diff --git a/notebook/problems.es.ipynb b/notebook/problems.es.ipynb
index 5ced765e..c3f242d1 100644
--- a/notebook/problems.es.ipynb
+++ b/notebook/problems.es.ipynb
@@ -43,35 +43,130 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "bb3e954e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# importa las librerías\n",
-    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "# Define la función de distancia"
+    "def distancia(tiempo):\n",
+    "    return 2.5 * tiempo"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "dbc4c780",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# \"Graficar la función de distancia en el dominio (t)"
+    "tiempo = np.linspace(0, 10, 50)  \n",
+    "dist = distancia(tiempo)\n",
+    "plt.plot(tiempo, dist, label='d(t) = 2.5t', color='red')\n",
+    "plt.xlabel('Tiempo (s)')\n",
+    "plt.ylabel('Distancia (m)')\n",
+    "plt.title('Distancia vs Tiempo')\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "4c4d4f20",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Tiempo (s)</th>\n",
+       "      <th>Distancia (m)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.204082</td>\n",
+       "      <td>0.510204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.408163</td>\n",
+       "      <td>1.020408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.612245</td>\n",
+       "      <td>1.530612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.816327</td>\n",
+       "      <td>2.040816</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Tiempo (s)  Distancia (m)\n",
+       "0    0.000000       0.000000\n",
+       "1    0.204082       0.510204\n",
+       "2    0.408163       1.020408\n",
+       "3    0.612245       1.530612\n",
+       "4    0.816327       2.040816"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Crea un DataFrame"
+    "df = pd.DataFrame({\n",
+    "    'Tiempo (s)': tiempo,\n",
+    "    'Distancia (m)': dist\n",
+    "})\n",
+    "\n",
+    "df.head()"
    ]
   },
   {
@@ -100,22 +195,126 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "ec1f8bd7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Define y grafica la función cuadrática"
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "a = 0.5\n",
+    "\n",
+    "def distancia_acelerada(tiempo):\n",
+    "    return 0.5 * a * tiempo**2  # Como V₀ = 0\n",
+    "\n",
+    "tiempo = np.linspace(0, 10, 100)\n",
+    "dist = distancia_acelerada(tiempo)\n",
+    "\n",
+    "plt.plot(tiempo, dist, label='d(t) = 0.25t²', color='purple')\n",
+    "plt.title(\"Distancia con aceleración\")\n",
+    "plt.xlabel(\"Tiempo (s)\")\n",
+    "plt.ylabel(\"Distancia (m)\")\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "ba5c497b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Tiempo (s)</th>\n",
+       "      <th>Distancia (m)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.10101</td>\n",
+       "      <td>0.002551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.20202</td>\n",
+       "      <td>0.010203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.30303</td>\n",
+       "      <td>0.022957</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.40404</td>\n",
+       "      <td>0.040812</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Tiempo (s)  Distancia (m)\n",
+       "0     0.00000       0.000000\n",
+       "1     0.10101       0.002551\n",
+       "2     0.20202       0.010203\n",
+       "3     0.30303       0.022957\n",
+       "4     0.40404       0.040812"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Crea un DataFrame"
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.DataFrame({\n",
+    "    'Tiempo (s)': tiempo,\n",
+    "    'Distancia (m)': dist\n",
+    "})\n",
+    "\n",
+    "df.head() "
    ]
   },
   {
@@ -193,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "4ff7e11a",
    "metadata": {},
    "outputs": [],
@@ -225,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "633a54fd",
    "metadata": {},
    "outputs": [
@@ -274,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "e26dbdf0",
    "metadata": {},
    "outputs": [
@@ -305,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "2bdd54f1",
    "metadata": {},
    "outputs": [],
@@ -347,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "0350981e",
    "metadata": {},
    "outputs": [
@@ -367,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "f8e01e2d",
    "metadata": {},
    "outputs": [
@@ -396,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "b699d1fb",
    "metadata": {},
    "outputs": [
@@ -415,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "0b76ee22",
    "metadata": {},
    "outputs": [
@@ -452,12 +651,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "7c67d8b7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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l9Tvz9s/05s2bXLhwgYoVKwI4/M0EBASwbds2/vnnnwc+HpFHQcmWiAvx9/cHzB8NSfHnn3/i5uZGSEiIw/rAwEACAgLsYziqV6/O888/z7Bhw8iePTvNmjVjzpw5dx03dedrABQpUiTBtqJFi9oTons9PiQkJMF/xHc+35EjRwBo164dOXLkcLjNmjWLW7duJWlcyWOPPeZw32azERISkmDMQd68eRMUkvjzzz957LHHEiQyRYsWtW+P/zd37txkzJjxnsfkjGPHjpEnTx6HhDaxfdzc3ChWrJhTz53U44qXP39+h/vxyUhi464Sc+dnkDFjRnLnzm3/DO7Wpry8vChUqFCCeBL7rJLLgxxr/N9d4cKFHdYn9vlHRkYyePBggoKC8Pb2Jnv27OTIkYMrV644tOf4z+hOSW1TL7zwAm5ubnz99deA2W1r8eLFNGzY0P69EhwczBtvvMGsWbPInj079evXZ8qUKck2XiuxH9sP+v4m9b04cOAAzZs3J3PmzPj7+5MjRw7atGkDYD+u+PZ053N6enpSqFAhh3VHjhzhwIEDCb6DHn/8ccDskg1mclulShU6depErly5aNWqFYsWLUpy4pXY8T3++ONERETw77//OqwPDg52uJ/U7/27HXeOHDkcTjA4Iz6Zv9s0AHfjzHfmpUuX6N27N7ly5cLX15ccOXLY34Pb2+ro0aPZv38/QUFBVKhQgaFDhyb5hJDIo6QxWyIuxN/fnzx58rB//36nHne/CVdtNhtLlixh69at/PDDD6xcuZIOHTowduxYtm7dmuA/QCvE/0j5+OOPKVOmTKL7JGecSRkLkxxsNpt9nMXtEhvA7yrc3d0TXZ/YcTwKznxWzr7fKX2sr7/+OnPmzKFPnz5UqlSJzJkzY7PZaNWqVbKWxs6TJw9Vq1Zl0aJFvPPOO2zdupVTp04lGBs1duxYXn31VZYtW8aqVavo1asXo0aNYuvWreTLl++uz+/j43PX+f3ir5LdeaUDUvb9vXLlCtWrV8ff35/hw4dTuHBhfHx82LlzJwMGDHig9zcuLo6SJUvyySefJLo9fnyXr68vGzduZN26dSxfvpyffvqJr7/+mlq1arFq1aq7HveDuFv7T+pE20lxt+ey4nuqZcuWbN68mX79+lGmTBkyZsxIXFwcDRo0cPhMW7ZsSdWqVfnuu+9YtWoVH3/8MR999BHffvutprYQl6IrWyIupnHjxhw7dowtW7bcd98CBQoQFxdnvyoU79y5c1y5coUCBQo4rK9YsSLvv/8+v/32GwsWLODAgQMsXLgQuPt/tvHPcfjw4QTbDh06RPbs2e9ZirhAgQIcO3YswY+rO58v/iqBv78/derUSfSWlOIMd74XhmFw9OjRexbxuD3WI0eOJPiRFt8dKf69KFCgAGfOnOH69ev3PCYwz+QnVnHrzqs3hQsX5p9//uHSpUt3ja9w4cLExcVx8ODB+x7L7ZJ6XMnlzs/g+vXrnDlzxv4Z3K1NRUVFceLEiYeKJ6nv98OI/7u7vcseJP75L1myhHbt2jF27FhatGhB3bp1eeaZZxLEGP8Z3Smx57ybF198kT179nD48GG+/vpr/Pz8aNKkSYL9SpYsyXvvvcfGjRvZtGkTf//9N9OmTbvncxcoUIDTp08nmnDFx5hc7Sip78X69eu5ePEic+fOpXfv3jRu3Jg6deokuGoTH9edzxkdHc2JEycc1hUuXJhLly5Ru3btRL+Dbr8S4+bmRu3atfnkk084ePAg77//PmvXrrV3NbyXxI7vjz/+wM/Pz14Z9m6S+r1/t+P+999/E1xZjH/P7myXiX1PAU6fEEzqd+bly5dZs2YNb7/9NsOGDaN58+bUrVs3wRXIeLlz5+a1115j6dKlnDhxgmzZsvH+++87FZtISlOyJeJi+vfvT4YMGejUqRPnzp1LsP3YsWNMmDABgEaNGgEwfvx4h33iz8rGV0e7fPlygmQn/upRfFfC+Kpod/5nmzt3bsqUKcPnn3/usG3//v2sWrXKHsPdNGrUiH/++YclS5bY10VERCSYlLlcuXIULlyYMWPGJPgPGUjQteZuvvjiC4dumEuWLOHMmTNJOtPZqFEjzp49a++KBWaJ5UmTJpExY0aqV69u3y8mJoZPP/3Uvl9sbCyTJk1K8JyFCxfm0KFDDvHv2bMnQdWy559/HsMw7BNL3y7+s3v22Wdxc3Nj+PDhCRKne10pSOpxJZcZM2Y4jA389NNPiYmJsX8GderUwcvLi4kTJzrE/dlnn3H16tVEq/olVVLf74cRfxwTJ050WH/n3yGYV3bu/GwmTZqU4IpBo0aN2Lp1K7/++qt93b///suCBQuSHNfzzz+Pu7s7X331FYsXL6Zx48YOJ0LCw8OJiYlxeEzJkiVxc3O7b5fiRo0aER0dzfTp0x3Wx8XF8emnn+Ll5UXt2rWTHOv9Xisp70X81aPb39+oqCimTp3qsF/58uXJkSMH06ZNc6jCN3fu3ATfdy1btuTvv/9m5syZCeKKjIy0d5lO7KTInd+p97JlyxaH8UenT59m2bJl1KtX775XxZL6vR9/gmrSpEkO71Fi7TQ+ibq9/PyNGzcSTA9Rr149MmXKxKhRo7h586bDtvt9ByXlOzOxzzSxmGNjYxN0f82ZMyd58uRJ0vsv8iipG6GIiylcuDBffvklL774IkWLFqVt27aUKFGCqKgoNm/ebC/ZDVC6dGnatWvHjBkz7F1qfv31Vz7//HOeffZZatasCcDnn3/O1KlTad68OYULF+batWvMnDkTf39/+3/cvr6+FCtWjK+//prHH3+crFmzUqJECUqUKMHHH39Mw4YNqVSpEh07drSXfs+cObPDXFyJ6dy5M5MnT6Zt27bs2LGD3LlzM2/evAST0rq5uTFr1iwaNmxI8eLFad++PXnz5uXvv/9m3bp1+Pv788MPP9z3/cuaNSvPPPMM7du359y5c4wfP56QkBA6d+5838d26dKF6dOn8+qrr7Jjxw4KFizIkiVL+OWXXxg/fry98EaTJk2oUqUKb7/9NidPnqRYsWJ8++23iY596dChA5988gn169enY8eOnD9/nmnTplG8eHGHIgE1a9bklVdeYeLEiRw5csTeZWbTpk3UrFmTnj17EhISwrvvvsuIESOoWrUqzz33HN7e3mzfvp08efIwatSohzqu5BIVFUXt2rVp2bIlhw8fZurUqTzzzDM0bdoUMMeMDBw4kGHDhtGgQQOaNm1q3++pp56yj7l5EEl9vx9GmTJleOmll5g6dSpXr16lcuXKrFmzhqNHjybYt3HjxsybN4/MmTNTrFgxtmzZwurVq8mWLZvDfv3792fevHk0aNCA3r1728udFyhQgL179yYprpw5c1KzZk0++eQTrl27xosvvuiwfe3atfTs2ZMXXniBxx9/nJiYGObNm4e7uzvPP//8PZ+7SZMm1KtXj759+/Lrr79SuXJlIiIi+P777/nll18YOXLkfa/IJFVS34vKlSuTJUsW2rVrR69evbDZbMybNy/BD3VPT09GjhxJ165dqVWrFi+++CInTpxgzpw5Ca6YvPLKKyxatIhu3bqxbt06qlSpQmxsLIcOHWLRokWsXLmS8uXLM3z4cDZu3EhoaCgFChTg/PnzTJ06lXz58tnnwrqXEiVKUL9+fYfS70CiJ1vulNTv/Rw5cvDWW28xatQoGjduTKNGjdi1axf/+9//yJ49u8Nz1qtXj/z589OxY0f69euHu7s7s2fPJkeOHJw6dcq+n7+/P+PGjaNTp0489dRT9vm/9uzZQ0RERKJzEkLSvzP9/f2pVq0ao0ePJjo6mrx587Jq1aoEVyCvXbtGvnz5aNGiBaVLlyZjxoysXr2a7du3M3bs2Pu+hyKP1COufigiSfTHH38YnTt3NgoWLGh4eXkZmTJlMqpUqWJMmjTJXtLYMAwjOjraGDZsmBEcHGx4enoaQUFBxsCBAx322blzp/HSSy8Z+fPnN7y9vY2cOXMajRs3dig9bBiGsXnzZqNcuXKGl5dXglLOq1evNqpUqWL4+voa/v7+RpMmTYyDBw8m6Vj+/PNPo2nTpoafn5+RPXt2o3fv3sZPP/3kUPo93q5du4znnnvOyJYtm+Ht7W0UKFDAaNmypbFmzZp7vkZ86fevvvrKGDhwoJEzZ07D19fXCA0NNf7880+Hfe9VxvrcuXNG+/btjezZsxteXl5GyZIlHcoSx7t48aLxyiuvGP7+/kbmzJmNV155xV5a+87958+fbxQqVMjw8vIyypQpY6xcuTJBaWTDMEshf/zxx8YTTzxheHl5GTly5DAaNmxo7Nixw2G/2bNnG08++aTh7e1tZMmSxahevboRFhbmcHx3lj9PynHFl0P/+OOPExzvne0hMfFlojds2GB06dLFyJIli5ExY0ajdevWDtMGxJs8ebLxxBNPGJ6enkauXLmM7t27G5cvX3bY516f1d0k5f125lgTK4sdGRlp9OrVy8iWLZuRIUMGo0mTJsbp06cTPPby5cv29z1jxoxG/fr1jUOHDiUod24YhrF3716jevXqho+Pj5E3b15jxIgRxmeffZak0u/xZs6caQBGpkyZEpTmPn78uNGhQwejcOHCho+Pj5E1a1ajZs2axurVq5P03Ddv3jSGDh1qPPHEE4a3t7eRIUMGo2LFig5TCtz5nt1ZGv3OUuKGkbD0uzPvxS+//GJUrFjR8PX1NfLkyWP079/fWLlyZaLfLVOnTjWCg4MNb29vo3z58sbGjRsT/VuJiooyPvroI6N48eL2v7Fy5coZw4YNM65evWoYhmGsWbPGaNasmZEnTx7Dy8vLyJMnj/HSSy8Zf/zxx33fR8Do0aOHMX/+fOOxxx4zvL29jSeffDJBvHd7Dw0jad/7hmEYsbGxxrBhw4zcuXMbvr6+Ro0aNYz9+/cn+p7v2LHDePrppw0vLy8jf/78xieffJLo52UYhvH9998blStXtv9/UKFCBeOrr76yb0/s+y2p35l//fWX0bx5cyMgIMDInDmz8cILLxj//POPw9/WrVu3jH79+hmlS5c2MmXKZGTIkMEoXbq0MXXq1Pu+/yKPms0wLBrxLCKSjNavX0/NmjVZvHixQ5l5eXTmzp1L+/bt2b59u30SZhFxZLPZ6NGjB5MnT7Y6FBF5BDRmS0REREREJAUo2RIREREREUkBSrZERERERERSgMZsiYiIiIiIpABd2RIREREREUkBSrZERERERERSgJItERERERGRFOBhdQCpQVxcHP/88w+ZMmXCZrNZHY6IiIiIiFjEMAyuXbtGnjx5cHO797UrJVtJ8M8//xAUFGR1GCIiIiIi4iJOnz5Nvnz57rmPkq0kyJQpE2C+of7+/hZHA9HR0axatYp69erh6elpdTiSCqjNiDPUXsRZajPiLLUZcZYrtZnw8HCCgoLsOcK9KNlKgviug/7+/i6TbPn5+eHv7295Y5PUQW1GnKH2Is5SmxFnqc2Is1yxzSRleJEKZIiIiIiIiKQAJVsiIiIiIiIpQMmWiIiIiIhICtCYrWRiGAYxMTHExsam+GtFR0fj4eHBzZs3H8nrSeqXWtuMu7s7Hh4emnJBREREUiUlW8kgKiqKM2fOEBER8UhezzAMAgMDOX36tH6ESpKk5jbj5+dH7ty58fLysjoUEREREaco2XpIcXFxnDhxAnd3d/LkyYOXl1eK/5iNi4vj+vXrZMyY8b4TqYlA6mwzhmEQFRXFv//+y4kTJ3jsscdSTewiIiIioGTroUVFRREXF0dQUBB+fn6P5DXj4uKIiorCx8dHPz4lSVJrm/H19cXT05M///zTHr+IiIhIapF6fnW5uNT0A1YkNdHfloiIiKRW+hUjIiIiIiKSApRsiYiIiIiIpACXSbY+/PBDbDYbffr0cVi/ZcsWatWqRYYMGfD396datWpERkbat1+6dInWrVvj7+9PQEAAHTt25Pr16w7PsXfvXqpWrYqPjw9BQUGMHj36URySyzMMgy5dupA1a1ZsNhu7d+8G4OLFi+TMmZOTJ08m6XmioqIoWLAgv/32W8oFKyIiIiKSyrhEsrV9+3amT59OqVKlHNZv2bKFBg0aUK9ePX799Ve2b99Oz549HcZwtG7dmgMHDhAWFsaPP/7Ixo0b6dKli317eHg49erVo0CBAuzYsYOPP/6YoUOHMmPGjEd2fK7qp59+Yu7cufz444+cOXOGEiVKAPD+++/TrFkzChYsmKTn8fLy4q233mLAgAEpGK3p1KlThIaG4ufnR86cOenXrx8xMTH3fMz7779P5cqV8fPzIyAgwOnXnD59OnXr1qVcuXLUr1+fS5cuPWD0pnPnzvHqq6+SJ08e/Pz8aNCgAUeOHHmo50xOR48eJVOmTAneq7lz52Kz2RxudxasMAyDwYMHkzt3bnx9falTp06CY0vKCRIRERGRtMDyZOv69eu0bt2amTNnkiVLFodtffv2pVevXrz99tsUL16cIkWK0LJlS7y9vQH4/fff+emnn5g1axZPP/00zzzzDJMmTWLhwoX8888/ACxYsICoqChmz55N8eLFadWqFb169eKTTz555Mfqao4dO0bu3LmpXLkygYGBeHh4EBERwWeffUbHjh2deq7WrVvz888/c+DAgRSKFmJjYwkNDSUqKorNmzfz+eefM3fuXAYPHnzPx0VFRfHCCy/QvXv3B3rd9u3bExYWxo4dO4iNjWXbtm0P9DxgJiPPPvssx48fZ9myZezatYsCBQpQp04dbty48cDPe6e5c+dSo0YNpx8XHR3NSy+9RNWqVRPd7u/vz5kzZ+y3P//802H76NGjmThxItOmTWPbtm1kyJCB+vXrc/PmTfs+9ztBIiIiInKnGzfAMKyOwnmWl37v0aMHoaGh1KlTh5EjR9rXnz9/nm3bttG6dWsqV67MsWPHeOKJJ3j//fd55plnAPPKV0BAAOXLl7c/rk6dOri5ubFt2zaaN2/Oli1bqFatmsOEqPXr1+ejjz7i8uXLCRI8gFu3bnHr1i37/fDwcMD8IRodHe2wb3R0NIZhEBcXR1xcHGA2hJSc39gwDG7cADc3A5stzmGbnx8kZZqv9u3b88UXXwBgs9koUKAAx48f58cff8Tb25sKFSrYj2fEiBFMnz6dPXv2kC1bNgAaN25MZGQkYWFhuLm5kTlzZqpUqcJXX33F8OHDk/eA/99PP/3EwYMHWbVqFbly5aJUqVIMGzaMgQMHMnjw4LtOejtkyBDATEAA+3HFu9/xeXh4EBcXx6xZs8iRIwf16tVL8BxJ9ccff7B161b27t1L8eLFAZgyZQp58uRhwYIFdOrUifXr19OgQQPCwsLsSc/HH3/M2LFj2bNnD7ly5brv68TH91+bNOz/3iv2d999lyJFilCrVi02b97ssG9cXBw2m42cOXMm+lqGYTB+/HjeffddmjRpApjvee7cufn2229p1aqV/QTJtm3b7H+3EyZMoHHjxowePZo8efIkeiyGYRAdHY27u/t9j10eXvz33J3fdyJ3ozYjzlKbEWf16mVj69Zq5MgRQ4UK1sbiTLu1NNlauHAhO3fuZPv27Qm2HT9+HIChQ4cyZswYypQpwxdffEHt2rXZv38/jz32GGfPnk3ww8/Dw4OsWbNy9uxZAM6ePUtwcLDDPvE/Vs+ePZtosjVq1CiGDRuWYP2qVasSzKXl4eFBYGAg169fJyoqCjAz73z5ApL4LjyoxJ//r7+ukCHD/R89fPhw8uXLx9y5c1m7di3u7u6Eh4ezdu1aSpUqZU8wAXr27Mny5ctp37498+fPZ+bMmWzevJlNmzY5dP8qVaoU69evd3jsnfLly3fPuF544QXGjRuX6LYNGzZQrFgxfH197a9RpUoVwsPD+fXXXxN0Q73TzZs3MQwjQXz3O76oqCgGDRpEhgwZmDJlCteuXbvn69zLxYsXAYiJiXGIw9PTk/Xr19OyZUvKli1Lt27deOWVV9i0aRN//vkngwcPZs6cOQ7Hfr9jvfM1gHvGvnHjRhYtWsTGjRv58ccfE7xXN2/e5Pr16xQoUIC4uDhKly7NoEGDKFq0KAAnT57k7NmzVKxY0f44m81GuXLl2LhxI40aNWLdunVkzpyZxx9/3L5PhQoVcHNzY/369TRu3DhBXFFRUURGRrJx48b7dhmV5BUWFmZ1CJLKqM2Is9RmJClOnvRn/vwaGEYWNm/eyIULly2NJ8KJqyqWJVunT5+md+/ehIWFJTpRafzZ8q5du9K+fXsAnnzySdasWcPs2bMZNWpUisU2cOBA3njjDfv98PBwgoKCqFevHv7+/g773rx5k9OnT5MxY0b7cVh58t3f3z9JyZa/vz/Zs2fH09OTxx57zL7+zJkz5M+fP8Fxfvnll5QtW5ZRo0YxadIkZsyYYb8yEy84OJhly5YleOztdu7ced+47vb4y5cvkzt3bofthQsXBswk4l6vC+Dj44PNZkt0v3sdX58+ffj666954oknaNSoEW+88QYtWrS452vdTfny5cmfPz8ffPAB06ZNI0OGDIwfP55//vmHCxcu2GMbPXo0mzZtol+/fhw4cIC2bdvSqlWrJL+Oj48PHh4e9uczDINr166RKVMmbIlc+rx48SI9e/bkiy++IF++fIm+V6VLl2bWrFmUKlWKq1evMnbsWBo0aMC+ffvIly+fPfEuVKiQw+Py5MnDpUuX8Pf35+rVq+TKlSvBZ5A1a1auXr2a6Gdz8+ZNfH19qVatmiY1fkSio6MJCwujbt26eHp6Wh2OpAJqM+IstRlJKsOARo3cMQwbzzzzFz16lLe8zSTlxHc8y5KtHTt2cP78ecqWLWtfFxsby8aNG5k8eTKHDx8GoFixYg6PK1q0KKdOnQIgMDCQ8+fPO2yPiYnh0qVLBAYG2vc5d+6cwz7x9+P3uZO3t7d9XNjtPD09E3y4sbGx2Gw23Nzc7IU7MmaElBzvHxcXR3h4OP7+/gkmfPXzc0tSN0LA/qP79ueI/2F75/OGhIQwZswYunbtyosvvkibNm0SPJ+fnx8RERH3nIT28ccfT1pwd4k3/r2OF798+/t/N7fve6d7Hd/EiROZOHHifeNr2LAhmzZtAqBAgQKJjl/z9vbm22+/pWPHjmTPnh13d3fq1KlDw4YNMQzDHpuPjw8LFiygVKlSFChQgPHjx9/z+E6dOuXwtxITE0N0dLRD8tK3b1+GDRuW6PN07dqVl19+2T7OK7H3qkqVKlSpUsV+/5lnnqFo0aLMnDmTESNG3PWzuP1zS6zNxbvbZxj/uMT+/iRl6T0XZ6nNiLPUZuR+VqyANWvAy8vglVd+x9OzhuVtxpnXtyzZql27Nvv27XNY1759e5544gkGDBhAoUKFyJMnjz3pivfHH3/QsGFDACpVqsSVK1fYsWMH5cqVA2Dt2rXExcXx9NNP2/d59913iY6Otr8xYWFhFClSJNEuhMnBZiNJV5ceVFwcxMaar3Gf/MJp2bNn5/LlxC/Nbty4EXd3d06ePElMTAweHo7N59KlS+TIkeOez58xY8Z7bm/Tpg3Tpk1LdFtgYCC//vqrw7r7Jc7OuN/x3c+sWbPs0xLc64+wXLly7N69m6tXrxIVFUWOHDl4+umnHcYeAmzevBkw39dLly6R4R6NKk+ePPbS/QDffvst33zzDQsWLADMBP1eMa1du5bvv/+eMWPGAP+N7fLw8GDGjBl06NAhwWM8PT158sknOXr0KPDfZ3Du3Dly585t3+/cuXOUKVPGvs/9TpCIiIiIAERHw5tvmsuvvx5HrlwpWBQhhVhWjTBTpkyUKFHC4ZYhQwayZctGiRIlsNls9OvXj4kTJ7JkyRKOHj3KoEGDOHTokL1SXtGiRWnQoAGdO3fm119/5ZdffqFnz560atXKPtD+5ZdfxsvLi44dO3LgwAG+/vprJkyY4NBNUP7z5JNPcvDgwQTrv/76a7799lvWr1/PqVOnGDFiRIJ99u/fz5NPPnnP59+9e/c9b/cqrlGpUiX27dvn8GM9LCwMf3//BFdAnZWU47ufvHnzEhISQkhICAUKFLjv/pkzZyZHjhwcOXKE3377jWbNmtm3HTt2jL59+zJz5kyefvpp2rVrd8/CFh4eHvbXDgkJIWfOnPj6+jqsu9fJhS1btiT4HDJlysTu3btp3rx5oo+JjY1l37599sQqODiYwMBA1qxZY98nPDycbdu2UalSJcDxBEm8O0+QiIiIiADMnAmHDkH27PD22w9WnMxyhgupXr260bt3b4d1o0aNMvLly2f4+fkZlSpVMjZt2uSw/eLFi8ZLL71kZMyY0fD39zfat29vXLt2zWGfPXv2GM8884zh7e1t5M2b1/jwww+diuvq1asGYFy9ejXBtsjISOPgwYNGZGSkU8/5MGJjY43Lly8bsbGxD/U848aNMwoUKOCwbu/evYaHh4dx6dIl+7rTp08bWbJkMSZOnGgYhmH89NNPhoeHh7FlyxaHxxYoUMD44osvHiqme4mJiTFKlChh1KtXz9i9e7fx008/GTly5DAGDhxo32fbtm1GkSJFjL/++su+7s8//zR27dplDBs2zMiYMaOxa9cuY9euXfZ2ktTjSy6LFi0y1q1bZxw7dsxYunSpUaBAAeO5555zOM6KFSsazz//vGEYhvHPP/8Y2bJlM0aPHp3k15gzZ45RvXp1+31n28ycOXOMzJkzO6wbNmyYsXLlSuPYsWPGjh07jFatWhk+Pj7GgQMH7Pt8+OGHRkBAgLFs2TJj7969RrNmzYzg4GCHv48GDRoYTz75pLFt2zbj559/Nh577DHjpZdeumssVvyNpXdRUVHG0qVLjaioKKtDkVRCbUacpTYj93PlimFkz24YYBhTprhWm7lXbnAnl0q2XFV6SrYMwzAqVKhgTJs2zTAMw4iLizNq165t1K9f34iLi7Pv8/rrrxuFCxe2JyybN282AgICjIiIiIeK6X5OnjxpNGzY0PD19TWyZ89uvPnmm0Z0dLR9+7p16wzAOHHihH1du3btDCDBbd26dUk+vuQ0YcIEI1++fIanp6eRP39+47333jNu3bpl3z5s2DAjd+7cxoULF+zrvvnmG8PLy8vYvXt3kl4jJZKtPn36GPnz5ze8vLyMXLlyGY0aNTJ27tzpsE9cXJwxaNAgI1euXIa3t7dRu3Zt4/Dhww77JOUEye2UbD16rvQfmqQOajPiLLUZuZ/+/c1Eq2hRw4iOdq0240yyZTOM1Dg92KMVHh5O5syZE62WdvPmTU6cOEFwcPAjq5R2rwIZyWH58uX069eP/fv3J/n5X3zxRUqXLs0777yT7PHIw0vpNpOSrPgbS++io6NZsWIFjRo1snwQsqQOajPiLLUZuZcTJ+CJJyAqCpYvh0aNXKvN3Cs3uJPlkxqL6wkNDeXIkSP8/fffBAUF3Xf/qKgoSpYsSd++fR9BdCIiIiKSlr39tplo1akD/18XL9VSsiWJ6tOnT5L39fLy4r333ku5YEREREQkXdi8GRYtMqt7jx1Lkqc0clWpqz+RiIiIiIikSXFxEN9RqmNHKFXK2niSg5ItERERERGx3Ndfw6+/mnPJPsAsPC5JyVYyUZ0RkZShvy0REZG0LzLSHKsFMHAgBAZaG09yUbL1kOKroUREpL4ZrUVSg/i/LasrD4mIiEjKmTABTp2CfPn+60qYFqhAxkNyd3cnICCA8+fPA+Dn54cthUfyxcXFERUVxc2bN1NdGW+xRmpsM4ZhEBERwfnz5wkICMDd3d3qkERERCQFnDsHH3xgLo8aBX5+1saTnJRsJYPA/7/OGZ9wpTTDMIiMjMTX1zfFEztJG1JzmwkICLD/jYmIiEjaM2QIXLsG5cvDyy9bHU3yUrKVDGw2G7lz5yZnzpxER0en+OtFR0ezceNGqlWrpq5VkiSptc14enrqipaIiEgatn8/zJxpLn/yCaSSDjhJpmQrGbm7uz+SH4bu7u7ExMTg4+OTqn44i3XUZkRERMQVvfWWWfL9ueegalWro0l+aSx3FBERERGR1OCnn2DlSvD0hI8+sjqalKFkS0REREREHqno6P+qDr7+OoSEWBtPSlGyJSIiIiIij9TUqXDoEGTPDoMGWR1NylGyJSIiIiIij8yFCzB0qLn8/vsQEGBlNClLyZaIiIiIiDwygwfDlStQujR07Gh1NClLyZaIiIiIiDwSe/fC9Onm8oQJkNZneFGyJSIiIiIiKc4woE8fs9R7ixZQvbrVEaU8JVsiIiIiIpLili6FdevA2xs+/tjqaB4NJVsiIiIiIpKibt40JzAG89+CBS0N55FRsiUiIiIiIilq/Hg4fhzy5IG337Y6mkdHyZaIiIiIiKSYM2dg5Ehz+aOPIGNGa+N5lJRsiYiIiIhIinnnHbhxA55+Gl5+2epoHi0lWyIiIiIikiK2b4e5c83lCRPALZ1lH+nscEVERERE5FEwDOjd21xu29a8spXeKNkSEREREZFk99VXsGULZMgAo0ZZHY01lGyJiIiIiEiyunED+vc3l995x6xCmB4p2RIRERERkWQ1ejT8/bc5n9Ybb1gdjXWUbImIiIiISLL5808z2QIYMwZ8fKyNx0pKtkREREREJNm8+SbcvAk1asBzz1kdjbWUbImIiIiISLJYvRq++Qbc3WHiRLDZrI7IWkq2RERERETkoUVFweuvm8s9ekDJktbG4wqUbImIiIiIyEObNAkOHYIcOWDYMKujcQ1KtkRERERE5KGcOfNfgvXRRxAQYGk4LkPJloiIiIiIPJQBA+DaNXj6aWjXzupoXIeSLREREREReWC//ALz5pnFMCZNAjdlGHZ6K0RERERE5IHExkLPnuZyx47w1FPWxuNqlGyJiIiIiMgDmTEDdu82x2h98IHV0bgeJVsiIiIiIuK0Cxfg3XfN5ZEjzSqE4kjJloiIiIiIOO299+DyZShVCrp2tToa16RkS0REREREnLJjh9mFEGDyZPDwsDYeV6VkS0REREREkiwuziyKYRjQujVUrWp1RK5LyZaIiIiIiCTZvHmwdStkzAijR1sdjWtTsiUiIiIiIkly9Sr0728uDx4MefJYG4+rU7IlIiIiIiJJMnQonD8PRYpA795WR+P6lGyJiIiIiMh97d8PkyaZyxMngpeXtfGkBkq2RERERETkngwDuneH2Fho3hzq1bM6otTBZZKtDz/8EJvNRp8+fezratSogc1mc7h169bN4XF3brfZbCxcuNBhn/Xr11O2bFm8vb0JCQlh7ty5j+CIRERERETShi++gJ9/Bj8/GD/e6mhSD5eoiL99+3amT59OqVKlEmzr3Lkzw4cPt9/38/NLsM+cOXNo0KCB/X5AQIB9+cSJE4SGhtKtWzcWLFjAmjVr6NSpE7lz56Z+/frJeyAiIiIiImnMpUvQr5+5PGQI5M9vbTypieXJ1vXr12ndujUzZ85k5MiRCbb7+fkRGBh4z+cICAi46z7Tpk0jODiYsWPHAlC0aFF+/vlnxo0bp2RLREREROQ+3n0X/v0XihWD2zqhSRJYnmz16NGD0NBQ6tSpk2iytWDBAubPn09gYCBNmjRh0KBBCa5u9ejRg06dOlGoUCG6detG+/btsdlsAGzZsoU6deo47F+/fn2H7op3unXrFrdu3bLfDw8PByA6Opro6OgHPdRkEx+DK8QiqYPajDhD7UWcpTYjzlKbST1++83G9OnugI2JE2Ow2Qys+Nhcqc04E4OlydbChQvZuXMn27dvT3T7yy+/TIECBciTJw979+5lwIABHD58mG+//da+z/Dhw6lVqxZ+fn6sWrWK1157jevXr9OrVy8Azp49S65cuRyeN1euXISHhxMZGYmvr2+C1x01ahTDhg1LsH7VqlWJdmO0SlhYmNUhSCqjNiPOUHsRZ6nNiLPUZlxbbCz0718dwwigRo3TXL++kxUrrI3JFdpMREREkve1LNk6ffo0vXv3JiwsDB8fn0T36dKli325ZMmS5M6dm9q1a3Ps2DEKFy4MwKBBg+z7PPnkk9y4cYOPP/7Ynmw9iIEDB/LGG2/Y74eHhxMUFES9evXw9/d/4OdNLtHR0YSFhVG3bl08PT2tDkdSAbUZcYbaizhLbUacpTaTOkyb5saxY+5kzmwwb14guXI1siwWV2oz8b3eksKyZGvHjh2cP3+esmXL2tfFxsayceNGJk+ezK1bt3B3d3d4zNNPPw3A0aNH7cnWnZ5++mlGjBjBrVu38Pb2JjAwkHPnzjnsc+7cOfz9/RO9qgXg7e2Nt7d3gvWenp6Wf7i3c7V4xPWpzYgz1F7EWWoz4iy1Gdd17hzEX9P44AMb+fK5xufkCm3Gmde3LNmqXbs2+/btc1jXvn17nnjiCQYMGJAg0QLYvXs3ALlz577r8+7evZssWbLYk6VKlSqx4o7rnWFhYVSqVOkhj0BEREREJG3q1w+uXoVy5aBrV6ujSb0sS7YyZcpEiRIlHNZlyJCBbNmyUaJECY4dO8aXX35Jo0aNyJYtG3v37qVv375Uq1bNXiL+hx9+4Ny5c1SsWBEfHx/CwsL44IMPeOutt+zP2a1bNyZPnkz//v3p0KEDa9euZdGiRSxfvvyRHq+IiIiISGqwYQPMmwc2G0ydColcA5Eksrwa4d14eXmxevVqxo8fz40bNwgKCuL555/nvffes+/j6enJlClT6Nu3L4ZhEBISwieffELnzp3t+wQHB7N8+XL69u3LhAkTyJcvH7NmzVLZdxERERGRO0RFwWuvmctdu0KFCtbGk9q5VLK1fv16+3JQUBAbNmy45/4NGjRwmMz4bmrUqMGuXbseNjwRERERkTRt/Hg4eBBy5IAPPrA6mtTPzeoARERERETEeqdOQfzsRx9/DFmyWBtPWqBkS0RERERE6NsXIiKgalVo29bqaNIGJVsiIiIiIuncihXw7bdmMYypU83iGPLwlGyJiIiIiKRjkZHw+uvmct++cEfBcHkISrZERERERNKxkSPh+HHImxcGD7Y6mrRFyZaIiIiISDq1fz+MHm0uT54MmTJZG09ao2RLRERERCQdiosz59KKiYFmzeDZZ62OKO1RsiUiIiIikg7NmgWbN0PGjDBpktXRpE1KtkRERERE0pmzZ2HAAHN55EgICrI2nrRKyZaIiIiISDrTty9cuQLlykHPnlZHk3Yp2RIRERERSUd++gkWLgQ3N5gxw5xbS1KGki0RERERkXQiIgJee81c7t0bypa1Np60TsmWiIiIiEg6MWIEnDhhjtEaPtzqaNI+JVsiIiIiIunAvn0wZoy5PHmyWYVQUpaSLRERERGRNO72ObWaN4emTa2OKH1QsiUiIiIiksbNmAFbtkCmTJpT61FSsiUiIiIikoadOQNvv20uv/8+5M1rbTzpiZItEREREZE0rG9fuHoVypf/rxKhPBpKtkRERERE0qgVK+DrrzWnllWUbImIiIiIpEHXrkG3buZy377w5JPWxpMeKdkSEREREUmDBg6E06ehUCHNqWUVJVsiIiIiImnML7/A1Knm8owZ4OdnbTzplZItEREREZE05NYt6NwZDAPat4fata2OKP1SsiUiIiIikoa8/z78/jvkygVjxlgdTfqmZEtEREREJI3Ytw9GjTKXJ02CrFmtjSe9U7IlIiIiIpIGxMaa3QdjYqBZM2jRwuqIRMmWiIiIiEgaMGkSbNsG/v4wZQrYbFZHJEq2RERERERSuZMn4d13zeXRoyFvXkvDkf+nZEtEREREJBUzDOjaFSIioFo1syuhuAYlWyIiIiIiqdi8ebBqFXh7w8yZ4KZf+C5DH4WIiIiISCp1/jz07WsuDxkCjz9ubTziSMmWiIiIiEgq1bs3XLoEZcrAW29ZHY3cScmWiIiIiEgq9MMPsHCh2W1w1izw9LQ6IrmTki0RERERkVTm8mXo1s1cfuMNKFfO2ngkcUq2RERERERSmTfegH/+McdoDR9udTRyN0q2RERERERSkf/9D+bONSctnjMHfH2tjkjuRsmWiIiIiEgqcfUqdOliLvfuDZUrWxuP3JuSLRERERGRVOKtt+Cvv6BwYXj/faujkftRsiUiIiIikgqEhZlVBwFmzwY/P2vjkftTsiUiIiIi4uKuXYNOnczlnj2hWjVr45GkUbIlIiIiIuLiBgyAU6cgOBhGjbI6GkkqJVsiIiIiIi5s7Vr49FNzedYsyJjR2ngk6ZRsiYiIiIi4qOvX/+s+2K0b1KplbTziHCVbIiIiIiIu6p134MQJyJ8fRo+2OhpxlpItEREREREXtHEjTJpkLs+cCZkyWRuPOE/JloiIiIiIi4mIgA4dzOVOnaBePWvjkQejZEtERERExMW8+y4cOwb58sGYMVZHIw/KZZKtDz/8EJvNRp8+fezratSogc1mc7h169bN4XGnTp0iNDQUPz8/cubMSb9+/YiJiXHYZ/369ZQtWxZvb29CQkKYO3fuIzgiERERERHnrV8P48ebyzNmQObMVkYjD8PD6gAAtm/fzvTp0ylVqlSCbZ07d2b48OH2+363TZUdGxtLaGgogYGBbN68mTNnztC2bVs8PT354IMPADhx4gShoaF069aNBQsWsGbNGjp16kTu3LmpX79+yh+ciIiIiEgSXbsG7duby507Q8OG1sYjD8fyK1vXr1+ndevWzJw5kyxZsiTY7ufnR2BgoP3m7+9v37Zq1SoOHjzI/PnzKVOmDA0bNmTEiBFMmTKFqKgoAKZNm0ZwcDBjx46laNGi9OzZkxYtWjBu3LhHdozJbeNGG5GRLpEni4iIiEgyevNNOHkSChaEsWOtjkYeluW/2Hv06EFoaCh16tRh5MiRCbYvWLCA+fPnExgYSJMmTRg0aJD96taWLVsoWbIkuXLlsu9fv359unfvzoEDB3jyySfZsmULderUcXjO+vXrO3RXvNOtW7e4deuW/X54eDgA0dHRREdHP8zhPrSPPnJj0CAP6tUrTtOm1sYiqUd8u7W6/UrqoPYizlKbEWepzSTup59szJxp/jyfNSsGHx8DvUUmV2ozzsRgabK1cOFCdu7cyfbt2xPd/vLLL1OgQAHy5MnD3r17GTBgAIcPH+bbb78F4OzZsw6JFmC/f/bs2XvuEx4eTmRkJL6+vgled9SoUQwbNizB+lWrVjl0Y7SCzZYNm60Kq1YVZNSoLZQvf97SeCR1CQsLszoESUXUXsRZajPiLLWZ/1y75kmvXrUAD5o0Ocb16/tZscLqqFyPK7SZiIiIJO9rWbJ1+vRpevfuTVhYGD4+Ponu06VLF/tyyZIlyZ07N7Vr1+bYsWMULlw4xWIbOHAgb7zxhv1+eHg4QUFB1KtXz6EboxUaNYKzZ2OYPNmTmTMr0qNHDNmyWRqSpALR0dGEhYVRt25dPD09rQ5HXJzaizhLbUacpTaTULt27ly+7MbjjxvMn58fX9/8VofkUlypzcT3ekuKJCVbzz33XJKfMP6q0/3s2LGD8+fPU7ZsWfu62NhYNm7cyOTJk7l16xbu7u4Oj3n66acBOHr0KIULFyYwMJBff/3VYZ9z584BEBgYaP83ft3t+/j7+yd6VQvA29sbb2/vBOs9PT0t/3AB3n8/mqVLr/HXX5no1cuTr78Gm83qqCQ1cJU2LKmD2os4S21GnKU2Y/rmG/jqK3Bzgy++sOHvr/fkblyhzTjz+kkqkJE5c2b7zd/fnzVr1vDbb7/Zt+/YsYM1a9aQ2Ym6lLVr12bfvn3s3r3bfitfvjytW7dm9+7dCRItgN27dwOQO3duACpVqsS+ffs4f/6/rnRhYWH4+/tTrFgx+z5r1qxxeJ6wsDAqVaqU5Fhdja8v9OmzAw8Pg8WLYeFCqyMSERERkQdx7hzEz2z09tvw/9cWJI1I0pWtOXPm2JcHDBhAy5YtmTZtmj0hio2N5bXXXnOqi12mTJkoUaKEw7oMGTKQLVs2SpQowbFjx/jyyy9p1KgR2bJlY+/evfTt25dq1arZS8TXq1ePYsWK8corrzB69GjOnj3Le++9R48ePexXprp168bkyZPp378/HTp0YO3atSxatIjly5cnOVZXFBJylXfeiWP4cHd69IBq1SBvXqujEhEREZGkMgwz0bpwAUqVgiFDrI5IkpvTpd9nz57NW2+95XDlyd3dnTfeeIPZs2cnW2BeXl6sXr2aevXq8cQTT/Dmm2/y/PPP88MPPzi87o8//oi7uzuVKlWiTZs2tG3b1mFeruDgYJYvX05YWBilS5dm7NixzJo1K03MsTVgQBzly8Ply9Cxo/kHKyIiIiKpw/z5sHQpeHrCF1+Al5fVEUlyc7pARkxMDIcOHaJIkSIO6w8dOkRcXNxDBbN+/Xr7clBQEBs2bLjvYwoUKMCK+5RqqVGjBrt27Xqo2FxR/B9m2bKwciVMn/7fZWgRERERcV1//QWvv24uDx0KpUtbGo6kEKeTrfbt29OxY0eOHTtGhQoVANi2bRsffvgh7eOnu5ZHpmhR+PBD6NPHnASvTh0ICbE6KhERERG5G8MweyVdvQoVKkD//lZHJCnF6WRrzJgxBAYGMnbsWM6cOQOYBSv69evHm2++mewByv29/josWwbr1kG7drBxIyRSX0REREREXMD06bBqFfj4wOefg4elM99KSnJ6zJabmxv9+/fn77//5sqVK1y5coW///6b/v37J1pBUFKemxvMmQOZMsHmzTBmjNURiYiIiEhi/vjD7I0EMGoUPPGEtfFIynI62bqdv7+/5ZP8iqlAAZg40VweNAj27rU2HhERERFxFB0NbdpARATUqgW9elkdkaS0B7pouWTJEhYtWsSpU6eIiopy2LZz585kCUyc166dWdFm2TJ45RX49VdIZG5mEREREbHAyJGwfTsEBJjdB90e6rKHpAZOf8QTJ06kffv25MqVi127dlGhQgWyZcvG8ePHadiwYUrEKElks8GMGZAjh3lla+hQqyMSEREREYAtW8xkC2DaNMiXz9p45NFwOtmaOnUqM2bMYNKkSXh5edG/f3/CwsLo1asXV69eTYkYxQk5c5qDLgE++sgsliEiIiIi1rl2zex1FBcHrVvDiy9aHZE8Kk4nW6dOnaJy5coA+Pr6cu3aNQBeeeUVvvrqq+SNTh5I8+bQvr1ZVvSVV+DKFasjEhEREUm/+vaFY8cgKAgmT7Y6GnmUnE62AgMDuXTpEgD58+dn69atAJw4cQLDMJI3OnlgEyZA4cJw6hT06GF1NCIiIiLp09Kl8Nln5nCPL74wx2tJ+uF0slWrVi2+//57wJzguG/fvtStW5cXX3yR5s2bJ3uA8mAyZYL58835tr780ryJiIiIyKNz9ix07mwuv/UW1KhhaThiAaerEc6YMYO4uDgAevToQbZs2di8eTNNmzala9euyR6gPLiKFc0y8EOHQvfuUKWKWSJeRERERFKWYUCHDnDhApQuDSNGWB2RWMHpZMvNzQ232+pUtmrVilatWiVrUJJ83n0XVq40K+C88gqsW2de7RIRERGRlDNtGvzvf+Y0PAsWaDqe9CpJydZeJ2bILVWq1AMHI8nPw8PsTli6NGzaBKNHw8CBVkclIiIiknYdPgxvvmkuf/ghFC9ubTxinSQlW2XKlMFms2EYBjab7Z77xsbGJktgknwKFYJJk8wKhYMHQ926UL681VGJiIiIpD3R0WZ598hIqFMHevWyOiKxUpIKZJw4cYLjx49z4sQJvvnmG4KDg5k6dSq7du1i165dTJ06lcKFC/PNN9+kdLzygNq1gxYtICbG/AK4ccPqiERERETSnmHDYMcOyJIF5s4FN6fL0UlakqQrWwVuq6rwwgsvMHHiRBo1amRfV6pUKYKCghg0aBDPPvtssgcpD89mMyc73rIF/vjDvLQ9bZrVUYmIiIikHRs2wAcfmMvTpkHevNbGI9ZzOtfet28fwcHBCdYHBwdz8ODBZAlKUkbWrPD55+by9Onw/xX8RUREROQhXboEbdqYVQhffRVatrQ6InEFTidbRYsWZdSoUURFRdnXRUVFMWrUKIoWLZqswUnyq137vwGbHTua8z+IiIiIyIMzDHM+rb/+gsceM8fKi8ADlH6fNm0aTZo0IV++fPbKg3v37sVms/HDDz8ke4CS/N5/H1avhj17zKIZy5erP7GIiIjIg5o5E779Fjw94auvIGNGqyMSV+F0slWhQgWOHz/OggULOHToEAAvvvgiL7/8MhkyZEj2ACX5xc/3UL48/PQTTJgAfftaHZWIiIhI6nPwIPTpYy5/8AGUK2dpOOJinE62ADJkyECXLl2SOxZ5hIoXh08+gddegwEDoFo1fTmIiIiIOOPmTXjpJbPMe7168MYbVkckriZJydb3339Pw4YN8fT05Pv7VFVo2rRpsgQmKa9bNwgLg+++g1atYOdOyJTJ6qhEREREUocBA2DvXsiRwyxCpmEZcqckJVvPPvssZ8+eJWfOnPcs7W6z2TSpcSpis8GsWfDbb3D0KPTs+V+1QhERERG5u+XLYeJEc3nuXAgMtDQccVFJyr/j4uLImTOnffluNyVaqU/WrOb4LTc3+OILmD/f6ohEREREXNuZM2Z5d4DeveG26WdFHOhip1C1KgwZYi537w5Hjlgbj4iIiIiriouDtm3hwgUoXRo++sjqiMSVPVCBjDVr1rBmzRrOnz9PXFycw7bZs2cnS2DyaL37LqxZAxs3mgM9N28GLy+roxIRERFxLWPHmlPo+PqaZd69va2OSFyZ01e2hg0bRr169VizZg0XLlzg8uXLDjdJndzdze6EWbPCjh3wzjtWRyQiIiLiWn777b/fSBMmQNGi1sYjru+BJjWeO3cur7zySkrEIxbKlw9mz4ZnnzXP2tSuDQ0bWh2ViIiIiPWuXTN7/8TEwPPPQ6dOVkckqYHTV7aioqKoXLlySsQiLqBZM7MqIUC7duYAUBEREZH0zDCga1ezenNQEMycaVZ1Frkfp5OtTp068eWXX6ZELOIiPv4YSpWCf/81B4DeMSxPREREJF2ZNcscn+XuDgsXQpYsVkckqYXT3Qhv3rzJjBkzWL16NaVKlcLT09Nh+yeffJJswYk1fHzML5Ly5c0BoB9/bE7aJyIiIpLe7NsHvXqZyx98AOrgJc5wOtnau3cvZcqUAWD//v0O22y6nppmFC1qTtTXqRO89x5UqwaVKlkdlYiIiMijc/06tGwJN2+a49jfesvqiCS1cTrZWrduXUrEIS6oQwfzytbChfDii7BrF2TLZnVUIiIiIo9Gjx5w6BDkyQOffw5umqFWnKQmI3dls8H06fDYY3D6tFkwQ+O3REREJD2YOxe++MJMsL76CnLksDoiSY2cvrJVs2bNe3YXXLt27UMFJK7F3x8WLYKKFWH5chgzBvr3tzoqERERkZRz8KB5VQtg+HBzOIXIg3D6ylaZMmUoXbq0/VasWDGioqLYuXMnJUuWTIkYxWJlypjjt8CcyO+XXywNR0RERCTFRESY47QiIqBuXRg40OqIJDVz+srWuHHjEl0/dOhQrl+//tABiWvq3Bk2bIAvvzTHb+3eDdmzWx2ViIiISPJ6/XU4cAACA2HePI3TkoeTbM2nTZs2zJ49O7meTlxM/PitIkXg77/hlVc0fktERETSlvnzYfZs83fPl19CrlxWRySpXbIlW1u2bMHHxye5nk5cUMaM5vgtHx/46ScYPdrqiERERESSx+HD0K2buTx4MNSsaW08kjY43Y3wueeec7hvGAZnzpzht99+Y9CgQckWmLimUqVg8uT/5t+qUgWqVrU6KhEREZEHFxlpjtO6ccNMsvSTVpKL08lW5syZHe67ublRpEgRhg8fTr169ZItMHFdHTqY47fmzYNWrcz5t3LmtDoqERERkQfTsyfs3Wv+nlmwANzdrY5I0gqnk605c+akRBySiths8Omn8Ntv8Pvv5vit//1PA0hFREQk9Zk927y5uZnjtHLntjoiSUv081geSIYMsHgx+PrCqlUwapTVEYmIiIg4Z/dux/m0ate2NBxJg5RsyQMrXhymTjWXBw+GdeusjUdEREQkqa5cgRYt4OZNCA3VfFqSMpRsyUN59VVo394sA9+qlVkWXkRERMSVGYb5++XYMShYEL74QsMhJGWoWclDmzwZSpeG8+fhhRcgKsrqiERERETubswYWLoUvLzMYRFZs1odkaRVTiVb0dHRFC5cmN9//z3ZA/nwww+x2Wz06dMnwTbDMGjYsCE2m42lS5c6bLPZbAluCxcudNhn/fr1lC1bFm9vb0JCQpg7d26yx5+e+fnBN99A5sywZQu89ZbVEYmIiIgkbuPG/7oMTpgA5ctbG4+kbU4lW56enty8eTPZg9i+fTvTp0+nVKlSiW4fP348Npvtro+fM2cOZ86csd+effZZ+7YTJ04QGhpKzZo12b17N3369KFTp06sXLkyuQ8jXStc2Jx1HWDSJLNsqoiIiIgrOXMGXnwRYmOhTRvo2tXqiCStc7obYY8ePfjoo4+IiYlJlgCuX79O69atmTlzJlmyZEmwfffu3YwdO5bZs2ff9TkCAgIIDAy033x8fOzbpk2bRnBwMGPHjqVo0aL07NmTFi1aMG7cuGSJX/7TuDG8+6653KUL7N9vbTwiIiIi8WJizPHlZ8+aRb6mTTOnsxFJSU4nW9u3b+fbb78lf/781K9fn+eee87h5qwePXoQGhpKnTp1EmyLiIjg5ZdfZsqUKQQGBt7zObJnz06FChWYPXs2hmHYt23ZsiXBc9evX58tW7Y4Havc37BhULcuRETAc8/B1atWRyQiIiIC771ndiHMlMkc/pAhg9URSXrg9KTGAQEBPP/888ny4gsXLmTnzp1s37490e19+/alcuXKNGvW7K7PMXz4cGrVqoWfnx+rVq3itdde4/r16/Tq1QuAs2fPkitXLofH5MqVi/DwcCIjI/H19U3wnLdu3eLWrVv2++Hh4YA5Zi06Otrp40xu8TG4QiyJ+fxzePppD44csdGuXRyLFsXqzJHFXL3NiGtRexFnqc2Isx51m/n+exsffWT+7J0xI4ZChQzUXFMXV/qecSYGp5OtOXPmOPuQRJ0+fZrevXsTFhbm0O0v3vfff8/atWvZtWvXPZ9n0KBB9uUnn3ySGzdu8PHHH9uTrQcxatQohg0blmD9qlWr8PPze+DnTW5hYWFWh3BXr78ewDvvPMOyZe507vw7zz131OqQBNduM+J61F7EWWoz4qxH0Wb+/jsj/fpVA6BJk2P4+u5nxYoUf1lJIa7wPRMREZHkfW3G7X3unPDvv/9y+PBhAIoUKUKOHDmcevzSpUtp3rw57u7u9nWxsbHYbDbc3Nzo3r07U6ZMwe22SQ9iY2Nxc3OjatWqrF+/PtHnXb58OY0bN+bmzZt4e3tTrVo1ypYty/jx4+37zJkzhz59+nD1Ln3cEruyFRQUxIULF/D393fqOFNCdHQ0YWFh1K1bF09PT6vDuauZM93o0cMdNzeD//0vlpo1H6ipSTJILW1GXIPaizhLbUac9ajazLVrUKWKB4cO2XjmmThWroxFTTR1cqXvmfDwcLJnz87Vq1fvmxs4fWXrxo0bvP7663zxxRfExcUB4O7uTtu2bZk0aVKSr/zUrl2bffv2Oaxr3749TzzxBAMGDCB79ux0vaNETMmSJRk3bhxNmjS56/Pu3r2bLFmy4O3tDUClSpVYccfpi7CwMCpVqnTX5/D29rY//naenp6Wf7i3c7V47tS9O/z6K3z+uY02bTzYuRPy5bM6qvTN1duMuBa1F3GW2ow4KyXbjGGYBbsOHYI8eWDxYjf8/DTFbGrnCt8zzry+08nWG2+8wYYNG/jhhx+oUqUKAD///DO9evXizTff5NNPP03S82TKlIkSJUo4rMuQIQPZsmWzr0+sKEb+/PkJDg4G4IcffuDcuXNUrFgRHx8fwsLC+OCDD3jrtomeunXrxuTJk+nfvz8dOnRg7dq1LFq0iOXLlzt76OIkmw2mToXdu2HPHnPC4w0bzAkERURERFLSRx+ZhTA8Pc1/71FrTSTFOJ1sffPNNyxZsoQaNWrY1zVq1AhfX19atmyZ5GQrOXh6ejJlyhT69u2LYRiEhITwySef0LlzZ/s+wcHBLF++nL59+zJhwgTy5cvHrFmzqF+//iOLMz2Ln/C4XDnYuhX69DETMBEREZGUsmrVf9PRTJ4MFStaG4+kX04nWxEREQmq+wHkzJnTqcFiibnbOKx4dw4va9CgAQ0aNLjv89aoUeO+hTYk5cRPeNy0KXz6qZl4dexodVQiIiKSFp04Yc6nFRcHnTqZXQlFrOJ0x9VKlSoxZMgQbt68aV8XGRnJsGHD7jkOStK3xo3NObgAXnvNvMolIiIikpwiIqB5c7h8GSpUMK9qiVjJ6StbEyZMoH79+uTLl4/SpUsDsGfPHnx8fFi5cmWyByhpx7vvwq5d8N135oTHO3ZA7txWRyUiIiJpQXxBjD17IGdOcxhDIvXORB4pp5OtEiVKcOTIERYsWMChQ4cAeOmll2jdunWiEwSLxHNzMyc8PnwYDh6E55+Hdev0RSgiIiIPb+JEWLAA3N1h0SJVQBbX4HSyBeDn5+dQhEIkqTJlgmXL4KmnYMsW6NULpk+3OioRERFJzdavhzffNJfHjoXq1S0NR8TugSYbOHz4MD179qR27drUrl2bnj172q9yidxPSAh89ZVZGn7GDCVbIiIi8uBOnYKWLSE2Flq3Nk/kirgKp5Otb775hhIlSrBjxw5Kly5N6dKl2blzJyVLluSbb75JiRglDWrQAD74wFx+/XX45Rdr4xEREZHUJyICnn0W/v0XypQxT+LabFZHJfIfp7sR9u/fn4EDBzJ8+HCH9UOGDKF///48//zzyRacpG0DBsDOnbB4sTl+a8cOyJvX6qhEREQkNTAMaN/eLL6VIwcsXWrO7yniSpy+snXmzBnatm2bYH2bNm04c+ZMsgQl6YPNBnPmQMmScO6cWaHwthkFRERERO7qgw/MQhienmblwQIFrI5IJCGnk60aNWqwadOmBOt//vlnqlatmixBSfqRIYN5JipLFvj1V+je3TxTJSIiInI3y5bBe++Zy1OmgH6Ciqtyuhth06ZNGTBgADt27KBixYoAbN26lcWLFzNs2DC+//57h31F7qdQIfj6a3Mc19y5UKoU9O1rdVQiIiLiivbvhzZtzOUePUAFssWVOZ1svfbaawBMnTqVqVOnJroNwGazERsb+5DhSXpRt65ZqrVvX3jrLXjiCWjY0OqoRERExJVcvAhNm8L161CzJowbZ3VEIvfmdDfCuLi4JN2UaImzeveGTp0gLg5atTInPhYREREBiI6GF16AEycgONgssOXpaXVUIvf2QPNsiaQEm83sd12tGoSHm2euLl60OioRERFxBW+8AevWQcaM8P33kC2b1RGJ3J+SLXEpXl5mRaGCBeHYMWjRwjyTJSIiIunXzJkwebK5PH8+lChhbTwiSaVkS1xO9uzwww/mmav1681Jj1WhUEREJH3atMkshAEwYgQ0a2ZtPCLOULIlLqlECfjqK7Nr4fTpZvdCERERSV+OHYPmzf8br/Xuu1ZHJOIcJVvisho3ho8+Mpf79IGwMEvDERERkUfoyhXzt8DFi1CunDk9jM1mdVQiznG69DtAbGwsS5cu5ffffwegePHiNG3aFHd392QNTuStt8z5NL74wjyjtW0bFClidVQiIiKSkuKvZB06BPnymQUx/PysjkrEeU4nW0ePHiU0NJS//vqLIv//q3fUqFEEBQWxfPlyChcunOxBSvpls8GMGXDkCGzZAk2amAlXlixWRyYiIiIpwTDM8dqrV0OGDOY47jx5rI5K5ME43Y2wV69eFCpUiNOnT7Nz50527tzJqVOnCA4OplevXikRo6Rz3t7w3XeQP7+ZdD3/PERFWR2ViIiIpIQJE8zx2jYbfPkllCljdUQiD87pZGvDhg2MHj2arFmz2tdly5aNDz/8kA0bNiRrcCLxcuUyuxBkzGjOsdGtmyoUioiIpDU//mjOpwXw8cfmnJsiqZnTyZa3tzfXrl1LsP769et4eXklS1AiiSldGr7+GtzcYM4cGDXK6ohEREQkuezZA61amSdTO3f+L+kSSc2cTrYaN25Mly5d2LZtG4ZhYBgGW7dupVu3bjTV6QdJYY0awaRJ5vK775rJl4iIiKRuZ86Y47Jv3IBatcwpX1R5UNICp5OtiRMnUrhwYSpVqoSPjw8+Pj5UqVKFkJAQxo8fnwIhijh67TWzFDxAu3awebOl4YiIiMhDiIw0Jyo+fdqsOLxkCXh6Wh2VSPJwuhphQEAAy5Yt4+jRo/bS70WLFiUkJCTZgxO5mzFj4PhxcxxXs2ZmhcJChayOSkRERJwRF2eeON2+HbJmNcdsqeKwpCVOX9kaPnw4ERERhISE0KRJE5o0aUJISAiRkZEMHz48JWIUScDd3axQVLYsXLhgdi+8fNnqqERERMQZ777rxuLF5pWs774DnbuXtMbpZGvYsGFcv349wfqIiAiGDRuWLEGJJEX83Bv58sHhw/DccyoJLyIiklqsWFGQsWPdAfjsM6hWzeKARFKA08mWYRjYEhmxuGfPHody8CKPQp48sHy5WRJ+/Xro2lUl4UVERFzdDz/YmDWrFAAjR8Irr1gckEgKSfKYrSxZsmCz2bDZbDz++OMOCVdsbCzXr1+nW7duKRKkyL2UKgWLFkHjxjB3LhQuDO+9Z3VUIiIikpjt26FNG3fi4mx06BDHO+84fe5fJNVIcrI1fvx4DMOgQ4cODBs2jMyZM9u3eXl5UbBgQSpVqpQiQYrcT8OGMHmyWalw0CDInx/atrU6KhEREbnd8ePmydHISBtly55j0qSs2GxKtiTtSnKy1a5dOwCCg4OpUqUKHh5OFzIUSVHdu8OJE+aM8x07QmAg1KtndVQiIiICcPGiWdDq/HkoXdqgX7/f8PTUf9SStjl9KiFTpkz2ku8Ay5Yt49lnn+Wdd94hStUJxGIffggvvQQxMfD887Brl9URiYiIyM2b8OyzZkGroCBYtiwGX98Yq8MSSXFOJ1tdu3bljz/+AOD48eO8+OKL+Pn5sXjxYvr375/sAYo4w80N5syBmjXh+nXzDNrJk1ZHJSIikn7Fz6X188+QOTP8739mgSuR9MDpZOuPP/6gTJkyACxevJjq1avz5ZdfMnfuXL755pvkjk/Ead7e5lwdJUvC2bPQoIHZdUFEREQevbffNgtZeXrCt99C8eJWRyTy6DxQ6fe4uDgAVq9eTaNGjQAICgriwoULyRudyAOKP3MWFGR2WWjaFCIjrY5KREQkfZk82RxLDTB7NtSqZW08Io+a08lW+fLlGTlyJPPmzWPDhg2EhoYCcOLECXLlypXsAYo8qLx5zYQrIAA2b4aXX4bYWKujEhERSR8WLYJevczlkSOhTRtr4xGxgtPJ1vjx49m5cyc9e/bk3XffJSQkBIAlS5ZQuXLlZA9Q5GEULw7LloGXFyxdCr17a9JjERGRlLZ6tZlcGQb06AHvvGN1RCLWcLp+e6lSpdi3b1+C9R9//DHu7u7JEpRIcqpWDebPhxdfhClTzK6FAwZYHZWIiEjatHMnNG8O0dHwwgswYQLYbFZHJWKNB54sa8eOHfYS8MWKFaNs2bLJFpRIcnvhBfjnH+jTxxyoGxhoVkYSERGR5HP0KDRsaFYErlUL5s0DnYuX9MzpZOv8+fO8+OKLbNiwgYCAAACuXLlCzZo1WbhwITly5EjuGEWSRe/e8NdfMGaMOelx1qzQpInVUYmIiKQNZ89C/frmpMVlypiVgb29rY5KxFpOj9l6/fXXuX79OgcOHODSpUtcunSJ/fv3Ex4eTq/4UZAiLuqjj8wrWrGx0LIlbNxodUQiIiKpX3i4eUXr+HEoVMgsUOXvb3VUItZzOtn66aefmDp1KkWLFrWvK1asGFOmTOF///tfsgYnktzc3GDWLLMU/M2b5pWt3butjkpERCT1unkTnn3W/P80Z05YudLsri8iD5BsxcXF4enpmWC9p6enff4tEVfm4QELF5qFM8LDzUmPjx2zOioREZHUJzYWXnkF1q2DTJnMK1r/X6haRHiAZKtWrVr07t2bf/75x77u77//pm/fvtSuXTtZgxNJKb6+8P33ULo0nDsHdevCmTNWRyUiIpJ6GIY5j9aSJf9NsaJ6aSKOnE62Jk+eTHh4OAULFqRw4cIULlyY4OBgwsPDmTRpUkrEKJIiMmc2uzoULgwnTpiDei9ftjoqERGR1GHQIJg61SzrPm+eWX1QRBw5XY0wKCiInTt3snr1ag4dOgRA0aJFqVOnTrIHJ5LScuWCsDCoUgX27TPHcK1aBX5+VkcmIiLiuj7+GN5/31yeOtUsOiUiCT3QPFs2m426detSt27d5I5H5JELDjavcFWrBr/8Ys7JtXQpJDI0UUREJN2bPh369zeXP/wQunWzNh4RV5bkboRr166lWLFihIeHJ9h29epVihcvzqZNmx44kA8//BCbzUafPn0SbDMMg4YNG2Kz2Vi6dKnDtlOnThEaGoqfnx85c+akX79+xMTEOOyzfv16ypYti7e3NyEhIcydO/eB45S0qWRJWL7cHMu1YgW8+qo56FdERET+89VX0L27uTxwIAwYYG08Iq4uycnW+PHj6dy5M/6JTJqQOXNmunbtyieffPJAQWzfvp3p06dTqlSpu762zWZLsD42NpbQ0FCioqLYvHkzn3/+OXPnzmXw4MH2fU6cOEFoaCg1a9Zk9+7d9OnTh06dOrFy5coHilXSrsqVzUG+Hh7w5ZfmfyaGYXVUIiIiruGHH8zKg4YBr732XzdCEbm7JCdbe/bsoUGDBnfdXq9ePXbs2OF0ANevX6d169bMnDmTLFmyJNi+e/duxo4dy+zZsxNsW7VqFQcPHmT+/PmUKVOGhg0bMmLECKZMmUJUVBQA06ZNIzg4mLFjx1K0aFF69uxJixYtGDdunNOxStrXqBEsWGDOxzVzJrzxhhIuERGRdevMbvaxsdCmDUyaZBbGEJF7S3Kyde7cuUTn14rn4eHBv//+63QAPXr0IDQ0NNECGxEREbz88stMmTKFwERmx9uyZQslS5YkV65c9nX169cnPDycAwcO2Pe587nr16/Pli1bnI5V0oeWLeGzz8zl8ePhtgulIiIi6c62bdC0Kdy6Bc2awZw55klJEbm/JBfIyJs3L/v37yfkLjPV7d27l9y5czv14gsXLmTnzp1s37490e19+/alcuXKNGvWLNHtZ8+edUi0APv9s2fP3nOf8PBwIiMj8fX1TfC8t27d4tatW/b78ePUoqOjiY6OTuLRpZz4GFwhlrSqdWu4ds2NXr3cGTkSfHxi6d8/9U7arTYjzlB7EWepzaRd+/ZBw4YeXL9uo1atOObNi8Uw4GE/arUZcZYrtRlnYkhystWoUSMGDRpEgwYN8PHxcdgWGRnJkCFDaNy4cZJf+PTp0/Tu3ZuwsLAEzwfw/fffs3btWnbt2pXk50wuo0aNYtiwYQnWr1q1Cj8XqgkeFhZmdQhpWv780K5dCJ9/Xpz33nPn5MkDNG58wuqwHorajDhD7UWcpTaTtpw5k4F33nmGy5c9KVLkEp07b2bt2uStHqU2I85yhTYTERGR5H1thpG0ESnnzp2jbNmyuLu707NnT4oUKQLAoUOHmDJlCrGxsezcuTPBVaS7Wbp0Kc2bN8fd3d2+LjY2FpvNhpubG927d2fKlCm43XadOjY2Fjc3N6pWrcr69esZPHgw33//Pbt377bvc+LECQoVKsTOnTt58sknqVatGmXLlmX8+PH2febMmUOfPn24evVqorEldmUrKCiICxcuJFog5FGLjo4mLCyMunXr3rNrpySPYcPceP99s53OmBHDq6+mvkFcajPiDLUXcZbaTNpz4gTUru3BX3/ZKFnSYPXqGBIZWv/A1GbEWa7UZsLDw8mePTtXr169b26Q5CtbuXLlYvPmzXTv3p2BAwcSn6PZbDbq16/PlClTkpxoAdSuXZt9+/Y5rGvfvj1PPPEEAwYMIHv27HTt2tVhe8mSJRk3bhxNmjQBoFKlSrz//vucP3+enDlzAma26+/vT7Fixez7rFixwuF5wsLCqFSp0l1j8/b2xtvbO8F6T09Pyz/c27laPGnViBEQGQmffAJdu3qQKRO0amV1VA9GbUacofYizlKbSRtOnYL69eGvv+CJJyAszEbOnCnzuarNiLNcoc048/pOTWpcoEABVqxYweXLlzl69CiGYfDYY48lWkXwfjJlykSJEiUc1mXIkIFs2bLZ1ydWFCN//vwEBwcDZgXEYsWK8corrzB69GjOnj3Le++9R48ePezJUrdu3Zg8eTL9+/enQ4cOrF27lkWLFrF8+XKnY5b0yWaDMWMgIgKmTTOrMPn6moOERURE0pK//4ZateDkSXjsMVi7Fpw4ly4id3igWjJZsmThqaeeokKFCg+UaCUXd3d3fvzxR9zd3alUqRJt2rShbdu2DB8+3L5PcHAwy5cvJywsjNKlSzN27FhmzZpF/fr1LYtbUh+bDaZMMecXiY01Kxb+9JPVUYmIiCSfs2fNROvYMShUyEy0nKx9JiJ3cOrKVkpbv379PbcnNrws/mrbvdSoUcOSQhuStri5wezZZpfCJUvg2Wdh2TKzq4WIiEhqdv481K4Nf/xhFohauxby5bM6KpHUT7MkiDjBwwO+/BKaN/9vvpFVq6yOSkRE5MFdvAh16sDBg5A3rzmBcYECVkclkjYo2RJxkqcnLFxoJlrxCdfq1VZHJSIi4rzLl6FuXXM+rdy5zUSrUCGroxJJO5RsiTwALy9YtAiaNIGbN81/16yxOioREZGku3rV7Aq/axfkzGn+P/bYY1ZHJZK2KNkSeUBeXrB4MTRu/F/CtW6d1VGJiIjc39Wr0KABbN8O2bKZPTSKFrU6KpG0R8mWyEPw9jaLZTRqZBbOCA2F+9R5ERERsdSVK1CvHmzdClmymIlWyZJWRyWSNinZEnlI3t7wzTfQsOF/CdfGjVZHJSIiktClS2YxjF9/haxZza6DZcpYHZVI2qVkSyQZ+PjAt9+afd8jIswrXZs2WR2ViIjIfy5eNMu779gB2bOb5d2ffNLqqETSNiVbIsnExweWLjW7Zty4YfaFX7vW6qhERETg33/NCYt37zaLYaxbB6VLWx2VSNqnZEskGcUnXA0amFe4QkPhp5+sjkpERNKzc+egZk3YuxcCA82xxSVKWB2VSPqgZEskmfn6mglXfFn4Zs1g2TKroxIRkfTo7Fkz0TpwwJxHa/16VR0UeZSUbImkgPgqhS1aQFSU+e/ixVZHJSIi6ck//0CNGvD775A3L2zYAEWKWB2VSPqiZEskhXh5wVdfQevWEBMDrVrB/PlWRyUiIunBX3+ZidbhwxAUZCZamrBY5NFTsiWSgjw84PPPoUMHiIuDtm3hs8+sjkpERNKyY8fgmWfgyBEoUMBMtAoXtjoqkfRJyZZICnN3h5kzoXt3MAzo1AmmTLE6KhERSYsOHICqVeHPPyEkxJz3MTjY6qhE0i8lWyKPgJubmWD16WPe79kTxo61NCQREUljduyA6tXhzBmz2uCmTZA/v9VRiaRvSrZEHhGbDT75BAYONO+/9RYMHWpe7RIREXkYmzaZVQcvXoSnnjK7DgYGWh2ViCjZEnmEbDZ4/30YMcK8P2wY9O5tjucSERF5EKtWQf36cO2aeWVrzRrImtXqqEQElGyJPHI2G7z3HkycaN6fNAlefRWioy0NS0REUqHvvjPndYyMhIYNYcUKyJTJ6qhEJJ6SLRGLvP46zJtnFtCYN8+ci+vmTaujEhGR1GLePHjhhf/mc1y6FPz8rI5KRG6nZEvEQm3amGclvb3h++/Ns5Lh4VZHJSIirm7iRHM6kdhYs3fEV1+Z8zuKiGtRsiVisSZN4KefzG4f69dD7dpw4YLVUYmIiCsyDHj3XXO8L0CvXub8jR4e1sYlIolTsiXiAmrUgLVrIVs2+O03c46Uv/6yOioREXElMTHQuTN88IF5f+RIGD/enF5ERFyT/jxFXET58mbp3nz54NAhqFIFfv/d6qhERMQVREaa47I++8xMrmbMMK9w2WxWRyYi96JkS8SFFC0KP/8Mjz8Op07BM8/A5s1WRyUiIla6csUs7b5smTnGd8kS8wqXiLg+JVsiLqZAAfjlF3j6abh0yRzDtWyZ1VGJiIgVzpwx587atAn8/WHlSmje3OqoRCSplGyJuKDs2c1JKUNDzXLwzz0H06dbHZWIiDxKR45A5cqwdy8EBsLGjWbiJSKph5ItEReVIYM5Z0rHjhAXB926weDBZiUqERFJ27ZuNROtkychJMTs8VC6tNVRiYizlGyJuDAPD5g500yyAEaMMPvpx8RYG5eIiKSc776DmjXNaUDKljXH8hYqZHVUIvIglGyJuDibDYYNg2nTzApUn30Gzz4LN25YHZmIiCS3iRPh+efNLuShobBhA+TKZXVUIvKglGyJpBJdu8K334KPDyxfbp71PHvW6qhERCQ5xMXBG2+YkxUbhtl1fOlSyJjR6shE5GEo2RJJRZo1MwtnZM0K27ebFQv377c6KhEReRiRkfDCCzBunHn/o49g6lSzK7mIpG5KtkRSmcqVzYHTjz1mzsVVpQqEhVkdlYiIPIgLF8wpPr79Fry84MsvoX9/TVYsklYo2RJJhR57DLZsgapVITwcGjY0C2mIiEjqcfQoVKpkfp8HBJgnzl56yeqoRCQ5KdkSSaWyZTP/Y27TBmJjoUsXePtts9+/iIi4tvXrza7gR49CwYKweTNUq2Z1VCKS3JRsiaRi3t7wxRcwdKh5/6OP4MUXzf7/IiLimj77DOrWhUuXoEIF88pW0aJWRyUiKUHJlkgqZ7PBkCEwbx54esKSJWalwnPnrI5MRERuFxsLb74JnTqZ8yW2amVe4QoMtDoyEUkpSrZE0og2bWD1arNS4bZt8NRTsGuX1VGJiAjAtWvmHImffGLeHzrULIbh62tlVCKS0pRsiaQh1aqZ3VGKFIHTp81KhYsXWx2ViEj6dvKkWUn2xx/NuRIXLjR7JKjioEjap2RLJI15/HGzNHyDBubYrZYtYdgwNxXOEBGxwObN5ris/fvN7oIbNphja0UkfVCyJZIGBQSYZ1DffNO8//777owe/RTXr1salohIuvL55+YY2n//hSefNCejr1DB6qhE5FFSsiWSRrm7w5gxMHcueHkZbN2ah+rVPTh50urIRETStuho6NULXn0VoqKgeXPYtAny5bM6MhF51JRsiaRx7drBmjWxBATcZN8+G089BRs3Wh2ViEjadP481KkDkyaZ94cONavEZshgaVgiYhElWyLpwNNPG4wZs4GyZeO4cAFq14bJk8EwrI5MRCTt2LEDypc3T2hlygTLlpmFMNz0a0sk3dKfv0g6kT37TdaujaVVK3N+l9dfN696RURYHZmISOr3xRdmBdjTp81CRb/+Ck2bWh2ViFhNyZZIOuLnZ87rMnasOaZr3jyzHPHx41ZHJiKSOkVHQ58+5smrW7egcWMz0XriCasjExFXoGRLJJ2x2eCNN8wJkHPmhD17zG4v//uf1ZGJiKQu585BvXowYYJ5f9Ags+tg5szWxiUirkPJlkg6VaOGOb7g6afh8mUIDYXhw9F8XCIiSbBpk1nOff16yJgRvv3W/A7V+CwRuZ3LfCV8+OGH2Gw2+vTpY1/XtWtXChcujK+vLzly5KBZs2YcOnTI4XE2my3BbeHChQ77rF+/nrJly+Lt7U1ISAhz5859BEck4vry5TMn2OzWzSyWMWQINGsGV65YHZmIiGsyDHNajZo14cwZKFrU7DbYvLnVkYmIK3KJZGv79u1Mnz6dUqVKOawvV64cc+bM4ffff2flypUYhkG9evWIjY112G/OnDmcOXPGfnv22Wft206cOEFoaCg1a9Zk9+7d9OnTh06dOrFy5cpHcWgiLs/bGz79FGbPNpd//NHsVrhzp9WRiYi4litX4LnnoF8/iI2F1q3NRKtoUasjExFXZXmydf36dVq3bs3MmTPJkiWLw7YuXbpQrVo1ChYsSNmyZRk5ciSnT5/m5B2zsgYEBBAYGGi/+fj42LdNmzaN4OBgxo4dS9GiRenZsyctWrRg3Lhxj+LwRFKN9u3hl1+gQAE4dgwqVYKpU1UeXkQEYNcuKFcOli4FLy/zJNW8eWYXQhGRu/GwOoAePXoQGhpKnTp1GDly5F33u3HjBnPmzCE4OJigoKAEz9GpUycKFSpEt27daN++PTabDYAtW7ZQp04dh/3r16/v0F3xTrdu3eLWrVv2++Hh4QBER0cTHR3t7CEmu/gYXCEWSR2S2mZKlYJt26BTJ3d+/NGNHj1g7do4pk2L1YDvdETfMeKstNxmDAPmzrXRq5c7t27ZKFDAYOHCWMqVM4iJsTq61CsttxlJGa7UZpyJwdJka+HChezcuZPt27ffdZ+pU6fSv39/bty4QZEiRQgLC8PLy8u+ffjw4dSqVQs/Pz9WrVrFa6+9xvXr1+nVqxcAZ8+eJVeuXA7PmStXLsLDw4mMjMTX1zfBa44aNYphw4YlWL9q1Sr8/Pwe9HCTXVhYmNUhSCqT1DbTsSPkzFmIzz8vzjffuPHLLxH06/cbhQtfTeEIxZXoO0acldbaTGSkOzNmlGLduvwAlC9/lt69d3LuXDQrVlgcXBqR1tqMpDxXaDMRTkxSajMMazoJnT59mvLlyxMWFmYfq1WjRg3KlCnD+PHj7ftdvXqV8+fPc+bMGcaMGcPff//NL7/84tBV8HaDBw9mzpw5nD59GoDHH3+c9u3bM3DgQPs+K1asIDQ0lIiIiESTrcSubAUFBXHhwgX8/f2T4/AfSnR0NGFhYdStWxdPT0+rw5FU4EHbzK+/2mjd2p0//7Th5WUwZkwcXbvG8f8XjiWN0neMOCsttpldu6BNGw+OHLHh5mYwfHgcb70Vp2qDySQtthlJWa7UZsLDw8mePTtXr169b25g2ZWtHTt2cP78ecqWLWtfFxsby8aNG5k8eTK3bt3C3d2dzJkzkzlzZh577DEqVqxIlixZ+O6773jppZcSfd6nn36aESNGcOvWLby9vQkMDOTcuXMO+5w7dw5/f/9EEy0Ab29vvL29E6z39PS0/MO9navFI67P2TZTpYpZKKN9e/j+e7MbzcaN7syapXlk0gN9x4iz0kKbMQyYOBH694eoKLNq64IFNqpVcwfcrQ4vzUkLbUYeLVdoM868vmXnZ2rXrs2+ffvYvXu3/Va+fHlat27N7t27cXdP+IVmGAaGYThcdbrT7t27yZIliz1ZqlSpEmvWrHHYJywsjEqVKiXvAYmkUVmzmgPCP/kEPDxgyRIoUwY2b7Y6MhGR5PXvv9CkCfTpYyZazZrB7t1QrZrVkYlIamXZla1MmTJRokQJh3UZMmQgW7ZslChRguPHj/P1119Tr149cuTIwV9//cWHH36Ir68vjRo1AuCHH37g3LlzVKxYER8fH8LCwvjggw9466237M/ZrVs3Jk+eTP/+/enQoQNr165l0aJFLF++/JEer0hqZrNB375QuTK0agUnT5o/PgYNgnffNZMwEZHUbN06s5T7mTPmNBhjx8Jrr6Fu0yLyUFy257GPjw+bNm2iUaNGhISE8OKLL5IpUyY2b95Mzpw5AfMS3pQpU6hUqRJlypRh+vTpfPLJJwwZMsT+PMHBwSxfvpywsDBKly7N2LFjmTVrFvXr17fq0ERSraefNs/ytm5tzjEzdChUr24mXyIiqVFMjHniqHZtM9F64gmzKmuPHkq0ROThudT56PXr19uX8+TJw4r7lPpp0KABDRo0uO/z1qhRg127dj1seCKCOVZr/nxo2NA867t5M5Qubc7J1bq11dGJiCTd0aPwyiuwdat5v1MnGD8eMmSwNCwRSUNc9sqWiLi21q3Nq1xVqkB4OLRpY96uqjq8iLg4w4Bp08wTRVu3mieRFi6EmTOVaIlI8lKyJSIPLDgY1q+H4cPB3R0WLDCLZ2zaZHVkIiKJO3MGQkOhe3eIiIBatWDfPnjxRasjE5G0SMmWiDwUDw9zvMOmTWbydfKkOY7rzTchMtLq6ERE/vPNN1CyJPzvf2YRjHHjICwMgoKsjkxE0iolWyKSLCpVMrsVdupkdtH55BN48klzoLmIiJWuXoW2baFFC7h40fxu2rHDLPGuSYpFJCXpK0ZEko2/vznmYflyyJ0bDh82y8W/8w7cY3o8EZEUs3q1eTVr3jwzsXr3XXOcVvHiVkcmIumBki0RSXaNGsH+/WYRjbg4GDUKnnrKvPIlIvIoXL0KnTtD3bpw+jQULmx2dx45Ery8rI5ORNILJVsikiKyZjVLxC9ZAtmzmwPQn3rKLKYRHW11dCKSli1fbl65mjXLvN+zp3myp3JlS8MSkXRIyZaIpKjnn4cDB+C558zJQ4cMgXLlYPt2qyMTkbTm0iVzbFbjxvD33xASAhs2wKRJkDGj1dGJSHqkZEtEUlzOnOYVrvnzIVs28ypXxYrwxhtw44bV0YlIWvDdd1Cs2H9js958E/bsgWrVrI5MRNIzJVsi8kjYbOYYrt9//28s17hxUKIErFxpdXQiklqdPWvOkfXcc3DuHBQtCr/8AmPGgJ+f1dGJSHqnZEtEHqkcOcwrXCtWQP785rxcDRqYXX8uXrQ6OhFJLeLi4NNP4YknYNEic2L1d96BnTvNK+ciIq5AyZaIWKJhQ3MsV69e5lWvefPMM9JffmnO0yUicjd79pjFLl57zaw6WK4c/PorvP8++PhYHZ2IyH+UbImIZTJmhAkTYPNms3LYv/+aXQzr1DG7G4qI3O76dXjrLTO52rYNMmWCiRPN5bJlrY5ORCQhJVsiYrmKFc2uPyNGmGel166F0qXh7bdVQENETN9/bxbAGDsWYmOhRQvzpMzrr5tdCEVEXJGSLRFxCV5e8N57cPCgWbY5Oho++sjsWvjNN+paKJJenTgBzz4LzZqZkxMXLGjOo7V4MeTNa3V0IiL3pmRLRFxKcDD88IN5FrtgQfPHVYsW5hivI0esjk5EHpUbN2DQIPOEy7Jl4OEBAwaYYz0bNbI6OhGRpFGyJSIuqUkT80fVe++ZV71WrjTLxL/3njluQ0TSJsOAr782qwyOHAm3bkHt2rB7N3z4ocq5i0jqomRLRFyWn585jmv/fqhXD6KizGpjjz8Oc+eapZ9FJO3Yswdq1IBWreCvv8yr2998A2FhZhEdEZHURsmWiLi8xx6Dn36Cb7+FwoXhzBlo3x6eego2bbI6OhF5WBcvQo8eZkXBjRvB1xeGDzfHcD73nDk9hIhIaqRkS0RSBZsNmjc3uxZ+/DH4+5sVDKtVgxdeMAfRi0jqcvMmjB5tnkSZOtW8Wt2yJRw6ZI7X8vW1OkIRkYejZEtEUhVvb3OenSNHoGtXcHODJUvM8R1vv21OcCoiri0uDubPhyJFzKIXV69CqVKwbp05Xit/fqsjFBFJHkq2RCRVypkTpk0zB83Xrm2O5/roI/MM+SefmGfMRcT1rF1rdgF+5RU4dQry5TPHYO7caY7XEhFJS5RsiUiqVrKkOXj+++/Nq1sXL8Kbb5pnzOfONSc/FRHr7dsHoaHmyZGdOyFTJvjgA/jjD2jXThMTi0japGRLRFI9m80sFb9vH8yaZU50euqUWUSjVClzjh5NiixijSNH4OWXoXRpWLHCnC+rZ084dgwGDtS4LBFJ25RsiUia4eEBHTuaP+5Gj4YsWcxqZs8+C888Y44HEZFH488/zb/HokXhq6/MEx4tWphFbiZNghw5rI5QRCTlKdkSkTTH1xf69YPjx/87c755M9SqZY4JWb/e6ghF0q4zZ8wrV489BrNnm115Q0PNroOLF5vz5ImIpBdKtkQkzQoIMMeEHD1qzuHj5QUbNkDNmuZtwwarIxRJOy5d8mbAADcKF4YpUyA62hyftXkz/PgjPPmk1RGKiDx6SrZEJM3LkwcmTzbHiLz2mpl0rV9vXuWqVcucRFVEHsypU9C7txtdu9Zl3Dh3IiOhUiWz6uDq1eayiEh6pWRLRNKNfPnMM+5Hj/6XdK1bB9Wrm7f//U+FNESS6uhR6NTJnG7h00/diY52p2LFOFasgF9+Ma8ei4ikd0q2RCTdCQr6L+nq3h08Pc2rW40amV2dvvoKYmKsjlLENR04YM6RVaQIfPaZ+bdSs2YcI0b8woYNsTRsaFYIFRERJVsiko4FBcHUqXDihDk3V8aMsGePWab68cfh008hMtLqKEWsZxhmt8BGjaBECZg/H+LizPu//AIrV8ZSsuQFJVkiIndQsiUi6V7evDBmjFmqesQIyJ7dTMBeew0KFjTXnT9vdZQij150NHz5JZQrZxa7+N//zKtWzz0Hv/0Gy5dD5cpWRyki4rqUbImI/L+sWeG998yka9IkyJ/fTLIGDzaXO3Qwr3yJpHXh4TB2rDkeq3Vr2LXLnEKhRw/44w/45hszARMRkXtTsiUicgc/P3OeoKNHYcECeOopuHUL5syBMmXMgf9Ll5rzB4mkJQcOmFd08+aFt96C06chVy4YOdJcnjwZQkKsjlJEJPVQsiUicheenub4rW3bzLmCWrYEd3ezbHzz5uakrWPGwIULVkcq8uCio2HJEvMkQokS5ljF69ehaFGYNQtOnoR334Vs2ayOVEQk9VGyJSJyHzabOVfQ11+bY7kGDIAsWczlfv3MqwCtW5sVDVU6XlKLM2dg+HBzXOILL5gnEdzdzfFYa9aYV7k6dgQfH6sjFRFJvZRsiYg4ISgIPvwQ/voLZswwx61ERZlFBKpXh+LFYfx4uHTJ6khFEoqJge+/h2bNzLY8ZAj88w/kzGmOVzx50hyPVauWyreLiCQHJVsiIg/Azw86dzYrsm3fbk7umiED/P479O3739WuVas0tkus98cf8PbbZoLVrJmZcMXGmpUEFyyAU6fMqpv58lkdqYhI2qJkS0TkIZUvDzNnmlcIpk6F0qXh5k3zalf9+lCggPlD9/ffrY5U0pOrV82iLtWqmRMQf/QRnD0LOXKY88odOGDOkfXyy+DtbXW0IiJpk5ItEZFk4u8P3bubZbK3bTOrumXJAn//bf7QLVYMKlSAKVNUVENSxq1b8N135hisXLnM6Qo2bQI3N3MC4m++MbvAjhljtkcREUlZSrZERJKZzfZfUnXmjFnprWlT8PAwuxz27AmBgeZVr88+0/gueTixsbBundmVNVcus8DFkiVm4vXEE/D++2Y3weXLzW1eXlZHLCKSfnhYHYCISFrm7Q3PP2/ezp+HhQvhiy9gxw5zPNeqVdCtG9Sta5aWf/ZZCAiwOmpxddHRsGEDfPutOefbmTP/bcubF156yeweWKaMCl2IiFhJyZaIyCOSMyf06mXejh6FRYvM25498L//mbcuXaBOHfNKWJMm5g9nEYDISAgLMxOs77+Hy5f/2xYQAC1amEVZqlY1S7iLiIj1lGyJiFggJATeece8HT78X+K1f/9/iVf37mZp+aZNzVvp0rpKkd6cOQM//QQrVpht4saN/7Zlz25eCX3uObNUu4pciIi4HiVbIiIWK1IEBg0ybwcPmlctvv8etm41uxvu2GHOhxQUBKGhZpfDWrXU3TAtio01i6vEJ1c7dzpuDwoyk6vmzeGZZ3QFS0TE1SnZEhFxIcWKmbe33zbHeC1fbiZeq1bB6dMwbZp5c3Mzi3DUq2cmX08/DZ6eVkcvzjIMs0vpunWwdq35Od/ePRDgqaegYUNo3NicZkBXN0VEUg8lWyIiLipnTmjf3rxFRv73Y3zVKjh0yLzytXUrDB8OmTJB9ermeJ2qVc3uh6o655pOnvwvuVq3zpwa4HZZsphJdKNGZsXKXLksCVNERJKBy5R+//DDD7HZbPTp08e+rmvXrhQuXBhfX19y5MhBs2bNOHTokMPjTp06RWhoKH5+fuTMmZN+/foRExPjsM/69espW7Ys3t7ehISEMHfu3EdwRCIiycfX1+xCOGGCOTnyqVNm2fhWrSBbNrh2DX78EQYMgMqVzS6GNWvC4MFmUYVr16w+gvQpKsos9z9xolkdsGBBCA4257+aP99MtLy8zImHhwwx58SKr1rZtq0SLRGR1M4lrmxt376d6dOnU6pUKYf15cqVo3Xr1uTPn59Lly4xdOhQ6tWrx4kTJ3B3dyc2NpbQ0FACAwPZvHkzZ86coW3btnh6evLBBx8AcOLECUJDQ+nWrRsLFixgzZo1dOrUidy5c1O/fn0rDldE5KEFBZk/2Dt0gLg42L3bLAW+aZN5u3AB1q83b2B2PSta1OyG9tRT5q10afDxsfAg0hjDgBMnzEmtt26FLVvM8XY3bzru5+Fhvv81a5pj7ypVAj8/a2IWEZGUZXmydf36dVq3bs3MmTMZOXKkw7YuXbrYlwsWLMjIkSMpXbo0J0+epHDhwqxatYqDBw+yevVqcuXKRZkyZRgxYgQDBgxg6NCheHl5MW3aNIKDgxk7diwARYsW5eeff2bcuHFKtkQkTXBzg7JlzVvfvuaP/kOH/ku8Nm2CP/80i28cPGjO8wXmj/6SJc0uhyVKmLeSJc1ubHJvUVHme7l7t5lc7d5t3sLDE+6bNStUrGjeKlUyx9dlyvSIAxYREUtYnmz16NGD0NBQ6tSpkyDZut2NGzeYM2cOwcHBBAUFAbBlyxZKlixJrtv6WdSvX5/u3btz4MABnnzySbZs2UKdOnUcnqt+/foO3RXvdOvWLW7dumW/H/7//3tGR0cTHR39IIeZrOJjcIVYJHVQm0l/QkLMW/v25v2zZ2HHDhu//WZj507z33//tbFrl5ks3C57dg9y567MypVQvHgsISEGhQoZBAWlv+p3V67A4cM2Dh+O/9e8HTsGMTEJK1V4eRkUKwbly8dRsaLB008bPP54wqIWae1PUd8x4iy1GXGWK7UZZ2KwNNlauHAhO3fuZPv27XfdZ+rUqfTv358bN25QpEgRwsLC8Pr/Ud9nz551SLQA+/2zZ8/ec5/w8HAiIyPx9fVN8JqjRo1i2LBhCdavWrUKPxfq6xEWFmZ1CJLKqM2kbzbbf10Iu3SBCxd8OXIkgJMnM/Pnn/6cOpWJs2czcOGCjQsXcrBvn+PjPTxiyZUrgty5bxAYeIPAwAiyZo0ke/abZM0aSZYst3B3N6w5uAcUGenOv//6cf7/2rvzqKjO8w/gX4ZlQGYAQYFBENGgaHBBUTLBqFXca6K2iaXUoEGtCBGlcal7alXUaK3BatRWmtZE8aRERW3CUYMLiMqSuEtwQwRJZF8UZN7fH/Pz1nFQmSSTGfD7Oeeemfve977z3MtzkMd77zvFdigubvX/71vhu+/scPduK5SXP/0+S3v7Ovj4lKNjx3L4+FTAx6cc7dpVwtr6f+cgL0+7vCj4O4YMxZwhQ5lDztTU1DS5r8mKrfz8fMTExCAlJQW2z3hoICwsDEOHDkVhYSE++OADvPXWWzh58uQz9/mx/vjHPyI2NlZar6iogJeXF4YNGwYHBwejfW5T1dfXIyUlBUOHDoU153qmJmDOUFPV1DzE+fMPkZh4CUB3fPutJfLyLHD9OlBXZ4mCAiUKChq/B04mE3B3Bzw8BFQq7cQdzs4CLi6Ai4uAs7O2zclJwN4esLfXPqvUqtWPv2LW0KC9ha+iAigvByoqLKT35eUWuHtXO/HE3bsWKC4Giou1bdXVz59HvV07gS5dHi1Aly4CnTsLeHpawMLCCYDTjwu+BeDvGDIUc4YMZU45U9HYPeNPYbJiKzMzE8XFxejdu7fU1tDQgGPHjiE+Ph4PHjyApaUlHB0d4ejoCF9fX7zyyito3bo1kpKSEBoaCnd3d5w+fVpn3Lt37wIA3N3dpddHbY/3cXBwaPSqFgDI5XLI5XK9dmtra5P/cB9nbvGQ+WPO0PM4Omq/v+v77/MxalR3WFtrJ61taABu39Z+J1Renvb1+nXtbHoFBcCdO9rb6u7cAe7cMfyLoORybfFlZ6ctvGQy3eVRMVZXp7s8eKB9fWISWoM4OQHe3tqZAh+9Pnrv6wsolRYA+OVWTcHfMWQo5gwZyhxyxpDPN1mxNWTIEJx74h6VyZMnw8/PD/PmzYNlI//NKYSAEEJ6nkqtVmPFihUoLi6Gq6srAO2lRQcHB3Tr1k3qc/DgQZ1xUlJSoFarjXFYREQtkqWltvjw9gaGDNHfrtForxzdvq0tvgoLgXv3Gl9KS4GaGu3yyIMH2uXHsrUFHBy0RaOj4//eu7pqp1FvbDGDGxaIiKiFMlmxpVQq4e/vr9Nmb28PFxcX+Pv749q1a9i9ezeGDRuGtm3b4vbt24iLi4OdnR1GjRoFABg2bBi6deuGiRMnYs2aNSgqKsKiRYsQFRUlXZmaPn064uPjMXfuXLzzzjs4cuQIEhMTceDAgZ/9mImIWiqZDHB31y6BgU3bR6PRToteXa0tvKqrtV/erNHoLg0N2lchtN9JJZdrXx9/L5drZ/hr5KYEIiIikzH5bIRPY2tri+PHj2PDhg0oLS2Fm5sbBgwYgLS0NOkqlqWlJZKTkxEZGQm1Wg17e3uEh4fjT3/6kzSOj48PDhw4gNmzZ+Ovf/0rPD09sX37dk77TkRkYjLZ/57ZIiIiaonMqtj66tG3bwLw8PDQu/2vMd7e3s/tN2jQIGQ/ObcxERERERGREclMHQAREREREVFLxGKLiIiIiIjICFhsERERERERGQGLLSIiIiIiIiNgsUVERERERGQELLaIiIiIiIiMgMUWERERERGREbDYIiIiIiIiMgIWW0REREREREbAYouIiIiIiMgIWGwREREREREZAYstIiIiIiIiI2CxRUREREREZARWpg6gORBCAAAqKipMHIlWfX09ampqUFFRAWtra1OHQ80Ac4YMwXwhQzFnyFDMGTKUOeXMo5rgUY3wLCy2mqCyshIA4OXlZeJIiIiIiIjIHFRWVsLR0fGZfSxEU0qyF5xGo8GdO3egVCphYWFh6nBQUVEBLy8v5Ofnw8HBwdThUDPAnCFDMF/IUMwZMhRzhgxlTjkjhEBlZSU8PDwgkz37qSxe2WoCmUwGT09PU4ehx8HBweTJRs0Lc4YMwXwhQzFnyFDMGTKUueTM865oPcIJMoiIiIiIiIyAxRYREREREZERsNhqhuRyOZYuXQq5XG7qUKiZYM6QIZgvZCjmDBmKOUOGaq45wwkyiIiIiIiIjIBXtoiIiIiIiIyAxRYREREREZERsNgiIiIiIiIyAhZbRERERERERsBiq5nZtGkTOnToAFtbWwQFBeH06dOmDonMxKpVq9C3b18olUq4urpi7NixuHLlik6f+/fvIyoqCi4uLlAoFPjVr36Fu3fvmihiMidxcXGwsLDArFmzpDbmCzWmoKAAv/vd7+Di4gI7Ozt0794dZ8+elbYLIbBkyRKoVCrY2dkhJCQEubm5JoyYTKWhoQGLFy+Gj48P7Ozs0KlTJyxfvhyPz83GfKFjx45hzJgx8PDwgIWFBT7//HOd7U3JkZKSEoSFhcHBwQFOTk6IiIhAVVXVz3gUT8diqxnZvXs3YmNjsXTpUmRlZaFnz54YPnw4iouLTR0amYHU1FRERUXh1KlTSElJQX19PYYNG4bq6mqpz+zZs7F//37s2bMHqampuHPnDsaPH2/CqMkcnDlzBh999BF69Oih0858oSeVlpYiODgY1tbWOHToEC5evIh169ahdevWUp81a9Zg48aN2LJlCzIyMmBvb4/hw4fj/v37JoycTGH16tXYvHkz4uPjcenSJaxevRpr1qzBhx9+KPVhvlB1dTV69uyJTZs2Nbq9KTkSFhaGCxcuICUlBcnJyTh27BimTZv2cx3CswlqNvr16yeioqKk9YaGBuHh4SFWrVplwqjIXBUXFwsAIjU1VQghRFlZmbC2thZ79uyR+ly6dEkAEOnp6aYKk0yssrJS+Pr6ipSUFDFw4EARExMjhGC+UOPmzZsn+vfv/9TtGo1GuLu7i7Vr10ptZWVlQi6Xi08//fTnCJHMyOjRo8U777yj0zZ+/HgRFhYmhGC+kD4AIikpSVpvSo5cvHhRABBnzpyR+hw6dEhYWFiIgoKCny32p+GVrWairq4OmZmZCAkJkdpkMhlCQkKQnp5uwsjIXJWXlwMAnJ2dAQCZmZmor6/XySE/Pz+0b9+eOfQCi4qKwujRo3XyAmC+UOP27duHwMBAvPnmm3B1dUVAQAC2bdsmbb9+/TqKiop08sbR0RFBQUHMmxfQq6++isOHD+Pq1asAgK+//honTpzAyJEjATBf6PmakiPp6elwcnJCYGCg1CckJAQymQwZGRk/e8xPsjJ1ANQ033//PRoaGuDm5qbT7ubmhsuXL5soKjJXGo0Gs2bNQnBwMPz9/QEARUVFsLGxgZOTk05fNzc3FBUVmSBKMrVdu3YhKysLZ86c0dvGfKHGXLt2DZs3b0ZsbCwWLFiAM2fOYObMmbCxsUF4eLiUG439W8W8efHMnz8fFRUV8PPzg6WlJRoaGrBixQqEhYUBAPOFnqspOVJUVARXV1ed7VZWVnB2djaLPGKxRdQCRUVF4fz58zhx4oSpQyEzlZ+fj5iYGKSkpMDW1tbU4VAzodFoEBgYiJUrVwIAAgICcP78eWzZsgXh4eEmjo7MTWJiInbu3IlPPvkEL7/8MnJycjBr1ix4eHgwX+iFwdsIm4k2bdrA0tJSbyawu3fvwt3d3URRkTmKjo5GcnIyjh49Ck9PT6nd3d0ddXV1KCsr0+nPHHoxZWZmori4GL1794aVlRWsrKyQmpqKjRs3wsrKCm5ubswX0qNSqdCtWzedtq5du+LWrVsAIOUG/60iAJgzZw7mz5+P3/zmN+jevTsmTpyI2bNnY9WqVQCYL/R8TckRd3d3vcniHj58iJKSErPIIxZbzYSNjQ369OmDw4cPS20ajQaHDx+GWq02YWRkLoQQiI6ORlJSEo4cOQIfHx+d7X369IG1tbVODl25cgW3bt1iDr2AhgwZgnPnziEnJ0daAgMDERYWJr1nvtCTgoOD9b5S4urVq/D29gYA+Pj4wN3dXSdvKioqkJGRwbx5AdXU1EAm0/1T09LSEhqNBgDzhZ6vKTmiVqtRVlaGzMxMqc+RI0eg0WgQFBT0s8esx9QzdFDT7dq1S8jlcpGQkCAuXrwopk2bJpycnERRUZGpQyMzEBkZKRwdHcVXX30lCgsLpaWmpkbqM336dNG+fXtx5MgRcfbsWaFWq4VarTZh1GROHp+NUAjmC+k7ffq0sLKyEitWrBC5ubli586dolWrVuLf//631CcuLk44OTmJvXv3im+++Ua88cYbwsfHR9TW1powcjKF8PBw0a5dO5GcnCyuX78u/vOf/4g2bdqIuXPnSn2YL1RZWSmys7NFdna2ACDWr18vsrOzxc2bN4UQTcuRESNGiICAAJGRkSFOnDghfH19RWhoqKkOSQeLrWbmww8/FO3btxc2NjaiX79+4tSpU6YOicwEgEaXHTt2SH1qa2vFjBkzROvWrUWrVq3EuHHjRGFhoemCJrPyZLHFfKHG7N+/X/j7+wu5XC78/PzE1q1bdbZrNBqxePFi4ebmJuRyuRgyZIi4cuWKiaIlU6qoqBAxMTGiffv2wtbWVnTs2FEsXLhQPHjwQOrDfKGjR482+vdLeHi4EKJpOXLv3j0RGhoqFAqFcHBwEJMnTxaVlZUmOBp9FkI89jXeRERERERE9JPgM1tERERERERGwGKLiIiIiIjICFhsERERERERGQGLLSIiIiIiIiNgsUVERERERGQELLaIiIiIiIiMgMUWERERERGREbDYIiKiJuvQoQM2bNjwzD4WFhb4/PPPf9TnLFu2DL169fpRY5iTQYMGYdasWT/ZeE05P5MmTcLYsWN/ss9sTF1dHV566SWkpaU1eZ9XXnkFn332mRGjIiIyHyy2iIhauKf9oZ+QkAAnJyeDxjpz5gymTZv20wRGP9h7772Hw4cPmzoMbNmyBT4+Pnj11VebvM+iRYswf/58aDQaI0ZGRGQeWGwREVGTtW3bFq1atTJ1GCZRV1dn6hAkCoUCLi4uJo1BCIH4+HhEREQYtN/IkSNRWVmJQ4cOGSkyIiLzwWKLiIgA/O+2sw8++AAqlQouLi6IiopCfX291OfJ2whzc3MxYMAA2Nraolu3bkhJSdEbd968eejcuTNatWqFjh07YvHixTpjAkBcXBzc3NygVCoRERGB+/fv642zfft2dO3aFba2tvDz88Pf/vY3aVtdXR2io6OhUqlga2sLb29vrFq16rnH+v7776Nt27ZwcHDA9OnTdQqqQYMGITo6GrNmzUKbNm0wfPhwAEBqair69esHuVwOlUqF+fPn4+HDh9J+1dXVePvtt6FQKKBSqbBu3Tq9z2/sVksnJyckJCRI67dv30ZoaCicnZ1hb2+PwMBAZGRkANC/jbChoQGxsbFwcnKCi4sL5s6dCyGEzvj//e9/0b9/f6nPL3/5S+Tl5f3gc5iZmYm8vDyMHj1aavv444+hUCiQm5srtc2YMQN+fn6oqakBAFhaWmLUqFHYtWvXU8cmImoprEwdABERmY+jR49CpVLh6NGj+PbbbzFhwgT06tULU6dO1eur0Wgwfvx4uLm5ISMjA+Xl5Y3erqhUKpGQkAAPDw+cO3cOU6dOhVKpxNy5cwEAiYmJWLZsGTZt2oT+/fvjX//6FzZu3IiOHTtKY+zcuRNLlixBfHw8AgICkJ2djalTp8Le3h7h4eHYuHEj9u3bh8TERLRv3x75+fnIz89/5rEePnwYtra2+Oqrr3Djxg1MnjwZLi4uWLFihdTnn//8JyIjI3Hy5EkAQEFBAUaNGoVJkybh448/xuXLlzF16lTY2tpi2bJlAIA5c+YgNTUVe/fuhaurKxYsWICsrCyDnkGrqqrCwIED0a5dO+zbtw/u7u7Iysp66q1369atQ0JCAv7xj3+ga9euWLduHZKSkjB48GCpT3V1NWJjY9GjRw9UVVVhyZIlGDduHHJyciCTyQw+h8ePH0fnzp2hVCqltrfffhvJyckICwtDWloavvjiC2zfvh3p6ek6V0T79euHuLi4Jp8PIqJmSxARUYs2cOBAERMTo9e+Y8cO4ejoKK2Hh4cLb29v8fDhQ6ntzTffFBMmTJDWvb29xV/+8hchhBBffPGFsLKyEgUFBdL2Q4cOCQAiKSnpqfGsXbtW9OnTR1pXq9VixowZOn2CgoJEz549pfVOnTqJTz75RKfP8uXLhVqtFkII8e6774rBgwcLjUbz1M99XHh4uHB2dhbV1dVS2+bNm4VCoRANDQ1CCO15CwgI0NlvwYIFokuXLjqfs2nTJmm/yspKYWNjIxITE6Xt9+7dE3Z2djo/g8bOkaOjo9ixY4cQQoiPPvpIKJVKce/evUbjX7p0qc75UalUYs2aNdJ6fX298PT0FG+88cZTz8F3330nAIhz584JIQw/hzExMWLw4MF67SUlJcLT01NERkYKNzc3sWLFCr0+e/fuFTKZTDrXREQtFW8jJCIiycsvvwxLS0tpXaVSobi4uNG+ly5dgpeXFzw8PKQ2tVqt12/37t0IDg6Gu7s7FAoFFi1ahFu3bumMExQUpLPP4+NUV1cjLy8PERERUCgU0vLnP/9Zug1u0qRJyMnJQZcuXTBz5kx8+eWXzz3Wnj176lxtUavVqKqq0rma06dPH71jVqvVsLCwkNqCg4NRVVWF27dvIy8vD3V1dTrH4+zsjC5dujw3nsfl5OQgICAAzs7Oz+1bXl6OwsJCnc+0srJCYGCgTr/c3FyEhoaiY8eOcHBwQIcOHQBA+lkYeg5ra2tha2ur1966dWv8/e9/x+bNm9GpUyfMnz9fr4+dnR00Gg0ePHjw3OMjImrOWGwREbVwDg4OKC8v12svKyuDo6OjTpu1tbXOuoWFxY+aNS49PR1hYWEYNWoUkpOTkZ2djYULFxo02URVVRUAYNu2bcjJyZGW8+fP49SpUwCA3r174/r161i+fDlqa2vx1ltv4de//vUPjvsRe3v7Hz1GYywsLPSeqXr8OTY7O7uf/DPHjBmDkpISbNu2DRkZGdLzX49+FoaewzZt2qC0tLTRbceOHYOlpSUKCwtRXV2tt72kpAT29vZGOU4iInPCYouIqIXr0qULsrKy9NqzsrLQuXPnHzxu165dkZ+fj8LCQqntUfHzSFpaGry9vbFw4UIEBgbC19cXN2/e1Bvn0R/+jY3j5uYGDw8PXLt2DS+99JLO4uPjI/VzcHDAhAkTsG3bNuzevRufffYZSkpKnhr/119/jdraWp3PVCgU8PLyeuYxp6en6xRKJ0+ehFKphKenJzp16gRra2ud4yktLcXVq1d1xmnbtq3OecvNzZUmkACAHj16ICcn55nxP+Lo6AiVSqXzmQ8fPkRmZqa0fu/ePVy5cgWLFi3CkCFD0LVr10YLJUPOYUBAAC5fvqxXNKalpWH16tXYv38/FAoFoqOj9fY9f/48AgICnntsRETNHSfIICJq4SIjIxEfH4+ZM2diypQpkMvlOHDgAD799FPs37//B48bEhKCzp07Izw8HGvXrkVFRQUWLlyo08fX1xe3bt3Crl270LdvXxw4cABJSUk6fWJiYjBp0iQEBgYiODgYO3fuxIULF3QmyHj//fcxc+ZMODo6YsSIEXjw4AHOnj2L0tJSxMbGYv369VCpVAgICIBMJsOePXvg7u7+zO8Rq6urQ0REBBYtWoQbN25g6dKliI6Ohkz29P+HnDFjBjZs2IB3330X0dHRuHLlCpYuXYrY2FjIZDIoFApERERgzpw5cHFxgaurKxYuXKg35uDBgxEfHw+1Wo2GhgbMmzdP56piaGgoVq5cibFjx2LVqlVQqVTIzs6Gh4dHo7dqxsTEIC4uDr6+vvDz88P69etRVlYmbW/dujVcXFywdetWqFQq3Lp1S+/2PkPP4S9+8QtUVVXhwoUL8Pf3BwBUVlZi4sSJmDlzJkaOHAlPT0/07dsXY8aM0blKdvz4cQwbNuyp55mIqKXglS0iohauY8eOOHbsGC5fvoyQkBAEBQUhMTERe/bswYgRI37wuDKZDElJSaitrUW/fv0wZcoUnZn8AOD111/H7NmzER0djV69eiEtLQ2LFy/W6TNhwgQsXrwYc+fORZ8+fXDz5k1ERkbq9JkyZQq2b9+OHTt2oHv37hg4cCASEhKkK1tKpRJr1qxBYGAg+vbtixs3buDgwYPPLJyGDBkCX19fDBgwABMmTMDrr78uzSj4NO3atcPBgwdx+vRp9OzZE9OnT5cKtkfWrl2L1157DWPGjEFISAj69++v9+zXunXr4OXlhddeew2//e1v8d577+k8P2ZjY4Mvv/wSrq6uGDVqFLp37464uDid5+ke94c//AETJ05EeHg41Go1lEolxo0bJ22XyWTYtWsXMjMz4e/vj9mzZ2Pt2rU6Yxh6Dl1cXDBu3Djs3LlTaouJiYG9vT1WrlwJAOjevTtWrlyJ3//+9ygoKACgndExLS0NkydPfua5JiJqCSzEk9f/iYiIWrhJkyahrKxM77uuyDDffPMNhg4diry8PCgUiibtM2/ePJSWlmLr1q1Gjo6IyPR4ZYuIiIh+kB49emD16tW4fv16k/dxdXXF8uXLjRgVEZH54JUtIiJ64fDKFhER/RxYbBERERERERkBbyMkIiIiIiIyAhZbRERERERERsBii4iIiIiIyAhYbBERERERERkBiy0iIiIiIiIjYLFFRERERERkBCy2iIiIiIiIjIDFFhERERERkRGw2CIiIiIiIjKC/wOnxA/aDdWWUQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Definir y graficar la función"
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def costo_por_unidad(x):\n",
+    "    return 0.1 * x**2 - 9 * x + 4500\n",
+    "\n",
+    "x = np.linspace(0, 100, 1000)\n",
+    "y = costo_por_unidad(x)\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.plot(x, y, label='f(x) = 0.1x² - 9x + 4500', color='blue')\n",
+    "plt.xlabel('Unidades producidas (x)')\n",
+    "plt.ylabel('Costo por unidad')\n",
+    "plt.title('Costo de producción por unidad vs Unidades producidas')\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -503,25 +728,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "9e200c32",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matriz A:\n",
+      " [[1 2]\n",
+      " [3 4]]\n",
+      "Matriz B:\n",
+      " [[4 5]\n",
+      " [6 7]]\n",
+      "A + B =\n",
+      ", [[ 5  7]\n",
+      " [ 9 11]]\n",
+      "A - B =\n",
+      ", [[-3 -3]\n",
+      " [-3 -3]]\n"
+     ]
+    }
+   ],
    "source": [
-    "# import numpy as np\n",
-    "\n",
-    " \n",
-    " \n",
-    "# Crear la primera matriz\n",
-    "\n",
-    " \n",
-    "# Crear la segunda matriz\n",
-    "\n",
-    " \n",
-    "# Imprimir elementos\n",
+    "import numpy as np\n",
     "\n",
-    " \n",
-    "# Sumar ambas matrices\n"
+    "A = np.array([[1, 2], [3, 4]])\n",
+    "B = np.array([[4, 5], [6, 7]])\n",
+    "print(\"Matriz A:\\n\", A)\n",
+    "print(\"Matriz B:\\n\", B)\n",
+    "sumaAB = A + B\n",
+    "print(f\"A + B =\\n, {sumaAB}\")\n",
+    "restaAB = A - B\n",
+    "print(f\"A - B =\\n, {restaAB}\")\n"
    ]
   },
   {
@@ -579,68 +819,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "681930a3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original list 1 : [1, 3, 4, 6, 8]\n",
+      "Original list 2 : [4, 5, 6, 2, 10]\n",
+      "Comprensión listas: [5, 8, 10, 8, 18]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Usar comprensión de listas para realizar la suma de las dos listas:\n",
-    "\n",
-    "\n",
-    "# Inicializando listas\n",
-    "\n",
-    " \n",
-    "# Imprimir listas originales\n",
-    "\n",
-    " \n",
-    "# Usando comprensión de listas para sumar dos listas\n",
-    "\n",
-    " \n",
-    "# Imprimir lista resultante\n"
+    "list1 = [1, 3, 4, 6, 8]\n",
+    "list2 = [4, 5, 6, 2, 10]\n",
+    "print (\"Original list 1 : \" + str(list1))\n",
+    "print (\"Original list 2 : \" + str(list2))\n",
+    "sumacomp = [a + b for a, b in zip(list1, list2)]\n",
+    "print(\"Comprensión listas:\", sumacomp)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "a3a8a425",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original list 1 : [1, 3, 4, 6, 8]\n",
+      "Original list 2 : [4, 5, 6, 2, 10]\n",
+      "Suma por map: [5, 8, 10, 8, 18]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Usar map() + add():\n",
-    "\n",
-    "\n",
-    "# Inicializando listas\n",
-    "\n",
-    " \n",
-    "# Imprimir listas originales\n",
-    "\n",
-    " \n",
-    "# Usando map() + add() para sumar dos listas\n",
-    "\n",
-    " \n",
-    "# Imprimir lista resultante"
+    "list1 = [1, 3, 4, 6, 8]\n",
+    "list2 = [4, 5, 6, 2, 10]\n",
+    "print (\"Original list 1 : \" + str(list1))\n",
+    "print (\"Original list 2 : \" + str(list2))\n",
+    "sumamap = list(map(lambda x, y: x + y, list1, list2))\n",
+    "print(\"Suma por map:\", sumamap)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "1708d7ee",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original list 1 : [1, 3, 4, 6, 8]\n",
+      "Original list 2 : [4, 5, 6, 2, 10]\n",
+      "Suma por zip: [5, 8, 10, 8, 18]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Usar zip() + sum():\n",
-    "\n",
-    "\n",
-    "# Inicializando listas\n",
-    "\n",
-    " \n",
-    "# Imprimir listas originales\n",
-    "\n",
-    " \n",
-    "# Usando zip() + sum() para sumar dos listas\n",
-    "\n",
-    " \n",
-    "# Imprimir lista resultante"
+    "list1 = [1, 3, 4, 6, 8]\n",
+    "list2 = [4, 5, 6, 2, 10]\n",
+    "print (\"Original list 1 : \" + str(list1))\n",
+    "print (\"Original list 2 : \" + str(list2))\n",
+    "sumazip = [a + b for a, b in zip(list1, list2)]\n",
+    "print(\"Suma por zip:\", sumazip)"
    ]
   },
   {
@@ -666,10 +915,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "840e7d0e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[24, 33, 28], [33, 36, 23], [25, 29, 23]]\n"
+     ]
+    }
+   ],
    "source": [
     "# Usando un bucle for para ingresar dos matrices de tamaño n x m\n",
     "matrix1 = [[1,7,3],\n",
@@ -694,21 +951,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "db6c3355",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[24 33 28]\n",
+      " [33 36 23]\n",
+      " [25 29 23]]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Importar bibliotecas\n",
-    "\n",
-    " \n",
-    "# Ingresar dos matrices\n",
-    "\n",
-    " \n",
-    "# Esto devolverá el producto punto\n",
+    "import numpy as np\n",
     "\n",
-    " \n",
-    "# Imprimir la matriz resultante\n"
+    "matrix1 = np.array([[1, 7, 3], [4, 5, 2], [3, 6, 1]])\n",
+    "matrix2 = np.array([[5, 4, 1], [1, 2, 3], [4, 5, 2]])\n",
+    "multi = np.dot(matrix1, matrix2)\n",
+    "print(multi)"
    ]
   },
   {
@@ -729,11 +992,8 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "d3463682613d55fcbb64853e38cc3520a7f67bdf8d6940e781ddcdc423122719"
-  },
   "kernelspec": {
-   "display_name": "Python 3.9.12 ('calculus-project')",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -747,7 +1007,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,