Step 7.1_Classifier (Russia) - by classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# General\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "\n",
    "# Classifer\n",
    "from sklearn.neighbors import KNeighborsClassifier #kNN\n",
    "from sklearn.naive_bayes import GaussianNB #Naive Bayes\n",
    "from sklearn.tree import DecisionTreeClassifier #Decision Tree\n",
    "from sklearn import svm #SVM\n",
    "from sklearn.neural_network import MLPClassifier #Neural Network\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis #Linear Discriminant\n",
    "from sklearn.linear_model import LogisticRegression #Logistic Regression\n",
    "from sklearn.linear_model import SGDClassifier # Stochastic Gradient Descent\n",
    "\n",
    "# PR curve\n",
    "# from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import plot_precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Decision Tree plot\n",
    "from sklearn.externals.six import StringIO \n",
    "from IPython.display import Image \n",
    "from sklearn.tree import export_graphviz \n",
    "import pydotplus\n",
    "\n",
    "#Cross Validation\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def file_reader(path):\n",
    "    df=pd.DataFrame()\n",
    "    chunksize = 10 ** 6\n",
    "    for chunk in pd.read_csv(path, chunksize=chunksize, index_col=[0]):\n",
    "        df = df.append(chunk)\n",
    "    return df\n",
    "\n",
    "country='Russia'\n",
    "data=file_reader('/Volumes/BECCA/Project/Data/Input/02_Preprocessing/05_Final/%s.csv' %country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_text</th>\n",
       "      <th>polarity_number</th>\n",
       "      <th>subjectivity_number</th>\n",
       "      <th>repeat_tag</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>suspend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barcelona is so sunny and nice at Vmworld, tre...</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear -up and –out scaling on disk and node l...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>open for a surprise https://t.co/YrU8x3AhGC</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MY HEART 😭❤️ https://t.co/iKrYLK5apF</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cows are just big grass puppies https://t.co/6...</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54165</th>\n",
       "      <td>RT @spacebongmilk: My girl can be butt ass nak...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54166</th>\n",
       "      <td>Not a win again, but people that understands f...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54167</th>\n",
       "      <td>RT @alizawhoo: @lanascouture A girl can walk o...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54168</th>\n",
       "      <td>People don't understand what football means to...</td>\n",
       "      <td>0.396875</td>\n",
       "      <td>0.443750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54169</th>\n",
       "      <td>@Hb_0L 😂😂😂 nah we’re fair people.. Liverpool d...</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54164 rows × 8 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                                              tweet_text  polarity_number  \\\n",
       "0      Barcelona is so sunny and nice at Vmworld, tre...         0.700000   \n",
       "1      Linear -up and –out scaling on disk and node l...         0.600000   \n",
       "2            open for a surprise https://t.co/YrU8x3AhGC         0.500000   \n",
       "3                   MY HEART 😭❤️ https://t.co/iKrYLK5apF         0.500000   \n",
       "4      cows are just big grass puppies https://t.co/6...         0.466667   \n",
       "...                                                  ...              ...   \n",
       "54165  RT @spacebongmilk: My girl can be butt ass nak...         0.500000   \n",
       "54166  Not a win again, but people that understands f...         0.600000   \n",
       "54167  RT @alizawhoo: @lanascouture A girl can walk o...         0.600000   \n",
       "54168  People don't understand what football means to...         0.396875   \n",
       "54169  @Hb_0L 😂😂😂 nah we’re fair people.. Liverpool d...         0.875000   \n",
       "\n",
       "       subjectivity_number  repeat_tag  day  hour  minute  suspend  \n",
       "0                 0.600000           0    6    10      32        1  \n",
       "1                 0.200000           0    1    12      30        1  \n",
       "2                 0.500000           0    5    11      27        1  \n",
       "3                 0.000000           0    1    12      21        1  \n",
       "4                 0.133333           0    4    20      26        1  \n",
       "...                    ...         ...  ...   ...     ...      ...  \n",
       "54165             0.233333           0    2    18      19        0  \n",
       "54166             0.400000           0    3     2      29        0  \n",
       "54167             0.400000           0    2    16      56        0  \n",
       "54168             0.443750           0    3     7       4        0  \n",
       "54169             0.825000           0    2    22      45        0  \n",
       "\n",
       "[54164 rows x 8 columns]"
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    "data"
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  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling data\n",
    "from sklearn import preprocessing\n",
    "\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "for i in data.columns:\n",
    "    if i != 'tweet_text':\n",
    "        x = np.array(data[i]).reshape(-1,1)\n",
    "        data[i] = min_max_scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
       "      <td>Barcelona is so sunny and nice at Vmworld, tre...</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.542373</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear -up and –out scaling on disk and node l...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.521739</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>open for a surprise https://t.co/YrU8x3AhGC</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MY HEART 😭❤️ https://t.co/iKrYLK5apF</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>0.355932</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cows are just big grass puppies https://t.co/6...</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>0.440678</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>54165</th>\n",
       "      <td>RT @spacebongmilk: My girl can be butt ass nak...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.782609</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54166</th>\n",
       "      <td>Not a win again, but people that understands f...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.491525</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54167</th>\n",
       "      <td>RT @alizawhoo: @lanascouture A girl can walk o...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.949153</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54168</th>\n",
       "      <td>People don't understand what football means to...</td>\n",
       "      <td>0.396875</td>\n",
       "      <td>0.443750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54169</th>\n",
       "      <td>@Hb_0L 😂😂😂 nah we’re fair people.. Liverpool d...</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.762712</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54164 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              tweet_text  polarity_number  \\\n",
       "0      Barcelona is so sunny and nice at Vmworld, tre...         0.700000   \n",
       "1      Linear -up and –out scaling on disk and node l...         0.600000   \n",
       "2            open for a surprise https://t.co/YrU8x3AhGC         0.500000   \n",
       "3                   MY HEART 😭❤️ https://t.co/iKrYLK5apF         0.500000   \n",
       "4      cows are just big grass puppies https://t.co/6...         0.466667   \n",
       "...                                                  ...              ...   \n",
       "54165  RT @spacebongmilk: My girl can be butt ass nak...         0.500000   \n",
       "54166  Not a win again, but people that understands f...         0.600000   \n",
       "54167  RT @alizawhoo: @lanascouture A girl can walk o...         0.600000   \n",
       "54168  People don't understand what football means to...         0.396875   \n",
       "54169  @Hb_0L 😂😂😂 nah we’re fair people.. Liverpool d...         0.875000   \n",
       "\n",
       "       subjectivity_number  repeat_tag       day      hour    minute  suspend  \n",
       "0                 0.600000         0.0  1.000000  0.434783  0.542373      1.0  \n",
       "1                 0.200000         0.0  0.166667  0.521739  0.508475      1.0  \n",
       "2                 0.500000         0.0  0.833333  0.478261  0.457627      1.0  \n",
       "3                 0.000000         0.0  0.166667  0.521739  0.355932      1.0  \n",
       "4                 0.133333         0.0  0.666667  0.869565  0.440678      1.0  \n",
       "...                    ...         ...       ...       ...       ...      ...  \n",
       "54165             0.233333         0.0  0.333333  0.782609  0.322034      0.0  \n",
       "54166             0.400000         0.0  0.500000  0.086957  0.491525      0.0  \n",
       "54167             0.400000         0.0  0.333333  0.695652  0.949153      0.0  \n",
       "54168             0.443750         0.0  0.500000  0.304348  0.067797      0.0  \n",
       "54169             0.825000         0.0  0.333333  0.956522  0.762712      0.0  \n",
       "\n",
       "[54164 rows x 8 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k=6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier Selection:\n",
      "Score of knn = 0.65 (+/- 0.16)\n",
      "Score of SVM = 0.64 (+/- 0.08)\n"
     ]
    }
   ],
   "source": [
    "combination_name='All'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_classifier\"\n",
    "path = os.path.join(parent_dir, combination_name)\n",
    "# os.mkdir(path) \n",
    "# os.mkdir(path+'/PR_plot') \n",
    "\n",
    "X=np.array(data)[:,1:7].astype('float')\n",
    "y=np.array(data)[:,7].astype('int')\n",
    "        \n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "gnb = GaussianNB()\n",
    "dtree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=4)\n",
    "SVM = svm.SVC(kernel='poly')\n",
    "nn = MLPClassifier(activation='identity',solver='adam', max_iter=1000)\n",
    "lr = LogisticRegression(random_state=0)\n",
    "sgd = SGDClassifier(loss=\"hinge\", penalty=\"l2\", max_iter=1000)\n",
    "\n",
    "scores_1 = cross_val_score(knn, X, y, cv=10)\n",
    "scores_2 = cross_val_score(gnb, X, y, cv=10)\n",
    "scores_3 = cross_val_score(dtree, X, y, cv=10)\n",
    "scores_4 = cross_val_score(SVM, X, y, cv=10)\n",
    "scores_5 = cross_val_score(nn, X, y, cv=10)\n",
    "scores_6 = cross_val_score(lr, X, y, cv=10)\n",
    "scores_7 = cross_val_score(sgd, X, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Classifier Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "std_score = std_score/len(scores)\n",
    "\n",
    "for i,j in zip(['knn','gnb','dtree','SVM','nn','lr','sgd'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        if j.std() < std_score:\n",
    "            print('Score of %s = %0.2f (std = %0.2f)' %(i, j.mean(), j.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Classifier=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[] \n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "name = ['knn','SVM']\n",
    "classifier = [knn, SVM]\n",
    "\n",
    "for clf,clf_name in zip(classifier, name):\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Classifier.append(clf_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    #PR curve\n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_classifier/%s/PR_plot/PR_%s.png' %(combination_name, clf_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Combination</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All</td>\n",
       "      <td>knn</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>All</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Combination Classifier    TP    FP    TN    FN  Accuracy    Recall  \\\n",
       "0         All        knn  4309  1038  4167  1319  0.782424  0.765636   \n",
       "1         All        SVM  3792  1549  3656  1836  0.687529  0.673774   \n",
       "\n",
       "   Precision  F1_Score  \n",
       "0   0.805872  0.785239  \n",
       "1   0.709979  0.691403  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Combination=[]\n",
    "for i in range(len(Score)):\n",
    "    Combination.append(combination_name)\n",
    "result={'Combination':Combination, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result=pd.DataFrame(result)\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier Selection:\n",
      "Score of knn = 0.63 (+/- 0.16)\n",
      "Score of SVM = 0.60 (+/- 0.13)\n"
     ]
    }
   ],
   "source": [
    "combination_name='k=5'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_classifier\"\n",
    "path = os.path.join(parent_dir, combination_name)\n",
    "# os.mkdir(path) \n",
    "# os.mkdir(path+'/PR_plot') \n",
    "\n",
    "\n",
    "X=np.array(data)[:,2:7].astype('float')\n",
    "y=np.array(data)[:,7].astype('int')\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "gnb = GaussianNB()\n",
    "dtree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=4)\n",
    "SVM = svm.SVC(kernel='poly')\n",
    "nn = MLPClassifier(activation='identity',solver='adam', max_iter=1000)\n",
    "lr = LogisticRegression(random_state=0)\n",
    "sgd = SGDClassifier(loss=\"hinge\", penalty=\"l2\", max_iter=1000)\n",
    "\n",
    "scores_1 = cross_val_score(knn, X, y, cv=10)\n",
    "scores_2 = cross_val_score(gnb, X, y, cv=10)\n",
    "scores_3 = cross_val_score(dtree, X, y, cv=10)\n",
    "scores_4 = cross_val_score(SVM, X, y, cv=10)\n",
    "scores_5 = cross_val_score(nn, X, y, cv=10)\n",
    "scores_6 = cross_val_score(lr, X, y, cv=10)\n",
    "scores_7 = cross_val_score(sgd, X, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Classifier Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "std_score = std_score/len(scores)\n",
    "\n",
    "for i,j in zip(['knn','gnb','dtree','SVM','nn','lr','sgd'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        if j.std() < std_score:\n",
    "            print('Score of %s = %0.2f (std = %0.2f)' %(i, j.mean(), j.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Classifier=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[]\n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "name = ['knn','SVM']\n",
    "classifier = [knn, SVM]\n",
    "\n",
    "for clf,clf_name in zip(classifier, name):\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Classifier.append(clf_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_classifier/%s/PR_plot/PR_%s.png' %(combination_name, clf_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Combination</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All</td>\n",
       "      <td>knn</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>All</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=5</td>\n",
       "      <td>knn</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=5</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4485</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1143</td>\n",
       "      <td>0.647466</td>\n",
       "      <td>0.796908</td>\n",
       "      <td>0.626309</td>\n",
       "      <td>0.701384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Combination Classifier    TP    FP    TN    FN  Accuracy    Recall  \\\n",
       "0         All        knn  4309  1038  4167  1319  0.782424  0.765636   \n",
       "1         All        SVM  3792  1549  3656  1836  0.687529  0.673774   \n",
       "0         k=5        knn  4297  1151  4054  1331  0.770885  0.763504   \n",
       "1         k=5        SVM  4485  2676  2529  1143  0.647466  0.796908   \n",
       "\n",
       "   Precision  F1_Score  \n",
       "0   0.805872  0.785239  \n",
       "1   0.709979  0.691403  \n",
       "0   0.788730  0.775912  \n",
       "1   0.626309  0.701384  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Combination=[]\n",
    "for i in range(len(Score)):\n",
    "    Combination.append(combination_name)\n",
    "result={'Combination':Combination, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result = clf_result.append(pd.DataFrame(result))\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier Selection:\n",
      "Score of knn = 0.63 (+/- 0.17)\n",
      "Score of gnb = 0.61 (+/- 0.11)\n",
      "Score of SVM = 0.60 (+/- 0.13)\n"
     ]
    }
   ],
   "source": [
    "combination_name='k=4'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_classifier\"\n",
    "path = os.path.join(parent_dir, combination_name)\n",
    "# os.mkdir(path) \n",
    "# os.mkdir(path+'/PR_plot') \n",
    "\n",
    "d=data[['subjectivity_number','day','hour','minute','suspend']]\n",
    "X=np.array(d)[:,0:4].astype('float')\n",
    "y=np.array(d)[:,4].astype('int')\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "gnb = GaussianNB()\n",
    "dtree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=4)\n",
    "SVM = svm.SVC(kernel='poly')\n",
    "nn = MLPClassifier(activation='identity',solver='adam', max_iter=1000)\n",
    "lr = LogisticRegression(random_state=0)\n",
    "sgd = SGDClassifier(loss=\"hinge\", penalty=\"l2\", max_iter=1000)\n",
    "\n",
    "scores_1 = cross_val_score(knn, X, y, cv=10)\n",
    "scores_2 = cross_val_score(gnb, X, y, cv=10)\n",
    "scores_3 = cross_val_score(dtree, X, y, cv=10)\n",
    "scores_4 = cross_val_score(SVM, X, y, cv=10)\n",
    "scores_5 = cross_val_score(nn, X, y, cv=10)\n",
    "scores_6 = cross_val_score(lr, X, y, cv=10)\n",
    "scores_7 = cross_val_score(sgd, X, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Classifier Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "std_score = std_score/len(scores)\n",
    "\n",
    "for i,j in zip(['knn','gnb','dtree','SVM','nn','lr','sgd'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        if j.std() < std_score:\n",
    "            print('Score of %s = %0.2f (std = %0.2f)' %(i, j.mean(), j.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Classifier=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[]\n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "name = ['knn','gnb','SVM']\n",
    "classifier = [knn, gnb, SVM]\n",
    "\n",
    "for clf,clf_name in zip(classifier, name):\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Classifier.append(clf_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_classifier/%s/PR_plot/PR_%s.png' %(combination_name, clf_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Combination</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All</td>\n",
       "      <td>knn</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>All</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=5</td>\n",
       "      <td>knn</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=5</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4485</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1143</td>\n",
       "      <td>0.647466</td>\n",
       "      <td>0.796908</td>\n",
       "      <td>0.626309</td>\n",
       "      <td>0.701384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=4</td>\n",
       "      <td>knn</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k=4</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4487</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1141</td>\n",
       "      <td>0.647651</td>\n",
       "      <td>0.797264</td>\n",
       "      <td>0.626414</td>\n",
       "      <td>0.701587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Combination Classifier    TP    FP    TN    FN  Accuracy    Recall  \\\n",
       "0         All        knn  4309  1038  4167  1319  0.782424  0.765636   \n",
       "1         All        SVM  3792  1549  3656  1836  0.687529  0.673774   \n",
       "0         k=5        knn  4297  1151  4054  1331  0.770885  0.763504   \n",
       "1         k=5        SVM  4485  2676  2529  1143  0.647466  0.796908   \n",
       "0         k=4        knn  4284  1155  4050  1344  0.769316  0.761194   \n",
       "1         k=4        gnb  3888  1974  3231  1740  0.657159  0.690832   \n",
       "2         k=4        SVM  4487  2676  2529  1141  0.647651  0.797264   \n",
       "\n",
       "   Precision  F1_Score  \n",
       "0   0.805872  0.785239  \n",
       "1   0.709979  0.691403  \n",
       "0   0.788730  0.775912  \n",
       "1   0.626309  0.701384  \n",
       "0   0.787645  0.774194  \n",
       "1   0.663255  0.676762  \n",
       "2   0.626414  0.701587  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Combination=[]\n",
    "for i in range(len(Score)):\n",
    "    Combination.append(combination_name)\n",
    "result={'Combination':Combination, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result = clf_result.append(pd.DataFrame(result))\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier Selection:\n",
      "Score of knn = 0.57 (+/- 0.16)\n",
      "Score of dtree = 0.54 (+/- 0.21)\n"
     ]
    }
   ],
   "source": [
    "combination_name='k=3'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_classifier\"\n",
    "path = os.path.join(parent_dir, combination_name)\n",
    "# os.mkdir(path) \n",
    "# os.mkdir(path+'/PR_plot') \n",
    "\n",
    "X=np.array(data)[:,4:7].astype('float')\n",
    "y=np.array(data)[:,7].astype('int')\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "gnb = GaussianNB()\n",
    "dtree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=4)\n",
    "SVM = svm.SVC(kernel='poly')\n",
    "nn = MLPClassifier(activation='identity',solver='adam', max_iter=1000)\n",
    "lr = LogisticRegression(random_state=0)\n",
    "sgd = SGDClassifier(loss=\"hinge\", penalty=\"l2\", max_iter=1000)\n",
    "\n",
    "scores_1 = cross_val_score(knn, X, y, cv=10)\n",
    "scores_2 = cross_val_score(gnb, X, y, cv=10)\n",
    "scores_3 = cross_val_score(dtree, X, y, cv=10)\n",
    "scores_4 = cross_val_score(SVM, X, y, cv=10)\n",
    "scores_5 = cross_val_score(nn, X, y, cv=10)\n",
    "scores_6 = cross_val_score(lr, X, y, cv=10)\n",
    "scores_7 = cross_val_score(sgd, X, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Classifier Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "std_score = std_score/len(scores)\n",
    "\n",
    "for i,j in zip(['knn','gnb','dtree','SVM','nn','lr','sgd'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        if j.std() < std_score:\n",
    "            print('Score of %s = %0.2f (std = %0.2f)' %(i, j.mean(), j.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Classifier=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[]\n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j8vsIhhQsMjOrUsni1Zwy7pX6DqNaQ4s6MKKvHyTVENR0RLBpbKHrCx2ImeVnaFGH+g6hRiWLVwM4ETQQ1SaCiNg0sNxs4NNNj5CU1ITkfgIzq2Mj+nbe7new2/vRipWX78nimUCLnOmdgKdrPxwzM6tr+SaC5hGxZtNE+r5FNfXNzKyByDcRfCKp96YJSQcBnxYmJDMzq0v53hT2r8CDkjaNL/Q14JTChGRmZnUp3/sIZknaB+hO8mCav0TE5wWNzMzM6kReXUOSWgD/DlwcEX8Gukry0NRmZo1AvucI7gQ+Aw5Np0uBqwoSkZmZ1al8E8FeEfEr4HOAiPiUpIvIzMwauHwTwWeSdiIdZkLSXsD6gkVlZmZ1Jt+rhi4HngQ6SZoI9ANGFiooMzOrOzUmAkkC/kLyUJpDSLqELo6IZQWOzczM6kCNiSAiQtKjEXEQXz6UxszMGol8zxG8KunggkZiZmb1It9zBN8CfihpAfAJSfdQRMQBhQrMzMzqRr6J4JiCRmFmZvWmpieUNQd+CHwD+DMwISI21EVgZmZWN2o6R3A3UEySBI4Bbih4RGZmVqdq6hrqERH7A0iaALxe+JDMzKwu1XREUDbC6NZ0CUk6WtI8SfMl/byS+TdJmpu+/ipp1ZZuw8zMtk1NRwS9JK1O3wvYKZ3edNXQLlUtmD7XeCwwmGSQulmSpkZEyaY6EfFvOfUvBA7cumaYmdnWqunh9U22Yd19gPkR8R6ApAeAoUBJFfWHkwxlYWZmdSjfG8q2RgdgYc50aVq2GUldgD2AZ6qYf56k2ZJmL126tNYDNTPLskImgsqGqY4q6g4DHoqIjZXNjIjxEVEcEcXt2rWrtQDNzKywiaAU6JQz3RH4sIq6w4BJBYzFzMyqUMhEMAvoJmkPSTuS7OynVqwkqTvQGnilgLGYmVkVCpYI0stNLwCmA+8AUyLibUljJA3JqToceCAiquo2MjOzAsp3rKGtEhHTgGkVyi6rMH1FIWMwM7PqFbJryMzMGgAnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8u4go41ZGbZVbJ4NaeM2z4HFR5a1IERfTvXdxjbDScCM6t1Q4sqfRjhdqFkcfIYdieCLzkRmFmtG9G383a7o91ej1Lqk88RmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxhU0EUg6WtI8SfMl/byKOidLKpH0tqT7CxmPmZltrmDPLJbUBBgLDAZKgVmSpkZESU6dbsClQL+IWCmpfaHiMTOzyhXyiKAPMD8i3ouIz4AHgKEV6pwLjI2IlQARsaSA8ZiZWSUKmQg6AAtzpkvTslx7A3tLelnSq5KOrmxFks6TNFvS7KVLlxYoXDOzbCpkIlAlZVFhuinQDRgADAdul7TbZgtFjI+I4ogobteuXa0HamaWZYVMBKVAp5zpjsCHldT5Q0R8HhF/B+aRJAYzM6sjhUwEs4BukvaQtCMwDJhaoc6jwLcAJLUl6Sp6r4AxmZlZBQVLBBGxAbgAmA68A0yJiLcljZE0JK02HVguqQR4FhgVEcsLFZOZmW2uYJePAkTENGBahbLLct4H8JP0ZWZm9cB3FpuZZZwTgZlZxjkRmJllnBOBmVnGORGYmWVcQa8aMjPbHpUsXs0p416p8+0OLerAiL6d63y7NXEiMLNMGVpUccizulGyeDWAE4GZWX0b0bdzveyM6+MIJF8+R2BmlnFOBGZmGedEYGaWcU4EZmYZ50RgZpZxTgRmZhnXKC4f/fzzzyktLWXdunX1HYrZdqV58+Z07NiRZs2a1Xcoth1rFImgtLSUnXfema5duyJV9qhks+yJCJYvX05paSl77LFHfYdj27FG0TW0bt06dt99dycBsxyS2H333X2kbDVqFIkAcBIwq4T/X1g+Gk0iMDOzreNEUEtatWpV9n7atGl069aNDz74gCuuuIIWLVqwZMmSSutW5dhjj2XVqlXV1hkwYACzZ8/erPyuu+7iggsu2ILo83f99dezzz770LNnT3r16sU999xTbSxbY/bs2Vx00UUArF+/nkGDBlFUVMTkyZM555xzKCkp2ab133zzzWVxA2zYsIG2bdty6aWXlqs3YMAAunfvTq9evejXrx/z5s3bpu0C3H333XTr1o1u3bpx9913V1rnlFNOoaioiKKiIrp27UpRUREAn332GWeddRb7778/vXr14rnnnitbZtCgQaxcuXKb47NsahQni7cnM2fO5MILL2TGjBl07pwMbNW2bVtuuOEGrrvuurzXM23atEKFWK2IICLYYYfNfyPcdtttPPXUU7z++uvssssufPTRRzz66KO1HkNxcTHFxcUAvPHGG3z++efMnTsXSHaSW2Ljxo00adKkbHrDhg3ccccd/N///V9Z2YwZM+jevTtTpkzhmmuuKdedMnHiRIqLixk/fjyjRo1i6tSpW92uFStWMHr0aGbPno0kDjroIIYMGULr1q3L1Zs8eXLZ+0suuYRdd90VgN///vcA/PnPf2bJkiUcc8wxzJo1ix122IHTTz+dW2+9lV/84hdbHZ9lV6M7Ihj92NucMu6VWn2NfuztvLb94osvcu655/LEE0+w1157lZWfffbZTJ48mRUrVmy2zH333UefPn0oKiriBz/4ARs3bgSga9euLFu2DIArr7ySffbZh8GDBzN8+HCuv/76suUffPBB+vTpw957782LL75YVr5w4UKOPvpounfvzujRo8vKb7zxRnr27EnPnj25+eabAViwYAH77rsvP/rRj+jduzcLFy5k5MiR9OzZk/3335+bbroJgGuuuYZbb72VXXbZBYBdd92VM888c7M2nX/++RQXF7Pffvtx+eWXl5X//Oc/p0ePHhxwwAH89Kc/LYt/09FF//79AXjuuec4/vjjWbJkCaeddhpz586lqKiId999t9yRx4wZMzj00EPp3bs33/ve91izZk3ZZzdmzBgOP/xwHnzwwXKxPfPMM/Tu3ZumTb/8DTRp0iQuvvhiOnfuzKuvvlrZV0v//v2ZP39+pfPyNX36dAYPHkybNm1o3bo1gwcP5sknn6yyfkQwZcoUhg8fDkBJSQkDBw4EoH379uy2225ln8WQIUOYNGnSNsVn2eUjglqyfv16hg4dynPPPcc+++xTbl6rVq04++yzueWWW8rtlN955x0mT57Myy+/TLNmzfjRj37ExIkTOeOMM8rqzJ49m//5n//hjTfeYMOGDfTu3ZuDDjqobP6GDRt4/fXXmTZtGqNHj+bpp58G4PXXX+ett96iRYsWHHzwwRx33HFI4s477+S1114jIujbty9HHHEErVu3Zt68edx5553ceuutzJkzh0WLFvHWW28BsGrVKj7++GM+/vjjcgmuKldffTVt2rRh48aNDBw4kD/96U907NiRRx55hL/85S9IKuv2GjNmDNOnT6dDhw6bdYW1b9+e22+/neuvv57HH3+83Lxly5Zx1VVX8fTTT9OyZUuuu+46brzxRi677DIguX7+pZde2iy2l19+udzn9+mnnzJz5kzGjRvHqlWrmDRpEoceeuhmyz322GPsv//+m5X/+te/ZuLEiZuV9+/fn9/85jflyhYtWkSnTp3Kpjt27MiiRYs2W3aTF198ka9+9at069YNgF69evGHP/yBYcOGsXDhQubMmcPChQvp06cPrVu3Zv369Sxfvpzdd9+9ynWaVabRJYLLT9ivXrbbrFkzDjvsMCZMmMAtt9yy2fyLLrqIoqIiLrnkkrKymTNnMmfOHA4++GAg2Sm1b9++3HIvvfQSQ4cOZaeddgLghBNOKDf/pJNOAuCggw5iwYIFZeWDBw8u2yGcdNJJvPTSS0jixBNPpGXLlmXlL774IkOGDKFLly4ccsghAOy555689957XHjhhRx33HEceeSRrFmzJu8rUKZMmcL48ePZsGEDixcvpqSkhB49etC8eXPOOeccjjvuOI4//ngA+vXrx8iRIzn55JPL2pKPV199lZKSEvr16wck/ee5O/CqupAWL17MvvvuWzb9+OOP861vfYsWLVrwne98hyuvvJKbbrqprDvp1FNPZaeddqJr16789re/3Wx9o0aNYtSoUXnFHBGblVX3mU6aNKnsaACSI8t33nmH4uJiunTpwmGHHVbuyKZ9+/Z8+OGHTgS2xRpdIqgvO+ywA1OmTGHQoEFcc801/Md//Ee5+bvtthsjRozg1ltvLSuLCM4880yuvfbaKtdb2c4j11e+8hUAmjRpwoYNG8rKK+5gJFW7rk3JAaB169a8+eabTJ8+nbFjxzJlyhTuuOMOWrZsyXvvvceee+5Z5Xr+/ve/c/311zNr1ixat27NyJEjWbduHU2bNuX1119n5syZPPDAA/zud7/jmWee4bbbbuO1117jiSeeoKioqOxcQE0igsGDB1fZHZLbnlw77bRTuevqJ02axMsvv0zXrl0BWL58Oc8++yyDBg0CvjxHUJUtOSLo2LFjuRO8paWlDBgwoNL1btiwgYcffpg5c+aUlTVt2rSsmw7gsMMOKztagOR+mk0/GMy2RKM7R1CfWrRoweOPP87EiROZMGHCZvN/8pOfMG7cuLId9sCBA3nooYfKrihasWIF77//frllDj/8cB577DHWrVvHmjVreOKJJ/KK5amnnmLFihV8+umnPProo/Tr14/+/fvz6KOPsnbtWj755BMeeeQRvvnNb2627LJly/jiiy/KfiFvOrF66aWX8uMf/5jVq5NH7q1evZrx48eXW3b16tW0bNmSXXfdlX/+85/88Y9/BGDNmjV89NFHHHvssdx8881lO/x3332Xvn37MmbMGNq2bcvChQvzat8hhxzCyy+/XNZvv3btWv7617/WuNy+++5btszq1at56aWX+OCDD1iwYAELFixg7NixW9TXPmrUKObOnbvZq2ISADjqqKOYMWMGK1euZOXKlcyYMYOjjjqq0vU+/fTT7LPPPnTs2LGsbNP3Bsn327RpU3r06AEkifEf//hHWUIz2xI+Iqhlbdq04cknn6R///60bdu23Ly2bdty4oknlv2q69GjB1dddRVHHnkkX3zxBc2aNWPs2LF06dKlbJmDDz6YIUOG0KtXL7p06UJxcXHZVSTVOfzwwzn99NOZP38+I0aMKPtVO3LkSPr06QPAOeecw4EHHliuSwmSvuyzzjqLL774AqDsiOX8889nzZo1HHzwwTRr1oxmzZqV6+qCpB/7wAMPZL/99mPPPfcs67r5+OOPGTp0KOvWrSMiyj6DUaNG8be//Y2IYODAgfTq1Yvnn3++xva1a9eOu+66i+HDh7N+/XoArrrqKvbee+9qlzvmmGM4/fTTAXj44Yf5l3/5l7KjKoChQ4fys5/9rGydtalNmzb813/9V1lX4GWXXUabNm2A5Lv44Q9/WPY9PfDAA+W6hQCWLFnCUUcdxQ477ECHDh249957y+bNmTOHQw45pFxXkW1/Shav3qZHVvb4+i4F6f5WTV0P25vi4uKoeL36O++8U67ft7FZs2YNrVq1Yu3atfTv35/x48fTu3fv+g6rwTrxxBP51a9+Va5bpaG7+OKLGTJkSNlVRbka+/+PhuL+1z7gD3OrvjggH9uSCCTNiYhK+zn986EBOO+88ygpKWHdunWceeaZTgLb6Je//CWLFy9uVImgZ8+elSYB236M6NuZEX0713cYlXIiaADuv//++g6hUenevTvdu3ev7zBq1bnnnlvfIVgD1mhOFje0Li6zuuD/F5aPRpEImjdvzvLly/1Hb5Zj0/MImjdvXt+h2HauoF1Dko4GbgGaALdHxC8rzB8J/BrYdAbldxFx+5Zup2PHjpSWlrJ06dJtjNiscdn0hDKz6hQsEUhqAowFBgOlwCxJUyOi4tCRkyNim4bKbNasmZ/AZGa2lQrZNdQHmB8R70XEZ8ADwNACbs/MzLZCIRNBByD3NtHStKyi70j6k6SHJHWqZD6SzpM0W9Jsd/+YmdWuQiaCykbTqjISAPYAAAXISURBVHg29zGga0QcADwNVPqkjogYHxHFEVHcrl27Wg7TzCzbCnmyuBTI/YXfEfgwt0JELM+Z/D1Q45Nb5syZs0zS+zXVq0JbYNlWLttQuc3Z4DZnw7a0uUtVMwqZCGYB3STtQXJV0DBgRG4FSV+LiMXp5BDgnZpWGhFbfUggaXZVt1g3Vm5zNrjN2VCoNhcsEUTEBkkXANNJLh+9IyLeljQGmB0RU4GLJA0BNgArgJGFisfMzCpX0PsIImIaMK1C2WU57y8FLq24nJmZ1Z1GcWfxFhhfc5VGx23OBrc5GwrS5gY3DLWZmdWurB0RmJlZBU4EZmYZ1ygTgaSjJc2TNF/SzyuZ/xVJk9P5r0nqWvdR1q482vwTSSXpXdwzJVV5TXFDUVObc+p9V1JIavCXGubTZkknp9/125Ia/MMs8vjb7izpWUlvpH/fx9ZHnLVF0h2Slkh6q4r5kvSb9PP4k6Rtf1JVRDSqF8mlqu8CewI7Am8CPSrU+RFwW/p+GMnAd/Uee4Hb/C2gRfr+/Cy0Oa23M/AC8CpQXN9x18H33A14A2idTrev77jroM3jgfPT9z2ABfUd9za2uT/QG3irivnHAn8kGb3hEOC1bd1mYzwiyGewu6F8OZzFQ8BASZUNidFQ1NjmiHg2Itamk6+S3OndkOU7qOGVwK+AdXUZXIHk0+ZzgbERsRIgIpbUcYy1LZ82B7BL+n5XKoxg0NBExAsk91VVZShwTyReBXaT9LVt2WZjTAT5DHZXViciNgAfAbvXSXSFke8Af5t8n+QXRUNWY5slHQh0iojH6zKwAsrne94b2FvSy5JeTZ8J0pDl0+YrgNMklZLct3Rh3YRWb7b0/3uNGuMzi/MZ7C6fOg1J3u2RdBpQDBxR0IgKr9o2S9oBuInGdbd6Pt9zU5LuoQEkR30vSuoZEasKHFuh5NPm4cBdEXGDpEOBe9M2f1H48OpFre+/GuMRQY2D3eXWkdSU5HCyukOx7V0+bUbSIOAXwJCIWF9HsRVKTW3eGegJPCdpAUlf6tQGfsI437/tP0TE5xHxd2AeSWJoqPJp8/eBKQAR8QrQnGRwtsYqr//vW6IxJoKywe4k7UhyMnhqhTpTgTPT998Fnon0LEwDVWOb026ScSRJoKH3G0MNbY6IjyKibUR0jYiuJOdFhkTE7PoJt1bk87f9KMmFAUhqS9JV9F6dRlm78mnzB8BAAEn7kiSCxvzgkqnAGenVQ4cAH8WXg3dulUbXNRT5DXY3geTwcT7JkcCw+ot42+XZ5l8DrYAH0/PiH0TEkHoLehvl2eZGJc82TweOlFQCbARGRfnh3huUPNt8CfB7Sf9G0kUysiH/sJM0iaRrr2163uNyoBlARNxGch7kWGA+sBY4a5u32YA/LzMzqwWNsWvIzMy2gBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgVkFkjZKmivpLUmPSdqtltc/UtLv0vdXSPppba7fbEs5EZht7tOIKIqIniT3mfy4vgMyKyQnArPqvULOgF6SRkmalY4DPzqn/Iy07E1J96ZlJ6TPu3hD0tOSvloP8ZvVqNHdWWxWWyQ1IRm6YEI6fSTJuD19SAb+miqpP7CcZAynfhGxTFKbdBUvAYdEREg6B/gZyV2wZtsVJwKzze0kaS7QFZgDPJWWH5m+3kinW5Ekhl7AQxGxDCAiNg1g2BGYnI4VvyPw9zqJ3mwLuWvIbHOfRkQR0IVkB77pHIGAa9PzB0UR8Y2ImJCWVzZWy2+B30XE/sAPSAZDM9vuOBGYVSEiPgIuAn4qqRnJwGdnS2oFIKmDpPbATOBkSbun5Zu6hnYFFqXvz8RsO+WuIbNqRMQbkt4EhkXEvekwx6+kI7iuAU5LR8O8Gnhe0kaSrqORJE/OelDSIpJhsPeojzaY1cSjj5qZZZy7hszMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMu7/AzBQWK4s27/6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "name = ['knn']\n",
    "classifier = [knn]\n",
    "\n",
    "for clf,clf_name in zip(classifier, name):\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Classifier.append(clf_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_classifier/%s/PR_plot/PR_%s.png' %(combination_name, clf_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Combination</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All</td>\n",
       "      <td>knn</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>All</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=5</td>\n",
       "      <td>knn</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=5</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4485</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1143</td>\n",
       "      <td>0.647466</td>\n",
       "      <td>0.796908</td>\n",
       "      <td>0.626309</td>\n",
       "      <td>0.701384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=4</td>\n",
       "      <td>knn</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k=4</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4487</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1141</td>\n",
       "      <td>0.647651</td>\n",
       "      <td>0.797264</td>\n",
       "      <td>0.626414</td>\n",
       "      <td>0.701587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=3</td>\n",
       "      <td>knn</td>\n",
       "      <td>4065</td>\n",
       "      <td>1323</td>\n",
       "      <td>3882</td>\n",
       "      <td>1563</td>\n",
       "      <td>0.733592</td>\n",
       "      <td>0.722281</td>\n",
       "      <td>0.754454</td>\n",
       "      <td>0.738017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Combination Classifier    TP    FP    TN    FN  Accuracy    Recall  \\\n",
       "0         All        knn  4309  1038  4167  1319  0.782424  0.765636   \n",
       "1         All        SVM  3792  1549  3656  1836  0.687529  0.673774   \n",
       "0         k=5        knn  4297  1151  4054  1331  0.770885  0.763504   \n",
       "1         k=5        SVM  4485  2676  2529  1143  0.647466  0.796908   \n",
       "0         k=4        knn  4284  1155  4050  1344  0.769316  0.761194   \n",
       "1         k=4        gnb  3888  1974  3231  1740  0.657159  0.690832   \n",
       "2         k=4        SVM  4487  2676  2529  1141  0.647651  0.797264   \n",
       "0         k=3        knn  4065  1323  3882  1563  0.733592  0.722281   \n",
       "\n",
       "   Precision  F1_Score  \n",
       "0   0.805872  0.785239  \n",
       "1   0.709979  0.691403  \n",
       "0   0.788730  0.775912  \n",
       "1   0.626309  0.701384  \n",
       "0   0.787645  0.774194  \n",
       "1   0.663255  0.676762  \n",
       "2   0.626414  0.701587  \n",
       "0   0.754454  0.738017  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Combination=[]\n",
    "for i in range(len(Score)):\n",
    "    Combination.append(combination_name)\n",
    "result={'Combination':Combination, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result = clf_result.append(pd.DataFrame(result))\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier Selection:\n",
      "Score of knn = 0.56 (+/- 0.18)\n",
      "Score of gnb = 0.51 (+/- 0.16)\n",
      "Score of dtree = 0.54 (+/- 0.20)\n"
     ]
    }
   ],
   "source": [
    "combination_name='k=2'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_classifier\"\n",
    "path = os.path.join(parent_dir, combination_name)\n",
    "# os.mkdir(path) \n",
    "# os.mkdir(path+'/PR_plot') \n",
    "\n",
    "X=np.array(data)[:,4:6].astype('float')\n",
    "y=np.array(data)[:,7].astype('int')\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "gnb = GaussianNB()\n",
    "dtree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=4)\n",
    "SVM = svm.SVC(kernel='poly')\n",
    "nn = MLPClassifier(activation='identity',solver='adam', max_iter=1000)\n",
    "lr = LogisticRegression(random_state=0)\n",
    "sgd = SGDClassifier(loss=\"hinge\", penalty=\"l2\", max_iter=1000)\n",
    "\n",
    "scores_1 = cross_val_score(knn, X, y, cv=10)\n",
    "scores_2 = cross_val_score(gnb, X, y, cv=10)\n",
    "scores_3 = cross_val_score(dtree, X, y, cv=10)\n",
    "scores_4 = cross_val_score(SVM, X, y, cv=10)\n",
    "scores_5 = cross_val_score(nn, X, y, cv=10)\n",
    "scores_6 = cross_val_score(lr, X, y, cv=10)\n",
    "scores_7 = cross_val_score(sgd, X, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Classifier Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "std_score = std_score/len(scores)\n",
    "\n",
    "for i,j in zip(['knn','gnb','dtree','SVM','nn','lr','sgd'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        if j.std() < std_score:\n",
    "            print('Score of %s = %0.2f (std = %0.2f)' %(i, j.mean(), j.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Classifier=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[]\n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "name = ['knn','gnb','dtree']\n",
    "classifier = [knn,gnb, dtree]\n",
    "\n",
    "for clf,clf_name in zip(classifier, name):\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Classifier.append(clf_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_classifier/%s/PR_plot/PR_%s.png' %(combination_name, clf_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAADU7Vnsu5G/L7drZz7TU8gnQCS8Tz2zc913PC/kezl89ur1iHsPn8cKuxEAAPipxMc8XzVjlJl1x6HjZwq7l9qkJSeu2XP6+x4o8gNcO334fljI62cP679kxIgRVVVVZmZmXbt27dKli7Ozs5pajSEeAAAAAAAA+CIENQAAAAAA6tDdoUPKxwwul9sYzvru4Vjbr/kKEY/H43K51hYm8rIywu4FAAB+Eu07d89MS+FxudQIPoJr18GpxkeSNWY8Ho/H5Zq0sZaWq/6klZrk5eXdvXuXzWZHRkYGBgZyOBwDAwMXFxcXF5du3bqpqn7hmWsAAAAAAABQDYIaAAAAAAB1WO/TqH+Ft5EY69Z3rFtfYXcBAAA/lWm+AcJu4SfX231sb/exX7VEVlaWiWUQUVFRUXR0NBPaOHDggGBoo3v37ioqNT5MBwAAAAAA4BeHoAYAAAAAAAAAAAB8NTk5uc9DG2w2e+/evURkamrq6OiI0AYAAAAAAMDnENQAAAAAAPjHtfDowqJi977dhd1IvXC5XBEREWF3AQAA8N+6F3a9pLCga393YTcCdRAMbRQWFt67d48f2mCxWCYmJkxow8XFRVlZWdjNAgAAAAAACBmCGgAAAAAA/9i093jCh9RGHtR4l/Qh8Ni5Szcj84uKOlm3mTnWo6u9jbCbAgAA+K8EBW5KS05oQkGNMV3bWNp1nue3Q9iNCJO8vHwtoY127do5ODg4Ojr26NFDSUlJ2M0CAAAAAAAIAYIaAAAAAABNRmlZ+dCpPmmfMof166GipHA+JNxtqvfFPRscO1gKuzUAAACg66ePpCbHW9p1FnYjjYhgaCMzMzM6OjoqKorNZm/btk1ERIQf2ujZs6eioqKwmwUAAAAAAPhBENQAAAAAgF8Rj8cjIhaLJexGvs7yP/e8TXx/fneAq5MdEc0Y695hgOckn7Wx7CBhtwYAAPAdNNEP6Mz01MNb1rx5/ig+9oWwe2nU1NXV+/fv379/fyLKyMgIDw+PjIyMioratm2bqKiopaWli4uLg4ODs7OzgoKCsJsFAAAAAAD4DyGoAQAAAAC/luev47z9tz968bqCw7EwMfSdMY4JPXwu4v6Ts9fCbkY9KC0v72TTtnOHduPd+4uKihBRWXnF+t1HT1wMSU3PbKGt0cXOxm/RNHlZGWZh7bMNceTs321MDPkNa6gq9+jc8dj5aw+exXSwNGv4/gAAAMISH/ti1xrv188fVXIqDEwtxs72te3S8/OyZ9ER4VfPPowMrSgrtejQydK2c9/fxomIijKzFeVlx3duYJ8/kfkxVbNZC6tOzpMX+8nIytc+1XClRYUpiXGy8oombW3ePH/0Xfb86WloaLi7u7u7uxPRp0+fIiIiIiMj2Wx2QEAAP7TB5DakpaWF3SwAAAAAAMB3hqAGAAAAAPxCIu4/GThpgYqS4tihfQsKi8+HhA2d6nPj6DY7K4tqleH3nvQdP0dBTu63fi6qyoo37zyctWJj4oe0tQumEtHslZuOXbg2YoCrpZlx4vu0/acuvXwbH3ZyJ7O29tlvlp2bn1dQOMatj+CgccvmRPTo5RsENQAAoOl6Fh3hPW6QgrJKH4+xxYUFEdfO+U4aujkoxNz6X2HKp3fDF4zpJyuv0H3AMEVl1UeRoX8unfXxQ6KX9xqmYMvS30POHesxeISRmeXH9wlXTh5IePNq2+lbtU81nK6R6eaTIUSUmhw/pmub77LnL0VTU5Mf2khPT799+zabzb506ZK/v7+YmBg/tOHo6CglJSXsZgEAAAAAAL4DBDUAAAAA4FfB5XIXrN0mKSEecmSroa4OEc2ZMNyq7+jA4+c+D2oEX2GLiYrG3jipqCBHRPMmjWztMuzqrai1C6aWV3BOXAzp7Wy/28+HKdbXbTZ/zdZ3SR+MW7aofVbwEtm5+YHHz9XU7SBXZzMjfcGRt4nviUhbXVVwsJW+LhFl5uR+07cEAABA+Hhc7vbVC8QlJDaduK6jZ0hEHl6/j+9pfeHI7mpBjdBLwaKiYkfDYuQUFInotynzRjmb3b15lQlqcCrK2edP2HXttTAgkKnX1jXYvmp+SuI7TR3dmqaa6xtX6yc/N/vikcCauu3ce3BL49bf7+6hOi0tLYQ2AAAAAADgp4egBgAAAAD8Kp7Fvnv+Om7UoF5MSoOITAx0N/rO5nK5nxfP8hw2dZQbk9IgogoOR0lBLr+wmIiquFVEFHH/6bOYd5ZmxkQ0deQQz6H9pCQk6pwVlJmTt3rb/pq6NW7ZolpQI/59KhEpK/7rkHbdZlpElF9Q9BXfCAAAgMbkXcyz+NgXPd1GMSkNItI1NJmxfOPnH9BDJ8waPHYak9IgokpOhayCUnFhPvOWW1VFRE+jb8e9emZkbklEg8dM6eMxVkJSilNRXtPU5/3kZ2ce/POPmrptrm+MoMYPIxja+PjxI/NslODgYH9/f2lpaWtra0dHRxcXl86dO0tKSgq7WQAAAAAAgK+AoAYAAAAAfCMWiyXsFr5OfHIqEVmYGAoOTh055IvFJga6OXkFWw4ERT95mZyaHp+cUlBUrK2hRkQyUlJLZniu+HOv3ZAJpoZ6zh2tXZ3senS2FRUVqXO2+iWe3qipWwlx8WojkuLiRJSbXyg4WFxaSkRK/05vNH48Ho+a4L9CAACNAfOHJ4/H+2n+FE1LiiciAxNzwcFBY6Z8XqlraFKQm3Nq75aYx/fSU5JTkuJLigpUNbWZWUlpmTGzF+/fuHJyf3tdI1MrOyfbrr06OLmIiIrWMvX5VVoYmlyNya6pW3Hx6snLX4hQ/63T1tbmhzbS0tKioqLYbHZQUJC/v7+MjIyVlRVCGwAAAAAA0IRU/8tiAAAAAIB6kpOVZVICTUVmbh4RNdNQq0/x5n0nDJ2H+O04WFlZ2a1T+z1+PvbW/3vk/KIpY16FnPCZNlZaSmrPyQtDpiyy7jvmU1ZOfWb5WCyWtJRkTa/Pgx2a6ipElPghTXCQyW2oKSt93fdC2AqLS4hIQUFB2I0AADQ9cnJyRFReWiLsRr6bvJwsIlLTbFZnZdDuzcM6GR3Z5ldZybF27LZo/W4LG3vBgpHTFx0Jezl6po+UlPTF43uXTBgyvqdNTuan2qeqYbFYklLSNb2+mO34RZQUF8nJN4psaLNmzdzd3QMDAxMTE1NTUw8ePGhubn7y5MkePXqoqKj06NFjxYoVbDa7oqJC2J0CAAAAAAB8GU7UAAAAAIBvpK2tlfIxQ9hdfAU9HS0iuv88xr1vd/7gsfPXuDze6MG9BSuzcvJ8NwaqKSu+DDkhLyvDDK7bdYT5ooLDKS0t19PRXjZrwrJZEz5l5fjvPLzz2NkdR86snDOp9lnBq3zKyvHbcaimbse69bEyNxEcMW7ZgsViVQtqPH8dR0S2lk3sDPa0T1lEpKWlJexGAACaHm1tbSLK+Jiia2hSZ3GToNVcj4hinz3o2t+dPxhy9hiPy3UdOpo/kpeTtSdgqZKK2uFbL2Rk/4kLHNvuzy+o5FSUlZZq6eh5zlnqOWdpTuanY9v9zx/ede7QzrGzF9c0NWH+imr95GR+OrrNr6Zue3mMbWVh9T3uu+nJSk/V1Gx0n91MaIM5aSMhISEyMjIqKurAgQMrV66UlZW1t7d3cXFxcHDo2LGj+GfHlQEAAAAAAAgLTtQAAAAAgG/Upq3l05h3wu7iK7Rv01paSjI8+jF/JDYuaZKP3+37T6tVvk9L53K5g3o681MaKR8znr/+52bDoh9r2fYJvsJm3mqqqcyZOJyI8goK65wVlFdQdODU5ZpeCe/TqtVra6g5treMfPgs4X0qM8KprAy6dKOZpnq1SEfj9zTmrbi4uKmpqbAbAQBoelq3bi0uLv7uVfUPr6bLpK2NpJT0kzth/JHkd7EBC7ye3Y8ULPuU+p7H5XZ2HchPaWR+TImLfc4veHInfGA77dBLwcxbFXXNYV5ziKgoP7eWqc/7KSrIuxJ0sKZXWnLC97v1JiYu5lnbtm3qrhMeAwODMWPGBAYGJicnx8fHb9q0SVtbe/v27Z07d2ZO2vD394+MjORwOMLuFAAAAAAAfnU4UQMAAAAAvlHXrl19vBdVcDgSTeTXEzVUlWeMcV+/++jM5RvGufeLjUvaciBITFR00m8Dq1Ua6+vKyUifuhra06mjiYHe3ccvVm7ZKy8rW1xS+jbxvb11G3VV5bXbD+poqVu2bpXwPoU5bKOXsz0R1T4ryMRAN//Fza+6hYVTRg/2Wjjy9+XeU8coKchv3HMsMeXj2V3rhPjA+G8TcvteJ3t7PEIeAOAbSEpK2nfq9CD8RvcBw4Tdy/ehrKbhNm7G8Z3rN/vO7DNsXPK72FN7t4iKivUfMVGwrIWBsbSM3K0rp227uOoatnr58O6BTatk5eRLi4s/JLxtYdDKvL2dkqr64a1+6lo6RubtUpPjmfM2OnbrXcvU5/3oGppcf5P3Y+69CeFUlD+9Gx7gv07YjdSXgYGBl5eXl5cXESUkJLDZ7MjIyG3btnl7e8vJydnZ2bm4uLi4uFhZWYmI4DfZAAAAAADgR2PxeDxh9wAAAAAATVJKSkrLli0PbVjm1rursHupr6oq7ootezbvO1FVxSUiLXXVjUtmDen1T/89R89K+JAaF3aGiM5euzV58bqiklIiUlZUWO8zQ1ZGeuKiNeUVnMJXt0LvPJzovfZjRhazUEpSwmfa2IWT/zmevfbZBjp15eZUX//i0jIiUlSQ850xfsaYod9l5x+msLjE0NntjzVrZ82aJexeAACapC1btizxXXrybhz/bImmjltVtX/jyqA9m7lVVUSkqqE1fdkG5z5DiGjucNe05ISTd94RUfjVs+sXTiktKSIieSXlab4BUjKy/vMnccrLQ94VENHjqNB18ydlf/rIbCshKTV6pveIaQtrn/qOUpPjx3Rt02eY5zy/Hd93Z6ELu3Jm7e+eSUlJzZs3F3YvDcKENths9q1bt7KysuTl5Tt27IjQBgAAAAAA/GAIagAAAADAtxs4YMDH5PjwoJ1N60SH4tKyl2/i5eVkjPSa13IcSE5ewdOYt1oaqq0NWzI3mJNXkFtQaKirQ0QlZWUv38R/SMtQVVY0N9ZXV1UWXFv7bANVVlU9fvmay+V1aGsmKtr0fpywed+JP7YfTElJVVb+nt8WAIBfR25urk7z5qNnLWGe3/HTKCspTnj9UkZeoXlLQzFxiS/WFOTmxMU8VdHQ1jMyZT6dC3JzCgtydfQMmYLy0pL41y8z0j4oqqjqtzJXUlXnr61lCmrH4/Fmujkb6Ta7eOGCsHv5nvihjdDQ0OzsbIQ2AAAAAADgh0FQAwAAAAC+3atXr9q1a7fzj4WjBvUSdi/QNGRk57TtNer3ufNWrFgh7F4AAJqwFStWbNi46QD7maqGlrB7gZ/f9dNHNvpMe/TokaWlpbB7+a/wQxs3b97MyclRV1fv2LGjo6Oji4uLtbV10wolAwAAAABA44egBgAAAAA0yPTp08+eDn529YiCnKywe4EmYPKSdWH3X8S+fi0jIyPsXgAAmrCSkhJT09bmdl0W+O8Sdi/wkyspKhjn0s5j6JDt27cLu5cfgcvlxsbGRkVFMbmN3NxcDQ0NW1tbhDYAAAAAAOA7QlADAAAAAL5RRUWFhIREdna2uZlZewvj4L/W4IBoqN3R89e8fPxOnz49ZMgQYfcCANDknT2JghYWAAAgAElEQVR7dujQoQsDAnu6jRJ2L/DT4nG5yyZ7xL14FBPzSlVVVdjt/GhVVVVPnz6NjIyMioq6ceNGXl6epqamk5OTg4ODo6MjQhsAAAAAAPDNENQAAAAAgK/GZrOXLFnSo0ePP/74g4gePnzo7OQ06bcB6xZNF3Zr0HjdefS8z7i58+bPX7NmjbB7AQD4SSxevHj9hg3+By+2s3cWdi/wc9q5xvvSsd23QkPt7e2F3YuQ1RTacHFxcXBwMDc3F3aDAAAAAADQlCCoAQAAAABfITQ0dNmyZVFRUX379l25cqWNjQ0zfuLEiZEjRy6e7rlkuid+sxA+F/XwuceMJV27dw8OPoWTVwAAvhcul+vu4cG+eWtVYFCbDg7Cbgd+Kjwe7/DWtUe2rj127Njw4cOF3U7jwoQ22Gx2ZGTk7du38/PztbS0OnfujNAGAAAAAADUE4IaAAAAAFAvd+7cWbZs2c2bNx0cHNasWePsXP03d/fs2TN9+vTBrs6Ba7ylJCWE0iQ0TkfPX5u+dH3/Af2PHDkqLS0t7HYAAH4qpaWlo0aPvnTp0tw1f+EZKPC9VJSXbVg0JeLvc9u3b580aZKw22nUKisrnz17xoQ2IiIiCgoKtLW1HR0dXVxcevTooa+vL+wGAQAAAACgMUJQAwAAAADqcO/evT/++OPy5csODg6rVq3q1q1bTZU3b950HzpUV1tjk++sTjZtf2ST0DhlZOcs3bT7yNm/vb2916xZg9NWAAD+Czweb8mSJevWrXMdOnriglXKahrC7giatpcP7/y1Ym5m2ofTp091795d2O00JfzQBpvNjoqKKi0t5Yc2evbs2bJlS2E3CAAAAAAAjQWCGgAAAABQoxcvXqxevfr06dMdO3ZcvHhx//7961wSFxc3fdq0G2y2Rz8X3xnjjPSa/4A+oREqLC7Ze/LCup1HlJSV/9yyZfDgwcLuCADgJ3fu3LnZs3/PycsbOX1R/5ETZWTlhd0RND2pSXGHtqwJvRjs4tJjx47tRkZGwu6oCRMMbURGRpaVlfFDG66urnp6esJuEAAAAAAAhAlBDQAAAAD4glevXq1cufL06dNt2rTx9fV1d3f/quUXL16cN3dufEJCZ1urfl07dbQyN9RtrqwoLyIi8h81DI1BQVFxanrms9h3IbfvXQ6NquJyFyxYuHDhQhkZGWG3BgDwSygpKQkICAhYv15ERLSTS78Ozj2MzdupaesgtAE14XG5Bfm5qYlxMU/v32VfeXbvtoGB4aZNGwcMGCDs1n4qn4c2DAwMHBwcHB0de/XqpaurK+wGAQAAAADgR0NQAwAAAAD+JTY21s/P7/jx461bt162bNnQoUO/7XEVVVVVV69ePX78+PVr13Lz8r57n9BoiYmJOTo4DB4yZPTo0crKysJuBwDgl5Obm3v48OFz585HRUVWVlYKux1oGpSVVVxde44cObJ3796ioqLCbudnVlpa+ujRo6ioKDabffv27fLycgMDAxcXFwcHh65du7Zo0ULYDQIAAAAAwI+AoAYAAAAA/CMpKcnPz2/fvn0mJiaLFi0aNWrUdzkAg8fjJSUlJSQk5OXlcbnchm/YVHh4eMyZM8fe3l7Yjfw48vLympqaZmZmkpKSwu4FAACovLw8Jibm06dPhYWFwu6lsbh79+7mzZuDg4OF3UhjISIioqSkpK+vr6+v/23ZXGiIkpKSO3fuREZGRkVFRUREVFRU8EMb3bp1a94czxAEAAAAAPhpIagBAAAAAJScnLx27dr9+/e3aNHC29t7woQJ+E3KhmOxWEFBQR4eHsJuBAAAAP4RHBw8bNgw/G0YNELFxcV37979PLTh4uLStWtXNTU1YTcIAAAAAADfk5iwGwAAAAAAYfrw4cOGDRsCAwO1tbW3b98+fvx4MTH8JyIAAAAAwA8lKyvLxDLo/0MbbDY7MjLywIEDHA6HH9ro1q2bqqqqsJsFAAAAAICGwt/CAwAAAPyiMjIyNm3atGXLFg0NDX9//ylTpuBxFQAAAAAAQicY2igqKoqOjv5iaKN79+4qKirCbhYAAAAAAL4FghoAAAAAv5ysrKwNGzZs3bpVVVV13bp1kydPlpKSEnZTAAAAAABQnZyc3OehDTabvXfvXiIyNTV1dHRkCpSVlYXdLAAAAAAA1BeCGgAAAAC/kOzs7G3btm3evFlSUnL58uWzZs2SlpYWdlMAAAAAAFA3wdBGYWHhvXv3+KENFotlYmKC0AYAAAAAQFOBoAYAAADAL6GwsHDHjh1+fn5iYmKLFy+eOXOmjIyMsJsCAAAAAIBvIS8v/8XQxp49e0RERNq1a+fg4ODo6NijRw8lJSVhNwsAAAAAANUhqAEAAADwkysqKtq+fbu/vz+Lxfr999/nzJmjqKgo7KYAAAAAAOD7EAxtZGZmRkdHR0VFsdnsbdu2MaENFxcXBwcHJycn/I8AAAAAAEAjgaAGAAAAwE+ruLh47969fn5+xcXF06dP9/b2xq/TAQAAAAD8xNTV1fv379+/f38iysjICA8Pj4yMZLPZAQEBoqKilpaWTGjD2dlZQUFB2M0CAAAAAPy6ENQAAAAA+AlVVFQcPHhwxYoVBQUFM2bMWLRoER5TDQAAAADwS9HQ0HB3d3d3dyeiT58+RUREfB7aYHIb0tLSwm4WAAAAAODXgqAGAAAAwE+FiWisXLkyLy9v4sSJPj4+Wlpawm4KAAAAAACESVNTkx/aSE9Pv337NpvNvnTpkr+/v5iYGD+04ejoKCUlJexmAQAAAAB+fghqAAAAAPwkOBzOiRMnVq5cmZKS4unpuWLFCm1tbWE3BQAAAAAAjYuWlhY/tPHx40fmmA2ENuD/2LvrsCi2/w/gZ9kFpDslpFUMQkRkQYUFExvsDjARE7sD65pXQWxRQVGvrawiZSCgGJikgBISS8PG74/1t18k1OtVB/T9eviDPXN25jM8j56Z3fecAwAAAAC/EoIaAAAAAM0en88PDQ1dsmRJenr6hAkTVqxY0bJlS6qLAgAAAACApk5LS6t+aCMkJMTPz09KSsrKyorJZLJYLAcHB0lJSaqLBQAAAAD4fSCoAQAAANCMCSMay5YtS01NHT58+PXr142MjKguCgAAAAAAmp/aoY3s7OyYmBg2mx0cHOzn5yctLW1paSkMbTg6OkpISFBdLAAAAABA84agBgAAAECzJBAILl++vHz58qdPnw4ZMuTy5csmJiZUFwUAAAAAAL8DbW1tUWgjJSUlOjo6Jibm9OnTfn5+MjIydnZ29vb2TCYToQ0AAAAAgO+DoAYAAABA88NmsxcuXJiYmDhkyJDg4GAzMzOqKwIAAAAAgN+ToaGhoaHh2LFjSa3QxuHDh1evXi0MbbBYLHt7e1tbW3FxcaqLBQAAAABoHhDUAAAAAGhO2Gz24sWL4+Pj+/bte+jQIQsLC6orAgAAAACAP0Wd0AabzY6Ojt6zZ4+vr6+srGyXLl0Q2gAAAAAA+BZiVBcAAAAAAN8kOjq6W7duLi4uioqKcXFxly5dQkoDAAAAAACoYmhoOHXq1GPHjr179y45OXnbtm1aWlq7d+92cHBQVlZ2cXHx8/OLj4/n8/lUVwoAAAAA0OQgqAEAAADQ1EVHRzs5OTk4OEhISMTGxoaFhVlZWVFdFAAAAAAAwCei0EZmZqYwtKGkpLR169ZOnTopKioitAEAAAAAUAeCGgAAAABN171799zc3BwcHKqrq+/cuRMWFmZjY0N1UQAAAAAAAI0ShjZCQkLy8vKSk5O3bt2qpKS0ZcuWOqENgUBAdaUAAAAAAJRBUAMAAACgKXrw4IGbm1vXrl0LCwtv3bolXPeE6qIAAAAAAAD+BVFoIz8/XxTa2Lx5c6dOnTQ0NNzc3BDaAAAAAIA/E4IaAAAAAE3L06dPPTw87Ozs8vPzL168KFz3hOqiAAAAAAAA/hNRaCM3N/fZs2fr1q2TkpLy8/Pr1KmTpqamh4fHzp07EdoAAAAAgD8Eg+oCAAAAAOCTpKSkVatWnT17tn379sHBwe7u7lRXBAAAAAAA8IPR6XRzc3Nzc/OpU6fyeLzHjx9HR0fHxMSsWrWqqKhIQ0PD0dHR3t6eyWRaWVnRaDSq6wUAAAAA+PEQ1AAAAACg3suXLzds2HDy5Mk2bdoEBwcPHToUH0cCAAAAAMBvj06nW1tbW1tbe3t71w5trFy5sri4WFNT08HBgcVi2dvbm5ubU10sAAAAAMAPg6AGAAAAAJXS0tI2btx46NAhU1PTQ4cOjR49WkwMi9MBAAAAAMAfp35og81mR0dHL1y4EKENAAAAAPjNIKgBAAAAQI2MjIz169cfOnRIV1d37969kyZNotPpVBcFAAAAAABAPVFoY9GiRVwuNzExURjaWLBgAYfD0dLSYjKZLBbLxcXFwMCA6mIBAAAAAP41BDUAAAAAfrXMzMwtW7b4+/tramru3bt34sSJDAauygAAAAAAABrAYDDqhzbYbLa3t3dlZaUotOHq6tqqVSuqiwUAAAAA+Cb4SgAAAADg18nLy9u2bduuXbtUVVX9/Py8vLwkJSWpLgoAAAAAAKB5+MbQRs+ePfX19akuFgAAAACgUQhqAAAAAPwK+fn5W7du3b17t6ys7MqVK729vVu0aEF1UQAAAAAAAM1V7dBGRUVFfHx8TEwMm82ePXt2VVWVoaGhvb09k8ns1auXnp4e1cUCAAAAAHwGQQ0AAACAn6ugoGDXrl1//fWXpKTkihUrZs+eLSUlRXVRAAAAAAAAvw8pKSkmk8lkMhctWlReXp6QkFAntMFisezt7Xv06KGrq0t1sQAAAAAACGoAAAAA/DQlJSV///33pk2b6HS6j4/PvHnz5OTkqC4KAAAAAADgdyYtLV07tHH37t3o6OiYmJgjR45UV1eLQhvOzs4tW7akulgAAAAA+EMhqAEAAADw45WWlu7du9fPz4/L5U6fPn3x4sUKCgpUFwUAAAAAAPBnkZaWZrFYLBaLEFJWVnbv3r36oQ0Wi9WjRw9VVVWqiwUAAACAPwiCGgAAAAA/UllZWWBg4MaNG8vKymbMmOHr66uoqEh1UQAAAAAAAH86GRmZOqENNpsdHR19+PDhmpoaUWjDyclJRUWF6mIBAAAA4DdHEwgEVNcAAAAA8Duorq4+cuTIqlWrOBzOpEmTli5dqq6uTnVR8EtNmzbtxYsXopdJSUk6Ojry8vLClwwG49ixY9ra2hRVBwAA8CfKzs4eO3Ysl8sVvuRwOJmZmW3bthV1aNOmzb59+yiqDgCahNLS0vv37wtDG7GxsbVDG87OzsrKylQXCAAAAAC/IcyoAQAAAPBfCSMaa9asKSwsnDx58uLFizU1NakuCiigrq6+f//+2i15eXmi342MjJDSAAAA+MW0tbXT09Pfvn1buzEiIkL0e7du3X55UQDQtMjKyopm2hCFNthsdmBgICGkdevWTCZT2EFJSYnqYgEAAADgN4EZNQAAAAC+X01NzalTp1avXp2ZmTl+/PiVK1fim/g/2cuXL9u0adPgJnFx8RUrVixbtuwXlwQAAABr165du3ZtTU1Ng1ufP39ee4INAACRkpKSBw8eCEMbjx49otFoFhYW9vb2TCbTxcUFa1wCAAAAwH+BoAYAAADA9+Dz+aGhoUuWLElPT58wYcLy5ct1dHSoLgqo1759++fPnzd4jf369WsTE5NfXxIAAMAfLjk52cTEpP7oTKPR2rVr9+TJE0qqAoDmJT8//969ezExMWw2OyEhQUxMTBTacHV1VVBQoLpAAAAAAGhmxKguAAAAAKCZ4fP5Z86cadOmzahRo+zs7F68eOHv74+UBgiNHTuWTqfXaaTRaJaWlkhpAAAAUMLIyMjCwoJGo9VpZzAYY8eOpaQkAGh2VFVV3dzcNm3aFBcXl5OTc/78eRaLFRMTM2zYMBUVlU6dOvn6+l66dKm4uJjqSgEAAACgecCMGgAAAADfSiAQXL58efny5U+fPh0yZMj69evx1TvUkZ2draOjU+cam8FgbN261dvbm6qqAAAA/nA7duxYsGABl8ut3Uij0TIyMhC3BYD/Ijc3NyIiIjo6OiYmJiEhgU6nd+zYkcVi2dvbd+vWTV5enuoCAQAAAKCJQlADAAAA4Juw2exFixY9fvx4yJAha9euNTMzo7oiaKLs7e3v37/P5/NFLTQa7d27dy1btqSwKgAAgD/Z+/fvdXR0ao/OYmJiXbp0iYmJobAqAPjN5OTkREZG1g9tCHMbUlJSVBcIAAAAAE0Ilj4BAAAA+Ao2m925c2dXV1dtbe24uLiQkBCkNOALxowZU3tydTExMUdHR6Q0AAAAKKSlpcVkMmsvT0aj0bDuCQD8WBoaGu7u7jt37oyLi8vOzj558qS1tfWlS5dcXFzk5eWFy6Ow2ezKykqqKwUAAAAA6mFGDQAAAIBGRUdHL1u2LCIigsVibdq0ydramuqKoBkoKCjQ0NAQTa5Op9P9/f0nTZpEbVUAAAB/uMDAQC8vLx6PJ3xJp9NzcnJUVFSorQoA/gTv37+Pjo5ms9nR0dFJSUkMBkM00waTyWzRogXVBQIAAAAABRDUAAAAgD9RcXFxXl6esbFxYx1iYmJWrFhx+/ZtFou1YcMGGxubX1keNHe9e/dms9nCrAaDwcjJyVFWVqa6KAAAgD9aUVGRurp6TU0NIYROp7u4uFy7do3qogDgj5OdnR0TE8Nms8PCwlJTU6WkpKysrJhMJovFcnBwkJSUpLpAAAAAAPhFsPQJAAAA/HHKysp69eo1d+7cBrfev3/fzc2NyWRWVVWFh4eHhYUhpQH/1ujRo/l8PiGEwWD07t0bKQ0AAADKKSoq9urVi8FgEEIEAsGYMWOorggA/kTa2tru7u7+/v4pKSlZWVlHjx41NzcPDg52cXFRVlZ2cXFZtWoVm82urq6mulIAAAAA+LkwowYAAAD8WSorK/v06RMRESEQCB4+fFh7NZMnT56sW7fuzJkzXbt2XbNmjbOzM4V1QrNWVlamqqpaWVlJo9FOnz7t4eFBdUUAAABAgoODR4wYIRAIJCUl8/PzZWVlqa4IAOCTlJSU6OjomJiYGzdupKeny8jI2NnZ2dvbM5lMR0dHCQkJqgsEAAAAgB8MQQ0AAAD4g9TU1AwcOPDmzZtcLpfBYDg7O1+/fp0Q8uzZszVr1pw9e7Zz585Lly51c3OjulJo9oYNGxYSEiIlJZWfny8tLU11OQAAAEDKy8tVVVUrKio8PDyCg4OpLgcAoGGi0Ma1a9fevXsnDG2wWCx7e3tbW1txcXGqCwQAAACAHwBBDQAAAPhT8Hi8UaNGnT17lsfjiRpPnjx57dq1oKAgc3Pz5cuXDx06lEajUVgk/DYuXrw4YMCAkSNHBgUFUV0LAAAAfDJq1KiTJ09evHgRwVwAaBZSUlLYbHZ0dHR4eHhmZqasrGyXLl0Q2gAAAAD4DSCoAQAAzduDBw8uX7587+7dpKTnhUVFlZVVVFcEP1GLFpJKiorm5u262Nn169fP1tb2298rEAimTp166NAhPp8vahQXF5eWltbX11+9evWAAQMQ0fjhKioqrl27duPGjYSEhJSUlKKiotp/f/j9yMnJaWhodOzY0cnJqX///jo6OlRXBAAAhPz/NXPM3XvPk5KKCwurqiqprgioJCMrp66uYWnZ0RnjNUCzIgpt3L59OysrSxTaYLFYlpaWYmJiVBf4p/vf/W98fEpqSlFRMe5/f29ycrIa6hodLSxw/wsAAN8HQQ0AAGiWBAJBUFDQpo0bnie9aKWj6Whl3tZYT0VRvoUEnib5nVVW13ws4iS9zYhMeJ6W+cG8bRvfxUtGjRr1LQGLefPm7dixo8FPSW7fvt2jR4+fUO8frbi4eOPGjQEBARwOp5NN5862XQyNjJWUlf+oTw9DTp8a4u5Bp9OpLuTXKeFwsrOzHj9KiLwTXl5e3q9fvzVr1nTo0IHqugAA/lDCa+b1Gze9THquqq2va8FUN2grraDMkGhBdWmUEfD5z+9caOc0mOpCqFRVVlKSn/3hzZPUR5HVlRV9+/ZbtxbjNUAzIwxtsNns8PDw/Px8OTk5W1tbhDao8v/3v/4cTomNRXtb645GrfSUFRXExPA0yO+MU1KW/SHn0bMXd2IelFdU9uvXd82atRhPAQDg2yGoAQAAzU98fPzsWbMexD4Y0ae7p0dvyzZGVFcEFHj0Itk/5Nqpq3dsO9vu2r3b2tr6C50XL17s5+fX4GUPg8Ho1KnTvXv3flqlfxw+n3/48OElS5bwBYKZs+eMGT9BXV2D6qKoUVVVJSkpSXUV1Kiurr5y6eKuv7Y9Soj39PRcu3atsrIy1UUBAPxZ4uPjZ86aHRv7oKPrMJtBk7VNLaiuqKng1lQxxP/QAboOXk31y5hr90P2ZL167IXxGqDZEoU2bt++/fHjR3l5+c6dOwtDG1ZWVpg58qf6dP+7eLGAz5s9Zcz4YYPV1VSoLgp+teqamks3bv/lfzThyXPc/wIAwLdDUAMAAJqZTZs2LV26tKtF283zJ3QwNaC6HKDYk9epC7cevvs4af369b6+vg32Wbt27YoVK768Hzab7ezs/BMK/OMUFRV5eHiEh4dP8Zy2ZPlKRSUlqisCKgkEgpMnjq9avkTA558/f97Ozo7qigAA/hTCa2b9Dl16ztyoadye6nKgSRMIBIk3TocHrpUQE/xzAeM1QDPG5/NfvHgRExPDZrNv3bpVUFCgpqZma2vLZDIR2vgZioqKPDzcw8PveI4dtnzeDCUFeaorAioJBIITZy8u37STT8j58xcwngIAwFchqAEAAM1GdXW1p6fn8ePHNvlM8BrWB58vgJBAINgffNX3r8Njxozx9w+QkJCovXXHjh0+Pj5f3gONRrOxsXnw4MHPLPOPkJyc3K9fv5KS0tOh5y0sraguB5qKEg5n0vgx4bfYhw4dGjFiBNXlAAD85qqrq6d6eh4/frzn9HWdB0/FNTN8o6qykvMbPFPj7hw+jPEa4HfA4/FevnwpDG2w2ezCwkJ1dfVu3brZ29szmUyENv675OTkfv36lnCKQw/usmzflupyoKnglJSOn+17K+o+7n8BAOCrENQAAIDmgcfj9Xdzi46KPLLBx7UrvgCGum7eTRi/5C+mg+PFS5fodLqw8cCBA56enrWvdsTExBgMBo/H4/F4hBBxcfGWLVuamZmZmZmtXLkSU1P+F8nJyXZ2drp6+qdDz2tpaVNdDjQtPB5v+eJFe3bt8Pf3nzJlCtXlAAD8tng8Xj+3/hGRUYNXHDSxZVFdDjQzfD4vbP/K+2f2YbwG+M3weLzHjx9HR0fHxMSEhYUVFRVpaGg4OjoitPHdkpOT7ey66Glrhh7apaWhTnU50LTwePzF67ftOnAM4ykAAHwZghoAANA8zJ49+2DggWv+a6zbGlNdCzRR8Ulve3uumDR5yq5duwghQUFBY8aMEV3qyMrKGhgYtG7d2tjY2MjIyMjIyNDQUEdHR0xMjNKqfxNFRUV2dnbSMrLXwm5Ly8hQXQ40URvWrt7qt/HatWtYaQgA4CeZPXt2wIGDY3dcatnakupaoLm6c8QvOmj7dYzXAL+p2qGNmzdvFhcXa2pqOjg4sFgse3t7c3NzqgtsBoqKiuzsushIioedOSwjLUV1OdBErd3+t9/uA7j/BQCAL0BQAwAAmoH9+/fPmDHj2MZ5A50pWN/x7M3oGi5vRJ9uv/7Q/xGfLxAT+9bHYrg8Ho1GozeeWigrr5SRbtHYVoFAUFxSpigv+6+r/KEu3Lo3dvG2vXv3uri4bN68WU9Pz+j/YbaMn4fP5/fs2fPFi5fhMfea/lwaz54+iYy4M3LUGEUlJaprIYQQPp//56SFBALBpHGj2TdvxMbGGhsjdQcA8IPt379/+owZ7isPte3Wn+paviIn+Xnqo6iOPYdLySlSXcsnAj6f9oNGZD6Py+NyxSUbvnLm87iERhMTo/+QY/0MAoHg3LqpGfG34x5ivAb4zQlDG2w2Ozo6OjIyksPhaGlpMZlMhDa+gM/n9+zp+iLpecylk01tLo2nL15H3I0dNbS/koI81bV88h03vCWlZdU1NSpKTeUK4bsJBIJxsxbdjLgbG/sQ4ykAADQIQQ0AAGjqsrOzzUxNZwzvs3waNSs7Oo5dUFpemXB2NyVH/w5vM7L9Q65diYgtLi2369h65ki37p07fKF/8LXIgDPXEl+mcHl8Ax1Nr2G9pwztLUp4PH6ZsnLPifikt0WcUnVlxX7dO6/3HisnIy16exGndNmuY8HXIiuqqmWlpVztrf5aNEVFkbIPBdbuO7X39NVXr19razf1xMBv4+DBg15eXuHR9ywsm8GyRAf275vrPTMu8ZlZ6zYUlvH2zeuAfX9fuXSxmFPcxa7rTG+f7j2cGux5J/z2Ah/vBjdZWlkFHDrK5/PtO1tzudzam/T19c/+c/nH1/2fVVZWOjvaa2lqXL9+nepaAAB+K9nZ2SamZp0GezlNWkJ1LV/38MLBKzsWzDh6X03flNpKPr5Ljr0Q+Cr6amUZR6+dbRePaYZWDeezBXz+/ind+LzPBlxFTb1Rm4JFL5MfhrMDVuemvuDzuAoaul2HzbAZMEmU/3gSdubhhcD3b57weTxl7VadB0+pvbVJ4VZXHZrham6gffMGxmuAPwWXy01MTBSGNiIiIkpKSkShDRcXFwMDA6oLbCqE97/Rl05atm9LdS117T962nvpusTwi61NDKmt5E1K2r4jpy7dDOdwSuxsLL2njO3B7PItb/xYWGTNGqwgL5sYfrH+1rbMPt3sbPZtWS18GR5932fFxgb3Y9W+7aGdDW/6lSqrqhz7j9bU0b1+/QbVtQAAQFPUFO8GAQAAalu4cIGakvyCSUOpLqR5qKiqdvfZeOziLZad5ZShvd5mvHf32RCTkNRY/5NX7kxesbOQUzp9RL8p7ivkVPgAACAASURBVL3KKirmbQ7ccviscGtCUnIfrxWPXiR79HLwnewuLyt96NzNftNW8fmfgp7VNdzB3uuO/nPLo5fj3uXTPXo5nAuL8Zi76VecaiMWThqqqaq4aNFCCmv4o3A4nGXLlnlOm9EsUhpNREVFhcfggceOHnZ2dZ081Sv57VuPQf1joiIb7Eyj0cTr4fF4L18kcTgcQkhWVuazp0/odLqqmproR6mpTiHTokWL7Tt337x58+LFBj53AwCA77Zg4UJpRVXHMfOoLqQ5qamqPLVkxKOrJ4w6O9kMmPgxK/nU4hHpiXcb7MzJy85Jfi4mRpdRUBX9SMn9b4KulISIEwuHFn3IsOg90mbgJG5VxdWdi+4c3Szcmnjj9PkNXhUlRV2GetkMnFRdUXZ156KooO2/4jz/PYaEZG/vLewwjNcAfxAGg2Ftbb1o0aJLly4VFBTExcV5e3sXFhZ6e3sbGhpqa2t7eHgEBASkpaVRXSmVOBzOsmVLp40f0QRTGk1HRWXV4Akzjwafd+1mP3Xs8LepGYPGz4h6EPct7/Wcv+J9Tm6Dm46FXEhOy6jdQqPRxBmMOj88Hu/F62ROadkPOJP/rIWk5M71S2/eDMN4CgAADcKMGgAA0KQ9fPjQ1tY2aPPC/j1sqarhX82o8a9WG/kZfLcf3nPy0rldy1y7WhFCcguKugyfK91C8tnFfQ327zLCp7KqJur4ZuEkGe/zCsz7eynJyybfOEQI8Vy1+8yNqDtH/TqYfnp6pt/0VXdinxzfNH8Qqysh5PD5sFnr922YM3726E8zbHtv2H/w3M3IY1us2hr9gvNt0MXwB6MWbn7w4IGNjQ1VNfw5fH19Dx469OjpiyaykshXNYUZNRYvmLdn147Qi1dce/YihOTm5thZW0pJSz979fYb9zB/zux/zp+Ljo3T0NCMuBPeryfr7sOE9h06/syqf6RJ40YnxD18+fIlnd50J34HAGhGhNfMHmuOtnHoR3Ut36SJzKhxY+/Se2f2jfILMbFlEUJKC/P2T2SKt5D2PvWofufUhMijcwd6HYzUNGrX4N6OeLulJcbMPhmvrG1ACKkqL93ubi7g8XyvpouJ0fdNcuBWV07df1tSRo4QUpL/YccICyk5xfnnXv7MU/xPzq2bWpL6+M0rjNcAfzTRTBvCyTYqKysNDQ3t7e2ZTGbPnj319fWpLvCX8vX1PRR44Gnk5aaztkhtTWRGjQWrN+86cOzi8X09ezgQQnLzPlq7DpaWavHq7ldmlfA/FrxglZ+MtJS6mopoRo2s9znrtv8dl/jsSdIrQsjEEUNEM2o0aM7yDeevhMXeOKOhpvqDTui/GjdrUdyTly9fvcJ4CgAAdWBGDQAAaNJ2797dsbURhSkNkRcp7yav2GnSe3K7/tNmrP27oqq69tZXqZmDZ6/TZ41XZw53HLvgn9v3RJumrNg5afmO2p23HTnnMnkpl8cjhMzfEjh9zd6s3I8+fgH6rPHfXk9uQVFAyLX4pLpf6564HN7ORF+Y0iCEqCsrsuws0rJzHj57XX8nnNLypOQMV3sr0VImWmrK3WzaF3JKa7g8Qsj9Jy87mBqIUhqEkDFuToSQuOdvhC9PX4tQU1bwGtZH1GH+xKEH1nirKlH5mUX/HrYdWxvt2dNsVqtpvioqKgICAmbOntOUUxrxcQ/HjPBoZ2bs1svFf99eAfksphwVGTHXe6aFeevWhvoTxow8GODP4/EIIetWr3R16paWmlK7s+ek8QP79qqzyMgX5ObmBOz/Oz7uYZ32E8ePtmvfQZjSIISoq2uwXF3T01LjYh98y27Dbt444L8v8OhxDQ1NQkjymzc0Gs3YhOKp4/+VpStWJScnX716lepCAAB+E7t2725p2qEppzSyXj4KWTl+5wjLY3MHxZ4/UOepobTH0Vd2LNg92ma7e7uzaybHXTzM5/OEm8IPbTw0q09hdlrt/uc3Tj++YEidVUgaU1qYF3s+MOtlA9mLx9dPaRiZC1MahBBZJTWjzs6F79MzX8TX7/wxM4VGo6noNLrGfHFelryatjClQQiRlJZt2dqKx+Nyq6sqyzi5qS9MbF2EKQ1CiJyqpoGlQ0VJEY9b8y1nQYnuE3zTUjBeA/zpRDNthIWFFRQUREVFTZ069f3797Nnz27VqpWRkdHYsWMDAgIyMjK+vq9mrqKiIiDAf/aUMU0npRGX+GyE51yzrj17DZ+87/BJUu+h3Mh7D72XrjN36Gto4zxmxoKA4yE8Hp8QsnrrHqfBY1MzMmt3njRnSd9RU7lc3jcePTfv4/4jp+ISn9VpPx5yoX0bU2FKgxCirqbi2s0+LSMr9tGTL+wt6fXbRWs2b1g6V1NdrXZ7SWnZm5R0BTm5Th0bDkrWdvNOtP/R00d3b2o6KQ1CyIp5M5JTUjCeAgBAfQhqAABA01VZWXkuNHT8ACeqCyH5hZzenssfPns9rLejoa7m0X/YXqv/FwK49/iF49iFr9IyJw12XTjZnS5GH7Vwy6bAM8Ktj16mPHqRXHtvyRnv7z1+IVw95Nmb9HuJL4d4rztw5rqu5tdvIz8WcQ6eu9l32kqTXpPnbj6Q9SG/ztYiTmmPzp89VW+sr00IeZT0WQ1CdLrYzQPr544bJGrhlJY/e5Pu3MVCnEGv4fJYXSw9a4UwCCGZOfmEECV5WdG5uHa1khBnpGblXIl8+OhFspaa0og+3fS0Pruv/vXGD3AKPRtaVVVFbRm/vWvXrnE4nDHjJ1BdSKOiIiP6uDhF3Anv7uRkYGS0dtWKndu3ibZGRtxx6+VyNiSY5eI6buKkzMzMObOmr1y2hBBiatb6Xkz0+dCzos4ZGeknTxxXVFJiMBhfPujH/PxDBwL69WSZttKd5z0rKzOzztaiwsIeTs61G4Uxi4SEBr4WqqPg48fpUyYNcffo1r2HsCUl+a2url5Zaen1q1eOHT704N5dYdakKTM0MnZw7Hbq1CmqCwEA+B1UVlaGhp7r2GcM1YU0Ku1x9JE5/VIfRRlYOSq1bHX74Ia7wXtEW1MfRR2bO+jZ7XNGNk5WfcdwcrMub593K2CNcKuqnknG0/vP7/wj6l+U8y7xxmkpOUUx+pdG5PLij3EXjxydO3D7kLZXdy7k5GbW71BRUmRo3a12o4qOESEku6FUR0FWioK6TnVF6et7NxKunHj3LFaUJhFq49CXk5f95n6Y8GX+u7dpj6INLJgSLaTF6IwJu67Yj/AWda4s4+SkPDfq1IPOEP/CWVBLuaWhoSXzJMZrAPh/UlJSTCazwdCGvr6+kZGRp6fnsWPHMjPr/pf7e7h27RqHUzJ+2GCqC/kk8t5Dl6Hj78Q8cLLvYqSvu2rL7u3+R2p3iLgb22v4pJB/rrl0t584Ykhm9odZi9cs2/QXIcTM2CAmNiH08v+muMjIzD5x9qKSgjyD8ZVZH/ILCg+cCOk5bFKrTj28l63PzP5QZ2thMcfJwa52o4lhK0JIQuLzxvZZWVU1ZvoCe1vrGRNH1dnU2sSQHXqEHXrk2N7NXy7sY2HRlLnL3fv36m5P/eNetRm10nPs2hn3vwAAUN9XPmUGAACgUFRUVFl5eW9H6levKCguWTJ12JKpw4Qvu49bdOt+ovB3gUCwYOtBCXEG++AGLTVlQojP2EEDZ63ZfPDMUFd7Yz3tr+78TXoWy87i2Mb5pq1aNtaniFP6T/j9c2F3Ix4+5fJ47U1bLZw0tG+3zhatDT/fVTYhRFP1s7kNTPVbEkLyCovr71ZGqkWXjq2Fv+89eTnjQ+716Hgejz9/wmBCiDiDvm3h5Nr98wqKA0KuiTPovR06EULKyis/5BeqKyu6+2y4FvVptVHTVi33r5zVuT3FD/f3drSZsykgKiqKxWJRW8nv7caNG51sOqura1BdSKMWzfORkJSMvv9QT78VIcTbZ15XGyvR1jPBpxgMxtMXbxQUFQkhPvMXtjczvnbl8rqNfv3c+svIyl44F+ozf6Gw8z/nzxFCho8c3dixigoLL144f+5sSMSdcC6X275DxwW+S/q69bewtKrd7c3rV4QQDS2t2o0mpmaEkLzchtfirW2u98zi4qLV6zeKWpKT33JKOOamhuXl5cIWSyvrA4ePUri2y7fo3c9t84Z1AoGARqNyuSgAgN9AVFRURXmZWddeVBfSqOu7lzDEJT0DwhU19QghXYfN3D/ZUbT12a1QMTrD+2RCC1kFQghz5JydIyxe3b3u4rWaEGLG7CMhJZMUcZE58lPK4UXEJUJIBxePBo9VUVL0IvLy8zvnUxOi+DyuplE7hzHzWtv31jKtu0BY/ru3hBA5Fc3ajap6JoSQsqJ8Uk9BVmpVecmO4R1rKiuELdqmFoOW7het3tJ58NSU+MiTi4frmndmSLRIfRwlp6LpNGUZIUSihbReu09fGt0/u6/oQ+ab+zcFPJ7DKJ9v/zNSwtiu17WgrRivAaA+aWlpJpMpzG2Ul5ffvXs3Ojo6JibmyJEj1dXVhoaGLBbL3t7e2dm5ZctGP+toXm7cuGFj0V5dTYXqQj6Zt3KTpITE/Wsh+rotCSE+XuNtXIfU7hB84SqDznhx97qivBwhZP6MSWZ2va6E3dm4dJ6bq5OsjPS5yzfnT58k7Hz+ahghZORgt8YOV1jMuXCVffby9TsxD7hcXoe2Zr6zPd1ce1i2b1u72+vkNEKI1uezYpgatSKE5H4saGznvuu2vc/Ju3Iy4L8MN95L1xVzOOsXN8WxtR+r24ZdARhPAQCgDgQ1AACg6YqPj9fRVG+pTv09sJgYbd74/z0zYdHGKO75m6zcjy3VVR6/THn8MmWgs50wpUEIEWfQR7s5RTx8evt+4rcENQghy6eNbCylkVtQNG31ntsPEgUCYm/VdqPP+L7dOjc2X0Xyu/eEECUF2dqNulpqhJCikrIv17D676DyyipCSBtD3RaSkvU7XIuKm752b34hZ/O8iebG+oSQ5Mz3hJC/T1821NHctnCybYfW9xNfLt91bNi8jbGnd6gpK3z1xH+eluoqLTXVEhISENT4qeLi4uwdun29H0UePrj/9EniAt8lwpQGIcTI2GT4qNGHDgQIX87y9vGaPlOY0iCE1FRXKygqcoqLCSHSMjL9Bww8FXQiIz1N+PbzoWdVVFWdXVzrHyg3N2f6lEm3b7EFAoE902HD5q193frr6TW8WnNycjIhRFlJuXajrp4eIaS4qOjLZ/Qi6fm5s2fmL1qsq6snakxJTi4tKVm5Zp3bgIH5eXlBJ44dO3xo2JBBd2PjpWVkvvZHooytbZfCwsK0tDQDA4Ov9wYAgMbFx8cra+jIq33TZeevl5kU9yH5meOYecKUBiFERceoo+uwuItHhC/tPGZ0HjxVmNIghPBqqlvIKlSWcYQvJVpIt3Hol3gzuOhDhnAPz+9ckFZQMbKpO+teaWHeP34zU+LuCAQC/Y5de05fa8bso6ih21hhBZkphBApuc8izgoauoSQytIGIs4FWSlV5aXOk5e1duhbXvTx8Y1Tj66cOL10lGdghEQLaUJIC1kFRQ3dD2+fZr16RKczBHy+GJ1RXV5aZz+3AtcJox7qrVozJFt8/S9IKV1zmxtFGK8B4CukpaVZLJbw7rusrOzevXv1QxssFqtHjx6qqk1oQYp/K+7hQweburE/qjxISHyS9Mp39lRhSoMQYmygP2pI/wMnQkR9vKeOmz5xlDClQQiprq5RVJAr5pQSQmSkpQb0cg4KvZT+Lku4h9DLN1WVlVy629c/Vm7exynzlt+KvCsgAqZtp80rFrq59tDTafjCIzktgxCipPjZJ0LCzkXFnAbfcpUdse/wyZDAnXUWPflXkl6/PXvpxqJZU3Rban299y9na92xsLAI4ykAANSBoAYAADRdaWlpRnpN4v5KW01FUuJ/kxIryskQQsrKK8n/ZyMcrM1r9xdOdPEmI/tbdq6qJG/dttG1rvMKim/EJDDodM9hvce4ObczafirXyFhkYXFn30cXF5RSWotVtKY3OhTyRnv7z5+sWrvie7jFr68ckBD5dO316mZHxZtP3w18qGhrtahdT49OncQtgsPVFVdE7R5oTBoYtHaMPdj0eZDZ8/ejJ42vO9Xz/2nMtbTTk1NpbaG315aWtqY8ZOorqJRr1+9IoR06GhRu7FN2//9azU1a13w8eOuHdtj79/LSE9/+/ZNCYejpfXp86ZhI0efCjpx/lyot8+8zMx3cbEPpnhOExdvYH7yvNzcG9evMRgMz2kzRo8b3659hy9UJSkpSQgpKPzsWSLhZBiKSkoNv+f/7di2RUJCYpb3Z08I7Q88JCkp2da8HSHEyNjE1q6rvLzCzu1b/7lwfsSoRuf/oJyRsQkhJDU1FR9UAQD8R2lpaUo6hl/vR5H8jNeEEE3j9rUb1Vq1Fv2uqmdSzim4G7w383ls0Yd3H7OSq8pK5FT/N9FFBxePxJvBSREXuw6bWZyblfUi3mbgpPorhpQV5r25HyZGZ3QeNMWy90gNI3PyRQwJSUJIRUlh7caayjJCiJScYv3+AxfvZYhLqhu0IYSo6BjptuvcQkY+5vTuF5GXOroOI4QcntUnJyWpr8/Wdk6DGRIt3j4Iu7h1TpDvsBlH7olCKoSQpdezPmYmZzy9f+vA2sBpLj4hT2WV1b9cKoWUdQwJxmsA+DdkZGTqhDbYbHZ0dPThw4drampEoQ0nJycVFeofy/lX0tLTxrv3o7qKT169TSWEdDRvXbuxrZlR7ZdmxgYfC4t2+B+5H5+Ynpn1NjWDU1KqpfFp0Bk52C0o9NK5K2E+XuMzsz/EPnriOW64eEMLfeZ+LLh+O5LBoE8bP3LcsEHt23xpClVJCQlCSGHRZ5HH8vIKUi+9IfQhN2/K3GUTRwwZ0Mu5/tZvt+3vQxLi4t5Tx/2Xnfw8xgb6BOMpAADUI0Z1AQAAAI0qLi5WkJGiugpCCJGWamCGCYFAQAj5WMQhhOhpffbpalV1DSGELtbwOFvI+SxIIdnQ974iZgY6oTuXDevtePzi7S4jfNr1n+a7/XBMQhKPz6/fWRitSM3K+exwxaWEEFVF+QZPgc8XiF4a6WmN6e+0ZtaYGi7vRky8sPH01YguI+dGxj1bN3vsw+AdopQGIURbXZkQ0rm9ae3pQHo7diKEvEqlfklaBRmpoq/NTwD/EYfDEU1H0QQJwxB0+mcr7Lao9ejqzu1bzQz1/Dasq6mp6e7k7B94uEvX/z0/1L2Hk4aG5oVzoYSQC+dCBQLBsBEjGzyQWes2Z/+5PGzEyOPHjth1smxnZrx4wbyYqEgej1e/s4amJiEkLTWldmNhQQEhRFXtS88PvXuXEXL6VL/+A5SUP5uNw9LKWpjSEOnZuw8hJOn5sy/sjXLyCgqEEPwjBQD474qLiyWkG7jSayIqOEWEENrnF8YMif8NxzGnd28fah55bAuPxzXs1G2g717RKiFCBtaOssrqSXf+IYQkRVwUCATtWe71D6SmbzpqU3AHF/fH14L2TXLYOcLyxt6l6Yl3+fwGhmNCiDAeUZid9nm1hYQQaYUGvjvUNrUQpjRETLq4EEJyU18QQvLSX+ekJLWyYNoMmCglpygu2aKNo5tFrxE1lRUvIi8LBAJBrat3FR0jy96jWFNX8rg1b+6HNVheE9FCRp5gvAaA7yUMbWzatCk6OrqgoCAsLMzd3T0+Pn748OGqqqpGRkaenp5nzpwpKGh0RYwmhcMpEc1OQTlhEqLO3a7k59Ojbt932LCT84Yd+2u4XCemXeBf67vaWIq29mB20VBTPXflJiHk3JWbAoFgxKCGYyitjQ3/Ofb3iEH9jgWf7+Qy2KxrzwWrN0c9iOPxGvhgSlNdlRCSmvGudmNBUTEhRE25gScT/I8F5xcUFpeUTpm7TPiT/SHn/YfcKXOXbd5z4Nv+GORd1vvTF6707+Wk3FAWpClQkJMjGE8BAKAeBDUAAKDp4vF4dHpTH6r0tTUIIXcfvajdGPvkFSHEQEeDEEIjpHYYghDyOj3r2/fPoNN72lv5r5qVFnYkePti245mRy6we05dZug60Wv1njqZDGM9bRqNlpb1oXbj0zdphJBO7Rp44mHbkXPynYfciEmo3aiiKE8IyfyQTwi5FhU3ZeWudsatHobsnDN2YO1pRQghOppqhJAa7meffVdWVRNC5GWlv/0cfxI6XazBr8nhB+JyuXU+GGpSWrUyIIRER0bUbkxPTxP+kp+Xt2LpYnl5hVfJ6cGhF9as3+g2YGBVZaWoJ51OH+oxLP5hbGbmu/OhZw0MjTp3sWvwQAwGo2ev3vsDD6dmfggOvdDFzu7I4YO9WD2M9LS9pkysk8kwNjGh0WhpKZ81Pn2SSAjpZNP5C6dzOPAAl8sdO+GzKUwyM9/9c/7cu3cZtRuFO1dTb7qP5xJCGAwGIYTL5VJdCABAs8fj8cSa8HCsqKVHCElPvFu7sejDp5GrrCifHbC6hYy8z5lnI9YHsaaubOPQj1tdVbuzmBi9ndOQrJcJxblZSXcuKGsb6Jrb1D+QGJ1h0sVloO/eBRdej1gfpNuuc8KV44e9+20d1PrCppl1AhmEEBUdYxqNVqf9Q/JzQohOW+s6nYtzs5IiLxXnfJZFLsxOJ4TIKKoRQnKSnxNCWll8NmO8UacehJCKkqLokztWO6nWyWRIK6oQQopzqc83f4EYHeM1APwYsrKywtBGXFxcUVFR7dCGurq6ubm5MLRRWFj49X1RhMvlNp0PqVrp6RBCIu89rN2Y/u5/HzflfSxcuvEveXnZ5Ie3Qg/tXr/EZ0Av58qq/42wdLqYx4DeDx8/zcz+EHr5hqG+bhfrhhd2YTDovZwcA/9an/kkKvTQbjtry8OnQllDxutZdpsyd1lqxmcDmYmhPo1GS0n/rPFJ0itCiI1VA3NPqqkodTRv/TY1PfH5S+FPVXV1ZVV14vOXwlVUvkVg0Bkulzdh+JBv7P/rMRh0gvEUAADqaSoXFgAAAM1URzMDCXHG7QePazdGxj+ji4mxulgSQvS11TPe54rSDC9S3qW8e/8dB5IQZ/R1tDm4dk4a+8jJzQu7d25/nn33yavPvuvVUlO2t2wbk5CUmvkpq1HD5QVfj9JWV7Zs08CE2ObG+oSQOsUfOR9GCGlv2ooQsmpvkLysdNDmBbqaDawjKyUp0c2m/aMXyckZ/zujS3diCSG2HVvX7w/wi1laW4uLi0fcCRe1cLnckNOnhL9nZKTz+fz+gwbJyn16KCoz892TxM/+OQwbOUogEPy9a+fDB/e/ZRkRCQmJPv3cAo8cT838EBR8tnsPp/OhZxMff7ZPLS1tewfHmOio1JRkYUtNTU3I6VPa2i0trep+LVTbbXaYkrJy9x5OtRsLCwpGD3ffsmlD7cbQM8GEEHt75lcLBgAA+Nm0zSzpDPHUhEhRC5/Hfco+K/y9OOedgM9v4+gmKf1pnb7i3KwPb5/W2UkHV3eBQHD/7L7MpLgOrh5fPiJdXMLMvvfgpf4LLrwZtuaoobVj0p0L7+vtU05VU79D1/QndwuyPy2Wx+PWPGWflVfV0jK1qNO5oqQwZMW4yBPbajc+Cz9HCNHvYEcIUWtlRggRTvsh8jz8AiFEw7CthmFbQkhy3J3aWxMuHSOEaBp/Ni0WAMCfQE5OrnZo4/r1625ubsLQhpqaWqdOnby9vc+cOYPpB77AuoO5OINxJ+aBqIXL5Z2+cFX0MiMrm8/nD+rNkpOVEbZkZn9IfP6y9k5GDnYTCAS7Ao8/SHgyaojbVw8qIS7ez7XHkd2bMhMjgw/s6MG0Db184/Gzz55c0tJQd7C1jn4Ql5L+aVKNGi739Pkr2prqVu3b1t/n9AmjYm+crf3T2sTIQF8n9sZZ/61rv/GvwY64q6yo0INp+/WuAAAATUkDS44BAADAt9NSU/b06LM76OKcTQFT3XuJM+gh16Mu3Lo3ql8PIz0tQkindqbXo+M9V+2aMMgl+d2H7UfPy8vKCBdM+Rb5hZxjF2/Vb+9oZmiip62pqlynfcHEIUO814/x3bpw4lBFedntR8+lZX04u2MpjUYTdjh07qbPpgDfKR6Lp3j0tLc2N9bff/qqgqwMy87yfd7Hc+y7V6MeWrc17u3QqYhTmpSc0dHMYNeJf+ocxcG6XW+HToSQNbPGdB+3aIzv1lUzR+loqEY8fHoo9IadRZu+jg085gjwi+no6E71mr53987pnpOnek2n0Wgb163hcD6tlWtqaiYjKxt6JsS1Z29TM7P7d++uXbVCTl6+tKz0zetXJqZmhBBLK2tTs9Z7d+8khIwcPaaxA+Xn5R0/erh+e0cLS2MTUy0trTrt8xctHjqg39iRwxf4LlFUUvpri19aasqZC5dE/04PBx7wmT3Dd+ly36XLhS1FhYWPEuJ79+0n9vnU8e3ad+jcxe7IwUBlZZX+AwcJ+PzTJ4NuscMGDBps/cX5OQAAAH4NBfWWNgMn3z+775/NszoPnExotIgjm6vKPl0Mq+iaSEjJPAs/b2zLUtUzeffswe2DGyRl5KoryvLfvVXVNRZ20za1UNUzuX92PyGkY8/hDR6orCj/0dWg+u2axh1UdIzlVDTrb3IYPTfId9iZVRMdR89tIacYc3Jn4fu0kRtPC0fk+EtHr/w1v9u4Bd3GLdQwNNc1t0m4fExaXrmNYz8BX/AkLCT5YXhbR7eWbawIIeqtWhvZ9Eh+GH5iwdAOrh6Kmnovoi4/vXVWvVXr1sw+YnSGhmHb2HMBLWTljTs7c/LfJ9258Ore9ZatLU3tev6IPzMAQHMlDG2wWCxCSF5e3v3792NiYths9u7du8XExCwsLOzt7ZlMpqurq4JCE13VghI62ppe40fsDjzuOX+517gRNBpt3V/7OJwSUQdTw1ayMtJnLl7v2cPBzNjg7sNHq7bslpeVLSsvf52cZmrUihBi1aGtmbHB7sDjhJDRQwc0dqy8j4VHg8/Vb7doIEadKQAAIABJREFU18bEsJWWRt2pHBfNmjpg3LSRXvN8Z09VUpDfsvdgakbmhaN7hcNrYNCZ2UvWLp0zbanPtB/xlyCFxZyEp0l9XbqLNbIAMQAAQJOFoAYAAMB/tXrmaB6f//epy4FnrwtbJg/puXn+p+UJZo/uH/vkVcj1qJDrUdrqyiP6dCeEbDvSwC1ug3I+Fq7YfbyxrcJ5L2pz7mIRuNZ7xtq/Ry7cTAhRkJPZ5DPBtauVqIOAEB6fLxAICCFiYrTT23wnL9+xISB4Q0CwsEN/py5b509m0On3El8KBILHL1Mev0ypcxQajSYMali3NQ7dudRr1Z7Bs9cJN/V1tNm/atY3nh3Az7Z6/cay8rIjBwOPHzlMCOnew2nL9p1TJowlhMjKye0LODht6iSPwQMIIUrKypu2bJeRkZk6aXxnyw6FZZ+mhB0+ctSalcudWS76rQwaO0pOzocVSxc3trV9h7rzxzqzXA4cPjbDa8qoYUMJIQqKihu3bHPt2UvUQSAQ8Hg84b9TociIO3w+37be2is0Gu302XMzPKds27xp2+ZNwsbJnl4b/LZ+/a8DAADwS7A8V9RUlsVfPiYMUhhades9e9O59V6EEElp2QGL9vzjN/PUkpGEECl5pV4z1otLyVzYMO3v8V1X3MoV7aSDi8ftg+uNbHooaek3eJTSglx2wOrGatBoaOIKI5seg5fuv7h5dvCKcYSQFrIKPaevM7FlCbcKBAI+/9NwTKPRhq87cXGLd1TQX1FBfwk72AyY6Dr90zUwTUxs6PLAq7sWPbsV+vbhbWGjfseuAxbtpotLEEKGrztxbr3nnSN+d474Cbe2cXTrPXuTcG0RAAAghKipqbm5ubm5uZFGQhssFsve3r5bt27y8vJUF0u99Ut8yssrDp48e+T0eUJID2aX7WuXTJjtK9wqJysTsG3t1HnLB0+YSQhRVlTYsmqRjLTUpDlLLJ0HlKUlCruNHOy2cvMuVreurfRaNnagnLz8pRv+amxrh7ZmdVpY3boe3rnJa8GKYVPmEEIU5eW2rFzYs4eDcKtAIODx+LXvdv+jiLuxfD6/sXVbAAAAmjLaDxwRAQAAfiwPDw9uYdbxTfOpLuSb5BUUP3mdKikh3s5YX1Fets7W/EJOdt7H9iatRE/M/1RcHi8hKVkg4HdqZ0r/2iMFfL4gPTvnVVqWlKSEiX5LbfW6s3R8VQ2Xl5SckV9YbG6sr6mq9L1V/2BjfLcylFqGhIRQXcjvjEajHQ06PXioO9WFfEVm5rvnz561bt26ftii4OPHxMTHmpqardu0Ff7zLPj4saio0NDo0yO8l/65MNJjSFDw2f4DB/3Yqrhc7qP4OD6f36mzLZ1O/497y8hIf/P6taKCglnrNqLFXJo4OUl6cHCwh8dXZrAHAIAv8/DwSMqrdl/VwNxOTUpxblZuSpKqvmn9pEU5p+DDm6eyKhpq+mbC4bicU1BZUqTc8n+L972Iuhy8fOywNUfbOH59bvZ/hc/jZr96LODzW7a1FhP7yohclPPuY8bbFrIKqvqmouVaauPkZeemveRWVajqmaroGte++Bfw+YUf0vMz3ohLtFDRM5FXrTvnVtO0qrsyxmsAoFZubm5ERER0dHRMTExCQgKdTu/YseOvD23QaLSgfVuHuvX6etdfKDP7w7OXr1sbGzWYtPhYWJT47IWmhlobEyPhkPSxsKiomGPUSk/Y4Z/rtzwmewcf2DGwN+vHFsbl8uKfPOPzBZ0tO9DpmOuCSOq0w3gKAAB1ILYPAADwY6gpKzh3qbuatYiqkryq0q974INBp3dub/qNncXEaAY6mgY6DUwH/Y3EGfSOZo1ONgBAOR0dXR0d3QY3Kauo9HByrtOirKIienn08EFt7ZZ9+v3g74QIIQwGw8a2y4/am56evp5ew08YAwAANAUK6i0V1Bt+WldaXtnQuludFmn5z9LDj66ckFfVMrPv/cMLE6MzdNp2+sbOihq6ihoNX1QIyatpy6tpN7iJJiamrG2grI3LZgCAf0ddXd3d3d3d3Z0QkpOTExkZGR0dzWaz/fz8GAyGMLQhzG1ISUl93yH4fH4zXThDR1tTR7vRz3NUlBSdHOzqtKgoKYpeHj4Vqq2p3s+lxw8vjMGg21phlgsAAIAvQVADAAAAAKABWzZtyM7Ounn92pa/djIYuGwGAACgRuTxbSX57988CMNCIQAAoKGhIQptfPjwISoqis1mX7p0qU5og8lktmjR4tt3y2KxZsyYMWTIkJ9WeJOzaVdA9oec67ej/lqzmMH4r1M8AgAAwHfA/S0AAAAAQAMOBx4oLSsdN3HShElTqK4FAADgzxV/+Wh1RZlV3zHW/cZRXQsAADQhmpqaotDG+/fvhdNsnDlzxs/PT0pKysrKislkslgsBwcHSUnJL+wnJyfnzp074eHhHh4ee/fuVVVV/VVnQKXAoDNlZeUTRwyZNLqpL2YKAADwu0JQAwAAAACgAUlvU6kuAQAAAIhP8BOqSwAAgKZOS0tLFNrIzs6OiYlhs9khISF+fn7S0tKWlpZfCG1ERETQaDSBQHD+/PmwsDB/f3/hfn5vbx+EUV0CAADAn65ZrrsGAAAAAAAAAAAAAABQh7a2tru7u7+/f0pKSlZW1pEjR8zNzU+fPu3i4qKsrOzi4rJq1So2m11dXS3sHx4eLlzssqampqioaNiwYX369MnKyqL0JAAAAOD3h6AGAADAn47PF3x7Zy6PJxD8i/71lZVX/pe3A/yB+Hz+N/bkcrlf/hf65Q5ffTsAAACICL55gObzuDVVjV4D83lcPp/35T1UV5T9i8oAAOD/iUIbaWlpr1+/3rFjh7q6ekBAgIuLi6qqap8+fTZv3nz58mVRaEMgEAgEAjab3bp164CAAGqL/zP9m/vfr3xCxeXyKiqrvvvtAAAAPxuCGgAAAN/pwZOXfgfP5BYUUV3Id3qbkb1g68G2bp66zmOHzll/J/Yrc0rfiEmwHzVPw2GkgeuEcUu2xSQkiTbx+YIuI3w6eXjX/hniva722x+/TBkwc42O01gNx5GGrhNnb9hfUlb+U04M4P89uHd388b1ubk5VBfynd6+eb1w7hxzE0M9LbWhA93uhN/+Qucb168xbTtpKssb6mqNHz0iJiryX3X46tsBAAB+lHfPYiOPby0tzKO6kO/08V3ytd2Ldwzr6Nff6KTv8JSEiC90Tn4Y7j+l+/qeLTf0arljuEXs+QO14x1Pws4cnNFzQ2/ddS5ae8Z0rrOVEPL+deLxBUP83Aw39NbdOsjs0jafqrKSn3ViAAC/OxMTkylTpgQFBWVnZycnJ+/Zs0dXV3fnzp2ZmZl1etbU1JSWlnp5efXq1av+1qbpXtzjjTv9c/M+Ul3Id3qTkjZ3xUaTLq5a7ewHjpseHn3/C52v34607eWubGqja+E4evr8qAdxdTqwI+7a9nJXNLZWMulkatdz3+GTtfMfX307AADAr4GgBgAAwHeKefRi7b5TH/ILqS7ke1RUVbv7bDx28RbLznLK0F5vM967+2yonb2o48yNqKFz1heXlM8ZM7C3Q6frUfFDfTa8Sf80EWhW7sdnb9LpYmKqivKiHyUFOdHbE5KS+3itePQi2aOXg+9kd3lZ6UPnbvabtupfTeYB8G/djYleu2rFh/fvqS7ke1RUVHgMHnjs6GFnV9fJU72S3771GNS/sfzEmeDT7gPdioqKvOfO79Wn77WrV9wHD3jz+tU3dvjq2wEAAH6g9Cf3bh/cUPrxA9WFfI+aqspTS0Y8unrCqLOTzYCJH7OSTy0ekZ54t8HOKQkRJxYOLfqQYdF7pM3ASdyqiqs7F905ulm4NfHG6fMbvCpKiroM9bIZOKm6ouzqzkVRQdtFb89+9eioz4D3rxLbs4Z2G7tAUlY+/tLRY/MGfvtMHgAA0BhDQ8OxY8f6+/tv3bpVTKzhb0kEAsHt27eFU2s0/akXYmLjV23Z/T63WeYgKyqrBk+YeTT4vGs3+6ljh79NzRg0fkZj+YngC1cHjptRxOHMnTahD6v7VXbE4PEzXyeniTqER9/vN9oz/V3WuGGDPMcNr6isnLN8w7q/9n3j2wEAAH4ZBtUFAAAA/Cn4fIGYGI3qKj5ZvTfoTXrWuV3LXLtaEUKmj+jbZfhcz1W7n13cV79zdQ136c6j0lKSMUFbFeRkCCFrZo4x7TN53OLtd09uI4SkvHtPCAlc493etFWDh/MPuVpZVX3nqF8HUwNCyDKvEf2mr7oT++Sf2/cGsbr+tLME+Bf4fH5jH89RYs2KZW9evwq9eMW1Zy9CyPRZs+2sLT0nT3z26m2dntXV1ct8F0rLyMQ8iFNQVCSErFm/0cxAb/zokTGx8V/t8NW3AwAAUEjA59Oa0gB9O3Bt/ru3o/xCTGxZhBDboV77JzIvbJrhfepR/c6RR7cKBIIp/reUtQ0IIc5TVmx3N78XvKfbuAViYvS7IXuVdQyn7GNLysgRQpgjvHeMsIg9H+g4Zr7w7bHnA2uqK6fsC9M0bk8I6TFx8bG5g1ISIpIiL5l3H/DrzhkA4LcWHh5Op9MbW26jpqampqbGy8srJCTkFxf2UzW1+98VfjtfJ6ddPL6vZw8HQsisSaOtXQdP9ln66u6NOj2ra2p8122VkZZ6cP2sorwcIWT9Yh+DTk6jp8+PvXFW2GfDjv0CgeDu1WBDfV1CyDrfOYY2zjv8jyydM43H53317QAAAL9MExqMAQAAKFFcUjZnU4CNh7eh68SRC/xuxCSINs1c9/fczQfe5xVMWPpXm35T2w+YNm3NnrKKSkLIrPX7DobeIIRMW71n/pZAYf/5WwKnr9mblfvRxy9AnzVe2PgqNXPw7HX6rPHqzOGOYxf8c/ueaP9jF2/bcij0wZOXYxdv02eN7+Thvf3oedEkE+v2n3KZvDQ167NVG6au3DVg5hou7yuLWH/Vicvh7Uz0hSkNQoi6siLLziItO+fhs9f1O79MfZedW9DT3lqY0iCEqCkrOHexePI6lVNaTgh5++49jUYz1tdu7HD3n7zsYGogTGkIjXFzIoTEPX/zH08EfnvFRUU+s2d0tuxgpKs90mPIjevXam+dOW3qPO9Z799nTxw7qq2xQfv/Y+8uw6JK3waA3zPDMHR3N4h0SCighIGBIAKCCiIqoSK2Ynfn2higGBhgBw0qCkqJgMDQKd05M++Hw47jwKCuu+7/XZ/fNR+Y+4lzH6+dPRP3eR4NVb/FC7s6OwFgmf+SkPPnAMB/sc+aoEAAWBMU6L/Ep6qqcmXgUgVpcWyGT/l5s2ZMVZAWFxfktTI3uR95j3F+Tw+3Q/v3vk157enhpiAtbqynffTQAewrvF3bt060tiotKWbsv2Sh18ypkwcGBn7+xK9dDdXS1sGqNABATEzcduLEstKSd6lvmXrm5+VWV1dNmjwFK7MAAFFRMWtbu+yszLbW1m92+OZwBEEQBBmqp6P18dHVp73MDzmq39o8v/BNNL3pwcHAJ8fXtjfU3t256KirznF3g/v7l/X1DO559/DQincPLgPA/f3Lnp5YDwBPT6y/f2BZW33142NrDjioYt3qywrC17kccFDdPVn6/BKb3KSHjEe/vd07+dqRipzU29u9DzionvIye3n9OLbORPylvZeW2TdXlzL2j9zrf3XNLCrlb7hAZz67Ia48GqvSAAAeQVHlMTbNNWWVecNUN7bWV/GJSmFVGgBA4uKR1jCgUAYG+np7Ots+l+SpmthhVRoAwCsioahv0d3eQhnoxyIVOakSKlpYlQZGb4o7AFQNdywEQRDkr3nx4kV/f//IfXA4XGxsLADEv3r7jy6t0dLWvnzjTn3rmbL6Vi4+gc/ivlpS0W/N1sBNu2vqPs9fulbFxE7DfPLiVZs7u7oBwH/ttvNXIwBg8arNQZv3AkDQ5r1LVm+uqqkLDN4lrWOBzZBfWDxjnp+0joWgqrG5vWvkk2jG+T38Vu0/eSHlXaaH3yppHQs9a4dDpy9in3+3H/rD2ml+SflXu8AsXLFxqsfigYGf/YYKAK5GRGmPUsOqNABATFR4otXY0vKq1AzmXXrzCsjVtZ8nW1tgZRYAICoiZGtlnvUxv7W9A4tUVNdKS4pjVRoAwMvDbaSn3d8/0NPb+z3DEQRBEOSXQYUaCIIgyG+t6nOjuceqG48TxhqMnjfDuqz68+yg3aeuP8JaswtKnr98b+W5trKuwXmihbS4yNUHcYu2ngAAFTkpCRFBAFCRl1KWlcT65xSWpWTlzwrcdeH2M1kJEQBIycyznL/2U2nlQqeJa31mE/AEj7UH94XcxvonpGaHPYh1XL6rv3/A28mOi4N9y8mry3YPrmmhpiCTkpkXGfNlIeXymvrrjxME+LjZCISfOevGlraWto4JY3QZg1iZRUYueWj/mvpmADAarcoYNBytCgB5xeUAUFxRIysh0tnV8zT5Xej9mLfZ+RSGm1H6Byi2pvpLXO0Zh1fWNQCAIB/Pz5wI8p9XVVVpPsbw+rWrYy0s5np6lZeVuTjOOHXyOL3Dh6ysZ0+fjDc3raysnOXiKiMrey30yiJvTwBQUVWTkJQEABVVVSUVFQDIyfnw5vVrZ4fpF86ekZWVA4CUVy8tzU3y8/O9fRav3RCMJxDmus3ev2cXff6E+LiwK5edHKb19fUtWLiIi4trS/CGZf5LAEBNXSPl1cvIu1/uuSkvL7t+7aqAoCAb28+uWtfY0NDS3DzB2oYxqKKqBgDp6cy/zdTU1ACAofEYxqCR8RgAyMv9+M0O3xyOIAiCIEza6qvP+lhlPb8pr2uuN8Wjpbb8+sY5b+4MvomtLcopSHlxwdem9XOVlrUTv6h0xtPwyD1+WKuwrAqvsDgACMsqC0krAkAd+WPFh7fh613Toi7yi8sAQPmHNxeWWNeXFRjN8LKctxqPx0ds8UwMO0hPoOR9UsaT8PB1LpT+fsPpnkQSV8z57Q8PrwAAETnV8g9vPibcp3duqavIen6Tk1cAT/jZC3RXa2N3e4uSoRVjUFhGGQCq84dZUWOUxdS2+mp6FUtDRVFpxktFvXHsHFx4AtuCE4/Hzgmkd+7pbKsr/qhsNIHARgQAykC/srH1GMdFjBO21VcBACef4E+eCIIgCIKpqqoqKysjEAgkEolIJDI2sbOzS0hIGBgYTJ06dcGCBZs2bQIAThKpsbnln0qmpm7MJOdrdx5YmBp6ujiWVVY7ei09GXKV3iHrY/7T2CTzqW6V1bUuDlNkpSRCb0V6B24AAFUlBUkxEQBQVZJXUZQDgJy8T6/TMhzm+50NvSkrLQkAr1LTzae65hcV+8ydvSFwCYFAcFsctOfYWfr88S/fXrl5z2Geb1//wEIPZy4OjuA9R/3XbQMAdRXFV6npdx99Wd+ivLL62p0Hgvx8bGw/9Q0VADQ0NTe3tllbmDEGVZUUACA9i/kzaU3dZwAw1tNmDGJPc/9cftJhim1VTR29zKWAXJr4+q2V+RhuLs7vGY4gCIIgvwza+gRBEAT5rW05ebWs+nP8lX3GWmoAELzEzXH5zs0nw9ynjcdqCMqqP6/0dNy+dC4Oh6NSaZbz1ySkZgNA4DwHCpX6NvvTKi8nxoUiCsuqbM30wvauVlOQptFoaw5dZCeyxVzcIykqBABB8x1nLttx4OJt54ljVeSkAKCksnbfygVL3acDwGZf92n+W8MexPo4T9IfpTzVypibiyMq9vVKT0dscmw1DrcpVkxn0djSdv72M1bnONPGbJSSLGOksKwaALBCEzo1eWkAqG8e5gZ6JRlxAEhMy14+dwY9mF9SAQC55AoTHY3iitq2zm7N6Uu6enqxVv1RyiE7AtUVZQCAyEY4vNaHccL6ptbzEU+JbIQpFkas0kYQANgavKG8rDQ++bXRGBMACN6yzWm6/ZaN69095gkKCWF9ystKg1av3b5rDw6Ho1KpVuYmCfFxABAYtIpKoaS+SVm5Zp2Orh7WubDgk63dxNDwG2rqGjQabe2qIBKJFJOYLCkpBQArVq1xnDblwN7ds2a7YFURAFBSTN578PDS5SsAYPO2HdMn2129ctlnse+06TO4eXii7t0NWr0W64mtxuHmPnfoiTQ2NFw4N8y+QhgHR6dRmqMZI4UFnwBAXFKSMaiqpg4A9Z8/Mw1XUlICgKT4uOUrVtKD+Xm5AJCXm2tiZj5yh7EWFiMPZ5U2giAI8tuKOb+9pbbc50y0zChDAJiwYP21dS7R57brTnTDaghaasvHuQfaLNqCw+FoVOp5X5uS94nYWHPXpVQKpeJj2jj3FfS1IhoqilSMrWdvvSQip0qj0Z6e3EBgJy384xmviAQAjJ2z/Nra2UlXD2tNcBKWVcaGNFWXTArYbTbbDwCsvTeGrXLMeBJu5OCtPs6enZM7N/HBOPfBGoi8xIcAoGPnwnQWXa2NaVEXWZ3jKKsZYgoaTMGGiiIA4BWWYAyKyKkCQGdLw9BJxjgtLn6fdH2Dm+zoMWzsHCWZybzCEtaLNgEAOweXnJYJ1u3NnTMttZWFb17QKBQLjyAsSGAj2gfuZ5yts7k+NTKEwEZUM5vEKm0EQRDkhxQXFzs4OEhISIiLi4uJiUlKSoqJiYmKikpJSfHy8jJ13rVrl6mRnojQP1UtF7znaFlFVfLD62P0dQBgy+qA6XN9N+4+4uE8Q0iAH+tTVlG12n/hrg0rsM+/5lPd4l++AYAgXy8KlfLmfdaaAB/d0YPXrwJyqZ3V2PAzh9VVFGk02qqt+0js7IlRVyXFxQBglb/3NI8le4+fmz1jMlYVAQDFZRUHt65dvmg+AGxbs2yym8+Vm5GL57lNn2jNw81179GL1f4LsZ7YahzuTtOHnkhDU/O50JusTtNxqp2mmgpjpIBcCgCSYqKMQTVlBQD43NjENFxJXg4A4l+lrljiRQ/mFZIBILegyMxIDwACFrjHv3wz0zPAzEiPRCIlvk6VFBfdsS7wO4cjCIIgyC+DCjUQBEGQ31dzW0fEs2RDTRWsSgMA2IlsCxztEtM+3I974zXTFgA4Sewbl7jhcDgAwONxproamfnFVZ8bpcWEWU272c9dTUEaADLzizPzi2famGFVGgBAZCPMnW6dmPYh7k0WVqjBz8sdMGca1orH49Z4Oye9y4l9k6k/Spmbk2PGeJMbTxLLqj/LS4kBQGTMa2EBPlszfaYjNjS37T7H8jOwqrwUU6EGuaIGAAT5v1rNQlZSFABa2juHzqAsK2WgqZyQ9uFKVIzzxLFUKu3mk0RsqQ9s5QxyRU1HZ/fWAPcZ400bWlrDHyWERsW4rNr7OvwwNycH02xPk9/57zzV0Nx2YJX3aBV5VmkjSHNTU8TNG4ZGxliVBgCws7N7LVyUmBD/ICrS03vw6yFOTs6Nm7f++SLFm5qZZ2akV1VVSkvLDDvtpm071NQ1ACAzIz0zI32m0yysSgMAiESix3yvxIT4uNgYeqEGv4BAwLLBX3rwePzq9RuTEhPiYqL1DQxnOMy8EX6tvKxUTl4BACLv3hEWEbGxmzj0oA0N9bt3bGN1piqqakyFGmQyGQCEBIUYg7JycgDQ2sJ8/5ayiqqBoVFCfFzopYuzZrtQqdSbN8KxpT4oFMo3O3xzOIIgCIIw6m5r/hBzR1pDH6vSAAACkd1w2vyS9KS85EcGU+cBAJHEMd5rPXZ1xuHxclomNQVZbfXVfKIsd8qbsDAYq3ioKcyqKcjStJqBVWkAAIGNqDd5Tkl6EvldPL1Qg4OH39TZF/sbh8dbzF1ZkpFMTouXUtMbZTEt68WtltpyAQk5APiYEMXFL6xsbM10xM6WxvjL+1jlIyyrMrRQo6myGAA4eb/6iY5fXBYAejqGKXfm4OEXEJetLfpQ9SmDQGCjUal4AltfF/PK6rEhu/p7ugFATEGDjcT85hlTkPL8/oHlXS0Nk5fuEVfSZJU2giAI8kMsLCwsLCz+7SwAAJpaWm9GPTbS1cKqNACAnUhc6O6c8Opt1NMY7zmzsCAnB2nzKn/6518zI/2MD7lVNXXSkuLDTrtt7TJ1FUUAyPiQl/Eh12nqRKxKAwCIbGzzXWYmvHobm5RCL9QQ4ONd5jMP+xuPx69ftijxdWpM0isDHU2HyTbhdx+WVVTJy0oDwN1HL0SEBO3Gjx160IbG5h2HT7E6U1UlBaZCDXJpOQAI/lmMgpGTkQKAltY2puEqinKGOqPjX765dOPu7OmTqTTajbsP7z56AQAUyuDarvz8fHIyUlkf899l5hCJbFQqlY2NraOz8zuHIwiCIMgvgwo1EARBkN9XQWkVjUbr6O6Zv+EwPdje2QUAJZW12FNRIX4O9i+rX2LLbHR29bCaU0SQz1Bz8AMnVg9hYfjV7696GkoAUFhejT1VkZXEPmBjsIqKkso67KmbvdWNJ4lRsSmB8xwq6xrScgoXzZ5MHLKqpJqCdP0rloUa7ETmyz2JnQgAza1ffUfc1d0DLPYiweNxZ7YsnR20Z+mu02sPXaRSqVQabcFMu4v3XmgqyQLAuW3LSOxETWU5AFCWkzTR0eDj4ToWFvUg/u0c+y/rf5RU1q47cvlJUpqSrOSlXUETxuiwyhlBAKCw4BONRuvo6PD0cKMH29vaAKC4+MsePaKiYhwcX37SEBAUBIDOjuE3lxURFTU0Msb+JhcVAcA4y6+WqNHT1weAooICekRFRfWrF6mmJj0BV/e5N8KvRd67Gxi0qrKy4l3q20VL/JjWy8WoqWt8bmG53y07OztThEQiAUBT81c3D3V1ddFPkBEejz99PsTF0WGp3+K1q1ZQqVQqleq10OfShfNYtiN3+OZwBEEQBGHUUFFEo9H6ujtvb/emB3s72wGgqaoEe8otIMrGTqK3cvAKAEBf9zAFwX/2F5HWGKxFxoohFPS++uFHUk0XABorv7wBEJZRYrxAiypoAEBzdQkA6Ni5ZL24lZv4wNx1aevnqqq898YzF2JD2KINAAAgAElEQVT7iTASkVMNfl7NKiXCcBd07KS625sZg/09nQDAySswtP/lZfZ1xblTgw5pWTuxsXMUvY1+cGhF+HrXgCspWBEJJvhZVWMlufzDm9gLO0P87IIiPvAIidFbm6pLnv8R/On1MyFppVmbzjNtvIIgCIL8NxSQS2k0WkdXl4ffKnqwrb0TAIpLK+gRURFhDtKXK6ygAB8AdHR2DTunqLCgka4W9ndRSRkAWJp+taypvvYoACgoLqVHVBTlGS+vmuoqAFBcVgEA7k7Tw+8+vPc4OsjXq7K6NjUje4mnG3G4fT/VVRRbipi37KRjH3KFJbGzA0Bzy1clj11d3TCkegMA8Hj8+SO7HL0C/NZsXbVlL5VKo1KpC92dL1yL0FQbrOa0dpyXk194Ys9mF4cpHCTS8/hkvzVbHeb7Zcbdl5eV/uZwBEEQBPllUKEGgiAI8vtqam0HABKRyFj6IMTP6zrFkr4EBQeJ+QdUAKDRaKzmJDF84GxsaQMAOUkxxg69ff0AQMDjsadM+49wc5IAgEQanGT8GB1xYQGsUCMqNoVGo7lOthx6UBwOxzlcnqyICwsAQElVHWMQq9sQEeAbdshoFfnUW8fuRr/KL66UEBG0NtFNfp8DAKOU5QBAfxTzp9lJYw2OhUXlFpXTIzefJAbuO4cD3K7l8/3cppLYh/nuG0EYNTU1AQDTVsFCwsKuc9wZ15/g4OQcOpbVi5TE8KNRY2MDAMjLKzB26O3tBQAC4cv/E8QlvlrenJuLGwA4SBwAMH6Ctbi4RNS9u4FBq6Lu3aXRaK5z3Ic9Lg6H4xwuT1awg5aWFDMGm5uaAEBEVHRo/9Fa2m/Ts+7duZ2flysuKWltY5uclAgA9H+okTt8cziCIAiC0HW3NQEAgUhiLH3g4hfSsZstpji4BMWwy0KM8BaaQPzyVrartREAGOsYAIDS1wsA+D/fQgMAz9f7j7BzcAEAGzsHACgaWvIIieUm3Dd3XZqb+IBGo2nbzh56UBwOR2SxfAUrWP1Ec3UpY7C7rRkAuPiZ19urLyuoK85V0Btn7DBY0TLKcnp5ztuUiNN5SY9MZ/sBjYb784yEZZSFZZRxOHzUvoDCN9H69h5YPDs64tGRVTgczs53u8msxWxEEiAIgiD/RU3NLQBAYmcnMlxehQUF5jhOw6olMJwcw1wIWF1hGW8JaGxqBgBsMQy63t4++Przr4T4V583uTg54c8bCSaMMxUXFbn3+EWQr9e9xy9oNNocx2nDHheHww2bJysSYiIAUFJewRhsamkFANHhNprR0lBNj4m88/B5XiFZUkzUxtIsKSUN/iwryS8szskvtDQzXjLfFes/c4rt67SM4+dDo57GBC72HHk4giAIgvxKqFADQRAE+X0pSosDgLKc5MWdK+hBCpXa0dn9Qx8pWZGXEgeA1xl5Uyy+3LKQmv0JABRlBhelJP+5dAemrLoeANTkBz85E/B450njTt94XFnXEBnzWlFGwkRHfeiB6hpb9oVEsEpj/gwbpkIKFTkpHA5XWvXVoT8UlgKA0Z+7wDDq6x8oq64TFuDzdLClBw9fuSchIijIx1NZ1/D+Y6GBpqqshAi9FasCERUavPXhafK7RVtPjNFWv7JnJWM3BBmBgqIiACirqIRcuUoPUiiUjvZ2Ti6uv2F+BUUAePUyebL9VHow9e0bAFBQUqJHislkxlFlZaUAoKquDgAEAsHZxfX0HycqKysi795RVFIeY2o27LHq6mr379nFKpN5ngv0DQwZIyqqqjgcrrT4q0KND9lZAGBkPIZpeF9fX1lpibCwyPwFX+5sPnJwv4SEpKCQ0Dc7fHM4giAIgjASlFQAAGEZJafgc/QglUrp6+ogkn6gKpEVAUl5ACjLTlEzm0QPVnxMAwBBKQV6pKnqq6tkS205AAjLqgAAHk/Qsp719u7Z1s9VuQlRQlKKsqONhx6oo+lzYthBVmno23tIqTFvVC8so4LD4ZgKNWrJHwFARtOQqXMd+SMMWRpE2WhCSsTp7vaWl9ePxV7Y6bHvlqqpHb2VS0AYAFo/V2JPC1KeR+7xkxlt7Lw5hF98+D3dEARBkP8GRTkZAFBRlL9y8su2XBQKtb2zk2vIlrJ/gYKcDAC8TH1vb/tlZaa377MAQEnuyyUG24WErqyyGgDUlRUAgEDAuzhM+ePitcrq2ruPnivJy5oa6g57rLr6hj3HzrLKxNPVyUDnq7UbVZXkcThccVklYzA79xMAGBswL8Xa199fWl4lLCSwYI4TPXjwVIiEmKiQAD8AfMgrAABLs68u/baWZsfPhza3tn1zOIIgCIL8SqhQA0EQBPl9KclKigjyxaRk9g9Q6ItqHLp8d+eZG9Ehu830Rv3k/LrqiuxEtri3mTthHj2Y9D6HgMfbmg6u7VxUVk0ur1GWk8SeXn0YCwA6agr0/m5TrE5df3Tq+qPUDwUbFrkMe6DW9s7QqBhWaVgYjGYq1JAUFRqrr/kqPbekslZRRgIA+gcot54lS4kJ6Y9SGjpDd0+v/qxlsydZXN4dhEWqPjfej0uZP8MGAJpbOzzWHvR2mnhioy99yN0XrwDAXH/w33DbqXA+Hq7wA2uYVhBBkBEoKauIiIrGRL/o7++nL6px+MC+ndu2vIhLNBs77ifn19HTY2dnj4/96rWTnJhAIBBs7SbSI0WFBeSiQmUVVezptdArAKCjM/iFlKu7x6mTx0+fOJ729s2GTVtYHau1pSX00kVWreMsrJgKNSQlpcZaWL56mVxSTFZUUgaA/v7+iJs3pKSkmXoCQHdXl4G25mxXt0th4VikqqryfuS9eV4LvqfDN4cjCIIgCCMhaUVuAZGitDjKQD99UY2X4UfjLu7xPvlETtv0J+eXVNEhENmL3yXAki/B0syXeDxB2diaHmmsIDdWkoVlBt/lZjy9DgASKtrYU52Js9/cOfPmzpnK3HdWnmuHPVBPR2v646vDNgGAgu7YoYUavCIS8jrmZdmvm6pLhKQUAYAy0P8h5g6fiKTkkM6iCuoAkJtwf7zXOnrwY3wUAIgraRI5OAGA/C6BsVAj/WEYAEioDC5TH3thJ4mbz2V7KK+wOKs8EQRBkP8GZUU5UWHB6MRX/QMD9P1EDvxxYdvBk3H3wsaOMfjJ+fW0NNiJxNikFNj4JZiYkkYg4O3Gf6kpLCwuLSopU1GUx56G3ooEAB3NwRWz3J2mnwy5eiLk6tv07E1BfqyO1dLafun6XVatFqbGTIUakuJiFiaGL9++Ky6rUJKXBYD+gYGbkY+lJMQMtJm34+zq7tG2muY60z7sjwNYpKqmLvJxtJfbYOHFKDVlALj36MXmlf70UXcePgcALQ21bw5HEARBkF8JFWogCIIgvy92Itv2pXMDdp5euPnYSk9HPm7OR4lpBy7esTbRNdXV+OZwOUlRALh0L3reDGtDzWEWSJQUFVriYn8y/MGKfecXz55MZCNEPEuOik3xmDaBXplBoVJdV+/b4ueuKi91P+7NmZuPnezGmut/+SCqP0pZTUH61I2HAOA+bfywmagpSDelsFxRY1hrvGfNCtw9b/2htd7OAnw8R0LvlVbV3jkWTN+L9NK9F0H7zq9f5LJhkQs/L7eVsXZU7OsJJjozxpuQK2uX7TojLSaye4UnAGipypvoqF+OjBbi550xwZRKo958khj7JtPB2sxotCoAtLR15JLLddUVT1y7z5SGhaEW43IjCMKInZ19+849Ab6LfLzmBa1ey8fH9+jhgwN7d1vb2Jqaj/32eABZOXkAuHzxwtz5XoZGzLfSSkpKLfELOHn8aNDygEVL/IhEYsTNG1H37nrMm08vywAACoXi5uy0ZftOFVXVB1GRZ06ddHKebT7OAmvVNzBUU9c4dfI4ALjPnQcsqKlrNLZ3/9Dpr163wdlh2nx3tzXrNwoICh49uL+0pPh21EPsRXo55ELQ8oD1wZvXB2/mFxCwGj8h6t7dCda20x1mkslFy/19paRldu8d/OJp5A7fHI4gCIIgjAhEdptFWx4cXH5v95JxcwJJ3Lz5L58mhR1WNhovq2XyPTMISMgCwPuHoXpTPKQ19JlaeUUkxjj6pEScfnx0tfHMhXg24oeYO7mJD/Qmz6GXZQAAjUq5uWmu9cJgYVmVvKSHb++dGz1hprzO4NJWUmp6InKqb+6cBQDdSW7DpiEip7o5unbYphFYzF0Zvt719jZvy7krOXgFXl0/3lxT6r73JnaBfv8w9PHR1Vaea6w814opaCgbTyCnxV9b46wz0UVAQi4v+dGH2DtiChoa4+zxBDZxJc3Ue+c5ePhUxti0NdTkJkR9SnkmraGPLSXS3d7yuSRPQlUnJeIPphwU9MYxLjeCIAiC/AewE4k71wf5rtnitWzdav+FfLw8D5/H7T1+zsbSzNyY+Vo5LDlpKQC4GH57vqujka4WU6ukuJjfAvfj50OXb9y5xNONyEa8GfX43uMX82Y70MsyAIBCoTovXL597XJVJfmoJzGnLoU7T580zmTwbgEDHU11FcWTIVcBYK6zA6tM1FUU20syfuj01y1b7ODp5+67av3yxYL8fAdPXSwpr4wKPYVdXkPCby/fuDN4hV9wkJ8AH+/4sSb3Hr2wHmfmMMWGXFruv3abtJT43k2rsKk01ZRtrcxjEl9P81jiPmu6vIzU/Wext6KeaKqpzJhszU4kjjwcQRAEQX4lVKiBIAiC/NY8HWy7e/o2HQ+9F/0KANgIBK+ZtlsDPOj1CiOwNtEdo60WcufZp5LKp+d2DNtn+9K5FCr19I1HIXeeYRGfWZMOrF5I7zDeWFtKTHjuugNUKg0ALAy1jq1fzDSJm73VjtPXbUz1FKT+tnvpbEz1QnYGBuw87b72AADw83LvC1ow0fzLLRo0AAqVSt/o9MyWpQuCj/jvOOW/4xQA6GkoXdodxMPFCQA4HO7mofUBu04funz30OXBeyZ8nCfvDfLC/k7JyqfRaJn5xZn5X61QjY1FhRrICOYv8O7q7tq8Yd29O7cBgI2NzdN74dbtu77nFQoA1ja2xiamIefOfsrLexIdN7TDtl17KBTK6T9OhJwbXJd14eIlBw4fY+wzfoK1lLT0XLfZVCoVACwsrY6eOMXYwc3dY8fWzTa2dvIKin/tNIdlY2t34XJYgO8iD1dnAOAXENh78PDESZOxVhqNRqFQ6K/Q0xcuLpjn4b/Ex3+JDwDo6RtcDrvGw8tLn23kDt8cjiAIgiCMDKbO7e/tij67FVsfAk9gM5g6z8Zn03deoJWMxstoGqXdv1RfVuB17MHQDraLttCo1Dd3zqbdv4RFjGYsmLJsL2MfRQNLPlGpiK1eNCoVABT0xk0NOsTYQcfOJe7ibmXjCYKS8vD3UTae4BR89sGB5be2eAIABw//JP9dqiaD+wPSaDQqdfACjcPjnTeHPDmxLif2blHa4PsQeV1zh3UnCUR2AHDbde3e7iUJV/YnXNmPtY6ynD5l+T48gQ0AKnLe0mi0moKsmoKsIVngUKEGgiDIf8+COU7dPd0bdh3Bln9gYyN4z5m1fV3gd15ebSzNTAx0z4Xdyissjr59eWiHXRtWUCiUPy5eOxd2C4ssnudyeMcGxj4TxplKS4i5LQ7CPv9amhmf2LOZsYO70/StB07YWpkryEn/tdMclq2V+eXj+3zXbHFdtAIABPh4D25dO2nC4A0SNBqNQvnyDdWFwzvnBaxZsnrzktWbAUBfWzPsjwO8PNxYKx6Pv3rqYNCmPbfuP4lOfIUFLUyMzh/ZyU4kfnM4giAIgvxKOPrlDUEQBEH+17i4uAw0V13dt/qfPlBHV3dWfklHd89oFTkZcZEfGltT38TLzYmVLLBS39SaXVBCYidqqcgL8PHQ43I2noajVSJPbG5p60jPI0uJCmkoyQ4d/iD+rfua/dcPrJ1h/bNLSTMZoFDSc8k0GtVIS42Ax4/cmUajfSwqL6mq1dNQlpUY5l+pvKa+sKyKn5dbQ1Fm5H+QX2Pe+kNsgtIRET+21gjyQ3A4XGj4TSfn2f/oUTra27MyMzo6O0dracnIDPMaGVlNTTUvD+8IZQf19Z+zs7JI7Oxa2joCgl/tziMvJWZoaHTv4ZOW5ub09PdSUlIao5hXXn14P8rdZVb4rTszZjr+aG7fNDAwkPH+HZVKNRpjQiAQRuhJo9E+5nwoLSnR1deXlZX70Q7fHP5P4CURbt265eIy/KZOCIIgyHdycXHJre+bvW2Yn2T+Ub1dHbWF2X3dnWJKmvxiP/xrTXtDLTsXD4mLh1WHzub62qIcAju7uNJoTl4BxqYDM1SkNPTnHrjd3d5S/SmTT0QS22eEUV7yo1ub57vuCB1lOf1Hc/smKmWg+lMmjUqV1jTE40e6QANAW33159L8gd5uETk1YVkVxt/baFRqc21ZQ3khkZ1DWE6VT0Tyb0/1520bL4Su1wiCIACAw+HCzxxynj75Hz1Ke0dn5se8zs4uLQ01GSmJHx1eU/eZh5t7hLKD+oamrI/57CR27VFqgvx8jE1S2uMMdbUeXjvb3NqWnvVRSkIM20aE0f1nsS4+gbcuHJs5xfZHc/umgQHK++wcKpU2Rl+HQBjpSyoajZaTX1hSVqGvrSkrPfzVs6qmLregqLu7R11FSU1Z4avr73cM/9uRZLTQ9RRBEARhglbUQBAEQRDg4eIca8D84+t3khQV+mYfUSF+G1PmXasZCfDxWJvosmoNux8jJSZkb8W8ccPPYyMQxmirfWdnHA6npSqvpcrylkQ5SVFsOxgE+Xvx8PKOtbD8y8MlJaVG7iAqKmZjazdyHwFBQWub4b+HCr18UUpK2n7a3/8jEACwsbEZm3xXhRYOh9PS1tHS1vlrHb45HEEQBEGYkLh45HXN//JwXpFv/PjELSiqbDxh5D6cvALKRuOHbcp4fI1PRFJ97JS/lt7I8AQ2Gc3vXRaOT1SKT3T4dyM4PF5ISlFI6u9clAtBEAT5f42Xh9vC5K+vPCopLjZyB1ERIVurb1y+Bfn5bCzNhm26fOOulITYNLtvXKD/GjY2gokByy/HGOFwOO1RatqjRvpGS1pSXFpy+IVpv2c4giAIgvwCqFADQRAEQf53Hbh0p/pz0/NX6YfWLGQb8WZ6BEF+vYP79lRXV7149vTg0eNsbOh9NYIgCIL8T0i6eri9oabwbTR9GxEEQRAEQX7SvhPnq2vrnsUlH92xgY0NfUOFIAiCIH8D9HkVQRAEQf41EiKCwgJ8I3S4fO9FR1eP10zbBY4Tf1lWCILQSUhICouw3A7pcsiFjs4OT++FCxYu+pVZIQiCIMhvjkdYnItfmFXr+0ehfd2dBlPnGU7z/JVZIQiCIMj/axJioiJCAqxaQ8Jvd3Z2ec+ZtXDuP7v5KYIgCIL8PlChBoIgCIL8a1JvHRu5Q96j878mEwRBhvU2PWuE1tyikl+WCYIgCIIgdP6XX43QGnQr+5dlgiAIgiD/GemxkSO0Fr2N/mWZIAiCIMhvAv9vJ4AgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCPK7QIUaCIIgCPKrPX+VfufFy387ix/Q2dUzQiuNRmtp6/iZ+Tu6upta239mBgT5Rz1/9vROxK1/OwtmHe3tTY2NwzZ1dvzUSxJBEARB/gcVvonOibv3b2fxY3q7Orramli19nV3smqiUgaoVMo/kxSCIAiCfPEsLini/tN/O4sf09HZ9ZdbBwYoFAr1784IQRAEQf4itPUJgiAIgvxqR0MjSyprnSeO+7cT+ULHMcDScPQfm/wZg5n5xVv/uPY+t6ilrUNMSGDa+DG7A+fzcnPRO7S0dWw6EXbraVJ3bx8PF+fEsQZH1y0SFuD7nsnpmlrbTdyC+Hi43t8+8befF4L8LY4dPlhCJju7uP7biXzR1NhoaqjHx8//LiuHHszMSN+2aeP79+9ampvFxMSnzpixe+8BXr7BlySVSh07xnBgYIBxHnl5+Tv3HzFNrqupbmFl9ccZtPUSgiAI8j/k1Y0TTdUlWtZO/3YiX5zwMFLQGztjzfFhW7vams54j+Pg5gsIfcMYrynIirmwozo/o7u9hUdQVH2c/UTfHSRuXqw1O/p2WlRITWE2lUIRklIY47TI2GEhDj94nxWNSj27yIpK+epqLiAh57Hvf66iFEEQBPl/4fDpS+SyCheHKf92Il9ojrO3MjM+c3A7UzzjQ+6mfcfeZ+Y0t7aJiQrPmGi9d9MqPl6e72kFgBuRj85euZGZkz9AGVCSl/X3cl/i6YbHM9/JzOroCIIgCPJPQCtqIAiCIMjv7trDuOKKGqZgei7Z3ndLRh7ZZbLFep/ZfDxcl+69mOa3jUqlYR36+gecAneF3o91mWx5arO/y2SLe9GvXFbu+57JGfnvOFVTz/JGQwRBhuW/xKemppoxkv7+3dRJthkZ6S6uc9Zt3MTHz3855ML0KROp1MG7haqqKnM+ZBMIBBFRUfpDUEiIaebwsNBictEvOg0EQRAE+X8r89n1pqriETo82L+8vaGWKVj9KSM0yKHmU5a2rbPV/DUkHr73D0PDVs2kUakAkPX8ZuQe3+72FlNnX+OZC/u6O58cX5ccfoQ+vK2+uo78EY8ncPOL0B+cvIL/xAkiCIIgyK8XFhFFLi0fGn+f/XGSi3dGdq6r49SNK3z5eXlDwm9PcfPBPvCO3AoA1+48WLB8Q3Nr29KFc5fMd+vo7Fqxec/+kxe+8+gIgiAI8g9BK2ogCIIgyG+q6nPj3vO33ucWfSgoHdp6LuJJT29fQuh+HTVFANjkO2ea/7aE1Oz7cSmOtuYAEP4oPvVDwZ4VXsvnzgAATwdbHMDFey/Sc8kGmsojT04XcudZdEqGIB/PCH0QBGEScu5szIvnTDUW58+c6unujn+ZoqOrBwCbtm6fPtkuIT7ufuQ9x1nOAFBMJgPAhcuh2jq6Q+esqqrcu2tH+rt3H7KzfslJIAiCIMj/S2311QlX9lfnZ9SSc0bolnb/UlFqLCcfcwlFamRIf1/PojPREiraADDBe0PYSsfi9MTcpIejxzu8jjglJKO06EwMtsDGuDmBx+bopUaGWM5bjQ3HSkMcg89KKGv9I6eHIAiCIP+Gqpq6XUdOv8vKyc79NGyHM5evd/f0vnx0RXe0BgBsXb10sptP/Ms3kU+iZ02bNHIrABw7d0VFUf7lwxvYGhtrAhaqmU46c+XGhsAl33N0BEEQBPmHoBU1EARBkN9FT1//7nM3tR38hMxcdBwDlu8529HVTW9Nfp8TtP+8rlOAuv0ir41HQu4+p1C/bFq5dNfpxVtPFFfUBOw8rW6/yN53y80niQBwMvzBuLmrFey8HJfvJJcPrhsxf8Phg5fuvs3On7/hsLytl5FL4JHQSPpCFEO1tneu2Hfe2CVQaaK3+5r9z1+lf2fOP6mjs7uwvJqPh8tQU2Vo65vsfB01RaxKAzNvujUAvPtYiD29+TRRVIjf19We3mG1t/OFHYEignzfnByTV1yx4eiVncvmSYiguwCRYfT09OzesU1bQ1WYl1NXU315gG9Hezu9NTkpcWXgUr3RGhpK8gvmuV88f45C+bKV+1K/xUsWehWTiwJ8F2koyU+daHPzejgAnDx+1MLUWFFGwmm6Pblo8D9mTw+3Q/v3vk157enhpiAtbqynffTQASqV5ba1rS0tQcsDxujrKMtKubvMev7sy4a+I+f8t8jL/bhx3eode/ZJSEgyxt+kpGjr6mFVGpi5ngsA4H1aKvaUXFiIw+FUVNWGnbajvb2osJCPn9/QyPjvTRhBEAT5f22grzf+8r7j7gY77SROeBg9PBzU29XB2KE08+XjY2tOzjU+Mlvrzg6fdw8uU6mDV+QHBwMj9/o3VRU/OLj8yGyt0CCH7OgIAEiJOH1u8YSDM9WurZ3dWEmmT3V7u3fytSMVOam3t3sfcFA95WX28vpxGosrck9H6+Ojq097mR9yVL+1eX7hm+gfSvsv6+3qaKwkk3j4pDX0WfX5XJr/4vQm2yVbeYXEmZoqclIlVLSwKg2M3hR3AKjKe9/T2fa5JE/VxI6+DQqviISivkV3ewtloB+LNFYW43A4YRmWb7ARBEGQ/y96ent3HD6lYT6ZV1Ffc5x9wLrt7R2d9NaklLTA4F2jLaYqGdvMC1hz/moEhTJ4QfRbs3Xhio3k0nLfNVuUjG0munhfv/cIAI6fDzWd4iKjazF9rm9RSRl9Kg+/VftPXkh5l+nht0pax0LP2uHQ6YusPvC2tLUv37hT33qmrL6Vi0/gs7ik78/5Z7R3dBYWl/Hz8hrpDl+JmPIuU3e0BlaHgfF0nQkAaZkfvtna2t7x8VPRpAnj6DuhSIqLjR9r0tzS2j8w8D1HRxAEQZB/CFpRA0EQBPldBO07d/1Rwpyp43XVFYsra69ERn8sKou9tBcAkt7lTPffxsfD5TLZUliAN+5t1oq950qr6nYtn4+NzS4oqaprjE/N5ufltjTSuhv9Kvn9x4hnyXFvsyaNNZCTFH328v00/60fH5zD43EJqdkZeeSjYZFWRtreTnaxbzK3nLxKLq85tdl/aFZVnxsn+gQ3NLfNmTqen4crJiVjdtDuvSsWBLhPGznnn6euKPP8/C4AKK6o0XEMYGzqH6DYmuobaakyBivrGgCAvvoFubxmorkBO5GtpKoul1wuJSqkraYwx97qm5Njevr6vTYeMdfX9HObejkyemgHBAlaHnDj2lU3j7m6evolZPKVSyEfc3JiE18CQFJiwowpE/n4+V1c5wiLiMTFxqxY5l9SUrxr735s7IesrKrKyvjYWH4BAcvx4+/ejkhOSrx983pcbMzEyVNk5eSeP30yffLEnAIyHo9PiI/LSE8/evigpdX4BQsXxcVEbwneUFRUeOos8zqoAFBVVTlxglVDQ7373Hl8fPyx0S9cHGfsOXAoYFngyDn/LXp6ehbM8zAfO84vYNmViyH0eKXLY6IAACAASURBVH9/v63dREPjMV+lWlkBAPSFN4rJRbKycp0dHYnxcZ/r6tQ1NIzGmBAIBKxVXWPUs5h4rJuupvrflTCCIAjy/93jo6uznt/UmegqqardVF2a/ij0c3HuwlPPsdaSjOSrq5xIPHzaNs5c/ELF7xIeHVnVXF1q57sdAGqLctrqq4rfJ3Dw8CvqW+TER5ZmvvwQc4f8LkHV1E5AXLbgzYuwVY4rbmTi8HgAKHmfVPMp69WNEwr6FobTPclp8THntzdVkWesOcGUVVt99aVl9l0tDbqT3EjcfOS0uOsb50zy32nq7Pc9af8MUXm1BccfAUBTVfEJD6OhHQb6eu/u8JHTMTOZtST9URhjE2WgX9nYWnqUwdfnUgUAnHyCeALbghOPBSUV6E09nW11xR+VjSYQ2IhYpKmqmF9Mpq+7oyQ9saOpXlReTVrTEI8n/Px5IQiCIL/Y8o27rt257zFrhp7WKHJp+aXrd3LyCxLvhwNA4uvUKXN8+Hl5XR2niggKxCanLNuwo6S8Ym/wKgDI+phfWVMXm/xGgJ93vPmY2w+eJaWk3Yx8HJv8erK1pZy05NPYpMluPgUpz/F4PADEv3ybnp17+PRFq7EmCz2cYxJfB+85WlRSdvbgDqaUqmrqJjjNb2hsmus8g4+XNzrxlaPX0gNb1izzmffNnH+ShqpSzN0rAEAuLdccZ8/U2j8wYDd+rLGeNmOwsroWAIQE+EduBQA2AiH2bqiSvAy9tbW940Nega2VOZGN7ZtHRxAEQZB/DirUQBAEQX4LvX39N58kTh5neHbrUiyiJCOx5tDFovJqFTmp28+T2dgIOffP8PNyA8BKLyetGb5PktLohRoAUNfYssXffa23MwDMnmzhtHxX8vucd7ePq8hJAcCSbSfDH8UXV9ZgT0sqa/etXLDUfToAbPZ1n+a/NexBrI/zJP1RykyJbTl5taz6c/yVfcZaagAQvMTNcfnOzSfD3KeN5+IgjZAz0zyNLW3nbz9jdfozbcxGKcl+/z8XkY1weK0PY6S+qfV8xFMiG2GKhREAdHb11DY0iwkJzA7a8zT5HdZHTUH67NZlY7SHv1+fSfCx0Jr6pvt/bMHhcN+fGPL76O3tvXU9fNIU+7MXLmERRWXltStXFBUWqKiq3b51g42N7UNeIb+AAAAErV6rra7y9PEjeqEGANTV1W7ZvnPN+o0A4Ow6Z9aMqclJiWmZH7AlJXx9FoRfDSsmF2FPS4rJew8eXrp8BQBs3rZj+mS7q1cu+yz21TcwZEpsa/CG8rLS+OTXRmNMACB4yzan6fZbNq5395jHxc09Qs5M8zQ2NFw4d4bV6Ts4Oo3SHD00vmn92pqa6qjHT5leOEQi8dCxr37Eqq//fP7MaSKRONl+KhYhk4va2ttGqyl1dXVhEX0DwwuXQ9U1RrFKA0EQBPnNDfT3ZkdHqJpNnLn+DywiJKXw9OSGxgqysKwyAOTE3sUT2AKvp3Pw8APAOPcVx+fofXr9DCvUAICOps/WC4Mt560CAC2bWeHrXEozXwZcScGGR+0LyHx2o6mqBHsKAE3VJZMCdpvN9gMAa++NYascM56EGzl4S6npMSYWc357S225z5lomVGGADBhwfpr61yiz23XnejGySf4zbTpulob06Iusjr9UVYzxBQ0WLWy8uLMlvaG2rkH7w59l0tgI9oH7meMdDbXp0aGENiIamaT2Dm45LRMsPibO2daaisL37ygUSgWHkH0/k1VJb1d7cfcdPt7BpfZk1LTcww+Kyr/Xe/AEQRBkP8RvX191+8+nGJjdeHILiyirCC3csvewuJSVSWFW1FP2Ahsea+fCfDxAsDqgIXqZpMfRydghRoAUFffsH3t8vXLFwOA60z7GfP8klJSM+PuqyopAIBPUPDV2/fJpeXYUwAoLqs4uHXt8kXzAWDbmmWT3Xyu3IxcPM/NQEeTMavgPUfLKqqSH14fo68DAFtWB0yf67tx9xEP5xlCAvwj58w4T0NT87nQm6zO3XGqnabajy0NRWRjO7ZzI2OkvqHpzJUbRDY2e9vxI7cCADcXp7nx4DpYJ0OullVWP41NolApa5cu+qE0EARBEORvhwo1EARBkN8Cto9J0vuPWZ9KdNUVAWCJi/18BxsOEjsALPOY4etqj1VpAEB/fz8/L3dbRxfjDAQ8fsW8mdjf2qoKAGBlrE0vmLAwHB3+KD6vuAKL8PNyB8yZhjXh8bg13s5J73Ji32QyFWo0t3VEPEs21FTBqjQAgJ3ItsDRLjHtw/24Ny6TLUbImUlDc9vucyw/BqvKS/1QoQaTp8nv/HeeamhuO7DKe7SKPACQK2sA4PTNR0oyEofX+pjoaLzJyt98Isx11d7Um8dEhfi/OeG5iCfXD65Dm54grGD7mCQnJWZlZujq6QPAEr8ATy9vEgcHACwLDPL1X4pVaQBAf18fv4BAW2sr4wwEAiFw5eCG7to6OgBgOX4CvWDCwnJ8+NWw/Lw8LMIvIIAtiQEAeDx+9fqNSYkJcTHRTIUazU1NETdvGBoZY1UaAMDOzu61cFFiQvyDqMjZbnNGyJlJQ0P97h3bWJ2+iqra0EKNZ08enztz6nrEXaZNT4Z69uSx/2Kfhob6/YePjtYavK+omEzuaG/fumPXdIeZDfX14dfCwi5fcp3l+Dr1PRc398gTIgiCIL8nGoUKAKWZL2sKsyVVdQBgjOMi/anz2NhJWAczl4AxTouxKg0AoPT3cfDw93S20WfA4wlj3ZZhf0soawGAor4lvVpCQW9s5rMb9WX59AgHD7+psy/2Nw6Pt5i7siQjmZwWz1io0d3W/CHmjrSGPlalAQAEIrvhtPkl6Ul5yY8Mps77Ztp0nS2N8Zf3sTp9YVmVHy3UKEh5nhp5wXVnGK8w86Ynw3a+f2B5V0vD5KV7xJW++qksNmQXVoohpqDBRvryRqKpqri3q8PGZ5OGxdSulsbM5zcyHl+7GeyxJCSRnYPrh1JFEARB/kXYPiZJKWmZOXl6WqMAwM9rjpebEweJBACBiz39vT2wKg0A6OvrF+DnbW37socXgYBf6bcA+1tHUx0Axo81oVdLWJoZX719P6+ATI8I8PHSV8XA4/Hrly1KfJ0ak/SKsVCjqaX1ZtRjI10trEoDANiJxIXuzgmv3kY9jfGeM2vknBk1NDbvOHyK1bmrKin8aKEGkycxiYtXb25obD68fb2WhuoPtW7Zf7yruwcANNVUODmYM0cQBEGQXwwVaiAIgiC/BS4O0obFrjtOXx/rsUpdUcbSSGvSWENbMz0CHg8AagrSTa3tJ649eJv9qbzmc1F5TXtnl6SoEOMMkqJC7MTB6yZWKsHYAds+oL9/AHuqIivJeAsdViRRUlnHlFVBaRWNRuvo7pm/4TA92N7ZBQAllbUj58xETUG6/hXLQg165j+qpLJ23ZHLT5LSlGQlL+0KmjBm8ON6c2sHAPT29YcfWKumIA0AehpKnxtbDly6c+fFSz+3qSPMWdvQ7Lv9D6+ZtjMmmPy1rJDfARcX14ZNW3Zs3TzOxEhdY5Tl+PGTJtvb2E3EXmtq6hpNjY0njh1JfZNSXlZWVFTY3tYmKfnVSjOSklLs7INVTRwkDgCQlPrSAU8gAEBfXx/2VEVF9avXrKYmABQXk5myKiz4RKPROjo6PD3c6MH2tjas88g5M1FT1/jc0jE0jqFnTldbW+O7yNvTe+F0h5msRgFASTF53eqVTx8/UlJWuRh2bYK1Db3pbMglEomkOVoLAJRVVE3MzPn4+I8fOXQ/KnKOx9wR5kQQBEF+W0QOTivPtXEXd59bNF5UXk1B30LV1E7F2Jq+14aInGpXW9PrW6cqP6a21FY0VpF7O9t5RSToM/CKSBCIgxc1rE6CsRWHJwAApb+fHhGWUWK8IosqaABAc3UJY1YNFUU0Gq2vu/P2dm96sLezHQCaqkq+J206ETnV4OfVrE6fQCR+1z/Tn9ob66L2LTWYOm+UxbSRezZVlzz/I/jT62dC0kqzNp1XMrRi6hD8rKqxklz+4U3shZ0hfnZBER94hMQAYOaGU2xEkpjiKAAQllGW1RrDwc336ubJvKSHuhNdfyhbBEEQ5F/ExcmxaaX/1gMnTCbP1lBVGm8+ZrK1pZ3VWAIBDwDqKoqNzS3Hzl158z6rrLKqqKS8rb1DUlyMPlxSXIz9z4sUiUQCACmGVuwTaN+f31ABgIqiPOPlVVNdBQCKyyoYUyogl9JotI6uLg+/VfRgW3snABSXVnwzZ0bqKootRe9ZnTv7D15eGRWXVazetv9xdIKyglzYyf3WFmbf34ppLnxXVFL2KjV9877j46bNIafFiIuK/OV8EARBEOQnDfNLD4IgCIL8J631dv4QdXq9z2wuDtLFu8+dV+w2mh1Y19gCAMfCotSm+OwLiRgYGJgwRufctmWmusw3z3FxMhfa41nv2cG0UAQ3JwkASCTmz6JNre0AQCISiWwE+kOIn9d1iiVW2zFCzkxwOBwniZ3VY9jajm+6+STR1H1l0rucXcvnp906Rq/SAAApMSEAGKOthlVpYKZYGgHAp5LKkacNufOssaWttaPLd/sf2KO6vqmmvsl3+x+HLt/9C3ki/1Vr1m/MzitYt3ETFxfXxfPnnGdON9bTrqurBYDjRw6pK8nt37Orv79/vLXNuZDLpuZjmYYPXSUCz/qFIC4hwfiUm4sb/izvYNTU1AQAJBKJyEBIWNh1jju2AMYIOTPB4XCcrA2t7Qg5d7axoaGttdV3kTf2qK6uqqmp9l3kffjA4K3AN6+HmxkbJCcl7tq7PzUjm7FKAwD0DQyxKg26SVPsASD3Yw6rfxYEQRAEsZy3avn191bz1xBJXO8eXL6+3u2Ul1lH02es9dXNk0ecRyeFHaRQBpSMrGauP0XfvANDHLLMA27E96U8wl9dkbFVItjYv7oid7c1AQCBSCKwEekPLn4hHbvZYooa35P2l2RwOCKJg9VjaGHHyN7dv9TV2tjb2Ra1byn2aGuoaW+ojdq3NDn8KL1bdnTE2YWWpZkv7Xy3+195Ra/SoNFoNCqV3k1YRll/ioft4q2Ugf7CN9FYUEpND6vSoFM1tQOAzyV5P5QqgiAI8q9bv3xx3qunG1f4cnFynL8aMdPTX8/aoa6+AQCOnLmsZGSz59jZ/oEB63FmIUd303fuwHBzcTLNNsKnXQCQEBdlfMrFyQl/VnjQNTW3AACJnZ3IRqQ/hAUF5jhOwwo7Rs6ZEQ6H4+QgsXoMLez4TtfvPTKeOCspJW1v8KqMuCimOowRWmk0GpXhCquiKO/p6rh7Y1D/wMDT2OS/lgyCIAiC/C3QihoIgiDIb6Gvf6C7p1dOSmyT75xNvnPqGlsOXLxzLuLJ2VuPA+ZM3/LHVREB/uyoUzx/ftY9eOnOzxyOXPnV77Jl1fUAoCYvzdRNUVocAJTlJC/uXEEPUqjUjs5uTg7SCDlv9fdgmqqusWVfSASrfObPsGHadeWbnia/W7T1xBht9St7VspKMN9eICMhCgD9AxTGYE9vHwDw8Xxj1WURQX4dNUVyeQ090tfXT6XRsj+VjFD7gvxu+vr6uru65OQVNm3dvmnr9rq62oN795w7c+rsqT8ClgVuCd4gIiKalfuJh3dwMdiD+/b8zOGKyV8tnlFWVgoAqurqTN0UFBUBQFlFJeTKVXqQQqF0tLdzcnGNkPPWHbuYpqqrq92/hzlIN89zAdOuKyKiojq6euSiInqkr7eXSqV+yMrCvpJ79uTxYm/PMaZml6+Gy8rKMU1YWVnxPi3NwMiIsam0uBgARMXEAEEQBEGGQ+nv6+/tFpCQm+C9YYL3ho6mz0lXD6dGXnh777yNz6bOloaY89u5+YWXhb8ncfFgQ5KvHvmZIzZVFTM+baktBwBh2a8WSBeUVAAAYRklp+Bz9CCVSunr6iCSOL+ZNuNUHU2fE8MOskpG396DccuVb+ISEJZQ0W6s/HIKlP5eGpVWW/QBhx98l1uQ8jxyj5/MaGPnzSH84jKMw19ePxZ7YafHvltY7QV9TgBo/VwJAK2fq6ry06XV9RkHNleXAQC3wFe/wCEIgiD/4/r6+7u6e+RlpLeuXrp19dK6+oa9J86fuXz91KXwZT7zg/ceFREWzE1+wsszePvBvhPnRp5wZOTScsanZZXVAKCurMAYVJSTAQAVRfkrJ79sCkahUNs7O7k4OUbOece6QMap6uob9hw7yyoZT1cnxi1XvtOTmETvwA2mhrpXTx2UlWbeDHTk1oOnQjbvO34/7PRka0t6UERIEAAqq2sAQRAEQf49qFADQRAE+S0kpn1wXL4zZEegm70VAIgLC6yYP/NcxJPmts7ymnoqlTbD2pRepVFZ15D9qURMWOAvH66orJpcXqMsN/jh8OrDWADQUVNg6qYkKykiyBeTktk/QCGyDd6xd+jy3Z1nbkSH7O7o6mGV89AjtrZ3hkbFsMrHwmD0jxZqbDsVzsfDFX5gDdPqIBhOEruVsXZi2gfG03yYkAoAJkMWI2Hi62rv62rPGBk3d3V3b9/r64dZDUF+Q4kJ8U7T7S9cDnNz9wAAcXGJwFWrz5051dLSXF5eRqVSZzg60qs0KisrsrMyxcS+vRk8K0WFBeSiQmWVwf1rr4VeAQAdHV2mbkrKKiKiojHRL/r7+4l/rtd6+MC+ndu2vIhL7OjsZJXz0CO2trSEXrrIKp9xFlZMhRq+/kt9/ZcyRixMjbu7u1+lDq4ou21zMB8//7WbERISzF9LAUBzU9Nct9kLfBadOPXl+7K7t28BwNix41ilgSAIgvzmSjKSr62d7RR8VsfOBQB4hMTGui1LjbzQ094CAK11FTQqdZTldHqVRuvnqtqiD9xCf71ooLGC3FhJFpYZfOOa8fQ6AEioaDP2EZJW5BYQKUqLowz0E9gGL8cvw4/GXdzjffKJnLbpyGkz6uloTX98FVhQ0B37Q4UaJk6LTZwWM0bOLZ4w0NvtG5JIj8Re2Eni5nPZHsorzPy+RVxJEwDI7xIYCzXSH4YBgISKFgB0tzdHbPE0nO45fdWX9Tly4u8BgLzOMKu7IwiCIP+zEl69nT7X9/KJfe5O0wBAXFRkle+CM5evt7S2lVdVU6lUxym29CqNyurarI/5YiJ/fYeOwuLSopIyFUV57GnorUgA0NH86tsbZUU5UWHB6MRX/QMDRLbBn40O/HFh28GTcffCxo4xGCFnpsO1tLZfus5ywVQLU+O/UKixed8xfl6em+ePSogN8zZj5FYtDTUAiElKYSzUuHj9DgDoaDLfnoEgCIIgvxIq1EAQBEF+C2Z6GqJC/HtDIqTFhXXVlcgVNdiaGZPHGaopSHFzcdx98XKiub66gkxKVt7OMzd4ebg6unoKy6pUhyyD8T0o/8fefcdTvf8PAH8djs3BsWVvkj2SRKikIioNFWlpp72Xhnaat512krQpDS2VIhGFY6+yjmOvc35/nH6n08Gh6KvufT0f/XG93uPz+pyuPudzzuvzftPpY5cErZs1QVtV8cajV0cu3/EcZNvPlPNGlJ+PuHHuxDmBh6eu3bfIx4MkInQ7Jm7HyTBHa+O+xno1dfXt5dz6iDpqvcpj211R42dRadUplFxjXfX9529wNNmZGw61swCATfMmOfgsn7Ri14a53kpy0jFxSaeuRdmY6A8bYNldaaD/MhubfjIyskFbAnspKRkbm1AoGcw1M1yGDtPR0RURFb12NXTwkKE6urqvXr4M3LBOjESqrqlOT/usrfMrn7O0tLSMG+25bmOglrb2zYjrRw4d8Bw9pl9/O45u/Pz8GwO3zvGfPs13UsCSZSQS6fatmzu2bXF0cu7bz7amurq9nFsfUUdXr6yq7hdSbRO1oiLlY7KxiemBvZzPMdvZO7i4DjPsY2TV1+bMyRNkspTbSA8GnX754oWH0Q/cPTzNLa26Kw2EEEL/MsqG1iKSMjEhO0kyivLaRuUFmcwFM7RtBgOAlLI2v5BI8uPrWtbO0iraecmvH53cKiAi1lhXU5qXIf3jMhidxKC3XF4z0XHqaillrdSnt16HH+09cCRHFQIvH7/T9HU3d84P3zKz//gFAiJin57fe3p2t6aFg7KhdYdps5NW0V77oI0dyn6Tuirq16xUeW2j2NCDHE1qJv21rQfJaRi8CT8mKErSsnKilRalPIn4HBvZS89Ux2YIAMhp9FbubRl/+6wwiaw/YDiDzvjwIJQS99hgwIhe+mb/s7NACCHUdTYWpjLS5C17jygpyBkb6lOycplrZgx1stfRUBMVEb56M3LIQDtdLfWXcQkbdh4giYrW1NamUbJ1flwGo5NaWuijp87fuGy+toZqxN3oQ6cujB4xpL/1Dx8u8fPxBa4I8F+6znfe8iWzp5LERG9FPdoWfNRpgA1z4xUuOXMcTldLvSor4ddemTZVVNI+fs4wMdTfezSEo8nextLG0pRLq6uzvYujnaGe9uHTFyRIYoMcbAuLv167HXXnwRMLY0NXZ4duzBMhhBD6WViogRBC6D9BVFjo1OaAGev3D525jhkR5OdbP9ubWfTwz7q5/psOei3aBgCSJNHti/1EhARmrD9g6bWQ+vrqLxzOwbKPoqzUxOU76HQGANiZG+5bMaPNnj7uznX1jWuCQ8IfvAAAIi+v70jn9XO8CQQC95x/q9jETwwG4/2nzPefMjmaCAQCs1DD3EDrWvBq/w0HPed/275h2ADLfzbM+925of8IUTGxk2fPz/TzdR3kyIwICgqu37R5yFBXADhy7OSsGVO9PN0BQJJMDtq5R0REZMZUXytTo4qahl84nMNAR8VevSaOG8PcudZugP3e/Yfa7Dl5il9tXe3alcvDw64CAJFI9PGbun7jZgKBwD3n3+pV7EsGg/E+If59QjxHE4FAcHEdRiAQLoeFz5k5ffeOoN07vq1kO22m/9btu353bgghhP5eAsKio9Ycu75t1pmFbswIkV/Aadoanb6Dma3uyw/e2D730qoJACBEknSZs4VPSCRi66zDvv3WPfz6C0dUNxtAklEMXe/LoNMBQM2k/7CANi5VZsMmNjXUPvhn/cfHEQDAw0s0GzbJadoaAoHQYdo9KC/5NYPBKEpLLEpLbNVI0LEZMm7z+fAtM5+c2f7kzHZmVH/AiKHzg3h4iQBAIBDGbT5/c+eCZxf2PrvwbVENS3e/wbPb3UwNIYTQn0lMVOTsge1+C1cPGjOFGREUENi0fMFQpwEAcGx34IzFaz2nzAUAsoT4zg3LRYSFpi5cZerkXpPd+grSsYH9+/aSlx03I4B5wzvAxnL/1rWtu00Z71lXX7dy856wW1EAQCTy+o0ftXH5AubllXvOv1VsXAKDwUhISklISuFoYu6gy6XV1dmeh4cn7NQB33nLA/ccDtxzmNk0cqjz3sBVxP9f3RYhhBDqEQQGg9HTOSCEEEJt8/Lyaq4oOBe0pLsmrK1vSE7PyS8ukZIgGWiqyJDFWU3llVWJn7PkpSX11JWYt6DllVVUWrWGchubCHCn4uRj3lvr+v61VFp1fCpFUYasp6HMfUh1bV3ip6zquvreWipKcj+sZskl5z9BU3NLCiW3tKKyt5Zqm5uk9KBJK3YRJXuFhnbbQiOoNQKBEHLhsufoMb9p/tra2o9JH/Ly8qSkpQ1695aRkWU1lZeVJSa+l5eX19M3+PY7W1ZGpVZoaP7087uqirLm5hbht+5SKyri498pKirq6XewEGt1VVXi+4TqmprehoZKSj/8gnPJ+U+Qm5uTnpYmIS6uq6fP2jump4gJ8F65csXLy6tn00AIob+dl5dXSknjmA2nf9P8TfV1XzI/Vn7JFxYny6rri0j+sKh4La28OD1JVEpORlWXeUWupZXXV1HJvTR+9kA73LQU9Uwn7rhaV0Ut/PyeJK0go8ZtoayG2uri9A+NdTWyGgbispzL4HFP+4/FoNMrinNKc9P5+AWlVLRJ0m3cjFC/5JXlZgiKikur6rD2nfl9NjiQ8XqNEEIAQCAQLhzZNXqES3dNWFtXn5T6Oa+gWJos0VtXW0aazGoqq6AmJqfKy8noa2syL69lFVRqJU1TTeVnj6LYp7+5seGt8/9UVNLiEz8qysvq63DbG7equub9x9SamlpDPR0lRfnO5/yHo9Pp2XkFnzOyhAQFdDTVFeX/17fqAkqGeD1FCCHEAVfUQAgh9B8iLChg1UfHqo9O6yayuNhAKyOOCFm8S99iSpBEHa2NO9NTVFjI1qztL4a55Pwn4CPyGuuq93QW6F9LWFjY0rqvpXXf1k1kKamBjk4cEbKUVFcOJyEp6ejk3JmeomJitnZtPzbEJec/gYqKqoqKak9ngRBC6G/CJyikZGChZGDRZqswiaxhbs8RESZ16WsbITEJTQuHDrsJCIuqGvdrr5V72n8sAg8PWVGdrMjtDbaEnLKEXAeF4AghhP58wkKC1mbG1mZtfHAkJSnhaGfDEZGSlOjK4STFSU4DbDrsJiYqYmfd7tWTS85/OB4eHg1VZQ1VvIAihBD6g/D0dAIIIYQQQgghhBBCCCGEEEIIIYQQQv8VWKiBEEIIdTN5aUkpCVJPZ4EQ6ix5eQUpaemO+yGEEELodxKVkhMW79LiWAghhBDiIC8rI03u0lIcCCGEEPpNcOsThBBCqJu9ubKvp1NACP2E1/GJPZ0CQgghhGD26Rc9nQJCCCH0bxP/8HpPp4AQQgihtuGKGgghhBBCCCGEEEIIIYQQQgghhBBC/yO4ogZCCKH/rqgX8VU1taMH9+/pRAAAToXfL6XSAEBPTcnNsW8nRzW3tPDy8BAIBC59amrrRYQFuXSorq1rbGomi4t1PtvODGcwGJVVNRIk0TZHdSZzOp3Bw/O9w8NX79+lZACAkAD/PG+3X8sW/V2iIu9V0Wijvcb2dCJw+sTx0tISANDV03cb6cHeVFNdLSLa9v/nTHQ6nYenZ8qjUGb2cgAAIABJREFUO8yNi+bmZl5eXu6/pNVVVY2NjWSpb8vUP4x+EP82DgAEhYTmLQj4teMihBD606S/etBQW2Xo6NnTiQAAvLsVUkMtBQAZVR39ASNYcXpLMxAIPDy8XZm8oba6pblRmETuapY/iUGnE7i+VeCSGL2luaW5mU+g7Xf7bbZS4h4XfIoHAD4BIRuv2V1IHCGEUHeKfPSUVlXj5T60pxOBExeulpZVAICetsbIoc6sOIPBoNKqJMXb3e23ubmlqblZSFDg147b3NxCIBB4eX/L7XNXJm/zvKJjXr5NTAYAIUGBBTN8uidLhBBC/zFYqIEQQui/a2/I9az84j+kUOPw5Tu5hV/lZciD+5mSRIWX7DrZZjdTPY3jmxYAQNSL+E2HL3zKyhcTEbK37DNj9FBbMwP2nu8/Za4/eP5dSgaVVi1LlhjuYLVlwWQxEWGOCcsrq6zHBZBEhd9d3f8Labc5nEqrXrP/7JV7T+saGkWFhQbbmu1dPl1K4tudfIeZZ+QWHg29dyfmTWV1rY2x3twJIxysjAAgLjn94p0nX8uofEReLNT4j9i3e2cWhfInFGocPrg/NydbXkFx8BAXZqHG+4T4DWtWvXv3llpRISsrN8zNbcu2HWKk759YZaSnHTty+M6tm5W0yr42/eYuCHAY6MhsotPptlbmzc3N7IdQVVUNu3EbAJ48frQ0YEGbaZiamR07FdJhth3mxmJsoGtnb3/wyDH2YFTkvcD1az+lpoiRSPYOA6fPnGVrN6D12PKysr7mJiRxceaHUwDw9s3ri+fPff36hY+PDws1EELoX+PFpf3lhVl/SKHGq2v/UItzxaQUtK2dmYUaHx5cjYs4UZT+gd7SQlZUs/Kcbuk+lcDDkxkfc2//ijYnUdQx8Vh1hCNYSys/4tdfUIQ0J+QVAPzscO72e1uomdi6LQ1mD5blUd5EnPj8/G59DU3F0Lqv1ywNM/vWYzkSY6HEPY4+tvFrViq9pVlcTrnf2DnME++wNT/1bWLUlZqKrzxEPizUQAihP8fuw6coOXl/QqHGwZPnc/IKFORkhwy0YxZqVFTSVm3efen67br6BjFRkSED+wdvWSNNlmQNiY55uXrb3o+f0ptbWlSUFANm+Mz0Gcd8YoFOp1sNGd3c0sJ+CFWlXjfOHmb9eOn67X/OXHqf/Km5pVlDVXm27wTWcO66Mvnj568C1m1rc1qzPgangrdxP683CR/Oh938WlrGRyRioQZCCKFfg1ufIIQQQn8KWzODD9cP7Vo6jUAg8BF5Of60tNA/ZeZV1dQBwNWoZ6MXbqmsql04aeRQO4vIZ+9GB2xNzylgTRWfQnH1X5eQSvFysVsxbQxJVPhU+P3hszbQ6QyOg87edKiopPyXc249vLGp2XPB5pAbD71cBhxaO9vLxS78wQuvRUHM1g4zr2toHBOw7ezNh842ptNHu2TkFo0J2PoiPgUAVkwb8+H6IbeB1r+cLUJdYdvfLjHl8869wQAQ/+7tsCHOCQnxXmPHL1+1hiQufvrE8RFDB9PpdGbnuro6L8+RZ0NOOw0ePG2GPyUjw8vD7cWzp8zWgoL85KQPvLy80jIyrD+S5G+PyRIIBL5WWlpaPqWm0Gi0DvPsMDeWC2dDMikZHMGrVy6PGTmCSqUuWLTExXXYvbt3xni6p6d9bn2g2TOnFRUVskeWr1qTmPLZzX1kx68mQggh9KtUjfrNv/B26PwgAEiMunx9q39dFbXvaH/LkVMb62ruBi9/dmEPABCAwMvLx/GH0dJSkv25oaaq9bQ3t8+vKi1m/fizw7l4H3mxvCCTI9jUUH9p1fiEu+c1rRwt3f3KCiiXVo7PSXzZYWJMmfEx55eNphbnmgydYDlyanND3d3g5U9CdnSm1X7y0vkX3urZDf+ps0AIIfSf0t/aIuX53b2BKwGgsanJfdKs05fDx3kM+2fnprHurmG3okb7zWN1fvz81fCJM3PyCnzGesz0GVdXX79w7dbNe78VNRYUfUlKTePl4ZEhS7L+kCW+P0hwPuzmlPkrKyppc6dOnDl5XHVN7cK1W7cfON6ZPLsyOYFA4CMSOf60tLSkplFo1TUdnteqhf4pz++6uzh19bVGCCH0H4YraiCEEEJ/HHvLPrEX93AEF+84UVVTG7zKv7GpeXVwiLCQwIsLu8TFRABg09xJOq7TfFbueXlxN7Pz0dC79Q2NT0K2G+moA8Aa//HDZ2948ubDjUexHs79WHOeCIt8EJsg2c7uJB1qc/iF24/fJKVtXeg7f6IbAPi4OxMATobfj0+hGGqrdpj5xkMX0nMKwvevGdzPDABmjx/Wd9yimRsOJN/8uccWEfqtjh05VF9X9/h5rJGxCQCsWb9xhMugJ48f3bge7jFqNABsWrcmPe3ztZt3Bg9xAYDZ8+bbmJvOnOaX/DkDADIpFAA4fjqkj5Fx68ntHQa+jIvnCC5ZOL+KRgs+1PEvQoe5FRTkb9u8Kf7t26QPiRxjGxsb16xYJiwi8uL1W3EJCQDYtGWbrrqK78QJL968Y+954ug/0fejWMUlCCGEUI94GXqIrKQx/Ui0gIgYAPQfv2DfeJM3108MmLRE3WyA/8mnHP3vBi9vqK0avpjznXbcjVMZbx4Kkb4/GfxTw9tEKyl8cmZ74aeEYkpy69ZHJwJL8zK8t4dqWzsDgPVo/3/8+kcEzVlwKYF7YkxPQ3YxGIzpRx+SFdUBwGn6uj1jesdeOWjvs5SHh5d7a2eSRwghhFjOXb3xOj5x+9olC2f6AsCU8Z4EAuH4+dB3Hz6aG/UGgK37/mEwGC/vXtFQVQaAzSsWalg67Tt6ZvXCWby8PJTsXAA4vT/IyEC3zfn3HT2jpa76/NYlkpgoACydM1Wn75AjZy6tXDCzw9y6MrmDrXXc/Wsc/Reu3UqrqjkUtK7D8+rci4cQQghxg5cThBBCf7fFO04Mmra6uLSCPThvyxG3ORsbm5oB4Nm75IDtx4w95+i6TvddtefEtaiWVo+VM01fFzx17T72yO4z4YOmrWatoFhZVbMw6Jil1wKNwX4Tlm6PesH5Terv8+BlwvGweycDF8pJSXzKyiv8Wj7E1pxZ6wAAMmRxp74mH9KyaNW1zMirD5+MdNSZVRpMk0Y4AsDbj+msSGpm3sq9ZwLnTZKX5vzktzPaG375XowMWdx/rCsrssRv9PFNC6QlSZ3J/Pztx4baqswqDQCQJUs425hkF36JS077hSRRj1uycP5gR/vi4iL24LzZM91dhzQ2NjJ/fPY0ZtGCuSa99fQ0VKdMmnDy2NGWH5ctZZrh5zPNdxJ7ZM/O7YMd7dk3EKmkUgPmz7EyNdJUVpzgNSoq8t5vOCcAgFexsX2MTZiVEEwTfaYAwLu4N8wfz58LMexjxKzSAABZWTnnwYNzsrPevnkNAJT0dAKBoKWt08nDPbgfdfzokRMh5+Tk5LueW3VVVUZ6Oklc3NzCkmPsp9SUwsKCIS5DmVUaACAjI+voPOhD4ntaZSWrW2rKx1XLl2zaGiQvr9DJU0AIIdSz7gYvPzXPtarsC3vw1q6F55Z4tjQ1AkD2++d39i09MNFyzxjDsE3T3t48Tae3cTkGgOtbZ4Vv+eGLk+cX952a50pv+XZFrq+uvLN3yWHffrs8dK+snZz+6sHvOSeor6F9zUrVth7ErNIAADFpeXVTu7oqaktzU+v+GW8ext046bn6qChZlj3+NfvT/cNrnGeuFyPLcTlce8Pb01BbXZZPERAl9dIzbd36PvKSnGZvZpUGAIhKymhaOVUU5eSnfq+M5JJYZUkBSUaRWYcBAALCor30zFpampsbGzpsRQgh9JssXLvV0XNy8dcS9uDsZRtcJ0xvbPp2YXoaG7dg9ebedsM0LJ0mzVl67FxoS0sbH1L5LVjpO++HHbh2Hjrh6Dm5ufn71ZlKq5q/KtDUcaSyqb3XtAWRjzjrC7vLpWu3ZaTJs/28WZHl86afCt4m8/9bn+QVFvdSkGNWMwCAmKiIhUmfpqbm+oYGAEjPyiEQCNoaam1OXllV/fFzxpCB/ZmFFACgICfrYGtdQa1s+nG30DZ17+T3nzw/GnI55ECQnIx0h+eFEEIIdR0WaiCEEPq7aSrLx75Pvfno+4bNRSXlITeiJcVF+fmIT98mD5+1ISzquXNfU5+RzvlfShduO7r+4Pk2p0r4lJmQSmGPUHKLYt+nMrcLKfha1s978aU7T2zNek9yc8wp/DomYMuhi7d/69kxlVdWzdp0cNSg/vaWfQCgqKQCACx6a7P3Me+tDQCpmbkA0NTc4tzXdCZbqQQA5H8pBQDW6hf1jU2+q/b0MzWYNW7YL6TEZTglt2hwPzN+PmJWwZc7T+MSUikKMpLjXe1VFGQ6zLyMSqPSqgda/bDGgJaqIgAkpPzwV4P+FhpaWrEvnt+MuM6KFBUVnj19SpJM5ufnB4CnMU9GuAwKC73iPGiwj9/U/Pz8hfNmr1+zqvVUCfHxCfE/VEdlZKTHvnjO2tGjoCC/n5X5xfPnbO3sJvr45ubkeHm4HToQ3HqqLmpqanIeNHjmrDnswYL8PABgrjBRVlpKragY6PjDCqjMsoz4+HcAkEnJUFZWqamujrx75+zpU69jX7ZZm8JUXlY2e/rUUWO87B0Gdj03ANDV04+MfhwZ/fjUWc5/DIuKigDA3NKKPWhhaQUAqSkfmT/W19dPmeTdz7b/rDnzACGE0F+C3Es9N+lV6tNbrEhVaXH83fNCJElePv6shGdnF3kkPwrXtHQ0GzaJ9rXg9p7FD49tanOqwrT3hZ/fs0fK8im5Sa8YdDoA0EoK/5lmnxh1WdW4n8lQb2px7sVV41+F/Zal0Xh4iVP237Edv4AVqa+hfcn8qGkxkJfIx9G5llZ+Y/tcw4Ee6mYD2OPNjQ3XNk1TMbKxHsXtsd32hnMho6ozJfj2lODbo9ZyLt5eW1lWV0XVMLdnD0opaQJA4aeEziSmbzeMVlLIKoIpzcvITniubtKfX1C4w1aEEEK/iZaayos38RF3o1mRoi9fT18OJ0uI8/PxAUDMyzcu46aG3rg3yMHWb/yo/MLieSs3rQna23qq+KSU+KSP7JGMrJwXb+LpjP+//y36YjVk9Pmwm3Z9zX28PHLyCz185x44ce53nFd6Vo7LQDt+Pr6s3Pzb9x/Hf0hRkJP1HjVCRUmR2cF9qHNB0RdWpUgaJTvm5Wv7flYiwkIAQMnOVe6lUF1Tezc65vSl8Ni379lrU4i8vA+vhSydM5UVqayqTkpNc7bvx0fseD34bpy8rII6fdHaMW4uDrbWnTkvhBBCqOtw6xOEEEJ/Ny+XAav2hVx/GDvDaygzEv7gJZ3OmDTCCQCuRj0jEnmTbxxhLuGwyNfT0M3/7tO4zfMn/+yB1h04l1P49fGZIEtDHQBYPXOcx/zAtQfOThjuwLH3RxmVduxqZHvzjHSy0ddQ/qlDB2w/VllVs2net7UENJTkACAm7gNzbxGmT1l5AJBCybM20uMj8u5eNo19hpLyymOh9/iIvEPtLJiR1ftCikrKbxxcRyAQfioZ7sNrauuLSytkyRJjArbee/aWGdRR6/XP+nlWfXQ6zDw9pxAAOJbo0FHtBQAlFZWA/kJeY8evXr404lrYDP/ZzEh42FU6nc5c4wEArl65RCQSk1LTmUs4BCxZ1kdX696d25u3bf/ZY61fvTI3J/vxs5cWVtYAsHrdBs8RrutWrZjgPYljh46y0tLjR9v9xsjdw1PfoDeXA/Hx8e3at589UlLy9diRw3x8fC6uwwAgPe0zAMgp/LDahLaOLgCUfP0KABRKBq2K1ltHo7b221oypmbmx0+H6Orptz7cogVzKyupG7ds43bync6NOw0NDQB4+vjR/IWLWMFPqSkAkJqSYm3TDwDWrFhWVFQYcefer/3rgRBCqEf0cR59/8i6lJibVh7f3iV+fHKdQaebDPUGgOSH13h4iQsuxguKigNA/wkLg8ebfH4ZOch/488eKPrYRmpx7rQjD5T0zQFg4JQV55d7PTi60XjwOI79O2ory+IiTrY3j769m6yaHvdj8QsKqxh++yrlVdgRanF++qv7jJYWO++A1p3v7ltaX13pPGM9R/z+kXVVpcUTd17jfl1rb/ivKc3LAAAxqR8WypJW0QaAGmppZxKz8pyR+e7pxZXjlHtbEfkFs94/E5OSd5y+pjOtCCGEfpOxI4ctD9x57c59f9/xzEjYrSg6ne4z1oP545WIu0ReYurLSAmSGAAsmTNV18blzoMn21Yv/tljrd66Nyev4Nmti1amRgCwbsmcERP9V23Z4z3ajSwhzt6ztLziaMjl9ubxGDbIQEeLy4Gqa2qLv5bIykh5+M65Gx3DDOpqqR/fs9na7NtjNnOmTHj8/NVInzk2FiYCAgIxL98oyMlsWv6tkpKSnVdVVa3Td1BtXT0zYmZkcDo4SE9bAwBEhIX6WX5beurAiXM5+YX3Hj5tobcsmzu9M69DN06+YPXmShpty8rv7yK4nxdCCCHUdViogRBC6O8mLUkabGsW9fxdSXmlDFkcAMLuP1eUJTtaGwPAPG83/7GurI02mpqaxMVEWLtsdF4FrTo08pm5gRazSgMA+PmIUzwGxcQl3Xj0ynekM3vn0gralqPt3gNrqyr+VKFGamZe+IOXS6eMUpaXZkY0lRXNDDSfxCWdiYgePdiWTmdcvhtzPfolALS5q8u9Z29nBx4qraDtWOzXW0uVGTkaevfizuW/tukJl+GU/CIAOHz5toaS/O5l06yN9F4lflq7/+zYxdveXN7XYeaUvCIAkBT/ofBFWUEGAKhVNb+QKupx0jIyg4a43I+8V1LyVUZGFgDCQq8oKvZydPr2WzNvQYD/7LmsjTaaGhvFJSTYd9nopIry8tDLl8wtLJlVGgDAz8/vO3V6zJPHNyOu+/hNZe9cWlqyZdOG9qbS0tbhXqjBIfLundkzppWWlmzfvbe3YR8AoFAoAECW/KE6RFlFBQAqqVQAyKRQqquq1m/aPMJ9ZGlJyYXzZ8+ePjV2lMfLN++ERUTYR6WmfAwPu7pk+UplZZXOp8QlN+40tbTNzC2ePH4UcurkqDFedDr98qUL16+FAQBzzY/Iu3eOHjl0MfQabnqCEEJ/FxEJaW1r5/RXD2oqSkQkZQAg+WE4SVpB09wBAGy85lh5zmBWaQBAS1OjoKh4fQ3tZ49SR6tIig7rpWfKrNIAAF4+fvPhk7Pin6Y+u2027IctzGqoZY9PB7U3lZSyVoeFGuwentjcVF8HALJqekQBQY7Wr9mfPj6OsJu4SFxOiT2eFhv15vrxsYFnxaS4bXrS3vBfVp6fCQBCYj+8lxaXUwaA+urKziQmKCouIadcnJFU8DmBl5fIoNN5eImNtdWdaUUIIfSbyEhJDhloF/noaUlpuYw0GQBCb9xTlJd1srNhdlgww2e2nzezSgMAGhubJMTFKmk//e9zObXycsQdC2NDZpUGAPDz8U2dMPrJi9cR96L9xo9i71xaVrFp96H2ptLWUONeqEHJzgWAgyfOa6qp7Atc1dfCJPZtwqote0b5zUuIjmCeprg4SUVJMfHjp7fvk/n4iHQ6nUgkVtd8+xiHkpVbVVOzadkC96FOJWUV56/eOH352ii/eW+iwjiWpli3PZhZb2GgoyUkKNCZl6K7Jk9Jywi7FbV83nTlXt9vdbmfF0IIIdR1WKiBEELor+c93OHu07ibT15P9RycU/g1LjltyZRRPDwEANBR61VeWbX//M3XHz7nFn3NyC2qqqlVkCF3OCeHtOwCBoNRXVc/eeVuVrCqphYAsvKLOTrrqPUqedFuoQY/389dfPeGXOfnI85jW4KCh4dwZN3cMQFb524+vGzXSTqdTmcwpowcdDL8vsGPJSBZ+cXL95y++zROQ1nh1OaAgVZGAFBcWuG/8aDvSGe3gdY/lQkT9+EVldUA0NDYdGHHMh21XgBgoqfxtYy641RY2P3ns8YN4565AD8faxIW5o00x7Il6C/iPcnn3p3btyIi/KbPyM3Jfvvm9eJlK3h4vm3Ap6OrV15Wtn/fnjevYnNzcjIy0qtoNAUFxZ89SnraZwaDUV1d7eM9jhWsotEAIDOTc98cHV29r9R2Pwtj7snSGVmZlOVLFt27c1tDU+vk2fOsvU4EBAQAoLyinL0zc/EMCUlJAPjnxCkBAQGD3oYAoKmlbW3Tj0QSD96z60bE9fHeE9lH7du9k5+ff96CNp4M/rXcuOPh4Tl87ISXh/vcWTOWLV5Ip9PpdLrv1Gmnjh/TNzAoLi7yn+7n4zd1hPvIn80HIYRQjzN2Gf/5ZWTqszsWbr7U4tz81Hd23gEEHh4AkFbRrqWVv7xyKP/jG2pxXlkBpaGmSkxavsM5OZTmZTAYjMa6mqsb/VjBhpoqACgvyOLoLK2ivTqqsL2pePk49y7hbnVkAXMHlofHA0/MGhQQmiRKlmW1vri0n5fIbzNmNvuQqrIvEUFzzYZN0rcbzn3yNod3BZFfAADqqirYg031NQAgJCbRmcROz3P9kpkyLGCXoaMnkV8w4/WDm7sWXlgxds6ZWAl5Fe6t3XUWCCGEWps0xv3OgycRkdHTJ3rl5BW8SfiwbO501v2vrpZ6WQV139Ezr94l5uQXZGTl0qqqFeRkuc/ZWholm8FgVNfWes/6vhQHraoGADKz8zg662qpUzPetTcVf0cX3ApqJQA0NDZePrZXV0sdAEz7GHwpKQvafyz05r05ft4A4OgxKflT+v6ta73chwoKCEQ9fjZr6Xr3ybPeP7qhqtzrxN4tAgJ8vXW1AUBLXdXGwoREEt1z5HTEvWjvUSN+OFb6W+YOL2uDgvsPH0+Ji5aTkeaeXndNvvvwKX4+vgUzfNiHcD8v7okhhBBCnYGFGgghhP56Lv0tJEiiEdEvp3oOvvbgBQBMHDGQ2bTvbMTmfy7x8/PZmfUeaGW01G/0/vM3cgq/dnLmiv9/rKG8sgoABPj4+Ii8rFayuNjYoQNaL49BIBCEBDr7RS93ecWloZHP3J36cpQp9NZSfXNl37UHLz5l5stLSzpaGz97lwwA+prfP3i9fDdmQdBRAhA2z588a9wwZg0EAJwIiyyj0iqra/03HmRGCkvKGQyG/8aDWioKS6b88OBFa9yHuzv2BQCrPjrMKg2moQMsdpwK+5yV32HmclISAJBV8IX9iMy6DWkJ0k+/fOjP4OI6TEJSMiI8zG/6jGtXQwFg4uTvn30E79m1eeN6fgGB/nYDHBydlq5YtX/fnpwszu9y2lRR/r0Sory8HAAEBAT42D5mIktJjR0/ofXyGAQCQUioq3vKXr54YeG82QQCYfO27f5z5jGLM5jk5OUBIDsrs3W20jIyAGBqZs4x25ChrsF7dqV8TGYP5uXlhl6+5O7hybF1S1dy61Bvwz6v4xPDw65+Sk2RU1BwdHJ+9jQGAPQNeh86EFxWWkqrrPSf/u3rt8LCAgaD4T/dT1tbZ/GyFT+VJEIIof8xHZvBQmISKTE3LNx8kx9dBwATlwnMpheXDzw+tZXIJ6BqYqthYW83aVHslUMVxTmdnLmORv3//ygHAF4+AV7i98uxsDjZaNAYWXXO5TEIBAJfq6UvfgqDwQAGg/D/335JKWlKKWkSCDwRQXPSXz0wdfVmxiu/5CdFhxnYj+DYe+XtjVO1lWUNNbSIoLnMCK20CBiMiKC5UsqarP1T2hveFcwikorCbPZgHa0CAITFpTpMrCQn7UtmippJf0v3b1dk/QEjcpNfx4YeTn16W8vamUurjVe3lZsghBBqzdXZXlKcFH7nwfSJXldvRQLAZC93VuueI6c37joowM9nZ2Pp2N9mxfyZ+46eycot6MzM5dTvK12VV1ABQICfn4/tgislKTHeY7iBLufyGAQCoZOrU7RJUV4OAKzNjJlVGkzDBjkE7T/2KZ0CAJ/SM5M/pQ+wsZw5eSyzdeRQ55dxCcHHQiLuRS+Y4WNmZMAx51DHAXuOnP74OR0AGAwGg8Fg1bJoqatqqavy8PBMC1h97+Ez33Ee3NPrlsnzCoouR9zxcB3EvmtMh+fVudcPIYQQ4gYLNRBCCP31BPj5Rg+yPRMRXV5ZFRb13NpIT0tFEQBKK2jrDp6TlhD/EHFI9P8XPNx5Kqy9eQgAdDqDPZKW8+1uWb2XHABoqiicDFzIam2h06tr6lrf7n4powadCG3vKJPdnEz1NTt5aqfD7ze3tPi4/7C1SmNTc07hFykJEnt895lweWlJVj3HvWdvp6/fb9VH98zWRaw9U5ikJcWNdNQpuUXfJ2xsojMYHz5n8XDdmbszw5XkZQCgqbmFfUh9QyMAkESFO8xcS0WRQCBkF/ywSElSejYAWPz/pjPoryMgIDBqjFfIqZPlZWVhoVesbfppaX/72ywtKVm3eqW0tExiymdRsW+rv+4M2trmPAQCgf7j5j7paWms/1ZTVwcATS2tE2fOsYItLS3VVVVCwsIcU335Urx96+b2Ep7kM6V1IQWHyLt3Zvj5WPW1OX3uQutNSbS0tQkEQnbmD4UaSR8SAcDC0io/P+9dXJyZhQX7QGZnGdkfHqU6feJ4c3Pz5Ck/7NvSIe65cdfY2JiTnSUlJT15yvcnoffs3C4vryBJJkvLyBgZm1AyMr73b2ig0+lJiYmsT74QQgj9sYh8Ar0HesTfOVdLK09+FK5saCWlrAkANdTS6GMbRcSl5l14JyD87c3ks3N72puHAAQ644crclleOvM/JBXUAEBKScNz9VFWK53e0lhbzSfAWSJZXf415uzO9o5i6uqtqGPC/YyeX9z38Higd9AV7b6DWEFhCSkAqPyaz4q8ux1Cb2k2dZ3EMVxYQkpeq09Z/vfrdUtTA4POKM5IIvAQOhzeFVJKWgQCgaNQo5jyEQCUDMzzPsZxT+wL5SMAqJnYsg/XtBgYG3q4rorKvbUbzwI5h/mpAAAgAElEQVQhhFBrAvz8Y9xcTl26VlZBDb1xz8bCRFtDjdlUUlaxetteaSnJlGd3xUS/bXkZtP9om/MQgMD5CRXl+/MM6ipKAKClrnrmwPdNxFpa6FU1NcJCnEWQX0pKt+77p72EfcZ6tq51YMfcCqSpuYk9WF/fAAAkMTEASEpNA4ABNpbsHZwH2AQfC6mopOUXFse9T7IwNmTfUiQzJx8AZKXIALDz0Im1QcE3zh52cRzA6iBNlgSA/MIi4Kq7Jj9x4Wpzc8uUcT88ucT9vLgnhhBCCHUSFmoghBD6N5gwfOCJa1F7Qq5/SMs6uObbU2K5RSV0OsPNsS+rSiP/S+mHz1myUhJtTqKqKPvodWJTcwtz2YzUzLzMvG+3bRrKCtKSpOjY96xWANh1+lrgkUsPTmyxMdFnn6eyqiYkIrq9VO3Mene+UOPhq/eSJFEHSyP2YF19g+moeWOG2J3e8u05v4KvZTcexU52+761wYZDF0iiwhd2LJWX5nzyz3+sq/9YV/ZI/4lL6hoaX17cDZ3Q4XB7yz4xcUmU3CJNlW/3ybeevAEAa2O9DjNXkCHbmhq8iE/Jyi9WV5IHgKbmliuRzxRlyab6Gp1JD/2ZvCdOPnns6J5dOz4kvj945BgrnpubQ6fT3Tw8WFUa+fl5HxLfy8q2sR27iqrq44fRTU1NzDUzUlM+ZlK+VwxoaGpJy8hEP7jP6gAAu3cEBW5Yd/9RjI1tf/apKqnUkFMn28u2v519h4UaG9auJomLn78cKi+v0LpVQUHR1m7Ai+fPsjIp6hqaANDU1BR6+ZKiYi9TM/PkpA8Tx42ZMm36/kPfPyy7dvUKANj+mOej6AeSZLLDQEfuyfxUbtzV1daa9TEYM3bcqbMXmJGCgvwb18Mn+U4BAP/Zc/1nz2Xvb9fXsq6u7sWbdtfRRQgh9EcxcRn/9ubpFxeDizOS3JYGM4OVX/IYdLr+gBGsKo3KrwXFGUkiZJk2J5GQV6G8e9LS3MRcNuNr9ifWtibkXuoiEtIZcY9YrQDw/MLeRye3+h24q9KnL/s89dWV8XfOQTvUjG07LNSQ0zAAAMrbJ+yFGvG3zgKAvJYhK0KJeyREktQwG8Ax3NpzhrXnDPbI0RkDmxvq/E/EsAfbG94VYtLyqkb9cj68LC/MIiuqA0BLc1NSdBhJWkFBx0RR15R7YjJqugCQ8uSGg+9yVp+PjyMAQE7DQFpVh0trN54FQgihNk0c437sXOiuwycTP346snMjK55bUEin0z2GOrOqNPILixM/fpKVbmN3D1VlxYdPY5uam/mIRABIScugZOWyWjXVVWSkJB/EvGB1AIAdB49v2HngUfhZWysz9qmolVWnLl5rL1u7vpbcCzWEBAUcbK2fvHidkZWjpa7KDN6IfAgANhYmAKCvowkA4bfvr130fdGmsFtRAGCop1NOrRw3I2Ca95hD29ezWq/eugcAttbmzD4AEP00lr2W4uTFMAAwMtDlkhgAdNfk0TEvyRLiA/v/sMMv9/PinhhCCCHUSViogRBC6N/Aqo+OlorigfM3hQUFRg3qxwzqqCmKCAteu/98cD9TXTWl2MTUwCOXxESFq2vr03MKtFU5t5O0MNSJfP5u5ob9UzwGUfKK94RcJ4mKlFFpAMDPR9w4d+KcwMNT1+5b5ONBEhG6HRO342SYo7VxX2POZZx11HqVx7a7okbnUWnVCZ8ornaWPDw/LHQhLiZib9kn4uHLgdZGbg7WlPzieZuP9JKV3rLQhzUwhZJrrKu+//wNjjntzA2H2llwP+7Bi7dW7wtZMd1r5XSvX0h707xJDj7LJ63YtWGut5KcdExc0qlrUTYm+sMGWAIA98wBYKnfqFELtkxasWuZ32gJkuiekPDsguKwfasJnVjtA/2xLK37amnrHAzeKyws7Dl6DCuuo6MrIip67Wro4CFDdXR1X718GbhhnRiJVF1TnZ72WVvnhw9lLK2so+7d9Z82xXfq9MyMjD27dpDExctKS5mt/Pz8GwO3zvGfPs13UsCSZSQS6fatmzu2bXF0cu7b74cnSgFAR1evrKrul0+HWlGR8jHZ2MT0wF7Op43t7B1cXIcBwJLlK0e7D588YdzSFaskJCX37tyenZV5NeIWgUAw7GNk1dfmzMkTZLKU20gPBp1++eKFh9EP3D08zS2t2I+SEP9u6LDhrRerOLh/35oVy1asXrti9dpfyI0LcQkJe4eBEeHXBjo6j3AfSaFkzJ/tr9hLacu2HZ1/fRBCCP2xlAwspJQ1Y0MP8wkK9XYYyQxKKWvzC4kkP76uZe0sraKdl/z60cmtAiJijXU1pXkZ0sqcK6j3MjBPe3U/ImiO+fDJ5QVZzy8GC4iQaivLAICXj99p+rqbO+eHb5nZf/wCARGxT8/vPT27W9PCQdnQmmMeaRXttQ+KoQu0rQfJaRi8CT8mKErSsnKilRalPIn4HBvZS89Ux2YIs09dFbUwLVHXxoXwS4s/cRkee/XIgyPr7H2W2vss+4WZ7SYuurBi7NUNfgMmLhIUk3hxMbiiKHvCtsudedMrq6anaTmQEvf4/NLRRoO9JORVUp/dTnoYJqump9fflYeXyKX1F1JFCCH0U6zNjLU11IKPhQgLCY4ePoQV19FQExURvnozcshAO10t9ZdxCRt2HiCJitbU1qZRsnU01dgnsTI1uvfw6bSFq6d6j87Izt116KQ4Say0vILZys/HF7giwH/pOt95y5fMnkoSE70V9Whb8FGnATb9LE058tHVUq/KSujKGW1ZFdB/+PgJ/osDVyxQUpR/8uLNifNXba3Mhg8eCAAGOprO9v2iY14O9545YdQIVSXFG5EPr0TcNdDRcnNx5CMS+5obn7wYRpaUGOnqTKfTL167HR3z0sN1kKVJHwBwcbQz1NM+fPqCBElskINtYfHXa7ej7jx4YmFs6OrsAAD7j59dsXnX6oWzVgfM4kisj75OFycHgIpKWnxSyrBBDhy33tzPqyuvJ0IIIcSChRoIIYT+JcYPsw88csnNsa+YyLdtDkSFhf5ZN9d/00GvRdsAQJIkun2xn4iQwIz1Byy9FlJfX+WYYf5EtzcfPodGPguNfKYoSx7v6gAAu8+EM1t93J3r6hvXBIeEP3gBAEReXt+RzuvneP++AoKnb5PpdIaVURsPEBxZN3fK6j2zNx2avekQAJjoaZzaEsBaOCQ28RODwXj/KfP9p0yOgQQCocNCDTqd0UKnMxgM7t3aY26gdS14tf+Gg57zv20tMWyA5T8b5nUmcwBw6mtyInDBnMDDE5btAABxMZGggCmD+5m1Og76y4z3nhi4YZ3bSA8xEokVFBUTO3Ls5KwZU7083QFAkkwO2rlHRERkxlRfK1OjipoG9hnmLVz05lVs6OVLzKUpxnlPBIA9O7ezOkye4ldbV7t25fLwsKsAQCQSffymrt+4udt/SV/FvmQwGO8T4t8nxHM0EQgEZjGEk/Og46fPzvGf7j12NACIS0hs27l78BAXZp/LYeFzZk7fvSNo945vC9VOm+m/dfsu9qmexjyh0+nWfW1aJ0Cn01taWtr8Je1MbtwdPn5yyiTv2TOnzZ45DQBMTM1Onz3PWvIEIYTQ38548NhHJ7ca2o0QEPn2b7uAsKj78oM3ts+9tGoCAAiRJF3mbOETEonYOuuwb791D79yzNDPa27+x7ik6DDmChBGg8cCwPOL+5itZsMmNjXUPvhnPXMJBx5eotmwSU7T1vyO98wEHp5xm8+Hb5n55Mz2J2e+vSXQHzBi6PwgHt5vn3dlJzxj0OlKvS3bn4YbLsMZdDqd3vbluDM0LQd6rv7n5o75V9b5AICgqPiQ2Zu1rZ07HAgABB6e0WtP3N2/PPnhtYy4R8ygqnE/9+UHePn4AYB7K0IIod/Ne9SIDTsPjBzqTBITZQXFREWO7Q6csXit55S5AECWEN+5YbmIsNDUhatMndxrshPZZ1g40/fVu8TLEXcuR9xRlJf1HuUGADsPnWB1mDLes66+buXmPcw1HohEXr/xozYuX/A7LrgWxoY3zh6etmiN26RvpRLDBw88vufbZz48PDznDu0MWLP1yo27D2JeMIN21hbH9gTy8/EBQNjJAzOXrttx8PiOg8eZrTMnj92+bhlreNipA77zlgfuORy45zAzOHKo897AVUQiL3y7/237QyoCgdDFyQEg5uUbOp3e19yYY/IOzwshhBDqOsIv31IihBBCv5uXl1dzRcG5oCVdmaS8sirxc5a8tKSeuhLzfrW8sopKq9ZQbntTgNIKWmFJWR9ttTZvbqtr6xI/ZVXX1ffWUlGSa2N1yl9m4bVAWV76+n7O5+Pbw2AwPmbkZhUUm+hpKst3ZyYAsONUmHovuTFD7H55hqbmlhRKbmlFZW8tVY7tVzqTeXNLS3wKhcGgWxjq8P74TMOM9fvvPXub9+hshzlMWrGLKNkrNLQbVjdB7SEQCCEXLrMvkvELysvKEhPfy8vL6+kbfPslLSujUis0NDkf4QWA0pKSwsKCPkbG7X38VF1Vlfg+obqmprehoZKSclcSY2dp0kdZWTn81t2fGtXc3Jzw7i2dTrewsubl5eVozc3NSU9LkxAX19XT/9lKiJ1BW9XUNcaMHfdTozqJwWB8TE7KzsoyNjVVVlb52eEzp/reu3snt6ikw55iArxXrlzx8vqVxXsQQgixeHl5pZQ0jtlwuiuT1NLKi9OTRKXkZFR1mRfZWlp5fRWV3Kvt7edqqKVVpUVymoZtXpEbaquL0z801tXIahiIy3KuY9cVh3xtxGWVJu74XnLNoNMrinNKc9P5+AWlVLRJ0j+989cve3put6SiWh+nUR13bQe9pbnw83sGnd7LwJyHh/OtQodoJYVfsz81N9RJq+hIKWtx/F1wb72+bXZabNTym5QOj7LBgYzXa4QQAgACgXDhyK7RI1y6MklZBTUxOVVeTkZfW5P5L3NZBZVaSdNUa+POq6SsorD4i5GBbnv3v1XVNe8/ptbU1Brq6SgpynclMXYmju7Kigq3zv/DHmxqbv74Kb20rMJQX1teto390QqKvqSkZdTV1etqaehocn6qlptfmEbJFhcX09PSYG3+wkKn07PzCj5nZAkJCuhoqivKy7K3Bu0/pq6iNHZku+tCdWXyDnE/r6kLV92NjilKftHhPAJKhng9RQghxAFX1EAIIfQvRxYXG2hlxBEhi7f7nai0JElaktReq6iwkK3ZH7G1M4FAMNRWNdRW7faZM/OKzt14eO9YYFcm4SPyGuuqt9nUmcyJvLxWfXDLz/8KspTUQEcnjghZSqrNztIyMtIybXwkxCIqJmZr1527yHcFkUi0tO7bXquKiqqKyq/8CmdSMs6eOX0v+lEXUuOGuT+LYR+jjrsihBD6txAmkTXM7TkiwiRye/1FJKRFJNqtFRYQFlU17ted+bWPwMNDVlQnK7b9zvP3KS/ITLh3wXffra5MwsNLVDLoYLk7LkgyiiQZxV9rRQgh1COkJCUc7Ww4IlKSEm12lpGSlJGSbLOJSUxUxM76168jP4WPSDQx1OfSoZeCXC8FufZaVZQUVZTavSrx8PBoqCprqLbxrAUlO/fM5fDosDNcDv3Lk3cG9/NCCCGEuuJXduhECCGE0O/w4XPWpBW7Dl7s0qe9XZeZ/+Xq3lXdu15Itzh389GkFbuex3/s6UTQf9SHxMTJE8Ye3L+vpxOBzMzMq9dvdON6Id3lfMiZyRPGPn/6tKcTQQgh9G9WnJF0dcOU2KtHejaN8sLsCVsvde96If8bCfcuXN0wJed9x8/+IoQQ+s9K/Phpgv/i/cc7Xs30d8vMybt+5lA3rhfSXUKuXJ/gv/jpq7c9nQhCCKG/GK6ogRBCCP0RnPoa5xeX0emMHt+VzNnGpGcTaA8DGHQ6w1RfS0xEqKdzQf85Ts6D8vPz6PS2d8b9H3MeNLinU2gbg8Gg0+mm5uY/u5MLQggh1EmaFo60r/kMOh16+oqsZenYswn8OgYw6HRFXRN+YbxeI4QQaoPzgH75hcV/yP3vIHvbnk6hbQwG0Ol0c6PerTdbQQghhDoJCzUQQgihP8L2RX49ncKfbrKb02Q3p477IfQbBO3a09Mp/AUm+U6Z5Dulp7NACCH0b+Yyd0tPp/DXM3X1NnX17uksEEII/bl2bVje0yn8BXzHefiO8+jpLBBCCP3dcOsThBBCCCGEEEIIIYQQQgghhBBCCKH/ESzUQAghhP53ol7Eh91/3tNZdBad3vNLXCL0PxYVeS8s9EpPZ4EQQgih79JfPUh+FN7TWSCEEEL/NpGPnobeuNfTWXQWnU7v6RQQQgihboaFGgghhND/zt6Q66v3hfR0Fh3IyC1cuuukwYiZyk6TRy/c8uTNh57OCKH/nX27d65Zsayns+gsYwPdubNm9HQWCCGE0O/14tL++0fW9XQWP2G/t8XNnQt6OguEEEKoA7sPn1qxeVdPZ9GB9MzsReu2afcdrGBoO9Jn9uPnr3o6I4QQQqjbYKEGQgghhL6ra2gcE7Dt7M2Hzjam00e7ZOQWjQnY+iI+pafzQghxunA2JJOS0dNZIIQQQugH7yMvlhdk9nQWCCGE0L9BXX2D55S5IVeuD7a3nTF5XEZWrofvnGev3/Z0XgghhFD3IPZ0AgghhNC/FoPBAAACgdDTifyEjYcupOcUhO9fM7ifGQDMHj+s77hFMzccSL55pKdTQ6ib/Y2/oQBQUJC/bfOm+Ldvkz4k9nQuCCGEUPf7Sy/QtJLCJ2e2F35KKKYk93QuCCGEEKe/9PK6bntwGiX75rkjQwbaAcC8qRPNB3tOC1j9+WVUT6eGEEIIdQNcUQMhhBDqfklp2cNnb1B0mCRjO87BZ/n9l/Ftdnv2Ljlg+zFjzzm6rtN9V+05cS2qhW3HzfrGpi1HL/dxn0W28TLymDN/6z/VtXUdNnXR+duPDbVVmVUaACBLlnC2Mcku/BKXnNYt8yP0J0j6kDjCZVAvWbKshKhDf5v7UZHt9Xz2NGbRgrkmvfX0NFSnTJpw8tjRlpYWZlN9ff2WTRv66GlLiQkZG+jOn+NfXVXFGsi9tSuqq6oy0tNJ4uLmFpbdMiFCCCH0hyimJJ9d5BE0XG3LEMXjs5zTX0e32S37/fM7+5YemGi5Z4xh2KZpb2+eptNbWK3NjQ2PTwcFTzALHCS/39vi1u6AhtrqDpu6rqG2uiyfIiBK6qVn2l1zIoQQQl33IeWzy7hpsgY2EloW/YePj3r8rL2eT2PjFqze3NtumIal06Q5S4+dC21p+fYhVX1Dw6bdh/T6uYipmxr0d52zfGNVdQ1rIPfWrjgXGtFHX4dZpQEAsjJSg+1ts3ML3iTgLr0IIYT+DXBFDYQQQqibPXuX7DEvkCwh5uPuRKuujXgU6xWwLep4oLWRHnu3p2+TR8zeQBIV9nIZICUh9uh14sJtR7MLvmyeP5nZISDo6MXbT8YPczDWVc/MLz5z/cHHjJyHp7Zxb+qKMiqNSqueNMKRPailqggACSkUS0OdLs6P0J/g2dMYzxGuZLKUj69fJa3yxvXwsZ7ukdGPrW36cfR8GvPEbehgkri419jxUtLSjx5GL5w3Oysrc/O27QAQMH/OpfPnxnlPNDYxzaJQzpw68TE5+WHMc+ZY7q1doaunHxn9GAAyKRnGBrpdnxAhhBD6E2S/f35+2RhhEtnMdWJ9DS015talVROmBN9WNrRi75aV8OzcYk8BUVIfp9HC4uTMt09u71lcUZg9yH8js8OdvUsSoy4bDR6roN2nvDA7/nbI18yUqYeiuDd1nYyqzpTg2wBQXpC539uiW+ZECCGEuuhpbNyIif5kSXHfcZ60qqrrdx54TpkbHRZiY2HC0TPm5Zuh46eJi4mN9RgmLSnx8FnsvJWbsnLztq1eDADzV20+H3bDe5SbiaE+JTv31MWw5E9pMTcuMMdyb/1lpeUVFZW0yWM92IPaGmoAEJ/40crUqIvzI4QQQj0OCzUQQgih7kSnM5btPsXPzxd5NFBDWQEAFk4eaT5m/rGrkRyFGlejnhGJvMk3joiLiQDAIl9PQzf/u0/jmIUaDY1Nl+/GuPQ3/2f9XGZ/DSX5pbtOZuQWKsvLtNekpaLIfogyKu3Y1XaXChjpZKOvocweSc8pBAB5aUn2oI5qLwAoqaj8xVcEoT8JnU5fvjiAX0DgXvQjDU0tAFi4aImFseHxo0daF2pcvXKJSCQmpaaLS0gAQMCSZX10te7dub152/aGhoYrFy8MGer6z/FTzM7qmprLFi3MSE/T0tbh3sp+iLLS0uNH291XyN3DU9+gdzeePkIIIfRnYtDpkQdWEfkEfINvkXtpAIDtuPmHfPrG3TjJUaiR/PAaDy9xwcV4QVFxAOg/YWHweJPPLyOZhRrNTQ0fHoRq2wweueIgsz9ZUe3egZVleRRxeaX2mqSUNTnyqa0si4s42V62+vZusmp67bUihBBCfwg6nb54fZAAP1902BlNNRUAWOTvZzzQ7WjI5daFGlci7hJ5iakvIyVIYgCwZM5UXRuXOw+ebFu9uKGx8eK1W0Od7I/v2czsrKmmsmjdtvTMbG0NNe6t7IcoLa84GnK5vWw9hg0y0NFij6RRsgFAQVaGPaijqQYAX8vKf/71QAghhP44WKiBEEIIdafEz5lJadnewwcyqzQAQEet166lU+l0BkfPed5u/mNdmVUaANDU1CQuJkKrrmX+yNwD5em7j4mfs4x11QFgppfrZHcnQQH+hsam9po4DlFaQdtytN17YG1VRY5CDUpeEQBIiouyB5UVZACAWtU9q1Yi1LMS3yckfUj0njSZWaUBADq6ejv3BtPZdh1imbcgwH/2XGaVBgA0NTaKS0jQKisBgLkByrOnMYnvE4xNTAFg5qw5Pr5+AoKCHbayKy0t2bJpQ3vZamnrYKEGQgih/4KijA/FlGQTl/HMKg0AkFbRHjo/iNHqAm3jNcfKcwazSgMAWpoaBUXF62tozB8ZLXQAyH7/vCj9g4K2EQBYeUw3HTaJyC/Q0tjYXlPrfGqoZY9PB7WXrZSyFhZqIIQQ+vO9T/70IeXzpDHuzCoNANDVUt+7aSWdwfkJFQAsmOEz28+bWaUBAI2NTRLiYpW0agBgboDyNDbufXKqiaE+AMzyHe87zlNQQKDDVnalZRWbdh9qL1ttDTWOQg1Kdi4ASEqIswdVlBQBgFpJ+4kXAiGEEPpTYaEGQgihPxcvL29DW9+e/sky84oBoLeWKntwppdr6546ar3KK6v2n7/5+sPn3KKvGblFVTW1CjJkZquwoMDKGWM3Hb5o671YV11pgIXhEFtzZxsTXh4eLk2tD1Hyot1CDX4+zrcBAvx8AFBR+cNe3bV19QAgSRKFv01LC12Al7ens/iXIxKJzKKEv0UmhQIABoZ92IMzZ81ps7OOrl55Wdn+fXvevIrNzcnJyEivotEUFBQBQFhYeOWadZvWr+1vbaGrpz/AwWGIi6vToMG8vLwdtnIc4iu1uo1jAwAAPz9n9dW/SXNzMwAQiXg/ghBCXcXLy9u6oOHvUp6fBQCy6gbsQSuP6a17Sqto19LKX145lP/xDbU4r6yA0lBTJSYtz2zlExSy91n26OSWo9MdZFR11EzttPsO0rJ05OHh5Wm/qc2jrI4qbC9bXj6+Lp3tX4XegtdrhBD6hkgkMosS/hbMQgdDPW324KwpE9rsrKulXlZB3Xf0zKt3iTn5BRlZubSqagU5WQAQFhJcs2j2+h37rV3G6GlrOPSzcnEcMMjelpeXp8NWjkNQM961ly1/q8urAD8/AFRQf1jhtba2DlpVb/z5mptbAK+nCCGEWuG8WCKEEEJ/DnFxcVpNXU9n8XNKqZUAoChL7rDnvrMROkOnBZ0IbW5uHmhldHTDvL7GPzyZt8xvdFLE4RXTxggLCpy8FjV64RaLMQu+lFG5N7EjEAhCAvzt/Wld2CEnJQEAWQVf2IPMug1pCdJPvxY9rbKmTuL/10JAv4m4uDhzhYm/RWlpCQAoKvb6P/buPC6m/f8D+JlmKu2LFEW7SAhRQnYSSkgRxrWVtbFLLiJLZS1rUuhaytYmcUUoW6QsRXvKUqF9n2bm98fcX9+u7Vqqz0y9no/+qHM+zbymx3TOnHPe5/35kcFee3Z10Vb32L6VzWYPHT7C59jx/gMG1q9d7ezy7GXqWpc/JSUl/Y762Fhb9uvVIz8/70fW1qPRaBLf9mVhR0vCf+fgnxQA4PfJycnVVgj3faWVJR8pipJt1+E/R94N3L/HxuBOwE4Op0677xBr54Pq3U0aDhg8c6XTmfghzNWi4pKPw46fcZ568A/T8sKC76/6DI1GExVv862vr9Z2tFT8biXYXwMAUBQlJytbUvbNOnsB9PFTIUVRqu1VfmTwnsPHtfuO2L7vCLuubvgg02N7tw3o17t+rbOTw8u7kS7LFkhKtDn61znrWYt6DZ+Q/+Hjj6ytR6PRJNqIf+vry8KO9spKFEVl5eQ2XFhYXEJRVDtFBUqolJSVUdifAgDAF1DBBwAAgktLS+ty6CXSKX6OegdliqIevUi1GT2ofuGZiFtcLneG5fD6JR+LSjce+EtJXu5ZyEFpSQn+wp3+F+oH1LLrqqpr1FWV/1ww7c8F0/I/FXv6XfA5d+VIUMS6+XbfWrVp0fSGYfI/FbsfO/etqEyrEb31/zUht666Ko1Gy377r2vJz9OyKYrq213v5/8YhKW9fjvORpt0ihZOS0srPS2VdIqfoKGhSVHU47iHNrZ29QvPnPqLx+VOZ85qOPLjhw8b169TUmr3NDlFWuaf7q873bfzv6mtra2qrFTX0Pxz0+Y/N23Oz8/buWO7z+GDRw4e2LRl6/fXNnyW/Pw8j+3/WtLQzFmze/cxaqSXLnDSUlMoitLWxj8pAMDv0tLS+hRymXSK3yLfXp2iqDfJ8d2HT6pf+PRaIMqq0XkAACAASURBVI/H7TXmfzf+VhR/jDq6WUqu7dLT8eKS//R7i/lrT/0ADruWXVMl31592Jx1w+asKy8suPPX7rhg34eXjg6dteZbq0bM+/OzPOWFBbcDdn4rbe+x01X1ejXKCxd8n3LSKeyvAQAoiqIoLS2ttMxs0il+gkYnNYqi4hKe2U6wqF946kIYl8tl2lo3HPnhU9H6HXuV2iokx1yRkf5nil53bx/+N7VsdmVVtUZHtU2rlmxatST/w8cd3kcPHz9z0P/0lrWs769t+Cz5Hz5u33fkW2ln2U3q0/NfvbU6a2vQaLTM128aLnyWnEJRVL8+PX/hD0JQakY2hf0pAAB8AR01AABAcBkZGb3N+/C24BPpID/ByEBXQlzs9qPn9UteZeY6uu6PfZLccFjO+w9cLs9qeP/6Ko03+R+fpWTVD7j96LnasJnnr8bwf1RpK7+MaU1RVFFpxXdWfRampKziZEjUt76y3nx+c3+HdooDe3e7+yS5fhW7jhN0NUZVWbG3vpAdTL4t+PQu/2Pv3r3/eyj8hj59+jyKe0g6xU/o07evhITE7VvR9UtevUxeMG92TMztz0bm5LzmcrlWEyfWV2m8eZP77Gki//vbt6I7qrQ9H/TP1EIqKu1ZK1dRFFVcXPSfaxsqKS4+6e/3ra+szMzGffkC5dGjOAUFBQ0Njf8eCgAA32VkZFSU/7b0wzen6hB8ql37iIq3yUq4U7/kQ3ZKiPvi7MR7DYeV5OfyuFz9wZb1VRolBW/z0v/3wTsrIcZ9vNaLGxf5P0orKg+cupSiqOqy4u+s+jJPdXnJk4i/vvVV9Da7EV+7gHvzMl5OHvtrAACKoqg+RkZxT56RTvET+hp2l2gjfuvu/47ZX6ZmzFu+PubB489G5rx9x+VyJ1qMrK/SePMu72nSK/73t+4+VOlmGhR6hf+jSjullQtmUxRVXFL6n2sbKi4p8z9z8Vtfma9zPxvfQUXZzMQo9uHj+lXsurrA4AjV9sp9enSjhMqjhGcKCvLYnwIAwGfQUQMAAATU69evMzMzpSQlI+88mmczhnScH6WsKL/Y3nLX8Yus7UdmWY96lZXrfSqUQReZO3l0w2F6mqpSkm0u/h07ekDvLpod7z996Xb4rIy0ZHllddrrt5011Ex7dW2nKLfj2Dk1lbaGXbQzct/z+22MGWT0nVWfhdHTVCu8/82OGl+1es7kyaxtM513rZljIy8rvefkpey3eRf2rafRaL/3h2luV24/kpKUNDMzIx2khTM3N/fz8ysoyFdW/qFmqsQpK6ssWsra7enOWrJw1uy5r16+3L9vD4PBmDff8bORenpdpKSlL54/N9rcQq9Llwf37rm5bpSRlS2vKE9LTTE1HdCunbL7Nje1jh0NDXtlZKTzm22MsRhHUdT31/7rWbp0/VQmZBM8NZYr4WFjxowRum0LAIAAGjRokISkVMq9q/0mzCGd5RdJK7Trb7Mw5vTey3tW9Bk380N2yr1zB0XojL4TZjcc1rZTZzEJqRfRwbomI5XUO+e+eHjTb7u4lExtVcXH3HSlTrqduptIKbS7fXKnbDvV9p17Fr7N5Pfb6Gw6+jurvsyjpN55w/XPa5pbp7R7kRYW2F8DAFBU/fHvh0/K7dqSzvJDlNu1XTqP6XnAd4nzltn2k1+mZuzzOcGg0+fPtP1spJ62prSU5Pmwq+bDzLroat17lOC6c7+stHRFZWVqRrZp397tlBS37T3csYOKYXf9jKwcfrMNixFDKIr6/tqGuuhqlWUl/NRLWLvUYcKshfYLVjo7OSjIye486JeV8ybk5EGh2zGFX781ZoyF0MUGAICmRuPxeKQzAAAA/EtCQsKuXbvOnTvXqVOn/v37v0x8FPuXJ+lQP4HD5W45dGZfQAiHy6Uoqr2Sws5VcyeOHEBR1BiHDVlv8lKu+FIUFRx1b8GWAxWV1RRFKchKe6ycIyUh7rBpf00tu/jheYqiouOeOWzyfv+hkP+wbcRE186zXT1n8vdX/b4Lf8cudjtUUVVNUZScjNR6B7tF08Y3yiM3p4EzVhsaDzh5MoB0kBauqqpKTU2NtWLVyjXOpLP8KA6Hs2XTBq89uzgcDkVR7dt38Nyzb+JkG/5ai1HDszIyXmW+pigq+OKFhQ5zK8rLKYpSUFR037lHSkrKYe4ftTU1RRU10TdvOM754/37f25fbtOmzVqXP1etXcf/8ftrG0VmRrphty6z5sw9cPhoIz5s88hIT+vdXT80NNTS0pJ0FgCAlmAmk3kr7tk8n+j/HiqouFzOzWPb7gXu53I5FEXJtFUZs9TdYOgEiqJOsCwL32WtOP+CoqikW6GhHktqqyooipKQVRizeJuohFTI9oV17NqNNwooisqMvx28Y2HZx3/KLBhi4kOYq81mrPj+qkZU+DbTe3rfPuNmWq32atxHbn6f3mQcmGmM/TUAAF9VVZWamuoKx1lrlswnneVHcTjcTZ7ee474czhciqLaK7fbs8V58nhz/tpRNn9kvM7NfHSDoqiLl685rNxQXlFJUZSivNxO17VSkhJzl7nU1NZWZD+9GXN/zrL17/ML+L/YRlzcZdmCtUv/+Tt8f+1vOhcauWD1xorKKoqi5GVlNqxcvGTujEZ55GaTnvW6++Dx2J8CAMCXUKgBAAACJDY21sPDIyIiokePHitXrpw2bVpiYqKJiclpzzVWw0xIp/s5FVXVL9Jey0pL6nTqICb69RZWhSVlT1Oy2ispdNXqyC+rLywpKy4t1+7UgT+gsrrmRdrrN3kf2srLdtNRb6coV/+731n1++o4nCfJGTwet293PbqI8E2UFhb9cPoaz4cPH/br1490lpbP2dnZz98/4flLeQUF0ll+QmVFxYvnz2RkZXV0O4uJiX1rWOGnT0+fJrZv376rfrd//kk/fSouLtLW0aUoqrKyMun5s9zc3LZKSt0MDNq1U/7XU3x3bSs3d9aMJ48fvXr1ik6nk84CANASPHr0yMTExHbLSX0z4auvbai2ujI/I0lcSqatmjZd9Os76MrSwry059JtVdppdOHvnStLC6vLihXV/pmqj11dlZ+ZVJL/RlJOUVlLX0qhXf3vfmcVfOnSVoeyrMS0FOyvAQD+4ezs7H/M9/mdywpysqSz/ISKyqrnL1NlZaR0tTTEREW/NexTUfHTFy/bq7TT76zD38N+KiouLinV0VSnKKqyqvr5y5Tct3lKivIGXTq3U1Js+LvfX/ub6uo48c9ecLk849496XThO0k1a+nax89evUpJwf4UAAA+g0INAAAgj8vlRkREuLm5PXr0aODAgWvXrh0/fnx9P8CZM2fcvXXz0bl9bcS+eTAJUK+mlm0ybYXp4GEBAX+RztIqlJWVdenSZeLkKR6795LOAsLh4f17o4YNxu1EAACNa8bMmdeiYxccv88QEyedBVqC3Bdx/kstsL8GAGiorKysSxe9yWNH7t4sNE0lgaz7jxOHTZyJ/SkAAHyV8JUfAgBAS1JeXu7l5aWlpWVtba2iovLw4cPY2FhLS8uGszZ6eHh+KCrd6XeBYE4QIp5+F/I+Fru7e5AO0lrIyMi4ubkdPXIoMeEJ6SwgBKqrq1ewlo4ePRpnqQAAGpenh0dl8cc7f+0mHQRagrramkiv1SNHYX8NAPAvMjIybm5bj5wMTHieTDoLCIHqmhrW+m2jR4+ytLTkcDhmZmYeHh6VlZWkcwEAgKBAoQYAAJBRUFDg6uqqoaHx559/WltbZ2ZmhoeHGxsbfzlSVVV1565dO49fDLlxv/lzgnAJuXF/5/GLO3ftUlVVJZ2lFZk9e/bQoUOnTp74/v070llAoPF4vEUOc3NzXh84cIB0FgCAlkZVVXX3rp0xp/ck3w4jnQWEG4/HC/VYUvHhzaGD2F8DAHxu9uzZQ4cOmTzX6X1+AeksINB4PJ7Dyg05794fOHCQoqiqqqp+/fpt2bJFR0dnz549FRUVpAMCAAB5mPoEAACaW3p6+v79+319fWVkZBYuXOjk5KSo+N9TVzo5Ofkd84302WLUTbcZQoIwik9Ot3DcOHfefG9vb9JZWp3i4mJTU1NJKenI6zclpaRIxwEBtd1t8y6PHZGRkSNGjCCdBQCgZXJycjrq68fcF67WtTfpLCCsbp3wiD295yr21wAA31BcXGxq2l9KXPT6+eNSkhKk44CActtzyGO/72fHvx8+fDh48ODevXvFxMQWL168fPlyOTk5giEBAIAsFGoAAEDziY2N9fb2vnTpkqam5tKlSx0cHCQkfvSAlsPhWFlaxsbcObF9+egBfZo0Jwijv+89+cNl7yCzwWHh4XQ6nXSc1igjI8PU1LSTukbgxeAOHdDRBP6Fw+FsWLf2gPc+Hx+f+fPnk44DANBicTic8ZZWt+/ETNro19lkJOk4IGS4XM71I5senD+M/TUAwPdlZGSYmvZXV21/0d+7g4oy6TggWDgc7rptu719A761P/348eOBAwf27dvHYDCWLFmybNkyeXn55s8JAADEYeoTAABoclwuNzw8fODAgWZmZpmZmf7+/ikpKSwW68erNCiKotPpwSEhk2ymTFm+/XBgBAoNoR6PxzscGDFl+fZJNjbBISGo0iBFR0fn/v37lRXlwwaaJiY8IR0HBEhZaem0KZN8fQ6fPn0aV30AAJoUnU4PDQm2nTL5rMu0hxd98JkZflxNRdm5DTOfhPpjfw0A8J90dHTu339QUcMeaGmf8DyZdBwQIKVl5VPmOfkEBH1nf6qkpOTq6pqRkbFkyRIvLy8NDQ1nZ+fi4uJmjgoAAMShowYAADShmpqaoKCgHTt2pKamjh071tnZeeDAgb/5mO7u7uvXrx/Qq5vnqtk99bQaJScIr2epWWt2Hb+XmLxt2zZnZ2fScYAqLi62tbWNjo6e77jQZcMmeQUF0omAJB6Pd+bUX64bXHhcbnBwsKmpKelEAACtBf8zs0bP/uZLdrTX7UE6Dgg0Ho/39Fpg9DE3MRFeaAj21wAAP6q4uNjWdkp09C1Hpt2GlYsV5GRJJwKSeDzeqQthG9y9uBQVHBzyg/vT0tLSw4cPe3h41NXVLVq0aO3atQo4kQIA0GqgUAMAAJrEx48f/fz8vL29P336ZGtru27dOn19/cZ68Pj4eKelSx/GPZw2dqjDFIs+3XQa65FBiDxJzjh6PvLslVsmxibe+/cbGRmRTgT/4HK5x48fd3Fx4fJ4S5yWzZj1h4pKe9KhoLnV1tZeDgvdv29PwpN4R0dHNzc3RUVF0qEAAFqX+Pj4JUud4uIeGo6262c9V7VLb9KJQOBw2LWvYq88OH/wbUriAuyvAQB+3j/Hv+vW8bgcp/kzZ9lNVGmnRDoUNLdaNjvs6o19RwOePEv6tePfsrKyQ4cOeXp6stnsRYsWrVmzBntkAIDWAIUaAADQyLKysvbt2+fn58dgMGbNmrV27VpVVdVGfxYej3f69Gn3HduTkl9qqLUfbNTNQFejrbxsGzHRRn8uEBxVNbWfikuTM3LuxCe/fpvX3aDbWud106dPp9FopKPB50pKSnbs2OHr61tcXNy3n7GxSX8d3c7yCgqYm6ZlKystffv2zdPEhDu3oisrK8ePH79ly5aePXuSzgUA0ErxPzNv2+H+KjmpbQd19V5mytrdJOUUGWJtSEcDkmoqyko/vM1Pf56VcKe2umrcuPFb3bC/BgD4df9//Hu0uLikX+8eJn0MdbU0FORk6XRMPd+SlZZVvH2fl5j06tbdh5VV1ePHj9uyxe139qfl5eV+fn47duyoqKiYM2fOunXr2rfHfS8AAC0ZCjUAAKDRJCQk7N279+zZsx07dly2bNm8efOkpKSa+knj4uLCw8Mf3L+flPSiqLi4urqmqZ8RCGrTRlxBXt7AoHt/U1NLS0tjY2PSieA/VFVVXb169dq1a/Hx8VlZWcXFxRwOh3QoaEIyMjIqKiqGhobDhw+fMGGCmpoa6UQAAEBR//+Z+d79By+SkkqKimpqqkknApKkpGWUlVV69zYcgf01AEDj+d/x7+PHWVlZxSUlOP5t2WRkpFWUVQx79Wrc419+uYa7u3tZWdncuXOdnZ07dOjQKI8MAACCBoUaAADQCGJjYz08PC5fvtyrV6/ly5fb29szGAzSoVo+W1tbiqLOnTtHOghAS7Zp06Zjx47l5OS0jF4gpaWlgwcPrquri4mJwcS3AABAVmlpac+ePXv16hUSEkI6SxP68OFD9+7dJ06ceOTIEdJZAAAAmgqNRgsKCuKfqoLfV1FRcezYMQ8Pj9LS0rlz5zZRx2IAACALrbcAAODX1dbWBgQE9OjRw8zMrKioKCws7MmTJ0wmE1UaANBiBAYGTp06tWVUaVAUJSsrGxERUVZWZm1tXV2N26kBAICk5cuXV1ZW+vj4kA7StNq1a+fj43P06NHIyEjSWQAAAEA4SElJsVisrKysPXv2XLhwQVtb29HR8e3bt6RzAQBAY0KhBgAA/IqysjIvLy9dXd158+bp6+s/evQoNjbW0tKSRqORjgYA0Gji4+NTU1Pt7OxIB2lMampqV65cef78+axZs7hcLuk4AADQSl2+fNnf3//w4cMqKiqkszQ5a2vrqVOnzps3r7CwkHQWAAAAEBri4uIODg5ZWVne3t4RERH8co03b96QzgUAAI0DhRoAAPBz8vLyXF1dNTQ0NmzYMHHixIyMjHPnzvXt25d0LgCAxhcUFKStrd2vXz/SQRqZgYFBSEhIWFjY0qVLSWcBAIDWqLi4eOHChUwmc/LkyaSzNJNDhw6JiIiwWCzSQQAAAEDIiImJOTg4ZGZm7t+/PzIyUkdHh8lkZmRkkM4FAAC/C4UaAADwo54/f+7o6KilpXXkyBEnJ6fXr197eXl16tSJdC4AgCbB4/HOnz8/derUFtkraPDgwSdPnjxy5Mju3btJZwEAgFZnwYIFXC533759pIM0H3l5eT8/v9OnT1+4cIF0FgAAABA+/HKN9PR0X1/fe/fu6evrM5nM9PR00rkAAODXoVADAAD+G39aE0NDw+joaHd39+zsbFdXVwUFBdK5AACa0L1797Kzs6dOnUo6SFOxtbXdv3//6tWr//rrL9JZAACgFQkODj537tyxY8da2wHF6NGj58yZs3Dhwvz8fNJZAAAAQCiJiYkxmcyXL18eO3bswYMH3bp1YzKZaWlppHMBAMCvQKEGAAB8E5fLDQ8PNzU1NTMzKyoqCg0NTUlJYbFYbdq0IR0NAKDJBQYGdu3atUePHqSDNKFFixatWLFi7ty5169fJ50FAABahQ8fPixYsMDR0dHCwoJ0FgK8vLzk5OQcHR1JBwEAAAAhJioqymQyk5KSjh079vDhw65du9ra2qakpJDOBQAAPweFGgAA8BU1NTUBAQH6+vrW1tZKSkr379/nN9Vokf3/AQC+xOVyL168aG9vTzpIk9u5c+fUqVNtbGwSExNJZwEAgJZvwYIFUlJSnp6epIOQISUldeLEifDwcLSzAgAAgN/EL9d4+fJlYGDgs2fPunXrZmtr++rVK9K5AADgR6FQAwAA/uXDhw+urq5qamoODg4mJibJycnh4eH9+/cnnQsAoFndvHnz/fv3dnZ2pIM0ORqN5ufnZ2JiMm7cuNevX5OOAwAALZm/v39ISMjx48dlZGRIZyFm0KBBS5YsWbp0aW5uLuksAAAAIPRERESmTJmSnJwcGBj44sULAwMDS0tL3IkBACAUUKgBAAD/yMzMZLFYmpqahw4dmjdvXlZWVkBAQJcuXUjnAgAgICgoyMjISE9Pj3SQ5iAqKnrhwgVlZeWxY8cWFhaSjgMAAC3T27dvV61atXz58iFDhpDOQtiOHTvat28/Z84cHo9HOgsAAAC0BPxyjRcvXoSEhLx9+9bIyMjS0vLJkyekcwEAwPegUAMAAKgnT54wmUw9Pb3Lly9v3749Ozvb3d29Q4cOpHMBAJDBZrODg4OnTp1KOkjzkZWVjYiIqKioGDt2bGVlJek4AADQ0vB4vLlz56qoqLi5uZHOQp6kpOSJEyeio6OPHj1KOgsAAAC0HCIiIpaWlvHx8SEhIe/evevbty//R9K5AADg61CoAQDQevF4vPDw8FGjRhkZGSUlJfn7+6ekpLBYLElJSdLRAABIunbtWmFh4ZQpU0gHaVaqqqpXrlxJS0ubNm0ah8MhHQcAAFqUgwcP3rhx4+TJkxISEqSzCIT+/fuvWbNm5cqV6enppLMAAABAi0Kj0SwtLR8/fhwaGpqXl9evXz9LS8tHjx6RzgUAAJ9DoQYAQGtUW1sbEBDQvXv3CRMmUBQVFhYWHx/PZDIZDAbpaAAA5AUGBg4cOFBDQ4N0kObWrVu3K1euREVFLVmyhHQWAABoOTIzM9etW+fi4mJsbEw6iwBxdXXV0dH5448/UB8JAAAAjY5frvHo0aO///47Pz/f2Nh41KhRcXFxpHMBAMD/oFADAKB1KS0t9fLy0tbWnj9/vpGR0fPnz69fv25paUk6FwCAoKiurg4PD7ezsyMdhAwTE5OzZ8/6+vp6eHiQzgIAAC0Bl8v9448/dHV1169fTzqLYBETEwsICHj06JGXlxfpLAAAANBijRw5Mi4u7vr162VlZSYmJqNGjXrw4AHpUAAAQFEo1AAAaD3ev3/v6uqqoaGxYcOGyZMnZ2RkBAQEGBgYkM4FACBYwsPDKyoqWtu8Jw1ZWVkdOHBg3bp1J0+eJJ0FAACE3s6dO+Pi4k6ePCkmJkY6i8AxNDTcsGGDi4vLixcvSGcBAACAlmzkyJEPHjyIiYnhcDimpqaDBg26efMm6VAAAK0dCjUAAFq+p0+fMplMDQ0NHx8fFouVk5Pj5eXVsWNH0rkAAARRUFDQ8OHDVVRUSAchacGCBWvWrJk/f/61a9dIZwEAACH28uVLV1fXzZs39+zZk3QWAeXs7NyrV69Zs2ax2WzSWQAAAKCF49dnxMTEiIuLjxgxYtCgQTdu3CAdCgCg9UKhBgBASxYbG2tpadm7d++nT58eO3YsJyfH1dVVXl6edC4AAAFVVlZ25cqVVjvvSUM7duywt7efMmVKQkIC6SwAACCU6urqZs2a1bt371WrVpHOIrgYDMaJEydevnzp7u5OOgsAAAC0Cvz6jJiYGAUFhZEjRw4aNCg8PJx0KACA1giFGgAALRCXyw0PDzcxMTEzMysqKgoNDU1MTGQymaKioqSjAQAItODg4Lq6uokTJ5IOQh6NRvP19R0wYIC5uXl6ejrpOAAAIHzc3NySkpJOnDhBp9NJZxFoXbt23bZt25YtWx4/fkw6CwAAALQW/PoMfrmGlZUVyjUAAJofCjUAAFqU8vJyLy8vLS0ta2trZWXlBw8e8Jtq0Gg00tEAAIRAUFDQmDFjFBUVSQcRCKKioufPn+/YsaOFhUVBQQHpOAAAIEwSEhJ27Njh7u6up6dHOosQYLFYgwYNmjVrVnV1NeksAAAA0Irw6zPu3r3LL9cYMGBAeHg4j8cjnQsAoFVAoQYAQAtRUFDg6uqqoaHx559/jhkz5tWrV/ymGqRzAQAIjaKioqioqKlTp5IOIkBkZGSuXbtGUZSlpWVFRQXpOAAAIBxqamqYTKaZmdmSJUtIZxEOIiIix48fz83N3bx5M+ksAAAA0Orw6zPu37/ftm3bCRMm9OnT5/z58yjXAABoaijUAAAQeunp6SwWS1NT8/Dhw0uXLs3Ozvbx8encuTPpXAAAQub8+fMMBsPKyop0EMHSrl27yMjI7OzsqVOn1tXVkY4DAABCYP369Tk5Of7+/mjs9+M0NTV37tzp6ekZExNDOgsAAAC0Rv379w8PD09ISOjcubOdnV2vXr1QrgEA0KRQqAEAIMRiY2NtbW27du0aERGxY8eO7OxsV1fXtm3bks4FACCUAgMDx48fLy0tTTqIwNHV1Q0PD4+Ojl60aBHpLAAAIOju3bu3b9++vXv3amhokM4iZBwcHMaMGfPHH3+Ul5eTzgIAAACtlKGh4blz5xITE7t06WJnZ2doaBgQEMDlcknnAgBogVCoAQAgfLhcbnh4+MCBA83MzDIzM/39/VNSUlgsloSEBOloAADCKi8v786dO3Z2dqSDCChjY+PAwMDjx49v3bqVdBYAABBcFRUVf/zxh4WFxZw5c0hnET40Gs3X17e4uNjFxYV0FgAAAGjVevbsee7cuadPn/bq1WvOnDn8cg0Oh0M6FwBAi4JCDQAAYVJTUxMQEGBgYGBtba2oqBgbG/v48WMmk0mn00lHAwAQbkFBQZKSkhYWFqSDCK7x48cfPHhw48aN/v7+pLMAAICAWr169YcPHw4fPkw6iLBSVVX18vI6cODAtWvXSGcBAACA1q5Hjx4BAQFPnz7t3bs3yjUAABodCjUAAIRDSUmJl5eXtrb2/Pnz+/Xr9+LFC35TDdK5AABaiKCgoIkTJ6I10fc5ODi4uLgsWLAgMjKSdBYAABA4N27cOHLkyJEjRzp27Eg6ixCbMWPG5MmT582bV1xcTDoLAAAAAGVgYBAQEPDs2bM+ffrMmTNHT0/v6NGjdXV1pHMBAAg9FGoAAAi6rKwsFoulpqa2ceNGGxubrKysgIAAfX190rkAAFqOnJycBw8eTJ06lXQQIeDm5jZjxgwbG5sHDx6QzgIAAAKkpKRkzpw51tbWmEfs9x0+fJjNZq9atYp0EAAAAIB/dOvWLSAgIDU1deTIkYsXL0a5BgDA70OhBgCA4EpMTGQymXp6emFhYRs2bMjJyfHy8lJVVSWdCwCgpTl79qyCgsKIESNIBxECNBrNx8dn8ODBlpaWqamppOMAAICgcHJyqqqqOnLkCOkgLYGSktLRo0f9/PyCg4NJZwEAAAD4H21tbR8fn9TU1FGjRi1evLhz585Hjx5ls9mkcwEACCUUagAACKLY2FhLS8vevXs/f/7cz88vLS1t7dq1cnJypHMBALRMgYGBU6ZM7M9mzQAAIABJREFUERMTIx1EOIiKil68eFFXV3fs2LH5+fmk4wAAAHlhYWEBAQFHjhxRVlYmnaWFsLKymjlz5oIFCwoKCkhnAQAAAPgXLS0tHx+ftLQ0KysrJyenzp07e3l51dTUkM4FACBkUKgBACBA2Gx2QEBAjx49zMzMioqKwsLCnjx5wmQyGQwG6WgAAC1WSkpKYmIi5j35KZKSkmFhYSIiIuPHjy8vLycdBwAASPr48aOjo+Ps2bMnTZpEOkuLsn//fnFxcRaLRToIAAAAwFdoamp6eXmlpKRMmDDB2dlZT0/Py8ururqadC4AAKGBQg0AAIFQVlbm5eWlo6Mzb948fX39uLg4flMNGo1GOhoAQAt39uzZDh06mJmZkQ4iZNq1axcZGZmbm2tnZ4dZaQEAWrNFixbR6fTdu3eTDtLSyMnJ+fv7BwUFnTt3jnQWAAAAgK/T0NDgl2tYW1vXl2tUVVWRzgUAIARQqAEAQFh+fr6rq6uGhsaff/45ceLE9PT0c+fO9evXj3QuAIDW4vz583Z2dnQ6nXQQ4aOjoxMeHn779u0FCxaQzgIAAGScPn36woULfn5+CgoKpLO0QCNHjnR0dFy0aFFeXh7pLAAAAADfpK6u7uXllZ2dbW9vv27dOk1NTQ8PD5RrAAB8Hwo1AACISUtLY7FYmpqaR44ccXJyysnJ8fLyUldXJ50LAKAVSUhISE5OtrOzIx1EWPXr1y8oKOjkyZOurq6kswAAQHN79+6dk5PT4sWLzc3NSWdpsXbt2qWgoDB//nzSQQAAAAD+g4qKiru7e3Z29uzZs7ds2cIv16isrCSdCwBAQKFQAwCAAP60Jl26dImMjOR/eHV1dcUtaPCfKisrixqora2tra1tuARHPgA/KygoSF1d3cTEhHQQITZu3LjDhw9v3rz54MGDX64tLy9v/kgAANAU/Pz8UlJSGi6ZP3++vLz8jh07SEVqDaSkpE6cOBEZGXn8+PGGy4OCguLj40mlAgAAKC4ubnhKiqKoioqKhkswRWarpayszD/jvXDhwu3bt2tqarq6upaWlpLOBQAgcFCoAQDQfLhcbnh4uKmpqZmZWVFRUWhoaEpKCovFatOmDeloIBwCAgIUGwgNDQ0NDW24JCAggHRGAGHC4/GCgoLs7e1pNBrpLMJt3rx5mzZtYrFYISEh9Qs5HI6Dg4ObmxvBYAAA0Ig8PT179uzp5eXF5XIpivL19b169eqJEyekpaVJR2vhBg4cyGKxli1blpOTQ1HUp0+fpkyZMnXq1PDwcNLRAACg9bKxsWl4SoqiqDlz5tT/qKysXFhYSDojkNSuXTtXV9eMjIxFixbt27dPR0fH1dW1pKSEdC4AAAFC4/F4pDMAALR8NTU1QUFB27dvT0tLGzt2rIuLi6mpKelQIHw+fvzYvn17Dofz1bV0Oj0vL09JSamZUwEIr/v37w8YMCAxMdHQ0JB0lpZg8eLFx48fj4qKGjBgQGVlpZ2d3eXLl+Xk5PLy8lCSCAAg7N69e9exY0cejyciImJqauru7j5u3LiFCxe6u7uTjtYq1NTU9O3bV1lZmcVizZ07t6SkhM1mGxsbP3z4kHQ0AABopY4dO/atmblERESGDRsWFRXVzJFAYH369Gn//v379u1jMBhLlixZtmyZvLw86VAAAOShowYAQNP68OGDq6urmpqag4ODsbFxcnIyv6kG6VwglJSUlEaNGsVgML5cRafTR40ahSoNgJ8SGBjYtWtXVGk0Fm9vb3Nz8wkTJsTFxQ0fPvzq1asURZWVlZ0/f550NAAA+F1RUVH8BlRcLjcuLm7EiBHq6uqurq6kc7UW4uLi+/fvz87OnjBhQmFhIZvNpigqPj4eXcQBAIAUGxsbUVHRb61lMpnNGQYEXNu2bfndNZYsWeLl5aWhoeHs7MyfMQcAoDVDoQYAQFPJzMxksViampoHDx6cN29eVlZWQEBAly5dSOcC4TZjxgx+r+nP8Hi8mTNnNn8eAOHF5XIvXLgwdepU0kFaDjqdfurUqU6dOo0fP/7Jkyf1ExJ7e3uTDQYAAL/v+vXrdDqd/z2bza6trU1KSho/fvybN2/IBmsloqKi7O3t+VOf1B8OcLnc27dvE80FAACtl7y8/JgxY751N9GECROaPxIIOH65Rk5OjouLy9GjR/nlGpgiBwBaMxRqAAA0vidPnjCZTD09vfDw8O3bt79+/drd3b1Dhw6kc0FLYG1tLSYm9uVyUVFRKyur5s8DILxu3br17t07W1tb0kFalMzMzLdv3xYXF/Pv9KUoisvlPn78+MmTJ2SDAQDAb7p27Vr9tp2Px+PduXOna9euR48eJZWqNaiqqnJ2dh49enRBQUF9ESSfqKjojRs3SAUDAACYPn36l/PzMhgMS0tLOTk5IpFA8MnIyKxdu/b169fr16/39fXV0NBgsVj5+fmkcwEAEIBCDQCARsPj8cLDw0eNGmVkZPTixQt/f//U1FQWiyUpKUk6GrQcUlJSlpaWn/WWZDAYEyZMkJaWJpUKQBgFBQX17t1bX1+fdJCW48aNG/3796/vx15PVFT0yJEjpFIBAMDvS0pK+vDhw5fL2Wx2RUWFo6PjgQMHmj9Va8DlcseOHevh4cHj8b68ElZbWxsREUEkGAAAAEVREyZMkJCQ+Gwhh8OZMWMGkTwgROrLNbZu3Xru3DldXV0Wi5WXl0c6FwBAs0KhBgBAI6itrQ0ICOjevTu/rV9YWBi/qcZXu/8B/Kbp06d/di8dh8OZPn06qTwAwojNZl+8eBHznjSigIAAc3Pz6urqzzZQFEWx2ey//vqrpKSESDAAAPh9UVFR32psTqfTPT09Fy9e3PypWgMREZHz589bWFiIiHz9DF56evrbt2+bORUAAABfmzZtJk6c+NndRBISEmPGjCEVCYSLtLQ0i8VKT0/funXr+fPn+eUa79+/J50LAKCZoFADAOC3lJaWenl5aWtrz58/38jI6Pnz59evX7e0tCSdC1oyCwsLGRmZhkukpaXNzc1J5QEQRtevXy8sLMS8J42lrq4uMTGRRqPR6fSvDmCz2QEBAc2cCgAAGsu1a9e4XO5nCxkMhrKy8u3bt1evXk2j0YgEaw2UlJQiIiL27NnDYDC+LJcRERHB7CcAAECQvb19w5aKoqKitra2X7bZAPgOKSkpFouVlpa2bdu2CxcuaGlpOTo6vnv3jnQuAIAmh0INAIBf9P79e1dXVw0NjQ0bNkyePDkjIyMgIMDAwIB0Lmj5xMTEpkyZUn+/gqioqJ2dnbi4ONlUAAKuurq64Y+BgYGmpqaampqE4rQ0DAZjz549GRkZtra2NBrty8tIXC7X29ubx+MRiQcAAL+jrq7u9u3bnxVq0Gg0CwuLFy9eDBw4kFSw1oNGo7FYrAcPHqipqX1217KIiMj169dJBQMAABg9erSCgkL9j2w2297enmAeEF78co3MzExvb++IiAh+uQY6hwFAy4ZCDQCAn/bs2TMmk6mhoeHj48NisXJycry8vDp27Eg6F7QiDe9XwDEwwI/o2LHjjBkzIiIiamtrq6urw8LC7OzsSIdqadTV1U+dOvXgwYN+/fpRFNWwSTuPx0tPT799+za5dAAA8Ivu379fWVlZ/yODwRAVFd27d29oaKiioiLBYK2NkZHRs2fPJk2a1HBhXV1dZGQkSiEBAIAUBoMxdepUMTEx/o8KCgrDhg0jGwmEmri4uIODQ2Zm5v79+69cuaKtre3o6Jibm0s6FwBAk0ChBgDAT4iNjbW0tOzVq9fTp08PHDiQnZ3t6uoqLy9POhe0OkOHDm3Xrh3/eyUlpcGDB5PNAyD4ysrKzp49O378eCUlJTs7Ozab/dl1DmgsxsbG9+7dCwsL69ixY8OZUBgMxoEDBwgGAwCAXxMVFVV/9UVUVLRTp05xcXEsFgvTnTQ/WVnZwMDAkydPiouL1/ev+vTpU3JyMtlgAADQmk2bNq22tpaiKDExsZkzZ37ZYRHgZ4mJiTk4OGRkZPj6+t64cUNXV5fJZGZkZHz/tzgcTvPEAwBoLCjUAAD4b1wuNzw83MTExMzMrKioKDQ0NDEx0cHBAZNNACkiIiIzZswQExMTFRWdOXNmw0uhAPAt/J7tZWVlV69erays7N69O5PJjIqKwk2oTcHS0jI9Pf3QoUPy8vL883R1dXUhISGYZRYAQOhERkbye7nRaDRbW9tnz5716tWLdKhWjclkJiQk6Orq8vewoqKimP0EAAAIGjhwYPv27SmKqq2tRetKaERiYmJMJvPly5e+vr7379/X19dnMplpaWnfGm9jYxMdHd2cCQEAfhMKNQAAvqe8vNzLy0tbW9va2lpZWfnBgwf8phq4ewyI49+vgHlPAH5Qw+02/16fkpKSoKCgUaNGderU6erVq+SitViioqL8O2CWLl3KYDAYDAaHw/Hz8yOdCwAAfkJpaemTJ08oimrTpo2/v/+pU6ekpaVJhwJKX1//8ePH/AMBNpt97do10okAAKD1EhERmTlzJkVRqqqqpqampONASyMqKspkMpOTk48dO/bgwQMDAwMmk5mamvrZsAcPHoSEhIwdO/bu3btEcgIA/AIa7iAE4VVTU5OUlFRQUFBWVkY6CzQhEREReXl5LS0tLS2t5iyPKCgoOHTo0P79+2tqaqZPn75y5Uo9Pb1me3b4WYWFhUlJSUVFRTU1NaSzNJ9FixZRFHXo0CHSQZqPuLi4goKCgYEBJkSHnyUpKVlVVfXlcjqd3q1bt4cPH0pISDR/KkHTdNvS/Pz8U6dOPXz4UEFB4dChQ+gDRBa2pdAy4HiweTx+/NjT01NNTW3VqlVqamrNH4DU8eAPIv4+jImJ8fHxoSjq5MmT2L02HQF/HwKAQOHxeFlZWVlZWUVFRa3n4ktWVtbatWsnTpw4bdo00lmaj4yMjIqKSrdu3dB0udlwudyLFy9u2LAhLS1t8uTJbm5uXbp04a8yNzePjo7mcDht2rS5efOmiYnJLz9L6zzP3Arh3AgIAhRqgPApKioKCAgIvnTx7r37dXV1pONA81GQlzcfY25vP33s2LG/fAaqvLycwWC0adPmO2MyMjK8vb19fX1lZGQWLly4dOnStm3b/trTQVNLSkry9/e/fDk8NfWbXe+gRdLT62xpaTV79mwDAwPSWUA4fLVQQ0RERFpaOiEhQVtbm0gqAfHPtjQ8LDUtnXQWaFZ6nXUtrSZgWwrChX88eOFS8P17dzk4HmxNZOUVLMzNp0+3/53jwcbCfx9eCg6+d/cuzku0KvIKCmPMze3tBeJ9CAAChcPhREREnD179urVa8XFRaTjQPNhMBgDBgycNGkik8lUUFAgHadV4JdrbNy4MTU1dfLkyZs3b66srOzXrx//ciedTufXahgbG//Uw+LcSKuFcyNAEAo1QJhUVlZ6enru9PSki9DGm/UZadKzVxdN1XaK0pLfu+gOwo7L5RWVlWe+yX/4Ii3ybmLMk2Qdba3de/ZaWVn97EMVFxePGTNm9uzZjo6OXx0QHx/v5eV15swZTU3NpUuXOjg44AZrgZWenr5y5cqwsDBdLU3rceZDB/Xvrt9Fqa2iuJgY6WjN51VqOkVRXfV0SQdpPjW1tR8/Fb54mXIr9kFIxLX0rGwrK6vdu3fr6raiPwL8mq8WatBotCtXrowZM4ZIJEGQnp6+csWKsPBwHXW1CSMGDOnX06CzVlt5WXEx0SZ6Rh6Pl/UmT7tThyZ6fPgRNbXsT8WlSWlZtx89C71xLyPnrZWl5e49e7AtBQHHPx708NzJo4mo9bNo33u4onYPCcUOohKYiaMJFWY8VdQxJBiAx+PWlhWX5WV9THn8Pv7vvBd3NbV09u3d/QvHg42C/z703LlTRIQ+dIzVgGGj9Xv2btdeVUpahkgevtqa6tzsTJ0u3QhmaNm4XG5pcWFuVsaz+Id3/r78+N4dbR2dPbuJvQ8BQNCEhYWtWLEyMzPD0GRwv+EW+r1NVDV0ZOQUaCKtaOr5e3+HDRjduraKVRXlH/PfZSQlxt/5+37UZR6Xs2b16jVr1khKSpKO1ipwOJygoKCtW7empKRoaGjk5ubWl8/S6XQJCYno6Oi+ffv+yEPVnxvR7tTealDvwX26dtNWaysnIy7KaMpXAITVsOs+lZQlZ7698+RVWGxCZm4ezo1A80OhBgiN4ODgZSyn4qLCNbMmzLUegeKMVivzTf42v0vnr98bNXLkwUOHfnyv+enTp2HDhj1//lxLSys9PV2kwZESl8uNiIhwd3e/d++ekZGRk5PT9OnTcXOMwKqurt68efPevXt1tTXdN641Hz4EvWdbJx6Pd+3mbectHumZ2cuXL9+0adP3m+VAKyctLV1RUdFwiYiIyPbt29euXUsqEln/vy3do6uutm3ZnFEDjbAtbZ14PN71u/Hr9/mn57xdvnwFtqUgsIKDg5c6LftUVKQ/ebnu6Fkozmi1yvKyXgR5ZscEjxg56vChg818FjU4OJi1bFlRUfHcZetsmPPJFmcAQblZGUd2bbkaHDRy1KhDB5v7fQgAAiU9PX3RosVRUdeHWk6Z7vSnqoYO6URARlVFecTZY0GHPBTk5b299k2cOJF0otaCy+Xu3LnT2dn5s+X8Wo3bt2/36dPnO7/+z7mRPXt0Oqm4OU4eadwd50ZaJx6PFxX3YoPPxYzc/OUrcG4Emg8KNUAI8Hi89evXu7u7Tx87ePMCW2VFOdKJgLz7z1JW7T2VW1B0/sKFESNG/Of4/Pz8YcOGpaens9lsGo128eJF/sflmpqaoKAgd3f3lJSUsWPHOjs7Dxw4sOnjw68rKCiwtrZ+mZy02XmFw6zpDAbqaVq7ujrO0ZOnN7nv0e9mEBISoqysTDoRCCgZGZny8vL6H0VFRc3NzcPCwlrnEXhBQYH1hAkvk5M2Lpoxz3YcA7WJrV4dh3PsXMSWQ6f0uxmEhIZiWwoCpf54UHvYVMPp69vItyOdCMj78PJhgr9LbeHbSxfO/8jx4O+rfx9a2TGXuGxt2w7bSaAS4+56rl+e9zbnwvlmeh8CgKC5ceOGjc0UJdVOjht2G/QdQDoOkFf8seDE7o3XL55ydnbetm1b6zzh0PysrKyuXr3KZrM/W85gMGRkZO7cudO9e/ev/mJBQYH1BKvkpBcb5ljPmTCUQW9FLXDgq+o4XP/QW27+Id0MuoeEhuHcCDQDFGqAoKuqqpo5Y0Z4eLj32jnTLcxIxwEBUl3LXrTdN+RW3MGDh+bPn/+dke/fvx86dGhWVhb/4xqdTjc0NLx58+aJEyc8PT0/fvxoZ2e3bt06fX395soOvygpKWn8+HEMGi3klG+XzrhHAf4nJS3Desb8Oh7v8uUIzCYIXyUrK1tWVsb/nsFgqKurJyQkyMrKkk1FRFJS0vhx4+i8ugvem7podSIdBwRISlaujdNmDo1+OeIKtqUgIKqqqqbPmBkeHt53wS7toXak44AA4bBr4g4uy70ffujQwe8fD/6+qqqqmTNnhoWH/7nzkKXtzCZ9LhAutTXVrssdbly+dPBgk78PAUDQ+Pr6Ll68eOCYict2HBYTx43X8D9Rl07v/3OJpZXlqb/+wrTaTe3p06e9e/f+1oVOfq1GbGxst26fzxCXlJQ0ftxYEW7tue1L9NQxPSv8T2rOe9t1B7h0MZwbgWaAQg0QaFwu13bKlJs3rgfuWDbAsAvpOCBweDzeDv9g9+PBp0+fnjZt2lfHvH79evDgwe/evaufo45PXV29vLx84cKFS5cuVVFRaZa88Ftyc3NNTIy11TtdDDjSVkGBdBwQOJ+KiiYzF2Tm5D58GNepE649w+fqCzVoNFqbNm0eP3785VF6a5Cbm2tibKylqnRu7wZF+dZYpwLfV1hcarvcLevdx4dx2JYCeVwu12aK7dWomwPXnFDW7086DggeHu/5uV0vzu/+zvHg7+NyuVNsbW/cjN5z/Hxvk0FN9CwgvHg83tHdW4/u2dak70MAEDRnz56dPn26/ZJ19ktd0DUBvvTi8d2tC+1Gjhh+/ty5hhNwQ6ObOHHi5cuXPzvz3xCDwVBQULh7927nzp3rF+bm5poY99NUlj+7bZGiLCZVhM8VlpZPW38ou6D4YdwjnBuBJoVCDRBoLi4uu3buDNm7ZnCf1ngpBX6Qy/7TvsE3b0ZHm5qafrYqNTV18ODBhYWFn7U+YzAYpqamV65ckZbG5zDhUFlZOXTokLKS4piIC/JyuLIIX1dWXjF4/BSGmHhMTCz+u+Ez8vLyJSUlFEXRaLTz589PnjyZdCICKisrhw4ZUlb4ITpgt5yMFOk4IKDKKqpG/LGKISEdE3sX21Igy8XFxXPnrqEbglS6Y3ZC+KaEE5sy/j5xK/rml8eDjcLFxWXnrl0Hz0b0GzikKR4fWoY9rmvOn/SJvtlU70MAECiPHz8ePGTI2Gnz5jrvIJ0FBFfS43vrZ41ftWrltm3bSGdpsVJTU7t27cq/yslgMOh0OpvN5nK5nw1jMBht27a9e/eujo4O9c+5kcGlH/OiDjrLSUsSyA3CoLyyetQSd1FpBZwbgSaFQg0QXJcuXbKxsTm83kEAZzx5kZ5z58lLe4tB8gJzkYPL5YmI/Gj59vcH13E47DqOhLjYt9bSaDS6IBUCc7m8qev2PUnNSUp+2bZt2/rlL1++HDJkSFFR0Vcramk0WlJSEqY7ERb29vY3oq7fvXpJs1NH0ln+w/PkV7di78+wnaQgL0c6yz+4XO6PF++XV1RKSwnxIUp27puBYyaNGDnqzJkzpLOAYFFUVCwqKqLT6WvXrm21Z0ns7adF/X3tzqm9GqoEWkmdv3qbXVdnP174ZnD/qU9Z//lJqbyySlrym81veTxeSVmFPOkbel6/yx88Y/nI0eZnzpwlmwRaM/7xoMkSL1IznryODeFy2FpDphB59t/B43FptJ84XmNXlXPr2OIywtqyjsfjxnr8UfU68VVyUsPjwUbBfx+67vMVzBlProWcq6tjj7OZTjrIT/upIxShwOVyV862efn0cXJS478PAUCgfPr0qVs3A+3uRhsOB9FIbMpuXz7Pqasbbi18LXx4XO6P/8U4nDoOmy3W5uuHThxOnYgIXfB7mURdOr3X2fHChQuTJk0inaXFqq6ufv1v6enpmZmZHz9+5Fds0Ol0Op1eW1vboUOHe/fuaWpq2k+bFvV3ZPRhF/X2SqTjf+5FxpuYhFfTzAfIywjE6dmfPB/CpYvQBP8f88fl5H0ctnD7yNEWZ87i3Ag0FRRqgICqrKzU79plcE+dwy6COMen76WoFbtPPD7t2UVTlWyS9Ny8oxevR8TEl5RX9u+pt8TOYmjfb06a9Z+Db8Q933Q4KDkzt47D7dReyWna2PkTR9bviYP+vnv0YtSz1Ow6DldLTdnRZnTDtWSVVVT1sV87yXbqwYMH+UsSEhKGDx9eXl7+rb5noqKis2bN8vX1bcaY8Itu3bo1bNiwsDP+FiOHks7y344cP7V07cbnsX931dMlmyQtI+uQ/1/hkddLysoGGBuxFswdbjbgW4MTnr1Yv3Xn48RnRcUlKu2ULC1GeWxaJyvzz5VCLpfbd/j4Og6n4a9odlILO+PftK/hl0RG3bKynxMdHT106FDSWUCA8As1hg4dGhUVRafTScchgL8tDT6wxdysH5EAg+ydKiqrE0KOEnn2X5D2+q1PYPjl6Psl5RWmvQyWzpw4zKTXd8YHRkT7BIU/fZVRV8fR7tRhwVRLBzvL+k9KiS/TN3odf5yUWlxartxWYfyw/ttXzJNtUBhXXFrustcvKOJmVU2tjJTE6IH99q1f3Jbc9DTXYh5NXLIR21IgpbKyUq+LfpsuA00W7SOV4eoa87rqivHesaQC/KyydxmpV4+/iYtkV5YpdTXuaunYvsd/3/NQU1Z0ZcVQUUmZ8V7/e6U8Hjdy5Qge519HUlLK6kPXn857HhN/zOWrD6WoY2jqdOA3X8WvYVeWRS4bONNu8qH/Px5sFJWVlV319fsMGLZpr4DuvGaMGVBZUX4p5hnpID/qdWbaueOHb10NLy8t7WVsOt2BZWw27FuDuVzutFHGnH8f0at20vA+Fdr0SX9FRVnpJLOeUyZPPNio70MAEDSLFy8OunDJ51qipLQMkQCsSWbVFeU+1xKIPPsveJuVdvnU0fs3LleUlRj0MbWevbTXgKHfGf8k9sbxnRtepyZzOHXKquqT5jqNn+5QX+Hx6Na1gL2bc9JfSUrLGJoOHT99fvd+X5mYbN7Inj1MzFjbyG+Q961bkPzwdsqrl5KSAnHdvfVgs9m5ubn11RvZ2dlZWVkMBmPRokWTJ0++4MEa3b8n6Yxf4RsSvXLvqUcBW7todCAYIz03/2jwzYjYhNKKqv49dBdPGT3U6Hv3u/794JnbseBX2e9kpCSG9NGfZz1soKHel8N62a8z6911/+pZ9Utuxb9c4/31e+166WkcXT/vN1/I7/v7wTObtV44NwJNh0E6AMDXeXh4FBUWbnJ0Jh1EoFXV1Nqu2f3uQ5Ht6AGKstKht+Js1+wO3rNmYK+uvzD41uOkiSs85aQlZ4wbIsqgh0THrdpz8mNR6fp5kymKOhMZu2CbT2f1Dgttx1TX1Ibeilu152RJWcWaP6yb9TV/g4yUhOuCKYt3+Dg4OBgaGt6/f3/06NHV1dXfmZ2OzWafPHnSzc2tffv2zRkVfhaHw2GxnMabjxCKKg3BUVVdbT1z/rv3eVMnT2irIH/p8lXr6fMigk6YmRp/OTg+8bm5zQwGnTF1kpWigvy5kMvHAs4mPk+6G3mJf6Pbm3d5z5NfddfvoqggX/9bivLyXz6UILAYOXTc6OFLFi9OfPqUwcBHnf/A4/GysrKysrKKiopadv1uXV1d27ZtmUzmpUuXSGUQERGRl5fX0tLS0tJq5jsMOBwOy8lp7ND+pKo0hE5VTe0UJ9d3BZ9sxw5hrofLAAAgAElEQVRtKycbEhVrs3RT6OFtg4y6f3X86fAbDht262l2XDx9QlV1bUhU7Ar3w8VlFc4O0yiKepKUNtZhHYNBtxs7TFFW5sK12/4XIp++zLh9ah+/kqOWXWe9eMOj5ymzrEcbG+o/fpHifyHybcHHmyd3N+vLbsDcrJ/FEJMlixclPn2GbSk0Pw8Pj4+FhWOnrSMdRGhwaqtv72BWFr7XNJskLqOY8+Dy7e0zhm0IVO72H7MwPDy0rKowT1TyX5eaKj+9L36dLK+hLyb9vzYbYtLyFEXRKJoIQ/SLZ68pfZcuo6rdSK/mp4lKynS3X+9zaLmjg4OhoWFjPayHh0dhYdFi5y2N9YCtXE111XLmpIK8d2MmTpVXVLxxOZjFnHjwbHif/l+vKCp4/zYt+bmufnc5ecX6hbINvhc0UjKyS1zctqxwdGjU9yEACJSkpKSjR4+yth8mVaUhdGqrqzY7TvmU/36ola2MvOLda6GujjZb/UO+Wl1BUVTivVsb5kyQkpUbZTOTzhCNvRp8eMvKksKPM1h/UhR16/K5nSvmqKhp2Mxb9jH/XcyVS49vX9t3KaajVueGD3L90ql3rzN6mAhEl+4/Vrk5jDL09PR0dXUlnaV1ERUV1dbW1tb+1wdUDofTp3cvi4G9BbNKQ0BU1dTauXi/+1BkO7K/opxU6O14u3Xel3Yu/2rtBUVRF248nOvmq96+LWvamHcfii9FP/r74fNbR/7srP6vyy6nI+9mvi0w6/2vS1c0GiXK+PxmqupadlpOnk5HAr1gvzS6f88xA3rh3Ag0HXTUAEFUVFTUUU3NZY41y34c6SxfJyAdNdZ5nz4QFHlx9+rR/Q0piiooLDGd5SIhLvbiwt5fGGyxZFtswstn53ZrqalQFFVeWa1nvZTD4b677ksXETGd5VJdU3vHz01GSoKiqPcfi7rbLFeQlUoPI1+YzMfj8YY7blbV1l+xcqWFhUV1dXXD7RudTmcwGDwej81m1y8XExPbsWPHihUrCEWGH3Lq1Kk5c+Y8i7mmq61JOssPEZCOGqs2bvU64h9+1n/MiKEUReV/+Gg0dKyEhETa49tfDp67dHVgcNi9q8GG3bvxl5hPnnEz5t7ZYwdsrMZSFBUdc3/05Onx0RE9DYRjtqD0zOyeZub+/v4zZswgnUVAcTiciIiIM2fORl69VlpSRDpOqyMrp2Axxnz6dPuxY8c2T3uPU6dOzZk9Oz74iK66WjM83Vf9VEeNn+qu2RTW7jq6/6/gkINuowf1pSiq4FOR8ZTFkm3Ek68c/+p44ymLampqY85685tkvP/wSd/iDwU5mawbZyiKctiw+1zkrTunvXp2+eck0TiHddEPE0/tdJk02oyiqOMXry7e4rVj5TwWczJ/wFK3/X4XrsSe8e5j0Pmrz9gM0nPeGk1c4H/8OLal0MyKiopU1TrqT1mlP2ExwRg/3lHjZ6caaQpPTmx8Fe4zdP0Z1T4jKIqqLv5wZeUwhpiE1eFH3/mttGsn4o9vYIhLtpFv17CjRv7z2Buuky1231TQ/Ga/xoYeH3PJfXDZYldUG3nl33whv47Hi3IZa9ylY3hY47RbKCoqUuvY0WHlhlmLBPeA8ac6ahCfbWT3ptWnj3rvPx02cLg5RVGfPhRMHWHURkIy/GHKV8c/ir3lOMU8MOqRnoHQXErh8XizxplpdeoQFiqgbT8A4DdZTZiQ9vrd7vO3CPb2//GOGj811UgT8d3uHHx8/5ZjwX2HjKYoqvhjwSJLkzZtJP2jk746fu30Mc/jYvxuPO+grk1RVFVF+cxBulwO90LCey6XM3uYQWVZaUBMqpSsHEVRxZ8+MAd1Vu+sfyDsPkVRH/Pe/h97Zx0XVdPF8bNJLN3d3VKCioIgKIgt2I3YHZjY8djdYgciKCioKIJ0d3cu3c2y9/1jeRdYFkEfFPS5349/cGfOnXvuujt37sxvznl65URmQnROWiIAWNktGwkRNQDA7e7F51eOFxcV8fL+qZnm/hpocyMRD4+MEBFAX0ZCRI09115ec/30+p8tk0ZrAkB5Tf2Y5c5srMTEF6f7Grd3ULTmOdU1Nae5neXmYAeAipp6lTk7VGXEg+45A0BxRc2pB54xabmJWYUAsHTq+J4RNZiy49JTz4DowLvOwnwjIrF4dlGZ4dKD6NwIyi8Clf+gjEQePXqEw2JWzhhBKcyjU3MuPn0Xm5YrKy40dbw+w0g8MDbVwy/cLzKpta3dWEt53CiVZdPMaLnJj911+xadevuAo4xY94yV47GbZVV1bmd34AexNlNeXffma4SemryeKuP+pCfe3zQUpGjCCwAQ4uO2GK35zCcoKiVbX03+R42LyqrEhfhoKg0A4GBn1VeTD45La2vvoFA6U3IK18yxpKk0AEBUgHeCnlpAdEoHpbOv5nFYwGAwG+dNWe587ZOvb2trKwBgsVheXl5BQUEpKSlRUVEREREREREhISExMTEhISERERE+vpG7FweFzs2bN6ZPmTSSVRpRsQlnr96Kjk+Sk5acbm3J8K4eEBzm5un9OSCotbV17GiD8WMMVy6aR1uXPXT6gn9QqMvVs7LSUnT7FRt2lJZXeD67jx/EL6usotLdy8dglLb+KMa5y0cvXmuqqdBUGgAgLChgaTb+sat7REycoS5j6P6QyGhtDTW6SgMAls6f6xcYEhkTTxNqZOXkYjAYRXnZwX8sw4uCnMz0KZNu3bqJDqCZ4unpuXnrtvzcHF7VMXyTN0sp6LEJyeBJPDDci0y/lLqUb9xq44fZCYRKaaptKc9rzIr2TfR1nT5dWlbu0oXz06ZN+9VXvnnjhu3EMcOo0qCTmp1/9p6rf0Q8C5Fgaqh9bs86NhYivTYtp9Dp3O3opIymllY1BekdK+xmWHRttFq17ywVod4/sYtufPa+64dvER/uncbjcNtP3Whuad2/bvGZey9ff/xWGPBykP6UV9V4+AbpayrrqffaGvL4ra+GkixNpQEAQvy8k8boPvX6EpmYbqCpzNBIfWNTSlbe2vnT6alMRAX5JxjqBETEdVAoBDw+LC5VS1mertIAgMXTLb+Gx0UlpdOEGs/ffxHk41k7fzrdYNcqe+NRagLDOiuhICVuO3HMrZtoX4ryu3n06BGCwSpYDjB/93uoK0xPdr9clhSEwxOFNcfprzqJI7LSquqLMmMeOldlxVJamrilVNVnbZQ0mkqrCr28AUGoYzZfp7eT4nG5OPqzxWF3DA4PAFH39nW2NmvO25nsfrkg5O1sl9TBONNaW1EQ6sWvqMuvwDicy/n6kkdajabSAABWHkFRHbNcf9eqzBh+Rd3+bi3mgfOoxQezfB8jCLVnVQM5BzAYLjHGl0qmkGP9Mj+4THR+NZwqDQDAYJRs13hfXFtUVCQhIfHv23v06BEWi5uzZCQmY2UgOz3F5cqZyOCvRCKLwTiz3ccvsLCy0WtzM9POH9qVHBfd3NSooKK+fONOc5uZtKoDG1cgVOqxaw/oxi5XzgR+9rnz+hMOj/9n/9aW5uY1Ow+6XP7nk6ebX3LxIP2pqij/8s5dfZS+uo5+z3Kvl48V1TRpKg0A4BcUMja1fPfqSVJMhIYuk7B/BbmZGAxGWn7YBIs/AQaDWbxmy951S4bqe4iCgjKiKCoq8n7/fteFB8Oo0qCTn5n66tbZ+NAAApFF23jC2oPniP/v/Auz0++cdMpIiG5tbpJWUrNz3D7Wqjsi8tmdqxAqdee57lS2rrfORfp/OPXEB4fD3zy6o7W5adHm/a43z37zfv0iomCQ/tRWlgd98FDS0lfS0utZ7uv+WFZFg6bSAAAeASE9E4svHs/S4yOVtZmEe6wgFwmIiNNUGgDARuJQ1tJPjAxub28tycuuKisxsZ5NU2kAAA+/oO448wj/D00N9SROrpamxuLcLHZObiUtvYyE6EF6/huwmb/qxbVTjx8/3rRp03D78l/n5o3rU01GjSiVRkxa7sXnH2LT82TFBKeO04XevUtgXPqbr5F+USmtbe3GWopjtZWX2Y7HYbHH77/5FpN2a+9KGTFBurHjiXtl1XVup7fgcYOa3yuvqX/jH6WvKqur0mvK96lPkIa8BE2lAQBCvFzmhhrPP4ZEpeToqzEuUaXll5RU1swyM6CpNABAkJdrooH6x9CE+qYWLhJbY3NrVmEpF4lNV0U2Ji13QK8+RyTdffP17bntI0SlAQDyEsJTTUbdunkDnRtB+RX8zdPxKH8uHu7uU010OdhZh9uRLgJjU603HAuITjHVV5cVFz56+9Wl59702m8xKbabTrp9DrMw1Fxqa1pUXrXljIvzja7lASUpsdCEdA+/cLp9QWnlM58gHk7S91UaVXUN99/4Td10Qmn6xu3nHxaXV/U1qG1oMtPvtcNJQVIUAGJSc37CeNoE/eLy6o+hcbTDzALyt+iU8bpq7KwseDzu47UDWxfZ0s+tb2xOyiqcaKg5QlQaNCaP0WEhEFatWpWQkEAmkzs6OiorK1NTUz9+/PjgwYNTp05t2bJlwYIFpqamampqqErjj6C0tDQ0NGzh3JnD7Ui/BASHmc+Y/zUodOL4MXKy0s6nzp+7dode6x8UajVnseubd5Zm41cstC8sLlm/88C+Y2dotcoK8sHhUW6e3f1JflHxY1d3Xh7u76s0Kqtr7jx8Zjl7obSW8SYn58IScl+Dmto68wljexbSZBbRcYkMxh0dFEuz8etWLulZWFRCBgB6opOs3HwpcbHGpub3n/xcnrqGRsZ0dnYO4uMZThbMmRESElpWVjbcjowssrKyJllazZgxo1lIS+dEkPJ2V1FLB045XTwH39+t0gCA4VdpAAAGi+fg45TTFbV0UN7uqnMiqFlIa8aMGRaWVllZWb/usqWlpaFhYQumTvx1lxgkFTV1Vit3Ryamz7Mxk5cSe+Dx0fFAd2qPkNhkkwWb0nMKV8213u0wH4fFLth+/OTtrmSlsSmZsSm9PqXs/OKQ2GQqFQGApMzc0LiUmRsO3n75TlJ04JXCqtr6e27e1g5O8haLtp68XlRawVBbW984cfSonoUK0hIAEJOc0bc1HA7n63J2+4q59JL6xqakjFxzYz0CHt9BoViM1Vsz37bnKUVlFQDAy90VMDkrv8RqnD6RgM8tKn3vHxabkikqxL9gqrnUIO7llzLfxiwkFO1LUX43bu4e4gZTCGwcw+0ItNVXfj44syozRsZkNoeobPaXZ2FXu6bXK1LDP+yyrCvKUJi0RGPuNgwWG3hmZdKr87Ta6uz46uxeQQ4aSnIrUsPpsf1q81Mq0iL8jy/M/OBCEhhgNbetvjrr06Mvh2Z7OGhH3d3TXMm4Xt5WX93eWCui1etJR5NZVGXFMW2zs6Mt+IKjkKqRsjWTxM8NpbkkAXFKa1NxlG/2l2eV6ZEIlfnYr62hJuzaFqmx04U1mQcw/51IGFoTWNg8PT2HpDV3Dw/TydNIIz6yfU1V5erZlkmxEdaz5kvKyr955uK8pVtcEhcRvGjymNzMtNmLVzls3YPF4XaumnfnwglabWpCTEpCTM/WCnKz4iKCqQgVADJTkuIjQjYtmu764KaIuOSAntRWV75+fMdxrpXVKJlTezeXlRQx1NbX1Yw26TUaoYkwUuJ7+UCnMDdbRFyyuakx0Nf7zTOX+MhQ6oh/BwEA0ynTWNnYh+p7iIKCMqJ4+/YtKzvJ2GLqcDsCddWVTosmp8VHmU2zF5WW+/jq4XmnNbSq5KiQzTNNCrPSreevnLduNxaLO75h4fNrp+jnZiXFZiX1isZRkpeVHBWCUKkAkJuWlBId6uww693T20JiA3f+9TVV3i/u7VlivWicwvXD2ypKixhqG+tqdcb06vzFZRQBIDOReec/xtK2srQ40v8j7bAoNzM+/Ju20XhWNlJVGRkAlLV6qQCVtPUBoCAzBQAk5ZX/efbxn2cfd51nHgdxuGAjcRhbTH39etjSsKLQKC0tDQ0Ln2c5QGbA30lgXPqUzf8ExKSa6qnJigkdvedx+cVHeu232LRpW8+6+UWYG6ovmTq+qLx66/nHh26/BgAlKdHQxEwP/yi6cWFZ1fOPIbycpAFVGlV1jfc9A2y3nlWetX3HxadF5TUMtbUNzaZ6aj0LFSSFASAmPa9va6WVtQCgp9pL6kE7TM0tBgBlaVGfy7t9Lu++f3D1gB9IdX3julP3Z5kZTtAdWUGd7ScZhYSGoXMjKL8CNKIGyoijtbU1JDTk5t6Be+3fxu6Lj4kEQtD9o1KiggCweb71mGV76bWvfEPxOFziq/M0zeDWRbaac7b6BMceWz8fAKaO1yOxsb75GkFXObz1jwSAeVZjmVwJoLahyTMg0v1LeEB0CqWzU1NBaufS6TYmejrKMgyWmflkABDm5+lZqCglCgAVNfU/Yew4x9I/KnnuznOjNRVZiYRvMSmiArwHHecCADsri5FW1zbTay8/FJZVfgiJ66RSdyz+5TuAfwh2VpaJozXJ5BJNTc3h9gVlaPD398fhcBPHjxluR/pl2/6jLCzEiM+e0pISALB9nYOeWXfOphceXngcLj3Cn4ebCwB2blqjpD/h3acvp5ydAGDaFAsOEvtrL5+dG7tepz28PgDAgjkzmFwJoKa27s37j6/evv8aFEKhdGqpq+7Zun7aZItRWhoMlhlZOQAgKtxreU9JQQ4AyisZVV8EAv7SyUM9S8orq27cf0Qg4G0su96ls3Pz6xsbFXRNmltaaCW62hoPr50f3vQu38d8wlgcDufv729vbz/cvowUvnz5Mmv2XOARV9/tzqnIZNsiym+GVUhG3uGqkOmSyOcH9PQN3V+/Mjf/JeHE/P39cVis6WjG7de/n+ra+n1rF+1bs5B2OH7hls+hXdNzCILsOH2TSCT4PTonKsgPANuWz5m+dv/p28/nWE1QlB44FkhGXpHFGL3H/+xRlu13PrG2vvHNl2D3j9/8I+IpnZ2aynK7HeZNNTPWUVVgaAoARAR7aTqVZCQAoKK6tm+zJDZWY52uWYyrT94UkMs+fIvspFJ3rrQDAAIef95pbU/7iuraWy+8CHi89fjRANDY3FJaWS3Ezzt74yGfb13qXmVZyVtHthlqqcCwYmY0CofFon0pyu+ktbU1LCTEcMPl4XYEAKCtoUbTfqem3Q7a4UenyeT4AAAABIm+vx9LIFoef8fGJwIAqjPWfz06L8ntvPTY6ZyDC0RRX5IlqmM2bvttLnHmAQPaG2sLw70Lgt+WJgUhnRReGXX12VskDCfzyTGGUqsvyQIANt5eWwNpbrTWVTJtPPbh4ZbqUrMDL4HZnuAGcm5HS+PbNXqUtq6xH5+89phN17gkGF2NurO7valOZ/GBgW/414PFE4Q0x3354rdu3bp/2VRra2toSMihi3eHxLFfSl1NleOOA47b99MOl1iPC/P/TPsbQZB/9m8nEllcPAMERUQBYOm67esXTL174aTl9LnScgNHqsjLzjA2nXT61lMZBcZoUnTq62r83r/x9XSLCPbvpFCU1LVWbXYynWyrojmKoSkAEBDuFcRbWl4JAKory5m2XJib3dTQYGOg1NrSTCtR1dI9dtVFVnGYH47fh0AgGowz9fMbgu8hCgrKSMPv61et0ePxBOLApr+Y+trqhZv2LdzYNUG9dfaEmKDPAIAgyK1jOwlE4lnXL/xCogAwZ/XWAyumP792erz1bHHZQcUoKsrN1DOxcLr0WFJOqT+bxrra4E9vA71fx4cGdHZS5FQ0563dZWQxVUG911tnUU4GAPAJivQslJBTBIDaql5aeTrTFq+NC/E/tHq2qq4RkYUlPuwbv5Do0m2HAEBUWg4A4sP8Z63sjktRkJUGAPmZqaq6RoO5u+FC12TSRSfHtrY2FhaW4fblv0vX3MhIUgA4XXnOQsAH3jkoJSIAAJvmWY1ZeYhe6/Y5HI/DJjw/1bXwtGCK1rzdPsHxR9fMtRmnQ2JjeeMftXXBFJrx24BoALCf1O8Pobah2fNbtPvXyG8xqZROqqaC5M7FU23G6WgrSfc0yywoBQARhoUkSREAqKxp6NusrJgQAATEpG20t6IXpueRASA1r2S0xo9NHW+/8LSuseWI45wfOus3YKanhs6NoPwiUKEGyogjNTW1o4OirSQz3I50EZmclZhVsHPpdJpKAwDkJUXmTR53/40f7XDjvClr5ljSIzt1dFC4OdnrG7vms9hZWaZN0H/+IaiAXEFrwcMvnJ+b03w04+RaeXXduhN3/CITEQTG6iif2LjAxkSP9oRmSnZxGQDwcfXaZyYpIgAAdY3NP2HMw8EuKSKQkJkfk5qDx+GoVASHwzU2tzI0deS2a3NrOwCoykqwshD6c2+40FaUcvs2qAS9KH8ECQkJSgpy7GxsA5sOB+HRsQnJqXu2rqepNABAQU5mod3MOw+79n9vXbNyw6qlNJUGALS3d3Bzc9XXdw1qSezsM6ytnrzyyC8sorXg5uktwMdraca477+sotJh867PAUEIgpgYG545vM92yiRpiX6XLbNy8wCAj6fXkJpmX1vHKORi4P0nv9VbdldUVZ8/dkBDVZneYENj49G9O2ZYW1VUVT96+drlqevMJauj/N6R2NkH/KCGBXY2NiUFucTERHQATePOnTvr1q3n07eRXXYeS0DnBUYQnIqGqns8c122TZ485fr1aw4OQx9iPSEhQUlWkp11+P/fsVjMjhV29MNRagpRSenFZZXiwgJxqVlxqVkzJ42jqTQAgIDHL5o+yT8i3i80ZjBCDQBw3rCkP5VGeVWNo/MFv9BYBEHG6Wmc2uEw1cy4v3gV2QUl0CPcBQ0pMSEAqG1o+r4Ph648aG5tAwBVeWk2Zp+5z7fwNc4XK2vqzuxyVFeUAYCcQjIAXHv6Rk5S7LzTWiMdtdC4lP0X7s3dfDjq9Q1BPp6+jfw22FlZlGQl0b4U5XeSmppKoXTwyY4I4TUGg1Wf2b0SwCenXZUZ21xV0lpbUZ2TIGVsS1NpAAAWR5Azm1eWGESODxikUAMAtOY7MVVptNZWhF3bUhofgAAipGaku/SQhOEUkmC/gTcaSnMBgMjZq7sgCUoCQEczk7FfcZRvhs89k10uDNoOOo2luR0tjdoL9kiOtm6rr8r5+jL7y7OAU0umnPuCZ+ke+9UVpueHeKrP2kwSGP7UWjR4ZDTjot/8+3ZSU1M7OjpUNIdf4zggWCx2+cad9ENVLd2k2MgycrGwqHhaYmxaYqzF1Fk0lQYA4AmEafZLIoP8wwO+DEaoAQDrdh/qT6VRVVF+eKtD2LcvCILoGo3b5vyP6WRbUQkppsaFudkAwM3D27OQZtxQz0QECQCFedlNTQ0bnI6YTZleU1Xh5frkzXOXrctmP/eNYGMnDcb54UJZQ8fv7WCzsKGgoPxBxMcnGFnbDWz368FgsXaO2+mHChqj0hOiKkuLayvLs5Ljxk2eSVNpAAAeT5g0a3F8aEBssN8ghRoAsHjLwf5UGrWV5Rf2rIkN8kMQRMNw7Kq9J43NpwqJM+/8S/JzAICzd+cvJCYFAE31dUxPIXHxCIlL5aQmZCRE4/EEhErF4fDNTY0AIC4tr6ipGxfi/9H1wXibOVQq9evb54E+7gBA7Sf018hBQV2no6MjLS1NW1t7uH3575KQkKAoLcbGOvxaKxqRKTmJWYU7F0+lrwHJSwjPtzS+7xlAO9xgb+k427zXwhMHe31TCwCws7LYmui++BRaUFpJO93jayQ/N4e5IeN2PgAor6lfd8rla1QygiBjtZVPrLe3GTdKUpifqVc5xeUAwMvVa6wlJcIPALV9Vp1oPo9SlgmITnn47tvsiYZUBHnxKdTDPxIAOjupfe2/Q2puifvXyB2LbCSER1wodDZWoqK0GDo3gvIrQIUaKCMOMpkMACOnL87ILwEALcVeukJV2e45MiVpseq6xsvPvSOSMgvIlVlFpQ1NLaIC3QNQe6uxzz8EeXyN2LzApqi8Kiol22GWRd+MIRU19R9D4/A4nOOcSYusx2soMB/g0mEh4AGgur6xZyFtYYCHk3HOYjDGluuOJmcXXtixbI6FMQuR4BsWv+HUvTk7zkQ+OU0XqQBA2Zf72YWloQkZh269NHNwTnW/LMw/UrKFAYC4EB+ZXDrcXqAMGWQyWVJMdGC7YSI9MxsAtDV6xYJTV+5+71VWlK+qqblw/W5YVExeYVFWTl59Q6OYSPeE+IK5M5688njt5bNtnUNhMTkiJm7N8kUEAuPTuaKyyuezPx6PW7dy6bL5czTVBthDxkIkAkB1ba8Zz6bmZgDg5en3B5uTV7D9wNF3H7/Iy0o/unnRfHx34J/7V86ysBDVVZQAQEFOxthAl5uT89y12x7vPy4awYlpJERFaM8UlOfPnzs6OorbbpWcto3pxlmU4QVLYJF3uEoUlnN0dOTg4Jg/f/7Qtk8mk8WF+5V+/k7EhARYiN0qTx5ODgBobG4BgKyCEgAw0eu1NDtKVQEAMvMZg/wzRYCXW0+93y1fFdV1HwMj8Tjcmvm2S6ZbaijJ9mcJADQna+p6bRZpamkFAF6uAXIxVIa/ySooDolJdr7yYPzCLRkfHwn/f1iYU0jedeaWd0C4vJSYy8ldE426thpX1zUAQFt7x7Nz+2hCEx1VhfKqmtN3Xrz6ELBuwfQB7/2XIiYsgPalKL8T2veNXUBsuB0BAGDjF8H22DVLJHEDAKW1qYGcCwBC6r3CJtMCXTSQGdNQ9gcLFz+/AnMdQGtdZUnMZwwOrzxlhdzE+TzSakzN6ODwLADQ3tBr7Edpa6b73JOWmrKwa5vkLRZKjrbur0GjDZexBBYeKRUA4BSVE1A2ILBzpb69Vhj2XnZCd5qnlDdXsXiC6rS1/bXz+2HnF8spHYL3Qdr3UFhsgKw0IwFBETEisVsXyMnNDQAtTY0AUJCTBQB6xr1U4LRAF/k5TDJ59YWXX1BdR7+/2prK8qAvH3B4vLsb3qcAACAASURBVP3ytdPnLVVU+56+iuZkXW2v2Notzc0AwMXNy/SUw5fuEogsCirqACAlp6BtYMzJxfXw+nk/7zc2cxYOxv/hQlhUnFyKPjpRUP5CyOQSQdER8WgQEBYj9Oj8SVzcANDS3FSclwUAmoa98pHJq+sAQHHuYLNtcvMJKGnp9VdbW1UR6f8Rh8PbLl4zafZiWRUmq8J0CEQiADT07vxpcZI4uJnr0XfOt8hLT15/+OIEm7lEFpbIb58u713v7DDrpk+UsLj01pM3DznOubRv/c1jOxEqlUqlTrFb7v3inpTiCIqRwBQBEXEAIJPJqFBjGCGTyRKCw7kRgoGMfDIAaCr02m2iKtstgFaSEq2ub7zy8mNEcnZ+aWV2UXlDU4uoQNctzLM0fvEp9I1/9KZ5VkXl1VGpuQ4zzJjmqa+oqf8UloDHYVfPMl80ZZyG/Pf6MSIBDwA19b32qDS1tgMALyeT3XpYLOa603I7p8sbzzzcdeU5QkWoCLJs6vj7ngGqsj/2TnfxuQ8Rj9tgZ/lDZ/02xAV40LkRlF/BX56MHOVPpKmpCQBGwqZPGtX1TQCAw/b6sbD2WGO49Oy98oyNpx+86aB0mhpo3NrnSM8SQsNUX12Yn/vN1wgAePM1AkEQe0smeRyUZcTczu6wtxzz+P0346V7NeZs3XP5aXBcWieVufCQlsckr6RXmLia+kYAEOBhTKM7oHF6XklydqHJKNVVMy14OElsLMRpEwwWWZs0t7a/DYhCEISWAJ6GvKTIIpvxh9fO66B0fgplnvN4uCCxsTY2DbDVFeUPorm5mcQ+QsNpAEB1TR0A4Hpn/mPtEcPw3LXbMlpjjp+/0kGhmI8fd+/KmTGGvd51J5qMERESfO3pAwDuXj4IgiyYw2RBTkVRwfPZ/QWzZzx8/krX1FpRf8KOg8cCQyM6+0nSLCIkCAA5eQW9vK2tAwBBfuYyuKdub/TMrAOCw045O8UHfuyp0gAAXW0NmkqDzhQLUwBITh3UDO9wwUFib2xsHNjubycqKmr5ylVilqslp29HVRojFwxGcvp20UkOy5avCA0NHdq2m5ubSWwjYmRFYmPtW4ggAABVNXUAIC3ea293W3sH9Olm6VTX9xJS9JSA9EVZTtLj6pF5NmaP3vgazl2nZr1899nbQdFJTAdaNGlFblGvN3CabkOAl4ncjWGkpCAlvmSG5dHNKzoolA9BkbTC5+/9jOzWB0YlHN+6Mur1TbpKAwDEhPgBwFBLpWc4EOsJRgCQllP4nZv6PXCwsaB9KcrvhPY+2DNswzCCZ2G2ax+BtoYqACAJ9tLWd3a0AQAGy7zLamusYSjB9R/giltC0XTfU1mT2dl+L7y3mXmuNYh5cLA8JRTpZ7coK68QADSW5fcsbG+oAQAWLsatcpkfH7TVV3c0N4Rd3Uz711Jd2lJdFnZ1c7L7JZoNn7w2TaVBR0zPAgDqCtLoJU2VxXmB7pKGU4gcI2i+G89Kom26/ZfQvocjPGwDDaZOIggCALXVVQAgJtlr20lHexsAYLFMJvEBoL6muuchkfi9LaeyiiqXn7y1njXf8+Uje3N929HK55x3xoQFUpm9pPALiQBAcX5ur8vVVgMAL79gX3sAUNXSpak06Iw1nwIAWWnJ3/FqJMBO4mhCH50oKH8jLc3NrGwjYojC3A0Eqa+pAgBhCWY9P455zw8ADXW9Rik9JSB9kVRQPnzH3Wy6ve/rR+ttR68wU79zwikpMohp588rKAIApYW9Ov+GumoA4OZjspegMDs9Lz1Za7SJzQIHDm4eIivbWMvpFrMXt7U0h3x8CwAyyuo33kduPnHdev6qxVsOXnT/JqmgDADSigOoWocdVnYSADQ0MMkcgfLbaG5uZh8x4TQAoKahCfrMePSc2bj0/IPK7B2nH3p1UDrN9NRu7llhpNmdSWSCnqowH/ebgCgAeOsfhSCIXT95T5SlxdxOb7abZPTEO2jMCmfNebv3XHsZHJ/BfD6EnxuYLCQ1AQB/n1UnGupyEuEPjlzdtWzlNNP9K2d+vblfWVoMAFRlfiDqXlFZ9avPYVNNdBmCeYwcSGxEdG4E5VeARtRAGXHQJhQwI2YlSUZUEACC4lJtJ3TvI8kndz2oKmvrD954IcDDFf/yHAd719rDmYdve7aAw2LnWBhfd/1YVF7l4RchKy5sqMEk0Bweh7My1rEy1rncQfkcnuD+JfyB19erL334uTknjx3ltHyGjFiv6NwKkiIYDCavpFcy18TMAgDQV2dM/TWgcVJ2AQCMG9VLfWxmoHnlhU9tQ9P5J16Hbrq6nd1hZdy95UuAmxMAisqr+vnkhgcMBkP7CqH8HSAIMnJ6g77ISEsAwLeQiBnW3Un48gqLaH9UVFXvPfqPID9favhXTo6uIebJC9d6toDD4exm2l657VJYTHbz9JaTkTLS1+17ITweN8XCdIqF6Y32Ex/9Atw8399/8vLSzfsCfLzWlub7t2+Qle61TqAoL4vBYHLzey3vJSSnAoChLpONm+8/+S1fv91If9STW5elJBjFzoXF5MjYeH0drZ5VtMaFBJhHyRshoB0CAFRVVU22nsqpMlZq7v7h9uXPBKEC5keUzT9q3xtpuwPtFbm202empybz8w/Z72uE96U0ZCREACA4JnnK+NH0wvD4VACQFRcBAMBgGOb+MvOKBt8+HoezMjGwMjG4epDiGxz1+tO3B+4frjz24Ofhsp4w2mn1AlmJ7sTJitLiGAwmt6jXnuzE9BwAMNBkEtPo7H1X58sPPK4esTIxoBcK8HIBQFFpBQD4fAtfte/saG3Vh6d2S/bJt0Ir6aD0uruWtjYA4Ga2YeU3g/alKL+Zru/byO61OISkAKAiNUxcfxK9sDI9CgA4hKUBADAYBlFFfclgt7ECAAaHF9O1ENO1MKR0lMT6FYS8zf78NM3rFgsXn7iepcbcbV1X+T9conKAwTAINWrykgFAQIlxbMnKxc8rq9Ez8kdnRxuCUGvykjAYLAA0V5ZUZcXwKYzqmdCksSwPAFi4u9dUsj49Qjop8uYjK7ABBgND0mWNtHmJn0NMSgYAYsKDTCZ1R09JiAoHAAlpOaD18L0n6POyf0CHjcPjx5lPHmc+eX9He4jfJ19PtzfPXJ7evszDJzB+krXD1r3i0t3xq6TlFDEYTFF+r5AzGckJAKCha8DYNEBZSVFSbKS6jr6IeLeKsTg/BwD4BJhnLhs5oI9OFJS/lZH/YiUsKQMASZHBhmZT6IVpseEAICIpQzvEYDCdvfuoosGFWaKBw+ENTK0MTK0ox9qjvvkGer/+4Ori4XKFi5d/tNmU+RucRCS7O39xWQUMBsMg1MhNTQQAZW0mnX9uWiIAaBqa9CzUHTvR4/7lxrpaSkd7aVE+Fy+/1dyl9NpXt87xCYpw9hOcaeRA++agT4fhBUGQEfUDlhYVAICguHRbk+4RewG5kvZHZW2D8203AW7OuGcnuxeeHr+jW+Kw2NnmhjfcPheVV3v4R8mKCRmqM8/AiMdhLY20LI202ndQPkckuX+NfPju2zXXT/zcHJPHaO9eYisj1q2aVZAQxmAwueReQo2krEIAMFCT69t4ewcln1zJz8OxxKb7l3vhmbcIP/cPSS5cvAIondSejYw0MOhPGOXXgEbUQEEZgFGqsgQ8LiA6hV5C6ex09e3a6lpQWkWlItMmGNAflkXlVQmZ+QyN2FuNRRDk+ssPkclZ8yePhe9CJOCtx+nedV6b+/7G0xObTfXVPfzC4zMY2xQV4B2roxIcl5ZbXEYr6aB0uvqGiAnyjlKW+VFjFRlxAKCF/aDj7hcGAOrykupykgDwNTKpZ62L51cA0FToNUWIgvKfQk9bi0DAfw0MoZdQKJ3PX3vS/i4oLKZSqTNsrOgqjcJicnxSKkMjC+fMQBDk8u374dGxA6YRIRIJtpMtHl6/UJIa5epyY+L4sa89vWMTUxjMxESETYwNA0Mj6EE1Ojooz1+/FRcV0dVmEpRy//Ez3FycL+9f76vSAICa2lr7FetOXuwlMXF98w4Axhkxea9GGVEcOHiwhQJyK6/8G/XAT9CQFVX07lJHfcXApiOS1rKcvOcHY3aNjtyknnZpSV1q0NDa9wsGK7fySjMFDhw8+JMt/LFoq8gTCfgvoTE9C79FJeCwWIuxegAgLSacX1LWQaHQqlKz87MLfibmJJGAtzE1un9iV/7XF8/P7zcbreP+KTA+rdcCqqgg/zg9jeDoxJzCrkt0UCgvffzFhPhHqTHKYQFAQ1EGAL6E9XL+/usPAKClJAcABy8/4OJgf3ZuX1+VBgCwsRBNDbVjUzKzCrqTvHj5hQKAkfZI3xmGgvLfhFdWE4snkOMDehaWJ4dgsDhRHTMAIAlJNZUXUDs7aFV1hemN5LyfuBAWT5AwsBqz+foslxSTnfdFNE0KQjxrchMZzNj4RITUjMtTQhtLu65C7ezID3Rn5xPlk2MMr61kvWrK2S89/3FLKHEIS085+2X0+gsA0NZYE3hmZbLbhZ5nFQS9BQAh1e5deqXx/kQOHmGtkTuXiqKioUMgEMMDvvQsjAoJwOJwxqaTAEBMUrqkMJ/S0fVFzU5PKczL/okLEQjECVZTj1178CWp6Oy9l4YmZr5er9OSekXfFBQR1TUyiQkLKsrr0mpQOjp8PF4IiYipajGRqtfVVO9cNe/uxVM9Cz+9fQUAo0YPMKmCgoKC8p9FXk0bTyDGBvv1LEwID8TicHomFrRDYXHpsqJ8CqWr88/PTCXnDzZxW0/wBKKRuc3Oc/efh+fvu/ZMZ4xZoI9Hdkp8Txt+IVENg3GJkcHkgv93/pQOfy9XfmExBY1RfduUUlAFgKAPHj0Lv3m/BgAZZfW2lpbVljo3D2+nV1WWFgd/eGNkbvMT/qOgDDujlGUIeNy3mO6QdZROquvncNrfhWVVVCoybbxej4Wn6sTMXrvy5k0yRhDk+ivfyJSceVa90jIyhUjAW4/VubvfIeftxSdH15vqqXl8jYrP7BWSWVSAZ6y2Ukh8Ru7/N/12UDpdP4eJCfDqKDFZCWppa9dbvG/HxWf0kuKKmrcB0dZjmSd57I8vkcm8XKQJuiM9jREKypCDRtRAQRkACSH+1bMnXXv5Yd3JO6tnTcJg4OR9j/rGZlqtkpQoiY319ZcwS2MtJSmxsMSMo3fcOElsjS2tmQVkRSlRmtkoZVklabFrrh8AYMEU5jNZlbX1j98F9C3XVpJWkBKh5x7ryY4l0+bsOLvkwJWdS6fzcJIuPHmXV1L+6swOmkTX5a3f1rMPnFbMdFo+c0BjVVkJc0PNLxGJM7f9Y281VlpUwCsgys03VFVWYqqJHh6HU5eXvPnqEzcHu8VorZKKGg+/cJ/gGD1Vuck/+MRFQfmbkBQXXbtiyeVb9x227F63YjEGgzl65nL9/8MYKinIcZDYX719P9ncVEVRPiQiyvnUeS5OjsampoysHCWFLg2yrraGsqL85VsuALDYfhbTC1VUVT945tq3XEdTTVFeVkyEybKf05Z10xasmLdqw56t63m5uc5cuZWbX/j26V3aT/7uo+cbdh/cv33j/h2bamrrktMydDTVL9y4y9DIhDFGNpYTNdVUjPR17z1+wc/LM9NmMpVKfer2xtc/cNbUyQa6aGbNEU1ycvLtW7dll53DsTEPTvjrqM8ML/T4h1fLgsDFPJb1SIba3pp2eVl7TamA0Uw8ibcq+n3a5aWqW59yKTGPIfmj9t8Hx8YpPmvPrVvbHVev/k8lrxUV5F8zb9rlx+6bj191tJ+Kx+Ndvb96+AYtmmahICUOAAZayh8CI1YfOL989uScgpJzLq+4ONirausH2X5lTd1Dj099y7VVFBSlJUQFGeOX7Fw1b9b6g4t2ntjtMI+Hi+Pc/Ve5RWT3K0foW+juu/lsPn51z5qFex0XWI0zVFeUufHMk5uTY9IYvZLySvdPgd4BYXrqSlMmGNbWN6Zk5WuryF966M5wlfEGmrQIIke3rBi/cMuiHSeObFouISLgHxF/z817zCh1G9Of+RahoKD8atj4RJSmrEzzuhl5e7fi5OVYPD7vm3tBqJecqT2nqBwACCjqlkT7hl3ZJD9pcSM5N8XjCoHE2VZfPWDLNNrqq7K/POtbziurySkmz8Yr0rdKffZm/+MLg845qM/eQuTgSfG40liWP2HvE1pskizfx5G3d2vabdeYu73vuYxXkVYTUNbP+vyEhZNP0sgaoSK539zI8f6SRlP5FbvWVNoba6uzE8QNLDG/VwmK8kMIiojar1j75Nalk06b5i5zxBMIH9xffH7nbmu3WEpOAQA0dA0DP/s4b1k1c+HKwtysB9fOcnBy11ZXDrL9mqqKt88f9i1X0dCRllMUFBZlKF+xefemRdN3rV6waosTJzfvg6tnivNzLz1+Q3+2uj+5e8Jp0+pt+1Zv26eopqmlb+Tx9B4PH99E6xlUKtX79bPQgM/mNjM1RqFicRQUFBTm8AuJ2i5e43H/8jXnLVMXrsYRCP6eL4M+eFjMWigm06U4V9YxiPD/cH7X6sn2K8j52a9un2fn5KLlTBkMddWVn9we9S2XV9MWl1HgE2Ls/O3X7nReNevkpsX263ZxcvG+un2OXJh7+PZrWufv8+L+tUNbFmzYs2DDHgCQVlTVHWceE/TlwIrpZtPnC4tLhfh6Bbx7Ja2oajzJFk8gahtPCPrgoTPWbMykaSX52Zf3bxAQEV/pdOLnPzIUlOFDQojPYebE66981592WT1zImAwpx541jd1LTwpSoqQ2Fhef42YZKSpJCUSlph17J4HJ4m1saUts6BUUUoEAHSUpZWkRK+7+QLAAqsx/V2osrbhsTeTDUVailIKksJ9F552LLKZs/viEuebOxdP5eFkv/DMJ49c8erUZvqYzcUrYNv5J7uX2TotncbNwT5BV/VtQNRjbzVbE92c4rJNZx6JCfIeW2s3+I+itqE5LiNvyhgdLHZEBT1BQfkdoEINFJSBObzWvqml7YHnV5qQwlRf/czWJQ5HbgAABzvrjb0Oa0/csdt1HgB4uThObVpEYmNZffSm4SKnmm/dcxbzrMYeuf3K3FBTWpT5klVZVd3BGy/784Fp4ApzQ807B9esP3l34d5LAMDNwX5y4yJLo65FHQSBTiqVHo7p+8ZYLMbl8PodFx698g39HJ5AKxyro3JjrwORgAeAF6e2rjx848Q99xP3utYYpk0wOLN1Cb7/BIcoKP8FThzY1dTcfO/xiwfPXgHARJMxF044L1u3DQA4OUh3Lv3jsHnXzMUOAMDHy3P26H4SO/uKDTu0x1u1lGTSG1k4Z8bBk+cmmZrISEkyvUpZecXeo//054OWOhOt8SRTkwfXzjtu3WO3fC0A8HBznT2yb7K5Ka0WQZDOzk5a/xASEY0gSGxCUmxCEkMjGMDYWE7EYDCvH91y3Op0+tKN05du0Kocly08c2TfID8llOHCac9eDhlNQePZw+3IIPh3GUOGlgKPUy2l2apbHvNoTgQA0Ukr450nZd3bqns6dEjsB0TQeE6F/8P9Bw56eb4d2Pov4sjm5Z1U6rWnb+64vqeVrJprc3b3Gtrfm5fMDo9Pfen99aX3VzEh/gVTzQHg7H0mIjamlFXWHLh0v79aTWVZhhILY917J3auO3Rx/rZjAMDNSTq9Y7XluO5EeAgg9IEWFotxvei8Yu8/x288OX7jCc1guvnYc05r8ThcaFwygiBxqVlxqYyJDzAYoAk19NSVPK4eWX3w/Iz1B2hVNqZGt49sG+TdoaCg/H50Fu1HqJ3p7+9kfnxAK1G0Wqq34jjtb5VpayszovIC3fMC3dn5RGUmzAWAFI/Lg2y8paY87smx/mp5ZdT7Fopqm47ZdC38+tbAMysAgEji1l1+REzXvKsaQRBq52BD9WIw43c/DL++Ndn9UrL7pf/f3TLdZYfpJmVJwQhCFVDS76cJlJHCxn3HqNTOZ3euvnp4i1YyZ8nqncfO0f5evGZLQlSYj/sLH/cXQiJiNnMXAoDLlTODbLyqvOzy8X7fCJTUtRhKjCdYHLvicmT7mh0r7QGAk4tn26EzYyd2J5FEEIT6/5cUDAZz3sXtyHbH+5f/uX+561Vo7lLHbYdOD9I9FBQUlP8my3ccpnZ2vn147f2zO7QS6/mr1hzo7ttnrdiUGhvu7+VKi2xhPmM+ALjeOjfI9msqylzOHOivVk5Vk6FEd5z5jrN3L+1df3z9AgAgcXGv3ntKf4IlrRaB7p4fADBY7O6LD28c3hbw7lV04GdaoYbBuK2nbuAJRADYevLm6a3LLu5Ze3HPWgBQUNfZdcGFjcQxSOdRUEYahx3nNLe0PXj3jSakMNVTPbNpgcPxuwDAwc563WnFulP37fdcBgBeLtKpDfPYWVkcT9wbvexAtV/XD9ze0ujoXY+JBuq0RCpMKauuc77l1l+thjzjdPREA/Xb+xw2/PNg0YFrQFtIWj9v0ujuX/f/F566Dq87LV9++Nb60y7rT7sAgLaS9P2DjvRAIIPhW2walYr0l7oFBeXvBk2aiDLicHV1tbe3bwh+MtyOMFJUXpWcXagiI95XaVFd1xifkSciwKMiI07TFVbXNdY2NMlJCNNtvAKiFuy9+PTE5mkThnjzB6WzMzYtl0pF9NXlcdgBVrkGNC4ur07NLWpt61CSFlWUEu2ZeZFKRfLJ5Rn5ZFYWoqKUqJjgSEz+5+4XvvTAFbRn+2uws7ND2pqe37063I4MQGExOSk1XVVJvq/SoqqmJi4xRVRYSFVJgfaDqqqpqa2tl5ftVl+98f44d9laV5cbM22sYEihUDqj4xOoVMRQVxv3r2VV+UXFGVk5PFxcKkoK9HwuI5n5qzZgWEiuroNdx/3LKCoqkpaRUVh9nV9/6k83QmmuL3A/2ZAe3tFYzamgL2SygFdrIq0q++FOLJ4obrMp3/VIQ2YkBofnUjaSXXAMy8Ke83BXbcq3tspCkpQGp6Kh7IKjAJD77AC1rVlyxo7i91cqI70MLiUCQAs5M+/lkcbcOGpbE7u4ipj1en69rpilGTfXkCTVuJTHkD/frUsNJnAJCI6ZIz55LU3PUfjmTF1aiMLKi6yC3T+lrHubO+oqVLY8wmD/lRY5cqMakU9M+/Dnni1XhLhp7nvHIcckOuuP2g+GqkivrDvr8/PyJCQkfq6FntjZ2XXWlT45s/ffN/UbqKiujU/PYSEQNJVkebgYZ9wqa+pKyqs0lWR/T3JoSmdnTHImlUo10FIZcJRFpSJ5xaUZeYWsLCxKMhJiQoxROgakg0JJycqvqKnTUJQREeD7Wa+HmEU7T+C4Rf6zfSnK74f2PrjgddlwOzIoWusqa/KScHgij7QakYNxL1pbfVVzFZlXRh1+Vz57pJNSlR0PCJVfUReD/bdjv6aKovqSLCI7N5eEIoHtz1gCKQh5G3Ru9b9/H6R9D2PIbUPi1bBTXVmRnhxPJBIV1TS5uBlf5GuqKspLS5TUtH7Ps7WTQkmJj6ZSqZq6hthBvKGQiwrysjM4ubhlFVVIHL87StzP4evptttxITovgYLy94HBYPZcemRi/QfshaitqshJTSAQibLKmhzcTOI011VXVpWVyKpo/qbOv5OSmRiDUKnK2gaD6fwrS4vzM1PbW1sk5JUlZBV7OokgSF56cmlhroK6jqAY8/1OIxNrRdLLly/t7H4gzADK0GJnZ0epyH14eO1wO8JIUXl1Sk6RsrRYX7FFdX1jfGaBCB+PikzXSk11fWNtQ7OceFd8Za/AmIX7rz05un7aeCa55P4NlE5qbHoelUrVV5MbcD4EQZDknOK8kgodJWkJ4ZEymzG0LHW+gReURedGUIYcNKIGCspgkRDil+hnwp2Pm8PMQIOhhI+710zWw3f+YoK81uOG+HkJAHgczkCdSbr0nzMWF+ITF2L+KMViMbLiwrLiwkxrUVD+y0iKi0qKM0Z3pMHPy2s+fixDCT9vr+lRl6eu4qIitlYWQ+4YHo8brfeT68R9kZYQl5YQH6rWUH41b9++xbOw8+n8vPqnvYacdGpmR0OV4Jg5eDau2iT/tMtLZewPik5yAIDmguSOxurq2A8sAlL8o6c35sSWB72kNDcor7/DKiJHLE5rqyxkFZFjFZKhtdZclNpRV556cXFzUSpJWhMAGjIjUi4sJHDyC5suwhHYquM/ZVxfLTljp4TtFgCoSw1qyk8o9rnOrTJWeMKiuuSAArcTrWW58svOAgCbqEKR18WqqHfiU9bT2m+rKqoIceM3nPYvVRqUxmpKc53gOPuehWzC8gDQmBffV3jxo/aDhG/UZDyRzdPTc926dT/Xwp+LIB+PhXG/4yUBXm4BXu7f5gwehzPUUhmkMRaLkZMUlZNk/jgYDAQ8XlsF3UGCgvInwcotIKpt2l8tCxc/C9cPa7b+DRgcXkBJb6haIwlKkASHQC+IMuzwCQgaT+j3XYOXX5CX//flqsPh8Zp6owdvLyohJSoh9ev8QUFBQfkr4eEX1B1n/h0Dbj4Bbr5+998POTgcXkXHcPD2AiLiAiLMJ6AwGIysioasigbTWhSUPxEJIT6JflZk+Lg4zPTUGEr4euxpefQ+UEyA1/oXpKfH47AGanKDNMZgMBryEhry6LsDCsoPgwo1UFB+OWcevi2pqPkUGo8mCkFBQWHg5IVrxeRSn8/+F0844/Fo/4AylHzx+8qlMgaDJ/x0C/luJ9oqC+lhISSn70i9uDDf7bjgmLl4Eg8AtFUWik9ZLzV7D2AwgFATjlrXpQYCgJjVGoTa2ZAdLT5lA0mqOzx7S2k2j4ap0pqbbKIKgCC5zw9i8USNPW+JPMIAIDZlXeqFBUXvLgoYTmMVlgOA1vJ8GftDopYOAAAzd6actS8PeiFitoQkrcWnY4VjIVVHvacLNaqjvQFA0IhxbxOlsbrUj0n2dBp8+tbsYso9S1pK0j262wAAIABJREFUswGA5hIdVhF5AOhoYJKy/UftBwkGT+BSHfv5i99/UKiBgoKCgoKCgoKCgoKCgoKCgvIdzjx+R66o/RSWeGbTAjxupGQTRkFB+VFQoQYKyi/H5a1fY0vbUlvT5dPMhtsXFBSUkcXdx88bm5pXLLJftXj+cPuC8rcRGxfPpjn9p0+nNNVWhntwyOrQY0Jg8ASh8QvrUoOro72Fxi8AACyRVWL69q5w7hgsp4JBU35iew2ZyNtvRAHJGTvZRBUAoKkgsSk/kV/fhi5xwODwgmPt6lKDa5O/iQjLAQCenUt00qquMzFYcZtNdWkhtckBJGktLAs7n+6UilC3tspCFgFJAKiK9MJz8PFomDJcsaOhqvDt2f78YRWRYxBqtJbnAQBNiUKHhV8cADqb6/u28KP2g4dNUiMu3vPftICCgoKCgoKCgoKCgoKCgoKC8vfh4hXQ1NK21MZkme344fYFBQXl50GFGigov5wU90vD7QIKCsoIJTsmaLhdQPlrKSsli00Q++nTW0qzAUE6W5sybq6hF3a2NAJAa0U+7ZDAKYAlsNBr8SRuAOhsbeqvTQInP4dsVzDGlrJcAOBSNu5pQEuJ0lqWQztkFZaDHplo2cSVAaC1vOvqAsazKkLdqqLfi1mtaa8uaciNFTFbisExDm7ZRBRG38juz6W+EUcweCIAUJpqexZS21sAAMfOJOPGj9oPHiKvKLms9N+0gIKCgoKCgoKCgoKCgoKCgoLy95Hiema4XUBBQRkC0Hg4KCgoKCgoKCh/Ia0tzTgi+0+fTmmsAQAsgYjBEej/8By8Akaz2MSUaDZYIiuzU5H+2qRpGv7ffjUAsPBL9jqzox0AANs1QCVwC/WsxRHZAIAuDeFWHUfgFqqKegcAVVHvAUEEjGYxuyoGS2Tt7x8Gy5hyiMgtBD3EKF3eNtUAAIGTv2/zP2o/eHAspJamxn/TAgoKCgoKCgoKCgoKCgoKCgoKCgoKysgEjaiBgvLfhUpFsFjMwHYAlM5OHBaLwTA3pnR2YjAYHHYA4Vdjc2t7B4WPm+OHHUVBQfnFUKlU7EA/YToUSicO13+H8N1aOg2NTe0d7fy8vD/mKMqPgCAIDPQf8R1YBaUAgFVIVtHhSneb1M7O1iaaYOJfwiIgBQD1meG82hb0wobsaABgFZSmHdKyitBpqyoCADYRedohBosTMJxO/ny3vbqkKsqLVUiaU16v74U66sqLvC7254aQyTyStFbPEloYj7aKgp6FTYUpAEDPAvNv7H8ADAZB+lW9oIxABj+ygoEGVwiC1DU08XANMGpqaGrp6Ojg4+H6MUdRUFBQAAAAQagYzBDs3kE6KRgsbsBRR0dLI5XSwcKJDv9QejH4N5FOCgWLw/X36OykUCiUDhbWfoepCII01NdycaPfQBQUFJSRDkKlYgb5aOikYLH9PhoAAEGQpvo6Dm6e/gxamhvZ2NHJahSUoeQH50aoOCzmO7/ippY2EhtLf7UoKH8BqFADBeWHCU/MDIhOXjbNTIjvX4U0Hy6yCktvv/Z9Hxhd19hspKW0wX6Kqb56f8YfQ+OO3nZLyy3mJLFN0FNzmGUxVkeFXvvyU/Dt158TMvIonVRZcSHHOZYOMy2YPoar6xqNluzh4mCLevrPL7krFJThIDQy5mtgyMrF84QFBYbbl58hMzv3+v3HXj6+dQ0NYwz1Nq9ZOdFkzHfsfT77Hzx5LjU9k4uTw8xkzJrli0yMDQdZ25OqmhrdCdZcXJyJQZ+G+JZQhg5WIVkCJ39tcgDSSaHnEyn2vlro8Y+GkwenIvP/3MFDktLA4Al1yd9gzj56YX16CAaL41E3pR22lua0luWyCsvSDsuDXgIAu2T3M0vQeBbZ906J752GnBjJaduYXojSXF8W+Kw/N7iUjRmEGkQeYS4lo/qMsNbyfFYhaQBAOimVYR5EXhGO3pY/Z4/Sl7C4FP+I+BWzJwvx/5FrJ5n5xbdeeL37GlrX2GSso75x8Uyz0Trfsf8YGHno6sO0nAJOErupofZqe9txehr02tr6xr0X7r1879fS1s5JYrMca3Bx33p+ZlKM6tp6gznruDnZYzxuD/1doaCg9E9lemRpYpCCxSJWHsHh9uVnaCjJzvjgUhTh09HcIKBiqGLrKKJpwtQSQag+282RTkrPQpKQlOm+p7S/S2I+xz87VVeUTmDjFNEcpzh5uZCaMbOWoK2hxnubKYGdc+olNPHf0BAfGRoZ7D9z4Up+QaGBrUce+TmZri43/D94NdbX6xgaL1y92dDErD/joC8frp9yzslIJXFyGYwztVvmqGvU/aUNDfh85di+rPTkTgpFVEJq8Zqtc5c59hR/1NfVXDq61/v187bWFhIH55iJVntOXuLh+yPf4FBQUFC+Q2pMWFxYwBS75TwCf+SjoTg3892T26Ff3jU11KnrGs9YvlFnjGl/xpH+Hx9dOFyQlcbOwaltbDp1oYOGwbieBo11tff+2ffV82V7awsbiUN/gtX6Qxe4eLvCXmYlxz04ezAjMbqxrpZHQMjYYurK3SfYOTh/6Q2ioHyf8KSsgJi0ZbbjhXj/yM0YWYVltz383gfF1je1GGkqrJ9raaqn+h37T2EJR+96pOWVcJLYJuiqrpphNlZbiV4bn5HvfPt1TFpubUOzEC+XzbhRx9bO5SQxkeTqLNhjMkrlys6lQ39LKCi/BTT1CQrKDxMSn3b0jltpVe3ApiOPlrZ2u13nHr0LMB+ttWqmRXZhqd2uc8FxaUyNX/mGzt15rrahafNCm8ljdXyCY+fuOpdZQKbVPvMJcjhys7ahaa3dZIdZFk0trTvOPzz76C3TptadvEOurPlVd4WCMkwEhUU4nzpfWlY+3I78DC2trTMWOzx45jpp4njHZQszc/JmLFwVGBrRn/0Ld6/pC1fW1tVv37Da2nLi+09+MxatysjKGUwtA6s3O5WUlv2Su0IZOjB4gtTsPZ0tDZl3NjTlJ7aW55V8vFXsdZFbbTyngsGAp7PwSwBA2bcnjblxTA2IPMIiE5c3FSTlPNnTXJzWUppd+PZsVdR7AaNZdGUGgnSmXV1RHePTXJJe5HWR/Pkev4Etl9JoeiMkaS02UQWy710AEBwzh+mF2EQVjG7l9feP38C27yniNhsRCiXjpmN1jHddWkja5aWtFQXyS8/Q9wqXBTwJc5Aq8rwwSHuU7xMck3zk2qPSP3Oc0NLWPnfToUdvPlmM1VttNzW7oHjORueg6KT+7F19/GdtdK5raNq6bI71hNE+3yLmbHTOyCui1bZ3UGasP/DQ46O9tdn1Q1vmTjF9/enb3M2HmTa15tBFckXVL7krFBSU71KeEpbw/FRLzR85mOlsbw04uST7yzNRHTNFq2UN5JyAE4vKU0KZGjdXkWvzUzA4HAu3AP0fkaNrT2p+kIf/iUUdTXVq09eL61sWR/kGnFhcX5LFtKnw61taqkt/1V39J4kND75++lBlOXm4HfkZ2lpbti6Z9fb5Q2NTy7nLVhfkZG1eMjMmLJCp8QePl5sXz2ior12ybpvJJOtAX+/NS2blZWfQaiMCv26YP7WkMH+a/dK5yxzbWltO79ty+9wx+ukdHe0bF05/88xlyqx5B8/dtJph7+vptnUZ83EjCgoKyh9NUlTI4wtHqiv+yAdue2vLYce5n9we6ZlY2CxwKM7PPuQ4JymSub7T/53rodWzm+rr5qzaYmg2JcLP59DqOUW5mXQDSkf7wVUzP716aGZrt/nEdVNbu0Dv14fX2NFqMxNj9iyekpUUa2prP3+9E4mT2+fF/b1LbRAq9XfcKgpKP4QkZB675/HnrjrZ77382DvQwlBj1QzT7KIy+z2Xg+Mz+rN3+xI+1+lybWPz5vmTJxtr+4TE2+25nFnQ1X3FpufZbDkTl55vZ2G0e6ktFwebi1eA7bZzVCpj3NmnPsE5xX/kzDwKCh00ogYKyq/lhwI9/QaO3HqVWUB+fW6npZE2AKyzszJeutfx2K0ktwsMlu0dlP3XnrGzsgQ/OM7NwQ4AR9bOU56xcdnBq8EPjgPAlRfe8hLC/ncO05SMWxdN1Ziz9ba7765lMxiauuvx+XNYAu9A4btRUP56fijDyK/mwImzGVk5Xs/vTzY3BYCNq5frmVqv2LgzMyqgr3F7e4fT4ZMkdrbIL1483FwAcOLAbhlt4wWrN0X5vft+LUNTtx48/egXwMfbb9hJlJGDkMl8antL/qtjVZFeAIDB4oXGz5ea5TQY/QGP2nhOOd2yr49aSjLVd7kxtZGevReoVPLnu2VfH9FKhE0Xy84/SjfgVh1H5BFJv74aECoAcCkbyy06ydCIoNHsAo/TPOoTaLlUhgQe9QkKDpezH+xIv+YAAHh2Lpl5zjyaE3vaINROAGTw9ihDyIgaXB268iAjr+jNtaOW4/QBYP3C6YZz168+cC7F26WvcXsHZe/5uyQ21tCXV7k5SQBwdPNyhUmLl+w6GeZ6DQCeen6OSEg7uX3V5iWzAWDZTCsMYO65ecckZ+qqK/Zs6o7re9/gKF5udMsXCspIZ6jSiwwV8c9O1Jdkme57JqZrDgDKNg7e283CrmyadiOyr3EjORcAjDdd45VhDMFIpXTEPjyMZ2GffPYzkcQNADqL9r9x0Ak+7zjl7BcG48yPD0pi/egKD5Tfz4h6DQGAqycP5mVnXHnqOXaiFQDMX7Vxnrme8+ZVXuHpDJYdHe0XjzixsZOe+YZzcvEAwKZ9xyfryu5xXPT8cwQA3LlwAkGQJx9CJGTkAGDj3mOTdeUe37y4ets+LA4HAF6ujxOjw7c6n168ZgsAzFiwHIPB/I+9sw6Iann7+LNJd+3S3Q0iIEiaqKAgICmKioko2AV2dxe2IhIWBhKKICgISnd3d+6+f5y9y7KAcfV3733vPZ+/2GeeM2dGd8/MmfnO8zy6dTk7I1VVa4yseSgoKCj/EX48vchfw41jgZUlBUFXwvXNpgKAnefKFbMnHtuw7FpsFpPn4ED/1QNbWdk4TkcmcnDzAIBXwG4PE4UDvh5nHtO0p9Fhd3LTU7w37Z+3eA0ATJvviQHM8/tXC76mKWjoPrl9oa+398SjeFkVTQBwX7t9i6dNemJcwssI0xnz/tJuo6D8Av+otZGgK+EF5bWPDq2dMlEDAJY7TDH22umz/+rX+wdHO/cPDG4795CdlZhwZSey8RS4zF7Zwd8r8GLC1Z0AcDHsTW//QMyFDZrykgCwdZHdnHVH4lJzIt+mzjXXB4CqhpYDwY/Tcku+Flb8pf1EQfkfgAo1UP6jtHV277oQ8j49t6mtY6KGguds82lGtCDVqw5cYSES/D3mbD1z98OXfBwOZ6KjfHSdJzsrCwCsPng15mMmAKzYd9lIU/Gwn0fA8ZtdvX1bF9sfvfU47E1y6fPzAJBXWr3l9J3UnOKunl5VWfF17nNszYfPH3tuP62hIGWqo3Iu5EV8arYQH7fLDBNfl1lYLGbPldC3qTmXti+TFh0OUrdsz4W6prbQI/54HO4XO377+Vt1eUlEpQEAwvw81hM17kYlfMou0leVY/TMLa2qbmiZZzkRGSwBQIiP29JA42VientnNwBkF1f4OEylx5siC/KZ6anGp2YPDA4R8MPtzCmp3HL6btAK5+DHsRQqKkxG+SfS2ta+be/hd0kpTc0tRhP0Frk5zbA2R4qWrdvMQiRuWrti4659iSmpOBx+srHByf27ONjZfdZviY5LAABv342TJuqf2LcTANZuCezq7t65we/gyXOhkc9qclMBIDe/MGDnvk+fMzq7utVUFDesWT5v1nSkfpclqzXVVMwmGZ6+dD32XaKQkKC749z1K5ciC6m7Dh6PS0i6fuaIjNTwDvSiVf619Q2P717D43/pgXDz/iMNVWVEpQEAIkKCUy0m3woJS0lLN9BlDtqfk19QVVM739YG0WEAgLCgwBRz0+evY9vaO0rLK75RysM9vI+YnVcQsGPv/h0br9y6T6Eyi6BR/oGQrBYJTXLsKs8c6u3mEFcm8ovSizS2P2dylrD1l7D1R/7Gc/Kpb33S31qHY+VALKPlGhg8QXpBoJjN6q6KLCyeyC6himcfkVMMg8XLLz4hvSCwqzSDyEtiE1WEUbCJKgCAiLnbr3WUGUEDWwF9m87SL0ChcMrqYLAjfm4iZm4iZm4/7v8foa2ja8ep6wmpmU2t7YZaKl7zpk8zpU1+VgSeZCESNng7bT56JSk9G4/DmuprHt20nIONdWXQyZikzwCwbMcxYx21o5uWA8D6A+e7e3q3rXA/fPXBo5dvK+IfAEBuccWmo5dSM/O7enpV5aX8FznaWdNi27oH7NdQkpmsr3n2TmRcSrowP6/LbGu/hQ7ImsXus7fiP2Zc3uMvI06it3bJtiN1jS1hZ4N+cXJ1K/K1uqIMotIAAGEBvinGuneevPn4NW+ChhKTc25xeXV9k/3UyYhKAwCE+HmtjHRfvEtp7+zi5uS49+yNED/v8gW29Es2eDsZ6agKjky3l1NUtunIpT1rF1179IKKTq5QUP4U/V1tGXf21Wcn9XU0CylNkLN2FdW1RoqSz6/DEVjU5vmm3djVmJuCweGF1Yz1vffhWdgBIOXC+pqMeAD4cHatkMpE/cV7P13dOtTbreEckBV2qjwx0v56DgC0Vxak3djZVPh5sKeLR1JFbd5qCcNZ9LsnHF3KJ60mom6c+/RyXeY7Vm5BGXNHFbuVGAz2y/2DdZnvjVaf5hSRovsnnV7d21JvvvUOPRnZn6Y49gGvlCqi0gAAVl4hsrZFSVxIU0GagIIuk3NHTTFgMNyicqOqgbaKvO7mGkljW0SlAQCsPIIkbfPq1NcD3e0Edm5Gz7TgnTruOwpf30IfWUx0tLee2bcj7cO71uZGTX2jua6LTKxorwm7/ZcTiMTFazYdD9qYnpKIw+P1jCZv3HucjZ0DAPb4r/jwNhoAAtcu1Z5ovGHP8UPb/Hq6u30Cdlw/dejV49CYrCoAKCnIPbZrQ1Z6andXp7yymtfqACubufS7b1zmqqiqqW88+e6VMx8T4vgFhWbNd/NYsQ6LxZ4/FPjxffzuU1fFpGTo/jvWLG6qrz11OxKH/9Xv4ZMHtxRUNRCVBgAICAkbmU99+vB2ZlqKuu6I/HrF+Tn1tdVT5jggKg0A4BcUMjKzfhcd1dnexsnNU1tdIUIWQ1QaAMDByaWuo5+WlNDX14v8Wz0PvccvKOS8eAW9zsW+G7UNjPgE/l+mLkJBQfnX09XeFnx0Z+bHhLaWJlVdw2nzF04wn0YvPbl1JYHI4rQ84Or+LVlpSTgcXsPAZPnOo6xsHKe2rfqcEAMAxzf5qOkb+2w/cmG3f293l5vvtpALR94+f3Q/pRwAKoryLu/flP8ltbe7S0pR1XHZ+knThk/c7ff1kFXW0JxoGnHjXEZSHK+AkNVcVwfvtRgs9taJ3V+S364/dIkkMTw0HN2wpKWhLvBKGO6Xpyivw27JKKsjKg0A4BUU1jO1fhN+Ny/jo5LWiKCe5YW5TXXVpjPtEZUGAPAKCOmaWKXEvejqaOfg4gaAmMj7vAJCczx86Fc5LQ9Q1TPi4RcEgOy0ZDkVTUSlgTDF3iM9MS7/yydUqIHy67R1dgdeCnv/Ja+ptXOiurznLNOphrQv2+rDN1gI+PVuNlvPhXzILMDjcCZaSkfWurCzsqw5fCPmUzYArDxw3VBD4bCvCwAEnLzb3du3ZZHdsdvPwmI/ljw+CQB5ZTVbzj5Iyy3p6ulTkRFb5zrT1oymPV2464K6vISpttL50Oj4tBwhPu4F04x9nacjayN7r0W8Tcu9uGWxtOjwLGjZvqt1zW2hB9ficb+k3LoTlaAuJ46oNABAmI/bykD93svET9nF+qqyTM65ZdXVjS3zLCaM2HiaoPYy6Ut7Vw83B1tyZpGGvASi0kBwm2ESl5qTmlOMCDU6u3sLK2q5Odh0lWXSckt+peUoKH87/yDVJArKX0ZVfbPxwq13o95N0lZ2s5lcXtPouOHo2QcvkNKvBWUvEtPNvXdU1jXbWxuJiwjcfvZ2SdAFpFRegkQS4EX+kBUXAYDMoooPX/IdAo5cDouWIAkAQNKXvMmLt+eWVS+ys9yw0A6LxbptPXnwegS9AXGpWTefxs/zP9w/OOhla8HOStxx/sHqg1cBQFFSNOlLXnhMMt25vLbxblQCLxfHr6s0mto6Wju6LPRHHIeSlyADQFoOc4YCJFOJ3kj1BiLmyCmpwuNxL89u93Mbjhjf3tmdWVhhaaDBqNLo7R/w2nnWWEtp+fypv9h4FJT/EZXVtfqWs26HhJsaGXgucCitqLRz8z518RpSmpGZ/fx1rPG0uRVVNY52syXEyDfuhXqtXA8AinIyZBFh5A95GdpK+tfs3MSU1Dkuiy5cvy0hLgYA75M/GU61y8kvXOLpsmXdKhwW57Roxd6jpxH/mLeJ1++GzF7g1d8/4O2xgJ2NdcvuQz7rtyClSvJy75M/hT4e3g4vq6y6FRLGx8vziyqNxuaWltY2K7NJjEYFORkASE3/Otq/urYeACboaDEakY/ZeQXfLqVbevv6XJeuMTGcsGrJwl9pPMpfDI6Vk1vRkE/TklGl8YMQeUVwrN8Jp0TgFuRVM+NWMmJSadDBs/PwqE4eU6UBAPXv7hH5SHzav3+UwWDxXLK6XPL6P6i6+Fn/fxlVdY2GjivvPnljoqfuYTulrLrOfs3OM7dpk58veUUv3qaYuvhW1jbMn24mThK6GfHKe+sRAFCQEicJ8QOAgpSYnCTtO5ZZUJKUnj131Y5LD55KkIUBIPFzlqnLmrziCu/5MzcuWYDDYl3W791/6S7iH5uSfjPild3KHf2DA4sdZrKxsWw/eW1V0EmkVFFGPPFzVtir4YDq5TX1d5684eXm/MXJVVNre2t7p+VEHUajvJQ4AKRljRHhs6a+CQD0NUZ8mfU1lAAgu7AMAArLqqeZ6BMJ+JLK2mdxHz5nF5CFBVxmWUmSh/W7vX39HhsPTNJVX+FiCygoKH+K7qbqKH+rkrgQYVUjWYsFnfUV8fvcc59eQkpbSrKqUl+/3Di9u6laymQuu6BYccy9pFOrkFIushwbnwgAcIvKcpGkAaC1LLshNyVur2vBi+scguIA0JCT/GLD1LbKfPkpHurz12Gw2HeHF2c+PEZvQN3Xt0Uxd2P3uFAG++WnuONY2NNv70k5vx4AuMUUGnKSyxMf0527GipL4kKInDy/rtLoa2/u72wlaU5mNCI6jKbCMVKVddSWcAiKDfZ2VX16XfTmbmPeRyplCCnqaakFAAGFEQ9AQQUdAGirGA6KMDTQ9/74MmEVQ6WZ3r/Y+H8fdTVVC6wNnj68rWtoOsfZs6aibK3H3LuXaa8JeZkZCdFR7jOMa6sqp9k5kkQlHt+/sX31IqRUUk5BUISM/CEhLQ8ABdmZGSmJa9xsQ4IvkMQkACA95b3bdOOSglx7d+8lfpuxOFyAt/Pl4/voDUh5Fxt5P3i1m+1gf7+9+2JWNvZTe7fuCVgBANLySukp718/eUR3rqksf/rwNhcv36+rNFqbG9vbWiaajog9JiWnAADZGWlMzg21NQCgrjNii05NZwIAFOVlA4DlDNu6mqqEN7T1nNKi/I/v4/UnmSEqDQAoLymYZDmdQCBWlZXEv3ya8yVNSIRs4+BKFv9twdhQUFBQfheNtVWr5hi9Cb+rPsFkqr17XWXZrmUOEcFn6A7FOV8+xr5YO29yQ22lmY2DEFn89aNbR/2XAICYtAK/MAkAxGQUyFKyAFCSm5mdmrRzybyndy4Ji0oAQNanRN+5phWFeTMXLHZesRGLxe1d5Xrv7AF6/RmJsa9Cb+7wnjs40D/DeRELG/v1w9tPbVsFABJySlmfEt9FhdOd66vK34Tf5eTh+3WVRntLU2dbq7bxiKFBTFoBAAq+Mg8NTXU1AKCkqc9oVNTSB4DygmzkY3Vpob7ZVDyBWFtR8uHNs8LMz/wiZEu7BcJikoODA3qm1rPdfRgvb6ipBAAuHv5f7AgKSlVDy6TFgXdfJk7SVHKbaVJe2+i4+dS5h6+R0i8F5S+Svlgs21NV3+xgOVFcmP92VMLSvVeAtuvEg/whJ05bAcgqrvyQWTh/44nLEbHiIgIAkPS1wGzp7ryymkVzzAM8ZuFwWPcd5w7eeIL4x6Xm3HqWYL/hRP/goNdsMzYW4s6LoWuO3EBKFSXJSV8LwuM+0VtbUdd072UiHxfHL6o0mto6Wzu6zfVUGY3yEiIAkJZXOtq/trEVAPRUZBiNyMeckqqBwSErA7Vl86wYSyvrmwGAj4u2xqgkRY46tTHq1MZrO5b+SstRUP4JoBE1UP6L7Dz/oLymIfZyICI72LrYft76QzvO33eZYYKk5yivafBzmx3o44jBYCgUqpn39rhUWpg1XxcbCoWSklmwzn22pgJta7agvMZ6ouaNoFWKUqJUKnXDiVssREL0hR1kQT4AWOs6a+66g4duRNhbG8pL0I5yllTV7V/jusppBgBsXzJ/tu/+W8/ivedZzZqsx8HGGhGbQtdARMZ9BADnaSP2UwGgqa3jclj0eH20NZ+gIiPOZCwoqwEAEYERIWcVJMkA0NDSzuQsKyYCAG9Ts9csmEk35pZUAUBOSeVEDQVDTdo2w9kHLyrqGl8kpg9RKP7ucxgr2Xbmbk1jS8TxjZgfiJOPgvK3sHXPwbKKyvcvwpAwEjs3+M1asHDz7oNujvOQ9BxlFZUBq332bgvAYDAUCsVwql3Mu0QAWLdiydDQ0IdPaRvW+GipD89E8wuLp1pMvnf5tJKCHJVK9dsaxMJCfPvsoShJBAD8Vy2zcfbcd/yMo90sRBhRXFp+JGibr88iAAjctG6ag3vw3Yc+C910tdTnzLDm5GB/9CQqYDXtBTL8yQsAcHFgTjDU2Nxy4dqt8fo4b/YMVaURMfPzC4sBABGa0FF6Y3FZAAAgAElEQVSUlwWA+sam0TXISUsCQGxCot+K4UX2nPxCAMjOzTc1MvhGqdEE2unMjbv219TWPw+5gT4QUH4LVU9P9bXWtHyNkXHZjcGic9q/me0nr5VV18XfPoGEkdi2wt1uxbZtJ666zrZC0nOUVdetXzQ/aI0XMrkycVkTm5wOAGs97YeGKMkZOf6LnTSVho9Z5JdWWhvr3Tq0WUlGgkql+h+8QCQSYm4eJQsJAMA6Lwfb5dsOXrrnMM1MQUoMAIorag76L13tPhcAdqz0sFm6+UbEqyWONjqqCrMsjDjZ2cKj361fNB+pPCI6AQAWzGJOT9PU2n7xAXPCJjpzrSepyEkxWvJLKwEAEZrQUZQWB4CG5jHSyspIkAEgLiUDyWyCkFtUBgA5ReXqijK1jc3CAnz2q3dFvaVpdpVkJC4GrTPQVKb7bzl+taah+cmFveizFAXlT5N+e09XfcW0A1FIDAlN5w2xe5zTbwXJmjsi6Tm66itU567Wdt0KGAyVSnm5YVrtF5rYS8V2BZUy1Jj3SXXuGj4ZdcTYXl1I1rYwWX+JW0wBqNTUa9uwBOLUvU/Z+EkAoGK3Mna3c2boMalJtlx/RKforC3VXRikPHsZAGgu2BSzy6Eo5p7CtIXiBtPxrBzlSU9V565GPCs+PAUA6cnzmXrR195c8HKMLEsIEoazeCSY4/q0VxcCACI0oYM0qbetcXQlHTUlAz2dkT56g309iIVfTst4zVlucQVOkjQA1H1NUJmznO6PSDRay/MElWjb6p9vBPY011psf/AjedP+a5zes7W6ouzms3dIDAmfgB2rXGaf3L3FZr4rDy8/AFRXlC1c5b96yx7kNcRtunFKQixyrcdyP8rQ0JdPH7xWByip0dTSpUX5RuZTDl68Iy2vRKVSD21bTySyXH8cL0QiA4DnivUrXWZdOb5/qu18KVnaq0FlafH6wMOuS9cAwPINu5Y7To+8Fzzfc6n59NnsHJzRT8MWrqIFS3vzLBwAbOxdmHrR2twYEnxxvD5a2cyVU1JlMpYW5QMAIjShIyWnCADNjcwpxpFQGR8TYpHEJQjF+TkAUJSfrTXByHnxypR3sb7udlr6RkRWlk/v44VEyKs2ByGe3V2djXW1/ELCvh5z372myd+l5ZUCT1zW0Js4XrNRUFBQ/i6uH95eV1V2PDQOiSHh5rtt+2K7a4e2W8115eLhQ3zqqsrmL1230D8Ig8FQKRTfeabpSbEAYO/tS6EM5XxOdly2nh4rorKkQM/UetPJWxKyilQq9eKeAAKReCTkjYAwGQAclvptX2R77+zByTPtxWRoQ0NNefGSLQfmeq0GAI+1OzZ72rwKvWnjssTQ2oaNnTPhRfj8pesQz/cvIwDA0tZ5dEfaW5qe3rk0XjcnTbOTUlBhtFQW5wMAvxCJ0SguqwAArU0NTJcjMpSMD3FIWhOE8sJcACgryFHRNezp7mxuqOUVEN611CElNgpxkJBV9Dt4UVnbAI8nLN9xlLHC1qaGp7cv4vEEA4sZ47UZBeUH2XkxtLy2Meb8ViSMxBYvW/sNJ3ZcDF0wzZiPmwMAymsb/Vxm7FpqT9t4WrY7LjUHANY4TxuiUFKyivxcZzBGkigor7UyUA/e5aMoSaZSqRtO3WMh4l+f3UwW5AWAtQtmzAs4fvjWU3tLA0QYUVJdv3+l00rHqQCwbfHcOeuO3Hqe4G1roa0kZWOizcHGEhH3yc+F9lWPjE8FAKcphky9aGrrvBwRM14fbSfrq8iMOMpVUF4LACSmXScJEgA0tnSMrkFGVBgA4tNyVzsNRwzKK60BgJzS6onq8kd8XRn9G1raL4fHEPC46caagILyrwNd1Eb5z9HS3hnyOlFPRZae6YNIwC+cYxGfmv04/pPnbHMAYGMhblk8D1n+xmIxhhqK6XmlVfXNYsLj6mq3LXFQlBIFgPT80vS8UjsLA0SlAQAEPM515uT41OyYlK90oQYPJ/tKR1pQUywW4+8x521adkzKVx0lmTlm+vdeJJTXNEiShQAgPCZZgIfLaiLzINTY0rH3yiMYB3kJ0mihRlFVHQDwc4843CxBEgSAts5uJmc5cZKuskzcp6wbT+LsrQwpFMr9l+/DY5MBYIgyImJt0KWQ7t5+AFCREWdlIdDtL95/vvjo9d19a5kGaRSUfw7NLa33Hj3W19GkJ/sgEgmL3Zxj3yVFPHu5yM0JANhYWXds8P3jgYA1NtD7/CWzsrpWXJQ0XrWBm9YpKcgBwOcvWZ+/ZNrPnoGoNACAQMB7ODvEvkuKjk9AhBq8PNxrlnkhpVgsdtPaFXEJSa/j3ulqqXOws9vNnHb7YXhZRaWUhDgAhD5+LsjPN9ViMtMdGxubAg+dGK89CnIyTEKNwpJSAODnHfHblBIXA4DWNmbZFgDIy0rraWvEvE28dvvBfLtZFArlbmgEEupjiDL07VKkhmevYs5dvfkw+DyTOgQFZTyIPCIErm8dZ6mLvz3U1yVi6iIy+TfnPUH5WVraOh48j9NTU6Qn+yAS8F72M+JSMiLfvF84bzoAsLEQt/q40SdXRtqq6TmFVXWNYiKC41W7c5WHkowEAKTnFKbnFM6dYoKoNACAgMe72U6JS8mISUpDhBo8XByr3GgiNiwWE+DtFP8xIzopTUdVgYONdY6l8d2nb8qq66RERQAg7NU7AV5ua2PmzPQNzW17zo0relOQEmMSahSVVwMAokShIykqDACtHV2ja5CXFNNVU4hLTg8Oe2E/zYxKpdx7GhP2+h0ADFGGiitqAODsnQhZCdFjm5YbaqsmpWdvO351vm/gp0fnhfh5ASDqbfKFe4/vH99OEkQPe6Gg/En6O1tL34UJyGvTM31g8QR5a/e6rwkVH57JWbsCAI7IquEUgGgLMBisoLJBc/GX7qZqdoFxg0tpLtjELaYAAM3FX5qLv0gazUZUGgCAxRFkLZzrvibUZMTThRpEDh7lWbTjXxgMVs1+bV3m+5qMOH45LYmJM0viH3bVV3AISwBAeeJjFm5+so450x372pu+3D80Xnu4yHKjhRodtSUAQOQaMQPkEJIAgIHuMWaAnbUlAz2dWi6bJSbO7GtvKo59UPTmbvwBjxlH33CRZfnltGu/vi2KviM5yRaolJL40PLEJwBAj7pR9el1ftRV0w3XmaQhKADQ1tocFX5fTVufnumDQCDOc138MSEu5lnEXNdFAMDCyubjv53+GqJtYJT79XNdTZUIWWy8alds3CUtrwQAuV8/5379bD1rHqLSAAA8gTDHyeNjQlxy/Bu6UIOLm9dlCU0ShMViF63Z9PF9fFJctIqmrsUM22ehd6orykQlpADg9ZNHvPyCRuZTmO7Y0tR44XDQeO2RklUYLdSoKCkCAB5ePkYjEt+io51Z5igpI6+qpZfyLjb8zrWptvOpFMrzR/einzwCAMrQEABwcfOQJSTzsjKy0j/hCQQKhYLD47s6O2n3Ki0CgHuXz0jIyG3ce0JrgmF6StLJPVv8FtqHxH7mF0Szn6CgoPyD6GhriXsSoqipR8/0gScQpzt6ZSTFJ76MnOa4EDESWdlc12xFRgcMFquqZ1SYld5YWyVIGnt0cF+7Q0JWEQCKstILs9JNps9FVBoAgMcTpsxzz0iK//w+hi7U4ODmsVtICySGwWKdlm/48uFtWsIbeXUdo6mzYyLu1VWViYhJAcC7qHBuPgFdU+vRN21rbrx9cs94PRWTlmcSalSXFQMA18ihQVhUEgC62tuYL5eSU9DQTU+MexkSPNnGgUKhxEbeexcVBgAUyhAA1JQVA0DkjXOiUrLLdxxV0TXMTk26dnh7kI/juWcfeUemvkqJjTqxeXlbc+PSbYekldQABeUXaGnvehidrKssQ0/2QSTgPWdNjk/Lefw21XPWZABgYyFu9rId3nhSl8/IL6tqaBET4huv2u2L7RQlyQCQkV+ekV9mZ66PqDQA2XiaMSk+LSfmUxYi1ODhZF8xnzZhw2Ix691t3n7OffMxS1tJip2VZbap7v1XSeW1jZIkQQAIj/0owMNpZaDOdMfG1o591yLHa4+8OIlJqFFcVQ8AiBKFjiRJAABaR+06AYCcuIiOknR8avaNp2/tLQ0oVOr9V0nhcR8BYGiIOVXii8SMlYeCG1s7Dq52VpNl3vBCQfkXgAo1UP5zFJTXUKnUzp5ez+2n6caO7h4AKK6qQz4K8XGzEocFB7xcHADQ1dM7Xp2CvNx6KrTRt6iiFgBMdEZMN7WVpAGgsKKWbpGXIDGeg1SRFQeA4sp6AHCaNunei4Tw2BRfF5vK+qZP2UVL5lkTRqU5UJQi18dcG69JRMIYv24WAh4Amts7GY3dvX30PjKCxWLObVnquOHoqgNXNpy4SaFQKVTqwjkW1yJimCQgdW+uFVXUJn3J33XxgcWSnTlhp0QEeGqbWn32XvKcbT7bTB9QUP6p5BcVU6nUzq5u+gIlALR3dAJAUWkZ8lFYUICVhYVeysfLAwBdXWPswCEICfDr69CUVQXFJQAw2XjEaS1dTXUAyC+i5c+Tl5VmfBogigr63V3m291+GP7oSdS6FUsqqmpS0tJ9vNwIo37gSgpy7eXZ4zWJSCAwWViIRABobh2xEtrV3U3vIBNYLPbKyUN2rt7L1m322xpEoVAoVMpid+fLN+6qKil+uxQAaurqvdcELHJzsps5bXTlKChjohX05tsOuodT/pqWoHyX/NJKKpXa1dPjHrCfbmzv6gYARHwAAEL8vKwsRHopEsOss7tnvDoF+Xj01GixuwrLqwHAVE+D0UFHRR4ACsqqkI/ykmIjnqXyUgBQ8sfdnWdZ3n36Jvx1wlpP+8raho9f85Y6zSKMCt6uJCPelDLuYsToyRULkQAALW0jDoggM0Y+7jGS/mCxmIuB6+xX71wReNL/4AUKlUqhULzmzbga+lxFTqq5rQMA+voH7h7diihUtFXk65taDl6+//BF/AoX29rG5qXbjy2cN32OpfF4jURBQfku7VWFQKUO9HYlHB0OkzvQ0wEAHbWlyEdWHkEcYXj6R+TgAYDB3nGnfyzcAgLyNNVvR00JAAirGTE68MtqAkBHzXC6SS6yDGOQCURU0VlbCgDSZg4l8Q/Lk56o2K7obqxuLEhTnO6FxTFP57jF5J3ulY3XJCye2R8AcHgWAOjvGDEDHOzrpveRCcNVp7AEFl5JZQDgIssKKk0gsHPnRJ6t+PBMxmy+4coT8fvdks+vS722lUqhUKkUuSluha9u8kgqAUBPS92Hs2vkrF0lJs4cXTNKWWE+lUrt7urcuGz4vGBXRzsAVJbRvif8gkJEFlZ6KTcPHwD0dHXCOPAJCKlp096+y4sLAUDPaIS8W1lDBwDKioeTc0nKyjOOnoioAmnATAeXZ6F3op+GeSz3q6uuzExLmb9wGX7Ua4W0vFJSyRhBpBAIBOJoI5HIAgBtrS2Mxp7ubnofGcFisbuOX/L1mLvbf/nh7espFAqVQpnruvjRrctIaxfZWRbmZG4+cGqanSMLC+v7mJe71y9f42YbGp8uKiHV3tICAP39fYev3EckLMoaOk0NdVdPHngZEbLAe+V4LUdBQUH566kszqdSqT1dnft9PejG7s52AKgpH55C8AqMGB04uXkBoKd77FkKD7+goiZNnl5VWggAGgYmjA5yatoAUFVSSLeISY8YGhBFBdIAS9sFMRH3EqIi7L19G2oq8zI+2rguxY815RCXVYz4OkawLgT8qNGBQCQCQMfIoaG3pxsAOHmYj/9hsFi//Rd2LXM4uXXlhT0BVAqFQqHMcPR6fv+qpIIKvZ6B/r4tZ+4iIhV5Ne3Wpvr75w7FP3to67ECqaemvPjS3o3JMc9FpeQCjl3XMbYYr8EoKD9IQUUtlUrt6ulduOsC3dje1QMAJdW02DCCvFyMG09839944tJVpqUIKaysAwATrRFqbC0FKQAorKBtbMmJi4zYdZIWA4CSalrQMuepRvdfJUXEpa5xnlZZ3/wpp2SJncUYG0+SpLpX58dr0ui1EcTS0j7iQdTV2w8AfFzso2vAYjHnNnk5bjq1+vCNDafvUZGNp1mTrz2OZ5SAlFTXbzp9PyoxQ1ZM+Mr2JRZ6zPJfFJR/B6hQA+U/R3NbJwCwEAiMq/P83FxOUyfR9QeMGwl0qNRx62QhDlfV1NYJAFKkEcdD+/oHAQCHHc71xZR/hIOVBQCQcBTm+moiAjwRsSm+LjYRsSlUKtVp6hjL8RgMhm2sdn4D5Kal1SNCxrW0dwKAIC/XaH81OYnkWwfCYj7kllSJCPBaGmi8S8sBABUZMSqVSqUCFksb9eUkSHISJAwW47Pn4qukdPdZZlfCo5vaOtq7enz20iLdVTc0UwF89l5SkCStH5khBQXl76KpuRUAWIhERumDAD+vi4OtqjJtg5CVjXX0hdTxnwgsDD/MpuYWAECCYdDp6+sDANwfyf+YIkxwsLMDAF0aYmlqTBIWevQ4at2KJWFPoqhUqouD7eibYjAYNtYx2jkeJGEhACguLWc0Nre2AYCQwNintNVVlD6/fREa+Sw7v4AsImxtZhL//gP8oSz5dunF4DuNzS3t7R2L12xAaquuraNSqYvXbFCUk9nou3zMO6KgoPx/obmtHQCIBAKBMPx6L8DL5WxjoSJPC0HBxsoy+sJvTq6GVy6aWtoAQEpsxIHsvv4BYHiWMuUfYWdjBYYHssVEbRFBvvDX79Z62oe/TqBSqc4zx1iG+9nJlYggHwCUVNYwGhHdhiDfGFueAKCmIP3x0YVHr97mFpWThPitDHXefvoCAKpyUg0tbQBgoKmMqDQQZpoZHrx8P7e4AgAuP3jW1Nre3tm1bMcxpLS6vpFKhWU7jslLiQUsdvrxlqOg/Jfp62gBAByBBcvwPsjCxSc92Z5XkrboiSOyjXHl+I8sRlVHX0cTAHAISTI6DA30AQCG4X2QKcgEnpUdAHBEVgAgaZiy8gojQo3ypCdApUqb2sNoMBjE/8dh5RMGgM66EfKO/o4WAGDhFhjtzy+nxWQR1bPOiTzbVp4LALxSKjOPx5cnPm6ryGPjEyFpmdVnJQIAr4QyABS8DO5rbx7o7vhwxhe5tqe5lkqlfjjjyyUqqzbP96da/u+jraUZAIhEFgLD/hYvn8DMeQvoIShYWMf4Hn7jNYRIHB7CWpubAAAJhkFnoL8PALDY4cFaUGREjEA2dnYAILKwAMBEEwsBYRFEqBH9NIxKpc6ct2D0TTEYzJjt/AYCwiQAqCorYTS2tzYDAJ/AGCEu5FXUQ2LTXj8OLc7PERQhG062+pT4FgBklVRLCnILczL1jSfP91yGOFvOtEtPSbx98WTM8wi3Zb5CZFEA0NCbiKg0EMym2lw9eaCkIPenmo2CgoLyv6ajtRkACEQWPMMUhZuX32KOk6TC8PYkC8tYo/84owOBODxFaW9pAgAR8bGGBtzw0MCUf4SVjQMACCysAKBtZM4nJJLwItze2zfhRTiVSrWYM/Y7CAaDIf7M6MAnRAKA2ooRQ0NHWzMA8PCPEYJRWknt/LOPb58/Ki/M5Rci6ZhYfk1+CwBSCqoAIEASBQBlbQNEpYEw0XLm/XOHKgrzkI8xkffP7vAFDCzauNfWYznjPxQKyp+GvvGEZ5A+8PNwOk4xVJGm6Q/GXHP41toIw5I1Uj8SqYJO/8DItRGBEWsRHKxEAKBLQ8z0VET4eSLiP61xnhYZ94lKpTqOynsCf2JtRIAHxth16gIAgbF2nQBATVY8OTgoLPZjbmk1SYDXQl81IT0P/lCWAMCDV0lrj93CYDC7feb7OFizjHUsGQXl3wH65Ub5zyEtJgwAchKkKzuHtwaHKJTO7t6f1T2MXT9ZCADeZ+RNn6RDN6ZkFtBvjVBcWcd4VVlNAwAoSJIBAIfFOlgbnQt5WVnfFB6TIiMmYqA+ImcBQl1T28Hg8PGa4T7LTEdJhsmIhPEorR6R9vVrQTkA6KvJMzn3DwyW1TQI8HB5zDKnG4/dekIS4OXj5jx66/GuCyGhR/ynGWnTSwV5uACgsr4JAAR5uTUVpIoYgoj0DwxSKNSvBWV0eQcKyt+OrJQEACjISt84d5xuHBoa6ujsYmf7uQXHMZGWlACAhA8fbaZa0o0fPn0GAFkp2vJ9YcmItfKyikoAUJKnBenB4XCOc2efvnS9oqom9PFzWWlJQ33d0TeqrW/Ye/T0aDuCl4ujrtaIKHYKcjIYDKakrILR+CUrBwDoWWAY6e8fKC2vEBDg83J1pBsPnbpAFhHm5+P9dikACAnwa6mrFhSX0kv7+vopFEpGZjb6QEBhouVLzFBvh6DBGIKkvwUqZZA6OIgdfyfsuw4AMNTbSR0cwHOOG8fy/zvS4mQAkJcSu7ZvA904RKF0dvWMqc/4+fpJAPA+LWvG5OEARckZOQAgI0ZbRiwur2a8pLyqDgAUpWk6ORwWO3+62dk7kZW1DWGv38lKkCdqjQh+hlDX2LL/0t3xmuFpN1VHdcSUTEFKDIPBlFTWMhq/5hUDwAQN5dE19A8MllbVCvJyL5w7HF7oyLUQkiA/Hw8XKysLAAwMDjFe0tPXBwA8XOwAIMjPo6kkW/hHEBEA6O8foFCpGblFWAz6LEVB+VE4SVIAwEWWNfY9RzdSKUMDPZ14ljHOe/10/cKSANCQ80FMfzhJRGPeJwDgFBneGkECb9Dpqq8AAG5ROQDAYHHSJna5zy53N1aXJz3mJEkLKo0RpLC3tf7rw2PjNUPOcsFomQU3WRYwGCahRktpFgAIKjLPMLsbq5sK0/jldTgEh0Opd9aVAgALjyBlcKCzvoyFS0DOyoVemh1+io1PhMjJCwCs3AJ8MuqMQUSGBvqoVEpLaSYGMyxY+c8iJiUDAJKy8nvOBtONlKGhrq4OVrbf8D0UlZQGgLTkBNMpwxFNvnxKBgBxKVm6BclCQqe6ogwApOWUAACLw02zdbx35UxddeXrJ4/EpWU19cdYx2+qr7t8fN94zbBd4KmiyfzVkpJVwGAw9MAhCPlZXwBAXXcCk/PAQH9VeSkfv4CdixfdeP3MYUEREg8vf3L8GxgVOMTQzPr2xZPtrS0AQBaTAIDBgQFGh97eXgDg5OYer9koKCgofwskCRkAEJOWDzg6HEGZMjTU09XJ8jtWqEQkpAEg8+N7A4sZdGPu52QAIElI0y3VZSOGhrrKMgAQl1EAACwOZ2YzP/LG2YaayoSocLKkrIrOiAiydFoa6u6dPTBeS6Y6eMir6zBaxGTkMRgMk1CjJOcrANATwdAZHOivrSzj5hOYNt+Tbnx48Si/EImLhw8AhMniADA4OOLh39fbCwAcXNwAkBIbdTTAW1ln4qbjwUKiEoCC8puQFhUCADlx4SvbltCNv3HjSYosCACJXwqmGw/P85OzigBAhkxTuyJZSOiU1TYBgIIkbeUEh8XaWxmcD42urG8Oj/skIypsoCY3+kZ1zW2HbjwZrxnuM021lUZIvuTFRTAYTEnNCKFGZmEFAExQlYVR9A8MltU0CvByetiY0o3H7z4nCfAg+VNeJGYs3XfVQE3u+o5l4iJo4leUfzmoUAPlP4esmIggL3d08peBwSF6WKejNx/vvhz66vx2I03mPL4/i6aiFJGAj/2YyWh89zkHh8VaGwyH7C6sqC2qqJWToI2Rt5+/BQBNBdoI5zRt0tkHL849ePExq3Dzorlj3qits+vGk7jxmmGiozJaqEEW5Jukrfw+Pbekqk5GTAQABgaHQl4nigrx6ShJMzn39PXrLgiYP8Xo2i5aONCq+ubIuBT3WWYAoCYrAQCxHzMZhRrXH8cCgIa8FAD4OEz1cZjKWKHpom09ff3vg/eO12YUlL8eORlpIQH+V7FvBwYG6UE1Dp48v/PAsbgnIZMm/mriHm0NVSKREB2fsB820o3x7z/gcLipFrT1xIKi4sLiUnlZaeRj8L1QANBSHz4t4epgd+ritVOXriWnft7uv2bMG7W1tV+782C8Zkw2nsgk1BAliZgaGbxLSikuLZeVlgSAgYHBe48ixcgkJk+E7p4eNWNr53mzb104iVgqq2vDnkYtdHH8bikArPT2XOntyVihgfWcnt7eTzFPx2szyn+W6hfneutL/wlCjdas+PLQfd1VuVTKEIuAuOi0ZSQLT2DYW/quA8JgZ0vGTiscG7f2nri/tAN/IXISooJ8PNHvUwcGB+kRyw5feRB09mZ08BFjnV9N9KulLEck4N8kpe1Zu4hufPvpCw6LtZ5EC+RbUFZVWF4lL0nbTbwZ+QoANJWGVwQW2FieuR1x5nZEypfcLT6uMBatHZ3BYS/Ga4apvgaTUIMsJGCip/4+9WtxRY2sBBkABgYHH0TFiQoL6KgyS2ABoKe3T9t2ieMM8+ADtEGhqq4xIvq9h91UAGBjIZobaMWlZDB25ElMEgAYaqkCwPIFc5YvGBGTzNh5dW9f34eQs+O1GQUFZTRcJBkWboGaz7GUoQF6PpGssFNf7h2YsuexkMrY+w0/Dp+MBhZPqMmI13bfTjfWZyVisDiy9nAsn/aa4o6aYi4y7TFVHHMPAHhlaNMw6ckOuU8v5T692JifquHoP+aN+rvaiqJvj9cMETWj0UINNn6SsKpRfXZSZ20pJ0kaAChDA2Xvwtj5yfyyzM59nS3vDi+Wn+Ju4HOEbixPiAQAYRXDwb7up6snSZnMneRHC+zc3VRdkfRM1ooWdEFxprfiTG/GCl8ETBns75lx5Dupzf4jSMjI8QkIJca9HhwYoOcTuXb60LmDu65FxmgbTPrF+pXVtQkEYnL8G9g2bPyUGI/F4YzMhyVEZcUF5cWFkrK0MSvy/g0AUFKnpXG0cXC5e/n0nUunvqYmL13PUBEDHe2t4XfGTcmqZ2Q6WqghRCLrGpqmfUioLC0Wl5YFgMGBgajw+8Ik0dHOvT3d80w0ps912nfuJmKpq6l68yzc1nkhAMgqqhOeAUUAACAASURBVABA9NOwZf7DP7dXj0MBQEFFHQBYWNkmmJh/TIhj7Gbci0gA0NIfkZ8IBQUF5W9HVEqWh18w9d3rwcEBej6RBxeP3DoedPjeazX9X81+KKeqhScQP7+PYTR+SX6HxeH0TK3plqqSwurSQlFp2jPz9aNbACCrShsaLG2dI4LPRASfzU1PcVm9Zbx7dXa0vQgJHq9Uw8CESaghIExWn2Dy9eP7mvJisqQsAAwODsQ9CREQEWXyBIC+np6lU7XNZzluOH4dsTTWVr1/ETHVgZYyhsjKpmVklpEUz9iRpNdPAEBF1xAAgo/uZOfi3nrmDlP4EBSUX0RWTFiQlyv6Y9aIjafbz/dcDX95ZpORxhhncX8KTQVJIgEf+ykLwIFuTEjPw2GxVga0hZfCitqiyjo5cVr0vttRCQCgIT8sSHKeYnTu4etzD19/zC7etHDsqOdtnd03nr0brxmTtJWYhBpkQd5JWoqJGfkl1fUyosKA7DpFfxAV5NNWlBpdQ09fv577Vgeridd20JJRVjW0RManus+k5WYKvBzGzcF2K2gFU4AQFJR/JahQA+U/B5GAD1zuuHL/Fe/Ac35us7k52J6+Sz0UHGk5Qd1QQ/H71wNIkAQB4HpkrJvNZD0VZkkgWZBvmf2U0/ej/I4EL5lnTcDjQl4lRsSmuM4wpcsyAGCIQnHedHzH0vnykqTHcZ/OP3w5z3Ki8R8JxnSUZBSlRM+GvAAAlxmmMBaKUqJNccE/231/jzkO/kc8tp8O8LTl5eI4fvtpaXX9w8P+SOqy65ExfkeCNy2au8lrLg8nu5meakRsioW++mwz/aLKujUHr4oK8e9d6QIAU4201eQkLjx8xcPJbj1Rs7qhJTwmOep9mp6K7PRJYxzHR0H5Z0IkEvZu27DUb5PnCr+A1cu4ubgeR73ed+yMtZmJsYHedy+XFBcDgMs37y1cMF9fR3O0gyhJZMVijxPnr67asH35IncCHn8v7PGjJ1EeTvZ0ZcbQEGWe57KgzesU5WTDn744czl4vq2NieHwiQFdLXUlBblTF68DgLvTvDFboqQg11WZ91N937R2xRyXRc7eqzb7reTj4T58+mJJWUXknSv0RIZXbt5btXHHtvWrt/mv4eXhtjA1evQkymqyie3MqUUlZT7rt4iRyQd3bgaAb5eioPx/pC0nIee4K56NW9jEGYPDN316VnJn20BHk4St/w860CkKXt/fWsfG9m8+tUkk4IN8vVbsOrFo8+H1i+ZzcbA/jUs6ePmelZGukfb3c4hKigoDwNXQ5x52U/XUxpiMkYUEfJznnLoV5rv3zDKnWXg8PuR5bPjrBLc51nRBwxCF4uQbtHO1p4KUWMSb9+fuRtpPnTxJd1h5pqOqoCQjceZOOAC4zrYasyVKMhKtn8Y9NTImAd7O81bucAvYt3GJMy8359FrD0sqa8JOB9GfpddCo3z3ntns47plmQsPF4e5gVb46wQLQx1bS+OiiuqVgSfFRAT3raPtZe5eu2iy61o3/31Ba7zESYJxKRlXQ58b66jZmI9xiBkFBeXPgcUTtN22JZ/zSzyxUnXuKgI7V2XKi8zQYyQtMyFlgx+pgUNIAgAKX9+StVwgIM/87sPGT1KcsTj3yYWPlzYqTPfC4vGlb8PKk57ImjvRZRkAQKUMvT3gqemymVtUruLDs7xnVySNbYVVaD92fjktbjGFvKeXAEDG3BHGgltMwflB5c92X83eN26va8LRJWr2a4mcvNnhpzvrysy23AYMBunUx0sbNRzXq89fzyelKqikXxh9m4WLX8JwJpVCLXkbWpMRJ2E4S0BBBwBENEwqkp4Wad2VMJjZUVuacmE9uwBZx2PnzzbpvwmBQFy9ZXfQep+tqxZ6rfLn4OSOe/nkyvH9hmZWWhN+aCuOLC4JAGG3rs5x9lDTZtaXC5HITouW3754cv+mNfMXLsMTCC/C7kc/DZvt6E7XKwAAZWhonZfDyk2BkrIKMc8j7l89O2WOg85E2hK5iqautLzS3cunAWDWfLcxmyEtr5Rc3vGz3V/ku3GNm+2GpS7eazdx8fAFnzlcVVZy8lYEMnqG3b6yb9Oapeu2Ll23lYubd4KJefTTsImmlhYzbStKivb4rxAhi63dsR8AZJVUjcysk+KjVy6YZePgIiohFfM88kX4AzklVfMZtI2HNVv3esw02bjUZdWW3SKi4h8T4kJvXtE2mGQ2bdbPNhsFBQXlfwqeQFzoH3Ryy4rD6xfPX7qOnZPrQ/Sz+2cP6kyyVNX7IW2ZsKgEAETdvzbF3l1Rk3lRS0CYPNvdJ/zaqbM7185yXYojEOIeP0h4EW49z5WuZgAACmUoaLmTh99OMRmFxFeRkTfPmc60V9enKQjl1XUkZBUjgs8AgNVcFxgHCVnFx9ktP9V9p+UBO73n7V/j7rRiAxc338NLR2sqSgIvPUKGhqj7187uWuuyarPLqs0c3DxaRmYJL8K1J1kYT5lTXVZ0atsqQZLY4k3DEZ68/Hf7OZjtW+O+cH2gEFk8/UNc1P2ravrGhlY2nW2tZfnZcqpaYVdPMrVBc+JkxnAjKCg/C5GA37XUftWh4CV7Lvu5zODiYHuW8PnwzScW+qqG6mOc5WBCQkQAAIIfv3WbOUlXmfkILgCQBXmXzrU8E/Jq3bHb3nMtCDjcw+jkiLhPLtON6cqMIQp1wdYz273nyouTHr9NvfAoep7FBGPN4ZUWbSUpRUnyudDXAOAybexpp6IkuTH64k/13d/NxmHjCY+dFwLcZ/FysR+/G1Va0/DwgC99beT6k/h1x25vXDh7k+ccHk52M12VyPhPt56rzjbVLa6qW3P4pqgQ357ljgDQ2tGdXVKlqSB5+sFLpruYaisxRhNBQfl3gAo1UP6LeMwy7+7t3372XlhMMgDgcTjP2eY7l83H/FjUaMsJGhPU5K+ER+eVVj0/s3W0wy4fpyEK5VzIyyvh0YhlsZ3VobXujD7m+mqignxu205SKFQAMNVROe7vxejgPG1S0KWHVgYaUuQxsrT+aawMNC7v8Fm5/4rrlpMAwMPJvn+121RD2vBGpcIQhULPentuy1KvnWdX7L+8Yv9lANBWkr4euJKTnRUAsFjM/QN+iwPP77satu9qGOI/x2zCYT8PPENeQxSUfz5ero7dPT2bAg88jHwGAHg8bpGr8+4t63/kgWBtbjJRT+di8J3cgqLo8LGj5e/dumFoiHL60vWLwXcQy1JP1+N7d9AdLE2Nxcgkp0UrKRQKAJhNMjx9KIipElcHux37j04xN0VyqfwWppibBp89tsxvs6PXcgDg5eE+ErR1upU53YFKpQ4NDdEfCJdPHnJb6rtk7cYlazcCgI6m+u2LJ7g4OX6kFAXl/x2VT04AlaqxPYpVWAoAJO03p/rrV7+8KD7bD4PF/YgDQl3szdavsXgO3r+rI38ZC+dO6+nt23r86qNXbwEAj8MtnDc9cLXnjzxLrQx1DDSVL4c8yyuueHH14Jg+Qb5eQxTK2TsRl0OeIRbv+TZHNvrQHSwmaosKC7is34PMrCbra57cupKpEmcby8AzN6yNdKXFftvBKWsj3av7AlbsOrFg3R4A4OHiOOi/dKrJ8IYZFaiMk6sLQes8Nx5YvvP48p3HAUBbRT74wEYuDlooYz01xfAzQUt3HLNbSTsZbGNueClo3e9qLQoKCoKclctQX/fnm0HliZEAgMHh5a1dtVy2wI+9D5K0zAQV9QpeBrdX5lsFjZGMUtttG5UylPfscsHLYMSiMM1Tb9GIyIIkDVN2fnLC4cVUKgUARNSMJywd8QCUMXPIuLufrGWO5FL5XZC1zI3XnE0+5/fu8CIAIHLw6HoFier+IV+jUqmUP6Z/GMzkjTeSz/llhZ3MCjv5R0cW6i4MRP42XHny/fFlyWf9ks/6AQC/rKax3wUCG+dvbO2/GzsXr96enhO7N79+HAoAODx+rsuilZsDf3BdwtDMSkNv4sMbF0sKci49ej3aYfXWPRTK0N3LZx7eoK2zO3gsDdhzlNHHwNRCmCQW4O2MvInoG0/ecuAUo4ONg8vZAzuNzKzFJKX/VC/HxsjMes/p60HrffwXOwEAFzfvul2HJ1nS8oJRqVQKw2vIruOXNy93D1y3LHDdMgBQ1tDZd+4mBycXAGCx2H0Xbh3a6vci/EFSHO0fQdfQdNfxSwQCLcC4mrb+qduRu9Z6r3alSTfMps3adeLyb+wOCgoKyu9i2nzPvp7ua4e2vXv+CABwOPw0x4We63f94NCgY2KlrG3w7O7liqLcA7fHiBTo5R9IGRqKvHH22V3aY3DmAm+f7YcZfbSNzAVIontXu1IpFADQnGi6MvAEo4OF7YKbxwN1TaxI4tJ/qpdjo2ti5X/kysktK5EjghzcPEu3HNA3o0VrpsKIocFv/4WDfgtPbF5+YvNyAJBX095w/Dobx/AkRFFTL/By2LFNy3Z40yJVG1rZ+B28CADZaUlUKrUwK70wK52pDRgMBhVqoPwiHjamPX39288/DIv9CAB4HNZz1uQd3vN+5FdsOUF1gqrslcjYvLLqZyc3jOmza6n9EIVyPjT6SmQsYllsa35w9QK6g7muClmI133HOdquk7bSsXXMclunqYa7r4RbTlBDcqn8FiwnqF3aumTVoWC37WcB2XVa6Txl4nCA+T82nmgfz23y8gq8uPLg9ZUHrwOAlqLUtR3LkI2nD18LqFRqRn5ZRn4Z010wGECFGij/PjD04Q0F5R9CSEiIk5NTx/txg7j+Ljq7ezPySzt7etXkJMSFBX728prGFi52NmTwGJOGlvYvBWUsBLy6vCQv14jdSqmZPnoqsmFHN7R2dKXllogK8SlLizFd/iT+k8uWE3f2+c4xY07F9+sMDg19zi2hUKj6anI47LcyBFOp1KyiitLqBi0laUTUyQiFQi2rqc8vq2FlISpIkkWF+H57U3+WsJhkz+2n0SfbvwZHR0dqX9e9K2f+1zfq6OxK/5rV2dWtrqIkIUb+qWura+u4ODm/LUqob2zKyMxmIRI1VJX5eIeDtpGU9PR1NJ/ev97S2paa8VWMJKKiNEYcvIjnL+cvXB5y/fxcm2k/1bbvMjg4lJrxhUKhGuhq4b6nsqJSqZk5ecVl5Toa6pLioj9V+rewwHsVhoUjJCTk727I3wMGg1H0uSAwYfYP+lMG+qqen25MCutrqWbhF+NRMZFy3I5jpa10tOclNX180pr9ltLfy61gwK1kJDzZBVEkFN0IoA72i8/2q3p+pjUzjk1ERsjEWcjIvubVpYYPYf3N1RxSmjIuu1lFaKcB8i/4cEiocisZ10Rfact5T+AWFDJ2EJu+HEkaknXIobe+VO/IJ8R5sLu9PGx/R17yQGczl7y+sKkLn6blD7b5V/i8yZgy2E9vBgBkH3Vuz/9gcCoby8L+Iw4A0F2d93X3TEn7zfXxd6lUys+mPmn6+CT/gs9vGdEcHR2H2mpvHx43Mu3voqOrJyO3qKu7R01BWpz0c0rTmoYmTnZ2umRhTBqaWzPyilkIBA1FGV7u4f9ocTMnPTXFyHO7W9s7U7PyRUUEVWTH2Nd8HJPo7Lf73rFttla/GlKeicGhobSsAgqFMkFT+dszK0AmVwWlJZW12ipyEmTh0Q4Dg4PZhWUNLW3qCtIkwb8/G6tbwD4cD+k/+yxF+etB3gddHtX9r2800NPZUvJ1sLeLV1KVXfCnpy49zbV4Ns5v6BJ62xpbSjNxeCKvlCqRc4Ri79FCZX55HYtt9/o7W5uLMtgEyDzizPGEKpKfvzvkZRpwTcLQ5mfb9l2oQ4NNRRlApQgo6DLqC8ekq6GyvbqQyM7DLa7A3F8qtbU8p7OujE9Wk0OQ+ZX2b6E8MTLh6NJfHz2R72FaTd9vadU36OrsyMtM7+7qUlBRFxEV/9nLG2pr2Dk5EdXCmDQ3NuRlZRCJRAVVDW6eES/sFqqiatp6Z+4+aW9ryU5PEyaLIplEGImNily/yPHI1QeWM+1+tm3fZWhwMDsjlUKhaOgaYL/5JkKlUgtzMivLS1Q0dEhiYyjX62qqivOye3t7ZOSVpOQUR++FDA4MFOZltTQ1KiirC4r8zwPdv34cunGZK7ougYLy7wODwWw+edN0pv3/9C49XZ1F2ek9XV3SSmpC5J8eGprqa9g5uBhVC0y0NjUU53whEIkyShqcPCOmKM4TJBQ09XZfjehsay3ITBMQEZWUV2a6PPHV4z0rF2w9e3fS1N+fsXRoaLDgaxqVQlHSmvDdoaE0L6u2okReTVtIdOxDTYODA2X52W3NjdJKav+ELCczFTgePHjg6Dh2sDSUvwBHR8fBhpIbgcv/1zfq7O7NKCjv6ulVlRUXF/65l/qaxlYudtZv7DoBQENL+9fCCiIBry4nwcvFTrdLz/bVVZYOO+zX2tH9Oa+ULMirLD3Ga86Td2mu287e3r1yzmTmrHO/yOAQ5XNeKYVC0VeV/aG1keKq0uoGbUUpcZG/f+nju3juPI8XkkHXRlB+O2hEDZT/LpzsrJO0meeaPw5Z8DuiBCE+bisDjW/78HJxWE5QH7PoxtM4USG+mSa/ebBEwONwE9S+H24LADAYjLq8pLr82Ke4sFiMjJiIjJjIb20dCsrfABcnh6nRDwW7Ho0o6fs/AWFBgSnmY6cxQuDj5bE2Mxmv9PqdEDEyafY06/Ec/jR4PG6iHnO+z/HAYDAaqsoaqmM/Ob9divLPp+T25obEUEEje5Kkel9DWd3bO92VOepbHgNAW25izlFnHBuX4MS5BC7+1qy3xbc29TaUSc3fBgDd5Vl9LTVt2e9w7Dw8ypMaP0a25SU1Jke0Zb3l1bRkERBv+RKdfcRJ99AHRIrRlpPQVfalKuocj/IkETO3tqz48tB9vXUlcguPMDWpv6Um88DcgY4mIWMHPBt3a2Zc7ilPaacd5ClLvtvmX4Rfd3r1y4stX2IQXUhPbVF7biKPqildhPFdB8pAX8HFFdwKE8lWi+vjx4648++Di4PNRG/sic13IQt9XzUrxM9rbfStqREvN6fV+A7BYS9FhQX+F2lE8DicgeaPPv0wGIy6ooy64hiBTBEIeLyWstxvahoKCsq4ENg4hVV/KJD4mLDxf2e5n5VHkKxl/m0fIicvSctszKKiN3fZ+cniE36zSBcBg8MLKn4/zR8Ch5A4h9A4u0T/x95Zx0WxvX/8WTZgWXaX7u7uRkJRsFBRUMQAFTGwsBsbW+zELhQLRQykQ8AApFS6u3th9/fHcJcF3YXv9V71/pz3a/9wznnOmc8AzszO+czzYDD8cpr8ckNXuUJhB4mPbGjO6WsCZ0TEh7CYCwqLWNgO8SWCQhUwt/1+UbDHt6+IikvaOvwrVUKwOJyOkdlwIjEYjIqmjoom2wcsYhJSYhKcrEI4PF5dGy3SioKC8t+ASOLTNmH7gGhIhESHuDTwC4kYjvj+aZ8JH5XfwGrUd7te3b8mJCZpbv/PG0kBAIvFqesP6+kcBoNRUNdWUOf09ROHwytpoi/fo/wC+Hh5rPS+U9d1OEgID52TVUSAMspEi0MAP5l3pDHbW/TrobGSwgLj/4X69Tgsl4mm4tBxAIA8G1GS1lb6n+1oKCj/z0CNGigovx2Hrj0pr2l4lZiGlhFBQUHxP3a6rKIyLDwqYJ8fDoeeEFD+Leg93TWJD/h17ZXnH0NauEXkCu9s76zK5xFTrEt6jOHCGuxPxPFSAEBynM/HDeYNaa8RowYA0JqqZZ03SE1cAQDCZpOzA+Y0f07Q3xPJI6YIALmBq2oS7ndWFyKbANBZXSQ/Y4eEw0IAAOd1WYdnVMfdFR85lySny6qqKHhfV22JzpZnfIoGACAzeW12wKyi4L0ilq44Ej9nzazz9LTWV0ZcY3fsgsbjeSXVBjWK289vyo7LOTGXrGTMheduykkg8IvJTt0w/ICie7u7G6s0Vt8ZZiJ9lH+VAxfvllfXvoxLObJhMXpzhYKC8puT+SCgva6i/EO48YK9GCz60Abl1xB4fH91RXncmxfr9xzD4tC/QxQUFBQUuHvmYF1VeUr0y8XbDmPRWxQUlP8gh248q6hpfPX206EV7jjsEBkvUFBQfg7oBRUF5RcgLsQvRGWbm/TKk4jWji4PJ7t5k0b+TFUoKCg/H3ExEWFBTul5Lt2409rWPn/2DK85MzmEoaD8KPReAGj+nNhWnEGS1QYACft5YtYzMXhuAJBw8Ba3n4+4NACA0dON5aX2djQzR2O4sJJj+1JH8spoAQBV3YrplqCqW9Yk3G8v/8JswfFSJMZ4MQdLTVjRlJPQmBnNatToaWusTXrEp6CPuDQAAIPDi9rMasqOr3//XNTGnbNmVmgtdSVPBqfrYMIjrvitUQPHS+UWkm4rzmwtTMVg8cCgY7C43s62YQY0pIVXRlxR87lEoH6nsAXKP464sKAwP4VDwOUHYa3tHZ5THee7jP9pqlBQUFDYQRQQ4yazze6b++o6rbNNefQs5TFzf6YqlD8NETFxfkG2hckf3LjU0dbm7D5/6pwFP1MVCgoKCsovREBUnCrANtNhWNDlzrZWx+me49zm/0xVKCgow0RciCrEz3bVCQCuPI1u6+jymGDt6WTz01ShoKBwBjVqoKD8ApJu7OfQm/Xw+E9TgoKC8mtJjXnBOSDvQ9zPUYLyh8NFIMpMWlP86ED6TkeihApV3ZJf155f2xapW0+UUO5pbSh/eb41731nbUlndUFvRwuBv7/oD55fDIPD902F5wYAAj9LQnguLgBg9NCYDTxiiqx5JohSagDQWV3EKqmjMg8YjN7Oti/nFjMbeztaAaCzpmhIzawQxZXNzuaxO3amclYy9k9pL81RnO0vZDaZC8fdmBGRd3VddsAc/d2R3MIynAMweO7cy76iNu6ChuPY7RTln+Xdg7OcAz6/YJtSBQUFBeXnM/5YNIfeyec//DQlKH8y9yI5/aU9f5f705SgoKCgoPwmnA1N4dB7LTrnpylBQUH5G7y9uotzQNa9Qz9HCQoKyvBBjRooKCgoKCgoKCggNXGFkOnkmoR7DekRlVE3KiOv8Ygpam94gKeKlr84W/L4EAZHoKhZ8Gta801cUf7yfFdtMXMslpt38HQc633gB+aZwBKI8JfDg0lPawMAcOEJGGy/kQLHJyBsPpUoqTqk5kFiuAg8w/1BAHRUfG0vzaGoWYiN7HuVWdBwfPPXlIpXF+o/hEk4eHMO6Glv6mmt721vzr3si/R2N1YwGIzcy75EcUWp8cuHrwQFBQUFBQUFBQUFBQUFBQUFBQUFBeX/JahRAwXl1/AyMbWlrcNltMWvFtJHT28vlosLw35dbciAIWnr6CQR/4d1MhSUP4ew8KjmlpYZzk6/Wkg/rW3tfKRvlt4BAIDBYDQ2NQvwU9mNHTKAw+QovwpGD623u4NbWFpmyjqZKetoTdWlz05URlypeHNFYoxXUfA+PFnIwD8Oy8OHxJc+O/Eju+usLmTd7KorBQCiuBJrI4+ILADwiCqoLDzZr5Pe29vZhhg7OGiWnbqBdSpaU3Xp0wB2YkSt3VhLrgBAe0k2AFDUBlyj+bVsKl5d6GlrHDIATxEmyWp1Vhcwu+g93UCnt5dkYjBoBdB/kZexKc1t7a5jbX+1kD56ensxGAyW6/u/dM69AMBgMJpa2vgpfP+aQBQUlF9J+YdwWnur3Igpv1pIH4zeHnpvD5ajr7Gnsw3HQ/rbw1F+Q+LevGhraXacMv1XCxkuvT09XFjsjzyXoNPpXOwvvigoKCgoAJAS9bK9tdl2ouuvFjKAjrZWGq2bws+2eNzfg8FgtDU38VH5/9lpUVB+Ia/epje3dbrYm/5qIcOCwWA0tXbwk9EHxSh/LqhRAwXl1xBwK7SgrOp3MGq8TEzdfSE4p6CMTCLaGmkunDraSl99mAF0OsNq3paenl7WeDkJkeDDa5mbqZ8Ld5wLep+d39jSJipInWBttNdnJplE/AmHhoLyX+HwqfP5hUW/g1HjY3rGlj2H3qWmNzQ2iYkIO40bc8BvE4Xct0zY0Ni0cdf+O8FPOjo7yXyksfZ2Jw7sEhYUYA7nHMB5cpRfS1NOXPax2cpeJ0QspgEAnioqOW5JZcSVnvbGrrpSYNAFjcYxXRrd9eXtJZl4Ctui5kPSWZnfWVXAI6aAbFbHBQEAr4wWawyPqAKeLNSYGc3o7cFg+25Zy56fKnl0UHvjI7KKKQfNg3bX095cFXubnRiKmsUgowaSsaP+XajM5DXMxrqUpwDAK60xZICQiZO4/YCSvem7xtK7O3X9Xg3nh4Pytzl69X5+ScXvYNS4Gxp5PuhpWk5eT0+voozEYjcn7xlOXFyY4fQCQGNz6+ZjgUGhER1d3WQS0cHKJGCLjxA/JTIpde3+7xd5MdBUubR37Xe7UFBQfk+yHp9urSz8HYwaFWlRqTf2NBXn0Ok9JBFpjUlLVMbOY3UW1uenp93cW5eX2t3ayMMvIm0y1sDDD08kD2c4g0EPW2PP6O1h3SNJVNZuy62fdoAoHLh2+khpYd4vN2rQ6fSZY0x7ewb8nUjKyJ24+YS5GffmxZn9fvlfsklkiskIu+meiwzNrYc/vCj/670rZ6NePG1tbtY3tZjlvdLUeuS/eUwoKCgo/2GCLx6rKM7/rYwazY31PhNMecmU8y/66nYx6PRlky0GnfzFpOV2Xnw4zIDWpsbAg1siQ4K6OzuIJD5jW0efHccoAkL//tGgoPy7BNx5UVBW/WuNGlHvs9ef+P6DOH1VuQtbvACgsaV927n7916/7ejq5uPlcTDTOeI7W4jKN5zhdDpjhNeOnl46a5esuHDwgZX/9KGgoPzroEYNFJQ/mvuvExfsPCMrLrxy1oTymvqHb5JevU2LvrRLRVZiOAFlNfUZucVaSjICLK97ClD637L6kFPgtNIfh+Wa7mApQCE9CH975UlE+pfCiAs7WRckQnEW6wAAIABJREFUUFBQfgfep35ydJmNw+Lcpk4SFOC/9/jZpet3Uj9lxoc95OLi6u6mOc2cn/wh1dPd1cLYMPlj2qXrd0rLK2JCg5HhnAM4T/5LjxsFAICsbIKnCJc+PcYtKEmS1e6sLkByZgjojiaKK2G5SXXJIQI6o4jiyi25KcWPDmJ5+Hq72jsq8walwRgmDEZvzqn5ss7recQV69+HVYQHCpk4UVTNWGMwOLzstE15V9d+vbhMapwPlkiu//iy7GkAVdOGrGzCWfOg3REllM3PFw5fHq+UKr+WbWNmdPaxWcLmU3mEZeo+hNUmPeaVVBM0GDucAJQ/mVtP33hvO6IqL+0za3JHZ/fj8LjV+882trRt9J45ZC8AdNN6pvhsS/n02WOKg6mexruMz5eDw8qqayOuHcFgMDj84K9vXV3dXwpLleSkfvZxoqCg/L+g8lNs5G43AomqOMqNC4cvTnz67tLmzqY6Xbf1SEB9XuqbHa5cWKy89VQCH39R/JPc1zcaCj457A/DYLiGHN5eV9FYlMUvp0Hg67f2EvjQl1ZRBlBdUfY165OyhjaV5T1p1nemXzwK2uLjISkjN3fp6urK8tchwfERL2+ExcsrqQ5neFdnh+/cqdWV5WOd3fgFBd88e7RyrvPpO0+ZVg8UFBQUlN+cgE1L6qoreMkUZkttZVlBToa8mhaZ2n/CJ1MFhhnQQ+ve7uX8OS3FwWWuuoHZl/R3YXcv11aWHQl68+8fDQrK/38wGMDjsIMaO7tpX4srlaTFAKCb1jNt/bF32QVzxo8w1VJ6n11w5Wl0WU3D69ObhjO8vKYhI69US1GadSmK9d8oKP8hUKMGCsqfSzetZ+vp27w83PFX91L5eAFg1xI3tSnLPbefir+6dzgB+aVVAHBx+xIdZdnv7uJC8KvOru7Iizt1VeQAYKuXi9NK/6h3mU+ikp1HmX13CAoKyq/iTOD1js7OhBeP9LQ1AWDHBl/HabMjYhMePnvhMmn8jaAHSe8/Htyx2XepFwDMmzUdg8FcvHb7feonI30dAOAcwHnyX3rcKAAAWB4+lYWncgNXZh50QVq48NyyUzcI6NoDgNL8o3mXV+ec8AQAHIlf3m0nFzcxN3BV2rZR5heL/sbuqBojCPzin894A4MOABQ1C8XZ/t+GiVrPpHd3FN3fgySrwHDhRG1myk7dCBjMkJp/CAyXyqIzBbe21iY/bsyIQtooquZK845icPhhBaD8wRy//kBZVjL6VgCFxAsAa+a7aozzPB/0FLFicO4FgFsh4cnpOf5rvFbOnQYAns6OGMAEBj//kPnVzlQv6d7pQbtbvf9sc2v7ya3Lf+pBoqCg/H8h4/5RYDDGHnjJJy4PAHqztjxeqJ8TclZn+hoMFxYAvjy/3NvdMXr/CwEFbQDQddsQscOl8lNsSeIzWctJQw5vrSgAAIsVpwXktdirQPnTKSnIA4A9J6+oaul+20ujdQfs2kjkJd1+nUSm8APAii17xxoqbFo0+0548pDDAeCU//bCvC8nb4VYjXIEgJley93sjfxWej1N+vzvHRQKCgoKyj9F6O2L72Nes5owAKC8KB8A1h4OVFTX+e4ozgHhD2/lpCZ7bfSfumAFADi6emAA8/xu4NdPH1R0DP/5Y0BB+cOwNdSID9wxqHHt8VstbR0Ba+YAwO2XCSlZ+XuXTl8+wxEA5k6wxmDgckj0x8+FBmryQw7PK6sCgAtbvHSUZf79o0FB+XdBjRoofxyd3bQj10Puvowvr6mXFhOyNdLat8ydj7evmG7sx+xHEUkRKRmdXd0WumojDNQ9J41Eyocv23+J1tOzwdP5yI2nb5LTlaTF50y0dXO0Onk37N6r+NKqegN1+UOr5irJiCNTeWw7qaMiZ22gcebei+j3WSICFPdxI1a6T/xuJomm1vYd5+7Fp+bUNbWY6ah4ONk5WugPU/PfJqewrLymYeooM8SEAQAiApRRpjovE1KbW9spfLxDBuSVVGIwGOW/Dvlb3mZ81VGRQ1waCLMn2ES9y3yfnY8aNVB+OZ1dXQeOn711/3FZRYWMlORIa8uDOzaT+fq9t9Hxb4NDnodHx3V2dlqZmdhYmi6Y7YbFYgFg0epNtG7aljXLD5w4+zoyRllRwdPddZbLlICzgbcfPCktKzfU0wnY56esKI9M5b5wua6Whq2V+ckLVyJjE0REhOdMd17j4/3dZBKNTc1b9x6KTUyuq2+wMDGaP3vGuNF2w5f9t0lIea+nrYkYKRA8ZrpGxCakfEhzmTT+VvBjUWEhHy8PZu/GlUstTY2EhfteTeAcwHnyHxeP8uNQNa0N/OPbSrO768pwfIK8UmrM4iZCxhOp6lZtxRl4flFeCVXEJ0FVs+xpbwIAnW3PWefBkfgtAstYW0QsXEQsXFhbMFw45QUB8jN3thWmEfjFkUoiCFrrg1kjxe3ni1hNbyvO6O1sJ0mrEwQlh6n5B8GR+FW8T8m5bmkv+0yndRLFlYniSsBSEH3IAFZ0t7/4R1T9nnR2dR8ODLoTGlFWVSsjIWprque/eiFrjbOYd+kPX8VGJH7s6OqyNNCyNtaZN20ccnO1dOdxGo220dv9yOV7rxPeK8tKzXV2mDlh1IkbD++GRpZW1hhoqhzZuFhZti9dxJx1/jpqCjbGuqdvPYlKThUV5Hd3Gu3r6fL9m6uWtu0nrsS9z6hrbDbX05g3dayjtcnwZf89mlvbsnILl8ycjPgwAEBCRMjWVD86OZXW09PR2cWhF4/DAcCd0DcigvxLZk5mzrnea4aFgaawIPXb3b2Of3ch6Omz8/5iwgLf9qKgoHyXXlpX5oPjhTHB7XXlJGFpMZ0RBh478MS+BIFVmQnFCSGVadG93Z0iGqaimpbKY2YjnoOks6vpPTRt19VZD09UpEaRJRSU7N3lbVxynp4rjHnQVlsmqKRnvGAvWUIRmSruiLeAvJaYtmXOs4tVGbE8FGEFu+kaU3xYq4ow6W5rSru1rzorsaulXkTNRGn0LEnD0cPU/CO015bxCkkiNgsAwBP5hFQMqrMSe2ldOG5eAKj5nCKgoI24NBAUR82s/BRbl/tR1nLSkMNbKvIBg6FI/p38W/+/6e7qvHziYOiD29XlZeLSMiZWI3399pP4+grKvEuICX/64G1MeFdHh76ZlZGF9dRZC7iwWADYvXYJrbt7oe/mK6cOJUa+klVUnjzTc/w095vnjz9/cKeqvFRD12D9nmOyisrIVBsWzVLV1DW2tLl96VRKXJSgsMhE19lzl67+7teQlubGU/u2f3gb21hfq2ts4Txr/gj7scPU/CMUF3zFYDBySirf7c3/kl1dWT5mkgvi0gAAQWERC9vRseFhrc1NfBQq5+EA8DTohoqmDuLSAAAhEVELO4dn929mfEjWNvxvlG9HQUH5E+ju6rx37nDEk7u1lWWikjJ6FrZeG/2JpL7LfXpSbFzYww/xEd2dHVrGljqmI8ZOn4dcGgDg+BafHlr3TJ+N988feR8bLimv5ODiMWqy26PLJyJDgmoqSpW1DZZsOywprwwA/ivnKqrr6JpZP752Ji0xil9IxN55lovXKgybdKdtzU1Xj/hlpMQ1NdRpGpo7unqa2DkOR/M/QtHX7Iv+m+av3/0i6Aqd3l/moKwoF4PBSMsrsxvIOSDiyV1+IZFJcxczW2YsWadpZEEV/GceKaD8gXR2047efH73dWJ5TYO0qKCtocbepdNZV3BiUz8/jkyJeJfV2dVtoatipafm6WSDPBtZfuhaN61ng4fT0VvP3yRnKEmLzRk/YoaDxal7r4Jevy2rrtdXlTu00h1JJgEAnjvOaSvLWOurnQ0Oj/6QLSJAmeloudJtLLuFp50XHsanf65rbDXTVvaYaO1g3u9tHVL2P0V4csalx5FPjqwRE6QCQNCrRBEByqKp/a9arZ090VxHRZj/+/eWg4bnlVZjMBhlGbF/XCcKys8HNWqg/HH4Hr5yJyzObewIPVW5grLqqyGRmXklb877AUDMh6xJK/dT+Hinj7EQ4idHpGSsOnSloKx6j89MAPj0taispj4yJZPKx2tjqPngzdvYj9n3XyVEpGQ4WOjJiAu/TEh1WumfERyAXBGj3md+/Fx47NYzG0ONeZNHRiR/2n42KLek6vQmr0GSyqrrHZburm1odh9nTeEjvkn6NH39kX3LZvnMGDuk5h+horYBAIw0Bzw1M9ZUepmQml1QZqajMmRAflmVjJhQW0dn9LvM6oYmNTkpYy0l7F+39bSe3tGmOoOGl1XVA5qHCuX3YNn6bTfvPZrl6mygo5lXWBx4425G1ufY532LxFFxiWNd51IpZLepk4QFBcKj43zWbcsvLNnvtxEA0jKyysor38TEU6kUOyuLe0+eRce/vfsgJDw6dtzokXLSUs/DIxxdZn99F4M8A42ISXif9unwqfN2VhZec2e+jordvPvg1/zCC8f2D1JVWl5p5zS9tq5+9nRnKoX8KjJ2ymyvQzs3r1g0fziy/zY0Wo/DSBsTQ72BYioAQFCAHwBy8wvH2tsRCPiCouKM7C9SEuK6WhqzXZ2ZwRwChpwc5TeBi0AkKxqC4ndeH8HxCVA1rQe14Ph+aG0Yx0ulatoMGYbl4aOomrPr5aD5xyEISBAEJH4k4E9g1b7Tt0LC3Z3s9dSV8ksqrjwIy/xaGHn9KNIbnZI20XszhUyaMX6kED8lIvHDij2nCkor9/ouAID0z3llVbURb1P5ySRbE73gl9Ex79KDnkdGJH50tDaRlRB9EZs8wXtz9vOryM1VZHLqx+yvR68E25rqLnAZH574ftvxy3nFZWd2rBqkqqyqdrTn2tqGJncneyof6XXC+2kr/Pav8V42e8pwZP9tsFjs6yuHFaT7PazNrW0ZXwrsLYzwOFw3todDL9KSW1TuOMKYgMcVlFZm5RZKigrpqCm6T/xOnpj6xuZF24+5ONramep924uCgsKOlAsbCqLuKdi6Cihot1YV5b6+0Vic7bAvFACqMuIjdrriecny1tO4KYKVadEpF9a3VhUZzN0OAA0Fme115ZXpMQReipi2VVH846rMhMLYhxVp0VKG9iQR6bL3ryN2uEw69w6xYlR9iqnPT8t6fEpM20p5zJyK1OjUm3taKvLNlh4bJKm9rvz11kldTXUKdtPxvJSK1MjofXMMPHeqT/QeUvMPImM2PjvkbPmHcMQX0lyeW5URL65rg9gs6L00Cf2RQioGA9WWwV/lSzgPB4CWygKSsFRPZ1tlemxnUw1VWkVIxRAxvvzh7Nu44tn9mxNdZqlp65cW5T28eTk3J+Pq02gASImPXjJjHJlMHTt1Br+gcFL0m30blpcVFazc5g8AnzPSqipKk2LfkCn8xlZ2r57cf5cQE/bw7tuYNyPsx0pIy8aFhy2ePvZZ8hfka0hybGR2+odrZ46YWNpOm7MgMSr8xN4txQW524+cGySpqqJsweSRDXW1E11n81EoiZGvV811Xr3joPvC5UNq/kFKCvLEpWTa21qTYyPraqoUVNR1DE2Zq481lRUAoG1gwjpEy8AkNjws73OWnokF5+GN9bXNTQ2T3OayDkdcHVlpH1CjBgoKyu/Dab9Vbx7ftp88U1FLr7KoIOzelcLPmUfuRQBA+tuYzZ4TSXwUu0kzKAJCH+MjTm1fWVlcMH/DXmRsfnZ6bWVZanwkiULVNbeJCX3wKSk26mnQx7gIEztHUSnZ5MgXmzwmXo3MwnBxpSVE5mZ8DL54VNfcdpzb/A9xb64c2lZemLty35lvVdVWlq1zG9NUX2vv7E4iU97Hhu9Y5LJwk/8Uz2WcNf8jdHd1HvD10Da2nDR36YugK6xdFUV5IpIyHe1tqYlRDbXVskpqanomzJP/kAHlhbnGtg44PKGypKDwS5awmKSChs6oKTP/KeUofyCrj9688zLBzdFCT0U2v6zm2rOYzPzS8DObkd6YjzmTVx+h8BFdR5sJUcmR7zJ9j94orKjZvdgVANK/FpfXNES9z6Ly8VobqD+MTIlN/XwvPCnyXaaDua6smNDLt+lOqw9n3D3418JT9sfPRQG3w2wM1ec52b5JyfQ7H5xXWnVqvecgVWU1DY7L9tc2trg7WlL4iG+SM6ZvOrFv6YylrmOGI/ufor65den+y1NHmtoaaiAteaVVY8y0CXhcYXlNVkGZpLCAtrKMm4PFMIfnl1VJiwq2dXRFv8+ubmhWk5Mw1lTEosW1Uf6boEYNlD+LLhot6GW8o6X+uS19z7wUpETXB9zILalUlhG//zoRh8V+un8USSDhO9tJx8U3LP4jYtQAgKq6pu3erus8JgOAyxiLaWsOxX7MTrl1AEkpsXjP+VthsfllVcwMEwVlVf4rZi2bMQ4Ati10dVrpfyM02muqvYGaAqsqv7NBxRU1kRd3GmsqAcCWBdOmrjm4/exd93EjBCh8nDWzzlPX1HLxYTi7Y59sZ6KhIM3aoiglBgAx77NWzOx/nT2noAwAsgtKzXRUhgzIK61sbuvQmraqvbMb6TVQU7i4fYmavCQA4HHYw6s9WHYINQ3NFx6+xuOwYy0HPOxDQfn5dHV33w5+PH7MyMATB5EWJXlZ3y27vuYVqCgpAMDdR09xWOzn5Ch+KgUA1q1YrGps++zVG8SoAQCV1TW7Nq3Z5OsDADOmOjnNnB8d/zY99hUyfMHyddeDHuQVFCGbAJBfWHx419aVi+cDwM6Nqx1d5ly9fX+x52xDPW1WYVv2HCgqKY1/8dDUUB8A/Nb7TpzpuWn3gdnTpwoK8A8pm0ltfcO5yzfYHf5Up3GaagPeOcPjccf9d7C2VNfWnb18HY/HTXAY1drWXlFVLSoiPGW2V+irvq/caipKgScOmhkZAADnAM6Ts/0loaCg/PZ0ddPuPIsYa2N6ftdqpEVRRmLtgXNfi8pU5KQA4F5YFA6HzQq9QiWTAGDNPFfN8fOeRychRg0AqKpt8FvmsWGhGwBMH2c3xWdbbEr6+0fnkeHe247cDAnPKylHNgEgv6TiwFrv5XOcAWC7z9wJ3puuPX61cPoEA80B57Rtxy8XlVdF3www0VEDgK1L50xZunVrQOAsJ3sBKnlI2UzqGpvPBz1jd/jOo600lORYW0hEHgv9vtRBp24+Lq6oehGT0kunr1swfcheAGht76isrRcVEpi2fEdYTBLSqKYgc37XalNd9UF7X7XvdFNL6+6V89j/flBQUAZDp3UXRgdLGY02X3YcaeETk3t/eWtLeR5ZUqko7iEGi510JplAogKApvPykCUmZe9eIUYNAOhsrNZz36Q1bRUAyI1wjtrrXpURPzEghiypBABvT67IjwpqrSgg/5VAorWy0NBzl7rTIgDQnbkxYodLXsQdFUdPQaUB/qrUm3vaqksc94cJqRgCgK7b+sg9bqk3dinaTSfw8XPWzDpPV3P915cD1jBYkTGfSJVRG9SoOt6r8lNs1L7ZImomWDx3VUY8UVBcz30T0suFxRt77WON72yq/RJ2hQuLlzJyGHI4ALRUFNA6Wp8sNurp6kBaBJX0LFecpkizTX7wJ9Dd3fX8wW3r0eN2BFxEWqTllA5tW12U/1VOUeXloyAcFheSlI0kkJi3bO1EM7XoV6GIUQMA6qqrfDbuXLByIwCMdZ6xfNakdwkxwdGpcooqAOC30uvpvRslhXnIJgCUFuav2XlolvcKAFiyfseS6WOf3Lnq6uGtoTvA53pyz5bykqLrobGId2Hxuu3L3J2O7948wXUWlV+Qs2bWeRrra+9dPc/u2O0nOCupaQ5qLCnIa2tpmWCi2tnRjrRo6BruOXVFQUUdAKTlFQEgJS5yzuJ+X2b+l2wAyPvSZ9TgMLww7wsACIsNcNbKKakCQH1tNTudKCgoKD8ZWndX5JO7pnZjfQ/0nULF5RTO715XVvBVSkEl6tk9LBZ3OSKTRKECgKv36vkjtZIinjONGgDQUFM119fPbel6ALCbOH27l3N6Uuy5sHdSCioAcHSDd/jDW+VFechmRXH+ws37nectB4C5q7Zv8pjwKvj6BPeFytqDH9heObStqqzoWHCUmp4JAMxeuXXbgimXD26zd57FQ+TloHnQPM0Ndc9uXWB3+FaOU+RUNL5tDzywpb6qYs+VEMw32SvLi/LbW5s97TS6/jr5K2sbrDscKKOkNmRAR3trfU0lv5DoDm+X5MgwpFdGUdX3wHl1fdTAh/J36KL1BL1OdLTQPbux70U7RSmR9Sfu5JZUIVkfgsOTcFiu9Dv7+xae3Mfpum0Ii09DjBoAUFXftM3Led2ciQDgOtps2vqAuNTPydf2IMMX+wfefpGQX1bNzCFRUF7t7zPDZ7oDAGxd4Dxp9eEbz+O8Jo/UVxvwjMLvfHBxZW3E2S3GmooAsHne5GnrA7afD57paClAIQ0pm0ldU+vFx2w9WJNtjDUUJNn1AsCaY7eaWjt2LerLttvW0VVZ1yQqQJ2+6cSLhDSkUVVW4uym+SaaikMOB4D8suqW9g6tGes7/lqW0leTu7hloZrcn/4yFcp/EdSogfJn0dvLAIDYj9lpXwr1VOUBYNE0Bw8nO24CHgCWu41b7OLALPNBo/VQybzNrR3M4VgurpXuE5B/6yjLAoCNkSbTLWFtqHErLDanoIzZQuXj9ZnelxWDiwuzdu6kmA9ZEcmfWI0aDc2t914nGGkoGv+VeYKAx3lOGhn9Pisk+p2Hkx1nzazUNrTsvfSA3bEry4gPMmooSYsbqitEvcu89jRqmr05nU6/+zL+UWQSAPTS6cMJyC+tam3v9Fvk6mRjXNvYciss9vrTqBkbjyZc3cvLwz1IwIv4j0v9L9Y2thxYOVtLCS0ehvKL6e3tBYDo+LepnzL1dbQAYOmCufNmTefh7vvT9V28YJmXB+LSAIDubhqVSmlubmHOgMVi1/j02ad0tTQAYKS1BdMtYWtldj3oQdaXr8wWfiplxaK+5TQuLq6Nq5ZGxSW+joplNWrUNzTeeRBibKCLuDQAgEDAL5jtFhmb+Dj05fzZM4aUzaS2tm7nwQB2h6+ipDDIqDGI0FcR3qs21NTVH92zTVtDLS0jCwBOXriipCB33H+HhYlhQvL7TbsOTJ3j/THmhaiwUF5BIecADpNzkIHy/xICVQxPFvzVKlD+GZD7gdiU9LScPD11JQBY7DbJw9mRh5uABKyYM3XpzMmISwMAunt6+CmkppZ25gxYLi5fz74v2zpqCgBga6rPdEtYG+veDAnPyStmtlDJJGZWDC4uzDqvGdEpaeGJH1iNGg1NLUHPo4y0VBGXBgAQ8Lh508ZFJac9eRPvOXXskLKZ1NQ37TnD1vSmIic1yKjByo6TV9s7uwBAQ0mO+M190Xd780sqAOD0rceKMpJHNy4x19dMTM3aeizQdeXOdw/Oigj2pyDKzit68Cp2vdcMGQlRdgJQUFC+hU7vBYCqjISGgk8CCjoAoDp+gdLoWVg8NwCoOy1WHeeFuDQAgN7TjSdRaO39t38YLqzGZB/k3wLyWgAgpmPNdEuIalvmRwU1lX5hthBIVGZWDAyGS2vaqqqM+Iq0KFajRndrY2HsQyFlfcSlAQBcOLzy6DlVn+JK3oYqjZ7FWTMrXc116XcPsjt2soTSt0YNAolCEpFuKMioy/3IhcUzGHQMFkvrbPvuDGXvXiedWdXZXGc0bze/nMZwhrdWFtA6WvXcN8mYje9qrsuPDMp7czt6/9xxR94ws278gdB7ewHgXUJMTkaqurY+AMyYv2SKuyc3Nw8AzF680m3BUmaZD1p3N5nC39rSxBzOhcXOXdpnNFTV1AUA0xF2TLeEkaXN03s38r9kM1vIFH5mVgwuLq75KzamxEcnRoWzGjWaGuvDHt3V0jdmZpjA4wlTZy1IiYuKCH3sPGs+Z82sNNTVnju0i92xyymqfMeoUZjX1taybOOukeMmN9TVPL138/GdK76e0+68TibykmQVlDX1jJJjIx/duuww2ZVBpz9/cCf86QPmT5Lz8JKCPACg8g/IAychLQsALc2N7HSioKCg/GSQE1p6UkxeVpqSph4ATJq92NHVg0DgAQDn+SsmzVmCuDQAoIdGI1Go7S3NrDNwYbEuC/sMbQoaOgCgb27LNEzomNmEP7xVnJuDtJAoVCQlBgBguLhmLFmf/jbmQ9ybQUaNlqaGqKf3VHWNEJcGAODwhLHT56UlRie8fGLnNJ2D5kE01dfePL6H3eFLySt/a9RIjgx7euPc1tN3BEW+U3S7vCivo7XVY80OyzFOTfW14Y9uvbx/befi6adCEniIJM4BFUX5APDk2hlJOcUl249oGJpnvU+8fGjbrsXTz4Sm8AuJsNOJgsIOei8dAGJTP6d9LdZTkQUAb2f7uROsmSs4y2Y4LJpmP2DhiY+3uW3gwpNb30KStpIMANgYqjPdEtb66rdfJOQUljNbqHy8zKwYXFyYNXMmxHzMeZOSyWrUaGhuux+eZKiuYPyX+4GAx3lMtIn+kB0S895jos2QspnUNrbsu/yE3eErS4tzMGpkF5Q/jExZO3uCtFjfw8C8sioAOBv8WlFa9PDKWWbaSm8zcrefC3bbfPLtlZ0iAhTOwwEgv7S6tb1z+8KpTtaGtY0tt18kXA+Nddt8Mj7Q79tlKRSU3xzUqIHyZ8HLQ9g0f+quC/dHzNuqJi9pY6jpaKFvb6aDpEVSlZOsb2o9ced5csbX4ora3NLKlrYOCZaa3xLCAgR83/8aHgIeaWH2IplFu3t6mC3KMuKshl8NRWkAyC8d8NLG1+IKBoPR2tHpse0ks7GlvQMA8suqhtTMiqqcRHXEZXbHzlTOIhhzZrP39PVHlu2/tD7gOp3OoDMYnpNGXn4cgVg6hgw4t3URNx6vqSgNAEoy4mY6KhQS8fjt0CdRKTPHjmDuqKCsasPxW2HxHxSlxQL9lo400QYUlF8NL5G4be3K7f5HTOyd1FWV7azMx407XRcdAAAgAElEQVQe6TDSBvtXFkQ1FaW6hoZjZy69ffehsKQ0N7+wuaVVUrzfTSwpLkr467YV8UlIsPQi2RS7u2nMFmVFedYTAuKTyCssYlX1JS+fwWC0trUzn6UCQHNLKzNySNlM1FSUmouz2B0+AT/4hptJfmHxmm27n718o6Qgd/1cgL2NFQDUNzQBQFd3973LZ9RUlADAQFe7qqbW/9jpe4+eLlvoOWQAh8lR/jT0dr351RJQ/jF4ebg3L56189Q1ixnL1BVlbEz0xlqbjLY0Yt6lqCnI1Dc2H7/+ICktp6i8Kq+orLmtXUKk37wlISrEcnNFAAAJ0f7v3lgscnPFci6VlRpwLlWWA4CCkgpWVV8KSxkMRltHx5x1/szG5rZ2+MsJMaRsJmoK0nXJbB9GfHtzxUpt0uPc4rKED5l+J6/azFr15eV1MZb7xu/21je1AEBXN+32kS1qCjIAoK+hXF3XcODi3fsvope6T2YOP3rlPgGPWzFnKgcBKCgo34LjJurMWJt22z9s7WiKtIqY9ggpQ3sJ/ZFIMQ6KlEpXS0N2yNnaz+/aakpayvNpHS1Ewf61AaKgGBeu7yYKS+ABAF7B/ts/ZBJ6TzezhSyhACynLMQn0VpZyCqpuSwXGAxaZ1vcEW9mI62jBQBaKguH1MwKRUp5xp0iYANTOSuvt05qLMo28T4gZzUFS+Ap//Am6ezq6L3uEwJiSaL93vrWysL3V7aVvXtFFlewXHVWXNdmmMPNl53gwnPzy6oDAFlCUVjNBM9LyX5yuuRtqIKtKzup/+/hIfIuWrP19H4/9zFmCirqJlZ2VvZjLe3GIN8g5JXVmhrqbpwLSH/3trykqLggt62lWUS8/+1AETEJPL7PWUjg4QYAEbH+J+PI94Ke7v6/Q1lFZdZLJ+KTKC3KZ5VUlPuFwWC0t7VuWDSL2djW0syM5KyZFXlltcQCtgYIpnJWdh6/hCdwK6trIWr1TCzIFMq1M0cjnj+e4DKLi4trx7ELK+c671675NC2NXQ6nUGnO89a8ODGReRYOA8nELgBoKmxgXWPHe3tAECh/lAVPxQUFJR/EG4ir/vyzdeP7Vw+2VJGSU3P3NbYztHIejRympVRVG1urH8YeCL7Y1JVWVF5YW57a4uQ6IAXx4VEJXDMqwM3DwAIsiQTwnJhAaCH1nd1kJIfcGlATBIVxQMuDQBQmv+FwWB0tLX6r+wvINXe2owEc9Y8CGlF1cefatkdPu6bq0N9TeXRDYscp3taOkz67pA1By7gCdxyqpoAICmvrGFoTuKjBF8KSHgZglQw4RAgJCYJALTurs2nbssoqgKAspZ+Y1313TMHo0PvT567lJ1OFBR2EHkIGz0n7b70yNprp5qchI2BuoO5rr2pNvMhg6qsRH1z68mgl8mZeUWVtXml1S1tHRLC/S9jSAjzD154Eurv/evZSP/Ck5K02ICFJ3kpACgoH7jwVFLJYDDaOjo9d/TXvEPcIQXlNcORzURVVrzq1Vl2h8/52UjAnTACDrtsugOzpaG5DQC6aD03di1VlZUAAD1Vuer65kM3nj2ISF48bTTn4QBwbtMCAgGnqSCF/CjMtJUpfMTjd16ExHxgVz8FBeW3Ba3Zg/LHsc5jcvq9IxvmTeHl5g58/MZl3WGTWRuq6poA4PjtULUpyw9cfUzr6bUz0T6/ZZG5rirrWF7iYDseUhWMHWIsV1MAIPFwAwAP94AHZPVNrQDAjcfjcTjmR5BCnuFgxUyAwUEzKxgMhshNYPf5bo0uLSWZpBv7T2/yWjDFfutCl6hLu9TkpABAQ0FqOAEGagqIS4OJo6U+AGTllzJb7r6Mt/DYEvsxa4/PzOSb+1GXBsrvwyZfn8/JUVvWLOcl8ly4dnvyrAW61g6V1TVI75HTF+R1LfcePUnr6bG3GRF48pClqRHrcF7ewW8BcnGshCchNuClZxIvL/zl8GBSV98IANwEAh6PY36EBPndXSZrqqsORzYTDAZD5OFh9/nW2IFwK/ix0cjx0fFv9/ttTIt9yTRSSEmIAYCZkQFiwkCY6GgPANlf8oYTwGFyFBSU/zQbFrplPLu8ydudyMNz6X7o1GV+Rs6Lqmr7lkOOXQ1Wdpjjf/4OradnlLn+hT1rmLU/EEjEwa9bcWE4nUvFRQakY+El8gAA98BMGPVNzQBAwOPxeCzzI8RPdpswUkNZbjiymfyvN1cMBoNOZzA3lWWl5k5x2L1yPq2n50VcCudeAJAUFQIAU111xKWBMN7WHABy8kuYLSUV1UHPo5xGWghQyRx+VigoKN9Fa9qqSaeTtF1X47h5c19ei9o3+9kqm87GagDIfnL68UK9jPtHGb00cV0b8+UnRNQHpL/GcZMGT8fxlEUUGJAxGMfDC385PJh0tTQAABbPzYXDMT/cZAF5m2n8smpDah4oBoMl8LD7fGvsaC792liULaZlqeLoSeDjxxJ4ZMwnKI506+nqKEkKZYYVxgQ/XzOqOjPBYO72CQExTJfGcIYLKukhLg0mkkajAaCpOIfDz+1PYMHKjSGJ2Qt9N/MQeYOvX1g5Z4qLrX5ddRUAXDtz1NFA8eKxfT09NDObUbuOX9I3sWQdS+Qd/HeI4fg1RFhswIvIRF5eACAM/BrS1FAPAAQCN8u3EDy/gND4qTOZCTA4aB4gBoPh5iGy+3x39U5D1xCxWTCxsh8HALk5mcimsob2vcgP24+cc5m7cOmGHTfC4pGyJopqmkMOFxIVB4CyogLWgObGegAQQN+ZRkFB+Z1wW7o+8M2nmT4buYm8oXcu7Vg4bfE444aaKgAIvhQwd4TKndP+vT00A8uRqw9c0DQavBLJQ/zmIRX7u5RBOSqQFBT4b5IktTTWAwCewI1jgcIvOHLSDFkVTc6aB4HBYAg8RHafb68OobcuNjfUtbc0H9uwCPnUVpXXV1cc27Do3rnDAKCsbYCYMJgY240FgMIvf1072AcIiUsCgLq+KeLSQDAbNR4ASnI/s/uhoaBwZt2ciWl3/Dd4OBF5CIEhUa4bj5t6bK2q71vBOX7nhfq0tQeuPaX19I400jy3ab65jjLr8G/zQHBeeBIXorJukngI8JfDgwlz4QmHwzI/glS+6WPMNeQlhyObyd9YeEIoraq/H/52orWhAKX/DlZSWAAATDQVEZcGwjhLPQD4XFQx5HAA0FeT01QYULjWwVwXALLyy9gpQUH5bUEzaqD8WXTTejq6umUlRLZ6uWz1cqmqazp07fH5B6/PBb/0mTF2+9m7wvyUtKAjfLx9N6aHrrF9h3I45JcOuDEtqqgBABXZAX5neSlRAFCSEb/kt4TZ2Eunt7Z3ErkJnDX7LZrOOlVVXdOBq4/YiZkz0Za15Aoyc1FFjRCVPHeiHbPx6I2n4kL8AhS+IQNKq+veZ+UbaijKiPW/F1tYVg0AzPxUL+I/eu8+Z6qtfGXnMtYwFJRfTnc3rb2jQ05GascG3x0bfCura/yPnT4TeP30pWu7N6+tqavfvPugiJBgdlIkma/vRtD/2Okf2WNuwYAXHItKSgFATXlA4T1FORkAUFGUv3bmGLOxt7e3pbWNl0gcUjbrVJXVNXuPnAQ2zHOfzlpyBSH0VcQ8nzXmxgY3z5+QlR6QsE5GWhIAaCwvtQNAR0cnAFAp5OEEcJgc5T9KQ3pEb2eLsOnkoUP/Zaqib/a01AMAUVJZ0HD8gD4GnfMSGgcY9B5GTw/X97K2DjMAAHo7Wxk9NByfAAA0Zka3FaQBABeBR8LBm8Oo/xDdtJ6Ozi45KbFtPnO2+cypqm04cOnuuTshZ++E7FjuUdvQtO34ZWEB/k9PA8kkIjLkwMW7P7LH/OJy1s3isioAUJUfYBuVl5YAAGU5qcv71jMbe+n01rYOpMgIZ9msU1XVNvhfuM1OjMcUB9aSKwBw+PI9vxNXH53a5WhtwmwUFqAAQGllDedeAEDqmNB6elnn7OjqAgAquf/B6+UHYT29vR7OjuyEoaCgsIPeQ+vpaieJyui6bdB129DZWJ0RHPAlLPDz80vqExel3tjDTRVyOvUWT+RD4jMfsC0kNxxaKgasDbdVlwAARVKJtZFPXA4AyBKKlivPMBsZ9F5aRytSHISDZj33zaxTdTZWf7p/lJ0YpVEzWUuuAEBDURYAiGoNMAFI6NnmPD3X3dqXEaHs3euEE8uEVY2tVp8nCUv9T8Pba8vrcj8IKhuwDmytKgQAbqowO51/AjRad2dHu6SM3JL1fkvW+9VVV1067h90+ezdy6fdFy4/uXeLgJDw44QsEl+fG+9SwP4f2R1S+4NJeUkRAMgrDaiDIyWnAACyisp7Tl9lNtJ7e9vaWpCVPw6afTYOKHRSV1118dg+dmImz/RgLbkCAFXlpRkfU7T0jcWl+k2KZUX5ACAoLIrsuqy4UEBQaIr7PGbAlVOHhMXEqfyCQw6XU1TBYDCDMoh8yUwHAG1DE0BBQUH5PeihdXd1dIhJyc1ZtW3Oqm0NNVV3zx58euNcyI2zUzyXXTm0jSooHBj+iUjqu0W5e5ZtsbPhUF404NJQVVoEANIKgyvkissoAICUvPK6I/0pnOm9vR1trdxEIgfNHqt3DJqqoabqzmm2lzMHl7mDqq5QBYUVNXTLCnOZLbTubgadnpedjuHiqqko/ZL+TlXHSESy/+RfWVIAAPxCogDAOUBUQhoAegY+wurq7AQAEnlAzQUUlGHSt4IjLrxl/pQt86dU1TcduvHswsOI8w/ebF84tbaxxe9CsDCVnHrbv3/h6cazH9ljftkA03ZRZR0AqMgO8GDJS4oAgJK06KWtC5mN31l4YiObdaqq+qaD156yEzNnvDVryRVWrjyN7umlz51gzdqIFDGh9Q589NFNAwDKX8+OOAwvra5/n11gpK7AWgylsLwGAEQE0LdZUP57oBk1UP4sot9nSTt633+diGyKCVFXzpoIAI0t7cWVdXQ6Y5KtCfNiWVpdl/6VbebY4ZBbUplXUsncvPk8BgB0VQZctBSlxIT5KeFJ6awP5Y9cD5F29H6fncdZ86DdNbW2XXsaxe5TUDb4jauOrm7DmevWHrvGbCmrrn8SlTze2nA4AQ3NbbO3HB/kZXnw5i0AWOn1PfTZcf4ehUS8uXcl6tJA+d2IjEsQUdG/+6jvFlNcVGSNjzcANDQ2AUBxSRmdTp8ywZHp0igpq0jLyP6RPX7Ny8/NL2RuXr0TDAB62gPc/UoK8iJCgq8iY2i0/lx2B46fFVHRT/mYNqRsVpqami/fCmL3GVRyBWHr3kNUCjno8plvjRREHp6R1hYf0jJYD+FJ2GsAsDAxHE4Ah8lR/qOUvzhTdG/3r1YBAFARHlgaeqI6Pqgpp+9a2VmVX3hn+4f1ZikrtHKOz23KjvufJmzMjE7f6Zi0WClpqfKHDeaVEVeAQf+fAhB6WhtSt9hk7HdGNlvzP1bHB5WFnS59dvxvHejvSHRyqsQIl3thUcimmLCAr8c0AGhobgGA4vJqOp0x2d6S6dIoraxJz8ljM9mw+FpUllvc/4bE9SevAEBXbYDpTUlGUliAGh7/nsaSF/TQpSCJES7vMj4PKZuVxpbWqw9fsPvkl1YMitdWkQeAN28/sDZefvACAHRVFTn3AgCRm2Bnqvcx6yvrMT6NSAQAc73+60V4wgcBKnmk2YBHmSgoKMOh6lNs8FzVorg+dzsPv6jGFB8A6G5taqspYTDoMmYTmC6N9tryhoKMH9ldc0V+S0X/8nB+xB0A4FcYYJYliytwU4QqPkbSe/tXCzIfngieq1r39QNnzYN2193WlBd+k90HcUiwQpVWBYDixAGPXIsSQgCAX66vTnza7b0EXor1usBBLo3hDO9qbYg9tCAz+BhrQHHcEwAQ1TD/zs/rjyElLspWTezF4yBkU0hUzGPpGgBobmysKC2m0+mjxjszXRpV5aWfM9N+ZHdF+V+L8/sXup7cvQYAatq6rDEyCkoCQiIJUa97aP1/h5dPHrRVE8v8mMJZ86DdtTQ3Prp1md2ntHBwXv2mhvp1Xm6DzCivntwHAAMzKwDo7GifOkLnwBZfZm9VRdmb0Ee2Dk7DGS4iLmFobv3hbRxz1z00Wtiju6LikoMsIygoKCi/kLTEaFcjyahn95FNARExF69VANDa1FhdVsyg060cJjNdGjUVpfnZ6T+yu7KC3HIWD8TrBzcAQFFTd1CYpJwiVVD4fexrVk9D0PnDrkaSn9PecdD87R5bW5pe3LvK7vNt1ZVJc5ecCklk/cgqqYnLyJ8KSVzlf7alqWHvslmD3CoxocEAoGViCQCcAwg8RD0L29yMj6w/hMTXTwFAw/CPvkVB+dvEfMyRmbA8ODwJ2RQTpK5yGwd/reCUVNXR6YxJNkYsC0/1n76WsJttOOSWVOaxvCR8MywOAHSUZVhjFKVEhfnJ4SmZAxaebj6XmbD8fU7BkLJZaWptvxYay+6TX/5Nqr+/eJOSKUAh2RpqsDYSuQm2hhqpn4tYD+FZ7AcAMNNWHnJ4Q3PbnO1nBjldHkQkA4DlwAT5KCj/CdCMGih/Fha6qiIClP1XHkmJCuqpyOWVVSE+g7GW+qqyEiQiz4M3bx0sdFVlJd9++rL7YjCZRGzt6PxaXDEoDcYw6aXT3TYe2+7tqiwrHhL17uz9l1NHmVnqDXhzhYDH7Vwy3cf/ktfOM76znSgk4rPY9wevPhllom2uo8pZ86DdqcpJ1kVdHb48Kh+vrZHm48jkkcbaTrbGeaVVKw4ESooI7vVxH06AtpKMqbbK1ZBIQQrfJDsTBp1+92X8m+RPk+1MjDSVAKCxpS0rv1RPVe7kneeDdm1toDHWCl1dQPmVWJoaiwoL7Tl8QlpCXF9HK6+gCEmYMX7MKABQVVbkI/HefxI61t5OXUUpIfmd3/6jFDJfa1vbl9x81YFpMIZJby99qseiXZtWqyopPnr24tTFq66TJ4wwH/AWF4GA37t1vbfvRo+lvuuWL6KQySFhr/cdPTXadgRSeIWzbFbUVJTaSv+HhI0NjU2ZOV/0dbSOnb00qMvW0nyCw6h9WzdYjnV281q2Z8taGSnJyNiEi9duW5kZO43tKxzIIWDIyYevEwXlu1BUzTV8byL/pnd35pzw7G6oFDZ3xpEE6t6H5pzw0PC9RVEd1gOXpuy47GOzcESK6Ag3DBZX9y604NZWWkudzOS1wwxgknd1TXdjFZHY90aOtNMqaadVuYErG9LC/6Hj/vWY62uJCPL7n7stJSasr66UV1yOJMwYZ2MKACry0ny8xOCXMY4jTFQVpBM/Zu06fZ3Mx9vW3vGlsHRQGoxh0kunz1i5y2+5h4qc1OM38WduP5nmYGNlOGDVk4DH7Vo5b+mOgPmbDq2Z70om8T6LSjxw8Y69hSFSeIWzbFbUFGQa37F9a+RbHEeYaqnIn70dQiXzjbE0Kq+uffgq9nn0WyMt1XG2plwYLg69yAy7V823mbVq9tp9u1bMkxYXjkpOCwx+bmmgNcGu7w+4sbn1Y/bX8bbmnPOgoqCgfBdhdVMeqvCne0eIQpKCCtotFQVIzgwpo9FkSWUcD6ko/rGkoT1FSrkmJzn9zgE8L7mno625PJciqTzk5N/CoPfG7PfQdd9EkVQqeRv6OfSSrOXkQR4FLhxef/bWpDO+CQE+ms7L8Lzk0uQXGcFHxfVskcIrHDQP2h1FSsUtqBSGDVVWTULPriItKnK3m4KtC0lEpiTpeVHsQ6qMmrTpeADobm1sLM4RVNDJCRlckVpUy0rSyJ7zcAE5TWE149zwm9xkQRnz8Qw6oyAmuCItSsZ8opDKH/1lUM/EQlBY5MLRvaIS0uraeiWFeYjPYMTocXJKqrwkvldP7luNcpRXVktNSTh7YAcfH6W9ra0w74u80t959Ezv7V09z8Vn405ZRZWI54/vBp4eM8nFwGwEawweT1i+efeuNYu3LPOct2wtiY8S9fLppWP+5rb2eiaWnDUP2p28slpS8WDjIwdUNHV0jc0f3QrkFxQcNX4KnU5//uB2YnS4/QRnbQMTACBT+E1G2IU/e2hmPWrk+MklBXl71i4Vk5Batd1/OMMBYP7KDStmT17v7e61aiOZKnD11KGyooLjNx6zVnZHQUFB+bVoGlnwC4ncPuUvLC6lpKlXUZSHmAxMRo6VVlQl8vLFhAYb2zrIKKpmfnh749guXj5yR3tracHXb9NgDAc6vXfXkhlzff2kFFQSXj15cv2M9fhp2saDC9Ti8ATPtbuOb156aM0CV+/VvHzkt+Ghd08fMLAapWlk0dnexk7zt3uUUVQNyRpcaPJvo6CmrWFg9iLoCplf0MphMp1Bj3xy90PcGyvHKWq6xsMJmLd2t6+L7b4VczzX7BSRkE59GxV2N1DL2NLcfsI/JRLlj8JcW1lEgLL/2lNJUUE9Fdn80mrERuBooQsAKjLiJCL3g8jkMeY6qrLibz/l7gl8RCbxtHZ0fS2uHJQGY5j00hkzt5za5uWsLC0eEvP+3IPwqSNNBtkUCHjcDu9pyw5eXbjnoq/7ODKJGBr38dD1pyONNc21lYeUzYqqrERt+Pn/VWRjS3vql8JxlvrfPr7YuWjayMV7PfzO+XlPlRIVjPmQfSUk2kJHZbyV/pDDtZWkTbWUrj6LEaSSJtkY0emMu68SI1IyJ9saGWkoAArKfw3UqIHyZ8HHyxPot3TRnvPjl+1FWngIeL9F0x0t9QHg7OaFS/ZdnL7+KAAIUPj2r5hNInJ77z5nOntjQ8w1TvOywc5YS1JYYPbW40g9cmsDjWNr530bNneiXXtn97bTdx5GJAEADov1cLLzW+SKPDXgrPkHObPZe57f6aX+F5f6XwQAfTX5Kzt9mNZOzgEYDObufl8f/0tHboQcuRGCxHs5j963vM/n8Tb9C4PBSP1cmPq5cNB+MRgMatRA+bWQ+UjXzwXMX7Z2tHPfXywPN/fuzWvHjxmJ9F48fnDhyvXOcxYCgKAA/+HdW0m8vPOXrdWzcewo//o39jjK2lJKQnzGfB86nQ4AtlbmJw/u+jZs3qzp7R0dG3fuv/8kFABwOOz8WW67N69BTgicZf8ICcnvGQzGx/SMj+mD3xzFAGaCwyhjA92Q24FeK9Y7zZyPtDuNHX3peP/bCRwChpz8B8WjoLBS/Gh/R2Wexqob/DqjAEBizII0vzG5gb6GBxKHM7z0aQAwGDrbwnhE5QBAdtqm92uNy1+el3byxXBhhxOAUBV5vfFTJI7E/68c5G8DmUS84r9+4dYjYxdsQFp4uAk7lnuMtTZFes/t9F3sd8xlxQ4AEKCSD65bRCJyL9x6xHjq4uYPfyfP50gzfUlRIfc1e5CbKxtj3eNbfL4N83R27Ojs2nIs8MGrGADAYbGeU8fuXO7Rdy7lKPtH4OLC3Avwm7/54N6zN/ee7TMPTba3OrJxCQ6LBQDOvQBgpKX66NQu7+1Hp/hsQ1om2Jlf2LWauYvolDQ6nWGmO+CFEhQUlGGCJ/JZrjqbeHL5m+196Y6weG49982SRmMAwNwn4O3pVdH+cwCAwMdvNG83joc38cTy0FW2M+/9nWrH4jrWvIIScYcWMBh0ABDTsjTxPvBtmJK9e29X+8fru4oTngAABotTHj1Lz30zYDBDav4RMBguq9Xn313aVBj3qCI1EmkU1bQw9wngwuEBoCYnBRiM+vz0+vxvXtvFYKSMx3AeDhiMzYZrSWd8Mx8ez3zYl01KxdHT0HPnDyr/r0PiI+89fX37yvne0/p+iQRuHp+Nu6xHjwMAv2MXdvp6r/KYCgBUfsE1uw4ReUnbVyxwtTNIKWn7G7sztR4pKi61zssN+RpibGmzef+Jb8OmuM/r7OgI2L3pdUgwAGBxOGf3+T6bdiKXTs6afwQMBnP0SvCuNYsunzh4+UTflwtXj0Wr/4+9s4yLauvC+BqYGXoYurtDaQUVCREVWxFbbLG7u712t9ioiIKiIAaCgSKIhHR3Mz3UxPvh8A4wwDCIXr33nv+PD7D32vs8B2XOOXs/Z61drX8su05c2bx45u41i3avWQQApn1sDpy/hSQdEWa4k4vHvjPX96z1WzdvMgDIEIhrdh0Z6I5WEENBQfmDkJCSXn/8+vH1CzbNaHE54MXEfdfs6uc6HABWHbpwctPi3YsmAYCMrNzCrYfFJSWPrV+4eIR9aAZ/hi1hsHZyVVBV3798OpfDAYC+/Z2X7u683NuwSb6N9Uz/w9vehz0CAFFR7DCf2b5rd2EwGMGafykYDGb7hQentiwJvHg08OJRpHHktAULNh8UMsC4r93uK4+Pb1q0Y37L/ZXjkJGr/+rxPjQKCoK0pPjV7Qv8DlwbubLlbkQcj9uxYALieJCWFD+/ae6SQ/6TN58GADmC1KFlUyTFxRYduNZ/9va6yCs/cERXWzM1JeLMHedbNp6sTY6vmdExbNZI5/rGpu0XHj5+GwcAWFER31GDd8yfwNt4EiC797z7lsHhcPtZGHTssjXVC/pr5eKD/hM3tHz4eA20vrBprjDDMRjMvf3Llh2+cexO2LE7LW8Izx/rtn+pz0+RjYLyN4Phcrm/WwMKSjsCAwMnT55M+3jn1x2C2dCUmltUXFmrICtjrq+pJNdafK6OQk/KKlBVJJrqaiCXqzoKnUxj6Guq9PQoOl5+dmb6j49tINMYCRn56kpyprr82WLbQmc2JGUV0OsbLAy0NJX5C4UI0NxLuFxuam5xQVm1lYlupwVKug0oqqjJLionSkua6Gq0NXn8Fh5HxvpuP4N+sv1r8PHx4TYy7l09+4vmZ9bXp6RlFJWUKcrLW5gZKyu2+x9eSyIlpqSpqSibGRsiHwi1JBKZTDXQ67zqngBUTezsbfo+u3+dRKZ8TUrRUFUxMxH00gONzkhMSaUzmJZmJloa/Bl9BMv+pTQ3s1IzMqtr6yzNTNRUlH8g4O9k6vxlGDGpwMDA3yvjd4HBYIz9Lio4jO4qIHfZFXcAACAASURBVP/uNkZxqvHiS3jZ1n+pvJsbGmqKzVbewmBxAEDN/FQbF0pOe8dpaiAY9SOYOCkPnobYEVIPezdUFdgdjQeAnKsruVyO0YIzvHlKw86Skt9YbHiIEWmxBbOY1KLHB2mZsc30OhlDe2XnaXJ9f45HJ3G7m5i8Bi+jRtxyc7y8utXu1qwVOddWVscE9dn6TFq/e4/gt00DOKwm5LwQ0o5NoWZ97nc6TURMUpgAAGCWZabs9dKeuLkqOoDL5Vjvi2orhpT02uF0ardKauNCsy76/ZQrmo+PD5tScefIlt5P1SnMhsbvWfnFFVUKRFkLQx0l+Xb2lDoyNTEjV01J3lRfu+XmikwlUekG2j0uhKTpMtnOwvjJ+b1kKv1rapa6iqKZvraAeBqjPikjl8GstzDS1VRV6pHs3sDhcAtKK7IKisXFxIx1NdXb39cJ7kVoZrHScgqrSRRLI11VRfmOAX8nM9YfEJVV/c9+lqL8/SDPg9MeVXYf+qOwGuvJhWnMmhIxGQVZbVNxWUVeVyONRMpPkZBTkdU0RnwSjTRSE4Mso9rjd7MezTaVN7Rx23aviU6uy02SUFBDaoV0RXM9nZSfwmpgELXNJRX5PyEFaO49zNoySnEmu6mBoGFIUDeEHqYZ6HY4o7qEWpaDl5QlaBrxKsv8Iopinnw4trD3V0/k/2FCeeNPUdUpDfXM7LSUitJioryigamFvGLrdYpCqs1ISVJUUdU3NkMunRRSLZVM1tLrZJlbMG7m6hbWdmcDQqkUUlpigrKaur6xIKsfg07L/J7IZDCMzCxV1PlzXwnQ3HvKS4oKcrNkCLJ6Rqa8yi88uFxuTvr3kqJ8sz42qhpaPR0OAGwWKy3pK4fD6WPbT0RUtGPAT+TV06CNi6aj6xIoKP8+MBjM5lO3nL0m/qL5G+uZ+Znfq8uKCXKKOsbmRIXWj1kquS43LUleSVXb0BS5NFDJdXQKSV2nx5eGKQ5aRn3t9l4LoVPI2d8TFFTUtQ1NBQ+pZ9Bz0xLrGQxdEwsltXZXBwGa/waqSotK8rOlCLLaBqa80jDCB7BYzYVZaZS6Gl0TC3mlH8lq0CO8jKQePHjg44PuJf82fHx8WNX5N3cv/kXz1zc0fc8rKa6sVZCVNtfT4NvBqaPSk7KLVOWJprpqLWsjVDqZxtTX6PHyqe7olbamuo+PrCbTmN8yC9QUiaa6ghZY6MyGpOwiRn2Dub6mpjL/CoNg2b+UZhY7Lb+0lkwz19dUVZDt6fDiytrsogpZaUkTHbW/YVvKd+cFrJIeujaC8tNBM2qg/BeRFMc7WBg6WHSSvVZeVtrNwZKvRV62V2tJRBkp9/Zzdoq0pPhA6y5viwVo7iUYDMbSUNvSsMt9jm4DtFUVtVV/5kIhCsrfhqSERH87m/52ne/dKsjJDRk8kK9FQU6uN0eUI8p6uAzqNkxGWsrZqcsXuwXL/qXgcFjrPha9CUD5cxBX0a2IvF73NUzVfTbS0kSurHx/T8FhFOLSoGTEpB+bIioho9h/PE5Gnpz6Lu/2pobqQp1J2/imohcmA5fTtqWhMp+W/QU4XBABAGgilX8/NL6ZVqs0wBsrQSB/j8o47as7eYfa0AU/96RY9DoWk6I0aHLbRgkVAwCgFyQJY9SQtx1eFnGJlByJ+EjqK3KpGTGy5s48E0a3AZzmxuxLSwhG/dWGzKuKDvi5J/hnIiku1q+vab++nd/GyBMJ7o42fC3yxF49+RMJ0kOcuq8rLyMlMciuy3swwbJ7g4gIRl9LTV+r88J5gnsRcFislWmPl1xRUFCEBCsmoWhsB8Z2HbvEZORU+w7maxGT6dXtH16aqGrl0m0YTkJa2dypq14BmnuPpIK6pEKPzXPCD5dS0pRS+pFyV/9uxCUk+9j172PXv2OXrJxC/8HufC2ycr0yZxNk5RxdhnQbJiUtY+vo3FWvAM29R01TW01T0LqEkXkfI/M+PzYcAESx2F+kHAUFBeVnISYhaWrdz9S6k+UgAlHeZoAbXwuB2CtLt7Qs0WagUK9PSEhJWzp0vpYlQPPfgLKGtrKGoA9/wQFYLM7A3OoX6EL5jyIhjncw13cw77xktjxB2s3OnK9FntDLjSdJN3vzbsOkJcUHWnVpGRcs+5eCw4paGQn6ExaMlopCp+8Vo6D8s0CNGigoKCgoKCgo/0UU+48vfLC3Nv4Zz6hRG/cUuBzlgS0uh9rYEIyIqM2hT1hJAgCoj1j6baMjKelVR6NGtxQGHWisKebltNAauy795PTCoP1KAybxFQdh0esqIrssNyZv7yWpbiLgQPUVuQCAJ7bLgyWuagAAzbQaYaSqDplLSf+QcXqWjIG9CE6MkhGDJ6poT9gofEBh4N4mcqXZmns9fSMZBQUFBQUFBQUFBQUFBQUFBQUFBQXlPwJq1EBB+VWoKhAVZDvJt4mCgvIfRFVFSVG+V+9ioqD8dHAyCsQ+buSUyGZqDY6gCAA1X57g5VSJFi1vEqt5LlQdMhdxaQAAl9UkKinLrqf29EAsBrkmNlhaz5qX0AKDxSkPnk5J/1j3NUx58LS2wc202uInR7uaSlxVX7BRo6GqAAD4zB9iChoAwGYKpRwrKSumoMkoSqUXJGJEccDlYESx7AaGkAGkpNcVkddNll5tW1AG5Wehqiiv2LtUHCgoKCh/GxJyKmIyv7l0EQqKkooqUR7Nf4mCgoKC0oqcsmovszShoKD8XlQVZBWI6MYTCsq/BNSogYLyq4i9feh3S0BBQflTSHz34ndLQEHpBKWBk0hJr+q+vVBxmdFYU0zP+6bhtRwwIkivhJohi04qi7hEz/3aUFPcUJXPrqfxJasQhvqKXOBy2Q2MrIt+vEZ2PR0AGqoL+YIlVA37X8jtaiqkJosAMFg8ALAY5LaNnKZ6ABCVFKra5fdD45glGfozDir0HyuCFSN/j8y9sT795EzrvW/FFLUEB2BwYjn+q5UHT5O3HSHMsVB6SvyjC79bAgoKCoqweJ2I/t0SUFAg8G3C75aAgoKCgvJnceF53O+WgIKC0is+39jzuyWgoKD8NFCjBgoKCgoKCgrKfxQ5q6FYSdna+GcqLjNq4p4CgPIgH15v2YsLxSFHMFg8wcSJaO4sPWpFWcSlxpoiYWZua5Vg0UkAIILDY0RbbRZYaTlFxwkS6h1qZGIwInjxHz4jJI8Fn/+DxSABAE6m+3eG6suzmSUZBBMnFbdZSIu8rRc1O6785eW6hHA1z4WCA1hMCotex2ZSc/xXI71N5HIul5vjv1pCVV/Da/kPnxcKCgoKCgoKCgoKCgoKCgoKCgoKCsq/CdSogfJfJOJTIo1R7+3h9LuFwPUnkTVkGgCY6KqPcXHgtXO5XAqdSZSREjCWzmxoambJy0r/cpXtYbHZGAxGVEREQIwAbSw2u5nFlhDDC3/EN19SEtLzAEBcDL98CvqOMsrPJPx1FJVGmzx+9O8WAgBw9da96to6ADA1Nhw/cliPxtLojKbmJgW5HldXYbHYGAyIior2dKCQ0BlMaSnJrno5HI5I1x8mLBZbVFQEg8HwWl5FvY//lgwAEuLiqxbP+7lS/5uIYPEK/cZUvbvHopNqvzyRMbQXV9FHuppptYVBB3AyCjYHP4iKt3yelzw73ek8GMBwOJy2LfUVrVkxxJW0AUBcWc9owRleI5fDZjcwRPESfFM1U6pKQk92JVjZeYqUTl8BZySuog8YTGN1OzcJozgNAHiFVwTALE4HAIJJuzsEosXg8peXEeuJ4AAcQVFK26KhKp/XxWE1AYfDLE7FYARdN//pRLyPozKYk4a7/G4hAAD+QeHVJAoAmOprjR0ysG0XnVkvLcn/X44Hh8MVEcF01fsbEebWSzB8p/b6U8LX71kAICGOXzFzwk+QiILyT6Ms4XUzk64zaNzvFgI5r243UmsBgKBhpOU48mdNy2WzMCKigOnyM43VwMCKC3rY7NXRuRwBVz3B2rhsFmAwGJF2t6blSVF1OYkAIIoXNx3t1+nAfyIf3rxg0KjDxvl0H/qLeXznKqm2BgD0jEzdvdr9XTAZdEmpv3vNQRjYLBYGgxHp4ilGcC8SICIqiun6b6Qjn6JfpyXGA4CYuMSMRSt7KhgFBQVFGOKiIph0qsuoSb9bCITf96fU1QCAlqHJQM+xP2taLpfLoFKkZYmCYjgczI8++9Qz6RKSP3jZYrNZIiJdXho6VZ7w4U1W8lcAEBMXHz93xY8dF+XfxMvPyVRGg/eQfr9bCADA9dDolo0nHfUxg22FHyh4bYTF5oiKYLr6S2GxORgM9Gb5glHfKCUh9gMDWWxOM4sleNeJb/LIuNSvGfkAICGGX+bj+QMHRUH5KaBGDZT/IifvPs8vrfwTjBrnAyOKKqpVFeU8Ha0QowaZxth27l7gy5j6xiZpSXFPR6vj62YryPKXHKuj0B1nbSZIS8TfPYy0RMWnrj9xq9Oj2JjqXd7ebj3LavJaZxuzs5vm90jtg5cfLz96nZxVwGJz9DSUF3l7Lhjv0fGy3VEbwpsvKTsvPEjLK2axOVqqiiumenU6vKO2+NTcgBfvq+ooOCwWNWqg/FyOnr2UV1D4hxg1Tl+5UVhUoqaqPHyIK59Rw7S/m8tAx0vHD3Y6sJZEsnXxIhBkUj685DVyOBx791EsNrttpK6WxtMAf+T7gKAnF/xvJ6akstgsfV2dpfNm+c2ZIcA20RWdavuW/H3rviPxickkMkVFSXH0iKF/7dxMkGl5Ws7OzT/vfzs0/BWFRhvQz26l3zx35wFth4e/jtpx8Fh6ZjZBRtrNeYDfnBnOTv0A4MvXxNuBj6uqa3A4HGrU+FkoDfSpjLpdGn6OUZRq4HuE195YWwJcjrzdCJ5Lo6mujFmciiN0UuZcTFGTnPaOy2ZhRLEAwCzLbGtWEFfWw8kokFOjeQEAUBp2tjj4sOWmYBmjdk+wLCa18n1AV2oJJk6CjRp4ogrB2JGa9bmhqlBcWQcAuGxWzedgvJyqtMCBCEiGj7r451pj1/Iaa+NCAUBS06zbAAWH0apD5radMHnPcE5TQ9+dL+FfzfEbD/OKy/8Qo8a5uyGFZZVqygqeA+0Ro0Zies6OU9fjU7PIVLqygtwoN8cDa+YT/u8hyy4svXQ/9NnbTxQ6w8naYvnM8W79rXmzcThcx8lL2ax2n6XaGirBZ/e8jU1cd6jzUiw25kZX96/rkew+o+c52/c9v7Pdxs/9528vPQhNyshlsdj6Wmp+U0YvnDy6471Tp2MFnFpcSkZA6JuqWhIWi0WNGij/TdJCztErCv4Eo0bms8uM6mIJOVU12yF8Ro3QpY7KlgP6Lz7e6cCuessSXicFHKKUZOIkZFT7DDIaPkfZvPWxty4vOenO/trcxCY6WZyopOkw3MZ3J06i+/rWFSnvv17d0mmXvIGV04qzAEAry816cb3kS3gzk6Zo2s909CLVPs7Cayt49yjrhT8pL4XDYcuo6Bp7zTUaPgcxfNRmJeRHBTZQqjGi2H+TUePmuWMlBbl/glEj4MrZsuJCJVW1ge7DEKNGRsq3M/u3pSZ+pVJICkrKLsPGrN5xUEqGAAAcDmfq0H5sFqvtDOpaOqfvPEG+FxzQ7fBuCXt8L/D6xYyURDabpaWjP3nukkmzF/GeYgT3AsCHNy/OH9qZl5UuJUNwGOTqM3uRraOzMMq/J3x59vBOXXUVFotDjRooKCi/iKArJ8qL8v4Eo8aTm+cqS4sUlNXsXTz5jBrzPfr26e+8cv85XktiTNTFvWs7zAEAYGhps+7IVQCgU8jXDm99+/RBU0O9hJS0vcuwpbtOEORa016W5mc/u3P505tnDBrFwtZp3Jzl1gNchVSbk5p44+iOrJSvdAqZqKjs5DFq3sYDktIyQmqLi4q4dWJ3UU6GpLSMlZPrqOkLLB0G8cIEKM9MinsTHECqqcRicahRAwUATt57kV9a9YcYNS4EvS6sqFFTIA7t32fMYFsOhzto/i4Wu91LVtqqikF/tdzV5BRXXg6OfP7hG5VR79jHcOkkT1c7s7bBLz8n770anFFQJiMl4WJrNn+c20Cr1iy5ga8+Xw6OTM4uYrHZehrKi8YPmT/OTfiXYZKyCndefpSQkU+mMZXlCCMH2exbPElGSgIAulUeGZe681JQWn4pi83RUlFYMXkY36G7mjwuLe9eREwViYrDiqJGDZTfyL/53T4UlH8EA61Nkx4cO7J6FgA0NbMmrD1y61n0pKEDzm2eP2nogMeRsZM3drI2t+TglfIaUtsWDAaDw4ryfbE5nIyCUiqjvm3k3bB3eSWVPdUZEP5hwZ6LZBpjsc/wBRM8GPUN647fPHqrk8WUjtoAICo+dfyaw4Xl1TNGuiyY4NHQ2LTu+M2D/o/5wjrVtnHOuKQHx8a42PdUMwrKPw5np34ZsW9PHtjZtvHW/aDc/MKuhgDAwpWbyir4/3BKyipS0jJERUSUFOR5X/LEFu//7cDHs5euIVEoyxfO8Zszk8Fgrty869DJ8z0V3Km2r4kpQydMT0j6PmXCmK1rlxMIMldv3RvmPQPJuFDf0DBu5oIbAYFD3Qcvmj09O69g3PT57z994Q2//zh07PR5ZAp17bKFXp7uz19GjpsxPysnDwC2rl2eEft2nFfP0o2gCEZG31ZcRb/85WURvISCQ6tpSULVQFRMqvbLU1LSq4bK/OqPgSkHxoiKS7MbmW2zZSBI69tyWc0511ZRMz9VvQvIPDNXVILA68VgcdoTN7PradlXljEKUxqqCsoiLpWGnpQ1Hyxj6MA3lYSaoeOlgq6+2irsCo2Ry7ksVtbFRXUJYZSMmIzTvg3VRQa+R3gv75a/vPJ5gXbJ0xMdx0pqGBMtXJhlmeknpld/ekTL/lLwYHdNbIikuom8zXBhAlD+BAba9UkJvXZs02IASEjNHj5/U0J6zmQvt80Lp8lKS/oHhY9csJnD4QJAfWPTpBW7boW89Bhot9BnVG5RqffynR++fudNVVpZ/T0rX0RURFFelvclT5ABAAwGg8Vh+b7YHE56XhGVweyR4NtPXuUWlfE13g19M2/rETKVvnT62IWTR9GZ9WsOXTh89b4wYwWf2uaF01JCr41xH9BxFAoKyt+PkrnT6HOf7eftb9uY9/Y+rSK/qyFd9RZ+CI46MKOZQTEfu1TD3rM0/lX0gZnUshykty438c3OiXV5SbrOEywnrcFJEnJe3Y7c5c3lcjpOxQcGMCJYHN8Xl8OhlGQ119MAgN3UEH1wVu6bADVrN6Nhs2nledEHZlSlfRJSW35UYMzppU10ismohcbD57AaGPFXt6Q+OoX0Wk5aM/rcZ83+Xt3qRPlhbB0HPYlJ27DvBACkJX1d6D0sLfnb8AmTF6zeIi0j+/jOVT+fEcjNfFV5aXZaioioqJyCEu+LQJTnTSU4oNvhgnn28M72ZXOoZNK0Bct8Zi9iMuh/bV3lf/ovYXoB4EXwg5Uzx9Go5FlL1jgP9Xr/KmzlrAkFuVnCaFuwesuTmDS3ET/ttXIUFBSUPxxLh4FXXyf7bT/atvHV4ztlhfwLAhgMhv+5CIvjsNlFORlMOg0AWM1NO+aPf/nwptton5UHzruO9nkf9mi3X6tVsamhfveiSS+Dbtk5e4yctqC0MHfXIu/vcR+E0ZmdkrB55oic799cR0+eunSTlIxs+H3/Lb4juRyOMNqingXuWjiRQaV4z1/Vz23El8jwXQu9S/KzkckFK5+6dNPV18kDPMf84K8YBeUXM7CvcWLAwSMrpwFAWTXpe26JqIiIIlGG9yVHaMmxV9/YNHnL6dth7z36Wc4f55pbUjl58+mPSVm8qYLexE7adJpMZ66cOny4k1V4TJLP5tPZRRVI772ImAX7r5JpzMXeHvPHuTOYjetO3T1657mQOr9lFoxcdSQxs9DHw3Gj72iCtMT10OjRa44h6zaClUd9TR+//kRhRc2MEYMWjHNraGxad+ruoRtPhZl8o+/oxICDo517kG4EBeVXgGbUQEH5gwgIfx+XmrN/2bQVU70AYNYoVwwG/EMiEzLybU31eGFXg1+//pwsR2iXyc3Fzjzm5gG+Cdcdv0lj1J9aPxcASqvqDvo/TkjPS8kpgp5z5n6YgaZK1JXdiJNx9YxRlt6rLz9+tWF2uxfROtUGAH/dCOFyue+u7dHTUAGA3X6TjcctP30vbNPc8aIiIr3UhoLyr6SkrGLv0VPx35KTU9MFhF26cTciMlpejj9vJOKfuHn+eF8Ls46jTpy/aqivG/MiGMlysX75IiP7wRf8b29Zs6z32s5fu1Xf0BDzItjK0hwAdm1cPWzijMj3MY+fvfAe47X9wNGsnLzQe/7Dh7gCwPKFc+xcveYuX58dHw0ATU3Nm3YflJKUiHsTSpQlAMCB7Rt1rZymLVwRH/lMGG0oP4DSAO/i4MOKdl6ibd6pFRWXNph7PNd/Tcbp2QCAlSLqTtktIiaRc21V0nZ3xyvtDDrqwxbRcr/WxAbXxAbj5VSVnLwBoDTsLC9A2Xkqp6m+8OE+JPkERgSrPHiq9oRNArKy/zBECxfDBadzb6zLPLcAALCSBN0pO4l93HkBXC6Hy2EDcDsZjBExWnQ+/+62mi8h5O9RSBvB2NFgznEMFidUAMofxsX7TxsaG9/5n+prog8A25fOHLlw89vYxJDXHyZ4Ou86cyOroCTk3F7PQfYAsHT62H6Tli7cfiwt7DoyPLe4HAD896/vY6LPN7NrP6vYwHN8jWsOXaDSmWe2LRdGW2llzf6Ld7+mZqVk5nXsPXXrkaG2evTdk0jyj7VzJ5mNmH3pQeimhVO7HQsA3Z4aCgrKHwiztiwl8GhdTiKpILWnvRxW87ebu7FiksOPvsZLyQKA9YxtIQusPx5fNOLoGwDICvNnN9V7HHohp2cJAH2nbIzc5V2R8r740zPtAd1sMKj0GTTiWCRfY/zVLc31tH6LjgBAUsABalmO69YAddshAGAyckHYWrfPZ1aMuRAnjLb0pxdk1PSH/RWOpPcwH7fsyWL7rHB/S+/VPf0dovSeB/4XGuvrb4V/MLGwAoDFG3b6+Qz/8v7tm+fBQ0dPLM7PBYB9Z64bW3Seq0xwQLfDBXP74kltPcPbYR+Q9B6zl64f1c/4wfUL81dt7ra3ubnp5J5NEpJSAa9iZQhEAFixdf9wW73Ni2bce/2l99pQUFBQ/sXUVJTePXMgO/lrXkZKx14rJ5ezoZ/5Gi/sWcuk05bvOQ0Arx/fzUj8Mn/TwQnzVgDAsEm+GMCE3b+WnZJg1McWAG4e312Sn73narC9iycAjPNdumR0/+MbFvm/7eSeh4/QOxcbGxpOPorWN+sLADNXbd/iOzIxJupDRIjziAmCtbGam64d2iouIXXmSYwUQRYA5qzfO2uQ0aGVs84+/SSMchSUfwq5pZUAcHnr/D6GWh1791wNzi6qeHR41dD+fQBgsffQAXN2+h28lnL/LwBoamZtO/9QUhz/4epOWWlJANi9aKKp97o5uy99uLYTAM48iDDQVH57cWvLztG0EX2mbLwSHLlh1ihhtF16/KahqTny4oa+htoAsHXuuDFrjkZ9TX/y7ut4V3vByg/fCuVyudGXt+upKwPAroUTTbzXnX4QsXH2aKQCi+DJf+h3iYLyk0EzaqD8I1l3/Kbn4r0VteS2jcv/ujZ21aGmZhYAvP+WvubYDesp60zHr5iz89y1kDdsTufvCS3ce3H+7nZpq4/fDvVcvJdXL4BCZ64+eqPfjE0Go5dO23Iy4lPirzknAID7ER+V5Ah+3q15ltbNGnt5u58isXXnLD2/ZMuZgD1LpqgqCKrnBwCvPidfCX59decSFQVZAKAzG3KKKwjSknZm/DsN3UKlM9Pyij2drJBrLQCoKcq52JmTqIzmNsnABWgrqazVUJZHXBoAIC0pbm9uwGKzG5uae6kNBWXl5l2uo33KK6vaNvqt3TJi0qympmYAiP74efnGHWaO7nrWA2YsWnn55l12+4IgPOYsXeu7pN1y8OHTF11H+7D+//+cTKEu27DdynmYpkW/SbMXh7+O+iWnBAAAdDo9OzdfliBjb9PlQmFaZvb6HfsP7tioqqzE15WTl4/BYIwM9DqOolBpqRlZw4e48mqRqKuquA0aUEcmNzezOsb3VFtM3FcrS3PEpYHgO3USAMQlJAHArfuP+pibIi4NAFBRUvR0G1xQVPwlIREA0rOyS8srRni4IS4NAFBWVBjq6pz0PY1CpQmjDeUH0By10ulaqeH803ztCvajbA/Hmq+9b7U30uHUd6UB3gp2I+0Of7HeHw0AFhuC7I7GI5EieAmzVbftTyb33fnS7ki89sTN2hM3O10rbetdUB0y1/5kssXGR6Yrb9v+9Ul/5iGsVDcXsh9Gsd/YfmfTLbeGWm5+Yn/qu5pHu1Jf6sMWaY/fKK7S+RUHK0U0WnjW7kic2eq7JsuuWe+LttgQhFRRETKgLX13vLDeF/XzzuwXsubQBY/Z6ypq6to2Lt1zarTfVuTm6l188qoD5/qOnm/kOdN346GrD593dXM1f+vRuVvalT876h/oMXtd680VjbFy/1m7CX667tOmrN4b8T7u15wTAMDnxPS+JgZ929gsZo71BID475kAcPvJK0tjPcTKAADKCnJDB9gWlFbEpWQiLblFpRgMxlBXU5hjvfoYf/lBqP/BDSqKcsLE0xj1OYUlstKSdhbGfF1UOiMtp8BzkAOvRIuakoJLP2sShdbMYgkei9DtqaGg/KOJv7rl1bYx9aR2GcW+XFwbuceHw2oGgMrUmLgrm0KXOYUstPl4YlF2xE0up/Pbv0+nl8WcWtK2JS349KttY7jslpuiJgYl7vLG56sGP55n+f7wnLKE17/mnAAAWPUMWlkeTpKgYGjd015KcSazrlzd1gNxQgCAuKyiqrUrKf97M5MKANWZcXJ6lohLA0HffSoA1OZ8+wGpEl1byQAAIABJREFU5d8is19cH7DyvDhRGQDy3j4g6pgjLg0AECcqqVm70auKarMTutXWzKRSijLUbdx5RVgk5FVV+jg30UkcdvMPaPvb+Gvrqrlj3WsqK9o27lu3ZMkUr+bmJgCIj3l3aPPKcQMtRtjqb148M+jWZU4XjyHbl8/dtnR225brZ47MHevOK8NBo5IPbloxydVmaF+ttXN9Prx58UtOCQAAkuI/mVhaIS4NhDFTfAEg9VscABTlZ2MwGB0Do66GCw7odrgA6FRKbkbqwCHDEB8GACipqjkMcqWSSKzmZsG9AJCXlV5VUTZwyHDEpQEA8opKTi4emalJdCqll9pQUFBQEC7sWbt+6tC66naXhtPblm2dPZrV3IT8mBz7/vyu1fOHWs1yNv5r9eywe1c7vTocXT//yNp2hTUDLx1bP3Uom926bsOgUs7tXLXYy36ak96+pVPjoiJ+wTkBANQz6KX5OZIyssZ97YSJ//ru1bO7l9cf85dTUgGAyCf3iQpKY2a11i+bvHj9uiNXZeVbKqu+enxbz9QScWkAAFFR2c7Zo6KkIDOp+0fFtIRYA7O+iEsDYejEWQCQlRzfrbainIzayjJ712GISwMAiApKtoOG5KUnM2hUYZSj/PtYd+rusGWHKmopbRtXHLk5du2xlo2nxMy1J+7YTN9i5r1u7p5L155EdbnxtP/q/H1X2rYcvxs2bNkhXhUPCp255vid/rO3G45bPX3buZefk3/NOQEA5JZUYTAYQy2VTnvvhn+wNNBEXBoAoCxHGNLPsrC8Jj4tDwAyCsvKakjDHPsiLg0AUJIjuDtYJOcUURn1VEZ9Wn7p0P592uwcEQfbmJJo7XaOBBD7PbePoRZipECYMWIQAHxNz+tWeUlVnYaSHOLSAGTXyVSPt+vU7eQoKH8CqFED5R+Jvqbqp+TMp1Gt92rlNaRbz6LkCNJ4HPZdQtroFQeDXn/26NfHd7RrSVXtqiPXd1540OlU3zLyv2W2SxubU1LxKTkTSaxUWlU3YPbWgPD3A61NZ4wcXFRe47Ph2LkHv2pNJLe4wtPRCo/DFpRVhX1I+JaZr6ZInDp8kLZqy81fQ1PznJ3nBliZLJ7UTdGsOgp9yYHLE4c4uti17JWa6Kq/OLftxblt/ruW9lQYFisacW776hmtCeepdOb3nGL3fn1wWFFhtI1xsS+tquPZXLKLyt99TRtsay4pLtZLbSgohno6H2PjQ563Po6WVVRevxsoL0fE43FRHz4N854ZGPLM023w3OmTi0vLlq7fvnXfkU6n+pr8/WvS97Yt2Xn5H2PjkTS/JWUV9u6j7gQGOzv1853qXVBcMm7G/NOX/H/ReZkaG0Y+uR/55P6di6c6DWhobJy+cMUgR4dlC2Z37M3JL9TWUKczmM9fRl6/G/gpLoFnT8FiRd8+fbB++SJeMIVKS0nLGOrqjMMJlWpLgLbmZpan2+Al82a1bSwpKwcAeTliTR2JRKYMcRnYthdxk3xNTAGAsooqAHCwsWobgPyYlpktjDaUnwtWWk7W3FlS3YSX+gIrLSeurNtpME5GQUrbQkCSDFFxaYKxo1xfd7y8+q9Q2xaMCFZG31bG0B4jIsrX1VBVUPXhPsFIUNFQvJwa0dJV3ma4hJphp2fUbcA/DgMttZhvqU9ef+S1lFfX3gyOQG6uouOSRi7Y/PBFtMdAu9kThpdUVK/Yd3bHqc5zM3xLy/6WltO2JbewNOZbasvNVWWNo8/SgNA3g+wsZ40dWlhWOXHFzrN3Qn7FSTWzWB4D7fymtquYU1JZDQBysjK1ZCqZSnfvb9O211BHEwASUlsyfOYWlWmpKjGY9eHvYm8ER3xOTOtqCaaOTF2044T3MBfXfladBnTEVF/rpf+Rl/5HbhzayNclKir66vrRtXNbq1NT6YzvWflDnOxwWKzgsQAgzKmhoPyjkVHTq06PLf7cmk23vq4i93WAmDRRBIur/P4xcpd34YdgNWs3A4/pzJqyuMsbEu/s73Squtykutx2S6K0svzq9FgulwsAzNqy8HVD8qMClc2d9N2m0quKow/MzHh2+RedF0HTyGNviMfekAGrL/a0t55UAQAKRu3+8BWNbACAUpzJYTerWbsZj5jXtpdZWwoAeOke+yYbaaTP51ZpDxyr0mcQADRS65roZNW+g9upVTcAgNqcxG61YUSxHvuemI9vzUXUzKSSC9PUrF1FRP/obFVauoaJXz5GhrVewqorykPuXScQ5XE4fNzHaD+f4REhgU6uQ8dNn1tZWnJg4/IzB7Z1OlV6ckJackLblqL8nMQvHzlcDgBUlpdO9ej37OEdW0fnMVN8y4sLV80aH3DlzK84KVZzs5Pr0MlzF7dtrCwtAQCkCEhxfq6qhhaTQX//Kiwk4HpS3Ce+/UXBAd0OF4AoFns15M3spet5LXQqJTs9xdHVA4vDCe4FgOqKcgCwtGlXd8/CxgEAcjPTeqkNBQUFBUFdRz81PiYmorVUdG1VecTDmzJEeSwODwDJn99t8R0Z/eyhnbPHMJ/Z1eUlZ3esvHF0R8epcr5/y/nezkxZVpCTGh/D/f/zSE1F6bIxTm+CAywdBnlOnFlZUrhrkXfIjbMdp+o9WgYmhwMiDgdEbDjefX4+KrnuxCY/Fy9vKycXnnJ7F08sDl9RnP/5zfOc79/kVdTcx01V1tAGACqplk4hWw9wbzuJhq4RAGSnJHScvy0sVrOds8fomX5tG6vLSwBARraTulp82morywHApG+7t+qNrewBoCg7rVvlKP9KDDRUPqVkP333lddSXkO+FfZeHtl4+pYxZvXRoMgvQ/pZzBo1uKSqbvXx27suP+p0qsSswsTMdvloc0sqP6VkI+vMpdWkgfN2B0TEDOxrMsNrUFFFjc/m0+cfvvpF55VXWqmpLM+ob3wRk3Tr+fvY7zm8xY1aCp1MY7rambeNR4wRCZkFAFBRQwYAO7N2bwMiP6bnl2JFRV6c3rR6WmuZQiqjPjWvxN3BgrdzJIBmFntIP4tFE4a0bSypqgMAORlpwcoBYLSzbWk1iedxyS6qePctw9nGFNl16nZyFJQ/AbT0Cco/Eh9Pp61nA0Leflk4cSjS8vhNLIfDnTFyMAA8fPUJKyqa8vA4YvFbPWN0H+/V4R+/7Vs6tacH2nnhQVF59dsru+3NDQBg67yJE9Ye3nHh/rQRgzpW96il0K487vL9qrGuDmZ6gl7HZNQ3VNSSleRlJ2049uJjy424sY76xa0LHSwMkR+3nQ0oryGFnNiI6W5PaM2xGxQ6c/fiKd2eozBIios59m15X/PcgxfFlTUvYhLZHM66ma05cgVrW+TtGRWfOmn9sf59jMTxuHcJaWqKcjsWTeoYiYLSU6ZMHLth14FHoeGL585EWoKePOdwOL5TvQHgfnAoVlQ080sUkqFh/Qo/Y3uXZy/fHNq5qacH2rrvr8Liko8vHveztQaAnRtWj5o6e/Pev2b4TOCrPFJTR7rof7ureSaMHmFu8hPe09q462B5RVVY4M1O/+hy8wupdLqhrTOzvh5psbWyvHnuuKmxoZSk5IB+Le9AnL7kX1hSFvYqks1mb1y5pOM8PQWHw546uKttS1VN7QX/WzgcdqSne1ZOHgCoqSi3DTA21EfCAMBAVxsA3n6IWb2kNQVCelYOAKRlZDk5oIklUTqHUZyadWGRjIG9mueCboMbqgpMV9z4G8wifFR9eEBOiaTn/8LUXL1hspfb5mNXg199WDSlxdbwKOI9h8OdNc4TAALDo7BY0bTn12VlpABg7ZxJ5l5zwqJj96+eJ2jSzth+yr+wrDL6zkmHPiYAsG3JzHFLtm07eW366CFysjJtI2vJ1EsPuqx5NN5joJlB54lMeOCw2OOb2m01VdeRL90PxWGxXoP7ZxWUAICqUrv1O2NdTSQM+TG3uIzKYJoO92U2NCItNuZG1/avN9XnT7m56sA5Co2+d+UcwZKEREpC3Mm6ZZXk7J2QovLKF+/i2BzO+nk+ggciCHNqKCj/aHSdJyTc3FX8KdR4RMubpoUxT7lcDpIiovDDY4yo6JjzX5D8Debjlz9d7FAa/9JmVie7IIJJvLOPUVU87FC4gpEtAPSdsuHtvimJt/fou/p09Dc0UuuyI7rcvdByHCWrZdJTAcIjraoLAJUpH8zGtH7uUYozAYBclKlo4mA/v12hzAZKTVb4dRFRnIZdN+8AdCT+ysYmBsV65nbkR2pZDgBIyLV7y01G3QA5ijDalExb3JMZzy4zq4tLv77mctgWE1b2VNjfzIgJk0/s2fj62SOfOS37Q69CgzgcztgpvgAQEfwAK4p9GpuO5G+Ys2zdqP4m0S+fr9x+sKcHOrNva1lx4a3n7y1t+wGA3/ody6aNPrV3y8hJ02WJ/LtQ5LqawBuXuppqyMjxBibmXfUCABaH27j/ZNuWuprqwOsXsDjc4KFeAFCcn8ug0UY6GDfUM5EAs762+85e1zMyRX4UHNDtcAFISEpZOwxAvg+4cqasuPDDm3AOmz13+YZuewFAU1cfAOI+vJ3pt4o3Z15WOgDkZqVZOTj1RhsKCgoKguvoyVcPbvnwInjUjJZ3Y96HPeJyOEMntixYRT0LFBXF+kemIikcJi1cM9fNIjYybO7Gzh2lArh+ZHtlaeGJoCgTKwcAmLFy2/Z54/wPbx8yfrqMbLsMf1RS7bO7XdpMBw4bp2PUSc3cH+b8rtV0GmXO+j3Ij/VMel11BVFBeddC7y9vw5FGLX3j1X9dMrXuBwAleVkAIK+k2nYSTX0jACDXVgs+FhaLW7zjWNsWcm31szuXsFhcP7cR3WpT09EHgKTPUUhlE4SinAwAKMxO1zW1FKwc5V/JJI/+W88/eBIVv3B8i3ko+G0ch8OdMWIgAAS9jsWKiiTfO9Sy8TRtRN8pG8M/Ju316/E2x85LQUUVNZEXttqb6wPAljljJ244ueNS0NRhA+QIUm0jayn0KyH8VQh5jB1sb6bX/QJXXmkVjVlvMXlDfUNLdh9rE50rWxeY6KhlF1UAAF+KdCMtVQCoIdEAAMlXEZ2QsXzyMF5AZkE5AKQXlPW3NHTs07J7df7hq6LK2ohPyWw2Z+10LxACHFb06MrpbVuqSdQrwZE4rOjwAX0FKwcAv4lDor+mT9p0ur+lgTge9+5bhpoCcef8CUJOjoLyJ4AaNVD+kSgSCUMdrV5+SqwmUZXkCAAQ9PqTupKcu0MfAFg+ZYSftycvEVNzM0tWRpJKr+/pUUhUeuCrGDszfcSlAQB4HHb2GLfor2lPo+N9R7vyxdeQaPuvdm6fBABDLVXBRo3ckkoAuBAYoa+pcnSNb39Lo88pWTvO35+88Xjs7UNKcoQXH79devQq4MCqbouepOeXPI6MXTdrjJaKgjBn2iP2XA5kNjQBgJmeprhYyxtO3WojSktqqSomZxcmpOdhRUU5HK6oqCid2fDT5aH8B1FSkB8+xDX8dVRVTa2yogIAPAh+pqGm6uEyCABW+81bNt+XV0ejqalZVpZA7XkRjToS+d6jp/Y2fRGXBgDg8bh5M6a8ff8p5HnE3BmT2wbX1NTuPnyys2kAAIwM9Hpv1Hj+MvL8tVsPb1zgMz3wyMkvoNHpe7esG+c1rLq27taDR9fvBo6ftTA+8pmUpCQvbPuBY4iTw9zESEJcrJeqOtW5cNXG6tq64/u2W5qZ3HrwCADkie0+KHQ0NQCATKECgKG+rp11n8h3Mf53HkwaN4rD4QQEhQQ9DQMAdhcZy1FQiBYuTXVlwOUCcIWKt3T9xYq6ggtcrrSulYi4VPexfzuKcrKeg+wj3sdV15GV5IkA8PBFlLqygrujLQCsmDlhydSxiEsDAJpYLCJBikJj9vQoJArtQViUnYUx4tIAADwOO2fiiKgvSU/efJw9YXjb4Oo6yr7zXZrejHQ0ujVq8BH+LtZv58kaEuXIhkUWRrp3nr4GAD53iLa6MgCQaQzkx9yicjqjftdy3zFDBtTUUe6Evr75OMJn5e5PgWelJMR5o9JzCx+9fL9h/mQttc4/k3vDrjM3EJuImYGOkB/UuUVl0N2poaD8oxEjKKjbuJclvGmg1IjLKgJA4YdgSXk1VSsXADAd7Wc8Yj6vygaH1YSTIjQze3z710QnF7x/rGBojbg0AEAEizP0mFmZ8qH483MDj+l88Y3U2uT7hztM04KMmsEvNWrIqOnLG1hXpLzLfX1Xe+BY4HLyo4OKYkIBoGPZl9L4V7HnVzVQa+3m7CXq9GxvhlKcWRjz1GLCSilFDaSFVpEPAHiZdjd4UkpaAIBUXRFeW3LAAVZjPQDIapmI4sXhz0ZOQWmg+7APb17U1VTLKyoBwIuQQGVV9f6DhwDADL+VU+Yt4VXZaG5qkiEQ6TSKoBk7g0KuCw++b2Ftj7g0AACHw0+YPi/uQ1Tk85Dx0+fyxZNqay4e2dPVbDr6RoKNGny8fxW2e81CUm3Nuj3HDM0sAaC4IJfBoC3btMdtxFhSbXVo4J2Qe9dXz55479UXCUmpbgO6HS4kZw/uQOwUBibmYhISwvRq6xmaW9l9ef82+K6/59hJXA4n7NG916GPAADJnPGztKGgoPyXkZVXtHfxjIuOINdWExWUACD6WZCCirrtwJYd3/FzV4yZuZhXaIPV3CxFkGXSqD09EI1CigoNNO5rh7g0AACLww/3mZP0KTom4skwn9ltgyl1NXdO7etqKg1dw59o1CjMTn8f9mjy4vVK6i3W9vLCPAB4cvO8uo7+4h3HzGwd075+8j+yfY+fz/nncUQFpbLCPACQIbYzlyirawMAg9qz6+aXt+EnNy+m1NUs3HZY18SiW20aOgZGfWwTY6IiAm8MHunN4XDePrn3PvwxAHA47G6V/8DvB+XPR5EoM7R/n5efU1o3niK/qCvKudlbAMCyyZ6LJg5pt/EkLUll/MDGE+Ph61hbUz3EpQEAeBzWd9Tg6IT0p++++o5ql6muhkw74P+ks2kAAAw1VYUyapRU0ZkNOxZMGO1sW0OmBbyIufX8/ZQtZz5e25lXWgUAfO4QbVUFACDTmQBgoKliY6Ib/TXt5rN3E937cbjc+y8/BUfFAQCb3S7n6O6rj+tbdo7UxcXwwv0y2vEiJmnp4Rs1ZNpfy6dY6GsKVi4pLiYrLamlqpCcU5SQUcDbdaLVd77r1HFyFJQ/AdSogfJPZbqXc/jHhNDo+Lnj3IvKq+PTctfOHCMiggEAYx31Ogr99L2wL9+zi8prckoqaIx6NeGKhbclu6icy+XS6xt8t7fmFKUx6wEgr7SyY7yxjlpVZJdFEPDdFRQgURkA0NjcfGf/CmMddQCwNtGtqqMcufkk6PWn8e79/fZf9h3tOtrFXvA8AHDy7jM8Frt8SifG4d5T+cY/t7jiU3LWrksP3BbsTH98mgvcbrV5Ltmbmlt8Yt1sbw8nMTzu1eekZYeuea87EnfnL2019L4WpbfMnDzxWcSbJ88jFvhOKywu+ZKQuHHlYhEREQAwMTKoJZFOnL/6OT6hoLgkJ6+ASqOrq3Ze1k4AWbl5XC6XzmBOW9CalplKowNAbkEhX7CJkQG1KK2rqfC43uZwLq+smr9i/dwZk8d5Desqxv/MUTExvIWpMQAY6us6OdjKysgcO3c5+HnEjEnjeWGUwtScvIKPsfHb9h8ZMHx83rePqso/508yr6Bo7fa9zyLeGOjp3Lp4csjggQAghscDQB253fvcDCYTAOSIsgAgIiJy9dThcdPnL1qzefXWPRwOh8PlzJs55crNAHMT458iDOXfh+6UXb9bglAoD5qiPOjnZLr6RcwY4xEWHfs0Mmaet1dhWWVcSub6eZORmysTPa06MvXUrUexSRmFZZW5haVUBlNNqcd+0KyCEi6Xy6ivn7m+9X1iKoMJAHnF5XzBJnqatV+6XIzo9s6qLXnF5RuOXAqLjjXQVr9+cIO7ow0AiOFxAECitNu7ZdQ3AAAvcdqVvWvE8DhzQ10AMNTWcLQ2l5WWOnEj6Mmbj9NGtSbPPH79IR6HXTFzgvCShKcmNiSnqDQmIXXnmRuDp6/Kiril0t1trTCnhoLyT0ffbXJp/MuS2DBDz1mMquLa7ASLCSsxGBEAIGgYNdJI6U8v1GTGM6qLaWV5zfU0CXnVbufkg1qaA1xucwPjw7GFvMbmehoA0CoKOsYTNAwn3+O/LeQhgv21VTwwGBHHpSejD86IvbDmq/9WLofD5XIMhs7IeXlLVrvVIEKvKPh6fXtp/EsZVb0Bqy7w1SsRhrSQsyJYXNvcGKJYMQBoorW7wWM1MgEAscsIqQ0AfAIKaOV51emxiXcPRGwcMe5ygjjx5xvgfiKjfWa+e/n8bXjIxJkLyooLvyd8mbtiA/IYomtoQiHV3r54Mjn+c1lxYVF+DoNGVVJV6+khCnOyuFwuk0HfuKjVG8SgUQGgpLCT0tq6hiaf8rtMnoTDCbtiXlKQd3Tnuncvn2vpGew/d6v/4JYtxt2nruLwYoamFgCgrW9o5eAkQyDcPH88MixkpPf0bgO6HS4kMXmkorycxC8fzxzcPstrUHh8roKyiuBeERGRXScur5w1fu+6xUe2r+VwOFwOZ/z0eY9uX0HMKz9LGwoKyn8cjwnTYyPDYl499Zoyr7K0MDMpzsdvHUakpQC9lr4xlVz3+Nrp9G+xlaWFZQU5TDpNQbnHV4eSvCwul1vPoB9c2Vp5lkmnAkB5Ef/VQVPfOCSlpqupsEJfGoQh6MpxLA4/fm5rggoamQQAzU2NW84GaOkbA4ChhTW5tur++cPRzx+OnbUEh8fzwnggfjtpWWELtJUX5V3evzE2Mkxdx2D98es2A9yE0YYREVl98OKuRd6nti69uG89l8PhcDgjfOaE3b+mbWTWrfIe/m5Q/jFMGz4wPCYp9P23uWNciipq4tPy1s7watl40laro9LPPIj4kppbWFGTW1JFY9SrKfa4kmB2cQWXy2XUN8ze1VrTEDF85JfxJ5Ix1latfHmhq6mEXBu5uHkeHo8119MAAANNlf6WhgRpiVP3Xjx9l4DMgGxO8WA0NAGAnIwkAIiIYM5vmuOz6fTyIzc3nLnH5XA5XO7sUYP9n0bzeUQqIy4g5V12X37s7rcv7eERFXlZYeQBQH5Z1aYz98NjkvQ1lK9uX+D2/1IsApRP8XQatuxQal7J8TUzvN37i+Gxr2K/Lz9yw3vjybibe7VVFbudHAXlTwA1aqD8Uxk+0JooIxUS9WXuOPdHbz4DAFL3BABOBTzfdyUIj8cNsjZ1dbBc7zv29P2wwg5XuK4gUenIN3UUOgCI4XBILXAEeYLMZM+BnebGwGAwEj/kE0RQV5IDAAcLQ8SlgeA1yPbIzSeZBWVXg1/XUmhURr3f/pZUdWXVdVwAv/2XjbRV17YpQVJcWRv48tNYV4efuBDP5XK5XEBuRwDAQEvVQEsVI4Lx23fp5afEwopqwdoyC8pSc4udbczmj/dAAsa4OHxOzjpzP/xJdPwvMpSg/KcY6ekuR5R9FBq+wHdaYMgzAPCd4o10HTt3edehE2Ji+MED+g8ZPGjz6qUnzl8tKCoRcmYSqcW8X1tHBgAxPB7X5vZXQZ44zXusuSm/gQCDwUiI/8JXAC/duFtTR6JSafNWtCTyLauo5HK581ZsMDbQ27hyMQDYWlnyjRrh4Xrs3OXU9Cwul8vlckX+v0xgqK9rqK+LEcHMW77+xeuo2dN+Qk2iu0Ehy9Zvw2Awh3ZuWrZgNuLPAADEBZJXUNQ2uI5MAQAlhZa8zZZmJt/evQh68jwtK1tNRdnDZVD0x88A8FPqxaCgoAhgxOD+RIJ08KsP87y9gl68A4AZY1tqzJ24EbT3/G08Duds38fd0Xrjgimnbj4q6My32il1/89jVEehAgAeh8PhWiuVKhBlpox0MzPkT4/RyzsrHveeR67cdxaDgf2r5y2ZNhYxMQAAYnfIL2lnEEHMDYpyLUsJNub8nzzDnB1O3AhKy2ndiy0ur3oQFjXOYyBfBovewHfrZaitYaitISIisnD7sRcf4nzHdVOnQJhTQ0H5p6Nh54mXJhZ9CjX0nFX4MQQA9N1aMpylPzmXfO8vEZyYioWTat/BFhNXZTy9QK8qEjhfK430ln2CRhoJAERxYiJtngfFZOR0B08kaneWGwOD+b1JIIg6Zl4nootinlKKMyXkVFStXKpSYwCAqNVSuKHgXdCXSxswGIzNrB0mXgtEer4xw6gpLXj/WNtxZNvKL+JyygBAr2xnUmmikQBAjKDQvTYulwtcxGQDADJq+jJq+iAi8vnMitKE1wbu037gV/G34TzUiyAr9/rZ44kzF7x88hAARk9u2TC7ef74hcO78WJidk7O/Qe7z1+16faFk6XF+ULOTCXVId9QSHUAgMeL4dp4fYhyCl4TpnaaGwODwYiJ82eY6ClhjwIObFyOwWBWbj84df5SPL41n5NZX/5ahAOHjLh5/nhORqowAd0OFwDfU4y2vqG2viFGRGTnyvkf3oSPmeIroHfs1NkAYGhmGfg24dXToLysdEUVNcfBQ+Jj3gGAvol5L7WhoKCg8OjnNkJalvgxPNhryrx3zx8BwNAJM3i9QVdP3jm5F4fH9+nnbDPAbcriDY/9T1cWFwgzM43SamWgkesAAIcXw7a5SyEQ5d3GTNY24r86YDAYfK8vDcJQXVYc9TRw4PBxbWuvKKiqA4CpdT/E64DQ393r/vnDxTmZACCnpAoAFe0vkTRKHQDIyiuCEEQ+uX9ux0rAwNyN+8fOWozDd5KGsFNtAKBrYnHhedy7sEdFORnySqo2g9xTYt8BgI6ROaWuRrBylH8rwwdYEWUkn0TFzx3j8igyDgCmDx+EdJ2692K/fwgehx1kbeJmZ75+5qgzDyIKy7s0QvFB+n+GS97GExbbujYiLyvtM9TRTJc/PcZPWRuxNuFfcvF07Hvq3ou0vNKhjn1eVPTdAAAgAElEQVQAoKD99hni21AgtqxyWOhrxt7Y8/htXEZBmaoC0c3e/ENiJgCY6Wrw7xxpqhhoqohgRPwOXnv5OWWm1yBh5D14+WnV8dsYDGav3yQ/bw+xNsvvApRnFpan5pU4W5vMH9vizRoz2PZzSvbZwJdP3yUs8/HsdnIUlD8B9H8kyj8VMRxu4hDHm6FRdRR60OvP/fsYGSJ1s8jUHRfuKxIJSQ+OSUu2LJMdudnl65gYDAbJcskju6hlUVtXQxkADLRUr+5sfVuIzeHQmQ2dXhorayl/3Qju6kAzR7nYmOgJOCNNFQUAYLHaialvbAIAgrSkIpHQ10gnt7iC19XUzOJwuCnZhbyrIML1J5EsNntWh8osveH4ndBdFwODjq4b5mTNa1SUlQGAkqrabrV9zy0CgEE27dLouTn0OXM/HM2/jfJTEMPjJ40d5X/3fi2J9CD4mZODrZGBHgBU19Zt2XtYSUE+PfatjHRLAreDJ851NQ8GgMNpl7EtM7flRQR9HS0AMNLXvXn+BK+XzWbT6AzJDrl2K6qq9x87A10wZ5pPRxdFj1BSkLeyNM/OK+C1NDY2cTicpO9pyB9dcWl53Lcke+u+2pqtN/f5hcUAoKyocPj0xW37jzwN8B/h4crrVZSXB4Di0rLeCEN4/jJyztK1jvY2dy6dbisAAIwM9DAYDKKER3JqOgAgNWWampoLiooVFOTmTPfhBRw+fVFNRVlersf+dJQ/DVJyJLuBpthv7O8WgtI5Ynic9zCXG49f1JGpD19EOVqbG+loAEANibL9lL+iHDEl9JqMVMsn3l9X7nc5Ucebq4IWe5yuphoAGOpo+B/YwOtlczh0Rn3Hoh6VNaSDlwO6OojvOM+OLoqOhL+Lnb/1aH8rs5uHNvLVJTHS0cBgMPklFW0bUzLzAMChjykAlFRUx3/PtLMwbjsQiVdq81KI/6NwFpvtO77LFEc/wFH/wJ2nbwSf3TPM2YHXqChHQFR1O7zbU0NB+RcggsPrDByb8/puI41U+DFE0cRBRt0AABqptYm394nJKow++xkn0eJcT33UZU06wGD4qm9Qy3KQb6RVdQBARk1/wMrzvF4uh91cT8eKSUIHGshVKQ+Pd3UcA/ep8gZWwp9gT+GwmulVhWIyCgZDWp0NacGnJeRUEFNFafyrmNPLFI3tB665xKta0lNyXt7islkGQ9plFyCo6QMGw2fUIBWkAoCisW232lIfn0q6e8B16111Ww9er7iMPAAwa37CrekvBY8X8xw7KTjAn0KqjQgJtHJw0tE3AgBSbfWZ/VvlFBRDYtKkpFsWuK+ePNTVPBgMhtv+MaQgNwv5RkNHDwC09Q33nbvB6+Ww2QwGTVyik/+HtVWVV04c6OpAY6f6drQj8PH+Vdj25XP72jsevHBbVUOrbVdlWcn3b3EW1vZt20sL8wBAXlG524Buhwvm+pkjZw9uP33nyaAhrbXSiPKKAFBRViK4FwCam5tKiwrk5BXGTZvTOufZI4oqqrJE+V5qQ0FBQeGBw4sN9vKOCLxBJddFP3toZuuoodfy2EKpq7l+ZLusvOK11ykSUi13KfcvdF43DYPBsLntinuW5GXxvlfV0gMADV3D9cdasztz2Ox6Br1jQShSdeW9c11egzy9Zxla2vTgDLsm/IE/m80aNsm3baOymiYAsFjNbRsbGxoAQEqGAAAaeoYYDIbPqJGfngIAvMIuAvjyNvzY+vmmNv03nbjBq2kipDZWc1NFSSFBTqFt+8NLx+SVVGVk5cTExAUrR/m3IobDTnTvd/PZuzoq/dGbL/0tDQ21VACghkzbeTlIUVYmMeBg68bT7WddzYPBAIfb7gYvu6jlIV1XXQkADDT/x95ZxzX5fXH8jNzGgu7uBukUFWzFQkSxAztQsRW7ELEbW1EE7EYECwOV7u7ujme/P8YPYbCBig783vdrf8g9Z/f5PHO7z33uPc85ohe2LGizMtt4KiitOHjlIbOjzBhl3TmUgYHswtKvcWmG6grSYoJtjfTIDBEBsrK0GA6HS8vrsNQQnZwFAMaaigDQ2NSckVcsxE+aOdq6zcHr5hNxIaoAhc/z+pMd5/39DqwcZqbbZhWikujHZS2MzrMPES57vU20lC5tW9heYbfKo1OyAMBKv0MM/RBjrRO+L9p2nVh0jkD0EVCgBqIf4zzK2vveq8PXH0YmZZzYMJ/emJlfgmE0exvjtotldmFJZFKGKJMkS7ISwq+/RDc1t3BzcQJAXFp2anbr46GKUmLC/JTAT5FtVgDwvPpg13m/F6e3musyPkRVUV1z5WEwM7VWAzRYB2oQeHlsDDVDvsamZOUrybQm5n30JgwAzHRURlkZLHLo8PSk9dwtdQ2N7y/vYegn6HOUAIU0yJCxDt/voKUoAwCvv0S3D9S49OA1AOgoy40ZaMham7q8FADce/1507wfKcEDgj4CgJYS0wk0AvFTzHSaeO7KDY9jZyOiY88ebs2on5mVg2HY+NHD26I0snLyIqLjxES6DsmXl5UODHnX1NRMT5sRm5CUkpZONykpyIsICb54/abNCgAHjp523384+KGvpWmHuj8VFZUXb9xmJnWghelvBmosnT9r6fwO95YmdvZ19fVhQa33BmXl5VPmLpk/c+rpQz+GCHquESsz48LiEgAIDHnXPlDD+9otANDV7oW6pFv2eFAp5NsXT0mIMa5pSoqLWZubvA39nJqeqSgvCwBNTc0+/velJMTpn0ltXZ2WhZ3TxLHXzhylvyU7Nz/g0dPZ0xwB0f/JfXaqvjC9vwRqfN9oSVG3UJrlwW4hf5Xp9nYX7jz2vHQnMiH1lPtKemNmbiGG0cbZWrRFaWTnF0XGp4gKdV2AQ05SLOjjt6bmZnpOsriUjJT/R8EqyUgKC1AD339tswKAx4XbO09eDbx8yGJAh9lLeVX15YBnzKRaG+n0JFBj27HLFBLxpudmcWHGG3IJESErQ+33X6NSs/IUZSQAoKm5+fbTYElRoQGaygBQVlE1bc2euQ4jT2z9kR3X/1kIAFga/BjGAz98E6CSB5v2zuImHW0VeQB49fFb+0CNi/7PAEBXVbHbt3d7agjEv4HCoClJz6/E3j1elhZturg1QqKmKItGw2RMR7dFadQW55alReP5uy7uxicqmx8RjLU0cXByA0BFVkJ1XjrdRBZX4KUI5X1/3WYFgJiAY5E++4fufiCiYcrQVWNNRUrgdWZqxbTM/2igRnND7aPllnJWEyxdW/Mn15bkZoU+VrSdSv8z4uYeHiLF2s2bIPDTRQDbyI8I5iHxi+lat28kCIqLapoXxoZW56eTxOUBAGtpyngbQBSUEFTU61Ybv6wmAORHhLQP1EgOvA4AAvK9eVf7hxjrON3v6rnLJw4lxERsPdSalTovOxPDsCGjJrRFaRTkZifERAiJdL3lLykj9zHkVXNTExc3NwCkJMRmpafQTTIKSgJCIh+CX7ZZAeDi8YOnDmy/eD9I38SSoauqyvK7N5iWZDU0t+42UOP4vq0kMtXj/C1hMcaCQRVlpW7znSZOn7/F40fsOz2VyABTy24dun07a1Q0tAHgY0hg+1CMuze8AUBVS5eLk4uFFQDq62onWumMmDBl76mrdGtBXs6rx3fHOc3uyakhEAhEz7Gb6PzE54LfWc/UuMiVe36MKoU5mTQMsxw2ri1KoygvOzUuUkCoi6uDmJTct/dBzc1NXFzcAJCRFJfXruKVpJwiVVD469uXbQ4AcPvsoWteOz18XmoZWbTvqrqq4pnvZWZqdUyseitQ49u7V2SqgL75oPaNPHiCnrlNRGhIbnqypHzrzUjoy4cAoGFgBgBCohLaxlZRX97nZaZKyCoCQHNzU/BDXyExyZ4Iu+zpTiRTNp+4ISjCqs5dl9oa6upchukPGuO4zusSvaU4P+f9s3vDHGb2RDniH2baCEvv+8FeN59GJmced2tdg80qKMEwmv1Aw3YbT6VRSVmigl0H7siKC78Oi2238ZSbmlNINylKiQrzkwO/xHTYeLr+ZLf33ecnNpjrdFjrqKiuvfL4LTOplvpq3QZqlFXWzNh2as5Ym6NrfxRL8g/6DAAWuqoSwvyWeqofIhLTcgsVJEUBoKm5xTfwo6SwgL6qHADUNTQaztjsYGt6cVtrRcicorL7IV/p2TK0FKUAICgstn2gxuVHbwBAp2dbPzvOB1D4CNd2LhEXYtzCY60cz8sNAPdCwjbO+bHSGBD0BQA0FaW77RyB6COgQA1EP8ZYS1lZRvzEradEPM/EIa3LZKqyEnwEvP+rj8PMdVVlJT9GJe4670fmI1TX1Sdl5qnIMtb8M9ZUfv4hfNGes7PtB6dmFxy+/pDCRyypqAIAHm6uHYsdl+67MH/HKdfpYyl8hEdvvx68fH+IsbaZDmOlAwBQlZMsCb78O2e0Y7HT4AXuM7ced1/kKC0qFPI15uK9IHNdtVFW3ayktFFeVfM9IW2kpQFDmo2ecOL20y0nfDbMnbBhzgQG0zBzfS0lmTN3XlBJRDtT3dyisrtBn56+/2aooTjCUr/L3tqjoSBta6Lz6nPUhNUHpwy3lJMQfhgS5vcyVENBeoy14c/qRCC6xNRwgIqSwpEz3kQCwWHcaHqjqrIiiY945/7jEbaD1FWUPnwOc99/mEImVdfUJCanqioz7nWZGOg/efl63oq186Y7paRlHDx+hkomF5eWAQAPD/eeLetcXDfMWuLqtnwhhUx+8PTl3sMn7GysLEwYv8ZqKko12ezMgqijqW5mZOB97ZaQAP+E0SMwDLvhd+9l8NuJY0YYG+hhGKatoXbywhV+CnnYkIE5eQX+D548evHKaIDu6KG2AHD0zMX1O/ZtWbN8y9oV3R6LgbLyipj4RH0dLa/TFxhMNhZmo4cN2bBqif20uU7zl210XSpApXgcP5uWkXX/xgUcDgcA/FTKYGtz/4dPbQdajRs1LCUtY9GaTVISEgfcN/bKJ4NA9JCi9771hekUdYvuXf8tTHTVVeSkjl0NIOJ5Jw5vLSqnIi9NIhL8nr8ZbmWsqiAd+j1258mrZBKxprYuMT1bVZ6xHpyxrtqzt59dth6eM2lEamau56U7FBKxpJxe9IRr58o5S7YfmbvRY83cyWQ+4qPg0APnfWzNDcz1GTP0qinIlIcxfWqkJ5RXVscmZ+ipKx29EsBgGmisM3Kgqdt8p4lLt01327t+gRM/heR58U5adl7A8Z30EUlbVcFUT+OS/zMhKmWcrSVGw3weBQWGfhtvZ2WkrdZ2iO9xSaNszDpPvY5fu7vp8IWNi5w3LfzpvP3DrUy0VORP33xAJZOGWhjmFhYHvHj7JOSjoZbqSBuTnvTA+tQQiH8DYVVDsqRS/MMzXLwEWcvWWpBkSWUuPF/G+3uSBrYUKeWi+M+RPge4ieTmuprK3GSKJGOskrCKQe7Xlx+Pr1AaOqM6Ly327nFuPnJDZSkAcHBx60/f8umU64cjSzUnLOMmkrM/P4v2OyyuZyOi3sUvkSKl4nS7p/X1eh0ePqqYjlVW6KMUvZsyJqOq8tM/n1lDFJIYMNMdABqry8sz4wUVdOIfMNa3FtWylDIaCgDxD89+v7pDx3GN9uQ1XR6isbq8NCVSynhYW5mSNrQmrQze4/zOc4HWpFU8JP7Yu8erCzJsNl0HHK5bbZKGtvxyGglPvLn5qBL6g+tK8zI/PMj58kJIWV/KcGivf1C9jo6hqZyiyrWzR/EE4jD71vKLckqqRD7Si/t3LIcMl1dWC//y4fSB7SQSpbamJj0lUV6JcT1B28DkbeBT91XzJzjPy0pLvnzyEIlMLS8tBgBubp7lm3btXLNo87LZc5at5SNRgp8/vOC1z8zGVs+4i4mKvLLap8yqXz6dyoqylPgYNW39a2e8GExGFjZWdiN1jczu3vDmFxQcMmo8hmFP/G+GhgTajp6gPcAYAFQ0dVg40Gg01m8HgBvnjnnt3OCyerPL6s0MAixtRyhraN+6eIpM5bcYNLQwP/flQ/83Lx5r6RsNtBuF4+BgYQUAMoXf2GpQ4KMAU+shg0eNy0pL2b12iZiE1Kpt+7pV/sufJwKB+G+irm8ipaAScOk4L4FoPWpSW7u0oiqBSHrz2M/IZpiMomrMt4/XvHYSSeS62urstCRphQ5bs2r6xp+Dnx1e5zJiyty8jJQ75w4TyZTKshK6lYubZ/banUc3LfFYM2+yy2oiifwx8PGtkwcGWA7RNDRn0COjqPogtgz+MNUV5UnR302HjMJxMM4T5qzd5epgs3fFjNlrdohISId/DH56y1vLyMLMtnX5bspiN/f5E/etmDFlyToyReDOOc+8rLQd5/zpNy93Lx33PrB52rKN05YxLg1VV5RnJMYqaeoFeB9lMOmaDjQZPJK1Nj4KVc/c5t2zu/qWgy2G2udmpBzbskxYXGrehr09VI74VzHWVFSWETtx+wUBzzNxcOtMQEVGnI/A6//681AzHVVZ8Y9Rybu975L58NV1DUmZ+SqyjKFCRhqKz0MjF+3znj3WJjW7wOvmUwofoaSiGgB4uLm2u0xadvDygt3nXaeNJPMRHr/77nH14WAjTTNtxhsWVVmJ4sCzv3M62krSJlpKlx+9EaTy2Q80xDDarRehQV9ixtkYGmooAMDa6aMd1h+Z6X7GbcYYfjLR6+bT9LyiO/tX0n+DVBLRxkDjfkjYtSeaY60NUnMKVnhclRQR2L3YEQCGmelqKUqf9X9FJRHtTLRzi8ruBYc9/RBuoK4wwkIPAE76vthy+s762WM3zLLvrK28qjY2LUdXRfb47ecMJmt9teHmuiyUYxhtiLFW0JeYCW5eTkPNZCWEH7755vfqk4aC5BirAd12TpeHQLAdFKiB6N9MHWG167yfvY1x2yOeJCL+9KYFi/eed1x3GAAEKKT9K6bzEXhddp0xmb6h7M0Vhh6WTx31OTrJ98UH3xcfJEUEnEZYAcDha627AjPHDKqtb9x60icg6BMAcHFyzho7yH3h5D+0xm2ooeh3aO3iPecmrWl9fneUlcGZzS497+HNt1gMo5l2upz3BAyjtWAYrWNWPTocHLhb+13n7Ti91ztgr3frVoe9jbGH60wuTs7O/p3ffmnH0rVeV++8DA38FElvtNRXP71pAQ8qCYboPaZPnuC+//CE0cMp5NZHE8gkvvNHDy5YuW7CjAUAICjAf2jXFj4ice6ytXoDh9flJjH04Lpk/sew7z7+D3z8H0hJiDtPHg8AB4+1Pvk3x9mxtq5uw479d+4/BgAuLs65zk67Nq3pg5teOBzO/+rZha4bDhw9feBo63L8wtnOHjs3AwAHB0fA1bMzF6/e6XF0p0frreyE0cO99rrTKyNiGNbS0tLlaNAtHz5/pdFo3yOjv0dGM6oC3OhhQ4YOsr588vBC142OcxYDAD+Vcmjn5hG2g9rczh89ON1l5YJV6xesWg8AA3S1r5890pYTBdGfoH+F+t4PhAWNZXlZDw7XpIXXZMWyWwvbmDrGdufJq+NsLSl8rQnVyXyEMztcF7l7OazYDgACVPJBt4V8BN4FWzyNJi6q/MaY53PlzEmfIuJuP3l9+8lrSVGhaWNsAeDQRV+6dfaE4XX1DZu9vP1fvAEALk7O2RNH7Fg+60+MpaHhMTQaLTwuOTwumcGEw8HIgaZ25gbee92WbD8ydfVuAKCS+Q6sdRlmZfR/H5zvkW2Ltx/x8L7t4d2aJ2mB4+j9a3/MzUK+RGAYzVS3i3REGA1jNrPqFg4OnO8R97mbDu45fX3P6dYH9MfZWnpuWNyTqRcAsD41BOKfQcFmcqTPfmnT0dyE1rwF3ASS2dIjH0+uCtk3AwB4SPyGc3Zx4Ymhx5Y/XmUz1TeHoQd1+8XFiWHpbwPS3wYQBSXkbSYDQOzdY3Srku20loba71d3Zn64DwA4Ti5lO2e9aZv65tXNbOnR914LP510/XTSFQAEFXUtXM/QM4sUxX8BGq00NbI0NZLxbTgcPVCDRsNoGKsZYEH0exoNE1btYiSR0BtkseLkp1Oubz3mAgAPH9Vgzk5JA9ueaMPhOAauv/Lh6JKo2x5Rt1tvhGXMRhvO24Pj7B93i6MnO586sH3IqPFtidD5SGR3r3M7XF1WzZoIAFR+wTU7PQhEvm0r5k0eNOBLFmMB0BmLVkWGfXwacOtpwC1RccnRk50B4NLx1k9j/LQ59XV1R3ZtfPnADwA4ubgmTJu7dOOOP3HpjPgcSqPR4qO+x0d9ZzDhcDjroaMOX/LbuWbhxWMHLx5rzdU/edbC1dsPtPmwcGBtpYNhGMbkToSDg8Prkt/mZbPPHtp19tAueuOQUePX7fHi5OICANZWANjudX7j4hk7Vi/csXohAKjrDNh76io960lPtCEQCETPGTJ+6jWvnZbD7In/T60EAAQ+0qr9p49sWLxj4WQAIFMFXDYfxBOJnm4ui0caPYyvaN/DxLkr4r5/Cn7oS88tYTt+KgD4nvVscxg+eVZDXe3Fg1vePvEHAE5OruGOs2et2c6uRaqITyE0DNMYwJhyDABUdQ13nA84vGHhtvmtDwea2Y52PfBj49nAynbtoQtHNy3ds3QaAPBRqC6b9hvZtOZvpmE0ZpeG2G+hNBotOSY8OSacwYTD4doCNVhoc9135oDr7CMbFx/ZuBgAlLX013ldakt50q1yxD+M0zCL3d537Qcatt94OrVh7pL9F6dsPAYAAhS+/cuciHjehXu9TWdvLQ06z9DDiinDP8ek3An8dCfwk6SwgNNwcwA4fOMJ3TpztHVdQ+PW03cCXn8BAC5OjlljBm6bP/FP/IRxOJzPnmXLDl72vP7E83qrgPnjBu9Z2prAeIix1rnNC5YdvDx960kAoJKI+5Y6DTXVaevh1IY5c3acXXrg0tIDlwBAT1Xu4raF9MwiHBw4nz3L5u8+v+/S/X2X7tP97QcaHFw5jYuTAwAwGn3XqWttH6OSaDRaRGJGRGIGgwmHgxEWeiyUc3DgLm1buPboDb9Xn199bl2IttRTPbVhDn3XqdvOf+HDRCB6HdyvLR0iEH8OX1/fKVOmVL1nmjO2J5RWVEckposL86vLS9GvbaUV1eVVNYrSXWd5LS6vzC0q01GW7fJCWF1bH5GYXl1Xr6UkIy0q9DvCGDB2Xi8jLhTgua59Y1NzS2xqVnF5lZaSjLgQfy8erls8rtyXlxSdPJQx8poOhtEy8goTM/LwvDwqshKSIl0nPGdBTmFpXFp2fUOTqpyEiqzET007Fu4+8/R9eObTM6zdAoI+zdp6HI1s/wyOjo60hhqfCyd+p5OSsrLwqFgJMVENVWX6t66krKy8vFJJoeu8cEUlpbl5+bpaGl1+Rauqa8KjYqprarU11GSkGJP0/A661sNlpSQf3brUi31mZOckJqfyUyjqqsoMsQ4YhqVnZscnpRDweFVlBSmJDnHf+7xOKsjJOk0c24ti2tPc3PI1IhLDaCYGepydNh1pNFp0XEJqRuYAHW1ZaUkG69xlax+/DCpI+NbtUabOX4bj5fP19e013f0KHA6nuuiMkPGf+k9kQU1WbMbtHdXpEbTmRqK0hsy4Nfw6QwAg5qBDfWG64aEwultlQmjJl4flsW+wxnqKiglFzVx04DQcR+v3AWtqyHlyvDg0oKEsl1dQiqphJee4lRNPYm36TeryklKvbgAArKm+Oi1cdOA0NpY+KfnyMPHMol65ojk6OrZU5F/32PQ7nZSWV4bHp0iICKorts6XSssryyqrlWQZf6R0issqcgtLdFQVuh5La+oi4lNqauu0VOSlxbuuR/BrGE5YKC0hev/Urp6/pbml5VtMEoZhxrrqnJ0e/wKAzLzCxPRsfjKfmoJs2xpNTzhw/paCtLjjyEE9f0t7MIyWnpOfmJ6F5+VVlZeW/PlZKItTW7Dl0JM3n3PedD9CTnfby0kV/8+OpYi/D/1+cJp/we900lBVVpYWRRAQo0qr0oMqGqrKGmvKyeJdF6NsqCypLckTkNfqMgKjqa66LC2qub6GX1aTKNz1oPdrPF5pTRSRHrzFp9d6pNHKM+OqCzIEFHX5hKV+9t0x/kdIYnJyVowZFnt68JbmkpQIoGFCKgZtF/QeaqPRsJqCzMqcJE4eAllKiSjIOM0OPb48J+yFw5VustZlfrj/ztPl96+e9O/ht7yG3+mkoqwkPipCWExcUbX1zqKirKSyvFxGQalL/7KSosL8XFVN3S4vnTXVVQnR4bU1NSoa2mKSjOmsfgcHG31xKZkTN38ii1VedmZ6SiKZQlVQUedrtwfZQwfWVu+j+6VkFUZMmNLloTEMy81MT0tOwOMJcsqqouKSPbcCAI1GS46Lzs5M09AZIC7VRTpu1tq2rZj39uWT13F5XWpr4+UDv/ULndG6BALx74HD4TYevdo+ScYvUFlemhIbISgiLqusTh/wK8tLqyvKJOW6uDpUlBaXFOQqqOswW0Stq6lOiQ2vq6mRV9MSkei1q8OikYYikjK7vO/1VocA0NzclJEYW1FaLK+m1WWZkpaW5qSobzQMU9Mz5ui4UnTr1EFxWflBY/5IVVwajZaeEJOflaaspS8i2cWlgbVyz3ULPgc9vR3WfVq1USp8t2/fdnREtX3ZhqOjY3NR2pUdi3+nk9LK6oikTHFBfnX51t2N0srq8qpaRamuK9wVl1flFpfpKMkw3XhKyqypq9dUlJYWZSzY+juYzNoqLSoY4OHavjGroCQpM59KIqrJSbQVcGmjuQX7npCOYZiRpmLntREajRaTmpOeW6SvKictxigVw2gZ+UWJGfl4Xm4VWXFJ4Q47Rx7XHilIijjYdhEs1UNYK88pKotPy6lraFKVE1eREf+5Xae93s9CIzIeHuvWc5b7aS4RBbQ2guh1+sfTCQjEzyJIJQ021mZoEaQy3cIR5qcI83ddSwwASES8pb56b+pjCTcXp56q/F87XBup2QVXH4ibZpoAACAASURBVIU8PcmYX7QNDg6cgpSYgtSvlzSWEhWU6tUJBwLRE4QEBGwHWjK0CAkwjTQSERIUEWL6RSWT+KzNe5R2vi8gJy0lJ931Gj0HB4eivKyivGxnU0paxqWbd17d6709g05wcXGaGjItNYrD4XQ01XU0/97Ai+hFKhNC47ycuUgCotZOLbVVJV8fxx+bo7Xen6zc4QHcivgPcZ5OnASysOkEbrJgecyb1Gsb6osy5CZvoTukXd9Y9MFP2HySuKx2Q1FGwZsbtdlx2psesDb9JgQJFa31/gBQX5j+fSOqR94BQX7KELMBDC2CzKdPwgJUYQGmFUDJfAQrQ21m1r8MFyeniS6rAUdWQlRWous1FxakZOZevff8uffBXxbGwYFTlJFQlPn1oMBuTw2B+FfhJQuI6w5kaOElM53+8VKEeClMY6G4CSRRza5j2fscOBy/nCa/HGMxqZ5QlZ+W8uqm3c5f35LBcXIJqzIvbclSGw7HQRKXJ4nL//LR+yBUASHTgUMYWqgCTL9pAkIiAkJMgxf5SGQDM+ve1PcbSEjLSkh3cR/RQwcW1qy0lHs+ly8EBDJ7LwcHh7S8orQ8YxXLnlgBAIfDqWjqqGjqMHPo9tQQCATiN6HwCw6wGMzQQuHvehmKKihMFRRm0RuBj6RtbNWb+v4YXFzcSpqsnl/n5ORS1+9iwS03I+WF39UDN579IWE4HE5BXVtBnen9abfKEf8pBCmkwYaaDC2CFBYbT2Rh/i6iWumQiHhLPcaieH8OGTEhGTGmc1EuTg5jTVaTKG0laW2lrgPCODhwCpKiCpJdrJyk5hRee/LuydF1nU09h7VyKREBqZ9/qBiB6COgQA0Egs1EJmXM3HrcRFt52ZSR7FWSmlNwx2NN76YM6RWuP37z4mPE19gUdgtBIP44EdGxU+cvMzMyWLloLhtlpKRn3L9+vnfzhfQKV3z8nr0K/vItgt1CEMyhYek+23BcPFrr/PGi8gAgOWJx+NZB+a+vMARqlHy6h+PgHLA/lItIAQDJkUu/rzcri3hJD9TAmhuLQv35dW2V57bWR+cVkUv32VZfkMojJM3MhBfrcD/ZXF2aH8RY8qwNQaNRREm13jx3RJ8hMiFlutteU12N5TN+8aHwXiEtO8/v+I7ezRfSK1y99+LF+7CwqG6eSkcgEH+HsrTod54LhFWN1McuZK+S6vx0m03XezdlSK+QGuST+z2oJKn7bGqIXyYhJmK9yzRdIzNnlxXsVZKdkXr06t3eTRnSKzy4deV90PPo71/YLQSBQCD+EqlxkftWzFAfYDJhznL2KsnLTNt+zq8X84X0Fi/9r4WFvEiIDGO3EASia6KSs2a5nzbRUlrqOIyNMtJyCn33rejdfCG9wvWn715+jAqLS2O3EMR/HRSogUCwE1tTneyCEox5ja6/iZ2pLrsldA2NRsMwbIC6Aon4E8nGEYh+x9BB1tk5uRiGsT1H7rDBA7t3Ygf00cBQX4dE6oUiF4g/QU1mdE1WrIjFZHqUBgAQJJQVpu2iYRiDp8QwF3HbufQoDQCgNTdyEqktdZWtZqwFACoTQmsyo/lktQFAwnaOmPVUHDcvrbmRmYnhEE1VJVn3DzGTihdXRIEa/yS2FgbZ+cUYRqMBm8dSOwvmj5WzFRoAhtEMtFR/qowLAoH4E0joD6opzqVhGLB7yAIACf3B3TuxAxrQaBgmqKTPTUAzwD+CmY1dQW52X7gNAQDzQUPZLaFr6HcimnqGXZZ6QSAQiH8MAyu7orxsDMOgD1waDK3t2C2ha+iXBhVtAyIfmqIg+hxDjLVyCkv7wvTO1qSvpFNlgEYDjEYzUJfvXEsFgfiboEANBIKd7F8xnd0S+gEzxtjMGGPDbhUIxB/Hc9cWdkvo68yeNnn2tMnsVtFv4OHFY82/VVj9F6gvSAcAorRG+0bxIXM6exIklJury3Kfn61O+VpfnFVfmNZSV8XD31pdi4OHIGO/JvPugcgdwwkSKlR1C35dW35tGxwHJ465ifEQ4sqmp5lmY8Jxcf/myf5psKZ6XjzaR/9pDrqx+ZH0vs+s8cNmjWfn8zQIBKINgzm72C2hH6A0ZJrSkGnsVvEvs3Yn08BWRBvjps4eN3U2u1UgEAjEX8Jl8wF2S+gHDHOYOcxhJrtVIBBds3+ZE7sl9HVmjLKaMap/lI5C/NugQA0EAoFAIBCIfxCqgEBzVdlfPmhTdQkA8AiId+uZ++x01j0PHBcPRc2cX9OaNGZF7vOzDcWZbQ5SY1YImYwr+uBbFhmUH3wt//UVvJii9np/bqooC1OHY+BwHDz9OCi+ubqMKoBKbCIQCAQCgUAgEAgEAoFAIBAIxD8ICtRAIP44z0PDq2rqHOzM2S2kp2AYjYMDx24VCMS/ydPA4MqqqikTxrJbSI/AMIyDg4PdKhC/iJaGRmxO/F8+KK+QDABUp34XNhnX1lj0wQ9omIilY1tLU1VJht9ebrLQgH3vOPGtOUKzHx1rc6A1N7U01vEKS8uMd5MZ79ZUUZj96Fh+0KW8V5dk7FczM8lOXN9eTFNFYfbDI8ykilo78cn10ZpfdGpz4rU0NLr3+0/y/O2XypraySP6R8ItNLNCIP7j5H4LbKqtlrMaz24hiP807149q6mqHD7esXtXBAKBQPxn+BL8vLa60mYMyl2KQPRLXnyMrKypd7A1YbeQHoHWRhCILkG7LwjEH+fIjcdbTvqwW0X3JGflrztyTWvSKtmRCx3cDgWHxbBbEQLxD3LoxNkNO/axW0U3JKWkuW7eqWxgLaZmMM55XtDbD+xWhPgVrCwtahPf/+WDkhT0OHjwFXHv2lrqchOTL66qSAht79ZQkg00TNBwZFuURmNpbm3Wj+tORfy7L8s1ij/do//JTRWVHLkYAJpry1mYGMQ011YWvL3J7FVfmNHLJ9/b1CZ+sLToN1Gef5nDl+9sOnyB3Sq6ISkjZ+2BM+ojZkkNnDxxmfvrT+HsVoRAINhD7L2T36/uYLeKn+DhUrNPp1ezWwWil7ly0vPIzg3sVvETjLPQ3LV2MbtVIBAIxD+O33kv7wOb2a2ip8y30z26eSm7VSAQfYgjPs+2nvZlt4puSM4qWHfMR8txndzYFZM3HA3+GsduRQhE3wJl1EAgEAAAdQ2Njus8c4vKHIdZCFJI94M/O67zvHt4naW+OrulIRCIv0pdff34GQty8/KdJo0TEuAPePRsvPP8x7cvW5v3j+hsRBtjxozZvXt3dXoESV7vrx2UmyIiYbcg58nx1KvrRQdOq8tNyn1+BsfBKT6oQ91WgrgSJy9fyecHAjpDCOLKVclfMu8e5MSTWhpq6/JTCOJKZGVjbopw9kMvXkFJPlnt+sI0er4NAV07FiYGMQQJZbOz6X/r1HuZ6rTw6sKssWP7R/YdRGfqGhonr9ieW1jiOGqQEJVyL/Cdw3L3+6f3WBlqs1saAoFAsCL19a2q/DRRbQt2C0H8p3lw+2pWWoqRRf9InYVAIBCIv8DLgOu5GSk6ptbsFoJAIH6CuobGKZuO5RaVOdqZCVL57od8nbLxWICHq6WeKrulIRB9BRSogUD0MjQaDQBwuH6WxGnn2TtJmXn+nm7DzPQAYInjcPNZmxbuPhvt58VuaQhEP6Y/Dghb9x5KTE596HNxhO0gAFjuMsdw0Ki5y92SwkLYLQ3xc5iamqppaBYEXSbN/asjucwENwBa7rPTBSHXAYCHKqricpKkOKC9DyeepDT3cMrF1fHHZgMAFx+/vNMODl5CsveqiK1DzM5ncOJJKgtOJHuvjDnoQH8LBzev7MT1Arq2AMDC9M9Q8PqKuqaWiQkKkALon2Pp9uOXE9Oz753cNczKCACWOo8zmbzUZatn7JNL7JaGQCD+MDQaAEC/GrIAoLYkN8r3UGlyeFk6Sqz4L9AfL50AUJCXc85zd0x4WGJMJLu1IBAIxL9GP700FOfn3Di+Nynya2p8FLu1IBBspj/+indeuJuUme9/cNVQUx0AWOww1GKO+6J93lG3DrBbGgLRV0CBGghErxGVnLnp+I2vcalNTc1ayrKb5k2kBz0w8PZ73N2gT0FfousbGs111awGqM+2H8zJ0VqHqL6xyfPqg1vP3+cWlUqLCdkYau1dNo1ExLM2/T7Xn7zRVpZtEywqSLUz1bn59F1YbIqRplKvHAKB+E8RGRPntm1PWHhkY2OTjpb6NreV9LgHBkLef/R78CQw5F19fb2lqfFAC5N50504OTnp1vqGhgNHT9+4cy8nL09GSnKwtcXB7ZvIJD7Wpt/k6i1/HU31NrViIsLDBg+85hvw+Vu4iYH+7/eP+Jts2bRx1qzZ4nbz+WS1/tpBcRycspM2So9ZWZMdx0kgEUQVcVzcdJPWOr82NyGjMVR1y5rMaG5+UaKEKn1Di6pm0VxbQXegaloP2Pe+JjuusSSHiyRIlFLjpgh3a+ot8KLy5t45vdtnz6nJjCkO9fe8cpldAvoOUQmpGzzPf41ObGxu1lZR2LJ4Oj3ugYE3YZEBL94GhX6va2iwGKBlbaQzZ9LIH5OrhsZD3rd9HgflFBTLSIjamOjtW72AzEdgbfpNrt1/qa2q0KZWVEhgqIXBjYevvkQlGOuo/X7/CASiD1KWHvP9sntJcjjW3Mgvr6nj6CZp0EUQYUHMh8wPD/IjQloa60U0TEQ1LZSHTsdxtE7/WpoaYvyPpr/xqy3J5ROWFtOxGjBrOzeBxNr0+zTX1VTlpnITKULK+iXJqFRTPyYxJvLwjnUx4V+bGhtVNXUWrt1qOWR4Z7ewD28CH/p/fBPYUFenb2ppaG490Xkex/9vQxob6i8eO/jY/2Zhbo64tIyx5WBX9/18JDJr0+9TW12VkZJEIlO19I1iwsN6pU8EAoFApMZHXdi3ITHya3NTk4KatvOKzUY2w7r0jPz09t3TgG/vgxrr67SMLHRMrEY4zqFfHRob6n3PHAq6f6s4P0dUUkbP3Gb+hn0Evv8XM2Vp/R3qaqpz0pKJZKqqrmFi5Nff7xCB6I9EJWdtPnX7a3x6U1OzlpL0pjnj6HEPDLwNT7j3+ktQWGx9Q6O5roqlntrssQPbbzwdvv7k1svQ3KIyaVFBGwONPUsc2zaemJl+kxtP32krSbepFRWg2Jpo+zz/EBabaqSp+Pv9IxD/AChQA4HoHd5+j5u4+qAglTRrzKCKmtr7wV+mrDv87OQWUx2V9m5vvsXar9xPIREdh5oL8ZODvkSv8riUllO4e+lUuoProUs+T985jbDSU5VLyym8/OB1TErWq7PurE2/SUlFVXlVzYzRA9s3KstIAMC3uFQUqIFA/Cwh7z+OcZojJCgwx9mxorLq7qNnE2YsCLp/29zYoL1b8LvQEZNnUilkp4n2woICgSHvlrptTU3P2u/eWj162bqt133vOk+eMEBHMyU90/varejYhLdP/Fibfofi0rKy8opZUx3aN6ooKQDA1/AoFKjR73B2dj595lycz2b1dXf/8qO9HLxEspIhax8ukgBV05qhhYsk8KMTHgJZ0QAUDTq9lZXpHyDL193Q2MTZ2ZndQtjMm7DI8Uu2ClLJsyYOr6yquRv43mHF9hcXD5rpa7Z3C/kSMcZlE4XMN2XUYCF+SlDotxW7T6Rl5+9xnUd3WLX35I0HgdPG2uqpK6Vm5V3yfxqTlP766mHWpt+hpLyyvLJ65rgOq5/KctIA8C0mEQVqIBD/JAUxH4J3OfGSBZXspjXVVGV+fBSyf+bQXfeE1Yw7uEW/D9oxmZtIlreexEsRzI8I+XJuXXVBxoCZ2+gOX86tTwv2VbCZLKCgXV2QkfzyWnlm3LC9j1mbfh+KtIrdrnsAUJWf9nCpWa/0ifj7hH14s2zaWH4BwfFTZ1dXVb56fHfVrIkXAgL1jM3bu315H7J4ykgymTpi4hR+QeFPIa/2rl+ek5G2cus+usPeDSse3bk+xsFZTVs/OyMl4PrF5Pjoyw9DWJt+HwUV9Qt3AwEgKy1lnIVmt/4IBAKB6JbIT2+3zRtP5hccPnl2TVXF++f3diycfPDmcw0Dxst95Mc3m2aP4SNRBtlPoQgIfX8fdGLbyvzMtLnr9wDASfdVr+7dtB03VVFLLz8j7anvpfSEGE/fIPp7WVt/BxkltYM3nwNAbkbKfDvd3+8Qgeh3vA1PmOTmJUghzRxtXVldS68e8vTYelNt5fZub77Hj1vtSSERJtuZClHJr8NiXA9fS88r2rVoMt1h9eHrPs8/OA0311ORTc0puvLoTUxqduCpTaxNv0NJRXV5Ve30kVbtG5VlxADgW0I6CtRAIOigQA0EohfAMNr6I9d4eLifntiiKC0GAKumjTZyXn8+IJAhUOPOy1AuTs6oO4epJCIAuE4fq+Pg+vT9d3qgRkNT0+3n74db6J/Z7EL3V5ASXXfkWnJWvoy4EDOTsow4g56SiqrzAYHM1I4bZKyhIN2+JSkjDwDEhPjbN6rISgBAUVnlr3wiCMR/GAzDVm/ZxcvL8+qej5KCHACsXeqiYzXszKVrDIEat+4+5OLkTPgczE+lAIDbikWqRjaPXryiB2o0NDbe9Ls3auhg72MH6f5K8rKum3cmpaTJykgxM9GDKtooLi07c/EaM6kTx47UVOswRiUmpwKAhJho+0ZVZUUAKCwu+cVPBME+cDjcsaNexiYmea8uStjNY7ccRI/IC/QuT/h0+vPn/pXNstfBMJrbwbM83NzPvQ8qyUoCwKrZDgYTFp7zfcQQqOH7NJiLizP28SUqmQ8A1syZrDlqzpOQT/RAjYbGJp9HQSMGmpzduZrurygjsfbAmaSMHFkJUWYmFTmp9ocoKa88e/sRM6kT7Cw1lOTatySmZwOAuIhg+0ZVeWkAKCot/8VPBIFA9GFoNOzbxS0c3Ly2u+6SxRUAQGP8kkcrrROfXWII1Mh4F4Dj5LQ/9ZmHjwoAmhOWP1hsnBP2gh6ogTU1pof4SRnamS07Svcnicl9vbilKjeFT0SGmYksyRhY31BZmvScaaElGbMxVBkUMfYPgmHYoW1reHh5zwcEyigoAcCsJasnDdS7c/ksQ6DG87u3uTi5HnyKI1P4AWDOsrVjTNVCXjymB2o0NjY88b9pbTdy+5HzdH9pOSWPraszUpMkpGWZmeQUO9xWAEB5abHv5bPM1NqOnqCkhkIxEAgE4s9Cw7Bzu924eXgO3HgmKacEAJMWuC4aYfDoxrnOgRrBj3w5ObkuBsXwUagAMNll9dzBWp+Cnsxdv6epseH1/Vsmg0a4Hmgd2MXlFM7ucstJS5JSUGFtbX+IyrKSRzfOMVNrOXy8nIpGL54+AvEPgGG0Dcd9eLi5nhxbpyglCgArp44wnrn1/L3XDIEafoGfuDg5In32t248TRup67T+6fsIeqBGQ1Pz7Zehw811T2+YS/dXlBJZd8wnOatARlyImYkeVNFGSUX1+XtMA7DGDTTSUJBs35KUmQ8A4gy7TjLiAFBcVvWLnwgC8c+BAjUQiF4gIik9KjnTeaQ1PUoDAFTlJD1cZ2IYxuC53GnkIodh9IslADQ1NVPJxMrqOvqfLS00AHj7PS4iMV1PVR4AFk4aNmvsIF4e7obGZmamznqKy6r2XPBnplZZRpwhUCMlpwAABCkd8tHJiAsDQEV1bQ8/BAQCQSc8KjYyJm7mlEn0KA0AUFNROrLXvfOA4Lpo3rL5s+hRGgDQ2NhEpVIqK1vnqS0tLQAQ8v5jeFSMvo4WACyZN3OOsyOel7ehsZGZieEQxcUlOw4eYSZVRUmBIVAjOS0dAAT5O0yg5aSlAKC8AoVt9UsMDQ337N69ZctWvIicgJ4du+UguqE8OjjTd+fePXsMDbvJR/LPExGfHJWQOt3ejh6lAQBqCjKe6xdhNBqD54oZE5dMHUeP0gCAxuZmfgpfRVXrBKYFwwDg7ZfIiPgUPXUlAFjkZD9rwnA8L09DYxMzE8Mhikordp9iGvSmIifFEKiRkpkLAALUDnngZSVFAaC8quZnPgYEAtE/KEuNKkuPURw0hR6lAQAUKRWjeXtonaZ/6mMXqY6cT4/SAACsuZGbj9JU2zr9w7AWACiI/lCWFiWgoAMAqqPmKdk5c3LztjQ1MjN11tNQWRJ56yAztWQJJRSo8U+SEB2eGBM51nEGPUoDAOSV1dbt9up8GzJ90UqneUvoURoA0NTYSKbwV1e1lp/DWloAIOzDm/jocHVtfQCYMnfx+GmzeXnxjY0NzEyd9ZSVFJ/x2MlMrZyiCgrUQCAQiD9NSmxEanyU3URnepQGAMgoqi7aegijMV4aAGDC3BX2MxbTozQAoLmpiY9Cra2qhP9fGiI/vUmJjVDS1AMA++mLhk+excOD79banorS4utHdzNTKyWvjAI1EAgGIpMyo5Kzpo2woEdpAICqrITHimmdf8XLpgxbOMm2w8YTiVhZ07rxhLVgAPA2PCEiKVNPRRYAXCbYzhxtzcvD3UjfeOrKxHCI4vKqvRfvM5OqLC3OEKiRmlMIAAKUDqW6ZcWFAKAc7TohEP8HBWog+hx4PB4AGpqaeLm7CEHom6RmFwCAppJM+8aFk4Z29lSVkyytqD7m8+RzdFJmXnFydn5VTZ2EcGuadyKeZ+PciTvP3bGas0VNXnKggeZwc31bUx1ODg4Wpq6OIlEYdJGZWh5uxh8+LzcXAJRWVrdvrK1vAAB+Mh/0K+oaGgn4Xiifhugj4PH48qoydqv4OeixDtoaHZa/l8yb2dlTTUWppKzM69SFj2Hf0rOyk1PTK6uqJcVb472IBMLWtSu37fM0th2rrqo8yNJspN3gYYMHcnJysjB1PkRlZiwzqTydhlleHh4AKC3v8MB3TW0tAAjwU3v2AfQh6urrhfiF2a2C/WzcuDE+IfHWhWVqq2+RFFD9mr5LdVp4ytlF06dP37BhQ+/2jMfjS4uae7fPP01KVh4AaKnIt29cNNW+s6eagkxpeeXRq/6fIuIzcgtSMnIqa2olRIToViKed9Mi5x0nrphPWaauKDPQWG+EtbGdhSEnBwcLU6dDSJd8ZroY0cXMiocbAMoqOjwgUlNXDwAClF6o0/yXqWtoFCEQ2K0C8R+Cfj+INTVycDNGTfVZqvLTAYAq12FrQXVkF7msKFIqDVVlcQ9OFyeE1RRlVeWmNtVVEQRbUyRy8RJ0pqyNuLnv6Vo7irSKmLaVlIGthP5gHAcnC1NXR1Ge4pPBTC0HV7+50f4FmhvrefG9MGTRv4eNjQ08PF2EwvRNstJSAEBZQ7t945S5izt7yiurVZSVXDtzJDLsY25WRmZack1VpYi4BN2KJxAXrtlycr/7tKGmCirqxpaDLG1HWAwaysHJycLU5VFC05jmkeLuPz/wX6C+vg6PLp0IxL8ILx7f1NjIbhU/QW5GCgDIq3a4NIydsahLZxlF1cry0gDvY3HfPxXkZOSmJ9dWVwmJSgAAL4E4bfmmq147lo+zkFFS0zOzMRo03NDajj7+s7a2R1pR9V5UMTO1XP/0paGxvg4ACOjqwFbweHxJcwu7Vfwc9CdstRQ7PHbrMnFIZ09VWYnSyurjt59/jknJyC9OyS6sqqmTEG4NzCXgeTbMtt914a71/B1qchIDB6gPM9O1NdHm5OBgYep0CPGCF6eZSe28NkJvKavs8LxKTX0jAAiQiT38BPoOdY3NougnjPgDdLHFi0CwFyEhIQAoKa/u1rPvUFxeBQCSIgLdeh69+Vht/PIDl+81NbcMMtY+u3mhma5qewe3WeMifT3XzxlP5OX1vvfKwe2QsfP6gpIK1iYGcDgcgZeH2avzJZZe9CQ9t6h9Y1llNQAI85OhX1FaUS0o2P1/BKK/ICgoWFRSym4VP0dxSSkASEqIdevpefKcvK7FnsPHm5qbbQdaeR/3sDDp8Az9RtelCZ+DN69ZTiTgz125Oc55nq71sPzCItam9uBwOAIez+zVObBDXFQEAFLTM9s3lpZXAICIkCD0N4pLywUF+5/sP8H5c2cH21jHeziUhPVOJXtEr1MS9jjOw2GwjfX5c0yThP8ygoKCxeX9LClOcWk5AEiKdh9r5XXZT3nYjH1nfZqam4eY6Z/bvca8Y22U9Qucoh9d3OgyjYDHX7jzeOIyd8MJCwuKy1ib2vPTMythAQBIy85r30iP2xAW6H9BbyXlVWgsRfxN6PeD9VX9qeZaQ2UxABAFJbr1jLt/8t4Cveg7h2ktTeK6A82WHxNRN2nvoDVplf3JT9qTV3PxEpOfXwneO/3RqoH15YWsTYzgcJw8eGavLmM7/hkaq8r4BXrhfpD+PSwv7U/fw7KSYgAQFZfs1vPKqcPDByie99rb3NxkOnDIzqMX9I0t2jvMW7nhQWjcAtdNeALR7+q5lTPGO9jolxQWsDYxgMPhePEEZq8uYzv+GSrKSgUF0KUTgfgHERAQrChjGmfQB6koLQYAIbHupygA4HfhyEwrFZ+T+1qamwZYDF594Jym4Y/KWU5L1nm/ipq6dAMvgfjY58L2BZMWjTQqKyroibUNHA7Hgycwe/3bl4bK8lL4/wQDwS4EBQWLK/rTrhP8f5us7UFfFhz1eaY+ae2BKw+bmlsGG2qe2TjXTKdDbRS3GWMifPatnzWWgOfxfhA8ecNRk1lbCkorWJva8/O7TlToYtepBgCE+tuuEwCUVNagtRHEnwBl1ED0OdTV1QEgJiWrJ3EPfQQ5cWEACItNcbD7MX+9+fQdjYY5jxrY1lJcXrnt9C1hfkrEbU8SsTXrg8eVHw9oNjY11zU0ykqIbJnvsGW+Q0FJhceVe2f9X57xe75x7kRmJveFjgx6CkoqDly+y0ztjDE2A9QU2rcoy4jjcLj03A5rfFFJmQBgpNXhct73iU3N0tBAWfL+HTQ0NC5dvEij0XA4HLu19BQ5GWkA+PwtYsqEsW2N13wDaBg208mhraWopHTTroMiQoJxn16TSa2pa/Z5UaZgxgAAIABJREFUnWxzaGxsqq2rk5OR2r7edft61/zCon1eJ095Xz154crWtSuZmXZtWtteTH5h0R7P48ykzpnmaKDX4bkKFSUFHA6XlpHVvjEyJg4ATAz6WSYGGo0Wl5A0Z74Lu4X0CXh4eB4/fODq6nrixMLaMSulRq/g6CpTOoItYE0NOY+P5Tw6unTZsiNeXp0jqH4fDQ2NS94X+tlYKiUGAF+i4iePsGlrvPHwFYZhM8b9SFpWXFax9ehFYQH+qIfeZL7WJxsOnL/V5tDY1FxX3yAnJbZ16YytS2cUFJcduHDrjM+D0z4PNi1yZmbavnxWezEFxWX7zt1kJnXW+GEDNDuUkVKRk8LhcGnZ+e0boxJSAcBYR/0XPg02QqPR4lMy5i7uZ7IR/Rr6/WBFRlxP4h76CHwisgBQkvRVzmp8W2NasC+NhikOdmpraagsCb+2m5cqNPbER25Ca36dGP8fVeqw5qbmhlo+URldp/W6Tuvrywuj/Y4kPvVOeHJBx9GNmUlv2iYGPfXlhVF3DjNTqzRkqqCSXq+ceB+kIjNeU7MXCmrQv4fJcdE9iXvoI0jKyAFA1LfPw8f/WCJ4dOc6hmH2U36k9ysrKTq+Z7OAkPC9D7F8pNYl8gtH9rc5NDU11tfVSsrILV7nvnide0lhwYWj+25fPH3r4kmXNVuYmZZuYKxyUlJYcN5rLzO146bO0tA16JUT74Mkx8egdQkE4p9EU0MjI5Fp1tI+iJi0HAAkRITZjJnc1vjq7k2Mhg2dOL29Z0Vp8SWPrVRBYe/AKAJf6yzl1unWSmrNTY0NdXViUnIzVm2dsWprWVHBrdMHH1478+Da6Vmrt7O2tj9KWVGBz8n9wIRhDjOVtQf00qn3OejfHPoEA8EuNDQ0Lnmf719rI/RCIWFxqQ62P8K7fZ5/wDCa80jLtpbi8ir3c37CVHL4zX0/Np6uPWpzaN14EhfePHf85rnjC0orPK49OhcQdNb/1YbZ9sxM2xZMbC+moLTi4JWHzKTOGGWtr9ahLKyytBgOh0vL6xCoEZ2cBQDGmoq/8GmwERqNlpCeMw/9hBF/AJRRA9HnEBISUlVRfvOtP816DTQUCbw8IV9/aI5Pz1m05+zb7/Ht3TLzSzCMZm9j3HaxzC4siUz6kZM25Gus9HCXOy9D6X+KCVFXOo8BgPKqWhamznoqqmuuPAxm9krLYXzoSkJYwFJf/X14fFpOa6RzU3OL78sPkiICA9Tkf/1zYQdvvyeYmVt074foJ5iZmVVWVX0Nj2K3kJ/AaIAuAY9//fZDW0tcQtK85W4hHz63d8vMysEwbPzo4W1RGlk5eRHRcW0Or999EFHRv3W3dforLiqyZqkLAJSVV7AwMYipqKi8eOM2s1dKOmNObElxMWtzk7ehn9uSajQ1Nfv435eSEGcI6ej7fA2PqqyqMjc37971vwEnJ+exY8dOnTpVEnQhxn1w6ben7FaEAAAo/fY0xn1wSdCFU6dOHT927E9EaQB9LK2u+Rab9Cc6/0MYaqkSeHmCP0e0tcSlZrps9Xz3tcMVITO3EMNo42wt2qI0svOLIuNT2hxCPodLWDn4Pg2m/ykmLOA6axIAlFVWsTAxiCmvqr4c8IzZK7Vj5gwAkBARsjLUfv81KjWr1dTU3Hz7abCkqNAAzX4WAvstNqmyugaNpYi/iZCQkKKyakH0e3YL+QmElPU5efD5Ue/aWiqyE0NPrCiMCW3vVlOURaNhMqaj26I0aotzy9Ki2xwKot76zVTNeNcac4/nF9UYvxQAGqsrWJg662msqUgJvM7sVV2Q3ovn3tcojn1vaW72+/0ICQmpqKh+eR/8+139NTT1jXjxhPaaUxPj3FfO/xb6tr1bXnYmhmFDRk1oi9IoyM1OiPlxwf3yLthGTezZvdv0P4VExWYtWQMAleXlLEyd9VRVlt+9cZHZKzs9tRfPva/x9X2weW98DxEIRF/DwsI88mMIu1X8BKo6hjx4QsTH4LaWzOT4w+tdoj+9ZfAszMmkYZjlsHFtURpFedmpcZH0f0eEhkw2lAx+dIf+p4CImMP8VQBQXVHerbU91VUVz3wvM3vlZf7Ll4aI0GBlFVX0OD57MTMzq6yu/Z6Qzm4hP4GBhgKBl+fNtx8rxvHpuYv2XXwXkdDeLaugBMNo9gMN2208lUYl/XgS7833eJnRy/0CP9H/FBOkrnIaCQDlVbUsTAxiKqprrzx+y+yVmtt514nfUk/1Q0Ri2v9NTc0tvoEfJYUF9FXloF/xPSG9sroWrY0g/gQoowaiLzJmrP0935s7F0/pL7GNooLUJY4jPK89WOlxcdbYwfFpOcdvPeHi5Jg/wba9m6qsBB8B7//q4zBzXVVZyY9RibvO+5H5CNV19UmZeSqyEua6qiIClP2X7kqJCuqpyKXkFNDzbYyw0Gdh6qxHVU6yJPjyT53C2pn2DmsPzdx63G3WOH4yn9f1R+m5hXc81vaX/wI6X+NSM3ILxo4d270rop+gq6srKysT8OiZ0QBddmvpKWIiwisWzjlw9PQSty3znKfEJiZ5nbrAxcW5cPa09m6qyookPuKd+49H2A5SV1H68DnMff9hCplUXVOTmJyqqqxoYWIkKiy0+9AxaQlxfR2tlLQMer6NUUOHsDAxiFFTUarJToCfYcOqJfbT5jrNX7bRdakAleJx/GxaRtb9Gxf612gAAP4Pn8rJyerq9ptvzt9h0aJF9vb2buvW+5xaQJHTFrSaKqg/jEeg3zyy/M/QWJZXGv6i9J1PZUb01GnOHgcPSEr+wQd2dXV1ZWWk7wW+M9RS7d67byAqJLBs+gQP79vLdx2fM3FEXGrm0av+XJyc8yePbu+mIi9NIhL8nr8ZbmWsqiAd+j1258mrZBKxprYuMT1bVV7aTF9LRJB/35mbUmLC+upKKZm59HwbIweasDAxiFFTkCkPY/rUSJe4zXeauHTbdLe96xc48VNInhfvpGXnBRzf2e/G0rsv38nJorEU8bcZbz/m4q0A/elboJ/8ZPD8IupjFsYEHP181k3ZzrkiKzHuwWkODi6V4R3S85AllbnwfBnv70ka2FKklIviP0f6HOAmkpvraipzkymSysLqJniqcJSvJ0FIUlBBuyovjZ5vQ8rQjoWpsx6KlIrT7ey/c+59ipLk8Ir8zN66Hxw7dsydgLsrNu/pL0O3kIios8vyi8cO7lm3bILznNTEuGtnjnBycTnMXNDeTU5JlchHenH/juWQ4fLKauFfPpw+sJ1EotTW1KSnJMorqeoZmwsKi5w7vEdUQlpdWy8rPYWeb8PKbiQLU2c98spqnzIZYx//C8SEh+VkZaB1CQTin2TMmDG7d+9OivqmotM/cgLxC4uOn73U98yhE1tXDHecnZkcH3DxKCcn16hp8xk8pRVVCUTSm8d+RjbDZBRVY759vOa1k0gi19VWZ6claRqa8wuJ3DyxT1hcSklTLy8jhZ5sw3jwCABgbW2PjKLqg1jGQpP/BWgY9uHlg6kOE9gt5L8OfW3kfshXA3WF7r37BqIClCWT7TyvP1nleXXWmIHx6bnHbz/n4uSYZz+ovZuKjDgfgdf/9eehZjqqsuIfo5J3e98l8+Gr6xqSMvNVZMXNtJVFBCj7rzyUFBXUU5FNzS6k59sYbq7LwsQgRlVWojjw58r1rp0+2mH9kZnuZ9xmjOEnE71uPk3PK7qzf2V/mWC3cS/4q5ysDFobQfwJcDQajd0aEAhGYmJitLW1/Q6tHW7ebzLtt2DYznN3jt543IJhACAuxH9w1YwJQ0wBYOSyPWk5BfF3jwHA3aBPi/eer6mrBwABCmn/iul8BF6XXWcam5rL3lwBgNdfohfuPpv3/+LoeB7u9XMmrJ1pz9rUK/gFhi7dd6G2vgEAqCTipnmTljgO763O/w6L9p6LSC+Oio5htxBEb7J9+/bTp04mhYUQCQR2a+kpLS0t2/Z5ep4839LSAgASYqKH92xzsB8FALbjp6amZ6SFfwAAvwdPFqxcV11TCwCCAvyHdm3hIxLnLlvb0NhQl5sEAK/evJ+7bG1ufmuqGzwv7+Y1yzesWsLa9PvcvvtwoevGmtpaAOCnUra5rVzuMqdXev5r1NbVKRsOXLpsubu7O7u19FHCwsKOHj3mHxBQV1vDJyzFKyqPI/D3l/2wfgwNo9VVNBSm1RTnEoh8DpMmrVix3MjI6C8cefv27adOHIt7comI7zdVb1owbMfxK16X/VonV8KCh9YvmjjMGgCGz1uXmpWX9OIaAAS8eLvI3au6tg4ABKjkg24L+Qi8C7Z4NjQ2VX57BABBH78v2OKZV1RC7xbPy7PBZeq6+U6sTb/PnWchS7Yfoc/6qGS+LYunL3Ue3+27+hS19Q3qI2cvW7EKjaWIvwz9fnDQ5huSBl1EIfRNaFhLxM39cfdP0rAWACAIiBnO3S1rYQ8AgdsmVOenjz/3HQAyPzz4eHJVc30NAPCQ+A3n7OLCE0OPLW9pbpzqmwMA+ZFvQo8vryttrZ3Eyc2rPXmN1qSVrE29SFV+2sOlZkp2zqaLmRZP6bN8OrmSJz8qNia6e9ceQP8eHrt+38qWcZ+pz4K1tJw84H7l1GGspQUAhMXE3XYdHjp2EgAsmDg0Oz3l6bdUAHj50H+Hq0ttTTUAUPkF1+z0IBD5tq2Y19jY8CWrBgA+vQnatnJuUX5rXigeXvwC103zVq5nbepFstJSxlloTnCeu/XQ6d7t+S+wfdWClOhv0dH9KSskAoHoOZpa2tLqA1wP/NxWJRvBWlqueu3wu3CEfmkQFBFfuNXDemRrOYP1ziPyMlOvvk0EgLdPA45sWFxXWw0AZKqAy+aDeCLR082lqbHhYXzF9w+vD7stKCn8Mf5PXbphymI3+p+srb1CbkbKfDvd4Y6zV+452b13H+NL8HP3BROjo6O1tLTYreW/zvbt208dPxLts5+A52G3lp7SgmG7Ltw96vPs/xtP1AMrpk0YZAQAo1YeTMspjPM7BAB3g8OW7L9YU9cAAAIUvv3LnIh43oV7vRubmkqDzgPA66+xi/Z65xW3prrB83CvmzV27fTRrE2/j9+rz8sOXv6x6zRn3GKHfnOXR6euvlHLacOyla5obQTxJ0CBGog+iv3YsSlxke8v7eL6MznA/xC19Q3RyZlkPoKStDgPd9cZa0orqiMS08WF+dXlpeiRg6UV1eVV/2PvzgOhXNs/gM9qJ1IphdJOZcmSSCmKSot9S7QvB6dNe2k5RbtUUifJfihRhBSSJSEjdCpFiELCYAyz/f6Y83N6pXPqZOae5fr8VWOa5/u+53HP/dzP9Vx3p+oohf//kJ7ytzW1Dc3yg6TVVEcNlZP54vO/+aMBQWcwil9WMZksHfWxeByfbY30vKLaePX+kJAbLi4u//5uwD8aGxsnTBj/y5qVPju3oM7yYzoplNIXL2WkpceNGS0iQuz3Pc0tLaTSFyMUhk2eMI49IDS3tLS2kseO+av/G6Wrq/TFy5r39UMGD1afPGHYEPnef/sPP/p5dDqjqOQ5k8nS09bg0F4MHOXjd/bC7zdev64YNmwY6iw8jUqlZmdnP3v2rKqqqqWlhclkok4k4HA4nKysrKqqqra2tpGRkZiYGNcO3djYOGH8+E0Oi/dvXsG1gw6Izi5q2esqGSmJscojvzm5aiWTXr4dMXTwJFXlvyZXreQWcsdY5b/6lFCo3WWvq2o/NsrLDlIfpzJ0sGzvv/2HH/08OoPxrLyCyWTqTpvEdzMrDAZz5GLYpejE1xUwlgIEFlsueVL2Zv6JB1g8P/UBpXdTWt+9IEpIS49QxRH6n/51t7e0VJWKyykMGjWBXSLZ3d7S09kqPXzM/39IV2v1C8qn96LS8oOUJ4kNGvLF53/zR6ClqizVe/6NGyEDeD1ouWTJq4rKyLSneAI/nYddlM6KF6WS0jLKquOIxP5vQrS1NL8sLRmiMFx1wmT2V2dbSzO5tVVpzFj2G6hdlIoXpR/ramUHDxk7SX3wkKG9//YffgRelZe4LDAICRnI8xAAwFPCw8NXurmdv52tOpmfnqumdnVWvSyTkJIZOXos4RtfDRgMhtz6+e2LksFDhyuPm8T+diC3fu5oa1FUGYvBYLq7KFWvyprqa2XkhqhMUJOV/5/x/59/KswYDLrnEgP1CWPv3r2DOgv4a21kw/LZe1fx33MUZW/fS0uIjx017JtrI+SOkoqa4YNlJ40e8dfaCLmjtZ2iOvKvK/ouak9Z5fvahmb5QVJqY0Z+eXfpH3708+gMZvGrd0wmU0dNlR/XRn4Ljr98O/N1xRtYGwGcAIUagEe9fft2irr60c0O663NUGcB/MHC4xhDRDonN4/vGmeBf3XmzJm9e/eUZt8frayEOgvgAzXv66camh397bctW/isuAcAjjpz5szePbuf3Q4aPXI46iyAD9R+aNRavv7ob8dgLAVIvH37Vk19yrQVByZYrEadBfCHjIPLlaRYT3JzBvB68O3bt+pTpvy639d+1caB+kwg2NZZmxFYtNycgTwPAQA8hcViGRvPbumin4hKg9908D3uhAZe89tTVlo6YQLfbEUq2NhrIwU3jqiMgKJn8O/eN3zWcd139NhxWBsBHAKFGoB37d69O/DihfSgAxNUOLhrOxAMgbGpu85HPH36dPr06aizgIFHo9GmTZs6Yqh8UnQI8RslwwCw0Wj0RQ5uH5qanz8vJRL7f5IVAOFEo9GmTZ06XFY84dIRIl89GQy4j0anL920/2Nr1/NSGEsBMrt37z53IdD0WJLMyPGoswBe9yrp6rOQAwUcuB7cvXv3pcDLIYlZo8dNHNhPBoIn6veLpw9uh3UJAAReUVGRnp7e2r1+S10HZv9ZIMBqK19vtzXZvGnD8ePHUWcBf6HRaNOmTlGQIsSd+JVI4L8uwoCbaHSGlfe5hg7689IyWBsBHMJ/TWaA8Dh48KCa+hTrHac/tZJRZwE87UH+8z0Bkb/99hushggqIpF48+atopKyTTv2oc4CeN2vew4VFD+Pjv4DZs8A9EEkEm/euvXsxVvPoxdQZwG8bpvf5cKyiug/YCwFKB08eHDaFLXHx5y7yc2oswCe9oGUUXzD5xhnrgcPHjyopjbZ03lJS3PTgH84ECR5mWlnD3nDugQAwmD69OlHjx69emzX04xk1FkAT2tvazmy3nbC+HH79+9HnQX8jUgk3rwVV/y69tczYaizAF7nfT6y6GV19B8xsDYCOAcKNQDvEhMTi09IwBBEnfacb+/sQh0H8KiiF29XHrjgssJl165dqLMADlJXVw8PDw+NvnnsDNxfBN907MyF38OiIiMjNTQ0UGcBgBepq6uHR0SEJaT5XolCnQXwLt8rUcE3kyOjomAsBWiJiYndTYiXEcHmnHSnUdpRxwE8qrmiOPfMWhcXTl0PiomJJcTH47GY7avtO9vhGRLQv7Ligp3rnDh3HgIAeM3u3btdV6w4uW3Vq+eFqLMAHkXpaD+ywQ7Hot+7lyQhIYE6Dvgf7LWRiOScE6GJqLMA3nUiNPH63SxYGwGchvfx8UGdAYBvkpSUNDObHxAYFHs/Z8GMaYOkJVEnArwlPuOp4+5zxrNNwiMi8HjoVCbgJk6cOGzYsF37Dr7/8HHB3Nl4PNQagr/RaPTN3vv8g4IvXLjg7OyMOg4AvIs9lu4+4lfX8Gm+4XQ8DsZS8Dcane559MKF8HgYSwGPkJSUnD/fLCToQuXj2yO05olIDkKdCPCWmry72SdWmsyZHRURzrnrQUlJSTMzs0sXzqfc/sNwnrn0IFkOHQjwqQeJcdvcbWfPNo4I5+B5CADgNQsXLszLzfv9zG8jx4xXHjcZdRzAWxrqqvetXERu+pCamjJmzBjUcUA/2Gsje46fq29qMdWbAmsj4Es0OmPLmfCLsfdhbQRwAYw+gNepq6vnPy0QkZKds+5Qah4JdRzAK6g9tKO/33TdH7B67bo7d++KiIigTgS4YePGjbdv3/7jduIiB7d3te9RxwG84l3t+0UObn/cTrx9+/bGjRtRxwGA17HH0tiUrKWb9lfXN6COA3hFdX3D0k37Y1OyYCwFPEVdXb3wab6ijOiD3Rb1zx6gjgN4BYPW/TzaL+f02vVr1yTdvcPp60F1dfWn+fkSYsSVC42yH6Zw9FiAj/R0UwNPHNq5zmnNmtV373D8PAQA8BQREZG7d++sXbP6uOeKsHNHerqpqBMBXlGQmbrVera0uMjTp/nq6uqo44BvYq+N3EwvtPI+V/PxE+o4gFfUfPxk5X3uZnrh7dvxsDYCuAAKNQAfUFJSepydYzrf3Gb7KTvvM29rP6JOBBC7+6hQz2X3xZi0S5cunT9/Hp5ZESqWlpbZ2dkfmpqnGc338TtL6YJ9kYQapavLx+/sNKP5H5qas7OzLS0tUScCgD9YWlpm5+R8bO3SXr7+yMUwCrUbdSKAEoXafeRimPby9R9bu7JzcmAsBbxGSUkpN+fxYnPTzN+cH/uuaP9QiToRQKw2/17KllmV965cunQpgFvXg0pKStmPH5uZzfN0Wfqr6/KayjdcOCjgZRnJCTazNSOvnod1CQCEFh6PP3/+/KVLl+7cuLBpoU7u/TuoEwHE6t+9ObTe5uBaqwXzTXOyHyspKaFOBP4Fe22koYOuu/LAb8HxXdQe1IkASl3Unt+C43VXHmjooMPaCOAaLIvFQp0BgO+VmZnp6fHLy5evFhlPd1xgOEdnioQYPK8gROoaP9/LfnYj8dHz1++cnZ38/E4oKiqiDgXQoNFoAQEBhw8fEiESVzrYWFtaTNecisViUecCXMJisYpIpbfuJt+IvtlDox04cNDDw4NIJKLOBQCf+WssPXSISMC5LjVbbmakrTYexlLhwWKxnr2ouJ2WHZqQRqMzDxyEsRTwuszMzM2/eL569XKUnrmKse3wacYEUXHUoQD3UJrr6wpS36VHfKosc3JyPnHCD8n1YGZmpoeH58tXL+cssFxk46xvPFdMHDaeFyINH+oepd5NiLz+sqzE2dnZzw/NeQgA4Cn19fXeO3dGRkSMVZs239ZtxrxFQ4aPRB0KcE93F6U4NyMjPirvYeKkiZMCAs7PmTMHdSjwA/5eG8FjXcxnLpszXWviaFgbER4sFqv41bv4zKLwlFwagwVrI4DLoFAD8Bk6nR4dHR10OTA37wkehxunoihGJIxWHIo6F+AgBoPZ2tH19v3HuoZPkhIS1jY2Hh4eOjo6qHMB9BobGwMDA4ODr9XU1MpIS6tNGj9ksJyYqOgAHqKTQhEhihCJhAH8TN5Bo9H4btJJ7e5uav7856s35PZ2FRVld/dVGzduHDZsGOpcAPCxv8bSa7/X1L6XkZKcPE5FfpCMmOj/DA6NzS3D5OVQJQQDjtpN+9RKfvm2mtzRqaKs7L4KxlLAN9jXg5cuBz3Jy8Xh8HKjxonJDceJS/d5G4vF7GlvEZWRRxISDCwWk8GgtHV8qCQ31YtLSNrYWHuivh5kn4eXg4LycnNxePyYcROGDleUlJJBGAlwApPJbP3cPHjIUAwGw2AwOtpaaqrefKyvk5CUtLG2hnUJAEAfhYWF58+fvxUXR+nsHKY4SlFlrJSMHBaHoKM5racHi8MSCHy24POfUSmdYhKSSA7d1Ulu/lhfW1XBZDAMDGZu3LjB3t6eQBDMVUSB979rIxKTRo+UHyQpJqBrwsKsvbOLQMCLi4pgMBgqjf6prfPVuzpyB0VFWcl91WpYGwHcB4UagF81NDSEh4efOnWqubnZwsJCdEBvzfKy2traJ0+e2Nraog7CPTgcTlZWVlVVVVtb28jISExMDHUiwHNKSkqePHny4sWLlpYWKnXAtgWlUCgZGRkKCgoCuQBXXFxMJpNnz56NOsiPERMTk5OTU1NTMzAwmDZtGuo4AAiUfsdSGo1WWFhYV1dnamoqKyuLNiFHtbe3379/38zMTEZG8O+0wVgKBEBDQ0NmZmZJSUlDQ0N7e/uXP+rq6nry5ElbW9vChQtFRASzBWN9fX1OTo6NjY0wPOrHy9eD/3AeCqra2tqCggIrKyvUQbihpqYmPz9//Pjx06ZNIxAIPHseAgB4CpVKzc7OfvbsWVVVVUtLC5PJ5H6G7OxsBoPBdws+/83bt2/LysrMzMwkJBA0uJKWllZQUNDQ0JgzZ46CggL3AwBO4NA6M+/Ly8vDYDAGBgaog3BWbm5uU1OTjo7OyJEjYW0E8AIo1AB8icViXb16dcuWLVOmTAkNDZ04cSLqRNwTExNjb28Pv7kAcFpjY+Ps2bOJRGJGRoa8vAA+jnnr1i07O7vq6upRo0ahzgIA4FHPnj2zt7cnk8mhoaELFixAHYezqqurR48enZ+fr6enhzoLAOC/y8jIcHJykpGRuXnz5tSpU1HH4ZTExERLS8vOzk4k9ySAMAsJCdm0aROFQkEdhEtu3ry5du3akSNHRkVFCfCQAgAQJBcvXvT09ExPTxeSQg0qlaqrqysnJ5eRkYHH41HHAYCP2dnZYTCYmJgY1EE4i0ql7ty58/z58ytWrAgMDJSURNOPB4BeCFpvAfCTqqur582bt3nzZg8Pj+zsbKGq0gAAcMenT5/mzp3LYDBSU1MFskoDg8FYWlrKyspGRUWhDgIA4FGhoaFGRkZKSkokEkngqzQwGIyUlBQGg+ns7EQdBADwH7FYLD8/PzMzMwMDg6dPnwr2LVVxcXEMBtPV1YU6CBA6XV1dQlUeZGNjQyKRZGVl9fX1/f39UccBAIB/8eeff3p7e+/bt09IqjQwGIyYmFhoaGh+fv7p06dRZwEA8AExMTF/f/+4uLikpCQdHR0SiYQ6ERB2UKgB+ExsbKyWllZDQ8OTJ098fX2JRGHZbA8AwDVkMtnCwoJMJqelpY0YMQJ1HE4RERGxtraOiIhAHQQAwHPIZLKDg4O7u7unp6dgj4RfYj9F0dHRgToIAODL0LfBAAAgAElEQVS/+PTp08KFC/ft2/fbb7/FxcUNGjQIdSLOYt8ph0INwH1dXV3sOiHhoaKikpmZ6e3tvW3bNmtr68+fP6NOBAAA/evu7nZyclJTU9u3bx/qLFylpaV1+PDhffv2FRQUoM4CAOAPy5cvJ5FIw4YNMzAw8Pf3hwb2ACEo1AB8o7Gxcfny5fb29ra2tgUFBdOnT0edCAAggCgUiqWlZU1NTVpamoqKCuo4nOXs7FxSUlJaWoo6CACAhzx79kxbWzsjIyM5OdnX11d4mseKiYkRCAToqAEAPyooKNDV1S0vL8/Kytq5cyfqONwAHTUAKkJYqIHBYAgEgo+PT1paWn5+vqam5uPHj1EnAgCAfuzbt6+ioiIiIkIIn2zcsWPHrFmzVq5cKTybcwEAfpKSklJGRoavr++OHTvMzc0/fvyIOhEQUlCoAfjDvXv3NDU1i4uLHz58GBQUJFSdNgEAXNPT02NjY/PixYv09HRh2FbJ2Nh49OjRkZGRqIMAAHjFlStXZs6cqaysTCKR5s+fjzoOt0lKSkJHDQD4zpUrV4yMjNTV1UkkkoGBAeo4XMK+Uw63IgD3CWehBpuJiQmJRNLU1DQxMdm1axeNRkOdCAAA/paVlXX27NmAgIAJEyagzoIADocLDQ1taGjYsWMH6iwAAL6Bw+G8vLweP3785s0bTU3NlJQU1ImAMIJCDcDryGTy+vXrFy1aZGRkVFxcbGJigjoRAEAwMRgMFxeXnJyc5ORkdXV11HG4AYvFOjg4hIWFMZlM1FkAAIiRyWR7e/tNmzbt2rVLeLY76UNSUhI6agDAR9rb29kD1+7du+/cuTN48GDUibgHOmoAVCgUitAWamAwmCFDhty5cyc4ODggIMDY2Liqqgp1IgAAwGAwmNbW1hUrVixZssTd3R11FmRGjhx55cqVwMDAu3fvos4CAOAn+vr6xcXFc+fOXbhwoZeXV09PD+pEQLhAoQbgaTk5Odra2vHx8bdv346JiZGTk0OdCAAgmJhMpqura1JS0t27d3V0dFDH4Z4VK1bU1dVlZWWhDgIAQKmoqEhbWzszMzM5OdnHx0d4tjvpQ0pKCjpqAMAvSCRS7z5NPj4+OJxwLW5AoQZApaurC1qcurq6FhQUUCgULS2t6Oho1HEAAACzYcMGBoNx9epV1EEQs7a2XrFixerVq2ELAwDAD5GRkYmMjAwJCQkODjYyMnrz5g3qRECICNdaBuAjVCp1165dxsbGEydOJJFIy5YtQ50IACCwWCzWpk2bbt68efPmTWNjY9RxuEpNTU1TUzMiIgJ1EAAAMuztTlRUVEpKSszMzFDHQQk6agDAL0JDQw0NDRUVFUkkknAOXOw75VCoAbhPmLc++ZKamlp+fv7KlSsdHR1dXV1h/gAAQOj69euxsbE3btyQl5dHnQW9ixcvysrKurm5sVgs1FkAAHzG1dW1sLCQTqdra2uHhYWhjgOEBRRqAF70/PlzfX39wMDAwMDApKQk4Wy+DQDgmp07dwYHB9+8edPCwgJ1FgScnZ1jYmJgoR8AIUQmk+3s7Ni7BqSlpQ0fPhx1IsSkpKTgRgsAPI5Kpa5bt87NzW3NmjUPHjxQVFREnQgNMTExDBRqABSgUKOXmJiYv7//7du37927p6OjQyKRUCcCAAijysrKX3/9devWrfPmzUOdhSdISUlFRESkp6dfunQJdRYAAP+ZOHFiXl7epk2b3Nzc7OzsWltbUScCgg8KNQBvodPpfn5+urq60tLSz549W7duHepEAAABt3///tOnT9+4ccPS0hJ1FjScnJw6Ozvv3buHOggAgKsKCwu1tLSysrJSUlKEcNeAfklKSsLWJwDwsoqKCn19/T/++CM2Ntbf359IJKJOhAwejxcREaFQKKiDAKEDhRp9LFu2rLi4eNiwYTNmzPD394cHuAEA3ESn011cXJSVlY8cOYI6Cw/R1dXdvXv39u3bS0tLUWcBAPAfUVFRX1/flJSU7OxsLS2t3Nxc1ImAgIMFWcBD3r59O2fOHB8fn8OHD2dlZY0dOxZ1IgCAgDt37txvv/0WGBjo6OiIOgsyioqKJiYmsPsJAMKDxWL5+/sbGhqOHj2aRCKZmpqiTsQroKMGALwsPj5eT0+PQCAUFxdbW1ujjoOeuLg4dNQA3EehUKBQow8lJaWMjAw/P78dO3aYm5t//PgRdSIAgLA4cuRIcXFxZGQku9UW6LV//34tLS0nJycqlYo6CwCAL5mZmZFIpMmTJ8+ePdvHx4fJZKJOBAQWFGoAnsBisa5cuaKhodHe3p6fn79z5054rBMAwGnBwcFbt249ceIENO9xdnZOSkpqbm5GHQQAwHFtbW329vbbtm2D7U6+Bh01AOBNdDp9165dVlZWlpaW2dnZqqqqqBPxBCjUAEhAR41+4XA4Ly+v7OzsN2/eaGpqpqSkoE4EABB8BQUFx48fP3Xq1NSpU1Fn4TkEAiE8PLympmb//v2oswAA+NWwYcOSkpJOnTp1/PhxMzOz+vp61ImAYIJ74QC9mpoaU1PTzZs3//LLLwUFBdOmTUOdCAAg+MLCwtauXXvkyJHt27ejzoKelZUVgUC4efMm6iAAAM4qLCzU1tbOzc3NyMiA7U6+Bh01AOBB79+/nz179sWLFyMiIkJDQ+EOcS8JCQko1ADc19XVJSEhgToFj9LT0ysuLp43b97ChQu9vLx6enpQJwIACKyOjg5nZ+e5c+du2rQJdRYepaqqeu7cuTNnzjx8+BB1FgAAv8JisV5eXrm5ubW1tZqamomJiagTAQEEi7MAsdjYWC0trerq6szMTF9fXxEREdSJAACCLz4+ftWqVZ6ennv37kWdhSfIyMhYWlrC7icACLDe7U7GjBlTWFg4a9Ys1Il4EXTUAIDXpKen6+jofP78OS8vT5g3qusXdNQASEBHjX8mIyMTEREREhISHBxsaGj45s0b1IkAAIJp8+bNra2tISEhWCwWdRbe5e7ubmtru3LlSuggCwD4GdOnTy8qKlq+fPmSJUvWr18PV2FgYEGhBkCmqanJ2tra3t7exsampKTE0NAQdSIAgFBIS0tzcHBYt27d2bNnUWfhIc7OztnZ2VVVVaiDAAAGXltbm52d3fbt23fv3n3//n3Y7uRbJCUloaMGADyCxWL5+fmZmZmZmpoWFhZOmTIFdSKeA4UaAAko1Pgerq6uhYWFDAZDW1s7LCwMdRwAgKCJi4sLCwu7du0aXNn9q8uXL+Px+LVr16IOAgDgb9LS0kFBQTExMTExMbq6uqWlpagTAcEBhRoAjZSUFE1NzcLCwgcPHgQFBUlKSqJOBAAQCjk5OcuXL7e3tw8ICECdhbdYWFgMHTo0KioKdRAAwAArKCjQ0tJ68uQJbHfyr6SkpKCjBgC8oKmpydzc/ODBg2fOnAkPD4erxX5BoQZAAgo1vtPEiRPz8/O3bt3q5uZmZ2fX2tqKOhEAQEDU1dWtXbt248aNlpaWqLPwAVlZ2bCwsDt37ly/fh11FgAA37OxsSGRSLKysvr6+v7+/qjjAAEBC7WA28hk8vr16y0sLAwNDYuLi+fOnYs6EQBAWDx9+tTCwmL+/PnXrl2DW5V9EAgEW1vb8PBw1EEAAAOGvd2JkZHR2LFjCwsLjYyMUCfiddBRAwBe8PjxY01NzZcvXz569MjLywt1HN4lLi5OoVBQpwBCh0KhQKHGdyISiT4+PqmpqdnZ2VpaWrm5uagTAQD4HpPJdHV1VVBQOHnyJOosfMPY2HjLli0eHh6vX79GnQUAwPdUVFQyMzO9vb23bdtmbW39+fNn1IkA34PbVICrcnNzp0+ffvv27Vu3bsXExAwePBh1IgCAsCgtLbWwsDAwMIiKiiIQCKjj8CJnZ+c///zz2bNnqIMAAAZAc3OzpaUle7uT1NRUBQUF1In4AHTUAAAtdnnZvHnztLW1SSSSvr4+6kQ8TUJCAjpqAO6jUqlQqPFDTE1NSSSSmpra7NmzfXx8mEwm6kQAAD528uTJx48f37hxQ0JCAnUWfnL06NHx48c7OzvTaDTUWQAAfI9AIPj4+KSlpeXn52tqaj5+/Bh1IsDfoFADcAmVSt21a5exsfG4ceNIJJKVlRXqRAAAIVJRUTF//vzJkyfHxcWJioqijsOjDAwMxo8fHxERgToIAOBnFRQU6OrqlpSUZGZmwnYn309SUrKnpwcW7wBAgkwm29nZbd++fc+ePQkJCXJycqgT8TrY+gRwH41Go9PpcHfwRw0bNiwxMfHUqVPHjx83MzOrr69HnQgAwJeKi4sPHDhw9OhRXV1d1Fn4jKioaGRk5IsXL44ePYo6CwBAQJiYmJBIJC0tLRMTk127dsFSEvjPYNEWcENpaamBgUFgYOClS5eSk5MVFRVRJwIACJHa2lozMzMVFZXk5GTY4/yfOTo6RkVFMRgM1EEAAP8R+3l0Q0NDdXV1EolkaGiIOhE/kZKSwmAwsPsJANxXXFysra2dlZWVkpIC5WXfCQo1APexTznoqPEfYLFYLy+v3Nzc2tpaDQ2NxMRE1IkAAHyGSqW6urrq6+tv27YNdRa+NHny5BMnThw9evTRo0eoswAABMSQIUMSEhKCg4MDAgKMjY0rKytRJwJ8CZY/AGfR6XQ/Pz8dHR0JCYmioqJ169ahTgQAEC4NDQ1mZmaDBg26d++etLQ06ji8bsWKFR8/fnz48CHqIACA/6J3uxP28+jy8vKoE/EZdjEfFGoAwGWhoaGGhoajRo0ikUjz5s1DHYdvQKEG4D4KhYKBQo2fMH369KKiIisrqyVLlqxfv579/ycAAHyPrVu3vn//Pjw8HI/Ho87CrzZt2rRw4UJ3d3cymYw6CwBAcLi6uhYUFFAoFG1t7ejoaNRxAP+BQg3AQZWVlXPnzvXx8Tl8+HBWVta4ceNQJwIACJdPnz7NnTuXyWSmpqYOHjwYdRw+MG7cOF1dXdj9BAB+9PTpUx0dnefPn8N2J/8Zu6NGR0cH6iAACIuurq41a9a4ubl5eno+fPhwxIgRqBPxE3FxcbjLC7gMOmr8PGlp6aCgoJiYmNjYWD09vdLSUtSJAAB8ICUl5fLly4GBgcrKyqiz8DEsFhscHNzV1eXh4YE6CwBAoKipqeXn569cudLR0dHV1RWe/wE/BBZwAUewWKwrV65oaGi0trbm5eXt3LkTqn0BAFzW1tZmbm7e0dGRlpY2fPhw1HH4hrOz861bt2BCCQAfYW93YmRkNHXqVNju5GdARw0AuOnVq1f6+vrx8fFJSUm+vr5wwfijJCQkoKMG4DIo1BgoNjY2xcXFsrKy+vr6/v7+qOMAAHhaU1OTu7u7q6urg4MD6ix8b+jQodevXw8LC4On3gEAA0tMTMzf3//27dv37t3T0dEhkUioEwG+AYUaYOB9/PjR0tJy8+bNmzdvLiws1NTURJ0IACB0KBSKpaXlx48fMzIyVFRUUMfhJ46Ojj09PXfu3EEdBADwXT59+rR48WL2difx8fHQPehnQEcNALgmLi5OX19fVFS0oKDAwsICdRy+BFufAO5jn3ISEhKogwgCFRWVzMxMb2/vbdu2WVlZff78GXUiAAAvYrFYq1atEhcXP3/+POosAsLc3Hzjxo0bN26srq5GnQUAIGiWLVtWXFysoKAwY8YMf39/FouFOhHgA1CoAQZYbGysurr6n3/+mZ6e7uvrKyIigjoRAEDo9PT0WFtbv3z58v79+6qqqqjj8JmhQ4eamprC7icA8IWnT5/q6uqWlpY+evQItjv5edBRAwAu6O7u9vLysra2tre3z8nJGTNmDOpE/AoKNQD3QUeNgUUgEHx8fNLS0p4+faqpqZmVlYU6EQCA51y8eDElJSUiIkJGRgZ1FsFx+vRpJSWlFStWMBgM1FkAAIJGSUkpPT3dz89vx44d5ubmHz9+RJ0I8DpYzAUDprW1dcWKFfb29jY2NiUlJbNmzUKdCAAgjGg0mo2NTV5eXnJyspqaGuo4fMnZ2Tk1NbWhoQF1EADAN/XZ7mTmzJmoEwkCCQkJHA4HHTUA4Jza2to5c+Zcv349Ojo6KCgIyvp/BhRqAO6DQg1OMDExIZFIWlpac+fO3bVrF41GQ50IAMArXrx44e3tvXfvXgMDA9RZBIqYmNiNGzfy8/NPnTqFOgsAQADhcDgvL6/s7Oy3b99qaGikpKSgTgR4GhRqgIGRmpo6ZcqUBw8e3L17NygoiN04GgAAuIzBYLi6uj58+PDu3bvTp09HHYdfLV++XExMLCYmBnUQAED/Pn36tGjRItjuZMBhsVgJCQnoqAEAhyQlJWlqara1teXl5dnb26OOw/egUANwH4VCwUChBgcMGTIkISEhODg4ICDA2Ni4srISdSIAAHrd3d3Ozs5TpkzZu3cv6iwCSEtL68iRI/v373/69CnqLAAAwaSnp/fs2TNTU9OFCxd6eXn19PSgTgR4FBRqgJ9FoVC8vLwsLCxmzpxZXl6+aNEi1IkAAEKKxWJt3LgxLi4uLi4Omvr8DAkJieXLl8PuJwDwpqysLE1NzfLyctjuhBMkJSWhowYAA47BYPj4+CxZsmTRokUFBQXq6uqoEwkCcXHx7u5uaNkNuKmrqwuHw0EvHA5xdXUtKCigUCja2tpRUVGo4wAAENu7d29FRUVERASRSESdRTBt37591qxZzs7OcAEIAOAQGRmZiIiIkJCQ4OBgQ0PDN2/eoE4EeBEs7IKfkpeXp6mpGRoaGhoaGhMTAw90AgAQ2rFjR0hIyK1btxYsWIA6C99zdnbOz89//fo16iAAgL+xtzsxNTWdPn16cXExbHfCCVJSUtBRA4CB1djYaG5u7ufnd+bMmdDQUElJSdSJBAS7qwGVSkUdBAiRrq4ucXFxLBaLOojAUlNTy8/PX7lypZOTk6urK8xJABBajx49Onv27IULF8aPH486i8DC4XChoaGfP3/29vZGnQUAIMhcXV0LCwsZDIa2tnZYWBjqOIDnQKEG+I9oNJqPj8+sWbNUVVXLyspcXFxQJwIACLU9e/acO3cuNDR08eLFqLMIAlNT0+HDh0dGRqIOAgD4S+92J0eOHIHtTjhHUlISbooAMIDYTYAqKiqysrK8vLxQxxEoEhISGAwGdj8B3MQu1ECdQsCJiYn5+/vfvn373r17Ojo6JBIJdSIAALe1tra6urouW7bMzc0NdRYBN3LkyKtXr16+fPnOnTuoswAABNnEiRPz8/O3bt3q5uZmZ2fX2tqKOhHgIVCoAf6LsrIyfX39kydPnj59Ojk5eeTIkagTAQCE2tmzZ319fS9fvuzg4IA6i4DA4/EODg7h4eEsFgt1FgAA5tGjRxoaGuXl5VlZWTt37oQnWTlHSkoKOt8CMCB6mwDp6uqSSCRdXV3UiQQN+345FGoAboJCDa5ZtmwZiURSUFCYMWOGn58fk8lEnQgAwD0bNmxgMBhXrlxBHUQoWFlZubq6rlmz5uPHj6izAAAEGZFI9PHxSU1Nzc7O1tLSys3NRZ0I8Aoo1AA/hslk+vv76+joiImJkUgkLy8vuFUAAEDr4sWLW7duPXXq1Jo1a1BnESjOzs5v377Nz8/vfaWxsbG8vBxhJACEEPtOp5mZGftOp4GBAepEAg46agAwINra2mxsbHqbAMnKyqJOJIDY98spFArqIECIUCgUKNTgmlGjRqWnp/v5+e3fv9/CwgLuIAIgJIKDg2NjY2/cuCEvL486i7C4cOGCrKysm5sbPKoEAOA0U1NTEomkpqY2e/ZsHx8fKMYFGAyGgDoA4CdVVVVubm55eXl79uzZv38/Ho9HnUgotLS0PHjwoPevT548wWAwsbGxva9ISkouXLgQQTIAuOvDhw8jRozo82JoaKinp+exY8e2bt2KJJUA09HRUVdXj4iImDZtWnx8fGho6IMHD/bt26euro46GgDCoqmpydXV9eHDh0eOHPH29obqWE4gk8mfPn3q6Ojo6upqb28nk8llZWVXrlwhk8lUKrWjo8PY2BgmWgD8kKKiIjs7u+7u7szMTENDQ9RxBAeTyXz9+nVnZ2dXVxeVSn39+jUGg4mKilJUVGxpaWEwGO3t7T4+PqKioqiTAsFRVFQUFxcnIyMjIiIiKSn59OnT7u7ue/fuiYiIyMjI4PF4RUXFr6/RwEDB4XBeXl4GBgZOTk4aGho3btwwNzdHHQoAMGCYTObixYvPnj07ceJE9iuVlZW//vrrtm3b5s2bhzabUJGSkoqIiDA0NLx48eIvv/zCfpFKpe7cuXPZsmUmJiZo4wHAOc+fP3/16lXvX9+/f4/53xtPEydOnDZtGoJkAm3YsGGJiYnnz5/39vZ+/PhxWFiYoqIi6lAAJSzUCYLvFBoaunnz5tGjR4eGhmppaaGOI0SoVOqwYcPa29u/9QYXF5ewsDBuRgIACQsLizFjxly8eLH3VmVcXJy9vf3OnTuPHj2KNptAYjAYa9asSUhIoFKp3d3dOBwOg8Hs3r378OHDqKMBIFC+1UX80aNHTk5ORCIxOjp6xowZ3A8mJEJCQtzd3b98BYfD4fF4HA7HZDJpNNqDBw9gnRSAr7W0tMjJyX39+pUrVzw8PAwNDSMjI4cPH879YIJtypQpfdqbsccrHA5Ho9HU1dWfP3+OKhsQSG/evBk/fjyBQMDhcCwWi8Fg9Hnsz8fH5+DBg6jiCQ8ymbxx48aoqCgPD4+TJ0+KiIh8/R4mk8m+ZAMA8Iv8/PwZM2aIiYn5+/uvW7eOTqcbGxu3t7cXFBSIiYmhTid0fHx8/Pz88vPzp02bRiKR7OzsKioqtm3bdurUKdTRAOCUhISEZcuW/cMb4uPjly5dyrU8wqaoqMjJyenz58/BwcGWlpb9vgcmeMIA/gODvyUmJvb09Hz9ekNDw5IlS9zd3VetWlVYWAhVGlwmJiZmbW3d73U4m5OTEzfzAIDEixcvUlNTL1++vHLlSgaDgcFg7t+/7+TktGHDBqjSGHDl5eW7du0aMWJESEhIe3t7V1cXk8mk0+l4PB7qOwEYWA8fPjQ3N6fT6V++yGKx/Pz85s2bp6enV1xcDFUaHGVnZyclJfXlK+z6jO7ubhqNJikpaWxsjCobADwrLi7O1NS0u7v7yxc7OjocHR03bNiwZcuWtLQ0qNLghLVr1/ZZp2MwGOwhi0AgWFtbowoGBNW4ceMmTJhAp9N7enpoNFqfKg0cDrdq1SpU2YSKjIxMRERESEhIcHCwoaFhRUVFnzd0dHSYmZm1tLSgSAcA+I+Sk5NFRESoVOqGDRssLS137dpVXFwcGRkJVRpI7Nu3T0tLy8nJydfXV09Pr6qqCoPBJCQkoM4FAAdZWFjIyMh866fS0tLQyoujpk+fTiKRnJycli5dun79+q83tXz06FFvmx8gwKBQA/ylrKzMxsbm6ychYmNj1dXVy8rKMjIy/P39oY0qEk5OTv3W0GAwmEGDBpmamnI5DwDcd+rUKQKBwGKxoqKili5dmpqaunTpUgcHB39/f9TRBAqLxVq0aNGUKVPOnDnT1NSEwWD63D9mV8kAAAYEmUx2dXXNyso6cOBA74tNTU0WFhYHDx48ffp0XFxcvw+sgwEkISHh5uZGJBK//hGBQFi4cGG/PwJAmH3+/HndunXPnj3z8vLqffHly5czZsx48OBBcnKyr68v7JLJIS4uLt/6/7anp+efn4cD4L+xtbXt97kRPB5vamqqpKTE/UhCy9XVtbCwkMFgTJ8+vU9f1XXr1qWnp385LAMAeF9CQgJ7vZfFYqWmpgYFBW3YsGHq1KmocwkpAoFw9uzZjo6OvXv30mg09mrYmzdv3r17hzoaAJwiIiJia2vb76IHkUi0t7eHu4GcJi4u7u/vHxMTExsbq6enV1pa2vujT58+2dnZXb58OTExEWFCwAVQqAEwGAyGQqFYWVnRaLQTJ07k5uayX2xra1u/fr2dnd3ChQufP38OTxMiNHfuXHl5+a9fJxKJzs7OcP8ACLzGxsbw8HAajYbBYOh0ekpKipWV1eLFi69duwa9vwYWFos9fvy4mJhYn/qMXn2eogMA/Ixt27axK6J8fX1TUlIwGExmZqaGhsbLly8zMzO9vLx6d3oCHLVp0yb2V0wfTCYTmnwC8DVPT08ymYzBYIKCgkJDQzEYTHh4uI6OjoSEREFBwYIFC1AHFGTy8vLLly/v9wJQUVFRQ0OD+5GAwFu6dGm/z40wmcz169dzP4+QmzhxYn5+/tatW93c3Ozs7FpbWzEYTFhYWFRUFPsPcXFxqDMCAL5LU1NTSUlJ719pNBqFQjl37ty6deu+fqgacEFcXNyCBQs+fPjw5cIXgUBITk5GmAoATnNycup3PYRGo0Efd66xsbEpLi6WlZXV19dnP5XKYrHc3d3ZzdLc3NzYi4dAUMH9LYDBYDBeXl5VVVVMJhOLxTo4OLS3t6elpU2ZMiUhIeHOnTuhoaF9OkIDLsPj8U5OTl8/xUKj0RwdHZFEAoCbzp8//+VfGQxGT0/P+/fvOzs7UUUSYNOmTYuMjPzWT2HrEwAGyoMHD65du8a+HsZisY6Ojt7e3qampkZGRiUlJbDdCTdNnjxZT0+v34fUoc8nAH0kJSVFRET0jl3r16+3tbV1dXVdv359Tk7O6NGjUQcUfGvXrv16LVVERMTOzg5JHiDwdHR0hg0b9vXrcnJy39pLG3AUkUj08fFJTU3Nzs7W0tKKjY3dsGEDu7oXh8OtXr36w4cPqDMCAP5dSkpKn7p8dn3A9evXtbW1v3yoGnBae3u7m5ubtbV1e3t7n9pEJpMJz7IDwTZnzpyhQ4d+/fqQIUPgyW1uUlFRyczM9Pb23rZtm5WV1cmTJ5OSkmg0GovFIpPJsNugYINCDYC5devW77//zn54msFgfPjwYebMmQsWLDAyMnrx4gVcePMIR0fHr59iGT58+MyZM5HkAYBrKBTKhQsX+qxH0yK7RokAACAASURBVOn0oqKi2bNnNzc3owomwJYvX753795+u5VARw0ABkRbW9uKFSt6f8uYTGZnZ2dMTMy5c+diYmIGDRqENp4Q2rRpU59CNCwWq6+v329LMwCEVltb2+rVq3vHLhaLRafT09LSwsLCTp8+DX3+uGPevHnKysp9Xuzp6YEOQIBDsFistbV1n19wIpG4Zs0a+K1HyNTU9NmzZ+PHj3d3d2cv4mP+f0rp4uIC5fUA8L6kpKR+V13odPqrV69MTEyg6IprVq5ceePGDUx/S15MJjM9Pb27uxtFLgC4AYfDubi4fD3Tc3V1he0suYxAIPj4+Ny/fz8nJ2fPnj290zkajZaUlHT9+nW08QDnQKGGsKutrV21atWXBbx0Or2srMzb2zsqKmrw4MEIs4EvGRgY9FmPIxKJK1euhH0fgMALCQnp6Oj4+nUajVZeXm5sbNzY2Mj9VALv0KFD5ubmfabpLBYLCjUAGBBeXl6fPn1iMBi9r9BotPfv33/8+BFhKmFmZ2cnKSn55SsEAsHKygpVHgB405YtW5qbm7+cDNDp9M7OzoSEBISphA0Wi127di2BQPjyRRkZGSMjI1SRgMBbunRpn7p5Go3m5uaGKA74y/DhwzU0NKhU6pf/dWg0WkZGRlBQEMJgAIB/xWAwkpOT+91zFo/Ha2pq5uXljRgxgvvBhFNUVNTOnTuxWGy/y+xUKjUrK4v7qQDgGkdHx69netDHHZWZM2fKysr2abnEYrE2b9785s0bVKkAR8EtXqFGp9NtbGy6urr61NrjcLjg4ODPnz+jCgb61ae2Eb4vgTBgMpknT578h+KAqqoquDfACTgcLjo6esyYMX1uA0ChBgA/Lykp6caNG1+vyjEYjGPHjqWmpiJJJeTExcXd3d2/3GaORqNBYzkAvpSenh4SEvL12EWn02/evBkYGIgklXBavXp1n93Tly5d2mfOBsAAmjt37pf74eJwOAMDg8mTJyOMBDAYTGZm5unTp78s/GVjsVi//vrr69evkaQCAHyP/Px8Mpnc50U8Ho/D4bZv3/706dPx48cjCSacREVFfX1909LShgwZ8nWzKBERkeTkZCTBAOAOXV3dMWPGfPmKsrKyjo4OqjxCzsvLq7Kyst/rbhcXl68nfkAAQKGGUDtw4EBhYeHXG9wymczW1lbY94jXuLi4fPkfS1VVVUNDA2EeALggPj7+3bt3XzduJRKJRCLR3d29srJy7dq1SLIJPGlp6bt374qJiX35SAEUagDwk1paWtzd3b/VEAuLxTo4ONTX13M5FcBgMBs2bPhyoqWkpDRx4kSEeQDgKe3t7V9u2NQHi8Xy9PQsLCzkciqhNWLEiAULFvRWZjAYjGXLlqGNBAQbkUhcuHDhl8VAGzZsQJgHYDCYpqYmOzu7bw3LDAbD1tb26+U+AACPSE5O/rJGHIPBEIlEBQWFjIwMX19f2FgKiXnz5pWXl5ubm/d5vaenJz4+HkkkALhmxYoVvSMPe8kdbR6hdfv27StXrvTbb4lGoxUWFp46dYr7qQCnQaGG8Hr48KGvr++3brnRaLSEhIQ//viDy6nAP5g8efKkSZPYf4bvSyAk/Pz8+uyHJyIiQiAQ3Nzc3r17FxQUNHz4cFTZhMGECRPi4uK+fAUKNQD4SR4eHq2trd/6VcLj8a2trd7e3lxOBTAYzOTJk/X09NhfOiIiIra2tqgTAcBDdu7c2djY2O/jO1gslkAgMBiMs2fPcj+Y0Fq/fn3v+h2BQDAzM0ObBwi85cuX944A4uLiNjY2aPMIORaL5e7u3tTU9K0pJZ1OLy8vP378OJeDAQC+U0JCQk9PT+9fsVjskiVLysrKjI2NEaYCQ4YMuXPnzo0bN8TFxb8sl6mqqnr79i3CYABw2pe7n9BoNDs7O7R5hFNtbe3q1av7bHryJQaDsW/fvpKSEm6mAlwAhRpCqrGx0cHB4Vu/8+ySXhERkfj4eLgnx1NcXV3Z00QajWZvb486DgCcVVBQ8PTp094FQSKRiMfj7ezsXr16deXKFUVFRbTxhISZmdmxY8fY3xcsFgu+FAD4GXfv3o2IiOjzdCMWi2V/uSspKW3cuPHx48dhYWGIAgq7zZs3s0e5np6exYsXo44DAK949OjR5cuX+zzWg8Vi8Xg8FovV1dU9depUbW1tREQEqoRCaNGiRUOHDsVgMHg83tTUVFpaGnUiIOAsLCzYtYxEInHlypUSEhKoEwk1LBZ7/vz5oKAgc3NzAoHQO5n8EoPBOHToUH5+PpKEAIB/0NjYWFZWxv4zkUiUkJAIDQ29efOmnJwc2mCAzdXVtby8XFNTs/fJMQKBALufAME2adKkKVOmYLFYLBY7depUNTU11ImEkaKiYlpa2oEDB6ZMmYL5//2wvn6bra1tV1cX19MBDoJCDWHELr1va2v78n5b76+9srKym5tbTEzMp0+foqKivtVHESDh4ODAXiHV1NSE3QqBwOvt90gkErFY7NKlS//888+wsDBVVVXU0YSLt7e3nZ0dgUBgMpmwEx4A/1lzc/OXm56w73FiMJjx48fv2bOnvLy8pqbG39/fyMjoH8rnAUfZ2tqyb3ZKSUkZGRmhjgMAT6BQKCtXruxdp8bj8QQCAYfD6evrnz59uq6uLj8/38vLa+TIkWhzChsCgbBmzRr2VNnKygp1HCD4Bg0aNHv2bCwWS6PRVq9ejToOwKiqqq5bt+7evXutra0JCQlubm7y8vIYDEZUVLT3PVgs1t7evqOjA11MAEA/kpOT2Vd8OBxOV1f3xYsXLi4uqEOB/zFmzJicnJwdO3awL9uZTGZiYiLqUABwlqurK/tab8WKFaizCCk8Hj99+nQfH5/S0tLKyspLly6x97v8siSXTqdXVVXt27cPbVQwsLAsFgt1BsBtZ86c2bZtG+b/7xDQ6fTBgwdbWFgsWLDAzMwM9hHgcbq6uoWFhefOnfPy8kKdBQAOqqqqGjduHPtLytbW9vDhwxMnTkQdSnh1dXXNnDmTRCKtW7cuKCgIdRwA+JKDg8Mff/zBLtTAYrGzZs2ytbVdunQp3N3kKVu2bDl37pydnR3sAAgA29atW8+ePcseu3A4nImJiYODw9KlS9l3BAFClZWV48aNw2Aw9fX1cBUPuODSpUubN2+eNm0a9FvmTQwGIy8vLzExMS4urqKigl1nz2QyN2/efOHCBdTpAAB/s7W1vXnzJg6H279///79+/ts+At4Sl5enr29fW1trYiISEtLCzSUAgKstrZWRUUFg8FUVVWx/wB4QVtb2/379+/cuZOYmNja2ioqKtrd3Y3FYh8+fGhiYoI6HRgY/6VQo6GhITMzs6SkpKGhob29nROxAOe0tLSkp6czmUwcDjdkyJDhw4cPHz580KBB/b4Zh8PJysqqqqpqa2sbGRmJiYlxOe0/ENrz8M2bNyQSadGiReLi4qizcAmch8KJRCJVVFQoKipOmTLlW2PUDxETE5OTk1NTU5sxY4aGhsbPf+AAKikpefLkSXl5eUtLS3d3N+o4/aNQKGlpaSNHjtTR0UGdBSVpaWkFBQUNDY05c+YoKCigjvM3KpWanZ1dVFRUVVXV2toKm9Twmrq6utzcXBwON3z48FGjRo0YMYK901y/YLxCiEwmp6am6uvrKysro87CWTC/At+jubk5IyMDi8UqKCgoKSkpKir26a4P4xVaGRkZTCZz3rx5qINwFoxXPKKrqysxMVFbW3vs2LGos3CEIM3zOzs76+vr6+vrm5qaWCzWrFmzoJyLXwjSeQj6xWKxEhISREREZsyYMXjwYCQZYP72Q3p6eoqKit6/fw9j6cCC85AHpaenYzCYuXPnog7CPXx0HrJYrObm5g8fPtTV1bW3t0tISMyfP//rze8AEj85f/uBQg06nR4dHX05MDDvyRM8DjdBVVlx6GBpCR66Qgb/is5gFJVVSIiLKsjLycvJ4P9tWxMmk9XS3vG29sP7D42SEhJW1taenp5ob9H9/3l4Ke9JPh6Hm6CiqDhkkJS46L//S0HRTaMX/Vk1c5oQ7XvCZLJaOiiVdU3vGz5JSkhYWVt5enrxxnkYmPfkCR6Pmzh2jOKwIdKSwlI6wwU9NHpR6Z+Txo6WGzRg+21Tu2mf28jlr9+S2zuUlZRWrV69cePGYcOGDdTn/weNjY2BgYHBwcE1NTUyMjJTpqjLD5bnqaXnPj41N9dUV2tra6MOglJ7e3tdfd3Ll68YDIaBgcGGDRscHBwIBALCSAUFBQEBAXFxcZ2dnUojFceqjh4sKws7l/EUBpP54s9X8vJyCkOHfs/zUtTu7ubPLeV/viK3tysrK61axTPj1e+/17x/Ly0prjZ6hJyUuBhRMJ/9yi6t1JusIkIQzP91vZgsTEsntaq++X1jM0/Nry4FXs5/kofD4UeMmTBo6AgRCSmEkYQdi/Wh8qWYlIzMEAU8vv9vOnpPd1fb57q3Lygd7aOUlNesXsUj49WV34Pr39eISkrLKk8iSMphiYJ5wUj5VMfooUorCuZd814sFovR2Ur5WEVuqhOXkLS2tvLilXWJv9bHJqoqjxgmLwzrY1kFzw201IhIp76c006hfmhsflVZw2AyDWbM2LBxIy/M888HBMTdiqNQOoeOGKWgpCohI4f9kXk+g05va26ktLeOVJ30Q/8QoNJN6WhprK+rfM1kMvRnGGzayBPXm+fPB9yKi+uidMoMHSk5fAxBUhaDhdPpP+pu/9zZ8E5uzDTsNyZXXMCid9M7PrfUvOzubB85SnntGl6ZvwVfu1ZTWysjLaU2XlVeVkZU9JvPNnBf3cdGcnvn5PFjUAcRHN3dPc2t5BcVlTy3Tisc6x79qvrYjMVgRg8XotaJVBqjpaPrxbsP7Z1dyqNGrVqzhkfOw9+vBb+vrZGQkh45Vk180GCCyP9cz/Z0Udo+fcQTiINHKKHKCb7UQ+loa/rwoeo/zt++t1AjMzPT08Pj5cuXliYzHBeZzNHXkBATzJUO0K+6hk/3sp6GxD8o+fONs7OTn98JRUVF7sfIzMz09Pjl5ctXi2dpOZjpz9GeLC7GQzM2rnld83GCsjAW8NY1tSTnloTeyyl5XY36PPR4+erlEtPZTsvMTQx0JMQFf0mOy9hdfzjxySwW61nZy9spGaFxSd099AMHD3p4eHC/+JRGowUEBBw+fFhUVNTdzc3G2nr6dG32HqU8rrq6GtrfYTAYCoXyMD09LCw8PiFh0qRJ58+fnzNnDvdj1NfX79y5MyIiQnPalNUujovMTUcpjuB+DMAhLBbrWUnprTtJNyJjuntoBw4cQDleHfIRwWOd501fajRNc9xIvhiv/rOK903jRw1FnYJ76j+1Jee/CE0rfP6m1tnJye8EsvnVLx6er16+1JizSG+hwyTdOSJiUALLN1gsVs1L0rOH8fmJEUxaj89BlOPVwUOHmTii0myHkTMs5VSnYQR6vGLQurtbmySGjkIdhEu6mj98KEqtzYhsrix1cnI+ccIP5fXgy5eWc2c6LZ47R19TeNbHXr97P2G0gJ9vFGp3Zj4pMjH9bnrupEmTzgcEoJrne+/cGRkRMWayxmyrldqzLQYrwGZ5QqSHSinLf5RzN6owPXHipEkXApBdb3p774yMjBiiOlVlrrOiznwJeQSjroChUzsJYpKoU2AwGAyGxfpcWVKbl1iTGY1l9hxCOn87fOiQCJHgar3QytxES30ib15vdlC6pCTgCmWAsVis4vJXcSkZobfu9dBQr9P+te6hvcRwqsCve/SrmdyJwWDkZXhjjOIiFotFelN3J6c04uGzHgbrwEEfhOehz6HDOKKI/mJnrXnLlCdpCuF5yL96qF2vCjKf3osuyUz6ofnbvxdqdHR0rFu7Nio62sJYz3fb6nHKMCETanfS8/acu970mXzy1KkNGzZw7bi956H5TI3jm+zGjkJZ1AaQu/u4eN/lW02tHQjOw3Vro6KiF841OrHbc9xoqFjkY5Qu6qmgsLPXIlVUVKL/+IObzc1KSkocHByqq6u3b9u2a6c37HDJ1yoqKrZu256YlOTo6HjlyhUpKe49+X358uUdO3YMGyLvd3jfskXmXDsu4D5KV9eJcxfPXAxSUVGJjub6eGVvV/3unYeV8RbbueKi0FNRkCXmle0LvveprfPkqdNcnl+tXbsuOjpq2ixz6y3HhikJeG8AwdZD7Uq9ceZheICKikrMH9FcHq9s7RzeVb8bt3jTxGUeeFFYRhdk9U/vvQg/RGtvPn3qJJLrQYvZ+n7b145ThhvnguxNTd3OU1eTH+U7OjpcuXKVy/P87dt3SA8e6rj1N515llw7LuBBH2veRpzc/exRsoOj41WuX29u276DKCM/1cVnlP5Crh0XcB+ju+vF7fOv714arTI6Nobb8zcHe/vq6upfVztuX+cCD8IJM0oX9dSV8HPXotCs09rbVb9798vyWVtsTWDdQ5h1ddPOxmZcuP1YZfTo6D9iuHwe2tk7vKuuNnXxmL9yKzy4wtcaa9/Gnd3z/HGKg4Pj1av/Pn/7l0KN2traJZaWde9rgnw8FxgJ9Z70oBe1p+fk77F+v//xyy+/nD179ns6eP+k2traJZaL62qrA71Xzp8xldOHA3yB2kM7HX7vRFgSd89Dy7r3tVd995rPmcnpwwHuqK77sH73scLnf0ZGRVlacmMJ7O7du05OTnq6utd+vzp69GguHBFwwb3kZPdVqxUVFe/cuaOkxPESLgaDsWXLlgsXLuzZ5rlrq4eYqLA8xCnk3tW8X+e1veBZSWRkJPfGK0cH7fGjLnjaKCvIceGIADlqD/1MzMOT0Q+5Ob9abLmkurbO5cClKYbzOX04wB3NH2rCj/xS+6IoKop745WDo5OMqqbWhrMSQ6GWWigwaN2v4vxfxp3d/Msv57h6PVhz5dCWBbN0OX04wCNSHxesO3h25CilO3cTuTnPX7bOe+ma7URRuGcJMBgMhvQ49eqBjcqjRibe5er1ppr1FjXrX/ECun0Y6KOzsbbw8pa2t8XRXJy/OTk56kydfPnYLpWR0BwUYDAYTHXdhw17fLm9TuvooD1+ZICHNax7ALaahhaPgFvPKuoio6K5dh46OjopqU133n9BfoQyF44IuKAs537E4U0qSv8+f/unQo3y8nIzU1M5abGb5/arKEIDA/A/bj/IWXfg3Jw5Jrfj40VEOLj/SHl5uZnpPDkJkZhjm5WFaX8s8D3iHxVtOH59jslcrpyHpoNlpOKunICLBwFDo9O9fE7duJkYEBCwceNGjh4rMDDQw8Njlbv7xQsB3O+fBjjq3bt3lkuXNjd/TktLU1dX59yBenp6li9fnpmZEXzxrPWSRZw7EOBBNBrdw3tvSMQf3BqvfnEx0zu1cRmRIER7sgIMBpOQ/Xzj2Zg5Jia34xM4Pb+aZ2pGlJLbcOYPWIwQMAw6LfrEttw74Re4Ml794uEx2sRRY/VxHB7mV8Kl7kli0SWPuSYmd+Jvc+N6UFr85vmDKooKnDsQ4EHV9Q02noc+t3elPXjA6Xn+smXLMzIz1x8N0jNbxrkDAX7UVF9zxsO2u73l4QOOX28uXbb8YUaG3uYAJQNo6CJcmAzas993VaZHcWf+5uHhsdJm0bmD24gEAkePBfgLjU7/1ef0jVtJXFz30D25YSmse4Av0eiMHZcTwtMKAgIucGc8NFjiYu99Gk+A61mB0vyhJmirPa3jX+Zv3yzUaGxs1NfTVRwsHRdwQFoSurKDfhSWvbbceMDKxub69RAOHYJ9Ho4YJHbz+C/SktDtB/Sj6M+qJdvPWdvacvg81FMcKhd/9ZSMlNBt0iYkjl+6fsT/9/j4eM7Vyd69e3fZsmWHfA7u27uXQ4cAaJHJ5EWWlu/f1+Xn5w8bxqkKV3d397hbt1LiInW1NTl0CMDjjp0+f8j3NBfGq93OZjscTDl0CMDjil7XLtt31drG7npICIcO0djYqKunLyo3YtO5WDFJaQ4dBaCVfO1k4pVjnB6vli5bpma7Y5L1Fg4dAvC4z2+Kc4/a2dta3wi5zqFD/HU9KC8dd+GQDKyPCSVyJ8Xql4P1zeT8pwWcm+e7ubvH3ozbdeXO2CnTOXQIwNe6OtpP/WJNaf5Q8JSD15tubu7RN28Z74+VH6fFoUMAHld+82xZzIkEzl9v7vdcvWuTG4cOAfid76WQI+evcWPdw8l0u8M8Dh0C8LtT0Q+PRz7gwnm4aN0ei9U7OHQIgBa1sz3wV9vuln+av/VfqEGlUueamHx4X/0o9OQQuUEczimMmEwWDof9nnfSGQw8DofFftebB/yf/6sHuc+svQ4fOXJ0165dA/7hVCp1rsmcDzVV6Zd2DZGF1duB9yPnIROPw3LuRPpJDwvKbXcFHDnKufPQ5ENdbfbN34cMlh3wzxc2TCYTh8N955v/eQSjMxg0Gl1cbMCacHocOBl1JzU7J4cT+8+Vl5fPnDnTxtr62u9XB/zDwQ/5oZPwR33+/HnGTMNBgwY9evRIQmLg1/GPHz++f//+W2G/L1oAt88HwPefDHQ6HY/Hf3M4+sefYjAYFovV2kaWkx2wSfXm7XsiY29nZ2dzarwymLHEQO2Cl+2Af7gQYrJYuO+bQdEZTDqDISbCK89PPHz2yt7nOufmV3NM5lbW1ntfT5eSGzLgny/wWEwm9vuGLyaDjsX90wD1Q5/2H0T5bilKicnJ4dR4pW8wc5juIu0NZwf8w4UQi8XEYgfsTKBTOwli/de4sxh0DBaLxQ3YY4sNJRm5vit+O3qEg9eD76uzws/C+tjP+/5VCMy/XQ+yWKy29k5ZmX/Z8rm9s4tGow2WlfmxoF9paWufvWKr7BCFR1lZHJrn79u/f+u5aK3Z5gP+4eD7v+kYDDruH783GQw6FovFDdwI9kM62loOucwdMUQ2K4tT15v79u032nlDcbrZgH+4EBrYL1ZuKrrqXZd9K5dj87eZBgbLF8y+fGz3gH84GMB1V07/83/lefBU1J37HFynNZixxGBygKfNgH84+P5lEMy/rYSwWKy2TqqsFLJHuLdeuh376Hl2Ti6HzsMZBjOnmSx12X9hwD9cGAzgwghHV0U6yS2n3ef9H3dXHVbV8rUX3SXS3SGCAkoIkgKChIRISAtII1gYhN1ioKKICDYqCoqC0t0h0t3doMQ53x8bj3CAq1eOv3vv9z7nuQ/sWXud2d5h1sy731mLlYEufZn129JCjUOHDt0KuZ4ccV6Qm/0P9ez3kFNamZpXZmuoyUj/n3xdWtvcHvr0bVxK7vDYuNw6UXdLfeWNy/55f8goCLwRWdXQSkVBrrxRfPcObQVJMaQJhULL7fSYmZ2db8/FyvTymv+v3I5bhDyOPXDhbl5enpQUjiX/hw4dunnj2qcbBwU5mXHreYXI/VyfWlxls02RkW6lW/1/BHWt3aExyW8zSkbGJ2XF+F13qCtLiixnnJBTfjwspqqpg4qCTElS2EFfeZOE4GKzdRaHFdcJXdtnhbmSUlS5/+qTJX2uE+QK9bNb+YPMx60Xnw7eePaHxuGtkBupz+8I8XLh1vPfQk5ReXJOgf0OfcbVq/7Bbvw2ahtbbkW9iP2UNjI6Jicp4WG7U0Ve+i/s36dkBVy+XVnXSEVJoSIn7WRhpLDhR/KAjxm5R86HVNQ0zMzOcrIye9mbO1kYrvzV+/TMjK6dd9fAaFl5OW7rkkxPT4uLi7OysLyPf/evqniSlZWdlJy028GBiem/ncBZQEhYWUnpTujtv7Cpqam5EXLz9Zs3w8PDm+Tlvb291FRV/0Rnqqqq5DYpODs7nz59GreeCwsLN27cePGkv5sjjqfQv4vsvILk9Cx7K3Mmhv/k69Xa+oaQuxGx8QnDIyPyMhs89zioblZYzjg+MenYqfOV1TXUVFQqivLOdtaK8jK/2AoAg0PDBwNOPn7+avLrVypKSi11lavnTqymX+lMPj09o7PDsrOnr6zsD8xXa8UYyeBFoN0/m/kzt7IprbTOWkuG8b+p1q1r770bl/U2p2Jk/KusKLeLgaLSOoHljJOKagLuv6ts7pqZRXEw0rptV3LYJo/wGqkltQduv17yrnX87Ld8ds6/Irn7rMJavqu4Y5puvcnwuxP7h9ZX10Ju+oZ9ZOZeYmH559BQlludn7ZpuzX1qv9kZc/ulrrU53dKU99Ojo3wSciqmbsKb1BazvhzZsKbm8c7G6tJKaiEpTdvNnEQWL/pF72hUaiTlgqomQX7TXpWTtcrz/9un2dnpm94GM2OdH8uL8P5fLVGTHyUZLWc3+N/tuJJf3V+7+cMHnVLEhqGf7Abv42xzvr69+EdBe+nJ0bphTYI6DgxrlVc0hKNRn3ar46enZl/kZyBY9Ohh5hfhxrLPz86OVhXPDU+TELDwLpBa+2uY0RkczN5S/qLhg/hQ43lKNQsJRMXn5Y9r6YNTl5i1cXfLYs4lv/H9oPJDy4K8fxVXeE/jZySLyl5pXZGWoz0/8nq6bXN7befxMYlZw+PjcutW+O+a7uKzF8lh/uQnh9wPaKqoYWKglx5o4Sjqa6C1A9Ga2hkzO9y2NO3SZPfpqgoyDQ2bbhy2JV+KSnGwNDIBmMXGiryolehK3+K6sZWFSsfZxfXP7TOt9x3RtPiz+bW/ruoKcn9kpeqYmRDQ/+fjJudzXWJT0ILk99OjA4LrZfdusttjYzycsYl6R+eXzve3lBFRkElKqO0xXS3sNSCuJn59mnik9CmqjLUzAwjB4+GmdMW0914+PgVuSkRZ5Y+Bcsjun7PSRyMvfnoaKwJ3KXq5rLnT4zDDRs3rrcJEtTejVvPfxd91fnd5Rl86paktP/JwDraUV/7Prw9P356YnS18EahbU5MywfWD75qqIWBlZKRc7Pfw+7y9MIwvyXvWsUnIeuO/Uovzk2WaY38hj2XcPIIqNnp9JNmlF97Kz7jfv0mvnYt8yqqN2EX/1UVT3KK3l0fUgAAIABJREFUylNyCu126P1Xedem1ttRL2I/pQ+PjslLirvbmqrI/SXvmpodcDm0qq6RipJCWVbKycIQ4V2Tswt8ji8tgF4vJhx27uhf345bTM/M6Nn7/Cmedq0YIxk6OsD2X1XxJK+yOa20zkpLhpH2JzrUfyfq2vvuvs16l/NlZPyrjCiXi4GikgT/X9gnFdcE3o+vbO7+zoRstteRwyg8hsYm/cPfPUsu/jo1TUlGskVa6PweA3rqOSE4Co3e7B48g0LNd8jJSPcswBa3DzU9M2scEN4ziVdW/hnn41BsrTg+NZPr1Rf/44on/3VipGcRlSH0l8RI7HdiREh6s5KJA/88YuSvXaFRqFNLESMuf58Y6WqquWiv7r7M+m2J/XB9ff3ly5eOuVj+21QaAJBVVBEUEtXVN/BPd+R3MPltysTr+IPXierykrtNtOtbOow9gjKKPi9p/Ox9mpFH0PDouJe14dbNG+LT8o09jtc2tSOt7T19n2ubCAgIGOhoMB86GqpfvB23cDHT3SS5xt3NbbkyOr+H+vr6y5cuHbXT/7epNAAgq6z2RFhMV//wP92R38Hkt2lTv+uR7zLUN65x0Feub+82PXQts7RmSePoT3kmh64NjU147tTUkhOPzyrb4Xe9trULy+zh+6yG9h6si3iAR0RAgPWZnUVVNXWMjk/i/LmcjdTkxQXd3VxxPw4vXwrwdvxnVRoAkFFQEng5tLO3/5/txu9h8us3I6d9EdGxWxRlHc0N65pbDZ18M/JLlrN/Gpe43dF3aGRsr4OltorCu6RMQ0ffmsYWpDU5q0DXzru5vdPKeJuThdHkt2/eQRdPXgtbeT+JCAnvnj3S0tJy7dq1lXubj6tXrzY3N4fdvfOvUmkAQHpG+tFj/p2d2H/U/y3cj4ioq6v7a5vJyUk9g+33wsM1NTT2ODvX1tXp6umnpaX/if4ICwsfDwq8dOlSTc3SU+vvAY1Ge3t7y26Qct2N4y3HbyAjO8//1Pmuru5/uiO/g8mvXw3M7e4/fLpFVcnJ1qq2vtHAzDY9K3dJ4ycvXuub2QwND/u4OWtrqL398MnA3Kamrv5XWgFgampa19QqPOrJTmOD0ODzpkb6z2NiDS3tV/4URESEYdcv/an5qqnpuofxP85WZFc0noz80D0w+s924/fwdWraLCg8MjFfTVLIXkeuvqPPNDA863PDksapJbVGx+62dA9YqG9w0JH7+m1m/62Ysw8TkVY8PDxCAgKszywKVdXSPTLxdb6fRx8LGjr6cPsgznoK8mK8f2J9denyZV3no/9jlQYA1BVnv7l1YqTvPzl9TX+bvLl3Z9abSFFZNSUj+96W+hBv09rizCWN8z9Eh3jvmBwd3rLLY62CVnnG+xBv0+7m2l/0NtjT0V5bgU9AQEm3GvMhp/6d18MEhES7/G82/5n5qqm5ab3z5X9WpQEAfVW5FU/PTg7+J8fV7NTXrLNWTcmPmSRUeDWsxzobs87u6qvMWdJ4sr9zuPkLHj4BCTU95kNM+WNgDNaXpgUaDTaUcigYihjtJSKnbvwYmR5kgkajAKA59Vn+dbep8SF+7d18GjYzXydK7vlVvwzGyYPwb3VgFJF1cXX/E/tBf9dd/6xKAwAyiyqCbjzo6hv8Z7vxe5j8NmXiEfAgJkF9k5Tjjm31Le3G7v4ZhUuzYQDwLD7F0N1/eHTc28ZYW0kmPi3P2N2/pqkNaZ2anjFwPRrx6oOptkpIgJfJVuUXCWkmnoFLunIOuILDTbQQD8cxF8tLly7ifJ3v5eUtKLFRw9wZh25xguqirOfXjw/1/ic3jFPfJi+670h99UBcXk3d1KGruf6C246qwqXjZlb88wtuJuOjQzo2nuuVtIpT4y+4m3Q2/Yib6bGPbvrtHh8Z0rLYo75z99eJ8YjTvq/vXgAAwMMjICTC+qBmZ9vrqybHRnD+XKw8gkauRy/+gf2mh5c3o5C04FYHHLr9PfRW5pQ/OTM59F8NrGlnrBqSHjGvU+HXtBntbEg7bdn7JXtJ48n+zqHmL/j4BKTUqzEfYkrkeCoePiER1geNQo201UxPYG+RGpOfjHU14vAp8AmINrgEN7c0/yF+7Napg/8qlQYAZBaUBl650/Wf5V2NnfZHRMdtUZBxNNte19Rq5LT/L3jXZ3GJho77hkdGvR0stFU2xSdnGjntR3hXPDw8IkJCrM/sLKqyrml0bPynt+MWRISEd84cbvlD47Cp6Zq70T/Oe2Ahq6LxZFRC9wDuY8f/AF+nps2P349KyFeTFLTTlm3o6NsZeD/r87JTU2ppnfGxey3dgxbq0vY6sl+/Te+/9frso49I69TMrEnAvciEfBPldVc9jI2V1r1KLzM/HoG5vaNvuKKpkwAfbzU1BeZDR4X7xBtEhAQhXsYtzX9kHDY1N1scvf4/VmkAQF1xduz/C2Jks5F9T0t9iLdp3TLESMGH6JveOyaWIUZ+6gohRvBwQYwwcwvqOB1Zbv22REYNPV3d+urP2Y+vEBL8u+YpALgUHn30akT2k2BxId6fGv+thIr/Axy8ePda1OtX1wM0NkkBQE//kIypOzkpSUXcXSzLqemZNdscRsbGa97fp6GiAIDegWEBTRsRPs7sJ8EAkJpXpu10OOfp1bWCPIu/6Ke34xxl1Q0KFt7370dYWlriyqeerm59ZWlG6BFCgn9djrjLj977h77IuHtMnP/nZM2/bRweuvHsxvPEF2c9t8iIAUDP4Ii8XSAZKXH5Y2wl19T0jLjZoeHxyarn52goyQGgd3BU2GSfCDdrxt1jANDeO3jmfmxRVVN5fSsAWOsozs+osSR8gx+/SStMv3OUaRXuc8aW1bUqOZ3A8TjU022orsp9c/8fnw8vhEYeOR+S++aBhMiy53Hn448Wd/i72H8q+Gr4k9d3L2kqyQFAT9+AtO4uclLSquQXi42npqeFVYxGRsfq0t8gaWx7+wd5FfREBXhy3zwAgC0WLul5xV8+RfNysgHA6PgEn4Le7Cyqp/gjAS6mi6Ard25ERtfU1uKq5mtPT4+goKCHu3tQYABOHOIQZ8+dO3jIr7iwcN26n2dv+1cNKgBoa2sLDDqeX5BfWloGAA729n+RUWOvj+/lK1fevY3bqqUFAN3d3RLrJcnJyRvqape7ZSWYmZlZLyXFw8P75s0bXPmMioqysbHJ/fRWYu0aXPn8bZwPDvELOl2Q8v5XOvNvGzm+R4KCb96JffpAS10FALp7+6QUt5CRk9UWZWFZTk1NC0rKD4+MNpbn0dJQA0BPXx+32AZRYaGClPd/3Yp4CHvwyNn7wLmgo96ujsgVF59Dd+5H5Xx6K7VOfOXPEnD6wvU792tqanA5XwnwO+nI+Flq4sThSnAlOjkg/F36Ne+1vKw/Nf5beTX/B/C7ExsSk/Y80H6LtDAA9AyNKrheJiMhKr23RHZfnYM3M8sbiu8e5GGhB4CxyW8iVidmUajW58cJlvnb2X8r5k1meepVLyY6qo6+4TOPEotqWj83dgCAlaYMDjNqAEB5Q4eyZ/D9CFyur3R19Uqq6g9FpeMT/K/52YSIK6+u+x9+mMEuuPanxn809eVvIPqy36dHN9yCo9fIbwGAkYGek2abiEnJjr8uw7KcmZ46qi8xOT58Oq6SjIoGAEYHeg/piLDwCh9+mPEr3qrz06646B5+lMkugJukjLG3T6Y/u11Xi8v5ik9AkGOLvajpfpw4XAmqX1///PCE2rmPtNw//+f6t2VBL4vwr317e9OhR8zrVQHg23Bvoq8qIQmZ1vW8xca9nzPSgozVz3+i4Vp6AVBww6M185XKqXjMP0X6cZOe8nQZ7zvscrof96nOTn1TPfMeSbDxdbAr3nUDMSWdTij2GP49DDV9Tj6oGRFxH7e8REN1RfbTa//4fvDivedHg+/lPLvxK2wY/MuIiAMXQq9Fvoq5cVxDQRoAevoHN5q4kpOSfHkXvth4anpGVNtmZGyiNiHyO6M1xL9llwgfZ86zGwAQ/uK9a1DwaR8HTysj5Bb349fCot9lPLoquWbBZvnOs7f7z98mJyNloqfFSUYNAJiZnZUzdecVWvMmNhYnDgEgKirK2sbmxOM0LmEcrA9xi9h7l59cOXbqWeav9O3fFjejzh+Kj7y+P+SFhIIGAAz39xwyliMmJbsSjy0Smpme8tq6dnJs+FpiNTkVDQCMDPS6qQux84ucejZH1h8ylpv+9vX44zQySioAGOzt9NISo6Shu5G09JmBiNO+eR9fn3yaQbsa99krZ2dnjuzYJC7EFxuLy/2mtbXNlnMJdL8Qy/40KmOulUad0Lzw6Vc6828LrMX3j1XH3VY6/IhlvRoAfB3qfe+rQkBMphuSv9i4uzwjOdBI60ISLfcvbfMLw/zacuI0z38kpWUEgIn+jopnF/rrS4aaKgCAT80CVxk1EJQ/OducEFaP0/WboICAyy6jY57/vB4ICxdDo45cuJn7+r74f5F3PX31WvjTmLsXNTfLAkBP38AGPWtyUpLKpOjFxlPT0yKqJiOjY3VpMTTfeVc+RQMRAZ7c1/eX9L836NKrDyk5MeFMDPS/cfsKERR8NyTyBY55WgF+R+2NfpYaOHGIQ1yJTgm8H5921fNXaBD4lzEhh+/GhcSkPw+wU5cWAoCeoTFFt8tkJMQlYQeWtN928Hbm54aiO/sxTIio9alZFKrlWSABPn7Ehzyvay+O2+u4bd+M2O+98TI8Pjfpsvt6AXYASCut1z8cmn7NS4yH5X/wdKeiEkLf5dXU1uFwHPILCCrucNrmdBgnDv8WEiKuxFz39/vPEiNJj264zqMyTpltIlqGGDmmLzE5PnxqHjHipyPCwivs9zDjV1xV56cFu+j64YgYQc3OnLFUlBDmi1u0fsP+962oqIiNizvhYf3ndqHDo+Nep25KG7vyqO8y8zn1IaMA0+QadG3vmVudvQO2h84Lb7UT093tHBA8Pjl3Vszt+PW70fEA4BwQ7Hs2FAB8z4buCbza3t3nffomp4oFYlbd2LrdLYBTxYJBzljRYm/MpwW0u9WBs+fDnuWUVlodOMupYiFl5HIpPBqFQgPA8ZCHW+wONLYt0InvPnpZz+UYVpGR30Pkm09iAtyISgMAGOlp1eUkm9q788ursSyrGlo6evo1FaSRTSkAMKyiUZNbX1bdMDI2DgB1LR14eHj8XGxLftFPb8c5xIV4zXRUz57BWc49ZBwG7d7+51Qaw2MTey8/lLHx59/uY3E0JCGnHNPkfv6Bb/Cjzr4hu+N3RE0PiJv7uZy9P/H1G9LqceFB2JsUAHA9e3/f1ccAsO/qY9dzEe29gz5XHvLoeyNm1c2dRgeCefS9mbVclZxOvk4rmv/tNoG3L0S9y/1cbxN4m0ffe6ON/+VH75FxePLea033s00dvfPtnU7fM9h3ZWZ2QSqn38PD95lifOyISgMAGOmo1Tauae7sK6jEljdWNXd29A1pyq5FVBoAwEBHpSq9pqyudWR8EgDGJr7WtXVTU5JJCnP/yld/zPt893XyncMOf0KlAQDi/Bw7NeRwPA5j407u24PD+XBoZMzD/7yktgWnnI6p68H3KT8mqD2HT3sFXuzs6bP2PiagZCCiaux48OT45CQAuBw5c+fRKwBwOnhi7/G5Tdfe45ecDp1s7+rxDLjAtnErcrGqvknfYS/bxq2rxFU2Gdq9+pCM8W/peeTszYiconJLzyNsG7eu32p+ITQS9T0/WNCVO6pmzo2tC/Lu2O8P2mbrtfIJMPLl27XC/IhKAwAYV6/aoijT1NaRX1qx2Liyrqmju1dLWR5TbJiBnk5dQaa0snZ4dAwA2jp72JgZEZUGAFBRkEuLi07PzHyd+rbCfiLwddpFQkx48+ZNnHgDgJCQEBISkoMH/tRbhKGhIRdXNzFxcWZWNkMj43fx8Zim3Y5Obu4eHR0d5haWXDy8fAKCdvYO4+NzgcDRyfnW7VAAsHOw9/D0AgAPTy97h91tbW2ubu4MTHPZjCorK7V1tjEwMVNQUW+QkXnx8uX8bzfdaXbq9OmsrGzTnWYMTMxr1oqfPXcOGVfH/AMUlZQaGhacILe2sdXU2jozsyCx5+9hdHS0praGhoZmw4a/SueI4H5EhLj4WkSlAQBMTEyaGhqNjY25uUu8hFg5CAkJz545ExsbW1GxxCD/PZw5c9pihyEOVRpDwyNu+/wkNqmxC683sdodn5iEaXLy2u+x/0hHV/cuRzc+CVlBqU0O7j7jExNIq7P3gdv3IwHAwcPX6+AxAPA6eGy3h29bR6f7/sMsgnOin6qaOl1TKxZBCRp2QVk1nZex7+Z/u7m9y5nL17LzCsztXVgEJcTlVc8HhyAjJ+D0BWUdw8amBacx7Fy8tY0tcDJyHjx+tnaNCKLSAAAmhtUaqspNza15hcVYlpXVNe2dXVu3qCI6DABgXL16i4pSaXnF8MjoX7ciVx4+f8m4evX8JCgHvd3Cb15ZeekTBPu9XEmIiXA7XxET4Hmb4Kwq0PD4pE/IS1mXC4IWQZYnIhLyq+a3elyN3nfzVdfAiMO5R2I2J9fZn3G98mzi6xQAeF6LvvcuGwBcrzw7cCsGAA7cinELftbRN+wb8orPLADxUN3aY+IfxmcWwGrop+IV/CazfL5/2zNRF59+yq1ssj0TxWcWILvnwpXoZBQaDQCnoj5o7Q9p6lpwXsr50hPDo3dwstx69DF/DQ8LotIAAEZaKlUpwebugYLqJY4ZtfcOs66mQbgJAKAkI5ES5JiZmf02tfSA/1hYfTcuK9TXjImOCgBGJ7/VtfdSU5BKCv6R095reVlN1aTO4i63dkVFRVxcrL5b4G+rNCZHhx+f3RtkKntAU+D2PsvPmQnzW6NOejw55zvc23nviP1h3TVHDSQeBLl+m5wAgIcnPdJehAHAgyCXZxf2A8CzC/sjj7sN9nQ8Oeuzb8vcu8+uxurrnsb7tvB6KrKcsVIuTlqwhb57yOZ9+MWGsty7h2z2beEN2iGTEHEFjUIBQOztkxd3a/W1N823jwhwvuq+HSuj9e8hO+4hm8AahEEAAOpVjKJyan0dzY2fC7Asuxqrh3o7xOQ1EDICAKhWMYjIqrbVlGOO9v61t57Wejw8PCZOvpV3G4Gm9V58ImLczlcofCIhA3dcOZweHy6+ezDRRylu99rsC3ZdxZ/mtxbd9ikJO/R1sCsveE+8i9R7d5nCm14z3yYAoOi2b0NCBAAU3vQuCT8MACXhhwtvek/2d5aEHYyzF0U8jLbXZp42j7MXfW3Jk3RIsz03br7/3MuOVa+C+6vzcy87xtmLJu7dXP36OpKF4svTc6nH9Me7m+fbF9zwyDi5E42LcdWc8oSGSxRRaQAACQ0D8zqV8Z6WgdqixcajnQ2Ah0fJsuzA6K8poOUWmy9Y4VLeCQCDdcXTEyMjLVXM61UxZVBI6ZgZxBSnxgZRs9MrfxAAoOUW49xsfPL0GZx4Aww/5mmLw/3g8Oi458nrUobO3KrmO72Pf0j/8d7OJTDY+3RIZ2+/zcGzQlrWa3Rsnf0vI4SYa1Dw3edvAcDp2CWfM3N/Rz5nbu7xv9ze3ed16gaHkilysaqh1cD1KIeS6WpZAwVzj5iPGRj/u/adPnf3SU7Jl137TnMomUptd7p47znCQgDA8RuR6ja+2ITYkQt6zodxsB98nSgmyIOoNACAkZ5ui7xkU3vXYjYMfjBaG+YxWrRqcpIYRuvx208Mq2j3mOljbtnvYHr3pO/qhTxDZX3zwQuhJ7zsmHGaxJ6QgOCEp21sXBwO1/mnTp9R2Lbzz6k0JkaHw094H9i+0UWF77K3eUn6h/mtdwPc7p/yGeztvHHAzkNT1Ft7beixPUjcvBvo/unZXQAIPeby4Mw+AHhwZl+ov8tAd/v9k3udN3MjHtobqs+5GDlv5raVYTpqppT3cUHJtmv7rF/fvVBTknttn7XzZu792zfE3ruMxM3oGyeCrDV62prm29867HTG2WAWF/Nb2usoTkExRKUBADT0jOLy6r3tzXXl2HGzvb5qsKdjnaIm+fe4Sb2KQVxetbmqbGJsBAAmxkZa675IKGggKg0AoGNgWbNRaWx4cHZmiRmsNDMx8ekdl9N3/4RKAwAICAhNvYLi4nC53zx56jS3kgkOVRpT48MFdw7Ee2+OcRDLOG/bUfQR05R/c2/h3UOTA13ZV5zfOEvGuW7MvTEXVQEg/5ZPXUIEAOTd8Cq6dxgAiu4dzgvxnujvKLxz8JXtXO3mkbba1JPmr2xFos25Ew5otuYsCKxZlxy/vAzuq87PuuT4ylbknZdiZcw1JLCWPzn76Yje2MLAmnPNPeW4KU4Ca2PyU1ouUUSlAQCktAzMEirjPS39SwbWrgbAw6Ni/aUVV2dxUt37cFmPEESlAQAzk+MjnQ1E5NSr+HFfdQIARLd7oPFxvH4jJiL0dcSZnnIxhkfGPAMuSOlYcsnrmrr6vU/9kcvE5fAZ78BLnT191nsDBJUNRdVMnA6dmouzR87eeRwDAI6HTmEKf/gcv+zsd7q9q8cr8CK7jDZysaq+ycDBh11Gm15CbZOhfcyHFIx/S6+j5249yCkqt/Q6yi6jLaltcTE06gfvGnxXzWxPY2vH/N467D+ha+eNC9713VohfkSlAQjvqiDT1NaZX/plsXFVXVNHd6+mshzNAt51Y9l33hULCWk5tx+9Cr/oz8RA/xu3rxy+jpbERDjmaYkJ8LxNVHDlcDGGxyd9Ql7JuVwSsjy+62RkYsEPJsTz2ot9t2K6BkYczj9ea3t6vcNZtyvPERrE69qL8PgcAHALfn7g9twm9MDtN+7B0R19w743Y/jNg5CLNa09JgH3+M2DWI2OqHpfm8+E2J19eOlZUl5ls93Zh/zmQbIul65Ep6C+n9U/FZWwdf/Npq4F9Qr2XHpmdDRs5UzIo48Fa7hZEJUGADDSUqpKCi1HgwBAe9/QXzAhz5KLGGgpHXV/lKjYu0P11l7T1TRz68P6jj48PDx+tv9RRWZvExViAjzcjkN8ImIN672/7WFydPjJ2b3HTWUPagqELiRGHp70ePqdFTmiu+aYgURkkOvU5ASmNf1FGABEziNGoo67DfV0PDnrs38eMXLD03j/Fl4vRZazi4iRsO/ESNghm/1beI/PI0biliFGruGIGMlZRGWIyKn1dzQ3/X1i5KeucEuM4BMQ6rkFvl1q/Yb9FvzevXt8nGwYMQHO0d7dJ7fT41FckoKk2C499eaObmPPoOsP5xbxZdWN79PzN1vubevuM9bazM60OvL1x91H56KjABcbsr8S4GLj5WQBgM+1jdklXwzdA0OfveNgYQCArOIvihZ7qxtb7Y21Duw2JSDAt/A9febOE0wHknNLH8R83O4WODU9Y2ekRU5KevRqhNvxawAgxMOeVfzlVeKPfWxLZ8+juCQ6asqVb8v7h0aGRsZUZRcsmwS42ACg6Au2+LqzdwAApMUWpAJGfv1S3wIA9a0dHMwM4xOT8Wn5ETGJOaWVs/OqMf309j8BJ1PtzxVf8vJw88br3r17vBzMGDEBztHeO7jJIejRh+xNEoKWWze1dPXv8LsWEj23Zyira32fXabifLK9Z8BYdSM7A11UfKbjqXtIKz8HEzM9LfIDHxsjAFTUt+WU15kcvHonJoWdaRUAZJfXKTmdrG7utNNT2rdLhwAfb9exm2cf/NgzpBRWRb7LMDoQPDU9a6u7mYyE2D/0hcfFBwAgyMmcXV73KqUQY9za3f/4QzYdFfnKZSv9w2NDoxPKUiLzL/KzMwFAUVUTlnFX3xAASAkvyNoiJcINAJWNHQAgxMUSH7wvPnjfvaM/r145MDLmcva+ocoGJUnhlTzCX8PRQBm345CPmwMjL1g52rt6ZPSsHr6KV9iwztpoW3Nbp6HTvmv3nyKtZZW18cmZmwztWrt6duhsYWdhfPAizt43CAAEuDmYGekBQICHk49rrihVeVVddmGZwW6f2w9fcLIyAUBWQemm7XZVdU27zQwOutji4+ObufmdujE3dJOyCiKiY/Ud9k5NTzvs1CcnIz1yPsTlyByPKcjHlVVQ+iL+x5valvauh6/i6WioVjgB9g8ODQ6PqspvmH9RgJsTAArLKxfbd3b3AoC0uOj8i9ISogBQWdsIAPoaSu1dPRiNS01jS2puoZKsJAUZbpKbkZORWhnqhN+7hxNvaDQ6PDzc1saGnJwcJw6x0NbWtl5K+kFk5GbFzbY2Nk3NTbp6+leC55InlZSWvH33bqOsXGtb605TUw4O9vD7962sbZBWQUEBFhZmABAUEOTn5wOAsvKyzKwsHV3dkJs3OTk5ACAjI3ODjGxlVZWTo+ORw34EBATGJjuOnziB6cCnpKR74fe1t22bmppy3L2bnJzs4CE/RydnABAWFsrIyHwe/UO/39zc/CAyko6OjhAXKS5FRERSk5NTk5MfRUX9tWVfX9/g4KC6mvr8i4KCAgBQUIi9hsMVtmpp8fHxhYcvcUzwN5Cbm1tR8QWHRU/aOjqllTWjnrxQlJextjBtam0zMLe9emsuxVdpecW7xE/y6tta2zt2GOpxsLFGPHpmu8cLaRXk42VhYgIAQT4efl5uACj/UpmVW6C30/pW2AMONjYAyMzJl1XTqayp3W1t4efrSUBAYGrjdPLCj7ReSWkZ4Q+f6ppaTU1NOVhZkJOR+QWddvY+AABCAvyZOfnRr38EzebWtsin0XS0NCsfOX39A4NDw2pKCvMvCvDzAkBhCbbyuqOrGwA2SC5YvCG/fqmq/utW5Ne6+kYtdRViYqLGppbY94lFpeWszEyWO4y4OHBTXpCcjMzafEd4OO7mq7AwCzUpMhLcJF3s6BtWdL/8+FPhJjFeiy3SLT2DOwPv3Xz9o+RQeUP7h/xKFa+rbX1DRkrr2BhoHibmO118AgD8bAzMdNQAwM+2mod1NQB8burM/dKOzKbxAAAgAElEQVRkEhB2920WOwMtAORUNKp4BVe3dttulfXdqU6Aj2916sG5xz8o4NSS2qjEfBP/sKmZWZutsmQkRAHh7zyvRgOAADtjTkVjTMaP/+mtPYNPPhXSUpLhYLk1Mj40Nqm8bsFJLH42BgAorm1bbL9NXqyjbxijYqlt600vq1cU5ycnJV5sPDA64XblmeHmdZu/l3oV4mB8d3bPu7N77u4zX2HPl8NuHfnPX3C5vmLm5MXshP8uBns6Tloo5L59LCApL6dn0d/ZHLLXNOlxCMagrbrsc8aHM9aqg93t0hrGdEzs2bFREf6OAMDIxU+zmhkAmDj5Gdh5AaCttqK+NOeGl3Fq9N1VzOwAUFeSfcZauauxWtHQdqv9PnwCgtADu97dPYfxX5WfmvUm8rqn8czMtMJ2G2JSslfX/R+e8gQAZi7BupLsoo8xGOOBztact48pqGhXnjtkbKh/YmRIZOMCPpGRkx8AWiqxdWZDvZ0AwL1mwaYe+bWzofJXvPW2NtAxs3+bGC9Pf5/5+kFDWS4KtSIGmZiUTGabRdg93IRFNBp9JyycQ2knAQluln+T/Z0f96u3pD5bLSLHrbJzorc168yuurc/zt8PNX3uLPqYdGjrZH8H+yYDcnrWpuQnBdfdAYCSlZeMjgkAKFl4KZl5AGCk+Ut/dV7mGYv6D/fJVrMBQF9VbtJBzZG2Wp4tVsJG3nj4BDkXHSpf/Djz2vM5vSnpceZpC9TMNI/6LgISss8PTxTd9gUAKjb+vqrctpwfZ/cnetuaU58RUdDgrXhcfRsdmBofZly7ef5FShZeABhsKF1sP97VRL6abebreGdhYlPSo/7qfPS8gYGanWaSUObTspt/y2R/BwAQU9LiERAqBcUI6bthmqYnRoabvzBJKOOweA2vpl3Vlwpc7gc52TDygpWjvbtPdofro9hPClJiVvpbmju6jTz8r0fNTRpl1fXv0/IUzT3bunpNtJTYmRkexCQ4HL4AAAJc7MwMc2wYH+fcCUuEENvudiz0aRwHCyMAZBVXKJp7VDe0OphoH9htRoCPb+5z8nToI8Q+Oa/kQUyCgeuxqZlpe2NtMjKSo8H33ILmlmeCPOxZxRUvE36E6ZbOnoexn2hXTIjNsWEy6+df5OdiB4CiiiUS/3b29AOA9NqFjNZaIQD4UtcMAHXNHZoK0sREhI1tXW9Tcoq/1LIw0ptvU+Nk+XHA8eu3KasDZzZJirmY6wOuoaEgzcvJhsN1fuWXCg0zJ5x4W4yB7nY/E/n02EfCUpuUDCz72lsuuO94H3UDY9BcXV6S9v6omXJ/V5uclhE9M3tqTNTNw7sBgIWLn5aBGQCYufmZOHgBoKXmc01xznlX48Snd+hZOACgujj7qNnmjoYqNRM7A8f9+Pj4wXstX90+i/H/OTcl9dWD8y6GM9NTqsa2JKTkT64cuxvkDgCsPILVxdm5Ca8wxn0dLemxjyioaQlWPL+NDvWPjwyJyS6IdMzc/ADQWIH9vnywtxMA+MQWxE1eMWkAaKurBAACAsJj4R907bwxrRNjIy01n9fKqy5OVz42NBB6zEVO02jNxmXLpa8cEgoaLJy8OByHVZVf+Lfa/dz01zDR3/Fhn1pTyjMGUTkeFbPxntb007uq4+YC62BTRUdRYsJBrYm+Di6F7eT0bI3Jj3OuzgUIKlY+MlomAKBi5aVk5gaAoaYvvVV5aacsaj+EkzOwA0BvVW7CAY2R9ho+Das1xnvx8PEzL9hXRP8IrN3laQ1Jj1JPmqNmpvi27CIkIS+NOpF/ywcAqNkEeqtyW7N/vHAa721rSn1GTImrwDrEJL4gsFKz8gHAQP0SdSjGOhspVrPNfB3vKExs+PSob2FgXeh5MC/Ei3OTPtPaH5tZanYBtaAYtaAYea9bK+z5kiAgIeNU3nknDGfrt/B796yMtMnJSHHicDHau3pk9G0Q3tXKSKe5vdPIaf/177xraWVtfEqWgpFDW2e3iY46OwvTgxdv7fcFAYAAz3felZuDF8O7VtdnF5Zt373v9sOXHKzMAJBVWKZgaF9V3+yw0+Cgiw0BAb6Z++HTN+b+fZKzCiOi4wx2+05Nzdib6pORkh65cNP16Nx8KMTLmVVY9hKLd42Jp6VeOe86PDQ8qiK/YLkiwMMBAIWfl+BdO3r6AGADFu8q/oN3nY+BoWGnQ6eMtdWUZaV+43acgJyM1MpIG5c8bViYhZokrniPxejoG97sEfwkqUhejMd8i3RLz8DOwPs3X8+9cyxv6EjIr1L1vt7eO2S0WYJtNe3DjwXOl54CAB8bAxNCg7Ay8H6XL1Q0duZWNu0ICA97mz3HhHxpUvG+VtPaY7NVZp+pGgE+vvXpqPNP5lTmqSV1UYkFJgH3pqZnrbVkyEmIAu/He12by2ktyM6Y86UJmwlJwgET8p0G4Z9/EVFRlNS2L3nLNjmxjr5hjIrlOxPChzAh9R196lJCxIQETV0D8blfSuramVdRm6pKcjDOVZ1o7OxjZ6Adm5z6kFcZmZCfV9k8/w0pzkFGQmShJhkehl0b4feARqPD7oXLbLMgJv3N/exQT8cpC4WcecTIzXnECMKKnLVWHexul/rOitz3n8v1y/SdGGH8Toy0fydG0qLv0jGzA0D9d2JEwdB2q/0+PAKCO4uIkew3kTfmESMx34kRJi7B+kXESO7bx+S4I0aEF1IZTJz8ANC8DDHCtQwx8iuu5hMjWbggRtbIb2Hi4Fm8fsP+d4mLfaOvJof3xxLmHL0a0dzRk/Lgwoa1QgBwZI+FgZv/0eD7FttU6WioAKC5o2evrXGQuxUeHh4KhVa09E7OnSMIPK22z87O5pZV+dgaY5I91ja1q8tLRp47IMjNjkaj950PJSYm+nT/PAvDKgDwtjHUd/U/e+epkYaiwPf8Ew1tnWd8HNwt9QHgmIuljvORB68/7t6hraMsQ0lO+upj1l7buXTBMR+zAGCnNra8rn9oJPTZO1gGBmryInycWBdrmtoBAEvIL8DNBgC9A0NYxrzsLACQklfmsWs75mJVQwsAVNa3yEqINLR2joxPiGjbY9I8rBfhDzu5Fymb+tPbl+v5SiApKsDFxhwbG7tx48aVe4uLfaOvuO7PjUP/0BctXf1JN/2kRXgAwM9Wz+hA8LHbL8w05OioKQCgpavf21wrYLchMg6VnE+kFM6tbDxMNWdn0XkV9d7mWzGlT2pbu9Q2rLnv7yTIyYxGo/dfe0xCTJh4/SDLaloA8DLTMtx/5XzkWyOVDfwcc2r6xo7e0647XE22AMAROwM9n0uR7zId9JV1FNZRkJHEpBZ6m8+d/H6dWgQApltksZ6if3jsTkwyLAN9JSkRbuwcWbWtXQDATL/goIkAJzMA9A1hFzjkYWMAgNTiKnfTH3nAqps7AaCyqUNG7O/pyHyuPBoemwhyNPpbd/1drBfi5mJhxN04jN2uoYzDcXjkfEhze2d69N0NEmsA4King5793sPnblhs37qKhhoAmts7fR13Hffdg4eHh0KhNhnaJWcXAIC3g8UsCpVb/NnXyWp+6ZOaxpYtijJRwSeEeLnQaLTPicskxEQpz0JZGFcDgI+jpa6d95kb90201QV4OAGgoaX9nJ+nh+1OAPD3ctxq7RERHedobigpJqyrrkhJTvbqfbKv4y7EOZKNw0xfC+sp+geHbj18Cctgu6ayqMCCZLw1DS0AwMxAP/+iIC8nAPT2L1FiGdkRpeQUetn/eOFUVdcIAF9qG2Ql17rsMknOyt/u6CsruZaUhDg1p4iFcXXgXlxW8zXQVLkQGllWViYuvtJTTWVlZS0tLcZGf2rkHzzk19TUlJOVJSOzEQACA/y3auscOHjIateuVatWAUBTU9OB/ftPnzqJDKoNMjKfkua2hb4+PrOzs9nZOQcPHMCUPqmurtbU0Hj6+LGwsDAajfb09iIhIclMT2NlZQWA/fv2aW3VPnHylOmOHYKCc+RpfX39pYsXvL28AOB4UKD6Fo174eF7nJ319fQoKSmjX7w4sH8umwiSjcPS0gLrKfr6+kKWlyQbGRqtWSO6XOuvoLq6BgAQVQoGQkJCANDT07v0PSsGHh6ekaFhzOvXFy5cWLm3uLg4Hi5OSYmf56P7RRwOOt3c0paZ8Gaj1HoA8D/gs23HrkOBpyxNjVfR0QJAc0vbPk+Xk0cPIiNHVm1bUtrcrnKvm9Ps7GxOfuF+T1dMho+aunoNVaXHYSFCAvxoNNr7kD8JCXFafAwrMxMA+Lrv0TGxPHUxeMd2XQG+uSmiobH5woljnnt2A0Cgn6/mdrP7D58621npaWtQUlC8ePN2n6cLYvkqNh4AzE0MsZ6ir3/g1r0Hyz2joa62qLAg1sWaunoAQIQmGAjy8wJAT28fljEfDxcAJKdnYgqXAEBldQ0AfKmqUZSX+YtWuY3SY+Pjnd09jIyrDcxt336YUw8ICfCHXb8oIy25XLf/Lgx1tc8Hh+Bsvmpr01fA2XwVcP9dS/fgx0vu0kKcAHDIUtP42F3/e293qkrRUc1p11q6B72MVfxttuLh4aHQaBXP4NTSWgBwN1SaRaHyqpq9TVQxOT9r23rVJIXCD1oKsjOi0egDt1+TEBEmXHBjXkUNAJ5GykbH7l548tFwswSiigCAxs7+U7t1XQw2A8DhXZr6frejEvPtdeR0ZNdQkJG8zijzMp5b5yNnUHaoLKFW7x8Zv/sWuzIOBvry4sJcC0ZUbVsvACC9wkCAjQEA+oaWOGPkpLsptaTWNPCejAgXCTFRemkdMz31UWvs+IvAN+Tl0NhkgK32cv35E1gvwM7JvBpX66vXsXHiKnq/vb6Kuebf39myP/wTj5g0AGxz9LvuafTqmr+MjhnF90Kh/Z0tGtZeBq4BeHh4aBTqjLVyVX4qAGyx9EDPohrK8zRt9mIyfHY314rKqTmcus/MLYhGo59fPEBIRLIvLIGGgQUANKw8r3kYxt87L6VhiOzYAaC3rdHY+5SauSsA6DkfueKql/UmcrORvbiSDgk5RdGnGA3rOWVbUdJrANi41XTxg4wN9adGL8vySKrqsfAu2LUhVVSpFx7MZeISAIDRAexYxsDOAwDVBWnqlj8STnQ1VAFAR0MVr7jMT731tDV8HR89rCc29XUSaeUUWWcbGMrMI7Rcn38KSVX9hIgruJqvOtpaVF11V+gHg8+PTkz0tqqcfLdKQBIARHfszzhlVv7wOKfSju914mGit1VI303M/DDg4aHRqKSDmj3l6QAgqOuCRqH6awqEDNwxmSRGO+qZJFRkvEKp2PgBjS4NP4JPRKxyIpaUjhkABPVdM06aVb24zCGvj8lOMd7dJG4dKKDjBACiOw+kB5k0JT/m1bBh2aBFSErRnhOLkTgg2Tg4FZcocvRtdKDhw7JvU9hktlFzLPg/ONZRBwCkdAvy91Kx8gPAt2HssAgAY12NMxOj8a7Ss9/mBgYdr/gG9xtUbAIAgE9AtM7u1IL+DPfVf7iHT0DELLWFkIScXmhuDql7Gzre19ZV9BGNmhUy8Fiuw78BOj4JaiZOHPISBuryONwPHg2+19zRnRp1ZY4Qc9ll4HLkyJUwC12174RYt4+dSZCHLUJEKJh7JOeWAICXtdHsLCq3tNLX3nR+6ZOapjZ1eanIc4eEeDjQaLTv2VvExERJDy6yMNADwF5bY/09R86GPjbWVEIIsYbWzrO+ju67tgPAMVcrHcdDETEJu3forBcV2KYiR0lO9upjuo+dCeIcycZhtg07z1b/0Mjtp3GwDLarbxLh45p/paapDQAQoQkGgtzssBQbBgA8HAijVYqpbAIAVfXNAFBZ3yImyNPVN8BIT2fkHhCflou0CvFw3A7au1H8x5kQv8thnb0DsbdO/glaCQ8Pz0BNPvYNztb5TOzcPKLrf276W3hyxb+3oyXwYTL/WmkAMHI5fG6P4ePLxxR0zSlp5uJmb0eLrp23qWcgEjePmClV5KQAgI6NJwqFqi3N07Pbi0n40dlUKy6v7n4+gpVHEI1GPzizn4iYxD/yIx0DCwDo2nqddd4eE3pOVsuIhWsubna3NlruO711lxsAmLgdPeWom/oqUn2Hg5TKNlJyirzEGIwAAsnGoaCzc/GDjA71Jz65s9xjbtyiz863IG52NtYCAFZCC1ZuAQAYWRQ3ERlKRV6atvWPGam9oQoA2usrBdfJkJCRC66fI+XeR93o7WgtSXuPQs3q2fsu7kz4qb0TI8M7vQKX6y1OgIeHJ6Wm9/pNLK7GIQ0z1yren1dE/UWURp0Y72ndcjqeXkASANaa7k89ubM0KohHeS6wjve0ihi4S1jMRdWEA5rd5XNCMWE9FzRqtq+mQGS7B92PwFrHsk5Ffm8oNZsAoNFF947gExGrn4gjW8UMAMIGrqkndlZEX+KU18dkpxjralpvEyS0zQkA1podTA40bkh6zK9pw7ZRi5CUojU7TuR7Xq62nDgA4N5sgvUU30YH6t4vG1XZZbfRcGCvi0bb6wAAUXBigHTp6zKBdXpi7M0eKUxgXcUrIetxg5odu/pG4d0DU+PDEpZHl+vPHwKH7LaEmGs422+2thpq/cE0Bkcu3Gxu70x7fmeDhCgAHPWw17f3OXz+psX2rXTfeVcfR8vjPs5zvKuRQ0p2IQB42ZvPzqJyiz/vc9olvoh3jQwO+s67XiEmJk5+egvhXffuttCz23smJMJYR12AmwMQ3vWQh7utKQD4e+3WtvGMiH7raGa4Xkxom/pmSnKyl++Tfb4nFIlJSAEAM33sqqP9g8O3l+ddDTSVRQUWHKqsaWwGABbGhbwrz/K8KycbAKRkF3ramWEuViK8a12jrOQC3skz4OLw6NgJ3z2/dzuusF1T+WJoFA55D71N239u+rsIuB/f0j2YeNF1jgmx0DDxDwsIf7dTVRJhQlq6B72MlY9ZayE0iKrXtdTSOgBwN9w8i0LlVzV7mSjPL31S29arJikYftBCgJ0BjUYfvP2GhIjww3kXhHPwMFIy9g+78OTTdkUJRBjR2Nl/0mGbi4EiABzepWFw+E5UYoGdttw6fjZtWVEKUuI3meVexsqI8zdZnwFghwr2OqR/ZDzsbTYsA71Na4U5F0x0dW29AMC0kAbhZ2cAgN7hpVOtOOrKp5bWmQbe3yjCRUpEmF5Wz0xPfcRKCwDGv051D4wy0lLuDLr/IW/urZwAO0OI1w5p4bkXrw0d/aMTX8XtTk9+m0tttY6f7bbPTkEO3JQmWQzdTWuvRKfgahy2tbZYqBn8tgcMMcItJg0AOo5+NzyNYuYRIwgroj+PFanOT0XuVbf0QC1DjNh/J0aeXTxARETi+50Y2WLled3D8P2989IahowLiRFVc1cA0HU+Euyql/0mcrORvYSSDgk5RfE8YqR4GWJkbKg/bXlWZP0iVgS+EyM0f4cYqVlIjHQ2VCH/RaPRP3XV29bwdXz0yEJixGYFxAgeHp64iv7i9dsCoUZ/f39Nbd1mrz+VfmpwePRZfKrUGgFkUwoAxESEtoaaqXllr5OybbZrAAAZCfFhZzNkK4WPjycnIVpSWd/e3cfGtGwSm2Mulsger6SqvqSyfrv6JpbvO0AiQsJdeuqpeWVJOSUYoQYNFYWbhR7yMz4+3n57k7T8sk/ZxetF+HVV5B6/TW7u6OFiZQSAV4kZ9LTU6vLY81Tf4PCJmw+X6w8/F+tioUZDawcA0H1PCYUA0fsPj2KXI+HjZJUUFUjJK73/KsFYQxGFRj1+m/IyMQMAEF1YfWvn2PhkgNsuXVW5vsHhh7FJEa8SdnidyHoSTEFG+tPb/xA2S4vlZC87ff86kHF4ZrfOyl0ticGR8ecf8ySFuRGVBgAQExFab9ucWlT1Jr3IWkcRAMhIiA7Z6GHGoawYf2lNS3vvIBsD3XJuj9obCHIyA0BpbUtpTYuBkhSi0gAAIkICCy351KKqpIIvGKEGDSW5i/Hc8W58fDwfS+204qpP+RXrBLl0FSWfJGS3dPVzMtMDwKuUAnoaSrUN2Onu+4ZGT4UvW42Sn4N5sVCjoa0HAOi+ZxBFwMlEDwBDYxNYxnxsTOuFuFMLKyPephupbECh0U8Scl6lFMDfH0iVTR0vkwt8LbWRjCN/FIrrBHKyl32b8uvo7++vqa3dfNBl5a4QDAyPPIlNkBYXQVQaAEBMRGS3Qy8lu+D1hxTbHXoAQEZKcsTD4fvAw5eTEi+uqG7v6mFjXnaF4e/lJMTLBQDFFdXFFdWGWqrIbgEAiAgJrQy1U7ILPmXmIUINWmpKd5u5iIiPj39gj3VqTuHHjDxJMWEKMjI9DeVHMfHN7Z1cbCwA8CI+iZ6OdouiDNY39g4MHQ9elh8R4ObAEmrUN7cBwCraBfIgTlYWABgaWWKhxs/FLrVWJDkrP/zZG2MddRQK9fj1eyTVBzLwaKgpOdlYSitrC8sqiQgJUSgUIQHB2Dj2AF4JpNYKU1NRZmdnr3zhlZ2dTU1NLSWFs5ey8zEwMPDo8eMNG6QRlQYAEBMT797tkJSc/PLVKwd7ewAgIyML8D+GGVSb5DcVFRW3tbWxsy97oP94UJCwsDAAFBUVFxUVGxsZISoNACAiIrKxsU5KTk78+BEj1KClpfXy9ER+xsfH9/M7lJySkpCYICUlud3AIDIqqqmpiZubGwCeR0evXr1aUwO7BmRvb69/wLI0lqCg4AqFGnX1dQCAKFcw4OLkAoChoSXYYVxBRUX53PnzAwMDWF/9G8jOztq8CVuu99sYGBx6HB0jvV4CUWkAADExkb2VeXJ6ZkxcvN0uMwAgIyU9dmAvZuTIy0gXl5W3dXSysy5b+jHQb5+QAD8AFJd9Li4rN9LTQVQaAEBERGhlbpKcnvkxJR0j1KClofZwnitMi4+Pf3CvW0pGVmJyqqTEWgMdrahnL5pb2rg42QEg+nXcavpVGqrKWN/Y1z8QeObicv0R4ONdLNSoa2wCAESMggGS32JoeATLmJ+XR2qdeFJaxr3IxybbdVEo9KPnL6NfvwWA2dnZv24FgPqGJgC4djuMj4c7+OxxuY1SWbkFhwJOGVrYF2cmMq7GTXpGqXXi1FRUuJqvqCjI1vEvXVnv72JwdOJ5SrGkIAfCTQAAMSGBtZZMWmldbFa5leZccCElJjpooTE30vDwZEW5S+vbO/qGWVcvXSXt8C5NQXZGACitby+tb9dXEMfoIYgICczVpdNK65KLazFCDRoKsj36isjP+Hh4PqZq6WX1SUU16/jZdeXEniQVtnQPcjLRAUBMRhk9NYWaJPaYAYC+4fHTUQmLryPgZ2PAEmo0dvYBAB3lgkRKyLGP4fHJxR5oKMg4GOnKGzqKaloJCQlQaDQhAf7Y5BL1vKqau1+ll/nsUEVO0vwvobiWB1frq/ramq3uv1lIZXxkMP/Dcy5RSUSlAQCERMQKBjbV+WklSW82GVgjF4lIyLY5HkLGFR4+Pp+EbEtV6WBPBx3j0oV+9ZyPMHMLAkBrVWlLVamkmj5CRgAAASGR3DaL6vy0qtxkjFCDjIpG1WxuiYiHj7/V1remIP1LbhKnyLp1yrq57570d7bQs3ACQNGnGEpaelE5tcVfOjbYF3f71OLrCJg4+bEoid7WBgDAiFEQIKeZJ8aGsW5n5ODjEllflZ+aGRMhpWGERqHy4p8WfooBANTs7K94621t+Do+pu9ydJ2K7thgX3bco6zXkTd9zPweZpCQ/WaSME6R9eSUOJuvSCio6HhxUyZgamyoJeMlHd86RKUBAPiERDxqlr2fM9pz3/KozQlMCYhJRXbsA2Rc4eHTC28caiyf7O8ko186Mq7ZeYCKjR8ABhvLhxrL2WS3ISoNAMAnIOJWNu39nNFdloYRahBR0Ahozyn/8PDwhQ09eysye0pT6HjFWTdqt6Q9n+htJWfgAIC27FgSqlVM65SXeJaR/i/Pzi/3pJQsfNhCja4mACCmXDASkMPK0+PY4wqQ90lfx8V2HmLduPXbSH9z6rOmpEdZ56zVzn0kJMEeGJ2FiYW3vL+N9EvYHKfhXDCePz85jbyRouYQIiDG8bHaVaLymdk5K/czx495W6/cFYLB4dGn71Kk1gguIMSMtqbklb7+lGljqAVzhJjlD0JsnWhJZd1fE2L+blbIiZ2SyrqSyrrtWxRYvkvkiQgJLfW3pOSVJmUXIYQYDRWFm+UcI4yPj7fPwTQ1v/RjdtF6UQEKMlI9VflHcZ+aO7q5WJkA4GVCOj0ttbo8toSxd2D4REjkcv0R4GLDEmrUt3QAAKJEwYCTlREAhhaxYQDAz8kmuUYgJbfk/sv3RppKaDTqcVzSy8R0AJhFzTa0dgLAjYcxvByslw7ukV0nml3y5cjlMBPPwIIXNxlW0QJAfFrurcdvnlw+ituiJ/OhtEH8UvhznKzzM7OyhaUVfm73WxgbHsx694xXTApRaQAAIRGxirFNRV5q/qc3KoZzw5uYhMxojx8mbgqtl22qLBnobl/FtPSy0MTtCCuPIAA0VZY0VZZs3GJANy9ubta3qMhL/ZydhBFqkFPRaFm6Ij/j4ePrO/h+yUsrz0riEV0vraaXEfu4t6OFgZUTAHITXlHR0otvWiJujgz0vQg5udyTsnDzYwk1ulvrAYCSZsH/oNUsHAAwPoo9vzFz8vGukazITUl+GSGnaYRCozLjniCpPlCLShI8vRo09XUCANj5RIhJsWewtvrK3A8v9R18kaj6RyG6cXNc+BWcjMOMrGx6EXmc9AoApsaGmjNeruJfRz8vsPKp7+ouz2jNfcunZgEABMSkYqY/oiqD8MbBhrKJ/g5y+qVXawCwdudBajYBABhoLBtsKOOQ00VUGgCAT0DEo7yzuzyjqywVI9QgpqAR0vkRWNcYevV8zuwqSVnFK8Euo92U+ny8p5WCkQMAWrLfkFCtYl4UWL8N95c/PQfLgIqVbwmhRlcjAGBUnggoGDgAYHoce34L4oMAACAASURBVL+J2E9/HRM3O8Quo/1tpL8x5WnDp0fpZ600L3yaH1iHW6tbst6IGnqSr8bNZu3XsYpPgoQCZ+s3airK9Wt+X/X71xgcHnkamyi1VgRRaQDCu5rqpeQUxiSk2proAsK7utv/IDok15b8nHfdjfCuJRU1JRXV27VU5vOuuwy1U3IKP2XmIUINGmpKN5sdSCs+Pv5+Z6vUnKKPmXnrxYQoyEj1NJQexbxfyLvSLMW7Dh6/uuybSwEeDiyhRn1zOwDQ0Sx4Qc7JxgwAw8vwrpJiwsnZBeHPY4211VAo1OM3H16+TwaA2YXFL77UNr6IT9rvbMXByvQbt+MQkmK45GlxyHssxuDoRHRqiaQA+wImRFMmrbQ+NuuzleZGQGgQ8y0YGkTmZzQIAPjt0hRgZwCA0vqO0vp2/U1rFzAhatJppfUpxbWIUIOGgmyP/tzSAh8Pb+8OlfSy+uTimnX8bOSkxNvkxZ4mFWExIaqLmJD+4fHTDxOX6w8/GwOWUKOhsx8A6KgW5If4CxoE5pgQ2vKGjuJFTEhDRx8A3HqTycNCf85Zf6MIV+6XJv/wePMTEZnXvRloKZFvHJv8dsRKa5vcmv7h8cefCiMT8s2PR6Rd9VwyO+nKsZ6fjYqCDFfjkJySilP4N6tWYYgR7nnEyKaFxAgRCZnOQlaktap0qKeDdhlWBAB05xEjrVWl6xcSI7LbLKrz0ypzkxnnESMq84gRLVvfmoL0yr9DjPw1K8K4iBWB71QG+UIqYxULBwBM/hoxUvSdGPkVVxhiREJFd2ywLyfuUdbryFs+Zn4PM4h/lxgRklZMfIC9flsg1KisrAQA0YV7KhyiprkdjUaPTXy1OvAjCd7I2CQAIHstAGBYRUtK/OMPiZaaAgCQamFLYjUdjdSaOZ1jXUsHAChILaiXsU6YDwDqmn8k2OHnZJ2vqUdEFUgHzHRUHr9NjvmY6Wm1va2rL/9zjeMObaJFebYFudn7cl4s1yVioiXytxATEwHA4MLoODH5FQBoF6o3AAAfH+9WgIex53HXoGv7zoWiUGgUGmVr+H/snXVAU90bx58lOTZgg9HdjZSAknYXqChid3fHa742dmMrkiaKYGEHYtOdUkrHdn9/3LFiG6BDf6/u85eee+7dudvhPOd8z3Oep9eJsBgzfW0AOLphnhSBYG6og76Oi42ZgrzsntMRV+Ofjurn1ebtnYS5gU78JaGBRtoP2g/N9DrLXqbmFiEIUlPXELT+CLvwe009AGTms1ylqBQFaSIn/hXq2VAjSDFvqU+yN9VF/52WVwIA7rY85s3GWAcA0vKK2SUGmio8/VBXHQAyC74CwMgezpfuPI168GqOf6+8kvJXnzMnD/Yk4PnDoBlr04tvHwQhEAkCwqax+iGvGlJT3wAA7LOtbLBYzMGlQX7L983efmbJvksIE2EiSFD/7ievPmjtAiKaPRdjiHjcrBE/GOO6Q5jradyLetR2vbZA+6GFsX6bNdtJakYOgiDVNXVj5q5iF6JJdjNyWAMUTVlRWoozACoqkACgulbwbAYAqEoUB2uWuUrLygWAbk48jmW2FiYAkJqZi/7XUFeLu9ehM/uMHFYY9tGDel2IuhVxK37+pIC8wuKXSR+nBgxrPQCa6OtUvL8vrElEAn/gOLTXlVfyWMraujpopdahYLHYI1tWDp26aPrKLQs37mYymUwmMsFv4PFLUWiDfUZN+5CcHrx+8Yh+PaSliLcfPp2xYsvgyQsTb11Alzo/DwaDMTPU+/LlS9tV2+Lz58/m5madFB8oOTkFQZDq6hr/kRw39u/fvwNAenoG+l8VFRVpLvFIUVERAKqrhaaNpNFojo6sSV5qWioAeHjwBO20t7MHgJSUVHaJkZEh9wtamJuzGzBmTMDZc+fCwsMXLVyYm5v7/PmLGdOnE1p1ElNT09pq/qA+bIjEn51eS0lJAUB5OU8KxpraGmj5QjoJSwsLAPjy5Yur689qXp8/f+7t2XaSqXaSkpaOIEh1Tc3oiRxftO9VVQCQnsXK1KtCo0pLSbGvKlLIAFBTI0BMR6FRlR3sWAewUtMzAYDPs8Te2goAUtIy2CWG+no8I5KpCQCkZ2YDwGi/oedCw8Ov3lgwa2pufsGL14nTJgQSWs2vTIwMvuenghBaD0cAIEWUAoDyCh4HnZraWvY7coPFYo/v2zl49Pip85bMX76WyWQyEebEwNHHQs6Zm5qIvgoA5ZWVANDQ0BgacgR1YbGztiou+bpl177QiKuzpognsDAGgzEzMRbXeGWqQxfXeJWa/xWdbo3fyklOVFVbDwCZhWXsEhpFXprI+WUp8rIAUF0veMZFJcvZG7Mk7/SCUgBws+Qx07YGmgCQns9xojfQoHK/ESoloA3w87K/FP86+vG72UM98r9WvkrOmdSva+vpFgAYa9KKIoUuHVvfgq4FKnhdYGsbGgGAIi8grGXvJQc/ZRXunDF0mIetNBEf++rLnOCwEWtPPD+0GJVO2OwJv0fE42YO6d76IZ2NuQ79/o2XP/8cdH6lbvCD7nfF2akIgjTU1RxfHsQurK+pAoCv+ZxAuyQlKoFr61eWRAGAhlrBtk9ekapjztpFKMlNBwAje54NM21TGwAozuFkq1TRMuDuV2r6pgBQmpcJAM59/J/fvPQmLqrHmDkVxflZH151Hz6pdVB0AFDVNQ5OKBL2pjgCv+3DE6UAoOY7z6m4hrpa9gtyg8Fix645cHCB/7lNc0J3LkWYCIIw3QePexRxSl3ftD1PG7f2EJ5IRH8pFS0DfWtnGXly7Nm9b+9dde4r4KBze8BgMOr6puIar8iaxiCm8aqqIA0QpLm+5vluToSkprpqAKgpzmKXSJGpOALHMhLlKADQXC/YMkopKCsasHS36sIMAKCZ80wGKHrWAFBdkM4ukafrcb+RgqYJAFQXZwGAdrdhOQ+v5D27ZjxgRl1ZQXnaG4OeQQLThZDUDQefy2pdjoJt1RWxeCIANFbz9ITm+loAIMgLcAhzmBmMIxAVtEwBQF5NX9nEkSBLSrl6sOD5DW2ug8g1xVlJIWsKX9+Rp+s5zTnIl1oFAAafzawuzCj98uLjxc33VvTtc+i1NEVsR98UtEw/xR5tu15bsPQxQ92ffxRKSlYegiA1dXVjF3Oc1b7X1AKfIMazHpQH0etBRXIXC5bygApi3brwnGS1MzMEgNQWQcxQW4Nn9mWoAwCZLZ8+sr/3hetxkbEJ88YNyyv6+vJ98hT//gLWg3qaZS+ihTWptSAmhaoQ33hm+6jKp9hKDQMALBZzZP2CYbPXzli/d9G2w0wEYTKZ44f2ORF208xAp/xbFQA0NDZd2LkS9VCxNTMsKavYduzSlZgHM0YPKiotn7J6V9DQ3gO9xbbr3Bq0Y4hrnu8TIMAvQSwUZqUiCNJQW71vMcflqK66CgBKcjnTcgVlGkGKYzflFCgAUF8reHxTUKTqt6QIKcpJBwAzXkcTXXNbACjM5thNug7PghH1qCjOywAA937+CdcuvrgT2S9obllRXvr7V77+kwXaTXU941MvS4S9KV6I3az+xrMGRC0d+oLcYLDYKRsO7pjtd3zdrDNblyAIE2EyvYcFxV05qWnIv0Nw6kVxUU568punocHr1oz2Cr7zmTtux/WTe/AEYp/A2dD5aBmag5j64adPnzR6iW2G+b3FsD7ZxW1Yq6DFQRAApHmtKkGODMKtKgBIKSgrGbINayYAqJjz5ClG3TerCjgdW16N17BqcQyrbvfhWQ+u5D67ZjpwRm1pQVnqG6Ne41sbVgUNwxEXsoU1qbVVBQD0pRqredabzQ21AECUF7AF6zwrGIeXImubAgBJTZ9q4kiQVfgSfSDv2Q1dD45h/Ry1H4snmA6Y3voJnQ4GQ9EyEdt601Cvk/QxAEjJyEEQpKa2bsw8TtyRqupaEK67Usht665drFp012yhumsaW3fV4dNd9YFLdx01sNeFqJjImHvzJo7OKyx5mfRp6uihAnXX8nfxIITWQkeLneXxBKqprWe/IB9YLPbI1hXDpi6ZsXLroo17WuuubHYdO08k4OdOGPljt4sRDAZjKj6d1lRbbLpHa9LyvyIIUl3fOGEb55j399oGAMgsYikhNIq8FI8MIgMANfWNwp5JJcvZG7GO26EeDG5WPEqIjaEGAKQVsJQQA3Vlnl0nbTpw6TB+XnaX499EP34/e2j3/NLK18m5E/u5tJY1jDRphREbQQit60uhMkgVz19Tbb1QGQQA+iw99CmraOeMIUO720gT8bGvkufuC/dbd/LZwYXocxqams+sGIt6qNgYaJRUVu+8HB/xMGnqQDcAODjfT4qAM9OhA4CBOtXJTEdBTjo4/MG1Jx/8vTvlwCQGgzHVpourH6rpm/5wP2QLIye4hJG6mioAKG0RRgSqIvVCVBFotzBS0pYw8jUvEwCc+vg/v3kpMS7KV6QwoqprvLcjqgi0TPBqOyiMnN805wqvMKKmb4omRhH9qEAhwkjiTwgj6NP45m/8ETUAgKoo1HXrJymvrAIAKSKBO8W4EoXk39fTvCUEBbexZIMGIRGIFNduelnFdwDQUedx5mpobAIAHFeOJT6fejkZafbnejrbqCorRt59PDdwSOTdxwiC+Pf1bP2hGAxGRlA7RUBXVgSAzDyeboeuMAV+4RZGui+v7A+PTfiSkUOnKnm72D589R4A0C8KXWxz09vdYc/piI9p2e25vZOgKZFLy8rartcWrH5IETCTEAvl32sAQIqAx3NZFCWynF8PZzM9lv+BwCxlovohlxiB/qzadJ4jL42NzQCAw3L1Q978I3LSRABAvUM8upipKpGj7r+e498r+sFrBEH8fPm9a4HVDzuWTQ0NP5VVwBNtr+J7DQAoC5q6WehrPD+1LuLeqy/ZBXRlspeDeUJiCrS4lbSTvOLyK3efD/LogqaV6WyoFPnSUvH1QyWxbeKWVX4DACkigXsKrkwhjxrYiz2RleHaFmUjcgDkDERlFd8AQEeTx1OBbwCk03i6payMDACw92K9ujqq0pQjYu7NnxQQEXMPQZBRA/nj7wHa8aQFtFMYaNKTzNwC7sLyb98BgKok+ECwpYnB6xvnw2/GfU7LpNOUfdydHj5/AwBmRvpf0rM+JKd3d7afMpqVBGFwT8+nr9/tPXkx6vZ97rB7PwlVkVImpgFNhdZZIdfKyssAQEqKyO36oKysHDB6NDsEhYyMgNmwqE7F1QlLS0sBAA2GwaahoQEAcFwZNNXoPL1OTk4OAFDvEB9vbzqdjjpqhIWHIwgSMHo0tAKDwQhsp7ig0+kAkJHBkywT9dug0cQT1UAgNBoNWr7Gn6SsrJxGVW67XjufVl4BAFJSUtyuD8pKiqNHDDE3YWn90q0OhwGA8I7DOxyVl0NLmAo2DY2teo4qz5+GnKwstIxI3t3d6Co01FEj4uoNBEFGjxAQlxKDwcgIaqcI6Ko0AMjI5pHbUL8Ngd+wpblpYkJsWNT1T8kpaqqqvp7dHjx+CgBorA7RVzXU6ADg7GCPemmg9O/dY8uufZ9T0lp/1g9DoyqJa7yiKvygP3hryr/XAgCRgOdewCspyPl52ZvqcPIQyRAFzrgEP5N776fsWw0AaPNG6mpobgYALNd0C83wyqZluoUHAA9bQ1VFUnTCu9lDPaIfv0MQZISX4MU8BoORFtROYagqkgAgq4jnR6moqgUAZTL/jlRybsmnrEJ3K4OJ/Vha8wBXq2efsg5EPrz25D23T0be18qw+4kD3axaO9f+ApTJcqVl5W3Xawu0r5IoPzig1VSWAwCeQORe4cuRlZx6+6lznbQgSgmyKUI6FoFr8V9dWQYA6JkPNk2NjQCAxXJ6MprPlY2UjBy06AUmTh4KyqqJcdE9xsx5ExeFIIhTHz+Bn4vBYAgC2ykEBWVVACjNz+IuRD0tSIoCbJmGocXqS89ex0YUZSYrUFXNnL1TXj8CADUDs/Y8TduM/3CPpVvP2LN7C9IFJLpuP7IUqrjGK4KC2Cx4Y1UFAOAIUtw7LlIkRe1uw7jjTwgJ/CC4X2G5+lVjVTkAoMEw2DCbGwEAw9WvpHnDpOOkZaFlv0fFqps0RSX/2XXjATPynl0DBNHqJiRNFQbToQAVqHtETXEOdyHqtyGlIODvtHUUE7qdb8rVg99yOXplzqOwxGNLAYOxGrPGsM8kzleBIAggGAxriJZX05dX08dgsa8OzClKjNP1Ets0XkpBuUKMuoT49DF0BUQkEAhcZyqUKaSR/bzMDFnHpQSus0ROwLgFsW8AoKMhShDjyz8iKyMNAFIt6paXs60qVTEy9tG8ccMiYxMQBGmdCBg6LoipUlE1rJC7sEK4GgaoohV+OPzOwy/pOXSako+L3cNX7wDA3EDna8U3AHCyNkW9NFD6erhsO3bpS0YuABy7fKOs8vv36pqpa3ahVwtKShEEpq7ZZaijsXiigFxUPwDacrHM88vLyxSUaD//HIFUo3aTKMVtN+UpSm79/DUMuO2mwJm/4J6H2juUqooyAKCp85z3a25sAF67yZd/BLWb6OaBhYsnhar6IjaqX9DcF7FRCIK49RP8G2EwGMH2XQgUZVUAKMnL4i6s/lYBAAqC7KaWkcW28OfPbkfkZ3yhUFWtunp/evUIANAvCkEQQBBMyySTrm1A1zbAYjGHV01LenTHYwgrgWxZYe6Tm6GOPQax08p0KqjJFks/rCwv1yeL27DipTBcqeiJ8oo63YaxQ1DgiAJna0Kfye3V0fC9DADkVHgmbMymBgDAcC0E+PKP4FmGVRoAVK26SVNUcp9eMx04I/fZNUAQne6CDGsHrSq0GNbqYp71pgjD2jrdjLq975foA9yGtbY0PzshQsulH1GQD+UvgEBSFps+JkQGFAuo7krk1V2VKAojB/Y0NxSD7lrKsrM8C4FGdKXQYmf58o+gdlaayNZdHVDddd7E0ZEx9xAEGTmQP+IsdFx3VaUK0F0rROuuxgavrp8Nvxn/OS1TTUXZ242lu3J7WuQWFF++dmdwL0++WB3tvF3s0BTJYtM9yJ24oEaVECkCHs8lfymRZEd42qEOE9CiSPAhoh/yKCHfawBAW4XHyjQ08Ww88eUfkZUmAADbNcTDxlBFkXT18fvZQ7tHJ7xHEMTPU0D+tY7KICrCZRCqIMfclNyST1lF7lb6E/qyTnkNcLV8/inrQNSja08/9HQwBQAHU23USwOlt5PZzsvxybksr83WYVF6OJgGhz/4nC104//nUVaQEVc/lKf8+PRPoDAiT1Zy6u3HDkHRIVUEeH1eBQojzY38C1iBwgiBSxh5ExftO2ZOYlwUgiCOgoSRjqoiIETKQJ0t5AVN8NQNLVZdevYmNqIwM5lMVTV19k5tEUaYTGabj2otjFj8tDAiT1GGVvM3nkEB3X2R6shfYIfQ06QDgKG2+slNC9mFDCazuqauQ+ZHGLoaqgDwOPFjn+6O7MLn774AgC6XEU3ntVvZBSUAYKSrAQA4LHZE7+4HLlzNKyqNjE3Q11Rz5kpyyaa4rGLr0UvCmhE4uEdrRwpDHQ0MBpOZzzNMvE/JBABHK/7IQo1Nzdn5xcqKCuMGcyIQ7DwZRqcqKZJJeUWlrz6mdLEw0qJz/phRFxAVJXKbtwtr9s9DJBAaGoS6/rUfVj8UFJhELOiqUQHAQFP1+MpJ7EIGk1ldW99R/xuB6KhRAeDJu9TeXTky1vOP6QCgp84ZLDLyeXImZReVAYCRFh0AcFjsMG/HQ+FxeSXlkfdf66nTnCwMWn9Qcfm3f88ITQ07tq+7rTF/dBxDTVUMBpNZwPPRH9LzAMDRnH8u1djUnF1UqkyWD+zH8Z7bff4WXZncIZeLU9cfNjOYgX07K5gnH0QCoaFRfP1QfOOhnpY6ABjqap3auY5dyGAwq2pqZWXEMQBqqQHA45dv+3q5sQufJ74HAD0t1sQFzULCJjuvEFrSFgIADof16+e7/3RoXmFx+K04fW0NZzueAEUoxV/LNh8QmqRz3PD+9pY8w6aRnjYGg8nMzecufP85FQDYWWC4aWxqysorpCqSg0Zw8o7vOHKWTqMqkRXiEl4AQHdeB3YfN6e9Jy9WfhMakuEHkJYi1NcLDebUfhobG6UErQPFgr6eHgAYGRqdO3uGXchgMKqqqmRlxbDq0NPTA4BHjxL69+Pkonr67CkA6OtzRoy09HTuu7KysgHAxMQYAHA43Eh//73Bwbm5uVfCwgwMDLp2FZC/o6io6J+NQsPYThg//idzxxgbGWEwmIyMDO7CpKR3AODsJMANTlygP71YOlJDQwNRfMORvq4OABjp650+HMwuZDAYVdU1suLwmNHV1gKAhGcv+vXyZRc+e/kGAPR1ObN8NAsJm+ycXAAwMTIAABwO5zd00L4jJ3LzC8Kib+jr6bg48ofdBoCikq+bduwR1ozxASPtbfjToxoZ6GMwmMwsnh2pdx8+AQA7EQybxsamrJwcZSWl8WM4XtL/7j2gpqqipEgRfRUAtDQ1AKCpqYn7mXX19QBAVhDnfExaSkps45WgeBI/hi5dCQAM1KlHF3H23hhMZnVdg3imW3QlAHj6MaO3E2eb4cXnbPZHo6BZSNjkFFcAgKGmCgDgsNih3W0PX03I/1oZ9eidnpqyk6ngsILFFVXbL94V1pIxPR1tDXnckgw1aBgMJquQx63hQ0YhALDDn7L5mFkIAO68B2K87IwPRD6srOY5jBJy61kzgzm2p5OwlnQqUgS8GOdX3Hs8HYKqqQsAKtoG4//hZGFjMhkNNdUEaTEMX1R1HQBIe/vUqltvdmHm+xcAQNXQZZd8zeUxKGUFOdCSyhSLxTn0HHbv0qGK4vw3d6Nomnr6VoJ/su9lxTePCw2m7TpwLJ8ioKptgMFg+ESE/JT3AMCOd8qmuamxrCBbnqLsNiiQXXg7ZBeZSkfTnYh+WkVxftbH1zrm9kp0Tt9Gj+YIdAppP3ii2MYrDF5s8WzlVHUAQF5Nz3H2AXYhwmQ011XjWqXz+JHnq2gDQOmXZ2pdOMvzspRX7I9GqSni8SitLckFAJK6IQBgsDhNt8FpN4/VlRXkP70mp6qrbMz/o6PUV5Z8Cd8lrCU6XqP5PC1I6vqAwdTw7id9y/4EAEqG/LMvNJiHooEdd9z1mpJsAJBu8ZspfB37cv9sZWMHp7mH+cKzJ0ft+3Bxs9vy83Q7TvAAKZISANSV8qwUfhIsQaqxUWgszPYj9vWgrqYaABjqaJzcvIRdKE5BTJMOAI/ffOzTnTO/fZ70GQD0WgSxjBweNSwnvxgA0FTCwBLEPA6cj84r+hoR+0hfS83Zhj+WAAAUl1ZsOXpBWDPGDe5pZ27EXWKEqmG8x5beJ2cAgKOVAMGtsak5K7+ISlEIGsI5NrDjZCiqaElLSwFAUzNPNoq6hgYAIJNkAYCqRLY20eeOqtvY2MREkKQv6VjxHZ9FO4Z4BrSGhtbRIMSFiqYuANC1DWZs4UTRZzIZ9TXVRHHYTZqGLgB8efPYzoNjN1OTXrA/GqWY125+LcgGADVdlt106T389vmDZUV5z+9EqmrpGdkItpuVpcVRR7YJvAQAHkPG6pnzTObpuoYYDIbPUSMn5T0AGFgJsJtf87NJFGXPoRy7efXELgqNjrpcXDux63LwusUHwmy7cbqlPIUKAGVFHJklPuwUg9HsOSQQfgnoXoiYDGuDwPgQP4Y8y7Dqd53LCQOMMBlNddWt82T9AKhh/fr5mTqXYS1NecX+aJSqQh7DWsMyrAYAgMHidNwHJ984VltakPvkqjxdlyrIsNZXlny8ItSq6vmMau1mQVITYFgrsj4CADsRDJva0oKytDfKhjyGFY35IcXlkJoWewZhNOt7BwhrSWeDwYtt/iZGWaM1qPhpqKt1asdadiGDwayuqZURi+6qqQYAj18lceuuz95+ABG6a34hABjpc3TXEf18D5wOzSssCY+JF6G7bjkYIqwZ44b1t7PkSR9jpKeFwWD4HDXefUkFAKc2dNf+7MIdR87SacrcPhknLkc3Mxjc2myHbhc7UlLE/0PdozVcSghHKRKnEqKKKiGZvbiUkJefc4BLCcko5PEkyCmpAACjlvywOCx2WHebw1cf55dWRiW811NTdhSkhJRUVP17KU5YM8b0cOTzkzDUoGIwmOwiXhkksxAAupgIyAX2IasQWoUG8bIzOhD1qLK6TkuFAgDNvPO9+sZmACDLSQMAGgvE3liLOy0s6iZCpQjwCxEX0gS8uPoh7icCSLOFkaBfKIxkdIIw0lFVBIRIGXltCSOuXMLInRZhpM1HdZIwghc0f+usvXCB6GupURXJsU/eNDU3s30bd5y4suHgudiT21ztfioDPQDYmBoQCfj4Z4kwN4hd+OjVexwW6+vKmQylZRek5RQYarNCApyNvgsA1i0JDkb289x/PvrAhegX75NXTBV8quNbVU1IpNBM1e5drFo7aqjRlNztLR6//pCRV6ivqQYATc3NobceqKsot65cV99gO2SaX+/up7YsRkvyi0uj4p4EDvYFgIrvVQGLtkwY1nvfqpnsW8LuPAIAVzuLNm+XoK+hQqWQ7r782NTMYJ/y3Hn+1sYTUbf3Le1qxf9zdBRrQ20iAX/v1SeYynHHTnibjMNifRw506+03KL0vBIDTdZ54nO3HgOAlSHLbo3s6XIw7O7BsLsvP2UsG8c/JUL5Vl13+kaCsGa42Zi0dtRQo1LcrI2evEvJLPiqp04DgKZmRujd5+pUSuvKdQ2NXcauHu7jdHI1K95+/teK6IdvxnbQ5SLu5UdFBTkPewESz1+FgY4mVYkS++g59wD475HT63cfjb942NWBf3XXUWzNTYgEQtzjF5uAMzI8fJ6Iw2HZ+Q5TM3PSsnINdVnd7Ez4dQCw4dLRRg3qvS/k8r6Qyy/eflw5e6LAD6qsqj4VelVYM7o52fE5aqipUN0deWnVmQAAIABJREFUbRNevs3IydfX1gCApubmS9fuqKvS+Gqi1NY1WPf09+/f4/TuDWhJflFJZMy9ccP7A4CZoR4ARMTcWzWH42gVdjMOACxMBPgz/dkYGhrSaLTbd+40NTWxg2ps2bp19Zq1jx48cHd3E317m9jZ2hKJxNi7sduAE5/5/v0HOByuV0+O139KSkpqaqqREasjnQoJAQBbG9ZEakxAwJ69e/fsDX727PnaNZz4k9xUVlYeP3FCWDM8PLr/pKOGurp69+7dHj56lJ6ebmBgAABNTU0XLl7U0ND4ySf/RzHQ06VRle/E329qamYH1di258Dazdvv34hwc3EUfXub2FpbEomEu/cfbeHII/Ag4SkOh+vp7cEuSU3PTMvINGxx+gm5EAoANpYsHSHAb2jw4ePBh48/f/Vm9ZL5Aj/o27dvJ89eFNaM7q4urR011Omq3VydHz15npGZra+nAwBNTc0Xw6I01OitK9fW1Vk4e44cNujs0f1oSV5BYcS1m0EBI9u8CgAy0tJe3dzuPXrM/ZrRN24DQFcnwRtsfxL66lQqWS7uTQr3dGtXaPyms7dj/p3hYvGzR21sDDSIeNy9xNT14zmFCe/TcVisTxeObpWW/zW9oNSgxVP2XOxLALDWZ60C/L3tD0U/Ohj96GVyztLRQhO0faupO3P7ubCrblb6fI4adCUFV0u9xx8yMgvL9NSUAaCpmXHlQaKaMrn1mQ9TbRUAiHr8blkAZ1yNfJQEAOa6PKcT4hNTFEmyHrZG8BdD09SXV6R+ehrHaG5inx25fWrX1cMbFx6LMbTtKvr2NtEyscYTiJ+fxw+ZvZ5dmPL6ERaL406nWpyTVpKbrqLFmng8uXYOALSMWWOIcx//+IsH4y8ezPzwst/kZcI+q7bq2+PoM8KuGnVx55MkyDQ1Qzu31DdPvuZl0jT1AIDR3PTi9hUKTb11atum+rp1wx0cew2fsJFlXitKChLjr7oOHNOep+WnfTy6dKz7kKCAFXvZz3wVGwEAhnadmErgdyFP15NSUC5+e5/JaGKHPU+ODP54eZvHhmiq6c/6dFL0LLF4Qsm7h8C1w/L142MMFqdq48kuqSrMqC7MkFdj6RJZ9y8BAFmXtX7U7jY87cbR1BtHy1Jfmw/nnHvho6nme2bceWFXqeaufI4a0op0mplL6eenNcVZcqq6AMBkNOUmRMgoqbUOntFYXfFs5yQ937H2U7azC/OeRAOAshnLE/fjxc0EWQWXBcf5AoQAgIK2GQAUv3vA7aiRGXcOAMi6AnYR/jwMtNSpiuS7j19zrwe3H7+84cCZuyE7UDHnZ0AFsbinbzbO46RXe/jqHQ6L9XVjObymZuen5eQbarOM0ZnoOwBgbcKRyEf1895/Lmr/uagX776smCZ4U7CyqjokIkZYM7o5WPE5aqjRlN27WD5+/T4jt1Bfi6WGXb51X11F2c5cgPxSV99gO2iyXx/PkK1L0ZL84tKou48DB/cEABkpoqeTzf0XSdwvci3+KQC42JgDwPRRA6ePGsj9QNeRs+sbGp6FHoC/D1VtfQVF6rsnd7nt5tXjO6/s/2fN6Tsmdj9rN3XNrPEE4oen94Brtv7p5SMsFmftxlEgi7LSinLS6dosu/kg6hwA6JqyBhn3/v4x5w7EnDuY9u7l0GnLhX1WbdW3exGnhV01dXDnc9RQpKmZdnH78vpxcW6mqhbL0j2+Gaqoos5XEwAa6+sWDbR37TNi5raTaEl5cf6Lu9GeLaEytIwsAOD903vcjhr3wk8BgLYJZxHx7mm8PFnR0tlTWDv/ElDDWvT2Hrdh/RQR/P7SVp+NV2k/bVgV9ayweEJR0gObMRyFoeTjEwwWR7flxAGqKsyoKswgtRjWzHsXAUBRj2VYdboPT75+NPnGkdLU15YjFgn8oMaab+lx5wReAgCaRdfWjhoySnQVs64ln55WF2XJ03UBgMloyn4UIaOk1rpyY3XF4x0TDXqMdZy6g12Y8zgaAGjmnCMuRUn3ifIUVetuwloiAcVAR4OqRLnLq7tuP3Jm/Z5jcRcPuXbhn9h0FFtzYyKBEPf45abFnEKW7urO2oBMzczl1V1vAoCNGccsjh7Yaz9bd50lOB1qZVX1qdBrwprRzdGOz1FDoO56+VqsuirNzsKk9RPq6hpseo3y69/j9K51aEl+UUnU7fuo7srmbsILRbKCV1f+MzPtvP2vRU+QErI79N6mc3du/TvdxVz3J59vbaBOxOPuvU1dx1WIKiHe9qzT4Om8Msj52FcAYKXPiY/u52V/KDrhUFTCq+ScpaME7xh+q6k/e/uFsGa4WerziRt0JQVXC73HHzK5ZZCw+4JlEAAw1VIFgOiE98u4pJjIR+8AwFyHLk0kdLcxeJiUzv0iN55+BAAnMx0AqKiqG7flXFBv592zhnJuf/gOALr+tNz0/48wYeTa4Y0Lj8UYiEkY+fI8HjoijDy9dg4ANFuEEScuYaSvEGGkturbk46oIiBEyngpXBhZP9zBgUsYqeQSRtp8VM33imNLx7oPCRrNJYy87hxh5Jc6ahAJ+A1zxs1YHzxx5c4FQcNJ8rI37j/bdvyyt4ttV9t2beJqqasAwMnw22MH+Xax4Jcp1WhK00b2Dz4bNW/zoSl+fQl4/OVb9yPvPg4Y4MN2ywAABpPpP3/j2pljjXQ0ouOfHLx4dVhPdzd71qrYzszQWFdz//loABjd31tgM4x1NSteRHb09RdP9Bs6e/3YJduWTPRTVJDfGRKemV8UHrwWTeRzMjxm3uZDy6eOWj5lJJkk5+FkHXn3iZdz7EDvrum5hbP+2a+hqrx53gQAsDTSdbY2PRVxW4lMGuTTlclELt24H/c0cbCPq4OlMQCIvl0CkYBfN3norO2nJ286Pn9UH5Kc9I2Et9vPXPdyMHexbNdGrxZdGQBCrj0c08fN3lSX76oalTJliNf+0NgFu89PGuxJwOOu3H0R9eD16N6ubLcMAGAwkVGrDqyeONhQS/XqwzeHI+KGejm4WrN6ta2xjrE2/WDYXQAY3Uvw2GqsTS+NPdTR1180pu/wZcGB6w4vHtOPQpLdfSEmq/DrlS1z0H546trDBbvPLx3Xf9m4AWR5WQ970+gHr892MRvQzT4jv2TOjjPqNMWN04a3/+Mqq2rfpmT36WqDxXZW7rf/CkQCYeOiGdNWbB6/cN3CKWMV5OWu3X249cApHzenru1YLWir0wHgxKWowGH9HawFDJhqKtTpY4fvPXlxztrtUwOGEQj4y1dvR8TEjxnal71CYDCZI6YvXTd/qpGeVtSd+wfOXBne18fNgWPD7C1NTfR19oVcBoAxQ/oIbImJvs73Tw879O5Lp48bPGlhwJyVS2cEKSqQdhw9m5lbEHl0BzuN2YlLUXPWbV8xa+LKWRMoCvKeXR0iYu55u10f2MMjPTtv5qotGnSVLctmA4C5kZ6vu/PdhOcDJswbPai3joZadOyD0Ot3zI30B/YQW0bV/wpEInHL5k2TJk8ZMzZw6ZIlCgqk6KtXN27a3MPX182tXdMFHR0dADh67Nj4oCBHR/6dY3V19VkzZ+7avXvGzFkzpk8nEPAXLl4KCw8fFxjIdssAAAaDMXjosI3/bDA2Mo6IjAjet89vxIhu3VgeXV262Juamu7ZuxcAAseOFdgMU1NTNAOcGDl67NiMmbPWrF61ZvVqAFixfHm//gP8Ro5cuWKFoqLitm3/ZmRkXL92tfPSUv4/QyQSNq1eNmXu4nHT5iyeO12BRLp6887mnXt9Pbu5OrfLgUBbSxMAjp05HzTa38GuVbBWuuqMSUF7Dh6btXjF9AnjCAT8xbCo8Ks3AkeOMOSKxcJgMIaOmbhh5RJjA/3I67f2Hz05YvAA964sgcPexsrEyDD48AkAGOsv2O6YGBnWFGYIvCSCZfNnDxw5buSE6csXzlakkLfvPZiZnRN9MQTtDMdPn5+1eOWqxfNWLZ5HISt4dXMLv3rDx6PboH690zOzps1fqqGutm39SgAQfRVl89rlrj0GjJwwfeOqpVoa6vcePT4Wcs7NxXFAb6E+AX8MRDxubVDf2XuvTNlxcd4IL5KM1M1nH3dcivOyM3ZunzahpaIIACExzwJ8He2N+U9g0JUUJg9wOxD5cOHBiEn9XPE4XNj9xOiEd6N8HAy4ApgxmEjAPyGrAnsbqtOuPnl/5GrCkG427HW7raGmsabKoahHADDKR0DUFhRjTZWS6K0dev2Ffj5+604EbTm7aKQPRV52z5V7WYVll9dNQLtZSMyzhQcil4z2XTqqh6kO3dveOP5NyrDVx/287XVUFK89/RD+INFUR7VfV46Db2V13dvUvN7O5mI8DfxfBE8gDp659tzG2adWT+45br6MHCnpwY2bJ7ebOXsZ2AiI2NQaJTUtAHgUecp1wBh2BlY2ZJqap9+Uu+f3X9y2wGP4JBye8DLmypu4aJf+o9nqAwAgTMbhhaMHzlilqm2YGH/t3uXDXXyHsBfq2ma2dF3juIsHAcC5n9BsDnRd431Pvgq7KpA+4xfunzfi+PJxvScsliNRbp/ZXZqfNXN3KNqvEiJDLm5d0Hfy0n6TlsqQyCaO3d/ERZs6edp6Dvial3Fu0xxFFfWhcze252kahhb6Vk6Po07Lk5VsvQciTOaLW5c/P4u38x6oayH0L+W/CxZPsBy98vXhBS+DZ5oMno2XkS98GfM5YreKtQfVpF0xbGSpmgCQefecrtdIRQN+eUhakW7Qe2Lq9cOJx5cZ9ArC4Ai5CRH5z67rePix3TIAAGEynm4Pshi5TF7NIP/FjfSbxzW7DqSasXazFPWtSRqGaTeOAoC2h+B8OgBA0jAcciG3Q69vMmTu461jnu2abDp0HlGekhy1v6Y423XZOcBgACDz7tnE48vMhi8wG76QrG2ubOyQGXeOSFLUcOqHIMycR2HFSfc1nPsrGdoBQGPNt2+5Xyi6linXD/N9Cs3clW7vQ9Y2S791giiroGrrVVdemPf0WuGrO4oGtmr2f75ZBFQQmzt+xro9E5ZvXzhhBElO9vr9p9uOXfTpat/Vtu1jS9rqKgBwIuxm4OCeXSz4w7ICgBpNedrIgcFnI+Zu2j/Vvz8ejw+9eS8yNmHMQF+2QwODyfSfu2Ht7HFGOhpRcY8PXoge1rO7mz3H3NiZG5noae0/HwkAAQN8Wn8KAJjoaVW+ErqBJJDFk0YOnblmzOLNSyePpCjI7zx5JTOvMGLfBvZU/GTYrbmb9i+fFrBi6mgySc7TySYyNsHLxW6Qt2t6bsHM9Xs1VKmbF7A89f+ZN6F7wLwxizZvmDNek069/yLpRNhNVzuLfp7tMgR/FXgC0X/u+mPrZh5cPmnAhPkycgqv712POvqvVVdvY9t2fV1UdS0AiA875TF4jL4lvwlQpKn1HDX15pl9pzbO7zFyMg5PeHIz9EVsVLeBAWy3DABgMhm75o70m7WGrmv48u7V2xcOufQaamLPspt65nbqesa3zh0AgG4DBSTKRFHXMz79umPBxgdNWrR95vDgRYGDJy+WU6BcO7m7JC9r8f4raMeLDzt1auP8IdOWDZ22TJZEtnDyeB4bZeni5eAzoDgn/fiGOUqq6qMXsOI+2nbrqWVkcefCYTkS2drNt7yk4PmdyDcPbulbdmFHE6n5Xpn5KdHeow939o2/EyyeYBOw6sWh+c/2zjQbPIsgS8p7EfMxfBfdxoPWPsMqR9MCgPTYs/peo5QM+Q2rjBLdqM/E5GuHXx1batRrPBaPz3oUkfv0mp6nP4nXsD7aNs561HKSukHe8xspN45ruw6itXgWKunbKGgYpVw/CgB6noINq4KGkd+lPIGXRGA+bO6DzQGPd022GDaPKEf5HLWvpji7+wqWYU2PPfvq2FLLEQstRiyk6JhTjR3S756TIilpOvdFECT7YVhR0n0tl/7Khix3osaayor0d+oOPdm5wyQIg0gg/LNw+vSVW8YvWr9o8hiSvNz1uEdbD4b4uDl2tec/ldEabQ06AJy4HB04rF8XK8G667Qxw4JPXZq7bseU0UMJBPzla3ciY+6NGdKHW3f1m7Fs3fwphrra0XfuHzxzZVhfbzeus3l2liYm+jr7T4cCQIBw3fXbx/sdevcl0wKHTF4UMHf1sunjKAqkncfOZeYWRBzdztFdL0fPXbdjxcwJK2aNJyvIe7p0iYy55+3qMKiHR3pO3oxV2zTotM1LOQf/Kr9VJX5M7ufthm01oLXn9r8ZIh63ZlyfOcFhU3demjvckyQrffPZxx2X473sjJzNBIfw5EabJYM8D+jhaG+k2boCXUlhcn/XA1GPFh6MnNSvKx6HC3vwNvrx+1E+XdhKCIOJBGw8s2psT0MN2rUnH45cfTykmzW3+4KtoYaRJu1QdAIAjPQRfIDNSJNWHLW5Q+++wN/Lf92p8VvPL/T3psjL7A27n1VUfnnteHY/DIl5vuhg1JJRPktG+ZrqqHrbGccnpgxfc8LPy15bVfH6kw9hD9+aaqv262oBAGuD+vou2D9+6/k1gb01aOSHSemnYp65mOv2cTYHAAtduqOpzunbLxRJsgNcLZkIEnovMT4xZaCbVZdW8tGfB1sYCVk9uee4+dJypKQHN26d3G7m7KXfEWEkIfJUVyHCiIfflLjz+y9tW9C9RRhJFCSMHFk4eoBIYST+4kEAcBEijNB1jYM7qIoAQO/xCw/MG3F8+bg+ExbLkih3zuwuzc+awSWMXNq6oO/kpX1bhJHEuOgnLcLI+U1zKCrqQ1qEEdGPYgsjcp0vjPxSRw0AGDe4R219/ardIeF3EgAAj8MFDem5btbYdu6U+LjYOVmZHLty80tmbswxASPF+tnjGAzmgQtXj125iZZMGt5n+5Ip3HU8nWzUVZQDFm9hMhEA6OZgtWfFDO4Ko/p5rT9w1qerna4G/ymQn8Gnq93xTQtmrt83etEWACCT5LYunNSz5WQDggCDyWQnozq8bm7Qsu3T1wdPXx8MALZmBqe2LJaXkwEADAZzefeqGRuCd5y8suPkFbT+5BF9tyzkHH8XcbsEAAjs517X0Lj6cFjEvVcAgMdhx/XrtmbSkHb2Q28HM0dz/ePR95OzC2/sEeB2vW7yUAaTeSgs7nj0fbRk4kCPbbNHctfxtDdVoymOXXuI1Q9tTXbN5zmw4t/D5Z8TUd6OFmguFXHh7WhxdOWkWf+eHrPmEACQ5WW3zPDv4cySZhAEYTCZ7GRVB5cGjd9wbOa/p2f+exoAbIy1T66eLC/bgQSNDxO/MJmIwNQtfyFBIwbU1tWv+Hc/GgECj8ON9xu4fuG09nQ8X3cnJ1vLoxcivqRn3Tkn+DDQP4umMxjM/acvH70QgZZMHjVk52rO2Ravrg7qdNqo2SvQFFzdne2D1y/me8ioQb3X7T7i6+6sq6kOYsLX3fnkjrXTV2wZOXM5AFAU5P9dMaeXB8cDCQFgMDgD4NEtK8fOXz11+aapyzcBgJ2FyZnd60lysgCAxWLP7NmwYP3Oy9djYx+xzje7O9oe3bqKSOjECIr/t0ycMKG2tnbJ0mWhV64AAB6PnzRx4qaN/7RzNOvh6+vi4nzo8OHPnz/fixcQ0W7L5k0MBmNvcPChwyzBfdrUqXv37Oau4+PtraGhMXyEH9qvPD08Dh7Yz11hTMDoVavX9OzRA82l8mtAEITBYLA7Vc8ePc6eOT1p8pRhw0cAAIVC2bVzR5/evUU+409m/JiRtXV1y9ZuuhJ1DQDwePyEsaP+WbmknT3H17Obs4P9kZNnvySn3b0a2rrCptXLGQzmviMnjpw8i5ZMGT929+b13HW8u7trqNH9g6aiPcfDreu+7TwZcAL8hq7Z9G8Pr+66OuJcZfXw6h5yaO/UuYv9xk0BAApZYcfGNb19WUev+HrOsX07xkyeNXnOoslzFgGAnbXVuaP7SfLy7bkKAA52NlcvnZ40a8EAf1aQvQG9exzfLzR87h/G2J5OtQ1Na09eR+ND4HHYwF7OqwN7t7ObedkZO5rqnLjxNDmn5PrWaa0rrA3qy2Aih6MfnbjxFC2Z0Lfr1qmDuOt42BiqKZMDN51BJzfuVgY7Zw7lruDnbb/xTIy3vTEaQVRceNsbH1k0avbeK2M3nQEAspzM5skDejiw4ki1TPsBALAYzIklAUsOR4U9eBv3Jhmt4Gqpf2CeH5ErIuujd2lMBHFqh7Lzx+M2KLCxvi4yeM3ru5EAgMXh3QcHDpyxpp39yszZS8/K8WHYiaLM5PmHb7SuMGjmWiaDEX/p0MMw1pGL7sMmjFjIE2vdxNGDoqJ+dGkgwmQCgHEX91HLeP6unfr4Xz30j5mLNxoyVFyYuXiP33D03MZZR5eMAQAZEnn4/M0WrqwdbgRBmEwGO+ls4OqDJ1ZOOPvPrLP/zAIAbVObCRtPSMvKt+dpGAxm2o4L5zbOjgnZFRPCerXuwycOnyc0T9l/HV3v0YyGuvfnNuQ9vQoAGBxezzvAYtRyaF+/UrX2UDLqknEnpCo/pfvaiNYVLEevRJiMtJvHMu6EoCX6PcbZjN/IXUfFspuMEv3ZzkkIwgQAmoWr3SSejqfdbfjHS1tVbTzRkO/iQtXG03HW/jeHFzzbOREACHJk63Hr6XasIysIgiDsfoXBdF0c8vrwguTI4ORIVvY0/Z5B1oHr0H+XfXkBCFKZ+b4y8z3fp2AAo9alR9fFIS/3zfx0ZcenK6yjwxrO/WzGb8TgfrUw9bsIGtKrrr5h5e4T4XceAiqIDe29fva49oxgPi52Ttamx0JvJGfkxpwQnABiw9zxDCbzwPmoY6Gs8W3SiH47lnJsqJezrbqK8uiFG1EVoruD9d6V/NsqI/t5r99/2rerPXcG4Z/Et6v9ic2LZ6zbM2rBRgAgk+S2LZrS053jH4wAwiOIbVgwbunW6Wt3T1+7GwBszQxDti4ltShaXSyMI/dvmLJm1+CZrMP0/Txdjm5YIK7W/mF4Dg1sqK+9uHv1s9sRAIDD4T2HjfObvbaddtPKxdvQ2vFu6PH8zORVJ262ruA/dx2TwYg5f/BuKCu7io/fxMClPFGsLZw9FVXU9ywcg9pNM8du41fyrCjd+o+8sm+DlasPTUOcdtPK1Wf6lmPH1s7csyAAAGRJ5DGLt9i4s2KY8dnNKRsO7l86/ujaGUfXzgAAXTPbWdtOScux7CYGi12w99LB5RPDD20OP8SSox19B45bth3XMoJ9evEQYTKNbDoxt+Z/CH2f0c0NtUlnN+Q8iQYADA5v4BNgPXpF+w2rsnGXtNsh3/NSvNcLOCppE7AKYTJSbhxLux2Clhj2HGc/gWeiomrVTVZJ7fGOiahhVbFw7TKZZ/DU7T783cUtdHEbVrqNZ9c5B14cnJ+wfQIAEOXIdkEb1FqiSSGAIMyW9SYG47709MtD8z9F7P0UwTqta9gryG4cZ+Fc8uExgjCpJn9+REaxEDSif219/cp/D4TfjAeW7jpg/YKp7bKzbo5OthZHL0R+Sc+6fXa/wDr/LJrGZDL3nw49eoHVLSePGrxj1Tx2Ba+uDuqq1FGzV7XornbB6/g3DkYN6rVu91Ffdyc0l4pY8HV3OrljzfQVW0bOWgEAZAX5bctn9+rO2a9FEIRbdz2yZUXggrXTVmyZtmILANhamJzexdJdUR48f8NkMp1tBWRmac/tfzljezrWNTSuOXUTjQ+Bx2HH9nRaHdirPf3Q087IwVT75M1nKbkl17ZMFVhnTVAfBhM5fDXh5M1naMn4Pi5bp3LCiXnYGKopK4zbfK5FBtHfMWMI30P8vew3nr3tbSdOJcTbzvjIwpGzg8MCN58FALKczKbJ/X0dOGFd+JSQ40tGLTkSHf4gKe5NClrB1VJv/9wRqBJib6QZunb8zD2hI9axgl31cTY/MG8E+m8MBnN+VeCc4LDdV+7tvnIPLZzQ12XTpL8lsotrK2HE7UeFkXnChZF7XMJINyHCyDEuYWSkEGFEWdzCSFBbwgh7uBu7+uDJlRPO/TPr3D+zAECLVxgR/SgMBjN1x4VzG2ffDtl1m0sYGdYJwgiG3WIACA0N9ff3r0nsmGv8D1BdU/c2Ob2mtt7CUFeT3uFN6MKv5SRZGRFuB1/Lv71LziASCVZGuhQFnqREWp6ju1gYRx1YV/m9+s2nNHUVZVN9fvX/avzTUQs3X9ixfJCP+CO7NjMYbz6lMZlMRysTnEgnawRBPqZlZ+YV2ZoZaNFprSvkFJakZuWTSXKmelqtv402bxc74XcSApdu4+5RPwbaD7/fP9Z21Z+jurY+KTWnpq7BXF9DU6XDNqmwtJIkKy3Ca+FrRdX7tBwikWCpr0kh8UxWdAfOtzfVjfh3bmVVbWJylhqVYqrLvyl+7VFiwOqD5zZMH9hd/JH5mxnMxOQsJhNxMNdrux9m5GcVfrU10tEU6x5GJxFx71XQ+iPi6of1qU/F0ipuqmpqkz6lVNfUWpoYaKp1zBussKRUXk5W9Nz3a1lF0ucUKSLR0sRQkUxil6s79nawNrt6YnfFt6o3Hz6rq9LQNCJ8RN954D9z2aUDWwb39OxQ29qkmcF48/4zk4k42ljgcG0cBUAQ5ENyemZugZ2FiZa6gG8pv6jkU2pmXX2DiYGOsZ622OMiBMxZiZVXDg0VsAPdIfz8/ABBQi9fEkurhFFVVZWY+La6ptrK0lJLq8Nb2gUFBSQSiUQiCatQUlLyNilJiihlbW2lqKjIfYmqouro4HDr5o2KiopXr19rqKubm/OfCIyMiho6bHh42JWhQ/gXBr+Y5ubmV69eM5lMZ2cnHK4Tc1KyweDwly9f9vMTevK1vc/BYC6cODhisOBMWD9MVXX123cfq2tqLM1NtTQ67JtVUFRMkpfj9kvgo6S0NOn9Jyki0crCTJFC5r5EN7J2sLO5Hnq2ovLb67fvNNToZib8kdKibsSMCJwcevrokP6CD5r8DM3Nza+CU7XaAAAgAElEQVTfvmMymU5d7ER3BgRBPnz6kpGdY2dtqa3JH7BR9FWUpqbmj5+/fC0rtzQ3VVNVEVjnZxg1YTqGKCOW8aqpMDlkueDINz9MdV3Du/T86rpGC126Blf20HZSVP5dXkZKXnhS4a+V1e8zCqQIeAs9NYo8z3xYf+Rae2OtsA2TKqvrElNz1ZTJptr8BuX60w9jNp4+uzJwgGvbp6w6SjODmZiay0QQBxNt0dMtACgo/fY5p6i+oclIS8VIg/b/Fu8n8lHS+K3nxDW/OvTy208+p762Ojc5qaG2RsPQQlFV8J+eCL59LZSSI3F7LfBRVf41L+U9nkjUMLSUVeDpt4t89XTN7WcFh9d+r8z+nEhRUVPT40/l9vb+tSOLx0z595ydl5jHbQBgMpqzPyUiCFPX0gGLbWP4Kkj7VJqfqWVqy51Utf1PKy/MLc5OlSGR6XomIr6u9nNs+TgDRYJYxqunefXO88W8YGyuq67M+tBcX0PWNpNR7rBZrK8owkvL42WEflEN30orsz5gCVJkHXOiHI9ZvDbRTNHAzn3Fhcaab5Xpb6WV1BQ0+UMmFLy4+XTHBJeFJzSc+3W0bW2CMJorMpIQJlPJyB4jsl8BQO3XvKqCNIIcWUHDSMT7Cv4ghFlbklOVn4YjSsurG8goiW2Lgk3e06vPd08R13hVm3RLLK3ipqqmLulLek1tnYWRrmYHtZrCr2XysrIkkYdwvpZXJiVnSBEIVsZ63IKYpod/Fwvj6IP/VH6vfv0xRV2VaqYvYG/yavyTkfP/ubhr1SCfn02kyEczg/HmYyqTyXS0Nm3TLCII8jE1i6VoqQmYPjU1N39Ky/5a8c3SSJdO/Q1KhaxNH3HN82dvP+3Sa2jbVX+C+prqrC9J9bXVWkYWykIsgggqvhbKyJLYXgut+V7+NfvLOzxRStvYUo7Xbk7trmNg0WXJoYia75UZH98oqahrGPDbzVdx13bPHz1v13lH34EgbhiM5syPiUwm09C6bbuZm/qxJC9Lz8xGWU3AshphMkvyswsyU4jS0uq6RooqYjvc8sMEWJPE1Q9dFxzVdh3UdtWO0FxXXZH5vqm+hqJtLkvt8NdVV15EkBFlWOtZhpVI0TEnyvF0vMjxpkqGdh4rLzbWVJanJckqCzCsec9vJmwf7774pGbnGNby9CQEYSq3w7DWtBhWcscN6y/g8c7JLppSYpm/MarLzu/9RyytEgFLd62tszQ20BRkQUTQbt01VYpIsDIxpHDprhpOfR2szaKP76z8VvX6wxd1VZqZoW7r26/GPvCfueLS/s2Denq0vvozNDMYb95/YTKZ7dVdU9KzcgtszQXrrp16+w8QMHc1Tkw6bVNh8qllgvO7iZHquoZ36QU19Q3munQNaseUkDZlEECVkMwCKQLeQpdHCTEYtd7OWCts/YTK6rrE1Dx1ZQWTVjIIAFx/+mHsprNnVowd4CrYHeeHaWYw36bmMRGki4lWm/M9YCkhxfWNTcaaKoYaVD4lpKmZ8Tm7uPR7tYWOmqqSAOE6t6QiNf8rWU7GREtF9DcmFsZvPU9QMxFLP0yvaJq0RWhOt3ZSX1udl5xU/zuEkcW+ejrtEEaOLh4z5d9ztv/fwkibjxK7MDLDkcw3f/s9Bxfk5WTc7X98CFCjtbEGoymRfbryZxzkg6Ig7+3CHzwN5XRUrLqKcj/PTnGCxuNwTlYC0oO1BoPBWBrpWhrpCqugraaiLXy20ebtEuRlpd1sBIQMbSdqbZlYmiLJ27GNRLMUkqyXg+Awp2duJKhTKX3dBPfSnwSPwzqa67ddD+1IBpqWBh1eyUsQBklO1t3xB39WNZW2Pdtoyoq+7qKGL0UyycdNaLTJkCvX1FVp/X3En/wSj8M5CfHFbg0Gg7EyNbQyFZC0GEWDrqJBF/9+538UEonUvfuP/2Tq6m0oJioqKj17tBGSWlFRsYev4LyGJ06c1NDQGDhA/HOyjoLH411cJCecOJDk5bu5/vgXok5vYzWuQqX28GojJ5EihezrKbj3njp3SUON3klZQvB4vLNDu/wgMRiMlYWZlYXgJH2ir6IQCHhbazEvff9DyMtIuVq2a8ohELqSgugKNIo8OxWrMCjyMl52guucvf1CTZncx7mNCduPgcdhHU3be25AnUpWp5LbricBAACkZeWN7H58B5FMa2NvmKREM3MRnASTjawCxczZS+Clx9FnKTR1m+7idzIDACwOr2fl2J6aGAxGw8hCw0hU9xb9NCU1LSVBO1V/KngZearZj6dOkFZsI/yAFJmqauMpug5RjqxiLVi1z4y/IKOkpu7Q68eaJxoMDq9k1N7wrbI0TVnaDy4PMRisnKqunKruj93+Z0CSk3Hv8oMTAzWacpt1aEoU366iJjkUBXkf4RVCIm6rqyh3RhoRPA7nZM0v4AoDg8FYGutZGgsNyEfA421MJZE724u0nLxplx+3m4pt2U0FJZqVq+BcOWzkFChWXQXb1vuRpxVV1O29+v5g+0SCw+ENrdtrN7WNLbWNhf55YrBYVS09Va1fFyfyvw5eRp5mLjibc3uQUWrDsEqTqfS2DSuFbiPYsGbEX5BRUtPoNMOqbNxewypH05T7UcMqoTW/RHcVlcSHQib5uAkddkKuXFdXpfXzcf+R9okEj8M52bZ3YYvBYKxMDK1MhOqunXr734C8jJSr5Q/aizZlEECVECEqBwpFXsbLjv9AFJuzd16qKZPRNCLiBY/DOph2IEyRaCWEgMdZG4hSrbVUFNG0uX8n0rLyhv/HwsiT6LMUmrr1/70w0uajfoEw8rdEmGwn/x4PLSgpu53waseSKfhfctZWgoTWbD97o7C08s7z99vnjMS35QArQYK42HowpKD4a8yDJ7tWL5AMgBLEwqbNm/PzC27euhW8dw8eL5lySGgvW3btyy8svBUbv2frBknPkdBJ7LgcV1j27c6rL9umDpJMtySIi1snd1R+Lfj4+I7fon+xf002BwmdzZeIPXXlRUWJcbbjN/09WUIk/Hq2HbtUUFJ6O+HlzqXTJOtBCb+GqGPbK4oL3j66E8iVQ0SChM7mU/ie2vLCgjd3u0yQGFYJv45th04XFH+NefB05+r5Ejsr4Xex83J8Qdm32FfJ26YOlCghEjqJmBZhZIREGGkff913RKcpKSsKdUk7GR5TXVcfNKTnhGF/b956Cb8AujJZmSw0SM6p6w9r6hrG9XMP6t/GQWQJEtqPmoqysqKoMDAnLkfV1NSN9xs4caSYw11K+INRU6NTqUIPHBw9dry6unrSxIlTJk/+la2S8P8PXVWFqiw0QNrx0+era2omjB01KbDTw1FK+INRVVJQVpATdjXk1rOa+sbAXk5BfcR/aFjCHwyZSpejCD3RnhB5qqG2xm1QoPuQoF/YKAn/eaQpqlIkoWYx8+7Z5voaPe8APV8xZ6eS8FdBpypRKaIOaJ4Mv1VdWxc0tNeE4Z0S2EDC3wmFSicpCrWb8WGnGmqrPYeO8x4+/le2SsIfj7SiKMOaFnumub7GwCfAoEfgr2yVhD8bNRVlZUVRIRJPXI6urqkbP2LARH/xp3mSIAFFVUlBWUFU7p6QmOc19Y2BPR3H9ZaEHJbw45CpdPm2hBFXiTDSbv46R42XV/aLuPrl1slf1hIJfzPPTq0TcfXT5W2/qiES/iJe3zgvukLqg6hf0xIJfxLvk5JEXM3OzPhlLZHw3+Jtwl0RV9OTnv2ylkj4g3l6cKGIqx9CVv6ylkj4k1h96amIq5uuffxlLZHwJ9Fj530RV/scfP2rGiLhT+ZV+CHRFZJjfjZPtgQJrdkW8VzE1eDbn35ZSyT8VfTZ9UDE1YGH3/yylkj4e3h1/azoCin3I35NSyT8zTw5MF90hfenlv+alkj4s1klUhjZKBFGOogkuI0ECRIkSJAgQYIECRIkSJAgQYIECRIkSJAgQYIECRIkSJAgQcIv4m901Lid8OrK7Ye/uxUcmhkMBpP5M7fXNTSKqIAgSOX36h9+voRO4s6z92HxL393KzggCFJZVSuiQjODKaKjtnl7mxUkdBIx95+EXo/93a3oGFU1teWV3353KyRwuHnr1qXLl393KzpGdXUbhq+qqqqsrOzXNEaCQG7Fxl+OuPq7W9EBmpubEQQRUaG6puZnbpfQSdx5+SX84dvf3YqOUV3XUC5k1iR6PtbMYNY3NnVauyRw+PD4zqs74b+7FR0AEbneRBCk9nulsKtMRjOTyeiERkngpygxLvfxfynEXXO9KMPXZoU2b5fQGdx+9PJKjKiD5n8SbcplEn4Zbx/dfhoT9rtbwU99TXV1ZfkP3MhgtDGxr6+VjG+/n4I3d3P+z6xqm4YPQX58gwAQpLFG6HROwi8j5sHTKzdEBRD99VTX1v3kE0TotMyf2NWS0HnEvvoS8VBUCORfDIIgldU/1Q+ZEj3tv8D/m07CFDlhEy2DoLc3Nfzs+NlO/rrUJwCwOyQ8I69oRK/uv7shcPnm/SOXbyR9yWhmMPS06NP8+0/x64fFYtCrTCbSdeScZgaPKKajrhqxby3677iniauDQz6l5TQzGNpqtDljh3DfDgCV36tX7jl1+eb9uoZGeTmZXm5ddi+friwyKamEX8aeizGZBV+Hezv+7oZAZVXt6sNhoXef1TU0yctK93S23DkvQJksz64QGvv8aNS9d6k5zQyGnrrK1KFekwZ5sXtam7e3WUFCp7Lz2LmMnHy//j1+d0M4WPiO6O5sf2iT4Ehr5ZXfuvQbQybJv4252M7bmUym86Bxzc28o6WmWtSxnWJs9t/Mv9u3p6dnjPT3/90N4WBkYurp4XHs6BG+8jdvEpevWPHy1auKigpVVdVBAwdu/3ebggK/4SsrK7O2tSOTyZ8+vEdL4uLj58ydJ/CzutjbnzkdIu43kAA79h3KyMz2H/p/lJzV1KGbh3vXI3v+5Su/FRu/ZvP2z8kpCiSSVzfXaRPGdXPl5PJMfPd+5YatrxKTKiq/qdKoA/r22rZ+lQJJvp23S+hs9obfyywsG9bd9nc3hIP95G3uVgbBc4YLvFpeVes6YydZTvr54cXc5aH33hy//uRdRn4zg6mnpjy5v9uk/q5YDGs+Fv8mZV3Izc/ZRc0MppYKZdYQD+6rEsTOnTN7S/MyHHoO+90N4bBmqJ1xl25jVgZzFxbnpD24cizpwY266u8GNi4+o2eaOnpwV6j9Xhmxb82LW6FNDXXSsvIWrj1GLt3JzjX7Iib0Qeix3OR3DEYzTVPP02+Kx/BJGOzfeNjj15ASvb+6OEvLbfBvbEPJ+0dJpwRnhqLo2zjO2gcAlZnvP1zYVJGW2FjzTYpMU3f8H3tXHRZV074fls6lpbulBUW6BEVCQVJBULGwCxvF7ha7EUUEm+7ubpbepZbuXYTfHwfWZRHQ7/3e9/d+6n1xcZ0zZ57hGXb2zMw99zyzWMn1KC0jKynnzBlmNf+DvxWXn7ypbmiyX2wwe9Z/CkpWa/U0lG/7bEduY9Pz9pz9/iktagrSD07tAYDR0TEtR6+vkyeAIoJzQm76ItdRqTlHrj4qqaob+fpVhJ93u5vtekcrcrrsD/5hfHp8taWhZuHi7w9+/l/Q19Wx306LiZXt/LusqU93WaooaOitO0Z5fHZeYvibGyew1WWMzKwKCwwWOXrKzdMhPa0tzXt17Vh1UXZ/Txeai3ee0VKXXacYWf683/5/UPbuVl9Lrcj/a6+KoLO6IN//VEdVHqG/i4GdR1BzsaqbD3nH14vDVIY9xmaGEgd6ueXmy1pumKOkN7WcT1u05szV1tx0mSKd0N+V/8y3NvHtV8IQDSOLgJrJPM+z9Kycf2+t/mAaXL7vX12PtV9q+v/tCOQVlx+5dCersLSru5eXm9PKRO+0txcbCzMAjI6Oatl4jHwdIc8vKsgfcv/i1HI6uro1LN3QrCy5od/O1K6sbbj74u3H6MTu3j5tdeWtHo5GCzX+7hr9wY/jWlB8TXO7rb7K/7cj0NU36PP4S2Bs7hCByMJIv0hD9sKmZVxszPH5Vd53v793S1VK8M6ucQq6Cot/8DnlS1pJT//QAgXRzcv0DFSkAOAHzf/gH0bks2tt/w6epCg54qPfiaaacgZmVlkNfQP7dVJq3wZsAz1dIZNpEEcyGgQAStNi3t30wWFKR7+OcPIJm6za+nczIb+jUONfgpefYtYfvSotKrh5pfXQ0PC76JTd5+529/Z7e46/R7Ct+KLK2rnSYpxs37h+DvT4MC4uI9/GywfNyuxqY0pLQx0Slbz73F18Z/fhTSuRDATiyPItxzKLKtxsTBeoyGcVVTx6G4ZtaY9+Qrn88Ae/MwjEETvva1mlNa4WOvMVJLPLah5/TMC2dUbe3I9kCAhP3Xj2sbTwnE0rTAaHiR/is/dcC+jqG9znuvRHzGfN8Ae/G54Hf8bUNeovUJ8uw4YDp5ta8WjW70t5vmuObW4rLKtSlJXkQH9bjye//oNfDE+ePq2qqjI0oOSXs7KyTc3MaGhoXJydOTk5XwcG3rt/PzcvNy0lBTV5LLV2nScOh0Oj0aQUKioqWlrKQdHQ0HB5ebm0lNTfVJE/+FfhWcAbTE2tge5CivRXb9+7bdgqKiK0e8tGbFNz0LtPYVGxqVGfZKQkASA7r8B8uRMNDY2T3TJODvbAkI8PnvrnFRQlR3xAWt3M5n/wG+JlVFY1Dq+rNG0D2HI1sLmjB83MQJ74Kjp705XX0oI8G631hgjE98mF++686+4f3OtkCgDxeZV2Rx+gmRlWmmrS0qDeJxXuu/Ouvbv/wCqzv70+f/DvQOon/7aGapl5kzh94vCg3y6nrjacprk9C5ozN+bD7Z2OW2+8lZ5gKEaIhJs7VtQWZWlbu4oradYWZyeFPOlsxe19GAEAaZ8Dnh3fNEdU2sh5I3F4KDf6w+sLewd7u5es3fsdD/7gVwEVFRUVNS1F4ihxqBeHYeGXAIBOTH7iCXsqamphXVs6Fo6GlPc1Uc+7agqNTn+hokLNmmFW8z/43fD8fSSmHqenoUxKoaKiopkyLB8eJlTUNkqKCiK32Ja2ooqaudJinOhvK52cbOPXsel5NpsOo1mZXZctoqWhCYlM2nXWD9/ZfXiz699cmz/4X8I9n82dbU1MrN/hDRLe+7fUVytoUK6Up4S+ub1/LbeAyFL37Z2tuLTw4PykiBMv4/nFpAGgujjnjKcVioZG28KBBc2RFvY2JuhxbVmB74uYPxrH3xkdmLzY4/YoampRPVs6Fvb6lPeYyOedNYWLzoQiHd9XwlDCWbfBjiZRPVt6Vs6GtE8JZ1YZHn7FozBpZloT+6qvuWbOXG2K8kdHiPGnXNorcySMnbllNNurcjCRzwfacaanPv1zlfyDfx9yisosVm+npqZ2sjTjYGcL+hz18PX7vJKKhDf3UCgUtrmtsLxKUUaSg2xDL8c0m3s3HjxDwdMODg2v2LAP19LmaGXGyc72LjzObsO+Dw8v62r+i3ZH/MG/AYSRr/bHHmWXN6xapKEpJ5pT0fAkLB2L7w6/sJkKqGipKTvHYeJIZWObpMD4evkQgehy4gkO321vqMbByvQxpdDp+JO3vmu1FcV/xPwPfltkhQc9PrKOk19kkeu2rtam7Kjg4pRI76exc0SlAWCESLi1Y0VtUdZCa1cJMhpkz8MIxLw8M/7mNltGVvRCq5XUNLS50e8DL+zt68Rbbjj49/n8R6jx/4Zrz0KkRAQSXlxiZWYCgF0eKxSWrr37+jNJqFFd3wQAD0/uUpIRn2p+9t6rsbGxRP/LEkL8AOC7dbWUufv15+8ObHCmRqEAwP9jdEZh+Zlda7a5LgeA1csWUVHBw6CwnJJKdQXpf6yaf/Avx8vw1MyS6lOb7Lc6mgGA21JdKiqqRx/ic8tr1WTFAOBGYISkEG+s30FWZkYA2Om8WMn5wP2QWESoMav5rBn+4DcBtrn15I2H2QWlBWWVM2S79zI4IiGVc4rGYmZzTF0DADy66KMs9+fl9iujsbHxuO+JzKzM/PyC72a4eevW4OBgemqqqqoKAPgeP2a6yCw6JuZtcLD9im+bt/zu3AkLD+fknLS/xNjIKC8nh6LArdu29/T03PG7/d+uyh/8i9CIazpx/kpWbn5BUcnUpwQCcb/PSWYmpszYMHY0GwCc9jkgpqjpss4rKy4MAG7ffzI4NJQS8VFFaS4AHDuwx3y5c0xCUvDHLytsLGc1/4PfBzh899mXkTkVDUU1uBmyPfycGp1dzsHKRJF+IyReUoA7+spWViYGANhhb6TscfrBpxREqHH+VdTY2Fjs1e3i/FwA4ONuIe928kZI/D4XU+o/CwO/NDpbcZ/vn60rzm6sLJr69P3tEy11lVuuBc3VXgQARs6bTjnrPDu26cT78W407XNATWGm3faTpqu2AoCOjRsVFVVi8OO60lxRebUo/xu8wpLeT2IYmFkBwNxtx2Eb5bg39/8INX5t8Cjqml6IpkjMe3SQONin5nkeADBhD78SBo1Oh7KLKQKAguO+xBP2rYWJ2LTPQgutZs0wq/kf/CbAtuBP3fHPLq4oLK+meGQ4XyU98BZF4q6zfj19AzcOb0VuMQ1NAPDo1F4lWYmphZ+993JsbCzp5XUJYX4A8N3mIb1o1bVnwQc2rvzTLf4BgqjABwXJUSxoDvLEjhZssN8ZTHFOfXnhVJMRIuHlpcP0jEynA5OZWNEA4LTDd4up7I197qcDkwEgIuAeYXjI91GsqJwyAKzwOnza06o4PS4j6v0Cs+X/SLX+4N+IytBHXwmDxmfDOMQUAUDJyTv2+IqWwsTGtE/CC60BoODl6V5clcGhl/xqJgAgY+EZtsco7eY2q9uZADDQjisOvNiOyeuqLf5u+TVxr9srslXdjslZbwIACRMXKqCqinzWgcnjlPyzav77wu950ODQcGLQfWV5aQA4un2dxertsalZ78LjbJcYY+obAeDhxaPKcrNsTLr3MiQiIY1iL5zPlbsVNfXvHlwy19cCgC2rHTStV3t6nyyN+dcdcfUH/78IiM7OKqs/sXbpluX6AOBqpklFBY9D03MrG/VVJBNvUAY23nfnfc/A0BUvW+T2xLPwysa2N8fWmGrIAsBGG129LVc2XwnMe+j9I+Z/8HtihEgIvnaEjpHp4ItERlY0ACzbcuzgUvmHBz0O+icBQPrngJrCTNsJGkR7Mg0CAF8enB8bG/N+GscjJA4ANluOHbSQi/K/YeHpjUJR/01u/6NCjSEC4eLDN6++xGFb8MJ8PAbzVc7sXMPCzIg8TcwqDI5MiknLGxwmaKsq6GooetiaI5MoL98bBOLI/vVOlx69iUrNlRQRWG1j6rTU6Przd69D4xqb8WryUhe910uJCCBFuXmfU5IR19NQuv3yQ1xGAQ8neqWl8Y7Vdt+Nc9jd2+9z41lSTlF7V4+Wirz7cjNzXY0f9Pk/Rk9ffwmmbpOTFaLSAAB+Hk4DTZX4zHziyAgtDQ0AVNXjqKiopCZ2DFCgsQUvOIcbUWkAAAszo4aiTHJO0dAwgZmRAQBefY7j4URvdPpGduxd67BQVYGbA/3dAn8fDBGIl/1DX0Wm4do6hXg5DdTlTm2yZ2Ea37aYmFf+Li4rJqtkaJi4UElKR1XW3VIPaYdbLzwjjIx4u1lefhkanVEsKTTH1ULHcZHWzcDI11Fp2NZOVRnRC9ucJYV4kaLcj99VlBTWU5X1exsVn1PGw8HmbLZwu5P599th38Dx+yHJ+RXt3X0LFCVXL9Uz01L6QZ//Cl5HpvFwsG6wNSal7FlpoaUoyc3OCgA9/YMlNdgNtsasE22en5tdX00uIbeMOPKVloZ6ZvNZy/+FMTRMuHDn6cv34djmVmGBOYYLNc7u30r6viek57wNjYlOzhgcGtbRUNGbr7bGwYZ6Qge66dAZApF40GvNhbvPIhPTpcSE3VdYOtssvvYo4NWH8IamFnVFuctHdkmJCQPAqu2HleSkDRao33z6OjY1m5eLY+XyJbvWrURNw0B19fQdveSXlJmH7+xaqK7kYW+92FD7R3z+i+jtH6isqWdjZdZQls8qKP1unpLKau8z10/t83r0+gPFGYczm1fVNVJRUUmLifxXXP1XYWho6MzZcy/8/RsbG0VERIyNjC5eOM/KOhFaKT7+zZugyKiowcFBXR0dAwN9z3XrqKmpAcBz/QYCgXDk8KGz586HR0RIS0mtWeOxauXKy1eu+L982dDQOE9d/fq1q9LS49IWRydnFRVlQwPDa9evx8TG8vLyurmu2rtnz3cbUldX18FDhxMSE/D4du2FC9etW2uxZMkP+vxX0NvbW1FZgUajNTU1MjO/E582JTVVVVUFUWkg8PBwj46JycjIJAk1iotLdu/Ze+7smfsPHoyOznTAYVh4+G0/v8iIcD4+vr/u/L8KQ8PD567c9H8TjMU1CQsKGunrnPc9zMoyvj0iPjk16P2nqNjEoaEhHS1NfW2ttW4uSLvasGMfkUA8tHf7uau3ImPipSTE3Vc6rnSwvXr7/ss3IY04nLqK0tWzvlIS4wJTl7WblRXlDXQW3rj7KDYxmYeby9Vxxe6tG7/frrp7Dp88m5iS3t7esXC+xhpX5yWLjH/Q57+Cvr7+Skw1mo1VQ00lK5fyCM/S8gpsU7P9Miv2CWKCl5t7kZHBl4jo7p5eNBtrSkaWiuJcRKWBYLWLQ0xCUmZO3goby1nN/7r//1oMEUauBMa8js3B4buEeDn0laVOrrNkYaRHniYVYt4lFsTmVgwSiAsVxHWUJFYvXkBaO9l2PYgwMrLPyfTKm9jonHJJAe5VZvMdjdRvhSQExuZg8V2qUkLnNi6TFOAGAI+zLxTF+XWVJe+8T0rIr+JhZ3EymbfNznC6Uz+6+wd9n4YmF1V3dA/Mlxd1M19gpin3Iz7/RfQODldh29iYGdRlhHMqGr6bp6yu5dCDj8c9LJ6Ep0r+Td4AACAASURBVI+RvaB6+odKa5vXW+uyToz9+DjZ9FWkEvKrkPEYtq1bgBuNqDQAgIWRfp6McEpR9TBhhImB7r/i/78ERMJQ+OPL6aGvu1pxHHxCshr6djtOMTCNvwoqspNyokJK02OJw4OSqgul1XV1l68mTaRfnNr2lUiwWLsv/OmVktQoHhFJHWvX+Usco/xvZoYFdrRgReVUHfae5xWWBIAHB9yFZJRk5unGBPiVZyWwcvBoLXVe5Lptuh2xg73d724fr8xJ6e9ql1BeoLPMTVHH7Ed8/osY7u9tratiZEGLKqjXlVDKDVM/+QtKz0VUGgDAxsmrsNAk7XNATVGWuKIGAGR8ec3KyWPouIFksthjj6SKFis792BfDw5TauSwAVFpAACah19WQ788K+HrCJGahjLiwv80vhKHy0Ou1ycGDbbjmLiFeBR1lV19aBjHP6O24hRs2oeWgoSvhEFuuQXcCgvFTVZRTbSrnLu7R0eIcnY7y9/daMmPZeGTEDN2FtFbUfnpTn3i28F2HLuEiqrHSSQWRfqV9WixuTwK2lVf7rcVJdGjuUUMHGSsN08XSYLY310UcAZfmjrc08ElqyluspJPzeRHfP7voiUvpjr8id6RQAZ2XgBor8hiF1NEZBYIRA2dWgsTO6tyEaXFzBlmNf9VMTRMuPjwdcDnGGwLXpif12C+ypldnqxkzFJCVkFwRGJMau7g8LC22lw9DSUPuyVIz7j5+DUikbh/vculR4GRKdlSIoJuy82clxpffx786nNsY3ObmoL0pf0bpUTGuSPXvWeUZMX1NZRv+b+Py8jj5WR3sTLd6b5iOkLs6PXHSdnjhJiH7WJzvW/Hs87q9n+M3v7BqrpGNAvTvLky2cUVM2eOTM669/rjp7tn5nCPL6tj6rFUVFRSYkLfzd/Y3CY4hxtRaQAAKzOjhpJsUnYhiS77fUAcHvrw8FLSp1cdLTgufqG58w1W7j7NwDz+rijNTEyPCClMjSEMD8mqLZTX0DVa4U7qNx8c2zJCJC7f4P3h0aWC5Gg+UUmDZa66lk5fnt1I/hzY0dworqDmduACn4gkANzYu1pEVkleQy/c/3ZxejwbF4+elYul+/bp+s2B3u7X146VZSf3drVLqy4wsl2tqmf+Iz7/V9CIKfW/cNBpp29s0JOxsW/Mw2B/X1NdFRMLm4TivOqibAorLKassxWnZW6LqDQAgI2TR1nbODchfKCvh4mFrTIvTVRWCVFpIDBYtqo4PQ5TlP27CTW+EodLgq/VJQQNtOOYuIXmKOmquR0jdVKtxSkNKR+aC+K/Eoa45ebzztWWNB3vWDP9dn0dISra7yoJud6cF8fCLy5h7CKmv6L8453axLcDeCynpIr6mlOs/OMKrZTL69nF5vLO1a74fL+lMJEezS1u6CBn4/XdjpXQ313w8nRbSepwbwe3rKaEyUoBddMf9PmvAF+eySGmyEHW8UkYO7cUJrZX5iJCjZrY1+yiCvwTXTwDOw+filFtfGB7ZQ6XtPrIYH9PUzUtExunlGpHVd7U8msTghjQ3DIW60gpCnY7uOXm07Nx/3Xn/4cwNEy4cPdZwPtwbHObsMAcQ615Z/ZvIac0E9Jzg8NiopMzB4eGtecp681XW+NgjTCxmw+dJRBHDni5X7z3PDIxQ0pMaLWdpbON+fXHrwI+RDQ2tagryl06vAOhYQFg1Y4jynLS+vPVbj4LjEOY2GVLdq5z+S7R0d3Td/TynaTMPHxnt5aakoeD1WKDb7FSZnX7P0ZabpGyvDSi0kDgZrc0NjUrs6DUdolxVW0DFRWV9ESNpkNJZc3+szdO7t38OPADOYH2PPiLkqwUotIAAF5uzkW6C/zfhWbml2iqKPx15/93MUwYufwmNjA2B4fvFuJh11eROrF2KTmlkFRY/S6pIC63cpBA1FIQ01WScDOfj4z3tt94SyB+3etscvVNbHROhaQA96pFmg5GarfeJb6JzcXiu1SkhM5tsEZoEABYc85fUZxfV0nyzoekhHwMNzuLk7H6NjuD7zIh3f2Dvk/DUopqOnr658uLuZlrLtKQ+3G3/2MExubwsLOst/p25MQuB+MF8mLcaOapmaOzyx9+Tg056cnLMT4DfRmVNVeMH1FpAAAvO4uxuuyrmOys8noNWcpVgKnmvw8QziGDjHOwncKTlKXHEoYHpVQXSqvr6pDxJP6nto2Q8SS8IpLa1q7zlzhG+9/MCAvsbMGKkPEkDw+4C8ooyczTjZ3gSRbMxpO8J+NJtKfwJNP5/FfQXFPe1YabZ7qccWLAxsrJI69lXJQUPtjXw8jClj6FBjH32COhosXKPv7l6mxpZOcVQFQaAMDAxCI2d15VbsrI8DAd439npWwq/lGhxo7Tfi8/xjhbGqnISdY0ND0ODi+urI15egEAEjILLDceYWNldlxiwMXOFpOWu/3U7ZrG5lM7PACgoLwG24KPTc9DszLrayi9jUhMzCp8HRofk5Znrqshws8blphpueFwyeeHyMwzNj0/twRz+UmwgabSGrvF0am5R64/rarH3fbZRuEStgW/aI03vrPHxdKYjYUpKjVnxXbfM7vWbllpM6vPfwXU1NQRD8+JC80hpfT09RdV1pgsVENUGgCAacAJ8/H0DwzGpee3dnTJigtpKsmSKGwro4XXn4eEJ2UhspLKWmxCZoGRlipp2llVjzPT0aCjpalpbC7B1AnwcinJiDsvNfqLnv8C2HXFPyA81clMS0VapBrX9vRTQnE1NurWfgBIyC2z2X2FjYXR3mQBF5olNqtk5+UXtbi2ExtXAEBBVQOurTMuuxTNwqSnJhccm5mYVx4YlR6bVWKmpSQyhys8rdBq96WigLNIO4zLLsstr7saEKavJudhpR+dWeJz7y0G23Jz72oKl7BtneZbz+G7+lzMF7IxM0ZnFjscvHF6s8PmFaaz+vwXgWlsWbRAiY6WphbXVlKDE+BhV5QUdjIbHzLSUKPCru8T4+ch5e/pHyyubjTWUKCloZ7V/Ecy/KrY5nPB/90XF5slqnNlquuwjwLfF5VXxQfeB4D4tGwL921oFhZHazMuDvbo5IytR8/X1GNPe29BbAtKKxubW2OSM9nZWA205gV9jkpIz3n1MSI6KWOxobaIIF9obMqS1VvLY4NRKFRMSlZOUdnl+y8MtNTXOdlEJWUcvnC7qrbhzunvxGLCNrcaO23Ed3StXL4EzcoSmZhmu2HvuQPbtro7zuzzX4ecpFjUSz8AwNQ1zjW1n5phaJjgtvOojoaql5vDo9eUh8zNbI6paxQWmNM/MBibmtWK75CTFNNUmUs9JQDa/yI2e2159vy566pVamqqGEz1/QcPCosKU5KSACA2Lm6RmTkajXZxdubm5oqMitq02au6uub8ubMAkJef19iIjYqOZmdnNzI0fB0YGBcf//JlQGRUlMWSJaIiop+/fDE1M6/BVCEzyeiYmOycnPMXLhoZGq739IyIjNh/4GBlZdWD+/coXGpsbNQzMGxra3NzdUWj0eER4VbWNpcuXtixffusPv9FyMvLx8fGAkBVVZW0rBzFUyKRaG5mNn++JnliQ0MDAHByjlO6Q0NDzitd9HR1t23dev/Bgxn+Vnt7+5q16xwdHIyNfsF+c8uegy9ev13pYKumrIipqXv47GVRSVli2DsAiEtKWWzrgmZjdbJbxs3FGRWX6LXnYHVd/dljhwAgv7AYi2uKjk9Eo9kMdbUD332IT0599fZdVFziElMjUWHBL5HR5sudK3PHz5qJSUjKzi+4eN3PUFd7ndvKyNj4g75nKqtr7l2jHEc14poMl9ri8R2rnOzQbGwRMfHLXDwunDiybeO6WX3+i5CTkYr5GAQAmJpauSnxjXHNLQCgqT5pK5KmuuqXiOiSsnINNVUzYwPNeZOeNmJxAMDJwT6r+cL5v/IZrrtvB7+KznY0VleWFKxtan8Sll5S2xRxaQsAJBZglh26x8bMYG+oxsnGHJdbsetWcG1zh++apYhtYTUWh++Oy61kZ2HUV5YKTshLKqwOisuNza1YpCEvzMsRkVlqc/BuweODKCqq+LzKvKrGa2/j9JSl3JdoxeSUH3v8BYPF39j+nb4Gh+9evO8Wvrvf2WQeGxNDdE6F0/FHpzytNtnozezzX4esMO+Xc5sAoBqHV/c8NzXDEGFkzXl/bUXxDda6T8LTyR/RUKO+nN8sxvctDlBP/1BRTZOxugwyHrPUVrwZHB+RWYaITiob2xILMIaq0r+YSgMAAs7uTv8csMDCSVhWuQ1bkxTyFFdVsvdRJABUZCVe87JhZGHTXGzPws5Vmh4bcHYnHltru80XsW0sL+hsxZVmxDGxoGU09LMjgyuzkzLC3pSmxyrqmHHyiRQlh1/bbH3yfSEVClWWGV9flhfx7KqMhr7ucvfStJiQmz6tDZhVh29M9aqzFXdpnXlfF37BUmdGFraS1OjbuxxX7Dhl7Lx5Zp//OvjEZXfd+wIAbQ3VR23VyB/1dbUP9HRpW60iT+QVkQKA+tJcRKjR2oCZq72IhpYOj63FYUrYeQSEZBQXWDgBwPDgwO57odyCYiTbwb6exqpieS3jX0ylAQB5D7zr4t+I6K9gF1fqb66tiX7RU19qePITALQVJyeecKBlYhXWtaVn5WwpSMi9793fUqe06ihi21VbNNje1FKQQMfMxjNXtzHlXVtJSkNScEtBAp+aCROPcHNOZOIJ+8W3MqmoUK1FiZ3VBRXvb/HM1RE3dW0piCvyP9nXVD1vI+Vh8wAw2N4Ud9Sa0NMuYuBAy8Takh+XctZV2e2Y1NL1M/v83wWhtzPLb6eQtg2Poi4AjH4lzlEx5JSa1NgG23EAQMfCPmuGWc1/Yew4fcv/Q5SLlYmKnGR1Q9Pjt6HFlbWxz8Y/+vjMfMv1B9lYmR0tjLjY2WJSc7advFnT2Hxq51oAKCjHYFvwMWl57KzMBpoqQeHxCVkFr7/ExqTmmutpivDzhiVmLF1/sPTLk3FCLCMvt7Ty8uMgg/nKa1dYRKVmH7n2CFOPvX2Mct8htgVv6r4H39ntYmWCZmGOTMm22+Zzdvf6LauW/YjbfwVyEsIRjy4AAKYep2S1doacHV09G45eWWFuYDj/mxobU0+iy3Jb2rvkxIU1leVIdJm1sc61Z2/DEzMR0UlFbWN8Rr6RltrvptIAgMendiZ+CNC1chKTU2lprIkNetJQWXzseTQAlGQknFlvzcTKpm3hwMrOVZgW8+jkjtbGGuddJxHbuvLCjhZsUVosExtaYb5+Wtjb0szElC9vilJjVPTMuAWE8xLCT3taXQstokKhitLjakpyPz26ojBf33iFR2FqzKurR5vrqzyPUQZHAYCOFqzvarOeTryelQsTK1tBcvTFrQ6r9pxevMprZp//KyAOD93c5yE7T9vcZVNs0BPyR4ISskcehwFAS331LksVCsPOtiYAkFScR54ooaiRmxDeWFUqqaiurGNK8bS9GQsALGyT4nb8Dsi6510bHyhmYM8hrtjXXIeJet5dV2p6+jMAtBYlx/ra0zKxiurZ0bNyNhfEZ93b19dSp+p6FAA6a4sH2nEthQm0TGy8ijr1ye9ai1PqEoObC+IF1E2YeYRw2ZGxx1dY+WUhUoyWwoSO6vzSdzfnKOpILnJtzo/Pf3Gyt6l6/qYrFC4NtOOij1gPd7eLGTrQMrE158UmnnFVXX1c1nL9rD7/FYx+JfKpGnFN7vgG8FiY6PiGezsI/V3ixk7kGdgEJAGgA5PHJa3OJiRt4vsOAPqaaz5t0Zr6J/qaqvnVTFA0tH0tdd31ZYxcfByic8UMvjMV+rWx/dhF/3ehLjaLVRRkauqxjwI/FFVg4l7fRZ7Gp+Us9djOxsLiZLWIi4M9Ojlzm8+Fmgbs6X1eAJBfWoltaYtJyWRnYzHQUg/6HJ2Qnvv6Y0R0cuZiw4UiAnxhcSkW7tvLYoIQoiM2JTu3qPzyfX/9BeprHW2ikjIOX/SrqmvwO3WAwitsc6uJ82Z8R+fK5UvYWFkiE9PtNuw7t3/LFnfHH3H7PwZxZGSR7nwN5UmaicamFgDgZGcDAEwdloxH7ZSTFJ3Kow4NE1bv9NHRUPFys38c+I2nbe/s7urudbNdSp5ZWlwYALKLSn9zocbu2yGvYnIcjdWVJQRqmtufhmUU1zZHXNyMPE0swCw//ICNmWGFgSoXG3NsXuWuWyG1zR3HPSwAoLAah8V3x+VVolkY9ZQlQxLykwqr38TnxuVWLtKQQ2iQZYfu5z/aj0gx4vOq8qqw197G6ylJrl68IDa34viT0Goc/vq2FRRe4fDdS7z98N39TsbqbMwMMTkVTsefnFxnuclG90fc/ivA4PCm82TpaKhrmztK65r5udCK4vyOxt85D72jd8Dr2pvl+ir6KuOnxLb39Hf1Da40ncSbSQlyA0BeJZZCqDHV/LfCq7O70z8HzLdwEpZVxk9wDnsmeJLrEzwJMztX2QRPsnwyT1KWEcfEgpad4EkyJ3gSLj6RouTw65utT5DxJJFkPMm76XmSrlbcpXXmvV14rQmexG+Xo90ETzKDz38RXW1NACA6d9KQTGzuvKKk8KbqUgnlBW3T0yAIVI2sol7cKEqOQGQlLXWVFVmJcgsM/z6VBvyTQo1hAvHV57jFepp3j49PDsWF+feev1dZh5UWFQwMS6ChoS7+eB/NygwAuzzs5i71/BKfgQg1AKClvdPHy3XfOgcAcFhisHzLscSswqy3t6RFBQFg/dGr/h+jMQ046Yn4E9WNTWd3r9u6ygYAjm5etXTj4WfvozwdLNTkJwV0OnL9aR2uNe7ZRU0lWQA4vGnlsi0+R649WWlpzIFmndln8nLau3ruBX6Zru7LTLTlJSe9O5gZGRaqyiPXN/3fNzS1hSVmfh0d3bPm20CquqGpp39A3mLtwNAwkqImL/Xw1C5ZcWEA2ORsGZeRb7fNV0tFjp6OLiGrgJ+H69iW8RM3+waGmvEdvFzsK7b7hiZkIokyYkJ3fXfMV5Kd9cP6hTFMHHkdmWa+UMlv/3jTkhDg2XfjVVVDi5TwnKDoDBpqVMHL02gWJgDY6bJY2flAaEo+ItQAgJaO7iNrl+11XQoA9ibz7byvJeWVZzzxlRKeAwAbzz5+GZZSjW1FbgGgBtd2xsvBy34RABxes8x69+XnX5LX2RiqyoiSe+Vz7219c3uM30ENeXEAOOhhbed97ejdt85mCznYmGf2mbyc9u6+++9ip6u7jcE8eTEB8pT+weHm9m5eDjaHAzfCUsdjIMuI8Pnt99BUkAAAJgZ6LcXxr8ztoKj65vbwtIKvX0d3r7T4EfNZM/yqGCYQA96HLTHUuX/uMJIiISq4+8SVypp6aXGR158iaaipS2LesrOxAMCe9a5yRnafY5JIQg0AaGlrP7Zzw/7N7gDgaLnIZt2uhPSc3NCX0uIiALDO+8SL4C+Yukbktroee/7g9m0eTgDgs2P9ktXbngZ9Wu9iq65IuZJ9+MLtOmxTYtADTZW5AHBk+zrrtbsOnb+1cvkSZkbGGXymKKe9s+uOf/B01V9ubqgg/dOf74FzN3At+E+Pr1JNswd6BmDqGnr7+mUMlw8MDiEp6opyjy76yEmK/WxR/yoMDw+/8PdfamHx+NFDJEVSUmL7jp0VFRUyMjIBAa9oaGgwlRXs7OwA4L1vn4SU9MdPnxChBgA0NzefPOF76OBBAHB2drJYahkXH19cWCAjIwMA7h5rnj57VlVVhdwCAAaDuXzp4s4dOwDghO9x00Vmjx4/3rRx47x5kwbQ+w8crK2tTUtJWbBgPgAcP+azxGKp9/4Dbq6unJycM/tMXg4ej7/t5zdd3e1s7ebO/bnZHS0t7Y3r18hTWltbb932o6WltVxqiaTs3eeNwzWFh4bO2sy8tmzt6uo6e+b0T/nwP4HhYcLLN8EWi4wf3hwn2SXFRXce8KnEVEtLSrx6+56Ghro8OxmJALF3+2YZNe1PYVGIUAMAmlvbfA/tO7BrKwA42tlYObrFJ6UWpERLS0oAwFqvXc9evcHU1CK3AFBdU3fx5NHtmzwB4PjBPebLnZ/4v964xk1dRYncq0O+Z+rqG5MjPsyfpwYAPt67LR1cDxw/vcpxBScH+8w+k5eDb++48+jZdHW3tbJQkJOZ7ul3ISkuCgCxick7vdaTEkvLKwCgpKxi4XyNa+dOkOdvxeP9Hj6lpaVZamb6I+Y/5cz/EIaJI4GxOWaa8rd3jjNQYvxc++++r8K2SQnyBMXn0lCj8h7uRzMzAsCOFUaqa8+EpZeQhBoA0NLZe9ht8R5HEwCwM1C193mYWIBJ89sjJcgDAJsuvw6IzqrG4ZHbmqb2055Wm5fpA8AhV3Obg3dfRGauXbpQVYpyo+2xJ1/qWzqjLm9FZvUHVpmvOPrA59FnJ+N5TAx0M/hMUU57T/+DzynTVd9GW1lOdM50T6fD0Uefmtu7g0+sm/qCYmKg01IQQ6793ifWt3RGZJaOjo7tdBiPOrPBSic+r9Lx+KMF8qL0dLSJ+VV8XGxHVi/+WR/+5RghDGeEvlbUNXfzGT+RikdQPPCSd0t91RwRqczwNygamhPv8pFtE2ardxyxUSlMDCUJNQCgp73FetORJWv2AICm+Yqb21dUZCcdDUyfIyIFAE+Pb0r79LK1sRq5bWusWbHztImLFwBYbzx81cs65cNzfbu1IvKUQaTf3fBpb6rf9zgaUT9Yrj94c7tdyA2fBUud6RmYZvCZopy+rvb4oGlFhOrG1vwS8j/+72qpqwQANu5JTRE5k7W3ow0Ahgf6u/HNbJy8t3c5FiaOH8bEJybjdvS2uJImPSOTpMr4YkBMwO32poaipPCxr18Xu+/6cR/+JzBKJNQnvOVTN9XYPD6KYOYTy398uK8Jw8Iv2ZAUQkVNvfhGOi0zGgBklm0N2zK/KTuCJNQAgKGu1rlO++VsdwCAsM7y5DMubcUpZpfjWfglASDr1ra6+MD+5hrktr+lVnn1cemlGwBAwck70de+NjZAwsydQ0KZwrGilycH2hqMTn3hlFYHAAWHfUmnnQv9T4gYONDQM83gM0U5w70d1eGPp6u+4AJLNuGZ+IHch/uJ/d2KK8cnCChqWtU1k0ZHw914TPgjFDUt37xFs2aY1fxXxTCBGPApZrH+/Lu+498gCWH+PefukJilwNA4Ghrqks+PEUJst4e9goXHl/h0RKgBAC34Tp8tq709nQDAYYnhMq8jiZkF2SF3xwmxI5defIiaRIg1NJ3bs36r63IAOOrltnT9gafvIjwdlqpNPgb3yLVHdbiW+BdXxwmxza7LNh8+fPXhSisThBCb2W0S2rt67r6eVie03FRHXlJ0uqezYsfpW929fSe2e5AnYhpwPf0DcotXf6PLFKQfntorJyEMAJtcrGPTc223+mipyDPQ08Vn5vPzcB3fSrlh5pcHkTCc9Om1qr75hhN3kJQ5QuLPzu1rqqviF5VKCX1DTUNz5UshEh/Cas3OHRZKOfGhJKEGAHThW+y3Hl3muRcAtJesOL/ZrjQz8dy7TH5RKQC4c3hj4gf/5oZq5LaloWbV3jNLXLcAgP2WI6fXW8WHPDd1WCeuoEbh2KurPm24+uP+sVJKGgBgt/nQ+U22AVeO6lq50DMyzeAzRTm9Xe2Rr6bdWDJ/kY2Q5Hf6zZeXD3e2Ne2/8+5nmYc5whIAUJyRYLH62yZAbHUZAGAxpTKqC1YfuEiev6ejLfLVPWoaWjWDX21INjNGiYS6hCCBeaYLvMY7KRY+0ZxHh3txGFYBybqkYCpqastbGXTMaACQX77102ZNXFYEItQAgKGuVmXnAwp2OwBAVHd5/CmX1uJkiysJrAKSAJB+c1tN3Ou+phrkFgD6mmvV3H1lLTcAgJLz/tjjK6pjAqTM3TklJklt8l+c7G9tWHQmlEtaHQCUHPfFn3LKf+ErbuhAx8I+s8/k5Qz3dlSFTdurCmlZoif3qihq2nlrJ3V8Q934yrDHKGpaAQ0zAOjFVgEAI8ekARvyR4e68bP+t0eG+gc7WxjYeRLOrMJlj69vsQlKL/C6xiUzb2bbXwnDBGLA+/Alhtr3zo4TF+IigntOXq2sbUCCRgR+iqShpi6NfoNmYwGA3etXyRuv+BKTjAg1YJyGXe+9aTUAOFguWrZud0JGbk6oP2Lu6X3yRUgoph5LCkFRXY89f2DbVg9HAPDZ4Wnhvv1p0Of1zrZqipMawOGLfnXYpoQ39xH5wpFta23W7j50wW/l8iUcaLZZ3SahvbP77vQ07DJzQwVpcfIUWhqay0cnDdrb2jvv+AfT0tAsMdIGAExdY09fv6yRHYlHVVOUfXThKDmPevDcTVwr/uPjKxRvy4qaOgDg5+UiT5QRF0H+ynRO/g4YJo4ExuWaacrd2jG+wCfOx7X/3ocqLB6RF7yNz6OhRuU+2DfBhBiqrDsbml6CCDUAoLWz97Cr+W5HYwBYoa9qf+xRUkF16u3diPnmK4EB0dnVuHbkFgBqmtpPrbPcvEwPAA65mi07dP9FZNYai4WqUpPGaceehNa3dEZe8hqnQVaa2fs8PPb4i5OxOgcr06xuk9De0//wc+p01bfWUZITmfQq6x8itHT08rKzOPk+Cc8YD48tLcRze4eDhhzlWsPe2++6+4aOuX8L2FzV2AYAczgnHbsjJcQDAG3dfbOa/z6YypNwC4q/ueTdWl/FO8GT+JLxJEdtVAoTQ5dP4UkWr9kDABrmK25N8CTINo9nxzelfXrZ1ljNS8aTGLt4AYDVxsPXvKxTZ+NJxBQ1AGDp+oO3ttu9m4YnIfeZopy+rvaE6XkStSk8CRIJoyIrATnZBEFTdRnyW1BKsRvfzMrJ6zeZBnE9eltcaXzzp6HDhrKMOL+dDhLKC2jo6CuyE9HcfNabjsLfiX9OqPF1dBQAErIK88uqVeQkAGCjo+XqZYsYd252GgAAIABJREFU6OkAYJvrsk1OlsikFACIxBE0K3NPXz/JnBqF2rF6/HghJRlxADCYr0KaHOprKPl/jC6rbiCloFmZt6y0Rq5RKKp9a+0TMguiU3PJhRqd3b2BofHz5kprTmgX6GhpPGzN4zMK3sekui83m9lncuA7u0/6+U9XdylRAQqhBjmO33yOzC3lJUUY6b/FFMI0NPX1Dx7b4mplvBDf2e3/MeZpSITDjpMpr64xMzKwszKL8PMUlFdnF1fS0tCMjo7RUKP6+gcR2+oGHADcfvlBQpj/kvcGLRX51PySI1efOOw4kfnmFg/n73v6yejXUQBIzKvIr6xXkRYBgPXLjd2W6tLT0QLAFgezDbYmiEoDkHbIwtQz8V8FAGoUarvTeDxGRUkhANBXkyepJfRUZV+GpZTV4UgpaBYmUlQMFIpq9yqLhNyy6MxicqFGZ0//m6gMdTkxRKUBAHS0NKst9eNzyj4k5qxeqjezz+TAd/WefkwZioAEKWE+CqEGBtsKAH5BURKCvBe3Oy+YK5VWVHX0bpDToZtpj47zTI4TdfxByOAQAQDkxQQY6Gl/xPynyv+V8PXrVwBISM/JK6lQVZABgE2rVrjbWzHQ0QPA9jXOm13tEZUGABCIRHY2lu7eSSMMamrULs+VyDUSp85QS4MkmDBYoP4i+EtpVS2Sws7GsnVCi41Cobw3rY5Py45KyqAQanR097z6GKGhLI+oNACAjpZ2jYN1XGrW+/A4RyuzGXymQFtH14lr0xIi0mLCPyvU+BKb7Pc86PWts3w8/0lsRkxdY2/fwPHdG20WGeA7up6HfHny5sOKjfvSPzxlZvwvBOb9/wLSkOLi43Nz89TUVAFgi5fX2jVrGBgYAGDXzh1bt2xBVBoAQCAQ2NnZu7u7SebU1NR79+xBrlWUlQHA2MiIpJYwNDR4+uxZSWkpKYWdnZ0UFQOFQh08eCA2Li4iMoJcqNHR0fEyIEBTUwNRaQAAHR2dp+e6mNjY4JCQdWvXzuwzOdra2nyOHZ+u7jIyMj8r1KDAp8+f167zbGtru3rlspKSIpJy89at4LdB/Pz8M9sWF5cEvnlz8MABEZFf8Dydr6NfASA+OTWvoEhVWREANq9z91jlxEBPDwA7N3tu8fQgndNBIBDRaLaenl6SOTU19e4tG5FrZUUFADDS1yGpJQx0Fz579aakvJKUwo5mI0XFQKFQ+3dtiUtKiYyNJxdqdHR2BQS901BTQVQaAEBHR7vWzSU2Mfndp9A1rs4z+0wOfHvH8bOXpqu7tKTEzwo1pCTE56kqxyQkPXoeYL/canR07OWb4KD3n2HiG0qOz+FR67ftbWtvv3z6mKKC3M+a/0pAwqImFWIKMFhlSUEAWG+l42Y2n56OBgC8lutvsNJBTwROJ46MoJkZuvuHyEugRqG22Rki10oSAgBgoCJFEkzoKUsGRGeV17cgKWhmRiQkBgCgqKh2O5okFmBicioohBqdvQNv4nLVZYRJey/oaKhXL16QkF/1MaXQ3lB9Bp8pgO/uP/MiYrrqSwny/KxQIyyj9N7H5BeHV/NNpiGmwvdp6OAwEQDkROcwTgwF0cyMwrwchdW4nIoGGhrq0bExGmpU3+DwT/nw7wdyLFpFdmJDeYGwrDIAGDqs17Zxo6WnBwCTlVsMHTeQglt+JRIZWdGDfT3kJaBQ1ItcxxdXhKQVAUBWU58kmJCZp5v26WVzdRmSwsiKRrZ6AAAVCrXEY09FVmJJegwFAdHf05kZ/kZUQR1RaQAADS2d7jL38syEvJgPmosdZvCZAn2d+E93pxUIzhGR+imhRltDNQAwT97Fy8UvDAADfd0A0NZYDQAxr/x4hCQc916QUF6AyU8LueHjt8f5SEAqK+c3fdL7276EoUEA4JeQp6X/Hx5ZfRdjo18BAF+c0lVTyC6uBACSi9eIGbtQ09IDgLTlRsklaxGVBgCMjhBomdiIA73kJVChqGWsx1l+djEFAOBR1CUJJnjm6tTFB/Y0ViAptMxoaYtx6R4VFUrOdntbcXJrfhyFUIPQ11WfFMwhqYqoNAAARUMrbrKqrSgJm/5ZRNd2Bp8pQOhpLwmcNiAoC7/kDEKNnobyxtQPcsu3M3F//zzWpuzI7Ds7h3vaVdxPoEW+0z5nzjCr+S8DhFlKzCzIL8OoyEkCwEYn69XLzUnM0jZX283ONiRCjDAyws7G3N07QCqBGoXa6T6+e0RJFiHEVEn0l56G8osPUWWY+kmE2ERUDBSKau86x/jM/KjUHHKhRmd37+svcfPmykwixOyWxGXkv49OdrddPKvbJLR1dJ+8/Xy66kuLCv7HQo1STN3biMR96xyF+XnJ0zH1TX39g8e2rrY20cZ3dL/4GPU0ONxh+/HUwJvjdJnAnILy6uziigm6jLp3YHC6v/KrAnm/lWYl1pbli8mpAICZ8wZD29VIH2ThttXcZSPpFI8RIpGZFT3QS9lvWrqPzxBFZJUAYO4CfZJgQkFTL/GDPxZThqQwsaKRkBgAQIVC2azbU5KRUJgSQyHU6OvuTPkSKKE4D1FpAAANLZ3RCvfijPjM6A86FvYz+EyBng7829unpqs+v5jUVKFGbnxYRMDdnVdesvP89OmWfCKSEnPVi9PjYoOfLjS3Gx0bTf70Kj0iBABGpwzsc+PD7vls7u3Eu+47Jyw993vl/bJAGl5rUUpnTSGHuBIASC9ZK2GyEumkZK02SlusoyPvWJkndaxUKGo5m4leVXQuAMxR0iOpJXjnatfEve5urCCl0DGjZZd+61jn2u5oLUpuzosjF2oQ+rrqkoI5pVS5yHpVSVPXlsKkhvTPkiYrZ/aZHMPd7YWvz09Xd1YBSfSM8kdcdmTG7R1DPe3qHifYReQBoLe5BqaElWLmEQYAYn/PdwshB2Je/vkeK5/4vLWnuWU128oy8l+cSDzntvhyPAP6dzn9ZIKGzc0vqVAZpzTt3O2tGOgm+tk1TpvcVqAnMbGs5EwsNTVq5zoX5FpZTgoADLXmkdQS+gvUX4SEllXVkFLQbCxb3B2QaxQKtW+jW3xaTlRyBrlQo7O75/XHyHlK8qQgE3S0tGscrePSst9FxHvYW83qNgltHZ0nrk+7TiktLkwh1KDAl9jkjQfP4Du6LhzarigjCQDV9Y19fQPHd22wXqSP7+h6ERL6JOij/ab9ae+fINGnvsQm+714+/rWaT4eLorSMHVYAOBAT5q3igjyAUB3D+Xy+W+FCRqkugCDU5YUAABPK21XM00SpeC1XH89GRNCGBlBMzP2kDEh1CjUVjsD5FpRgh8A9FUkSWoJXSWJgOjs8oYWUgqamZEUFQNFRbXLwSixABObW0Eu1OjsHQiKz1OXFppEg5gvSMjHfEwpcjOfP6vbJLR395/xnzbggZQgD4VQoxqHB4A7H5LF+bnOb7SZLy+aXlLr8zjU5eTT5Js7edi/HXJRVt8SklSwy8FIiOfby7C6qR0AOFgnTT+FeTkAoLt/0qDuu+a/D77Lk+jYuNFM8CRGP8CTmE7hSXgn8yRN1WW8EzyJERlPsthjT0VWYun0PIkYGU+iMyNPQvKZAjPzJLxTeBJeYUlRebWyzPjkd0/nmdmNjY5mhL7OiX4HAKNfvyI0SOwrPx4hCYe9FyQnaJA7e5wPT9AgjKxoTn6RxorCupIcFA3N2OgoNTXN0MDf+37754QaTAz0Bzc4H7/1XNt5u6y4sIGmkrmuhqm2OhKcUEZMqKO79/rzkPT8sjpca1U9rrd/gJ/nW4Bffl5OOtpxbxnoaAGA/CkSmolAHCGlSIkIkMv9EJ1EdUMTuUsVddixsbG+gSE372/xh3v6Bkk5Z/aZHDJiQvi0t9PVneT5d9GWGlRVj0vNLfG58czAdXd52KM5XBwAcM93Bz0trYKUKFIdLRV5Nhamq0+DP8SkOi81Ml2zv7iy9urBTfbm+vT0dBFJWV4nbtpuPZ719raoAG9ndx8ADBOILy8ekBETAgBVecnW9q7zDwLfhCdsdv6VD3+dGYwMdPtXW514+E7P84SsKL++mqyZlpKJpuJ4OxTh6+jpu/E6IqMYU9fcjsG29vYP8nN/e8Xzc7NTtkPub6oXahQVABCI32ZokkK8k9qhmAAA1ODayF2qbGgeGxvrHxx2P/4tshnSSddg22b1mRwyInwt4benqzsdLTVFSmdPPwAME0ee+26SEeEDABUZkdbOngvPP7+NydhoZ0KeuSXsFqaxNbWw8vj9EONNp0sCz81q/lPl/0pgYmQ4tHXtsSt3tWxWy0mKGWjNW2y4cJGeFvKmkpUQ7ejqvvrwZXpuUR22qaq2oaevn5930vSJn5eHjnZ89YWeng4A+Od8y4B89AQiEbmVEhMmb2bI6Ly6vpHCq8rq+rGxsb7+wVXbD5MSET1cdT12Zp8pICsh2lkYN131SZ7/IJrb8J7eJz0crG3MDH7KkIT7547Q09HNlZEAACkxYS11JTQL8+UH/u/D41yW/Q9raZmYmHyOHjl85Ki6hoa8vLyRoaGFxRJzMzNqamoAkJOTa29vv3T5cmpqWm1dbWVlVU9Pj4DANzGWgIAA3cTsDtFJkD9FCiEQCKQUaWkp8oY0V0EBADCYanKXyssrxsbG+vr6HZ2cSYk9PT2knDP7TA45ObmBvl6YBnRT5qU/DgwGs3PX7o+fPklJSfm/eG5qYgIATU1NHmvWrlu7dvmyZbOWcP7CBTo6ul07KaND/xpgYmQ8sm/X0VPnNY2WyMlIGeppLzE1NjM2QD4jWWmp9o7OK7fupWVm19Y3VFXX9PT2CfB9m2sJ8M2hm1gbRnQS/GRPUch4jKxdSUmIT3pByckCAKamjtyliirM2NhYX3+/y9pvkRV7ensBAFNbN6vP5JCVluzBVk5X9599OwEACoV6cOPSMhePDTv27TzgMzo6Ojo2utbN5f6TF0hdEFTX1O0+fPxTWKSkuNize9dNDPR+yvzXAyM9rbfLopPPwvS3XZUV5tVTllqkKWeiPn6En4wQb0fvwM3g+IyyuvqWTgwO3zswRCFQ4ONio6MZ/3zpaWmQFNJTJLQ7YWR8xCUpyE3ezBB2oKapncKrSmwbMuLyOPuClNg7MIRkntlnCsgI8TSHTDtRpKWhbJkzo7mjx+vKazfzBZYLFWfN3BR8GoPDpxXX+D4NNdl1o+jJoTkcrIv33S6pbbq02dbOQJWBjiYyq2zb9SB7n4fpfntF5vw60bbpGBiXeu7/4Hfi9Co9PnFZ2Xl6ijpmCgtNkNNV+cRk+rs7ol7cqC7MaG+qb63HDPX3onkmKfPQPHw0tOP9Cw09AwCwc39bp0HKGRkZH1/xCkuStyt+CTkAwDfWUHjVUlc5NjY2PNj/4IA7KXGovxcA2rA1M/tMgTliMteTmqerPjXtz/WMNHT0ANDfM2lb2/DgAAAwsbIDQH93JwCMEIbXn3vGJyYDACJyKr0draGPLmZFvDVy2kiyupbY3NqAweSlvrvle87d+PSnYjaun44Z868FNT2jvP3u4ldno70XsQpK8yrq8KmZzlExpEJRAwCroBSht7Pyo197RfZAW31fUw1xsJeBY9LyHgPHHNTEcTAoWgYAYCTLgJzXOzrRrlj4xIGsXbEJyQJAX0sthVe9uCoYGxsZ6k+/8i0gE3GwDwD6W2pn9pkCrAJSy15Qlk8CasaDbCo+3ELR0Epbbpz6qL+lNv/J0absCBY+8fnbbvMq6f9UhlnNfzEwMdAf3Ljy+M2nCx23yEkI62uqLNbTNNWeR+piZMWFO7p6rj17m55fVodrwdRhe/oH+MmWRvh5uciICDoA4OedQohNNDMAkBIRnDQAkxIFgBoKQqy2cWxsrH9w0HXvGVJiT/8AkBNiM7pNgqy4UHvG++mqPzMhNjMuP35DR0uzzdWWIv3+iV30dLQKUmIAICUiqKWqgGZhvvIk6H10souliYn7nuLK2muHttgvNqCnp4tIytx8/Jqt19HskLuiAr/O62tW0DEw2W468OaG7yEHXUEJWQVNfRU9c2Wd8T5IQFymr6vjy9PrlfkZbbj65vqqwb5ejsn9JgcvP6nfpKVjAAB2sgzIEQAjxPGRP5/opOkkIpJoaZw0nQSAptrKsbGx4YG+G2SHAg/29QJAa0P1zD5TQEBc5nFm63TVp5nSb3a1Nd89stHIdrWGyX9CilKhUOt9b1/c6vDg2JZnZ/eNjY2OjY4a27lHv3kkJPVthaCloebFee+c+NA5IhJeZx8qav2CJ2nODGp6RkWHPQUBZ8L3mrIJSc+Zq8uvbsKvaoR0UmyC0sO9nWUf/NorsvpbG3qbqomDvYyc3/pNRs5vvSo1HdKrfvvaIoWMjnybb7LwT+5Yhb/TsfZM9Kopl8l71V4A6GuundVncrAJStm/rINpMEOv2tdcm/vkCDYrgoVP3Gi73xzl8Y4P0YIQ+rrIM48MDwAAHcvs2ywJvV0AMEok6Ox5yCYoDQAcEspD3W0lb6/WJ4fIWHjOWsKvASZGhkNb1xy7ck9rmYecpJiBlrq5wcJFegtIlCbCxF57FJCWW1SHbcLUNlIwsfy83CS6ANEjkj8dp2EJZAtPohRMrARMYWIrquvHxsb6BwZX7ThCSuztGwCA6nrsj7hNgqyEaEdBzHTVn4HoqK7H7jt97XNMsqSo0JNLPsba4/vF7589TEdHS86jsrEyX3nw8n14nMuyxc1t7ev3n/Kwt7Je9B2eFtk42tk9aaG3f2AIANjRv+yezB8BIz2tt7PpyefhBtuvyQjz6ilLmmnIGavLkAZO0kI8Hb0DN0MSMsvq61s6MLh2CiaEj5OSBuEnezqxHEC28CTANWnhSYQPpjAhVdi2sbGxviHCmnPfNpn3DAwDQE1z+4+4TYK0EE9T8EmYBlNpkM7eQQAYJo48O+gqLcQDACqSgq1dfZdexwQn5G+w1iHlvBYUT0dD7bVs0mHEyH8AKYSEgSECALCzTFJvfNf89wGJczizSo9PXFZmGp6k5md4EjQ3+Xx2dp6kbXqe5CEZTzLY3wsAeDKe5Ls+U2COmMy1n+FJqFAo16O3bu9y9D+17c0l77HRsbGxUd1lqxODH/NLyJFoEM8JGkRYTqWnozWMjAa55LkYV1Xs5H1Zw8yOlp6+OCXK/+TW2ztWHAnM4OL/u7Z0fod5/Puwb51D4cd7+z2dmBjoHwSF2m3z1bDzamnvBICrT4OlzdzP3HtNHPlqpKV6z3cH6WQQBExTNuMiFO104OPmJL9FxIAUwv+Orl4AoKejpSEDJzuro4WhwkQAjBl8JgcVFRUjPd10P1Pfa2NjY4haDYGUiICrjemJ7auJIyPhiVlIopq8FDKdJmGxrgYAFFfVldc0FFfW6mkoedpbsLOxMNLT2Zhou1qbDAwNv49OAQABXi4AmK8ki6g0EFjozweA8uqGGf5vvwP2ui7Nf3na282SkZ7u4Yd4+/035rsfbenoBoBrr8LlVuw79+wT8etXIw35O/s9SAd/IJh61Dfqe/Q9CXxck0bVzAx0MKHwIKGjpx8A6GlpaGioST+caGaHRQvkxQVm9ZkcVFRUjPS00/1MbYcCPOwAoKkggagoECzRVgGA8rommNJQJYV4Vy3ROb7eljjyNSKtcFbzWTP8wti/2b0kOuig1xomRob7ASHLPfeoLXFpaWsHgMsP/CV0rc/cekwcGTHW1rx/7sjCeZSxjqeenouaPiwnRRQKJkZGmFhAJUd7VzcA0NPR0tLQkH642NHO1uaItmMGnylARUXFyEA/3c93tR0z4J5/SHtnV09vn6f3SeQH19La1Ir39D55/s60RxiQQ11RDpldkLDYUBsAiisoWaH/ORw6eLCqovzI4cNMTIx37t61tLKeq6Tc3NwMABcuXhQSET1x8hSRSDQ1MX3y+JGOjja5LTMzM0VpM7+v/o+9s45r6v3i+BkM2MYCttHd3SAlIIKi2AV2Y2F3C3br1xYLW7C7FbtIBenuHrAgFr8/hnMbbIBYP9z7df9gTx7G5T7P/dxzz1FTFdilcbsLRcKorKoEADk5WRk+SCTSmNGjeQEwxNjMDwKBQIum5QP4dnLu/Hlbe4eoFy+2b9ua+DmB66UBAIePHK2oqKipqZk0eQr3KCwsKioqmjR5ypatW/lHyMvLu3Dx4uBBg4hEYmszdAVWLJyTGv161eJ5GDQ67NS5QaMmWrv5lJSVA8Cu/Ud0LZ027dzb1NTk08PjxME9bs5O/H0xGOGEfG2cVyoCrz/KYzDQ4gJVWVUNAHJycjIySN5BIiqOHjHE3MS4TZv5QSAQaBRK1PFj55WluWnc68dh/+2YNmls6Mol7x7fMTM2BABecI7zkdccvPxevH63NWRVwpunPC+NdnbvqiwO9Ik7vnzpKF+0nOzJe+8CQ066zNxZWl0HAPuuRpmP37D90pMmJruHrdHhhYG8vB485Fu8rStmHVRRFHDy+LbdEn4sVFVLBwBZGaQMUpp3EPHyAd72pjqq4m0WAoFAoGRlRB2t+naI4eS9d5W1tFoaY9aeCO5RXFFbXFU7a0/E7shnwN2Pcfj2Y+rkMb2cQib5NzFZjz+lpOaXfc0p7m5lMKWfqwIWjZKVGeBmNdrXkdHQdPvtlw5Z8vfTd/Li9dfj/aculUWhX147eXBBwPoA59rKUgB4fPa/Ff5m905sZzGZpt16TAg5zMvcwUMOLbwyIkT/sfi1CV5fZItIYzRKFQAgZWSlkTK8Q55A7NYnQF3fTLzNwsYgEDJyaFFHq5qFGLi+FBWFOQLW1lYDAE6RDAAKymoAoGflxJUnuFh59AWAkpxUDofDYbN55cpaBq4Dxg6ZHcJiNiW+ERlO5v8U06Hz++x/bzZsIVIOnfXozJutYx8t9KqnlAFA2q1D92bYJl/dw2Y1KVt5Ogb/RzLpJtQdiRJeGcWcVyjB2ObSKAx8e0jDT2NdNbdcCinDO+Rwitoew7iPoMTYLAwCIS2LEnW06tvBhV5RmPf6mrpTX6HXfAEg79WVJ0t8yr++tRq7tteuFy3dLMQ3aLN7l2RZ0MjEOydXTBuNRqGOX747dPY6hyHTSyualaU94VcMe4/bcvRiE5PZ08U2bOMiV1uB2HKt3R6KFSKUBPaxGDQKvr0AwKOqphYAZGVkZGSkeQdJATeyn7fZNyVKvNk8OiqItZP84rKIe1EDvF0VWzz4sTM34npp8PDzcAKArxm5KVn5Sek5no7WQQH9vsll7uMG9qLXN9x8+ubHLPn/ZXDQkt13Pw+ZvkwWhXly+cTO2cOXDXaiVJQCwJ3w/2b3Mrl+dBuT2WTp0mPGhqPGdi3XzQ7s/BUEk21x102uewc/VO66KSvHv25iFYju/QI1DMzE2ywEAoGQlUOLOlqum08ij9dRKunU2qNrZnCPqrKi6rLio2tm3DouMiYfP1pGFtuufggKOegbMGVE8OoNF6LU9U0AQONb6I7Xdy6tHOH6NfrVqIUbt137+A96aXAxHza//4EPFsMXImUxGY9Ov9wy9t4CT+4ilXLz4K1pNklXdrNZTSrWns6z95FNBRZWpFyL3ZrYy51Q0hBk88IqcOI1r6pIOYQ0knfIYhV1PIbxAmCIsVnImh9YVXNeXnmwuGdp0lvbcWv997zkeWkAAEpBGQCopQLOH43UagCQwwtHMmgJhqQKACRjB66XBhcNRz8AqC0Q+QJDl2TZzAlJTyJXBE9Co+WOXbwxdNoS+75jeJLmnuMXDDwGbzkYzmxWYle52gsosS3D8bYhdAgm/uCus0IxibkyrKygDEtUwI8c2NvcUK89ZvP4MRn24s2HzgMnvPwQt3lpcMzdczwvDQCwszQR1lG93AAgKT0LAMIuXK+srqmh0qYt38Q9CkvLi8sqpi3ftOPoWRUyCQCy84v4u3P9NsjEfzGeAT+LAnvGHlu6ZKQPRk7m1L33gaGnXGftKvsmKey7+sJ8wqYdl542MVk9bI0OLRjhLKiEYFDCPjfiH4AKpQXhdheKhMGVQeRkkEhpad5BxGFG9LAz+/a8RrzZPDoqg6iR8ADgaKrN9dLg0qebGQCk5n+/uhaUU668iOvnYqGIE9h1KCviACCnRODfobqODgBkPLbN7v8UfSYvDv2mOby6dvKQoE6y0t/s/jedZHz7dBIxF8BWdRKZ9ukkWAKxW58AbgAMMTYL8QM6ibqhxepL78eu3u8xbMqAmauWnn6uqmcCAGoGZq3KINbfZBAAKMlOLcpIMnbo7jl8CgavICOHtvUe4DJgTGM9I/6ZyFQGnef3RdRobGIy6ht01FXWzBqzZtaY0srq7ccjj1y6c/jindljBq7ZF05WJHy5GYb9Fvxn+/HIzkyXKbha5BaVAYCRrkDETj1NVQAw1FY/uWkRr5DFZlNpDDRKTrzNIbPH8Q9VWlm9NeySKGPGD+7Fn3IFAHadurJu/5lr+9f5df+enpykgAeAgtIKACgoqYhOSnOwMNJS/X4hyy4oAQBlIuFLWg4AeDgIvHXX08V239kblDoqAGiqKQFAE1Mg+F59QyMA4HHC/3j/FI1NTEZDo7YqadXkQasmDyqtqtlx9l7Y9WdHrz2bNdx3XdhVMgEXf34TFtO8p99x9m5npssqFHiGlFtSCQBGWgKXM101MgAYaKocXzWVV8his6n0erScrHib104dwj9UaVXN9jMiU8OO8+/On3IFADSVSdDiPGFwzxMsGgB2X7gfeuz6la1ze7t8j1FPUsACQEFZdZvd22zQVWlsaqIzGnQ01NbOD1o7P6i0vHLr4fDDZ68cOnt5zsSRq3ccIhMVkp5cxsk3byC2HQ7vzHSZuQIu27kFxfAtOyA/elrqAGCoq3VqVwivkMVi19HoGLScGJtDFwq/RVdaXrn5oMisnBOG9xfKuiIeMlHBxswoI+e7D1lDYxObzf6cnCZ+P8qloLj00+evjlbmWnxvR3FvGJRI/9+vETc2NtLpdF1d3fWKhfNEAAAgAElEQVShIetDQ0pKSjZt3nLg4MH9Bw7Onzd3+YqVSkpK6akpOFyzcLlps8jXu9tDRmYm/8ecnFwAMDEReJasr6cHAEaGRufOfvehYbFYdXV13Of3YmzetHED/1AlJSUbNooMVDt50iT+lCvt5M7du+MnTHR1dbl4/rxQ1hIlJbKtrU16xnepoqGhgc1mxyfEC+0+w44dZzKZU6ZM7ujs/y80NjbRGQwdbc2QFYtDViwuKSvfsmvfoePhB8NOzZ0xZeX6LUokYnL0Kxy2+bZny659nZkuIzuH/2NuXj4AmBgJJPrV19UBACN9vdNHvs/FYrHqqDSu55kYmzesXso/VElZ+aade0UZM2nMSP6UK+2hsbEpJy+PRCROGjuSV7j9v4NqKspERQUAuPvwyaRZ812cHM4dO6CtKRwZvs3uXZVGJovR0KitorhyrN/KsX6l1XW7Ip6G3X4TdvvNzEEeIafukQnY2OPLsOjme7mdl552ZrrsYoH8zXml1QBgqKks1ExXlQgABurksMXfYwKx2GwqowEtJyvG5jXjhTOLl1bX7bj4RJQ9Y3s7CWVdEQ+JgLXSV88s+v5bNDQxORzOl6wi7jq45/Lz9afvR4ZM6e30fXkl4uUBoKCCgsmWBYDuVgJCm7ed8cHrLynULhXmndnU2FTPIKlrD5i+asD0VbWVpfdP7oyKDHsecdRn1KzrB0JwCuTQ63EoTPPl6/7JneIHFA83dQiPyqI8AFDRMRJqRtbUBQBlbYNJG75nhWOzWQ00qgwKLcbmQbOEU5zWVpbeOy4ymLbbwHEt076KQUXbAIFACDlqFKZ9AQBu9FGiqiYAsPhewQeApoZ6AEBh8Q9P77l5MDR472VL9968WqwCCQCqSwvbb8bfD5vZxGqgY5S0zAOXmgcuraeUpVzbm/ngZOb9E4b9piWe3yiLJ/XZ9w6Jbj6vUq6JXGXaA61E4GUjelk+AODUhRPxyqvoAABWTc9pzkFeIYfNYjKo0nIYMTZbjFohNFQ9pSzl6m5R9uh4jxbKusIj+8lZDoup13O0UHlxzONPB+aQjB27zTvSakoU8Q3a7N4laVaWNFTWBI9bEzyutKJ62/FLRy7eOnzxVsicCRXVNWv+O0lWVPhy+wTumyC27ZhIfak9ZOUJCGJ5haUAwP8aDwDoaqoBgKGOxsnN33dTrQhiIszmH6q0onpL2AVRxkwY3Js/5Ur7OXn1PpPFmjDET6i8oKQ8OjHVwcKYPx8KVy5TIhIS07MBwMNRYMvn42q37+w1yj8Wkp3Z1NhYz1DS0B4evHp48GpKRenNYzseXTz66MKRPuOCL+1di1ck776TgJJvvr7dOCYyTVJ7KBVcN8uLcgFATVf4T6+sqQsAqtoGs7Z8D+PPZrPqaVRZFFqMzQFz1wkNRakovXF0G4jAa8g4oawrOEWyjql1Sd73215mYyOHw85N/SLGwe5746bG8sJcnAKpx9DxvMJbJ3YrKKliCYoAEPfiwZFV04xsus3edoqba+zfhM1sYjbQ5ZW1rEYusxq5rJ5SlnR1b/r9E2n3jpv0n55wbqMcgdR//3vewvr1aqcW1rpigYWV1rywCtxvYptXVX3Xed+DEHPYrCYGFSmHEW+z9eiV/EPVU8qSLotcVfV8RvGnXOFSFPP4/f7ZZGNHtwVHWy58ODV9QCBogo4a1TlJAMBL1CIGDFkTADhMJn8hq7EeAGQwbWRU7Eo0NjUxuJLmvKlr500tLa/cdvj04XNXD529ErpwekUVZfXOw2SiQuLjCJ4Su/Xw6c7MKKzEFhYDgJG+gAylp6UBXBl25/fLF4vFptLoaLRcm2bzD1VaXrnlULgoYyYM68+fcoXLvedvpizd4GxneWZ3qJZgNKmC4rLoz18drMwEddRCAFAmKQIAmahgLajTNjY2sdnshOR0KSmpsUP6IhAIIUeNzynpANDN5t/K9CQEn6TQe+XY3mXVdTsjnh278/bo7bdrxvtV1NBCw++TCfIxx5bylJBdESIDpbSHLMHgGXll1QBgpKHEX8gng3xXpXgySJtm8w9VVl23XbR0M7aXE3/KFQDQUlYAAKbQA8pGJgAQ5L+704U/+MBkscf2FnhJDAAMNcgIBCK3pIq/MDG7GAAcTLTa7P7vwNMc+k9f1X/6qtrK0gcnd0ZFhkVFHO05ataNAyE4BXIIn07y4PfqJBPF6iQtbR7YaZ2E2dRYWZSLVSC5Dfq+YXsUvptAVpXHK8rKoaCFDNLYUA8AaCweAAozkgDAyL47fwMzZ++n5w/Q6wTCX/1cfl9EjRefEtQ9R16+/4L7UYWkOH/CUACg1FLzisvYbM6gnm48L42CkorPqZ16EzojtyiD79b07M0nAGAt6Cqor6VGViQ8fhvbxLeh2XnisrrnyOjENPE2C01XU0cLv/5I1JGVL/wmsYWhLgA8ex/PX3jq2kMAsDLWA4Dq2roxi7cIeatcefQKANzsLMz0tQDg+hOBtwGuPnrNGxktJ+vVzTouOYP/S7j9/D0AuNh04AFq1+NlXIpW/3lXnn7kflQhEuaP9AMASh09v7SSzeYM9LTneWkUlFV9yehUAJKM/JLMgu8egufuvwEAK0OBGzZ9DWWyAu7JpyR+h4Zd5+9r9Z8Xk5Ij3mah6WqojNN3X4s6hLxGAAAtJ+Nlbxqflstv5J3XcQDgbGEIABb6mgDwLPorf6/w268AwMpQs83ubTboqkS9i1F16BV5p/l1QxUl0sKpYwGguqYut7CYzWYP7t2Dd29QUFyakJzWmenSs/P4d89nrt4BAJsWWpiBjiaZqPD41Qf+K972o6dVHXpFJ3wVY3PLGSl11FORt0Qd3Ah+7WfW+BEfbp3hP0wNdPW0ND7cOnN0y6o2u1fV1I6avXKroLPL5btPAMDdqQPPM/5Cnj1/rkgiX7zUrNKqqqouWbwIAKqrq3Nz89hs9tAhQ3heGvn5+fHxCZ2ZLi0tLT39ux/DqfBwALC1EfgODQ0NlZSUHj561NT0fUOzZetWRRL548dP4m0Wmo5CoRw/cULUkZmVCR1n5apVBALhSmSkkJcGAMyZPTsuJob/MDMz1dfXj4uJOXH8GH/LR48fEYlEn549f8CA/wuev3qjpG9x6WpzeGpVZaVFc2YAQHUNJa+gkM1mDx7Ql+elkV9YlJD4VeRY7SA9Mzsj67t2Fn4hEgBsLAXu3g30dJXIpEfPopr4ctht23tQSd/iU2y8eJuFpqupqTl59qKoI1PQa6Q90BkMC+ce85d/D1VaUFR87fa9/n2bn1yu3riNgMdFhB9t6aXRnu5dlZcJGToBa6+8aN7oqiji5g7rAQAUKj2/rJrN4Qxwt+RpE4XllC9ZRaKGag8ZheX8Xg7nHn8CAGt9daFm+upkMkH+aWwa/45rd+QznYC1sal5YmxuOWMNjXHm4QdRR8usK+KZPsD91f4F/IeJlrKuKvHV/gUH5gUAgLmuGgA8jxPYLZx+8AEArPTUTLWVAeDGm8/8tddfJQCAuW6H86//zaRGv1zYUzv64RXuRzxJpde4eQBAr6NUluRz2GzbngN46kN1aWFBaqcCipTmZZTlf1+M3t4+BwBaxsLOXkqa+lhF8td3T/lv9R+e2r2wp3ZOUowYm1vOSK+reXPzjKijvFA4mqh4CEpqhnbu6bFveWFIWcymjw8vKyipa5vaAoCMHNrEyTMvOZ7/10yIugMABtbOGgbmAJD84Tn/mK9vnIZvaWu7DOWJr25NMsl/c537EaWgbDwwGAAaaTX08gIOh63h3I/3MIlRWUTJSerMdHXFWdTi70JHTtQlACDoCn+lWFU9OTypND6Kzfp+XqVe33drkklVRqwYm1vO2ESrzX56XtRBa5F1hUdpQpQsVkHJSjiGcNLFzTIYvMvC46LcLMQ3aLN7l+TFx3i17sMj70dxP6qQFRdMGAYA1bV1AJBXVMZmcwb5uOG+C2Lln1N+ZDPMIz23MIPvpuzMzUcAYG0iIIgZaKmTFQlP3sTw3x7uOB6h1n14dGJqm2bzQ6mjhl97IOrIKvjBaJpP3sYqEnDeznZC5dU1daMXbdp2XMCX5eqDFwDgbm9ppq8NANcevxaoffQKACyMdH/Mkv9Tkj6+CHLXfHvvMvejAlml/8R5AECrpVQU5XHYbCefgTwvjcqSgtyUzyLHagclORn8PhAvbpwDAF1TYVcwFW19vCL589sn/OvmreO7gtw1MxNjxNjcckZ6Xc3za6dFHaX5wuum3+gZmyPf8B/q+ibKmrqbI99MCxWZRJhHYz1j8UD701sW80qqSgs/Prnp0MOf+zFiXwgai5+369y/7KUBAKVfXl2bYJz7+vsiZTaoeZGiledzOGxNvoWVXlFUnZPYmenqirPq+BbW7OcXAUBRT2Bh5a6qJfHP+VfVr9f2XZtgXJkRK95moekaaTWZT8+JOriJVIT4fH6TLAbvvvhEqwsfmqiqbOZa9vUdry+b1ZT76hqaqNbS56Ml0rIoFavuVVkJ/F9Cwcd7AEA2+YceW0a9i1F19Iu885j7UUWJtGDqGACgNK+zJWw2e3BvLz4ltuxzcqcijqRn5wsqsfcAwMZMQIk10NEgExWeCMqwO46eUXX0i/6c3KbZ/FDqqKcib4s6WpVh1+06isfJX9y/SatFzq+qmtpRc1ZtE3RVuXLvKQC4O9oAwKxxwz/cDOc/TLg67c3wI5tXqCmTuzvZvv4Uz5u3icmMuP1YXUXJzqIrJ3htk1cJGbqBIVdfNGuzyoq4ucO84JukwFVC+rvxKSEVlC9ZnYo4nimohJx/HA0AVoJKiF5rMsieyOe6gSGxafltms1PDa3+7MOPoo6WMghKVsbTxiA+o5DfyLvvkgCgm9n3d4mfx6Yp4jBetsKPilSJeDcLvTeJ2byRm5isK1FxaiQCv0eIqO7/DmnRLxcJag6+v1EneXf7HABotlsnWdRTOzcpRozNLWek19W8vXlG1NFSJ2mqZ4QOd4zYsYRXQikrint2y9qzL4iVQfStnQFATc8EAGKfCmR1jHl8HQDUDX+hL9rvi6jhamOuRCRsCbukoUK2MdXPzC/meiH08XA01tHEYlBXHr3q7e5grKf5Pj55/aFzOCyGSq9PzykUCoPRTlhsduCCjeuCxxnpaNx89vbQxVvDend3txf4KmVlkOvnTpgVum/Kql0LJw7HYTF3o95vOx7R08WWm3hFjM1C0xnralZ/vN5+8/y6O1oY6R6+eJuAk/d1sy8qq7z++PW9lx8dLIz6ejoBgKWRrrO16alrD4kE3CAfVzabc+lu1NN3cYN93Bwtjdlsjo+r3dN3cYOC143y76GtrnL7+bvL91+aGWgP8G6OXbNh7kSvcYvGLd0WOme8pgo56tPnE1ceuNmZ9/Ny/oHvs8vgYmmopIjbevq2upKijZF2VmEZN2aGn6uVkZaqPFru6vNPvZwtjbXV3idmbDxxAyePojIa0vNLhMJgtBMWmzNq9cE1UwYbaqncehl75NrTod6ObtYC+zZZGWRI0NDZO04HbTq+YFRfnDzq7uv4HWfueDuau1gaiLdZaDpjbdWKx4c7ZGHotGHeMzdPCDmyLmiohrLiy9iUU7deuloZ+rvbAEBvZysLfY2j154RsBjfbhZFFZQbUdH33yXYm+r2cbVps3t7GnRJXB2slUiKmw6c1FBVtjE3zswt4MbM6OvtZqyvg8Wgr9x94uflaqKv8y72c8ieMDxWnkZnpGXntQyD0R5YbPaImctCFkw30tO68Sjq4JnLw/193B2FfRRkZWQ2Lp41Y+XmSYtCFk0bh8fK337ycuvBUz7u3VwdrKl0hiibW85ooq9T+/XlD5j6K7AyMXS2szwZcZOkgB/Uuwebw7l488GT1x+G+Hk7WZu33f8vxt3NTVlZef2GjZqamna2thkZmdyYGf36+ZuYGGOx2IjIyL59+5iamLx5+3bN2nV4PJ5KpaamppqY/MitEYvFGjx02MYN642NjK9dv7Zv//6AESM8PAQcSGVlZbds3jQ1aNrYceOXLV2Kx+Nu3rq1cdPmXr6+3MQrYmwWms7U1LSB0crjzx+muro6MTHJzs521+49QlU9enj179evnYPExMQO6N9ffJTL/2vcnB2VyeSNO/ZqaqjZWllmZudwY2b49/IxNtTHystfvn67j4+3qbHh2w/R6zbvwOOwVBotLSPT2NCgzcFbwmKxho6dsn7VUmMD/et37h8IOzli8IDurgLhbWVlZTatWT5t3pIJM+YumTcTj8Pduvdo867/fHt4uDk7irdZaDoTI0Na8c/MeaRAwHt7uF+9ddfHy2NQvz6Z2TkzFizTUFfbFroKAKopNUnJqbZWlnsOHhXq6OXu2s/PV3z3LoyLua6SAnb7hccaZIK1vkZWcQU3ZkZvJzMjTSV5tNy1lwm9HEyNtJQ/fM3ZePYBDiNHq29MLyjnD4nZflhszpgN4avH9zFUV7r19svRW6+HeNi4WugJNZNFSq+b6D/nv8vTdl6cP8Ibh5a79z5p56Wn3nbGzua6tPpGUTa3nNFYU7ns5taW5b+I3k6m5rpqYbffELBoH3vj4sraG68THnz4am+s5dfNXEoK0dPe+Fls2rA1xwN62usoK95+l3j1RZypjko/1y71QN3AxgVHVLp7bJuCsoaWiXV5fhY3ZoaVu5+KjpEcRj7m0TULt16qusaZCe9vH96IwuIa6NTS3PSWr3e0Bw6bdWTR6IGzVqtoG8Y9u/084oiD7xBDO+F9EVJGdnDwunMb55xaE9R7wgK0PC7hxd17J3eYOXsb2Lg0MGiibG45o6qu8f63wn7VnaHvpEUH5o84vmJCn8lL5HEKD8/sqSjMCd4TycspO2R26LaJPY8tnzA4eJ2iikbqp5evrp0ytHW19vTnsNkahhZREUcxOIK5iw+lvDj2yfXPr+7rmNtbeQjHmPm/hmTSTY5ATr6yG01UV9CzpJbkcGNmqNn74tQNkCj5grc3VO164tQNK1M/JUVslUFjWfW0uqJMobd12wmHzXq3Y6LFyOVYNYPCj3cz7x3XdB1INhO+Q5dCyliOXhVzZOGnfcEmg+cg0djiTw+Sr+1RtvYim3Rj1tNE2dxyRpyG4ZALHX73oJFWQ8n6rObYWyjgfCOtpiY/RUHXMu3OEaEuSuZuag69xDcgmXYT372jdv6/4GJroURU2HLkgoYK2dbUIDOviBswo69nNwAw0tXEYtBXHr706+5krKf5Lu7r+oNncFgMjc5IyykQCoPRTlhsduC89evmTDDS0bjx9M2hCzeH9fZ0txdYFGRlkOvnTZoVsnfyih2LJo/AyWPuRL3bduyij6s9N/GKeLP5MdHTokTf/rEvRxSUWmpccrq/l0vLIIuWxnrONmanrj4gEfCDfNzZHPbFO8+evIsd7Nvd0dKEzeb4uto/eRc7cObqUf176qir3Hr6NvJ+lJmBzoDW7m27MCa2rnii0vUjW0kqGjpmNqV5mdyYGbaefdR1jVEY+fcPr9p49FbXM06Le3/5wAY0FlfPoBbnpLcMg9Ee2GzW7nkjA2avVdU1/PTk1sMLh138hprYt7JuBs4LPRYSfGjF1AGTF6Dl8THP79wI227l2tPY1qWBThNlc8sZ1fWMT8d0zDu2M2BwBItuXh8e37B08Xb0GVCal3l8/VyiivrohZsAgFZLKcj4qmNqc/fMfqGO5o4edl5daukUj5JpNxSBnHR5F4akrqhnWVeSzY2ZoW7vi1c3RKLk897cULPzwWsYVqR8/HJpmwwax2TQ6ooyWsaXag8cNuvVtgnWo1bg1A0KPtxNu3tc222QkplAXHcppIzNmNUfDy94/1+w2eDZMhhcwccHSVd3q9p4KZl0E2+z0HR4DaOASwXQbhppFEp+iqKeVeptYbVW2cJd3aEXAJgPm/di85g3u4Mshs2XlVdIvrGfVprrufIciM78yI/NmDWPVvR5uyvIesxKDEmjNPFV5qMzSqbOGk6t7Da7KlwZdvPBUxqqyjbmRlm5hdyAGdy0yEZ62lgM+vLdp709XU30td/FfgndewzXaSU2YNbykAXTDHW1bz6KOnTm8jD/nlwvBx6yMjIbFs2cuWrLpMWhi4PG4rDyd56+2noo3MfdydXeqk2z+THR16lJimq/eZSauqT0LBtz470nLgpVeTrb9e3h5mxneTLyFlEBP9ivB5vNvnjz4ZPXHwf79XBsn466dMb4IUGLx8xbs3zmBAU8btexc9n5RdfCdiDad9J2VZy5MsjFJ+pkgrW+elZxJTdghp+TGQAYaSrJo2Svv/rcy9HUSFPpw9ecTece/QQlZOOZ1eN6G2oo3X6bePTWmyEe1kJKiCxSeu2EvnP3XZm+69K84T1wGNS990k7I5552xk5m+m0aTY/RppKpTc6Fst53UR/34UHJm09v3Z8Hw0lwsuEzFMP3ruY6/Z1bj7TKFRGfEZhH2ezVnPdLgz0Dgw5NWnr+UWBPRWw6P+uROWUVEWsm8Q708R3/0fQt3HBEZXu8WkO3JgZloI6iYqucVbC+1s/Qyc5umj0gHbrJOFrgnpPWICSxyW8uHv/5A4zZ2/9bzpJqza3nFFV13hfR3QSNI5g4uQZ9/Tm2249bHsMKC/IOr9proKy+pB5G7kNBs8O3T6x5/HlEwZ9k0FeXztlYOtq7ekPAGr6ZmYuPZPfPzswd2i3voEkNe34qDvRD6+o6ZvZeLXr4cKP8fscNbDy6FObFwet2dMnqDloGEpWNmT2uD4eTgBwOGTezJD/RszfAACKBNz2xVMxaNS0NXschwfXRN/4gel6dLNRVyaNWbKFzeYAgIej1d6Vs1o2mzC4F72+fvWecG44CqS09MQhvUNmj+P+t4u3uTNISSEidq+asmrXpiMXNh1pDhE5yMdt59JpSGlpAEAgEBF7Vs9av2/nycs7Tzb7kgeN8N+yaAq3++ktSxZuO3r5wcsnb2O5td3tLQ+HzpWVaf6bOlgYXdu/bvq6vUNmh3BL+nk5Hw2d10nL/9/BYlDHVwfN2HKy3/zmID8oWZm1U4f4uVgDwKFlE2dtCw9ceQAAFPHyW4MDMWi56ZtPOk9cV/VU+BlMe+hhb6qmpDhu3eHm89DWZPeCMS2bje/XndHQuObIlWvPowEAKS01oZ/H2qlDms9DsTZ3EntT3Stb587cemrYsv+4Jf7uNoeXTeL+LCWFuLgxeOqmE1vCb20Jb07CNNDTfvvcUUhpqTa7t6dBlwQnjzm9O3TKkvW9xwZzS1BysqELZ/Tt4Q4AR7eunr5847DpSwCASMDvWDUfg0FNXbLBvu9oasprceOKwNvVUV1VadSclWw2GwA8ne33hS5pteXEEQPojPqV2w9wHaWR0tKTAgaGLpqBQCDE2/w3g0AgrhzePmPl5u1Hzmw/0pyPY9roodtWzP2zhnUeHA53/tzZCRMnefdsfhqNQqE2bdzQz98fAE6eOD55ytSBgwYDAJFI3LN7l7y8/ISJkyytbbhxyzuKT8+eGhoaw0cEcE+kHl5ehw4eaNlsyuTJdDp96bLlkZcvAwASiZw6ZcqmjRu41yvxNv9S3rx9y+FwYmPjYmPjhKoQCEQ7HTWeR0Wx2WxXV+F0fV0JHBZ7Jmzf5FkLfAcGcEtQcnIbVi/17+0DAMf27wyas3jImMkAQFRU2LlpnTwGM3nWAht3X0Zpx17m5tLTs7uGmmrgxOnc88rL3XX/jlZS3kwaO5LOYCxft+nyjdsAgEQiJ48btWHV0ubzSqzNv5pj+3eODZodNHdx0NzFAGBnbXUu7AA36MjbD584HE7c5y9xn4Ud0hEIRD8/X/HduzBYtNyxJaNn7LrUf3nzoziULHLN+D7cW/2D8wOC90aOXH8KABRxmC1BAzEo2Rm7L7nO2llxS2T8ajF42RiqkQjjN51hczgA0N3KYFfw0FZbjuvdjd7QtO7kHW7ACaS01Hg/5zXj+yAQCPE2/1mkEIgLayZM23Fx6/lHW883x74a4Ga1fcYg7n7sxNIxS4/cuPIi/mlsKrfWzVL/4PwAWWTrebL/T0FhsJM3HA8PmbFnRvMlXUYWNWjWWsvufgAwfs2hMxtmHV44EgDk8YrDF26RQ8ufDpm+PtDl4PsfeYpj4uSloKwetmw8h80GAGOH7qOWtx7p2n3Q+MZ6xvV9a2OeXAcAKWlk98HjB85ai0AgxNv8qzFz6Tlpfdi5jbPDlo4FADSOMHzBZgu378/Cdcztg/dePhM668C84dwSa0//8esOAQBCSmrGzgun1gTdCdtyJ2wLt9bOe0DAku1S0r9PQ/gNINHYbnMPfTow92Vo83VDWkbOYtQKVXtfAHCYuTfm8Py328YDgCxWwXrCeiQKE31g7uNFXkMvduA5DQ9lSw80UfX9rqkcDhsAlCzc7Ka2ft3T7Tma1cD4cm59wbtbAICQRur1HGMxagUgEOJt/imUJ77mcNhEY+EXVCpTPgKHQ8n+QslusfABQs2hl/gGACC++8+y/28DJ48+tWVp0OpdfaYs45ag5GRD5kzo49GNW3skdMGMdXuGzw0BriC2ZLo8Wi5o9S7HoTNqY0XmNhWDt7OtujJp9KKNXCHC09H6v1XBLZtNHOLHqG9YtefE1UcvgSuIDe0TOmdC8wZMrNm/mhefEthsjrN1K6swAoGI3Lt2ZsjeHScidpyI4BYGBfTbungacOWybcsXbj0ceT/qydsYbm13B8sjoQt5ctk/AkoeG7z1xJFV0zdOab4Xk5FDBcxdZ+fpBwDT1h8OWztz15wAAMASFMcu3SqHlj+yatqyId3OxAlHQ2wPFs49FJXV9y4ay103zZw8Jq0SdqDn0mPo+IZ6+sU9a94/vAYA0tLIHsMmBMxZh0AgxNv8x5m2/tCBZZPC1s0KWzcLAHTNbGdvO8WNSpIW957D4eQkx+ckxwv1QiAQ/5SjBhKNdZ13+P2BOc/WNSdrlhAQqNkAACAASURBVJaRsx69kuuU4By898PB+a+2jgMAWayC/aQN0nKYD/vn3FvgFRjxI7nVVKw8MES1NzuncBdWZQs3h6BWFlZ9n9HMBnrC2fV5b28CAEIaaeAzxnr0Sq4zhHibO0NFyifgcKqzPldntYxYg+COr2rTw3XuwY+HFrzeMRkAZOUJdhPXq9m191aXaGjrtfL8h4PzXmxqzlam4eTnHNypBKb/d+DkMeG71k1dutFv3GxuCUpONnTh9L493Li1R7esnL5i8/AZSwFAkYDfsWquPBo9dekGB/+xdck/8h6at6ujugp51JzV35RYu30hi1s2mziiP72+ftX2g1fvPYNmGXZA6MLp39ZZcWZ3hrexnzkcTnxSanxSqlAVAoHw93a/fGjrzFVbdhw9u+PoWW75tNFDti6f087xfbt3O7lz7cyVW0bOXgkABDx224o5fp5dWUlrD1i0XNjikTN3Rw5Y0fwUSU4WuWa8HzeBKRYtd2D+iNl7L49aHw4AijjM5qD+GDnZmXsi3YJ3l9/c8gMzetkYqpHwEzaf+6aE6O+cNaRls3G9nRgNjWtP3bv+6jMAIKWlxvXutma8X/ODJ7FmdxJ7I83IdZOC90aOCDnJLenrbH5w/gheg1efM9kcjpOpTqvde9oZH100cs6+K+M3nwUAgjx6U1B/X0eTdnb/R0BhsJM2HD8dMmMvn+Yw8JvmMG7NobOCOoksWv50yPQNgS4HOqGTHOPTSUaK0EncWugk7nw6iRibO8+4NYdOrpp8bsPscxtmA4CWqc3kjSd4YUV0zO1n7b18NnTWQT4ZZNy65uBqCCmpyZtORu5YEv3wytd3zbl+DO3cx609iJSR/SnmtQqCw+HwPkRGRgYGBtLifrIjPD/0+obE9Jz84nKyAt7cUEeJSOBVVdXUJaRkqpKJpvpa3MtEVU1ddS3VQEuto7No9RjtYGF842AIpZYa+zVDXZlkqi8u7hyVxohPzaTR6y0MdTVVye23uZOw2ZycopK07AK0nJyRroa6Mqllm7zisvScQgJO3lRPi5cahkdhaUVyZh6jodFET9NIR6Ol32ITk/k1I6+iusbCSEeVTPxZlrfK1Uevxy/bxn9G/Rjc87A26ljbTX8URn1jYlZBfmkViYA119NQUsTxqqpqqQnp+aokgqmOWvN5WEul1NH1NYQznbeJ7sAF9qa617bPo9TR41Jz1MgKprrCUbj5odLrE9LzaIwGc30NTWXhP5YYmztPE5P1NbuwklJnrq+pShI+w9lsTm5JRVpeMUpW1khbVZ2s0KHu7WnwE7n2PHpi6NGfdR7Wp7/74RHojPrE1Iy8olKyIsHC2ECJpMirqqLUxH9NU1UimRnqNZ9mlJrqmjoDnQ6/L6Xu1MfR2uzWiT3VNXWxicnqKkpmhsLvEAtRR6MnfE2j0uiWJgaaagIR8MTY/PeTV1iSlp1LwONMDXR58Qx/mDFzV0lhSZGRkW03FUtAQABwOJERP55kmk6nf/78JS8/j0wmW1pYKCt/vxZVVlbGxcerqaqZm5txT6TKysrq6mpDww6/iUJWVnFydLx/7251dXV0TIyGurq5uTg/+rq6uri4eCqNamVpqaUlvMKKsflfAyGNjIiICAgI6Ow4CMSFE4dGDB7wU6wCADqD8SUpOa+gkEwiWpiZKJO/b34qq6rjvySqqaiYmRg1n1dV1ZSaGgM93Y7Oompk7WhncyfybDWlJib+s4aaqpmJOH/tOio1/nMSlUazNDfV0hBeMcXY/KvhcDiJX1OycvPsrC1bTXHyS7u3yajJMxGy6J9yvWoqTg1fMe6nWAUAjIamxOyignIKCS9vpqOqpPDdPaWqjv45s1BVEW+irdy8DtbRKXV0ffUO/1n1R66zN9a6sn4qhcqIS89XIxFMtYWDuwpBZTR8ziykMhotdFU1lAR2NWJs/uOwOZzckqq0gjK0rIyRppJaiw1VUUVNcl5JfUOTkZaykYbSz32f6fqrhElbz/2s/dXhT60kaGgnjfWMwvTEqtICLIGkbmCGI35/94hWU5Wf+plAVlHVM+X++rSaKnotRUlLX/R4rbPYV0/X3H72vqv0WkpucpyCspqaXhtCVT2dmp+a0ECnaRhaKKoI/KeLsfk3wGYxc7/GcThsXUtHKalWfHdYzKaizGRqdYW6oTmBLBBEkMNmVxTlluakycihVXQNFZTE3cu0h2MrJhgoyvyU69W7gnrnBT/thpHVwKjJ+0qvKJTDEfFapnKE79eixrpqSs4XlIIKXtOY+zinsa66kUbBqrax5W7J7SlmigZ23VdeaKTVUDLjUUQ1vKax+C5MBpWSk8ispxG0zdAkge9fjM3/OAXvbn3YM+1nXa/oCfd/ilXAVZbSsvNLykgKBAtDHSWiwAJURamNT8lUUyKa6mt/u0Osra6lGmh3+P9O0yvQwcL45qENlFpqTFKaugqZmw1EFHU0RkJKJo3OsDDS1VQVvkCJN/vPkldclpZToICTN9HTxrUul+Uy6huN9TSNdTV/+mu+GJu+P2ufP2fHaRe/1n1MO09jPT0vLamiOB+nSNIyNMfzrUFUSlVOSoKCkqqGfvO6SaVU0WopKtodXjene+oYWDgsPXyNVkvJSoolKqtrGLS1btKoOSkJ9XSqlpEFSVVACRFj8x+Hw+HkpyeVFeTomdn8DSlOxljjftZ56LYwTNtt0E+xCgBYDQxK7ldaRYEcjkTQNkXxLVINddWU7C8oRRXCt4W1oa666YcW1uuTTImGdl6rLjbSKFUZCRhSGwsrk0Gtzv7SVE9T0DbHkIWvrmJs/g1wWMyqzAQOh00yske0tmETD5vVVJOX0lBbSdA2Qyu2cTfUUd7sCnLRlPsp+zcWtfL8fxt+ilUtoTPqE1Mz84tKSUSChZG+kKT5TYklmxnqdlKJ1ejm72htdvP4LkpNXUxiirqKkpmhrpj2zTIsnWFpbKCpJqyPiTf7l5JXWJKWnaeAx5r8kI7KZLFiv6Sw2WwnGwtp6V8elXbMvDXSP0mnbSpOPbW8lTdpfwqMhqak7OL8ZklBRUhSaFZCiHgTLT4lhMrQV2vlsaB4DEaF2hlrXQmdTKEy4tIL1El4E7FKCJXR8DmziFbfYK6rqtHi4Y54sztJE5OVnFtaUUu10FFTIXb4kRaTxY5PL2BzOA4mWtJ/Ov7xpK3nZdRMfsp5mFndNHXL6babto/fo5Ms8dXT6bhOUpCaUP/bdRIOh1OU8bWiMFvL1Jao2sp1XowMwoVSVlSUmdzUwFDRNVbRMfq5txKznAhC+7ff7U6OQcl1szLpZtVKVHYiAeftbCtUQiR06mm0Ah7b00U4/n9LsPLo7vYiIwOLsbmTSEkh9DXV9DXFeaJoqylrt1jCeWiokDVUxG0cZZBIG9MO/8t1edAoWSdzfSfzVr4ZIh7r7WAmVELEd2pxUsBhvB3bDh2GxaDcbUTeUYixufPIIKVtjERqN1JSCD11JT11kddK8d3b06BLgkGjutladrNt5dpCVCD0dHMSKiEqdMqFRZGA83Fv17tNOHlMd6fWL4xibP770dZQ1db4kRRFfzkYDMbFxdnFpZWsVSQSydfHR6iEROrw5p4fRUXFXr5tv46Jw+E8PYVzlvMQY7OEvwQMGu3saO/saN+yikRU9PHyECohETslFigqEHx7iDxheOCwWA83kaeNGJt/NQgEwsrCzMriByMrdLL7/y9oORknU51WX24g4jA9bI2ESoi4TvnYKWDR3nZtPPLkgkXLuVm2vqESY/MfRwqB0FMj6YlWcNTJBHXyr3WH/RuQRaH1rJz0rFoJcChPIJp26yFUIk/olKs6Bq9g5uzdnpYoDNbIrvU4ZGJs/g1ISSPFTy2NlNEyaT1QH0JKSklTT0mzww9O/u+QlkMTjRyIRg4tq2RxispWnkIlsrhOLYuy8gRla6/2tESisWSz1l9PFGOzhL8TDEqum7VpN+vW1UyiAr6ni51QCVEB35kZFfBYH9e2N044eXR3B7GCmGiz/yydlMv+EWRRGENrJ0PrVhYCrALR0sVbqASr0Kl1Ux6vYOXasz0tUfJYUwdR66ZIm/84CARC29hS2/j/UjP5nUjLoUnGDiTjVhYpOZyiirWnUIlcZxdWBVWbthdWJBqrZO4qqlaMzb8BhDSyM1NLScso6gknyP7XwKBR3WwtutlatFr7K5RYBQLOx73tK5UYGRbaMvuX0kkdFSkt/UfM/stBy8k4mmo7mrb+HOSXKSFt57DAouXcLEXe1ok3u5PIIKWtDX7c4x8pLfWLDOtK/M06ieGf0EkQCISGkYWGkchrlBgZhIuCsrqCcmffVGk/XTYFuwQJEiRIkCBBggQJEiRIkCBBggQJEiRIkCBBggQJEiRIkCBBwt9G13TUUFUikhQ79eaBBAmdR5VEIBH+oojZErokasokkuJfFH5Wwv8pamqq5N+YSELCP4KqijKZ9GvTrkmQoELEk/Dyf9oKCV0NAllVXqFTEaokSGgJSkFFDidZFiX8clTJRHLnQnFIkNBRFMiqOEXJuinhd4NSlCysEv4AasokkmLXD2Qo4S9HhYgn4Tub+FuChA5BIKtiJTrJr+F3pz75PXy6fOBPmyBBArw/FfKnTZDQ9Ym5e/5PmyChK/AlIeFPmyChCxL/+smfNkFC1+fdoUV/2gQJXZA1l979aRMkdEF67Yr60yZI+CeIvnr4T5sg4Z9j27UPf9oECf8ifXe/+NMmSPgXib5z9k+bIEECvD244E+bIOGfY7VEJ/lldM2IGhIkSJAgQYIECRIkSJAgQYIECRIkSJAgQYIECRIkSJAgQYIECX8hvzuixsPX0bU0+gg/z988b0tOXn1QUV0LACZ6moN83PirqPR6LAYlqqP4WvFwOJyaOpoC/pekw2CyWAgEQlrqx51v2GyOlBSCv+Tpu7iYpHQAQMnJzh03uLMm/jU8ev+lll4/vKfTnzYETt1+WUGpAwATHbWBnvZCtVR6fSOTSWzthKExGuTRcr/DxNb4zbM/+5QUk5IDAGg52dkBvX7bvL+aB1Fva6m0gP5//jc6celGeRUFAEwNdQf37sErZ7JYTU1MNErk35pKZ2Ax6N9gYUuYLJa0lBQCgRDVgM1mSwleD5+8/hD9ORkA0Ci5eZNH/XITfwv37t+vra0dGRj4pw0BAAg7dqy8vBwAzMzMhg4ZwivncDgUCkVRUVFURyqVisWKXBmZTGZTUxMa/SNnWptTi4fJZEpLS/OfZo8eP/706RMAoNHohQu6jvf6/cfPauuogUMH/mlD4Pjp8+WVVQBgamw4pH/fDvVt+V/Pg8lkIhAIaWlpUX1b/q3bD5PJbGIy0SiRm8M2Bxey/PHzl9FxCQCARqHmzwr6AZP+Th59Sqlj1A/ztP3ThgAAhD94X1FDAwATLeUBblbt78jmcKR+6DwBACaLzWSxULIyP9adC5XR0MhkEXGtxDgVvz1rafmz2LTY9HwAQMvKBA/583dnP4vEN4/qaXWOvYf9aUPg9fXwuuoKAFDVM7HzHsArZ7OYgEBISYm8IgFAPZ3KamqUJ/yBaN4NdJocRmQ6oZa1ye+f5XyNBQAZOZTvmNm/3L7fQknc0yZ6nZb7X3H/m/3kbENtJQDgNIw0nPvxyjksJpvFlJb9QWmiU3A4jfRaWXmRQb85LCYgEAixJ3mHKE2Iqs6MBwBpWZRR/xk/a9g/zsNXn2pp9BF9vP60IXDyyv3y6hoAMNXXGuTj3v6OLUWk34Z4eY3JYjUxWWg5WVHdWwpoT97FxiSmAQAaJTt33NCfbvCfIv7VQwatzrXP8D9tCDy7cqq2ugIANPRMnHw7fN9RT6MymxqxCsIrI4vFZDGbZOXE3S1y2GzEj4ql9XQaSvSy2ElaDv7l7dPMpFgAkJVD+Y+f84vm/f0UxT5hMqjaf8HCmvm4eVXFaxpp8q2qAMCspyFRP/637mT3zsNkUFnMJjlc6+oHf21JQlRVRvOqajKg66yqD168q6PSRvTz/dOGAACciLhZwRVaDXQH9e7YQl9Hozc1NREVWtloiddp21RxO4kYyaU9CGnIT15/jPnSLNLOnTTyJ9j3d/A4OqWO3jDU0+ZPGwIAEP7gQ2UNDQCMtZQHuFkK1YrRFsQjXtlgstgIBHTmGaX4qaWlEGLkNVp9ozxK5PaPC/8v/iwuLS6tAABQcjLBgz1+rrV/kL9HFQGA19fDqd+EEdsOCiPi+eENHofDYdTVYPAKPzYvm8VkMZtk+Dafye+f5X5TRXx+SBX53Y4ae8KvZhWU/A2OGgcv3MorKlNTIvZ2d+Q6asQnZ67dfzomKZ1SS1UmKfTv4bJ5wSScfPOlSnwtP9YDp3k4Wh1cK7CfptRSV+09FXEvitHQiJVH+7k77Fkxk9TxvKGtDh5xL+poxN2ElCwmi6WnpTojsP+0gH7cu+WojwmLth1tdSg7M8PjGxcCQHpuYVjE3TtRH2qoNFdb8zljB/Xo1ryWfEpMvXD7WWkVRQaJ7EqOGnsvPsguKv8bHDUOX32aW1KhRlLo5Wwp5KhRVUt1nRyKl0d/Or2eV5iQlrfu2LXYlGxKHV1ZEd+vu+3GGcNx8s0XBTab0z1oPZPF5h9HW5V0ZevcNmvbg/jZediOWeVha7J/yXhR44hv0LL2U3L2xYfvyqprZZDSXclRY9exc1l5hX+Do8aB05G5hcVqymQ/L1euo8aT1x9W7ziUlJbFZLG01VXnTxk9fcxQ3nY8Lil1zc7DMV++VtfUKZOJA3w9tyybjcc235Gy2WznQROYTBb/FDqaajeO7WqPMW12fxD1NmTP0eSMbBxW3tvVcfqYYd2dvj/2S8/OO3Lu6u2nL2vrqK72NnMnjfR2c+RWfUxIOn/9fllFlQwS2WUcNbbv2JGZmfWXOGr8t29/Tk6Ourp63z59uI4a1dXVS5ctP3/hAoPBwOFwffv0OXhgP5lM5raPjY1bsXLlp+jo6upqFRWVQQMH7ti+DY//vjI+evx4+YoViYlJTCZTR0dn0cIFs2bObOdtofip+TEyMe3h5XUsTGCtvHf//uo1a75+Tcbj8T29vWfNnOnp6QEAHz58OHP2XGlpqYyMTFdy1Ni5/3BWdu7f4Kix7+iJ3LwCNVWVPr7eQo4apo4eXt1dj+7dLtQlPTPr0PHTt+8/qqmtdXN2mjdzak/P7rzaC5evHz5xOv5zIpPF0tfVCQ6aOGPyeP6z6P7jZ2s370hOTcPjcN4ebjMmT/Bwc26ntY+fv1y5fktScgqTydLW0lgYPL1Dg4uy/GNM3NlLV8rKK2RkkF3JUeO/q8+ziyv/EkeNIzdf55ZWqZEIvRxMhBw17IO2dbcy2DdX4HlGRmH58Ttv775PqqXVu5jrzhrs4WVr1OrIrXZ/FpsWEn4vObeEyWJrKSvMHuI1tb/bDzh8VNXR3WbtIsijPhxZwitMyCwMDb8Xm5ZPoTKUFXD+LhYbpvTDfXMrF2N5dGrepWcxZdV1MkjpruSo8ejMfxUFWX+DJPHs4uHK4lyCkpqlWy+uo8bHB5EvIo/lp35msZhKmno9AqZ5DZ/aUlmg1VRtGOWGxuLXRX5sdeS1Q+2MHTzGrtrXTktSPr2I3LG01SptM9uJoUcBIC8l4cbBkNyvsfRaCp6obO3lP2zeRpQ8jttMTG12YvT7exfrKsukkTJdxlEj7eYBamnOX+KokXHvGK08H62oqmLXk+uoUZoQlXhhU21eCpvNxJA1jQfM1PebiEBIAQCHw3661JfDYvKPgFHScl/RSrbEh3NdlSzc7Ke3a4vOpZFWk3hufd6rq6zGeiQaq2rb03bqVjnc9+emea+uZj08Rcn+wmazsCo6Bn2mcG0r+/Iq4dSqVsdU0Ldxmr1fvGFVGbF5Ly7X15RLSSO7kqPG7vDLWfnFf4OjxsHzN3KLStWUSb3dHYUcNawGTPFwtD60bh5/YXpu4dFLt+88f1dDpbnaWswZN8TbuXl9f/4hfvHW1nOv2JkbHd+0mM3muAQGswRv97Q1VK4fWN9qr5ZQaqkr95yIuPuM0dCIk0f3dnfauyqYJ689eRe7Zu/Jrxm5TBZLW0153vih0wIH8HuTXLr7/GjE7YSUTCaTpa+lNmPkAG6DT19SLtx+WlZZjUQiu5Kjxp1Te0vzs/8GR40H5w+VF+YpKqvadu8t5KixsL+NuaPH1BCRaayplKrlw1wwOPz2G9G8wi9vn17auy4/4yubxSSraflPmNsrMIh/VS3OzXh8KSzm+V16XY2JnUvfcbMtnHu009qc5PhL/4VkJcbQaikEkrKDd7/RCzehsTgASPoQdXrrklZ76ZnbzdwU1uavJmbwjC/Rr25fqKksQyJlupKjRsqNg9TSnL/BUSP1bhh3VVW39+E6alRnfU44v6kqI76RRkEpKGk49bEdv04GjQOA0i+vYk6sbHUcooGNy5wD4rt3iDuzXVQs3Jxm7uZ+bM/U/DTUVT9Y1EMGg/Pf+7plF6HayvTY7KhI7qralRw1dh87n5VX+Jc4ahw8HZlbWKKmTPbzdBFy1LDsFejZze7QpuWtdqyi1Dj2H0/AYePuC2zenrz+uHrn4a/fdNp5U0ZNHz2EJz6Ir20/rdqWnpN/9NzV209f1dRR3eyt50wK9HZtFlrZbLbLoElMwZ2njoba9WM7eR/jk1LX7DoS/SWZwtWQfTw2LwvGY+U/JSSdv/GgrKIKiUR2JUeN/668yC6p/EscNY7cepNXWqVGxPs6mgo5alTV0d2D9+DlUR8OLwKAFwkZy47eanUQW0ONIwubZedncWmh4feTc0u/KRueU/q58pSNy1Fxx+68+5xVyGKx9dRIQf3d+Gvbj0PQ9u7WBv/NEbijfxydsvHsw9S8MhxGztPacEo/VzdLPV5tQmbh+vAHselcVQTr72KxfrI/rrWX7YV+8ZjU/EtPY8ooVBmkdFdy1Hh85r/yv0MVAYDn34QRC7dett+EkZd8woiXCGFk3VA7YwePMS10j7K8jBeXjyW8uMug1hrYuPiMDjZxau/9FL2Wcn3/2o/3I5saGCgM1sKtV+CyXVgFEreWw2ZvHtudLXifQlLXnrX3Mvfn5PfPbhxYV5SZzGYxiapaPmPncC3PToz+8E0V+TFHjX869Ym7veXnW2E7l00DgNiv6X2nrYr7mhHY12t50EgCVv7k1Qf9pq9mszlt1vJz7tbTzPxiocLGJuaQ2SGnbzwO6Ot1aN3cgD5eVx+9Dpi/saMGtzr4hTvPpqzeXV1LnTVm4LQAfxq9ftG2oztORHJrEYCQQSKFDhabnZKVX0ejAwCjoXHE/A1nbj72dbMPGuGfmVc0fO7617GJ3O7Lg0Z+vhU20Nu1o6ZKaD/u1sbx5zftmCv82Dh42+niCgp/SVxqTr8FO+NTcwN8nZeN74/Hok/dfjlg0W7eeVhUXp2YWSAthSATsLxDESffnto2aXN2LucfvM0qLBMzjvgGrdYuG98//vymAR527TRVwg/Q3ck26cnl3WsWAsDzt9EDJi/ILSweP7z/9DHDGA0NC9bv2rT/BLdlzJfkPuNmxyamBA7wWxk8mYDDnrh0w3/CHDa72QeosKT8S0qGtLQUmajAOxQJ7fVLE9894s7jIdMWU2qpC6eO9ffufu/Zm6HTFqdl53FrGfUNw6YvOX3ldi8Pl2mjh2bk5g+dvvj1p3hu7crgyUlPLg/soCe7hA7h6eGRnpqy77+9ANDY2Ojfv/+JkydHjxp1/FjYqJEjIy9fHvQt0kZ0dExPX9+Y2NjRo0atWb2aQCCEHTvm27s370R6+uxZn77+OTm5kyZOnDVzJoPBmDN3Xuj6De0xQ/zU/ISfPp2RkSFUePHSpf4DBlIoNUsWL+7fr9+du3cHDBqUmpoKAGtWr05PTRky+M8LTF0YDzfnlOhXe7cKiPVnLl7OzM5p2ZhRXz949OTw8xG9enpNnzQ+PTN78KhJr942J8k+G3Fl4sx51ZSaOTOmzJg8nkajzVu2Zuue76rWpas3B42aSKmpWTR7hn9vn7sPnw4ePTEtI7M9dj57+brfiLG5efkTRgfOmDy+nlE/b9maDdv3tHNwMZavWjwvJfrV4H59OvbFSegg7pb6sceWbZsh8O984Ul0VlGFUMv6xqZR60+dffzJx95kSj/XzKKKwNBTbxOzWo7ZavcX8enD1h7PK60a4+s0tZ9rfQNz6ZEb284//gGbZ++NLKmq5S+JSy8YsOJIfHrBiB52S0f54uVR4Q/eD1oVxuZw2rR86Sjf2GPL+rd4sUbCT8TIzn39tbiAxdsB4P3di+Frp9HrKN6jZniNmNpAp0XsWPLgVCvPyM9umF1TLnzTx+PdnfPl+a2cfmJAAEIaiRQ62GxWcXZKPa0OAHKT4/bO7J+XHO/kN8J/6lI0Fv/6evjeWQM5bHabtf5Tl66/Fsf/ZoyEnw7ZzNVv3zvbSZsAoOzLq9ebR9HL8nS8Rxr0nshqrI8/uTL5cvOJxKgsrsn9ipCSlsOTeIcstpWXa3OjIqgl2R0yg81serN5dPazC1rdhzrM2K3lPqTg3a132yd8H/NF5KcDsxtpFEP/IIPeE5n19PiTK1Ov/QcACAQCIS0jdHDYrNqCNCajrk3DzIYt9Nv3TqObf4cMltAh3B2svtw+sWv5TP7CszcfZ+YVCbVkNDSOmBty5sYjX3eHaQH9M/MKh89Z9zqmWURCIBDCCpQMksVmJ2fl1dLoAFBYWp6Yli0lLUUmEngHEd/eh5qNTczBwWtOX38Y6O99KGT+iL49rj56OWJeKLf2+Yf4QTNX5xaVjhvca1pgf0ZD48KthzcfOcfrfv720ymrdlBqqcFjBk0L7E+lMxZuPbz9+CUAWDFt9JfbJwb2dGt9Ygk/AzMH9913EsYv38Ff+PLm+dK8Nta1sHWzqgVXxqQPUdtmDikvyvUaPNY3MKixof70lsXXjmzhNWhsYOya+X4hGwAAIABJREFUE/Di+hlrNx/fwKkluZk7ZwekxLxpj51ZSbGbpvTL/hrn5h8wZPoyDBb/7MqpzdMGcBc+QCCkkTJCB5vFKsxMYVAF9mmt/mriBx8yfdnuOwlOPf+8A30XRtnctf+B9/aTNwFAVWb8s5Bh1VkJOh5DLYYvlMHgMx+ffR46nMPh6hIIKaSM0MFhs2sL0prode3o3l6yn19qsfa1MbUQHw/NZ1SViBpfqNZi+ML+B95rSlbVX0x3J9vExxG71gi84XP22r3M3AIxvWas3FJcJnxH+fxd9MApC3MLi8cP6zd99FBGQ8PC9bs3HTjZntr206ptjPqG4dOXnr5yp1d352mjhmTk5A+bvpQntBaWlH9JzZCWkiYTFXmHIt+bybGJKX3Gz41NTB3Zv/eK4EkErPyJiJv+E+ax2ewVwZMSH0cM7CURaX8tbpb6MceWbpsuvKzM+e8yv7aAAISMtJTQwWazU/NK6+j13DYvEjKGrz2ZV1o9xtdxSj+X+oampUdubrvwhFt76VnM9F0RFCp95sDuU/q5UhmNS4/c3B3xrKMGX3gSnVVcKVR49UV8YGh4DbV+zjAvPyezB5+SR64PTy8o59bGpRcMXBkWn1EwvIftkpE+eHlU+IMPg1cd46oi4n/xJSN9Yo4t7e8qUUV+LYZ27qHfhJEPdy+eFhRGIlsTRt6L0D2aGhiHF458e+usuYuP57ApZXmZhxYEZsS1a4PHbGo8OH/425tnnPqMGLt6v6Pf8Jgn148s+v5YtrqsqDA9CSEtjVUk8w4MvvluOvXTiwNzh1YW57kOGOM5fGpTQ33kjiV3/8feWwc02b3/49dgG0p3d6NgUCoGIm2AYieCgYKKYrdgt9giYGIgqaQoEtLd3c2osRES2++Pe+I2xoAHfR6/v8/79Zc7cZ1zz5td57zO61zX0ysAsHj7EWe/jOkTYEX+7Ygafy0evwvu/fEj+tXNaSryAHDafuMSu1NRyVkBX+OsjOcxrwWAuqaWS0/epuWV5BQzIDu8Pn1Nzim67GS7b/MKALBeboxCgYdPWHp+ieYUxrfxqMHcuOtLf0Vp8ZjXN5HwHk42q6Ys2fbkffDRHWsBQF93WuJ7es3RwatPCMSeu6ccAMD5/suSyjr/++dM5moBgMMGi1lr99qduZMX5D6+b/B/+K1wD4z6kpzLx02jonji/623rz/y0eFpilIAcNLW0sLpVlR6QWBM2oqF2gBQVtcMAG4nt2koSA23ybx2VDAfvQ7XfuX5p/TCypyyGobdmTcYtfv/8G/i0gNPMpkc5/dMXloCAM4f2q0wz+KOx9sTe7axsrI8eu3T0/sj1tdjupoSAJzZv8Pceu+3+FT/8KiV5osAoKyqBgA8b5ydpjr679twMOne199//Mo9jsmTEgNfIEFuLx62l59nsdnxVNLHlwBw9tbj4orqQPdbpvpzAGCP9VrtZZu3Hzlf+M13Il/I//DP8OLly8TEpBvXrx10cgKAbba2KBTqiZtbamqatrbW/QcPenp6khISZsyYDgAuzueMjE2+Rkb6+vmtXrUKAM5fuEAmk1OSEhUUFADg8qWLktIyN2/dOnP6FJMEFmMZGgBqa2udXc6npKZkZWXT9e3r6zt85CgHB0d6agovLy8AXLl8SVJaZt2GDRlpab//a/ofmKK2vuH8tdupGVnZufkMG5y+cK24tOzT+5dmRgYAsHfXNq35xrZ7DpSkxwPA7QduivJy8RGfuLk4AeCwo73SzDmP3J+fOLgPAPr6+o+dvcDBzp7yLYyXhxsALp09Lquus2G7Q2pU2Khzu3jDlUwmJ34JlpeTAYCLZ47JquvcfuB26vB+VlbWUY0zn/n/8C+jvgV/5U1EenFNbgX9cRQAuLwIK6nFfXDeZqytCgC7LOfNc7i9+9b7LM/jY+l+7d0XMpn87Y6jnJgAAJzdulhty4V7/tFHNhiNKxyoR3DC17QiPtrApG6f4np/9H+9vU9DXhwATmwytTzpFp1Z8jEuZ/m8aaPO/H/4N/HF656wlMLR55FIIArTLftPWU6L+vDUfBvNrdwYH4+8hC8c3PQn6+3N9cFPr1TlpdWW5I53aBWdBSff0DMX768f7u0ibDh+GwCivN36f/QefR4pqawBAMvsTrraWxSmRGdEftQ0Ws68dryT+R8miELfW0AmL7oSziEiCwDqG06G7JpZ8umR2ionFAtrV2MFAOjsvc8jM5Vh957WhgKfG22lmfiqvPEOXRX9vq0kbdrms0rLdgOA7KINKBSqPOJle1kWn8J0ACgJeswpKm9wKRS5SayyfE+og05Z+DPVlQeE1OcZXf9KZzDT80R/D3HmjmsTnNj/8NtR19Ry8bFXWl5xThEDevTcvefFlbUBD86bzNMGAIeNlrqrHXaevpkf8gwAFupOT/J+QNfF6cqjTmL3vVN7AQC5feR58bCGivw/mJvXxy/J2YWXD2533LISALauMEUBysMnJD2vRHOq0hW3N2Qy+fubu/JSYgDgss9GyXiT60u/47s2Ij7X9aWvorR4tNcdbg52ADhou1rNfOuT95+O7fz/ScDF/4fQ1lTn9+hyWV56dVEO85ZfvN2z475w8tB4Rv8nV8lk8vm3MSJScgCwztF5j7Fy8Iu7K3YdQ2Joe991aagsOfLQd/o8EwAw22h/fNWcx6fs7oSO7kY/v3Xr+9Hr4vlNRnUaAKxyOHVpx7K8pKjkL4GzTFZM1dW//IF+xf7i8qGeLoLtaddRH4258VHn9j/8XpSEeg729Sy6EsYnqw4AGuuOfnNe1ZQTW5sYJDXHQkRjntkN+lPGNI8TAz0EHbvro3YfdfTu1vo87xutZZkdlfS+b9ShqVEa/rwhMxLLyTiEO/Pa/+HfQV1j88V7nmk5BdmF9DeFqOH2xv9zTOLwq26XHzwnk8lxvh4IT+tyaJfi/OWuHm9PONiysrIwr53g3M7eflJcUR3gftN0wWwA2GO9RsfCesfRCwWRPgBQVl0LAB43zkxTVWRo/NErn57eH7E+T6chHLLj9sXWjt8SUgPCo6zMF406t//hD8EzJPFrWjE1t7BgukLsvf10zY48Duzs7r3tQAkzdv3tVzKZHHl7L4XZsDafYn3pvn/MkfWGrCws9/1iFcQFvt7ag8Sx2L9q4XTbK0+DEw6tMxzLlOpb8FfffkkvrsmtGHYHfmDwtGcIOxsm+u4+Ho7JAHB2q/nUrZe2XXsTc9cRAJ4Gxff+6P9ya89PVsRk+cmn0VmlCCvC/MH/h38fX7zuCUkpHPlJjJhs2X/aclr0T2KkYzTeI/Dh+aaqEgdXn6l6xgBgsH73pfVzX5zbfT6QnuEfjqTgtxU5KVaOF4w27QUAPcstKBQq1u9ZVUGGjNpMAECkIVtd3CSVGGh3Qtyvkcnkoy+ihCTlAMByz7kTi1W/eN1bvOPoRBK4IBh3RI2DV58Y2x5tbGmjLtxz/v6y3af7+gcAIDY158DlR9Mt7ZTNbLYeu+7uEzpIYiwj3X7qlu1JGqXMzWc+xrZHBwYpoUXwhK79lx5pr3KQM9q8/uCl8O+pjMz8HiRlFUxTkZ9GtVHcYmkEAKm5xaPWAgCxu6e0qp6Hk0NrKoODyXfBUUL8PLvW/RLUHN62xv2CkyDfiFldqcHEeCexK7+symSu1lASFjEhfn2d6e2dhP6BgWGWAAAi4tPcvIM9Lx4UEeADgFcfv6orySIqDQAQFuA1mqNZWdeUklM0lrn9Vzjk+tZ079XGVjx14b4bLy0P3aa8h5lFB+94zdx0Um31EVsXN4+P0SO9hzsveW6/SKNKufUm1HTv1aEUIXhit9Ntr1lbzyquOLjx9MPPiaPsISeOgsr6kw+9XexWifLTvCFJuaUailKITgLBJvO5AJBWUIl8LKttQqFQipKiDM0yrx0VzEcndveW1jZxc07WVJVl2J15g1G7/5044HJz0fpdjTgapbP9qStLtjr29fcDQExSuuO5G+rGaxTmW245cObpW//BQcbvoe1hF5uD56hLrj95uWj9rqHfw45O4r6z1zUXb5Ses2Stw7GwqD94klfb0CwhKoys7wGAi4Nde9qU/oGB3r4fAJCYnjNdTQlRaSDYsnIpAKRmU45RS6tqUSiUkqz0PxudSfeC0sr6JpzZQr2hVMRCAnxG82ZlFZTgCUQAeOUXrKGqiKg0AEBYkN94/qzK2vqUrL+a8N27z3G+vn5DA81KdKfdLhNTs76+PgCIio522LNXWVVNSkZ2/YaNj588GRwcZGhqi/XWTZtpkgpduXp1vr7+wE+P0NHRYe+wR33aNFFxCauVq0JCQ//MMwEAvPbyEhYW3rvnV7yvE8ePvXzxXEhIEADiExJmzJiOqDQQ2NhsBYDk5BTkY01NraSkJKLSAAAuLi5dHZ3+/v7e3t4JDg0ABAKhuKSYh4dHR0ebrm9+fkFdXd1ic3NEpQEAwsLCJsbGmZlZeDwe/lY4Hj29cIlVQxNNRKJdB46ar9zQ19ePfIyOS9h75KSazgI5Dd1NOxzcnr1i+CLZ7N5vvYsmJda1Ow8WLrEaoFpXdOA79xw+MX2uoaTqzNVbdoRGjFsjP0YQiV0lZeU83FzaMxnHjXz51ltjqhqidQAAESFBk0ULK6tqktMy8J2EvIIiMyMDRKUBAOKiIgbz57Z1dPT3DwBAQVFxXUOjufEi3p88iLCgoLGBflZOHr6TwS0lOtTU1UuKiyEqDQDg4uTU0ZzZP9Df++PHWIwzmfk/+qr+JRx5HGB25CFdUAfHez4rTj3tGxgEgO85ZYce+mvtuDrF+sK2q16eIQkjLcB23Xy388Zb6pLbH76ZHXn4awHW1XPwod9s+xvKG102XXjxOaXwzzwTAACh50dpHY6bY5KmMgMx65svKVPlxBCtAwAI83It0lKuampLLaoeS/c6HF5ckAfhMgCAczKblrLUwMDgjz7Ga3WGKKxqOun+ydlmsQg/zZ3j5IJKDXlxhI9AsNFIGwDSiqrHMvO/E++vH765wwzfQnMp0Ovivrt7lg/09wFAcdr3d1cPnrXSPLFEzeOkbYyvJ4nE2C0+P2v37DRN/qDw57dv7jAj/QzP20PAv73q5LJ29lFTpSeHN+XGff4zzwQ9xM76soKpesZDyUR4hMRUtBd0d7YPDvQPNWsoL/C5c3LFXmduQfoV+48uQnNV6WROHpkpmjBh5CV8ifZxt3F5yi0gAgDlWUmSyhqIDgPBHItNAFCZlzZq7V+LTM8T0Wcse9ubqAvTnxyKvbCWNNAPALi8+EyPY+GOeiG7Zya77iqPeEEe4UVKub835Z4DdUlRwL3oM5ZDGUb6u/AZ7sciDuoH7dBIuGHbmEGvSPiN6G6tnywghqg0AAA9mZNPcSZpsH+w/wcAEBrKAYXiFFMYqftAL5FQX4Zh5+JTGHciquoYHzYeQQXz7UMlKiscdfbcY+MWAID+7s7O6kLRmYuG4r1P4hMVUp/fR2wnDfYPt9aUGVke/lx374NJvMITnNh/CKcrj4y2HqLjxxxcXJftOonwEjGp2fsvPZi2bLuSyWbro1fcPwSPyI+dvGF7gia/2w1Pb6Oth6j5MceL97Wsdsku2rDuwPnw2JQ/80wAAISuntKqWh5Odq2pysNrXwVGqCvLISoNABAW4DPW06ysaxyJRIqIS3V7/8nz8hERQT4AKKuuQ6FQirKS/2xub4O/CvHz7l5vOVRyZPta94uHBPl5AKC2ESchIoioNACAi2OytoZK/8BA748+QAi00kqTeTrcvwg0AX3dGe34EQm0vwEvLh9ysTbpwNG4RXfnvVfsLBG3CAAFKbHPLzodXDpjr7Hq/SM2X709GHrGRyd3Pjy+nbrko8ctF2uTQaqo9d0E/LMLB46u0LU3ULh9YENmbPgfeCYAgJ4uYkNVKTsnt7y6FpNmtWUFXtdPrDvgwkvrGVsba/lFJBCVBgBM4uBUUNceHBjo//EDKYkJfC2trI6oNACAR0B4mp4Rrq6qNGd0brkkM1FGRQMRUiDQX74JAMpyGTu+rLiIiPdP7S+78wqKjPpo4zX+lyDN48TXUxY9tF415fHBqPNrSD9XMs158WlPjwXvnfPRbmb8bbvSz4wda+K9PQmu9tQlBf53v56yoM7b1deFT316NPTAgoDt6t+v29Snf/kDzwQA0FKUwierjsgsEMgvWg8ArSWMN2UNGZGlYc9m73uIOK/xdqfDQE9XZ0M5hp2bX3F030c39BDwNUUZL87O2HRmMp/I8F7Ma/9COLncMly/uxFHc5/e4dTVpTb7fxKtGfudb2qYrFNcsGLLgbNP3waMRLRuO3Le5pAzdcmNJ68M1+/+5Vg7iY7nbmgt2SSjt2ytw4mw6IQ/80wAAISu7pLKGm4uTi0NtZHa5JdUHLty78JhezFhAbqq2oYmRjztYG9f36i1E5zbK78QDRVFRKUBCNE6b1ZlbUNKVj4AlFbWoFAoJdkRb4QmZuROU1OaRsMhLwGAlOyCscztv8KRx4HmRx41tdHwQvvv+Vqddv/Je5QfehSgvfM6Ihd4Fpo4Iu9x6/3OG++oS+58iDI/8oiW9/CfY39LZdP5zRdfRaT+Qd4DQWF100n3oHM25iJ8zOKZfU0r8ghOcDu4Xvhns7qWjpGYjc6u3oKqRiMt1aFsI6L83POnK3YQuvsHGO+w6EAcIlWU6JeIRdVNDa14Ex1VRKUBAEK8nAYzlXLK6zu7egEguaCKnhUx1gaA9GKa+8BjfPC/B94jECP3aImRc1aaJ5aoeZ60jR2ZGHlx1u75aMTIu6tO59fOPmaq5PavEyPKVMRIbxehqap00si8R2KQl4TSVESlAQDc/MJqcwxb66sqc0df4CWFvOfiF1q41m6oxNTmkLXzEy5eynlBc00ZCoUSkWa8m25vquUVFkdUGgAwiZ1TdqoWaWBg4OficyIYt1BDXkosPiM/8Osv19WAa3sR8JmfhwuLQcekZC+xO/UhPNZIT3PrCpPaJpzjxYdn7r5gaCqjoDQjn0apV1pdH5+Rj2RSqGtqmbNu35ugyHma6pstjKrqm1Y5utz3ChzvhMeC/oEBIz3NXeuWUhfWNrUAAD8PF/Na5KOKnFS4x+Vwj8vPLjPIU1haXW8yVxuLQVfUNgZHJ2UUlIoJ8a9fYiAtJjy88XAwMc7KyvrZ46qTza9sQ53ErtySCsM5MzFoBuFS2vCEXWfvrjKZr687DQBaOzo7OomLZtMsB5VkJAAgPZ+ZwPM/h4KEcEJO6ceY9KGShpaOlyHf+bk5sBh0TEahhdMtn8gUQx31LUvm1za3H7j1+pybH0NTmcVVmUVV1CVltc0JOaVI+P06XPvc7S5vwhPmTlfeZD63urF1zYl7D33+1A4BAHr7+m1d3OZMU961kkZY2j8waKijbrfCgLqwFtcGAHzcFJahvA4nKczf1dMblpD9Mvh7Um4Z9SqBeS1zjDq6ioxYqOvhUNfDnrQ/+kNg3mDU7n8nFGQk41OzAsKjhkoamluef/jEz8uNxWCiE9PMrfd+CIownj/LZo1FTUPT3jPXTt94yNBURm5hei7NUqy0siY+NYvyHjY2z7LY4uUfOk9nhvXKpVW1DVZ2h+89f/+HnsvSRL+usXlIC1JcUR2dlKY/W5Nj8uT+gQHj+bN2b6ZJcFvb0AQAQ4rvsqpaKXGRru6ekG9xzz98SkzPGWnXxBBMujc04QBAe9oU6vba06cAQEFJRWt7RzuesEhPh7oWEXyk5fzVGwBFRYXv3+P8/P2HSurr6z08Pfn5+bFY7LeoKCNjk3fv35uamGzfZltTW7Pb3uH4CcZJvtPS09PS06lLSkpKv3+PQ16k2tramVraL1+9WjB/gc3WrZVVlcssLO+4uv6h5yopKTU3M8NiseXl5R8/fUpLSxcXF9+8aZOMjEx/f7+pickeB5ozj5qaGgDg56fclFqxfHltbe2QlKSoqOhbVJTBwoUcHKMnbGIyNNJATU0t+tu36G/f3rx+Tde3vqEeAHR1aV4k5GNeHuOgDn8DFOVl4xJTAoJ+KW/qG5uevX7Hz8eLxWIAIOp7vOmK9d5+H00W6dtuXl9T1+Bw6MTJ81eGm0rLyk7LpJEklpRXxCWmDCW6qq1v0F5o+vqd73y9WdYb11bW1C7fYHP38R8JxKWqrBj5ySfyk8/rpwxSVre0trV34A3151EXKinKA0BaZjYazfotyOew4y8SEN9JyMkrMDbQx2DQAFDf2AQAOpo0qyDkY37h6HLV5UvMausbhkQqxaVlUd/jF87T42BnH9U485mPOvR/CHkxgcS8ik/xv4TtjW2dr8KT+bgmY9GssdlllifcfGMyDbVUtpjOqmvpcHrg5/ycsSAss7Q2s4QmsGpZPS4xrwKJTlnfgp+/9/bbr2lz1eU3GmtXN7evc/Z8FBj7h55LRUo45OrukKu73Q9voKtq7ezqIPYsnEGjllaUEAKAjJ/zZ9IdAJbqqde34IeEJiW1uNjssvnTFNknYcc4vd6+AdtrXnrqcnYWNO9M/8CgoZbKjmVzqQvrWvAAwMfFPpaZ/50QkpIvzUzI/PZpqASPa4j7+IqDmw+NwRanxrraW6R+9p0yx3Ducuv2prq3Vw4E3HdmaKq6ILO6MJO6pLmmrDQzAXGL7c31FzfOSwp+q6SpN8diY2tD1UOntZFvGS/VJggWVvRBt1AT61+3lHqInbWleWqzF7GiMUhJf1+vx8ltijPnGKxlkCxcVE7FyS3EyS1k2wWPCU6mC9/2ysVB29hKRWcBAAwO9E+ZY7hwzU7qNu2NdQDAzsPHvHaCM/mj4BSVaylMqksOGSrpbW+sjHyD5eRlQWNweXGx51fXxAWITF8ot2hjd0t9xtOjuW8uMjTVUZ7VXp5FXUJsKG8pTCKTyQDQ09rw5YhRdbS3oNocWYN13bia+CubS4Pd/tBziesu7mltGNKCEOrLcHlxQlPnodnYAaCrsZJdUGKgt6shLaIy8k1rUQrdIRmXhJK+c4C+c4Cu46PxDk1srBCdsYgFjelqqmpIDW8vz57MJyK9YDW7kCQAoFjR+i4BKpa/ZLL93Z34qnyR6QtZWDF0pvoI7amPDkjqWQqpz5v4xP5DKEiJxWfkBX75FbGmAdf6wj+cj5sTi0FHp2Qt2XH8Q1i00VytrVZmtY24fRfun3F9xtBURn4JHT9WVlUXn5E3xI/NXuPw5tPXeVrqWyyNq+qbVu47e/91wB96LlV5qc+e1z97Xn9+5ShdFYVEmkWTllRRRhIA0vOKh5tq6+i0O3N7lan+Ql2K6Lasul5KVKiruyc0Jum5f3hiZv7YeQkAKK2qN533k16LSszILxETFtiw1BCh1ywWza1rahlSsRRX1kYnZy3Qmc4xeRIAsLKyRjy7cdB29ZC1TmJXbnGF4RwthgTaXwIRafmijISUr78yx7fjGqL8X3Lw8KExWADIT465tGNZQpjPtLlGBlbWrU21nhf2v79zdripivyMinya8+PG6tKijATyz/+Ctqa6E6v1Yj+9UdWaq798U0td9Y29a8Je04dI+S2QkFc5/Szs9LOwPVdGjM/f/6P3/hEbFS090w276aq0DS3amuqGdCQNlSX5yTFTdBawTWYHAEJHa1dnh/psGgpLVFYRACry0oEpBgf6p801MllvR13Y2lgHAJzD4l0BALGjze2M/RzTlVN19Ud9tPEa/3vAJSqHK0yqTQoeKulpayz/SvGqANCcG/fNeVVVnL/oDAN5w43drfWpbkeyvBg41rayrPZymi0PoaEC99OrAkB3a334YcPKKG+hKXPkDNZ3NdfEXt5cFPT7HStpsF90hoGS+Tbqwu6WOgBgGH/iB6E9+eF+6bmWIhrz/kH34eCWVDJ0CTB0CdDb/5h5S7qhhzDY/yPhtp2Q2mzlxduH92Je+3dCXkYyPi074HPUUElDc8tznyA+HoRoTV+8dZ930Bfjebo2q5fVNjTvO3v99E3GK4eM3MKMXJp9fWlVbXxa9i+i1XIrQrRuWbmkqq5hpd2R+3+MaFVVkI3wehDh9eDFrXMMG/T+6LM+cHau9nSHLauH11oY69c1Ng9JSah42kmj1k5kbq3t+A48wUCP5oKTkpwUAKTlFgBAWVUdFYUbRMcA9w8MGM/T3b1pJXV3hEPm5x1rguz/BPLiAon5lfS8x+cUPk52hPdYfvKpX0zWIk3lLSa6dS14pwf+Li8Yh2XNLK3LLKXjPVoS8yuHeI8F+1zfRabrqcttMNaubm5b5/z8UeD3P/doP/oGtl17ozdVzo6WRqBDG6HbwfXDigXTF0z/dWK9dI56fQt+SEryk9lQYJ+ERbOyhFzdtX/VwqHGnV29eRUNBprKGPSYIg0oSwkHX9kVfGXX0yP0pApyU4juVoyWshQAFFY39Q8MLtJU3rGUJnVdHY7Cioz3wf8qCEnJlw0jRuI/vmL/SYzctbdIoyVGAsdMjOBqysp+EiMdzfWXNs5LpCJGHv0xYoSVFe00jBipoyJGhngPW0a8B7GjtbuzQ1WXZoEnIq0IAFUFo0skcTVlU/WM0RhsS11ldkxIdUEmr5DorMXr+MWkfjYo5xOV/NHdlRMbFh/4sjw7iVr7MsNgWUdz/ZCKpamqpDg1Vll7Pnbyb4jRMu6tyFpz/RO3PP2/xNmtXYKU+H6OJZHImy2MAMA7LAaNZs379JSHiwMAnGxWTl2yIyQ6+eJ+m/EOdPrui6r65qiXN3Q0VADg1O6Ny/ecPe36fOPSRXw89KKn1o5ON+8QRmYAAJYb6qkpMLvbjUGjbx6lWSXj2vBP3gdj0Gjz+TrMa0d9EGJ3b2NLm7AA7ypHl9AYynZRWVbyict+XQ2VUbszB8fkSXNmUNSO970CaxpwYbEpgyTSIVsGTh0ADlx+hCcQXRy3Ih+LK+sAQFSQn7qNkqwEAODaOiY4tz+K1Ua6Jx95B0an7fwpHfCPSiWRyEg1jHE3AAAgAElEQVSMB5+vyWhWluw3l3g42QHgwAazaeuPh8Znnd+1iplRRjjr5lvd2Br56IS2mhwAnLCxWHnU9cwT3/Umc+jykgBAK574NODbSKYs9bXUZMVHqh3CqUc+DS14/+sHUCgUdTkGzXrDkSYaJ66d8NT/GwbNajaHQnmU1zUTunumrjvW00vRzM5Qlnl6cpuKjNiotcwxltH/D2LdMtNjl+/5hX3btYnyavmEfCWRSEiEifdBEWhW1vxIXyT8w6Gdm1UNVgZHfr90dA8zo4xw6vrDqrqGWB93nelTAeC043aLbU4nrz3YuMKcf1hAvNb2jsdejGVJALDCdOEUpVFizNpvXv0tPmXFzkOzNTUmsWGjE9PFhAWdnXYBAAaNvn3mIHVjXGv749e+GDR6sQFloVNWVUMgdikvXNHdQ4l8oKmu6nnjrKqC7Fgelkl3eRlJAIhKTNu/7deirbC0AgDyS8qRXb2oEI3wXFleGpnkWIb+r7Bh/fpDh4/4+Pg62FNOlL0/fCCRSEiEibdv36HR6LKSYiTAw9EjR+QVlT4FBV27yuCInTmOHT9RWVmZGB8/a5YuADifO2u+eMnRY8e3bN7Mz0/jCFpaWh4+GpElX2m1curUKSPVIiASiQ0NDSIiIsssLIOCKWyOqqrqMw+P2bNnYTCYe3dpBCLNzc0PHj7CYDBLl1BkkXv3OHyN/Lp0mYWe3pxJbJO+RUWJi4tfvHBh1MdkPvSo3RXk5QEgMvIbkjYFQX5+AQDk5efp6c0Z1cJ/gnUrlx85c8E3MHj3NkqqeJ+ATyQSyXrDWuTjO99ANJq1KC0OCfBw2NFeeaZeUNiXK+cYi36Y4KTL5arq2rjPH3W1ZgLA2aMHl67ZfNz50qa1q/j5aAiplta2x54vR7JjtWzxFFUG1zTHjuLSMgAQE6G5G6SsKA8AzbgWDnZ2vVmUddrdx+5VNXUhn78ODg4e3U9RCCnIyQDAt9i4Aw6/DiALiooBIL+weI4ufbQVOjjssImMibNcv3WOrvYkNrao7/HioiLnTx0di3Hk92qkmY/3e/g3sWqh5imPoMDv2UPbYP/YLBKZvNFYBwB8ojPQrCyZHseQiw77VxnM2HY5LCnfxXbJeAc69zykuqn9y6292irSAHB8k+mqM+5nPYPXLdKiC1PZ2tnlHjxilClLvWmqMhO6PYbkPRXlp/G2ShJCANDSQRyLBbtlc6MzS9Y6e85Sk2HDYmKzSkUFuE9bm419Dmc8gxpb8X7ntw9fHF7bRZN4AtdBfBoUh0GzmumqTXzm/xV0TVf73jmV/jVAfzVFv5v2xZ9MIs1ZtgkAUsI/sKDR5wOyJnPxAICJ9f7TltNzYkOt9rmMd6CAe2dbG6qPPPsqp64NAEt3nrjvuNL/3tlZS9YPzzxC7GiN9hlRkaa5yEJMfsTrcQDANpldYTrlIlrk24etDTW538PJg4NmW3/5Gj/X0x24hr33/Oj+o3873l092E3AL99DIXFY0Zi1h2liaBPacNEf3FjRGI15psxr/+g8JwipeVbZr5zrEj8pmFK4iNr4j2QySdZgHQDUfPdHsbKa3UvCcPAAgPLyvWF7dBvSPmtsOjPegXLfXOjG1RhcDOFX0gSAKWuOfL+0PsfrvLT+Grpzmh+EtvJwxof0ACAxaym31Oj8gKLZNlxObNyVTQLKOqxYNlxu3CR+kanrKfmMiI0VA92EUAftwR89SAmf/DSdvQ+4JP5JRkJqDPR29bY3sfEKxV/d3JAWgRRySShq27vyK2kBAJqNXUBFFykvDXbraqltTP9CJg2qLN833FqGx7H+Lrz6xlMTnNV/jrWLDY7fdPeP+G73M36qb3gsiUTestwEALxDo9Bo1vzgZwg/dtBm9ZTFNiHRSRcPbGNmlBFOu3pW1TdFv75D4cfsNy+3P3XqjsfGZYYM+bEn74NGMrXCaK6agsx4JzCE4spaABAVotk7KMtKwggk0v5LD/AE4nnHX5RgWU19Z1e3qpl1dy/l8tnMKUoeFw+ryo+eoZXY3dPY0iYswLdy77nQmCSkUEVO6omLk+40VQDYvcHiW1KG1d6zs6erTWLDRqdkiQkJOO+lLI85Jk+aM4Oyi7n/OqC6oSksJmWQRDq8bc04v4Z/FXqL17y5cTIpIsB4HWVVmRTuRyaRkDAMABAf+oEVjb4dksPOxQMAy2wP7F+skR4dut5p9K0THd7dOYurr3b2+qaooQ0AK+1PXttt9fb2mXnLNtBlHiF0tEa8ezqSHV1jS0kFZm5xjHhz61Q7ruHY44DhntF0vV1eUtSNPauVZszCYCflp8TwCYmt2Uf5DW+oKAEAJL7FEMRllQCgsw3HfFBWNMb6+A3qks42XMQ7N1Y0ZqY+g/Xbs0tO3Z34dfsZH41M0PjfA5n5Vhkvz9UkfFIys0VKquM/kskkOQMKVVj13Q/Fyrr0QTKWgwcA1FbsDbLXqU/9PGPzuB1r1usLXc01xpdDBZQ0AUBj7ZHoi+uyXrvILWTgWEvDRnSskrOX8jB1rCysGK1tl6hLevEtJWHPWFgx4tomw9unuR/t68JP33T6n3WfCOiGHkLmS+ee9kb90++B0eqRee3fiXXLTI5fue8f9m3XRsrpvi+FaF0CAN5BEWhW1oKvH3i4OQHg4M5NaotWhUTGXTriwMwoI5y68aiqriHmw1Od6VMA4PS+bZbbDp68/mjjCnO6zCOt7fgnI7Osy00XTlGSG+/ow3Hi6v365pZPz24z3AjYb171LSHVaufh2TM1JrFho5PSxYQFnQ/YjaV2IiiuqAIAuggfynK/iNayqtpOYpeKwcohCnemuorn9TMIA4xBo2+dcaLui2ttf+zlh0GjzQ1ojtX/NqzSn3naIzgwLnv7UgoH6B+bTSKTNxhrA4BvdCaalSXD/chP3mPh9O1XQpPynW0Wj3egc89Dq5vaI246UHiPjSarz3qcexaybpHmcN7DI3jEoC8WczVUpcfEe5z2DG5s7fRzoecW6HD4YQCe2Htuqzl14c5letFZpWudn+uqyUzCoGOzy0QFuE9tMQMA9knYWVNkkWaPAr/XNLd/TikkkUhOqw2GGx8vZEUFACAmq2zPigVDhUXVzQBQUN2kqyZzbZcldXtcB/FpcDwGzWqq82spMsYH/6ugY7ra986pDKbEiAsVMXLGcnpObOiKCRAjsuraALBk54kHjisDGBEjxI7WmJFZkZmjsSIAgKUlRtp+EiOmW52Yd0TQVFUCADy0CzwRGSUAIIy2wPvR3YVvaeTiF37ktDYnlqKsEpVV3nzmoZwGhT3G1Zb3dhFOWaj39VJ209JqM7Y6u4nKqQDAwjV2hclRjw6skZ82C41lK06L5REUtdg97nUOQ4xbqCHIx2MyTys8NhXXhhfi5wEAn/BYcWGBRbNnAsC+zct3r1uK7EIBoL9/gIeLo5PYNd5R2vEE79BoralKOj+lDFgM2sbKNDo5OzAyYesK+uVOSzv+wiOvkawpyogzF2rQITQmZbeza0t757XDO6YqyY6rdjjKa+oB4OGbj/JSYjeP2s2erpaQlX/6zvM1+8+nfHggxD+m7CdjgfP9V8hGV01BejIb2/AGBWXVvp+/H962WkpUiHpufD+TCCBALiLgCeP+X/s3IcjLZTxL43NiDq6dIMTHBQA+X5PFBXkNtKYAwJ41JnZWhohKA5D3kJO9s6tnvKO0d3Z9+JKsqSqLqDQAAItBWy9dEJ1e+DE23XrJfLr2LR2ES88+DjNDgaKU6KhCjbCEbDf/SK/z9qICo7wYYQnZDteet3QQr+5ZO1WeEtysvK6Z2N17ZvuKZfM1WzoIb8LjXwZ/X3fyfpz7GfZJbMxrR/kuxjD6/0EI8vOa6s8Ji47HtbYLCfABgHdQhLiIkOFcHQBwtF1vv3n1UJKOvv5+Xm5OJEPHuNCG73z36bP2NDVEpQEAWAzGdo1FVEJqYHiUzRr6tJe4to7zriMyJkqyUqMKNXi4OaUlxLIKStKyCzBoNIlEQrOyEru6h7cM+RZnd/xiS1vHjVP71VUoGtuyqloCsdv54C5LY/2Wto5X/iHPP3xctetI0scXHJMnj/q8TLorykhqaah9i0955v1x1RIjEon0NjDMNzQSAAZJpLKqWgDg56X525EWFwOAjs6/+kRKSEjI3MwsJDS0ublZWFgYAN69fy8hIWFsZAQATgf2792zZygNR19fHy8v7z/IwdHW1vbm7VsdHW1EpQEAWCx2x47tkd+++fn7b99GwxfjcLiz50ZkmpSVlUcVapSWlgGA6927iooK9+666s3Ri4uPO3rsuOWKFTlZmchjDiEoOHjb9h04HO7O7VsaGpSooby8vDLSMpmZWSkpqRgMhkQiodFoAnH0hBTjGno4lJSUtLW1vkZGunt4rF2zhkQivfby+uDjAwAjZZz5GyAkKGBmZBAaEdnc0iIsKAgA7/0+SoiJGi2kuKoD9jv27LAZSsPR19fPw8PdOYYEH3Roa+946xOgPXM6otIAACwWs23Lhm+xcQFBobabaSR9La1tzlduMjIDAKCkID9BoUZpRSUA0KlDZKQkAaADT5Ob4/SFa909PQAwRVV58iTKtRJFeTmtGdMiY757vnq7esUyEon85oOfT2AwjO3/mpeHR0ZSIisnLzUjE4PGkEgkVjQrgUgci/Gxz/xvgyAPh5G2akRKIa6DKMTLCQC+0ZliAjwGM5UBwGHFArtlc4fCUfYPDPBwTMJ3jZ6uiA7thO4PURmaylIIWwEAWDSrtdmsmKzST/E5W0xpFFct+K7Lr0eMyqgoITRBoUZFQwsA8HHSsCRSwnwAgB/b2pKHY7KUMF9OeX16cQ0azUoik9GsLMSesYZGDEsucPsU9/qUNZ3kgmHLva7eLfiuyzstpsiKvf2aOsGZ/1fg5BNU1zPOjftMaMNx8QsBQOpnX14hcbVZBgBguHHPwrV2CBkBAIP9/ZO5eHqI4/7D6epsTwn/IDNFE1FpAAAag523fGtRSkxm5Me5y63p2hPbW4KeXBpmhgIRacVRKYkhBD50Qbb9YvJqGDbK30tObFiUt5vd9dc8w5Ke/F40lBekffE3sznIL8o470BObNirC3uI7S2rna5IKE4dV+1fBTZuAdEZixozvv7At7DxCAJATXzAZH4x4Wn6AKC0dJeC+TZEpQEApIE+DDt3f/e43WIfsaP6ux+fwgxEpQEALGiMnOEmXO73uqRgOcONNI07W/O96fPKD4FTTGEsQg0MBze7kGRHZW57WQYLK4ZMJqFY0AM9FO9DbKzo7+1SX3dcXNf8R2drVbR3ZeSb+GvWhte+ICE3/jGIjRUAUBrylFNUbobtJQEVndbC5Byv8/HXrI1vRCHf8BBy311GlCLcUiqsWPrbnJ01RbUJH1VXOLIL/j+/kRTk4zGZpx0em4Jr6xDi5wWAD2FR4sICi2ZrAsC+zVb26y2H+LG+gQFebg48gcGuijna8YT3IVFaU5Vp+LGV5lHJWYFf47Za0R/u4trwFx6+GsmakozERIQaZdX1AECnDpEWFwaAjmEkUkFZle/n2CPb10pRRZMtq24gdvWc22ttYajX0oZ//enLC7/wNY7OCd73R737W17TAAAPvALkpcRvHds9e8aUhMz8U7c9Vjs6p/o+EuLn5eXikBYXyS4qT8srxqDRJBIZzcpK6Gbg9c7de/6TQJOZPE4+5F8GN5/g9HnGmbGfO9tw3PxCAJAQ6sMnLK4xmxKEdfGWvaYbdrH/9IwD/f0cXDzdhHF7RiK+PT7EW15dC1FpAAAagzVYtTUvOTrl60cDKxrP2NnW4vuQcRQiABCTVZy4UCMjOuzz2ycHbr/hFWLgGdm5eQXFpKoKs8tz01nRaDKJxMrK2ttF+TFsqikDAE4eGkWRoJgUAHQRxreVzogOcztrT2hv2XzkqpQSveOrLStICvez3H5IQGx0pdF4jf9VYOMWEJ+5qD79ay++ZRKPIABUx/lP5hcTnU6JI6KybJfS4u1YasfK8Q8da9V3P37FGQJUjlXBaHNTzveapGAFWsf6A9+a8/4aIzMAAFziCsyFGnSoT4tIfri/t7NV0+Y8rzT9C4yvKaqO/zjFakTnxbz7RDDS0PVpESWhHvMOP2OY1oR57V8LQX5eU/3ZYdEJv4jW4C9DROs+23W7t6zioSFauf4B0dqO73z/KUJLQw1RaQBCtK61iEpMC/gcbbN6GXVjXFv7+bsjHkwqyUlNXKgR8i3u0Wvf9w8u0d09GwIPN6eMhGh2QUlaTgEGzYrwtISfPC3z2omgrKoOqCIoI5CWEAUAfCcRAMqra4nEbmcnOwvjBS1tHa/9Q5/7fFq9+1hi4PPhPj3kW9yuE5db2jqun3RUVx4xT9/fAEEeDiMtlYjUoiHewy8mS0yAx2CmEgA4rFiwk4r36BsY4OGY3PmPeA+f6ExNJUka3sN0VkxW2af43C2mutSNW/Fdl70iRjKlKCE0FqFGeHLB06D4Vyc30yVUpUNhdZP/92ynNQaSQjScFQ/HZClh3pzy+gymzMb5l2E9P/oBQFVaZBKWPq7eP4CChOBMJcmYrNKX4clWC6aTSGTvbxkB37MBgDQsKlt4csHeuz4t+K7LO5ZNkRUdKhzLg/9t4OQTnKpnnDcyMWLwW4kRWSpiZO4IxAhzVkR4PKwIAHxkRIwwB66mHADYaeUjSDyMHuIoCzxcbTkAfHv3SEhSfs3h6wrTZpVlJfrfO/v40PpTbxOQbxhXU97bRbS0Pz3dYBmxvSUx6E184KvHB9ef8PqOncw+mYuHX0y6tjinKj+dhbL4RPd2/55Tp38S3G/jMsOQ6OSPkQnbVplV1Ten5BQdsl3NwoICAGVZyTY84e4r/6Sswqr65tLqekJXtxit1n4sKK6qI5PJxO7eLUevDhV2Envg58aMDsqyki2JviNZw2LG+pjltQ1Hb7iHRCcrSIk9u3TIYNaMsdeOhHY8EQB+9PW/uXEcuWcwQ02hubXjmrv3h/AY+/XLRjMwVuASfEqr6xMy8s/ee6m/+WBRmKeIAM0re+u5LxaD3rfp18U7JBx6O+0RJiKB5KVVb/yF2GCmFxqf9Sk23dZCv7qxNbWg4uDGxZT3UFq0rZN47/3n5LyyqsbWsrpmQlePmOCY4s5Ro6SmkUwmd/X82Or8ZKgQ8bsVdQwkWsrSok3hIwYFwmJGCfTU2IrffeWZ9ZL5y+bPZNKsoh537P770PgseQlh91M7DLR+/fw9Pm6DxaCnyEkAgIKk8Cx1BW6Oya7vwj/GpK8zmcO8lvncxjL6/01sslocHPk9MCJ6+7rlVXUNKVl5R3ZtYWFhAQAVeZm2DvwdjzdJGblVdQ2llTWdxC4xYcFRbdKhpLyaTCYTu3o2Of66c4YI4Mqr64a3V5GXac+JGskaFjP6Cslw/a7corK7zodXLzGexIYNj0mwP3F5+Y6DGaFvZCQo8VfKq+sOX7wTHPldQUbyxS1n6oQjT6+eZsNipyrLA4CirNRsTQ0eTo5b7l6B4VEblpszHpIKzLs/uXzSyu7Q7pOXD164TSKRSCSy7RoL93cBU5Tk6hpxANDWQeOVkaPZ4ffM/jZYW2/5FBTkHxBgt3NnZWVlUlLy8WPHkBdJVVW1tbX15q1bCQmJlVWVJSWlnZ2d4uKjh+ehQ1FRMZlMJhK71q77dZTe2dkJAGVl5XSNVVVVu0eWRGCxo4frb2tvA4AfP374eHurqqoCgKbmzKam5ouXLr17/37f3r1Is7KysgNOBz8FBSkqKnq9fmVkaDhkYb6+fk5O7sMH99etXTtp0qTQsLAdO+2WLF2Wl5MtKys78aFHAgsLi6e7xzJLyx077Rz3HyCRSCQSacf27U/c3KZO+au5s83rVgWFRQQGhe3YuqmqujY5LePo/j3IWwQAKkqKrW3ttx+4JaakVVbXlJZXdBKI4qLjpmyKS8vIZDKxq2vDtl8pRToJBAAoq6yia6yipNBZVzKSqbH8HDEHG5YNANraaa5vdnV3AwAfrWYLX1tcWl4Rl5hy6vwVPZNl5dnJosJCLCws7vduLt9gY7f/yIHjZ0kkEolM2rZlw9Pnr6eojs7oLVxilZtfeP/6pTVWFpMmsYV9+bZr/xGLtdbZ8ZEy0pLMjdc1NI5x5n8hNhhqhyXlByXk2pjPrm5qTy2qdlqziAWFAgBlSeE2Qvd9v+jkwqrqpvay+hZCd++o8oLhKKnDIQswmyu/khMRunsBoKKhla6xsqRQo/+IG8UxhtlkAmQf0U6k4bm6f/QBAC/nmHaSZkce5lc23LS3Wqk/YxIWHZFauO+uz+qzHkmPDkuLjBLjurGt0+H2+y2ms5bOUWfSrKKh9fjTj2FJ+fLigk8Pb0DSnUx85v8hZi/dkB0bmhkVNN/KprWhuiI31WyrE4qFBQBEZZW78G1fXt8rz0lubahuri7r7SLwCI0eH44OTVUlZDL5R0+X+/GtQ4W9XQQAwNVVDG8vIqt893vj8HIErJixJrIBANfYxuaasrLMhIAHLle3LroUlEcmk1+62M9dbj1j4W/bG46Ezy9dWTFYw40MArzhait8bh3Pjg0VkpK3Pe+uqrtw7LV/J2QWrmlI+1yXHCJvvKUbV9NWkq6yYh8KxQIAXBKKfYT2kk+PWovTunHVxIaK/h7CJL5xq2QI9aVAJg/0diXd/hU/qb+HCABdTZV0jbnEFZe/pi8cAgt6TG4x+owlvrpg5vYrknNXsGLYGjO+pj85GHdlo8mtGHYhKW2Hu6wYLLeUKgBwiskLqOhg2LmKPz6sTwqWXsA46OYY0U/sAABSf99sJw8uCUUA4JXT6MXjCv3u1MQHKJrTxFRf/qqC2FDeUpic9/bStxOLzR+lTeL9dU5f/PEBCxqjtJRBip//F7HJwigkOuljZPy2VYur6ptScooOb1uL8BIqclJtHZ2uL32Tsgqr6pvKquo6u7rFRjiDYYLiyloymdzV07P58OWhws6ubhiBH1ORk2xNHjFr8Nj5MYZgQ0gkPM1OoaunF4ZdAQKAW88+YDHofZutqAufnndiw2KmKMoCgKK0xOwZU3g4OW4/9wn8GrdhqSEwRRueAAi9dvOkipwUAMxQU2xubb/69N2HsGj7DZaGWw/llVS6ntyz2kyfjQ37+XuKvbOrlcOZNP8nMuI0696WpIDS6rr49Lyz954v2Li/OPyliODfm3hivuXG9OjQlK+fDFfb4uqrS3NSLbYfRP1c54vLKRM72kJe3C3JSsbVVzdWl/YQCXzj94wNlSVkMvlHN/He4V+kfA+RAADNNfQbRnE55WcpzSOZQo/HLTJEB67xyeldBlbW2oaMPaOLtUlNSZ7NqdtzzFZh2NiyYiPcnfdcc1h1LSBFSFwajWUDACK+jbrLj55uAODgHis32FRT8fra0fToUBFpeYcrHnSJVBAEed5BY7DmW0bZY/4z438bZBeurUv9XJscomi8pau5prUkfYqVI+JVAYBbQukHob3w46PW4tSu5hpCQ3l/D2Ey/7gda+dPxxp/i9qxEgCA2FhJ15hbQnH1G/pN6BDG6FgRyxnPT9elfuYUlTNwfCQybcHwNgUB91nQGNVl9Fl4xth9ImA4dE97U9L9fQqGGyVnMbjEz7z2L8emFYuDI+OoiNb8w3abqYlWV8+3iRm5VXUNZZW1/4xoLS6vJpPJXd09m/b/ClJCIHYDI6JVRV6mLTtyJFMTpzUaca07j120Wb3Mwlh/pDZG6+1zi8tczx1as8SIjQ37OSZx98krK3YeSg95LSMhxrx2InP76fRpjn67unsBgJeHCwCeXjmFxWKoKVxuLo7b7m8Cw6M2LP8lJC2vrjtyyTU4Mk5BRvL5zbN0Sav/Tqw31ApLLghKyLMxn4XwHgdWGyC8h5KkUBuh+75/TEphdXVTW1l96z/jPUrrcGQymdjbZ3v11/3zzu4fAFDRSM97KEkKNfiNGChrLLxHUxvB4c6HLaa6zLkFAHD1icaiWR2W019RNj/6KL+y8ab9CqsF0ydh0RGpRY73fNec80x8eJCa2aj3vYCkd3F5EWZ08H7us+PCfBOi5VlQqPuOq9e5PHe853vM7SOJTCaTyNZmus9Ck6jlKRUNrSeefgpLLpAXE3A7tG4oCezYH/wvxOylG3KoiJHK3FTTYcRIxW8iRjyoiJGeLgIAtAwjRkRklV1/EysCAHd+EiOBD1yubV10MSiPW2AUmhpZ4HV30oRORxZ47FyjLPC68O0AMND3Y8fVl6KyygAgpTq9s605zPNG6mdfg3W7AGDL2UdoLFZcYQoACEspyE+bNZmTJ+KVa8a3j7MWr7u5w6y+NG/d0VvaJisxbGx58V+8Lux9uH/Vae9kAbFxxIlg/Gj/oI/5fB1ebk7/L3HbVpn5hscAwCYLym7qzgu/8w+9sFjMfC11g9kzjmxfc/eVf2Vd0xgtD2322joIAMCGxaCp8kTy83KtXbxwCqPYGCgUajLbRLcB74K/OV56CCjUxf02u9cvY6MVfDGvZQJxYQEA0NVQQVQaCBYv0L3m7l1UXjPBOZPJZDIZEBYAABSlxRWlxVlYUDvP3AmPTd2y3HioZU0jzjs0ermRHvWBpagAHwBU1NL8dSFbX0G+v/2cwGzONF4u9sDoNFsLfd/IFADYaEaJmuX6LvyiZyAWg543Q9lAW+3w5iX33n+uahxrLPH2TspFkLbOLgBgw6DRVN6On4djjfEsNTkGp6QoFGoy2z9fn3kERrXiiZ1dPbuvUAL31bd0kMnk3VeeKUqJHNy4GADeRyTuv/UahUKd37Vq10pDNlqSZYYy/c0Yk9karu/C8yvqR60dC5iP/n8Tiw3m8fFw+YVGbl+3/EPwFwDYbEUJ8H7L3cvljhsbFjtfd+YiPZ2ju7fe8XxTWTPWb3toHdzagQcANiyGOm+uAC/PegtThqptFAo1kStBhWWVuUVlC2Zp7j73PAwAACAASURBVNxAIdSWmyxMSMt29XwbEB7laLseAN4Ghu09cw2FQl06usdhyxq630NNdVU6m2YL9W65e+UV05M7DMG8u7qKQlqwl2/I14LSClEhAcN5ujFJ6QCgpiSP5EGsoP2G2/CdACDIP26d1r+MpUuW8PHx+fj42u3c+d7bGwC2Wm9Bqq7fuHHm7Dk2Njb9BQuMDI1Onjhx89atiorKMVpua6OQU61trQDAxobFUG0jBQQENm7YMDw8BgqFmjyG8CdMICEuDgCzZ89CpBIIli1devHSpYICSjrD115eu+0dUCjUtatX9u3dy0YVC6qgoCAnJ3ehvv7uXRQS32rFiri4+Fu3b/v5+zsdODDBoZlDQ0M9JyvT+8OH/PwCMTFRYyPjqOgoABg1jsh/iyUmRny8PL4fQ3Zs3eTt/xEArDf8OpK5ee/xucs32NiwC/RmGy6cf/zgvtsP3CqrqsdiuZ1KT9Da1g4AbGxsGCoXIMDPt2H1iikq9OExUCjUUPiKPwFRESEAKK+i4eYQ9YOQoACZTCaTyUNSFUV5OUV5ORQLapuDU1hE5NaNawFAfYpqxvcIn4Cg/KJiMRERo4Xzo+MSAGDUUB+FxaW5+YX6c+fY2W5GSlYsNY9PSrnz8Kl/UOh++x3MjQ+SBpnMfOLfzB+Fqa4aL+fkwO/ZNuaz/WIzAWCDEUV3f9c36tLrcCwGPVddYeEMpUPrDO/7RVc2tjG19wvtBMqF17bObgDAYtDUdAM/N8caA01VGXq2F4VC/ZZbGiNBhI8LACppiZJ2QjcACPCMLmsuqmnOr2yYp6GwbQlFFLtMTyMxv/KBf8yn+ByHFaPQuJ4hCa2dXZ1dPfa3KfmSG1o6yUC2v/1eUULIac0iAHj/Ld3pgR8KwMV2iZ3FvKHl2QRn/t9CY54pOzdv+teA+VY2aZ/9AGD2MsoFyohXrp8eX0JjsUqa81R1F5rbHvry+n5L/YgcPR26fm7puzraAACNwbJSkfgcPPy6ZmvEGd0CQaFQY7zkwRBkMhnI5KFDNWEpBWEpBRSK5YXz7ty4z60N1cSO1h4i/qUzRQPXgasHMvmls72wjKLZ2KKAjgVtjbXJYR80F1kMz+2SHPr+zZUDKEBZ7XMxWLsLYUDGWPvXQkzTBMvBU5cYJG+8pSY+EABkF1IyghV/fJj//ioLhk1wyhxhjQWqVvuLPz3uah6TWwSAPiLFM/YR2gGAFcNGfRrExsUnPX8lg/AYKNTw8BLjAqGuBF9dIDRVT95kK1IiMWtJa1FKSdDjuqRgpaW7+OSn0XURnWlU/PEhvmZMSyAmmMQvBgD8SlqISgOBmJZJod8dQm0JAOUtHzqx4xST5xSTR7GwpD7Y15jxVfZnbPzulrrq734Ss5bQha//fxfmC2bxcnP6R3zftmqxT1gMAGyypPAwt5/7nH/4CovBzNfWWDR7xtEd61xf+I6dH2v7GfkM2dFgMRgM1d0PAV6udUsM1BQZxMb4LfzYSEDUDBW1NAKRdkYkUk1D8/uQqOVGc+lE8zOn0CfiMZ2vc/u5T37p6L/kFHptmiqi0kCwWH/21afvCstrCstr8koqF2hP27GGsiW3NJybkJF/95Vf4Ne4fZuthhFoEorSEiwsLDtP3wz7nmK9/DenKviNmLnAjIObNzkiwHC1bWKYLwAssNw0VBv03NXnwQUMBquqPU999sLlOw4Hv7yLqx2TZ0Q4awRExDNi2ag9Iycv/9wlayWGhcdAoVDYCbjFUfHF253Q0dpN7HxymrIfbGuuBzL5yeldYjJKWouW1pTkqenMN1pDUYnpGFkUZyaGvLyX8iVw8Za9vAIiANBcW0ltk4hvBwBuvjGd6X4Pevfswn5AodY7XTDdsAvDyPG1NtTEh3jrGFvS5YX5Lcb/QkhomWA5eGsSPikab6mODwAAuZ9eFQAKAx/kvLvKgmETnjpHZNqCKSv3F356NEbH2kf89R5SHCuaDcX6a7+J5eSTmb+SQXiMCTtWAKiM8Ul1OwIo1IzNZ5QX72BhdM7U3VJX9d1PajYD5zWW7hPBSEOXhj//QWjr6yYkPXBESnraGslkctIDRy4x+cG+Xia1U6wcf+8kfy/MDeby8nD5h0VtX7fcJ/grAGxeSflVv+3+xsX1KRsWO193xiI9nWO7re94vKusHSvR2tZBQ7RiaYlWfl7udRYmUxTpidYJsqyjwu2Nf2s7Hk/s2nmMEqaorgkHZNh57KKSnPRhu82FZZW5xWULZs3cuWEF0sDSRD8+Lfvus3eBn6NNFsxmUrvPZt1E5iYiKADDiNZ2KqJ1pjr9X6WZvt5t9zd5Jb8Y4LeB4fvOXkehUJeOONhvWT32M7X/Fgjv8TEux8Z8ll9sFtDwHtGXvD6zYdBz1eUXzlA6uHbRff/YqnHwHpSrFAjvwYZBo1mpeA8u9tULZ6pJ/37ew4PCLfQ63PmAlDS0dpKB7HDng6KE4IGfOUpqcR0+0RkWehp0uVeKa5rzKxvnacjbLqbkrVimp56UX/kgIPZTQq695TwyAMvPrCIK4oIK4oIsKJT9be/PqYWbjCcqzZkiKxr/4IB/bHZRdZMIP7fBTKXvOWUAMCTU8P6W4fTAD4VCOdsstrOYS31oNcYH/zuBECMZVMTIHCpiJIiKGDGzPfT19xEjnDz8umZrhofHmCArAiMTIy+dd+fGfdaz2My8O6LkaKmrpC5EdBucoy3weIXFAEBOQwdRaSCYNt88zPNGY2UR8lFajT40w9S5JhGvXOvLChoriupL85S15i1YRYlKPsNgWVlW4lev+5mRHxnehBkX/sk5KxsWs8pk/nP/z214wofw2NnT1ZRkJACgpR1/+u5zQT6enEA3zp/Bf665e49kB4VCkck00WmKKymiRTlJUQBQlBb3vHhwqHaQRCJ29TB0jU2t7Vfc3o000JblxjPVFEeqRRAak7L99O1Z01SfXzk8lBlkjLXMISkmBAD9AzQxtHt/9AEA988YmP8YN5/5nL330u/eWdN5vzKpC/ByA0BtE40uwdM3bGBwkG7nqSgjgUKhKupohBo5xRUAoKMxoWjk/wLYMOiVBjovgmPbOom+kcmz1BUUpUQAoKWDcNbNV5CHK9PrIic7Zb1+/VXwSHZQACQymbqkpIbyhciKCQKAgqSI+8lfN4QGSSRidy9D4qOpDX/t5Yi5YDcvnjdcKkENQV6uaYpSZbW/iJu+/n4SiZxTWoOcM4UlZO+85Kk7Vf7Z6Z2SIvSBamqb29IKK7VUZKmrKutxACDEy8W8lsmshsB89P+zYMNiVi02eub9sa0D/yEoYramhpKcNAC0tHWcuv5QkJ8378sHLg7Kyubqo+cj2UGhUHTvYXE5ZUMrJyUOAIqyUs9unhuqHRwkEbq62Scz+j3EtV56MGKSTutVS4crIaiRU1gKAAt0acK6GM7VdfV824EnAEDItzjbwy6zZqq/un1eSpxe51jb0JSSna+tMYW6ClnTCwmMTl4w797X319Z2yDIx7OVKg7hjSevRIUE+Xm4leSkUShURQ2N+D2noAQAhrLG/LVgY2Nbu2aNu4dHa2vru/fv9fTmKCsrAwAOhzt2/ISQkFBJUSEXF+VP9eKlEe+Oo1D0Yd+KiouRf8jLyQGAkqLS61cvh2oHBwcJBAI7O30o7MbGxvMXRoxka2tjo6WlyfyJpKWlAaC/v5+6sKe3BwB4eHgAICg4eIv11jlzZr/18kIaUyM7JwcA9PVp7hMYGxvdun27vZ1B9utxDc0cfX19FRUVgoKC22xthwqvXL0qJibGz/9X//SxsWFXr7DwfPW2ta39vf/HObraSgqUPEe4ltYTLpeFBPgLUmO5OCkHtJdv3mVoB4VCkUg0i5ai0rKhf8vLygCAkrzci8e/ug8ODhKIXezDxD2NzbiLN+6MNGGbjes0p2uM4wmHQUlBHoVCVVTS0H/ZufkAoKs185rrg1Pnr35898LceNFQrSA/PwDU1NUDQF9ff2V1tQA/v82mX+TFNdcHYiLCdElJhiM7rwAAFsydTV1o9P+xd5YBUXRdHD9Lx+7S3d3dIQgotmIriih2dze2otg+FigmDYp0CiJId3dLd+++HxYRlt0FBV3w3d+n55k7c+fMcp1z53/PPWe60e2HTxubmkbtnLTlv/Qj/H3oaWmWGKk6BcQ2tHa4RyTryIlKCnABQF1z+zlHX04WZOKzo8gf7snufQjRjhD4E7D88oF9maK87AAgwc/55NDPDED9GExbZ/fICVhNY+uNd8HEbrLWXEtVknB9hzEiKcCFQCCKq4bJLumFVQAwmKGUBBlFVQBgqDSs6JiJmvQDz89NbaPXH+FgQSqJ8xdU/pzVd/f2YbHYtMJK3DqT/7esbTffa8uKPD+6Bi8x6TgtJy80dPSaM5dEeTm1NzfEBbqJK+vwCEsCQFtjnef9cyhWzvOeSQxMA28zPwc7oh0hENj+YW4RV9MUADgFRQGAW1hiw4WfBeMwmP7u9jZaBgLSQ0t9je8zorm19RdajfykH0rAS3vvB+d33nZVNPj5OYZk5QCAxpoKJCunoLTS99KfL9u+nh4sFlOWmzooYUwIUZ6OmP4+/UXr8I6nRfq/OLtVTEl74yWHkSVRSLdOZqho6QT1LYpC3/S0NpZ/8eKQ0ULySQBAd0t9+puLdGiO2Xe/0jAODKRsD6IOCxAIbP+w91VrZT7uP5h5RAAAySemtfvBYCsW09/X2UY9otRIV9P3bPdbxG4iYmI5MswCj+aSTADglB9W1ZtH2SjP57+e9ubO+sqG/EQ2CbWhqdHbv5cAAAP6lzeb4oHrE9s/bH7V39MFALRMKADI8bqX/u6ywfE3vGo/kyLQo9gBoLPu5/y8KPgVtr9PzNRynPZMHujpaJfNMn7h4d/Q1OLqH66rKv9TH7vjwMnGmvbxOeqHPnbtKVHZChAIzPDKa3nF5bj/EBXkAwBJEQGHy0cGW0npY3WNV568JXYfawvzkaESY0cKJyIN3+2TllMIAFpKwz4zHdz9+vr7rRfPGnqwvLo2Pj1HQ0F6aDEUXG9jqQuMuwpPXuvs7gYAFhRTel4RAEzTHDa9NNNTu/vKA1cK087B5ezdF573bWdN+7lgwMmGxhk26t3JCC0dve7speHuL9uaGr76u0mr6vCJDIicLY1172+fQbNx3vJJYWAeeKF5PSVcZQkBCLyvxarin9nvuAVFAYBXWGLHlZ9J/jGY/q72NroRnrGprsbr8TUggvFiKzH5cc1pUWycIrLK1SM8Y0lOGoKKqiw3HQDkNA2HXqKoZ+LrdK+9pQkAeEUlEQgEXqBGaW4aAEgoacJoJEX4/3dyi5SK9q5rjiRqmoS6Ofb3901fjO9VJ6TzSQgVLZ2wwaKCkDfdrY2lUV6cMloo/oHiBd0t9SmvL9KzcMy/FzPoWDPdCTvWkd+bLT+8KgAgBxyruN7enxmLsZj+3s62kTW8upq+Z7gSdaxiZqvZxVVIP1RlQlDMvV2c0pr6+x+TKMiVH+SE7e8TN12Dd3yMl48HYremR3OwiSq2Vf1cDu/v7cZiMU1F6QgEFauoAonWP2HnBEJPR7t8rpmjy8eGpmaXT8G66kpSokKAE1rtHnGys6YHOQ8KrVcfvSTWz8iRllc0KLQKAE5otTs72Nrfj2lr72AcIbTW1NZfefiC2F2sl84fGazwS3CysyrLSeUX/9zN29PTi8FgUrLycMsB6TkFADANX6fVuuv4vrG5lXTreAwDACkxIQQCgReokZqdBwDaKgrlVd/jUzM1lOSGS7gVAMD9QwH2Dfuy8cgFHTVFp1vnR2rIkxl6WprF01ReBX5raO3w+JyiLSciKcAJAHXN7edf+HGyMCc8PTKoe9x0JppzBYFbmR5C3o807UN0j5+qETHd43tj63Xi6sramVqqkqO8gjhZmIlqCz8CLADghX9sXz9mrTl+aEV6cRUAGOArG1IPvCKb2jpvu4XbvvR3ObdhpubP2SAHmhkAKmp/uXg3Hj19/SXVDRwszFZDrLJ3DeNhR+GiSQK+ZW275awlK/z8iCWeKjL2B5+c0NDRa8xc8sXLqb25IT7QTVxZh/uHMOJ1/xyKlfPcEGHEn6QwghlNGFk/BmFknKoIAAS+tPd+cH4HEWGE9LUAwCMsgUAg8AI1ynPTAGCwdAsxcFJGf9+w79me7i4AYESicQYUZySIyKsPFT1waUVQbJwV+RkAIKU+bPIpp2MS8uZ+R+soSxVj4Tc3xK9ZaPbMze+mo1tqTuGDMwMZ3kqrvmMw2EWm+oNRGuXVdak5hdwchMVuEX6e0Jik3r4+XPRiVkHpYNpGcSE+TjaWoOjEwVYAsHvuavvwdZDDNX01/C2tza3tLzyJVqc21FAaNVDj7H0nNJLpjd0xXk4CyzCkW0nDSE9nrK0c8S01v7RSUnggDcPHsBgA0FUhtVw6FhQkRQEgNCZ5aKCGo0cAAChJD4sADfmaxMaCmq49bIbKx8VuqK7wJSG9sLxKXJAPAHr7+lz8Ivi5OUb9xSYDlrP1n3+IsH/rn5pfdu/wwKdRWU09BoNdaKQ+GKVR/r0hLb+Mm0gGKmFezrCEzN6+ftyuzaziysKKgXUCcQFuTlZUcFzGYCsA3Hzjd/G5V8C9o3pK+D9Rc1vny09RxKw1UJEhHaixdYnp1iWmQ49M23Khq7s36tkZ3P+ef+qBZmZ8dX47LwcBCaOxtd3qzKMNC4zuHPwZeuYeFg8A+spSpFtJWDUI6bv/P2O1ZO7Td552j1+lZOU9unQcd7CkogqDwViYTx/8eCivqknJyuXmIPwOERHgC/nybfCNl5lXWFAyIMxJiAhysrMGRcYOfR9ef/zyvP2T0Hf/6Wvif3Y2tbY5unwgZu00bTXSgRpykmIA4OEfdmrPz/gkN98QAFCQkQCAMzcfsaCY39+/zMtFQPBtaG5ZvevExlUWDy4cHTyIyzVioDV6uSjSl3d0diubr1w5f+ZLe1tcU0X1d0//MOtl8wGAj5vTUEs1Ki65sLRCXFgAAHr7+t5/DOTn4SL9yJME63Xr/nv8+Nr1G8nJKU+fDJRbKikpxWAwSxYvHozSKCsrS05O4eEh/HkjKiIaFBzc29uLS5uRkZGZnz8geUhKSnJxcQUEBg62AsCVq1dPnzkbGRFhaGgwtJ+mpqZnz58TM9XY2GjUQA1GRkZTE5PQsLC8vDwpqYGXjJeXNwDo6+sBwImTJ1lYWNxcXPj4CKRlk5eTBwA3d7dzZ88MHnRxcQUAJaVRUtWNemvSdHR0yMorrF616u2bgZoL5eXl7h4eNhs2jHot2Vm3atkTx1c37j5MSct4fPvn1Lm0vAKDwVgsmDMYpVFWUZmSnslD6F+xqLBQcPjn3t4+XM6MzOzcgiEZXCTERLk4OQJDwwdPAIBrtx+cvXwj/JOHge6wb7nm5maHV++IWWukrzvOQA1+Xp5p+jqR0bGFRSXiYiIA0Nvb987NS4CPV11FqbrmOwAEh0cODdR47vQWAJQV5QGgo7NTQWf6qqWLXj25j2str6zy+Oi7fs3om07kZaQAwP3DpzNHf+50d/X6CACK8rKjdk7a8vH8Jn+H1WYaDr5fb7uGpRVW3t2zDHew7HsjBotdYKA4qFZU1DalFVbiSrqORISbPSwpd3CKlV1SU/ijrIk4PycnC3NIYu7QCdgtl9BLrwL8r+/QVRg2xW1u73QKiCVmqoGS+DgDNXjZ0fqKYl/SC4uq6sX4OACgt6/fNSKJj4NlVCkEAGSFuQHA60vqsTU/v0I9I1MAYLBgKgm2LjDYumDY+9l4z+2unt7IewOJhWxf+qGZGF6esBqZanWclpMd3XmWn90dAl7eLs9NW3tyIDKsvroMi8Gomi4YFCMaayrKc9JQHNwEO+HgF86ODevv68XtDqkqzKr9kbydS1AcycaZ+TVksBUAAhxvffjv4sGn/pKq+P6io7X5i7cTEEFKw5C0JCEgIQ8AWbFhQ/WIKK+XACAopagyfb7Jyq1Dz79iZdTT3XXyDdEvi98jMyaUGc0mq4WfV9n7oS0DEr3lmhMLJ4FhSbp1kiMyfUVh0Msc73tNxenqW2/iDnbUlmOxGAGdeYOLSZ31lU3FGfQshLdkMHEJfU+NwPT3UlHTAkBLWU77j+zrSF4xejRHTXL4YCsA5HjezXC+ZmzrzSmrM7Sf3vaWopA3QAROef1RAzVQgtIAUBHzUX75ocGD5V8/AACLsFxPW2PMzU1iM6zUt/xcoy2P9gYADjndEZ39GtR0DFyKhrXpUW1VhUi+AYm2Ms4PADhktAAALSwHADWpEUMDNYpCXgMAi+jPmOmalHA6JCuXEn4u5SnN2oUznrl+uunomppT+PDswDbl0srvGAx2kZk+6qc+VpuaXcBNJHhdhJ8nNCZxiD5WUlA6oI9JCPFzsrEEf0kY+j1445mz7QOn4Bd2+mr4IelNrW0vPPyJWTtNU2k8gRp8XByGGopfEtIKy6rEhQZEJGe/cH5uDjX5YQpJcHQiGwvKRGfY0lFjc6vlwUs2y+bcP71n8KC7fwQAGKiPno+akZ5uurZK+LeU/NIKSeEBX/Yx9CsA6KrIiwrwAoBHUNTJ7T+zTbgHRgKAgpQoAChKiQJASEzi0EANB3d/AFCWHrbwMAkxWrgmxOX5R4dbJdmpm87dHzxeV1mKxWC0zBYORmnUV5eXZKeyEPKMXALCaV9/usXygqzq0p/ruDzC4mg2ztTo4KGe8cOzm673L5x5GSijNswzdrQ2h3kQXR+V1TQcZ6DGLMttsyyHFUg6uXJab3fnZZcvAFCWlwEA3wK9lm4/MXhCbIAHAAhJKQAAGxefrIZBdsKXmrIiHiExAOjv6/3i68LGzT8Ww5zvnmNEovfefM3KRcrxpX4NRbKwKepM/6VHG2PnkxOx6SvzA19med1rLE7X2v4zQqK9tgyLxQgOcawddZWNxekMrAQcKzO3cHXKT7/ZXJbTVlU82IpzrNXJYUMda6bH3bT3V80ufuAa7lh72psLQl4DEbgU9EYN1Eh9c4mOCW1w6DkjG6kl5OqUcDokK48yvvMa4+Xjgditpedukp47rO5YwJGZ/T2ds+xCBk8g0TrJWbt47tN3XnZPXqdm5T28dAx3sLSyGoPBWJgbDxFav6dm5ZEUWuOGCK1FQ4RWAU521uDhQuuNx07nbz8NefdIX2PYfKyptc3R5SMxU6dpqY0zUGOH1bIdVsuGHtFbbNPV1R3r/QL3v7KSogDg6R92avfGwXPc/UIBQFFaQkZShETreAwDIkKr88cgfh4uNQWZtJyC1btPbly56P6Fn4GkOAHZ4IdYffbmYzSK+d29S7y/XvqN7FiaaTj6xdxxC08rrLyzeynuIE73mK8/RPeoa0orrCKmewjzsIUn5f3UPUprBsu5ihHSPexdwi69DvS7vl1XXnRoP83tXa8CvhEz1UBRfNRv/C0LDLYM1xam773b2dP7+e6wFDthiblsKCZjVfxlL1khHgDwjko7Zvkzhb9nZCoAyIvwMtHTAkBYUt7QQI2XAbEAoCg+rvo7ANDZ3aO9zW6pseqzwwM7eSrrmj9Gp6+dObAeauvkj2ZicDpuxcNOYDfyGB980qI7zzLS3SHw5e3y3LQ1f1EY+fjfxYNP/SVU8ad/0eNQRQAAV1Ukm4gwQvpaAGDh4pNUM8hLjK4tL+ISHJjgxQW4snLxC8uOcmtaekYZLaOcuM/fywq4hQZejynhPgAgrqwDAO0tjU+PWhkuXm954s7gVQlBHgAgqaZPR88AAIkh3vO2HB/S6gkA/JITsEP4NwM1tJVkpEQE7r3yYmKgX2o+EEUiLSKIZGJwC4w0N9CQFhOMSc6yffgahWRq6+jKK66QEsV/WWgpSftHxm05c9tmyayCsqpbjm5oJFN9Ey6pI43tHusd5+9uPHnzwPplKCTTp/CYa8+cTXVV9VQJpKKVFhVs/Ob5e88CAE0tbZn5JSqy4necvPCajDSV9FTlSLTOMRo9dc+FPeuNrQ5aHbl2fvc6QR7O8LjU527++mry84wH5pf3XnuftHc4vnX18S2/lpBqlqGmgpToo3cfWVDMM/TVK7/XewZF+X7+pqEgNdSwppa2pKz8uUbagzkeBzm8ccWS3eetjlw7snEFGxp584V7UUW1+92ziEkfUAYAWvLikkI8912CGBnolkwfeDVLCfEyM9K7h8XN1FGUFuaLSc+/+NwLxczQ1tmdV1YtJYT/IaQpLxYQk7rtquP6+dMKK77bv/VHMzPWN7cBAB0tzbnNS3bdeLn50rP9q+egmBk+RSXfcPIx0ZTXVSQw15EW5q0LevSHHraptSOzqFJZSuieC35M0jRVmdl6yorigtoKEi98ItnRyIVGahgM9n1QTGhcxiIjdQ05MSwWS6IV188D16BTj9yOWs8/Zo1fBHTUu/+hp54SaKsqSokJ33F8x8TIsHTugC4pLS6CZGJ0+xQ8y1hPRlzka2LqOfsnaCRze0dnblGptBj+TlYtVXm/8C+bj1ywWbmooKTc7skrNApZ39gEAHS0tBcP7dh24vKGg+cObrFCI5k/Bn+++sDRzEBbT4PALy8jLtKS+fm3H0deSmyGoU5wVOwCm32Wi2aLCPB5B0W4+ATKS4kvnGnU2NyakVuoKi99+zn+4quRjvpcEwMlGUkdNUUHZ28OVvQi8+kYLPadt39wVOziWSZayvIAcNfx/fFr907s2nhyl83Iu496+XQ9TQ//MFMDn4UzjQtKyneeuiLAy33l2EC84NHt1habDq7Zc/LojvVsaJTdk1dFZZWeT+ymxAtNV1dHWlr6lr09ExPTiuUDFStkZKSRSKSzi8ucObNlZWS+REefPnMWjUa3tbXl5OTIyOB/DeroaH/y9V2/wWbz5k35+fnXrt9gYWGpq6sDADo6uiuXL23avGWt1bqjR46g0SjvDx8uXro8c8YMAwN9vH5k7ETCeAAAIABJREFUZWVxBd7Gw9UrV3T09FasWnX50iUhQaHQsNDHT54YGhosXLCgsbExPT1DTU315i17vKumTzeeP2+egoK8+cyZgUFBs+fMXbt2jaiIqKeX17v37xUU5C0WLQIA+9u3Dx85eub0qTOnT//SrUc1m5WV1dTExM3dfcYMs8UWFvn5BVu2bRUUFLxxneiOscmDjqa6lIT47YdPmRgZl1n8fFhpSXEkM7Or58fZZiay0pLRsfFnL99Ao5Bt7e25+QXSksM8mraGmm9gyMZd+zeusywoLL5+5yELGl1XP7Apn46O9tLpY1v2Hrbetufw3u1oFOqDb+Dlm3dmTJ+mr4MfvCwjJdleNaaaR7/Nsf27F66yXmWz/fjB3WysLDfuPCwqKfV+9wKBQMyZaaooL/vgqSMrC9rc1Liiqtrd28cnIFhTTWWeuRkAsLKgTaYZuH/4ZGY8bdG82QVFxdv2HxXg57t2/iSu8zuPnh49e+nU4X2nDu/Du6+8rPRME6OgsM/zlq+1XL5EVFjI65Ofs7u3vKz0ormzx9I5Ccv/6C82IWjJikgKcD3w/MxIT7t42oAEIyXIxcxI7/E5ZaaGrJQQd2xm8cVX/igm+vaunrzyWilBfJVWQ0Y4IC5rh72z9Wydwsq6227haCaG+pZ2AKCjoT67fu7uO65b7N7tW26CYqT3jcmwex9ioiatM1ytAABpQe7v3lf/6PMeXGG24tzz9VdeHVplxopkuu0aVlxV73zOZix/LFkRXlN16dDE3KWnn60wVRfhZvv4Nd09IklWhGeeniIAPPT6fPr5pyOWM46unjlqb3g0tXVmlVQri/Pf98T3+4ZKErO15cZjOdkRU9LiEZYMeXOfjoFRY+ZAOTYeESl6JuaEQA8F/Zm8otIFKTEfH11kQKK6O9pqSvJ4RPCXHsUUNNOjAl6e325osb62rCDA6TYjEt3WVA8ANLR0FjvPvr642/H0ZnPr/YzMqJSIT74ON+R0TCRUCCxp84pK34v+/f3WigbmApIK4c6PmVAs8rpmTbVVicGeqZF+IvLqStNmj379aIS8feBx5/TczUfnbTpK7JyOlqbS7GTlaXPwsnR0tDRVFmQKySgHv7mPd4m0uqGEii6J1gkx/o/CLqWB5JPI83lMTc8oqL8QdxDFL0HDwFwe7cWrZoril6zPictwvkrLiOzvam+tLBjcHzykE/XqxOD4B3vFzNa2VRflet+nZUJ1tzYAABUNraLlyYT/DsTd3SljsZuGEVkV55/lYc+tbMwpo43XD0pAcvHbcRU/RQvJ8KhMr0kJj7q0WthoKROXUOU3v7IoT7SQDL/WHCpqGg5pzaKQ13QoNgHteVgspjTSrSYlXEBnPrvkmFZM8z49TntlK7fsgNyygyNbldacCj0xJ9Z+i8LqE0yc/N/To4qCnDhldfg0ZwEAr7oZi7Bcgd9zOiY0j6pJZ0NV+dePVfGBbBKqfOoD77ee9uamwlQ+TfPJv533l9BWlpUSEbjr5MHEQL9k1kBNKylRQSQTo1vA51mGWtJigl+TMm0fOKGQTO0dnbnF5UML4+LQUpbxj/y25fStDUtnF5ZW3nR0HaaP7d2w49xtm+M3DtosRzEz+YR/vfb0nZmeup4qgcJ8MmJCTfFEl5TGz+FNq5bsPLP28OWjm1exopE3HVyLyqs87tkOdS5NLW1JWXlzjXXxNChFaTEdFTlHd38OFvQiMwMMFvPOJzT4a6LFDEPNHwtd9155nrj17Pi2NSe2Esi8cmGfjdGafWsPXbbds0GQlzP8W8pzN199NYV503UxGOwMPfXgr4kLt59aPd9UhJ/nQ0i0i1+4nITIAhN9AJhlqK0gJfro7QcWFHKmvkbl9zqPwEjfiBgNBek5xvj/YCcbkspafCKSvk736RiYdM2XDB7nF5VmYGKOCXBXmWbOLyadmxTjev8CIxLV1dlWVZzHJzrMM0ooaSV9Dvjv1DbTpeurywo/OtxiQqJbmwZWjGho6VbuPf/03M6HxzctsNnPyIxOCPPxenJdSc9UWhXfM/KLSb9MqAcyISghp6RvlhYdcm3bYoP5K7kEROJDPkb7uQlKyGmazseds2jToRs7l909tM5i82FmNOtHB/vv5cWH77viBqrfq/tvb55avO3Ykm3H8Dpvb2kqz88UkVX55HQPr0lec5qa8ezB04oyk9SN8b0qacbY+aSFQ1oDxS+R8/E/anpG4R9eFQDQ/JI0DMylX7z41MzQApJ12d/S3l+jZUT1dba3Vuaj+Ict+HFIqVcmBMXe3yMxw6q1uijL896gVwUAKhpalTWnvj3aH3Nnp5zFLlomVPk3/wz3W7wqxlwjHCtaQGrF+/Lffpye9qamsmw2MaWcj/haLreCAb/GoPNqaixI5R/hvMZyeY7P42Sn84rLDyosJ+BYx2IhwVv/82irKkiJCt11fM/EyLB0zsC+CykxYSQTo+unEHMjPRlx4a+JaedvP0WREFpV5P3CozcfvWSzYmFBafnNJ6/RKOb6xmYAoKOlvXBw+/aTVzYcOn9o81oUktknJPLqwxdmBlp66vgbJ2TERZozwv/8QxNFXlJshqF2cNS3hRsPrF44S0SQzzswwsUnSF5KbMHMaTTU1CRaAeCeo/Px6/dP7LQ5set3dh8d2bZu8eZDa/aePrbdmhWNuvn0dVFZpceTGwgEQklGQkdN0cHlAzsr2mLWdAwG8847IDjqm8Ws6ZrK8gDQ1NyakVeoQlhDVptrYkDohpMITVlhSQHOB56RjPS0i6cNKPBSglzMDHSekakzNWWlBLliM4svvQ4koXtoyggHxmXvuO1iPUunsLLuznDd44z1nD133bbefL932XQUE4NvTIadc6iJmpSOHP5eXylBrhovovmVJ4qmts7k/IrZOnIjU03IivCYqkmHJuUuO/N8hYm6MA+bT3S62+dkWWGeeXoKNNRU8qK8Tz5GszAzmqlLV9Y3e0el+cdmqUsJztKSA4CHXpFnHHyPrDY7snrGr1rFwsxopCLx4Uvaa1XJ+XqKhVX1++6583Ow2NrMgwFVpIaIKiI+S5vAUvLUQkxJi5ukMMIjKl2YEvOBpDAiqqCZHhXgdH67gcX62rKCQELCyIvTm82t9zMwo1IiPvk53JDTMREfIYzwikrfHYcqAgCKBub8kgrhzo8Zhwgjab8ijMzecPDBvuXPjlvPsTnMhGINdLKvqyjeYe+Cm+CF/hBG5hISRix2nb++3vTZMetFO8+y8QjkxH2O8nCUUNVTNpoLAAKSCuJK2l+8XjKzsKuaLsRiMN/8nLNiQtVMF4oqaGAxGDld06yY0Pt7lmjPWcnBJ5wc7hMf4MYnLqdiPG88vwmO3wzUAIDV80xsH75eaKY/GMaIZGZ8dG7v9nN3lu+7AABsLKjrhzYxMTJsOW2vuWxnczx+lMMeq8WxKdkufhG4FA6r55kAwE1HN1yrtcXMjq6uU/Yv3AOjAICGmnr9YvNzu6z+hLD4NTkLi8UmZxUkZxXgNeHuRqJ1LIEaGgpSHvfObj17e/Guc7gj84x1Hp//GbSFxWL6MRi8PEhjgYoK4Xzr5MaTNy/99/bSfwPJLReZ6dsd2TK0ulVEXCoGg9VRJrCn3ExP7dmlAzvP37M8dAUAWFDMVw9uMjfQ+FVLyMUqc72Lz70WTlMf3KeCZGJ4eHT9jmsvVp64DwBsaOarO1cyMdJvveygs/5sQ8hjvB72rDD/llHgGhzrGhzLz8m6ylwPAG699cO1rptn2Nndc/o/N4+weACgoaaynjftzKbFf1/gjknPx2KxKbmlKbn4JR4RgJitp4xAIN5d3Lnrxsubb3xvvvHFNW1aNP3SjhUAQLoVBwaD7cdgCA7DUe8+QU85VbG0mH3e/ski8+lo5EA9IxQz0+Orp7Yeu7h062EAYGdB3zi5j4mJYdPhC+pzLNuy8fdH7rOxjE1Kf/8xEJcBYo3FHAC48XggRHH98gUdnV0nrt/HBSbTUFNvWLHw/MFtf2IcUlFROd22PXD+prNPUFDkwB5lQy3VJ1dP0dHSBid+w2KxSRk5SRk5eBciEIi5JgYIBMLt0fVtJy5f/8/p+n8D9m+xXHLt+MCWKQwG099P9HU36uVPrpy02n966/FLW49fAgA1BRkn+/ODbmiGoY6D3dntJ66s2nkcAFjRyOsn9swyHj2JwiTBau2a02fOLlm8GI0e2BiNQqEcnj+z2bhp4SILAGBnZ7e/dZOZmdl6/QZFZZXe7i68Hg4eOPD1a8zbd+/evnsnICBgtXYtAFy9NhBhsNHGpqOj48jRYy6urgBAQ0OzaePGSxcv/KEXmpaW5iefjxtsNs6dNyCWLVywwNHhOQB8iY7GYrGJiUmJiUl4VyEQiPnz5lFRUb17+2b3nr3v3r8PCByIDzMymubw7BkdHR0MDKR+YgOJxK3HgsPzZ6vXrNm4afPGTZsBQF1d7e3r14NJTSY5a1cuPXv5xuIFc9Con+H8KCTy6T27zbsPLV5jAwDsbKx2l84yMzHZ7NivYjCjs6ZoaA/7d26JiUt45+aFS/CwZuVSALh++2dG9w1rV3V0dh47ewmXQIKGhsbGavWFk0fIsvQ708ToxaM7W/ceXmG9BQBYWdB2F8/MnmECAFRUVB6vnq/btsf22i3bawP7vRbPn2N/1Zbmx6aZp/fs1m7etXnPoc17DgGAmrLS6yf3B/OOYDBYYsOMiorq9dMH+46dfu/uHRgagTs4TV/n2d2bdD/qhpLunITlU4KVpuqXXgUs1FdF/UhghmSkf7Bvxc7bLqtsHQGADcV0ZfNCJga6bbfe6+2wq/uAH+q0e4lxXHaJa3iSa3gSHwfLKlN1ALB3DcO1Wplrd3T3nnXwwSWfoKGmWjdL5/S62WQZZqbq0o8Prd59x9XqkhMAsDAzXt68YOhOERJQIRDPj6w58p+XW0RySOKA69RXFH+wbwUdDTWQnH2NSkxmMRaLTSmoSCnATxGJAJitLTceyycDOnNXffjvoqrJQgbmgTcwAxNy3emHThd2PDqwCgCY0WzLDlyhZ2R+eW6r7UrdBzH4K0Yz1u4uTPsW5+8a5+/KysWvM28lAAS8GIgRNFi0rqer0/PumYRgTwCgoqYxtFi3cMeZPzHMEFRU2+zeOp7e7PPkis+TK7iDaiYLVhy+TkX9+5/kg2AxGAymH0iOpJyESCwGI66Ev8hRkBKDxWJLs1NKs1PwzQYEAJBonfyBGgAgYrQsw/magPY8WsaBgUTDiNTYfjvh0b7oa+sAgA7JqmxtS8PAFH9/T9BB4yXv8Jd8pOdvb8iNL4vyKIvyYGTnEzZaBgA5XgNrbKKmlv3dnWmvbXGZLRDUNGKmaxRWH4c/MZAQVNp7/0t2OFH2xbMmZeCFySmnq7njNhUNLQDoHX6R8N+BHM+7OZ4D263Ezdcrrzs31htgMFjiA4lNQtXg2Jv4h3u/XBlYPufTnKW5486gbXqHX8Td25npapfpOpB3V0BnnsqGi4gfg7w2PQqLxbBLj153YMqxer6Z7QOnRWYG6B8fJihmxv/O79921n7ZnnOA08cOb2VmpN986qbmkm0tifglU/euWxqbkuXsG+bsG8bPzWE53wwA7BwGSgmvXzyrs6v7pP1z98DPgNPHlsw+v9uaLG5xhp7688uHd5y7vfrARQBgQTFfO7TF3HDYnzUiLgWDweooE6hs7XL7zPZzt288d77x3Bl3cPOKeVcPbRk8B0NSH9NQkPa8b7vlzC2LnQPh2vOm6z6xPQAAVFSIl9eOHbj6yMUvPDg6AddqqKH43/kDdLQ0uBNcbp+1OXH90qPXlx4N7MJfZGZw89j2oQLapMVwwWrX+xe0ZyxkRP78MGFgRm6xffTkzPabu1cAAJKFbe2Rq/SMzP+d3HJ0sbZTUuPQHuZZ785L+Rbt6xLt68LGzT9twSoA+PD8Z16E6UvWdXd1vLM/HRPgAQDU1DTTl1qv2D3ptnIhqKh2XXN8eeXQVz/X1OiBEniyGgZbbB/R0A6ki1fSN9t+5enTsztvH1gDAEwolrWHr6gYDmzfJOE3c5NisFhscVZycVYy/n0RiMFYisxvn7EYjJSKzogOSDHGziczokbL095fFdL56VUBgIYRqbPzduyDfZFXrQCADsmqvuECNT1T7L3dvvuNVzoPm6nKLthelxNfEulREunByM4narwcALI8fxbWFDez7OvuSHllWxrtDQAIahoJszXKlicm3LHWZccBFttYmNpYmDqiETEYqPE9/QsWi+GUwXdeY7kci8FgMUSFi1Ehduv/BywtZp+//XSRufEwofXKia3HLy/bdgQA2FjQN07uYWZk3HTkgsbcta1Z+Gu0e21WxyalO38MdP4YyM/DZWkxGwDsHr/Cta5fPr+jq+vk9QfuvqEwILQuOH9g62R73QFOp7U/v9/W3mW4Tvv4ygk6WloAIN2KwZKSYUdlhqG2g92Z7SeurNp1AgBY0Mhrx3fPMtIFAAQC4frw6vaTV248fnXjxw+7xXLx1R+76aITU7FYbHJGTjIRDfn3TPqbrDRRv/Q6cIG+ylDd4/6+5btuu662fQEAbCimy5vnM9HTbbd30d95q9b7Cl4PuxYbfcsucQtPdgtP5uNgWWmqBgC3XcNxrVbmWp3dPWccfXGpKWioqazMtU+vm0WucRiZWoDBYrVkCaSEp0Ignh1ZfeSxt3tESkjiQJVtfUWx+3uX45SNN6est9i9v/o26OrbIFzrAn3Fa1sX0lBTAQAG+/u6BwDc37t84/V3u++47b7jBgAqEgLPDq/GJTWJJaGKIOAfCNQAAJ25qz6OEEasTj98NVwYoWNkfnlu64WVuvcJCSNFxIUR/RHCiMEfFkZenN786cmVTz+EEdVfEUbkdE3X2z55fXHXkyNrAYARxbJs/2UF/QGXjcFgMMTdroi8+o7brq/O73iwdyCPkbLRXKuzA6XWEAjEVru3ry/uDnhxK+DFwPTYaNnGpfsu4Sy3ueTgcuNwfIBb5teB9FSSagZWZx4MTj7HA2Ko0S4uLitXrmxPGlfofUNza0p2AS8nu6y4EO5v2dDc2tjSJiFEOMtNXWNz5fd6JWkxgn/4tvbO5JyC9o4uBUlRQd7x1lUdisbSHUK83F4Pzk1gn6Tp7evLzC+ta2xWkBIZWULl+jMXUUHeFbONfqNnDAZbXFmdW1TOSE8vJSrAz/3LuaT6+vsTM/MxGIyWkgz1iGDwzaft/SLjysOJVjnF4R4Yte7otd92/IPgxmFL+NPRTyVOQ0tbSl4ZLweLrAjfwDhsaWtq7RAXIJz/p66ptbKuSUlCkPA47OhKyStt7+yWFxcQ5P7l8jck0F5/VpCb3eP6RKZaKqupzyutZkEyyYjwDdZ/GWPrjVefxPi5lplNxt0kW684+H9NLflAvHo0AAB4hMWvP/94osZhV97X8XTS0NScnJnLy8UhJznwimtoam5sbpUQIZyGva6hqaKmVllWkuA4bG3vSMnMbWvvUJSREOSbyLSKanMshfh5Pjwfltugovp7Zl5RZ1e3jISItJjwr/rm0orq3KISFjRKVkJ0MJACx9WHL8SEBVbOJ7V1mMTlWCw2PaegqKxSTUGGYIHDvv7+xLQsDAarpaJATY3/Qtt4xNYv9EtlfABp+9fsOUmF5HBxcSF92qisWLECsFgXZ+LlqMdAfX19UnIyHy+fvLwc7g9RX1/f2NgoKUm4RlVtbW1FRaWKijLhgdTampSU3NbepqSoKCQ0kTVxFZSUhYWE/Hw/DT3Y29ubnp5RW1erpKhIsMoJacrLyzMyMzs7O2VlZGRkZIY+0aXLl8XFxVevIpqJajy3xmKxaWnphUWF6mpqwsL4OzOs12/w+fSpvvb7qP0gqGmcnZ1XrFgx6pmj9INAvH3+cLnF6BlBSFDf0Jicls7HwyMnIzUwkBoam5qbJcRER55cW1dfWVWtrChP7N9+a1tbcmpGW3u7oryskAD/eAwbirK+qbCggI/Lq1+6qq+vLyE5FYPBaGuoUQ/X2TEYTHFJWXZePiMjg7SkhAAffmItLBabnpldWFKqpqwoLIif/u3KrXtiIsKrli4iduvyyqrM7NzOri5ZKQlpSQm8n4t056Qtt9mx/1NgcE1+2qiPv9pmO4KOcULeV71VOS+OW41+KnEaWjtSCyp42dAywtwDjq+1o6m1Q5yf8DS+rrm9qr5ZUYyP8ASsszu1oKKts0dBlFdgRKnR8aC73U6Qi9XNdtPop/6grx+TlFeGwWI1ZYRHzpZHpbKuOau0uqu7V0qIW0qAa+jz2jmHiPKyLzMeV4ZwEpC2fNut9wHfsorenyfdiWdkyoarrydqfvUoblx1atubG8pyUlk4eXjFZHG/ZHtzQ0dLE5cQ4cT1bY11TbVVAlKKBIdZV0dbWU5Kd0e7gKQCG89EFoWxXaHDziu466774BEsBlNXWVJTnEtLz8gjKsnKNWEvTwDwc7DjFBDVmrVs9FP/AC/PbUuL9LcLKSZ92tPj1hJstBPyvvpa3qWzf1wfjD2tjU3FaQysPGhBadzaT09rY097E5JXjOD53S31nQ1VrCIKBBeK+jrbmorT+7raWYTlGDkm8i8bdMCIkVPQ8MSwj/HO+qqW8uz+ni4UvxSKXwLPpI7a8tbKfFpmFrSA1GAW+jGS7XGbmUdEyGAxsRMw/b0tpdndLfUswnIMIzK9Y7GYju+lrRX51HQMSH4JRvbx5joeSvyDPVUJgQscskmfVv71Q6z9lol6X3Wk+I2nk4amluTsAj4udllx4R/fgy2NLW0SwoQHCWl9rLW9MyW7oL2jU0FKVJCXcKWe30Nj8VZBPm7vhxfGfklff39iRh4Gg9FSlv0Nt1ha9T23uJwVxSwjJjy462aQa0/fiwnyrpgzndjlvX19mfkltY3NilKiI+W1ipq6rIKSzq4eaTFBaVF8kQeDwRZXVOcWlzHQ00uLCuIJaJtP2fl+/lbxefTXFJPKnIma5+++8VJ31pLRTyVOW1NDcXYKKxevgPiAZ2xramhvaeIRJuAZWxrrGmsqhWWUiM3zu9rbirNTujrahKQUOHjHVUJuKEcWa3HyCh155DFRHQJAQ01FeUFWT1cXv5g0n6jUyCfq7+8rykjCYDCSyppUVMMm215Pb3ALiurPWT6B9kwU/53cmvTZ73Ek/kapkaxRRk3UONQ/8ERYn+hXz1jobm1sKkpjYONh+eFYu1sbe4k41u6W+o6GKjYiXhUA+jrbGovServaWYXlmTgnzLH67pvGzCVofJJogc4JJ9P9NjOPiIghUcdKLmLu7a5MCFzyAn8FfSRfbm7WFaSfkPlbf1v9mzu/4GtG8kNo5ZSTFB2j0FpZU6tEWmjt6FSUlhDkI7xk8Huoz10jxM/r/ezmBPaJ02m7urulxQnotCRarz16KSbEv4KkDEuavv7+xLRsDAZDUGgtrajOLSplRSNlRki4o7LpyEXfsC+4anqkWbP3NPUE6bS9VTmOx9aMp5MB3YMdLSM0RPdo6xTnI7wqNwbdo7K9q1telFeAc0J1jx23BLlY3c4TyGn921TWNWeV1nT19EoLcksKcA59IgwWW1LdkFdey0BHKyXIycfBMvTCm86horzsS41HL4xOECwWm1lSXVzdoCIhIDih6hAAbL/lEhCXVfju7Khnbrj6hpZPZkLGYUFj76YrRKvIjYUJF0bKc1K6/oAwcmGFDttECyOY/r6SzCQsFiOqiD/B83ew4xQQ1SQujPT39VYWZLU11vFLyhOs7tpQVVZTkseIYuEVkxksLjNI0/fKyoKs3u5OHlFpHpFhk8+X57alR/rfGE0VAYAdWix487cJ2L6DBzsLykRHFe8IOwvRfaicbCycbCzEWpHMjIZjKFQ5JaCloVGRJfyPpKCsysk7yP8pfszdGKGiQogL8okL/r4IQkNNra00roJqkw12NNJEQw7vCDuaqErFyYriZCU6SpFMDAYq0hNp359EiIdDiIdosA6J1sKK76/8vvjePkSwlcJvwM7KYqqvhXeEnZXoG4+TnZWTnehUA8XMZKj1m7OZ30CAl1uA9/e/UoQFeIUFCLi6gpLyl24+QW8ejGway+UAgEAglGQllWQJxygA7oWm+o84DhwcHBwzzMzwjnBwEP1nzsXFxcVFVLpFoVBGRn+vLjgtLa2a2u+PW0FBQUFBAt/b+fn5Do4vwkNJFVgdz60RCISyspKyMn7SyykNBzubmfE0vCMc7IQLpXNxcnBxkor7RCGR0/R/bQPZn4OGhkZHU51gExUVlbiYiLgYgQ0BOBAIhJKCnJICgSj7gqJixzfvQz64kri1ID+fID/RCRiJzke1fIrCjmKariqFd4QdRVSs4WRh5mRhJtaKZKTXV5wsNeNpqKkI7iwZI/ycLPycBCYAhZV1rwPjPl3dNrJpohin5ZMQZhZ2We3peEeYWYjGUiPZOJFsRCP+GZiQUmp/aTsXgoqKS1AMV0t1YqktK4z+8OrAY98J7/kfhg7Fxq1khHeEDkXYLQIAPZqDHk3UM9IwIjnlCBTN+UMwcvAxchD1Pkxcgkxcv7O22lZdVBz2zvgcqaqyVNS0rGJEJ0gIBBUzjygzj+hv3P2fhJ0VbaqrhneEnRVN7HzS+hiKmdFQY7J85tBQU2sTSto6RoT5uIWJLIkVlFY6eQUEPL9O4nJaGhoVWQLlaHEI8HAK8BB97VNRIcSF+MSJ7CWboiBZ2RV1TfCOIFkJe0Y0GyeauFsEAAZmpKzGFNjoDADsPALsJNcSqKlpJJUJJEKuKS2M8HQ65TCuSCwKeNCj2HiUjfCO0BNxrKS9KgDQMCK55KdMelRitFUXFYa+NT2Pn2Wcwm8zpYXWcUJapyXWipNhA1+PIsOShoaaWltVgVgrCQn3X+UP6B4T/4X4hyCmbAAAFQIhxschRihapbCq/nVQnM/Vrb99XwQCoSDKpyBVsr/4AAAgAElEQVT6T83fxsmECyOSU0cYoaKmEVMiMMHDCSP7SQoj1DS0QjKk6gOw8wmx8xHd2srKzc/KPZEbM3BMfKDGFCI1p9DqyDVtZdnda8cVMjx+isqq3O6cmdiUIRPCK+/gwC8J8em55DbkXyYtv8z63GNtBfGdy38/snVCKKqsdbm8a2JThkwIr/2+BMWmx2cVjX4qhd8lJStvzZ6TOmpKezYQzU8wIRSWVng8uTGxGUHGiJO7T0DE17jUzL9/6/8fklNSVqxcpaenu3/fvj96o4LCwo/eXhObEWQsOL544efn/y0u7i/f9/+KlLSM1TbbdbXU927fTF5LCopKvN++mMB8IWPk5VsX/+CwuET8fMgUJpa0wsr1V15py4nssPidhHYTRVF1/fuzGyY2X8gYeRMUFxSfnZBb9vdv/f9DWW7a0+PW4kraZpY7/+iNasuLdtg7T+zGlzHy9ePr9Oig4oyEv3/r/x+ai9Nj7TezS2tKzft9cXMstNcU6x99NbEZQSaE4rD3NckhDfn4JfMoTCCpOQVrD1/WUZbbbUXmbd9F5VVu985PbMqQseDkFRj4JT4+bfTN5RR+m5Kc1LuH1kmpaM+x2kVeS2rKCw/dc53AfCETRYTX65SowIJ0ilf9gzQWpX+5uZlTRlNm/p/1qgDQWl1sdPz1BGYEmRCKwt5VJoY25CeS25B/nJSsvDV7T+uqKu7esJKMZhSWVrg/vjGx+UImBCf3TwGfY+IpIu0fJq2wcsPVN1qywjss/t6evZEUVdW/P7N+YvOFTAhvguOD43Moqsifpjw37dlxa3ElbdM/L4xsJ4cw8vXj64zxqSL/v4EaM/TUy6trMcQrX/5VY/Qn6WZKLBaLwWDU5SWRI3JRUpgQTDXlK743YMZRo2sCMdMiGhtLXgbGoYzIyFotFCaEGYba5VXfMRjsX3gfzpxGti34WCxgMFgNRTnkLybiozBGzGfOLCsr+zuOdZa5+Z++BUFwryNNDQ0UimgSJgrjYaaJcXlF5SRxi+amxmS5L26YaagqI5FEt1xQGCcmatIVdU1/xe+Ngpk62XLaYbGAwWLVpARxtV0pTDhyuqaNNeXYv+IW5fXMRj/pz4DFYrEYjIicGgMTxTP+EbhVpnfWVWIxWPjzA4lHxWT0k8gDFovBsomr0jBSPOMfwUxfvby6DoPBYoHcfhFghr4GWe6LBcBgsOoK0iNLsVCYEJT0zBqq/5JbHBVl/RnkNoEIWCwWgxGXV2Ng/rXCVRTGCJ/q9I66SsBi/oJXBQA+1cnoWLFYLGAx7BKqv1ofjcLYMTPUKa+qwWAwZHesZJRhSTOg9ivK/mq1FApjx1RNqqKueTL4XTP1SZqrHovFUlSRP83/gzAyqIrQ/64q8v8bqHHt0C8Upf6/ZZ3FzHUWZE7z8G9zdRc5g2qnClZzDa3mGpLbin+ZGyf/bPKDSYL1svnWy+aT24p/GftbE1l9c3Jis2GDzYYN5LbiX+bmpdFLQv7zrF+zcv0ayvTgz3Jly0Jym0B+1pprrTUnkCuSwkSx/MBv1rWcWugvtNJfaEVuK/5lVKxtyW0C+RE1WS1qsprcVvzLXD/8x7eVT36sLcytLcgTC/5/gtWRq+Q2YQpgvNjKeDHFq/5B1NZfILcJ5Efc1FLc1JLcVvzj3Dixh9wmTHYoIu1f4PLmBeQ2YbKzdqbW2pkUVeTPsuz/QBgZvypCNVGmUKBAgQIFChQoUKBAgQIFChQoUKBAgQIFChQoUKBAgQIFChQoUCDNPx6oERAV7xrwmdxW/AIYDNkTEVGYeAJj0txC48htxS9AGYdTFP/waBefIHJbMVYwGAy5TaBAAF8/v/fOzuS24hegDKQpgV9QqLPHB3Jb8QtQxtUUJTAu2/1zMrmtGCuTosAPhTGQ/iUwPtCd3FZQ+NeoTgop++JFbiso/OMERMa5+keQ24qxQlEh/hmSIwO++ruR2woK/19UJgaXUrwqhb+Of8RX10/B5LZirFBUjn+VoPhsj88p5LZirFBkkCkKRRX50/zjpU/sX7gXllcvn2VEbkNGIa+k4onzJ5/w2Oa2dj1V+d1rF03XViG3URQmjNvv/Isqa5eZTvY0SvllNU+8wj5FJbe0d+oqSu5cMWO6uhy5jaLwC9x8+rqwtGLF/EldriivqPS/1+4fQz63tLbpqavs2bDKRF+T3EZR+Mn1GzcKCgpXrZzsZRdyc3MfPHzk/eFDc3Ozgb7+/v37zExNyW0UBaLY3XtUWFSycslkLzaRV1D48NnLj36BzS0t+jpae7dvMjWiFN6aStxxDyuqql9qpEpuQ0iRX1H7zCf6U0xGS3uXrrzoDotpxqpS5DaKAikCne7UlRdqmi8ltyFj4swSNWmNaWtP3iW3IRRGIdf7fltNsZCBBbkNGRMBe/S4FPTVt/77Re7+MW69cC0sq1o+25jchpAir6Ti8fuPPmFfm9va9VQVdlstNtGZ1H6cwqj4ON6uKSvSm72M3IaMiQPzVeQ1p206d5/chlAYF9leD9pqioWniFcFAJ9dujwK+lrbb5HbEArj4tbTN4WlFcvnzSC3IaTIKy57/Nr9Y0hkc2ubvrry7g0rTfQoMuw/xR23iKLq+iVGk3oxMb+i7tmnaN+YzJb2Lh15kR0W04xVJMltFIVfIMjpTu3UUUUA4OwSNWmNaWumjjDyj2fUmBJ0dvcs33fByTtohr765uVzC0orl+2xjUpMJ7ddFP6/6OzuXXni/ivfqBnaCpsWTS+oqFl5/N6XlFxy20Xhn6Kzq3vp1sMv3T7OnKa7xXJJfknZkq2HouKmzO5nCpOEzs7OhRaLHRwdZ5mbb9+2LS8/f8HCRZ8/R5LbLgpTm86uLgtLmxdvnGeaGm/dsC6voMhi9YbI6Fhy20Xhn6Krp3e1reOroDgzdZmN8/QKKutWnneMTi8kt10U/hG++rypLaMMJwoTTEm4c1t1EbmtoPBv0tnds3zPOSevwBkGGltWzC8orVi2+2xUAkUNo/CX+Oz9pqaU4jcp/G2Kwt5THCuFv0NnV/eyrUdeuvnMNNTZsnpxfnHZ0q1HKDIshb9MV0+v5YUXrwPjzNSlbebqFlbWrTr/Ijqd8hqk8KeImYLCyD+VUQOLxQIAAoEgtyG/xvn7TnnFFZ73z5kbaADATsuFOit3bz1zO8PnGblNo/A7TNFxaPvMM6+s2v3a3pk6igCwfZmZvs35bVcd095dIbdpFAgwRYfZ2Vv/5RaVej+7NctYDwB2Wa/UXGC16ciF7DBK7izyMEUH0slTp3Nycnw/+cyZPRsA9u7ZraKmvt7GpjA/j9ymUQCYsuPq9MXrufkFH52dZs8wAYDd2zZqTJtps2t/XmI0uU2jQIApOsxsX/rnlde6nt84U1MWALYtMjTcab/9lnOKw3Fym0YBYMqOq8bvlZ+eXi3JSCjPo6xuTkpwCX6n2rjqrK/KcrNryE9uLskgty0UxsRUfIOdu/cit7jc68EFc0NNANi5ZpH28p1bTt/M9HUkt2kUxsRUHHUA0FBT4fHoSkFGYmlOGrltofBbTE3H2lFfmeFiV1+Q3FRMcaxTkqn4xjtr/zi3qNTr2c1ZRroAsMt6hdZC681HL2aFUgpUTVWm4ji84BSQV17res5mhqYMAGxbZDhtl/0Oe5fk50fJbRoFwkzFYQYATVNZGPlHAjXScouO3XyekJHX29unKC16cpslLugBj8j4NI+gqNCY5M7uHn1VeUNNxQ1LZlFTDaQV6erpsXvu+t43vKKmToiXy1hb5cp+GyQzI+mm8fPqQ4iilOigwdwcrDP01N/6hMal5WgpyUzILSj8HdIKyk4+cE3IKe7t7VOQEDyxfiEu6AGPyOQcr/D40PjMru5ePSVJA1WZ9fOnDRmHvbfe+L0PiqmsbRTkZjdWl720fTmSiYF00/h54/9FUUJw0GBuNrSZtsK7gK/xWUWacmITcgsKE0Jqdt7RK3cTUrN6enuVZCRP7dmEC3oYyefYRHe/0JAv3zq7ug00VaZpq9msWERNTQUAXd09N/57+dY7oKL6uxA/z3Q9zavHdqOYmXAXkm4dD688PinJSg4azM3JPnOazhtPv7iUDC0VhfH3T2HspKSkHjx0KC4+vqenR1lZ6dzZs7igBzzCIyJcXd2CgoM7OzsNDQyMjY02b9pETU2Na+3q6rpy9drrN2/Ky8uFhYVNTUzsblxHoVCkm8bPi5cvlZWVBg3m4eGZZW7u9OpVbOw3HR3tCbkFhd8jNT3z8OkL8UnJPT29SgpyZ44ewAU94BHx5aubt09wWGRXV5eBrpaRvu7GdZY/x1V39zX7+29cPSoqq4QEBEyMDK7bnkIhkaSbxo/TOxclBblBg3m4OM1Np79ydvuWkKStoTYht6AwIaQXVZ585pOYW9bb16cgyndsjTku6GEkUWkFXpGpYUm5nT29evJiBkri1rN1cDOurp4+e5dQ57DEyromQW42I2XJi5vmIxnpcReSbh0Pb4PjFMT4Bg3mZkWZaki/D0mIzynVlBEef/8UfpvyvHR3+xPFmYn9vb0CUgrztxxX0CdcSy43ISox2DMrNqy3u1NCVU9K3dBwsTUVFTUA9PZ0BTjeivVzbvpeycYrKKNptHTfJQamgXcU6dbx0N3e+r0knxHJIiKvXpKZOP4OKUwUzSUZqU7nGvOTMX09LCLycssP86oRrtRWmxFdEfOhJvVzf08np6wOp7yemNlaBBU1APT3dud43i2NdOusr2TiFORSNFS2OkvDODBySLeOh76uttbKAlomFJuEamMBZeflpCYtp/DYzacJ6bk9fX2KUmKntq/FxT3g8Tk+1SMwMvRrUmd3t76awjRNpQ1L5/wUIrp77J47v/sUWlFTJ8THbaytcuXAZhROECPeNE5eeQcpSosNWsvNwTZTX/3NxxCKGjb5Kc1Je213ojAjoa+3V1hKYemOEyqG5gTPzIqLjA30TPsa2tPdJaOmJ6dpaLJs/YDf7O768PxmlM/7hppKDj5BBW3jNQcvMzD/8JskW8dDZ3tbVUk+ExItrqhRmJ4w/g4p/DWaijOSXp5tyE/G9PWwisgrrjzMp2Y28rTvGdFl0R+qUyP6e7o4ZbW5FfQlZgx4VQDo7+3O9LhT8tmto76SiVOQR8lQbd05nOsk0TR++jrbW6oKaZnQ7JKqDfkUxzqVSM3OP3blXkJaVk9vr6KM5Kk9G3FxD3h8jk3y8A8N+RLX2dWtr6E8TVvNZsVCnAwLOK31sdM774CK6lohfp7puhpXju3Caa0kmsbJKw9fJRnJQWu5OdlnGuq88fKLS8nUUpEff/8U/ibpRVWnnvkk5pX39PUpivIds5yJi3vAIyqt0CsqNTwpr7OnV1de1FBJfN0s7cH5XndP3y3XMJewxMq6ZkEuViMVyQsb5+G0DhJN4+RtcLyCKN+gtdysSFN1mfehFBlkMoITRkp+CCPziAgjOFUkOzasp7tTUlVPSt3Q4IcqAj+kj29DpI8lP6QPEk3jp6u9taYkn2FqCiP/QqBGZHyaxc5z7Kyo9YtnNrd1eAVHL997IeD5FV0VuaGnfY5Lnb/tNBrFvHKOMQcrOjQmae+lh0Xl1Zf2bcCdsO/yo7cfQ1fPN1GRlSgqq3L0CMjIKw59eYN00zipb2ppamlbt2hYMTMpEQEASMzMp3yaTiEik3OWHrnDjmZeN9ewpb3TOyJh5Yl7fneO6ChKDD3tc1L2ooP2aCTjcjMdDhZkWHzm/luviytrL2wbqN95wP7Nu4Cvq8x1VaSECytrX/p8ziisCH5wjHTTOKlvbmtq7Vg7x2DoQUlBHgBIzC6mBGpMHj7HJi7cuJ+dlWX98gXNrW1eAeFLtx4OfvtIV10J78yImIS56/ewIJErF5pzsLGGfPm2+8z1otKKy0d3AcCeszfeePlaLpqjqiBdWFLh4OKdnpMf4fIUdy3p1t+mvrGpsbl13dL5Qw9KiQoDQEJaFiVQ428SHhExZ+48Dg6OjTY2zc3N7h4eCxdZRISF6esPC/oJCw+faT6LhYXFcvVqTk6OoODg7Tt2FhYWXb92FXfCjp27nF69slq7Vk1NtaCg8OmzZ2npadFRUaSbxkldXV1jY+OG9euHHpSWlgKA+IR4SqAGGYn48nX+cisOdrYNa1c3t7R4fvRdvMYm9KOrnvawpYLwqOjZSyxZ0KhVSy04OdiDwyN3HjpRWFJ69dxJ3Am7Dp147ey+ZsUSNWXFgqKS505v0zOzI/29SDeNk7r6hsamZmvLFUMPSkmKA0BCciolUGPyEJVWsOzMMzYUs5W5dktH54cvaattHT9d264jJ4p3ZmRqgcXJJ2hmhuXT1djRzOFJuQceeBRXN9jazAOAgw893ockrDRVV5YQKK6qf+Efm1lcFXhzF+5a0q2/TX1Le1Nb55qZWkMPSgpwAUBSXjlFoSAjuQlR9/csZWZhN1i0rrOtJSnU++GBVQef+Ior6+CfGR95Z+ciRiRaa/ZyJCtHVmzYu6v76yqKl+yxBYB3Vw/GfnqnM3eVkIxybUVRlOfLyvzMww5BuGtJt44HXjGZA098AaC2rPDMEsr7arJQmxH95fJqOhS7qKllb0dLReyn6OvrjM95cshojTjzS+SFFbRMKCHDJfQo9prUz0lPj7bXlCitPQMAyc+OlkS4ChstYxVTaq8uLgp53VKaNf2iD+5a0q3jASUgZXzeCwDaqosC9hCOC6cwGfgcn2qx4zQ7C8p6yayW1nbP4C/L9pwLdLiuqzpsASYiLmX+lhNoFPPKuSYcrOjQr4l7Lt4vKq++tH8j7oR9lx+8+RBsucBMRVaisKzK0d0vI684zOkW6abx8EMNG7a6LykiCACJGbkUNWwykxUXeW3HEiQL+/TF1h1tzXFB3jd3rzzl6C+tiu83M799vrJlIRMKrT93BYqVIy0m1OHivu/lRasPXAQAx0v7Iz+8M1ywSlRWpaa8KMztRVlexrlXIbhrSbeOBwFxmdOO/gBQU1p4YL7K+Duk8Hf4nhEdcXEVHYpd3Myyt6O1LMbn89V1ZrZenMMd6/f0L2G2y2mZUCLTltKj2KtTI+KfHGmrKVG1OoM7If7J0eIIF1Hj5Wxiim3VJQXBr5pLsmZc/kS6afygBaXMbAccq88uAsv8FCYnn2OTFm06wM7KYr18fktru1dA+LKtR4LePMBTYiNiEudt2ItGIlctmMnBxhryJW7P2RtFZRWXj+zEnbD3nN0bLz/LRbNV5KWLSiscXD6k5xaEOz8m3TQe6hubm5pb1y2ZN/SglJgQACSkZ1ECNaYWUWmFy84+Z0cxW5lrtbR3fYhOW33hxaer27TlRIaeFplasPjUMzQzwzJjVQ40c1hy3oEHnsXVDec3zMWdcPCh5/vQxJWm6sri/EXV9S/9v2UUVwfa7SDdNB4GZJAZw4RBSQFOAEjOq6DIIJOK3ISoB3uWMrOw6y9a19XWkhTq/ejAqgMjhJHc+Mi7P1QRZlaO7B+qyOI9trgT3l89GPvpnfbcVUIyynU/pI9DDkGkm8bPUGHk7FQTRqZ8oAYGgz184ykdHa3/sysSQnwAsN96ifqSHU9cfPECNVz8P9PQUGd8fMqCYgaAAxuWKszb7BvxDReo0d3T+/5T+OxpWo/P78OdLybEd/j6k7ySCmE+bmJNuIiKodQ3tTxx8SVmrYWZvpzEsLdPbnEFAPBysg89KCUqAAC1DU2/84tQIAcYDPbYPWc6WhrfO4fFBbgBYO+qWVrWZ556h+EFariFfKOhpkp9e5kFyQQA+y1nK68+7hedggvU6O7tcw6KmaWn9OjYQPyQOD/XkXvv88tqhHg5iDVJCvHg2VPf3PbUK4yYtYuMNeRE+YceySurBgBeDpahB6WEeQGgrqn1d34RCn8ADAZz6OJtejq6oDcPJUQEAeB/7Z1nXBNPE8eHGnovofciKGBXVERR7NgVFMWOvaHYFSsqImLHv70LVsCKDbBXutTQewkQEki7PC+CmAQS9PECAe/7uRfkZtld4rg7+7u53bULPR1GeoRcu9M8UeNWZJSkhETKyzsqSgoAsG7RLOshkx++fLNvw3IqjX7jwZNRzgP+O7CVXdjUSM9nd1BGdp6FiaFgK2cTlcTq09fu8uvtxBHONhamnHfSCXkAgNdU57xpaWoIAOWVxP/rK8H4f0AQZPWaNTgc7vXLF+bm5gCwfp2PTdduJ0+d4knUuHHjpqSkZFZGuoqKCgBs8PU1NbeIiIxkJ2pQqdSr166NGT36wvlz7PJmZqarVq9JT083MjLiZ7K0tOTpT0VFxclTp/j1dvKkyba2XKvHtLR0ANDRwXPetLKyAoCysvL/5xvBQAMEQdZu9sPhpF9EhJmZGAPAuhWLu/Ufevr8ZZ5EjZt3HkhKSqR9fauirAQA61cttezuGPnkOTtRg0qlXQ+7O3r40HPHG9V/MxOjNZt2ZGQRDPX1+ZkszLhGGwCoqKw6ff4yv95OGjfaxprLFdMzswBAR5trPrU0NwWAsvKKP/8+MIQCwmJtDAmXlpJ8uH+xqa4GAKyc7Nx38aFzD983T9S4Hf1dUkI87txGZXlZAFg9ZYjDfP8nH1N2zRtDpTNCX31z7d3l5Jrp7MLGOuobQx5kFpab62kKtnI2UVlLPvuQ78k44x3trI24PCqjoBwA8GpKnDct9DQBoKK67v/5RjDQgIUgYYEbJKWl14Y81DQwBYDhs1bumtYnOuxs80SNz0/DxCUld9+Pl1VUBgBXr9Xbxtsnxj6etHIXg0b99PhW14EjZu84yS6sqWcSGrihNC9T29BcsJWzibrqyujbfE/A7DHUTce0Cz8rhujAYiHxF7eJS+Gc/O4q4E0AwNJt2bO1gwjPLjZP1Mh/c09MQmLksY9S8soAYDlhxZPlfYq/PuvmuR2h0/Ji7uB7DOu1NJhdWB5vHH9ha11xloKOmWArZxNUUhXhKd+zJPT6jlUywB6Kd0gQhLX+YIi0lNTTcwfNDHUBYPWcKT0mep8JjeRJ1Ah9/FpSUiLl4QW2IOYzd6rN6LmPoj+yEzWoNPqNyJcjnfqE7FrLLm9qoLPuwGm2IMbPxCOIVVbXhtzimyQ0cdiALmZczxLScwoAAK/JpYZZGusDpoaJNiwEuXxwg5SU9Lbzj7UNTQFg7JzVvhN6Pb/1X/NEjXePwyQkJYMeJcopKgPAuHlrVo/u9i36scfaPXQa9U3kLQenEd67T7MLa+ubXD7gW5ybqWNkLtjK2QSpujLqJt+3SvoMH69vhs2bnQEWC/l2Yau4FM5l1z32xGo9fumj1YMynlzgSdTIfXNXTEJi7IlP0vLKANBl4orIpb2LvjxjJ2ogdFpuzG3dnsP6LmucOhXwRt/ObyUVZclrGvAzKepyzaoAQCVVZT7hO7Hq9xurjE2snQIEQdbvPYKTln529ThbiV2zYEb3UTNDrt/lUWJDI6MkJSR+vAhTVlIAAJ9Fnl2GTnn08i07UYNKo9948HSUs+OZ/Y0vqJgY6q3bcyQjJ99QF8/PZGFswNlEJbEmhL8MO2GEs40F15uW6dm5AKCjxS3DmmAybMcDYbE2nQnHSUlG7vc21VEHgJWTB/ddEnj24XueRI070XGSEuLfz/r+lEGc7Rfsf/wxhZ2oQaUzQl9/d+1tfWL1VHZ5E7z6xjPhmYUVBloq/EzspIomKmvJ5x6+59dVtwHdrA25ZJDMgnIA0OaWQcz1NQGgvAaTQUQIFoLcDtwgKS295qcwMmzWyt3T+sQ0E0bYqsguDlVk+3j7xNjH7ESN5tKHhp5JWOCGsrxMNbwBP5MWtyoCAHXVlTH8hZHunU4Y6fCJGvFpWYnp2TPHubCzNADA0lj/kO8ihH1eHQcrZ01Y4j6WvSgFADqdoawoX1tHZn9kIggAxHxJjE8l2FubAsDi6WO9JgyXwUlTaXR+pub9qSDW7Dl1jV9vzY10eRI1CPlFAKCqxLW7i6GOFgDUkMi//TVgtDMJmXmJWfkzRjqyszQAwNIQH7DSHUF4/XD5NFfvSS7sLA1g+6GCXC25nv0RYSIAEBuXHp+RZ29hCACLJg6dPWYgTlqKRmPwMzXvT0U1ad+FcH69NTfA8yRqEArKAED15/8ONoba6gBQXUf57a8BQ7jEpaQnpGZ4ThrNXhsAgJWp0eFtaxEW0rzwqnkeS2dNVfk5ttDodBUlhRpSHQAwmUwAiPn4LS4l3cHGEgCWeE6ZM3WcjDSuVSsn5VXVu4P5CiIWxgY8iRpZuQUAoKbClQ9kqKsDANW1WGTWdnz/Hhcfn+A1ezY7SwMArK2tjwYfQRBeR1q7ZvWK5cvZWRoAQKPRVFRUampq2B/ZrvI6Ovr797ju3R0AYPmyZfPnzZORkaFSqfxMzftTXl6+w28nv95aWlryJGpkZmUCgJoal6RrZGgEANXVmKTbbsQlJickpcx2n8rO0gAAKwvzI/t3NferNUsXLl84l52lAQA0Gl1ZWam2tjEpkIkwASD67fu4hCQHu64AsHTBnLme7jI4HJVG42dq3p+Kyqqd+wP59dbCzJQnUSMzOwcA1FRVOG8aGegDQHVN7e99BxhCJyGrMCm7yMOlFztLAwAs9bUOeI9vHvYDwLKJTt7jBij/3JidzmAoy8vUkBsAgB2evUnMSsgqtDPTA4BF4wbMdu2Dk5Zs1cpJRQ3Z/+ozfr0119PkSdTILq4AAFUFrj1sDbRUAaDmZyiI0fbkpyUUZCT1GzuDLUYAAN7Yctq6g6xmwxcAuMxc7jzdm61HAACTTpdVVK6vqwUA9nCX/jU2Py3BwMoOAJynLXIcP1sKh2vVykkdsSIyZB+/3mobmncyPaKzUp2dVJObbDR4GvthEgAo6pk7zN3Lailutxi72GzUfHaWBgAgDJqUnBKdQgIAFsIEgIrkd9XZiSom3QDAbOQ846EzJKRwrVo5odVWpoTy3RBUQQ13pvMAACAASURBVMcMS9TooMSnZiamETzdhrGzNADAysQgcMPilgSxSUs9xjcJYjQGQ0VJvobUuNhnC2KxnxPiU7Psrc0AYLG7m9fEEU2CWIsmnibKq2r2nLzCr6sWRno8iRpZeUUAoKrMdTaioa4WAFRjapgIk5Man5eWOMhtJjtLAwB0TSxnbwxocd4cPXvFiBmL5X7Omww6XV5RmUKqhZ8j2I8vsTmp8cbW9gDg6uHtPMmLPTMKtnJSW1Vx5+Refr3VMTbHEjU6B8TsxOqcZBPn6U0Tq5KeRc95LUysVuMWW4xeIM05q8o3zqrw07XKkt4RsxNVTboBgMWo+aYuMyWkcAidxs/UvD/UmsrEWwf59VZR1wxL1OgcxKdkJKRmek4cxanEBm5b01zoWDnPfcnsKcpcMqwiW4aFX1rr9/iUdPtGrXXynKnjZKSl2UJHiyaeJsqriLuP8n1saWFiwJOokZVbCACqylwPyA318ABQg8mwHYqErKKk7GIPl57sLA0AsNDXPODt1vzB07KJTos4ZBAag6EsL1tLbmB//Kl1EBKyiuzMdAFg4TjHWa69cdKSNDqTn4mnicoasv81vvsfmOtp8iRqEIorAUBVkevIPEwGEUFaFEamtiSMuMxcPoSPKgJ8pI8B42dL4nAMGo2fqXl/BAsjWp1OGOH6nyYmJgYALBaL/UOHICuvGAC6WnCt9xa7j21e0tJYv6qGdPTKvY/xqblFZZl5RSQyRedn8r6cDG6zt8fOE1ccPVZZmRgM7t1txMBewxx7SIiLCzC12ErFhzv8eistxTu0SUtLAQCRe3ak1DcAgIoSOmfztBloeU6H9MOCMgCwNeF6oWTRxBZOILY0xFfV1h279exTclZuSWVWYRmJXK+j0fhkSFZGeqPXuN3n7g9auNvKSMepu5Vrv24uvbtKiIsLMLXYSunTk/x6Ky0lwXuH7Yfcagi5gQoAqoooHInXlnRiP2QnOnS15ErkXzJrSouFrUyNqqprjpy7/vF7Um5hcWZOfm0dWUdLAwDkZGW2rJjvFxTSb7yXtZnx4H49Rzr3Hz6oH/vcRMFWniaIia/59VZaijeFiO1mVdU1nDcp9fXQTJ4TBVgsQMuRmq/f2hd2okO3bl05by5ftqx5SWtr68rKysDDh9+//5CTm5ORkVlbW6ur26gCy8nJ7di+beu27T169erSpcsQZ+fRo0eNcHWVkJAQYGqxFUod3517pJstTXE4HABUVVVx3iRTyACgqqr6O99Am8FisQBQc6S/r0SoZBJyAKCrjTXnzaUL5jQvaWVhXllFDDpx5sPnrzl5+ZmE7FpSnS6+cSEnJyu7zXft9r0Hew8ZZW1p7jzIcdSwoa5DB0tISAgwtdSKWW1hBr/eNh+gcNI4AKgicuX6kCkUAFDlTi8TQVgsljhKbtZCvoMoQSiqBABbY64NdRaNG9BiYUt9rSoS5fjd6E+puXmlxKyiChKlgb2bhSxOasOM4XsuP3FaecTKQGuQnfnw3tYuPazYMZVgK3cTmiX3+K4bpSSbhVtSkgBA5E6BpVBpAKCiIAuiDYrTIohYfFVWQAAAPTOupEDnaYtaLIw3tiTXVD2/eoyQ+KmyOK8sL6uBTFLW1AEAaRnZMQs3hp/avc9zEN7EyqrnoK4DXG36u7APahVs5UTb2PLomxJ+vZWQauFVgY4EioF6SxlaogO5JBsAlA25xCOzkfNaLKyoZ04jETMiTlWmf6WU59UVZ9PrSTKqeACQwMl2meqTfHP/iw3DFfUstLoOwHcfpm3vLCYu0aqVqwld8wlXc/j1VlyyhdT/DkznXQ82Jyu/GABsLYw5by72cGte0srEoKq6NvjynY/xqblFpVm5hbVkis7PzQ7lZHCbF8/cefxS/+nLrU0NnHrbjxzUe5hjz0ZBjI+pWRP6lZ8e8OtqczWM/c4JsYZrIUCub4Bm7zKJIP9UnM9DaR4BAAwtuOZNVw/vFgvrmljWVVc9unQ0I/5TeVFeSV5mfR1JtXHelJu0ZFPYsV1bpg3UM7Wy6e1kP2iE3YCmeVOQlaeJC5/L+PVWsqPPmwL5p/ywrjgHmk2sFqPmNy+ppGdBJRFTw09Vpn8hl+WTign0epKsWuMKQgIn23XauoQb/k/XD1PSt9C2HajTw0XHYYiYuIQAU0utmE+9nsuvt51tYgXUJlaWaMdvzcnKKwCArlbcSqzn5OYl2TJs8PkbH74n5RYWZ+UUNMmw0Ki1zvMLOtNvwlxrM+PB/XqMGNx/+KC+EhLiAkwtNJHwkl9XW1I52PMs15snZEoDAKiIngzbKigK/h3ODwnFFQBgw62ELBzr2Lykhb5mFYly/F7M59S8vNKqrKLKJhkE2FqHx7A9V54OXhVsaaA1yM7MtZf10B6WEuLisjhxfqbmTRTf3cOvq81lEBxbBiFx5WRQGjqGDNKcTuyH5QUEAND9DWGkSRXJbqaKAIf04e85CG9iZckhfQgwNW9F29gyuJMKIy3Gb1z/0xQUFACA0kBty279JRXEGmi2iVOLHLl018J1jv+ZW3QGc0g/hzO7Vvd34ArvfBdMS4w4s3Ghu5wM7uztx5NX7uo1eVlpJVGwiQcxMTFZnDS/q/nQhldXBYDsAi6fq6ohAYCGqqg/GOChjlKvqIDCcprth/VU2t9X1WZU1pAAQEez9WeEwTefWk/xPXA5ks5kDunV5fTGuf26cm3ss37WmPjr+zbMHiuLkz4XHj1147E+c7aXVtUINvEgJiYmi5PidzX3Q/b2UzlFXLu7E2vJAKDe0UK3uvoGRQX51su1xs/xsOHvq0KLiqpqANDV1my1JAAcPnvNdKCb/4kLdAZjqGPv/w5s69/Trsm6cemclBe3Ny+bJycr89+NexMXrus+akZpeeXvWJsQExOTlcHxu5qvKNiHnmTnF3HerKqpBQANNRUQMUgUiqIiCs6voKBAJovWC2Hl5eUAoKfHe3RXcwIOHdI3NNq9Zy+dTh/mMuzihfMDBnAtA7Zs3pyZnrZt61Y5OdnTISFjx7nZdrMrKSkRbOJBTExMlj/Nn8Hj8XgAIBCyOW+y8zY0NTVAlCCRSACgpKTUaslWUVBQoFBEOtO8orISAHS5j6RpkcBjp4279t576AidTndxHnTuRJBjX669ajetXZH25c2WdavkZGXPXLg63mOOnaNLSVm5YBMPYmJisjIy/K4W/EpbEwAIuVxaGztvQ1Oj9SCzfSHVkdEaryhU+t/XIzwqauoAQEfjtyLko3de28zeffDmczoDcXawOLV2ej8b4ybruuku389u9PUYJouTPv/o/XS/8/2WHColkn7H2oSYmJiMtBS/q4VwS1URAHJKuOZTIokCAOrKov5ECt34itYgQgNaHbECAFS0dFstCQBRV4I3je7y6NxBJoNh3cfZy++Umf2vI8ZHzVu3617c6AW+0jKyMXfPn1gzbde0vrWVpb9jbUJMTEwKJ8vvalHC6EDQKHVojVcITYS8qDnU2koAkFHT+Z3C6eEnHy12+HEnCGHStbo59VoWrG7Vp8lqPWn1yGMfukxeK4mTJTy7/Ha/57O1gxuqy37H+gsxMQlpGX5Xi4+gOi70+jo5BXTcDEReH2tcIWq1HgMHXbxt7jrLP+QGncEY2s/hzB6f/txno2xY6J4UeX7TohmyMjJnwx5OWr6j50Tv0gqiYBMnf6qGaWuw1bBizpvEDqKGkSj1gFKcLy+vQK3vSPuY1hIrAEBV+7fmzciLwcuHW90LOcBg0Lv2c168O8Sy+695c8LC9YcfJkz03iAtI/c87Nyh5VM2TOhdXVH6O9YmxMTEpHGy/K6OPm8KpoFcByj5oZy8ApMq0n5Ira0AADn11ifW1AcnwhfZJ98+jDDp2nZOfZcf1bDuw1nAZvLqscc/2k5ZKyktl/nsUoy/56M1TuypU4CJl39pYkUaUIvf2O+mdiDKf1uJDTp73WzQBP8TFxmNMuyW/j3sOAtsWOKV/Dx007K5srK4/27cn7RofY9RM9laqwATJ38qw2prtCDDEkVVhm2VOnI9an5IY/x9PW1JZQ0ZAHTVW4+Ojt6JtvHaG3DzBZ3BdHawOLlmal8OGQQAfKYP/faf73p3Fzmc1IVHH6bvvNB/aWAZkSTYxMmfyiBa/GUQDZFPzG1OXQMdLT+kN4jWtEv6bWEk6krw5tFdHv9URWZzqyIAMHLeup0/pY/Yu+dPckgfAkw8dGJhhEppIX7jSmnX0dEBgMLSCvapkB0CI11tAPiSmD51hFPTzeuRLxGE5enm0nSngliz7ehFDVXlxAdnFH5u/nPwbGhTARqdUd9ANdLV3rZ05ralM0sriQfPhp6+GXnqRuRmbw9+Jr/ls3j6U1pJ3H/mJr/ezp4wvHsXrqfy5kZ6YmJi2YVcz64S07MBoHc3rk25RZ+isko8Xrv1cq3B9sOCMqKlYevPe0QEQ7wGAHxJIUwZ+utp042n7xEWa+bIX881K6pJO87c0VBWjLu2V0Gu8QiAgCsPmwrQ6Ix6Ks0Qr75l3vgt88aXVtUEXHl05t7LkLsvN3qN42favmAiT39Kq2oOXuZ7Ouys0QMdLLk2oTHX1xYTE8su4nrWlZRVAAC9bbi2TRN9isqr8dro+WFxmZWpUauF2wYjfR0A+ByfPG3s8Kab1+49RljIrEljOEtWVFVvDTipoaaS/DxMUb5xT5QDpy6yf6DR6ZR6qpGezvbVC7evXlhaXrn/1MVTV26fvBK2c+1iwVbOVkrLK/ed4Hskp9eUsT26cr1bb2FiKCYmlp1fyHkz8UcGAPS2t/3z70O4FJVW9BmEwhCEx+M/ffr49/WgiLGxMQB8/PjJffr0ppuXr1xBEGSOl1fTnfLy8o2bNmtqamakpTbFoHv3/Xp3nEajUSgUY2PjXTv9du30Kykp2bvP//iJE8eOn9ixfRs/0949u3n6U1JSsnsP371q582d27NnD847lhYWYmJiBAKB82Z8fAIA9O3Deyhy+1JYWAg/M0v+Eh0dfH5hUevl2g/2KSGfvn6fPunXS5xXbt1mIazZHlOb7pRXVG7e5a+prvbjS2xTcqd/4NGmAjQanVJfb2So77dpnd+mdSVl5f6BR0+evXjizIVtvmv4mXZv9eXpT0lZ+d5DR/j1du5M9x72XAfKWpiZiomJZefkcd5MSEoBgD49u//pt9HGFBaX9O7f8q4SfwQej//wSqTPeTHUVgOAL6l5k50cmm7efPEVYbFmDOvFWbKihux34ZGGssK3sxsUZBs3UTx08wX7BxqDWU+lGWqrbvYcsdlzRCmRFHjrxZmIt2ci3m6bPVKwlbOVUiIp4MZzfr31dO3tYM61pDLX0xQTE8sp5toQKIlQDAC9rLjORhRBiiprUYyviKUFeGNRWemo6xgCQHbSl16uv96N+/DwBouF9B87k7NkHbHi3nE/RRWNnfe+y8g1jmCPzx9i/8Cg0+gN9eq6huO8t4zz3lJbWfr4/KHXoWde3QoZv3S7YCtnK7WVpY/O8t1J29FtlmEXB35W0aemvBiP79d6udbA4/HUKr5HI4sCcpoGAFCV+c1gwISmm7nRocBiGTlP5yxJra1MurZHWkl95NH3krKNfpV6t3EKQxh0JpUip2lgM93XZrpvQ3VZ6t0jWU/OZz0+Z+uxSbCVs5WG6rLUO4f59dZoyAxVUzt+1g5HQ1WxNorrwZJyKxODVgu3F0Z62gDwOTF16sjBTTevRbxAEGTW+F9rxgpizbbg8xqqKokR5xR/CmIH/vulXDUKYnra25bN2rZsVmkF8cDZm6dvhJ+6Eb558Ux+Jr8Vv9YOAFBaQfQ/c51fV70muHa3seC8Y8FWw7hfW0pMIwBA725cC0kRpKi0ElCK8/E6OpUlBX9fT5uhqWsEAJkJX/qP/LW7Z2zEdRbCchrPNW/WEituHtmupKpxODJeRr5xfLv/X+MxTAw6jdZQr6lnOGXZ1inLtlZXlD74L+DZjZBn109PW7lDsJWzleqK0vshB/j1dvDEWSY2oh7M/99UlRUBSn6ojcdTKkR6vSmvZQgAlRlfDTkm1pzoUBaCmAxxb7pDra2Mv7oHp6w+9tiHplk15c6vhSHCoDOoFHktg27uG7q5b2ioLku+cyTj8bn0R2e7TlvPz2Q3YzNPfxqqy5LD+E6sJi4eaqb2qPzhogCVWILHt/Du/p+Cx+M/vo35+3raEiM9PAB8ik+ZOmZY081r9x8jCGvWpNFNdyqqqrceOqWhppIUdatJht1/6lJTARqdXs/WWlct2L5qQWl55YFTl05dvXPyyu0tK+bxM+1cy7VZUWl5pf/Ji/y66jV5bPeuXAfuWJgYiImJ8SRqJKRmAEAf0ZNhW6WorKKPEzo6rYjrHs0x1FIFgC9peZOcfg0sN19+RRAuJaSihrzz4mMNZfmv//k2ySCBt37twsKhdbhu9nQtI5IO3Xr5X+S7kIh3G2YM42faNnsEZ2fKiKSDP6WV5ngO7+1gzvVmoLmehpiYWG4JtwySXQwAPa1EN8bmR3EVqR8q4R8eXxPz4e/rQRG2MJLDLYx8fHiDxUL6cQgjdcSK+8f9FFU0/DhUkSc/VRHgEEbGem8Z672ltrL0yflDr0PPvL4VMnrhRn4mN25VBDq1MFLdUvzGleLUpUsXKSmpuB9Zbdqvv6OnrYUsTvr1p/imO6mE/EXbj8R+TeQslldchiCs8UMdm7I0CkoqEtJ+PeaJ/hyv6+Qe9jia/VFbXXW11yQAqK6tE2Bq3p8aEvnivWf8LkI+78vEOppqA3vYvv2aRPj5GgGdwQh9HK2rpc6T0iH6xKcRutmhoO906dJFSkoyPp3vDnIiSA9rY1mcVMz31KY7qTlFi/dfeBOXxlksv7QSQVhuTj2asjQKyqoSM/ObCsR8TzUYu+r2i0/sj9pqyqvdRwBANYkiwNS8PzV19ZcevuF3EQp5Xz7W0VAZYGfxLiG9KVeDzmCGPv+oq6HCk9Ih+iRk5nezR2E5xB4PvyentV60rejVrYusDO71h69Nd35kZi/YsDvm43eekrmFxQiCTHB1bloeFBSXxv9IZ//8+v1XfM/hoZHP2B+1NdXXLvCEn28vCbZyUk2quxAazu8i5BXylNfR0hjY2+HN57gmE53BuBnxTFdbkyelo90h19enE3K6devWetHWsLOzS0tLp1BEKEm2d69esrKyL1/9itRTUlLmzJ0XHc21YM7NzUMQZNLEiU1ZGvn5+XFxv2bbl69eqapr3LjZqPDi8fj163wAgEgkCjA17091dfXZc+f4XVkE3phEV1fXyWlQTGxsVlajiU6nX79xQ09Pjyelo9359v27lJSUtTUK7t2tm933hKS/r0d49OrhICsj8yr2bdOdH2kZ85etjX7L9SAtr6AQQZAJ40Y1ZWnkFxbFJ6U0FXgV+1bT1PbmncZ9s/Famj4rFgMAsaZagKl5f2pqas5fucHvysrO4Smvi9ce5Ng39t1HQnZj+EGnM27cvq+ng+dJ6RA1yBRKemYWWuNVRn5JvQhvqtHDUl9GWiomIbPpTmpe6ZKgW28SeQeK/DIiwmKNG9C1SZ4oLK9OJDRKVDHxmUbTtt+OjmN/1FZVXDnZGQCq6yitWjmpIddffvqR35VdzPsKFF5NybGrydskQpOJzmCGRX/XUVfm0TJEkARCUTc7dOIrSSmp/NT41ou2FUa2PaRwsmlfopvuFGenXt65JOPrW56SlSX5LARxGDquSY8glhYWpDWuOtO+xKwdavjl6W32RyV17eGzVgEAhVTdqpUTCqnm7YPL/K7ywmzosFDrKcU5GWiNV9UFmUyq6G6qoWbuICEtU570pulObUH6l5OrylPe8ZSklBewWIhe3zFNz5PqK4uqc5LZP5cnxYbPtcp/e4/9UUZFy9JtGQDQyDWtWjmhk2uzX1zjd5FLc1D969uZmtwkB9R0Cam4H5mtF20/etpa8ghiPwh5i7YFvuERxIrKEIQ13sVR8ZcgVp6Q+mv2jP4UpzNwSujj1+yP2hqqa7wmAwCxliTAxNOZalLdxbtP+F0E7p0zAEBHU31gz65vvyYS8n+pYbcev9bVUu9uI+pqWNyPTLTifDu7brk/4v6+njbDtGsPaZxsyqdf82ZhVmrI1sU/vsTylKwoymMhSG8Xt6YsjcqSgtzUBPbPyZ+iFw7Qf/cojP1RRUN77JxVAECurW7VygmFVPPq7iV+V2l+B543WyXnR5wkSn7oYGdXnZ3w9/UIDzUzBwlpmdJEron1w/GVZSlc601yeT6LhehzzKqUiiJizq+ldGli7F0vy9w3v6bOLuMbp04Bpub9oZFrsl5c5XfVleSg+Le3LwwqhViQiVb8lk7I6VibarCV2Oj3nEpszsINe2M/cSmxeUUlCIJMcB3MIcOWJfz4dRjr6/df8b1GhEZGsT9qa6qvWTATAKprSQJMPJ2pJtVdCI3gd/2mDHsrIkpXW7O7rRV0KNDVaUVc92hOd0sDGWmpmPhfkVtaXunSoLC3SVxzHFsGGevIIYNUVCcSfgVgsfGZxtP97kQ3xo1aqoorJw8GgOo6igATT2dqyA1Xnn7id7Usg9iavE3K5pRBbr/uGDIID5QGWkZ+CVp+WJyTIVJbjfITRtK5hRHBqggApH+J8eGWPob9lD4EmJr3h0KqeffgMr+rQwsjeWnxzeM3rh01cDicY//+z959mzZqMHQQtNRVls0cf+h82Mq9J+ZMdE0l5AdfvicpIbFgyijOYpZG+gpyMrefxboO6Glpov8h7seuk1cVFeTqKA0ZOYUWxnr97W001ZT9z9zU09awtzbNyi9m77cxclAvAabm/bE01id+uvdHf8L6+dMmrdg5y/eA7/xpqkoKgRfvZBeW3Dm6Q/QPCOSESqNHf0rYf5B3i5H/A7YfRn1KnjpMtN6NFoCWqtLSKcMDrz1affiq15iBqTnFx0KfSUqIzx/vzFnMwgAvL4u78+rz8L5dLQ11PiRl7jl3X1Fepq6empFfYmGA79fVXFNVcf+lCF1NVXsLQ0JhGXu/jRH9uwkwNe+PpSG+IurUH/0J6zxHT9l4dLbf6fWeY1QU5YKuP8kpLg/zX9nB/JDOiPmeuv/ggr+vqnE8jPngPs7172tDBS0NtRVzph88fXn5tgNzp7mlZuYcOX9dUkJi0QzeLVUsTY0U5GRvP3w+YnB/K1Oj998S/ILOKCnIkyn16dl5/Xvaaaqr7j1+Xg+vZW9jmZVbwN5sY9QQRwAQbOXEytSoNuXPcuE3LPGasMBn5sotG5bOUVVSPHTmSnZ+0b0zh0TNzV69+8JkIs7Ozn9f1eDBg5lM5vMXL9zGjfv72lBBW1t79apV/vv3L16ydMH8+Sk/UgIPH5aUlFzszZWqb2VlqaCgcCs0dNSokdZWVm/fvdu2fYeSklJdXV1aWpqVldUAR0ctLa1du/fo6+t3d3DIzMxi77cxZsxoAabm/bG2tv7TzX43b9o0Zuy4ae7uWzZvVlVVPXDgIIFAiIwIFzVHevLkqaOjIw6H+/uqhgwZsmnjRhqNLi0toofdamtqrFy84MCR40t9Ns2f5ZGSlh504oykpIT3XK7AwNLcVEFePuxexEiXIdaW5u8+ftmxL0BJUaGOTE7PzLI0N3Ps20tLQ2NPwBF9PR2Hbl2zsnPY+22MHu4iwNS8P1YW5uRiQvP7Ati4ZoWbu5f7vCWbfFaoqigHBJ/Mzs17cOOiqPkVDy9j3jKZTNTGK4T1Oi59VF8Rfb1GS0Vx6YRBh0Nfrjl+Z/aIvml5pcfuRUtKiM8b3Z+npIW+prws7m5M/PCe1hYGWh9TcvZceaIohyM30DIKyvvZGGuqKBy8HqWnoWxnqkcormBvtuHauwsACLZyYqmvVfZg/x/9CT7TXKb5nZvjf2Wdu4uKgtyRsFc5xZW3/OaJuJtR6YyY+KwDnkv+viocDte/v2PK++e9R05tvXSboKSm5eKx5MnFw9f9Vw+Y4FVMSH1+7Zi4hOSgyfN4SmobWeDk5L8+u2vrOBxvbJkV/yHi1B4ZBUUqpa40N8PMvp+imubD/w6oaOkZWNmV5xPYm210GzACAARbOcEbWx5718KJTp2AtM+vEQS18QpBmGWJMTq9eL9AEQGnrGk+ZlHavaPf//M1HjqztiAtI/K0uLik6XAvnpKKumaSMvIF7+7juw9V1DWvTPucfGu/lKwCs4FMKspSt+qDU9b4cfuwrJquiknXupIc9mYbOj2GAYBgK1creuYTr+fDPwBCp1UkvXHx4vt6/e/TqEu8+zp99JC/r01IaKmrLvecGHDu1ordx+ZOGvmDkBd8+Y6khMSCqVwbLloY6yvIyd5+GjNiYG9LE/3331N2nbisqCBHptSn5xRYGuv3c7DVVFPxP31dT1vDwdosK6+Ivd/GKKc+Akw8nbEyMaj+EvFH/V+/wH3Ssu2e6/dtWOiuoqQQeD4su6D47rFdIj4tAkDUuy+O/fujEucPHTLEd8MmBp0m2UEO21ZW1xo5a2n42cDzu1c5T/IqJKQ+unRMXELSZRqvDqNrbCkjJ//h6R37Qa66Jpbp3z+EHd8tq6DYUF9XnJNh5dBfSU3z3un96tp6Rl3sS/Oy2JttODiNBADBVq5WTCwvfeV9LPSPkPD2ef/+6Kw3hw4d8tB3I8Kgi0uK6HpTRkXTaqx3yt3gLyHrTYfNrC1ITw0/JS4uae7KNbEq6ZpLysjnvb2v091FSc+8IvVT4s0DUrKKjHoyqShTUddc07qPjLJGclignLquqklXUkk2e78N3R7DBJia90dJz2LazY60F87/TWlCLIrxG5OJvHz3ZazLwL+vrW3Q0lBb7jUtIOTKiu0Bc6eN+5GZHXzupqSExEIPLiXWwsRQQU427OELV6f+VqaG778l7jzyn+JPGdbSxJCtte47cUEPr2VvY0HILWTvtzHS2VGAiaczVqZGNcmv/6j/votnGVvFVwAAH+ZJREFUT1y4buaqbRuXeKkoKQb+dzU7v+jumQDRn2d5ePXuK5o6LcJ6HZcxqq9N66VFAy0VhSXjBwaFvVp74u4s1z5p+aXH78ZKSojPHcX17MxCX1NeRvpebMLwXtYW+pofU3L2Xn3WJINY6Gv2ZWsdN57raijbmeoSiivZ+22M6N1FgImnMxb6mqX398GfsHb6kOl+F+buv+YzfaiKgmzw7dc5JVW3dsztcH4YHZ/JRFgormdTP72yc2pBKm8XlNS0hnoseXrx8A3/1Y4TvEp+CiNO3MIIpyqibWxJiP8QzqGKaBtZmNr3U1TTfMQhfbD32+g6YIQAU/P+4I0tj3ZSYSTlfQvxmxiLxeL8HBwcvG3L5synF5t2nhB9mAiy8/iVI5fuMhEEAPAaagG+CycNHwgAIxdsIhSUpD+5AAB3o94s8QuuozQAgKqy4sF1C+RkZRZtC6LS6DVf7gPAq49xC7cFFZc37sMjIy29cdH09fOnCTahQtjTmGU7j5HrGwBAWVF+y+IZy2a4tfpbIsWdZ2/mbj6Uk5Ojr4/CuTnBwcHbtmxKCzvYtPOE6MNEkN1n7wfffNroh+rKB1a4T3TuBQCjVwVkF5X/CDsIAPdef1l64CK5ngoAqkry+5dNl5PFee87T6PTq16EAMCrrz8W+58vrmjMI5ORlvKdPXad52jBJlS4/fLz8oOX2KfwKivIbZ7jtmRKC0+/RJm7r77M33MWTT/cuoXwJrwpIbrdYTIRv6CQw2evMpkIAOA1NQK3rZk8aijbOnzmUkJeYVbsAwC48/il98Y9dZR6AFBTVgrYslpOTmbB+t1UGq0u9c3Ld5/nr99VXFbB/kUZnPSmZfM2LGlc3wq2/iWhkVFLNvuT6+sBQEVJYevKBcu9prf6W23MjBVbykjU2DdvWi/6GwwcOFBXRyf0Ft9TsdoeJpO5ddv2gEOHmEwmAOjo6AQfCZo6ZQoAOA8dmpVFyM/NAYCw27fnzV9QV1cHAGpqakGHA+Xl5b3mzKVSqXRqAwA8f/HCa87coqLGl9RlZGS2bd2yedMmwSZUuHnr1oKFi8hkMgCoqKj47di+auVKtCpHBRKJpGdguGfPnpVodKygoMDY2PjKmWNTJ4hKxk9zmEzm9n0BgcdON/qVttZh/51Txo8FABe3qYTs3OzETwBw+0HkwhXr6shkAFBTVTm0d4e8nNy8pWuoNFp9aTYAvIiOnbd0TVFJ4wmFMjjclvWrNq5ZIdiECrfuhnuvWk+mUABARVlp+4a1K7zno1W5kHCfu7i0qiY2lvcVxv+PgQMcNSUoFzd6olKbMGAiyJ7LT47eiW4Mt9SU9nuPnzCw8c3pMRtPZRdXplzaCgD33yQsOxLaGHEpyvkvdJOTkV58+CaNzqgIP/A6LmNx4M2SqsYtT2WkJde7D/OZ3hj2CLb+JXdi4lYEh1EaaACgLC+7aebwxeMHoVKz8LgXG78g4HpOTi5a8dWmLVv3PUpregOj3UEQZvip3VGXjyIIEwCUNfDTfA70GNa4sfZh7zEVBYR9D38AwLfn9y/vXkqlkAFAXkl1ylp/nKz8JT9vOo124kNl6qfXF/0W15Q3vrQkJS0zeoHvyLk+7I+CrahQnk/YPqn7gAlenluOtl66zTm7cTauofLtG3TGq/6OAwtBvc+aM6jUJgxYCDP55v708JMshAkAMqra9nP26PdvnMdj/CbWleaMPvUdAAreR3w9tZrRQAYAaQUVO69dkjJyX46vZDJok24UlCXGfD6+soHYuDenhBTOespa64mr2B8FW1GhriT76cr+Ji4ze3gHolitkCh4H/756JJcNHWJzZlRVxVFWB9jIsjOY5eCLt5uEsQObVg8yXUQAIyY70vIL854dgUA7j6LXbwjiL08VFVWPLjeW14Wt3BrIJVGr/0WCQAvP3xfuDWwuLzxgbcMTnrjIg/fBe6CTX9P2JPopX5HmtSwrUs8l82c0OpvtS8kcr35cM89+/xRjPOX7j/fb8Skv6+tbUAQZtixXZEXgtnzpoomfvaGg31dGx9b7pk3qjQ/+1hUKgB8fHbvzPYlDRQyACgoq3r67sfJyp/esohBo13+Tkz68Or0Fm9i08yIk5novWH8gnXsj4KtqFCaR1g71n7IJK8FfsdRrLZtaCDXrRhm6b8PtfWmkbFxv1WnDB3H/31tQoKFMBNu7E99cII9scqqaveYt8egvxsAvNw+sa40xy3kOwDkvw//eOLXrNpj7m4JnNzHYyuYDNr0W4UAUJoQ8+H4ivqqX1On7VQfm0mrBJtQpK4kO3J5PzOXmb2X8D08RXR4H7hAV4z47i1a680B2soy14J5j8QVZZhMxO/ImaCz134qseqBW1dPGjUUAFw9lxPyCjNj7gHA3ccvvTft+znPKgVsWSkvK7vAdzeVRif9iAGAl+8+L/Ddw621zvVdPFuw6e8Je/h8yWb/xnlWSWHrivnLvVB7pNVmzFi5rRxFnXaAo6YE5cKGGajU1jYwEWTPlafH7sSw4z1tNcX9i9zYSsjYjSHZJZXJFzcDwP03CcuPhJEbaACgqii3b+FYOZz0kqBQGp1R/sAfAF7HZSw5HNqkdeCkJX3dXdZOGyrY9PfcjYlfcfR2kwyyceawxW4dJmGriTn7r1WyFGLf8G69+f/hOGAgTU5zvv+l1ou2FQjCjOAWRqb+FEaCvMeUc6giV7hVEWlZ+Ut+3gwa7fiHSgBI/fT6Erf0Meqn9CHAhCLl+YQdk7oPmOA1U/SEkQZK3ZbRVvv37eWJ33gTNYhEor6e3hZvd/bpHh0Icn1DUkaOkrycmaGutJRki2WqakjxqVl4DTVrUwN2xlZVDYlYW2dmoMMuQGmgJmXk5BeXa6go2ZgbaaopN/2uABMqMJjMbymZCIL07mYlIS7e+i+IEiwWy9lrva6x5YPwcFQqZPvhJq8xq9xF9B0pflAaqElZBYrysmZ6Wnz9sLYuPiMfr65sbaTT6Ie1ddUkiqmeFrtAfQMtiVCQX1qlrqxgY6KnqarY9LsCTKjAYCLf03IQhNXLxqQj+uHQZQf0zGzQ9EN9va0r5q9dMLP10m0Iub4+KTVLUUHO3NhAWorv6w5V1TVxKel4TfUu5iaNnlZdQ6whmRnpAwClviEpLTOvqFRDVdnW0kxTXZXzdwVb/xIGk/kt8QeCsHrb20pIiJybZebkdx894/z5C56e6DyqvHr16rx585ITEywsLFov3YaQyeSEhEQlJSULC3Np6Zbf36qsrPweF6eD17Gx6cL2osrKSiKRaG7euB0xhUJJSEjMy8/T0NDoamurpaXV9LsCTKjAYDC+fPmKIEjfvn0kJCTQrfzvCTh0yG/nroKCAlVVdP77jB8/vrgg7+0zkds4hAcyhZKY/ENJUdHc1ITf/h+VVcS4xCQdbe0uVhaNflVFrK6pMTMxZheg1NcnJv/IKyjUUFez7WKlpaHR9LsCTKjAYDC+xiUgCNKnZ3cR9CseMgnZdo4u58+fR3O8mjv3wykfM12Uv1h0oTTQknKKlWRlTPU0pCX5/jNVkSgJWYV4VSUrQ63GeZBEqSZRTHU1AKCeSk/KLioor1ZXku9ihNdU4UoaEGz9SxhM5HtGPsJi9bIyFP1wi8ViDVt3Us+i64PwP3tPmh9EIlFPT3/Uwo3ssz9EB2o9pTAzSVZeUdPATMBrzeSaqvy0BGUNbbyJNduvyDVVlNpqTQNTAKA11BdmJFWVFigoq+uadVFU0+T8XcHWzk1ZftbuaX0vXEBzvJozd96wwGgFHVNUKhQSDCqlJjdFSlZRQcdUwGvKNBKxOidRRkVbSd8SxMTYd2jkagW8CQAwqfU1eSmUikKcopqSgTVOmWuIFmz9t2CxYraO6WutHxH+AJX6GvWxxTPWzJmCSoXCg1zfkJSeraQgZ2aox1eIqK6NS83S0VSzNjX8uTysJdbWmRnqsgtQGqhJ6dn5JWXqKsq25kaaaipNvyvA9PcwmMxvyRkIgvS2sxb9aREAgi7e3nv6ekFhIVpxvpvb+B85RX5XX4p4nM8DtZ6Sl54kq6CINxQ0b9ZVV+Wkxqto4vVMG+fNuuoqcm21tiF73qTkpSdXFOcrqqobmNso8c6bgqz/OJEXg++f2ldYiNp6c5zb+E9pBUP3PgLR9kMGlVKdmyIlq6jIf2KlkojV2YkyqtrKP2dVKolI/zmrAgCTWl+dm0KuKMApqisbWstwTJ0CTP8gpGLCkzVOF1GN3+bNm/vt4VVzYwNUKmwzyPUNSamZigry5sb6/JTYnzKsRhdz4+YyLDRqrVn5RaXqasq2FqacWqsA09/DYDK/JaYiCCKaMmyrZObk9xjjibJOO3fu+5NrRFz3aA6lgZacU6woJ2Oqy1cJaZRB1JSsDDhkkLp6Ux11doF6Kj05uzi/UevQ5tQ6BJj+HgYTicsoQFisnlYGHSLe4yGrqKL/0qDzF9D0w7lz520N/ahlYIZKhWhBq6cUtCaMCFZFQKD08S+rIgAQdSX4yX/7m8dvvIkaAODn53f4UEDc/VN4DbU27CFGB+Zq+Iulu459/frV3h6Fs6vZ+Pn5HT508Ovl3Xh1lHNiMDor1568Wx5wGX0/DDyU+OwmXrODhW4Y/zcTF63LKyXGxcdLSrYscf4pTCazZ8+eBvqoScYYok9paalVF5vVq1f7+fmhVWdycrKDg0NI8MHZ7qJyXgBGuzPeY05uYXFcHKrjVY/uOrLIze1zUKkQoxNw/fmXFUfDvn79hm58dfBQ4I7bX5U18GjViSHinFozjV5VkBAfh+J45dC9JxGn1c/3CioVYnQCcl/f+hay9hv6ukRAfPh/mD6Gwaaskmg/ftHqtT7oxvn2Dg4L/Y4PchOtV0QwRJaayrL1bt3XrV2Dsh/aO/RactjEWeQ2PcVoL974eypQChMT0F1v9tDTULpzGoVDyjD+ESYt8s0rQ1un7dFdRxa5sQ2dbUsw/gXcd10qILHiEhJQ9MPuPXqKKessPnwLlQoxRJ/aqrJdU3qubyl+ayF3ydfXV1VN3e84pndg/BYkMmXH8Sve3t4oqiHw0w93nb2PYp0YnRgSuX7n2ftC8UNVte2HQ1CsE0OUefL63eNXb4+fOIFW1AUAEhISR44ciXz48NHjx2jViSHibNq8RVlZ2dfXF8U6bW1tFy1atHX3gVpSHYrVYnRcHke9fPTsxfHjaI9XwUeffEx+9jkVrToxOjQkSsOuy0+9vRejHl+pq6mFn+pImx5j/A1Jb58lvHl68sRxdMerY0ePFH6JKvn+Aq06MTo09HrSj5v7hKRL+B0ToU2JMdqX7UcvKquooB7ney9aFBrsV19HQrFajE7MreAdqsLwQ+9FyTf20usxP8QAACj69rzga9Spk6ivN4MfvXzzJPo9WnVidG6eRL9//FoIOm3w0Scfk6O+YLoHxm8R9SX16aeU4ydPouuHR4OPJMQ+SXr7DK06MUSc8BM71fjEby0kasjJyQUdOXI1/MW1CEzywGgFBGHN23KYJSaxa9cudGuWk5MLOhJ87cnb60/eoVszRucDQVgL9p1niUsKxw+PXLnz8OrdR+jWjCGC5BYWL9y418PD3dnZGd2anZ2dPTw85s6bn5OTg27NGCLIpcuXL166FBQUJCcnh27Nu3btQljgtXglgiDo1ozR4cjJK1iwwsfDw0Mo45W7+7LgsLxSIro1Y3Q4EBZrUeBNloSUMOKr4CNB7yOufYi8jm7NGCJIZXHe1V1L3d2FMl5Nd/f4fmoVpTwf3ZoxOhwsFvL12DJZCdgtpPXgg6ir4c/RrRmjI3I1/PmVB1FBR4KFEedLiLFObZ7PwuJ8jNaIDb8W8+Ba8BGhrDdlJODT0WUsFuaH/zrksvyvJ1dNF0785uHhvmjjvtzCYnRrxuh85BYWL9q4T1g6rbv7suA7mO6B0Sp5pcRlwXc83IXih+7uHtd2La0szkO3ZgwR5EPk9fcRfOO3Fo4+YbN58+ZDAQHhJ3c69bYTcg8xOjCbDp87E/r45atX/fv3F0b9bD+8F7DKqbu1MOrH6BxsPhl69kGMcP3wUEDk+SOD+/UURv0YogCJTBky3VtSRj72zRsFBTQP4WNDoVCcnZ3r6urevYlVUUHzUGcMkeLNm7fDXF19fHz27t0rjPq/fPkyePBg77meB3dtE0b9GB0CUl2d06hJktLSsbFCG68GO9WWFT47tFRZXhb1+jE6ClvORpx79FGo8VVAwKEVx+5Z9hokjPoxRIEGSt3hBa6qclJv38QKabwa5OScXVYzaFeElDx2Yua/S+Jlv5yoi69fvRSqLhF+es/g3mhu14HRsXj3PXnMos0+69YJL853cho8dNqCGT5CqR+jc5D2/b3/wnHr1wlxvTnIabDxcC+H2X7CqB+jQ8Cor3u1bayuEu7dW2HFb86DB5OqK1/dPK2ihH79GJ0DEpky1H2JcHXawU61ZYVPAxZjugcGP+rqqSN9T0spasS+fSskP3Qa7FxcVetzNkpWEVvPdlqy4t4fXTZeQPzGN1EDQZBpU6e+fPH81uHNA3rYCrOTGB0SFou1L+SG/5mb165d8/DwEFIrP/0w6sbuJY52FkJqBaPjwmKx9l+K2H8pUuh+OG3qqxcvwk7tH9DLQUitYLQjVdU1U5ZszC4o+fjpk4GBgZBayc/P79u3r5mZ6f27d9XV1YXUCkY7Ehv7ZsKkSUOGDAkNDRUXb2HTMlS4cePGzJkzt65fvc13jZiYmJBawRBZKquIk2ctIOTmf/z4UbjjVZ/exhoK17bOVlNE+V09DNGHxWIduB514MZzYcdXU6dOe/bipXfAdfPujkJqBaMdIddUhaybUVuS8/mTcMerXr37gpph33UXpRVVhdQKhujCYqXcDky9Hdgm68Hnt4K2DejRVUitYIgyb78lTV+ze4jLsNDQMGHH+RMXb5y0eBMW52M0J+3bu6DV7sNdhoYJf71pO9Wn69R1gPnhvweVRHwX4MWqzPvyWdjrzT4m+tphJ/3VVLBnkxi8VFXXTF26KbugVOg6bZ/eRhry17bMwnQPjOZUkSgz917JrSB//PRZqH7Yu09fJbzxokPX5ZXVhNQKRjuS+f3dmfUzXF2GhoXxjd/4RnXi4uJXrl4dOmzY2MXbsDNQMHhooNHmbQkMOB8WEhIiPDUEmvzQZbibz2HsDBQMHhpo9Pl7zh269rgt/PDK1SEuLqNmr8TOQOl8pBFyB01dWFROjHr+XHhRFwAYGBhERUUVFBT2cxyQmoqdg9jZuHT58jBX1yFDhly5ckV4qhkAeHh4hISE7A86Ptt7ZQOVKryGMESQtIzMgSPGF5aURkVFCX28ev6imEQf5nMivaBMeA1hiCANNMbCQzcDQ1+1QXx19eoV12FDg5eNx85A6XyU5KQHzHOhEotfPBf6ePXyRRSOXBqzbTSpMFN4DWGIIEw69fOxpRn3gttqPThszKLN2Bko/yBXw5+PWbR5yNBhV65cbYM4P/zsoVOb5tOpDcJrCKMjEht+zX/huOFDh15tk/Vm6t3gD0eXMunYevPforYw49WWUdJ1JS9ftMF683lRebXTNO80Qq7wGsLoiKQRcp2meheVV7eFTvv8RQmJMXzdqYyCcuE1hNERySgoH77uVEktI+r5C2H74YvnUVRi8aF5LiU56cJrCKNd+BB5/eiy8a4uQ69eFRS/Sfj5+fGzSUlJTZ06rb6hYeOewLzisj7drOXlZITSWYwOxbvvKTPW7/+emv0gPHzKlCnCbk5KSmrqtGn1DQ2bDhzPL63qbWsqL4sTdqMYos/7xMxZO0LiMgsehEe0kR9OnVbf0LDBb19eUUlfh67yctiuaB0eBpMZcu3uHB8/YxOzFy9fmpiYCLtFLS2tGTNmRERE7D9wUEFBvkePHkJVWDDahtLS0pWrVu/ctcvX1/f06dNSUlLCbrFnz56Ojo7+BwPuhD+0tbYy1NcTdosY7Q6DwTh9/vJs75VGJiYvXrxoq/FqZuSjJ4evPZTHSdmb64uLY2/UdX4+JGfP8r8an13SZvHVtKlTGxrqT+zdVFWSb9KtN05WXtiNYggbhMmIvn324vYFlqYmr1620Xg1c+aMZ48jP90KlsDJq5p0E8Piq3+AitSPX4LmU3ITI8IftOV6cOPugPzisj521th68F+grJLoc+D03tPXfH19T4eEtE2cP8DR8UTggY9R9/XMbTR0hPhgAKOjUFNZdnn/urun/H19fUNC2mi9OWCA48XjB/PfRygaWMtr6Au7RYx2h8VkZD698OnoUhsLk9dtFb/NmDEjIvLhoZPn5eVku9taYfoYBoPJDLl+b67PTmPTttRp2brHI3kZKXszPUz3wGAwkXOP3i8MDDUxt3rx8lUbrWdnzHj8MPLBuUBpWXlDa3tsPdsJqK0qCzvk+/C//Rt+I34TlKgBAGJiYi4uLnZ2diEXrh65GCYpId7VwlhaWugRIYZokplX5HPgzKbD5+y797z/4EGvXr3apt1ffnj5RvD1RxLi4l1N9aWlJNumdQxRI6ugbP3RG5tPhtr36N0ufnj67MXD/12RkJDoamWOw8bDjgmCIE+j33us2BL6MGrFylUXLlxQVm6jvRbl5eVnzpxJJpN37doddvu2sbGxmZkZtrFtB4VEIh09dmzqdPfi4uILFy4sX768zf4pTU1NJ0+eHB0Tu32Pf0YWoZttFzVVbL/3zgmCIE+ev5o+d/Gtu+ErVqxo6/HK05NMqd938vKDt0kGmiomuurYeNVZySqq2HD6wZazEfY9+9x/EN728dXNCyFPLgWLS0joWXSVlJJum9Yx0IWFIMnvos5unP312Z3VK1dcbNvxapbnTGo9OTxkf9HHSFlNA3m8MTZedVbqignxF7ckXvZz7OUQ/uB+O6wHz18JuhAqISHe1cIEWw92Vkjk+pPXH3iu9y8h1l24cLGN4/wpUya/fxNz8fDOktxMA8uuCtg+2P8qDeS6J9dPHVs3u4FYerHN15tTp0z++j725bm95GKCslEXHHa+WCeFxUKKv7/4cHhB/tv7a1e1dfw209OTTKHsOXT07uNXhnp4U0M9LH77N0EQ5GnMhxkrtoY9fN4OOm2j7nHlwbskQy0VEx1M9/hHQVis51/T5uy/ficmYcWq1RcuXGxLP/T0nFlPIV8I3vf9xX1VHUNNfRPMDzsoDZS6VzdPnd/kRasu+834TYzFYv1O1RQK5eDBgwEHD0qIi41x7uvq2MOhi5meloaCPPYOQWcGQVjEWlJmXtGnhLRHMZ9ivySamZoEHg5yc3Nrl/5w+CGMHmA/vHdXe0tDPU1VBWyvl04NgrCIJHJWQennFMKjdwlv4tJEwg8DDkqIi491GeTq1K+7rZUeXktRHjvNTqRpoNIqidXJ6YToD1/vPYsm5Oa7uY0LDDxsbm7eLv3JzMz08fEJDw83MzObPGnSkCHOXW1tNTQ0ZGSwAU2kqa2tLSgo+B4X9+TJ0wfh4Uwmc/369b6+vnJy7TMChIeH+/iszcoiOA3oN27k8H69e5qZGKupqmAvo3RoGqjUisqq5B9pr9+8uxfxOCs7x83NLTAwsD3Hq7VrwiMiTXS13BxtBtmZ2xjh1ZTkZaSxrNkODMJiEUmUrKKKL6m5jz6lvk3IFIX46uDBABAXtxs8xqb/MEMrexVtPRk5hXbpD8ZvQqc11FVXFWWlpH+JjX/1oDQ/e9w4t8OH23O8WrPWJzIiXFnHWLvPGC3bAUoGXaSV1CSksE0ZOzAsFkKrq64rJlRlfC37+rQ0+b2xidmRoEAR0CXExg7pN9yxl0MXcz1tDUVMH+vg1JIphaUV8T+yot59iXz1gYmw1vv6tm+cv2atTzYhy6bXwO7Ooy3s+2obmiooqWIvWXZu6utIVaWFOanxCW+ff3v1kIUwfX3beb25eo1PTnYW3tYR32uEhmUvBR0TaQUVMTHMDzswTDqVWltZk5dalvy2+GNkdXHO2HFuQe0av/n4rA0PjzA10p/gOnhw3x62lqbqqioyOCx7uzPzS6f9+O3+s2hCbkH767SNuoemW3+bQXZmXTDd4x+ggcaoqiX/yC2JTcgKf5+SXVTuNm5s4OGgdvTDtWt9IiLCtQ1M7IaMt+o1SNfMRl5FTUoae14g0jSQScSywvy0hJT3zxOjHwKC/Fn8xvoTqqqqjhw54jx4sKQkNjz9W6ipqrq7u0dERDAYjD/yGWHw0w+dJCUl2vuLwWhT1FRV3N2ni5gfYuNhx8PSwmLdunXJycnt7UQsFouVlJTk4+NjaWnZ3t8Kxp8hKSnp7OwcHBxcVVXV3k7EYjAY4eHh7u7uqtimGp0OS0vRG6/Mzdr7W8FAGRGMr5wGO0tg8VVHw9zCUtTGK1Nzi/b+VjBQRllVbbrI6RLYerCzISkp6Tx4sEjF+dPd3VVUsDj/30JSUtJpsGitN6dPd1fC/LDTYWoucvGbpQUWv/1ziKJOi+ke/x6W5mai5ofmFtjzgg6GxP8bv/3ujho8UKnUlJSU0tJSEomE+h+DITqIi4urqKiYmJiYmIjiTjuYH/4jYH6IgQo4HE5VVdXW1lZNTRR3jq2qqkpJSSESiQ0NDe3dFwxBKCoqamtr29jY4HAi924ui8XKyckhEAjV1dUIgrR3dzD+f7DxCqMNwOIrDFTAxiuMNgAbrzDaBizOxxAFMD/EaAOw+A1DFMD8EEMUwPwQAxX+Mn77PxM1MDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAw/hTsPDkMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMjDYCS9TAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwGgjsEQNDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDIw24n+Ky4cxdX8/SwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()\n",
    "feature_cols=list(data.columns[4:6])\n",
    "export_graphviz(dtree, out_file=dot_data,  \n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True, feature_names = feature_cols,class_names=['0','1'])\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "graph.write_png('/Volumes/BECCA/Project/Data/Result/Russia/By_classifier/%s/Decision Tree.png' %combination_name)\n",
    "\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Combination</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All</td>\n",
       "      <td>knn</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>All</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=5</td>\n",
       "      <td>knn</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=5</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4485</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1143</td>\n",
       "      <td>0.647466</td>\n",
       "      <td>0.796908</td>\n",
       "      <td>0.626309</td>\n",
       "      <td>0.701384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=4</td>\n",
       "      <td>knn</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k=4</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4487</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1141</td>\n",
       "      <td>0.647651</td>\n",
       "      <td>0.797264</td>\n",
       "      <td>0.626414</td>\n",
       "      <td>0.701587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=3</td>\n",
       "      <td>knn</td>\n",
       "      <td>4065</td>\n",
       "      <td>1323</td>\n",
       "      <td>3882</td>\n",
       "      <td>1563</td>\n",
       "      <td>0.733592</td>\n",
       "      <td>0.722281</td>\n",
       "      <td>0.754454</td>\n",
       "      <td>0.738017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=2</td>\n",
       "      <td>knn</td>\n",
       "      <td>3864</td>\n",
       "      <td>1312</td>\n",
       "      <td>3893</td>\n",
       "      <td>1764</td>\n",
       "      <td>0.716053</td>\n",
       "      <td>0.686567</td>\n",
       "      <td>0.746522</td>\n",
       "      <td>0.715291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=2</td>\n",
       "      <td>gnb</td>\n",
       "      <td>4268</td>\n",
       "      <td>3258</td>\n",
       "      <td>1947</td>\n",
       "      <td>1360</td>\n",
       "      <td>0.573710</td>\n",
       "      <td>0.758351</td>\n",
       "      <td>0.567101</td>\n",
       "      <td>0.648928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k=2</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3247</td>\n",
       "      <td>827</td>\n",
       "      <td>4378</td>\n",
       "      <td>2381</td>\n",
       "      <td>0.703868</td>\n",
       "      <td>0.576937</td>\n",
       "      <td>0.797005</td>\n",
       "      <td>0.669347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Combination Classifier    TP    FP    TN    FN  Accuracy    Recall  \\\n",
       "0         All        knn  4309  1038  4167  1319  0.782424  0.765636   \n",
       "1         All        SVM  3792  1549  3656  1836  0.687529  0.673774   \n",
       "0         k=5        knn  4297  1151  4054  1331  0.770885  0.763504   \n",
       "1         k=5        SVM  4485  2676  2529  1143  0.647466  0.796908   \n",
       "0         k=4        knn  4284  1155  4050  1344  0.769316  0.761194   \n",
       "1         k=4        gnb  3888  1974  3231  1740  0.657159  0.690832   \n",
       "2         k=4        SVM  4487  2676  2529  1141  0.647651  0.797264   \n",
       "0         k=3        knn  4065  1323  3882  1563  0.733592  0.722281   \n",
       "0         k=2        knn  3864  1312  3893  1764  0.716053  0.686567   \n",
       "1         k=2        gnb  4268  3258  1947  1360  0.573710  0.758351   \n",
       "2         k=2      dtree  3247   827  4378  2381  0.703868  0.576937   \n",
       "\n",
       "   Precision  F1_Score  \n",
       "0   0.805872  0.785239  \n",
       "1   0.709979  0.691403  \n",
       "0   0.788730  0.775912  \n",
       "1   0.626309  0.701384  \n",
       "0   0.787645  0.774194  \n",
       "1   0.663255  0.676762  \n",
       "2   0.626414  0.701587  \n",
       "0   0.754454  0.738017  \n",
       "0   0.746522  0.715291  \n",
       "1   0.567101  0.648928  \n",
       "2   0.797005  0.669347  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Combination=[]\n",
    "for i in range(len(Score)):\n",
    "    Combination.append(combination_name)\n",
    "result={'Combination':Combination, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result = clf_result.append(pd.DataFrame(result))\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# L1-based Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier Selection:\n",
      "Score of knn = 0.65 (+/- 0.16)\n",
      "Score of SVM = 0.64 (+/- 0.08)\n"
     ]
    }
   ],
   "source": [
    "combination_name='L1-based'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_classifier\"\n",
    "path = os.path.join(parent_dir, combination_name)\n",
    "# os.mkdir(path) \n",
    "# os.mkdir(path+'/PR_plot') \n",
    "\n",
    "d=data[['polarity_number','subjectivity_number','day','hour','minute','suspend']]\n",
    "X=np.array(d)[:,0:5].astype('float')\n",
    "y=np.array(d)[:,5].astype('int')\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "gnb = GaussianNB()\n",
    "dtree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=4)\n",
    "SVM = svm.SVC(kernel='poly')\n",
    "nn = MLPClassifier(activation='identity',solver='adam', max_iter=1000)\n",
    "lr = LogisticRegression(random_state=0)\n",
    "sgd = SGDClassifier(loss=\"hinge\", penalty=\"l2\", max_iter=1000)\n",
    "\n",
    "scores_1 = cross_val_score(knn, X, y, cv=10)\n",
    "scores_2 = cross_val_score(gnb, X, y, cv=10)\n",
    "scores_3 = cross_val_score(dtree, X, y, cv=10)\n",
    "scores_4 = cross_val_score(SVM, X, y, cv=10)\n",
    "scores_5 = cross_val_score(nn, X, y, cv=10)\n",
    "scores_6 = cross_val_score(lr, X, y, cv=10)\n",
    "scores_7 = cross_val_score(sgd, X, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Classifier Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "std_score = std_score/len(scores)\n",
    "\n",
    "for i,j in zip(['knn','gnb','dtree','SVM','nn','lr','sgd'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        if j.std() < std_score:\n",
    "            print('Score of %s = %0.2f (std = %0.2f)' %(i, j.mean(), j.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Classifier=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[]\n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "name = ['knn','SVM']\n",
    "classifier = [knn, SVM]\n",
    "\n",
    "for clf,clf_name in zip(classifier, name):\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Classifier.append(clf_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_classifier/%s/PR_plot/PR_%s.png' %(combination_name, clf_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Combination</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All</td>\n",
       "      <td>knn</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>All</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=5</td>\n",
       "      <td>knn</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=5</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4485</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1143</td>\n",
       "      <td>0.647466</td>\n",
       "      <td>0.796908</td>\n",
       "      <td>0.626309</td>\n",
       "      <td>0.701384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=4</td>\n",
       "      <td>knn</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k=4</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4487</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1141</td>\n",
       "      <td>0.647651</td>\n",
       "      <td>0.797264</td>\n",
       "      <td>0.626414</td>\n",
       "      <td>0.701587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=3</td>\n",
       "      <td>knn</td>\n",
       "      <td>4065</td>\n",
       "      <td>1323</td>\n",
       "      <td>3882</td>\n",
       "      <td>1563</td>\n",
       "      <td>0.733592</td>\n",
       "      <td>0.722281</td>\n",
       "      <td>0.754454</td>\n",
       "      <td>0.738017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=2</td>\n",
       "      <td>knn</td>\n",
       "      <td>3864</td>\n",
       "      <td>1312</td>\n",
       "      <td>3893</td>\n",
       "      <td>1764</td>\n",
       "      <td>0.716053</td>\n",
       "      <td>0.686567</td>\n",
       "      <td>0.746522</td>\n",
       "      <td>0.715291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=2</td>\n",
       "      <td>gnb</td>\n",
       "      <td>4268</td>\n",
       "      <td>3258</td>\n",
       "      <td>1947</td>\n",
       "      <td>1360</td>\n",
       "      <td>0.573710</td>\n",
       "      <td>0.758351</td>\n",
       "      <td>0.567101</td>\n",
       "      <td>0.648928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k=2</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3247</td>\n",
       "      <td>827</td>\n",
       "      <td>4378</td>\n",
       "      <td>2381</td>\n",
       "      <td>0.703868</td>\n",
       "      <td>0.576937</td>\n",
       "      <td>0.797005</td>\n",
       "      <td>0.669347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L1-based</td>\n",
       "      <td>knn</td>\n",
       "      <td>4321</td>\n",
       "      <td>1051</td>\n",
       "      <td>4154</td>\n",
       "      <td>1307</td>\n",
       "      <td>0.782332</td>\n",
       "      <td>0.767768</td>\n",
       "      <td>0.804356</td>\n",
       "      <td>0.785636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L1-based</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3787</td>\n",
       "      <td>1545</td>\n",
       "      <td>3660</td>\n",
       "      <td>1841</td>\n",
       "      <td>0.687437</td>\n",
       "      <td>0.672886</td>\n",
       "      <td>0.710240</td>\n",
       "      <td>0.691058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Combination Classifier    TP    FP    TN    FN  Accuracy    Recall  \\\n",
       "0         All        knn  4309  1038  4167  1319  0.782424  0.765636   \n",
       "1         All        SVM  3792  1549  3656  1836  0.687529  0.673774   \n",
       "0         k=5        knn  4297  1151  4054  1331  0.770885  0.763504   \n",
       "1         k=5        SVM  4485  2676  2529  1143  0.647466  0.796908   \n",
       "0         k=4        knn  4284  1155  4050  1344  0.769316  0.761194   \n",
       "1         k=4        gnb  3888  1974  3231  1740  0.657159  0.690832   \n",
       "2         k=4        SVM  4487  2676  2529  1141  0.647651  0.797264   \n",
       "0         k=3        knn  4065  1323  3882  1563  0.733592  0.722281   \n",
       "0         k=2        knn  3864  1312  3893  1764  0.716053  0.686567   \n",
       "1         k=2        gnb  4268  3258  1947  1360  0.573710  0.758351   \n",
       "2         k=2      dtree  3247   827  4378  2381  0.703868  0.576937   \n",
       "0    L1-based        knn  4321  1051  4154  1307  0.782332  0.767768   \n",
       "1    L1-based        SVM  3787  1545  3660  1841  0.687437  0.672886   \n",
       "\n",
       "   Precision  F1_Score  \n",
       "0   0.805872  0.785239  \n",
       "1   0.709979  0.691403  \n",
       "0   0.788730  0.775912  \n",
       "1   0.626309  0.701384  \n",
       "0   0.787645  0.774194  \n",
       "1   0.663255  0.676762  \n",
       "2   0.626414  0.701587  \n",
       "0   0.754454  0.738017  \n",
       "0   0.746522  0.715291  \n",
       "1   0.567101  0.648928  \n",
       "2   0.797005  0.669347  \n",
       "0   0.804356  0.785636  \n",
       "1   0.710240  0.691058  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Combination=[]\n",
    "for i in range(len(Score)):\n",
    "    Combination.append(combination_name)\n",
    "result={'Combination':Combination, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result = clf_result.append(pd.DataFrame(result))\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tree-based feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier Selection:\n",
      "Score of knn = 0.62 (+/- 0.15)\n",
      "Score of gnb = 0.62 (+/- 0.12)\n"
     ]
    }
   ],
   "source": [
    "combination_name='Tree-based'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_classifier\"\n",
    "path = os.path.join(parent_dir, combination_name)\n",
    "# os.mkdir(path) \n",
    "# os.mkdir(path+'/PR_plot') \n",
    "\n",
    "d=data[['subjectivity_number','day','hour','suspend']]\n",
    "X=np.array(d)[:,0:3].astype('float')\n",
    "y=np.array(d)[:,3].astype('int')\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "gnb = GaussianNB()\n",
    "dtree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=4)\n",
    "SVM = svm.SVC(kernel='poly')\n",
    "nn = MLPClassifier(activation='identity',solver='adam', max_iter=1000)\n",
    "lr = LogisticRegression(random_state=0)\n",
    "sgd = SGDClassifier(loss=\"hinge\", penalty=\"l2\", max_iter=1000)\n",
    "\n",
    "scores_1 = cross_val_score(knn, X, y, cv=10)\n",
    "scores_2 = cross_val_score(gnb, X, y, cv=10)\n",
    "scores_3 = cross_val_score(dtree, X, y, cv=10)\n",
    "scores_4 = cross_val_score(SVM, X, y, cv=10)\n",
    "scores_5 = cross_val_score(nn, X, y, cv=10)\n",
    "scores_6 = cross_val_score(lr, X, y, cv=10)\n",
    "scores_7 = cross_val_score(sgd, X, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Classifier Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "std_score = std_score/len(scores)\n",
    "\n",
    "for i,j in zip(['knn','gnb','dtree','SVM','nn','lr','sgd'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        if j.std() < std_score:\n",
    "            print('Score of %s = %0.2f (std = %0.2f)' %(i, j.mean(), j.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Classifier=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[]\n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "name = ['knn','gnb']\n",
    "classifier = [knn, gnb]\n",
    "\n",
    "for clf,clf_name in zip(classifier, name):\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Classifier.append(clf_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_classifier/%s/PR_plot/PR_%s.png' %(combination_name, clf_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Combination</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All</td>\n",
       "      <td>knn</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>All</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=5</td>\n",
       "      <td>knn</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=5</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4485</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1143</td>\n",
       "      <td>0.647466</td>\n",
       "      <td>0.796908</td>\n",
       "      <td>0.626309</td>\n",
       "      <td>0.701384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=4</td>\n",
       "      <td>knn</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k=4</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4487</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1141</td>\n",
       "      <td>0.647651</td>\n",
       "      <td>0.797264</td>\n",
       "      <td>0.626414</td>\n",
       "      <td>0.701587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=3</td>\n",
       "      <td>knn</td>\n",
       "      <td>4065</td>\n",
       "      <td>1323</td>\n",
       "      <td>3882</td>\n",
       "      <td>1563</td>\n",
       "      <td>0.733592</td>\n",
       "      <td>0.722281</td>\n",
       "      <td>0.754454</td>\n",
       "      <td>0.738017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k=2</td>\n",
       "      <td>knn</td>\n",
       "      <td>3864</td>\n",
       "      <td>1312</td>\n",
       "      <td>3893</td>\n",
       "      <td>1764</td>\n",
       "      <td>0.716053</td>\n",
       "      <td>0.686567</td>\n",
       "      <td>0.746522</td>\n",
       "      <td>0.715291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k=2</td>\n",
       "      <td>gnb</td>\n",
       "      <td>4268</td>\n",
       "      <td>3258</td>\n",
       "      <td>1947</td>\n",
       "      <td>1360</td>\n",
       "      <td>0.573710</td>\n",
       "      <td>0.758351</td>\n",
       "      <td>0.567101</td>\n",
       "      <td>0.648928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k=2</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3247</td>\n",
       "      <td>827</td>\n",
       "      <td>4378</td>\n",
       "      <td>2381</td>\n",
       "      <td>0.703868</td>\n",
       "      <td>0.576937</td>\n",
       "      <td>0.797005</td>\n",
       "      <td>0.669347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L1-based</td>\n",
       "      <td>knn</td>\n",
       "      <td>4321</td>\n",
       "      <td>1051</td>\n",
       "      <td>4154</td>\n",
       "      <td>1307</td>\n",
       "      <td>0.782332</td>\n",
       "      <td>0.767768</td>\n",
       "      <td>0.804356</td>\n",
       "      <td>0.785636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L1-based</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3787</td>\n",
       "      <td>1545</td>\n",
       "      <td>3660</td>\n",
       "      <td>1841</td>\n",
       "      <td>0.687437</td>\n",
       "      <td>0.672886</td>\n",
       "      <td>0.710240</td>\n",
       "      <td>0.691058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tree-based</td>\n",
       "      <td>knn</td>\n",
       "      <td>4426</td>\n",
       "      <td>1278</td>\n",
       "      <td>3927</td>\n",
       "      <td>1202</td>\n",
       "      <td>0.771070</td>\n",
       "      <td>0.786425</td>\n",
       "      <td>0.775947</td>\n",
       "      <td>0.781151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tree-based</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3897</td>\n",
       "      <td>2001</td>\n",
       "      <td>3204</td>\n",
       "      <td>1731</td>\n",
       "      <td>0.655497</td>\n",
       "      <td>0.692431</td>\n",
       "      <td>0.660732</td>\n",
       "      <td>0.676210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Combination Classifier    TP    FP    TN    FN  Accuracy    Recall  \\\n",
       "0         All        knn  4309  1038  4167  1319  0.782424  0.765636   \n",
       "1         All        SVM  3792  1549  3656  1836  0.687529  0.673774   \n",
       "0         k=5        knn  4297  1151  4054  1331  0.770885  0.763504   \n",
       "1         k=5        SVM  4485  2676  2529  1143  0.647466  0.796908   \n",
       "0         k=4        knn  4284  1155  4050  1344  0.769316  0.761194   \n",
       "1         k=4        gnb  3888  1974  3231  1740  0.657159  0.690832   \n",
       "2         k=4        SVM  4487  2676  2529  1141  0.647651  0.797264   \n",
       "0         k=3        knn  4065  1323  3882  1563  0.733592  0.722281   \n",
       "0         k=2        knn  3864  1312  3893  1764  0.716053  0.686567   \n",
       "1         k=2        gnb  4268  3258  1947  1360  0.573710  0.758351   \n",
       "2         k=2      dtree  3247   827  4378  2381  0.703868  0.576937   \n",
       "0    L1-based        knn  4321  1051  4154  1307  0.782332  0.767768   \n",
       "1    L1-based        SVM  3787  1545  3660  1841  0.687437  0.672886   \n",
       "0  Tree-based        knn  4426  1278  3927  1202  0.771070  0.786425   \n",
       "1  Tree-based        gnb  3897  2001  3204  1731  0.655497  0.692431   \n",
       "\n",
       "   Precision  F1_Score  \n",
       "0   0.805872  0.785239  \n",
       "1   0.709979  0.691403  \n",
       "0   0.788730  0.775912  \n",
       "1   0.626309  0.701384  \n",
       "0   0.787645  0.774194  \n",
       "1   0.663255  0.676762  \n",
       "2   0.626414  0.701587  \n",
       "0   0.754454  0.738017  \n",
       "0   0.746522  0.715291  \n",
       "1   0.567101  0.648928  \n",
       "2   0.797005  0.669347  \n",
       "0   0.804356  0.785636  \n",
       "1   0.710240  0.691058  \n",
       "0   0.775947  0.781151  \n",
       "1   0.660732  0.676210  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Combination=[]\n",
    "for i in range(len(Score)):\n",
    "    Combination.append(combination_name)\n",
    "result={'Combination':Combination, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result = clf_result.append(pd.DataFrame(result))\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_result = clf_result.reset_index()\n",
    "clf_result = clf_result.drop(['index'],axis=1)\n",
    "clf_result.to_csv('/Volumes/BECCA/Project/Data/Result/Russia/By_classifier/Total_Result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>Combination</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All</td>\n",
       "      <td>knn</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>All</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k=5</td>\n",
       "      <td>knn</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k=5</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4485</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1143</td>\n",
       "      <td>0.647466</td>\n",
       "      <td>0.796908</td>\n",
       "      <td>0.626309</td>\n",
       "      <td>0.701384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k=4</td>\n",
       "      <td>knn</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>k=4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>k=4</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4487</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1141</td>\n",
       "      <td>0.647651</td>\n",
       "      <td>0.797264</td>\n",
       "      <td>0.626414</td>\n",
       "      <td>0.701587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>k=3</td>\n",
       "      <td>knn</td>\n",
       "      <td>4065</td>\n",
       "      <td>1323</td>\n",
       "      <td>3882</td>\n",
       "      <td>1563</td>\n",
       "      <td>0.733592</td>\n",
       "      <td>0.722281</td>\n",
       "      <td>0.754454</td>\n",
       "      <td>0.738017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>k=2</td>\n",
       "      <td>knn</td>\n",
       "      <td>3864</td>\n",
       "      <td>1312</td>\n",
       "      <td>3893</td>\n",
       "      <td>1764</td>\n",
       "      <td>0.716053</td>\n",
       "      <td>0.686567</td>\n",
       "      <td>0.746522</td>\n",
       "      <td>0.715291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>k=2</td>\n",
       "      <td>gnb</td>\n",
       "      <td>4268</td>\n",
       "      <td>3258</td>\n",
       "      <td>1947</td>\n",
       "      <td>1360</td>\n",
       "      <td>0.573710</td>\n",
       "      <td>0.758351</td>\n",
       "      <td>0.567101</td>\n",
       "      <td>0.648928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>k=2</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3247</td>\n",
       "      <td>827</td>\n",
       "      <td>4378</td>\n",
       "      <td>2381</td>\n",
       "      <td>0.703868</td>\n",
       "      <td>0.576937</td>\n",
       "      <td>0.797005</td>\n",
       "      <td>0.669347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>L1-based</td>\n",
       "      <td>knn</td>\n",
       "      <td>4321</td>\n",
       "      <td>1051</td>\n",
       "      <td>4154</td>\n",
       "      <td>1307</td>\n",
       "      <td>0.782332</td>\n",
       "      <td>0.767768</td>\n",
       "      <td>0.804356</td>\n",
       "      <td>0.785636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>L1-based</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3787</td>\n",
       "      <td>1545</td>\n",
       "      <td>3660</td>\n",
       "      <td>1841</td>\n",
       "      <td>0.687437</td>\n",
       "      <td>0.672886</td>\n",
       "      <td>0.710240</td>\n",
       "      <td>0.691058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Tree-based</td>\n",
       "      <td>knn</td>\n",
       "      <td>4426</td>\n",
       "      <td>1278</td>\n",
       "      <td>3927</td>\n",
       "      <td>1202</td>\n",
       "      <td>0.771070</td>\n",
       "      <td>0.786425</td>\n",
       "      <td>0.775947</td>\n",
       "      <td>0.781151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tree-based</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3897</td>\n",
       "      <td>2001</td>\n",
       "      <td>3204</td>\n",
       "      <td>1731</td>\n",
       "      <td>0.655497</td>\n",
       "      <td>0.692431</td>\n",
       "      <td>0.660732</td>\n",
       "      <td>0.676210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Combination Classifier    TP    FP    TN    FN  Accuracy    Recall  \\\n",
       "0          All        knn  4309  1038  4167  1319  0.782424  0.765636   \n",
       "1          All        SVM  3792  1549  3656  1836  0.687529  0.673774   \n",
       "2          k=5        knn  4297  1151  4054  1331  0.770885  0.763504   \n",
       "3          k=5        SVM  4485  2676  2529  1143  0.647466  0.796908   \n",
       "4          k=4        knn  4284  1155  4050  1344  0.769316  0.761194   \n",
       "5          k=4        gnb  3888  1974  3231  1740  0.657159  0.690832   \n",
       "6          k=4        SVM  4487  2676  2529  1141  0.647651  0.797264   \n",
       "7          k=3        knn  4065  1323  3882  1563  0.733592  0.722281   \n",
       "8          k=2        knn  3864  1312  3893  1764  0.716053  0.686567   \n",
       "9          k=2        gnb  4268  3258  1947  1360  0.573710  0.758351   \n",
       "10         k=2      dtree  3247   827  4378  2381  0.703868  0.576937   \n",
       "11    L1-based        knn  4321  1051  4154  1307  0.782332  0.767768   \n",
       "12    L1-based        SVM  3787  1545  3660  1841  0.687437  0.672886   \n",
       "13  Tree-based        knn  4426  1278  3927  1202  0.771070  0.786425   \n",
       "14  Tree-based        gnb  3897  2001  3204  1731  0.655497  0.692431   \n",
       "\n",
       "    Precision  F1_Score  \n",
       "0    0.805872  0.785239  \n",
       "1    0.709979  0.691403  \n",
       "2    0.788730  0.775912  \n",
       "3    0.626309  0.701384  \n",
       "4    0.787645  0.774194  \n",
       "5    0.663255  0.676762  \n",
       "6    0.626414  0.701587  \n",
       "7    0.754454  0.738017  \n",
       "8    0.746522  0.715291  \n",
       "9    0.567101  0.648928  \n",
       "10   0.797005  0.669347  \n",
       "11   0.804356  0.785636  \n",
       "12   0.710240  0.691058  \n",
       "13   0.775947  0.781151  \n",
       "14   0.660732  0.676210  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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