diff --git a/.ipynb_checkpoints/TP1_prog2.py-checkpoint.ipynb b/.ipynb_checkpoints/TP1_prog2.py-checkpoint.ipynb
index 5398024..a1385c7 100644
--- a/.ipynb_checkpoints/TP1_prog2.py-checkpoint.ipynb
+++ b/.ipynb_checkpoints/TP1_prog2.py-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "id": "530f620c",
    "metadata": {},
    "outputs": [],
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "68b6a517",
    "metadata": {},
    "outputs": [],
@@ -864,7 +864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 26,
    "id": "98107e41",
    "metadata": {},
    "outputs": [
@@ -872,37 +872,59 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Matrice de confusion K-NN :\n",
-      " [[51  0  0  0  0  1  0  0  0  0]\n",
-      " [ 0 56  0  0  0  0  0  0  0  0]\n",
-      " [ 3  1 45  1  0  0  1  1  0  0]\n",
-      " [ 0  1  1 35  0  1  0  1  1  1]\n",
-      " [ 0  3  0  0 48  0  0  0  0  2]\n",
-      " [ 0  1  0  1  0 38  0  0  0  0]\n",
-      " [ 0  0  0  0  0  2 44  0  0  0]\n",
-      " [ 0  2  0  0  3  0  0 47  0  0]\n",
-      " [ 2  0  0  0  0  3  1  0 42  2]\n",
-      " [ 0  0  0  0  4  1  0  1  2 50]]\n"
+      "Métriques pour K-NN :\n",
+      "Paramètres : (n_neighbors=3,p=2,n_jobs=1)\n",
+      "Taille de l'échantillon : 10000\n",
+      "Proportion des datasets : 90%\n",
+      "Temps d'entraînement (secondes) : 0.01596\n",
+      "Temps de prédiction (secondes) : 0.30718\n",
+      "Précision pour chaque classe : [0.942, 0.891, 0.962, 0.959, 0.988, 0.944, 0.961, 0.97, 0.989, 0.918]\n",
+      "Précision : 0.95\n",
+      "Erreur : 0.05\n",
+      "Matrice de confusion :\n",
+      " [[ 98   0   1   0   0   0   1   0   1   0]\n",
+      " [  0 114   0   0   0   0   0   0   0   0]\n",
+      " [  2   2 102   0   0   0   0   0   0   0]\n",
+      " [  0   1   1  93   0   3   0   1   0   0]\n",
+      " [  1   5   0   0  82   0   0   0   0   5]\n",
+      " [  0   1   1   1   0  84   3   0   0   1]\n",
+      " [  0   0   0   0   0   0  99   0   0   0]\n",
+      " [  0   3   0   0   0   0   0  97   0   2]\n",
+      " [  2   1   1   3   1   2   0   0  92   0]\n",
+      " [  1   1   0   0   0   0   0   2   0  89]]\n"
      ]
     }
    ],
    "source": [
     "### Create vector of 5000 random indexes\n",
-    "rand_indexes = np.random.randint(70000, size=5000)\n",
+    "rand_indexes = np.random.randint(70000, size=10000)\n",
     "### Load data with the previous vector\n",
     "data = mnist.data[rand_indexes]\n",
-    "# print(\"Dataset : \", data)\n",
     "target = mnist.target[rand_indexes]\n",
-    "\n",
     "# Split the dataset\n",
     "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
     "\n",
-    "# Training on xtrain,ytrain\n",
     "clf = neighbors.KNeighborsClassifier(n_neighbors=3,p=2,n_jobs=1)\n",
+    "# Training on xtrain,ytrain\n",
+    "t1 = time.time()\n",
     "clf.fit(xtrain, ytrain)\n",
+    "t2 = time.time()\n",
     "# Predicting on xtest\n",
     "pred = clf.predict(xtest)\n",
-    "print(\"Matrice de confusion K-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
+    "t3 = time.time()\n",
+    "#Calcul de différentes metrics\n",
+    "precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
+    "\n",
+    "print(\"Métriques pour K-NN :\")\n",
+    "print(\"Paramètres : (n_neighbors=3,p=2,n_jobs=1)\")\n",
+    "print(\"Taille de l'échantillon :\", 10000)\n",
+    "print(\"Proportion des datasets :\", \"90%\")\n",
+    "print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
+    "print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
+    "print(\"Précision pour chaque classe :\", precisions)\n",
+    "print(\"Précision :\", clf.score(xtest, ytest))\n",
+    "print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
+    "print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
    ]
   },
   {
diff --git a/.ipynb_checkpoints/TP2_prog1.py-checkpoint.ipynb b/.ipynb_checkpoints/TP2_prog1.py-checkpoint.ipynb
index a895d5b..acbb173 100644
--- a/.ipynb_checkpoints/TP2_prog1.py-checkpoint.ipynb
+++ b/.ipynb_checkpoints/TP2_prog1.py-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "3eb7a65b",
    "metadata": {},
    "outputs": [],
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "a8812842",
    "metadata": {},
    "outputs": [],
@@ -1233,7 +1233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "abb0fcf1",
    "metadata": {},
    "outputs": [
@@ -1241,23 +1241,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Matrice de confusion A-NN :\n",
-      " [[59  0  0  0  0  0  0  0  0  0]\n",
-      " [ 0 60  0  0  0  0  0  1  0  0]\n",
-      " [ 0  0 42  0  0  1  2  1  2  0]\n",
-      " [ 0  0  1 44  0  1  0  0  0  0]\n",
-      " [ 0  0  0  0 46  0  1  0  0  4]\n",
-      " [ 0  0  0  0  0 31  0  0  1  0]\n",
-      " [ 0  0  0  0  0  0 48  0  0  0]\n",
-      " [ 1  0  0  1  0  0  0 49  0  0]\n",
-      " [ 0  1  1  5  0  1  0  0 48  0]\n",
-      " [ 2  0  0  1  1  2  0  0  2 40]]\n"
+      "Métriques pour A-NN\n",
+      "Paramètres : (random_state=1, max_iter=300, hidden_layer_sizes=((85,)*15),\n",
+      "solver=adam, activation=relu, alpha= 0.0000001)\n",
+      "Taille de l'échantillon : 10000\n",
+      "Proportion des datasets : 90%\n",
+      "Temps d'entraînement (secondes) : 27.9214\n",
+      "Temps de prédiction (secondes) : 0.01396\n",
+      "Précision pour chaque classe : [0.972, 0.974, 0.926, 0.97, 0.919, 0.939, 0.971, 0.967, 0.951, 0.927]\n",
+      "Précision : 0.952\n",
+      "Erreur : 0.048\n",
+      "Matrice de confusion :\n",
+      " [[103   0   1   0   0   0   1   0   0   0]\n",
+      " [  0 114   0   0   1   0   0   0   0   0]\n",
+      " [  0   0 100   0   2   1   1   1   1   0]\n",
+      " [  0   1   3  97   0   1   0   0   1   1]\n",
+      " [  0   0   0   0  79   0   0   0   0   4]\n",
+      " [  2   0   0   3   0  93   1   0   1   0]\n",
+      " [  0   0   0   0   0   3  99   0   1   0]\n",
+      " [  1   1   1   0   1   0   0  89   0   1]\n",
+      " [  0   1   3   0   0   1   0   0  77   2]\n",
+      " [  0   0   0   0   3   0   0   2   0 101]]\n"
      ]
     }
    ],
    "source": [
     "### Create vector of 5000 random indexes\n",
-    "rand_indexes = np.random.randint(70000, size=5000)\n",
+    "rand_indexes = np.random.randint(70000, size=10000)\n",
     "### Load data with the previous vector\n",
     "data = mnist.data[rand_indexes]\n",
     "# print(\"Dataset : \", data)\n",
@@ -1266,10 +1276,6 @@
     "# Split the dataset\n",
     "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
     "\n",
-    "best_training_time = 0\n",
-    "best_precision_score = 0\n",
-    "best_zero_one_loss = 0\n",
-    "\n",
     "r = 1\n",
     "max_i = 300\n",
     "nb_hl = 15\n",
@@ -1281,31 +1287,27 @@
     "\n",
     "#Entraîne le classifier\n",
     "clf = neural_network.MLPClassifier(random_state=r, max_iter=max_i, hidden_layer_sizes=hl, solver=sol, activation=act, alpha=a, verbose=False)\n",
-    "t1 = round(time.time(),5)\n",
+    "t1 = time.time()\n",
     "clf.fit(xtrain, ytrain)\n",
-    "t2 = round(time.time(),5)\n",
+    "t2 = time.time()\n",
     "#Prédiction sur le jeu de tests\n",
     "pred = clf.predict(xtest)\n",
-    "# Probabilités des prédictions sur xtest\n",
-    "pred_proba = clf.predict_proba(xtest)\n",
-    "# On sauvegarde le temps de calcul, la précision et \n",
-    "# les taux d'erreurs par classe\n",
-    "best_training_time = t2-t1\n",
-    "best_precision_score = clf.score(xtest, ytest)\n",
-    "best_zero_one_loss = metrics.zero_one_loss(ytest, pred)\n",
+    "t3 = time.time()\n",
     "\n",
-    "# print(\"Paramètre :\\n\")\n",
-    "# print(\"random_state = \", r)\n",
-    "# print(\"max_iter = \", max_i)\n",
-    "# print(\"nb_hidden_layer = \", nb_hl)\n",
-    "# print(\"hidden_layer_size = \", hl_size)\n",
-    "# print(\"solver = \", sol)\n",
-    "# print(\"activation = \", act)\n",
-    "# print(\"alpha = \", a)\n",
-    "# print(\"Temps d'entraînement : \", best_training_time)\n",
-    "# print(\"Score : \", best_precision_score)\n",
-    "# print(\"Zero-one loss : \", best_zero_one_loss)\n",
-    "print(\"Matrice de confusion A-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
+    "#Calcul de différentes metrics\n",
+    "precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
+    "\n",
+    "print(\"Métriques pour A-NN\")\n",
+    "print(\"Paramètres : (random_state=1, max_iter=300, hidden_layer_sizes=((85,)*15),\")\n",
+    "print(\"solver=adam, activation=relu, alpha= 0.0000001)\")\n",
+    "print(\"Taille de l'échantillon :\", 10000)\n",
+    "print(\"Proportion des datasets :\", \"90%\")\n",
+    "print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
+    "print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
+    "print(\"Précision pour chaque classe :\", precisions)\n",
+    "print(\"Précision :\", clf.score(xtest, ytest))\n",
+    "print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
+    "print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
    ]
   },
   {
diff --git a/.ipynb_checkpoints/TP3_prog1.py-checkpoint.ipynb b/.ipynb_checkpoints/TP3_prog1.py-checkpoint.ipynb
index fcbea2b..586496a 100644
--- a/.ipynb_checkpoints/TP3_prog1.py-checkpoint.ipynb
+++ b/.ipynb_checkpoints/TP3_prog1.py-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "3eb7a65b",
    "metadata": {},
    "outputs": [],
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "a8812842",
    "metadata": {},
    "outputs": [],
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "id": "6ec263be",
    "metadata": {},
    "outputs": [
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "3b1a54ef",
    "metadata": {},
    "outputs": [
@@ -84,17 +84,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Temps d'entraînement :  0.28424\n",
+      "Score échantillon de test :  0.906\n",
+      "Classe image 4 :  7\n",
+      "Classe prédite image 4 :  9\n",
+      "Précision pour chaque classe : \n",
+      " [0.96078431 0.88235294 0.89830508 0.89361702 0.94117647 0.87179487\n",
+      " 0.92592593 0.90243902 0.9        0.875     ]\n",
       "Matrice de confusion SVM:\n",
-      " [[44  0  2  0  0  0  0  0  0  0]\n",
-      " [ 0 56  0  0  0  0  0  0  0  0]\n",
-      " [ 0  0 46  0  0  0  0  1  0  0]\n",
-      " [ 0  1  2 48  0  2  0  0  2  0]\n",
-      " [ 0  1  0  0 42  0  0  0  1  3]\n",
-      " [ 0  0  0  0  0 36  0  0  1  1]\n",
-      " [ 0  0  1  0  0  1 54  0  0  0]\n",
-      " [ 0  0  0  0  0  0  0 54  0  2]\n",
-      " [ 0  1  1  5  1  0  0  0 43  0]\n",
-      " [ 0  0  0  0  1  0  0  1  1 45]]\n"
+      " [[49  0  0  0  0  0  1  0  0  0]\n",
+      " [ 0 60  0  0  0  0  0  0  0  1]\n",
+      " [ 1  0 53  0  0  0  0  0  0  0]\n",
+      " [ 0  1  2 42  0  2  0  0  1  0]\n",
+      " [ 0  0  1  0 48  0  1  0  1  1]\n",
+      " [ 1  2  0  2  0 34  2  0  2  1]\n",
+      " [ 0  0  0  0  0  1 50  0  0  0]\n",
+      " [ 0  2  1  0  0  0  0 37  1  2]\n",
+      " [ 0  2  1  3  1  2  0  1 45  0]\n",
+      " [ 0  1  1  0  2  0  0  3  0 35]]\n",
+      "Zero-one classification loss :\n",
+      " 0.09399999999999997\n"
      ]
     }
    ],
@@ -108,20 +117,23 @@
     "\n",
     "#Prédiction sur le jeu de tests\n",
     "# print(\"Predicting...\")\n",
+    "t1 = time.time()\n",
     "pred = clf.predict(xtest)\n",
+    "t2 = time.time()\n",
+    "print(\"Temps d'entraînement : \", round(t2-t1,5))\n",
     "#print(\"Prédiction : \", pred)\n",
     "# On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
-    "# score = clf.score(xtest, ytest)\n",
-    "# print(\"Score échantillon de test : \", score)\n",
+    "score = clf.score(xtest, ytest)\n",
+    "print(\"Score échantillon de test : \", score)\n",
     "\n",
     "#Infos image 4\n",
-    "# print(\"Classe image 4 : \", ytest[3])\n",
-    "# print(\"Classe prédite image 4 : \", pred[3])\n",
+    "print(\"Classe image 4 : \", ytest[3])\n",
+    "print(\"Classe prédite image 4 : \", pred[3])\n",
     "\n",
     "#Calcul de différentes metrics\n",
-    "# print(\"Précision pour chaque classe : \\n\", metrics.precision_score(ytest, pred,average=None))\n",
-    "print(\"Matrice de confusion SVM:\\n\", metrics.confusion_matrix(ytest, pred))\n",
-    "# print(\"Zero-one classification loss :\\n\", metrics.zero_one_loss(ytest, pred))"
+    "print(\"Précision pour chaque classe : \\n\", metrics.precision_score(ytest, pred,average=None))\n",
+    "print(\"Erreur pour chaque classe :\\n\", metrics.zero_one_loss(ytest, pred))\n",
+    "print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
    ]
   },
   {
@@ -260,9 +272,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 12,
    "id": "5726fcb1",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -281,8 +295,10 @@
     "####### Variation du paramètre de tolérance aux erreurs C #######\n",
     "\n",
     "list_training_times_tol = []\n",
-    "list_precision_scores_tol = []\n",
-    "list_zero_one_loss_tol = []\n",
+    "list_precision_scores_train_tol = []\n",
+    "list_zero_one_loss_train_tol = []\n",
+    "list_precision_scores_test_tol = []\n",
+    "list_zero_one_loss_test_tol = []\n",
     "\n",
     "kernel_train = xtrain\n",
     "kernel_test = xtest\n",
@@ -297,35 +313,38 @@
     "    t2 = round(time.time(),5)\n",
     "    #Prédiction sur le jeu de tests\n",
     "    pred = clf.predict(kernel_test)\n",
+    "    pred_train = clf.predict(kernel_train)\n",
     "    # On sauvegarde le temps de calcul, la précision et \n",
     "    # les taux d'erreurs par classe\n",
     "    list_training_times_tol.append(t2-t1)\n",
-    "    list_precision_scores_tol.append(clf.score(kernel_test, ytest))\n",
-    "    list_zero_one_loss_tol.append(metrics.zero_one_loss(ytest, pred))\n",
+    "    list_precision_scores_train_tol.append(clf.score(kernel_train, ytrain))\n",
+    "    list_zero_one_loss_train_tol.append(metrics.zero_one_loss(ytrain, pred_train))\n",
+    "    list_precision_scores_test_tol.append(clf.score(kernel_test, ytest))\n",
+    "    list_zero_one_loss_test_tol.append(metrics.zero_one_loss(ytest, pred))\n",
     "print(\"Done\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 11,
    "id": "741f82ca",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(36.0, 0.5, 'Zero-one loss')"
+       "Text(38.625, 0.5, 'Zero-one loss')"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAALACAYAAADPIDvhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAADu8UlEQVR4nOzdd3iUVfbA8e8hoSSUAFKkJQgovYPorstiwYq6YsPFiogFFXfVFVfXtov6Q9eKiliwIei6KoKIIgoqriICIiKK9N57qMn5/XHvJJNhZjIhk0zK+TzPPMnb71vnzL33vVdUFWOMMcaYeKiQ6AQYY4wxpuywwMIYY4wxcWOBhTHGGGPixgILY4wxxsSNBRbGGGOMiRsLLIwxxhgTNxZYGGOMMSZuLLAwxhhjTNwkx2tFItIY6Af8AWgI7AHmAx8CH6lqdry2ZYwxxpiSSeLR8qaIjAYaAROBWcAGoApwDHAi0BUYqqpfFHpjxhhjjCmx4hVYtFPV+VGmVwLSVfW3Qm/MGGOMMSVWXAKLsCsWqQU0UdV5RbIBY4wxxpQ4ca28KSLTRKSGiNQGfgBGi8hj8dyGMcYYY0queL8VkqaqO4C+wGhV7QqcEudtGGOMMaaEindgkSwiDYCLcBU5TSkkIh+JyBXxnrewROTvIvJicWwrShrqi8gXIrJTRP6dyLSEU5znw2+vl4isKobtqIi0OIzlRERGi8hWEZkZh3RcKSJfFXY9iSYi14rIE/7/pv74xu0twcNMU9yObX7Xi4j8S0Q2icg6f0//LCKV47FtE8fXTb0HgI+Br1T1OxFpBiyK8zZMGCKyK2gwFdgHZPnha1V1TKzrUtUzimLeghCRXsAbqto4aFsPFsW2CmgQsAmooUVVQakQiup8lGInAL2Bxqq6O3SiiFwJDFTVE4o7YYniK9PfDRyX6LQkgog0AW4FMlR1gx/3Oe7efjqRaSsr4ppjoar/UdUOqnqDH16iqufHcxsmPFWtFvgAK4Czg8blBBWJ/lVSBmQACw4nqCjKY+9/mVuDd4fKAJaFCyqKm4gkJToN3rnAQlVdHc+VloRnS4xpyAA2B4IKbwxwbdGkqvyJy4NIRJ4WkacifeKxDXN4AlnVInKHiKzDVaitJSITRWSjzyKe6Bs4CywzTUQG+v+vFJGvRORRP+9SETnjMOc9KqgY4VMReUZE3giT5qrAR0BDEdnlPw1F5L7A/EHZt1eJyEq/vetEpLuIzBORbSIyImS9A3yW51YR+VhEMvx4EZHHRWSDiGz3y7cLk65XgCuAv/k0nSIilUXkCRFZ4z9PBLJUwx37kPVV9ulsFzSurojsEZF6MZ6nYSIyA8gEmoWcjwoicreILPf79pqIpAWnLSQ9y0TkFP//sSIyS0R2iMh6ibEStj9P//VpXioiNweN3yOuYndg3s7isqMrRjs/MW7zAxHZIiK/icg1fvzVwIvA8f583R+yXGtgZND0bX58mj9WG/2xu1siBG0i0kpEpvht/yIiFwVNe0VEnhORSSKyGzhRRM4SkTn+uK4UkfuC5g9c01eIyAp/bO4Kmp4krjhwsbh76Htxv76jpiOMM4DpUY7n+f5aaOevoaF+m5tF5O3AOQxK79UisgL4TPJ/BqSJyEsislZEVosrkihUwOXTMFhEFpE3h/xMEVnij+Mjfl9OAaaQ+2x5xc/7Le7+iemaM/lQ1UJ/cA/bK4BRwFfATf7zBfB4PLZhnwKdj2XAKf7/XsBB4P+AykAKcARwPq7IpDrwH+D9oOWn4bKHAa4EDgDXAEnA9cAacl9VLsi8/wMeBSrhsqh34Io7wu1DL2BVyLj7AvMDTQHFfTFUAU4F9gLvA/VwDbZtAP7o5/8T8BvQGlcEeDfwtZ92GvA9UBMQP0+DCOl6BfhX0PADwDd+m3WBr4F/Rjr2Ydb3MjAsaHgwMNn/H8t5WgG09ftUMeR8DPD73AyoBrwLvB7l+C4j97r5H3CZ/78acFx+5wn3Q+V74B5/jpsBS4DT/PTPgGuCln0EGJnf+fHTFWgRIQ3TgWf9ddAJ2AicHHRNfhXlXjlkOvAaMN4f86bAr8DVofMDVYGVwFU+zV1wxWRtg66V7cDv/bGp4o9Xez/cAVgP/Cnkmn4Bd592xBVptvbTbwd+BFrirtOOuGskajrC7PN3wIVBw4HtJvt1/BY41sAtuOu7Me4afh4YG7Lcaz4NKeT/DHjfr6Mq7p6ZiSuqPeRcAPOAbRE+z4ZcG1OA2vh7zI/73I9L9+cwcF/0IuTaD9reOYl+fpeFT3xX5k5kxaDhisDnid7J8vbh0MBiP1AlyvydgK1Bw9PIGyz8FjQt1d+0RxZkXn9zHwRSg6a/QeEDi0ZB0zcDFwcN/xe4xf//Ef7LwQ9XwP3KzwBO8g+e44AK+RzbV8gbWCwGzgwaPg2X9R7rsT8FWBI0PAO4vADn6YGQeYLPx1TghqBpLXEP/eQIxzf4uvkCuB+ok8/xyFkP0ANYETL9TtwbYgADgc/8/4L7MuyZ3/nxw2EDC6AJri5R9aBxDwGvBF2TMQcWuC/DfUCboHHXAtNC5wcuBr4MWd/zwL1B18pr+Ry/J/A/vsi9phsHTZ8J9PP//wKcG2YdUdMRZv5FwOlBw4Ht3gYsCNn+z/ggzQ83CLqGAss1CzmekZ4B9f2xTQmafgn+OyK/cxXlGCpwUphxwft4AzA19JoNWSbivWefgn3iXSbbEBflB1Tz40xibVTVvYEBEUkVked9Nu8O3JdIzShZkusC/6hqpv+3WgHnbQhsCRoH7oulsNYH/b8nzHAgnRnAk+KKHrYBW3Bfbo1U9TNgBPAMsF5ERolIjRi33xBYHjS8nLzXfJ5jH8ZnQIqI9PDZsJ2A9yDm8xTtGIZLWzLuAZ+fq3FN8i8Uke9EpE8My2Tgspi3BR3nvwdt7x1csUNDoCfu4f9l0LJhz08+2wxcVzuDxi2PYblI6uByW0KPW7j1ZQA9Qva3P+5LNCDP+fHn+XNfzLIduM5vM9i6oP8zyb2Gm+AC2cNJR7Ct5H1OB9wOPKOqwUVkGcB7Qev9GRfIBV9DoddgpGdABu7H5tqg9T2Py7korHD3QfC40PsynOq43BBTSPGubPMwMEdcDVuAP+J+ZZrE0pDhW3G/Xnuo6joR6QTMwT3Ii8paoLaIpAY9bJpEmT80zYW1ElfkEPbtGFV9CnhKROoBb+Mesv+IYb1rcA/Mn/xwuh+Xs+poC6tqtoi8jfvlth6YGPQlGct5irb+QNoCArlG63EP2dTABB+s1A1K1yLgEl+3oC/wjogcodErQa4Elqrq0RH2dZuIfIJ7Hb01Lktdg5aNeH7y2cfaIlI96LilA7FWTAw9fptwv8gzcL/eo61vJTBdVXsXYP1v4oLYM1R1r7hXPkMDi0hWAs1xnTsWNB3B5uGCxlCnApNFZJ2q/jdo3QNUdUbozCLS1P8b6726EpdjUUdVD+Y3s4j8RN7rN9gbqnpd0HC4NDQh8n0Zuq1koAWuYUdTSPF+K2Q0Ljv0Pf85XlVfjec2TFxUx/2a3+YrYt1b1BtU1eW4DuruE5FKInI8cHaURdYDR4ivbBgHI4E7RaQt5FQiu9D/393/kqwI7MbV1ciKvKo8xgJ3i6t0WQdXv+CQCqn5eBOXnd3f/x9Q2PM0FviLuEqz1YAHgbf8Q/1XoIq4yoQVcXUact7jF5FLRaSuul6Jt/nR+R2TmcAOcZVVU3xlw3Yi0j1kXy/H1R0J3teI5ycaVV2Jq9fykIhUEZEOuNyWWAOU9UBjca9goqpZuMBymIhU97lIfyX8OZ0IHCMil4lIRf/pLq5SaCTVcTkse0XkWODPMaYTXEXUf4rI0eJ0EJEjDiMdk3A/+kL9BJwOPCMi5/hxI3HHIgNyKhefW4A051DVtcAnwL/FtdBcQUSai0i4tKCqbTXobbeQz3Xhlglxu7gK0E2AIcBbUeY9FleEuTzKPCZGRfF6WhKu8tRW3MXeswi2USREpKWIzA367BCRW0LmaSUi/xORfSJyW8i0l8XVvo/YIVsJ8QSuotUmXMWsycW03f7A8bi6EP/C3ej7ws2oqgtxX4xLfLZpoYrUVPU9XCXKcb5YYT6udjxADVyFua24LNPNuEqmsfgXLmCah6tYN9uPK0javsUFNA1xdQ0CnqBw5+ll4HVcEcpSXMB0k9/mdly584u4X+O7geAs8NOBn8S1j/Ikrpw/WpFO4Ev5bFxxzlKf7heB4ODwA+BoYL2q/hC0bLTzk59LcOX9a3A/aO5V1SkxLvsZ7gt1nYhs8uNuwh2PJbjK6G/ijmUePofkVKCf3/Y6civqRnID8ICI7MQFoW/HmE6Ax/z8n+AqPr+Eq69Q0HRMAFqFu6f8OekDvCDubY4ncefsE5/mb3A/Hg/X5biipgW4++0dXL2NojAeV5l4LvAh7nhF0h8XRJk4iGsnZCLyf7hfXj8B2X60quo5kZcqmXzW8GpcNvTyoPH1cNlzf8JVpHs0aFpPYBeuwtYhryuavETkLdz79EWeY2KMySUig3AVVG9JdFoSzT/TpwOd8wueTWziXcfiT0BLVQ37K7SUORlYHJo1pq5RlQ0iclboAqr6RVC5ownhs8S34H7NnoprqOfhhCbKmHJIVUclOg0lhX+mRyu+MgUU78BiCa7Wb1kILPrhsuJN/ByJa0vhCFy2+/WqOiexSTLGGBNP8Q4sMoG5IjKVoOBCVW+O83aKlK/IdQ7uHXwTJ6o6AVe+a4wxpoyKd2Dxgf+UdmcAs1V1fb5zGmOMMSZHXAMLVX3V/9oPvCP9i6oeiOc2isklWDGIMcYYU2DxfiukF/AqrmlgwTVQcoWqfhGvbdSpU0ebNm0ar9UdIjs7m3nz5tG+fXuSklwDhxs3bgSgbt26HDhwgJ9//pmsrCxEhAoVKtC2bVuSkpJYsmQJO3fu5ODBg1SsWJGGDRtSp06sbd8YY4wxJdP333+/SVXr5j9n/ItC/g2cqqq/AIjIMbhf/l3jtYGmTZsya9aseK0OxoyBu+6CFSsgPR2GDYP+/eO3fmOMMaaUE5GYGw+Ld2BRMRBUAKjqr75Vv5JpzBgYNAgyfQvTy5e7YbDgwhhjjDkM8W55c5aIvCQivfznBVzLZyXTXXflBhUBmZluvDHGGGMKLN45FtcDg4GbcXUsvgCezW8hEfkDrknVZFxrcL+Lc7rCW7GiYOONMcYYE1W8cyySgSdVta+qngc8hes7JCpV/dJ3KjMRV/mzeKSnR0oQnHQSTJvm/jfGGGNMTOIdWEzFdZoUkAJ8WoDl/0yY1zxFZJCIzBKRWYE3NOJi2DBITc07LiXF1a/4+Wc48UTo2RM++cQCDGOMMSYG8Q4sqqjqrsCA/z81yvw5RCQd2K6qO0KnqeooVe2mqt3q1o3pbZfY9O8Po0ZBRgaIuL8vvABvvAFLl8KIEbBsGZx2Ghx3HEycaAGGMcYYE0W8A4vdItIlMCAiXYE9MS57NTA6zunJX//+LnjIznZ/A2+DVKkCgwfDb7/B88/Dhg1w9tnQtSu8+66b3xhjjDF5xDuwuAX4j4h8KSJfAm8BN8ayoKreq6pfxzk9hVe5snsF9ddfYfRo2LULzj8fOnaEt96CrKxEp9AYY4wpMeIaWKjqd0Ar3NshNwCtVbXkvm5aEBUrwpVXwoIFrv2LrCzo1w/atoXXX4eDBxOdQmOMMSbh4hpYiEgqcAcwRFV/BJqKSJ94biPhkpPhz3+G+fPhP/9xORqXXw6tWsFLL8H+/YlOoTHGGJMw8S4KGQ3sB473w6uAf8V5GyVDhQpwwQUwZw68/z7UrAkDB8LRR8Nzz8G+ffmtwRhjjClz4h1YNFfV4cABAFXdg2soq+yqUAHOPRe++w4mTYJGjeCGG6BZM3jyyUNb9jTGGGPKsHgHFvtFJAVQABFpDpSPn+4icMYZMGMGfPqpy7m45RY46ih49FFX6dMYY4wp4+IdWNwLTAaaiMgYXINZf4vzNko2ETj5ZNdq5/Tp0KED3H47NG0KDz4IOw5ppsMYY4wpM+L9VsgUoC9wJa4FzW6qOi2e2yhVevaEKVPgf/+DHj1c52YZGXDffbB1a6JTZ4wxxsRdvN8K+T2wV1U/BGoCfxeRjHhuo1Q67jj48EOYNQt69YL773cBxt//Dps2JTp1xhhjTNzEuyjkOSBTRDoCtwPLgdfivI3Sq2tXeO89+OEHVx/j4YddgHHbbbBuXaJTZ4wxxhRavAOLg6qqwLnAU6r6JFA9ztso/Tp0cK12/vQT9O0Ljz/uKnnefDOsWpXo1BljjDGHLd6BxU4RuRO4FPhQRJKAinHeRtnRurVrtfOXX1yjW889B82bw/XXw/LliU6dMcYYU2DxDiwuxr1eerWqrgMaAY/EeRtlT4sWrtXORYvgqqvc/y1awNVXu07QjDHGmFIi3m+FrFPVx1T1Sz+8QlWtjkWsmjaFkSNhyRKXa/Hmm9CyJVx2GSxcmOjUGWOMMfmKd46FiYfGjeGpp2DpUvjLX1w37W3awMUXw48/Jjp1xhhjTEQlIrAQkQoiMkxEnhaRKxKdnhLjyCNdq53LlsHQofDRR67iZ9++MHt2olNnjDHGHKJEBBa4t0ga4foYsdciQtWt61rtXLYM7r0XPvvMvbrapw98+22iU2eMMcbkiHsDWSIyRUR+FZElIrJURJbEsGhL4H+q+lfg+jDrHSQis0Rk1saNG+OZ5NKldm3Xaufy5fCvf7kWPY87Dk49Fb78MtGpM8YYY+KeY/ES8BhwAtAd6Ob/5mcVEGjjOit0oqqOUtVuqtqtbt268Upr6ZWW5poHX74chg93DW717AknnuhyM1QTnUJjjDHlVLwDi+2q+pGqblDVzYFPDMu9C5wmIk8DX8Q5TWVXtWqug7OlS+GJJ+DXX10HaCecAJMnF1mA0bRpU9q3b0+nTp3o1q3bIdPHjx9Phw4dcqZ/9dVXeaZnZWXRuXNn+vTpUyTpM8YYkziicfzyEZGHgSRcoJDTXbqqxq2mYbdu3XTWrFnxWl3ZsncvjB4NDz0EK1dCt27wj3/A2We7XlfjpGnTpsyaNYs6deqEnb5r1y6qVq2KiDBv3jwuuugiFga9LvvYY48xa9YsduzYwcSJE+OWLmOMMUVDRL5X1UN/SYYR7xyLHrjijweBf/vPo3HehomkShXX/sVvv8ELL8CWLXDuudC5M7zzDmRnF0syqlWrhvhAZvfu3Tn/A6xatYoPP/yQgQMHFktajDHGFK94N5B1YpjPSfHcholBpUowcKBrKvzVV2HPHrjwQmjf3jW6lXVINZYCERFOPfVUunbtyqhRo8LO895779GqVSvOOussXn755Zzxt9xyC8OHD6dChZLyQpIxxph4isvTXUQu9X//Gu4Tj22Yw5CcDJdfDgsWwNixrjikf3/XR8krr8CBA4e12hkzZjB79mw++ugjnnnmGb744tBqMeeddx4LFy7k/fff5x//+AcAEydOpF69enTt2rUwe2WMMaYEi9fPxqr+b/UIH5NISUnQrx/Mm+eKRKpWdX2StGzpikz27y/Q6ho2bAhAvXr1OO+885g5c2bEeXv27MnixYvZtGkTM2bM4IMPPqBp06b069ePzz77jEsvvbRQu2aMMaZkiWvlzeJglTfjQBUmToR//hO++w6aNIE77nCdnlWpEnXR3bt3k52dTfXq1dm9eze9e/fmnnvu4fTTT8+Z57fffqN58+aICLNnz+bss89m1apVeepaTJs2jUcffdQqbxpjTClQ7JU3ReRuEakdZfpJImLvFpYUIu5NkW+/da+lpqfDjTdCs2bw+OOQmRlx0fXr13PCCSfQsWNHjj32WM466yxOP/10Ro4cyciRIwH473//S7t27ejUqRODBw/mrbfeyhNUGGOMKbvikmMhIucCfwP2ArOBjUAV4GigE/Ap8KCqFrrZTMuxKAKqMG2ay8H4/HPXhPitt8INN8AHH7jGuFascAHIsGGunoYxxphyoyA5FvFux+Jo4PdAA2AP8DPwharuidc2LLAoYjNmuADj449dXYz9+/NW8kxNhVGjLLgwxphyJGGBRXGwwKKYzJwJvXq5V1VDZWS4DtGMMcaUC4lsIMuUFcce61ryDGf5cnj3Xdi5s3jTZIwxpsSzwMJElp4efrwInH8+HHEE9O4NTz4JixcXb9qMMcaUSBZYmMiGDXN1KoKlprrWPKdPh1tugdWr3d8WLVzDW7ff7qYdZuNbxhhjSre4BhYiMlxEaohIRRGZKiKbAq1ymlKof39XUTMjw+VSZGS44csuc920Dx/uWvX87TeXa9GkifvbqxfUqweXXAJjxsDmWDq4NcYYUxbE+62QuaraSUTOA/4E/AX4XFU7xmsbVnmzhNu5E6ZMcQ1wffghbNgAFSrA734HffrAWWdB27Zx7W3VGGNM0Upk5c2K/u+ZwFhV3RLLQiLSS0S+FJGRItIrzmkyxal6dejbF15+GdaudY1w3X23a3Rr6FDXEdpRR7kGuSZPjlxB1BhjTKkU78BigogsxHWdPlVE6uIazcqPArtwjWqtinOaTKJUqODeLrn/fvj+e1i1yhWldOzoAo8zznAVQP/0J3jxRVizJtEpNsYYU0hxb8dCRGoBO1Q1S0SqAtVVdV0+y1RQ1WwRqQ88pqr9Q6YPAgYBpKend12+fHlc02wSYM8e19rnxInus2KFG9+1qysy6dMHunRxwYkxxpiESmTLm6nAX4F0VR3kW+Jsqaox9TQlIpWAN1X1gkjzWB2LMkgV5s/PrZfxv/9BdjbUr+/qZPTp415rrVYt0Sk1xphyKZGBxVvA98DlqtpORFKA/6lqp3yW6wucBtQEnlPVaZHmtcCiHNi0ydW/mDjR/d2+HSpVcm+bBCqANmuW6FQaY0y5kcjAYpaqdhOROara2Y/7wd4KMYftwAHXf0mgyOSXX9z4Nm1yi0yOPx6SkxObTmOMKcMS+VbIfp9LoT4hzYF9cd6GKU8qVnQ5FY8+CgsXwq+/uq7dGzSAxx5z7WnUq+fa3HjzTdgS04tIxhhjiki8cyx6A3cDbYBPcD2dXhmtaKOgLMfC5NixI2+bGRs3usqev/99bm5G69bWZoYxxhRSQns3FZEjgOMAAb5R1U3xXL8FFias7Gz47rvcIpO5c934o47KDTL++EeoXDmhyTTGmNIo0YFFB6ApkFPorarvxmv9FliYmKxa5XIxJk6ETz91DXFVrQqnnuqCjDPPhCOPTHQqjTGmVEhk5c2XgQ7AT0C2H62qOiBe27DAwhRYZiZ8/nlukcnKlW58t265uRmdO1ubGcYYE0EiA4sFqtombisMwwILUyiq8OOPuUUm33zjxjVokNtmximnuNwNY4wxQGLfCvmfiBRpYGFMoYhAhw7w97/D11/D+vWuG/gTToC333bNix9xhGtu/JlnYNmyRKfYGGNKlXjnWPQEJgDrcK+ZCq4opEO8tmE5FqbI7N8PX32Vm5uxaJEb365dbm7GccdZmxnGmHInkUUhv+Ga9P6R3DoWqGrcOvewwMIUm19/za0A+sUXcPAg1K7tcjP69IHTToNatRKdSmOMKXKJDCw+U9WT4rbCMCywMAmxfTt88okLMiZNcs2OJyW5IpRABdCWLa3NDGNMmZTIwOJZXH8fEwhqcdNeNzVlSlYWzJyZW2Qyb54b37x5bpDRs6fr38SYEmjv3r307NmTffv2cfDgQS644ALuv//+PPM88sgjjBkzBoCDBw/y888/s3HjRmrXrs3jjz/Oiy++iIjQvn17Ro8eTZUqVRKxK6aYJDKwGB1mtL1uasq2FStcLsbEiTB1qmszo1q1vG1m1K+f6FQak0NV2b17N9WqVePAgQOccMIJPPnkkxx33HFh558wYQKPP/44n332GatXr+aEE05gwYIFpKSkcNFFF3HmmWdy5ZVXFu9OmGJVkMAirrXQVPWqeK7PmFIhPR2uu859MjPhs89yczPe9Zl1xx6bm5vRqZMVmZiEEhGqVasGwIEDBzhw4AAS5ZocO3Ysl1xySc7wwYMH2bNnDxUrViQzM5OGDRsWeZpN6RGX101F5G/+79Mi8lToJx7bMKZUSE11wcPIka4hrjlz4J//dIHEvfdCly7QuDFcey1MmOACEWMSICsri06dOlGvXj169+5Njx49ws6XmZnJ5MmTOf/88wFo1KgRt912G+np6TRo0IC0tDROPfXU4ky6KeHi1Y7Fz/7vLOD7MJ98iUhVEfleRPrEKU3GJJaIy524+27XENfatTB6NPzudzB2LJxzjmsz46yz4NlnYXncXp4qlL1793LsscfSsWNH2rZty7333nvIPNOmTSMtLY1OnTrRqVMnHnjggZxpTZs2pX379nTq1Ilu3WLKOTUJkJSUxNy5c1m1ahUzZ85k/vz5YeebMGECv//976lduzYAW7duZfz48SxdupQ1a9awe/du3njjjeJMuinh4lIUoqoT/L+Zqvqf4GkicmGMq7kDeDse6TGmRKpfH6680n3274cvv3TFJRMmuDoagwdD+/a5RSY9erg3T4pZ5cqV+eyzz/KUv59xxhmHlL//4Q9/YOLEiWHX8fnnn1OnTp3iSK4ppJo1a9KrVy8mT55Mu3btDpk+bty4PMUgn376KUcddRR169YFoG/fvnz99ddceumlxZZmU7LFu+XNO2Mcl4eInAIsANZHmD5IRGaJyKyNGzcWMonGlACVKsHJJ8Pjj7uGuBYuhEcfdTkYw4e7rt/r14fLL3ctgm7bVmxJK2j5uyl9Nm7cyDZ/Te3Zs4dPP/2UVq1aHTLf9u3bmT59Oueee27OuPT0dL755hsyMzNRVaZOnUrr1q2LK+mmFIhXHYszRORpoFFI/YpXgIMxrOJEXFfrfwauEZE86VLVUaraTVW7BaJkY8oMEdcGxq23us7SNm2CceNcQ1wffggXXwx168JJJ8Fjj7mGu4pYLOXv//vf/+jYsSNnnHEGP/30U9DuCKeeeipdu3Zl1KhRRZ5WE4MxY6BpU9fRXtOmrH3xRU488UQ6dOhA9+7d6d27N3369GHkyJGMHDkyZ7H33nuPU089lapBfef06NGDCy64gC5dutC+fXuys7MZNGhQAnbKlFRxed1URDoCnYAHgHuCJu0EPlfVrTGu50pgk6qGz1/FXjc15UxWlqufEWgB9Mcf3fgWLXKLTP7whyJrM2Pbtm2cd955PP3003myyXfs2EGFChWoVq0akyZNYsiQISzyTaCvWbOGhg0bsmHDBnr37s3TTz9Nz549iyR9JgZjxsCgQXkrCqemwqhR0L9/4tJlSpVEtmNRUVUPxG2FYVhgYcq15ctzg4zPPoN9+6B6dde8eJ8+LpejXr24bvL++++natWq3HbbbRHnadq0KbNmzTqkXsV9991HtWrVoi5r4kTVtaGSmZn72bMHTj/ddbYXKj29xFQYNiVfItuxKNKgwphyLyMDbrjBfXbvdg1yBdrMeOcdV6zSo0dubkaHDgVuM2Pjxo1UrFiRmjVr5pS/33HHHXnmWbduHfXr10dEmDlzJtnZ2RxxxBHs3r2b7Oxsqlevzu7du/nkk0+45557ImypnMjKcl/wgS/60C/+eA3v2VOwdK1Y4XK+GjaERo3c38AneDg1tWiOiymzrJtGY0qrqlXdK6vnnON+rc6Z4wKMDz90r7jefbdrMyPQM+tJJ8X0JbF27VquuOIKsrKyyM7O5qKLLsopfwe47rrreOedd3juuedITk4mJSWFcePGISKsX7+e8847D3CNKP35z3/m9NNPL9LDcNgOHCjaL/rA8L59+aclnJQUd75SU/P+n5rqKvkGD4dODx6+9lrYsOHQ9VevDt27w5o1MGsWrF4dPjipWTN8wBE8fOSRULHi4e2nKXPiWhRSHKwoxJgYrFsHH33kAo1PPoFdu6BKFfcmSp8+Ltho0sTNO2YM3HWX+wWbng7DhiWu7F3VvYpblF/0gf8PxlKvPESFCvl/kcdjuEqV+LXOGmsdC1XX2d6aNS7IWLMm9xM8vHbtocdOxBXBRcr1CPxfp447hqZIxdIXzMKFC7nqqquYPXs2w4YNy1NcuW3bNgYOHMj8+fMREV5++WV+97vfJayOxQQgdIXbcQ1nPa+qewu7DQssjCmgfftct++BNjOWLnXjO3Z0bwp8/LErmw8I96WTne3mKY5f+NnZBd/H5OSi/aIPDFeqVDqbY49n8JidDRs35h+AhMslqVgRGjTIPwCpUaN0HucSIpa+YDZs2MDy5ct5//33qVWrVp7A4oorruAPf/gDAwcOZP/+/WRmZlKrVq2EBRZPAnWBsX7UxcA6IAWooaqXFXYbFlgYUwiqrs2MQJHJ9Onh50tOdtnbwXUDDkflykX/Cz8lxbLhS6L9+13OWbigI/j/7dsPXTY1NXq9j0aNXICSklL8+1XKZGZmcsIJJ/Dcc8+FfW08tIL1jh076NixI0uWLMnTfk3CKm8CnVU1+L2yCSLyhar2FJGfIi5ljCkeItC6tfvcfrvLlg734+LgQejdu3Bf/CkpCWk51JQQlSq53JH09Ojz7d4dOddj9Wr49lv3/94wGd61akXO9Qj8X7++C5TLmaysLLp27cpvv/3G4MGDI/YFE2rJkiXUrVuXq666ih9++IGuXbvy5JNPFmjb8T7adUUkXVVXAIhIOhB4/2x/nLdljCmsSK8cZmTAyy8Xf3pM+VO1Khx9tPtEoupan42U67FmDfz0k8shycrKu6yICy7yC0COOKJMFb8E+oIJtEUzf/78sE22hzp48CCzZ8/m6aefpkePHgwZMoSHH364QNuOd2BxK/CViCwGBDgKuEFEqgKvxnlbxpjCGjYsfMW+YcMSlyZjQom43IlatSDal2NWlqv/Eanux4oV8L//udZtQ1WqlFv/I1oAUr160e1nEcivL5hQjRs3pnHjxjk5HBdccEFiAwtVnSQiRwOtcIHFwqAKm0/Ec1vGmDgIVOArKW+FGFMYSUmubtCRR0LXrpHn27fP5W5ECkDmz3eVmnfuPHTZatWiVzxt2NAFKFWqFN1+5iOWtmgiOfLII2nSpAm//PILLVu2ZOrUqbRp04ZJkybFvP24v24qIr8DmhIUtKjqa/Fav1XeNMYYUyx27nSv10arfLpmTfi2So44Iv8ApH79IqmHNG/evEPaornnnnvytEWzbt06unXrlqd5/gULFlCjRg3mzp2b80ZIs2bNGD16NLVr107YWyGvA82BuUCgoEtV9eZ4bcMCC2OMMSWGKmzZkv/bL+vWHfoqdYUKLnclvwCkdu38638UcXs0iXwrpBvQRktbq1vGGGPM4RBxuRNHHAHt20eeLyvL9dkSKddj6VKYMQM2bz502cqVo796+/338I9/5L4Wvny5qzsFCSnWjHeOxX+Am1V1bdxWGsJyLIwxxpRZe/fmFr9EaoRs9Wr3mm5+MjJg2bK4JCuRORZ1gAUiMhPIKXRS1XPivB1jjDGm7KlSBY46yn2i2bkzN+g4+eTw86xYEf/0xSDegcV9h7OQiLQGhuACk6mq+lw8E2WMMcaUKdWrQ6tW7pOREb49mvwaJysi8X7dNEL7wPku9zNwnYhUAF6IZ5qMMcaYMq2EtUcTl27mROQr/3eniOwI+uwUkR0xruMc4Ctgaphpg0RklojM2rhxYzySbIwxxpQN/fu7jgMzMlxl0oyMQzsSLEYlrtt0EflQVc+KMn0jECbPp9DqAGGaYzOeHZ+yz85x2WfnuOwrqnOcoap1Y5kx7j2ziEgSUJ+8DWRFrUEiIr2AvkBlIGrzXrHuWEGJyKxYa7yWR3Z8yj47x2WfneOyrySc47gGFiJyE3AvsB4ItASiQIdoy6nqNGBaPNNijDHGmOIX7xyLIUBLVQ3Twocxxhhjyrq4VN4MshLYHud1FpdRiU5ACWfHp+yzc1z22Tku+xJ+juPd8uZLQEvgQ/I2kPVY3DZijDHGmBIr3kUhK/ynkv8YY4wxphwpca+bFhURqQo8C+wHpqnqGD/+TOBGYJKqjvDjHgZSgUxVHZqgJBe7KMdoKK7X2iOB61V1lYgsBqYAs1U14VlvJjZRzvF9QGtgK/CAqq4pr/dBaRflHF8NdAeaAPNU9U67j0snEWkG3AWkqeoFQePbAXf6wYdUdX4i7uN4NZD1hP87QUQ+CP3EYxtx0Bd4R1WvAXL6LlHVScDwwLCIpAMVfVfvSSLSpNhTmjiRjtHDftzLwIl+9C4gBVevxpQeYc8xcBD3RXQA2FbO74PSLtJ9/JKqXgf8CrziR9t9XAqp6hJVvTrMpCHAYOAG4KZE3cfxKgp53f99NE7rKwqNgR/9/1lR5mtE7k22wi9XXm66iMdIRKoBFwG+L146A4KrT/NRcSXQFFqkc/ygqmb7FnAHAt9Rfu+D0i7afVwFOEpVf/Gj7D4uW9JUdRuAiFQnQd9nccmxUNXv/d/p4T7x2EYcrMIdVIi+36uD5mvilysvwh4jEakBPAf8TVV3AqhqtqpmAXt9Hy+mdAh7jlU10O7MBqAa5fs+KO2iPesuAN4NDNh9XOZsF5E0/8zeSYLu43i/FXI08BDQBqgSGK+qzeK2kcPkyx1HAHtxfZKcrqqXicjxuDKpWsATqvpfEXkI1wroPlW9M+JKy5gox+hdoCLuIn3b/73DLzZPVZ9IQHLNYYhyjv+Oe/DUAW5W1bXl9T4o7SKdYz9tPNBPVfeISEvsPi6VROQIYBjQG3gRaOPv43bA7bhcqOG+jkWx38fxDiy+wrW8+ThwNnCV38a9cduIMcYYY0qseGd9pajqVFwwsVxV7wNOivM2jDHGGFNCxbsdi0A53SIRuRGXZV4vztswxhhjTAkV76KQ7sDPQE3gn0AN4BFV/SZuGzHGGGNMiRW3wMJ3l/6wqt4elxUaY4wxptSJVwNZyf6Vpa4iIvFYpzGm+IlITRG5IZ95morI/OJKkzGmdIlXHYuZQBdgDjBeRP4D7A5MVNV3Iy1ojClRauJa7Xs2XisUkST/w8MYUw7E+62Q2sBm3JsgfXCvnPaJ8zaMMUXnYaC5iMwVkUf8Z76I/CgiF4fOLCJJfp7vRGSeiFzrx/cSkc9F5E18K5Ai8r6IfC8iP4nIoKB17BKRYSLyg4h8IyL1/fj6IvKeH/+DiPzOj79URGb6ND7vi2GNMSVEvHIs6onIX4H5gOIa5wgoH72cGVM2DAXaqWonETkfuA7oiGs46zsR+SJk/quB7araXUQqAzNE5BM/7Vi/rqV+eICqbhGRFL+u/6rqZqAq8I2q3iUiw4FrgH8BTwHTVfU8HzxUE5HWwMXA71X1gIg8C/QHXiui42GMKaB4BRZJuGaAw9WvsMDCmNLpBGCsL8ZYLyLTcb1jzgua51Sgg4gEelhMA47GdWg2MyioALhZRM7z/zfx8232807047/HtSYILufzcgCfhu0ichnQFReYgOtAa0N8dtcYEw/xCizWquoDcVqXMaZkiKUitgA3qerHeUaK9CKonpUfPgU4XlUzRWQauc3+H9Dc19OyiP5cEuBVa2LcmJIrXnUs7E0QY8qGnUB1//8XwMW+HkVdoCeuonawj4HrRaQigIgc4/uqCJUGbPVBRSvguBjSMhW43q83yXesNBW4QETq+fG1RSSjYLtojClK8QosTo7TeowxCeTrPMzwr5Mejyv2+AH4DNe77bqQRV4EFgCz/TLPEz7HYTKQLCLzcI3nxdJo3hDgRBH5EVdE0lZVFwB3A5/4dU0BGhRwN40xRSiuLW8aY4wxpnyL9+umxhhjjCnHLLAwxhhjTNxYYGGMMcaYuLHAwhhjjDFxY4GFMcYYY+LGAgtjjDHGxI0FFsYYY4yJGwssjDHGGBM3FlgYY4wxJm4ssDhMItI/qHvoaPONFJF/FEeaEk1E/iUim0QktNnnhIv1fMV5m8tE5JQi3sYrIvKvw1z2PBFZKSK7RKRzHNKiItKisOtJJBGpKyK/iEgVPzxNRAaWgHTF5djmd72IyO9FZJG/Jv4kIu+KyOmF3W5xEZE/iMgv8Z63sETkPhF5ozi2VRKUycDCP9D3+JtjvYiMFpFq8dyGqo5R1VNjmO86Vf1nPLddEolIE+BWoI2qHpno9ISK9XyVM48CN6pqNVWdEzqxLAQKh2EoMFpV9yY6IQnyADDCXxPvAw8Dw4pjw/H48lXVL1W1ZbznLU6F+bFQUrZTJgML72xVrQZ0AbrjOi7KQ0Ti1W18mXMYxyYD2KyqG4phWyVq/aVYBvBTohNRUs6PiFQGrgDi+stSnIQ/a0UkKYbZ8lwTqjoTqCEi3YosYTEqKcfR5K/MnyRVXQ18BLSDnF9hg0VkEbDIj+sjInNFZJuIfC0iHQLLi0gTnx24UUQ2i8gIP/5KEfnK/y8i8riIbBCR7SIyT0QC28sTFYrINSLym4hsEZEPRKRh0DQVket8VuRWEXlGRMJ2SS8ix4rILBHZ4XNlHguadoLfj20+q/tKPz5NRF7z+7JcRO4O3Kh+f2b4/dgC3CcilUXkURFZ4bcxUkRSwqTlFFwvkw19LtErfvw5IvKTT8c0EWkdtMwyEblDXA+Vu0O/XPy2Hg0ZN15E/ur/Hyoii0Vkp4gsEJHzguYLty8558vP8zsR+c6fr+9E5HchaTslaDjnl5SIVBGRN/y1sM0vWz/cOQpJe4WgNG8WkbdFpLafNllEbgyZ/wcR6ev/byUiU/w184uIXJTf9oK2ebc/1xv8uU/z53UXkAT8ICKLwyz7hf/3B39OL/bjI16/IctHvHZEpJeIrPLnfx0wWkRqichEf21u9f83DlrfNBH5pz+vO0XkExGpEzQ90jUf0zXs9QC2qeqqCPvUQNy9fZsfPi5omz+ISK+Q9A4TkRlAJtBM8rm/RWSAiPzsp30shewOXtyz5zkRmSQiu4ET/aQ6/nraKSLTA9vx10EzYII/55X9/NOAs6Jsp9DPNHHFLX8HLvbb/sGPD3ccr/LHaaeILBGRa4PW00tEVgUNLxOR2/x52y4ib0luMVfM8/rpfxORtSKyRkQGSpQcPRE5yh/bnSIyBagTMv0/IrLOb+cLEWnrxw8C+gN/88dhgh8f7XnXwm9ru7ii6LeCpoV9dkTaTtyoapn7AMuAU/z/TXAR+D/9sOK+BGsDKbgcjQ24h0oS7hfLMqCyH/4BeByoClQBTvDruRL4yv9/Gq5b55qAAK2BBn7aK8C//P8nAZv8NisDTwNfBKVbgYl+PenARuD0CPv4P+Ay/3814Dj/fzqwE7gEqAgcAXTy014DxgPVgabAr8DVQftzELgJ1+11CvAE8IE/VtWBCcBDEdLTC1gVNHwMsBvo7dPxN+A3oFLQOZrrz09KmPX1BFaS2wNvLWAP0NAPXwg0xAXHF/ttNYiyL8HnqzawFbjMT7/EDx8Rev344fuAN/z/1/rjkIq7ProCNWK4Dm/BdRXe2J/754GxftrlwIyg5doA2/x8Vf1xuMqntQvuGmoben2F2f4Af8yb4a6Rd4HXQ663FlHuozzTie36beH/f4II1w7uWjkI/J9fTwruOj3fH9fqwH+A94PWPQ1YjLuuUvzwwzFc8xHTEWZ/BwMfhoybBgwk934Z5Mc3AjYDZ+Kuwd5+uG7QciuAtv68VSTK/Q38yZ+r1n7+u4GvIxzbof76CPsJWuYVYDvwe5/GKn7cTtz9VRl4En9fhLv2/bi/Au9GOGbxfKbdh7/PQo5/6HE8C2iOe9b+ERdwdInwHFoGzMQ9K2oDPwPXHca8pwPrfDpSgdeJcv/gns+P+WPS0x/zN4KmD8Bdj5Vx1+jckPP2r5D1RXvejQXuCjrHge+ow352FPaT8CCgSHbKXSC7cDfacuBZ/JeXvxhOCpr3OXzQETTuF3/BHu9vhOQw27iS3C+qk3APneOACiHz5Zw84CVgeNC0asABoGlQ2k4Imv42MDTCPn4B3A/UCRl/J/BemPmTgH24OhCBcdcC04L2Z0XQNPEXb/OgcccDSyOkpxd5b9J/AG8HDVcAVgO9gs7RgCjnUHAPlJ5++BrgsyjzzwXODbcvYc7XZcDMkOn/A64MSlukwGIA8DXQIcbrMBBY/AycHDStgT/3ybgHzG4gw08bBrzs/78Y+DJkvc8D94ZeX2G2PxW4IWi4ZWCbQddbQQKLWK7fFvldO/5a2Q9UibLtTsDWoOFpwN1BwzcAk/O55gt6Dd8FjAsZNw33BbEMuCRo/B0EBWl+3MfAFUHLPRDmeIa9v3G5qleH3C+ZQddE1HMVYX9eAV4LM25c0HA1IAtoEu7az+/ei/GaiPWZdh/hA4sHws0fNM/7wJCgays0WLg0aHg4MPIw5n2ZoIAUd52HPSe4AOogUDVo3Juh+xY0raZfV1rQOYr6hU/e591rwCigccg8h/3sKOynLBeF/ElVa6pqhqreoKp7gqatDPo/A7jVZ2duE5FtuF/RDf3f5ap6MNqGVPUzYATwDLBeREaJSI0wszbEBTqB5XbhfuU0Cpon+I2KTNyNGs7VuF9vC8Vlx/fx45vgftmFqgNUCt6+/z9428HHpS4uMv8+6LhM9uNjEbqv2X79kbaXh7orfxzuVyjAn4ExgekicrnkFl9twxV1BWc3Rlx3aNq80GMRyeu4L5BxPkt0uIhUjGG5DOC9oPT+jHug11fVncCHQD8/bz9y9zUD6BFyffYHYqkgG7qfy3GBTL5FN7GsL8L1C7FdOxs1qIKkiKSKyPPiim124ALnmpK3XkCkeyPSNV/Qa3grLsgL1R8XFL8TNC4DuDDkvJyACxgDwl2DkfYhA3gyaF1bcIFRLNdkNOHSkDPOn8MtuHMbSXXcj7Rw4vlMiyTPPojIGSLyjc/e34bLNaoTdsmCbz/SvA1D0pHf82Wrqu4OGpdzjEQkSUQe9kUbO3ABDUTZh3yed3/DXSszxRU9D/DjC/PsKJSyHFhEo0H/rwSG+SAk8ElV1bF+WrrEULlMVZ9S1a64rLJjgNvDzLYGd7IBEJGquGzb1QXeAdVFqnoJUA+XpfyOX99KXDZhqE24XxIZQePSQ7atIfPvwWWbBY5LmroKsbEI3VfBfQFE2l44Y4ELxJUB9wD+69eVAbwA3IgrvqgJzMfdXLGsO0/avOBjsRv3hRSQcyOq6gFVvV9V2wC/A/rgijLysxI4I+Q6q6KuDlBgXy8RkeNxWf2fBy03PWS5aqp6fQzbDN3PwC+p9TEsm+/6oly/sVw7oefnVlyOSg9VrYHLPoa85zSSaNd8Qa7hebh7N9R9fl1vBgU6K3E5FsHnpaqqPhy0XH7Xd+g+XBuyvhRV/Tp0RhH5uy8XD/sJmT1cGpoErasaLtt/TZS0tcYVCYcTt2cakY9Xznhx9T7+i3ujqb6/9ycR23VSGGtxxZgBTSLN6Oet5Y9FQHrQ/38GzgVOAdJwxWyQuw95jkN+zztVXaeq16hqQ1wu9LO+7kd+z46CXJ8FUl4Di2AvANeJSA9xqorIWSJSHVfethZ42I+vIiK/D12BiHT3y1fEfSntxf0aDfUmcJWIdPI3yIPAt6q6rKCJFpFLRaSuzwnY5kdn4X7pniIiF4lIsogcISKdVDULlw05TESq+4v1r0SoAe/X+wLwuIjU89tsJCKnxZjEt4GzRORkf1xuxRXFHPKgjETdK5AbgReBj1U1sJ9VcTfFRp+uq/CVc2M0CThGRP7sj9HFuHoNE/30uUA/Eakorjb8BYEFReREEWnvv2B24IK1cOc61Ejcsc/w66krIueGpCkD97rfW/7449N0jIhc5tNT0V9vrcnfWOAv4iqSVcNdb2/llwMXZD2ufkZATNfvYV471XFBwDZxlVrvjTGNEPmaL2g6ZuJySUJzCQ7gyrirAq+Lq/D8BnC2iJzmf4FWEVcZsDGHZyRwp+RW4ksTkQvDzaiqD/oviLCfGLZ1prjKrpWAf+LOYbRf4H/EFdWEE7dnGu56ayrR3/yohKuXsBE4KCJnAMXxGvnbuP1sLSKpwD2RZlTV5cAs4H4RqSQiJwBnB81SHfcs3Iz7AfNgyCpC77uozzsRuTDoutvq580i/2dH6HbiptwHFqo6C1eGOAJ3Un7Dlcfjv4zPxpWnrQBW4cqtQtXAPcC24rK8NuMi6tBtTcXVPfgvLmBpTm72d0GdDvzkf6E8CfRT1b2qugKXNXgrLotzLtDRL3MTLvBZAnyFeyi8HGUbd+COxzfisuw+xf2qzJeq/gJciqvMtQl3HM9W1f0F2EdwX46n+LQG1r0A+DeuXsR6oD0wI9YVqupmXE7Drbhz9Tegj6pu8rP8A3dutuLqsbwZtPiRuCzxHbjijOnE9nrik7hKhJ+IyE5cRc4eQWnah6tcGbqvO3EPzn64X4fryK30mJ+XcUU3XwBLcQHvTTEsF3Af8KrPRr2ogNdvQa+dJ3A5NZtwx2ZyrInM55qPOR3+2nwFd92Gm9YXl0P4Mu4X+bm4Nxk24n4d3s5hPlNV9T3ceR3n0zkfOONw1hWDN3GB2xZc5eP+kWYUke7AbnWvnR4izs+0//i/m0VkdoTt7QRuxn3Rb8X9+v/gMLcXM1X9CHgKl5P4G+7ZAy5ACOfPuPt7C+5YvxY07TXc98RqYAHueg/2EtDG33fvx/C86w58678LpuAq7E7BVUYOfXb8G3hG3Nt4ZwBdAtsRkb+IK0qZLyJjJe8bMTeJe6vkJxEZnt/xCtS4N8aYck9E6gJfAp1D6mWVSyLyX+AlVZ2U6LSUJP5X/3ygcgFyAIuUz0X9FfeG0irgO1yF4wVB8zwC7FLV+0WkFfCMqp7sc+m+wlXu3yMibwOTVPUVETkRV7H5LFXdJyL1NJ/2isp9joUxxgSo6kZVbWVBhaOq51tQ4YhrAr+SiNTC5S5NKClBhXcs8JuqLvE5bONwuWrB2uDeFkNVF+KKngKVuZOBFHF1ClPJrXdzPe7V7n1+uXwbQbTAwhhjjMnftbhir8W4OgyxVKAuTo3I+7bKKg59q+gHXJEeInIsrl5XY1+J/FFckf9aYLuqBvpWOgb4g4h8K64hru75JcQCC2OMMSYfqnq6f6uotqqep6prE52mEOHejAmt6/Aw7o2Vubj6VnNwlWBr4XI3jsK9LltVRAJ1jZJxDRQeh6tH9LZI+BahCVrAGGOMMaXbKvK+BtuYkNeIVXUHriXOQBMAS/3nNFzDcYE3T97FvU7/hl/vu+oqZM4UkWxcGxobIyXEciyMMcaY0u874Gj/enkl3Nsged6YEZGafhq4puq/8MHGCuA4cQ3VCXAy7q03cC2bnuSXPwb3yu8moih1b4XUqVNHmzZtmuhkGGOMMSXK9u3bWblyJapKnTp1aNCgARs3uoyFunXrsmvXLpYtWwZASkoKGRkZJCe7gos1a9awZcsWRITU1FQyMjKoUKEC2dnZLF++nC1btmThGpG7TV1r0xGVuqKQpk2bMmvWrEQnwxhjSq3JkyczZMgQsrKyGDhwIEOHDs0zfevWrQwYMIDFixdTpUoVXn75Zdq1c20yPf7447z44ouICO3bt2f06NFUqVIl3GZMMRozBu66C/bvh/R0GDYM+kdsoaTgRGSuqnaLZV4rCjHGmHIkKyuLwYMH89FHH7FgwQLGjh3LggUL8szz4IMP0qlTJ+bNm8drr73GkCFDAFi9ejVPPfUUs2bNYv78+WRlZTFu3LhE7IYJMmYMDBoEy5eDqvs7aJAbnwilLsfCGGPM4Zs5cyYtWrSgWTPXmnO/fv0YP348bdq0yZlnwYIF3HnnnQC0atWKZcuWsX6962Lm4MGD7Nmzh4oVK5KZmUnDhtH6LzMFdeAAbN8OW7fCtm2x/Z0zBw6GtKiRmelyMOKZaxErCyyMMaYcWb16NU2a5L480LhxY7799ts883Ts2JF3332XE044gZkzZ7J8+XJWrVpF165due2220hPTyclJYVTTz2VU08tjq46Sg9V2LUrejAQbdqu0G7kQlSsCLVqQc2a7m/t2ocGFQErVsRttwrEAgtjjClHwlXYD22WYOjQoQwZMoROnTrRvn17OnfuTHJyMlu3bmX8+PEsXbqUmjVrcuGFF/LGG29w6aWHdK9Sqh04EHtuQbi/Wfl0S1ijRt7goHnzvMPR/qakQGgrEk2buuKPUOnph44rDhZYGGNMOdK4cWNWrsxtoHHVqlWHFGfUqFGD0aNHAy4QOeqoozjqqKP4+OOPOeqoo6hbty4Affv25euvvy5xgYUq7Nx5+MFBZmb09Veq5L7oA1/2devC0UfnDkcLDNLSICkpvvs7bJirUxGc7tRUNz4RYg4sRKSqqu4uysQYY4wpWt27d2fRokUsXbqURo0aMW7cON58880882zbto3U1FQqVarEiy++SM+ePalRowbp6el88803ZGZmkpKSwtSpU+nWLaYXBQps//7Yg4Fw47Kzo68/LS3vl/4xxxQs16AkCdSjuOsuV/xRFG+FFES+gYWI/A54EagGpItIR+BaVb2hqBNnjDEmvpKTkxkxYgSnnXYaWVlZDBgwgLZt2zJy5EgArrvuOn7++Wcuv/xykpKSaNOmDS+99BIAPXr04IILLqBLly4kJyfTuXNnBg0aFHY72dku1yCWegXh/u7Jpxu4KlXyftnXrw8tW8YWHFSvHv9cg0Tr3z9xgUSofBvIEpFvgQuAD1S1sx83X1Xb5bNcVeBZYD8wTVXH+PEXA2cBB3CdnvwKvA7sxrXodZWqRow1u3XrptaOhTHGxC7QxkFBf83u23f49Qy2b4+eayASudggllwDazqjeInI97G2YxFTUYiqrgyp3JNP1RTA9aD2jqpOEJG3gMAbtecD/XGdmjwI3ALsU9VrROQZoCqwM5Z0GWOMie611+C663JzAJYvhwED4MMPoVmz6EULe/dGX3dKSt4v+wYNoHXr2HMNKlhLSmVSLIHFSl8cor6N8ZvJbUM8msbAj/7/4EDkUeBpYB0uuMj06/4QWKuqFlQYU8wOtyXGX375hYsvvjhnviVLlvDAAw9wyy23cPvttzNhwgQqVapE8+bNGT16NDVr1izmPSt7VN0X/4YNsX22bDl0Hfv3w9ix7os9NIegUaPoQUHw/5UrF9dem9IklqKQOsCTwCm4blk/AYao6uZ8lrsM2KqqE0VknKr2C5neArgR13vamar6gIjcDUxU1bkh8w4CBgGkp6d3XR7uvRpjzGHJysrimGOOYcqUKTRu3Jju3bszduzYPA0m3X777VSrVo17772XhQsXMnjwYKZOnXrIeho1asS3335LRkYGn3zyCSeddBLJycnccccdAPzf//1fse5baZGZCRs3xh4sRGq3oHZtqFcv7+fZZ8PPK+LWY7kGJhZxKwoRkSTgCVU9nCoh7wIjROQsYIKIvK6ql4nImcDZuMqgtwM7gFtF5FlcV6yPh65IVUcBo8DVsTiMtBhjIihMS4z169fPmWfq1Kk0b96cjIwMgDwNJx133HG88847xbE7JcLBg7BpU2xBwsaNkRtFqlo1N0Bo0gS6dj00cAh8jjjCNZ4U6sMPI7dxYEGFKQpRAwtVzRKRuiJSSVX3F2TF/tXUq4JGjfHjJwGTQma/pCDrNsbET2FaYgwOLMaNG8cll4S/lV9++eU8RSaljaqrjBhrjsLmCPm5SUl5g4EWLSIHCnXrusCisEpaGwem7IuljsUyYIaIfIB7cwMAVX2sqBJljCk+hWmJMWD//v188MEHPPTQQ4esa9iwYSQnJ9O/pLwL5+3ZU7DihwMHwq+nVq3cYKBNG+jVK3KwULNm8ecSlLQ2DkzZF0tgscZ/KgDVizY5xpjiVpiWGAM++ugjunTpkicHA+DVV19l4sSJTJ069ZBgJd4OHnQ5BfkVOwT+3xmhmnhKimsToV49V5Gxc+fcHITQQKFOHdcKY0lXkto4MGVfvoGFqt4PICLV3aDm00WKMaY0KUxLjAFjx449pBhk8uTJ/N///R/Tp08nNTW1wOlShR07Clb8EK4uelJS3qCgR4/IOQr16sWn+MGY8iyWljfb4Rqwqu2HNwGXq+pPRZw2Y0wxKExLjACZmZlMmTKF559/Ps96b7zxRvbt20fv3r0BV4HziSdGFqj4YX+Eml01a+YGAq1aQc+ekQOFWrWskqIxxSmW102/Bu5S1c/9cC/gQVX9XZGnLgxredOY+DrcVhnB9eIYKH6IJWDYsSP8eqpUiZ6LEFqpsTQUPxhTlsS75c2qgaACQFWn+ea6jTGl3Jgxed8YWL4crrkG1q+H447LP1DYtCl88UOFCnmLH7p3z7/4oYirYBhjikksgcUSEfkHrjgE4FJgadElyRhT1LKy4JdfYMiQQ7uI3rMHbr310GXS0nIDgWOOgRNOiBwo1K5txQ/GlFexBBYDgPtxDV4BfEHe9imMMSXYvn0wfz7Mng1z5ri/8+bl33vkRx/lLX6w5puNMbGI5a2Qrbj+QYwxJdzOnfDDD7kBxJw58NNPuU1A16jhXp+89lro0gXuuAPWrj10PRkZcPrpxZt2Y0zZEMtbIVOAC1V1mx+uBYxT1dOKOG3GmCg2bXKBQ3AQsWhRbp2HevVc8HDmme5v585w1FF5iygqVLBWGY0x8RVLUUidQFABLgdDROoVXZKMMcFUYfXqvEUZc+ZAUJtWZGS44OHSS3ODiAYN8q8Qaa0yGmPiLZbAIltE0lV1BYCIZADWEZgxRSA7GxYvPjSI2LTJTReBli3hD39wwUPgU7v24W/TWmU0xsRTLIHFXcBXIjLdD/fEd2FujDl8Bw7Azz/nBg9z5sDcublNTVesCO3awTnn5OZCdOgA1aolNNnGGBNVLJU3J4tIF+A4QIC/qOqmIk+ZMWXInj3uTYzgXIgff3RvbICr19CpE1x+eW4Q0batNQRljCl9Yqm8+XtgrqpOFJFLgb+LyJOqurzok2dM6bNtm8t5CA4ifv7ZFXOAa2K6Sxe46abcIOLoo12fFsYYU9rFUhTyHNBRRDoCtwMvA68BfyzKhBlTGqxff2h9iCVLcqc3bOgCh7593d8uXVwFSWtl0hhTVsUSWBxUVRWRc4GnVPUlEbmiqBNmTEmi6pq7Dg0igtuAaN7cBQ4DB+ZWqgzpRdwYY8q8WAKLnSJyJ64p754ikgRULNpkGZM4WVnw6695K1XOmQNbt7rpSUnQujWcckpuUUanTq7Ja2OMKe9iCSwuBv4MXK2q60QkHXikaJNlTPHYt8+1TBmcC/HDD7kNRlWu7N7EuPDC3CCifXtISUlsuo0xpqSK5a2QdcBjQcMrcHUsTDkxefJkhgwZQlZWFgMHDmTo0KF5pm/dupUBAwawePFiqlSpwssvv0y7du0AaNq0KdWrVycpKYnk5GRCu7x/9NFHuf3229m4cSN16tQp0v3YtSt8c9cHDrjp1au7wOGaa3LrQ7Rq5V77NMYYE5tYciwOi+9a/VlgPzBNVcf48RcDZwEHgEdV9WcRGQB0Brar6t1FlSZTcFlZWQwePJgpU6bQuHFjunfvzjnnnEObNm1y5nnwwQfp1KkT7733HgsXLmTw4MFMnTo1Z/rnn38eNmhYuXIlU6ZMIT09Pe7p3rz50Oauf/01t7nrunVd8HD66blBRLNm1iOnMcYUVpEFFkBf4B1VnSAibwFj/Pjzgf5ALeBBEfk7rrhlDhCmOySTSDNnzqRFixY0a9YMgH79+jF+/Pg8gcWCBQu48847AWjVqhXLli1j/fr11M+n5uJf/vIXhg8fzrnnnnvY6VOFNWsOrVS5YkXuPOnpLnj4859zg4iGDe3NDGOMKQpFGVg0Bn70/2cFjX8UeBpYhwsumgFbVHWoiAwXkeaqujh4RSIyCN/aZ1H8ujWRrV69miZNmuQMN27cmG+//TbPPB07duTdd9/lhBNOYObMmSxfvpxVq1ZRv359RIRTTz0VEeHaa69l0CDXaOsHH3xAo0aN6NixY8xpyc52r3IGV6qcPRs2bnTTReCYY+B3v4Mbb8x9M+OIIwp/HIwxxsQm1gay7gMy/PwCqKo2y2fRVbjgYi6Qk8GsqjOBmSLSArgRWA1s8ZO3AYc0WKyqo4BRAN26dbN+SoqR6qGHW0J+6g8dOpQhQ4bQqVMn2rdvT+fOnUlOdpfWjBkzaNiwIRs2bKB37960atWKbt26MWzYMD755JOI2z140DUqFZwLMWdObnPXycmuZco+fXJzITp2tOaujTEm0WLJsXgJ+AvwPXlzHvLzLjBCRM4CJojI66p6mYicCZyNCyBu92+abBGRx4BKqvpDAffBFKHGjRuzMqgbzVWrVtGwYcM889SoUYPRo0cDLhA56qijOOqoowBy5q1Xrx7nnXceM2fOpFatWixdujQnt2LVqlW0a9eFv/51JosXH5nT3PXevW79KSkuaLjsstwgom1b98aGMcaYkkXC/SLNM4PIt6rao5jSk69u3bpp6JsFpugcPHiQY445hqlTp9KoUSO6d+/Om2++Sdu2bXPm2bZtG6mpqVSqVIkXXniBL7/8ktdee43du3eTnZ1N9erV2b17N7179+bWW++hTp3T8xRlzJ/fFJgF1KFmzdzgIfD3mGOsuWtjjEkkEfleVbvFMm8sORafi8gjuByIfYGRqjr7MNNnSpHk5GRGjBjBaaedRlZWFgMGDKBt27aMHDkSgOuuu46ff/6Zyy+/nKSkJNq0acNLL70EwE8/radfv/PYswcyMw+SnPxnLrjg9Jx1N2jggoeVK+GJJ+CPf4SmTa1SpTHGlGax5Fh8Hma0qupJRZOk6CzHoniNGQN33eXeskhPh2HDoH//vPOouunB9SFmz3ZvawQ0a5ZbmTKQG3HkkcW7L8YYYw5PXHMsVPXEwifJlEZjxsCgQbmtUC5f7obXrIHGjfNWqtziq99WqOAalTrppNwgolMnqFkzUXthjDGmOMWSY5EG3Av09KOmAw+o6vYiTltYlmNRfJo2dcFEJJUqueatg+tDtG8PqanFlkRjjDHFIN51LF4G5gMX+eHLgNG4BrBMGRbcyFSouXOhTRtr7toYY0xesQQWzVX1/KDh+0VkbhGlx5QAP/0E99yT2/x1qIwM9/qnMcYYEyqWnhH2iMgJgQHfYNaeokuSSZTFi11bEe3bw5Qp0Lfvob14pqa6CpzGGGNMOLEEFtcDz4jIMhFZDowArivaZJnitHKlq5TZsiX8979w222u6ez//hdeeMHlUIi4v6NGHfpWiDHGGBOQb+XNnBlFagCo6o4iTVE+rPJm/KxfDw89BM8954o9rr0W/v53176EMcYYExCXypsicqmqviEifw0ZD4CqPlaoVJqE2bIFHnkEnnoK9u2DK65wdSoyMhKdMmOMMaVdtMqbVf3f6sWREFP0du50LVw++qj7v18/uO8+12S2McYYEw8RAwtVfd7/vb/4kmOKwp498Mwz8PDDsHkznHsu/POfrpKmMcYYE0/5Vt4UkeEiUkNEKorIVBHZJCKXFkfiTOHs3w/PPgvNm8Ptt0PXrjBzJrz/vgUVxhhjikYsb4Wc6its9gFWAccAtxdpqkyhHDwIo0e7Io7Bg11gMX06fPwxdO+e6NQZY4wpy2IJLAJtK54JjFXVLUWYHlMI2dnw1lvQrh0MGAB16sDkyfDFF9CzZ/7LG2OMMYUVS2AxQUQWAt2AqSJSF9hbtMkyBaEKH3zg+uvo1w+Sk+Hdd+G77+C006wbcmOMMcUn38BCVYcCxwPdVPUAsBs4t6gTZvKnCp9+Cscd5ypkZma6Hkl/+AHOO88CCmOMMcUvWjsWJ6nqZyLSN2hc8CzvFmXCTHQzZsBdd7m6E02auBYyr7jCOgUzxhiTWNHasfgj8BlwdphpigUWCfH99/CPf8BHH0H9+q6Rq0GDoHLlRKfMGGOMid6Oxb3+71XFlxwTSaDH0XffhVq1XJsUN94IVavmv6wxxhhTXGJpx+JBEakZNFxLRP4Vw3JVReRVEXlBRPoHjb9YRF4TkZdEpHXQ+JEi8uhh7EOZ9ttveXscvfdeWLoU7rjDggpjjDElTyxvhZyhqtsCA6q6FffqaX76Au+o6jXAOUHjzweuBu4EbgUQkQsA61ksSKDH0VatcnscXbrUNcGdlpbo1BljjDHhRatjEZAkIpVVdR+AiKQAsZToNwZ+9P9nBY1/FHgaWAfUEpH6QGfgBaBVuBWJyCBgEEB6enoMmy69Qnscvf5663HUGGNM6RFLYPEGrv2K0bhKmwOAV2NYbhUuuJhLUM6Iqs4EZopIC+BGXCXResA9QEcROUZVfw1ekaqOAkaB6zY9hm2XOqE9jl55paukaT2OGmOMKU3yDSxUdbiIzANOAQT4p6p+HMO63wVGiMhZuEa2XlfVy0TkTNybJtWA21V1HfC2iDQFbgwNKsq6nTvh8cfh3/+2HkeNMcaUfqKafwaAiGQAR6vqpyKSCiSp6s4iT10Y3bp101mzSn91jNAeR//0J3jgAesczBhjTMkjIt+rardY5o3lrZBrgHeA5/2oRsD7h526cm7/fhdQhPY4+t57FlQYY4wp/WJ5K2Qw8HtgB4CqLsLViTAFENzj6I03Wo+jxhhjyqZYAot9qro/MCAiybhKnCYGoT2O1q1rPY4aY4wpu2IJLKaLyN+BFBHpDfwHmFC0ySr9QnscrVjRFXfMnGk9jhpjjCm7Ygks7gA24tqkuBaYBNxdlIkqzVRdC5mhPY7OnesqaFpAYYwxpiyLGliISAXgR1V9QVUvVNUL/P9lqihk8uTJtGzZkhYtWvDwww8fMn3r1q2cd955dOjQgWOPPZb58+cDsHLlSk488URat25N27ZtGTLkSU48EU49FRYuvI+aNRuRmtqJ4cM78fHHk4p7t4wxxphiF7UdC1XNFpEfRCRdVVcUV6KKU1ZWFoMHD2bKlCk0btyY7t27c84559CmTZuceR588EE6derEe++9x8KFCxk8eDBTp04lOTmZf//736h24c47d/LUU1054ojePPVUG9avh5o1/8Jtt92WwL0zxhhjilcsRSENgJ9EZKqIfBD4FHXCisvMmTNp0aIFzZo1o1KlSvTr14/x48fnmWfBggWcfPLJALRq1Yply5axfv16tmxpwLBhXejWDWbNqk7r1q0ZPXo1N90EybG0aWqMMcaUMbEEFvcDfYAHgH8HfcqE1atX06RJk5zhxo0bs3r16jzzdOzYkXfffRdwgcjy5cu58spVeXocnTZtGbt2zeGPf+yRs9yIESPo0KEDAwYMYOvWrcWzQ8YYY0wCRQwsRKSKiNwCXIjrHGyGqk4PfIorgUUtXHURCalhOXToULZu3Urbtp3o3/9psrI68/nnydx+u+tx9LbbdnHVVefzxBNPUKNGDQCuv/56Fi9ezNy5c2nQoAG33nprseyPMcYYk0jRMuxfBQ4AXwJnAG2AIcWRqOLUuHFjVq5cmTO8atUqGjZsmGeePXtqkJY2mt9+g+xspXr1o5g16yiOOQYOHDhAnz7n079/f/r27ZuzTP369XP+v+aaa+jTp0/R74wxxhiTYNECizaq2h5ARF4CZhZPkopX9+7dWbRoEUuXLqVRo0aMGzeON998E8jtcfTJJ7exb18qV11ViRYtXmTBgp4cc0wNVJWrr76a1q1b89e//jXPeteuXUsD39f5e++9R7t27Yp934wxxpjiFi2wOBD4R1UPhhYPlBXJycmMGDGC0047jaysLAYMGECTJm05++yRfPop7Nt3Haec8jO//no5X32VxJYtbXjppZcAmDFjBq+//jrt27enU6dOgHuD5Mwzz+Rvf/sbc+fORURo2rQpzz//fJRUGGOMMWVDxN5NRSQL2B0YBFKATP+/qmqNYklhiHj3bjpmDNx1F6xYAY0bwwknwCefWI+jxhhjTEBBejeNmGOhqknxS1LJNGYMDBrkWscEWLkSxo51/Xp89JF1DmaMMcYUVCyvm5ZZd92VG1QE27nTggpjjDHmcJTrwGJFhLZEI403xhhjTHTlOrBITy/YeGOMMcZEV64Di2HDIDU177jUVDfeGGOMMQVXrgOL/v1h1CjIyHDdmWdkuOH+/ROdMmOMMaZ0ivi6aUklIhuB5UWw6jrApiJYb1lhx6fss3Nc9tk5LvuK6hxnqGrdWGYsdYFFURGRWbG+o1se2fEp++wcl312jsu+knCOy3VRiDHGGGPiywILY4wxxsSNBRa5RiU6ASWcHZ+yz85x2WfnuOxL+Dm2OhbGGGOMiRvLsTDGGGNM3ETrNr1MEZGqwLPAfmCaqo7x488EbgQmqeoIP+5hIBXIVNWhCUpysYtyjIYCzYEjgetVdZWILAamALNVNeFZbyY2Uc7xfUBrYCvwgKquKa/3QWkX5RxfDXQHmgDzVPVOu49LJxFpBtwFpKnqBUHj2wF3+sGHVHV+Iu7j8pRj0Rd4R1WvAc4JjFTVScDwwLCIpAMVVfVmIElEmhR7ShMn0jF62I97GTjRj94FpAAriz2VpjDCnmPgIO6L6ACwrZzfB6VdpPv4JVW9DvgVeMWPtvu4FFLVJap6dZhJQ4DBwA3ATYm6j8tNjgXQGPjR/58VZb5G5N5kK/xy5eWmi3iMRKQacBEwyI/qDAjwIfBRcSXQFFqkc/ygqmaLyDnAQOA7yu99UNpFu4+rAEep6i9+lN3HZUuaqm4DEJHqJOj7rDzlWKzCHVSIvt+rg+Zr4pcrL8IeIxGpATwH/E1VdwKoaraqZgF7RaQ8XUelXdhzrKrZ/t8NQDXK931Q2kV71l0AvBsYsPu4zNkuImn+mb2TBN3H5eatEF/uOALYC3wFnK6ql4nI8bgyqVrAE6r6XxF5CKgM7FPVOyOutIyJcozeBSriLtK3/d87/GLzVPWJBCTXHIYo5/jvuAdPHeBmVV1bXu+D0i7SOfbTxgP9VHWPiLTE7uNSSUSOAIYBvYEXgTb+Pm4H3I7LhRru61gU+31cbgILY4wxxhQ9y/oyxhhjTNxYYGGMMcaYuLHAwhhjjDFxY4GFMcYYY+LGAgtjTA4RqSkiN+QzT1MRmV9caTLGlC4WWBhjgtXEtdoXNyKSFM/1GWNKNgssjDHBHgaai8hcEXnEf+aLyI8icnHozCKS5Of5TkTmici1fnwvEflcRN7EtwIpIu+LyPci8pOIDApaxy4RGSYiP4jINyJS34+vLyLv+fE/iMjv/PhLRWSmT+PzFrgYU7JYYGGMCTYUWKyqnYBvgE5AR+AU4BERaRAy/9XAdlXtjuvg6hoROcpPOxa4S1Xb+OEBqtoV6Abc7Bv5AagKfKOqHYEvgGv8+KeA6X58F+AnEWkNXAz83qcxC+gfr503xhReeeorxBhTMCcAY32Tz+tFZDoueJgXNM+pQAcRCfSwmAYcjevQbKaqLg2a92YROc//38TPt9nPO9GP/x7XmiDAScDlAD4N20XkMqAr8J2IgOtAa0N8dtcYEw8WWBhjIpEY57lJVT/OM1KkF7A7ZPgU4HhVzRSRaUAVP/mA5jYBnEX055IAr1oT48aUXFYUYowJthOo7v//ArjY16OoC/QEZobM/zFwvYhUBBCRY3xfFaHSgK0+qGgFHBdDWqYC1/v1JvmOlaYCF4hIPT++tohkFGwXjTFFyQILY0wOVd0MzPCvkx6PK/b4AfgM17vtupBFXgQWALP9Ms8TPsdhMpAsIvOAf+Lqb+RnCHCiiPyIKyJpq6oLgLuBT/y6pgCh9T6MMQlknZAZY4wxJm4sx8IYY4wxcWOBhTHGGGPixgILY4wxxsSNBRbGGGOMiRsLLIwxxhgTNxZYGGOMMSZuLLAwxhhjTNxYYGGMMcaYuLHAwhhjjDFxY4FFCSMiV4rIV4lOR1ERkT+IyC+JTkckIvJ3EXmxmLepItKiiLcxTUQGHuay14vIehHZFdTV+eGmo6nf31LdAaKItBGRWUHDy0TklASnKW7HNr/rRUTOE5GV/proLCIzRaRtYbdbXESkv4h8Eu95C0tEXhGRfxXHtopSqQgs/IndFeajInJPotNnnFi+IFX1S1VtWVxpKihVfVBVD+sLuCzynYs9BpyqqtV8XyLB08tEoHAY/gk8muhEJNCjwI3+mpjjhx8ojg3H48tXVceo6qnxnrc4FebHQlFvp1QEFv7EVgv+ALcA64EXCrq+cvgQLBFK+nEv6elLkPq47s1/SnRCSsr5EZEGwInA+3Feb8L3T5xYvhcyyHtNfIDrMC7hHcKVhONY7qlqqfsAnXHdO/cKGpcGvASsBVYD/wKS/LQrgRnA48AWPy0NeA3YCCzH9ZhYIco2KwNPAGv85wmgsp/WC1gF3Aps8Gm4KmTZR4EVuGBoJJASYTtXAl8FDf8O+A7Y7v/+LmTeJf5YLAX6+/EtgOl+mU3AWxG21RRQ4CpgJbAVuA7ojuvVchswImSZAcDPft6PgQw//gu/rt3ALuDioONyB7AOeD0wLmh9TYB3/XnYHNheAfZhMu6XU/C4H4C+/v8n/b7twPWQ+Yeg+e4D3gHe8NMH+nFvBM1zDu4Bug2YBrQOmqZAi6DhV4B/+f/rABP9cluAL4lwfQWvJ9q14o97n6Dlkv2x6eKHjwO+9tv8gbz3xzRgYEGubeAYfz7Vn9PPwiy7Imj6LlyPqBVw99Ny3P3wGpAWcs0lH+Z92xzX0+pmv+9jgJpB6VkG3Ia7frcDbwFVgqafC8z153sxcHp+6Qizz5cDn4aMWwac4v9vhbsf+/nhPn6b2/z56RCy3B0+vftw170CV/hjuwm4K2j+CsBQn/bNwNtA7XDHtgDP02nAMH+s9/g0TAMeAmb64zgeqO2vi13k3uuLg9YzBbgiwjZiuSbC7nPIegYBB4D9Ph0TIhzH5KDjtBPXA+95UZ6zinv2LcI9254ht5POgsybBPzb78NS4MZo5wT3XTbbp/EtYBy5z5BauGfIRr+diUBjP20YkAXs9cch8NyM9rw7Fpjlp60HHguaFvbZEWk7+V5TBbkAS8IHqOkvljtCxr+P67K5KlAPd0NcG3RhHARu8hdcir+wxwPV/YX9K3B1lO0+gOvquR5Q15+Ef/ppvfz6HwAqAmcCmUAtP/0JXERf229vAvBQhO3kXMR+/q3AZT7dl/jhI/x+7gBa+nkb4LqVBhgL3IW7masAJ0TYVlN/0Y/0853qL6D3/X42wj0E/ujn/xPwG9Dap+du4OuQGy74izZwXP4P90BKISiwwN2EP+C+OKoGp7UA+3A5MCNouA3u5ggEfZf645WMC/zW4b9ocEHEAb9fFXz67sMHFuR+sfb25/Vvfv8rRdjfV8h9KDzkj2tF//kD/uETZh+CA4sniHCtAPcAY4KWOwtY6P9vhPuiOdPvS28/XNdPn0bkwCLatd2U6A/GQ6bjgs/fgGZANVzg+Hq4+Sn4fdvC71tln9YvgCeCtr3Mr6OhP4Y/A9f5acfiviR7+2PUCGiVXzrC7PMjwDMh45YBpwBdcF+Offz4Lrh7qAfuer/Cz1s5aLm5uAA7Jej4vOCHO+K+KFv7+W/x56qxPwbPA2MjHNtncfdCuM+8oLRP82lu649zRT9uNdDOH5P/kjfgznPt+3FPEfRlFTItlmsi7D6HWdcr+Pss5PjnHEc/7kLcdVAB90NnN9Ag9DkbtD8Tcd8v6bgv89MPY97rcEFMY1xg8CkR7h+gEi7Q+os/5hfgnkeBZ8gRwPlAKu5Z8B/g/ZDzNjBkndGed/8DLvP/VwOOK+yzI9In4YFCgRILggsGxhP0kMZl1+4jKBcA9yX8edCFsSJoWpKfv03QuGuBaVG2vRg4M2j4NGCZ/78XLtIPfrhuwEWB4i/o5kHTjgeWRthOzkWMCyhmhkz/n5+nKu4BcT4huR+4oGkUPrqNsk9N/UXfKGjcZuDioOH/Arf4/z8iKPjyF2EmubkW4QKL/eT9xdiL3MDieNxNGe6mi3UfqvvjG0jDMODlKPNvBTr6/+8DvgiZfh+5gcU/gLdD9nc1udF8tMDiAdx12iJa+oPXk9+14ufZCaT64THAPf7/O/AP6qBlP8b/giR6YBHt2g5cIwUJLKYCNwQNt8Q9MJOD56eA922E7f8JmBM0vAy4NGh4ODDS//888HiYdURNR5j5XwAeDhm3DLgfl0N3YtD45/BBWtC4X8gN1pcBA8Icz8ZB42aSm/vxM3By0LQG4Y5tftdcSHqmAQ+EGfdw0HAb3L0cyE0KF1hEvPdivCbC7nOYdb1C+MBiQKR99PPMBc4NurZCg4UTgobfBoYexryfERSQ4oLNSIFFT1wOYfB32deh+xY0rROwNeQcRf3CJ+/z7gt/jdYJmeewnx2RPqWijkWQO3AR9BXq99jLwEV8a0Vkm4hswz1E6gXNszLo/zrkRosBy3GRGyIyMqiC6N/99IZh5m8YNLxZVQ8GDWfiosK6uIjz+6C0Tfbj8xO6zZx0qupuXBR+nd/vD0WklZ/nb7gvqZki8pOIDMhnO+uD/t8TZria/z8DeDJoP7b47TSKsu6Nqro3wrQmwPKQ4xYQ0z6o6k7gQ6CfH9UP94ULgIjcKiI/i8h2n+Y03PkPCL4uQuU5/qqa7eePtr8Bj+B+oX0iIktEZGgMy0S9VlT1N9wXy9kikoorpnnTL5sBXBhYzi97Au6LJz/5XdsFFW59gUAiWEHvW0SknoiME5HVIrIDV4wVfD7B/UoLCNyH4K63xWHSG0s6gm3FBbShrsPl4H0esu5bQ85LE/Ie33DXYKR9yADeC1rXz7is6tBjW1Dh0hA8bjnuGIUe62DVcT92wonlmoi0z7EKvVYuF5G5QceqHdHTX5DtR5q3YUg68nu+rA75Lss5RiKSKiLPi8hyf61/AdQUkaRIK8zneXc1Lhd2oYh8JyJ9/PjCPDvCKjWBhYj0wmWNX6Cq20Imr8T94qijqjX9p4aqBr/+FHzyNuGi5Yygcem4X6Oo6nWaW1H0QT99TZj518SQ9E24L+e2QWlLU1cBNT+h2wxN58eq2ht3ASzEV2RV1XWqeo2qNsTlxDyb39saMVqJi8ZrBn1SVPXrKMtolGkrgfRwla0KuA9jgUtE5HhcVurn4F5txQWjF+GKpWrissIlxvTlOf4iIrgvhdV+VCYuEAg4Mij9O1X1VlVtBpwN/FVETo6yLYjtWhmL+zV9LrDABxvgjuXrIeemqqo+nM82D9lPYr+2IfzxC7e+g+QNWANpLsh9C66ISXH1FGrgsn6F2KzE1dEINz6/dASbh3tAh7oOdz0/HrLuYSHnJVVVxwbNE+0aDJfWM0LWV0VVV4fOGPIDKfQTWhk3XBqaBP2fjntmboqStta4os1wYr0mYhHpeOWMF5EM3PPwRuAIf+/PJ/Zr5XCtxRWDBDSJNKOft5F/rgSkB/1/Ky5np4e/1nv68YH58xyH/J53qrpIVS/BBcz/B7wjIlXJ/9lRkOsTKCWBha9pPA6XJT8ndLqqrgU+Af4tIjVEpIKINBeRP4Zbn6pm4bKvholIdX8R/hX36yeSscDdIlJXROrgyrujzR/YVjbuAn9cROr5/WkkIqfltywwCThGRP4sIskicjEuS3KiiNQXkXP8hbEPV7Emy6//QhEJXNxbcRdGVgzby89I4E7x76uLSJqIXBg0fT2uDDVWM3E318MiUlVEqojI7/26C7IPk3APrQdwlTyz/fjquIfXRiBZ3KvJNQqQvreBs0TkZHGvXd6KO9aBQGou8GcRSRKR04Gc601E+ohIC//Q2OHTHvUcxHitjMPVhbme3NwKcNfi2SJymk9PFRHpFXQMozmsa9vbCGST97yPBf4iIkeJSDXgQdx5yZMzVdD71quOu9a3iUgj4PYY0wmucuZV/nxW8Me21WGkYwrQRUSqhIzfCZwO9BSRwEP5BeA6EekhTlUROUtEwuV4xGIk7rmVAeDP2bnhZgz5gRT6iaXNiUvFtdeRiru33vHPzkOISGWgK+7YhBPTNRGjWJ4zVXHPjI0+fVfhciyK2tvAEH9t1cR90UfyP9zz6Wb/fO+LqwcUUB33Q2ObiNQG7g1ZPvQ4RH3eicilIlLXP2e2+dFZ5P/sKOhzvXQEFsA1uCyzJ8NE3iP9PJfjijcW4L6I3iF6Vs5NuPLsJbiasnWAoRImy9p/OdTCfamvwWU/zgbeEpG5wItAPRHZISK3+MVqAuP89F64i/wbcVlan+Ii0ajUtRnQB/eFthlXPNBHVTfhzt2tPj1bcF9qN/hFuwPfisguXEXAIaq6NL/txZCe93CR7ji/H/OBM4JmuQ94VVx22kUxrC8L92u+Ba7y2Cpc8U6B9kFV9+Eqg51C3i/bj3H1Qn7FZTHuJXrWZOh6f8H9In4a90vtbOBsVd3vZxnix20D+pP39cOjced5F+4B8qyqToths3fgilDCXiv+S/B/uLeF3goavxKXi/F33INlJe5LN5Z7/F+4e2Ae8CPu2o6pnQBVzcS/UeDP+3HAy7g3gL7A1Yzfi7vfwinofXs/rkLkdlwR2LuxpNOndSbuDajH/fLTyf0VHXM6VHU9riz9kC90n5vaGzhDRP6pqrNwz68Rfr2/4crsD9eTuPvhExHZiavI2aMQ64vmdVx9hnW4CtQ3R5n3HFwdtUg5XQW5JvLzEtDGX2/vh5tBVRfg3s74H+6LsT3urZei9gIuSJ0HzMH96DlImB8V/jnSF3c9bMU9+4Kv5ydwObCbcOd5csgqngQuEJGtIvIU+T/vTgd+8s/UJ3F1WPbG8OwI3U6+Aq/IlFviyqt+xT0MVuFe6bzEX5iBec7E3QRn4m7iJ1W1R5j1rMZlWy0XkfuAXapanhvRMaZMEpE2wKvAsVreH6KAiHyLq9g9P9FpKUlE5Axc5eHQIu0yrbTkWBSlY4HfVHWJjyDHcegvkXOB19T5BleBJvTXzMm4d7pDK1saY8oYVV2gqt0tqHBUtYcFFSAiKSJypi/aaIQrvngv0ekqbhZYuBr+wdlFqzi01n8s8/TDlSMGu1FE5onIyyJSKx6JNcYYU2IJrrhuK64o5GdcnaVyxQKL8LWEQ3+FRJ1HRCrhyhj/EzT9OVwN9E64Cor/LlQqjTHGlGiqmulzsqqraj1VvUpVdyQ6XcXNAguX+xD8SlBjDn3VLr95zgBm+0pdgKvgpapZQTX9g2v7GmOMMWWSBRausubR/jWoSrgijQ9C5vkAuNy/LnYcsN3Xzg+4hJBikJA6GOfh3qAwxhhjyrRS91ZInTp1tGnTpnFd5/bt21m5ciWqSp06dWjQoAEbN24EoG7duqgqK1euZPv27VSoUIGmTZtStWpVALKzs5k3bx7t27cnKSm3QbSlS5eSmZmJiFCpUiUyMjKoWLFiXNNtjDHGFIfvv/9+k6rG0mI0pa572aZNmzJr1qy4rW/Mj2O4a+pd7N++n/S0dIadPIz+7fvHbf3GGGNMaSciMb/xWOoCi3ga8+MYBk0YROaBTACWb1/OoAmDACy4MMYYYw5Dua5jcdfUu3KCioDMA5ncNfWuBKXIGGOMKd3KdWCxYvuKAo03xhhjTHTlOrBIT0sPO15RBn4w0AIMY4wxpoDKdWAx7ORhpFZMzTMuJTmF05qdxuvzXufop4/m5o9uZt2udQlKoTHGGFO6lOvAon/7/ow6exQZaRkIQkZaBi+c8wKTL5vMopsWcUXHK3j2u2dp9mQz7phyB5szNyc6ycYYY0yJVurasejWrZvG83XT/Py25Tfum3Yfb/74JtUrV+evx/2Vvxz/F2pUrpH/wsYYY0wZICLfq2q3WOYt1zkWsWhRuwVv9H2DH6//kVOancJ90+/jqCePYviM4Ye8UWKMMcaUdwUKLESkloh0KKrElGRt67Xlvxf9l1nXzOLYRsdyx6d30Pyp5oyYOYJ9B/clOnnGGGNMiZBvYCEi00SkhojUBn4ARovIY0WftJKpa8OufNT/I7686ktaHtGSmz66iWNGHMNLs1/iYPbBRCfPGGOMSahYcizSfLevfYHRqtoVOKVok1XynZB+Ap9f8TmfXPoJ9avWZ+CEgbR5pg1jfxxLtmYnOnnGGGNMQsQSWCT7njovAiYWcXpKFRGhd/PefDvwW96/+H2qJFfhz+/+mY4jO/L+wvcpbRVjjTHGmMKKJbB4APgY+E1VvxORZsCiok1W6SIinNvqXOZeN5ex549lf9Z+znvrPHq82INPFn9iAYYxxphyw143LQIHsw/y+g+vc//0+1m+fTl/SP8Dw04axh8y/pDopBljjDEFFtfXTUVkuK+8WVFEporIJhG5tPDJLLuSKyRzVeer+OXGXxhxxggWbVlEz1d6cvobpzNrTckOiowxxpjCiKUo5FRfebMPsAo4Bri9SFNVRlROrszgYwez+ObFPNL7EWatmUX3F7rT962+zN8wP9HJM8YYY+IulsCiov97JjBWVbcUYXrKpNSKqdz2u9tYMmQJ9/e6n6lLp9LhuQ70f7c/izZbdRVjjDFlRyyBxQQRWQh0A6aKSF1gb9Emq2yqUbkG9/zxHpYOWcodv7+D9xe+T+tnWltPqsYYY8qMmCpvikgtYIeqZolIKlBDVRPS5WdpqLwZq3W71vHQlw8x8vuRAFzb9Vr+/oe/c2S1IxOcMmOMMSZXvCtvVgQuA94SkXeAq4F8u/kUkaoi8qqIvCAi/YPGtxORMf7Tzo8bICJPi8i/Ykl0WXFktSN58ownrSdVY4wxZUYsRSHPAV2BZ/2nix+Xn77AO6p6DXBO0PghwGDgBuAmEakHXAzsBtbGnvSyIz0tnVFnj2LhjQvp27ovj3z9CM2easb90+5nx74diU6eMcYYE7NYAovuqnqFqn7mP1cB3WNYrjGw0v+fFTQ+TVW3qep2oDrQDNiiqkOBDBFpHroiERkkIrNEZNbGjRtj2HTpZD2pGmOMKe1iCSyygr/sfcubWVHmD1iFCy5Ct7NdRNJEpAawE1gNBN402QZUC12Rqo5S1W6q2q1u3boxbLp0s55UjTHGlFb5Vt4UkZOB0cASQIAM4CpV/Tyf5aoCI3BvkHwFnK6ql/l6Fbf7dQ1X1fki8k+gKlBJVW+Mtt6yVHkzVl+t+Iq7P7ub6cunk56Wzj097+GKTleQXCE50UkzxhhTDhSk8masb4VUBlrigoGFqpqwn83lMbAAUFU+XfIpd39+NzNXz+To2kdzf6/7ubjdxVSQWDKejDHGmMMTl8BCRPpGW1BV3z2MtBVaeQ0sAlSVCb9O4B+f/4N56+fRrl47/nniPzm35bmISKKTZ4wxpgyKV2AxOspyqqoDDidxhVXeA4uAbM3m7Z/e5t5p9/Lr5l/p3rA7/zrpX/Ru1tsCDGOMMXEV96KQksQCi7ysJ1VjjDFFLa4NZJmSzXpSNcYYU5JYYFFGFGVPqpMnT6Zly5a0aNGChx9++JDpqsrNN99MixYt6NChA7Nnz84zPSsri86dO9OnT588459++mlatmxJ27Zt+dvf/laoNBpjjCkZLLAoY6L1pPrblt8KvL6srCwGDx7MRx99xIIFCxg7diwLFizIM89HH33EokWLWLRoEaNGjeL666/PM/3JJ5+kdevWecZ9/vnnjB8/nnnz5vHTTz9x2223FXxnjTHGlDix9BWSKiL/EJEX/PDRItInv+VMYoXrSbXViFZc88E1BepJdebMmbRo0YJmzZpRqVIl+vXrx/jx4/PMM378eC6//HJEhOOOO45t27axdq1rnX3VqlV8+OGHDBw4MM8yzz33HEOHDqVy5coA1KtXr5B7bIwxpiSIJcdiNLAPON4PrwLKVWdhpVntlNo8dMpDLL55MTceeyOvzXuNo58+mps/upl1u/LvoHb16tU0adIkZ7hx48asXr065nluueUWhg8fToUKeS+1X3/9lS+//JIePXrwxz/+ke+++64wu2mMMaaEiCWwaK6qw4EDAKq6B9dQlilFjqx2JE+c/gS/3fRbnp5Uh346NGpPquHeGgp9nTXSPBMnTqRevXp07dr1kOkHDx5k69atfPPNNzzyyCNcdNFFYddjjDGmdIklsNgvIimAAvh+Q6zDilKqSVqTPD2pDp8xPGpPqo0bN2blypU5w6tWraJhw4YxzTNjxgw++OADmjZtSr9+/fjss8+49NJLc5bp27cvIsKxxx5LhQoV2LRpUxHttTHGmOISS2BxLzAZaCIiY4CpgFXhL+XC9aTa7MlmPDLjkTw9qXbv3p1FixaxdOlS9u/fz7hx4zjnnHPyrOucc87htddeQ1X55ptvSEtLo0GDBjz00EOsWrWKZcuWMW7cOE466STeeOMNAP70pz/x2WefAa5YZP/+/dSpU6f4DoAxxpgikW9goapTgL7AlcBYoJuqTivaZJniEtqT6t8+/VuenlTf+vktdp+ym2bdmlG1YVVa9nSvh44cOZKRI0cCcOaZZ9KsWTNatGjBNddcw7PPPpvvdgcMGMCSJUto164d/fr149VXX7UWQ40xpgyItROyRrheTXO601TVL4owXRFZy5tFK7gn1doptdm1fxf7s/bnTE+tmMqos0fRv33/BKbSGGNMcYprk94i8n/AxcBPQLYfrap6TuSlio4FFkUv0JNqn7F98gQVARlpGSy7ZVnxJ8wYY0xCFCSwSM5/Fv4EtExkV+mmeIkIvZv35kDWgbDTl29fzg0f3kCXBl3ofGRn2tVrR+XkysWcSmOMMSVRLIHFEqAi9iZIuZOels7y7csPGV85qTJjfhzDc7OeA1x/JW3rtqVzg850ObILnRt0pmP9jlSvXL24k2yMMSbBYgksMoG5IjKVoOBCVW8uslSZEmHYycMYNGFQnrdEAnUsLml3CUu3LmXOujnMXjubOevmMGnRJF6Z+woAgnD0EUfn5Gp0PrIznRt0pk6qvflhjDFlWSx1LK4IN15VXy2SFOXD6lgUrzE/juGuqXexYvsK0tPSGXbysIgVN1WVtbvWMmdtbrAxe+3sPLkeTWo0yQk2ujRwuRuNqjeyN0KMMaYEi2vlTb/CSsAxfvAXVQ1f+F4MLLAofbbs2cLcdXPzBBu/bPoFdW2uUTe1Lp0bdM4NNo7sTPPazakg1keeMcaUBPF+K6QX8CqwDNeUdxPgCnvd1BTGrv27mLd+Xp7cjfkb5nMg28Ws1StVp9ORnXKLUhp0pnWd1lRMqpjglBtjTPkT78Die+DPqvqLHz4GGKuqh3YAkXe5qsCzwH5gmqqO8ePbAXf62R5S1fl+/Ehgl6pG7T/bAouya3/Wfn7a8FOeehtz183NqeNROaky7eu3z6kg2qVBF9rXa09KxZQEp9wYY8q2eAcW81S1Q37jwix3GbBNVSeIyFuqerEf/wJwO67vkeGqeq2IXADUBFpZYGGCZWVnsWjLokPqbWzduxWAJEmiVZ1WeeptdDqyE2lV0hKccmOMKTvi3Y7FLBF5CXjdD/cHvo9hucbAj/7/rKDxaaq6zSe0uojUBzoDLwCtwq1IRAYBgwDS09Nj2LQpK5IquMChVZ1WXNL+EsBVEl2xfUWeQGPq0qm8Pu/1nOWa12p+SL2N+tXqJ2o3jDGm3Iglx6IyMBg4AVfH4gvg2fwazPI5FltVdaKIjFPVfn78C8BtuByLR3CdmvUGkoCOwCWq+muk9VqOhYlk/a71zFk3x+VurJvNnLVzWLx1cc70htUb5rz6GngjJSMtw95IMcaYfMT9rZCgFTdQ1bUxzlsVGAHsBb4CTlfVy3wdi9txQcrwoDoWTYEbrSjExNP2vduZu25unnobP2/8mSx1mWi1qtTK07BXlwZdOLr20SRVSEpwyo0xpuQoysBitqp2OeyUxYEFFqaw9hzYw48bfnSBxto5zFk3h3nr57Evy2XCpVZMpWP9jnnqbbSt15ZKSZUSnHJjjEmMogws5qhq58NOWRxYYGGKwoGsAyzctDBPvY256+ayc/9OACpWqEi7eu1yXn3t0qALHet3pGqlqglOuTHGFL2iDCxuUNVnDztlcWCBhSku2ZrNkq1LcnI2AvU2NmZuBFyz5S3rtMxTQbRzg87UTqmd4JQX3OTJkxkyZAhZWVkMHDiQoUOH5pmuqgwZMoRJkyaRmprKK6+8QpcuXVi5ciWXX34569ato0KFCgwaNIghQ4YAMHfuXK677jr27t1LcnIyzz77LMcee2wids8YU0hF0fJmfaC7H5ypqhsKkb5CscDCJJKqsnrn6jyvv85ZN4cV21fkzJORlnFIvY0G1RqU2EqiWVlZHHPMMUyZMoXGjRvTvXt3xo4dS5s2bXLmmTRpEk8//TSTJk3i22+/ZciQIXz77besXbuWtWvX0qVLF3bu3EnXrl15//33adOmDaeeeip/+ctfOOOMM5g0aRLDhw9n2rRpidtRY8xhi+vrpiJyEe7tjWm4CpdPi8jtqvpOoVJpTCkkIjSu0ZjGNRpzdsuzc8Zvytx0SLPl4xeOz2m2vF7VenlyNro06EKzWs1KRLAxc+ZMWrRoQbNmzQDo168f48ePzxNYjB8/nssvvxwR4bjjjmPbtm2sXbuWBg0a0KBBAwCqV69O69atWb16NW3atEFE2LFjBwDbt2+nYcOGxb9zxphiF0s7FncB3QO5FCJSF/gUsMDCGK9Oah1OaXYKpzQ7JWfczn07mbd+Xp5g45GvH+Fg9kEAalSukafn1y4NutCqTiuSK8RyW8bP6tWradKkSc5w48aN+fbbb/OdZ/Xq1TlBBcCyZcuYM2cOPXr0AOCJJ57gtNNO47bbbiM7O5uvv/66iPfEGFMSxPIEqxBS9LEZsN6hjMlH9crV+X367/l9+u9zxu07uI/5G+bnaW/j+e+fZ8/BPQBUSa5Ch/od8rS30b5+e6okVymydIYrDg3NSclvnl27dnH++efzxBNPUKNGDQCee+45Hn/8cc4//3zefvttrr76aj799NM4p94YU9LEElhMFpGPgbF++GJgUtElyZiyq3JyZbo27ErXhrld7WRlZ/HL5l/y1NsYN38cz3//POCaLW9Tt02eehudjuxEjco14pKmxo0bs3LlypzhVatWHVJsEW2eAwcOcP7559O/f3/69u2bM8+rr77Kk08+CcCFF17IwIED45JeY0zJFjWwEPeT5Clcxc1Ay5ujVPW9YkibMeVCUgUXOLSp24b+HfoDLodg2bZleRr2+mTxJ7z2w2s5y7Wo3eKQN1LqVa1X4O13796dRYsWsXTpUho1asS4ceN4880388xzzjnnMGLECPr168e3335LWloaDRo0QFW5+uqrad26NX/961/zLNOwYUOmT59Or169+Oyzzzj66KMP4+gYY0qbqIGFqqqIvO97Mn23mNJkTLknIhxV6yiOqnUUfVvn5gKs3bk2pxhlzro5zFozi/8s+E/O9EbVG+UJNLo06EKTGk2iVhJNTk5mxIgRnHbaaWRlZTFgwADatm3LyJEjAbjuuus488wzmTRpEi1atCA1NZXRo0cDMGPGDF5//XXat29Pp06dAHjwwQc588wzeeGFFxgyZAgHDx6kSpUqjBo1qgiOlDGmpImlr5BngFdU9bviSVJ09rqpMXlt3bP1kGbLF25aSLZmA1A7pXaet1E6H9mZo484mgriqkqN+XEMd029ixXbV5Cels6wk4fRv33/RO6SMaaEiXe36QuAlsAyYDeuOETz6za9qFhgYUz+Mg9kMm/9vDz1Nn7c8CP7s/YDULViVTod2YnU5FSmr5ieMx5ck+ajzh5lwYUxJke8A4uMcONVdflhpK3QLLAw5vDsz9rPzxt/ztOw14wVM3La2ghWOakyZxx9BvVS61GvavhP7ZTa1llbKVUULa2asq0oWt48AThaVUf7diyqqerSQqbzsFhgYUz8VLi/QtjAAqBdvXZs2L2BTZmbcopV8iwrFaiTWidvwBElEKlWqVqJaBCsvCuqllZN2RbvljfvBbrhikNGAxWBN4DfR1vOGFPypaels3z7oZmPGWkZ/Hj9j4B7HXbLni1s2L3hkM/GzI05/3+/5ns27N7A9n3bw26rclLliEFH6Kdual0qJ1cu0n0vr4qqpVVjAmJpx+I8oDMwG0BV14hI9SJNlTGmWAw7eRiDJgwi80BmzrjUiqkMO3lYznBShSTqVq1L3ap1aUvbfNe57+C+PAFHpM/8DfPZsHtDTnf1odIqp8UciNROqZ1TGdVEV1QtrRoTEEtgsd+/dqoAImL9RBtTRgQqaMbzrZDKyZVz+lPJj6qyc//OfIOQXzf/ylcrvmJT5qawRTcVpAJ1U+tGzQGxYhmnqFpaNSYglsDibRF5HqgpItcAA4AXizZZxpji0r99/4S9ASIi1KhcgxqVa9Cidot8549WLLNh9wY2ZLq/3635jg27N7Bj346w66mSXOXQACRC/ZC6VetSKalSvHc9YYqqpVVjAvINLFT1URHpDezA1bO4R1WnFHnKjDEmREGLZfYe3MvG3RGKZXwQsm7XOuatn8eG3RvyvHYbrGaVmjFXUq2VUqtEF8sUVUurxgREDCxE5DRV/RjABxJTgqZdqKr/ibSsMcaUBFWSq9AkrQlN0prkO6+qsmPfjnwrqf6y6Re+3P1lxGKZJPHBT5SimeBP1YpVi7VYpqhaWjUmIOLrpiKSBXwBXKqqq0OmzVbVLsWQvkPY66bGmJIgKzuLzXs251s/JPDZuX9n2PWkJKfEXEm1TmqdwyqWsdZVy4fDbZ8EYMCAAUycOJF69eoxf/78Q9YtIquAxkBdVd0ULR3RikLmAW8C34jIX0NyKPINr30lz2eB/cA0VR3jx7cD7vSzPQQcAO4AqgA/qOr/5bduY4xJtKQKSTlf+LHYc2BPvm/LrNm5hrnr5rJh9wYOZB8Iu55aVWrFXEm1Vkotxs4fm+fNn+XblzNowiAACy7KkKysLAYPHpynfZJzzjknz6vAH330EYsWLWLRokV8++23XH/99TlvBF155ZXceOONXH755Yes29e3qQGsiCUt0QILVdUXRGQ6MEZEzgQGq2omRGhRJ6++wDuqOkFE3gLG+PFDgMF+HcNV9VpchVBExDo6M8aUSSkVU0hPSyc9LT3feaMVywTXD/l5089MXz6dzZmbIxbLKHpIA2eZBzK5cdKN7N6/m1pValGzSk1qpfi/VWqRViWN5Aqx1O03JUVh2yfp2bMny5YtC7vuv/zlLwCrgJjeCo2l8uavInI88C9gjogcGs6E1xj40f+fFTQ+TVW3AQS3hyEi/YBPwq1IRAYBgwDS0/O/KY0xpjQTEdKqpJFWJY2jj8i/u/mD2QfZnBm+WObBrx4Mu8y2vdu4duK1EddZvVL1QwKOmlVq5vk/dFpguLjrjZj4tU8S6oMPPqBRo0YAe4hDYJFzVajqQWCoiEwGxgJ1Y1h3oDxmLhBcRXq7iKThcix2Qk5QkRGpGERVRwGjwNWxiGHbxhhTbiRXSKZ+tfrUr1b/kGljfhwTtnXVJjWa8PXVX7Nt7za27tnq/u51f3PG7cudtnTb0pzxkeqLBKcnagASYXzgUzGpYtyOTXkRj/ZJQmVmZjJs2DA++eQTnnrqqZjTEi2wuD90hKpOE5GuQOQwN9e7wAgROQuYICKvq+plwJPAU7jAZbiIdAYeBSaKyGOqau8wGWNMnERqXfWhUx6KuSGzUAezD7J97/Y8wUhocBIamCzftpyte7eydc/WiPVHAqpWrBo+N6Ry5FySwHB5bfyssO2ThLN48WKWLl1Kx44dAdoDScBsETlWVddFWi5iYKGq70cYvxV4OGJKcufbDVwVNGqMHz8fuCJk9oJf2cYYY/JVFK2rJldI5ojUIzgi9YgCL6uq7Dm4J3IwEjzs/67YvoJ56+exde/WiI2eBSRJUky5JJGCk9KaW1KY9kkiad++PRs2bABARH4E6gDdCvNWiDHGmDIgka2rhhIRUiumkloxlYbVI/9ajiQrO4vt+7bnCUTyC05W7ljJ1j1b2bp3a8RG0AJSK6YWuAgnMFy9UvWE5ZYUpn0SgEsuuYRp06axadMmGjduzP3338/VV199WGmJqdv0ksTasTDGGHO49hzYk38Rzt5teYpxAtO2790e9u2bgApS4dCKrjEW4dRKqVWopuOLuq2SuHabbowxxpQVKRVTSKmYQoPqkYsAIsnKzmLn/p0xF+Fs3bOVNTvX5AzvPbg3etqSU2KuWxIcnHy6+FOGfDykxLRVYjkWxhhjTDHYe3Bv3rduYgxOAstEyy0JJyMtg2W3LItL2i3HwhhjjClhqiRX4chqR3JktSMLvGy2ZrNz386wgcjVH4SvC7Fie0wNZcadBRbGGGNMCVdBKuQ0mhbqgekPhG2rJJZWXotCye3b1xhjjDH5GnbyMFIrpuYZl1oxlWEnD0tIeiywMMYYY0qx/u37M+rsUWSkZSAIGWkZjDp7VMJeMS51lTdFZCNwaJ5P4dUBojb6Uc7Z8Sn77ByXfXaOy76iOscZqhpLdx6lL7AoKiIyK9Yar+WRHZ+yz85x2WfnuOwrCefYikKMMcYYEzcWWBhjjDEmbiywyDUq0Qko4ez4lH12jss+O8dlX8LPsdWxMMYYY0zcWI6FMcYYY+Km3LS8KSJVgWeB/cA0VR3jx58J3AhMUtURftzDQCqQqapDE5TkYhflGA0FmgNHAter6ioRWQxMAWarasKz3kxsopzj+4DWwFbgAVVdU17vg9Iuyjm+GugONAHmqeqddh+XTiLSDLgLSFPVC4LGtwPu9IMPqer8RNzH5SnHoi/wjqpeA5wTGKmqk4DhgWERSQcqqurNQJKINCn2lCZOpGP0sB/3MnCiH70LSAFWFnsqTWGEPcfAQdwX0QFgWzm/D0q7SPfxS6p6HfAr8IofbfdxKaSqS1Q1XAchQ4DB/9/encdHVZ2PH/882ReysUUgEDYFwSoiIC7Ffa+2blWLWksV10pbv1atXdSKC/XXaqVqcV8QtNa6FVGLotUWIiogooiyJQhhMQl7SDLP749zMkyGyWQCk/15v155Ze6527nrPHPuuecAVwE/a6nruMOUWAAFwKf+c02U6Xqx6yJb5efrKBddvftIRDoBPwTG+6SDAQH+BbzeXBk0e62+Y3yHqgZE5AzgUuBDOu510NZFu47TgH6qusQn2XXcvuSoajmAiGTRQt9nHanEogS3UyH6dq8Oma63n6+jiLiPRCQbeBD4lapuBlDVgKrWADtEpCOdR21dxGOsqgH/cR3QiY59HbR10e515wAv1g7YddzuVIhIjr9nb6aFruMO81aIf+44GdgBvA+crKoXichhuGdSecC9qvoPEbkTSAUqVfWmehfazkTZRy8CybiT9Hn//wY/20JVvbcFsmv2QJRj/GvcjacrcK2qrumo10FbV98x9uNeBs5X1e0iMgi7jtskEekCTAROAB4Bhvjr+ADgelwp1CRfx6LZr+MOE1gYY4wxpulZ0Zcxxhhj4sYCC2OMMcbEjQUWxhhjjIkbCyyMMcYYEzcWWBhjgkQkV0SuamCaviKyqLnyZIxpWyywMMaEysW12hc3IpIYz+UZY1o3CyyMMaHuAgaIyHwR+aP/WyQin4rIeeETi0iin+ZDEVkoIpf79KNF5B0ReRbfCqSIvCQiH4nIZyIyPmQZW0RkoogsEJE5IpLv0/NF5J8+fYGIHO7TLxSRIp/Hv1ngYkzrYoGFMSbUjcDXqjoMmAMMAw4Cjgf+KCI9wqb/KVChqiNxHVxdJiL9/LhRwM2qOsQPj1PVQ4ARwLW+kR+ATGCOqh4EvAdc5tP/Arzr04cDn4nI/sB5wBE+jzXA2HhtvDFm73WkvkKMMY1zJDDNN/lcKiLv4oKHhSHTnAgcKCK1PSzmAPviOjQrUtXlIdNeKyJn+s+9/XQb/bSv+fSPcK0JAhwLXAzg81AhIhcBhwAfigi4DrTWxWdzjTHxYIGFMaY+EuM0P1PVN+okihwNbA0bPh44TFW3ichsIM2PrtJdTQDXEP2+JMCT1sS4Ma2XPQoxxoTaDGT5z+8B5/l6FN2AMUBR2PRvAFeKSDKAiOzn+6oIlwOU+aBiMDA6hrzMAq70y030HSvNAs4Rke4+vbOIFDZuE40xTckCC2NMkKpuBD7wr5MehnvssQB4G9e77dqwWR4BFgMf+3n+RuQSh5lAkogsBP6Aq7/RkAnAMSLyKe4RyVBVXQz8BnjTL+stILzehzGmBVknZMYYY4yJGyuxMMYYY0zcWGBhjDHGmLixwMIYY4wxcWOBhTHGGGPixgILY4wxxsSNBRbGGGOMiRsLLIwxxhgTNxZYGGOMMSZuLLAwxhhjTNxYYLGXRGSsiLwZw3QPichvmyNPLU1EbheRDSIS3vxzi4v1eMV5nStE5PgmXscTInL7Hs57pogUi8gWETk4DnlRERm4t8tpSSLSTUSWiEiaH54tIpe2gnzFZd82dL6IyBEistSfEz8QkRdF5OS9XW9LE5HvisiSZlrXLSLyTHOsq7Vp14GFv6Fv9xdHqYg8LiKd4rkOVZ2qqifGMN0VqvqHeK67NRKR3sB1wBBV3ael8xMu1uPVwdwDXKOqnVT1k/CR7SFQ2AM3Ao+r6o6WzkgLuQ2Y7M+Jl4C7gInNseJ4fiGHn7uq+h9VHRSPZcfT3vwwaI3radeBhXe6qnYChgMjcR0Y1SEi1n18PfZg3xQCG1V1XTOsq1Utvw0rBD5r6Uy0luMjIqnAj4G4/toUp8XvuSKSGMNkdc4JVS0CskVkRJNlzLQbLX6SNxdVXQ28DhwAwUj2ahFZCiz1ad8TkfkiUi4i/xWRA2vnF5HevjhwvYhsFJHJPv0SEXnffxYR+bOIrBORChFZKCK166sTKYrIZSLylYh8KyKviEjPkHEqIlf4osgyEfmriEik7RKRUSIyT0Q2+VKZP4WMO9JvR7kv6r7Ep+eIyFN+W1aKyG9qb3h+ez7w2/EtcIuIpIrIPSKyyq/jIRFJj5CX43G9Tfb0pURP+PQzROQzn4/ZIrJ/yDwrROQGcT1Vbg3/cvHruics7WUR+aX/fKOIfC0im0VksYicGTJdpG0JHi8/zeEi8qE/Xh+KyOFheTs+ZDj4S0pE0kTkGX8ulPt58yMdo7C8J4TkeaOIPC8inf24mSJyTdj0C0TkLP95sIi85c+ZJSLyw4bWF7LO3/hjvc4f+xx/XLcAicACEfk6wrzv+Y8L/DE9z6fXe/6GzV/vuSMiR4tIiT/+a4HHRSRPRF7z52aZ/1wQsrzZIvIHf1w3i8ibItI1ZHx953xM57B3KFCuqiX1bFMPcdf2//nh0SHrXCAiR4fld6KIfABsA/pLA9e3iIwTkc/9uDdkL7uFF3fveVBEZojIVuAYP6qrP582i8i7tevx50F/4FV/zFP99LOB06KsZ6/vaeIet/waOM+ve4FPzxGRR0VkjYisFve4NdGPG+jzXyHuEexzPn23c7f2nAtZ3woR+T9/PCtE5Dnxj7/8+F/5dX4jIpdKlNI7Eenn87FZRN4CuoaN/7uIrPXreU9Ehvr08cBY4Fc+n6/69Gj3tojb7MdFvE/Ut54moart9g9YARzvP/fGReB/8MOK+xLsDKTjSjTW4W4qibhfLCuAVD+8APgzkAmkAUf65VwCvO8/n4Tr3jkXEGB/oIcf9wRwu/98LLDBrzMVuB94LyTfCrzml9MHWA+cXM82/g+4yH/uBIz2n/sAm4ELgGSgCzDMj3sKeBnIAvoCXwI/DdmeauBnuO6v04F7gVf8vsoCXgXurCc/RwMlIcP7AVuBE3w+fgV8BaSEHKP5/vikR1jeGKCYXT3x5gHbgZ5++FygJy5IPs+vq0eUbQk9Xp2BMuAiP/4CP9wl/Pzxw7cAz/jPl/v9kIE7Pw4BsmM4D3+O6zK8wB/7vwHT/LiLgQ9C5hsClPvpMv1++InP63DcOTQ0/PyKsP5xfp/3x50jLwJPh51vA6NcR3XGE9v5O9B/vpd6zh3cuVIN3O2Xk447T8/2+zUL+DvwUsiyZwNf486rdD98VwznfL35iLC9VwP/CkubDVzKrutlvE/vBWwETsWdgyf44W4h860ChvrjlkyU6xv4gT9W+/vpfwP8t559e6M/PyL+hczzBFABHOHzmObTNuOur1TgPvx1Eenc92m/BF6sZ5/F8552C/46C0l7CXetZALdgSLgcj9uGnBzyLYdGeXcPZq696cVflk9cefG58AVftzJwFp/7DKAp8OXF+Fe/Ce//WP8/n0mZPw43LmXijsf54cdo9vDlhft3hZxm9mL+0Q8/1r8y79JN86dNFv8hbYSeAD/5eVPkGNDpn0QH3SEpC0BjgIO8xdCUoR1XMKuL6pjcTed0UBC2HTBAwo8CkwKGdcJqAL6huQt9OJ4Hrixnm18D7gV6BqWfhPwzwjTJwKVuDoQtWmXA7NDtmdVyDjxJ/SAkLTDgOX15Odo6l64vwWeDxlOAFYDR4cco3FRjqHgbsxj/PBlwNtRpp8PfD/StkQ4XhcBRWHj/wdcEpK3+gKLccB/gQNjPA9rA4vPgeNCxvXwxz4Jd9PZChT6cROBx/zn84D/hC33b8Dvw8+vCOufBVwVMjyodp0h51tjAotYzt+BDZ07/lzZCaRFWfcwoCxkeDbwm5Dhq4CZDZzzjT2Hbwamh6XNxn1prAAuCEm/gZAgzae9Afw4ZL7bIuzPiNc3rlT1p2HXy7aQcyLqsapne54AnoqQNj1kuBNQA/SOdO43dO3FeE7Eek+7hbpfyPm4e1Z6SNoFwDv+81PAFKAghnP3aHYPLC4MGZ4EPOQ/P0ZI8Ik7pyPuf1ywVA1khqQ9S1iAFDIu1y8rJ+R4RP3Cp+69LeI2sxf3iXj+dYRHIT9Q1VxVLVTVq1R1e8i44pDPhcB1vjizXETKcb+ie/r/K1W1OtqKVPVtYDLwV6BURKaISHaESXviAp3a+bbgfuX0Cpkm9I2KbbgLNZKf4n69fSGuOP57Pr037pdduK5ASuj6/efQdYful264aP2jkP0y06fHInxbA3759a2vDnVXw3TcjQTgR8DU2vEicrHsenxVjnvUFVoEWe+yw/Pmhe+L+jyN+wKZ7otJJ4lIcgzzFQL/DMnv57gber6qbgb+BZzvpz2fXdtaCBwadn6OBWKpIBu+nStxgUyDj25iWV495y/Edu6s15AKkiKSISJ/E/fYZhMucM6VuvUC6rs26jvnG3sOl+GCvHBjcUHxCyFphcC5YcflSFzAWCvSOVjfNhQC94Us61tcYBTLORlNpDwE0/wx/BZ3bOuThfuRFkk872nhCnElPWtC9svfcCUX4EpBBSgS98h1XIzLbShfPam73xq6l5Sp6taQtOD+EJFEEbnLP9rYhAtoIOxxSagG7m31bfPe3CfipiMEFtFoyOdiYKIPQmr/MlR1mh/XR2KoXKaqf1HVQ3DFZ/sB10eY7BvcCQCAiGTiim1XN3oDVJeq6gW4i+xu4AW/vGJgQIRZNuB+SRSGpPUJW7eGTb8dV5RWu19y1FWIjUX4tgruC6C+9UUyDThH3DPgQ4F/+GUVAg8D1+AeX+QCi3AXXCzLrpM3L3RfbMV9IdUKXpyqWqWqt6rqEOBw4Hu4RxkNKQZOCTvP0tTVAard1gtE5DBcUf87IfO9GzZfJ1W9MoZ1hm9n7a+r0hjmbXB5Uc7fWM6d8ONzHa5E5VBVzcYVKUPdY1qfaOd8Y87hhbhrN9wtflnPhgQ6xbgSi9Djkqmqd4XM19D5Hb4Nl4ctL11V/xs+oYj82j8rj/gXNnmkPPQOWVYn3KOAb6LkbX/cI+FI4nZPY/e8FuNKLLqG7JNsVR0KoKprVfUyVe2JK319oL56EI20BvfIslbv+ib00+b57a7VJ+Tzj4DvA8cDObhHarDrvK6zzQ3d26Jsc0P3icaci3usowcWoR4GrhCRQ8XJFJHTRCQL9wxuDXCXT08TkSPCFyAiI/38ybgvpR24X6PhngV+IiLDxFWMugOYq6orGptpEblQRLr5koByn1yD+6V7vIj8UESSRKSLiAxT1RpcMeREEcnyJ/AvqacGvF/uw8CfRaS7X2cvETkpxiw+D5wmIsf5/XId7iax242yPupegVwPPAK8oaq125mJu1DW+3z9BF85N0YzgP1E5Ed+H52Hq9fwmh8/HzhfRJLF1YY/p3ZGETlGRL7jv2A24YK1SMc63EO4fV/ol9NNRL4flqdC3Ot+z/n9j8/TfiJykc9Psj/f9qdh04BfiKtc1gl3vj3XUAlciFJc/YxaMZ2/e3juZOGCgHJxlVp/H2Meof5zvrH5KMKVkoSXElThnntnAk+Lq/D8DHC6iJzkf5WmiasgWMCeeQi4SXZV7MsRkXMjTaiqd/gvjYh/MazrVHGVXVOAP+COYbRf5UfhHtVEErd7Gu586+v3L6q6BngT+H8iki2uMvIAETkKQETODdnfZbh7Qk3IsvqzZ57327S/iGQAv6tvQlVdCcwDbhWRFBE5Ejg9ZJIs3H1vI+7Hyh0Rtjk0n1HvbVG2uaH7xN7sj5hZYOGp6jzcM8TJuAP1Fe55PP7L+HTcM7ZVQAnuWVa4bNwNrAxXDLYR10ZA+Lpm4eoe/AMXsAxgV/F3Y50MfOZ/odwHnK+qO1R1Fa5C2XW4Is75wEF+np/hAp9lwPu4m8JjUdZxA25/zBFXjPdv3K/KBqnqEuBCXGWuDbj9eLqq7mzENoL7cjze57V22YuB/4erF1EKfAf4INYFqupGXEnDdbhj9Svge6q6wU/yW9yxKcPVY3k2ZPZ9cEXim3CPM94lttcT78NVInxTRDbjKnIeGpKnSlzlyvBt3QyciDtPvsEV39ZWemzIY7hHN+8By3EB789imK/WLcCT4opWf9jI87ex5869uJKaDbh9MzPWTDZwzsecD39uPoE7byONOwtXQvgY7hf593FvMqzH/WK8nj28t6rqP3HHdbrP5yLglD1ZVgyexQVu3+IqH4+tb0IRGQlsVffa6W7ifE/7u/+/UUQ+9p8vxj3CXYy7Hl9g1+OmkcBcfw98BZigqsv9uFsIOXcbkwlVfR34C67U8CvcfQZcgBDJj3DX8re4/fpUyLincN8Jq/02zAmb91FgiM/nSzHc2yJuc9h9Yj3uWn8XuCLCev4lIv8U90ZMkfg3GCH4tsyn4h7FzGt4b9VVW9PeGGOMJyLdgP8AB2vdelkdkoj8A3hUVWe0dF5aiv/VvwhIbURpX4vwJalf4t5SKgE+xFU6XhwyzR+BLap6q4gMBv6qqsf5cSuAESE/shrFSiyMMSaMqq5X1cEWVDiqenZHDCrENXefIiJ5uJKkV1t7UOGNAr5S1WW+lG06rmQt1BDcG2Oo6he4x097WqG7DgssjDHGmMguxz1S+BpXhyGWytKtQS/qvsVSwu5vFi3APdZDREbh6nbV1ttQ3OPaj8Q1rNUoFlgYY0wHICIni2uJ8SsRuTHC+Lz6nrn78Yki8omIvBaSNkxE5tQ+i/dfUO2Gqp7s3yDqrKpn+oqkbUGkt6jC6z3chXuTZT6uztUnuLfFAI5Q1eG4+j1Xi8gYGsECC2OMaef8M/e/4r4ohuBeaR4SNtmvca1BHoirLHlf2PgJuIrKoSYBt6rqMNxbE5PinHWzZ0qo+3psAWGvEqvqJlX9iT92F+PadVnux33j/68D/ol7tBIzCyyMMab926tn7v7VxtNwr3yHUtzbcODaZ4jWDoZpPh8C+/pXzFNwb4m8EjqBiOT6ceCaq39PVTeJa1Ihy0+TiXvLZFFjVt7m3grp2rWr9u3bt6WzYYwxbUZZWRkVFRXU3js3btzI1q1b6dNnVxtOq1evJhAI0Lt3b7Zu3coXX3zB4MGDyczM5Ouvv2afffYhEAhQWlrKwIGu/ant27ezdOnS4DIGDRpEamosb0CbplZRUUFxcTGqSteuXenRowfr168HoFu3bmzZsoUVK1YAkJ6eTmFhIUlJSVRWVvL1164BW1Wlc+fO9OjRg48++miDqsbU4nKTdVPsI50HcH0BzFbVqT79PFzkWwXco6qfi+tN7y3gY1WdEm25ffv2Zd68Rr9Wa4wxHdbf//533njjDR55xBU4PP300xQVFXH//fcHp9m0aRMTJkzgk08+YcSIEXTq1IlHHnmE4uJiZsyYwQMPPMDs2bO55557eO01V83i2muv5bbbbuPss8/m+eefZ8qUKfz73/9ukW3s6EqnlrLs5mVUrqoktU8q/Sf2J39sXF7yAEBEwrs/qFeTBRa42qYvqOqr4rp0re3z4GxcQyx5uNbHLsV1FJZO9LbYjTHG7IGCggKKi3fdXktKSujZs263INnZ2Tz++OOA+6Xar18/+vXrx/Tp03nllVeYMWMGO3bsYNOmTVx44YU888wzPPnkk9x3n6uKce6553LppZc230aZoNKppSwZv4TANtdQb+XKSpaMXwIQ1+AiVk0ZWBQAn/rPoU0d34NrhXEtLrgAOBhXi/Vf1N9krDHGmD0wcuRIli5dyvLly+nVqxfTp0/n2WefrTNNeXk5GRkZpKSk8MgjjzBmzBiys7O58847ufPOOwGCJRbPPOMame3ZsyfvvvsuRx99NG+//Tb77rtvs29ba6QBJbAj4P62B4Kfa7bX7JYW/BxpunrGh0+7c81OCNTNQ2BbgGU3L2t3gUUJLriYT0glUd8kbJG4DlOu8WkBABHZISIJIf0j4NPHA+OBOs8EjTHGNCwpKYnJkydz0kknUVNTw7hx4xg6dCgPPfQQAFdccQWff/45F198MYmJiQwZMoRHH320weU+/PDDTJgwgerqatLS0pgyJeqT7GalqmiVxv7FHvI54hd7I4IA3bl3dRclRUhISyAhPcH9T0sgMT0xmJbcLbnO+LWPrY24nMpV9bU+3rSarPKmr2MxGddW+fvAyap6kYiciusvohOuTf0cXDv+AAtV9d5oyx0xYoRaHQtj4mvmzJlMmDCBmpoaLr30Um68sW4zB2VlZYwbN46vv/6atLQ0HnvsMQ44YFczBzU1NYwYMYJevXoFn7/Xuueee7j++utZv349XbvW20u0aUJN/fw9Gq3RPf4lvre/7veqL88Egl/cwS/1sC/74OdIAUA94xucNi0BSYilM99d/tf3f1Su3D2ISC1M5bAVh+3FTthFRD5S1RGxTNtkJRbq+qX/SUjSVJ8+A9eDY621wDiMMS2ipqaGq6++mrfeeouCggJGjhzJGWecwZAhu5o5uOOOOxg2bBj//Oc/+eKLL7j66quZNWtWcPx9993H/vvvz6ZNm+osu7i4mLfeestKGltQxOfvly1hxzc76Hx8570ujm/o171W7eWv91SJ+mWdkp1S7y/7qMFAA9NKkiDSuC/4ltJ/Yv86xxggISOB/hObvCPTiGIOLEQk0wcLxph2pKioiIEDB9K/v7sJnX/++bz88st1AovFixdz0003ATB48GBWrFhBaWkp+fn5lJSU8K9//Yubb76ZP/3pT3WW/Ytf/IJJkybx/e+HN5lg9kTN9hqqy6upLnN/VWVVdYary6qpLvfpfnjr4q11a7kBge0Blv9qOctZHnlFYSRp96L50M9JuUmx/bIP+RxTKUBK43+9d0S1pU8tVSoVrsHAQkQOxzWK0gnoIyIHAZer6lVNnTljTNNbvXo1vXvvaqSvoKCAuXPn1pnmoIMO4sUXX+TII4+kqKiIlStXUlJSQn5+Pj//+c+ZNGkSmzdvrjPPK6+8Qq9evTjooIMwjqpSs6WmUYFB7fiqsiq0Mvqv/8SsRJJyk0jKc3/pA9LZ+mk9vwcFDvjnATEV2SckWVuKrV3+2PwWCyTCxVJi8WfgJHyrXaq6oLHthhtjWq9I9azCi4BvvPFGJkyYwLBhw/jOd77DwQcfTFJSEq+99hrdu3fnkEMOYfbs2cHpt23bxsSJE3nzzTebOvvNTmuU6oo9Cwyqy6t3Kz2oQ6gTGCTnJZPaKzU4nJSXRFKuSw8drv0fKQCo9/l7n1S6ft/qvJj4i+lRiKoWh91ool0axpg2pCnaOLjhhhtYvnx5sLSipKSE4cOHU1RUxD777NN8G1ePwM7AngUGZdXUbIp++5NkqRsEdEkmfWB6nSAgGBiEBRGJWYlxL/pvbc/fTfsXS2BR7B+HqG9X/Fp274jGGNNGNVUbB+vWrQvOX9tibrzeClFVAtsCexQYVJdX1/mSjSQhI6FOEJDaO5XMAzMbDAyScpNIyEhoVZX+Wtvzd9P+xRJYXIHr5a4Xrm2KN4GrmzJTxpjm01RtHDREA0rN5poGg4D66iM09LZBYnZinSAgY7+M3YKAiI8YcpNISG1fdQpa0/N30/5FbcfCd7X7pKpe2HxZis7asTAmvvamjYNAdaBuiUEjAoPqiurdWgusI5G69QkilA7UW/cgJwlJbD2lBsa0dXFrx0JVa0Skm4ik+K52jTHtSKQ2Dr746ReUv19O5pDMBl9trNnSQH2DVKkTBKTkp5AxOKPeCoihaYmdElvVIwVjTGxieRSyAvhARF4Bgu8tqeqf6p3DGNOqBaoDbJ63mS+v/nK3+gZaqax5aE1wOLFT3VcY0/qlkXRwA4GBH05MT2zuTTPGtLBYAotv/F8CkNW02THGNAVVZetnWymfVU7ZrDLK3y2P/naDwOGlh7v6Bsntq76BMaZpNRhYqOqtACKS5QZ1S5Pnyhiz17av2B4MJMreLqOqtAqAtAFpdD+/O3nH5fH1dV9TWRK5jYOUbinNnWVjTDsQS8ubBwBPA5398AbgYlX9rInzZoxphJ3rd1L+tg8kZpWxY9kOAFL2SSHvuLzgX1phWnAerVJr48AYE1exPAqZAvxSVd8BEJGjgYeBw5suW8aYhlRvrqbivYpgILF1oasClZidSO7RuRRMKCDvuDwyhmTUWwnS2jgwxsRbLIFFZm1QAaCqs32X6MaYZhSoDLBpzqZgILG5aDNarUiqkHNEDv0m9iPvuDw6HdKpUX07WBsHxph4iiWwWCYiv8U9DgG4EBruEs8HHw8AO4HZqjrVp58HnAZUAfeo6uc+/SFgi6r+X6O3wph2SGuULfO3BAOJiv9UENgegATIGpFF71/1Ju+4PLIPzyYxzd6+MMa0DrEEFuOAW4EX/fB7wE9imO8s4AVVfVVEngOm+vSzgbFAHnAHcKmInAPMAwY3Iu/GtCuqyvYvtwcDifJ3yqkuqwYgY0gGPS7tQd5xeeQclUNybnIL59YYYyKL5a2QMlz/II1VAHzqP4e+13YPcD+wFsgTkXzgYFy9DQssTIdSuboyGEiUzSpj52rXDl1qn1S6/qArecflkXtsLqk9Uls4p8YYE5tY3gp5CzhXVcv9cB4wXVVPamDWElxwMR/XBgYAqloEFInIQOAa4CigO/A74CAR2U9VvwzLw3hgPECfPn1i2jBjWqOqb6son73rzY3tS7YDkNQlibxj3Vsbucflkj4g3VqdNMa0SVH7CgEQkU9U9eCG0iLMlwlMBnYA7wMnq+pFInIqcDrQCbheVdf66fsC1zRUx8L6CjFtSc22Gire3/XmxpaPt4BCQmYCuWNyg4FEpwM7xb27bGOMiZe49RXiBUSkj6qu8gsvBKJHI4CqbqVuXYypPn0GMCPC9CsAq7hp2rRAVYDNH24OBhKb/rcJ3alIspA9Opu+v+9L7nG5ZI/KJiHFWrQ0xrQ/sQQWNwPvi8i7fngM/rGEMR2dBpSti7buenPj3QrXMZdAp2GdKLi2gNzjcsn9bi6JmfbmhjGm/Yul8uZMERkOjAYE+IWqbmjynBnTSm1fFvLmxtvlVK13TWWn75tO/oX55B6XS94xeSR3sTc3jDEdTyyVN48A5qvqayJyIfBrEblPVVc2ffaMaXk7S3dS9rYPJGaVs2OFbyq7RwqdT+rsAonj8kjrndbAkowxpv2L5VHIg7i3NQ4CrgceA57Cvc1hTLtTvama8nfLg4HE1kWuqeyk3CTXVPZ1vqnswfU3lW2MMR1VLIFFtaqqiHwf+IuqPioiP27qjBnTXGp21LDpf5uCgcSmDzdBDSSkJZBzZA7dx7qeQLOGZyGJFkgYY0w0sQQWm0XkJlxT3mNEJBGwh8emzdIaZfPHm4OBRMX7FQR2BCARskdm0+fGPq6p7MOsqWxjjGmsWAKL84AfAT9V1bUi0gf4Y9Nmy5j4UVW2fbEtGEiUzy6nutw1lZ15QCY9LndNZecelUtSdiyXhDHGmPrE8lbIWuBPIcOrcHUsjGm1dhTvCAYSZbPK2LnGNZWd1jeNrme7prLzjs0jJT+lhXNqjDHti/08M+1C1cYqyt7ZFUhsX+qayk7ulkzuse6tjbzj8kjvn97COTXGmPbNmv4zDZo5cyaDBg1i4MCB3HXXXbuNLysr48wzz+TAAw9k1KhRLFq0CIDi4mKOOeYY9t9/f4YOHcp9990XnGf+/PmMHj2aYcOGMWLECIqKihqVp5qtNWycuZGvr/+aecPn8UG3D1h87mJKnyklfb90BvxpACMWjODwtYczdPpQel7W04IKY4xpBg32FdLaWF8hzaumpob99tuPt956i4KCAkaOHMm0adMYMmRIcJrrr7+eTp068fvf/54vvviCq6++mlmzZrFmzRrWrFnD8OHD2bx5M4cccggvvfQSQ4YM4cQTT+QXv/gFp5xyCjNmzGDSpEnMnj273nwEdgbYVLQpWCKxac4mtEqRFCH7sOxgiUTWyCwSki1eNsaYeIprXyG+gaxbgEI/vQCqqv33JpOmbSgqKmLgwIH07+8O9/nnn8/LL79cJ7BYvHgxN910EwCDBw9mxYoVlJaW0qNHD3r06AFAVlYW+++/P6tXr2bIkCGICJs2bQKgoqKCnj171lmvBpQtC7cEA4ny98oJbA24prKHd6LgF64tiZwjc0jMsDc3jDGmtYiljsWjwC+Aj4Caps2OaW1Wr15N7969g8MFBQXMnTu3zjQHHXQQL774IkceeSRFRUWsXLmSkpIS8vPzg9OsWLGCTz75hEMPPRSAe++9l5NOOon/+7//IxAI8MEHH7Dtq227Aol3yqna4JvKHpTOPj/ex725cXQuyZ3tbWdjjGmtYgksKlT19SbPiWmVIj0qC29t8sYbb2TChAkMGzaM73znOxx88MEkJe06tbZs2cLZZ5/NvffeS3Z2NgAPPvggk343iWPSj+HZR57l3MHn8sdK9xZzSq8UOp/a2QUSx+aSVmBNZRtjTFsRS2Dxjoj8EXgRqKxNVNWPo80kIpnAA8BOYLaqTvXp5wGnAVXAPcASXLPh6cA2Vb1iD7bDNJGCggKKi4uDwyUlJbs9tsjOzubxxx8HXCDSr18/+vXrB0BVVRVnn302Y8eO5fRjT2fDyxsom1XGY399jDMDZ/I5nzMidwS/rfkt+/51X/fmxn7p1lS2Mca0UbEEFof6/6GVNhQ4toH5zgJeUNVXReQ5YKpPPxsYC+QBd6jqpcDlACLymIgkqGog1g0wTWvkyJEsXbqU5cuX06tXL6ZPn86zzz5bZ5ry8nIyMjJISUnhkUceYcyYMWRnZ1O9rZqLvn8RPct7ctRzR/HB9R9AABLSE+ie3p11Y9dx6vhTmbtxLoNuGkSvq3q10FYaY4yJl1gayDpmD5ddAHzqP4fWzbgHuB9YiwsuEJEhwO3AmkhBhYiMB8YD9OnTZw+zY/ZEUlISkydP5qSTTqKmpoZx48YxdOhQHnroIQCuuOIKPv/8cy6++GISExPZd599mfjdicw/bj7/+c9/eK7qOfrTn/9m/JfEbonc8otbOOfn5/DUh08xYcIE7phzB2lpaUyZMqWFt9QYY0w8NPi6qYjkAL8Hxvikd4HbVLWigfkuAsp8d+vTVfX8sPEDgWtU9echaQ8Ad0frkt1eN21epVNLWXbzMipXVZLaJ5X+E/uTP9ZVylRVti12TWWXzSqjfHY5NZtcDJl5YGbwFdCcMTkkZVlbbMYY01bF9XVTXDfpi4Af+uGLgMdxjzqieRGYLCKnAa+KyNOqepGInAqcDnQCrheRnsBNuMa6qoHiepdomlXp1FKWjF9CYJsrRKpcWcmSy5ZQNruMwLYA5W+Xs3Otbyq7fxrdz3O9gOYek0tKd2sq2xhjOqJYSizmq+qwhtKai5VYNJ//9f0flSsrI45Lzk8m71hXIpF7XC7pfa1VS2OMaa/iXWKxXUSOVNX3/cKPALbvTQZN21C5KnJQgcDhaw63NzeMMcbsJpbA4krgSV/XQoBvgUuaMlOm5agqZbPKWPmHle7dnwhS+6RaUGGMMSaiWN4KmQ8cJCLZfnhTU2fKND9V5ds3vmXlbSvZ9L9NpPRKoftF3dnwwgYC23e9qJOQkUD/idaauzHGmMjqDSxE5EJVfUZEfhmWDoCq/qmJ82aagaqy8bWNrLxtJZvnbSa1Tyr7PrgvPX7Sg4TUBEpPqv+tEGOMMSZctBKLTP8/qzkyYpqXBpQNL21g5R9WsmX+FtL6pTHokUHkX5RPQsqu3kHzx+ZbIGGMMSZm9QYWqvo3///W5suOaWpao6x/YT0rb1/J1kVbSd83ncFPDKb7j7pbd+PGGGP2WoPfJCIySUSyRSRZRGaJyAYRubA5MmfiJ1AdoHRqKR8e8CGLz1+M1ij7T92fUZ+PYp8f72NBhTHGmLiI5dvkRF9h83tACbAfcH2T5srETaAqwJon1vDh/h/y+YWfI8nCkOeHMHLRSPJ/lI8k2tsdxhhj4ieW102T/f9TgWmq+q29atj6BXYGWPvkWlbduYody3fQ6eBODH1xKF2/3xVJsONnjDGmacQSWLwqIl/gGsW6SkS6ATuaNltmT9XsqGHtY2tZddcqKosryRqVxcC/DKTLaV2s7QljjDFNLpZ2LG4UkbuBTapaIyJbge83fdZMY9Rsr2HNw2tYdfcqdn6zk+zDsxn08CDyTsyzgMIYY0yzidaOxbGq+raInBWSFjrJi02ZMRObmq01fPPQN6z64yqqSqvIOSqH/Z/en9xjci2gMMYY0+yilVgcBbyN64k0nGKBRYuq3lzN6r+upuT/lVC1oYq84/MofL6Q3DG5LZ01Y4wxHVi0dix+7///pPmyYxpSXVFNyf0llPy5hOpvq+l8SmcKf1tIzmE5LZ01Y4wxpuE6FiJyBzBJVcv9cB5wnar+poH5MoEHgJ3AbFWd6tPPA04DqoB7gABwA5AGLFDVu/d4a9qxqm+rKLmvhJL7SqipqKHL6V0o/G0h2SOzWzprxhhjTFAsb4Wcoqq/rh1Q1TIRORWIGlgAZwEvqOqrIvIcMNWnnw2MBfKAO1T1UmAcgIjY45UwOzfspORPJayevJqazTV0Pasrhb8pJOtga2ndGGNM6xNLYJEoIqmqWgkgIulAagzzFQCf+s81Ien3APcDa3HBBX655wNvRlqQiIwHxgP06dMnhlW3fTtLd1J8TzGrH1xNYFuAbj/sRuHNhXT6TqeWzpoxxhhTr1gCi2eAWSLyOK7S5jjgyRjmK8EFF/MJaeFTVYuAIhEZCFwDwaCisL7HIKo6BZgCMGLECI1h3W1W5TeVFP+xmG/+9g2BygDdL+hO4c2FZO6f2fDMxhhjTAsT1Ya/p0XkZOB4QIA3VfWNGObJBCbjGtN6HzhZVS/yj1FOBzrhmgbvAbwKvAZsU9Vf1rNIwAUW8+bNazDPbc2O4h2sunsVax5Zg1Yr+1y0D31+3YeMfTNaOmvGGGM6OBH5SFVHxDJtLCUWAJ8D1ar6bxHJEJEsVd0cbQZV3QqEvlEy1afPAGaEpK/FlWx0SNtXbGfVnatY+/haAPa5ZB/63NiH9P7pLZwzY4wxpvFieSvkMlz9hs7AAKAX8BBwXNNmrX3b9tU2Vt25itKnSiEBelzWgz439CGtT1pLZ80YY4zZY7GUWFwNjALmAqjqUhHp3qS5ase2LdnGyokrKZ1aSkJKAj2v6kmfX/UhtVcs9WGNMcaY1i2WwKJSVXfWNg8tIkm4SpymEbZ+tpWVt69k3XPrSEhPoOAXBfT+v96k7mMBhTHGmPYjlsDiXRH5NZAuIicAV+EqW5oYbFmwhZW3r2T9C+tJ7JRInxv6UPDLAlK6pbR01owxxpi4iyWwuAG4FNcmxeW4ipePNGWm2oPNH21mxR9WsPHljSRmJ1L420IKJhSQ3CW5pbNmjDHGNJmEaCNFJAH4VFUfVtVzVfUc/7ndPgqZOXMmgwYNYuDAgdx11127jS8rK+PMM8/kwAMPZNSoUSxatCg4bty4cXTr3I2BWQP5aMRHVLxXQd9b+zJ65Wj63dbPggpjjDHtXtTAQlUDwAIR6RDNXdbU1HD11Vfz+uuvs3jxYqZNm8bixYvrTHPHHXcwbNgwFi5cyFNPPcWECRMAKH+/nCM/PZKJZRMJbA/Q745+jF4xmr6/60tyrgUUxhhjOoZYHoX0AD4TkSJga22iqp7RZLlqIUVFRQwcOJD+/fsDcP755/Pyyy8zZMiQ4DSLFy/mpptuAmDQoEEs+2IZs46cReIHiQzqPghugoyXMii8qbBFtsEYY4xpSbEEFrc2eS5aidWrV9O7d+/gcEFBAXPnzq0zzUEHHcQ//vEPhmwfwr+u/xervlnFyp0rOeHPJ9BzfE+K1xXDK82dc2OMMaZ1qDewEJE04ApgIK7i5qOqWt1cGWsJkaqO1L5mWzt+/PDxXHvltYy6dxQDMwbynT7fYdjfh9F7VO/d5jXGGGM6mmglFk8CVcB/gFOAIcCE5shUSykoKKC4uDg4XFJSQs+ePVFVNr6ykRV/WMGWj7ZwU+FNFE4sJP/ifAYMHsDAwQNbMNfGGGNM6xEtsBiiqt8BEJFHgaLmyVLLGTlyJEuXLmX58uX06tWL6dOnc/8l9zPv4HlsXbCVtAFp7DN5H/pe0pe0zDQefvhhxowZQ3Z2dktn3RhjjGkVor0VUlX7ob0/AqmVlJTE5MmTOemkk9iv934cvvZwAr8M8MLaF5h7yVxGfTGKsuFlfGfYdxg8eDCvv/469913X3D+Cy64gMMOO4wlS5ZQUFDAo48+2oJbY4wxxjS/ertNF5Eadr0FIkA6sM1/VlVtkZ/p8e42vXRqKctuXkblqkpSe6fS+dTOlL9TzvYl28kYkkHhbwvpfm53JFEaXpgxxhjTDsWl23RVTdzLTGQCDwA7gdmqOtWnnwechisRuUdVPxeRvwGH1z56aS6lU0tZMn4JgW0BACpXVbLmoTWk9E5h6AtD6XpmVyTBAgpjjDEmVlEbyNpLZwEvqOplQGibF2cDPwVuAq4DUNXLgSVNmJeIlt28LBhUhBIRup3dzYIKY4wxppFiacdiTxXgXlMFqAlJvwe4H1gL5MWyIBEZD4wH6NMnfo2AVq6qjJxeHDndGGOMMdE1ZYlFCS64qLMeVS1S1SuAZ4DiSDOGU9UpqjpCVUd069YtbhlM7RO5y/L60o0xxhgTXVMGFi8CZ4vIg8CrIvI0gIic6tN+D9zl0yYCB4vIQyLSbN/q/Sf2JyGj7i5IyEig/8T+zZUFY4wxpl2p962Q1qpJ3wrpk0r/if3JH5sft+UbY4wxbV1j3gppc4GFiKwHVjbBorsCG5pgue2F7Z/2z45x+2fHuP1rqmNcqKox1UVoc4FFUxGRebFGYx2R7Z/2z45x+2fHuP1rDce4KetYGGOMMaaDscDCGGOMMXFjgcUuU1o6A62c7Z/2z45x+2fHuP1r8WNsdSyMMcYYEzdWYmGMMcaYuGnKJr1blSidop0KXAPMUNXJPu0uIAPYpqo3tlCWm12UfXQjMADYB7hSVUtE5GvgLeBjVW3xojcTmyjH+BZgf6AMuE1Vv+mo10FbF+UY/xQYCfQGFqrqTXYdt00i0h+4GchR1XNC0g/A9cMFcKeqLmqJ67gjlVhE7BRNVWcAk2qHRaQPkKyq1wKJItK72XPacurbR3f5tMeAY3zyFiCdGJtlN61GfZ0DVuO+iKqA8g5+HbR19V3Hj/ruFL4EnvDJdh23Qaq6TFV/GmHUBOBq4CrgZy11HXeYEgvq7xQtXC92XWSr/Hwd5aKrdx+JSCfgh/jO4ICDAQH+BbzeXBk0e62+Y3yHqgZE5AzgUuBDOu510NZFu47TgH6qWtubtF3H7UuOqpYDiEgWLfR91pFKLCJ2ihbB6pDpevv5OoqI+0hEsoEHgV+p6mYAVQ2oag2wQ0Q60nnU1tXXOWDAf1wHdKJjXwdtXbR73Tm4fpwAu47boQoRyfH37M200HXcYd4K8c8dJwM7gPeBk1X1IhE5DPdMKg+4V1X/ISJ3AqlApareVO9C25ko++hFIBl3kj7v/9/gZ1uoqve2QHbNHohyjH+Nu/F0Ba5V1TUd9Tpo6+o7xn7cy8D5qrpdRAZh13GbJCJdgInACcAjwBB/HR8AXI8rhZrk61g0+3XcYQILY4wxxjQ9K/oyxhhjTNxYYGGMMcaYuLHAwhhjjDFxY4GFMcYYY+LGAgtjTJCI5IrIVQ1M01dEFjVXnowxbYsFFsaYULm4VvviRkQS47k8Y0zrZoGFMSbUXcAAEZkvIn/0f4tE5FMROS98YhFJ9NN8KCILReRyn360iLwjIs/iW4EUkZdE5CMR+UxExocsY4uITBSRBSIyR0TyfXq+iPzTpy8QkcN9+oUiUuTz+DcLXIxpXSywMMaEuhH4WlWHAXOAYcBBwPHAH0WkR9j0PwUqVHUkroOry0Sknx83CrhZVYf44XGqeggwArjWN/IDkAnMUdWDgPeAy3z6X4B3ffpw4DMR2R84DzjC57EGGBuvjTfG7L2O1FeIMaZxjgSm+SafS0XkXVzwsDBkmhOBA0WktofFHGBfXIdmRaq6PGTaa0XkTP+5t59uo5/2NZ/+Ea41QYBjgYsBfB4qROQi4BDgQxEB14HWuvhsrjEmHiywMMbUR2Kc5meq+kadRJGjga1hw8cDh6nqNhGZDaT50VW6qwngGqLflwR40poYN6b1skchxphQm4Es//k94Dxfj6IbMAYoCpv+DeBKEUkGEJH9fF8V4XKAMh9UDAZGx5CXWcCVfrmJvmOlWcA5ItLdp3cWkcLGbaIxpilZYGGMCVLVjcAH/nXSw3CPPRYAb+N6t10bNssjwGLgYz/P34hc4jATSBKRhcAfcPU3GjIBOEZEPsU9IhmqqouB3wBv+mW9BYTX+zDGtCDrhMwYY4wxcWMlFsYYY4yJGwssjDHGGBM3FlgYY4wxJm4ssDDGGGNM3FhgYYwxxpi4scDCGGOMMXFjgYUxxhhj4sYCC2OMMcbEjQUWxhhjjIkbCyxaMRG5RETeb+l8NBUR+a6ILGnpfNRHRH4tIo808zpVRAY28Tpmi8ilezjvlSJSKiJbQro939N89PXb26Y7QxSRISIyL2R4hYgc38J5itu+beh8EZEzRaTYnxMHi0iRiAzd2/W2NBEZKyJvNtO6nhCR25tjXc2hzQUW/mBvifCnIvK7ls6fcWL5glTV/6jqoObKU2Op6h2qukdfwO2R72jsT8CJqtrJ9ysSOr5dBAp74A/APS2diRZ0D3CNPyc+8cO3NceK4/WFHOncVdWpqnri3i473vbmh0FzrafNBRb+YHcK/QN+DpQCDzd2eR3wJtgqtPb93trz10LycV2df9bSGWktx0dEegDHAC/Febktvn3ixPIdUUjdc+IVXOdx1jlcR6WqbfoPOBjX1fPRIWk5wKPAGmA1cDuQ6MddAnwA/Bn41o/LAZ4C1gMrcb0nJkRZZypwL/CN/7sXSPXjjgZKgOuAdT4PPwmb9x5gFS4YeghIr2c9lwDvhwwfDnwIVPj/h4dNu8zvi+XAWJ8+EHjXz7MBeK6edfUFFPgJUAyUAVcAI3E9XJYDk8PmGQd87qd9Ayj06e/5ZW0FtgDnheyXG4C1wNO1aSHL6w286I/Dxtr1NWIbZuJ+OYWmLQDO8p/v89u2Cddb5ndDprsFeAF4xo+/1Kc9EzLNGbgbaDkwG9g/ZJwCA0OGnwBu95+7Aq/5+b4F/kM951focqKdK36/fy9kviS/b4b74dHAf/06F1D3+pgNXNqYcxvYzx9P9cf07QjzrgoZvwXXO2oC7npaibsengJyws65pD28bgfgel3d6Ld9KpAbkp8VwP/hzt8K4DkgLWT894H5/nh/DZzcUD4ibPPFwL/D0lYAx/vPg3HX4/l++Ht+neX++BwYNt8NPr+VuPNegR/7fbsBuDlk+gTgRp/3jcDzQOdI+7YR99PZwES/r7f7PMwG7gSK/H58Gejsz4st7LrWvw5ZzlvAj+tZRyznRMRtDlvOeKAK2Onz8apP7wn8A3cfWQ5cGzLPKGCeP+alwJ+inLuXUPf+q7h74lLcPe+v7OrIMxH4fz6/y4Frou1/3PfWx7j79XPAdHbdL/Jw94v1fj2vAQV+3ESgBtjh81l7j4x2b4u4zdHuE/WtJ+bzqDETt7Y/IBd3Ud0Qlv4SrvvmTKC7vyAuD7lBVQM/w92M0/2J/TKQ5U/sL4GfRlnvbbhun7sD3fyB+YMfd7Rf/m1AMnAqsA3I8+PvxUX0nf36XgXurGc9wRPbT18GXOTzfYEf7uK3cxMwyE/bA9fFNMA04GbcxZwGHFnPuvr6C+EhP92J/qR6yW9nL9xN4Cg//Q+Ar4D9fX5+A/w37CIM/aKt3S93425I6YQEFrgLcwHuiyMzNK+N2IaLgQ9ChofgLpjaoO9Cv7+ScIHfWvwXDS6IqPLbleDzdws+sGDXF+sJ/rj+ym9/Sj3b+wS7bhR3+v2a7P++i78hRdiG0MDiXuo5V4DfAVND5jsN+MJ/7oX7ojnVb8sJfribHz+b+gOLaOd2X6LfLHcbjws+vwL6A51wgePTkaan8dftQL9tqT6v7wH3hqx7hV9GT78PPweu8ONG4b4kT/D7qBcwuKF8RNjmPwJ/DUtbARwPDMd9YX3Ppw/HXUOH4s73H/tpU0Pmm48LsNND9s/DfvggXMCxv5/+5/5YFfh98DdgWj379gHctRDpb2FI3mf7PA/1+znZp60GDvD75B/UDbjrnPs+7S+EfIGFjYvlnIi4zRGW9QT+OvPDCbgv1t8BKX4dy4CT/Pj/ARf5z52A0VHO3UvYPbB4Dfe90wf3xV8bjF4BLPbHIg/4d/jyQpaTgguqfuH37zm4e0/t/aILcDaQgbvu/w68FHaMLg1bZrR7W33bvMf3iYb+Wjw42NM/QHDBwMuE3KRxxbWVhJQC4L6E3wk5WVaFjEv00w8JSbscmB1l3V8Dp4YMnwSs8J+PxkX6oSfoOlxkKLgvpwEh4w4DlteznuCJjQsoisLG/89Pk4m7QZxNWOkHLmiago94o2xTX38h9ApJ2wicFzL8D+Dn/vPrhARf/sTcxq5Si0iBxU7q/mI8ml2BxWG4CzXShRjrNmT5/Vubh4nAY1GmLwMO8p9vAd4LG38LuwKL3wLPh23vanZF+NECi9tw5+nAaPkPXU5D54qfZjOQ4YenAr/zn2/A36hD5n0D/wuS6IFFtHO79hxpTGAxC7gqZHgQ7iaaFDo9jbxu61n/D4BPQoZXABeGDE8CHvKf/wb8OcIyouYjwvQPA3eFpa0AbsWV0B0Tkv4gPkgLSVvCrmB9BTAuwv4sCEkrYlfpx+fAcSHjekTatw2dc2H5mQ3cFiHtrpDhIbhrubY0KVJgUe+1F+M5EXGbIyzrCeoGFoeGnyfATcDj/vN7/th0jeHcvYTdA4sjQ4afB270n98mJPjEBZb1BRZjcKWBod9b/w3djrDphwFlYccj6hc+de9t9W3zHt8nGvprc3UsQtyAi6B/rH4veIW4KHCNiJSLSDnuJtI9ZJrikM9d2RVB1lqJi+YQkYdCKoj+2o/vGWH6niHDG1W1OmR4Gy5S7IaLQj8KydtMn96Q8HUG86mqW3GPG67w2/0vERnsp/kV7kuqSEQ+E5FxDaynNOTz9gjDnfznQuC+kO341q+nV5Rlr1fVHfWM6w2sDNtvtWLaBlXdDPwLON8nnY/7wgVARK4Tkc9FpMLnOQd3/GuFnhfh6ux/VQ346aNtb60/4n6hvSkiy0TkxhjmiXquqOpXuC+W00UkA/eY5lk/byFwbu18ft4jcV88DWno3G6sSMurDSRCNfa6RUS6i8h0EVktIptwj7FCjye4X261aq9DcOfb1xHyG0s+QpXhAtpwV+BK8N4JW/Z1YcelN3X3b6RzsL5tKAT+GbKsz3HF1+H7trEi5SE0bSVuH4Xv61BZuB87kcRyTtS3zQ0pBHqG7eNfhyz7p7jSxy9E5EMR+V6My20oXz2pu48aupesDvveCu4PEckQkb+JyEp/Xr8H5IpIYn0LbODeVt827819Iqo2GViIyNG4ovFzVLU8bHQx7hdHV1XN9X/Zqhr6+lPoAd2Ai5YLQ9L64H6NoqpX6K6Konf48d9EmP6bGLK+AfflPDQkbznqKqA2JHyd4fl8Q1VPwJ0UX+ArsqrqWlW9TFV74kpiHmjobY0YFeMi9NyQv3RV/W+UeTTKuGKgT6RKa43chmnABSJyGK4o9R1wr7bigtEf4h5L5eKKwiXG/NXZ/yIiuC+F1T5pGy4QqLVPSP43q+p1qtofOB34pYgcF2VdENu5Mg33a/r7wGIfbIDbl0+HHZtMVb2rgXXutp3Efm5D5P0XaXnV1A1Ya/PcmOsW3CMmxdVTyMYVBwuxKcbV0YiU3lA+Qi3E3bTDXYE7n/8ctuyJYcclQ1WnhUwT7RyMlNdTwpaXpqqrwycM+4EU/hdeGTdSHnqHfO6Du2duiJK3/XGPNiOJ9ZyIRXhei3GleqH7JEtVTwVQ1aWqegEuULwbeEFEMiMsp7HW4B6D1Opd34R+2l7+HlKrT8jn63ClOIf683qMT6+dvk5eG7q3Rdnmhu4Te7xP2lxg4WsaT8cVyX8SPl5V1wBvAv9PRLJFJEFEBojIUZGWp6o1uCKtiSKSJSKFwC9xv37qMw34jYh0E5GuuOd50aavXVcA94X/ZxHp7renl4ic1NC8wAxgPxH5kYgkich5uCLJ10QkX0TO8CdLJa6yTY1f/rkiUnvCl+FOlpoY1teQh4CbxL+vLiI5InJuyPhS3PPNWBXhLri7RCRTRNJE5Ai/7MZswwzcTes2XCXPgE/Pwt281gNJ4l5Nzm5E/p4HThOR48S9dnkdbl/XBlLzgR+JSKKInAwEzzcR+Z6IDPQ3kk0+71GPQYznynRcXZgr2VVaAe5cPF1ETvL5SRORo0P2YTR7dG5764EAdY/7NOAXItJPRDoBd+COS52SqcZet14W7lwvF5FewPUx5hNc5cyf+OOZ4Pft4D3Ix1vAcBFJC0vfDJwMjBGR2hv1w8AVInKoOJkicpqIRCrxiMVDuPtWIYA/Zt+PNGHYD6Twv1janLhQXHsdGbhr6wV/79yNiKQCh+D2TSQxnRMxCr/PFAGbROQGEUn35/8BIjLS5+1CEenmr69yP08Nkc/dxngemODPo1zcF319/oe7F13r7+Vn4er81MrC/agoF5HOwO/D5g/f5qj3tijb3NB9orH38KA2F1gAl+GKte6LEHk/5Ke5GPd4YzHui+gFohfv/Az3PHsZrvZsV+BGiVBk7b8c8nBf6t/gih8/Bm4XkQnA40C+iPy8nnXdgCsWnyOumOvfuOg0KnVtBnwP94W2Efd44HuqugF3HK/z+fkW96V2lZ91JDBXRLbgKgJOUNXlDa0vhvz8Exf9TvfbsQg4JWSSW4AnxRWx/TCG5dXgfs0PxFUeK8E93mnUNqhqJa4y2PHU/bJ9A1cv5EtcseMOohdXhi93Ce4X8f24X2qnA6er6k4/yQSfVg6Mpe7rh/vijvMW3E3lAVWdHcNqo54r/kvwf7i3hZ4LSS/GlWL8GnezKcZ96cZyvd+OuwYWAp/iz+0Y5kNVt+HfKPDHfTTwGO4NoPdwteV34K63SBp73d6KqxBZgXsE9mIs+fR5LcK9AfVnP/+77PoVHXM+VLUU93x9ty90X5p6AnCKiPxBVefh7l+T/XK/wj3H31P34a6HN0VkM64i56F7sbxonsbVZ1iLq0B9bZRpz8DVUauvpKsx50RDHgWG+PPtpZD7yDC/7A3AI7hHA+CCvc/8veQ+XN2NHfWcu43xMC4gXQh8gvuBU02EHxD+nnEW7tiX4e5zoefuvbjS1g24YzozbBH3AeeISJmI/IWG7231bXND94nw9cSs9lUZA4h7hvUl7mZQgnul8wJVXRwyzam4i+BU3EV8n6oeKiIH4H5BjsJVbJoJXKmqS5t3K4wxzUlEhgBPAqPUbqiIyFxcxe5FLZ2XliIip+AqCoc/vu4Q2mKJRVMaBXylqst8VDmd3X+JfB94Sp05uEo1PXDPFOeo6jZfpPcucGZzZt4Y0/xUdbGqjrSgwlHVQztaUOEfu5zqH230wj2++GdL56ulWGBRVy/qFiGVsHut//qmWYR7ntrFP4c8legVeIwxxrQPgns0V4Z7FPI5rn5Sh9Tizca2MpFqlIf/Cok4jap+LiJ34yosbcHViN6TykjGGGPaEF9HY2RL56O1sBKLukqoW8pQwO6v2tU7jao+qqrDVXUMrhKl1a8wxhjToVhgUdeHwL7+NagUXANLr4RN8wpwsX9dbDRQ4WvnE/JaYB9crd9pGGOMMR1Im3srpGvXrtq3b98mW35FRQXFxcWoKl27dqVHjx6sX78egG7duqGqFBcXU1FRQUJCAn379iUzMxOAJUuWUF1djYhQUFBAdnZjmkkwxhhjWqePPvpog6rG0kp009WxENdY0wO4Vy9nq+pUn34Aru12cC3nLcP1A7EJ+EZVo74z37dvX+bNmxe3fJaWTmXZspuprFxFamof+vefSH7+2Lgt3xhjjGnrRCS8S4l6NeWjkLNwrbNdhmswpdYE4GpcA04/w72muURVrwJ6iEizvUlRWjqVJUvGU1m5ElAqK1eyZMl4SkunNjivMcYYY3bXlIFFAbteywxtfSxHVctVtQLXFOknQKqI/AnXOUssnTrFxbJlNxMIbKuTFghsY9mym5srC8YYY0y70pSBRQm7OmUJXU+FuH4lsoHNqhpQ1d+o6i9x7wAvC1+QiIwXkXkiMq+2vkM8VFaualS6McYYY6JrysDiReBsEXkQeFVEnvbp9wF/wbWXfz+AiDwgIg8D81R1XfiCVHWKqo5Q1RHdusVUdyQmqal96hmjLFhwAuXl78VtXcYYY0xH0ObeChkxYoTGq/JmbR2L0MchCQnpdOnyA8rLZ1FVtY6cnKPo2/d35OYeg0iktrGMMcaY9k1EPlLVEbFM26HbscjPH8ugQVNITS0EhNTUQgYNepihQ59l9OjlDBx4L9u3f8mCBcfxySff5dtv36CtBWLGGGNMc+rQJRaxqKnZwdq1j7Fq1V1UVhaTlTWKvn1/R+fOp1oJhjHGmA7BSiziKDExjV69ruLQQ79iv/2mUFW1jk8//R4ffTSC9etfQjXQ0lk0xhhjWo1GBRYikiciBzZVZlqzhIQUeva8jFGjvmTQoMeoqdnEZ5+dybx5w1i37u8WYBhjjDHEEFiIyGwRyRaRzrgeOx/3bU50SAkJyfTo8RNGjvycwYOfJhDYyeLFP+TDD79DaemzqNY0vBBjjDGmnYqlxCJHVTfhWtJ8XFUPAY5v2my1fgkJSeyzz4WMGvUZ++8/DRA+/3wsRUVDWLv2KQIB6zHdGGNMxxNLYJEkIj2AHwKvNXF+2hyRRPLzz2fkyIUMHfoCCQnpfPHFjykqGsSaNY8SCOxs6SwaY4wxzSaWwOI24A3gK1X9UET6A0ubNlttj0gC3bqdzYgRn3DAAS+TlJTHkiWXMnfufqxe/RCBQGVLZ9EYY4xpcva6aRNRVb79diYrV97Gpk1zSEnpRZ8+N9Kjx6UkJqa1dPaMMcaYmMX1dVMRmeQrbyaLyCwR2SAiF+59Nts3EaFLl1M4+OD/cuCBb5Ge3o+vvvoZc+f2o7j4z9TUbGt4IcYYY0wbE8ujkBN95c3v4ToW2w+4vklz1Y6ICJ07H8+wYe9x0EHvkJGxP19//UvmzOnHqlWTqK7e0tJZNMYYY+ImlsAi2f8/FZimqt82YX7aLREhL+9ohg17m2HD/kOnTsNYtuwG5szpy8qVd1Bdvamls2iMMcbstVgCi1dF5AtgBDBLRLoBO5o2W+1bbu6RHHTQGwwfPofs7NEsX34zc+YUsmLFrVRVlbV09owxxpg9FlPlTRHJAzapao2IZADZqrq2yXMXQVupvNkYmzd/xMqVt7Nhw0skJmbTq9fP6N37FyQnd2nprBljjDFxr7yZDFwEPCciLwA/BTbGMF+miDwpIg+LyNiQ9ANEZKr/O0BEEkXkWT/dkyLS4fovyco6hAMO+CcjRsync+cTWbXqDubM6cvXX9/Azp3rWjp7xhhjTMxi+RJ/EDgEeMD/DfdpDTkLeEFVLwPOCEmfAFwNXAX8DEgHKv10W4DMmHPfznTqdBBDh/6dkSM/pUuX0yku/iNz5vTlq6+uo7JyTUtnzxhjjGlQLIHFSFX9saq+7f9+AoyMYb4CoNh/Du1AI0dVy1W1AsgCtgEqIv8CUlV1c2M2oD3KzBzKkCHPMmrU53Trdi4lJfcxZ04/li69lh07Slo6e8YYY0y9YgksakRkQO2Ab3kzlp62SnDBRfh6KkQkR0Sygc24EpAVqnoasEJEhoUvSETGi8g8EZm3fv36GFbdPmRkDGL//Z/k0EOXkJ9/Id988yBz5w7gyy+vZMeOlS2dPWOMMWY3DVbeFJHjgMeBZYAAhcBPVPWdBubLBCbj3iB5HzhZVS8SkQNw7WAIMMkv91GgDOjql721vuW2x8qbsdq+fQWrVt3F2rWPAco++1xCnz43kZ7ev6WzZowxph1rTOXNWN8KSQUG4YKBL1S1xTq+6MiBRa0dO4opLp7EN988jGo1+fkXUlj4azIy9mvprBljjGmH4hJYiMhZ0WZU1Rf3IG97zQKLXSorv6G4+B6++cZ1cta9+/kUFt5MZuaQls6aMcaYdiRegcXjUeZTVR23J5nbWxZY7G7nzlKKi/8fq1c/QCCwjW7dzqGw8Dd06nRgS2fNGGNMOxCXdixU9SdR/lokqDCRpaTkM2DAJEaPXkGfPjfx7bczmTfvIBYtOovNmz/Z6+XPnDmTQYMGMXDgQO66667dxqsq1157LQMHDuTAAw/k448/BmDJkiUMGzYs+Jednc29994LwPXXX8/gwYM58MADOfPMMykvL9/rfBpjjGl5Ha4xqvYsJaUr/ftPZPToFRQW/p6ysrf56KPhfPrp6WzaVLRHy6ypqeHqq6/m9ddfZ/HixUybNo3FixfXmeb1119n6dKlLF26lClTpnDllVcCMGjQIObPn8/8+fP56KOPyMjI4MwzzwTghBNOYNGiRSxcuJD99tuPO++8c+823hhjTKtggUU7lJzcmX79buGww1bSr9/tVFT8l48/PpQFC06mouK/jVpWUVERAwcOpH///qSkpHD++efz8ssv15nm5Zdf5uKLL0ZEGD16NOXl5axZU7dBr1mzZjFgwAAKCwsBOPHEE0lKSgJg9OjRlJRY+xzGGNMeWGDRjiUl5VBYeDOjR6+gf/+72LLlYz755Ajmzz+e8vJ3Y1rG6tWr6d27d3C4oKCA1atXN3qa6dOnc8EFF0Rcx2OPPcYpp5wS62YZY4xpxWLpKyRDRH4rIg/74X1F5HtNnzUTL0lJWfTpcwOjRy9nwID/x9ati5g//2g++eQoyspmEe2V40jjRKRR0+zcuZNXXnmFc889d7fpJk6cSFJSEmPHjt1tnDHGmLYnlhKLx4FK4DA/XALc3mQ5Mk0mMTGT3r1/yejRyxk48C9s3/4VCxYczyefHMHGjTMjBggFBQUUFxcHh0tKSujZs2ejpnn99dcZPnw4+fn5deZ78sknee2115g6depuwYoxxpi2KZbAYoCqTgKqAFR1O66hLNNGJSamU1DwMw499Gv23fcBKitX8+mnp/Dxx6PYsOHVOgHGyJEjWbp0KcuXL2fnzp1Mnz6dM844o87yzjjjDJ566ilUlTlz5pCTk0OPHj2C46dNm7bbY5CZM2dy991388orr5CRkdG0G2yMMabZxBJY7BSRdEABfL8hLdbypomfxMQ0evW6kkMPXcp++z1MVdVGFi06g48+OoT1619ENcDGjc9x9dVbGTOmP/36ZXLqqYMYOnQoDz30EA899BAAp556Kv3792fgwIFcdtllPPDAA8F1bNu2jbfeeouzzqrb3to111zD5s2bOeGEExg2bBhXXHFFs267McaYphFLXyEnAL8BhgBvAkcAl6jq7CbPXQTWQFbTCQSqWLfuWVaunMj27UtJSSmgqmodqjuD0yQkZDBo0BTy861OhDHGdBRN0VdIF2A07hHIHFXdsHdZ3HMWWDS9QKCa9euf54svfoxq9W7jU1MLOeywFc2fMWOMMS0iLi1vhknD9T66CRgiImP2NHOm9UtISCI//0eo1kQcX1m5kvXrX6Kqqrx5M2aMMabVS2poAhG5GzgP+AwI+GQF3mvCfJlWIDW1D5WVKyOMET777EwggaysQ8jLO47c3OPIyTmCxMT05s6mMcaYVqTBwAL4ATCosV2li0gm8ACwE5itqlN9+gHATX6yO4H1wK1++FTgAFXd1Jh1mabRv/9EliwZTyCwLZiWkJDBvvs+QHp6f8rKZlFePovi4ntYteouRFLJyTk8GGhkZY0gISGWU8wYY0x7EctdfxmQTOPfBDkLeEFVXxWR54CpPn0CcDWu1GOSql4OXCEi3YB0Cypaj9oKmsuW3Uxl5SpSU/vQv//EYHpu7neBW6iu3kJFxX+Cgcby5b8BfkNiYja5uUcFA43MzKHWXoUxxrRzsQQW24D5IjKLkOBCVa9tYL4C4FP/OfRhfY6qlgOISFZI+k+AJyMtSETGA+MB+vTpE0OWTbzk549t8A2QpKROdOlyCl26uGa5d+7cQHn5O8FAY+PGVwFITs4nL+/YYKCRnt63qbNvjDGmmcUSWLzi/xqrBBdczKduJdEKEcnBlVhsBhD3M/YY4I+RFqSqU4Ap4N4K2YO8mGaUktKV7t3PpXt314T3jh0rKSt7OxhorFs3DYC0tP7k5R3nA41jSUnp1pLZNsYYEwexvm6aAuznB5eoalUM82QCk4EdwPvAyap6ka9jcT3u1dVJqrpIRI4BDlPVOxparr1u2rapKtu2fU5Z2SwfaMympqYCgMzMA4OBRk7OGJKSshpYmjHGmOYQ13YsRORo3COKFbhgoDfwY1VtkbdCLLBoXwKBarZs+TgYaFRUvI9qJSJJZGWNCgYa2dmjSUhIbensGmNMhxTvwOIj4EequsQP7wdMU9VD9jqne8ACi/atpmYHmzb91wca/2bz5nlAgISEdHJyvhsMNDp1GoZIYktn1xhjOoTGBBax1LFIrg0qAFT1SxFJ3uPcGRNFYmKar+B5LDCRqqpyKireDZZoLFt2AwBJSXnk5h4TDDTS0/ezN06MMaYViCWwmCcijwJP++GxwEdNlyVjdklOzqVr1+/Ttev3AaisXEN5+dvBQGPDhhcBSEnpFQwy8vKOIzW1V0tm2xhjOqxYHoWk4tqdOBJXx+I94IHGNpgVL/YoxNRSVbZv/5ry8lk+0Hib6uqNAKSnDwp54+QYkpPzWji3xhjTdsW9E7KQBfdQ1TV7nLM4sMDC1Ec1wJYtC4OBRnn5ewQCWwGhU6fhIW+cHEliYkZLZ9cYY9qMpgwsPlbV4XucsziwwMLEKhDYyaZNRcFAY9OmOahWIZJCdvZhwUAjK2skCQlWbcgYY+rTlIHFJ6p68B7nLA4ssDB7qqZmK+Xl/wkGGlu2zAeUxMQscnLGBAONzMwDEIm1419jjGn/4v1WSKiH9yA/xrQKiYmZdOlyMl26nAxAVdVGysreCQYa3377LwCSk7uRm3tsyBsn/Vsy28YY06bEFFiISD4wElglIt1VdV3TZsuYppec3IXu3c+he/dzANixozjY7HhZ2SzWr38OgLS0vuTm1r5xciwpKfktmW1jjGnVYnkr5Ie4Pjxm494K+S5wvaq+0OS5i8AehZjm4Joe/yIYaJSXz6a6uhyAzMwDgoFGbu5RJCVlt2xmjTGmicW75c0FwAm1pRS+e/N/q+pBe53TPWCBhWkJqjVs3vxxMNCoqHifQGAHkEh29shgoJGTc7g1PW6MaXfiHVh8qqrfCRlOABaEpjUnCyxMa+CaHv9fMNDYtOlDoIaEhDRyco4MBhpZWcOt6XFjTJsX78qbM0XkDWCaHz4PmLGnmTOmPXBNjx9DXt4xwO1UV2+ivPzdYKCxfPlNLF8OSUm55OYeHQw0MjIGW9Pjxph2LWqJhbg7YAGu4maw5U1V/WfzZG93VmJh2oKdO0spK3s7GGjs2LECgJSUnuTlHRsMNNLSerdsRr2ZM2cyYcIEampquPTSS7nxxhvrjFdVJkyYwIwZM8jIyOCJJ55g+HDXpE3fvn3JysoiMTGRpKQkQq/P+++/n8mTJ5OUlMRpp53GpEmTmnW7jDHxEbcSC1VVEXnJ92T6YiMzkQk8AOwEZqvqVJ9+AHCTn+xOVV0kIuOAg4EKVf1NY9ZjTGuUkpJPfv4F5OdfAMD27cuC/Zt8++0blJY+A0B6+r6+Euhx5OUdQ3Jyl2bPa01NDVdffTVvvfUWBQUFjBw5kjPOOIMhQ4YEp3n99ddZunQpS5cuZe7cuVx55ZXMnTs3OP6dd96ha9eudZb7zjvv8PLLL7Nw4UJSU1NZt85eJjOmI4jlUcgcERmpqh82ctlnAS+o6qsi8hww1adPwPU9osAkEfkt7vHKJ0CLNhduTFNJT+9Penp/eva8DNUAW7cuCgYapaXP8M03D+GaHh8WDDRyc79LYmJmk+etqKiIgQMH0r+/a6/j/PPP5+WXX64TWLz88stcfPHFiAijR4+mvLycNWvW0KNHj3qX++CDD3LjjTeSmuoqs3bv3r1pN8QY0yrE0rzgMbjg4msRWSgin4rIwhjmKwCK/eeakPQcVS1X1QogC+gPfKuqNwKFIjIgfEEiMl5E5onIvPXr18ewamNaL5EEOnU6kN69f8GBB77GEUd8y8EHf0DfvreSmJhNSclf+PTTU3j//Tw++WQMK1bcSnn5+wQCVU2Sn9WrV9O7965HMgUFBaxevTrmaUSEE088kUMOOYQpU6YEp/nyyy/5z3/+w6GHHspRRx3Fhx829reJMaYtiqXE4pQ9XHYJLriYT90ApkJEcnAlFpuB1cC3flw50Cl8Qao6BZgCro7FHubHmFYpISGZnJzDyck5nL59f0tNzTYqKt4PlmisWHErcAsJCZnk5o4Jlmh06nRgXJoej1TPKryCabRpPvjgA3r27Mm6des44YQTGDx4MGPGjKG6upqysjLmzJnDhx9+yA9/+EOWLVtmlVeNaecaDCxUdaWIHAnsq6qP+3Ysdvvyj+BFYLKInAa8KiJPq+pFwH3AX3AVQSeparGIfCsifwJSVHXBnm+OMW1fYmIGnTufSOfOJwJQVfUt5eWzQ+povA5AcnJXcnOPCQYa6ekD9uhLu6CggOLi4uBwSUkJPXv2jHma2v/du3fnzDPPpKioiDFjxlBQUMBZZ52FiDBq1CgSEhLYsGED3bp1a3QejTFtR4OBhYj8HhgBDAIeB5KBZ4Ajos2nqluBn4QkTfXpi4Afh03720bl2pgOJDm5M926nUW3bmcBsGNHCeXlbwcDjfXr/w5AamqfYP8mubnHkZq6T0zLHzlyJEuXLmX58uX06tWL6dOn8+yzz9aZ5owzzmDy5Mmcf/75zJ07l5ycHHr06MHWrVsJBAJkZWWxdetW3nzzTX73u98B8IMf/IC3336bo48+mi+//JKdO3fuVsHTGNP+xPIo5EzcGxsfA6jqNyKS1aS5MsbUKy2tgH32uZh99rkYVWX79i+DQcaGDS+xdu3jAGRkDA0JNI4iKSkn4vKSkpKYPHkyJ510EjU1NYwbN46hQ4fy0EMPAXDFFVdw6qmnMmPGDAYOHEhGRgaPP+7WUVpayplnnglAdXU1P/rRjzj5ZNfJ27hx4xg3bhwHHHAAKSkpPPnkk/YYxJgOIJaWN4tUdZSIfKyqw/1rpP9T1QObJ4t1WTsWxtRPtYYtW+YHA42Kiv8QCGwHEsjKGhkMNLKzDycxMQ2A0tKpLFt2M5WVq0hN7UP//hPJzx/bshti4q4p2iq5/vrrefXVV0lJSWHAgAE8/vjj5ObmNvemmWYQ7ya9/w/YFzgBuBMYB0xT1b/sbUb3hAUWxsQuEKhk06Y5wUBj06a51DY9np19BElJndm48VVUdwTnSUjIYNCgKRZctCM1NTXst99+ddoqmTZtWp1XimfMmMH999/PjBkzmDt3LhMmTAi2VdK3b1/mzZu326OsN998k2OPPZakpCRuuOEGAO6+++7m2zDTbOLapLeq3iMiJwCbcPUsfqeqb+1lHo0xzSAhIZXc3KPIzT2Kfv1uo7p6MxUV7wUDjfLyWbvNEwhs48svr2T79uUkJ+eRlJRLUlJeyF8uycl51tlaG9JUbZWceOKJwc+jR4/mhRdapNNr08rUG1iIyEmq+gaADyTeChl3rqr+vRnyZ4yJo6SkLLp0OY0uXU4DYPbsBNyb33XV1GxmxYrodaoTEtKDgUZSUp4PQuoGIvUFJomJmVbfohlFaocktOXU+qZZvXo1PXr0CLZVIiJcfvnljB8/frd1PPbYY5x33nlNtxGmzYhWYjFDRN4DLlTV1WHjbgIssDCmjUtN7UNl5coI6YUceuiXVFeXU11dRlVVGdXVZcHhXWm7hisrS9i6dRFVVWXU1FREXa9IckjA0djAJDsu7Xd0JE3VVkmtiRMnkpSUxNix9vistWiq/n9iES2wWAg8i2t185dhJRT2U8OYdqB//4ksWTKeQGBbMC0hIYP+/SeSkJBCSkp3UlIa3xS3ag3V1RWNCEy+ZceOr6muLqeqqoy6jfWGE5KScuqUgMQemOSSkJDc+B3VxjVVWyUATz75JK+99hqzZs2yUqhWoqn6/4lVtMBCVfVhEXkXmCoipwJXq+o2IpWdGmPanNoKmvF+K0QkkeTkziQnd270vKpKTc2WOoFIQ4HJtm2Lg2mqlVGXn5CQGRKENC4wSUxM39Nd0qKaqq2SmTNncvfdd/Puu++SkZHREptmImiqOjWxiqXy5pcichhwO/CJiFy812s1xrQa+fljW9UbICJCUlIWSUlZQJ9Gz19TsyMYfNQGI7sCk92DlR07VlBd/QnV1WXU1GxpIG+pwcqrjQ1MEhOzWuwXfVO1VXLNNddQWVnJCSecALgKnLXLNC2nOerURBMtsAheAapaDdwoIjOBaYC1yWuMaZUSE9NITOxBamrjf3kFAlX+EU5sgcnOnWvZtu2LYHr0wtxEH2TsSWCSi0jinu4SSkunkpd3M1Om1JZK9QVcQFFLRPjrX/+627z9+/dnwYLIPS189dVXe5wn03Saok5NY0QLLG6NkNnZInIIcHmj1mKMMW1AQkIyKSldSUlp/LNl1QDV1ZvqPK5pKDDZsWNlcFg1eu+1iYnZdV73jTUw2bhxJkuXXhWsR1NZuZIlS9wv0NZUUmXipynq1DRGvYGFqr5UT3oZcFej1mKMMe2cSALJybkkJ+cCfRs1r6oSCGzb7U2baIHJtm1fBodDK9/GIhDYxhdf/JTS0mdISEgjISE97P+uz4mJu6fVN0/ttCIpVpGzBTVVnZpYxdJXiDHGmCYkIiQmZpKYmAkUNHr+QKAyJACpG5gsXXpNxHlUK6mq2kggsINAYDuBwA5qarYHhyGwN1sUNRjZk2CloQCodtq9eWTUXjRVnZpYNdikd2tjTXobY0zs/ve/vvW2VXLYYSvqnS8QqPJBxq7AY9f/ukHI7tNFClbqm7buMht6q6chIklxD1aijd/1ObVFS2maus+fuDbpvReZyAQeAHYCs1V1qk8/ANfAFsCdqrpIRL7Gtez5sapOaao8GWNMRxOtrZJoEhKSfZsfzduZtWqAQGBnxMAj9sAm0nQuvbq6jJ07v4m4TPeewp6Ld7ASawC0fv1zdY5xS9ejacpHIWcBL6jqqyLyHDDVp08ArsZVn56Eqwi6BUgHiiMtyBhjzJ5pqrZKmopIgn+zJ63Z1x0IVDdJKUxoUFPfPPHflm0sW3ZzuwssCoBP/efQZvRyVLUcQERqQ+GDca+3/gt4PXxBIjIeGA/Qp0/j32s3xpiOrLW1VdJaJSQkkZDQCejUrOt1lXcr9yBYcekrVkSuXFlZuapZt6NWUwYWJbjgYj4Q2rB/hYjk4EosNgOoagBARHaISELtcC3/eGQKuDoWTZhnY4wxplm5yrt7XkqzZs2j9dSjaZkf4k3Zk8+LwNki8iDwqog87dPvA/4CTAbuF5FBIvKYiDyGq4uxN1WRjTHGmA7F9e1Tt0n1WOrRNBV7K8QYY4xp41rTWyFtLrAQkfXA7mU+e68rsKEJltte2P5p/+wYt392jNu/pjrGhaoaU3cebS6waCoiMi/WaKwjsv3T/tkxbv/sGLd/reEYN2UdC2OMMcZ0MBZYGGOMMSZuLLDYxVr8jM72T/tnx7j9s2Pc/rX4MbY6FsYYY4yJGyuxMMYYY0zcdJhu06N0inYqcA0wQ1Un+7S7gAxgm6re2EJZbnZR9tGNwABgH+BKVS2xjuPapijH+BZgf6AMuE1Vv+mo10FbF+UY/xQYCfQGFqrqTXYdt00i0h+4GddFxjkh6ZE6+Wz267gjlVjUdop2GXBGbaKqzsB1hgaAiPQBklX1WiBRRHo3e05bTn376C6f9hhwjE+2juPapojHGKjGfRFVAeUd/Dpo6+q7jh9V1SuAL4EnfLJdx22Qqi5T1Z9GGFXbyedVwM9a6jruMCUW1N8pWrhe7LrIVvn5OspFV+8+EpFOwA/xncHRQMdxptWq7xjfoaoBETkDuBT4kI57HbR10a7jNKCfqi7xSXYdty/hnXy2yPdZRyqxqO0UDaJv9+qQ6Xr7+TqKiPtIRLKBB4FfqWqw4zhVrQF2iEhHOo/auojHOKSPnnW4rh078nXQ1kW7152D68cJsOu4HaoQkRx/z95MC13HHeatEP/ccTKwA3gfOFlVLxKRw3DPpPKAe1X1HyJyJ5AKVKrqTfUutJ2Jso9eBJJxJ+nz/v8NfraFqnpvC2TX7IEox/jXuBtPV+BaVV3TUa+Dtq6+Y+zHvQycr6rbRWQQdh23SSLSBZgInAA8Agzx1/EBwPW4UqhJvo5Fs1/HHSawMMYYY0zTs6IvY4wxxsSNBRbGGGOMiRsLLIwxxhgTNxZYGGOMMSZuLLAwxgSJSK6IXNXANH1FZFFz5ckY07ZYYGGMCZWLa7UvbkQkMZ7LM8a0bhZYGGNC3QUMEJH5IvJH/7dIRD4VkfPCJxaRRD/NhyKyUEQu9+lHi8g7IvIsvhVIEXlJRD4Skc9EZHzIMraIyEQRWSAic0Qk36fni8g/ffoCETncp18oIkU+j3+zwMWY1sUCC2NMqBuBr1V1GDAHGAYcBBwP/FFEeoRN/1OgQlVH4jq4ukxE+vlxo4CbVXWIHx6nqocAI4BrfSM/AJnAHFU9CHgPuMyn/wV416cPBz4Tkf2B84AjfB5rgLHx2nhjzN7rSH2FGGMa50hgmm/yuVRE3sUFDwtDpjkROFBEantYzAH2xXVoVqSqy0OmvVZEzvSfe/vpNvppX/PpH+FaEwQ4FrgYwOehQkQuAg4BPhQRcB1orYvP5hpj4sECC2NMfSTGaX6mqm/USRQ5GtgaNnw8cJiqbhOR2UCaH12lu5oAriH6fUmAJ62JcWNaL3sUYowJtRnI8p/fA87z9Si6AWOAorDp3wCuFJFkABHZz/dVES4HKPNBxWBgdAx5mQVc6Zeb6DtWmgWcIyLdfXpnESls3CYaY5qSBRbGmCBV3Qh84F8nPQz32GMB8Daud9u1YbM8AiwGPvbz/I3IJQ4zgSQRWQj8AVd/oyETgGNE5FPcI5KhqroY+A3wpl/WW0B4vQ9jTAuyTsiMMcYYEzdWYmGMMcaYuLHAwhhjjDFxY4GFMcYYY+LGAgtjjDHGxI0FFsYYY4yJGwssjDHGGBM3FlgYY4wxJm4ssDDGGGNM3Px/tC9z/qpuVx4AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<Figure size 504x720 with 3 Axes>"
+       "<Figure size 576x720 with 5 Axes>"
       ]
      },
      "metadata": {
@@ -338,24 +357,32 @@
     "##### x coordinates\n",
     "x = tols\n",
     "training_times = list_training_times_tol\n",
-    "precision_scores = list_precision_scores_tol\n",
-    "zero_one_loss = list_zero_one_loss_tol\n",
+    "precision_scores_train = list_precision_scores_train_tol\n",
+    "zero_one_loss_train = list_zero_one_loss_train_tol\n",
+    "precision_scores_test = list_precision_scores_test_tol\n",
+    "zero_one_loss_test = list_zero_one_loss_test_tol\n",
     "        \n",
     "training_times = [round(i,2) for i in training_times]\n",
-    "precision_scores = [round(i,3) for i in precision_scores]\n",
-    "zero_one_loss = [round(i,3) for i in zero_one_loss]\n",
+    "precision_scores_train = [round(i,3) for i in precision_scores_train]\n",
+    "zero_one_loss_train = [round(i,3) for i in zero_one_loss_train]\n",
+    "precision_scores_test = [round(i,3) for i in precision_scores_test]\n",
+    "zero_one_loss_test = [round(i,3) for i in zero_one_loss_test]\n",
     "\n",
     "### Create plot\n",
-    "fig, figs = plt.subplots(nrows=3, ncols=1, figsize=(7,10))\n",
+    "fig, figs = plt.subplots(nrows=5, ncols=1, figsize=(8,10))\n",
     "fig.tight_layout(pad=3.0)\n",
     "figs[0].plot(x,training_times, marker='o', color='r')\n",
-    "figs[1].plot(x,precision_scores, marker='o', color='b')\n",
-    "figs[2].plot(x,zero_one_loss, marker='o', color='g')\n",
+    "figs[1].plot(x,precision_scores_train, marker='o', color='b')\n",
+    "figs[2].plot(x,zero_one_loss_train, marker='o', color='g')\n",
+    "figs[3].plot(x,precision_scores_test, marker='o', color='m')\n",
+    "figs[4].plot(x,zero_one_loss_test, marker='o', color='y')\n",
     "\n",
     "### Add every x coordinates\n",
     "figs[0].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
     "figs[1].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
     "figs[2].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "figs[3].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "figs[4].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
     "\n",
     "for i in range(len(x)):\n",
     "    figs[0].annotate(training_times[i], # this is the text\n",
@@ -363,13 +390,23 @@
     "                 textcoords=\"offset points\", # how to position the text\n",
     "                 xytext=(12,3), # distance from text to points (x,y)\n",
     "                 ha='center') # horizontal alignment can be left, right or center\n",
-    "    figs[1].annotate(precision_scores[i], # this is the text\n",
-    "                 (x[i],precision_scores[i]), # these are the coordinates to position the label\n",
+    "    figs[1].annotate(precision_scores_train[i], # this is the text\n",
+    "                 (x[i],precision_scores_train[i]), # these are the coordinates to position the label\n",
     "                 textcoords=\"offset points\", # how to position the text\n",
     "                 xytext=(12,3), # distance from text to points (x,y)\n",
     "                 ha='center') # horizontal alignment can be left, right or center\n",
-    "    figs[2].annotate(zero_one_loss[i], # this is the text\n",
-    "                 (x[i],zero_one_loss[i]), # these are the coordinates to position the label\n",
+    "    figs[2].annotate(zero_one_loss_train[i], # this is the text\n",
+    "                 (x[i],zero_one_loss_train[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "    figs[3].annotate(precision_scores_test[i], # this is the text\n",
+    "                 (x[i],precision_scores_test[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "    figs[4].annotate(zero_one_loss_test[i], # this is the text\n",
+    "                 (x[i],zero_one_loss_test[i]), # these are the coordinates to position the label\n",
     "                 textcoords=\"offset points\", # how to position the text\n",
     "                 xytext=(12,3), # distance from text to points (x,y)\n",
     "                 ha='center') # horizontal alignment can be left, right or center\n",
@@ -377,22 +414,30 @@
     "figs[0].set_xticks(x)\n",
     "figs[1].set_xticks(x)\n",
     "figs[2].set_xticks(x)\n",
+    "figs[3].set_xticks(x)\n",
+    "figs[4].set_xticks(x)\n",
     "    \n",
     "### Add title and axis names\n",
     "figs[0].title.set_text('Training times for various level of tolerance (kernel=rbf)')\n",
-    "figs[1].title.set_text('Precision score for various level of tolerance (kernel=rbf)')\n",
-    "figs[2].title.set_text('Zero-one loss metrics various level of tolerance (kernel=rbf)')\n",
+    "figs[1].title.set_text('Precision score for various level of tolerance (kernel=rbf) on training dataset')\n",
+    "figs[2].title.set_text('Zero-one loss metrics various level of tolerance (kernel=rbf) on training dataset')\n",
+    "figs[3].title.set_text('Precision score for various level of tolerance (kernel=rbf) on testing dataset')\n",
+    "figs[4].title.set_text('Zero-one loss metrics various level of tolerance (kernel=rbf) on testing dataset')\n",
     "figs[0].set_xlabel('tolerance')\n",
     "figs[1].set_xlabel('tolerance')\n",
     "figs[2].set_xlabel('tolerance')\n",
+    "figs[3].set_xlabel('tolerance')\n",
+    "figs[4].set_xlabel('tolerance')\n",
     "figs[0].set_ylabel('Training times (in seconds)')\n",
     "figs[1].set_ylabel('Precision score')\n",
-    "figs[2].set_ylabel('Zero-one loss')"
+    "figs[2].set_ylabel('Zero-one loss')\n",
+    "figs[3].set_ylabel('Precision score')\n",
+    "figs[4].set_ylabel('Zero-one loss')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 5,
    "id": "62c7302a",
    "metadata": {},
    "outputs": [
@@ -400,47 +445,65 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training...\n",
-      "Predicting...\n",
-      "Score échantillon de test :  0.9506666666666667\n",
-      "Précision pour chaque classe : \n",
-      " [0.99324324 0.97633136 0.93377483 0.95375723 0.92546584 0.91549296\n",
-      " 0.96527778 0.95833333 0.96402878 0.91472868]\n",
-      "Matrice de confusion pour C=1.0 et kernel=rbf :\n",
-      " [[147   0   0   0   1   0   0   0   1   0]\n",
-      " [  0 165   0   0   0   0   0   0   1   0]\n",
-      " [  1   2 141   0   0   1   1   1   0   0]\n",
-      " [  0   0   3 165   0   3   0   1   1   1]\n",
-      " [  0   0   1   0 149   0   1   0   0   5]\n",
-      " [  0   0   0   4   2 130   3   1   1   1]\n",
-      " [  0   0   1   0   0   3 139   0   0   0]\n",
-      " [  0   1   3   0   4   0   0 138   0   4]\n",
-      " [  0   1   1   3   1   4   0   0 134   0]\n",
-      " [  0   0   1   1   4   1   0   3   1 118]]\n",
-      "Zero-one classification loss :\n",
-      " 0.04933333333333334\n"
+      "Métriques pour SVM\n",
+      "Paramètres : (C=1.0,kernel=\"rbf\")\n",
+      "Taille de l'échantillon : 10000\n",
+      "Proportion des datasets : 90%\n",
+      "Temps d'entraînement (secondes) : 15.8407\n",
+      "Temps de prédiction (secondes) : 3.68326\n",
+      "Précision pour chaque classe : [0.98, 0.99, 0.958, 0.958, 0.955, 0.967, 0.949, 0.958, 0.94, 0.947]\n",
+      "Précision : 0.96\n",
+      "Erreur : 0.04\n",
+      "Matrice de confusion :\n",
+      " [[ 99   0   0   0   0   0   0   0   0   0]\n",
+      " [  0  98   1   0   0   0   0   1   0   0]\n",
+      " [  1   0  92   0   0   0   2   2   0   0]\n",
+      " [  0   0   2  91   1   0   0   0   3   0]\n",
+      " [  0   0   0   0  84   0   3   0   1   0]\n",
+      " [  1   0   1   2   0  87   0   0   2   1]\n",
+      " [  0   1   0   0   2   1 111   0   0   0]\n",
+      " [  0   0   0   0   0   0   0 114   0   1]\n",
+      " [  0   0   0   1   0   2   1   0  94   3]\n",
+      " [  0   0   0   1   1   0   0   2   0  90]]\n"
      ]
     }
    ],
    "source": [
     "####### Meilleur modèle de SVM #######\n",
     "\n",
-    "#Entraîne le classifier\n",
+    "### Create vector of 5000 random indexes\n",
+    "rand_indexes = np.random.randint(70000, size=10000)\n",
+    "### Load data with the previous vector\n",
+    "data = mnist.data[rand_indexes]\n",
+    "# print(\"Dataset : \", data)\n",
+    "target = mnist.target[rand_indexes]\n",
+    "\n",
+    "# Split the dataset\n",
+    "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
+    "\n",
     "clf = SVC(C=1.0,kernel=\"rbf\")\n",
-    "print(\"Training...\")\n",
+    "#Entraîne le classifier\n",
+    "t1 = time.time()\n",
     "clf.fit(xtrain, ytrain)\n",
+    "t2 = time.time()\n",
     "\n",
     "#Prédiction sur le jeu de tests\n",
-    "print(\"Predicting...\")\n",
     "pred = clf.predict(xtest)\n",
-    "# On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
-    "score = clf.score(xtest, ytest)\n",
-    "print(\"Score échantillon de test : \", score)\n",
+    "t3 = time.time()\n",
     "\n",
     "#Calcul de différentes metrics\n",
-    "print(\"Précision pour chaque classe : \\n\", metrics.precision_score(ytest, pred,average=None))\n",
-    "print(\"Matrice de confusion pour C=1.0 et kernel=rbf :\\n\", metrics.confusion_matrix(ytest, pred))\n",
-    "print(\"Zero-one classification loss :\\n\", metrics.zero_one_loss(ytest, pred))"
+    "precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
+    "\n",
+    "print(\"Métriques pour SVM\")\n",
+    "print(\"Paramètres : (C=1.0,kernel=\\\"rbf\\\")\")\n",
+    "print(\"Taille de l'échantillon :\", 10000)\n",
+    "print(\"Proportion des datasets :\", \"90%\")\n",
+    "print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
+    "print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
+    "print(\"Précision pour chaque classe :\", precisions)\n",
+    "print(\"Précision :\", clf.score(xtest, ytest))\n",
+    "print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
+    "print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
    ]
   },
   {
diff --git a/TP1_prog2.py.ipynb b/TP1_prog2.py.ipynb
index 5398024..a1385c7 100644
--- a/TP1_prog2.py.ipynb
+++ b/TP1_prog2.py.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "id": "530f620c",
    "metadata": {},
    "outputs": [],
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "68b6a517",
    "metadata": {},
    "outputs": [],
@@ -864,7 +864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 26,
    "id": "98107e41",
    "metadata": {},
    "outputs": [
@@ -872,37 +872,59 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Matrice de confusion K-NN :\n",
-      " [[51  0  0  0  0  1  0  0  0  0]\n",
-      " [ 0 56  0  0  0  0  0  0  0  0]\n",
-      " [ 3  1 45  1  0  0  1  1  0  0]\n",
-      " [ 0  1  1 35  0  1  0  1  1  1]\n",
-      " [ 0  3  0  0 48  0  0  0  0  2]\n",
-      " [ 0  1  0  1  0 38  0  0  0  0]\n",
-      " [ 0  0  0  0  0  2 44  0  0  0]\n",
-      " [ 0  2  0  0  3  0  0 47  0  0]\n",
-      " [ 2  0  0  0  0  3  1  0 42  2]\n",
-      " [ 0  0  0  0  4  1  0  1  2 50]]\n"
+      "Métriques pour K-NN :\n",
+      "Paramètres : (n_neighbors=3,p=2,n_jobs=1)\n",
+      "Taille de l'échantillon : 10000\n",
+      "Proportion des datasets : 90%\n",
+      "Temps d'entraînement (secondes) : 0.01596\n",
+      "Temps de prédiction (secondes) : 0.30718\n",
+      "Précision pour chaque classe : [0.942, 0.891, 0.962, 0.959, 0.988, 0.944, 0.961, 0.97, 0.989, 0.918]\n",
+      "Précision : 0.95\n",
+      "Erreur : 0.05\n",
+      "Matrice de confusion :\n",
+      " [[ 98   0   1   0   0   0   1   0   1   0]\n",
+      " [  0 114   0   0   0   0   0   0   0   0]\n",
+      " [  2   2 102   0   0   0   0   0   0   0]\n",
+      " [  0   1   1  93   0   3   0   1   0   0]\n",
+      " [  1   5   0   0  82   0   0   0   0   5]\n",
+      " [  0   1   1   1   0  84   3   0   0   1]\n",
+      " [  0   0   0   0   0   0  99   0   0   0]\n",
+      " [  0   3   0   0   0   0   0  97   0   2]\n",
+      " [  2   1   1   3   1   2   0   0  92   0]\n",
+      " [  1   1   0   0   0   0   0   2   0  89]]\n"
      ]
     }
    ],
    "source": [
     "### Create vector of 5000 random indexes\n",
-    "rand_indexes = np.random.randint(70000, size=5000)\n",
+    "rand_indexes = np.random.randint(70000, size=10000)\n",
     "### Load data with the previous vector\n",
     "data = mnist.data[rand_indexes]\n",
-    "# print(\"Dataset : \", data)\n",
     "target = mnist.target[rand_indexes]\n",
-    "\n",
     "# Split the dataset\n",
     "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
     "\n",
-    "# Training on xtrain,ytrain\n",
     "clf = neighbors.KNeighborsClassifier(n_neighbors=3,p=2,n_jobs=1)\n",
+    "# Training on xtrain,ytrain\n",
+    "t1 = time.time()\n",
     "clf.fit(xtrain, ytrain)\n",
+    "t2 = time.time()\n",
     "# Predicting on xtest\n",
     "pred = clf.predict(xtest)\n",
-    "print(\"Matrice de confusion K-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
+    "t3 = time.time()\n",
+    "#Calcul de différentes metrics\n",
+    "precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
+    "\n",
+    "print(\"Métriques pour K-NN :\")\n",
+    "print(\"Paramètres : (n_neighbors=3,p=2,n_jobs=1)\")\n",
+    "print(\"Taille de l'échantillon :\", 10000)\n",
+    "print(\"Proportion des datasets :\", \"90%\")\n",
+    "print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
+    "print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
+    "print(\"Précision pour chaque classe :\", precisions)\n",
+    "print(\"Précision :\", clf.score(xtest, ytest))\n",
+    "print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
+    "print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
    ]
   },
   {
diff --git a/TP2_prog1.py.ipynb b/TP2_prog1.py.ipynb
index a895d5b..acbb173 100644
--- a/TP2_prog1.py.ipynb
+++ b/TP2_prog1.py.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "3eb7a65b",
    "metadata": {},
    "outputs": [],
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "a8812842",
    "metadata": {},
    "outputs": [],
@@ -1233,7 +1233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "abb0fcf1",
    "metadata": {},
    "outputs": [
@@ -1241,23 +1241,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Matrice de confusion A-NN :\n",
-      " [[59  0  0  0  0  0  0  0  0  0]\n",
-      " [ 0 60  0  0  0  0  0  1  0  0]\n",
-      " [ 0  0 42  0  0  1  2  1  2  0]\n",
-      " [ 0  0  1 44  0  1  0  0  0  0]\n",
-      " [ 0  0  0  0 46  0  1  0  0  4]\n",
-      " [ 0  0  0  0  0 31  0  0  1  0]\n",
-      " [ 0  0  0  0  0  0 48  0  0  0]\n",
-      " [ 1  0  0  1  0  0  0 49  0  0]\n",
-      " [ 0  1  1  5  0  1  0  0 48  0]\n",
-      " [ 2  0  0  1  1  2  0  0  2 40]]\n"
+      "Métriques pour A-NN\n",
+      "Paramètres : (random_state=1, max_iter=300, hidden_layer_sizes=((85,)*15),\n",
+      "solver=adam, activation=relu, alpha= 0.0000001)\n",
+      "Taille de l'échantillon : 10000\n",
+      "Proportion des datasets : 90%\n",
+      "Temps d'entraînement (secondes) : 27.9214\n",
+      "Temps de prédiction (secondes) : 0.01396\n",
+      "Précision pour chaque classe : [0.972, 0.974, 0.926, 0.97, 0.919, 0.939, 0.971, 0.967, 0.951, 0.927]\n",
+      "Précision : 0.952\n",
+      "Erreur : 0.048\n",
+      "Matrice de confusion :\n",
+      " [[103   0   1   0   0   0   1   0   0   0]\n",
+      " [  0 114   0   0   1   0   0   0   0   0]\n",
+      " [  0   0 100   0   2   1   1   1   1   0]\n",
+      " [  0   1   3  97   0   1   0   0   1   1]\n",
+      " [  0   0   0   0  79   0   0   0   0   4]\n",
+      " [  2   0   0   3   0  93   1   0   1   0]\n",
+      " [  0   0   0   0   0   3  99   0   1   0]\n",
+      " [  1   1   1   0   1   0   0  89   0   1]\n",
+      " [  0   1   3   0   0   1   0   0  77   2]\n",
+      " [  0   0   0   0   3   0   0   2   0 101]]\n"
      ]
     }
    ],
    "source": [
     "### Create vector of 5000 random indexes\n",
-    "rand_indexes = np.random.randint(70000, size=5000)\n",
+    "rand_indexes = np.random.randint(70000, size=10000)\n",
     "### Load data with the previous vector\n",
     "data = mnist.data[rand_indexes]\n",
     "# print(\"Dataset : \", data)\n",
@@ -1266,10 +1276,6 @@
     "# Split the dataset\n",
     "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
     "\n",
-    "best_training_time = 0\n",
-    "best_precision_score = 0\n",
-    "best_zero_one_loss = 0\n",
-    "\n",
     "r = 1\n",
     "max_i = 300\n",
     "nb_hl = 15\n",
@@ -1281,31 +1287,27 @@
     "\n",
     "#Entraîne le classifier\n",
     "clf = neural_network.MLPClassifier(random_state=r, max_iter=max_i, hidden_layer_sizes=hl, solver=sol, activation=act, alpha=a, verbose=False)\n",
-    "t1 = round(time.time(),5)\n",
+    "t1 = time.time()\n",
     "clf.fit(xtrain, ytrain)\n",
-    "t2 = round(time.time(),5)\n",
+    "t2 = time.time()\n",
     "#Prédiction sur le jeu de tests\n",
     "pred = clf.predict(xtest)\n",
-    "# Probabilités des prédictions sur xtest\n",
-    "pred_proba = clf.predict_proba(xtest)\n",
-    "# On sauvegarde le temps de calcul, la précision et \n",
-    "# les taux d'erreurs par classe\n",
-    "best_training_time = t2-t1\n",
-    "best_precision_score = clf.score(xtest, ytest)\n",
-    "best_zero_one_loss = metrics.zero_one_loss(ytest, pred)\n",
+    "t3 = time.time()\n",
     "\n",
-    "# print(\"Paramètre :\\n\")\n",
-    "# print(\"random_state = \", r)\n",
-    "# print(\"max_iter = \", max_i)\n",
-    "# print(\"nb_hidden_layer = \", nb_hl)\n",
-    "# print(\"hidden_layer_size = \", hl_size)\n",
-    "# print(\"solver = \", sol)\n",
-    "# print(\"activation = \", act)\n",
-    "# print(\"alpha = \", a)\n",
-    "# print(\"Temps d'entraînement : \", best_training_time)\n",
-    "# print(\"Score : \", best_precision_score)\n",
-    "# print(\"Zero-one loss : \", best_zero_one_loss)\n",
-    "print(\"Matrice de confusion A-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
+    "#Calcul de différentes metrics\n",
+    "precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
+    "\n",
+    "print(\"Métriques pour A-NN\")\n",
+    "print(\"Paramètres : (random_state=1, max_iter=300, hidden_layer_sizes=((85,)*15),\")\n",
+    "print(\"solver=adam, activation=relu, alpha= 0.0000001)\")\n",
+    "print(\"Taille de l'échantillon :\", 10000)\n",
+    "print(\"Proportion des datasets :\", \"90%\")\n",
+    "print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
+    "print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
+    "print(\"Précision pour chaque classe :\", precisions)\n",
+    "print(\"Précision :\", clf.score(xtest, ytest))\n",
+    "print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
+    "print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
    ]
   },
   {
diff --git a/TP3_prog1.py.ipynb b/TP3_prog1.py.ipynb
index e992c42..586496a 100644
--- a/TP3_prog1.py.ipynb
+++ b/TP3_prog1.py.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "3eb7a65b",
    "metadata": {},
    "outputs": [],
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "6ec263be",
    "metadata": {},
    "outputs": [
@@ -132,8 +132,8 @@
     "\n",
     "#Calcul de différentes metrics\n",
     "print(\"Précision pour chaque classe : \\n\", metrics.precision_score(ytest, pred,average=None))\n",
-    "print(\"Matrice de confusion SVM:\\n\", metrics.confusion_matrix(ytest, pred))\n",
-    "print(\"Zero-one classification loss :\\n\", metrics.zero_one_loss(ytest, pred))"
+    "print(\"Erreur pour chaque classe :\\n\", metrics.zero_one_loss(ytest, pred))\n",
+    "print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
    ]
   },
   {
@@ -272,9 +272,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 12,
    "id": "5726fcb1",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -293,8 +295,10 @@
     "####### Variation du paramètre de tolérance aux erreurs C #######\n",
     "\n",
     "list_training_times_tol = []\n",
-    "list_precision_scores_tol = []\n",
-    "list_zero_one_loss_tol = []\n",
+    "list_precision_scores_train_tol = []\n",
+    "list_zero_one_loss_train_tol = []\n",
+    "list_precision_scores_test_tol = []\n",
+    "list_zero_one_loss_test_tol = []\n",
     "\n",
     "kernel_train = xtrain\n",
     "kernel_test = xtest\n",
@@ -309,35 +313,38 @@
     "    t2 = round(time.time(),5)\n",
     "    #Prédiction sur le jeu de tests\n",
     "    pred = clf.predict(kernel_test)\n",
+    "    pred_train = clf.predict(kernel_train)\n",
     "    # On sauvegarde le temps de calcul, la précision et \n",
     "    # les taux d'erreurs par classe\n",
     "    list_training_times_tol.append(t2-t1)\n",
-    "    list_precision_scores_tol.append(clf.score(kernel_test, ytest))\n",
-    "    list_zero_one_loss_tol.append(metrics.zero_one_loss(ytest, pred))\n",
+    "    list_precision_scores_train_tol.append(clf.score(kernel_train, ytrain))\n",
+    "    list_zero_one_loss_train_tol.append(metrics.zero_one_loss(ytrain, pred_train))\n",
+    "    list_precision_scores_test_tol.append(clf.score(kernel_test, ytest))\n",
+    "    list_zero_one_loss_test_tol.append(metrics.zero_one_loss(ytest, pred))\n",
     "print(\"Done\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 11,
    "id": "741f82ca",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(36.0, 0.5, 'Zero-one loss')"
+       "Text(38.625, 0.5, 'Zero-one loss')"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAALACAYAAADPIDvhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAADu8UlEQVR4nOzdd3iUVfbA8e8hoSSUAFKkJQgovYPorstiwYq6YsPFiogFFXfVFVfXtov6Q9eKiliwIei6KoKIIgoqriICIiKK9N57qMn5/XHvJJNhZjIhk0zK+TzPPMnb71vnzL33vVdUFWOMMcaYeKiQ6AQYY4wxpuywwMIYY4wxcWOBhTHGGGPixgILY4wxxsSNBRbGGGOMiRsLLIwxxhgTNxZYGGOMMSZuLLAwxhhjTNwkx2tFItIY6Af8AWgI7AHmAx8CH6lqdry2ZYwxxpiSSeLR8qaIjAYaAROBWcAGoApwDHAi0BUYqqpfFHpjxhhjjCmx4hVYtFPV+VGmVwLSVfW3Qm/MGGOMMSVWXAKLsCsWqQU0UdV5RbIBY4wxxpQ4ca28KSLTRKSGiNQGfgBGi8hj8dyGMcYYY0queL8VkqaqO4C+wGhV7QqcEudtGGOMMaaEindgkSwiDYCLcBU5TSkkIh+JyBXxnrewROTvIvJicWwrShrqi8gXIrJTRP6dyLSEU5znw2+vl4isKobtqIi0OIzlRERGi8hWEZkZh3RcKSJfFXY9iSYi14rIE/7/pv74xu0twcNMU9yObX7Xi4j8S0Q2icg6f0//LCKV47FtE8fXTb0HgI+Br1T1OxFpBiyK8zZMGCKyK2gwFdgHZPnha1V1TKzrUtUzimLeghCRXsAbqto4aFsPFsW2CmgQsAmooUVVQakQiup8lGInAL2Bxqq6O3SiiFwJDFTVE4o7YYniK9PfDRyX6LQkgog0AW4FMlR1gx/3Oe7efjqRaSsr4ppjoar/UdUOqnqDH16iqufHcxsmPFWtFvgAK4Czg8blBBWJ/lVSBmQACw4nqCjKY+9/mVuDd4fKAJaFCyqKm4gkJToN3rnAQlVdHc+VloRnS4xpyAA2B4IKbwxwbdGkqvyJy4NIRJ4WkacifeKxDXN4AlnVInKHiKzDVaitJSITRWSjzyKe6Bs4CywzTUQG+v+vFJGvRORRP+9SETnjMOc9KqgY4VMReUZE3giT5qrAR0BDEdnlPw1F5L7A/EHZt1eJyEq/vetEpLuIzBORbSIyImS9A3yW51YR+VhEMvx4EZHHRWSDiGz3y7cLk65XgCuAv/k0nSIilUXkCRFZ4z9PBLJUwx37kPVV9ulsFzSurojsEZF6MZ6nYSIyA8gEmoWcjwoicreILPf79pqIpAWnLSQ9y0TkFP//sSIyS0R2iMh6ibEStj9P//VpXioiNweN3yOuYndg3s7isqMrRjs/MW7zAxHZIiK/icg1fvzVwIvA8f583R+yXGtgZND0bX58mj9WG/2xu1siBG0i0kpEpvht/yIiFwVNe0VEnhORSSKyGzhRRM4SkTn+uK4UkfuC5g9c01eIyAp/bO4Kmp4krjhwsbh76Htxv76jpiOMM4DpUY7n+f5aaOevoaF+m5tF5O3AOQxK79UisgL4TPJ/BqSJyEsislZEVosrkihUwOXTMFhEFpE3h/xMEVnij+Mjfl9OAaaQ+2x5xc/7Le7+iemaM/lQ1UJ/cA/bK4BRwFfATf7zBfB4PLZhnwKdj2XAKf7/XsBB4P+AykAKcARwPq7IpDrwH+D9oOWn4bKHAa4EDgDXAEnA9cAacl9VLsi8/wMeBSrhsqh34Io7wu1DL2BVyLj7AvMDTQHFfTFUAU4F9gLvA/VwDbZtAP7o5/8T8BvQGlcEeDfwtZ92GvA9UBMQP0+DCOl6BfhX0PADwDd+m3WBr4F/Rjr2Ydb3MjAsaHgwMNn/H8t5WgG09ftUMeR8DPD73AyoBrwLvB7l+C4j97r5H3CZ/78acFx+5wn3Q+V74B5/jpsBS4DT/PTPgGuCln0EGJnf+fHTFWgRIQ3TgWf9ddAJ2AicHHRNfhXlXjlkOvAaMN4f86bAr8DVofMDVYGVwFU+zV1wxWRtg66V7cDv/bGp4o9Xez/cAVgP/Cnkmn4Bd592xBVptvbTbwd+BFrirtOOuGskajrC7PN3wIVBw4HtJvt1/BY41sAtuOu7Me4afh4YG7Lcaz4NKeT/DHjfr6Mq7p6ZiSuqPeRcAPOAbRE+z4ZcG1OA2vh7zI/73I9L9+cwcF/0IuTaD9reOYl+fpeFT3xX5k5kxaDhisDnid7J8vbh0MBiP1AlyvydgK1Bw9PIGyz8FjQt1d+0RxZkXn9zHwRSg6a/QeEDi0ZB0zcDFwcN/xe4xf//Ef7LwQ9XwP3KzwBO8g+e44AK+RzbV8gbWCwGzgwaPg2X9R7rsT8FWBI0PAO4vADn6YGQeYLPx1TghqBpLXEP/eQIxzf4uvkCuB+ok8/xyFkP0ANYETL9TtwbYgADgc/8/4L7MuyZ3/nxw2EDC6AJri5R9aBxDwGvBF2TMQcWuC/DfUCboHHXAtNC5wcuBr4MWd/zwL1B18pr+Ry/J/A/vsi9phsHTZ8J9PP//wKcG2YdUdMRZv5FwOlBw4Ht3gYsCNn+z/ggzQ83CLqGAss1CzmekZ4B9f2xTQmafgn+OyK/cxXlGCpwUphxwft4AzA19JoNWSbivWefgn3iXSbbEBflB1Tz40xibVTVvYEBEUkVked9Nu8O3JdIzShZkusC/6hqpv+3WgHnbQhsCRoH7oulsNYH/b8nzHAgnRnAk+KKHrYBW3Bfbo1U9TNgBPAMsF5ERolIjRi33xBYHjS8nLzXfJ5jH8ZnQIqI9PDZsJ2A9yDm8xTtGIZLWzLuAZ+fq3FN8i8Uke9EpE8My2Tgspi3BR3nvwdt7x1csUNDoCfu4f9l0LJhz08+2wxcVzuDxi2PYblI6uByW0KPW7j1ZQA9Qva3P+5LNCDP+fHn+XNfzLIduM5vM9i6oP8zyb2Gm+AC2cNJR7Ct5H1OB9wOPKOqwUVkGcB7Qev9GRfIBV9DoddgpGdABu7H5tqg9T2Py7korHD3QfC40PsynOq43BBTSPGubPMwMEdcDVuAP+J+ZZrE0pDhW3G/Xnuo6joR6QTMwT3Ii8paoLaIpAY9bJpEmT80zYW1ElfkEPbtGFV9CnhKROoBb+Mesv+IYb1rcA/Mn/xwuh+Xs+poC6tqtoi8jfvlth6YGPQlGct5irb+QNoCArlG63EP2dTABB+s1A1K1yLgEl+3oC/wjogcodErQa4Elqrq0RH2dZuIfIJ7Hb01Lktdg5aNeH7y2cfaIlI96LilA7FWTAw9fptwv8gzcL/eo61vJTBdVXsXYP1v4oLYM1R1r7hXPkMDi0hWAs1xnTsWNB3B5uGCxlCnApNFZJ2q/jdo3QNUdUbozCLS1P8b6726EpdjUUdVD+Y3s4j8RN7rN9gbqnpd0HC4NDQh8n0Zuq1koAWuYUdTSPF+K2Q0Ljv0Pf85XlVfjec2TFxUx/2a3+YrYt1b1BtU1eW4DuruE5FKInI8cHaURdYDR4ivbBgHI4E7RaQt5FQiu9D/393/kqwI7MbV1ciKvKo8xgJ3i6t0WQdXv+CQCqn5eBOXnd3f/x9Q2PM0FviLuEqz1YAHgbf8Q/1XoIq4yoQVcXUact7jF5FLRaSuul6Jt/nR+R2TmcAOcZVVU3xlw3Yi0j1kXy/H1R0J3teI5ycaVV2Jq9fykIhUEZEOuNyWWAOU9UBjca9goqpZuMBymIhU97lIfyX8OZ0IHCMil4lIRf/pLq5SaCTVcTkse0XkWODPMaYTXEXUf4rI0eJ0EJEjDiMdk3A/+kL9BJwOPCMi5/hxI3HHIgNyKhefW4A051DVtcAnwL/FtdBcQUSai0i4tKCqbTXobbeQz3Xhlglxu7gK0E2AIcBbUeY9FleEuTzKPCZGRfF6WhKu8tRW3MXeswi2USREpKWIzA367BCRW0LmaSUi/xORfSJyW8i0l8XVvo/YIVsJ8QSuotUmXMWsycW03f7A8bi6EP/C3ej7ws2oqgtxX4xLfLZpoYrUVPU9XCXKcb5YYT6udjxADVyFua24LNPNuEqmsfgXLmCah6tYN9uPK0javsUFNA1xdQ0CnqBw5+ll4HVcEcpSXMB0k9/mdly584u4X+O7geAs8NOBn8S1j/Ikrpw/WpFO4Ev5bFxxzlKf7heB4ODwA+BoYL2q/hC0bLTzk59LcOX9a3A/aO5V1SkxLvsZ7gt1nYhs8uNuwh2PJbjK6G/ijmUePofkVKCf3/Y6civqRnID8ICI7MQFoW/HmE6Ax/z8n+AqPr+Eq69Q0HRMAFqFu6f8OekDvCDubY4ncefsE5/mb3A/Hg/X5biipgW4++0dXL2NojAeV5l4LvAh7nhF0h8XRJk4iGsnZCLyf7hfXj8B2X60quo5kZcqmXzW8GpcNvTyoPH1cNlzf8JVpHs0aFpPYBeuwtYhryuavETkLdz79EWeY2KMySUig3AVVG9JdFoSzT/TpwOd8wueTWziXcfiT0BLVQ37K7SUORlYHJo1pq5RlQ0iclboAqr6RVC5ownhs8S34H7NnoprqOfhhCbKmHJIVUclOg0lhX+mRyu+MgUU78BiCa7Wb1kILPrhsuJN/ByJa0vhCFy2+/WqOiexSTLGGBNP8Q4sMoG5IjKVoOBCVW+O83aKlK/IdQ7uHXwTJ6o6AVe+a4wxpoyKd2Dxgf+UdmcAs1V1fb5zGmOMMSZHXAMLVX3V/9oPvCP9i6oeiOc2isklWDGIMcYYU2DxfiukF/AqrmlgwTVQcoWqfhGvbdSpU0ebNm0ar9UdIjs7m3nz5tG+fXuSklwDhxs3bgSgbt26HDhwgJ9//pmsrCxEhAoVKtC2bVuSkpJYsmQJO3fu5ODBg1SsWJGGDRtSp06sbd8YY4wxJdP333+/SVXr5j9n/ItC/g2cqqq/AIjIMbhf/l3jtYGmTZsya9aseK0OxoyBu+6CFSsgPR2GDYP+/eO3fmOMMaaUE5GYGw+Ld2BRMRBUAKjqr75Vv5JpzBgYNAgyfQvTy5e7YbDgwhhjjDkM8W55c5aIvCQivfznBVzLZyXTXXflBhUBmZluvDHGGGMKLN45FtcDg4GbcXUsvgCezW8hEfkDrknVZFxrcL+Lc7rCW7GiYOONMcYYE1W8cyySgSdVta+qngc8hes7JCpV/dJ3KjMRV/mzeKSnR0oQnHQSTJvm/jfGGGNMTOIdWEzFdZoUkAJ8WoDl/0yY1zxFZJCIzBKRWYE3NOJi2DBITc07LiXF1a/4+Wc48UTo2RM++cQCDGOMMSYG8Q4sqqjqrsCA/z81yvw5RCQd2K6qO0KnqeooVe2mqt3q1o3pbZfY9O8Po0ZBRgaIuL8vvABvvAFLl8KIEbBsGZx2Ghx3HEycaAGGMcYYE0W8A4vdItIlMCAiXYE9MS57NTA6zunJX//+LnjIznZ/A2+DVKkCgwfDb7/B88/Dhg1w9tnQtSu8+66b3xhjjDF5xDuwuAX4j4h8KSJfAm8BN8ayoKreq6pfxzk9hVe5snsF9ddfYfRo2LULzj8fOnaEt96CrKxEp9AYY4wpMeIaWKjqd0Ar3NshNwCtVbXkvm5aEBUrwpVXwoIFrv2LrCzo1w/atoXXX4eDBxOdQmOMMSbh4hpYiEgqcAcwRFV/BJqKSJ94biPhkpPhz3+G+fPhP/9xORqXXw6tWsFLL8H+/YlOoTHGGJMw8S4KGQ3sB473w6uAf8V5GyVDhQpwwQUwZw68/z7UrAkDB8LRR8Nzz8G+ffmtwRhjjClz4h1YNFfV4cABAFXdg2soq+yqUAHOPRe++w4mTYJGjeCGG6BZM3jyyUNb9jTGGGPKsHgHFvtFJAVQABFpDpSPn+4icMYZMGMGfPqpy7m45RY46ih49FFX6dMYY4wp4+IdWNwLTAaaiMgYXINZf4vzNko2ETj5ZNdq5/Tp0KED3H47NG0KDz4IOw5ppsMYY4wpM+L9VsgUoC9wJa4FzW6qOi2e2yhVevaEKVPgf/+DHj1c52YZGXDffbB1a6JTZ4wxxsRdvN8K+T2wV1U/BGoCfxeRjHhuo1Q67jj48EOYNQt69YL773cBxt//Dps2JTp1xhhjTNzEuyjkOSBTRDoCtwPLgdfivI3Sq2tXeO89+OEHVx/j4YddgHHbbbBuXaJTZ4wxxhRavAOLg6qqwLnAU6r6JFA9ztso/Tp0cK12/vQT9O0Ljz/uKnnefDOsWpXo1BljjDGHLd6BxU4RuRO4FPhQRJKAinHeRtnRurVrtfOXX1yjW889B82bw/XXw/LliU6dMcYYU2DxDiwuxr1eerWqrgMaAY/EeRtlT4sWrtXORYvgqqvc/y1awNVXu07QjDHGmFIi3m+FrFPVx1T1Sz+8QlWtjkWsmjaFkSNhyRKXa/Hmm9CyJVx2GSxcmOjUGWOMMfmKd46FiYfGjeGpp2DpUvjLX1w37W3awMUXw48/Jjp1xhhjTEQlIrAQkQoiMkxEnhaRKxKdnhLjyCNdq53LlsHQofDRR67iZ9++MHt2olNnjDHGHKJEBBa4t0ga4foYsdciQtWt61rtXLYM7r0XPvvMvbrapw98+22iU2eMMcbkiHsDWSIyRUR+FZElIrJURJbEsGhL4H+q+lfg+jDrHSQis0Rk1saNG+OZ5NKldm3Xaufy5fCvf7kWPY87Dk49Fb78MtGpM8YYY+KeY/ES8BhwAtAd6Ob/5mcVEGjjOit0oqqOUtVuqtqtbt268Upr6ZWW5poHX74chg93DW717AknnuhyM1QTnUJjjDHlVLwDi+2q+pGqblDVzYFPDMu9C5wmIk8DX8Q5TWVXtWqug7OlS+GJJ+DXX10HaCecAJMnF1mA0bRpU9q3b0+nTp3o1q3bIdPHjx9Phw4dcqZ/9dVXeaZnZWXRuXNn+vTpUyTpM8YYkziicfzyEZGHgSRcoJDTXbqqxq2mYbdu3XTWrFnxWl3ZsncvjB4NDz0EK1dCt27wj3/A2We7XlfjpGnTpsyaNYs6deqEnb5r1y6qVq2KiDBv3jwuuugiFga9LvvYY48xa9YsduzYwcSJE+OWLmOMMUVDRL5X1UN/SYYR7xyLHrjijweBf/vPo3HehomkShXX/sVvv8ELL8CWLXDuudC5M7zzDmRnF0syqlWrhvhAZvfu3Tn/A6xatYoPP/yQgQMHFktajDHGFK94N5B1YpjPSfHcholBpUowcKBrKvzVV2HPHrjwQmjf3jW6lXVINZYCERFOPfVUunbtyqhRo8LO895779GqVSvOOussXn755Zzxt9xyC8OHD6dChZLyQpIxxph4isvTXUQu9X//Gu4Tj22Yw5CcDJdfDgsWwNixrjikf3/XR8krr8CBA4e12hkzZjB79mw++ugjnnnmGb744tBqMeeddx4LFy7k/fff5x//+AcAEydOpF69enTt2rUwe2WMMaYEi9fPxqr+b/UIH5NISUnQrx/Mm+eKRKpWdX2StGzpikz27y/Q6ho2bAhAvXr1OO+885g5c2bEeXv27MnixYvZtGkTM2bM4IMPPqBp06b069ePzz77jEsvvbRQu2aMMaZkiWvlzeJglTfjQBUmToR//hO++w6aNIE77nCdnlWpEnXR3bt3k52dTfXq1dm9eze9e/fmnnvu4fTTT8+Z57fffqN58+aICLNnz+bss89m1apVeepaTJs2jUcffdQqbxpjTClQ7JU3ReRuEakdZfpJImLvFpYUIu5NkW+/da+lpqfDjTdCs2bw+OOQmRlx0fXr13PCCSfQsWNHjj32WM466yxOP/10Ro4cyciRIwH473//S7t27ejUqRODBw/mrbfeyhNUGGOMKbvikmMhIucCfwP2ArOBjUAV4GigE/Ap8KCqFrrZTMuxKAKqMG2ay8H4/HPXhPitt8INN8AHH7jGuFascAHIsGGunoYxxphyoyA5FvFux+Jo4PdAA2AP8DPwharuidc2LLAoYjNmuADj449dXYz9+/NW8kxNhVGjLLgwxphyJGGBRXGwwKKYzJwJvXq5V1VDZWS4DtGMMcaUC4lsIMuUFcce61ryDGf5cnj3Xdi5s3jTZIwxpsSzwMJElp4efrwInH8+HHEE9O4NTz4JixcXb9qMMcaUSBZYmMiGDXN1KoKlprrWPKdPh1tugdWr3d8WLVzDW7ff7qYdZuNbxhhjSre4BhYiMlxEaohIRRGZKiKbAq1ymlKof39XUTMjw+VSZGS44csuc920Dx/uWvX87TeXa9GkifvbqxfUqweXXAJjxsDmWDq4NcYYUxbE+62QuaraSUTOA/4E/AX4XFU7xmsbVnmzhNu5E6ZMcQ1wffghbNgAFSrA734HffrAWWdB27Zx7W3VGGNM0Upk5c2K/u+ZwFhV3RLLQiLSS0S+FJGRItIrzmkyxal6dejbF15+GdaudY1w3X23a3Rr6FDXEdpRR7kGuSZPjlxB1BhjTKkU78BigogsxHWdPlVE6uIazcqPArtwjWqtinOaTKJUqODeLrn/fvj+e1i1yhWldOzoAo8zznAVQP/0J3jxRVizJtEpNsYYU0hxb8dCRGoBO1Q1S0SqAtVVdV0+y1RQ1WwRqQ88pqr9Q6YPAgYBpKend12+fHlc02wSYM8e19rnxInus2KFG9+1qysy6dMHunRxwYkxxpiESmTLm6nAX4F0VR3kW+Jsqaox9TQlIpWAN1X1gkjzWB2LMkgV5s/PrZfxv/9BdjbUr+/qZPTp415rrVYt0Sk1xphyKZGBxVvA98DlqtpORFKA/6lqp3yW6wucBtQEnlPVaZHmtcCiHNi0ydW/mDjR/d2+HSpVcm+bBCqANmuW6FQaY0y5kcjAYpaqdhOROara2Y/7wd4KMYftwAHXf0mgyOSXX9z4Nm1yi0yOPx6SkxObTmOMKcMS+VbIfp9LoT4hzYF9cd6GKU8qVnQ5FY8+CgsXwq+/uq7dGzSAxx5z7WnUq+fa3HjzTdgS04tIxhhjiki8cyx6A3cDbYBPcD2dXhmtaKOgLMfC5NixI2+bGRs3usqev/99bm5G69bWZoYxxhRSQns3FZEjgOMAAb5R1U3xXL8FFias7Gz47rvcIpO5c934o47KDTL++EeoXDmhyTTGmNIo0YFFB6ApkFPorarvxmv9FliYmKxa5XIxJk6ETz91DXFVrQqnnuqCjDPPhCOPTHQqjTGmVEhk5c2XgQ7AT0C2H62qOiBe27DAwhRYZiZ8/nlukcnKlW58t265uRmdO1ubGcYYE0EiA4sFqtombisMwwILUyiq8OOPuUUm33zjxjVokNtmximnuNwNY4wxQGLfCvmfiBRpYGFMoYhAhw7w97/D11/D+vWuG/gTToC333bNix9xhGtu/JlnYNmyRKfYGGNKlXjnWPQEJgDrcK+ZCq4opEO8tmE5FqbI7N8PX32Vm5uxaJEb365dbm7GccdZmxnGmHInkUUhv+Ga9P6R3DoWqGrcOvewwMIUm19/za0A+sUXcPAg1K7tcjP69IHTToNatRKdSmOMKXKJDCw+U9WT4rbCMCywMAmxfTt88okLMiZNcs2OJyW5IpRABdCWLa3NDGNMmZTIwOJZXH8fEwhqcdNeNzVlSlYWzJyZW2Qyb54b37x5bpDRs6fr38SYEmjv3r307NmTffv2cfDgQS644ALuv//+PPM88sgjjBkzBoCDBw/y888/s3HjRmrXrs3jjz/Oiy++iIjQvn17Ro8eTZUqVRKxK6aYJDKwGB1mtL1uasq2FStcLsbEiTB1qmszo1q1vG1m1K+f6FQak0NV2b17N9WqVePAgQOccMIJPPnkkxx33HFh558wYQKPP/44n332GatXr+aEE05gwYIFpKSkcNFFF3HmmWdy5ZVXFu9OmGJVkMAirrXQVPWqeK7PmFIhPR2uu859MjPhs89yczPe9Zl1xx6bm5vRqZMVmZiEEhGqVasGwIEDBzhw4AAS5ZocO3Ysl1xySc7wwYMH2bNnDxUrViQzM5OGDRsWeZpN6RGX101F5G/+79Mi8lToJx7bMKZUSE11wcPIka4hrjlz4J//dIHEvfdCly7QuDFcey1MmOACEWMSICsri06dOlGvXj169+5Njx49ws6XmZnJ5MmTOf/88wFo1KgRt912G+np6TRo0IC0tDROPfXU4ky6KeHi1Y7Fz/7vLOD7MJ98iUhVEfleRPrEKU3GJJaIy524+27XENfatTB6NPzudzB2LJxzjmsz46yz4NlnYXncXp4qlL1793LsscfSsWNH2rZty7333nvIPNOmTSMtLY1OnTrRqVMnHnjggZxpTZs2pX379nTq1Ilu3WLKOTUJkJSUxNy5c1m1ahUzZ85k/vz5YeebMGECv//976lduzYAW7duZfz48SxdupQ1a9awe/du3njjjeJMuinh4lIUoqoT/L+Zqvqf4GkicmGMq7kDeDse6TGmRKpfH6680n3274cvv3TFJRMmuDoagwdD+/a5RSY9erg3T4pZ5cqV+eyzz/KUv59xxhmHlL//4Q9/YOLEiWHX8fnnn1OnTp3iSK4ppJo1a9KrVy8mT55Mu3btDpk+bty4PMUgn376KUcddRR169YFoG/fvnz99ddceumlxZZmU7LFu+XNO2Mcl4eInAIsANZHmD5IRGaJyKyNGzcWMonGlACVKsHJJ8Pjj7uGuBYuhEcfdTkYw4e7rt/r14fLL3ctgm7bVmxJK2j5uyl9Nm7cyDZ/Te3Zs4dPP/2UVq1aHTLf9u3bmT59Oueee27OuPT0dL755hsyMzNRVaZOnUrr1q2LK+mmFIhXHYszRORpoFFI/YpXgIMxrOJEXFfrfwauEZE86VLVUaraTVW7BaJkY8oMEdcGxq23us7SNm2CceNcQ1wffggXXwx168JJJ8Fjj7mGu4pYLOXv//vf/+jYsSNnnHEGP/30U9DuCKeeeipdu3Zl1KhRRZ5WE4MxY6BpU9fRXtOmrH3xRU488UQ6dOhA9+7d6d27N3369GHkyJGMHDkyZ7H33nuPU089lapBfef06NGDCy64gC5dutC+fXuys7MZNGhQAnbKlFRxed1URDoCnYAHgHuCJu0EPlfVrTGu50pgk6qGz1/FXjc15UxWlqufEWgB9Mcf3fgWLXKLTP7whyJrM2Pbtm2cd955PP3003myyXfs2EGFChWoVq0akyZNYsiQISzyTaCvWbOGhg0bsmHDBnr37s3TTz9Nz549iyR9JgZjxsCgQXkrCqemwqhR0L9/4tJlSpVEtmNRUVUPxG2FYVhgYcq15ctzg4zPPoN9+6B6dde8eJ8+LpejXr24bvL++++natWq3HbbbRHnadq0KbNmzTqkXsV9991HtWrVoi5r4kTVtaGSmZn72bMHTj/ddbYXKj29xFQYNiVfItuxKNKgwphyLyMDbrjBfXbvdg1yBdrMeOcdV6zSo0dubkaHDgVuM2Pjxo1UrFiRmjVr5pS/33HHHXnmWbduHfXr10dEmDlzJtnZ2RxxxBHs3r2b7Oxsqlevzu7du/nkk0+45557ImypnMjKcl/wgS/60C/+eA3v2VOwdK1Y4XK+GjaERo3c38AneDg1tWiOiymzrJtGY0qrqlXdK6vnnON+rc6Z4wKMDz90r7jefbdrMyPQM+tJJ8X0JbF27VquuOIKsrKyyM7O5qKLLsopfwe47rrreOedd3juuedITk4mJSWFcePGISKsX7+e8847D3CNKP35z3/m9NNPL9LDcNgOHCjaL/rA8L59+aclnJQUd75SU/P+n5rqKvkGD4dODx6+9lrYsOHQ9VevDt27w5o1MGsWrF4dPjipWTN8wBE8fOSRULHi4e2nKXPiWhRSHKwoxJgYrFsHH33kAo1PPoFdu6BKFfcmSp8+Ltho0sTNO2YM3HWX+wWbng7DhiWu7F3VvYpblF/0gf8PxlKvPESFCvl/kcdjuEqV+LXOGmsdC1XX2d6aNS7IWLMm9xM8vHbtocdOxBXBRcr1CPxfp447hqZIxdIXzMKFC7nqqquYPXs2w4YNy1NcuW3bNgYOHMj8+fMREV5++WV+97vfJayOxQQgdIXbcQ1nPa+qewu7DQssjCmgfftct++BNjOWLnXjO3Z0bwp8/LErmw8I96WTne3mKY5f+NnZBd/H5OSi/aIPDFeqVDqbY49n8JidDRs35h+AhMslqVgRGjTIPwCpUaN0HucSIpa+YDZs2MDy5ct5//33qVWrVp7A4oorruAPf/gDAwcOZP/+/WRmZlKrVq2EBRZPAnWBsX7UxcA6IAWooaqXFXYbFlgYUwiqrs2MQJHJ9Onh50tOdtnbwXUDDkflykX/Cz8lxbLhS6L9+13OWbigI/j/7dsPXTY1NXq9j0aNXICSklL8+1XKZGZmcsIJJ/Dcc8+FfW08tIL1jh076NixI0uWLMnTfk3CKm8CnVU1+L2yCSLyhar2FJGfIi5ljCkeItC6tfvcfrvLlg734+LgQejdu3Bf/CkpCWk51JQQlSq53JH09Ojz7d4dOddj9Wr49lv3/94wGd61akXO9Qj8X7++C5TLmaysLLp27cpvv/3G4MGDI/YFE2rJkiXUrVuXq666ih9++IGuXbvy5JNPFmjb8T7adUUkXVVXAIhIOhB4/2x/nLdljCmsSK8cZmTAyy8Xf3pM+VO1Khx9tPtEoupan42U67FmDfz0k8shycrKu6yICy7yC0COOKJMFb8E+oIJtEUzf/78sE22hzp48CCzZ8/m6aefpkePHgwZMoSHH364QNuOd2BxK/CViCwGBDgKuEFEqgKvxnlbxpjCGjYsfMW+YcMSlyZjQom43IlatSDal2NWlqv/Eanux4oV8L//udZtQ1WqlFv/I1oAUr160e1nEcivL5hQjRs3pnHjxjk5HBdccEFiAwtVnSQiRwOtcIHFwqAKm0/Ec1vGmDgIVOArKW+FGFMYSUmubtCRR0LXrpHn27fP5W5ECkDmz3eVmnfuPHTZatWiVzxt2NAFKFWqFN1+5iOWtmgiOfLII2nSpAm//PILLVu2ZOrUqbRp04ZJkybFvP24v24qIr8DmhIUtKjqa/Fav1XeNMYYUyx27nSv10arfLpmTfi2So44Iv8ApH79IqmHNG/evEPaornnnnvytEWzbt06unXrlqd5/gULFlCjRg3mzp2b80ZIs2bNGD16NLVr107YWyGvA82BuUCgoEtV9eZ4bcMCC2OMMSWGKmzZkv/bL+vWHfoqdYUKLnclvwCkdu38638UcXs0iXwrpBvQRktbq1vGGGPM4RBxuRNHHAHt20eeLyvL9dkSKddj6VKYMQM2bz502cqVo796+/338I9/5L4Wvny5qzsFCSnWjHeOxX+Am1V1bdxWGsJyLIwxxpRZe/fmFr9EaoRs9Wr3mm5+MjJg2bK4JCuRORZ1gAUiMhPIKXRS1XPivB1jjDGm7KlSBY46yn2i2bkzN+g4+eTw86xYEf/0xSDegcV9h7OQiLQGhuACk6mq+lw8E2WMMcaUKdWrQ6tW7pOREb49mvwaJysi8X7dNEL7wPku9zNwnYhUAF6IZ5qMMcaYMq2EtUcTl27mROQr/3eniOwI+uwUkR0xruMc4Ctgaphpg0RklojM2rhxYzySbIwxxpQN/fu7jgMzMlxl0oyMQzsSLEYlrtt0EflQVc+KMn0jECbPp9DqAGGaYzOeHZ+yz85x2WfnuOwrqnOcoap1Y5kx7j2ziEgSUJ+8DWRFrUEiIr2AvkBlIGrzXrHuWEGJyKxYa7yWR3Z8yj47x2WfneOyrySc47gGFiJyE3AvsB4ItASiQIdoy6nqNGBaPNNijDHGmOIX7xyLIUBLVQ3Twocxxhhjyrq4VN4MshLYHud1FpdRiU5ACWfHp+yzc1z22Tku+xJ+juPd8uZLQEvgQ/I2kPVY3DZijDHGmBIr3kUhK/ynkv8YY4wxphwpca+bFhURqQo8C+wHpqnqGD/+TOBGYJKqjvDjHgZSgUxVHZqgJBe7KMdoKK7X2iOB61V1lYgsBqYAs1U14VlvJjZRzvF9QGtgK/CAqq4pr/dBaRflHF8NdAeaAPNU9U67j0snEWkG3AWkqeoFQePbAXf6wYdUdX4i7uN4NZD1hP87QUQ+CP3EYxtx0Bd4R1WvAXL6LlHVScDwwLCIpAMVfVfvSSLSpNhTmjiRjtHDftzLwIl+9C4gBVevxpQeYc8xcBD3RXQA2FbO74PSLtJ9/JKqXgf8CrziR9t9XAqp6hJVvTrMpCHAYOAG4KZE3cfxKgp53f99NE7rKwqNgR/9/1lR5mtE7k22wi9XXm66iMdIRKoBFwG+L146A4KrT/NRcSXQFFqkc/ygqmb7FnAHAt9Rfu+D0i7afVwFOEpVf/Gj7D4uW9JUdRuAiFQnQd9nccmxUNXv/d/p4T7x2EYcrMIdVIi+36uD5mvilysvwh4jEakBPAf8TVV3AqhqtqpmAXt9Hy+mdAh7jlU10O7MBqAa5fs+KO2iPesuAN4NDNh9XOZsF5E0/8zeSYLu43i/FXI08BDQBqgSGK+qzeK2kcPkyx1HAHtxfZKcrqqXicjxuDKpWsATqvpfEXkI1wroPlW9M+JKy5gox+hdoCLuIn3b/73DLzZPVZ9IQHLNYYhyjv+Oe/DUAW5W1bXl9T4o7SKdYz9tPNBPVfeISEvsPi6VROQIYBjQG3gRaOPv43bA7bhcqOG+jkWx38fxDiy+wrW8+ThwNnCV38a9cduIMcYYY0qseGd9pajqVFwwsVxV7wNOivM2jDHGGFNCxbsdi0A53SIRuRGXZV4vztswxhhjTAkV76KQ7sDPQE3gn0AN4BFV/SZuGzHGGGNMiRW3wMJ3l/6wqt4elxUaY4wxptSJVwNZyf6Vpa4iIvFYpzGm+IlITRG5IZ95morI/OJKkzGmdIlXHYuZQBdgDjBeRP4D7A5MVNV3Iy1ojClRauJa7Xs2XisUkST/w8MYUw7E+62Q2sBm3JsgfXCvnPaJ8zaMMUXnYaC5iMwVkUf8Z76I/CgiF4fOLCJJfp7vRGSeiFzrx/cSkc9F5E18K5Ai8r6IfC8iP4nIoKB17BKRYSLyg4h8IyL1/fj6IvKeH/+DiPzOj79URGb6ND7vi2GNMSVEvHIs6onIX4H5gOIa5wgoH72cGVM2DAXaqWonETkfuA7oiGs46zsR+SJk/quB7araXUQqAzNE5BM/7Vi/rqV+eICqbhGRFL+u/6rqZqAq8I2q3iUiw4FrgH8BTwHTVfU8HzxUE5HWwMXA71X1gIg8C/QHXiui42GMKaB4BRZJuGaAw9WvsMDCmNLpBGCsL8ZYLyLTcb1jzgua51Sgg4gEelhMA47GdWg2MyioALhZRM7z/zfx8232807047/HtSYILufzcgCfhu0ichnQFReYgOtAa0N8dtcYEw/xCizWquoDcVqXMaZkiKUitgA3qerHeUaK9CKonpUfPgU4XlUzRWQauc3+H9Dc19OyiP5cEuBVa2LcmJIrXnUs7E0QY8qGnUB1//8XwMW+HkVdoCeuonawj4HrRaQigIgc4/uqCJUGbPVBRSvguBjSMhW43q83yXesNBW4QETq+fG1RSSjYLtojClK8QosTo7TeowxCeTrPMzwr5Mejyv2+AH4DNe77bqQRV4EFgCz/TLPEz7HYTKQLCLzcI3nxdJo3hDgRBH5EVdE0lZVFwB3A5/4dU0BGhRwN40xRSiuLW8aY4wxpnyL9+umxhhjjCnHLLAwxhhjTNxYYGGMMcaYuLHAwhhjjDFxY4GFMcYYY+LGAgtjjDHGxI0FFsYYY4yJGwssjDHGGBM3FlgYY4wxJm4ssDhMItI/qHvoaPONFJF/FEeaEk1E/iUim0QktNnnhIv1fMV5m8tE5JQi3sYrIvKvw1z2PBFZKSK7RKRzHNKiItKisOtJJBGpKyK/iEgVPzxNRAaWgHTF5djmd72IyO9FZJG/Jv4kIu+KyOmF3W5xEZE/iMgv8Z63sETkPhF5ozi2VRKUycDCP9D3+JtjvYiMFpFq8dyGqo5R1VNjmO86Vf1nPLddEolIE+BWoI2qHpno9ISK9XyVM48CN6pqNVWdEzqxLAQKh2EoMFpV9yY6IQnyADDCXxPvAw8Dw4pjw/H48lXVL1W1ZbznLU6F+bFQUrZTJgML72xVrQZ0AbrjOi7KQ0Ti1W18mXMYxyYD2KyqG4phWyVq/aVYBvBTohNRUs6PiFQGrgDi+stSnIQ/a0UkKYbZ8lwTqjoTqCEi3YosYTEqKcfR5K/MnyRVXQ18BLSDnF9hg0VkEbDIj+sjInNFZJuIfC0iHQLLi0gTnx24UUQ2i8gIP/5KEfnK/y8i8riIbBCR7SIyT0QC28sTFYrINSLym4hsEZEPRKRh0DQVket8VuRWEXlGRMJ2SS8ix4rILBHZ4XNlHguadoLfj20+q/tKPz5NRF7z+7JcRO4O3Kh+f2b4/dgC3CcilUXkURFZ4bcxUkRSwqTlFFwvkw19LtErfvw5IvKTT8c0EWkdtMwyEblDXA+Vu0O/XPy2Hg0ZN15E/ur/Hyoii0Vkp4gsEJHzguYLty8558vP8zsR+c6fr+9E5HchaTslaDjnl5SIVBGRN/y1sM0vWz/cOQpJe4WgNG8WkbdFpLafNllEbgyZ/wcR6ev/byUiU/w184uIXJTf9oK2ebc/1xv8uU/z53UXkAT8ICKLwyz7hf/3B39OL/bjI16/IctHvHZEpJeIrPLnfx0wWkRqichEf21u9f83DlrfNBH5pz+vO0XkExGpEzQ90jUf0zXs9QC2qeqqCPvUQNy9fZsfPi5omz+ISK+Q9A4TkRlAJtBM8rm/RWSAiPzsp30shewOXtyz5zkRmSQiu4ET/aQ6/nraKSLTA9vx10EzYII/55X9/NOAs6Jsp9DPNHHFLX8HLvbb/sGPD3ccr/LHaaeILBGRa4PW00tEVgUNLxOR2/x52y4ib0luMVfM8/rpfxORtSKyRkQGSpQcPRE5yh/bnSIyBagTMv0/IrLOb+cLEWnrxw8C+gN/88dhgh8f7XnXwm9ru7ii6LeCpoV9dkTaTtyoapn7AMuAU/z/TXAR+D/9sOK+BGsDKbgcjQ24h0oS7hfLMqCyH/4BeByoClQBTvDruRL4yv9/Gq5b55qAAK2BBn7aK8C//P8nAZv8NisDTwNfBKVbgYl+PenARuD0CPv4P+Ay/3814Dj/fzqwE7gEqAgcAXTy014DxgPVgabAr8DVQftzELgJ1+11CvAE8IE/VtWBCcBDEdLTC1gVNHwMsBvo7dPxN+A3oFLQOZrrz09KmPX1BFaS2wNvLWAP0NAPXwg0xAXHF/ttNYiyL8HnqzawFbjMT7/EDx8Rev344fuAN/z/1/rjkIq7ProCNWK4Dm/BdRXe2J/754GxftrlwIyg5doA2/x8Vf1xuMqntQvuGmoben2F2f4Af8yb4a6Rd4HXQ663FlHuozzTie36beH/f4II1w7uWjkI/J9fTwruOj3fH9fqwH+A94PWPQ1YjLuuUvzwwzFc8xHTEWZ/BwMfhoybBgwk934Z5Mc3AjYDZ+Kuwd5+uG7QciuAtv68VSTK/Q38yZ+r1n7+u4GvIxzbof76CPsJWuYVYDvwe5/GKn7cTtz9VRl4En9fhLv2/bi/Au9GOGbxfKbdh7/PQo5/6HE8C2iOe9b+ERdwdInwHFoGzMQ9K2oDPwPXHca8pwPrfDpSgdeJcv/gns+P+WPS0x/zN4KmD8Bdj5Vx1+jckPP2r5D1RXvejQXuCjrHge+ow352FPaT8CCgSHbKXSC7cDfacuBZ/JeXvxhOCpr3OXzQETTuF3/BHu9vhOQw27iS3C+qk3APneOACiHz5Zw84CVgeNC0asABoGlQ2k4Imv42MDTCPn4B3A/UCRl/J/BemPmTgH24OhCBcdcC04L2Z0XQNPEXb/OgcccDSyOkpxd5b9J/AG8HDVcAVgO9gs7RgCjnUHAPlJ5++BrgsyjzzwXODbcvYc7XZcDMkOn/A64MSlukwGIA8DXQIcbrMBBY/AycHDStgT/3ybgHzG4gw08bBrzs/78Y+DJkvc8D94ZeX2G2PxW4IWi4ZWCbQddbQQKLWK7fFvldO/5a2Q9UibLtTsDWoOFpwN1BwzcAk/O55gt6Dd8FjAsZNw33BbEMuCRo/B0EBWl+3MfAFUHLPRDmeIa9v3G5qleH3C+ZQddE1HMVYX9eAV4LM25c0HA1IAtoEu7az+/ei/GaiPWZdh/hA4sHws0fNM/7wJCgays0WLg0aHg4MPIw5n2ZoIAUd52HPSe4AOogUDVo3Juh+xY0raZfV1rQOYr6hU/e591rwCigccg8h/3sKOynLBeF/ElVa6pqhqreoKp7gqatDPo/A7jVZ2duE5FtuF/RDf3f5ap6MNqGVPUzYATwDLBeREaJSI0wszbEBTqB5XbhfuU0Cpon+I2KTNyNGs7VuF9vC8Vlx/fx45vgftmFqgNUCt6+/z9428HHpS4uMv8+6LhM9uNjEbqv2X79kbaXh7orfxzuVyjAn4ExgekicrnkFl9twxV1BWc3Rlx3aNq80GMRyeu4L5BxPkt0uIhUjGG5DOC9oPT+jHug11fVncCHQD8/bz9y9zUD6BFyffYHYqkgG7qfy3GBTL5FN7GsL8L1C7FdOxs1qIKkiKSKyPPiim124ALnmpK3XkCkeyPSNV/Qa3grLsgL1R8XFL8TNC4DuDDkvJyACxgDwl2DkfYhA3gyaF1bcIFRLNdkNOHSkDPOn8MtuHMbSXXcj7Rw4vlMiyTPPojIGSLyjc/e34bLNaoTdsmCbz/SvA1D0pHf82Wrqu4OGpdzjEQkSUQe9kUbO3ABDUTZh3yed3/DXSszxRU9D/DjC/PsKJSyHFhEo0H/rwSG+SAk8ElV1bF+WrrEULlMVZ9S1a64rLJjgNvDzLYGd7IBEJGquGzb1QXeAdVFqnoJUA+XpfyOX99KXDZhqE24XxIZQePSQ7atIfPvwWWbBY5LmroKsbEI3VfBfQFE2l44Y4ELxJUB9wD+69eVAbwA3IgrvqgJzMfdXLGsO0/avOBjsRv3hRSQcyOq6gFVvV9V2wC/A/rgijLysxI4I+Q6q6KuDlBgXy8RkeNxWf2fBy03PWS5aqp6fQzbDN3PwC+p9TEsm+/6oly/sVw7oefnVlyOSg9VrYHLPoa85zSSaNd8Qa7hebh7N9R9fl1vBgU6K3E5FsHnpaqqPhy0XH7Xd+g+XBuyvhRV/Tp0RhH5uy8XD/sJmT1cGpoErasaLtt/TZS0tcYVCYcTt2cakY9Xznhx9T7+i3ujqb6/9ycR23VSGGtxxZgBTSLN6Oet5Y9FQHrQ/38GzgVOAdJwxWyQuw95jkN+zztVXaeq16hqQ1wu9LO+7kd+z46CXJ8FUl4Di2AvANeJSA9xqorIWSJSHVfethZ42I+vIiK/D12BiHT3y1fEfSntxf0aDfUmcJWIdPI3yIPAt6q6rKCJFpFLRaSuzwnY5kdn4X7pniIiF4lIsogcISKdVDULlw05TESq+4v1r0SoAe/X+wLwuIjU89tsJCKnxZjEt4GzRORkf1xuxRXFHPKgjETdK5AbgReBj1U1sJ9VcTfFRp+uq/CVc2M0CThGRP7sj9HFuHoNE/30uUA/Eakorjb8BYEFReREEWnvv2B24IK1cOc61Ejcsc/w66krIueGpCkD97rfW/7449N0jIhc5tNT0V9vrcnfWOAv4iqSVcNdb2/llwMXZD2ufkZATNfvYV471XFBwDZxlVrvjTGNEPmaL2g6ZuJySUJzCQ7gyrirAq+Lq/D8BnC2iJzmf4FWEVcZsDGHZyRwp+RW4ksTkQvDzaiqD/oviLCfGLZ1prjKrpWAf+LOYbRf4H/EFdWEE7dnGu56ayrR3/yohKuXsBE4KCJnAMXxGvnbuP1sLSKpwD2RZlTV5cAs4H4RqSQiJwBnB81SHfcs3Iz7AfNgyCpC77uozzsRuTDoutvq580i/2dH6HbiptwHFqo6C1eGOAJ3Un7Dlcfjv4zPxpWnrQBW4cqtQtXAPcC24rK8NuMi6tBtTcXVPfgvLmBpTm72d0GdDvzkf6E8CfRT1b2qugKXNXgrLotzLtDRL3MTLvBZAnyFeyi8HGUbd+COxzfisuw+xf2qzJeq/gJciqvMtQl3HM9W1f0F2EdwX46n+LQG1r0A+DeuXsR6oD0wI9YVqupmXE7Drbhz9Tegj6pu8rP8A3dutuLqsbwZtPiRuCzxHbjijOnE9nrik7hKhJ+IyE5cRc4eQWnah6tcGbqvO3EPzn64X4fryK30mJ+XcUU3XwBLcQHvTTEsF3Af8KrPRr2ogNdvQa+dJ3A5NZtwx2ZyrInM55qPOR3+2nwFd92Gm9YXl0P4Mu4X+bm4Nxk24n4d3s5hPlNV9T3ceR3n0zkfOONw1hWDN3GB2xZc5eP+kWYUke7AbnWvnR4izs+0//i/m0VkdoTt7QRuxn3Rb8X9+v/gMLcXM1X9CHgKl5P4G+7ZAy5ACOfPuPt7C+5YvxY07TXc98RqYAHueg/2EtDG33fvx/C86w58678LpuAq7E7BVUYOfXb8G3hG3Nt4ZwBdAtsRkb+IK0qZLyJjJe8bMTeJe6vkJxEZnt/xCtS4N8aYck9E6gJfAp1D6mWVSyLyX+AlVZ2U6LSUJP5X/3ygcgFyAIuUz0X9FfeG0irgO1yF4wVB8zwC7FLV+0WkFfCMqp7sc+m+wlXu3yMibwOTVPUVETkRV7H5LFXdJyL1NJ/2isp9joUxxgSo6kZVbWVBhaOq51tQ4YhrAr+SiNTC5S5NKClBhXcs8JuqLvE5bONwuWrB2uDeFkNVF+KKngKVuZOBFHF1ClPJrXdzPe7V7n1+uXwbQbTAwhhjjMnftbhir8W4OgyxVKAuTo3I+7bKKg59q+gHXJEeInIsrl5XY1+J/FFckf9aYLuqBvpWOgb4g4h8K64hru75JcQCC2OMMSYfqnq6f6uotqqep6prE52mEOHejAmt6/Aw7o2Vubj6VnNwlWBr4XI3jsK9LltVRAJ1jZJxDRQeh6tH9LZI+BahCVrAGGOMMaXbKvK+BtuYkNeIVXUHriXOQBMAS/3nNFzDcYE3T97FvU7/hl/vu+oqZM4UkWxcGxobIyXEciyMMcaY0u874Gj/enkl3Nsged6YEZGafhq4puq/8MHGCuA4cQ3VCXAy7q03cC2bnuSXPwb3yu8moih1b4XUqVNHmzZtmuhkGGOMMSXK9u3bWblyJapKnTp1aNCgARs3uoyFunXrsmvXLpYtWwZASkoKGRkZJCe7gos1a9awZcsWRITU1FQyMjKoUKEC2dnZLF++nC1btmThGpG7TV1r0xGVuqKQpk2bMmvWrEQnwxhjSq3JkyczZMgQsrKyGDhwIEOHDs0zfevWrQwYMIDFixdTpUoVXn75Zdq1c20yPf7447z44ouICO3bt2f06NFUqVIl3GZMMRozBu66C/bvh/R0GDYM+kdsoaTgRGSuqnaLZV4rCjHGmHIkKyuLwYMH89FHH7FgwQLGjh3LggUL8szz4IMP0qlTJ+bNm8drr73GkCFDAFi9ejVPPfUUs2bNYv78+WRlZTFu3LhE7IYJMmYMDBoEy5eDqvs7aJAbnwilLsfCGGPM4Zs5cyYtWrSgWTPXmnO/fv0YP348bdq0yZlnwYIF3HnnnQC0atWKZcuWsX6962Lm4MGD7Nmzh4oVK5KZmUnDhtH6LzMFdeAAbN8OW7fCtm2x/Z0zBw6GtKiRmelyMOKZaxErCyyMMaYcWb16NU2a5L480LhxY7799ts883Ts2JF3332XE044gZkzZ7J8+XJWrVpF165due2220hPTyclJYVTTz2VU08tjq46Sg9V2LUrejAQbdqu0G7kQlSsCLVqQc2a7m/t2ocGFQErVsRttwrEAgtjjClHwlXYD22WYOjQoQwZMoROnTrRvn17OnfuTHJyMlu3bmX8+PEsXbqUmjVrcuGFF/LGG29w6aWHdK9Sqh04EHtuQbi/Wfl0S1ijRt7goHnzvMPR/qakQGgrEk2buuKPUOnph44rDhZYGGNMOdK4cWNWrsxtoHHVqlWHFGfUqFGD0aNHAy4QOeqoozjqqKP4+OOPOeqoo6hbty4Affv25euvvy5xgYUq7Nx5+MFBZmb09Veq5L7oA1/2devC0UfnDkcLDNLSICkpvvs7bJirUxGc7tRUNz4RYg4sRKSqqu4uysQYY4wpWt27d2fRokUsXbqURo0aMW7cON58880882zbto3U1FQqVarEiy++SM+ePalRowbp6el88803ZGZmkpKSwtSpU+nWLaYXBQps//7Yg4Fw47Kzo68/LS3vl/4xxxQs16AkCdSjuOsuV/xRFG+FFES+gYWI/A54EagGpItIR+BaVb2hqBNnjDEmvpKTkxkxYgSnnXYaWVlZDBgwgLZt2zJy5EgArrvuOn7++Wcuv/xykpKSaNOmDS+99BIAPXr04IILLqBLly4kJyfTuXNnBg0aFHY72dku1yCWegXh/u7Jpxu4KlXyftnXrw8tW8YWHFSvHv9cg0Tr3z9xgUSofBvIEpFvgQuAD1S1sx83X1Xb5bNcVeBZYD8wTVXH+PEXA2cBB3CdnvwKvA7sxrXodZWqRow1u3XrptaOhTHGxC7QxkFBf83u23f49Qy2b4+eayASudggllwDazqjeInI97G2YxFTUYiqrgyp3JNP1RTA9aD2jqpOEJG3gMAbtecD/XGdmjwI3ALsU9VrROQZoCqwM5Z0GWOMie611+C663JzAJYvhwED4MMPoVmz6EULe/dGX3dKSt4v+wYNoHXr2HMNKlhLSmVSLIHFSl8cor6N8ZvJbUM8msbAj/7/4EDkUeBpYB0uuMj06/4QWKuqFlQYU8wOtyXGX375hYsvvjhnviVLlvDAAw9wyy23cPvttzNhwgQqVapE8+bNGT16NDVr1izmPSt7VN0X/4YNsX22bDl0Hfv3w9ix7os9NIegUaPoQUHw/5UrF9dem9IklqKQOsCTwCm4blk/AYao6uZ8lrsM2KqqE0VknKr2C5neArgR13vamar6gIjcDUxU1bkh8w4CBgGkp6d3XR7uvRpjzGHJysrimGOOYcqUKTRu3Jju3bszduzYPA0m3X777VSrVo17772XhQsXMnjwYKZOnXrIeho1asS3335LRkYGn3zyCSeddBLJycnccccdAPzf//1fse5baZGZCRs3xh4sRGq3oHZtqFcv7+fZZ8PPK+LWY7kGJhZxKwoRkSTgCVU9nCoh7wIjROQsYIKIvK6ql4nImcDZuMqgtwM7gFtF5FlcV6yPh65IVUcBo8DVsTiMtBhjIihMS4z169fPmWfq1Kk0b96cjIwMgDwNJx133HG88847xbE7JcLBg7BpU2xBwsaNkRtFqlo1N0Bo0gS6dj00cAh8jjjCNZ4U6sMPI7dxYEGFKQpRAwtVzRKRuiJSSVX3F2TF/tXUq4JGjfHjJwGTQma/pCDrNsbET2FaYgwOLMaNG8cll4S/lV9++eU8RSaljaqrjBhrjsLmCPm5SUl5g4EWLSIHCnXrusCisEpaGwem7IuljsUyYIaIfIB7cwMAVX2sqBJljCk+hWmJMWD//v188MEHPPTQQ4esa9iwYSQnJ9O/pLwL5+3ZU7DihwMHwq+nVq3cYKBNG+jVK3KwULNm8ecSlLQ2DkzZF0tgscZ/KgDVizY5xpjiVpiWGAM++ugjunTpkicHA+DVV19l4sSJTJ069ZBgJd4OHnQ5BfkVOwT+3xmhmnhKimsToV49V5Gxc+fcHITQQKFOHdcKY0lXkto4MGVfvoGFqt4PICLV3aDm00WKMaY0KUxLjAFjx449pBhk8uTJ/N///R/Tp08nNTW1wOlShR07Clb8EK4uelJS3qCgR4/IOQr16sWn+MGY8iyWljfb4Rqwqu2HNwGXq+pPRZw2Y0wxKExLjACZmZlMmTKF559/Ps96b7zxRvbt20fv3r0BV4HziSdGFqj4YX+Eml01a+YGAq1aQc+ekQOFWrWskqIxxSmW102/Bu5S1c/9cC/gQVX9XZGnLgxredOY+DrcVhnB9eIYKH6IJWDYsSP8eqpUiZ6LEFqpsTQUPxhTlsS75c2qgaACQFWn+ea6jTGl3Jgxed8YWL4crrkG1q+H447LP1DYtCl88UOFCnmLH7p3z7/4oYirYBhjikksgcUSEfkHrjgE4FJgadElyRhT1LKy4JdfYMiQQ7uI3rMHbr310GXS0nIDgWOOgRNOiBwo1K5txQ/GlFexBBYDgPtxDV4BfEHe9imMMSXYvn0wfz7Mng1z5ri/8+bl33vkRx/lLX6w5puNMbGI5a2Qrbj+QYwxJdzOnfDDD7kBxJw58NNPuU1A16jhXp+89lro0gXuuAPWrj10PRkZcPrpxZt2Y0zZEMtbIVOAC1V1mx+uBYxT1dOKOG3GmCg2bXKBQ3AQsWhRbp2HevVc8HDmme5v585w1FF5iygqVLBWGY0x8RVLUUidQFABLgdDROoVXZKMMcFUYfXqvEUZc+ZAUJtWZGS44OHSS3ODiAYN8q8Qaa0yGmPiLZbAIltE0lV1BYCIZADWEZgxRSA7GxYvPjSI2LTJTReBli3hD39wwUPgU7v24W/TWmU0xsRTLIHFXcBXIjLdD/fEd2FujDl8Bw7Azz/nBg9z5sDcublNTVesCO3awTnn5OZCdOgA1aolNNnGGBNVLJU3J4tIF+A4QIC/qOqmIk+ZMWXInj3uTYzgXIgff3RvbICr19CpE1x+eW4Q0batNQRljCl9Yqm8+XtgrqpOFJFLgb+LyJOqurzok2dM6bNtm8t5CA4ifv7ZFXOAa2K6Sxe46abcIOLoo12fFsYYU9rFUhTyHNBRRDoCtwMvA68BfyzKhBlTGqxff2h9iCVLcqc3bOgCh7593d8uXVwFSWtl0hhTVsUSWBxUVRWRc4GnVPUlEbmiqBNmTEmi6pq7Dg0igtuAaN7cBQ4DB+ZWqgzpRdwYY8q8WAKLnSJyJ64p754ikgRULNpkGZM4WVnw6695K1XOmQNbt7rpSUnQujWcckpuUUanTq7Ja2OMKe9iCSwuBv4MXK2q60QkHXikaJNlTPHYt8+1TBmcC/HDD7kNRlWu7N7EuPDC3CCifXtISUlsuo0xpqSK5a2QdcBjQcMrcHUsTDkxefJkhgwZQlZWFgMHDmTo0KF5pm/dupUBAwawePFiqlSpwssvv0y7du0AaNq0KdWrVycpKYnk5GRCu7x/9NFHuf3229m4cSN16tQp0v3YtSt8c9cHDrjp1au7wOGaa3LrQ7Rq5V77NMYYE5tYciwOi+9a/VlgPzBNVcf48RcDZwEHgEdV9WcRGQB0Brar6t1FlSZTcFlZWQwePJgpU6bQuHFjunfvzjnnnEObNm1y5nnwwQfp1KkT7733HgsXLmTw4MFMnTo1Z/rnn38eNmhYuXIlU6ZMIT09Pe7p3rz50Oauf/01t7nrunVd8HD66blBRLNm1iOnMcYUVpEFFkBf4B1VnSAibwFj/Pjzgf5ALeBBEfk7rrhlDhCmOySTSDNnzqRFixY0a9YMgH79+jF+/Pg8gcWCBQu48847AWjVqhXLli1j/fr11M+n5uJf/vIXhg8fzrnnnnvY6VOFNWsOrVS5YkXuPOnpLnj4859zg4iGDe3NDGOMKQpFGVg0Bn70/2cFjX8UeBpYhwsumgFbVHWoiAwXkeaqujh4RSIyCN/aZ1H8ujWRrV69miZNmuQMN27cmG+//TbPPB07duTdd9/lhBNOYObMmSxfvpxVq1ZRv359RIRTTz0VEeHaa69l0CDXaOsHH3xAo0aN6NixY8xpyc52r3IGV6qcPRs2bnTTReCYY+B3v4Mbb8x9M+OIIwp/HIwxxsQm1gay7gMy/PwCqKo2y2fRVbjgYi6Qk8GsqjOBmSLSArgRWA1s8ZO3AYc0WKyqo4BRAN26dbN+SoqR6qGHW0J+6g8dOpQhQ4bQqVMn2rdvT+fOnUlOdpfWjBkzaNiwIRs2bKB37960atWKbt26MWzYMD755JOI2z140DUqFZwLMWdObnPXycmuZco+fXJzITp2tOaujTEm0WLJsXgJ+AvwPXlzHvLzLjBCRM4CJojI66p6mYicCZyNCyBu92+abBGRx4BKqvpDAffBFKHGjRuzMqgbzVWrVtGwYcM889SoUYPRo0cDLhA56qijOOqoowBy5q1Xrx7nnXceM2fOpFatWixdujQnt2LVqlW0a9eFv/51JosXH5nT3PXevW79KSkuaLjsstwgom1b98aGMcaYkkXC/SLNM4PIt6rao5jSk69u3bpp6JsFpugcPHiQY445hqlTp9KoUSO6d+/Om2++Sdu2bXPm2bZtG6mpqVSqVIkXXniBL7/8ktdee43du3eTnZ1N9erV2b17N7179+bWW++hTp3T8xRlzJ/fFJgF1KFmzdzgIfD3mGOsuWtjjEkkEfleVbvFMm8sORafi8gjuByIfYGRqjr7MNNnSpHk5GRGjBjBaaedRlZWFgMGDKBt27aMHDkSgOuuu46ff/6Zyy+/nKSkJNq0acNLL70EwE8/radfv/PYswcyMw+SnPxnLrjg9Jx1N2jggoeVK+GJJ+CPf4SmTa1SpTHGlGax5Fh8Hma0qupJRZOk6CzHoniNGQN33eXeskhPh2HDoH//vPOouunB9SFmz3ZvawQ0a5ZbmTKQG3HkkcW7L8YYYw5PXHMsVPXEwifJlEZjxsCgQbmtUC5f7obXrIHGjfNWqtziq99WqOAalTrppNwgolMnqFkzUXthjDGmOMWSY5EG3Av09KOmAw+o6vYiTltYlmNRfJo2dcFEJJUqueatg+tDtG8PqanFlkRjjDHFIN51LF4G5gMX+eHLgNG4BrBMGRbcyFSouXOhTRtr7toYY0xesQQWzVX1/KDh+0VkbhGlx5QAP/0E99yT2/x1qIwM9/qnMcYYEyqWnhH2iMgJgQHfYNaeokuSSZTFi11bEe3bw5Qp0Lfvob14pqa6CpzGGGNMOLEEFtcDz4jIMhFZDowArivaZJnitHKlq5TZsiX8979w222u6ez//hdeeMHlUIi4v6NGHfpWiDHGGBOQb+XNnBlFagCo6o4iTVE+rPJm/KxfDw89BM8954o9rr0W/v53176EMcYYExCXypsicqmqviEifw0ZD4CqPlaoVJqE2bIFHnkEnnoK9u2DK65wdSoyMhKdMmOMMaVdtMqbVf3f6sWREFP0du50LVw++qj7v18/uO8+12S2McYYEw8RAwtVfd7/vb/4kmOKwp498Mwz8PDDsHkznHsu/POfrpKmMcYYE0/5Vt4UkeEiUkNEKorIVBHZJCKXFkfiTOHs3w/PPgvNm8Ptt0PXrjBzJrz/vgUVxhhjikYsb4Wc6its9gFWAccAtxdpqkyhHDwIo0e7Io7Bg11gMX06fPwxdO+e6NQZY4wpy2IJLAJtK54JjFXVLUWYHlMI2dnw1lvQrh0MGAB16sDkyfDFF9CzZ/7LG2OMMYUVS2AxQUQWAt2AqSJSF9hbtMkyBaEKH3zg+uvo1w+Sk+Hdd+G77+C006wbcmOMMcUn38BCVYcCxwPdVPUAsBs4t6gTZvKnCp9+Cscd5ypkZma6Hkl/+AHOO88CCmOMMcUvWjsWJ6nqZyLSN2hc8CzvFmXCTHQzZsBdd7m6E02auBYyr7jCOgUzxhiTWNHasfgj8BlwdphpigUWCfH99/CPf8BHH0H9+q6Rq0GDoHLlRKfMGGOMid6Oxb3+71XFlxwTSaDH0XffhVq1XJsUN94IVavmv6wxxhhTXGJpx+JBEakZNFxLRP4Vw3JVReRVEXlBRPoHjb9YRF4TkZdEpHXQ+JEi8uhh7EOZ9ttveXscvfdeWLoU7rjDggpjjDElTyxvhZyhqtsCA6q6FffqaX76Au+o6jXAOUHjzweuBu4EbgUQkQsA61ksSKDH0VatcnscXbrUNcGdlpbo1BljjDHhRatjEZAkIpVVdR+AiKQAsZToNwZ+9P9nBY1/FHgaWAfUEpH6QGfgBaBVuBWJyCBgEEB6enoMmy69Qnscvf5663HUGGNM6RFLYPEGrv2K0bhKmwOAV2NYbhUuuJhLUM6Iqs4EZopIC+BGXCXResA9QEcROUZVfw1ekaqOAkaB6zY9hm2XOqE9jl55paukaT2OGmOMKU3yDSxUdbiIzANOAQT4p6p+HMO63wVGiMhZuEa2XlfVy0TkTNybJtWA21V1HfC2iDQFbgwNKsq6nTvh8cfh3/+2HkeNMcaUfqKafwaAiGQAR6vqpyKSCiSp6s4iT10Y3bp101mzSn91jNAeR//0J3jgAesczBhjTMkjIt+rardY5o3lrZBrgHeA5/2oRsD7h526cm7/fhdQhPY4+t57FlQYY4wp/WJ5K2Qw8HtgB4CqLsLViTAFENzj6I03Wo+jxhhjyqZYAot9qro/MCAiybhKnCYGoT2O1q1rPY4aY4wpu2IJLKaLyN+BFBHpDfwHmFC0ySr9QnscrVjRFXfMnGk9jhpjjCm7Ygks7gA24tqkuBaYBNxdlIkqzVRdC5mhPY7OnesqaFpAYYwxpiyLGliISAXgR1V9QVUvVNUL/P9lqihk8uTJtGzZkhYtWvDwww8fMn3r1q2cd955dOjQgWOPPZb58+cDsHLlSk488URat25N27ZtGTLkSU48EU49FRYuvI+aNRuRmtqJ4cM78fHHk4p7t4wxxphiF7UdC1XNFpEfRCRdVVcUV6KKU1ZWFoMHD2bKlCk0btyY7t27c84559CmTZuceR588EE6derEe++9x8KFCxk8eDBTp04lOTmZf//736h24c47d/LUU1054ojePPVUG9avh5o1/8Jtt92WwL0zxhhjilcsRSENgJ9EZKqIfBD4FHXCisvMmTNp0aIFzZo1o1KlSvTr14/x48fnmWfBggWcfPLJALRq1Yply5axfv16tmxpwLBhXejWDWbNqk7r1q0ZPXo1N90EybG0aWqMMcaUMbEEFvcDfYAHgH8HfcqE1atX06RJk5zhxo0bs3r16jzzdOzYkXfffRdwgcjy5cu58spVeXocnTZtGbt2zeGPf+yRs9yIESPo0KEDAwYMYOvWrcWzQ8YYY0wCRQwsRKSKiNwCXIjrHGyGqk4PfIorgUUtXHURCalhOXToULZu3Urbtp3o3/9psrI68/nnydx+u+tx9LbbdnHVVefzxBNPUKNGDQCuv/56Fi9ezNy5c2nQoAG33nprseyPMcYYk0jRMuxfBQ4AXwJnAG2AIcWRqOLUuHFjVq5cmTO8atUqGjZsmGeePXtqkJY2mt9+g+xspXr1o5g16yiOOQYOHDhAnz7n079/f/r27ZuzTP369XP+v+aaa+jTp0/R74wxxhiTYNECizaq2h5ARF4CZhZPkopX9+7dWbRoEUuXLqVRo0aMGzeON998E8jtcfTJJ7exb18qV11ViRYtXmTBgp4cc0wNVJWrr76a1q1b89e//jXPeteuXUsD39f5e++9R7t27Yp934wxxpjiFi2wOBD4R1UPhhYPlBXJycmMGDGC0047jaysLAYMGECTJm05++yRfPop7Nt3Haec8jO//no5X32VxJYtbXjppZcAmDFjBq+//jrt27enU6dOgHuD5Mwzz+Rvf/sbc+fORURo2rQpzz//fJRUGGOMMWVDxN5NRSQL2B0YBFKATP+/qmqNYklhiHj3bjpmDNx1F6xYAY0bwwknwCefWI+jxhhjTEBBejeNmGOhqknxS1LJNGYMDBrkWscEWLkSxo51/Xp89JF1DmaMMcYUVCyvm5ZZd92VG1QE27nTggpjjDHmcJTrwGJFhLZEI403xhhjTHTlOrBITy/YeGOMMcZEV64Di2HDIDU177jUVDfeGGOMMQVXrgOL/v1h1CjIyHDdmWdkuOH+/ROdMmOMMaZ0ivi6aUklIhuB5UWw6jrApiJYb1lhx6fss3Nc9tk5LvuK6hxnqGrdWGYsdYFFURGRWbG+o1se2fEp++wcl312jsu+knCOy3VRiDHGGGPiywILY4wxxsSNBRa5RiU6ASWcHZ+yz85x2WfnuOxL+Dm2OhbGGGOMiRvLsTDGGGNM3ETrNr1MEZGqwLPAfmCaqo7x488EbgQmqeoIP+5hIBXIVNWhCUpysYtyjIYCzYEjgetVdZWILAamALNVNeFZbyY2Uc7xfUBrYCvwgKquKa/3QWkX5RxfDXQHmgDzVPVOu49LJxFpBtwFpKnqBUHj2wF3+sGHVHV+Iu7j8pRj0Rd4R1WvAc4JjFTVScDwwLCIpAMVVfVmIElEmhR7ShMn0jF62I97GTjRj94FpAAriz2VpjDCnmPgIO6L6ACwrZzfB6VdpPv4JVW9DvgVeMWPtvu4FFLVJap6dZhJQ4DBwA3ATYm6j8tNjgXQGPjR/58VZb5G5N5kK/xy5eWmi3iMRKQacBEwyI/qDAjwIfBRcSXQFFqkc/ygqmaLyDnAQOA7yu99UNpFu4+rAEep6i9+lN3HZUuaqm4DEJHqJOj7rDzlWKzCHVSIvt+rg+Zr4pcrL8IeIxGpATwH/E1VdwKoaraqZgF7RaQ8XUelXdhzrKrZ/t8NQDXK931Q2kV71l0AvBsYsPu4zNkuImn+mb2TBN3H5eatEF/uOALYC3wFnK6ql4nI8bgyqVrAE6r6XxF5CKgM7FPVOyOutIyJcozeBSriLtK3/d87/GLzVPWJBCTXHIYo5/jvuAdPHeBmVV1bXu+D0i7SOfbTxgP9VHWPiLTE7uNSSUSOAIYBvYEXgTb+Pm4H3I7LhRru61gU+31cbgILY4wxxhQ9y/oyxhhjTNxYYGGMMcaYuLHAwhhjjDFxY4GFMcYYY+LGAgtjTA4RqSkiN+QzT1MRmV9caTLGlC4WWBhjgtXEtdoXNyKSFM/1GWNKNgssjDHBHgaai8hcEXnEf+aLyI8icnHozCKS5Of5TkTmici1fnwvEflcRN7EtwIpIu+LyPci8pOIDApaxy4RGSYiP4jINyJS34+vLyLv+fE/iMjv/PhLRWSmT+PzFrgYU7JYYGGMCTYUWKyqnYBvgE5AR+AU4BERaRAy/9XAdlXtjuvg6hoROcpPOxa4S1Xb+OEBqtoV6Abc7Bv5AagKfKOqHYEvgGv8+KeA6X58F+AnEWkNXAz83qcxC+gfr503xhReeeorxBhTMCcAY32Tz+tFZDoueJgXNM+pQAcRCfSwmAYcjevQbKaqLg2a92YROc//38TPt9nPO9GP/x7XmiDAScDlAD4N20XkMqAr8J2IgOtAa0N8dtcYEw8WWBhjIpEY57lJVT/OM1KkF7A7ZPgU4HhVzRSRaUAVP/mA5jYBnEX055IAr1oT48aUXFYUYowJthOo7v//ArjY16OoC/QEZobM/zFwvYhUBBCRY3xfFaHSgK0+qGgFHBdDWqYC1/v1JvmOlaYCF4hIPT++tohkFGwXjTFFyQILY0wOVd0MzPCvkx6PK/b4AfgM17vtupBFXgQWALP9Ms8TPsdhMpAsIvOAf+Lqb+RnCHCiiPyIKyJpq6oLgLuBT/y6pgCh9T6MMQlknZAZY4wxJm4sx8IYY4wxcWOBhTHGGGPixgILY4wxxsSNBRbGGGOMiRsLLIwxxhgTNxZYGGOMMSZuLLAwxhhjTNxYYGGMMcaYuLHAwhhjjDFxY4FFCSMiV4rIV4lOR1ERkT+IyC+JTkckIvJ3EXmxmLepItKiiLcxTUQGHuay14vIehHZFdTV+eGmo6nf31LdAaKItBGRWUHDy0TklASnKW7HNr/rRUTOE5GV/proLCIzRaRtYbdbXESkv4h8Eu95C0tEXhGRfxXHtopSqQgs/IndFeajInJPotNnnFi+IFX1S1VtWVxpKihVfVBVD+sLuCzynYs9BpyqqtV8XyLB08tEoHAY/gk8muhEJNCjwI3+mpjjhx8ojg3H48tXVceo6qnxnrc4FebHQlFvp1QEFv7EVgv+ALcA64EXCrq+cvgQLBFK+nEv6elLkPq47s1/SnRCSsr5EZEGwInA+3Feb8L3T5xYvhcyyHtNfIDrMC7hHcKVhONY7qlqqfsAnXHdO/cKGpcGvASsBVYD/wKS/LQrgRnA48AWPy0NeA3YCCzH9ZhYIco2KwNPAGv85wmgsp/WC1gF3Aps8Gm4KmTZR4EVuGBoJJASYTtXAl8FDf8O+A7Y7v/+LmTeJf5YLAX6+/EtgOl+mU3AWxG21RRQ4CpgJbAVuA7ojuvVchswImSZAcDPft6PgQw//gu/rt3ALuDioONyB7AOeD0wLmh9TYB3/XnYHNheAfZhMu6XU/C4H4C+/v8n/b7twPWQ+Yeg+e4D3gHe8NMH+nFvBM1zDu4Bug2YBrQOmqZAi6DhV4B/+f/rABP9cluAL4lwfQWvJ9q14o97n6Dlkv2x6eKHjwO+9tv8gbz3xzRgYEGubeAYfz7Vn9PPwiy7Imj6LlyPqBVw99Ny3P3wGpAWcs0lH+Z92xzX0+pmv+9jgJpB6VkG3Ia7frcDbwFVgqafC8z153sxcHp+6Qizz5cDn4aMWwac4v9vhbsf+/nhPn6b2/z56RCy3B0+vftw170CV/hjuwm4K2j+CsBQn/bNwNtA7XDHtgDP02nAMH+s9/g0TAMeAmb64zgeqO2vi13k3uuLg9YzBbgiwjZiuSbC7nPIegYBB4D9Ph0TIhzH5KDjtBPXA+95UZ6zinv2LcI9254ht5POgsybBPzb78NS4MZo5wT3XTbbp/EtYBy5z5BauGfIRr+diUBjP20YkAXs9cch8NyM9rw7Fpjlp60HHguaFvbZEWk7+V5TBbkAS8IHqOkvljtCxr+P67K5KlAPd0NcG3RhHARu8hdcir+wxwPV/YX9K3B1lO0+gOvquR5Q15+Ef/ppvfz6HwAqAmcCmUAtP/0JXERf229vAvBQhO3kXMR+/q3AZT7dl/jhI/x+7gBa+nkb4LqVBhgL3IW7masAJ0TYVlN/0Y/0853qL6D3/X42wj0E/ujn/xPwG9Dap+du4OuQGy74izZwXP4P90BKISiwwN2EP+C+OKoGp7UA+3A5MCNouA3u5ggEfZf645WMC/zW4b9ocEHEAb9fFXz67sMHFuR+sfb25/Vvfv8rRdjfV8h9KDzkj2tF//kD/uETZh+CA4sniHCtAPcAY4KWOwtY6P9vhPuiOdPvS28/XNdPn0bkwCLatd2U6A/GQ6bjgs/fgGZANVzg+Hq4+Sn4fdvC71tln9YvgCeCtr3Mr6OhP4Y/A9f5acfiviR7+2PUCGiVXzrC7PMjwDMh45YBpwBdcF+Offz4Lrh7qAfuer/Cz1s5aLm5uAA7Jej4vOCHO+K+KFv7+W/x56qxPwbPA2MjHNtncfdCuM+8oLRP82lu649zRT9uNdDOH5P/kjfgznPt+3FPEfRlFTItlmsi7D6HWdcr+Pss5PjnHEc/7kLcdVAB90NnN9Ag9DkbtD8Tcd8v6bgv89MPY97rcEFMY1xg8CkR7h+gEi7Q+os/5hfgnkeBZ8gRwPlAKu5Z8B/g/ZDzNjBkndGed/8DLvP/VwOOK+yzI9In4YFCgRILggsGxhP0kMZl1+4jKBcA9yX8edCFsSJoWpKfv03QuGuBaVG2vRg4M2j4NGCZ/78XLtIPfrhuwEWB4i/o5kHTjgeWRthOzkWMCyhmhkz/n5+nKu4BcT4huR+4oGkUPrqNsk9N/UXfKGjcZuDioOH/Arf4/z8iKPjyF2EmubkW4QKL/eT9xdiL3MDieNxNGe6mi3UfqvvjG0jDMODlKPNvBTr6/+8DvgiZfh+5gcU/gLdD9nc1udF8tMDiAdx12iJa+oPXk9+14ufZCaT64THAPf7/O/AP6qBlP8b/giR6YBHt2g5cIwUJLKYCNwQNt8Q9MJOD56eA922E7f8JmBM0vAy4NGh4ODDS//888HiYdURNR5j5XwAeDhm3DLgfl0N3YtD45/BBWtC4X8gN1pcBA8Icz8ZB42aSm/vxM3By0LQG4Y5tftdcSHqmAQ+EGfdw0HAb3L0cyE0KF1hEvPdivCbC7nOYdb1C+MBiQKR99PPMBc4NurZCg4UTgobfBoYexryfERSQ4oLNSIFFT1wOYfB32deh+xY0rROwNeQcRf3CJ+/z7gt/jdYJmeewnx2RPqWijkWQO3AR9BXq99jLwEV8a0Vkm4hswz1E6gXNszLo/zrkRosBy3GRGyIyMqiC6N/99IZh5m8YNLxZVQ8GDWfiosK6uIjz+6C0Tfbj8xO6zZx0qupuXBR+nd/vD0WklZ/nb7gvqZki8pOIDMhnO+uD/t8TZria/z8DeDJoP7b47TSKsu6Nqro3wrQmwPKQ4xYQ0z6o6k7gQ6CfH9UP94ULgIjcKiI/i8h2n+Y03PkPCL4uQuU5/qqa7eePtr8Bj+B+oX0iIktEZGgMy0S9VlT1N9wXy9kikoorpnnTL5sBXBhYzi97Au6LJz/5XdsFFW59gUAiWEHvW0SknoiME5HVIrIDV4wVfD7B/UoLCNyH4K63xWHSG0s6gm3FBbShrsPl4H0esu5bQ85LE/Ie33DXYKR9yADeC1rXz7is6tBjW1Dh0hA8bjnuGIUe62DVcT92wonlmoi0z7EKvVYuF5G5QceqHdHTX5DtR5q3YUg68nu+rA75Lss5RiKSKiLPi8hyf61/AdQUkaRIK8zneXc1Lhd2oYh8JyJ9/PjCPDvCKjWBhYj0wmWNX6Cq20Imr8T94qijqjX9p4aqBr/+FHzyNuGi5Yygcem4X6Oo6nWaW1H0QT99TZj518SQ9E24L+e2QWlLU1cBNT+h2wxN58eq2ht3ASzEV2RV1XWqeo2qNsTlxDyb39saMVqJi8ZrBn1SVPXrKMtolGkrgfRwla0KuA9jgUtE5HhcVurn4F5txQWjF+GKpWrissIlxvTlOf4iIrgvhdV+VCYuEAg4Mij9O1X1VlVtBpwN/FVETo6yLYjtWhmL+zV9LrDABxvgjuXrIeemqqo+nM82D9lPYr+2IfzxC7e+g+QNWANpLsh9C66ISXH1FGrgsn6F2KzE1dEINz6/dASbh3tAh7oOdz0/HrLuYSHnJVVVxwbNE+0aDJfWM0LWV0VVV4fOGPIDKfQTWhk3XBqaBP2fjntmboqStta4os1wYr0mYhHpeOWMF5EM3PPwRuAIf+/PJ/Zr5XCtxRWDBDSJNKOft5F/rgSkB/1/Ky5np4e/1nv68YH58xyH/J53qrpIVS/BBcz/B7wjIlXJ/9lRkOsTKCWBha9pPA6XJT8ndLqqrgU+Af4tIjVEpIKINBeRP4Zbn6pm4bKvholIdX8R/hX36yeSscDdIlJXROrgyrujzR/YVjbuAn9cROr5/WkkIqfltywwCThGRP4sIskicjEuS3KiiNQXkXP8hbEPV7Emy6//QhEJXNxbcRdGVgzby89I4E7x76uLSJqIXBg0fT2uDDVWM3E318MiUlVEqojI7/26C7IPk3APrQdwlTyz/fjquIfXRiBZ3KvJNQqQvreBs0TkZHGvXd6KO9aBQGou8GcRSRKR04Gc601E+ohIC//Q2OHTHvUcxHitjMPVhbme3NwKcNfi2SJymk9PFRHpFXQMozmsa9vbCGST97yPBf4iIkeJSDXgQdx5yZMzVdD71quOu9a3iUgj4PYY0wmucuZV/nxW8Me21WGkYwrQRUSqhIzfCZwO9BSRwEP5BeA6EekhTlUROUtEwuV4xGIk7rmVAeDP2bnhZgz5gRT6iaXNiUvFtdeRiru33vHPzkOISGWgK+7YhBPTNRGjWJ4zVXHPjI0+fVfhciyK2tvAEH9t1cR90UfyP9zz6Wb/fO+LqwcUUB33Q2ObiNQG7g1ZPvQ4RH3eicilIlLXP2e2+dFZ5P/sKOhzvXQEFsA1uCyzJ8NE3iP9PJfjijcW4L6I3iF6Vs5NuPLsJbiasnWAoRImy9p/OdTCfamvwWU/zgbeEpG5wItAPRHZISK3+MVqAuP89F64i/wbcVlan+Ii0ajUtRnQB/eFthlXPNBHVTfhzt2tPj1bcF9qN/hFuwPfisguXEXAIaq6NL/txZCe93CR7ji/H/OBM4JmuQ94VVx22kUxrC8L92u+Ba7y2Cpc8U6B9kFV9+Eqg51C3i/bj3H1Qn7FZTHuJXrWZOh6f8H9In4a90vtbOBsVd3vZxnix20D+pP39cOjced5F+4B8qyqToths3fgilDCXiv+S/B/uLeF3goavxKXi/F33INlJe5LN5Z7/F+4e2Ae8CPu2o6pnQBVzcS/UeDP+3HAy7g3gL7A1Yzfi7vfwinofXs/rkLkdlwR2LuxpNOndSbuDajH/fLTyf0VHXM6VHU9riz9kC90n5vaGzhDRP6pqrNwz68Rfr2/4crsD9eTuPvhExHZiavI2aMQ64vmdVx9hnW4CtQ3R5n3HFwdtUg5XQW5JvLzEtDGX2/vh5tBVRfg3s74H+6LsT3urZei9gIuSJ0HzMH96DlImB8V/jnSF3c9bMU9+4Kv5ydwObCbcOd5csgqngQuEJGtIvIU+T/vTgd+8s/UJ3F1WPbG8OwI3U6+Aq/IlFviyqt+xT0MVuFe6bzEX5iBec7E3QRn4m7iJ1W1R5j1rMZlWy0XkfuAXapanhvRMaZMEpE2wKvAsVreH6KAiHyLq9g9P9FpKUlE5Axc5eHQIu0yrbTkWBSlY4HfVHWJjyDHcegvkXOB19T5BleBJvTXzMm4d7pDK1saY8oYVV2gqt0tqHBUtYcFFSAiKSJypi/aaIQrvngv0ekqbhZYuBr+wdlFqzi01n8s8/TDlSMGu1FE5onIyyJSKx6JNcYYU2IJrrhuK64o5GdcnaVyxQKL8LWEQ3+FRJ1HRCrhyhj/EzT9OVwN9E64Cor/LlQqjTHGlGiqmulzsqqraj1VvUpVdyQ6XcXNAguX+xD8SlBjDn3VLr95zgBm+0pdgKvgpapZQTX9g2v7GmOMMWWSBRausubR/jWoSrgijQ9C5vkAuNy/LnYcsN3Xzg+4hJBikJA6GOfh3qAwxhhjyrRS91ZInTp1tGnTpnFd5/bt21m5ciWqSp06dWjQoAEbN24EoG7duqgqK1euZPv27VSoUIGmTZtStWpVALKzs5k3bx7t27cnKSm3QbSlS5eSmZmJiFCpUiUyMjKoWLFiXNNtjDHGFIfvv/9+k6rG0mI0pa572aZNmzJr1qy4rW/Mj2O4a+pd7N++n/S0dIadPIz+7fvHbf3GGGNMaSciMb/xWOoCi3ga8+MYBk0YROaBTACWb1/OoAmDACy4MMYYYw5Dua5jcdfUu3KCioDMA5ncNfWuBKXIGGOMKd3KdWCxYvuKAo03xhhjTHTlOrBIT0sPO15RBn4w0AIMY4wxpoDKdWAx7ORhpFZMzTMuJTmF05qdxuvzXufop4/m5o9uZt2udQlKoTHGGFO6lOvAon/7/ow6exQZaRkIQkZaBi+c8wKTL5vMopsWcUXHK3j2u2dp9mQz7phyB5szNyc6ycYYY0yJVurasejWrZvG83XT/Py25Tfum3Yfb/74JtUrV+evx/2Vvxz/F2pUrpH/wsYYY0wZICLfq2q3WOYt1zkWsWhRuwVv9H2DH6//kVOancJ90+/jqCePYviM4Ye8UWKMMcaUdwUKLESkloh0KKrElGRt67Xlvxf9l1nXzOLYRsdyx6d30Pyp5oyYOYJ9B/clOnnGGGNMiZBvYCEi00SkhojUBn4ARovIY0WftJKpa8OufNT/I7686ktaHtGSmz66iWNGHMNLs1/iYPbBRCfPGGOMSahYcizSfLevfYHRqtoVOKVok1XynZB+Ap9f8TmfXPoJ9avWZ+CEgbR5pg1jfxxLtmYnOnnGGGNMQsQSWCT7njovAiYWcXpKFRGhd/PefDvwW96/+H2qJFfhz+/+mY4jO/L+wvcpbRVjjTHGmMKKJbB4APgY+E1VvxORZsCiok1W6SIinNvqXOZeN5ex549lf9Z+znvrPHq82INPFn9iAYYxxphyw143LQIHsw/y+g+vc//0+1m+fTl/SP8Dw04axh8y/pDopBljjDEFFtfXTUVkuK+8WVFEporIJhG5tPDJLLuSKyRzVeer+OXGXxhxxggWbVlEz1d6cvobpzNrTckOiowxxpjCiKUo5FRfebMPsAo4Bri9SFNVRlROrszgYwez+ObFPNL7EWatmUX3F7rT962+zN8wP9HJM8YYY+IulsCiov97JjBWVbcUYXrKpNSKqdz2u9tYMmQJ9/e6n6lLp9LhuQ70f7c/izZbdRVjjDFlRyyBxQQRWQh0A6aKSF1gb9Emq2yqUbkG9/zxHpYOWcodv7+D9xe+T+tnWltPqsYYY8qMmCpvikgtYIeqZolIKlBDVRPS5WdpqLwZq3W71vHQlw8x8vuRAFzb9Vr+/oe/c2S1IxOcMmOMMSZXvCtvVgQuA94SkXeAq4F8u/kUkaoi8qqIvCAi/YPGtxORMf7Tzo8bICJPi8i/Ykl0WXFktSN58ownrSdVY4wxZUYsRSHPAV2BZ/2nix+Xn77AO6p6DXBO0PghwGDgBuAmEakHXAzsBtbGnvSyIz0tnVFnj2LhjQvp27ovj3z9CM2easb90+5nx74diU6eMcYYE7NYAovuqnqFqn7mP1cB3WNYrjGw0v+fFTQ+TVW3qep2oDrQDNiiqkOBDBFpHroiERkkIrNEZNbGjRtj2HTpZD2pGmOMKe1iCSyygr/sfcubWVHmD1iFCy5Ct7NdRNJEpAawE1gNBN402QZUC12Rqo5S1W6q2q1u3boxbLp0s55UjTHGlFb5Vt4UkZOB0cASQIAM4CpV/Tyf5aoCI3BvkHwFnK6ql/l6Fbf7dQ1X1fki8k+gKlBJVW+Mtt6yVHkzVl+t+Iq7P7ub6cunk56Wzj097+GKTleQXCE50UkzxhhTDhSk8masb4VUBlrigoGFqpqwn83lMbAAUFU+XfIpd39+NzNXz+To2kdzf6/7ubjdxVSQWDKejDHGmMMTl8BCRPpGW1BV3z2MtBVaeQ0sAlSVCb9O4B+f/4N56+fRrl47/nniPzm35bmISKKTZ4wxpgyKV2AxOspyqqoDDidxhVXeA4uAbM3m7Z/e5t5p9/Lr5l/p3rA7/zrpX/Ru1tsCDGOMMXEV96KQksQCi7ysJ1VjjDFFLa4NZJmSzXpSNcYYU5JYYFFGFGVPqpMnT6Zly5a0aNGChx9++JDpqsrNN99MixYt6NChA7Nnz84zPSsri86dO9OnT588459++mlatmxJ27Zt+dvf/laoNBpjjCkZLLAoY6L1pPrblt8KvL6srCwGDx7MRx99xIIFCxg7diwLFizIM89HH33EokWLWLRoEaNGjeL666/PM/3JJ5+kdevWecZ9/vnnjB8/nnnz5vHTTz9x2223FXxnjTHGlDix9BWSKiL/EJEX/PDRItInv+VMYoXrSbXViFZc88E1BepJdebMmbRo0YJmzZpRqVIl+vXrx/jx4/PMM378eC6//HJEhOOOO45t27axdq1rnX3VqlV8+OGHDBw4MM8yzz33HEOHDqVy5coA1KtXr5B7bIwxpiSIJcdiNLAPON4PrwLKVWdhpVntlNo8dMpDLL55MTceeyOvzXuNo58+mps/upl1u/LvoHb16tU0adIkZ7hx48asXr065nluueUWhg8fToUKeS+1X3/9lS+//JIePXrwxz/+ke+++64wu2mMMaaEiCWwaK6qw4EDAKq6B9dQlilFjqx2JE+c/gS/3fRbnp5Uh346NGpPquHeGgp9nTXSPBMnTqRevXp07dr1kOkHDx5k69atfPPNNzzyyCNcdNFFYddjjDGmdIklsNgvIimAAvh+Q6zDilKqSVqTPD2pDp8xPGpPqo0bN2blypU5w6tWraJhw4YxzTNjxgw++OADmjZtSr9+/fjss8+49NJLc5bp27cvIsKxxx5LhQoV2LRpUxHttTHGmOISS2BxLzAZaCIiY4CpgFXhL+XC9aTa7MlmPDLjkTw9qXbv3p1FixaxdOlS9u/fz7hx4zjnnHPyrOucc87htddeQ1X55ptvSEtLo0GDBjz00EOsWrWKZcuWMW7cOE466STeeOMNAP70pz/x2WefAa5YZP/+/dSpU6f4DoAxxpgikW9goapTgL7AlcBYoJuqTivaZJniEtqT6t8+/VuenlTf+vktdp+ym2bdmlG1YVVa9nSvh44cOZKRI0cCcOaZZ9KsWTNatGjBNddcw7PPPpvvdgcMGMCSJUto164d/fr149VXX7UWQ40xpgyItROyRrheTXO601TVL4owXRFZy5tFK7gn1doptdm1fxf7s/bnTE+tmMqos0fRv33/BKbSGGNMcYprk94i8n/AxcBPQLYfrap6TuSlio4FFkUv0JNqn7F98gQVARlpGSy7ZVnxJ8wYY0xCFCSwSM5/Fv4EtExkV+mmeIkIvZv35kDWgbDTl29fzg0f3kCXBl3ofGRn2tVrR+XkysWcSmOMMSVRLIHFEqAi9iZIuZOels7y7csPGV85qTJjfhzDc7OeA1x/JW3rtqVzg850ObILnRt0pmP9jlSvXL24k2yMMSbBYgksMoG5IjKVoOBCVW8uslSZEmHYycMYNGFQnrdEAnUsLml3CUu3LmXOujnMXjubOevmMGnRJF6Z+woAgnD0EUfn5Gp0PrIznRt0pk6qvflhjDFlWSx1LK4IN15VXy2SFOXD6lgUrzE/juGuqXexYvsK0tPSGXbysIgVN1WVtbvWMmdtbrAxe+3sPLkeTWo0yQk2ujRwuRuNqjeyN0KMMaYEi2vlTb/CSsAxfvAXVQ1f+F4MLLAofbbs2cLcdXPzBBu/bPoFdW2uUTe1Lp0bdM4NNo7sTPPazakg1keeMcaUBPF+K6QX8CqwDNeUdxPgCnvd1BTGrv27mLd+Xp7cjfkb5nMg28Ws1StVp9ORnXKLUhp0pnWd1lRMqpjglBtjTPkT78Die+DPqvqLHz4GGKuqh3YAkXe5qsCzwH5gmqqO8ePbAXf62R5S1fl+/Ehgl6pG7T/bAouya3/Wfn7a8FOeehtz183NqeNROaky7eu3z6kg2qVBF9rXa09KxZQEp9wYY8q2eAcW81S1Q37jwix3GbBNVSeIyFuqerEf/wJwO67vkeGqeq2IXADUBFpZYGGCZWVnsWjLokPqbWzduxWAJEmiVZ1WeeptdDqyE2lV0hKccmOMKTvi3Y7FLBF5CXjdD/cHvo9hucbAj/7/rKDxaaq6zSe0uojUBzoDLwCtwq1IRAYBgwDS09Nj2LQpK5IquMChVZ1WXNL+EsBVEl2xfUWeQGPq0qm8Pu/1nOWa12p+SL2N+tXqJ2o3jDGm3Iglx6IyMBg4AVfH4gvg2fwazPI5FltVdaKIjFPVfn78C8BtuByLR3CdmvUGkoCOwCWq+muk9VqOhYlk/a71zFk3x+VurJvNnLVzWLx1cc70htUb5rz6GngjJSMtw95IMcaYfMT9rZCgFTdQ1bUxzlsVGAHsBb4CTlfVy3wdi9txQcrwoDoWTYEbrSjExNP2vduZu25unnobP2/8mSx1mWi1qtTK07BXlwZdOLr20SRVSEpwyo0xpuQoysBitqp2OeyUxYEFFqaw9hzYw48bfnSBxto5zFk3h3nr57Evy2XCpVZMpWP9jnnqbbSt15ZKSZUSnHJjjEmMogws5qhq58NOWRxYYGGKwoGsAyzctDBPvY256+ayc/9OACpWqEi7eu1yXn3t0qALHet3pGqlqglOuTHGFL2iDCxuUNVnDztlcWCBhSku2ZrNkq1LcnI2AvU2NmZuBFyz5S3rtMxTQbRzg87UTqmd4JQX3OTJkxkyZAhZWVkMHDiQoUOH5pmuqgwZMoRJkyaRmprKK6+8QpcuXVi5ciWXX34569ato0KFCgwaNIghQ4YAMHfuXK677jr27t1LcnIyzz77LMcee2wids8YU0hF0fJmfaC7H5ypqhsKkb5CscDCJJKqsnrn6jyvv85ZN4cV21fkzJORlnFIvY0G1RqU2EqiWVlZHHPMMUyZMoXGjRvTvXt3xo4dS5s2bXLmmTRpEk8//TSTJk3i22+/ZciQIXz77besXbuWtWvX0qVLF3bu3EnXrl15//33adOmDaeeeip/+ctfOOOMM5g0aRLDhw9n2rRpidtRY8xhi+vrpiJyEe7tjWm4CpdPi8jtqvpOoVJpTCkkIjSu0ZjGNRpzdsuzc8Zvytx0SLPl4xeOz2m2vF7VenlyNro06EKzWs1KRLAxc+ZMWrRoQbNmzQDo168f48ePzxNYjB8/nssvvxwR4bjjjmPbtm2sXbuWBg0a0KBBAwCqV69O69atWb16NW3atEFE2LFjBwDbt2+nYcOGxb9zxphiF0s7FncB3QO5FCJSF/gUsMDCGK9Oah1OaXYKpzQ7JWfczn07mbd+Xp5g45GvH+Fg9kEAalSukafn1y4NutCqTiuSK8RyW8bP6tWradKkSc5w48aN+fbbb/OdZ/Xq1TlBBcCyZcuYM2cOPXr0AOCJJ57gtNNO47bbbiM7O5uvv/66iPfEGFMSxPIEqxBS9LEZsN6hjMlH9crV+X367/l9+u9zxu07uI/5G+bnaW/j+e+fZ8/BPQBUSa5Ch/od8rS30b5+e6okVymydIYrDg3NSclvnl27dnH++efzxBNPUKNGDQCee+45Hn/8cc4//3zefvttrr76aj799NM4p94YU9LEElhMFpGPgbF++GJgUtElyZiyq3JyZbo27ErXhrld7WRlZ/HL5l/y1NsYN38cz3//POCaLW9Tt02eehudjuxEjco14pKmxo0bs3LlypzhVatWHVJsEW2eAwcOcP7559O/f3/69u2bM8+rr77Kk08+CcCFF17IwIED45JeY0zJFjWwEPeT5Clcxc1Ay5ujVPW9YkibMeVCUgUXOLSp24b+HfoDLodg2bZleRr2+mTxJ7z2w2s5y7Wo3eKQN1LqVa1X4O13796dRYsWsXTpUho1asS4ceN4880388xzzjnnMGLECPr168e3335LWloaDRo0QFW5+uqrad26NX/961/zLNOwYUOmT59Or169+Oyzzzj66KMP4+gYY0qbqIGFqqqIvO97Mn23mNJkTLknIhxV6yiOqnUUfVvn5gKs3bk2pxhlzro5zFozi/8s+E/O9EbVG+UJNLo06EKTGk2iVhJNTk5mxIgRnHbaaWRlZTFgwADatm3LyJEjAbjuuus488wzmTRpEi1atCA1NZXRo0cDMGPGDF5//XXat29Pp06dAHjwwQc588wzeeGFFxgyZAgHDx6kSpUqjBo1qgiOlDGmpImlr5BngFdU9bviSVJ09rqpMXlt3bP1kGbLF25aSLZmA1A7pXaet1E6H9mZo484mgriqkqN+XEMd029ixXbV5Cels6wk4fRv33/RO6SMaaEiXe36QuAlsAyYDeuOETz6za9qFhgYUz+Mg9kMm/9vDz1Nn7c8CP7s/YDULViVTod2YnU5FSmr5ieMx5ck+ajzh5lwYUxJke8A4uMcONVdflhpK3QLLAw5vDsz9rPzxt/ztOw14wVM3La2ghWOakyZxx9BvVS61GvavhP7ZTa1llbKVUULa2asq0oWt48AThaVUf7diyqqerSQqbzsFhgYUz8VLi/QtjAAqBdvXZs2L2BTZmbcopV8iwrFaiTWidvwBElEKlWqVqJaBCsvCuqllZN2RbvljfvBbrhikNGAxWBN4DfR1vOGFPypaels3z7oZmPGWkZ/Hj9j4B7HXbLni1s2L3hkM/GzI05/3+/5ns27N7A9n3bw26rclLliEFH6Kdual0qJ1cu0n0vr4qqpVVjAmJpx+I8oDMwG0BV14hI9SJNlTGmWAw7eRiDJgwi80BmzrjUiqkMO3lYznBShSTqVq1L3ap1aUvbfNe57+C+PAFHpM/8DfPZsHtDTnf1odIqp8UciNROqZ1TGdVEV1QtrRoTEEtgsd+/dqoAImL9RBtTRgQqaMbzrZDKyZVz+lPJj6qyc//OfIOQXzf/ylcrvmJT5qawRTcVpAJ1U+tGzQGxYhmnqFpaNSYglsDibRF5HqgpItcAA4AXizZZxpji0r99/4S9ASIi1KhcgxqVa9Cidot8549WLLNh9wY2ZLq/3635jg27N7Bj346w66mSXOXQACRC/ZC6VetSKalSvHc9YYqqpVVjAvINLFT1URHpDezA1bO4R1WnFHnKjDEmREGLZfYe3MvG3RGKZXwQsm7XOuatn8eG3RvyvHYbrGaVmjFXUq2VUqtEF8sUVUurxgREDCxE5DRV/RjABxJTgqZdqKr/ibSsMcaUBFWSq9AkrQlN0prkO6+qsmPfjnwrqf6y6Re+3P1lxGKZJPHBT5SimeBP1YpVi7VYpqhaWjUmIOLrpiKSBXwBXKqqq0OmzVbVLsWQvkPY66bGmJIgKzuLzXs251s/JPDZuX9n2PWkJKfEXEm1TmqdwyqWsdZVy4fDbZ8EYMCAAUycOJF69eoxf/78Q9YtIquAxkBdVd0ULR3RikLmAW8C34jIX0NyKPINr30lz2eB/cA0VR3jx7cD7vSzPQQcAO4AqgA/qOr/5bduY4xJtKQKSTlf+LHYc2BPvm/LrNm5hrnr5rJh9wYOZB8Iu55aVWrFXEm1Vkotxs4fm+fNn+XblzNowiAACy7KkKysLAYPHpynfZJzzjknz6vAH330EYsWLWLRokV8++23XH/99TlvBF155ZXceOONXH755Yes29e3qQGsiCUt0QILVdUXRGQ6MEZEzgQGq2omRGhRJ6++wDuqOkFE3gLG+PFDgMF+HcNV9VpchVBExDo6M8aUSSkVU0hPSyc9LT3feaMVywTXD/l5089MXz6dzZmbIxbLKHpIA2eZBzK5cdKN7N6/m1pValGzSk1qpfi/VWqRViWN5Aqx1O03JUVh2yfp2bMny5YtC7vuv/zlLwCrgJjeCo2l8uavInI88C9gjogcGs6E1xj40f+fFTQ+TVW3AQS3hyEi/YBPwq1IRAYBgwDS0/O/KY0xpjQTEdKqpJFWJY2jj8i/u/mD2QfZnBm+WObBrx4Mu8y2vdu4duK1EddZvVL1QwKOmlVq5vk/dFpguLjrjZj4tU8S6oMPPqBRo0YAe4hDYJFzVajqQWCoiEwGxgJ1Y1h3oDxmLhBcRXq7iKThcix2Qk5QkRGpGERVRwGjwNWxiGHbxhhTbiRXSKZ+tfrUr1b/kGljfhwTtnXVJjWa8PXVX7Nt7za27tnq/u51f3PG7cudtnTb0pzxkeqLBKcnagASYXzgUzGpYtyOTXkRj/ZJQmVmZjJs2DA++eQTnnrqqZjTEi2wuD90hKpOE5GuQOQwN9e7wAgROQuYICKvq+plwJPAU7jAZbiIdAYeBSaKyGOqau8wGWNMnERqXfWhUx6KuSGzUAezD7J97/Y8wUhocBIamCzftpyte7eydc/WiPVHAqpWrBo+N6Ry5FySwHB5bfyssO2ThLN48WKWLl1Kx44dAdoDScBsETlWVddFWi5iYKGq70cYvxV4OGJKcufbDVwVNGqMHz8fuCJk9oJf2cYYY/JVFK2rJldI5ojUIzgi9YgCL6uq7Dm4J3IwEjzs/67YvoJ56+exde/WiI2eBSRJUky5JJGCk9KaW1KY9kkiad++PRs2bABARH4E6gDdCvNWiDHGmDIgka2rhhIRUiumkloxlYbVI/9ajiQrO4vt+7bnCUTyC05W7ljJ1j1b2bp3a8RG0AJSK6YWuAgnMFy9UvWE5ZYUpn0SgEsuuYRp06axadMmGjduzP3338/VV199WGmJqdv0ksTasTDGGHO49hzYk38Rzt5teYpxAtO2790e9u2bgApS4dCKrjEW4dRKqVWopuOLuq2SuHabbowxxpQVKRVTSKmYQoPqkYsAIsnKzmLn/p0xF+Fs3bOVNTvX5AzvPbg3etqSU2KuWxIcnHy6+FOGfDykxLRVYjkWxhhjTDHYe3Bv3rduYgxOAstEyy0JJyMtg2W3LItL2i3HwhhjjClhqiRX4chqR3JktSMLvGy2ZrNz386wgcjVH4SvC7Fie0wNZcadBRbGGGNMCVdBKuQ0mhbqgekPhG2rJJZWXotCye3b1xhjjDH5GnbyMFIrpuYZl1oxlWEnD0tIeiywMMYYY0qx/u37M+rsUWSkZSAIGWkZjDp7VMJeMS51lTdFZCNwaJ5P4dUBojb6Uc7Z8Sn77ByXfXaOy76iOscZqhpLdx6lL7AoKiIyK9Yar+WRHZ+yz85x2WfnuOwrCefYikKMMcYYEzcWWBhjjDEmbiywyDUq0Qko4ez4lH12jss+O8dlX8LPsdWxMMYYY0zcWI6FMcYYY+Km3LS8KSJVgWeB/cA0VR3jx58J3AhMUtURftzDQCqQqapDE5TkYhflGA0FmgNHAter6ioRWQxMAWarasKz3kxsopzj+4DWwFbgAVVdU17vg9Iuyjm+GugONAHmqeqddh+XTiLSDLgLSFPVC4LGtwPu9IMPqer8RNzH5SnHoi/wjqpeA5wTGKmqk4DhgWERSQcqqurNQJKINCn2lCZOpGP0sB/3MnCiH70LSAFWFnsqTWGEPcfAQdwX0QFgWzm/D0q7SPfxS6p6HfAr8IofbfdxKaSqS1Q1XAchQ4DB/9/encdHVZ2PH/882ReysUUgEDYFwSoiIC7Ffa+2blWLWksV10pbv1atXdSKC/XXaqVqcV8QtNa6FVGLotUWIiogooiyJQhhMQl7SDLP749zMkyGyWQCk/15v155Ze6527nrPHPuuecAVwE/a6nruMOUWAAFwKf+c02U6Xqx6yJb5efrKBddvftIRDoBPwTG+6SDAQH+BbzeXBk0e62+Y3yHqgZE5AzgUuBDOu510NZFu47TgH6qusQn2XXcvuSoajmAiGTRQt9nHanEogS3UyH6dq8Oma63n6+jiLiPRCQbeBD4lapuBlDVgKrWADtEpCOdR21dxGOsqgH/cR3QiY59HbR10e515wAv1g7YddzuVIhIjr9nb6aFruMO81aIf+44GdgBvA+crKoXichhuGdSecC9qvoPEbkTSAUqVfWmehfazkTZRy8CybiT9Hn//wY/20JVvbcFsmv2QJRj/GvcjacrcK2qrumo10FbV98x9uNeBs5X1e0iMgi7jtskEekCTAROAB4Bhvjr+ADgelwp1CRfx6LZr+MOE1gYY4wxpulZ0Zcxxhhj4sYCC2OMMcbEjQUWxhhjjIkbCyyMMcYYEzcWWBhjgkQkV0SuamCaviKyqLnyZIxpWyywMMaEysW12hc3IpIYz+UZY1o3CyyMMaHuAgaIyHwR+aP/WyQin4rIeeETi0iin+ZDEVkoIpf79KNF5B0ReRbfCqSIvCQiH4nIZyIyPmQZW0RkoogsEJE5IpLv0/NF5J8+fYGIHO7TLxSRIp/Hv1ngYkzrYoGFMSbUjcDXqjoMmAMMAw4Cjgf+KCI9wqb/KVChqiNxHVxdJiL9/LhRwM2qOsQPj1PVQ4ARwLW+kR+ATGCOqh4EvAdc5tP/Arzr04cDn4nI/sB5wBE+jzXA2HhtvDFm73WkvkKMMY1zJDDNN/lcKiLv4oKHhSHTnAgcKCK1PSzmAPviOjQrUtXlIdNeKyJn+s+9/XQb/bSv+fSPcK0JAhwLXAzg81AhIhcBhwAfigi4DrTWxWdzjTHxYIGFMaY+EuM0P1PVN+okihwNbA0bPh44TFW3ichsIM2PrtJdTQDXEP2+JMCT1sS4Ma2XPQoxxoTaDGT5z+8B5/l6FN2AMUBR2PRvAFeKSDKAiOzn+6oIlwOU+aBiMDA6hrzMAq70y030HSvNAs4Rke4+vbOIFDZuE40xTckCC2NMkKpuBD7wr5MehnvssQB4G9e77dqwWR4BFgMf+3n+RuQSh5lAkogsBP6Aq7/RkAnAMSLyKe4RyVBVXQz8BnjTL+stILzehzGmBVknZMYYY4yJGyuxMMYYY0zcWGBhjDHGmLixwMIYY4wxcWOBhTHGGGPixgILY4wxxsSNBRbGGGOMiRsLLIwxxhgTNxZYGGOMMSZuLLAwxhhjTNxYYLGXRGSsiLwZw3QPichvmyNPLU1EbheRDSIS3vxzi4v1eMV5nStE5PgmXscTInL7Hs57pogUi8gWETk4DnlRERm4t8tpSSLSTUSWiEiaH54tIpe2gnzFZd82dL6IyBEistSfEz8QkRdF5OS9XW9LE5HvisiSZlrXLSLyTHOsq7Vp14GFv6Fv9xdHqYg8LiKd4rkOVZ2qqifGMN0VqvqHeK67NRKR3sB1wBBV3ael8xMu1uPVwdwDXKOqnVT1k/CR7SFQ2AM3Ao+r6o6WzkgLuQ2Y7M+Jl4C7gInNseJ4fiGHn7uq+h9VHRSPZcfT3vwwaI3radeBhXe6qnYChgMjcR0Y1SEi1n18PfZg3xQCG1V1XTOsq1Utvw0rBD5r6Uy0luMjIqnAj4G4/toUp8XvuSKSGMNkdc4JVS0CskVkRJNlzLQbLX6SNxdVXQ28DhwAwUj2ahFZCiz1ad8TkfkiUi4i/xWRA2vnF5HevjhwvYhsFJHJPv0SEXnffxYR+bOIrBORChFZKCK166sTKYrIZSLylYh8KyKviEjPkHEqIlf4osgyEfmriEik7RKRUSIyT0Q2+VKZP4WMO9JvR7kv6r7Ep+eIyFN+W1aKyG9qb3h+ez7w2/EtcIuIpIrIPSKyyq/jIRFJj5CX43G9Tfb0pURP+PQzROQzn4/ZIrJ/yDwrROQGcT1Vbg3/cvHruics7WUR+aX/fKOIfC0im0VksYicGTJdpG0JHi8/zeEi8qE/Xh+KyOFheTs+ZDj4S0pE0kTkGX8ulPt58yMdo7C8J4TkeaOIPC8inf24mSJyTdj0C0TkLP95sIi85c+ZJSLyw4bWF7LO3/hjvc4f+xx/XLcAicACEfk6wrzv+Y8L/DE9z6fXe/6GzV/vuSMiR4tIiT/+a4HHRSRPRF7z52aZ/1wQsrzZIvIHf1w3i8ibItI1ZHx953xM57B3KFCuqiX1bFMPcdf2//nh0SHrXCAiR4fld6KIfABsA/pLA9e3iIwTkc/9uDdkL7uFF3fveVBEZojIVuAYP6qrP582i8i7tevx50F/4FV/zFP99LOB06KsZ6/vaeIet/waOM+ve4FPzxGRR0VkjYisFve4NdGPG+jzXyHuEexzPn23c7f2nAtZ3woR+T9/PCtE5Dnxj7/8+F/5dX4jIpdKlNI7Eenn87FZRN4CuoaN/7uIrPXreU9Ehvr08cBY4Fc+n6/69Gj3tojb7MdFvE/Ut54moart9g9YARzvP/fGReB/8MOK+xLsDKTjSjTW4W4qibhfLCuAVD+8APgzkAmkAUf65VwCvO8/n4Tr3jkXEGB/oIcf9wRwu/98LLDBrzMVuB94LyTfCrzml9MHWA+cXM82/g+4yH/uBIz2n/sAm4ELgGSgCzDMj3sKeBnIAvoCXwI/DdmeauBnuO6v04F7gVf8vsoCXgXurCc/RwMlIcP7AVuBE3w+fgV8BaSEHKP5/vikR1jeGKCYXT3x5gHbgZ5++FygJy5IPs+vq0eUbQk9Xp2BMuAiP/4CP9wl/Pzxw7cAz/jPl/v9kIE7Pw4BsmM4D3+O6zK8wB/7vwHT/LiLgQ9C5hsClPvpMv1++InP63DcOTQ0/PyKsP5xfp/3x50jLwJPh51vA6NcR3XGE9v5O9B/vpd6zh3cuVIN3O2Xk447T8/2+zUL+DvwUsiyZwNf486rdD98VwznfL35iLC9VwP/CkubDVzKrutlvE/vBWwETsWdgyf44W4h860ChvrjlkyU6xv4gT9W+/vpfwP8t559e6M/PyL+hczzBFABHOHzmObTNuOur1TgPvx1Eenc92m/BF6sZ5/F8552C/46C0l7CXetZALdgSLgcj9uGnBzyLYdGeXcPZq696cVflk9cefG58AVftzJwFp/7DKAp8OXF+Fe/Ce//WP8/n0mZPw43LmXijsf54cdo9vDlhft3hZxm9mL+0Q8/1r8y79JN86dNFv8hbYSeAD/5eVPkGNDpn0QH3SEpC0BjgIO8xdCUoR1XMKuL6pjcTed0UBC2HTBAwo8CkwKGdcJqAL6huQt9OJ4Hrixnm18D7gV6BqWfhPwzwjTJwKVuDoQtWmXA7NDtmdVyDjxJ/SAkLTDgOX15Odo6l64vwWeDxlOAFYDR4cco3FRjqHgbsxj/PBlwNtRpp8PfD/StkQ4XhcBRWHj/wdcEpK3+gKLccB/gQNjPA9rA4vPgeNCxvXwxz4Jd9PZChT6cROBx/zn84D/hC33b8Dvw8+vCOufBVwVMjyodp0h51tjAotYzt+BDZ07/lzZCaRFWfcwoCxkeDbwm5Dhq4CZDZzzjT2Hbwamh6XNxn1prAAuCEm/gZAgzae9Afw4ZL7bIuzPiNc3rlT1p2HXy7aQcyLqsapne54AnoqQNj1kuBNQA/SOdO43dO3FeE7Eek+7hbpfyPm4e1Z6SNoFwDv+81PAFKAghnP3aHYPLC4MGZ4EPOQ/P0ZI8Ik7pyPuf1ywVA1khqQ9S1iAFDIu1y8rJ+R4RP3Cp+69LeI2sxf3iXj+dYRHIT9Q1VxVLVTVq1R1e8i44pDPhcB1vjizXETKcb+ie/r/K1W1OtqKVPVtYDLwV6BURKaISHaESXviAp3a+bbgfuX0Cpkm9I2KbbgLNZKf4n69fSGuOP57Pr037pdduK5ASuj6/efQdYful264aP2jkP0y06fHInxbA3759a2vDnVXw3TcjQTgR8DU2vEicrHsenxVjnvUFVoEWe+yw/Pmhe+L+jyN+wKZ7otJJ4lIcgzzFQL/DMnv57gber6qbgb+BZzvpz2fXdtaCBwadn6OBWKpIBu+nStxgUyDj25iWV495y/Edu6s15AKkiKSISJ/E/fYZhMucM6VuvUC6rs26jvnG3sOl+GCvHBjcUHxCyFphcC5YcflSFzAWCvSOVjfNhQC94Us61tcYBTLORlNpDwE0/wx/BZ3bOuThfuRFkk872nhCnElPWtC9svfcCUX4EpBBSgS98h1XIzLbShfPam73xq6l5Sp6taQtOD+EJFEEbnLP9rYhAtoIOxxSagG7m31bfPe3CfipiMEFtFoyOdiYKIPQmr/MlR1mh/XR2KoXKaqf1HVQ3DFZ/sB10eY7BvcCQCAiGTiim1XN3oDVJeq6gW4i+xu4AW/vGJgQIRZNuB+SRSGpPUJW7eGTb8dV5RWu19y1FWIjUX4tgruC6C+9UUyDThH3DPgQ4F/+GUVAg8D1+AeX+QCi3AXXCzLrpM3L3RfbMV9IdUKXpyqWqWqt6rqEOBw4Hu4RxkNKQZOCTvP0tTVAard1gtE5DBcUf87IfO9GzZfJ1W9MoZ1hm9n7a+r0hjmbXB5Uc7fWM6d8ONzHa5E5VBVzcYVKUPdY1qfaOd8Y87hhbhrN9wtflnPhgQ6xbgSi9Djkqmqd4XM19D5Hb4Nl4ctL11V/xs+oYj82j8rj/gXNnmkPPQOWVYn3KOAb6LkbX/cI+FI4nZPY/e8FuNKLLqG7JNsVR0KoKprVfUyVe2JK319oL56EI20BvfIslbv+ib00+b57a7VJ+Tzj4DvA8cDObhHarDrvK6zzQ3d26Jsc0P3icaci3usowcWoR4GrhCRQ8XJFJHTRCQL9wxuDXCXT08TkSPCFyAiI/38ybgvpR24X6PhngV+IiLDxFWMugOYq6orGptpEblQRLr5koByn1yD+6V7vIj8UESSRKSLiAxT1RpcMeREEcnyJ/AvqacGvF/uw8CfRaS7X2cvETkpxiw+D5wmIsf5/XId7iax242yPupegVwPPAK8oaq125mJu1DW+3z9BF85N0YzgP1E5Ed+H52Hq9fwmh8/HzhfRJLF1YY/p3ZGETlGRL7jv2A24YK1SMc63EO4fV/ol9NNRL4flqdC3Ot+z/n9j8/TfiJykc9Psj/f9qdh04BfiKtc1gl3vj3XUAlciFJc/YxaMZ2/e3juZOGCgHJxlVp/H2Meof5zvrH5KMKVkoSXElThnntnAk+Lq/D8DHC6iJzkf5WmiasgWMCeeQi4SXZV7MsRkXMjTaiqd/gvjYh/MazrVHGVXVOAP+COYbRf5UfhHtVEErd7Gu586+v3L6q6BngT+H8iki2uMvIAETkKQETODdnfZbh7Qk3IsvqzZ57327S/iGQAv6tvQlVdCcwDbhWRFBE5Ejg9ZJIs3H1vI+7Hyh0Rtjk0n1HvbVG2uaH7xN7sj5hZYOGp6jzcM8TJuAP1Fe55PP7L+HTcM7ZVQAnuWVa4bNwNrAxXDLYR10ZA+Lpm4eoe/AMXsAxgV/F3Y50MfOZ/odwHnK+qO1R1Fa5C2XW4Is75wEF+np/hAp9lwPu4m8JjUdZxA25/zBFXjPdv3K/KBqnqEuBCXGWuDbj9eLqq7mzENoL7cjze57V22YuB/4erF1EKfAf4INYFqupGXEnDdbhj9Svge6q6wU/yW9yxKcPVY3k2ZPZ9cEXim3CPM94lttcT78NVInxTRDbjKnIeGpKnSlzlyvBt3QyciDtPvsEV39ZWemzIY7hHN+8By3EB789imK/WLcCT4opWf9jI87ex5869uJKaDbh9MzPWTDZwzsecD39uPoE7byONOwtXQvgY7hf593FvMqzH/WK8nj28t6rqP3HHdbrP5yLglD1ZVgyexQVu3+IqH4+tb0IRGQlsVffa6W7ifE/7u/+/UUQ+9p8vxj3CXYy7Hl9g1+OmkcBcfw98BZigqsv9uFsIOXcbkwlVfR34C67U8CvcfQZcgBDJj3DX8re4/fpUyLincN8Jq/02zAmb91FgiM/nSzHc2yJuc9h9Yj3uWn8XuCLCev4lIv8U90ZMkfg3GCH4tsyn4h7FzGt4b9VVW9PeGGOMJyLdgP8AB2vdelkdkoj8A3hUVWe0dF5aiv/VvwhIbURpX4vwJalf4t5SKgE+xFU6XhwyzR+BLap6q4gMBv6qqsf5cSuAESE/shrFSiyMMSaMqq5X1cEWVDiqenZHDCrENXefIiJ5uJKkV1t7UOGNAr5S1WW+lG06rmQt1BDcG2Oo6he4x097WqG7DgssjDHGmMguxz1S+BpXhyGWytKtQS/qvsVSwu5vFi3APdZDREbh6nbV1ttQ3OPaj8Q1rNUoFlgYY0wHICIni2uJ8SsRuTHC+Lz6nrn78Yki8omIvBaSNkxE5tQ+i/dfUO2Gqp7s3yDqrKpn+oqkbUGkt6jC6z3chXuTZT6uztUnuLfFAI5Q1eG4+j1Xi8gYGsECC2OMaef8M/e/4r4ohuBeaR4SNtmvca1BHoirLHlf2PgJuIrKoSYBt6rqMNxbE5PinHWzZ0qo+3psAWGvEqvqJlX9iT92F+PadVnux33j/68D/ol7tBIzCyyMMab926tn7v7VxtNwr3yHUtzbcODaZ4jWDoZpPh8C+/pXzFNwb4m8EjqBiOT6ceCaq39PVTeJa1Ihy0+TiXvLZFFjVt7m3grp2rWr9u3bt6WzYYwxbUZZWRkVFRXU3js3btzI1q1b6dNnVxtOq1evJhAI0Lt3b7Zu3coXX3zB4MGDyczM5Ouvv2afffYhEAhQWlrKwIGu/ant27ezdOnS4DIGDRpEamosb0CbplZRUUFxcTGqSteuXenRowfr168HoFu3bmzZsoUVK1YAkJ6eTmFhIUlJSVRWVvL1164BW1Wlc+fO9OjRg48++miDqsbU4nKTdVPsI50HcH0BzFbVqT79PFzkWwXco6qfi+tN7y3gY1WdEm25ffv2Zd68Rr9Wa4wxHdbf//533njjDR55xBU4PP300xQVFXH//fcHp9m0aRMTJkzgk08+YcSIEXTq1IlHHnmE4uJiZsyYwQMPPMDs2bO55557eO01V83i2muv5bbbbuPss8/m+eefZ8qUKfz73/9ukW3s6EqnlrLs5mVUrqoktU8q/Sf2J39sXF7yAEBEwrs/qFeTBRa42qYvqOqr4rp0re3z4GxcQyx5uNbHLsV1FJZO9LbYjTHG7IGCggKKi3fdXktKSujZs263INnZ2Tz++OOA+6Xar18/+vXrx/Tp03nllVeYMWMGO3bsYNOmTVx44YU888wzPPnkk9x3n6uKce6553LppZc230aZoNKppSwZv4TANtdQb+XKSpaMXwIQ1+AiVk0ZWBQAn/rPoU0d34NrhXEtLrgAOBhXi/Vf1N9krDHGmD0wcuRIli5dyvLly+nVqxfTp0/n2WefrTNNeXk5GRkZpKSk8MgjjzBmzBiys7O58847ufPOOwGCJRbPPOMame3ZsyfvvvsuRx99NG+//Tb77rtvs29ba6QBJbAj4P62B4Kfa7bX7JYW/BxpunrGh0+7c81OCNTNQ2BbgGU3L2t3gUUJLriYT0glUd8kbJG4DlOu8WkBABHZISIJIf0j4NPHA+OBOs8EjTHGNCwpKYnJkydz0kknUVNTw7hx4xg6dCgPPfQQAFdccQWff/45F198MYmJiQwZMoRHH320weU+/PDDTJgwgerqatLS0pgyJeqT7GalqmiVxv7FHvI54hd7I4IA3bl3dRclRUhISyAhPcH9T0sgMT0xmJbcLbnO+LWPrY24nMpV9bU+3rSarPKmr2MxGddW+fvAyap6kYiciusvohOuTf0cXDv+AAtV9d5oyx0xYoRaHQtj4mvmzJlMmDCBmpoaLr30Um68sW4zB2VlZYwbN46vv/6atLQ0HnvsMQ44YFczBzU1NYwYMYJevXoFn7/Xuueee7j++utZv349XbvW20u0aUJN/fw9Gq3RPf4lvre/7veqL88Egl/cwS/1sC/74OdIAUA94xucNi0BSYilM99d/tf3f1Su3D2ISC1M5bAVh+3FTthFRD5S1RGxTNtkJRbq+qX/SUjSVJ8+A9eDY621wDiMMS2ipqaGq6++mrfeeouCggJGjhzJGWecwZAhu5o5uOOOOxg2bBj//Oc/+eKLL7j66quZNWtWcPx9993H/vvvz6ZNm+osu7i4mLfeestKGltQxOfvly1hxzc76Hx8570ujm/o171W7eWv91SJ+mWdkp1S7y/7qMFAA9NKkiDSuC/4ltJ/Yv86xxggISOB/hObvCPTiGIOLEQk0wcLxph2pKioiIEDB9K/v7sJnX/++bz88st1AovFixdz0003ATB48GBWrFhBaWkp+fn5lJSU8K9//Yubb76ZP/3pT3WW/Ytf/IJJkybx/e+HN5lg9kTN9hqqy6upLnN/VWVVdYary6qpLvfpfnjr4q11a7kBge0Blv9qOctZHnlFYSRp96L50M9JuUmx/bIP+RxTKUBK43+9d0S1pU8tVSoVrsHAQkQOxzWK0gnoIyIHAZer6lVNnTljTNNbvXo1vXvvaqSvoKCAuXPn1pnmoIMO4sUXX+TII4+kqKiIlStXUlJSQn5+Pj//+c+ZNGkSmzdvrjPPK6+8Qq9evTjooIMwjqpSs6WmUYFB7fiqsiq0Mvqv/8SsRJJyk0jKc3/pA9LZ+mk9vwcFDvjnATEV2SckWVuKrV3+2PwWCyTCxVJi8WfgJHyrXaq6oLHthhtjWq9I9azCi4BvvPFGJkyYwLBhw/jOd77DwQcfTFJSEq+99hrdu3fnkEMOYfbs2cHpt23bxsSJE3nzzTebOvvNTmuU6oo9Cwyqy6t3Kz2oQ6gTGCTnJZPaKzU4nJSXRFKuSw8drv0fKQCo9/l7n1S6ft/qvJj4i+lRiKoWh91ool0axpg2pCnaOLjhhhtYvnx5sLSipKSE4cOHU1RUxD777NN8G1ePwM7AngUGZdXUbIp++5NkqRsEdEkmfWB6nSAgGBiEBRGJWYlxL/pvbc/fTfsXS2BR7B+HqG9X/Fp274jGGNNGNVUbB+vWrQvOX9tibrzeClFVAtsCexQYVJdX1/mSjSQhI6FOEJDaO5XMAzMbDAyScpNIyEhoVZX+Wtvzd9P+xRJYXIHr5a4Xrm2KN4GrmzJTxpjm01RtHDREA0rN5poGg4D66iM09LZBYnZinSAgY7+M3YKAiI8YcpNISG1fdQpa0/N30/5FbcfCd7X7pKpe2HxZis7asTAmvvamjYNAdaBuiUEjAoPqiurdWgusI5G69QkilA7UW/cgJwlJbD2lBsa0dXFrx0JVa0Skm4ik+K52jTHtSKQ2Dr746ReUv19O5pDMBl9trNnSQH2DVKkTBKTkp5AxOKPeCoihaYmdElvVIwVjTGxieRSyAvhARF4Bgu8tqeqf6p3DGNOqBaoDbJ63mS+v/nK3+gZaqax5aE1wOLFT3VcY0/qlkXRwA4GBH05MT2zuTTPGtLBYAotv/F8CkNW02THGNAVVZetnWymfVU7ZrDLK3y2P/naDwOGlh7v6Bsntq76BMaZpNRhYqOqtACKS5QZ1S5Pnyhiz17av2B4MJMreLqOqtAqAtAFpdD+/O3nH5fH1dV9TWRK5jYOUbinNnWVjTDsQS8ubBwBPA5398AbgYlX9rInzZoxphJ3rd1L+tg8kZpWxY9kOAFL2SSHvuLzgX1phWnAerVJr48AYE1exPAqZAvxSVd8BEJGjgYeBw5suW8aYhlRvrqbivYpgILF1oasClZidSO7RuRRMKCDvuDwyhmTUWwnS2jgwxsRbLIFFZm1QAaCqs32X6MaYZhSoDLBpzqZgILG5aDNarUiqkHNEDv0m9iPvuDw6HdKpUX07WBsHxph4iiWwWCYiv8U9DgG4EBruEs8HHw8AO4HZqjrVp58HnAZUAfeo6uc+/SFgi6r+X6O3wph2SGuULfO3BAOJiv9UENgegATIGpFF71/1Ju+4PLIPzyYxzd6+MMa0DrEEFuOAW4EX/fB7wE9imO8s4AVVfVVEngOm+vSzgbFAHnAHcKmInAPMAwY3Iu/GtCuqyvYvtwcDifJ3yqkuqwYgY0gGPS7tQd5xeeQclUNybnIL59YYYyKL5a2QMlz/II1VAHzqP4e+13YPcD+wFsgTkXzgYFy9DQssTIdSuboyGEiUzSpj52rXDl1qn1S6/qArecflkXtsLqk9Uls4p8YYE5tY3gp5CzhXVcv9cB4wXVVPamDWElxwMR/XBgYAqloEFInIQOAa4CigO/A74CAR2U9VvwzLw3hgPECfPn1i2jBjWqOqb6son73rzY3tS7YDkNQlibxj3Vsbucflkj4g3VqdNMa0SVH7CgEQkU9U9eCG0iLMlwlMBnYA7wMnq+pFInIqcDrQCbheVdf66fsC1zRUx8L6CjFtSc22Gire3/XmxpaPt4BCQmYCuWNyg4FEpwM7xb27bGOMiZe49RXiBUSkj6qu8gsvBKJHI4CqbqVuXYypPn0GMCPC9CsAq7hp2rRAVYDNH24OBhKb/rcJ3alIspA9Opu+v+9L7nG5ZI/KJiHFWrQ0xrQ/sQQWNwPvi8i7fngM/rGEMR2dBpSti7buenPj3QrXMZdAp2GdKLi2gNzjcsn9bi6JmfbmhjGm/Yul8uZMERkOjAYE+IWqbmjynBnTSm1fFvLmxtvlVK13TWWn75tO/oX55B6XS94xeSR3sTc3jDEdTyyVN48A5qvqayJyIfBrEblPVVc2ffaMaXk7S3dS9rYPJGaVs2OFbyq7RwqdT+rsAonj8kjrndbAkowxpv2L5VHIg7i3NQ4CrgceA57Cvc1hTLtTvama8nfLg4HE1kWuqeyk3CTXVPZ1vqnswfU3lW2MMR1VLIFFtaqqiHwf+IuqPioiP27qjBnTXGp21LDpf5uCgcSmDzdBDSSkJZBzZA7dx7qeQLOGZyGJFkgYY0w0sQQWm0XkJlxT3mNEJBGwh8emzdIaZfPHm4OBRMX7FQR2BCARskdm0+fGPq6p7MOsqWxjjGmsWAKL84AfAT9V1bUi0gf4Y9Nmy5j4UVW2fbEtGEiUzy6nutw1lZ15QCY9LndNZecelUtSdiyXhDHGmPrE8lbIWuBPIcOrcHUsjGm1dhTvCAYSZbPK2LnGNZWd1jeNrme7prLzjs0jJT+lhXNqjDHti/08M+1C1cYqyt7ZFUhsX+qayk7ulkzuse6tjbzj8kjvn97COTXGmPbNmv4zDZo5cyaDBg1i4MCB3HXXXbuNLysr48wzz+TAAw9k1KhRLFq0CIDi4mKOOeYY9t9/f4YOHcp9990XnGf+/PmMHj2aYcOGMWLECIqKihqVp5qtNWycuZGvr/+aecPn8UG3D1h87mJKnyklfb90BvxpACMWjODwtYczdPpQel7W04IKY4xpBg32FdLaWF8hzaumpob99tuPt956i4KCAkaOHMm0adMYMmRIcJrrr7+eTp068fvf/54vvviCq6++mlmzZrFmzRrWrFnD8OHD2bx5M4cccggvvfQSQ4YM4cQTT+QXv/gFp5xyCjNmzGDSpEnMnj273nwEdgbYVLQpWCKxac4mtEqRFCH7sOxgiUTWyCwSki1eNsaYeIprXyG+gaxbgEI/vQCqqv33JpOmbSgqKmLgwIH07+8O9/nnn8/LL79cJ7BYvHgxN910EwCDBw9mxYoVlJaW0qNHD3r06AFAVlYW+++/P6tXr2bIkCGICJs2bQKgoqKCnj171lmvBpQtC7cEA4ny98oJbA24prKHd6LgF64tiZwjc0jMsDc3jDGmtYiljsWjwC+Aj4Caps2OaW1Wr15N7969g8MFBQXMnTu3zjQHHXQQL774IkceeSRFRUWsXLmSkpIS8vPzg9OsWLGCTz75hEMPPRSAe++9l5NOOon/+7//IxAI8MEHH7Dtq227Aol3yqna4JvKHpTOPj/ex725cXQuyZ3tbWdjjGmtYgksKlT19SbPiWmVIj0qC29t8sYbb2TChAkMGzaM73znOxx88MEkJe06tbZs2cLZZ5/NvffeS3Z2NgAPPvggk343iWPSj+HZR57l3MHn8sdK9xZzSq8UOp/a2QUSx+aSVmBNZRtjTFsRS2Dxjoj8EXgRqKxNVNWPo80kIpnAA8BOYLaqTvXp5wGnAVXAPcASXLPh6cA2Vb1iD7bDNJGCggKKi4uDwyUlJbs9tsjOzubxxx8HXCDSr18/+vXrB0BVVRVnn302Y8eO5fRjT2fDyxsom1XGY399jDMDZ/I5nzMidwS/rfkt+/51X/fmxn7p1lS2Mca0UbEEFof6/6GVNhQ4toH5zgJeUNVXReQ5YKpPPxsYC+QBd6jqpcDlACLymIgkqGog1g0wTWvkyJEsXbqU5cuX06tXL6ZPn86zzz5bZ5ry8nIyMjJISUnhkUceYcyYMWRnZ1O9rZqLvn8RPct7ctRzR/HB9R9AABLSE+ie3p11Y9dx6vhTmbtxLoNuGkSvq3q10FYaY4yJl1gayDpmD5ddAHzqP4fWzbgHuB9YiwsuEJEhwO3AmkhBhYiMB8YD9OnTZw+zY/ZEUlISkydP5qSTTqKmpoZx48YxdOhQHnroIQCuuOIKPv/8cy6++GISExPZd599mfjdicw/bj7/+c9/eK7qOfrTn/9m/JfEbonc8otbOOfn5/DUh08xYcIE7phzB2lpaUyZMqWFt9QYY0w8NPi6qYjkAL8Hxvikd4HbVLWigfkuAsp8d+vTVfX8sPEDgWtU9echaQ8Ad0frkt1eN21epVNLWXbzMipXVZLaJ5X+E/uTP9ZVylRVti12TWWXzSqjfHY5NZtcDJl5YGbwFdCcMTkkZVlbbMYY01bF9XVTXDfpi4Af+uGLgMdxjzqieRGYLCKnAa+KyNOqepGInAqcDnQCrheRnsBNuMa6qoHiepdomlXp1FKWjF9CYJsrRKpcWcmSy5ZQNruMwLYA5W+Xs3Otbyq7fxrdz3O9gOYek0tKd2sq2xhjOqJYSizmq+qwhtKai5VYNJ//9f0flSsrI45Lzk8m71hXIpF7XC7pfa1VS2OMaa/iXWKxXUSOVNX3/cKPALbvTQZN21C5KnJQgcDhaw63NzeMMcbsJpbA4krgSV/XQoBvgUuaMlOm5agqZbPKWPmHle7dnwhS+6RaUGGMMSaiWN4KmQ8cJCLZfnhTU2fKND9V5ds3vmXlbSvZ9L9NpPRKoftF3dnwwgYC23e9qJOQkUD/idaauzHGmMjqDSxE5EJVfUZEfhmWDoCq/qmJ82aagaqy8bWNrLxtJZvnbSa1Tyr7PrgvPX7Sg4TUBEpPqv+tEGOMMSZctBKLTP8/qzkyYpqXBpQNL21g5R9WsmX+FtL6pTHokUHkX5RPQsqu3kHzx+ZbIGGMMSZm9QYWqvo3///W5suOaWpao6x/YT0rb1/J1kVbSd83ncFPDKb7j7pbd+PGGGP2WoPfJCIySUSyRSRZRGaJyAYRubA5MmfiJ1AdoHRqKR8e8CGLz1+M1ij7T92fUZ+PYp8f72NBhTHGmLiI5dvkRF9h83tACbAfcH2T5srETaAqwJon1vDh/h/y+YWfI8nCkOeHMHLRSPJ/lI8k2tsdxhhj4ieW102T/f9TgWmq+q29atj6BXYGWPvkWlbduYody3fQ6eBODH1xKF2/3xVJsONnjDGmacQSWLwqIl/gGsW6SkS6ATuaNltmT9XsqGHtY2tZddcqKosryRqVxcC/DKTLaV2s7QljjDFNLpZ2LG4UkbuBTapaIyJbge83fdZMY9Rsr2HNw2tYdfcqdn6zk+zDsxn08CDyTsyzgMIYY0yzidaOxbGq+raInBWSFjrJi02ZMRObmq01fPPQN6z64yqqSqvIOSqH/Z/en9xjci2gMMYY0+yilVgcBbyN64k0nGKBRYuq3lzN6r+upuT/lVC1oYq84/MofL6Q3DG5LZ01Y4wxHVi0dix+7///pPmyYxpSXVFNyf0llPy5hOpvq+l8SmcKf1tIzmE5LZ01Y4wxpuE6FiJyBzBJVcv9cB5wnar+poH5MoEHgJ3AbFWd6tPPA04DqoB7gABwA5AGLFDVu/d4a9qxqm+rKLmvhJL7SqipqKHL6V0o/G0h2SOzWzprxhhjTFAsb4Wcoqq/rh1Q1TIRORWIGlgAZwEvqOqrIvIcMNWnnw2MBfKAO1T1UmAcgIjY45UwOzfspORPJayevJqazTV0Pasrhb8pJOtga2ndGGNM6xNLYJEoIqmqWgkgIulAagzzFQCf+s81Ien3APcDa3HBBX655wNvRlqQiIwHxgP06dMnhlW3fTtLd1J8TzGrH1xNYFuAbj/sRuHNhXT6TqeWzpoxxhhTr1gCi2eAWSLyOK7S5jjgyRjmK8EFF/MJaeFTVYuAIhEZCFwDwaCisL7HIKo6BZgCMGLECI1h3W1W5TeVFP+xmG/+9g2BygDdL+hO4c2FZO6f2fDMxhhjTAsT1Ya/p0XkZOB4QIA3VfWNGObJBCbjGtN6HzhZVS/yj1FOBzrhmgbvAbwKvAZsU9Vf1rNIwAUW8+bNazDPbc2O4h2sunsVax5Zg1Yr+1y0D31+3YeMfTNaOmvGGGM6OBH5SFVHxDJtLCUWAJ8D1ar6bxHJEJEsVd0cbQZV3QqEvlEy1afPAGaEpK/FlWx0SNtXbGfVnatY+/haAPa5ZB/63NiH9P7pLZwzY4wxpvFieSvkMlz9hs7AAKAX8BBwXNNmrX3b9tU2Vt25itKnSiEBelzWgz439CGtT1pLZ80YY4zZY7GUWFwNjALmAqjqUhHp3qS5ase2LdnGyokrKZ1aSkJKAj2v6kmfX/UhtVcs9WGNMcaY1i2WwKJSVXfWNg8tIkm4SpymEbZ+tpWVt69k3XPrSEhPoOAXBfT+v96k7mMBhTHGmPYjlsDiXRH5NZAuIicAV+EqW5oYbFmwhZW3r2T9C+tJ7JRInxv6UPDLAlK6pbR01owxxpi4iyWwuAG4FNcmxeW4ipePNGWm2oPNH21mxR9WsPHljSRmJ1L420IKJhSQ3CW5pbNmjDHGNJmEaCNFJAH4VFUfVtVzVfUc/7ndPgqZOXMmgwYNYuDAgdx11127jS8rK+PMM8/kwAMPZNSoUSxatCg4bty4cXTr3I2BWQP5aMRHVLxXQd9b+zJ65Wj63dbPggpjjDHtXtTAQlUDwAIR6RDNXdbU1HD11Vfz+uuvs3jxYqZNm8bixYvrTHPHHXcwbNgwFi5cyFNPPcWECRMAKH+/nCM/PZKJZRMJbA/Q745+jF4xmr6/60tyrgUUxhhjOoZYHoX0AD4TkSJga22iqp7RZLlqIUVFRQwcOJD+/fsDcP755/Pyyy8zZMiQ4DSLFy/mpptuAmDQoEEs+2IZs46cReIHiQzqPghugoyXMii8qbBFtsEYY4xpSbEEFrc2eS5aidWrV9O7d+/gcEFBAXPnzq0zzUEHHcQ//vEPhmwfwr+u/xervlnFyp0rOeHPJ9BzfE+K1xXDK82dc2OMMaZ1qDewEJE04ApgIK7i5qOqWt1cGWsJkaqO1L5mWzt+/PDxXHvltYy6dxQDMwbynT7fYdjfh9F7VO/d5jXGGGM6mmglFk8CVcB/gFOAIcCE5shUSykoKKC4uDg4XFJSQs+ePVFVNr6ykRV/WMGWj7ZwU+FNFE4sJP/ifAYMHsDAwQNbMNfGGGNM6xEtsBiiqt8BEJFHgaLmyVLLGTlyJEuXLmX58uX06tWL6dOnc/8l9zPv4HlsXbCVtAFp7DN5H/pe0pe0zDQefvhhxowZQ3Z2dktn3RhjjGkVor0VUlX7ob0/AqmVlJTE5MmTOemkk9iv934cvvZwAr8M8MLaF5h7yVxGfTGKsuFlfGfYdxg8eDCvv/469913X3D+Cy64gMMOO4wlS5ZQUFDAo48+2oJbY4wxxjS/ertNF5Eadr0FIkA6sM1/VlVtkZ/p8e42vXRqKctuXkblqkpSe6fS+dTOlL9TzvYl28kYkkHhbwvpfm53JFEaXpgxxhjTDsWl23RVTdzLTGQCDwA7gdmqOtWnnwechisRuUdVPxeRvwGH1z56aS6lU0tZMn4JgW0BACpXVbLmoTWk9E5h6AtD6XpmVyTBAgpjjDEmVlEbyNpLZwEvqOplQGibF2cDPwVuAq4DUNXLgSVNmJeIlt28LBhUhBIRup3dzYIKY4wxppFiacdiTxXgXlMFqAlJvwe4H1gL5MWyIBEZD4wH6NMnfo2AVq6qjJxeHDndGGOMMdE1ZYlFCS64qLMeVS1S1SuAZ4DiSDOGU9UpqjpCVUd069YtbhlM7RO5y/L60o0xxhgTXVMGFi8CZ4vIg8CrIvI0gIic6tN+D9zl0yYCB4vIQyLSbN/q/Sf2JyGj7i5IyEig/8T+zZUFY4wxpl2p962Q1qpJ3wrpk0r/if3JH5sft+UbY4wxbV1j3gppc4GFiKwHVjbBorsCG5pgue2F7Z/2z45x+2fHuP1rqmNcqKox1UVoc4FFUxGRebFGYx2R7Z/2z45x+2fHuP1rDce4KetYGGOMMaaDscDCGGOMMXFjgcUuU1o6A62c7Z/2z45x+2fHuP1r8WNsdSyMMcYYEzdWYmGMMcaYuGnKJr1blSidop0KXAPMUNXJPu0uIAPYpqo3tlCWm12UfXQjMADYB7hSVUtE5GvgLeBjVW3xojcTmyjH+BZgf6AMuE1Vv+mo10FbF+UY/xQYCfQGFqrqTXYdt00i0h+4GchR1XNC0g/A9cMFcKeqLmqJ67gjlVhE7BRNVWcAk2qHRaQPkKyq1wKJItK72XPacurbR3f5tMeAY3zyFiCdGJtlN61GfZ0DVuO+iKqA8g5+HbR19V3Hj/ruFL4EnvDJdh23Qaq6TFV/GmHUBOBq4CrgZy11HXeYEgvq7xQtXC92XWSr/Hwd5aKrdx+JSCfgh/jO4ICDAQH+BbzeXBk0e62+Y3yHqgZE5AzgUuBDOu510NZFu47TgH6qWtubtF3H7UuOqpYDiEgWLfR91pFKLCJ2ihbB6pDpevv5OoqI+0hEsoEHgV+p6mYAVQ2oag2wQ0Q60nnU1tXXOWDAf1wHdKJjXwdtXbR73Tm4fpwAu47boQoRyfH37M200HXcYd4K8c8dJwM7gPeBk1X1IhE5DPdMKg+4V1X/ISJ3AqlApareVO9C25ko++hFIBl3kj7v/9/gZ1uoqve2QHbNHohyjH+Nu/F0Ba5V1TUd9Tpo6+o7xn7cy8D5qrpdRAZh13GbJCJdgInACcAjwBB/HR8AXI8rhZrk61g0+3XcYQILY4wxxjQ9K/oyxhhjTNxYYGGMMcaYuLHAwhhjjDFxY4GFMcYYY+LGAgtjTJCI5IrIVQ1M01dEFjVXnowxbYsFFsaYULm4VvviRkQS47k8Y0zrZoGFMSbUXcAAEZkvIn/0f4tE5FMROS98YhFJ9NN8KCILReRyn360iLwjIs/iW4EUkZdE5CMR+UxExocsY4uITBSRBSIyR0TyfXq+iPzTpy8QkcN9+oUiUuTz+DcLXIxpXSywMMaEuhH4WlWHAXOAYcBBwPHAH0WkR9j0PwUqVHUkroOry0Sknx83CrhZVYf44XGqeggwArjWN/IDkAnMUdWDgPeAy3z6X4B3ffpw4DMR2R84DzjC57EGGBuvjTfG7L2O1FeIMaZxjgSm+SafS0XkXVzwsDBkmhOBA0WktofFHGBfXIdmRaq6PGTaa0XkTP+5t59uo5/2NZ/+Ea41QYBjgYsBfB4qROQi4BDgQxEB14HWuvhsrjEmHiywMMbUR2Kc5meq+kadRJGjga1hw8cDh6nqNhGZDaT50VW6qwngGqLflwR40poYN6b1skchxphQm4Es//k94Dxfj6IbMAYoCpv+DeBKEUkGEJH9fF8V4XKAMh9UDAZGx5CXWcCVfrmJvmOlWcA5ItLdp3cWkcLGbaIxpilZYGGMCVLVjcAH/nXSw3CPPRYAb+N6t10bNssjwGLgYz/P34hc4jATSBKRhcAfcPU3GjIBOEZEPsU9IhmqqouB3wBv+mW9BYTX+zDGtCDrhMwYY4wxcWMlFsYYY4yJGwssjDHGGBM3FlgYY4wxJm4ssDDGGGNM3FhgYYwxxpi4scDCGGOMMXFjgYUxxhhj4sYCC2OMMcbEjQUWxhhjjIkbCyxaMRG5RETeb+l8NBUR+a6ILGnpfNRHRH4tIo808zpVRAY28Tpmi8ilezjvlSJSKiJbQro939N89PXb26Y7QxSRISIyL2R4hYgc38J5itu+beh8EZEzRaTYnxMHi0iRiAzd2/W2NBEZKyJvNtO6nhCR25tjXc2hzQUW/mBvifCnIvK7ls6fcWL5glTV/6jqoObKU2Op6h2qukdfwO2R72jsT8CJqtrJ9ysSOr5dBAp74A/APS2diRZ0D3CNPyc+8cO3NceK4/WFHOncVdWpqnri3i473vbmh0FzrafNBRb+YHcK/QN+DpQCDzd2eR3wJtgqtPb93trz10LycV2df9bSGWktx0dEegDHAC/Febktvn3ixPIdUUjdc+IVXOdx1jlcR6WqbfoPOBjX1fPRIWk5wKPAGmA1cDuQ6MddAnwA/Bn41o/LAZ4C1gMrcb0nJkRZZypwL/CN/7sXSPXjjgZKgOuAdT4PPwmb9x5gFS4YeghIr2c9lwDvhwwfDnwIVPj/h4dNu8zvi+XAWJ8+EHjXz7MBeK6edfUFFPgJUAyUAVcAI3E9XJYDk8PmGQd87qd9Ayj06e/5ZW0FtgDnheyXG4C1wNO1aSHL6w286I/Dxtr1NWIbZuJ+OYWmLQDO8p/v89u2Cddb5ndDprsFeAF4xo+/1Kc9EzLNGbgbaDkwG9g/ZJwCA0OGnwBu95+7Aq/5+b4F/kM951focqKdK36/fy9kviS/b4b74dHAf/06F1D3+pgNXNqYcxvYzx9P9cf07QjzrgoZvwXXO2oC7npaibsengJyws65pD28bgfgel3d6Ld9KpAbkp8VwP/hzt8K4DkgLWT894H5/nh/DZzcUD4ibPPFwL/D0lYAx/vPg3HX4/l++Ht+neX++BwYNt8NPr+VuPNegR/7fbsBuDlk+gTgRp/3jcDzQOdI+7YR99PZwES/r7f7PMwG7gSK/H58Gejsz4st7LrWvw5ZzlvAj+tZRyznRMRtDlvOeKAK2Onz8apP7wn8A3cfWQ5cGzLPKGCeP+alwJ+inLuXUPf+q7h74lLcPe+v7OrIMxH4fz6/y4Frou1/3PfWx7j79XPAdHbdL/Jw94v1fj2vAQV+3ESgBtjh81l7j4x2b4u4zdHuE/WtJ+bzqDETt7Y/IBd3Ud0Qlv4SrvvmTKC7vyAuD7lBVQM/w92M0/2J/TKQ5U/sL4GfRlnvbbhun7sD3fyB+YMfd7Rf/m1AMnAqsA3I8+PvxUX0nf36XgXurGc9wRPbT18GXOTzfYEf7uK3cxMwyE/bA9fFNMA04GbcxZwGHFnPuvr6C+EhP92J/qR6yW9nL9xN4Cg//Q+Ar4D9fX5+A/w37CIM/aKt3S93425I6YQEFrgLcwHuiyMzNK+N2IaLgQ9ChofgLpjaoO9Cv7+ScIHfWvwXDS6IqPLbleDzdws+sGDXF+sJ/rj+ym9/Sj3b+wS7bhR3+v2a7P++i78hRdiG0MDiXuo5V4DfAVND5jsN+MJ/7oX7ojnVb8sJfribHz+b+gOLaOd2X6LfLHcbjws+vwL6A51wgePTkaan8dftQL9tqT6v7wH3hqx7hV9GT78PPweu8ONG4b4kT/D7qBcwuKF8RNjmPwJ/DUtbARwPDMd9YX3Ppw/HXUOH4s73H/tpU0Pmm48LsNND9s/DfvggXMCxv5/+5/5YFfh98DdgWj379gHctRDpb2FI3mf7PA/1+znZp60GDvD75B/UDbjrnPs+7S+EfIGFjYvlnIi4zRGW9QT+OvPDCbgv1t8BKX4dy4CT/Pj/ARf5z52A0VHO3UvYPbB4Dfe90wf3xV8bjF4BLPbHIg/4d/jyQpaTgguqfuH37zm4e0/t/aILcDaQgbvu/w68FHaMLg1bZrR7W33bvMf3iYb+Wjw42NM/QHDBwMuE3KRxxbWVhJQC4L6E3wk5WVaFjEv00w8JSbscmB1l3V8Dp4YMnwSs8J+PxkX6oSfoOlxkKLgvpwEh4w4DlteznuCJjQsoisLG/89Pk4m7QZxNWOkHLmiago94o2xTX38h9ApJ2wicFzL8D+Dn/vPrhARf/sTcxq5Si0iBxU7q/mI8ml2BxWG4CzXShRjrNmT5/Vubh4nAY1GmLwMO8p9vAd4LG38LuwKL3wLPh23vanZF+NECi9tw5+nAaPkPXU5D54qfZjOQ4YenAr/zn2/A36hD5n0D/wuS6IFFtHO79hxpTGAxC7gqZHgQ7iaaFDo9jbxu61n/D4BPQoZXABeGDE8CHvKf/wb8OcIyouYjwvQPA3eFpa0AbsWV0B0Tkv4gPkgLSVvCrmB9BTAuwv4sCEkrYlfpx+fAcSHjekTatw2dc2H5mQ3cFiHtrpDhIbhrubY0KVJgUe+1F+M5EXGbIyzrCeoGFoeGnyfATcDj/vN7/th0jeHcvYTdA4sjQ4afB270n98mJPjEBZb1BRZjcKWBod9b/w3djrDphwFlYccj6hc+de9t9W3zHt8nGvprc3UsQtyAi6B/rH4veIW4KHCNiJSLSDnuJtI9ZJrikM9d2RVB1lqJi+YQkYdCKoj+2o/vGWH6niHDG1W1OmR4Gy5S7IaLQj8KydtMn96Q8HUG86mqW3GPG67w2/0vERnsp/kV7kuqSEQ+E5FxDaynNOTz9gjDnfznQuC+kO341q+nV5Rlr1fVHfWM6w2sDNtvtWLaBlXdDPwLON8nnY/7wgVARK4Tkc9FpMLnOQd3/GuFnhfh6ux/VQ346aNtb60/4n6hvSkiy0TkxhjmiXquqOpXuC+W00UkA/eY5lk/byFwbu18ft4jcV88DWno3G6sSMurDSRCNfa6RUS6i8h0EVktIptwj7FCjye4X261aq9DcOfb1xHyG0s+QpXhAtpwV+BK8N4JW/Z1YcelN3X3b6RzsL5tKAT+GbKsz3HF1+H7trEi5SE0bSVuH4Xv61BZuB87kcRyTtS3zQ0pBHqG7eNfhyz7p7jSxy9E5EMR+V6My20oXz2pu48aupesDvveCu4PEckQkb+JyEp/Xr8H5IpIYn0LbODeVt827819Iqo2GViIyNG4ovFzVLU8bHQx7hdHV1XN9X/Zqhr6+lPoAd2Ai5YLQ9L64H6NoqpX6K6Konf48d9EmP6bGLK+AfflPDQkbznqKqA2JHyd4fl8Q1VPwJ0UX+ArsqrqWlW9TFV74kpiHmjobY0YFeMi9NyQv3RV/W+UeTTKuGKgT6RKa43chmnABSJyGK4o9R1wr7bigtEf4h5L5eKKwiXG/NXZ/yIiuC+F1T5pGy4QqLVPSP43q+p1qtofOB34pYgcF2VdENu5Mg33a/r7wGIfbIDbl0+HHZtMVb2rgXXutp3Efm5D5P0XaXnV1A1Ya/PcmOsW3CMmxdVTyMYVBwuxKcbV0YiU3lA+Qi3E3bTDXYE7n/8ctuyJYcclQ1WnhUwT7RyMlNdTwpaXpqqrwycM+4EU/hdeGTdSHnqHfO6Du2duiJK3/XGPNiOJ9ZyIRXhei3GleqH7JEtVTwVQ1aWqegEuULwbeEFEMiMsp7HW4B6D1Opd34R+2l7+HlKrT8jn63ClOIf683qMT6+dvk5eG7q3Rdnmhu4Te7xP2lxg4WsaT8cVyX8SPl5V1wBvAv9PRLJFJEFEBojIUZGWp6o1uCKtiSKSJSKFwC9xv37qMw34jYh0E5GuuOd50aavXVcA94X/ZxHp7renl4ic1NC8wAxgPxH5kYgkich5uCLJ10QkX0TO8CdLJa6yTY1f/rkiUnvCl+FOlpoY1teQh4CbxL+vLiI5InJuyPhS3PPNWBXhLri7RCRTRNJE5Ai/7MZswwzcTes2XCXPgE/Pwt281gNJ4l5Nzm5E/p4HThOR48S9dnkdbl/XBlLzgR+JSKKInAwEzzcR+Z6IDPQ3kk0+71GPQYznynRcXZgr2VVaAe5cPF1ETvL5SRORo0P2YTR7dG5764EAdY/7NOAXItJPRDoBd+COS52SqcZet14W7lwvF5FewPUx5hNc5cyf+OOZ4Pft4D3Ix1vAcBFJC0vfDJwMjBGR2hv1w8AVInKoOJkicpqIRCrxiMVDuPtWIYA/Zt+PNGHYD6Twv1janLhQXHsdGbhr6wV/79yNiKQCh+D2TSQxnRMxCr/PFAGbROQGEUn35/8BIjLS5+1CEenmr69yP08Nkc/dxngemODPo1zcF319/oe7F13r7+Vn4er81MrC/agoF5HOwO/D5g/f5qj3tijb3NB9orH38KA2F1gAl+GKte6LEHk/5Ke5GPd4YzHui+gFohfv/Az3PHsZrvZsV+BGiVBk7b8c8nBf6t/gih8/Bm4XkQnA40C+iPy8nnXdgCsWnyOumOvfuOg0KnVtBnwP94W2Efd44HuqugF3HK/z+fkW96V2lZ91JDBXRLbgKgJOUNXlDa0vhvz8Exf9TvfbsQg4JWSSW4AnxRWx/TCG5dXgfs0PxFUeK8E93mnUNqhqJa4y2PHU/bJ9A1cv5EtcseMOohdXhi93Ce4X8f24X2qnA6er6k4/yQSfVg6Mpe7rh/vijvMW3E3lAVWdHcNqo54r/kvwf7i3hZ4LSS/GlWL8GnezKcZ96cZyvd+OuwYWAp/iz+0Y5kNVt+HfKPDHfTTwGO4NoPdwteV34K63SBp73d6KqxBZgXsE9mIs+fR5LcK9AfVnP/+77PoVHXM+VLUU93x9ty90X5p6AnCKiPxBVefh7l+T/XK/wj3H31P34a6HN0VkM64i56F7sbxonsbVZ1iLq0B9bZRpz8DVUauvpKsx50RDHgWG+PPtpZD7yDC/7A3AI7hHA+CCvc/8veQ+XN2NHfWcu43xMC4gXQh8gvuBU02EHxD+nnEW7tiX4e5zoefuvbjS1g24YzozbBH3AeeISJmI/IWG7231bXND94nw9cSs9lUZA4h7hvUl7mZQgnul8wJVXRwyzam4i+BU3EV8n6oeKiIH4H5BjsJVbJoJXKmqS5t3K4wxzUlEhgBPAqPUbqiIyFxcxe5FLZ2XliIip+AqCoc/vu4Q2mKJRVMaBXylqst8VDmd3X+JfB94Sp05uEo1PXDPFOeo6jZfpPcucGZzZt4Y0/xUdbGqjrSgwlHVQztaUOEfu5zqH230wj2++GdL56ulWGBRVy/qFiGVsHut//qmWYR7ntrFP4c8legVeIwxxrQPgns0V4Z7FPI5rn5Sh9Tizca2MpFqlIf/Cok4jap+LiJ34yosbcHViN6TykjGGGPaEF9HY2RL56O1sBKLukqoW8pQwO6v2tU7jao+qqrDVXUMrhKl1a8wxhjToVhgUdeHwL7+NagUXANLr4RN8wpwsX9dbDRQ4WvnE/JaYB9crd9pGGOMMR1Im3srpGvXrtq3b98mW35FRQXFxcWoKl27dqVHjx6sX78egG7duqGqFBcXU1FRQUJCAn379iUzMxOAJUuWUF1djYhQUFBAdnZjmkkwxhhjWqePPvpog6rG0kp009WxENdY0wO4Vy9nq+pUn34Aru12cC3nLcP1A7EJ+EZVo74z37dvX+bNmxe3fJaWTmXZspuprFxFamof+vefSH7+2Lgt3xhjjGnrRCS8S4l6NeWjkLNwrbNdhmswpdYE4GpcA04/w72muURVrwJ6iEizvUlRWjqVJUvGU1m5ElAqK1eyZMl4SkunNjivMcYYY3bXlIFFAbteywxtfSxHVctVtQLXFOknQKqI/AnXOUssnTrFxbJlNxMIbKuTFghsY9mym5srC8YYY0y70pSBRQm7OmUJXU+FuH4lsoHNqhpQ1d+o6i9x7wAvC1+QiIwXkXkiMq+2vkM8VFaualS6McYYY6JrysDiReBsEXkQeFVEnvbp9wF/wbWXfz+AiDwgIg8D81R1XfiCVHWKqo5Q1RHdusVUdyQmqal96hmjLFhwAuXl78VtXcYYY0xH0ObeChkxYoTGq/JmbR2L0MchCQnpdOnyA8rLZ1FVtY6cnKPo2/d35OYeg0iktrGMMcaY9k1EPlLVEbFM26HbscjPH8ugQVNITS0EhNTUQgYNepihQ59l9OjlDBx4L9u3f8mCBcfxySff5dtv36CtBWLGGGNMc+rQJRaxqKnZwdq1j7Fq1V1UVhaTlTWKvn1/R+fOp1oJhjHGmA7BSiziKDExjV69ruLQQ79iv/2mUFW1jk8//R4ffTSC9etfQjXQ0lk0xhhjWo1GBRYikiciBzZVZlqzhIQUeva8jFGjvmTQoMeoqdnEZ5+dybx5w1i37u8WYBhjjDHEEFiIyGwRyRaRzrgeOx/3bU50SAkJyfTo8RNGjvycwYOfJhDYyeLFP+TDD79DaemzqNY0vBBjjDGmnYqlxCJHVTfhWtJ8XFUPAY5v2my1fgkJSeyzz4WMGvUZ++8/DRA+/3wsRUVDWLv2KQIB6zHdGGNMxxNLYJEkIj2AHwKvNXF+2hyRRPLzz2fkyIUMHfoCCQnpfPHFjykqGsSaNY8SCOxs6SwaY4wxzSaWwOI24A3gK1X9UET6A0ubNlttj0gC3bqdzYgRn3DAAS+TlJTHkiWXMnfufqxe/RCBQGVLZ9EYY4xpcva6aRNRVb79diYrV97Gpk1zSEnpRZ8+N9Kjx6UkJqa1dPaMMcaYmMX1dVMRmeQrbyaLyCwR2SAiF+59Nts3EaFLl1M4+OD/cuCBb5Ge3o+vvvoZc+f2o7j4z9TUbGt4IcYYY0wbE8ujkBN95c3v4ToW2w+4vklz1Y6ICJ07H8+wYe9x0EHvkJGxP19//UvmzOnHqlWTqK7e0tJZNMYYY+ImlsAi2f8/FZimqt82YX7aLREhL+9ohg17m2HD/kOnTsNYtuwG5szpy8qVd1Bdvamls2iMMcbstVgCi1dF5AtgBDBLRLoBO5o2W+1bbu6RHHTQGwwfPofs7NEsX34zc+YUsmLFrVRVlbV09owxxpg9FlPlTRHJAzapao2IZADZqrq2yXMXQVupvNkYmzd/xMqVt7Nhw0skJmbTq9fP6N37FyQnd2nprBljjDFxr7yZDFwEPCciLwA/BTbGMF+miDwpIg+LyNiQ9ANEZKr/O0BEEkXkWT/dkyLS4fovyco6hAMO+CcjRsync+cTWbXqDubM6cvXX9/Azp3rWjp7xhhjTMxi+RJ/EDgEeMD/DfdpDTkLeEFVLwPOCEmfAFwNXAX8DEgHKv10W4DMmHPfznTqdBBDh/6dkSM/pUuX0yku/iNz5vTlq6+uo7JyTUtnzxhjjGlQLIHFSFX9saq+7f9+AoyMYb4CoNh/Du1AI0dVy1W1AsgCtgEqIv8CUlV1c2M2oD3KzBzKkCHPMmrU53Trdi4lJfcxZ04/li69lh07Slo6e8YYY0y9YgksakRkQO2Ab3kzlp62SnDBRfh6KkQkR0Sygc24EpAVqnoasEJEhoUvSETGi8g8EZm3fv36GFbdPmRkDGL//Z/k0EOXkJ9/Id988yBz5w7gyy+vZMeOlS2dPWOMMWY3DVbeFJHjgMeBZYAAhcBPVPWdBubLBCbj3iB5HzhZVS8SkQNw7WAIMMkv91GgDOjql721vuW2x8qbsdq+fQWrVt3F2rWPAco++1xCnz43kZ7ev6WzZowxph1rTOXNWN8KSQUG4YKBL1S1xTq+6MiBRa0dO4opLp7EN988jGo1+fkXUlj4azIy9mvprBljjGmH4hJYiMhZ0WZU1Rf3IG97zQKLXSorv6G4+B6++cZ1cta9+/kUFt5MZuaQls6aMcaYdiRegcXjUeZTVR23J5nbWxZY7G7nzlKKi/8fq1c/QCCwjW7dzqGw8Dd06nRgS2fNGGNMOxCXdixU9SdR/lokqDCRpaTkM2DAJEaPXkGfPjfx7bczmTfvIBYtOovNmz/Z6+XPnDmTQYMGMXDgQO66667dxqsq1157LQMHDuTAAw/k448/BmDJkiUMGzYs+Jednc29994LwPXXX8/gwYM58MADOfPMMykvL9/rfBpjjGl5Ha4xqvYsJaUr/ftPZPToFRQW/p6ysrf56KPhfPrp6WzaVLRHy6ypqeHqq6/m9ddfZ/HixUybNo3FixfXmeb1119n6dKlLF26lClTpnDllVcCMGjQIObPn8/8+fP56KOPyMjI4MwzzwTghBNOYNGiRSxcuJD99tuPO++8c+823hhjTKtggUU7lJzcmX79buGww1bSr9/tVFT8l48/PpQFC06mouK/jVpWUVERAwcOpH///qSkpHD++efz8ssv15nm5Zdf5uKLL0ZEGD16NOXl5axZU7dBr1mzZjFgwAAKCwsBOPHEE0lKSgJg9OjRlJRY+xzGGNMeWGDRjiUl5VBYeDOjR6+gf/+72LLlYz755Ajmzz+e8vJ3Y1rG6tWr6d27d3C4oKCA1atXN3qa6dOnc8EFF0Rcx2OPPcYpp5wS62YZY4xpxWLpKyRDRH4rIg/74X1F5HtNnzUTL0lJWfTpcwOjRy9nwID/x9ati5g//2g++eQoyspmEe2V40jjRKRR0+zcuZNXXnmFc889d7fpJk6cSFJSEmPHjt1tnDHGmLYnlhKLx4FK4DA/XALc3mQ5Mk0mMTGT3r1/yejRyxk48C9s3/4VCxYczyefHMHGjTMjBggFBQUUFxcHh0tKSujZs2ejpnn99dcZPnw4+fn5deZ78sknee2115g6depuwYoxxpi2KZbAYoCqTgKqAFR1O66hLNNGJSamU1DwMw499Gv23fcBKitX8+mnp/Dxx6PYsOHVOgHGyJEjWbp0KcuXL2fnzp1Mnz6dM844o87yzjjjDJ566ilUlTlz5pCTk0OPHj2C46dNm7bbY5CZM2dy991388orr5CRkdG0G2yMMabZxBJY7BSRdEABfL8hLdbypomfxMQ0evW6kkMPXcp++z1MVdVGFi06g48+OoT1619ENcDGjc9x9dVbGTOmP/36ZXLqqYMYOnQoDz30EA899BAAp556Kv3792fgwIFcdtllPPDAA8F1bNu2jbfeeouzzqrb3to111zD5s2bOeGEExg2bBhXXHFFs267McaYphFLXyEnAL8BhgBvAkcAl6jq7CbPXQTWQFbTCQSqWLfuWVaunMj27UtJSSmgqmodqjuD0yQkZDBo0BTy861OhDHGdBRN0VdIF2A07hHIHFXdsHdZ3HMWWDS9QKCa9euf54svfoxq9W7jU1MLOeywFc2fMWOMMS0iLi1vhknD9T66CRgiImP2NHOm9UtISCI//0eo1kQcX1m5kvXrX6Kqqrx5M2aMMabVS2poAhG5GzgP+AwI+GQF3mvCfJlWIDW1D5WVKyOMET777EwggaysQ8jLO47c3OPIyTmCxMT05s6mMcaYVqTBwAL4ATCosV2li0gm8ACwE5itqlN9+gHATX6yO4H1wK1++FTgAFXd1Jh1mabRv/9EliwZTyCwLZiWkJDBvvs+QHp6f8rKZlFePovi4ntYteouRFLJyTk8GGhkZY0gISGWU8wYY0x7EctdfxmQTOPfBDkLeEFVXxWR54CpPn0CcDWu1GOSql4OXCEi3YB0Cypaj9oKmsuW3Uxl5SpSU/vQv//EYHpu7neBW6iu3kJFxX+Cgcby5b8BfkNiYja5uUcFA43MzKHWXoUxxrRzsQQW24D5IjKLkOBCVa9tYL4C4FP/OfRhfY6qlgOISFZI+k+AJyMtSETGA+MB+vTpE0OWTbzk549t8A2QpKROdOlyCl26uGa5d+7cQHn5O8FAY+PGVwFITs4nL+/YYKCRnt63qbNvjDGmmcUSWLzi/xqrBBdczKduJdEKEcnBlVhsBhD3M/YY4I+RFqSqU4Ap4N4K2YO8mGaUktKV7t3PpXt314T3jh0rKSt7OxhorFs3DYC0tP7k5R3nA41jSUnp1pLZNsYYEwexvm6aAuznB5eoalUM82QCk4EdwPvAyap6ka9jcT3u1dVJqrpIRI4BDlPVOxparr1u2rapKtu2fU5Z2SwfaMympqYCgMzMA4OBRk7OGJKSshpYmjHGmOYQ13YsRORo3COKFbhgoDfwY1VtkbdCLLBoXwKBarZs+TgYaFRUvI9qJSJJZGWNCgYa2dmjSUhIbensGmNMhxTvwOIj4EequsQP7wdMU9VD9jqne8ACi/atpmYHmzb91wca/2bz5nlAgISEdHJyvhsMNDp1GoZIYktn1xhjOoTGBBax1LFIrg0qAFT1SxFJ3uPcGRNFYmKar+B5LDCRqqpyKireDZZoLFt2AwBJSXnk5h4TDDTS0/ezN06MMaYViCWwmCcijwJP++GxwEdNlyVjdklOzqVr1+/Ttev3AaisXEN5+dvBQGPDhhcBSEnpFQwy8vKOIzW1V0tm2xhjOqxYHoWk4tqdOBJXx+I94IHGNpgVL/YoxNRSVbZv/5ry8lk+0Hib6uqNAKSnDwp54+QYkpPzWji3xhjTdsW9E7KQBfdQ1TV7nLM4sMDC1Ec1wJYtC4OBRnn5ewQCWwGhU6fhIW+cHEliYkZLZ9cYY9qMpgwsPlbV4XucsziwwMLEKhDYyaZNRcFAY9OmOahWIZJCdvZhwUAjK2skCQlWbcgYY+rTlIHFJ6p68B7nLA4ssDB7qqZmK+Xl/wkGGlu2zAeUxMQscnLGBAONzMwDEIm1419jjGn/4v1WSKiH9yA/xrQKiYmZdOlyMl26nAxAVdVGysreCQYa3377LwCSk7uRm3tsyBsn/Vsy28YY06bEFFiISD4wElglIt1VdV3TZsuYppec3IXu3c+he/dzANixozjY7HhZ2SzWr38OgLS0vuTm1r5xciwpKfktmW1jjGnVYnkr5Ie4Pjxm494K+S5wvaq+0OS5i8AehZjm4Joe/yIYaJSXz6a6uhyAzMwDgoFGbu5RJCVlt2xmjTGmicW75c0FwAm1pRS+e/N/q+pBe53TPWCBhWkJqjVs3vxxMNCoqHifQGAHkEh29shgoJGTc7g1PW6MaXfiHVh8qqrfCRlOABaEpjUnCyxMa+CaHv9fMNDYtOlDoIaEhDRyco4MBhpZWcOt6XFjTJsX78qbM0XkDWCaHz4PmLGnmTOmPXBNjx9DXt4xwO1UV2+ivPzdYKCxfPlNLF8OSUm55OYeHQw0MjIGW9Pjxph2LWqJhbg7YAGu4maw5U1V/WfzZG93VmJh2oKdO0spK3s7GGjs2LECgJSUnuTlHRsMNNLSerdsRr2ZM2cyYcIEampquPTSS7nxxhvrjFdVJkyYwIwZM8jIyOCJJ55g+HDXpE3fvn3JysoiMTGRpKQkQq/P+++/n8mTJ5OUlMRpp53GpEmTmnW7jDHxEbcSC1VVEXnJ92T6YiMzkQk8AOwEZqvqVJ9+AHCTn+xOVV0kIuOAg4EKVf1NY9ZjTGuUkpJPfv4F5OdfAMD27cuC/Zt8++0blJY+A0B6+r6+Euhx5OUdQ3Jyl2bPa01NDVdffTVvvfUWBQUFjBw5kjPOOIMhQ4YEp3n99ddZunQpS5cuZe7cuVx55ZXMnTs3OP6dd96ha9eudZb7zjvv8PLLL7Nw4UJSU1NZt85eJjOmI4jlUcgcERmpqh82ctlnAS+o6qsi8hww1adPwPU9osAkEfkt7vHKJ0CLNhduTFNJT+9Penp/eva8DNUAW7cuCgYapaXP8M03D+GaHh8WDDRyc79LYmJmk+etqKiIgQMH0r+/a6/j/PPP5+WXX64TWLz88stcfPHFiAijR4+mvLycNWvW0KNHj3qX++CDD3LjjTeSmuoqs3bv3r1pN8QY0yrE0rzgMbjg4msRWSgin4rIwhjmKwCK/eeakPQcVS1X1QogC+gPfKuqNwKFIjIgfEEiMl5E5onIvPXr18ewamNaL5EEOnU6kN69f8GBB77GEUd8y8EHf0DfvreSmJhNSclf+PTTU3j//Tw++WQMK1bcSnn5+wQCVU2Sn9WrV9O7965HMgUFBaxevTrmaUSEE088kUMOOYQpU6YEp/nyyy/5z3/+w6GHHspRRx3Fhx829reJMaYtiqXE4pQ9XHYJLriYT90ApkJEcnAlFpuB1cC3flw50Cl8Qao6BZgCro7FHubHmFYpISGZnJzDyck5nL59f0tNzTYqKt4PlmisWHErcAsJCZnk5o4Jlmh06nRgXJoej1TPKryCabRpPvjgA3r27Mm6des44YQTGDx4MGPGjKG6upqysjLmzJnDhx9+yA9/+EOWLVtmlVeNaecaDCxUdaWIHAnsq6qP+3Ysdvvyj+BFYLKInAa8KiJPq+pFwH3AX3AVQSeparGIfCsifwJSVHXBnm+OMW1fYmIGnTufSOfOJwJQVfUt5eWzQ+povA5AcnJXcnOPCQYa6ekD9uhLu6CggOLi4uBwSUkJPXv2jHma2v/du3fnzDPPpKioiDFjxlBQUMBZZ52FiDBq1CgSEhLYsGED3bp1a3QejTFtR4OBhYj8HhgBDAIeB5KBZ4Ajos2nqluBn4QkTfXpi4Afh03720bl2pgOJDm5M926nUW3bmcBsGNHCeXlbwcDjfXr/w5AamqfYP8mubnHkZq6T0zLHzlyJEuXLmX58uX06tWL6dOn8+yzz9aZ5owzzmDy5Mmcf/75zJ07l5ycHHr06MHWrVsJBAJkZWWxdetW3nzzTX73u98B8IMf/IC3336bo48+mi+//JKdO3fuVsHTGNP+xPIo5EzcGxsfA6jqNyKS1aS5MsbUKy2tgH32uZh99rkYVWX79i+DQcaGDS+xdu3jAGRkDA0JNI4iKSkn4vKSkpKYPHkyJ510EjU1NYwbN46hQ4fy0EMPAXDFFVdw6qmnMmPGDAYOHEhGRgaPP+7WUVpayplnnglAdXU1P/rRjzj5ZNfJ27hx4xg3bhwHHHAAKSkpPPnkk/YYxJgOIJaWN4tUdZSIfKyqw/1rpP9T1QObJ4t1WTsWxtRPtYYtW+YHA42Kiv8QCGwHEsjKGhkMNLKzDycxMQ2A0tKpLFt2M5WVq0hN7UP//hPJzx/bshti4q4p2iq5/vrrefXVV0lJSWHAgAE8/vjj5ObmNvemmWYQ7ya9/w/YFzgBuBMYB0xT1b/sbUb3hAUWxsQuEKhk06Y5wUBj06a51DY9np19BElJndm48VVUdwTnSUjIYNCgKRZctCM1NTXst99+ddoqmTZtWp1XimfMmMH999/PjBkzmDt3LhMmTAi2VdK3b1/mzZu326OsN998k2OPPZakpCRuuOEGAO6+++7m2zDTbOLapLeq3iMiJwCbcPUsfqeqb+1lHo0xzSAhIZXc3KPIzT2Kfv1uo7p6MxUV7wUDjfLyWbvNEwhs48svr2T79uUkJ+eRlJRLUlJeyF8uycl51tlaG9JUbZWceOKJwc+jR4/mhRdapNNr08rUG1iIyEmq+gaADyTeChl3rqr+vRnyZ4yJo6SkLLp0OY0uXU4DYPbsBNyb33XV1GxmxYrodaoTEtKDgUZSUp4PQuoGIvUFJomJmVbfohlFaocktOXU+qZZvXo1PXr0CLZVIiJcfvnljB8/frd1PPbYY5x33nlNtxGmzYhWYjFDRN4DLlTV1WHjbgIssDCmjUtN7UNl5coI6YUceuiXVFeXU11dRlVVGdXVZcHhXWm7hisrS9i6dRFVVWXU1FREXa9IckjA0djAJDsu7Xd0JE3VVkmtiRMnkpSUxNix9vistWiq/n9iES2wWAg8i2t185dhJRT2U8OYdqB//4ksWTKeQGBbMC0hIYP+/SeSkJBCSkp3UlIa3xS3ag3V1RWNCEy+ZceOr6muLqeqqoy6jfWGE5KScuqUgMQemOSSkJDc+B3VxjVVWyUATz75JK+99hqzZs2yUqhWoqn6/4lVtMBCVfVhEXkXmCoipwJXq+o2IpWdGmPanNoKmvF+K0QkkeTkziQnd270vKpKTc2WOoFIQ4HJtm2Lg2mqlVGXn5CQGRKENC4wSUxM39Nd0qKaqq2SmTNncvfdd/Puu++SkZHREptmImiqOjWxiqXy5pcichhwO/CJiFy812s1xrQa+fljW9UbICJCUlIWSUlZQJ9Gz19TsyMYfNQGI7sCk92DlR07VlBd/QnV1WXU1GxpIG+pwcqrjQ1MEhOzWuwXfVO1VXLNNddQWVnJCSecALgKnLXLNC2nOerURBMtsAheAapaDdwoIjOBaYC1yWuMaZUSE9NITOxBamrjf3kFAlX+EU5sgcnOnWvZtu2LYHr0wtxEH2TsSWCSi0jinu4SSkunkpd3M1Om1JZK9QVcQFFLRPjrX/+627z9+/dnwYLIPS189dVXe5wn03Saok5NY0QLLG6NkNnZInIIcHmj1mKMMW1AQkIyKSldSUlp/LNl1QDV1ZvqPK5pKDDZsWNlcFg1eu+1iYnZdV73jTUw2bhxJkuXXhWsR1NZuZIlS9wv0NZUUmXipynq1DRGvYGFqr5UT3oZcFej1mKMMe2cSALJybkkJ+cCfRs1r6oSCGzb7U2baIHJtm1fBodDK9/GIhDYxhdf/JTS0mdISEgjISE97P+uz4mJu6fVN0/ttCIpVpGzBTVVnZpYxdJXiDHGmCYkIiQmZpKYmAkUNHr+QKAyJACpG5gsXXpNxHlUK6mq2kggsINAYDuBwA5qarYHhyGwN1sUNRjZk2CloQCodtq9eWTUXjRVnZpYNdikd2tjTXobY0zs/ve/vvW2VXLYYSvqnS8QqPJBxq7AY9f/ukHI7tNFClbqm7buMht6q6chIklxD1aijd/1ObVFS2maus+fuDbpvReZyAQeAHYCs1V1qk8/ANfAFsCdqrpIRL7Gtez5sapOaao8GWNMRxOtrZJoEhKSfZsfzduZtWqAQGBnxMAj9sAm0nQuvbq6jJ07v4m4TPeewp6Ld7ASawC0fv1zdY5xS9ejacpHIWcBL6jqqyLyHDDVp08ArsZVn56Eqwi6BUgHiiMtyBhjzJ5pqrZKmopIgn+zJ63Z1x0IVDdJKUxoUFPfPPHflm0sW3ZzuwssCoBP/efQZvRyVLUcQERqQ+GDca+3/gt4PXxBIjIeGA/Qp0/j32s3xpiOrLW1VdJaJSQkkZDQCejUrOt1lXcr9yBYcekrVkSuXFlZuapZt6NWUwYWJbjgYj4Q2rB/hYjk4EosNgOoagBARHaISELtcC3/eGQKuDoWTZhnY4wxplm5yrt7XkqzZs2j9dSjaZkf4k3Zk8+LwNki8iDwqog87dPvA/4CTAbuF5FBIvKYiDyGq4uxN1WRjTHGmA7F9e1Tt0n1WOrRNBV7K8QYY4xp41rTWyFtLrAQkfXA7mU+e68rsKEJltte2P5p/+wYt392jNu/pjrGhaoaU3cebS6waCoiMi/WaKwjsv3T/tkxbv/sGLd/reEYN2UdC2OMMcZ0MBZYGGOMMSZuLLDYxVr8jM72T/tnx7j9s2Pc/rX4MbY6FsYYY4yJGyuxMMYYY0zcdJhu06N0inYqcA0wQ1Un+7S7gAxgm6re2EJZbnZR9tGNwABgH+BKVS2xjuPapijH+BZgf6AMuE1Vv+mo10FbF+UY/xQYCfQGFqrqTXYdt00i0h+4GddFxjkh6ZE6+Wz267gjlVjUdop2GXBGbaKqzsB1hgaAiPQBklX1WiBRRHo3e05bTn376C6f9hhwjE+2juPapojHGKjGfRFVAeUd/Dpo6+q7jh9V1SuAL4EnfLJdx22Qqi5T1Z9GGFXbyedVwM9a6jruMCUW1N8pWrhe7LrIVvn5OspFV+8+EpFOwA/xncHRQMdxptWq7xjfoaoBETkDuBT4kI57HbR10a7jNKCfqi7xSXYdty/hnXy2yPdZRyqxqO0UDaJv9+qQ6Xr7+TqKiPtIRLKBB4FfqWqw4zhVrQF2iEhHOo/auojHOKSPnnW4rh078nXQ1kW7152D68cJsOu4HaoQkRx/z95MC13HHeatEP/ccTKwA3gfOFlVLxKRw3DPpPKAe1X1HyJyJ5AKVKrqTfUutJ2Jso9eBJJxJ+nz/v8NfraFqnpvC2TX7IEox/jXuBtPV+BaVV3TUa+Dtq6+Y+zHvQycr6rbRWQQdh23SSLSBZgInAA8Agzx1/EBwPW4UqhJvo5Fs1/HHSawMMYYY0zTs6IvY4wxxsSNBRbGGGOMiRsLLIwxxhgTNxZYGGOMMSZuLLAwxgSJSK6IXNXANH1FZFFz5ckY07ZYYGGMCZWLa7UvbkQkMZ7LM8a0bhZYGGNC3QUMEJH5IvJH/7dIRD4VkfPCJxaRRD/NhyKyUEQu9+lHi8g7IvIsvhVIEXlJRD4Skc9EZHzIMraIyEQRWSAic0Qk36fni8g/ffoCETncp18oIkU+j3+zwMWY1sUCC2NMqBuBr1V1GDAHGAYcBBwP/FFEeoRN/1OgQlVH4jq4ukxE+vlxo4CbVXWIHx6nqocAI4BrfSM/AJnAHFU9CHgPuMyn/wV416cPBz4Tkf2B84AjfB5rgLHx2nhjzN7rSH2FGGMa50hgmm/yuVRE3sUFDwtDpjkROFBEantYzAH2xXVoVqSqy0OmvVZEzvSfe/vpNvppX/PpH+FaEwQ4FrgYwOehQkQuAg4BPhQRcB1orYvP5hpj4sECC2NMfSTGaX6mqm/USRQ5GtgaNnw8cJiqbhOR2UCaH12lu5oAriH6fUmAJ62JcWNaL3sUYowJtRnI8p/fA87z9Si6AWOAorDp3wCuFJFkABHZz/dVES4HKPNBxWBgdAx5mQVc6Zeb6DtWmgWcIyLdfXpnESls3CYaY5qSBRbGmCBV3Qh84F8nPQz32GMB8Daud9u1YbM8AiwGPvbz/I3IJQ4zgSQRWQj8AVd/oyETgGNE5FPcI5KhqroY+A3wpl/WW0B4vQ9jTAuyTsiMMcYYEzdWYmGMMcaYuLHAwhhjjDFxY4GFMcYYY+LGAgtjjDHGxI0FFsYYY4yJGwssjDHGGBM3FlgYY4wxJm4ssDDGGGNM3Px/tC9z/qpuVx4AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<Figure size 504x720 with 3 Axes>"
+       "<Figure size 576x720 with 5 Axes>"
       ]
      },
      "metadata": {
@@ -350,24 +357,32 @@
     "##### x coordinates\n",
     "x = tols\n",
     "training_times = list_training_times_tol\n",
-    "precision_scores = list_precision_scores_tol\n",
-    "zero_one_loss = list_zero_one_loss_tol\n",
+    "precision_scores_train = list_precision_scores_train_tol\n",
+    "zero_one_loss_train = list_zero_one_loss_train_tol\n",
+    "precision_scores_test = list_precision_scores_test_tol\n",
+    "zero_one_loss_test = list_zero_one_loss_test_tol\n",
     "        \n",
     "training_times = [round(i,2) for i in training_times]\n",
-    "precision_scores = [round(i,3) for i in precision_scores]\n",
-    "zero_one_loss = [round(i,3) for i in zero_one_loss]\n",
+    "precision_scores_train = [round(i,3) for i in precision_scores_train]\n",
+    "zero_one_loss_train = [round(i,3) for i in zero_one_loss_train]\n",
+    "precision_scores_test = [round(i,3) for i in precision_scores_test]\n",
+    "zero_one_loss_test = [round(i,3) for i in zero_one_loss_test]\n",
     "\n",
     "### Create plot\n",
-    "fig, figs = plt.subplots(nrows=3, ncols=1, figsize=(7,10))\n",
+    "fig, figs = plt.subplots(nrows=5, ncols=1, figsize=(8,10))\n",
     "fig.tight_layout(pad=3.0)\n",
     "figs[0].plot(x,training_times, marker='o', color='r')\n",
-    "figs[1].plot(x,precision_scores, marker='o', color='b')\n",
-    "figs[2].plot(x,zero_one_loss, marker='o', color='g')\n",
+    "figs[1].plot(x,precision_scores_train, marker='o', color='b')\n",
+    "figs[2].plot(x,zero_one_loss_train, marker='o', color='g')\n",
+    "figs[3].plot(x,precision_scores_test, marker='o', color='m')\n",
+    "figs[4].plot(x,zero_one_loss_test, marker='o', color='y')\n",
     "\n",
     "### Add every x coordinates\n",
     "figs[0].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
     "figs[1].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
     "figs[2].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "figs[3].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "figs[4].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
     "\n",
     "for i in range(len(x)):\n",
     "    figs[0].annotate(training_times[i], # this is the text\n",
@@ -375,13 +390,23 @@
     "                 textcoords=\"offset points\", # how to position the text\n",
     "                 xytext=(12,3), # distance from text to points (x,y)\n",
     "                 ha='center') # horizontal alignment can be left, right or center\n",
-    "    figs[1].annotate(precision_scores[i], # this is the text\n",
-    "                 (x[i],precision_scores[i]), # these are the coordinates to position the label\n",
+    "    figs[1].annotate(precision_scores_train[i], # this is the text\n",
+    "                 (x[i],precision_scores_train[i]), # these are the coordinates to position the label\n",
     "                 textcoords=\"offset points\", # how to position the text\n",
     "                 xytext=(12,3), # distance from text to points (x,y)\n",
     "                 ha='center') # horizontal alignment can be left, right or center\n",
-    "    figs[2].annotate(zero_one_loss[i], # this is the text\n",
-    "                 (x[i],zero_one_loss[i]), # these are the coordinates to position the label\n",
+    "    figs[2].annotate(zero_one_loss_train[i], # this is the text\n",
+    "                 (x[i],zero_one_loss_train[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "    figs[3].annotate(precision_scores_test[i], # this is the text\n",
+    "                 (x[i],precision_scores_test[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "    figs[4].annotate(zero_one_loss_test[i], # this is the text\n",
+    "                 (x[i],zero_one_loss_test[i]), # these are the coordinates to position the label\n",
     "                 textcoords=\"offset points\", # how to position the text\n",
     "                 xytext=(12,3), # distance from text to points (x,y)\n",
     "                 ha='center') # horizontal alignment can be left, right or center\n",
@@ -389,22 +414,30 @@
     "figs[0].set_xticks(x)\n",
     "figs[1].set_xticks(x)\n",
     "figs[2].set_xticks(x)\n",
+    "figs[3].set_xticks(x)\n",
+    "figs[4].set_xticks(x)\n",
     "    \n",
     "### Add title and axis names\n",
     "figs[0].title.set_text('Training times for various level of tolerance (kernel=rbf)')\n",
-    "figs[1].title.set_text('Precision score for various level of tolerance (kernel=rbf)')\n",
-    "figs[2].title.set_text('Zero-one loss metrics various level of tolerance (kernel=rbf)')\n",
+    "figs[1].title.set_text('Precision score for various level of tolerance (kernel=rbf) on training dataset')\n",
+    "figs[2].title.set_text('Zero-one loss metrics various level of tolerance (kernel=rbf) on training dataset')\n",
+    "figs[3].title.set_text('Precision score for various level of tolerance (kernel=rbf) on testing dataset')\n",
+    "figs[4].title.set_text('Zero-one loss metrics various level of tolerance (kernel=rbf) on testing dataset')\n",
     "figs[0].set_xlabel('tolerance')\n",
     "figs[1].set_xlabel('tolerance')\n",
     "figs[2].set_xlabel('tolerance')\n",
+    "figs[3].set_xlabel('tolerance')\n",
+    "figs[4].set_xlabel('tolerance')\n",
     "figs[0].set_ylabel('Training times (in seconds)')\n",
     "figs[1].set_ylabel('Precision score')\n",
-    "figs[2].set_ylabel('Zero-one loss')"
+    "figs[2].set_ylabel('Zero-one loss')\n",
+    "figs[3].set_ylabel('Precision score')\n",
+    "figs[4].set_ylabel('Zero-one loss')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 5,
    "id": "62c7302a",
    "metadata": {},
    "outputs": [
@@ -412,47 +445,65 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training...\n",
-      "Predicting...\n",
-      "Score échantillon de test :  0.9506666666666667\n",
-      "Précision pour chaque classe : \n",
-      " [0.99324324 0.97633136 0.93377483 0.95375723 0.92546584 0.91549296\n",
-      " 0.96527778 0.95833333 0.96402878 0.91472868]\n",
-      "Matrice de confusion pour C=1.0 et kernel=rbf :\n",
-      " [[147   0   0   0   1   0   0   0   1   0]\n",
-      " [  0 165   0   0   0   0   0   0   1   0]\n",
-      " [  1   2 141   0   0   1   1   1   0   0]\n",
-      " [  0   0   3 165   0   3   0   1   1   1]\n",
-      " [  0   0   1   0 149   0   1   0   0   5]\n",
-      " [  0   0   0   4   2 130   3   1   1   1]\n",
-      " [  0   0   1   0   0   3 139   0   0   0]\n",
-      " [  0   1   3   0   4   0   0 138   0   4]\n",
-      " [  0   1   1   3   1   4   0   0 134   0]\n",
-      " [  0   0   1   1   4   1   0   3   1 118]]\n",
-      "Zero-one classification loss :\n",
-      " 0.04933333333333334\n"
+      "Métriques pour SVM\n",
+      "Paramètres : (C=1.0,kernel=\"rbf\")\n",
+      "Taille de l'échantillon : 10000\n",
+      "Proportion des datasets : 90%\n",
+      "Temps d'entraînement (secondes) : 15.8407\n",
+      "Temps de prédiction (secondes) : 3.68326\n",
+      "Précision pour chaque classe : [0.98, 0.99, 0.958, 0.958, 0.955, 0.967, 0.949, 0.958, 0.94, 0.947]\n",
+      "Précision : 0.96\n",
+      "Erreur : 0.04\n",
+      "Matrice de confusion :\n",
+      " [[ 99   0   0   0   0   0   0   0   0   0]\n",
+      " [  0  98   1   0   0   0   0   1   0   0]\n",
+      " [  1   0  92   0   0   0   2   2   0   0]\n",
+      " [  0   0   2  91   1   0   0   0   3   0]\n",
+      " [  0   0   0   0  84   0   3   0   1   0]\n",
+      " [  1   0   1   2   0  87   0   0   2   1]\n",
+      " [  0   1   0   0   2   1 111   0   0   0]\n",
+      " [  0   0   0   0   0   0   0 114   0   1]\n",
+      " [  0   0   0   1   0   2   1   0  94   3]\n",
+      " [  0   0   0   1   1   0   0   2   0  90]]\n"
      ]
     }
    ],
    "source": [
     "####### Meilleur modèle de SVM #######\n",
     "\n",
-    "#Entraîne le classifier\n",
+    "### Create vector of 5000 random indexes\n",
+    "rand_indexes = np.random.randint(70000, size=10000)\n",
+    "### Load data with the previous vector\n",
+    "data = mnist.data[rand_indexes]\n",
+    "# print(\"Dataset : \", data)\n",
+    "target = mnist.target[rand_indexes]\n",
+    "\n",
+    "# Split the dataset\n",
+    "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
+    "\n",
     "clf = SVC(C=1.0,kernel=\"rbf\")\n",
-    "print(\"Training...\")\n",
+    "#Entraîne le classifier\n",
+    "t1 = time.time()\n",
     "clf.fit(xtrain, ytrain)\n",
+    "t2 = time.time()\n",
     "\n",
     "#Prédiction sur le jeu de tests\n",
-    "print(\"Predicting...\")\n",
     "pred = clf.predict(xtest)\n",
-    "# On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
-    "score = clf.score(xtest, ytest)\n",
-    "print(\"Score échantillon de test : \", score)\n",
+    "t3 = time.time()\n",
     "\n",
     "#Calcul de différentes metrics\n",
-    "print(\"Précision pour chaque classe : \\n\", metrics.precision_score(ytest, pred,average=None))\n",
-    "print(\"Matrice de confusion pour C=1.0 et kernel=rbf :\\n\", metrics.confusion_matrix(ytest, pred))\n",
-    "print(\"Zero-one classification loss :\\n\", metrics.zero_one_loss(ytest, pred))"
+    "precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
+    "\n",
+    "print(\"Métriques pour SVM\")\n",
+    "print(\"Paramètres : (C=1.0,kernel=\\\"rbf\\\")\")\n",
+    "print(\"Taille de l'échantillon :\", 10000)\n",
+    "print(\"Proportion des datasets :\", \"90%\")\n",
+    "print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
+    "print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
+    "print(\"Précision pour chaque classe :\", precisions)\n",
+    "print(\"Précision :\", clf.score(xtest, ytest))\n",
+    "print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
+    "print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
    ]
   },
   {