{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Análisis de la sensibilidad en la topología Sallen-Key\n",
"\n",
" \n",
"\n",
"#### Por Mariano Llamedo Soria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Resumen \n",
"\n",
"En este notebook se analizará la respuesta en frecuencia de un **filtro pasabajo activo**, mediante el uso de las siguientes funciones:\n",
"\n",
"* Análisis de la respuesta en frecuencia: [analyze_sys](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.analyze_sys), [bodePlot](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.bodePlot), [pzmap](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.pzmap), [pretty_print_bicuad_omegayq](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.pretty_print_bicuad_omegayq), [GroupDelay](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.GroupDelay)\n",
"* De presentación algebraica: [print_latex](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.print_latex), [a_equal_b_latex_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.a_equal_b_latex_s), [print_subtitle](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.print_subtitle)\n",
"* Dibujo de redes: [dibujar_elemento_derivacion](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_elemento_derivacion), [dibujar_puerto_entrada](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_puerto_entrada), [dibujar_espacio_derivacion](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_espacio_derivacion), [dibujar_elemento_serie](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_elemento_serie), [dibujar_puerto_salida](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_puerto_salida)\n",
"\n",
"Finalmente se presentará un *análisis estadístico cualitativo* de la sensibilidad de dicha respuesta a los elementos circuitales."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introducción\n",
"\n",
"\n",
"En el siguiente documento analizaremos la dispersión de parámetros $Q$, $\\omega_0$ y la ganancia $K$ correspondiente a una topología Sallen-Key en su configuración pasabajo\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Como se analizó en clase, la transferencia de tensión está definida por la siguiente función\n",
"\n",
"$$ T(s) = \\frac{ K·\\omega_0^2}{s^2 + s · \\omega_0/Q + \\omega_0^2} $$\n",
"\n",
"siendo los parámetros de la transferencia en función de los componentes de la red:\n",
"\n",
"$$ \\omega_0^2=\\frac{G_1 G_2}{C^2} $$\n",
"\n",
"$$ K=1+R_B/R_A $$\n",
"\n",
"\n",
"$$ Q=\\frac{ \\sqrt{G_1 G_2}}{G_1 + G_2(2-K)} $$\n",
"\n",
"Los primeros dos parámetros, $\\omega_0^2$ y $K$, no llaman la atención, pero sí la selectividad $Q$ que depende de la ganancia $K$ de una forma muy singular. Como se observa en el denominador, si la ganancia iguala o supera el valor de 2 (o 3 en el caso que $G_1=G_2=G$), el valor de $Q$ podría incrementarse demasiado, incluso en teoría comprometiendo la estabilidad de la red. Esta sería una forma de analizarlo, sin embargo también hay otra forma, si despejamos $K$ de la expresión simplificada para $G_1=G_2=G$\n",
"\n",
"$$ K= 3 - \\frac{ 1}{Q} $$\n",
"\n",
"Si necesitamos un filtro con un $Q$ elevado (no mucho más grande que 3), la ganancia requerida estará apenas por debajo de 3. En consecuencia, implementar $K$ mediante dos resistores ($R_A$ y $R_B$) con tolerancias comerciales resulta difícil o impracticable.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Análisis de la Sensibilidad\n",
"\n",
"Para este tipo de circuitos con dependencias restrictivas (o marginalmente problemáticas), resulta necesario estudiar en detalle cómo afectan el valor de los componentes a la transferencia total $T(s)$, o más precisamente a algún parámetro en particular: $K$, $Q$ u $\\omega_0$. Este estudio se denomina **análisis de la sensibilidad**, que según se estudió en clase, analiza la dependencia lineal entre el valor del componente y el parámetro en cuestión. Para este caso, la sensibilidad de $Q$ respecto a $K$ se denomina $S_K^Q$\n",
"\n",
"$$ S_K^Q = \\frac{dQ/Q}{dK/K} \\approx \\frac{\\Delta Q/Q}{ \\Delta K/K} = 3Q - 1 $$\n",
"\n",
"y se aproxima a la relación lineal entre el error relativo entre $Q$ y $K$\n",
"\n",
"$$ \\frac{\\Delta Q}{Q} = S_K^Q . \\frac{\\Delta K}{K} $$\n",
"\n",
"es decir que un error relativo $\\Delta K/K=5\\%$, para un valor de $Q = 5$, se convierte en $\\Delta Q/Q= 3 \\times 15-1 = 44\\%$.\n",
"\n",
"Restaría analizar $S_{R_A}^K$ y $S_{R_B}^K$ para poder calcular \n",
"\n",
"$$ \\frac{\\Delta K}{K}= S_{R_A}^K \\times \\frac{\\Delta R_A}{R_A} + S_{R_B}^K \\times \\frac{\\Delta R_B}{R_B} $$\n",
"\n",
"Esto se deja para quienes tengan interés especial. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Análisis de Monte Carlo\n",
"\n",
"Sin embargo, no es el único método para estudiar la sensibilidad de una red respecto de sus componentes. Otro enfoque muy utilizado es el denominado [análisis de Monte Carlo](https://en.wikipedia.org/wiki/Monte_Carlo_method). El mismo puede resumirse mediante los siguientes pasos:\n",
"\n",
"1. Definir las variables independientes: sus posibles valores y distribuciones estadísticas. En caso de incertidumbre, se puede asumir una distribución **uniforme** entre dos valores límite, lo que equivale a no favorecer ningún valor en dicho intervalo, o desconocer cómo se distribuyen los valores de un proceso de fabricación.\n",
"2. Samplear o tomar al azar un valor para cada componenente (o variable elegida en **1.**), y calcular determinísticamente la transferencia $T_i(s)$.\n",
"3. Repetir $N$ veces para acumular una cantidad significante de realizaciones. Una regla adecuada para asumir un valor de $N$ adecuado es de *10 veces la cantidad de variables independientes* definida en **1.**.\n",
"\n",
"Con los $N$ resultados acumulados de $T_i(s)$, se procede a calcular los valores más representativos de centralidad, dispersión, y eventualmente, los casos más y menos favorables. \n",
"\n",
"Estos serían los resultados del análisis de **Monte Carlo**, comportamientos de la red desde un enfoque más generalista, y una persepctiva más amplia, ya que podemos hablar al menos de 3 comportamientos:\n",
"\n",
"* El más probable, dado por el comportamiento central que denominamos $T_0(s)$. Podría ser el comportamiento medio o mediano.\n",
"* El menos favorable para la red, o el peor caso analizado. Habría que definir una función de costo, en este caso podría ser el valor del parámetro $Q$. Tener un $Q$ elevado implica un costo elevado para esta red, dado que el $Q$ objetivo por lo general será más bajo.\n",
"* El más favorable para la red, o el mejor caso analizado, sería simplemente el más cercano al $Q$ objetivo.\n",
"\n",
"Todo esto, se contrasta lógicamente con el análisis de sensibilidad donde solo podemos aspirar a tener un intervalo de incertidumbre, o error porcentual $ \\frac{\\Delta Q}{Q} $"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Experimentación\n",
"\n",
"Realizaremos un estudio de la dispersión, *estilo Monte Carlo*, de los parámetros $Q$, $\\omega_0$ y $K$ muestreando valores de $C$, $R$ y $R_B$ con una distribución uniforme, definida por los valores centrales y la tolerancia de cada componente, de la siguiente manera:\n",
"\n",
"* $C = 1$\n",
"* $R = 1$\n",
"* $R_B = R \\times (2-\\frac{1}{Q}) = 2-\\frac{1}{Q}$\n",
"\n",
"Siendo $\\omega_0 = 1$ y $Q = 10$. Elegimos un $Q$ elevado e impracticable para acentuar las limitaciones de esta red.\n",
"\n",
"\n",
"Para ello se calculará la función transferencia $T_i(s)$ a partir de los 3 valores sampleados $C_i, R_i$ y $R_{Bi}$ de cada componente. Finalmente se calculan las $N$ respuestas de módulo y fase, y los valores de los parámetros de cada transferencia $Q_i$ y $\\omega_{0i}$.\n",
"\n",
"Se procede a inicializar la simulación de la forma usual."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"code_folding": [],
"scrolled": true
},
"outputs": [],
"source": [
"# Inicialización e importación de módulos\n",
"\n",
"# Módulos externos\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from scipy.signal import TransferFunction\n",
"\n",
"# Esta parte de código la agregamos SOLO en los notebooks para fijar el estilo de los gráficos.\n",
"fig_sz_x = 13\n",
"fig_sz_y = 7\n",
"fig_dpi = 80 # dpi\n",
"fig_font_size = 13\n",
"\n",
"mpl.rcParams['figure.figsize'] = (fig_sz_x, fig_sz_y)\n",
"mpl.rcParams['figure.dpi'] = fig_dpi\n",
"plt.rcParams.update({'font.size':fig_font_size})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Separamos la inicialización del módulo pyTC2 para enfatizar las funciones concretas que se necesitan."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Ahora importamos las funciones de PyTC2\n",
"\n",
"from pytc2.sistemas_lineales import pzmap, GroupDelay, bodePlot, pretty_print_bicuad_omegayq\n",
"from pytc2.general import print_subtitle, print_latex, a_equal_b_latex_s\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ahora se definen las cuestiones más generales de la simulación, como:\n",
"\n",
"* la cantidad de iteraciones $N$\n",
"* el comportamiento esperado de la red, es decir los parámetros $Q_0$ y $\\omega_{00}$ de $T_0(s)$.\n",
"* los componentes de la red sujetos al análisis Monte Carlo y sus valores centrales $C_0, R_0$ y $R_{B0}$\n",
"* la tolerancia de cada componente (5%)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#################################\n",
"## parámetros de la simulación ##\n",
"#################################\n",
"\n",
"# Cantidad de iteraciones o experimentos\n",
"NN = 1000\n",
"# Tolerancia de los componentes\n",
"tol = 5\n",
"\n",
"# Q y \\omega_0 proyectados\n",
"QQ = 10\n",
"W0 = 1\n",
"\n",
"# Valores de los componentes \n",
"CC = 1\n",
"RR = 1\n",
"RB = (2-1/QQ)*RR\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La simulación continúa con el sampleo de $C_i, R_i$ y $R_{Bi}$; y finalmente, cálculo de las transferencias $T_i(s)$"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"code_folding": []
},
"outputs": [
{
"data": {
"text/markdown": [
"#### Transferencia sampleada al azar"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle T_N(s)=\\frac{2.872 \\cdot 1.025^2}{s^2 + s \\frac{1.025}{7.818} + 1.025^2}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Transferencia deseada"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle T_0(s)=\\frac{2.9 \\cdot 1^2}{s^2 + s \\frac{ 1}{ 10} + 1^2}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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ggw++1Xl9nf9cAuNgvmoJDK/Xq/30pz/VcnNzNbPZrA0fPlx7+eWXO39H/ttzzz2nDRgwQDMajVqvXr20Bx54QHv44Yc1QGtqaurcb/+yFv+9DMn+383/rOGrlsBwu93axRdfrDmdTk1RlC7vh8Nh7ac//amWlpamWSwWberUqdrGjRsPWAJD0w6/H4QQQhwaRdP+x6fLhRBCfC8pisKll17K888/392liKPg5ptv5h//+Ader1cmjhFCiO8JeSZRCCGEEITD4QOeX2xqauK5555j6tSpEhCFEOJ7pMeHxDlz5qAoygE/+9d0gn2TIEycOBGz2UxOTg633XYbsVis+4oWQgghjjFbtmxh4MCBzJkzh6eeeoo77riDYcOGEQwGmTNnTneXJ4QQ4ig6Jiauyc/PZ82aNV227Z8YoaamhlNPPZVzzz2XJ598kj179nD11VcTj8e5//77u6NcIYQQ4piTm5vL4MGDeeqpp2htbcVsNjN+/HjmzJnD2LFju7s8IYQQR1GPfyZxzpw5PPPMM13uHP6n22+/nXnz5lFbW9s5FOaRRx7hl7/8Jc3NzV+7eLQQQgghhBBCiAP1+OGmAI2NjRQUFJCfn8/pp5/OypUrO99bsWIFp556apdnJU477TRCoRDr1q3rjnKFEEIIIYQQ4pjV44ebjh07lnnz5jFw4EA6Ojp47LHHOOGEE1i0aBGnnnoqDQ0NjBs3rstnsrOzAWhoaPjKNufOncvcuXM7Xzc0NJCamnrkTkIIIYQ4RJqmfe2ahFEtij/hRx8HRwASZjOawYDO6933v83mo1ytEEKI71JHRwfhcLi7y+j5IfGMM87o8nry5MlUV1dz3333ceqpp/5Pbc6ePZvZs2d3vs7Pz6e2tvZb1SmEEEJ8FxYvXsyMGTO+8r2H1j/EP7b8gyGVCe54OUH2nDuxTT6BPdOmkXr11WTd9uujXK0QQojvUn5+fneXABwjw03/k6IoTJgwofMZxZycHBobG7vs09TU1PmeEEIIcbxo8O8bIdOLfZO36VKc6BzJAMQ7PN1WlxBCiOPLMRcSAdavX09BQQEAkyZNYsmSJV3Wdlq0aBFms5lRo0Z1V4lCCCHEd67B10CKKYXCRAoAiiMZ1WYDVSXR0dG9xQkhhDhu9PiQOHv2bD755BMqKirYuHEjN954I0uXLuWWW24B4MYbb8Tn83Httdeybds2FixYwB133MFNN90kM5sKIYQ4LjR4grT7IzT6G8mx5ZAe2ffsYYdFQ0NDSUoi7JI7iUIIIb4bPf6ZxIaGBq644gpaWlpwOBwMHTqUJUuWcPLJJwNQUFDAhx9+yOzZsxk1ahQpKSn86Ec/4t577+3myoUQQohv74MtDdz4wnrG90qlydxE/9T+OMOtAOxu2M3bf/sbg2Iqwd115AbDNO7cQvHwUV87+Y0QQgjxTXp8SHzppZe+cZ/x48d3WRZDCCGEOF48vnwPAKsq2knqp5KTlIM9uC8kVuzYSmNIJddkJz3o4ek//YlQ2Vpmzf4tpeMmdWfZQojjRCKRoIcvq35MUBQFVe3xgzg79fiQKIQQQnxfxeIJdjZ6O1/Hg/nk2HKw+DcQNIK7rpEqWxG9TCHy/Y2EytYCsPvLLyQkCiG+FZfLRUtLC/F4vLtLOW7odDoyMjJwOp3dXco3kpAohBBC9FAVrX7CsQSji5ys3esiHiwi25aNwRuiwW7Aa8iiJMdIe1MK3qDx35/bsIZ4LIZOL1/zQojD53K5aG5uJi8vD7PZLMPXvwOaphEKhairqwPo8UFRvj2EEEKIHmpb/b7JaC4aU8DavS4S4Rxybbkong7q8/JJmG2oQCDTgc+1LyTmDBhMw46teJobSc3tGettCSGOLS0tLeTl5ZGUlNTdpRxXkpKSyMvLo76+vseHxGNnYKwQQgjxPbN/qOnIQic2c4REJI1saxax1jY8aVmd+yWcVnxmAwDWvsMBaK+rPer1CiGOfYlEgng8jtls7u5Sjktms5l4PN5l+b6eSEKiEEII0UNVtvjRqQpFaVbMlg60SAaOoArRKD5HGlo0Sk3cgWbR4TMbiWDEZd0XHtvrJSQKIQ7f/klqZIjpkbH/uvb0yYAkJAohhBA9VFWbn3ynBb2qEI6H0BImmqobiRgMhM1JhMNx2jQbAH6TkbhmpDaxb3iYq6GuO0sXQghxDJOQKIQQQvRAiYTG3rYAxWk2HlqyC19HLgC7q5vxOBwAeDQ9roQFJRohoSqocYUqL5jtyTLcVAghxP9MQqIQQgjRAzV0hAjHEhSkWnji03Iu6LsAgOWNa2lPzUOJG2hRzbg1C2o4CIA9GqeyzU9qTh7tcidRCCHE/0hCohBCCNEDVbX6AVCAXsk7GZG5FYCaYJC6nKtwto2knVTCehP6WASA1ECAmvYAKbl5hLwdBDo83VW+EEIc0wKBAAUFBXz++eeHtP+PfvQjZs+efYSrOnokJAohhBA9UMW/QmKbL8KIzC2kW9pQSFDf2g8UI7qEiV7hAhxJESzsmyUvs8NNNK6hd+6bvMZVL3cThRDfP8uXL0dRlIP+nHXWWQdt4//9v//HkCFDmDx58iEdc86cOTzxxBPs3r37uziFbichUQghhOhhEvE4NR++wczGhTTV1zM0ezttCQMWQ4KWiB1dUjVxJUBJzILZ4kEfDZPQ6XGE9wXLgCUVgPYGeS5RCPH9M27cOBoaGjp/TjjhBKZNm9Zl2/PPP/+1nw+FQjzyyCP8+Mc/PuRj5ufnc8opp/D3v//9uziFbichUQghhOghNC0B1FO+6nNM2z6hJLiXPi1vk2ZoZ7t+Mk6jGbcKpuxthHStpCRUkuJuYr4OEkYz4SQb1miINv2+iW3kTqIQ4vvIbDaTnZ3d+bNt2zYmTJjQZVtycvLXfn7RokV4vV5OP/30A957/PHHGTp0KDabjZSUFCZNmkR1dTUA5557Li+88EKPX97iUOi7uwAhhBBC7FO+6w/o9K/w5bsnEld0+Ex2Mmsaifr17LUV4ozFmbZjISX166h0ZGCjkKyOGPFwmESKHU+Kg/Sgm5pYX5J1OlkrUQjxnWpubqalpeWQ9zeZTPTp0+eA7bt37yYcDh9SGxkZGWRmZh7yMf9bRUUF7e3tjB49+pA/s3z5coYPH47RaOyy/YknnuDee+/l0UcfZejQobhcLj755BPS09OBfXcwW1tb2bp1K0OGDPmfa+4JJCQKIYQQPUR21jlUlL1JS2Ude2x9UHI0HLvddNTYSC+t5aS1NUzYsxyAXsPW4neOJt277x8xCZMFj8NBdshFVXuIE7NyaK+r6c7TEUIcZx599FHuuuuuQ95/4MCBbNu27YDtZ599Ntu3bz+kNu68807mzJlzyMf8b2vXrgU4ICQuWbKEn/3sZ0SjUS6++GLuvffezvcqKyvJy8s7oK2FCxdy0kknMWvWLACKiooYPnx45/v5+fnAvmB6rIdEGW4qhBBC9BAOx3A6asYCsNdSiCknCoC31sYY/0ZGV31BbVIG/mQjQ8vL8Cgx0kPZAKjmfSGxJNxGVVuA1LwC3I2NxKLRbjsfIYTobmvXriUnJ4fc3NzObfF4nJ/85Ce89957lJWVsWzZMlauXNn5fjAYxGw2H9DWWWedxYsvvsiECRO4//77qays7PL+/s8Eg8EjdDZHj4REIYQQogeIuUKEKz34anUA1FjySUtxEUsz0FyXQePuARjiEZaUjmVLaSGWYARjcDfWWAZgxJmZg8fhIN/b9K9lMPLRtARuWS9RCPE9tnbt2gPuIq5Zs4aSkhJ69+6NTqfjiiuu4M033+x8PyMjg/b29gPauvbaa6moqOCyyy7j3XffpbS0lJdffrnz/f2fycjIOEJnc/TIcFMhhBCim4WrPLQ8tQXiGsGmZqJWPX69jTpHIR25dkZvWcX8gvMZ7dhJSqGe+nAfYDeF7i20ZPdHteSR3as3jT4/JcFWYgkNzbHvGZ62uhrSC4u79fyEEMeHn/zkJ1x44YWHvL/JZPrK7e+8885hPZP4v9I0jfXr1x+wfmFtbS0FBQWdrwsLC1m2bFnn65EjR/Lggw9+ZZuFhYXcdNNN3HTTTYwfP56VK1fygx/8AIAtW7agqiojR478n2vuKSQkCiGEEN1I0zTcb+/ZFxBjXsKBKO1ZmUSGOPnEejo52TWM3rKK9LZG5p19EUURN5hMRG0qBe17aMkGkvLJzs2FzZsxqTEA2g1OANpq5blEIcR3IzMz81tNIrPfV01mcyTs2rULj8dzWJPWAJx++unceuutVFZWUlJSAuxbNzE1NZVx48Zhs9n44IMP2Lx5M3/+8587P7ds2TImTJiA0+n8Ts+jO8hwUyGEEKIbRaq9RBv9rMjbyl8dD+PXWfgk42TQKRg8EfrXbAGgX8UOPh82hucsY9mbSKE+MwdHSy26iI+ELr3zH24JswE0jb1xG4BMXiOE+N76uklr8vPzqan5938bq6uru0xUM2DAAE466STmz5/fuS0cDvPAAw8wfvx4Ro4cycsvv8wbb7zB1KlTgX1/8HvhhRe44YYbjuAZHT0SEoUQQohuFFjfBMALxndICoZ4K/tsWv1OjBvbGf3RUvrt3DcDYFHtbrxWGxHVyNJoKSuSB6NoGg73RtRYeueQLH9yMikRH7vaIyRnZEpIFEJ8b11yySVomnbA3c8xY8ZQUVHBnj17iMfjzJ8/n3POOafLPnfffTePPvooPp8PgDvuuINt27bh9/txu9189tlnXdZRfOWVV0hKSuocenqsk5AohBBCdBMtoRHY1kqNqRFdtgV6XYDL6CSebUZPjA22YawcfiKaoqBLxLAEfCTSTOiJ81nqYABSXNswxu34AxHMioLH4WBwqImyRi+peQW0N9SRSMS7+UyFEKLn0Ol0PPLII5x55pmUlpZy4oknMnny5C77TJo0iXvuuYeKiopDajMcDvPMM8+g1x8fT/MdH2chhBBCHIOiDX40X4zVqVu4dMClRPL68aqrCTVdx4iyjaxxjGZdwUiKGqrIa6pm5KaVrB0+iXi5Qm1yOj6DmZSOagC276ggw+GgIRKhX0UVX7T1xVGaT9XGdXiam3Bm535DNUII8f0xffp0du7cedB9rrvuukNu78orr/y2JfUox9SdxE8++QSdTkdxcXGX7eXl5cyYMQOr1UpaWhrXX399561hIYQQoqcKle+bLn1TcjkzimcQyc4mMjqdPhU7GObegkICc5WPFaNPAqD/7q2ELTbiZgOZ+gBb0nqT7Pegi4WoLSsjKz+fmMFArreWeEIjnvyvGU5rqrvtHIUQQhx7jpmQ2NjYyJVXXsn06dO7bPf5fJxyyimoqsqKFSt47bXX+PDDD7nqqqu6p1AhhBDiEPl2tBBUwqT0ycZutONqr0EfjdC/eQcmLUKJs5qwV091RgkJFFL8LnSxKFquCZMSYWtaCSoa6Y4lpOY8hjn9KWy2duxaCAC3ZV9IbKrc3Z2nKYQQ4hhzTAw3TSQSXHbZZdx0002EQiF27NjR+d6LL75Ic3MzL774Yud0s4888ggzZ85k9+7dR22KXSGEEOJwJIIx4jV+Ntp2cmLRFABuanmHecv6UtRWRW1eEeOzV1LhKiZzjw+PLQWn30VxzS6a0rNorbBTk5dOwBInZfq7oNdQEo0MHFJPcEMv0DSqEnZMej1NFRIShRBCHLpj4k7i3XffjaIo3HbbbQe8t2LFCsaNG9dlPZLp06ejqiqff/750SxTCCGEOGShXS4UTWFt0nb8MT9/3/B3Vqga6XEPukSCtuJMnFVDUTTIrAii2vcNOR29cyUxu5leGeX86Kx/4r4mjmJIUPv5TbwYuBGzMUDqwCou3L2MHU1+0gtLaKrYjaZp3XzGQgghjhU9/k7i0qVLefzxx9mwYQOKohzwfkNDA9nZ2V22GQwGUlNTaWho+Mo2586dy9y5cztfu91uFi9e/N0WLoQQQhxE9g49aRjZq1Wy4b0vaUwPY9YVYCi9iHljfoqOZEbtDFFkcFFpMjEzUEQME3n1NSTpfPx46LNEEwb06r6ZS1OKVjBgaT/0QxVio31c9dZ7vL0gSGRoEgGPm/ffehODLambz1oIcSzIyMjA4/Gg0+m6u5TjTjweJxgMsmTJku4u5aB6dEhsbW3lsssuY968eQcEwW9j9uzZzJ49u/N1fn4+M2bM+M7aF0IIIQ5G0zT2fvk5KzoWMapKZfE40FRoTplByD4cNR4nodPx2iQ9A5Ztp8pcQpklQWlgAIQ3cmXwH5gsUaw7LsOqBKlLWoi9cAOnvL2dDr8NLvLRPl7POR8vxXXyjawu20b/gjz6jBnf3acuhDgGlJeXYzQaSUqSPyx913w+H0lJSYwcObK7SzmoHh0St27dSn19PTNnzuzclkgk0DQNvV7Pk08+SU5ODpWVlV0+F41GaW9vJycn52iXLIQQQnyjaGOAxuZyats28eVAF02pYfrtTSJ1wxbqi3T0bWhn04Dp7EhRyM4bCMEgNUqcAcYhqLo1DDTvxNo6mIKafc8ymqNJ1Ob9A/WSDiZsC7K9wU7jRA3nJwrGj5aCFZoqdklIFEIckoyMDOrq6sjLy8NsNn/laD5xeDRNIxQKUVdXR2ZmZneX8416dEgcM2YMW7Zs6bLt0Ucf5Z133mHx4sXk5eURi8V4+eWXcbvdpKSkAPDRRx+RSCQOWBRTCCGE6Al82xrZ0PYxfnOc8kIvBZY8rs6czvo9q+jXsIhQ+kUEtrdh0sF69k0gsEcfB30aqX2DKIpGtG0i5SfdgGX5tQyxbCTYHqE1zUiHUWP4LhcbBsf5YvhoJm/YQWr/Ipm8RghxyPbP9VFfX088Hu/mao4fOp2OzMzMLnOp9FQ9OiTabDYGDx7cZVtmZiYGg6Fz+yWXXMLdd9/NJZdcwh//+Efcbjc//elPOf/882VmUyGEED3S+k/eJxDvYNnYFhIq3DJ2NpNTx/F6vRs1YWNXMINGXYIMs45Gqx7awwRUaNIlKB3gJerXU5a0E92HGaS53mZC4SYy6vvSlubio8wxnFv/OQPKfXw+WYMN0CcUZ/OeXWiaJncEhBCHxOl04nQ6O0fxiW9HURRU9ZiYMxQ4RmY3PZikpCSWLFlCLBZj4sSJnHfeeZxyyik888wz3V2aEEIIcYCoL0R51RckjHraksPkGrMwrmhkydPvEEnrS7s2nGpDgqFhHXesfIXoiFQSVh2gYU39iGJPE/4dSYQWNeGrtTE+dzfxhIrS9EsIWHDm7OETbSLmSIJppvXsSs0nta6JqNtNW211d5++EOIYo6oqOp1Ofr7lz7EUEOEYDIlz5syhqqqqy7Z+/frx4YcfEggEaG9v56mnnpIHbYUQQvRIZYuWEox7KSuNktBB3uYo6xa8yq4vXmXI6s8oi8awJeAUTxvVtgb67t2BszQBKNTELfTb42eKt5FMYwBQ+Ghvfxp3JROIJJNcfyIGa4Ddzgw69BZ6NXWwflwhajRKrttH7fat3X36QgghjgHHXEgUQgghjmUbln2Agsq2jAZ0cYV+DdkY7OfTZ9w5rM2aSECFCSEDicg6wno9o7at5rXq3zBV3cAHibGcFruH0dEnuTvr5+wuGkJrxMYyTwku21LSGvetpViQvZV1yiBMUY1hvXYRN5ooaO+gZvuWb6hOCCGEkJAohBBCHDVttTU0t1QQy3Lis0QpaUjCariAkTMmkzdgGglHMrkxhcGhIK22dkIWPVfYl1Ia2Mto605AYWesF6eqaylRGvhAncz69LE02JOoz/0UxZ+Ovr0Ae0oba+IjiKoKJ4V2sKXfSFICYdrXrpFni4QQQnwjCYlCCCHEUbLhrQUArM7f92zgoMbx6LIthL98BMPGHzHDsJRLfWa02CZeO/NK9pw/kRFqNa2GJL7w75uwTSXBL7RPeMd4B5PVLXxhH8kuWx9qN9ppS91OatNUFIOGI2cv5dZ8UvwRmgeGAEirrqe9rrZ7Tl4IIcQxQ0KiEEIIcRREwyG2rV6K0eKgIq2NpICBj4es4bE+v+WOAR/xlrmDuCuDOGHemfA+JtfNXFb+BAqwKjSQFdoQ0uJtJFD5o/8qjMR42PB3nHhZnjYVjzuZGvV9khvHQUKlwL6dlZGxAJxsX080NY08l5eazRu690IIIYTo8SQkCiGEEEfBzhWfEouGWF/YREyv4bNGCetCKJpCXLPyRmqQ1/p8wLNj7sRr8NM/FOZkfyttmp67Q5dhSEQ4u/ZdbDEfG4xWtnYMxYGf3xmeJ6QzsM4xml1bzHQoAWwtQzFmhGhS0mhOsjLQ30DNmJGYY3GaF3/Q3ZdCCCFEDychUQghhDgK1r+3AE2FHbmtADgCmTxcrXBG44mcWjuDzGAmfqMHveonoij8tt1Fg6rj94HTadI7GBPbQEq/izAlwvj1STzmHsEm9yzOVT+nt1LLJsdggjE9S8Of42iYjKJAr9Q1bDH0QaeBrqACAP2a9UQj4e68FEIIIXo4CYlCCCHEEdawu4zWuiq2FbnxW+KoCZUf7TmBjcpozKF0dJqO0S2jGRhJEFBVfuTt4FFnCtOL8vh08CaS+t5L3zw354aySDOmg6ax2TGUlc1BAjE7v9W/TEzRsTZlNNG6DvSt/VEiFpxpjazxjSViUBgXXku4uJBMl5fqL7/o7ksihBCiB5OQKIQQQhxhq999E4BtJV7QNM7aMInkhh0Ya6PoYjFUDSxxC32bZnCyT+UVSxJfmk2ktOQRbj2ZpISJN9OW8ve01xlrKidf56HWnEONLY1FDZM5WVnPEPawzd4fX8LHutAukpvGYUyNojN6qXCm4YwGCU0sRKdpNL3yajdfESGEED2Z/mBvfvrpp4fd4Lhx4zCZTP9zQUIIIcTxxOdqZ/eXK9hR6CEpFOc3r2kMqF3a+X5CUWhzDmDLxSP4LLCLcmsCTVGYuSWVxVzPtJiJMdFZuFU/cWMHPls1vVSV2kQKG7PGkdyxmnJvLr9IeoNror9mvWM4qS0VjGyYhKdgGb2t61inDaEfS0mxbCSmU9Gt3rcUhqIo3XhlhBBC9FQHDYlTp05FUZRDXlNJURR27dpFr169vpPihBBCiGPdsvdeRNM0XClu/vhsHEcAojodhnicuE6HmoiT0b6dKU/sZNlVGcQUuKkizlr/BVyKAxMKQTWCTtNjDOShC6XRmlSNXomxJ5bKqNQ83nNN4RblZfqzl63JAxnlWU99y1j0gQxshS62buxLm3MFpa5yNg+eRPKmSuo/XUbelJO6+/IIIYTogQ4aEgFWr15NRkbGNzakaRqDBw/+TooSQgghjgfRSIStSxZTl9nOrW/FsIUgqtejJhK8ePrZ7CjoxbC6Ks74ZBGWYJA5r9TRdJ6fdXt/wGDLIOKKhiOpid3WcsxuF7FQFIv5dE7v6I0vKUS1XmVPKBljapx3fZO5xfwGN8RmsyF5OLlt2xjdMJFY73fI1u9gR1I+J7gqsIzSkdgEDc8+KyFRCCHEVzroM4nDhg2jd+/eFBUVfeNPcXExw4YNk6GmQgghxL+89d7jKJEIM9e7SfVBTKcjrqr89qZf849ZF2MzaMTtRl445wJCRjO6oI5g2RjMljMIK2C1u9htK8caDKNvqmBk2Xr88b8T1UU4J2AgI6axK5YLkTCbrCMZyF760MDW5EE0hnej7h0JmkJW9l7Wd4wgZFTJjawlZDGgX7eeRCTS3ZdICCFED3TQkLhhwwacTuchN7ZixQry8vK+dVFCCCHEsS4Q8bNt0SIyfY0UtoLLpscYS3DP1TcRtpm5YOWHhPUG1uSX8v6ISfz2pl8R1Duo019FHIWtNh/tljKMcQWldgfRjGb6p7ZyzvJqPu31JnpNx8ygkQ69FZcnDxSF1xJncrP+NaKqgY2OQWxtqMbWMgxbYYCAR8/eDDv2WADzCbmYwlH2Pvdsd18mIYQQPZDMbiqEEEIcAX9/489ktPkYvyuCK0mPLQzPn3MBwwNh0iMKb46fxrL+I1nXeyAeu4NNpQP5bPyPiRodbKCR4ZYqIkoMY30FvpQAL44K8MSU3qwcNZxT1lSxJv8D0uN6Tgzq2WrMxehy4zU6sQDFtLApeQhVwW0kV09BUaC3YwPrtcEkAEdhE3EF3C++3N2XSQghRA90SCHR6/Wybt06fD4fAJs3b+aqq67ioosu4uWX5QtGCCGE+E87XXvxfbiSkVWthPUKrowC3jnnbBI2M68PHsQnfUuxhwNM27GBP31Rwf+tKKP33nIePGcon/RLoOYm6NC5SPZGMIWi3JS+nayYxifOVjL6TMGWVsKOHBf19t2MihiIqGZiwREo4RBfqGP4sX4BUdXIRntfKvYkMAQycZR2sKehD61pRnJ8FQR7OzHX1ePbvLm7L5cQQoge5htD4sqVKykoKGDMmDGUlJSwfPlypkyZwqpVq9i+fTuXXnop8+fPPxq1CiGEED1eNKHxm5d+x6iqdpx+jT19Slkzbixem4PFw0+iKTmVYTXl3LjyH/RvLmNvZDNWKnh8W4L0rbvZmqOjJF6JOaEnUb+Nk7MsZKpeZroHEdJFmFPyCA+MXU2HLc7Hvd4mqsSYHjSy1ZKKpTVIQtUTVuwU4GJT8hB2+DbiqD4JvSVGrmErW5MKUYCcyToA9j70YLdeLyGEED3PN4bEO+64g+nTp7N582auuOIKLrzwQq677jp27tzJ1q1bueOOO3j44YePRq1CCCFEj3fTmreY9nEFpQ0hGrKy2DJsGKGEwvL+I2i0WZhUvplrqj9geIaHkdnlpCU3Ux0L83H+Un7ZVsM12zeQUBKY6vbQJy+N6MClPJFdRGW0ioEVdtLdRvp19OL8uhRaUieytmAhqQkVs2JCZQL6jnaqdCVcaVhMWGdmva0Ad3k2xA2k92piQ8swPHY92eGtBFL1sGo1MZeruy+bEEKIHkTRvmERxLS0NJYtW8aQIUMIBAIkJSWxbt06RowYAUB5eTljx47F7XYfjXqPiPz8fGpra7u7DCGEEMe4V+sbee0fl/Db1xpRVAOLTj8dQ4ePd8afRGVOLy5YVUlpgwEtYfuPTyWI2lrxm+qxmQK4tRjpvjhqcCWl51Sh6P69Z6DZTP3qTPb6jNjtpSTHM4jXv0885VfodE4qIjvoG9hKS2kGdvy8GRlFe9zE9Q0LOWGaiqdwKds+HcC4kj2Mq6ynKtyf4FsdWK64jOLbf3fUr5cQQoiuekou+cY7iS6Xi6ysLACsVis2mw2Hw9H5vsPhwOv1HrkKhRBCiGPADl+Q3696lOsWtWOJaqwdM4asunqWjR6G31rEze+30LcuBQMGCvQeBuoaGairJlnnxuDPIKV9OIFAKoaEQrhhA86+Hio/zuWTj3N4dc8YNniGY0qL0efMaqbQwGkfLuXkxS9x6pYOhuychwokmfswYcsqinaV41WTudDwOUGdhbWWHILb+0NCpbD3Xta0j8Jv0ZFrrSBkVfG9+hoJv7+7L6EQQogeQn8oOymKctDXQgghxPeZLxbnmg1fcs0bCyloD1NZUow1EGDlmAFEGMrVH3vQazr6m1V6mawkNAMKDvSqgb5AfdTL2nAch6c/EUMO8eQ9bN8a5bNBHgKmEXjC+XxRa2ZS6+mMrdET1mWxZ5IeJe6j0R7m85z19GnbxVBPKe+PvIbz1j5Oe3YuHcnpFCvtbHAMZ33N20xoHAd5XxAu97K7TwrDKtuwTbYQ/9BP49P/JPdnN3f3pRRCCNEDHFJIvPnmm7FYLACEw2F+/etfY7fbAQgGg0euOiGEEKKH0zSNX+ysJmXNA5y+MYjfaqUxK4uGNDst+lM4+0s/ZlVhlCVKg/9LFnlraTeG0BSFtKiNXvoC+tpHMsum57NgG21RCwFLf1ZnTqGpPpnkWIgp0SQGR+3oFJWo4kOLN2DK8RL2p5Lpz+O8itOoSi7Dp8SxJg3k6Wn5nLluHZ+efDKz4uv4m34a65IK6LdVhyH3C0pKKlntHUWpaQlFGdXssKTjeuZZsq+7HtVs7u5LKoQQopt943DTE088kcbGRiorK6msrGTSpEm0trZ2vm5sbOTEE088YgU+99xzjBo1CqfTicViYcCAAdx3333856OUq1evZuLEiZjNZnJycrjtttuIxWJHrCYhhBBiv3/WtbKq/GV++2oVaiJB5Yk5qIMa2ZN7AVO2hTDaOihKX8qWjg+IpmQyIms65zl+yDkpFzA68yTSrIXs9W2jJVTLidZ0hpkDbAtoDK/4nOtq3+Uyn4VhMQdKvA21/U0qQm/xQIaF+1M388TQ+3hp+L2Up62juKMfDfoYZk0larqEuTPbya2uxGtN4XzXajY4RrF9z1YMzYNxFPvxNAbZlefAFA9jm6Ci+gO0PP98d19OIYQQPcA3TlzT3RYvXkwwGKRfv36YTCY+++wzfvKTn/CHP/yBW2+9lZqaGgYNGsS5557Lr371K/bs2cPVV1/NNddcw/33339Ix+gpD4gKIYQ4tqzz+Ln+y7f5yT/mMnFTAM+pGk3nGHhvy18ZvlOPllHLoFH/IKtqJsmNEwCIalE64h0YVAtWxYhe2Teoxx1podK3nSrvRiKJEHpjP/S2UwE9KU0fovrX0ep0ECFC2BBnXT83O9JT6E0ul1i38ojrHs7cFcSlWCiK6fgidxHt1iVMbJ+BPhZnS1MKUXOE080bKTx7C8YmhW1NQ7k8vAxjSGHnO5moqo0By5ej2mwHOWshhBBHSk/JJT0+JH6Vc889l1gsxrvvvsvtt9/OvHnzqK2tRafbNwXcI488wi9/+Uuam5s7h8UeTE/pDCGEEMeO9miMH614nlO+uJ+T5sdxF+momJJE5d6R2Dx+NBXsaowcS1/yLKWYc114Sj7Ea99D08aLqW7qQ3nGWhod5bgsTQT1QewxK/aQifOqTyTmORmjFmHwpsdJ8ddgmTybN4u38nl0GSPKU7CGdVTb01nknIU1pZbM/E001A1mbGsBgwKpxBVYm/UAyYqeXG04vcvLeN02irEdq+g3sxVnXg3xL2ykFfkZtbuVPe25RD4Ex3U/IvfWW7v78gohxPdST8klBw2Jf/jDHw65od///vffSUEHo2kaa9asYebMmfzmN79h9uzZTJkyhaKiIubPn9+53549e+jTpw9Lly5l6tSp39huT+kMIYQQx4aEpnHbqicYWzOXzMdsVNqctNms/3pXBX0yFhLE4iGiWmTfVmMSOWOaCfWpZqHXyI7Qvj9sGqMG0nwpJMUsGK0quY0T6N08HrsWYeiX92CKewmdPYXH8ytYad5LYTiH26uupqzlc1wde3GZTCzIuABfsguD40vMSpChlVcyPmQmoERo1u7HmD4Io+pg8tIlPNP3BM4MfUnaNbXYfXEathdxinETNp/Gtg+z0XsN9P14CYbMzG66ukII8f3VU3LJQSeumTdvXpfXLS0tBAKBziUwPB4PVquVzMzMIxoSPR4PeXl5RCIREokEv//975k9ezYADQ0NjBs3rsv+2dnZne99lblz5zJ37tzO1263m8WLFx+h6oUQQhxv1upWMbnjRTyv5bA20w5oJJwFmKJjiKcUcaoaJEXnoFyp58twGaa2IIS3UbfCSl1ZEdVDWhmWGmGCIUH03WzifhMAOc7LcZGOIxFg6Kq7McQChHrl8If8j9ltVhlqjnNCMI/tmo0U0wXEbevBv5TL6t9ge+hsdsSn0mJoZmPJ8xSXX0Nm3ECy6WIM1YsJlaSwdfgorvlyIR/2Hcj4bSHUIc2MtO+k3J7GaF8zqZOCeN+F9bfeivuyy7r3IgshhOg2Bw2JlZWVnf/7tdde47777mPevHkMGjQIgG3btnHttdd2BrYjxW63s3HjRgKBACtWrOC3v/0teXl5XHvttf9Te7Nnz+5Sc35+PjNmzPiuyhVCCHEc+2DVb+m35QOqPu5FTNWRrvMSyjmNmH8cpKjM0EWwqQ6qlRZimo5TdENIKdjBcstIIi43vesbyFlmpvDEBtL7e/i0YCvzX9QRU2wU6/+GQ+ekd8t69qgKvcdcxBun7WC3WWVC7TCG1V1AWNET14Xo0DdiTC7AYJyB5v6YIa1vMSp8AXXW4ayLa3zs3MUPW0tJCveiLbkvlvYmXGnZuHL6MKF2B01hB5b+esp6Oxm8xkNzupF8XGwtNmFfu45h//d/mPv16+7LLYQQohsc8jOJ/fr1Y/78+QfctVu1ahWXX345u3btOiIFfpW7776bRx55hMbGxq8cblpRUUHv3r1luKkQQojv1MoPLqTsi3ray5wYY3H6B5rx5J9Hg24CdpvKVD3oUA+6nrCmaTSHayhzf0mrMZldDQb+/t6dX7u/ta+VU084m3Hpk4iYXGjqgbN3q5qC6nOj97gwcyoGfQ7b7XtxR7KZGDaRQCMYXkA420zcYOK0RYspywiTOkjBeloHVDvp39RIdtiLL2al7nUH+r796PvWWyjqN06ELoQQ4jvSU3LJIa2TCLB3716sVusB261W61E/kUQiQSgUAmDSpEk888wzJBIJ1H99kS1atAiz2cyoUaOOal1CCCGOU/EYq16ewMYvkgm2OEnzBxjS2Exz77Np0k9gmE1HiV5BQSFCjAraWZn6Acudu2gzuEn2G5i2chC9bOPpmzSAHFMBWdmFtIXqsRStwW5Pwuv1feWhA7sCvLPrJbb1WcMFJ01lSGYpm1Uz/QsWYUwK460bRMJTTFOSRszuIBzZijfgZYC3L1+YwjTqEmTFFaymmaiNC/EVmvnkxImc/f5iltgN9G20YMj30NSUhckeJt0ToGm0jfiX5bhefZXUH/zg6F5rIYQQ3e6Q7yROnDiRlJQUXnzxRVJSUoB9z/JdeumluN1uVqxYcUQKvOOOO5g6dSolJSVEo1GWLVvGL3/5S6655hoeeuihziUwzj//fH75y192LoFx9dVXyxIYQgghvjXNXcPSV2aw5YtCYkE9JR3t9Kty0VB6Oq2Fsxhj1WFSFBKaxkZ9JV/q9vKl4zXiOo1mp0JptQHLu715Y9MHDCoYw7UTzqAqbw2l1iGcEjgBFYVf7bqPjxpWEvVHiXvjaB0aUU+UeCx+QD0/GKzn3pOzcSVPYUv/egpy9hKsHUqv7T9mvbaT3cZW0OnpiFpI9fbhE72VswNGFEAhgl+3lFCGlZw9ZZy4ZiNvnaBn1MUB/B2ZjNzcTrapGX1Yo/zDLAjaKP3oQ/Tp6Uf/wgshxPdQT8klhxwSN23axJlnnonb7abfv55RKCsrIzk5mQ8++IBhw4YdkQJvvvlmFi5cSF1dHWazmV69enHNNddwww03dC55sWrVKmbPns369etJSUnhyiuv5N5770WvP7QbpT2lM4QQQvQs8U2v8N6SP7FnbR6KojGoo4mC3QFa+s5AV3ouRYZ9I1j8RHjPuAZXtIaCsqW8dqJKk1OhdFUmG9+qp6JpZ2eb154whgE5+2YOtRtSGZNxBhmmPJoTjdyXPw99NI1e3l5EIlEWbd7BpqUfoQX8nZ+3GQ18cW0OQzLdJDQ96535+AZ2UNU+kBM2/oL6YBWfsoGoM4OYptAeddIcKOWkkJEEGiR8dDjXETErjF6xnOL6Jt48R2XSKSGqd43gnMaVOPHTFrLS8o4Dy8mnUPzII0f3wgshxPdUT8klh7VOYigU4vnnn2fHjh0ADBw4kEsuuQSLxXLECjwaekpnCCGE6CE0jcCrl/Pm9j007UzDmBRhWEcDGVtieAZdSGrvaVjVfc8dNuDhQ9MG0up2YW1cy9/PUglpGknzLHy5ZgPxRKJL08NGFTH09Gkk9FmYwwmyWxuZpPRlUMoklhm2UaFvJqTXWDD0ZNrtDhIdHoIvPo3v7VcgEqbgtrs51zyaK7wbGGJ7DaO6h4DOREUfAyvUIZyw8VbavZWs9C8jmFuCZjDQkLDgaB9GUcwIQCLRjitrOyE0Zr3/JvpEhPeuhUn9E1SuncwVsbexxGPsKksjtsFE7v334Zg586h3gxBCfN/0lFxyWCHxeNVTOkMIIUQP4Guh7cmpvFmbQUddErZsP0NdTTjXKETG3EhqzlAAFEWhjEY+N22nT9kOKmxbeG2yStYGPVtfqKKx3d2lWUO6idSrzsEy+CRMei9xc4Co0UHMUIwhnsoNnyzlVN1Q2g0hXi5O59PCVDqM/5o0RtOIt7YQ3bYJ84mngKrSr6aZyzbrOMfwCanG59ApHprSjSxKG0rvLb8i6tnDuvYPCeaXErfZcCfMZLcNwRY3o6IQ05pxZZfjDipc++6LtDrg058lGGJNJbipNxer76EkYNdHmSSCSfT94AMMWVlHuTOEEOL7pafkkoOGxPXr1zN8+PDOCWG+yaZNmxg4cCAGg+E7K/Bo6CmdIYQQopvtXEjlmzfwflU/wh4Taf1dlFa5Sd2cgm7iL7DYs0hoGqqisJZyNppq6L99E68PqaDZocP2eoSPv9hBNP4fdw8VGHBGOpdeloXVYAVvCh5fLu1+JxXhJELJ5TSlNhIzjqaXdyzDYzk8X2wiHtRIRDT0WhyH2U/cpseld6Ip+76Ts7V6BmhbmdhQT8u7ezgrp43RKdsIGVUW5/cjtvNO7K6dbG7/mFBWH2KpKUQSejJcA9FHHagoRHT1eNJ309EU4bplb7EzH3bcGCfTNZTSWjcnsIaOqImGt50YR46j5NlnZLZTIYQ4gnpKLjloSNTpdDQ2NpKRkXFIjSUnJ7Nx40Z69er1nRV4NPSUzhBCCNFNEgl47+es37KQT8tKiUdU8iY0kbchSvbeQRhHXYtObySuaSQUWKltZJelHUf1el4fsZeiXVa2vFnJ1rqmLs068i384dJiZmWESIt4SYpEUP/rW1cDGnXJ7FQdLHHAWn0fqJ5BUYaCmu+mPcVMmy4VP0kE2fd4RwgzPuzEFT3qqg9p+N1t2Kwqf/xtCWfkBshtCrLOmc+Xe/9I//Zt7HR9TshZSCA7A52mkuLujzGcjoJCyFSNx1GFoayBCzZ9yucDFbxXxWD3NM7rWEox9dTWJ+P9NIn0W24h44YfH6VOEUKI75+ekksOOrOLpmncf//92Gy2Q2osEol8J0UJIYQQR01HI4l5p7HEk2DL9v6ohgS9ZtSQ9omNvMjZGMdNJaZpaJpGQKfxqfYljUYvdYmVfNnPz5QvMnjsnRW4A6EuzZ4xOZU3p0Qx6evAC22kUE4+LsWE2xQiZvOSow+QF4vh8Ps4KdSBEh+NO20iH548iE265M62LLEAlkgEXVxHSGcmaNARN6jEG+povvdu0MDvT/CLOZU0/jqdU052kt7uYjo/5zn+zMREiD2utSS0GL6cPDwpO0jy9sYayMMcLiQWiNHeX88K9wAmb9/B6wv0pJz5KS9sPYufx58lP7eD3X0MtDz0ENaRI7CNHXu0e0kIIcRRdNA7icXFxQddEPirfPrppxQUFHzrwo6mnpLYhRBCHGXb3ib05vW85e9FfWU6xuQIJdNqSH2jL3lpl6Bz5BFMaFhUhUZ9lGXKl/jwsiZ1HSVVEUoarUT0ep6rqmLHyk0AWO065p9tZlZvAxvUHKrCSTR77YR9FhJRAwlNBQ0Sqo64XsVtDRHPaWFEjpttSdcxP3Mqfb0RTmhOMMQdpyCgoQJVNpUqm8q6VD3rnTpCiobVF6Hxyf9H4IO3upzW6ZdN5RdX7UVVFOzuOG9t/wkjqjuo8WykJTWXSEYxyWoEiz8Pm7cXCgodjjLqjF5O//h9erU389hZCn1GJhPbOYGbeQYFjYqP0onFM+j77gL0hzjKSAghxKHrKblEJq6h53SGEEKIoyQWgQU/o3nzO7zZNBB/u5WkPB+FE+rJfnMG6XnngM5AIKFh06mUG/ysVNfio4M69jBwdxBjTKU6J5ftfXZSb2mj+u4KBlkSvHKuGWuWAU0FcziBKZJAjYOagJCip0Oz0hBPpSyaT1k0n3XR3gQTJoyJCCelZDLckIExGiegV4mjoqLHnjCQH1UwsO8Pt3ES7DQGWJBvYkFJCr6X/oF73mNdTjHjjPO4/5oQec6dKAmNsuqR2FYV0eIpY2dWMWZHEWk6P8ZgOsme/oBCh3Mru4lz7aLnsUeC3HeJyoi8PFKr8rmSNwjF9dQsSEPtM5ReL76AajId/b4TQojjWE/JJRIS6TmdIYQQ4iho3Q3PncMmd4illX2Jh3VkjWwhPTNGyedXYssYQTgeB0XFpCqssLSzI7GRVp0fa902DHVBjGk2agcksSV7M35F5Qyfn2vr2+mjT6D+xwgct2KhkVQ8mg1/3EKSEsKJjwKlGbMSBSChKWzUerMwPo5lhkFEDDp+svJNhlRXo0b3tZVAoTE5ncasQQRzRpOTnMcQnQVVUYlH/WyNVfO7UAWbnp4LsWjn8Y0TTmTmLZdwvfkR9El+gkELTe+Nwd3ewoq8fuRYCskwuNCHHSS7B6JqOjzOLZRFNGYvfpqoLs4DlysMtw1kcFOUM1iGy2em8X0nSaedQf7cvx72iCMhhBBfr6fkEgmJ9JzOEEIIcQRpGqx+gtii21kcKWbnnhx0xgSFJ9fjaOxL74ZL0VvT8cTi2HUqMUXhDWctnmAZreF6csqqeHvtVrY3tzDrriLKsi2kxuP8X7OLXi1OKiKFNChptOlT8eushFUjMU1HSDXQobPQZknGZ7MSTtZR7KjgZG05oyPbyHSFSHVFMcQ1ykts1BT8e+3hUMiCvyMZ6o04N4TJW+9CiSskgOrcPrT2OYPhSSU4jTa0WIQF29/h55/OI+73dbah7zeInLvv5xeJVxiespyYqlC5YAje5hjvFo1jsC6DdFMjuqiVZNdgdAkDntQt7PFGueXjZwmaNB68XGGAMo5Znp2MYDuNDUm4lttJv/lmMn7yk27oTCGEOD71lFwiIZGe0xlCCCGOEF8LvHo5rVUbeLt9AJ6WJCxpIYpPbiRj9SVkq5PQNA1PQsGpV2k2aLydtJO2UD1pezdRW17Lq+s34w/um6DNWmrl+ptymNRYyjbjaIymAPbkVmwmN2ntLdhbAljaIhjbEqg+0PmAmIIGJHQqYYuJ9iQnO3N7Ey2MUVK0g/7GauzeKAlVpbnRRkKnEMvWiOZo8K+VpaJxAzWuEkzrDAxZVIk5HCNsMFA1ZCa9MsaRakllZ8N2Lnrn/3B5WjtPX5eTT8pf/s6YFC8/izyMIamdPe+X4G0y83LxdCZqSaRZ96LEjThcg9HHLbhSNlPljvCL5fPxWmDu5SoDopO4xv85xdRRtzuZjrVJ5M39K8lnnHH0+1QIIY5DPSWXSEik53SGEEKII2Dn+2hv/Ii1oWRWVPUiHtGRPqid/GIbhRuux2LKJhAJgc6EVaewzp5gmbqWWPtuMnbt4a2NW9m4t6FLkzarnl/OnsHAUi+p4Rbs5VFMZSqGSgU18u/hl5pBI5EECatGwqiATkOJgtqhoOsAJf7vfV1OO/4BCpZBLirW9iG9zo81FMYWCWLIChHprxEakiDaa9/Xti+aREXtQAa81kBRVRMxVUfzwNMoyp+KOxrl8td/yc7mis729b1LSX3yZSxamF80P0l/++dUfJiPu9nBS4UzOSlhwWnbhaIp2N0DMUSTcTm3UN8c4mcrnseVBHOvUBkamMwNwSVk00rNRgfeXckUPvEESZMnHeGOFEKI419PySUSEuk5nSGEEOI7FGiHd27Cv2Mx7/r7UVebht4So3BKIzkNp5Pnng4otMfiOA0G4orCS5leqn0rKdi1hfKKWt5cvwVfKNql2RHDkrn9mhQKd5uwbFTRN/9rMhmdQjhZT5UtlyWZY6hxDsSid5KZUMjWFJxxSE00Uah9jk2/CV+ql0gmWIIRjHsUjDtUDC372rrhpF/QlJSGPebDEfWQEW6l1FXNsOY9FNJGYlQM/+Q4CSfE4zp2tg+h8DUv/bbWENMb8Q06G0fWGG58924+rVqDarWR8tDTGHqXomgamqIw2b2GS2NP0bzSRnNjOq8WnMWMqBm7fSeakiDJU4opnEZ7ynbamjzcsPJlWpPhoUt0DAlO5JbwQhx42bsqhUB9CsXPPIN15Iij3ctCCHFc6Sm5REIiPaczhBBCfAc0Dba8jvbuzWwN2Fha15to0EByoZe+w8zkbvoRVl024bCPkM6KQ69SY4TnnWtw7NqIWl3Hgo3b2FLX1KVZq1Xl1unpnOd3YKrWAeA22Wg32QnozXzSZwa+lH70TwTpozSRrLaTpGvDrHoxan6MCS9KLEYioSehSyFMAfHGKOb2L2kb6iU4LoFV82LaobLceBLrfaXUahnUk95Zg6Jp5EbcDHSVcWLtl/TPqSR4coJokUY8rrK1fQT9n2+lZFcD0aR0EkMu4f4NH3DKgCmsPf0E3sg3oGigqfvCaHLMx02tT2BeV0N9QzZv5p3D6WEzScnbSegiWL3FWPy5uFLKcDe2cu3qV/FY4YEf6hgcG80vQ4uwEqT6ixQCLemUvPQi5n79jl5fCyHEcaan5JKDhsSSkpJDnrWsoqLim3fqoXpKZwghhPiWPHXwxo/wVq3hPW8p9fWpqMY4hePclHjPJMN9AmgarmgUh9GEqsBrebU01i8ndVc1n5ZV8PGO3UTiiS7NnlBg4y5LNtmKgaDOyJc5A4gZ9ahJTtTcDEqM9ZgMbag6H1EV/FiJo8K/lqwwEMVGgCQCJOMjk1ZS6EBh3+ym7S2ptO0qxKO4aJ1gw1ZfxPAT+uHTewmGm6lrbGO3386Xif58lhhKADMAWREfJ9Sv4Sw+ghn+fWExpmNb7UjG/KOKtHYPkYLROAZeiN7i5MtUHXcOMdNuVIgpoI+FiBssnNa+jFFrP6KhJpUFuWdxRmjfHcW4wY85kI2toxcex26aW9u4/osXCRjh/h/o6K8M5TfBj7EQouaLFPztWZQ8/zzmfqVHueOFEOL40FNyyUFD4pw5czpDYjgc5pFHHqG0tJTJkycDsGLFCsrKyvjpT3/Kvffee3QqPgJ6SmcIIYT4H8UisOJBEsv+H1+GM1hdV0QspCe52MuA0jxyyy/FqCQTCHqIG+zY9Sp7HHUsDS7FXlYPaCzcso1PdlR1aTbZoPJ/6VmcaU9me1oJqwsHkeUMkOOIkzAkcKlWXIqDfcvdHx6jFiFbaaGIWnpRTQENJLzgqbGz1VBE9rqWzn0NeXnYBhdj6GemQ6lmT6OPJbGRvBcfjx8Lei3BiPYKroy/hH1aC7Fs8AeTqN40gCnPb0anmlCG/QB7wUT8OphbauKlFS8S+vJz0u78E1pKBjnhZs7c+AaJrUHezZnFaSEbKUnlRE1uDOFk7O6B+JL2Uu1p56YV84no4b4LdRSb+vN7/2eYCVG3KgVfayYl85/FPHDgt+xUIYT4/ukpueSQh5v+5Cc/wWazcd9993XZftttt+HxeHj88cePSIFHQ0/pDCGEEP+DssXw7s/Y6w6yuK0P3nYbekuMvqNUilvPJzlcSiIWxh0Hp9GIz1bJEt8SElVuYoqGTgdFedXMT/OxYM5eEuF9dxHPczj4WWY2m4vGsLF3P3LSQth0cSLqvxeQT074SIr4aAuZWKPvR53BSQgDMU0hVQmTT5BUJUCSGsSohlGVOF0H6Gjsv9uoJ0ZvqhjIbirdBZx85vVEa+sI7dhOaNt2IhUV+4bSAqbiHJJOzMVvqWdtm42X4iezXtt3925wRy3XJj2L48QmNCs0u3MwvmNl4OoatKxBGEddxYqa7Vz9xm/QNA1ddi6Oex7EWlxMVDVwUuVSCpZvYlHmmUwPJpNu2UvIVo8aM+FwDSZkbmVPoJWffjYPDY2/naViT+/NvR2rMROiYX0yntpMSp6Zh2Xo0KPxGyCEEMeNnpJLDjkkpqWlsWrVKvr27dtle3l5OePGjcPlch2RAo+GntIZQgghDkPbHnjnJnyVa1kU6M3e2gxQNHIHhhlgPYm0ln2jXryhABazjah5O5vbFtLcpOEjjk6n0iujiZV9XOwImzljbYLdn7ayyN3BzQW9qBo8g20lvcm3BcnS+QGNbK0NcyyOz6NjRaiAtan9iap6TMTopXNRonaQovgxKOEuYVBJ6NHFzahxM7q4ETQd6CKomkpC09AZaokRIWSwgKIwLN3LuT/9a5fTjft8BFavxvfZZ/g//YxofT0Atv4ZqJOTqfO1MS8yncWJ0WioDAlX8ePsp7GMcqMBFTUDGPJUIw5PjB80tbOlrebf9ZktJN9+L2kTxuLX2SjoqGX0x0v4zDiRqeEUivXN+JJ3o2g67O4BJNQo5fFGbvjsn5hCceafotLSP4cH2raTjJ+WnTZad2RS9MTj2MaPP9K/CUIIcdzoKbnkkENiSkoK8+fP56yzzuqy/Z133uGqq66SkCiEEOLo8DbCR78nvPF1Pgvns7U+j3hER3JuhKElA8muOxOdYib4r6Glin4Vuxo+oTZgojUeZvnOCjbWVHPzrbnUhqxMX5dg4L/y0hZnAe+WTKausIBBxhZyVRfFiXr0USuVnmzWBpPZkFFCVNWTpvgYam4iCy+mRKwzFKqqSmpqKoWFhfTp04eioiJsNhshf5SWGi8te73U73ZTV+4mFo7v+5A+AnEVC7uxKF9ScMWZTB5/4ddeAk3TCG3diufdd+lY+AHx1lZ0VgXLmfl4DO08H57C6/EpxNAxRVvHpf1fRlcUJhww07C6L4Ne3sav27x85uo6OY/tsh+RdvmVxPQW1ESCUes+o7Ipi1GxDIbgoyNlG5oSx+YrQRdJokzfxNUrnsLREWLhaIUVE5080lJDjubBVWOm4Ys0cv/yF1JmzToSvwlCCHHc6Sm55JBD4o033sgbb7zBPffcw4QJEwBYuXIld955J+eeey6PPfbYES30SOopnSGEEOIgQh5Y9idiq//B6mAm6xoLiAYNmO1xhvXvS17bDAxaMtFIgIBiQBdbTGX9Wip1DlyRIF/sqeLT8j14/7WkxWU5adyenEFUVVmeN4J3e00kkW5lmK6W0Uo5aRGNsuBAVgQz2ZJkJ2Cwkql4GWaqJxM/ev49uY3T6aRPnz6MGDGCnJycQ5r0LR5L0FTpoXJzG3vWNeNtD+17Q41ywW3jyCpKOaTLosVi+D79FNfzL+BfuRJFn8ByYir+nAj/CJ7KW4l96xdenvw2Jw5djmLVaGnKIfkFjddW7eaZtuYu7ZlGjMZ++x9JSzbhNiST31BJYrOP0lAmE6MxPClbiRuCGENpWHwF1JibOHP18+S2tLKmr8Irp1m5r93NwHgLXreR2sWppN9yK+nXX3fIk+EJIcT3VU/JJYccEsPhML/5zW94/PHHiUQiaJqG0Wjkhhtu4M9//jNms/lI13rE9JTOEEII8RVCHfDFI8Q/f5BNQQdftBQR6jBhsMQZ0q8XRb7pGOOpxKJBAlqchO81qpqqqUhOw+P3sHJXJZ/tqsAfjnVp1qyq3Hjaj1jedwoZthDTdRsYEm/BFejLouggNhtUWk1WMhQ/g40N5Cke9Oz7ytTpdOTk5DBs2DBGjBiBXq//VqeoaRrNVV7K1zSyY3UN1/xpKnqj7rDbCVdU4nr5JTyvv4EW8WGcbMebA4+Fz2RRYizJBg+35P+Tor5VaFGFmu39qP3rRu5paCKSiHe2o0tNJ/mOP5M3sIgWQzqmSJDUTTXkNiQzI6TgT95F2NKKGjNj9/TDY2pj4Ob3GFJdRk06PHKOgeuiGjPD1QSjBmred5J01iXk/P4OlG95rYQQ4njWU3LJYa+TGAgE2LNnDwC9e/fGarUekcKOpp7SGUIIIf5DoB0+f4DYqidZH0phTUshoQ4TegMM6deHovCJmGIZxGNh/LEOIm3PsdsbZa8zGb+7lc/KK1mxq5JgNH5A09lZJZhP/TFjC61cqHyBI5TGkuhEvsRMpdlAmhqgv6GRYtWN4V93DFVVJT8/nwkTJtC/f/8jdlds0aLFnHbajG/VRtztpv2FF3DNfw4t4MJwgoW2DBP3RS7kS20AQx3buK7vc1hTA4RcNrY/b+FvCzdREwn/uxFVR9K1N5F2wYUY0egw2HFWN+DYoXCBR0WzNOG37wFUkjx9iKsx9HUbOG3zIoJG+NtZKgNTrPzaW05E09P4STKxgokUPfJ39E7nt7tIQghxnOopueSwQ+LxqKd0hhBCCPY9c/jp/UTWPMvqSCYbm/KI+IwYjTqG9h5AYWQyhoSDeCxCMFyLu/FVylQrTTYDrQ11fFZexfq9tQesdQiQktUL59QfcmHvGNNj9ewJjeEDpRfbjAksaph+hiZ669ow/SsYKopCTk4O48aNY8iQIajq4S91cbgWL17MjBnfLiTul/D7cb32Gu1Pz4OOBpSTLJQnZfLn2A+pJ5UL8t/nlL5LUfUae7dkMf8P21ne7unSRupNt2I4/zJyws00mDIx+QNYt/j5QQ04dH46UraQ0MUxBbMwhpy0B+u4aPU/0Yc1XpmiUj3ExqNtuzAkFNo2W3C19qbkqScx9+//nZyjEEIcT3pKLjnkkHjNNdcc9P2nn376OymoO/SUzhBCiO+1+o2w7E94d3zC5/F8yusziQUN2K1WBhcNJi88Dp1mJRYNEfStp7rpc8pTUwmpQVRvgBavn798sOwrm07KH0jpiWdwTWGA4qCThYmxrDXq8Ogi9NM109/YjIV9dxwVRSEjI4MxY8YwcuRIdLrDH/b5bXyXIXG/RCSC+5VXaX3iCdRYI9rJFparg3godh4Wq48f93mO4uy9xAMqzz2s8NKS3cQ1jRJnPv+48Wlun+CgymbEEfMQUsyEVSOWSg8zN0UojSbwOrcSMfpQY2aSOnrhUTxMX/0EqW4/G0sUXj7NxB99jQyMBvC0mqlbnkH+X/4fjjPO+E7PUwghjnU9JZccckg86aSTuryORqPs3LmTWCzGyJEj+eSTT45Igffddx9vvvkmO3fuRNM0Bg8ezP/93/9x2mmnddmvvLycn/3sZ3z22WdYLBbOP/985s6dS1JS0jceo6d0hhBCfO8k4rDjXVj2R+oa6vg0XEhDnRMtrpLjzGVg5jDSwoNRFJVY1EdH2zK2+yupSbOhC7ShhRMoaDgVD/m1Hfx2/W7WBYOdzdtKhjNl8gQuy7RSFx7GMn06u41R8nTtDDfUYlf+/ZxiWloaI0eOZOzYsRgMhu64GsCRCYn7JYJBXC++SNuTT6Ha24lNsvBy7ESej09jRNZmruj7ClZrkA2fKfz1/9Vw1azrOK//xWSH4MFSI68Wq0TRkRlpp9mUjr4jxKCNfs5s0AjbavAn7QUUrL5CEgkdRWULGV6xCZcNHpmlcpI9xrXeRvwxM00f2jGffhk5v7sd1Wg8IucrhBDHmp6SS77VcNNgMMjVV1/NtGnT+NGPfvRd1tXp9NNP57zzzmPMmDFYLBaefPJJHnroIZYvX86kSftmbPP5fAwYMIDBgwfzxz/+EZfLxTXXXMPo0aN5/fXXv/EYPaUzhBDie8PbCKufILr6aTaHjKz35NHRlIReMdI3pzd9bKOxxnIBCAWqqG9bzRazj6ApwN7yGtbvbeCiEX3JCHsprfBhi8Zx2eBvViOvfLGdjEETuXhUKSPtA/mU/mw2JtDrOxht2Eu6GuosIyUlhWHDhjFx4kRMJlN3XY0ujmRI3C/u89H+7LO0z5uHrtCLf0gSf4uezefKQM7t/T6nFH5KPKbh35mDabGdpqmzme6yUJ6kcEsvL3XuVjJ7F+DR20koKo5KP5dvDGLXgnidm4jp4hjCDszBbGLuSk5f/xy6CLw1UaFipIEH2vdiihtwbzTjjg6l16OPYCwuPqLnLIQQx4Kekku+9TOJW7duZebMmVRVVX1HJX2zgQMHcvrpp/PXv+5baPjJJ5/kZz/7GY2NjTj/9TD8+++/z8yZM9m1axd9+vQ5aHs9pTOEEOK4lojDro/gs7/SULWVlYl8auvSiIX0pFty6Z89gGyGoMNEIh7B07GRHeE97E0OEGipZc32OtZW1dLU4QPgobw8Trbb2VRkZHHJMFY5TmNcoo5BHR48htGsN5hoMvoZrK+nt669cx1Du93O4MGDOeGEE3rk5GtHIyTuF3e7afvn07hemo8yJEJDQRp/if4Ab5KeK/u9TElqNQmvjtiHTsqsFzE6aSx3PvcbllaswnbhpVivuoF0QrSZU1EDMU5a52NCfRS/cxtBkxslocPqKyQaizF57T/IaHezMw+eO93Ar2LNjAsF8bit1C9PI++uP5LyX2sxCyHE901PySXfOiR+8cUXnHbaaXg8nm/e+TsQj8cpLi7m6quv5g9/+AMAV155JZWVlXz66aed+0WjUcxmM//85z+56qqrDtpmT+kMIYQ4LrXuhi+fJLTuJdZGktjmzsHXbMWkWumT0Z9etqFYtSwAwqEGar3b2WRtIBiuZ9PmGtZV1bGruZX//rbqlZ9GyuW/wJHIYmLMjSFcwCa9g73GEPn6JgbpmzAq+yagsdlsDBw4kMmTJ+NwOI72FTgsRzMk7hdraaH1iSfxvvMC8fEKmx29+WvsAvrmlXNxn7exmoKEq5NY8GiMJ9Zs7/ycOScfy6/vJHNAX3yqhZjeSEZNkEvXBjApbXgdO0moCQzhFIzBDDL2fsG4HR8TMsALJ6mYeif4nbuBRNxE+woLsf4zKbr3HnQpKUf1/IUQoqfoKbnkkBcrmj9/fpfXmqZRX1/Pk08+ydSpU7/rur7WPffcg9fr5frrr+/c1tDQQHZ2dpf9DAYDqampNDQ0HNDG3LlzmTt3budrt9vN4sWLj1zRQgjxPWOMuMlv/pjs+iXsjurYFMyivW4I+oSZfHtfxhX3I43eKIpKPBakwbeerfoaWtlNWUU16yua2FrXRDR+4PIVADqzjeTswZzjzadMzeYjUxbO5GaG6PcwVNm3lq/BYCAlJZX8/HwsFgsAq1atOpqX4X8SCoW65ztpzGh0vXvhWLSIEWtXMu/E+/mwaRR3Nd3GmaWLOLFwFcpIN/r1CrH4vsQeaqgl9IvriMw8H+uPbyEz0kFLQToPZxqZssnAhKoUQqmbCJjcxAxemkvG8G72IKaueYJrPwyzuVjhshkF/DbcxPATOvA0v8XWaStw//AyQoMHH/1rIIQQAjiMO4n/Pe23oihkZmYybdo07r//frKyso5Igf/p0Ucf5Ve/+hULFizglFNO6dw+ffp0UlJSePXVV7vsn5GRwezZs/ntb3970HZ7SmIXQohjWtAFW98ksepJyluaWBfNoaUuBSImciy96ZVWSpauL6piQNPiuANVlKs11Om3QlsHSzfX8MGWMsKx2NccQCG7qB+j+5+Ao/dMqq0qBn0rAwwtpCl+UDSSbEmUlpYyefJk0tLSjubZf2e6407ifwtXVNDy8N8Ibl2If7yVFxLTWJ9UwA8HvIni2sNf/18rO8pCXT5jSsvEfPNtOMePI6bpiRpNpLRFuHBtEGeoHl/yLhJqAn0kGVMwndyKLxhZtoygEeafrOIsifArdwuRuI2OzwwkRl1IwZw70R3CBHRCCHG86Cm55JDvJCYSB643dTTdf//9zJkzh3fffZeTTz65y3s5OTlUVlZ22RaNRmlvbycnJ+dolimEEN8vvmbY9ArRtfPZ3t7OlmgmbfVOlFAe2dbejE/vRba+D3rVjKZpeEK1VKp7qdVvJuZzEQns+xoyxRL0Dse+MiA60nMY3v9E0ktn0pLqIKRvpcBQxkmKH0UBh8PBgAHjmThxIsnJyUf7ChyXTL16kf/gAwS3/YiWhx7gR63v0zbYwd9Wn42SG+Cu+95h2aI6/vnPdsLhfX9rDrc1E77zVqJjJmK5+TbSU+24U1N4arqBIXuMnLolHexbCBg6iBm81JaOoCl3GJO+fIIfLwqxI1/PNafmc73SyglTgnjaXmLn9GUU3vP/sJ980jdULIQQ4rt0yHcS58+fz8UXX3zA7G+RSISXX36ZK6644ogUCPD73/+eBx98kPfff58TTjjhgPf3T1zT1NREyr+eY1i4cCFnnnmmTFwjhBDfNVcVbHiB4LoX2eSPsT2Qiac+CWPCQZ61DwXJvckwFKMq+wKgJ9xIpb6CDS2fsm7rHrZUtzCiMJcpRXnkuHzkun3oEmFWFcOt7+8iHotjdaTRv/c4svudji+3EIuxlV76djIUH4qyb7mKIUOGMG7cuM6hpMeLnnAn8b8F1q6l5cH78eu3UlGYy99iZzGo11aGGj/kbw81sn59sMv+qt6A5QdXkvTDq7DG4wSSkjGEE0zfFGBwbTN+53Ziagw1ZsbizyGzZgtjtr5PXIV3xis0DE/wu45mrFEL/nV6AjnTKb7nXgxZmd10BYQQ4ujoKbnkkEOiTqejoaGBzMyu/4Fua2sjMzOT+Nc8N/Jt3XLLLTzxxBO89NJLjB8/vnO70WgkNTUV+PcSGEOGDOGPf/wjbreba665hpEjR8oSGEII8W3FwlD5GYn1z9K0cyUbNRvVHWkEGq2k6HLItvYiP6kEpyEfgIQWoy1aTznb+KxsKevL6tjZ0Ior8O8gMc5u555Beazvo+eL3L5U2kYwyJdB/eZt4OxLPD+XVFM7BaoLpxLAZDSSn5/PiBEjGDhwIHr9IQ+EOeb0xJAI++Yi8H/+Oa0P/wVvdg1rHf14QTeFaf2W4Nq0gieeaMPj+feoI4vRwtDHX6eqOBdL2E8EI3GTgYz2KKdvCJAV247X1AyKhjGUhilgY/j6F8htrafeCc9NU5ni9PDDDj8dwWTcn1px3vhb0i+9FEWn68YrIYQQR05PySWH9UxiU1MTGRkZXbbv3r2bUaNGHbHZTZX9c5b/lylTprBs2bLO12VlZfzsZz/j888/x2w2c/755/PAAw+QdAjPMvSUzhBCiB7DXQ1bXiew9hW2dbSzI5KJp8mGGnCSbSkhx1JCtqUYo27fEhKxRJiq2B4+r/+E1eXr2V7bTlWri8TXfMUoqsroX/+eoZF84rFMmnU6gkY3WXoX+To3NiWCw5FCnz59GDdu3AF/oDye9dSQuJ+WSOD98CPanvoLrl4eVhgH8bF9IKfkv8fHb2zh3QUdJBJw+vk38GDpJXyUo+dvpQbcRj0Wn5egNQlUhdLaMCdvcWM2bSSkC4KmYA5mY2/zMXnV0xjjCdb2UVgyBW6ItzIsqOBtMNFS04/ef7oPy/Dh3X0phBDiO9dTcsk3hsRrrrkGgGeffZaLLrqoy7CeeDzO2rVryc7O5uOPPz6ylR5BPaUzhBCi2/iaYedCwhtfZXfdLnZoKbS67ERaHKQbC8g0F5JjLcJh/PdM0p5EC41qJe3+Hbz95VpeXbuF2Dc8v66oKjn5A8komUzq0BOwJAXJ0XnIUr3YTAZycnIYPHgww4YNw2g0Humz7pF6ekjcT4vF8Cx4l/Zn76d1QIwl6nC2pGczRPcGyxfu5efX59BYNZKI/iamNsP8EiPz04OEw2GsJhthRxJKQmPM7jDjd9WRsO4kpkZR40ZMgSwKKjYzYseHRHXw/hiFhuExZvvaSI5YCW5WCeSeSfGdd2I4ChPnCSHE0dJTcsk3hsSTTtr3sPjy5cuZMGFCly9to9FI7969ue222ygqKjqylR5BPaUzhBDiqAm0w64PiWx4mT3V29mmJdPqSibSkkyaoZBMcyFZlgKcxmwUZd/s1h1RD8sal2EwtJOIevDFwgAoCY222kb+uGr9Vx7KlpxGTtFY7CWjSOlVQI4tQo7OjUOfICMjk/79+zNixIgev37h0XKshMT9EpEI7ldfo+21h2kZrOO9xFga84zM7P0hDpOXaJuRXQ2nkh2+mLfeeIhXNyzAfPEV2M6+BIPBRMxuwRhJcML2IEMbdxIy1aOpcfTRJEx+J0M3vkVh4x7ak+CVKQp5hX5+7OkgFE7Bv0KP5ZybyL7hBlSzubsvhRBCfGs9JZcc8nDTq6++moceeui4nDmup3SGEEIcEZoG7RWw8318G95hp6uOXXEHHW4r8bZ0UvX5pJvyyPhXKFQVFU3T2OXaw+cNX7CuZj1baquodrUTT2ic0r83P+xdTKo/iD0YoiUlxM5slXtf2EE8FsdotpKZN5jk/GGk9ulHZqaFHL2XdF2QtNQ0evfuzciRIw9Y31bsc6yFxP0SgQCuV1+l7b3HaBxg5I3ERBJFPk4rXopZH6Zig8oNv67onC3dYHdguuxa7KecBRYrCYuBpECcE7b7KXVvJKx3gaJhCKdg9RoZv/o5UrxuqjLhrRNVTnR6ONsbwu9LwrU+hexb/kDKzJko/7VklxBCHEt6Si455JB4POspnSGEEN+JWBhqviS+5U0ati9lezRGbTCVYJsFSyCfdHMeaaY80ky5JBlSAPCEvKxr2MTKulVsqN3G9sZafOHwVzbfN8XCpeeXsimjhGZLPwoC+RDOo3zXl1jS7eTkJpOt95FhCJOZkUWvXr0YNmzYUVlP93hwrIbE/RLhMO7XXqN14aPU99azQBuLoVc7Heve5/FHmw/Y35ieiemK67GPn07CbkUz6UjxxTlhu4eSjg1E9F5QwBhKI7k9yqQvnsEcjbKtEBaeoHCWxc3J3jj+dgttuwopuv0ekk444WvnNBBCiJ6sp+SSg4bEa665hoceegi73d75bOLXefrpp7/z4o6WntIZQghx2OJRaNxGomwhbZsWUe5vpTLuIOCxoLqySVZycRqzSTPlkGrKQa8aOj/qow13pJq/LHmet7esOeRDmixJzPzR00T1JuKGDpIMHjJVL1nmODnZOfTp04dhw4bJ8NH/0bEeEvdLRCJ43nyLlgUPU9PLwEJG0qbuYsOCj9m8KXjA/qbcQkxXXk/SsMkknEloBh1pHXFO2N5GgX8jUX0ANAVTMIO0Zhfjv3wBQ0JjdT+F5ZM0LsXFKL+eQL0BV+sges25F+uwYd1w5kII8b/rKbnkoCHxpJNO4q233iIlJaXz2cSvs3Tp0u+8uKOlp3SGEEIcVDwGzTtJlC2kdcNCyn0tVMSTCXos6D3ZJJOH05SN05iF05SNQTFS19HEtuZdbG7cyeamLVw99iR0xhjt4XoiiRBoGmt27OaVreVfe9jk1BySswbgzOtFWkE2xTkOskxB8pzJ5OcX0r9/f/r06YPBYPjaNsShO15C4n5aNIrn3fdoeeNBqksUFjGMmtBO1r+7nF3lB96tNuYVYr7kWpJGnEgiPRlNr5LpjjG+rJV830YS+hBoKqZgOpkN9Yxf8waaAp8OUdg8Os4VCTf9gkZCe/W4YuPpc8ddmPuVdsOZCyHE4espuUSGm9JzOkMIITr522DvSsJbP2BvxWp2xeO0hJKIeW0YfXnYycFhSCfFmInTlIUvHKa8tZLy1irKWvewvaWMsua9dIQDXZq9ZuRgpqWlYg+GCZnCtDqjrI4neOatHQAYzVac2f1x5pTiKMgnPy+dPHuC3CSV/Nx8evfuzaBBg7Db7d1xVb4XjreQuJ8Wj9PxwSKaX3+YvXkRlihD2OXazJp3V1JTEz1g/5Qzzsf0s9+i7wgRd1rQ9CppHXHG7m6i2LMFTQ3+KyymkVNXw5h176Ap8Nlgha2jY/wADwMDJkJ7FVzxifT5vzmY+/XrhjMXQohD11NyiYREek5nCCG+h6JBaN5OvOwjGjctYnegjeqYnbDXjMGXhSWSi8OYSYohA4cpgyR9SudHNU3jitd/xdbGMloDh7ZW7bjhORRMPwGfuYTUcB7hSB7hRJSG3R+TW5BNUYaF/GQd+Tl5FBcX079/f9LS0o7QyYuvcryGxP00TcO/ciXNLz1Apb2NFWovVjeUs2HRahoa/h0W/3nDY7QPGc6zJQZaNA1dRxgt1ULCoOLwxRm7p4U+bZvQ/rXGoimUTnZtLWPXvQOKxmeDFDaNiXOR4mGw30SoWkdHZAy9fncnloEDu/EKCCHE1+spueSwQuLSpUtZsmQJTU1NnbOT7SfPJAohxEEEXVC7ltD2j6gu/4zKaICmqI24z4ohkI05nE2SLp1kQyomNRl3IEyVq47K9loq2mvQ0Pj9qVfhDrXiirTiibTgjrZw9/vv0eDxfuPhLTYHjsze5JaOp2TEaCyql3RjgMIUEwV5BZSUlNCvXz+SkpKOwsUQB3O8h8T/FNq+ndZ5D1KulLNBl8+Shjo2LVlJcaGeO24vxFs7noy2i9nkSOGZXkZqdm/HZElDKcwkbtRhDyQYvaeVvm2bURUfaArGUCrpTS1M+PJNVC3OigEKG0bHOVfvYaTfSKhBj8s7gt63z8E6ZEh3XwIhhOiip+SSQw6Jf/7zn7n99tvp06cPubm5B8waJs8kCiG+9+JRaNtNvHIlrZsXs7e9nOqYHq/fiup3YgplYYlnYjekYdE56AjGaPF6qXLXUtleQ0V7LRWuGmo9jSS0rn+IM+l1/OmsU7GHo9hDEUyxCAFzhAe2V7C25t93EVWdnuTUXJLSikjJzCYtN4PinFR6ZSVRlJNFfl4BvXr1oqCgAL1ef7SvkDgE36eQuF+kto72Zx6hrGUFO60ZrE9O5pShm0mzuIhG9PhqBmFrv5gr7r+Vdr8L82nnkjTlPAylRcTMOkyRBMOq3Axs2oI54QLAEE7B2RZg4hevYYqG2NBL4YsxGifaPUzxKUTbTLhqSii65fckTTlRZkMVQvQIPSWXHHJILCgo4Ne//jU/+9nPjnRNR11P6QwhxDEgGoSm7YTLl9O0/WOqfLXUxUxEAlb0gTSMoUxM8XSsuhRiMT2uQJg+zt7k2DO7NPPG1g+45f0/Hdahb7t4ON6sfJqsBRiUXAzhXHbv2onXVYUjK5W8LAd9clPonZNBfn4hJSUlFBcXy4Qyx5jvY0jcL+5243rleXZveYOKZAfr0vMY0nszBfZ6li31cc89/7GEhqpimjCVpFPOxzxqNDG7ETWhMaDWz9C67Tgi9QDoI3bsHhi36g0cfhe7c2DJWIUBWV5meWPEvXZ825PJvPo20s45B0X+/yKE6EY9JZccckhMTk5m06ZNlJSUHOmajrqe0hlCiB4gHgPXXuI1a2jbsoTalq3UhsN4QlYIJmMIpGGIpKFLOAiGFTqCMdzBEK0+Dw0dzdR1NFPvbaauoxF/ZN80/7856Uqm9x+BN9pGR6Qdb7SN8oYqHly26hvLMVuTsKfkYnPmMuTEmaQ4DTgMQfKT9RTl5pKXl09RURG5ublyZ/A48X0OiftpsRjejz6katFjVFqjrLEVsfzzj/h8ceVX7q/v0w/btAtIPnEGkex9kyoVN4UYVrubbO8eFEVDF7Ng8VkYtnEx+Y0VNKbAorEKqUUBzg+GMQXshLbqMZ96E7lXX4UuyXYUz1gIIfbpKbnkkEPiFVdcwZQpU7j22muPdE1HXU/pDCHEURALQ8tOgrtW0Lx9CbUde6mLKIQCNvQhJ8ZQKrpoKqGQkVhcx4CMflgNyVh1dvTKvhB25rPXs6WxDI1De6R7VlE+V5XkEzXECFuiBGxQoZq474UvAdAbjDic2dicuSSnZpGcnkJmup1eBWkMLMonP6+AgoICioqKsNnkH67HOwmJXYW2b6fhxYfZEa1gcZuRJZu3sHNdNf81NQIAakoq1pPPIWXaucRK89BUhVRvjCE1dfRu3Y5RC6Mk9JiCyZRUbGfwtuUEzLB0qELHoDCzNB8FgSSieyCQNYveP/8FxqKio3/SQojvrZ6SSw45JP7tb3/jj3/8IxdeeCHDhg07YPjSFVdccUQKPBp6SmcIIb4FTYOQB615B74dn9JYuZJabw3NIR2xgA1dOAVd2EHQZ6MjoOANRvGGwniCIdxBP21+D82+dpp8rbT424kmYhSm5LL8uvn4Yx34Yx4CsQ4CsQ5mv/MYZc31h1xaUWkp08/6MTHNSVgxgS6ETu0g1FROn8IshpT2Ji+vkLy8PPLy8jCbzUfwQomeTkLiV4u1t+N6+Wm2lC9mfczI6+W1bP1iG35f/IB9Fb0e6zlX4jznGrR0MzGjiiGqMbCunf6N23GG20Db99xiWqubcasXYIwFWdtXYePwBBOSvUz06Ym3GnE39abkptuwnzgFRVW74cyFEN8nPSWXHHJIVA/yH0ZFUYjHD/yP9LGip3SGEOJrRPxobXvw7VlN6+7PqG8rpzEQJeS3oYRsxAN2fF4L/oAOfziOPxzjxJIxFDuLsOiSMKn7Qtcz69/kjo8ePOTD6lWVJ6ZPwRqLYYjHiOtixEwxHt5cyeoad+d+iqJgszuw29Ox2NOxOFKxOeykp1opyE5jSGkxxYXF5OXlUVBQQGpqqkySIb6WhMSD06JRvEs/ZveH/6AMHy9WeFm3ZjMNtb4u+1118S9ovPASNhg1dJ4IxiQjQce+P3AXtgQYVF9BgXsPKhq6qBWrT8/w9UvIbamkOgM+GaWQWeDnzEAUS8BOeIcRy6k3knvlFehkFmAhxBHSU3LJIT/A8t9LXgghxLcSDaK17cZbtpKWPZ9R276LZm+caMiKPmxHF01BjaWh15LRYea9rWvxhkN4QkE8wRjuoI/2QD2tARehWPiA5kuS+9DbWYI73EQo7iMQ8+FP1B1WibFEgk/z8rA4CtEp6US0JEKqgeTkbUwNNZHptNG3IJvB/UvJzy8gKyuLnJwcWUZCiCNIMRhInn4aI6efxuDqaia//ChbetlY2BDhw6272b2lDrtd5dxL30XXupe492zed/bnA5NKfNVGTAmoGT+M6ozB2AMDGFDXQGnLDuKGDj4/aQLG0Azya/Zw2eJPCJmsvD9YoW1AhOkTWujT9FdqfjYXr2UqfW7+JZb+/bv7cgghxBFxWOskHq96SmIX4pilaRDuIN5UhmfXCur2fkFtcwUdHhV/wITfY8DvN+MP6PGHFALhGP5wBH84jC8SxhcO0REK0BHyMTxnII+ePee/mtconTv9K8Pg17myX29m5mWjKTE0fZy4McaOgJ+7/z97/x0nV1n///+P06Zt772kd9IIhBB6Qg9FBBWVYi+IGjvv30cQeetbweBXVFRsgIAiIIRO6BBKCBAgvffN9untlOv3x8xuEhLIBoFswuvObZiZa8655jrnzGb3Odd1rvPEmv5lDMOgoKCIgsJSggVl+IvKCBYWU1Tsp7KsgNbGGiaPH0NDfRPV1dVUV1dTXl7+riMrhPhvSU/i/lPZLLFHH2TZs7ewKBrlRUfnxDNchpVuAiDVU4PZeTxz//g0K954EXPoaIqOPZPQGWfiVJSAUrR2xhm9Yx1N4U0YSmFmCyiMukx57QmqerayshFengjNdQlmJT0C8RDxtUWUnvUNas8/H13OFxZCvA8GSy4ZcEh89tln916BphEIBBg+fDhlZWXva+M+LIPlYAgxKHguxDtIbnmDrWufZeO6xXRt6ySSMEkkdFKpAKm0j3TaJJ2hP/DFM2m+feyFlAXL8RsB/EYQnx7gx4/dwD+WzB/w24+rGcJvzv0GaTdB2k3i2DG8TJRv3H8XXankgOowTZOjjzyGadNPJaMFSWlBsrqHcrqJ71hDU30VY4YPYeTwkVRXV1NTU0N5eTmGYbzXvSbE+0ZC4n8ns34D7XfeyJvdS1lTUEay1WFS/VvEwwku/NTuE95ogSCBI06kfPa5ONMng6ETSruM2d7GiM5VlKZjaJ6BL11E4+b1THrzSdI+l+fGaXSNzXKclWB0vBBvq0bcOprhl80lOGHCgdt4IcRBb7Dkkv06J7HvHJq+VXZ9rus65513Hn//+98JBoMfUHM/GIPlYAjxvvFcvGgbveteZs3Sx1i78k3C3WmSaZ100iCZCpDKWKTSGumsy7iaoUyqH4mhWxiahc/ws6F7B9+6bx6xTJKsaw/obZ/70h00lFSRdVNkvCQZN8UNL/yTW199ZMBNryvwM+/kMWQDGsmAn6hVRliv4l8PPUMskSJYUEywoBhfqARfQREFhQFKi/zUVZczvKWRkSNG0tTUREVFBZWVlYRCofe6F4U4ICQkvj+UbRN/agHLnvwbm8jwQGecf9/2LJnM3udQMJuHUTzzDEKnnYHdkLuuaWNXnNE71tPSuwnLczGzBYTiisPefIaGHetZXQ8vToS6xgQnJz1CiQISa4MUzPoqjZ/5tJy7KITYb4Mllww4JD744IN8+9vf5jvf+Q5HHXUUAC+++CLz5s3jf//3f0kmk/zwhz/k05/+NNdee+0H2uj322A5GEIAuKko29cs4tWF97BxxRskIwnslEY6o5HO+MhkTTIZnYytCJhBzhk/E0Oz0DQDXTMxdZP/efBPrO3aRspOk8xm9nmphm/NuITvHPO53cpWda5j1l8v3a+2/+zwKYwo8YPmguGC5XH/xnZuWbpzJlBNg4DfTzAQIhQIEQgVEggVYgUKMYOFFBYXM2nyeIpDPqrKC2moraG6qoaKigoqKiooKyuTHj9xSJOQ+P5zurrouPvvvLB8AfPXdvHkso1s29Sz94V1A/+EIyg75nQ4/WRUwIfP9hje3snwzrXURTvRlIYvU0xlRxdTXluA4cZ4eZTGttEOk0viTE740bpMwpHRtF7yLUqOPx5N/t0SQgzAYMklAw6JM2fO5Ac/+AFz5szZrXz+/Pn84he/YOHChdx55538z//8D2vWrHmHWganwXIwxMHFcz3ikTAr1y1h1ZInWP3qQpxwDDfj4GQ9MrZOxrbIZnVsWydrQ8Z2yTouGcfhM4fPor60Gl0z0DUDUzO5763nuOnF+aSd7IDa0FRSywtfuXOP8jNv+RJvtK0c8LacOWI8n58wGbIJyCZx3Ti9mQRffP71d10v4DPx+wKEAiGCoRAnHH86FTUtZHQfaU3H0LO4iU50L0NzYz3DhrTS0tJKRUUF5eXlFBcXS+AT4m0kJH5wlFKk33qTdff+gUfXLeXu1e28/sYG0ilnj2XNgiIq/ng/fsfELPaRKM3NjFqSyDKifRsjOtdSkk6guxaBVJDWDasYu/x5eos8Fo7TyI7IcIyRZki8AHeTTqZyFkO//A0Co0Z92JsthDiIDJZcMuDZTV999VVG72UWrzFjxvDaa68BcMQRRwyKjRIfXUopkvEEPd3dbN6ylnWbVrNx81q6tq3Bi3QzvDxE0PEwlY6u+UFZvLy6g65IGtsFx1HYjkfW8ci6DrbjknFtso5DxnXI2FlmjTqci6efjqmbWJqFqZtUxmq46KZn9qutlx/5JSaWTtytrMq/fsABESCSjvDGpjvxnBTYKXBSeE4KL7Njn+tqGgR9FgG/n3hxkGV1NSjLwvH5cTSThKs4xt+EP+CjuMBPeXkxdTVVNDY00NrSQn19A2VlZZSUlBAKhWQyFyHEoKdpGsHDJjL+sBsZm8nwhQUP8sKT/+De1Zt4bOVWNq7r6F929kyLrzt3sEQ7iX/qQ0htS1DgQlyLsHjoEBYPHUJdT5wRHRsZ1r2J5IQ6Vo2+hMKYx+RlL9P84gpW1/v4y3ioGJ/k2Ox8dvx9Aen1AawjL6bl4osxKyoO4N4QQoh3NuCQ2NDQwM0338w111yzW/nNN99MY2MjAN3d3ZSXl7+/LRQHFcdxSCdS9IZ76e1uJ5aMkLIT7OjZSnfndqId7aSiPWxcvQU966DZHspTKAccF1xXe9u9wnEUcyYcSXNZDbpmoGGApvH8umXcsfhxMk4uxGXzYe6dhlaWBotY+PWbMQMWBrlz70zN4qa13+TFze/eY7YrL+tjVGhy7rHycJWN7uve7321fMtDBFOLwc3kgp2bon37+n2uZ+pavgfPIugP8lYFKK0YzSrHMX24usFwXwEViSSmP0Ao4KOoMEBleRnVtdU05q/VV1dbR1FREYWFhRQWFkqPnhDiI0X3+yk582OcdubHmN3bS9f8f/DQC/OZv2o7C1ds4cgTqwg0LWA6C5gSriHlHsv82ARu/uZFWK2jKJtxMm1nnE7b6PEsdMfS2tnLsK71NPvbeHHmRBZnZ1LSG2PW4ucofWIbr47QWD/KY9TUbqbFf8PWn/+B+LYays++jNqz5qDL+dNCiEFkwCHxpz/9KZ/5zGd48sknOeqoo9A0jRdffJGXX36Z2267DcjNgHrqqae+rw189tln+dWvfsWSJUvYvHkzV155JVddddVuy6xevZpvfOMbPPfccwSDQc477zzmzZt3SF2rTClFNpslk0qTSiaJxaJEo2G6ezvp7umkp7eT3mgP4WgPhSET3DR2OoGXzaI7EO2KsWVrF5rrggLyAczzwO1/rOG5CscDQ9P50rGnoatccNA0E03T+evCR1i+YzOO62C7LrbrYLsOjueSdRw8tfv1NE8fP50fnPxZhmgmwxmKwUhS2SzH3f+l/dr+SydexJEVR+5WttaKsz0y8HCWsbN4jkdSxXGVg6tsHM9BMfCeO4BtHa/y2pKr8LwUSrPRDQ+bvU+EEDB1/KaBzzLxWRaW5cPy+bBMHx11JSxvqMQ1LVxdx0VHNY/k1JbN+P1BCgoDVJSWUFlTRV1dHY319dTX11NeXk5RUREFBQVYlrVfbRdCCLEns6yM2ou/wecu/gaf2bKVTf/5C0s3vsKK5wtINLmMqV9JSem/sf55E3ge9voVdKxfAbf9Bv+Iwyg+5mTWnHYK68ZPw3JchnZ0MrRrA42BGF01x+LLFFLZ0c2cx5/CtDWeHqmxY6TDmCnrmbzqR2z64Y+JdbdS/8nLKD/lFHSf70DvEiHER9yAQ+KnPvUpRo4cyfXXX89TTz0FwNixY7nhhhuYMmUKAN/+9rff9wbG43HGjh3LhRdeyLe+9a29vn7SSScxfvx4Fi5cSG9vL5/73Oe45JJLuOuuuwb2Hokov7j+W2RScZxMmu6OXrZu6cBzXJSjcF3QXAWeQrkempd/7IFSGprSOHfaVAx0NGUABrqm88gbb7KhswOlwPU8XE/lep48D7fvfpeyyc0j+PRRp6A0DTSF0jQybpYv/OlnuJ63z+3o83/nfI1jh83AwEDHQDcNHuxeyA3PPDHgOvymj1vO+f0e5X9IL2BT976HMvbX4xUx1JqQ73Fz8JRLSk8NeP0+G7tfY1UoivJslJtBuTZd3cv3q460k2Hj5qvRTQ1NB0wDpeuUhrqoLwlimSaWZWGYFqZlYRm5e9Ow8Fk+TJ+F6fNTVV3L4uGjcDQdw7QoCPooKi7i58fFqKquora6htra3EQrBQUFFBYW4vf7ZTimEEIcBHxNjYy4/EqGK0Vm5QpW/+cm1izPsqSigEcef3j3hZUis/oNOle/AX/9FcExUyg87mRWnHIyq+qPIpB1GNrRzrDuDaQDFm2Np+NLh6jZ0c6cB5/E0Q0eGa3RM8LmsKZlBF6dS/QRnVhiDM2fuYzS445Dky8DhRAHwIBDIsDUqVP5xz/+8UG1Za9OP/10Tj/9dAB+8IMf7PH67bffTkdHB7fffnv/dRp/97vfceaZZ7J27VqGDx++z/ewHJ0jtg1Hz0UqFmxezO8femjAbbR0k9/O/lVubU1HQ0fXdO7ofYtFGwY+ic/U2okcXXLCbmW26+B617zDGntXSi11+lA85eLh4imPkL5/w1hs12ZD90so5eJ5NspzUJ5DJtu1X/V09LzK62/MRTNc0BS6rkDfORxU18Bn6Fi6hmXoWIaGZRhYho5pGpimiW6YxCo6WF1ugTLwlImLheav4zTfUZiWhW75MUwT3bQwLD++UIiSgkJKyospKa+korKcyvIKmluHUlxcQCAQIBAIEAqFOM2y+i/nIoQQQkD+OtBjxnLYmOuZ4HmkXl3EhLCff7+2hCfXtbFhQ+5L4H7KI7V8Manli+FPvyA0dhqcfi7LTzmZ5Y0NFKRthna05QJjKMiWlnMIpEPUtm1h5r1Pk/SbzB+tkRhuMzX4CoXPfYXeuw0i9mSGXnoZxUfNQJMvG4UQH5L9ComD0cKFCznyyCP7AyLAySefjK7rPP/88wMKiSY+Rpi5CUSUUlQZ2/exxu5sz6E31ZZf383fPGw3sV/1hBNbWL3xP6Cc3AXNPQfP3b+hkAAbNv+FFd7daLpC0wAdouEIQVPH1DUsXcPIhzJT1zDz94ZhYBg6pmGg6wYbfQtRloWtW3iYoOkMH1dHqCaEZVqYlolumhimhW6Y6IaFYZgYPh9GIEQwVEBdbS2xEcMpKCmiuKiEwsJCCkIFrPi5oqioiFAohN/vx+fzYZoH/cdRCCHEIUjTdULTpnPytOnM9jxSi1/isX/9lntfX8pT69vYvOltX6C6Lsm3XqKkpRHGHU2xA9lKP281N/NWczOFqSxDutoY0r2ZREExm4achy8doHZHG8f/5xkSfpN7R2pkh2aZUPAcxY8tpvsfBhF7EkM+81VKZs5Ek9+ZQogP0ID/hXEch5///OfcdtttbNy4Edve/eLarrv3c7I+aG1tbdTW1u5WZlkW5eXltLW17XWdefPmMW/evP7nthNm6fJvAy6apmjfEUbXwNC0XHjSNAwdTC0Xrgxd2+2xoWuEY79FN0w8Xc+dX6aZtFbHmE41um6g63ruZui550ZfmYFhGuiaRmNZGevrI3i6jtItHEwUfj57xinouoFmGGh6rrcMnx/DF8DvDxAI+QmEQgQCueelpWWEC0K58Gfm3udwXefffW34L76JbD75Pa9KNpOiJ5Oip7vzvVcihBCHuHQ6zaOPPnqgmyH2IXjyxXxqlsfn1q9mw5LHeXzNRp5et52tW3def/GHJ7/E0NDPWO8ezV3ZaazdFsK/bTO9618jcuJJvDXpGEIZm9auHQzp3kKisIhNQ87Gnw5SvaOd8fc/h62HeWCkRnyozejihQSffo2ufxt0dDSSPnwW9vgJIIFRCPE+26+Ja/74xz/yne98hx//+MdcddVVrF+/nvnz5+8xkcxgN3fuXObOndv/vLa2lpG/vgufL3dO2uGmyXfz56eZppkb8vgegtXp72ejhRBCfCTIdRIPNqdxLN/kIs8j9cpLPHDrr7nnrWW80RmGynFYJWsYp69krPorWX0Iv38lw4N3Pwd/uo7AkDHYM0/kzVmzWX7YDPy2Q2tnB0N6NhMvKGRL6xn4MiEqOrs5fMHzmNkOnhip0TPMYUTzSiZkNhJ83CDe1ULV2V+m5vTTZJZUIcT7YsAh8fbbb+fGG2/k3HPP5eqrr+bCCy9k2LBhXHvttbz66qsfZBvfVV1dHRs2bNitzLZtenp6qKurG1AdpmkydGjrB9A6IYQQQnwUaLpO6MgZXHDkDM73PNJvvMbrD/+dLa8VsLXSh94QZ1TNWpa9tq5/nfSGFaQ3rIBbf4evfgiFM05g6YknsnLsEfg8RUtXJ609W2gqTNLWOAsrW0Bpb4Sjn19EUXQjzw/X2DHcYej45Uxa/j3SL11BrL2KkhO/QOPHzsUoKTmAe0QIcTAbcEjctm0bU6dOBSAYDBKPxwE499xz+fnPf/7BtG4Ajj76aP75z38SDocpLS0FYMGCBXiex8yZMw9Yu4QQQgjx0aTpOsHJhzNj8uEAZNauYdn8v7H8FR2DbjStc/dJb4Ds9g303LUB7vorRkkFBYfPZOnxx7Nm6pEYow+nvreHlp5ttHQ7dNROx7JPojCaYcoby2jcvIQ3WmHTMI/KsVs4bPtVqJ//jNT2ArQR59L6mc8SGDLkw98RQoiD1oBDYnV1NT09PTQ3N9PU1MTixYuZOHEimzdvRr39X7r3UTweZ+3atQBks1l27NjBkiVL8Pl8/ZfG+OlPf8qFF17Iz372M8LhMJdddhnnnXfegCatEUIIIYT4IPmHj2DK3J8xBfjEj9p57bbfc8uj9/P0xjZWrW/HdXf/O8qNdBN94j544j7KP/5lys+7lLbKcrZWVLBwxGFUxOK0dG+jtaeNnsqRLJkykWBSZ+i2jYx+6gW2VqZ5Zjj4hkcYa/0F/a+3obUbpAJH0fKpL1B8xBFohnFgdoYQ4qCgqQEmvIsuuohx48bxgx/8gGuvvZZrrrmGk08+mWeffZaTTjqJ22+//QNp4NNPP80JJ5ywR3lLSwsbN24EYNWqVXzjG9/g+eefJxAIcN5553H99ddTWFg4oPdobGxk69at72ezhRBCiPdEzkn86HCjUdbe/Q/+dNfNPL1+G0s3tJPNOLstc8MNM0k1nMg97hR2xCsJGTq9dgcZJ4U5fBSFGZuW7jZaerbTEO7EZ5v40n4qu7sYvexFXNpZMlwjOcRmeFGasRk/gR6TeG8LlWd8nrozzkAvKDhAe0AI8XaDJZcMOCSGw2EymQw1NTUopfjVr37Fs88+y+jRo/l//+//UVRU9EG39QMzWA6GEEIIISHxo0nZNp3PPMGf/3Y9T69Yxysb2jB8iltubcJv5oJjJlnDtuzh/OIvK1j30OOYJRUEpx6FPvMYfFOn4wsW0NjbTXPvdpp62ilKZbCyBRTFUgzZsIzabW/w1hDYMcSltjrFWE+nJuYn3V6AGvExhn7q0zIsVYgDbLDkkgGHxEPZYDkYQgghhIREAZBZv45nbr+RSHgjO8pNsrUZWms3UuKP8JlPb6G9ffceRzSdwMjxmDNm4jtyJubwUVQkkzT1tNHc205tpBtfNkAgrVPdvp2xS1+goyTKymGgNWYZEbIZnvZjdpgknPE0nHMpFSeeiB4IHJgdIMRH1GDJJRISGTwHQwghhJCQKN7OjSfoWPAfFi98gJXZGP/zh0ex7Xe/PrVRUk7g8KMwj5qJb9oMAsEQDT1dNPe25XoZ0zZWNkRxJMmQ9W9R2f4WS4dodLa6VFalGKN81EZNMu2F6CPPofWTnyYwVHoZhfigDZZcss+QaAzwxGbXffd/rAazwXIwhBBCCAmJ4t0oz6Pn5YX87Q/X8sSyFby6qZ3Orti7rlN7yQ8oOeNjRMotlKYBUBGL0dTbRnNPOzXRHnzZIIGURnV7G6NXvkhnUZgVQ0FvtBkeyjIq7cfoNomnx1B/9ueomjVLehmF+AAMllyyz9lNlVK0trZy6aWX0tzc/GG0SQghhBBC7IWm61QcdQzfPeoYvgu4vb08f8vvufX+//DK5u2s2NSBnd39i/ufH/8UESvFI72TWBcbhmVadG1fxdbu7bw69UiC4ypoCHfS2NtOY7Vi47DTsbIhCmMpml9fRfXW13ihVdHZ4lLZ+iqJN18n/oJFtqMAp/E0hp7/aUJjx6LlA6gQ4uC3z57E+fPn86c//YkFCxZw0kkn8aUvfYk5c+YMuIfxYDBYErsQQgghPYnivVJKEV7yKjf/7v9YsORNFm/egeGH3/5pKCX+XG+j6/roTY/mJ9etZcWzSwGwGlqxph2JNXU6volTKdUNGsPtNPZ20NDbRTCj48/4KOvpYcTqV3HdTawYApkmh4aSDKMcPyVhk3h3DUXTP0nTuedi1dQcyF0hxEFrsOSSAZ+TuHXrVv7yl7/w17/+Fdu2ufTSS/nCF77AkENgFqzBcjCEEEIICYni/eIlk6x/8C5WvPIo3b4snTVQUtNDc9lmPnXBBsLhvZwqpBv4R47DPGI6vilH4hs9nppUksbwDhp7O6mO9ODPhgikFDXt2xi2+mV2lEZYPwSs+gxDgh7DU36sboOkPY6aMy+i+qST0EOhD38HCHEQGiy5ZL8nrlFK8fDDD/OLX/yChQsX0tXVRWlp6QfUvA/HYDkYQgghhIRE8UHJbt7E8vm38PKyxfz03mfZtiO8z3U0fxD/xKkEL/gsvilHYDkO9eEumnrbaeztpDSRyg1NjWdo2LaO+i2vsLrBpr3Fo7Qmw0hdoznuI9vlJ11yHEPP/yzFU6aiHUIj0oR4Pw2WXLLPcxLf7qmnnuLmm2/m5ZdfZsaMGQTkpGUhhBBCiEHP19zCpMv+H5OAL/3WZeMTj3DTn2/ghRWreGNLB+FIco91VCZFetHzNM88ArNiLG6hj83lNWyqrEW5LkVZm/pIBw3hThoaDIrHD8PK+imOxGl4fgVe+C0ea1YkGzNUl95H+rGHqLnbJN1VhNtyJkM//glCo0fL+YxCDDID6kns6Ojgb3/7GzfddBORSISLLrqIL37xi4wePfrDaOMHbrAkdiGEEEJ6EsWB4KZSLPrXLdxyx19ZtHEbyzd3kE7bAOg63H1PC8FQAZ2Z0bzoHMbLmYlsmf8kPU/egzn5cHyTpuGbNI3yggLqwx00hLtoCHdSkNLxZUxKwxGGrn8LlV7F2hbINLrUlKUZ7vqoiJikusuxxpxDy3nnERgyVEKj+MgaLLlknyHx4x//OA888AAzZ87ki1/8Iueeey4+n+/Dat+HYrAcDCGEEEJCohgMMl1dPPTHX/PvR+5neyrOGZdOpqymm+bSrRi6B8D//E8nL7+0++U3zLomrCnTsCYdjm/S4VT5A/2hsT7SRShl4ssYlPX0MnTdEjJqPetawGlwqCvNMjzrpzSqk+qpJjDpY7Scex7+pqYDsQuEOCAGSy7ZZ0jUdZ2mpiaGDRv2rhU9+eST72vDPkyD5WAIIYQQEhLFYGRv28qqB25n6erX6Sn1iJa7XP39e0kl3/062WZjC9bkafgmHY5v4uHUmGb/8NS6cDeBjB9/Wqe8u4th614nam5mQwuoepuGYpuRGYvCqEkyUkto6gU0n3suvtq6D2mrhfjwDZZcss9zEi+66CLp8hdCCCGE+AizGhoZ/+XvM57cJIbJtavQXjd58tXXeGt7O20d0b2u52zdhLN1E6n776LirIuIfPWbdDaV8EbTCHTPoyoWpj7SRV04xPqhRYTSPvwZKGvrpnzhEtZZm9jcDEbtVhrD/0fij/MojJnEI/UEp15Ay1ln4a+v/3B3hhAfAfs9u+mhaLAkdiGEEEJ6EsXBRinF1lde5uYbf8Wzr7/Om9s6aO+K7bHcNT8bTumE6TzpHM6m5EgSRjk9HauJPXov1mFT8E+YTE0oRH2kk7pIN3XhbkJpC39ap6y3h5b1b5DU1rOlCbx6m9oSm6G2RVnUINlbg2/82TSffS7BIUOkg0MctAZLLpGQyOA5GEIIIYSERHGwU0qx6aWF3Hrj9Tz9xhLe2tZBTzjBH/5xBEOqO/uXs51Cfn+Lx/23Le0vM6prsQ6binXYFHwTJlFdXpHraYx0Ux/uojBl4MsYlEQiNG9cjsqsZGOTh93gUFlqM9SzqI4apLvL0IeeRvPZ51EwbqyERnHQGCy5REIig+dgCCGEEBISxaFGKcX2xS+zdeHDrNu2mu5Sj3SlQ0VVF7/72Su89mrqHdfVS8qwJk7BN2EK1mFTqKypoyHek+9p7KIkCVbGojgWo3HzanzRN1nf5JKpdykvzzIUk7q4QbqnEK96Fs1nf5ySyVPkOo1i0BosuURCIoPnYAghhBASEsWhTimFvXkjbz70L678y828vmX7O57T+Hah6cdT9LPr+58Xp5LUhXPDU2uj3ZTFHHxZHwWJJPXbN1DU9TobazMkGlzKKjK0GjpNSRO7N0AqMJ2m0z9Jxcyj0f3+D2pzhdgvgyWXSEhk8BwMIYQQQkKi+ChqX7mCf/3heh57/jmWtnWwZUcvnrfnn6ifvHgYNR/7OEuyh9FjN5EoDBIjReLWP2GNm0TxqDE06FAb7aE20k1VNI4/EyCUsqns2E7t1tdoK+2ht9EjUJWlPqgYmjaxwiaJzAjKjj6fhtNOw6qsPAB7QYjBk0skJDJ4DoYQQgghIVEICG/fzgN//i33PfIgb25rY/32bhzH42fXDuWIKTuXi9m13PlSBXdcdX9/mVHfiDV+Eta4iQTGTKC+vIy6WC910W5qImEKUj78aUVZbzeNm5YS19fR1gjU2lQXOwxxfJRFddLhSsxRc2g+8yxCo0fLeY3iQzFYcomERAbPwRBCCCEkJAqxp2QkwpO3/BV76zKiRoyucjAqUtRW7eD+f67h9tvC77iuVlCINe4wrHGT8I2bSHVzC/XZJLWRbmojPZTGFb6sSVEsTv32dfjCb7ClziZd71BSYdOiGTTGDexwELvwaBpPO4/yo2WIqvhgDJZcss/rJAohhBBCCHEghUpKOPMb3+5/rlyX1Fuv8/Jjd5HdnKGsNEtvOLnXdVUiTnbRC2QXvUACiFZU0XvnoyyvHwJAQSZNbaQrN0R1eDGV0TH4M36CqQzWsu04bUt4obib3sYUwdDDNCx6iKHPmFhRi2RqKCUzPkHD6afiq6z6MHaFEB8K6Ulk8CR2IYQQQnoShXhvNr72Knf/5bc88eKLLG/vYMuO8F7PazxyRhmzr/gUizOT6bBbifpLiBWbxG78FUZ9E4FR42iorKA2GaU22kN1JEJRysSfURSHw9RvX0XSW0lHnZsbolpi0+JaVMYMUpFSVMOptJ55DkWHHSazqIr9NlhyySETEh966CGuuOIKVqxYQV1dHZdddhnf/e53B7TuYDkYQgghhIREId4fse5uFtx8E/fMv5u3tmxjTVsXqZTNhZc087nP7hxMZ7tBXusazv9ceF9/meb3Y44cizX2MKwxE6gYMowGS6cm2kNNtJfKaAZ/1iKUSFPdsY2ijiW0l4eJ17sUVGap92sMSRloER9pZzRlR59H3cmzpbdR7NNgySWHREhcvHgxRx11FHPnzuWSSy5h0aJFfPnLX+a6667jsssu2+f6g+VgCCGEEBIShfhguK7LG48+xPbFzxCNtdFd6pEpz1Ba2cuW5au5+qrt77q+Xl2LNWYC1tgJBEaOpaG+jrpUjJpoLzXhcK63Ma0oiYSp27aatLuc9noPah3KSxyaXYOamEkmXIKqP4nG08+mdPIUNMv6kPaAOBgMllxySITECy+8kLVr17Jo0aL+su9973v885//ZPPmzfucjWqwHAwhhBBCQqIQHx43HGbr0w/xh3/8lTtfWsKmHb24rjegdWtvewhVV9f/vDiZoCbWQ020h9poD5WRDP6sj2AyTVXndko636CjuJtYvUuo0qY2AENTBmbMIpUeSvER59Fw2mn4a2s/qM0VB4HBkksOiZDY0tLCxRdfzNVXX91f9sQTTzBr1iw2bNhAa2vru64/WA6GEEIIISFRiAMnnUjw4n/u4t///gevr1rD6vYuesKJPZarrDS48bYjedk5gqWZw+hwmggHSmn7y3V4nTvyvY3jqG+oo97O5IapRsIUJU18GSiORqhuW4ebWUZHbRa31qGizKZZmdTFddKRQrJlx9B8ynlUTJ+O5vMdgL0hDoTBkksOidlN29raqH3bty59z9va2vYIifPmzWPevHn9z8PhMI8++ugH3k4hhBBiX9LptPxOEuJAqqrl7K99l7PzT3u3bmXlcwt4ffkyVrV3sL6tm/qWajJpj1ML53NqYD4Armdx4Utb6G5PknnhGeJAl66zvHUY1ujxmKPHUzpkGI3lpdQko1RHq6iOHEYwY+FPOWRXdtLdvpS1/s3E6pIEtAfYvPA+Wp8yKYwZ9PaU0116BPqEiai6epDrNooP0CEREvfX3LlzmTt3bv/zxsZG+dZWCCHEoCA9iUIMQp//fP9D27bpfOM1Nr/0BOs3LKe71CVdauOZnXS3r9p9Pc/DWb8GZ/0aeOg/xIAt/gDWiNFYo8YRPPlMamprqY71Uh2rpiZaR0XEwW+b0JsiuWobG3vfoLMsTKquh8KS+dS230/regM96iPljKRs+rnUzZ6Nv7YOId4vh0RIrKurY8eOHbuVtbe3978mhBBCCCHE+8GyLOoPP5L6w49ker7MS6dZ8ej9LDk2ybItW1m7o5tUyt57BZk09tIl2EuXUD1sNImmEayoL2VF/mWtp4uKaC+NuqI6Vk11dAglcR1fBqxtUVLtG1iWfIvOmgxezVJKNr5Bw59/SkNCx4kEsUNHUnviWVQeexxmYeGHsUvEIeiQOCfxwgsvZN26dbz88sv9Zd///ve54447ZOIaIYQQBxXpSRTi4Od5HsteWMj9t9zEky+/zJrOTra273ndxn/+q5nSigCr7MN4xZnK5uwIVt33JO23/D43m+rocVijx1M4bCSNtTXU2elcr2M4QUHaJJBxKQn3ULljFQl9LT21Dma1TXmRR7OrUxkzSEdLUXWzaTrlTEqnTpXZVAe5wZJLDomexG9/+9vMmDGDH/3oR1x88cUsWrSIG264gWuvvXafAVEIIYQQQoj3k67rTJh5DBNmHsMV+bJkLMai+/7D3XffzqurVtGRTLCp4wi6nSgt5asYF3oNQnD1pnbaAa9jB5mOHWSefYI4sEPTMJqHYI0ejzV6HOUtQ2isLKc2VUNNtIbKyHQCWYtgIkt6czvtXUtZEdxOvC5MwL2d9U/fxpBHTUIxg2SiDmvUHFpOO4PQyJHy97LYwyHRkwjw4IMPcsUVV7By5Upqa2u57LLL+N73vjegdQdLYhdCCCGkJ1GIjw4vk6Hnled46cn76Il30VUK18ybT293cmAVWBbBMz5G8eU/xHBdKhIRqmJhqmJhqmNhKqIu/qxBKJmkvGs7gcgyuou6yNY6FJZnqfFDS8rEjFsks0MonHwujbNPJtDUJMHxABksueSQCYn/jcFyMIQQQggJiUJ8tG1csYJH//FXHntyAcvb2tiwo4dMxnnH5SddcDTln/oanaqecKiIrM/ASyZI3PxH/MNHUdPYSENBiOpEhOpohPKYhy+rU5BIUtGxFV/8LbpLI9g1NoUVNrWWRkvSQIv6SLujKJ52NvWzZxOor/8Q98JH12DJJRISGTwHQwghhJCQKITYled5LH/pRR649S889dKLrO7oYEt7GNf1APjJ1TUcfXQBAK7SWeuMZ/7r5Tz6o7/216EVFGKOGIM1cgyB4aOobajPBcd4hKpIhPI4+LIahfE4lR2b0RJv0VMRx6lxKCpzqDU1mpMGKuInw3jKZ5xF7Qkn4H/bJejEf2+w5BIJiQyegyGEEEJISBRC7Es6nea1BY8w//ZbaC3T8WoNkqUewbI4teUdPP7ARv7wh553rUMrLMIaMQZz1FiCQ0fQPGkyNclobrhqJEZpHPxZKIrFKO/aBKmldJcnUbU2haUu9YZOU9zAiwbIGBOomHkWtcefgK+q6kPaC4emwZJLDomJa4QQQgghhPioCAQCzJhzDjPmnNNfplyX9KplLF5wL/HVD1BZnqWrJ/6Odah4jOzri8i+voh0QSG+e55me+XOS8cFshnKtm+mDofqeB1VkXGUJnSsrELfEqW3cyOd6eX0ViZRNS9TuvIF6lfrNCZ1nGiQtDmRqmPOofb447HKyz/I3SE+ANKTyOBJ7EIIIYT0JAoh3i+9XV0s/M9dzJ9/F2+sXsP6rp69BsfJU4L87y+bedM5nCX2JLbaQ+k2aljzzYtxe7r6exwLhwyjvq6ehoCPqkSE6kiC4qSOlfUojkYo696InVlOuDKFVmNTWuLQqBnUxXWy8QAZ63CqjzmL2hOOxyop/dD3x8FgsOQSCYkMnoMhhBBCSEgUQnyQejo7ef7uO5l//128tXY96zp7mDC1gfMvaaG+rIOiQBSAVMrj7LM24nl71qEVl2KNHIM5cgyFrUOpraujIRSkKhGhKhqnJK7hzyqK4lFKujajMssJl8eh2qak1KVe12hIGrjRABnzMMqPnEPdiSfgq6r+kPfG4DNYcomERAbPwRBCCCEkJAohPmxuNkt8+Zu88vg9tLVvprvUY033dn5//TMDrkMrKKTkJ9fhn3IkgWyGqniYyniYyliEqmiMsjj4slAYj1HWtRUtuZRwWQyvxqao1KHG0mhO6BDzk1ZjKDn8TOpOPJFAQ+NH6nIcgyWXyDmJQgghhBBCfIQZPh8lkw5n1qTD+8uivb3MGnE7D8z/N2+sW8/6jm56I+98/UaViHN00Xqi0Sq6tWq2l1SxpbyG9LOPk339FQJDhlPT0EBjWR3V6QaqomOpiHn4shqqLYHRvZ1wbBm9JT24NW8QWv8aldt+SmtSx4r5SNtDKJyYC46hYcM/UsHxQJCeRAZPYhdCCCGkJ1EIMVh1tLXx/L1388C9/+bN9evZ0NVDTzgXHANBnfnzW9D1XHizlckKdyK/u2496xa8urMS08RsHYY5YjSBoSOpamigsaqSGjtNVSxKRcTFn9UJJZOUd+/Aii0nEmonW+MQKrepDGm0pHQCMYtUqh7/6NNpOHE2RePGoen6gdgt76vBkkskJDJ4DoYQQgghIVEIcTDpam/npfn38tKzjzGipoBwsUO6xM1djqOskyvmvsG6tdl3r0TTMJpasUaMJnjUsTQdfgSVsTCV8QhVsTCVEQd/1iSYSlPW00EwspKIfwvpGptQuU1FATRldIoSJslEFUbTbBpnnUbppElolvXh7Ij3yWDJJTLcVAghhBBCCPGeVNbUcOYXv8yZX/xyf5nyPDLrV/Pa4/fRHAyTrumkrTOC571D35RSuJs34G7eQGEwQHjmLNqLd142Q0un8L/0DPUttTSYdVTGWqmM2ISyBnYsi7ali3R4DQljI8mqLgLxmyl7/O+0PKJTGjdIJUpxK4+j8cQzKD/iSIxA4IPeLQc96Ulk8CR2IYQQQnoShRCHolQqxZKnnuDhe/7JC4sXs66zi60dYRzH3W2573+/itmnFLHda2axM4219mjWr06y6rvfAECvqMQcMQZr+ChKm1qora2hIRigMhmlKpyiKKXjy7gUR8MUhzeSsVeSqEhhVmcpKfKo13Rq4xp2soCMfypVR51BzbHH4auoOBC7ZQ+DJZdISGTwHAwhhBBCQqIQ4qPCcRyWvvwyT9x1G08+/zxr2ts56/wp1I0MUFbRS11JOz4jy4MPRrl+Xtc71qMVFGIOG4k5fBSFLUOorqujqaKCqmSUymiCspjCb0NBPEZJzza05AoiJb1QbVNY6lDl02hO6RgxH2l3OAUTzqDuhBMpGDbsQ58gZ7DkEgmJDJ6DIYQQQkhIFEJ81HmJBJ2LFvLSMw/Qk+7m788v5tmFqwdegd9P9QML0QwDAJ9jUxGPUNrTSU02RWU8SmXYIWDrBBMpSsPtBCKriAS2ka2xCZbZlBdqNKU1iuIWqWQ1Vssp1B5/ImWTJqP7/R/Qlg+eXCIhkcFzMIQQQggJiUIIsTulFFs3bOC5/9zFww/PZ9mmjWzo6CEcTe11+dFjAnzl16fxpjuJTfZwOqmlJ1DCjssvwuvtwRw2Et+wkZQ2NlFXU0Nj0E9VIkZVJENBWseftimJ9FAQXk+KNSQqM/gqbYqLFI2eRnXcIBsvxi4+gpqZp1F19Eys0tL3ZVsHSy6RkMjgORhCCCGEhEQhhBiYzvZ2Xnn4QR6+/25eW7mKjV3d7OiKcOSxzXztm41UF7Zj6B4Ajqs484yNOPae0UcrKMQcOgJz2EiKmlupqaujsayEmkyKykiSkqSGP+NRGI9R0rMFN7OcSFkMoypLYalLjaHTlNRRcT9pbxQlk0+n7oQTCLa07vdw1cGSSyQkMngOhhBCCCEhUQgh3rtUKkVkw3rCyxbz6uKFJIw4kRKP9mQXv7r6iYFXpBuU/L+fEzhuNoFshsp4hMp4mIp4hMpYnPKoR8DWCCZTlPbuwIqtJBLagVOdpaDcpiKQuyxHQcIikarBajmFhhNnUzpxEpr5zheYGCy5RC6BIYQQQgghhDgkBINBgmPHUTt2HKPPv7i/vHf7dkabN7LgiQWs3rqVjV29hCPJd67Ic5lZsZFEfBVdWg1tJZVsLa8m/cwCUo8+gH/YcMoamqgbVkNDqJ7KxEiqIlkKMjqZcBY32kM0soG0to5URTuBnr9Q+shN1D+sU53QSScKsQuPovaYM6maMQOrpORD2DsDJz2JDJ7ELoQQQkhPohBCfDi6OjpY9PD9PDT/HpasWMmG7h52dOWu52gYGvfNH0LfJRU9dDZ6Q/ntb7t5875Xd6tHCxXkZlfND1etrqmlsaKM6myaimiSsrjCn1UUJOIUhbehJVcRK+qG6ixFpQ4Vfo2mJFhxP7PujA6KXCI9iUIIIYQQQoiPnMrqak6/+POcfvHn+8vS6TRvvrCQRY/MJ/5cim16kkiJg16SobQsgrd+xR71qGQC+63Xsd96nRTQASzVdYzGFgLHnkT5RV+iPBGhIh6lIt5MRXw8lRE71+vYncEOdxGOrSOpr//wNn4fJCQKIYQQQgghBBAIBDjixJM44sSTdiv3Mhl6X1/Ewvr/H2ZqHZu6eukNv9twVQ938wZKO9dSno7QEyplR0klAMp1iN84j6LGJqpra2gcM44aexgV0TS8/KsPcvMGTEKiEEIIIYQQQrwL3e+nYvox/PWRZ/rLuru6WPzYIzx23128vHQpG7u7aevMDVftc+7Itzin4MsooMOr5i13Kq+sL+OJe+4gCbQDb+k6RkMz5tARVH7oW7Z3EhKFEEIIIYQQYj9VVFZyyoWf4ZQLP9NflslkWLZoEc/efzdPPvcsFfF6Xn+5BK84S1FZjGklL2Js28pu86x6Hu6WjbhbNkJDw4e9GXs1qEPismXLuOqqq1iyZAnr1q3joosu4u9///sey7W1tfHNb36TRx55BIBTTz2VG264gZqamg+5xUIIIYQQQoiPKr/fz5RjjmHKMcfwrV3KleOQXLOcV59+iN4lj9BQk2VHVxTX9Q5UU9/VoA6JyWSS5uZmzjrrLObNm7fXZTzP48wzzwTgscceA+BrX/sac+bM4aWXXkLX9Q+tvUIIIYQQQgjxdpppUjDmMI4dcxjHfvWH/A7IZrMsW7SIp/7zL55Z+Dxr23cQsQ90S3MGdUicNm0a06ZNA+Avf/nLXpd5/PHHee2111i6dCnjxo0D4NZbb2X8+PE8+eSTzJo160NrrxBCCCGEEEIMhM/nY/LMmUyeOZO5+bLGxsYD2qY+B30328KFC2lqauoPiADjxo2jsbGR559//gC2TAghhBBCCCEOPoO6J3Eg2traqK2t3aO8traWtra2va4zb9683YavtrW1DZrULnJSqRTBYPBAN+Og81Heb4fqth/M23UwtH0wtjEej1NYWHigmyGE+C8Nxn9fxODX3t5+oJsAHICQeN111/G9733vXZe5+OKL9zpBzftl7ty5zJ07t//5BRdcwJ133vmBvZ/Yf3JM3puP8n47VLf9YN6ug6Htg7GNjY2NbN269UA3QwjxXxqM/76Iwe+CCy440E0ADkBI/NznPtc/0cw7KSkpGXB9dXV1/bOa7qq9vZ26uroB1XH++ecP+P3Eh0OOyXvzUd5vh+q2H8zbdTC0/WBooxDi4CT/voj3YrB8bjSllNr3Ygfe8ccfT2tr6x49jI899hinnHIKy5cvZ8yYMQAsX76ccePGsWDBApm4RgghxEFFehKFEEIcaIP6nMRsNsvy5cuB3DkaPT09LFmyBIBJkyYBMGvWLKZMmcJFF13Eb3/7WwC+/vWvM23aNE488cQD0WwhhBDiPdv1dAghhBDiQBjUPYkbN25kyJAhe31t12a3tbVx+eWX9w87PfXUU7nhhhv2OqGNEEIIIYQQQoh3NqhDohBCCCGEEEKID9dBf51EIYQQQgghhBDvHwmJQgghhBBCCCH6SUgUQgghhBBCCNFPQqIQQgghhBBCiH4SEoUQQgghhBBC9JOQKIQQQgghhBCin4REIYQQQgghhBD9JCQKIYQQQgghhOgnIVEIIYQQQgghRD8JiUIIIYQQQggh+klIFEIIIYQQQgjRT0KiEEIIIYQQQoh+EhKFEEKIQeySSy6htbX1QDdDCCHER4iERCGEEDz99NNomtZ/03WdsrIyZs+ezYIFCw508waNefPm8fe///1AN+O/9ve//323473rbfTo0Qe6eUIIIQ4w80A3QAghxODx+c9/nuOPPx7XddmwYQN//OMfOfXUU3nsscc46aSTDnTzDrh58+YxfPhwLrnkkgPdlPfFD3/4Q8aNG7dbWUlJyQFqjRBCiMFCQqIQQoh+06dP5zOf+Uz/8/PPP5/x48czb948CYmHoJNOOolZs2Yd6GYIIYQYZGS4qRBCiHc0btw4KisrWbt27R6vPfDAAxx33HEUFRURCoWYMWMGDz744B7L/fnPf2by5MkUFRVRWFjIiBEj+PKXv7zbMpqm8ZnPfIaHH36YKVOmEAgEGDJkCPPmzdujPk3TuOqqq/Yof6dz91544QVOP/10ysrKCAQCTJo0iZtvvnmP5e69915mzJhBWVkZoVCI1tZWPvOZzxCLxfrfd9u2bTzzzDP9QzP73s/zPK655hpmzJhBZWUlgUCAMWPGMG/ePJRSe9u1e3Bdl6uvvprm5maCwSCHH344jzzyyDsuP9Dtei/2Z3u6urr4+te/TmtrK36/n4qKCo466ijuvPPO3ZZLJpNceeWVjBo1Cr/fT3V1NRdddBFbt259X9oshBDi/SM9iUIIId5ROBymt7eXESNG7FZ+ww03cPnllzN79mx++tOfomkad9xxB3PmzOH222/nk5/8JJA79+2LX/wiZ599Nl/60pfQNI3169dz//337/FeS5YsYf78+XzlK1/h0ksv5a677uI73/kOqVSK//mf/3lP7b/33ns5//zzmTJlCldccQWhUIj58+dzySWX0N7ezve//30AnnzyST72sY9x7LHHcvXVVxMIBNi0aRMPPPAA0WiUoqIibr31Vi6//HJqamr621NYWAhANpvl+uuv5xOf+ASf/OQn0XWdxx57jO985zuEw2Guvvrqfbb1m9/8Jr/73e845ZRTOPPMM9m4cSPnn3/+XoPvQLdrX6LRKF1dXbuVlZSU4LrugLfn4x//OK+99hpf+9rXGDFiBOFwmCVLlvDiiy9ywQUX9O+f2bNns2TJEj7/+c8zfvx4Nm/ezO9+9zuefvppXnvtNSorKwfUZiGEEB8CJYQQ4iPvqaeeUoC6/vrrVWdnp2pvb1cvvfSSmj17tgLUb37zm/5lt27dqnw+n/r617++Wx2O46gjjjhCNTQ0KNd1lVJKnXPOOWrs2LH7fH9AAWr+/Pn9ZbZtq6OPPlr5/X7V2dm527JXXnnlHnVcfPHFqqWlpf95MplUlZWV6swzz1Se5+227HnnnaeCwaDq7e1VSin1rW99SxUXFyvbtt+1nQ0NDeq4447bo9zzPJVMJvcov+SSS1RhYaHKZDLvWu/y5cuVpmnqnHPO2a2t8+fPV8B73q538re//a1/n7/9dv/99w94e8LhsALUL3/5y3d9v1/+8pfKNE31wgsv7Fb++uuvK8Mw1A9/+MN3XV8IIcSHS4abCiGE6Pftb3+bqqoqampqmD59OgsXLuSqq67isssu61/m7rvvJpvNcskll9DV1dV/6+3t5YwzzmDbtm2sWLECgNLSUrZu3crChQv3+d4jR45kzpw5/c9N0+Tyyy8nk8nw2GOP7fe2LFiwgK6uLi699FK6u7t3a+sZZ5xBKpXqb1dpaSmJRIIHH3xwwMNDd6VpGsFgEADHcejt7aWrq4uTTjqJeDzOypUr33X9+fPno5Tiu9/9Lpqm9ZfPmTNnj9lG92e79uWXv/wlCxYs2O02ffr0AW9PMBjE7/fz5JNP0tHR8Y7vc8cddzB16lRGjBixW3sbGxsZMWIEjz/++IDaK4QQ4sMhw02FEEL0mzt3LqeddhrpdJrnnnuOefPm4brubsGlLyBMmzbtHevp6Ohg3LhxXHHFFTzzzDPMnDmTxsZGjj/+eObMmcPHPvYxTHP3X0GjRo3ao56+sg0bNuz3tvS187zzznvXdgJcdtll3HvvvZxzzjlUVlZy/PHHc8YZZ3DBBRcQCoUG9H7/+Mc/+NWvfsVbb72F67q7vRYOh9913b7t29vlJ0aPHs3rr7/+nrZrXyZPnvyOE9cMZHt8Ph/XX3893/zmN6mrq2PKlCnMmjWLCy64gMmTJ+/W5lQqRVVV1V7fq7m5eUDtFUII8eGQkCiEEKLfmDFj+kPDmWeeSWlpKVdccQXTp0/n9NNPB+jvabvnnnsoKiraaz0TJ04EYMSIESxfvpwFCxbw+OOP88QTT/CPf/yDKVOm8Oyzz1JQUPC+tf3tQaavnTfeeCPDhw/f6zpjx44FoKKigldeeYWnn36axx57jKeeeopLL72Un/zkJ7zwwgvU1dW963vfeeedfPazn+Xoo4/mxhtvpKGhAZ/Px2uvvcYPfvADPM97H7Zw/7frvdqf7fnqV7/KnDlzuP/++3nmmWf44x//yC9+8QuuueYarrjiiv42H3XUUe94bmYgEPiv2iuEEOL9JSFRCCHEO5o7dy5/+MMf+N73vsepp56Kruv9waSuro7p06fvs45AIMCcOXP6h5L2TXrzr3/9i8997nP9y61atWqPdfvKhgwZ0l9WVlZGb2/vHsuuX79+t+d97SwtLR3QZR5M02TWrFn9y95///2cddZZ3Hjjjf3hZtce1V3dcccdBAIBHn/88d0Cz9vb9E76tm/lypUcffTRu7329qGq+7td78X+bk9jYyNf/epX+epXv0oqlWL27NlceeWVfOc738Hv9zN8+HB6enrkchtCCHGQkHMShRBCvCO/38/cuXNZvnw5d911F5CbzdKyLH784x9j2/Ye6+w61PHtM2cC/cMQ3x70Vq9evdusp47j8Jvf/Aafz8fs2bP7y0eMGMFTTz2127ovv/wyL7744m5lp5xyChUVFVxzzTXE4/E92tHZ2dnfKzfQdhYUFOw1oBqGgaZpu/VmptNpbrjhhj2W3ZuzzjoLgOuuu263cyLvv//+PULi/mzXezXQ7UkmkySTyd3KgsEgo0ePxnGc/vZdeOGFrFq1ir///e97vJdSis7Ozv+qvUIIId5f0pMohBDiXX3xi1/kpz/9Kddccw3nn38+LS0t/PrXv+ayyy5j4sSJfPKTn6S+vp7t27fz0ksvsWzZMjZt2gTAySefTEVFRf85ie3t7fzxj3+koKCAc889d7f3GTduHJ/+9Kf5yle+QlNTE3fddRcLFy7kJz/5yW7nsn31q1/l0ksv5cwzz+y/VMRNN93EhAkTiEQi/csVFhbyl7/8hfPPP58xY8ZwySWX0NLSQkdHB6+//jrz588nkUhgmiZf/OIX2bFjB7NmzaK5uZloNMpf//pXDMPov5wHwOGHH87tt9/OT37yE0aOHElhYSFz5szhnHPO4e6772bWrFl89rOfJRaLcfPNN/dP/rIvY8aM4Stf+Qp/+MMfOO2005gzZw4bN27kD3/4A+PHj++/VuP+btd7NdDtWb16NccffzznnXce48aNo7i4mFdffZW//e1vnHrqqVRUVAC5HumHH36YSy+9lAcffJCZM2diWRYbNmzgvvvu44ILLuCaa655z+0VQgjxPjtQ06oKIYQYPPougXHTTTft9fUrr7xSAeqee+7pL3v88cfVqaeeqsrKypTP51NNTU1qzpw56vbbb+9f5k9/+pM68cQTVXV1tfL5fKqhoUFdcMEF6s0339ytfkB9+tOfVg899JCaPHmy8vv9qqWlRV177bV7tMXzPHXVVVepuro65ff71bRp09STTz65xyUw+ixevFidd955qrq6WlmWperr69WsWbPUb3/72/5LSNx9993qjDPOUPX19crn86mamhp1+umnq+eee263ujZv3qxOO+00VVRUtMelKX7zm9+oESNGKL/fr1pbW9VPfvITtWDBAgWop556al+HQDmOo6688krV0NCg/H6/mjp1qnr44Yf/q+16J32XwFiwYME7LjOQ7enq6lKXX365Gj9+vCouLlahUEiNHj1aXXXVVSoej+9WXzqdVv/3f/+nDjvsMBUIBFRRUZEaM2aM+vrXv67eeuutfe4fIYQQHx5Nqf9yTIoQQgjxX9I0jU9/+tP84x//ONBNEUIIIT7y5JxEIYQQQgghhBD9JCQKIYQQQgghhOgnIVEIIYQQQgghRD+Z3VQIIcQBJ6fHCyGEEIOH9CQKIYQQQgghhOgnPYnkLha96zW4hBBCiAMlk8ng9/sPdDOEEEIcAJ2dnWQymQPdDAmJAFVVVWzduvVAN0MIIYTg0Ucf5ZRTTjnQzRBCCHEANDY2HugmADLcVAghhBBCCCHELiQkCiGEEEIIIYToJyFRCCGEEEIIIUS/QyYkPvTQQ0yaNAm/309rayvXXXfdgW6SEEIIIYQQQhx0DomJaxYvXszZZ5/N3LlzueOOO1i0aBFf/vKXCQQCXHbZZQe6eUIIIYQQQhyUPM+Ta9m+DzRNQ9cPnv65QyIkzps3j8mTJ/OLX/wCgDFjxrB06VJ+8Ytf8PWvfx1N0w5wC4UQQgghhDh49Pb20tnZieu6B7ophwzDMKiqqqKsrOxAN2WfDomQuHDhQi6++OLdyk499VSuu+46Nm3aRGtr67uuH42FufKnX0YD0DR0LZfyNTTQcmWa0kGjP3BqmoamtJ1lWn55FBp6bp18ff3Lo+WeaqCh518nv7zKl+XfU++rT+sv0zQNXQM0PVeugdbXVk3vb4Om59bs+7ZCQ+9vA5pCx+hvh46BQqEZet87odDy76WjUOianm9tvj1a33b0Pdby2wCarqGjg5ZfQ9PQtdwGaXpf+4x8+wAMdDR0w0DTNQzdwtANdD3XHh09tz1a337Ibzfazm3v34+51/v3A9ou5bytDm3nlwe7rKvtUv/OY6vtcvx2qV/P7w991/2i9b93rpz88djldW3Xcn2313dtp5bfB331vG1j2fnx2vn87a/v+pkVQgghhBiI3t5eOjo6aGhoIBAIyN8S7wOlFOl0mm3btgEM+qB4SITEtrY2amtrdyvre97W1rZHSJw3bx7z5s3rf264Gk2bi/tD2YptG7n31efzIWdn2On/Qx/ywYdciNHguyeej64MwENTgIKHVr7Cmq5t/eFL32V9TctFt9x9bomhZTWcPGQyKK8/rikF/1j6dP8PZ986O+vJtWVnOUyuaaa+oIT+hiiNjmSU5fm2GH3ZV8+vlw+cen9AyT0+rLoWTQMXhSK3Tls8SszO5NbTd61H9W8XOrnnOvhNk+JAIF8Duf9rEMtm8yE3vy25huS2W98ZvBUeSiNfu8r9p4HSFErL1elpgPJQupZ/va985/IAnq5QSuXXBaV5KE3rf+71raMplA6epqGUl3+88zUvn5ddPNAUbt9zXeHq4OkargaOofA0hWOAp4OjgWsoXE3h6eDqGm7uwOFpuX3Tt/9RfTlP63+c36zc8ep7XeU/B0p722s7l9GU3r++ho6ucq/p6Ll7lfsSQFN67riqvoDeV6b1l2loufXZ+bnVVN/jXYJ9X/27/KcrfbfPe996urZL3bvWvMvPnK52+aKgr419Ab5vW/P16FruS5pc3QaaZuS+itB0DN3ItVAzcl9+9N1j9P9M9wVtlat0lzJQffl7ly8L+o6byn9HobSdz/u+YGCXx32v7/ZlRe4HKf+z0LfduR8wXTfy76Xnf0jzn+n8e/W1q6+s/wsCcdBKp9M8+uijB7oZQoiPsIqKChobG3Fdl0QicaCbc0gpKSlh3bp1dHd3H+imvKtDIiTur7lz5zJ37tz+57VFVRxfNhvIxZB4+zOs67hzv+qcUDstn8dUfty24i+vPMnLm1cOuI7jrKlcOGR4Pgp5uf8rj3v/88p+taWitYWGkS2gKTylUJrH2lVL+cOS5/arnt9/7zt9GSXfJrj1vldZsmrNgOs4bNRwPnvOaQB4qP4A871f3rDPdXVdR8//EX3B6bMZN3J4vi25Olav38T8J57K/7GvoesGuq7tfK7p+Tp23i4757OYmPlwlAsSTyx5kQ0dWzHQMDQdI7+sgYZB7nnusYahabQUV3N8w3g0V6F7Ck15aMrjvvWv4NcUugJDAwPQUJjkQnRfmQmYms7QghIqrAAaufVRHkknS5edxtTA0DRMLRfqDU1H18m3Jd8Lq7x8SOhLjwqlvNzDXOTNHTWlAA9Ny32u+vag0rzc/tyZPvtq6qd2CRyKncv1hdqdgTtftmuY1nOPvb5QbICrKRxDw9UVjg6eoXB0ha3nQrWjKxwzf2/klrUNRdZQOCbYhsI2FVljl8emImN6ODo4JriGhtLfW1LSvL5gnQ+eSkPvv9d2fm6U9ray3QP3ztf1/s+ZoXR0pec+U8rEVDqmMjCViaUMTMzcYwwsZWJiYnlmvtzAUhYmuXsLE1NZ+DCxsLCUL7c8FpayMDQDAwNDNzA0ExMdTwelq9yXJbrKPwdl5L4UQQcMDWWAZmhg6GCAbhjopo5hmOimgWma6JaJ6bcwfRaWz4fp9+Hz+fH5/Vh+C80ycjdTyz/Wd+8RF+/q0Ucf5ZRTTjnQzRBCfER5nseqVauorKzEND+SUeEDVVBQQG9vL1OmTBnU5ygeEke+rq6OHTt27FbW3t7e/9q+KOUSSe/I99ZppOzYfr2/hobp6Dt7OfL15HoWB65QK6RFG7pLj8l7+6NqjDmGGcbM3cp62b/x5Boac/Sz9yh/WL3KEgYeEpu1Jj7J+btWjFKK77HvkOh5Hl7+8UxtOmcaJ/S/ppTHfOdJbo7cN+C2AEz3j0FpHh6Kvv/f2rGDl9YsGXAdR408jOkzD8/VoCk8XDzl8fsHHtuvtnx2zslMHFOFp9xcqFKKt1at4/b5T+5z3dzJz7kToL/7nU+j6Ubu86YZ6Og8uuAF1m/YnAsJRm74bm4Ybz446AaGbqLrOqZu0lrfyglTj0f38mHGywWd+QsfwNQMLN3E0ozcY83EpxlYGPg0Ex8GPk3H1DSaiqoosQLgOSjPBc/GtrNknDQBTcOngaXAQKEpB91z0ZSLrlw0z3nbvYuunPy9u8vyu6znOfl7le/Ty1GAq+t4uo6n6ShNyz/W8r3Hu97I3ZO79/qe67l6PF31B2LXyPX+OoaGa4Ct53qH+0JsxvTI+CBtKVIWpPyKhE+R9CsSAY+IXxEPuiR9Hq6p7eyp/BAYrobhaRiejuFpmJ6B6emYnoHlGViuic828alcOPUpK3fzLPzKR0D5CXoBQipISIUoUEEKVEH+FiKoAgQ9Pz5l5Yacv42juTi6i2t4uKaHaypcS6F8GsoHmt/A8JsYAQszYGEFAwQLCigoKMQXDKCHLIyQiR6y0KzB+0v1v+WlUiDn/wghDqC+SWpkiOkHo2+/DvbJgA6JkHj00Ufz6KOPcvXVV/eXPfLIIzQ2NtLS0rLvCuwY/iU/6+syY1gszWdry3K9cOQ6YpQi91zRX+apna+5j16xc7xXvpdlfLQLVVyc+0Ozry7o78tx82Ve/tYQWcmOBd9GofX34DjAyGBwt+V2W1f11an6H/esvYe1vc/ne1Ny5we2d7QR0I3c0FGV709Sao+eoz6aBqtW/g3I98rkxqISSWwa+IEB4pkdrOh4OD82Nbd/vPfwM9GV3szG+Fv5oYi5oYNdqbb9qkPXdAq8YP+QxL4hfT7l2696qowqjjSO2q3M8Zz9qgPgGOMYTtOO2+38wfvUE9zOvkOiUgrXVSgPvuJdiue5eLm4iofLi5EldHb1DLgtRZ7O8CkGrubiallczcEmy/2vPLxf23T+mccwZlgTSnl4moereaxYvYX77nt5t+V0XUM3dAxDxzCMXe5zty98+RMYmoWmmRjk7hcveoMdO9rxmT4sI4DP9GMZfnyGD58RwKf78Rl+/HoAvx6gPFhBU0kLmqeB8vBcF+XZpDMJLBQmLrprY3g2ppvFcLOYThbDy6J7DoZro3t9NwfdtdHt/ONdy71cHbuWa+/4k7XLMSQfZDU9N1S6L7QCaDtrULqGq+s4ho5j6tj5W8bSyFgaaZ9Gyq+RCEAioBELKOIhRTjoEQ4qEkEHR/fyQ7dzYwJcXWEbdv/w7L4eYC8/7Pu/ZToalmdgujqWZxBwLQKuj6DrJ+QFKHRDFHuFlGSKKU+VUuVVUuIVU+iFCLmFBJQCsrhEie6lfttwyPgcbL+LF9DQQya+ggCB4gIKyksIlRVjlvgxin3oBdZB1YvZe9ttNPzhj7S/tZSyT34C30B+hwkhhBDvs0MiJH77299mxowZ/OhHP+Liiy9m0aJF3HDDDVx77bUD+hbE9vl448hp+XzngYLT+vJe38l49N3nz0LStPx5ch66prFS8/qHDeqeh47iOKU4XuWHAGq5oYeqr8cjPyRVU7nz7jSl0JSiS/X1iHjoHoDH/zdjWn7Z3A0UusoPc4RcUsyX9SVYje7+55pSzG5UzJ48IX+aouq/x1N4ucSI5+XP2fPAcxWF2iu593NBcxSaBz8Z7SM9bDgohevklsXLr+MplNP3GJQHxVqahsX3gAu5U/g0PKX4c2NTf7DtGxjZF3Ld/ONd749c8zQVGxfudtwmZzJ8q7IKF4ULO29q53MnH4j7Ilz0if/Xf45j3/2Q5A5mlBbj5df18m3Y+f755udf82dXsWzlj1G6DpqB0nWyCkr8vtwyKr9e/+Pc7e02Jl/i1Z4u0A20fC/guvjyfX5ed2VoOtuTa9DyPYi58KuTsdP7VU+FUcEEfeJuZRknu191AJxsncqpxjG7lf3HfYz72D0kep7C81wc2wXs3V4zdIPZmanYOLg42MrGweGB1etZtGbZgNsyemQDZ86ZSlZ3cHSHrObg6h5/+9nTQO5cWtPUMazcEErLl7v5/D4snw+fz4fP8jNt+uE0N7Riuham58P0fMR6Emzbtp2gWUDAKCSoFxI0igjoRfj1EIYVQOkaWeXi5j6JoBw0ZaOpfMB0sxheBsPJYDoZLCeD6aSw7DQ+O4XfTuLLpghk4vjtFAWpNIabGVAAfTuP3XtNPcPANQwcw8AxTWzLJGuZpP0mKb9JPKgTD+mEC3S6ixTtJYpY0MEhi6vlfiKU2nnbec7sznOPlZ4bXmwbNkl/hoyVO793IAxXw+8Y+B2TgOsj5Pop9EIUe0WUU0alW0G9XUNjooaKaAkFno/cvyQx0sTY9dPvah6ZoINbqGGU+ymoLqWktgJfVQFmRa6HcjAxSkvxAgF6/vY3em65hdKPfYyqud/GHOQTHAghhDi0HBIhcdq0adx7771cccUVzJs3j9raWq6++uoBXyMxVFLGp6+99QNu5b4pT+X/eFYoN3+ff+46Hq7r4roetmvjOC6242A7Do7tYDs2mWw2f0uRSqbJpjNksjZ2xiabsclms7i2g5e1cZ0snpdFcx10L4uhHHRloysHQ9nompsrw0XXPHTloecnatH7JsTRVP/EKSo/EYfKh+e+W6cGnfk+VE1T6J6LoVyKlIvmefmhgvnz6JSTv/fyNwXKQ1eKjPLYgEL3vFzNShFUijPUzoDdd9PzXbwauRDcH5a9XHjDy9eTD7hfaClH88rRPIXmkrvl02Hu+c7y/rK1bbnHu5y092Lr0Hc+tn3BVSmc/H1g41p8m9bttly163J0S2t+OYWrckHXyYdWW8tNjmPj5SbJMaDw9evwzNyEP8rI/XF+anGEUcMrcfBwFPm6cu9tq1y9tlI4CrJKUVuwlGXt38LVDVzdh6dbpFyN5vIiHE9hux6Op3BcD8fzcFwPey9D4rZmlvJGxEFXBrpmYGCyJb52v34OfLpJs9uEoZsYmtn/JU3I+fd+1TPeN55vmt/d5SBAPJ3gbzyde+op7KyLnXWBdw7EMxuGU+UvwVY2WbJkyPLW2jXc8/BL7/r+uqlh+U2sgIkVsDj9OydhGX78yo/P8+N3A2x8cwvJWJoCq5ACq4jCYBGFVhUho4Qio4xCowIfIVx07Pwxsw1wdK//pjQHhQPKRlNZdDcXJk0njekk8dkJ/JkYgUyUgmSYwmSEolSSgnSKkOe96zbsqn84rq7jGga2aWL7LLJWiFTARzLkIxby0VvsY0eZRVu5RtKXJkUKz3Vx01lwbTTPwXI0LBtMBwwvd54n5IbuZiyPlN8l7fNI+2wiwXSuh/MdWLaGzzEIeD4KtBAlRinVVFGTraA6VUZJpoAKp5Sq7jKKOizUyh7C7Oxlz/odnCqdUGMZZS01+OoLMatCB6z3sfTjH+flwkJmlpbSecNvCf/738QXPk/j9dcTnDhx3xUIIYQQ74NDIiQCnHHGGZxxxhkHuhn/ldwlIDT270zGg4vnKTzXw3PVLrfcc9fxyNo2WcfuD8B21sZxHDJZm0w2TTqdJpVOk06lSSdTZFI2dtYmm3FwMjaem0HzMuDZmGQwVAaDLCY2hnIw8NB1Jxco85O5QN+pYWrnuWnoKE3HI38eG/nH9E1U6aLhYnoOhudgeDaG56C7Dhoepueh4+TCtXIx+sKs56LnHyfyvcG6UrnA7OVCbq2r0Lz8pDj5x33BVXPUzuDqgObken21dP65nSs7xy1hvz5IUeDF5B7Fj1Q19D9WhsrPupO7eYbCM8HWFLbmYWuKEncRgW2voBugGx6GrigpzjL5xGpswFYeWSCLIuvlH3uQVZD2IOPm9n175nLSyiCLQVb58ZSfstIdjG+oIOsosq6H7SiyrovtetiOS9Z1dguuCaeXFeGXMTUzHzgtkpn97x2dGTiWY61pu5WZ3v3cw7uHRM9RZBybTMLGZzmctGMMtpcl62XIqjQZFea+519izYZ3HzZt+HXMAhOrwKJuYg0TTx9H0AsQcAOE3BB+O8jmt7ZR7CulxCqnxFdJob+KoL8B0w7heSbZAgiTu7kaZHyQsSBtQdbwyBounu6gVO7nx3ATmE4cXyZKMB2mKBmjJJ67lcajlMRjFCaTsOdHpl9fqHR1HduyyPp8ZAIBEgUlRIoK6CgPsbkmQLjIJWbFSFgJMloaDwfDVpSmdAq7oCgGoYyJ5Wp4miLt90gGXBIBl0TAIRF0SARcukNJurUk0AWsBT9oBeR6JDUfRYES6orqaaaRslgB9DoUxv20ZOpo3VYPWyP0vhQBwDFdnEaLilENFAyvwKovzE3m8yHYvPQN2hY9j/rGXFr+cSuRu+9mx0+vYdNFF9N0058oOOKID6UdQgjxUZdMJhk1ahR33HEHM2fO3OfyX/jCFyguLt7tCgoHM00N9rMmPwSNjY1s3br1QDdDDCK5Ybe5IbWu4+E6+SBrezh2rhc3m7XJ2FmyWZts1iGTyZBOZ0lnkiSTCVKJFMlEhkw6g511cLIOnp1FUxl0LYOh0phkMFUWU7NzoVJz+8Or1n/5Di0/oUourLroeJqBi46Lkb/l/oDVcTBw8Ks0fjeNz8vgc7KY+Z5iIx9SDS+/trdziLSmPHQ3F1j1vnDqqNxjN//YyQ091m3QbJULpdlcONWz//0f0Z4FygLlA0wNZdEfSjUTNFNh9AVQ08M0PEzTwzQdLMNFM108wyOruRgmFPp1XE/DUQa20olndZ7emCVpayRtjYQNSRtitkbMhqStkXI0UrZG2lakHcWF04+itaweAx+G8mPh44Flr/CnFx8a8HZVF1bw6tf/s0f5x267jFe2vjngeqZPGMPZs44kRZKYipHQk6SyWW797Yt7LKv7NIwiE6vYIljkp6AoRGFhISWhIsaOG01dYQNBuxB/tgi/XYiVLcLKFqEcPxm1+0y3tqFI+xVJv0YsoBEN6sQCOlnTwVMpdC+GPxOhOBGlLBqhMhKmMtxDVW83lZFeihPxXI/+XijAMU2yPh+pYJBYUSG9paVsqy5me3WAqC9OxB8h6ouSMBIYDhSn/ZRGTcp6HcpjPooSuR7nRMAlHnSJFrhECzJECmyiBTaxkLPbUFfdg6DyU1dUz9jaCQwtHkqqM0p8SzeBLp3RySGMSrVi5b9HtX0e/jGllE1sJDCi7AOdOOfmX/6SrlefxbB8TDltDked9ymc1avZfOnn0CyLIffdi1VT84G9vxBCuK7L6tWrGTlyJIZxcHZdPPPMMxx//PHvusycOXOYP3/+O75+1VVXsWjRIh56aGC/77du3cqoUaN44403GD58+Dsut6/9O1hyiYREBs/BEOLtPNfDsb18ON1579gu6Uwmf8uSTmfJpLMkUynSqRSpRIJUIk0ymcXO2LhZB8+10UmjkcbQUpikMVUaQ88FVE1T+ctk5Gf3zPekuloujObip5m/GbiagYGLT6UJkiDgpQi4KQJuBktlMF0b08vNRKorF8PLBWDdzZ+36+bDqKPQHYVmK/S+W1ahZ/IBNJO/qf0PoZ4P3ICG59Nys2haueCpWaAbCsNSuaBpufgtl4CZxedzMC0XzeehmTp9pyXreLnJbvDIegZx26A3rRPO6ETyt1gGolmNWEYjkoVwVidqW6AHOW3iZHQVxPQC+PBjaX6uefSfrOrcRiKbwh3A0M+vT/80Pzzuy/3PXVxWRtdz6o2f36/9MvOSMRSMK+jvjbMtRfTVKF0Pd+Ev81FUWkhpcTEVxeXUFFZRG6qnxtdIyCnHypRipUuwXYtdW6xQ2JZHyq+IBhWRkE5vgcmOEj9dJRZKxSmLhakM91Ld001ddweN7W00dO6gpqebUGbP82gV4BgG6WCQWFER3RXlbK+tYVtVgGggTo+/h15/LxErQtDxURYvoKrborpLURbTdg5j1SBcBD3FaXqKkvSUZOkuzuKYqv+N/MpkSMlQzhg5h5FlI1nevoxtK9dTsF1nWmwcwzJNubosj9CUGkqPasKqLdiv/T4Q3/j1f1i6bhtHRV6hItNNcVU1p3/jexRv2cbWy75B0exZNN6w79mhhRDivToUQmI6nSYcDvc/v+CCC/D7/dx6687Ty0KhEMXFxe+4flNTE3/+8585++w9Z/t/J2eddRZDhw7l17/+9TsuIyHxIDJYDoYQB0rf+XlO1sPJn6fnZD2yGZtUOkMylSaVTJNKZUkl06RTKZKxBKlkhnQqi5NxcB0XVAZDT6JpKQwSmKRyvaaaS/7Cg/khvPngqeUCp41FFgul7eyhMZRNQKUoVDFCKkHATRJwU1iujaHs/DBfNxdCPS933UrXywfN/H0mf0uDngItBbo98LDpmeAGNdyAjhvQ8Xw6ytLQfQrT9PD5XAI+m6CZJeTPEPRl8Hw6tp7bLi9/KQgTFwuHADaG5qEUZD2DtGuSdk1Srkk0a9Cd0ulO6XRkTNozFj1pi2jaIJmGVNplYt0Qjmgai98IETBCBIwCNnZ38Kk7rtiv4/3Lr3+P4uoQMRUl5URJ2QmefWEJC597l+u6amCWmFjlFr5qHyW1RUw+ZTzldhlldhml6QqKMpX4khXoyQpcd/dZg3Mh0iEecAkXKHqKDHaU+thYHaK3wE8gk6G2p5O6rg6ad2xn+NaNDNu6mfqudvy2/ba6IOvzES0uprOyki1NTXRUFBAOROgIdtDt7yZiRSjNFFEVKaKh3U9th4Oxy3BkTwvQW5ylvTRMe3mG9vI0aX/f+dHQEKrnwvGf4YwhZ/BK+yu8sOxZjNVpTghPY0gmNwxbGxai8pQR+Jv3/kfGe3HdzT9g62KbBf4jGZbYwHHRRejK46TPfYXy+x4itmABrf+8g+CkSe/bewohxK4OhZD4dhUVFXz961/f7UoI7+bee+/lk5/8JNFoFJ9v999nf/jDH/j973/PunXrsCyLcePGcccdd9Dc3Mzf/vY3vv/979PR0fGOk2ceLCHxkDknUQjx3mm6hi9g4gu8f3UqlRuem0272BkHO+OSTbtkUjaJZIpEIkUymSaRTJNOpEnFUqTjuSG7dsbGczwUGVJmkoweRSeOaSbRLTvX46mR7+U0sTUzHzR9eNrbhwIqAm6KYhWhQMUo8OL43SR+N5Pv6XTyQ2y9/nvDVugZDyPtYaQ8zISHrzsXMt/eo5nFIEuQCMH+Mieo4YR03KCBF9DAp2H4FZbPocCXpTCQptCfojCQIWC5WErDFzIoKdVpRcPAxkeSkJahgDRG/txZpdaScp8i5fpIOhYJ12J4lZ//fHUaW1N+OpN+EimTdFojm3JJpW3i6SzRdJpoOkVvKk5PMsop+jGUJ0t3246t8Xks5F1CogIn7OCEHVLrU1DtkDw1ysai7bjazj7FHf/eAS6UVhbTUF7D0IpWmnzNlKVqCSWq8cerKI8ZDO2/tG0ST4+S9KeJFHh0FVeyflgdjx91HD2FhSg0yqIRWnZsY8SWjUxYu5LRm9bnhrN2dVHV1cXYlSvxNI1EKERHTTUbW8fQWVVJzIrTFmrj1THbiR4WpSJdRl1vCUO3FlAcTlAR8aiIFDN2U663t6u4ly1VMbZWp9iqbeeXr/yS6165jilVk/nRUVdQM6uGR9Y/wj2Lb2fqlhEcu24qnb9/A2NUEVVnjcKsCO511+2PImMopRVtnNv7JI8FpnN78Bw+FX2CBTf9luPPOp/QggX03iEhUQhxYHR0dNDZ2Tng5f1+/16HXq5du5ZMJjOgOqqqqqiurh7we77d+vXr6enp4fDDDx/wOs888wyTJk3aIyD+8Y9/5H//93/5/e9/z2GHHUZvby9PPvkklZWVABx55JF0dXWxdOlSJkyY8J7bPBhITyKDJ7ELIfakPIWdccmkHDJJh2zKIZO0iSdSxGJJ4vEkiUSaVDxFOpomnbCx0x6e7eAqB80XRzej6FoEgwS6ZoPm4Wk6jmaS1XK9mI6290sh+Jw0pV4vxfRS7EUIqSSmm80FSs/NTybk5nsvc8HSTCj0mIYRAz357j2Xrg+ckIEb0lEhHS2g8AdcQoEsxYEURaE4voCHp0GEQqKqgCR+bEwU4FM2haQp0RKU6LvPJJNxDeKOn6jtZ4cqoV2V0OUWk3AKMZ1iQk6QYoIEjSJe2bSOpW2b6U7G6Iz3siPWRVey9x3bPWxMMydecjgxO4qVsSj2goQMi9/96l6Sid1/8RsFBv4GP/56P6H6II21dYypHk6z1kJZspZArB6VqOjveYXcpYFsK0EslKGzBDZXWmyqLqKrsBTHMAlk0gzfuonxa1dx+Io3GbdhDYHszomJPE0jWlTE5pZm1g0bRjrgo9vfzYaiDbQVtFGULaIxUk3r9kIqu1OobCK/ZgDNKCAcbGd9XZy1DXESIRcUtBY185OZP2VS9SSe3/Y8d7/wT45YM5LjYlPxDEXZqUMpnNnwX12AOhaLce1vr0XP6Jg923nAmE7UKOJLsYdwI10cQYCqFWsY8dyzGO8wTEoIIf4b79bTddVVV/GTn/xkwHWNHTuWZcv2vHzVuHHjWL58YJf9uvLKK7nqqqsG/J5vd+edd/KJT3yCbdu2UV9f31/++OOP841vfAPbtvnEJz7B//7v//a/ds4552AYBnffffdudZ199tmUlJRwyy237PW9otEoJSUl3Hvvve84TFV6EoUQ4n2g6Rq+oIkvaFJUvn/rKk+RSTmk4zbpRO6WjGWJRuNEowli0STJWIpMLEs24WKnc9dv9Iwk+KIYZi8mKRQBwnoNPUYdtmaS0XxkNf87vSmF2Ti1XhsVdBPQkhieDV7usi84uUBpZDzMtIeR9DCiHmZEw9i+M1xkMOikkE4KURo4BTpOoYEX1DGCHsFghqJAipLCOMECG8OncJRGJ6V0e8XECJHFBFMjYGQoJcFwfQMVWpRdr+7geBphJ0BtRSUTvUra3Tqi9jCsbBGhtIWX8EikIZ62CafSdMWjbIns4MiqiXyj57MA9OoRNvvbWKat3yMgArgJl+TqJMnVSXrpZRvbeZlX8VX7qLmghpLDSyhwgwxNNdKaaqEm0Ugo2oQvWYEvUkRFBEZvBk+zyfrXESlMsanKYGNNIQ+ecBL/POUsUIrWtq1MXf4WR7+5mPHrV1MajVL61lImvLWUZDDI5pZmVo+aQCo4jbAVZk3pGh6ctIaqdAVDeppo3WoSiIRRTjclcYsp60cxeX2MreXdLB0SYaO2mYsfvpjRZaO47oRfcf35v+XB9Q/y86f/zmc3nYb+oEZ8TRc1F45HD7y3X69FRUVMHTeVl5e9jFdey5wdC/l30Wz+XnwqX/Du57VEnBnKI/HiSxSfcvJ7eg8hhPgoWbx4MXV1dbsFRNd1+drXvsbDDz9Ma2srxx57LC+88AIzZswAIJVK9fcO7uqss87iy1/+MmvWrOG8887jvPPOY8iQIf2vBwKB/vUPdhIShRCHLE3XCBRYBAoGfsF0z/VIxW1SsSzJaJZUNEsskqKnN0a0N0E8kiQTtXES4NoKT8/i+aLg68XSu9GJo3SHbqOaHXoDaS2Ao+39n1rTyVCf2UGt6qBYj6J0G5SLp1w0x8XIuJhpFzPuYUY8fGEXvT035FUBUQJEyf1CcgMa2WIDr1DHLFDU+HsoDcUpKEzjK3TRdEgrizWqkTavjAiFZJWFiUOpFqfe6Ga2vpwCbfeQl3RN1mo1rFV1bHGqKM+WMzHVQE0mwBs9T1FsVVLsq2CsM5TaZCUnj5jJpt5tbApvJ+28+1CibEeW2fZoSsIVbLairAlt4q3CNWTsDMlwkqoh5UwuHsfwxAiqIsPwR+uJpSsJpDVqumDqqgxZ/ybioQjr60w21lXywAmzuHvW6ZiOw8TVy5m96HmOfmMxhakUY1auYvTKVUSLi1kxejQVLVNxjMPZHtrO0qplvFFjMyIynGHb6ynq7kRPtgPQ1NNKU0+CzsJuFo3pZaW2ijn/mcOFoy/ke9O+x/FNx3Ptwl8y9KUSZq0+kq2/f4WGL0zFKPa96/a/k4A/wGkfO41H//ko2YY6Tl3zKHfWn8eLzacxZdk/WdpUReNLL0pIFEKIAVi8ePEeQ01feeUVhgwZwrBhwwC46KKLuOeee/pDYlVVFd3d3XvU9fnPf57Zs2dz//33c+edd/KjH/2IW2+9lU9+8pMA9PT09K9/sJPhpgyebl0hxMHF8xSpWJZEOEO8N0MinCHcHae7O0q0J0GqN4sd11AeeEYGz4qh/N1Yejs6CTzNxdZNMrqfzF57JhXFboyadAd1dicFegzX7+DoHh4uuu1ipRysqIvZA0a3htG753mTSodsiYFbquMr9CgLJigrjhEocjD8uV8B7aqU9V4dW1QV3RRjK5MCUgyljaF6Gw16N+Yu5x66CtboNSxTzaxTDfRkQpTG/bSkfVSaVZT6akhndDb2tLGqawOruzaysnsdq7s2krF3Dgs94mdTadKG0+A0MdSnUaSn+ffjL/DIo68CuclyCsYUUDi2kPJRZUwsGM3I6GgaesegxatJenp+T3lkfb1kAl10lCfZ2FzO0soRxKxCNM9j/LpVnPPMAo5ZsggrP4GNYxisGzaM5ePGkvH7iFpRllQsIeaLMCYylNb2Joo6OzDiuesnakYDymtnW0WMZyd1kfF5VAer+fupf6epuIl/r/o3qx98hYs75uCW6jReNg2jcP+D4qOPPsopp5zCN2/+JmUbyigly7PtBbxWMokfFS8l/sZzTEvDsfc9sN91CyHEvrzbcMiD7ZxEpRRlZWXMnTuXH//4x/3ld911F4888gh//vOfAXj44Ye55ZZbuOOOOwCYN28ev/71r9m8efO71j99+nSOOOIIfvOb3wCwYMECTj31VLq6uigrK9vrOjLcVAghDnG6rlFQ4qegxE91y96XUUqRTTnEwxkSvRliPWkiXSm6OiL0dsVIdtv4kwYKF9dIowIRlL8Di3YUaRxdZ3OwiTUFe/6SDVhpKn091FkdVJX0YoxMk/F5ZHUPhYvpOPjjNmYnmO0K3ybQPI0YAWL5HkgnqOOU6/hKHFoLtzOpZC3+EgfdyoXHzaqKZV4r/3GOoVsV42gGNaqbCdoGRhlbOE9/BbRXIAAEoNsIsphhPOsNZbtTQWHIx+jSYk4cPZvKwCUUmZVsDrextH0Nq3s2MDs1nRfKlvFa+Ws86evA7/jYsrWtfxudiEPkpQiRlyJsYxurqldTOOFJiqcU0zyhnsPTh9HaO5bS3uFEs+X4sxUURV0a27qZFnqVcGmCVUNreWPoWH464nJ82SyzFj3PZx75D3XdXYxavZoRa9awsaWFNydN5Dj7OJJGklcqX2HNiE1MKx9JVddIgu1b0NPbQCuisbeUjz/l56VxPaxr7OCse+cw74TrOX/U+bxQ1MAf//0vvtz2cbbe9CpNX5uG7h/4r9otyzuJPrYdb7ZiznFzuH/b/WCXMTX2JitLx3O3NoEztIUsJ82MaAyzuGjgH1ghhPgvVVdX/1eTyPR5t+sIvp/WrFlDJBLZr0lrAE477TS+853vsGHDhv7hpL/85S8pLy/nyCOPpKCggIcffpg333yT//u//+tf7+mnn+aoo456x4B4MJGQKIQQHyBN0/CHLPwhi4r6wr0u42RdYj1pot1pYl0pIl0pOtvD9HbESXW5+Bwdpbm4RgoCXWhWG5rWjYtHl1HO1uL6PeosJkodHdQZXZRVRXAaHSJFYGseumvjS9r4elysdg9fm4a+TSNMAWEKcpeYKDPQKxQlRQlmFr7J7IpXMQO5nsReVcASbzj/ck9kpdNEWvlo1XYwiTWMNzdykr6cU1gKBnglsL66klcYxRNqGPFEISP9OsPqCjlq6ClUZuqZ1D4egLDqZUlgOd93r89dnHIv41yyHVl6nuih54ketoa28sbEZZTOKKVqcgXT4hMY2zOFoq4RaJlqAplqCiMZKjvbmBJ8gmgDPDV0Ig8ffQIPHX0Ch61ZwVfuuY0xm9YzdONGWjZtZtWo4awYN55j248lYkV4seZFKoq2MK1wIr5Om0DnNpQbw2+2csybOo2dQZ6d1MXlT17Oj6f/mPNHnY/6uOKOfz7Ip9pPo/3u5dRdeNiAPy/d//dzRr/yGCtHVnDsF07l11W/5vBth1M8chRj295ksZrC58ob2Na9hbVPPMboc88bcN1CCPFRs3jxYoA9QmJjYyNbtmzpf75582YaGhr6n48ZM4YTTjiBW265hSuvvBKATCbD9ddfz8aNG7EsiwkTJnD33Xdz/PHHA7kvhW+77TauueaaD3irPhwy3JTB060rhBBvpzxFIpIh3J4k3JEi3J6gsy1Cz4446V4PlIanOXhWDEJtmFobmoqSNiziZgGetnMoi45LBWFq3A7qIj2UJuPYQYdwOWSDLpZtEwxn8LWBtVXH6Nx96Gq2WEer8Sgrj1NWlCZUmkXPf9W4yaviFW8UC70JvOqNoFKLMk1byRH6ciaZ66kg3l/PNl8RL1hjeEU/jGi8hEndilFOCbXBoRRZuW9fu9Jd3LvtEe7veI4VGzeS2pLaa2gEOObow6n9dB0rQhsACLp+jopO4bD2o3DCDTjKROGRCXSSLNiMXhrlifFjWeEfg9J0Jq5axjfuvJlh23N/MGRNHy8fMYXtzS0oFOuL1rO8ZDmn9jRj9Y6iYOta9GwazahDuR10F6V48Og2PB1+dMSPuHDMhfxn9X9w79zO9PhhlF4wgsIptQM63ptf/jMbX7+W8hstmv6/3/ML/RESzyeodCpwV6/g5sZP81V3Jcamp2hqaOGC638/4M+SEEIMxKF4ncS3c12X0aNH88gjj/RPXPOLX/yCmTNn9i+zcOFCPvaxj7Fu3ToKC/f+Je+u/vnPf3LNNdewZMkSTPOd++EOluGmEhIZPAdDCCH2h+t6RDpS9GxP0L0tTte2GO1bwqR6HEBD4eFacbTQFgx9G3gJkqafhLX7L7tiojSoHTQkd1AZTqJ5Nl1lBvEqD9PMEoyk8W8Ga6OOtWNnaFQ62FU6hbVJSstTlJalMXy5XynbVRmL7FE8rSbxgpqAjsd0bRkn6q9zpLWCGqL99azyV/KkfyqvMIGqHosZYZOhVitl/hoAMm6Sl2Mvc0v4QRZvX0v4jQhu1O1f/6Lzv8nJxcMoKEqysTbGsyWvs8JdjxtzGVU2hBndM6jaMZFMthiFIhPoIFmwmVBpLw9PGMNSX66n7/hXX2Lu7X+mKJW7lMi2mloWHXUE2UCQjJ7hmbpnGJMsZmzXSOy2XqxYL5pRgXIjREMZ/nPsVpQO846fx+yW2fzy6Z9z6oIJFOtFNP3gqAGdn/j8iuvIbvs9vkV+yv4VYPu8r3PD8vlM6Z5CYc8OHg5OR4s7XLT+NrqKC/nqn28jWCSXwhBCvH8+CiER4LHHHuPyyy/Htm0uuOACfv7zn++xzE033cSRRx7JYYfte0TIzTffzLhx4/Y5tFVC4kFksBwMIYR4P9gZl562XHDs2ZagfUuEzk1R3Px8MUpz8QI7MP3rQPWQ1g1iviKUtvM6heX00qy20hxto7zHJu6DzmqddJVDwEkTas/i26hjbdAw4rngqAC7RqewLkFpeZrS0p2hcb1bzdP2YTzEUbzGKOq1bo7hTWaZrzDdXEmByjWu2/DzXHAsjwWPh95STujwGGsOocSXm4q8N9PB69mX+avzGCs3tRFdG2PoFUOpTjUztHsKo9tbGeUmmb/2Wf78xN0Ujy6i7PRyqsZWcFLvMbRsnY6XrkahSAfbSBRspqgyxq3jZ9CmN1KQTvHVu27ljBeeBiBj+nnquMOJVDWiULxZ/ibKjHNaeyM9HRq+ng40owzlhukqcXhgxlZ0TefOOXfSXNzMNX/7EV9ddx76tFLqz9v3hZX/eNMPuKDzZt6cGiT0bz/Fa2v50sfDHN9zNsWGxYr1vTxbfgy/e/5nvNlczewvfYPDTjrl/fngCCEEH52QeKBISDyIDJaDIYQQHxTlKcIdSTo2xejYGKV9U5TOzVE8J/+65qAKNmMZ61FehIQZIO7fOSlKiCTNbKMlvZW6jihuUmdbrUF8iIcvmKZgWwb/Gg3fan1naNTAbfIobExRWZaksNBG0yCmArycHcnjzmTu1Y/D1iwma2s4U3+BWb7XaFS5KcTDhsWTobE8WHAaRT1lnNahM8Iags8IkHXTrIst4Yngcywc1k6sIIurO+iewZCuibzwy8fp7Nre3/6ihkJKziilfFoZx8SmM2rz8eipWjzNJlG0Ac8XQY3q5saGj+NoPo597WV+ePONBLK52fdenDSSzaMmomk624Pb2VC8ls+1V7ChowR/9458j2I3axsyPH/YDiqDldx/7v281v4amb9uYGS6lfq507CqQu96nF792jSmVq9mR2kBS0cVUnO1zsraAp6bOYOKWAXampX8temz3P7M//FSUwlDpx3JOd/7f+/nR0UI8REnIfGDJSHxIDJYDob4/7N33/FxnWXe/z/nTC8azaj3ZsuSLNmWe4ljO3ZIc4pDCCQkkFBCJ8uWwLIsCYTlAQIEwsNSdgNhSXZJIwnB6YnT3KtsS5at3vtoRtPrOb8/lDW/PGHZxHEs2Vzv12tesqSZe65znT/GX5373LcQ4kzS0hqTwxFGuqYY7vAz2O4j7PvT9hSayYfF1oKujxJVDEzZ3CevNjoIU0UfVdEe8sciREIWBgpUwnU6VmsIe28KS6uK5biCmpgOjSmXgqUihrsgQm5mFMUAcYzsjs3j+eQSnlDXETY4KVdG+ID6ClvM2yl9IzCOGyw85VzBVueVrOszsCmSQ6YpC03X6AsfY2/yFZ5oaCdqsxDwT9L+1Xb01Fs/3uxZNjIvc5O3LpcN/nWU923AnPSQNE0RyGgn16rx+IoSjpjrKfCOc9dPvkPp+AgA++eV0r54JapiwG/20+pu4TOjDk4MZ2Ka8qKobnTNz/PLJhjKC3NJxSV8f/33+e6j3+DG/ZtIz7dS/tHlf/Gc/PzvbuCDgZfILonSUWFnMFVK7g8CPLwpH3IvwDbUzda8y/nytnsZz1cJZ2bw+V/9DlX+IyeEOE0kJL63JCSeRWbLyRBCiJkWnIwx1O5nuMPPUIcf33Dk5O90QwirvQldHyKkWpmyu0/+Lhcvc/Vuqid7sAwZGTEbGaszolXFcflDWFsULM0qpvE3AqNNwVgdx1MUIT8jgmKAqG5iV6yGJ2MreM68mqjBwTyln2sNL3O1eTs52vTiN/ssBTzi3kI6vJAPjBgpNRah6Ro9oWYOso3HF3QQiJsZeX2AiRcmSAVSbzlOR56dnBtyyW/IZePARRQNXQCKRjijExWdWP0YPy+9Ekckwr/84ocs6jgOwLHSXJrWrMWgmAkbw3RknuATEzotfU6MkSAodnRiPHBRD2mDzq8v/jUWg4Xen+9hYXQexV9ZhdH95/bEnHbfHz9HmfI8y14P4LQn2bvUheHRTNRDSZ6/bAsWo8KuKQ9XHN6J2+ilI8fFh7/9Qwrn1pyO0y+EEBIS32MSEs8is+VkCCHEbBMNJuhvnaT/2CR9rZNEpv77SqOGwTyM1XSQuBbGb3UTtUxPpbTrEeYpXdREO8jqj+GddDLYYCDWmMap+LEfVrAeNGAemA6MaRsYa+PklIbItsVRFBjSsnghtICtieUctc0nbjCzVjnKJy1bOY9jGNEZNVh4xLmRJuu13NyjUKHkktZTnJjay2H38zxZN0I4YmNizzijz42QGEm85fg89W5ybsil1j2X5Z1XkxWaQ9Q2TNzspSAzxLdWXkA6beT2e+9h7ZEDABwtz+LwqnWYFAthY5gx+wBXeeN0dmuoaQ3QGMkx8+yKdoocRWy9eit3P/gv3HD0QjjPTckV//O9iX98/WfYkz/E/Tos0Sbwu0wcqK6i6MsBOuvraFm8hN7OceYPTNLoO8beOUWsu/HjLL/i/af1vAsh/npJSHxvSUg8i8yWkyGEELOZrutMDoXpa5mk75iXoXY/WloHdHQlToZlF7o2gM/kYipjeisLo55kntLNgmgrWb0RvJNOBhpMxJemcaqT2JtUrPuMmN+4fTCdp2GrjVGUE8RmTpNCZV98Ho8FlrDbuIh+WynZTHGT8VluMj1Pph4lpKg8Y1vBtsxPc0uXiTzFTSQV5PDUy3QueIlns+IEIw58R7wMPzlErD/2puNyOG1U3j0H1aSwamgd9f1XoRnDhJyd5OpufrGunAmDizv/7UesamkCYFdtFl2NGzBiImAKYFcnqfQm8PcHQLWBFuWJ88fxZ0T4+qqvU+oowfqLCewmO/O+vglFVfhz/nXLdcy5rgmTO0nZ8waqXaO01DjpedlJxXYDz1x6KaGxEYyTTq7s3s4LCyqpPW89m2+97b067UKIvzKapnHixAmqq6v/4lYO4tSkUina29upqalBVdW3/H625BIJicyekyGEEGeTeDRFb/MEXYcm6GuZIBnX3vhNggzzXgxaBz7VyURWHgBWPcZ8pZ36qVZsnQrDUSsj662wIEiGL4Jtl4ptnwFDZHp7DeYnyZ4TIs8aRVHgeLqMrYH57IzXc9xZS8po5mrDa3zB8jil2iRRReGP5tW0Oj7HDQNWrKqVwXA7R5Q/MLSmnacTKnokk+FdfQw/Nkw6NL2Nxjcu/CIXrF7FDwr/g277EKXhEta23Ywz6WQqs5XscCVPrFc54Sjixz+8k9q+LgB+vzaHZMlGFBTGLeMsS0wyOajDVAhQiNlcPHjBEbIsWbzwgRf4zb/ezWXD5+H5RB2O6pw/29OH/uEX1OZNMbHs37DsNLAsGUCzJdhal03DVwz0VlRwuCSXiD+H65ufZceGFRhsNj72o1+8x2dbCPHXpK2tjaKiore1P6B4Z0KhEENDQ8ybN+/P/n625BIJicyekyGEEGerVDLNwHEfnQfH6Dw0TjKW5o1NMchWt6Gn+xi15eN3T19h9Oh+lmhHmTfYSajTTX+RhamNBuwl49hbdGyvmrC1T4+dLknjqotQ7AliUGFAz+OxwAL2hao5llGP15zFReo+vmZ5gHJ9gpCi8kfLZZiS17Eq7CaejnLA+zzxxpc5UBxmW9CEzeui8/kTRFojfPLmf+DTkfOwG83cm/0wf8zdgTVt4YL2j1IcmEvAfYzM4FyePl+h15zBz7/7z+T4pxfUufeKUjIdawAYsY7w/nAPRzosKDqgp3lmdZhRzwR3rL6DxFCIdc/OYWqBTv0N6/5sH3/zu71sOhzj+NK/w+D04XyphhXOHbRWO+l5JofSgyleOH8lvmA21x/cyvH3X0ZXVxtf/M3DmK229/gsCyH+Wvh8PsbGxiguLsZqtaIof372g3j7dF0nFosxODhIXl4eHo/nzz5vtuQSCYnMnpMhhBDnglQiTc9RL217R+ht9r4xJVXDpPjJST9NNBmhL7+CuNWKqqepUbpYMnUY2wmNobCVkUudGBdO4piMYX/ZiG2viiGpk/Zo2JZEKc0OYFZ1evVCHgnX0e4tocW1gCFbIVeqO/iy5XcU6X7GsbDV+hXO8y3ErljpCh7muPFJzGv7eTSlMBJ2QVInbo9QFJjD3w98hPlKHjvMe7kj+G+oVQZW+65k0ch6ptzNZPrreHKDET0Q4J67vwm6TsQC/3HNMnLTVQBEjD7mj/fhHUkCELMX8OD6PRQ6C/nlhb9g/IeHcBlc1Nx+4Z+dctr/DxcRNdxGsPAV/AsfJP5KORuV4+jWKPcX5nH+d1VOzK9lv6WMGw49T+/119Jy7CDXf+v7FM2rO5OnWQhxjvP5fIyPj5NOp2e6lHOGwWAgNzf3fwyIMHtyiYREZs/JEEKIc00snKR93yjNrw8yORie/qGeJkd/HWusiSF7EaMFBQDkahOcl9hHQdsE4z0Oei/0wNoAdiWA/VUDjm0mjBENLVPHuihKef4URoNOu17Cr0N1TE3mcyhzMePWXD5ieJ6/Nz2MkwTH9BomUl+hKp3HZHyEXd7HyFrbSkt+lD/4TWRECvDZR7Ek7dzc/2Fq+jO5/P5PoThUij5bxHL3Razu20zQ3YrHP59fX5TJhp0vccsfHkIHDleaOLzyUixYSSkp5ml9TLQH4I1w/Id1QXzOSX598a85/tAOLhheSvYXF2IrznxLvx74zhcp00qpiCyh7fzPo/rAsX8dKzMeZm9dJoYf5aHG0jw2ZzEfbtlF9/s/QGvnIS753N9Sv37TmTuxQoi/GpqmIXHh3VMU5c/eg/j/mi25REIis+dkCCHEuUrXdUa7A7S8Nkj7/lHSqekAZdf6KYw+xmTaSVdlFSmTCacWYnXqAJUn+vB2OejdkI22LojT4sP6uhHHCybMoTRpj4ZjeYwKjx9dUdjOPH7nr8Toy2J31kp0i5mvmh/gGnUHMd3A/tQ/Up5aRVyL8vrooyiVx1CXj/FAwEwwmEPCGCWWDjP09WH8Yz4AFFWh5LMlLKncwPk9VxHJ6MA9Vc89m/O46547qentQkXnV5tzcGZMh7SIIcJi3wmGBqf/+j6eW8pTy7dzWeVlLA3NZ81rVYTWW6i9dMVb+vSBRz9EINDLv534DgPV9xKv2s3Iyx/jesMPmPQYORBeQ9lv29lePoe1vZ20v+8K2seOseqa6zjvgzeeqdMphBDiPTJbcsn/HmeFEEKId0lRFAqqMtl083xu/t5aVl89B3umlYhaTqfjS4QdV7Oyu5kFzYdJx1ReMK/nN/UfxHtpPot7eqm4PUbopQom19iY+HYU31WgxYzEnrfTsj2f0Skn6zjBXe7XqKpuZlXoBRaNH+Sfox/ng/GvM6Fkstb0bVKGn2BUjFxQcD3WvhUEnyrnM9Y0jTmjaKTJDOSTLvzTvoq6pjPwswEOtL3MK1W/xxYuJehq5zMvjPPDD38SXVWImUxcs20Cr3EQAHvazmBGFboCOgq53ilMKQsv9D5P0fwqkkqScNvEn+3TCn8eZd31nHCcoGDkMgAchXvpSSwh25fEWOtHB6oC069P+6evzvpHht/DsyeEEOKvjYREIYQQZ5TVYWLJxeV89Ntr2PjROjyFDhKGHNpctzLh+SjL+k6wYt8ezKE4r5jP4/4FHyRwSQ6LjvZTeAf49s3Bv8HAxJ0x/OsMMGLE/0wGLUdyUeIp/jZ1hFtKm0iUHuXaoUdJ+BNcFP0+96c2UWl+gWzzV9GIc17e1eQnltL3hwo2RY18MC9IJGecFR9fT/4H80/Wq+s6A/8+wKHm19hZ+jxq0knaMMyyQTuPXnAp1mQSVxQqTuxDR0NDw686yCqwoqCDFqBmuIaklqI/NkC7vZ/MMQu69taJPJmHM6hrG+U19SiWUClawIWjtI3m0GUoQKWvg9HiHPL9AQDSkRhOTxa+4aEzdfqEEEL8FZCQKIQQYkYYjCp1awq5/vaVbP78QvLKXaQMmRx338pwwU2s7Gpi+Z49GCIpXnRs4KFVW1DXmFn8VD+Z/8fJ+OAcQh+MM3FHnKn5FtRjJnqfyaZrIJsFqQF+QRMsOUGhcoTNQ09zd+T9fCTxjyQNA5RZv0Ran2RlzmaKzUvofaqc4kEHX8iPEczspnHNGopuKob/XltGh8FfD7L7yPMcyt1PwuynbMzL3kUXE7Q5CNvsvO9AnDH1BCoqRt3IuKuS/46BizrzQYdtfdsI5Maxps2Eh3xv6cmyyy6c7s2QSlSJkzGyDNWRZCLDSdhgI38ixviKDAxv3CmSisZwFxThHxmSe4aEEEKcNhIShRBCzChFUahYkMMH/nEZmz+3kOxiJzFTHkcKvoY3/2rWH32Vpfv2k06o/CHnMl7YuJ684ikW/N9BovdXMWnII/yFIOOfgLjVRHy7hZbdBaTDcGewiytLj9BW3cs1w48T8ybYHP8OR3BQYv0yMMGqrEsozFzM+MtFpNsy+Zv8GOGsNhYuXkXJx0v/FBSB4fuHea3pSfqsXkKudi5oTvDg+64kIxohYbLR0NKKhkZKSRFWbZhzM9EBU7QPZ9zD3pG9pEpMAPS2tr2lF3VlGihm3P4pdjmOkO2b3l7DUdhCR3o+jmgae2UQgDQK6ViMzLwC4pEwiWjkvT5VQggh/kpISBRCCDErKIpCxcIcPvS1FVx8SwMZWVaC1rnsq/guaVcdl7z8NLWtrYwZcvnP2g/RdvE8Grp7KP12nJGD9UQXafhvjzC+xoahT2XgeQ+9/dlcEhvkp7RzcFUn1YljrB7azs3R23hAW0Sh5SuoygRrMy/ElVVPaGcBA03Z3JofI5F9nAUNKyn/VMWbPi1HHhzh6Y7f4gfimUdJOlYz6cokaTKy/kiSKa0No25ERSWaVTSdMfUY80Zq0HQNb04IgGDP+Ft68N0nXuXB0veT1KboD/qxTlWhx6xk5O+lI7gWgCplAL87ExUdJZHAkukGIOSbfC9PjxBCiL8iEhKFEELMKoqqMHdpHjd8czWrr56DwWxgMPNS9jZ8i+LoFJc8/SxFQ4M0uRbx0LotWOfpLP5VO9H75+KP5ZG8cYrRT6skbAYiOywcO1BEYSzAb31DjC4/TDTLz4cGHuVXgU18M30ZOabbMShRNmVejJJdjeFQLkcPZfH5vCix7OM0Vq+l4rMVKIY/XVIc/q9hnkk9SMgQpTQwwPMrL8IdCuJ1eajqPI6OTlJJElHtpCzTm9zPG8wBoD85yITRj3FMe8uxN8eyGDd46LOVYhtNECeJZWIeluwJRrV5xFUjxYEAY3OzUQBXIkzK4gQgLCFRCCHEaSIhUQghxKxkMKksubicj/zLampXFZAyZXCk6Da65n2I1U07WbN9B0oafl97Fcc2NTD/eBd5P4ChE/WkFsaZ+GqCiUYbagccf6UQ3Q+/HBslv6qJlrkJrhh9ilZvNp9LfZBM47cxo3Cp+zIS7kIyD+ax76iHz+dHmcw9wpLyCyj/QvnJqad6WsffOcm+3H2EnF1o9sVELRaSJhPnH0kQYBCjbkRBQSsqRgdsoem9GHcO7mTEOUl2yIWefvN9hDXKGADdzjko8X6azb24pxYD4MgepNNSRGYwRbJ2+nWZ8TBRowOQkCiEEOL0kZAohBBiVnNkWth083y2/O1inFlWvK4l7FzwXRwmuOSpZ6ns66Q5p57HL7wcjy3Ewp/1MPTKchJmC4lbphjaYkP16/S/lMV4r4tvTI7xPncLry8Os2zqIMYRL59LXEyG4SdkaFbWFl5JxJlBzr58mtoz+EJBmKGcJlbnbSb/mnxMNiM/f/83uXLJ1Yw5++lwDmIxdbBj0XqKJ8YY9xSQ19+CgkJKSRK3ZKIrCnp6jOKpOfjiPgayJzDpRnxDo2861kWjx6iaGqTTVk5KCzEVDuCYnD/dh6wjdCaqASh0j6IDjlSUgGIHIOR/60I4QgghxKmQkCiEEOKsUFzj4YZvrKLxfaWkTA6ayr5CT83lLNu7n3WvvoKCwiNr349/XhbLHmki/FAtPn8BXDTF4GctpB0K/l12OlvyuTng5Sa1l5dW+ChP9lM43Mk/Rmqwqk9RGXNTXnsNCbMF144iBses3FIQpCN/L5uWbKHq23M4vL6Tv/ddQ1a4kObcA4zZBwhmTYc5r9vDmpY4MSWChg6KQjIrD4CqkVIABjzT+xz2d3W96Rjr7CNcad9JXLUwaCsi6othiuZB1IEjp5XO6EIASrUx4iYTlnSSgGYG5EqiEEKI00dCohBCiLOG0WzgvGuqufary3Bk2RjM3sT+xq+QHfXxvqefp2RygJcXbODQmsU07Gsh414nI0NzUetDjH4JYmWQOGqgbV8h7w9O8PexYV5aNolLCZI71se/TQ5jVNrY6M0ntuRK0FRCL5RgjKtcm+en23OcRmUp2zMP8oesbXys53pUXeVAzkHimSO0l8ylrruDtOpAD7Ri1s3o6KQ80yExf3J6vuqQeXrRmsDAxJuOryIjxOWOPQB0OOYQifcTVsJY/JWYsqaI48ZntZEXChF2mqc/xEenp6jKwjVCCCFOl3MiJHZ1dZGbm0t5eTmNjY3k5ORQUVFBf3//TJcmhBDiPZBX7uKGb6xi3op8Qs4ydtd/m1huNmte2MnyE/voLJnDtg0bKB3sp+jfY/R0LIbcBJNfSBNaAFqXwonXi3hfcIxvhEZ5eVkAqzFOYirCKxNPoRDiUyMeji+/AHvMSOvzJcy3aKzI8hFJ65TGCvnPvKfIs+ksG7iQicAYB9L76S9twJpM0Fo5l8r+OAAxNYpmspA2WzBHR7EnXLRHOwHQvIk3HdeL5ip+qyYp00bos5WipQYIJCfJCM5HUcHlmmDAnIs1oZGennmKreM4VmcGYb+ERCGEEKfHORESq6qq2LBhA7/61a9oampi1apVPPDAA5SWls50aUIIId4jJouB9328nos+WY9mdXBwzlcZqlpG1aEuLtz9IqHMDJ658BJygpPU/XqQzqPnodl1pj6Rwr9egRE48VoRayLDfG1qnFcWKlhNSTqDOp2Bp9DJ4g5fimO1i8jx2nhtVx6bs0Po2R0sGjkfk27g+yW/oeCgka47utj2ny/RUxolZrZQODHGwi4/Yd2HUZ/eFzHpyUdPT1AYqGQiNkGntR9bwPDmY9o1ztW/t9FgfoqgMYOANrZG8QAAjnxJREFU0UFvaBLbVBUAmRndDKXzAXCUxQBw9nXicHsI+/1nrvlCCCHOaedESARobm6moaEBgKNHj578txBCiHNb9bJ8rr99JXa3lRNlH6Nt3lW4+/1ctO157IYoT73vMmzpGIsfaKXz8Fo0g0LkmgS+S0CZ0Gl7tYjzIz38Q3iI/QucGIw6O739hJLNqKnVfFYdYzIrh9xjHpp6nNxcNMlrhc/wgaGr6Wru5dtPfJdUJEW0J8rTnS/QUV5HXW8XB2sbMASCmHQTaVIkXR50dAp9BQAczezEE8lA1/+0wumx8iX8ccsWanq6QEkxYC1mMNWLNVCBroPD1kNnYi4AWc7p/RZzB7uwOp3Ew6Ez33whhBDnpHMiJMbjcfx+PwUFBfh8PnRdx+12z3RZQgghzhB3vp0bvrmKomo3A0UXcbT+k1hCcda/8ArliX6e23gRBtIsfrCF9sPrSWMgujmJ/3INvDrtrxSyMXKc68OTdC9yoyoaLw6/gqZHKIpuZk6ZTtpgIv5qIem4gWvzpzhsa+Pi0rWY880n6zix9QT7y5MATLizqO0NogMJNQZGE5rVQY5/+spiV8YgNs3C5MT4ydfHrEY0g4Eqbz5Z1tcYtBaTSA2hp1QMYQ/mTD9jyTxiJpUswgDkTg5htViJhUJvCpxCCCHEqTonQuLx48epq6sDoLW19eS/hRBC/PUwW41s+bvFNKwvZiJnMYfnfw49pdD44gHqY628uPFCjFqKZQ8fpq3pAtIYiFyUZuqqFPig67U8tiR3siSUIrLUSiwd5pD3VTSyuSaUyURdFba4kUOv5bMgI4bB1c8FUxdSdW3FyRrSgTS/a99D3GhkXl83rnCE3gwDRn06SCZdHjKnQiiayqBlesGZob7ek69XAwEAxguKuKr3JQZteSixEMcYxBYqw5QZQzFoeG1OXPEEuqqh6jquaBwtnSIZj525hgshhDhnGWe6gNOhpaXl5PRSi8XCsWPHOHToEIsXL/6zz7/77ru5++67T37v9/t57rnnzkitQggh3mNZkN2g4G2eT1PD39DYcg/VL50g44IwL12wkU3btrH0sSYOGtcxb+ErhDfpqMk0GU8b6N2ew8fPf5bvhz6EaWE7nUeOUuVaAuYtfC3xd/yf/JXk98GR9iAfmtPPryJ/4MuWT/LFum8Sbp2+sjewbZDfbSnmI82t3H/Z1ZhDaUy6GQ2NlCsLy0QX7lge/bZhAJoPNjE0Mb3ojEWDeDTEUFEhG5/fw39taWNqxMUxrZNNwSrCBYdxuEcZ190U6wHMJSmSfWYivdMLtT331FOYnRkz03chhBDnDEWXuSmUlJQwMDAw02UIIYQ4jToOjvLcvzfj8nezpPlujKSZ2pjNDpazads2JnNyab66nNr611BSCpmPqDi2G3CWxXCvgP9jup784YNkRSvYWHgDFnU3PerveKmrjKQ5xuJruzgyWURVxw08mPw9f/juc/DGJ2puo4dXovn825brqRzso79uLmpqArvmxN7dyu4GO+15+3n4xPcZrA9x4Q0fAOA7n/oWuqmPRG4xG7a9zH+sTMLkGuY6ImyqKCXceC/9bTUUxGNc4jtAb7+LyA4n4RXLeTU+yUfv+r/kllfOYNeFEEK8G7Mll8za6aY/+MEPUBTlLz5uvvnmmS5TCCHELDV3ST5X3rqYgLuSw/O/QDqt4n51gvPUPWw/7zxyR0aY+/wkbW1rwKTj/4BGdLFGqM9K5GiKz8deZaKyjvF4P32hVuLaKhYoDmxFNuxRI3t35bGyuJ8XXK/zdeUzZK/LPvne400+HsiKMq+/m6xggLbcfHRFAyDlzCQ77AGg2zqINvWnbTCKSGMJTwEwWFLERc1+evMTGKIheqPTH9kuxUt/qhgAW3aSmMGEYcILQEwWrxFCCHEazNqQ+PGPf5zW1ta/+PjOd74D8L+GSSGEEH+dSuuy2PKlJfiyajhadwupuIGs17wszTjEvuXLKO9oI3e7gb7eBShmHe9H0kTnwuQJJ86hTq4en8RYpXDY9wqanmYk9XE+Z3uGQIabjI5MxsYcnF/Ux4DRx6fWfwjV+qeP1Z/1jbGo9Qi7GxrR42msaSdoGilnJu7A9D2K7dY+jME/fU55y/7IpHMMJZlgtCSfxm4do60NUyhIeyKKoqlYbWHGooUkjQpWe4phezamsen7G2Oh4JltsBBCiHPSrA2JWVlZ1NbW/sVHYWEhALquo+s6qVSKxsZGNm/efPJnMptWCCH+uhXXeLj675YwkbeI4/NuIBk2kvu6l+ryNlrm11F/YA/pg1VMjJehWnV8n0wQ96gM7/MwP/4U5dFFRHU/3cGjGPW5xFjK5VlHUXWF4ztzKMnrZ5+lhY8ltlC6+U/7804NRnkwNkR/QRG1Pb2Mu7JJKSk0m4PM4PQCM122ARxRy8nXHFVyaM9NYIgECdjdxCwW1vb047fGSGkp9IgHqztGMmUjaDdhU1KM2d0YQiFMqbRcSRRCCHFazNqQeCruueceWdlUCCHEWxRVe7j8C4sYLlxNV8WlxP0myneNYG/wMVxYyIqXX2DkyCpCIQ+4IPDpOGlVpX97FhsND2D15HPMv5O0nqY38SmW2XowZppxjdrp7fCwoPwoY4YAX1l4C6Zc08n3/VXfBKVdbRR4vZzILyOpTk8tNagJTGkzA9YxsuMuEqnpn6uqB0/6PIzh6VVO+0sKOb9F40h5AlMsTjDiRndopE0pfBY7Jl0nnjF9VdIRTxIPSUgUQgjx7p0zIXFgYICnnnqKT37ykzNdihBCiFmovD6HTTfPp6f8MsbyFhEcsLGsuZ2pFRbiFgurXniJtqPrSSYsJEt0/B9JkI4amNxl5HxzP5F0gO7QYdxKEe2pS/hozg5SqoGhvVlk5XXQqg6yObKW+VvqT75nQtExHXmV0exshjPc6G/cApG2m3BHCxgxTmDVLYx5p1c6XR5cxMKxS1ES01cXxyvyKfCDWfXimorji9pBUSiztOEzuACweqavSjriCWLh8BnsqBBCiHPVORMSv/SlL3HXXXehqufMIQkhhDjNalcVsvL9c2ipuYmwKx/vsQzWTTbRs6IQRyjMvD2dHD+xFl2B+DKN8Y0mol4z+T3HcdsdHPPtIq2niMTeT6YpTpUngD1kprMll8ziJuJKkr8r/gSeDR4WfWg5NXfXYKkJcLBmPjW9PXhdZSiJOGmHE3c4l4AaIqxG8U6MA2CJTW9foVlrUJNxAlnTC9ysPpFg2NJLNDr9+3mmNiZ0NwA52dML3TjiSZluKoQQ4rQ4JxLV1q1bycvLY+nSpTNdihBCiFlu2cWVVJ5XyKH6W0lbLUzsdLE6fx9dteXMazuB/YSN/v4GUCC5JYy3zIm31cla7RDRdIiB5FFyTEW8HL+SK7MPk1YNTBx24yrbyTFllAuiC1ly7Uq4OEVW0sprC3R0RaPxRBvduSXoehLdaCI74gag3zxCYHJ6n8SSxB4AHNSjhkJMGTLpyinkvGM6rQXDpKPTr8m1+hjTcgHIdgbQFWV6uqmERCGEEKfBORESd+zYwZNPPklFRQXXXXcd27Zt49prr53psoQQQsxSl9y0CGOVm0N1n0fXVOJPO6nc1MxYbg6rX32JifYFTPlzwQjJT0ySsJhQDit4SHJ88gAAtvBawgYby7IHsEWM9LVmobt6UFC4evQy0moSWyKTkE0he3wbKCo9OYVETWkA3PHpexdHzBNE/NOrkoYNtejoGDBjeeOqYefcalxRsKQiOMN5AMQdRpJRG2kV8k1TRD25OBIpErHomW6lEEKIc5Dx7T7x4x//+Dse/K677iInJ+cdv+6d+s53vnNyO4xXXnmFH/zgBzzyyCPv+fsKIYQ4OymKwo1fXs+//WOC7qrLqOx8Gs/WJJPXekn9ysTSHTvY61jLkmVPQk6agQ9bqbovxfLhPp4vMDJpaKPEXsNz/nVck/UshyaLGGvKovCKZ/AeWMJFsQYeDZUwljmCManjt7Zwonw5KVTG3HmUB+JY9en7E0dMXtxT0+HvgHEuHrxY4jmY9TrCdBLNcwCwrF2jv6qH4rQBn9VMeWycqNVAVixEryeXrKkJkrHYjPVUCCHEueNtX0n8zW9+w4kTJ+ju7n5bj/vvv59AIPBe1i6EEEKcMpPFwHX/uIbO0vcRyikl2G1n7rEpJs83UjgyQm6nn87OFaCAdfkk3Y35GMeg0B+iM7YXVVHJjS2h31jAmuxerHEY7IVxwtgUI5tGLiatpsgJmujVfLw+3sny1nYGc6pQ4lEMxjeuJJq8aKEUAGb7AAHPMRKGEAa1EDWVAqvKiN3DijadA65W4tEM4jaYm+onYjOQocUIuLIwpTXSQfncFUII8e697SuJAI8//jh5eXlv67kZGRmnVNC7tWHDBjZs2DAj7y2EEOLskp3vYvVNlRy49wucH/oawdedFH9yFO+xYlbu2sXW/C1M5vSSlT2E+bpBYm12FgyN84rTyoIFI1S5Gnl9uJoP577OrokyRo/mULjyJfS+69kQrefRTiv7/tjO1OEpVPM9zL35a/zx8g0YYlFSmVlkxDMZMI1heGNR0oG4kUI7BO0jZAfnYg67iLsUdhQt4JqO1yAVIBjJJzenlwxiBIwW8kiQyJwOnKpvaga7KYQQ4lzxtq8k/uhHP8Llcr3tge+66y5yc3NPqSghhBDiTFm2ugbTEhNH6m5BTytYfm9B/9goaYOBFTt30ta+mnTSCBkaR68rwJjSaRj0MqS8jlm1kpGo57B5Liuy+nDEUgwnT+BLRyk2mpg3tpSppinQQYsn2de2n0lHJmHL9BYXxaEyRsxeLDEDANaRYQwpO5ptFJ00lkQZmmLgcGntdK3tGu0xI6qq024uR09Ov053Tt/naAgEZ6CDQgghzjVvOyT+zd/8DVar9W0P/NnPfnbGriYKIYQQ78RHb7mIweJSfOUNxCbMlL6sEbw8QeHICO6BEO0dq1AUKF7WQUdDCQVTYXwHm9HUOFWuRezrzWOJZwhF0elvymPMMoIJA1dbrsJR7Tz5Pnuad2EPJeksKgcgJ5aDz+THHreg6zpxNYUSKQA1TdQ6himdDTqoHgtBk5UlHTpHU9NBsNtSgCWuAWC0Raa/BmV1UyGEEO/eObG6qRBCCPFuGM0GrvzCEg6V3wROI9HddjIKo4QKzJy3fQfe0TKmJvMBSH7YR9KkUtY1RtiynxxLMcZoDgfsdTRkjuCJhGi27UbXdUossHjJ+SffJzLupfzxJ+kqrEJJJXElHWiKhoZGJBXBYo3jTeWCrhK2D6FgwJR0UWzwcTCvhqpRGIkHCKRh0uzEEUkC4DRPAGAOR85884QQQpxz3vY9iRs3bnzbg27btu2UihFCCCFmSnVFObsuPkJz5OM0HPglzgdNDN4Swn6XhfnNLbTZV7Fs2ZNkun3seN9SNjx9lHjTbjJqzqMqq5HmPi/XZbdx1F9Af3ecKU+EEoONy7NvZrf7BVL+6cVp+l9+jNiHr0KJR7EaMlB0hZAhijc0QVZunJagSn40l5h9lJQxhCWag8vVzr78OtYPHmZht86xUgO51hhjej4upYdsZRwAazxBOpU8uSiOEEIIcSre9pXE8vLyk4+ysjIOHDhAW1sbmZmZZGZm0tbWxoEDBygvL38v6xVCCCHeMzdevZn2ylwic+aQmDRRuDNNaH2K+cdaUSaN9PUtBKDy4iP4HVbMrSdIG4aoyqgh5jPQ4ppHlXOSwuAYzyh9mDAwR82ialXdyffo7DlGcmCQkMmKoihkJjIZMo0z6Z8ga1ELAWsQa7QQgKhtCHM8B1A5nDcXgMVdOsdiBizmCB1UkDIq5OsTpB12bMkUCdkGQwghxLv0tkPifffdd/JRWlrKlVdeSXd3N48//jiPP/443d3dbNmyhaKioveyXiGEEOI9o6oqW25ZwY7ST2BwQnqnFX1pkpRdYc2OnfT3zScRtWM0Jjm4sQSAdMd2TOkMSnNrODiUy2LPEApwPNBJCp1sa4L3zb0exaD86X0eepChnEoAPHEPveZhgr4p9k0Vo2eMYUg6MSRtxGxjqJoZVbOg2IwMO90s7tRpi6gYTAE6qEAH8tJ+0m4XtkSKZCw6A50TQghxLjmlexLvvfde/umf/gmT6U/TWUwmE//4j//Ivffee9qKE0IIIc60+WXz0NcH6KrZgp5WyHlYJfjhBDleL9ljPk60r0FRYO6GE/Rlu0gc34Oup6ktqCA5mqAvr5xMU5Q5Ux08lg5SZrBRk1xO9tLsk+8x+drTtOdPXy3MimfRb5ogHAjQOpWHYh3Hp4IllguqRsIyiSnuIUuJ0JpThiMOxcMKo2qASTykkyZMaKg5diypNAnZo1gIIcS7dEohMRgMMjIy8pafj4yMEInITfNCCCHObp98/7VsryhHLXOQ7DfjCKSJVeis3rkTv68Av7cAoy1N3woH8XQYzXuMzHAdDrebPVPlLPIMY9HibI+eQMFAtsHA8voLT46fiARo7++BdIqseDajpkligTDuhMKk4mfMoGGJFoCuE7OOYk54KFPCNOdVAzC/T6eDBABesgBQPNPLDMT6+85ss4QQQpxzTikkbt68mVtuuYXnn3+eYDBIMBjkueee49Of/jSbN28+3TUKIYQQZ5TD7GDt9dXsq7wJ1axhe9JM8PIk9liM8p5e2jtXoutQumGQtvwsEr37UHQjDbWZ6F1+lEIHBkWjOnCUP5Ikx5Jkne0ajJl/Wi8use15oqqJjKSTgCFIMhSn0m9nSgkybtAxalasik7COokx4SKfCAdy6tGAxl6dEylATzNIAQBJ+/R01uTg0Ax0TAghxLnklELiL37xCxYsWMAll1yC2+3G7XZz6aWXMn/+fH7+85+f7hqFEEKIM+7i+o20NiaI11WixVSy90BkSZpl+/eTCDsYGarGnJEiuEIn6j+Cnk6QH16ExWHjVRZQ6xonLz7Ok/EhCoxGMtJZlC740+Juse0vM2bNQUHBoKukwwkKKxejlm5m1Di9/2GGQQVFJ2nxYdVMjJkymHJYqOnX8SYVUtY+uigDIGlOT38dkpAohBDi3TmlkOjxeHj88cdpb2/niSee4Iknnjj5b4/Hc7prFEIIIc44RVH42Ieu4pmiy7Fkp1D3m4itSqOSZv6x4/T0LiadNlC4ZpymUg+pkaNYgzXMWewj3B4iN296D8PSUDMvqRo2BVZWXI7RZSRrUxbvu/FzDOdP773oSrqIxCIc0mL4sisZKDQD4DQYQNeI2UYxJ9yU6wmC2TbMSagcgWF7P6PkAqAZplc1TY+NzUC3hBBCnEtOKST+tzlz5nDFFVdwxRVXMGfOnNNVkxBCCDErzM2ai3ZhktGataArZD1lJLw+zfyWZpSIQm/PQsyOFMryIKnAHhRFpTQ+D5em8mrOcjzmCPXh4/ynHsVjglWOS2n4QQNFHynCVRmko3B6RfDMRCaBRAjryCiGdIpwhYOYmiIVKiTDNELKHMCYclCrxRgqdAGwoE9j2DpFEAc6kKFOAJD2Ts5Uu4QQQpwjTjkk+v1+HnzwQb773e9y5513vukhhBBCnCs+tfGjPFRRhr1Cg14VrThN2qrQcPQYQ0N1JGJW8hsneWmOjp6M4O5dTXldL73DNuZ6fKjpNK5ID0csKYy6gZqJxQD0ZB/GmDCRUsCdcBPQQgxF3NR2HEXPstKapRCfKiGvaHohmrQhRhEx9uSXoCuwtF9n2JwgBcSwkKN60RTQfb4Z7JYQQohzgfF/f8pbHTx4kIsvvphEIkE4HMbj8eD1erHb7eTl5XH77bef7jqFEEKIGZFty6ZhczltoxspG9hG5h/MBDYmqX76BK318+nqXkJt3U488zpJHziEMfs8Cuw6Jc0TtC+tQxmdYl1oP3+0z+GjioV53rW0FO0jYglQ19NJyGwnM5HCb/RxpWkX4RMKLTWLOVjtYPGuGB53GHp04tYJbHEPPr2BlGM3VX1pdF0n7ujGF84kW5lk0JiJaUq2wBBCCPHunNKVxNtuu43NmzczOTmJzWZjz549dHV1sXDhQu65557TXaMQQggxoz7SeAOPzLPhqFUgAGoaUnaVhiNHGR+vIhp0kFXlY0/V9DZQuXuXUVHQx/7UHModPjIiE0TSYXqsGnmhckxpBwCO0H58TjdG3UiYBBY1yqb4QewTUwwX2whZFJKBIjLMk6TMAdBVchIVaNkKpoRC5QiMWQfxkYmZFAmziiL7JAohhHiXTikkHjp0iNtuuw2DwYCqqiQSCSoqKvje977HV7/61dNdoxBCCDGjLAYLV1y+kb2VGzE5Uji3GYmuT1DV1YEjmqKrdxmqUcdS8Sx6OorJtoyCwmE8HaMoxU5AYU34EDvMSdRUjNrRZcTH4uzc/RhP/OSHJJNJkrrOVk8xFQ4fxW0d6AaFg3MshIbrKJrTAoCmJshN2hiaGwdgUZ9Ov3mKABkogDErhRoKo6fTM9csIYQQZ71TCom6rmOz2QDIyclhZGQEgOLiYjo7O09fdUIIIcQssbnqMv5Yr2Ott0ASDFNGkhkG6psOMTlZSnAyE2eulxHHIIbsOeS86GGe0sZrxSuxqEkWh44xquq0m9LkH5tD+5fb6d7aTaCjjba2NlRdZTRjBV9d/BVK+jpRoin2z7UyNV5Pds4A6AopcwCPkmR3mQtd1Vk8oDFmSTKq2AFw5UdRdJ203JcohBDiXTilkFhXV0draysAS5Ys4cc//jEtLS18//vfp6Ki4nTWJ4QQQswKqqJy06YP8WzZcqyeBI7dEFsbp7K3G1ckRU//MhQVEmWvA2DSlzEnd4ipISjyRIglUpTER9nlMFJlX4Al33py7OaWFiyahVRWGS8Vnkcqx4ihL0zYptKUV4AWV7AZUqTMQdKGCEOR9aTzdcoHdUCnzTl9ZdHlmf6akm0whBBCvAunFBJvvfVWJiaml9r++te/zu7du1mwYAG//e1v+e53v3taCxRCCCFmi/Ul69m+2IC5wQEaGAfMRDNN1Bw+hH+qiKnxLOLFe9BIYSxeTPZ2WDi0n+NVDQDcGNnOiFGn3xCmqn7+yXHb29sxhg1ceiwIwERZOYbBCIa0zuFKK972YnLyugFImKcomFpIvETDElUo8kJHRggAk1MHIDU+fibbIoQQ4hxzSiHxuuuu46abbgJg4cKFdHd3s3//fvr7+7nqqqtOa4FCCCHEbKEoCp9b8ykeLZ+DozCG/ahGclmMyt5u7NEUPQNL0U0xgp7jGHNqUCYyWGTuYY9tPg5jAsLDqJrOXrPGRQU3nhw3nUox0NpPAT48kwk6iuejJDVKB6fozzNxdHITheUH3phyGiQvVsx42fQC5fV90G2OAqCZTSgGXUKiEEKId+Udh8RkMsmiRYtOTjcFsNlsLFmyhKysrNNanBBCCDHbrChcwbEluZgbrICOqdNC3GliXstRAsECgpNuIoW7QTFgKlpAQXOawv5uDAU2wkkDN8Zb6bGacbkXYC2ynRy3paWFJTEbnx/SSNqmx87tmg57O+esJDFuwpiykTIFURSNDtc8ABp7YETRGFeNpBUjVk9CQqIQQoh35R2HRJPJxNDQEEbjKW2xKIQQQpz1vrD8i/xXUTGZFVFsPSmSCxPM62jDFIvR3beUcF4TOhq2uYtQxgxcPPgKu+auBmBJdDcA+yxRyhqqT47Z0dHBmP11LhxJYtJ1FBMkfTFyp1J0FWcxfCIXiykBCsQtXiajK0hl68wd1gDYbs1ARcOekyAp9yQKIYR4F05puun111/Pb3/729NdixBCCHFWWJC7gL7ldRjrzSiqjrnTQMJmpKCnm6lAIcGEQsRzHC1jEYrZzLzeMIMJJ05rgsFQmPmpJCdMChvyP3JyzHQ6TdOeFzCn+9g4miJtMzNotrK0I07KqLLXuBqHaxB0iFkncHkbSFTpuAM6zojOPrsFmx5HzZF7EoUQQrw7p3Q50Ol08pOf/ITXX3+dlStX4nA43vT722+//bQUJ4QQQsxWn196K789tJ+PVA8wecLJ1BJY0NzKYEUFnZ3LKMo/iMM3H9eCOUwdaOWi5lfxlpVgaRvjY/FXuc1xIVNZC7AUWYkPxQA4tH8cLfEKW7Iq2WY1EDTaWNQzwYuL7DTNX8na3n/DmJxLyuLH5K8jlJ+FHT/1gzpNlUYcSoTh3FJMXXIlUQghxKk7pSuJv/vd78jKyqK3t5eHH36Y++677+TjN7/5zWkuUQghhJh9qj3VTCw7D1OtEcWoY+/RMCs69rFB/FPFeF3T9+4nK1aCqnPRiaO8XLkWAHO0hexkgiZLioKGkpNjvu71Eu7bzeLxAFajwvTc0nHq++IM55XSHSzCkLKBopGw+PFZFgJQ36fQb1SZVBW8liw03+AZ74cQQohzxymFxO7u7v/x0dXVdbprFEIIIWalTy/5PL/KyyS7NohpEiLVKqV9vSiJOCcG5hDL6AEWkVkZwzimUN92HKsLekIGrkyMEFOgqujyk+PFkgkeznKS7tpOfVwBYNRqYFnn9P6H+wqWAdM/j1uHGYufh2aGmt7p1++3WfHjwqIOo+v6GeyEEEKIc8kphcTZpquri9zcXMrLy2lsbCQnJ4eKigr6+/tnujQhhBDnsMrMSoLLL8FaraBaNFx9cbIiccwTI3gny5jKOoIh7UBdVA2Kzg27n6d1zgJSuoF12msY0ZjM3oDR/ae7P37tzCPUv5M1gekFabpz3BROpsmaCnFs7kLitgCGpJ2k1UciVEy0xEjpeBpDWmef1UIEG1Z3jLTPN1NtEUIIcZZ72yHxscceI5lMvu2Bn3zySaLR6CkV9U5VVVWxYcMGfvWrX9HU1MSqVat44IEHKC0tPSPvL4QQ4q/XLY2f56ksG7nzgxgCCmoemPwTGFMpjsWmPze9WWvJLI/iHIkTTGcDOr7YIHV6EL/RSP6GMnKvyOWyWy/k5mvd/GFxPdWj3QB0ZHtQgMaOMEmzhfZ8F+aEB03RSZkCBHIKMWqwYFxjr9WKjTjhfCep8YmZa4oQQoiz2tsOiddeey1+v/9tD3zjjTcyPDx8KjWdkubmZhoaGgA4evToyX8LIYQQ76VSVymdKz6Ao0pDceoU9AZwR2OYxofo9GeSsE5gDy3AsSgMwEde/SPpbAe9IScfCu9DQadw5TXkX5PPqrWjXFi1nYlSM+7u1wGYUEFHZ0mfhikRp6VoDmrKAkAqsxO/dREAS4c0eswmNMMUY5l5pEaHZqYhQgghznpve3VTXde59dZbsdls//uTgXg8fspFvVPxeBy/309BQQE+nw9d13G73Wfs/YUQQvx1+9jSL9L1+m/Ib/AzutuDzahjmPJiyi9lwDxEVXAhHdWNFJS1Q98Yj65eR7X3EGV6Ey77JibC9Th5hhdiaVZqKrW1RxndbgBAiWsEbDq5sRTz2w9zuH4FQ54JPEkzcbOfcX01dWylYkCBJdBvDZJMV1HbfxDYMKN9EUIIcXZ62yFx3bp1jIyMvO2B16xZ87YD5bt1/Phx6urqAGhtbT35byGEEOJMKHIWsa92HXPjLzHcoVM+5GWkwkb2aC+teVlUAcb0QlwLmwj02dnY1MyATWc0kiTHGWRqLBslZcNnjNI2lktdQScPL78WezyBOWhiOMtG5iAsa2vicP0KDldmctHRbGKGBHEbBNxWCvsSAHRaYyTDRaSGD81sU4QQQpy13nZIfOWVV97DMt6dlpaWk9NLLRYLx44d49ChQyxevPjPPv/uu+/m7rvvPvm93+/nueeeOyO1CiGEODdp5ktR1OfIXBaEZxVUXccwMsJYvo+EGsMxsZjORU/gbkqw4MRxTmxcRr8Xbhh7ll/o1xAI1mLyHGLHZJy6AphT28qB/bVoMQvjBQZqB1XmxbIpHB+grbCAC5uiwDDkNuPNKqayq5P5kxrHnApOPEyO7mSvfLYJIYQ4BYoua2RTUlLCwMDATJchhBDiLNd3TwMFk0Mc35VLk6GI8Qw7+Z5cirMvo1orpHv139H4SjcDz+TyUmMjcT3IqtxBvpF/O93qCfTEvxM8EMDYFOHyzVa6Cu6gO17JosocNh2KUmHew0OeGM9vuJqNh8ap8e/C7vBTvtfBgpZHeWVzip8ttHJZ32XcEH+Nhf+ya6ZbIoQQ4h2YLblk1m6B8YMf/ABFUf7i4+abb57pMoUQQoiTspbcjIk0xnVR8qfCoCjkHGujzzAGgN23hIFFdozFadYfPgyqQm8ogxqlFXtgLqO/H2P8qQmGByPs3BHm/Lm7SaMyLzK9HcaIzUVdx1FMqSRHqpyY4tmEoy5C+TkAlA9ML2jjsw4xYsyG0PjMNEIIIcRZ7W1PNz3TPv7xj3P55Zf/xedkZmYCoCjKX3yeXCwVQghxJjgbPgAvfYs8Z5SpvOnVTKcsRgzeY6RcDThHljK85AWW1vsYGMzBqJsYjrq4IvAsE+l6xhrKCLccA+DYsTg11l14LJeR7Z9CUwoYMJRSgJHa4U6OltYyaSvGyTjJql5iO8w4BkxAnCnLEP3hKhjcDzWXzmBHhBBCnI1m7ZXErKwsamtr/+KjsLAQgL6+PjZs2EBdXR319fX8+Mc/Rtf1kw8hhBDijPBUkMqtxeVPoV8WxZFIMJ5lp2bvHobUSWz+GnTdwWijCb1QZWFXHwCZkUnyLCM45646OZSuw77dIS6qeJndajsRm4qSdFGda2FJ72EAjpbmAxA1RJl0l5E9GWFOOMmIdZIh8kn17T3zPRBCCHHWm7Uh8Z0wGo388Ic/5NixY+zevZuf/exnHDt2bKbLEkII8VfIWLsZMxpZthgec5i4YkRPpZiK9qCi4hhpZLDYQmn9OHnBMArQFshhAbtpiF6GuWDOybFefi3F+pKdDMRzsKppPCGN4xWlVI53kheYpKXMgqZ5mArkECvOwqDrLO8zM2lJETQk6DvRPHONEEIIcdY65ZA4Pj7O3XffzWc/+1kmJiYA2LFjB93d3aetuLersLCQJUuWAOB0OqmtrWVwcPCM1yGEEEJQcxkAnvE0tlVBAEbKrWTv24qOjnHgfFImFW+jCnkWiicDjMedrEttpzFhZHnV8pNDHT4cQkvEWFHRiiM0iKrDK57zsEWnmD/cQ8qo0pldja4b8NclAcjqzQZg3DbOkDcAWvoMN0AIIcTZ7pRC4pEjR6itreVnP/sZ9957L4FAAIDnnnuO22+//bQW+E719PRw8OBBVq5cOaN1CCGE+CtVtAQceWREFDxzgqiKxmi+nYyJYXyaF3ewGiVmoq/ERknDMMWT00FyImQjy3KE9cUXnhxKS6Z4abuBTWWvcdQQmn4eOdjcWVSMDmFKpjhalosO+N0mNEXFNTy9R/GEdYwBPQfGj5/xFgghhDi7nVJI/Pu//3tuvPFG2tvbsVqtJ39+8cUXs3379tNW3DsVCoW45ppruPvuu3G5XDNWhxBCiL9iqgo1l2BPRcn3xXDmRQgGbUSLgZ49mDES791I0qziXwQZTiOWZIrWqVyWGp9DLYvizM49OdyTL1rIMIdRaqdn6uQG0jTPW4uehqrRIcbcJsYz8pn0FxPKdzHPO0xOzITXMkY/Rej9cl+iEEKId+aUQuL+/fv5whe+8JZVRUtKShgZGTkthb1TyWSSa665huuuu45rr712RmoQQgghgJNTTrPHNZxVIXRNZXCVBXPn9L6FpoG1KEnoK7VTWj9MoT/EVMpGhdbDQaOB+Q31J4fqPtLFRMDKeVXb0ElSPJlma/ES0BIsHOkAoDVvHum0mfEGJ85kjKLBfILmGGNG8HUdPPPHL4QQ4qx2SiFRURQSicRbft7f339yW4ozSdd1PvGJT1BXV8dtt912xt9fCCGEeJPK9WC04dYzyM2dnk46abaTcIwSj4xRls5jbKKShEUltChJpjr9R9fjk7lcpOxiwdyak0PpiRj3PVtGls2PrXwnJb4EQzYbhkwH2eEA+b4Y7QUe4gYTI4XT+yVm9ecBMG4dp69/5jdlFkIIcXY5pZC4ceNGfv7zn5/8XlEUYrEY3/rWt7joootOW3Fv144dO7j//vvZtm0bjY2NNDY28uSTT57xOoQQQggAzHaYcwGGiJfK2BQmZ5LgoIPQRWkYOIAdC/72i1CT0Ftmp2HuMPZ4kuNTedxoeJ7FC9IY3QUnh9v5eoBUUiGn9gXMsTRqWqe/cAEAS3omSBlVjhdWMEYJOjB3KA5Mh8T+oAKxqZnoghBCiLPUKYXE733ve/z+979n1apVxONxPvOZz1BdXU1LSwvf/va3T3eN/6u1a9ei6zpHjhyhqamJpqYmrrzyyjNehxBCCHFSzaWgaxSMJ3CVhohPWfDPNRALNQFQmixkwltOwqKSbAziSmnEVBNTUxaK1SHstWtPDpWY8rPd58SZOUpGUTPLBmPsyC9AV0yUTYxiSWi0FlQRTzoYr/SwZLILcyQTr3WMPgpgUKacCiGEePtOKSTOmTOHw4cPc+mll3LRRRdhMBj42Mc+xsGDByktLT3dNQohhBBnn3mXAAoO1U1uzvQq4MExJ4HzOtCifirT2ZzoWo4hqdNbZqO2xAfA631zIJbCsXAjZauv4v3v/xgFN97F81OlaCkDOfV/ZGFHLxGjSjC7jpQhRENfAL/Dxqgri/55xeQEg6i+KsKmGN1GO9HufTPYCCGEEGebU94nMS8vjzvuuIOtW7fy9NNPc+edd5KXl3c6axNCCCHOXs48KFkG8SC1Bi8oOoEBB5E1aaK+Flxk4Ehk4/OWE7cayF3cjzWlMWW0oCYLsBeWYtt4M/WqDoA/lsOhwFysnj4qlNcxp3WaC4vRibLsRDsAR4rnMOguRweq+xwATFgn6O9qnakuCCGEOAudUkg8fPgwLS0tJ79/6qmnuPrqq/nnf/5nksnkaStOCCGEOKvVboZ0ghJfFEd+hNCgA02DkcpjAFSnXBzrXo4xAT3ldvKzoiSMJgZ8EaxGjahqpsI3BoCedPP7dC5aykTBwoMsGPRyJMuCy55HsXeU8rEk3TmFTFiy8Xk8bJgcBGDcNk7P6BTo+oy1QQghxNnllELipz/9aZqbmwEYGBjg2muvJRaLcd999/G1r33ttBYohBBCnLXmbwHAolvJzQqipVTCQ05Sq/egpWJUBzNJJu14x6pImlUWLTvBtg9dy0R+Fk5THJI6SWcIACWVTSA9ykTv+diyxlg9+p8AdBVW47BorGiLoasqx4oq6assYYm/m3Q8jzHrOD3pHJjsmqkuCCGEOMucUkg8ceIEixcvBuCxxx5j+fLlPPPMM/z2t7/lkUceOa0FCiGEEGetrEoobAQtRYNl+orgVL8DzCkGjYPY7UXkJOK09C7DGLUwVGnhywP3kzbayTAHUXQ4NC8bUzqJIeIg2dND0/jlaCkTi8r3UhiMs63IidlVRM3YCO5QmuaiKroqKrENJbGHC4kZo7QbncS6d89sL4QQQpw1TikkJhIJLBYLAC+//DKXXnopAPPmzWNkZOT0VSeEEEKc7eq3QDrB3NgUZmeCqd4MdB3CpTtRFJWVPZBOWxgdqyZlVCnJPEr1ZCcOixeAV7PchF74Ge0/upOuO9roIIm/ax323BirJ54lZFJoL6nEZOxhRVuMuMnMkbJa/BlZXOGb/kwetXrpOLp/5noghBDirHJKIbGmpoZHH32Uvr4+XnzxRS688EIAhoeH8Xg8p7VAIYQQ4qz2xpRTg8FMfk6AZMhEdMQGFTtI6mly7dW4Y15a+xZgDGXRX+QknHbgMIYBCOBm5PALpEJ+9JTO8fYX8B6/BC2lcpHr91hTSV4sc2OLx2jsjmNKaRwtrqKvrJQL3rgvsd/qp2dofKY6IIQQ4ixzSiHxjjvu4J/+6Z+orKxk7dq1LFu2DIDnn3/+5DRUIYQQQvCnKae6ziLz9JW9wIADxRZjQOnAmFPN0oN96LqRwZEaHGNLyc7rJys2BYCpYB4Z2fknhxs49DLpmBvv8UVkZIXZMvUUxzINKJ45WFJQ3x1h0pnJjsXLcXVqWNMuJm2jDCYdEAvMRAeEEEKcZU4pJF511VX09fVx4MABnnrqqZM/37RpE9///vdPW3FCCCHEOaF+C2hJqlUfBnOKqd4MAKJZrwNQZKkkJzhK21AVxuPv51iigaKJCQC0dAZ5DdUnhwo2d+BNBRhp30wqprLJ/jgmPcFrtXMxmoMsbkug6Dp75zYwFc5nqRpCM4Y5anTiPfLcGT90IYQQZ59T3icxPz+fxsZGVPVPQ6xevZr58+eflsKEEEKIc0b91QCoqoH87CBRr5WEz4Q2dx8xLQ4Va2jcdQRQ2eM8hDZeQPFUEAAl5SRjVfHJofS0zs6RnZAuYfRQDnZriGtif+D5QhOlNjMlISgZDdGXXcCuxmWsH48C0GIL0H7g1TN+6EIIIc4+xlN9od/v59lnn6Wnp4dEIvGm391+++3vujAhhBDinOGpgLI10L+bRaYRhvDgH3CSt8DHcLyFSscScpJWisf7GMwtw2QZoCAYBxuQtpEuC2EumEtipAOA5raXuKLkEiZay8leGOB9lid4Ur2MPXM8VDVFyD0eo78gg6dXrec7rz+LKU9hwj7I0Jh7JrsghBDiLHFKIfHgwYNcfPHFJBIJwuEwHo8Hr9eL3W4nLy9PQqIQQgjx/1p0HfTtpNo2ybMGjanuDPIW+PBl7aUyugSlYg2Nu58h55IgBl3HbdQA0FJG0g4v9trzT4bEoY5DTC4fRcuoZXjfCJUb+rkm+QiPlH2Un7fFWOBNMzIxTntBGccTy6iz7eOIPsCr2kI2jfWTmVc6k50QQggxy53SdNPbbruNzZs3Mzk5ic1mY8+ePXR1dbFw4ULuueee012jEEIIcfar3wIGCyYDFHgChIftxANGDDWHCKeDmEpX4QxHKHrmBK8UJ7C4UyiKDkkd3aTinL8aFAUAXdfZfuyPTObU429zEAq5udDwNAbzGM1lFuo0A9GOJLqi8PtVl7BYS4Ois9MGHa8+NLN9EEIIMeudUkg8dOgQt912GwaDAVVVSSQSVFRU8L3vfY+vfvWrp7tGIYQQ4uxnzYTazaBrLLYNAwr+bhfWvDi9sSbMZheGvHosKYVlfQqp7CQmUwolkUYz5eDwmHHNXXZyuB3Hn0bRXKArnDi2GKOa5ibt1/xnhZlaq4HicYWiyRGaqmrJ7FiEgk7S1caB490z1wMhhBBnhVMKibquY7PZAMjJyWFkZHpJ7+LiYjo7O09fdUIIIcS5ZNH1AFTbvRiMafwdLgD8udvRdR1TxVqOz5vD8bxaEp4oVjUOCQ2zUobB5Me2bMvJocKxKYb3vcSop5D4gImxsQoWqwfIcDbRU2Ti4kSSso4eNFVla84WyswaiqOTF6NVjI2NzcTRCyGEOEucUkisq6ujtbUVgCVLlvDjH/+YlpYWvv/971NRUXE66xNCCCHOHXM2giMPo0GhONtPdMJGzG/CUd3NcGoIY8EiTJqNuXY7cXcQVzKMktRwKVWkLONYSheSkec4OdyupifoK12MORqgu2sJekrlJv1X3DcHLrBmcsSbT5FvnH3VdWSpNaTQ6HP7OLhr2ww2QQghxGx3SiHx1ltvZeKN/Zu+/vWvs3v3bhYsWMBvf/tbvvvd757WAoUQQohzhsEIi28AXaPRPgyAvzMTa36CntRBFNXAfArps5pIO5MU+b0oKR3dUABmL4qiMG/xEgCqSgq5aPH1xJ1VqNEwiYSDSG8d+coo5ZnP0JNv4qZkmk2HXkZTVfqTNwFQWPAafzzQi67rM9YGIYQQs9s7Com//vWvicfjXHfdddx00/SHzcKFC+nu7mb//v309/dz1VVXvSeFCiGEEOeEpR8DFOZYfJjMKXztLnQd4jk7SaUiOIpWsbijHRQoSCQBiBpcKCYfACV1VzH3W3O56dJGFletp3okjM/kgmSSnoGVGCOwRX+Uh6p9XGHKJTBqptQ7SmtROTZLBaPKJMeMTto65d5EIYQQf947Com33HILU1NTJ78vKyujt7cXm83GkiVLyMrKOu0FCiGEEOcUTzlUX4SqQEWOl/iUhciYlewaL33xDlRnPmlHBQBZ1umXhHUbqmUcgLSxDGuplaCpH8UQotib5EReLcZoiEmSFLYUYlESrHP9kr4cA3muZVy6/1VQFCK2TxDUFBbPeZKfPXNwhhoghBBitntHIfH/nZri8/lIp9OntSAhhBDinLf8k9NfbEMAeFs9WHIStDoP8Hv1VVS1B4Bc5/ReiaGklSKMgMYURkwpI/35GkZ9BJOSjT8jFzUWBgWC/o/gGlWZTws7617jBoOL0p5x5owNMOGqIGGpJ2zppTfcgy8QnImjF0IIMcud0j2JQgghhHgX5m6CzFIKzCFc7gj+ThfpuEpm4RGSHbvQvdkA5GZP74tIXCdfq0Uxe5k0aOSEy+goVMn0D4FioG5knKmUGYBhxUpOq4tIKoN1jv/kUPEU+XmXsPHoXgzpNBHPzTRFzVxc/TQ/fXLPTHVACCHELPaOQqKiKChvbOT7//+ZEEIIId4B1QDLP4kC1OeMoKVUfO2ZZM3zoxp1+vKnZ+7kFE5/VWJpDJZiDOYxwpjJD1QTN4MePMKRnp0898R9vNQ9CbpGizqEHr+WurYANmL4a3/NAkcuyztHWDTQQdJcgNe+kZS1n47xHcSTMiNICCHEmxnfyZN1Xefqq6/GbJ7+a2U0GuXDH/7wyT0T/9u2bbK0thBCCPEXLb0JXv0eS/Vh9pjKmWh1k13vo666H7u7ihTgqUjD0emQmM62o070kArV4whVA8/xjQMvcGL4cQDsisKVS0qIWMCgbCI1/p/0TM1jXmYTr9e/zLLIZaxs38/xgjLCmR/gVd9uPlDzFD9+bB1f+dAFM9oKIYQQs8s7upJ40003UV1dTXl5OeXl5XzkIx+htrb25Pf//RBCCCHE/8LmgeW3YFE0SgsniU1aiYxZsayMUNN4IQBm9xRmPQGxNAlXEpchBIASLQYdMhZ5Tg4XGWrnRP8YiqryvHOMkeSlXHCkn3E9n7zC35EuCNPoVVnd1YJucLLfegN2kxdv8EGCseSMtEAIIcTs9I6uJN53333vVR1CCCHEX5/Vn4fd/8py+yC95DLRnIVj0xBB9yiGsJN4YpycdJjBmI2wwUllCiYAn6qTGcsidqkB24sjRFPTIe/513cxt7qS5tQJNqUvw5d+kZYTH2Vt7U/oavxX6oJfo2ZoD635pQxlnc8D4QN8ovJlfvLIY3ztIx+a2V4IIYSYNWThGiGEEGKmZORD442UK0Ey80L4ulwMTRWQyr0aiyWPRGKcAmMaJZZmUC+i/I0rfsM2P3mhMuKZaS6vqTw53MRAB8ePH8cT9vGHEhM96oVc2XWAZyPX47SM0r/gQeoTeWxoO4yqJdhhuYE0BtyO+xiaDM9UF4QQQswyszokrlu3jsbGRurr6/nMZz4j220IIYQ496z9EqCwJHcANAW9RaN0fBiLOY94fJRSlw00GEvkkZlKoxj9eFWF4qk6AMovKsNl+9O00+dfeIG0wcjzhjbKU5uJ2bpx7lhLk74E8ndSUT6CJxqlsaeVtDGXb2t/S627nV8+9u2ZOX4hhBCzzqwOiVu3bqWpqYnm5mbGx8d55JFHZrokIYQQ4vTKqoRFH2JRehyrO4b3uJuYIY7ZkkcqFaCiIHP6eTGdcLYZu9FPOO2kcGoeAK1VJj6y6n0nh/P7fOw5eIgFJ/bzq2orPYZVrI3sYlf3ZxihAF/NQyzJUFnW34Mz0kencQU7Wcvyij/w4s5dM9EBIYQQs8ysDokulwuAVCpFIpGQ7TaEEEKcmzbdgcFgpKF4GLs7zlRXGxZLHgCl5U4AlEiKcJaTQi2IrpsIWmK4EjZGbGN8oMFJSc7ck8O99tprGANT7DUN4zRfhs/ez+rDKv8e/RJxxYy14VE85iiXNB9D0WL8UvssKdVIx/gdxGQRGyGE+Ks3q0MiwAUXXEBubi4ZGRl84AMfmOlyhBBCiNPPVQTLb+E8fYCPFrQzZ8N1WMzTIbGwaPoPpEo0zXhmAWXBCQA6nONkBwqIG+MMZI1yzerPnRwuHo/zwqGjLD+8g+83OEgpteRrAyzf5+Znyq3o5jALFmyjIOmnduAlUgYrd6a+xdyMTn768D+c+eMXQggxq8xoSFy6dCkNDQ1vefzhD384+ZyXX36Z4eFhotGo7L8ohBDi3HXBVzEabFhTARg8gPmNK4l5dh8AxnCcYVsJJWN9AHQbkuSEpqec7slMsrjYzcKK804Od+DIUexHDxBMjHOoai2RjB4qRgwYu3P5D+XjKA4vC+tf5YLeKPbgQYZMZfwqcQsLip/hqWf/8wwfvBBCiNlkRkPigQMHaG5ufsvjqquuetPzbDYbW7ZseVN4FEIIIc4p1kxY/2XiJg9YnFitRQCYGcSmxzEFYvQbyshIhzGpYSaTbmrDdRh0hVZbjCrrPq5e9RkUw/TuVrqu80pHL+v2bON3VWaS9gbMaoDNTWkOTq1kq3YVjswxaute54bmPtSon5dNF/NabB1hw48Z6joxk90QQggxg97RPoln0tTUFIlEgtzcXFKpFFu3bmXDhg1ves7y5cvZv3//n3394sWLOXjw4J/93d13383dd9998nu/389zzz132moXQgghToWi1RCf/33MTQPAFEYTtLXtIke5gKGom4jiJJFvJ0fzMhwvxp+5H0/EzJjdh9PRSm7mB1i84HKajj3LBeevYXXjAjL7jpHrHeU3S8q58fXDGKfK+VLL83xz5XUUxEdYlr2H+enX2dKm8Vj9Fn5t/Cy3Re7k8ZYvUXnkFgw2x0y3RQghxBmm6Lquz3QRf05vby/XXHMNiUSCdDrN+973Pn7wgx9gNP4p1w4MDFBZWckvf/lLLrvsMgCuueYa5s2bx49+9CPcbvfbeq+SkhIGBgbei8MQQggh3pHnnnuOiy++GF3XeOXVejyeNdzz0qW8PGYjtrGQG5vupbcrh33ulcwr+D3W5DjdeT18csyGo/NvGU+Y+bktyudKjxCPW3AcP8K4p4L/uPZjnNcXZ+PuSTJUPwcK23h0zWa+MXU7VZknmByr4pmhS3h68SbUYIK/iX8XUgY+f/6PcJaUzHRbhBDir8JsySWzduGa8vJy9u/fz5EjR2hpaeHHP/7xmwIigNfrJZVKsW7dOgoKCigoKKC1tZWVK1e+7YAohBBCzEaKomK1lhCNDlBbmj/9s1CSkYJiiqJjAPTqZupii1B0eNWpUmXZjsvsYY41m2R0DrpuIFpYRJ63kwtbTrCjzMJorkownc/H/S2sPbCPb2Teych4JVl5XWwpfJErdu5CyzBzj/Jlwikzdz/1Zcb37J7JVgghhDjDZm1IfDuamppwOp3MmTMHgJ6eHnw+H42NjTNbmBBCCHEaWK3FxGID1JVNh0RDME5n1jzyEmOATjpSjmqNkB0y0mmLUO16EYCGhMrRZC4qOvYiSKNSvmsrJaEk95+XS1pNcSDyQe44+nPm9vby5dzvMDpRgSO/jQ/O3cr7DjWhZVu5V/08w+Txo+2/oP3f/41ZOvlICCHEaTZjIVFRlHf1ADh8+DALFy48+f3BgwdRVZWFCxfO1GEJIYQQp43NWoKmxagpsQJg9wbps1diJI1H86OFq9nl3k9GwoWm6GzL1LHoPcxNGjiWMlKoewiG8uh3xvjlk49R/eufoasKryzIIKplEcxZw7f+44dk+/zckXEHI2OVqLnH+XjFA6zoaEMrsvOk/VoOaQv4+eQBdn3xM6RD4RnuihBCiPfajIVEXdf/7COVStHY2MjmzZv/x+f8918ym5qaWLRo0ckxDx06RHV1NXa7faYOSwghhDhtbPZyAHLto5j1BCZ/lLRiZDS3iIrUCJpmYyztYJ7egKrBk84M5lm3YUSlJmEmmSzk8OEj/PL+l/CGIjzx1AN89MVD7K624HWq7I+8n7lVU3z3p3eRVgz8VPkb+voa0Nzd3FrwE5b1tJMucXAkbyWvxM7j3lIPf/jIBwgdOjTDnRFCCPFemnXTTe+55x7q6ure1nMPHz78pqml7e3tVFZWvkeVCSGEEGeWwz4XgGikgyJDgETcCLrOYGU5cyLTW1SkQzWMW8fJm7LSZktQkPMiuh5lccLIXgyYdSPJpAZALJni3//9y3y+OcDTyxyAkW3Oz1Mb7+L//PT7DGbl8vv0tbS3rQT7CF8s+g6LR7pIlzkZm1PFq4FV3LvyYu7/xm10f/tf0GKxmWqNEEKI99CsCokDAwM89dRTfPKTn3xbz52cnHzTlcT6+npefvll7rjjjveyTCGEEOKMcDim77kPRzqpdKZIKiaUSIrukmryfKMYSWMOLOSQ4zi1qRoAHsl0kK3sxKOpjCSsXFd/OUuXLj055ojPy1O//DKrIxpHy81MJWoZWLSaRR3HufOBH3GwvJqdybU0N29EJc4X87/Gct9xUsVO0ouzaAnO495lN3N/82F2fOBqIv/DdlNCCCHOXrMqJH7pS1/irrvuQlX/97JKSkrQdZ2VK1ee/NnXv/51YrEY3/zmN9/LMoUQQogzwmotRlWthMMd1BdmAmCeDDPoKQMVyggQSuSiaWbSZgOZYRN/dNpZ7HoSXddYFLMQSmdz6cWXUFJYcHLcXcf30fmT2zFlGwhbFA4YboZqByv3HOHb2+7i5ZpGOhPz2H/oEkxTWXzR/XU2hvYQy3ZhWW1hIpHN/VU38G8ljfzhK39L7z//Mymfb4a6JIQQ4nSbNSFx69at5OXlvemvnUIIIcRfM0UxYLdXEQ53sKJm+nYK21iQtMFEf2El85Q2dFSyJtazw9VEbbSKsAEO5U+ia8cpSxnYrkWoUUu45oMfwmEznRz7j4efZ+Cn32Swwog5pfJEzZ0oVp01f2zmW03f5On5SxjX89h59ALUnno+7riLq1JPEnBkYz7fhjFT53X3Wu5u/Bj/0dHOzi1XMvnb+9GTyZlqlxBCiNNk1oTEHTt28OSTT1JRUcF1113Htm3buPbaa2e6LCGEEGJGOR3ziMeHaazLx5qOYvBN3wfYVjWfefEmFF0n5V2NpmjYjRlYEwZ+lZnJEvuDaOh4wh7KE2V4Mt184pobcNgNJ8d+4uhz9Pz7nYzmKbhCNh6/8CtoSZX1Wzv4+/Y7ebp+IWGDg9f6FpN6dQPXpR/lFv1fiRochJYWM7d+gHFDLr+puJF/rd3IE7/7D46+/2pCr74q22UIIcRZbNaExO985zsMDAzQ09PDgw8+yMaNG3nkkUdmuiwhhBBiRmW4GgBI6j0Up8eIpKyQTNNW1YA+nqZCTzGMlSJ/Ha9mHmBJsJZRk4Ghkm6CWg+laQOvxOLM0QrwzCnnlgs34nL+KSg+2foi+x76JiFrmoxQOTtWXUFszMw1r3fyqe47eLa2nLjRxE6lDP8jK7l0bITbla+RQZDmkpXMX99HltPHHs8KfthwCz/NnctLX/9Huq7/MOHde2aqbUIIId6FWRMShRBCCPFWrowFAAQDR5nnCKMrKqovQdTmYNBTxmJrK7qi4Bm4Bk3RCVkS2JIqv/K4aDTfTxIdNelgQbICRVcoql/Jp9etwpP5p6mnz7a9ymvPfRfdAAHnJbRVNjLZ4uSWo718pPfbPFuTQdRspqm0mKMvzGH5jhq+k7yDev0IB62rSa7J4YKGnSQsFp7Pex/fXvopfmm0s+Nvv0jPzTcT2b9friwKIcRZZFaGxA0bNrB169aZLkMIIYSYcRkZ8wGVQPAIa8pdANiGpwA4UdVAtXkXRh169Qyq/HUccbazNLyQQZORnspuBunFrRl5PZiiPl1C2Gwgu6SaL69qJMttBsBoUDm/dhFrrQYsKTg+75OMe4oZ2J3Frf0jfKT/J7xQGSBksTFc6ub30Xzm/LSMb/e/wI36fUySw3NF7+f8DU2szD/IuDWXh0vez50rPsEvkyp7P/dpeq67nuBLL6Fr2ky1UgghxNs0K0OiEEIIIaYZDHaczhr8/gOsXLgQRyqMYSIGukZLzWLGJp3MJ86wUae66wbMmolmZyf5cQu/yXKy3Pbv+BWNVNpGdawCq24iXVCG0VPI9+tryM9zcN3552EwtzMSb2Gp3YAzrrBz5W1EDG7aXi/ib4a9fGzkEV4s2cOYMwOjOcUvzl9B4H4fNz/l5K7Av1JKL08btnB80XI+uvpRapzdDNqK+V3pNfzTms/wc0sGe//pK3RefgW+Bx9CC4dnurVCCCH+BxIShRBCiFnO415JMuklu9xOebSPRGp6v8SY1U63Yw7n5xwA4KjBQom3Hr8xSF6ylKiq8vrccXpoxoTCq+E0KxLVJBQFvWwuU8UlPFZewXmXZRA22mkZf5rR2EEarCqOqIHnVt5GMumk6fVKPj3i41PeA2zPeYhut5WcqJ/nNp3Pg7rCwh9O8YvdLdwYfxw/bn6Z8SUsawzcVvtz5lgGGbYW8HDRFr583q3cVVTH9p/ew/H16xm5805ibW0z3F0hhBD/LwmJQgghxCzn8UzvCRxNNjOHUQDU0TiKpnG4fgVeJUhFUqXNlGJxz4ewpSwcdXawJOLiFaeN2qL7OGCOY9CNxEJZlKdzCdsySLhyOFJTwHkPdFM4N0jQmEGH9yW6oy9wtPkhfvzQrTxQ9QEMMRu7ds3n0gk7t/qGOej+Tw7lTmBLxFFdJu68fgtH9zXzqZ+O8avWbaxIHWKvsppvl32T0vWjfLPgRzRo3QTMLp7PuYDbVv89ty+/ij/u3UPLB66h54Yb8f/+MdKh0Ey2WQghxBsUXe4kp6SkhIGBgZkuQwghhOC5557j4osvftPPkkk/r72+nJycjex/pozvTdSRzLCirXITN9nZ/PLDZCvVPBzPY0HcgCtvK8cLdqDoGk49TVzRqW3eRGn8SvLTKo0One3OPSSJY+5qId8zxtJXpzi4IZcWfwnhgTZ+/vJutDf+i7CiehNfc0CmNcr4FfkYzLv5Zk4W7ngeK8Yvwp6KMZrhYdzi4aYnH6bWVs3uzdX8oqqYTkM5Nj3MxdozXHzkBZ7quox9jlqmFCeKrlEaHWBp6BiXtuyhNJ7EtXEjmVddieO881CMxpk4BUIIMWNmSy6RkMjsORlCCCHEnwuJAAcOXk8gcJQM7uGbD+/nqKuB5AoPabeNsqFuVg32cGisjn6jxocjGs803oFmSNEYhUM2jYpEGu/xf+LqaCF2XaE+I8or9gOY00lMnUcprh9hwUsmevPS/P0BLyc629/0/majlRtKG7jJlWbrjVew3PB7fpypE8XE2uHLyUoa0RSFIyVzsAWS3Pjs41Q76njhsvn8qqKYYUMOFj3GBfqLXNXxJN3b5/F0znq6zAVoioolHac22s6aiSOsP36ULIeLjAsvJOPCC3GsXIFiNp+pUyCEEDNmtuQSCYnMnpMhhBBC/E8hsbfvXjo6vkNt9f/lJ199iIdKPki60IatSiHg9LBhx3OURqt5JGWhPKVS49jJ3srHSasKVwXi/MFlocZvZ7Tvdq4NW7EpUOiaYL+tFXMsjrmnhfJ1A1QecBNr9fMPWDm4dy/JdPpNdeTaPfx9dibD138Ee+0Yx1Kv0Gk2UeerpMa3FoMSIWy2sq+ilkxflI8+/SgNFLLtkvX8dp6bbnMuBj3JKnZxse8Zql4Y4ffxKziUNZcRUzYAzlSQ2lgXqyaaWXfsKJkWG84LNpBx4YU4165FtdvPxKkQQogzbrbkEgmJzJ6TIYQQQvxPITEa7Wfnrg3k513OI/+a4gVzI0P2IkzrrIRNmWT5J/jQriH2aEU0W9JcGjbQMv9uAtYh3JrGoniCbQ4bueOVGIc+y5URMzZFwZE5wHFrF6ZIFEtfC2XnD5ODgYLfebi3LJMXdu2juafnLfWUWBysr6+n528/R0X81xw2TuJIKazvvwyrnoGiJJm0Z7C3sg57RGPLS39k40icg2sv4YEluRxxTAfCCr2LC9PPsb51B8FdOTzhuIhmdwWTxkwAHKkQtbEuGgMnuKD5MNkpDceyZTjOPx/n+Wsxz5mDoijv6TkRQogzZbbkEgmJzJ6TIYQQQvxPIRH+e8rpYQ61fJkTBw7xXN5FpKoyyM30M5xXRn3LYdZ0O3nMYCWpwLVRL1sX/xBIc0E0SkhV2WOzYpxopHDgOi6LWLGoCpbMPrqsPRiiMWx9rRSvHCKrNkTu4wvo7QpznyODQ689y4Bv6i01eYwmFtz8aWKXlWLw/xdBNc28kJOFg1ejm2KgaEzaMzhUNo8JRxYX73qNK/fvJ12xit+vW8xzhZlEVRMWPcZS9nJ+4jWWHjmCt6mQp60XcMg9lwmzBwCTlqA8PsD8RA/n9Rxlfu8A1rx8nOevxb5yFfblyzHl572Xp0cIId5TsyWXSEhk9pwMIYQQ4i+FxOHh33Os9cv4lK/Q/suneaj0WnwmN67VkExbCTozue3+x+nJXMcT9hTFKZVlhl1sm/84iq7zpUkf2+129tmsaP4Gins/xJVhJyZFxZ4xQqejDWM8iaXvGLlzfBSf3482kE/hH+fzkNPAwc5j7Gk6gC8SPVmTqqjct/p6ChwxvnHDtcSsL5OK7gB01nuLKBi/jKQlCEqakNlGc3EVxwvLKB+Z4KJd21g7FGLfygt4tr6EZpcTgEzdx2p2sCK5m/oTbYRb3bwWX81+Rx2d9mJ0ZXpx9uyEl8rUIDWRPtZ2HKV8xIu5vAz78uXYly3DsXw5puLi9/ycCSHE6TJbcomERGbPyRBCCCH+UkjUtAQ7d11ANKnw8AOrsKSibC24jFSJnVprJy2VS7BHw3zr10/zWNkG9ltTNMQNZLse5GDVAYyaxi/GxvmvjAy2Oeykw1UUdF3P1UEPqmIiwxqgK7MJQ1rDPNiOzRyi+vI2TLY0xiN18FoZD2WbGDmxj70tzQz5Ayyfu56bNt2OJTZJZe8zaG4j/3zNSoYP/hJbkR9Hjol13mIKxy4hZo6gG5KkUejKLaa5uJIJh5tFnZ1s2red6riFPSvP57k5+QzYLAC49CmWsI+l7KVx5CiWZuicrOIVfSWHHdX4za6T/clOeKlMDzIv1s+y3hPU9A5iLijCtnAhtoULsC1ciHX+fFSH44ycSyGEeKdmSy6RkMjsORlCCCHEXwqJAH3999He/i88sO+r1B98gj9UXEG/UoJ5iYmawRPsazyfkpFhPru1jwcKyug2aayKKajuX3O08gSZqTS/HR/jPx1OHnZloCUzsPdcz4fHSjCrmdgMSUbdR4kbQ1gmhlB9o5Qvi5CzqIt0yojt8FImDlbxjDNGtHM/xVYDBXnnY7GuQFesWKPj5Pa9xHWv/4ZoOomt2oNrsRnPYhcXWnMoG7mMhGonaQ4AEDZZOV5YRlt+KQGrk3l9/aw/tJvSlImO+oW8XlZCl3M6MFr0ODW0sIAjNGiHqRzoJ9Vtp8Nbxc7UEo7Y3xwanakQJakRKtKjzAv0saztOLlTUSxz52JbtBBrwwKsdbVYqqtRbbb39sQKIcTbMFtyiYREZs/JEEIIIf63kJhKhdm5az0v9a1m7EXISvp4sPRaUhYLZdUTGEMpWuc10tDexub9Fh5ymRgx6qyO62RY/oNddcdxp1I8OjHGXoOZO7JzSCgq2vj5XNvRSJ5SiQKkHENMOrswxeOYhjvAaqBhfQ+WgjHSSQOm4wvwHlrKc6YQudF+spNhDNYlmCyL2d3+Kg+88v231G4pspCxOIP583O4yLoaY2wpccsUmiEBwJTFSUd+MZ25RUw6XGRPBVl8vIU6r5dQSSlNpZUcdTtJqdPTTd26n3oOM59m5unHKRkbRuu3MjBZyJFEHfvTDfRZC09OT1V0jZykl5L0GMXpCaqCQyztaCPPF8JcXoGltgZrTe3019pajPn5siiOEOKMmi25REIis+dkCCGEEP9bSAQYGPwv9hz5Pt9+8e/48MDDtJXM5XnThaTzrayK7WWwsIqu8hoWtrWzqcnBY04YNegsSEBN8lFeatyPQ03zoG+MWBT+NreQHgtoiWzq29azIbAMXXFgUFP4nR3ErGOYA14M4wMY8zJZuLoTNWcITVNgsILQkfW87ksT071URAZ58dgoL7XsRtO1//EYVKuKs8bJ/Mp8VmevJ8eziKTNj66mAIgaLfRlFdCeX8xIZhYpg5HS4VEaO47jMhuZyi/kWE4RXU7ryTEz9CDVtDKP48zjOFWpTqyDGqGRTPoDRRxN1bBfb2DMmPOmWlypAIXpCYq0CUriY9RM9NPQ0Y3dkoF57lwsVZWYK6uwzKnCXFWFqagIxWB4F2dZCCH+vNmSSyQkMntOhhBCCPF2QqKuaxw8+GG+9UojWa1e5gdb2Tb/Ao5Fa9HKrFze9RSHFqyku6yGktEJtuzUeNGUpsekUZhS2Bjew565TxHOiXJvxEvNaJSfu/L4d48DXU2jBiq5snUVRdpyNAxgiBFw9pA0j2Hyj6J4x1Dzsli0ZAhjyXEAUgE76Z6lHOlYRFt8gozJEwz1j9M8NE77cDtpLfU/Ho/RY2TRDxezMJhP3Vg9WrycuNWPZohPHy8KPlsGvdkF9ObkM5bhQVNVPFMBqgd6caspki43g65cOp0O0m9c/TPqaUr1fsqVdirppoIuyrReLN4U0XEH41O5dEQrOJqcR7NeTVo1naxJ1dNkpfzkaT7ydR8FyUnKAqPM7+sh3xfFXFGJuaoKS1UlptIyzKUlmErLMObmoLxxpVMIId6p2ZJLJCQye06GEEII8XZCIkA43MmvnvkyP917Ax8bexhDIswf5l/JYKgIpdTEB48+yKGFazhStwxrPMHV24MMhFLss6Yw67Ap5kU3PsTh+b183Rrgfd0BxjUDf5NZT2umH0XRcXkrubRrPdmpRWio6GqcsGOAuHUEY2AMw+QwIVsejXURPDVN6NYgAKnJTCa6z6e5p5TRhJdsfx+jI/9fe/cdZ0V973/89Z1y6vZdll3aghRpgooNY40FNDZiMDEhwRJjzDU3RuNNYsMkXo2KGDWWn7k+bMk1xhK78QYEjFgJQgwiTZC2tO1nT5vy/f0xZ8/uCosLqCz4eeYxOWdmvvOd7xTceZ/vnDlJFq+v5YN175N2kp225fDhwyj5z2rWx+oAsIgwINmb92f8i94FA+hd1Y/KfiVU9q7Asix8FE3RQjYUl7OmvJJNRWWkQ2EMz6dq6xZ6p5qxIhat0SI2RgupD4Xy6zK0T1+/lhpjBf35mL6spS/rqHC2QL1ForGQLYkKPs70ZZk7kKXuYJrdok7tLXATlPuNVOhmyv1mKt0G+iS2MLi2lgFbGgn1HUCoX3/s/v0J9e+P3b8fdt++2H36YBYU7O4pIoTYh/WUXCIhkZ5zMIQQQojuhkSAzZtfYfIfVuHVGUxa/yx+kcGTA7/OppZKVKXFN5b9hU1V/Zl51On4hsG45QmGfZDmlXCWpAF9XTi6ZT6rq1/hkFGbmbK5maotGWZaffhV0WAaitailE9BU2+OX3Us/VOH4WMDPunIVtLRjShvE0bjBpyWLOE+VRw0bDOqZiEqlAbAbY2xZvURfLRqKFsSLRSmGvC2JFi5pZlltStZvWkJ5x57OV+pGUdxej3ZyAbeGrSEmZGlLP/58k7baxgGleVVVFX2oayymF6VFVRUVFBaWooXjrI1XkRtUTm1JeVsLSgmHQoeeFPS3ERFupmwqciEYtSHCzoFR4CQdujr19LHWJ0PjlXUUskmQkmPdH2E5pYiNqcr+NjtyyqvH2udPjSkS/F0+62ntu9Q6jVS7jdRTgulfivl2SaqEnXUbN7EgOYE0YpqrKpq7Opq7Oqq3Psq7OpqrKoqjHB4N84gIcTerKfkEgmJ9JyDIYQQQuxMSAR48vWH+dkLFRyfmMXoLcswylyeGXgmHzcMgLjByVtnUZHeyl8nTqGhpIJw1uP4f7bSsCXJv0IeGtjfdRjb/Cbm6Jl8rc8Wxq5OUNjq8QSjmV40iNaSD1FmBtMNcdDHRzC27ivYXhUAvnLIRLbihLainLXQtAk/6RKvqmDU0BZ0v8UYBfUAaB/qNu/Hh2sOZ+O6KH6qhaqUR4HZi2hkKMoszm/X/CXP8NBrd3VrHxiGQVlpOVO++x2Ki9vr8AyTpmicTYWl1BaX0RArojFWgGMFt5VGM0lKsknCaFwrTLMVpdm2t6m/zG+iN7VUqvX0ZiOVbKQ3m+jNRuJ+K25LmNaWGA2txWzO9GKdW8U6v5q6bCkN6RJa3RgQ3AKrtE+hl6DUb6FYJyglQanfSpnbTEVrA33r66hJpimKl2FV9MLq1QurV0XwWhG8mhUVWL0qMeIxebCOEPuYnpJLJCTScw6GEEIIsbMhEeDCB55k1vIol3z4R6xwC2ahw7sHHMa82iPQCgaa6zllxYu8P/IQ5oyfiGfZFLV6HPSvVmrrW1ljBQ+YGeS6jE4vYvShTzE+Wsegj5PEEz4v6wOZERnFhpJ1WPGPAChureSgdUcwpOlgLK8UAF+5OKFGnFADyt+I17oaqzlJyI4zoMageGgtTq+PsELB00x9rVi3aQgfrTuALesLsRIe/RxF1Cxj1dZm3lr2OuvqVlDbsBrf93a8ExScMf1s+mX6UZbsRdgpAjPD8zOfYO3atZSUlFBcXExJSQlFpWWEKnvj9htAfWUf6uLFtETitESi+EbQKxhxMsS8LCbgGRatZojMdh5WE/UzlOsGytRmytUmytlKGXVUsIVy6iijDtt1ySQiJJJxGlNFbHXKqXV7Uev2pilbRHO2gOZMIWkvQluYDPtpCv1WinSSIt1KoU5RpFMU+a2UZlqoSDZSnWhhgK+Ix8uxKnphlpVilpZilZZhlpZilpVilZZilgXj0kMpRM/XU3KJhER6zsEQQgghdiUktqQdvnbHK6yv8/nZv++ntQgM0yc5roA/tX6TVGsE0/Y5vP4dRjR/yFvjjuO90YfjmxaRrM+QpUky6xOsw0MriPswVq3hsKEvcFTZ+/SvTVGxJcsSv4YHGM//xYvwC5dhxlahlKY41YsRtYcwpGEM8WwViuDBLZ6RwQk14ZvNKHcjbuojQokURYaiso9BaFgTXu+1ROKtAGgN9ekSVtUOZ+P6/ni1NmVJk7gfYXNLivUN9ayrW8PGxo/Z1LiW5mR9fh9UFJXx/amT+bhiIyuqNpAKp4h5MZbftJyGFQ1d7rtwOExJSQklJSUUFhYyaOhQBow9kMZonLpYUS5AxmiJxGgNRdC5h9JYnktYuxiAp0wyysTr4oE1xX4rpbqBEuooNrZSTCPFNFKSf22gmEbCbhYnFaE1HaU5U0CjU8xWt5StXhnN2cJgyBSScOK0OjE8bQEQ9VLE/BRxUsR1mgLSwatOU+CnKXCTlKRbqMgmqfY0vQ2TWEEZVkl5ECCLizGLizCKijCLgvdmURFGcTFmYSHKsnbqfBRC7LqekkskJNJzDoYQQgixKyERYOnGFr5+x2wc1+fI7EOMbs6ikgaRqjRv73c4/9hyJNqFkJHl4Pr3GJZcwb9HH8KCA44gHYkB0GtThuKVCRqaM7Sq4PKghCwHlr7Pof3e4ij3A/pvbSHcbDLLP5hn9VjeiIXR8dWY8ZUYdjMhN0Kf5iEM3noA/ZqHEXFKUbneMY2HayfwrFaUbsZ3NqKSa4imk5SGXGJ9fLxBjVhVW4nFUygV9DbWp0tZ11jN5rX9MNYWUtgSIqLjOK7NptY0m5sbAcX44RPbd4ifBK+eax+/jIbWrkPiJ31l/LGcdOKJ+Z/iAGhubuYPf/gDhYWFFBQWEi0uxi4qhpJSvLJeuGW9cMt7kanoTbaiN268AJQCrbG1h6FBK4WrDPwd3B4a0g4lfjMlNFBk1FNAE4W0UEgzBSQopDk3tFBIC6Gsg5sNk8xGac7GaHQLafKKaHYLaM0FyYQTD0JlNhhPeRG0r4h6aWI6TURniJIhRoYoWWI6S1Rniek0MS9D3ElR7KYpxaNCayotm+JwHLOoDLOopD1cFhZixAswCuKYBQUYHYdPfP9TCNG1npJLJCTScw6GEEIIsashEWDhB2v43v+8RcKOUFj6Ct9reR+9sgilITwww9zex/Buw8HggMKnJrWGoS0rCBVp/nnwUaytHohnWaA1RRvSxNYlSTVlyOQuFUx89itaw9jS9xnHcg5LraCiKcWb/gH8wx/NHLMfm2PNmNE1GNG1mOEthNwolYka+jUNpX/jUIrTVVh++22PGo1npvDNFBhJ8JvQ2S2YqQ3E/UZi8TRUZVBVrcR6NxErzGAY4PoGW1MVbGytoHldKdaqEiLNcUJ+HNMsQlklaKuMuR+8wtamDdQnNlLfspm6lo2ksoku9+G4CWPofWJvtA0xCinKlpD4KMkT9/2528fBtm2+/6P/oKB3FalQiEQoQks4xuoVy6nbsB6vqBS3pBSvuAy/uAQKi1FFxRjRKJYy0Ao8VBA0d8DQHoU6SSEtFNBEgWqigAQxWonRSpxkh/et+fcRJ4vhKDJuhKQbocWN0eTFSbpRkm6UlBsh5UZJOtHgNTctGI/gOwYRL0vUTxMhS4QsYRzCOERwCOMG03yHqJ8h6jkUaIcCXIoUlBmKMsumNBQmHCvCLCgJwmQ8FyrjMYxYHCMWxYhGMWIxVDQaTItG5DcqxT6tp+QSCYn0nIMhhBBC7E5IBHj7xt/x87VxVhf3wYys49TeLzBoqY+uzf12YLnDB31H85o+lmTCRBE8TKVfej190xswChUfD9+f2uqBJGMx0BrV4hDfkMbemibT6uB3WF9VbBP7R1cxWq1iRHYdVakmVvr9WcgQ3tX9+TiiUdH1GOGNGJGNGPYWCpwiKlr7Ud0yiMrW/pQmq4g4RfnbVNv4ysUz02BkUKTQOgFuE4azFYvNGNF67JJWzLI0sfIkhRUpQhEfx7fYmiqjrqWU7JpC1IZirOZCTKcAgzhp36IulaUulaShtZG6lo00JDbRlKxnwkHfZtSAwwHQfhrtJ/jX6n/wP7N+v1PH4bIrrqQwVoChNOR6ZV988UXmz5/f9UJKEY5ECEVjWPE4xUP2Z9AFF5MKRUjbYdKWTdayaZz5Er5lo+MFqGgcIxbDiMYhHkfF4qho7FODlKE9YqSI61ZiqpVYPlwmiZAmQoooKSKkOoync+MpQq6L7foYno/jhUh5EVJemKQbptULk3LDpL0IGS9E2o2Q8cKkO753w2RcG+0YGI7GcHxC2iOEkxtcQrjYyiOMi609wjiEdHCbbwSPiPaIKY+YggITii2TIsOmJGwRD8UIxwoxYkXB/skFThWJYkTCHV4jGOHcaySCikgIFXtWT8klEhLpOQdDCCGE2N2Q6CVaWXrmJB4oP4in9/sKGpPCwvVMqlpAbNkmjLUuphf86XcqbP5ZchT/Dg0hlbYgd0Vg+1mq0xsp1s04JREa+vahrk81iYI4aFBNWYwmh3BdBrM5jZPt3IaYmaQmtIHB5gb6u1sxHU0Lcbb6RazQvVkdsvEidRjhzRh2HUaoDtOup8iJU5HsQ2VrP8pSVRSlK4hmiwm5sW0CJIDGxzczaMNBkQWVAT8FuhXtt+AbdZihOsz4FqJFzUSL08RKsoQKHZLEaGwpIVNbgL+xGBoLUck4hhsDFQcjDmYcbRayKdHIv9b8k6bWrTQn62lK1tOabiKRbiSRbkZrv1O7FIoZ590LRgZPpciaKbJWiidmPcvipUu7fSwHDx7MlClTtpl+44034jjODpc1Q2HMaAQjEqPfdy8kfuSxZC0Lx7RwDYvUmlUkXn4WIlGIRFHhCCoaDUJSONI+Hm6fZ1RWobbzvUtbZzsEyCRRUoRJEyZDhDQhsoTIECaTf21/nyVMmhAZbM/D8n0MT2N4GuVpfN/A8UJkPDsInH6IlGcHgdO3yXqh3GCT9UNkcuOuZ6JdhXYUhgt4GsNVKF+jPLDxsHGx8LGVh608LDxsfGzc3KtPGA9b+YSVJgJEDYiamgLDpCBkEbctCu0IheEw4XAcKxpHRWKoSDgIn6FQbgijQnb7tA7zjNwrliVPqxU9JpfIN5GFEEKIfYhZEGfQHbdz0Xe/yynr5nPXhHNY1FLFIy2nY4dSjDumnuGtH+Ks2kxpQyNHbJ3NEczGNU0+Lt6fpcVDqA2Xs9bozxoUOMBqCH20mRqnkbDp4MVtUuXFJKtKaDmgEg8Do8VBJVxCiSQkTFYmaliSGrJN+6IqTT+9hbJkiFCiGuhDRts06ShbVZj3Qw4qtBWzdD3K/gDDasSwminwfcqzRZSle1OcrqAwU0Y8W0LEKSTkRTG8wvx3H/M8IBUMrXUuCeWC4YJyQLko7QAZtE6j7SSUJNBWE1Z4HXa0kWhhPUY8ScyP0D8RQTcW4bf0QyWHQSaK9mL4fpiUo2jOurQ4DgknS9JxsEMDQdnYQCTXllJzCSXxOpKZBFk3/anHsogSCjYW4plZPMPBNx1cw/vUgAjgZTN42Qw0NTF21RJGxzv3ji1ZsoS/PPOXT62no6MefgI3EiVrWrhmEDg33Hs7dfPfglAQegiHUaEw5IIRdntIIhTCHjaC6IQztqk7NetllG3nlrFzoSqEsm1CtiJse4RDPmFLE7Y8IhFFyPSxcbDJ5l+LcbBp6TTNzvVOdpxm+hrL9zFzg/I1hgf4GjyF9sHzLRzPIuvbZH2LpG+R8W0yvkXGh4xnkPV9HN/B8cFrzUJzI3gqV4cKzkEflBcM+KByg+GDoTUGGkv5WPiYysdSPiY+ttJYBOM2Clv5hAywFYQNCBmKiGEQtQ2itkXctonaNvFQhFjIJmpHsEMRjFAk2J+2jbKt3GvnAatteih3HHLz2qZL7+qXjoREIYQQYh8THT2KmocfwvyPS/ntY7+j4cQTuG3YMN7bWsxbq/vyFn0xijxqalLs524gtnEV1en1DGn5N4PrPwDAR1EXKmN9wQA2xKups0qoD5fiEIIMsAHYkMYmeIJmWDmYlo8XtXHjEXR5FDcawVcGISdDodNIOJ1Cp3w2p0pZle6Drz9x4amBDKhMX2IqRZQMlvZQaDxMNmCy0tBkrSwq0ooq2IBhNaPsJkyziRgOZb5FsRujMFtIQbaEmFNE1Ckk7EaxvQiWF8b0CjD0tr+HCEAWSILbAC0E35nUykMrF608UC5EPIh6KFzI3RZZQZYKlQGVxlcZPD0X5WcxfBflKgxH851DBqMOGgbaxvMsklmflOPT6rq0Oi5JJ0vSSQev2RR9y4cS5aAgaHiAAxknRTxSRDqbxPPd7W/DJxS3DqBoSw1aZfGVE/S8Nm3/SaxdUUrx1ZXvb9PT9fiqpXy4ZVO36+kz9kCO711KNnfrbMayySiTl/77qp1qT9l1NxE55kS0MvBVsC3pebNpuWN6LvR0CD+WHQTPTq8Wyg5R/IvfbFN3es7f8TaswTBNTNvAtAxMU2GaYJoKywLLBNMEy/SJVYQpHVyNhYuJi42DqR1aP96AbfpYpiZk+kGoNTUhy8c2PcKWj21oLKWx0Rjax/Q1htYoX2P6GqU1ylfgAzoIntqDjG+Q0dDoGfi+wtUWrm/i+BaONsm6Fm7WwG1ReL6B5ys8H7Sv8H0FvkJ7KrgzIDcdHwxfgx88dEl5BPPbhraAqw0M38dAYSiNmQuzhgJTgWFobAWWoYJ5CmylsAyFbRqEDYVtGNimQcgyCFsmITNE1DYI2zYRO0zEsgjbNrYdImyFMMNhMK324JobgmlWEGI7TA/G7fw8ZVn5+RiG9Nh2g4REIYQQYh8UHTOGgU8+wcZf/RpmzuKGWa9iH3k4fx/Vn6dMm1XNBayqH8gqPQyiwyAKoXKHam8LVZmN9PHWU+5uZWxyIQfWv5evN2OEaLSKabRL2BoppyFUSotVQMqIknDj+AkDEsCmDBaZ/HIthEhiYJk+ygIrrvBDBlhgmg4hnSXkZzF9F+X5+K7CcU0STpyUG0G33W7qA9n8nbF0/PXENJpmPFRwRY3Gx1MaHfIgkkUZjSgj+I6jqdJElUOh1sQ1FPgGMd8k7ttEvRBh3ybshbE9G8sPYfkhTN/G8MMY2sLQXV9CfbLPRQOeDdht40HwDCmPkPIpUl4QQPGCq/C27iffw2UNaB9ygcGyNTd991bQ4LhZnGyGdDZDxknnXjNk3AxpJ03GyZJ2MvSP9yecjoMqAWWDYVOqUuxX9W+ybpqskybrZoL3bhrX27anMmxF6bXp6Fxg9tDKD9qceu7TT8YOihyDgbWt6Fzi0EqTdbO8tFO1wMnLFrG/SrfvX2Ww6P1/8cLWzd2uw7AsDj3nXFzTzN2Ga+KaJotfeorE/Le7XU/sxImU/OI3+MpA5wKrdrJsvvDw7m+QaVHyq+mEjzy20+T067NJ3Dsjl0hz4act9Ji5kGSaKNNAWSZGOEzVNddh4nUa6p95lszq1RiGyg1gmGAYCtMIXg1TYxpBGC4Z1Jv+RwzDwsPI1aE8hxX/9x6mAZahMQxy73P1KQPTUBhKBfUYit4DyokXhsnkQiZakU5kaKlrRaEwDANFsJxSBoavUFkD5Sh0ysBQwXTTNIN/Fhq0r9G5YKu1wtNB8PV98HJB2tc6P02TC8H5QNzWlvYAjK+CQK41Rn6eRmmF0goIwrrSCkO33bMQbKtBEI6VQX7blWFgKZXbR8H2mcoIPmgwDGxDYZkmlmlgGSa2aWCZJiG75/TYSkgUQggh9lF27970v+duWt95h/qHHyExezbHzXuL4wBz6GC2Dizn9fIS3gxZrPCjJL3efJyp5uNIH+DgoJLi4CEnxU4zvdwtlHv1lLjNlGRbqG7ZQtjNEPLTKDQaSJpREmYBCauAVjNGyoySMiMk8++jpI0IjmEFQQFwMckQBaJdbIlG4WOYGmVoTMNHKY2pPJTyMXLzlSboAfFBa4XjW2T9EI4XA689WEJwvZkFmna0A83cgE/7fYIpgmePeoTxCKOJoglrCGtNGE0ICGkIaYWtFTaKkK+wtYGtDSxtYmkjGHwTU5tBANUmhjZQuf91KZeXwyaEw1Cwo23Y7t70GVVzKCMHjiMXWduulAHwfRenLTQ6QXD0fBeflmD/ap1rn+Yrw05k/6rRuJ4ThFbPyb0PXl3f7TS9b3wMxU2HoFR7T2Yyk8AwTHzf2257t6e4dRjFdWNzQTO4qo82rt+p/WApg/HLt6Dxc6E3CK2b6hto3Il6hm3dyJn/eAHInSmGQcpxmbEzjfFcDtiwiqplRXjKxDcMPKX4ePUy/lXb/e+nGeEwZS0t+MrANxS+MvBUiMa3FpJ4t/vBN3LyaSwf/+NO07STYvP0G7tdB0DJf99BePwxnaal3nyO5lumdb+ScITeL7+5zeTGay4jM/8tlBEkNBUkVVBBSGt7VblpZcceyYAfTsXEw8DHwEdn0iy89DqUoVBK5V7JLxe8b5sXhOnR50+k1/B+wX+TCP49bFq4kmXPvY1hqHx5o9OyQaBsezVtk6MvOjEXRIPbjkGx5NV/7dT+/TxJSBRCCCH2cfHDDiN+2GG4dXUk/vEPWl97jeSC9yj9+0pOB05vK2hbZHqVsLGimJUFRayOxlkbKmCTGaclVMj6SCGr9VAcXYSmw+2aWhPx00S9NFE/RcTLvfdSxLwk5dkGIn6GiJch4mewdRZDu7jKJGuEyBphMkaITIf3WSNExgjnXx1l4Rg2rrJwlI1jWLjKxjHC+Oqz/vTdDx5iojxM5WHmeibRQW9EEElNfG2TwaA1d9mpOz5cR+WGNp/axFwQJViVQXCRZmkwUZi683jwCpbWWOQGHXRWWmjM/HvyZY1cPSZg6qAHxMi9BuuwUIBp2BihGBFbEYuS6zlRmNsJrgftN3GbaTsrFi7gzov+D8/3cL1sEDK9LK6Xzb06eL6HlwudnufQv3QYIae4Uz1DSg9l0ngjF04dPM/F8x1cz8Xz24a2cQfTsChsHAF0PlQV9kAai1rwfBfXc3E9B639YHntbRNmo+kqyjeNB9UeuFszLTu9H/avjbB/2EYT9CZrpQmtTbMzscHScMbbKwHQHdrzaF0jXf/wy7aGbt3IxHl/QyuFr8A3FOlMhvt2ZoOAsetXUv1hPFePQqP4qPZjFuxEHaaC0ZuXB8sr8nW9n2wik83kP/z5tCdxZhrTtDglwfIYwW+XOmlaP1q7U9uUOfvHhNVR7RMUpDY9Q/NrD3S7DhWNUXvpvdtMb3h9Nr12qjWfn70+JB566KFdPk76oIMOYsGCnTkNhRBCiH2XVV5OyVlnUXLWWQC49fWkP1hCZtkynHXrcNavJ7R+HdEVtdQkV3ZZjwaSVpiGcCEtoRiNsQKaojEaI1Fa7AgtdoSEFSFpxdhoF5GORMiaITJmmKxh58Oej8LWLiE/22mwtYOl3WDwHaJuirCfJeKlCftZQr4TPJlSu8FtcMrHR+EpA19ZuBh4qj24eSh8ZeIpC08ZuMoK5n9icLcZD54E2rZcUEfwHThf5fovlUHQB0CuL3Xnv+ukcqHUyPVMmMrPv1dKE1xaa4I+wODCv2NM0fla2koG74Na2t9rgtvzOpYLcq/qHHB3cOAVbYGTfOA0tOo0zSAIpAow8uO5IVeHgcqPG7RNI+hNxUIR7TS/bVC5ULsSULidy1TVMKCqJuiZ2c462/pnVYdp9R3WHfwcDJxx/JUdpqlO5RWA1qB9tO+htR/cNqntTimlwLC5evIDeNoLemH9IFx2DJrBuIevg9dB8QOJJss77fKhReM5/TCz0/Jt5duXba/fMCzK6kdtc+gGFY7F6BPB93209vHbhlwdvs5N94PpVf4QatYd1qmO1kwLISvSaflPPtn3k0asLWO46tWhtxrU2tBOhUTbN5jwppNfXuf+LWysd3eqx3fwxlYmzVyRqyeoJZ1N8t87UQfAhPc/YFBSow3y/34WLV3G/+1EHZb2OX3BW/nQ2/Zv8ZWmhp1szednr/8JjHXr1jFo0CD+3//7f5x66qkAnH322QwbNozbb7+dkpKST62jpzxqVgghhNjdn8D4rPjpNF5DA259PV59A15DPV5zC34yid/a2j50HE+n0dksOpNBZ7P42Sw6m0FnsuBteyuhqwzSVoi0GSJjhsiYQQ+hY1rBq2HhGGb+fdYwSVshMnaITNuDT8zcYNm4ufKeYeCaBr5h4ioDzzDwtQLVFuYC+aiUuxRS2g8eGqKDGGX6Pob2MH0P23eDB4vooGfRIHgapdIeptLBtLZbNlVw860y2gOczsfI9gvCILiRD2haKfz8VVmHIEdwMYrO9cZolRs3gotMHYS8oIclFy+VgacMtGHmemAUvhHEHt9QaGW096gYwfJ+LgZpRe5hMLnl2tqjgrag2iJr0Ib8fNoCp86FLJ2Pp2ZbhA4WyvWWanRuXOXKqA5bTq4O8mXaI3jbfDpM+2RfUscS+eOQq669rXQIy5+Y1un9tq/kQnfwLnd+aSP3Gkw1clvRHjDbgmr7TcVKt4fXjrcaG7m6jdy4yq+L/DpzuanDUu3/31Yfuf2utpnXsa72MttM71hWdx7XucCM7wWvWuP7PsGXAX3C4RiWGepUXybdTLKlLgja2kdrD/y297ng6fto38fXHoYy2K/msG3a/PHaBSRa60BrtPbybdFa54a2acF4ZflABg84tFNbHDfD6/P/Nxd2Oy+nc3WRC8Tkph059mx6lw3ssO9hxboFvPPBC52W0/nlcuO0zwtZYS469aYO525Qz+NzZ7C2ZVGPyCV7fUhctGgRBx54IMuXL2fIkOBR22VlZdx444388Ic/3O4yM2bMYMaM9jvFGxsbeeqpp76Q9gohhBA7kk6niUQie7oZnz3fR7kuuC6qw4Dvo3IXlflXL3fR2TbN81G+Fzy8ZbvT/dx3EXWulyc38IlpuQtROl6M5i8i2y9sde5BMfn3aPB1vrz2gws9X+tc6NB4ufDhYwTzCMKfr3WH3rxgdZ4Ogp2b64X0lMo9HiRXThm5B0wqPMMIXvO3yJEPc8FzQNpu4wvCWtt30XRunlYKbQRlADyjLfwY7WVywaCtfKdQ1dbLYeQChgraGFwhtwfe9qANaD/4HlZbn2Xbgz8I9rWRz31BoG678PfJBcdcCG2L1oFczFb5d7m2kmu/zveQtjVEtbU9932v/FU95D8t0EqjtJGLsG0zyG1T7jRSbfE+F847BOEOLQvK6SC46Q5X/5rgoSgdA/YnP6pQqn1K+zYEx6RtB7V/E5T8ftP5Dz5y7Tbag2Zwu2lQNmCgcyEPo33f5Veeb3PbOts/UtEY7Smx4/mRr77DBwd88k3wFNS2mvJHS+Ub33Gr88tpOjSrLZC17Vzlt20xGpU7m9v36Cf2fu5A0qFMx23xOwRond9HbXPbWgxBNUrlTt18qzucPbptrHMNOr/WtvOxwzmiO74o3nzkXgmJ2zN9+nSuvPLKHZaZOnUqDz30EAAPP/wwl156Kc3NzSilWL16NYMGDeLNN9/kiCOO6NY6pSdRCCFET9FTehKFEGJf8qmRpzuRaHfr6Mb8/vvt1yNySY/7TuIFF1zAaaedtsMyxcXF+feLFi1izJgx+d87WbBgAYZhMGbMmM+1nUIIIYQQQoi9w6f+NqL8dmInPS4klpWVUVZW1u3yCxcuZOzYsfnx9957j6FDhxKLxT6P5gkhhBBCCCHEPq0bj7Lq2dq+k9hm+fLlDBo0aM81SAghhBBCCCH2Ynt1SFy3bh319fWdehJHjRrF7NmzmTZt2h5smRBCCCGEEELsnXrcg2v2BHlwjRBCiJ5CHlwjhBBfXj0ll+zVPYlCCCGEEEIIIT5bEhKFEEIIIYQQQuTJ7aaAbdv07t17TzdDdJBKpYhGo3u6GXudL/N+21e3fW/err2h7T2xjYlEgoKCgj3dDCHEbuqJ/30RPd+mTZtwHGdPN6Pn/QTGnjBp0iT+8pe/7OlmiA7OOeccOSa74Mu83/bVbd+bt2tvaHtPbGNP+T6KEGL39MT/voie75xzztnTTQDkdlMAJk+evKebID5Bjsmu+TLvt3112/fm7dob2r43tFEIsXeS/76IXdFTzhu53VQIIYToQaQnUQghxJ4mPYlCCCFED3L55Zfv6SYIIYT4kpOeRCGEEEIIIYQQedKTKIQQQgghhBAiT0KiEEIIIYQQQog8CYlfkKlTp1JZWcno0aP3dFNEjhwTsSvkvBFCCLEnzJw5kxEjRjBkyBCuvvrqPd0csZfY1fNGQuIX5IILLuBvf/vbnm6G6ECOidgVct4IIYT4onmex49+9CNeeOEFli5dypw5c3jjjTf2dLNED7c7542ExC/IscceS1lZ2Z5uhuhAjonYFXLeiD1JehKE+HJ69913GTRoEIMHD8Y0Tb73ve/x9NNP7+lmiR5ud86bHhcSH330UcaNG0dpaSnRaJQRI0Zw66238nk8hPW1117jzDPPpKamBqUU119//XbLvfTSSxx44IGEw2EGDhzI9OnTP/O27C1effVVTNNk4MCBn0v9ckz2LXV1dVxyySX06dMnf6zuu+++z3w9ct6ILwPpSRDis3Prrbcyfvx4SktLKSkp4aijjvrc7hL5LP5GrVu3jv79++fHBwwYwPr16z+X9oqu7W05ZXfOmx4XEisrK7n22mt54403WLx4Mb/4xS+4/vrrmTFjRpfLzJ8/n6ampm2mu67L3Llzu1wukUgwcuRIbrnlFqqqqrqs+8wzz2TChAksXLiQX/3qV1xzzTX8/ve/z5cZN24co0eP3mZ49tlnd2LLe76NGzcydepUTj755E8tK8dEJBIJjj76aFasWMFjjz3G0qVLeeyxxxg5cmSXy8h5I0TXpCdBiM/Oq6++ygUXXMDs2bN5++23OfzwwznttNOYN29el8vs6b9RYs/bG3PKLtN7gbPOOkufdtppXc4/4YQT9KGHHqobGhry07LZrJ40aZIePHiwTqfTn7qOmpoaPW3atG2mn3vuufrQQw/tNO1nP/uZ7tevn/Z9v9vboLXWq1at0qNGjdqpZXoKz/P0CSecoG+66SY9bdo0XVNTs8PyckzEddddp2tqarp1rNvIeSP2VXPnztVnnHGGHjBggAa2e45qrfWLL76ox44dq0OhkK6pqdG33nprft4TTzyhL7zwwvz4Sy+9pL/1rW993k0X4ktjxIgR+vLLL+9y/p78G/Xmm2/qk08+OT/vvvvu01dcccWnb5T43PXknLI7502P60nsSGvNO++8w7x58zj++OO7LPf0009jWRYnnngiDQ0NZLNZvvGNb7Bo0SJeffVVwuHwLrdh3rx5TJw4sdO0iRMnsm7dOj7++ONdrndv85vf/AalFD//+c+7VV6OiXjqqac46qij+OlPf0p1dTX7778/V1xxBa2trV0uI+eN2Fft8U+EhRA75HkeLS0txOPxLsvsyb9Rhx56KB999BErV67E8zweeeQRzjrrrF1en9h9e0NO2Z3zpkeGxKamJgoKCgiHwxx55JFceumlXH755V2WLyoq4pVXXiEcDvPVr36VSZMmsXjxYubMmcOAAQN2qy21tbXb/EFvG6+tre12PZMnT2b8+PEsXbqUfv36cc899+xWu75Is2fP5r777uPRRx9FKdWtZeSYiJUrV/Lkk0/S3NzM888/z6233soTTzzBBRdc0OUyct6IfdWpp57KTTfdxDe/+c0uLwhmzJjBQQcdxM0338yIESOYOnUqP/7xj7n55pvRWtOvXz/Wrl2bL79mzRr69u37RW2CEPu0G264gZaWFn7wgx90WWZP/o0yTZO7776br33tawwbNoxjjjmGo446arfWKXbN3pRTdue8sXarZZ+TwsJCFi5cSDKZZN68efzyl7+kb9++XHjhhTtc5plnnmG//fZj4cKFLFiwoNMXNfe0J554Yk83YZds3bqVKVOm8OCDD3b56XdX5Jh8ufm+T3l5OQ8++CC2bQOQzWaZPHkyd911F5WVldtdTs4b8WU1b948pk6d2mnaxIkTmT59+jafCA8cOJBHHnmEm2++eQ+1Voh9xz333MMtt9zCc889R79+/XZYdk/+jTr55JP58MMPv5B1ia7tbTllV8+bHhkSDcNgyJAhAIwZM4atW7dy9dVX73Dnp1IpvvOd79C/f3+qqqr47ne/y6uvvtrlhWh3VVdXs3Hjxk7TNm3alJ+3r/v3v//Nhg0bOO200/LTfN9Ha41lWdx///1d9gzJMflyq66uZuDAgfmACDBq1CgAPv744y7PAzlvxJfVp30iPHDgwPwnwo7jcM4550hPghC7afr06Vx//fU8//zzfPWrX/3U8vI3SnxZckqPvN30k3zfJ51Odzk/mUxy2mmnsWHDBubMmcOLL75Inz59OO6447bZcTvrK1/5Cq+88kqnaX/729/o168fNTU1u1X33uDQQw/l/fffZ+HChfnhhz/8IX369GHhwoVMmjRpu8vJMRFtTzZ1XTc/benSpQBd/oSKnDdC7FjbJ8IrV67kpptu2tPNEWKvdt111/HrX/+al19+uVsBUf5Gie3ZZ3NKtx5v8wW65ppr9MyZM/XKlSv1hx9+qO+77z5dUFCg//M//7PLZU455RR9wAEH6M2bN+enpVIpPWHCBD18+PAunxrU0tKi33vvPf3ee+/p6upqffHFF+v33ntPL168OF/mnXfe0ZZl6V/84hd6yZIl+uGHH9aRSETfddddn91G72W683RTOSZi4cKFOhQK6YsuukgvWbJEv/rqq3rw4MH629/+dpfLyHkjvgy6ekrdgAED9LXXXttp2qxZszSgV61a9cU0TogviZ/85Cc6Eonov/71r7q2tjY/1NXVdbmM/I0SX6ac0uNC4o9//GM9ePBgHYlEdElJiT744IP173//e+26bpfLvPbaa3rLli3bTE+n0/rFF1/scrnZs2drYJvhkwHohRde0GPGjNGhUEgPGDBA33LLLbu8ffuC7oREOSZCa61nzpypDznkEB0Oh3VNTY2+/PLLdSKR6LK8nDfiy2BHjzI/7LDDOk278sord+lnWoQQO7a9vxmAPvbYY7tcRv5GiS9TTlFaa717fZFCCCGE2JFEIsGKFSuA4EmnZ5xxBj/84Q8JhUKMHDkSgHfffZcjjzySn/3sZ0ydOpV33nmHiy++mFtvvZVLL710TzZfCCHEl4yERCGEEOJzNmfOnO3+jlZNTQ2rV6/Oj7/44otcddVVfPjhh1RVVXHppZdy5ZVXfoEtFUIIISQkCiGEEEIIIYToYK94uqkQQgghhBBCiC+GhEQhhBBCCCGEEHkSEoUQQgghhBBC5ElIFEIIIYQQQgiRJyFRCCGEEEIIIUSehEQhhBBCCCGEEHkSEoUQQgghhBBC5ElIFEIIIYQQQgiRJyFRCCHEXuO8887juOOO29PN+MytXr0apRRz5sz5XOp/6KGHUEqhlOK00077XNYBMGfOHJRSrF69+nNbR3f97ne/y2/zeeedt6ebI4QQexUJiUIIsZc477zz8he9HYf/+Z//2dNN+8LccccdPP3003u6GXut2tpa/vjHP37h681kMhQVFfHGG298Yev8wQ9+QG1tLePHj//C1imEEPsKa083QAghRPeNHz9+m5BUXFy83bKO42Db9hfRrC9MV9sqAtlsllAo1OX8qqqqHS7v+z6+72NZn+3lwd///ncKCgq+0MAWi8WIxWI73B9CCCG2T3oShRBiLxIKhaiqquo0RKPR/O2Ec+fOZdy4cYRCIZ599lkA7rrrLoYPH04kEmHo0KH85je/wXGcfJ2u6/KrX/2KwYMHEw6H6du3Lz/+8Y/z85VSPPTQQ53acdxxx21zC9+nradtmRtuuIGqqirKysr43ve+RyKR6FTP448/zrhx44hEIpSXl3PKKafQ0NAAbHu76YIFCzj11FPp3bs38XicsWPH8thjj33qfrzzzjs56KCDKCwspLKykjPOOIMVK1Z0KjNw4ECuvvpqLrvsMsrKyujduzc//elPcV03XyaVSvGDH/yA4uJiSktL+dGPfsQvf/lLBg4cmC9z/fXXdxqH7t2Wec011zBq1Cji8Th9+/ZlypQpbNq0qVMZpRS33347U6ZMoaSkhLPPPvtTt72jtrY9+eSTjBw5klAoxMKFC1m1ahXf+MY36Nu3L9FolBEjRnDnnXeite60/F133UW/fv2IxWJMmDCBNWvWbHc9Tz/9NGeddRZKKZqbmzn//POpqqoiHA7Tv39/Lr/88m3q3dG5VFdXx+TJk4nH4/Tu3Ztrr72WqVOn7pO3IgshxJ4gIVEIIfYhl19+OTfffDMffvghRx99NNdffz3Tp0/npptuYsmSJdx555384Q9/4Lrrrssvc+GFF3L33Xdz/fXX88EHH/DUU0+x33777dR6u7MegL/+9a/U1dUxZ84c/vd//5fnnnuOm2++OT//wQcfZMqUKZx11lksWLCA2bNnM3HiRDzP2+56m5ubmTx5MrNmzWLRokV873vfY8qUKcydO3eH7fV9n1tuuYVFixbx0ksv4TgOp512WqcgAnD33XdTVVXF22+/zZ133sldd93Fww8/nJ//85//nGeeeYZHHnmEt956i+LiYu65556d2nddCYfD3HvvvSxevJg///nPLFu2jHPPPXebcr/+9a8ZN24c8+fPZ/r06Tu9nk2bNnH77bfzwAMPsGTJEoYOHUoikeC4447jxRdfZPHixfzXf/0Xv/jFL3jkkUfyyz377LNcdtllXHbZZSxcuJBvfvObXHnlldvU73kezz33HF//+teBIPwuWLCAZ599luXLl/P4448zYsSIfPnunEvnn38+7733Hs8//zyzZ89m3bp1PPPMMzu97UIIIbqghRBC7BWmTp2qDcPQ8Xg8P/Tt21drrfWDDz6oAT1r1qx8+dbWVh2NRvWLL77YqZ6HH35YFxQUaN/39fLlyzWgH3/88S7XC+gHH3yw07Rjjz1WT506tdvraVtm1KhRncp8//vf10cccUR+vH///vqSSy7Z4T449thju5yvtdannHKKvuiii3ZY5pM2bdqkAT1v3rz8tJqaGn3qqad2KnfiiSfqb33rW1prrROJhA6FQvq+++7rVObwww/XNTU1+fFp06Z1Gtda69mzZ2tAr1q1Smut9apVqzSgZ8+e3WUb3377bQ3o9evX56cB+rvf/e6nbl/b+fFJ06ZN04BesWLFp9ZxySWX6JNOOik//pWvfEWfc845ncpcccUVnbZLa61nzZqly8rKtOM4WmutzzjjjPy580ndOZeWLVumAf23v/0tPz+bzep+/fpt99zoeK4KIYToHvlOohBC7EUOOeQQ/vSnP+XHTdPsNP/www/Pv1+8eDGpVIrJkyejlMpP9zyPdDrNxo0bWbBgAQAnnXTSLrepO+uprq4GYOzYsZ2W7dOnD7NmzQJg8+bNrF27dqfaUldXx/XXX8/MmTPZuHEjjuOQyWQ4/vjjd7jca6+9xm9/+1sWL15MfX19/jbK1atXc+SRR+bLHXjggdu0d+3atQCsWLGCbDbLEUcc0anM+PHj+etf/9rtbejKs88+y+23387y5ctpbm7G9/18G/v06ZMv1/GY74qKigoGDx7caVoqleKGG27gueeeY8OGDWQyGbLZbKfbZj/44AMmT57cabmjjjqK2267rdO0p59+mtNPPz3/Pccf/ehHnH322cyfP58TTjiBiRMnMmHCBAzD6Na59MEHHwB02u+2bXPIIYfkb0sWQgixeyQkCiHEXiQajTJkyJAu58fj8fz7tlDx5z//udPtfG169erVrXUqpbb5LlrH2zJ3Zj2ffJCOUiq//K6YOnUqK1eu5MYbb2Tw4MHEYjF+8pOf0Nra2uUya9as4ZRTTuGb3/wmv/zlL+nVqxee5zF69Giy2Wynst1pb8cwsz2GYexw/23P22+/zde//nWuvPJKfvvb31JaWsrq1auZOHHiNm3seMx3xfaWv/LKK3nqqae47bbbGDlyJAUFBdx666288sorO1W31ppnnnmm0y24bd9dfOWVV5gzZw5TpkzhgAMOYObMmTt1Ln3afhdCCLHrJCQKIcQ+atSoUUQiEVauXMnpp5++3TIHH3wwEDx98pxzztlumcrKSjZs2JAfT6VSfPjhhwwdOrTb6+mOyspK+vXrx9///ncmTZrUrWXmzp3LjTfemC/veR7Lli2jb9++XS7z7rvvkkwmueOOOygsLMzXs7OGDBlCKBTizTffZMyYMfnpb731VqdylZWVbNmyBdd1871pbT24XXn99dcpKSnht7/9bZf1fp7mzp3Lt7/9bb797W/npy1btqxTmZEjR/LGG2/wk5/8JD9t3rx5ncq8/fbbNDU1cfLJJ3eaXlZWxrnnnsu5557L+eefz/jx43n//fe7dS6NHDkSgDfffJMJEyYAwcOX/vnPf+70d2mFEEJsn4REIYTYRxUUFHDVVVdx7bXXYpomJ598Mq7r8u9//5v58+dz6623MmTIEL7zne9w6aWXks1mGT9+PPX19Z0u/k888UTuv/9+jj/+eIqKivjNb37T6Qmf3VlPd02bNo1LLrmE6upqvvGNb+D7PrNnz+Zb3/oWFRUV25QfPnw4jz32GMcccwyGYXDLLbewefPmHYbEYcOGoZTitttuY+rUqSxdupSrrrpqJ/ZsIB6Pc/HFF3PddddRXV3N8OHDefjhh1myZAmlpaX5cscffzyZTIZrrrmGiy66iH/+85/ce++9O6x7+PDh1NfXc//993PSSScxf/58brjhhp1u464aPnw4zz//PJMnT6akpIQHHniABQsWdNquK664gsmTJzNjxgxOP/105s2bx6OPPtqpnqeffppTTjmFSCSSn3b11Vczbtw4Ro0ahWEY/OlPfyIWi1FTU9Otc2no0KGcfvrpXHrppdx///1UVVUxffp0mpubpXdRCCE+I/J0UyGE2Idde+21/O53v+OBBx5g7NixHHXUUcyYMaPTd8sefPBBLr74Yq655hpGjBjBpEmTWLVqVX7+9OnTGTNmDBMmTODUU0/lhBNO4KCDDtrp9XTH97//fR566CGefPJJDjzwQI455hhefvnlLn+376GHHsIwDMaPH8/EiRMZNmzYp/ZCHnDAAdx77708+OCDjBgxgmuuuYa77rprp9rZ5uabb+bMM89kypQpHHbYYTQ0NHDeeed1CkX7778/f/jDH3j88cc54IADePjhh7nxxht3WO/XvvY1pk2bxrRp0xg5ciR33303d9xxxy61cVf87ne/Y8iQIZx44okcffTRZLNZ/uM//qNTmUmTJnHbbbdx2223MWbMGP74xz92elItBE+zbXuqaZtIJMJ1113HuHHjOOSQQ1i0aBEvv/wyZWVlQPfP2bFjx/K1r32NY489lr59+3LSSSd12u9CCCF2ndKf/KKEEEIIIXbZV7/6VQoLC/O/U9kTPPTQQ5x//vnbfDfy8/Svf/2Lww47jC1btuRv6/28eJ7H8OHDOfXUU7cJ08cddxwDBw7c5rc+hRBCdE1uNxVCCCF20eLFi3n33XcZP348juPw6KOPMnv2bJ5//vk93bTtKigo4JRTTuGJJ5743NeVyWT4/e9//7kExNdff53a2loOPvhgWlpauP3221m1ahXnn39+vsw999zDf/3Xf5FKpXa6R1sIIb7spCdRCCGE2EVLlizh/PPPZ8mSJfi+z/77789VV121zS2We1pLSwubNm0Cgu9Stv0kyd5q7ty5XHbZZaxYsQLTNBk9ejQ33ngjxxxzTL5MU1MTW7ZsAaCoqIjKyso91VwhhNjrSEgUQgghhBBCCJEnD64RQgghhBBCCJEnIVEIIYQQQgghRJ6ERCGEEEIIIYQQeRIShRBCCCGEEELkSUgUQgghhBBCCJEnIVEIIYQQQgghRN7/B1qEFIaRSj9pAAAAAElFTkSuQmCC",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"# Valores de los componentes para cada iteración:\n",
"# Cada valor es muestreado independientemente de una distribución uniforme,\n",
"# limitada por la tolerancia impuesta.\n",
"all_C = np.random.uniform(CC * (100-tol/2)/100 , CC * (100+tol/2)/100, size=NN )\n",
"all_R = np.random.uniform(RR * (100-tol/2)/100 , RR * (100+tol/2)/100, size=NN )\n",
"all_RB = np.random.uniform(RB * (100-tol/2)/100 , RB * (100+tol/2)/100, size=NN )\n",
"\n",
"plt.close('all')\n",
"\n",
"fig_hdl = plt.figure(figsize=(fig_sz_x, fig_sz_y), dpi= fig_dpi, facecolor='w', edgecolor='k')\n",
"axes_hdl = fig_hdl.subplots(2, 1, sharex='col')\n",
"fig_id = fig_hdl.number\n",
"\n",
"# analizaremos cada iteración resultante\n",
"for (this_C, this_R, this_RB) in zip( all_C, all_R, all_RB):\n",
"\n",
" this_KK = 1 + this_RB/this_R\n",
" this_QQ = 1/(3-this_KK)\n",
" this_w0 = 1/this_R/this_C\n",
" \n",
" num = [this_KK * (this_w0**2)]\n",
" den = [1, this_w0/this_QQ, this_w0**2]\n",
" \n",
" my_tf = TransferFunction( num, den )\n",
" \n",
" _, axes_hdl = bodePlot(my_tf, fig_id)\n",
"\n",
"plt.sca(axes_hdl[0])\n",
"plt.ylim(np.array([-10., 50]) )\n",
"plt.xlim(np.array([3. * 10.**-1, 3. * 10.**-0]) )\n",
"\n",
"# visualizamos la última realización a modo de ejemplo\n",
"print_subtitle('Transferencia sampleada al azar')\n",
"\n",
"print_latex(a_equal_b_latex_s('T_N(s)', pretty_print_bicuad_omegayq(num, den, displaystr=False)))\n",
"\n",
"# finalmente ploteamos también la transferencia con los valores esperados\n",
"# sin incertidumbre alguna sobre sus valores.\n",
"KK = 1 + RB/RR\n",
"QQ = 1/(3-KK)\n",
"WW0 = 1/RR/CC\n",
"\n",
"num = [KK * (WW0**2)]\n",
"den = [1, WW0/QQ, WW0**2]\n",
"\n",
"# visualizamos la transferencia esperada o media\n",
"print_subtitle('Transferencia deseada')\n",
"\n",
"print_latex(a_equal_b_latex_s('T_0(s)', pretty_print_bicuad_omegayq(num, den, displaystr=False)))\n",
"\n",
"my_tf = TransferFunction( num, den )\n",
"\n",
"w, mag, phase = my_tf.bode(n=300)\n",
"\n",
"(mag_ax_hdl, phase_ax_hdl) = axes_hdl\n",
"\n",
"plt.sca(mag_ax_hdl)\n",
"plt.semilogx(w, mag, '--k', linewidth=3, label = '$T_0(s)$' ) # Bode magnitude plot\n",
"plt.legend()\n",
"\n",
"plt.sca(phase_ax_hdl)\n",
"plt.semilogx(w, phase*np.pi/180, '--k', linewidth=3, label = '$T_0(s)$') # Bode phase plot\n",
"plt.legend()\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En la respuesta de módulo y fase podemos observar la notoria dispersión de las N iteraciones de $T(s)$. En trazo negro intercalado observamos la transferencia esperada $T_0(s)$. Notar la gran dispersión en torno al valor de $\\omega = 1$, donde la transferencia de módulo debería valer $20 \\times \\log{Q_0}$ = 20 dB. Más abajo observaremos que se alcanzan valores de $Q_i$ bien por encima de 50.\n",
"\n",
"En la respuesta de fase se evidencia claramente la dispersión de $\\omega_0i$, ya que este tipo de transferencia atraviesa los $-\\pi/2$ radianes exactamente en $\\omega_0i$.\n",
"\n",
"Se continúa ahora con el análisis estadístico de cada parámetro $\\omega_0$ y $Q$."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Ahora vamos a hacer un estudio estadístico de los parámetros Q y \\omega_0\n",
"# calculo los valores de los parámetros para TODAS las iteraciones\n",
"all_KK = 1 + all_RB/all_R\n",
"all_QQ = 1/(3-all_KK)\n",
"all_w0 = 1/all_R/all_C\n",
"\n",
"plt.figure()\n",
"plt.hist( all_QQ, 20 )\n",
"ylims = plt.ylim()\n",
"plt.plot(np.array([QQ, QQ]), ylims, '--k', linewidth=3, label = '$Q_0$' ) # Bode magnitude plot\n",
"maxQQ = np.max(all_QQ)\n",
"maxQQ_idx = np.where(all_QQ == maxQQ)\n",
"plt.plot(np.array([maxQQ, maxQQ]), np.array(ylims)/10, '--r', linewidth=3, label = '$Q_M$: peor caso' ) # Bode magnitude plot\n",
"plt.title('Q para cada experimento')\n",
"plt.legend()\n",
"\n",
"#plt.figure(figsize=(fig_sz_x*6/10, fig_sz_y*4/10), dpi= fig_dpi, facecolor='w', edgecolor='k')\n",
"plt.figure()\n",
"plt.hist( 20*np.log10(all_KK), 20 )\n",
"ylims = plt.ylim()\n",
"plt.plot(20*np.log10(np.array([KK, KK])), ylims, '--k', linewidth=3, label = '$KK_0$' ) # Bode magnitude plot\n",
"plt.plot(20*np.log10(np.array([all_KK[maxQQ_idx], all_KK[maxQQ_idx]])), np.array(ylims)/5, '--r', linewidth=3, label = '$KK_M$: peor caso' ) # Bode magnitude plot\n",
"plt.title('Ganancia K para cada experimento')\n",
"plt.legend()\n",
"\n",
"#plt.figure(figsize=(fig_sz_x*6/10, fig_sz_y*4/10), dpi= fig_dpi, facecolor='w', edgecolor='k')\n",
"plt.figure()\n",
"plt.hist( all_w0, 20 )\n",
"ylims = plt.ylim()\n",
"plt.plot(np.array([WW0, WW0]), ylims, '--k', linewidth=3, label = '$\\omega_0$' ) # Bode magnitude plot\n",
"plt.plot([all_w0[maxQQ_idx], all_w0[maxQQ_idx]], np.array(ylims)/5, '--r', linewidth=3, label = '$\\omega_{0M}$: peor caso' ) # Bode magnitude plot\n",
"plt.title('$\\omega_0$ para cada experimento')\n",
"plt.legend()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Como se puede ver en los histogramas, el parámetro $Q$ es el más afectado o sensible en esta topología, razón por la cual habrá que ser extremadamente cuidadoso en el diseño de este tipo de redes activas. El $Q$ depende de la ganancia $k$ que a su vez depende de la relación de dos resistores. Esta alta sensibilidad puede mitigarse, o bien utilizando resistores con tolerancias bajas ($\\le$1%), o asumiendo que el Q a implementar sea bajo de forma tal de relajar el valor de la ganancia a implementar.\n",
"\n",
"Notar también que el peor caso de $Q$, no necesariamente coincide con el peor caso de $\\omega_0$, como se observa en el histograma de frecuencia."
]
}
],
"metadata": {
"_draft": {
"nbviewer_url": "https://gist.github.com/ac51adac5315287242cfd3dfb27a0196"
},
"gist": {
"data": {
"description": "mariano/Docencia/TC2/scripts/digital filters/python/Filtrado digital. Aplicaciones electrocardiografía.ipynb",
"public": true
},
"id": "ac51adac5315287242cfd3dfb27a0196"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
}
},
"nbformat": 4,
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}
![](\"./img/logo_UTN.svg\"){kind=logo}
\n",
"\n",
"#### Por Mariano Llamedo Soria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Resumen \n",
"\n",
"En este notebook se analizará la respuesta en frecuencia de un **filtro pasabajo activo**, mediante el uso de las siguientes funciones:\n",
"\n",
"* Análisis de la respuesta en frecuencia: [analyze_sys](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.analyze_sys), [bodePlot](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.bodePlot), [pzmap](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.pzmap), [pretty_print_bicuad_omegayq](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.pretty_print_bicuad_omegayq), [GroupDelay](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.GroupDelay)\n",
"* De presentación algebraica: [print_latex](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.print_latex), [a_equal_b_latex_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.a_equal_b_latex_s), [print_subtitle](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.print_subtitle)\n",
"* Dibujo de redes: [dibujar_elemento_derivacion](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_elemento_derivacion), [dibujar_puerto_entrada](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_puerto_entrada), [dibujar_espacio_derivacion](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_espacio_derivacion), [dibujar_elemento_serie](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_elemento_serie), [dibujar_puerto_salida](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_puerto_salida)\n",
"\n",
"Finalmente se presentará un *análisis estadístico cualitativo* de la sensibilidad de dicha respuesta a los elementos circuitales."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introducción\n",
"\n",
"\n",
"En el siguiente documento analizaremos la dispersión de parámetros $Q$, $\\omega_0$ y la ganancia $K$ correspondiente a una topología Sallen-Key en su configuración pasabajo\n",
"\n",
"![\"Sallen](\"./img/sallenkey.png\")
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Como se analizó en clase, la transferencia de tensión está definida por la siguiente función\n",
"\n",
"$$ T(s) = \\frac{ K·\\omega_0^2}{s^2 + s · \\omega_0/Q + \\omega_0^2} $$\n",
"\n",
"siendo los parámetros de la transferencia en función de los componentes de la red:\n",
"\n",
"$$ \\omega_0^2=\\frac{G_1 G_2}{C^2} $$\n",
"\n",
"$$ K=1+R_B/R_A $$\n",
"\n",
"\n",
"$$ Q=\\frac{ \\sqrt{G_1 G_2}}{G_1 + G_2(2-K)} $$\n",
"\n",
"Los primeros dos parámetros, $\\omega_0^2$ y $K$, no llaman la atención, pero sí la selectividad $Q$ que depende de la ganancia $K$ de una forma muy singular. Como se observa en el denominador, si la ganancia iguala o supera el valor de 2 (o 3 en el caso que $G_1=G_2=G$), el valor de $Q$ podría incrementarse demasiado, incluso en teoría comprometiendo la estabilidad de la red. Esta sería una forma de analizarlo, sin embargo también hay otra forma, si despejamos $K$ de la expresión simplificada para $G_1=G_2=G$\n",
"\n",
"$$ K= 3 - \\frac{ 1}{Q} $$\n",
"\n",
"Si necesitamos un filtro con un $Q$ elevado (no mucho más grande que 3), la ganancia requerida estará apenas por debajo de 3. En consecuencia, implementar $K$ mediante dos resistores ($R_A$ y $R_B$) con tolerancias comerciales resulta difícil o impracticable.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Análisis de la Sensibilidad\n",
"\n",
"Para este tipo de circuitos con dependencias restrictivas (o marginalmente problemáticas), resulta necesario estudiar en detalle cómo afectan el valor de los componentes a la transferencia total $T(s)$, o más precisamente a algún parámetro en particular: $K$, $Q$ u $\\omega_0$. Este estudio se denomina **análisis de la sensibilidad**, que según se estudió en clase, analiza la dependencia lineal entre el valor del componente y el parámetro en cuestión. Para este caso, la sensibilidad de $Q$ respecto a $K$ se denomina $S_K^Q$\n",
"\n",
"$$ S_K^Q = \\frac{dQ/Q}{dK/K} \\approx \\frac{\\Delta Q/Q}{ \\Delta K/K} = 3Q - 1 $$\n",
"\n",
"y se aproxima a la relación lineal entre el error relativo entre $Q$ y $K$\n",
"\n",
"$$ \\frac{\\Delta Q}{Q} = S_K^Q . \\frac{\\Delta K}{K} $$\n",
"\n",
"es decir que un error relativo $\\Delta K/K=5\\%$, para un valor de $Q = 5$, se convierte en $\\Delta Q/Q= 3 \\times 15-1 = 44\\%$.\n",
"\n",
"Restaría analizar $S_{R_A}^K$ y $S_{R_B}^K$ para poder calcular \n",
"\n",
"$$ \\frac{\\Delta K}{K}= S_{R_A}^K \\times \\frac{\\Delta R_A}{R_A} + S_{R_B}^K \\times \\frac{\\Delta R_B}{R_B} $$\n",
"\n",
"Esto se deja para quienes tengan interés especial. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Análisis de Monte Carlo\n",
"\n",
"Sin embargo, no es el único método para estudiar la sensibilidad de una red respecto de sus componentes. Otro enfoque muy utilizado es el denominado [análisis de Monte Carlo](https://en.wikipedia.org/wiki/Monte_Carlo_method). El mismo puede resumirse mediante los siguientes pasos:\n",
"\n",
"1. Definir las variables independientes: sus posibles valores y distribuciones estadísticas. En caso de incertidumbre, se puede asumir una distribución **uniforme** entre dos valores límite, lo que equivale a no favorecer ningún valor en dicho intervalo, o desconocer cómo se distribuyen los valores de un proceso de fabricación.\n",
"2. Samplear o tomar al azar un valor para cada componenente (o variable elegida en **1.**), y calcular determinísticamente la transferencia $T_i(s)$.\n",
"3. Repetir $N$ veces para acumular una cantidad significante de realizaciones. Una regla adecuada para asumir un valor de $N$ adecuado es de *10 veces la cantidad de variables independientes* definida en **1.**.\n",
"\n",
"Con los $N$ resultados acumulados de $T_i(s)$, se procede a calcular los valores más representativos de centralidad, dispersión, y eventualmente, los casos más y menos favorables. \n",
"\n",
"Estos serían los resultados del análisis de **Monte Carlo**, comportamientos de la red desde un enfoque más generalista, y una persepctiva más amplia, ya que podemos hablar al menos de 3 comportamientos:\n",
"\n",
"* El más probable, dado por el comportamiento central que denominamos $T_0(s)$. Podría ser el comportamiento medio o mediano.\n",
"* El menos favorable para la red, o el peor caso analizado. Habría que definir una función de costo, en este caso podría ser el valor del parámetro $Q$. Tener un $Q$ elevado implica un costo elevado para esta red, dado que el $Q$ objetivo por lo general será más bajo.\n",
"* El más favorable para la red, o el mejor caso analizado, sería simplemente el más cercano al $Q$ objetivo.\n",
"\n",
"Todo esto, se contrasta lógicamente con el análisis de sensibilidad donde solo podemos aspirar a tener un intervalo de incertidumbre, o error porcentual $ \\frac{\\Delta Q}{Q} $"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Experimentación\n",
"\n",
"Realizaremos un estudio de la dispersión, *estilo Monte Carlo*, de los parámetros $Q$, $\\omega_0$ y $K$ muestreando valores de $C$, $R$ y $R_B$ con una distribución uniforme, definida por los valores centrales y la tolerancia de cada componente, de la siguiente manera:\n",
"\n",
"* $C = 1$\n",
"* $R = 1$\n",
"* $R_B = R \\times (2-\\frac{1}{Q}) = 2-\\frac{1}{Q}$\n",
"\n",
"Siendo $\\omega_0 = 1$ y $Q = 10$. Elegimos un $Q$ elevado e impracticable para acentuar las limitaciones de esta red.\n",
"\n",
"\n",
"Para ello se calculará la función transferencia $T_i(s)$ a partir de los 3 valores sampleados $C_i, R_i$ y $R_{Bi}$ de cada componente. Finalmente se calculan las $N$ respuestas de módulo y fase, y los valores de los parámetros de cada transferencia $Q_i$ y $\\omega_{0i}$.\n",
"\n",
"Se procede a inicializar la simulación de la forma usual."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"code_folding": [],
"scrolled": true
},
"outputs": [],
"source": [
"# Inicialización e importación de módulos\n",
"\n",
"# Módulos externos\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from scipy.signal import TransferFunction\n",
"\n",
"# Esta parte de código la agregamos SOLO en los notebooks para fijar el estilo de los gráficos.\n",
"fig_sz_x = 13\n",
"fig_sz_y = 7\n",
"fig_dpi = 80 # dpi\n",
"fig_font_size = 13\n",
"\n",
"mpl.rcParams['figure.figsize'] = (fig_sz_x, fig_sz_y)\n",
"mpl.rcParams['figure.dpi'] = fig_dpi\n",
"plt.rcParams.update({'font.size':fig_font_size})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Separamos la inicialización del módulo pyTC2 para enfatizar las funciones concretas que se necesitan."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Ahora importamos las funciones de PyTC2\n",
"\n",
"from pytc2.sistemas_lineales import pzmap, GroupDelay, bodePlot, pretty_print_bicuad_omegayq\n",
"from pytc2.general import print_subtitle, print_latex, a_equal_b_latex_s\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ahora se definen las cuestiones más generales de la simulación, como:\n",
"\n",
"* la cantidad de iteraciones $N$\n",
"* el comportamiento esperado de la red, es decir los parámetros $Q_0$ y $\\omega_{00}$ de $T_0(s)$.\n",
"* los componentes de la red sujetos al análisis Monte Carlo y sus valores centrales $C_0, R_0$ y $R_{B0}$\n",
"* la tolerancia de cada componente (5%)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#################################\n",
"## parámetros de la simulación ##\n",
"#################################\n",
"\n",
"# Cantidad de iteraciones o experimentos\n",
"NN = 1000\n",
"# Tolerancia de los componentes\n",
"tol = 5\n",
"\n",
"# Q y \\omega_0 proyectados\n",
"QQ = 10\n",
"W0 = 1\n",
"\n",
"# Valores de los componentes \n",
"CC = 1\n",
"RR = 1\n",
"RB = (2-1/QQ)*RR\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La simulación continúa con el sampleo de $C_i, R_i$ y $R_{Bi}$; y finalmente, cálculo de las transferencias $T_i(s)$"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"code_folding": []
},
"outputs": [
{
"data": {
"text/markdown": [
"#### Transferencia sampleada al azar"
],
"text/plain": [
"