX5ZKTVuKF4r0WjmQC4Xkqlpl5IcRhSCRHUtlThP5bI+Ur8y5w8vbmJ4mZMvTaob0H5KKX75tUMJmpr12/ewYuNOdnV0JX0d2EF5+6/wxAJomA5f+DGo9J3wvdzyDju6Wjl95Mx+t2tpOJJxbz+Eu3kz3toD0/b6EVNqpnDVUVdx29rbuOjZizjr4LP4ryP/iwpn8tO4i0HvtmrS6Ap+/8L7PZYx/P6F9/nl6VOpc8jMDJF9voC2jNFrv5rRSYtC5ISsd1yToZS6BLgEYOzYsYM+TmS0pXfG3shoy0DXl2Zq2uVQTecUmZeu2M03ieoaDD7OU10HLvUrOQOJ3dZOP69/1MLXP9eAwzbwTuDo7mngrXvDSbI+3NWZuY7ru4/CY9+H0VPh2J+CzZG2Q2utueOTZ6h1VnBk5YR+t22pP5yxax+lduNzbM1AxxVgfOV4rp15LQ9teoi/vPcXnv7oaS6fdjlfO/BrGGnsrOeSgba5vduqmjInz63fyXPrd/bY7prTZB28yKxkY1drHSdG5dKJovDlRcdVa30bcBvA9OnTB10zE422yPpSkW7pit18k8mRTamnQ2Mgsfv6R81o4LD9KlN6zdGV4Q7sh7s6mXFATUrHsvTeU/Dw96DuEGi8Guzp/QHjjd3vs7b9Y87brxG76j8eQ84yWuunUrvxWT49+kJMR2ayqpTYSzjnkHP4wn5f4L4N93HNK9fw4MYH+dnnf8bUuvRdjidXDLTNtbqUl7QvIhuSjV35DhTFrDB/cu1Hf5e6kPVvQqRPpi4rI/U097yyuRmX3WBCXVnijftRU+6kxG7w4a6ONJUsxvvPwd/Oh+EHwPHXgCO9I7qmNrnxw0eodpTzheopSe2z84AvYPd3UrPxubSWxcr+Ffvz06N/ysWHXcynHZ9yzlPncNOqmwiawYy/dq7rfSkvaV9ELpPvQFHM8mLEdajI+jchcp/U09zzygfNTBo1DPsgpgnHMpRiVKWLjzydaSpZt3cehkcugepx8OXrwFmacJeBenTHq6zv2MolY+bgMJL7au2oHkfH8HHUv/UXPAd/BW3P7ImnUoqZ9TM5YsQR/G3j37jr3btY61nLDV+6gbrSga1NLlTSvohcJzEqilnWR1yVUn8BXgUOUkptU0p9N5vlydQokRAifaSe5o6d7T4+2NXBlPrUpglHjKpwsTldI65awyt/gIe+C3UHwYm/hJJh6Tl2jC3enfx280McVLYfn686KPkdlWLbwXNwdnoY8e7ytJcrHrfdzXemfIeLD7uYdzzvcOYTZ7L6s9VD9vq5TtoXkeskRkWxynrHVWt9ltZ6tNbaobVu0Fr/KdtlEkIIkZxXNzcDMKU+PdlqR1e62drqxR80UzuQvzM8yvrc/8DYGd0jralNZbayO7CXBe/ehlKK7405aUCXAgJor51I28jJ7PfG3Tj3bE97+fozs34m//P5/8Gu7Fz07EXct+G+obkUkRBCCDEIWe+4CiGEyF8rP/BQ5rQxviY9ncL6KhchU/NxcwrThbe8ArfOgnV/gyPPg9k/BXv6sxR7/Lu5dN3v+cj7GfPGfoWawVxqRim2TD0DUBz43P+HEfClvZz9aRjWwMIZCzms9jB+/Z9f85N//YR2f/uQlkEIIYRIhnRchRBCDErI1Pxjw06mjqlK21S1SSPDU3lf+7B54Du3fBgeZb3rZAh64aRfwtRvpfU6rRC+HMWzu1bzzdW/4oO9TXx/7MlMGbb/oI/nL61m87RzKPV8wMSn/wdb19B2HEsdpfzgyB9wxsQzePajZznt0dN47IPHCJiBIS2HEEII0R9JziSEEGJQXnxvJy2dfo7af3jajjmywsXIihKefXcH35k5LvEOHbvg43+FEzC9/wwYNphyOhx+FqTxEjMBM8iGjq283raRR3e8ylbfLsa66vjhhNMY4049sdHuUVP48HNnM37NXzn0rxexfdo5tI7/IoGyDFwWyIKhDE454BSm1Ezh3g33cvXKq1m8ejFfO/BrzKyfyZSaKZQ60p/USgghhEiWdFyFEEIM2P8+tYE/v/ox9VUujhpfndZjH3fQCP7yxlZ++sg6fvX1w/Y98ffLYc+nEPBCVzu0fQLelvBzrio4+LRwp7V08B3pxR8t54O92/GbQbrMAN5QF5/522j2t6MJr/+cVLYfF485ic9XHYSRxtHcljHT8A0byf7rHmH/f/+B/f/9B/xltfjLagm6Kgg5y/jwy/+TttezMq5yHD/7/M9Y51nHC1te4E/v/Inb190OQI2rhpFlIym1l+K2uymxlXDqhFM5fuzxGS2TEEIIAaDyLRGDUmoXsCWJTWsBT4aLkw3yvgbHo7Wek8HjJzSA2B2sXI0NKdfAxJYr63ELQxK7veXDZ5MrcrVM72U7dhPEbS7+v/Un38oL+VfmSHmz3u7KuW6/5D3Hl/XYzbS867gmSym1Sms9PdvlSDd5XyKeXP0/lHINTK6Wayjl6v9BLpZLyjQ4+VDGWPlWXsi/MudbeSE/y5wqec/FTZIzCSGEEEIIIYTIadJxFUIIIYQQQgiR0wq543pbtguQIfK+RDy5+n8o5RqYXC3XUMrV/4NcLJeUaXDyoYyx8q28kH9lzrfyQn6WOVXynotYwa5xFUIIIYQQQghRGAp5xFUIIYQQQgghRAGQjqsQQgghhBBCiJwmHVchhBBCCCGEEDlNOq5CCCGEEEIIIXKadFyFEEIIIYQQQuQ06bgKIYQQQgghhMhp0nEVQgghhBBCCJHTpOMqhBBCCCGEECKnScdVCCGEEEIIIUROk46rEEIIIYQQQoicJh1XIYQQQgghhBA5TTquQgghhBBCCCFymnRchRBCCCGEEELkNOm4CiGEEEIIIYTIadJxFUIIIYQQQgiR0/Ku4zpnzhwNyE1uA71lncSu3AZxywkSu3IbxC3rJG7lNshb1knsym2Qt4KXdx1Xj8eT7SIIMSgSuyJfSeyKfCRxK/KVxK4Q1vKu4yqEEEIIIYQQorhIx1UIIYQQQgghRE6TjqsQQgghhBBCiJwmHVchhBBCCCGEEDktYx1XpdQYpdQKpdR6pdS7SqkfWWwzWym1Wym1pvv280yVRwghhBAiGa992MyL732W7WIIIYSIYc/gsYPAFVrr1UqpYcCbSqnntdbre233L631qRksR1EwtUmLrwV/yI/T5mS4aziGsv5dYiDbpmM/IRIJmkE8Xg+BUACHzUGtuxa7Eb95klgUxSJerEceN00TExNTm1IX0ujbt70GwMe/PiXLJRFCFKtAKIDH6yFoBrEbdmrdtThsjmwXK6sy1nHVWm8Htnf/3a6U2gDsB/TuuIoUmdpkU+sm5r84n6bOJurL6ll83GImVk/scwIzkG3TsZ8QiQTNIO+3vs+CFQuisbWocRGTqidZdl4lFkWxiBfrE6omsLltM7e8dQtnTz6ba1ZeI3UhQ0xTYxgq28UQQhSZQCjAprZNfc6NJlZNLOrO65B8symlxgFHAq9bPD1TKfW2UupppdSUoShPoWnxtURPbACaOpuY/+J8WnwtKW2bjv2ESMTj9UQbZgjH1oIVC/B4ra9jJ7EoikW8WPd4Pcx/cT5zJ86Ndlpjn5e6kBqtdfTvPb5AFksihChWAz03KhYZ77gqpcqBh4HLtdZ7ej29Gthfa3048HvgsTjHuEQptUoptWrXrl2ZLXAe8of80cCOaOpswh/yp7RtOvYrdhK7iQVCAcvYCpjWJ4wSi0NDYjf74sV6wAzXmUpnpdSFXtIRt76AGf270x9KV9GE6Je0uSJW0Axatu9BM5ilEuWGjHZclVIOwp3W+7TWj/R+Xmu9R2vd0f33U4BDKVVrsd1tWuvpWuvpdXV1mSxyXnLanNSX1fd4rL6sHqfNmdK26div2EnsJuawOSxjy2FYT4WRWBwaErvZFy/WHUa4zuz275a60Es64navf9+JYWdXcZ8kiqEjba6IZTfslu17f/k/ikEmswor4E/ABq31TXG2GdW9HUqpo7vL05ypMuULU5t4vB6aOprweD2Y2ux3++Gu4Sw9YSlLjl/CXSfdxZLjl7D0hKUMdw233HbxcYujlSGyJspq23TsJwrXQOM03va17loWNS7qEVs3N96M03BaHltiUeS7ZOpO0Ayitebmxpv7xHqtu5bFxy1m+ablXDfrOqkLabY3ZpS1QzquQogsqHXX9mn/b268mVp3n/G9opLJbvss4DxgnVJqTfdjPwPGAmitlwLfAL6vlAoCXuDbOnZxSREabOIZf8jP9a9d32OfeJw2JwtnLMRtd+MNepP6dd5QBhOrJ3LfKfdJJlcx4Djtb3u7YWdS9SSWfWUZATOAw3DgDXo568mzLI8tsSjyWTJ1JzZhWa27loUzFjK2Yiyl9lJq3DXROvDzY36OaZos+8oyySqcRt7Avo7r3i6ZKiyEyI7BnK8XOpVv/cTp06frVatWZbsYGePxejjnyXN6zGuvL6vnvlPui/sry0D2GczxC0TW00IWUuwONI4kRgct63ELhRW72ZZMfO/o3MH5T5/fZ5tlX1nGqLJRQ17mQcp67A42btdsbeNrt6wE4LbzpnHilLz5PxfpkbexKwrH9o7tXPDMBX2+B+6eczejy0fH2y3rsZtp8rNsjhlM4pmhSM4kRKyBxpHEqBBhycT3QBOWifSKXePqD/W/BEIIITJBkjNZk45rjhlM4pmhSM4kRKyBxpHEqBBhycT3QBOWifTyxqxx7QpIx1UIMfQkOZM16bjmmMEknhnIPpLYRqTDQONIYlSIsGTi2yph2aLGRcU4VT4rYte4yoirECIb5HvAmqxxzUGmNmnxtSSVeCZ2W0MZGIST1xiGgS/os9w/aAbxeD0EQgEcNgdOw4kv5Iv+mu8L+nDYHNS6a1P+Zaf3a6XjmIOU9Xn/+Ri7/cViojjt/dkPLxlOS1dL9H51STUtvhaCZhC7YafWXYvD5rDcN4txk21Zj1vIz9jNZZH4jnz/ajSGMnAoBwEdwNTmvvY45MOu7DgNJ1ppqkqqaOtqi9a73vcHkpxpIN81g5D12B1s3D64aitXPbQWgOu+OoXzjxmX5pKJHJe3sSsKiy/oo9XXGj1PqnZV47K7+tsl67GbaUV5JpjrDGUk9YtKvOyUTpuTec/Ps8xYaWqTzW2be+xz/azruXn1zXi8nh5/L2pcxKTqSYPuMMRmxoy8VqrHFEMnUfbT/uI03me/dM1SVmxbQWNDI/OOmNfn+YlVE7EZtj4xmkxmbSHyQaQNvuWtWzh78tlcs/IamjqbaGxo5NLDL+XHL/24T3v+vee/F93Gqt5E6tVA6spgM9gXgx5ThYOSVVgIMfR8QR8f7v6wT3t/QOUBiTqvBa24v53yXIuvJXrSAeFF2/NfnM+29m19HmvxtcTdZ+HKhVx02EV9/l6wYgEer2fQ5fN4PdEKF3mtVI8phk68+IrEUn/iffZzJ84FYO7EuXFjI5XXFSLXReJ77sS50U4rhOtEpNMK1u15vHoTqVcDqStSz+LzxU4VDspUYSHE0Gv1tVq2962+1iyXLLtk2CuPxctO6ba7+zwWyVgZb59KZ6Xl36lksZTMmPktley+8T77SGxVOivjZsvTaMkqLApWpF71rgPx6kRsex5vm0i9itxPpq5I9u74AjHrWqXjKoTIBhWBufYAACAASURBVMkqbE1GXPNYvOyU3qC3z2ORjJXx9tnt3235dypZLCUzZn5LJbtvvM8+Elu7/bvjZsuTrMKikEXiu3cdiFcnYtvzeNtE6lXkfjJ1RepZfJHOqsOm6JKOqxAiCySrsDXpuOaxeNkpx1eOZ8nxS7jrpLtYcvwSln55KaZp4vF6qCqp6rPP9bOu5851d/b5O9XsZZIRLb8lyn5q6nBMNXU04fF6MPW+E7x4n/3yTcsBWL5pedzYSEdW4f7KJkQ2ReJ7+ablXDfrumicL9+0nJtm39Qj7n/X+DsmVE1gyfFLmFo7NW69idSrRHUltl4YypDs3XH4Qxq7oXDYDOm4CiGyotpVbdneV7uqs1yy7JKswnmud1bIqpKqPoltfvXFX3HjqhvxeD0sPm4xE6om7MtCaTijGYhjs1g6jNQzuZrapKmjKZrx2NRm+Ff+8vpsJP/Ieqa1fIzdeFlHEyV2sfrsSx2l2JUdvxk+VoWjgmZfs2VW4VSynRZY0pmsxy3kZ+zmskh8m6aJiYmpzT5ZhUM6xA3/uSGadOnmxpvxBr08tukxThh3AvtX7E+pvZQqV3JZha3qxdITljLMMSxaJyWrcNgvnljP/a9vwWm3ccrU0fzv6YdloHQih+Vt7IrCEQgF2NO1hy6zi5AZwmbYKDFKqCipiJ4rWch67GZacY83F4DemV09Xk+fhBs//ddPueroq7h8xeXMf3E+951y35CMerb4Wvjus9/tMUe/vqx+yF5fpC5e5uB4iV0in21/n319+b6pL6PLRw/odZORqGxCZFui+PZ4PZzz5Dk9YvjyFZdz1dFX8ejmR3l086PR+hT50ScRq3ox7/l5feqkCK9xtRtGeKpwQEZchRBDz+P1cMEzF/Q5j7p7zt1xz52KQd4NP4j+JZN8aaiSb0jyj8KV6LPN5mcvcSfyXaJ2PHJ/IDEt9SJ5gZCJ3RaeKuwPScdVCDH0JDmTNem4Fphkki8NVfINSf5RuBJ9ttn87CXuRL5L1I5H7g8kpqVeJM8f1NhtBnabwi/XcRVCZIEkZ7ImHdcCY5XYJjbh0lAm30hHkh2RmxJ9ttn87CXuRL6ziuGBJGFK9phSL6yFpwpLciYhRPZIglNrkpypAAXNIB6vh4AZwKEcuOwu9gb3RpNvAPsS38QkZ3LZXZim2SNRR49tk0zeEZtYx+qYWUqQk/UF64UWuz3izCKZlz/o75F8abhrOO2BdstYSiUZk5V0Hy+Lsh63UHixm6ti49ZQBgYGXaGu8GWiDCcBHcDAwMTEwMAwjAG1ybHJoIagXmQ9dgcbt9+/903e+XQ3pU47NeVO7r94RgZKJ3JY3sauKCy+oI9WX2v0PKraVY3L7upvl6zHbqYV93hzATK12SercO9sr70zS14/63r+vvnvnDbhNBauXNhjP6fNybzn5yWdnbXAMrqKOBLFWSAU4IPdH7BgxQKaOptobGhk3hHzovdjtwfSHjOpJHcSIhvitc03r74Zj9fDdbOu4/719zPviHksXbM0mm1Y2uT0C4RMbIbCYVPRa7oKIcRQ8gV9fLj7wx7nTYsaF3FA5QGJOq8FTb61Cky8jKotvpa4zy9cuZDzDz0/2mmN3W9b+7a4xxrM64vCkOhz9ng90cYWYO7EuT3ux24vMSNE/Lb5osMuoqmziWtWXhOtR3Mnzo1uI21y+nUFze41rgY+WeMqhMiCVl9rn/OmBSsW0OprzXLJsktGXAvMYLO92pTN8nG33R33WIN5fVEYEn3OvbPhVTor+91eYkYUu2Qywkfq0UCyC0ubPHCRNa52Q+H159dyKiFEYZCswtZkxLXADDbba0iHLB/3Br1xjzWY1xeFIdHn3Dsb3m7/7rjbS8wIkVxG+Eg9Gkh2YalfAxcI6mhyJrkcjhAiGySrsLXMZWVQaoxSaoVSar1S6l2l1I8stlFKqcVKqQ+UUmuVUp/LVHlykalNPF4PTR1NePZ6aPG1hP/2ejD14L4se2eObGxo5I4T78AX9LGjcweVzkrLrMPL3lnG9bOu75NxsmFYw4CyUFq+/kl34A/5e7yvHu89hfcr0ivZzyVRhtJady1LvryEJccv4a6T7qLCWcGSLy/ps72hDEzT5HeNv0trtlOJL5FLYuNxR+cOdnbuZEfnDpr3NrOjcwdNHU2gYekJS+NmhL9u1nUs37Q8qezCsa8Xe9yptVNZcvwSbjvxNtBIvYjDH+qeKmwoAtJxFUJkQbWrmlu/fGv0PGrJ8Uu49cu3Uu2qznbRsipjWYWVUqOB0Vrr1UqpYcCbwNe01utjtjkZ+CFwMvB54Hda68/3d9xCybSWKBFHKskzYjNINvuauXzF5T0Wdk+smshu/+6MZxU2zfC/P1rxox5JQSZUTeg3sU+GZD3TWq7H7kCSuATNIFv2bKGpIzyd3Bv0Ul9ez/4V+2M37HGPVVdahy/ow2k4aQ+0RxN/NTY0cuXRV2JTtpSznRZYMpqsxy3kfuzmMqt4vG7WdazcupI5B8zhxy/92LKORLIK+00/dmUP3zcMqkqqaOtqi9smx4v/kaUj2d65vcf3QYbrRdZjd7BxO+fmlykvsTPMZWf99j28/rMvZ6B0IoflbeyKwuEL+vho90c92uybG29mfOX4/pIzZT12My2pjqtSqhqoB7zAx1oP/GdapdRy4A9a6+djHvsj8JLW+i/d9zcCs7XW2+Mdp1Aqs8fr4Zwnz+kxf72+rJ6rjr6Ky1dcTn1ZPfedcl9KmVF3dO7g/KfP7/May76yjFFlo1IqfzLivcdlX1lmWa5U328CWa/MuR678T4vq88lUWwlOtZAXiuT7yMPZD1uIfdjN5fFi8clX17CZf+4LO1xmkPtbtZjd7Bxe9yNLzFiWAnlJQ5Wf9LK6qtPyEDpRA7L29gVhWN7x3YueOaCPm323XPuZnT56Hi7ZT12My3uz6xKqUql1M+UUuuA14A/Ag8CW5RSf1NKNSb7IkqpccCRwOu9ntoP2Bpzf1v3Y733v0QptUoptWrXrl3JvmxOSyYRR6rJMwKhgOVrBMxASsdNVrz3GK9chZgsJJ9idyBJXBLF1mCThKUjBiQZTXrkU+zmsoEmxEs1TuO2u2ZxtLvpiNtA0MRuGNjlcjhiCEmbK2JJciZr/c0Peohwp/KLWuuDtNZf0FpP11qPAX4NzFVKfTfRCyilyoGHgcu11nsGU0it9W3drz29rq5uMIfIOckk4kg1eYbD5rB8DYfhSOm4yYr3HuOVqxCTheRT7A4kiUui2BpskrB0xIAko0mPfIrdXDbQhHipxmncdtcojnY3HXEbCOloVmFZ4yqGirS5IpYkZ7IWt+OqtT5Ba32P1rrN4rk3tdaXa63/1N/BlVIOwp3W+7TWj1hs8ikwJuZ+Q/djBc8quU1sIo5Uk9NAOEHOosZFPV5jUeOiIZsuGS+BT627tt/EPiI7EiVcipUothIdayCvlcn3IUSmWcXjdbOu47H3H+Om2TelPU6l3U1dODlTOKuwdFyFENlQ7aq2PM+S5EzJrXGdCowj5rqvcTqisfsoYBnQorW+PM42pwA/YF9ypsVa66P7O24hzfuPJDHqkyTJ5sJv+gmEAjhsDmrdtQl/YUl4LDOAw0juWIN+D3EShVg9n2i/DMj6vP98iN2gGcTj9cSNvUAogMfrIWgGcdgcGBh0hbqwG3Zq3bU4bPtG8wcbG+mQhfjKlKzHLeRH7Oay2IR1IR0ipEPYlA2bYSNkhtDoaDImwzAGFK9WsQ7WSfWGuF5kPXYHG7eHXfsssybUMsxl529vbmPz/56Mzcj62xFDJ+sftrS5AsIJmlp9rQTNIHbDTrWrur/ETJADsZtpCXswSqk7ganAu0Dkp0cN9NtxBWYB5wHrlFJruh/7GTAWQGu9FHiKcKf1A2AvcOEAy5/XDGX0Gf0MmkHeb32fBSsW9MgEPKl6UtwOZyYzFPcnmeytVu+xv8dF9pja7DfbcyAUYFPbph6x2V+cJfqMMxkDEl8ilxgq3Bm1yi58//r7mXfEPJauWcqKbSsGlO23vzZY2t3BC4RMbN1Thffdt2W5VEKIYtIV7OLD3R/26Q9MqJxAib0k28XLmmR6MjO659yfr7W+sPt2UaKdtNb/1lorrfVUrfUR3bentNZLuzut6LD/0lpP0FofprUu+p+XPF5PNEghvBB7wYoFeLyeuPu0+FqiJy6RfRauXMhFh11EU2cT81+cT4uvJe1ltXrdTL2WyLxEn6dVbA5FnAlRCKzq1zUrr2HuxLksWLGAuRPnRh9Pti5JG5wZwZDGblPYbeFTpC5J0CSEGGItvhbL/kCxt+/JdFxfVUpNznhJBDC4TMBDkaF4IK9baFkqi0WizzNehrtMx5kQhaC/djq2HkUeT6YuSRucflprgua+5EyArHMVQgw5ySpsLZmO658Jd143KqXWKqXWKaXWZrpgxWowmYCHIkPxQF630LJUFotEn2e8DHeZjjMhCkF/7XRsPYo8nkxdkjY4/QKhcN4Pu2Fgs0nHVQiRHZJV2Foy7/5PdK9VZd8aV5EhkWytkekBjQ2NXHnUlZhmkB0d2zGULZzEozv5ktNwYjfs3Nx4M5evuDw6D/6m2TcBcOdJd9IwrAF/yI/H6+k3cUcisYk9DGXgUA5+1/g7frTiRz3WV0mWyvwUyUbae71c5POsddf2ibPIGtfItlprtu7ZisPmoKqkqkdSgeGu4bQH2qNxV+WopM3nwW8GcRp2hrvrMGzF3SCLwhMMBvD4PGiI1p9ady3zDp9Hw7AGPF4PS768hN+9+TsgfGLyp5P+RCAUYOuerdgMG6X2Usqd5bR1te2rPyVVGMroUycXH7cYQxk0dTTle2KyrIh0Um2GwmGE/98CwcRJLIUQGWaasHcXBP1gd0JpHRiF27YNdw1nyZeX0NTRhNvuxhv0Ul9eX/Tn2MmcJe7SWj+e8ZIIIPwLy6TqSSz7yjI0mjZfG9977nt9knmcN/m8aFKc62ddz983/52FMxYytmIsADe+cWM00UfvBDpOm5N5z8+Lm1DJilUSkOtmXcfKrSu546Q7sCmbnCQVAKfNycIZC6ONZOzIjc2w4bK7WDhjIRUlFVSXVIfXvU5bgDfoxW7Y+cWrv2DFthXc8IUbGFs1tk9Sgac3P83dG+6O3u+RkGb2IiZWT5LOqygYwWCATbs/iHYsGxsaufOkO9nj39Ons3ntrGv5f8H/R6m9NLqWKfaHyApnRfS7oLGhkXlHzGPBigXUumtZOGMh+1fsj9vupiPQwVlPnDWg9l3sE4yMuNoU9u4RV7+MuAqRXaYJO9fDX8+Ctk+gaix8+y8wYnLBdl41Gn/Iz/WvXR9tz29uvBlNcf+Qlsyn/ZZS6n6l1FlKqa9HbhkvWRGzG3ZGlY3CoYme3EDPZB6xSXEWrlzIsWOP5bIXLuOTPZ9wyXOXsGLbiug+vRPobGvfNuBkHvESixwx6gi+9+z3cNqc1Lpr5eQoj7X4Wpj3/Dwue+EyLnz2Qi574TLmPT8vGhuxz3u8Hi5+7mLOf+b86LaX/eOyaIKZqSOnWiYV+Nqkr/W43yMhzUsLaPHuysI7FyIzPD5PjzZ8xbYVfLj7wz7t+vwX52Nqk/ryenwhX5+68+OXfkzA3Jf/IJLQqamzibWetVz2wmVc/NzFhHQo+qNk7LGLPZnHQPhjRlztkRFX6bgKkV17d+3rtEL437+eFX68QLX6Wvt8V1y+4nJafa1ZLll2JTO04Qa6gBNjHkvmcjgiRf5+kuH0TooT+dttdydMoOO2u/s8nyiZR6LEIpIMJP8lSvQS+3zkc++9bSTOQmbI8nmbslluH32tIk86IApLwKINj9dGR+pZvAR9sT8Kxqt/sZ1bq2OLxIJmuJNqN4xocia/ZBUWIruC/n2d1oi2T8KPFyhJzmQt4fBYzCVwLhzI5XBE6pz9JMPpnRQn8rc36E2YQMcb9PZ5PlEyj0SJRSQZSP5LlOgl9vnI595720ic2Qyb5fMhHbLcPvpaRZ50QBQWh0UbHq+NjtSzeAn6TL2v8xSv/jkM632lfU5eZD2rzdg3VVhGXIXIMrszPD04VtXY8OMFSpIzWUvYcVVKLVNKVcXcr1ZK3ZnZYgmA4e46Fs9eFA3cyLrS5ZuWc/2s67lz3Z3RNayRvytLKvnVF3/VY5/Y5xcft5iGYQ09nk8moVIkcY9VWSQhU2Gw+oxjP9vY5+9cdyfXz7q+x7aLGhexfNNyANZ+tpZFjYv6PP/Y+49Zbh9Z4zrcXTek71mITKp1hROaxdaD4SXD+zzWOwla77pz0+ybenRKl29a3mebxcctptZd228dFokFoiOu+67jKmtchciy0rrwmtZI5zWyxrW0cM8Zql3VludR1a7qLJcsu5TW/S/yVUq9pbU+MtFjQ2X69Ol61apV2XhpoGdm3aFIRmSGgrR4d+E3gxiG3TKrcORvhQIFdhX+NSZgBno8Hykvphk95kCyufbOKmwQLkeeJGRS2S5AtmM3GUEziMfrIWAGcBgOat21PX7di40Bl92FaZr4zXBdqHRW0uxrju7bN6twNe2BDskqPDBZj1vIj9jNtnjfDZGswgEdxKZsOJSdKmzs1n78aMvvkaAZxLM3XA/7yyocez9yjKH+jupH1mN3MHH73o49zLn5X1x+/ESqy5xc8/i7LLvoaI6dVLgnyKKPvIzdghcKQscOCAXA5oDyUVDg5wy+oK/HeVS1qxqX3dXfLlmP3UxL5hM3lFLVWutWAKXU8CT3KzhWmXUznbHRsNmpLR+dvgOaJux6j9pBZGYzlEGtuzZ9ZRE5xdQmm9s29xvfiWJgVNmo7oOFMwCO7hVnJb3iLK2xLUSW9PfdYLc7GFU+uk9WzNrYtrfX94fdsDOqfFSf1+ld96zqorTTqYlOFbap6BrXgKxxFSK7us9diymrsKlNtuzZMqR9jnyQzDu/EXhVKfULpdQvgFeA32a2WLnJKrNu3mVsLMLMbCI5aY1viTNRRJKqO1In8oJMFRYiBxVh+1kQfY4MSDhyqrX+s1JqFXBc90Nf11qvz2yxclOirKt5oQgzs4nkpDW+Jc5EEUmq7kidyAuR0dXYrMKSnEmILCvC9rMg+hwZEHfEVSlVHvlba71ea/2H7tt6q22KQaKsq3mhCDOzieSkNb4lzkQRSaruSJ3IC0EzJquwXA5HiNxQhO1nQfQ5MqC/EdflSqk1wHLgTa11J4BS6gCgETgTuB14KOOlzBGRrKq955sPScbGRIvSTTM8ZSLoD1fk0jrref+RzGy91wkkkZkth5J+iAxIKr4HEGfmuY/SsnsL/pIynF2dDK/cHyNenPU+rrsGvM2JX0eIHBC37phA29Z9MR1pe8tHwLE/geETwldFN82E8S3t79CITAuOnSocCPWfxFIIkWGldXD+kxD0gVKgNdhdBZ1VeLhrOEtPWMq29m247W68QS8NwxqKPkt83I6r1vp4pdTJwKXArO6kTAFgI/AkcL7WesfQFDM3GMpgYvVE7jvlvqE9eQgF4bN34MHz9nU2z7wHRh4a7rz2SvrR76J1wwg//r1/DKhTkI3EVGJoJYzvAcSZqWCTEWL+W7/tGS/KYppH7+MedAoce1XPeC/wJAwivxkaJpo27jvyqn0/1AQ1xrPzYeOT+2K47mC4eAXsaYIHzkk6vqX9HTrB7k6q3WbIdVyFyBVmCLwtfc+Dh40u6PMCf8jP9a9d36PdL3b9ftpa66e01udorcdprSu01jVa62O01r8stk5rRCRjY315PbXu2qE5aejYsa+yQvjfB88LPw4DX7RuGFA+EqrGhP9NotLLIvHi0G98DyDOBhQvvY97xFl9473AkzCIPLd3F8a9p1N7z9epv+Mkau/5Osb93wzHMuyLYW8z6NC+Tmvsc/3Et7S/QyfSSY2dKiwdVyGyLNF5cAGSdt9aUV7WJu+EAtaL0kOB8N9DsGhdFomLgcTZgOKl93Hd1UWXhEHkuXh1w13d834khgcY39L+Dp1A7FTh7h91u2SNqxDZZQat200zmJ3yDAFp960V7vh6IbE5rBel2xzhv4dg0bosEhcDibMBxUvv43pbiy4Jg8hz8eqGt7XnfbtzUO21tL9DJ7Ke1WYomSosRK4w7NbtplG442/S7lvLWMdVKXWnUmqnUuqdOM/PVkrtVkqt6b79PFNlyXvlo8Jz+SOVNjK3P3KB+kjCpdjnk0y4lKxI8pFIJRrSxFQiNwwgzgYUL72Pu+YvfeM9zfEsRFpZ1Y0z7wnHcuR+JIYH0V5L+zt0gjEjroZS2AwlHVchsi3ReXABknbfmtI6cbY8pZQNGEnM1GKt9Sfx9wCl1JeADuDPWutDLZ6fDfy31vrUgRR4+vTpetWqVQPZJTdYZWOFxBlau/czDSctIS9+HcSp7Ax312LYnfueR9Gig/uet5didLWDwx1e1B5K4nUTZIzN86yWKtsFyEbsmqEgLd5d+M0gTsPOcHcdhq2fXygTZQ2Oed50uMMxZwbCx3bVYvhaovuaripavM0xMTsco9OzLzN22UiIbN87TiWrcETW4xbyuN3NhHh1JOiDjl3hqWuGHVxV4GsL37c5wbBjmgFaDAPTsGPqEGay9ZK8bH+zHruDidt7Xv2Yq5e/y63nfI6qUicX3PUfzj9mHD87+ZDMFFLkoryM3YLXu40trwtnFi5ggVAAj9dD0AxiN+zUumtxRGZbWst67GZawjF2pdQPgWuAz4DIz44amNrfflrrl5VS41IsX2GIl43V7oJ7T4+fWbJ7P/PtB9j0uW8x/59X7MssNnsRE6smYng2xnn+Ria++xTG2Jmw/LJ9r3Huo+HK37ssdQfDrvf6zRgbSdwj8oMZCrKp9X3mv7SgZ9xUT7I+SU4ma3B3Yi/rY9/ExH/8L8Z7T8BBp2AcexW1kWQKVpmCz7wH1j0Mry62fq3ykUP3nyVEMuLVkdoDYed7++LbIt7Nby5jkwpyy4Z7OXvy2Vyz8poBZQiW9ndoBGKyCof/VXIdVyGyLejr2cZGziFGHFywndegGWRT2yYWrNh3nrWocRGTqidhL+Ap0okk83Ptj4CDtNZTtNaHdd/67bQOwEyl1Nvq/2fvvuPbqu7/j7+OlmcSJ7YTyIIAARpoCGB2KaRQdkkpUCCBQGgLlBYKfL90fdsCLR2UX1ltwyyEESh7tLRAB9CyAiHsQJiBhJDEjrM8Ne75/XElWdayPGRJ9vv5ePhh6y4dS597fI/vvW8Z83djzE4DtM3ikymNdf2H2ZMlo+s17zE3PiiFaLLYU+fT3J5t/v/QvOtJXYPW2HOs/zB9W1pW9y6ZWIpec3tjfGAJSXWTTm9Sg9Nu+wKad5vjLpCcDJwuKfieU2DXOT0+l0jRyLSPtDT2WO/NHes597mfMmvqrPigFZQUWWwSw5kA/B5P/LNdRaRAkvvYeKrw0D1maGpvig9awf1bcf6T59PU3lTglhVWLgPXFcDGPDz3EmAra+0uwO+BhzItaIw5wxiz2BizuLGxBIs0U+KkvzJ1WmKyZHS9oMeXPlnMhrPPx6Y+r78yc0KxklwHXCFrN+iE09dFphS+3qQGZ9p2ZfTei+Rk4ExJwR5vj88lhVHy/W4+ZNpHkhMv09R7sKyKVa2rGBUYpaTIPOpv3Yad6BnX6MDV5zWEdMZVBoH63CyGYapwKBJK+7ci5IQK1KLikHHgaoy5wBhzAfAh8JQx5kexadHp/WKt3WStbYn+/DfAb4xJex2UtfYGa22Dtbahvr4EA1oypUiG2lKnJSZLRtcLOOH0yWLGl30+JvV5Q22ZE4qV5DrgClm7AY8vfV1kusSkN6nBmbbdFj1rlJwMnCkp2In0+FxSGCXf7+ZDpn0kOfEyTb0HOlsZXzWejcGNSorMo/7WbeyyYG9s4OrxKJxJBoX63CyGYaqw3+tP+7fC78l6j+uQl+2M64jo1yfAP4BAwrTq/j6xMWYLY4yJ/rxntC3r+rvdouA40LIGNqxwv1fUpk+RHL1N9mTJaPrkmJdu45oDfhcv4JkTZ3LTITcRtBGavvEYNSvf6DY/do/rmFfuglnz3W1PbIA590L95+CEhd2f94SFYLww9xH33qzk6Y7+aJeiMRX1XHPglUl1cSVjKjL8QUyTduqc/CBNHi+rWlbR1N6EEwlDyxrGOHDNzKuStn0FY5YsdNd99S6Yfa9bc6c96p6Bmn1vUiLgHfDKwq7HSg6WYpNLX37qo264WCzxcmKDW++nPOTW/8QGqJnMmBETuObA3/Hwew9zyX6X9JgU6ViHpvamrn3Pqh8eDKGIg9djiB6euGdcIz2HWIpIHlXXdz+mmHOv+7h66B4z1FXUMf/g+cw/aD63HHoL8w+az/yD5w/7rIMeU4WNMcdba+/taVqa9e4CDgTqcIOdLgL8ANba64wx3wW+DYSBduACa+1zPTW46JPWMoV31O+YmpIKuacKR1ODHWNo7tzA9548r1sozraV49kQaSdoI25KpacMT7DVPaAC2LQK7p7TFRzy5Z9DsAUC1fCPn8GyR7sGqxWjofFtePoyaFmbGppTmgqetFZqqcKOv4L3Otdx7r/P7R7u9M9f4mlZjXP45TR3rCdYVkWgs5Uxo7fBs+lTdzuBKjAeuPvkrv3ghDvAOhBsjZ753wram92zrqE29x85Y7Yp9TobaAWvWyiBfjcfcunLA1Vd91pN+SLsfyF0bkyt+1AbPPETnOotaD781zi+chwcHOukTQh2rMN769/rvu/lEOBUZApeu32p21/97W1ufW45C+btCcCPHnidqeNGcOPchnw0UYpTSdbukBbugMZ3u45jY8er9dsP2XCmPv4dKHjt5lsuA9cl1trdepo2WIp+Z25ZAzcd3P1a/JrJ8M1/DkhKatPmVcx5fF63697HV41n4aG3UDdifPqVMrVp9r1w5/Gp04/8HSw8Pi/tL6CC78xFX7tJmtqbmPPoCmoOvAAAIABJREFUnNRam/496iIRePzHqbVz6K/cg/azF6Wvrdn3wvy9UpePPS79OhtoBa9bKL3aHRC59OUbV8Ith3ctc8Id2feLdNtII+O+d+TCUvpve8Frty91e/Ejb3Hv4hXcdOoeAPz04TeZOLoiPpCVYaEka3dI27ACFhyR2ree9jeomVS4duVRH/8OFLx28y3jqRdjzOHAEcAEY8w1CbNG4p4llXR6EXDTF0GbIRTHZnlLMrXJ4+1baJQMC8FIMHMAUzhDUELFaPfnTLWVHMYUWz72WHUmxSKXvjw51C5TCFkv6zzjvqcAp7wLO078/lZwQ5r0cTgiBTYMw5n0dyC9bNccrQJeBjqi32NfjwCH5r9pJaoXATd9ETAZQnFMlss/M7XJifQtNEqGhYA3kDmAKVPYUvt69+dMtZUcxhRbPvZYdSbFIpe+PDnUrqf9It020si47ynAKe9CYRv/DFdwQ5oUziRSYMMwnEl/B9LLOHC11r5mrV0AbGetvTXh6wFr7fpM6w17aQJuBjJ0xg3cuSI1FCdT4E6mNs2aD8//viu8KbGtPYVGybAwpnwM13zpmtRwpyUL4dmrUmvn67e7oUzghi7FwmoS5yeGMSUurzqTYpNLX169Rfc6f/Wu1Lo//rZe13nafS9NgJMMvJDjxD8KB8Dv9RBUOJNIYVXXpz+mGMLhTPo7kF7Ge1yNMW8AGXtra+30fDUqm5K47j8h4CZt6FIkDC2rAeOG1ThhN+Qj3On+7PG5B0S+9JHXTjhIc3sTQRsm4PEzxvjwhDrAGDcF2HjcSzJD7V3Pbx33OSMh8AbAV+bO91e4Z8EivQyNKj0Fv+6/KGo3VnuRkHu2qHoLSAxrSqpdp6KW5uAGgpGgGyLjH4WndY27vr/S/e6EojVb734YeLyGkx+Pg471XXVVUZsaWFb6dTbQCl63UCS1m0+Z+uzYdIz7sxN0+1LjdfefSCj62OP2v9ZC2Ujo3AQ2El0u4PavWLffzbHOHevQ3NHcte8lBTiVgILXbl/q9py7XmHx8mau+PoMAP7fE8toD0b42/f2z0cTpTiVZO0OeeGO1GOMIRrMFNOHvwMFr918y3aO/ajo9+9Ev98e/X4yWQa0gntQkil4IxKGNW/C07+Fvc6ER77rplHu8U24Z25XWtrXb4exO6UOXh0HT9O71P35JKgeCwddDA+f3bXe0X+ARdfD3t+Gf13clQrsK4c7jumejpktKVgBOUNPrPbuOaV7nY3b2R28pklR9Zx4F3WxOkmcn672vn67W9exhOqv3w5v3A/PX5O55lRnUmiZ0oNjtVo+Bpo/hNa13ev9+NvgP5d31Xus7/3ihd2n9zGV3WM8pRTENGSEI93PuPp0qbBI4YU7YO07qccvY3cc0oNX/R1Ile1S4Y+ttR8DX7bWft9a+0b06wfAIYPXxCGmZbW74804yR20bvgE9jmna9AKXR+v0LI6df22xq4DrP3O6zqQiq33yHfdbT98tjt/wyfu8us/7L7cn0+KnkmQYSNWe5nqLLG2YvMT66Sn2ovVdeLjXeek35ZIseip7ltWw8aPU+v93rnd6z3W9yZPV92XlNjnuMYonEmkCLQ0Zjh+Ud863OTyL2BjjNkv4cG+Oa4n6cRSKBPTJzMlsKZLS0tMusyWYJmYZKmkYIHUBFRwH0dC7s89pajmWnuJj5NThFVzUmx6qnsn7PafudR7ct+bvC0peqFI93Amn9dDUGdcRQprGKYKS3q5DEC/Acw3xiw3xnwMzAdOz2+zhrBYCmVi+mSmBNZ0aWmJSZfZEiwTkyyVFCyQmoAK7mNv9HL0nlJUc629xMfJKcKqOSk2PdW9x+f2n7nUe3Lfm7wtKXqhiIPX6FJhkaIyDFOFJb0eB67W2pettbsAuwDTrbUzrLVL8t+0Iap6C5h9L1TVwddvc3e853/f9TN0Xbvv9bsfcB9J+I9SZT2c/CDMuRdGTYTjb+2+3tF/cBMsZ813k1+VFCwxyQmo8VS+LdzH6VJUT33UPSPb/JEbPHPine70nlKFY48jnXDao269nvygak6KT7b0YMcBfznUbp/a1yanBX/9Nrdfn32vO31ig1v3pzzkpkI4GvyUgnDE4vMmDFy9HkJKFRYprGGYKizpZfxXhTHmZGvtHcaYC5KmA2CtvSLPbRuajMdNYX3gDDfg5sjfQf3n3DNTc+7vSqa0Ebh7jhuulBigA+5N6o/+j3uZxA5HwtxH3EsyjcdNsTzqSvfxcQu6JwV/859KcB3OvD63jub9PX2qsMfjhsjE6iRQ1XUfSSwMYfa9cNwtbg16vDD3YWhb555hGjEeDr8MDv2l+19Q68CtR3UPvBEpNsl1n9hnrl0KT/4K9vkOPP9H+Mo1XVcSWAcOvhgOuRQ2r4K//a/bX5+wEI6+GjZ95vbhuQbiSVEI6oyrSBEy7j8GT/1rV2K7x8swCNGVJNnOsVdFv48YjIYMG22N8OfZ7sHMhk9g4fHuf+VjA9GYmslw6K/g7pPdgcO8v7tnWJODRJY9CmvecA+6ekpoVYKreH1uHWWSmIi9cWVqGMKdx7uD15sOcqcl1mnN5GgdToKWNXDTwamBN7nUqchgS5cE37LGrdlDfwUPneXW8IyT4Pavpu+rV0Y/uuLuOW5/HRu0guq/hIQiDmW+xHtcNXAVKbiWtbDgiNS+97S/Qc2kwrVLBl3Ggau19vroj5dZazsGqT1DX7ogkJ6CP3oToCMyUDKFOSUHLiXWabogp8RlVadSKmI1nBhElmsoWaZ9R/Vf9EIRh8pAVx/nnnG1OI7F49HZHZGCUDiTROVyzdKbxphnjTG/McYcaYwZlfdWDWXpgkB6Cv7oTYCOyEDJFOaUHLiUWKfpgpwSl1WdSqmI1XBiEFmuoWSZ9h3Vf9ELRSy+hMu5Yz+HdI+ySOEonEmicgln2g44CXgDOBJ4zRjzar4bNmSlCwIZvU3qtMRwpZ4CdBS0JPmQKczplYVdj5NDwGJ1qDqVUher4VfvckPvcg0lO/Eud99R/ZeklM9xjQY1KaBJpICqx2YIZxpb2HbJoOvxXxXGmInAfsD+uMnCbwHP5Lldpcdx3PtPewo/8nigfseugByPD3xlYGrgG/+ESLDr7OrXbuo5QEdBS9IfyXVbPgZa13SFN42d1j3MqWoc7Lcl7HUGeAOpIWCxOlSdSqHl2idnW7ey1g0cMx73Xiqse2tHrK/2BaCiFr5ypbtc4vOo/ktSKOLgSxy4xs64hh0oK1SrRIY5XxmM3dHth52we+xcXe9Ol2Ell3PsnwAvAb+y1p6V5/aUJsdx0ydjoUnZEiQdBxrf6b7s0X+ARdfDzB/nljqZLkhEpLeS63aHI+GA73dPEU5OtIbca091KoXSmz45l3UT++iRE1O3ka7OVf8lKfXjcGJnXHWpsEjBhEOw9p3U45OxO4HPX+jWySDK5d+/uwK3AbONMc8bY24zxnwjz+0qLclJv7EEybbG3JZ95LtuWmWmdUTyIbkWZ5yUmiJ8zynQsrpwbRTpi970ybmsqz562HAvFU68x9UduHaGNXAVKZiW1To+ESCHM67W2teMMR8AH+BeLnwycADwpzy3rXT0JkE107KxtEqlTspgSa7FTImpsURrkVLRn1Rr9dHDmhvOlHjGNXqpsM64ihSOUoUlqsczrsaYxcDzwDHA28AXrbVb5bthJaU3CaqZlo2lVSp1UgZLci1mSkz16jIcKTH9SbVWHz2sBcMOfm/qGVeFM4kUkFKFJSqXd/xwa22vr40yxtwMHAWstdbunGa+Aa4GjgDagNOstUt6+zyDLjHwI1DlfpQNwAkLuz5wPluCZGU9nPwgrP/QDfkItbnhHm89BHMfcbe7eY0behNq7wr1gL4HjcjQli2EJnleRS20r3MfewNw6qPQ9I5bix4vfP0OuOfkrjo+4Q7wlcOGFanrqw6lWMUSgZPvca2sh0jYvbwsEuoKGAu1d4WPWeCUh6D5A3j6MveD72P3uJ6w0F1/8xqoUu0PNeGIQ8Ra/LrHVaS4VG/hHo/cnXR8EvvEDRk2crlUuK839CwA/oB7f2w6hwNTo197AddGvxevxNCOKV+EPb4J98ztCraZ+4h7EOQry35AH+6AR/8nYedbCLudArcd3TVt1nz418XuQdOJd7mDhzuO6X3QiAxt2UJoILfwpcULYNmj7uM598G8x6Lp1gH3gP7Gmd2Xf/q3XcurDqUYZUr1tQ6sebP7PhDra6vGpe4fJyx0P27BCcGXfw7/+JlqfwiL3cfa/Yyr+3NQA1eRAoqAtwyO/F3XSR9vmTsdXRU2nOTtL6619j9Ac5ZFZgG3WdcLQI0xZst8tWdAJIZ27HNO16AV3IOZ2452Pzahelzmg5l0wR93z4ENy7tPe/hs2O+8rlCR9R/2LWhEhrZsITS5hi/NOKnr8cLjAAtjprhJwrF/lmRaXnUoxSqW6lszqatPThfwEetr0+0fd89x76Fa+7a7Lyx7tGuean/ICaYduEbPuCqcSaRwWhrhzuNh4fGw4Ej3+53Hu9NlWCnkv4onACsSHq+MTkthjDnDGLPYGLO4sbGARZoY2uHx9i3IJlPwh78ydVrF6OzzFRJS9PJeu9lCaHINX4rVWexxrIazhdQkP5cMOUXT7w6kSChzTWcLJ/NX9j3sSQZVf+q264xr6qXCOuMq+TYk+9yBonAmico4cDXGfC3b12A20lp7g7W2wVrbUF+f5r7RwZIY2uFE+hZkkyn4I3avbOK09vXZ5yskpOjlvXazhdDkGr4Uq7PY41gNZwupSX4uGXKKpt8dSF5/5prOFk4Waut72JMMqv7UbWc4AnQ/4xr7OagzrpJnQ7LPHSgKZ5KobGdcv5Ll66gBeO5PgUkJjydGpxWvWOBHzWR4/vfw9du6dqTY/X893SieuI3YeifeBaO36T5t1nx49qrM8zOFP8nwkqmeKutT5716l1ujyTX76l3dH8dqON22k5dXHUopqd4idR+I9bXp9o8T73LXGb2Nu5z64CEt3aXCZT7357ZgpCBtEhGguj798Uu1+uDhxlibv4h3Y8zWwF8zpAofCXwXN1V4L+Aaa+2ePW2zoaHBLl68eIBb2gvpUoUjIfe/8tVbuPcF9mYb6VKDE5MulSo8UEzPi+RX3mq3r6nCvgCUj4HWNZlruKf1VYf5VvC6hSLodwdST6nCHc2p9e040N7sLmcj4KtQqnDPCl67va3bNz/dyFG/f4b/+fL2NGw9BoCmlk7OuesVLjv285ywx+QetiBDRMnV7rAQ7nDvaXXC7pnW6no3uFQSFbx28y2nc+zRQeZOQLxCrLU/72Gdu4ADgTpjzErgIqLRX9ba64C/4Q5a38f9OJx5vW9+AcQCP+LGDMA2otJN6818GZ4y1VOmecmPR03s3bZVh1LKvL7sNZ+uvj0eqKrLX5ukKKS7VLjc5wV0xlWk4HzlbtieDGs9DlyNMdcBlcBM4CbgOODFntaz1p7Uw3wLfCe3ZoqIiIjkTzycyZcwcPXrUmERkWKRy3VO+1pr5wLrrbWXAPsA2+e3WSIiIiKDJz5w9SSmCnvweQytnUovFREptFwGru3R723GmPFACCjuz1sVERER6YXOUOoZV4Ayv0dnXEVEikAu97j+1RhTA1wOLAEs7iXDIiIiIkNC7LNaE+9xBSj3e3XGVUSkCOQycP2ttbYTuN8Y81fcgKaO/DZLREREZPB0hqLhTJ7uwZzlPq/OuIqIFIFcLhV+PvaDtbbTWrsxcZqIiIhIqUsXzgRuQFNrUGdcRUQKLeMZV2PMFsAEoMIYsytdnw00EjdlWERERGRICMbDmZLucdUZVxGRopDtUuFDgdOAicAVCdM3AT/OY5tEREREBlV79FLhMn/3gWtFwMvGtlAhmiQiIgkyDlyttbcCtxpjjrXW3j+IbRIREREZVK2dYbwegy/pHteaCj/Lm1oL1CoREYnJ5R7XZ40xfzLG/B3AGDPNGPONPLdLREREZNC0doap8HsxpvvAdVSln+bWIOFo6rCIiBRGLgPXW4DHgfHRx+8C5+WtRSIiIiKDrDUYodyfelhUU+HHAs1twcFvlIiIxOUycK2z1t4DOADW2jCglAIREREZMtqCYcr93pTpNRUBABo3dw52k0REJEEuA9dWY0wtYAGMMXsDG/PaKhEREZFB1NIZST9wrfQD8NkGfYS9iEgh5TJwvQB4BNjWGPMscBtwTl5bJSIiIjKI2jrDlPlSD4smjq7EAG+t2jT4jeqPzWvgrhPhN1vBgq/AJy8UukUiIv2S7eNwALDWLjHGHADsgPtZrsustcqFFxERkSGjNRimMpB6WFQR8DJhdAWvrdxQgFb1Uft6WHAEbFwJU74Iq5bALUfAV66G3U4pdOtERPqkx4GrMaYcOBv4Au7lwv81xlxnrR3218w4jmVda5BgOELA56W2KoAnKUZfRPpP+9rQofdSilVrZ4QxVWVp500dO4KXljcTcSzeUqjXf14MzR/Bob+EcTtDsA2e/g08cg6Uj4JpRxe6hdIP6kdluOpx4Ip7afBm4PfRx7OB24Hj89WoUuA4lmVrNvOt2xazcn07E0dXcOPcBnYYN0Kdh8gA0r42dOi9lGLW2hmmPM2lwgCfnzCSJ5et5Y1PNzJjUs0gt6yX1rwFLy+AaV91B60AgUqY+WN4/P/g4bNhy+kweutCtlL6SP2oDGe53OO6s7X2G9baJ6Nf3wJ2ynfDit261mC80wBYub6db922mHWtissXGUja14YOvZdSrKy1bOoIURlIDWcC2H7cCADe+LQEsimfvQZ85TD9xO7TfeVwwPfBOnD/N8HRB0SUIvWjMpzlMnBdEk0SBsAYsxewOH9NKg3BcCTeacSsXN9OMKw/BCIDSfva0KH3UorVpo4woYhlVPSjb5KNqQpQVeblnc+6Apo+aGyhI1Rktbt5Nbx5H0z9MpRVp86vHgd7ngUrX4JXbh/89km/qR+V4SyXgevuwHPGmOXGmOXA88Aexpg3jDGvZ1vRGHOYMWaZMeZ9Y8wP08w/zRjTaIx5Nfr1zT79FgUQ8HmZOLqi27SJoysI+NL/t1ZE+kb72tCh91KKVVOL+xmtIyvS30FljGHymEre+WwzAO+v3cxBv3ua7965ZNDamJM37gUnDDscmXmZbQ6EcTvBv34OnS2D1TIZIOpHZTjLZeB6GDAFOCD6NSU67SjgK5lWMsZ4gT8ChwPTgJOMMdPSLHq3tXZG9OumXra/YGqrAtw4tyHeeUwcXcH1p+yO4zg0bu7EcWyBWyhSOhzH0ri5k0/Xt6XsP+n2tRvnNlBblf7MiBSvtP3mybvj9aA+UwqqabM7cB1V4c+4zISaCt5vbMFay7/fWQvAP99eWzy1ay28eifU7QCjJmZezhjY/XRoWweLbx689smA0N9EGc5y+Ticj/u47T2B9621HwIYY/4MzAKW9nF7RcXjMewwbgQPnr0fwXCEiGO59NGlPLF0rW6UF+mFnoImkvc1JSiWrth7+cDZ+9LWGeGjplZ+8tCbNLZ0qs+Ugmpqce8PzDZwHV9Twcb2taxrDcbPvAI0tnQybmR53tvYo9VvwNqlsNe3e162fgcYvys8dw3sdRb4NOgpJWU+D7+YtTOVAS9twUjazx8WGYryWekTgBUJj1dGpyU71hjzujHmPmPMpDy2Z8B5PIb6EWUEfF5m37SIJ5a6/4HVjfIiucslaCK2r00YXUn9iDINbkqYx2MwGE7+0yLmLXiJV1ZsUJ8pBde42f2Ev6wD11HuGa4P1rbw7pqugevK9W35bVyuXrsLPH73c1tz8blZ0NoIyx7Nb7tkQK1rDTL35heZt+AlTrjhBeYteIm5N7+o/lOGhUL/i+YvwNbW2unAP4Bb0y1kjDnDGLPYGLO4sbFxUBuYC90oL5kUe+0WA+0/xSmftav3XPKlr3X7UVMrFX5vj2dcAd5b28IHjS1MnzgKIKWWCyISgjfugUl7QNmI3NYZv6sb1rT4lvy2TXKSa+2q/5ThLJ8D10+BxDOoE6PT4qy166y1ndGHN+EGQaWw1t5grW2w1jbU19fnpbH9oRvlJZNir91ioP2nOOWzdvWeS770tW7fXdPCxNEVGJP5ao7a6gBlPg//ebeR9pAT/zzXFc1FcMb1/X9BaxNsc1Du63i8MPUQ+OhpWPdB/tomOcm1dtV/ynCWz4HrS8BUY8wUY0wAOBF4JHEBY8yWCQ+PBt7OY3vyRjfKi/Sd9p/hR++5FJtlazanDAaSeYxhfE0FTyxdA8B29dVUl/lYu7kz63qD4pXbobwGJqb9/39m2x0MxgOv352fdsmAU/8pw1mP4Ux9Za0NG2O+CzwOeIGbrbVvGWN+Diy21j4CnGuMORoIA83AaflqTz44jmVda5BgOMLYEQHuOXMfQhEHn8cwtrr7fXihUIS1LZ2EHRuf7/d7025L4TMynKQLX6op97F6UwehiIPf62FsdRm+LOETyfvP6Ao/69tDBMMR/F53vfZQJKdtZaP9NHfJr9WoMi+NrcF4Hzi60s/dZ+yNx4DX474fn21s1+sqg25dSyfNrUEmjq7scdktRpXzUVMrHgNb1VZRU+mnsdAD182rYdnfYdos8PTysK6yFsbtDG8+AAf+yE0c7gXHOrzd/DbN7c2MqRjDdjXbUeYt610bpFc8HsOU0ZXcfcbe8f60tkJ9pgwPeRu4Alhr/wb8LWnazxJ+/hHwo3y2IV8Sk1Drq8v4/mE7cOF9r8dTUa87eXd2HDcCn89DKBThnbUtfPuOl+Pzrz15d3YcW43f7+0xVVVkqIuFLwGEww7vrNnMWQn7S+L+lCx5/zlk2ljOPWj7butfftx0fvvYMhpbOrNuKxvtp7lLfq3O3H9rjpoxsVsfeNmx07n1uY84e+Z2hMIO59/zml5XKYh317ifZdrTGVeAidH7XN1gRg+jKopg4PrqQrARmHpo39bf+gvwwnxY8xZssXPOqz30/kPMf3U+n7V+Fp9W6avk4K0O5qvbfZWGcQ1ZL72WvunoCPPeutaUY8qptVWUl+f1sF6k4AodzlSyEpNQzzpw2/igFdyb5M+642XWRj/QfG1LZ7yDic3/dsL8XFJVRYaLtS2d8UEnpO5PyZL3n2N3n5Sy/oX3vc5ZB27b47ay0X6au+TX6riGySl94A/uf51jd5/E+tZQfNAam6fXVQbTe2vdhOBczrjuu20dtdUBjpo+HoCaCj9rNnXktX1ZRcLw8q0w7vMwKt0HN+Rgq/3AeOGtB3Ja3LEOlzx/CT999qdU+iv51ue/xY/3/DFn73I2DeMa+MfH/+D0x0/n2EeO5f5376c9XAThVUPIuvZg2mPKde3qM2Xo079m+igx1a2mwp824S0ccQAIOzb9/OiHlishTqRLKOJk3Z+SJe8/mfbHmmhaaLZtZaP9NHfJr5XXY3p8T5Ln6XWVwfLxujYCPg+jKzMnCsdsMaqcP5y0W/zxqMoATZ9swFpbmLOLbz0AGz6GA3/c922Uj4Itp8NbD8KXftrj5cJXLbmK+969j8OnHM6xU4/FY7rOgTRs0cDsz81m0epF/Ovjf3Hx8xdz5ctXcuz2x3LSjiexRdUWfW+nAD0fU4oMZTrj2keJqW4b2kNpE9580XvrfB6Tfn70MjglxIl08Xs9WfenZMn7T6b9cUN7qMdtZaP9NHfJr1XEsRnfk7ZgRK+rFNSK5jbGjSjr08CzpsJPeyhCa7AA/2gJd8JTv4GarWHy3v3b1uR9oflDWLs062KPLX+MW968hZmTZnLc1OO6DVpjAt4A+0/Yn4v2uYgf7PEDpo6eyoI3F3DIfYcw+9HZXPfadby17i0ijv451Rc9HVOKDGU64xqVGCRijMFrwOPxxENC0oW/3Hb6nny8ro266gDz5+zG2QuXdLsnb2y1e8/e2Ooyrj1595T7EWLza6sC8W1VBry0BSNsVVuphDgZcIMZLtTX5xpbXcZ1J+/e7R7VBfP2wBj4eF1rSsBSLGExdmnq/S+vSFn/6hNncOlf307ZN3sj+XmU5JhZbVWAu761F51hS7nPYIxh4Tf34sPGVq7513s0tnTG73G98NAduHbObnw7of/U6yqD6ePmtvg99r1VEz1L27i5k+qyQT6keuZKaP4ADrrITQbuj8l7u/e5Ln0Exu2UdpGm9iYufeFSpoycwuwdZ/c40DfGsMOYHdhhzA40tTfx3KrneL3xdea/Op8/vvpHRgZGsscWe7Dv+H05dOtDGVU2qn+/wzBRWxFgwbw9WNHcHj9mnDSmgtoK9Zky9GngSvrQldhB1flf3oGp9dW819iScsBa5vPw04ffjAfCLPzmXjjWEnEg4Ovq0P1+LzuOre6WAJecKtwZduLbim1fZCANZrhQf57L4zGMqvCxYN6eeAz4vYaN7WFOu+X5tGFNyanEfq+HjnCEX8zaOf5HfWSFn+tP2Y2wQ59ThdOlHyv9Nj3HsWxsD3PNv97l1H2n8IP7uwfX1VYHcKzl9C9sw/fve4P6EQHu/OZeeD1Gr6sMKmstK5vb2H9q3z6reFRF18B1Sl3VQDYtu7f/Ck9fBlMOgIl79H97FaNh3DR4+xGYmT4z8/8t/n+0hdq4sOFCvJ7eXRFRV1HH0dsezdHbHs3Gzo28ve5tljYv5bXG1/jXJ//ishcv47jtj+PMXc5kTPmY/v8+Q1xHqPsx43Un9/JjkERKlC4VJn3oSiw45Fu3LWZtS2faUJaP17XFpz2xdC1zblrEu2taOPiKp5l946JuATB+v5cJoyvZqraKCaMruw1aFfoig2Ew66w/z7WuNchJNy7i4Cue5ku/e5qOkNNjWFMslXjC6ErCjmX2jYuYt+AlTrjhBeYteIl5t7xE2IHxNRV9/iic5OepH1GmwVUGsYCtY3efFB+0Qtd79/rKjZxw/QtsbA/xyooNPLF0LbNvWkTA59XrKoNqfVuI1mCEsSOwMWm/AAAgAElEQVT7esbVPcs1aMnC1sJzv4d7ToHaqbDPOQO37cn7upcKr/sgZdbrja/z6IePcujWhzK+eny/nmZU2Sj2Hr83p+98OpftfxkX73Mxe225F39e9me++tBXeebTZ/q1/aFuXXsw7d9EhTPJcKCBK5lDV2IhL5nCYioD3rTrxH7ONQBGoS8yGAazzvrzXLkG+2Tav3ob7iQDL/YeZAvKSuwvY9PV58lgW7m+DYD6Ptw+AMRruHHzICQLt6+Hu+fAEz+ByfvAIb8Af/nAbX+rfd3vSx/uNtlay2UvXkZNWQ1HTDli4J4P93LiySMnM2/neVy8z8VU+as4+59nc++79w7o8wwlCmeS4UwDVzKHrsRCXjKFxbQlhTH0NQBGoS8yGAazzvrzXLkG+2Tav3ob7iQDL/YeZAvKSuwvY9PV58lg+2yjO+Cs7ePAtbrch8dAYx8+YqtXWhrhT4fCu49DwzfggB+Cv+eP7+mVqnqo2x7e/ku3yY8tf4zXm17nmKnHUO4bwIFykokjJvJ/e/8fO9ftzM+f/zn3LLsnb89VyhTOJMOZjuToCl2JdQSxe1zvf3kFN85tYGx1Wcr8G+c2sFVtZbdplx83neue+qDXATDpnl/hJDLQBrPO+vNcyevet/gTrj15927byrZ/xcKdcl1eBl7sPbj/5RVcduz0tH1rbH5suvo8KYTV0YFrLh+Fk47HGGoqA/m9VDgSgj/PhvXL4eCfw07H9PiRNX02eV9YtQQ2uPtmZ6STK1++kskjJrPf+P3y85wJyrxlnLPrOUyvm84vF/2Sp1c8nffnLDW1FYGUv4nXnry7wplkWDDWltalBQ0NDXbx4sUDsq1QKMLalk7CjsXv9VDmM4Qilohj3Wkeg9/noS0YobrcS0fQIeJYvB6DxwMVfg+tnU58WZ/XQ3sogs9jKPd76Aw7WAsWCETP9rSHIlSXeekMW0IRJ56Q6vGYeAKr3+fB5zG0BxUAM4AK/gIOZO32VT5ThZO3PTLgpaktGA8kq60IsK49/eNynwcwhB2HgM/LCL8347Kxx80dofg+VFfpp6mt63Fthb/bcyeGofX0Ggxm8nIOCl630HPtJr5m5QEPwZClMmBo6XQo93voCDnx98LjAccBjwHHQmXAQyh68Uoo4hTDay4Do+BvYC597mWPvcON//mQW0/fE08fB4P/99AbbDWmklvm7dmn9Xv01GXw1K/gixe6YUz5tOlTePBMOPTXsM/ZLHhzAb97+Xf8b8P/Mq12Wn6fO0FHuIPLXrqMpvYm7j3qXiaNnDRoz00J1G5HRzjlb2J5ufJWpfC1m2/DtspDoQjvrG3p9hE1t8zbg86EIJjYWdQHl3zKMbtN4JZnP4onZO67TS0n77NVt4/Aufy46fz2sWU0tnRy82kNbGoPc97dr3abv/ijZg7YcWzKR+fsOG4E9SPKBjX5VYafWLjQQEuu2zP335qjZkxM+Qio3//rXZ5YupaLj9qR3afU8e07Xqa+uozvH7YDF973etplMz3+66sruf6/yzlk2ljOOWj7jM+VuA8BWfcv7X+9l/iaxd7LZZ9tZPcpdfz11ZUcucuEbv1dLLH91H2ncOtzH3HOQdszutLHiTcs0msug271xg7GVAX6PGgFN1k4b5cKb1gBz/wOtt4//4NWgJETYPQUePsRNu42hxveuIHP131+UAetAOW+cr4z4ztc8vwlXPD0BdxxxB2UeXXVDLiD1vfWtab8zZtaW6XBqwx5w/ZS4bUtnfGdHtwb21c2t6cktV143+t864vbcOF9r3dLyPzWF7eJH4wlLnvWgduycn07n67viA9aE+fP2m1iynqJCalKGJZSlFy3xzVMTtm/vh1NmQX40rQt4/PPOnDb+KA13bKZHh/XMBmAY3eflPW5EvehnvYv7X+9l/iaxd7L2Pt7XMPklP4ultge+/7tO14GjF5zKYjPNrYzurJ/l1iOKvfTuClPA9enf+MmCe8+Lz/bT2fyPvDJC1y/+Epag60cv/3xg/fcCeoq6vjGzt/gneZ3uPylywvShmK0rj2Y9m+eUoVlOBi2A9d0qWyVAW/apLZYqmliQmampNNYwmCmbVmbIQ0umniqhGEpRbkmAcf2DydhP8iWPJvtsTd6Ni7X9YPhSI/7l/a/3kt8zWLvRez9zVYHid8jSWmYes1lsMTOuPZHTaWfptYgzkCnum5eA6/fA9t9GarHDuy2s5lyAB/7vNz1/oN8YcIXmDhi4uA9d5IZY2dw6NaHcveyu/n3J/8uWDuKiVKFZTgbtgPXdKlsbcFI2qS2WKppYkJmpqTTWEpmpm0ZkyENLnoPrBKGpRTlmgQc2z88CftBtuTZbI9jg51c1w/4vD3uX9r/ei/xNYu9F7H3N1sdJH73Jl0SrNdcBoO1ltWb+j9wHVURIOJY1rcN8BmvF29wg5mmzRrY7fbAjhzPz8dPotyJ8NXtjh7U507n2KnHstXIrfjZcz9jbdvaQjen4JQqLMPZsBu4Oo6lcXMnZT5PSirbxDEVKWmklx83nRv/8yGXH+cmYf5h9q7cctoeRBwn7bKxVOFM23p4yUrmz9ktY+KpEoalFOWSBHxtQorsv5d+Fp9/3VMfcPlx0zMum+7xLfP2oLUzzN1n7M2YykDKvpa8fGwfSrd/3Xb6nlgsn65vw+tB+18vJb6msfcy9v7et/iTlP4ulioc+37tybsDVq+5DLqN7SE6Qs6AnHGFAf5InGArvHQjTN4bRo4fuO3m4KE1L/CiN8L5zeuZ1LR8UJ87HZ/HxxnTz6Aj3MGPn/kxjh3en8mtVGEZzoZVqnC6AJlT9p1CJCF5FOiWNOzzGjqCbtJvxHHY2BbmzOi9BYdMG8v/HTkNA/i8HnwG2sMOXo/h9uc+4sXlGzj3oKlMqauiMuCePegIRaiKpgqHIw6+aKqwz+fp1s4iSjUdKgr+AhZDqnA+JdZtVZmXTR1hgmEbT48dWeGlrdPBsRbHQm21j7ZoKndvUoXLfR7WtQY54/auYIrb5u1JRcBLKLpP1VcF2NARTrsPJbazIuBlzabObmFMt52+J9XlPkLhoki4LXjdQs+1Gw47rNrYztrNnXg9UFMZYGS5j46Qg99rCEbT2r0eg9dAxLqpwlioLPPgWEPYscXymsvAKPgb2FPdvrN6E4dd9V++d9BU9t6mts/Ps2z1Zi7+y1vcctoezNxxgC7pXXQD/P1COPy3MHbwgpE+alvDCUt+zeTyOu587w1ax0/n/cN+MWjPn81/Vv6HBW8t4ILdL2Deznm957eoa7ezM0yHE6aloyutvbrcQ7nHR1mZwpmGuYLXbr4NqwpPDl65/r/LefTNNTx49n7dklYnjO7+od6Nmzs5Zv6z/PSoafzir0vj6z+xdC1LP9vMPWfuw/iaim7LxpaZt+AlJo6uSHmObPKV/CqST4l1u2pDO7NvXNTtPpyJoyu45bQ9+PKV/4k/fvDs/ZiQodYnJKUjxh43bu6MD1rBvbdn7i0v8uDZ+7FlTdflU/X+9JeaJrazcXNnShjT3JvdbSX3A5LZ+vYQs29Kfb/vPmNvvnbt8ynTY33pPWfuQ01leSGaLMJn0c9w7e8Z1wnRfufdNZsHZuDqROD5P0L9jlD/uf5vL0ebwm2cv/QGfMbLGVsdQWN4FBOWPU7Fug9pr91m0NqRyf4T9ufNpje5esnV7LnlnuxUu1Ohm1QQTW1BTrjhhbT97QQNXGWIG1aXCvc1eCW2XqYQmFiwUn+eQ2QoCUWcjEFniY/7sl8M5D6m/XVgZHodM4WIxPrSxL5TZLCtHqCBa3W5jzFVAZat2TwQzYJ3/goblsNOx0A/PqanN9oiHZzz5rV83L6GMycfzmh/NWu22Z+Ir5yJi24elDb0xBjD3GlzGVU2iu8//X3aQm2FblJBKJxJhrNhNXDta/BKbL1MITCxYKX+PIfIUOL3ejIGnSU+7st+MZD7mPbXgZHpdcwUIhLrSxP7TpHBtqK5Da/H9PvjcAC2GlPJyx+vp9+3X1kLz1wJI7aESXv3u125WNO5gdNeu5JXN33INycdxrQR7keNRQKVrNr+YGo+eYFRH78wKG3pSXWgmm99/lus2LyCy166rNDNKQiFM8lwltejBmPMYcaYZcaY940xP0wzv8wYc3d0/iJjzNb5bE9fg49i68UCRTIFK/XnOUSGkrHVZWkDk+5b/En8cV/3i4Hcx7S/DoxMr2O6OoiFMiX3nSKD7eN1bYwdUZaSat0Xu06u4eN1bbyzup9nXT98Cla9AjsfC578/gPNWstf1izimMW/4MO21Zy79dHsWbN9t2XWbPtF2kdswdZP/Q5f2/q8tidXO4zZgSO3OZIH3nuAxz56rNDNGXR1lenDmeoG4B8wIsUub+FMxhgv8C7wZWAl8BJwkrV2acIyZwPTrbVnGWNOBI6x1p6Qbbv9Dbjpa/BRbD3HcW+Gjzg2bbBSf55D8qrgb8BQD2dKFg47btBZDoFJvTWQ+1iR769F0ZBcajfT65hYB16PwWPAYtL2nTKkFLx2e6rbI6/5L36vhx8ctmO/n2tTe4jz7n6VXSaN4rbT9yLQl9q2Fm49Cta+DV+7Cbz+ntfpo1c3fsA1yx/hpY3vsV3leL4x6cuMKxuddtmKTauY9vTVtNdO4Z2vXI4TqMpbu3IVdsL89qXfsmLzCm485EZ2HbvrQG6+6Gu3szNMU1tXgGFdZUDBTAJFULv5ls8q3xN431r7IYAx5s/ALGBpwjKzgIujP98H/MEYY2weo477GnzUm/UUriQCPp8nHloWkykwqbcGch/T/jowMr2O6epApNAijuXDplYO2L5+QLY3ssLPaftuzbVPf8APH3id3x2/Cw+9+ikLnl3OoTtvwdkHbtfzRt59HJY/A3uekbdB69stK/j98kf4b/NbjPRVMmf8gcysnY7HZB5ot48cz/t7zGW7Fxew/aM/4v3DLiFckX6QO1h8Hh/n7HoOv37x13znX9/h1sNuZeroqQVt02AqK/MpiEmGpXxW/QRgRcLjlcBemZax1oaNMRuBWqApj+0SERGRYeyjphbagxGm1A7c2cMvbl9PY0sn9728krc+3cSyNZupKvPy2mPLmFBTwawZEzKv3NkCj/0QRk6EHY4YsDbFvNOyghs/eZwnmpZQ5S3n2C324+C6GZR5chsgb9xiJz5sOJltXr6TafedzUdf+gGbJ8wY8Hb2xojACC7Y/QJ+tehXzHt8Hn886I/sUr9LQdskIvlVEtdpGWPOMMYsNsYsbmxsLHRzRHKm2pVSpdqVUpRr3b66YiMAU+oG9rLXr+06ga83TKKlM8ysGeO5ds7ubDe2ml/8dSmbO0LpV7IW/va/sH457PMd8AzMOQVrLYs3vMdZb/yB45f8mv82v8lRY/fksh1P48ixe+Q8aI1ZP34X3t7/HCwOO/7lf9n6ycvxt64bkLb2VV1FHT/c84eUeco4/bHT+fM7f+5/QFaBqM8V6Vk+73HdB7jYWnto9PGPAKy1v05Y5vHoMs8bY3zAaqA+26XCw+0+QRkwBb/uX7UrfVDwugXVrvRJwWs3W92evfBlnnt/HX+cvVve72n/sLGFnzz0Jqd/YQo/PWpa95mRMDz+Y3jxetjlJJgxp9/P1xTcyKNrX+Kh1c/zfttnjPRV8uW6GcysnU6lt/+fm+wJBxm/7AnGffAU1uOjcaejWbPzLIIjt+z3tvtqU3ATN71xE282vcmM+hmcu9u5NIxrwPTt44SKunZFsih47eZbPi8VfgmYaoyZAnwKnAjMTlrmEeBU4HngOODf+by/VURERIa399Zs5h9L1zBzh7GDEsS2TX01X9pxLAueXc4+29Sy37a1+Ns+w/fJs/Dc72HNm/C5WbBL8iFSdhHrsDHUyqrOdXzQupr32j7lxQ3v8k7LSiyWbSu3ZO6Eg9h39OcIDNBZXADHF2DlTkfRuNVejF/2D8a9cT9bvH4fm8bPYOOkPWjZcmc6Rk0kXD5y0D6HdmRgJOftdh7PfPoMD7z3AKc/fjpbj9ya/Sfuzx7j9mDKqClMqJ6AP4+BVyKSf3kbuEbvWf0u8DjgBW621r5ljPk5sNha+wjwJ+B2Y8z7QDPu4FZERERkwG3uCPH165+n3O/lmF0nMFgB4rP3nMwHjS18/7Z/81TZ+VSYdnfGyAlw4I9g6/0zrvvShmVc8u5CwjZCxDqEnQhBG2ZzuB1L1//6fcbLtpVb8tUt9mWPUVPZsrw2r79TcMQWLG84hU+nHUXdJy8y5tMlTFp0Y3y+4/Xj+MpwvAE8kRAfHHIRmycMaPpvN17j5YCJB7D3lnuz6LNFvLj6Re565y5uX3p7fJlybzkjAiOo8FVw9cyr2W50DqFZIlI08napcL4YYxqBj3NYtI6hGfKk36tvmqy1h+Vx+z3qRe32VbHWhtrVO4ntKnjdwqDUbrJSeG+KRbG26Z1C124PdVuMr1s2pdZeKL02x9pb8H5Xx7pZ6XfOrOC1m28lN3DNlTFmsbW2odDtGGj6vSSTYn0N1a7eKdZ2DaZifQ2KsV1qU9+UQhsTlVp7ofTaXGrthdJsc3/pdx7eSiJVWERERERERIYvDVxFRERERESkqA3lgesNhW5Anuj3kkyK9TVUu3qnWNs1mIr1NSjGdqlNfVMKbUxUau2F0mtzqbUXSrPN/aXfeRgbsve4ioiIiIiIyNAwlM+4ioiIiIiIyBCggauIiIiIiIgUNQ1cRUREREREpKhp4CoiIiIiIiJFTQNXERERERERKWoauIqIiIiIiEhR08BVREREREREipoGriIiIiIiIlLUNHAVERERERGRoqaBq4iIiIiIiBQ1DVxFRERERESkqGngKiIiIiIiIkVNA1cREREREREpahq4ioiIiIiISFHTwFVERERERESKWskNXA877DAL6Etfvf0qONWuvvrwVRRUu/rqw1fBqW711cevglPt6quPX0NeyQ1cm5qaCt0EkT5R7UqpUu1KKVLdSqlS7YqkV3IDVxERERERERleNHAVERERERGRoqaBq4iIiIiIiBQ1DVxFRERERESkqOVt4GqMmWSMedIYs9QY85Yx5ntpljHGmGuMMe8bY143xuyWr/aIiIiIiIhIafLlcdth4H+stUuMMSOAl40x/7DWLk1Y5nBgavRrL+Da6Pei5ViH5o5mgpEgAW+AMeVj8Ji+jf+7bcsTwOPx0BHuoNJXSXu4nbATpsxbhjGGkBPCYAAwxuDFS0ekg3JvOREihJ0wXuONb9tnfHREOijzluHgEIqE8Hl81JbXYrE0dzQTdsL4PD7KvGW0hloJeAMYDJ2RTnweH3UVdXg93ngbq3xVtIXb4uuVe8uxxuI4DkGn6/UA4utU+6ppDbfG16ktryXgCwzo6yiZ9fZ1DoaDrOtYF3+/RpePZkPnBkKREH6vn5qyGtZ3rCfshFPqJduysfmZHifX6ZjyMazvXB/fVl1FHT6PL+ffK+yEaWpvyri+yEDLVJOJtVjmLSNiI3iMB8c6RGykW79d7i2nI9IR30e8xkvERuLb9xgPPo+PsBOOr1vmLaMz0tmtj20Jt9AR7sCxDuXecmrKa9jQuQFjDUEnSNgJ4/f6qS2vZWNwo/rhYSBdfTrW6dZPJtdDTZlbN4nrBCPBrP36mPIxWGyPfb/XeGlqb8rp70PseATotk5dRR1+r7/Ar6yIDKa8HclZaz8DPov+vNkY8zYwAUgcuM4CbrPWWuAFY0yNMWbL6LpFx7EO761/j3P/fS6rWlcxvmo813zpGqaOntrrP/bptnXpfpeyZM0SvjDxC1zw1AXUVdRx3m7n8ZNnfxJf5pL9LuHOpXdyyrRT+MsHf+Er236lV/Ovnnk1fq+fs/95dnzaFQdewd3v3M2i1Yu4dL9LuWrJVTS1N3HlzCsZUz6GU/9+Kl/b7mvsP2l/zn/y/Ph68w+eT2uolQufvrDb6xHwBjjrH2dx5ufPZMe6Hbutc+XMK9l21LYs37R8QF5Hyay39RoMB3l/4/sp79d1r17HkyufZObEmZw146xu85PrpcJTwdGPHM3fv/p3Puz4MGVbr6x+hd8s/k23bSXXebrnuXLmlWw/ent8Hl+Pv1fYCfPu+nczri8y0DLV5DajtuH9De9z3pPnxev89qW3M3vabC569qJu/fazK57l8G0P71a3v97/1/g8vm597BUHXsH1r13PkyufTNlHx1eN56qZV2GM4Xv//l58natmXsWS1UuYMW4GFzx1Qdr9W/3w0JWpPn0eX7djgeR6SH58/1H380nrJxn/RsSOC4KRIOc9eV7GZa6aeRVl3jK+/c9vs6p1Fdd/6XrWV65P2e4nGz7hwmcuzLrdqTVTNXgVGUYG5a+TMWZrYFdgUdKsCcCKhMcro9OKUnNHc7zjB1jVuopz/30uzR3NA7Ktnzz7E47c9sj4gcXpnz89fjAfW+aiZy9i1tRZ/OTZn3Dqzqf2ev73nvweq1pWdZt2wVMXcOrOp8bbcPrnT2dV6yrOf/J8wk6YVa2rOHLbI+N/VGLrrWpZFT+gSnw9Vm5eyarWVew9Ye+Udc5/8vwBfR0ls96+zus61qV9v2ZNnQXArKmzUuYn10uZvwwAj9eTdlsHTD4gZVvJdZ7uec5/8nya2pty+r2a2puyri8y0DLVZFN7U/xAO1bns6bOig9aY8te9OxFfHX7r6bU7Y/++yM2dm5M6a9j+2TyPrqqdRXnPXkejW2N3dY578nzOGDyAfG/LZnWVT88NGWqz+RjgeR6SH68Kbwp69+I2HFBrOYzLXPek+fxacun8WW2Hr112u1OHze9x+2qXxcZXvI+cDXGVAP3A+dZazf1cRtnGGMWG2MWNzY2DmwDeyEYCcY7zZhVrasIRoIDti3HOvHpowKj0i4Tm+413j7Nr/BVpEyLXa4WWz+xPUC3dsVU+Cqybj/iRNLOjw2Gk6f35XUsdoWs3d7Wa6b3JVYP2eox9nPEcS9rzPTex+opcVvJ2830PCEnlNPvFYqEsq4vuSmWfrcUZKrJxH0qVteZ6rs3/XVsn8v0ON066frwdOuWej+suk2VqT57W1uZ+vXEZTIdF2Sr0Uzbjf09ybbdsBNO/0uXINWuSM/yOnA1xvhxB60LrbUPpFnkU2BSwuOJ0WndWGtvsNY2WGsb6uvr89PYHAS8AcZXje82bXzVeALewIBty2M88ekbgxvTLhObHrGRPs1vD7enTIvdRxVbP7E9QLd2xbSH27Nu3+vxpp3v8/gG7HUsdoWs3d7Wa6b3JVYP2eox9rPX4/4DJNN7H6unxG0lbzfT8/g9/px+L7/Xn3V9yU2x9LulIFNNJu5TsbrOVN+96a9j+1ymx+nWSdeHp1u31Pth1W2qTPXZ29rK1K8nLpPpuCBbjWbabuzvSbbtDqXbP1S7Ij3LZ6qwAf4EvG2tvSLDYo8Ac6PpwnsDG4v1/laAMeVjuOZL18Q7z9h9IrFAov5u69L9LuXRDx7ligOvYHzVeG5+42Yu3e/Sbstcst8lPPzew1y636Xc+uatvZ5/9cyrGV89vtu0Kw68glvfvDXehpvfuDl+/0jswOvRDx7lyplXdltvfPV4Lj/g8pTXY+KIiYyvGs8Ln76Qsk7svtmBeh0ls96+zrXltWnfr4ffexiAh997OGV+cr10hjoBcCJO2m09/cnTKdtKrvN0z3PlzCvj4Rw9/V51FXVZ1xcZaJlqsq6ijqtmXtWtzh9+72Eu2e+SlH77oXcfSqnbX+//a0aVjUrpr2P7ZPI+Grt/sL6yvts6V828iqc/eTr+tyXTuuqHh6ZM9Zl8LJBcD8mPR/pGZv0bETsuiNV8pmWumnkVE6onxJdZvn552u2+vub1Hrerfl1keDFuLlIeNmzMF4D/Am8ATnTyj4HJANba66KD2z8AhwFtwDxr7eJs221oaLCLF2ddJK8KnipswBBNFXY6KPcMUKpwuJWAZ0inCpt8bjwXhajdAUsVdkL4PTmmCqdZts+pwtFt9TlVOMP6JaTgdQuF73dLQY+pwk6IMk+GVGEDWCj3ldMR7iBsw/hMNFWYCNjUVOHY4z6lCtswfk/eU4ULXruq2y5ZU4Wj/aRSheNUu1KqCl67+Za3gWu+aGeWPir4zqzalT4oeN2Calf6pOC1q7qVPlLtSqkqeO3mmzLvRUREREREpKhp4CoiIiIiIiJFTQNXERERERERKWoauIqIiIiIiEhR08BVREREREREipoGriIiIiIiIlLUNHAVERERERGRoqaBq4iIiIiIiBQ1DVxFRERERESkqGngKiIiIiIiIkVNA1cREREREREpahq4ioiIiIiISFHTwFVERERERESKmgauIiIiIiIiUtQ0cBUREREREZGipoGriIiIiIiIFDUNXEVERERERKSoaeAqIiIiIiIiRU0DVxERERERESlqGriKiIiIiIhIUdPAVURERERERIqaBq4iIiIiIiJS1DRwFRERERERkaKmgauIiIiIiIgUtbwNXI0xNxtj1hpj3sww/0BjzEZjzKvRr5/lqy0iIiIiIiJSunx53PYC4A/AbVmW+a+19qg8tkFERERERERKXN7OuFpr/wM052v7IiIiIiIiMjwU+h7XfYwxrxlj/m6M2anAbREREREREZEiVMiB6xJgK2vtLsDvgYcyLWiMOcMYs9gYs7ixsXHQGijSX6pdKVWqXSlFqlspVapdkZ4VbOBqrd1krW2J/vw3wG+Mqcuw7A3W2gZrbUN9ff2gtlOkP1S7UqpUu1KKVLdSqlS7Ij0r2MDVGLOFMcZEf94z2pZ1hWqPiIiIiIiIFKe8pQobY+4CDgTqjDErgYsAP4C19jrgOODbxpgw0A6caK21+WqPiIiIiIiIlKa8DVyttSf1MP8PuB+XIyIiIiIiIpJRoVOFRURERERERLLSwFVERERERESKmgauIiIiIiIiUtQ0cBUREREREZGipoGriDDnxo0AACAASURBVIiIiIiIFDUNXEVERERERKSoaeAqIiIiIiIiRU0DVxERERERESlqGriKiIiIiIhIUdPAVURERERERIqaBq4iIiIiIiJS1HIauBpjvmeMGWlcfzLGLDHGHJLvxomIiIiIiIjkesb1dGvtJuAQYDRwCvCbvLVKREREREREJCrXgauJfj8CuN1a+1bCNBEREREREZG8yXXg+rIx5gncgevjxpgRgJO/ZomIiIiIiIi4fDku9w1gBvChtbbNGFMLzMtfs0RERERERERcuZ5xtcA04Nzo4yqgPC8tEhEREREREUmQ68B1PrAPcFL08Wbgj3lpkYiIiIiIiEiCXC8V3stau5sx5hUAa+16Y0wgj+0SERERERERAXI/4xoyxnhxLxnGGFOPwplERERERERkEOQ6cL0GeBAYa4z5JfAM8Ku8tUpEREREREQkKqdLha21C40xLwMH4X5+61ettW/ntWUiIiIiIiIi9DBwNcaMtNZuMsaMAdYCdyXMG2Otbc53A0VERERERGR46+mM653AUcDLRO9vjTLRx9vkqV0iIiIiIiIiQA8DV2vtUdHvU3q7YWPMzbiD3rXW2p3TzDfA1cARQBtwmrV2SW+fR0RERERERIa2nO5xNcYcA/zbWrsx+rgGONBa+1CW1RYAfwBuyzD/cGBq9Gsv4Nro9+LmONDWCOEgBKog1AaREPgr3PmREHgD4PFCqB18Aaisd+clrhdsBWPAWrARCIxwt+WEweMDfyV0bor+HN12qN2d7ysH60Ak6P6MdX82XnebAB6/uz1vAHxl7vLhTnf9shFd2/L4utqT2G5j3O15PG77PbnmeMmgS6zJWL3F3q9IGFpWR+vSD1X10LK2672vroeWxr49TqzD/m7L63frNNTh/g4VtdC+Lv3vJJKLTPtFt30i1ud1uN99ZW49WweMx50f7nS/WwecUHSe163ZSKhr2Vh/7i93t5c83XjcLxtx17cRd7rqW7IJd6T2m/+/vTsPs6Ms8z7+/fWSpJMASSAwSIgsKr6MYkIiI+DggjqM4wt6ARJMQNRREQFnUQedeRWdVZlx1EFBcBAhMZFFxiiKILIpqEkgBAibQCBBxiSQsGTrdPf9/lFV3dWnz+k+nT4np07n97muvrrWp+5T566n6qnt0JrkcMW6dW8ghq5/YYhp9oGtG/pvQ1B5f2NmVmfV/o7r5yPiuqwnIjZK+jxQseEaEbdLOmCQMk8AroiIAH4taZKkfSPimSpj2vl6emDtSlh0Khx4DLz+L+Gq05OdxLHnww/Pgo1PwaTpcMI34ebzk0bCvOuSnU9+vtsugD/5KCw+G2aeBq98B1x1Wt/8770SHr0R7rkS3js/OaD63sn9l1VuucdfCL/5FrzhY33LP2V+coB01Wn94+5d1hWw5NvwxO39487KestnYe9DvXMqonxOZt/nnIXJ9xU98If7+/LqyHPhtScOzLP7roW7vl55/G1fhoevh0P+At706WR8udzLT1tadtb/5K/hZ39Xfvos9ybs07ec0s/kHLRqVNou9joE1j7QP7fyeXfMp+DqtG485C+S/vt/ADPnwqZ1/fP95Cvg9gv68vf4C+HRnw/chrJ69MiPJychb//3vrrf+W2D6doKax/qn0/vuxq6t8H351Wuez/wU9i0fmBdvv4x+MEHk/5TFiQnZL53cvV1+JyFyQnL+e9x7ppZQ1Rb05SbrtpGbyX7Aatz/WvSYcW1eV3fgdCR5/Q1/o7+q74DGkj+//CsZPjGp2DD4wPnm3Fq34HLYe/t28Fk8191WjJ841Nw1Tx4/qmByyq33MVnJ2Xnl//SH3KNl1zcvcs6PRleGndW1qJTk89uxZPPSUj+Z9/XS//bP69mzi2fZzPnDj5+xqlJ/4xT+8aXy738tKVlZ/2vPq7y9Fnu5ZdT+pnMqlFpuyjdJkrz7upc3Zj1z5yb1L+l+X716f3zd/HZ5behrB79nzNh8/r+dX8+Nue3lXpp3cB8ev6pvkZrNqy0Lu3pLl+XT399X//35/YdV1Qqp7QOX3Rqcjzj3DWzBqm24bpU0lckHZz+fYXkhU07haSPSFoqaem6dQ2sILs6+yrslta+7o7Jfd2ZjU8lwyG57bd0vvw80VN+/ujp624fP3BZgy13qOWXztPSOjDufFldnUOvHxug7rmbz8lM9n11b+8/bqjvvtL4LB+qzb1yZWf9WU5Xmr5jcuWynYM7VWHq3R1Rabvo6aqct6V5l/W3tPavQ0vny/cPtg1l9bjzu66aOm9LlcvXanIxuivkf3f//uy4olI55erwcvM4d2tiVOWuWZ1U23A9B+gEvg8sArYCHx/hsp8G9s/1T0uHDRARl0TE7IiYPXXq1BEudgTaxiS3xkCyA8i6t2zo685Mmp4Mh+RZ09L58vOopfz8aunr3r554LIGW+5Qyy+dJ9uh5efLl9U2Zuj1YwPUPXfzOZnJvq/W9v7jhvruK43P8qHa3CtXdtavlsGn37KhctnOwZ2qMPXujqi0XbS0Vc7b0rzL+nu6+9ehpfPl+wfbhrJ63PldV02dt6XK5Ws1uajWCvnf2r8/O66oVE65OrzcPM7dmhhVuWtWJ1U1XCNiU0ScB7wpIl4fEZ+NiE0jXPZi4HQl3gA8X+jnWyF5CcGchUlFfdd/Jc+GTpoOv/pq8pxUtqPInpv61VeT7skHDZxv+cLk2adJ02HFVcmzJPn533tlMnzS9OQZ1z2mD1xWueUef2FSdn75E/fpKz8fd++yrkiGl8adlTVnYd9LGaxY8jkJfc8cjZ8KE/+of17ds6B8nt2zYPDxy9Ofb16+sG98udzLT1tadtb/0A2Vp89yL7+c0s9kVo1K20XpNlGadyfn6sas/54FSf1bmu8nX9E/f4+/sPw2lNWj774Yxu/Vv+7Px+b8tlITpw7Mpz2mJ++tGKzubWktX5c/taSv/5QFfccVlcoprcPnLEyOZ5y7ZtYgSt6NNMRE0lHAt4GJETFd0uuAj0bEWYPMsxB4M7AX8Afg80A7QERcnP4czoXAcSQ/h/OBiFg6VCyzZ8+OpUuHnKx+/FbhZqVGB1C33PVbhUezhuctFKDe3RHDeatw19akvqvqrcLpG4L9VuGhNDx3mzJvS/mtwo3g3LVm1fDcrbdqX7D0n8CfkVwlJSLulXTMYDNExKlDjA9GfrvxztfSklTmvaZUP29+vvHl5tuzf++EPctMUydl47GmMCAnc1rbYI9p/YdN2r+2/fUqq9JnMqtGpe2i3DZhVlRt48rXk9XUpbWYptw25LrZzBqk6tNkEbG6ZFB32QnNzMzMzMzMaqjaK66r09uFQ1I78AngwfqFZWZmZmZmZpao9orrmSS39e4H/B6YQTPe5mtmZmZmZmZNp6orrhGxHphb51jMzMzMzMzMBqjqiqukgyT9SNI6SWsl/VDSQfUOzszMzMzMzKzaW4W/B1wF7Au8DLgaWFivoMzMzMzMzMwy1TZcx0fElRHRlf7NB8bVMzAzMzMzMzMzqP6twj+VdB6wCAjgFOAnkqYARMRzdYrPzMzMzMzMdnHVNlzfm/7/SPpf6f85JA1ZP+9qZmZmZmZmdTFow1XS64HVEXFg2v9+4ERgFXC+r7SamZmZmZlZvQ31jOu3gE4ASccA/wp8F3geuKS+oZmZmZmZmZkNfatwa+6q6inAJRFxLXCtpOX1Dc3MzMzMzMxs6CuurZKyxu2xwC9y46p9PtbMzMzMzMxshw3V+FwI3CZpPbAFuANA0itIbhc2MzMzMzMzq6tBG64R8c+Sbgb2BW6MiEhHtQDn1Ds4MzMzMzMzsyFv942IX5cZ9kh9wjEzMzMzMzPrb6hnXM3MzMzMzMwayg1XMzMzMzMzKzQ3XM3MzMzMzKzQ3HA1MzMzMzOzQnPD1czMzMzMzArNDVczMzMzMzMrNDdczczMzMzMrNDccDUzMzMzM7NCq2vDVdJxkh6W9DtJ55UZf4akdZKWp39/Wc94zMzMzMzMrPm01atgSa3AN4C3A2uAJZIWR8TKkkm/HxFn1ysOMzMzMzMza271vOJ6BPC7iHg8IjqBRcAJdVyemZmZmZmZjUL1bLjuB6zO9a9Jh5U6UdIKSddI2r+O8ZiZmZmZmVkTavTLmX4EHBARhwE3Ad8tN5Gkj0haKmnpunXrdmqAZiPh3LVm5dy1ZuS8tWbl3DUbWj0brk8D+Suo09JhvSLi2YjYlvZ+G5hVrqCIuCQiZkfE7KlTp9YlWLN6cO5as3LuWjNy3lqzcu6aDa2eDdclwCslHShpDDAHWJyfQNK+ud7jgQfrGI+ZmZmZmZk1obq9VTgiuiSdDfwMaAUui4gHJH0RWBoRi4FzJR0PdAHPAWfUKx4zMzMzMzNrTnVruAJExE+An5QM+1yu+zPAZ+oZg5mZmZmZmTW3Rr+cyczMzMzMzGxQbriamZmZmZlZobnhamZmZmZmZoXmhquZmZmZmZkVmhuuZmZmZmZmVmhuuJqZmZmZmVmhueFqZmZmZmZmheaGq5mZmZmZmRWaG65mZmZmZmZWaG64mpmZmZmZWaG54WpmZmZmZmaF5oarmZmZmZmZFZobrmZmZmZmZlZobriamZmZmZlZobnhamZmZmZmZoXmhquZmZmZmZkVmhuuZmZmZmZmVmhuuJqZmZmZmVmhueFqZmZmZmZmheaGq5mZmZmZmRWaG65mZmZmZmZWaG64mpmZmZmZWaG54WpmZmZmZmaF5oarmZmZmZmZFVpdG66SjpP0sKTfSTqvzPixkr6fjv+NpAPqGY+ZmZmZmZk1n7Z6FSypFfgG8HZgDbBE0uKIWJmb7EPAhoh4haQ5wJeAU+oVE0BPT/Dspk46u7oZP7aFzdt66OoJ2ltbGNsmtncH3T2RDGsR7W0tbO7sZrdxrWzp7KG7J2htERJ0tLewubNv/lbB1q4e2lpEx5gWtm7vIQICaG9pAYKtXT1MGNNKZ1ewvaeH9tYW9p44FoC1L21je3cybPyYFjZt62ZMWyt7ThhDS4vquVpsF5HP/1rnVmnZu7W38uyWTrp6grYWsWfHmIr949paCGBbV5L/U8a1Vz1vNf0Tx7awcUs37a0tTJ0who1bu3rjnNzRzoYt2yv259dRPddfM+vpCdZv2sbW7d2Ma22hK2DCGPHSth7GtSd1YfZdtLRATw9IEAG7jWuhsws6u6O3/tt74lja2nxDkO0c27Z1sX7z8OoXYMCwF7d396sbANcXZmY1VLeGK3AE8LuIeBxA0iLgBCDfcD0BOD/tvga4UJIiIuoRUE9P8PAfXuTDVyzlqIP2ZN6RL+esBXezZsMWpk3u4Bvvm8nW7T387dX39g674KTDWPrEc7zp1Xv3m/Zbp83iuYAz5y/rN+2Xb3iYdS9t4zsfeD0vbuni3EX3DFnWxfNmsXtHG++79De9w74593Dm3/Ukdz7+LJeePptD9tnNOzwbkXz+Z3lWq9wqLfuKD8xi8sQOPpbbPi6aN4sfL1/Dt+5YxfnvejWzDtyLj81fxtSJY/n0cYfwqWtW9Jv2v25+hBtXruWjf3oA75oxrWJZ7zh0b8459lUDxmfzZ/0PPr2Rr9/yWL9x7zh0b8499lW923Fpf34dAXVbf80s/91n3+X2ri4mT+xg2RPre7/nbJ196cTD+O6dT/D+ow7ku3c+wbnHvopx7S2c8Z0l/erEV++zmxuvVnfbtnXxyPpNvTn603OP4rnN2wfUJ6vWvcA5i1b05mdpzl40bxbLnljP+T9+iGmTO7jig0ewravH9YWZWQ3V86hgP2B1rn9NOqzsNBHRBTwP7FmvgJ7d1Nm7E/nwMQf1Nh4B1mzYwnObtvc2WrNhn7pmBSccPm3AtGtf2NZ7cJuf9sw3H8yaDVtY89yW3kbrUGWdOX8ZnV3Rb9hZC+7mw8cclMR6xVKe3dRZr9Viu4h8/gM1za3Ssg/ee/feA79sWR+bv4yTZk8H4K2H7ts7/sw3H9zbaM1Pe+Ks/QE4afb0Qcs6cdb+Zcdn82f9R71y6oBxJ87av992XNqfX0f1XH/NLL9esu8y+/7z3zMk6+zvrl3BibP27/1/5vxlrH5uy4A6ce1L2xr5sWwXsX5zZ78c3b1jbNn6ZObL9+ztL5ezWb5n/U8+u9n1hZlZjTXF6WxJH5G0VNLSdevW7XA5nV3dvTuR1hb1dmfGj2kdMGzNhi1ERNXTTupoH3ZZazZsofQE7JoNW2hNB67ZsIXOru5hfFIrilrlbi3k8z9Tq9wqLburp3yeZzndk9sOJnW0D7otldtW82UNNX/W390TA8aVzluprM6u7rquvyKqNnfz6yVbf9n331Ohvsumy/6PH9M6YJqu7p7afygb9YZb55bWVZXqrqz+yPrL5Wz+ZrFKxwCjtb6wkSvS8YJZUdWz4fo0sH+uf1o6rOw0ktqAPYBnSwuKiEsiYnZEzJ46deoOBzSmrZVpkzsA6O6J3u7M5s7uAcOmTe5AUtXTbtyyfdhlTZvcQU/JzdHTJnf07iinTe5gTFv/naQ1h1rlbi3k8z9Tq9wqLbutpXyeZzndktsONm7ZPui2VG5bzZc11PxZf9bQzY8rnbdSWWPaWuu6/oqo2tzNr5ds/WXff0uF+i6bLvu/ubN7wDRtrU1xXtUKZrh1bmldVanuas2dXa6Us1LfNJWOAUZrfWEjV6TjBbOiqueRwRLglZIOlDQGmAMsLplmMfD+tPsk4Bf1er4VYM8JY7j09NnJsya3P8435x7eu2OZNrmDKRPa+Y+TX9dv2AUnHcYP714zYNq9dx/LxfNmDZj24lsfY9rkDqZN6eDrc2ZWVdbF82Yxpk39hn1z7uFcevvjvc/FZC96MNtR+fwHappbpWU/tvYFLirZPi6aN4trlj4FwC9WPtM7/uJbH+OCkw4bMO21y5InDa5Z+tSgZV27bHXZ8dn8Wf+dj64bMO7aZav7bcel/fl1VM/118zy6yX7LrPvP/89A73PuF67bHXv/4vnzWL/KR0D6sTspXVm9bTX+DH9cvSFLdvK1if3PPlsb3+5nM3yPet/+Z7jXV+YmdWY6thORNI7ga8CrcBlEfHPkr4ILI2IxZLGAVcCM4HngDnZy5wqmT17dixdunSHY6r2rcLd6ZsC29ta2NLZzcTsrcIRtKo2bxXu6umhreStwl3dyTC/VbjmGr4CR5q7tVD0twp3diX5X4+3Cj+/pZu25nurcMPzFobO3b63CvcwrlVl3yqcvZF9sLcKZ/Wf3yo8KjQ8d6utc/1WYSvR8C+pCMcL1pQanrv1Vs+3ChMRPwF+UjLsc7nurcDJ9YyhVEuLmLpb35n8yeOHMfOEgYMmlxm2o142qf9tRZOGE5tZFUrzv95l7zeubVj9I5l3qP49ctvT1Pb+t+uVxl1pHdVz/TWzlhax927jBgzfw3WYNYGxY9vYb+zw66rSYePKTOP6wsysdnxK28zMzMzMzArNDVczMzMzMzMrNDdczczMzMzMrNDccDUzMzMzM7NCc8PVzMzMzMzMCq2uP4dTD5LWAU9WMelewPo6h9MI/lw7Zn1EHFfH8oc0jNzdUUXNDcc1PPm4Gp63sFNyt1QzfDdFUdSYHmp07g6Rt0Vcb4Nptnih+WLO4m14vetj3UH5M1fW8Nytt6ZruFZL0tKImN3oOGrNn8sqKeo6dFzDU9S4dqairoMixuWYdkwzxJjXbPFC88XcbPFCc8Y8Uv7MuzbfKmxmZmZmZmaF5oarmZmZmZmZFdpobrhe0ugA6sSfyyop6jp0XMNT1Lh2pqKugyLG5Zh2TDPEmNds8ULzxdxs8UJzxjxS/sy7sFH7jKuZmZmZmZmNDqP5iquZmZmZmZmNAqOy4SqpVdI9kn7c6FhqSdIkSddIekjSg5KObHRMtSDpryU9IOl+SQsljWt0TM1C0v6SbpG0Ml2Hn2h0TBlJ4yT9VtK9aWxfaHRMmaLWEZJWSbpP0nJJSxsdz85W8HwuXM4UcZ/QDPW5pOMkPSzpd5LOa3Q8g5F0maS1ku5vdCzVKPI2XEmR91WVNFMO10qzbQu10IzbU72NyoYr8AngwUYHUQdfA26IiFcDr2MUfEZJ+wHnArMj4jVAKzCnsVE1lS7gbyPiUOANwMclHdrgmDLbgLdGxOuAGcBxkt7Q4JgyRa4j3hIRM3bRV98XOZ+LmDOF2ic0Q30uqRX4BvDnwKHAqQXKsXIuB5rpdxmLvA1XUuR91QBNmMO1cjnNtS3UQjNuT3U16hqukqYBfwF8u9Gx1JKkPYBjgP8GiIjOiNjY2Khqpg3okNQGjAd+3+B4mkZEPBMRd6fdL5IcuO7X2KgSkXgp7W1P/xr+UP1orSNGg6LmcxFzpsD7hKLX50cAv4uIxyOiE1gEnNDgmCqKiNuB5xodR7WKug0Ppqj7qkE0VQ7XSrNtC7XQjNtTvY26hivwVeDTQE+jA6mxA4F1wHfS29W+LWlCo4MaqYh4Gvh34CngGeD5iLixsVE1J0kHADOB3zQ2kj7p7ZXLgbXATRFRhNiKXEcEcKOkZZI+0uhgGqlg+VzEnCncPqFJ6vP9gNW5/jXs4geC9VKwbXhQBd1XVeIc3gU10/ZUT6Oq4SrpXcDaiFjW6FjqoA04HLgoImYCm4Cmf65B0mSSM4UHAi8DJkia19iomo+kicC1wF9FxAuNjicTEd0RMQOYBhwh6TWNjKcJ6og3RsThJLeAfVzSMY0OqBGKlM8FzpnC7RNcn1umSNtwNYq2rzLLa7btqZ5GVcMVOBo4XtIqklsn3ippfmNDqpk1wJrcWcBrSA5amt3bgCciYl1EbAd+ABzV4JiaiqR2kgptQUT8oNHxlJPewngLjX8+pdB1RHrFiohYC1xHckvYLqWA+VzUnCniPqEZ6vOngf1z/dPSYVYjBdyGq1agfdVgnMO7kGbenuphVDVcI+IzETEtIg4geSHELyJiVJztjYj/BVZLOiQddCywsoEh1cpTwBskjZckks9VtBegFFa6zv4beDAivtLoePIkTZU0Ke3uAN4OPNTImIpcR0iaIGm3rBt4B7DLvD0RipnPRc2Zgu4TmqE+XwK8UtKBksaQfKeLGxzTqFHEbXgoRdxXDcE5vItoxu2p3kZVw3UXcA6wQNIKkjff/UuD4xmx9GrBNcDdwH0kOXlJQ4NqLkcDp5FcBVqe/r2z0UGl9gVuSfN1CclzQ4X5KZEC2gf4paR7gd8C10fEDQ2OaWcrcj4XUaH2Cc1Qn0dEF3A28DOSRvVVEfFAY6OqTNJC4C7gEElrJH2o0TENoRm34abaVzVbDtdKE24LtdCM21NdKaLIL04zMzMzMzOzXZ2vuJqZmZmZmVmhueFqZmZmZmZmheaGq5mZmZmZmRWaG65mZmZmZmZWaG64mpmZmZmZWaG54dpAkt4sacAr1ysNr8Hy3i3p0Fz/rZJmVzHfvrWIJ/2ttF3t5z1GtR3NVUkvk3RNhXG9eSnps7nhB0iq6ndNJf2VpNOHG1eZcs6W9MGRlmP1JekMSS+rYrrLJZ1U7fAaxOX8taqMNIermO/McjmVz0tJM/I/tSHpfEmfrKJsSfqFpN2HG1eZsn4uafJIy7HikfT3kh6QtCL9WZc/qWHZP8l+i9dGNzdcdy3vBg4dcqqB/ga4dKQLj4h1wDOSjh5pWdbcIuL3EVHNwddnh56kP0ltwAeB7w07sIEuI/mtTCu2M4AhD/obwPlr1TqDOuZwRFwcEVcMMdkMYEd+I/KdwL0R8cIOzFvqSuCsGpRjBSLpSOBdwOERcRjwNmB1rcqPiHdGxMZalWfF5YbrICRNkHS9pHsl3S/plHT4LEm3SVom6WeS9k2H3yrpa+mZpPslHZEOP0LSXZLukXSnpEOGGcNlkn6bzn9COvwMST+QdIOkRyV9OTfPhyQ9ks5zqaQLJR0FHA9ckMZ3cDr5yel0j0j60wphnAjckJbdKunf08+3QtI56fBVkv41LXuppMPTdfOYpDNzZf0PMLfaz28j06gcTpd5WNp9j6TPpd1flPThkrP8HZIWSXpQ0nVARzr834CONJYFadGtaU4/IOlGSR1lFv9W4O70R9qR9Ir0LP69ku6WdLCSK8W3SfqhpMcl/Zukuem2cF+2fUTEZmBVth6s/tLceEjSgjQnrpE0Ph03IG+VXH2aDSxIc6VD0uckLUlz+BJJGsbyB9s2vlRaX0oaL+kqSSslXSfpN5JmO393XTs7hyXtLWlZ2v06SSFpetr/WJqjvVdP0xjulXQv8PF02Bjgi8ApaQynpMUfmub+45LOrRDCXOCHuXhOV3J8cK+kK9Nhl0u6SNKv07LerOTY5kFJl+fKWgycOsxVbsW3L7A+IrYBRMT6iPi9kmPHL6f11m8lvQJ679C7Nt0Glii94CFpoqTvpNOvkHRiOnyVpL3S7nlpWcslfUvJcWtrmoP3p/P+dYPWg41URPivwh9Jg+3SXP8eQDtwJzA1HXYKcFnafWs2PXAMcH/avTvQlna/Dbg27X4z8OMyy+0dDvwLMC/tngQ8AkwgOTv7eBrTOOBJYH+SM7argClprHcAF6bzXw6clFvOrcB/pN3vBH5eJpYDgWW5/o8B1+Q+z5T0/yrgY2n3fwIrgN2AqcAfcvPvB9zX6O92V/lrYA6fR3JAtAewBPhZOvwW4BDggFzZf5Nb/mFAFzA77X8pV+YB6bgZaf9V2bZRsuwvAOfk+n8DvCftHgeMT+PeSLIzHQs8DXwhneYTwFdz8/898LeN/i53lb/0ew7g6LT/MuCTVeTt7FwZU3LdVwL/N+2+nFwdmJvmcuCkKpYxoL5MY/tW2v0a56//GpTDD5DU02eT1LlzgZcDd6Xjzwc+mXavAI5Juy+gry4+g/R4ITfPnWmO7QU8C7SXWfaTwG5p9x+THKfslf8cadyLAAEnAC8AryW5gLIs2y7SaR8F9mz09+i/mm4TE4HlaW58E3hTOnwV8Pdp9+n09YIhKgAABqhJREFUHft+D3hj2j0deDDt/lJJ/TY5V85ewP8BfpTlabqs04FZwE25+SY1ep34b8f+2rDB3Af8h6QvkWxMd0h6DcnByU3pCdBW4JncPAsBIuJ2Sbsrued+N+C7kl5JsjNrH0YM7wCOV99zJuNINmKAmyPieQBJK0l2UnsBt0XEc+nwq4FXDVL+D9L/y0h2tqX2Bdbl+t8GXBzp1YBsOanF6f/7gIkR8SLwoqRtkiZFchvHWop5S99o1agcvgM4F3gCuB54e3rF4cCIeFjSAblpjwG+ni5zhaQVg5T7REQsT7sHy9kHASTtBuwXEdel5W9NhwMsiYhn0v7HgBvT+e8D3pIrby3w6iE+r9XW6oj4Vdo9nySXbmDwvM17i6RPkzTyppAc1P+oiuUeMsQyytWXbwS+BhAR9zt/LbWzc/hO4GiS+vRfgONIGol35CdK6/NJEXF7OuhK4M8HKff6SK6SbZO0FtgHWFMyzZR0fw/JHQNXR8R6GHCM8KOICEn3kZzQvi+N6QGSbSHbNrLjhGcHicuaSES8JGkW8Kck9dP3JZ2Xjl6Y+/+faffbSK72Z0XsLmliOnxOrtwNJYs6lqSRuiSdt4Mkn34EHCTpv0iOSW7EmpIbroOIiEckHU5ydv2fJN0MXAc8EBFHVpqtTP8/ArdExHvSA/ZbhxGGgBMj4uF+A5OH2rflBnWzY99nVkal+beQNJaHU1ZPSWw9ubLHpWXaTtDAHF5Ccuvb48BNJCdUPkxysD4SpTlf7lbLanO2NEfz+ZvfFpyzO1+5HBSD5y0AksaRnGWfHRGrJZ1P9XXYUMsYqr4civN317Gzc/h2kkbBy0lu2/27dJnXDz/0fqo5zuiS1BIRPVWWNdgxAjhnR6WI6CY5drg1PXnx/mxUfrL0fwvwhuxkXWaQO+Z7JwG+GxGfGTBCeh3wZ8CZwHtJ3iVgTcbPuA5CyRv+NkfEfJLbaQ4HHgamKnnQHEntkv44N1v2DOEbgefTK6J7kNzKBcmtOMPxM+Cc7PkWSTOHmH4J8CZJk5W85OPE3LgXSa6cDccj9L8qcBPw0bRsJE0ZZnmvAqp6s6aNXKNyOCI6SV68cDJwF8lZ/0+SHFyVuh14X7rM15DcLpzZLmk4dyhAcrXqFWkcLwJrJL07LX9seuV3OJyzO9/0LD9JcuOXDJ63+botO8Bfn56hH84bWIfaNsr5FclBEEre2v7a3Djn765rZ+fwHcA84NG0AfkcyQnLX+YnSu982pjW79D/nRM7cowAyec6KO3+Bcm7M/aE4R8jpMc6f0Ry66eNEpIOSe/YyswgucUc0mOO9P9dafeN5F4sJ2lG2nkT6XPZ6fDSN1DfDJwkae90/BRJL0+ff22JiGuBfyA5FrIm5Ibr4F4L/FbScuDzwD+lB+QnAV9S8mKD5cBRuXm2SroHuBj4UDrsy8C/psOHe5b+H0luy1yR3k7zj4NNHBFPk9wm9FuSA6pVwPPp6EXAp5S8LOfg8iUMKG8T8JjSB+aBbwNPpfHcS9rgGIa3MPIzwFa9RubwHcDaiNiSdk+j5La11EXAREkPkrwcJH9V9hKSXFtQZr5Kfkpyu1zmNODc9BbOO0kOiobjaJKdpe08DwMfT3NiMnDREHl7OXBxmufbSN6Cfj/Jib8l1S60im2jnG+SNEZWAv9EcktnVuc6f3ddOzWHI2IVydWm7OTgL4GNZW6lBPgA8I10WflLWLeQ3J6ZfzlTNa4nee6aiHgA+GfgtvQzfmUY5UBym+evs8eRbNSYSPK40cq0LjuU5BlqgMnpsE8A2UuTzgVmK3kB00qSq6SQ1LGTlbxk6V76PxZBRKwkaZjemJZ5E8njF/uRXOldTnLr/oArstYcFFF6N4vtKEm3krz8YGmD45iYPk/QRnJb6GXZM1I7WN57gFkR8Q81iO124IQKO1NrsKLk8EgpeTvxpyPi0RGWMxP4m4g4rTaR2VDSW9F/HBGvaXAoVZHUSvIikK3pCcGfA4ekjZQdLdP528SaLYdHSsmbt6+IiLfXoKyvAYsj4uaRR2ZFJ2kVyS3x6xsdizUHP+M6Op0v6W0ktxvdSPITNDssIq7LbvsZCUlTga+40Wo7wXkkZ1lHdOBP8mzu/xt5ODaKjQduSW8JFnDWSBqtKeevNY2IeEbJzzztHiP/Ldf73Wg1s0p8xdXMzMzMzMwKzc+4mpmZmZmZWaG54WpmZmZmZmaF5oarmZmZmZmZFZobrmZmZmZmZlZobriamZmZmZlZobnhamZmZmZmZoX2/wHp5pV/z/PzWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 946.375x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris, hue = \"Species\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the labor dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/site-packages/ipykernel_launcher.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "labor = pd.read_csv(\"Nov2019_labor_market_majors.csv\", skiprows = 13, \\\n",
    "                    skipfooter = 3, index_col = \"Major\")\n",
    "labor[\"Median Wage Early Career\"] = labor[\"Median Wage Early Career\"].str.replace(\",\",\"\").astype(float)\n",
    "labor[\"Median Wage Mid-Career\"] = labor[\"Median Wage Mid-Career\"].str.replace(\",\",\"\").astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "labor.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the labor data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "labor_scaled = scaler.fit_transform(labor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Put the data into a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Unemployment Rate</th>\n",
       "      <th>Underemployment Rate</th>\n",
       "      <th>Median Wage Early Career</th>\n",
       "      <th>Median Wage Mid-Career</th>\n",
       "      <th>Share with Graduate Degree</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Agriculture</th>\n",
       "      <td>0.308824</td>\n",
       "      <td>0.687702</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.172185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animal and Plant Sciences</th>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.744337</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.403974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Environmental Studies</th>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.613269</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>0.360927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Architecture</th>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.245955</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>0.447020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethnic Studies</th>\n",
       "      <td>0.691176</td>\n",
       "      <td>0.626214</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.645695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Communications</th>\n",
       "      <td>0.426471</td>\n",
       "      <td>0.673139</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.391892</td>\n",
       "      <td>0.213576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journalism</th>\n",
       "      <td>0.397059</td>\n",
       "      <td>0.503236</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>0.246689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mass Media</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.708738</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.130795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Advertising and Public Relations</th>\n",
       "      <td>0.397059</td>\n",
       "      <td>0.587379</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.418919</td>\n",
       "      <td>0.130795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information Systems and Management</th>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.432039</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>0.225166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Computer Science</th>\n",
       "      <td>0.544118</td>\n",
       "      <td>0.195793</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.729730</td>\n",
       "      <td>0.362583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General Education</th>\n",
       "      <td>0.102941</td>\n",
       "      <td>0.174757</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.612583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Early Childhood Education</th>\n",
       "      <td>0.102941</td>\n",
       "      <td>0.126214</td>\n",
       "      <td>0.055263</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elementary Education</th>\n",
       "      <td>0.132353</td>\n",
       "      <td>0.072816</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.605960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Secondary Education</th>\n",
       "      <td>0.191176</td>\n",
       "      <td>0.195793</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.121622</td>\n",
       "      <td>0.629139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Special Education</th>\n",
       "      <td>0.279412</td>\n",
       "      <td>0.077670</td>\n",
       "      <td>0.184211</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.834437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscellaneous Education</th>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>0.184211</td>\n",
       "      <td>0.094595</td>\n",
       "      <td>0.743377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General Engineering</th>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.195793</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.635135</td>\n",
       "      <td>0.427152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aerospace Engineering</th>\n",
       "      <td>0.455882</td>\n",
       "      <td>0.249191</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.797297</td>\n",
       "      <td>0.703642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemical Engineering</th>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.165049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.837838</td>\n",
       "      <td>0.635762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civil Engineering</th>\n",
       "      <td>0.132353</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.662162</td>\n",
       "      <td>0.451987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Computer Engineering</th>\n",
       "      <td>0.220588</td>\n",
       "      <td>0.140777</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.878378</td>\n",
       "      <td>0.488411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical Engineering</th>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.176375</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.797297</td>\n",
       "      <td>0.569536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrial Engineering</th>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.095469</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.621622</td>\n",
       "      <td>0.485099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mechanical Engineering</th>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.155340</td>\n",
       "      <td>0.868421</td>\n",
       "      <td>0.770270</td>\n",
       "      <td>0.506623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscellaneous Engineering</th>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.291262</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.594595</td>\n",
       "      <td>0.557947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foreign Language</th>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.563107</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.655629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family and Consumer Sciences</th>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.537217</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.121622</td>\n",
       "      <td>0.365894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English Language</th>\n",
       "      <td>0.632353</td>\n",
       "      <td>0.634304</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.581126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liberal Arts</th>\n",
       "      <td>0.838235</td>\n",
       "      <td>0.760518</td>\n",
       "      <td>0.089474</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.288079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychology</th>\n",
       "      <td>0.455882</td>\n",
       "      <td>0.619741</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>0.660596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criminal Justice</th>\n",
       "      <td>0.455882</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.184211</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.195364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public Policy and Law</th>\n",
       "      <td>0.102941</td>\n",
       "      <td>0.831715</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.569536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social Services</th>\n",
       "      <td>0.367647</td>\n",
       "      <td>0.325243</td>\n",
       "      <td>0.034211</td>\n",
       "      <td>0.043243</td>\n",
       "      <td>0.612583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anthropology</th>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.771845</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.604305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economics</th>\n",
       "      <td>0.455882</td>\n",
       "      <td>0.459547</td>\n",
       "      <td>0.657895</td>\n",
       "      <td>0.662162</td>\n",
       "      <td>0.526490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geography</th>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.357605</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.391892</td>\n",
       "      <td>0.397351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Political Science</th>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.648867</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>0.683775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sociology</th>\n",
       "      <td>0.426471</td>\n",
       "      <td>0.721683</td>\n",
       "      <td>0.121053</td>\n",
       "      <td>0.202703</td>\n",
       "      <td>0.410596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General Social Sciences</th>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.661812</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.455298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Construction Services</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.365696</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.594595</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Art History</th>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.729773</td>\n",
       "      <td>0.234211</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.509934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fine Arts</th>\n",
       "      <td>0.676471</td>\n",
       "      <td>0.760518</td>\n",
       "      <td>0.092105</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>0.200331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Performing Arts</th>\n",
       "      <td>0.397059</td>\n",
       "      <td>0.878641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.229730</td>\n",
       "      <td>0.450331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Commercial Art and Graphic Design</th>\n",
       "      <td>0.573529</td>\n",
       "      <td>0.401294</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.008278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Services</th>\n",
       "      <td>0.308824</td>\n",
       "      <td>0.555016</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>0.697020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medical Technicians</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.639159</td>\n",
       "      <td>0.331579</td>\n",
       "      <td>0.310811</td>\n",
       "      <td>0.230132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nursing</th>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.391892</td>\n",
       "      <td>0.264901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pharmacy</th>\n",
       "      <td>0.397059</td>\n",
       "      <td>0.279935</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.801325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment Therapy</th>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.349515</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>0.574503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General Business</th>\n",
       "      <td>0.397059</td>\n",
       "      <td>0.728155</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.391892</td>\n",
       "      <td>0.221854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accounting</th>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.187702</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.418919</td>\n",
       "      <td>0.302980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Business Management</th>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.779935</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>0.213576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Business Analytics</th>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.422330</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>0.635135</td>\n",
       "      <td>0.221854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.668285</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.445946</td>\n",
       "      <td>0.107616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>0.367647</td>\n",
       "      <td>0.414239</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.594595</td>\n",
       "      <td>0.332781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>International Affairs</th>\n",
       "      <td>0.544118</td>\n",
       "      <td>0.619741</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>0.533113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>0.455882</td>\n",
       "      <td>0.674757</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.337838</td>\n",
       "      <td>0.645695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering Technologies</th>\n",
       "      <td>0.632353</td>\n",
       "      <td>0.477346</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.527027</td>\n",
       "      <td>0.230132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscellaneous Technologies</th>\n",
       "      <td>0.794118</td>\n",
       "      <td>0.754045</td>\n",
       "      <td>0.184211</td>\n",
       "      <td>0.418919</td>\n",
       "      <td>0.105960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Unemployment Rate  Underemployment Rate  \\\n",
       "Major                                                                         \n",
       "Agriculture                                  0.308824              0.687702   \n",
       "Animal and Plant Sciences                    0.294118              0.744337   \n",
       "Environmental Studies                        0.529412              0.613269   \n",
       "Architecture                                 0.485294              0.245955   \n",
       "Ethnic Studies                               0.691176              0.626214   \n",
       "Communications                               0.426471              0.673139   \n",
       "Journalism                                   0.397059              0.503236   \n",
       "Mass Media                                   1.000000              0.708738   \n",
       "Advertising and Public Relations             0.397059              0.587379   \n",
       "Information Systems and Management           0.588235              0.432039   \n",
       "Computer Science                             0.544118              0.195793   \n",
       "General Education                            0.102941              0.174757   \n",
       "Early Childhood Education                    0.102941              0.126214   \n",
       "Elementary Education                         0.132353              0.072816   \n",
       "Secondary Education                          0.191176              0.195793   \n",
       "Special Education                            0.279412              0.077670   \n",
       "Miscellaneous Education                      0.029412              0.098706   \n",
       "General Engineering                          0.588235              0.195793   \n",
       "Aerospace Engineering                        0.455882              0.249191   \n",
       "Chemical Engineering                         0.235294              0.165049   \n",
       "Civil Engineering                            0.132353              0.098706   \n",
       "Computer Engineering                         0.220588              0.140777   \n",
       "Electrical Engineering                       0.529412              0.176375   \n",
       "Industrial Engineering                       0.352941              0.095469   \n",
       "Mechanical Engineering                       0.485294              0.155340   \n",
       "Miscellaneous Engineering                    0.485294              0.291262   \n",
       "Foreign Language                             0.470588              0.563107   \n",
       "Family and Consumer Sciences                 0.485294              0.537217   \n",
       "English Language                             0.632353              0.634304   \n",
       "Liberal Arts                                 0.838235              0.760518   \n",
       "...                                               ...                   ...   \n",
       "Psychology                                   0.455882              0.619741   \n",
       "Criminal Justice                             0.455882              1.000000   \n",
       "Public Policy and Law                        0.102941              0.831715   \n",
       "Social Services                              0.367647              0.325243   \n",
       "Anthropology                                 0.823529              0.771845   \n",
       "Economics                                    0.455882              0.459547   \n",
       "Geography                                    0.588235              0.357605   \n",
       "Political Science                            0.470588              0.648867   \n",
       "Sociology                                    0.426471              0.721683   \n",
       "General Social Sciences                      0.529412              0.661812   \n",
       "Construction Services                        0.750000              0.365696   \n",
       "Art History                                  0.411765              0.729773   \n",
       "Fine Arts                                    0.676471              0.760518   \n",
       "Performing Arts                              0.397059              0.878641   \n",
       "Commercial Art and Graphic Design            0.573529              0.401294   \n",
       "Health Services                              0.308824              0.555016   \n",
       "Medical Technicians                          0.000000              0.639159   \n",
       "Nursing                                      0.147059              0.000000   \n",
       "Pharmacy                                     0.397059              0.279935   \n",
       "Treatment Therapy                            0.323529              0.349515   \n",
       "General Business                             0.397059              0.728155   \n",
       "Accounting                                   0.264706              0.187702   \n",
       "Business Management                          0.470588              0.779935   \n",
       "Business Analytics                           0.411765              0.422330   \n",
       "Marketing                                    0.294118              0.668285   \n",
       "Finance                                      0.367647              0.414239   \n",
       "International Affairs                        0.544118              0.619741   \n",
       "History                                      0.455882              0.674757   \n",
       "Engineering Technologies                     0.632353              0.477346   \n",
       "Miscellaneous Technologies                   0.794118              0.754045   \n",
       "\n",
       "                                    Median Wage Early Career  \\\n",
       "Major                                                          \n",
       "Agriculture                                         0.263158   \n",
       "Animal and Plant Sciences                           0.131579   \n",
       "Environmental Studies                               0.157895   \n",
       "Architecture                                        0.394737   \n",
       "Ethnic Studies                                      0.210526   \n",
       "Communications                                      0.263158   \n",
       "Journalism                                          0.210526   \n",
       "Mass Media                                          0.131579   \n",
       "Advertising and Public Relations                    0.263158   \n",
       "Information Systems and Management                  0.526316   \n",
       "Computer Science                                    0.842105   \n",
       "General Education                                   0.157895   \n",
       "Early Childhood Education                           0.055263   \n",
       "Elementary Education                                0.131579   \n",
       "Secondary Education                                 0.210526   \n",
       "Special Education                                   0.184211   \n",
       "Miscellaneous Education                             0.184211   \n",
       "General Engineering                                 0.789474   \n",
       "Aerospace Engineering                               0.894737   \n",
       "Chemical Engineering                                1.000000   \n",
       "Civil Engineering                                   0.789474   \n",
       "Computer Engineering                                0.921053   \n",
       "Electrical Engineering                              0.921053   \n",
       "Industrial Engineering                              0.894737   \n",
       "Mechanical Engineering                              0.868421   \n",
       "Miscellaneous Engineering                           0.789474   \n",
       "Foreign Language                                    0.131579   \n",
       "Family and Consumer Sciences                        0.052632   \n",
       "English Language                                    0.131579   \n",
       "Liberal Arts                                        0.089474   \n",
       "...                                                      ...   \n",
       "Psychology                                          0.105263   \n",
       "Criminal Justice                                    0.184211   \n",
       "Public Policy and Law                               0.263158   \n",
       "Social Services                                     0.034211   \n",
       "Anthropology                                        0.078947   \n",
       "Economics                                           0.657895   \n",
       "Geography                                           0.315789   \n",
       "Political Science                                   0.315789   \n",
       "Sociology                                           0.121053   \n",
       "General Social Sciences                             0.157895   \n",
       "Construction Services                               0.684211   \n",
       "Art History                                         0.234211   \n",
       "Fine Arts                                           0.092105   \n",
       "Performing Arts                                     0.000000   \n",
       "Commercial Art and Graphic Design                   0.263158   \n",
       "Health Services                                     0.157895   \n",
       "Medical Technicians                                 0.331579   \n",
       "Nursing                                             0.526316   \n",
       "Pharmacy                                            0.263158   \n",
       "Treatment Therapy                                   0.157895   \n",
       "General Business                                    0.394737   \n",
       "Accounting                                          0.526316   \n",
       "Business Management                                 0.263158   \n",
       "Business Analytics                                  0.710526   \n",
       "Marketing                                           0.315789   \n",
       "Finance                                             0.578947   \n",
       "International Affairs                               0.394737   \n",
       "History                                             0.157895   \n",
       "Engineering Technologies                            0.526316   \n",
       "Miscellaneous Technologies                          0.184211   \n",
       "\n",
       "                                    Median Wage Mid-Career  \\\n",
       "Major                                                        \n",
       "Agriculture                                       0.256757   \n",
       "Animal and Plant Sciences                         0.256757   \n",
       "Environmental Studies                             0.324324   \n",
       "Architecture                                      0.459459   \n",
       "Ethnic Studies                                    0.216216   \n",
       "Communications                                    0.391892   \n",
       "Journalism                                        0.324324   \n",
       "Mass Media                                        0.256757   \n",
       "Advertising and Public Relations                  0.418919   \n",
       "Information Systems and Management                0.459459   \n",
       "Computer Science                                  0.729730   \n",
       "General Education                                 0.054054   \n",
       "Early Childhood Education                         0.000000   \n",
       "Elementary Education                              0.027027   \n",
       "Secondary Education                               0.121622   \n",
       "Special Education                                 0.054054   \n",
       "Miscellaneous Education                           0.094595   \n",
       "General Engineering                               0.635135   \n",
       "Aerospace Engineering                             0.797297   \n",
       "Chemical Engineering                              0.837838   \n",
       "Civil Engineering                                 0.662162   \n",
       "Computer Engineering                              0.878378   \n",
       "Electrical Engineering                            0.797297   \n",
       "Industrial Engineering                            0.621622   \n",
       "Mechanical Engineering                            0.770270   \n",
       "Miscellaneous Engineering                         0.594595   \n",
       "Foreign Language                                  0.256757   \n",
       "Family and Consumer Sciences                      0.121622   \n",
       "English Language                                  0.256757   \n",
       "Liberal Arts                                      0.256757   \n",
       "...                                                    ...   \n",
       "Psychology                                        0.202703   \n",
       "Criminal Justice                                  0.256757   \n",
       "Public Policy and Law                             0.256757   \n",
       "Social Services                                   0.043243   \n",
       "Anthropology                                      0.216216   \n",
       "Economics                                         0.662162   \n",
       "Geography                                         0.391892   \n",
       "Political Science                                 0.459459   \n",
       "Sociology                                         0.202703   \n",
       "General Social Sciences                           0.256757   \n",
       "Construction Services                             0.594595   \n",
       "Art History                                       0.256757   \n",
       "Fine Arts                                         0.189189   \n",
       "Performing Arts                                   0.229730   \n",
       "Commercial Art and Graphic Design                 0.256757   \n",
       "Health Services                                   0.189189   \n",
       "Medical Technicians                               0.310811   \n",
       "Nursing                                           0.391892   \n",
       "Pharmacy                                          1.000000   \n",
       "Treatment Therapy                                 0.351351   \n",
       "General Business                                  0.391892   \n",
       "Accounting                                        0.418919   \n",
       "Business Management                               0.324324   \n",
       "Business Analytics                                0.635135   \n",
       "Marketing                                         0.445946   \n",
       "Finance                                           0.594595   \n",
       "International Affairs                             0.459459   \n",
       "History                                           0.337838   \n",
       "Engineering Technologies                          0.527027   \n",
       "Miscellaneous Technologies                        0.418919   \n",
       "\n",
       "                                    Share with Graduate Degree  \n",
       "Major                                                           \n",
       "Agriculture                                           0.172185  \n",
       "Animal and Plant Sciences                             0.403974  \n",
       "Environmental Studies                                 0.360927  \n",
       "Architecture                                          0.447020  \n",
       "Ethnic Studies                                        0.645695  \n",
       "Communications                                        0.213576  \n",
       "Journalism                                            0.246689  \n",
       "Mass Media                                            0.130795  \n",
       "Advertising and Public Relations                      0.130795  \n",
       "Information Systems and Management                    0.225166  \n",
       "Computer Science                                      0.362583  \n",
       "General Education                                     0.612583  \n",
       "Early Childhood Education                             0.460265  \n",
       "Elementary Education                                  0.605960  \n",
       "Secondary Education                                   0.629139  \n",
       "Special Education                                     0.834437  \n",
       "Miscellaneous Education                               0.743377  \n",
       "General Engineering                                   0.427152  \n",
       "Aerospace Engineering                                 0.703642  \n",
       "Chemical Engineering                                  0.635762  \n",
       "Civil Engineering                                     0.451987  \n",
       "Computer Engineering                                  0.488411  \n",
       "Electrical Engineering                                0.569536  \n",
       "Industrial Engineering                                0.485099  \n",
       "Mechanical Engineering                                0.506623  \n",
       "Miscellaneous Engineering                             0.557947  \n",
       "Foreign Language                                      0.655629  \n",
       "Family and Consumer Sciences                          0.365894  \n",
       "English Language                                      0.581126  \n",
       "Liberal Arts                                          0.288079  \n",
       "...                                                        ...  \n",
       "Psychology                                            0.660596  \n",
       "Criminal Justice                                      0.195364  \n",
       "Public Policy and Law                                 0.569536  \n",
       "Social Services                                       0.612583  \n",
       "Anthropology                                          0.604305  \n",
       "Economics                                             0.526490  \n",
       "Geography                                             0.397351  \n",
       "Political Science                                     0.683775  \n",
       "Sociology                                             0.410596  \n",
       "General Social Sciences                               0.455298  \n",
       "Construction Services                                 0.000000  \n",
       "Art History                                           0.509934  \n",
       "Fine Arts                                             0.200331  \n",
       "Performing Arts                                       0.450331  \n",
       "Commercial Art and Graphic Design                     0.008278  \n",
       "Health Services                                       0.697020  \n",
       "Medical Technicians                                   0.230132  \n",
       "Nursing                                               0.264901  \n",
       "Pharmacy                                              0.801325  \n",
       "Treatment Therapy                                     0.574503  \n",
       "General Business                                      0.221854  \n",
       "Accounting                                            0.302980  \n",
       "Business Management                                   0.213576  \n",
       "Business Analytics                                    0.221854  \n",
       "Marketing                                             0.107616  \n",
       "Finance                                               0.332781  \n",
       "International Affairs                                 0.533113  \n",
       "History                                               0.645695  \n",
       "Engineering Technologies                              0.230132  \n",
       "Miscellaneous Technologies                            0.105960  \n",
       "\n",
       "[73 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labor_scaled = pd.DataFrame(labor_scaled,columns = labor.columns, index = labor.index)\n",
    "labor_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the first two principal components using the unscaled labor data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca2 = PCA(n_components=2)\n",
    "pca2.fit(labor)\n",
    "labor_rotated = pca2.transform(labor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use k-means to cluster the scaled labor data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters = 4)\n",
    "kmeans_clusters = kmeans.fit_predict(labor_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new dataframe with the principal components and the cluster labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>cluster</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-9397.382267</td>\n",
       "      <td>-2021.829909</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-11870.884897</td>\n",
       "      <td>2323.493809</td>\n",
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       "      <td>-7030.863971</td>\n",
       "      <td>3927.920096</td>\n",
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       "      <td>6112.082079</td>\n",
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       "      <th>4</th>\n",
       "      <td>-12993.972301</td>\n",
       "      <td>-1767.797265</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-706.746142</td>\n",
       "      <td>2925.172045</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-6041.461115</td>\n",
       "      <td>2189.782494</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-11870.884130</td>\n",
       "      <td>2323.483547</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1031.382796</td>\n",
       "      <td>3914.562809</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8585.579684</td>\n",
       "      <td>-3291.971886</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>31903.259711</td>\n",
       "      <td>-3826.734024</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-24412.126538</td>\n",
       "      <td>-5966.114318</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-29817.710998</td>\n",
       "      <td>-4555.579814</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-26644.951880</td>\n",
       "      <td>-6086.461064</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-19077.408478</td>\n",
       "      <td>-5230.735998</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-23917.424226</td>\n",
       "      <td>-6835.178736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-21310.233928</td>\n",
       "      <td>-5351.079058</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24830.414058</td>\n",
       "      <td>-5551.508596</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37237.977096</td>\n",
       "      <td>-3091.343965</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>41823.970740</td>\n",
       "      <td>-5083.502497</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>26568.543213</td>\n",
       "      <td>-4562.116034</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>42947.061111</td>\n",
       "      <td>-992.220660</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>37732.678898</td>\n",
       "      <td>-3960.416354</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25940.153711</td>\n",
       "      <td>-9522.465772</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>35005.151585</td>\n",
       "      <td>-3211.695357</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>22223.221332</td>\n",
       "      <td>-7035.597464</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-11870.881132</td>\n",
       "      <td>2323.485555</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-22045.617290</td>\n",
       "      <td>-16.339269</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-11870.882601</td>\n",
       "      <td>2323.489618</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-12662.405675</td>\n",
       "      <td>3713.992234</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>-15841.836925</td>\n",
       "      <td>1213.752199</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>-10881.489605</td>\n",
       "      <td>585.384832</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>-9397.385257</td>\n",
       "      <td>-2021.806653</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>-27432.471880</td>\n",
       "      <td>-2277.267851</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>-15467.476770</td>\n",
       "      <td>2577.526453</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>24095.034295</td>\n",
       "      <td>-216.770655</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>282.660986</td>\n",
       "      <td>1187.025494</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4627.972858</td>\n",
       "      <td>3660.559197</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>-15545.018865</td>\n",
       "      <td>692.316355</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>-11376.182885</td>\n",
       "      <td>1454.425887</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>20244.418631</td>\n",
       "      <td>-3559.356677</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>-9941.553452</td>\n",
       "      <td>-1065.848075</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>-16958.253563</td>\n",
       "      <td>1153.582450</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>-16082.516296</td>\n",
       "      <td>5679.418613</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>-9397.376323</td>\n",
       "      <td>-2021.858697</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>-15721.498589</td>\n",
       "      <td>-1019.077534</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>-4634.906190</td>\n",
       "      <td>-2302.594268</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>4240.270707</td>\n",
       "      <td>-5765.508753</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>38401.123186</td>\n",
       "      <td>25186.663895</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>-5292.731351</td>\n",
       "      <td>4917.303924</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1766.754174</td>\n",
       "      <td>-1420.135479</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>5978.393987</td>\n",
       "      <td>-4776.091263</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>-5052.066262</td>\n",
       "      <td>451.680171</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>23346.308481</td>\n",
       "      <td>-2944.307741</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>3758.908862</td>\n",
       "      <td>3165.844403</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>18265.616444</td>\n",
       "      <td>-83.093519</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>6112.074338</td>\n",
       "      <td>1053.365093</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>-6161.801664</td>\n",
       "      <td>4422.633456</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>12930.896656</td>\n",
       "      <td>-818.466887</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>-452.721514</td>\n",
       "      <td>6521.763970</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             PC1           PC2  cluster\n",
       "0   -9397.382267  -2021.829909        3\n",
       "1  -11870.884897   2323.493809        3\n",
       "2   -7030.863971   3927.920096        3\n",
       "3    6112.082079   1053.330863        2\n",
       "4  -12993.972301  -1767.797265        1\n",
       "5    -706.746142   2925.172045        3\n",
       "6   -6041.461115   2189.782494        3\n",
       "7  -11870.884130   2323.483547        3\n",
       "8    1031.382796   3914.562809        3\n",
       "9    8585.579684  -3291.971886        2\n",
       "10  31903.259711  -3826.734024        2\n",
       "11 -24412.126538  -5966.114318        0\n",
       "12 -29817.710998  -4555.579814        0\n",
       "13 -26644.951880  -6086.461064        0\n",
       "14 -19077.408478  -5230.735998        0\n",
       "15 -23917.424226  -6835.178736        0\n",
       "16 -21310.233928  -5351.079058        0\n",
       "17  24830.414058  -5551.508596        2\n",
       "18  37237.977096  -3091.343965        2\n",
       "19  41823.970740  -5083.502497        2\n",
       "20  26568.543213  -4562.116034        2\n",
       "21  42947.061111   -992.220660        2\n",
       "22  37732.678898  -3960.416354        2\n",
       "23  25940.153711  -9522.465772        2\n",
       "24  35005.151585  -3211.695357        2\n",
       "25  22223.221332  -7035.597464        2\n",
       "26 -11870.881132   2323.485555        1\n",
       "27 -22045.617290    -16.339269        3\n",
       "28 -11870.882601   2323.489618        1\n",
       "29 -12662.405675   3713.992234        3\n",
       "..           ...           ...      ...\n",
       "43 -15841.836925   1213.752199        1\n",
       "44 -10881.489605    585.384832        3\n",
       "45  -9397.385257  -2021.806653        3\n",
       "46 -27432.471880  -2277.267851        0\n",
       "47 -15467.476770   2577.526453        1\n",
       "48  24095.034295   -216.770655        2\n",
       "49    282.660986   1187.025494        1\n",
       "50   4627.972858   3660.559197        1\n",
       "51 -15545.018865    692.316355        3\n",
       "52 -11376.182885   1454.425887        3\n",
       "53  20244.418631  -3559.356677        2\n",
       "54  -9941.553452  -1065.848075        3\n",
       "55 -16958.253563   1153.582450        3\n",
       "56 -16082.516296   5679.418613        3\n",
       "57  -9397.376323  -2021.858697        3\n",
       "58 -15721.498589  -1019.077534        1\n",
       "59  -4634.906190  -2302.594268        3\n",
       "60   4240.270707  -5765.508753        2\n",
       "61  38401.123186  25186.663895        2\n",
       "62  -5292.731351   4917.303924        0\n",
       "63   1766.754174  -1420.135479        3\n",
       "64   5978.393987  -4776.091263        2\n",
       "65  -5052.066262    451.680171        3\n",
       "66  23346.308481  -2944.307741        2\n",
       "67   3758.908862   3165.844403        3\n",
       "68  18265.616444    -83.093519        2\n",
       "69   6112.074338   1053.365093        1\n",
       "70  -6161.801664   4422.633456        1\n",
       "71  12930.896656   -818.466887        2\n",
       "72   -452.721514   6521.763970        3\n",
       "\n",
       "[73 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labor_rotated_df = pd.DataFrame(labor_rotated, columns = [\"PC1\",\"PC2\"])\n",
    "labor_rotated_df[\"cluster\"] = kmeans_clusters\n",
    "labor_rotated_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the colored-by-cluster data using the principal component coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f243b3b14e0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.125x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x = \"PC1\", y = \"PC2\",data = labor_rotated_df, hue = \"cluster\",\\\n",
    "           palette = [\"blue\",\"orange\",\"green\",\"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even find the first three principal components and plot them in 3D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca2 = PCA(n_components=3)\n",
    "pca2.fit(labor)\n",
    "labor2_rotated = pca2.transform(labor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "labor2_rotated_df = pd.DataFrame(labor2_rotated,columns = [\"PC1\",\"PC2\",\"PC3\"])\n",
    "labor2_rotated_df[\"Cluster\"] = kmeans_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vr8PtdqOlpQWHDh2CxWLJW6eefHi9aFZCrvf7K6V5xNze7XbD4XDAarUimUwiHo9jbW0N6XQ6TyFCyLqSF8V2RMha5pC1zkkX5pDLRdQk/1/Ymv+Zz3wGHMdhamoKQ0ND2LdvH7q6umr622YyGdxxxx3IZrPgeR5vectb8KlPfQqLi4s4efIkgsEgZmdn8cgjj6hSPqnBy5qQlRridDqNixcvYnZ2tqY/GomQqyEWi8l52ra2NpjN5prUGbUSPyE8ZUdfa2srZmZmioiYgJB4ozcW2e/LHURlQSI35d9bOdNPOYGksNhFXs13WsqiGnieV1XAVub/lXj44YfxsY99DC0tLfj+97+PL37xi/j+979f0zGaTCY88cQTsNvt4DgOt99+O173utfhS1/6Ej70oQ/h5MmT+P3f/308/PDDeP/731/zdyyFlyUhE+maIAhyMweZylFrdFSNKCORCFwuFwBgdHQUbW1tWFtbkzvNtNpPKXAch2effRZOp7NsR58SxGCo3htLkiT4/X4sLi5CkqQipzdCLmRfzTjC6XopI8oZ2xc2ZJBXc4qiIAgCfD5f3qt5vbgZCL5c4KAGpIHoHe94B2ZmZuraBkVRstKIdINSFIUnnngC3/jGNwAA73rXu/DJT35SJ+R6UGkyRz2EB5SOkJXOayzL5hnvAPX7G6tJWRBS9Hg8EAQBx48fr0rEte6jcH8A5H12dHTg8OHDMrkTXwpigEQMhKxWK7LZLOLxeFmVQj3H0Uyoh9zLNWSQtmYACAQCeWZMhc0uas6l1hHtdkTIjW4vGo3mFWTrgSAImJ2dxcLCAj7wgQ9gbGwMTqdTTqcMDg7C5/M1tA8lXhaErGYyR71RkZJclRIypfNaqXXUpDnK7acUlB4X7e3tmJ2dxQsvvKCajIHaPZGBa0Tc1tYmt3MLggCO40rm/YhKIR6PY2NjQ+5CJCoFZTR9I3wpmlU7TIavDgwM5G2f4zi5kOjz+ZBKpSAIQp4ZE/lRXvM3KmURTnH41XIEWUHCvj4HdreXjoK1KDpqQcgMw+Ds2bOIRCK4//77ceXKlYa2Vw07mpCraYi1AMuyyOVycp7Wbrdj//79FeVj9UbIpUhcFEX4/X4sLS2ho6OjqsdFtX2ojcJJlBYOh3H48GHVxE9UCkRSNjU1JW8znU7LEXUgEEAmkwFFUXm+FOX8fZsR203uFEXBaDSivb29yIwpm83KRB0KhYqkealUCtlstsg1r16oIeRomsNXf76EZE4AS1N4xh3Cu47vwkR38b2iRWNIMpnUTMbpdDpx11134dlnn0UkEpGPb2VlJe8h2Sh2JCGXImK1N0YtN5EoiojFYlhbW0NPT09J5UIp1DsjL5fL5e17dXUVS0tL6OzsxJEjRxqu9FYzqVcqNVpbW+FwOLBnz56iXKZa2Vvhv61WK6xWq6xSAIqnOnu9XlnuREhaqY5pJtyoxhCKomA2m2E2m/PMmJQPvUAgAJ/PB7fbDQBFpvb1jImqtvyLq3HEszwGnVv3SCzD4an5YElCbjQFQt70GnnYbGxswGAwwOl0Ip1O48c//jE++tGP4q677sK3v/1tnDx5El//+tfxpje9qe59FGJHETJp5nC73ejs7ITNZqvpolKrMiBkuLy8DKvVip6eHkxPT6veTyNDS0VRhM/nw/LyMrq6uqqaDdVCCtW0yy6XCy0tLbJS4/Tp09ueuy1X/FK+qsfjcYRCIfh8PphMprxoutYIsFlTFlqkGJQPvZWVFUxNTckKDqL4KJTmFSo+Gnk7EUQJtGJdmqLACaUDAC0i5EYHQvj9frzrXe+S77sHH3wQ9957L6anp3Hy5En86Z/+KW655Ra8973vbeg4ldgRhFzYzJFOp5HL5Wr2YiAFunKETJzXVlZW0N3djSNHjshttbWg3gJiJBLBs88+i+7ublWub7XqigtzyCQn7nK5YLfbcejQobwmEi0tPishmuYwv5GEmWUw2WsHS2+pYtra2tDW1gZRFGE0GtHT05P3qq70TS7U/GoxKLQampXcgXyCr2TGpHw7UU4fKZyTqAZTvXY8ORdEMJmDgaEQTfO4e29nyWWJPWgj36/R83Xw4EGcOXOm6PPR0VGcOnWqoW2Xw01NyKRAVGgIbzAYai6aAeW77nieh9frhc/nQ39/P44fPy4/vYlovBbUQsiiKGJlZQUejwcGgwHHjh2ryWColgubEKwkSQgGg3C5XLBarWW7+a5Hc8dyKI0//cFVpDkBogTs67PjE6+bgIEp/k7lXtVJ00+hHadS80sIqVlJ9EbI1CpJ80jX3MbGBjwejzzhvFDxoYxyux0m/M4rduFn8yHkeBGvme7GzGBL4W5lNHLuYrHYdfft1gI3NSGTP1jhqwnLskUTaNWgUMKmdF4bGBiQndeU2C4JmzIa7+3txb59++D3+2vSntYTwUYiEczNzcFisVQtTl4PQv7bny8hlRPgtBogSRLO++L42XwI90xeI9xqx6F89e7p6ZE/L0UssVgMly5dQktLS14EWE8+s5kJGaif8ArVM6Io4le/+hX27dsnn8+1tTUkEgnZdIicyxabDScP9257zj8Sidx0PhbATU7IQGnSUds9VwiyXi6Xg8fjwcbGRknntXL7CsSyuLIWh8PMYmawFTRd+oKvdCMIgiBH4729vXI0Ti7uWlALIYdCIQQCAVitVuzbt09VuqdaEVALBOJZWI35o+A3EllNtl1Klnf+/HkMDw/LZK2c60cUCoRcrFZrxb9ls0bbWoMU4CqZMVWak6g8nyRtpsW0EJ2QmwQGgwGZTKaudT0eD7LZLHbv3o3x8fGqUQkh5LPeKP7Xv16AKEoQIeH4cDs++xvTYMqQciEIEa+srBSlRYDti8TD4TAWFhZgMBjQ3d2Nzs5O1bn3enTLteJAfwuemg+iw2aAIEoAhZJVeS1hNBrR0tJSJCVTyvLW19eRTqcryvK0jpC1NAPSEpUK4UozpnJzEhOJBDY3N+V8v9lsRi6Xk02Z6tGj34xeyMAOJeRaI+RUKoXFxUVsbGygu7tb7jRTA3Lj/Z8/ugJeFGE2MJAkCc8thvDzhSDu3FO6aEGgzE8PDAzgxIkTJW+8etIPldZREjEZz+R2u2vaR6VUgVZE9Luv2IVwKofzq3HQFIV3HB3A7K7tvdFKHbtaWR4pfLEsi3Q6jUAgAKfTWbO5fSGaeaozz/M1H5vSjKkw3x+LxTA3N4dkMik/+IDa5iTejOObgB1AyKX+IAaDQVUOOZFIyIMxR0ZG0NLSUnN1liy7mcjBZKDlz0RJQjCZK7ueJElwu91YXV3NmwxSDo1I5ZSIRCJYWFgAwzBFc/JqTUFcjwjZZmLxqXv3IpUTYGCoksU8LVHr96kkyzt79iwA5OVT65XlNXPKQkvnOJqm5db6kZGRvH1UmpOoJGqj0ahHyM2EahFyPB6Hy+VCLpfD2NgY2tvbQVEU1tbWkEwm69rn/v4WnPNFYTMyst5yb0/xqz/P81haWkIymQRFUVWJmEDtBJDCdQjBRqNRLCwsgKIo7Nmzp8groXB5NahE4FoTCMkjXw9ocdwGgwEsy6K/v18uxJIOOvKarpTlFbaNF0Z/WkbIWj9Er4cvRi1zEj/96U/LxXBSnL7ttttqekPxer145zvfiUAgAIqi8L73vQ8f/OAHEQqF8Na3vhUejwfDw8N47LHHGm7PVuKmJ+RaIuRIJAK32w1JkmTnNSXqLQYCwKfeOIk//s5FzAUSYGkKH7p7DPv7r5Eex3FYXl7G2toaBgcH0draisHBQdUXcj03I03TSCQS8Hg8kCSpyOSo1PKNpiwikQjm5+fzZGWF+dVmhpZkVfhQUsrylOb2SllePB6H3+8viv5SqZRmMw5vButNteRZak7id77zHXzyk59Ee3s7WJbFt7/97aKaTDWwLIu/+Iu/wOHDhxGPxzE7O4tXv/rV+Md//EfcfffdeOihh/D5z38en//85/GFL3yh5u9Ydr+abamJwLJsXjRJpiizLIuxsbGypFQvIVMUhXarAX//zsNIZHlYDIxczFNK53bt2iUrNjY3N2saWlor4vE41tfXwTAMpqenVeXTaJquSS6oJPBYLIb5+XlQFIW9e/fCZDLlub0Fg0EsLS2B4zhZBsVxHOLxeJHxTa242c3ulbI8JUj0R4avejweWY+ufMjVKstrdqc3LbaXTqdx66234td+7dfqWr+vrw99fX0AAIfDgampKfh8Pjz++ON46qmnAGxZb9555506IStRrgAjiqI8qshsNhflS0uhUbmc0WiE3bR1SnO5HJaWlrC+vp4nnQuncvivS+twebKg22O4ZUS9G5sakHQMMcLv7OxUXdyoldgoikIqlcKZM2cgCAImJibQ2toqS51KRS/KmXSBQABer1dOEym76ex2e8UJGtsFraVqjTxolOcvHo/nDV9VOryVk+VZLJaS+99JEXI5aCl783g8OHPmDI4fP45AICATdW9vLwKBgCb7ILjpCbkQxAAnlUohEAhUbW5Qol5CVhbPKmmYQ8kcfv8bZxFM5JDN5fCkdw6fud+AY8ON56ASiQQWFhbAcRzGx8fR1tYGj8fT0NinSkilUlhfXwcATE9P50maKkEpgzIajbIHiPK1PRKJYG5xBSKfhdVkyCPpRtUK1xPBtICHHr+C5VAaY102/O93jaDTXl/KRkmi5RzeyEw/pSwPQFE3YqNtyYVoxghZC+tNYOu+euCBB/DlL3+5qO7SqFdGKdwcV7YKEGP2paUltLa2wmKxqJ5XR9BIhJxOp7G8vIzNzU3s3r27ZDPJf11ax2YihzarERlKAAcJX/u5pyFCTiaTWFhYQDabxdjYWF5ustZCoBpCzmQyWFhYQCKRQEtLC7q6ukqSca2RJnlt52kjvvJcGPMbAijKgLcc6sav99iRSCTg9/uRTCZlv19CMLlcrm7L0UaPuxwynICvns8hRydgNdA4uxLFnzx+BX9zcn9dSpFqx1Vqph8AOW1ErFJXVlaQSqXA8zyuXr2al/qo90GndeqN5/mafLxLIRaLNayy4DgODzzwAN7+9rfjzW9+MwCgp6cHfr8ffX198Pv9NY1hU4ObnpAlScLKygqWl5fR3t4ujyr65S9/WbPJdT3SMjL14sKFCxgbG8PExETZ6CPNKbZNAQyAVK62/ZEbM5lMwuVyIZ1OY3x8XFaKKKElIWezWbjdbkQiEYyNjWHfvn1YXFyUj8m9mUI4lUNfqxn9rfUb93z1F8uYW0/KjSDfOhvAnt4WzAwOyssURoObm5vgOA5+vz8vElSOjLre8IYzSHBAV+vW9dfK0AjEs1iLZbGrrfbRRPWmGWiaLpLlEdlYT0+PbMXpdrtlH5dCGVm1/TbjtJB4PF5SSaQWkiThve99L6ampvDhD39Y/vy+++7D17/+dTz00EOaW28CO4CQRVFEJpMp8gMmBkO1EHItJJLNZrG4uIhQKASz2YyhoaG8yKQUToy045vP+5DK8RBFICuIeHCq8jpKMAyDeDyOpaUlpFIpOSIud9y1FulK5ZBzuRwWFxcRDAYxMjKCycnJPA8RSZLwnTOr+OGFABiKAiDht28dwpFd9Rm7XF5LoNXCgqIosMyWnnsplMozoSmMBg0GA2ialgmm1MgoJUlX0v5qFSGbDTRECRClLQmkKEkQJcBiqI9otMz7kq4/p9OZl2ctlOWFQiE5v6/MTxfK8rYjZdFoWorMdKwXTz/9NB555BEcOHBAnsn32c9+Fg899BAefPBBPPzww9i9ezcee+yxho6zEDc9IRsMBoyPjxd9TgyGGn31KUQmk8Hi4iLC4TCGh4exd+9euFwuVbnXqT4HPv3GSXz15x6E4yJeN9qKd54YUrXfdDqNVCqFixcvYnx8HJ2dnVWJo1YZm3J5nufh8XgQCASwe/fukpE/TdPwx7L40YUwehwmMDSFLC/g6895cbBvEiYV5FNIgL0tJniCKbRaSNMJhQ6burxrtSJiofaXdH6RHy0leYNOM2a6aFyO8RAlgKaA1+/rrjuHfD2MiqrJ8sr5JSeTSZmwtTiHjRb1tFDc3H777WW389Of/rTh7ZfDTU/I5dCIprgUMpmM/MpeGCnWsq/jI+04PtIOr9e79RSv4nXeDeBWAAAgAElEQVSRTqfhdrsRi8VgNpuLPIkroR5C5nkei4uL8Pl8VY2VKIpCIiuApin5e5hYBqLEIZUTVBFyIf7nHbvxZ/82h0iagygCx4edODFSOcderYWbFBELSYZ0fkUiEbnlOZ1OY2FhAQ6HQ44I64m0KIrC/9hjQLZtDCuRDHa3W3DbaP21Ai0j5FojWqUsT5kzJbK8ubk5xGIxbGxsgOO4Gy7LI922zdrZWAk7gpBL3ZBq26cLQfKu5IJIp9NYXFxENBrFyMgIpqam8v7QK+E0/uV8BBlexJuO2jGj0meBYRhks+Vdy8gDIBqNYnR0FNPT0zh37lxNBFur73IgEMD6+jocDoeqDkKaptFlZWBkaMQyHBwmFqFkDl12E1os1S+tUgY8u9os+Mpb9mExmILZwGC8y5o3ZUIrlOv8On36NLq6upBKpeD3+5FIJIokZWoNb2iKwh0THRWXUQutUxZabIu8kZhMJoyMjMjjyyq5uxUOsS11HI1GyIlE4qb0QgZ2CCGXghYWnCQyHR0dLSJiAFiNZPBH334RkWQWlCTief9lfOy1E7h1rPpNWI4sSUqEROLK/TIMo7mMTTmbr729HR0dHXkeApVAURSshq2uxP/3vxfhj2Yx1G7B++8YUUWi5SJbh5nFwYH6CzKNgKIoOJ3Oik5vgUAg75X9ehQRm9lbubAIV86GkxRiif90oWkQIetGi3rRaLShgt6NxI4gZC0jZAC4fPky0uk0xsbGMD09XfZGeOLqBuJZAe1WA3iBh0hTePR5X12ErCwSFqZEyq1TDZUImcgEPR4POjs7cezYMXAch6tXr6rePjnv4912fPGB/eBFCQaGlvO2NyPKTXcu5/RWqohIlApk7p8Wk521JGSti3BqzIUqyfIK3fLI9JHCtIfah93NaiwE7BBCLgWWZSumBAqRSqXgcrnkiPjQoUNVbwBOEEHhJWISJdAMBV5UV1Ag5KpUMZAiYSXVRKMRsnJydFtbG2ZnZ2UN73osjXNrWXCeMA70t8BSxdBH6fZGURQMTO1Tipux5VktqhUR19bWZEUMULqIWI+zYKMgMwi1QiMRd6nU0enTp3Ho0CE5mg4EAkgmk3kPOyVRF+77Zp0WAuwQQm7EgjOZTMLtdiOVSmF0dBQsy8LhcKi6+O+Y6MT3z68hnuUh8AJoScAbD/RUXQ/YuohDoRBOnz6NkZER7Nmzp+o+G4mQlQNLHQ6HrNcmWAqm8Ll/d2EjnMbP1hcx6DTjodfugc1U/hIp5/YmSRJSqRTMZnPFyKlZCbkR4lMWEU0mE/bt2wegOBL0er3I5XJgWTaPpOsdF1ULmmkcVClIklRWlqdsGydt95IkyW33KysrmJ+fbyhCfs973oMf/vCH6O7uxoULFwBg213eCHYEIZdCtRyy0gtZqeeNRqOqSW+sy4bPvmka//TMIjbCUTx42yhePVXcubMWzeD0UgSSJGFmwI5MyI9AIACWZXH8+HHVN0ethEyWD4VCWFhYgNlsLjuw9Fsv+ABI6LDQ6HKasRJJ4xl3qOT3IShFqJubm7LfMklbVIoMtSLkZiR2JcoVEUlao7AARuw4lcNXtUIzm91XSs2UU8yQACCZTOL06dP40Y9+BJ/PhyeffBLT09P48z//87zlq+Hd7343/uAP/gDvfOc75c8+//nPb6vLG8GOJeRyk6cTiQRcLpfcalzY4VZrMXCqz4E/e8MeXLx4EbPTW9FxKJnDM64gRAkYbLPg7572IJXlkc5k8M98Dh+5exizs7O4fPlyTTdGrSkLIulaXl7G9PR0RfvGSJqD2cgimd668RmaQixd+TwoI2Riu2kymXDgwAGwLCv/vlRkaDAYiiZq1EsSzRppq4HBYEBbW1tRAUxpx7m2toZUKoXnn38+L5Kut4iodQ5ZS9RzbBRFyWmMD3/4w7KO+m1vexsuXbpUs+LijjvugMfjyftsu13eCHYEIZd6ohZOni5nSl9tPTVQkvh6PLulvEhtbSOe4dBuptBu4NHnsCMhWnEuROPQuEGTCSClEIvFsLCwIEuNSKdRJRzZ3Ybvnl0FJYhI5wQIIrCvv/KFTNM0stkszpw5A0mSZEc9ZVGvXGSYy+Vw7tw5cBxX5PjmcDi2pVnjeqKRB4SyiEh0v6dPn8bMzEzZImItU0ia2exeC6e3eDyO0dFRWK1WHDlyRJPj2m6XN4IdQcilQEgyHo9jYWEBPM/LRFxtvVqKgTleBKvwjPjBOT9iaR6ddiMy6QwCmRwkkcXkRDcoikI2mUOGE/PIlRdE/Gw+CF8kjdFOG14xVvphUU27rHR8m5iYQEtLC375y1+q+h73HuhBhhPwvVNRSAB+9/bdmOwtT8hEnx2PxzEzM1OUT6uWUzQajTCZTOjv75dz2SSajsfjJf2T1bQ+Nwu2Y+RSqSnZpN25VCdiqeEA5K2l2TTNBFo5vW1nUW87m052BCGXOjkk8X/16lWMjY2pTsCrTVn4Imn85RNu+CJpdNiNeKXzpVf3VA48l0M0ulXU6mqxIJoRkMhukW+GE3HbaLusUJAkCV/88QJ+4QpiywkCuO9gH973yuGifZZLWRCFSCqVwsTEhPzQIdtX9b0ZGiePDmKIW8Zttx0ou5zSZKivrw8Wi6XsuZUkSZbClUJhqqFcNF2NcLLZbNNF0tdrBp6y3blwWCiJpgtTRZlMBpubmxBFseEi4k73QibYbpc3gh1ByEpEo1HZW8JoNNb8ylI4baQUeEHEF3+8gFiaQ2+LCfEsj8eu5jA17IIz44coUTDZ7KAoCjTN4y2HexDN8IAEvP3YIG4ZunaxLIXSeHYxhE7bVvQiiBJ++OIaHpwdgNOanx8sTFlkMhm4XC7E43GMjY0V+VtoSQgcx8k+z0QnHYvFkEgkSi7/oi+Kbzy/ilROxN4eG95xbBAt5vout3Ktz8ppJJlMBoFAIC+adjgcZbvBthtaG93XilIub8BWquj8+fOQJCmviFg4HKDSRGcldrIXshLb7fJGsCMImagjFhYWAABjY2NwOp145plnar4x1ETI4RSHUDKHbodpy49CyCHD8dhI8nj3G27Hrqub+JfnfRAkCe86MYQHDveXPYYsL4JWvALR1NZPlhcA5BMyaesmXYShUKhq80qjEAQBy8vLWF1dxdDQEE6cOCETXLli2lo0g79/dgWtFgNazQbMr6fwz6d9eP8rd+ct10gxTkk4kiRBFEUMDAzIsqh4PI7NzU2k02m56HM9bTmbtbPOaDSCYRgMDg7mDV8tNdNPOROR/BRGr1qb3TdDhPybv/mbeOqpp7C5uYnBwUF86lOf2naXN4IdQcjkNbpwiCd5xa/liauGkK1GBhSASCwBLpuG0WQGRbOYHN0NhmHw69M9+PVpdXrk3e0WtFsN2EzmYDOySGR5jHRa0WkvNlyXJAnhcBjPP/981SaSRiGKIlZWVvDjcx4EJTtGBoawv7M37+Yrl0LxRTKQcM1qstthxNVAouTQTy1lb+VkUZU66gpz01qh0fFNSmidpy3cXqkiIrBFjsp2cZfLBUEQ8s4bgKaLkOPxeEM65EcffbTk59vp8kawIwjZZDLhlltuKfqcNIdoSciiKCIYWMXR1gSeWmNgMTuQBoU7dvHotNV+Ok0sjc/+xj789c/c8IbSODDQhvffMZrnAsfzPJaWlrC6ulqzdrlWSJKEtbU1uN1uXE1Z8UzYBruJhetKEOdW4vgTRbNIOUK1mxiIogRBEsFQNFI5AU6LYdseHmoaakp11Clz0xsbG0ilUkilUrh8+XLD0bTWKQstz51agldTRCSeyWRYbaN2plpEyMTv+WbEzXnUBSh3sdZjMMQwTMl1SMTo9XrR29uL33vjK3BfnMNaNIMOuxEhz+W6JpSIooieFhM+/capot8r0wWDg4OYmZmBy+XaFjImnXypVAqhUAiHDx/GNx+fQ2+LEUZ2a3+rkQyuBhI4/FIOvBwh7+lx4NiwE6eXoqCpLU3z7xekKyqtfz1Qrhh26tQpDAwMFEXTypFRatzeiAWkFmimzrrC82a32xGLxbB7925Zb65UyBArTuVbSKUAiby51ItaCtnNiB1ByIB2BkOFF6ooivD5fFheXkZPTw+OHTsmR0y72lh5HE9sha35Yiq0+lTuk5B/f38/Tpw4AYZhkMlkatYuA9UjrHA4jPn5eZjNZhhMZuwe2wOjgYYkAcrVKApQWnWUS1nQNIW3Hx3AiWEn0pyIfqe5pMm88m8mSRI2EjnkBBG9LWawVXyitwsURZWMppUjo5Rub0qyUeZYmzWHrDXI9cswTNkiIjlvXq8XqVRKLiIqm1xIEbHRaSFKb5WbETuGkEuhEZN6pS1ld3d3HhGXQj3z+MjxkVc75T57enpw/PjxvIuzWqdeNM3hyasbiGV4HBpowaFdzpKewwTxeBzz8/MAgMnJSZxezeIfLgbxr6sXMdXrwCsn2vFfl9bRYjYgwwlotbLY23NNklbNGH6sy1bxxiDrC6KE/+epRfz3QggURWGozYJPvmECrZYbMw+vEOWcygRByCNpkmM1m80wmUzIZrNIpVKqvJMr4XpJ6OpBtZxvqQnZygnjZL4fKSKS+5U87Gol51QqpXrKfDNixxCyVhGyKIrI5XJ49tln0dXVhaNHj6rKhdWbHhEEQc7bLi4uoqOjo+w+K5F+Isvjs/9+FevxHAwshSevbuDdtw7B8tI6yggrlUphYWEBmUwGe/bsgdPpxJW1OL5zZhWtJgq9LSZcCSRw1NyKtx/bhRdXY2izGPD6/b1wKKRrjaYcCMk8NbeJJ+dDaLeyoAAsBlP4u6eX8Uf3jNW97esBhmFK5liJzpdIMNPpNBiGKTIRUks2zR4h10qaygkkPT3Xit88z+PFF18ETdNFDzg1xvbAVgv/zWq9CewgQi6FWkhS6Q8siiJmZ2drmsdXT4RM0zQ2NjZw4cIFOJ3OPCvMcsuXi5Bf9MWwHs+h37l1zBlOwHfPruHB3RQePb2CF/1JmFkKxzs5dNBpTExM5A1IXQmnwdAUaJoGBaDdasDCRgq/dWI37tpbehBruePJZrPweDwwGAxyG3S5KEqSJLiD6ZfkflvHYjPScG2myp6HZgaJpp1OJxKJBKamtmoDRLFAvCkSiQQEQSgyESql/9VSsaE1iOpCC7AsC5qmMTAwIG+TPOCUKplUKgWapkuaVm13l952Y0cTssFgkLu6ykEZnba3t2N2dhbnz5+v+QaolfyDwSA2NjbQ0tKCmZkZefxNJVR6bRVECVt9flugKQpZUcDPVzh40iHYaQ6BUBr/FrXjE/fNoNOZv782m3Grq+6l49uS31V+9SuMkMlg1PX1dfT390MUxbwxSOQGIiRNzsXudgtE6dqreYoTcWigNglasxVyCtMM5RQL5fS/SqLheV7TfLSW2O7GEGW6SFl8FQShqIj4yCOP4PnnnwdN0/inf/onHDx4ENPT0w11cf7Hf/wHPvjBD0IQBPzO7/wOHnrooYa+XzXsGEKu1ShIkiQEAoGSRu3EKa6WP6TaCFlZQOvp6UFnZ6cqMq6G6T4H7CYDNuNZmAwMomkO9x3swQ9PBWFhMzA5WzHW3g5fNIPlcBr9BYQ8M9iK2V2teOpCBEI0A6fNhLccHqi4T0LIyiLk4OAgTpw4AZ7n80iJWCQSBzqv14t4PL5VoW914lCPEefXMmAYGgNOM37nFbtUf/dmzK+qyftW0v+S3LTf70ckEgHHcchms/IDTVkIqwW16vKr4UZ16pUqIt5yyy149NFH8cQTT2BjYwNf/vKX8bGPfQx79uyp+1g+8IEP4Mc//jEGBwdx9OhR3HfffZienq5re2qwYwi5FEpZcConZrS2tuLw4cNFqYl68sHVXOKi0Sjm5+fBMIxshel2u+tSTZRCu82Ih147ge+d9SOSyuFYL4PulAdWAw1bixMOh+0lSdC1hg0lGJrCb9+2GwMIYmDXLoz2Oiua0xPkcjk899xz6O7uLipCKqG0SCR5Q2IkzjAM3jMTx+I6EE+l0W1NI7DsQkrh+naz6UobKcQVmrOTfHRfXx8SiQRisZhcCKvV4L4ZzYAK0eiAgJmZGfzRH/1Rw8dx6tQpjI+PY3R0FABw8uRJPP744zohq0G1CFmSJGxsbMDlcqGlpaVoYoYS5bTIlUBkaYUgbnOiKGJiYiLvlbWevHMl9Lea8ea9FiwurqGrqwsjIycQpy/g3z0iViPpLYvMXgem+0q7uNE0hT4Hi5EOc1UyDgaDmJ+fB8/zOHHiBEwmEyIpDo8968FiMIlumwFvnulFX2v5PDxFUWBZVh6uOjy89TnprIvH43nFHYvFAoPZiuf8AuI8jUO7nHjluDZTnbWG1o0hJGdaGE2XM7gvzK+aTCbNnd4A7Qm50dSTljlkn8+HXbuuvakNDg6qdk+sFzuGkEuBqCwIEdvtdlX52nojZOU6yWQSCwsLyOVyGB8fL2l2Ui8hF97spKljYWGhqDg40mbCBwbbEBNNMBtoTPU6yrqvAdWldbFYDHNzc2BZFgcOHMC5c+dgMpkgihL+8dklBGJZdNpNCCYz+IdnvfjQ3aMlI3KgvEqjXGddLJHEHz8+B/fm1sPle2f9uGfYgNeNmsGyrNwt1gwFsOulQy5ncE/SQ9FoFD6fD9lsFizLwmw2I5vNIhaLaTIuSusUSKOIRqN5JHqzYccQcqnKdCQSQSQSQSAQKDu6qBQakbCl02m4XC4kk0mMj49XHB2jHHNUCEmS8LQrhCeubkACcNeeTrxyvKNIVxwOh3H56hxOrQPejBX2TeABRwYHB7cImaZpdFhZTHaqc78izSqFIFK5bDaLPXv2FEmL4lke/mhWjojbbUb4oxlsJnJy80whapHNURSF+TCP1biArhaL7Iz3izUebz5gRyaVzDO6JxIpUkDcbjOhQtzIxpBS6SFgK5peX19HKpWSo2nlPDpyrmoZvsrzfFN5dmgZIQ8MDMDr9cr/XllZwcBA5bpKo9gxhExAFAwulwtWqxUWiwX79++vaRv1ELIoiggGg4hGoxgbG0NXV1dDQ0vPrkTxvbN+dDmMoCjg8XN+2IysvE4ymcTc3BxomsYy1Y25RBS9rSZkeRH/39NL+MirxzHUbq1p7JM3nMYj5+PIvriI6YEoHjwyAAsjweVyIRKJFEnllDCxNChqaxK3gaEhiBJEUYKJ1S5a5QQJFHXt4UtTW7oSi8UKi8mIoaEhAPnWnOXanx0OR11FMbVoRi8L0sbscDiwd+9eANeaNOLxeJFvcmFuuhRZahkha5H+iMVimhHy0aNHMT8/j8XFRQwMDOCb3/wmvvGNb2iy7XLYUYRMiNhsNmP//v2w2Wx45plnat4Oy7JIp9Oqls3lclhcXMTGxgZYlsWJEydU3zyVCPmSPw6biYH5pdd9h5nFRX8MkzTw4osvQhAETExMwOl04rs/uIwuhwkGhoaBoRFLc1jYSNZEyIkMj7/7hQcZXkJ3mwGujQS+/KNzuLsng9HRUUxOTgIA/uNiAD+5sgGGpnDfgV6QW9RsYPDGg7343lk/AEAQRLxyvB1d9vJKlWoRsmsjiRdX47AZGRwfacN0rx02I4tohoeZ3TItOjHihJGlkc3mG92TCjwZu1Ou/ZlU67UeJtqsrdOF21I2aShR2PKsHLFVOHxVq2PTwlgoFotp1hjCsiz+6q/+Cq95zWsgCALe8573yFPEtws7hpCz2SxWVlaqDvNUAzURMjFsX19fx/DwMIaHh3H+/PmabsJKhOwwsy95Im8hleEQD64jbo5jcnIy79XJbmIRTORk8hYkwGKg8dMr63j8+U0YDRH85gkTZndvpS1EUYI3nEZOENHXYobdzCIQzyLLiWgxM0glk+DSacQZKw4dOQaHeYtUn7q6gcdeWEWn3QhBBP7+2WXc1SngtpeO4xVjHRhqs2A9kYPNAAy3VY5AKxHyuZUovvYLL2h6S2P9364Q/vieMfz5/ZP46i+WEYhlceeeDrz3tl2IhTarnuty7c9EYhaPx+Hz+ZBKpXD69OkizXStWtZmjJAB9VFouZbnwoG1yWQSZ86ckaV4Doej7hFbPM83nTn961//erz+9a/XbHvVsGMI2Ww249ChQ0WflzPwqYRKhMzzPJaXl+H3+zE0NIRbb71VjkK1HFp6x3gnLvhiWA5uvXqzYg53z44jGzPkFbsA4IFb+vHXT22Nk5IkYLjDgnROwD+fWoFJArICh7/5bw8+8moW4102fOtXq7i8FgdDbUW27751CGaWRjKdRjoTg9ViQVdPH2JZEWbDtUvk1FIYLRZWJv5UjsZCJD/63tVuxa52KziOq6sJIZnlEcvw+O65ABxmBvaX1B6+SAbnfDHcPtaO/+uNe/PWidW8l2solJglEgnMzs7KxBMKhWTnslL+yeWI8maJkGtBqRFbp0+fxr59++RoemlpqWjEllo7zkaNhYDtn6e33dgxhFzNgrNRQhYEAV6vFz6fDwMDA7IDm3L/tRJQxQjZROO+UQYvLETQO9qHW6d3o8VixIULkaL9jHXZ8NHX7IF7MwkTS2NfnwNfedKFFjMLWhAhSSIyEoUXliMQJQmXVmMYbNsqjIWSOXz7lAez9ggmnQzOr1shSUYIKR4njw7mKTJazAa4uRTwkpKNEyRYy3TNqiGjwgj5aXcIf/+MFxKA5VAa+/ockH36X8pPXw+UIh4ySTsej+e18JKpGsq0B8MwO5KQy6FcNK0csaVmYK0WKYtaG7qaDTuGkIHKBkO19NsrCVlpv9nX11e2+aGem68UISu73gYGBvCO174ij/jLkXhPiwk9Lde+o9XAIsdLsNCAKAKcKMJqYJDM8qDprZFR2WwWseAGQhLwrt+YwXGbDT8/N4+cRGN6ZFD2xSB448FeXFyNwRdOA9SWkuKWjvovIeXfaz2exd897UWLmYWJpRFL8zi7EsWJkTbwwlZxcKq3sVRUI1BOI1G28Co9KpRt4hRFwWg0wmq1wuFw1KRcKISWJKrlyKVK+fZSM/3IQ61wYC3pWCT5aOKjrOXx3CzYUYRcCo103fl8Png8HlX2m/VASa7E3GhxcbGk9SZBOVlaIe492ItLa3EEEhx4XsBApwV3THQilePB8zy8Pj8gchBNrbh9oluOBne3mcAwTBEZA8CA04I/u3cSF1djoGkahwZacOHM6ZLRoCAIVSvwSkLeiOdAUZBVGSMdFnDC1kPE2WrAfQd70Nui3uzpeqGcR4XH40Emk0E0GsXKyoqsXFBG0mpzrVpH21p1PdaaCiw3YotE016vF5lMBi+++GLJEVtqB9Y2Yyu9WuiEXADSZEFyUWrtN+sB6QhcX1+Hy+VStT8yZaQahjus+D9evxf/fWkZ2XQK957YC7uBwqbPiylzBJdFO1ijE4f7HPj16WudX9VUGZ12E16151pRrBQRLy8vw+fzydtTSs0Knd8IIXfYDZCka7K5ZE7AgNOCj79uoqpZ/Y2cPFIKJDo2Go15xVdlykMZHRamPAoJs1lTFlp16ZFo2m63o729Hb29vSVTRNUG1mYyGU18YW4kdhQhN+KJrGytbm1thdVqlbWatey/lgs+HA4jlUphfX29agdhlhNwzheDazWDkU4GPSpmqPa2mnH3ng4EAjwiAR8ura5i9+7dePtrbwewpV5gC7r2yCujWii/M4nw+/r6cPToUXmcjtIoR+n8JooiLBYLcrkcelvM+K1jA3jklA8UBRgZGh/6teHrOjlES1IvJQczGo3o6OgoO4C1sE2cEA7HcZqqLJqNkAl4npebt8qliMoNrD179izOnz8PSZJw9epVjI+PN3Rs3/rWt/DJT34Sly9fxqlTp3DkyBH5d5/73Ofw8MMPg2EY/OVf/iVe85rX1P+lC7CjCLkU1ETIwWAQCwsLsNlsMjHWo18mKYhqF3wsFsP8/DxompY105WQ40X80y+9WAmnkUll8MJqGkZ7mzzbrhxIk4zf78fo6GhRIZJlim9ymqZrIiaKohAKhWSPEBLh8/xWamQjyeHbZ8MIJXOY7mvFG2f2wEAD6XQay8vLSCQSuHjxIjiOQ4fJhA8esUJgzBjqbkNn6807+UFtmqFcm7jSljMSiSAWi8FkMuVF0vW0iWvdyHG9fTHKna/+/n7Z4P4Tn/gEFhYW8KUvfQl33nlnXceyf/9+fOc738Hv/d7v5X1+6dIlfPOb38TFixexurqKe+65B3Nzc5qd0x1FyKVuAIPBULbJIxwOY2FhAUajUW4kaQSE/Mvlmom/BcdxstGQGuL3BFNYjaSxq82CKJ1DhhPwkyvrZQlZGe3bbLaXjIZGVH2HWtQiJPJdXl7GgQMHis5fLMPj/35iETlegtVE46dXN5HMCnjXiUHYbDa0trbC4XBgcHBQnmZMXlH9y24svuRmRkioEY2rGmyHIVA9KLTlTKfTGB4ehtFolEm63jZxrdMfWkfI9eS3KYrC4OAgZmdn4XK58LWvfa3hYyGDBQrx+OOP4+TJkzCZTBgZGcH4+DhOnTqFW2+9teF9AjuMkEuhVISsjFAnJyeLBjMCtacfgPIKiEwmA5fLhXg8Lrcf14It8/mX2oVpGjR4cELpKDYSiWBubg4WiwUzMzPgeR6Li4uq96Wmsy+bzWJhYQGJRAJWqxX79+8vqWLxBNNIZQX0tG79rq+VxvPLEbz92EBRKkI5zVjZuMFxnEzSROOqzLs6HA5NHfO0gpZTp8mDopSRkFJetrm5WXVKdjPmkAkabQyJRCLbrkH2+Xw4ceKE/O/BwUG5XqIFdjwhK3PIiUQC8/PzcttxpRbLwgGkalBI/qStOhgMYmxsDNPT00U3qZqbZLDNApuRwUYiC4kTsZ7g8LqJ/AuPfDdRFDE1NSU/ZIgdYzWIooSL/jgue6NIJRJgnEn0O81yEwiQPxFkdHQU09PT+NWvflUyxUFRFAwMBRGSTCi8IIGhKZBMiZpinMFgKNK4Ku0518+t2WUAACAASURBVNbWEA6H5Y47EikSqVmtaMbuukrXRzl5Wbk28VwuJ5+XRt3etsOcvhEFSK1NIffccw/W1taKPv/MZz6DN73pTXUfRyPYUYRczhM5k8ng/PnzyGQyGB8fz7u5y6EeQiYRMs/zWFpawtraGoaHh7Fnz56yN6eavLPDzOLdtw3hp1c2sBbkcWzAjLsnt6JIEn0nEglMTEwUfTe1XhaPn/PjGXcIy5txJNJZXIi4cWykDW/Y3wurkcHKygqWl5fliSDkeEttX5IkCIKA4TYjRjusmN9IwcBQ4EUJJ2f75XNRrzqiMI+4vr6ORCKBnp4e2SRneXk5r7uOvM5XmgCtdVHvRrq9lWsTP3fuHAAUub3V0ya+HRHy9STkn/zkJzXvY7sd4HYUIRcinU7D7XYjHA5jZmamrFNZKdRrwen3+3HlyhUMDAzIbdXV1hEEoarGudNuwluPDCIctsHv90MSBcy5FrC5uVk2+lZuvxKCiRxeWI4gls5hM8mBEiV4gimMddrw7BUvrEk/Ojo6SmqxKYoCJ4jwRzOQJAndDhMgiZAkCWajAe+/fRdOL0UQzfAY7bBiT7cVgiDIZKwFCVIUlWc5SUDy0iTvSiJFMmWDENB2eCgTQr4aSODP/m0Oa7Es9vU58Ol796DDVlvkrlWagWVZsCybN0RU6U9Ra5v4dpjTN/I9Y7EYent7NTueUrjvvvvwtre9DR/+8IexurqK+fl5HDt2TLPt7yhCJhdMNpuF2+1GJBLB8PAwkslknnRGDWodWrq6uoqVlRU4nc6Ko4wKUY9JfTQaxalTpzA0NJQXrZaCmgiZF0VE0hzm11NbJClK8IXTuLzohS1nxf945S1lJXk5AfiXX/mxkRIBScIupxlvOdwPI8uAZVkYjcCdk1s3vyAIMgnzPI9oNAqz2YxcLreVG1dE3VpAmZdW/v3JlA1lcYxElblcDpFIpOGxUZIkIZoR8L7HXkQyx4OmKJxaiuAD/3IBj/72LTVFz1qbCxW6valtEyfLkgcZx3E1TWavhmaaFvLd734Xf/iHf4iNjQ284Q1vwMzMDP7zP/8T+/btw4MPPojp6WmwLIu//uu/1vShtKMImeM4XL16FcFgECMjI5icnARFUTUVtQjUELJSzdDW1obh4WEwDFPTjayWkEknn8vlAk3TRRK2clBDyB02I7KcAJqSkBMAjhNB0yIyTCvunN1Xloy9oRQeu5xEmM/h6EgHjCyN5XAav/JGcevotdSJkmjJOXO73ejq6sLg4GCeOZMgSkhxAqwGBixDy9GvlhFsqeKYIAiIxWKIxWJ5euDC13m1LfiSJOHKRhqiJIF96dglSYInmEY4xaG9hij5eisjqrWJE015MBgERVHY3NzMa/5ppE28EWhJyPfffz/uv//+kr/7+Mc/jo9//OOa7KcQO4qQJUmCzWbDxMREwxdwNUIOhUKYn5+HzWaT5/Otrq4im83WtJ9qhEy0xPPz83A6nTh48CDcbrfqpzJFUfAnBDx5dQM2I4ODg61yoU6SJISSHERJwondrdgIx5HgORhMFNocdtyyuxNDHaWnrLzoi+Jj37uEjXgWOS4N13oc94w5INAsVoIxSCNtRTclGf9ktVpxyy23FJHb4mYKf/OzRcSzPGxGGu97xS7sbtuKwEh0TdPbQ9IMw8But8NsNssNQcpRSErzdqPRKBfSyuWlJUmCzchClPIjXFGSYDHWHlE1Q7GxsE18YWEBbW1tMJvNReOiam0T1+It4GZ3egN2GCGbTCYMDg5qsq1yhEymR7MsW6RdZllW1oaqRSVCjkajmJubg8lkwqFDh2C1WpHNZmtKcbzoi+HHSxyGhTA4QcT51RjeeXwIFAU8enoFZ71RpFJJmMU0drXZkBCsWAvHYTYwuKPEAFFJkiCKIv72Z4ugAHQ5LIikOWQEEaspClaGA5UI4pe/XJMVABaLBeFwGKIoYu/evSVlhhlOwF/9bOtNpq/VjHiGx1efXsFn3jQFE0PJ+yX/BSCfB57nIYqi5k5mpUYhKV/nlXlppdG9w+GAKIo40GfD/n4HzvtiyPEijCyN3zo2WHbG4PWClukPlmVLjouq1CaufOMgb5NajW/S0gv5RmBHEXI51KO/LJwakkwm5SnL5SRz9U6rLiTYVCqFubk58DyPvXv35nUl1Zpz/umVDThNlOwE542k4QmmsBZL49m5NZiFJDptNiTQgX6HCYubCfTZGezpb8F/XAqgz2lGu80o5/dIpBrLcDCwNIwMDU6QEExmEeOAN84M4e7JLjA0hUwmg4WFBSwvL8Nq3SrmXbp0Sb4hW1pa5JsylOKQ4YStoiC2lCXr8SxCyRwGnBb5uxMQAia5+7Gxsa2Uh8KsiWGYa+OeNDLxqfQ6TwjI6/UiGAwiEongAwcceL63DVGOxszudty1t7vC1m8uVCrq1dombjabIYoiMpmMPCG7Vmg5vulGYUcRcjVP5Ho0xYRU1AwtZVm2IZP6bDYLl8uFWCxWtoGklhl5wJbtprJDmgaFSDSKp8+6wfMUuvv6YDKwYNIcXBtJHN7VivBGGoNtFvheIu82q0EmQJIueMVYB757zo9WMwurkQZgwv921yhmh5yQJEm2LB0cHMT09LRMiIIgyAU1v9+PeDy+tV2jBelUGjFKgN1qBi9utcI4zKUvUfKmQgyZDAaDfF7IsRZG0yTlQY7FHUzjheUorEYGd+/tbOhmYFk2Ly99+fJl9Pf3g6ZpdLbHX/rOXpw65YHFYsmLpm9UzrVR1No6XalNPBgMQhAEzM3NIVPQoalWCZPL5TQtMt4I7ChCBiobDNVCyKT4RJo61A4trSdCzuVyWFhYwPr6OkZGRjA1NVV2X7V6TRwZasO3llfRm+URTWUQC4cRXo9hlbdhLpzESiKIPT12MDSNdpsRSqoXRQkURPk7ETIGgHeeGMRSKIVn3GFYDDT+56uGMTvklOcatrW14ciRI0UyOYZhiuwqifTqzYIfj53bwFo4DlGU8LpxCzZXvci+lK81mUzIZDKYm5uDJEnYv39/3iTxcioNJUETjfSppSg+8W/z4AQJNEXh0ed9+KsH9mqaqyV56cKmjVQqhaX1CNZWgzAJ3jzjdpKbrqSXbhZoYeVJ2sRJwZDMbiylhAHKt4k3k9tfI9hxhFwKtUjYSDea3++HwWDA8ePHa9Iu1xIhi6KISCQik341CVs9eNWeTixcpRCKBGHgM/jdV+3FE540GDaGoXYL1uM5nFuJ4denu/EbM314/KwfwTQPJpxCq4XBaIc1j4gJfrkYBsvQuO9gDzKciEsrEbSlV2E3sZiY3Ad/UoQnnMNoJwumimMbkVPde3QCxyeHsJnMocNqgJ0VZXOd5eVlOZru7OyU9aZq0gzKqJjgL3/2IiQJsL5UYPNFMvjuC0s40g5Zhldr8ZAXJTw5t4lggoM5nYOlncPfPudCNM3j1/Z24K49neBFCQ/92yKecYdBURT2dtvwtbcdBotrbw7r6+tyXtputyOXyyEWi8Fut2+bj0c90NJcqLAppJY28ZWVFTz99NOQJAlutxsjIyMNHddHPvIR/OAHP4DRaMTY2Bj+4R/+QU6FbKfTG7ADCbleC05RFGUf38HBQRw+fBhXr16tKUpRS/ySJGFtbQ2Li4uwWq0YHByUR9hrCUEQ4PF4MGzJ4t79E+jr6wNFUfi75y+i22FCX6sZGU7EajSNO/d2YqrXAfsxGt9/cgW9ljRmdneAkXiUukyeW4ygr8UEGhIysTCWIxm8amwUfX0d+PgPrmIzkYMoSTgw0II/ee0EjIy6G6TLYUKX45r6wmKxQBAEbG5uYnR0FF1dXXkpj3Q6LVf0SV5ajQFRIsvLsjpRFJHjeIQTGYyfmMZpbxzJrICJLgsGnea8hywh6sLtC6KE//WdSzizEoMgShAFAXjuIgRpq338ibkgPnw3h2CCw3OLEeQECYCEK4EE/vwnbnzqDXuKjNtJO/jm5iZcSytIJhIwscX+yWoHJ2jprwFo2xiiZlvl2sSHhoaQSqXw5JNP4iMf+QgWFxfx0EMP4a1vfWtdx/LqV78an/vc58CyLD760Y/ic5/7HL7whS9su9MbsAMJuRQqESVp6vD8/+ydeXycZbn+v7Nnm0z2vc2+tIVuaQoIaFFKpSgIKBY4gj88ogi14EYV2c6xFFA8LAWKyM4BxIKWg6UuCKhAU9pS6JJlsu9pkklm32d+f4Tn7TuTmWSSTBDaXp8PH+0kM/PM5H3v536u+7qvu6ODvLw8qanD6/XGpUAXDiFhS01Npba2FovFgslkmtb7TAX5ZyooKCAlJYW8vDzpZszR6xi0uslM1qLTKNGplaQlaPD7/eSn6vjmF09hT+sR/nTwCB53F2UpfualJ0gBT6/XoyCIaXQMl91KRkYGORo9htRUnninmyGbWyoE7u8x89eGIc47KQYD5zAIoySDwRBCf4S3BLvcbmwfFdTa29ux2+0hN28kY/wzKjLZeegIyqAfXyCATqNmTW0Vd/ytk+ZBG6BArVTw0zUVLC7Uh1Ae8oG2gprY3Wlmf48FtQI0aiVmjw+XP4hep0KtUOIPBHn07W7Ks5Jw+Y4SQx5/kA97I49pVavVJCbreboJDo+NoUDBOQuyuLYqF6c91A9Y+CfLfTzCg+9czNOLV4CfjdNbXl4eq1ev5uWXX+bll18GZkdhnHPOOdL/P/XUU9m2bRsw905vcJwE5EgZcjAYlCZ1ZGZmTpjUMZPW6ckuTqHBVavVIVaVM+nUmwzDw8MYjUbS09OlVucjR46E3IyX1hXx0FttDJhd+INBTitNpyIrQTr+t5u9HDIFyM3Nxh8IMuj0UVucQSLjR+euri5SbVbeN6lIS0lkwOomP03DvDQdvWNOSdalUChQKRX0jbmm9RmcTqdkArVo0aKotqgur5//fq2ZN5pG0KgUfPezJVy64mTgaHZpsVjo6enBZrMBSFnl+fP99PX5aTArSUtJ4PrPl2Fx+WgatJGVPB7M7G4fv3m7i4cvXTxB4SH+VwRqi9ODgqPXQPiVoAB8/gBlWUnsah/9KEMGtRJKo2i9AR5/t5sDw36SE8avzb82DlOencSlK476J8j9k6NpgfV6PWq1+hNFecgx22w7XIMcr43i8ccflzLtuXZ6g2MwIEczGJI3bAhD+pSUFJYvXx6xMhuvP6jD4aClpQW3201VVdUEudxMA3I4d2q1WmlqakKj0Uia5fD3EBlIviGBjWuq6BtzoFUqyDfopKM4QOsRB2lJGqmBxOHxc8Thp8KgwWQykZKSwjfPW8IZQw4O9ZhQ+l3MT3Ty4ft70ft8GC1BspI1KFVq/IEgVTmx+Uz7/X7JHW8qRQvAvX9v483mEbRqBcEgPPRWO/PSEjijIhO1Wk1aWlrITSpkcu3t7Wg0Gr55cuJH00sS0WttvDcYIBg4+r3q1ErMzolUV6Ti4fKSTJTKDjy+AAT9KBTjwdb3EU2gUij40knZfOu0It5pM9Ez5kYBpCao+ek55VE/475uMwrF0esxGAzwfrclJCCH+ycLyPXSw8PD2Gw2PB4PTU1NUpCereNbvCCfFjITWCyWEPXGVIjF6W3Tpk2o1Wouv/zyGa9rujjmAnIkaDQa7Ha7JJXSaDRxMaSfDB6Ph9bWVsbGxiQJ20zNf8IhpG8qlQqXy4XRaMTpdFJdXR1RHy2Xyokjt1YZJCdFy+EBK8ZhO/PTkyjPHv8+EjQqRp1exLfjdHvobm9HoYeamhrJ92BRkY5FRaFC/IWL3dzxWiOH+234Ax5qsxXoTC0cPnwkRHscPlevv7+fzs5OioqKqKuriymT29U+ikoBSoUCbyCA0xvgTeMIZ0RoaBHqjEAgQG1trXTzB4NBycozS23C7XYxOOJEp1Zi8yn5TIkhJm1srl7HTavy+NUb3dj9Sk4pS+OyFYU89m4XVpePL1RlcsXKApQKeOYbJ3Ow34bPH+DkwlSStOqodEKBQcfB3qPfE0BRhAG0kRCuBbbb7bS1tZGbm4vNZpvg+Cand+I90HcqzDZDnq4X8lROb08++SSvvvoqr7/+uvR3n2unNzgGA3Kkm8br9dLX14fFYqGqqmpaO+l0EQwGaW1tZWBgIMRPIxpmql12u9309PQwPDxMRUXFpLI8Malabu7j8Qf4vwNHGHN6SdAoONRr5bNVmSwuNLB8voH/+9BJj8nB6OgoKp+TFadXUZQ/dVNDapKOzRctZszpRaNSkqJTS9pji8VCb28vVqsV4GhTiMlEWlpaRJncZMhI1nLE6sbm9uL2BQgC2/b18cWFOawoHr85/X4/O95r4h/NIxTlZ3H5aeUkJR2lphQKhURj5Ofnk5I1ysP/6MDi9HJKgY4LKnU0NDRIQVnOSwsHNIvFQlNTE/P0el5df3rIZzg9wuagDQRYWaqL2nko/mYKhYKrTytkd9sw7sB4MC5MS+DKU2fWjSpkapFODpHm1CUkJIRQHvJNKd4Fwo/benMy7Ny5k7vvvpu33norJGufa6c3OAYDshxOp1NqtEhKSqK2tnZaz59Oh18gEKC3t1dqEY3FehOmnyEHAgHcbjd79+6luLg4JqmcUqnEbrej1WolCdjAqAuTw0PhR9lWsjbAex1jLC40kJGk4dScIB+29rG4PJ9lVYtJ0sUeKBUKBemyoBdJe+xwOGhsbMRisZCcnIzVamXv3r2SgkAUDycL0D86u5xvP/uBVCRTMK52+PHLh/n79acxNDTEtnebecEYIKhQohga4e8tVp64YmlUc5+VJemsLIncfut2u7FYLFLLtN1ulwaQFhUVkZ2dHbPhk/heBOTBWV48VHnt3H6GHmdyPhq1iqVFqSFDA6aDaNeyvACan58PHDW5F5SH8GkRvLQwnIqXE91sp4XEMyBfd911uN1uVq9eDYwX9rZu3TrnTm9wDAZkhUKBx+ORfJDLy8spLy/n8OHD036tWDr85MXB7OxsDAYDhYWFMRdPpuP2Jt4nGAyyePHiSSeeiOcEAgFycnLo7u7GaDRKxjjmgA6vx0swmPARRwlBjvLrGRkZXPT5U2ct/A+HkOINDQ1RUVER0n4sGkQsFovkoie4RdHhJZ8EsjBfz3+sLGTrvzoJBo8W0kx2D3v37iUxMZG/D+rQaLzo1ON/j1GHh782DPH1FdM/aup0OrKzs8nKyqKvrw+Hw0F5eTmJiYlYrVY6OjokhUdKSoq03nCKJhLCg7TH45Ha50+qqUSn04U0tohrRmilY9FLT4cWkJvcR+KlR0dHcblc7NmzRzpliKA+E146HtNCysrKZvx8OVpaWqL+bC6d3uAYDMgWi4V9+/ZRWlpKdfV455WYgDxdTBWQR0dHaW5uDikO7t+/H5/PF7NNYyydd0L+lZSUxPLlyzEajZNmJSIQi6NwVlaWJBMTTQZ+0xhum4k9RwZJ1KrwBFVUpHjp6krm5JNPnlWBJdqaBgYG6OjooLCwkJUrV04IING8eUWQHhkZoaOjA4/HI7Uf12So0agUeD/KkoNAQbJS8hvx/KMeh8fHmCOIQgEqhSJEdjZdCMMnMWFbBJFwzwaRWQqKJhgMhnh4CNVDpO+pp6dH8ueIREWFZ9PhlEe0IB0P2ZvgpRMSEnC73SxatEjqshOfN5yXFp97stPOJ4my+HfimAvIer1+Al0wUyVDNH7XarXS3NyMUqlk0aJFIQEknjI2YTLk9/tD5uRFe4/wQKxQKOgedTJocZOSoKYiOxmtVktWVhZZWVnMKy7hvfYRWjq60SucLJ6Xidfr5YMPPkCtVkuBIzU1leTk5BkfTUUQ0+v11NbWTquFXe64Fn6ctlgszAtauahCy+8aXSgIkpmo4q4vl6HVjuug56Un0jniRKWEQAB8BJmXPn2/A9He7nQ6WbBgQcjfPBwqlSoqTyv8lltaWiS/ZXmzQ1tbmyRZjJZlTkV5yK8BeZD2+XxzYnQfbssp1uNwOLBardJGKh++KgJ1QkICCoVi1kW9Y8FYCI7BgBypTXY2/q/yzNrpdEo3ZVVVVcQLYCb65XDIFRpVVVUT5F/hBkPh3KPIjg70Wni71YROo8TjC9A2bGfNwhzUHz1/uL+HxLEBvry0hNzc3JDvyev1SnxpW1sbDodDspgU9MFUHXHCmMnj8UwZxKYD+XFap9Nx9nwTX15YQFp2LtrA+MimxsZGXC4XDquPfL0KszuIVqUkU6/F5o59wxRGSd3d3ZSWlk74nmJFtC4zh2O8cGo0GnG5XGi1Wmw2Gx0dHdLvi6A11etD9CBts9no6emhqKgIj8cT4oI3HUc8gakCaLTTjhi+KjotXS4XGo0Gl8vF4OBgTN7JkWA2m6ek8D4NOOYCcjwhgqvX66WtrQ2TyTSl0dBsMmS/309XVxd9fX2TKjTk7yE4RRGIxYUcCAbZ3TlGXqoO9Udty71jTo5Y3KjcFtrb28nLy6Ouri7ijaXRaCbYJ3q9XqxWKxaLJaQjLjyTDgaDdHZ2cuTIEcrLy8nKyvpovpyVvZ1mUChYWWygIidFWqvT45fsPGOBkPv5fL4JzSNyX97Xhg6jMNkpVwXxer0csTkY6GqnSWeW1h0t+zebzTQ1NZGenk5dXR2+oIJt7/czYHGxuNDAZysy4mKq3t3dTUlJieTPIYppQpUiArWc7og04y4cQl3T0dHB2NgYCxculL4nueJmMke8aIExlskj4ZBvpPJOS4/Hw549e3C73SHeyfIpJFPx8MeCFzIcgwE52kU6E09kpVJJX18fzc3NFBcXTzo9WmAmGbLIwjo6OsjPz59yPJNcxianJ+RrCwYhGAiilBn7uF1uPvjwQ0qyU1m+fHlU6sDu9uHxB0jRqdHIAqRGoyEjIyNksrXwAbZYLNKN7/F40Ov10jDNYDBI27CdVz4YJD1ZQzAIf/hggK8tzyczWcf/HRhg2OpBqVTw+epMFuZHlyUGAgE6OzsZHByUNsfJ8I1Ti7n7r0ZsvgBBlZpFJRl8/axS/O6jxUMxL04Eu8TERPr6+vB4PFKw9/gD3PD7gzQNjnf8/fGDAS6vK+Kqz8zMg8Rms9HU1ERycvIEuV+kYprb7Y5qii/fDOXX9/DwMC0tLRQWFrJixYqQ6yOcWxb/K7Jq+SYPE3npePpYaLVaNBoNxcXF0mNCKinGRdlsNgKBwAS3N3ENnwjIn2BEMhiajiey3M83LS0t5vl1MP0MWWQEY2NjE9q3o61NpVLR09OD2+0+6i0RtlGolAoW5Os52GchWQ3d/UfQKPysPGsRmWnRA94HPWb2dplRACkJatYszCY1IXoxRvgAq1QqhoaGyMrKoqSkRMryurq6sNls7Or34/BrSFEkkZCQSKJGSdOgHZvbzJjDS35aAh5fgL80DI8bDKWEFkWDweC4yU5rK3l5eRGLgpFQmZPMf3+5hqZBOzq1kiVFqeOt3ckJEzYWi8VCd3c3JpMJjUaDRqOhs7OT1NRUjGZoGbKj142b3vsDQf73vR6+cUpRyKY1Ffx+v6QAqqmpiVkTH8kUX5xYrFYrnZ2d2Gw2qWvPbrdPaNOPhslsS6MVD51Op8T9znacViR/78lsWsUUks7OTnp6enjwwQexWq3s3LmT2tpaiouLZ3xyufnmm9m+fTtKpZKcnByefPJJCgoKCAaDbNiwgR07dpCUlMSTTz7J8uXLZ/yZo0ExTROOT4XpqMfjmRCQ33//faqrqydVD8iHlmZkZEhOY6WlpTG/d29vL16vl5KSkkl/T2RISqUSt9s9acYq1iYv2Igb0WKxYLPZQjhKg8EwntX5/PxlTxOtA2NUzC/krJPnkaKLvgcfsbp55cMB8lJ1qJQKTHYP6cla1i6KbgzkdrtpaWnB5XJRVVUVcTwTwM5Dg3zYNUKKyo/L5WLA7KLMoKDXpaEwfbztV6fTMWj1cO6iXCpl7dZ2u52mpia0Wi2VlZUxK1imA6FkycjIoLS0VNpYRUPLP5qH+M0+CzrVR7yrUonTB/93TR36xKnXI7+2ioqKKCoqirvfcTAYpKuri+7ubjIzMwkGg1JmKZelTaV4mAxer5f29nZpQwn/W0xHhid/zYMHD7Js2bJprycQCNDS0sI3v/lNLrroIvbv38+qVau44YYbpv1aENqCff/993P48GG2bt3Kjh07eOCBB9ixYwf19fVs2LCB+vr66bx0TH/sYzJDjoSpLDjFDZmYmMjSpUtJTExkaGiIsbGxab2PaGeOBvkEkqqqKtLT09m3b1/UrDpSwS58OgUclVqFUwf5qaksX1mCwWAgSTO1JaVSgeRfbEjUMGB2EQiOm7jLIfjuwcFByRZzsgBTV5JOy5ADpy8ASYkUG1R8dXk+2/f1YLLZcbtHcTpdjLiCdCdZUTrSSdMnMzQ0JHVYzkUV3e12YzQaQ+gJAXmWdl5mHr83vo/V7UOhBKfHx4JMDQ0HPpCO0uK0Eh7wHA6H5DMyXZVJrLBarTQ2NpKWlsZpp502obgnZGlCyy7Xd4s1T7XRmUwmmpubKSgoYOXKlSFde5PJ8KYapzWbphClclziqFKpuP3222f0GnLITyx2u11a8/bt27niiitQKBSceuqpjI2N0d/fLyl/4oVjMiBPRlmEw2azYTQaCQaDIdKyyZ4zGaI9RxjfHzlyhLKyMhYtWiT9sSeTsUUq2EWCkFr5fD7pQikqKsLpdIYcaUUmbSeB+n4v7qCShfmpfL4qC32CmmBw3Gjd4wvw14YhXN4AvWNuvrosnwX5eqlBpa2tjfz8/Jipg8xkLf+xsojWYRsKFMzPSMTi8rG0NIs9nWrcvgC6xCDL9Fo63W72fzhIgs/GwkwVCTotPT09UvYy5lNjcvjITNFSnDEzvXQgEKCnp4fe3t6YJsKkJWm475KTuO/vbQxY3CyryeF7nysZnywdoaFFzIoTY8AqKytDOOF4we/309raitlspqamJuIJRX56KigoAI4qPESTR2dnpzQCSc5LJyQkuwpSNQAAIABJREFU4PP5MBqNuN1ulixZInXpyV8fJio8xPqmKh56vd5ZaZBnq2EOx0033cTTTz+NwWDgjTfeAMZPvvPmzZN+Rzi9nQjIM0R4huxyuWhtbcVms1FZWRnCJwrMJCCHB1fRUi3my0VqdQ5/TiQ98VTHW5vNRnNzMxqNhsWLF0s3TUJCQkgm7fP56D4yyjNvd6EMeMHv4dWufrq7e1l7Ug6Lc7UcOOKmvmMMl8fPsvkG/IEgz+3p5aoV2Zj6xmfCzSTTS0vSUDs/HavLxz1/a6XP7CIYDFKWlcxlKwtwegL8q2kAr2WI7OQkFMnlZMwzcFK+XqIOXtnTxp+NVoKASqVmTXUa5y0uiNpoEQmioScrK2tSvW84ijOS+PVXT5rweCSJ1/DwsKS9TklJobu7m9bWVhITE0NUKbOhX4aGhmhpaWHevHlUVlZOiwKR67vl01fk7eF9fX3YbDa8Xi9paWnk5+dPKPZFQyReWl48FNe4z+djZGQElUoltaFPl/Iwm81xdXrbtGkTmzZtYvPmzWzZsiUumXesOCYDcjQLTiFha29vZ3h4mPLychYuXDiphG2mGbLgDFtaWqQbPxpvJ95nJoFYaJbtdnvUadjh63OQQHKKnmy9Dp8/QCFB+s2OceG+00JZwMrbdh8laQm4HePFoVGTmX0NFs6pW4BHqaNlxI1W7aUkMxH1NAs62z/sp9fsJC913IO5ZcjOB11jYBvCPOpkYel8EhIScHr9DFjcLC40kJqailKXxHtvj1IxLxeNUoHT4+XNdjuVhj4UHvsE6iA1NTUkSLvdbqkdeS66EYGQmX/h1q7yhhYhd3O73VJWKtY9le7Y5XJJ9Yfly5fHjVNXKBQkJCSQkJBAamoqVuv48IHi4mIpUAtVilzhITadWDxV5P/rdDppaGggISGB8vJyqWs1UiY9WZCebpfeVE5vApdffjlr167l9ttv/1ic3uAYDciRoFKpOHLkCJ2dncyfPz8mU56ZZsiix1+n07Fs2bIJR7xIz5FPKIklEMs53Fhc5eTQqpUM2dw0DdoIAkoFLMrXh8iOdtmacXvc2Ow2HA4XTq8CnTKBvU2d/LPXP25UpFZTlpXMBUvyphWU+8bcJGmPXnp+r4u9DW1cUFuCVYsUYBweP/PTj3539o8aOoSqIVGnJSkpSGFxGUXpiRG5UtENJ0bQl5eXx/2YCUcpkL6+vqhyPLkOV2ilw7NSue5YvrGIa6i7u5u+vr6oU8lni2AwGPE9UlJSJoyXEoXlrq4uaQip3MNDr9dHPH2I9vDe3l6qqqoink6n4qXhaFNLPJtCjEYjlZWVwDhvLIaunn/++WzZsoV169ZRX1+PwWCYk+vomAzIoXrcca9dcVycjoRtugHZ6XTS1NSE1Wqlrq5uymOUuNCSk5MxGo309vZiMBikTrhIGXUwGGRwcFAaORUrhyuHPkHNmMOL1x9ApVTi8PoJyDj3YDDI6rJEHvvXANqERJLSc7moPIM1C7J45M0WEtV+lF4rbpuHdwaCGLwmFhZlxNxiXZGTRNOgDU3Qi9liwR3Q8rll1SypyMbZbqJx0EYwALmpOk4qPPodpidrMCRqxtUfSRrGnD6StSqyUsapk0hc6cjICE1NTSQlJZGZmUlPTw+dnZ1Sy/Jk33WsEAVhMXlmOgUqeVY6me7YZrPhdrtJTk5m3rx5kr47nkoNm81GQ0MDaWlpU36OaIVleRee8J+WO/ipVCpphNlk7zEdR7wdO3bEbXLHxo0bpdNHcXExW7duBWDt2rXs2LGDiooKkpKSeOKJJ+LyfuE4JgMyHNWttrS0kJaWRk1NDSaTaVo3SyzGP0BIJ19ZWZmkD55qfeJIlpubS25urlQYGh4epq2tDZ/PJx3BU1NTCQQCtLe3o9frp5TJTQab20dVbgoalRK/P4g+QY3F7cPrD+By2GlubiYhIYHbL65lzD0+mVnYdKLRkadPlrr/NCY7+rRkyWEvUot1eJD+fFkq7zd1YDQFSE7S88WqbD5XlY1KqSAIDNs8KFCAAtw+vzQZWqtScu3nSnjsnS76xlzkG3Rc9Zn5Ee0o5Yb04acUuSm9mF7s9XqjuspFg/C3cLlck46amgmE7jgtLY3W1la8Xi8LFy4cHxdlsUz4rqM1h8QCManFZDJNKGxPB5Nph81mM62trVitVnQ6HU6nk87Ozmlx6eFB+siRI/zwhz9EqVRy3333zWjN4XjppZciPq5QKHjwwQfj8h6T4ZjUIZvNZg4cOIBOp5N2NKvVSnt7O4sXL57Wa73zzjt85jOfifgz+aTq4uJiiVN69913oz5nOjyxCBwjIyN0d3fj9Xolk3R54JhuhXnI6ua3b3eRo9eiUSkZc3pRK4KsynHjcDgmNfH/e+MQ+7rN5KbqcHkDODx+rji1KMT/WN5ibbFYpMCRkpKCy+WSNMua5FSUCoWkje4dc7FtXy/5hoSjOugkLV9dXjBhHf5AUJLnySH+JgMDAxPsPSeD3FVOrF0Eafn3LUyL+vr66OrqoqysjJycnLhrigGJdpk/fz4FBQUR30NOHcg16bHaf46OjtLU1ER+fj7z58+fk89htVppaGggMzOT0tJSFAqFpP4R37fb7Uan04XQHYmJiRHXEwwGeemll/jlL3/J7bffzoUXXjgn644zjl8dslqtprq6OiSoxMP0R0BYSba1tZGXlxcTDTKTgp2QsI2OjrJgwQIyMzNDXMMGBgYwGo2S8F8epCdbT7Zex+qF2fytYQgAr9NGrcFJZmY5CxYsmHRdZ1RmggKaBm2k6NSsPSknJBjD+Hc94tPhUKeTU5xHXqqO3t5e2tvbpSzOaDSiUqmkNaempmJzBVAqlCE66CGbJ+I6IgVjMdE7Nzd32lRONFc5p9OJxWKRpGEulwuPx0NycjKlpaWkpaXFPRgI6kutVk+pZpmMOog04FUuZ2tra8PlckWUssUDgUBA6kpcuHBhiApFzAAM59JFkO7v78fpdEqG+DqdjuHhYfLy8rjxxhtJTk7mjTfeiHnD/bTgmAzIiYmJE7LGqRpDJoOcqxOSKb1ez4oVK6Y8as0kEAupXE9PD/Pnz6eioiJEVC8yCJGRCzcvi8VCX19fyIgkeZCWB6jaeQYyFE6aWjsoK8+honRxTHSOVqXk89XZfL46sodEMBjkjx8MsK/bjFIBHo+XpXobS+alc+qpp4ZwtfJMurW1lV6Tnd4+PwFHIilJSdj8Kkqzpz4+O51OmpubAeIaXOTDQ7OysqQiYVVVFYFAQDL/Efpd+Xc91Qy+SAgEAnR3d9Pf3x+12BULolEH4hoRQVKr1ZKWlsaRI0dm3cEXDrPZTGNjI7m5uRN8NCJBzqWHGw8Jx8GbbrqJxsZGsrKyOOusszhw4ABnnXVWXNb7ScExGZCnckibDkRmLSRTwAQP5EiQFx3klphTPWdkZITW1lapQBQLHSGMceQngkhZknDQ0ul0jIyMkJiYyFmnLos4dXum6B1zsa/bTF6KGpNpFI/bQ4M6ja9VV0/o9gs3K1oM5HaM8HrDAP1mJ4kKLym6Yfbs6Z1g+ynMooTRUKyqA48vQMOAFX8gSHVuCsmTtJLD0SJqe3s78+fPDzGYkmd3U8nZBE8a7RoQznIzKQzGAqVSiVarZXh4GK1Wy5lnnjkugYzQ0CKnaWLh0uXw+/20tLRgs9niIi3UarV4vV5+/etfU1payvbt29HpdHzwwQdzktX/u3FMBuRImOmxUqlU0tDQIHGrsWQtYoyUkOXEcnS2Wq3SiKUlS5bMOkhGypLEUdhkMkkGNB9++OE4jZCix6NKQK1NIEuvI1k7s0vD4fHhsNvotzpJT08nJyebvjEXPn8QrXrqv8HykkwWFaXh9QdJ0qrGJ0p/5M1ssVg4cuQIDocDGFciZGZmxjxB3O7xsek1I10mJzDeqHLr2iqy9ZFPOcJDIyEhYVLqYCo5m9xGU86TCtVBW1sbdrs97oVBAbnMLHzjms6ElqkaWkRrdVFRUUzOiFMhEAjw4osv8j//8z9s2rSJL3/5y9Jrnn766bN67U8qjpuAPF3IfWTLyso4+eSTY8pwxQy7PXv2SPSCkLJFkoS53W5aW1txOBwxNXbMBOIoLHyW5SbrPp+PMbOFvx0eoH2oB6/Xg1al4vOVaczLSQ/JSKfCyMgIvUYjGoUKfWYuCQnjXhjl2clo1bHzuTq1CnniKvdmFpsKILnKie9Po9GE0Abh6/7L4SE6Rhzkpo4Hk2Gbh9/t7eW6VaGz2OSqg5l6aESSs8l5UrPZLHlKC39gcYqJVsyaCaYjZRPrnmxCS6QTQFJSEsPDw/h8PpYuXRqXE1d/fz/XX389GRkZvPXWWzOmbz5tOCYD8mQX81TaTWG92dnZSWFhIfn5+aSnp0/5HDlPXFxcTElJSYhXsFymlJqaSkpKCjabLSbT+9lAHEVzcnIitgmr1WocikSc6hRqF4wXSEZtbrpdHnI+2izsdntIsBONCmK9gsNVKBScWruEqpPG/YJHHV6q8/R8+eTobnGxQmyQw8PDUVvdBd8oxiSFB+m+URtqWTEwQaNk0BpaNBTfV0FBAStWrJj1DDo5RJAWGauQLwopm9DvOp3OEEP68O87Fsg3lenYfEZbd7QTQE9PjySTDAaDHDp0aEJDy3TWHQgEeP7553nggQe44447OO+88z4NCoq44ZgMyDC5wVC0wsXw8DBGo1GaaabRaKRW20iYqmAXqQLu8Xjo7OykpaVFOgJ3d3dL3UZTcY2xQnhbaLXaKbMWt9cfEqiSEzR4fCpKSo7KzaIFu0AggMfjoby8nLy8PBQKBYWJcO3nYrcsnQoiSObn51NXVxc1SIoBnPIjuRjqarVa0XvHMJntBN1KNBoNFg+sKkuTMkAxJ3HZsmVzYvEpl+RVV1eHXBfZ2dkTilmC7gjfXESwi3ZykUvZ4r2pCHi9XsmU6zOf+Yx0LUcy0ler1SGcdLTmob6+PjZs2EBubi5vvfXWMWE4P10ckzpkmJ4nssVikYx5KisrQ37e1tZGYmJiSJvkTAp2MN7RZTQa0ev1lJWVhVzE4uYzm83ScVBkRwaDIebCitfrlQT4VVVVMVEgI3YPr344QHqyBq1ayYDZzUkFqawojnxUF4Uu4Rut0WiwWq0hHGkshaypIGwr1Wo1VVVVsw6SwWCQ3+/rZ/sHffh8fpbkajhnngK71YLf7ycrK4ucnJwZZXZTYWxsjKamJrKzsykpKZlRkJRvikLfLWRhYs29vb243W4WLFgwJ0UveZGzvLw8Jge7aLp0vV5PX18fOp0Oo9HI1q1bufPOOzn33HOPxaw4pg90zAZkr9c7YRLBgQMHKC4ulo5vIityuVxUV1dHDF5dXV0oFArJei/cEjOWC8fhcNDS0kIgEKCysnLKwo2csxP/eTweqZPMYDBMkCjJ7SRLPprPNp2LusvkYFf7KB5/gIrsFGrnGyJOwrBardLoofLy8gkbRfi6wzeXWLqy5PSE8IyOJ/yBIIFgEKt5fIPMyckhPz9fUqVYLBaJNpBz0jMJ0iKTdLlc1NTUxN3QSBQ8+/r6GB4eRq1WT1B3zGRoaCS4XC4aGxvRaDTjjT2zkMgJOu/FF1/k2Wefpbu7m+LiYpYvX87tt98uOdAdQzgRkMMDckNDA7m5uaSmptLW1sbw8DAVFRWT8rd9fX243W5KSkqmrScWznJjY2NUVFTMqjAhr36L//x+P8nJyahUKkZHR8nJyaGsrGxWkqloHLvcVa66ujrm9tpom4uo2od3wAl6orCwkKKiojk5bgtTeq/XS01NTdRMUk4bRArSosEiWjfZwMAAHR0dM9ogY4UIkuIUIWRi0TJSOW0Q63cr6io9PT1xMzUKBAI888wzPPzww9x9992sWbMGp9PJgQMH5syJ79+MEwE5PCALk+2xsTGp1Xmqi3JwcFAqvMm9WieDvLGjuLiY/Pz8ObkZRQU9EAiQlJSE0+kkGAxKDSEGgyEmW8TJIP8s4QqNmULeASfPpH0+HzqdjpKSEjIzM+PWpCB/356eHnp6emI+bodDTi9ZrdaIQToQCEhyucrKyrh/DvlniSRliwQxM1AEajExPNxzJPxacTgcNDQ0kJycTEVFRVyM4Ht6eli/fj0lJSXcfffdc6Is+gTi+A7IPp8vZITMkSNHOHz4MAaDgcWLF095YQmO2Ol00tjYGMKPyotv4c8RgzgFVxhvgT8cNTMym80TZFnyhhBxA8Yiv4sEk8mE0WiUPAjm4rMINcDIyAjFxcWS4kCcAOSGP+H+xtOBaLxIT0+f9SkiHCJIm81mBgcHcblcpKSkkJGRMWUmPROIjdhgMFBeXj7jzzKVD4bdbpcMh+IxPisQCPDUU0/xm9/8hl/96lecffbZxyJXHA0nArLf75eKKWJ8OIzrV6NhsoKdXIsZzutqNBoGBgZITEykvLw8rt1vAjPNvOXyO7PZHCK/ExtMuIxNVNCrqqrmrDgkRkFFG/opzJXkm4vf75+Wb4fX66WlpQWHw0F1dfWUHZYzhWipz8nJobi4OKSZxWKxRGwKmW6QFm5/IyMjs5ayRYPf75dMjcQcPIVCEUJ3TGZWFA1dXV2sX7+e8vJyfvnLX87YUe5TjOM7IFutVg4fPix5D+j1egYHB7FarVRUVER8znQLdsFgkLGxMVpaWiQjFNGeLKcM4pGNjYyM0NLSQmZmJiUlJbM+OoYHDFGxF5xvRUXFnPGeogNOp9NRWVk5rdZcubmSCNKBQCDk6C1omv7+fjo7O+eUw/V4PNKQ1Mn4aBjf0OXc7nRUKULKlpeXx/z58+eEWw8EAlIxVR7wxQBdeSYNoV4p0a7zQCDAE088wW9/+1vuuecevvCFL8zo79Dd3c0VV1zB4OAgCoWCq6++mg0bNnDbbbfx6KOPSpLBO+64g7Vr1wKwefNmHnvsMVQqFffffz9r1qwBYOfOnWzYsAG/389//ud/snHjRgDa29tZt24dIyMj1NbW8swzz8RzIO3xHZCHh4fxeDwh3JrJZGJwcJAFCxaE/O5MDID8fj+dnZ3S0FJRGJSbuJjNZqn7SgQMg8EQM2UA48HLaDRK03XnMlttaWkhPT0drVYbUSExHfldJPh8PmmEfDRVy0wg/84tFgu9Q6OYbG6y9TrKi4tIT0+fNZceDjH4oLOzc1bcujh1iWAXHqQTExPp6uqaUykbjEs/Gxsbyc7Opri4eMrvSk6NiWAdDAYlEysx4ujGG2+kpqaGu+66a1ank/7+fvr7+1m+fDlWq5Xa2lr++Mc/8uKLL5KSksKPfvSjkN8/fPgwl156Kbt376avr4+zzz5b8qKpqqrir3/9K0VFRdTV1fH888+zcOFCLrnkEi666CLWrVvHd7/7XZYsWcI111wz4zWH4fi13wTIyMiY4O4WbsE5k0Asr54XFhZOsHmUG/0UFRUB4xevCBbCt0DeQWYwGCYcX+UKjcrKyjkTydtsNpqamkhMTKSuri4k4MoVEmNjY3R1dU0pv4sEuXZ13rx5Ie518YD4zpOSkninbZQ/HPaTnGxAa1ZyaZoXm607hEufrIgVC+x2O42NjSQlJbFixYpZFe2itVcLC8qRkRHUajUpKSn09vbGRd8th9/vp62tjbGxsWl5aUzmKNfc3Mz999/PgQMHSE9Pp7CwkH/+85+ce+65M15nfn6+1Aug1+tZsGDBpFNCtm/fzrp169DpdJSWllJRUcHu3bsBqKiooKxsvF1+3bp1bN++nQULFvD3v/+d5557DoArr7yS2267LZ4BOSYcswE5EjQazYyHicL4sdFoNGIwGKZ1I6pUqogdeyJI9/f3h2RGXq8Xk8lESUnJtKcJxwp5A0m4d7RAtJbZSA5hycnJUtFQzuvKA/5MJlXHAiGX23e4hR1tKkrys9CqVdjcPl5udvHLixaiViqlo7fFYqGzsxObzTbldBM5BIcrjvRzoQ4Q793f349KpeKMM85Ao9GEqDt6enqm7SYXCYIGEW3is73OlEolIyMj3HbbbSxatIhXXnkFrVbLoUOH4no66ejo4P333+eUU07h7bffZsuWLTz99NOsWLGCe+65h/T0dHp7ezn11FOl5xQVFUkBXPQUiMfr6+sZGRkhLS1NogLlv/9x4rgKyCqVCo/HM6PGDqPRCBA3Ry6tVktWVpZksC2yyJaWFjQaDVqtlq6uLoaHh0Pm7M2WOxaa0u7ubkpKSqiurp7WjRjJfEbO64rR8WLDCwQClJWVkZeXNye8p+jm02q1FFUsILG/G616fDNI0akZtLixuf2kJSpRqVSkpaWFKAaEHCyS34i8sUIU7fLy8iZt354NJpOyTZZJhwdpecEzUnHZ5/NJhc54+Uf7/X5++9vf8vTTT3Pvvffy2c9+Vrquli5dOuvXF7DZbFx88cXce++9pKamcs0113DzzTejUCi4+eab+eEPf8jjjz8et/f7uHHMBuRIFXuFQoFKpWLXrl3SrDpx7I4U6OTystk2dkwGecCvra2VbhCRjZrNZonjFdNB5NlorMFBDONMT0+P2Ws5FoQPFx0YGKC9vZ2cnBy0Wi2jo6N0dXXNWH4XCaIANTQ0JHXzjdjHjYLcXj86jQqry0eKTkWKbvJhnXJPZggteA4MDDA2NoZCoSAnJ4eEhARcLlfcW6ttNhuNjY2kpk4+/FMgmpucKByGu7KJIC1khvPnz5/2ZhwNbW1trF+/niVLlvCvf/1rTixEYfzvcvHFF3P55Zdz0UUXAUc9qQG+/e1v86UvfQmAwsJCuru7pZ8JThuI+HhmZiZjY2P4fD7UanXI73+cOGaLesFgUPKzCKcnACnQiRtPVOpFgB4bG6Ovr29OGzvkRa5YA77g6cTaY9EZu1wujEYjPp+PqqqqObthRFt1SkoK5eXlEyid6crvokGMaoqkONjdMcpTu7oJBCFRo+S6VaWUZU3/88rn5pWXl2MwGEIUEtPp2psMcy1lE0FajKByu91oNJoJ+u6Z+IT4/X5+85vf8L//+79SVjxXCAaDXHnllWRkZHDvvfdKj/f390sntf/5n/+hvr6eF154gUOHDnHZZZdJRb0vfOELIVLO119/ncLCQurq6njuuedYtGgRX/va17j44oulot7ixYv53ve+F6+PcHyrLMRNL2aeTUVPiJE8fX19DA4OolQqSUxMlAoXkQpvM4WcNpg3bx6FhYWzel0R6ESQFkVDvV4vGdJUVlaGuInFE7Hw0ZM9N1x+JwJduPud8B4JBoNUV1dH1Xrb3T4sLh8ZyRp06ulLDkW2qtfrKS8vj3qSkFMG0WRsk+nRx8bGaGxsnFMpG4x3m7a1tUlqEAj1HBFDRhMTEydw0tHQ0tLC+vXrqa2t5Re/+MWMW52jydlMJhNf//rXpdbzH/zgB6xdu5aTTz6Z/v5+LBYL+fn5LFq0SPKbUalUjI2NoVar+fnPf05PTw+PP/44Pp8PhUKBWq1m7dq1rFmzhhtuuAG/389VV13FTTfdBIxn+uvWrcNkMrFs2TKeffbZeLr+Hd8Beffu3fzwhz/EbDZTU1NDbW0tdXV1UTkzq9Uq+bqKxg55sDCbzTidTun4J4LFdItUojCYnp5OaWlp3GgDOYQkSzjVKRSKkKPrTNce7X06OzvjepKI5H4XDAbxer0UFRUxb968OSkOztZDOJzXjSYdVCgUknZ9LgyHBNxuN42NjahUKsnnYrK1T+Y5otfr8fl85OTk8PDDD/PCCy9w//33c8YZZ8xqjdHkbE8++SQZGRls3LiRO++8k9HRUe666y527NjBAw88wI4dO6ivr2fDhg3U19djMplYsWIFe/bsQaFQUFtby969eyUr3fvvv59TTjmFtWvX8v3vf39Wio8Z4vgOyAJer5dDhw6xa9cu3nvvPfbv3y953i5fvpx58+axfft2Lr/8cmpqaibtIBI3nMhEzWYzPp9vAh8dif8TJu7iyDRXN6G86aKiokLa4cNvOLPZjNfrjYlLjwSr1RqSRc6FXwMczSLT0tIwGAyS9nUm8rvJIGiQgoKCuJoahX/vw8PDOBwOkpOTycnJkdYezw1GvlEK86zZrt1kMnH11VfT29tLSkoKl112GWeddVbch4xecMEFXHfddVx33XW8+eab5Ofn09/fz6pVq2hqauI73/kOq1at4tJLLwWgurqaN998U/rvkUceAZB+b9WqVZx11lk0NjYC8Pzzz4f83seI41uHLKDRaFi6dClLly7lu9/9LsFgEJvNxr/+9S9+/etf8/7771NVVcWtt95KXV0dtbW1rFy5MqLQX15IkcvA7HY7ZrM5ZOKzvINpcHCQ0dHRqJMu4gF5ATJS00U0CZtQRwwODmI0GkO63iKZEwl6wmazTbmBzQaiA87tdnPyySdP4L2nK7+LBjG8NhAIxG38kBzykUwDAwPSlBChTR8ZGaG9vT1kcxRrn8kGI7xXdDrdrAu3Yu0ajYbf/e53ALz00ksUFBSwZ88eOjo6ZvzakSCXsw0ODkrccF5eHoODgwD09vZOkK319vZO+rjoB5A//knFMR+QwyG65lJSUjj//PPZsWMHarWa/v5+du/eza5du3j00Uc5cuQIFRUV1NbWsmLFCpYtW0ZKSkrEIC0GRYqqrLjZxHgblUpFUlISIyMjeL1eDAZD3IT98uJT+FTkWL4LsfaCgvHpIIFAQCpedXeHNlQEAgHGxsYoLS2NW4U+0ucR/HpZWRk5OTkR3ydW+V009zv5+8wmi4zl8wgpW0VFhSRzBEhKSpJ8fyfbYORBOlqAlb9PrMN4Y0FjYyPf//73Of3003n77belDau0NH4TYWCinE2OWOWpxwKOu4AscOaZZ3LmmWdK/y4oKOArX/kKX/nKV4DxoNrU1ER9fT1//OMfufXWW/F6vSxevFgK0gsXLoyYxVgsFqmBRAj7RSOIyKSFdEquMZ5uRiRkbGlpabPuGBNQKpUTOrBMJpM0uSMlDs8RAAAgAElEQVQ5OZnOzk76+/tDeNF4bDCCBjEYDDPK7uTyO/nmKGgOscEIBY5er2fhwoVzYtID05OyRdtgRJCWn2DCzZVcLhcNDQ0xS+Zigc/nY8uWLbz88ss8+OCDnHLKKbN+zWiIJmcTCor+/n5J2hdNzlZYWMibb74Z8viqVasoLCykp6dnwu9/UnHMc8jxhMPh4P3332f37t3s3r2bw4cPo9frpQCdm5vLq6++yrp166acDCL4ObPZLHHSciczwS1G4jKFwbrH46G6unrOZGwej0cqPoU7pXk8nhDZoMvlIiEhIWSDiZUX9fl8ISqNuaJBRJvw6OgohYWF+Hy+WcnvomEupWziFCC++5GRETweDxkZGWRlZU1bmx4JDQ0NrF+/ns997nPceuutc+JcKBBNzvbjH/+YzMxMqahnMpm4++67+dOf/sSWLVukot73v/99du/ejclkora2ln379gGwfPly9u7dS0ZGxoSi3vr16yUDoo8RJ4p6c41gMMjIyAj/+Mc/uPfee2loaKCiokJqvKitraW2tlaS3k0F+c0mNyaSZ0PDw8MMDg5SXl5OVlbWnNMGsRrnhI+Kt1gsEi8qD9Ly7E3ucTGXem8YN5tqaWmRJpGEv8905HeTQdi95ubmzqmUzWq10tDQQGZmJsXFxSEOeMKNLZID3mTw+Xzcd999vPLKKzz00EPU1dXNeH1XXXUVr776Kjk5ORw8eBAgojNbamoqZ555Jnl5eZhMJhQKBTfddBPf+973uOSSS2hsbGRsbIycnBy+853vcOONN3Ldddfx6quvMjQ0REZGBqeffjrPPPMMzz77LHfccQcAN910E//v//0/APbs2cM3v/lNnE4n5557Lg888MC/gwI5EZA/LvzpT3+is7OTq6++GqVSSVtbG/X19dTX17Nnzx7sdjsLFy5kxYoVrFixgsWLF8esbxQa476+Po4cOYJSqSQ5OZm0tLQQuiBekBu5z1aWJy8ams3mEKvMhIQEhoeHSUlJmfV8tskgtMsw7vI1nWxvOsNnhe/yXEvZ5Nn3ggULop4m5L4dcvP5aOZKhw8fZv369Xz+85/nlltumfU19Y9//IOUlBSuuOKKkID8KXFmmwucCMifFHg8Hj788EMpSB84cACtVsuyZcukIF1RURExg7Hb7TQ3N6NWq6msrCQhISFkQoXQuiYlJc3K80KuaphLGsTr9dLc3IzJZCI5ORmPxyO5tYn1RxtvPx1MVkybzWtG0uqqVCqcTif5+fmUlpbO2eZiNptpbGyccfYt75S0WCyMjo7yk5/8hMzMTDo7O7nrrru4+OKL45bVd3R08KUvfWnKgLx582YAfvrTnwKwZs0abrvtNuk5f/7zn0N+b+PGjWRnZzMwMIBarebdd98N+b1PKE7I3j4p0Gq1UuC99tprpTFF7733HvX19dx66620traSn58v8dGVlZW88MILnHPOOVRXV4cY4uh0OrKzs6Wjn7xCL/e8kLdTRzuyyufMTaZqiAeEeqCgoIAFCxZI65Eb/AjTm5nQBQKiOJiWlha3IhdMlA6KAaPBYJD8/HwcDgf79++fkfxuMvj9flpaWrDZbJx00kkz3izVanWI6+DBgwfR6/WUlZVxzjnnsG3bNl577bU5Nec5lpzZ5gInAvK/AQqFAoPBwNlnn83ZZ58NHA2M7777Lk888QRvv/02CxcupLm5WQrmS5cujZg9RqvQi2yoq6tLspqUH7dFcVAUPuZiZh6Ma2ObmppQqVQsW7ZswnE4ksFPJCcz0TUmAl0krwyhxZ5LjbScY6+srJyQfU9HfjcVTCYTzc3NFBYWTkvSOBm8Xi+//vWv2blzJw8//DDLly+f9WvGgmPNmW0ucCIgf0KgUCiYN28eAwMDVFRU8Oyzz6LX62loaGDXrl38/ve/52c/+xnBYJAlS5ZIQbq6ujoiPSGXr4ksQ4yHF11pPp8PvV6PQqHAZDLNeiJIOAKBAJ2dnQwODk5bGxvpFCAmVY+MjNDW1obP55OCXCAQoL+/n/nz58+ZhzSMU0gNDQ3o9fqom1is8rvJTKG8Xq9EIcWzYeXAgQNS6/A///nPOWlBj4ZjzZltLnCCQ/4UQRTJ9u7dK0nvRAFOUB11dXUUFBREDEiBQEDiVcvLy8nOzp7AR3s8nkmVEbFCZHa5ubkxjQSaCQKBgOQNIm7OSOOy4vHe8mJavMZPRXO/02g0WCwW5s+fHzelhsfj4Ve/+hV/+9vf2Lp1a1w9iqMhnEP+FDmzzQVOFPWOB4h5eKJg+N5779Hf309paalkqLRs2TLefvttFAoFVVVVlJSURA2ykZQRYlaanI+OloEKGsTn81FdXT1nM+CCwSDd3d309fWFmLlHUhdEMpyfTgb9cUnZPB4PDQ0NeDwe0tLScDgcs+bTAT744AM2bNjAl770JTZu3DjjrDiSlC3cle3FF18kPT2dSy+9lFdeeQWHw4FareanP/0p7e3t7N+/n7GxMUwmEzk5Odx2221ceeWVbNq0iYcffliSsn3ta1/jvvvu47XXXuP666//uJ3Z5gInAvLxikAggNFoZNeuXbzxxhv86U9/IiMjg7q6OimLXrRoUcw3pjzImc1m7HY7arV6QpDo7e2lp6eH8vJyqbNqLiAGcmZkZFBaWjplBh9NYyw/BUSbrGE0GnE4HCxYsGDOpGxyPXak72468rvw5/3yl7/kjTfe4JFHHmHx4sWzWmckKdtPfvKTY82Vba5wIiAf7wgEApx33nlce+21rF69mv3790uudwcPHiQpKYnly5dLfHRJSUnM2Z8IcmazmZGRESwWCzqdjry8PNLT02fUCj4V5B19NTU1s5piLHftE9JBeSu7x+Oho6NjzhtWhFJDo9HErMeOJr9LSkrC7XYzODiIwWDg5ptv5itf+Qo/+clP4va3CKchhNvaMeTKNlc4dmVvN998M9u3b0epVJKTk8OTTz5JQUEBwWCQDRs2sGPHDpKSknjyySelCvJTTz3FL37xCwB+/vOfc+WVVwKwd+9eqYtn7dq13HfffVKRK9JR7NMEpVLJa6+9Jv37lFNOkTwJgsEgo6OjvPfee+zatYtt27bR0dFBUVGRFKBra2vJyMiIGIzE1GzRrLJy5UrUanWIg5mwJp2t/EsMMW1tbZ22gVI06HQ6cnJyQsYfOZ1ORkZGaGhowOfzodPpGBkZwefzxUW+JofcFKqqqipkft5UmGz47L59+3jkkUc4dOgQmZmZNDY28sYbb3DOOefEZd3hOF5d2eYKn8qA/OMf/5j//u//BuD+++/nv/7rv9i6dSuvvfYaRqMRo9FIfX0911xzjXRMuv3220OOSeeffz7p6elcc801PProo9IxaefOnZx77rnceeedfOELX5COYnfeeSd33XXXv/mTxw8KhYKMjAzWrFnDmjVrgKOqiPr6et566y1+9atfSdmo4KNFl6Go0JeUlFBTUyMFSHmQkLeCC2tSUXSLda6ekMyp1eo5m1otYDKZ6OnpoaamhqysrIjrB2ZdNHQ6nRw+fJjk5OS4zTZUKBQ0NTVx0003cfHFF/PnP/8ZhULB4cOH52QIQrQ1HC+ubHOFT2VAlpu12O126SLYvn07V1xxBQqFglNPPZWxsTH6+/t58803Wb16tSS7Wr16NTt37mTVqlVYLBZJlH7FFVfwxz/+kXPPPZft27dL7lFXXnklq1atOqYCciQolUpKS0spLS1l3bp1wDg1cfDgQXbt2sXTTz9NfX09o6OjLFq0iEsuuYT09HRycnKmlH8JCGtS+ZRnkW2LIJ2QkEAgEKCrq4uBgYG42klGglzKJg+Q0dYv+PSOjg7sdnvMxkTyQmR1dXXcTlwul4vNmzfz7rvv8tRTT7Fo0SLpZ7PljafC8erKNlf4VAZkGDcPefrppzEYDLzxxhtAfI9J0Y5ixxs0Gg3Lli1j2bJllJWVYTQa2bp1K36/n127dvGLX/wCo9FIdnZ2iPQumiGRSqUK6RaDUOe4np4eHA4HXq+X1NRUKioq5sweU0yuHh4ejlnKplKpSEtLC+mclBcNBwcHcTqdIbP1DAYDXq+XhoaGuHcP7tmzhxtuuIGvf/3rvPnmmx9bNixw/vnn89RTT7Fx40aeeuopLrjgAunxLVu2sG7dOurr6zEYDOTn57NmzRp+9rOfMTo6CsBf/vIXNm/eTEZGBqmpqezatYtTTjmFp59+mvXr13+sn+WTgE9sQD777LMZGBiY8PimTZu44IIL2LRpE5s2bWLz5s1s2bKF22+/fUbvYzQaqampQavVkpaWFiLT2rx5M4899hgqlQqfzyc9vnPnTjZs2IDf7+c///M/2bhxIwDt7e2sW7eOkZERamtreeaZZ9Bqtbjdbq644gr27t1LZmYmv/vd7ygpKZnRev+dOOuss1i9erV0RBfjewQfKgz+H3nkEYaGhqisrJQc75YvXx6VntBqtWRnZ5OWlkZrayuBQIAFCxbg8XgkPlpYk4pml1g73aJBSNlycnJYsWLFrF5Lo9GQmZkZwgPLne+am5txu90YDAbUajVjY2OzLnq6XC7uuOMO6uvrefbZZ1mwYMGMXytWXHrppbz55psMDw9TVFTE7bffzsaNG7nkkkt47LHHKC4u5sUXXwRg7dq17Nixg4qKCpKSknjiiScAyMjI4Oabb5ac5G655Rbp9PPQQw+FuLIdQwqLmPGpV1l0dXWxdu1aDh48OKPK7sqVK2lvb0etVnP++efT1dXF/v37KSkpITk5mX379rF//37OPPNMnE4nMH0HqoceeogPP/yQrVu38sILL/CHP/xBGolzrMLv99PY2Eh9fT27d+9m3759+P3+CQb/arVa4q4HBgaiqhoCgQA2m03KpEWn23RNiXw+Hy0tLdjt9jmVssFReV52djbz58+foIyQ+19Pp2goBvheeumlXH/99XHNiktKSqR1qNVq9uzZE7XAPZMi+nGMY1f2ZjQaqaysBOCBBx7grbfeYtu2bbM2r66trZUokM9+9rMkJSWxc+dO7rzzTh555BGee+45YPoOVMK96rTTTsPn85GXl8fQ0NBxVwARKgDRZdjQ0IBWq8XhcHDKKafwox/9aFqNF3JTItHpJqgCuT5aYGhoiJaWljmXsgkj/LGxMRYsWBBVnhduNi8vGkZqwnE6nfziF7+QlBQ1NTVxX3tJSQl79uwJ8eeIp9b4OMaxK3vbuHEjTU1NKJVKiouL2bp1KzD7Y1IgEOCGG24AxrPgd999l8rKSoqLizn99NNn7EAl57DVajUGg4GRkZG4WEJ+mpCUlMQZZ5whjY5/4oknuO+++/jGN76Bw+HgBz/4AT09PRQXF4dI7wwGQ8TgGc2USBj8d3V14fF4SEhIwOVyodVqWbp06Zx1DwKMjo7S1NREQUEBK1asmDToT1Y0NJvNUtHwoYceIhAI8P7773PZZZfx+uuvf6xccbQC93SL6OLkegLR8akMyC+99FLExxUKBQ8++OCExwUfLbqx7rnnHu655x6Jjz548CCbNm1iz549/Md//AcACQkJ/PSnP5X+/a1vfWvKddlsNhYtWkRDQwOvvPJKyM+2bNnCtm3bUKlUOBwO6fHjlY8G+NznPsfll18eImULBAK0trZSX1/PX//6VzZv3ozD4Qgx+D/55JOjtsnK9cXCQa+rq4usrCyCwSAHDhyQWsHj6Xchp0KWLFky46AfXjR0OBzk5ubS0NDAhRdeyKFDh6itreXNN9+ck4xToVBwzjnnoFAo+M53vsPVV18dN63xCUyNT2VAni7+9re/TfrzJ598kldffZXXX39dymhm4kDlcrl48cUXufbaazly5Ij0+waDgZdeeolDhw7R1dVFTU0NaWlp+P1+rr322hA++vzzz2fhwoXceOON3HDDDRIf/dhjj3HNNdfw2GOPkZ6eTktLCy+88AI33njjp5aPLisrm/CYUqmksrKSyspKaTP0eDx88MEH1NfX8+ijj3Lw4EF0Ol2IwX95eXlIULXb7TQ2NpKSksIpp5wSklEKa1Kz2UxnZ2eI34UoGiYkJMRMaYyMjNDc3Mz8+fPjOo37nXfe4cc//jFXXnkl999//5zZo8rxr3/9i8LCQo4cOcLq1asn0CIntMZzi+MiIE+GnTt3cvfdd/PWW2+FFHjOP/98LrvsMn7wgx/Q19eH0Whk5cqVBINBjEYj7e3tFBYW8sILL/Dcc8+hUChYvXo1Bw4cAMbHOgkJUGZmptT5tXfvXrKzs3nvvfcAqKiokALTunXr2L59OwsWLODvf/+7xFlfeeWV3HbbbVxzzTVs375dmqbw1a9+leuuu45gMHhM3yRarZa6ujrq6uqkz2s2myWD/5tvvpm2tjYKCgpYsmQJvb29pKenc9NNN4XI0wQiTdaWS9cGBgZwOp2SX4Tgc8ObUsT0E6/Xy7Jly+JmkWm32/mv//ovDh48yIsvvijVSz4OiCQiJyeHCy+8kN27d8dNa3wCU+O4D8jXXXcdbreb1atXA3DqqaeydetWqfFBKAEefPBBKUPZsmULa9askRyohBD/rrvuYt26dRw4cIDPfOYzEs1RUFAg+RxnZGTw2c9+9gQfPQsoFArS0tJYvXq19HcLBoNs376dH/3oRxQXF9Pb28sXv/hFqqqqQgz+ozVthEvXgsGgxEePjo7S0dEhtYLL/ZfLyspiGgIbC4LBIG+//TY33ngjV111Fffee+/HkhUL2O12adKM3W7nL3/5C7fcckvctMYnMDWO+4Dc0tIS9Wc33XSTZPcnIPhokS09//zzPP/88xIfvXv3bqnoIXhOtVrN97///Wnx0dPB66+/zi233DKBhz6eoFAoUKlUvPbaa1JG6fP5OHToEPX19fzud79j48aNKBSKCQb/kYKeQqEgISGBhISEEL8I4b/s9XpRq9V0dXUxOjoawkfPJDjb7XZuu+02Ghsb2bZtG+Xl5bP7QqaJnTt3cs0119DX1ycVSi+77DK++MUvUldXFzet8QlMjuM+IE8XU/HRkRDPiQjitYqKivD5fIyNjfGzn/0sIg99vOHLX/5yyL/VajVLlixhyZIlXH311ZLX8549e9i9ezd33XUXTU1NZGRkhHQZRpLEBYNB+vv76ezspKKiQppkIldFtLe3S9akguYQU8GjBelgMMg///lPNm7cyLe//W0eeOCBOfNbjgZRy3j99dcnaOthnHJ7/fXXJzwvWhEdxr2Tr7rqqjld97GIEwH5Y8BM+OizzjqLbdu2sW7dugnHxKeeeorTTjuNbdu2sXTpUtRq9QQe+ngMyFNBoVCQkpIiNQXBUS9iYfD/+OOPMzAwQFlZmWSoZDAYePXVV7noootYsWJFSIddpFZqj8cjaaP7+vpwuVwh1p56vR6tVovNZuOWW26hpaWFP/zhD5SWln7cXwkw3mgSqZZx4hr6+HEiIMcRf/jDH1i/fj1DQ0Ocd955LF26lD//+c+z4qN//vOfs2zZMonm+Na3vsU3vvENiY++8soref/996U1CB76BGKDQqEgLy+PCy64QNr0AoEAzc3NvPPOO2zevJlDhw6xcOFCenp6pCC9aNGiqK3PWq2WrKwsidcX/sVms5nh4WHuv/9+Xn75ZVwuF2eeeSabNm2ioKDgY/vM4YgkUztxDf17cCIgxxEXXnghF154YcSfReKjYZyHW7t27YTHy8rK2L1794THExIS+P3vfy/9e9u2bbNY8QlEglKppKamRjrJ7Ny5E6VSKRn8P/jggxw6dIjk5OQQg/9oswPl/sXJycmYzWbKysq4/vrr6evr49FHH+WCCy6YQLmcwPGHEwH5U47J+OnpzECLp7n/sYL/3975x0R5pHH8MzkrJuZOiN4fPSGR3QVK+aHWW6yxMVe1QoloVA7tVdPEBnuK1dr6o7XJ1SZWTGjSXs/YpjlsqqkS8ZrSiELxV9tETC090iiJCgLdSptTtpo0KWDhe3/s7nu7IhYqAgfzSSbZfd6ZyTubl+cdnpnnOzk5ORFO8uGHH3akWiXh9/sdgf+DBw/S3NxMXFxcRJZhTEwMxhgkcerUKbZu3UpBQQHvvPOO47zz8/MHZXwh7vQMWQYYSX0pliHGzZs3FR8fr8uXL6u9vV3p6ek6d+6cJOnTTz9VTU2NUlJSnPqbNm1SYWGhJKmwsFCbN2+WJJWXlysrK0tdXV2qrq5WRkaGJKm1tVXx8fFqbW2V3+9XfHy8/H6/JMnr9aq6ulpdXV3KysrSkSNHBnLoQ47Ozk41NDTogw8+0Pr16zVz5kylp6crNzdXs2bNUmZmppqbmwf7Nrtxp2fI0m/0ysdahzwMKC8vV0JCglwul7Zv3x5xrbGxMcIhJyYmqqWlRZLU0tKixMRESdKqVau0f//+bvX279+vVatWOfZQvZaWFiUlJTn2W+tZAnR0dOjLL7/UK6+8os7OzsG+nR650zNk6Rd65WMHdn+N5Z6QnZ3NxYsXaWhouG2cOhx7BtrAct999zFt2jS2bdt2T7azbdu2jYkTJzJlyhSmTJnCkSNHnGuFhYV4PB6SkpIcdUII7DlOSkrC4/Gwc+dOIPAMVVZWMmHCBN577z2WLl1KR0dHv9+v5c5YhzyC6asugc/nY8+ePRQVFZGXl8c333wDBOLSzz//PCdPnuSxxx5zMrQksW7dOjweD+np6Y78KQTi0iHNivfff9+x19TUkJaWhsfjYd26dYF/4yx3ZMOGDdTW1lJbW+ssENfV1VFSUsL58+epqKhgzZo1dHZ2OnuOjx49Sl1dHQcOHKCurg7A0U+pr68nJiaG4uLiwRzWiMQ65BFGSJcA6LUuQcg+atQoHnroId58800qKytpamqirq6OnTt34vF4WL58OXPmzHFmXeGHzr777rusXr0awDl0NiRe/+qrrzpOPHTobKhdRUXFgP02w4mysjKWLVtGVFQU8fHxeDweR4c6tOd49OjRzp5jSZw4cYLc3FwgoJ/y0UcfDfIoRh7WIY8wQoklQLeEk7179yKJM2fOROgSfPLJJ/zwww+MGTOGr776iszMTBISEhg7dizHjh2jrKyM77//noULF0b8Ifekl1tZWeno5cbExDh6ud99951z6Kwxxjl01nJndu3aRXp6OitXrnRebH0NQd1JP8UycFiHPIx54oknmDFjBhcuXCA2Npbi4mJefPFFqqqqSEhI4NixY47uRXZ2Ni6XC4/HQ35+Prt37wYidQm8Xq+jS9DU1ERUVBRvv/029fX1pKSk8Pjjj9u49D1g7ty5pKamditlZWWsXr2ahoYGamtruf/++3nhhRcG+3Ytd4HdhzyMOXDgwG3td6tL8OOPP7JkyRJ2797N4sWLiY6OZteuXRF99Ya2tjZmzZrlxKIvXLgA/E+c/8qVK3R1ddHR0fGL4vzhB9K+9dZbZGZmAj0fAPD/RG/1U/Lz85k/fz7Qv/oploHDzpAtfeLmzZssWbKEJ598ksWLFwO/Pi4dFRXFiRMnWLhwIUVFRdTU1HDp0iVncamoqIjo6GhncSlcnH/Dhg1s2bIF+HULWMOF0O8OgdT91NRUIBCCKikpob29ncbGRifr0Ov1OvopHR0dlJSUsGDBggj9FIgMZ1kGkN7uj5Pdhzzi6erq0ooVK7R+/foI+8aNGyOSTTZt2iRJOnz4cESyidfrlRRINpk0aZL8fr/8fr8mTZokn8+nqVOnKjk5WePGjVNHR4eysrL0+uuva968eZKkefPm6fTp05ICyQzjx49XV1eXduzYoR07djj3E6p3+vRpp62kbvWGA8uXL1dqaqrS0tKUk5Pj7DGXpO3bt8vlcikxMTEiaaenPccNDQ3yer1yu93Kzc1VW1vbgI5lmGMTQyz9y+effy5AaWlpmjx5siZPnqzy8nJdu3ZNs2fPlsfj0Zw5c9Ta2iop4MDXrFkjl8ul1NRUnT171umruLhYbrdbLpdLcXFxGjt2rDZv3qyqqiqNHj1aLpdLBQUFam5udhJbUlJS5PP5nD5cLpeuXr2qgoIC7du3z7GvXLlSpaWlKi0t1dNPP+3Y9+7dq4KCgnv9M/WZgwcP6sEHH5QxJuI3kgIvEbfbrcTERFVUVDj2o0ePKjExUW6323kZStLly5eVkZEht9utvLw8tbe3S5La2tqUl5cnt9utjIwMNTY2DsjYLA698rE2hmzpNY888kiP+4LvNi59/fp1Fi1aRE5ODnFxcc7BAeEhj+FKamoqH374Ic8880yEPTwU09LSwty5c7l48SLAiD6LcThjY8iWIUF0dDSPPvoo1dXVzuIScFtxfgicBnLjxg3Gjx/fLVbt8/nYunUrb7zxBj6fj8bGRqZPn85zzz3HZ5995mSgtbe3s3TpUjweD9OnT6epqcnpoy9ZbndLcnIySUlJ3ez9uZe4rKzMEYXKzc3l+PHjNulmCGIdsmXQuHr1KtevXwfgp59+oqqqiuTk5B4Xl8L3UB86dIjZs2djjOm2gFVTU8O0adOIjo7m0qVLrF27lmeffZbY2FgeeOCB/5tFwv7cS9zTWYyWIUZvYxu22NLfBUgH/g18DZwD/ha0u4AvgHqgFIgK2scEv9cHr7vC+noZaAiWWmA2cBjIBjqD9peBGUBlsE0lMCP4eRRwDTDAS8BLYX1XBts5bYP2iHq/MNZjwTHeWhaG1TkF/DHs+y5gedj3YiA3WP4ZZl8RrDsBqA+zxwHngp/PAbFh1xqACYP9DNgSWWwM2TJoSPoamHob+2Ug4zb2NuDPPfT1GvCaMeYQUAj8NnjpC6BRkgfAGBMHhDbYTgR8wfY/G2NuAOOD9jNh3X8b1sZ3i336Lw400P/c3tS7hSsEnGqI2KCNHuytQLQxZpSkn2+pH+rrW2PMKGBcsL5lCGFDFpZhgzFmPvAfSTWDfS/9xMfAMmNMlDEmHkgg8II5CyQYY+KNMaOBZcDHCkx9TxKYQQM8BZSF9fVU8HMucCJY3zKEsI3HTIoAAADfSURBVDNky3BiJrDAGJNNILzxO+Dv9H3W2NeZ6V1hjFkE/AP4PVBujKmVlCnpvDHmIFAH/AwUSOoMtllLIJTyG2CPpPPB7rYAJcaY7QTCQSHJtmJgnzGmHvATcOKWIYaxL0nLcMQY8ydgo6T5xphS4F+SSowx7wBfS9ptjCkA0iT91RizDFgsKc8YkwLsJxA2+QNwnMDs1AAXgTkEHPFZ4C9hztBiuSvsDNkyEujTrPFXzkwtlrvGzpAtFotliGAX9SwWi2WIYB2yxWKxDBGsQ7ZYLJYhgnXIFovFMkT4L+y421J6XevpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.scatter3D(labor2_rotated_df[\"PC1\"], labor2_rotated_df[\"PC2\"], labor2_rotated_df[\"PC3\"]);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}