diff --git a/module3/exo3/exercice_en.ipynb b/module3/exo3/exercice_en.ipynb
index 1c05f032e7fc74c819a754ad03d601187c14f444..4ed08fb3214812e57e093275ef16ceeee759ab8d 100644
--- a/module3/exo3/exercice_en.ipynb
+++ b/module3/exo3/exercice_en.ipynb
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -221,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -288,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -326,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -364,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -383,7 +383,7 @@
    "source": [
     "# Slow contribution. Creating the graph to show the average CO2 concentration per year over time\n",
     "plt.figure(figsize=(10, 6))\n",
-    "plt.plot(annual_mean_co2.index, annual_mean_co2.values, marker='o', linestyle='-', label='Mean CO2 Concentration per Year Over Time')\n",
+    "plt.plot(annual_mean_co2.index, annual_mean_co2.values, label='Mean CO2 Concentration per Year Over Time', marker='o', linestyle='-')\n",
     "plt.title('Mean CO2 Concentration per Year Over Time')\n",
     "plt.xlabel('Year')\n",
     "plt.ylabel('CO2 Concentration (ppm)')\n",
@@ -402,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -425,9 +425,16 @@
     "print(data.head())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A graph is made with the oscilation of the CO2 concentration over the time"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -444,7 +451,7 @@
     }
    ],
    "source": [
-    "# Periodic oscillation. Creation of the graph to monitor the oscillation of the CO2 concentration over the time\n",
+    "# Periodic oscillation. Creation of the graph to show the oscillation of the CO2 concentration over the time\n",
     "plt.figure(figsize=(10, 6))\n",
     "plt.plot(data['Date'], data['Oscilation'], label='Oscilation CO2 Concentration Over Time')\n",
     "plt.title('Oscilation CO2 Concentration Over Time')\n",
@@ -454,6 +461,13 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For a better visualization of the periodic oscillation, only the last 300 rows of the table are graphed."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 41,
@@ -473,7 +487,7 @@
     }
    ],
    "source": [
-    "# Periodic oscillation. Creation of the graph to monitor the oscillation of the CO2 concentration over the time (last 300 rows of the table)\n",
+    "# Periodic oscillation. Creation of the graph to show the oscillation of the CO2 concentration over the time (last 300 rows of the table)\n",
     "plt.figure(figsize=(10, 6))\n",
     "plt.plot(data['Date'][-300:], data['Oscilation'][-300:], label='Oscilation CO2 Concentration Over Time')\n",
     "plt.title('Oscilation CO2 Concentration Over Time')\n",
@@ -494,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -521,28 +535,28 @@
     }
    ],
    "source": [
-    "# Contribution lente. Modéliser une évolution lente en utilisant la régression linéaire\n",
-    "X = np.arange(len(data)).reshape(-1, 1)  # Variable indépendante : nombre de semaines\n",
-    "y = data['Mean_CO2_Concentration'].values.reshape(-1, 1) # Variable dépendante : concentration de CO2\n",
+    "# Slow contribution. Modelling slow change using linear regression\n",
+    "X = np.arange(len(data)).reshape(-1, 1)  # Independent variable: number of weeks\n",
+    "y = data['Mean_CO2_Concentration'].values.reshape(-1, 1) # Dependent variable: CO2 concentration\n",
     "\n",
-    "# Ajuster le modèle de régression linéaire\n",
+    "# Fit the linear regression model\n",
     "model = LinearRegression()\n",
     "model.fit(X, y)\n",
     "\n",
-    "# Paramètres du modèle de régression linéaire\n",
-    "# Obtenez les coefficients de régression et l'interception\n",
+    "# Linear regression model parameters\n",
+    "# Get the regression coefficients and the intercept\n",
     "coeficiente = model.coef_[0][0]\n",
     "intercepto = model.intercept_[0]\n",
     "\n",
-    "# Obtenir le coefficient de détermination (R²)\n",
+    "# Obtain the coefficient of determination (R²)\n",
     "r_cuadrado = model.score(X, y)\n",
     "\n",
-    "# Imprimer les paramètres du modèle\n",
+    "# Print model parameters\n",
     "print(\"Regression coefficient:\", coeficiente)\n",
     "print(\"Intercept:\", intercepto)\n",
     "print(\"R-squared (R²):\", r_cuadrado)\n",
     "\n",
-    "# Prédire la concentration de CO2 en 2025\n",
+    "# Predicting CO2 concentration in 2025\n",
     "weeks_in_2025 = (2025 - data['Date'].dt.year.min()) * 52\n",
     "predicted_CO2_2025 = model.predict([[weeks_in_2025]])\n",
     "\n",
@@ -560,17 +574,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pour amelliorer le document un plot est plus utile que les paramètres de la régression."
+    "To improve the document, a plot is more useful than the regression parameters"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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E119/Tfv27Zk+fTonTpzg+PHjfPXVV7Rr1y7TsZYqVYrQ0FCmT58OwMmTJzlx4gRXX301kyZNIj4+HoBdu3axf//+LO+7ZMmSHDt2LNP2Y8eOUaVKFU6fPs2UKVPOeF69evWIjY1l8+bNAHz44Yd06NAhyzGIiIiI5DYF19nMGMNXX33FDz/8kLpU3vDhw6latSpFixbl66+/ZuTIkdStW5fw8HCaN2/Ogw8+CLiVMuLi4hg0aBCRkZGZBpPvvvsue/fupVatWoSHh3PfffdRtWpVoqKiuPvuu2nRogUtW7akf//+NGnSJMvxfvjhh4wdOzZ1+b+9e/fStWtXbr/9dlq3bk14eDi33HJLloEzuJnpgQMHpr7QmNbzzz9Py5Yt6dKlC/Xq1Us93rt3b0aPHk2TJk1SX6YEl6Ly3nvvceuttxIeHk5ISEjAS6IiIiIieZE50z+/52XNmjWzy5YtCzi2bt066tevn0sjyl7a/jx4zudZ56c/c8EQExNDdHR0bg/joqBnHVx63sGjZx08etY+xpjl1tpmZ+qnmWsRERERCZrkZLiA53bPSMG1iIiIiARNp05w7bW5PYqck69XCxERERGRvGP3bkizcFq+o5lrEREREQmK66/31ZOTc28cOUnBtYiIiIgExYYNvvrRo7k3jpyk4FpEREREgiIlxVc/ciT3xpGTFFzngBIlSqQ7NmHCBD744INcGE3eMmPGDEaNGpXbw0inUaNGHDx4MMs+YWFhZ+wjIiIimUtM9NWXL8+9ceQkvdAYJDm9AYq1Fmttui3RvZKTkylQoMA5X/98z/fq3r073bt3P+/riIiIyIUl7fJ7t9ziO/bSS1CiBDz0UPDHld00cx0kw4cP55VXXgEgOjqaoUOH0qJFC+rUqcOCBQsAF8A+9thjNG/enIiICCZNmgRAfHw8nTt3JioqivDwcL7++msAYmNjqV+/PoMGDSIqKoodO3YEfGdYWBjPPfccbdu25fPPP2fLli1cc801NG3alHbt2rF+/XoAtmzZQqtWrWjevDnDhg1LnXmPiYmhY8eO3H777YSHhwPw0Ucf0aJFCyIjIxkwYADJyckkJydz991306hRI8LDw3n99dcBGDt2LA0aNCAiIoLevXsDMHny5NQdKbdt20bnzp2JiIigc+fObN++HXC7PQ4ePJg2bdpwxRVXMG3atHTPMzY2lnr16tG/f38aNWpEnz59+PHHH7nyyiupXbs2S5YsAeDQoUPcdNNNRERE0KpVK1avXg1AXFwcXbt2pUmTJgwYMAD/zZQyukcRERE5P/HxrmzdOn3bU0/B4MHBHU9Oyd8z10OGwMqV2XvNyEgYM+a8L5OUlMSSJUuYOXMmI0aM4Mcff2TixImULl2apUuXcvLkSVq3bk337t257LLL+OqrryhVqhQHDx6kVatWqbO/GzZs4L333mP8+PEZfk+RIkVYuHAhAJ07d2bChAnUrl2bxYsXM2jQIH766ScefvhhHn74YW677TYmTJgQcP6SJUtYs2YNNWrUYN26dXz66af8/PPPFCpUiEGDBjFlyhQaNmzIrl27WLNmDQCHDx8GYNSoUWzdupXChQunHvP34IMPctddd9G3b18mTZrE4MGDmT59OgB79uxh4cKFrF+/nu7du3PLLbekO3/z5s18/vnnvP322zRv3pypU6eycOFCZsyYwYsvvsj06dN59tlnadKkCdOnT+enn37irrvuYuXKlYwYMYK2bdsybNgwvv32W95++22ATO/xrrvuOpdfs4iIiHj8+acrb74ZfvkFKlfO3fHklPwdXOdhPXv2BKBp06bExsYCMHv2bFavXp06U3v48GE2bdpEaGgoTz31FPPnzyckJIRdu3axb98+AKpXr06rVq0y/Z5evXoBbvZ70aJF3HrrraltJ0+eBOCXX35JDWpvv/12Hn300dQ+LVq0oEaNGgDMmTOH5cuX07x5cwASEhKoVKkSN9xwA3/88QcPPfQQ3bp1o2vXrgBERETQp08fbrrpJm666aZ0Y/vll1/48ssvAbjzzjt5/PHHU9tuuukmQkJCaNCgQeq9plWjRo3UGfWGDRvSuXNnjDGEh4enPtOFCxfyxRdfANCpUyfi4uI4cuQI8+fPT/3ubt26UaZMmSzvUURERM7PoUOuvOIKuO462Ls3fZ/jx6F48eCOK7vl7+A6G2aYc0rhwoUBKFCgAElJSYDLm/7Pf/7D1VdfDcCxY8coWbIkkydP5sCBAyxfvpxChQoRFhZGoueNgOJn+BPobU9JSaFMmTKs/Isz+f7Xt9bSt29fXnrppXT9Vq1axffff8+4ceP47LPPmDRpEt9++y3z589nxowZPP/886xduzbL7zLGpNa9z8f7vRnx7xMSEpL6OSQkJOCZZvY9/t93NvcoIiIimdu+HapXhxkz4IYb0rd7g+ty5aBsWVi3zn32zPVRtSpk8urYBSUf3EL+cfXVV/Pmm29y+vRpADZt2sTx48c5cuQIlSpVolChQsydO5dt27b95WuXKlWKGjVq8PnnnwMuiFy1ahUArVq1Sp3d/eSTTzK9RufOnZk2bRr79+8HXD7ztm3bOHjwICkpKdx88808//zzrFixgpSUFHbs2EHHjh15+eWXOXz4MPHeZCuPNm3apH7flClTaNu27V++rzNp3749U6ZMAVwOeYUKFShVqlTA8VmzZqWmrWR2jyIiIpK1F190ZWbrFnTu7EpvcL11q/v8yCOu7NABihbN2TEGQ47PXBtjCgDLgF3W2uuNMaOBG4BTwBagn7X2sKfvk8C9QDIw2Fr7fU6PLyecOHGC0NDQ1M+PeP/UnEH//v2JjY0lKioKay3lypXjm2++oU+fPtxwww00a9aMyMhI6tWrd07jmjJlCg888AAjR47k9OnT9O7dm8aNGzNmzBjuuOMOXn31Vbp160bp0qUzPL9BgwaMHDmSrl27kpKSQqFChRg3bhxFixalX79+pHgWr3zppZdITk7mjjvu4MiRI1hr+cc//pGaeuE1duxY7rnnHkaPHk3FihV57733zum+sjJ8+HD69etHREQExYoV4/333wfg2Wef5bbbbiMqKooOHTpw2WWXZXmP1atXz/axiYiI5CcnTmTetmuXr162rMu5Bvdq3Jw5rp6QkHNjCyaT2T+5Z9sXGPMI0Awo5QmuuwI/WWuTjDH/ArDWDjXGNAA+BloAVYEfgTrW2kyXamjWrJldtmxZwLF169ZRv379HLqb4PKmheS0EydOULRoUYwxfPLJJ3z88cepK5JcLM7nWeenP3PBEBMTQ3R0dG4P46KgZx1cet7Bo2cdPGf7rFNSwH/F3sOHwX+urmZN+OMPVz9+HD74AB54AD7/HIYOdW3du0NeDj+MMcuttc3O1C9H00KMMaFAN+Bd7zFr7WxrbZLn46+Ad4r3RuATa+1Ja+1WYDMu0JYctnz5ciIjI4mIiGD8+PG8+uqruT0kERERuYCknXX2rPabyhtYg0v9uP12F4wvXQqXXuqOt8gnUV9Op4WMAR4HMpsSvAf41FOvhgu2vXZ6jgUwxtwP3A9QuXJlYmJiAtpLly7NsWPHzmvQeUVycnJQ7iUyMjJ1uT6v/PIMz9b5POvExMR0fw4lc/Hx8XpeQaJnHVx63sGjZx08Z/us582rADRK/fzVV+tJSPAtB9KuXUMWLKjo6euuV6pUG37//SClShmgCq1bx5Affq05FlwbY64H9ltrlxtjojNo/z8gCZjiPZTBZdLlrFhr3wbeBpcWkvafKtatWxeUVIpgCFZaiJzfsy5SpAhNmjTJ5hHlX/rn3ODRsw4uPe/g0bMOHu+zbtoUVqyANWugYcPAPvHx0LFj4LHSpesRHe17R2zXLihVCn7+GRo1igagTBkoU6YqKSlQqxZ06hSdk7cSNDmZFnIl0N0YEwt8AnQyxnwEYIzpC1wP9LG+pO+dwGV+54cCu3NwfCIiIiJyFlascGWzDDKOX3vNV2/WzKV9xMX5jk2d6tJCKlWCRr7JbYoVcy9Bxse7en6RY8G1tfZJa22otTYM6I17ifEOY8w1wFCgu7XW/73SGUBvY0xhY0wNoDawJKfGJyIiIiJn5lm5FwDPNhsBnn3WV583D8qXh4MHfcfuvtuV/foFnle8uNu1ccYMqJYuEfjClRubyLwBFAZ+8Gzi8au1dqC1dq0x5jPgd1y6yN+zWilERERERHLexx9n3ubZmiNVsWJQoULgzHWjRm6G+qmnAvs2bAjeVXi9S/PlB0HZRMZaG2Otvd5Tr2WtvcxaG+n5GejX7wVrbU1rbV1r7aycHlf8qXienfssFUdXJGRECBVHV+TZuc8Sfyr+zCdnoUCBAkRGRtKoUSNuvfVWTmS18GMGrrvuutRNTf6KmJgYFi1alPp5woQJfPDBB3/5OmnFxsYyderUv3TOypUrad26NQ0bNiQiIoJPP/00tW3r1q20bNmS2rVr06tXL06dOgW4dbgjIiKIiIigTZs2qZvcAISFhREeHk5kZCTNMvo3KREREckRWa3a7NlzLUCFCoHH4+Mho1eTqlTx1T3bUOQLF+0OjfGn4mn1biteXvQyB08cxGI5eOIgLy96mVbvtjqvALto0aKsXLmSNWvWcMkllzBhwoSAdmtt6oYrGZk5c2a6DVfORtrgeuDAgdx1111/+TppnUtwXaxYMT744APWrl3Ld999x5AhQ1L/wjB06FD+8Y9/sGnTJsqWLcvEiRMBqFGjBvPmzWP16tU888wz3H///QHXnDt3LitXriTt2uYiIiKSc9asCfzsv+Hy3r2kc/nlbit0cGtab9vmjqV1552+ekbbpV+oLtrgevTPo9ny5xYSkwKThxKTEtny5xZG/zw6W76nXbt2bN68mdjYWOrXr8+gQYOIiopix44dfPzxx4SHh9OoUSOGDh2aek5YWBgHPclKH330ES1atCAyMpIBAwaQnOwyZb777juioqJo3LgxnTt3JjY2lgkTJvD6668TGRnJggULGD58OK+88grgZpJbtWpFREQEPXr04M8//wQgOjqaoUOH0qJFC+rUqcOCBQvS3cMTTzzBggULiIyM5PXXXycxMZF+/foRHh5OkyZNmDt3brpz6tSpQ+3atQGoWrUqlSpV4sCBA1hr+emnn7jlllsA6Nu3L9OnTwfcduhly5YF3JbsO3fuzJbfgYiIiJy7mTMDP2/Z4qvv2+ere/4vnBo1XNCdkAALF8KpU76tz/35bzhtMloz7gKVGznXQTPkuyGs3Lsyw7ZFOxZxOuV0hm2JSYm8tPAl5m2bl64t8tJIxlwz5qy+PykpiVmzZnHNNdcAsGHDBt577z3Gjx/P7t27GTp0KMuXL6ds2bJ07dqV6dOnc9NNN6Wev2HDBj799FN+/vlnChUqxKBBg5gyZQrXXnst9913H/Pnz6dGjRocOnSIcuXKMXDgQEqUKMGjjz4KwBzvfqLAXXfdxX/+8x86dOjAsGHDGDFiBGPGjEkd55IlS5g5cyYjRozgxx9/DLiPUaNG8corr/Df//4XIHWTmd9++43169fTtWtXNm7cSJEiRTJ8DkuWLOHUqVPUrFmTuLg4ypQpQ8GC7o9eaGgou/z3RPWYOHEi1157bepnYwxdu3bFGMOAAQPSzWqLiIjIuTl6FF591eVEFy4c2JaQ4Nt28YEH4M033ZbljRu7Y99+68otWyAszNW95fbtvpchW7bM+LvHjoUGDbLlNvKMfB1cZyWzwPps27OSkJBAZGQk4Gau7733Xnbv3k316tVp1aoVAEuXLiU6OpqKFd2C6n369GH+/PkBwXVMTAzLly+nefPmqdetVKkSv/76K+3bt6dGjRoAlCtXLsvxHDlyhMOHD9OhQwfAzRbfeuutqe09e/YEoGnTpsTGxp7x/hYuXMhDDz0EQL169ahevTobN24kIiIiXd89e/Zw55138v777xMSEoLNIHHLpPnr6ty5c5k4cWLAxjY///wzVatWZf/+/XTp0oV69erRvn37M45VREREshYR4VI3KleGQYMC29asKZVaf/FFF1wfOOBrHz/eldWqQYgnH8IbXG/bBjt3uvWt/bdC9+cJJ7IUfyqe0T+PZvyy8cSdiKN8sfIMajaIx658jBKXlDiT1o1MAAAgAElEQVS7mwyifB1cZzXDXHF0RQ6eOJh5e7GKxNwdc07f6825Tqt48eKp9YyCzLSstfTt25eXXnop4PiMGTPSBaTno7Dnr6kFChQgKSnpDL3PbuwAR48epVu3bowcOTL1LxUVKlTg8OHDJCUlUbBgQXbu3EnVqlVTz1m9ejX9+/dn1qxZlC9fPvW4t0+lSpXo0aMHS5YsUXAtIiKSDbZtc+XBDMKi//u/8NR66dJQrhz8/nv6fv4z3t7gOjbWBdeXXZa+/9nyviPnn8rrfUfui3Vf8Gv/X/NcgH3R5lwPajaIIgUzTmMoUrAIDzR7IEe/v2XLlsybN4+DBw+SnJzMxx9/nDqz7BUdHc20adPY73nl9tChQ2zbto3WrVszb948tm7dmnocoGTJkhlu4V26dGnKli2bmk/94YcfpvuurKS9bvv27ZkyxW2suXHjRrZv307dunUDzjl16hQ9evTgrrvuCpglN8bQsWNHpk2bBsD777/PjTfeCMD27dvp2bMnH374IXXq1Ek95/jx46nff/z4cWbPnk0j/1XoRURE5JwcPeqr//FH+vbTp32hojFQu7bbbdGraFH45z8Dz6lSxc1i79wJO3ZAaOi5jy9Y78hlp4s2uH7syseoWbZmugC7SMEi1Cxbk8eufCxHv79KlSq89NJLdOzYkcaNGxMVFZUaZIILQuvVq8fIkSPp2rUrERERdOnShT179lCxYkXefvttevbsSePGjenVqxcAN9xwA1999VXqC43+3n//fR577DEiIiJYuXIlw4YNO+uxRkREULBgQRo3bszrr7/OoEGDSE5OJjw8nF69ejF58uTU2W+vzz77jPnz5zN58mQiIyOJjIxMnc3/17/+xWuvvUatWrWIi4vj3nvvBeC5554jLi6OQYMGBSy5t2/fPtq2bUvjxo1p0aIF3bp1S81jFxERkXM3bpyv7t2FMSulSvkC8oQE9+P3D80AFCjgluPbu/f8g+vxy8anC6y9EpMSeXPZm+d+8Rxizvaf+POiZs2a2bTLsq1bt4769euf1fneHJ43l71JXEIc5YuW54FmD+RqDk9ycjKVKlVi7969JCYmUrJkyVwZx8Xm2LFj5/ys/8qfOXHvEkRHR+f2MC4KetbBpecdPHrW2WfECBg+3NVLlHCBs3/mqbd+6JBbDeSWW2D9erc8365dLnB+6y1Iu85Aq1aweLGrv/46DBlybuMLGRGCJfNYNcSEkDwsOHsOGmOWW2vPuNlGvs65PpMSl5RgRMcRjOg4IreHkqphw4b079+fQoUKkZjRHqMiIiIi2STZLy6Nj4c9e8D7KlRKChhjefppk7rMXsmSvplr7/xmrVrpr+tZrwEIXHLvrypfrHyW78iVL1o+07bcctGmheRV69ev51//+lduD0NEREQuAmvXutK7ofPu3b62jRvBWkPlyr5j/mkhS5e6FJB27dJf94UXfHXP4mbnJLffkTsXCq5FRERE8qlly1zaRmYOHIAOHXxrTfu/rOjNePSu/gEuuD52zG2JfvCgWz2kUKH01/U/p3r1cx197r8jdy7yZXB9IeeRy4VFf9ZERCQva94cBg4MTP/wevBBWLAAypRx61RDYHDt5Z/2UbasSxfZuBE2bw5M//BXwu/VtUz2mDsrJS4pwa/9f+XxNo9TsVhFQkwIFYtV5PE2j+fJZfggH+ZcFylShLi4OMqXL5+ta0GLpGWtJS4uLtOdKUVERPKKFStcoO1lrW+lkNKlXZBcoIAvuPZue1Gq1Gnq1vVNTXt3ZnznHfdS43XXZfx9ISFug5nzSQnxyovvyGUl3wXXoaGh7Ny5kwP+2wddoBITExW4Bcm5PusiRYoQej5rDImIiOSQ+Hhf/dprAzeJSUjw1WvWdIF1lSq+4Pr661154427gLDUvp06ufL4cdi3L+tl9h7Ie+nQQZHvgutChQqlbgt+oYuJiaFJkya5PYyLgp61iIjkN+vW+eodOwa2xcX56jVrurJaNd8Ljd9/78py5U4FnGeMC8InTHCfvauIiE++zLkWERERudh5Z6GNcS8u+vMPrr172FWrlj7nOm1wDYE51Gk3kBEF1yIiIiL5kjegvuEG+N//AtvGj3fl99/7Xj7MKLguXjwp3XX37nWlMdCnTzYOOJ9QcC0iIiKSD3lTPCIj3drU/nnWf/wBBQsGpotceikcORLYr3Tp9MG1t/1vf3O52hJIwbWIiIhIPhMb69vW3Lvj4p9/+to3bYLevQPXqPa+nLhunQu0AWrW9HsrMo3XX8+24eYrCq5FRERE8pHDh+G993yfy5Vz5aFDrkxMhB070m9b3rKlK3/7zc10DxniUj/SevJJN+NdpUo2DnrlSnjkEbdG4AUu360WIiIiInIxu+oqWL7c9zltcL1tm4thvauEeF12mSuXLIETJ6B27Yyv/+KL2TjYI0fgmWfcotvly8PgwYHbO16ANHMtIiIicgH68kvo2zf9ZK9/YA3pg2vvS4tp16guVszFtz/95D5nFlxnC2vho4+gbl144w23jeSGDRd8YA0KrkVEREQuONbCzTfDBx+4NJCseNei9uZce4Nrby62v7p1Yf16V8+x4HrtWpdXcuedUL06LF3qZq7zyaLZCq5FRERELjDepfQA5szJum/amWvvKiIZBdft2/vq3jSRbHPsGDz6qFu+5Lff4K234JdfoGnTbP6i3KXgWkREROQC403dgMCXFzNSsqRbMs8bXG/fDmXK+Na39nfNNa5s2zYbl9mzFj77DOrXh1dfdbksGzbA/fdDSP4LRfPfHYmIiIjkc5dc4qvPnBnY1rx5YN0YSE6GH35wx7Ztc9kYGenQAb76ysXC580bVEdFQa9eUKmSm6l+912oUCEbviBvUnAtIiIiksfs3w/79mXe7h9cp7V0qSs3bgxMGVm6FOLjYdmyzINrgJtuyoZl9jZsgK5dXVCdlAQTJrgBtGp1nhfO+xRci4iIiOQhJ09C5cq+jVwy4r/5izenOq3atV1KCLiAGeCLL1zQvmhR9ow1nePH4amnIDzcBdNvvOHWsB4w4KLZzlHBtYiIiEge8ve/++rHj6dvT0yEiRN9nw8dgtOnXX3//oyvOWyYK6dNc+XQoec/zgDWunySBg3gpZfg9tvd7PXf/37RBNVeCq5FRERE8pDZs331+fPTtw8Y4Kt7A3FvUP3CCxlf07thzKpVrrzrrvMbY4DNm6FbN+jZ070puWABTJ7spt8vQgquRURERPKQnTt99bi49O2ffOKrX3WVK/fuhZQUGDvWfe7fP/CcUqXc+4Q7drjP3nSR85KQ4KbEGzaEhQthzBi3g03bttlw8QuXgmsRERGRPCQqyldPu0FMSgqcOuXqjzzimxzetw/WrPH1Gzcu/XVr1XJlSAgUKXKeg/zmG5cC8vzzcMstLgXk4YehYMHzvPCFT8G1iIiISJA8/DC8+WbWfbzblxcs6NvwxeuPP3z1oUMDg2v/uDaj1US8wfXll7vl+c7J1q3Qvbv7KVYM5s6FKVOyYXmR/EPBtYiIiEiQjB0LgwZl3n7woK8eFgZbtgS2v/qqK2vUcGke3ph21y73oiO4ncUz0qWLK89pW/PERDdL3aCB28Fm9Gi3Ckh09DlcLH9TcC0iIiISZJm9eFixoq9eqVL6nOsJEwLPL1rUnbN9O5w44Y499VTG1/buMl6q1F8c7HffuaX1hg1zM9br17ttzP3XA5RUCq5FREREguzpp8/cp0oVl0dtrfvsXW4PAoPuihXdZ29wXbRoxterXx8++ujMaSmptm+n4bBhcO21LlF79mz49FMIDT3LC1ycFFyLiIiI5IKEhKzb27RxudSHDrnP3hLcMtJe5ctDbCwcPeo+ZzUz3adP4Ox4hk6dglGjoH59yi1ZAi++CKtX+/JKJEsKrkVERESCpFgxX33hwqz7hoW5cvt2V3pXA/n668BdGaOjYcUK964hQOnS5zHAOXMgIgKefBKuvpolkye7euHC53HRi4uCaxEREZEgSUqCq6929Y0bM+83bx5cdpmrb9vmSu+a1uHhgX29udRLl7qyTJlzGNiuXdCrl/uSpCSYORO+/JKTWe3BLhlScC0iIiISJKdPQ5Mmrp72ZUX/3Rjr1YO6dV197drAfmnjXW8K9Jw57iXIv/TC4unTbgmSevVgxgwYMcJNkV977V+4iPjTSt8iIiIiQbBnj3s5cedOFwCnDa5vvNGVzZu7IBlcuW1bYH522hcWvcH1oUOZL8OXoXnz3P7pa9e67cvHjoUrrvhL9yTpaeZaREREJAg++8yVH30EFSoErmmdkODbjXH4cN/xatVcxob3ZcWMgmf/FxQbNjyLgezdC3fc4ZK14+NdEvd//6vAOpsouBYREREJAm8Q3KmTW+HDf+Z6zx5XDh4M113nOx4a6oLrI0fc53vvTX/dEL9ozptKkqGkJPj3v12nzz936wH+/rtbu1qyjYJrERERkSAoUMCV//53+pnrAwdc2bVr4DnVqrk0Em9wndlKINOnQ1QU3H13Jl/+88/uzcchQ6B1a5dX/fzzgcuXSLZQcC0iIiKSwz78EHr3dvVSpdzMdUbBddo1qKtVczPc+/e7z5kF1zfeCMuXQ4kSaRr274d+/aBtW5eUPW0azJp1jnugy9lQcC0iIiKSw/y3JK9UyQXJx475ji1Y4Mq0wbX3ZcX161151iuBJCfD+PEuBeSjj2DoUHeRm28GY87pHuTsKLgWERERyQZJSS7rIqP1q3fu9NWLFHHZGN7tyg8cgJdfdvXq1QPP8wbXP/zgyjPurgiweDG0aOFWAomKcrsrjhoFxYv/pfuRc6Ol+ERERESyQfHibufw33+H2bOz7lusGCQmQkqKe2HRKyTNtOfll7vy+++hfn2oUiWLix486HZTfPddqFoVPvkE/vY3zVQHmYJrERERkfNkrQusAY4fz7yfN1j2zlr/8AMUKuTqM2ak71+5sq9+3XWZxMkpKS6gfvJJ9+bjP/8Jzz4LJUv+5fuQ86e0EBEREZHz5A2swbddeUYGDHBlRIQrv/7a92JjjRrp+/vnWKdNGQHcW4ytW7sLN2oEK1fCK68osM5FCq5FREREzlN8vK++d6+byc6IN4C+4w6XGmItvP++O1ahQvr+/jPV3llvAP78EwYNcts5btvmliOJiXEBtuQqBdciIiIi58m7DnXDhm6hDu+Oiml5l+MDuPRSt2KId9bbu+V5Wt6A/LLLcCkg770HderAW2/BQw+5VUDuuEO51XmEgmsRERGRs7BnT+Yz0t6l8rp0ceXy5b62det8df/4t0QJN+NtLbRpk/5lRq/HHnNlrfiV0K4d3HOPW6d6+XK3I02ZMud2Q5IjFFyLiIiInMGqVW4BjswCYG9aSI8ervzf/3xtDRq4cty4wHNKlnTnxcVBuXKZf/fA246Q8tDDlOrY1K3zN2kSLFwIkZHndjOSoxRci4iIiJzBpElZt7/+uitDQ126x9q16fu0axf4uVw5l5+9dWsmLytaCx99hKlXF/PGf9xLixs2uB0XM4vyJdfpNyMiIiKSBWth7Nis+/z6qyuLFHF512mD6yuugPDwwGPh4a7fkSNQs2aaC65ZA9HRcOed7k3GJUvcjotZTXFLnqDgWkRERCQLixcHfs5qqb3Chd3PkiVux0Zwm8vcdFP6vv5ZHanB9bFj8OijrnHNGvfS4q+/QrNm53UPEjw5HlwbYwoYY/5njPmv53M5Y8wPxphNnrKsX98njTGbjTEbjDFX5/TYRERERM5k925XPvSQKzdvDmxPTvbVCxeGmTNdffFiN+udkABFi6a/bqtWvnrNKyx8+inUqwevvupSPzZsgPvvVwrIBSYYv62HAb/3ZHkCmGOtrQ3M8XzGGNMA6A00BK4BxhtjCgRhfCIiIiKZuvlmV0ZHu9K76YtXXJyvXrSob3WPokVdOklKClSsmP66l13myrqsp97gLm6dvksvhV9+gXfeyXjha8nzcjS4NsaEAt2Ad/0O3wh4lkvnfeAmv+OfWGtPWmu3ApuBFjk5PhERERFr4Y033MuFWalVy5Vpg+tvvnHlp59CgQK+IPz0aRgyxNXT5lsDcPw4R/7+JL8XjKDA/5a75USWLAmc0pYLTk7PXI8BHgdS/I5VttbuAfCU3iXTqwE7/Prt9BwTERERyTGLF7uUj4cfzrqfdzMX/+A6Nhb693f1pk1dWaiQK0+f9gXaV17pdyFr4csvoX59So0bRcgdfVwKyKBBLjqXC1rBnLqwMeZ6YL+1drkxJvpsTsngWLql2o0x9wP3A1SuXJmYmJjzGWaeFh8fn6/vLy/Rsw4ePevg0bMOLj3v4MnuZ/3ll9WA2hw8uI+YmHUZ9IgGYNmyGEqWvJJVq/YRE+MSrz/8sDrgou4dO2LYsQPWri0DRLJ06UpCQqoQGlqSX35ZAkDRnTupPXYs5ZYuJf6KK9g0dixHwsPh99/dTx6jP9d/XY4F18CVQHdjzHVAEaCUMeYjYJ8xpoq1do8xpgqw39N/J3CZ3/mhwO60F7XWvg28DdCsWTMb7f0rYT4UExNDfr6/vETPOnj0rINHzzq49LyDJ7uf9fjxrqxRozLR0ZUz7dexYzSVK0PhwqFER4cCsGCBa9u2DS6/3I3pkkvcsYYNI5k716VOR7doAS+9BC+/7N56HDOGEn//O00K5mQodv705/qvy7G0EGvtk9baUGttGO5FxZ+stXcAM4C+nm59ga899RlAb2NMYWNMDaA2sCSnxiciIiICvtU/DhzIuD001O04Di5Q9k8LGTbMlZdf7jvmTQv56Sf3smO35Blu8euRI+HWW10KyMMPQx4PrOXc5MZvdRTwmTHmXmA7cCuAtXatMeYz4HcgCfi7tTY588uIiIiInL+jR105Y4Zb2SPtyneJiW6yGVxwvXNn1tc7dcqVn/3rD94p9jCdT/zX7YE+d64vCVvyraAsnGitjbHWXu+px1lrO1tra3vKQ379XrDW1rTW1rXWzgrG2EREROTitXMnbNni+/zFF+n7HDsGJUu6uv/M9enTrhwxIrB/sZBEnuE51tKQlifm8knT0bBypQLri4RWJRcREZGL1q23urKyJ9U6IcHXlpwMXbrAyZOwZ4875g2urfVtLhOwhvWsWTS5sxHP8Swz6E491vNN3Ud9uSKS7ym4FhERkYvWvn2u9G7ocvKkr+3JJ+HHH1199WpXVqjg0kR27IDffnPHmjTBvdHYowdcdx0ULMivz/9Abz5lF6GMGxeUW5E8QsG1iIiIXLRSPDtxeCeW/XdbXLPGV58xw5V16rhyyRI4fBgu4SS1PnsR6teH77+HF1+EVas41f6q1HPLlMnBG5A8R8G1iIiI5Ftz5sCAARm3TZzoJpwBevVyqSErV/rak5OhcWNXelcDucoTM2/eDDMf+ZHVRFDh9f+Da66B9evddHfhwpQokXP3JHmbgmsRERHJt666Ct5+G+Lj07d5d1YEGDzYpYh8+qnv2O7dEBYWuHpIyZIQWWEnnd/+G1MPdKEAySR/M9PtuOi/Hp9ctBRci4iISL63O922dIHvGBq/faK9qSF79kCVKn4nnD4No0fz86F6RGz7hrEVnuPpG9dQ4Ppr0127USPo3j1wJlwuDgquRUREJF/y5kkD7NqVvt27ysdPP7nyww9duXOnm+mOi4Nq1TydY2IgMhIef5yNVTtSP2Utjx97hsrVi2T43ZdcAl9/7dJK5OKi4FpERETypXXrfPWYmMC2OXPcbHaZMtCxoztWqZIrjx1zLywCXHnFHujTx3U6cQJmzOD7B79hK1dw8qRf8C3ioeBaRERE8qUCBVxZujRMmeLWpvZascKVhw/7jnk3iomPh03rkniYMXQYUBemTYNnnoG1a+GGG6hZ03eOdwk/ES8F1yIiIpIvJSa6cvhwtwujf961N/Du29d3zBtcF1q8kJuej2IM/4A2bdyafM89B8WKAVC0qO+cVq1ybvxyYVJwLSIiIhekw4cD86rTmjLFleHhrty40dd29Kgr33rLdyys6D7e4246D2/HyX2H6ckXhHw3C2rXDrhu4cJ+54Sd+/glf1JwLSIiIhekfv3gxhshNjbj9vXrXVmjhiv9+40Y4crChXELWY8bR4mmdbmdqcyKfIL6rOMregYuI+Lx55+uvPHGDJvlIlcwtwcgIiIici62b3flgQNZzyCHhrq1qrdudZ+Tk/0af/0VBg2C//0POnWi1+5xnKxSjxMrA5fq89eypSsfffR870DyI81ci4iIyAXJmyPtv2W5l/flxccec8vi1a4Nq1e7Y3v2QHkO8kP1/tC6tds95pNP4McfOV2zXuq25//6V8bfe/nl7vpt22bv/Uj+oOBaRERELjiVKsG8ea4+a1b69t69Xfntt64MC3NBNYmJJIx5i43UodOOyfDPf7r8kV69wBiqVYMdO9w5DRrk8E1IvqTgWkRERPKclBTfah8ZOXDAVx87FpKSfJ/374fPPgvsV650Ml23vgXVqlH71YH8Rjjbv14Jr7zimwIHqlb1XSciIhtuRC46Cq5FREQkT7EWevRwS975r02dlbff9tW7dfPVL70UWLqUf81rxfMHBpLSKILrC31HNDFU6dIo3XX8A/pLLz238cvFTcG1iIiI5ClTp/qW2Nu8+ezO8Q/CvTnT5YhjXoOB0LIl5U/u4jamEjvpJ749fTVgApbU82rXzpXGaCUQOTcKrkVERCRP2bLFV//hh6z7ercpL1LEd6xMqRTuYSIbqEvZae/CkCH8/O56PuE29u4zVK4M99+f8fWuuw5++81tgS5yLrQUn4iIiOQpIX5Tf6dOpW/fsMGXI3355a48ftxzYMUKfjgxiEYsZgFtabdiHEREUHGla967120+U6ZM5t/fKH22iMhZ08y1iIiI5CnHjrnl8woUgIMH07cPHNgUgJEjoXRpdyz54J/w4IPY5s2pGL+VO/mAFa/PT30r0Zs/vW0bnDyZdXAtcj40cy0iIiJ5yrFjUKqUm8FOG1x7N44BKFcOChdKoS8fcO/ox+FUHLHd/k6Tb56jcfsyfDjE17dCBVc+8ogrFVxLTtHMtYiIiOQp06a5skIFt7+Lvyef9NVrHF2F6dCeyfRjTWItWL6cRb3GcoQyTJgQeF7BNNOJ3mBbJLspuBYREZE843//c2tTHzwI9erB778Htk+dCqU4wusM4eqnomDDBvoxibYshMhI/vjDrfJRo0b6azdrlnFdJDspuBYREZE8Y+JEX71mTZcjnbrMnrX04SM2UJeHzVjMgAGwYQOT6Yf1hDSbN0NoaODqIV6tW7uyUSO3Y6NITlBwLSIiIkE1d67bVTGttWth3Djf52rV3MuHcXG4xaujo/mIO9lGdcySJTB+PJQrR69ern9Kiguua9XK+HvvuANuvhneektrWEvOUXAtIiIiQZOcDJ06wcMPp19Leu/ewM9VqkAJjsGjj0JkJKxZwz9KvM1LN0wLyOvo2tWVW7a44LpmzYy/u0ULl8/dpk023pBIGgquRUREJGjef99Xf/nlwLbAlw4t4Ws/YT31KP/Ba3DPPZxcvYEx8fdRtnxSwHneFI9162D//sxnrkWCQcG1iIiIBI3/7PT+/YFt3uC6HuvYEnYV9Z+7jb1cSiv7C7z9Ns/82y3xUbHiyYDzvGtY//yzKxVcS25ScC0iIiJB83//56snJga2JR+JZxRDWU0EVxxegR03nhYsodCVLQEYPdr1K1kycObaG1wvXOhKBdeSmxRci4iISK5ISfFUrIVp02jetz5DeZkPuRM2bsQMeoCrry1AQoLrVqWKK1u1igu4TtmybtOZRYvc58xyrkWCQcG1iIiIZJsvv4Q9ezJuSwqccKZ8eWDjRrjmGrj1Vk6VKE8bfqbkZ5OgYkUAqlaFnTtd/xMn4KGHoFAhG3AdY6BfP9/nEiWy6WZEzoGCaxEREckWJ064pe4iIzNuX7PGVy/KCSr8+2lseDj8+iuMHUt0iWX8QhuuvNLXr25dl5u9fz8cOZL5zop//7sro6Ky515EzlXBM3cREREROTNv+sb+/W7JvQIFAtsHDgSw3MjXjGEIYWzjQOc7qTjpZZIqXMrKwa6fN4ca3IYw4NbAhtQJ7XRq13Z9vKkjIrlFM9ciIiKSLX780Vd/8sn07VfV2MJ/uZ7p9ICSJWnPPNY89gFceim//urrF+IXnXiD6RUrXJlV8Nyggcu/FslNCq5FREQkW/Tu7at7V/YA3JT28OEM+6wh7c0CeO01TixYwQLas2+f67Jxoyu9K354VarkykcfdWVmKScieYWCaxEREck5//0vNGwII0YwLaUnnauuh3/8g8qhhQBSg+vjx11Zt27g6d7g2su7YYxIXqXgWkRERM4oORlOn868PXVZPY8GRbfCjTfCDTdAkSJ888hP9GEqO5KrAi59o0gR2LHD9fcG18WLB17H/wXGa645z5sQCQIF1yIiInJGnTrBJZfAY49l3H7ihCsLk8jTPM/yxAYwZ47b43zlSrq/1hGAPn1cv5AQNwu9dav77M3RLlIk8Lr+W6KXLp099yKSkxRci4iIyBnNn+/KV17JuP34cbia7/iNcJ5nGN8VugHWr3fR+CWXpPYzxndOWBhs2xZ4Hf92r/vvd+Xgwec+fpFgUXAtIiIif4k3T9rr8OrtHOlyM99xLSmEMKn3bP5mP8NWc+voWb89X+64w1cvUwaOHvXNer/wQsbf99ZbLi2lTZtsvAmRHKLgWkRERLKUNp965UpP5dQpGDWKSyLrE/rbLJ7kRSJYzZ5GXTh9GjZtct2OHHHlK69A48a+6/z6q+szZ477fMUVmY8hRBGLXCD0R1VERESy9N13gZ8TE3GLWkdEwJNP8p29mvqsYxRPUqRUYQ4ccP0GDXLlqlWurF8/8Dqxsa6cMMGVNWvmxOhFgkvBtYiIiPDmm+nzn72Sknz1auwkclQv6NLFNZjEMvEAACAASURBVMycyX3lvmQ71QE4eBCGDnV9O7p3GNm925U1amR8/WXLXJnVzLXIhULBtYiIyEVu7143y9yjR8btiYlQkNMsvnU066lHteUz4LnnYM0auPbagC3JCxWCypVd/dQpV+7f78q0W5f/85++9lKloFy57Lsnkdyi4FpEROQi98ADrjx6NOP2Ir/MZSWRtPj8cX6iE/WSf4dnnkldN8+bU+0VEgIlS0JcnPu8ahWUL+9+/I0e7fqB27o8o5VCRC40Cq5FREQuYocPw/Tprp5ua/E9e+D22+k+phNFSeDUtBk8VmcGW1JqpK4AYi0cOABdu7qV97waNoS1a119wQK48sr0wbMxcOyYqzdokO23JpIrFFyLiIhcxPxnnb/4wpPKkZQEr78Odetiv/ySEQyjIWu55OYbuPde19e7fN7KlW6ZvB49Arcuv+wyl27yxx+weTN07pzx93u3N69WLdtvTSRXKLgWERG5iCUmujIszJU7pi6AqCh45BFo25bJ/1zDcEaQSFHAlxd96JArFy1y5VVXBV730ktdcL1unfvcvHnG33/nna5Mu+25yIUqy+DaGBNqjHnUGPO1MWapMWa+MWa8MaabMUaBuYiIyAXOG1zf020fk+lLzX7t3XT2V1/Bt9+SGForoL83uP7zT1cuWQJVq0KtwG5ceqlLOXnvPfe5Tp2Mvz852ZUFCmTDzYjkAQUzazDGvAdUA/4L/AvYDxQB6gDXAP9njHnCWjs/GAMVERGR7JcYn8SDvMnQSU8DCSzr+hTNvnwqdSo5Pt71mzLFlWXLutI7c71+fcb50t40jy++cGXalxm9WrVyZVTU+d2HSF6RaXANvGqtXZPB8TXAl8aYS4DLc2ZYIiIikqOSkuCttwh/+T/8hw3ENehC9G9vcHV4HZr5pWjs2+cWBbntNvfZPy3k9Gk3c/3gg+kv778TY9eumQ+jVy8XYFevfv63JJIXZJrakUlg7d9+ylq7OfuHJCIiItnFWpfh4d01EYBffnFJ0A8+SOzhMtzC52x763tOhdUJ2EgmIQFefRXKlPGt9OEfXM/3/Nv15RlMtUVGwnXXuXqxYlmPUYG15CdZzVwDYIy5HngeqO7pbwBrrS2Vw2MTERGR8xQV5Vb0ALD7D8ATT8CkSRAaCp9/TsNbbwYM79aE0qV9S+MBvPOOK/fu/X/27jtKqirb4/j3kDNNbKLknKM54phRUR/mEXWMKI4Jcx7GMaGIWcw5AGJEQUFEyUimGwHJqGRoMt3n/bHrcqu6qrtLpRtofp+1ap1bN9W9vebpfsd99g73BWkh69ZZwxjIuflMv35WfeT55/foK4ns0/IMroGngLOAmd4HVS1FRERkX/f11xZYFyGTfzEI3+wO3KZN0Lcv3HMPY6eVA+DSS212unRpm60OrF8ff8+yZS2oDtJCIOcyeu3bw4gRe/ilRPZxyQTXS4FZCqxFRET2L1OnQicm8xzX0pVJbGl6DGVefXb3CsQLLrDzgjJ5P/9sM9eZmVa947774u/pHBQrZrPa3bvbQsXSpQvohUT2A8kE132BL51z3wPbg53e+/759lQiIiLy12Vmwssvc+VTA7iDNFZSgwt4hyv+ez7HtrTk6a1bYelSO/2882wMUkJ++SV2Nnr69Njb79hh17/xBpx6aj6/i8h+Jpla1f2ALVgZvvJRHxEREdmLdu2CzZuz7Zw0ycpvXHMN28tU5nYepjlpvMcFLF8R9h8P0jUuvDDMow7Mnm2BM8ANN0DbtrHHBw2y0fvYrowiklxwXdl7f5b3/j7v/QPBJ6+LnHOlnHMTnXPTnXOznXMPRPa3d86Nd85Nc85Nds51jbrmDufcfOdcunPuxL/xXiIiIoXehRdCuXKRL2vXwtVXw8EHw/Ll8O67vHnlWB7hdub/UZGiRa0mdWD5chsffzzcN3myjYsWQYVI2YJrron/3ei61jk1hxE5UCUTXI90zuVSoTJH24HjvPftgPbASc65Q4BHgQe89+2BeyPfcc61BM4DWmFNap5zzqlfk4iISAJZWfDhh+DI4ufrX7Up5EGDbKo5LQ3OP59NGTZTXbWqBcRTp4bXB4sVU1LCfR07WrC+bFm4sLFCgtpgNWuG25q5FomVTHDdGxjunNvmnNsU+WzM6yJvIn2dKB75+Mgn+D/VisCKyPYZwPve++3e+1+B+UBXREREJM7s2dCOaYzlCDo8c7lFuVOnwpNPQoUKLFkCzz1nE9nOWeD888/h9StWWHOYUqXCfc5Zhb5ly2DLFtuXaLFi3bowdiy8+CIccUT+vqfI/ibPBY3e+7+cXx2ZeZ4CNAae9d5PcM79G/jaOfc4FtwfFjm9NjA+6vJlkX0iIiISbcMGivz7HqbwLGuoQi9e4/Ux/4Qi4ZzZ55/b7PQrr9j3Ro2sXvX27ZYr/cIL0Lhx/K1r146duc6pEsjhh9tHRGIlUy0E59xZwBHYrPMP3vtPkrnOe58JtHfOpQBDnXOtgSuBG733g51zPYFXgOOx5jRxt0jwLFdG7kFqaiqjR49O5lH2SxkZGYX6/fYl+lsXHP2tC47+1gWrQP7e3pM6ciSNnn+eFuvW8zzXcDf/YT2V6DUm9rd//vkgoCHLl49h1aosNm6sCTRj6NBxgGPXrkPo1Gkxo0f/GnNdsWLNmT+/Ep99lkHVquX46adxu7sz7iv0v+2Co7/1X+C9z/UDPAd8A1wa+QzHZqHzvDbbfe4DbgE2AC6yzwEbI9t3AHdEnf81cGhu9+zUqZMvzEaNGrW3H+GAob91wdHfuuDob12w8vXvnZXl/ZAh3h91lPfgfdeu/sgyk73NQdsnu9tv9754cbvUe++/+MLOGzfO+4kTbfvTT+Ove+SR8J63355/r/R36H/bBUd/6xAw2ScR8yaTc300cKL3/jXv/WvAKcAxeV3knKsWmbHGOVcam51Ow3Ksj46cdhzwS2T7U+A851xJ51wDoAkwMYnnExERKbxmzYJjjoGzzoJ58yzRedw4ftjSCYAHH7Rc6ei25QB//AGVK7N71jlYhLhyJaxebdvVqsX/XPRixa5a+STypyWTFpIOHAQsjnyvC8xI4rqawBuRvOsiwIfe+8+dc+uBAc65YsA2Iike3vvZzrkPgTnALqC3t7QSERGRA87CH5az+YY7aT3jHVzFivDSS3DZZVC06O624wCtW9s887x50KlTuH/uXGjePPweBM0rVliPGbAqItmVKRNud+u2595H5ECRTHBdBZjrnAtmkbsA45xznwJ4709PdJH3fgbQIcH+sUCn+CvAe98Pa1ojIiJyYNq5EwYOpNrN91GLnSzu0Yf6L90ZEwkHiw0hDJp//z3c5z2MG2dlrwPVqtl6x5UrLcAuXRrq1Yv/+YMOCrcTleETkdwlE1zfm+9PISIiIjBmDPTuDbNmMYZT6MPT7JrSiMXZZpiDMnnFikFqqm1HB9efRMoOtGgR7itaFGrUsOB62TJo0waKF49/hC5d4OabNWst8lclU4rve+dcDazmtAcmee9/y/cnExEROVD8/jvceiu89RYcdBCrXhrKaVeeAThYEn/6xki3iVdfTRxcf/+9jf/6V+x1u3bZNS1b5t78Jbpro4j8OXkuaHTO/QtbWHgWcA4w3jl3WX4/mIiISKG3axcMHGg9xN9/H+68E+bMYVH7M4muUBu0Kg8EM9MNGliOdPnyVsM6MGkSHHpobP402CJHgCVLrJ61iOx5yVQLuRXo4L3v5b2/BMuXvi1/H0tERKSQGzfOcjD69LGyHDNnQr9+ULZszCw0ELOAcdMmuC3yb+GmTcN9AwbYQsVNm2DiRDjqqPifDHKwMzKgYsU9/0oiklxwvQyILvCzCViaP48jIiJSyK1aBZdfDocdZtsffgjffBOTp7FypY1BB8SRI8PLJ00Kt7OX0luzBsaPtwnxRDnTLVuG2wcf/DffQ0QSSia4Xg5McM7d75y7D2tRPt85d5Nz7qb8fTwREZFCIjPTalQ3awZvvgm33GL18v7v/8jeAvHll23s3t3GK64Ij/0aaajYq1d42amn2rhuXZge0qBB/CNEtzI/7bS/9zoiklgywfUC4BPCVuTDgJVA+chHREREcjN5siVBX301tG0L06bBY49ZsnQ2CxbY7HT58lY6L7s+fWwcMCDc17u3jevWQd++tp2ojF6pUuH2vtbSXKSwSKZayAMF8SAiIiKFztq1cNddNmNdvTq8/TZccAE4h/eJA9y2bW0880yLxfv2tQwSsAWJQRm+cuXCaypXDn8umLlOELdTo4aNJUr8/VcTkcRynLl2zr3knGuTw7GyzrnLnHMX5t+jiYiI7KeysqzmXbNm1lmxTx9IT4cLLwTnuPRSm5VeuDD+0iB4XrzYAuSTT4bt223fzz/bWLZs7Kx2EFyvWhXui04BCQQ53Jdc8vdeT0RyltvM9XPAPZEAexawCigFNAEqAK8C7+T7E4qIiOxPpk2zPI2ffrIp5+eeg3btdh/2Hl5/3bZPOQXS0mIvT0mB9evh3/+279WqwZw54bUQu8ARrNNi6dIWfFepAueem/jRSpe22W9VChHJPzkG1977aUBP51w5oDNQE9gKzPXepxfQ84mIiOwXimZk2Az1s89ahPvaa/DPf8YlTkfPLqdn+7fprl0WWNevDz162L6qVWH1atseNMjG6NxpsDSPatXs2o0bY1NGssteYURE9qxkcq4zgNH5/ygiIiL7Ie/hnXc4uE8fi26vvtrqVVeqlPD0oNpHIhMn2rhoUbivalXYvBm2boXBg21f9uAabAHjtGlWE1sBtMjek2dwLSIiIjmYNctSQMaMYVvz5pQYMQI6dcr1kvnzY79v2xYGy8FsdZcu4fEgUF6xItyXaEHili1hDrdqWIvsPcmU4hMREZFomzZZner27S3AfvFFpj77bJ6BNcDw4bYgsV49+z5+vI27doXtyceNC88PWphH51GXLRt/3+jFkfXrJ/8qIrJnKbgWERFJlvfwwQfQvDk88QRceqklTl955e7c6iOOsBJ7W7cmvsWsWXD00WGKRzAjHeRi9+0LRYuG5wfbU6bYeOmlkJqa+2MGJfdEpODlGVw755o65152zn3jnPsu+BTEw4mIiOwz0tLghBPgvPMsuh03zlopVq26+xTv4ccfbXvChMS3WbfOLmnRwr4H+dW//25j9pSOs86y8YgjbOzQIe9HLV4873NEJH8kk3P9EfAC8DKQmb+PIyIiso/ZvBn+8x+bqS5TBp55xhYtRk8vRwQ1qgFWroy/1UsvWf1q7+1W1auHwfXy5TZmn5UuXtwC6smT7Xvjxokf8/ff4ZVX4Npr/9zricielUxwvct7/3y+P4mIiMi+xHv45BMrOL1kiXVeeeSRXHMygmofAN9/D+efH35ftQquusq2zz7bxnr1wuA6CJ5bt46/b1aWLXwEOPLIxL9dvTrccUferyUi+SuZnOvPnHPXOudqOucqB598fzIREZG9Zf58OPVUy8moWBHGjLHOL3kkO190Ubj94ouxxzp2DLfPOMPGevVsJhtg+nTL1U7U4GX6dBuLF8+9hrWI7H3JBNeXALcCPwFTIp/J+flQIiIie8XWrXDffTZ9PHYs9O8PU6fmPF2cTfPmNp50ko1BR0WAZctsPO+8cF+VKlYaG2Do0Njzo735po1B+3MR2XflGVx77xsk+DQsiIcTEREpMJ9/Dq1awYMP2ox1WhrceCMUswzKzMzcG8B8+GEdvvvOqvGdcILtCwJnCMvnPfFEuK9CBeuoGOjePfG9L77YAu9Wrf7Ce4lIgUqmWkhx51wf59zHkc91zjmtQxYRkcLh118tT6N7d+vm8t138O67UKtWzGlXXQUNG1rqRnQL88DQobUBW/8YZI8EFUDAFiKeeGLsbStWtFzqIMA+5JA9+WIisjckkxbyPNAJeC7y6RTZJyIisv/avt2qgLRsSeY3I/n9pkesf/ixxyY8/ZVXwu0rrog//ttvpQFbAxnUmf7tNxs3brT61p07x15ToYKNH39sYxF1nxDZ7yVTLaSL975d1PfvnHPT8+uBRERE8t3XX8N119nCxXPOof7H/VnWvy6/32ZVN/IStCsPjBkTbjdrZikkEAbXY8bYvm7dYq8LguvLL7cxUaUQEdm/JPP/I2c65xoFX5xzDVG9axER2R8tXQrnnGMrDp2D4cPZ9tZHLKMuEJcJslt0/eqyZWHt2tjjc+faGATHwcx1kBYyc6aNXbrEXhcE14HDD/8T7yIi+6RkZq5vBUY55xYCDqgHXJqvTyUiIrIn7dgBTz5pixWzsiwd5JZboGRJlv4SnpaZaYsQU1JiL3/7bRsvushyqtPSYo8HM9UjRthYqZKVzQtmrn/9FapViy+j16RJ7Pdg0aOI7L/yDK69998655oAzbDgOs17vz3fn0xERGRPGDUKeve26eXTT4cBA6B+/d2HlyyJPX3FivjgOlIwhHvugUcfDWeqAyNH2lilio3O2aLGILiePh0aNIh/tKjHAKBEiaTfSkT2UTmmhTjnjouMZwGnAo2BRsCpkX0iIiL7rhUr4IIL4LjjrCTHZ5/BsGFxEe3SpbGX3Xhj/K2++cbG6tXhxx9t+5eoGe+hQ20sHlVLq0YNSwvx3oLrgw+Ov69mqkUKn9xyro+OjN0TfE7L5+cSERH5a3butBSQ5s1hyBC49162T53N5mMT/6trwwYbv/7axiCQDpx9NnzwgW1XqGDrICEMrqdOTfwYtWvbrPiWLVaYpHbt+HOcC/OsG6qDhEihkGNaiPf+vsjmg977mLL5zrkE/3FLRERkL/vhB0sBmTnTFi0OHAiNG9OwtjVgyR44A2zaZOMxx4T7du4MZ6GHDAn3FykCp5xi28FixU6dbHziiWlA+93nNm0Kw4eHNbGDlJHshg61GfEzz/xTbyoi+6hkFjQOBjpm2/cxVu9aRERk7/v9d+jb1/qEH3SQRcRnngnOkZFhGSIrVliKhnPhZcOHWx51iRKx+c7r19sCxGhB8F25so3ZK4a0bLkx5nvTpjZjHbQszym4rlYNFi3KuVKJiOxfcgyunXPNgVZAxWw51hWAUomvEhERKUCZmfD883D33ZZ/cccdcNddMcnMvXqFp69fb5U8Aueea+OOHTa+/bZVBFm7Nj64HjzYxgoVLBBfudK+H3QQ1KsHpUplxZzfrJmN90X+O3DVqjm/Rr16SbyriOwXcpu5boblVqdgedaBTUCC3lQiIiIFaPx4uPZamxru1g2eecbyrKNs2xYGxQCLF8cG19Wrh63HIZxdDmalg7zq004LZ6ydgzZtLPMkLc3yqrcnqKEVBNdBjeuO2f8bsIgUSrnlXA8DhjnnDvXejyvAZxIREcnZ6tU2Qz1okOVSfPAB/N//xeZ7RERX9ACYMwfah2nR1KxpTRoDQVOXIOCePNnGIM860KiRxfSzZtn3IP86WrVqULGiLZg8+mhVBhE5UCTTofFn51xv59xzzrlXg0++P5mIiEi0rCx46SWbEn7tNbj5Zps67tkzYWANFocHiheHSZNijy9bFvs9CIA3b7bxrrtsPP/82PNq1rQa1rt22fegeUw056BzZ9sOZr1FpPBLJrh+C6gBnAh8D9TBUkNEREQKxpQpcMghcNVV1mN82jR4/HEoXz7Xy4Lg+uqrLQXktdfCY8uXW+fE8uVhXOS/zwYdFIPZ7BUrbMzeVKZ6dasyEsxcH3po4t/PyLAxqIMtIoVfMsF1Y+/9PcBm7/0bWEOZNvn7WCIiIsC6dZZX3aWLJTe/9RaMHm0BdhK++srGe++1YHrDBsvDBhg71sann7a4HcKZ69tus8oixYvDDTfE37d6dRsnTrRmMTmlfHTtauNTTyX1uCJSCCQTXO+MjOudc62BikD9fHsiERGRrCybZm7aFF58Ea6/HtLTrZRHthSQhQstEM4uMzOcqa5SJey8uHixjT//bFU/LrwwvCY11cbGja2ySEYG1K0bf++g8seIEYmbwwQee8y6r/fpk8Q7i0ihkExw/ZJzrhJwN/ApMAd4JF+fSkREDlzTp8ORR8Jll0GTJpYSMmCArQ7M5v33bXHhoEHxt9myJdwuUQLOOce2FyywMS3Nbh/dstw56z2TkgIzZti+gw6Kv/dJJ4XbuQXXJUtafewcUsJFpBDKtYmMc64IsNF7vw4YA6g5q4iI5I8NG6wo9MCBtgLw1VfhkkusLWIObrvNxkQtyKODa4AGkd7Cv0Z6Ds+dayX1sktNtaoi335r31u2jD+nVFS3h6ZNc3w8ETkA5Tpz7b3PAq4roGcREZEDkffwzjtWo/rpp+HKKy0F5NJLcw2svbeKHRAuHIwWBNfXXGNjjRo2S71smTWNWbAgriw2YNX9Vq60xYylSlnb9NycemoS7ygiB4xk2p+PcM7dAnwAbA52eu/X5nyJiIhIEmbPht694fvvbdHiZ5+F9evysH592Flx3rz44zfdZOOxx9ronDWQWbfOAuvMzMTBdZMmsHOn9ajJbVZ66lSb/Q7aoouIQHI515cBvbG0kCmRz+T8fCgRESnkNm2CW2+1ji4zZtiixXHjYgLrRYssuF24MPEtli+3sUoVy5/Ovqjxk09sLFky3JeSAj/+aOdD4uC6TBkbZ8+GhrkkQ3boABdckPNxETkwJRNct/DeN4j+AAky0ERERPLgPXz4IbRoYXWqL7nEpp2vvBKKFo059Y03rMPi1VcnvlUQXHfvbh0VJ0dN+wTl9iA2QC5Z0lK7cwuuo4NxtSwXkT8rmeD6pyT3iYiI5Cw9HU44Ac4913qD//STlfkI6tplc//9No4YEXZCjBYE1+edZ2PQCAbg5Zdt/O9/Y0tiH3mk5WKnpUGdOmHTmGglSoTbmzfHHxcRyU2OOdfOuRpAbaC0c64DEBQSqgCUKYBnExGRwmDzZujXz2aqy5SxaiDXXBM3Ux1t6dLY74sXW8m9aEFwffTRtlhxypTw2LRptjDxjjtir9mxA9assRrXiWatIXbmOlEZPhGR3OS2oPFEoBfW7rx/1P5NwJ35+EwiIlIYeA/DhlmLwyVL4OKLratK0KklFxdfHPt99erY4Hr7duu6CFbRo0kTy9EOrF9v1fyyC+phz5wJ3bol/u3o2eorr8zzUUVEYuSYFuK9f8N7fyzQy3t/bNTndO/9kAJ8RhER2d8sWGA16nr0gAoVrBrIm2/uDqyXLLEa1Tt3xl/qvZ0O4frGoDZ1IPuMdL16YedFsGA8JSX+3tHdFrt3T/zoWVk2XnghFEumppaISJRkcq4/d85d4Jy70zl3b/DJ9ycTEZH9z9atlizdqhX88AP072816446avcpq1dD27bw6KPw3HPxt/jgAxuLFbM86hIlLM0j2pNP2vjQQzbWr2/1q3ftssWNY8ZYWnd2I0aE24cfnvgVTj/dGkIGedsiIn9GMsH1MOAMYBdW5zr4iIiIhL74wlYPPvAAnHWWLWC88cbY/uLA8cdbxQ6Af/87/jazZtlYt64F2HXr2kx3InffbWO9ela3etmy8NyuXePPb9w43I7OrY5WpAj06QOlS+fwniIiuUjmP3jV8d6flO9PIiIi+6dFiyxKHjbMVgl++y0cd1yOp0+fnvvtgqD2s89szB5cb98ef02Qj/3DD1YFBODgg+PPK1oUTjzRqoaIiOSHZILrn5xzbbz3M/P9aUREZP+xfbtVAOnXz9of/u9/NlMdXcsuga5dYeJE205UjSOYjW7RIjxn9Ojw+E+RYrCnnRbuO+wwG5cuDWekE6WFAAwfnuvjiYj8LckE10cAvZxzvwLbsZJ83nvfNl+fTERE9l3ffAPXXWddXs4+25Kgo1cL5iK6WMjSpdbwpVSp+POKRBIX69SxsntBB8ZgUvzdd8NzS5YMG8SsWmX7cgquRUTyUzI51ycDTYATgO7AaZFRREQONMuWwf/9n+VWeG/TwB9/HBNYb9tm+c85KVrUuia+957dIuiWGKhUCa69NvyekmL327LFuqYHypePva5CBVvMGN0WXUSkoOUZXHvvFwN1geMi21uSuU5ERAqRHTusRnXz5vD551amY+ZMC7KzKV06vk51YOJE+OQTi9HbtLF9wQJGsDWQ69bFtiwPZrBnzgxnpW+/Pf7eFSrYzPXDD9t3ldETkb0hzyDZOXcfcBsQVBUtDrydnw8lIiL7kFGjoH176NvXOq/MmWOJ0QlyOYIZ6/fei5+RBhgS6ZKwY4c1fileHGbPDo9/+KGN554b7gtqXt98cxhcJ1qQmJlpjyoisjclMwPdAzidSPk97/0KoHyuV4iIyP5v5Uq44AJLct661cp3DBsGDRrkeEl02saYMfHHo9NFSpSAZs1ig+tvvoEOHcKKH2BrJgGOOSb3fOpFi+C33yzt5E71ERaRvSSZ4HqH994DHsA5VzZ/H0lERPaqXbvgqacs8h08GO65x2aro8tz5GDt2nC7aNH449nrVderZykigXXr4mP3pk0tv3rLFpg71/YlqjJy7LE2ZmZCzZp5PqqISL5IJrj+0Dn3IpDinLsCGAnk2bfKOVfKOTfROTfdOTfbOfdA1LHrnXPpkf2PRu2/wzk3P3IsPpFPRETy148/QqdOVlLv8MNtWvnBB5PuqPLMM+H2K6/EHw/SPmrUCMfffguPr1+fuG15tWqwcKEF17Vrx1YcCVxwQbjdsmVSjysissfludzDe/+4c+4fwEagGXCv935EHpeBle07znuf4ZwrDox1zn0FlMY6Prb13m93zlUHcM61BM4DWgG1gJHOuabe+1zWnIuIyB7xxx+WU/3GG1b5Y/Bg6NHD6ldHGTDAFhYOGpT4NkFb8rp1Yf78nH/uxx9tTE21n87Kshnn334LA+9oDRta85mGDRPPWkNsdZC2KhYrIntJMgsaGwA/eO9v9d7fggXJ9fO6zpuMyNfikY8HrgH+573fHjnvj8g5ZwDve++3e+9/BeYDCZrXiojIqhXuRgAAIABJREFUHpOZCc89B82a4d99l+UX327Tw2edFRdYgzVifOUVWLMm/lZZWTbWqwfXXGP50RkZ4fFggeNdd4XVQFJT7RHWrLGAOzPTcq6za9XKZrUXL7b7J1K1arhduXIS7y4ikg+SSQv5CMiK+p4Z2Zcn51xR59w04A9ghPd+AtAUONI5N8E5971zrkvk9NrA0qjLl0X2iYjI3/TOOzB2bLadEyZYu8TevaFjR/r1nEGdtx4mbWnipTXRbcejy+cFNm608YYboH592168ODx+0UU2ZkX9G6VSJRvXr4dJk2z7mGPi7x2U2Vu4MOfgOjpXu4gKxorIXpJMFdBi3vsdwRfv/Q7nXO69bcNzM4H2zrkUYKhzrnXkNysBhwBdsJzuhljnx7hbZN/hnLsSuBIgNTWV0dE9cQuZjIyMQv1++xL9rQuO/tYFJ/pvfdFFxwAwatRoim3YQMOXX6bml1+yo0oV5t9zDxPqncY9/2oOwODBMzn88Pip6eHDawB2ziWXbOb11yfFHJ83rxzQmZUr0yhRYgvQkU8/ncGqVbbKsWjRNkAV2rUbx+jRFqkvXVoFaMN3301h2LB61K5dhlmzJsb99vr1tbF+ZlC69CxGj16dw1sfQ506Wxg9Ov4e+U3/2y44+lsXHP2t/wLvfa4fYARwetT3M4Bv87ouwX3uA24BhgPHRO1fAFTD6mjfEbX/a+DQ3O7ZqVMnX5iNGjVqbz/CAUN/64Kjv3XBif5bg/eOTO9fesn7ypW9L1rU+5tv9n7jxt3Hg8/LLye+3yuvhOecckr88eDYxx97v3y5bT/7bHg8NdX77t1jr/n+ezvvnXe8b9TI+3PPTfzbY8eG98/Kyvmdly71fs2anI/nJ/1vu+Dob11w9LcOAZN9EjFvMv/h7GrgTufcEufcUqyhzFV5XeScqxaZscY5Vxo4HkgDPgGOi+xvCpQAVgOfAuc550pG8rybAAU/9SAiUsh88AF0ZArjOBSuvBJat4Zp06yAdPYe4sCKFYnv89RTNtarF5tLnV2xYrYosUQJqz0NlvLx++/WGj1aUBnkwgutTF9OJbSDutZVqyZMBd+tTh3lW4vI3pVMtZAFwCHOuXKA895vyuuaiJrAG865olhu94fe+88jKSWvOudmATuASyL/38Bs59yHwBxgF9Dbq1KIiMjfs24dJW66m0k8zyqqwZtvWvJztgj17LOtQAhY75hEZs608eij4zsh7toV+71IEVu0GNSl/uknG2+9Nfa8cuXC7Z07w1zt7Bo0sDLbQd62iMi+Ks/g2jlXEjgbqA8Uc5F/IHvvH8ztOu/9DCBuzbe3/O2E/3j03vcD+uX1TCIikoesLGoMHw49e3LG6jU8w3Xcy4Osuygl4cxv9Gz1kCHw/POxx4PFimCT3Zs3xx4fODDcPvhgG9u3DxcpLllipbKPPz72uurVY7/nFFwXL24NIkVE9nXJpIUMw/Ksd2Et0IOPiIjsi2bMgKOOovkjj0DjxnT0U7iBp9lAChMTJNtt2waTJ1tDRrC609nde6+NDRpA2bLWiTG6lflNN9nYv39Yp7p69bBd+eLFVp86e2AfPXMd3F9EZH+WTHBdx3t/rvf+Ue/9E8En359MRET+nI0brbNix46Qnk7arbfifxjLdNrvPmX8+PjL0tMtJeOBB8IgOnuax9atNl5yCfzyi22//rqNPqqu0403htvVqtkj7diRe33qaDk1iBER2V8kE1z/5Jxrk+9PIiIif4338O67NvU8YABccQWkp/PbKafw7ajwH/N16pBw5jpo7tKiBVSsaNvZ0z6KRZII77kHeva07WCGe61V2uO//429pmRJG6dMseA6p5SPaKVK5X2OiMi+LJk610cAvZxzv2ItzR3WgFHNZUVE9rJtU+ew+rze1PllNHTuDJ9+Cl267D7+j3/YeNNNFuQuXRp7vffhbHPt2mHxkE2bwkAbrHlLu3a2ULF7d9tXtKiNQUWQ5s1j7x1U7Rg2zNJD8pq5DoJ2EZH9WTLB9cn5/hQiIvLnZGTAgw9S4oknKZNVnqt4gRfH/yuMeLOpWNFyoKdPj92/alVYHaRSpdjgOvDDDzB8OPTqZd9Ll7ZxyxYbg+A6+8z0qafaGJTfyym4njTJuj8efnhOLysisv/IMy3Ee78YSAG6Rz4pkX0iIlLQvIePPrJp4sce4+e2/6QZ6bzEVfgi8YF1nTo2nngipKZareloq6MaHRYpkji4PuooG4PAvEgRS/kI8rBzCq7LRrqo//yzjQ0bJn6lzp0VWItI4ZFncO2cuwF4B6ge+bztnLs+vx9MRESymTfPouSePW214E8/0XnaK6zGOqysydax3HtYtsxyqQ8+2GauN2ywWeJAUM0jEFTvCILr6MWK550XbtesCQsW2PaiRVChQtgQJlCmjI1jxtjYuvWfe10Rkf1RMmkhlwMHe+83AzjnHgHGAQNzvUpERP6W9eshKwsql9piqwUfe8xW/D39NFxzDb5o7D/CN2ywDoaBuXPLR0b7XqGCjZs3h4sN+/a1MWggE1z/22/hMwD07h3bAKZFi9jgun79+DJ70RkqDRokbAYpIlLoJFMtxAHRnRIzI/tERCQfde3iubTKMDbXbwn9+sG551rdvOuvh2LFeOml2PPHjcv9fkGaRpArDWH1kKOPtrFJExsXLrRx+XIbjzwyNnhesMA6qG/eHAbXiQT1r1u1yv3ZREQKi2SC69eACc65+51z9wPjgVfy9alERA50Cxfy5PzTGMaZ/LqqHHz/vbUuDzq0AFdfbWOHSC/ciy+OvcWuXfaP+H//274HaRpBcL1smY1nnQVVqth2iRI2q52RYd+D4LpWrdh7H3GEjX/8kXtwHQTkjRvn/roiIoVFMgsa+wOXAmuBdcCl3vun8vvBREQOSNu2wQMP4Fu25CjGcBNP0IGfw1WFCQSpHdlt3Wp5GUGJu6DKx6hRNk6YkPj67dvh0Uct3zpoX96oUew5J0fqSC1ebIF4TsH1YYfZeOKJOT6+iEihkmPOtXOuC1DVe/+V934qMDWy/3TnXBHv/ZSCekgRkQPCl19aysfChezscS7Nhz7BCmoD1qglqBsN8OOP4fZhh0GPHmHnRLAFif/5TwuKFAlbireNdCdYscLG6dOt8ke7dokfZ/NmS/+oUyd+5jrIn54508acguuzzopdFCkiUtjlNnP9GDA3wf45kWMiIvI37G4xvnixRcennmp5GSNHMu/B91lBbS64wE4J0jYC771nY4UK1jJ80SKYNQvmzLH9118PGRnFqVw5zCRp1MjODfKp09Mt8M7eFTGYJF+zxhZDJmpJHpT4++YbG5PpvigiciDILbiu4r1flH2n934+UCX+dBERSdaSJVC2+HamnP1fK73xzTfw8MM2ndyt2+6UjRtuSHx9Nau+t7vjYlBL+osvbAyuP/TQ2OsaNAjrUqelxXdVBLjjDhunTbP7HHNM/DktWkDx4vD55/ZdwbWIiMktuC6dy7Gye/pBREQOJJfVG8kM2tJpyF2WwDx3Ltx+u81cE3ZNbN/eAtmgikdgzRqr/hGU1wsELcvT0mzMHpxXrgzr1kFmps1ct2gR/2zNmtn41Vc2JgrAixSxWteB7DWuRUQOVLkF1yOdc/2ci61c6px7APgufx9LRKSQWrYMevZkJP+gKJmcxFdkfjg4LvdiwwZL1yhRArp2jW38AtaSvFOn8PtDD9m4Y0dsjnO3brHXBcH1Rx/ZPRMF15Uq2fjiizZmr18dWLLExt69cz5HRORAk1twfTPQEJjvnBsc+cwHmgE3FcjTiYgUFjt3wuOPQ/Pm+M8+4x4epDWz+JqT+OOP2FO3b7dTt22z75UqWUAcmDfPUjaOPz7cd/PNNm7YEJ576qkr4h6jcmVbHHnRRfa9Xr34R80+Gx7dmTGRe+7J/biIyIEkx2ohkY6M5zvnGgJB+f/Z3vuFBfJkIiKFxejRNr07Zw6cdhrfnDKA/1zbcPfhpUtjUyzeeCP28pQUq/7x22+2OHHIENsfXde6dGm7x/z5YU51ly5rgdgyH2vWWNDepIlVFznuuPjHLZJt2qVYHr18U1NzPy4iciBJps71Qu/9Z5GPAmsRkWT99ptNER97rHVu+fRT/Kef8cRQC6yPPNJOi+6smJ4OV11l208/bWNQmWPWLBvXr7d0keyzzi1bWvw+frx9b9IkI+6RunSx8ZdfrBFMXukcY8bkfOznn8PfEhERk0yHRhER+TN27YIBA2xl4Ecfwd13w+zZ0L07ixfDiBF2WjBDHXRQBBg+PNwOZqaDRixr1ti4apUtXMweGNeoAatXW1fFokWhRo1tcY925ZXhdt26eb9K5845H2vfHg4+OO97iIgcSPL4j30iIvKn/PgjO/51LSXSZlhbwoEDY0p9BO3EISynF23VKhuPOCKswBHUuF692saffoKOHeOvrVABNm60JjE1asSnd0DsvtwC55QUmyEvnVvdKBERiaOZaxGRPeGPP+DSS+GII/g9bS1n8zGDzv4qrobexo3hdtmycO21YXUOsIC2cmWrBhIIOjOuXm2Bc1pafP1qCIPrlSvjOyom0rBhzsdmzbKS2yIi8ufkGFw759o458Y755Y6515yzlWKOjaxYB5PRGQfl5kJzz9vKSBvvw233UbnsmkM4WwGD4lPaD7llHDbOVsMuG6dldADqy29dm3sNcWK2Uzy6tUwdqztC3Kno1WoYPeZPTv34LpNGxuDtuiJ1K4dtksXEZHk5TZz/TxwP9AGmAeMdc41ihwrns/PJSKy75s40ZKOr70WOnSAGTPY8eD/2JRlfbY2bMj50hdesDGotBGU41uYw7LxqlUt5zoouZe9HTqEJfSWLUt8PDBoENx7bxhki4jInpNbcF3Oez/ce7/ee/84cB0w3Dl3COBzuU5EpHBbs8ZKehxyiOVpvPcefPsttGjBtGmwdavlU0+YYAVDErniChuD4Pr3320SHBLnSlepYvnYweLGrl3jzylVKtw+6qicH79rV3jggcS/IyIif09u/2h1zrmKwRfv/SjgbOAtIEHbARGRQi4ry6Z9mzWDV16xMh9padZlJVK646ST7NQWLez06AWMQerHrbeGgW10cB0sZhw4MP6nFy+GkSOtJnZOJfSi61Gfc87feE8REfnLcguuHwFiGuN672cA3YAh+flQIiL7nKlTbdr4iiuYuq0lW378Gfr3j2tnGHRHDKpsZESVmg5msaPXOAbB9aBBYR3rGjXifz64dty4nBci9uxpcf7PP9tiSRERKXg5Btfe+3e99+MBnHPlnHNlI/uXeO+vKKgHFBHZq9avh+uugy5d8L/+ysW8SafN3zNoQuKE5SAd48Ybbfz99/DY++/bGN3RMNgeOjSsEJKodvS//hVuJ1rMCFCmjGWotG+fxzuJiEi+yTXjzjl3jXNuCbAYWOqcW+ycu7ZgHk1EZC/yHt5801JAnn8err2WDRPSeZuLAUd6euLLtm6FE04Iuyf26xceW7LExuj60sEMc/v2VvquaVOr1JFd797hdqNG8cdFRGTfkFspvruB7sAx3vsq3vvKwLHAyZFjIiKFyrhxMGoUMHOmTUFfconlYEyaBAMH8u2UlN3nfvtt/PXffmunbtxoMTnAjBnh8U2brJV59jJ5p55q47BhcPjhiZ8tJfxpWrb88+8mIiIFI7eZ64uBs7z3uwtDRbZ7Av/M7wcTESloJx62kWnH3Yjv0AHmzrVE6B9/3N0OMVgkePTRsGCBLViMFqR9pKfHLjgMzps7Nwy6ozVsCNOm2famTYmfLbq0Xj0tKRcR2Wflmhbivd+WYN9WICvB6SIi+yfv2f76e6TRnBsYwA/N/mUR8uWXJ6xX16MH7NoVLl4M7NplY4cOsfvXrbMsk7lzE886H3RQuH3RRYkfsXx5eO01GD/+T7yXiIgUuNyC62XOuW7ZdzrnjgNW5t8jiYgUoDlzoFs3Sl56ASuoxSGM58GaL+TYhaVjx7CaR3QN67VrYfNm2377bRvPOMPG22+39O2MDGjePP6ec+eG24cckvOj9uqVeLGjiIjsO4rlcqwPMMw5NxaYgjWO6QIcDpxRAM8mIpJ/MjLgoYesnF758lzN87zMFWRRlMaLE19SoQIceWRsbepWrWz7hhvgo49su2ZNG+vWtfHDD8NZ7uj254G774ZXX7XtatX+/quJiMjek1spvtlAa2AMUB9oGNluHTkmIrLf6N8f3nkHy88YPNi6vDz6KFx8MaSn8yJXk0VRIHGdaYDt26FkycQz1xMmxJ9/++02nn66dU9s0ADq148/L8ihbtZMXRNFRPZ3Oc5cO+caA6ne+1ez7T/SObfCe78g359ORGQPyMiAm2+GxvzChW9eB998A+3a2QrEww/f3ejluuusrPX338ffw/swuK5d2xYsRpfja9wYfvkF3n033Fe7ts1ilyoFCxeGM9rZFSliv5koZURERPYvuc2RPAUkWre+NXJMRGTf5z3zX/yW/tzILFrjx4+HAQNg8uTdde+CIPnYY61yx7JlsHNn7G2mTrWxZElbXNi5M4weHR5fs8bqW59/fux1ZcrAli2wYkXOM+Jglf+qV/97ryoiIntfbsF1/Ui78xje+8lYmoiIyL4tPR1OPJH2txxPH57mQ3ryzj3p0KcPFAv/w93SpTYedJAFwN7DqlWxtwo6LwZBd5MmsHy5ba9eDRMnJs6XXrDAZrPnz09chk9ERAqX3ILrUrkcK72nH0REZI/ZsgXuvBPatIGJExl67NOkuI38k7cYNTd++nhxZAFjvXoWNIM1g8l+S4ANG2ysXh3++MO2b7nFxoYNc36kXbugU6e/+D4iIrLfyC24nuScuyL7Tufc5Vj1EBGRfYv38Mkntljx4YfhvPMgPZ3Xy19P/VZl6dzZZpKzW7TI0jeqVg1L4c2ZEx6PDopvuMHG6tWt4cvWrWG6x513xt/73HPD7a5d/9bbiYjIfiC3Unz/BoY65y4kDKY7AyWAHvn9YCIiycrKAj9/AUVv7ANffgmtW9sKwUgux7x51rylVi1rxOJ92EHRe3jmGavk4RyUK2etxoOUDwjzra+6Kqz2EeRHr1pl+dY1atjCxexuuAE++MC269TZ8+8uIiL7lhyDa+/978BhzrljsZJ8AF94778rkCcTEUnG1q283+4Rzp7/P4qULY7r39/KfhQvDlg6xoIF1tClRg1r9LJ2bdgjZswYO+eXX8Jb1qhhNazBKoQEnn463A6C65Ur7dycFiM2ahRuR7dEFxGRwim3mWsAvPejgFEF8CwiIn/Ol1+y69rruWDxQt7jPGq88ATHXlgr5pQlS2wRYpMmUKlSuC8IroNc6mipqWFwfc89Np55JpQoEZ4TzEI//TSMG2eVRhJRUxgRkQOL2hWIyP5n0SKLdk89lY1bS9CNkVzAe/yyuVbcqcGMdJMmYbOWxVEdGINGMF9/He5LTQ33B50Tt26NvW/79ja++65VCznmmMSP6pylovz4Y9JvJyIi+zEF1yKy/9i+Hfr1swTqESPgf//j8s7TWVi/G+XKJW7+8vHH1qSlZUsrtQc2cx14/30boxctRqeFBOXzBg6MvW/2FI8GDXJ+7F694LDD8nw7EREpBBRci8j+YcQIaNsW7r4bTjkF0tLgtttYuKwE7dpBjx7WeNH78JIVK2DQIFvwWLWqfUqXDmeuV6ywa3r1CtNEwDopbtxo3RozMqx9eVCiL9qTT4bbQeAuIiIHNgXXIrLP+ugjqOOW8UuHntb+MCsLhg+36ei6dQFL30hJsZnp1ath27bw+jFjbIycinOWfx0sTAyOn3567O8efbSNl1xiDWZyqvJRsWK4HfyGiIgc2BRci8i+accOJvZ8jDSaU2faZ/DQQzBzJpx44u5TRoywRi7p6RZgg802Bx56yMbomtW7dtnHe+uADnDyybE/3aaNjZ9+CuvWheX3souuYV2hwp9/RRERKXwUXIvIvmf0aGjfnsfoy7d0oyVz8HfdHVdIekqkAn+rVmElkF9/tXHDhjCoLlcuvKZvXxu3bYMnnrDt7PWpy5aN/Z5TcF2mjP32GWck/WYiIlLIKbgWkX3HypVw4YVW127bNu5s8xlnMoxFNOCTT+JPr1nTxjvvhHbtbDuoDvKf/yT+iaBiyOrVNpYpE3+Oc2GwDjkH1wCzZpHw2URE5MCk4FpE9oq1a+GxxyJ1pnftgqeestIcH38M994Ls2fz+urTdudDz54df4+gPF7p0uGCxJdftrFoURs3b469JkgfmT/fxmD2Ort//tPGkiWhefM//XoiInKAyrOJjIhIfqhWzdYnznt1LC8Xv9byqU86yWreNW7M00/bRPYJJ1iTlqVLY6/ftAmuuca2S5cOUzuCetLr1lm96uwz08EixPR0G1NTEz9fcN7BB0P58n/vXUVE5MChmWsRKXA7dkCVrD94jV68nHYkmWvXw5Ah8OWX0LgxEHZGvPhiq9axbFnsPUZF9Y0tU8ZmmKOtXQuVK8f/dvbgukaNxM8YLFDMnn8tIiKSG81ci0jBysxk2Z3Pkc5dlGUzD3M7p318N20OiY1ia9eGDh0syK1TByZOjL1NdNvyoC150aKQmWkz4mvXxuZNB4K0kHnzbMxp5rpY5J+OqgIiIiJ/hmauRSRfLF4cdjncbcIEOl1zDQ2f6M1UOvJO3xncycOs3hobWD/zDMydG84q16pl91q0KDxnxQob160L92Vm2rhoUd4z119+aWNOM9enn2553HfckdebioiIhBRci8geN2eOVdjYHbiuWQNXXAGHHEKJdet4v8f7nOBG0q23rRScOTP2+ocftjEIloPFhdHn3XyzjdGNXAJLl9oCyKZN448FM9eBRNVCwNqZr14dViERERFJhoJrEdnjjjzSRkcWO5592aLc116Dm29m4htv8O6ucyldxlG3bmw78sCmTTYGFT+C1uIrV9oYPVvtXLj9xRc2Ll5snRibNYt/tuia1xdc8NfeT0REJCcKrkVkjxo92lIyOjKFcRxKieuuhNatYdo0ePxxdpYqw2efWYk852x2O3slkMDxx9uYmmrnBsF1cP4xx8SeH6SBTJ1qY4MG8feMDsYPPfSvvKGIiEjO8i24ds6Vcs5NdM5Nd87Nds49kO34Lc4575yrGrXvDufcfOdcunPuxPi7isi+7vqL1vEMvZlEF+qxmLl3vmURd+vWACxZEpuH0batNWKJVrq0tSS//HL7Xry4le4L8qw//dSC5EGDYq8LKoYE5fgOOST3Z1VnRRER2dPys1rIduA4732Gc644MNY595X3frxzri7wD2BJcLJzriVwHtAKqAWMdM419d5n5uMzisiekpUFb77Jdyv6Upk1fFDteq5Z9QDdl6TwVtRs8VVXdQKgTx/7npJiixcDS5bAH3/AP/4RO8tcq1YYXE+fDk2aQKNGsY8Q1LqePNlSSXKqTz14sHV3rFv3b7yviIhIAvk2c+1NRuRr8cjHR74/CfSN+g5wBvC+93679/5XYD7QNb+eT0T+unnzLPVjtxkz4Kij4NJLWZ3SmE5M4YxFA9hACtu3x167Y4clUrdoYd/feMPGSZNsfPttG087Lfa6mjXDtJAJE8I87GjRta6DvO9EzjpLKSEiIpI/8rXOtXOuKDAFaAw8672f4Jw7HVjuvZ/uoqeloDYwPur7ssi+7Pe8ErgSIDU1ldGjR+fT0+99GRkZhfr99iX6Wydv2rQUbryxPQBjPvucBq+/Tu2hQ9lZvjwLb72Vsz+9k7KNspg4cTRNmnRiyZIdjB4dlvlo0aItc+dW5qCDfmD06EzgGAC+/noWmzevZsKEJlSoUJ3ly39k+fLwd51rxqJFlfngg6ksXXoorVqtZPTo9Jhny8goClhUnZIyn9Gjs3WeOcDof9cFS3/vgqO/dcHR3/ov8N7n+wdIAUYBbYEJQMXI/kVA1cj2s8BFUde8Apyd2307derkC7NRo0bt7Uc4YOhvnTzwHrL8BbztfY0a3jvn/dVXe79mjffe+9RU7y+7zM499ljvjzwyvDYry65v2DDc9+abtu+DD+x769bet2sX/7u33eZ9iRLef/KJnf/FF4mfb+RI7w85xPvff98DL7uf0/+uC5b+3gVHf+uCo791CJjsk4h7C6RaiPd+PTAaS/1oAEx3zi0C6gBTnXM1sJnq6AzIOsCKgng+EUnOunXQgjl8x3G8w0X4unWtdeLzz0PlyqxZY81eWra088uUsaoggQULbIzOlT7hBBtXrYJffrHFjcH10cqUsbbpQWpI27aJn7FbNxg3DqpX/3vvKiIi8lfkZ7WQas65lMh2aeB44GfvfXXvfX3vfX0soO7ovf8N+BQ4zzlX0jnXAGgCTMzh9iJS0DIyGNyoL9NpRzumcxUvMKrfOOjcefcpQf3patVs/OILK4sXBNhjx9rYv39426B83urVYYm9iy+O//kgd/v992N/Q0REZF+SnzPXNYFRzrkZwCRghPf+85xO9t7PBj4E5gDDgd5elUJECtznn4dVOQDLAvnoI2jenH+te4w3uIRmpPMSV7FgUdGYa5s0sXHXrth7Ri9ELFt2V8zMdPHiUKmSBddr1ti+OnXin+uHH2z8/nsboxcvioiI7Cvys1rIDO99B+99W+99a+/9gwnOqe+9Xx31vZ/3vpH3vpn3/qv8ejYRSWzjRujePaqSRno6nHgi9OwJ1apxKD9xBYO45X82bfzHH+G10QF50GHx9NNt3LLFRmuLvpki2f7JU7WqpYUEnRprxy1lhubN/967iYiIFAR1aBQRAKZMgYoVbXvVki1w113Qpo3lVA8cCJMnMx6Lus84AypUsPzqQPR2r142Xn+9jUsiFe3nzoV69bbE/XbVqjZzPW+ebQepItGeeCLcVhk9ERHZV+VrKT4R2X989BGA5wyGMYAb4L9LLPn5sccgNZWsrPDc5s2tJflvv4X7vor8t6aBQt+hAAAgAElEQVT+/cMgPUgTufdeC4hXrYJ69aJWOEaMG2fj5s3QrFni54tuCBOkmYiIiOxrNHMtIgAMeWwBn3Man9CDjVRg+B3fw5tvWhRNuFgxULt2bCrI/ffbeNRR4b569aBKFUsHmRkpdX3QQfEz10GO9fjx0KpV3s/6yitJvpSIiEgBU3AtcqDbuhXuv58ZWa04ijGM79mfjkxl+8FHxZwWVOno29fGEiXgxx+tPB5YajZAp06xt7/wQmtLPngwlC4NbdpsiHuE6GA5URm+7I49NpkXExERKXgKrkUOEBUrgnOxudF8+SW0bg0PPMBQetCMdKr990Z2UTxmVnrjRrjuOtv+5z9tjK7e4T1Mn25txbOrWhU2bIBRo+CII6Bs2fgiQFHV/HINrtu1szG2uauIiMi+Q8G1yAEgK8sCZLB4mkWL4Mwz4dRTbQp65EguKf4e5/67FvXrQ7lyFiwHotuQB8Hv55HCmqtWWdWQpUtjU0ICVarYOHs2dO2a+PmiFzDmFlxPmAAZGbm8qIiIyF6m4FrkALA6UvCyBNsp3b+fRbAjRsD//gfTp/NDiW7s3GkBddGiUKtWbI51MIv98MPhrPHBB9u4aBEsXGjbjRvH/3bVquF29Ax1TmrVyvlYyZJQtmze9xAREdlbFFyLHAA+/RSOZwRpJdpy3qy78aecYnXxbruN39eV2D3jfPbZNlauHAbkEFbniE77KFvWcqjXrYP5821fw4bxvx0dXGfPx05EKR8iIrI/U3AtUghkZEBaWg4Hly2j0yM9GcEJVKqQxUl8xZoXPoaDDgKsvnXgwgttbN7cKnwEguC6Zs3YW5cqBdu22W8XK5Z45jq6TXmizouBtLRc3kFERGQ/oeBapBA45xxo0SJb2/EdO6xGdfPmtFjwGU9WfojRA2fyNSftnmkOTgukpNjYuLHVsN661b4/9pjNUkfXmoYwuJ4/H+rXt1bm2QXdFhs0yH1WulmznGtci4iI7C8UXIsUAiNG2Li7Hfno0dC+PfTtS9Zx3Wjh53DT2rtp1KoUAJ98El6baIFgvXo2Lllin1Wr4LDD4s8rXdoC8AULoFGjxM9WubIF50GTGRERkcJMwbXIfu6009jdPXHMBystt+PYYy3q/ewzVj4/jEU04NJLrZs5wKZN4fW33GLjd9+F++rXt3HRorD5y513xv92qVJ5B9fBb2hWWkREDgRqfy6yn/viCyjKLq7jGU656V4osR3uuQfuuANKl2bZBDsvWIzYokVsretg++ijw31BcD15slXqA+jQIf63U1NhyBDbTrSYUURE5ECjmWuRfdzNN1uuclCnOrvD+JEpdOIpbmRKycOtoPSDD1rOBmGN6iD3uVKlsMze+vXhfYpE/dOgdm1bfDh1qt2uZk27LrvoWti5zVyLiIgcKBRci+zDFi2C/v1t++efsx384w/o1YsfOYJ65dfx0klDOLPEl/hGsSU7cguug4WNQ4fG3to5S+NYsSJs4pjIDTeE28Fst4iIyIFMwbXIXrRjR5gvncjFF4fbgwZFNjIz4bnnoFkzst55l4e5nbNbziXrjB5s3ORYtiy8xnvo08e2g3rT0cH1L7/Y2KRJ/G9XqWLl+Fatgn/8I/HznXxyuJ1bZ0UREZEDhYJrkb1k3jzrOBg0bklk7Nhw+9tvsf7fXbtC797QsSMtd83gTh7mh6lldy8YnDcvvOb1121s1ChM+6hUKWwQ88svNkudKKUjJSVMRWnXLvHzBW3Le/cOc7NFREQOZFrQKLKXXH65jZ98YjPMudWAPvPINVww43Y4ZJD1B3//fejZk/QidtHgwVC3rp0bpIEAjBtn48SJ4b569az83rBhFlzXrWtVP7KrWDHczqnSR6NGMGkStG2bx8uKiIgcIDRzLZJP6te3gDmmsUuU3TWpCZu1RNu8GRxZfHHmy7w1sSk9NrxmqxvT0uDcc2Oi8eOPD3Oqly4N7/HyyzYGM8wAvXrZ+P33uZfQK1o03A4C90Q6d9astYiISEDBtUg+WLQIFi+27SCvOdrOnbHpG19+GX/Oys+nMI5DOeWTK1lTozXtmcb2fo/vbpMY3Z68dGn7VKsW/u62bYmfrUoVq/6Rnm4z2zktRPztt3C7iP5JISIikhT9K1MkH+xefEhskBp46qnY7//3f1Ff1q2D3r1pdH4X6rGYBfe/xdd3jGY2rWNmuwcPtjGoMw02C71ggW0HQf0rr8T//sqVYUAffc9oQdqKiIiIJE/BtUg+SEsL0yp++in++Lff2linTtTOrCxbgdisGbzwAk/762lOGhV7X0RKJUsBWbgwPP2JJywzpEePcF/jxmF5vWBmvH373J81UedFgCOOsPzttWtzv15ERERCCq5F8sGkSdC8uW3ffXf88Vq1bLzrLitz17nEDPxRR8Gll0Ljxsx6Ywr/ZgAbSKFKlTC9OiiJl5EBGzbE5lKDpYUEwfCvv9qYV+fEQw7J+VitWombx4iIiEhiCq5F9rDMTFiyxDobpqTkfF6tWnD1+Rt4eNu/GbejI1lz0i2HY+xYBnwfTjc7B1u22PbOnTYGs9KPPhp7z/LlLfDOyrLgOiUl92coVUr51CIiInuS/rUqsod9/rmNHTvCNddAsWJWai8wfz689prnnO3vQPPmdPjhaV7mCo6vmw6XXQZFiuxejPjBBzYGtbAPP9zGtDQbDz449rcjHc/ZuNGC6wYNEj/jqFE25lYFRERERP48Bdcie9jHH9v4yCNQoYKV4ouu3PHl43P4juMYsOYiqFOHJ8+dwLU8T9ofYY7Hli3QqhX07Gnfy5Wz7SVL7Ht6us04N47tdE7x4jYOH26VQNq0SfyMwWx2mTJ/82VFREQkhoJrkT/pq68sVeOFFxIf37LFgt7jjw+D1wULsHyNvn3p/XI72jGdnQNfgPHjufS5LkAYSIMVDIlu4gLQooXVsN6504Lr+vWtw2O088+38ZFHYP16OOmkxM/YqhX885/w3nt/6tVFREQkDwquRf6kU06x8ZprEh+fOjVsF96yJYBn1n0f2QrHxx7jp8aXcFT1dIpfdxUULUrlyhYoB50Vp0+3tI1Fi2LvW62ajevWWXCdqGtiaqqN06bFfs+ueHF44w0L2EVERGTPUXAt8idkZeV+fO5cC4o7dbLvx9dN57viJ3LekJ4WHf/0E/0aDKJMvWox17VqFdanfvppG4NOioGgMsgvv+QcXEd3VQRrGCMiIiIFR8G1SJRly6yhy/r1iY9HN1ypXj3++Hff2Xhu9y1WZ69NGzpnTeTxegNh8mT8IYcye3Z8V8TUVPj9d9ueMgVOPhn69Ys9J8iTPuIIa5fetGniZ7zqqnC7atXE54iIiEj+UHAtEuWuu2xB4ltvJT4edFvs0sUC7RUrYo9PnOC5pOInNDi1Bfz3v3Deedx1djoD/XVs2V6UIkUsgD/iiNjrUlPtfpmZlhaSKJ0juuIIwJFHJn7GYFEjaOZaRESkoCm4lgNGWhqsXp37OStX2tinT+LjwezyaafZOHRo1MEFC7hsyGm8vqEHrkIF+P57ePNNitRMZd26MO0D4meda9SwwPqZZ+z73Lnxv92hQ7h98snQunXiZwyayJx0ktWxFhERkYKj4FoOCFlZtnivWjXYtCnn80aMCLc/+yz+eDBzfeihNj78MJajcf/9ZLVsRcfNYxhx0hO2qvGoowCbhd60CcaMCe8TdG8MBDPVs2bZmKglec2a4ULJevVyfody5Wy8+eaczxEREZH8oeBaDggPPxxuP/RQctdMnx77fdMmqx8NYXDddvmX+Fat4YEHSG/Rg2ak0/SFm2JyM4IZ52+/De910EGx965Z08YZM2w87rjcnyl7Cb5ojz4Kzz4L3brlfI6IiIjkDwXXUij85z9w4YU5Hx8yJNx+7LHE5wQ5zUcfbWOQIhK4/XZ4/33bLrdmMUM5ky85lV1FS8DIkTze6T2yUmvFzSpXqmTj0KG2KDEjI77leOfONk6caLPawexzdkEu+CWXJD4OVh/72mutFreIiIgULAXXUijccw+8+26YE53d1Kmx37dujT/njTds7NYN/r+9O4+yorr2OP7dTDIEkUEmQZlRQERUFCQOwSe+RFESB2KMJvg0RnyiiRExGoOsGOcYE6NRYzRxSkRRREWB0OQJCKICAjaDgMocUWQQWobz/thVVN3u2y0Nfbvp5vdZ666qW1W3bt0Tltkc9tm7Vy+YPz/z/P/9H9SigOHcBkccQf9q4xnG7cx9cjb068eiRdCxY9H7tmqV+b316hW9Jt0pMQ7us7noIv9LQDr/WkRERPYdCq6l0vv882S/uMYusbgRy/PPZx7fsQN+/GPfX7LEFxim7wvQ58vxzKE7t/FL+Pa3mffcB9zJMNZ8Xgvw2tOF25EDHHJIsl9QsBs/SERERCotBddS6d1wQ7I/bVrR89u3J/tx3nPhRY2TJydFqy+91HOad+VcL18O55/PQx+eTjV2Mm7oazBqFAd198Tp1at9VnvtWmjXruRnzbZIsrDvfvfrrxEREZF9U42KfgCRvfXll8n+6tWeNpHON160yLcHHwytW/v+1q3J+fXrYeTILoDnWTdv7jWka7CNNdfdR7OHRrD9qx38mpHUGn4dv7rN69sdeqgH4TNmJDPS551X8rPGJfyymTwZNm+G00/fnV8tIiIi+yIF11LpFa68sXx5EkRDkkv9+OPJQsHHH4drr/X9dJ528+a+PZk8HmAIze6ZDwMG8ON19/HklLas/N/k2lq1fCHi++/Dgw/6sfbtsz/jtGmZda6ziSr3iYiISCWmtBDZ5xUU+KxucW67zbczZvg2Xasa4MknfXv44VAj+uvkV18l55ct8+0rr+BT1z/4AXmcSl2+ZMoNL8NLLzHm/bZcfnlSMi924IGZiyPT3RHTTjih5GomIiIiUjUouJZ9XseOcMopcP/9Rc+l0zuOPdbL0L39duY1hx4Kffok+dAXXOALGGNjxkB1ttNr6n3QuTOMGsWma2+mC/OZ1epMPvsMNmzIXgnk/ffhnXd8/4or9upnioiISBWg4Foq1PTpSbvubDZvhk8+8f1//avo+TiQHjPG86yPPDKzhN4rr3hKRp8+ybGWLeGjj5K61lsnTmF+7R40+c21cOKJMG8ede66lWp167BgQdJ0platot+/fHmynw7YRUREZP+k4FoqzMqVni7RuHES6BY2ZUqyH7ceT1u61LdxO/FmzWDduuR8vIAwHRi3a+dpIfMmrWXHxT/iLwv60qja516f79VXoUMHqlf361asgG3b/HMXXFDy79m5s+TzIiIiUvUpuJYKs3hxsv/WW9mvGTw42Z8+vWgAu3Spz1jHixrr14cvvih6n3T6yOn9dvBT/kT7b3fGnnma33IDfx76otfAS5UZqVvXK5Fs2eK51s2aFb3v008n+3ffXcwPFRERkf2GgmvJmYIC+PTT4s9/9FGyX7gbYqyLV8hjwADfzpqVnNu82YPbli29JB74esTly5MGMD17+vbGG6MPTZ9Op4t68SeG8FHjnvx+8Bxu5Ld0Oz61wjESB9ePPZa0MC9s0KBk/6CDiv+tIiIisn9QcC0507Sp15ZON3FJS1cA2bw5+zULF3oAe911/n7VquTctdf6+RUrkmM9evh2+XIP7ufO9c82Zh1cfjn07g2rV3NVk2f5bb8JvLflcA47DBo02Fbku+vW9ftD0tmxsHQ9bREREREF15ITf/2rV9gAePfd7NcsXw5HHeX76UYwsY8+8lfXrklO9dy5yfm1a31bvXpy7NRTfbtxI+TlwbavdnLh5kegUyefgv7ZzyA/n4lNLmDLVuOTT6BVq+zPd+CBSZ731VcX/1vHj8++2FJERET2PwquJSfSudJvvJH9mo0boUkT38/LK3r+b3/zbbduPnN88MGwZElyPq5ZPXNmcqx+fd9u2AAfv/gO0+jN0Q9e7jeZNcsTo+vXZ+lSeO45/94OHbI/35FHJvvZ8q1jp52WBPUiIiKyf1OHRsm5dG51bPVqmDo1yWV+/fWi1/zqV749/njfNm+ezCRv2ODFPcySVBDw2eaD+JxWv72J0//9IJ9WawpP/N07uKRyOOJ25eAVS7I55phk/+ijv+ZHioiIiKCZa8mBrVs9jr3lFp+ZfvTRzPNjxyazwo0aZb9HOk+7cWPfNmyYVAKZM8e3p52W+tDOnTQf9zgL6MwR/36I0S2v4ofH5sNFF5WYHJ2tOQxA377Jft26xX5cREREZBcF11Lmli71utUdOiTVQtILFs86Kzn+zDMwfLjvp+tY33yzbwcPTmpUN2iQBNfxIsZ7740+MGcOnHQSTX7xYxbTgWN4h3NX3k+bHtlLeIwb56keffp498ds6tXz5xs2bLd/uoiIiOznFFzvR0KABx7wjoV74667fCL45Zezn4/rV3foAA8+6PvZUkPA1xnGwe3Uqb7dsAFuv933f/nL5NqGDZOgPL5f6wYb4JprvObeggXsePgv9OVNZuO5It27Z//e/v09mJ8yJXNBZGGDBiXPIiIiIvJ1FFzvR37yE7jqqsxW4Nn8+c9JvnM211/v2wED4P33i55/803fduiQ5EPHnRTTfvc7n40+6SQPcKdP9+M//KFvzz7buyTGunb1CiOffgoLFwR+Uv9pGvTqDPffD5ddBgsWUP2ywYTUH+vigmsRERGRXNCCxv3II48k+9u2Qc2aRa/57DO44grfv/jiopU0CpfM697d7xVX7igogDvv9MWHTZokudNxcB0C1K7tQfdVV/mx2rXhiCPggw/8/VdRP5ef/zzzu3r18u0Hz8/nylFD6LkxDzofC2PGwHHH7bouPz8p3Zeu+CEiIiKSa5q53o+k1/QV1znxoYeS/bFji57P9rl0rnRc0zquwNGsGdSpkwTXX3zhCx6/970kIAcPhvPzfX/cON9+85uZ39Oz0ybu4Hr6XHkUbTfO5m99HvK+6anAGjL/QqCuiSIiIlKeFFxXEfPnw9ChxbcRBw9m27f3/WxBckGBZ1j07+/5zYsWFb0mzot+4QVvPQ6ZixVnz/btiBG+NYM2bWDZMn8f17Pu1Cnzvu3bewC+c2eWBw8BnnuOA3sdzvXcxV93XkKnsIBl/X+SNWG6enVf6PjOO1nuJSIiIpJDOQuuzay2mc0ws9lmNs/MRkTH7zKzfDObY2ajzeyg1GeGm9liM1tgZv1z9WyVUQjFn/v4Y89Hvv9+nxHOpqDA0zfatPH32YLrJ5+ENWu8XXjz5kkHxLTvf9+3jRrBN77h+5s2JedHj/bSdt26JcfatElmrseP9zSQM87IvG/cJj1eqHjffdGJBQs82j//fDj4YHozlct4lE85mEMPLWZA8NboPXsWf15EREQkF3I5c10AfCuEcBTQAzjDzE4AxgPdQgjdgYXAcAAz6wIMAroCZwB/MrMS6jjsPzp3hmrVPB86m3Sg/J//ZL8mPn7EEUU/Az5jfM89ngvdr5+XoUvPSAN8/nmyX7t29uB6yRLPw66W+pOVDtTfeANOPjkprxeLa1nHM+8tD/rSS4UceSTMmAF/+AO8/TZv0XvXZ9IBvIiIiMi+IGfBdXBx2FUzeoUQwhshhLhFyFtAq2j/bODZEEJBCGEpsBjolavn25fs2FHyzPTChb599dXs56dMSfbXrcvMgY7FjVziToOrVmWef+01X1B43XWeyjFrlh9LP9fbbyf7HTp4AA5JcP3FF16Gr/CMce3aSUfEVaugS5eiz7cruJ4XOJsXOev6I+C227wW3oIFvvqxRub628MOK3ofERERkYqU05xrM6tuZrOAtcD4EML0QpcMBl6L9g8BPkmdWx4dq9IGDvSYsaSScXFAnA5u0377W99eeaVv77mn6DXxQsNBg7zbYOHSeHffDa1be/YFJFU+Pkn9L/LYY76dP9+D4cIz1/HMeosWmfc+4ACfKZ82zWfD47zvtCZNoB0f0mXYmbzIQKo1PBAmT4a//c1XRUbOOy/5jBYrioiIyL4mp6X4Qgg7gB5RXvVoM+sWQpgLYGa/BLYDT0WXZ+tPXWQ+18wuBy4HaNasGXnxCrlK6sUXTwFg7lyK/JZNmzbxxhuTee+9kwF47bWNDBxYdJXeqlV+j3PPzeMf/+jN9OkbyMubl3FNfv4xdO26kxkz3qNZs2OZMWMreXlzAfjiixrk5fVl8OClTJniSc8nn9yFyZObMnbsu3TpsgGAJUu606FDTdaseYc1a2DNmgOA3rz99gKaNVvFhAlNgS6sWDGXvLwk72TixB7AQfTvvw2oSUHBLPLy1u86X62ggEOefIZ5PMs2avIz7uGs+7tjO3cmKyAj9esfBrQFYMqUzHN7Y9OmTZX+z1JlobEuPxrr8qXxLj8a6/Kjsd4DIYRyeQG3ANdF+5cA04C6qfPDgeGp968DvUu65zHHHBMqsw8+CMETL/xV2KRJk8J3vpOcr1YthA0bMq/Jz/dzffr4+969Q+jXL/OalSv9mhtv9PcDBoTQrVtyfto0P//SS8mxN9/0Y+PGJcc6dQph4MDk/fr1fs3FF4ewaVMIXbv6+5UrM7+/U6fM3zl/furkK6+E0K5dCBBG1x4UWrAi61jENm0KYdKk4s/vqUm5uKlkpbEuPxrr8qXxLj8a6/KjsU4AM8NuxLy5rBZycFwJxMzqAKcB+WZ2BjAMGBBCSLckGQMMMrMDzKwt0BGYkavn2xf0T9VDSWU+ZHjlFd927uyLDtP51QDPP+/bOCWkQQNvH5728MO+7dvXt+3a+cLDOJ867rKYbrgSp1zEixhnz/bc73SFjgYN/Lrt2+HCC2FeNFleOC0krlASa94cLwsycCB85zu+unHCBB4+9RlW0ZKrr84+FuB53nG7dBEREZF9TS5zrlsAk8xsDvA2nnM9FvgjUB8Yb2azzOwhgBDCPOCfwHxgHDAkeFpJpXX77Unwm03cRbBHDy+BV9Kixksu8W3cxTAWdzOMc6WbNfOa0nG96KVL4de/9v24/F27dt5pMa7gMWsWHHhgZhDctq3Xi44D5rgd+g9+kPn969d7veutW/39vfcWffa4HjZAexZz0GXn+orIN97wQZo9G/r129U0pnANbBEREZHKIpfVQuaEEI4OIXQPIXQLIdwaHe8QQmgdQugRva5IfeY3IYT2IYTOIYTXir975TB8OJx7bvELEZs394oXBx/s7+++O/N8QUHyP8+wYd6ufM2azGtWrPD7xK3Me/f2sntxSbuXX04+H3dobNfOt0uW+Hb2bF9Qme7gWLeu186eOdPfz5/v8XChZoi7rF/vixyvvbboucaNoT4buJ1hzKMr9vrrcM01/jeFYcN21eW77DIfs4svzv4dIiIiIvs6dWjcQzt2JLO12cxLrSccPz77NXHKR9xk8IUXMs/7gkEvmFGtmndNXLcuOb92rZfYa9o0ORaXp4sreLz6qs+Q3357ck06uN65E+bMgaOOKvp8xx7rwfXGjV5i70c/Kv73zphRTPWOjz6CESNYSCeGcSdPc6Hnl9x1F4W7wDRp4tX36tcv/ntERERE9mUKrvfA9u0wYADUqVP8NXEbcCiaygHeLXHdOo89H3rIjxWu/zx+fHMgSR+Jg+k4qL/lFt/GrcUheaYvo2z2efPg+OMz7xunfyxZ4p/duDF7cN2tm5fQi4P+OCgvTkbTma1bYeRIf/gRI1hc43COYwbVn/hr0aRsERERkSpCwfUeGDw4aeiycWP2a+Lc5MMPL7oIEXwmOHbYYR7IFu7A+O67B9G7d9FUjLhF+MqVvh05MjkXB9fLlnnjltWroWXLzM/XqePx7bJlyV8CsgXXcWOXeMb6kCxVx9NdG3c1r3nlFf9Bv/oVnHUWfPQRLfLz6Hn5cZx5ZtF7iIiIiFQVCq73wN//nuwvWlTytSeemBmAxuJZ6tGjfdusWbLAMPb557WyzhbHederV8Opp5JRXSNeFHnppR7Ub98OJ5xQ9B7x982e7Skn2VqJN2qU+T5bAJ5OBRk5eCmcfTaceaZ3xhk/Hv75T2jdmvbt4c9/LnpPERERkapEwfVeyhZc/+lPyX6LFr7Yr7hKIHGGRNOmmYsVX38dVq2qs2uxY9rq1fDhh57n3Lp15rl0kDxunC90/Na3it6jSRNvXDNrFnTs6AsYC2vYMNm/+24vvZdNbbbwK0Zw41NdYOJEuOMOT+Q+7bTsHxARERGpohRc76WFCzPfhwBDhvh+375JKkXcfhx8LV8sri1dUOABc1yj+pFHoFq1wNChybVxGsiaNTB2bPIdafXqJVVHxoyBnj2TNuVpEyZ4WshLL2WfkYbMWeYjjsh+DWPHMpdujODXbDtjAOTnw/XX76oAIiIiIrI/UXC9lwrPXMdVOgB+9zs42TuXk5+fHL/xRt/+/e/JjHE8K7xwoVfwmDQJTj99dUbt6TgFY/PmpMTeBRcUfaajj/btggVJ9ZCS9OiR/Xh65jpeVLnLkiW+qvOss2jY7AAG1p9AzRf+Aa1aff0XioiIiFRRCq5LKU7vOO44T7dYsCDzfLwo8dFHvZRd27b+funS5JoTT/TPX3RRcmzQIN8WFHiFj88+gx491mfcu3Zt327Z4kF869be/KWwdPpGtkWIAI8/nux37Zr9mnRJvF2531u2eFeaLl3gX/+CO++k0cezGL2hH9X0p0lERET2cwqHSikEeOopbyneoYPnPadnr2+6ybdxYFq7tudVxw1b5s+HyZOLtiiPq3xs3ZqkkBx+eGYpEjO/35w5MH168aUA02kgxc1cp1uvd+6c/Zoiedgvv+yR+IgRcM45Ph3/i18oBUREREQkouC6lKpVgwsv9FSKY47xY3HeNMCTT/p2y5bkWJs2Sfm8t97y7TnnZN43PSs9a5YHzq1afZn1GV580bff/nb2Z0z3Zin8PbHmzZNOiPHsemFxx8aTWi3xknoDBviDTpwIzz6rFBARERGRQhRc74U437mgwLfrU1kcpyiC4FUAAA5bSURBVJ+e7DdtmqSPXHqpb2+9NfNe6Znrp5/2duRx58a0dFfIe+/N/lzpGe2Scq4fe8xn0IudeN6yhTVX3ELef7pAXp6vlJw9O3v5ERERERGhRkU/QGXWoIFP5o4Z4zPOM2f68dGjM5sQrl8PK1b4IsVY4YA2XqyYn+/1p/v0+frvj2eWszn33EIdE7OoXj2VVx2Cf+CLL/yBZ82Cm26i6bJl8P3ve4mT4hK4RURERATQzPVeGzjQt6tWwT33+H7PnpnXxN0aR43y7RNPFL3PIYd4QP6HP/j7bFVAICm99/DDJT/Xc88lXSRLtHUrPPigd5WpX99TPbp189WWdev6osWnn1ZgLSIiIrIbNHO9l+LW4itXetMWKBqHxosH4+Yy2apzmHkudxygH3oofPVV0evuvddTS4oLvndbCF7k+tprveD1N7/pixMbNPBp9Fat4Pjjs+emiIiIiEhWCq73Ujq4btHC3xeORxs3znxfuKtiLF3T+rjjvH15Yccd59VC9kp+PgwdCm+84ZH+uHGeJF5SnomIiIiIfC2lheyldHD9+edwyilFr6lXD8480/dr1fLW49nUq5fs16xZpo/pNmyA667z8ibTp8Pvfw/vvedT6wqsRURERPaaguu91LChtwl/6CFPX06XwUuLy/bt3EmxzVY+/DA3z8hXX8Ff/gKdOnleyY9+5K0gr746R1G8iIiIyP5JwfVeMvOmMnEHxsKLGWO9e/u2Q4fi7/U//+PbYcPK6OFC8FImXbv6zdu08RnrRx7x+oAiIiIiUqaUc10GzjgDnn8eHnggmaEurFcvOOmkpBpINm3aJO3V99qcOfDzn8OECXDEETB2rHedUfqHiIiISM4ouC4jZ56Z5FVn07Chtz3PufHj4Wc/g7lz/Uvvvx+uuELpHyIiIiLlQGkhVcXy5XD++V71Y+tWuO8+WLQI/vd/FViLiIiIlBPNXFd227Z5ID1iBOzYASNHer3qAw6o6CcTERER2e8ouK7M8vJgyBCYPx/OOstL67VtW9FPJSIiIrLfUlpIZbR6tbcnP/VU+PJLrwgyZowCaxEREZEKpuC6Mtm+3WenO3eG556Dm29OZq1FREREpMIpLaSymDoVrrwSZs/2jop/+AN07FjRTyUiIiIiKZq53tf95z8weDCceCKsWwejRsFrrymwFhEREdkHaeZ6X7VjBy3HjIHvfhc2bvS2jTffDPXqVfSTiYiIiEgxFFzvi95+G668kk4zZ/qixQce8C6LIiIiIrJPU1rIvuSzz7yb4vHHw4oVzL/pJpg4UYG1iIiISCWh4HpfsHMnPPaYVwF59FG45hrIz2dtv35gVtFPJyIiIiK7SWkhFW3WLK8CMm0a9O3rKSDdu1f0U4mIiIjIHtDMdUX54gu4+mo45hhYvBgefxz+/W8F1iIiIiKVmGauy1sI8NRTcN11Xmbvpz+FkSOhYcOKfjIRERER2UsKrsvTvHkwZAhMngy9esGrr0LPnhX9VCIiIiJSRpQWUh5WrYKhQ6FHD3j/fXj4Yc+xVmAtIiIiUqVo5jqXtmyBe+6B22+HrVu90+Jtt0GTJhX9ZCIiIiKSAwquc2HbNnjmGe+o+PHH8L3vwR13QPv2Ff1kIiIiIpJDSgspSyF4HnX37nDJJdCoEUyaBKNGKbAWERER2Q9o5npv7dgBY8Z4veo334R//Qs6dIAXXoBzzlETGBEREZH9iILrPTVjBoweDS+/7FVAzODQQ+Hee70iSK1aFf2EIiIiIlLOFFyX1mefwaWXwosvQs2angLy7LMwcKACahEREZH9nILr0mrQAFavhltvhWuugfr1K/qJRERERGQfoeC6tKpXh6lTlUstIiIiIkWoWsieUGAtIiIiIlkouBYRERERKSMKrkVEREREyoiCaxERERGRMqLgWkRERESkjCi4FhEREREpIwquRURERETKiIJrEREREZEyouBaRERERKSMKLgWERERESkjCq5FRERERMqIgmsRERERkTKi4FpEREREpIwouBYRERERKSM5C67NrLaZzTCz2WY2z8xGRMcbmdl4M1sUbRumPjPczBab2QIz65+rZxMRERERyYVczlwXAN8KIRwF9ADOMLMTgBuAiSGEjsDE6D1m1gUYBHQFzgD+ZGbVc/h8IiIiIiJlKmfBdXCborc1o1cAzgaeiI4/AZwT7Z8NPBtCKAghLAUWA71y9XwiIiIiImUtpznXZlbdzGYBa4HxIYTpQLMQwiqAaNs0uvwQ4JPUx5dHx0REREREKoUaubx5CGEH0MPMDgJGm1m3Ei63bLcocpHZ5cDl0dtNZrZg7590n9UE+LSiH2I/obEuPxrr8qOxLl8a7/KjsS4/GuvEYbtzUU6D61gIYb2Z5eG51GvMrEUIYZWZtcBntcFnqlunPtYKWJnlXg8DD+f4kfcJZjYzhHBsRT/H/kBjXX401uVHY12+NN7lR2NdfjTWpZfLaiEHRzPWmFkd4DQgHxgDXBJddgnwUrQ/BhhkZgeYWVugIzAjV88nIiIiIlLWcjlz3QJ4Iqr4UQ34ZwhhrJlNA/5pZpcCHwPnAYQQ5pnZP4H5wHZgSJRWIiIiIiJSKeQsuA4hzAGOznJ8HdCvmM/8BvhNrp6pEtov0l/2ERrr8qOxLj8a6/Kl8S4/Guvyo7EuJQuhyJpBERERERHZA2p/LiIiIiJSRhRclyMze8zM1prZ3NSxo8xsmpm9b2Yvm9mBqXPdo3PzovO1o+O/MbNPzGxTtu+R0o21mf3AzGalXjvNrEd0TmO9G0o53jXN7Ino+AdmNjz1GY331yjlWNcys79Gx2eb2Smpz2isv4aZtTazSdGf03lmNjQ63sjMxpvZomjbMPWZ4Wa22MwWmFn/1HGNdwlKO9Zm1ji6fpOZ/bHQvTTWJdiDsf4vM3sn+u/IO2b2rdS9NNbZhBD0KqcXcBLQE5ibOvY2cHK0PxgYGe3XAOYAR0XvGwPVo/0T8AWjmyr6N+2rr9KMdaHPHQksSb3XWJfxeAMX4t1YAeoCy4A2Gu+cjPUQ4K/RflPgHaCaxnq3x7oF0DParw8sBLoAdwI3RMdvAO6I9rsAs4EDgLbAh/rvds7Guh7QF7gC+GOhe2msy3asjwZaRvvdgBUa65JfmrkuRyGEfwOfFTrcGfh3tD8e+F60fzowJ4QwO/rsuhBVTwkhvBWiLpeSXSnHOu37wDOp+2isd0MpxzsA9cysBlAH+ArYEN1H4/01SjnWXYCJ0efWAuuBY6P3GuuvEUJYFUJ4N9rfCHyAdw4+G3giuuwJ4Jxo/2z8L44FIYSlwGKgV/R5jXcJSjvWIYTNIYQ3ga1Z7qWxLsEejPV7IYS478g8oLaZHRCd01hnoeC64s0FBkT755E00ukEBDN73czeNbPrK+TpqpbixjrtAlLBteyV4sZ7FLAZWIWX47w7hFA4WJTSKW6sZwNnm1kN8/4Bx5D9z718DTNrg8/gTQeaxQFFtG0aXXYI8EnqY8ujY1IKuznWUgb2YKy/B7wXQigor2esjBRcV7zBwBAzewf/55mvouM18H/y+kG0HWhmWUsYym4rbqwBMLPjgS9DCHOzfVhKrbjx7gXsAFri/3T+czNrVzGPWGUUN9aP4QHeTOA+YCreR0BKwcy+ATwPXBNC2FDSpVmOqSRXKZRirGUvlXaszawrcAfwk1w/W2VXLu3PpXghhHw8BQQz6wR8Jzq1HJgcQvg0Ovcqnmc5sSKesyooYaxjg9CsdZkpYbwvBMaFELYBa81sCp6qsKRCHrQKKG6sQwjbgWvj68xsKrCoIp6xsjKzmngA8lQI4YXo8BozaxFCWGVmLYC10fHlZP7LQCtgJbJbSjnWshdKO9Zm1goYDVwcQviw/J+4ctHMdQUzs6bRthpwE/BQdOp1oLuZ1Y1yU0/Gu1fKHiphrONj5wHPVszTVT0ljPfHwLfM1cMXxORXzFNWDcWNdfTfj3rR/n8B20MI+u/IbjIzA/4CfBBCuDd1agxwSbR/CfBS6vggMzsgSsPpCMwor+etzPZgrGUPlXaszewg4BVgeAhhSnk+a6VV0Ssq96cXPiu6CtiGz3BcCgzFV+ouBG4nauwTXX8RvnhgLnBn6vid0ed3RttfV/Rv29deezDWpwBvZbmPxrqMxxv4BvBc9Gd7PvALjXfOxroNsABfsDQBOExjXaqx7oundcwBZkWvb+PVmybi/wowEWiU+swv8SohC4D/1njndKyX4Yt7N0Vj2kVjXfZjjf+FfXPq2llAU4118S91aBQRERERKSNKCxERERERKSMKrkVEREREyoiCaxERERGRMqLgWkRERESkjCi4FhEREREpIwquRUSqgKhu+Jtm9t+pY+eb2biKfC4Rkf2NSvGJiFQRZtYNryF+NFAdr0d7RtiLjmpmViN4p0cREdkNCq5FRKoQM7sTb/hQD9gYQhhpZpcAQ4BawFTgqhDCTjN7GOgJ1AH+EUK4NbrHcuDPwBnAfSGE5yrgp4iIVEo1KvoBRESkTI0A3gW+Ao6NZrMHAn1CCNujgHoQ8DRwQwjhMzOrAUwys1EhaY++OYRwYkX8ABGRykzBtYhIFRJC2Gxm/wA2hRAKzOw04DhgppmBz1J/El3+fTO7FP//gpZAF7wlPcA/yvfJRUSqBgXXIiJVz87oBWDAYyGEm9MXmFlHYCjQK4Sw3syeBGqnLtlcLk8qIlLFqFqIiEjVNgE438yaAJhZYzM7FDgQ2AhsMLMWQP8KfEYRkSpDM9ciIlVYCOF9MxsBTDCzasA24ApgJp4CMhdYAkypuKcUEak6VC1ERERERKSMKC1ERERERKSMKLgWERERESkjCq5FRERERMqIgmsRERERkTKi4FpEREREpIwouBYRERERKSMKrkVEREREyoiCaxERERGRMvL/3aTZSio74JwAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -582,29 +596,38 @@
     }
    ],
    "source": [
-    "# Visualizar la evolución temporal de la concentración de CO2\n",
-    "plt.figure(figsize=(12, 8))  # Aumentar el tamaño de la figura\n",
-    "plt.plot(data['Date'], data['Concentration'], label='Concentración de CO2', color='blue')\n",
-    "plt.xlabel('Fecha')\n",
-    "plt.ylabel('Concentración de CO2 (ppm)')\n",
-    "plt.title('Evolución temporal de la concentración de CO2')# Graficar el modelo de regresión lineal y la proyección\n",
+    "# Visualisation of the temporal evolution of CO2 concentration\n",
+    "plt.figure(figsize=(12, 8))  # Size of the figure\n",
+    "plt.plot(data['Date'], data['Concentration'], label='CO2 Concentration', color='blue')\n",
+    "plt.title('CO2 Concentration Over Time')\n",
+    "plt.xlabel('Year')\n",
+    "plt.ylabel('CO2 Concentration (ppm)')\n",
     "\n",
-    "plt.plot(data['Date'], model.predict(X), label='Modelo de regresión lineal', color='red')\n",
-    "plt.plot(pd.to_datetime(['2025-01-01']), predicted_CO2_2025, marker='o', markersize=8, label='Proyección a 2025', color='green')\n",
+    "# Plot the linear regression model and the projection\n",
+    "plt.plot(data['Date'], model.predict(X), label='Linear regression model', color='red')\n",
+    "plt.plot(pd.to_datetime(['2025-01-01']), predicted_CO2_2025, marker='o', markersize=8, label='Projection to 2025', color='green')\n",
     "\n",
     "plt.legend()\n",
     "plt.grid(True)\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Periodic oscillation. For this step, we have already separated the oscillation phenomena in the \"oscillation\" column of the pandas table and now we characterised them by their amplitude and frequency."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Oscillation périodique. Caractériser l’oscillation\n",
-    "import numpy as np\n",
+    "# Periodic oscillation. Characterisation of the oscillation\n",
+    "# Importing the necessary libraries\n",
+    "# import numpy as np\n",
     "# from scipy.fft import fft "
    ]
   },
@@ -612,13 +635,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "fft de scipy.fft, a des problèmes de chargement, il se peut que nous utilisions une version de SciPy antérieure à 1.4.0. La fonction fft a été ajoutée dans SciPy version 1.4.0.\n",
-    "Pour résoudre le problème, nous pouvons utiliser la fonction fft de numpy à la place, puisque numpy fournit également des fonctions pour effectuer la transformée de Fourier"
+    "\"numpy as np\" is already included when determining the linear regression model\n",
+    "\n",
+    "\"from scipy.fft import fft) is having problems loading, we may be using a version of SciPy earlier than 1.4.0. The fft function has been added in SciPy version 1.4.0.\n",
+    "To solve the problem, we can use numpy's fft function instead, since numpy also provides functions to perform the Fourier transform"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -635,14 +660,24 @@
     }
    ],
    "source": [
-    "# Oscillation périodique. Caractériser l’oscillation\n",
-    "# Affichage des premières lignes des données pour vérification\n",
+    "# Periodic oscillation\n",
+    "# Displaying the first rows of data for verification\n",
     "print(data.head())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Periodic oscillation\n",
+    " \n",
+    " Characterisation of the periodic oscillation, we use the Fourier transform to characterise the oscillation\n",
+    " "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -650,51 +685,42 @@
      "output_type": "stream",
      "text": [
       "Dominant oscillation frequency: 51.661538461538456 week/cycles\n",
-      "Maximum oscillation amplitude in CO2 anomalies: 3207.2901558554163 ppm\n"
+      "Maximum oscillation amplitude in CO2 oscilation: 3207.2901558554163 ppm\n"
      ]
     }
    ],
    "source": [
-    "# Oscillation périodique. Calculer la transformée de Fourier de la série chronologique des anomalies CO2\n",
+    "# Periodic oscillation. Calculate the Fourier transform of the CO2 oscilation time series.\n",
     "co2_oscilation_fft = np.fft.fft(data['Oscilation'])\n",
     "\n",
-    "# Calculer les fréquences correspondant aux composantes de Fourier\n",
+    "# Calculate the frequencies corresponding to the Fourier components\n",
     "n = len(data)\n",
-    "frequencies = np.fft.fftfreq(n, d=1)  # Fréquences en cycles par semaine\n",
+    "frequencies = np.fft.fftfreq(n, d=1)  # Frequency in cycles per week\n",
     "\n",
-    "# Trouver la fréquence et l'amplitude maximales\n",
+    "# Find the maximum frequency and amplitude\n",
     "max_freq_index = np.argmax(np.abs(co2_oscilation_fft))\n",
     "max_freq = frequencies[max_freq_index]\n",
     "max_amplitude = np.abs(co2_oscilation_fft[max_freq_index])\n",
     "\n",
-    "# Calcula la frecuencia inversa en semanas por ciclo\n",
+    "# Calculate the inverse frequency in weeks per cycle.\n",
     "max_freq_week_cycles = 1 / max_freq\n",
     "\n",
     "print(\"Dominant oscillation frequency:\", max_freq_week_cycles, \"week/cycles\")\n",
-    "print(\"Maximum oscillation amplitude in CO2 anomalies:\", max_amplitude, \"ppm\")"
+    "print(\"Maximum oscillation amplitude in CO2 oscilation:\", max_amplitude, \"ppm\")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 22,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Comment caractériser l'oscillation périodique en calculant la transformée de Fourier donne des valeurs pas faciles à comprendre a priori, une autre manière est choisie pour caractériser l'oscillation\n"
-     ]
-    }
-   ],
    "source": [
-    "# Oscillation périodique. Comment caractériser l'oscillation en calculant la transformée de Fourier donne des valeurs pas faciles à comprendre a priori, une autre manière est choisie pour caractériser l'oscillation\n",
-    "print(\"Comment caractériser l'oscillation périodique en calculant la transformée de Fourier donne des valeurs pas faciles à comprendre a priori, une autre manière est choisie pour caractériser l'oscillation\")"
+    "Characterising a periodic oscillation by calculating the Fourier transform gives values that are difficult to understand a priori, so another method is chosen to characterise the oscillation\n",
+    "\n",
+    "Remembering the periodic oscillation in CO2 concentration (column \"Oscilation\" in the pandas table)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -711,7 +737,7 @@
     }
    ],
    "source": [
-    "# Oscillation périodique. Création du graphique pour montrer l'oscillation de la concentration de CO2 au fil du temps (300 dernières lignes du tableau)\n",
+    "# Periodic oscillation. Creation of the graph to show the oscillation of the CO2 concentration over the time (last 300 rows of the table)\n",
     "plt.figure(figsize=(10, 6))\n",
     "plt.plot(data['Date'][-300:], data['Oscilation'][-300:], label='Oscilation CO2 Concentration Over Time')\n",
     "plt.title('Oscilation CO2 Concentration Over Time')\n",
@@ -721,27 +747,34 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Characterisation of the periodic oscillation, determination of the amplitudes max and min"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Maximum oscillation amplitude in CO2 anomalies: 4.419807692307586 ppm\n",
-      "Minimum oscillation amplitude in CO2 anomalies: -4.191923076923047 ppm\n"
+      "Maximum oscillation amplitude in CO2 periodic oscilation: 4.419807692307586 ppm\n",
+      "Minimum oscillation amplitude in CO2 periodic oscilation: -4.191923076923047 ppm\n"
      ]
     }
    ],
    "source": [
-    "# Oscillation périodique. Calculez la valeur maximale et minimale de la colonne 'Oscillation'\n",
+    "# Periodic oscillation. Calculate the maximum and minimum value of the 'Oscillation' column\n",
     "maximum_value = data['Oscilation'].max()\n",
     "minimum_value = data['Oscilation'].min()\n",
     "\n",
-    "print(\"Maximum oscillation amplitude in CO2 anomalies:\", maximum_value, \"ppm\")\n",
-    "print(\"Minimum oscillation amplitude in CO2 anomalies:\", minimum_value, \"ppm\")"
+    "print(\"Maximum oscillation amplitude in CO2 periodic oscilation:\", maximum_value, \"ppm\")\n",
+    "print(\"Minimum oscillation amplitude in CO2 periodic oscilation:\", minimum_value, \"ppm\")"
    ]
   },
   {
@@ -751,39 +784,105 @@
     "From the graph it is observed that maximum oscillation values around 3 ppm are frequent and the same is true for minimum oscillation values around -3 ppm\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Characterisation of the periodic oscillation, determination of the oscilation frecuency\n",
+    " \n",
+    " Searching for concentrations equal to zero"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 61,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cette forme de détermination ne donne pas de résultats car les données de concentration de CO2 de l'oscillation ne valent pas zéro mais ont de faibles valeurs, à la fois positives et négatives, donc la méthode correcte doit détecter ces passages par zéro\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Oscillation périodique. Trouver les indices où la concentration est égale à zéro\n",
+    "# Periodic oscillation. Find the indices where the concentration is equal to zero\n",
     "zero_indices = data.index[data['Oscilation'] == 0].tolist()\n",
     "\n",
-    "# Calculer les temps entre deux passages à zéro consécutifs\n",
+    "# Determination of the time between two consecutive zero crossings\n",
     "times_between_steps = []\n",
     "for i in range(1, len(zero_indices)):\n",
     "    time_between_steps = data.iloc[zero_indices[i]]['Date'] - data.iloc[zero_indices[i-1]]['Date']\n",
     "    times_between_steps.append(time_between_steps)\n",
     "\n",
-    "# Calculer le temps moyen entre les passages à zéro\n",
+    "# Determination of the average time between zero crossings\n",
     "# average_time_between_steps = sum(times_between_steps, pd.Timedelta(0)) / len(times_between_steps)\n",
     "\n",
-    "# print(\"Average time between zero steps:\", average_time_between_steps)\n",
-    "print(\"Cette forme de détermination ne donne pas de résultats car les données de concentration de CO2 de l'oscillation ne valent pas zéro mais ont de faibles valeurs, à la fois positives et négatives, donc la méthode correcte doit détecter ces passages par zéro\")"
+    "# print(\"Average time between zero steps:\", average_time_between_steps)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This form of determination does not give results because the CO2 concentration data from the oscillation are not zero but have low values, both positive and negative, so the correct method must detect these zero crossings\".\n",
+    "\n",
+    "Searching for zero crossings\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "crosses_by_zero is of type : <class 'pandas.core.series.Series'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Periodic oscillation. Identifying zero crossings\n",
+    "crosses_by_zero = (data['Oscilation'] * data['Oscilation'].shift(1) < 0) & (data['Oscilation'] != 0) \n",
+    "# Displaying verification of type\n",
+    "print(\"crosses_by_zero is of type :\",type(crosses_by_zero))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    False\n",
+      "1    False\n",
+      "2    False\n",
+      "3    False\n",
+      "4    False\n",
+      "Name: Oscilation, dtype: bool\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Displaying the first rows of data for verification\n",
+    "print(crosses_by_zero.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\"crosses_by_zero\" is a pandas series which contains booleans values, True in the rows where a zero crossing occurs in the \"Oscilation\" column of the DataFrame and False in the other rows. This pandas series is then used to filter the DataFrame and obtain the rows corresponding to the zero crossings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally we get the Average time between zero crossings we are looking for"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -795,25 +894,15 @@
     }
    ],
    "source": [
-    "# Oscillation périodique. Identifier les passages à zéro\n",
-    "crosses_by_zero = (data['Oscilation'] * data['Oscilation'].shift(1) < 0) & (data['Oscilation'] != 0) # Série booléenne qui contient True dans les lignes où un passage à zéro se produit dans la colonne « Oscilation » du DataFrame et False dans les autres lignes. Cette série est ensuite utilisée pour filtrer le DataFrame et obtenir les lignes qui correspondent aux passages par zéro\n",
-    "\n",
-    "# Filtrer les lignes contenant des passages à zéro\n",
+    "# Filter lines containing zero crossings\n",
     "data_crosses_by_zero = data[crosses_by_zero]\n",
     "\n",
-    "# Calculer les temps entre les passages à zéro consécutifs\n",
+    "# Calculate the times between consecutive zero crossings\n",
     "times_between_crosses = data_crosses_by_zero['Date'].diff().mean()\n",
     "\n",
     "print(\"Average time between zero crossings:\", times_between_crosses)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -823,7 +912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -836,7 +925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -851,7 +940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -881,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -911,7 +1000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -933,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -963,7 +1052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -993,7 +1082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1022,7 +1111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -1051,7 +1140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1080,7 +1169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1114,6 +1203,13 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,