diff --git a/Algoritmos/Parcial 2/Nicolas Mantilla/MovimientoParabolico.cpp b/Algoritmos/Parcial 2/Nicolas Mantilla/MovimientoParabolico.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..d34b6e250d80614f7d0b3ad392e6d7290645d940
--- /dev/null
+++ b/Algoritmos/Parcial 2/Nicolas Mantilla/MovimientoParabolico.cpp	
@@ -0,0 +1,61 @@
+// Incluimos los headers necesarios
+#include <iostream>
+#include <vector>
+#include <cmath>
+#include <fstream>
+
+using namespace std;
+
+int main()
+{
+    cout << "Bienvenido al simulador de movimiento parabólico para el todopoderoso parcial de Algoritmos y Arquetipos." << endl;
+    // Definimos la aceleración por gravedad
+    double g = 9.8; //[m/s^2]
+    const double pi = 3.14159265359;
+
+    // Ingresamos los valores iniciales de velocidad, posición y ángulo
+    double v_0, theta, x_0, y_0; //[m/s], [°], [m], [m], respectivamente
+    cout << "Ingrese el valor de la magnitud de velocidad inicial en m/s^2: ";
+    cin >> v_0;
+    cout << "Ingrese el valor del ángulo inicial en grados: ";
+    cin >> theta;
+    cout << "Ingrese el valor de posición horizontal inicial en metros: ";
+    cin >> x_0;
+    cout << "Ingrese el valor de posición vertical inicial: ";
+    cin >> y_0;
+
+    //Definimos el intervalo temporal sobre el que se hará la simulación
+    int n, lineas=0;
+    cout << "Defina la cantidad de intervalos a calcular: ";
+    cin >> n;
+    double t_0 = 0, t_f = (sqrt(pow(v_0*sin(theta*(pi/180)), 2)+(2*g*y_0))+v_0*sin(theta*(pi/180)))/g, dt = t_f/n;
+
+    //Inicializamos los cálculos
+    vector <double> tiempos, pos;
+    vector <vector <double>> posiciones;
+    tiempos.push_back(t_0);
+    posiciones.push_back({x_0, y_0});
+
+    double v_x = v_0*cos(theta*pi/180), v_y = v_0*sin(theta*pi/180), x = x_0, y = y_0, t = t_0 + dt;
+
+    while (t<=t_f)
+    {
+        v_y += - g*dt;
+        x += v_x*dt;
+        y += v_y*dt;
+        tiempos.push_back(t);
+        posiciones.push_back({x,y});
+        t += dt;
+        lineas += 1;
+    }
+
+    //Exportamos los datos para verificar su validez
+    cout << "Escribiendo " << lineas+1 << " registros...";
+    ofstream archivo;
+    archivo.open("MovimientoParabolicoC++.csv");
+    archivo << "t,x,y" << endl;
+    for (int i = 0; i < lineas; i++)
+    {
+        archivo << tiempos[i] << "," << posiciones[i][0] << "," << posiciones[i][1] << endl;
+    }
+}
\ No newline at end of file
diff --git a/Algoritmos/Parcial 2/Nicolas Mantilla/MovimientoParabolico.exe b/Algoritmos/Parcial 2/Nicolas Mantilla/MovimientoParabolico.exe
new file mode 100644
index 0000000000000000000000000000000000000000..ac262aacb9d8ff57c132ec4fcaa833c91862058d
Binary files /dev/null and b/Algoritmos/Parcial 2/Nicolas Mantilla/MovimientoParabolico.exe differ
diff --git a/Algoritmos/Parcial 2/Nicolas Mantilla/MovimientoParabolico.ipynb b/Algoritmos/Parcial 2/Nicolas Mantilla/MovimientoParabolico.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..41d342bec1f5938a35db429726b49efab1b4a6df
--- /dev/null
+++ b/Algoritmos/Parcial 2/Nicolas Mantilla/MovimientoParabolico.ipynb	
@@ -0,0 +1,301 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cálculo del movimiento parabólico de una masa en la tierra\n",
+    "\n",
+    "Esta simulación representa el cambio de la posicion de una masa atada a la gravedad terrestre. Esto calculando la velocidad y la posición de la masa en diferentes instantes de tiempo $\\Delta t$.\n",
+    "\n",
+    "Para esto se definirá el tiempo inicial $t_0 = 0$ y el tiempo final $t_f$, esto buscanto la solución positiva a la ecuación:\n",
+    "$$ 0 = y_0 + v_0sin(\\theta)t_f - \\frac{g}{2}t_f^2, $$\n",
+    "donde $y_0$ es la posición inicial, $v_0$ es la velocidad inicial y $\\theta$ es el ángulo de lanzamiento.\n",
+    "\n",
+    "Luego, se divide este intervalo en $n$ partes iguales, donde $n$ es el número de iteraciones que se harán. Ahora, para cada tiempo $t_i$ se calcula la velocidad y la posición de la masa en ese instante de tiempo, suponiendo un comportamiento lineal. Esto se hace con las siguientes ecuaciones:\n",
+    "$$ v_x(t_i) = v_0cos(\\theta) $$\n",
+    "$$ v_y(t_i) = v_0sin(\\theta) - gt_i $$\n",
+    "$$ x(t_i) = x_0 + v_0cos(\\theta)t_i $$\n",
+    "$$ y(t_i) = y_0 + v_0sin(\\theta)t_i - \\frac{1}{2}gt_i^2 $$\n",
+    "\n",
+    "Finalmente se guardarán los valores de $x$ y $y$ de forma que puedan ser graficados, validando la parábola que se obtiene.\n",
+    "\n",
+    "En la Fig 1 se muestra un ejemplo del movimiento parabólico de una masa en la tierra.\n",
+    "\n",
+    "<figure>\n",
+    "    <center> <img style=\"padding: 2px\" src=\"https://media.giphy.com/media/121djZcUyvjwGY/giphy.gif\" width=60%> </center>\n",
+    "    <figcaption style=\"font-style: italic; padding: 2px; text-align: center\">\n",
+    "        Fig 1. Movimiento Parabólico :D\n",
+    "    </figcaption>\n",
+    "</figure>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Importamos las librerias necesarias\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Definimos la aceleración gravitacional\n",
+    "g = 9.8 #[m/s^2]\n",
+    "\n",
+    "#Ingresamos los valores iniciales de velocidad, posición y ángulo\n",
+    "v_0 = 20 #[m/s]\n",
+    "theta = 45 #[°]\n",
+    "x_0 = 0 #[m]\n",
+    "y_0 = 5 #[m]\n",
+    "\n",
+    "#Definimos el intervalo temporal sobre el cual se hará la simulación\n",
+    "n = 100 #Cantidad de particiones sobre el tiempo\n",
+    "t_0 = 0 #[s]\n",
+    "t_f = (np.sqrt((v_0*np.sin(np.radians(theta)))**2+(2*g*y_0))+v_0*np.sin(np.radians(theta)))/g #Solución positiva a la ecuación cuando y=0\n",
+    "dt = t_f/n\n",
+    "\n",
+    "#Inicializamos los cálculos\n",
+    "tiempos = [t_0]\n",
+    "posiciones = [[x_0, y_0]]\n",
+    "v_x = v_0*np.cos(np.radians(theta))\n",
+    "v_y = v_0*np.sin(np.radians(theta))\n",
+    "x = x_0\n",
+    "y = y_0\n",
+    "t = t_0+dt\n",
+    "while t<=t_f:\n",
+    "    v_y += - g*dt\n",
+    "    x += v_x*dt\n",
+    "    y += v_y*dt\n",
+    "    tiempos.append(t)\n",
+    "    posiciones.append([x,y])\n",
+    "    t += dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cSZmtAC7Ae3+zilnnCrxLaU3LUMOzGrjAzBoWUstxGt6Pwe0RrrM4a/HKuQ3H1v62i3BdER3/zrmFZrYQuMHM/oCXzO3i2OO+0JeXEEc2pTsvxWJZlfdzXZyi9lvBeWNfOc8bxdmGV771S7sNPzd4GXjZ/3H4N7wOKpfidcgpq3K/33i7nFqUPLyDIrSmJQWvhqUoz+OdjP6DN3jiC6EPOq9n4sfA5WbWN/zF5jnef24+3iXa08zspsKeG3J3r7/se4mJf/KdjneZt1PYa+/z775TzPspScEvr/BalJuBZqVcx3v+/7vD1nEZx37RzsebnudnZnZMwuW3ESksOQv3FZBuZrVDXtsIryalPArdH3g9/irzc1FUuXQirB2Yee32qoUu89tEFZz0CpLPgs9DUshrDa+WIVI/N6996JEY8C5F7sLrEVbceziPUrSbieKxX+p9W4Z1FPXe9gKNwj7zpd1OacusoPfgX8ysRviDIdsuaM/1SPiln1LE964fy/e+Q83sQryele+Xpq1vBD7w/4f26i3oudfh2KcXqyzH/0i8Ru3X4dUwvRHe3rAIBaMBFPZdFsl5KRbLqlyf6xLspfB9Ng6vuc1vCzs/mFktM6tXng37++IVoI+ZXVnYc8xvamV+m8qw1zu88xt8/z0U9Z6KU+73myg1cWPwGnV+YmZj8cZru47iBxR9Ba+B/g14v5YmFvKcn+M1RJ9qZqPwCi4JrxboUrzq9gf95/4e78P/gn+Qf4F38HfH288FQ4rMwBvq479m9pEf40y/Nuh2vBPj52ZWMMzCYLyu6K865wqLsbQ+wavOHW1mT+LVHJ6JV0W8mlIcC865cWb2ATDMP+A+xWtL+FO8hK1TyHOdmd2I15h4kX8J8Eu8RuWn4tV+3kfJI3Y/ifcLaJKZjcZrw3QzXg/J0iafhXkH74TxsZk9h3eZ41y82o2KrGEoyTK8/XKPn6iuwKvpLNinPUKeOwB4zsze9p+3F+iJd0l1pnOuoO3QGLyu7JP847YaXqeU2kRuO16X+5fwjucf4XXL/0lILccXeMfqP/zLDhvwaqVvxLsEU5oJ06Nx7Eeyb4sS6Xub4cf9pJlNxzsRTvIT1eKUusycc7PM7O/AvcBcM3vDj7EV3nAUfYBdzrm3/MeGAm3MG05jp78Pzifk81qIEXhDsdzrv++peJ/bW/E6AvyuhPcTqY/xTmg329HG/63wRhNYhPe5LK2yHP+vAI/gjTKQRNFNaMLNxru8+H/+j8t9eOOXziSC81KMllVFfK6LMgPvStjDeJ/TfOAD59w+MxuKl5iu8L93VuF977fHO28M4Wi7wrL6P7zz35tm9qYfzyG8RP4ivFEEhuO1J9zkl8d8vISrFV5usJOjPz4K3tM5ZnYv3riEzjn3enFBVMj7daXoDlyZf5Qw2G8Rr0nGSwgKBu1bh/eB7OCv68EiXvei//gfiomnCV6y9xVeFf0uvAP4CeC0sOc29Le7yj8gvsEbNya0S3sSXm+ZDRz9pTY85PGufoHu8N/LMooZ7LeYuI/p7gychffB/NZ/Hx/hfTlkUsSwCIWstxZeO4/NeG3IZuN9yRQaD96H4hm//Ar2yVy8L7fUUm7zbo4ONrsMb8yeguMkI+R5D/rL0gpZxzHHD94X+1yONnh/HS9BKfJYC3t9RmHHanHHa2Ex+vvoLbxq/v14l8iGhD8X78vjGX8f7PHjXoY3nlCDsO3cjNcp4ADeGGLP4f1KLLIbfBGfw3PwOqes9/f/EuC6Qp7fBS+pL2gAnYnXZu+Y46KYY6VUx34xMWdy7BAjpdq3Jaw3kvdWB+97ZQtHP98ZRcVXnjIDrsUbmuVb/1hYjtd2KXSw3yS8cQzncXSw40XAAyUdxxwdQHYN3md3K1674ZYRfA4KLesi3n9B26steN8tM/F+GI8B9pdU1uU9/vFOyA74qoTPREbY8mH+tg6Frp8Iz0uxWFZUwOe6iH15At74eTvwErjw78ROeD/eCwZi34JXW/8H4LjSHAclxYOX1P8B73z+nf/+CkaYON1/TnV/v87CO3cdxPtufwloE7a+NnjtH/cQNixOccddJO+3sL+C8YSqJDP7L94vvTR37GCIIhIFfo3qtc65RLkSIFFkZovxxlsrS5tOkYSWKG3iIua37bkBbxA/JXAiledEvJoCkSPMm9cyfNnFeLUUEyo/IpHYV+V+CfuNmrvjVYHXJWyARBGJDjM7A69X5Vl4lw5EQt1vZt3x5oPdjdf+6sd4l7FKHApCpCqqckkcXsPfB/CuPd/qnMsKOB6RquIWvEb/bwG/CTgWiT2f4zU2vxtvwO0deO2m/qCrJSKFq9Jt4kRERETiVdzWxDVp0sSlpaVFfTv79u2jTp06Ud+ORI/KMP6pDOOfyjD+qQzLZ+7cududc8dX5DrjNolLS0tjzpw5Ud9OZmYmGRkZUd+ORI/KMP6pDOOfyjD+qQzLx8zWVfQ6q2zvVBEREZF4piROREREJA4piRMRERGJQ0riREREROKQkjgRERGROKQkTkRERCQOKYkTERERiUNK4kRERETikJI4ERERkTikJE5EREQkDimJExEREYlDSuJERERE4pCSOBEREZE4pCROREREJA4piRMRERGJQylBByAiUpScrByyM7PZXX83ZHx/WVpGGqnpqYHGJyISJCVxIlLpCkvEcrJyWDtxLc17NadJuyas+3wdH97yIXmH87Bko1leMwAm3DOBvMN5JFdL5rKRl9HyrJZsW7qNjTM3kjbg++tTsiciiUxJnIhEVUEy1fLsljRKa8Syt5cx7jfjyM/Nx5KMpl2bsn/bfvbk7ClyHS7f8entn35vWd7BPN6+5u3vP9Hg+NOOp3aT2qyfth6X50iulsylIy7ltCtO4+u5XyuxE5GEoSRORCpEaM1X8x7N2bJwC0veWMLMJ2bi8lyhr3F5ju+2f0fNBjXZs2EPOCAJOgzpQPOezZny0BTyD+dDMlz4xIWYGZ/e8Sn5h/NJSknizPvOZMuCLax4f4X3WiD3QC7bl23H5XoL8g7lMfa6sYxNGus9x0FSShKD/jaIrkO7Uuf4Oqq1E5G4pCRORMpt5ScreeOyN8g7nAeAJdmxiZtB28FtOeXcU/jsns+8S6LVk7ni9SsAGDVoFHmHvGXpv0knNT2VtIw0sjOz2VF/B71/3huApl2bfi/hysnKYfX41UdeO2T0kO+tLykliTPvPZP1n68ne3I2APm5+Uy4awIT7ppAnaZ12L99Py7fkVIjhRsn3kiLM1oosRORmKckTkQitjZzLQtHLCTvUB7bl21n84LN33u8xZkt6POrPliyMfa6sUcSrH739SM1PZUTe514TII0dOLQY5alpqeSmp5KZmbmkXUXLAu9X9hrw5flZOV8L1E877HzOPzdYRaOXMi+LfsArxbv5fNfJjU9lezMbFy+I7l6MkMnDlUiJyIxR0mciBSroEaqeY/m7Nu6j/kvzmfdlHVHHm/WvRk9bu7BwlELyc/NJ7l6MoP+NqhUyVmowpaVVmnWV1Syl3pG6pHkzpKM1DNS2ZC1wbuMC+R+l0vm/Zmc/8/zObDnAOumrFPtnIjEBCVxIlKk1eNX8+rgV48kNADV61UHAxxYsnHaVafR/77+dPtRt0IvP5YnOatoRSV74cnd+unrGT1oNLkHczEz1ny2hqc7P+29byClRgpDJ6l2TkSCpSRORI4oGOYjpWYKG2dtZNnYZUfbthn0+lkvOl/fmdHnjj5yWTItIw2IrWQtUuGxtzijBUMnHU3sGqY15ONbP2b5u8sB77Lrm1e8yRl3n0HjNo3ZsniLaudEpNIpiRMRAL5860vGXjeW/Fyv1q1Ggxq0H9Kerz746shl0i43dinysmSiCU/szrjnDFaNW3XksmvNBjUZ/+vxRx4vaDvXol+LIMIVkSpISZxIFZWTlcPaSWtJqZHC2olrWfXpqiOPWZJxxl1ncNbvzyq0l2Y817qVVWHJ67hfj2PGv2aA84Yyee2S1+j3236c0PkENi/YnNBJrogET0mcSBW05rM1vHLRK0fautVqXItuN3VjyStLjgz90WpQK6BqJmxFCd8Xp111GnOemeMNZZKcRMOWDfns3s+OPJ5SU23nRCR6lMSJVCH7tu1j5r9nkvVY1tHOCknQ946+nPX7s+hxU4+Ev0xakQqrnfv0jk+Z+e+Z4Ly2c+/f9D6XvHAJGNq3IlKhlMSJVAHLxi5j+mPT2TR3E3mH82h5Vks2zNhwpK2bat3KLnyfdby6I3Ofm+u1nTNjz4Y9vHTmS94AyHgDCmvcORGpCEriRBLYt5u+5ZNffsKyt5cB3pAgV7xyBZ2u7aQZCaIkvHauaZemjLlmDCs/XAl4487Nf3G+9rmIlJuSOJEEk5OVw8qPV7Jr7S6WjV1G3sG8I+O6AezM3gmo1i2awvdt/9/1Z+3EteQezAUH81+cz661u+h4dUf2f7NfibSIlImSOJEEsnbyWl4+7+Ujw4S0Pr813W/qzrvD3j1mXDepPKG1c6npqWxeuJkpD01h7aS1gDpAiEjZKIkTSQDOOZa8toSPfv7RkQTOko2WZ7ek41UdqX9yfV06DVho7VxaRhoHdh5gyh+nHOkAMf4347n2g2up3bh2wJGKSLxQEicSx3Kyclj08iLWTV3HtiXbaNy2MbkHc490WEiE2RQSVevzWzPtkWnkHfIud2+YsYH/tPkPXYd1pXbj2rQa1EplJiLFUhInEqdWfrSS1y597ci0WP3u68fAPw1kw8wNqnWLA+EdIGrUq8F7P3qPmf+aCcDUP01l2ORhKkMRKZKSOJE44/Id81+azye//ORIAmfJRvV61bEkU61bHAkvq/ZD2vP1vK8hH/IO5vHxrR9zw/gbqHN8nQCjFJFYpSROJA4UDAfSsGVD5jw9h/VfrKdZt2ZsX779yAwL6rAQ/9IGpJFSI+XIGHNbFm/hyXZPcu6j59KkfRPWTV2nGlYROUJJnEiMy8nKYdSgUeQe8IanqF6vOpe8dAndhndjwwxdOk0k4ZdYazasyYc//ZAPfvIBlmQAJNdI1mDBIgIoiROJeYtfWUzud7neHYPTf3U63X/UHVCHhUQUXqbDM4fz1lVvsWysN2Bz7oFcsidnq9xFhKSgAxCRwuUdymPyA5OZ/fRsMLAkI6VmCm0ubhN0aFKJLMlIvyudlJr+b24HX775JTtW7wg2MBEJXKXXxJnZS8BgYKtzrlPYY3cBjwLHO+e2V3ZsIrEgJyuHJa8vYdUnq9ixcgddbuxC5+s7s2neJl02raJS01MZOmko2ZOzOfzdYWb9ZxbPdHmGXj/vRa3japE2QMeFSFUUxOXUEcCTwKjQhWaWCpwLrA8gJpGYsH7aekYOGEn+YW/A3kF/HUS/3/YD4NTzTw0yNAlY6GXWXj/txZtXvknWP7IAzfggUlVV+uVU59xUoLDrAP8E7uHIDI8iVcu+rft470fvHUngLNlwTh8HOVb9k+vT7pJ23py4eO3k5j0/L9igRKTSxUTHBjO7BNjonFtoZsU97xbgFoCmTZuSmZkZ9dj27t1bKduR6ImHMtw5dyfL/7KcQ7sPYSmGy3dYirGj/o6Yj70yxEMZVrbdDXaTVD3JS/odLPjfArbv307j9MbsWbKHBt0a0KBjg6DDPEJlGP9UhrHHgvilb2ZpwIfOuU5mVhuYDJznnNttZtlAr5LaxPXq1cvNmTMn6rFmZmaSkZER9e1I9MRyGa77fB2T/zCZdVPW0aRDE65840oO7T2kYUPCxHIZBqlg/MCT009m6VtLmfPfOV4nGLOYG4pEZRj/VIblY2ZznXO9KnKdsVAT1xpoBRTUwp0MzDOzPs65zYFGJhJFKz5YwRuXveHVuiUbFz11EU07NwWImROvxLbQdnKtMlpxcM9BFr+8GOcceQfzyM7UUCQiiSzwIUacc4udcyc459Kcc2nABqCHEjhJZOs+X8fY68bi8o/WhG+YsSHAiCQR9L6195GhSFy+Y/vy7eTn5QcclYhESxBDjLwGZABNzGwD8IBz7sXKjkMkCM45Zj4xk/F3jadu87rk5+Zr2iypMAVDkaz5bA1fz/maRaMWsffrvfS9sy+bF27WJXqRBFPpSZxz7toSHk+rpFBEKtWaiWsYd+c4ti7eSvvL2nPpiEvZtnSb2r9JhQq9xDr/pfl8+LMPWTNxTUy2kxOR8omFNnEiCW/Z2GW8eeWb4CCpWhLpd6dTs0FNTZslUdX9x93ZNG8Ts5+ajXOO3IO5aicnkkACbxMnkuhypufwztB3joyA6PId66asCzYoqTI6X9/56JRd+bB9+fbvtcUUkfilmjiRKFo4aiEf3PwBtY+vjctzav8mla6gndzaiWvZOGsji0Yt4tCeQwwZPYTqdasHHZ6IlIOSOJEoWD9tPZN+N4l1U9fRamArrnrrKrav2K72bxKIgsv2zjlm/WcW4+4cxzPdnqHD5R1oP6S9jkeROKUkTqSCrc1cy+hzRuPyvPHfzn7wbGodV0vt3yRwZsbpvzqd/Lx8xv96PNMfnc7Mf89k2ORhOjZF4pDaxIlUoP3b9/PusHdxeUfbHK3/Yn2AEYkcK/dALpbkTXGYdzCPuc/ODTgiESkLJXEiFWTnmp28dOZL7Nu8j+TqyViyqf2bxKS0jDSSa3jHKAYLRy9k3ovzgg5LRCKky6ki5ZSTlcPiVxaz+NXFmBlDJw3Fkkzt3yRmpaanMnTiULIzszmp90lMf2w6H/zkA3Km53Bc6+NIG6DjViQeKIkTKYecrBxGDhhJ3sE8MLjilStocWYLQPOfSmwLbaPZ8uyWvDHkDRa8tAAMUmqmaFBgkTigy6ki5TDziZleAgdYkrEze2fAEYlELrlaMqlnpIIBDnK/y2XNZ2uCDktESqAkTqSM5jw7hy/f+BJLMrV/k7iXNiDNGxTYPyuseH8Fh/YeCjYoESmWLqeKlMG0R6bx2b2f0ebiNqTflc6GrA1q/yZxLbSdXO53uXz+l88Zdc4orv/4emodVyvo8ESkEEriRCKwfro/iO+UdXS6phOXjbqM5GrJtMpoFXRoIuUW2k6ueY/mjLl6DM/3fp6OV3ek7Q/a6keKSIzR5VSRUlo/bT0jzh7BuinrsGSj9y96k1wtOeiwRKKi/WXtOfexc9m5Zidf/PULRg0cRU5WTtBhiUgIJXEipeDyHZ/e8Sku9+ggvus+1yT2ktgO7T10ZFDg3AO5LH9necARiUgoJXEiJXD5jvdvfp9NczaRlJKkTgxSZXxvUGBg4aiF7FyjHtgisUJt4kSKkZ+Xzwc/+YAFIxZw1v1n0fr81qybsk6dGKRKCO3sUK95Pcb/ZjwjMkYwbNIwjjv1uKDDE6nylMSJFGH9F+v55JefsHnBZs5+8GwyHsgAoMUZLYINTKQShXZ2aNa9GaMGjeLF9BfpMrQLp115mn7MiARIl1NFCrH+i/WMyBjB5gWbSUpJovV5rYMOSSRwzbo24/x/nc/+7fuZ8fgMdXYQCZiSOJEwLt/x6e2f4vK8TgzOObIzs4MNSiRG7MnZo84OIjFCSZxICOccH//yYzbNUycGkcKEd3ZY9PIi9mzcE3BUIlWT2sSJ+JxzTLhnAnP+O4cz7j6Ddpe1UycGkTChnR3qnFCHcXeOY9SgUQyfMpy6TesGHZ5IlaIkTsQ35aEpZD2WRe9f9Oacv5+DmakTg0ghQjs7NGnXhJfPf5nR547mnEfOYfP8zfrhI1JJlMRJlZeTlcOUh6awetxquv2oGxf++0LMLOiwROJCi34tuOb9a3jlwld49aJXsSSvCcLQiUOVyIlEmdrESZWWk5XDiLNHsHrcaizZ6P7j7kcabYtI6Zwy6BQ6X9cZHLg8R96hPHUGEqkESuKkSpv15CzyD+cfua+ptETKpudPe5Jc3Z9L2EHLfi2DDUikClASJ1XWmolrWPrmUizJ1AtVpJxS01MZljmMtj9oi8t3zP/ffJxzJb9QRMpMbeKkSvp6zte8cdkbNGnfhHMfO5dN8zapMbZIOaWmp3Lt+9eS+WAmUx6aQu0mtTn3kXODDkskYSmJkyolJyuHpWOWMv/F+dRqXIsbxt1AvRPrcer5pwYdmkjCOPuBs9m/fT/TH53Oof2HqH9SfXbX3w0ZQUcmkliUxEmVkZOVw6iBo8g9kAvA4OcGU+/EegFHJZJ4zIwL/30h25dvZ85TcyAJkqol0aNHD9V2i1QgtYmTKmP1uNVHEjhLNnau3hlwRCKJy5LsaBvTfMg/lK8eqyIVTEmcVAl5h/NY9ekqgCPjWKkTg0h0tRrUipRa/gUfB/VT6wcbkEiC0eVUSXjOOT669SM2ztzImb89kxr1a6gTg0glKJiia8V7K5j5zEwm3TeJVgNaUf8kJXMiFUFJnCS89a+sJ/vFbPr/X38G/mlg0OGIVCkFU3QdOPUAi3+9mFcvfpUfTf0RNerXCDo0kbiny6mSsHKycnjr6rfIfjGbztd3ZsDDA4IOSaTKqntqXX445ods+3Ibo88dzdQ/TSUnKyfosETimpI4SUg5WTmMHDCSpW8uBYMeN/fQfKgiAWt9XmvOuOcMNs7ayOT7JzNq0CglciLloCROEtKyt5eRdzDPu2OQM10nCpFYUL1udTDAQe6BXPVYFSkHJXGScA7sPsDSMUsBbyiRpGpJ6okqEiPSMtJIqXm0x2pyteRgAxKJY+rYIAklPzefMVeP4duN33LhkxdycM9BdtTfoZ6oIjGioMfq6vGrWfLqEqb8cQqnXnAqJ3Q6IejQROKOauIkoYy/azyrx63m4qcvps8v+tD/vv406Ngg6LBEJERqeioZD2QwdNJQqtetzms/eI19W/cFHZZI3FESJwkhJyuHN4a8wcwnZnL6HafT4yc9gg5JREpQ/6T6XPv+tezdvJdR54xiysNT1NFBJAJK4iTuFfREXf7ucizJOO3y04IOSURK6cReJ9L///qzdfFWMu/PVI9VkQgoiZO4t/yd5d/ribrui3XBBiQiEbFk83qsArkH1WNVpLSUxElcO7z/MMvfXQ54JwLNiSoSf77XYzUfzeYgUkqV3jvVzF4CBgNbnXOd/GWPAj8ADgGrgR8553ZVdmwSX5xzfHDzB+xYtYNzHzuXvEN5mhNVJA4d6bH66WoWjFzAlAen0O4H7WjQQp2SRIoTRE3cCOCCsGUTgE7OuS7AV8B9lR2UxJ8Z/5zB4lcXM+DhAZzxmzPof19/JXAicSo1PZWMhzK4YdwN5B3K440hb3D4u8NBhyUS0yo9iXPOTQV2hC0b75zL9e/OAE6u7LgkvqydtJYJd0+gw+Ud6P+7/kGHIyIVpEm7Jlz+yuVsmr+JN4a8wed/+VwdHUSKYM65yt+oWRrwYcHl1LDHPgDecM69XMhjtwC3ADRt2rTn66+/Hu1Q2bt3L3Xr1o36dqT0tk3dxvK/LKdao2r0erEXKbWLbxWgMox/KsP4F2kZrnhsBZs/2gwGSdWT6PKPLhrzMWD6HJbPgAED5jrnelXkOmNqxgYz+z8gF3ilsMedc88BzwH06tXLZWRkRD2mzMxMKmM7UjrZmdlMeXAKOMjblUe7Ru1KvISqMox/KsP4F2kZJk9P9pI4B+6w47g9x9E/Q7XuQdLnMPbETO9UMxuG1+HhehdE9aDEhQn3TAD/6Mg7nKehCEQSVNqANFJqefUMLt/RtEvTgCMSiT0xkcSZ2QXAvcAlzrn9QccjsWnByAV8PftrklKSNJyISIIr6LHa51d9SK6eTNZjWeTn5gcdlkhMCWKIkdeADKCJmW0AHsDrjVoDmGBmADOccz+r7Ngkdm1ZtIWPfv4RaQPSGPDQANZ9sU7DiYgkuNT0VFLTU2neoznvDX+PSX+YxDl/PSfosERiRqUncc65awtZ/GJlxyHx48DuA7x55ZvUbFiTK167grpN69Kif4ugwxKRStJtWDdypucw7W/TSE1Ppd0l7YIOSSQmxMTlVJGirJ++npfOfIkdq3dw5RtXUrepekaJVEUXPnEhzXs05+3r3mb83eM17IgISuIkhuVk5TAyYyTbvtxGUnISSSk6XEWqqpSaKfT7XT8O7ztM1mNZjBo0SomcVHk6K0rMWjR6EfmHvYbMLt+pJ6pIFffNV98cOWvlHsjVd4JUeUriJCYd2HWA5e8sB9PE9iLiSctII6VGChjeUEMWdEQiwYqpwX5F4OjE9vu372fws4PZv32/eqKKyJFhR9ZOXMviVxcz/e/T6XxNZxqmNQw6NJFAKImTmDP32bksHbOUc/5+Dj1v7hl0OCISQwqGHel8XWee7f4sb1/7NsOnDie5WnLQoYlUOl1OlZiyZdEWPr3jU1qf35oz7joj6HBEJEY1OqURP3j+B2yYsYFJv58UdDgigVBNnMSMNRPX8PY1b1O9bnWGjBqCJanBi4gUreMPO7J20lqmPzKdfVv20fOnPdXsQqoU1cRJTMjJyuHl819m//b9HNp7iB2rdwQdkojEgY7XdASDhSMXMmqghh2RqkVJnMSEmU/MxOV5M9vn5+Zr6AARKZUNWRvwp2v0hh2ZnB1sQCKVSEmcBG7Xul189eFXWJJpOBERiUhaRhrJNZKPnM32btkbbEAilUht4iRQ+Xn5vHPjO5gZV75xJd+s/EbDiYhIqRUMO5I9OZtVn6xi7jNz6f7j7jTr2izo0ESiTkmcBGra36ex/vP1XDbyMk678rSgwxGROFQw7EiPm3vwTJdnGHvdWG6eczPValULOjSRqNLlVAnMxlkbyXwgk45Xd6TLjV2CDkdE4lyd4+tw2cjL2LZ0GxPunhB0OCJRp5o4CcSaiWsY88Mx1Gpci8HPDD7SMFlEpDxan9eavnf2ZcY/Z3Dw24P0+lkvNc+QhKWaOKl0BcOJfLfjOw7sOsC2ZduCDklEEki7S9uBwaJRizTsiCQ0JXFS6Wb/d7aGExGRqMmZnqNhR6RKUBInlWrf1n189eFXYGg4ERGJiiPDjvitNA7tOxRsQCJRojZxUmmcc3z4sw/J3Z/LkFFD2J2zW8OJiEiFCx12ZOlbS5n1n1n0vLknDdMaBh2aSIVSEieVZtHoRSx/ZznnPnouXW5Qb1QRiZ6CYUc6X9eZp7s8zbvD32XYpGGak1kSii6nSqXYnbObT375CS36t6DvnX2DDkdEqoiGaQ254IkLWDdlHTOemBF0OCIVSjVxEnUu3/Hej94jPy+fy0ZcRlKyfjuISOXpNrwbK95dwcT7JlK3aV12rdulphySEHQ2lagbf/d41k5cS59f9KHRKY2CDkdEqhgzY/Bzg0mpmcLYG8Yy+Q+TGTVIQ49I/FMSJ1G15M0lzHjcu4Qx8z8z9aUpIoGo27QubQe3BQcuz5F3KE/DG0ncUxInUZOfl89n93x25L6+NEUkSL1/0RtL9jo2JKUkaXgjiXtK4iRqZj05i93rdpNUPUljwolI4FLTU7n2w2upVqcaDVo24MReJwYdkki5KImTqNixagcT75tIm4vbMGzyMAY8PIChE4eqIbGIBKrNBW0YMnoIO77awbS/Tws6HJFyUe9UqXAu3/H+Te+TXD2Zwc8Opv5J9WlxRougwxIRAaDDkA50uqYTU/44hXaXtqNp56ZBhyRSJqqJkwo3+7+zWTd1Hef/83zqn1Q/6HBERI5x4X8upGbDmrw3/D3yDucFHY5ImSiJkwq1c81OPrv3M1qf35puw7sFHY6ISKFqN6nNxf+9mE3zNvHJLz/h879+rt7zEnd0OVUqzPpp63nnxndwOH7w/A8w0/Q2IhK7TrvyNNIGpjH32blYkpFcI1ltdyWuqCZOKkROVg4jB4xk19pd5B/OZ8+GPUGHJCJSotS+XsLm8jV2nMQfJXFSIVa8v4L8w/mA92WoL0IRiQdtBrchuXoyAJZkGgZJ4oqSOCk35xw507y2JBoPTkTiSWp6KkMnD+W4NsdhyUa9E+sFHZJIqSmJk3JbNnYZ6z9fT69be2k8OBGJOy3OaMGNE24kKTmJj37+Ec65oEMSKRV1bJBy+W7nd3xy2yc0696MC5+4kKQU/S4QkfjTsGVDBv55IOPuGMeS15bQ+brOQYckUiKdcaVcPrv3M/Zt28clL1yiBE5E4lqf2/pw0ukn8entn7J/+/6gwxEpkc66UmbZmdnMe34e6b9Op3mP5kGHIyJSLknJSVzywiUc2HWAsTeO1dhxEvN0OVXKZG3mWt668i3qnViPjAczgg5HRKRCnNDpBLrc2IUF/1vA6vGrSamRona+ErOKTeLM7LQyrneVc+5QGV8rMS4nK4eXz32Z/Nx8kmsks3nhZn3BiUjCaNiqoXcjnyNjx+k7TmJRSTVxS4BIuumY//zewLyyBiWx7cs3vyQ/1xsTLj83X19wIpJQTjnnFD7/8+fkHczDTGPHSewqzeXU24ClEaxvfNnDkVjnnGNd5jpAY8KJSGJKTU9l2ORhfHzbx2xZuIUa9WsEHZJIoUqTxM11zs0qzcrMLBmvNk4S1MKRC9m8YDNn/vZMatSvQVpGmmrhRCThpKancuO4G3my3ZN89LOPGD5lOJak05vElmKTOOdcRL1XnXN5qMdrwtq/fT/j7xpP6pmpDPrzIH2hiUhCq92kNuc+ei7v3/Q+8/83nx439Qg6JJHvqfSEy8xeMrOtZrYkZNlxZjbBzFb6/xtVdlxSsgn3TODg7oMMfmawEjgRqRK6De9Gi/4t+Oweb0xMkVgScRJnZjXN7BQzOy38r5SrGAFcELbst8BE51wbYKJ/X2LIuqnrWPC/BaT/Jp0TOp0QdDgiIpXCkoyLn76Yg3sO8u6wdzV2nMSUUo8TZ2YnA88B5xf2MF6v1OSS1uOcm2pmaWGLLwUy/NsjgUzg3tLGJtG1buo63rzyTeo0q8NZfzgr6HBERCrVCR1PoNO1nVg0ehGrxq3S2HESMyIZ7Hc0cApeb9VVQEWOA9fUObcJwDm3ycwKreoxs1uAWwCaNm1KZmZmBYZQuL1791bKdmLV7i93s/D2hbg8h1UzPhnxCQ06Ngg6rIhU9TJMBCrD+BfvZbg7ebd3Ix9yD+Yy+aXJtDjYItigKlm8l2EiiiSJ6wVc75x7P1rBlMQ59xxebSC9evVyGRkZUd9mZmYmlbGdWDXhkwm4PH+owHw4bs9x9M/oH2xQEarqZZgIVIbxL97LMKdGDiNfHUneoTySkpMY8OMBVa4mLt7LMBFF0iZuKVA7SnFsMbPmAP7/rVHajkRo48yNgMaEE5GqLTU9lWGZw2jSoQmWbDRIja8rEpKYIknifgnca2ZnRiGO94Fh/u1hwHtR2IZEaNW4Vaybso6eP+3JgIcHqA2IiFRpqempXPfRdRjG+N9oXHsJXiSXUxcAs4CpZnYI+Db8Cc65ErstmtlreJ0YmpjZBuAB4G/Am2Z2E7AeuCqCuCQKcg/m8skvP+G4NsdxwRMXkFIjkkNFRCQxNWrViH6/60fm/Zn0uLkHp5xzStAhSRUWyZn5Bbzkagzl6NjgnLu2iIcGlWV9Eh1Z/8hix8odXP/p9UrgRERCnHn3mSwcuZCPb/uYny/6OcnVSxyYQSQqIjk7DwHudM49E61gJDbsXr+bqX+aSofLO3Dq+acGHY6ISExJqZnChf+5kFcvepWsf2bR795+QYckVVQkSdw2vEudksBysnL44OYPcHmO8/9Z2JCAIiLS5sI2tB/SnswHMvlux3e0v6y92gxLpYukY8MfgbvMrG60gpFg5WTlMHLASLZ9uQ3nHHs27gk6JBGRmNXlhi7kHcxj+iPTGTVolGZykEoXSU3cxUAbYL2ZzQF2hT3unHNXV1RgUvnWTlxL3sE8AFy+IzszW78sRUSKsH3F9iPzFeUdzNN3plS6SJK4JngdGgCqAcdXfDgSpL2b9wLeXIEaE05EpHhpGWmk1Ewh97tcnHO07Ncy6JCkiil1EuecGxDNQCRYezfvZeGohZx8xsm0HdyWtIw0/aIUESlGanoqQycOZfZTs1n8ymI2L9pMi/5VayouCZbGjhAAJv5uIrkHcrlsxGU0btM46HBEROJCanoqJ/c9mb2b9zL5D5PpdE0najeO1uRGIt9XbMcGM/tVUZPRl/CaJuULSyrTxtkbWfC/BfS9o68SOBGRCJkZFzxxAQf3HGTyHyYHHY5UISX1Tv0nUOqL/GaW7L9G9clxwuU7Pv3Vp9RpWoezfn9W0OGIiMSlEzqeQO9bezP32blsXrg56HCkiijpcqoBfzWzHaVcn5UzHqlki15ZxIYZG7j0f5dSo36NoMMREYlbGQ9lsPjVxXz6q08ZljkMM50SJbpKqombCiTj9UQtzV8T/zXHzKsqsWfNZ2v4+Bcf06RDE7oO7Rp0OCIica1Wo1oM/PNA1k1dx9jrxmrcOIm6YmvinHMZlRSHVLKcrBxeufAV8nPz2blmJxtmblBvVBGRcjqh4wlgsOT1JSx/bzlDJw7Vd6tETSQzNkgCWTZ2Gfm5+QDk5+aTnZkdbEAiIglg3efrjjQsyj2Qq+9WiSolcVXUpnmbALBkDewrIlJR0jLSSKnhX+Ryfs2cSJQoiauC1k9bT/akbLrd1I0BDw9Qdb+ISAUpGAA4/TfpWLKx/N3lQYckCUyD/VYxLt8x7s5x1DuxHhc+cSHV61QPOiQRkYSSmp565Idx1uNZ9PllH5p3bx5wVJKIVBNXxSx+bTFfz/6aQX8dpARORCSKzvr9WdRuXJvxvx6Pcy7ocCQBKYmrQg7vP8zE306kec/mdLmhS9DhiIgktJoNa5LxUAbZmdmseG9F0OFIAir2cqqZzQZK/fPBOden3BFJ1Ez/x3T2bNjD5a9ejiVpEEoRkWjreUtPZj81mwl3T6DNRW1Irp4cdEiSQEpqE/clESRxErtWvL+CqQ9PpWVGS1r2L/VMaiIiUg5JKUmc94/zeOXCV3h9yOuc9fuz1JFMKkxJg/0Or6Q4JIpysnJ44/I3cHmOjTM2kpOVoy8REZFKUqNBDSzJWPXxKrInZTN0kkYEkIqhNnFVwJLXluDyvArVvMN5GnxSRKQShX7n5h7UAMBScSIaYsTM0oAbgLZAzfDHnXM/rJiwpCJtnLkR0MC+IiJBSMtII7lGMrkHcsHBcaceF3RIkiBKncSZWU9gCpCDl8QtAhoAacAGYFUU4pNyWj1+NRtnbaTPr/pQt1ld0jLSVI0vIlKJCgYA/uqDr8h6PItlY5bR8aqOQYclCSCSmrhHgbeBHwOHgZucc/PM7AzgNeCRKMQn5ZCfl8+EeybQsFVDzn3k3KNTwYiISKUqGAA4qVoSU/84lb6/7svJp58cdFgS5yJpE9cNeBXI9+/XBHDOTQceAv5WoZFJuS16eRFbFm5h0F8GKYETEYkBZ9x1BnVOqMOEuydoAGApt0iSOAccct5RtxUIHaciB2hTkYFJ+Rz+7jCTfz+ZE3ufSMcfqtpeRCQW1KhXg4yHMlj/+XpWvK8BgKV8IknilgKt/dtZwJ1m1sbMWgL3AKsrOjgpu5lPzGTPhj2c+8i5GthXRCSG9PhJDxq3a8xn935G3uG8oMOROBZJEvcc0My//TugObAcWAOcDtxVsaFJWe3fvp8v/voFbQe3VU9UEZEYk5SSxDl/P4dvVnzDW1e9RU5WTtAhSZwqdUMp59zokNvLzKwDkA7UAmY457ZGIT4pgw9v/ZCD3x6k07Wdgg5FREQKUfv42liSseK9Fawev5qhEzUAsESuzIP9Ouf2OucmOOfeVwIXO75860uWvbUMgPd/8r5+4YmIxKB1U9YduZ17QAMAS9mUOokzsz+b2bNFPPaMmT1ccWFJWX3+58+9Gw7yDml2BhGRWFQwADAADpp1aVb8C0QKEUlN3LXA50U89jlwXfnDkfLYvGAzWxZuISklSbMziIjEsIIBgPve2RcMVo3XePkSuUgGDzsR2FjEY1/7j0uAJv5uIjUb1eTK16/k67lfa3YGEZEYVjAA8MFvDzLn6Tn0vaMvjVo1CjosiSOR1MRtBnoU8VgPYFv5w5Gyyp6SzapPVtHvvn60Pq81/e/rrwRORCQOZDyYQVJyEpn3ZwYdisSZSJK4N4H7zezi0IVmdhHwB+D1igxMSs85x2f3fka9k+rR57Y+QYcjIiIRqH9Sffr8qg+LXlnElkVbgg5H4kgkSdz9wEzgAzPbZmaLzGwb8AHe4L9/iEaAUrIV761g48yNZDyYQbVa1YIOR0REItTvt/2o2aAmE383MehQJI6UOolzzh1wzp0HXAi8iJfQvQhc4Jy70Dl3MEoxSjHyc/OZ+LuJNGnfhG7DuwUdjoiIlEGtRrU487dnsvKjlaz7fF3JLxAhso4NADjnxgHjohCLlMHkByazfdl2Bv5lIEkpZR72T0REAnb6L09n1r9n8dGtH9H52s6kDVDnNClesWd9M6sderukv+iHK6GyM7P54q9fADD14aka2FdEJI5Vq12NLjd0YduSbUz6wyRGDRql73UpVklVN9+aWUFL+b3AtyX8SSWa9ug0cN5tDewrIhL/qtev7t3I1/e6lKyky6k/BlaH3HbRDUdK69DeQ+RMz8GSDAwN7CsikgBaDWzF1OpTyTuUR1Jykr7XpVjFJnHOuZEht0dEPRoptZn/nsnBXQcZ/Pxg9m/br4F9RUQSQGp6KkMnDeXNK94kuXoyJ/U+KeiQJIZFMnfqGjPrWsRjncxsTcWFJcU5sOsA0x+dTtsftKXnT3pqYF8RkQTS4swWDH52MHty9rBw1MKgw5EYFkl3xjSgRhGP1QZOLk8gZnanmX1pZkvM7DUzq1me9SWy6Y9N58CuAwx4eEDQoYiISBS0u6QdJ/U5iSkPTSH3YG7Q4UiMKql3an0za2FmLfxFzQruh/y1Ba6h6HlVS2RmJwG/Ano55zoByf46Jcy+rfuY8a8ZdLy6I826Ngs6HBERiQIzY8CfBrB7/W7mPT8v6HAkRpXUseFO4AG8Dg0OeKeI5xnwmwqIpZaZHcar2fu6nOtLSF/87Qtyv8sl46GMoEMREZEoOuWcU2h5dks+//PndP9xd6rV1ow88n3mXNEdTs2sDdAWL0l7H7gLWBH2tEPACufc+nIFYnY78GfgO2C8c+76Qp5zC3ALQNOmTXu+/nr0p2vdu3cvdevWjfp2SuPgtoPMvH4mTQc1pd297YIOJ27EUhlK2agM45/KsGx2L97Ngl8toNUtrWhxbYuSXxBFKsPyGTBgwFznXK+KXGdJvVNXAivNrAYwFFjonFtckQEAmFkj4FKgFbALeMvMbnDOvRwWz3PAcwC9evVyGRkZFR3KMTIzM6mM7ZQkJyuHTx/7FPLh6qevpmFaw6BDihuxUoZSdirD+KcyLKMM2PvJXta/tp7UZqmcesGpgXVkUxnGnlJ1bPDnRX0eOC5KcZwDrHXObXPOHQbGAmdEaVtxJycrh1EDR/H1rK/B4NtNGldZRKSq6PjDjhz69hBTH56qWRzkeyLpnboY79JqNKwH+vrTdxkwCFgWpW3FnezMbHIP+L2THBrBW0SkCjnyw91pFgf5vkiSuDuBe8xssJmV1CEiIs65mcAYYB5espiEf9lUoFHrRt4NzcwgIlLlpGWkkVwjGfB6reocIAUiScbexes1+h7gzGwnYdNwOedOKGsgzrkH8HrCSpiVH64kuXoy6Xel03ZwWw3sKyJShaSmpzJs8jA+vOVDdqzZQeO2jYMOSWJEJEncU2ju1Eq3fcV2Fr+ymL539mXQnwcFHY6IiAQgNT2VK16/gqc7P03WP7IY9BedDySCJM4592AU45AiTH14Kik1UzjznjODDkVERAJ0QscT6HR1J2b+eyZ97+xLnePrBB2SBCySNnGANxyImfU3s+v8oUEws5pmFvG6pHjblm1j8auL6X1bb+qcoA+riEhVd/YDZ5P7XS7TH5sedCgSA0qdeJlZipk9AmwApgCj8cZ1A3gbtWercFP/OJVqtatx5t2qhRMREWjSvgmdr+vM7Cdns3fL3qDDkYBFUnv2Z+Bm4DbgFLxZHAq8B/ygAuOq8rZ+uZUlbyzh9F+dTu0mtYMOR0REYsRZfziL3AO5THtkWtChSMAi6dgwFPitc+5/ZpYc9thqvMROKkBOVg4f3vIhKbVSSP9NetDhiIhIDGnctjFdbuzC7Cdnk1IjhbY/0KgFVVUkNXEN8ZK1wlQHwhM7KYOcrBxGDhjJ1iVbyT+UzzdffRN0SCIiEmPaXNyGvEN5fPG3LzSLQxUWSRK3BG9+08JciDdQr5RTdmY2eQfzAHDOaWRuERE5xo5VO7xGTZrFoUqL5HLqn4C3zawW8BbemHHdzGwI8FPgkijEV+U0SG3g3dDsDCIiUoS0jDRSaqSQeyBXszhUYaWuiXPOvQdchzdZ/Sd4vwFeAIYDNzrnxkUjwKpm5UcrSamVQv//68/QiUPVzkFERI6Rmp7K0ElDadatGZZsHNf6uKBDkgBENLabc+5N51wa0B7oB5wGtHDOvRmF2Kqc7cu3ez1Sbz+dgQ8PVAInIiJFKpjFIe9QHtP/oXHjqqJIxokbYGYG4Jz7yjk33Tm33DmnqbgqyNQ/TaVarWqk/1o9UkVEpGRN2jWh0zWdmP3UbPZv3x90OFLJIqmJmwh8bWb/NrMzohVQVfXNV9+w5LUl9Lq1l6ZSERGRUjvr92dxeP9hsv6ZFXQoUskiSeI6A88D5wFfmNl6M3vUzHpGJ7Sq5fO/fE5y9WTOuEv5sYiIlN7xpx1Px6s6Mus/s/hux3dBhyOVKJKODV865+53zrUHegCvAEOA2Wa2ysz+FK0gE92O1TtY9PIiev6sJ3Wb1g06HBERiTP9f9+fQ98eYsa/ZgQdilSiMk1a75xb4Jy7zzl3Kt7QIrWA+yo0sirki79+QVJKkuZIFRGRMmnauSkdLu9A1j+ymHz/ZA3+W0WUKYkzs+PM7CdmNgEYC9QFXq3QyKqIZWOXseB/C2g7uC31TqwXdDgiIhKn2l7SlsP7DzP1T1M1i0MVEUnv1PpmNszMPgY2AU8AO4FrgBOcczdGKcaElZOVw1s/fAuX71j50Up94EREpMy+/fpb74ZmcagyIpmxYSveLA3j8Ab4fd85ty8aQVUVK95fgcvzRmjJO+x94DQ2nIiIlEVaRhrJNZLJO5iHJWkWh6ogksupPwOaOucuc869pgSu/Hat3QWAJZum2BIRkXJJTU9l2ORhNGrdiOp1q9OsW7OgQ5Ioi6R36gjn3J5oBlOV7N++n68++IrWF7RmwMMDNMWWiIiUW2p6Kpe8cAkHdh5g/ovzgw5HoiySy6lSgWb8awaHvzvM+Y+fz/Edjg86HBERSRAtz25J6pmpTHtkGj1v6Uly9eSgQ5IoKVPvVCmfA7sPMOvJWXQY0kEJnIiIVCgzo///9WdPzh4Wvbwo6HAkipTEBWD2U7M5uPsg/X7XL+hQREQkAZ16wak079GcL/72Bfl5+UGHI1GiJK6SHdp3iBn/nMGpF5zKiT1PDDocERFJQGZGv9/1Y8fKHSx9a2nQ4UiUKImrZPOen8f+7fvp/3/9gw5FREQSWIchHWjSoQmf/+VzXL4LOhyJgkgG+61mZneZ2XQzW29mW8P/ohloIsieks3kBybTrFszWvRrEXQ4IiKSwCzJ6HdfP7Yu3sq7w9/VgPIJKJLeqf8Efgp8CEwGDkUlogSVk5XD6HNHk384n23LtpGTlaMhRUREJKoapTUCg0WjF7F0zFINZ5VgIknirgJ+65z7R7SCSWRrJ60l/7DXuDQ/N1+zM4iISNSt+2Ldkdt5BzUzUKKJpE2cAeqrXFZ+cwRL0uwMIiJSOdIy0kipmfK9+5I4IkningeujVYgicw5x7Kxy6jfor5mZxARkUqTmp7K0IlDaX1+a1y+I6WGxvhPJJGU5hbgejObDEwAdoU97pxzT1dUYIlkzYQ1bJ6/mUtevITuP+4edDgiIlKFpKancuUbV/KvFv9i2t+nceUbVwYdklSQSJK4f/n/WwBnF/K4A5TEFeKLv31BvZPq0fn6zkGHIiIiVVDNBjXpdWsvpj8ynW9WfkPjNo2DDkkqQKkvpzrnkkr40+RshdgwcwPZk7NJ/3W6qrFFRCQwfW/vS1K1JKY/Oj3oUKSCaLDfKJv292nUbFSTHjf3CDoUERGpwuo2q0u3H3Vj4ciFfPv1t0GHIxUgoiTOzBqa2b1m9oGZTfP/32NmDaMUX1zbtmwby99ZTp/b+lCjXo2gwxERkSruzLvPJD83nxn/mhF0KFIBikzizKx92P3WwGLgj0AdYL3//4/AIv9xCTH90emk1Eqhzy/7BB2KiIgIjU5pRMerOzLn6Tl8t/O7oMORciquJm6mmV0Scv+feD1ST3HODXTOXeucGwi09pc/HrUo49Cyd5excORC2lzchjrH1wk6HBEREQDOvPdMDu09xJhrxmgqrjhXXBJ3PfC0mT3g388A7nfObQx9kn//IWBAVCKMQzlZOYy5agwu37Hyw5X6kIiISMw4vP8wlmSsGb+GUYNG6RwVx4pM4pxzHwJdgU4Fi4CieqAmcWROAln1ySryc70ptvIOe9OciIiIxILszGycf8rOPZirc1QcK7Zjg3Nuu3PuKv/uZOBhM2sZ+hz//h+BidEJMf7s27oPAEvWFFsiIhJb0jLSjg555aBl/5bFv0BiViQDl90BTAJWmtk8vBkcTgB6AjnArys8ujiUeyCX5e8u56TTT6Ldpe1Iy0jTFFsiIhIzCqbimvWfWSx5bcmRigeJP6VO4pxz2X6P1R8DvYHmwFLgf8AI59yh6IQYXxa9vIh9W/Zx+SuXc8qgU4IOR0RE5Bip6amc1OckNs7cyPRHp9N+SHvMLOiwJEIRTSHgJ2rP+H8SxuU7pj82nWbdm9FqYKugwxERESlSUnISfX/dl09u+4ScaTm06Nci6JAkQjE1Y4M/mPAYM1tuZsvMLD3omCLx1Ydf8c2Kbzjj7jP0i0ZERGJe9x91p1bjWkx7ZFrQoUgZFJvEmdlWM+vu397m3y/yrwLieQL41DnXHq9n7LIKWGelmfbINBq0bEDHqzoGHYqIiEiJqtWuRp/b+vDVB1+xbdm2oMORCJV0OfUpvA4MBbejNoyImdUHzgKGw5FLt3HTzi4nK4ecaTmc/6/zSUqJqQpOERGRIvX+RW+m/X0aWf/I4pIXLin5BRIzzLnYGN7NzLoBz+F1lugKzAVud87tC3nOLcAtAE2bNu35+uuvRz2uvXv3Urdu3RKf9+X9X7JrwS76vtGX5FpFDacnQShtGUrsUhnGP5VhbFv5z5Vs+mQTp792OjUaFz7Xt8qwfAYMGDDXOderItdZ6iTOzFKB451z8wp5rAewzTlX5mGfzawXMAM40zk308yeAPY45/5Q2PN79erl5syZU9bNlVpmZiYZGRnFPuebr77hyfZP0v93/Rn4p4FRj0kiU5oylNimMox/KsPYtmPVDv7T5j+0PLslg/46qNChsVSG5WNmFZ7ERXLd72nghiIeuw74bzlj2QBscM7N9O+PAXqUc52VYsI9E7Ak4+T0k4MORUREJGL7tu3Dko11U9ZpKq44EkkS1xdvsN/CTPYfLzPn3GYgx8za+YsG4V1ajWkrP17JivdW4PIdb131lg58ERGJO9mZ2UdavWsqrvgRSRJXm+I7NtQpZywAvwReMbNFQDfgLxWwzqjKejzLu+Eg75DmSRURkfiTlpFGcg2/Pbem4oobkSRxi4Fri3jsWuDL8gbjnFvgnOvlnOvinLvMObezvOuMptwDuWyatwlLMs2TKiIicatgKq7O13cGB3s37w06JCmFSGZs+BvwtpnVAEYAm/Cm3hoGXOH/VSmLXlnEgZ0HuPDfF3Jw70HNkyoiInGrYCquDVkbyHo8i9OuPC3okKQEkcyd+o6ZDQP+ipewOcCAjcANzrl3oxJhjHLOMePxGTTt2pTet/XWDA0iIhL3kpKTOP2O0/n0V5+Sk5WjiokYF9GotM650UAqcBrewLynAS2cc69FIbaYtnrcarYt3Ub6b9KVwImISMLo/qPu1GxYkxmPzwg6FClBxFMLOM9y59w0/39sjBZcybIez6Ju87p0urpT0KGIiIhUmOp1q9Pzpz1ZNnYZO9fGdNP0Kq/Yy6lmdivwlnNum3+7OM4593TFhRa7tizawpoJaxj4l4EkV9fsDCIiklj6/LIPWf/IYsa/ZnDhExcGHY4UoaQ2cU8Cc4Bt/u3iOLwBgRPejH/OoFrtavT6aYUOvCwiIhIT6p9Un07XdGL+i/MZ8NAAajasGXRIUohiL6c655Kcc7NCbhf3VyWqpL7d9C2LXllEtx91o9ZxtYIOR0REJCr6/rovh/cdZsy1YzSQfYyKuE1cVTfxvonkH86n5dkaCFFERBJX7oFcLMlY/elqRg0axe4vdwcdkoQpdRJnZv3N7NKQ+03M7FUzW2Bm/zCzatEJMXasnbyWhSMXAvDusHf1y0RERBJWdmY2zp+oKfdgLrsXKImLNZHUxD0ChHbFfAJvftMZwHDgoYoLKzbN/PfMI7c1xZaIiCSytIw0Umr4TecdNOjaINiA5BiRJHHtgLkAZlYbGALc7pz7GXAPcHXFhxc7nHNsnr8ZDE2xJSIiCa9gKq62P2h7dHh/iSmRTLtVHTjg3z7Tf+1H/v2v8KbgSlhrJqxh97rdnPWHs0iplaIptkREJOGlpqdyxatX8PjJj7NxzEb4RdARSahIkrjlwAVAJnA9kOWc+9Z/7ERgR8WGFltm/GsGdZrWof//9T9avSwiIpLgqtetTo+be5D1eBa71++mQQtdVo0VkVxO/SNwp5ltA64D/hby2AXA/IoMLJZsX7GdVZ+sovetvZXAiYhIldPntj4AzHpqVsCRSKhSJ3HOufeBDsDPgE7OuU9CHs4C/lzBscWMmf+eSXL1ZHr9TIP7iohI1dOwZUOa9G/CvOfmcWjfoaDDEV9E48Q559Y45952zn0Vtvw551xCzpR7+NvDLByxkM7Xd6bOCXWCDkdERCQQJ19xMgd2HWDR6EVBhyK+iJI4MzvFzJ42s8VmttH//18zaxWtAIO2+ePNHN5/mNNvPz3oUERERAJTv1N9Tux1IjOfmInLd0GHI0Q22G9PYAFwBTAbGOX/vwJYYGY9ohFgkPJz89n4zkbSBqTRrGuzoMMREREJjJlx+h2ns335dlaPXx10OEJkvVMfw+u8cKFzbn/BQn/MuI/9xwdWbHjBWv7ucg5uOcjpz6oWTkREpONVHZlw9wQyH8xk0/xNGm4rYJEkcX2AH4YmcADOuf1m9hjwRoVGFgOm/HEKKfVSqN24dtChiIiIBC65ejJtf9CWec/N4+vZX5NcI5mhE4cqkQtIJG3ivgMaF/HYcRwdCDghzH9pPlsXbyV3by6jzxuteVJFRESAOsd7nfxcvtMUlAGLJIn7CPibmfULXejf/yvwQUUGFrQdK3d4U4w4zZMqIiJSoM3FbbBkbw4uTUEZrEiSuF8Da4ApZrbZzBaa2SZgir/8N9EIMChtL2lLSs0USNJBKiIiUiA1PZVLXrgEgB4399Cl1ABFMtjvN865fsDFwFPANOC/eB0d+jvnvolSjIEomPi31Y9b6Xq/iIhIiG7Du9GiXwtWfriS/Lz8oMOpskrs2GBmtYCLgDRgEzDROfdplOOKCanpqbQ42EIJnIiISJg+v+zDmKvHsOqTVbQd3DbocKqkYpM4MzsF+AwvgSuwx8x+6JwbH83AREREJHa1H9KeeifVY9Z/ZimJC0hJl1MfAfKB/kBtoCPeWHHPRjkuERERiWHJ1ZLp9fNerB6/mu3LtwcdTpVUUhKXDvzeOTfNOXfAObcM+CnQwsyaRz88ERERiVU9b+lJco1kZj05K+hQqqSSkrjmeD1PQ63GG3xD81CJiIhUYXWOr0OnazqxYMQCDuxOqOFi40JpeqdqllsREREpVJ9f9uHwvsMsGLEg6FCqnNJMuzXOzHILWT4xfLlz7oSKCUtERETiwYk9TyT1jFSmPzqdw/sOkzZA86lWlpKSuIcqJQoRERGJW63Pb03mA5lM+sMkUmqkaHzVSlJsEuecUxInIiIixbIkbxou8o9OVakkLvoimXZLRERE5BitBrUiKcVLKZKraarKyqIkTkRERMolNT2VH779QyzJaD+kvWrhKomSOBERESm3dpe0o8MVHVj16SoO7z8cdDhVgpI4ERERqRC9f9GbAzsPsOT1JUGHUiUoiRMREZEK0fKslhzf8XhmPzUb5zTMbLQpiRMREZEKYWb0/kVvNs3bxMaZG4MOJ+EpiRMREZEK0+WGLlSvV53ZT80OOpSEpyROREREKkyNejXoOqwrX775Jfu27gs6nISmJE5EREQqVO9be5N3KI95L8wLOpSEVpq5U0VERERK7fgOx9NqUCtmPDED5xytBrbS2HFRoJo4ERERqXCtzmnF/q37mXz/ZEYNGkVOVk7QISWcmErizCzZzOab2YdBxyIiIiJl5/L8IUZC5lOVihVTSRxwO7As6CBERESkfFoN1Hyq0RYzSZyZnQxcDLwQdCwiIiJSPgXzqWLQ/nLNpxoNFisjKpvZGOCvQD3gLufc4EKecwtwC0DTpk17vv7661GPa+/evdStWzfq25HoURnGP5Vh/FMZxr+yluGXD3zJrgW7SH8rnaTqMVN3VOkGDBgw1znXqyLXGRO9U81sMLDVOTfXzDKKep5z7jngOYBevXq5jIwin1phMjMzqYztSPSoDOOfyjD+qQzjX1nLsMX9LRh9zmgab2lM1xu7VnxgVVispMRnApeYWTbwOjDQzF4ONiQREREpr1YDWnFcm+OY+8zcoENJODGRxDnn7nPOneycSwOuASY5524IOCwREREpJ0syev2sFznTc9iyaEvQ4SSUmEjiREREJHF1G96NlJopzH5a86lWpJhL4pxzmYV1ahAREZH4VOu4WnS8uiOLX17MwW8PBh1Owoi5JE5EREQST6+f9eLQ3kMsenlR0KEkDCVxIiIiEnUnnX4Szbo1Y87Tc4iV4c3inZI4ERERiTozo9fPe7F18VY++vlHmku1AiiJExERkUrRqHUjAOY+O5dRg0YpkSsnJXEiIiJSKTbO2gjm3c47lEd2Znag8cQ7JXEiIiJSKdIy0kiungx448elZaQFG1CcUxInIiIilSI1PZVhk4dR/+T61DuxHif3PTnokOKakjgRERGpNKnpqWQ8lMHudbtZ/8X6oMOJa0riREREpFJ1vLojNerXYN5z84IOJa4piRMREZFKVb1Odbrc2IUv3/qS/d/sDzqcuKUkTkRERCpdz1t6kncwj0WjNYNDWSmJExERkUrXtEtTTu57MnOfnasZHMpISZyIiIgEosctPdi+fDvrP1cHh7JQEiciIiKB6HR1J2o0qMHc5+YGHUpcUhInIiIigahWuxpdbuzC0jFL1cGhDJTEiYiISGAKOji8M/QdzaUaISVxIiIiEphDew9hScaqj1cxatAoJXIRUBInIiIigcnOzD7SOzXvYB7ZmdnBBhRHlMSJiIhIYNIy0kipmeLdMe++lI6SOBEREQlManoqQycO5cQ+J5KUnMTxHY4POqS4oSROREREApWansrFT11M3qE8Fr+2OOhw4oaSOBEREQlc857NadatGfOenxd0KHFDSZyIiIgEzszo/pPubJ6/mU3zNgUdTlxQEiciIiIxocv1XUipmcLc5zWDQ2koiRMREZGYULNhTU676jSWvLqEQ/sOBR1OzFMSJyIiIjGjx809OLjnIEvfWhp0KDFPSZyIiIjEjBb9WtC4XWPmvaAODiVREiciIiIxw8zo8ZMe5EzLYdvSbUGHE9OUxImIiEhM6Tq0K5ZsfPDTDzSXajGUxImIiEhM2bF6BwA5X+QwatAoJXJFUBInIiIiMSU7MxvnHAC5B3PJzswONqAYpSROREREYkpaRhopNVIAMIy0jLRgA4pRSuJEREQkpqSmpzJ04lDSBqThnKNBaoOgQ4pJSuJEREQk5qSmp3LJC5eAgwUjFgQdTkxSEiciIiIxqdEpjUgbkMaC/y3A5bugw4k5SuJEREQkZnW/qTs71+wke0p20KHEHCVxIiIiErM6XN6BGg1qsOClBUGHEnOUxImIiEjMqlarGp2v68zSMUs5sOtA0OHEFCVxIiIiEtO6/7g7uQdyWfL6kqBDiSlK4kRERCSmNe/ZnKZdmjL/xflBhxJTlMSJiIhITDMzuv24G1/P+Zoti7YEHU7MUBInIiIiMa/LDV1ISkniw59/qLlUfUriREREJOZ989U3OOfYMH0DowaNUiKHkjgRERGJA9mZ2TjnDfibezCX7MzsYAOKATGTxJlZqplNNrNlZvalmd0edEwiIiISG9Iy0kipkQKAYaRlpAUbUAyImSQOyAV+45zrAPQFfmFmpwUck4iIiMSA1PRUhk4cSsuzW+JwNExrGHRIgYuZJM45t8k5N8+//S2wDDgp2KhEREQkVqSmpzL42cGQD4teXhR0OIGzguvLscTM0oCpQCfn3J6Q5bcAtwA0bdq05+uvvx71WPbu3UvdunWjvh2JHpVh/FMZxj+VYfyLpTKcf9t8cvfm0ut/vTCzoMMplQEDBsx1zvWqyHWmVOTKKoKZ1QXeBu4ITeAAnHPPAc8B9OrVy2VkZEQ9nszMTCpjOxI9KsP4pzKMfyrD+BdLZVjv9np8eMuHtK3TlpP6VN2LdjFzORXAzKrhJXCvOOfGBh2PiIiIxJ6OP+xISq0U5v+vas/gEDNJnHn1oS8Cy5xzjwcdj4iIiMSmmg1q0uHyDix5bQm5B3KDDicwMZPEAWcCNwIDzWyB/3dR0EGJiIhI7Ok2vBsHdx9k+bvLgw4lMDHTJs459wUQH60TRUREJFCtBraiQYsGLBixgE7XdAo6nEDEUk2ciIiISKlYktF1WFdWj1/Nng17Sn5BAlISJyIiInGp67Cu4OC9m96rknOpKokTERGRuLRv6z4syVgzfg2jBo2qcomckjgRERGJS9mZ2RRMWpB3MI/szOxgA6pkSuJEREQkLqVlpJFS0++jad79qkRJnIiIiMSl1PRUhk4cSrNuzUipmULz7s2DDqlSKYkTERGRuJWansq5j57L4X2HWfH+iqDDqVRK4kRERCSupQ1Io95J9Vg4cmHQoVQqJXEiIiIS15KSk+hyYxdWjVvF3s17gw6n0iiJExERkbjXdWhXXJ5j8auLgw6l0iiJExERkbh3fIfjObH3iVXqkqqSOBEREUkIXYd1ZcuiLWxeuDnoUCqFkjgRERFJCJ2u6URStaQqUxunJE5EREQSQu3GtWk7uC2LX1lM3uG8oMOJOiVxIiIikjC6DuvKvq37WD1+ddChRF1K0AGIiIiIVJQ2F7ahdpPaZD2exZZFW0jLSCM1PTXosKJCSZyIiIgkjOTqybTMaMmyMctYN2UdydWTGTpxaEImcrqcKiIiIgmlbtO6ALg8R96hPLIzs4MNKEqUxImIiEhC6XRdJzDvdnL1ZNIy0gKNJ1qUxImIiEhCaXFGC3r+tCcAQ0YNSchLqaAkTkRERBJQ//v6A7Bt6baAI4keJXEiIiKScBq0aEBaRhqLRi/CORd0OFGhJE5EREQSUpcbu7Bj1Q42ztoYdChRoSROREREEtJpV55GSs0UFo1eFHQoUaEkTkRERBJSjfo1aHdpO5a8voS8Q4k3DZeSOBEREUlYXW7swnfffMeqT1cFHUqFUxInIiIiCav1ea2pfXzthLykqiROREREElZytWQ6XduJFR+s4MCuA0GHU6GUxImIiEhC63pjV/IO5vHlW18GHUqFSgk6ABEREZFoat6zOU3aN2H2U7PZv30/aRlpCTGLg5I4ERERSWhmRsuzWzL32blsXbyV5BrJDJ04NO4TOV1OFRERkYRXo34NAFy+I+9QHtmZ2cEGVAGUxImIiEjCaz+kPZZkACRXTyYtIy3YgCqAkjgRERFJeKnpqaTflQ7Axf+9OO4vpYKSOBEREaki+t3bj6RqSWxZvCXoUCqEkjgRERGpEmodV4s2F7VhyWtLyM/LDzqcclMSJyIiIlVG5+s7s3fTXnVsEBEREYknbQe3pXq96ix+ZXHQoZSbkjgRERGpMqrVqsZpV5zGsreXkXsgN+hwykVJnIiIiFQpna/vzME9B/nqw6+CDqVclMSJiIhIlZI2II26zeuy6OVFQYdSLkriREREpEpJSk6i07WdWPnxSr7b8V3Q4ZSZkjgRERGpcrpc34X8w/ksHbM06FDKTEmciIiIVDnNujejSfsmcd1LNWaSODO7wMxWmNkqM/tt0PGIiIhI4jIzOl/fmXVT1zHh3gnkZOUEHVLEzDkXdAyYWTLwFXAusAGYDVzrnCuyjtPMgg9cREREpHTmOud6VeQKY6Umrg+wyjm3xjl3CHgduDTgmERERERiVqwkcScBofWYG/xl32Nmt5jZHDObU2mRiYiIiMSglKAD8Fkhy465XOqcew54DnQ5VURERKq2WEniNgCpIfdPBr4u7gU9e/ZkzpzoV8hlZmaSkZER9e1I9KgM45/KMP6pDOOfyrB8zAqrryqfWLmcOhtoY2atzKw6cA3wfsAxiYiIiMSsmKiJc87lmtltwDggGXjJOfdlwGGJiIiIxKyYSOIAnHMfAx8HHYeIiIhIPIiVy6kiIiIiEgElcSIiIiJxSEmciIiISBxSEiciIiISh5TEiYiIiMQhJXEiIiIicUhJnIiIiEgcUhInIiIiEoeUxImIiIjEISVxIiIiInFISZyIiIhIHFISJyIiIhKHlMSJiIiIxCFzzgUdQ5mY2TZgXSVsqgmwvRK2I9GjMox/KsP4pzKMfyrD8mnpnDu+IlcYt0lcZTGzOc65XkHHIWWnMox/KsP4pzKMfyrD2KPLqSIiIiJxSEmciIiISBxSEley54IOQMpNZRj/VIbxT2UY/1SGMUZt4kRERETikGriREREROKQkjgRERGROKQkrghmdoGZrTCzVWb226DjkZKZ2UtmttXMloQsO87MJpjZSv9/oyBjlOKZWaqZTTazZWb2pZnd7i9XOcYJM6tpZrPMbKFfhg/5y1WGccbMks1svpl96N9XGcYYJXGFMLNk4CngQuA04FozOy3YqKQURgAXhC37LTDROdcGmOjfl9iVC/zGOdcB6Av8wv/sqRzjx0FgoHOuK9ANuMDM+qIyjEe3A8tC7qsMY4ySuML1AVY559Y45w4BrwOXBhyTlMA5NxXYEbb4UmCkf3skcFllxiSRcc5tcs7N829/i3cCOQmVY9xwnr3+3Wr+n0NlGFfM7GTgYuCFkMUqwxijJK5wJwE5Ifc3+Msk/jR1zm0CL0EATgg4HiklM0sDugMzUTnGFf8y3AJgKzDBOacyjD//Au4B8kOWqQxjjJK4wlkhyzQWi0glMbO6wNvAHc65PUHHI5FxzuU557oBJwN9zKxTwCFJBMxsMLDVOTc36FikeEriCrcBSA25fzLwdUCxSPlsMbPmAP7/rQHHIyUws2p4Cdwrzrmx/mKVYxxyzu0CMvHaqqoM48eZwCVmlo3XnGigmb2MyjDmKIkr3GygjZm1MrPqwDXA+wHHJGXzPjDMvz0MeC/AWKQEZmbAi8Ay59zjIQ+pHOOEmR1vZg3927WAc4DlqAzjhnPuPufcyc65NLzz3yTn3A2oDGOOZmwogpldhNcmIBl4yTn352AjkpKY2WtABtAE2AI8ALwLvAm0ANYDVznnwjs/SIwws37A58BijrbF+R1euziVYxwwsy54jd6T8SoK3nTO/dHMGqMyjDtmlgHc5ZwbrDKMPUriREREROKQLqeKiIiIxCElcSIiIiJxSEmciIiISBxSEiciIiISh5TEiUilM7PBZrZHcxKLiJSdkjiROGZmD5qZC/n72szeNrPWFbwdZ2a3RRjX9iIeawI8Bwxzzi2toPhGmNmcilhXMdso8j1Fm5mdZ2Z3lPG1w/3yq1vMc0KPo3fLGmfYOjND1lnqY0dESi8l6ABEpNx2442ID3AK8DAw0cw6Ouf2VdA20oG1ETz/BeCDIh57BnjBOfdOuaOqXMW9p2g7D7gSb+zKaCk4jipq3K9bgfpAVgWtT0TCKIkTiX+5zrkZ/u0ZZrYeb8Dci4C3KmIDIesv7fM34E1fV9hjV1ZETJXFnwYsv7j3lCByIy3n4hTUsnqTcIhINOhyqkjiKZi0Og28y5dmNtLMvjGz/f5lrl6hLzCzS8xsrpntM7OdZjbTzM4OefyYS2JmNsTMZpnZd/66Pzazlv5jx1x69Kexe9dvC/etmX1gZqeGPceZ2e1m9hcz22ZmW83sKTOrUZo3bmbnmtki/318YWYdwx6vbWb/NrPNZnbAzGab2Xlhz8k0szFmdouZrQYOACeGv6ewy4WhfyNCntPNzCb6+32nmb1iZk1DHk/zX/NDM3vWzHab2QYze8jMkgr2JfAboGX4Nsws3cze9y+j7zOzBWZ2fWn2VSn354Nmtt3MTjezOX5Zf+GX5Ql+ee41s2VmNrCitisipaMkTiTxpPn/N/v/3wXOB+4Crsb73E8uSKDMaz83BpgE/AC4HvgQOK6oDZjZjcBYYDXwQ+BHwFfA8UU8vwYwEegA3AwMB1oBU8wsfDu/AU4EbgAeBX4K3F7y26aF//w/A9cCJwBv2vergp73Y/0zMATIAT4yb7qvUGcCPwfuxdsnuwvZ3q14l5kL/m4CHN5+wMyOx5v8vTZwHfBL4GxggnlzMod6BNiLd8n0ZeB+/zZ4l3FfxSvPgm097D/WEpgG/MSP823gf2Z2bVE7qQxq47Vh/Cfefm0BjAZeA74ALgc2Am+ZWe0K3K6IlMQ5pz/96S9O/4AHge14TSNSgLbAZGAP0ByvjZMDzg55TR1gG/Csf/9K4JsStuOA2/zbSXgn7bElxRVy/2dALnBKyLKTgUPAfWHbmRq2rneBGSXEN8Jff5uQZZf562vv3++ANx/rsJDnJAFLgHEhyzKB74Bmxb2nsMca4CVvnwHJ/rK/AbuA+iHP6+PHdK1/P82/PypsfQuA10PuPwZkl7APzD8GnsWbsLxg+XB/G3VLW15hy8OPn1v9ZfeHLDvNX3ZhcceO/vSnv4r9U02cSPxrDBz2/1bgdW642jm3CS9p2Oacm1LwZOd1dvgQKKh9Wgw08C+5nmdmdUrYXju8mrL/RRBjH2Cec25NSBwb8GqRwmvBxofdX4qX8JUk2zm3Mux1hLy2N16ic6SdoHMu378fHsNc59xmSsG/7PkKUAO4xjmX5z/UBxjvnNsTsr1ZQHYh2yvTezazRv7l4XUcPQZuwUvmK8ohvDaWBVb5/ycVsuykCtyuiJRASZxI/NuNl6D0wjvxpznnPvEfaw5sKeQ1W/AvlzrnVgCX4iV/HwPbzexV/3JgYRr7/zdFEGOJcYTYFXb/EFCzFNso7HWEvLY5sNc5t7+QGGqHtbsrLNai/BEYBFzunAttB1gZ73kE3iXyR/F6sPYGXirla0vrWz/ZDY0NQmJ2zoXvaxGpBOqdKhL/cp1zRY2RtgmvbVi4poQMJeGc+wivbVgD4GK8oSz+A1xTyGu/8f83jyDGTUDHQpZ/L44o2wTUNbPaYYlcU2C/c+5gyDJXmhWa2RDgd8BNzrm5YQ8Xt+/DnxsxM6uJV1a3OeeeCVmuH+ciVYQ+7CKJbSZwgpmdVbDAb3x+MV6j9O9xzu12zr0KvIPXzqkwK/DaxA2LMI6eZtYqJI6TgDMKiyNKZuMlZ0eGOPE7PVxZlhjMm21iJPCMc66wS8szgfPNrF7Ia3rjtYOLdHuF1czVAJKBI8mnv61LIly3iMQp1cSJJDDn3Dgzmwa8YWa/xatFuwuohXcJDjP7KV6Px0+Br4E2wFXAqCLWmW9m9wCvmNkreL0UHTAQeK2IWsEReD09PzGz+4E8jnbKeLZC3mwJnHPLzOw14Ekzq4/XjutmoD1eT9RIvYt3Kft1M+sbsnybc2418Li/3nFm9negLl5nh8V4vUgjsRxoambD8TpibHfOZZvZbOB+M9uD12njt35M9cvwfkQkziiJE0l8Q4B/4F0irQnMAgY65woaoy/Cq715HK+t1ia8oTjuL2qFzrlXzewA8H94w5PsA2bg9Xot7PkHzewcfxsv4nUwyMRrR1ZZl1PBS9r+DvwBaIiXUA12zpWlNrCN/39K2PKRwHDn3DYzG4C371/Dq037GLgzpA1Zab0JDMAbiuT4gm3gDV3yHF7C/Q3wJN6QIJrmSqQKMOdK1fRDREQSlD+g8G1AM7zZKfKLf0Wp1pmMl6wfBn7pnHuyvOsUke9TmzgREYGjQ9WMraD1TfTXJyJRopo4EZEqzsxOxBv7D2Cn36avvOtsBxR06ljnnCv0UruIlJ2SOBEREZE4pMupIiIiInFISZyIiIhIHFISJyIiIhKHlMSJiIiIxCElcSIiIiJx6P8BFyjR8jEXIOUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Graficamos los resultados para comprobar la somulación\n",
+    "plt.figure(figsize=(10,8))\n",
+    "plt.plot([posiciones[i][0] for i in range(len(posiciones))], [posiciones[i][1] for i in range(len(posiciones))], \".-\", color = \"purple\")\n",
+    "plt.title(\"Trayectoria de una masa bajo la atracción gravitacional terrestre\", fontsize=18)\n",
+    "plt.xlabel(\"Posición horizontal [m]\", fontsize=15)\n",
+    "plt.ylabel(\"Posición vertical [m]\", fontsize=15)\n",
+    "plt.axhline(0, linewidth=4, color=\"Black\")\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100\n"
+     ]
+    },
+    {
+     "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>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.453195</td>\n",
+       "      <td>5.443131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.906390</td>\n",
+       "      <td>5.876198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.359585</td>\n",
+       "      <td>6.299202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.812780</td>\n",
+       "      <td>6.712141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>43.053526</td>\n",
+       "      <td>2.162141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>43.506721</td>\n",
+       "      <td>1.649202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>43.959916</td>\n",
+       "      <td>1.126198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>44.413111</td>\n",
+       "      <td>0.593131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>44.866306</td>\n",
+       "      <td>0.050000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            x         y\n",
+       "0    0.000000  5.000000\n",
+       "1    0.453195  5.443131\n",
+       "2    0.906390  5.876198\n",
+       "3    1.359585  6.299202\n",
+       "4    1.812780  6.712141\n",
+       "..        ...       ...\n",
+       "95  43.053526  2.162141\n",
+       "96  43.506721  1.649202\n",
+       "97  43.959916  1.126198\n",
+       "98  44.413111  0.593131\n",
+       "99  44.866306  0.050000\n",
+       "\n",
+       "[100 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(len(tiempos))\n",
+    "pd.DataFrame(posiciones, columns=[\"x\", \"y\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Leemos los resultados de la simulación en C++ (no correr sin primero ejecutar \"MovimientoParabolico.exe\")\n",
+    "#Pongo los markers de python más grandes para que se vea que estan ahí pero se tapan con los de C++ XD\n",
+    "dataCpp = pd.read_csv(\"MovimientoParabolicoC++.csv\")\n",
+    "plt.figure(figsize=(10,8))\n",
+    "plt.plot([posiciones[i][0] for i in range(len(posiciones))], [posiciones[i][1] for i in range(len(posiciones))], \".-\", color = \"purple\", label = \"Python\", markersize=12)\n",
+    "plt.plot(dataCpp.x, dataCpp.y, \".-\", color = \"green\", label = \"C++\", markersize=8)\n",
+    "plt.title(\"Trayectoria de una masa bajo la atracción gravitacional terrestre\", fontsize=18)\n",
+    "plt.xlabel(\"Posición horizontal [m]\", fontsize=15)\n",
+    "plt.ylabel(\"Posición vertical [m]\", fontsize=15)\n",
+    "plt.axhline(0, linewidth=4, color=\"Black\")\n",
+    "plt.grid()\n",
+    "plt.legend(fontsize=14)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Algoritmos/Parcial 2/Nicolas Mantilla/MovimientoParabolicoC++.csv b/Algoritmos/Parcial 2/Nicolas Mantilla/MovimientoParabolicoC++.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c003a8c8934bb9bdbabe4b8446f83b14e704e70e
--- /dev/null
+++ b/Algoritmos/Parcial 2/Nicolas Mantilla/MovimientoParabolicoC++.csv	
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