K-Nearest Neighbours and Decision Tree.ipynb

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   "cell_type": "markdown",
   "metadata": {
    "id": "oI2fVTvPwDfG"
   },
   "source": [
    "**Importing libraries** "
   ]
  },
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   "execution_count": 1,
   "metadata": {
    "id": "QV4lwZFEcFsM"
   },
   "outputs": [],
   "source": [
    "#import important library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import io #Importing input module\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model  # Importing linear model\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "\n",
    "#import model library\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Zq4N6OPwmX0"
   },
   "source": [
    "**Importing data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "base_uri": "https://localhost:8080/",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>5.431</td>\n",
       "      <td>2.8955</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>32.1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3.573</td>\n",
       "      <td>2.242</td>\n",
       "      <td>1</td>\n",
       "      <td>8.088</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NRB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>5.287</td>\n",
       "      <td>3.3732</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3.787</td>\n",
       "      <td>3.083</td>\n",
       "      <td>3</td>\n",
       "      <td>9.278</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NRB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>4.869</td>\n",
       "      <td>1.7670</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>44.4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3.848</td>\n",
       "      <td>2.576</td>\n",
       "      <td>5</td>\n",
       "      <td>9.537</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NRB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>5.158</td>\n",
       "      <td>1.6914</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>56.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>5.808</td>\n",
       "      <td>2.055</td>\n",
       "      <td>8</td>\n",
       "      <td>11.055</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NRB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>5.076</td>\n",
       "      <td>2.6588</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>54.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.009</td>\n",
       "      <td>2.206</td>\n",
       "      <td>0</td>\n",
       "      <td>9.130</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NRB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1055 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SpMax_L  J_Dz(e)  nHM  F01[N-N]  F04[C-N]  NssssC  nCb-    C%  nCp  nO  \\\n",
       "0       3.919   2.6909    0         0         0       0     0  31.4    2   0   \n",
       "1       4.170   2.1144    0         0         0       0     0  30.8    1   1   \n",
       "2       3.932   3.2512    0         0         0       0     0  26.7    2   4   \n",
       "3       3.000   2.7098    0         0         0       0     0  20.0    0   2   \n",
       "4       4.236   3.3944    0         0         0       0     0  29.4    2   4   \n",
       "...       ...      ...  ...       ...       ...     ...   ...   ...  ...  ..   \n",
       "1050    5.431   2.8955    0         0         0       2     0  32.1    4   1   \n",
       "1051    5.287   3.3732    0         0         9       0     0  35.3    0   9   \n",
       "1052    4.869   1.7670    0         1         9       0     5  44.4    0   4   \n",
       "1053    5.158   1.6914    2         0        36       0     9  56.1    0   0   \n",
       "1054    5.076   2.6588    2         0         0       0     4  54.5    0   0   \n",
       "\n",
       "      ...  C-026  F02[C-N]  nHDon  SpMax_B(m)  Psi_i_A  nN  SM6_B(m)  nArCOOR  \\\n",
       "0     ...      0         0      0       2.949    1.591   0     7.253        0   \n",
       "1     ...      0         0      0       3.315    1.967   0     7.257        0   \n",
       "2     ...      0         0      1       3.076    2.417   0     7.601        0   \n",
       "3     ...      0         0      1       3.046    5.000   0     6.690        0   \n",
       "4     ...      0         0      0       3.351    2.405   0     8.003        0   \n",
       "...   ...    ...       ...    ...         ...      ...  ..       ...      ...   \n",
       "1050  ...      0         6      1       3.573    2.242   1     8.088        0   \n",
       "1051  ...      0         3      0       3.787    3.083   3     9.278        0   \n",
       "1052  ...      4        13      0       3.848    2.576   5     9.537        1   \n",
       "1053  ...      1        16      0       5.808    2.055   8    11.055        0   \n",
       "1054  ...      2         0      0       4.009    2.206   0     9.130        0   \n",
       "\n",
       "      nX  experimental_class  \n",
       "0      0                  RB  \n",
       "1      0                  RB  \n",
       "2      0                  RB  \n",
       "3      0                  RB  \n",
       "4      0                  RB  \n",
       "...   ..                 ...  \n",
       "1050   0                 NRB  \n",
       "1051   0                 NRB  \n",
       "1052   0                 NRB  \n",
       "1053   1                 NRB  \n",
       "1054   2                 NRB  \n",
       "\n",
       "[1055 rows x 42 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import dataset\n",
    "\n",
    "data = pd.read_csv('biodeg.csv',sep=';')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xDb_ry0PxEuZ"
   },
   "source": [
    "**Data processing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TWgd3Z-udKTu",
    "outputId": "aeed4a8d-a092-4139-c7f2-7f6ae56408b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target variables  :  ['NRB' 'RB']\n"
     ]
    }
   ],
   "source": [
    "#Get binary classification of the response \n",
    "response = np.unique(data['experimental_class'])\n",
    "print('Target variables  : ', response)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "EDbz1Xi-dSkG"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#Extract features and response into respective y and X variable\n",
    "y = data['experimental_class'].values\n",
    "X = data[data.keys()[:-1]]\n",
    "\n",
    "#Normalize data\n",
    "scaler = MinMaxScaler()\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "YM-_LwREeWwq"
   },
   "outputs": [],
   "source": [
    "# Import the feature selection library\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AUhUKVfxddHC",
    "outputId": "ebdffbf6-7ef7-46e2-8122-c45843d89564"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 features selected based on the Chi Square method:\n",
      "--------------------------------------------------------\n",
      "    Features     Score\n",
      "1    J_Dz(e)  0.000139\n",
      "15  F03[C-O]  0.001195\n",
      "27  Psi_i_1d  0.006990\n",
      "11     SdssC  0.156340\n",
      "34     nHDon  0.185663\n",
      "8        nCp  0.387754\n",
      "17        Mi  0.427909\n",
      "29       SdO  0.686679\n",
      "16        Me  0.957503\n",
      "36   Psi_i_A  1.208577\n"
     ]
    }
   ],
   "source": [
    "#apply SelectKBest class to extract top 10 best features\n",
    "#chi square is used as the feature selection method\n",
    "#the lesser score acquired, the stronger the relationship with the response\n",
    "bestfeatures = SelectKBest(score_func=chi2)\n",
    "fit = bestfeatures.fit(X,y)\n",
    "dfscores = pd.DataFrame(fit.scores_)\n",
    "dfcolumns = pd.DataFrame(data.keys()[:-1])\n",
    "\n",
    "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
    "featureScores.columns = ['Features','Score']  #naming the dataframe columns\n",
    "featuresTop = featureScores.nsmallest(10,'Score')\n",
    "\n",
    "X_arr = np.array(featuresTop['Features']) #Assign the best features to an array for later use\n",
    "print (\"Top 10 features selected based on the Chi Square method:\")\n",
    "print (\"--------------------------------------------------------\")\n",
    "print(featuresTop)  #display 10 best features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "epSGdhvngcyl",
    "outputId": "235698f1-6b18-44ea-cdc6-7e165d6e3256"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(675, 10)\n",
      "(169, 10)\n",
      "(211, 10)\n"
     ]
    }
   ],
   "source": [
    "#Splitting the dataset into train, test and validation set\n",
    "X = data[X_arr].values\n",
    "seed_num = 10\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=seed_num)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=seed_num)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_val.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F2SjTGGQy2IS"
   },
   "source": [
    "**k-Nearest Neighbour**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "XmAfKSNdMQ3P",
    "outputId": "e30b4eab-9dc1-42d4-9f88-a05f42cb5b96"
   },
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best k score: 0.7751479289940828\n",
      "Best k value: 3\n"
     ]
    }
   ],
   "source": [
    "#find the best k to train the model\n",
    "#implement a validation test\n",
    "scores = []\n",
    "max_score = 0\n",
    "best_k = 0\n",
    "for k in range(1,20):\n",
    "  model_knn = KNeighborsClassifier(n_neighbors=k)\n",
    "  model_knn.fit(X_train, y_train)\n",
    "  score = model_knn.score(X_val, y_val)\n",
    "  if score > max_score:\n",
    "    max_score = score\n",
    "    best_k = k\n",
    "  scores.append(score)\n",
    "plt.plot(np.arange(1,20), scores)\n",
    "plt.xticks(np.arange(1,20))\n",
    "plt.grid()\n",
    "plt.show()\n",
    "print(\"Best k score:\", max(scores))\n",
    "print(\"Best k value:\", best_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_F0MHHKaExvU",
    "outputId": "522a8f1f-0f9f-482f-d721-859d8d7f983c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysis for k-Nearest Neighbors (k value =  3 ):\n",
      "------------------------------------------------\n",
      "\n",
      "Accuracy: 0.7630331753554502\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         NRB       0.82      0.84      0.83       147\n",
      "          RB       0.62      0.58      0.60        64\n",
      "\n",
      "    accuracy                           0.76       211\n",
      "   macro avg       0.72      0.71      0.71       211\n",
      "weighted avg       0.76      0.76      0.76       211\n",
      "\n",
      "\n",
      "Confusion Matric:\n",
      "[[124  23]\n",
      " [ 27  37]]\n"
     ]
    }
   ],
   "source": [
    "#model training \n",
    "#using optimized k\n",
    "model_knn = KNeighborsClassifier(n_neighbors=best_k)\n",
    "model_knn.fit(X_train, y_train)\n",
    "y_pred = model_knn.predict(X_test)\n",
    "\n",
    "print(\"Analysis for k-Nearest Neighbors (k value = \", best_k, \"):\")\n",
    "print(\"------------------------------------------------\")\n",
    "\n",
    "print(\"\\nAccuracy: \" + str(accuracy_score(y_test, y_pred)) + \"\\n\") #precision, recall, f1-score\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(\"\\nConfusion Matric:\")\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "I1jA2gYaNV42"
   },
   "outputs": [],
   "source": [
    "#convert the binary classification clasess into numerical values \n",
    "#so that it can be plotted \n",
    "#'RB' is equal to value 1 (true)\n",
    "#'NRB' is equal to value 0 (false)\n",
    "\n",
    "def y_to_numeric(y):\n",
    "  y_test_list = []\n",
    "  for i, value in enumerate(y):\n",
    "    if (value == response[1]):\n",
    "      y_test_list.append(1)\n",
    "    else:\n",
    "      y_test_list.append(0)\n",
    "\n",
    "  return np.array(y_test_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WKNQbPcb5F0j",
    "outputId": "d9de425e-cb1e-4611-d6b6-d83ae4ae1624"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set data size into two features\n",
    "X = data[X_arr[:2]].values\n",
    "seed_num = 10\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=seed_num)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=seed_num)\n",
    "\n",
    "model_knn = KNeighborsClassifier(n_neighbors=best_k)\n",
    "model_knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 503
    },
    "id": "Pv27r0NbDDQm",
    "outputId": "2dd60126-bdc1-422b-8a29-d206d0b9c3ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x131a63b6fd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph for visual representation\n",
    "from matplotlib.colors import ListedColormap\n",
    "cmap_regions = ListedColormap(['lightsteelblue', 'bisque', 'limegreen'])\n",
    "cmap_points = ListedColormap(['blue', 'orange', 'green'])\n",
    "offset = 0.05\n",
    "step = 0.02\n",
    "x1_min, x1_max = X_test[:,0].min()-offset, X_test[:,0].max()+offset\n",
    "x2_min, x2_max = X_test[:,1].min()-offset, X_test[:,1].max()+offset\n",
    "xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, step), np.arange(x2_min, x2_max, step))\n",
    "Z = model_knn.predict(np.c_[xx1.ravel(),xx2.ravel()])\n",
    "Z = y_to_numeric(Z)\n",
    "Z = Z.reshape(xx1.shape)\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.contourf(xx1, xx2, Z, cmap=cmap_regions)\n",
    "plt.scatter(X_test[:,0], X_test[:,1], c= y_to_numeric(y_test), cmap=cmap_points)\n",
    "\n",
    "leg_a = mpatches.Patch(color='green', label='RB')\n",
    "leg_b = mpatches.Patch(color='blue', label='NRB')\n",
    "plt.legend(handles=[leg_a,leg_b])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y3ivmO3D-rmy"
   },
   "source": [
    "**Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SWvfz-9i_HZ6",
    "outputId": "6a4b1d2f-fef6-4732-94c3-46c4a06d0a3a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=10)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model training\n",
    "model_dt = DecisionTreeClassifier(criterion='gini', random_state=seed_num, max_depth = None) \n",
    "\n",
    "X = data[X_arr].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=seed_num)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=seed_num)\n",
    "\n",
    "model_dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k82pgqI__3yQ",
    "outputId": "ee83dd6e-64e1-4a34-8d16-e8b8754b760d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree Algorithm: \n",
      "0.7582938388625592\n",
      "---------------------------------------------------\n",
      "Confusion Matrix: \n",
      "[[119  28]\n",
      " [ 23  41]]\n",
      "---------------------------------------------------\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         NRB       0.84      0.81      0.82       147\n",
      "          RB       0.59      0.64      0.62        64\n",
      "\n",
      "    accuracy                           0.76       211\n",
      "   macro avg       0.72      0.73      0.72       211\n",
      "weighted avg       0.76      0.76      0.76       211\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# data analysis \n",
    "y_pred = model_dt.predict(X_test)\n",
    "print(\"Accuracy of Decision Tree Algorithm: \")\n",
    "print(accuracy_score(y_test, y_pred))\n",
    "print('---------------------------------------------------')\n",
    "print(\"Confusion Matrix: \")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('---------------------------------------------------')\n",
    "print(\"Classification Report: \")\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "l20tQNuBABLe"
   },
   "outputs": [],
   "source": [
    "dt_models = []\n",
    "depths = np.arange(1,14)\n",
    "for d in depths:\n",
    "    model = DecisionTreeClassifier(criterion='gini', min_samples_split=15, max_depth=d, random_state=seed_num)\n",
    "    model.fit(X_train, y_train)\n",
    "    dt_models.append(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fj3glFWfAEoX",
    "outputId": "11b56866-9b5f-4d39-a9e8-6caac0be1f15"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree classifier with max_depth=1 achieves a mean accuracy of 0.6804733727810651\n",
      "Decision Tree classifier with max_depth=2 achieves a mean accuracy of 0.7455621301775148\n",
      "Decision Tree classifier with max_depth=3 achieves a mean accuracy of 0.7455621301775148\n",
      "Decision Tree classifier with max_depth=4 achieves a mean accuracy of 0.7455621301775148\n",
      "Decision Tree classifier with max_depth=5 achieves a mean accuracy of 0.7751479289940828\n",
      "Decision Tree classifier with max_depth=6 achieves a mean accuracy of 0.757396449704142\n",
      "Decision Tree classifier with max_depth=7 achieves a mean accuracy of 0.7810650887573964\n",
      "Decision Tree classifier with max_depth=8 achieves a mean accuracy of 0.7869822485207101\n",
      "Decision Tree classifier with max_depth=9 achieves a mean accuracy of 0.757396449704142\n",
      "Decision Tree classifier with max_depth=10 achieves a mean accuracy of 0.7633136094674556\n",
      "Decision Tree classifier with max_depth=11 achieves a mean accuracy of 0.757396449704142\n",
      "Decision Tree classifier with max_depth=12 achieves a mean accuracy of 0.757396449704142\n",
      "Decision Tree classifier with max_depth=13 achieves a mean accuracy of 0.757396449704142\n"
     ]
    }
   ],
   "source": [
    "#display mean accuracy for each depth\n",
    "best_acc = 0\n",
    "model_best = None\n",
    "for m,d in zip(dt_models, depths):\n",
    "    acc = m.score(X_val, y_val)\n",
    "    print(f'Decision Tree classifier with max_depth={d} achieves a mean accuracy of {acc}')\n",
    "    if acc > best_acc:\n",
    "        best_acc = acc\n",
    "        model_best = m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1Bqasvv0AOn4",
    "outputId": "c90ef52b-8147-4f4d-bee9-1ae5a01b70d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- Mi <= 1.14\n",
      "|   |--- F03[C-O] <= 0.50\n",
      "|   |   |--- SdssC <= 1.59\n",
      "|   |   |   |--- Psi_i_1d <= 0.05\n",
      "|   |   |   |   |--- Psi_i_1d <= -0.02\n",
      "|   |   |   |   |   |--- J_Dz(e) <= 3.12\n",
      "|   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |--- J_Dz(e) >  3.12\n",
      "|   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |--- Psi_i_1d >  -0.02\n",
      "|   |   |   |   |   |--- J_Dz(e) <= 2.44\n",
      "|   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |--- J_Dz(e) >  2.44\n",
      "|   |   |   |   |   |   |--- J_Dz(e) <= 2.47\n",
      "|   |   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |   |   |--- J_Dz(e) >  2.47\n",
      "|   |   |   |   |   |   |   |--- J_Dz(e) <= 3.02\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |   |   |--- J_Dz(e) >  3.02\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |--- Psi_i_1d >  0.05\n",
      "|   |   |   |   |--- class: NRB\n",
      "|   |   |--- SdssC >  1.59\n",
      "|   |   |   |--- class: RB\n",
      "|   |--- F03[C-O] >  0.50\n",
      "|   |   |--- Psi_i_1d <= 0.00\n",
      "|   |   |   |--- Psi_i_1d <= -0.02\n",
      "|   |   |   |   |--- J_Dz(e) <= 3.31\n",
      "|   |   |   |   |   |--- nHDon <= 1.50\n",
      "|   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |   |--- nHDon >  1.50\n",
      "|   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |--- J_Dz(e) >  3.31\n",
      "|   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |--- Psi_i_1d >  -0.02\n",
      "|   |   |   |   |--- SdssC <= -0.74\n",
      "|   |   |   |   |   |--- nHDon <= 1.50\n",
      "|   |   |   |   |   |   |--- F03[C-O] <= 7.00\n",
      "|   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |   |--- F03[C-O] >  7.00\n",
      "|   |   |   |   |   |   |   |--- J_Dz(e) <= 4.34\n",
      "|   |   |   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |   |   |   |--- J_Dz(e) >  4.34\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |--- nHDon >  1.50\n",
      "|   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |--- SdssC >  -0.74\n",
      "|   |   |   |   |   |--- Psi_i_A <= 1.93\n",
      "|   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |   |--- Psi_i_A >  1.93\n",
      "|   |   |   |   |   |   |--- Me <= 1.01\n",
      "|   |   |   |   |   |   |   |--- nCp <= 1.50\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |   |   |--- nCp >  1.50\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |   |--- Me >  1.01\n",
      "|   |   |   |   |   |   |   |--- J_Dz(e) <= 3.35\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |   |   |--- J_Dz(e) >  3.35\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |--- Psi_i_1d >  0.00\n",
      "|   |   |   |--- SdO <= 5.01\n",
      "|   |   |   |   |--- Psi_i_A <= 2.17\n",
      "|   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |--- Psi_i_A >  2.17\n",
      "|   |   |   |   |   |--- Psi_i_1d <= 0.05\n",
      "|   |   |   |   |   |   |--- J_Dz(e) <= 3.01\n",
      "|   |   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |   |   |--- J_Dz(e) >  3.01\n",
      "|   |   |   |   |   |   |   |--- Psi_i_A <= 2.25\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |   |   |--- Psi_i_A >  2.25\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |--- Psi_i_1d >  0.05\n",
      "|   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |--- SdO >  5.01\n",
      "|   |   |   |   |--- SdO <= 20.03\n",
      "|   |   |   |   |   |--- Me <= 1.04\n",
      "|   |   |   |   |   |   |--- Mi <= 1.14\n",
      "|   |   |   |   |   |   |   |--- Psi_i_A <= 2.28\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |   |   |--- Psi_i_A >  2.28\n",
      "|   |   |   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |   |   |--- Mi >  1.14\n",
      "|   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |--- Me >  1.04\n",
      "|   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |--- SdO >  20.03\n",
      "|   |   |   |   |   |--- class: NRB\n",
      "|--- Mi >  1.14\n",
      "|   |--- SdssC <= -0.01\n",
      "|   |   |--- Mi <= 1.18\n",
      "|   |   |   |--- J_Dz(e) <= 2.98\n",
      "|   |   |   |   |--- class: NRB\n",
      "|   |   |   |--- J_Dz(e) >  2.98\n",
      "|   |   |   |   |--- F03[C-O] <= 11.00\n",
      "|   |   |   |   |   |--- SdssC <= -0.17\n",
      "|   |   |   |   |   |   |--- J_Dz(e) <= 4.20\n",
      "|   |   |   |   |   |   |   |--- F03[C-O] <= 8.50\n",
      "|   |   |   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |   |   |   |--- F03[C-O] >  8.50\n",
      "|   |   |   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |   |   |--- J_Dz(e) >  4.20\n",
      "|   |   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |   |--- SdssC >  -0.17\n",
      "|   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |--- F03[C-O] >  11.00\n",
      "|   |   |   |   |   |--- class: NRB\n",
      "|   |   |--- Mi >  1.18\n",
      "|   |   |   |--- class: NRB\n",
      "|   |--- SdssC >  -0.01\n",
      "|   |   |--- J_Dz(e) <= 2.96\n",
      "|   |   |   |--- nHDon <= 3.50\n",
      "|   |   |   |   |--- SdssC <= 0.07\n",
      "|   |   |   |   |   |--- Me <= 0.98\n",
      "|   |   |   |   |   |   |--- Psi_i_A <= 1.55\n",
      "|   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |   |--- Psi_i_A >  1.55\n",
      "|   |   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |   |--- Me >  0.98\n",
      "|   |   |   |   |   |   |--- Psi_i_A <= 2.32\n",
      "|   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |   |--- Psi_i_A >  2.32\n",
      "|   |   |   |   |   |   |   |--- Mi <= 1.15\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |   |   |--- Mi >  1.15\n",
      "|   |   |   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |--- SdssC >  0.07\n",
      "|   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |--- nHDon >  3.50\n",
      "|   |   |   |   |--- class: NRB\n",
      "|   |   |--- J_Dz(e) >  2.96\n",
      "|   |   |   |--- Mi <= 1.14\n",
      "|   |   |   |   |--- class: RB\n",
      "|   |   |   |--- Mi >  1.14\n",
      "|   |   |   |   |--- J_Dz(e) <= 3.23\n",
      "|   |   |   |   |   |--- F03[C-O] <= 0.50\n",
      "|   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |--- F03[C-O] >  0.50\n",
      "|   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |--- J_Dz(e) >  3.23\n",
      "|   |   |   |   |   |--- Psi_i_A <= 1.67\n",
      "|   |   |   |   |   |   |--- class: RB\n",
      "|   |   |   |   |   |--- Psi_i_A >  1.67\n",
      "|   |   |   |   |   |   |--- Psi_i_1d <= 0.43\n",
      "|   |   |   |   |   |   |   |--- nHDon <= 4.50\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |   |   |--- nHDon >  4.50\n",
      "|   |   |   |   |   |   |   |   |--- class: NRB\n",
      "|   |   |   |   |   |   |--- Psi_i_1d >  0.43\n",
      "|   |   |   |   |   |   |   |--- class: RB\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#display result\n",
    "from sklearn.tree import plot_tree, export_text  \n",
    "print(export_text(model_best, feature_names=list(X_arr)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 833
    },
    "id": "j9sW7u8TAchK",
    "outputId": "8b3abfc7-f2f3-4d2c-9480-8bea89a7c41e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7vxMyLhGR/alnSkSKSjygxAfM7F5gKdE8OJe4+2nx/DhKpERygLu/7+7/dPeziEr/ugNvmtkDZna65qwSkVygnikRKQpmVgp8hugudyfgz8Dd7r4xaGAikjQz6whcTXQc7yTqTf67u+8IGZeIFC8lUyJS0MzsaODrwBeBWUSNr0nuvi9kXCKSungY9XOJkqrTgAnAn919edDARKToqMxPRAqOmbWI5615EnidaD6bU939Qnd/WomUSH6L56ya7O6XACOBvcBMM3vGzC4ys5aBQxSRIqGeKREpGCoBEile+5XyfhM4EhiPSnlFJMOUTIlI3jOz44gSqM8Ak4iSqBka0lyk+MQDU4wmOid8FHgU+JO7zw0amIgUJCVTIpKX9hs2+ZvAMWjYZBE5SDz9wZeInpssJ5qE+2F33xs0MBEpGEqmRCSvmFl34CvAtcByosbR4+7+ftDARCRnmVkr4GKimy/DgTuB2929PGhgIpL3NACFiOS8eG6o083sAeBNoAdwgbufGc9Do0RKROrl7tXu/ri7nwt8GOgIzDezh83sLM1ZJSKpUs+UiOQsM2sDfJbobnIbomeh/ubum0PGJSL5z8zaA58jOr/UEJ1f7nP3bUEDE5G8omRKRHKOmR1D9IzD1cBMolK+59y9JmhgIlJw4l6ps4iSqg8D9xHNWbUkZFwikh9U5iciOSGeG+oiM3sGeBmoBka5+8fi+WSUSIlI2nnkRXf/JHACsBWYamZTzOwSzVklIg1Rz5SIBGVmZcA1wHXAJqJeqIfcfVfQwESkaJnZ4cBlRL1VPYjmrPqLu28IGpiI5Bz1TIlIEGZ2kpndBawATiR6NmqUu/9ViZSIhOTue9z9fnc/FfgEMAhYZmZ/M7PRgcMTkRyinikRyRozO4zobu83gN7AbcBd7r4+aGAiIo0ws07AF4me53yXqBf9H+6+O2hgIhKUkikRyTgz60U0L9SXgUVEo2Y95e7VQQMTEWmi+BmqjxDdFBoB/AW4zd3XBA1MRIJQmZ+IZEQ8N9RZZvYwMB84EjjH3c9198eUSIlIPnL3fe4+0d0/ApwGlABzzOxxMztPc1aJFBf1TIlIWplZO6K5W75BdMNmHHCvu28NGpiISIaYWVvgSqLz3uHAn4nmxNsSNDARyTglUyKSFmY2mKghcRXwIlES9aLrJCMiRSLulTqd6Fw4BngIGOfuC4MGJiIZozI/EUmZmbWM52GZDLwEbANOdPdPuvsLSqREpJjEc1b9290vB4YDlcBkM5tqZpeZWevAIYpImqlnSkSazMy6AF8Cvga8Q9QL9U933xM0MBGRHBOPYvpxot6q/sDtwJ3uXhk0MBFJC/VMiUjSzGyUmf0NWEY078on3f1Ud79PiZSIyKHcfa+7P+TuZwAXAr2AxWb2dzP7oAasEMlv6pkSkQaZWQnwaeCbQBeiB6vvdvf3ggYmIpKnzKwj8AWi3qrtRHNWPeDuOwOGJSIpUDIlInUys75EZXxfAuYQlfI94+77ggYmIlIgzKwF0UAV3wBOBf4KjHf3FSHjEpHkqcxPRGrFc0Oda2aPEyVQJcDp7n6Buz+lREpEJH3cvcbdJ7n7R4FRQA3wipk9bWYfiZMtEclh6pkSEczsCOBqoruje4lKTu539x1BAxMRKTJmVgpcTlRa3YGotPoed98UNDARqZOSKZEiZmbHEiVQlwNTiJKo6RrSXEQkrHhgig8QJVUXAQ8TzVn1Rsi4RORA6j4WKTJm1jqe7+RFogRqPTDc3T8Tz4+iREpEJLB4zqpX3P0qYAiwGnjKzKab2RXxkOsiEph6pkSKhJl1Bb5CNKjEKqJeqMfcfW/QwEREJClm1gr4GFFFwTDgDuAOd68IGphIEVPPlEgBiweU+KCZ3Q8sAfoAF7n7h+J5T5RIiYjkCXevdvdH3f0c4BygM7DAzP5hZmdqziqR7FPPlEgBMrM2wBVEdy/bEz3A/Fc9wCwiUljiAYQ+T3S+ryaaxuI+d98eNDCRIqFkSiRFpaWllbt37+4aavklJSVVu3bt6pb43sxOJXr+6WtEk0G+QnRRnezuNUGCFBGRrIh7pc4mSqrOBO4lupHW2d1n7v+3uXb9EslnSqZEUmRmQcdqMDPc3eKvfwH8CNgM3A3c5u4rgwUnIiLBmFkf4Fqi52SPBP7k7tfv9/ucuX6J5Ds9MyVSGIYSPRN1vbt/T4mUiEjxcve17n4DUaXCEmBgXX/32GOP8dprrwEwceLEA343adIkHn/8cfbt+89c7XUlYO+//z7jx49nzpw5tV+/+uqrjB8/ntdee43777+fKVOmpG3dRHKNeqZEUrT/nb1bbrmF888/nxkzZjBgwAB27tzJ4YcfzqpVqygrK+Owww7joosu4vXXX6e8vJyjjjqKxYsX86Uvfan29dyd/Z8dnjx5Mhs2bODKK6/kwQcfZMCAAYwaNWr/5evOnoiINFni+jVp0iR27tzJtm3b2Lt3Lxs3buTcc89lxYoVtG3blosuugiAVatWMXv2bIYOHcrWrVvZsmULZ555JqWlpVRUVPDuu+9SUVHBCSecwLvvvsvChQsZPnw4FRUVmBkf/vCHKSkp2X/5un5JwVDPlEgatGjRgtLSUrp168aOHTs46qijqK6upmvXrrWJFMDatWu59NJLeeedd+jaNSpXf/7553nkkUfYsWMHkyZNqr1LWF1dzZFHHgnAUUcdxbZt28KsnIiIFKR27dpRXV1d2+PUs2dPdu3axcqVK2nRogVPPvkk+/bto2vXrpSWllJTU8O+ffuorq4G4O2332b79u3MnDmTTp06UVNTw8yZMznmmGOYOXMmXbt2Zc+ePQckUiKFplXoAEQKwdFHH02fPn2YMmUKAwYMYP369bRv3569e/eye/duJk2aRFlZGb179+bxxx+ne/fubNy4EYC+ffuydevW2gtUoqSiZcuWbNiwgRUrVtCpUyfmzJkTchVFRKTAnH766Un9vE2bNrU3BfdXWloKwODBg2t/9vWvfx2AD3zgAwCMGDEiLbGK5CqV+YmkKJkHeCdPnsyYMWOa/Lskl68yCRERaTINQCGSPirzE8mgIUOGALBt2zY2bTpwiqcxY8YwefJk7r//fgAWLVrEX//6V958801+9atf1X4WERHJprVr1wJ1X7uAA65dmzdv5o9//CPTpk1j3LhxtZ9FioXK/EQyYMKECWzfvp0+ffpwxx13MGbMGMrKymjdujXTp0+nrKyM0aNHH/BcVO/evZk6dSrDhg1j6dKltZ9FRESyIZVr19y5cxk4cCCdO3emvLycs846i/Ly8sBrIpI96pkSybBTTjmF+fPnA9GIfQc/F7Vx40ZWrFjB6tWray9GvXv3rv0sIiKSbcleu7Zv386SJUto2bIlgwcPrv0sUiz0zJRIihqqOZ89ezbl5eVccsklmVy+as5FRKTJ6rt+ZePaFS9f1y8pGOqZEsmAESNG1HkxOnhSxPqMHTuWiooK7rrrLiorK7nllltqh6IVERHJhOZcu1asWMHtt9/OsmXLmDBhAkuXLuX222/PRJgiOUXPTImk0a233sqll17KuHHjGDRoEJs2baJNmza0a9eObdu20b59e5555hlWr15NaWkpp556KkOGDOGFF15g7969XHDBBSxevJi+ffvSs2dPunXrRrdu3dizZ0/oVRMRkQKVjmvXgAEDeO2115g3bx6tW7emf//+mtJDioJ6pkTSaMCAAcycOZPevXvTv39/TjvtNEaNGkVVVVXt39TU1DB48GAGDhzIwoULgWiC3kTP08aNG6moqKCqqopZs2ZRUVFBp06d1DMlIiIZkY5r14wZM6isrGTIkCFs3LiRmTNnUllZiR4nkUKnZ6ZEUtSUeTqmTp3KyJEjadeuXTqXr5pzERFpsmSvX5m4dsXL1/VLCoaSKZEUadJDERHJR7p+iaSPyvxERERERERSoGRKJM2SGfXonnvu4e233+aWW25h/vz5jB8/nvnz53PzzTezfft2gNrR/BK/37lzJ2PHjqWqqirpUQFFRESSkey1K3FtSozeB9EItAkHj0S7YMECbr75ZtasWaNrlxQkjeYn0kxPP/00LVu2ZOnSpRx//PGsXr2ahQsXMnfuXJYvX861117LEUccccDs8V26dKFHjx7s2bOH1atXc9555/H2229jZqxdu5Zhw4bVjuZXXl7Oeeedx1tvvUXfvn3p2rVr6FUWEZE8l+q1K3FtSozelxiBNuHgkWjNjIqKCrp06RJwbUUyRz1TIs20d+9eOnToQMeOHVm7di19+vRh+vTpAPTr149WrVodMns8UDtKX9++fZkyZQp9+/alVatWbNmyherqat577z1mzZpFz549mTJlCtu3b6eiokLDpIuISLOleu1KXJsSo/clRqCdNm3aAdeuxDVu69at9O3blx07doRaVZGM0gAUIikqLS2t3L17d7BuopKSkqpdu3Z1C7V8ERHJT7p+iaSPeqZEUmBmLXfv3v1noBI4390tGx9AS+AGoGr37t3XBH0TREQkL+3atatbA9eYKcBPU7xGDQHeBYY09HdKpKSQqGdKpInMrBtwP9HNiCvd/e0AMZwRx/B34Mfu/n62YxARkcJiZt8CrgROd/eUZoo3s+uALwCn6dokxUA9UyJNYGbnAHOA6cC5IRIpAHd/CTgZOB6YamZ9QsQhIiKFwcyGAWOBz6WaSMXGA+8BP05LYCI5TsmUSBLMrKWZ/QK4l+hC81N339fY/2WSu28ALgKeBGaZ2UdDxiMiIvnJzA4jur7d4O5vNee14tmAvwh8zcxOSUd8IrlMZX4ijTCzHkTldNXAVe5eGTikQ5jZacADwD+BH7r73sAhiYhInjCzXxJVOnzM09QwNLNPAr8BTnL37el4TZFcpJ4pkQaY2fnAbOB5ooEmci6RAnD3GcBJwCDgJTM7OmxEIiKSD+KbcV8EvpyuRArA3R8BZgD/m67XFMlFSqZE6mBmrczsV8BfgCvc/f8LXdbXGHd/D/gYUe/Uq2Z2adiIREQkl5lZe2AC8DV3r8rAIr4NnG9mF2XgtUVygsr8RA5iZr2ISuZ2Ej0ftT5wSE0W16k/CDwOfE9lfyIicjAzuwvA3b+cwWWcQXQ9OiF+1lekoKhnSmQ/ZnYh8DrwL+Aj+ZhIAbj7K0Rlf0cDM8ysf9iIREQkl8TVCx8Grs/kcuLRZ+8F7jAzy+SyREJQMiUCmFlrM/sdcDvwKXf/lbvXhI6rOdx9E/Bx4D7glfhhYBERKXJm1hW4Dfi8u2/LwiLHAv0ATTYvBUdlflL04jmaHgQ2E11Y3g0bUfqZ2SiidfwX8P/cfXfgkEREJIC4d+gpYJ6735DF5Q4HXgQ+4O4rs7VckUxTz5QUtXhuplnAY8DFhZhIAbj7LGAE0A2YaWbHBA5JRETC+ArQHfh5Nhfq7guBXwETzKxlNpctkknqmZKiFE9Q+GvgMuByd385cEhZEd+RvA74KfAtd38ocEgiIpIlZjYQmAmc4e6LAyy/BTAFeM7df53t5YtkgpIpKTrxHEwPAVXAF9x9Y9iIss/MTiZ6D54Hrnf3XYFDEhGRDDKzVsB04H53vzVgHL2J5m+8wN3nhIpDJF1U5idFJR696FWi54cuKcZECiC+gI0AOhINTjE4bEQiIpJhPwK2AuNCBuHu64DvAPeZWWnIWETSQT1TUhTM7HDgd0ST2l7u7q8GDiknxGV/XwVuBL7j7vcHDklERNIsHoRoInCyu1fkQDxGNJ9jlbt/O3Q8Is2hZEoKXjzH0j+AdcAX4yHDZT9mdgLRe/QS8G133xk4JBERSQMzawvMAX7i7v8IHU+CmZUB84iuy1NCxyOSKpX5SUEzs8uAV4AJwCeUSNXN3ecBI4FS4FUzGxo4JBERSY/fAa/lUiIFEJfZXwPcHSdWInlJPVNSkMysBPg/4ALgM+7+euCQ8kJcevFF4DdE81H9LXBIIiKSIjP7CNHkvCe4++bA4dTJzG4iGqr9clejVPKQkikpOPHQr/8AlgNfdvctgUPKO/Hkiv8kGqzjG+6+I3BIIiLSBGbWmaiM7kp3nxo4nHrFg1C8DvxKz+1KPlKZnxQUM7ucaA6NO4FPK5FKTTy54sj421lxciUiInkgrjK4HXgglxMpgHhqjquAP5pZn9DxiDSVeqakIMR3tm4CziZKouaGjahwmNkXgN8DPwDuVhmGiEhuM7Orge8Co9x9T+h4kmFmPwTOA85195rQ8YgkS8mU5L14jqR/AG8C17r71sAhFRwzG0b0Hr8BfM3dt4eNSERE6mJm/YDXgHPcfX7oeJJlZi2BacCj7v6H0PGIJEtlfpLXzOwqohndxwGfVSKVGe7+JjAa2APMNrPjA4ckIiIHiROSvwG/zadECsDd9wGfA35oZseFjkckWeqZkrxkZm2AW4HTiMr68uqikc/iBPaPwA3AnSr7ExHJDWb2feAjRL1S+0LHkwozuwb4DjA6X0oUpbgpmZK8s1/J2Vzg6yo5yz4zG0K0DRah0koRkeDM7ERgCjDS3dcEDidl8eAZjwJvufv3Qscj0hiV+UleiQdDmAb8Afi8Eqkw3H0J8AFgK1HZ30mBQxIRKVrx3Ir3Af+dz4kUQFzt8FXgKjM7M3Q8Io1Rz5TkBTNrC/wZGEVU1rcwcEgSi4ejvxX4KTBeZX8iItllZn8AehFNUl8Q52Azu4joeegTNM2J5DIlU5Lz4jmO/kE0OpEmkM1BmihZRCQMMzuHaNCJE9z9vdDxpJOZjQfauPvVoWMRqY/K/CRnWeRLwItEIxN9QYlUbnL3t4BTgfXAHDMb2ci/iIhIM5nZkcA9wJcKLZGK/T/gVDO7LHQgIvVRz5TkJDNrD4wHTiQq63szbESSLDP7FFFpxo3ArYVSciIikmvM7O/Ae+7+rdCxZIqZfQB4AjjZ3d8OHY/IwdQzJTnHzE4AXgd2Ew2NqkQqj7j7P4l6qT4PPBLfORURkTQysyuAk4Dvh44lk9z9VeA24O54pD+RnKJkSnJGXNZ3LfAc8At3/7K77wwdlzSdu68gmgOsnKjsb3TgkERECoaZ9QZuBq4qkuvkL4EjgetCByJyMJX5SU4wsyOAO4ChRGV9SwOHJGliZh8Hbgd+A/xRZX8iIqkzsxbAZOBFd/9l6HiyxcwGATOAD8XTc4jkBPVMSXDxHEWzgU3AKUqkCou7PwaMBj4DPGFmZYFDEhHJZ/8FtAF+GzqQbHL3ZcBPgHvNrHXoeEQSlExJMHFZ3zeAZ4Efu/vX3X1X6Lgk/dx9NfAh4C1grpmdGjYiEZH8Y2bHAjcAn3P36tDxBHA70aixY0MHIpKgMj8Jwsw6AHcBA4jK+pYHDkmyxMw+SrTt/xf4P3evCRySiEjOM7PDgFeBce5+V+h4QjGzbsAbwMfd/eXA4YioZ0qyL56DaA5QBXxQiVRxcfengFHAx4GnzKxz4JBERPLBz4G1wF9CBxKSu1cCXycq92sXOh4RJVOSNXFZ37eBZ4Dvu/s33X136Lgk+9x9LXAmsJBotL/TA4ckIpKzzOxDwNXAVzSIT+2zuC8Bfwgdi4jK/CQr4rmG7gZ6AZ9x95WBQ5IcYWYXEu0bNwO/VdmfiMh/xKPdzgP+K+7ZF2rflzeA77j7k4HDkSKmZEoyLp69/EGiGcy/7+57AockOcbMegEPADuJHqxeHzgkEZGcYGZ3A9Xu/tXQseSauKrhn8AJum5IKCrzk4yJy/q+CzwJXO/u31EiJXVx93Lgw0RD5M8xs7OCBiQikgPM7BPAGcB/h44lF7n7dOCvwF1mZoHDkSKlninJCDPrRHSC6wJcHg+NLdIoMzufaN8ZD/zS3feFjUhEJPs0al1yNMqhhKaeKUk7M/sg0Wh9S4EzlEhJU7j7s8AI4Gzg2bhBISJSNOJelruBO5RINczd9wJXAb82s2NCxyPFR8mUpI2ZtTCz7wOPAd909/8Xn+REmsTd3wbOBWYCs83snMAhiYhk07VElR3/X+hA8oG7LwJuBCaYWavQ8UhxUZmfpIWZdQH+BnQAroiHvhZpNjM7F5gA3An8QmV/IlLIzGwQMAM43d2Xho4nX5hZC2AyMNXdbwwdjxQP9UxJs8XzX8wBFgBnKZGSdHL354CTgdOB58ysR+CQREQywsxaA/cBP1Mi1TTxtBpfAP7LzEYGDkeKiJIpSVlc1ncD0bCk17r79939/dBxSeGJZ7wfA7xIVPY3JnBIIiKZcAOwEfhz6EDyUTwy7H8B95lZm9DxSHFQmZ+kxMyOIrp7VkpU1lceOCQpEvGw6fcRlZX+1N2rgwYkIpIG8ZyMTwAnx8+NSorM7H5go7t/K3QsUvjUMyVNFjdm5wCzgA8rkZJscvepRGV/o4AX4gl/RUTylpm1Be4FvqFEKi2+CVwST7UhklFKpiRpZtbSzMYCDwBfdPcb1CsgIcQz3V8ATAJeN7OPBA5JRKQ5/hd42d0fCR1IIXD3TUTPT/0lnvdSJGNU5idJief6uZ8oAb9Sd84kV5jZGUT75t+BH+u5PRHJJ2Z2ETAOOMHdt4SOp5CY2R+A3sCnXQ1eyRD1TEmj4jl+5gDTgXOVSEkucfeXiMr+jgemmlmfwCGJiCQlnlbkTuDzSqQy4kfAUKJJfUUyQj1TUi8zawn8FPgy8Dl3fz5wSCL1iucY+R/gv4Evu/tTgUMSEamXmRnwKLDM3b8fOp5CZWYnAM8BI919Teh4pPAomZI6xXP5/B2oBq6Kh6YWyXlm9kGi5/oeBn7o7nsDhyQicggzuwb4NvABd98TOp5CZmbfAy4CztbE75JuKvOTQ8Sj38wGngfOVyIl+cTdZxKV/Q0EXjKzo8NGJCJyIDPrD/yO6GalEqnM+z/AiCoXRNJKPVNSy8xaAb8APk80yMS0wCGJpCwuobke+D7RpNKPh41IRKS2hH4a8Ki7/yF0PMUivrE2i+jZ73mBw5EComRKAIjn6nkA2En0fNT6wCGJpEU8EeaDRJNhfk9lfyISkpn9EDiPqFFfEzqeYmJmVwP/Dxjl7rtDxyOFQWV+gpldCLwOPAN8RImUFBJ3f5Wo7K8vMCMurxERyTozO4mox/xqJVJBTACWAjeGDkQKh5KpImZmrc3sd8BtwGXu/mud3KUQxRM4fgK4F3jFzD4ZOCQRKTJmVko0J9533H1d6HiKUTzX1NeAK8zsw6HjkcKgMr8iFc/F8yCwiegO2buBQxLJCjMbRbTv/wv4fyr1EJFsMLObgG7AFZpANiwzuwC4nWii5M2Bw5E8p56pImRmHyN6CPMx4KNKpKSYuPssYARRo2ammQ0MHJKIFDgzOw/4JHCdEqnw3H0SMBH4U+hYJP8pmSoiZnaYmf0BuBW41N1/r7I+KUbxnchPAX8hSqguDxuRiBQqMysD7gaucfeNoeORWv8DjDSzz4QORPKbyvyKhJn1IyptqgK+oBO6SMTMTgYeIppX7Xp33xU4JBEpEPEUDQ8Ale7+ncDhyEHisu+JwMnuXhE6HslP6pkqAmb2ceBVomTqEiVSIv/h7nOIyv46Eg1OMThsRCJSCMysBLgSOA74YeBwpA5x2fefgHvMrF3oeCQ/qWeqQMUT8A4FvgJ8FLg8HiJaROoQ30H+KtGQud8B3nD3RUGDEpG8ZWb3Ej0ndam7Tw4dj9Qtbi8tAnoBHdy9OnBIkmfUM1W4fgi8Agwk6r5WIiXSAI/cDpwL/BKYZ2bDA4clIvnrXGA7oGHQc9s+YAHQhqjNJNIkSqYK12eIno/6QzzHjogkwd3nET0svpWop0pEJBUfBnq4++LQgUj94htplwHDgSWh45H8ozI/ERERERGRFKhnSkREREREJAWtQgdQyEpLSyt3797dNdvLLSkpqdq1a1e3bC9XpBCFOo5Bx7JINmTzGNcxnRvUPpN0UplfBplZkInOzQx3t6wvWKQAhTqO42XrWBbJsGwe4zqmc4PaZ5JOKvPLgscee4zXXnsNgIkTJx7wu0mTJvH444+zb9++2p8dfIBv2bKFm266CYDVq1dz2223UVVVxS233MLKlSu57bbbWLVqVWZXQqTI/elPf+LRRx8FYO3atbU/T3w9e/bs2uP74OP84GN6+vTp3HnnnQd8nfi8YMEC7r333oyth4g07Ac/+AEvvfQSTz31FJs2/Wf8proa3z//+c8P+XrSpEncd9993HXXXbz11luZD1hSNnHiRB577DEmTpzInDlzAHjllVfYsWMHAC+99BLV1f8ZKf3gfWDbtm3cd999rF69mp07d3LnnXcyY8YMfvGLX7Blyxbuv/9+pkyZkr0VkiBU5pcFpaWllJeXs3jxYvbu3ctvf/tbzj33XFasWEHbtm259NJLAVi1ahWzZ89m6NChbN26lS1btnDmmWfSoUMHjjnmmNrXOvzww9m9ezfr1q2jZcuWtG/fniVLltCvX7+AaylS+Hr16sWNN97IiSeeSJ8+fQCYP38+ffr0YcSIEUycOJGHHnqI9evXA9FF+e233+a0005j7ty5lJaWcuaZZ7J161b69evH3r17a7/euHEj/fr1Y/DgwSxcuDDkaooUtR49erBy5Uq2b99Ov379KCkp4YknnqBbt250796dVatWcdxxx9GzZ09GjBhR+3+Jr7t06cKwYcO4/fbbGT16dKjVkCS1bt2aiy++mCeeeIInn3yS0aNHM2/ePF566SUuueQS1q1bR/v27Xnuuefo378/hx12GJWVlYwePZqysjK6d+9O27ZtKSkpoUOHDqxZs4bOnTuzc+dOysrK+NCHPhR6FSXD1DOVBe3ataO6urr2jkbPnj3ZtWsXK1eupEWLFjz55JPs27ePrl27UlpaSk1NDfv27au9G7Jr1y5mzZpFRUUFu3fvxsxYs2YNQ4YMYcuWLezevZvjjz8+5CqKFLyjjz6aE088kbZt2x5wtzph2bJlLF++/ICf9enTh5YtW1JdXU11dXVtD3T79u1ZuXJl7UV65cqVHHnkkaxcuZInnngiK+sjInXr378/I0eOrP2+pKSE7t274+7U1NQccD1ftGgRa9euZcWKFbVfr1u3jj59+jBkyBBVjeQ4M+P9999n4sSJdOzYkfbt29OpUyc2bdrE0KFD2bBhA71796ZDhw6UlZXh7rXtM3dn8+bNPPvsswBUVFRQU1NDv3796NatG2vXrmXPnj2UlJQEXkvJND0zlUGqyRXJf/Udx3PmzKGmpoYePXqwbNkyRo4cSbt27dK9bB3LIhmmZ6aKj5l5RUUFc+bM4cQTT6RXr17ZWq62fwFSz1Qgiecstm3bVudd7smTJ3P//fcD/3kW48033+RXv/oV06ZNY9y4cVmNV0QOdPLJJzNy5Eiqq6s566yzcPdGj+XNmzfzxz/+sfbzokWL+Otf/5rlyEUkWU25VieO58S1WnJbjx49uPjiiw9JpJqyzRPn8sQ2nzx5Ms8//3zmg5ecomemsmzChAls376dPn36cMcddzBmzBjKyspo3bo106dPp6ysjNGjR1NdXc2RRx4JUPssxrBhw1i6dCmdO3emvLw88JqIFLdUjuW5c+cycODA2s+9e/dm6tSpYVdERA6RyvGdOJ4T12rJL805pye2+cqVK+ndu3fgNZFsU89UQKeccgrz588HohFi9n+momXLlmzcuJEVK1bUPotRXl5O7969admyJYMHDw4ZuojsJ9ljefv27SxZsqT28+rVq+ncuXPI0EWkEcke34njOXGtlvzV1HN6YpsPHjyY1atXB4xcQtAzUxlUVx327NmzKS8v55JLLsnkclWTK5Im9T1PoWNZpDBk81qtYzo3qH0m6aRkKoM0AIVI/tOkvSKFTQNQFB+1zySdVOaXIw6e5LMuFRUV3HXXXQCMHTv2gO9FJDckcyyvWLGC22+/naqqKsaOHcu8efO4/fbbsxCdiDRHMsf3/Pnzufnmm1mwYAE333xz7QSwkp+a0j5btmwZEyZMoLq6mrFjx2YhOskFGoAiS2699VYuvfRSxo0bx6BBg9i0aRNt2rShXbt2bNu2jfbt2/PMM8+wevVqSktLOfXUUxkyZAgvvPACe/fu5YILLqBnz55069aNxYsX07dv39rvRSR70nEsDxgwgNdee42uXbty0kknccIJJ/Dmm2+GXjWRopeO47tz586JHggqKiowU0dELktn+2zevHm0bt26tp0mxUE9U1kyYMAAZs6cSe/evenfvz+nnXYao0aNoqqqqvZvampqGDx4MAMHDmThwoUAtZN9Arz33nvMmjWLjRs3UlFRQVVVFbNmzaKmpibIOokUo3QcyzNmzKCyshJ3x8wO+F5EwknH8d26dWtatWrFtm3b6Nu3r3qmclw622eDBw9m48aNte20PXv2BFknyS49M5VBydTkTp06Ne2TfaomVyR9kq2t17Eskp+yea3WMZ0b1D6TdFIylUF6wFEk/2kACpHCpgEoio/aZ5JOKvPLsmQeZLznnntqH2Y8+EH17du3A4cORlFVVZXUa4tIejT1WE48jL5o0aIDBptIPKw+f/58xo8fz5o1a3QsiwSW7PGduEYDBww4sP81W9fo/NDUc3rinJ34vHPnToBDBqHQti98GoAiw55++mlatmzJ0qVLOf7441m9ejULFy5k7ty5LF++nGuvvZYjjjjigNm1u3TpUvsw48EPqie6mw8ejKJr166B11SksDX3WDYzKioq6NevX+1kkEDtw+rTp09nzJgxqrEXCSDV4ztxjT54wIHENXvdunW6Rueo5p7Ty8vLOe+883jmmWe48MILKS8vZ9CgQYcMQqFtX/jUM5Vhe/fupUOHDnTs2JG1a9fSp08fpk+fDkC/fv1o1arVIbNrw38eZjz4QfVXXnmF6urqQwajUANMJLOaeyzv3buXvn37MmvWLCorK3n55Zeprq6ufVj9tNNOY8qUKfTq1SvUKooUrVSP78Q1OnEtnjZtWu2gBGama3QOa+45vWfPnkyZMoUzzzyTKVOm8O6771JdXc3QoUM1CEWR0TNTGZRqTe5zzz3H2WefTYsWyee6VVVVVFZWcsIJJ6gmVySNmlNbr2NZJPdl41qdOK5PPPFEHdM5QO0zSSeV+WVQSUlJlZllvX+3pKSkqvG/EpFklJSUbDKzIwMte32I5YoUk2xeq3V9zg1qn0k6qWcqh5jZn4AV7v7H+PvfAtvc/cawkYkUHzNrCfwY+BrweXefksVltwFuBU4DPuXuC7K1bBGpm5mVAJuAju6+Jz5HbAW6u/vWsNFJJpjZv4C/ufuD9fz+b8Acd785u5FJLtEzU7llOLB/o2kBcFygWESKVnzHchLwYWBENhMpAHff6e5fAn4FvGBmXzQzlYaIhDUEWOnuewDcfR/wJtG1WwqMmXUBTgWeauDPHgCuyE5EkquUTOWIuKF0HAcmUwvRSVokq8zsLGAO8Cpwrru/HSoWd58AnAl8F/irmbUNFYuIHHKNBl2nC9mngGfcfUcDf/M80N/M+mcpJslBSqZyR3egBtj/GYnFRAfp4WFCEikeZtbCzH5MdKfxi+7+Y3evDh2Xu78JjI6/nWVmx4aMR6SIDSdKnvanCpLCdQXw94b+wN3fB/6JeqeKmpKp3DEcWLD/8DJxKcEqotICEckQMzuKqKxvDDDS3Z8NHNIB3H2Hu18N/A6YamZfCBySSDGqq2dKyVQBMrM+wFBgchJ/rlK/IqdkKnfUdZIGlRCIZJSZnUFU1vc6cLa7VwQOqV7u/lei57i+b2b3xANViEh2HPxcM8TXaD3TWHAuBx51971J/O1M4AgzU1JdpJRM5Y7jOLR8AHTXSyQj4rK+HwL/AL7i7j/KhbK+xrj7QmAU0dQWr5nZsMAhiRQ8M+sIlAGrD/pVZfy5WzbjkYy7gqjHqVHuXoN6p4qakqncUdcdL+KfqWdKJI3iUZqeAS4iKuv7V+CQmsTdtwOfB/4ITDOzzwUOSaTQDQcWxQ3nWnFpvq7TBcTMhgBHAS814d8eAK5QD2VxUjKVA+K5KoYCi+r49ULUMyWSNmZ2OlFZ3xvAh929PGxEqfHIX4BzgB+b2V9U9ieSMXUNPpGg63RhuQJ4KB76PlnzgN3AKZkJSXKZkqnc0B9Y7+7b6vjdSqCzmXXIckwiBSUu6/s+8AjwNXf/QTwSU15z9/nASKAEeDW+qyoi6VXfc82gcvyCEfcsfZYkS/wS4h7Kv6NSv6KkZCo31HuSjksK3gQ0HLJIisysE9HEi5cAo9z96cAhpVV8I+Yq4Fbg32b22cAhiRSahpIpDRRVOEYARjQgUVM9AHzazFqlNyTJdUqmckN9g08k6K6XSIrM7IPAXKKbEme6+9rAIWVEXPZ3B3Au8DMzu8PMSkPHJZLv4t6Kxsr8hsUl+5LfrgAe2H+ammS5+3JgHdGIq1JElEzlhvoGn0jQw60iTRSX9f0P8BjwDXf/n0Io62uMu88jKvs7AnjFzAYFDkkk3/UAqt29qq5fuvtWYAPQL6tRSVrFyfDlNDJRbyM0ql8RUjKVGxoqHwA93CrSJGZWBjwBfBIY7e5PBQ4pq+LG3RXAeGCGmV0eOCSRfNbYDU/QdboQfAjY4O6Lm/EaDwGXmtnhaYpJ8oCSqcDiMpy+wLIG/mwBcJyG3BRpnJmdQlTWtww4w93XBA4piLjs7zZgDHCjmY03s5LQcYnkocZK8UHl+IUg6bml6hNP+j4P+EhaIpK8oGQqvCHA8kZm2a4CatCkgCL1ssh/A08C/+Xu301y9vqC5u5zgZOBTsDLZjYwcEgi+aax6hFQOX5eM7PDiCoZHkzDy6nUr8gomQqv0ZN0/CCkSghE6mFmRwKPE9W7f8DdnwgbUW6Jy/4+A9wFzDSzTwcOSSSfqMyv8J0HLElTJcMjwAVm1j4NryV5QMlUeMmUD4BKCETqZGajiSbhXQWc7u6rAoeUk+Kyv3HABcCvzWycyv5EGhYPSjCUaDTQhiwBjtYxlbeaPLdUfdz9PeAloqk4pAgomQovmTteoBICkQPEZX3fASYC/+3u31FZX+PcfTZR2V9XosEpBgQOSSSXDQAq47nc6hWfe1YQle5LHjGzNsBFwD/T+LIq9SsiSqbCS6YWG1RCIFIrLut7lGii2lPc/bHAIeUVd98CfAr4K9FzVJeFjUgkZyV7jQZdp/PVR4FX3X19Gl/zSeA0M+ucxteUHKVkKqC4QXgEkMwkoguBoZoUUIqdmY0CZgPlwGnuvjJwSHkpLvu7leiO7O/M7FYN5ytyiGRL8UEVJPnqCpo3t9Qh3H07MAnQjaoioGQqrOHAInevaewP4xKD9UD/jEclkoPisr5vAU8D33P3b7n7ntBx5Tt3n0VU9tcTmG5mOseI/EeypfigZ5vzTnxT+8NEk7un299RqV9RUDIVVlPueIFKCKRImVkHonr2LwCnuvvDYSMqLO6+mWhY4PuAV8zs42EjEskZTblO6xqdfz4BPBePeJpuzwLDzaxXBl5bcoiSqbCacscLVEIgRcjMRhCN1ldFVNa3InBIBSku+7sZuBj4g5ndFM+9IlKUzKwU6AMsTfJfVgNHmlnHTMUkadfsiXrrE1dOPEY0LYUUMCVTYTXlwVbQXS8pInFZ3zeI6s5/5O7fcPfdoeMqdO7+GjAC6EdU9nd02IhEghkKvOXu7yfzx3HJ/iJ00zMvmFl3onPd0xlcjEb1KwJKpgIxM6PpZX7qmZKiEJf1PQR8Gfiguz8UOKSi4u4bgUuBB4HXzEzzpUgxauo1GnSdziefBp50910ZXMZUoIeZDcrgMiQwJVPh9AR2u/uGJvzPUjQpoBQ4MzsJeB14j+j5qLcCh1SU4rK/PwAfA242sz+o7E+KTFNL8UGDUOSTjJX4Jbj7PuAfqHeqoCmZCqepJX77Two4NCMRiQQUl/V9DZgMjHX3r6usLzx3f4VotL+BwEtm1jdwSCLZ0uTrNCrHzwvxqKX9geezsLgHgCviiiQpQEqmwkmlfABUQiAFyMyOIBpG9uvA6e6e0buF0jRx2d8lwCNEZX8fDRySSDakXOanhnPOuxx4ONnn4ZrpNaA1cFIWliUBKJkKJ5XyAVAJgRQYMzuBqKxvG3CKuyc7cpZkkbvXuPvvgY8D48zs92bWOnRcIplgZmVAe2BNU/7P3dcD7wM9MhGXpM1nSfNEvfVxd0cDURQ0JVPhpFI+ACohkAIRl/V9FXgO+Lm7fzXDDwJLGrj7TKI7rMOAaWbWO3BIIpkwHFgYN4SbStfpHGZmxwFHADOzuNgHgMvNTO3uAqSNGoCZtQIGA2+m8O8q85O8Z2btiSaI/RbwIXe/P3BI0gTu/h7wUeAJYJaZXRQ4JJF0G05qpfig63SuuwJ4MB7KPivcfRGwCTgtW8uU7FEyFcYxwNvuviOF/10DdDSzI9Mck0hWmNnxRGV9u4APuPuSwCFJCuKyv98ClwG3mdlvVfYnBSTV6hFQOX7Oip9ly/gofvVQqV+BUjIVRqqDT+w/KeCxaY1IJMPisr4vE42e9Et3/7K77wwdlzSPu08nGu3veOBFM+sVOCSRdEj5Oh3/n3qmctMpwG7gjQDLfhD4lG46FR4lU2GkOvhEgu56SV4xs3bABOA7wBnuPiFsRJJO8Xx5FwFPA6+b2UcChySSsrj3ojnX6UXAUDNrmb6oJE2uAB5I8Vm4ZnH3VcBbwLnZXrZklpKpMJpTPgB6uFXyiJkNB2YB1URlfYsDhyQZEJf9/Rr4NHCnmf0qfj5UJN/0Ana5+7up/LO7bwcqiUr6JUfE56NPE6bEL0GlfgVIyVQYzXmwFfRwq+QJM7sGeBH4rbtfk+JzgpJH3P0lorK/EcALZtYzcEgiTdWcEr8EXadzz1lAubu/FTCGfwAfNbPSgDFImimZyjIzawP0JurqTdUC4DhNCii5yszamtlfgf8BznL3v4aNSLIpnmvnI8CzRGV/5wcOSaQpmluKDyrHz0VXkKW5perj7lVEAzBdHDIOSS8lU9k3DFjWnFm34+cT9gK64ys5x8yGEc34bsCoeEhYKTJx2d8viRowd5vZjSr7kzyRjp4pDUKRQ8zscKIJxx8KHQtRQqdSvwKiZCr70nHHC1RCIDnIzK4GXgL+D/iCyvrE3acSlf2dAjxnZj3CRiTSqOY+1wzqmco1HwHmu3tF6ECAx4BzzKxD6EAkPZRMZV86TtKgQSgkh5hZGzO7G/gB8GF3vzvEaEmSm+LSlvOBF4DZZnZe4JBE6hT3ng4G3mzmSy0D+ujZmJwRam6pQ7j7ZqJz4ccDhyJpomQq+5o7+ESC7npJTjCzoURlfYcRlfWl42aBFBh33+fuvwCuBP5qZr/Q0NGSgwYCFc3tVY9L+d8ChqYlKkmZmbUHLgAeDh3Lfh4APhs6CEkPJVPZl66eKZX5SXBmdhVRWd9NwOfiIYFF6uXuLxCN9Hc6MMXMugcOSWR/6SrFB930zBWXAP929/dCB7KficAoM+saOhBpPiVTWWRmnYC2wLo0vNybwBA90C0hmFmpmd0F/AQ4x93vUlmfJMvdK4HzgH8Tlf2dEzgkkYR0DD6RoEEockPOlPgluPtOooTqU6FjkeZTMpVdw4GF6Wh0xj0A7wADmh2VSBOY2WDgVaANMNLd5wcOSfJQXPb3U+DzwL1m9lOV/UkOSFf1CKhnKrj4JvbpwBOhY6mDJvAtEEqmsiudJ2nQIBSSZWb2WWA68CfgSnffFjgkyXPu/hxR2d9ZwLMqe5HA0lnmp2t0eJcB/8rREvQpwCAzOzp0INI8SqayK53lA6C7XpIlcVnfHcDPgfPc/Q6V9Um6uPs7RGV/LwNzzOzDgUOSImRmbYnmb1yeppdcA7Q3s7I0vZ40Xc6V+CXEg5Q8AlweOhZpHiVT2ZXOO16gQSgkC8xsEFEj9whghLu/ETYiKUTuXu3uPwGuAf5uZj9R2Z9k2TBgWdzIbbb4hpOemwrEzHoR3XCeFDqWBmgC3wKgZCpLzMxI37DoCSohkIwys88AM4DbgSvcfWvgkKTAuftkorK/c4F/mdlRgUOS4pHuazQomQrpM8Dj7r4ndCANmA6UmdmxoQOR1CmZyp7ewI40D825DOhtZm3S+JoimFmJmY0HfgmMcffxKuuTbHH3t4FzgFlEZX9nBg5JikO6n2sGleOHdAVRz0/Ocvca4CHUO5XXlExlT9pP0poUUDLBzI4hKuvrTFTWNzdwSFKE4rK/G4AvAw+Z2Q1mpmuWZFImkilVkAQQl6f3BKYGDiUZDwBXxBVMkod0YcqedA8+kaC7XpI2ZvYpokTqLuDT7r4lcEhS5Nx9EjASuAB4xsy6BA5JClcmyvwWAMPVUM66K4B/uPu+0IEkYQ5QDYwKHYikRslU9qR78IkEJVPSbGZ2uJn9CfgNcIG7j1NZn+QKdy8HPgzMJSr7+1DgkKTAmFlnornz1qXzdd39XWAX0Cudryv1ixPXnB3F72DxtVZzTuUxJVPZk6meKT3cKs1iZgOAmUB3orK+2YFDEjlEXPb3Q+Ba4J9m9gOV/UkaDQcWZugmkq7T2XUicBjR5PL54gHgMxrBND/pQpQFZtYaGAS8mYGXV8+UpMzMPklU1vdX4DJ33xw0IJFGuPszROUwHwUmxj0KIs2VieelEnSdzq4rgAfyqbrC3ZcClYAG28lDSqayYyCwzt13ZuC11wLtzKxTBl5bClRc1ncL8HvgIne/NZ8uPFLc3H0dcBbRHf85ZnZa2IikAGQymdIgFFkS91bnTYnfQVTql6eUTGVHpkr8NCmgNJmZ9Sea26I3UVnfrMAhiTSZu7/v7t8DrgMeNbPvqexPmiETg08kLEDX6Gw5Ddjs7pnalpn0IPAJMzs8dCDSNLrwZEemBp9IUAmBJMXMPg68AtwPfMLdNwUOSaRZ3H0iUdnfx4En1UsvTRUPWJDJ6/SbwGAza5Wh15f/yPm5peoT97gvAs4PHYs0jZKp7Mhk+QCoZ0oaYWaHmdlNwB+Aj7r7TSrrk0Lh7muJnjVYQlT2d2rgkCS/9AW2ufvGTLy4u+8A3iYq+ZcMiZ9Pv4yohydfqdQvDymZyo5Mlg+AeqakAWZ2NPBvoB9RWV8+jXAkkhR33+vu/w/4FvC4mX1Xc/tIkjJ9jQaV+mXDOcAKd18VOpBmeBj4iJm1Cx2IJE/JVIaZWVugB7A8g4tZiCYFlDqY2SVEw8M+BFyaqTuvIrnC3Z8EPgB8GnjCzMoChyS5L9PVI6BBKLLhs+RpiV+Cu28gmqrkY6FjkeQpmcq8Y4Gl7l6dqQXEkwLuJBpQQCRR1vcH4BaiJOoPKuuTYuHuq4EPEd3EmmNmp4SNSHJcxgaJ2o96pjLIzEqJpkv4R+hY0kClfnlGyVTmZXrwiQSV+gkAZtYXeImoPv8kd385cEgiWReX/f038B2igSmuV++91CMb12ldozPrIuB1d68KHUgaPA6coV71/KFkKvOyUT4AGoRCADP7KPAa8Ahwicr6pNi5++NEZX+fBR4zsyPDRiS5JB60YCDRiHuZtBzoGZf+S/rl69xSh3D3bcBk4JOhY5HkKJnKIDP7EPB5ogt5pumuVxEzs9Zm9ntgHPBxd/+9u9eEjkskF8QPpJ8OrCEq+xsdOCTJHY8Be4GRmVyIu78PLAOGZnI5xSi+9l1ENJpnoVgK/MzMzgsdiDROyVRmLQbKiHoKMk0PtxYhM2tpZncD04BhwMnuPjNwWCI5x933uPu3ge8CE83sDjO7MHRcEtwOoDWZ75kCXaczZTjQkmhbFooqosHL1JOeB0zPpGeWmV0APJvph//N7IPAVOBad78nk8uS3GFmNwA3AlOAC9QbJdK4uGpgMlANHKHBWYqXmXUFerv761lY1t+BPkQTpq/P9PKKhZmdAZS6+7OhY0knM/sOcI+7bwkdizRMs3FnmLtPytKijiC6u3ZslpYnuWEJ8DvgL0qkRJI2E7iKaPhhA5RMFal4wIJsDVowCDgZ2J2l5RUFd38pdAyZ4O43hY5BkqOeqQJiZrcBt7r7otCxiIiIyH+Y2QDgR+7+pdCxiEj6KJlqQGlpaeXu3bu7hlh2SUlJ1a5du7qFWLYkL+Q+AtpPRNIl28eyjt30CHEO1rZLj2I+5op53QuRkqkGmFmwUnozw901J0qOC7mPxMvXfiKSBtk+lnXspkeIc7C2XXoU8zFXzOteiDSan4iIyEEee+wxXnstGoh14sSJ3Hvvvdx9990H/M3BjaHVq1dz22238f7771NZWcm4ceNYs2YNN910U7bCLloTJ07kscceY+LEicyZMweAV155hR07ogHeXnrpJaqrq2v//uBtt23bNu677z5Wr17Nzp07ufPOO5k2bRr3338/TzzxBHfeeSczZszI3goVqYkTJzJx4kT+8pe/cO+997JlS/1jLxy8DSsrK7nllluorq5m6tSpPPnkk5kON+1+8IMf8NJLL/HUU0+xadOm2p/XlXj9/Oc/P+Trf/zjHyxcuDDzgcoBlEw14k9/+hOPPvooAGvXrq39eeLr2bNnM3HiRIDazwkH7/zTp0/nzjvvPODrffv28fOf/5yXX36Zxx57rN44SktLK83MM/VRWlpa2fx3q3ilcz8BmD9/PuPHj2fnzp21X1dUVHDXXXexbNkyJkyYcEDDQETSq7S0lPLycv72t7/xzjvv0LZtWzp37kx1dTWrVq3i4Ycf5s033+Tll19m0qRJ7Nq1i9LSUg4//HD27t3LihUruPzyy1m6dCnHHHNM6NUpCq1bt+biiy9m3bp1/OxnP2Pjxo3MmzeP3/zmN3Tp0oV169bx7rvv8uCDDzJr1izeeOMNJk2axMaNG2nfvj3du3enbdu2lJSU0KFDByoqKigrK+O8886jQ4cOrFmzpt5l6xrdPDfddBOPPvooixcvZvny5XTt2pWLLrqI8ePHM27cOCZMmFDbflqwYAGPPPIIq1ev5oUXXmDSpGicr27durFnzx4AOnXqxJ49e+q8vuayHj16sHLlStasWUNFRQW7du3iwQcfZNq0aSxdupRJkyZRUVEBwIgRI2r/L/H1zp07g8Rd7JRMJaFXr17ceOONzJ8/v/Znia8TO/BDDz3EqlWrgOhu2KOPPsr69euZNGkS06ZNA2Dr1q3069ePvXv31n49Y8YMjjvuOJYtW9Zg43j37t1d3Z1MfYR87qdQpGs/ASgvL+e8886jvLy89usdO3bQrVs35s2bR+vWrfPuIiGST9q1a0d1dXXtcbZjxw7effddWrVqRdeuXSktLaWmpoZ9+/bVnrtbt26NmfHuu+/Sq1cvHnzwQQYPHsysWbNqe0gkM8yM999/n4kTJ9KxY0fat29Pp06d2LRpE0OHDmXDhg307t2bDh06UFZWhrvXbjt3Z/PmzTz7bDSydkVFBTU1NQwdOpQ9e/bQpk2b2u/ro2t08xxzzDEcdthhDB06lGOOOYaqqiqefvppTj31VFq2bElZWRndu3cHomtt69atqampobq6uvb4q6iooFOnTrz11lt07NiR9evXs3t3fg2c2L9/f0aO/M/80SUlJXTv3h13r13fxDlp0aJFrF27lhUrVtR+vWXLFtav16j72aZnphpgZv7UU08xZswYxo0bR+fOnfnc5z4HRL0LF198McuWLeOZZ56he/furF+/nm9961u8/fbbzJo1i5EjRzJ37lzatGnD2Wefzb///W8WL17MOeecw9tvv83ixYvp378/L7/8MmPGjGHGjBl85zvfoUWLFofUtx5cX7t27Vr69OnDtm3bqK6u5sgjD5zXbfLkyWzYsIErr7ySzZs3c88993DNNdcc8Pn666/ff11VT5uCxHaZOHFi2vaTBQsWUFNTw8yZMznjjDOorq5m5syZfOxjH+OOO+7gk5/8JP/+97/5zGc+Q5cuXbTdRNJAzzDkJzPziooK5syZw4knnkivXr2yscxDtl1D+09Trtf7f93YMvNdMR9zxbzuhUjJVAPq2tnnzJlDTU0NPXr0YNmyZYwcOZJ27dplYtl1JlMTJkxg+/bt9OnTh1deeYUxY8ZQVlbG0UcfzfTp0ykrK2P06NE888wzAFx44YW8+OKL7Nixg7Zt2x7w+eKLL653eZKc+k6IofYTEUmNGjf5KVcGoKgrjlSu1/t/3dgy810xH3PFvO6FSJP2NtHJJ59c+3V1dTXt2rVL6m7T7Nmzeeedd9i+fTsDBgxg586dLFy4kG984xspx3LKKacwf/58zjrrLNyd6upq9u3bB0DLli3ZsGEDK1asYPv27SxdupTBgwcf8Hn/ZErSK9X9ZNGiRcyaNYujjz6aJUuWMGTIEJYsWcLXvva1bK+CiMRSqQQ46aSTdAwH1pTtljj3jho1ilmzZlFSUsKAAQMYNWpUWmJJ9nqd+LrYpXLMnX/++Tz++ONcd911h1Tf5JNU9tuhQ4eybt06hg4dyqxZs/jCF74QJvgipWQqBfvfbbrjjjtq7za1bt36gLtN+x8EI0aMYOLEiRx11FFs27aNrl27Ul5e3uRlH3vssZSXl9eZCO3/s/PPP7/26wEDBvDRj34U4JDPkjmp7Ce9e/dm6tSp7Ny5k61bt9Z+FpHsS+UYnjt3LgMHDtQxHFBzzr2Jz4nGbHOker0uZs055oYNG8bSpUtrv883zdlvR44cydq1a2u/l+zSABTNlLjbBNR5t2njxo2sWLGCZcuWsXz5cjp16sSaNWto2bIlgwcPbvLyRowYwSWXXFLv7w8eKa4u+w8fKtmR7H6yevVqOnfuzOGHH37Ah4iElewxvH37dpYsWaJjOEc09dyb+Jy4VjdHfdfrZK7TAGPHjm3W8vNdU4+58vJyevfuXft9PmvqfnvbbbdRVlZW+71kl56ZakB9Na2zZ8+mvLy8waQmDcuu85mpW2+9lUsvvZRx48YxaNAgNm3aRJs2bWrLyBIjGK1evZrS0lJOPfVUhgwZwgsvvMDevXu54IILAPj973/P9ddfT6tWrepcniSnobrnEPuJiKSmrmM5k8ewjt30yPZ2i5fZ4DNT6bhOL168mJkzZ/KlL32p3mXmu2I+5op53QuRyvxSMGLEiAPG98+mAQMGMHPmTHr37k3//v0pKSmhVatWTJ06lTZt2gBQU1PD4MGDOfzww1m4cCFDhgypc/jQ6urq2mRK0i/kfiIizadjOD+F3m7puE5v3LiRiooK9uzZU1Q9m6G3XUjFvO55L5PzIuT7R/T2JOepp55q9G/Ky8v9zjvv9Hfeecdvvvlmnz9/vt90002+ffv2Q/42XnbSsbz44ou+bdu2pONtbHn6SP8+kpDMvrJ8+XK/7bbbDtln3n///QP+TttNH/pIz0e6z/cvvviiP/HEEz5v3jy/6aabDjk/69jN/nZzT27b7b/NfvKTnxzy+7q2XTJxNOc6XYj7Sya2XWVlpf/kJz/xp556yp944okDfpdL72Em1j3RpnzjjTcOOefk0roX4oe6JZKUTLf9M88802C3fc+ePenWrVvtLN1mRkVFBWbN73k966yzmr+Skhbp2FcGDBjAa6+9dsg+IyKZl45juFOnTmzYsIFOnTphZqxdu5Zhw4aFXrWCl45t17lzZ8yMNWvW0Ldv37TFput0w9Kx7bp27cpJJ51E3759WbJkCe6eljZWpqVj3RNtyi5duuick2VKppKUjm779957j1mzZnHSSSfRqVMntm7dSt++fdmxY0fta0j+S8e+MmPGDCorKw/ZZ1SaKZJ56TiGjzzySNavX09NTQ2tWrWiX79+IVepaKRj27Vu3ZpWrVqxefPmoiy1CyUd2w6i54MSx9/u3bspLS0NtUpJS8e67927l759+9buvzrnZFHorrFc/iDD3fYNIYkyv2S6fe++++7aUrGERAlZZWVl7WscvDx9pG8fScjEvqLtpg99pOcj2+d7HbvZ227umd929cWR7HV63759B5QUzps3z//85z/76tWr/amnnirI/SXb2y6X3sNiXvdC/NAt7mbKZrf9008/TcuWLVm6dCnHH388q1evZuHChcydO5fly5dz7bXXcsQRRxwwH0GXLl1qS8USEiVkXbt2zVrsohIPkXynYzh/ZWvbpXqdXrp06QElheXl5Zx33nkq76a4j7tiXvd8onmmmiiZ+SHuueceKioquOuuu1ixYgW33347y5YtY8KECQeMqLf/76uqqhp97b1799KhQwc6duxYO0P29OnTAejXrx+tWrXC/cD5COA/5YUzZ86kurq6toQsulkhmdLUfWXq1Kk8+eSTVFVVMXbsWLZv3w5Q+/PEPpTMviIizZfsMZw4jyeO1YT95/QbO3asjt0sauq2mz9/PuPHj2fnzp0AB5yHm7LtUr1OJ0bvmzZtGtXV1fTs2ZMpU6bQq1evFN+B/JXKtrv55ptrr5kQzdGVj9fMZNc9sX+uWbOGu+66q/Z3ifbEggULuPnmm1mzZk3erHs+U89UI5rbG5ToBZo3bx6tW7euTWAO/n1jvUQlJSVVn/jEJ5rVlfSLX/zigO//+7//+4DXb85rS/P3lcQD60cddRQnnXQS7dq1A6j9eWIfKisrC7ymIoUp1WM4cR5PHKvu0UPviYFjFi9eTN++fVUNkEHN3XaJnqDy8nIGDRpUO5DBunXrktp2JSUlVWaW9g183XXX1b5+ul87VzR32yUGDEkMuJA43vLhmpnquu8/0Mb+lUeJ9sT+g1FI5qlnqhHN7Q1K9AINGTKEjRs38uqrr1JdXX3I7xvrJdq1a1c3d7dMfezatatbgwFIo5q7r3To0KH2gVkz45VXXqG6urr2QdrEPrRly5ZQqyhS0FI9hhPn8Y4dO7J+/fraYzcxp9/WrVtrBzKQzGjutkv0BL377ru1FSRmdsB8Tw3RNTp1zd12iQEXtmzZQnV1de02GzRoUM5fM1Ndd4j2z4MrjxLfJwaj2LFjR4jVKjqmUq/61TVDdTKee+45zj77bFq0SD5XraqqorKykhNOOCGxbFyzVee8VPeRhObuK9pPRNIjG+d7Hbvp15xzsLZdWNnedieeeGLObLdiXvdCpDK/BmSq2z7ZZYdYrjRNyH0ksfxQyxYpJNk+lnXspkeIc7C2XXoU8zFXzOteiNQzlUZmdj0wwN2/GX//YeAX7v6hsJFJSGbWDlgJnOHuS5L4++8BJ7n7FRkPTkTqZGatgenAve7+pxRfoy/wOnCeu7+RxvAkRWa2GjjX3ZfH3z9BtI0fDhqYNMjMLgM+5+6XxN8fAzzn7kcHDSwLzGwIMNHdj4m/bwlsBbq5+7agwQmgZ6bSbTiwYL/vFwLDLR+m35ZM+iowLZlEKjYeONfMBmYwJhFp2I+AzcCfU30Bd18DfBe4z8xK0hSXpMjMjgC6AKv2+/EComu35LaD21ergC7xNi10w4nakwC4+z5gMXBssIjkAEqm0us4DtzhNwB7gJ7BIpKgzOxwosbUr5L9n/hO0zjg+5mKS0TqZ2ajgeuAa9y9ppkvdy9Rw+eXzQ5MmutY4M24MZqwkOjaLbnt4PbVPuBNiiOhOI4DE0nQfptTlEyliZm1AIax38Ee0w5f3L4AvOHuc5v4f7cAHzez3ukPSUTqY2ZtgfuAb7r72819vfgp868BnzGzs5v7etIsBzTIYwvQNTofFHNCUd9+qx7VHKFkKn36Ae+5+8FjcGqHL1Jm1oqodynpXqkEd98I3A38T7rjEpEG/R54xd3/ma4XdPf3gC8BfzWzjul6XWmyg0vFAJYBvc2sNEA8koR42/Qm2lb7K5b2VV37rW4C5BAlU+lT150D0A5fzC4H1rr7jBT//w/AVWZ2VBpjEpF6mNmFwIXAt9L92u7+LPAkUQmvhHFI74a7vw+8RVRZIrlpGPBWvK32V/DtKzNrQ5RIvnXQrxZSHIlkXlAylT513TmA4umGlv3EZZ8/pBnPSbj7O8ADwPXpiktE6mZmnYE7gavrqDBIl+8BI8zs8gy9vtQjHgiqoZueapjmrgbbVwU+yNcwYFkdieQ7QMuQU7PIfyiZSp/6TtKLgMFxyZcUj0uBncBzzXyd3wNfNbMjmx2RiNQpbozdAfzd3adlajnuvhO4CrjZzHplajlSp0Sjs7KO3+mmZ26rr32V2JaFnFDUmUjGz2Jqv80RSqbSp66HI3H3HcDbwDFZj0iCiBtmNwC/THmK85i7rwaeAr6ZhtBEpG5XE52jf5zpBbn768CtRM9P6RqcPccBC+o5Jxd8uVieq6995RT+tqtz3WPqUc0ROpGnQTz8dT+gvnmEVNtaXMYAhxM9H5EOvwa+FU/+KyJpZGb9iHqAr3T3PVla7G+AtmTg2Syp1wFz9RxEjdLcVl+ZHxR++6qx/baQE8m8oWQqPYYAqxq4EGuHLy43AL9Kw/w0ALj7UmAq0eS/IpImZtYSmAD8xt3ra6ylnbtXA58DfmJmGvggOxq6w78WaG9mZVmMR5IQb5P2RNuoLoXevmpovy30RDJvKJlKj4Z2dlBda9Ewsw8RTdL8jzS/9K+B75pZSZpfV6SY/Q9QDfwx2wt29+VEg9Tcb2aHZXv5Rai+5270/EluOw5Y2EDJfMFuNzPrRNSDva6eP1kIHKty4fC0AdKjoW5YUAlBMfkR0V3u6nS+aDzp7xtEz3aISDOZ2UnAd4lG70tLL3IK7iJqKP0s0PKLQtzYHErD12nd5c9NjbWvFgJDCzShGE4DiWQ86uhGosdMJKBC3PlCaKxn6i2iSQHbZCkeCcDMRhDtCxMytIhfAt/XyJAizRNPAnofcL2711c+lHFxI+krwDVmdnqoOIpAf+Bdd9/awN8UerlYvmqwfRVv03eJtnGhaaxtCbpZnxOUTKVHYwf7+0Qzdw/NWkQSwo+A/83UQ+zuPpOobvyKTLy+SBH5FdEd7ftDB+LuVcC1wAQzOyJ0PAWqsd4NUDKVq5JJKAq1V1H7bZ5QMtVMZtYBKANWNfKn2uELWPwQ+elEk35m0i+BHxZoSYNIxpnZucBlwNebO3VBurj7k8DzwE2BQylUSTfIC3wC2LwSb4tiTiiS3W8Lcd3zihpkzTcceDOJmnvt8IXtB8DN8bximfQcsJ1oUmARaYJ48ut7gC+6+8bQ8RzkeuAMM/t46EAKUL2DTyS4+7tEE633zkpEkozewM542zSk4NpXTUwkC7FXLq8omWq+ZO4cgHb4gmVm/YELgXGZXlZ8J/2XwA26gyrSZH8GHnP3KaEDOZi7bycaLn28mXULHU+BaWieov0Vag9Hvirm9lVvYIe7v9fI3y0B+sfznUogSqaaL5k7B6CTdCH7HnB7PLJONjxFNCnw+VlankjeM7MrgBOB7wcOpV7u/jJRqfBfdLMkPeJGZj+iRmdjCvXZm3yVbPtqCdCvwBKKpBLJ+BntVcDgjEck9VIy1XzJ3jlZB7SN5w2QAmFmPYBPk8VnHeKS0l8RDXghIo0ws97AzcCV7r4rdDyN+AXQlWhQCmm+ocBKd9+bxN/qpmduSTah2AuspLAG+Wq0NHU/2m8DUzLVDE2oad1/UkDd9Sos3wX+5u4bsrzcfwA94kmCRaQe8WAtfwVucvc5gcNpVDz661XAjWY2KHQ8BSDZEj/QNTrXJNszBYVX6tfU/VbJVEBKppqnO1ADVCX597p7UEDMrDNwDfB/2V52PCnwb4Ebsr1skTzzbaAE+F3oQJLl7kuIJvK918xaBw4n3yVbPQKwCBis9zy8eBsMJtomySi09lVT9ttCSyTzjpKp5jkOWNCE4XV116uwfBt42N3LAy1/AnCsmY0MtHyRnGZmw4nKYT8X34DIJ+OATaict7mS7t1w951AOTAwoxFJMgYC5fE2SUbBtK/iRHIQsDjJfym0RDLvKJlqnqZ0QYN2+IIRT675daLeoSDiB0//F/hhqBhEclX8MPp9wA/cfWXoeJoqvkn3ReA6MxsdOp481pQ7/FBAjfI8V8ztq4HAuiYkkquATpr0OxwlU82T0klaozQVhOuASe6+InAcdwKnx5MGi8h//BxYDdwdOI6UufvbwDeA+8ysbeh48o2ZdQTKiPaDZBVSozyfNbV9tRooi7d5vmvK4BOJQaneRDcBglEy1TxN3eHfA3agSQHzmpm1Ab4D/CZwKImylJtR75RIrXhgls8DX21CGXZOcveHgVeA34eOJQ8dCyyKG5vJ0sP8uSGVhGIR0TbPd00ZfCJB+21ASqZSZGYtiYbhbEo3NOiuVyH4EvCyuzd122fKOOAj8eTBIkUtLnWZQJRIrQ8dT5p8C7jQzC4MHUieaWrvBuhh/lyRSkJRKO0r7bd5RslU6gYAVe6+rYn/p7sHeczMDgP+h2iep5wQTxZ8G9HkwSLF7mZgsrtPDB1IusTH+NXAXWbWJXQ8eaRJvRuxt4CeKqsMJ37vexJti6YolPZVKvttoSSSeUnJVOpS2dlBdw/y3eeAJe4+K3QgB7kZ+LSZ9QwdiEgoZvYJ4HSi+d8KirtPIxpQ43Y9d5u0JvduxKM+LqUwysXy1bHA0hRG4Mz79lWcSHYHljfxXxcCx+ncEIaSqdSl0gUNhXPnpOiYWSvgB8AvQ8dysHjS4L9RgI1IkWSYWXfgz0TDoG8PHU+G/ISoKuLq0IHkurhRqZue+alZ7as8TyhSTSSriOY97Zb+kKQxSqZSl0pNK0QjrgzSpIB56TKiE9ZLoQOpx/8CX4gnExYpGnHj6S/A7e7+Suh4MiWeDuEq4Pdm1i90PDmuO1Dt7lUp/K9KpsJKqX0Vb+tqom2fr1JKJOOBdrTfBqJkKnVNnQMBqB19bR2aFDCvmFkLoskzf5mro4O5ewXwT6LJhEWKydeAzsCNoQPJNHdfQDSS6IR4ICSpW6o3PEFzTYWWUvsqlu8JhfbbPKRkKgVmVgr0JaqrToVK/fLPxUR3vCaFDqQRvwO+bmYdQgcikg1mNhj4BVF53/uh48mSPwLvEw2GI3VLtcQP8r9Bnu+am1Dk87bTfpuHlEylZiiwwt33pvj/qsfOI3EJ0Q3Ar3K1VyohnkR4EvD10LGIZFpcLn0v8FN3T/XmVt6J59T5AvBdMzspcDi5KtXnbgDKgRKNnJh98XteQrQNUpHv7avm7Lf5nkjmLSVTqWnOzg66e5BvzgaOAB4NHUiSfg18J55cWKSQ/Rh4DxgfOpBsc/e1wPXAfXG1hBwo5d6N+KaZSqbCGA4sbMaNy7xtX8WJ5OFARYovsRAYqvLf7FMylZrmdEGD7h7kmxuA38R3g3Oeuy8CXga+HDoWkUwxs1OAa4Ev5nqPcQbdT3Q9yZl573JB3JgcSjTgU6rytlGe55rbvnqT/E0ojqMZiWQ87+l6oH9ao5JGKZlKTXMejoRo/oDumhQw95nZqUA/4O+hY2miXwL/E08yLFJQzKwdUXnfde7+Tuh4QokbXV8HLjOzc0PHk0MGAJVx4zJV6pkKo1ntq3ibVxLtA/mmuVVPoJv1QSiZSk2z7pxoUsC88iPgd/n2YLu7v050h+5zoWMRyYD/BWa4e76U3maMu28EvgjcY2ZHho4nRzTnIf4E9UyF0dyeKcjfhCId657vz4zlJSVTTRRfrDoAa5r5Utrhc5yZnQCMAO4JHUuKfgX8IJ5sWKQgmNnFwPnAf4WOJVe4+xTgMaJJiyV9d/iHx9NiSBbE73VzK38gf9tX6Vr3fEwk85pOEk03HFiUhudntMPnvh8Bf3D33aEDSdFLRJMMfyp0ICLpED+gfQfweXffGjqeHPN94EQzuyJ0IDmg2Xf44x6/rUCftEQkyegDbI3f++bIu/ZVnEgeS/OTKZWnBqBkqunS0Q0L+dsNXRTiuWs+DNwWOpZUxc9T/BL4ke6uSr6Lpyi4E5jg7v8OHU+ucfddwJXAzWbWO3Q8gaWjzA/ysFGe54q5fdWXKJHc1MzXWQocbWYlaYhJkqQGVtOloxsW8rcbulh8H/iTu28PHUgzTSKa3PPi0IGINNMXgaOBnwaOI2e5+xzgJuCvxXoDJR4mvg+wLA0vp7v82ZWu9tUyoE+eTRmQjtJU4vlPVxCNZilZUpQn22ZK152TCjQpYE4ys77AJcCfQsfSXHHv1K+AG+I7+yJ5x8wGAL8BrnL3PaHjyXG/I5r09NuhAwlkKLA8blQ2l3qmsist7at42y8nvxKKdLUtQTfrs07JVBPEjdF0HeyOTtS56n+Au9JQt50rHiWadPic0IGINFU8gMoE4Ffuno671gUtHi32c0TlvcXYoEp3o1TX6Owp5m2XrtJUyL91z3tKppqmJ7DH3Tek6fV09yDHmFk34LPAH0LHki7xYCm/JhpQQyTffA/YDdwcOpB84e4rgR8A95nZ4aHjybK0lEvFFgPHaL6+zIvf42OI3vN0yLf2VTr323x8ZiyvKZlqmnTeNQHt8LnoeuDv7l4VOpA0ewDoF09CLJIXzGwE8B3gC2kYQbXY3A2sBn4eOI5sS9sd/nhQj7XAoHS8njRoELA2fs/TIW/aV0ok85+SqaZJ550DUFdsTjGzMuDLRM8cFJR40uHfATeEjkUkGfHD4/cB33b3daHjyTdxKflXgc+b2Rmh48midN/0VMM0O4q5fTUYWJPGaVjWAB01iXf2KJlqmnTWtBK/1rHFOupSDvoW8IS7rw0dSIbcA5xkZieGDkQkCb8F3nD3B0IHkq/cfT1RQvU3MzsidDyZFt8QO4KoNyld8qaHI8+lu321Fjgi3idyXVoTybgXfxG6CZA1asQ3TVrveMXzCWwhml9AAjKz9sA3iUYMK0jxXa8/AD8MHYtIQ8xsDHApcF3gUPKeu08EJgO3hI4lC4YDC9NcEppPPRz5LN3tqxryZ2j7dPemgnpUs0rJVJLiEaUGA2+m+aW1w+eGa4EX3D0dc5PkstuBD8eTEovknPhO8t3ANWmYwFIi3wVOM7NPhg4kw9JdKgb50yDPd+maY2p/+dK+ysS6q0c1i5RMJe8Y4J0MTOKqHT6weKbw/yaaj6mgxfvvn4hG+hLJKfH0E7cB/3T350PHUyji4/5zwDgz6x46ngxKd6kYRPMVdYurFyQD4ve2G9F7nU750r5Sz1SeUzKVvEzc8QKVEOSCa4DZ7j4vdCBZcivwsXhyYpFcciVwLBrGP+3c/RWinum/FPAE3mlvlLr7PqJR1oal83XlAMOAxfF7nU45376KE8mjgJVpfukFwHEFfKznFCVTycvEHS9QCUFQZtYa+D5F0CuVEJdO3UU0ObFITjCzPsAfgavSODyyHOhGoDPwtdCBpFvcaMxEuRTkTw9Hvspo+yrHE4pjyUAiGc+HupdoflTJMCVTyctENyxoUsDQPgusdPeXQweSZX8APhtPUiwSVDyi6d+A/3P3uaHjKVTxFAmfA35RgM9N9gJ2x43IdMv5Ho48l5H2Vbwv7CbaN3JVptqWoFK/rFEylbyMlPnFd2DXEA1uIVlkZi2JRrb7ZehYsi2elPh+omfFREK7HmgF/D50IIXO3ZcCPwXujXvmC4UapfkrU49RQO4nwpnqlYPcX/eCoWQqCWbWBugNvJWhRajUL4yPA5uBFwLHEcrvgS/nyTwcUqDM7HiiAVE+n4FnJqRu44H3gB+HDiSNMlXiByrzy7RMJhS53r7KZCKp/TZLlEwlZyiwLC6RyATdPciyuIb6R8Cv3N1DxxNCPDnx40STFYtknZkdDtwH/I+7rwodT7GIz3lfBK41s1NCx5MmmeyZehtoZWZHZej1i1b8nrYieo8zIWfbV3E7RD2qBUDJVHIyedcEcvhgL2AfITqBTwwdSGC/Ab6pYX8lkBuJhkP+W+hAio27v0M0KfK9ZtYudDxpkLFGaZx86i5/ZhxHNNFypm5q5nL7qitRO7wyQ6//JjAknidVMkjJVHIyeecAcr8buqDEd4NuIOqVqgkdT0jxJMXPU4Cje0luM7OziAaA+Wqx9g6H5u6PAjOA/w0dS3PEjcXBRI3HTMnlRnk+y3T76k1gcI4mFMOBBZk6/8Xzy70DDMjE68t/KJlKTiZrWgFWAF3VO5A1ZxDN6/DP0IHkiF8D/x1PXiyScWbWAfgr8BV3fzdwOMXuv4Dzzezi0IE0w0Cgwt13ZHAZKpnKjIy2r+J9ooJoH8k1mU4kQTcBskLJVHIyWua336SAx2ZqGXKAG4Df6GH3SDxZ8etEz1CIZMOtwDPu/kzoQIqdu28FPg/cYWZdQseTokwOPpGgMr/MyPRjFJC71T/abwuEkqlGmFknoC2wNsOL0t2DLDCzUcAQ4N7QseSYXwLfK7ChkiUHmdmngA+gSaNzhrv/G5gA3JnjE5zWJxt3+BcCw+I50SQN4vdyGJlPKHK1fZWtnqlcTCQLik4KjRtOZh+OTMjVg73Q/Aj4vbvvDR1ILnH3V4jKTT8bOhYpXGbWE/gT8LkMl2RJ0/0UOBq4JnAcqch4o9TdNwObiN4jSY+jgU3xe5tJOde+2i+RXJThReXcuhciJVMNiCd1vZTMzS+1v1zthi4YZnYGcCrwl9Cx5KhfAT80s26hA5HCEzce7gb+7O6vhY5HDuTue4CrgN+aWf/Q8TRRxsul4h67pcDlmVxOkbkcWJqF3tBcbF/1A95z9y0ZXs5aoLeZnZ7h5RQ10yBK9YuHi90E7AI6ZnLkNzPrAcwDjtLIVulnZgOI3t+X3f280PHkIjPrCswH9rh7n9DxSOGIy6VfAvYCo9y9OnBIUg8z+yFwLfAzd/9r4HAaZWZtgXeBIzI4FyTx5OYVwD6gva7TzRMnUNuAlkAvd38vg8tqDWwFOudKj7iZXQp82d0zOvCLmX2CaLCtZ9z9o5lcVjFTz1QD4mEllwA3ZGEI7XeITiq9M7ycYnUY0bNvT4QOJIdtJhoIpWPYMKQAnUlU0lKlRCrnrQD6AF8JHUiSTgCWZTKRAnD3jcB44DUlUs0Xv4evArdlMpGKl/U+sIxoX8kVJ5L5Z8UAHouX83wWllW01DOVI8xsBPAcUO7uqm/NADPrHk9UKfWI7xZ2dfdMTSIoRSjuef84UYmfLjo5zsw+APSM56HKaWZWBdQAo919Xeh4JPeYWS9gFtDC3bvmQDzHAi8DS919VOh4pPlycRKzYrUA2A28HTqQQqVEqnFxQ1eJlKSVu78NjAsdhyTH3V8NHUMTbIg/lweNQnJZBVEpaK6MVLkUKAX+FToQSQ/1TOUQM+sMVGdhZBsREZG8Fw+Ysy1XnoWR3BQ/W9c+V6ou4ue4V6qnvjAUbDJVWlpauXv37mDduSUlJVW7du3SqGgpCrH9imWbhTo2iuX9zWfZ3je0T+SGYt3uIdsJufIeNFc238NCec/SQW2k3FKwyZSZBU34zQx3z5Uu5bwTYvsVyzYLdWwUy/ubz7K9b2ifyA3Fut1DthNy5T1ormy+h4XynqWD2ki5RaP5ZUlpaWmlmXmmPkpLS3Oi6zrdJk6cyGOPPcbEiROZM2cOAK+88go7dkQVHS+99BLV1f8ZHOzgk0tNTQ3//Oc/mT179gFf33jjjWzbto377ruP1atXZ219csVjjz3Ga69FU/1MnDjxgN9NmjSJxx9/nH379tX+rK6T9vvvv8/48eOZM2cO1dXV3H///UyZMoXf/e53bNmyhRtvvDGzKyEiaXXweeHee+/l7rvvPuBvDj4XVFZWcvvtt/P2228zbtw41q5dS2VlJePGjWPNmjXcddddvPPOO4wdO7bR5es6mZ+aez1J7C8H7zu33XYbVVVV3HLLLaxcubLe5Yfeb0IuP51tpESbaOXKlQe0laRxBT8AxZ/+9Cd69OjBJz7xCdauXUufPtH0OYmvZ8+ezTvvvMPFF1/MxIkTufji/wz57+7YQXPJzZ8/nxkzZnD11VezfPlyZsyYwRlnnMH06dMZPHgwW7du5WMf+9ghcezevbtrJu8iWDRHUEFq3bo1F198MU888QRPPvkko0ePZt68ebz00ktccsklrFu3jvbt2/Pcc8/Rv39/DjvsMCorKxk9ejRlZWW0a9eOli1b0qJFi9qvTzzxRNq3b0/37t1p27Zt6FXMutLSUsrLy1m8eDF79+7lt7/9Leeeey4rVqygbdu2XHrppQCsWrWK2bNnM3ToULZu3cqWLVs488wzKS0tZf369Xzwgx+koqKCk08+mbKyMk455RRee+212vdY8k9j58zly5fz4IMP8uMf/7j2fw4+V1ZWVvLEE0/wuc99jvfff5977rmHq6++moceeogLL7yQRx555IBzreSGg88LnTp1olWrVlRXV7Nu3bo6zwXdunWjc+fOvPXWW1x++eXMnj2btm3b1n7dokULWrRokdR5NsR18pZbbuH8889nxowZDBgwgJ07d3L44YezatUqysrKOOyww7jooot4/fXXKS8v56ijjmLx4sV86Utfqn2Ng/f/RLuie/fute2LyZMns2HDBq688sqMrV8ozb2erFix4pB9Z9y4cXTrFlWUrVu3jpYtW9a7/NDtq9DLT1cbKdEmat++/QFtJWlcUfRM9erVixtvvJH58+fX/izx9YgRIwB46KGHWLVqFRBl9Y8++ijr169n0qRJTJs2rfb/ysvLOe+88ygvL6/9unXr1hxxxBF06tSJPXv21HkX/2Br164FYNu2bWzatOmQ30+ePJn7778fgM2bN/PHP/6RV199lYcffpjJkyfz/PPFMWWAmfH+++8zceJEOnbsSPv27enUqRObNm1i6NChbNiwgd69e9OhQwfKyspwd/bt20d1dTXuzu7du9m3bx979uxh3bp17Nu3j5qaGmbNmsWGDRt49tlnD0mYi0G7du1q3yOAnj17smvXLlauXEmLFi148skn2bdvH127dqW0tJSampra9xXg7bffZvv27cycOZNOnTqxbt069uzZQ1VVFQDr16+vfY8l/zR0znzhhRfYtWsXEDWOHn74Yd58801efvllJk2axK5du2ob2K1ataJDhw4cc8wxLF68uLZRffTRRzNw4MAg6yb1O/i8sGPHDt59911atWpV77lgxYoVLFiwgIEDB/Lggw8yePBgevXqVft127ZtWbp0KXv27GHz5s1NjimVa+W0adMYNy65wSNbtGhBaWkp3bp1Y8eOHRx11FFUV1fTtWvX2kQqEcell17KO++8Q9euUdv2+eef55FHHmHHjh1MmjSptncm0a5IfAaorq7myCOPbPL654PmXk8GDBhwyL5z9dVXU1NTQ1VVFUOGDGHLli0pxdaU/Wf/r5sjlX028bOpU6dy2223Jb2sdLeRnn32Wfbs2XNIW0kaVvA9U0cffTQnnngiM2bMqHOnXrZsGcuXL6d79+61P+vTpw9VVVVUV1dTXV1d2z29YMECevbsyZQpUzjjjDNqvz7++OOprKzk9NNPZ/369ezevbveeCZMmMD27dvp06cPd9xxB2PGjKGsrIzWrVszffp0ysrKGD169AEn3rlz5zJw4EBGjhzJ2rVrWblyJb17F/7cvm+//TbuzqhRo+jVqxcAZ555Zr1/P2bMmDp/vv8d8MT7dvLJJwPwu9/9Ll3h5pXTTz89qZ+3adOmtjGxv9LSUgAGDx5c+7PEe3vDDTcA8POf/zwtsUp2NXbOXL9+PQAbN25stIF9yimn0K5dO2bNmsUXv/hF5s+fT0VFRVHewMgH9Z0XoP5zwYABA/jZz34GwDe+8Y3anye+7tu3LwBnnHFGk2JpzrWyc+fOlJcnN1L60UcfTZ8+fZgyZQoDBgxg/fr1tG/fnr1797J7924mTZpEWVkZvXv35vHHH6d79+5s3Lixdt22bt1au+8n2gqJdkXi84oVK2jZsmXBNkrTcT2pa9/52te+BsDxxx/f5JhS2X+am/A2Z59N/Gznzp1s3bo1qeVloo2UaBMlXi/RVpKGFd0AFHPmzKGmpoYePXqwbNkyRo4cSbt27TKx/AMe1EvEs//BBrB69WrOOuss+vbty7Rp0+jUqROnnnoqzz77LBs2bODUU0/lzTffZOnSpZSWljJkyBBatGjBm2++ecDJp9AeDNTDlZmjASikPnXtG5k8Z2qfyA25MgDF/nE051p58cUX88orr/CFL3yhweU2tN6TJ0+ut/HZ0O+SVSj7fi4MQFFXDKnsP8uXL2fDhg1cddVVSS334OU3Z5897rjj2LBhA927d2fhwoV8+9vfbnD5aiPllqJLpg6WeA5g27Ztdd6V2L/OOVEH3aVLF9atW8dll13W0PLrPGnPnj2b8vJyLrnkkmasXePLy3cNbb+mbLPNmzdzzz330L17dwYMGECLFi1qa9jrWGZBvYf1Sdd7u2jRImbNmkWPHj3YsGEDxxxzTIPHRbG8v/msvn0jlWPu/PPP5/HHH+ecc86pd7/QPpEb0n2+HTNmDLNmzWLo0KF1bvtkGsWZuFY2JZmCpq37q6++yrp166iurmbAgAGsWLGCAQMGMGrUqKRiyVfpPGdcc801B3y+/vrrD15W0slUOvefZJOpbLbv0nXMJvbbI444gpYtW9K9e3cef/xxfvSjHyUVh0QKvsyvPql0x44YMYKJEyfWltulYsSIEQfUUUvymtOF3qZNG7Zt28bZZ599yGhDktp727t3b6ZOnVr7s+YcF5KbmnPMDRs2jKVLl2q/yFPN2faJc0O+XitTWffEuh511FFs27at9nOxac5+c/Dn5gix/4Ru3zVnv008PpI4b0vTFMUAFI055ZRTah+udvcDap9btmzJxo0bWbFiRW3982233UZZWVna40imkV9RUcFdd91FZWUlt9xyywFDXhaTZLfZ9u3bWbJkCZ06dWLNmjW121Dql+x7u3r1ajp37lz7s0wdF5IbmnrMlZeX07t3b+0XBaCp2z5xbsjEtm/KdRJIakj2hiS77ol1TVxrEp+LWVP3m4M/p1sy+86yZcuYMGFCRtpWySy/qqqKsWPHsn379mYtq6n77eDBg1m9enXteVuapmjL/DLVHbvf8g8pJ7jlllu49NJLGTduHIMGDWLTpk20adOGdu3asW3bttpRWFavXk1paSmnnnoqQ4YM4YUXXmDv3r1ccMEFALVDuP/+97/n+uuvp1WrVgXX/Zrpbvt6lllQ72F9Qry38XKL4v3NZwfvGzrmikO2zwkNlWul8zo5YMAAZs6cWTuMebJlfjofJi+b54zGyvxuvfXWZu87//znP6muruayyy6jdevWDS43E8uHaN6uj3/84w2ut9pIuaVoe6ZGjBhxyA7XlDteK1as4Pbbb2fq1Kk8+eSTSS0zcWLv3bs3/fv357TTTmPUqFG1w0lDNIHa4MGDGThwIAsXLgSoHVUQ4L333mPWrFlUVFTQqVOnouqZqmubJSSz7RK9ec8991zS26xY1PfeNuWYSNxRW7BgATfffHPtpIGSv5p7zCXOk/v3FEh+SNf5tqnng3ReJzdu3EhFRQV79uxJatkJzV33xDrPmzeP8ePHs3PnziYtP581971L9AzNnz+fm2++uUk9NOnYd4YOHcrGjRtTGoo9HcsHUh7tNF377aJFi7j99ttTiqFYFdUzU8ncNXjmmWcavGvQs2dPunXrxoABA3jttdfo1KkTGzZsqHOC34NdeOGFdf58+/btDY6Qtf+oQZ06daodcvqLX/xiiu9E/knHtuvWrRt79uyha9euLFmyJKltVujSeUx07dqVk046CTPT8NcFIB37RuI8mdhHJD+k83zb1PNBuq+Tp512WtbXPbHOFRUVtfNSDho0qElx5KN0vHfz5s2jdevWdOjQATNj7dq1DBs2LKnlp2PfGT58OMOHD2/6yqdp+UDtJMdNkc79tl+/fgfMMSiNK6pk6uC7BiUlJbRq1YqpU6fSpk0b4D93DQ4//HAWLlzIkCFD6rzjdeSRRx4yt1Ri7p2mOuuss9K1igUrHdsu0Zt35JFHNnubFYp0HhMXXnghZsbevXvp27cvO3bsqH0NyT/p2DdmzJhBZWXlAftIixZFWxCRN9J5vt26dWtazgfZuk6mY90T58C2bdsyZcoUrr766qzEHlo63ruhQ4cybdo02rRpQ6tWrejXr1+z4wrdxsrG8tO5386aNYvKykrdcG4Kdy/Ij2jVkvPiiy/6tm3bkv77ZMTLbzCep556qtHXufvuu33evHl+0003HRDjT37yE6+srKx9jYOXl+8fyW6/dG67QnsP6/tI5r3NxjGhj9z7yPa+oX0iNz6yfb6tb7s35zr5xhtv+E033eTbt293d/elS5f63/72twavkyHbCYWy72fznJGJ/Saxn7z//vvu7j5v3jz/85//7KtXr066fdWc5S9fvtxvu+222p+9+OKL/sQTTzTavlMbKbc+iqpnqj7Zumvx9NNP07JlS5YuXcrxxx/P6tWrWbhwIXPnzmX58uVce+21HHHEEQcMYdmlSxc6d+58QHf34sWL6du3L127ds1K3Lks9B2nQqX3VeqjfaN4ZWPbp3qdbNmy5QHlhIlysXSNJqj9PnW5vN8k9pMoV4Dy8nLOO++8Jj9nl+ryE6XQCYlHR4466qi0vC/ab7OjaOstknkY75577ql9cPrgh+wTD0UmHthbs2ZNo6+5d+9eOnToQMeOHWsnVZs+fToA/fr1o1WrVrgfOIQlQOvWrWnVqhVbtmyhuro65YdqC0FTt1viAfiExPeJh6Orqqo07xRNf18hGnL44AFYEu9reXm53t8C0tT94+CpG5pynpTcka7zbWJQgWTOB6leJxMlSrNnz6a6urpZAwmkY90T61yM+36y793B1+P9B2EYO3ZsVvabxH7y6quvUl1dTc+ePZkyZQq9evVq0jqnuvxEKfTLL79cO/9T4jGEpkr2fa9vkI+Dz9+6fievaHqmUr1rkHhw+uCH7BMPEiYe2OvSpUujMSSGujz11FMb/LuLL7649uuSkhI6derEddddV/uz0047jdNOO42qqqqCnw+gudvt4Ls+ie8TD0d36tQp4NqF09z3NdE7evAALIn3tdjf33zX3P0jsf0TmnKelHAydb5tSi9RqtfJE088kZNPPrn2Z4mBBJK9TqZ73RPrDBT8vt/cXpmDzxeJ60s29pthw4YdMODECSecwAknnNDk9lWqyz/11FMPGCSlV69efOMb30hq+c3tjTt4kI+Dz9+6fievaHqmUr1rkHhwOvG5pqYGM+OVV16hurr6gIftG1JSUlJlZjT147zzzqNly5Z1/q5bt26ceOKJmBklJSVVDQaQp5q73Q6+65P4vhiHlt9fc9/XRO/oEUccwfr162uPh8T7WlVVVdTvb75r7v6R2A9ef/31Jp0nJaxMnW+HDBmSVC9RyOtkutc90eOxa9eugt/3m9src/D5InF9GTRoUM7vNyGX39zeuMQgH4mqp2Keeqe5inbS3mQ899xznH322UmNPlVVVUVlZSUnnHBCYvm4JjdLWXO2X6rbrVi2WarvbVPe14RifH/zWaaPO50nc1O2z7cnnnhiTmz35rYT1EbIzvUk1/abXJDN973Q9tlMKNgyv/hOQbARGgq1pyhbQmy/YtlmoY6NYnl/81m29w3tE7mhWLd7yHZCrrwHzZXN97BQ3rN0UBsptxRsz1RTmNkxwAygp7sf0q9pZicBjwL9m93dJRlhZuOAt9z9pvj7O4E57j4+aGB5ysyeB/7i7n9v4G8MmA3c4O7/ylpwkhPM7MdAO3f/Qfz9/0c0dO7YsJFJJpnZIOBZd+8Xfz8ceNjdh4SNLPPMrBWwFejq7tvM7DBgC3Ckuzd9xIAiYmYlwCagg7vvNbN2wHqgvbvva/i/JRVmdh7wK3cfVc/vPwF8w93PyW5khadonplqxBXAP+tKpGJvAHuAU7IWkTTVcGDBft8vAI4LFEteM7PjgSHAww39XXxj4SbgO5mPSnLQcRx6zA2v52+lcBx8rl0G9DWzYpgBfQBQ6e7bANx9L7ACGBo0qvwwBFgRv2e4+3bgHeCYoFEVtiuABxr4/TPAyWbWI0vxFKyiT6biu+tXAPXegY8bjX+P/05yTLwNjwMW7vfjhahhl6r/Av6cuOg14iHgeDMbluGYJPfUdczpBkbhO2C7x+eJ5USN5UJ38A0E0LUmWQefL0DvXcbEPYGXEl2j6xT3pj4BfDpLYRWsok+mgOOBNsDLjfzdA8Cn425+yS3dgWp337+edwFwXJxoSZLMrAvwSeCOZP7e3fcAtxElYFIkzOxwoB+wZL8fvwX0NLM2YaKSLDm4ZwqKpxJgOIcmBMWy7s1VVyKq9y5zPgLMc/eKRv5OHQVpoGQq2okebOxZKHd/CygHzspGUNIkh5yk3X0DsBfoGSSi/PVVoucfNjThf24jutHQ+GQgUiiGAKviZBoAd3+fqORLvZSFrb7emWJoFCshSF1dSXix7DchNFbil/ACcLSZDchwPAWtqJMpM2tB8jsc8d99NnMRSYrqKh8APcPRJPHD1NcBtzTl/+IewSeBr2QiLslJdTUqQY2jghY/F9WXKGneX7Gca1Xml7q6rtPFst9klZm1B84HHmnsb+OxAv6JeqeapaiTKeBUYDswP8m/fwi4NC5xkdxR1x0v0B3DpvoUsNjd63ovG3Mz8E0za53mmCQ3NXTMqXFUuIYAy+t4nrLgz7VxItmbQxPJ1UBHMzsy60Hlifi96Uj0Xu1vGdCnSAYvyaZLgZfc/b0k//4B4Ao9FpG6Yk+mrgAeSHa4c3cvJ0q8PpLRqKSpdJe8meKT6HeIkqImc/e5wErg42kMS3JXQ73BOuYKV33n2rXAEQWeUAwjmn7j/f1/6O41wCJ0E6EhxwKL4veqVvxevoVKg9OtKRVXEI0Z0Badu1NWtMlUPJDEp4EHm/ivKvXLIWbWkuhu6Zt1/Fp3yZN3KnAk8HQzXuMmNEx6sWjoBoaOucJV1wAMxZJQ1NcbC9rvG1PfzRfQdTqtzKwzcBpR6X1S4uP3QdS2TVnRJlPAOUQPUC9v4v89DJwf16RKeAOAqsS8Hwd5ExiiERiT8m3gloPvHDbRk0B3MxudppgkB5lZB6AMWFXHr9cBbc2sU3ajkiypL4mGwq8EaCwhKOR1b65i3m+y7TLgmXger6Z4ALhcpX6pKeZkqqndoADENaj/Bi5Je0SSinovcPtNCqhRahpgZr2Bc4F7mvM68Sz2txIlZlK4hgNv1pV4xyXTahwVrmLuYWgoISj0dW+uOns0Y3rv0uuzpNC2JXqEZSdRlYo0UVEmU/HDjpcA/0jxJR5AI5/kioZKL0B3DJPxDWBCPb17TXU38BHNqF7QGmpUghpHBSl+HuoIYE09f1Lo59rGyvw0r2Ed4veksUS0kPebrIlvjB4LPNvU/41vhGnOqRQVZTIFXAjMdfe3U/z/J4DT49pUCauxhp3ukjfAzNoCXyLqUWo2d98M3E80xLoUpobuMoMaR4VqOHUMIrCfgk0o4jn02hMNtHEId1+P5jWsTw9gb/we1WUt0F7zFKbFZ4DH9p//r4keJJozUo9GNFGxJlNXEGXgKYnLx/5FVJsqYSXTsNNd8vpdBUx395VpfM1bgK9ouNuClcwNDB1zhafBc2080ff/395/h9lVXQne8G+rJFSlrCploZyTSYIh2A0OgI1xg20ccM/3dk+7e9wzng7+Zr6e/mY+6GZevu4Ze+YxYGOEAQewETaYKGyCAYkGEYTAkkqhhEJJqpJKEsoSSqXa7x/nnMutq7p1T9gn7HPW73nuc6tu3Tp7r53X2mvvdYJ8KhTzgeYaN/9Ku++Z3lxDy12DpeyiE3Vtuwln5/lTxnJUEAqnTCmlhgBX4yOYWQ3E1S9legkgWY5YyavgBq3+O0Jeh14NrfX7wArkZqDc4e461HKtbQbm53GHouDUUqIhv+NtrwqBS15lj0qR201iKKVmAWOBZREfJWvbEBROmcIJZrZMa30g4nOew1kwTIieJSEkc+g5gGQ57wMTZJekR64GThJ98O2JO4C/kwV17hgLdAHVXHa8S3qOAROTypSQCH4UirzuMPhVCPIoe1RqeY9AfttNktwM/Nq9CCoKvwJuUErVG8hTYSiiMhX2ppNuuD6pT+L4qArpUMtC7gUF3IgEBeyJvwXu9Bu0OiAvAQr4ZAzPFtJjAbDGR5uRhWWO8LkjCfndYfAju5zP7RnZmYoZt3+GuqG6EvcugT/g3C0g+KRQypRSaiRwKQGCmdVAbj5JFz+DNMgkdxZKqdnAhRgYfHvCXWzfiQTxzRt+rMwgi6O8MR446Z6L6o3cjbVlimStdr8WiWvYDbcsZuOUTW+Ia3A0LgD6Am8bep64+gWkUMoU8BWcYGbHDD1vKTBOKTXT0POEYPhxOwGxkvfEXwM/1lqfiDGNXwKXKqWmx5iGkCxiwCgmfsfatcCsnCkUE4DjWusPevuSxDXskWlAR60Asm7ZHgfOTSRX+eNmYLFBL5PfANe4dwwIPiiaMmVkG9TD9U39NaLBp4Uf1wsQK3k33HgxNwP3xJmO1vpD4H4cxU3IB36VKTFg5AtfY61rqNwJ5MmA4neeATEiVOJ3vACZp0PhXiT1dcyubfcDr+LEYxV8UBhlSik1EefcTOBgZjVYDHxDtqeTxUcAyXJkguvON4ElWutdCaT1I+DfioXLfpRSdTiXvtRy2QFYB8xUSvWLN1dCQgRZFOdtvPW7KweiEFRS5HaTFB8HDmit/YzLQXgYuZHXN4VRpnA099/UuPktDG8B/XB8VoXk8AJI+tnW3g4MUko1xZynzOO63/w1hq9Dr4bWug14AfjzJNITYmUasFtrfaTWF7XWx4EdwIzYcyUkgd+zcpC/XckgO1N5kz0qRW43SREptlQvPA1c5t41INSgSMqUURc/D3cxL4f1kse3xUuCAnbjRmC71nplgmneAfyNu7Mh2EsQCz2IlT4XBLhEwCNvOwyyuxIeKbsYcXf+bwIeMf1s12X3d+7zhRoUQplSSs0BRuP4gMbBYuDrru+qkAxhFnaiTLnXoSeZoNb6LZy4RNcnma5gnCAWepDFUV6YDuwKcHFTbsZad7E6C8dt1Q8bkbiGACilBuBc3rHR57+sxXENztPlJXHzGeB9rXVrTM+XjQKfFGXxfzPwKwPBzHpEa90MHMTxXRWSIejCrvBWcqXURTiBVJ9MIfk7kGvSbSeIlRlytKguOEHHWi9Q+oCY8pMk04E29zKdmkhcw27MwVnon/bzZbeM2xHX4CAYiZvaC88B89w7B4ReyL0yVRbMLA6f0nIk5lRCuHUadGdKrOTOrtQPtdadKaT9G2CGUuq8FNIWzCBufsUkkBKdM4UiaJsHmWs8ghpfQMrON66x4nqcG6Vjwb1j4HHga3GlkRdyr0wBFwEKeCfmdB4BbpLbqxJhPHDCRwDJctZQ4KCASqmxOAPv/Wmk7y6w7sZR6ATLcN2WJgItAf5tMzBWKTUwnlwJCRHkEgGPvOxKBt2Vg/zIHpUit5sk+DywQmu9O+Z0xNXPB0VQpkwHM+sRrfVWYBOOD6sQL4EtXlrrfcAxHB/uIvJXwCNa6wMp5uHHwBeVUqNSzIMQjjnApiC3obo7oBuAebHlSkiCMDsMedmVlN2V8BS53SRBLJeq9cAyYIxSanYCaVlLrpUp9/Ywo8HMaiD38idDGNcLKOiNfkqpeuBbwF1p5sNVaB918yLYRRgrM8jC0mrKLhF4P+C/5mWsDTPXyO6KQ1gXSSm7GiilhgKfBp6IOy33roFfIbtTvZJrZQr4BLBXa+33Jp6o/Br4Qk4O3maZMK4XUFyr19eB97TWG9LOCM5Ngv9BKXVO2hkRAhHGygyysLSdOcBGv5cIlGH9WOu6p44juCJZ+LiGruyDgG0B//V9YLy4BtfkS8BLWuuDCaW3GLi5qMck/JB3ZSqpbVAAXN/VFTi+rEJ8hF3YFc5K7g5+f4dzm17quFHa1wJfTTsvQiDC7gZbv6guOGHH2h3AQMsVirlAS9ALe9wjBWspthFhPtAc9HiFW9Z5ubwkThJd2+Ksa/sAFyaYplXkVplyLd9fJoZgZjWQw3ox4sagCBL3o5wiWsmvBM4BXkg7I2XcAfydWLmsIuxucOEMGDkjlBKdk0DpYQ0IUMy5phwpu5hQSo0GLgaWJJWm258fQda2VcmtMgVcg2NVCrrNHJXHgU8rpYYlnG5RmA7sDBBAspx1OEEBi3Tj4t8Cd8V9AUtAfgcMAS5POyNCbZRSw4GhBHfZASduzDly6Yi1hFWiwf5dySLLHhUpu/j4KrBEa3084XQfBr6ulMqz3hCaPBdKErGlzsL1YX0Z+GLSaReEsG4nhQsKqJSaihNI+qG081KO1roL5zKMv0s5K4I/5gNr3XoLhKvEi6XZXkKPt9i/Kxlld8X2XbmoFLndxE1aa9t1wD6cuwiECnKpTLkXQHwe5+awNBBXv/gIe6uYR5EWdv8J+EnIXby4+TnwKaXUpLQzItQkysIIZGFpJe55p4E455/CYPtYG2V3pZmCxjV0ZY4yT9vebmJDKTUZxzvn9yllQda2VcilMgV8AXhLa70npfSXAJe4vq2CWaIu7ArhQqCUGgz8KU6g3MyhtT4C/Az4dspZEWojfa6YhLpEoAxrFQql1AigAWgL8/9a6w+A4xQzruEE4EO3DMLQBjS4dSB05+vAb0LcrmmKR4Avy228Z5NXZSrpm0664bqTPQN8Ja085JgorhdQHBeCP8O5OnV72hnphR8Cfy7X4GaeqLvBRelzeSPSWFsWKH2isRwlxwKiKZJQ3B2WqO3Gu7xExoyz+Qbprm1bcW5bvDqtPGSV3ClT7mHpT5JAMLMaLEYC+BrFdd8cT/C4H+XkfoJzD4j+LRm5Dr0aWuutwKvA/5V2XoSecXcVTLj5zZODy9YRxc3Nw9bx1pTsRVQIitxuYkMpNQ8YDryWclbE1a8H8ji5fQn4vdb6UMr5eBGY4fq4CmaYS7gAkuVsAsblfDfk88AB4I20M+KDO4G/lYV2ZjkXOKm13hv2AVrrA8BBQM7H2UVUJRrs3WGI6gEB9soelSK3mzi5GXgkzEVAhnkUuN41bgsueVzApOri5+Eu+H+D4+MqmCHyBOcGBWwh30EB/xa4I2PXoVfjVZyzBdeknRGhR0xYmUEWR1Zh4BIBD1t3GEwoBLbKHpUit5tYcPtjVta2u4G3gevTzkuWyJUypZQaC1wEPJt2XlweRrZDTWJqYZdb9wul1AIcRTGtmywD4Sp8dyLXpGcVE4tKkMWRbUwAjrnnnqJg3VjrLlznEV0hWAfMKlJcQ1fWWcDaiI+y9vKSmLgE6ATeSzsjLg8jx1i6kStlCieY2dMpBDOrxmtAo+vrKkTH1MIuz1byvwF+pLU+lXZGAvAIcL5Sak7aGRHOwoSVGSxcVBccU2PteuwLlD4JOKK13h/lIe5FVG04V1kXhRlAmyt7aNyyP4K4BnvcDDycIW+TJ4BPKqWGpZ2RrJA3ZSoT26Aerm/rr5DdKVOY8GOHnFrJlVIjgZuAe9POSxC01ieARTiKoJAtxIBRTIyMte6iegd2BUo31eaheEYEU8YXyOk8HRSlVB3ORkGW1raHgJdw7igQyJEypZSaCkzFqeAssRi4Wbaro+HGnBhA+ACS5eR1gvv3wONRLgtIkUXA15VSjWlnRHBQSvUFZhPdZQecHYppEp/EGky5VIN9461JhaBoRgRRRM1zJbBLa70x7YxUILf6lZEbZQrnoofHUgxmVo13cXxdL047I5YTNYBkOe1AvbuTkwvcRep/xDl/ZB1a6w7gaeAv0s6LUGI6sFNrfSzqg9zdx2045ymE7GPKCwDsUyhMKwRF2l0RRdQ8qcaW6oUlwEKl1Ji0M5IF8qRMZcrFz8Nd/IsGHx1jllK3TvI2yd0EtGitV6edkQjcCfwnd0dESB+Ti0rIX5/LJe75ppk4FyiYwLZ6N7krVzSFQBRRgyil+gNfxDkukincuwmewXFBLDy5UKbcG8yGAq+nnZcqLAa+5vq+CuGIY2GXi0nOdSH9OzIepLcWWut3gVacyUNIH5OLSshRn8s5M4AdUS8RKMOaencVyRk4bqkmeJ/8xzUEwJVxPI7MJliPE6vTpstLTHMtjkeOieMNcSAbBS65UKbITjCzHtFatwAdOL6vQjhMup3gPisvVq9LgSayExIgCnJNenaIo89ZsaguOKbrfTMw1hKFYhaw3dSNwAWJa+gxF8c7otPEw9w62I6zS1pUMulxVcbvgenunQWFxnplyrXKf51sNziQmFOhKQsgKVbynvlb4C6t9Zm0M2KAp4DxSik5Y5g+4uZXTIyOte7iegNO7KasY/LMj0dRjAimxwvI1zwdCKXUIOA64LG081IN946CR3HW4IXGemUK+DfAKeAPKeejFr8CvuT6wArBmIiBuB8VNAPzlFJW9wGl1ATgGuCnaefFBO7C64c4CqKQEkqpAcC5mHPZAdgCjFJKDTb4TME8cSyKbVEo4lIIimBEMG3wBHvaTRz8MfC61vqDtDNSA3H1Ix/K1DfIVjCzHnF9Xtfi+MAKwTDtdoLW+gBwGEdRs5lvAw9qrQ+nnRGDPAB8Xik1Lu2MFJi5wEaTt6O6O6frKcbC0maMj7fYo1DI7kp4itxu4uBmHI+mrPM6MEwpVdR6AixXptxbvzIVzKwGosGHIw6LF1g+ybm7B98EfpB2XkziKroPA/8h7bwUmDj7XKEn3SzjnmsaC2wy/Ghbxtq43PyK0OZFETWEUqoJ+CMct/dM495V8AgFX9tarUwBVwFtWmuTrihx8ijwOUsO4maJOAZpsN+F4P8FLNdab047IzFwF/DvlVL1aWekoMRhZYaCLo4sYh6wwdQlAmVkfqx13U/HYF6RbAMa3MDzucSN2ViPI6tJNgNjCuga/GXgea31kbQz4pOHgZvd8+2FxHZlKus3nXTD9X1djuMLK/gnDmshWGwldwetv8XSIL21cG/AXInjxiskT5wGDCv7XEGIa6xtB85RSo2K4dmmmIujSBq9yMc9gpB5ZTIi83Gu8DZ63KLMNbgItyGWY9XaFufOglM4dxgUEmuVqSwHM6vBYmSB6JsYAkiWY7OV/GqgE3gl7YzEyB3A3xXZ2pUisbrWSp1mlliUaEsCpcdlQIDsyx6VuMYLsHueDoxSajxwHvC7tPPiF7d/F/oYi7XKFPA5YLXW2vS2ctw8CfyRUqox7YxYwkycAJJG4n5UsAEnRsI5MTw7bv4WuCPrF69E5EWgL447r5AQrr/+QCCOQJEdOPPO6BieLUQnLvdOyP7uTNzKVJZlj0qR241pvgo8qbU+kXZGArIY+KpSqi7tjKSBzcqUbdugALg+sM/j+MQKtYnN4uUqaNuwLCigUmoWsBA7bvoJjasoShDf5InFZQes2aEoMnHvMGS53uNycYT8u7fKrp45voGda9uNOO68n0w7L2lgpTLlBjP7LBkOZlaDQm+HBiTOQRrstHr9DfBjCy1XYXgIuFwpNS3tjBSIOK3MkH8rvZW4lwj0x1kQxUHWx9o455pmYH4e3VtdmeYhO1ORUUrNACZgr/t+Yde2VipTwA3Aa1rrfWlnJCS/A86XODq+SGJhZ43VSyk1HMdydU/aeUkCrfWHOHGn/jrtvBQIMWAUkwXEtCPpktlA6Uqp0UA/YGccz3cDzh8BJsXx/JSZBBxxZYyDnUDfjF9eYoqvA4/GcJtmUvwK+KJ7p0GhyNyg5pNvYLGLk7uj8BTwtbTzYgFxup2AfVbybwLPaq1jmfQzyt3A/6WUGpJ2RgpCEn3OGgNGgYi13t34cQfJpkIxH1gT8xnUvLb7uNuN5xps0zwdGHeHz/a1bRtOXX0u7bwkjXXKlHs4+uNYEMysBg9T0O1Qv7junHEEkCzHGiu5G6T6P5HT69CrobXegXMZxb9LOy95x53Q4zw7gvvsuVncoSg4ce9IQnbH2yRkz6tCUOR2Y5LzcGJ1vZl2RiJSyLWtjZPZTcBzWuujaWckIq8AE5VS09POSIaJJe5HBZuBUZYEBbwBaNdar0g7IylwB/A3Rb0pKEEmAMfidKHWWh8G9gFT4kpDCEXcSjRkd3cmCdnzeglF3K74kN12Y5KbgUdycEPvY8BnXWN4YbBRmbLyFr9KXJ/YR3F8ZIWeid3iVRYUcF6c6Rgit0F6ffAmzgL882lnJOckYWWG/FrprcTdJYzzEgGPrNa77EyFJ263YMhv2QGl/vd18rG23Qe8hmP8LQxWKVNKqXOBj2FRMLMaLAa+kccbfgyRhMULLHAhUEpdBEwGHk85K6ngWuvuQK5Jjxvpc8VkEnDYPdcUJ5mr9wQVyfXADDcQfS5wZZmBI1ucrCWjl5cY4nKcC0qSMGQlwWKc81+FwZqG6R4+/wmwTGt9Mu38GGIFMBT4cdoZyRpKqX+DY9lIIrhnpl0IlFLnAf8buM/iW35M8BgwWylV1N25JEjCygwwEvgTpdTcBNISarMIOKSUGhFzOuuBaRkLlH4HcBoYH2ciblzD7VgW17AG9wCHcdyDY0NrfRDYj2NQzBVu2I9/AV7KgYufx8vAlUqpW9POSFJYo0zhXFt6NTAr7YwYZCTQCPxx2hnJIEdJbuDMugvBlcBVOJdxFJl6YBjw71POR55Jys2vHmcsj3snRPDHVOBc4FScibg32baSrXl8MjCYmGV3yfpcE5TJOGuYJAzceSs7j5E4l6pNTjkfJhkDNAA3ppyPxLBJmdoPPAf8UdoZMYV7vfWXsDf4cJysA+4C/iGBtD7Aift1WQJphWEjTtv/27QzkibuxQWfBd5KOy95RCnlGas+SCC5vwT+j9Z6VwJpCbW5Ffi628diw3XT2ka2DCL/f+CvtdbvJ5DWbpw4PHk5nP9/A9/SWm9LIK1dwFeVUg0JpJUka3GOrnw17YyYQmv9LvAfgd+knZekUPnZVRSEcCil/iNOLKN/0Vr/t7TzIwhpoJT6GfCnwM1a60dSzo6QQ1wlosP9dXCO3Jp8oZTainM+bazWenfa+bEJpdQunF2cEa7bnyBkBpt2pgQhLu4F3gOWpJ0RQUiRx4HXcaLYC4Jx3JAmtwDPF02RcvkmsFIUqVD8BfC2KFJCFpGdKUEQBEEQBEEQhBD0TSKRhoaGjhMnTiRxKxv19fW7jx8/PiaJtPxQZNkhOfmzJnuS9Q7Zkz8sSZebR17KLy3SqjeQuiuniONtGm0vS/JHReaqcBS53RVZ9moksjOllEpsR18phdY6M3Gbiiw7JCd/1mRPst7d9DIlf1iSLreydHNRfmmRVr25aUvduRRxvE2j7WVJ/qjIXBWOIre7IstejUTPTC1ZsoQnnniCJUuW8O677wLw5ptvcuzYMQBeffVVOjs/CqNTWVldXV08+uijrFy5kjNnznD77bdz5MgRfvGLX9Da2pqYHGGIKnulnN/97nc5evRo6d0WlixZwpIlS3jggQd46KGHOHToUNXvVpZBe3s7999/PwBLly7l6aefrpleQ0NDh1JKx/1qaGjoqJWXpGW3mR/+8Ic8/rgTn3j79u2lz72fN23axO23397tfyrL7NChQ9xxxx0AtLa2smjRIt544w3uvfdetm3bxl133cWWLVtilCI94m731dp7rXpbuXIlS5Y4RxO9d4/K+nvttde47777uv185swZbrvtNt544w2eeOIJQ6WVP/7hH/6BV1999awyhrPLGeC2224r/fyTn/yEZcuW8etf/5rm5uZuf6tFWu3uiSee4O233wbOblfPPfccTz75JGfOnOm1DE6fPs0999zDu+++W1pfnDlzhrvvvptt27bxy1/+khdffDFzspskaLvx2grAQw89xDPPPMNzzz3HL37xi9K7H2wuu7vuuouWlpZSWfzud7/j5Zdf5oEHHuCJJ57g2WefBeCdd97hySefZPny5TzwwAPdnlFZti+88AK//OUvAXjkkUdYsWJFXNmPhOl5+sCBAyxatIjt27fz/e9/nzVr1nD//feza1f2L31NxM2vnH79+nH99dfz1FNP8fTTT3PJJZewatUqXn31VW644QZ27NjB4MGD+f3vf8/UqVM555xz6Ojo4JJLLqGxsZFBgwZRV1dHXV0d559/PoMHD2bs2LEMHDgwaVECE1V2T87Tp09z+vRptNacPn06bbF8cccddzBx4kQ2b95Mv379mD59Opdffjn33HMPgwcPZvDgwZw8eZK//Mu/ZM2aNWzcuJELL7yQrVu3curUKT772c8yfvx4xoxxdnmbmprYu3cvWmuUqm6sOHHixOiELLVVt7zTkt12zj33XG6//XbOP/98Jk6cCMDq1auZOHEiL7/8MsePHwdg69atrFy5kjlz5nD48GEOHTrElVdeydChQ5k+fToAjY2NDB48mIMHDzJixAjq6+vZsWMHdXV1qckXJ3G3+97ae2/1dtFFF7FkyRJ+9atfsWfPHsAxKu3cuZMrrriC9957j4aGBq688koOHz7MlClTOHXqVOnn119/nQULFrBx40YGDBgQm3y2M27cOLZs2UJdXR3/+I//yLe+9S2GDx/OU089xZgxYxg7dixbt25lwYIFjB8/nosuuqj0v2PGjOHYsWN8+OGHAN3+Vou02l1DQwNtbW2sX7+eU6dO8b/+1//iM5/5DJs3b2bgwIHceOONQPWxoqGhgT179nD55ZfT3t7OhRdeyPnnn8/u3bs5duwYnZ2dNDY28olPfKJq3tLsc6YI2m68tgIwYsQIWltbueSSS5g7dy579+5l7lx/MbltLrs+ffrQ0NBQKovRo0ezb98+Ro8ejVKKz3/+84CjYHzpS1/iN7/5DaNHO9l56aWXOHjwINdeey2vvfYajY2NXHLJJXR2djJ8+HAARo0axZEjR+LKfmRMztPr16+nT58+DBw4kMmTJ7NgwQJWrFhBnz7Zvysv0RwqpTh9+jRLlixh2LBhDB48mKamJg4cOMCcOXPYu3cvEyZMYOjQoTQ2NqK15syZM3R2dqK15sSJE5w5c4aTJ0+yY8cOVqxYwd69e3n++eczv6iMKvvBgwd5/vnnAWfxAZS0dW9RkmWmT5/OOeecw5w5c5g+fTq7d+/m2Wef5bLLLqOurq6kLILTOfv160dXVxednZ2lHbt9+/axYsUKNm/ezLBhw9izZw8nTpwInBfPanLkyBEOHDg7Zmi5VcizpHvWobVr1/Kzn/3MWtltYfLkyZx//vkMHDiwxzry2vz+/fsZPXo0DQ0NdHV1lfoMwPHjx1mxYgXt7e2l8WPIkCGsWbOGffv2MXv27F53B/NKkPZf/rMfatXbxo0b2bRpU7fPJk6cSF1dXam9ezsIgwcPZsuWLSUj05YtWzh16hRr165l9uzZ7Nixg66uLt95KxJTp05l4cKFAEyZMoW+fftSX1/P2LFj0VqXxhdvAbt27Vq2b9/O5s2b2bVrF6dOneLQoUPs2bOn9LcoBGlzBw8e5Pvf/z5vvfUWjz3mLwTjoEGDuskzfvx4jh8/zpYtW+jTpw9PP/00Z86cqTpW7Ny5k6NHj7J8+XKamppK64t+/frR1NTE1q1bOXnyJPX19YnIvnTpUhYtWhQ4ragEbTe7du1iwIABbN68GaUUc+bMYceOHUycOLH0HpU4xysTTJ48mYkTJ5bKYs+ePSVDz4kTJ3juued4++23mTBhAk8++WRprgeYNGkSffr0KbVFb+yrq6tj//79bN68maamJrZtSyKMV3BMz9Pjx4+nq6uL9vZ2lFLs37+/tIGQdRI7M9Xe3s67777L+eefz7nnnhtnWpnyqyyy7JC+D395+g8++CBHjx5l4sSJvPnmm1xzzTU0NjYyefLkblah3/72twBcd911gOM24g2OCxcu5KGHHuLb3/52r+knJXe19G2lWrm9++67dHV1MW7cODZu3MjChQsZNMhc3Mu8lJ9HT+UYpv1X9oWy5/tq73HXW095KTJZGm8hXJt75ZVXOHbsGJ/73Od4/PHH+cpXvtJrukmPtX7zEUX2Pn360NzczN///d/3mqYhWVKfq5IerwzJ0Wu5vfDCC1xzzTWB/1YjzUyMdWmM91mRvRqJufmNGzeOcePGdfts+/btTJw4kSNHjnTb1vR44YUX2Lt3L3/yJ3/CW2+9xY4dOxgyZAh1dXX07duX5ubmsxa1WSSq7N7PF1xwAU8++SQ33ngjTz75JP/tv9kbXzaM/OPHj2fDhg184hOfYMWKFfzZn/1Z6PQvvfRSVq9ezVVXXYXW+iyr0N69e9m8eTNnzpxh06ZNfPKTn+Tdd99lxIgRjBgxIorogWQ/ePAgP/3pT7n22mt58sknWbhwIXV1dXz605+OlAfbuPDCCwGn7K666qqSldLPmDFlyhR27drF1KlTre83pvDb/r2fwxKm3rw2P2/ePPbu3cvs2bPZtWsX119/fXiBC0yYeXbkyJE0Nzdz1VVXRR5rPfy2uaNHj9LS0sK2bduYPXt2pDTDyO6NF2PHjjXW7oLKfsEFF9C/f//I6UYhzDzl9dnPf/7z/PSnP+U73/mOkbwkNV6ZYvv27VxzzTVVyw7gl7/85Vll5+V/2rRpXHzxxSnkPBoXXngh27dvZ9y4cQwePLjHHaXydrNy5cpuc/OnP/1pduzYwU033ZRC7qOR+Jkp6G5x+PGPf1yyOPTr16+q3+jChQvZvn07W7ZsYcKECUyePJm2trY0sh+JMLJ7P8+dO5eWlpbSu41Ekf/DDz/k8OHDTJgwgaVLlwZOe968ebS1tfU4OZZ/du2113b728yZMwE477zzAPjYxz4WOG0IJ/t7773HjBkzSnXutf+iEWXM8M7p2NxvTBCm/U+bNi1SmlHavPeZV39CMKL0mREjRtDW1hZ6rPUI2+a+8IUvhE4TzIwXUdtdVNnTMpiZ6LPe71FIY7yKSpSy8+b2rJ+RqkYY2SvnZq8P2kjqp7o8iwPQo8XB8xtdtGgRjY2NzJo1i9bWVurq6pg1a1aaWY+MX9m9n73JzXu3naDy9+/fn/79+9Pa2hpqd+iiiy7ihhtu6PFvfiZN70a9zZs3c++99wZOvxy/sh89epQNGzaU6txr/0Um6JjhndPJS78JS7X276ftd3R0cNddd3W7cTQoQdu891lP56yEYATtM978Gnas9eipzQUZa1evXs0999xTugwjDGHHi6jtLup8k4VbW8P2We/3KEQZr2wsO6+/ZfmMlF/8yl45N3t90EZSiTO1cuVK2traqg40EdPKlF9lkWWH5OSv5cP/gx/8gBtvvJG7776bmTNncuDAAQYMGMCgQYM4cuRI6UKQ1tZWGhoauOyyy5g9ezYvv/xy6UY9cAby66+/nsWLF3PzzTdXTb8nn+Ki1X0Yqvlix1l2brq5KD+P8nI01fa/973v8Z3vfIe+ffv6PjMVd725aeeq7qJQXg9pjDdKKX3XXXcZG2v79OnDzJkz6erqYubMmb7P26QxXpjuc2vWrGHDhg3cdNNNKKUSOzMlZedbjky0uzQosuzVSMXN76KLLgp03WqeKLLskJ7806ZNY/ny5UyYMIGpU6dSX19P3759Wbp0aelyia6uLmbNmkX//v1pbm5m9uzZPd6oN3z4cDo6OgJfTV70uo+ClF14TLT99vZ2mpqa6OzspG9f/9OG1Ft65GGs/dKXvsSLL77In/7pnwbKQ9rtzkQZDB8+vHRra0NDQ2J5l7ILT9pllyZFlh1wtuDifjnJ+OOZZ56p+Z1NmzbpRYsW6VdeeUU/9dRT3f7mppWIXCZl9yN3W1ubvu+++/SqVav0j370o9L7sWPHMim7afm9et+1a5e+88479enTp0t/qyZ7rfRfeeUVfeTIEV957I3K9IO0ea39ya+11rfcckupHfSWvq2vOMrNKy+v3XR0dOhbbrmlW73npfy8l59yjNL2o7Z3rYPV3erVq/Udd9yhjx49WjMvRX7FMd72VPZhxlsTY21P6cYxZnhjxDPPPHPWGiNKPkz2OVMv0+uzlpYW/fOf/1yfPn1a33LLLb7ksLHs4pyrVq1ape+4446z5M3KWBeH7F672bFjx1nruyzJXu2V+M6Uny3c3/72t71u4U6bNo23337bquClJuT2guS1tbVx9dVX89vf/pbrrruOtra20iUJWcVkvY8ZM4aTJ08ayddVV11l5Dm1MCH/+vXrmTRpUrcAvnnHZL/x2k1TUxMXXHCB8Su6bSPutm+y7pRSpdgjQm1Mjremyt6msXb06NFccMEFTJo0iQ0bNhhbYyRVBmExUXarVq2iX79+pfnKFEUoO2+8GzFiBEoptm/f7jvwcZqYbDejR482tr5LksQvoKjcwr3iiiu4+OKL2b17d+k73hbujBkzaG5uBui2hfv666/T0dFhVfBSE3J7rg/jx4/nxRdf5Morr+TFF1+MNXaVKUzWe7nLkS2YkH///v20t7eze/duVqxYUYiApSb7TXm7kUV5/Jisu1OnTjFp0iSOHTuWiiy2YXK8PXnypFVlb0J2cM5olLuLFQETZTdnzhz2799fmq9sXBiHweR454X/mTJlSiqyBMVku9m5c6d16zsgO25+cblbpf2qJbspubXOnuw6Qfmryd5T+n62nH/yk5+Uttwr3eq83zs6OkrPqkzfT5vXOr/tPuwr6XLzyEv5ea8o7b7SfbjcPTJqe9da6i7Ol83jbaX7rZ92Vy3Nnoh7rq2WD7/yt7e3d3Nv8twsPfnjaudJrs+0jqfsXnrppW7umN4Y1traGlvZZaXdpfEqsuzVXqlcQNETWd/CjYuiyu2RhPzPPvssdXV1tLS08LGPfYzW1laam5t577332LRpE9/61rcYMmRIt1gII0eOLG25V7rVeb+PHj06ct6KXv9hkXKrTdh277kRe+7D5e6RJpC6S48sj7eea53nfmtruwsr/7hx47rt4nhulibmmahkvexGjhzJvn370Npxx/TGsCzsihV5vCuS7KnGmfITL+CnP/1pKa6PF3uinFtvvZXdu3dbF9TRr+yrV6/mzjvvZPXq1Tz44IPdtj7zLntlfXvtwHv3K/upU6cYOnQow4YNK0V1f+211wCYMmUKffv2RevusRDgoy1373358uV0dnaWfg/jZhe0zVfGy/DKZOPGjTz44INW1n8YgrYZr/wq49R4f9+9eze33nor27Zty235hW33nhvxBx98QGdnpxG32qD1VxnXyusHRWnvJvFb9i+//HK3scbrQ15dxD3eguNa9+abbybe7rx59ujRowCl8TXMOBtWfk/ed955h87OzpKbpWOUT5ag/XXNmjXceeed3VxBb7311sDlF7bsPHdMr+14Y1haRyDCzlceNs9PQccbT9bKvmfbWJ/ozlRYq4NnoancIfAOOGbBclOLsLJ7BxGfeOIJZs6cWRpYiyB7ZX177SCoxe6LX/wiAJdddlmv3yuPql5fX8/w4cO57bbbAErvAE1NTdx2223s3r27ZhDYqG2+8pIVr0y8w5q2BrirRdQ245Vf5S5L+a6id8B8zZo1aYsbC2Hb/YIFCzjvvPNKn40fP54///M/99XePaLWX+UlM14/GDVqVJAiKCSmrPuVF/743SEK2+66urq48cYbS58l2e4qD/x742uYcTas/GPHjuXP//zPS59dccUVXHHFFYHkD0vU/lp5SYm3PglafmHLbty4cXz7298ufXbeeedx3nnnJVJ2YG6+8rBpfoo63owaNarbjrSta5tEd6bCWh08C03lDoFNBxzDyt6vXz/69u3Ll770Jfbv389bb71VGNkr69trB0EsdvX19bu9oH1BXldffTV1dXW9fmfMmDGcf/75KKWor6/f3VP6Udu8d8mKZ3HzymTWrFns37+fQ4cORaqbrBK1zXjlV7nLUr6rmOdLKOJo937au0fU+qu00hftIoAomLLuh7nwx9Z2582zhw4dorOzs3QYfuzYsb7H2bCy+5W/luxRiNpfvQtiVq5c2W19MnPmTF/lZ3PZmSg/r6+98cYbpX5my/wUdbw5ceJEtx1pr+9Zt7ZJ4mAWIWKQeLz44ov6zJkzvX6no6ND/+EPf9BaZ++QWpFljyK/7bLHXe8eXhlkTf6wr6TKzSNv5ZfWK0q9eQTt8x5Sd9HrwebxNmrbC9PusiR/1FecbcYjq20nyqvI7a7Isld7JeLm51odEvFHi9sCEZQiyw7JyZ812ZOsdy+9pNKKk6TLrTzdpNPME2nVm5d2GulmkSKOt2m0vSzJHxWZq8JR5HZXZNmroRyFLzsopd4E/ovW+jX390eBx7XWi9PNWfwopR4A3tZa3+v+/k9AP631f081YwmglPoOMFVr/dfu7zcB/1ZrfWOqGUsApdQ1wP9Xa/1J9/c5wDNa6+np5iz7KKVuBP5Sa/159/eLgJ9qrT+WasaEmiil/hW4VWv9ivv7w8BzWusH081ZvlFK9QcOAsO01ifdz9qBy7XW29LMWxIopdYDX9Nar3Z/fwG4U2v9bLo5yyZue9kJnK+13lHlOy8A92mtH000cxahlNqDU4Y73d9XAv9Ra/1WujmLH6XUK8C/aK1fcH9/EFimtX4g3ZyZI9Xb/CpRSvUB5gFryz5uBhakk6PEWYAjr8caYH5KeUmaStmLVO/zceraYxMwXik1MKX82MQCupfdOmCGUqpfSvkRfKCcAwFF7vNpMhvY4ilSLoUoe6VUPTAZ2FD28RoKIHsErgXWVlOkXBYDNyeUH+twd3H6ArvKPi5Kn/PG+vJ5Ondr20wpUziD3EGt9YGyz3JX6D3hKpJzKe7iorKzbQLGFkSh6Ca71vo00ILTHoTemU9Zn9FaHwe2A7NSy5Hgh3OBE1rrvWWfFWKszwCVSiwUp+xnA5u11qfKPmumGLKH5Wbg4RrfeQL4tFJqWPzZsZL5wBrd3RWsKEr8aEABHWWf5U72rClTlRZ6yGGhV2EKsE9rXX6FySZgjFJqcEp5SoSeFEmtdSeO9XBeWvlKkGqLmyK0+6hUKuEgiyMbKPJYnzZFLnsZawOglBoEfA54rLfvaa0PAi8DX0wgWzbS0zxVFAPGAs5WJHO3UZA1ZaqngW4LMCrvCgUVFnYArfUZYD3536GYCnygtT5c8XnuF8VKqTpgDme3+9zLHhXXl38q3V12QBZHNtDT4mI7MEQpNTyF/BSJIhsgelIk1wEzxTW4R/4YWK61/sDHdx9GXP2qcdb6jhwqFFXoSfZ24BylVG4CB2ZNmTproCtTKPK+Q9HTBAfF6HA9TXBQjEXxVGCP1vpIxedFkD0qPZ39gOJY/GymJ+NRF855Wam7eOlpcVMUheKseVZr/SHQBsxIJUfZ5mac81B+WAJcnNatnhmnp/XdTqBvnhSKKvTU5zQ5m6ezpkz1tDMFxVAoqsmeqwZXhSLXe08LGyiG7FEpsgHCdqTuUkApNRRoAraWf+6eNdwBzEwjXwlSbbwV41UFSqlG4I+AJ/18321DS4Cvxpgt66hysZqnUBRhvOttrM/N2jYzypRS6hxgGs4uVCVFUCiq7c4UubMVod6ryb4DqFdKjUw4PzZRbWFUiLOGtqKU6otzQci6Hv5chD6fJvOBde4uYCW5Lnv3coThQGsPf87Vws4QXwae78FrojfkVr+zmQzsd8+VVZJrJb7sPPzaHv6cK9kzo0zhTK6tWusTPfwtV4VeSS9nPyDnsrtUUyTbgf45Vyh63JUrs1rJBF+dHhVR1zV4A/k/a2gr04GdWutjPfytCONdmlQbayH/ZV9Lkcyz7GH4Bv5d/DxexAlNMdl8dqylmsEUcm7A4KPz8Id6+FuuNgqypExVc/XC/XyBe199HpkNbO3h7Afk3K/WVSSn4FwF3o2CbIP3trgRZap3ak1SeW43NtNbveV9rE+bmmWfYF6SpsiKZCCUUuOB84DfBfk/N6zHY8DX48iXpdRc2yaYl6SpNd7Mc3evrCdLQvRW6Ltw8ppLhYJeBvkCKBRz6PkSAY/cWm7KAkiepUi6yARfBffsRyMVZz/KkLLLLlUXF27cqRPA+ERzVBx6W9jldqx16W2NUaS4hn74KvBkFU+hWizG2dUSHGoZTHOjUPRANVd83HiyB4FJSWYoLrJUgb0Vet4Vit4mOMj3wrBqvbvkud7ncHYAyXLyXO9R6c1lB2RXL8v0triAfPf51HB3+3or+804CsWg5HKVKL0p8Z04Rq283xrslyC3+FXyGjBcKSVl6dBbuzsIHMAxquaR3gwYkKOxPkvKVK1Cz7PVzM/iIq+yS71Xx7NaicvT2dQqO1FEs4sf41Fe+3yajAW6gD09/bEsUHruzhr6UCRB2h0ASqkZwETglTD/7xq4foVcRFF+Hr6ni9U88jxXFabPZUKZUkoNAUbiBOitRp4bnB+Foqiy53kbvFfZtdb7gSPkZBvcMLUW5Lk+a2grSqkBOC587/fytTyPd2kyH1jjenpUI69lPw44rbXuUZF0yY2VPCJfBx51leuwPAzcLIZAZlH9PLxHLvtcb+fhy8iN7FlZoM4D1ru3cFUjlwNdtbgfFeRZoejVzc/1qz1MPhWKWgoB5HtXMgq1FNG8uwbbylxgo3tQvRpSb/FQy3AF+S17P2NtbhZ2YXGVn5txlKEovAd0ApdEzpTdFHmOr3UeHnI03mRlce53kJ+bQ4ViHr2f/citX60b96O3SwQ8crMVXEGtLXCQCf4sfLrsgJRdFvGzuFgLzHLjUQnm8KtQFHmszaPsQfgY0AC8GeUhrjFLYk4Ve57yI/t6YJobZ9ZqsqKY1FSm3Hvq9+FsG+YJP4ok5LPDzQfW9qZIuuROdqXUcGAYsK3GV3MnuwF6PftRhiyOskfNCdaNP7UTJx6VYI4iL+z8zLPtSKD0m4FHariC+mUx8FWlVJ2BZ9mKn3a3AZjqusXliZrGG/e2yFYcd0iryYoyVetGN488bof6lT2Pk1zR692PIplH2aPi5+wH5MiFIEf4NR5JuzeIu6Cdg7Pr1xs7gXNyeNbQz8JOU2ADjOv1E+UWv25orVtwwtpcaeJ5llJzjZMnhaICP8YbyMk8nboy5brsFHl3psiLiyLXu9+BZh1ORPl+MefHJvy4K0F+XYNtpsjGozSZCuzRWh/p7Ut5VChcRXI2tRVJyMnCLiSX4Vx45Gde8kthY065F6uNoPeL1TzyON75nadzMd5kYZExGlBAh4/v5mqgK1Mki7q48Luw2gBMz4NfbRm+FEmt9XFgO/mzWkXBb9kdJIdnDW1FKdUEDAR2+Ph6rsb6DODXcAX5K/vpwC6t9VEf383jPOuXm4HFhlz8PH4FfDGHLmx+mI9zsVot7xPImbE8wHl4yEmfy4IytQB/LjuQEw22jDE4Zz92+/juBpyDerkYlILsSLoKxTZgZtz5ShC/SjTkbKA1gN9dPcjfwtBmFgDNBR3r0ybIeJO3si+y7L5wL3v5CvCIyedqrXfg7Ahea/K5lhBknsqFQlHGPPwdY4CczNGZUaZ8fjdvB/V8K5KuX+1W8rNDMQ7o1Fr7USQhR4NNgNvoPHIje1QCnP3wKOTiKKMEafPvAxPcuFRCdIo83gQ1vuQ1DElvfBpo1VpvjuHZD1PMW/2CrG3z1ueCGDC2ACNdt0hrycKA4dfVC/e++q04/s95IEiDg3x1ON/17pIL64XLeOCU1nqvz+/LztRHTMPH2Y8y8tRubMf34sKNQ7URJy6VEJ0gC7u15Euh8D3P5jyuYW+YiC1VjceA65RSg2J6flYJsr7bCoywXaEoI8hYfwbnivR5seYoZrIwWAYZ5CFfluYgFjPI16Ja6t0/eVKioyJlZy9hjEd56fOpoZRqwFEONvr5vqtQHCQ/CkWYMaMw7U4pVQ/8MfDrOJ6vtf4AeN1NoxAE9T5xFYp15KfdFa7PpapMuS47c/HvsgP5sjQXeWcqqOx5q/cgA80mYIxSanBM+bGJoO0mb67BVhLCtRXy1efTZDawSWt9KsD/5KLsXUVyIj4VSZdcyB6A64D3tNa7YkyjaAF8x7jvfo8xQE7aXcCL1TysX9umvTM1BfjADcjrF+s1WOh29qNQDa6MoAurzcConCgUgQYa12q1AXF5goDtJscxPGxjInBEa70/wP/kYqzPAEGNN5Cfsp8LvO+6jfolL7L7xVhsqV54Evgj90bPIuA3FmI5eWl3Ywl2Hh5yoEimrUyFHeStLnQXX3E/KsiFX22ISwRy41frElSRhPy0+6gUeWFoM0HPSIK0eVMUuexlrO0Fdy1xDfCbONNx1znPA1+OM50MUeS1bWjZ3V0tK8mCMhV0kN8KNCmlhsaQnyQJLHuO/GqnA7sDKpKQA+uFewWt3wCS5RReIQh69qOMvExSNhNmgt0BDCyQNTsuwpS99WOtSxjZ8xjXsBo3Akvdc3JxUyRXvzBr22YsVyhcwsjuxZkdbTgviZG2MhXYauTeW78O+3cowljMIB+TXFjZ86BQeAEkjwX8vzzUe1TCnP0AKbssEMZ4pMnXpTtpEWZxsx4nrqHtCkWYdpfHuIbVSMLFz+N3wHlKqfEJpZcmYdY4HYDmo/NWthJmXe+N9dbO02krU2EGeciHpTmK7LYvLopc71EUSdtlj0qR+4ztSLtPAaXUcGAIjnLgmxydNZR2VwWl1EjgMuCZJNJz29RTwFeTSC8tQl6s5ikUeWh3hZynU1Om3Os4J+NsqQclD9bKIg/yUXblbK/3MG4nADuBfkqpUYbzYxNh280WcnDW0FaUUv1wrPzrQvx7Hvp8mswH1roeHUGxuuyVUo3AIGB7iH+3WnaffAV4NoSXRBQeBr6RYHppMAXYq7U+HOJ/rW53riIZ5hgDWL62TXNnajawxQ3EGxSrC71MkWwJ8e/NwHzL/WrDKhS7gDqllLV+tYS02uTIahWFUO3GXUiux+JJynJmAjtc96mgFL3NRyWslRjsL3tPkQxyo5qH7bL7IUkXP49XgAlKqRkJp5skYdc3YH+7m0a48/BguSKZpjIVZZC3/aDeHGBziLMf8NFBPSv9akPG/QDy4VdL+N0VsHwb3ABRF4ZFLrs0idLm82A8SpOoZW/zWBtlUWu77L2ilJqIsw55Icl0tdadwKPA15NMN2Eir20N5iVposo+VymV9vGjUKSZ6SiD/G6gC0sVCiLInoMdijmEu0TAw9pFsVJqADABeD/kI2wfaEMT9uxHGYUtuwwQelGrtd4HHMPpN0JwolrJrRxrXaIs7PIU17AnvgY8HmEejsJi4Bs5NpBENWDMcd3lbCTK2vYwsA8nbJB1pL0zVVSFIsoEB3Zvh0aV3eZ6nwNsDBhAshybZY9KlLMfYP/C0GbCxDkqp8jtPjTuYjVK2W/BboUiysIuT3ENe+IbJO/i5/EGUA+cl1L6cRNlbXsY2IulCgVm1ndWztNp70xFmWBttjRHsZiB3YuLotd7VCV6nq3b4BExsiDPsTU0y5ho97b2+TQZD5zUWu8N889lCoV1ixsDiiTktN0ppWYDo4BX00jfNYY/Qg5jTkU8D+9hu7G8kGvbVBZlSqlhQCNOAN6wWKvBEm0bGOzvbFFlt9WvNtJAo7U+CBzAGayLRtR2s9t9t9U12EqUUoOAsThuU2GxeaxPk6gLG7C37M8FjmutP4jwDFtlr8XNwK9cZTktFgNft3Qe743ZhD8P72GlQhHlPHwZ1q5t02rIUV12wN4GNxwYRvizH/CRQmGjX22kRbHW+hCOX+0UYzlKjqhKNFja7g0QVRH1XIOtHKgtZi6wwT14HpaitvmoFHm8iWp8gRzuTLk7dmm6+HmsAY4Cl6ecD9OYaHe29rmo5+HBXtlTVaaiWszWYudBvciKpK1+tW7cjyGEi/tRjq2TnAlLsbWWm7CUuezI4sg+TCwu1gMz3XhVgn+KrFCY2pXLm2vwRYAC3kkzE65x62Hy5+pX5DnexHjTAkxRSvU3kJ9ESUuZilzo7j32e7BMocDMohDs7HDzgeaIO5Jg4Q6DUqoJGEh0RdJay00EIp39KKOIZZc2kRcXWusPgR1AnmPTxEGRFQoT8+wunDVSngKl3ww8HDL2lmkeAb6ilOqbdkYMYqLdbQAmu+evbCKy7G7c2S047pJWYa0y5WLj4khkj46NsnuKZNRJzDpF0gAmFoVQzLJLG1PGIxv7fGq4C9RZOB4cUfDiGtoWKN2EwTYPcQ1LuF48Xyd9Fz8AtNabcc7NfzrtvBjERLs7hXPGdI6RHCWHyXnauj6XuDJl6JYdD2t3Zww8x8aFYZHr3dRAswGYppQ6x8CzbMHUgnwt9p41tBVTBhQb+3yaTAd2aq2PRXlImUJhTdm7iuRMYJ2Bx9k4z1bjE8BerfX6tDNSxmKcM1zW416sNoxo5+E9rOpzLkX2ukplZ2oc0Km13l3zm7WxSoN1FUmTiwtrZHcxJfsGYKplfrVGBhqt9QmgFQu3wSNgRBEtO2to4+Ul1qGUGgWcA+w08DirxvoMYMpwBfaV/Qyg3XUPjYqN82w1biYju1Jl/Ar4Y/cmONsxcbGah1V9zr1YzcR5eLBMdo80lCmTg7xtA9144JSBsx9gmV+tyR1J1692K3YpFKZ2piBf1lI/mLJ4gX1jhs3MB9YYOp8h9RYMU4YrsK/sTY+1NsneI64nw5dxzillBq31LuBd4Lq082IAk+3Ott2ZBZhTJG0bb4B0lCmTg3wLMMkWhQKDi0LXr9amg3rnAicMKZJgkUJh8DY6DysHmzC4LjuziX72wyMXiyNLMLm42ASMVUoNNPS8vFNk443psdbWuIblXI0TosCEC5ppFpOPW/1Mtjvb5imTsm8FGpVSQw09LxGsVqZchWIT9hzUM6lIgl0drsiyTwCOaa33GXqebYubKEwHdkU9+1FGkcoubUwajzpxduPnmXheASiyQmFyjXEI2I/9rsFZiC1Vjd8AV9u2eO4Bk2ucVmC46z5nA8aMN+7u1losm6dtd/MDu6z0Jq2FYNfCUOrdHDbJHhWTi0IoVtmljWkDitSdD5RSA3AMOO+beJ571nAf9oQhMT3X2GS4Owu3PXweeDTtvPSE1voAsBS4Md2chKfsPLxphcIW41EcY70ta1sgYWXKvUVrDuZcdsA+haKoi4s4dqZsqXfTsm8BRiqlhhh8ZlYxrYhaddbQVtxdjHkUd6xPk7nARq31aYPPtKLsXTfQ8RhSJF2sW9hV8AXgLa31nrQz0gu2u/qNwzkPb7KMrVDiDd/Q7WGF7OUkvTM1DehwA+6awopCj+HsB1giu4tpRXIr0GSJa4DRgUZrfQbn2l9brFZRMNpuLDxraCuTgIOu1dkUNo13aWJ6rAV7yt5TJDsNPtMW2atxM/Bw2pmowTPApe4NoDZi2mAKlhgwMH8eHuzaKACSV6ZMW5nBHqvRNMye/YCP/GqHGXymccoCSJqI+wGUtsFtUSjiGGitG2xCEtckVYSyS5O42rwNY33aFLns41IkbZD9LNwzN58Enkg7L73hrot+C3wl7byExPTODNgzx8fW59xdLytIQ5kyXejbgGEWHNQzLrtFB/VmYCCAZA9kflGslOqHuQCS5Vg7wfvF9NmPMjLfbnJAHIazdqC/Umqk4efmjTjK3pY+E8caw8a4hh5fAn7vnnvLOg9jr6tfbEY/CxQK4+ONG4f2DDDW5HPjJGllyrj2bpFCEccEB3YsquOw2oAdlpsZQJuhAJLl2LK4icIczJ/9AHus7DZj3Frpxquyoc+nTRyW4hZgigUKRRxrDBvjGnpkMVBvNV4AZiulJqadkRDEoVDsAU7jnMfKMnEokmDZPJ2HnSmwR6GIq8FlfXFR5HqPc6CxwWoVhTjbTdb7jO0U2XiUGkqpJmAgsMPkc12FwoazhkWea7qhlBoLXAQ8m3Ze/OCeZ30c+HraeQmCe7Ga6fPwHjbMVXGtbW2QvURiypRSqgGYCGyM4fE2FHqRF4axdraMKxRx7cp1uO+jY3h2Voir7Fqx4KyhrSilzsE5I7o+hsfbMN6lyXyg2d3FM02my14pNQJoANpieLwNRstKvgo8rbU+nnZGArAYJyaWTXixEI/G8OxMK/FxnIcvw6o+l+TO1Bxgk2t9ME2mCz3Gsx9gx0G9uKzUe4Ausu1XG4sSXRCXp7jKzhbXYFuZBbRqrU/E8Oy8t/moxGW0g+yXfWEVySrY5OLn8SpO2I85aWckAHGtbyD7fS6u8/CQcUWykiSVqTgH+awrFHGd/fD8ajvJqF9tTHE/gJJCkfUOF3e7z/JAG5U4J6m8l12axL24mOfGsRLOJu4+U9Sx1qrzG0qpqThBll9KOy9BcMN+/Aq7LqKIy/MGsj9PxeU9Ao7Bc47rRpl5kpyQYmtw7v32J3EW7VkkzkEest3hYlMkXTIru6tIjgU2xZRE1hc3oSk7+7E9piSsWhxZRpxj/QHgIE4cK+Fsirywi1OR3II9cQ3BUUYejXHejZPFwDcybByvJM713VqcSzmyqlDEJrsbj3YPjst45kl6ZyqugQ6yvR0at+xZXlQXud7nAS2GA0iWk2XZoxKnyw5kf2FoM3Ebj/Lc7kPjLj7jtBS3Ao0ZPmsYpxLvxTXM6jxbiY0ufh7vABpYmHZGfBLbGsc9h9WBcy4riySxUWBFn8vFzpRLlgs9btmzvLgocr0noUjOyanLU5yLQijGbYhpUWTjUZpMBI5prffF8fCys4aZC5SegCIJluxmK6UWAEOA5WnnJQyuAW0xFrj6uRerTSCei9U8sjzeJTJPx/h8YySyCHMD6g7BCbAbF1m2NBdZe49b9iz71caqSLqBGD/A8Y3PG7G2GzcooA0xPKxCKTUEGInjFhUXWR7r0yRuwxVkt+wnAke01vtjTCOrsldyM/CIq/zaymLgaxmd18uZS7zHGCCj7c49xnAu8Vys5pHltW03krJoLwDWxuiyAxm1GsUV96OCdWRXoYjVSl3mV5tFhSJuRRIsstwEJO7dDcjoJGU584D17kHyuMjkWJ8BkugzWS37pMbaLMpewt2hs9nFDwCt9XpgL/BHaeelBnHvzEB25/i4z8ODRXN0UspUEhYz76Be35jTCUrcZz88hSJzfrVu3I8BxKtIQnY7XBIDrTWWG7+UuewUfnFkIUnU23pguhvPSviIIu9MJWZ8ybhr8KXACeAPKefDBA+TfVe/JJT4rM7xSci+EZjkulNmmiR3pmId6Nx77neRMYWCZAZ5yGaHi12RdMmc5UYpNRKoJ54AkuVkTnYDTCDGsx9lZHVhaDOxT7Bu/KptOPGshI9IbCc8gwpFEorkbrIf1/BmYHECc24SPAJ8KeNGkyTWdxuBiRlUKJIY60/h3IY8O850TJCnnSnIrkKRhOzNwCUZc/Urcr1/nGQUySzKHpUkdvRw0/iYUmpwAmnlHjc4+XkU13iUGkqpMcBMnF272HDPGnaSIYXCPaf3MeI32HqB0jPZ7pRSg4CvYrmLn4fWejuwAbg+g8q7R+xrHNeN7n0ct7oskeQ8fXkC6UQidmXK7QRJ7s5kzdKchLUQ4IvAX5Mta21Ssmex3n+Gc45tVMzpbACmKKX6x5xOkiTVbm7AWfx/O4G0isCfAlcAX04grSz2+TR5AGc+/1wCaWWt7P8O56zeHyeQVtZkL+ctoA5n9ywvfAj8lAzuTCilGoFBxBcLsZwstrvY52l3c+CTwP92jXWZJYmdqXOBE25g3bjJlMtTQte1evwYpz43JJCWX5JSorPoV9sGrNJa74kzEXcbfAvZs1pFIal28yOchce7CaRVBJYDCvg/CaSVqbE+A6zHWXj+NoG0slb27wJncObAuMma7OXU4wS03pVyPkzyIs5N0EmsH4OSxMVqHplqd0mdh3cvMnoG56bOD+NMKypJKFOPAMeVUklErJ8NfEop9ZUE0vLDfTiWotitKlrrO4DhWbkOVSn1H3GC7iVxhm0WcJRsuTdcCHwm7kTc2yIV8GDcaSWBUup6HOt67Mqh1noHMFhr/ULcaRUBrfUqYJDWOs6YKx6zgCuVUlk/oJ4U/x9gnNb6eAJpTQP+k1JqRgJp1URrvQQYorVuTyC52ThuZ0nsAAblc8B5WV90BkFr/T3gfK31B2nnpRzXUH4fMMB1r4ybWcA3lFKXJZCWH34FHCGZ9d2/B5LQHyKRxM13A4GhQNyHycHZlRmKE3snC5wD9AeSGORJaCL1y2mgH87hwbjZjbPdnpmdKa31yYSSOoJjuetMKL242Qk0Ai1JJJaxPmM9CZbnBmAwEGdcIWtwreNJlf0hnLhOicxrfkiw3b0PNBH/DbWBSciIkTiukSZTaK21UmoYjiEzibbXjnNOMQmXQj8MwFl3xOp5A4mPbaFJQpn6NnBGa3007oS01r9y/ViXxZ2WT24BntBat6adkRR4CEBrHXtdaK33KKX+GOd6+EKhtT6llLoWx502D7wHfFtr/UDaGRGyi9b6CXf3+8W081JAvgkszdMOSAAW4Xj0rE07I0Lq/AWOm1+cMfU8bgHaEtp99cPfAPVa60NpZyQrqHzcoJlPGhoaOk6cODE6yTTr6+t3Hz9+fEySaWYRKXs7SaPeIB91J2WXPknVQZbKvOhjbZHlL7LsRSWteQbirXtRpjKMUirxcBFKKbTWWb2GNDGk7O0kjXpz07W+7qTs0iepOshSmRd9rC2y/EWWvaikNc+4acdW94EuoGhoaOhQSum4Xg0NDVXdtOJOu7c8pJk2wJIlS3jiiSdYsmQJ777rXDz25ptvcuzYMQBeffVVOjs/OjJT2VCPHDnCL37xC1pbWwH47ne/y9GjR/nlL3/Jiy++WPo9a+WehXaXJlko+yzn32++/+Ef/oFXX32VJUuWnPW3ngb12267rfTzT37yE5YtW1Z6f+ihh3jmmWcilEr2iTredHV18eijj7Jy5UpOnz7NPffcw8qVK7n99ts5c+YMt99+e808ZKXtpInXbp955hkOHDhQ+rxWm/V+/t3vfsfLL7/M/fffz/vvv+8rzTTLPWq7g4/mNm/Oe//997nnnnt49913S20yLfltaHNFJc9r21rtLu3080IgZerEiROjtdbE9ept6y/utHvLQ5ppe/Tr14/rr7+eHTt28E//9E/s37+fVatW8T//5/9k5MiR7Nixgw8++IBHHnmEFStW8Ic//IHnnnuO/fv3M3jwYMaOHcvAgQM5ffo0p0+fBqCxsZFLL7209HvWyj3tdnfXXXfR0tJSWkh7i5MHHniAJ554gmeffRaAd955hyeffJLly5fzwAPdj/pUTvjr1q3jn//5nwF44YUX+OUvf5npso9CmuNFOePGjWPLli0cOHCAf/zHf2Tnzp0cP36cRx55hGXLltHS0sJzzz1He7vjjn7RRReV/nfMmDEcO3as9D5ixAja2triKK5MEWW86dOnD4MGDaKuro49e/Zw+eWXs3v3bs4//3zq6uo4//zza6aflbaTJl673bZtG+3t7b7brPfzqFGj6OzsZOvWrRw/7u/8dtrlHqXdlc9t3px3zjnncPnll9PR0VFqk2nJ35vsP/zhD3n88ccB2L79ozsGvJ83bdp0lhGicm45dOgQd9xxBwCtra0sWrSI06dPc+utt3L48GG+//3v+1aqk8SE7B0dHdx77718+OGHHDlyhPvvv59du3Zx7733diuXauR5bVurz6WRfq06X7lyZcn4WWkEraz71157jfvuuw+AM2fOcNttt7Flyxbuueee0nsSJHE1uhARpRSnT59myZIlDBs2jMGDB9PU1MSBAweYM2cOe/fuZcKECQwdOpTGxka01pw5c4bOzk601hw8eJDnn38ecCx9AHv27OHkyZPs3r279LvQnT59+tDQ0FBaSHuLk9GjR3POOefw+c9/HnAGgBtvvJFdu3YxerQzbrz00kv85je/4dixYzz33HO8/fbbAMydO5c5c5xbvzs7Oxk+fHg6whWIqVOnsnDhQgCmTJlC3759qa+vZ+zYsWit6erqKvUVgLVr17J9+3Y2b97Mrl27GDBgQOldKVWqv7wSdbw5ceIEZ86c4eTJk3R1dbF8+XJGjx7NihUr2Lt3b+ld6J3ydgv4brPez3v27GHAgAHMnj2brVu3piWGb6K2u23btgHQ3t7Ojh07eP755xkwYADLly9n6NChnDlzhq6uTEQO6ZFzzz2X22+/ndWrV5c+835++eWXSwrx1q1beeyxx1i3bh1vvPEGzz33HMePH2fo0KFMn+7cVN3Q0ED//v3ZuXMnAwcOZMiQIUyePJkZMzJxm/1ZRJV9zJgxjBgxgr59+zJ48GD69OlDnz59GDFiBA0NDaVyEbJDb3XuGYR+9atflcauN998k8cff5w9e/bw3HPPsWyZc7fZ4cOHmTJlCqdOneKtt95iwYIFTJgwgWPHjpXekyDybX7bt29n4sSJHDlypMfF4QsvvMDevXv5kz/5Ew4ePMhPf/pTLrjgAjZs2MBf/dVfRU0+cB7KfzZFnOnv3LkTrTUXX3wx557rXNh25ZVXVv3+Nddc0+Pn3/3udwEYOXIkn/jEJwBnsgb47//9v/vKS0+kUfZh2ty/+3f/rtv7d77znZrpTJ48mYkTJ/Liiy8ybdo09uzZw+DBgzl16hQnTpzgueeeo7GxkQkTJvDkk08yduxY9u93bmqeNGkShw8fLk32Z844F/60tbUxYcIENm/eTF1dXaRFZdrtPgxJ1V05119/PQDz58/v9nl5PypXkP7rf/2vpZ+nTZsGwFVXXRUoTVsxNd54ZQ7wH/7DfwA+miDLXdKCYmObD0tP7dZPm/V+njhxIgAf//jHI+UjiT5rot01NTV1m8u8Oc9rf2EII/u8efOoq6tjzJgxrFixgj/7sz+rmc7kyZM5//zzef3117u5dHp4xs79+/czevRoGhoa6OrqKs0vAMePH2fFihVccMEFdHZ2opRi27ZtnDx5koMHD6JUNo8JmZB98+bNrFmzhksvvZTOzk4GDhzIhg0bWLNmDZ/97GdZsWIFn/zkJxk4cKDvfAWp+5UrV7Jr1y4GDRpkZG2bxjwZJQ9Bx9padb5x40Y2bdrE2LFjS59NnDiR3bt309nZ2W1NNXjwYNavX8+UKVP48MMPWbt2LQsWLKCpqYnW1laampoilIJ/Al1AodRHB8cefPBBjh49ysSJE3nzzTe55ppraGxsZPLkybz22ms0NjZyySWX8NvfOgHZr7vuOl555RWOHTtGnz59aG5u5u///u8rn4+ucjisPG2PMHko/7lKOmfloae040i/Mu1q6caJ3zzELXvF37TWOlKbGzhwYLf38sVekDoHZxCptojs7W+1yGLZR8HUeFGt7oLWW5zEVYZJkqWyq8yLLW0+KknVQTX5i9Jna8kfRXbPYPaJT3yChx56iG9/+9u+0izn3Xffpauri3HjxrFx40YWLlzIoEHmYsNmpf1nRfao7R4cd7Soa9uk+ly19MPmIeq6Nu46r5YXkxhx87v00ktL23Na625aY11dHfv372fz5s0cPXqUDRs20L9/f/r3728i6cB58H42TdLpe76lR44c6VGzLz+P4/mfvvXWWzz22GPd/FFNkEbZB21zle9RmD17NtBz2V9zzTXdyv7gwYN8//vfL52VWrt2LT/72c8ipV9O2u0+DGnVXZg+s2zZMu6+++7Se1EJUnZeG/c+8863mMLGNh+FIGVfOcabGutt6rPemOu9RyWo7LNmzaK1tZXW1lZGjBgRKs0LL7yQhQsX0tnZyVVXXYXWuqb83s9Lly5l0aJFxueapAgju1fXnuymxhy/de/tpJhc26a5xgmah6hjrVfn48aNY+rUqQwaNChUn/fqPw1C70ytXLmStrY2brjhBnOZCbgzlVQeqlnOTKfvR4OPYjX53Oc+x+OPP85XvvIVlixZ0m13JkgekpC94m9aax1LfVdL21TZe1aj66+/nieeeIJPf/rT3ayVveUjC2Uf8bmJjxem+8yUKVP4xS9+wb/9t/+WX/ziF/zLv/yL77zYhqmyO3z4MA899BBTpkwBnLM+AJ/61KeqpVuzHm1p81GJaqU+c+bMWWN8T2O9n10SW/tsb14IveWjPC9Jrm9Me90ANDc381d/9Ve+55q0MD3PejtD3lnDnsacWm0wzbVt0mlXph9HHuL0uIHufb7azmC1vJgk9M7URRdd1GNh+7GCtbe3c//997N69WruuecePvwwXCD1nvIQJP2lS5fy9NNPh0q7p/T9pN3R0cFdd93FmjVruPPOOyMdjgtqNVm0aBGNjY2l36MQtuw3b97MvffeG6ruq7W5IOl75V9+xW4YglqNPNePKNZKjzDtzlSbD0tW6i5on6mrq2PWrFml9yLjt+y8Nu591tTUVLocICxR5htvzLEZv2VfOcZHGett7bMmLPS9yR5kvPXeoxB0d8DbHTEx16RNWK+nKGNOmmtbE31u9erV3HnnnVXD3YTNQxD5oxK2z8fh9eaXwDtTd911FzfeeCN33303M2fO5MCBAwwYMKC0LefdwNPa2kpDQwOXXXYZs2fP5uWXX+bUqVN89rOfBT7yL505cyZdXV3MnDnTl/b+gx/8wFj6kyZNYsOGDdx0002lw5m9WS1Mpf29732Pz33uczz44IP80z/9EwMGDOgx7aR24ypkrZoHU/IvXryYoUOHplb33/ve9/jOd75D3759e5S5Um6PtMo+jTZvSB7jbae87rJSb266mbD0RiFLZWe67S9evJibb7651zSzQNxW6rJ0au6S5LnP9ia/yTVO+c5gkJ2ptORPmqzInoW1LZjpczt37uSxxx7jM5/5DHPnzvW9M2VyndHbmfSkPG6qyJqdnalp06axfPlyJkyYwNSpU7niiiu4+OKLS1dsgxO0cdasWcyYMYPm5maA0g0cAPv27WPFihWMHz+eF198sXR7T9LpDx06lD179nDixInE0m5vb6epqYnDhw8zadKkwDtTUS1mGzdu5MEHH6SlpSWwtdaE/K+//jodHR2p1b1X/mEspVHL3rOQe3XgNw9ptnlTpFl3Jixt5Rb2W2+9NVD6NmPKQh+0zXuYHHOCGA6zgKnxJuxudBb7LASb58L0V5Pjrfce9Ep2EzuxprwwkiZq3QPceuutgXdJ0l7bmspDv3796Nu3b8nNOg35w4QgSHNta4LQZ6YqWbp0aeQbOIKemTKdfrU8pJV2ZbomLAePPvoonZ2d3HTTTTz22GPdrLV+8hCX/GnWvZ86N2kh79u3b6kO+vXrVzUfabZ5E8TddmrVm2lL2/e+9z0++9nP8vbbb/PNb36zZl5sI642v2TJEo4fP95jm3fTTXzMzWp9xbEjN3/+fN+70Wn3WTA7z82fPz9Qf41zjZPkbqC3E1vphVErL0kTR92vX7+e5cuX881vftP3ecEsr22j5iHomSmTafeUfk/pJbG29VMWUYgcZ8oj7TgsaaafVNqVloP6+nr69u3L0qVLS66CnuWgf//+NDc3M3v27G6Wgzlz5rBs2TKWL19estZ6E2xYilD3Jsres5B/5jOf4dVXX+XQoUORfNrTLncTxCmDiTrzLG0XXHBBaUe5vb2dkydPpuabnRQmy+/LX/4y//qv/xq5zXvkoe33hsnx5uMf/3hpN7qhoSFy3uIue5Pz3P79+431V9vmmfJdwUplKquYkN+r8927d7NixQquu+46+vQJf3F1FsaavK9vs7q2DYTW2vfL+Xp3nnnmmbM+q+QnP/mJbmlp0T//+c/16dOnS5/fcsstuqOjo/QM9/nG025ra9P33XeffuWVV/RTTz1V+tuqVav0j370I93a2tprHqKk3d7eru+88069evVqfccdd+ijR4/6TrundHvilVde0UeOHPH13Vr4zYNf+T05jx07prXWetOmTXrRokVnyR933Xt46ddqd1kt+6TavIlX1LZTq+7C1pvJOvOIqwyTfKXR5rU2P+ZWG3P8zjVpvmrVgamyryZ/1D7rlbWH14ej9lmt4x1re8uLX9nPnDmjb7nlltJnHR0d+pZbbqk5x6UxZmWl/Wel7qOON3fccUcpf5VtvlqavaUdJH1vjemtrb21dm99zqTs5WOtH9n91rnWdrX7wOaKZ599lrq6OlpaWvjYxz5Ga2srzc3NvPfee2zatIlvfetbDBkypNtVhiNHjmTVqlX069fPK0zWr1/PpEmTGD16dOxpjx8/njFjxtDU1MTevXvR2tFY29rauPrqqzl58mRsaY8bN46TJ0+ilKK9vb2kKQdJuxZJWA7Cyu/J2dbWxsyZM5k2bRpvv/12YPmj1r2Hl36QdtcbcZd9mm0+KzJ4mKq7LFgabSap8jM95pjq82mS9bL3ytrD68Mmyj7rsre0tDBp0qTSc0aPHs0FF1zApEmTWLNmTaQ8FX3MyvIaZ8SIESil2L59O3Pnzg3d5qOuMT28tXZjY2PsaVeOtSb7O9jV7gPvfZ46dYqhQ4cybNgwtm/fzsSJE3nttdcAmDJlCn379kXr7lcZgrMFt3//ft566y06Ozu7bb/HnXbl4fs333yTzs7OQIcEw6ZdeeHEypUrA6edBcLK78n5wQcf0NnZGfoCiqh1v3z58m7pe0p91kmzzWdFBlvrToiG6TFH2o1/wpa9V9ZvvPEGnZ2dkQ6kp0VY2b01zbJly0quR4m6GQmRCFvv3oUPhw4ditTmo64x33nnHTo7O0tr7UOHDsWeduVYa2N/N0aQbSwCbM+V8+KLL+ozZ870+LeOjg79hz/8oeYWXBxp+81DWmn3lq7fbdiXXnpJP/XUU2e5GWp9tptl0DzUIoj8Sdd92DoP6qJWbRu8ciu+p3yk2eZNvOJqO721mZ7SDFpn5XXXk/uG5zppg9tYkJeJ8cYro127dnVzPfF+rxxvqpVdmnNNmq8o7bfS3cdzuQvi5piVPusRdJ7z8Pqo14fL3Zq1Ni9/WNl7SzOqO3RP9e6RlfZvSnbPpdIbq8vdTf0cJ7Fxbes3/Vp1nbTstdIL2ucr676n+dkjznYfyM2vvr5+t1IqNl+J+vr63b39Lc60e8tDmml7hN2GHTlyJPv27QPo5mbo180yC7Kn2e6iuqhV2wb3sxWfhbKP+tw06i1qnXnv1dw3PNfJUaNGxSVa6kQtwzFjxnTzOvB+b2pq8pV+mn0+bUy5+3gud0HcHNMu96jznNaOO7PXR5uamlBK+Q5BEqf8cclezR3aJvfWqLJ7LpXejXOe7FlZ52R1fZOF9KP2+VGjRnWr+7Tm50BufsePHx+jtVZxvY4fPz4mrbR7y0OaaXuE3YYdPnw4e/bsOcvN0K+bZRZkT7PdRXVRq7YNPmvWrJpb8Vko+yikVW9R68x7r6ur69F9I61YXUkStQwrXU+CxhtKs8+njSl3H8/lLoibY9rlHnWe89yZvd+7uroCxdyJU/64ZK/mDm2Te2tU2bu6ulBKlerfkz0r65ysrm+ykH7UPn/ixIludV/+eZIoGzpaUWloaOg4ceJEomal+vr63VleaCSFlL2dpFFvkI+6k7JLn6TqIEtlXvSxtsjyF1n2opLWPAPx1r0oUzlCKfU74Eda62fc3/8A/KXWekWqGcs5Sqk+wGFgvNb6kFJqGNAGDNFaF/AkZvZRSv0F8Dmt9Zer/P1uYJfW+vZkc2YPSqn/AkzQWv+t+/v/BvZprf8l3ZzlH6XUM8BPtdaPu7+vAP5Wa7083ZzFj1LqX4DjWuv/4f7+U2C51vq+dHMWP0qpPwH+WGv9Nff3bn0wzyilzgd+obWe7/7+OeA/a60/k2rGhNhRSrUAX9ZaNyvnrMoBYLrW+oOUs1YifCQzIYssAMrvYF0DzE8pL0ViCs4i8hCA1vogsN/9XMgmNwMP9/L3h4GblVzH1RvzkfEmLYpc9j3NcwtSykvSFHmOL3K9FxalVAMwEWgB9wYJaCZj7V6UqZyglBoODAG2l30sg00yLMDp3OUUaZKzCqXUWOBC4Le9fO0NYBDSf3qjst3LeJMASqnBwChgS9nHzRSn7CsVySLJXtnniix7O3COUiq/NwEJAHOA97XWp8s+y1y7F2UqP8wH1la4lWWuweWUyskdpOyzzFeBp7XWx6t9we1Hj+DsYAkVKKXqcCa5tWUfrwdmKKX6pZOrwjAPWK+1PlP2WSGMN0qpIcBIYGvZx2uABQXZRa6ca3YCfQuiUHSTPas7FIJxrDBWizKVH6xocDlFyt4uvgEs9vG9xYirXzWmAbu11ke8D1zldDswM7VcFYNKdydwjTcFaKvzgHUVimQHoIDs3wEeAaXUUKCJMkWyTKEoguFO5tliYoWxWpSp/NBTg2sDGpRSI1LIT5GworMLoJSahnOW7SUfX18FfAhcFmum7KSnBT1Iu0+CnhaVHe573m8pO0t2V6EowqLaUyQrLzXKvXute6nTMKC14k8y3uSfqkp0loxHokzlh7MWN7INHj9Kqf7AVGBDxZ/WA1PdvwvZ4evAoxX+1z3i9p/FiKtfT/RkQIBiLGrT5qyyL5BCUU2Jz71CQe+y573eezrGAMWo96LT09p2P3AE52KKTCDKVA5wtfOetHeQwSZuZgNbtdbdogK6v28FZqWSK+Es3H7i18XPYzHwVaVU33hyZS0y3qRA2Vhf1F3Bakp8EWSv1ueKLvs8NzyJkDOUUo3AYGBbD3/OVLuXBpgPxgMntNZ7e/hbphpcDqk2uYOUfdZYgDMw+47Fo7XehHMO6FNxZcpSirygT5PROPN2Rw9/y7Ui68NoWITdmWp9Lu8KRY+ya60PAIeASYnnSEgCb0eyp4C4merzee58RaLawgYy1uBySK2yz+3ixkJuBhaHCKT8MOLqV6Is7sfGHv68CRijlBqUbK4Kw3xgjQ2LixjwzoP1pEg2A3PzqlD0tiNZFtdwcrK5ShSZZ4uJNfWey4GngMynZ2sd7ueZOqiXM2qWfYJ5Eargtv+vE8zFz+NXwA1KqXqzubKWOcAmrfWpyj+4t6xtwDksL5in2s4MONfUz3Wvrc8j84HmnhRJrfVhYB/5DZQ+BugC9lT5e253hN2xW+bZYmJNvYsylQ+qau9lB/VkGzwerLGcFJxLgRM4N/QFQmu90/2/z5nOlKX01uYh/zskaVLVrdhVKPbiXIiTR/y0u7yOtwuoviMJ+ZZ9HHBaa11Nkcyz7EWntz6/DpiZlbiGokzlg96slSCLm1joKe5HBVuAEe73hHT5Bo6LX7XFSC0Wu88QercWgixu4qSWQpEpa61h/Mie13ZXa47Pe73LeFMwau1IunENd5CRuIaiTFmOe8vYLBwXj2rkeZJJk2pxPwBwP1+HuDylittHvkI4Fz+P3wDXKKWGmMmV1RR5UZsa7nmgufQ+1ud5YelHic+rQtHbRUeQ/3rvTfYNwDSl1DkJ5UdIhnOpfrGaR2b6vChT9jMd2Km1PtbLdzLT4HJGrUUl5HuSs4VPAju01u+HfYDWeh/wKnCDsVzZi7j5pcMUYJ/W+lAv38nleOOeA5tLcXcoavW5DeQ3rmGvsrs7FNuQMCR5w6r1lShT9lNrCxwy1OByht+yl4VlutxMtF0pj8IH8HXjfgzBuS6+GjuBfkqpUcnkqjD4WVzk1d1rCrDXPRdWjRZgSt4UCleRnEMvO5Ja6xNAK/lUKGSeLSa1diQhQ14QokzZj58Gtx6YkZWDejnCqs5eRNwb+G7EuZEvKk8DVyilRhp4lq14N6pVvV7ePZeW10V9mtRycwNnh2JyDm+erLmgdgOlb8EJpJ4npgJ7tNZHanwvd0ZTV5GcTe+urSDzbB6xSokWZcp+alor3W3w7WTkoF4e6C3uRwVrgAVyNX1qfA5YpbVuj/ogrfVR4LfATZFzZS9+DAiQw4VdBvAz1p8CNpM/hcLPWAv5bHdBZM/EwtIg04Fd7tjbG3ms96Ljp91vAsZmIa6hKFP248daCTLYmKZW3A+P3YDmo4CTQrKYcvHzKLqrnx9rIYilOA6KXPZ+57k87ogGkV3qXbCesovV1vX2vbK4hnOTyFdviDJlMUqpAcAEwM/B+jwOtGlSK+4HUHJ5EkU2Bdyb967FuYnPFM8D85RSEw0+0yaKbCVPDfcc0BSchUMt8jjeyM5UbYos+2ZglFJqcMz5EZJhBtBe42I1j0y0e1Gm7GYusFFrfdrHd2VxYxa/VmLISGcvIDcA/+rexGcE91zGE8DXTD3TFmrF/aigGUfplDnGDLOBLW77q0WurPTu+a/J+FMk82g09DvXbMWJa5in8A2+ZHd3KNaTo3ZfcPwq0ZCRPi8Tnd34Pb8AGWlwOSJo2csgnzw3Aw/H8NyHKaarn5+4HwBorQ8CB3EWwUJ0imy88RTJUz6+uxVozEugdFeRnIRzU2GvuArFOvI118g8W0z8Gu0gIxsFokzZTRDtfRMwJgsH9XJCkLLP2+Im87g37l2BcwOfaZbh9KW8HfKvRZA2DxmZ5HJCkEVlKzBcKTU8vuwkiu92594yuZb8tLs5wCafiiTkaK5RSjUAE4GNPv8lN7IL9q2vRJmyG9/ae9k2+LxYc1QA/MT9qGAtMMf9PyEZbgJ+5+MWqMC4fenXFG93Koi1EGQ33CS+d6bKFIq8jPVBduUgXzsUYfpcXmSfA7zv8xgDyHiTJ4L0+Z3AOWnHNRRlym6CWoplsDGD37gfALiBJve6/yckg+lb/CpZDNxcsCvvZWcqPYLsTEFGrLWGKLLsYfpcoWUv2JicO5RSA4Hx+LtYrfySr1TnGlGmLEUp1QQMBHYE+LfUG1xOCGophXxNcplGKTUBxyr/XIzJvA3UARfGmEbWCNrupc0bwD3/04RzHsgveTKcFdloGGZXLi8KRVDZdwEKGB1PdoSEmAu0BNiRhAz0eVGm7GU+0Fzrau4KZHFjhqCWUshAZy8QXwOe8HnzWSjcfleYmFNlcT/8uraCc/vaNKXUOfHkqjDMB9a57nt+yYXhzD33NRznHJhf1gDzc6JQBJ1rOshPXMNAsrtjssyz9hPWWC07U0IoglrrIF/+1GkSpuxT7+wF4hvE6+LnsRj4ekGu/w4S9wMArfUJnEXwrLgyVRDCGG/y4vI0D1gbRJHUWu8GzgBjY8tVAriK5DBgm9//yVlcQ5lni0no8S6GvPimCIuAvBJGe98J9Ev7oF4OEDe/jKKUmoWziFoad1pa67XAfuATcaeVAcK0eZB2b4LAi0qt9R7gNM7ZA5sJs6CGfOxQzCegIulivexKqUZgMLA94L/KeGM/Yfr8WlKOayjKlL0E1t5lGzw6QeJ+VNACTHL/X4iPm4FfuzfuJUFRYk6FsRaC7IabIOiNbh55sNIXXfYwfS4vsgc9xgAy3uSBwH1ea30AJ67hpDgy5AdRpizEdd0o8iSTJnOAzQHifgDgfn+z+/9CDLj9Iq5AvdV4BPhyAc4Fyc5UCrhtOuzuTB7KvuiyF7XPhZW9GZhbENfr3KGUGgEMINjFah6ptntpcHYyATimtd4X4n/zMNCmSVhrIciuYNxcCPTFuWkvEbTWrThXuF6dVJopEWVnStp8eMYCXcCeEP9rtZW+TJEMu6i2VnaXsH0uD3ENQ8mutT6E43otYUjsZAHhdiQh5T4vypSdhLXWgSxuohKl7GVXMF5uBh4JORBHIde3+rlxP87FZ9yPCrYAI5VSQ8zmqjDMB9aEbNO2G87GAafdCyWCYrVCEWVHMidxDWWeLSZRjNWyMyUEJqy1DmQbPCpRyt72xU1mcdvz10jWxc/j18D1SqkBKaSdBHOBjQHjfgDgnl1bh3MrmxCcKIvKdcBs91p7GwktuxtQfQ8wzWiOkmM8cEprvTfk/1trNI14jAFknrUZazcKZEFtJ6G1d631QeAAMNlgfopEVDc/sZjFw8eBA+4Ne4niWs7fBq5POu2EiNLmweKFXQYIbbxxFYoO7FUooiyowe4diqh9zmbZzwWOa60/CPn/Ms/aS5Q+v54U4xqKMmUnUbR3kMEmFGHiflTQCgxznyOY5WaSiS1VjTy7+kUdb2xe2KWNiUW1rYqsiXYnsttHkWUvLGU7kmE3ClKNayjKlGUopfoBM3G08LDIYBOOsHE/AHD/by2ysDSK2yduwrlZLy0eBz6llBqWYh7iwsQOgYw3AXHP+8zBGTPCYrPhzITR0NZ2F8WdHOyv9yiybwCmKqX6G8qPkAyTgCNa6/0RnpFanxdlyj5mADu01h9GeIbNk0yaRLUSg92TXFa5Gnhfa701rQy4t0i9BHwprTzEiJFFrWt5FPwzFdjjuuuFxUpF1lUkZ+Oc+wqLzTuiUeeaDcBkS+MaRpJda30S5+Kb2cZyJCRBVKMdpNjnRZmyj6gLG7B7kkmTqBYzsHRxk3HSdvHzyJ2rX8S4Hx4d7vvo6DkqFKbGehvHm+lAR0RFciNOoPQGQ3lKBPfCkNlE2JG0PK6hzLPFxMR4JztTgm9MDDQbSPGgnsWY6uyiyBrCvUHvCzg36qXNEuBipdSYtDNikPmEj/sBgPu/shseHBNj/UZggm0KBQas1K5CsQn7FIrpwC6t9bGIz7FurnFdtmcRzbUVLJRdMLYzJcqU4IvIrmZlB/VkG9wnUQ9HlrEGcXkyyfXAipCxaIyitT4OPA18Ne28GMSEAQFkNzwMJsb60zjxweYayVFyFLndmZhnwM7dmelAW8RjDGCn7EXHRJ9PLa6hKFP2YcJaCXZOMmkSNe4HAFrrPcAp93lCdG4mndhS1cibq58JayHI4iYMphQKG630JmW3rd2ZmuNtrXcZbwqGuyM5g2hnJL24hutJIa6hKFMWoZQaCIzFcV2Iigw2wTBlLQQ7J7nM4d6c9yngiZSzUs7vgelKqSlpZ8QQRV7UpobrljcJx00vKjaO9SaVeNvG2iLvTJmSfSvQqJQaauBZQvzMxLlY7biBZ6XS50WZsot5wAatdaeBZ8mCPhimLGZg5ySXRb4IvOwGos4ErlvVY8DX085LVMpcW01ZyecopWTO8cdsYJN77icqVo03riI5EWgx8DirZHcxZcBoBYZbFtfQyDzrhiFZh6xxbMFUm4eUDHcysdmFLOjTw2Rnt9FamkWycotfJQ+TD1e/icBRrfW+qA/SWh8GPsC57luojenFhU3jzRycUAenDTxrOzDEFoXCvVBnAs45t0iUxTVM3OUpAjLPFhOTnj+prG1FmbILkw1uKykd1LMU025+oshGwL0x7xKcG/Syxus4FmHbJ3KTCxuQdh8Ek4azbcBgpVSjoefFjTHZLQyUPgfYaEiRBIuMpu4xhvEYUCRdZLyxB+MbBUlf8iXKlF0YW9y4B/XWYZfVKhXK4n5EOhxZxlpgtvtcIRxfAZ4xcOuTcdwF3CPYvztl0oAAYikOgrGyL7ua3payN63EW6NQEI/sttT7XKDF0DEGsKvei47Jdt8BKBKOayjKlF2Y1N5BBhu/eHE/jpp4mPucDmCaiecVlKy6+HksBr5u+RX4pscbsRT7p8hlb+qcnodNshe53uPYCZ9v+Rice5RSg4AxOEGmI+MajxJf24oyZQlKqVHAOUC7wcfaNNCmienJHUSRDY17U94M4MW089IL7wGncVwRbaXIVvLUcM/3DMFxzzOFTeNNkdtdHLvBtsQ1ND3P7ga6cG5AFrKLyYvVPBLfiRdlyh7mA2tcrdsUNk0yaWJ6cgcp+yh8HfiNwXMFxnH76WLgG2nnJQym4n5UsAGYopTqb/CZeWQ+sNZ1FzWFFeONe65rMGYVyWbsUSiMzjVlcQ3HmXpmjJiW3duhyHy7LzimDQggO1NCL8SxO2LTJJMmcXR22RUMT9YC9VZjMfBVpVRd2hkJgcm4HwC413xvwTlkL1THtKsX2OPy5CmSxoyGbqD1E2Q8ULpSqgkYCOww/Ghb5hqZZ4tJHONd4kq0KFP2EMfuSIf7nuhBPQuJq7PLIB8QpdQ8YDjwWtp5qYXWeiOOW+5VKWclDHGMNyCWYj8YX1RqrT8AjuNcu51l4mx3WR9v5wPNhr1PwALZlVIjgAagzfCjMy+7EEufXwvMTTKuoShT9mB8QZ/WQT2bKIv7sdHwozcCE9wAlYJ/bgZ+ZdgFKk4WY+etfnHshIOMN36IS6Gw4Ua/OHYnwI4dijiMdmBHvS8gPkUy67IXnTiMR4eAfSQY11CUKQtwtet5xDfQZn2SSROTASRLuM97H+c6WMEHrotS1m/xq+RXwBctPCcU54JexpsquG08rkW1DYpsnLJnfVEdlyJpQ73HZbxZC8yx1NU695RdrLYzhscnakQQZcoOJgEHtdYHYni2DZNMmsS1qAQp+6BcAnQC76adEb9ordtwBvXPpp2XgMS5sJM2X53xwAn3nI9pMl32riJZZIUirrnGi2uYZYUiFtm11keAPSS4QyEEIo6L1TwS7fOiTNlBXNY6sGOSSZO4lSkpe//cDCyOaeCNk4exyNXPjfsxFkNxPypoBYYrpYbF8Ow8EOd4k/VdwXOB4+75LtOsA2ZlNVB6mSJpfJ4vi2s43fSzDSLzbDHJTb2LMmUHcVnr4KODelm2WqVJXO4HkP3FTWZw2+dXscvFz+Mx4LOukmIDccT9AMA967bOTUM4mzjHm7U4CkW/mJ4fldiMhlrrYziuRFlVKCYAx7TW+2J6fmYVCleRjOsYA8g8m2Xi3CgQNz/hLGLT3rXWh4G9wJQ4np8DxM0vG1wJdGitW9LOSFDcBdLrwB+nnRefxGm8gQwv7DJAnGP9hzi3pWVVoShyu4tznoFszzWTgCNa6/0xPT/LshedOPv8BmBqUueVRZmygzi1dxDLTY+4cT8GAdtjSmI7MNgNVCn0zjewI7ZUNWwK4CvjTXoUuexF9vgQ2YVMUXax2to4nq+1PkmCcQ1Fmco4SqlzgGnA+hiTEctNz8QV9wMoXU1vw7W1qeJalr6IczOerTwFfNxV0LNOEjsE0uYrcM/zzCKmxYVLlsu+yO1OZI+PFmCSUqo+xjSE4MR5sZpHYu1elKnsMwvYprU+EWMaWXZ/SJO4XS9Ayt4P1wJrtdY70s5IWNxbpZ4Hvpx2XnwQt6V4DbDAPSshfMR0YKd7vicuMmmlL1Mk18WYTCZld4m7z20EJmY0rmGs86zW+hSwiYR2KATf5Gp9JcpU9onbagPZnmTSJM7D4B5S9rWxLbZUNTIfwLcs7kd7XGlorffgXHE/Lq40LCXuBTVk13gzg/gVyfeB8W4g9szgXggykxgVyYzHNZR5tpgkMd4l5vkjylT2SUJ73wBMlm3ws0jKcpJV94vUcW/Auw54NO28GOB3wHlKqfFpZ6QX4oz7UY60+7NJwnDmKRQDY04nKLHL7ioUG8meQjED2OFeEBInmetzriI5g3h3JCGDsguJjHeyMyWUiF17d7fBNwOz40zHJuKM+1FBMzBfXJ6q8sfA6zHFnkkU90DsE8DX0s5LLyRhLYTs7pCkSezGG/e6+xayp1AkYbiCbO5QJNXnsij7LGC71vp4zOnIeJM9kujzW4HGJOIaijKVfWLX3l3rUAfwX2RRX2IC8GECi/g+OC5PfxdzOtahlJoH/BX52JXyeBT4d0qpS9LOSBWSsBYCnAC+oJQ6N4G0Mo9S6grgEuBUAsllykqvlJqGcy7yZALJZUp2l6T6XFZlT0KRPAlcpJT6ZAJpCTVQSn0FZ0cyzrsAEo1rKMpUhlFKDQZG4VzvGCf9gH+DPXFwkiApS+lEoBH4QgJp2cbngU+QvQVAFGbjDOx/mnZGqpBUu/83wB8BmTq/kiLzgPE4lvq4yZqVvhFHkVyYQFpZkx2S63NFln0GMBK4MIG0hNpci3M2d2YCaSXS7kWZyjbXAxu01mfiTMT11f5PwCMJnJXIPO7NUp8kfj9utNbvAPcBP487LQvZBKwG/nvaGTHID4HXcM6uZAql1HScRf2GBJL7M+A1rfXGBNKygSU4gZ3/TwJpbcCx0mflmv6VwFvAXySQ1gbgY0qpSQmkVRN3Z/Y8kulzO3DiGv6bBNKqiet6dTHJyH4PsAwnRIWQPr8EntRaP51AWuuBy+O+eEbJ2jm7KKU6gKHAXK311rTzUxSUUmOBbcB+rfWYtPMjCEmglPoFzm2D39Ja3592foR4UEo9AnwF+Hda6wfTzk+SKKX+38D/Bn6otf6bDOTn/wb+f8A/aq3/R8xpXQM8C7yjtb4szrR85ufLwK+B32mtr087P0I+UUqtwjkj+nGt9VtxpSM7U9lmDfCKKFLJorXeBRwAbk87L4KQIG8AHwAPpZ0RIVb+K3AaeCXtjKTAvcBBnF3ALLAcOAzcmUBav8fZ7X87gbT88ApOO/zPaWdEyDV/j3NmbmWcicjOlCAIgiAIgiAIQgj6pp0BIT4aGho6Tpw4MTrJNOvr63cfP3688K5xRS77pGXPitxQ7HqHZOXPktwgshe1zwvpklTbkzZ3NlL2HyE7UzlGKZX4fRJKKbTWhb9evchln7TsWZEbil3vkKz8WZIbRPai9nkhXZJqe9LmzkbK/iPkzFRCNDQ0dCildFyvhoaGjmppL1myhCeeeIIlS5bw7rvvAvDmm29y7NgxAF599VU6OztL36/sHF1dXTz66KOsXLmS06dPc88997Bs2TJ+8Ytf0Nrayv/4H/+DQ4cOxVFsRrC57AG++93vcvTo0W714L3fe++9/Ou//qvpIouNhx9+mFde6fmoRqXsK1euZMmSJQA88sgjrFixIvb8mcJknyv/2etr3u/VSLPNA/zwhz/k8ccfB2D79u2lz72fN23axO23dz+SWFkGHR0d3HvvvXz44Yeln9etW8eiRYs4ffp0b8mnSlyyb9u2jbvuuostW3qPlJFW3d911120tLTwk5/8hGXLlvG73/2Ol19+mQceeIAnnniCZ599FoB33nmHJ598kuXLl/PAAw/0Wg5Lly5l0aJFgL8xIM12n+e0a+UhifRrjTkA//AP/8Crr77KM888w4EDB0qf9zSv3nbbbWf9/Otf/5rm5ma+973v0dFRMzmhgiVLlrBkyRIeeOABHnrooV7XhT3Vya233go4/f7pp2tf9Jf2PFeOuPklxIkTJ0bHqcErpXrdau3Xrx/XX389Tz31elSNCQAAMzxJREFUFE8//TSXXHIJq1at4tVXX+WGG25gx44dDB48mN///vdMnTqVc845h46ODi655BIaGxsZNGgQdXV17Nmzh8svv5z29nbGjh3LwIEDGTFiBB9++CFDhw6NTb4o2Fz2gwcPLi0c+/TpU6qHQYMGobWmvb2dBQuyFjrkbO644w4mTpzI0KFD+eQnP8kdd9zB2LFj+drXvsZLL73EwYMHufbaa3nttddobGzkkksu4aKLLiopU6NGjeLIkSMpSxEMU32uvN69vub9Xo202zzAueeey+23387555/PxIkTAVi9ejUTJ07k5Zdf5vjx4wBs3bqVlStXMmfOHA4fPsyhQ4e48sorGTNmDCNGjKBv376ln5uamujfvz+nTiUR2zY8ccheX1/Pjh07eq13SK/u+/TpQ0NDA2PGjOHYsWOMHj2affv2MXr0aJRSfP7znwccpfJLX/oSv/nNbxg92nlUtTHgww8/5PDhw4C/MSDNdp/ntGvlIYn0/Yw548aNY8uWLRw9epQpU6ZQX1/PU089xZgxYxg7dixbt25lwYIFjB8/nosuuqj0f97PH374IQCjR4/myJEjjBmTac+yzODN75s3b6Zfv35Mnz6dyy+/nHvuuYfBgwczePBgTp48yV/+5V+yZs0aNm7cyIUXXsjWrVs5deoUn/3sZ1m/fj2TJjkRC5qamti7dy9aa5SqviGVhXnOQ3amUsazVh45cqSbJcXjhRde4Je//OVZPwdBKcXp06dZsmQJw4YNY/DgwTQ1NXHgwAHmzJnD3r17mTBhAkOHDqWxsRGtNWfOnKGzsxOtNSdOnODMmTOcPHmSrq4uli9fzujRo3n++ec5efIkY8aM6WaBzTpByvytt97iscceY926dfzzP/9zN0upH6KW/bZt2wBob29nx44dnDlzhg8//JAzZ85w6tQppkyZYoWSMX36dM455xwOHDjAsmXLuv1t0qRJ9OnTpyT3mTNOWLWNGzeyadMmNm/eTFNTU6ksbMBknyuv9zFjxrBx40bOnDlDV1dXqLwlMeZMnjyZ888/n4EDB/aYxp49ewDYv38/o0ePpqGhga6urlIZAGzevJk1a9awd+9eVq1axZo1azhz5gxKKfbt2xc4T0kRl+z79u1j9uzZob0A4q73yZMnM3HiRHbt2sWAAQPYs2cPAwY4oV1OnDjBc889x9tvv82ECRN48sknGTt2bOl/q40B/fv3p3///qHHgCAyHzx4kO9///tnvZsgiT6XtbTDzLNemYf1RJg6dSoLF34U+7m+vp6xY8eitaarq6s0vgKsXbuW7du3s3nz5tLPhw4dYs+ePTQ2Nlo136SNN7/PmTOH6dOns3v3bp599lkuu+wy6urqaGxsLPX3c889l379+pXqwxvz9u/fT3t7Oy0tLQwbNow9e/Zw4sSJ0HlKut3LmamEUKq7b+mDDz7I0aNHmThxIm+++SbXXHMNjY2NTJ48uZtl7re//S0A1113Xbefe3j+WT6lSind3t7Ou+++y/nnn8+5554bp4hV85E2XtmHKfMzZ87w+OOP85WvfIUnnniC/v3709zczN///d+XP1/Kvns+avpRL1myhOuvNxNaJCtyQ3bqvac6MDnmVCvzanX/7rvv0tXVxbhx49i4cSMLFy5k0KBBxuVOk6zIntZcU63Pv/DCC1xzzTWB/9YbtdpfGJlfeeUVjh07xsCBA7u9l49TQcodkulzSaTtt9zDpuvNsyNGjODYsWMlJfxTn/pUoLTjJGvjTRZIu+zT7HOVyM5UBrj00ktZvXo14PiRllvm6urq2L9/P5s3by79HIRx48Zx/fXXn7WoC6O1L1u2jLvvvtuoxS4t/Jb5okWLaGxspK2tjQkTJpQspX4wUfZr167lZz/7WWlnzLazQ5V4sl955ZW+rZVeu/N+zzom6t07L+Z9Zrre4xxzemLEiBEsXLiQwYMHc955552lTPRU717bLz87ZyNBZPdk9erbKwNTJF3vANdcc03Vtu8pUj3NN96YFxW/Mh89epQNGzac9W6KNMo+7bSDzrNemZvwRAgzz3qf2TLXZJEw81zlmG+KJNu9nJlKiXnz5tHW1tajdb78s2uvvbb087Rp0yKnW661//jHPy5p7f369eumtXd2djJ8+HCA0s8jRoygra2N9957jxkzZkTOS9KEKfNvf/vbpZ+9xfGnP/3pUOmHKfsJEyawdOlS5s6dS0tLC0OHDrXCra+SMLIvXLiQ7du3l9rdX/3VX1nlTuoRRnbvvJj3WX19feR6T2PMiVLvXtsvPztnE1Hq3TsfNGvWLJYuXRopH2nNNRBtvvHGvDCElfkLX/gCwFnvYUmz7NNKO+o865X5eeedFyr9KPOs95k3Bgn+iTLeVY75UUir3cvOVEpcdNFF3HDDDanmIajWXldXx6xZs4xb7JIiC2Xu4bfsW1tbS8rEhAkTrDs71BNBrZVeu/N+txm/snvnxbzPTNR72u0/aL17bd8rC5sJWu9efXtlEIW0690j6HzjjXlhyIrMaeYjrbSzUvYQfJ71PsvDXJMmQce7yjE/Cqm1P621vBJ4OUVdm2eeeabmd9ra2vR9993X7TP3+TXTfOedd/STTz7pKy9h6Ckfab9Mln1LS4v++c9/rk+fPt2rzEUu+6Rlz4rcOkP17rfNax1uzKlW5pXpFqXedYZkz8pco3V8ZeC3/VXDj+yrVq3Sd9xxhz5y5EjNdIOk7Td9v30uSNp+0t20aZNetGhR6b2cJMq9XO5bbrklcNpJt7kiv8rLPo3xLs0+V/mSCygSwjso94Mf/IAbb7yRu+++m5kzZ3LgwAEGDBjAoEGDOHLkSOnWr9bWVhoaGrjsssuYPXs2L7/8cukKSTj7AH/QQ8ExynlWPtLGZNk/+uijdHZ2ctNNN9GvXz/v+VL23fORqOxZkRuyU+/l+YhjzEn7MHhveUiLrMhehLmmVvszIfvOnTt57LHH+MxnPsPcuXN7Tbc8bUi2z5mWG2Dx4sXcfPPNpfcky92Te9q0aSxfvpxvfvObvtKOm6yNN1kg7bJPs89VIm5+CeMNEBMmTGDq1KlcccUVXHzxxezevbv0na6uLmbNmsWMGTNobm4G6HaF5L59+1ixYkXoq5Gr4edcwsaNG3nwwQfZtm0b999/v9H048ZE2c+ZM4f9+/cbD1Lsp+w3b97Mvffey+rVq7nnnntKMTFsJ0i7a2lp4d57700gV8ngR/bdu3dz6623smrVqlCyZ3XM8SN7e3s7999/v+8gjrYQpL/DR8Esg2BzvXv1HbbeTcjer18/+vbty5QpU1JJP0zZm0j39ddfp6Ojo/QeZLFsUm7vquyTJ0/6Tr83gvQ5b8w9evSokbSLThJry7THO9mZSohaGvzSpUsjXZnrx1poemfm+eefP+uQXxatN3kpe3AshkOHDmXmzJl0dXUxc+bMXvORBnG3u8cee8yXtTQN4qr3J554gi9+8YtnWYrdNEPtFERp90lbCidNmsSGDRu46aabSkEcs1TvEI/sixcv5vzzz+9moXfT8rVD0hNxj3cmZF+zZg0bNmxg9uzZZ9V7b/KnIbvftKOmn2ad18pDEukntSvmjbHemFsr/SITl9dP5doyyq5kXH2uEtmZyghXXXVV5IGuFiZ3Zvbv3x/L7lga2FL2nqVw/PjxvPjii4nEMDKByXa3fPnywNbSNDEhOziDehhLcW/E3e5NWgqHDh0aOYhjkpjs76Yt9DbU+/Dhw9mzZ4+R4J3lJDHWZzH9Ishtss9prbsp70LvZHltmVjb93u4Sl7mDup5+DkQ95Of/ESvWrVK/+hHP9LHjh3TWn90SK6jo6P0DCIcQn/llVe6HbKNQk/5SPsVpexfeukl/dRTT5U+k7IPV949YUr2rMitM1TvaYw31dKtJI/1rjMke5S6rzyA7bWF1tbWyONdEmNdFNnb29v1nXfeWbpcyLtsyE+7r5Z2kPRrlX1cdV7Z3ysvoAjb74PKXXnhRWX6YcecvI43WXilXfZR+1ytuS5InUucqQR59tlnqauro6WlhY997GO0trbS3NzMe++9x6ZNm/jWt77FkCFDut3JP3LkSNra2rj66qtpa2tj5syZjB8/njFjxjB69Ggj+brqqquMPCfLhC37kSNHsm/fPrR2LFVS9uYQ2eNFxpvskZTsYeveq2sPry2Y2BXLuuzjxo3rJueqVavo169f4Cuy0yp7U/192rRpvP3226V3v/0+qtxeeh5B069GkcebtMl6nzc514mbX4KcOnWKoUOHMmzYMLZv387EiRN57bXXAJgyZQp9+/ZF6+538gMlt64PPviAzs5OI4eC/RwI/OlPf8rLL7/M008/zZo1a7jzzjs5duwYAB0dHdx1113s3r3bioCaYcveczd58803Uyt7j8rD+LaUvUdQuT15Pbzfvfe8yu8dxK3sc+BcROBXbhvHG69uvfHFcwEpPxhuU52Df9nLL5yAj2QOI3vYuvfqevny5XR2dkZ2KQ4ruzfGhZlnwsre3t5OU1MT77zzDp2dnaEvG0qr7E3197AXUESV20vvjTfe6JYPv+mXE3aOjdLnBIegZd/TZR9B5jnIxlwnO1MJ4h1mvOyyy3r9XvnBu/r6ehYsWNAtGnhTUxO33XYbu3fv9h3UMOrODDiTjedHPGbMGE6ePElTU1OgMkiLsGU/bty4btHZ0yj7yl2xpqYm9u7dy6hRo0KURHJElXvcuHHdLLWe/KZ3SuIirPyeRRy697n169czadIk33LbON54deuNLx6mrNRJEFb2apb5MLKHrfvhw4dz2223lT4777zzOO+883zXvSnZvTFu9OjRgeeZsLKPHTuWP//zPy99Nn/+fObPnx+o3UdJP2rZm+rvV1xxBVdccUXp57jTL5fbS7c8H37SNzXHRulzRSVq2Y8aNYoLLrigdK4p6DwH6c51HqJMJUR9ff1upVRsPbO+vn53b38/deoUY8aMOUtzHzhwoK+dmXHjxjFp0iRWrlzJZZddxu7du2lqaup2SD6r2F72b775JhdffDGHDh1ixYoVfOxjH7PiMH5UuXfu3MmKFStKA7An/2WXXcaKFSu47rrrUpSuNmHlnzNnDsuWLeP48ePd+lyQiwhsbfOeZfCCCy4o7RIsXLiQt956y5qLR8LKXm6Zv/jii0syB7XQp1n3pmQvHwOCzDNpyp7ntGvlIYn042h3lXNs2D5XZKKW/YkTJ1BK8eabb7Jw4UKr5rly5Gr0HBM1oNrvf/97PvWpT9GnT3Vv0N27d9PR0VHS7uX6UIcil30U2f3I7eHJf/7552dCbih2vUP8dV8ue5bkBpE9Kdnd9DIlv5AeYdue7X0uC0jZf4TsTOWYpKxVlWkmmV5WKXLZJy17VuSGYtc7JCt/luQGkb2ofV5Il7R35YqMlP1HyM6UAIBSqh9wGGjUWh9XzkGNQ8BkrfX+dHOXb5RSnwC+q7W+zP39QuDnWusF6eYsfpRSjUArMFRrrZVSDcB+YIjW+nSqmUsApdRO4FKt9Xb39/eBG7TW69LNWfwopV4A7tBa/9b9/QngYa31o+nmLH6UUt8Hdmqtv+f+fjvQqbX+p1QzlgBKqb8APqG1/lP3968DN2mtb0o3Z0KeUUr9V2CU1vo/u7//J2CB1vpb6eYs/yilfgvcq7V+yv3918CTWuuH082ZOeQ2P8FjJrBDa30c3Mv1YS0wP9VcFYMFQHPZ7+uB6Uqpc1LKT5LMB9Z6vgJu+9uB0x5zjVKqCRiII6/HGpz2UAQW4Mjr0UyxZC/v81LvghAvRe5zaZP7Pi/KlOAxn+6NHWSwSYpuZe8qFNspgELB2YMs7u9FUOLnA80VTufNFEB2pdQIoAFoK/u4KPUOZ4+3uVtc9MJ8ui9qW4CJ7q60IMRFT31uvvKuSxViQSk1DBiO44HikbuxXpQpwaPIi9q0qVb2RVhcFVmJL3q9VyqShZBdKTUS6A+0l328EZhQEIWiW7t33XnfB+akliMh17jHGGYCJfdprfU+4CgwMa18FQTP+6Q8eFPuxnpRpgSPyi1wKJa1NBVcq1hPZZ+7waYKRW53Ra/3SkVyEzBeKTUwhfwkyQIqFElXodgIzE0tVwngHlbvC+ys+FNR+ryQDjOAdq31hxWfF2W8TZOexvqtwEil1JAU8hMLokwJHj3tEMg2ePyMB05orfdWfJ57dy+3XVW6/EBxdkR76nObgLFFUSjKPyhTKPK+Q9FTvUMxFIqediShOH1eSIfe+py0u3g5a47XWp/B2SWcl0qOYkCUKQGl1CBgLLC5/HN3gX8CODeNfBWEnnYnoBgWs3PpWZHcjKNQDEohT4lQTZHUWnfinCHJzSRThSIrFL31+bwv7HqyUkMx6l1IjyLPs2lTiD4vypQAjmvJBnchV4kMNvFSbVG5GRitlBqccH6SpMdB1m2HG8i3y9ME4Jjrt19JrhfVvexIQs5ld6nW54sw1vYme97rXUgPaXcpUDbW577sRZkSoLrlAHLW4DNINYXiDM4V6Xneoag2yEL+F5a99blcWex6YCJwpEr8ulzXu1KqD06f7kmRLILLUbUdgm3AEKXU8ITzIxSDauPtemCme0GFYJ5xOPHz9vTwt1yN9aJMCVB9goP8L+zSpreyz9Vg0wNFbndFr/eiKpKTgMNa6wM9/G07MMgNZJ07elMky+Ia5rnuhRRwz5+OwzmP2g33QopCxDVMiZpjfV7O5IsyJUCxdwhSQynVF5iFs4joibxbqou8I9pbnytCvVdTJHcADW4cqjxStd4LoFBMBg5orQ9W+Xve+7yQDnOBlirHGCD/422aVHPnBuhw30cnlJdYEWVKgN4XteuAWe7CXzDLdGCn1vpYlb/nVpEtUyTXVflKbmV36a3PtQH1bjyiPFJLocjz4qa3eod8KxR+ZM9znxfSQdpdelQte3esz03ZizJVcJRSo4BzODvuBwDuQr8dJ06DYJberDaQ74WVF/ejmiK5EzjHbZ+5oiyA5Pqe/l42yeS17mstbvKsTPnp87lYXPRAb7uxkO96F9KjyPNs2hSmz4syJcwH1vQQ96OcPE/waVJrUbkL6OsGuswbvcqec4ViBrCjhwCS5eTy7JCrSM6giiLpkufxxo8imWfZe1vU5uoMhZAZitznUkMpVYcTM7Ca9wnkaKwXZUqoNcFBjqwHGaPXss/bNngFtayFkN9Jzk+fy2u9zwS2a62P9/KdXNa7UuocHNfeDb18bQ35DZRey4AicQ2FOKg13nqB0nMb1zAlpgMdWusjvXwnN2O9KFNCrW1YyO/CLm38ln0eFdla1kLIr+xS772TV4ViJrCtN0XSjTt2DCcOWW5QSvUHptK7Ign5bfdCCrjnTutxzqH2SEHiGqaBn3muGZjj3vRpNdYLIETG7+JGlCmDKKUG4CyY3q/x1dxYbioocrvzI3szMC8Pk0wFNXfl3PhTR3DiUeUJP/UO+ezzs4BWrfWJGt/La58X0sHPMQbIZ59LGz9j/WHgAxxDi9XkbaIWAlAW96Pa1dwem4DxbrwGwQxzgY1a69M1vpe7xYXbjsbTQ9yPCtZSXIXiAHAYJy5RnvBjrYR8Lm78uHdCPndniqxICukRpM9JuzNLofp83hYpQjAmAQerBJAs4S74W5BtcJMEWVTOzZlC4cX96FWRdNvlQXKkULiK5FhqK5JQ7EV1HmX32+fzuLALInve6l1ID2l36VGoss/TAk0Ijt+FDeRzgk8TX2WvtT4E7AemxJ6j5Chyu5sHbOglgGQ5ubDYeSilBgNjgM0+vp63eoeCWWor8LtDIHENBZMUuc+lhlKqAcdNe6OPr+dirBdlqtj4uVHNQ270M0uQss+F5aaMIre7Itf7XGC91vqMj+/mqt5dRXIUsMXH19cBM91r5POCLyu1G3duJ85NYIIQGvcCm3n4G2/byWlcw5SYA7zv4xgD5GSsF2Wq2BR5hyBtilz2Irs/iix73hSKefhUJN34YzvISaB0pdQQYAT+FEnIX7sX0mEScLjWMQbIfVzDNAgy1m8Aprg3flqLKFPFxq/rBeTEepAFlFJNwECcBZMf8uaCEHRnKk+yB+lzG4DpbnyiPOC73t3rw3fgXCeeB4LUO+RrvJ2Po0h2+fx+3vq8kA5h+py0OzP4Lnut9SkcQ8ucWHMUM6JMFRR3gTYNWO/zX3YADUqpEfHlqjDMB5p9XNfqkRuLmdt+GvCvSK4HpuVMofBlsXMVim3kS6Hwa62EHLV7AtS7S552Z8LInpd6F9JD2l16FK7sRZkqLn7jfgClbXCx3Jgh6KJyAzDV9m1wlwUEUCTd9tmK016txg0g2R/HP98veepzQS3FeVIogvZ5qXdBiEaR+1zaFK7PizJVXIK4WnnkyfUkTQKVvdb6JLCVHCgUFLvdBd2RhBxY7ADcg919cS4X8Ete6h2Ct/tc1LtLUCu1F9dwQEz5EYpB0D6X10DpiaKUagQG43hV+MX6sV4aTXEJarWBHFgPMkKRy15kD0auZA+hSFovu1JqNFAH7Arwb5uAcbYHSndvVAvU7t0bwDYicQ2FkLgX18zA/zGGXMY1TImwRkOrx3pRpopL0G1YkG3wyLiLi7C7M3ko+yK3O5E9GJuBsUqpQTHkJ0kCubYCuHHIWnBuAbSZMe777oD/l5d2L6TDLGC7e+40CNLuohNmrG8FGpVSw4znJiFEmSouQV0vcL8/z1UIhHBMAI5prfcF/D/r3X7K4n6EaXdWy+4Sps9tBka5cYpsJrDsrkKxAft3KMLUO+Sj3c8n+I4k5EN2IT2K3OfSJsxY3wWsxWLjkShTBSRgAMkSWuv9wBFkGzwKYaw2kINtcJx2c9RtR0HYgqNQDIkhT4ng+uH7DSBZwo1LtB6LJxmXIrd7kT04eZBdSA9pd+lRyLIXZaqYeHE/agaQ7AHZBo9GmHMz4FxA0aSUGmo4P0kSSvacKBS+A0j2gNV9Lqwi6WK17C5h+7zILgjhkHaXAmXHGApX9qJMFZOwjR1kGzwqocre3QZfh90KRZHbXZFlnwzs11ofDPG/VsvuKpJzCW+ptVZ2l7DtXuIaClEI2+7yFtcwac4FjmutPwjxv1aPd6JMFZOwVhuwfCs2AxS57EX2cIjs9jIF2Ke1PhTif9uB/m58MutQStXhKJJrg/5vWVxDaxdXQjq4xxhG45w3DUSe4hqmROSx3tYz+aJMFZMwt8l5yAQXEve61pkEuK61AtvLvsjtTmQPx07gHDdOlY2Elj0HCsUUYK/W+nDI/7dZdiE95gIbQh5jAGl3UYgy3u0BOoGxRnOUEKJMFYwwcT8qWAfMcBUDIRgzgB1a6w9D/r+1VvqyuB/rQj7CaqsV0frcLqDOjVdkI6FldxUKm90/otQ7WNznKbbsQnpIu0uPwpa9KFPFYzROvXeE+Wc3bsMOZBs8DGFvufFoxl6FYhaOIhk07odHB067tU6hcP3vp+Nc8x2Ysh0KKycZDLV7Q3lJGpE9PDbLLqSHtLv0KGzZizJVPMLG/SjHZktxmkS5hACcwJddfBQI0yYiyW75DsVMYFsERRIslV0p1R+YSnjXVrBUdpeofb7wsltqPBLSo8h9LjWUUn1x5rrAZyTLsLbsRZkqHlG3YcHirdiUiVT2ZQqFjWVf5HZXZNlnAVu11icjPMNK2csUyVA7ki7NOIHSbZyro453XlzDicZyJBSBqOOtF9fQ9kDpSTMDaI9wjAEsHetBlKkiEnUbFuSAZliKXPZRLiHwsNUFQOo9GmuxU6GYTURF0o1LdhjLFAqlVD3OlfgtER9la58XUsA9V9oX55xpKMriGto43qaJiXluHTDHvQnUKmybnIToRN0CB4utB2mhlBqIc0vNpoiPsrXsTe3O2DjBmehztu5QRK53V6E4iBP42CZM1DvY2ednA5u11qciPsfWPi+kg4ljDCDtLgyRxzut9RGc89HTjOQoQWybmIUIRAwgWc4mYIxsgwdiHs51rZ0Rn2OdpdZtJ2OIrkg2A3MLqlAcAvbjXDdtEyaslWCnQmHCgAAW9nmKXe9CehS5z6VNofu8bYsSIRpTcQJIho37AZS2wTfgKGaCP0wNNM3Ytw0eNe4HAG673YfTjq3AVSRH4fjhR8VGa6mp3RkbFzcmFxdS74JQm0Iv6FOm0IqsKFPFwtQEBzLYBMVI2bvb4HuwSKHA3CAL9i0s5wHroyqSLlb1OaXUEGAEokhGxap6dzHV5yWuoRAEo31ObpL0h3uMYTzwvoHH2TjWizJVMIq8qE0bk2Vv22UERVbii1zvniLZZeBZVtW7Umoo0ARsNfC4DcB0N16ZLZi4eMSLa7gd58plQaiK6/49DzM7Ux2AwsK4hikxF2gxcIwBLBvrPUSZKhZGJjgXK7diU8Rk2ds22JhyvQD7FIqi17spRXIDMM0ihWIesM6EIukqFNuwRKFQSg0DhgOthh5pW58X0mEysN89XxoJy+MapoFJg+lGYKJSqsHQ8xJBlKliYXpnyqaFXWoopUYC/YF2Q4+0TZEtcrszrVBMdeMX2YAxJVprfQJncT7LxPMSwGS9g119fj6GFEkX2/q8kA5F7nNpY3KsP43jLjjHxPOSQpSpguAuwKYQPe6Hx06gn1JqlKHn5ZkFQLOB61o9rLGYue2jH057MUELMMUGhcL1tzc5yZzEcRuzRaEwaa0EuxY3JndjwaI+T7HrXUiPOPqctDt/FF6RFWWqOMwGtkQJIFlO2Ta4VQ0+JUwvLlqAyW5gzKyzADNxP4CSQrEFpz1nnVE4Y2zoAJI9YEWfK1MkTbb7IisUVtS7S5HrXUiPOPqctDt/FL7sRZkqDqYnOLCwwaeE0bJ3A2Fuxg6FwvQgC/YsLI0qki62nB/xDm7vNvhMK+o9JkXSlnoHs+cEwYlPN1YpNcjgM4X8YbrPrcXOuIaJopQaATQAbQYfa8VYX440kuJgegscLNyKTYk4yt6WwabI7a7o9R6HImmD7GOBLpwQBqbYDIzOeqD0OBTJsriG80w9U8gXrtv3VJx2YgT3Igur4hqmhOljDGDPWF9ClKniUOQdgtQwfF1rObYMNkXeEY2jz9lU76bb/BZgpBu/KsvMx7AiaZFCMQ7o1FqbVCTBnj4vpMMsYKupYwxl2LQjnBamd6LBub10sFKq0fBzY0OUqeIQx6K2GZgn2+C9Mgk4qLU+YPi5mV9cxKhI2qLEx9HntgBNtigUJh/oKhTryL5CEUe9gwV9nngMCGBPnxfSIc4+J+2ud4yXvWuIskqRlUVwAXDjfjRiLu4HAFrrg8ABnPgOQs/EYaEHOwb5ycABt52YpBVodNt1JnEVybk4fvfGcK+bXkf2J5m4Fjc27MwVuc8Xud6F9Chyn0sb6fOIMlUU5gFrDcb9KMcGa2maxGWp3QYMy7JCQUyyu+14LdneoZgC7DMRQLIHMm2xcxXJORhWJF1sGG/i6vOZrneXOFx+wI56F9KjyH0uNdwzknF4n4BlfV6UqWIQl9UGLLMepEAsZV+mUGR5sClyu4tT9qxbS6cCH2itD8fw7EzXu1KqjngVyczK7hKXlXoncI7ENRSqENd4a1ug9KSZBBzRWu+P4dmZHusrEWWqGMRltQE7Jvg0ibPssz7YxLWwguxbrYpe77Eqkq5FNItMBfZorY/E8OxdQF+l1Oia30yBMkVynelnl8U1zHKfF1LAPT/ahHOe1CiWxTVMg7h2osHt7xke67shylQxKPKiNjWUUucA04H1MSWR9bIvshIfe5/L8CQTZ713uO+ZVCiIsd4tUCimAR0xKZKQ/T4vpMN8YF1MxxhA2l1vxDnefQAcB86N4/mmEWUq55TF/YjLerABmOYqDkJ3ZgHbtNYnYnp+Zgd5tz1Mw2DcjwqayfYORZx9bjeggTExPT8qcSsUWd6Zi7PeIcN9nngNCJDtehfSo8h9Lm2kz7uIMpV/xuLE/dgdx8NdRaEV2QbviTgt9JBthWI20BqXIum2506c9p0pXP/6KcSkSIpCkendmUT6fIzPj0KR611IjyT6nLS7nonTzQ8s6vOiTOWfuC0HYFGDT5hYy94NjHkKJ1Bm1oh7goPsWgxnA1tiCCBZTib7nFKqHudK/Lh2JCG79Q7xj7eZrHeXJBa1EtdQqCSJPpfV8SY1lFL9gBnEcEayDGvKXgal/BO3tRCybS1NkyKXvcgeL1mV3VMkT8WYRiZlV0o14NxutTHGZNaSXYUi1nbvxqs7iMQ1FFwSOMYAsJWMxzVMiVnADq318RjTyORY3xNZHJAFsyS1Q5BVa2maFLnsRfZ4KbLszcCcDCoUs4FNcSqSrkKxn4wpFK4iORFoiTmprLZ7IR3GAF0450hjoSxQepbjGqZBEmP9WmCmUqpvzOlEJmuTkWCepNz8rLAeJIVSajAwihiua60gq2Vf5HaXhOxrgbnuddRZInbZ3fhVH+BcQ54lkqh3yGa7nwO8r7U+HXM6WZRdSI8FwBr3HGmcSLs7myTG+g+Bdhx3wkwjylSOcRdas4nXpxWcbfCRbrwHwWEesF5rfSbmdDK3De62g5E47SJO1gGzM6hQxH0o11Mo9pJNhSJuF0fI5qHw2OvdJXN9nmLXu5AeSfY5aXfdkbIvQ5SpfDMN2B1j3A8AXIVBtsG7k5SVei3ZUyjm4cT9iFWRdNv1bpx2ngmUUkNxAkjGrUhCNl2eknD9gGxaipPcmZJ6F4Ri7wanjZR9GaJM5ZukrIWQTWtpmiRS9lrroziBTKfHnVYAitzu4g4gWU6mZHcPaDfihEqIm0zJ7pLk7kxRZZe4hkI5ifa5jIYhSRz3GMMYYHMCyWVxvDsLUaZyitvY/wbQCQ0AfYE/U0plLu5P0iilLgKuBQYkkNYgYB/wv+JOyw9KqTHAn+G0h7jTUjjBa//abe+p4l6I8I/A0YQWe/XAl5RSsxJIyw//B9iFszMXNw3Ax5VSn0kgrZoopf4zjiLZkEBy/YAZSqm/TCCtmiilvgBcgtMe46YJZ7z7XgJpCRlGKfUnOMarJMbaQThz2i0JpGUD/xvHiDsigbQagMuVUtcmkFZoMn9DhhCak8DlONcUx304E5ydkX8DxBKk1TL6AjNxzg3FjcK5onR8Amn54SRwKU6+YkVrrZVSs3HODcUZ08kvGkf2E+7PcXMucB4Q97k8v1yCc6NbvwTSGowTsHloAmn54UKcST+Jc6NDcco4K9ba8cBwYGACafXHsYgvTCAtIdvMxWkPSfS5Qe7rogTSsoFLcOafJHSIQTiXeSWhuIVGlKmcorU+pZT6MfDjhJK8GfhHrfWBhNLLMiuAx4H/EndCWusjSqlvAlPiTssPWusDSqmfA7cllORXgG/FHNfIF65ydxfwSgK3mgH8BdCltd6UQFp++D5Qr7XemUBadwKXAc8nkJYf7gF2aq3fjDshrfXvlVI/Ah6IOy2fPA58Avh53Alprbcopf4B2BF3WkLm+TkwSmv9TNwJaa3/oJT6F+CVuNOyhDuAoVrrtgTS+hHO+LIkgbRCo5LZtBAEQRAEQRAEQcgXcmZKEARBEARBEAQhBKJMCYIgCIIgCIIghEFrLa+Mv+rr6ztwDrTH+qqvr+/IUtpZeCUhf2+yx51+mmlnud0VWfYit7siy552uadd9vJK71XUPpf2Kwuy56XPy5kpC1BK6STqSSmF1lpVfJZa2lkgCfl7kz3u9NNMu7c8pN3uiix7EnnIartLIv2syp52uSeRh6zOM0WnqH0ubbIge176vLj5Wcz27dsBOHLkCAcOHDjr7y+88AK//OUvAVi5ciVLlixh6dKlLFq0KJU8lP9sM0Fkfuutt3jsscdKn5ks/zTKPkiaBw8e5Pvf/z6PPPIIK1asYO3atfzsZz9LLH2vzXufefmISlptPky789rbunXr+Od//udE82CqvIOm69W71/68skg6H2m2N5P1HiZ9r69HHe/CjDeV74K9hKl/E3Ns2HnmpZdeMjrupUGY/u7Jntb6JgtlLlejW8iDDz7I0aNHmThxIj/+8Y+55ppraGxspF+/frz22ms0NjZyySWX0NnZyfDhwwG46KKLWLJkCR9++CGHDx9OJQ/lP9tIGJkXLlzI9u3bS5+ZKP80yj5Mmu+99x4zZsxgwIABHDlyhFmzZrF06dLE0vfavPdZfX09R44cSbQMTLT5KO3Oa29z586lpaUl0TyMGjUqUnmHTderd6/9eWWRdD6iyh+lvZmo9yjpT5gwgaVLl4Ye76KMN5Xvgn1Eqf8oc2yU8WbLli1MmDDByLiXBlH6uye7UiqV9U0Wylx2pizn0ksvZfXq1QBorens7OTMGSeGZ11dHfv372fz5s1s3LiRTZs20b9/f/r3759KHryf84BfmRctWkRjY2PpM9Pln0bZ+03z6NGjbNiwgaamJrZt20ZraysjRkSPuxe0zXufefkwQVptPmi789pbW1sbEyZMSDQPJss7SLpevXvtzyuLpPORZnszXe9B0/f6uonxLuh4U/ku2E3Q+jc1xwYdb2bNmkVra6vxcS8NgvZ3T/a01jdZKHM5M2UBlT6lK1eupK2tjRtuuMF0Or796E3nIYv+xNBd/iTLvaf0PUzmw2/accleLQ9ptvme0o8rD1mUPYk8ZLXdVabvIX0u3rQr85BG2kJ6FHm8SZMsyJ5m3RtFZ+BGEXn1/nKqqTbPPPNMze/s2rVL33nnnXrVqlX6Rz/6kT527Fjpb246odIOmn5ra6u+7777ek07Cy+TZd/W1qbvu+8+3dHRoW+55RZ95MiRmrLHUfenT58ufZZU2ps2bdKLFi3SLS0t+uc//3nNPMTV7irTrya/Sdm11vqWW27Rq1at0nfccUep3qulb1p2r+wr6z/pui8nqbS9Phek7edFdi9tv3kw3e5eeeUV/dRTT52VflbHO3ml94qr7Xlktc+l/TIpu1fmleubWrLHMdeWk1S5y5kpi/jBD37AjTfeyN13383MmTM5cOAAAwYMYNCgQRw5coTBgwfz29/+ltbWVhoaGrjsssuYPXs2L7/8MqdOneKzn/0sY8aM4eTJk7S2tnL11VfT1tbGzJkzE09//PjxjBkzJuYSM4cJ2T2ZR48ezQUXXMCgQYMSTd8r+zRknzZtGm+//TarVq2iX79+3iCauOxB0zeR9vr165k0aRIjRoxAKcX27duZO3du4mUfpv5Nph8Uk32uiLJ7aYfJg4n0m5qa2Lt3L1OnTmXIkCGJpx92vBPSxWTb01qjlL9NibT7XJqYLPNRo0YFXt+YyoM316aBKFMWMW3aNJYvX86ECROYOnUq9fX19O3bl6VLlzJgwAAAurq6mDVrFv3796e5uZnZs2fT2dlJZ2cnAO3t7TQ1NTFp0iRefPFF/vRP/zSV9Hfv3s2KFSu47rrr6NMn+0f3TMi+b9++ksx+B3iT6Xtl39nZSd++/ru+ibRff/11Ojo6+MxnPsOrr77KoUOHfJ+fMin7nDlzWLZsme/0TaS9f/9+2tvbqauro2/fvkyZMiWVsg9T/ybTD7KwMZW21+cuuOCCwsnupR0mDybSHz58OHv27GH58uWppB92vBPSxWTbO3HiBA0NDYmlG6XPpYnpMg8js8m59uTJk8bvBqhJEttf8or2osY26CuvvNJtSzUshHT9MJF+T2ln4ZVE2fcme9zpp5l2b3lIu90VWXY/echru0si/azKXvR2J6/0XnG3vaz2ubRfWZA9L31eLqCwgKIHckwTCdpbzHZXZNmTyENW210S6WdV9rTLPYk8ZHWeKTpF7XNpkwXZ89Lns+9fJQiCIAiCIAiCkEFEmbKUJUuW1PzOT3/6UzZv3sy9997L7t27ufXWWzl69CgAHR0d3HXXXezevdvXs8Kmv3PnTu666y62bdvG/fffX/rb6tWrueeee9i2bVuo9NMkaNl7tLe3c//997N06VKefvrpxMre8yc2UeZ+0/ZkrWx3q1ev5s477wydhzCy33rrraW/lfeHoOn7Tburq4tbb721JGu57FHKP2jZV6YPcOutt8be7rz0PcrbQlLtrlrfK4LsHl4ZpCW7N86llX6Uvi5kg7BzXdS6DzvHe78XYX3z8ssv8/TTT5f6uYeJfhe1/NPo83Iq0xKeffZZ6urqaGlp4WMf+xitra00Nzfz3nvvsWnTJr71rW8xZMiQbhGiR44cWbpVpvIGOe+mo6ampljTHzduXI+397W1tXH11VdbcdtS1LL38Mqg/NabONP3yt4jTJmHTbvazYXejXbHjh1LRPbK233K+0Ncabe0tPR4e1/Q8o9a9pXpe2XhR3YT6XuUtwW/RE27Wt8rguweXhmkJbs3zo0bNy6V9IP0dSEbmJrrgta9qTne+70I65uRI0eyb9++s25ODNPvTJd/Gn1edqYs4dSpUwwdOpRhw4axfft2Jk6cyGuvvQbAlClT6Nu3L1p3jxANZ98m9eabb9LZ2dntpqM406+8vW/58uV0dnYyfvx4XnzxRc4991yDpRQPUcv+jTfeoLOzs3Sz2NChQ0u33sSZvlf277zzTugyD5u2J2tXV1e3dtevX79AN9pFlf3w4cO0t7ezbNkyOjs7u/WHuNKuvL3v0KFDoco/atlXpl9+01ES6Xt9vbwt+CVq2tX6nt882Cy7l7ZXBmnJ7t3utXPnzlTSD9LXhWxgaq4LWvem5njv9yKsb7z+PWzYMPbs2VOa48P0O9Pln0qfT+KWC3lFe9XX13cAOu5XfX19R5bSzsIrCfl7kz3u9NNMO8vtrsiyF7ndFVn2tMs97bKXV3qvova5tF9ZkD0vfV5u8xMEQRAEQRAEQQiBuPkJgiAIgiAIgiCEQJQpQRAEQRAEQRCEEIgyJQiCIAiCIAiCEAJRpgRBEARBEARBEEIgypQgCIIgCIIgCEIIRJkSBEEQBEEQBEEIgShTgiAIgiAIgiAIIRBlShAEQRAEQRAEIQSiTAmCIAiCIAiCIIRAlClBEARBEARBEIQQiDIlCIIgCIIgCIIQAlGmBEEQBEEQBEEQQiDKlCAIgiAIgiAIQghEmRIEQRAEQRAEQQiBKFOCIAiCIAiCIAghEGVKEARBEARBEAQhBKJMCYIgCIIgCIIghECUKUEQBEEQBEEQhBCIMiUIgiAIgiAIghACUaYEQRAEQRAEQRBCIMqUIAiCIAiCIAhCCESZEgRBEARBEARBCIEoU4IgCIIgCIIgCCEQZUoQBEEQBEEQBCEEokwJgiAIgiAIgiCEQJQpQRAEQRAEQRCEEIgyJQiCIAiCIAiCEAJRpgRBEARBEARBEEIgypQgCIIgCIIgCEIIRJkSBEEQBEEQBEEIgShTgiAIgiAIgiAIIRBlShAEQRAEQRAEIQSiTAmCIAiCIAiCIIRAlClBEARBEARBEIQQiDIlCIIgCIIgCIIQAlGmBEEQBEEQBEEQQiDKlCAIgiAIgiAIQghEmRIEQRAEQRAEQQiBKFOCIAiCIAiCIAghEGVKEARBEARBEAQhBKJMCYIgCIIgCIIghECUKUEQBEEQBEEQhBCIMiUIgiAIgiAIghCC/wcGZFGf4e5q7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#displat visual tree\n",
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(15,15)) # increase the value if the size of the image is too small\n",
    "plot_tree(model_best, feature_names=list(X_arr))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oWTaREfyAopt",
    "outputId": "c7de0bfd-e0d8-4f29-96fa-60180120ae1f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=13, min_samples_split=10, random_state=10)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set dataset size into two features\n",
    "X2_train = X_train[0:,:2]\n",
    "index = np.where(y_train=='RB')[0]\n",
    "X2_train_normal = X2_train[index]\n",
    "y2_train_normal = y_train[index]\n",
    "index = np.where(y_train=='NRB')[0]\n",
    "X2_train_abnormal = X2_train[index]\n",
    "y2_train_abnormal = y_train[index]\n",
    "model_dt = DecisionTreeClassifier(criterion='gini', min_samples_split=10, max_depth=d, random_state=seed_num)\n",
    "model_dt.fit(X2_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 500
    },
    "id": "y5sin6JXAx6z",
    "outputId": "01164be1-208c-4d55-f315-287cac01645c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x131a5977e80>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph for visual representation\n",
    "from matplotlib.colors import ListedColormap\n",
    "cmap_regions = ListedColormap(['pink', 'lightsteelblue'])\n",
    "step = 0.02\n",
    "offset = 0.1\n",
    "xx1_min, xx1_max = X_train[:,0].min()-offset, X_train[:,0].max()+offset\n",
    "xx2_min, xx2_max = X_train[:,1].min()-offset, X_train[:,1].max()+offset\n",
    "xx1,xx2 = np.meshgrid(np.arange(xx1_min, xx1_max, step), np.arange(xx2_min, xx2_max, step))\n",
    "zz = model_dt.predict(np.c_[xx1.ravel(), xx2.ravel()])\n",
    "zz = y_to_numeric(zz)\n",
    "zz = zz.reshape(xx1.shape)\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "ax = fig.add_subplot()\n",
    "ax.scatter(X2_train_normal[:,0], X2_train_normal[:,1], c='blue', label='RB')\n",
    "ax.scatter(X2_train_abnormal[:,0], X2_train_abnormal[:,1], c='red', label='NRB')\n",
    "ax.contourf(xx1, xx2, zz, cmap=cmap_regions, alpha=0.5)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "ModelTraining.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}