{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 6, Heat transports\n",
    "\n",
    "> Two-dimensional advection-diffusion of heat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "\n",
    "from IPython.display import HTML\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Background: two-dimensional advection-diffusion\n",
    "\n",
    "## 1.1) The two-dimensional advection-diffusion equation\n",
    "Recall from the **last Lecture** that the one-dimensional advection-diffusion equation is written as\n",
    "\n",
    "$$\\frac{\\partial T(x,t)}{\\partial t} = -U \\frac{\\partial T}{\\partial x} + \\kappa \\frac{\\partial^{2} T}{\\partial x^{2}},$$\n",
    "\n",
    "where $T(x, t)$ is the temperature, $U$ is a constant advective velocity and $\\kappa$ is the diffusivity.\n",
    "\n",
    "The two-dimensional advection diffusion equation simply adds advection and diffusion operators acting in a second dimensions $y$ (orthogonal to $x$). \n",
    "\n",
    "$$\\frac{\\partial T(x,y,t)}{\\partial t} = u(x,y) \\frac{\\partial T}{\\partial x} + v(x,y) \\frac{\\partial T}{\\partial y} + \\kappa \\left( \\frac{\\partial^{2} T}{\\partial x^{2}} + \\frac{\\partial^{2} T}{\\partial y^{2}} \\right),$$\n",
    "\n",
    "where $\\vec{u}(x,y) = (u, v) = u\\,\\mathbf{\\hat{x}} + v\\,\\mathbf{\\hat{y}}$ is a velocity vector field.\n",
    "\n",
    "Throughout the rest of the Climate Modelling module, we will consider $x$ to be the *longitundinal* direction (positive from west to east) and $y$ to the be the *latitudinal* direction (positive from south to north)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2) Reminder: Multivariable shorthand notation\n",
    "\n",
    "Conventionally, the two-dimensional advection-diffusion equation is written more succintly as\n",
    "\n",
    "$$\\frac{\\partial T(x,y,t)}{\\partial t} = - \\vec{u} \\cdot \\nabla T + \\kappa \\nabla^{2} T,$$\n",
    "\n",
    "using the following shorthand notation.\n",
    "\n",
    "The **gradient** operator is defined as \n",
    "\n",
    "$$\\nabla \\equiv (\\frac{\\partial}{\\partial x}, \\frac{\\partial }{\\partial y})$$\n",
    "\n",
    "such that\n",
    "\n",
    "$$\\nabla T = (\\frac{\\partial T}{\\partial x}, \\frac{\\partial T}{\\partial y})$$\n",
    "\n",
    "$$\\vec{u} \\cdot \\nabla T = (u, v) \\cdot (\\frac{\\partial T}{\\partial x}, \\frac{\\partial T}{\\partial y}) = u \\frac{\\partial T}{\\partial x} + v\\frac{\\partial T}{\\partial y}.$$\n",
    "\n",
    "The **Laplacian** operator $\\nabla^{2}$ (sometimes denoted $\\Delta$) is defined as \n",
    "\n",
    "$$\\nabla^{2} = \\frac{\\partial^{2}}{\\partial x^{2}} + \\frac{\\partial^{2}}{\\partial y^{2}}$$\n",
    "\n",
    "such that\n",
    "\n",
    "$$\\nabla^{2} T = \\frac{\\partial^{2} T}{\\partial x^{2}} + \\frac{\\partial^{2} T}{\\partial y^{2}}.$$\n",
    "\n",
    "The **divergence** operator is defined as $\\nabla \\cdot [\\quad]$, such that\n",
    "\n",
    "$$\\nabla \\cdot \\vec{u} = \\left(\\frac{\\partial}{\\partial x}, \\frac{\\partial}{\\partial x} \\right) \\cdot (u,v) = \\frac{\\partial u}{\\partial x} + \\frac{\\partial v}{\\partial y}.$$\n",
    "\n",
    "**Note:** Since seawater is largely incompressible, we can approximate ocean currents as a *non-divergent flow*, with $\\nabla \\cdot \\vec{u} = \\frac{\\partial u}{\\partial x} + \\frac{\\partial v}{\\partial y} = 0$. Among other implications, this allows us to write:\n",
    "\n",
    "\\begin{align}\n",
    "\\vec{u} \\cdot \\nabla T&=\n",
    "u\\frac{\\partial T(x,y,t)}{\\partial x} + v\\frac{\\partial T(x,y,t)}{\\partial y}\\newline &=\n",
    "u\\frac{\\partial T}{\\partial x} + v\\frac{\\partial T}{\\partial y} + T\\left(\\frac{\\partial u}{\\partial x} + \\frac{\\partial v}{\\partial y}\\right)\\newline &=\n",
    "\\left( u\\frac{\\partial T}{\\partial x} + T\\frac{\\partial u}{\\partial x} \\right) +\n",
    "\\left( v\\frac{\\partial T}{\\partial y} + \\frac{\\partial v}{\\partial y} \\right)\n",
    "\\newline &=\n",
    "\\frac{\\partial (uT)}{\\partial x} + \\frac{\\partial (vT)}{\\partial y}\\newline &=\n",
    "\\nabla \\cdot (\\vec{u}T)\n",
    "\\end{align}\n",
    "\n",
    "using the product rule (separately in both $x$ and $y$).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This lets us finally re-write the two-dimensional advection-diffusion equation as:\n",
    "\n",
    "$\\frac{\\partial T}{\\partial t} = - \\nabla \\cdot (\\vec{u}T) + \\kappa \\nabla^{2} T$\n",
    "\n",
    "which is the form we will use in our numerical algorithm below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Numerical implementation\n",
    "\n",
    "# 2.1) Discretizing advection in two dimensions\n",
    "\n",
    "In the last lecture we saw that in one dimension we can discretize a first-partial derivative in space using the *centered finite difference*\n",
    "\n",
    "$$\\frac{\\partial T(x_{i}, t_{n})}{\\partial x} \\approx \\frac{T_{i+1}^{n} - T_{i-1}^{n}}{2 \\Delta x}.$$\n",
    "\n",
    "In two dimensions, we discretize the partial derivative the exact same way, except that we also need to keep track of the cell index $j$ in the $y$ dimension:\n",
    "\n",
    "$$\\frac{\\partial T(x_{i}, y_{j}, t_{n})}{\\partial x} \\approx \\frac{T_{i+1,\\, j}^{n} - T_{i-1,\\,j}^{n}}{2 \\Delta x}.$$\n",
    "\n",
    "The *x-gradient kernel* below, is shown below, and is reminiscent of the *edge-detection* or *sharpening* kernels used in image processing and machine learning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow([[-1,0,1]], cmap=plt.cm.RdBu_r)\n",
    "plt.xticks([0,1,2], labels=[-1,0,1]);\n",
    "plt.yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first-order partial derivative in $y$ is similarly discretized as:\n",
    "\n",
    "$$\\frac{\\partial T(x_{i}, y_{j}, t_{n})}{\\partial y} \\approx \\frac{T_{i,\\, j+1}^{n} - T_{i,\\,j-1}^{n}}{2 \\Delta y}.$$\n",
    "\n",
    "Its kernel is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow([[-1,-1],[0,0],[1,1]], cmap=plt.cm.RdBu);\n",
    "plt.xticks([]);\n",
    "plt.yticks([0,1,2], labels=[1,0,-1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have discretized the two derivate terms, we can write out the *advective tendency* for computing $T_{i, j, n+1}$ as\n",
    "\n",
    "$$u\\frac{\\partial T}{\\partial x} + v\\frac{\\partial T}{\\partial y} \\approx u_{i,\\, j}^{n} \\frac{T_{i,\\, j+1}^{n} - T_{i,\\,j-1}^{n}}{2 \\Delta y} + v_{i,\\, j}^{n} \\frac{T_{i,\\, j+1}^{n} - T_{i,\\,j-1}^{n}}{2 \\Delta y}.$$\n",
    "\n",
    "We implement this as a the `advect` function. This computes the advective tendency for the $(i,j)$ grid cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "xkernel = np.zeros((1,3))\n",
    "xkernel[0, :] = np.array([-1,0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ykernel = np.zeros((3,1))\n",
    "ykernel[:, 0] = np.array([-1,0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OceanModel:\n",
    "    \n",
    "    def __init_(self,T, u, v, deltax, deltay):\n",
    "        self.T = [T] \n",
    "        self.u = u\n",
    "        self.v = v\n",
    "        self.deltax = deltax\n",
    "        self.deltay = deltay\n",
    "        self.j = np.arange(0, self.T[0].shape[0])\n",
    "        self.i = np.arange(0, self.T[0].shape[1])\n",
    "        \n",
    "    def advect(self, T):\n",
    "        T_advect = np.zeros((self.j.size, self.i.size))\n",
    "        \n",
    "        for j in range(1, T_advect.shape[0] - 1):\n",
    "            for i in range(1, T_advect.shape[1] - 1):\n",
    "                T_advect[j, i] = -(self.u[j, i] * np.sum(xkernel[0, :] * T[j, i-1 : i + 2])/(2*self.deltax) +\n",
    "                                   self.v[j, i] * np.sum(ykernel[:, 0] * T[j-1:j+2, i])/(2*self.deltay))\n",
    "        return T_advect    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2) Discretizing diffusion in two dimensions\n",
    "\n",
    "Just as with advection, the process for discretizing the diffusion operators effectively consists of repeating the one-dimensional process for $x$ in a second dimension $y$, separately:\n",
    "\n",
    "$$\\kappa \\left( \\frac{\\partial^{2} T}{\\partial x^{2}} + \\frac{\\partial^{2} T}{\\partial y^{2}} \\right) = \\kappa \\left(\n",
    "\\frac{T_{i+1,\\;j}^{n} - 2T_{i,\\;j}^{n} + T_{i-1,\\;j}^{n}}{\\left( \\Delta x \\right)^{2}} +\n",
    "\\frac{T_{i,\\;j+1}^{n} - 2T_{i,\\;j}^{n} + T_{i,\\;j-1}^{n}}{\\left( \\Delta y \\right)^{2}}\n",
    "\\right)$$\n",
    "\n",
    "The corresponding $x$ and $y$ second-derivative kernels are shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow([[1,1],[0,0],[1,1]], cmap=plt.cm.RdBu_r);\n",
    "plt.xticks([]);\n",
    "plt.yticks([0,1,2], labels=[1,-1,1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow([[1,0,1]], cmap=plt.cm.RdBu_r);\n",
    "plt.xticks([0,1,2], labels=[1,-1,1]);\n",
    "plt.yticks([]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "xkernel_diff = np.zeros((1, 3))\n",
    "xkernel_diff[0, :] = np.array([1,-2,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ykernel_diff = np.zeros((3,1))\n",
    "ykernel_diff[:, 0] = np.array([1,-2,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as we did with advection, we implement a diffuse function using multiple dispatch:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OceanModel():\n",
    "    \n",
    "    def __init__(self, T, deltat, windfield, grid, kappa):\n",
    "        \n",
    "        self.T = [T]\n",
    "        self.u = windfield.u\n",
    "        self.v = windfield.v\n",
    "        self.deltay = grid.deltay\n",
    "        self.deltax = grid.deltax\n",
    "        self.deltat = deltat\n",
    "        self.kappa = kappa\n",
    "        self.i = np.arange(0, self.T[0].shape[1])\n",
    "        self.j = np.arange(0, self.T[0].shape[0])\n",
    "        self.t = [0]\n",
    "        self.diffuse_out = []\n",
    "        self.advect_out = []\n",
    "        self.t_ = []\n",
    "\n",
    "    def advect(self, T):\n",
    "        T_advect = np.zeros((self.j.size, self.i.size))\n",
    "        for j in range(1, T_advect.shape[0] - 1):\n",
    "            for i in range(1, T_advect.shape[1] - 1):\n",
    "                T_advect[j, i] = -(self.u[j, i] * np.sum(xkernel[0, :] * T[j, i-1:i+2])/(2*self.deltax) + \\\n",
    "                                   self.v[j, i] * np.sum(ykernel[:, 0] * T[j-1:j+2, i])/(2*self.deltay))\n",
    "        return T_advect\n",
    "                \n",
    "    def diffuse(self, T):\n",
    "        T_diffuse= np.zeros((self.j.size, self.i.size))\n",
    "        for j in range(1, T_diffuse.shape[0] - 1):\n",
    "            for i in range(1, T_diffuse.shape[1] - 1):\n",
    "                T_diffuse[j, i] = self.kappa*(np.sum(xkernel_diff[0, :] * T[j, i-1:i+2])/(self.deltax**2) + \\\n",
    "                                              np.sum(ykernel_diff[:, 0] * T[j-1:j+2, i])/(self.deltay**2))      \n",
    "        return T_diffuse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3) No-flux boundary conditions\n",
    "\n",
    "We want to impose the no-flux boundary conditions, which states that\n",
    "\n",
    "$u\\frac{\\partial T}{\\partial x} = \\kappa \\frac{\\partial T}{\\partial x} = 0$ at $x$ boundaries and \n",
    "\n",
    "$v\\frac{\\partial T}{\\partial y} = \\kappa \\frac{\\partial T}{\\partial y} = 0$ at the $y$ boundaries.\n",
    "\n",
    "To impose this, we treat $i=1$ and $i=N_{x}$ as *ghost cells*, which do not do anything expect help us impose these boundaries conditions. Discretely, the boundary fluxes between $i=1$ and $i=2$ vanish if\n",
    "\n",
    "$\\dfrac{T_{2,\\,j}^{n} -T_{1,\\,j}^{n}}{\\Delta x} = 0$ or \n",
    "\n",
    "$T_{1,\\,j}^{n} = T_{2,\\,j}^{n}.$\n",
    "\n",
    "Thus, we can implement the boundary conditions by updating the temperature of the ghost cells to match their interior-point neighbors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_ghostcells(var):\n",
    "    var[0, :] = var[1, :];\n",
    "    var[-1, :] = var[-2, :]\n",
    "    var[:, 0] = var[:, 1]\n",
    "    var[:, -1] = var[:, -2]\n",
    "    return var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x13a4a7d90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "B = np.random.rand(6,6)\n",
    "plt.pcolor(B)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = update_ghostcells(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x13a537640>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(B)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4) Timestepping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OceanModel():\n",
    "    \n",
    "    def __init__(self, T, deltat, windfield, grid, kappa):\n",
    "        \n",
    "        self.grid = grid\n",
    "        self.T = [T]\n",
    "        self.u = windfield['u']\n",
    "        self.v = windfield['v']\n",
    "        self.deltat = deltat\n",
    "        self.kappa = kappa\n",
    "        self.omega = 7.2921e-5  # Earth's angular velocity in rad/s\n",
    "        self.f = 2 * self.omega * np.sin(np.deg2rad(grid.lat_grid))  # Coriolis parameter f\n",
    "        self.i = np.arange(0, T.shape[1])\n",
    "        self.j = np.arange(0, T.shape[0])\n",
    "        self.t = [0]\n",
    "        self.diffuse_out = []\n",
    "        self.advect_out = []\n",
    "        self.t_ = []\n",
    "\n",
    "    def update_ghostcells(self, var):\n",
    "        var[0, :] = var[1, :];\n",
    "        var[-1, :] = var[-2, :]\n",
    "        var[:, 0] = var[:, 1]\n",
    "        var[:, -1] = var[:, -2]\n",
    "        return var\n",
    "\n",
    "    def advect(self, T):\n",
    "        T_advect = np.zeros((self.j.size, self.i.size))\n",
    "        for j in range(1, T_advect.shape[0] - 1):\n",
    "            for i in range(1, T_advect.shape[1] - 1):\n",
    "                T_advect[j, i] = -(self.u[j, i] * np.sum(xkernel[0, :] * T[j, i-1:i+2])/(2*self.grid.delta_lon[j,i]) + \\\n",
    "                                   self.v[j, i] * np.sum(ykernel[:, 0] * T[j-1:j+2, i])/(2*self.grid.delta_lat))\n",
    "        return T_advect\n",
    "                \n",
    "    def diffuse(self, T):\n",
    "        T_diffuse= np.zeros((self.j.size, self.i.size))\n",
    "        for j in range(1, T_diffuse.shape[0] - 1):\n",
    "            for i in range(1, T_diffuse.shape[1] - 1):\n",
    "                T_diffuse[j, i] = self.kappa*(np.sum(xkernel_diff[0, :] * T[j, i-1:i+2])/(self.grid.delta_lon[j,i]**2) + \\\n",
    "                                              np.sum(ykernel_diff[:, 0] * T[j-1:j+2, i])/(self.grid.delta_lat**2))      \n",
    "        return T_diffuse\n",
    "    \n",
    "    def timestep(self):\n",
    "        T_ = self.update_ghostcells(self.T[-1].copy())\n",
    "        \n",
    "        # Calculate Coriolis forces\n",
    "        f_u = -self.f * self.v\n",
    "        f_v = self.f * self.u\n",
    "        \n",
    "        # Update the velocities with the Coriolis effect\n",
    "        self.u += self.deltat * f_u\n",
    "        self.v += self.deltat * f_v\n",
    "\n",
    "        # Proceed with advection and diffusion as before\n",
    "        tendencies = self.advect(T_) + self.diffuse(T_)\n",
    "        T_ += self.deltat * tendencies\n",
    "\n",
    "        # Store the new temperature field\n",
    "        self.T.append(T_)\n",
    "        self.t.append(self.t[-1] + self.deltat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.         4.99923423 4.99693714 ... 4.99693714 4.99923423 5.        ]\n",
      " [5.52649688 5.52573111 5.52343402 ... 5.52343402 5.52573111 5.52649688]\n",
      " [6.05283158 6.05206581 6.04976873 ... 6.04976873 6.05206581 6.05283158]\n",
      " ...\n",
      " [6.05283158 6.05206581 6.04976873 ... 6.04976873 6.05206581 6.05283158]\n",
      " [5.52649688 5.52573111 5.52343402 ... 5.52343402 5.52573111 5.52649688]\n",
      " [5.         4.99923423 4.99693714 ... 4.99693714 4.99923423 5.        ]]\n"
     ]
    }
   ],
   "source": [
    "# You can instantiate the grid outside of the OceanModel like this:\n",
    "N_lat = 180  # Number of latitude grid points\n",
    "N_lon = 360  # Number of longitude grid points\n",
    "grid = Grid(N_lat, N_lon)\n",
    "\n",
    "# And then create the OceanModel with the new grid:\n",
    "windfield = {'u': np.random.rand(N_lat, N_lon), 'v': np.random.rand(N_lat, N_lon)}\n",
    "\n",
    "def create_wind_field(N_lat, N_lon, max_speed=10):\n",
    "    \"\"\"\n",
    "    Create a simple zonal wind field for an aquaplanet.\n",
    "\n",
    "    Parameters:\n",
    "    N_lat -- number of latitude grid points\n",
    "    N_lon -- number of longitude grid points\n",
    "    max_speed -- maximum speed of the wind (m/s)\n",
    "\n",
    "    Returns:\n",
    "    windfield -- a dictionary with 'u' and 'v' components of wind\n",
    "    \"\"\"\n",
    "    # Create a latitude array that goes from -90 to 90 degrees\n",
    "    latitudes = np.linspace(-90, 90, N_lat)\n",
    "\n",
    "    # Create the zonal wind profile - simple sinusoidal variation\n",
    "    u_wind_profile = max_speed * np.sin(np.deg2rad(latitudes))\n",
    "\n",
    "    # Create the u component of the wind field - it varies with latitude but is constant with longitude\n",
    "    u_wind = np.tile(u_wind_profile, (N_lon, 1)).T\n",
    "\n",
    "    # The v component (meridional wind) is zero in this simple model\n",
    "    v_wind = np.zeros((N_lat, N_lon))\n",
    "\n",
    "    windfield = {'u': u_wind, 'v': v_wind}\n",
    "\n",
    "    return windfield\n",
    "\n",
    "# Example usage:\n",
    "N_lat = 180  # number of latitude points\n",
    "N_lon = 360  # number of longitude points\n",
    "windfield = create_wind_field(N_lat, N_lon)\n",
    "\n",
    "kappa = 1e-6  # Example diffusivity\n",
    "deltat = 3600  # Example time step in seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gRiKxBsaQSGVIJFd/y5BIJFUt7j2lOuvlYUfUTfVF1E31xCTOpFQKYs9XRETkrjCbSjHo8zHqCzEZixEsRgSLGUGwIFhMCAjY2PkAWNMYCjEarGklEjkyuQqpTFn2I5Nd/V+F7MpvqUyBTK5CJrepNp13QbBQXHCZguwYTIZCJBKp1bi4YmQgkVKQdY70yzspzL2IxWxEsBhvOf9/87IaLtL//K3SuGBj54ONva/1t92V3/Y+yBXa+1hqERERERG5XOw+VwZiLdcA5HI5iA2iWlE2uqWQI0gf3NEtvS6HrORDGA2FyOUabBz8cHStzcWTf5B4fgUlhckYSvMqTyCJFIXSDoXKDpXGGVvHQGwdg3D2bIRnQDvg7nUjCNaFqpIKFqnGnfqT3PRT5GdHU5Adg9mku2FeCpU9Hv5t8Qt7BJlM/R9DzPpbIpVdMejMWCymK3+bsFjMVgPPYsYiXDl+9ZzZSGlJJsUFyaQnbKekIBnLf+JhKtXOSCSg1+XiHdyN5t2nIZOrbrse7jUPS5upiYi6qb6IuqmmSMQ+WWUg1rKIyENKaUkWhblxlBRcRq/LxWI2YDQUYdDloNfllM0S/D9SmRKL2YBv2KP4hPRAY+uJUu2EQmWHQmmLRKpAIpUivTLqD1Ccn4hEKi8zMBRKO+QKGwTBjNlswGLSYzbry1yhzGY9ZtO1/+tLsinKTyA/8yzZqUfITT9ZJlfngavRlWSAKQ/ahxBzdA7FxQWYDEWYTXoELCAIV1ILSCRynL0a4+zRkMKcC+RknCQ3/RR5GacRBAt2TrWu/ASjsfMi9ugvFOTEAuAZ2Ik6Ld/AwSUce9dwVGon68yRYC43i6RQO9zWHkeCIHBw4xskxay9aVqJVIFG647WwZ/C3Atlxw2lOWV/p1zciMlYUqGRIljMGErzMJv1Vn0obavNLJWIiIiIiIhopNQApAopEjEmd7Xi6oCWVCFDVsNGtwRBQBAsbJjbFovZOgIvV9oikymRK7WoNM6obFxw9Ykkovko3P1aoda6YTKWUJAVS2byQVy8GuHu1+qW72nn7Hfdcwo0FR43mw3sW/0yKXGbKzwvlSpQqh0pLckEYMuiPgBoNBp4YSEXT/6OySyzlk2hRnI1TohEggQJJmMJl84uAayGi42dD86eDajd4hWkMiWFORcpyLlIxuU96HU55e6ddmkraZe20rzHN9g6drjlergZFouJ1LhNt5RWsBgpKUy+7vnIrl9gMRu4cOJXDLpc9Lps9Lpc9CXW39YZMKEsvUQiRaGyR6l2QOvgT+OOH2HvEnKXJfqXmtxmHnRE3VRfRN1UT8Rw0JWDaKTUAKRymbhxUDVDdkUdMrms2i5mFASB7JSj7F/3JoW58QBIpHIEiwkbOx9UNi7oClMBcHAJJaL5C7j6RKKx9axwRF2hVKHRNscjoPl9lttCUV4iyRc2VWigeAa2o7ggmZKCpDIDJajeAEIaDUVj54WtnSNwjj4v7cNkvrFu9Lpc8jLPY+8Sgkbrdt10ZrMBk6EYfUkOqfHbiTu5mLzMc6hsHJHdw7YpQ8aTYy5gNBQjkUiRyVXXuJ1ZLCZ0RRmUFKRQUpiCrigdtdYNrb0Pmxf0K0t3ZNN7SGVKNLYeqDROqGxcsHX0x8W7kdUQ1TijtnHGZNKRmXSIzMsHyc+KxlCaR1FeAlsX9af/6NP3rmw1oM1UV8wmPaUlWRh0eVfcEQXsXULJyzhHfnYMcoUNdk5B2DsHI1fa3Hb+D7puSkuyyUjYS3bqcfKzY9EVpaMrTMNsKsXVJxKPgCg8/Fvj7NWwQjfPquRB101NxWSqagkeDkQjRUTkASQ/K5ajWyeSFr+j7JhHQBv8wnsilSrITD5M4rlVgNV9Ky/zHHtWvQSAXGGDrVMgeRlnK8xbY+uJrigNgGbdvySk0ZB7InNhbjznDswm4exKTMaSK7JokUrlGA2FCIIFiVROaUk27r7NsXcditrGBZWNM+7+rZDJlADIZOZbvqdK44SH/81nhGQyJTKNEpXGCXuXYMKbPndnhbxFFMrrL36XSuVo7b3R2nuXOy4IFsIin0MQzPiEdMHOKQgbex9MhkJSLm7DbNJjNpViNuuRyVTkZZwhKXYjuqL0K/kqcHSrjYNbBPbOtfAMbHdfy1hZ5KSdIubIXJRqe0qLsygtyaK0OBPBYsHeNQRbxwByUk+Qk3YSpdoRja07GlsP1Lbu2DsH4+hWG0f32qg0TpUue0H2Rdb90uG2rhk4NlF02wN0xZls/2toufeYjb0PTh51cfWJxMbWE4lURublg5zZN50TO77EzimI0CZPE1RvAEq1QxVKL1LdEQeOKwfRSKkByOUypGJM7mqF/Mpgm1wu+3c+vhqxa/lzFObGU7/NGzRs//Y1o4PhTYei7zqBtIR9FOcnUZR3mfzsCxTmxKMrSicv89x1875qoACUFqchv4NnsyD7IgU58ehLsiktySE79TiJ59ahsnGhTquXcPOJ5PCmj8nP+ndNjINrKOFNRxAWOeKGnbDqrpv7h4wWPSddc3TrwmfITDp8zXGtvQ9B9R7DxasRju61sXcOQiq7f7so3yu9GPVFJF/cSlFeIkW5iRTmJaArTMMvvCduvk0pKUjlwvE/KS5IxmI2YjYbMBuvDXBQq/4AigtSuRy9Dkf3CBq0fRODvsg6yl6UQVbyYS4cm1+WvveL23F0C7tjue8EW0cPQILKxhl9SXaFaexdQlCqHTAZS6jX+hUUytv/rD+IbSY/81Q5AyW82bM07/5phWktZiMZlw8Se/QPjm37lBM7J1Or/gCadP4ApcquskSukAdRNw8CBnEmpVIQjRQRkWpKdupJzh34CY2tO0qNExqtG3ZOAdg6BWBj53lDt4TmPT9n7+rRnNr9LRdP/oVK40Ru+hkAug1bhkdAK1Q2zgTUfuSaa03GErYuHEZ64r6yY1KZki6DFyKRyigtzsLJoy62jv43NBYEwcKqmW0ozL0EWNc82DkFYjQUl43eAyhUdmjtfWjW/VOCGz6FRCqjOD+J4IZPcmbfTPQl1vUg+VmxHPx7PCGNBiGTq2+rLh9m2g/4hZgjv3Fy1zQAVDYu9B21A5WNcxVLdmec3T+bk7umIpOrcbgyE6K19+bsvllYroR49giIIrzps8jkSkqLs0k8v46ivETAGtpZEMzEnVoKgFSuxpJ6ioLsOCxmAyZjCSaj7hrDRq64fTequ0WpdmDYB9Z1R4bSAjKTDpObfgaVjTMOrqG4+TYTZ02ug29oV4a8n0hxXhIXTizi9J7vyUk9hYd/S9z8mmHvHITGzhOFUotUpsAzMArPwCgiC9M5tWsaMUd/x7tWBzKTDpKffRGJRIqDSwhOHnVw822G1sFXrPuHGLk4k1IpiEZKDUAuk4pTi9UMudS64Fgul4Ll1nQTe3wx5w78QnbqSRQqOwSLiYDajxDVZ+o10ZcEQWD9nB7XzUsmU2HnHEhEs2cIbzqcotxE1s15BLNJj1rrgp1TILUaDMDG1oOSwlSSL2wru1altq3wBSsIAqnxuziz70fSE/eXO2cxG3D1qovK5tZdXgRL+Q+4IFjwC++GXKHByb0OEokEfWk+JQUpFOZdJuHsak7v/YGSglSuLuiWSGRWw8zRHzunAOq0eB6V+sb7gNyJbh5kVGotcaeWorZxoV2/GfiEdKwSOe6VXuq1fpHkC1sozInHL6wrEU1HYGPvScuen2IylCCRSlHZuCD9z6hzix4fU1qczbHt3xBzZD6C8K9LoJN7OO6+zTCbSlFrXZErNMiVNsgVGhQqO7yD2qLWutx5we8RclsnAiK6EhDR9d7n/cC2GRlK92CadR2Pi2cdLp1dw8UTizi9d3pZCoXKDhs7T9Q2LpiMOgz6AvTF2UhlSvatfRuJVIqbT2MkCMSfWcmZfTMB68BNYJ1HieozFbmi4uAf94IHVzc1G4uleq1delARjRQRkfuMYLGQfGEru1e+XnbMqC8E4OLJpTTtOgEbO49y10gkEmo3f5ZzB3+tME+LYCYvM5r969/l5K5v6fH0MkqvuIPYOQeSEreDlDjrepR2/WZSq14/Dm/6hNRLu1nzc3fkChuUageUansEwYLJqMOoL8RQmn/NvYLq9qVZt49vy0ApzE0gNW4XvqGdSIz+h+L8JDwDWtO8+0QANi0YQlKsdVG8UmWPo3s4to7+eAS0xM7Jv8wo0dp7I5WJr6m7IT/rAkV5l+n1zGo8AlpUtTh3jUrjSPfhf3Fs22TO7PuRk7unE1S3D35hXXHxaoC9cy0k0ms7EBmXD3H+0K9o7X1wdAujMC+RguyLZKecIDvlBCChTsuR2Dn64eLdEBev+pVfOJH7Rq36j1Or/uMIgkBR3mWK8i5TUphW9lNanI1caYNCqSU98QBZyccwCQIhjZ5CIpGiL83D0TUEiURKcX4SFrOBuFPLieo9paqLJiLywCJ+/WsAMoW4JqW6YdLnAXDwn4/Q6w0gkWLUF1FSkIpcaUO3YX+WuQLsXP4WMUcWVJiPq3cj7Jy9KzzXotdEnD1rlxkHFrOR4vwUslJOkJ1yiuLCNMxGHSWFaRiNBXR4cjYHN3xMTlr5iEw7l79c9neHJ2djMRsx6PLRl+ZjKC1AIpEgV2iRKzXoijI5s/fHctfHn1lF/JlV9HttJ86eda5bJ4bSQjIuHyY98SCnds3AbCrF3jUYj4BmBNT+iIDaPZBdeY4Vqn9nQwyGQkqLs5BIpZQUJpMUuwmTocS6y7xCjUrtQPsBM9E6eF333v9FdmXkUaaQIYgjjyjU1lHezORDeAY1rzKj717qxUbhQlTfr2jW/QOijyzg/MF5xJ1abj1n78XgcafK0qbG72HH0lcoykvCL7wb3Yb+ARIJcz74N5qbq08jspKPc3b/TwBIpDKem5TOw8LD1mYc3YNwdA+q8Fzapf2c2TcbALlSQ2r8LmQKq3upYDGBYLmy15MWtdaVI1s/o3mPj5Er7o8L6sOmm5qCRBB1URmIRkoNQKaQlXXuRKoOo6GEC8eWcuHkCvJSjtB3wXwykw5jNEkQBAtyhZqctHMIgoBcLi0bza0f9Ty6ojQMpYXkZcZi6+iLk1soDq7B2LvWIvnCZpzcw7F3CSx3P7nClvptnr+hTKUleZQUpKG198TGzpnB753g8vlNbPx96DVpFSo7dIWp2Dr6olCqcXANxDe0PQqVLWf2zeHiyVUUZMcBENr4Ceq1HkluRgzRRxaSGrcHMN5wkfySaV3Jz/p3U8FOA38krMmTFabtPnweJqOO3PRo9q//iOQLOynIib9u3nGnl9G4w+gb1sVV/vtRf1jdIwSLhezU02SlnCIz+TgAh/6ZxKF/JvH0hFg0tq6VLtP90Itc4UTjDq/SuMOrzJsYSmlxFiUFqexc/hretVqDRMqxrVOxsfOgde/P8I/ohlxhDQ7QotfHnD/4BzZ27hTlJ+MV1AoXr3rYOQfgGdjyjgJC1FRuRTdmk4GslFNkp5yitCQbfUkehtJCfELbE9zgsQdmfYZvaBTPTbqMTKEhK/kk+9Z9eOX9B3bOAbj5NsRiMZF4fhMlhWnkpJ0m+vAfNOowmnqtnyMnPRoACRKQWGfFFSo7nDwikN1BYArxfVY9MRiqWoKHA9FIERG5CfqSPHatfJvE6M0YSgvwCWlP405vAlCrfl8SY/eRlxlLYU4ChtICXL0bsHJmD6RyJXaOvtg6+VGrXh/sXQLxCGiOXKEmMXozW/4ciV6XZ72JRELPp//Ev3Z3LGYDUpmywo/+8R3TKcq9jCBYyEw+QVbyCSxmY9l5e5cgPANbVlgOo76QAxsmXnO886CfuHB8Oanxe8uOZSaf4NSen9DYuuLiVQ975wCOb/8OQ2khRn0RcqUNWgcvtPZeaB28kMlU5QwUgILsSzesV7lCg5tvI+q1foGCnAQKcxIAcPeLJKLZEJy96sKVjSeRSEi+sAuLxYRgMV35bUEqkyNXaJDJVag0jji4hSATXYU5tv1bDv79b6QvB9cQ9Lo8bOzc4AHpTP4/w8afIe3SARKjN3E5egsxRxYCoNa60H3479fMAjbuMPqWDd8HHYtJD0DM4YXk5aRhKC3AUFqIQV+AobSA0uIcctLOYjbpkUikV/ZqsXLu4G/kpkfTrNu7VSX+LWHUF6Erzr4SFKEUk1GHQqnF2asuJYXppMXvJ/XSPrJTTlnDnUuk5KZHo3XwouvQefiHd0ahsi3La+fytyjIScDWwQeTUceRzZM5snnyde8vk6tx822Iu19T/MI64hvWqVobdoIgUFqcTVFeMiZjMRKpHOmVH7lSi52THzK5sqrFrDLEgePKQSIIgnDzZCJVQUFBAQ4ODoz6Mh65qmZG4nkQKMxN4rdP6pb9L5OrUCqkLFy4kOEjRuLgXg8X73oU5CSQcG4TcrkSv/BOSCRSCnMvU5ibRHFBKggCCpUdQXV7oLFz58SOGahsHGnZ60MSzm7i0tm/UajsMOoLkUik1Gn5NB2f/LbsvoIgMOsd97Jd4gGi+nyKk0c4ZlMpJmMp+9dNwiMgkvptRpJ4fgtZyacoLkhFsFjQFWdTUpBGQO2u9BjxO7npMfw1tT0ALl51UartkCvUWCwWBIsJo6EYvS4fwWK27tIukSCRyJBIJJiM+rJ7mgwlCIKAs1dtvAKb4xnYHM/AFji6Bd/yR1gQBNLiDxB9ZDHxZ/6mOD/ljnSlUNniH9KCKZ+9xtz1JXjW6lDOtexhobQ4h71rPyI79RyZSSdQaRwY8fG5OxrJvVfIJCY6Bu5i26W2mIX7Pz5mvOIyKJXKq3VnsLKxmE1kJp8kPfEImUnHyUw6gS7vEgsWzGfw4CEg015Zq2amuCAdk6Gk3PVSmaLcwIiDWzA9R/yOq3e9yi7KNQiCQEF2PKnxB9AVZWHQF2HUF2EoLSD22HKM+qJrrpErNJiuRHJzcAvGw68JMrmqbHa8abex2N7E1VQQBE7u+gm1jSPu/k3KIi9au1cCpUXZpCceIS3hEOkJhynMvYybTwMef3U9SvWNQxxXdrsBSLt0iPW/DqakMOO6aSRSGfbO/ji6heDgGoyjewhuPg3wCmqBrjgHwWJCbeP8wK4pNOlzmP1uEPn5+djb21e1OA8sD+bTIyJyD7Gxc6f3C8vIy7yIrigDja0bjs7Whe5PTziDIFGxfelbxJ1cjVLtQNNuY5HJVZhNely862IxGcnPiiP6yGKM+kJiji4py1tfkseOpWPo+cwfuPrUJ+3SQTS2riSc20TsseXljBSJRIKDSyC5GTHl5LN3CcCoL0ZXlEVpSQ52Tr5kJZ/m2Lbvy3UmnDxrE9F0IEe3fktBdgLufo0Y8MYWUuP2kRK3l4Sz/2CxVBz8XSZXYWPngVylQq7UYGOvwWzSYzSUUJiThFFfgI2tKx0GTKtw0fLNkEgkeNVqiVetlrQfMJXc9GhKijKRy9XIFCpkchUymeLf0TyZHIlEdmUPDKuxpCvKJD3xCHlpJwD4Z/5z2LnV5rGXVqFUP1wfEbXWmU5PTef8oYXsWvmetW4SDuNd6+YbVz4oKO5g5/UHFZNBx7HtP5B8YRfpCYcxGoqRypS4eNXB3a8xPu2eAaBxpzdIiN5FTtr5fzuoEgl2jn44ugXj4FYLB9daOLpaf9s7ByBX3r/IVjcjNz2GmGPLMBt1FBekk3xhN0V5SYDVvVWptkWhskWpsiWsyQBCGj1eNvNqXYOXRdqlgzi4BOFVqxVae4+b3LFiJBIJDdu9eP0E7qG4+tTH3b8JKd712L9+Etlp56rd7vZgNWI3zBuO0VBCy14f4h/RuSwapcmow2TQYdAXkZ8VT35WHPlZcSSc28jJXbORSKQ89vIaVsz4N7S9WuuMja07WgdPNLZuFOUl0+qRj/CqVfGMv4jIfxGNlBqAXCa1hh8UqTR0xdmUFudg1Bdj1BcjlYKdkyfOHrXQOnjh4u4PHEGpVGAWpNjYOuPu14j87AT2rBqPTK5EJldhKC28Jm97l0DkChU5adFlxzbMLb+GRKlxoPuwH6/R++BxuzDqixEEC79/1ow9qz9gz+oPys7bOfvTpOPLzJlQu+yYd61W5KRHk5t2jq6DviPu1BoWT2mLnZMf9s5+2Dn74eHXCL+wdsgVGqRSKUaDDn1JHiZTKTa2rihUthj1xVZ3L0MRCqUtWgcPjAYdOannOL3vN5Jid5KXeR43n7sfUXX3vf4C/RsRWLsDMokJ2EFoo8c4uW8hv3xQi/DIAXR+6rsq7VBVNoIgcOnsBvQluQDY2rtU6XtEdqVDJpdLkQji+6yyEASBs0cXcWCDdSPDNn0/wTu4Ne6+DUlPOELc6fWc3jMHnvqMkztn4ebfggZtnsfJIwxnjzAc3YOrncFnNhnYtfIDTu76GYXaDo3WGZXGkdDGffELbYdPSBQqza3tGB8Q3va+yKjXFXDx5BpS4vaTlnCE7NSzCBYzCpUtter1pGG7F9Fob75RZGW3G7NEQmCdLsQcXc7+9ZOIPbYUrYMXeRkXKbzialwRUqmc7sN/QaEqH06/tDjH6i6Yfr7s2PnDC/ALa31fy3HfMYnvsMpAdPeqxlx193pzWhJytejuVVnkZlzgl48a3DCNRqNh4cKFDB32NHqDGZlMgYNLALXq9yAi8glcva1GQk5aDHMmNvr3QokER9cgXL3r4OpVB2fPcNQ2jihVtihUWhQqLUqVFrXWBcVNOtSlJflcOLEaiUSGs2c4cqUGmUyFVCbDUFrA5ZhdxJ36m5yMWPS6fCRSOfbOvoAEs7EUqUyBIAhYzAb0unxKirKsrl3XQaHSolTboVTZWtMXZiFXqHBwDcLOyYeQBo8S0uDRK6OX2nL7VFQmMomJlp5b2Z/WiZysZGKOrWLPmk9w863P4y8twaYKFo1XNnGn/2bZjH7ljtVvPZwew2ZXkUTl9VJZbisPM3mZ8RzZNoO40xvJy7yIQqWl38vL8A9rR05aDH//8RLJF/dhY+9O3cg+jB/9KLsSWyFVVO0O64IgoNflU5BjdZU1GUuRSmVW170rrmZJF/Zw8J+pAIycdBZH18Aqlfn/yUmPZc7HDcsdC4johGdAExQqLY3aPo/G1gWjoYTi/HQsFhNKtS1KtT0KpU0598SK2o3FbKK4IA2Nret9iypmNOi4dG4zscdWY9AX4uQegp2jD7riHAz6QhAEHN2CUSg1CIIFN596eAZEll1fUphJWsIRMpPPoNflYygtRKVxIDyyH24+9Wu8C6auKJMf3gkQ3b3uM+KXQkTk/3B0Cya0UR9ij6/G0a0W/V9ejkKtRamyxWwykJcZT3GeNQpWmz4fodcbMZsMJF3Yw771X7Jv/ZeAhG6Dp+MT3JKXv0qwLoaXylGq7SoclTQZ9eSkx5AUu4d//nwVALlCjclYSovub9O278Syl7rFbGLb0nGc3v9HhTM1AJ2fmoogWLh0fguObrXwD2+PrYMXFosJi9lISWEWcWc2IljM2Dv7MeqLC8Sd3siJXb+QmXya4oL0cq5iT725kQsn1pCdep7stPOUFGaCRELdlkPJSjnDpbObuXR2M5sXvQFYd5f3CWlNWKO+hDbqg72z3z3U0K3j4BJAsy6v4xvSmuUz+vPn153o/8oKnNyDq0Se+0nqpUPEHl+DnZMPRXmp5c49PuovPAOaVJFkIpWNIAis/20kyRetwTCeHL0e/7B2WAQzB/6Zyp41k7Bz8mXAa6sJjOiEXGYBtqJQajBX8bDlP3++xsndFe8PVRH5WfHVzkixdfCkbsuhFOWnIphNZKWdI+H8VhLOb0UqlbN79URUGsd/A6f8B4lEikJli6NbEH1GLsDB0e2aNDtWfsjhzd8B1nV4NrauSGUKQECmUBNYuzNhjR/DO7DZHbnfAiiUGkIb9ia0YW/AqperhuF/kcoUPD1+P65etcsdt7Fzo1a9HtSqd/1NiWsyCnGD7UpBnEmpxlydSRnzfbI4k1KJZKWeZ9GUHkgkEh4btRCf4Gt9Z2USE83dtrDumAfnjq3nzP6F5GZc5OpO6f/F0TUI7+AW5Gcl4Opdh3otB6HSOnF48w8UF6RTu+kA9m34iuzU89dcC9aO9oufn0MikXB63wLiz2zi7MHFADRoM4I6zQeSm3GBksJMdq2yRu8Kb/I42WnROLgG0v+VpUgkEswmIyd3zyU57gCpl46QkxZN3RaDaNdvElsWjSHm2Co8A5oQWLsTto7eHN/5C1kpZ/EMaEL65RPYOfng4dsAF68IXLwiKC7MYO/aLzAZS8sMGq/ASLJSz2PUF5crw/D3d+EV2PRu1HLLXNXNwczO5Ubs8zLjWfJ9X3TFuXQb/C0RTftXijyVQUF2ImvnPs/lmF1lx5w9w7F38qXd4xPxCoy8wdWVw/X0InJviTv9DztWTCDjsnVtVqcnJtOs6+skXdjLxj9eJzv1HE27vEbbPhNQqKwDJnejG5OxlK1L3uXiqQ1YzEZsHbysP45eePg3omHbZ5FIJMQeX0vs8TU0aDsC3+Drr41aMWsguZlxdB86HXsnX+vgkNmIxWzEYjEhlcpRaRyQKzU1ajS+pDALgKyUc2z841UimvbHyT0EWwdPpDIFBn0hBl0BhtIi9KUF7N/wDaUluWWz9sOGP4vRbB280uvycfYIo2nnVygpzERXlF22nrC0JI+LJzdQXJCOu28DhozdglJte1eyG/Ul/PhBXYrz0yo836jd87ToMQaNjRNKjX2N0sudYijOZtqbvuJMyn1G/FKIiPyH3Iw4Fk/rhcbWlYFvrbvuQsqUS4fBDf74qhM6XSkgYOfsS0B4B4LqdsU3uCXbl3/AuUNLKMhNwjbbC3sXP+LP/MOJXXMAsHX0xsElgLW/PluWb58X5mPS6zixZy4pFw+gdfDgmY8Ol730d6+eRH52Qll6lcaBgIj2BES0R19aSHriMS6d3Ur00RW4etch4dxWoo8sR2XjyK6VH5OWeAyvwEh8glvSY9gP+IW2YeeqicQeX8NjoxYS3uSxsrwjmvZn1U9DuRyzi8btX6DLwCnXRGqp23wgy2cNJDX+IA4uAWQknb5m8b1EImPHsg+IaP4k4Y37orF1uRsV3TGObkEMGbuVXyc2Y9VPQ/EKbIqDa0CVyHIvWTPnGc4eWFTuWHiTx3ls1J9VJJFIVWCxmEk4v53Ni8YA8OToNQTW7oREKuXQpu/ZumQcXoFNeXr8Hjz8G92z+25b+h4nd8+jcfuRKDX2FOenUZSXSvLF/Rzf+QvOHmH4h7fl7MHFnD+8lPNHljHg1RWUluSSdGEvRfmpmE0GLCYjSCRcPLkeQbAgkymxc/K5Z3JWNTZ2VjdT//C2jJx04qbpwyP7kZ5wDIlQCkBU7/HodCUYDdbF68H1e+Af3q7Cay0WMzFHV7Lqp6HsWfsZHQd8ccdyp8QfYtGUHhgNJTh5hAFQWpxLaUmudXNL4PjOXzi+8xfAGvVLo3VGbeOExtYZtdbZ+r/WicCIjgQ36HnHslQnpLIH3xCrDohGSg3g9IE/sHcNwSugCbaOXtVmlEIQBEzG0puunahJLP3hMYryUvEPb0tK3D58Q1th51g+/OShTT+wa8UHPLZwIVKZHCf3WvR/dTEefvXLpXts1G/0fu6nstFKsG6ylxizG11RDqGNeiGTKzm9byGrfhoBQHbKGTr0n0jjDk8Td3oTC6c8yoUTK2nYZjgAfUbO4cTu37lwYgMlhZmc2bcAMNOq19vYOnjSY/j3JMXuZf1vr+AXFkVBbhKrfrIuypcr1LTo/gZt+45H+Z+wvMkX9hJYuwN1W5Rfw+Dg4smQdzaQnngCr6DICp87RzcffIObkRp/ELPZgE9wC3xDWhLWpA/OHiEkXzxAUuw+Lsfu4Z8/XmPTn6NpEDWcZl1fJT87AV1RDmGNH0Vt43inKrsGKVY5ZXIJEsq7Omxf9h7FBWmENnqE1Ev7MZtLcPetW1E2NYKCnCTcfGpfc9ygL0CuqF4LO2+kF5G75/jO31k31xphquugrwlt1K3sXHFBChpbF4aO21jhqPqd6CY98SS713zB+cPL6TZkGs26vFx2zlBaxIrZw8hMPs2ZA3+wd93nJJzfDoBRX8zCKVbZ7F38cXILuhJoRInRUFy2MPu3z1rz8uRzOLnXuv3KeABw9aqFq1ctpBiBzTTr8iIWbjWEuJSUuH2A9V0gk0vK3t9JF/bzz59jcPEMI7h+N1y9a5ObcZGslPMYDSW4eIbh4hWOSuNA7PF15GdfwmgoISCiPUPe+bvMfUwQhCtRJbPRFeeiK85BV5RT9rv0P//nZV6k+GI6hzdPp37rITTp+ALetZpV2brFe0FOSlxVi/BQILp7VWOuunup1WpKS62jKV6BkTz70d6bXHnvESwWslLPly2WvnDyb84e+Iuc9Ato7d1xcg+mTvMBBER0oKQoC4VSg1Jth6t37WpjVN0K0UdXc2rPH6QlHic/yzpj0aTjC9g5+ZBx+SQZl0+RnRaDb62G/DB1IoMGDUKn012TzxvfJaG1v9aX+Hrs2zCF3PSLdB30TTmjZvnMwaReOsqTo5eXq0uLxUzKxYPEHFvD0e2/oNfll4U9BrBz8kZXlINa60SDqKGUluRTkH2Zi6c2YmPvzoBXF+MbYnVj27vuG7Yt+wCuvAoGvb2OWnW73LLsgsWC0VByU5eCovw0ti/7iBO75pU7rrX3oH2/j/APb4uTW627jqsvxUik02aO5HYp91EXBIHF0/py8dTGcuntnf1o2fMt6rZ8CpsqmuW5E/KzEvjhHevI5uMvLSCs8aNEH1nF3vVfk5VyjlGfn6pWHbzr6aW6cTlmD3FntiCTKZAplAgWC4W5yRTkJGEoLcQzsAl+oa3xDW1drZ6XtITjzPm4BQAyuRIXrwj6jPwVd5+6JF3cz/wvOqOycaBxu2eJ7DwKBxf/smtvRzdF+Wls+O1VYo6twdEtkKhH36Nh26fLvecP/vM9mxa+U/a/RCKl66CvSb10lFN7FwDQbchUmnV55Zr8DaVFnD+8guhjq+kx9DvsnLzvql5qOnfabgpyk9nw2ytcOLEBv7A2hDToiYtXKGvnvIC9iz8SiZT0xONl6a1RHLXlZuoVKi0KhYaS4mwAXvvmwpXgK7ePIAic3DOfrYvfo6QoC42tC8H1uhHSsCeOboFX3Nyu7mtThEFfhKG0GIXSBhs7V2zsXXHxCMXFK/yO7n+viDm2htU/P4dE0FNaWiq6e91nRCOlGlORkdKyx2h6DPn6vt87P/sy8We3kZeZQEr8ERJj91FanFt2XmXjQO2mj+EfFkVBThLpl09x7vDKso7uVSQSKU07v4hKY0dxfob14+kZiotXKM4eIdjYuqBU2yJXlA9bKAjCfTNuzFfcCmQyOWcPrSD98ikCwtsQVKdjuXsW5KZwau9Ctiz5EIVKi6dfAzz8G+Dp34CwBh1pE3CW7ReD+XZM/Wvu4RXUhKK8dCRSKQ7OvkilcmwdPXjk6enY2N16xyYt4QRzP+uMXleAg2sAoQ17EFyvM4G126PROgFQUpjNxdObKS7IxNbRA/+wKI5u/5UD/8xg9NRo1Jp/X6C5GfEs//EZUuIO0+e52TRsM5SSwmy+evnf2aJ2j71PQFgb7Jy9cXILuqczZWaTkZjj69Hau+Hg6g+CwD8Lx3HmwFLgaucqDA/fejTp8CyBtdvf9nMgxUgDu02cLOxa4Ue9MC+V37/sSWby2WvO+YdF8eyH2+6scJWI0VDKzlWfsWv1vztcax08CIxoS0FuCpdj9tLm0Xfo8tRnVShleW6ml8rGYrGQlxmPyViKXKmhtDiP7SsmEXNsHTZ2bkikUswmAxKJBDsnb+wcfVCo1CRfPERh7r+bjTq51+KZD7ZiXw060/k5SWQmnyM7LZb9f0+/sk4O5EoNJkP5wZRRnx7CM8Aagep2dHNq3yKWzRxOh34TaNt7HDL5ten1pUVsXjyeQ5tn0bTzi5zcswCVxoEhY1YSd3YbiTF7aN3zTfxrehjaSuBu2o0gCJw/sooj234lIdoavt7DvwHPjN+C2saBwrw08jIv4ewZgvaKO5pBX0J2WgxF+ekEhLVBqdZiNpswGUtR3eXaFrAOsCVdOEjsifXEnvibtIRrXd8kUhkqtR0KlRajoaRc3yOs8SP0eW42tg53tqfN3bJt2UR2rPysrF8mGin3F9FIqcZcNVJe+Xwvds6B2Dq43/d7lpbks+63Nzmx508sZhNaezc8/RsQENGGgPAoHFx80esK8fCrd41hkRJ/lIKcZNx8amM06Ni5ajK6ohwKclPR6wqwdXDHZNSTnRaL8f8+mDKZAnsXX7T2buRnXaa4MBNXr3C8g5rg6V8PJ7cgnNwDcXILQmPrdMMymM0mdEU5aGydkUplZKacJ/roOhLO7yYzJZrcjDhs7N1o1GYIBzfNRkDAqC8hMKIt3QZ9hkymZMvSieRmXuK1r06QkXSac0fWolCo0Tq4E1SnPc4untTRbOSsrjtx5/ez8c93uRx74Kb1O+CV32nUZvBt6cRoKCX+7Haij60n9sRGctKtHY/AiLY888EmZDI5GUln2TD/bVITTxJUux264lwSzu+hQ7/xNGwzGEeXf6NrFealMfkl62jY6G9O4+YTQW7mJdb//hbx53ZSWpxX7v4aW2eUKi0arRMqG3u0dm7YOXri4h2G1s46W3R1iv/c4dUU5afTuN0wnNyCCKzdFlevsJuWsTAvjYyks2QmnyMj6RyXzu8iI+kMtZv25anX/7zmWbsRUoxluqnoo56ddoFpb0bQdeBn1KrTnsK8dJIuHmTnqslIpTIGvPI7DVo/dcv3qwq2Lv2Ercs+uWGalz8/iHdQ9YnodTO93G8EQeDiqc3EnPiblLijpCYcR68rHx3PyT2Irk99Sr2WTyCVSkm6eIhjO37n8oUDpCWexNO/ARGRvQmMaMOvn3Ytu65x+6dxdq9F/VZP3NLzXhkU5qURd2YbRn0JRkMJhiu/SwqyUWsd6PD4eJRXZ20tpdTTbmbemgQ0dl7YOXlZDTMnL+RyZbl8dcV5TB/bEE//+gwft/a699eXFjHpGUd6DvuGQ5t/QiqV8/IXh26rLYvcu3ZjNhlJSzyJm3cESrX25hdUEgU5KeiKc1GqbVGpbVGqbZHJleUGp8wmIyVF2Zw5uJy1c1/nqdf/pH6rJxEEgeL8DEp1+VjMJixmE0qNHfZO3jcc+NQV57H4+0Fkp13A1sEdrb07tg4e2Dq4o7ZxxGwyYDKWYjLp0dq54e5bB3ffOji4+CEIFuLP7sBsKOH3rx8TjZT7jGikVGOuGikT5qSjtLn/bgWpCSfYuvQTLp7aTOcnJxLZ4VnUNve+8VksFgpyk8lOjUWvK0BfWoReV0Be5iWKC7NxdPHDxs6FzOTzJMcfITP5PIbSorLr1TYOePjVI7zJI9SO7IOrdzgWs4lT+/7i1P6/uHRuZ1nnQ6WxQ68rRKHU4B8ehYdvHVy9wrl4Zitxp7cS2rA7vZ+ZTmLsPjYt+oC0xJOAdZFjSWEWjq4BFOQklUWSuZqvX3Ajpk/5mDkrYnFwD0Wjdebnj62LGDVaJ9x8ItDau+PuWwcPv3q4eoXe1MAyGfVkppynMDcVQ2kRGlsn3LxrY+dkXYeUlRrLt2/9u/5AIpHy9vQ4vn41EAClSkv91gOJO7ON3Iw4FEoNFrMJiVRK615v0P6x91Cpbdn81wS2r/gcAKlMTnC9zjzx6nzOHV5NWMPuqGwcKMpPJz/7MjnpcWyY/7a1/iXW8McVIZMp0Ng64+5bB2ePYM4cXI6uKAepVEbDtkPp8+wPtzUjIwgCZw4uZ+mM4dSq25HBY5Zf01m6Hjf7qBcXZPLFi144uPhRq14njPoSkuOOkJth9TH2CmzMK18cumVZqwJ9aREXTvxD0sWDnD20kuy0C2XnlGpbXvniMC6eIVUo4bVUlZFiNhk5uW8xe9ZOJS3xJI6uAbj51EalscNQWkStuh3wDGgECATWblf2nBXkpDB9bENUGnuC6rTHw68eF05t4sLJTTRpP4K8rATiz+1EsJiRSmUoVDbodYX4hbagVt1OZZ0erb0btg4euHqHV1sf/A2/vc47o7pV6L7q6hWGX1grAsPbEN7kEWwd3Pn2rTpkpcZQt3k/2vZ+G9+Q5tfkmX75DNPHNiQisjfRR9fy3ISt+IW0rHDmReT6VLVxX53ISo3huzHWNYRO7rUoLshEryuoMK2NnStKtS0GXSH60kKkMjnNO79A2z5jObj5R7YunYhfaEtcvcMpzs+gKD+D4oIMSkvykcmVyBVqZHIlRflpGPUlgPXd6u5Tm8Da7Yho2JlfPuslGin3GdFIqcZcNVJ8AutSu9kAWvd6HRtbx3uSd0lhDktmPktG0jksZhNGg46i/HS09m70HPoFTTs+c0/ucy8QBIFZH7RFbeNA045Pk5MeT2LsfmJPbMJo0CFXqJErVJSW5BNUpz1hDbvi5hOBriiHkqIcPPzqEly3IwrVjTvJFouFMwdXIFjMhDfuybrf30Ft44CrVwgNowai0thRUpjDxdNbuXR2K++M6snTI54nP88aWlIikaJU26LXFSCRXF1caEGp0vLoM1PR2rlSqitAX1JIaUl+2Y+uKIfk+GPkZlyqcDdflcYOR1d/lGrbstmaHkO+ILhuB5w9ajHtrfoU5WeUu0YqlWG5sjFjm0feZPe6abTt/RaPDP8ak9FAasIJCnJTOL5zAWcOrkStdaSkMBuFyoY2j7xBlyc/QiaTYzGbmfScB+36jKFjv/ewmM0UF2SSm5XA2YOrOLRlDg6ufrw2+dA1bllGQykfDrGO2Ll5h9O295v4BjfDK7DhDV24DKXFbFjwHrEn/iErNRaZTMFb357BxfPW9jaRYiRM8Tcxxh7X/ahfOLWFc4fXEn9uF0qV1lpXgkBi7H56Df+adr3fuqV7VSZms4m8rEQuntrC+aMbuHBqC4bSIhxcfKnb/DHqtehHYEQbpLLq2RG+Fb3cS0qL8zm45Rf2rP+e/OwkfEOa0aLLSGwdPfntyz44ugWQn2XdQdvR1R+N1gmTSY+bdxiOrv4c27kAuULFG1NPor3iornw2yGc3r8Ms9mIi0cwviHN8PCvR/POz6PS2HHuyBqO7phP6qWTFBdkYDLqy+SxsXMhvHFPajd9lLAG3VBrr78j+qVzu5HKFTi7B6G1d7tll0eL2Uxqwgniz+0i/twuzhxYQe3IRxn6zjJ0RTllo835OUnEnNhE3Jkd+AQ1JiiiOf3b2jB48BCeemMxDs6+FOSmkJ+dREr8cRJi9pF66TggISAiisLcVJRqW1IvHUcmU9Ch33t0HvAh0v/syXH+yDrmfdkHn1qRJMcdAazvso/mZlfbZ7Q6UtntprqTkx5P/LmdpMQfx87RE1fvMLT2rkilMiRSmXX9ZU4KBTkpGEqLUGnsUGrsKMxJYd/GmQgWCwERrYk9sQmZTMGEuVmoNNd3Y7O6hSaQkXSW9KSzpCWcIvbkJkz6AtHdqxIQjZRqzFUjxUZrR0mxdQTfN6QZnQd8QFijHsjuYoHx7189ztlDqwHw8KtL3eaP4RfanLCG3avlSNfsCR1wcvXnqdd/Lztm1Ou4eGYb2WkX0RXnUadZH7wDG94gl3vH1Q9HtKE7eTkZZCSdIycjjtyMBArz07GYzRzbOR+wjr78dyZIJleitnFArlCjtnHAaCghJ906iq+xc0Fr64LGzpmUuKMIggX/0JZIpTJKinORSCQ0aT+cto++UU6erNQLTBvTAHNZp0iC2saeiMhHsXP0YNeaqTz7wQbCGnYrd93BzT+z/MdRRHYcQYe+Y9m67DOO7VrA618fxTuwIWvnjWHP+u8ZNWkHAeHl/cdP7VvK4ulPU6tOO579YMM1dSQIAnM/f4QLp7YilckxGayhmh3dAqjbrC/t+ozBweXaRZiFeen8OKEDWakxAHj616dh1FO4+9bGxTMEF88QFMrr77J8Ox/1pIuHmfvZIxQXWoM9NIwayOMvzr6rtnU35Gcnc2LvYopy07AIFory0sjLukxeViIFOclYLGYkEin+Ya2IiOxFeOOeeAU0qBHBKSqrs5WXmcju9d9zaMsvGPU6AsJbkZd1mdzMS2Vp1DYODHz9D84f28CxnX+g1xXg6OqPT60mlJYUkJkSTf2W/Wnb+y0cXf91lZzxfmtyMi/xyNCvaNRm0A0729ad0wuvzEomEXtyM+ePrCMt8RRSmZxaddoTEfko9Vo8Xu4epSUFfPz0vzOucoUKBxc/vAMb8tgLs8oMJrAOBCRdPET82V1cOreLhJh96HWFyBWqcgaSo1sAeZn/LogGUGsdCYpoQ9zZncilFhYs+INJUxbS+/nZFc6gF+VncO7wGs4cXMmFU1swGfU4uPhSkJMMSBg74wJObv+G9DYZDWxf+SXbV0xGsJgxm424+UQw5tszN1agSDlEI+XeUVKYw641Uzl/bAN+Ic2o3fRRakc+etv5WMxm4s9s5udJ4kzK/UY0UqoxV42UT+enc+Hsfvaun05ORgJZqbHYOXkS2X4ozTo9g4fftSFIb8bF0zvYs2EmKfHHyUqNxcktgJ5DP6Nx20HVssMzY3x7nN0DGTT6t6oWBQAJRoIlG7go9ESo4MMx94vHOXNwVdn/7j7hGAw6jHodRkMJRn2J1UdWKsXVM4TMFGuHfNg7f9Gw9QAASnWF7Fg1hZ2rpmIyGVAo1ZSWFCCVyfEObIjJqC/z573ajHsO+ZTakY+QGHuApbNGAdYOmU+tJoz6ZPM1ujUZDRTlp5d1klbPHcOuNd8y7J2/OLVvOcd2LeTxkdOJ6nVtFJ51v7/LthVf0eWJ8fQYPOma8wc2/cKSmS8Q3rgHel0Bl87vpfOA99EV5XJi71LkCiVvfHMEO8dr11oJgkBW6gWij28k+tg/xJ3ZUeZqZ2PnQp9nphDZYViFz+rNdHOVvKzLfDe2BY6u/vR5Zgp+Ic2QK27Npexeo9cVseLn1ziy/XdkChWOLn5IpFJsHdxxcvXHyc0fRzd/nNwC8A9tjo1dzdvc9Vb1cqcIgsDO1dNYN/9dVGpbIpr05OS+5ZhNeqQyOU+PW4bWzpXigkzsHD3xD7O6KBkNpRzfvYida74l9dJJ3pt9ERePoArvEXN8ExsXfkRCzH7cfWsz+qsDNxyFrYicjEucO7yOs4fXcuHUNiQSCV2fmkDn/u+Vpfln8UT+WTSRei0eR6114PDWeQC4eoUwdvo5tq/8mnNHN5AYcwCzyYBKY0dgRBS16ralVu22+IU2Y/Wvb7H371k4uvnjF9yUJu2HIFeokMrkaLRO+NRqjEwmp7ggm+M7f+Pp3iG3rJtSXSHRR//m9MFVZCSd59GnvyK0Qadr0p3ct4w/pw0lqtcrnDuygayUGN7+/jTuPlUboakmcb/bjcidoS/JZvwQN9FIuc+I+6TUABRyKY1a9aZRq94AJF08xoEtczm45Ve2r/wGgIat+6PS2KG2sUdtY4+nXx0atu5/3VmR8IbtCW/YHoDLF4+yeckX/DltKIe3zqVh1AAatHy8ws5jVSFBQCIBeTXxEpAIgAByKQgV2HQDX/2R6OMD0BXlkpOZgMlQemVhoBalWotKY4enXx0uRe9n4+JJOLr60aHvmzRs2aesjLa2djwy5GN6DppQ1hnPSI4h9uRWEmIOotbYYu/sjY2dM8IV167GbZ5Ca++Cf0hDls4ahatnMON/jLmu4SmXKVGr/x3F7dzvbdISTvH7V0+g0ToybMwCItsPuuY6g74E31oN0dq5kBC9r0K95GclIJXJyctKIDczEXsnTxq3GYBvrUbUqtOaP6YO4/DWOXR94r1rL0aCl18oXn6hdOj9KoIgUJiXTlbqRfZsmMWi70dwdMcfhDfqglKtxd07jPDGXW9JN1dZNnsUhblpPPveMoIirr8D9v0mNeE0cyc/SV7WZfq98D1NOwxFcwNXoJrKrerlTjDodSz+4QWO7FhAx8feon7Lx/lxYi9C6rVjxLi/UCjV112wLdeoadV1BN4BdZj2dkvM+sLrvmfqRHZFV5TFgmmHcHDyRKPR8N9lJlef05KiXFw9gys0et29AnHv/Qrte79CaUkB6+aPZ8Mf42nUuh8evtbOe256PADNOw/j4pmdAKht7PGt1ZjtK75g/YIJNGj5OH2e+YrgOm3xDmxwzazOky/P4MmXZ9y07hycXOjQ5zUQNtyybmxt7Yhs9wSR7Z6o8LxBX8KKX95k38afqdO0FwU5KWQmn6dph2G4evhXm/d4TeB+thuRO0d/8yQi9wDRSKkBCIKAQV9SFonFN7gxvsGN6fvM17zd37rOIvHCYeydvNCXFKArySc/Oxln90A69x9L884jrnGPMZuMpFw6yaXo/SREHyA14TQAMSe2EHNiC0tmvsTgN+bRvNPwyi3sDZBQc97Qdo7uNO0w5Kbpgmq3pt2jr5WFRK6I//p5e/iG4+EbTpteL9007/Gzo3F2D7ytmTEHZy9GTfybU/tXEhDWnOLCbKa81ZyAsOZ4BzYgPyeFjOQYzh5ei15XREB4S3oNqTjKVIfH3qJUV4hEKsXO0YNmHYZh7+zFP4s/ZcOfHxHWsAutuj1/XVkSYg5yKXo/hblpuPuE07TjMOydPKlVJ4rIDkNYM+9dtiybjK4kH5XGji8X5l43r4qI6vEi545s4PC2+ZVupBTmpnPywEpO7FnGhVPb8PCrzZiph/DwjbjjPGNPbSc57jgFuakANO88As8rs6wlRbnEntyKi0ctvALrk5pwmpN7lxFzcisSJJgtJnoO+pjakT3uRfEqldzMROZ83o/0y+cY/vaf+Ic157uxUXj4RvDse8tueabj6v48JtONux+xp7ZisZjp+PiYawyDlXPeYsfq78r+9/Srg2dAPbz86+LpXxevgHq4egZjNhvJSI4mMeYgsSetIa83LJiA0aBDbWOPm3co9s5ezP9mCMPeXsDBzfPQFedxfM8Sju9ZQt3mvXn2/WW3U02VRlbqRX7+tA/pl88BEH18E2aTkf4vTqftI9fOyIqI1ETk4n60lYJopNQAPn7Wn9LSUqYsK7zSmVUgVyiRy9UMfv1n1s7/kMef+5omba2jWoIgEH9uHyvmvM2SWS+zeekXvD/zFGobe47tWsKx3Us5d3QjpSUFyOQKfGs1JqJJVzKSo8vd989vRxDRsAPOHgEViVXJCEikIJdXD0NFIkjAaJVHuEv3uFuNWnW7ePnfaShUGZHt+pOblcRvXz1FYV4G+dlJ7NkwCztHD5zdA+jS/x2adhyCm9f1F7PbOzrz5EvflTt2eMci1i+YQM/BE+g5aMJ1ffotFgs/ftwTQ2kxdk4e5GZeZu/GHxn4yix8gxvRoEUv6jXtzrZV37L29w9o3nFo2bNxq7qxtbf6/cvkciQS831Zh1JSlEfa5XOkJZwhKf4EKfEnKchNIzMlFolESmiDDjzx0nRadH4apdrm5hleh9W/jeefxdaIba6etSjVFbJ1+ddENOmGSq3lzMF1mEwGwNoZt5hNaGwdqRPZA7lCxYHNv/HjxF58v9Zczii+l9zLNnOV6ONbmTt5IEqVDWOm7EWuUPHD+x1Q29jx0ifr0NrZ3TSPtMRzHNm5mKS440ilMn77aiCtuj9HZPuBFT7fT738A4W5qfz6RX9e+XQjofXbl51r1e0Zdq2bURYFT621pyg/nZ1rt1BcYN0QT65QYTYbESzlg2ScObyW0Hrtyc24xLFdi7FYzDz+/Dc0afM4dZt2I/bkduwc3XH1CkZ7j9397qVuEmP3k5VqjTantXeh56AJhNRvj2+tylkv+KBxP9qNyN0jzqRUDqKRUoP4YLgvuuJ8pFIZ7r7hDHtrHq17PE/rHv+ORi//eQzbV39f9pFUqrU0iuqPUmVDSVEu86c+jdFQip2TB8++9xeh9duXzbJ06PM6ly8cRSqTI5MrUKltcXTzq1CWqqA6rpV5UElPimb6e50pKc7DUFqMrhhs7Jxp2nEIUT1GlrmlVIQgCKxfMJG4s3sQLGYsggWToRRdcR7pSVZDuH6LPjdcdGw06LBYzKht7On0+BhcPINY89v7TB4dSb3mj9KgZV/2/P0zCdEH6NB3NL2H3/6mhUG1W9G5/9tsXTGVxJhDjPxwBQ7OXje/8BbIz0nlj2nPcO6IdXd7iVSKu08YPoEN8A9tiqd/HRq07Iutg+td30uvKyozUACy0uJo3mkYEY27snPdTEoKc+jzzJc0bP04eVlJJMYexsOvNmENOpa5I7l5hbB2/od8O7Ydw9+ej6tnxWsyqgsWi4V/Fn/OugUfEdagE8+MW0hGSizfv98ZB2dvXpn0N3YObjfMQxAEdq+fzfKf30Kp1uIT2ICWXZ9FX1rI2t8/YO3vH/DRnAvXGCoKpZpW3Z7jzKH1pCWeK2ek+NZqyPdrjKQlnuO79zoSWr89fZ/5EovFwt6Nv5CZEouNrTN2Dm6kJ0ezZdk3OLsH0L7P67Tq+gw2dlbDuSAnjeRLJ4m44sKoUmup1/yRe1yL94fmnYbStP0g8rKT0WgdHkjXRRERQTBWtQgPBaKRUgMY8OI0bB29SE04i5OrLwZ9CdtWfc+3Y9vhHxJJSP12tO31Ii4eAVw6vw//0Eg69R2NxtaRWrVblYUtViqd+ejn82xZMY09G35m1a9jadymP55+tVEo1RQVZGExm3F29yf21E6O7V5KelI0vYd9Qq8hH1ZpHUiu/MiqyRSr5Eq4CdkD6CecGHuUuV8PRanWoivOo9eQCWSnX6KkMJcDm+aydfkUPP0iqNO0B/Wa9SKicZdyBuS6BZPY8OdEGrbqi+KKi6LZZKAgNw2AyPZP4eUffkNd2tho+eins6z5fQLLf3kLqVRGWIOO+Ic0IT05hgXfPoe7TxhjvtlFcN2octfeqm5kSgUDRn5Nk6h+fD++G/v/mUOvwR/cWaX9B4Nex1ejm4IgMOytX/EPaYK7TxjKm4TAvlNstLYMeGEqS3/6N2zywa3zGfrGT7TqOqxcWnevQMLqt7kmj6btn+TIzkXEnd3DucPr6dDn3rvl3Ks2U5Cbzm9TRnDuyEZ6DZ5Ar8EfkpUWx9QxrXFw8ebtKbtuKVR7RvIFFs94mRadhzH49R9RqjSkJpxl45LJZWl0hVnIfK6dTVk9713sHN1p2XlIhc+xT2BtXNwDKMpLRyaF2R/34fTBdYB1JiswvDk+gfWRSCQoVRq69n+zXBtycvXEydXz9ivnDrnX7zOZVIabp//dZyTyQH9rahKHti3k+J7lBNdrQ9LF45w/sq6qRXooEI2UGkBk2ydQ27rQsFXfsmMNWvXl8I5FxJ/fz651s/nnry9RaewoLSmg+5Pv0qTdExW6r7h4BPDkqG9p1eVplv48hm2rvi9zQwDriK9gsaC1c6a4MAeAjJTY+19IkSojMfYoh3csQlecT0FuGqcOrMHTvw49Bo7j9ykjqN24KyH1rB3bgrwMZn3ch0vnD6DXFbF1xbfUbtKVZh2H0KzDIOQKJXFn9wJw4cxuTEY9el1Rufsd2bGYi2d2E9n2SZp2GEhAWLNrZskEQcDR1Ydhb82hz9OfcmzPMvZsnEP08S2M+WYXLp5B2Ng6oVDe/e7Vteq0wjeoIbGndiAI4+96xi4vO5n87BRenriW+i0qZ/S7ZdcRnD++pawjDDD3q6EMe/NXNNrykWeskdPiuHB6l/XnzG4ykmNwdg/ghQ+W0Sjq8UqR+XYpLsxl87Jv2LbyOxQqDa9++je1m3Rl87IpJMQeBiCicZdbMlDSLkez7o+PAQhv2InUhDP8vehzju9dgaOrD/1HTqFZx8E4OFdsKAx4YRpzvhzI9A968M7UPRWmcXTxYd+meVw4s5vMFKv7U+9hn6B1cCXmxDaO7VmGk5sfDVs9Vm43bBERkerH2j8+JiM5hqO7l+LmHUKr7s+xfuGXVS3WA48YgrgaczUE8fSVWahtr7/jfKmuiKO7llOUn4nW3gVP33C+HP3vnhaR7QYQUq8NPkH1qd342jCRhXmZGA2lmM0mFAoVB7cvIvbkTkp1hZw/tpUGLR/ltU/X3Jcy3iqTR7fBzSeEZ8fOq1I5riIRjLgWrydL2wtBUnPDQm5c/DVLfx6Lo4s3Di7eqDW2NG7TD7PZyJLZYwCo3aQLT700jUPbF7Nz3Y+UFOVhNhlx8w4mM+ViWV5N2vTjpY+XYbFYOHtkE/HnD6DW2KHW2qO5EnXOy782ORmJHN7xF0d2LiE/Jw3/kMZ8OPtoWT6FeZm8NcAaWa738I9p1XU4bl5BmM0mJo+OQldSwITZx69roNyJbo7uXsGsj/vRpd8b9B85+Y5DEedkXGbeN8+SGHuUT349i72Txx3lc6ec3L+O6R+Uj/uvsbHHyd0PZzc/pDI5l6IPUZCbjkQiwSeoPiH12hBavy2NWve9b7M9cGd6EQSBlISzHN7xF1tXfI/JqKfz46Pp/uQ7aO2d2bZqJn9OfwW/4EZo7Z155p15OLtf30X1UvRh/lk6hcM7/sLRxZsu/d8kM/kC29fMwsM3jB5PjaNll6E31b/ZbOLdIYFo7Zz5YNYR5BVEUSwpyuPMoY3Ent7F8b2ryM1MYvyMQwSGN72lslcmD8r77EFE1E31wGw2sX31TBbPfIN+z39Jh0ee47XHXMUQxPeZh95ISU5OZty4cWzYsAGdTkdYWBhz5swhMjISsH4kJ06cyE8//URubi4tWrRgxowZ1K1btyyP6Ohonn32WRISEnjhhReYMGFC2bnAwEASEhLYt28fLVu2LDv+xhtvcPz4cbZv335d2a4aKTPXZKG5gZHy/xzfu4bvxvep8Nw3ixJw8Sg/DZ986SxfjG5LcUFO2Wieh28oao0dAaFN6PHU23j63eki7HvDZ69G4eEbyvPvzqtSOcoQjDgVrCfXvhfUsA+HIAgc2r6EC2f2cmDrQmrVbsGrnywvN/O2Z+PvzPnqGVp1Gcr+zQuwWMxotPY0afM4KrWW1MvRZKcnIJcrMehLcPUK4okXJlMrotkty2E0lPJCd2unOKJxRyLb9qNe0254+Iby9ZgunDu2tSytf0gj6kR2QVdcwI61PzF1STJOrt7XKeCd6eafpd/x1+y38QtpxIsf/Imnb+gtX5uTmcSa+Z+ya8Ov1kXbExZTN7LLLV9/LzGZjORlpZCTeZmcjCs/mZfJzUzCoNcRGBZJSN3WhNRrjdbO6eYZ3ituUS+CIBB//hBHdq/g6K7lpF2OQa2xJarHCB4d8j6OLtZ1Q/rSEl5/3I2obsMZ/uasG9769KF/WPPHp8Sc3IWbVxDdnxhD6+7D+fa9R4g/f4gnXphM58deueWd0E0mI7M/GciRXcuxdXBl+Bszadah4nC8V8tUmJeJvVP1Cetejhr8PnvgEXVTrVjy07usXziZRwaNY93CyTc1UqpzH7Mm8FC7e+Xm5hIVFUXHjh3ZsGED7u7uXLx4EUdHx7I0X331FVOnTmXevHmEhYXx6aef0rVrV6Kjo7G7EjnmlVdeYdiwYTRr1oxRo0bRuXNnoqL+9ZNXq9WMGzeOHTt2VEq5GrXuzY9/lyCVyUm6eJKje1aiKy6gXrPu1xgopw/9w5yvnsHe0Z2R782/MsIqpVXXIVW26/b1EN0h7p7sjMvM+2Ykpw9txMs/Ar/ghnTtN/oaXUd1H05Ud2v46cZRfREsZhq17s24ISHkZiWXpWvW/gle/vgvLp49wLoFn+MbVJ9eg99jwfTXOLJzGUq1Dd7+tfEKqI2Tmy9SiZQzRzbh6OJN667D+PHvEg7vXMauDb+yaOZbmE1GXDz8iWjUkY59XmLbamvnM/HCcS7HnUSwWGgc1ff6Bspd0G3AaELrRzF70iA+HtmYVyYupX7zWwvJu+D71zixfx39nv2Ujn1fQmNz86hS9wu5XIGrZwCuntUhKt/tkZ+Txtyvn+fE/nXYOrjSuHUfBr40lTqRnSsMo24oLalwdvi/pCZGM/XdntSKaM4rE5fRJKovUpmMnevmEHNyF2O+2ki9Zt1uS065XMGrnyxj99/zmDP5GY7tXX1DI0UikVRfA0VEpIaTkRLHzI+fICstHrPZxIRZB/Hyv/Nw7lfJSrvEmcObOHN4EzGndqFUaXBy8wXgwNYFN73+Qe1jVibVqxdayUyePBk/Pz/mzp1bdiwwMLDsb0EQ+Pbbbxk/fjz9+vUD4LfffsPDw4M///yTF198EYC8vDwaN25MgwYN8Pb2Jj8/v9x9XnzxRWbNmsX69evp1avXbcsplwnIZbc34SW3sX7QQ+o0IaROk/+c+Tefc8d3MHVcD+pGdmHke7/i7Obzf7lUn0k2iYQrmzlWE5muTEDKZcK/KxurOZfjTvP56I4olGrGTF5Lo1b/fRavX4aWHa1rFCwWC2azNaKJk6s3PZ96i469X0AmtTB70kCy0i5xdPdKmkQ9wq71c2jWYQBefmGkJJzn/LGt5GalYNCXULtRBy6e2cuejb/hH9KQ3kPe5f1vN6PXFXP+xA5OH9pEzOm9pF2OKSdHzyffpNfAt9Fo7W/8HNyFbkLrRPLZnKNM/+hJfv5iOF/8dgqHW+hcNu/Qn6O7VxLZ5hHs7GypTm2n2nALepkytjv5OWm8PmkpTaL6IJP/9xNV/hqt1hqUITvt4g2fh9W/f4yTizfvf7cVpeqqoSPQoEUXFEoV8ef30ahl19sqSnFhHsvmfMjmFTMJCG3MkFe/qT7vpjuhBr7PHhoeYt1kp18mI+UiGSlxZKTEkZedQpfHXyEoPLIsTW5WCj9OGkhJUS4qtRaz2Yiru9cdtcfUyzEc2PoX6ckXiT21h/TkC0ikUoJrN6ddrxFYzGZyMpMIb9CWZu378cf0N2+YX03pY1ZnHmojZfXq1XTv3p0nnniCHTt24OPjw8svv8zIkSMBiI+PJy0tjW7d/h1lU6lUtG/fnr1795Y9QJ988gldu3ZFp9Px6KOP0r1793L3CQwMZNSoUbz33nv06NHjvu1DcLtsWTkbQRBo2fmpCgyUe0NBXiYbFk1hy6rZfDhjN3616pWdM5mMFOZl4ujiJc6S3GdSE6P58s0uuLj7MW7qJuwcbt198CpSqZQfVqZiNJQilyvLucb0GjiGtQsm07LLQILrNMfVMxBXdz+eGPlpuTyuLhAWBIFzx7az5o8vmTFxEAd3LMPDJxiVWkuz9v0Z+vq3CIJAQV4maZdjSLscQ1iDNjg43/81HmobW154fx7vPV2fL0Z3YtDLX+MX3ACwjojbObhes2Yh7txBNFr7Sl+D8qCh1xXRpvswmrXvd9O0MrmcVl0G8ddP7+PlH0Fk277XpMlKT2T/lkU8NuLD/xgoVlw9/AkKb8rluNO3LJ/FYmHXhnks/vE9jIZSBr3yDd36vfZ/xpSIiMjdUJCXybypr3Bo+1LA+t51cvVBECycPbqNz+edJOXSOTYu/Z6D2/5CplDSvtczbFo+g+fG/nxNsJAbIQgCsaf3sWHRFI7sXolGa4+XfwT1mnVh4EuTqd24I1o7x2uu0xVl39RIedj7mPeCh/rNGhcXx6xZs3jrrbd4//33OXjwIK+//joqlYrhw4eTlmYNmerhUb7j4eHhQUJCQtn/vXr1IjMzk4KCAtzcKo7N/8EHHzB37lwWLFjAsGHDKkxzPWRSkN+aq/QtYTIZSbscS7ueQ8nPTmH+96Np0LwzbvfYPWTzyh+Z8/W/O6NHH99GUGg9DPpSvhzTkwtn9mM06PGrVY/WXQdRv1kXAkIbVbgIVSIBfUkB+dlJOLv5VL1Rc2UfNrkUqObvA4vFwvcfDsDOwZX3p23E3un2DZR/kaCQX7u4uucTr9DziX/D1obUaU70yV0VPLeSst8NmnWkQbOOrF/8LdvX/kpCzFHyslM5sW8dk37eB0hwcXXHxdWduo3/DZubnX4ZJzef67+I74FuXFzdGf/tP8yd+hrfjC0focvRxYsnR35C+14jkMpkZKZeYsuq2fR/dgJOzndTtw84t6AXD59gMlPibvl9N/ilL9i3eSEWU2mF17i6e1G/WVfWL/wGD+9A2vV8uuy5EQSBxAsn8DEbOX9sC/Wadi53bUlxAfHnj5CRGo9eV0RxUT471/9GRkocbboPZfDLk3FyvTf76lQ5Neh99tDxkOlm/9Yl/DrlFQRB4IX3fiGiQRtcPQNQKFVkpMQzdngDxg4JJy87DXtHNxxdvSgtKWLT8hlEtulDp0dHIK3gXZB6OZaje9agKy6gc98XsXN0Zc/GBWxcNoP46CN4+YXx/NgfadN96DUDGhVxK9sh1JQ+ZnXmoTZSLBYLTZs25fPPrRuhNW7cmDNnzjBr1iyGDx9elq6i8Kj/f0ylUl334QFwc3Pj7bffZsKECTz11FO3JadcYkQuMdzWNddDEASe7VF+t2KlXMm7w+vRrsdQgiOakJp0EVt7Z7o8NhKNRnvb9yjIy2Ltwm/5e+kMNBoN9SI7MOLNadg5unHh1Hbizx/j0vmDREb1IKrLUxzds46/F09h9e+folLbUCuiKTa29pQUFSCVSAgIbQhmA6cObuCdwRtwdvMhMuoRHhs+ttyMQHFxAfk56Xj73fqC5ztGYt0sUy4xVvsp+KMHNpCdFseH0zfi7OwA3Jtn6UZ07DWIbz8czMXTOwiv3+qGafsMfJk+A18G4OevXiE5IbrC591sNvPXLxP5e8kPtO0+mOfenl6xsXqPdBMcVptJszZx4exBSooLQRAQLGb2blnC/O9eZ9Py73F08iQrPQFXNy8eferle9ZOH0huQS9+QWFcOHPwlupREAR2rvsJG62WRi06VHiNXAHjvlrC3Kmj+X3aqxza9hfvfrPS+txIYMhLn7B93e9MfbcPnR97HpXShsz0BJLjz5N6OYarcWUUKjUqtZbiglwcnV2JbN0FNzcXKqMtVQo16H320PEA6cZkMiGTySp8bxcW5PDbt2M4uGMlkW0eZcToqTg4/7dPZcDbx4eX35vFvm3L0GhsOXt8N8UFObTs2I/6TTthsZiJPr6Z4sIctq35DRcPH+wc3Ti+729SL8eiUKqQyxVsWvY9nn5hXL54igbNuzBk1EfUb9blPwNft9CuLTffzLGm9DGrMw91dK+AgAC6du3KL7/8UnZs1qxZfPrppyQnJxMXF0dwcDBHjx6lcePGZWn69u2Lo6Mjv/32203vERgYyBtvvMEbb7xBUVERISEhvPvuu1y6dOmWo3v9+eef2NjY3FVZRURERERERERE7p6SkhIGDx58w+he1b2PWRN4qGdSoqKiiI6OLncsJiaGgACr21NQUBCenp5s2rSp7AEyGAzs2LGDyZMnX5PfzbC1teXDDz/k448/pnfv3rd8neDWHsHO+eYJb5Pc7Aw0WlvUaqsBZDRYRw8USiXjnmuLRCIlrF4z9mz+C11xEY8NfxsfvzAK8jI5cXALpaXFjJu8tGy2xWKx8N7I9lyOO8NL788mon4r3hzSGEEQ8PEPp2Xnx2ka9Qje/mHIb9OHOy83i/iYY1w4d5itq3+jMD8bJzdvstMvI5VK8fCpRdfHniM3K401C78jrF5znnz+Q+o0jLp55neCxYQkYwuCe2eQVu9mtGTeF6xZ8G2ZS1+fwW/i5HL/Ig2tWjCNxb9MotcTLzN41Ce35R976cJp3h/ZjtoNo2jUshu9nniZ4qJ8NiyZwaoF03j2ral06T2CVX9+y+KfP+Gx4W/zxIj3yo861SDdPEjERR/HZDIgk8kpLsrHxz8MF/f/rHW7Bb0kxp3l3efaMOazP4ls/W9kNZPJxKZVv7Bs3peYTSb0pSUAjHxnOh17DbmuTBfOHWHnP4s4tncjMpmMT2Ztwd6h4nfpX79+xsr5U/D0DSYotAFuXgFo7ZzITEsg5VIM50/tw0Zrx9e/H8TB0fUOaqgaI7aZ6ks1041er2PVgqms/2sGBn1puXMarS0KpYbSkiIMel25cyF1ImndeQAWs5kdG/4kNyuF+k07UlSYS25mGkkJ58vSzloRc00bEwSBzat/ZcGsCTi7eTHi9a9o0KwTxcUFzPj0BU4e3MyE79fj6OyBRbDg6R2E0WhEJpPdlzUaxvzcm6apKX3M6kzVP/FVyJtvvknr1q35/PPPefLJJzl48CA//fQTP/30E2CdgnvjjTf4/PPPCQ0NJTQ0lM8//xwbGxsGDx58R/d84YUXmDZtGgsXLqRFixa3dpFUDtJ7Hx/d6f8WyyvU/94jomEbVv/5LRlplwmrF8WB7StZ+OOka/JQqLQgVVgXn507gMUCRpMZk8mCs7s/0xaews7BBfUduI39F0cXLxq38qJxq170Gfw2fy/7keW/f4VOp8PJ1YvAsEiSEi4ik8lo2u4xzhzdwfgXu9Cu+yBGT/zt/i0ku0+6uZe07PgE2RnpZKVf5p9Vc8nLzWb0x3NvfuEdkpqcgE6nI/FSDLk5WeU7qjchILQRA579kHMn9jDv+3fZtGoeqUkXECwWBjzzHl36Whcc9h36DmazwPwZ72PQGxjy0qfXuhDUAN08KKxaMJV5340td6zjI8N5/aNfr018A734BTfAwzeML8c+SdO2j1KnURtSEmM5eWgrqZdj6fb4CwweNZFVC6ay6o+p1G7c7oY6/umbN8lKu0zj1j3oN3zsDQMbuHuHoNPpyM5Mo32vp2nb7Smc3ayhrv+cPYFjh7by7aJTODg/IOtQKkJsM9WXKtaNIAgc3LmGOVPfJDcrld6DRhPRoDWOzu44Onvg4OyBSv3vekWj0YCuuJCS4nykEinu3oFl5yQyJZtX/UpmRiq29k6EN2pLVPfBuHr44RMQVtbGrMFTski9HMvSuV9yZM96evQfxYjRX6FS21BclM+4Z9uRm53GmM8XE1a//KCkQnX/6kt+CwvnakwfsxrzULt7Aaxdu5b33nuP2NhYgoKCeOutt8oiL8C/G+38+OOP5TbaqVev3g1y/Zf/TsVdZeHChQwePJj27dvfkrvXom1paO0rd0GuIAiYTEbkcgUSiYSCvGzSki5iNBpIT7nEtAnPMHX+PtQaW3b98xe7Ny/jctxZVGobzGYzJqMegBlLTuBfq859kbFUV8y+bSs5dXiHdRTXaMBsNqMvLSEnK5WA4Lpcij1J70Gv8fxb39zTewsWI5bkf5D6dENSgz7qq//8njnfjmXGXyfwDQy/L/cQBIG9W5bz09dvYmPrwJTf9mBje/s78h7bv5nVf35Pw+Yd6dBrCI7O187+rJg/jV+/HcvwVz/jiWesneSaqpuaysGd65j05mP0f/od6jVpy8TR1o1kw+u3wNnNmwFPv01Yvea3rJf83Cy2b1jA1rXzSbhwBp/AcGqFNeCxoW+i0drxz4pfKSrI4dCu9dRv2p63P5tfYT6luhIGtnfhhXem0euJUddJU8z2DX9yePffdOw1GKPRwJQPhmMN8CAQHNGY7v2eJz3lEsvmfc0383YTXr/mf/j/H7HNVF+qg25SL1/kp2/e4vDu9TRp1Y0Xx36Lt//9XfuZnZnCG0Oak5edDoCjszuvT/iZZm3/DbF75thu3n2+IwqliseHvcWQUR9XWmSr4oJsBnb0vOlmjtW5j1kTeOiNlOpMVRopFZGZdpk/Zn3E1rXzCQiuy7NvfMVnb/dHoVBRv2kH9m9fBYBfUG0ux58DYNyXC2nTdUCly7p701ImvzuIlh36sn/7Kp594yseH3bjcIG3Q3X4cNwJ+lIdrz3VGLPZhFKlITMtES+/YOo0iqJ2w9Y0adUNe8d786wlXYpmzPDWtO78OKM/+uXmF9whf/74CQt/msTrE36ia99naqxuaioTR/elMD+br+fu4tCudUx683E8fWrh4uHDmaO7eGX8THr0G3lHerFYLAgWC/Nnfkj06YPEnD5U5kby6gez+eHTUUycvo4mra/djPHUkZ28/0Jnvl90hKDQBtecNxmNjB/VlfMn9+HtH0ZyQjRd+z7DPyt/pXu/kTRs1pEta3/nyJ6/sXdyozAviw+mLqd5u0fvrsKqIWKbqb5UpW4M+lKW/fY1S+ZOxtHZg5FjptCyY9/7Hl2zuDCfHz57id2blvDCO9OoF9kOH/+wa6JuGY0G1v01k/3bV3Pm6C7+2JyCg9P1F5ffUxlv0UgRuTseanevmoJMIiCrwqgeqZcvMnf6ePZuW4VSqUat0ZKbncZHrz1CQEhdpszdicbGlvEv9eT4wa2kXI6ldce+DBk1gaDQ+lTFxnbtuvbj/InXWL3oB5xdPfn127HkZCbT4/Fn8a9V+67zFyQCFqy6kdSgiCs2GjVvfvwzy/+YhodXIG6eviTGn+fkoa2sXzIbuVxBy459GP7yRHwDwu7qXgFBYXTpPYzDezbe1+d34LNjWb9kNvu2rqDHYyNqrG5qKtGn9lO/SVtkEoFW7R9h/VEDJqORye8PRa5Q0qrdI8gkwh3pRSaT8NfvU1gxfyotO/RhwNNjWPb7FPSlOsLrNKZpVA+mThjBjEWHcXazuojkZKai0dqxbN5XuHsFEBRcp8Lnb9vGPzl7fA+vvj8DlVrDni3LObRrPV7+IQQF16Zx8/Z06D4AXYk1/PCYEe3Zt3UFrdo/ck1eNR2xzVRfqko3R/dvZuaXr5ORkkC/YW8y8Pn3/uO2fX/lmPLBMM4c38M7n/1Gx56D/nOm/H1lSgX9hryOSqni3PE9ODg4VVpfqSr7ZA8TopFSA5BJBeRSS6XdTxAEDu35h+1/LyY7M41jB7YC0K5rf4qL8jl2YCu9+j9H+279Ca/X7Mr0qsCXs9dy+tge/ALDcHK56vtdeXL/P6+M+5qOPfrz+bhh+PiHsGXtfFYu+I7wes14e+JPBATfubEiIGAE5FIBSSXq5l7QqGlrGjVtfc3xnKw0tm34i1WLZvLqwKa07tib2g1a0jyqOz4BIbd9n9hzx9iy9g+atel2X5/frOxkigvzOHN8Dz99/SZPjXgDDTVTNzWRp1/+iB++GM3vP4xn8AvvodFomT75NfZsWUFAcB0sJh1yqeWO28yl2BPUadiSj6cuIiM1kT9/sm4Q+su0ceRlp1NclM/8WRMY8/GPzP3hIxb+8u+C04nfLkWlkFDRe6ggJx2JVMoPn79S/kQ2zP76LWZ//RaNW3Sk52MjyMxIQYLAkb0bGflYHcLrNaVR8w6E121KQHCdGr95Wk1+nz3oVLZusjNT+XHKWLb/vYQGkW355Nul+NeKuHL2/t8/JyuNI3s38vr46XR95Kkb3jMzPYnRw9qTlZGMq7vPddu6SM1FNFJEysjNTmfPttVsXr2Asyf3X3N+56ZlePkG8eaEWXR/7OlrzkulUhpEtq0MUW+ZOg1b8viQ15jz3Xh6PP4MvoGh/DztPQ7t/eeujJQHEWdXT/oPe51eA55jybypHN23hT1bVjHrqzE0iGxLYEhdlCoNao0NDk6u+AaEEtmqyzX5ZKYnsWLBDyxfMJ2gkHqMHv/DfZXb0yeQeWvPsvavn1i//Fd2bV7CnJ9/4vTxvfgF1sbRuXKm/x8mCgtyOXFoB/pSHfWbWBerLp77DWkpCYyfPJ/Q2o0p6NSXcycP8Mnbg/h23jYUijv73KjUNuRmZ2A2mXj/5T44uVgX6J44tJ1aYfUxGQ1sXbeIlMsXOX10D/2HjSasThO0do40b9O9wjwtFgtSmQzBYmHYqA/p2nsIWjsH9LoS5Aol+tISDu39h9ULZ/Hj1HfJz82iZYdHCYloRHzsabauX8TW9YsA6Np7KO9Mun/ujNWZUl0J/6yez8FdG3Byccfdy5+AWrVp27Vf1W+4K3JbmM1m1i75ibk/fIRCoeKdSb/Q5dEhla7H3ZtXIpXKaNPl8ZumXTTna/R6HeM+m0tQ6K2t4bhXyKTiTEplIK5JqcZcXZOyencStvb3PgTxVRLjo/lqwijOnjhQ7rhabUOLdj1o3eERHJ3d0Grtqd2gWY37+OhLdcyb+Smb1y0kLzcLi9nCVz+uJrJlpzvOU7CYKLy0FbvATkiqQVjI+4W+VMfuLav5Z80CsrPSMeh16HTF5OdkYTIZWbc/E42NFkEQWPjrFJbN/4HcnAxUag3DXnyPJ4a/jkKhrDR5Cwty+XX6Rzwz6FEGDRqETqcjJKIBL709mcbN21eaHA8qFouFTWv+ZObX4ygssIbgtLVzxNXdi0sXrevQhr/4Hm069yEkoiExZ4/x2vBOtO/Wj6HPv4MjibfdZk4f38/opzujsbHFoC9FqVLz8juTmT3lfYoK81BrbKjfJAoHRxf8AkMZMnIcUqmU3OwMDu/bwpF9W0mIP4+PXzBtOvXGYNAz/YsxFBflI5PJMZutm+X1G/IKr477uuy+Jw7v5r1XHqdUV8znM5bTsu2/IZGzM1Ox0dozvHd99KWlrN6Tci+qt8q4lfeZrqSI7RuXceniOS5fiiUp4QKpyfEIFguNmrWnpLiQ9JREcnMymD5/G3Ub3jjAQGZaEk4uHsgV4hqYG1EZ35r8vGzefakv0WeOorW1Z8I3f9Cs9bUDUJXB8gUz+XHaeNbsSb3uzu9Go4FTR/fy9kjrIvpF/8Tg7ulbmWKSl5dHv3be4pqU+4xopFRjKstI+fqjUWxY8XvZ/42bd6Dn48Np06kPak3N3UQyKeECS+dPL9tltkffYYwd1RsnF3cmz16Nw10sEH9YjJTrMW/GJNYsmcOy7ZcAMJtM9GjujNlk4qkRbzL4+bexs3eqEtmu6ibH4k1c7DmW/fEDyYkXWbjxPBob2yqR6UHg7ImD/DD5bc6fPkzDpm05cXgXbbv0JT72LEkJsTz90niOHthOYnw0upIiPpm2iOZturFh5e9M++Q1VColCxYsuKM2079jIE7O7nh4+7Nvx3oA5HIFru7epKUk4OLmScceT9C9zxD27djAvh3rOX/6MIIgEBLRgFqh9dm/cwMF+TlleSpV1vV1FrOZ4qICtHb29B/8Ms2iuhFz7ihLfp+O1taOCV//ge913B1nfDWWZX/8wAtvfsYTw19HJrt5WNLqyNU2g1Njdm5ZTVZGKrnZGeRkp5OblU5OVjrZWWmYTUa8fIPwDQjBNyAUX/9gmrXphrdvEGAdjR/QKQiVWkNBbjauHj40adGBPk+OpFZYPQryc9i2YQnffW4NYvL2xzPp1W9EFZa8+lMZ35qMtCS+eP85sjJSyM3OwGQyMnHqQrx8A5k95T2KCvNxcHJl6MixhNeNRFdSzNhRvTl36hDr92de15i4Ey7GnGLkgBZM+WVDhQNLBfk5jBzQgsz0ZOzsnWjRrgdvfvBdpb/biwpy6NPGVzRS7jOikVKNuWqkrNubeF+NlJLiQs6dOoyPfzDunr413r/6Kovnfc/Mb97H0dmVvJwsPvzqV6QSKVM+GY1UKmPCV3NpFtX5jvIWLCby4rbjWKvDQ2mkrFk6l6mfjGbFjjgcnaybbuVmZ/D6iO4Ehdblk6l/VJls/6+btJREBvdqgJOzG+269OWVd74QR29vE5PRSI/mHnh4+zP2kxmYjAbeer53WeekeVQXxn4yg9V//YpEImHR3O+oFVaH7+dtBEBXUsyYkY/y2cTxOAS1RyqruP5NJhNnThzg2IEd5GRncGT/drIyUijVlRBepzE//bWL3OwMxr3Un/gLZ2nUvB3tuvQhPvYsG1cvtM6sqG1o0a4brdv3pHlUF5xdrevjSkqKOHviEEWF+ZTqisnPy8Fo0FvfdxIJxw/t4sCufwCQyeU0a9WZkaM/JiSi/nXrxWw2M+HNIezeuhaFQsn3v22kToNm97LqK4Wrbeb1N94m8dIFXN29cHJxx9nVA2cXD+tvV3eiOj6Cl0/ADfNaOn8Gxw7tonb9pqSnXmbdsnmYzWbadenDvh1/Y7GYkcnkGAx67B2ceWP8FDr3eqKSSlrzuKqbYkUASQlxZKQnYzToESwWfANCCKvTqOwZvxfoS3WMfqYn504dBsDbN4j6TVoRc/YYSQkXeXXcZEJrN+TlIVZPhI++nkennvcugqfFYuGxdoE8NvAFnn31g2vOb1y9kM/fH8mM+Zup26hFlXl2FBXk8Ehrf9FIuc88fL0rkWuw0doR2bJjVYtxz2nRths/f/cxeTlZANRt2AIvnwAaNWvLu688wR+/fEPT1p1qnPtadaB1h15M/WQ065f/zuDn3gLAycWdHn2H8tvsL9GVFKOxubsNPO8Vnt7+TP15DXu2r2fZHzPxDwrl8UEvVrVYNQq5QkHrDj2JPnucOg2aoVAo2XG6iKLCfB5rF0RE3SasW/Ybv/4wCZVKg05XTMPIfzdW09hoefaV8QD8OPVD6jZuTcOmUeVmM/ft+Jsvxr9Afl4O9g7OCIKA0ainXRdryNNXxn4BWJ+zn/7adY2MjZq24cM3hzBqzKQK9WtjY0vTVtd/z7Xp+AgHdv3DUyNGM3TkmOvuTP9fZDJZ2Wyz0Wjgj5+/4fPpi296XXVFV1IMwJSfVxN4h2v2Bgx7hQHD/g1GYDIaObhnE7nZmYwc/TFdHn0KF1cPMtKSmfH1u3wy9hlKS3U80m/4PSnDg8aluPM4As8PaI1Op0MikaBQqgDKdnz3DQih94BneHzwi6juclZDpdbww/zNXIw+xaWL52ndoSd29o4YDHpmfv0e0z59E5lcjrdvEClJ8WxcvfCeGilSqZSGTdtw9OAOnsVqpBQV5vPj1A+5GHOGuJjT1GnQjHqNW96ze4pUX0QjpQYgk1gqNbrXg0JIaBhzl+1maJ/mALzwZBtWbjuPu7sbj/YbwpRJYxjSqwE9+gxkxEvjbmsGySJY9SGTWJA+hLrxcHdjwNBRzJv1BZ269cY3IBiAth2789O3H3HqyA5at+9xk1zuDxXppmmLKJq2iGLP1rVkpCaK7ekOcHZxJSs9haL8TNzcvcjJymDKp2OQyeW06didFQt/wWKxMPXnFdRp0BS5XM5/I+00b9WO7Is72bJ+CYt++4E2HXsxeYZ18XlxcSHfTHyNkPD6vPjGBGrXj+SbiW9y9NAuPpr843+kuL7ePL2sIYgvRp+8I/1u2/AXAEZDCc5Ojje813+5um9L5579qV2vCX/89CWNmrapMIJedcVksa7LKSrMw8PLD7NRd8/ayPjPKgqcYcHb24tPp87j64lOTJn4Gp6enrRoUzXrIP6LIAjVauBqzvcfM+b1UfTuP5ynnn4NN08fZDIZgiCQmpxA9Jnj7Nyylp+/+5j0lHjGfDj1ru8pV0qpW78hdes3vHLEglyt4O0PvyE1KZ79uzbx7qTpxJ4/ycxvPkSK6Z56YLSI6sTUT8dQUpiNvYMTO/9Zxtpl8+j26JO07dSTLr36V/k7XCYRvyGVgWik1ADkEgG52CDuiNDQMBau3segPq0oyM/l8N7NtO/8CAMGPoN/QBDzf/mWOTO+YOgzr2CjvXWfVssVfcglFqQPqW5eHj2ePdvW89Kw7rw9/is6de/L3u3rkSsUhIZGVNkzW5FuMjNSmffjFNJSLpOfkym2p9tk97a/WbFoDhF1G7F57WL27drEsUN7kMpkfDblV9IuX2TVkrkAHNm3hcZNmiH5vzq+qpcxH3zFu6Ofxs8/sEwP82Z8TmF+LuMnfYetrT0T336GzX+vYPCIV25ZVw0aNmHwiFdY+ddvfDDpu9suo6G0BACVQsFLQ7thMhqwtXPAzcOLOvWaEBgchtbWHqVCiZePP0qVmjEvPcX+3VtwdvVgy4ZlbNmwDK2tPT9//ymfT5tH5x6P3bYcd8Kp44c4c/IwJpMJwWIhom4jGjdtfctujR+NfY5XRz2Dq7sXC1fvR6FUUimhXCUw9sOvyM5I5oM3hjN7/jrC6zS8+XX3id9//pY5s76iTYcedO7Rl4DAUPwDQ67UR9XwSF/rPiFePn74+l5dHG4BCfj7+ePv50/XHn2oU7ch3331AY/0HUi9hk3viyxFhfnExZ4BwEalxEajQRAElLIrMt0j2rbvwtcTzRzZu4UGjZuzctEv1G/cgomTZ/8nlfgOfxgQ16RUY66uSdm8Px47h6pZhPygYDIayc5Kx93Tp9wo2aolv/Plx2/w29LthNW+dlfq62Exm0i/sBuPkDZIZQ+vrZ+Zkco3k95h++a1hITVIe7CeQYMGcmY9yff/OL7xFXdXEgq4vDBPZw5eYQzJ49YZ1iuvO72n82tVqOl1Z2De7czc9pEMtNTKcjPpUnzNnTu3pf2nR/FwcmZCzFnGNI3qtw1B87llfv/ql4Wr9jIwt9mMXrcZ+TlZrNv5yZizp/C1c2TvLxsmrVsz75dmwkKjuCnBX9j7+B4SzKaTCa+/+oDFs+fzZRZi2nToeLww9fj9InDPDewCwqFgvZdeqPV2lJYmE9KUgKx0acxm0xlaZVKFQaDHoBnXnybWqF18A8MJjSiHvpSHR2bWjuTH34+g0cfH3JbctwOMedP8eN3n7J7+0ZUKjUKpRKLRaCkuBB7B0ei2nenfedHaNWuK2q1hvy8XI4f2YvJaMRsNuPjF0id+k3YuPYvGoZ5MGjQIAJqhfPtT0txcna9b3L/P7qSYl56+lEy0lJo3roDKcmJ9HvqGTp260N2VjpxMedw9/QmNKL+fWu3v/08jZlTJ9LtkQFciD5D3AVrtDqlUkV4nYa8+d4X1G0QeV/ufT2OHd7L2FcG8fvvvyF1CMbD0++6aU0mEy8M6U566v/YO8uwqLYuAL8zQ3e3gJQIioGKCHZhd3d3d3fH1Xvt7u7ubgwsQkWkpLth4vuBolxRMa9+zvs88ygzZ9dZJ/bae0U4O47cRFvn+88Zxg/ryq1rF1BUVMTVrTJ1G7Rk9KCO7Dh8A1sHp+/aVrfWNUhJTiIuNhp1dQ1mLt5AaVf379rGt5CSlECtikXlPik/GLmS8gvzVkm5cOcFWnIl5buTmBDP0D7tCAoMQCKRULJ0OVwreFCzbmOK2n4627pUIub1s5uYObj/0UrKWy6ePcaR/TvwqFqbxi3ao/TGZvp7IpPJyMrMNa0RCIUoKSkXOGEJCXqOMDOSdu3aoWdgjLqGJs/8HudNLAcMn0i3PsO+e//+ZF69fM6gnq2ICA9FIBBgaW1LBfcq6BsYE/TyGa9ePkcmETN/3mw6dOiAVCYjIz09NxqWQICNbTEcnUthZm7J9k0rEOfkMHbaQho2bfvZtsNDg9m5eRXHD+8mJTkJRSUlZFIZdsWcqFLDC5fS5SleovRnJ20ymYzGNUqjoanNziNX8v2WlZVJVEQ4sTFRPPN7THJyEkpKSqSnpdFrwKgPVtoXz5mIz71b+D5+QKceg+jRbzgSiQTgmyaPMpkM38cPsLV3ZM7UkZw4vAcLy6L0HTKO2vWaIhQKkclkBPg+4uLZ41w6f4LAZ36oqalTonQ5fO7eylOu3mJV1I7srExWrliWF7b71DVfDAy/nzN2YYiLjaZ7Gy8iI8IoWbo8PvduIRQKkUrfrZi3at+d0ZPnF0pRCQoM4NK5E1y9eBqhUES12g3Q0dXDxMQcVzfPfHVkZqRTpawVntXqsHjldmQyGbHRkYSHBuPv+5Cdm1djYVWUeUs3oqGplVcmJjqStNQUjE3N0dHV/64KVER4KB2b16B4iVKMHTmkUO+amKgIWni50bxtV4aOmf7d+vIWDxdzeg8ag46uPjMmDObgGW96tm9AtVr1GTdt0Xdt6+bVCwzp3YYmrToxZNTUvPP+q5CclECNCnZyJeUHI1dSfmHkSsqP5dXL57SsV7DzXaMW7RkwbOJHX9RyJeXncff2NaaPG0RUZHjeRA9AJFLAwMgYI2NTdHQNSE1JIjYmisSEWLZu2UJUEmjp6NGxRQ1KliqHz73cBKVLVu/Es1qd/2o4/3e8eOZH20aeeX+/nVgaGpmQlZVFUVsHbOyKoaOrR5N61VixehNnTx1m5MQ5aGhocezgTu7duU6rDj0wt7AmR5zDsoXT6D14LL36j/xk24f2bmXmxKEoq6jSoWs/qtT0wsTUnNPHD/LXnHeRgYRCIV17D6V1hx4YGJl8UI9EImH+9NHs37WJ4s6l2HrgwjefF5lMRpeWtfB94pN3TlTV1OnedximZkVwdimLhWXRL5rYvh2vjq4+iQlxDBs3kzYden7SrCs46AVnTx7i/p3ruHlUp26D5qhraCIUCnn0wJv9uzYSHxfD9CkTuHD9Po7Opang/t/kFIqJiiAmOhKnkmV4FfiMWzcuUcTSBlt7Ry6dO87CWeOxtLal96AxlC3vTmJCPDZ2jm/8n3IjQ129eJptG5bz4O5NVFTVcPesTk5ODjevXcjbDatc3Qv7Yk7o6Opj6+CIsrIqYwZ3RUVVjcPn7n3Qr307NzJ36khECgo4lShNbEwUEeGh+Y4pYmVDl15DaNi0zSflIc7J4dSxfWzbsIKsrEzqNmhOefcqvA4LwdmlDEVti5GUmMD44T25c+My524GkBrlV+h3zT8Lp3Fg92ZOXn2Kiorql5z+T5KZmYFnKQtmLFiFoZEJfbs05dDZu5w7dYSVS2ez++g1rG3sv1t7AOlpqV9khv0zkSspPwe5kvIL81ZJuewdIFdSfhCrly1k87plZKSno6auTt0GzThxeB85OdkoKCjQpmMPuvYaiJ5+/qzlUomY0IA7FClW4ZdWUlJTUziweytu7pUp5vTxUKq/IoHP/Tl+eB9bN67EvpgTbu6VkUgkhIeFcOHM8bzj9PQNMDUrgnVRWwyMjClSxIpypRwwtXWlW7tGZGVlUtzZhWOH9uSVadmuCxlpaVSt6UXNug3/b8Ju/xfExUbTp0tLpBIJHbv1RSzOwdahOK7l3enXrRXPA3ypU78pAb6PmDxxHF27dqOiZ3Umz1qMqqoaYrGYzeuWs37VX+Tk5CCTypBIxFSuVpvZi1ehoaH50bbv3LxKny4t6NC1D8PGTM2XpyQpMYHzZ44jk0o5fGAnAb5PkMlkVK9dnwoVPUlIiCM7OwuZVMbtG1d4/PAeoyfOpnHztqhrfL+J0euwEG7duIKysjK3b1zh2KE9vH3t6ujoMW/pWiq4V/5sPUGBz2lez4Pa9RqTmpLM8wBfTlx6gOJ3CKf9uzzPrl+5wN6dm7h8/lTed+UrVsajSg2SEhO4cPY4wUGBlCpbng5d+1K5Wq28ibpUKkUiFnP+zHE2rF5KamoK8bExZL2JkGXnUJw5i1dh51BwVLPX4aFcu3SWe963MDE1w86hOMYmZqiraxAREcapYwc5f/oYrhUq0aFLb0QKChibmKGrq89Dn7sE+D7mdXgIPve9iQgPpWpNLwyNjDm0bwfinByUlJRRVVNDS0ubmOgoZDIZbTp2Z8jIiV8km1cvX9CividKSsrY2jvi6FSCdp17YWvv+E3n/pHPXbq0rs+m3cdzrRD6dmLP0Uu8Dg9l6rihuJR2Zenq/y70/M8mOSmBquWLyZWUH4xcSfmFeaukXLvrJ1dSfiBSqZRzp4+zdOEsQkNeUcKlNE8e+QAgFIlQEInQ0tZBT/+djbaysjIzpk1h0pRpZGVlfaTm/55n/r4AmFkUYfE/6zA1s0BX7+uTWP4M0lJTGTW0N9cuF7yara2jSwmX0ly/cjHf99fvP0NTUwupRMwr/7us37SdY4f3sXXPcewcioEMdm3bwLpVf6OsooK2tg4vA58zdNREuvce+DOG9n+NWCzm6uXzPPf3JSw0GEMjE27duMLjh/fRNzCkvJsHfXt1xcjSqUDTDZlMRkJ8HEcO7ubsqeM8fngfyJV3jz6D6Nqz/wdlJBIJU8cP5/CB3TiVKEXfgcM5uG8nEa/DqVW3ARoaGqQkJ3P+zAn8/Z5gaWVDUlICSYkJqKtroKSsTE5ODqkpySirqOD9+NWPPk3IZDKSkxLZsmE1a1cuYfqcv2jast1nyy1fMo/Vy//in9VbqFqjzneNQvX2nrF2LPdLKymQ+7y+fPEsQoGA82dPcOncaRITEzA0Msa1fEU6dOlFqTKFcxyXSCSEhrwiIjyMsuXdvjl87907NxkxsAcJCfEf/GZsYoZFEUuK2tjTtmM3HBxzfTiioyIZPrA7kRGvGTNhJrduXsHIyIRW7Tqjp2/wVbLxffKQu3duEuD/lHt3bhIdFUn7Lj0ZPGwcSspfZ4q7fOl8dm7dwKVbT/h70WyOHNxDhYqenDp+KO+YzbuOUMa1wlfV/7uRkJgkV1J+Ar/200iOnJ+AUCikTr1G1KrbgG2b1rDsr3dO31KJBAVlFRIS4qlWsy5Kirm252/NC8qUrYD4PYfaH4UMGQK+fEJSrrw7z5/78/D+Xdo2y3Uk1tHVo0JFD1zLVyQ6OgoDAyNKuJRBKBRw5dI5VFXVaNikJUbGH5rF/Gji4mLo2bEFgS+e5X1XqXJ16jVsirGxKc+f+bNg9mT8nj7mnm8oQS9f8OTRA7S0tNH818T33OljTJ/zFy6ly+Z9173PILr1HohAIGDn1vXMmT6BJQtm0qR5G/QN8u+Wyfkyrlw8y9D+3dDW0cWiiCU3rl0iOioSoVDIyYt3UFJU5JX/XdTU1AosLxAI0NM3oGvPAXTp0R8/38fs37WNvbu2oKpacBmRSET33gO5d/c2Ea/DGNy3C2bmFjiXLM3aFUvIzs5CT98QDU1Nps9dgkURK+553+TW9Svc876FWCxGRVUVRUVFOnTqSVDgc3R09dDW0UUgEHDvzk1OHT/M7VvXUFBQoGGTlpR388h3TX0pAoEAbR1dhEIBSkrKeDVsWqhyVy6dz1P23tbzJyIUCvG5d4dd2zeSkZ5O05ZtOXpoH/oGRnTo0guX0oV3bheJRFgXtcW6qO136Vu5Cu6cuXqftNRUxGIxEa/DiYuNprhzSUzNLAosc/LYQR4/fMCazXtxc/fEs1rurtD7i2JfilOJUjiVyI2Slp2VxZaNq1mxdD4GBkZ5yn5UZASnTxwmwP8pz/39MDIxZdmarQXWl5KSzOkTR3D3qIKCggKREeFoaetw7vQxevYbgo2tPf5+TzA0+rl+TP8l8hDEPwf5TsovzNudlBt3n6JdyAg3cr6drKxMOrVtxtMnj/I5bi7+ezV16zUEclfgXvrfx8axbD4Tk1+VxMQEwkJDCA8L5fkzf65cOo/v08cYm5gSHxeX50yrpa1DVmYmYnEOVavXolWbDnhUrvZTxhgcHESLxnXISM8NBdu732Batm6PucW7iDb1anmgIFJg+ZrNWFpZF1hPfFwscZFBtGvXjq49+mJqZo5ELCY1NRVDIyMquFUiMuI1Wzevw8TUnI3rVjJm/FQ6d+v1w8f4/8wjn/u0a9WIYo5OVKzkSe269XFwKE5GZgYGBoZffM/4+z1lxNB+hLwKokatuqiqqlK6TDmMTEzJzMhAUVGRAH9ftmxcS1paKms37URdTR1bOwc0NDV5/PABPbu1AxmYmpmTkBBPbEw0IpGIojZ2tG7XiZzsLI4dOYif7xMEAkGeGZaWtg4SsZi0tFRsbO14GfiC0mXK8fTJI3Jysuneqz8jRk/4qvN07sxJ1q9ZzqOHD1BQUOCG99NCmZdNGjeCA/t2UbV6LUxMTBkyYux3ey/8bs+zdi0bIlJQoO+AoXh4VuXpk0dMGj+CZ/5+6Osb4ObuyZDhY7AoYvlfd/WztGhcB3+/pxRzdKKIpTX3vG+RkBBP2XIVmDB5JvYOjt9FNuNGDebSxfMUcyyORCLJvf5EIkxMzXgV9JIWrdoxffbCD8olJibQu3sHQoNfsWHLHoo7l2De7Kls2bgWgEs3HmBoaPTV/fpdSUhKxrNccflOyg9GvpMiR86/UFZWYeiIcfTq1o61m3ayZuXf3L55nZDgoP+6a1+Njo4uOjq6lChZirr1GjJwyEgkEgkikYjs7GyeP/NHnJODUwkXMjLSOX70IPt276Bfr86YWxShVZsOtGnfGS0t7R/Sv8MH9zJ+9NC8vzU0NNizayuWVtY0s2iT971zCReCXwWhrqFBVlZmPvMMmUzGlUvnKVXm3Urq6hVLkUqlCAQCVNXUSE9Ly9fu0BFjcXIuya0bV+VKyjdSwqU0cxYs5eb1K5w+cZTNG9bQoVN3Ro+f8sV1vQoKpEXj3OAGCgoK3PW+hZaWDlcvXyQxMSHvOGVlFYRCISKRCCenEujovssSf/jQPlJTUqhSrSY6urqoKKtQroI7tevWR+lNNK7g4CCW/b2I0mVcady0JdY2tiQmJHDtykVMzcxxdCqBZ+VqTJkwkjOnjrN6/VZmTpvIhrUraNaiDTa2doUe04vnAZw+eZyVyxYjk8lwKVWGISPGFtr/ZfrshTgWd+bMqeOcPH4EP98nbNl5EEVFRRIT4jl/7jS6evqUdClNdlYWhkbGeeP8nQkNCaZL++bo6uljZ18MM3MLnj8PoFOXHnhWrgZAiZKl2HvwFHfv3OTWzWscPXyAPj06sH3PEXR+QCje78mosZPYvnUjevoGhAQH4dWgMSVdSrNq+RLat2rM7Hl/YWtt+s3t9B04DIFAiEQiQSaTUbO2F81atKFXt/Zo6+gwZMTYD8rEx8XRo2sbYqKi2LB1D8WdSgAgleQu3KmoqDBl/EiWLF/3f3GtfQlCeZ6Wn4J8J+UX5u1Oyu17j+Q7KT8ZqVRKjSru1K3XgFat27Jg3mwmTZmetzInkUh47vcQ++KlfouVx69BJpPx+NFDdmzfwqED+wCYOXs+LVt/Pizsl5CUlIiba26OGoFAgI6OLtVr1iIsNJSw0BAuXLmZd+yxo4cZOWwQADa2drRt35GU5GQqV6nGpYvnWbFsKQsXLcXO1ipPNm+VlMePHtK6RWMAataqw/lzZzA0NCQmJoa/li6nXoNG33VcfzJSqZRtWzcxf85M6tZrwPyFSwAKdc8kxMfjXqE0ABs2b8e9Um642DOnTzJ6xBAyMzM/KOPg4IhL6dKYmJgS8fo1jx75kJycTEJ8HNt376dkyQ8TBEa8fk2Pbh2RiMUcOHISdXX1j/YpMzOTvr264vv0CV279+LvJbnhVvv0G8jNG9dISUlh5ZoNWL23u5eens6B/Xs4cewI8fHxBL/Kv8hRtXoNVq/d9NE2P8XRI4cYNXwwLVu3JT4ujqtXLiMW5/D+67xJ0+bMe3PeP8ev/DybP3cW+/fuoo5XfQJfPCcw8AXly7sxZ/6iD0w83/LqVRD1aldj9NiJdOvxeyw+PH3ymI0b1nL/3l1iY2Lydrc9K1dl5IhhP0Q2d71v07FdKzZt3UlFd48Pfp80YQxnz5xi24692Nm/C8s/bHB/Tp44lvf3lGmzaNeh03ft26/O2/eWfCflxyLfSZEjpwCEQiGOxZ149eoldvYOrF636b/u0k9HIBDgUqo0LqVKU9G9EmNHDWfi+NEc2L+HrTv2frcXpra2Djv2HGDLxvU89HnA6XOXUVJW5u8liwgNCc53bJ06XmhqapGSkszLwBfMnjEVgJMnjvHiea4fy6SJY9m5c2dembeRu1xKlebClZvIZDLMzMw5eeIoN69fw9yiCHW86n+XscjJRSgU0rlLd4yMjBkxdCD6+vqMGTepUGWTkpPy/r9vz26uX7vCkcMHiYmOzqu7ddv2NG7SDCfnkuzbu4tbN65z9PBBlJSV0dLUQk1NjajICNTU1GjVrBH1GzbGraI7Dx/c5+KF82RnZ5OWloqOji679h36pIICuSvGDRo24dbNG3kKCsDqlctwLlGSoJeBeNXKDduroqqKpoYmqmqqhIWGUrNWHcqULYe9gwNrV6/kZeALlq9cR83aXxcGOzMzEyUlJZSVlTly6AClSpdl+MgxNG7ajJSUFF48f8apk8e5cePaV9X/K5GdlcXB/Xtp2rwV4yZMLnS5xIQEZDIZziV+/YiGEomEE8eOMH7sKMzMzKjfoCFGxiboaOsQHR2FW0V3flR29SOHD2JoaEgFtw+TJO7cvpX9e3czYvS4fAoKgImpGQD6+gbExcWSnpH+Q/onR45cSfkNECFGxI93zpaTnyJFLLhx/fpHzn1uvo5c2fz/b0a2aNaURg3q07FDe+7fu0vThnWZOWsWZct+ewbmV6+COH/6BKdPnWDMmHGoKosAMZoaasTGxrJlwxoMjQxJiE8gMysTS0tLRAoi/P38yM7OBuDF82coKyvni7RWkGyKmL117JTQqEF9GjV4q5zIQH6PfXca1KvLxXONeHDPO+8++tw9Y2tdhLPnzjNyxHBOHD+CiooKDg4O1KxZk969+6Kuro6eXq5Z16tXQRw5tJ9HDx8CkJOTQ0pyMgBly5alU+cu3Lx5k5s3bnDi2BFEIhElS5bEysqaI0cOU6ZsGXS11Av1fFUQ5Ub1U1JSwtLSiqdPn9CpcxcmT55CeHg4nTt1JCQkmMyMDKpVq8aF8+dRUVEhNiaas2dOUbJkSUJDgqlTty7Vq1X+qmf68+fP6dypA7Gxsairq7Nl6zZcXErl/Xbi+DFCQ0MJfP4MYyOjfG3IZDLCwsK4f/8e9+/d4/6D+yQmJNKmbVvatWtfKNn8DKRSKcHBr3jy5Alnz5whISGedm3bfNH5CvB7DEBw0HOUFMDMzBxDQ8O8gCf/BSkpySgpKaOsrIxEIuHYsaOcOH4cb+87pKSk0LhxE+bMnfeB2ZREIsHf7+l3kU1ycjJHjx4hOzsbJUVF9u7eydix41AUSnmrCEkkEubPm8uGDevp3KUrPbp1/eDcq6koYWpqyvYdu/D396N8+Qp/3BzlTxvvf4VcSfkNECJFhOTzB8r5rmRlpKOmpvqRc/9WSZHwaxlH/DhUlUTs27MLb29vJk2ewtIlf7Fty+ZClb1+4wYLFi7Cz88vXwZpyH0pCgQCevboTs/uXRC+Obcd27Vh757dzJ4984P6xo8bx4C+fdmzdy++fn5ERESQk5NDw4YNGDpkCKlp6X+UbH5lUpKTMDI0zLuPCiMXW2tLHj58iK6uLrduXC8gF0huXfe8b/Po4UOmTJ7EtOkz8q4te3t77t+/z/379z+o28/Pj727d1HC2YlZs2fTq2d32rVpQ9WqVTAx+XhEu2aNG/LwwX0OHT7M06dPqFK5MlMnTUCABEtzE4o52BPyZuevQ7u2aGtqsnvPHh48yO3D48e5E+chgwaioijMG0NhCQ4OplPH9hgaGrJ29Srs7e1RU1MjMzONe/fv8/c//+Dr64eDvT2xsTG0a9s237Nr8NChHDuem1/I1taGsmVyI5StWrmCrVs2s2HDBqIiwihiUXAUqp/B9h07mDd/AampqQBYW1szY/o0HGyt+ZLz1axxQ44cPsSkie+CGwiFQhyLFWPgwAHUrVPnp0ZHu37jBj169EQilWJra4tUIuFFYCAVypenZ4/uuLu741q27Js+/Xuc3+dd8/z5c5o2b0FOTg4ikYisrCzc3d3p0e3dMzc9PZ1hw0dw/sIFJk+aSNcuXfL1AXKf13v37iE6Opoe3buyacN69HU0C+i3HDnfjlxJkSPnI4SEhGBZpMjnD/yDEAgEVKhQgebNm/H3P8sQi8WfXJ3MyMhg3vwFbNm6lXLlyjF+3FgUFBTyTRAEAgHVq1XLmyC+fh3BmbNnOHr0GC9fvkRDXR1bO1sePnxEnTq1MTM1Zeu2rYSGhmFlaUmN6tXx8KiEe8WKaGtrI5FIeOrn/8PPhZzCER0TQwln5y8uV9nTk+DgYM6eO8f58xfw9fPDq25d6nl54eCQm9k6MyPXP8XTw4OZM6aTkJDAosV/8fz5cwCcnZzYvGkj/gEBZKSnM2bsOOITEsjIyKBH925oa2sxesxYHj16hLOzM/v27Eb5vTwS0dHR3Lt/H3//AFxcSuJRqRK7du+mUaOGjBszJt91vHrVSlasXMXCRYvo1LkL6urqtG/fjrJlyuDk5MTRo8dYtXo1DRo2QldXl3lz5lCrVs1Cn4/klBTi4uJYtGABpUq987E5dvw4o8fkOj23a9eWWTNmFFj+1atX1K5VizmzZ+XtRAGMHjWS7Tt2ANCgYSNGDB9Gl86dC92v70lOTg6pqalMnTKFRg0boKv7dU7vGhoa7N29i/T0dIJDQoiIiCAqKooTJ0/Sf8BArKysqFu3DmNGjfopysq48RPIEYuZOWM6T319SU5KZt7cOZQpU+aHt/0WNTW13Ofx3Dm0aN6cqKgojIyM8sxho6Oj6dWnL4GBgaxetZKaNWoUWE9AQADR0dHMmzuHFStXUbdefZo2aYKVlSUXLl6kTevWNG/W7KeN679C7jj/c5ArKb8BImkOImnOf92NPwqZTEZwcDCupV0KPvfSN6tb0pw/Ml56hbKlycjI4JnvE0qWeDcBvXHrNkeOnSAnJ/ecPX8RiP+zZ0ydOJ4uHdt/MrP7hQvnmD1/Ic9fBKKoqEBlDw/WrviHWjWq03fgEOItLEhPTWXT5i3U96rD34sWUKaUS/5JhjTnj5fNjyQnJ4d9Bw+zZ/8BdHV0sLayzMsdpKung52NDSAgMysTRQUFkpNTCPD3p1H9unn3UUFykclkvAoOJjs7B8siFqiqqtK7W2c69ejNsOEj0NbSwtPDnbXr1rL0779Zt3IZtWvWoHnj+qzfsIH6DRvSvUtnxo0aga1VEfoPGY5NUWue+voyb+4cJo0bg7a2Nq8H9mfKjFlcPH+Wpo0a0qZZY0oVd6Ru46Y8ffoU55IuFLW2RlNDHUVFRR48fIRYLEZbW4ukpGRUVFQwMzVl2aL5uR3/17Ohf8+uONhYEZ+QSMP6Xmi+F7nLefhgWjZpwLKVqzl28jS9+/bFqbgjPbp0xqm4I+ZmpmhpaX100mxraU5pl5IMGz6c00cPYWycG/a1WqWKmBgbExkVRee2rT/6rhAJhRjo6WKoo5mv30a6Wgzu24tHAYF0aNuaadNn8MzfH2VlJaKiY0hPTyctLR09XR1GDR+Cg7194S+Y9+QrkUg+a27VoXUL1qzJdR7v1qHNB+f3S9FUUaSEgy0lHHLzoHRs3YKbt++w78Ah1qxZi6dbeapW9vymNj5HTk4OOdnZyGQyalXxpGPrFu9+LMz4vtPzzNLUCD1dXSLDwxBJc7AwNgCkIJUS+DKIzj17k5Odw94dWyjh5FRg38LCw7lw/hwikYgm9epQr2Y1tuzYyc7de9m1OwKhUIiWhgatmjT86n7KkfM+ciVFjpwCuO/zkIjISCqUL1zm4j+NuLg4gA9Mt/oNHkpiYhLlypZFIABVNVU2rFqOp0elT9bn8/ARfQcNpVzZMgwbNABPj0povxcxpVbN6gQFBxMbF8eG1SupWb3q9x+UnE/y4OFDOnbtSWpaGjWrV0MqlXLp8lVyxGIEAoiJjSU9PeODcmpqqigpKfHi5UsAIqOjMTMxQSAQkJySwsixE7jjfZeExMS8MkZGhmRlZaGlqUmzJo3o17snYWHhuJUrx7Zdu9l/6Ai1a9ZAW0uLM8cOsWb9Rv76ZzmODg40b9qYbVpaDBg6HID9h45w78FDmjVuyKKl/yAQCFi1dj0zZs+jfDlXpk0an9fu2JHDiYiMJD09g8ysTGrXqkHTRo0wMjRg6MjRHDp6nAF9Ph4tSiQSUbd2rY/+bmdry5KF83OV9p598Hn0mBFj37WvpqaKu5sbTRo1oE7NGigoKOSZumlqaDBr+hQaNG1JYFBQnpJibGzExdPHqVyzLuMnT6Vdm1Z41amd7/4BEIqESKT5TXIyMzNRUXkXxnv08KFoa2mxdsMmDA0NMDE2Rl1dHUMDA+77+FCvSQtGDBlE/0+cA6lUyvMXgaRnpFO8WDH8AgIYMmI0UdExVKroRoVyrqSkpCAUiVAQiVBVU0VHWxsDfX3KuZalmIM9R4+fYMyIoViYm3+0na/F3a0CFSuUJzAoiKkzZ7N+1QpUVJRRV1P7pJL4pchkMnz9/JkwdTpR0dE0ql8PLS3N71L3lyCRSNi9dz+qqqrEJyRw2/suTmXK079PL8qUcmHPvgMcP3Uaaysr9mzbjLmZWYH1/L1iFYuW/A1AaZeSqKqqoqqqyoA+vVFRVuHcxUv4+vrxIjDwZw7vP0O+cPxzkIcg/oV5G4L4yZ1r6GjLQ9z9TOYuXsrGrTvxvXu9wChWEomUB89eUcbBGpHo47sD/4+EhoXToGV7SjoXZ/XSRTx49JhnLwI5ceYct73vUaNqZbasWf5FdfYdMpKn/gGcObQHVVXVb+rfnyybH8n+w8cYMno8ndq2pn+vbhSxyD+BlEqlREZHIxQIUVFRQSzOIex1BLMXLuHO3fsoKiqyc+dO2rVrh421FXVrVefh46ecv3SFgX16ULF8OdTV1AgODSUoOJTs7Gx6de2EkaEBx06doe+QkQgEAiaOGs6M+Yvo36s7o4cOzFudHzB8NOcuXmH21Im0aNKQzKwsrly7wawFfxEY9AoAI0NDlJUUCQ1/na/vSkqKWBUpwtkj+wpc7Y+Li6dC9br06tqRscOHfLdzeu3mLZav3ci1G7co4eRIQ6+6nLlwkXsPcgMBCAQC7G1t6Nm1I+1btSAyKhrPOg2p7O7Gmn8W5/PVuXjlGktWrObeg4eUdHZi4aypPA98SWmXklhbFqFp287YFLVi8ZwZpKenM3P+Yrbs3EOpkiXo0KYlDiVKf/KeycrOZuT4KZy9cAn/+zcLPCYzK4s+g4Zz/vJVIDfHjZqqKnp6urRr2ZxLV6/jff8BRoYGCAQCxGIJ6enppLzxQbG0MCcuPoF6dWoyZ+rEb34WfIqXr4Jp1akHUW+ixgGoqqpgYmyMnq4O8QmJREfHoKurQzF7O1o0aYhXrZooKeX3j8rIyODhk6c8ePiYp34BpKenk52Tw/PAl4S/jsDC3Izli+bhWubDMNiF4WufZ1KplKd+/mzfvZ9tu/fmfa+upkbNalU4cuIUANZWlvTo3IHWzZqgrq5WYF1h4a+pXLchndq2xrm4I+OmzKBxAy96dO7AkROnWbV+E1U83HFyLEb1Kp5Uciv/VWP9nUhMSqZEBU95COIfjFxJ+YV5q6T43rqErlxJ+alcu3WHVt36cnj7BiqULf3B72KJlHsvwnC1s0DhD5sI9x85noPHT9GhVTOOnjpLckoqiooKVHQtS5vmjalfuwaq763OFoaRk2ewfe9BAO5eOIG56ccdmD/HnyybH03RMu5kZmZhZmJMNQ930jMyGNS7G07FHD5ZLj09A2+fR6jom/Lk7k2u3rjFnXs+KCoqEB0bR+D966h9YkJau3k7nvgFAHBm/w6u3/ZmxsKluJZ24Z+507EqYkFqWhrjps9l35HjlCheDDU1Vf6ZOwNzUxMuXL3B6k3buH7bGwANdXXS0tORyWSoqaqirKSErq42V47tRyKRIpVJUVFW5vCJ0yxesZZngS8RCAR4VizPng2rPuhfRmYmB4+d4tptbx4+fkpmVhYSiQQ1VVWaNvCifcumWJh9PCHfrgOHGTZhGid2b8HB1oaw1xEcPnkGIwN9Lt+4zanzFylqWYRBvbtjZGhAl/5DaexVm2XzZ35gQtm0Yw9u33uQ77sBPbrg/eAhRSzMMDMxZuvu/WRmZdOvWyce+/pxw/s+O3bs+Ow9s+CflSxesZYpY4bTt2vHfPJdt20ny9dtIjklFadiDiyeNRmfx748euJL7epV8KpZDSAviez7BL4KZvWmbew9fAw1VVXO7N/xTc+AwhITG4fP46coKCqQmprG68goIqKiiU9IRF9PFyMDfeLiE7jr84jb9x5gaKBP2VIlESCgpJMjKampbN29n7T03CArzo7F0NHSRFFREVNjI2pW9aRShXIof0Oiwy95ngWHhnHp2k2cHR1o12sgqWlpqKqqoKqiQnxCIk4O9uzdtBo9XR1Cw1+TmZWFjZXlZ8PJP/b1p06L9mxe8Rc79h3C5/FTFBUVCXsdAcC0sSPo3aXDV4/xdyQuJV2upPwE5OZecuQUQPk3iklwaFiBSsqfTPkypTh6+hyHT5yhc5sWtG3RhKKWRQod3lMmk3Hp+k02bt/N04DnGBno4/P4ad7vaenymPu/KmqqqhSzsyUpOZkrN28T9jqCkLBwju/e8ulyaqp4uJXn3oswalX1xMTQAGvLIqzbupMKrqVRec9ZvSA83MrzxC8ATQ0NHO1tKenkSNlSJRk4eiJVG7akdbNGONrbMn/aBGpV9WTVpm3cueeDW+1G7N+8hlpVPaldrTKjpsxi2579NG9Uj4PHTjJhxGCKmJmy6+BRjp46S8e+g7n/8DFSqYyGdWux68BhALq2b03gy1dcvXkH0+JlaVKvDm6uZdDR0SYxMYl/1m4kKiaWUiWcqFHFAy1NDURCEeGRUazZvJ2lq9dTzcOdWlU9qVzJDZFQSGh4BEpKisTGxbNtzwEA6rfpjIa6Ou1bNsXR3o6gkFCiYmIACAoJZfjEaXhWLI9QIODQidOoqamyYNrEfCZKRczNuH3vAcXsbAl4EUgF19IsX78ZfT1dQl+/JiIyGj1dHS4f3Yvlm92wtj0HADDv7+VIxGI6tW5BUSvLD+TQuF5dNu/ax5pN29BUV8dAXw9TYyM69R1CQmJiXtABRUUFlq5aT+e2LencpkW+/r2dEIe9juCxrz/PXwYx/++VKCsrUa9WDTq0bPpTFBQAQwN9alevUqhjA54HsnXvAV4FhyKWiFm8Yg0a6mr06NSWxl51KGZn88NDHMtkso+aowUGBdO6e19eR0blfTd74hieBb7k8Mmz7Nu0mjIuJfIWA4qYF2zWVRBFrYqgIBLRpf8wAOrVqs6av+Zx+sJlkpJTaN+y6dcP6jdFKJWHIP4ZyHdSfmHe7qT43zgv30n5ycQnJlLcozYr5s2gRUOvD34XS6R4v4ygvI3pH7lan5yS+saU48t2TOISEnHyrP3B95OGD0JLUwP3cmWxt7H+pr796bL5kYycOpsb3ve4cXw/D5/6Uad1Z/p368iUkZ83gXorl/bt25Oeno6utjZd27ZgRL9eKCp+fHK3ff9hhk/ODUOtqqLMqd2bcbTLdYROS89g+YYtHDxxhtDw1zja29KtXSvaNWvE8g1b2bhzL+GRUZRwdGDOhNE8exnEqGlz2LtuOS2698PSwoyQsFzTLwebosiQERYRSXZ2bvZ2kUhITo4YrxpVOXXhcr5+KSookCPOnag0rVeHsYP6UtTqw2iAqWlpHDh+mgPHT3HX53Femfep6FqG+rWqYWNlyQ3ve5w8f4mgkDB0tbVJSEpCJBIhkUjwqODKU//nlC/jQq2qHoyZPo8R/XrStW1LjAz0AfB99oLqzdqhr6uDgoICUTGxNKxTk4ioKFRVVJDJYHjfHni6vfO3u3b7HiI9E8aPGU14RATaWprcPL6/wEn31VvedBowjIzMdzmJDPX1MdDTwe95rj+CdRELRCIhga9CaFKvNmsWzkYmk+H3PJCAFy/R0lSn1/DxpKWnIxKJaN6gLvMnj/vi58l/SVRMLOpqqmh8JhHot5IjkXD3ZSSL583hsa8/21ctwdWlRN7vEVHRzFu2mr1HjmNpbs6CKeN4FvgSkUhE6ZJODBgzOXd3as+nFxI+hVQqpW6brjwLfImxkQGvI6LwuXgCA72vi772/0BcShpOFavJd1J+MPKdFDlyCuDhEz8ATI0N/+Oe/JpoaWp8/qACWLRyXd7/NdTVqVu9MgO6dcLZ8dPmQnJ+DV6FhmFmkpsQ84n/MwC6tWtVqLJvV3j1dHW4uH87VkXMC+WkbPFmVX3ZnGnMWbqCVZt3sGRGbvZ6dTVVRg/sw+iBffD2ecT8ZasZPnkmj339mTh8IOeuXCc8Moon/s9o1KknTerVzo3cFxoGgJqKCluXL+bY2QvsPnQsr83KFcvj6VaeOUtXAHygoOxYtYQanpVIz8gkOzsbXR3tj/ZfQ12dzq2b07l1c9LS0rnz4CEiBRGWZmZk5+Tk+p28p5jXrurJvYePCQoJY+msyXQeOAKJRMKOlUuoUblSvnM2feE/LFq5jkUr1xF8/xoqysq8XXeMS0ikmG1R2jZrRP+uHT/p11ixXBm8X0Zwdu9WAp6/oEaLDmzde7BA2VauWJ5X964hlUpZunYT+4+d5PnLV2i8589gZmKMzxNf9HS0qV+zGqcuXGbB8jV51wyAob4eVw7vxszE6JNR/35VjA0NfngbL4NDmDh3MYOGjyI9I4OUtFRGTpnNxYM7cpXfY6eY8/cqhCIhE4cNpHPrFqirqaKjpcmOg0eZvugfTIwMGD2wzzf1Izo2jke+fvwzeyq1qnpQqlp99hw+Rv9unb7TSH8/hFJ5XpifgVxJ+Q0QysQI5ZEkfirlShSjuJ0NwyfP5M7xPR/8LpRJ3/ybg1D6+71gfzYXrt8iKysbLbXclVL/KyfQ19V5d8B3vL7lsvkxPPZ/xvU795g2YiAhwa8YPnkmhnq6WJkYICiE/Jat30Szdp05tmkF5iZGIBNTmATaNha5SoqBjiYSqQRtddUCn4duLsXZv2YJi1ZvZP6K9dy+9wDf54EUMTPBuog5V2/fo15VD0JCwxg+ZRa6Otoc2bQCXW0tTp67kK+uzi0a09SrJjJxDnOXr8XC1JhlsyajqqKMvq4OVhZmIBOjqaIAKgqFvn41VRWpWamAiIH/Kt+nY2siIqPpPWI8+ro6xCUksnjlWuwszTh85gIhYa8ZN6g3RcyMiYlLoE+nNqgqCBBIc/LJ4sC6fzAy0Cuwjfd5/54p6VCU1o3rMXbmfK7dvsPsMcMKXKw5duYCj339SE1NQ19Xh6CQ0Lzf0tPTaVCrKjU83AgNC2PmkpUA7Fi+kHKlShAdG4eWhgamxvqAJC/MrpxcEpKSefbyFR0HjsLIMPfcGxvqIxZLKF/KmepN2+L34iUymQwFBRE1PCrSol5NNFUU8PX3p3brLhjq69GmcT0mDumTu9vzDc9YY11NlBQVSUlOwkBLnUrly3Dr7n0Gdmn7vYb82yGUyc29fgZyJUWOnALQ1tJkZL/u9BgxkYioGPmOylcSGByKV/ueCIVC4hOTABjSs3N+BUXOb8H85euws7akXdMGpL0JNWxpYVbokK3X79yjWbvOGBt92Qr02xX6tv1yQwp/zl9hRJ9uKIhEzFya6+Ae+jqS9YtmoqSohHMxO+rXrIpz9YZYGBvl+cL079KOHQePYV3EnFtHd+X5Tbz1j+rVoRUe5X9e4r2GtaoxcPwMVFWUiUtIBODuo6fUbd+ThKRkIHenxP9FEKvnTaV5/Tp5ZUsUs6dPxzas3rab2f+s5q+pY784rO6ymROp6eHG5IXLqNK8I9NHDaZlg7p5ZnnZOTn0GDERAFtrSwa368SBE2fQ1tIkJDwCn6d++Dz1Y+/R3AhSOlpaHFj3NyWL5+6Yys2XCyYrO5t+Y6dx9OxFAFyKF2PvmqX4xWVy4dotADbvPUTdap707tSG4nY2rN2+h33Hz3Dzng/NvGpx9c5dpFIp7Zs2YOzAXt9llyoyJjZvhy4tPYNLN+4wvHfXb65XjpzPIVdSfgOEUoncSes/wKNMSfR0tOk+fDwHVi9GXe1d9CGhVPbmX4k88+xHEIvFtOw1mMTkFJp71eTAqfOYGhkyaWAPBD/wepbL5seQmpaGUCDg2fMXlCnhiJ6ONtbmpoV6NiWnpKL2JuKbNDsL4b/CuH4KQ20NZo4cyMWb3rRt5EUtT7d8bYa+jiQzOxsdTU2SUlJ5HRXNq/dW9UsVd6CEnXVuhCWpGHUlEftWLqJpryEMnjCd9QumYWlsgJqKCq9Cwzl57iKNa1cDoHurxqzYsovY2Lif/gyuULoEMpkMHW0tjpy9hJWFGWoqykwb3p/BU+Zy+ZY3xe2K0rhm5Q/6Nq5/N5KTk9lx8BgLxg3Jiy6VlJxCdHwClmYm+SJOFXTPtPSqQU33coycuYjBk2axYOV6OjZtQMOaVXCwsWLhhBEsWLOJEg629G7blN5tm3Lg5Hl2HjlJz7bN8HAtjb6uDsmpqVRo3IEarbsyoHMbpo8Y8JPO4O/H5Ws3OXr2Il1bNqZhrapUKFUCZRUViMsEoLp7eQZ0aUt193chfieGhQPgXsoZgSQHdWUlyjg78tfazaSmpjB37NBv7teilevR1FCnTYNa9BwxHnVV1QKvuz8JubnXz0GupPwGCCU5CCVyc6+fjbGOBvuWzaFx7xE06DqA0xv/RlUld+U176UuyUEo+z7Jv/7fiI2JISwi1w/hwKnzAGyePxnRD36xyWXzY5jQryv1ug9h3NwlnNr4N/GJSQS+Cvnssyk9I5Ml67YS8iZcqZJA9sXPswHtmzGgfbN3X7wpn5KWTul6rT9azquKOzv+mpG7k/BemxWc7VkwZjADpy1gysDuWFuY4VrCkat3feg2cjLx984hEAjwfvAQiURCeloasuzMz4Zq/Z5kZ2fzMiQcvzN7yJg6iozMTPTe+r5IcpiwaCW6WppkpaWi8i8fMW0VRcwM9TAx0EMkFTN29jIOnL5Iano6Wdk5qKuq0LlZAzo3r49tEQuEb8b173tGX0OVjXMnMrx7O5Zv3cuSDduYs2I9XVs05K8Jw7hy6w4BgUGkJCZw6Oxlhs5cDMCFG3e4d2gLxjoaKCJh4bghrN5xgLU79zNjSK/vljDx/4067q5ULleaTfuOcP3uA3YumYmmhiYgpEuzBrRvXCd3YeDNtZyZlc39N/6T9Tr3QyAQEBoRhYmBPtbmpqzdeYCaFV2pW7niV/UnISmZkXP/5sDpi8wdNYAHDx9z7tptdv89m1L21vnuKTlyfgRyJUWOnE9QxqkYq2eNp8OwSRw8c5H2jT+M9CWnYMyMDLmxdz2VWvUAoHfbZpR3cfqPeyXna6lYugT9O7Rgxfb97Dl+FoByLsVJSUtH8yNJ4AJeBtOo93Bi4hPp1qrxJ+uXSCSIJRJkMjh64SrB4ZEIBFDNzZXsnBwOnL7IVe8HWJqb0qRmFZzsi6Kt8W5yrqGmSqv6tYhPTOLwuSuM69eVXq2bfHRC3LCGJyPmLGHfqQuULu7A1bs+ABS3K5p3TEuvGsQnJjF2wXKc7W3o1rLRl5yyD5DJZCSnpqH9RqmIS0ji7PU7JKemEhoRRcjrSG4+eIyBni4yqZTI2Dh8/J5RurhD3gIJQKem9dHV0qLTyCkUrdaUOp5uLJk4HBND/bx2pFIpkbHxlG7UkbiEJPq0a4apoQHO9jZcvevDuj2HWbljP8pKikwf1pdi7jU/2m+pREKOWJwX0evy7ftUbdeHRwEvcLIrinXVJgCYGuozvl83Bk1fyJ1HT9mw7wjLt+3Lq0dTXY0Dpy/SwqvGN53H/1cUFETsWDKTPSfOMWL2Eur3GIqiohLLV63mxOXrrNqRey7PbVmOawlHVJSV2LJwGqPmLCU2IZHMrGz6tGvO3FEDkMlktB0ygYFTF/D4xE5UlAuXq2XPiXNcuHmXWh4VmLh4JZlZ2ayZNZ6WXjU4fTXX5MylmN0POwe/C/KF45+DPATxL8zbEMRBF/ahp/V10ZTkfB/aDJvCk2cvubNvLeqqKoilMq6+zqKymTIKQvmq4MeQyWTolK8HQNyt4ygo/PiVaLlsfhydRs/kyIVr+b4rV8KRcxv/wv9lMIs27iY+KRmRUIhEKuVRQCCGujqsmzUGh6JWXIvIRhbuz5lrtwgKi+B1dCxRsfEkp6aTkZWFUChEQ1WF5LR0DHS1yc7OITkt1y/EWF+XSmVLcvDsFQBsLc25f2A9wa8j2XPyIntPXSQgKCSvX/G3j39252P43GXsPnEeOysLfPyeM3dEX/q1a/rBcf2nLeLoheuUdymOproaQzq3wt7KglHzVxCXmISBrjYju7fD1jI378hlbx9GzV9BEVMj4hKTKGJizAPfZ0hlUsKjYrG3skBFWQnfwFdIpTKUFBUQCgTIgN6tG5OZlc3pa7eJTUjC3tqCi5v/LtC3IDAknKv3HjFr1Rai4xIw0NXGxECP0MgYklJSUVVRplPjOnRoXIfSjvb5yqZlZOL92I/jl26y9ehZdu7cyYub52ha0xNDPZ18x3q2709CUgp1K7uhoaaKmooyT18EcfuRH1rqauhoaTBtUA/KlShGREw8pZp0BUAgEKCtqU5aeiY5YjH6OlokpaQxoW9nhndr80nZ/OmcvnabLYdOY6CnS9POvVi5aG6ekjBraC8GdmyR7/iVOw8xdtEqHKyL4L1vLQAvgsNwbdGTrfMn0riG52fbjIqNx7VFT1LTM5DJZNSuVJ5/Jg3F9I3yO2PFJlbuPMTzM7tQ/43CRf8I4lKzsKnWRB6C+Acj30mRI6cQzB3RF7fWvZm0dB2LxshtqguLQCBg+eThDJi+mMjYeCxM5AEIfmfmDO9Dv3ZNSUxOoayzAz5+L2gzbAquLXoSGBJOUQtTSjrYIpVKEQgE1PWowPi+nUjLyOTopRvoFitH+5HTMNDWxNm+KK7OxTA11EdbUx0NNTWysnOIiountVd17KwsyMrOZso/G1i58xD3DqxDQ02N8KgY7jzyY87w3gBYmZkwqkc7RvVoR0hEFCt3HMKmiGmhTLOmDOwGwPp9ueGHtTUKznkxfXAPBAhISU/HLzCY6p0Ho6OlQWJyKnU8ynPt3iMOnL2CvZUF8UnJRMTE4+ZSnCfPXhIZG8+jgEAkEikDO7bAsaglPv7PkUpldGvegEbVK9Fq6GSCQl+zfMpwGlX3AGD+qH5c8fahcf9xrNhxkL5tm36g5NtammNraU6j6pU4c82b0MgoImLiaVrLACdba8qVKIbx2+he/0JdVYVqFcpQrUIZPMqVAmDyPxtISEpidM8O5IjF7Dt1iSt3fXj87CVrpo+iTf38uy1v5fz+btWzN4qiproalcqUwNHGClNDfVydHSjtaM/CDTtZuGEnNd1dKWFf9Kea0P1O1PV0o45HBc7fegDAq9dRjO3VgVKOdtR0d/3g+MTkFACWTRqW952dlQUKIhEx8UkfbUcqlRKflIy+jjbTlm9EQUHE7T2rCQ6PpLZH+XyyPXjuKo1reP7xCgpQqIiGcr4duZLyGyCQShBI/lwHtV8BG1ND5g3rzdB5y9HVVGd8744ACCRiBHK/h0/ibJOb4C44NIwihj8++ZfgjU+KXDbfnyKGuvlkaFopd7IUGJLrvFvb3RUtDXV0NdVxdXIgOS2DloMn4hsYjKqqKjt37uTo8lmUd7L/vF+CRIyKSEidimVZufMQMTFxaBVRZs6QntTsMYKnAYF4/WuyZmWkz9yhPfLKfw4dVWX+GtWXKX07kpSSSqkWvZm4dC3NalZGVVkJqUzKtAFdMdTSYMXEwQCIxRJ2n75EaEQ0tSu54urkQFpGJvM37OLxsyCQyQiPiuWmz1Oir+zHqEoLDHS0ObpsFo42uVncOzfMP9lXU1aiZsWyNK7iltdvAVDNtSSdG9dhwpK1PA8K4e/xgwoch4GmOu3rVfvoefwcjSpX4EqUhIouxdly+DQqCgrc833G4Ys3sLM0Z9bg7rSq5fnBe0gEH4SRjktIAODEyjmUKmb7QVtdG9dh3b5jDJ/zN34vQ+jXpjFDOrX4qIL4pyKTyeg+cT4nr99l586d7Fk4iaJmRu8O+JcsujSqzdy12wkKCaNiidwoaokpqUhlMgSy/HOIszfvseXwaV6EviYw9DWZWdloa6iTlJrGX2P642hphqOlWb7Q0GlvcgJlZWXJ5yPkzsvk/HjkSspvgFAilts//gL0bFKLtLRUJizbTDXXkmBRIlc2coPJAsnMyua6jy9TVm3F1EAPJyuzn3IdC6UAArlsfgLnb/sAMG9IdyJjE9h89CzqqqrEJiaRkZUNgJOtJQcXT8LZrii+UijnUPSLgickJiYCoKEoQijJoWRRc9RUlEEq+W7Xk56aMnGxsUilUuISk1m3/3jeb9Gx8WycNjzvbyUBdPKq8q6wJIeQ0DD+2rLv/Srp0qgWojcTmV7NvHCyMi3Q0Tg7J4cnz4Oo5lqywPHM7N+RO4/9ePws8IfdP2/vmTuP/bExN2L6qq1oqKowd3A3BrR564cjBcnno+VpqeT6PgSHhlPGLlcpC4+O5dLdR1ibmeBgZc6F1XNYue84YVGxrNx1hJs+Tzj012RUlZU/VfUfxaYjZzlw7irzhvYEwMpQ95PyT0pKBMDCQIfbDx6xbNdRbjz0RVNNhfruZRBKcpBKpWw8cpbhi9bgYl+USi6OdG5QAxVlJZ4Fh6OqrET3hjVITU7iWXAYGVnZZGRmcd3Hl52nLpOYksqE7q3l8xE5Pw25kiJHzhcwuF0Tlu06SpdJC1m/cRPrDp6iXqWyWJkafb7wH8QD/0BajppFVFwCliaG7F0wHgMdud3u/xOJKWkMmreCauVc6N+6IQKBgBkDOgO5uw1+r0LpNW0JWhrq1K5YFrEUfGO/rI2g8Eimrd5OWUc7TA31yMjKomafsbn28hW/b94Se0tzQk9t5YH/C9bsP4m2pjqJyakMbtfks2WtzYzz/q+spEi3xrVZMKwnL0JfAzBz3U7KOztQ0610vnIRMfHM27SX5LR0GlWpUGDdK/ccJzQyhj3zx3/94AqJDDi0eDI6muooKih8URSu5LR0wqPjGLt0A45Fi1C3UlkAQiNjqNpzNNHxiTjbWvE0MDivTPcmdWhVpzLNh89g0NyVrJsy9DuP6PfFSE8HXU0N5m7cy8ZqDT97/FtJdRw/n4SUVJxtrejSqBbNqlfC3MiAO08CGDRvJU8Dg6ldsQyWJkacunGPiJgziCVS1FWVCT+9HW/f53QYP5+ouIS8unU1NWhRy5OezepSzNriB43490Io3036KciVlN8AgSQHgVi+cvErIACOLZ7IwSveAExasYVRi9cwtksLxnRujqLCn31LPQ4Mpu+cFTx8/gpXR1sOLxxPCRvL3MnOT7qGBTIApdz7Rp4m5buTkp7B5mMXWLDtIFnZOSwb2euDF7Yi4GJtTtOqbqzcfxJZdhYCgZAvlcvynYdJTUvn5JLJuc/AnBwCQyMw0tUmMz39uz8X9dSUqVnWmZplnfP/8Jl2NBRFxJ/bjm9QKI7WFqgqK4FEzOC5K1BXUSYtM4v4hIS8/gaFR7FoxyG2n7qMipIik3u0oaS1eYHjufs0APsiplR1KfbD3gNv7xmpVIqRphoCAYUyE3ufjuPmceHuIwDWThiImkgI4hwy0tKIjk9k+eg+lHdy4FlIOGoqytiYG2NnYYpAIKBz/eqc934kf8+9R4OKpXm6exn95q8GcvMLCRQ+npjRqYgJU3u3QyyWUNLWivoergiFQnLEYs5e96bZqNl5x5594+fyPmkZWYxdso71R85S3smevbNHoaWhhqqSEkZ6Oii9SeT5s57jcuSAXEmRI+eLcbS2YJSVBRcT4PbGhUxZtZWF2w7i7fucvXPGvHuY/2H4vwqjyYhZ6GlrsmJMX1pUd5c7WP6fcea2D92mLSU1I5O2dSozuUcbzI30P3p85dJOzNywB2/fF7g6O3xxewY6WmRkZ/MiLAJLE0PUVJRZPKwHS3ceoeWYufRu5kXdimVQVlLE2swIXc0vi4IYm5hMVnYOpga635yZW1lJkTLFbPL+vvnYn6s+vuhoqLFwSDda1/LkdUw809fv5r7/CyLjEpnUow09GtdGW6PgEM4APRvXps2EBczfcoDhHZr88IWQmMRkjHS1v6hMVHwiYdG522T75o6hXqV3vkLFrMyp5OLIluMX6dKgRp6P2vuUc7LjzG0f4pJS0NfW/LYB/B+hraHGsHZNSAQOXb5Nm5ruHz1WKBQyqmOzfN9JpVJc2g8hJDIGW3MTAsMjAVBRUiQz+52yoSASUd21BKsOnKJX0zrMH9T1j32PFRaB3OTtp/BtT+XfnJUrV+Li4oKWlhZaWlq4u7tz8uTJvN9lMhlTp07FzMwMVVVVqlWrxtOnT/PVERAQgIeHBxYWFkyfPj3fb9bW1ggEAm7dupXv+6FDh1KtWrXCd1QqkX9+xQ9gZazHtqlD2Dt7FOfuPKRm/4nU6j+J0h2GoF6lNfWGTCUiOua/7+sP/Iizs5m8ahtu3UahrqLMrhnD6OxVBXVlxf9UNv/1efl/+zzwe067CQtwK2HP051LWT2mD+YGOp8sU97RBhUlRcav2PJVcundpBauxWxpNHwmx67cBqmETnUrc3DeGLLFEv7Zc4yaAybh2WssFg26U6HrCOZt2sur8IhPj8X/BZV7jcWqcU8cWvbD2KsL9QZPJTwy+rudrzkb96KkICI9M5t7fi+YvGobLu0Hc/yaN+HRcTT0cGV424Zoqyl/sp6GHmUZ2rYh09fvxrnNQDYfPfdjZAzUKu+CkbbGF5ftP3clCSmprBjdGy+3Uh/8PqpDE7Jzcug7ZzkJSUn5fouJT2DriYtIpVLuPPb/z6/zX+1TxsEKgEkrtxEZE/dFZYXIsDY1xFBXi0VDupJ0bhs6Gmp5EdXqVizN7H4dOPP3ZA7NH0vwodUsGdoNJZHgPx/3b/H5DL/NHPMX5o9WUiwsLJg7dy53797l7t271KhRgyZNmuRdJPPnz2fx4sUsW7YMb29vTExMqF27NikpKXl1DBgwgE6dOnH48GGOHj3K9evX87WhoqLCmDFjfuq45Px8alcohbNNEYIjYyhqbkQ991x77CsPfLFvNZAaAyazeOcRnoW8LrB8WHQcx67dzftExiX+xN5/PWKxhLaTFrNk1zHGd2mO98b52FmY/tfdkvMD2HLyEoa62uyYNgyLT+yeZGXncO2hH569x2Po1RUEAio42X/0+E+hr63JkYXjcLGzosOUJVx76Ed6Zhaj/tlMemYWq8f05eLy6VxYPo114/vjVLQIi3Ycwa37GCLes6l/nycvQ6gxYDJiiYSNkwayb/YoJnZrydOXITi0Hki9oTMYuHAtjwODkUikSArhLP5vUtIzuPzAl2yxBH1tTS7cfcyyvSdoW9uTUR2b4mBpzrHr96g3dAZzNu//7P0+q28Hbm+Yh3uJYvRfsIZL9598cZ8Kw43HAWTnFN7MKzYxmbt+Lzhzx4cBLevRpX71Av1Y6riVplxxO45eu8vKA6fz/ZaWkYl/cDhmhrqf3JX701FUFNFq/ALSMjK/qNzykb0x0tWm2Zh5nLh5n8TUdA7OHc2mSYNYNqIXQ9o0xO3NLqfcb/D7Ip9jfjt/9H5eo0aN8v09a9YsVq5cya1bt3BycmLJkiVMmDCB5s2bA7B582aMjY3ZsWMHffr0AXIjz5QpUwYXFxfMzMxISsofj7xPnz6sXLmSEydOUL9+/a/qp0Ailm8t/mLk2nCr5trXv3knj2nfiM4zV9CrQTWKW1vw957cCEEaqsqkpKUze9N+Jq3eiUMRU9xLOCB6Y14iQ8bG45fy1W+ir8PGcX2pWubXztC+YOshztz2Yd/MYdR1y821UFAEo59JQbKR822kZmSy++x1ujeohqqC4JMy7jp9KUeu3cPJ2py/BnfGysSAmq4lkEpy+Bq5KAngyrIpuHQZzdqDp7n++BkJKansnDaYJp7l8o6r6FiU9jUrEp+cSolOI+k5cxnta3tQw7UEZgbvwibvP38dRZGIy/9MRllJEYD6biVp6lGWYzfucfVhAPvO32DjsQsACIUCyhWzoVb5ktSrWJoy9tbcDQhi0trdBAS/RiqTUdTMiJbV3GhQqQxWxob0mrUcsSR3pdWhiAlbJw+kcr8pbDx2AQWRiHKONtSvWJqktHSW7DrGgm2H6eRVmdEdGmNhWHBek5JWpmwa34eA4DBW7jtJ9VLFCn8SP8PbeyY9M4tGI2ayYEBHStlZFXisVCpl7dELHLh8h6sP/QEQCYW42ll99D31KiKGtYfP0qRyOfo2qp7vOGtDHUrZWmKqr4uxlpr8Xfcv3spm2+SBVB8whS3HztO/eZ1Cl7c10aNcMRtiE5NZuusoJW2K4OFs+06ZlJ/vr6IwYZh/lznmr8wfraS8j0QiYe/evaSlpeHu7k5QUBCRkZHUqfPuYaCsrEzVqlW5ceNG3gU0ffp0ateuTUZGBg0bNqRu3br56rW2tqZv376MGzcOLy+vr7J7FojFCMTySBK/EoI3oW0FYnHehKt5pbIstrNk5LJtHJ2dG7K0XLGimOjpcOzmA07OG0VqRibHbj7g8YtXbyoQIEBAWQdrmnmWo0tdT5LTMui1aD31Rs5laEsvZvVo+c328j+KA5du07F2JbxcneEXuUYLko2cb+OB7wuS0tJpVaX8R59FsUkp7Lt8hxuPA2hVrQLrRvZ8Z9culSKT5e5IfI1clAVQuaQDlx/6ERGXwN3V03G2tijwmtNXU+GfwZ2Zve0IvefnZt4uY2dFy2oV6FG/KprKSqRlZpGYmIyJ3jvfi6KGugxqUouBjWui17gvFYvb0rVeFTKysrn6KIDl+08ze8shdDTUSExNp0RRC/o2roFMBjO2HsLbL5AxK3fk1WduoEt0QjI1yhRHT0UZK2MDgqNiOThjCLVcS+Qdl5CSxtpjF1l28Cz7Lt7in8GdaVm14EhfIiAqPonHL0ORZmUjEn2f58Lbe2Z69xZM37iPgxdvUdravMBjx63exbKDZ7ExNaRTbQ+616+KvpYG9hYmH30GPPAPBGBU63oYqKvmO04ADGhSk9GrdtFk7Hy61KlMt3pVcsNMy8mTzcOAlwBkZ2V/8XyggoM1W05dQSyWsHV8X4SSz5sqyfm+/MpzzF+ZP15Jefz4Me7u7mRmZqKhocHBgwdxcnLixo0bABgbG+c73tjYmODgdyEU69evT0xMDMnJyRgaFpxNe+LEiWzcuJHt27fTqVOnL+6jWJb7kfPr8FYe+eQiFDK/XyeaTFzMqbtPKedoh2uxokzt1hyXHuO5+DCASZ2b4vWZ0Km62lqcWjCO1ccuMGXjAYqam9C9XpVPlvlW9l25y8Ldx4lLTkVFUQFDbS1ikpLJzpHQrEo5JnVqyqvIGJ4GhVHXzQVtNVUAJAIB2lqav9T1WaBs5HwTtkVybaaDouMpWcAK+6vIWJpM/IuYxBSqlnJkSKsGCBUU8sngW+VSq0Jpjt95jKWpEfZFzD9ZT9MqFWhapQLxKWlceODHGe9HzN9zisX7zpCelU272p7o62gXWEd6Zg6aGurUrViWDnUqA9CzUU1yxBJu+wVy2ccf12LW1HItgcIbJaGSiyMaqirEJCYTGZ/Ew8AQdl24hYKSElXLOCOWCRjXqSnek5cyd/dJqpV9p6RoaqgzvG1DujeswahVO+n912Yu+AQwq1crNP8VeCIiPpFyxe256ONLq+krMNTRxLmoOWXsiuLu/GHixMLy9jzIECKWCbCxMP3o+d19+S4j2jZkSpf8TtofO/7m0xcMWradhh7lcLaxKvC4muVcOL2oKOuOX+Lk3afM23sKkVCIg4UJvRpUo4mH6x+74PD2fP196DymBvo0rlz+i++hzvWqUtbRFlN9HfS1NOTPxu+ApJBmkb/DHPNXRiCTyf7oyzU7O5uQkBASExPZv38/69at4/LlyyQmJuLh4cHr168xNX1nY9+rVy9CQ0M5derUZ+u2trZm6NChDB06lOnTp7Nx40YCAgIYPXo0Pj4+XLp06ZPlk5OT0dbWZseOHaipfTz6ixw5cuTIkSNHjpyfQ3p6Ou3btycpKQktrY/78vzKc8zfgT9+J0VJSQk7OzsAypUrh7e3N0uXLs1zRIqMjMx3AUVHR3+g+RaG4cOHs2LFClasWPHFZT1EsegpyEO5/kqIZQIuSwypKopBQZBfzw+NSaDplL8JjYkHQFFBxJL+7Vm09xQWBrrsmzyg0O28ioql0cQlJKRmYG1iQEh0HIOa1uRuwCsuPvRnzfCuNHX/+M7MBR8/ei/ZjJJIRLXSjlga6nEnIIjM7BziU9IIj02gqIkh/qERAPismoaZvk6+Ou4/D2btycvUKutMcUtTWkxdjrWpPsemD+XOsyCaTP6bHeN6U+vfuSX+Iz4lGzlfhkwmY9el20zYeBBnazM2j+rB1nO3WHfyMlEJyZQvVhRHCxMqOttRt5wzWqqqH63re8il8eSl6GtpsHFkj68d0meRyWRUHDILTyc7FvVtW6gyaVlZFO04GoDXuxYTnZhC6b5T8n5XEAmZ1a0FzSu75u1CfoqgqFj6Ld3Cw8BQzAx0UFdWRiKVEhQRQ93yJTh++xGzujenWx1PXkXFUm3EfIa1qMOIlnU/W3dBvJXNpNHDCY6IRiKRIv3X+qWGqgoeTnbUdytJ88quKCsofrJOmUzG4OU7OHn3MTeWTMBIR/Pz446MwW3QTOb0aEEPr9zd41Pejxmzbi8R8Ul0r+vJuPYNC3UO/41UKuXgjQfM3nmMpLQMDLU1CXwdTVOPMqwe0uWLElf+TN6/b5pO/gtzA11WD+nyX3frjydeVLgABr/DHPNX5o9XUv6NTCYjKyuLokWLYmJiwtmzZylTJncSmJ2dzeXLl5k3b94X16uhocGkSZOYOnXqB85Un0NBko2C5P/LzvC3R5b7QlOQZH8w4Sqqp4734lFM2nqEvw9fIAPoPm8NjdxcOHr7IcsPnGRIk5qFasbOQIvbi0ex+dwtDt30YWmvFrSq7IpMJqPzwo0MXLIJT/simOjmX8m58vgZc/ec5uKjALxcndk0oiva6vlf7BlZ2aw8foXQ2HgalSuOlZE+RbRVEUiy8x1XwcaUCgPeTdY2D+9IzbF/cfrWPRq5uVDO1pyRK3fwcPmk72Yj/018QjZyCodMJiMhNZ2R6/ax85I3rSq7smJAe87cf8rE9XvoUsudYc1qUszCJH/Bf107+Sv9drkIJGIy09NR+FQ73wETTVXiEhIL3Y62goCSlsbcCXjF6sNnEAmFZGRk0KZKOSqXsMPV3orSb/ODFKJOewMtTk7px+ZzNwmJiSctMxupVIqemhL7Lt1GQSTkzuMA+tVxx9FYl641yzNvxxGalXfCweLLJzhvZXNgXC8qDJqBjoYKbauWY2jTWiSnZ2Kso4mK0vtKieyT45DJZEzddpRNpy6zelAHzDSVCzXuO4/9ycjIYNOJS2gqCGlTpRwNyxbD659xLDt6iXEbD7LqyHnaVyvPkKY1KV7EJM8GXyaTIRAICIyIwT80kqolHdBQzfVriUtOpfPCjVx4GECD8iUobeuCf2gU2soK7Dx3g5IWxqgqKdDMowzGv1qEqzeyCX39mhuPA5jbrdkPv/7lfJ6vlcGvOMf8lfmjlZTx48dTr149ihQpQkpKCrt27eLSpUucOnUKgUDA0KFDmT17Nvb29tjb2zN79mzU1NRo3779V7XXu3dv/vrrL3bu3Imbm9t3Ho2cX4n0rGwURSIUREKGNavFwv1niUxIZmSL2ozdcJDTd32Z0rEhbsWKfrYuLTVVBjWuzqDG1fO+EwgELO3bhpL9ptF98Wb2TeyDmrISAFvP36Lvsu2UtinCxuFdaOXpWqDyoKqsxPDmtb54bPqa6gAIBQIEAgEDG1WnzZy1hMcnYvmRyERyfh9OeD9mwPKdRCYko6SgwJaR3WhVOTc5X1JaOgALe7bMmwD+THxDIuhS6+MJ7f5Lto3qgUPPSYzdcDDvu91X7jK7a9MPdicLg6KCiJ5enh98v+r4ZfZcvcfea/eY1L4BNqaGTGrXgBN3ntB+/jpGt6xLvXIl0FT78t13E10tzs4eyrTtx5i/9wwtPMriUtSiUGXFEgmj1x/g4sMAlBUVeBgUxpxuzej8BfJqV608OhpqrD99jR5LtrBg3xmGNK1BmyrlGNq0Jk0qluLo7Ucs3H+GrRduo6mqgp2ZIeoqSjwOeo2TlSm3A4KQSnMVFlNdLcraWfLoVTjpmdkcnTqAWmWK57U3d88prvsGMm/vKZLSMjj3wI/tY3qgrPjpXaL/gorD5qGiqEjjiqX+667IKSTyOea380crKVFRUXTq1ImIiAi0tbVxcXHh1KlT1K5dG4DRo0eTkZFB//79SUhIwM3NjTNnzqCp+flt64JQVFRkxowZX34BSiS/TOQkOW8R5IbakYiBD1eFHweGsOjAWQB61KxIORsL2szfQO1SxVjVvy3zD5xj9Lp9XJo99KvNDPRUldk5shvN56xl4qaDLO7RgrTMLEas3Uu7KuVY3b9drnIik4L4y3M9fIy3r+/IuAQQi3G3t0JJQcSBq/cY+p4ilZUjRvk/yVr8adnI+TiPX72m9ey11C1TnHZVy1HGxgJNNRXiE5JIz85h8tajaKupcvPJc2qXcfzC2r9dLkKBAHGO+Mc/D2WyN/dN4dspoqvJ1Hb1mbrzBABDGldnz7X7dFu0ieNT+qHwJoHet9K3rgftK7tSatBsyg2ezalpA6jgYM3W4Z3ps3wnXRZtwsJAh63Du1DxvUWQkJgEbgUEYWmom+/7XN7JxtnciDmdGnH09iO2nL3Bgm7NPvuMys4R02XJVo55P6ZLDTeycsQMqF+ZTjXcvugcCoEGZYvToGxx7r4IYe7e0/RfvpMJmw5RxrYIxjqa1HBxwG/FJG4HvOL+y1CCo+PZd/0+SemZ3PR7Sd96nvT1qsytgCCu+gay8/Jd3IpZs3FIR6yM9PP6I5FIsTLQQUtNhaS0DMa0qM2Cg+cYtHwnawZ+3STxRxCVmAr65tRzdWZxt8YYaWvK5wO/AoWIkPbbzDF/Yf54x/lfmbeO8+EbZ6GnpvRfd0fOe4gRcl7ZlppZgShQsALw9/ErjNt6DKlMRseqruioq7HsxFXGNq9JOTtLWs7fSOPyJZjTsQE2JgZf3ZcxW46y69p9/JeNJyoxheKD5nB8Qi9quDh8dZ2fo+vfOzj/6BkPl4xGT0ONLn9vxzc0kgvTB2DUdVLece0ql2Vd/zY/NSxiYWQj50PiUtJoOnc9aZnZ3Jo3FLFEise4pfiHRyMUCJCRa6rwlj513Jnerj5ahVyx/1a5SKVSNNuPZXG3pvSpW+mLy38JDWauQVdDjW1DO35RubTMbBwHzsarrCNr+7flmu9L6k5fxehmNZjSxuu79jE+NZ1aU1YQHBPP6n5taOmeu8L+MjKWHst3cTcwlEblSlDZyYbzj55x/J5vXtktQzrQqlLpvL//LZvX8UnY9psJwKO/RmNvVnBUobf8c/wqo7ccYUST6sxs/31zNbyMjGXzJW+ev44lLC4R7xcheJVxpKSVGSWtTCluYUzbRVuQSCW8ik6gqJEeWWIJYokEdRUlgqLiWdarBT1qVcyr87p/EEPXH+BJSCQAqkqKRGyYzsC1+3geEculGQO/6xi+ha7L99Bu+ARKxz7CWPPLfXHk/Bjis6WYdxrzWcd5Od/GL2BALkfO/yeDG1TBf9l46pUtzrbL99h38yEVHayYf/ACrrYW7BzemWt+L+m4ZNs3tdOrdkUS0zKoMHox68/dAiA+LeN7DOGjzO3ckGyxhEk7cleNixjokpaVzcLDF/Mdt/PqfVIys35oX+R8O35hUZQYMg//sGiW926JglBIpyXbCAiPpr+XJ73ruNOzlhu9audO9OZ3bsS2y/coO3IhJ96b/P5ILj8NRAaIhL+mgzOAuooSgxtWYc91H4JjEvB0smFCy9osOHSRFxEx37UtPQ01Ls0YiJ2JAUuPXWb6ntPsvv4AGxMDzkzpx7S29XidkMTITYd5HhFDG493ATY2X7zz0Xr/OX4VhwGzURSJaFKhBCa6n1/VbeNZhqrOtqw5cwOp9PsuDNiYGDCtbT12DO/ElVmDWNG7JRnZOey4eo/OS7dTftRiAiNjCY1NwlhHEz0NNeqVdaRfXQ+aubkwtY0XzSq65Ktz/sHzpGVlc2nGQDJ2LyBm80yUFRWQychLsvsrcMbHnyPeT4B3ZrZyfhHku1k/hT/a3Ot3QSbOQSb+dV/MfyIyhKD8RjafWBW20FFn/4hOhMUl0emfnbyKjkdBJGTt6etMbFGLLfaW3A0M5bZfIBXsLb+qL7aGOpyd3JsVp26w4tQ1AK4+eU6L8j8uW72xhirT2tRh2KYj6Kmr8Ox1DCbamoxtXJV2lVww1FKnxcKtpGfloKWkgEz887IaF1Y2ct4xYdsxDDTVODe5D0baGlx8FMCJ+34AedfUW7RUlelf243GZR0ZtOEwLeZvpJW7Cws6NcRIW+OjbXyrXKLiE5HJZCgI+PHXk0yWm4DyK9rpU7MCq05dZ+CavRwe3ZWh9T3YcP42bRZu4vK0/qirfL9dcU0lEVaGuhy968vdF6EAeDpYYqarxbD6Hgyr70F6VjZX/IJovmAzAK425px/9JyifaZT1EiPMkXNKGtnjUFtWxKTU1h67DKNyzkxsUVNlBUViIhNIEQiISYplYjEFOJS0mjk6oSloW5ePwzVlRnWoDJN52/i8O2HNC1fosD+fg+6Vi1L16plAYhNSeP561i2X73PhoveyKRS7r0Mo6iRLos6NUBJQSHPof6tLGOSU7kfGEozt5JUsDFDJs5BCFx9+oLjd59Sr4zjT31efYyEtAwGrztAleK5pnkJyclkZmRioa/9mZJyfga/wjXyJyBXUuTI+QlY6GvTsmJJJu06zeB6niw4fJm2HqUZ07Q6vVftpdrUVcxuX4+hDSp/Vf3lbYuwcUAbzj16RuN5m2hU7scpKG/pXcuN0NhEFh65DMDCTg1RUVIkPiWDcmP+ZpBXJSa3qv3D+yHn25DJZPi8ek0HzzJ5SsbWK/cBmNXOiy7VyiGWSBFLpUgkUjRVlREKhVga6jKmaTXiUtI4dOcpJ+/7s7R7E9p6lP4h4VxbVyrF0Xu+LDxymS7Vyn33+r8XmqrKLO3ehFaLtnLg9mNaVHThwKjOuI37h02XvBng5fFd2xtcz4Ojd9/tZiWlZWD2XrS/XdcfMmTjYUx1NYlISGFYwyqkZWbzKiaBwMhYTvs8Y+MVH3bWboPD4HlkZWZy6M4TDt55UmB7IqGQv45d5dTEnti9Z6Za3dmWxuWc6PzPLgKWjsZU98ebwBhoqmNQTB0FBWGukgJ0rFKWbVfus+/W41zlRCZjcD1P5nbMNUObf/gSUpmMCc3fRVj0D4+m4dwNVCpmxeKujX94vz+HVCql+4o9pGRksqhLY14Ataev4VVkDNsGt6NB2eKfrUOOnP8H5ErK74BUUignLTk/kze2+RIJFHJVWENJkcwcMTcCXpEjkdBk3kYuTOrNvTmDmLznLBN3nkJVQUTvmhW+epJ34dFzzHS1qFG86A+/ZgTA1BY1cDI3RCyR0sGzNEgkbLtyD4lUypIT1xjdqAoqPz0s8ZfL5k9FKpUyfOtxIhKSqVXCFnF2NlP2nmPXdR8UhEKURUL0Cori9ebaOv/oGY9CIqhW3IbEtAx6rNzLzmsPWNatCZYGOv8q9G1yEQDtKpXiwO0nqHUYT/fq5ZjWsjb6mj8g0a1Mlvv5ynuovos9zSs402/tAYqbGlDSwpjS1mbsvOZDg9LFsH5vF+Jb8bC3xM2uCLff7KQ8DArHNyQCFSVFHgVHMOPABXpWL8+6i94oKYj469gVTo3rjrryux2dhMwcvIF/ujUmNDoWfQ01THW1UFdWQiQUoCAUYqilgYmOBimZ2dSfs4EOS7Zza+YAtly5z4WngQBkZucglki54feSFm4lv9sYP8fyE7m7faMbVaVbNVc6epQmKDqe8IRkZh64wMNX4aSmpSOVybj4+AU1nG0x0lDNk++9FyFkiyWs6tEMZQH/6fs2PjWdYVuOcebhMw6O6IS2Sm6okoxsMVk5YkKi4+XzgV8BqVwGPwO5kvIbIBPnIMuRm3v9SsgEb2Lzi3OQyQo34erkXhI9VWVG7sjNJBsUnUCT+Zs5O7Yr05pVIzMrm2FbjnEvMJQVXRt9lW20X1gUpS1NQCz+KXGtREC7Cm+SOEokyCQS+tcox8ZLdwF4ER6Fa1Hzn9CTd3yNbP5Upu4/z9oLd5jRsialzQ1pOHcj15+HMKd1bWYcuog4R4ws5+NmDeZaGkikUvYNaoOigohTD58xeOsJXMf9zbQWNelTvTzCNz4k30Mu9ZxtGNeoCnGp6ey+9Rjv5yFcGN8DNeXvGzJWJpOCTPrJsX+OlV0aYTxgLt1X7uPm1N7MblWTtsv2UHbs32zo1Ywmrt9vNfzM6M7MPnKFeceu0n3Vvny/DfOqRClLE9Zd9Kapa3GOPvDHZdQSPBwsqeFkQ9uKJdFQylVYWpUrjsJnZKOursLC9l40WrQNj0kr8AmJxM3WAtU3eVRqOttga6DzTefuS2nvXpID3k95/joaVaGAynYWFNXTxGnM3wD0quZKswVbuPkiFKlMxj+dG+TrX1RCMgCOwxfRuKwjOwe0/ml9f58TPgEM2nKcjOwc1vdsSu3i1ozdd5GafVzpWrk0sw6co5GL/U89t3Lk/JfIlRQ5cn4SAoGAhmWKUcXRmm5rDnDq0XMehUYy+8hlZreuzcL2XrgWNaP3+sOoKyuxqL3XF++opGRmfddV2n8jkUq5+TwU3/BoYlLSSEjLRENFCUNNdZqVK46ZrhZO5kYs6VifA95Pf2hf5HwbL6Pj+evUDSY3rc7weh5svebD1WfBTGpanfWX75GeLeZzl5+5nhYyGUQmpVJEXxuvUg7cdbBi8r7zjNxxin13nrC8SyMcPxMdqrAIBAImNq1GfGoGiemZnHsSiO2IxVQvXhSvUg50rFSK51GxTD1wkWsBwXT0KMXguu6YFiLb+fdGQ0UJN1sLFN4sNlR1LMqLRcPoue4Q/TcdpWrxouh8RS6TglAQiZjQpCobLt+njLUpM1rWxEBDDRlgqqNJi6U70VRRYkXXRkxsUo3l527z4NVrBmw+yiW/INb0bvFF7VUvXpS/OtRj1QVvBALwcrFjRH3P/8zpvHYJOzSVlVBSeBfm+eBdPyTS3KWaA96+RCSl4GZrQYsKzlS0K5KvfDPX4kilMvxex3Dkvh+X/YOo6vjxHFZZOWKWnLrBrluPmdi0GrWcbdH+BlkGxyYyaucpjvs8o25JO5Z1aZhnsrf79mNq9oHrz4KpXMwacz15JKlfAblPys9BrqT8BshyJMhE/30kibcOiHLeWxXOEX/xqrCmgogdvZvjMO4fYlPSKW6sjywnV75tyzmRnpHF4O0nKWakR+9qrl9Ud0xyGqWLmOTV9z057/uSoTtOERSbmO97awMdIhJTGLP7NN0rl8HR1ABjLQ2OD22faxP+A/ryKb5FNn8SJ+77I5HK6FfVFVmOmBsBwTibGbHtmg9Gmur0bFmW1q7FPyk/c63ciEOh0fFYvPm/poKIv9rWoYWrIwO3nsB92hrG1PdgUN3cxITfIpesHDFrLt9j3onriCVSulcujYaKMhf9gui/6Qirzt0mPTuHlMxsWpQrzqYrD1h9wZtOlUoxrE5FrD4wQfsIUhkyqeybr113GwuWnL3F1H3nmNioCmpCIX+1qUOJiSv4++R1JjWu+k31v48QGFSrApMPXuTM4xdoKCvhaGqAvbEe4fFJdPUojYpAgI2eFota5/qK7bvrS9d1h1BRVqH58DJfJJtelcvQtZILs49dZdrBS7yKTmB5pwbfbTxfSmePUmy+/pDZzWow/+R1Zh69goJQiFgq5eTDZ2TkiOlT1ZW+Vcp+IFcrHU2G13YjODaR6wHB1F+wFf/ZA7HQ0yJbLOH0kxdsvfGIoNgEsnMkhCUkI5HKKGFhROdV+wHY1LMpLQvpC7jvri9RSamceRrIy+gEQuOTMdJSZ0uvZjQr65jvuWn6xk/sxvNQ/mlb+6c/T+XI+S+RKylyCkXL5XsIjI7n/Ogu6KqpyJWVb0RJQcSxIe1JyczC/V+ret0rl+FhaCSTDl6gg3vJfLbjnyMhPRPVH5BAcY/3U7qvP5z3dzETfQIi4wDQVVPBycwQVSUFNl3zyQ2MJJNR38WeFZ0bYKDxA3wG5HwTwbGJrL18nzKWJmioKPEyJoFjD5/RpoIzJx+/oKKtBQNrVfhsPRZvVnvD3pjLvI+nvSU3J/Zg7vFrzD52lRNPXzFp0efrLAiZTMaBe35MOXSJ0PgkunqWZnzDyhhr5U7gxjeszJ47T5l97CqvYhOY3KQqw+tWYkqTaqy5dI/lF+6w6ZoPbSo4M8KrEg4m+p9tMyMnB583eTS+luauxUnPyWHhyRs8DY9mT//WGGtrUNnBikehUd9Ud0EMq1ORFq7F8YuIxe91TN6/IXFJHH7gz4zmNVB4z0esZTkn9nn7stf7Kc2/oj1FkYiRXpVYcPIGm68/5J8O9fPM+34mmTliHEz0Sc7IosFf27n6PASACjbm+EfEkvAmJHsJC6NP1mNloEOdErZsu/mIlMwsQuKSWHDyOhuv+aCiqECr8k7oqatirqtFdUdrHE0NuPoshPp/bedu0Gu8StgBoCASovKv57BMJuNWYBgbrj5g5+3coATO5oY0LeuIpb42bd1KoKnyof9XV4/SAJjraNK07JcmUJXzo5DlyH1SfgZyJeU3QCYWI/uPF09sDbS5EhCMb0gEXkt3UcXBksE1ylHH2eaPVFhkglyzAplYjEz2dQ8rZxPdN3V8uG08oGpZNl71YdnZW4z2ci90nQ1K2rHpug+Da7iip164xF9SqYxrL0K5/CwEn5BIpDIZWWIJWqrKrOlUHy1VZbZd9wFATUmR6U2q0N2zFM8i4zn++AWh8cmc8wsiLCEFNSVF9NVVGVyzPHNO3qDSzHVMaVSZGo7WmHwiPO335HvI5v+dhSev8yo2EUs9LUpNWsnL2AQ0lZXRUFJAKpXyKja+UOYMmopCNFWUCI9LLPB4FSFMbeRJs9L2DN9/CYBpBy8ytk551JQK50fyIjqBAdtPcSMwDK8Stuzt0wxH09yoUu+32aqsA63KOuA5dzPPImKRiXPQVhIxqk4F+lctzaYbj1h6zpsdtx/TtHQxRtZ1w8XCuMA2VRREhMYm4Tl7Q6H6WBhOPHrBi4hobA11UVEQkpyZ9UNMRix11LHUUaducau87/4+7820o1fJyspE9N55j0lJx+91DO42FsDX3TOKSCllYcTDsGhuPHuFh11uXU/DY7gRGEZ3z1I/zAxsy83H/HX2Ni9jEpG+STT6VkHZ0LUhZa1MKD1tHQBNStvjUdT0s+e8nnNRTj1+TvnpawFQeqPUGWioEpmYwor2dd8dLBHjaWNKz8qlWXb+Dssv3EEkECAUCnE2NaBXlTJkiyWcfBLIk/AYwhNTMNfRZGGrmhTR06KmozXK7ykzBfWtk5sT14A5zaujLJDJzYx+EWTyPCk/BbmSIqdQzG1Rg7ktahCTkgbAnaDXtFx1AIAdvZrSqJT9f9m9/zvsjfWwN9bjyrOQL1JSJtT3YPONR5x++vKdQ/snSMvKptbiHTwJj8FAQw1XKxPUlRRJy07j+KMXXHsRSv2Sdhwa0IqE9Mx8u2jO5oY4m+f6GmSLJdwMDOPmy3BWXLzHGd+XDK9dgcM+z+iz9SQA7rbmLGpVi5KfWc2U8+Pxj4hFLJUSmpCMAAGKIhEyZGy4/hCAIl8QPtZcR5PwxJRPHlOqiDHHBrfhBrDx+kMO333KsvZ1qezw8dxAUqmMtVcfMOnQZUy1NTgysBXVHa0/25/KDpYsu3CXNuWdqFYsd6KurqzEgOrl6OlZmu23n7Lk3G085m6hrrMNo+pWxM0mf3CHzBwxlvparOxU77PtFYaEtEwaL9vD6Scv6V/dlfJFzZh+9CpPwqMpYf7j74cjD59R09E6n2IokUppvGwPSRlZzGlejYivrFtRJOLk0Ha0XXOQpsv30sHNmSevY7j98jUAxc0M8PzXbvH34nJAMBnZYpa0rY2ZjiYmWuqUKmKczzRZRVGBzBwxvSqXKZSyVMfZBp/JPRmy6ww77zwlW5Jr/ja1UWV6bjnBuAMX0VJRonEpB5zNDREIBFR3tGL/fX8ys8WMq18JFUUFRu+7wLILd/GNiMXd1pxmZYvRoKQdlWwtvmi36W2fazkVBfmii5w/DLmS8hsgFYuR/iKLJ/oqyiT9NYyIxBQmHLnC/gfPmHLoMopA7eLWf8yuilSQu2onzREj/QEvjqsvQgmIjMPT1hzpF9ggG6ur4GppwuLTt6hTzArdzzhzXvIN4kl4DLt7NqGuU1EEAgFSqYymq/ajraJMCWP9vPZ1lBSRiSUFRg1TACrbmFPZxpwSJvoM3n2Os75BFDfR5+yQNrRcc4ibgeFUmruZXp6lWNC8OhKpDAWREKlUhlQmy2eG8i38aNn8PzDey5126w5Tq7g1m7s2LPCYwl535joahMYlf/Z40ZsdrjND2jB0xymaLN/L+aHtKFWA0hoSn8yAXWe48jyUXp6lmNawMurKioXq0/QGHqy98oC+W0/gM6F7PmdqRaCrmzMdyxVn/4MAFp+7Q63FO6hsV4SRtStQtogxgbGJJGdkoaOqjEshzMIKw8OwaABMNdWQ5ojp4+HCrttPab/2EPObVaeO08edtL+VsIQUbr98zer2dfPO34WAYJq9WWS6MLQddvraRPD194y6SMjenk2YfPQqV1+EYqihxsbO9em/8wwPgiKoZGX6PYcEgPerCM75BVHV3pIu7y3IvB3j2+dUl4olWHPVh9T0zEJdP4kZmcw9dQufkCiq2luypHUtlEQijLXUOFTSjoP3/UlMz+LM05ecH9qOI4+e03nTMeo727KoZY08H5KTj15w8VkIIqGALZ0bYqT1xuxVIvmi6LXy59mviVS+k/JTkCspvwPiHGTCnxFQtvCYqKuwvl0dLLU1+OvSfVqtPcTw6q5Mrlf4Vf/fGZkwd3VNJs5B9gPipRu/yU2hIhR+cbjJVa1rUmfFPgbvOsOWz6wEB8UkIBIKqGVnnhe2+OqLMC4/D2VX1waYa6h8cfv1ilkyoU4Fhh64hF9kHLWX7s73u39ELOVmb+JFbCK6asokpGcB4GCkS+1iVoytXQHNb8jK/aNl8/9AZWsThlQvy4Jz3kizs79pccFMU52nkbGfvU7eysVGR4N93RtSZcluqizaTjX7IqxrVwcDDVXCE1M58PA588/dQVtVmUO9mlDNPncVvrDXoRDwtDEnNDEFkURSoCO4CGhdyo6WJW059vQliy7cpcnK/eipqRCfngmAk7Hedwv1aq2dG1QgIzMLWU4OSsCmDnUZtO8CbdYdZlWbWrQuW+y7tAW5O0EXnoVy6PELTvkGoaGkiFcxy7zxLLtwFys9LaY3qERZM33Eb0yIvuWeUQbmNfLM991YFSVSMzK/a8jca4HhLDjvzeUXYZQ0M2Bx0yqfrH92g0qEJyTTdv0RvIpbs6mj1wf+IgCBMYlMPH4dn7Bo0rJzsNDRwPvVayLjk6hgZYJAImFbJy8SM7LovPUkV16EceLhM3ptO0UzF3vWtauDUCggKSWNXjvPcPFZrtmZRCpjytHLrGhd66vGK3+e/ar8GQuy/zVyJUXON2H6np/BwUcv/hgl5Udjb6TL+DpuzDl7m+TMbMbULo9FIcOo2hvpMsXLneEHL3PrVQQVrT9cxRRLpFwNDGP5FR9qOljmM4N4Fp0AgMmbaE1fQ41ilkzyqoilriYaykrEpWWgKBJhpadFcWM9Gq4+CICCUIhAAH0qufA6OY3lV31YftXnk5MJOd8HRaEQNSUFpFIZItHXv3DNdTQ44//qi8qoKSlyfXg7ll95wPRTt+iw+ThaKkqcDQhBQSiknasjMxt6oF1QIslC8DIuCa/i1p81qxEKBTQuaUujEjZceBbCiIOXcbMyYVxdN+wKGwmsEGipKFHcWI+Lz0No80YZsTfS5XjfZvTbc54h+y/i5VQUra9QznMkEsISU3kVl8TLuCRuBL3mrH8wyZnZOBrr0c+zFG3KFss7l8+jE7j4PJRGJWxoUtLuu43xY7z1Ffke7Pd5Rs+dZ3AxM2RTRy8albD5rAmXSChkbbs61Fm+j1N+r4hITqOovvYHx806c5v7oVE0cLZhSLWyvIxNpPn6I9RdsZ8n47vkPX9nnLrJlRdh1C5mxZUXYWirKrOyTa28a23OmdtcDwzn75Y10FRWpNv202h8QfATOb8Jct+gn4J8BvAbIM2W/LJ5sytbmaKnpoJHUTNmerkjzf4ztkClb3a2pNlipD9odWu4Z2k0FRVYePEe+32esa2DF1VtzQu16t3WxZ5t3n54rdhPUT0t+lVyQU9dhbuhUTyLScQ7JIqUrGzKFTFmbn2PPLmNOXqNtW8izzjp63yVPGPTMlh28T6p2TkgkdK0hC1F39ikjz56lVP+wRhq5Dr1x6TmRt1xNtLFXEsdqUTKMd8gTvm9ovnawyxpWpWFF++hIBJiqK5Kf49SeWU/xs+Qzf8DxfR1yMiWEB6XVGgFuCBM1VWJTk0nMz0rn2nVv/m3XBSBfhVLcj0wnISMLM4GhDCxVgV6uDnnTai/5vpLzswmKC6JksZ6X1S+elFz7A10EAAlDHS/uv2PUc/RivW3n3L35WvKvjFxEwATa5Rjz/0A9t31p2uFwoWwjUhOY9m1hxz3DSIsKTVPERAKBCiJhHSv4EwHV0eKG+vllZFmi9l5P4Ahhy4jlcmw0dXKG9+PumcsdTQJiIz7LufxpN8r+uw6R+tSDixvXj1XKRBLkRbi7agMHOzaEI+/9zDm0GV2dqyX7zm6474/Bx4+Z04DD/q4lwSgiIYao6uXY8X1RxirKCPNFuMbGYejgQ7FjHQ5GxDM68QUEtMzeRERl3euUzKy0FdXQSSVsej8XewNdZhVt+JXn4O3shmy9wKmqkpoqyrRz93lP4miJkfOz0aupMj5JooZ6fJsbBf5A/MHIBQK6ONeknZlitFh20mabzqGla4mpc0N6VvJBTdLk4+WVVIQcbBbQy48D+Xwk5eMPnYNABt9LewMdBhcuRTu1qY8jYjj8JNAZDIZex4+x//NLkojZ5uv9hHZfs+fNbee5P094+wdTvVuSgVLE26HRBKWlEpYUmq+MoMOXs77v4aSIqnZOVjparLb5xl7Hj7P+y1HKmVmvUpf1S85+bHQ0SBLIuF1cto3KSnm2hq5CR1T0rD8Aod7AGUFEXu7NOBBeDQ1Vx6gTjHLr949ecvTN6GxXd5EAPsSREJBXgLA7013N2fOPQuhzuoD/NWkKp3K5Wact9DRpFUpe2acvU0thyJY6Gh+MifV85hEaq7cj1AooF2ZYjga6WKtp4W1rhaaKoqUWbQToVCQT0EJjE1kk7cfux4E4FnUjB0/aZeyopUJ+x69+OZ6Nnn7MvroNeoVt+afZtW+6n2jp6bCX02r0H7bKS4HhlPtTRQygN0PngHQ0TV/iN+wxFQ0lBSot/YwUpmM+298i0w01ZhUuwIzzt7BRFONnnvOca5vc1QVFajtYMkJv1cMOHARJ2M9VrSo/k3RzV68yUv16HUs20Nzwxu0K1MMve+UCFTO1yHNli+A/QzkSspvgDRHjFTwq+6l5PKnLVjnrTzmiJFKf+zukYZIwL6OXlwNes3pZyFcevmaxuuO0LOCE4M8XDD6yM6CqgAaOBShvr0FXco6UERHA8v3JqPjT95kzR1fALSUFEAoxFpXk1cJKaxqWgXpV9qRdy3rQGh8Mjt9npEtkaKqqIA4JwdpTg6HOtdj6/1neUkAU7KyGX7sOiaaaixvWoVihroYa6jyNCoeRyNdHkXE4R8Zh5KCCDVFBfpWcPpsv36mbH5n4t9E6tNWFH21rAHM1HMnS6FxSVh8YpfrU3J564SaGyTk28wowuKTALDQVP3iuoRAtkTyzX34NzKZjHN+r9BWVkIqg4knb9DY0RLNN2ZA02uX50bQa6os28vceu6MOHYdB0MdBAIBIoEAF1N99NVUKKKjwca7/hhrqnGmZyO0C8ir0aN8cdbefsqQSiVRURCRkSOm3ppDpGbnUMJEj6m1yqGELN8Yf9Q9U97cgH+uPSQsLhGzrzAfjU7NYMyJGxz1e0WP8sWZ5VURoVTy1bs9tW3M0FdT4frLMKpYvQs/PaJKaa4GvWbbnaf0cnvnhK8iEhCRko6uqjImmmq0K21PxzLFGHjoMjvuB9DaxY5jfq+IT8tk6skbzKnnTgOHItQa1pbEzCwM1FQQCYXfdD2d9g2iTBU41rU+a677MOvCPWKSUtBR/PiupZwfjzTn156T/b8gV1J+A6QSCVLxr+U4/6cjfbOSJxV//QvzSxAB1axNqWZtSqZYzMpbT1l64zHr7vhSr5glnlYm1LYvkjf5/zfub8xLpOJ3fd32ZvVwXfNqVC1qiv2inZQx1SczJ9cU5/1jvwR1kYi5dd2YWtMVABUFhby2NRUU6P8vk5bqRc0QCQSYaOZGv5FJpDgZ6IBURmljPTa2qJ7v+M/162fL5nflTEAIqgoiBFIpe3yeYaCmStWipl/sRG/yZkU3PDEF6ZuQ1AXxKbkkvwmeIJVIv/q6e0tqZjYAKgLBF9clAsTSb+/Dvznw9CVDj17D08qE2XXcmHrem/W3nzK4kgsA+spKHO1cj/LL9zP1rDdpOWJMNNTQV1MmMTObG68ieBmfTJZEigA43rU+mgoKBfazWxkHlt94TIcdZ3gRl0R8Ru653dqqJnUdcs0u/13uR90zZYxzo6N5h0TSqBDho/9N622neBmfzJpmVWnqVBSksm/qn0wmQ1VBRGpmNlKxBJlMxtYHz1h/14/2peyYcPo2trpaVLMxA6BfBSdq21pQ3cYs330xtWZ5uuy7wNaWNbgVEolMBmvv+FLB3JAmTkVRBAxVlL+5vwBRyekAKCKgV7nizL14n1P+wdhW/PrdTznfA7n1yM9ArqTIkfOboaKgwDDPUnRzdWT34xfsfhTIcf9gJpy5Q6uSttRzsKRyUVNUFEScehbCmedhCAXg5WBJLTsLhAIBkSnpdClbjJW3n9LzwCUu9GwMwM3gKCpaGROVko6hhirBiSm8jEtGSUGEsYYq9vrahZ7EvlVOPsfHFCs5P5YLgWFkiiVUXHkw77sKFkYMrlSSOvaFz2uhoayIupIC115F0NzZ5ov7kSkWM/rkTRwMtLHT/zJzsX9z7kUYsy/do5iBDsKviFj2o8y9bodGU8xAhwMdvQC4Gx7NId9XeUoKgIW2BmOrlmbWpQfUL2bJppY18tUhk8k4ERCCs7Ee1roFT1ClMhld9l0gRyolJDGFJk5FcStihL2+NiW/UzjlL8FYUw0zTTXuh8cWWkl5nZzG9eBItvs840lUPHPquOUqKN+BwPhkwpLTqFDEiLCkVMadvs3p56Eoi4S4mOjjYWXCyJM3uNO/BUKBAEsdzXy7z28xfbOg4h0ezaqmVWiy5RRG6qr0OniZK68imFu3IorfIaT6zZBINj8IwItc2W64649EJqO8PNfUf45UIl8A+xnIlZTfAKlYjJQfk7FXztchfbPTLhWLkUr+G5MiLQURvcoUo1eZYqRm5bDZ5xkbHzxjx8PnaCgpoqmkSERqOk6GOmRLpGzzeU55c0PWNa5MzyNXeRQZh5JIiLmmOsI3D9xsqZQrQRGU/HsPyiIhWZL8W9puFoYsrFsR+wKi4/wq/Aqy+dVJyxYTm5ZBpSLGdChlR7WiZtx/Hcs/t5/Qac95trSohruFMVdDIilprIeSUMiV4AjCktN4G6xJhgyZLDcfRZZYQkpm9ifzULwvlxcx8bxOSScpK5u74TEExidzpH0dlGSFz8/yb7b6PGfUmdvUtDHj7/qVvqie+Iwshp64wd3XsTga6Hx1Hz7GtaDXlDc3zKu3np0FB54G8fe1hwyo4JSn+Jtr5E5+p1YtW2Af6tnmJp0s6DexVMpKbz98IuJY16QKDYvlT5RZWNl873umUhFjDj59yVA3pzzzto8RmpRKlfVH+R97Zx0exfE+8M+eS5K7uLtBcIpTHNpSSinUqdu3lLq7/Oru1N2dKqWleHG3ACHu7jnf298fFwKUBGKEBPbzPH1K7vZmZvedmZ135hWrS2RUZDAfzBjLmUmRnSaPmgYrKoXA/35ajlIh4KfX8tE5Y9lVWsX7m/Zw2YBEthWV47A70ChbNqfqF2Diwr5x3LlgNd9eMIkvz5vAFfOXMS4mlK+37cNbreKR8YM73N78ygNJUv/ak8PjSzZy+YBEBgf5dnoflWkr8pqsK5CVFBmZEwAvrZobh/dh7rAUMqvqmL87G4vTxZmJkQxpNMFZnVvC3N//5fTP/qS4wcob00ZxfuPOt93lonegmd1l1UyJD+fclFjKLTb89FpSgnzJqaojq7qON9btYsyHv/HZrPGcdpDjqUzPod7u5L5F66l3uHjpjBFNu/KT48OZGBfG1fOXM+fXfzktIYL5u7ObjBokQKUQMOu0CILH2EFAQBAg2MvA7P6Hh7OVJImlWUV8tzODerfATcPP4Z6/1/HVpt1N1xjVKsZGh3QocaIkSby7cTfTkqJ4f8aYNp+iPLlsM2vzSxkfE8b42M5NPJhTXce+ylruHzuQdfmlFNY2MDk+nBuHpfDk8i2syi3hlTNGEOJtYEmWJ0u7UdO6V7PF6WJ5dhH/ZBTwT0YBJQ1WxkaHMKVRmekO3DtmAOM/+p3Hl23mhdNHHPHan1Kzcbkl1lw3g9gWTos6wqDQADbNmcmijALqHU4u6Z+At1aDKEnU2p3MW5/K9UN6HVFBARAEgZfOGMHSrEKWZBby8PjBXNAnnmXZhTw4dhD/t2wzF/SNo1cHw1gHGg84x28uLCPcx8jzpw/vUJkynYOczLFrkJWUHoDb4UJ0y1p7d6Jp59HhQuxmu/UxXgZuH3rA70NsDH05PMSfn86fxDnf/oNSENALiqbv3t+4m91l1dw8NIX7Rvc/pLyMqlquOCj6FsCWgjImHSG62PGkO8umO/Dq6u38kJrFxX3iiDTqm/rAft44fQTnfr+YNbklnJUYycTGPDuRPkaGhwceMVLRwWU1OJzMnr+cjUXl9A4wodV5Flyrc0q4a2RfZiZH46PVYNZpPEqFWzqsLa1lb0UN6ZW1PDh6AJJTpC2GGAW1DXy1I4PHxg7iusYcJu1tR3NsLSgDYFlmAcuyi8mtbUCtUKAUBKJ8jKzLK2HsR7/xOHfscAAA3BFJREFU0fQxXNE3gR92ZbEqq4hpRzG521VWxXW//0tOTQPxvt7MSI7i3F4x9A3ybfOzPJZjJkyv46ExA7l/yUZmp8TR/6CoY/9FFEWMahWRBl2nyuBgAjQaLu59wHxMdLiI9zmQ7yvO5NVs3S63m0WZheytqCG/tgG9Wkl5g406q51xH/6GSiFg0mi4rG8cz6zcyoqMQhIPKrc9DAnyI9joCUbx0ZY03A47n2/ei0IQODspCoOcR+q44XbJjvNdgdzDZWROIqJNXiyYfRrX/vYv1/3+LymBZpSCwPbSKsxaDW9sSOXzHek8PWEITrfIK2t3kd0YLvj+0f3pFWDiil9WEiCHv+yxXDcomWXZxfybV8Ky7CJW5BZT0mBlWFggBrWK81NiuWlIb/63YDV3j+xHgl/b/UTW5pdy819rqbTY+XTGWCbFhOJWqdkMDA0L4LZhfQ7xbXK53Xy+PZ09FTXU2BxMiAnlrMRIjBp1q+rLqqpFo1Dwd2YBp7XxFGFJtiesa5DxyPl32otSEFAIMH9PDvUOF/eN6o9DFLG4RCqtdn5Ly8XmErn05+V8O2sC8b7e/Jme36ySUm1z8Nzq7SzKLKDCaifRz4cll00luRubXwJc0jeO51Zv5+/MgiMqKYl+PlTbHZRb7IecIhxrEv18CNBrCTDoeGTZZgYE+9Ev6NB2PrFyKx9sScNPryXSx0hJvQWlQsHnOzKI8DFidbkI9zKgV6kYFOLPmvxSrhmU1KF2aVVK3jlzFLbGv+2im7v/2eBpz4qt3DCkFzcNbV1uHRmZnoispPQA3E43bknW2rsTbndjNByniNDDduuDtRp+mDmOH/fksL20ik3FlQA0OBzEmb0orLNyy8LV7A8oNy4qmBcnDiHEqOeznRmoFALnJ0V1W5voniybrsBPreKBkX2Z/etKLvn5wAnZz3tzARgbHsiI0ADUCoG3NqTywoRTWh0sYd6mPby1eS9Wl4s+AWa+OXsssWYvJJeIW/KcwPy+L49h/t5cdJAz9Htb9vLU6h30CTCjVSq4c9F6nl21nesGJPJvQSmnhgdxYUosJQ1W5m3aQ4nFRoXFzuAQP+yimz/S8/DWavguNRN/nYZ7R/Rt9fMoaFTCJ0UGHZM+vaO4EqNaxY5rzsYuug/b/b5raAqzflpKUb2VS39eRr9AXzYWljW1ZUl2Ea9v2k2ZxU5BnQW1UsG5SVEk+flwcUoserWqw+0+1mNGAE4J9mNLUfkR2xp+UKQ4/1aavHUWk2NC+SuzAJNWw6db0liZX0qD08UNg5IZGurPB1vSeOzUAVwzILHpN9tLq/huTzbXDkjkt7Q8XtmQiuhwMizEn69TszqlP63LK2VA4791KiW2xshs1XYHz6zazv/6J6DqQB4WmfYhR7fvGmQlpQcgupyIso7SrRAbR47odIKrc3MqdAUq4MLESC5MjESSJNYUlrO+uIIFWYXYGp3oBeDHs8cwuHHnU3Q4sdmdKBBQiSKiu+Od0i1J7KmsJcbH2GmmCz1dNl3BwfbUvlo1AQYd9Q4nAwN9+XpHBoOCfTk3MZJvdmdzRe8YUvxNSJKEXXSjayGrvFuSmLd5DzE+Rs6MDeeSlBh8NGpEh0cG++XSP9DM3Us3sSS7kGv7JdAvwMx3qdlMigrmw0afhbw6Czct3sAza3fQP9CXF9bt4vl1OxEliUhvI7EmI0OC/VieV0KN3cl1/ROYt3UfakHg97Qc7mo022oN3+3OBkDtdje1tbOwulx8k5rF9LgIcIlo4bA6gjQqfj9nHGf+tJRyi43VjeZhDy/bxPriSlIraugXYGJabBiR3gYmR4cQtP8kU5I6pc1dMWb6+Jv4IjULu83e4qK6zuI5M3A4HJ0ui6Nxz5BefLs7mygfDZlVdeTXeUL/fpOaxZ5yT5LbU0P8D2lXH7MX/7dfIZbcGDQehXF/eIDOuIe+fgd8c04J8mVwsB9vNIaPB7hn8QZGhQVydnwESjmpctehaN0pr0zHkJUUGZmTHEEQGBUeyKjwQG47pRdlFhtDv/wLAbD8xzlwX1UdKoWAKEntCvF6MFaXiwdWbmN+ej6xPkZeGj+YD3Zk8OjIvk122DLHhj7+5qZ/qxQKBECrVLIsv5Rl+aVYG3dr/XUaLl2wmqv7xrEop5id5TW8OG4QM/9jiiRJEo+u3k69w8Xjo/szKKhlk577h/Xhd5OBL1IzWZFfRrKvN/uq63jq1AFN10R6G/jlnHFNmdfLrXb+zCpErVBwTkJEk6LkliTckoRKoaCgzsri3GKy66ysLSpnRCszzif5elNisbE8v5QJkcFH/0EzZNfU8+jqHeyqqOHGgYlc1TeeOoeT2X+sotbh5Kq+Rw6ha9ZpmJEQydd7snHYPQvbZXmlxJiMnBHTi7kDE4+4W74wq5Dt5dVc2y8ev2YSPHYHzogJ5Y3Ne/l2by6X9I5p9povdmcT42Okf4Bv1zYO0CmVSEC8yYvVReUAnBkbRn6dhYWNAQ2W5BaT8B+HfqvLxfd7c/kiNZuoxtDElXYHvkeJZNZaBgb5sqPx36FGPVf3jeeWwcm8ty2dN7bs5fu0PL5Py8MtSZybFHXEsmQ6D1HeAOsSZCWlB+B2uBDl09xuhdhoHiE6XHCCRfnwU6nYMft0rl+6kbn/bOD1sYMYHhKARqlgW2kVQ4L9ULjciLT9JKXO4WRTaRUWl8hzm3aTX28FoMRi4+I/VmEX3ZQ12JgQGcRVvWPRtCPXwIksm87CR6FgekwYv2UXMiMunFhvA+/tysTqEnnx1AFEehtwiG7e35lBidXOixv30M/fxIAAMw/+u41ePl4kHbRYW5BdxOep2Tw9sh/9zT7NOh7vl0uSt4HyBhs6pYpEsxd5dRa81Uqi9doWnaV9lUpmJzQqRv9xChcAETdPDO/D75n5KICdJVUMPUgROxIfTxpK8ud/8k9mIWOD2xdhbEFGAcvzSwFILatGdLh4c9Ne9lXV8f2Zo4gzGo7oCF5utfPhjgxGh/qztPEk5Z9zxh0wsztovNlFkXXFlewor6bYYmNtcQWZtQ0AJHgbmRHX9sheXTFmkhvb9uKGVCaEBhDcjF/b9tIqJkcGg6ttwQ86A0F046tVY3G6sLlEUny9eWPsIAAqbQ6uW7yeX9LzubpXTJNctpVVc83i9dQ4nEyNDuWWAYlU1VvZWFSBn1bdYef/ZfmlDArz5ER5fHhf7l62gR/25dHHz4dbByZxRe8Y3t2ZCcBHOzI4JyasQ/XJtIFW5gGT6RjyU5aRkTkMg1rFvPGnMOK7f7jynw3olAoC9FpKLXYivdp+ylFjd3Lvqm0syitp+izZ1xuFJ6gTloMyYNc7XbyyZS/L80v5YNJQHG43FTYHEV56tEolDtHN3uo66h1ObKKbUosNpSCwu6qW06JCGBLRPaOOdTeeHd0fnUrB13tzsIluYn2MPDWyH2fHhjeZjYwKDUCSpKad4T1VtUz/7V/e2pHOq40LOIDfswoJMei4sHEnd1VhOW/tSMcuijhEN0pBoFSUeGf6lVy1aD3r8ou5ZUAic/rFsyC7iLv+3Ya9A+aDFqeLaxdvaDwVEnC2oaxfswrx1qhYVVSO1eVC347FR/+DHNf1KhW5dRY+Ts3i6pRYUvyO7tQuIeFwu1laUIYCcAPf7Mvj4qQocusspFbWUNRgY3VROSsKynBJEipBwKxVc3p0CBcmRfHMxt0E6rvnKcp+Hhjam5WFZby5bR9PjOx32PeeMNfHZ0dOo1TQy9eHDSUeH71LkqObvvPTabhrcC8u/Xsd3+7L45y4cP7KLea9nRlU2Z3M7ZfAHYOSWFZQxnVLNlJtdzBv/Ckdas/G0kquWbyBG09JYcq5MCM+nP9btYV6p4tdlbX8b8nGQ67fVVmLXRTRHiV8skzn4D5G0edkDkVWUnoALrsLl3yS0q0QRc8izmV3IZ2gu/UGBK7pFcNbuzIZFuSHyy1RWG9laUEpWRW1RDYmnmsNj63ZwbriCu4ckEi8jxcaQeCGf7cwKtiff4srAPjptJGk+HojCAILcou5ffU2+n31V1MZcT5Gbu4bz/cZBawuqWi2npJ6GwMDPWY+J7JsOgMV8OSQPlyTFMP1KzajVyg4NyoUySniAqrsDipsDkIMOkxqFW6HiI/gWQCtKSzj293Z/JFTzKqSCiKNOkL0WmobbBhUKv7JKWJXRQ1TIoJQKxRYXCL6xpCd2ytqmB4dwo2948AlEW/09KPU0mqC1e0zkfliTzaby6p4e8wgnt6yl58z8rkyIeqoJonzswq4b91ORgX7s7qkghc37uG+gclsr6zhs7RcnKKbF0b2O2zhl1tnYWFeMdOjQ1EqFNyyfAspvt5Eehn4Li2X37MK8dNqOC8mDJf96H3QV6Hi0VN681V6HsUWK2NDAnhs7U7e3LqPYqvHT0OrVJDi68M9A5MZGGDimc17ifUx8Mig3iwu8JzieAmKVtX3X7pqPjMJSs6Pi+DLfbnc2z/pEP+mP3OLya2zIInudt1DR3FLEmuLK5AAtULg9LCgQ9ox1M/MrNgwHlyzg8fW7sQpSfT19WGQv5kkbwNXL1rPiqJyhgX58uHYU4jyNrT7PlxuN3cs3wJApcXu+czuIkivpb4FZ/zb+iWgdEm45DmvS5AjEHcNspIiIyPTIiOD/XlrVyYrGm20/552Ktct38RF/6zji4lDiT0oD0BadR07K2tJMBnp42s6xIlzTUkFF8RH8L/enuSR32fk43JLeB3kLG9UK5vMKKZGBqM5dSD1ThGtUoFBpeTl7fu4ffV2tEoF1/WO5fy4cF7fkc7vucVNZQToOscO/GQi1sfIbf0TuX31Nq5bsZnXRw3gjZ0ZfL4vB6dbIkCn4afTRxKs1xFi0DEq2I+sOgsPrN+FslFeVQ4nhRY7MxauRiEI1DlduCWJp4f1bZJpjSRQCIQb9fyTX0ZWbQOxPkaWNC6w43yM7Wq/JEn8nF3AQH8z1y7f3PT5lvJqTgls2beh2GLjqc17OCcmjGeH9+Xd1Cxe2bGPWTHh3PzvViSg1GpHXC0xMtgPb42aCWGB5NRZuHrZRmqdLt7bnUWoQYdCgA/HDUGnUnD+32tJr23g0wlD2qTIz06MIs7HyBVLN5Jk9qafv5lKu4MB/mYGB5jx1aoPjbJ20D8X5BaTbPait7nzEyB2NufEhPFOaiYri8uZEuHxAcqua+DutdtJ8DGyvaKGggYr4V3sl6YQBDQKBXa3m4nhQXg3E/76yaF9mR4dxtLCUj5Ly2VUiD/Xp8Rx7bJNZNTWM+/UQUwKD2x1NLzmcEsSd6zeTkFjEIGLEyKbDGsfGdybRzemklPvceqP8tLjEN3YRDdmrRp3J/gKysh0J2QlpQcgHgf7XJkjIzaGUxWdIjhPXOkMNvnw4IBkntq2F4BZf61hUlgQv+QWcfafq7m3fxID/c18mp7DwvwSHG5P3OIQvZYnB6cw0N/M3et3UGZzNNrle57Vz1kFSIDuoBeq0yE2fQ8wIehQx+dTJw6nyu5AEATMjQuIJwenMDzAl5XFFSwtKiOntsEjE0582XQmp4cE8vbIgdyxfgeXLF5PanUd58WEMTUihDvX7+DdnZk8OMATMau/2cSmsioWn3Eqvlo1kuTZeV5WVM6rqen0NnmTVd9AanU9BTUNhBo8i0292iOzccEBfFReyYLsQoYF+vFeahYzokIJ12gPkX9r+aewlL3V9UwJCwRgWmQI0yKC6evtdUh5Trebn7ILqXO6sLhEvs3KR69Ucm/fRNxON0GNfWpPRS0lVjs/TRxOdr2FuzfsZGlhGSF6Lfes3YG3WkWgVsMHpw7m+6wCiq02Hh3YC5NCCW4I1emoc7oYYPJp8/0M9TVzXVIMr+5I5/KEKM6NCSOhcSPA7TywddvgdFFYbyXJ24jT7mJtcQXnRIcdck1b6Mr5LEqnI9nkxZ/ZRUwMCvCE+V2xBadbItnHiz/ySzj3rzV8NX4o0W1Q8jpKdr2lyeRwS1k1n+3O5pL4w3PVDPczk+xl5Mt9eZRb7Jz31xrKbHY+PHUw/fxM7ZYBgMPt5rLlG9lRVQvAotNHE2LQk4FHNgPNPjgaQ30KwIejB1NmszN7+UYe27ibdcWVvDTscDM6mc5HfrN0DbKS0gOQXBLudjgpyxw7pEazF8nlxu06saeri6PDuCAqlCq7g8v+3cIfeZ6TC1GSeKJReQnWaTGp1ZTZHagEAR+1ihvXbMOsVlHnEnlqYC+mhgY2PatePl5sLK/GoFSS4G2kt8mLGL3uqM/S1Gh2s/86FTAzIoSZESF8kp7Li6kZPLFlN7MvPjlk05mMCfTlsQHJ3Ls5ldNCA3mkXxIKQWCov5kfswvwViq5KiGS4f4m3tkrsbmskqnhjdGw3BITg/2Z2Oh4Xmixcdo/a9hTVUuwxnO6tX/MFFmsDPYzsbGsil2VtUR76XmkXyKS6EZqY5vrnC4+Tcsh0dvIUwN7UW138kdeMX1NXowN9MPtPiD/LzPyeH5XOgB6pZKzI4O5NjEab4UCt0tEJygwKhQ8sGkXEQYdiUY9SV4GBk4ZiVahwOmW2FhRjUKA0UF+GFUqHu1/IFmf2yViF0VMGhUlVjsK0Y3b3dY7gpuTY1ABX2bl82l6LikmL6ZHhDA1PJgAnQa3JPHgxl3UOV1cERvB8sJSyu0OJgT5tbu/d/V8dnpoIG+nZXN2YSk/5BSRWddAko+RHZU1TAkNZHdNHQ9t3MUnowd16FSiLawp9pwWPzYgmT8LSnhuexoTg/wJbsbPR+2WMKlV/JRTSLKPF9+MHUKMl6HDz+78pevZV+cJgvDT+KGEajVNmc0llxuVW+Lt4f24ds02DCola0sqeHjrHgC8VSr+KSzF4XDKeVO6ghZCsct0LrKSIiMjc1SUgkCATstVCZE8vt0To18EErwMjAry45usAhySxFvD+zN33XbSGqMN+eo0vD6gF33+Y4ZyX99EbKLI/Nwi1p45tslsqCNMCg3kl7xi/sgrYTbwRWYel7czpOzJyuTQAARgsL+5yWzELUm43BLvpGVTbrdTZvPYyIfqW84IHqDVoBIEsustjPuPCJYVlzPUx4AEZDdYGB7g225n3+d3pbO3tp6Xh/TFoFLxwaiB3Lp+B19k5jMzMpRKhxOjSsmCghJeSs2gt8mLD0YOxNSMKc/k0ADMI/ozZ+126pwu1pZXMTLQj6CDQvqeER50xPZcu2YbWyprGBHg226zG4UgcGOvWK5LjGZFaQW/5RXzUmoGL6ZmMNTfTLXDyZ7ael4b2pcoLwNP7Eijr9mb/r4+7arveHBlfBTbqmq5fu12AJ4Y2Iuxwf6M+2sVN/WO47zoUK5fu53VZVWMPkI4685kvwnVq7sz+X7sEM5eup7f8ou5NjH6sGv1KiVPDurN1soa/pcUja6TnNX3Kyj/nnFq02nxwVTYHTy0dQ/ldgfY4eGte0jwNvLeyAFk11u4evVWchqsxHu3z3RSpvVIrrZvQMi0HVlJ6QG47C5c8klKt8Lt9uxUuewuFN008/qxYFZoMEN8fPgyp4AIg56X9maS3vhyD9NrGWU2cVl0OJ/nFAAwzNdEsl7frANpiEaDVXRTUmvBqPLkKPDqQFhHh93FvroGJkWEAvDGniwkq41LotsekvVkRQlMCvZn3p4s+hgNVDqcLC4u59LoMBpcIj/kFAEQb9TT12hs0TF4fl4RoiQxwmxqumb/mAnWaXGJbjQKBUpJQHS1zVHa5ZYQBKhxOvmroJRLY8IZZvJpKmOsvy+bKqqZ9PdqGg5KTHpOeDAPpCSgloQW6xvk7c2CsUO5YdNO7tiwi1/GnIKfpnV+Tk63my2VNQCEaDU4bM4O+QcogPF+voz386XG4eTvknK+zikgs8HKwykJjPPzZVtpFWvKqni2f3K7TOX209XzmQJ4sX8vntudQYhey7SgAGyNJwZOh8gwfz/8NWoWF5Qy3NQ1ytdYf18+y8yn2uHkro276O1jZE1pJVdEhjV7mjPKbGKU2QSd6Ky+Ycpo1AoFSBw2blx2Fx/tzWFndR1KAcTGNfLz/XthdAvsq6oDILO6nmhN947ydiLgQj6t6gpkJUVGRqZNRBn13J+SAMDYQF9m/LsJgEsblYHLYyPYXFVDodXG5TEtKwinBvjxSVY+py1f3/TZILMPrw/ug3cbs8+7JYk/izwO2Pt3vicHB/DCnkySvY0M8TO3qbyTmUf7JDF3004uX7cNX42aXt5G7kyOY0t1LRsqPXIdF3TkfCKLSsoZFeBLvNfhO7peKhUutwu9IGBzu9G2MheOJEn8XVLOozvS8NWoiTTosIgiF0WGHla3TqlglL8vJo2aOC8DYwP88Gtlcj1/rYYn+iYxZ9NOZq/ZyvtD+xFpOLoTt1qhYMXEEfxZVMYzuzOINuq5MiaiU8yVTBo1KkEgs8HK1bERnNt4z+9n5hFt0DMlJLDDdXQ1aoWCh/okNv1tUCkxqVUUWm0IgkA/kzeZjRsgXcEQPzObTjuVHdV1XLl+G318vNhcVcvtW1KZFRHC2KP0+Y7y8t5MNlbWcEtSDCP8mw/4sLzME9Uw2dsLh9tNer2F7AYLr6dlsazMEzrZKpu4ypxAyEpKD8DlFHF1IIeATOfjljwvApdDRNGBHcyeTrhKy9qxI1EIHjMVl13EFyVP9Epk1votrCquZFpI8yYyCVo9nwzuz7rKakxqFU63xEvpWcxes4XRfr4IAtS7XFwRGU6M8cgOtHvrGngnI5dgrYbpQZ76zgoKZHtJOXM27uSh5HgmB/pT7xLJtVrxUqlIMBrkSDjNoEfg/QF9mF9UwvvZ+ZTa7OytrKO/lxdv9OvNzPVb2FBRTb3F0ayZi1uSPAu9qHBcdvGgzz3/Tq9vIFGtZHdNHQ63xPKSCnLqrKgUAtdFR5DQjGID8H970vmtuJQx/r5YRJGcBisPJcfjg7KpnhXllaypqObR5ASmhx7a7w5uy9GI1+r5YnB/bti2i/u37uGTU/q36ncGFJwbHMze6npeS8vGCyUzwzpucriotJzHU/dxflgIN0RF4rKLbKyqYVlpBU/0TkRyuOnIXn53mc/6eXvzW0EJehRsqaplSpB/m+TWGfQ1GJkU4MeS8koUwO7aem7ZksqNsVFcERV+TOaMf0rL+Sy7ALNaxU2bdvHz8MGENG62HCyb+xLiyLZYmRUWzMbqGuZuS8XtkprMxACG+fh0+TM7GXEpZHOvrkBWUnoAos0lKyndjKYjeJsLhZzUCXfjf/sJElQoBYGsOgsuc8vPJ0KhJiLgwC6wb6KC/0vPZF1lNU7JTb7NjlYSuCs25oj1Ly/xOL0+lZBAjEpDKdBPZ+DLfn24e+8+HtuTzmN70g/5jUGhYJTZxAR/f8b5mmVn0/8w0z+Q0d4mbt+9l0s3befq8DAuCwvl3OAgfi4t48GdaTyXnHjY7yRJwkupJK22HpfN1fTZjloLQXhMfbbX1uOjVJJsMGBQKnCJIpkNNq7bspM3UnrRy3ioorK3oYHfiks5PySY26KjDvFh2l8HwC8FJcTq9Uwx+x7yeXvwR8mtUVHcuTeNW7emcm9cDEGtNP26JzqaeoeLtzJzSNHqm3LBtIfVVdU8lLaP0wP8uT0yEtEu4pIkXtiXST8vLyb5mDt8r91lPrsxIoLLduzktYxsRpnNXBUS2uF7a44GUWRDTQ0GpZIQjZYQrQbNQeP/0bg4VlZUEabVkm2zIQDzsnIpsdi4Mya6U535F5VX8HhGJpP8/Lg9JoqZW7ZxycZtXBASzPTAQAK8D8hmoN7IQL0Rt11koM5If28v7tq5p6msu2Ki8RIFXKL8TjrmGORw912BrKTIyMh0OjVOF6Ik4a8+3PnzSIwwm/lzyGAAlldWcW/aPib4Hd1xdkZQIL+VlnFf2j7izGYeaPzcLkn4qJQYlApujYqiwulkjK8vyyor+bCgkD0NFv6prKK/txfPJCbg38pF6MlCkEbDR/368HFBIe/nF7C3wcIj8XEIwJ/lFZ4EeNU1rKmpIc9qo8zpQCsoCNFqWFRRyUUh9dS4XHxVVMwup5OvgUvDQhlvNBKj1x2y2Kt3ubh+127ezMnjzZReh7Tjq8JiwrRabv2PgvJfrG6RSJ0WVQvXFNhsLCyvYKjJh35eXofUL0kS+XY7PxaXYHO72VpXR43TRbhWy7rqat7Nzeeh+NhWL1DnREYgShKX7djJ/EEDCNa23k9gXXUNuxsasIgi3xYVM9ps4qH4uKZd/C8Li8i0WPmob8oJdRoYa9Dz48D+mFSqTnNGb47FFZU8nZl1yGeT/f14LCEelSCgEAScksRws4m+ohe9jEaUwHPZOQz28WGif8ed+etcLl7KymZhRSXDTD48lhCHWqHg/b4p3Ld3H+/nF7CxppZ5gwY0+3uFIPBOSm+WVVbxbXExF4WGtGqulOkcxGOgPMscjqyk9ABcdhcuUT5J6U64pYMcTY9DduTujgmBALWaIout3Tuhu2vq8FepGaA1HFKGw+1GLQhNi0Wb283W2lp8lErSrFYa6usBj2zey87jr4pKrg0JZZrpwAv8vboG+hiMvJuUzI6Geh7KyuLy7Tt5NymZENnp9BCUwLWBIfTW6HksJ4sZW7bir1KhVyiYtXkbxU4H4RotcTodAwxeNIgi2+vr0QoC1+xKBSBGq+P/YmMBuCQgCIXdgfgfkxQdcHVwCA9mZ/FxTj5RWi2b6+vJsFrZ2lDPKG8fsItHNGvyFpRsqq2jweJA+5+TsT0WCzfu24sIvJ9fQH+jkQsDg4jV6VlQWcF3ZaU4JQmdQoFLkojQapkZEEiZw0GpRsMf5eVIbglvpZJCh50YrY7rww73uXK43bxZkM+/tTWIEvipVLycmU2ywcgFgUHo/tOuYocdnUKJt1LJHouFJdVVfFtWikmpRKdQcqrJzAOR0U33vrmujnfz8rksOIREla5TThq603zmjxKcEq5j6MCfqPKM8YFGL64ICSHDauXtwgK0ksB9kVEIgsBpvn78VVbBb337Nc01S70r+aqgiLHGjjnz14sit6SnkW3zJGyM1+hYUlKOSlAQodVS7HAAMNL7QECIlmQz1ujD2HhPe47FqZNMC8gnKV2CrKTIyMh0OhZRpNzpZK+l/Y6v3koldaKLOpcL78aoXzsbGpizz5ObZZSPD1UuF2kWiyccsk7P/ZFRfFRd1VRGhtVKf6ORK0MOda4O1GhItViwiCL9jF68lpDAVXt2c1HqLr5J6SMrKs0w2mTiy14pfFtWyh8V5UjARF9fpvkF0MdgOOyEweF2s62hHj+VmjidDkmnpfIodYwz+3J+QD3vFhUC4K9Sk6D3OK1vaahnbW0NI3xMLf7+/MBAltdU811ZKZcFhyBKEpvr6wjTaHmjIJ9onY43E5LYWl/PO0UFPJh9YDd9lI8P402+TPL19eymwyH39GFRIZ+XluClUFItulhJDSpBIEqrI0KnpczhJM9uY2V1NQVOB/2NXkRrtRQ47OTZ7ayprWVBZQV3RkQSp9OjEODl/DyWV1djUioJ1GhIs1pRAreER3B+wOGZywvsdh7JyWKglxfX/KdPy7SeRIOBh6OieTI3h+XV1dwWEYlZpeLJ3BxcksR9kVFMMJv5q6qSPLudKJ0n3Ha0VseautoO1S1KEk/lZJNutTaZyH5dVtr0/X4VdnZQMLODguW4nt2UtkQklGk/spLSAxDtEi45Yke3QpI8rw7R5sYtOykehqsx9CtuCZetfc/nVL0Pb0gFvJNfwO0hEQCEHTRl1TpdON1ubgkOZ7DRm8hGxeLbGk8YWJdVpI/WwLeVpXyYV8AVgSFNv53h7cdP5WUsLqtkqtmPr4tKUAkK3JLEj8WlXB8U1q42n+j4oeQG/1Bu8D90gSzaD19KKYBBamPT9xKtGzM3BoRxvikApyQRptYgCALVLhcPFmRxd2YG78YkkqRr3sejl1LPJG8zX5eUMFBj4MuKUv6t9ywqDQoFl/oHo3HCMK0XQ6OTqBRdZNisZNptnOMb4DnlcEhISIdllL7CN5hLzEEoAQn4tLyEL0tLcEgHHGiNCgV+ShX+ShXZViv/F3ogx0au3cazRXncnpHe1B61IHBTUBh/1VSRZrVyd0gEo7x88FWpD3umdaKLu3PSMQoKHgmJRrJ3zFn+YE7G+WySwYw1ROTl4nzSLRaej4jj4bAoni3Ko8Bm4+7gSIJUah7JymRedCJahYIqhxMDinbNaZIkUe5y8XlFCatqa7gnJAKL202qzUKG3ca86AS+rijli4pS5kUn0EdvxGUTT0rZ9ARE1YljZtmdkT1FO5m33nqL2NhYdDodp5xyCitXrmz6rri4mKlTpxIWFsbcuXNxy87wMicgu60WHi3MQScouKNRuWgPaTYrAJqDdpN9lCrGeJnoqzfwSFg0H8QmM8M3oElBaRBFCp0eUwlBEJjtH8SZJj8+rSihXnSxx2phl7WBNJuFBK2eRK2OYqeDP2squTYwhCkmX1bU1SBJcuSW40mQWkO4Rtt0kmBWqXgtKoEAlZo/qo98HnNjcBjBag235Gawur6Wx8KieSQsCovbja/ygJIrCAL+KjXDvHy4yP9wM6zmUDWaGSoEgasCQ4jUaJnkbeLVqHi+jOvFH0n9+Dy+N5f5B5PjsFPgsDf9Nkqr443oBF6LiufekEjO9Q3gxcg4+hm82Ge3cpFfINPM/viqDvfjqna5uD8/i2qXi+ci4zB3IJ+QzAHOMvvzWlQC+2xWPqkoYZKPL69GxVPsdPJoYQ73hEaSbrexqNZzOhun1ZPntFPpcrapnm8qSpmZnsr5Gan8Vl3B7SERnGH2J1CtYXFtNZN9zBgUSkqdTgwKBf7N9AGZnou8Lmw/8kzXiXz77bfcdtttvPXWW4wePZp3332XqVOnkpqaSlRUFA899BBDhw7l2Wef5f777+frr7/mkksuOWq5zgYR50kc5rY7Ijk98nDWiwjtPCk4EbFLbu4pygTgDnMYQXYVznbu/vnZFQjAtrp6nIYDZfRS6Hi/voYLMnbzmF8Uo/UH2YdLbrwVHodbZ72IwiYxWW3mdyq5KzuTPU6P4qPGE41sTs4+zAoVRkHJFKWZnaoGfndWsqeygQTN0XNjyLSejo4ZUZKocblYXFPFurpaikUnekFBqFKDUgCnJKEVFPTW6Bmn8SHLbmWS3sRowZtPakvQCwoGY8BZ13njtdjhIMNu4yavMLxEZVNfHywZMQgKFpRVcKXPoSGIU9CTotR7nH2csLChCjdwuS6o2bbtdVh5vDIXh+TmSf9ogjswplriZJ7PeqHjUu8gPqosQXBIXOETxLN+0dxelsV7RYWEKTW8WJzPjpp6zvby53sUvJifx//5H56J/mB22BvwVijxV6p5t6yICXoT4/QmEtQ6gpQanHUiX5eVMkBr5Dy1P846kf8Zg9naUM8r+fk8GeApvz2ySXVYiFPpWqV4y7QPp6Z1JynHal14siArKZ3Iyy+/zDXXXMO1114LwKuvvspff/3F22+/zTPPPEN1dTVTpkyhX79+xMbGUtNolnI0xFoXLjnMbbdCcngWwq5aF4LsrNjEl/Yy6iSRB7VhnOrywlXbtmdznyWXXLcdBQISHrOaNJeN7OoGIhRaREnC6PK8HHQIBNuUhzjYKoCRSi8A/qmsJL2+mn9ctTiQKHR6drUf1Ydjkdz85aimQHLih4o5+mDU9W76Sjp8BSU3l2XwtD6Kfqr2h46VOZSOjpkdLgsOJMYqvRGBYpyMUXpRLYmsdzWQotRjlBSstNRSIXnKPwMTrloXqxpqOVXpjU+DgKuTjKQyRRsNjaY41Im4hAOnbyogUaFju6UeF0dOAig2LkLdtS5cB50aOiQ3Xzkq+N5RQbxCx8P6KAJt6mPiHH2yz2czJTNujZvP68uotju5TRfKo7pw3rWXgtsj13+s1ey0NTBcZWShrYb8agshiuadp9e66vg/awHgyZ2jAC4W/NhlsfK328JAlZE6SSTVYeE+XRjuOhE3YABCULPOXkdptQ0/hapJNpU1Nkz/UU7dksTPziry3Q5u1AajFAQK3Q5ubfBsFF2rDeRczbFNQnnSEtC65fOxWheeLMhKSifhcDjYtGkT99133yGfn3baaaxevRqA++67j2nTpnHppZcydOhQnnvuuePRVBmZTqdeEvnNUcXXDk9G5N9d1WgVCoaqvNpUzi7Rggs4VeVNpEJDpmhjndjAR9ZSBqm9+NJRTo0kkqjQ8bIhutlQs+dqPFG83rAV43BaOV1tZrLahFuSUAkCyUrPCclE9eEO2GpB4AVDNNc2ZJLnttMPWUnpLmwSPQnrMt12nHgWjmdr/IhRaLnBkkWq6Dkle1UfjUIQyHXb6a3U45Ik8tx2zlKbO60tTkniaWshcQotj+kj0AmH7ljvFq1sFy3crA1poYQD7O/BBxsY5rvtPGUtJN9t51JNAOdr/FsMqyzTcZSCwAVazzP+yF7KJZoA+qgMvK6KAWCJs4aXbUXU4Wah07OIrJdaNst521ZCiKCiXHKRoNBiEJS8bCtmr9uKFgWfOjx5ncIFNSP+M0dOUpvYJlq4zZLN04Yo3G4IBq5pyCTBIRCu0OAlKFAisFu0slX0BCe5XBOAWVCx2eUZJ76Ckg/tZUxT+x7WP2U6jtiKDTh5XdhxZCWlkygvL0cURYKDDz3aDw4Opri4GIAhQ4ZQUFBAeXk5ISFHf3ntR7S7EW2ynWJ34mAnYMEqy+ZHsYJvpEqm4kOKQs/HYjmPWPOZIZgRgZGCFwMUR17wZ0n2pj3uwW4Dp2HCgZuPhHL+dFezxt5AL3Q8ogwjCR2CTULkcN8RX9SIwLPKSPyVLsySChwHvhePEC/HJUm86S5Gh0CKS48oh/7uNDo6ZpyiGwUQL2lRIjBYMBBmV4Eg8aIQyTdCJfOlKoIcKnwEJXFocLskGiQREfByKjpNnmvddRRIDt5URONnVx7Sp+olkWfFApLRMdHlfdQ6pcadetHqBkFgi7uBZ91F+KHiVWU0MW4ttNDXOwt5PvMwRfLhM8pYYq3hPMWBkOXj8KZM4eQTdzkTBG+mC2Zi7GpE4fBn5ZTclEou5gpBpApWVrjriEGLADytjKA3etKwoUQgHi1K24E5qUpy8aZYjIBnLtplaeBddxFf4zFP3SZa2CYeHjHxRkUQ3nYFIm7cjf1JkjyKr2h1I8r6bacjHkFJ3c+xXBeeLMhKSifz35CRkiQd8plKpWpzR5S0WiTh2CW2kmk7kk57yP9PZiRJIlWUSMHMXFUkAGOkQN51l7JZslKOi8XYiaKOCQofpgqmZhPQlbid6N16rlYEMFbwQRIUqIHrieQiKRQXEmZUTcn8Wlqy7ZdJrN4HQbC3aWlXKjnYK0oI6PhcqMeEgjGCD/0Usn9KR+nomCkQFQzBn1uUh0Zek/CYyRSJ1aRIZryUeqSD+leZZEcv6tEr9EiKzhmv5W4Lfm4D0Sqfw/rXP+4qHG4N9yijUArqo/Y/lVuH3q1HUmrZg50XxSr6C77crghGLyiPoWpyAHk+86AHxooB/C5Z6K+USBR0Td+dI4Vgdav5VaomXiGRqNA1LxtJwijqUSt03KkI5jZJOiwBaS8OlHtwGavcVvRuPZ8pYzAKSr4SK9DrPHNPf70ZBANqBBokN9nYMKFiosKHMxS+TeWkSGb0Yh12IAkNGqXukPEgSRJp2PFCIAxNq5OTyhyKpFFBfeuuPRbrwpMFQZLD2HQKDocDg8HA999/z8yZM5s+v/XWW9m6dSvLly9vc5m1tbWYTCa++uorDAbZ7ERGRkZGRkZG5nhjsViYPXs2NTU1+Pg0n9zzWKwLTzbkk5ROQqPRcMopp7Bo0aJDOuOiRYuYMWNGh8pW3/0SGtlxvlsh6bQ433gA9c1PI9jsR//BCcyVDRlYJZEvvRJR0fyu3B/OKj6xl3Ghxp+vHRWMVXlziy4E4T/XL3XV8KathM+94jHQvtPDzpTNK7ZC/nXVM1blTankIk20ogCGqLyYrDYxSGnsUPknEx2Ry0JnNe/bS/nIGIdJaPm1dU1DBqepTVyoCUBC4kN7KX86azhbbeYKbVBHbwEAFxLX1meiApxI1DdjPuiFgt4qA5dr/QkTjnw6sVu08pA1jzBBjUIQeMYQ2e6+317k+exQ7Li5vSGHKKWW+3QHTu72ua08Zy2iSnLxkD68xfG/2FnDW/YSbtQFM1HVcvLRg5nvqOQrRzkDlEYe0oeT6bZxtyWXfkYzD3zwNnOuvoYa6wFTr0BBxTvGuGbL2io28KS1gIf04QxUGrEgcll9BuAJOHKjLgSDoGSLq57fndXcogthnOrQhbYNN24k9CgOm6dlwOY+up/PsVwXnizISkoncscdd3DZZZcxZMgQRo4cyXvvvUdubi5z5szpULmCzY4gKyndEsFmP+lf6iqrnZEqAw6rFXUzZonbXRY+suYzSuXFeUojRreLN+qKGeXSMFBpoMjtJEihRo2A6LJjtVlRKh0IHXT27AzZTBMNjJA0jMIbBKhWuFjqquWfhhoedJdxty60WQd8mZZpj1yGuVW8b7XzjauQObpD7bvnOyrpqzQQr9BSZ7EgaXQIbjt/O6r5yV7MzdoQzpRM0EnjdKWzhlJbPXFoqMDJeLUP56j9UAsCWgQ0CGxyNfBpfTnX13kcly/RBuDTgsmuv9uFl81JhmTlYV04RpsLOi1NY9uQ5zMPOuBCtxfPWQqZ7tLTtzHKXxIK3lSEcl1DJl85Chikj2rWXGqCpGWjU8WL1mwiDdEkKY9sLtogibxfn8c0tZkblP4INjs7HVU47TYe9Aps9C2x4WzMUv8/bRBjVD4tymoQKn5URYMTcNoxAjdIZt6zlVCFmzXOcmZq/BiHlqU2J3tc1YxvNPXLEm18ai9nneixZQoV1JypMTNVbcYom503odK0bvl8rNaFJwuyktKJXHjhhVRUVPD4449TVFRE3759WbBgAdHRR46nfjSUPipUjqNfJ9N1SDoVDkDlo0LQnFx5BQ7GLUlU1rv421nD384avgxOIkjlCctZ7xZZbq3hrboi+moN3OoXgVqhYLrkz8ryOp60FqATFNRJnucXoFDRW6MnSKnGy9R8aM/W0Jmy6YP3IX8HoOJ8dJwnBXJzWSYv2IqYL1ahFxScovVisM6LJLUeEQlRAr2cp6CJjsglGBWXKgP5sLaEET4+DNF5k+6w8mFtCRvtnsVUnEpHPW6GmHxQqJV8X1LJGJ0PZ/sHdOp9rKuox1tQUCmI6CQFAVo1CaZDd9SnoGOc5Mv8+gq+qitjiauGS7yDONvLD81ByvdOewNPV+UjCfB/vlGM0jdvNnKskeezw5ko+fJzWRXznCXM841vkps3cKsmjEcqc7nSmsm5Xv7M8jq0j6mAO7wjWF60m2KtSIrhyEstlyhBPYzw9kGrV+OWJFaU15Oo1qHzVmEFTjWamG9t4DqfYM71Djxq+6tEFw2SiAqBQKWaqYI/Z0h+/NRQwTs1xSx21TZdm6twoPJRscRSzbN1+YQqNdxiDsMoKFhvq+MzaznfOCroozHQT2tkpM6baLXuCLW3TJ7Tzoe1Jczy8qe/tgefRNtad9mxWheeLMhKSiczd+5c5s6de7ybISPTJSgEgXt8I3i8Mg+AEtFJkErDhzXF/FBfgQuJcXof7vGNaHrJC4LAQ36RLLZWUy2KRKm1SEh8VlvKGls9AzXd3/9KEARi1Tr2Oq3EqnRYJJHv6sv5pK70kOv6aQxcbwolWU4M2WHO9Qpgq72BpyvzeSEwhterC9nttHKu0R+FIFDndnG2lx+9NQZynXYKRAc3mkM7vR06QUmd5CZIoabU7WxxsaYRFFzoHchpBl8+ryvl/dpifm2o4LmAWEIbFflv6sqpEJ3MC4yXk4d2MxSCwB2+4dxYmsGr1YXcZQ5vCvgxUu/DI36R/Gut5Z2aYpLVBvpoD523DAol3oKSd2uK+am+Am+Fkgu8A9hmb8BfqWaK3tyUbDGnMYdTcGO/yHLZSHVYuN83gq/qypgJLGmoZpLexOd1ZYzRm5r60H4WW6rZ5bCgFQT6qg08VpV3yPcT9CZuN4dT5nI2fRaj8kQd22Zv4PnKfFbYapioN3GXb0RTyOuJBjPXik7+aKhki62eD2tL+LC2hD4aA1MNvozS+zQl0G0NvzVUsspWyypbLQO1Rh7yjcSk7HlLUaWPCipad628Lmw/Pa9nnISojUrUWjm+QXdC0nomZbWXEkF9ch+BKyQFagQSdXoG+Xuzur6Wb+rLudgvkFm+gQSq1Yf9JgAlF5oPmO3stVkornJiVCiIMxpQe7f/mXaVbO7zjuI+opr+dkkSe6wWchw21IKAU5L4vrKcm8oyOMPky63B4ejb8DI/0eioXNTAI8YY7szN4O6yLKJ1OnDCZaEhmFWHvsrqLB4/Eb1BhdrYuc880KpBaYXrAkN4qjiPWrX7iP01CCV3miM51x7InOx9/O6s4gZzKOl2G5sd9VwTGEJv/7blE+ps5PmseZIxcr8qkicLcwnUafhf0AGld6K3H4NdPixJr6Fc7Wq2DzwUHsVmSz11osiKuhruKc/GW6Gk3i3yenUho7x8mGby48GKbAASfQ0IgkCCZCSgUs0zVfnE+5iYCfiq1Uw1+bM4r4b1Uj3n/ec05bvycjLtnu39nbrDwxQvtdYQotdwSWgwG3PrKXA4CNdpUQoCw9Q+/F5dQZRGxz2RUWj/cwIcipJrzWFst9RzS24GNwaGsbahlherC1BWFzDA4MWp3iammfwO++1/mWMMI6nWwHPFeWy1N3Be8R5+TEjBX3X4e6I7o1bJ46QrkJUUGRmZdrOxoY7HCnMASLVZKHY6+ai8mKFGb/4XGNqq8JZ5Djt/VlcC0OB2E6lpv6nX8UQlCPQ1GOlrOGDCcLrJjwXVlbxVWkiO3c7TETH49rCXcXfCW6nkpag47szNYLfVwlX+QWTYrcRKWopcTrSCArvk5pGCbGI0OnrpO/90IkarQwTmlRYy0GBkurl1Gb1jtDqmmf34rrKMMqcTNeCnVHGB39FNd2SOH5N8fClzOnmnrIgQtYbpZr+meS3d7kkgmqxv/vR3uJcPw708Jnzn+wVSL4qk6A3k2G1sstQzr7SQ1fUes6tYra6pXJUg8FxELNdkp1Ho9Nh6lzodvFNaiFoQeKu0kGlmv0M2Pe4MieCT8hJC1RpmmP2oc7uRJNApFOQ4bDxblEetKOKnUnN/aBRzcvaxqrFuvULBub4B9Ncbj5iPZ78D/TAvb873D6TM6WR1fQ3/1tfyVkkhv1VX8Hh4DJGaloNFaBUKRnr5EK/VkWm3IQFPF+byUlT8UWUhc/IhKyk9AKVWQCVr7d0Kt9azW6TUKVCcxM6EvxRVEKvTUe1yUeVycV1OGvWiyJWhoaj1R55eiux2Xi/MZ2VNDQogTqdjgNGL04MCUCnb/0y7k2xUwCx9EH1MXtyTmc7c3HQuCw4hSW8gWK3Gt5lTphOVzpKLL0peTkziqj2p/FJTyccVpYSpNU2LOYD+RiPPxMbjrer8V1ywS4OvSoWXQsETsXEY2yDD26Oj6OfjxbN5udjcbv4XGoZef/z7QHcaM92R2WEhFIgOXi7JZ2lDNfdFRhOu1VLe4AlwEOWtPywXyn9J1B3YvEjSe5Fk9iLSqCNIrcFPrUIjKA55zydpjYRqNFQ3/n2arx8/FxY0ff9ldRlzwsKb/h6g8+EV3+Z9mvrhzVnBB5ThvjpvnhLi8FYqSTIYeL+okK/KS/kC8FIoOcvfn3MDAgnVHqpsqMRGk12NApVOSahOybneOs4lmEyrlYeyM7k5J503ExOJ0R2+QZButeCSoFJ0kmE/4NSRarOg0ilJs1gI02rx6sD831UolXLEs65AVlJ6ACqtCpX75M0C3B1xN0b2UGlVKJrJOnyyEGHQs6W+np8GDcAmurl/3z4m+vkxKTig2YSN+ymw2bhs7268VUoejY9jrK8vxk5SxLujbProfHjf0IcXsrN5MS8XN6ARBF5ITmK4+eSIDtaZcglAxTPJSVy7KxUAs0bNeWHBbKipRZQknklKxHiMFjoxCi+qXC7uSUwg0LvtJzVnhAbxbXkpXkolV0WFd4tket1xzHQ3HkyKZ3J1AM9lZXNDehrzUnrhUgooBQFtOxXN8bqWgzrkWq0UORzEmDzzw5WR4ayvKKfQ7vFfqXS7UOnav4SbFOKpO89mw0erZsHgQZQ5nfxdUcEvJaV8V1bK2UGB3Bsb09RH1U5PfUqt8rC6k3TevO/dh9nbdvB+STF3xEQRrNWysaaWv8rLidDpeDsv/7B2nB0YwOywUOwqgavT9gCwYtgQNN096IhTHiddgaykyMjItJtRZhNfFxWztbaOsX6+fNi3T6t+92d5BU63my/6DcB0kpwmhOm0vNIrmUqnkzKHgxeysvmyqOikUVI6m77eXqQYjexuaMDqFvkovxABEICZW7YiIDDc5MPjiQmdWm/T0qmdukWR3c7uBgtPJMR3CwVFpvUMN5v4sG8KN+3ew2XbdyLgyR5+wdbtGJVKxviamRUchLkT5jSzWk0vo4Ech+eEMEir4dN+fbhtz16qnC7ODQk+Sgmt4/H0THbU1xOl0zE1MIBEo4Grw8P4vriEt/LyGWU2M9bP95DfuFuwCDOpVFwRHsq83HzO2bKNU3x8UCsE1lTXHHatEpgS4M99cbEoBIGD84q/kp3DPQcpR90RlVZePncF8lPuASh18klKd6Np51GnQqE4eWVTU+O59/cKCkjy9SZCf/SwlDZRZEVVFQNMPvi3Yyf6aHR32QTpVAShZ7IlkLezchHVCrTKbr5r2AkcC7kM8PUhtaGBeC8jZ3l7eRaNgIREVoOVBSVl3JYkEvQfs5V6l8dMx6sd5mAmtYBOoWB7QwNTwtqeIFJ0e6IrpdmtTNEoUSmO/0Ksu4+Z7kSgTsW7g/ryR3EZPxeVkGe1MdLfTK3TxaeFRbyXX8D1MZEkeRlRCgLzMnPw1agZajYx1NdEirfXEU+Z9+OHis+GDMCiVFGNRza+Ch0fn9IfoFVlHA1JkthR7wnhrdeqmk5HvFFxVVwkG+rq+LqkhIlhHlMxlcNzOtncScp+ZsdEcHZECHfv3MuG6hp6exk5PyyEP0vKqBc94a2Hmk08mBx/2PviroRYXkzPYn5pGcVOJ4PMPlweGYaqO56qOORx0hXISoqMjEy7OSM4kDCdjkf37OOKTdt5d2AfEryOHPv+qbRM0hos3BgbdcTrTnSG+5p4NcPN1ppahvuZj3dzeiRXRoXzT2kFCkHgiqjwQ76rc7pYWl7B1/lF3BofA0CJzc7/7U1nfZVnZ/eyyDCi9HpmhrV+V1qvVPK/mEhez8whyqDj/PC2hTmO1uu4NCKMz/MK6eXlxenBnZvHRebYY1arOT0ogNczshGB9AYLDybFc2t8DDdvT+Xd7EPD//ZVePFxbj7zsnKZFRrMA8mtcxJXCAIGlbLJL2X/Z52FIAgMNHlTZncwMdD/sO9G+Jl5JysXu+hGq1Q0HR5KRwk26qVSkVbfgFIQGOJr4tb4GG5PiCGtvgFvlYpoQ/ObUxeEh7Cjto6/SsvJs1pZU1XNW1m5fDKoH1VOJ6P8fY/q+9NVKDtgaifTeuSn3ANQqZWo2mtbIHNMcDeG6VRplCe9o+ngIDNfmAdy6bqtfJBXwIsDe7d4rVUUWVhSRoqPF9clHZtkVj1FNjEqjzJX5HKg0nbfdnYWx0IuwVo9F0SF8nFWPqJKOOREyler5PKYCD7IzCPF15tyu4Mf8oqpcjiJMerJbrAyv6iEepfI6RGB+LTBROfqxCgybVY+ySvgvJgw1G3c6R0SaOaL/EJiTYZuIfueMma6EyFaPQvGDeOv4jJeS8ti1vot+KpVRBr0BGk1BGg1DPY1MTU0kD4mb5xuN59nF/D6vmzWVdcQptfSz+TD7OgwArQHIho63G6UgtC0GG9ONk63u819riU+GTGw2c83VFTzdlYuRpWSOsGNUatGrWk8SdEomvptpd3Bd3lFLCmtwKRWcVpIIKcFBzApJIBfC0oIMepQahQYBCUD9eajtueZgb2Y02DFV6Pm3FWbqHA4eTUrh63VnkhkHw3rz2Df428iq6Ibnu6cgMhKSg9ApVWhQj5a7E641Qc5msrmEfhrVVyTGM1j2/byd1kFZ0Y0vzPtjYqBfiYcbjcKjbJTdwX301Nks7u8DoCBgb4nhX3zsZLLlIhg5qXnsKmujrHBh+4GX987ljWV1dy/fS9KQcAtSdyZEs+VCZ5TvO1VNcxeuZndFiujg45uemgXRT5Oz2N3TR0Z9RZKbA7ez87jhuTYNi0aq90es5feAaZuYcrSU8ZMdyNcq+Jqk5Hz4sLZUVXLlspasustDPQ3kVbbwNe5hXyRU0CoXkuUUY93oyJcYLVRZnewtbqWL3MKmJscw+XxkXybXcgnGbkUW+0M9PXhzj7xLCqvZRbwwM69JOs1fLQvlzqXi3/POBWzxpOdfkN5Nak1deQ0WFhcVM4j/ZOZEtax0NavpWcDEKTTcsaK9QTrtIwO8vimCGolKq2KtNp6Ll25GYAJIQFUOhw8uzuDL3IK+HbsELw0Kl7Yk4lBo+b8mLBW152o8zyn5WecitPtpsLuYPKiNQBcvX47V8VHsqumjucGpxCoaznc8THFLo+TruDEfzPKyMh0CedGhbKmrJIXU9OZHBqIpgU/ixuTY7h2zTZe2pXB3X0716m5J7GhvBoftYpEnyObx8kcGUHw+LDnW6yHfadWKPhk9CCy6y2EG/QoBQ6JGhTUuMCxuMRW1fVRei7z9mYzKtCXof5mevkYeW9fLt/lFDEu2J/ZMeH0NnsfVfk2NEYeS69roLfJu5V3KtNd8VGrGR3kz+igQ5XkaoeT1WWV7K6uo8hqp8bpZGSgLxV2B2m1DQDolAL1LpHPM/N5OTWj6bdbq2p5a082WxtszAKWl1Sw0HqgjxtVSnZW1/Lm7kz+Las6pN6Mugam0DElJbzRJEujUHBVfCR2t5uvsjwhkNeUVWJxuSi02rGIIp+NHsRgfzMA+2rrOX/5Rj5Kz+WevonUOl28sjsDl+TmrIgQvNVtW3aqFQpC9Dq2TR/P99mFPLkjje1VtWyqrOG9tBwe7J/UoftsLyfDxlJ3QH7KPQBBJaBAPoLvTkiqxnjxKgUKSZbNfm7uk8D0RWv4vbCU82LDm71mVGggvUxefJqZR4LZi3NjwsmobeDT9Bzu6JOAWXt4MkerS6TAYkUCEn2OnJ27J8gmvbaeTzPzmBoejLqNL+2eyrGSy6aqWiTgjMgQFM2EsdaiJNmvefMQR6MyodOomv3tYXVV1jLY38wHY05p+ux/1XX8VVDCL7lF/FNUhkuSmNsrluuSY1ss5/SoEOalZfN+ei6vDu9/1HqPNT1hzPRE/FRKzooO46xmLFvTaurJb7AyIsgPg0rJe3uzABjoZyLZ5EW5zcHiojKCfDxK7PiQAAKV0NvkzeAAM9/nFfH0trRDyhwXEsAl8ZGMDvLrcGSsV0Yc3i97mX14YtseXt2d2fRZuEFHmLexafwk+5mY2zuO11MzUCkV3NU/iVVllTy1Yx+fZOTx0ZjBRBqbT355JBTAgAAzAJsqPT5lX2cXcFliFDHex2Gjx3UUxxyZTuHkeDvKyMh0CXHeRiaHBfJBWjYzY8JadHK8LjmGLzPyeXTzbn7LLWZ7VQ020c20yBCGB/o1XVdmtfPU9r38XVAKeF5Um2dMbPGUpieQWdfA1Ss3E27Qcd+A47MLeCIxITSAp7YJLCwo4dL4tgVjiDDoSPLx4q3dmYwLOboDe19fb37LLT7ks15mb3qZvbkmKZorVmwi32Llp+xC+vmaGBHk12w5aoWCS+MjeWbbXjaWVzEkwLfZ62ROXJJMXiSZDmy4/C85lv8dpNjesnYb4AkAATAxNJBpIZ7+VGl3HKKgXBgbzg294gjSH1vTp/NiwzknOhSr6CavwUKl3cmIQN/DTBavT45BAF5LzWBbZQ0Xx0XyemoGBRYbp/+1mk/HnMLQwLb3+YNPQZWCgChJVDtcHb2tdiGoZD/hrkBWUnoASpWSHrwmOzFp3DVSqpUo5eRnh3B933jO+3stCwpLOSe2eTvks+LCmRoTxrxdGeTUW5hk0PJHbjGiQiDXZkOrVPDBniy+y8jHqFLx8OBe7K6u49fsInQ61ZF3CbuxbNJr6rly5SZ8dRo+nTAEn+NlT308OEZyCdMYmRoZzLt7spkWG0ZAG56pEiWjQ/35cl8eu+rqyau3EKTXMijA3KyvSKHNTrS3AaXm8NMGk0bJBQmR/N+m3QTpFfySX8zoiOZNbkS3hJ9ei0IQmLN6C+tmTjq+inc3HjMnK/PGDmZNSQXf5Xk2aIw6dVO/czlBrRBwS3BJYiT3D+p1TPz7mkOJEi3wd3EpTreEVtd8wIm5/RNI9PPmvdQsPtqXc8h3j2/bw4IzT21z3b0CTSydPpbUqjoKLVae2ryH2cs38Mqo/pwZ1bYoex1FKQ+TLkFWUmRkZDqVfn4mJocH8frOdM6MCmlx8aVUCNzSz+OTsqeqlj9yi7l2+SaMKiUNjT4CN/eN5/KkaHw0an7LLuSHzAKqHU58mzEJ6+5YXC6uWbYJP62GTycMxU/X8+6hu3LPwGTG/rqcdSWVTItu22LF6ZZwuN2cv2ht02d6pZJp0SHckBJPhJceuyiiVSrJqbPQy9yyD8nuqjoijXqGBvqyoqic+VkFxPsYSTR5U2yxsSC3mNSqWrZV1FBms9PH14ddVbWUWG1EerXdBEbmxGZksD/DwkNIA8aHBXq0EyDUqGfdzInolEqUbcyz8/K2NN7dncW40ADuGJB0xP7cElvLq3l4QyoA40IDifBqPujElIhgpkQEI0kSVQ4nv2YXklNn4aw2jtGDCTPqCTPqcUsSf+eVsKGsis1l1V2upMh0DbKS0gNQaVWo5JOUboWgOhANR6mUbVP/y91DejH11xV8l1PAlb1bts3fT59gXz6bMpz06jpe2LKXyZFB3DGoF8m+B16g6saEc0tLyrkgsWWznu4qm493Z1Jpd/DN1JEEeZ98C9JjKReD5FH41BpVmx1a5w5MZEJUMGatmp8zC/hhXx7jIgJZXlTO6pJKUvx8+CevhCdH9qPIasOs0+BUCmgUisMWiLuqaxka4o+/UYdLkrhv3c5DvlcIMCokgKkxocyIC6Pcauf6pZvwMmja3G67KLK9vIahwc2blLWF7jpmZFqWjakdjtsNThdVjYlMlxeVs7a0knPiwgkz6hkW7Mfe6jpSK2s5NyGCIS2YKgKsLK1o+vdv+cXcPCDxqHUH6dRc279zA6V8c+aoTi2vLajkk5QuQVZSegAKjUo29+puNL44lBoVSoX8Uv8vyUG+nJcYxbzt6cxKjsa3FacGY2NCGEsIFsnNq5v28ujoASg1B6aoqQkRrC2t5P7VO1hZVMGAQDPjIoPp5edzaEHdUDbbyqp4d2cGV/eLI8bf5+g/OBE5hnL5emcGAtAnyPeQPtMawjRehJk9vgFrS6uwiW5enzSU3LoG3tqSRk6dBYCH1uwAYFVROX2/XMjdQ3pz46BDfYoifYzk1FvIrmugT4CZ5ZOHsKeylp3lNcSajER7G4k8KJrbhmLPYq9GdBPSxnY/tHQbP6Xn8/L4wcxKjGzTbw+jG44ZmUY6QTaSJPHq5r28vXUfYmMmxomRwaT4m/g9s4Dsxkhj+/k1s5AVF00myKBrtqxv9+VxbmIkObUN/JSRz02nJHeLUNpdiu34+MKcbMhKioyMzDHhrqG9+SuniCfW7ODlCacc/QeNpPibcEkSRfVWIg86cdAoFTx96gD6+Jv4ek8Oy/NLeHnTHq7sE8dFvaKJNR056tfxYmFWIXev2EKKv4nbBvc63s054ViQWchLm/Zwy+Bk4sxt7wMX/76KjJo6BARKLDZUgoBGqSDB7N3Ub20ukQ3FFTyxdidpVZ78NqkVNYeUU2mzszSvhFmJkRQ32BAlCb1KxaAgPwa1sCsd3mjiVdhgJfm/yvYRkCSJZfkeX4WHV21jQmRwqzYCZE5OPk/N4rXNe5k7MJFgg45oHyPDQvwxqFXcNbQ3LrebNYXlhBh1vLhxD2uLyjG0EO2uwemizGonyKBjdu8Yzv9tJT+m5XFhr2OTnLe7omjjpoJM+5Cfcg9AqVKjlCXVvVCqG/+nlk+5WiBUo+b+kf24b9lmrhqYxMBWmqUUWOyoFQIjooKbdZC/YmASVwxMwuYSeWr1dr5Py+XjnZn46bVMjQvnvrGDge4hG9Et8dianZi1Gj6aNhpjMzuTJw3HYMzUOZw88O82psWHc+eIvu1yHl5XXM7E6FD6B/riliTizN4oNYc6Axs1asbHhRPt58PZPyyl2u7AW6c55LrF6fnYRTe3DevDd3uyeWl9Kh/vzuaa/gktBnow6j02Iy5BOKzOlthRVsXTq3dQaXPw8qQh3LN0E7/nFHFlvw6Y0sjzWfelDbKpsTvYVVbNoBB/9I1KRkmDlUdW78Cs1XD/6AHNVwGMjwvnnB+XsKm4kjdPG46pBR+p1MacLGcnR9E30Jc4szdpNfWt7r8nDDbn8W7BSYG89JWRkTlmDA7xJDfbU1HTaiWlwenCqFYfNc6/TqXkibGDuH9kP75KzaKwzsL72/YxOCKEDhq/dBq5tfUUN1j5fPqpBJzMCsoxYkFGATV2B4+MHtAuBcUtSbglOD02jItSju47FWv2Zv2V06izO/HTe04usmvqWZlXwtepWagUArN/XcG0hAhOjQji//7dRqBBx4wWzLH2B5VwiK03cP8zo4B/80u5YVAS5/eK4dMdGeworW7172VOPPZV1jLzp6XU2D0L5ygfI69OHsrQ0AAeX7WdAL2WT84afcQyau1O0iprmRQd0mJ/BbA6PUFNTI3BS0xaNRuKK7jtn/XcPjSF6G56ot3ZKFUnmVJ2nJCVlB6AQq1AIXvOdyukxozRCrUShWzD3SIpwX70D/Llw+3pTIgPJ/QoEYwkSWJNYTm+ei2KViY59FKr+N+Q3gB8sSuT5fmlXEr3kI228UWuVClbfT8nKsdizCzJLWZoWAARbTCVOhixUTloi3yMahVGvZZ3Nu3hs+37yKlpQKUQGBziz0V94liWU8TrG/cwITqUM+LDeXHdLma2oAB5qZQEGLTsrKjhnFYoSZuLKvh+bw6nRgbz0DjPiWGM2Zus2voO9S95Puu+HE02GwrLOee7fwCYEB3KrcNTeOrfbVz2279c3j+BX/flcfPQFAaFBx2xnhf+3YZDdPPMpKFH7Es+jZst1S6RaLWKywckcutf69heWsWW0iq+OGdct1FUNhaWc+Ofq3l+8lDGdSCiWHMo5FDdXYK88pWRkTlmCILAi1OGUW13MPWrv/ktLRdJankRZHWJLM8tZkpc8/lVjoRDFLG6RP7NK+lIkzuV/SYXtsaQyjKdh1N0s66gjFNCj56EsSX2OxG31ek3q7qOT7alYVSr+Xj6GHZeP4v5F0zmuUlDWXXlWZyZEMHeyhomxoSRU1OPu4U+X2GxU2NzEmg8+inbL3tzmfn9P4R5GXjltOEA1Duc7CqrZkNhOVnVdW26B5mez57yaub8sYqBwX4sv/xMvpg5jqFhgXw1czxjooL5fV8eQKvCFDtFEbvo5pe0XKzOlp3CTY2+T+UWGwDn9orh94umsPzyM8msquPTbfs6fmOdREZVLfl1Fi6Zv/x4N0WmnZzcW3s9BKdCaHPEGplji9ToJKTQqFDK688j0j88kEVXnMldf61jzoLVTIgN5enJw4j1PTw+f6XFCkCQt6HNfV6PihAvPb2DzACU2Z34KCW8j2NOFZXoMQkQ5THc6WNmaUYBFVY75/eL78Cz9SgParWy1WW8tGo7L67a3viXhVfW7yKvwcJ1p/RCEASUQJnVzvCIIDaWVCABFknCpD3cPOTlZbvQq5XMHph4WP0OUWRTYTlr8kr4cls6BXUWzusTyytnjETdaCb23fZ00qtqAdDrNO1+DvJ81n1pSTYZlbXM/H4xQUY9n507gaCDcpV4a1R8cu4Ez+8l6aimswBPnTYcL52Wp//dxryNu7ljVD+uPeXwJJH/5BRhUKsYFRPa1N+GRAUDEOfrjUOSus1c1zfUY24sAUVWGxGdeMKzr6Ky08qSaZnu0ZNkjkiZzUmYd/PJkmSODwpF44tDpeJki7zYHoLN3nx+4WT+Ssvjwb/XMe6j37hicDK3jOxH8EERvKIDzPQO8mVdQRm3tMN85cpTevHa+j3cCpz64a9YrVY23nguUe1IWNYZZDSGmI0P9D3pzb06e8zsLK/GV6+lT1hAqxZhzaFTKAgw6vhyRwZn941vWvw3h0MUuWvBGr7Zns6c4X2YGBfGwrQ8Kq02HlmyiVfW7OT+cYO48pRexPub+H1PDvUOJ7P6xGLy0h+y2HOIIm+t3cVnW/fx1GnD8PM2HlbfBxv38PiSjRjUKs5JieXUmBBmpsSiPOjhRTWauV3UP4FIf1O7ngHI81l35r+yKamzcMUPS9hcWA545ryQZjZ82opBreKJ04dzzbAU3lq7k0eWbGJVXikPTTiF5EBz03VLs4sYExOKTzMJHEdGhbA8pxhBpWz3mOxMBkYG8/Rpw3jg7/WM/eh3Mu++pF2+awfz3Y4MnliykRqXbBbZFZzcb80eQqTp8BeYjExP5PSkSMbEhvLOul28vW4Xn29J44rBydxx6gB89VosThfVVju9AsztKv/GEX0ZGR+JBEzvFc13W/YcsqjralJLq1ApBBI7sICUaZ5qmwNJkjj13Z/Jra4jwd/E1UN6E+Klxy2BKLkRJQm3W8ItSRg1aibFhx/SH9RKBR/MHM95X/3FTb+u5MaRfdlXXkOYj4ERkQeiy1mdLq7+cSkrs4t4cPxgbhrZF6VCwfi4cACuHFzMx5v2cM/CtcxIieXxyUPZV15Njd3BvLPHNC2MSuosfLJ5L59t2UtZg43bR/fn2kZ/qoNxSxK/7c6mX7Afv19xJvoWFNzVucUoBIGHJ7Y+xLdMz8Uhilx5kIKSFGDi5pF9O7WOGF9vnp86kikJEVz/8wrGvPczZ/WK5t1zxpFVVcua3BLemjGm2d9O6xXNl9v28fa6Xcwd0bntai9XntKLP9Py6NPBpKd2l0h2VR3PL99CSb0VnU4OhNIVyEpKD0CpUsmO890MhaLRmVGlROGWd1TagpdKyV3jB3PdiD68u3YXLyzfQkZVLV9cNIXPt+zzhJWddAqKFuL0Hwm9Ssnw6FDWAreOGchvO/Zxxsd/8O/cWfjqtZ1/M0dhb3kN8f4m9HIOi04fM9G+3tTaHZzZK5orh/Ti6637uHPB6iP+ZnhkMBMTwvHWavDRajBqVKRX1JAUYOaX1Czmp2YBnqhbyYG+jI8LI9bPhz/35rI6p5ivZ5/GuEbF5GBOjQ+n3iXyy+5sREEgyEvPgmumY3WJqDVqJEni9VXbeWbpJrRKJRcMSODqob3p3UL+lA25JWwpKue7S07HeIR++3d6Puf1iye4g2Ys8nzWfTlYNtsKqthUWM494wbxztpdXD+iL75HCUbSXk7vHcPm6BB+2JHBAwvX0v/17xCAIKOeGX3jm52fT+sVzdTkKL7fmclNpzYf7rir0aBk/hVndqgM0e3m7M//ZEujcjitVzSPnDGS4a9+0xlNlDkCspIiIyNzXDDptNwzfjD+Bh33/bmGIa9/R3GdBZdb4q+0POaM6NjpQ7y/iRU3zGLEmz/wW2oWl5/StYkUS+ut/LQzg7N6x3RpvScLwd4G3BJcOaQ3g8MDuWpobwprG9CpVCgEAaVCQNn4f0EQ2FZYzqOL1vPB+lRqbU7sosfAX2C/ZwooBAG3JGHWa9lZXEF+TT21NgcAH18wsVkFZT/1Dk/41/1hhZUKBV4aBZIk8cTijby+aju3jO7Praf2x6Q7ssKsbVwA+h/Bod7mclFc20DfkI7tEMv0HNbleRJ4XjGkF/eMH3zM6/Mz6Pjf8D6Mig7hu+3puEQ3c0f1a+qfzdE3xJ8Nje08UaizO5sUFPC8W/z0sgl+VyArKT0AhVqFQt32XWWZY0fT7pZahcJ9/G1vezLXjR7A0JgwZn3yOwPDAwkw6vl1dzY3nDqwXXbNB8vG19uIKEkYdFoU6q6Na7+hMI9qm4P7pgzr8rq7I509Zk5PiWNw+A6u+G4xi2+YRYi3kbgjmGCMSYxiSWJU098zPvyVrKpazukTx9CoEOL8Tby7ZgcXDUxiRHQo1373D4v35bH8xvMI9jbgbzzyomRdfimJgWb8fA6capTVW7nm20X8m1XIk1NHMreFZHr/JT7ID6VCYFtJFYOimg+dumRfPlaXyOm94zrcv+T5rPuyXzbLc4p5bNF6LhyYRIjZp0t9PvpHhtA/MqRV14aavam02lmTX8bo2LZHaeyO+KnVvDJjLMszCoj29ea2sYNwyxm2uwT5KfcAFBolim4SLUPGQ9NLXSO/1DuDwbGhDIoIwqhVc1afOOZ8+w9PL93Iw2eMPOpvRbeb77eksbWgjL0llRRZHDw7bwpzflxCgFaFVqVkXHJUl4+h3NoGvDRqwv1N3cKJ9HjT2WPGqFHx1VVnMfqVr3l91XaePbt5O/mWGyQwIiaUJ2eMbfrozcYoRQCvnj+RIc9/wWOL1vHlFdOOan5YZrERZvJq6mfZFTXM/nQB5fVWvrvqLE5rw4mar0bFuIQIPt+0h6tH92/2mvk7M+gbGkByePtDMO9Hns+6L4LgOZl7eOEaRsaE8vZFU1C0IqTw8eLiYSl8vCGV6R/+yvjESJ47ewzJHfQH6Q5cNbo/Vx00Fivq7cexNScPsqODjIxMtyDW30R2RS0XDk4m2s+H4lpLq373/uodzPn2H/7Zk4NRq2ZqSgwA2wvKeW/1DkK8DYR2cXKxaqudj9buZFBkkKygHENCfIxcMCiZH7akYTtCbofmcEvSESP9mPVa3r1oMiszCrjk0z+OmDsCPOZne0o8YUndbokp836g1mbn1+vPaZOCsp/BEcEU1dQ3+12tzcGfqVmcPyipzeXKdH/yquq4++fljH7la4Y8/yXgib7V3RUUAL1axbJbLuCTS88gv6qOsa99y6tLN+ESj33yw192pPPw76tIL6s65nUpNLJ1S1cgb8/3BFRqhJM8fGl3QxA8E5SgUiNIsq7fGZi99NQ7nJTbneRU1nL3acMRjmLG4hRFXli8kcuG9+XNi6YA4BKUrASW3nExY5/7FK1KedRyOhO3W2Lu9wuotNiYP2dWl9bdnTlWY+a6sYN4+99t/LAjg8uGtz6ikISAQnnkvjG5bwLfXjuDCz/4hV5PfMwfN51Pv/DAw65zuyUW7cklOcQfQa2moq6BsnorH1w6ld4Rwc2UfGQqG2x8uj6ViclRzbbvjy1p2EWR84akdEr/kuez40uD3cmX63exIaeIvMpa1mQVolQInDcomchAT66PP2+5EEMPEY0KmHlKb87ol8iTf67msT/XUG518NRBp5bHgs827GbxnhzeWLGFF8+dyLWj+x+7TSJH2zZFZNpHD+nyMjIyJzoGjZoGu5NALwMDI4L4duPuo/6mtM5CRYOVM/vGNVOehom9ounqYEVL03JZsDOTd2afTmw7QynLtJ74QF+m9onjxUXrsbZh4SC63a3KmTAhOZq/b7mQGpudBTszcDfToX7bkU5uVS33njYCgACjgQERQby2ZCOiu207yG63xDWfL6Cs3sLj05s3Yft52z5GxIQR0Qn5MWSOL6sz8hnw5Efc//NydhdX4HS7eXf26aQ+ci3vXTqV+xtNXjXKnrdzr9eoePLsMfQLC+TTNTsoqK47pvU9MX1sk1P/XT8u4aZvF7X5hLXVqOTNp65A3p7vAShUqpM+EVx3QyEc5Ggqde/j956CQqFAlCSUGjV3nT6CSz/8lT1lVfQKCeDt5ZuotdrpGx7EmMRIdheVU15vZV1WAQBD48ObxsjBsnHjeVEKKiXFNQ2EmIzH3Pxqc34JXlo10wYmyaZeB3Esx8yTsyYw8plPeGzBKp4/b+IRn/u+kkrmLd1IRnkNiSH+rZpbB8eFc/nIfjy9cA3vrNzKA2eOYuagZEx6LZtyirj+y4Wc2S+BMb2iEQQBBfDseROZ+uo3LE3P57Q+hyvRLbEkNYsle3O4aeIQwppRcmusdpbszeHJc8Z32ntBns+6ltLaBi7/6FesDhebc4sZkxjJ25dOJbqZfEongmx+ufl8Bj/+Ie/8u41Hp49B047w8q2hX3QIC2+7mPPf+YnyegtfrNvFzsJyvrt+FqHmzjX5VYidWpxMC8grXxkZmW5BVkUNsQGel/TpfeMxaNT8uTODz9bs4K1lm/A16KlssBLp60N+dS2SBGaDlhh/H/yOEHlpW34p4Xe/Tp3NQXygLx9deRaDo1sXqaYtSJLELV//zSertzNDVlC6lKRgP56ZOZ47v1/MtP4JjE+ObvHa37bv45PV2xkeG84ZfeNbXcerF01hYGQwzy9cw8t/r+Ou7xcDEO7rRZ+wAD656qxDZD46PgKAB+Yva5OSsqOgFKNGzdMzxzf7/cKdGThFN2cPTGx1mTLdi2s+/Z1V6fkAfHDFNGYNTkbdA09KWkugt5FzBiXz+uINvL9iC69dfBoXD+tzTOoaEhPK8nsu5YYvFrIiLZeteSXc9NVCfpx73jGpT+bYIispPQBBpUKQjxa7FfsjrggqlWzD3UlkllcTF+SHoFKjV6kZmxzNY7+uJMTHi0fOHsddZ4witbCMqS9/SajJmxX3X8Wnq7by9G8rOP/d+Xzxv1l467SHyObp86dwRv8kUgvLCPIx8spfa3lqwWp+uvnCDrfXKYpsyy3h3325bM0txuJwsmD7Pp6YOYGbJw9HUMr94mCO9Zj538RhPPnHKjbklDChT0LL7VAoMel1/H335a0u2+ESufvHf/hwxRaSQvy5adJQdGo15XUNPLtgFecMisRgODSpngAkBvsRZvZu0/wd4muiweGkyu7Cv5lEfX+lZjEoKqTJV6EzkOezriWv0mP29MX/ZnHO4CPnbzpRZHPH1NHU2BzM37yH6z9fwOxRA49ZXdFBAfxx+yXM37yHnzfv4aZJwzp9DSXIq+cuQX7MPQBBrZQd57sZTS8OtQpBOvZRS04GMsuqOTU5pqmvPzxzAgadhp82pHLeiH4oNGr6xoSx+IGrcLrchAaYuW/GeEYkRXPxvO844+Uv+eGWiwjyMwMe2fiojJw1JIWzGuvYWVjO0tRMSi02ArwNKBVtf+nbnC5+3byH+7/9m+KaevQaFYOjw9Br1Vw6egC3njm6XeWe6BzrMSMA3notNlE88nypONCO1mB3ujjnzW9Zsy+XVy6ZytXjTkF1kAKaXl7Nb9vSeG72GYedngmCgChJbZq/pwxIQqtS8umaHdx55uhDvnO7JZbuzuaqcYM79Z0gz2ddh+h2o1AouHLMIGa2ItDDiSKb+LBAvrjxAj7/dytzPvaY8vYOOzwIRWchAOeO6Me5I/odmwpszmNTrswhyCtfGRmZ4069zUFRdR3xjfH0JUmiqsFKenElfSKCiAs6EGc/KeTQvBDje8fy971XMvPVr0i861X6x0bw+CtvIkmHOzj3CQ/i7cXribvjZcYmR/PnPVe0uo37iiu479u/WbY7C5vTxfRBydx+xigGxYQdMxtrmbah16ipa8wQ31l89u9WVu7N5s+7L2dMcsxh3583rA8fLd/Muox8RiRENn3udImkFVcQ0sbw10E+Rsb2imF9Rv5h361Jz6W83sKUNpipyXQv/tq+j30lFbx2+ZnHuynHhWkDk0kJD2LS0x/xxdzzmZjSelPI7oQgJ9juEmQlpQcgqNRyGNNuhsD+I3g1Aj13d6u7kF5QBkByRAiCWs2Lvy3n0e//oV9UCK9ePv2o/b9/XARrnpzLou37+HNHJgDnv/EtN04exqS+8U0nGxePHYwT+GrVVlan57VqXEmSxDuL1vHgt38R7ufD/50/hQl94ujbygzMMh66Ysz0iQxmW17JEeUqKBU4RRGLW8Ko1bR4ndXh5KaPfuGXjalccuogxvZt3gdkbJ8EvHQaVu7LY2TvOCrrLXy0dCMfLt2AWqnkljPHtHn+DvE1sS49F7dS2dR3nS6R95dvJibQl1G94xA68bROns+6juJ6KwARgf6t6hcnmmz8fdUsfvg6Lpv3Lee88hVf3HQhM4akHO9mtZ1O3gyRaR7ZJkFGRua4s6+oHICkUM8pSUlNPUmhAax9Yi6jm9m9bo4gHy8uOXUQn9xwPgCVDVbOefEzhj84D3tjGEq9Rs31k4fTKzyISH8T7laEh3170Vru/OIPrho/hHVP3sjNZ4ySFZRuyrD4SDZnFeB0tRx6RyEIWBwu7v3qz6bPaq02Hvnub65790dW783B7Xbz/uL1fL16G3NPH8nLl01rsbzKeiv1NgehZm/u++pPkm57kad+XsqY3rGs/L/rOXNQcpvv48pxg9lXXMHg+17n3X/W8cDXC4m66Vm+X7uDG6aMQCGbE/ZYLh41AI1Kyct/rDzeTTlumAw6frrjUmYOTeHyed+xJi3neDepzch+wl2DfJLSE1Ao4QSO/NEzaVwkKJV4rF9lOkJaSQVBJi9M3kYA1CoVEh5n0bbjkc2SR+fw7sJ/ueeLP9hbUkn/6FDAczKyObOAgspaHvr+H56ePbXFkpbvyuCeL//ktmljjnidTGs49mNmdEocNqeLVel5jO/TvEmUWxDQqpV8tHQjV00cxoDoUG7+5DcWbN5DhL+JL1dtRRAEJEliSv8kHr/ojBbrW7oznZkvfIpaqeDXLbv5Y9Me7ps5gTlTRhLURjOvgxnZO46lj83h1d9XcOfnf+Cl03DtpOHMHN6XwbHh0OmR4+T5rKv4fWsaDpfIJ8s3Mef0UU3zUsucmLJRKZV8OPdCdj/4JvMWrWVk7x5m9qWQ12Rdgayk9AAElVrW2rsZJ9oR/PGmqLqeCH9zUz/PqajB7hLb1e/3y8YuCbz7zzpGJsfQLzaiyTwmu6SCXfklTDslhVf/WMkd50wk0Kf5BeX363aSGBrIk5ee1anmNScjXTFmTkmMISrQl9+37GXCgBaiJimUaFQqLHYnv23ewwNfL+Tf3Vm8c/35XDruFBZtSyO/opoIfzMT+yUitOBvJEkSd3z6O4E+XrjcItuziwG4YuIIgv19O3wvw3vF8XWvOIqrazFoNPgYdB0usyXk+azrmL9hV9O/UwvLGRAfdcTrT2TZaFRqZgzvx/uL1vTANY5s7tUVyG9d4O2336Z///74+Pjg4+PDyJEj+fPPA6YAkiTx2GOPERYWhl6vZ/z48ezateuQMvbu3cvo0aOJiIjg8ccfP+S7LVu2cNZZZxEUFIROpyMmJoYLL7yQ8vLyLrk/GZnuTkl1HSFmT/ZsSZL4Zf1Oymrqm3V+by2v/LaMjOJybp8+jv2l5JVXM/vlz/HSaQjzOzxx2n+prLfg184oYDJdjyAInNorljV7so5yHQSbvHh+/hJ25Rbz96NzuHzCUBQKBacP6sU1k0dw+qBeqI8QEEGSJAqraogIMGMy6CmurgXAfIScPe0hxOxzTBUUma7lpqljmv599tCjR/c60dFrNTjFnqd8tUapkteWHUc+SQEiIiJ49tlnSUjwxNb/9NNPmTFjBlu2bKFPnz48//zzvPzyy3zyySckJSXx5JNPMmXKFPbu3Yu3t2dhdeONN3LZZZcxdOhQ5syZw6RJkxg9ejSlpaVMnjyZ6dOn89dff2E2m8nKyuLXX3/FYrG0roEqFfS4XYYTncZjd5UKaP9CWsZDUXUdg+IiQaXmuR/+RpIkbp8xEUHdsmNzy3hkIwkCapWSC178FI1KSaDJm4KKaoJMXkQE+PLh4rXcM2sKgX4t73rHhQayMSNPHn+dQteMmTOH9eerlZ+wal8eo5szIVEoEBBY/NStFFRU0z82HLPx8HwkR0MBfH331Vz8wke4RDeJYYGYjHrMJp+O30SXI89nXcWp/ZO585xJvPbbUiRla97tJ7Zs3Ag4XCJlDXYCO2Ai2fUc/SSl268tewCykgJMnz79kL+feuop3n77bdauXUtKSgqvvvoqDz74ILNmzQI8HS04OJivvvqK66+/HoDq6moGDRpE//79CQsLo6amBoDVq1dTW1vLBx98gKrRvj42NpaJEyd24R3KyHRf9uaXsDWrgOtOH82aPVk88e2f3H/eaTx8Ucd8QB684AzunD6etXuz2FdYRlFVDeU19XyxbAOhfiaWPX0bQxNbzkwOsDOnkD6RR7MZl+lOzBzRn8HxkVz60icMiA1Hq1bz7o0XHaaIxIcGEh/asTwNkwYkk/vRk6iVSnrPfYIzh8g74zJH55yRA3jp58X8tWU35x7DpIY9gasmj+Slnxfz2q9LefKy6Uf/QXehFf6S8tqy48hKyn8QRZHvv/+ehoYGRo4cSVZWFsXFxZx22mlN12i1WsaNG8fq1aubOtLjjz/OlClTsFqtnHXWWZx++ukAhISE4HK5mD9/Puedd95hyb5ahVLZqgEh04VIjXJUqkA48Xa3upKXf11KiK8PsyeNYMoDrzE4PooHZk9rf7CIg2Tj5W1k8pC+TJIknvpmAR8vXsvVp43i1esvPKIpD8CfG3eyeFsaz109Sx5/nUEXjRkF8OPDNzDpvpf5Z+teRLebzOJybjhrHA02B2/8tpwQP1ObZbp8exqPfvEbPgYd795yKaGN5oI6lYqP/15NXnkVI1ISemZfkeezLmVQYgzj+iVx/byvOGvkALRHCkV8gssm0N9MckQI5fWWnjV2lK42Xd4t15Y9ANnQupEdO3bg5eWFVqtlzpw5zJ8/n5SUFIqLPc6QwcHBh1wfHBzc9B3AmWeeSVlZGYWFhcyfPx9l4wJrxIgRPPDAA8yePZuAgACmTp3KCy+8QElJSdfdnIxMN2ZHdgGnDU4hs6icDWnZ3HXuaag6OZrdk18v4Klv/uTxy6bz5tyLj6qgzF+9hVlPvMPUIX245vTRR7xWpvsR4uvD9w9ej1tyc+G4Ieg0am5++xvu/egnJg7sxS+Pzm1TeZ8sWs3UR95AEAQWbdnNmLteQBTduN1ubnrra+bO+4orJo/kTNnHQKYVKJUKXvnf+VgdTt76ffnxbs5xxeF0kVFU2iofwZ6IvLbsGD1IbT22JCcns3XrVqqrq/nxxx+54oorWL78wOTxXy1VkqTDPtNqtQQGHm4+8NRTT3HHHXewZMkS1q5dyzvvvMPTTz/NihUr6Nev31Hb5lJqcCnbY5svc6xwSQKIHtmciLtbXUlEcBA1NhdF9Tb0ej0pibEd6u//lc3d733Lp4tWc/8lZ3P7hWfRcgYND9X1Ddz36a/MGjecj++8CoVCQdv2zGSao6vHTFJsNNdNm8jv67az9o0HUSoVaFQqdI271q2VaVp+Mfd88guXnz6Gy6aM4uKn3qVvfBSSRser8xfx9cotvHP7VVwyaeRR+1Z3RZ7Pup7E2GhOHzaQ9ekFR5zvTnTZLN+ZidUlMX3MsB61znEpW+fs353Xlj0BQepI+JwTmMmTJxMfH8+9995LfHw8mzdvZtCgQU3fz5gxA7PZzKefftrmsh0OB4MGDWLIkCFH/H1tbS0mk4mvvvoKg6Htjp0yMjIyMjIyMjKdi8ViYfbs2dTU1ODj0/pgGd1hbdmTkE9SWkCSJOx2O7GxsYSEhLBo0aKmjuRwOFi+fDnPPfdcu8rWaDTEx8fT0NDQqutHa6rw09naVZfMscElwXK7H+O0lahOTFPQLqGspo4+V97H6zdfxid/rcRqc7D45ftRKdtviXqwbN788S9e+3EhK157mDvmfcny7XsweRl4+LIZXDp5NArFocL7fvkGbnz1E57730VcdVCoUJmOc7zGzLu/LeGRj39i/hO3MapPQrvKKCiv4o55XxEbGsBT11xAdUMDMx96DafbxV/P39vjQwTL89nxYcG6bVz57Hv88uTtjGyhb3aWbERRot5uw1unO2zeO57c/fY3bNibybJXHzjeTWkTlS5ru37XndaWPQFZSQEeeOABpk6dSmRkJHV1dXzzzTcsW7aMhQsXIggCt912G08//TSJiYkkJiby9NNPYzAYmD179lHL/v333/nmm2+46KKLSEpKQpIkfvvtNxYsWMDHH3/cqvYp1SqUallU3QlJAuyNsuk+832Pw+xjxGazUVhRxaptu/n4gTlodR078j9YNos2bGdE73hiwoP4/olbWZe6j08XruDGlz/iuyVreP/e/xER5I8ounnpm9957KMfuOyMMVx91jgUcm6UTuV4jZnrZ0zm55UbuO7599j5+Qvt8neKCg3khydvBSCvpJzp975AVV09f738AL49Kmxq88jz2fFh2qhBJIUH8cyXP/PHC/c1e017ZNNgtbN8ayr/bNzBkk27yC+rwGLzhMxNjAjh7tnT6RUdTr+4SLSa4xdeXZIklm3ZyZDe8T1ujdMahbG7ry17Aj2rVxwjSkpKuOyyyygqKsJkMtG/f38WLlzIlClTALjnnnuwWq3MnTuXqqoqhg8fzt9//90Ux/pIpKSkYDAYuPPOO8nLy0Or1ZKYmMgHH3zAZZdddqxvTUamW6PTaIgJCaSytg4/byN7c4s6tfx1qek8dvV5gMdZdVS/ZEb1S+biyaO57rn3GHvT/7H8jUe4+dVPWLRhB/ddOoOHr5x1wkZKORlRKhVcMHEkt7z6CVV1DQSa25/HZFt6Duc++DJqlZLFrz1MQkRIJ7ZU5mRDoVBwyWmjefj973G73e3eGNmWnsMnC5azOc2TxHR7ei52p5OY0EAmD+lHcmQoXgYdeq2G75eu5X/Pvw9AqL8vt5x3BtdOn4hRr+20+2otf6/fzp7cQl66ueethaRWRCKT15YdR/ZJ6cbs90nJ+/1D/Lxkn5TuhEuCpTV6JpissnlEB5Akib6zb2PqqMGUVFSRXVTGynef6lCZ+2Uz3seCeeJsXr/jGq45e/Jh1xVXVNP/kttIjg5n895Mvn7iDs4eM7RDdcu0zPEcM6fd8n+s2r6Hir8+Radt+0mdyyXy/q+LePS9b0iKCuP7Z+4m1L/lJKA9DXk+O348+9lPvPXDn+T88l6zmyNHks1bPy7ki4XL2bYvm5jQIHpFh+Pr40X/hGjOGDGIxMjQZsssLKskv7SCD379hy//WsHQ3gkse/uJQ675+PfFmLyMzBo/olPvdz9f/72S21/9mL5xUSx649EetzFUWW8h8qxr2uyTItM25JMUGRmZ40ZGQTHZRaVMOqUf9VYbVz7xBn+s2si00UM6XPayzbsAiAgOaPb77KJSGmx2MgqKGdE3iemndrxOme7JtNGnsHrHXmwOZ7NKiiRJSJJEUXkVQX4mFIKCqrp61u1KY+W23Xz3z2pKq2q4+qyJPDP3Uoz6nu2DItM92JWZx49L1xJg9jnqIr2mzoLZqEPZ6K+3MzOXu9/4lDNHDeaOi8/mnLHDUB0ltPp+wgL9CAv0Y0jveKrq6lmwejOfLljKFWdOAGDeD39yz5uf4e/jdUyUlJyiMq5/9h1mTRjBa7df0+MUFJmuQ1ZSegIKpec/me7D/vNHhRLk+bXdLFizFY1axdhT+mHU67jyiTfIL6vqWH9vlM3qHWkY9ToSIsOxOcXDFqcRoUHotGqCfM189tjtCEp5OjymHMcxkxIfjSRJfPPPaq6fdTqCIFBRU8e2fVn88e9Gvl20klqLFVF0o1Yp8TboqaytByDA5M35k0dzxVmT6Bcf3bUN7yrk+ey48MJXv7AnJ5+3772h2Tlv/rK1vPDFL/zf8y+RfOFNKJEIC/SjqLwKi91OclQ4n/3fHejbcToIoFAo+e6Ze7n1pfe55aUP6Z8Yx5MffcfCNZsB6BMffUzWHo9//D1mbyPz7pnTrRT+5sL/toi8JusS5LdyD0BSqpFUx8+5TeZwpMYQ6ZJSjST7V7cLt9vNB78sYvrYERi8vVmxZScAocFBHerv+2XT4HDSYLXRf/YtqJRKLjhtDAOS4jB5GbE5HLz8+U8YdDoWvvUkwf6+yHavx5bjOWZGDvLkDLjztY/4fslqBvWK5/2fFuISRYL9fbl8+mSiQoOICAogv7Sc4vIqesVEILrdzJ464cA9dG2zuwx5Pjs+xISH4nZLPP3JD5jNJiYMHYBGpWL+0tU88tbn5JWUMX38SADm3X8jhSWlFJZVEh7kj1GvY/q4EeiMxg73y9svP48Pf/2Ha5+eR3puIWeeOpQF/25gxZZdfLN4NReePq7jN9vIzowcvvl7JfPuvxGDt3e3GFNOl4uRV9zBnqw8tn33FvERoce7STKNyEqKjIzMceHvNZvJyC/ivYdvAeDBNz5laJ8kpnWSX8hNF05n2fot7MrIwSWKLN+0gx8Xr8Lh9KTxmzVxNA9ccyHBJ5BvgUzzGPU6qlZ8zw+L/uWpD7/h64XLuO/qC5g1cTTxESFNWZxlZLqSR6+/hAtOG8sDb3zMhfc+c8h3004dymNzLmHmpDGsqPTMV6pjpEBGhQTyf3Mu5aXPf2LC0P4s+HdD03ff/r2iU5WUz39fjK+3Fxef0XlldhSVUklV48np4Itv4oIpY3n93jnotS0HE5CU8sZxVyArKT0At1KFWx4Q3Qq3IAGiRzbdKOZ8T+LdnxYyqHciQwb0xS0IZOQXceeV54NaS+ty+TbPftkEBwex6os3uOqh51m3fTe7fvkYt1uipqEBq81ORLAng29H6pJpPcd7zCiVai48awoXnjXl8LZ1eWu6F8dbNiczyQlx/PDq4/y7eQdF5ZU4HE4iQgIZP3QgAC53F8hGCbdfdRFzLzmXjLwCsoueZXDvRL5duJS/12zmovuf440HbsbfbOpQNVt2p/PWd7/z0PWXotIZutW4++bFh5lw1R2IopuvFy5j8qihnH8E5cwtmwd3CfJTlpGR6XIKS8tZvG4Lr947F4AXPv6WmvoGgjr5VEOhUBATHsLPS1bx3g9/MOeC6fibfKBj71oZGRmZTkMQBMac0v94NwOtRk1KfAybvnsHSZKYNWUMO9Iyeee737jigef4/a2nO1T+1wsWExrgxx2Xn9dJLe48TklJ4otnH+Cy+59BkiSe++hrlEoFZ48f1eqABDKdj6yk9AAkhRJJ1tq7FVLjzqOkVCHJO49torqunpufmYdWrWbm6RPYm1fEE+98zh1XXchFZ52G1MFIL/+VzcM3XsWyjdv4delqrr94ZufchEybkcdM90WWTffleMrmjHGjOGPcKBJio7jyvqdJLywlPjKs3eWt2rqLSaOGoNBou4Uvyn+ZPnksjxeV8vBrH5CWnc+VDz7HZTNO582Hbz/sWkl2nO8S5JVvD0BSqHErZHOv7oQbTxpgt0Itm0e0kZ+XrGbR6g3MveRcvHzM1OeXADBjygQkpabDL6//yubnf1axJXUfrz50hzyOjiPymOm+yLLpvnQH2UwYNQKlUsEHP/zBk3fc0GwELEmScDidaDWHRhrLzC0gLTuX6tp6dqRlMnXc6G49D99w6QV8/9cytu9JB2DB8rUsmrKV5Lho3v/2Z9QqFTdffsFxbuXJg6ykyMjIdCleBk9i0nuvvwKA8OBABEFg175M+vdK7LR66hss/LnsX+59/k1mThnP5TPP7LSyZWRkZE4WfLyMPHrzdTzy6rts2rWXM8aOoHd8LCMG9sXk7cXStRuZNfdeACo3/YMgCGTnF3LfC/P4a+XapnKG9uvNBdMOT6zbnVAqlYwbNpja+ga+euVJrr7vCc676b5DrhkzZCD9eycdpxaeXMhKSg/ArVTLjvPdDI+jaaNs5J3HNhEeEQ7AnpxChvRPoaS6Hi+jgaXrt3LhOWe1qSyXy8U7X34PgI+XF2WVVZRV1jDtgks45ZzLqayqZsLIobz82L1Iqo6f0si0H3nMdF9k2XRfuotsbvz/9u47PIpqfeD4d3ezu9lNb2TTE0LvvUuTYsV2saAiilxREXvDxtUrdtSfV+xiu4J6VURFERUBpUpRamiBQArpvW05vz8Ca1ayECBlAu/nefJkd/bMmTPzzuzOO+XM9VcTFxfD/K++57k3P6K8shIfHwN9unZm3Z9b3eWGTpjK+PNH8/K7HxPgZ+WVxx9k5KC+WMxmAgP80el0mrph/u+KS8vYsG0nsTYb7du15dfP32fzjl3sO5jB2k1beHv+F/Ts0RVnVVVzN/WMIEmKEKJJ9ezUHj+rhaUr19KnWydun/kMtogwpl135QnXVVpewWOzX3O/DwsJIjEulvMvh2mTJnDZ2BHERkU2ZPOFEOKMNG7UcMaNGo7L5SItI4ufflvDkhWruHPyNdx+w9UsX7Oe1z76lMdmv8Y5wwbxnydmEBIU2NzNrjeHw8FZ/5jEwcxDvD7rYaCmU4N96RlMe/QpyisqueLCsfhbrRQ4tJxqnT4kSWkBXHoDLr2ESktch48F1cRGnn52Ir5dtoKy8go6tG9LZn4RG7fuYM6sR+nUscMJH2HzDwwiKCCAm669nLv+OQmDwYDD5WJNai43T5qAj16v6aN2ZxLZZrRLYqNdmoyNHuLj47k+Pp7rrxrvHjxm5DBGjxjKodw8IsPDNH/W5O+qlZOS0nIAvli8lNHDhxIY4M/7n3+DTqfjuYfv5dLzRuPS++CSG+ebhEbWeCHEmeKL75bQt0dXzj97GKt+3wTAWQP61GtcpRRFxSXu99m5eRSVlJCcECcP5BNCiGayO3U/v63bSElpGbaIcHQ6HeUVleTk5aOUti+0LS0rY9b/vcGaDX+yYfHnvPDoffy6dj0vvfU+23buYdXvm5j4j4u47vKLCfD3a+7mnlHk8HwL4NL5aLo3jDORSx0+uqUzaufoVguxNWUPo4cNwaU3snnnHqIiW9EqMqpeR9we/PezvDvvM7p37kjXju1J2b0XgPziMvc2IrHRJomLdklstKulxGbSnTNI2ZOKyWjkrAF9Sc86xJ7U/dgdDu745w08ePvNzd3EOlVX2xl15WT27Evjxbfep32b1vTv1YPyikoMRjP/vP8xWifGc//tt3rsh7l0svvcFGQptwA1p3nlKLGWuFTNDYyaOgXfApSVlbPvwEE6dWyPS28gJDSUwuJinDp9nd1a/t2PK1bSoW0bbLZINm3dQWJ8LG+++AwXjB2F6/D4Ehttkrhol8RGu1pKbMLCwmBPKnfeMoVfV6+lV7euXH/1FXz06Rfs2rdfs/swr77/Hnv2pTH734/y4ONPk7J7r/vg10tvvovVamHR/A8wWawt6tK104UkKUKIJrNlRwpKKe6fOYu16zeRsnsPFRWVFBYVExJ8/MfAJyclsD1lF77ZZiy+Zt6Y/TR6Df9wCyHEmeCpRx9g9CVX4Ws287/33gTAbrfz4SefU1mpzZ6wVq1bz9MvvcpNk67hyksvYvTwoVx3yx1kHspmysQJHMzI5OYbJhIbHXXUuFpNuk43kqS0AC69D065cV5T3Kfg9T44ZSe53n4/3FVlVXU1H3++gODgILp06kBASCjOepxJ+ddDDzD0nHFkZecA8MeO3XTr0smjjMRGmyQu2iWx0a6WEpvktu3o37c33/+8jBtvmIRSijsefoIt21O4787pmtuH2fjnZqbe/SA9u3flofvvocrp5IEnnmFryi4++/AdenXv5i7rrGN86cyoachSFkI0me0pOz3ed+3UkeLiEhwOB0bjse+7Ukrx1nsfAjB21Eguv+Qiunbu2GhtFUIIUX83XDuB62+ezsNPPEXagYMsXf4rAGcNGtDMLftLQWERz//fq3w471O6d+3M3Nf+D71ez8R/3spvq9fw+ksveCQoonlJktICOHUGnHKTlqY4dc7D/w2gk9O+9dW/f3/W/L6B0rIyjD4+XHn55dx6x1288uZcbp92yzHHPZSdzYfzP+PBe+9m6pTJAHVeIyyx0SaJi3ZJbLSrJcVm5NmjuHXqP3n3/Q9xOhw89tCDDOjXF73JUufZiKa0c9cu5n7wEd8s+r7mkuN77mTydRNZ8dtK3nz3PVatXgPAqNGj69VWp8ZjcbqQPd8WwKk34pTevTTFqWpOuzv1RpBrU+tt7of/5WB6BgAXnH8+UbFxAMz/3xdMm377McdNSd0PwIizRx1ze5DYaJPERbskNtrV0mJz9z33cvc99+JwOPDxqdnFbO4E5auFC3lwxkOEhYVx5VVXMvmGG4gID2fjxo1c/8+b6dmjB7dPn067dm1lX0tjJEkRQjSJyspKdu7cxZ133I7NZqNPnz41PcIAubm5dY7jcDh46+13+OijjyguKSE2NpakpKSmbLYQQogTdCRBaW6v/Oc/vPjSy1xy8cX8+4nHsVgs7s/efPtt4uPi+OzTT064AxZJZpqGNtYicUwu9DjR/hGUM4nT/d8AEpt6WfP7BpxOJ0OHj6RLl64AVFdXA3DbbdPrXMcfeewRPvvsU8ZffgXR0dEMHDgIDKZjHpmT2GiTxEW7JDbaJbE5eatWreTFl15m+vTbmXbbdHQ6nXt5/vbbryxe/APPPfcCSm9s9rM9om6SpAghGt3GDRu4ffptdO7chfbtO7iH79pZcyN9z169jxqnrKyMBQu+ZNKk63lwxkNN1lYhhBAt35YtWwBYvPh7dqTs4KqrJhASHML8T+bx6Sef0KdPH8ZddNFJ1e1Cuz2tnU4kSWkBnPjglFBpihPd4f8+yNGt45s5cybFxcUUFxfz9DPPMOPhmQD8tPQXrH5+dOvZ96h1XO/jS9du3Xn//fcwGM0MHTaCnr16H/cyAomNNklctEtio10Sm5N3xYTrqLa7yMnJZs2q37h+0nUABAYGcv+DD3PV1RNR+mOfmfdG9smahixlIUSj+79XX2f6tKls27qF7du2oZQiJyebue+8xYXjLsZkMh01jsls5r0P5/PKy7P54L13eOuNOdx7/wwmT5naDHMghBCiJfHz82PqLdMAcDqd7N61k8rKSpJatyYw8PgPDxbNT5KUFsCpDDiVHEHREqc68t8AEpvj2r13H9u2buHGm6Yx7fZ7cOHDj0t+pLS0hGm33+91/dYZDEy7435s0XH865H76dy113G3BYmNNklctEtio10SmwaiN9CmfRf32yPL9WTJPlnTkCSlBZAb57XHhTr8X4+cgj++jz54l05dunHb3Q+5b17MycvDaDRh9PXzun6nbN/KA3fdzJ7dOxl+9lh69B2E8zjXAktstEniol2nEpv0g2n88N3XdO3eizZt2xMcEtoILTxzyXYjzmSSpAghGt2BtP0MGzEKnU6H3W7nkftvZ9HXX3DNpH9i9fOrcxyXy8W/Hr4bpRQffPI1PXr1beJWCyGOZ/rU69iVst39/s77HuH6Kbc2Y4uEaHxy43zTkCSlBXAqvfuBTkIbXEcesKX0KInNcQUFh1BYWIRT6cnLL2TR119w4y13cfP0++pct8tKS/ngnVfZ8ucm3vrwS7r27F/v0/MSG22SuGjXqcQmJzvb473FL0B+rxqQbDfiTCZJihCi0QUHh5KVkX74dQgAOh3sStlG+45dPMru2LaZqZP+QWlJMVdPuok+/Qc3eXuFEPVzx32PsitlGxaLlcioaC4Zf01zN0mIRieJeNOQJKUFkBvntcelag7rO5UBJbE5rmGjLuCJh6az9KclDB15DmPOv5S3Xp3NW6/O5ttlfxJpi3GXfeWFWQQFh/LxV8uItMWc0A2OTqeTHVu3EGSEFct/oaqqihGjLzjhpwmLhifbjHadSmwuuPTopORUb0oWf5HtRptkn6xpSJLSArhcBpwu2SC0xOU6/MPhMqB0Epvj6T9kNHq9noz0DJwuAzOfeYve/Ybx1GO3k5WZTXireHfZxDYd2bF9MyFhMSe03v+0+CveePnfZGcdZN68eTx4x2QqKiro2WcQc97/RhKVZibbjHZJbLRLYiPOZJKkCCEanb26CqUU6QdScdjt+BiNnDPucuZ9MIebrj2XwKAQ/PwC8DEaKczPpSA/l3MGJ/Pdil34GI31msaMO2oe1GWxWDyGK6UkQRFCCNFgXHLguElIktICOJQOh1z/qClHbmZ0KD16ic1xtYppzYwnX+PJGVP5dsE8Jk29l8uunsqLby1g2ZKFpO7Zwa8/LyIv95B7nOKiQg6mHyQ2Ible07jn0dks+ORdxo2fCMD4a2+mfedeDBgySrYfDZBtRrskNtolsRFnMlnjhRBN4tyLrgKguKiA/3tmBpeO7Mjmjau56IobSNm6icKCXMIjbNz50HMYDD7o9HpKSorqXf8lV97I+1+u5JIrbgRgym0PM3z0OHwt1kaZHyGEEGcmh9I1dxPOCHImpQVwKj0Ol+STWqJqdQvpktjU20ffbOS6i/vidDjIyz3Eo3dfj06vR7lc6HR6Zr/zLQmt2/P6i//CPyCQR+6axE13Ps7QURdhMNTv9LrERpskLtolsdEuiY02Se9eTUOWshCiycQltmXhiv2MGHupe5hyufDzD+Tex/9DQuv27uFDR11EWISNmXdPZMJ53XjywSn89+0XKCzIbY6mCyGEEKIJyZmUFsDp0uOUIyiaog7Hw+nSo5Nc/4RY/EJ45LmP+MfEdbz/2pOs/fUHzL4WVi9fQsaB/Yw893JMZl8+/+9r7nGy0veTlb4fAKPJymXXeH+itcRGmyQu2iWx0S6JjTbJPlnTkCSlBXC45HIvrTnyw+GQH46T1rZzf/79n4Wk7d3Bt/97m9TdW1j46dt89OYzBASGEhQSTtHhsyaBwWH4GE106z2E88bfdMztQWKjTRIX7ZLYaJfERpzJJEkRQjSr+NYduPm+5wGw26tZs+I79u3eSnlpMT36jaD3gLMx+MhXlRBCCG2QA8dNQ375WwCnSycbhMYoV03PHg6XTo5uNSCdwZcBwy9hwPBL3MMU4HDVvw6JjTZJXLRLYqNdEhtxJpM1/rD09HSuueYawsLCsFqt9OjRg/Xr17s/V0oxc+ZMoqOjsVgsDB8+nK1bt3rUkZKSwuDBg4mNjeXxxx/3+Gzjxo1ccMEFtGrVCl9fXxITE7niiivIzZWbgIUQQgghWgqnq35dEMu+5amRMylAQUEBgwcPZsSIEXz33Xe0atWKPXv2EBwc7C7z7LPPMnv2bN577z3atWvHv//9b0aPHk1KSgoBAQEA3HrrrVx77bX07duXqVOncvbZZzN48GCys7MZNWoUF154IYsXLyY4OJjU1FQWLlxIeXn5cdvnVDqc0ie3pqjD8XAqHTqJjaZIbLRJ4qJdEhvtkthoU332ybS+b9kSSJICPPPMM8TFxTF37lz3sMTERPdrpRQvvfQSDz30EJdeWtN16vvvv09kZCQff/wxN910EwCFhYX07NmTbt26ER0dTVFRzYPoVq5cSXFxMW+//TY+h6+tT0pKYuTIkU00h0IIIYQQoqnIvuWpkyQFWLhwIWPHjmX8+PEsW7aMmJgYbrnlFqZMmQJAamoqWVlZjBkzxj2O2Wxm2LBhrFy50r0iPf7444wePZqKigouuOACxo4dC4DNZsPhcPDll1/yj3/8A53uxI6GOF06HE45gqIpLh06wOnUgRzd0haJjTZJXLRLYqNdEhtNqs/lXlrft2wJJEkB9u7dy2uvvcZdd93FjBkzWLt2LdOnT8dsNjNx4kSysrIAiIyM9BgvMjKS/fv3u9+fd9555OTkUFxcTEREhHv4gAEDmDFjBhMmTGDq1Kn069ePkSNHMnHixKPqrIvD4cThcDTQ3IoG4XJipCY26E+/L4YWTWKjTRIX7ZLYaJfERptcx98n0/q+ZUugU0qp5m5EczOZTPTp04eVK1e6h02fPp1169axatUqVq5cyeDBg8nIyCAqKspdZsqUKRw4cIDvv/++XtPJy8vj559/ZvXq1SxYsID8/HyWL19O165d6yxfXFxMUFAQH3/8MVar9dRmUgghhBBCnLLy8nImTJhAUVERgYGBdZbR6r5lSyJnUoCoqCg6derkMaxjx458/vnnQM0pNYCsrCyPFSk7O/uEstWwsDDGjx/P+PHjeeqpp+jZsyfPP/8877///jHHqwgeji4grN7TEU3AZcdS8CMVIaNAb2zu1ojaJDbaJHHRLomNdklsNKnClHfcMlrft2wJJEkBBg8eTEpKisewnTt3kpCQANTciGSz2ViyZAk9e/YEoLq6mmXLlvHMM8+c1DRNJhPJycmUlZUdv7DeKF9OWiWx0S6JjTZJXLRLYqNdEhttqUcsNL9v2QJIkgLceeedDBo0iFmzZnH55Zezdu1a3nzzTd58800AdDodd9xxB7NmzaJt27a0bduWWbNmYbVamTBhwnHr/+abb5g/fz5XXnkl7dq1QynF119/zaJFizx6ffDG6QKH85RnUzSkwxdJOlx/vRYaIbHRJomLdklstEtio0nOejxgWOv7li2BJClA3759+fLLL3nwwQd5/PHHSUpK4qWXXuLqq692l7nvvvuoqKjglltuoaCggP79+/PDDz+4+7E+lk6dOmG1Wrn77rs5cOAAZrOZtm3b8vbbb3Pttdced3yHU3r30pzDvaw4nDo4DXvUaNEkNtokcdEuiY12SWxaLK3vW7YEcuO8hh25cX7O17lY/OWeFE1RdkKKF1EQeB7o5BS8phyOzQ+/l7Jl4y84qqsYddl0ktr3ae6Wndlkm9EuiY12SWw0qaI0j1suDD/mjfPi1MmZlBbA6ZTLvbRGdzi1dzql63qtyE7fze6tv3Fw1zpuvW40c1+YQkVFBQCpKb/z+LvbmrmFZzbZZrRLYqNdEhtxJpMkRQjRIhUXZLNt/Q8U5WXyv7fucw+3WCzcet1oAPR6A226DObyqbObq5lCCCFOM045cNwkJElpARyOmj+hHUeObjkccnSrqZWVFLDg3ftZteQ9nA67x2dnX3InyZ36AXDxDc/QZ8S1WP2DAdmGmptsM9olsdGupoiNy+mkMC+d0FbxjTOB05D8njQNSVKEEC2Cw17Nn2u+5vt5T5J7KJVx1/2b3VtWsC9lLbc+sYj4Nr3Q6XTolB2qFjHsgqkouYZbnIEc9mpyMvdQmHuQ8tICzL7+BAS3Ijg8lqBQW3M3r8Wpqizjh0+fYf+u30nqMIDeQ6/AFtf+pOpy2KtJ37eZVtFtsPgFNXBLT87vyz9h7jNX077H2dz6+DcYTb7N3SQhAElSWgSnql93d6LpuK8TdsmRx8bmdDpY+f1bLJ7/JIV56SS068vUx77GYDCy/NvXKSnMprgwF5fSgZLYaJXEpXGl7ljNpt8+Z+cfP5Oe+icuZ92HeqMTu3LxDc/Sqc857mEtOTYup5NvPnyE5M5D6Nz3vAatu7qqgvXL5vPtR49SWpRDm67D+PHzF/jmw8foPugSzrt6JjFJ3Y5ZR352Gmt+fI8/Vi2gqqKEovxMqivL0On1dB94CRdd/zQR0W28jv/32JSV5FOcn4neYCTcloTBp+ZAjN1eRVVFKXqdHot/MLrDPYEppSjKyyA7fSelxblUV5WjXC6Uy0lJUTZF+ZmkbPoRgJRNP7HsmzcYcfHtDbD0Tm9O6XKqSUiS0gI4HQqHQ7YILdEd7hTP4VAoncSmsezZuoLP5tzMoYPb6T3sakZecg9ZB7Yx95kJFOYecJfTG8zubURio00Sl4ZVWV5MYe5BXC4nq5e8w/Kv/4+A4Eg69BxL/1GTscV3IiQ8HmtAKFWVpZQUHiI3czffvP8gb/37EsZNepY+I67F4hfUomJjr66kMPcgOr0egJ+/eI6V378BwAXXPUXPIVcQEh4HOh2lRdkopfALCMPHaMJeXUlJ4SEsfsF1nsUoLy3A6bDjFxjOpl8/ZcE7d1FckEWX/hdx8eQXCLe1xl5dyYYV81k873GeurU7nftdSLvuZ2MwGNHpdFRVllKYe5CCnDRyM3eTuX8LJrOVboMuIzDEhn9gOAntB5C5fzM/fPokj09pR2irRBz2KmwJXeg+8FIS2w8gMq4jBh+jOzYpfyzn569eYcuar9zt1esNBITYMBp9yc/eh8tVc6OEj9FMUFgMfoHh5KTvpKKssM5laQ0IJSg0mqDQWK667V3WL/+YL966k7RdG4hp3R2HvYrqyjKcTgcup52K8iLsVZUAKOXC6bCj0+vxMZoxma1Y/UOwxXWix5Dx+BjNDRZzceaSLog17EgXxM/My8HsJ10Qa4lO2UlQ37Ffd65cUtRI0lP/4Pk7epHYYRCXTnkJ/6BWfDT7WvZsXU6XfuMYffkMQiLiCQiOdB81BImNVklcTp7DXkXarnXoDUaUcrH2p/dY/8t/qa4qB0Bv8GHIeTdz8Q2z0RsMx6wrJ2M3X717D9t+/xaj2Up0Yjda2eJ58PYr2FU9DKPvXzvv+dn7+e271zGaLSS060+4LZmQiLhG3wF1OuysW/ohm9d8RdrOtSil8A8Mp7Q4h9KiHI+yBh8TF173FHmHUlnxzX+Amofk6Q0+Hves6XQ6au/uhNuSCY6IIy9rL05HNQ57FeWlBYfrNOJ02OnSbxwX3fB8nWc6nA4765fPY+mXz5ObuQenoxoAo9lKcHgsweFxhEUm0rrTWXTpdyG+1qO7qa2uqmDD8nnkZOzEx2hm15+/kLrjN5Sr5tIJq38I/oHBvDnnRa666ioCwhIYduF0ohO7Ya+uJCdjJ8UFNWdmIqLb4R/cCpfDTlFBJkV56ZQW5RAelUx0YjciotsRGGLDaLKg19esI39fV1wuFz9/8SwbVnxCTnoKRrMVk9kPg48RvcEHizUIo8kCOh06nQ6DwYhS6nAyU0pZaT4F2fsJCI7kiQ8yTzjuLUlVWR73XxUhXRA3MklSNOxIkvLkf3MwWyVJ0RIddpJ137FHnYtCdrgaQ8qmH3jrX+cw7alfSewwiHdnXUTKxsXc+Mgi2nYb6XU8iY02SVzqz+V0kpu5i11//sSOjYvZvfln7IcTEoCgsBj6jZpMu26jUCiiE7vXuRN8LEX5Gaz98R1yMnZRXpTB0zOnM/G6G4hO7o9fYAT52ansT1mF2RIASrmPxut0OgJCoggIjsTiF4JOr6dd91GMuOS+Y0/wOMqK89i9ZSl7ty4jZeNi8g7tJanjWbTuNASDj4my4lz8gyIICosjODwOAHt1BYntB2INCAWgtCiHtF1rKSnIxG6vJDgsDr3eQFlJLg57FQaDkcDQaCrKCti7bQVlxXmERyXX7LgbfAiLbI3BYKQgZz/x7fqT2GHQKc3TyaiqLOPgnvXkZuykrCQPXFX8c3wPftoeTHz7szwOyGjR20+cz44N35HcZTiJHQaR0H6gR4xOF85KSVKaglzuJYTQhKqKUnxMvhgMNV9LtrjOABzcu4HEDoMICo3G5XKQun0FQWExRES30/wPtjiz2KsrWfbVC+xPWU14VDI9h15NfNu+xx0vOz2FLau/5MCe9eRl7iY7IwVHdSV6gw+JHQYz+vJHaNd9NDq9nurKMhLaDTjuGZPjCQqNZvTljwA1CSR8x4hL72fnn8vJy9pNQLCNi298md7DrsVotpJ/KJWCnH0UZO8nP3sfpUXZVJYXUVlRwrcfPIB/UCv6jpx0wu2oqijl2w/uZ/WSt3A5HYRHtSGx4xCuvecTYlr3PKG6/IMi6NTn/HqV7XnWVSfc1qZg9vUjufNQkjsPBf6KTWKHgSi0/3036YEv2LDsv2xdu5A1S97mp//NAqg5s2RLJrRVIsER8YREJNAqpj1BYbHoDT7s2PAdWfs3077nObTtdrb7Xhutcsh9wk1CzqRo2JEzKY+/n41JzqRoih477Yzfs9N+Di45KnxKtq5dwLcf3Ev+ob2E2dpw9d2fYjL78f4zF1FZXsT0Z34nIMSGvbqSxfMeZvXiOTjsVcQk9WLk+Ifp3Pcij/okNtp0uselvCSfD569hAO71tC6y3By0lMoLTpExz4XEmZrQ0VpAfHtB9BjyAQMBh+qK8v48s2b2b3lZ0oKMjGarcS3HUCYLZlWsZ2IjOtEfNv+NWcyGtmpxuaTV65j86rPePCNA/gF1P+3qqKskPkvX03qthWM/MfD9DxrAkFhsSc8/dNZS95ulFLkH9rL/pSVZKfvID9rDwU5+ynMTaOkMOuo8oGhMRTnpxMQEkWPIVeR3Hk4weHx+AW1wi8g/JQT84ZUXZ7Ho9e1kjMpjUzOpLQADodCLzfOa4oeBcaa2LiQ2Jys7IPb+PC5y+jQ6wKGXfQgK7/7Pz56fjzhUe2pqihh8iM/YQmIxOFQ6PRmzrn6OUZeNpM9W35i9eJX+ei5y7jyjk/p3O8Sd50SG206mbhUVZaSuu0X4tsORG8wUllRRHBY3FHlSgoyKchJxWiyEhAchX9wJPmH9lKQU3PE315djsthR2/wweTrT2BoNH6BrfC1BOIfbDvhM3LVlWXs3fozGakbyc3cSXFBBgd2rcbH6MvkR38mvt1AHPYqfv3mBVI2fsuBXesw+fqzZskbfP/fh4hr25+Du9dRUZbPwHNuI77tAJK7jqq53v9vmqLTlJPdZpxOBzs3LiLv0N7D938Yj2qvy+Vk04r/kpX2B5XlxdiryiguyKAgex/F+QcxWwK46q7PaNttDNA089uStPTvs6Dw1nQLb33UcHt1BXmZuygpzMTlchLSKomI6A5k7tvE7z+/w6Zf57Pi678ewqvXGwgMi2PU5Y/Tpf9lcmP+GUKSFCFEsykrrrkJ9pxrniM8qi2JHc/izcfOIm3nb4RHtyc08ugbVk2+fnTsM472vS5g3ov/YOE7t2JL6EZYZHJTN180sKqKErLTt5GXtYeDu9eydc3nlBRmetz03HXgFZx/3Yv4BUawb/tyvn3/TrLS/nTXodcbsCV0JyN1g0fdOp0epY6+RiM2uS/nT3oJ/6BIcjNSqCwvxmGvPDw9hcvlxFFdQWHufgqy91GUd4CstM047JVYA8JpFdORgJBoxlz5JN0GXUlASBRQ08PS8EtmMPySGe5pZe7/g/VL55K5fxNdBlxGt0FXEpt8/MvBtCgjdQMfPX8pxfkHCWmVxDlXP3PUWR+Xy8Vn/5nIltWfEmZri69fMEaTleCweBI7DCE8ugNJHYcSHC4PETzTGE0WbAndsCV4duEcndSTcZP/w4U3vFJzkKEwi7LiXEoKM9n1x2L+9+pEvnzjRmKS+9B/9M207jwC/6DIJr/0V5LppiFJSgvgcLrQywWQmqKnJh4OhwsXEpsT4XBUk5+1C3t1Bft3rgbAx+SPw+EiKLw1V921gHceP4uDu9fy02f/YsRlj3mt6/xJr/LO48OZ82Bfrn/4Z2zx3SQ2jcDlcqLT6T12BPKz97Lux9cxmq3EJPWhfa8L3J8ppaiuLKW0KAt7dQVm3wCCQ21gAbvdSUHeQXIydlCUm0Zh7n6KctPIydjOobTN7kQipFVr2vW6gN4jbuRQ2p/4GM1UVZTy02eP8OKdHTD4mCgvySW+3WAuu+UDWsV2wWGv5I9fPyInfTuXT59PZHw3/IP+6tFIKUVVZQkl+emUl+ZRWpjF0i/+xRuPDD7m/OsNRoLC4giJSCI8phOd+o+nfa8LCG2VfNTOkeMY39URMV0555rZHsOOVb6pnMw2k52xi+L8g0DNfSX9xkz3mJeM1A38/L+Z7N68mPHTPqZzv8u81qWFZaBVZ/L3WWBoAoGhCe73PYdNJiN1PQf3rCVlwzd89p9rAfAxWWoOQricmHz9MZqsh7tjVpgtgVj8QwkKiyO0VWtiWvcltk1N5xBC+yRJEaKFKS7IoCjvwOHLWmz4+Jiau0n1cnD3Wtb+OIddfyymojTPPbxjn4vxD4p0v49N7kv3wdeQsuEbli14klBbG7oPvrrOOv2DIrnp32t469GB/PbN81x2yweNPh9nkvxDe1gy/0FSNn6LUi4sfiH0OfufBIclsPCdm7D4h2HwMbF8wSyiknrhFxBBadEhCrL3UlVR7FFXTFJXXn3xCV6+ux35ORkA6PQGAkNiCAqPJyqhJ31H3Ux0Yk9CI9t4HJWPTvzrBuqOfS5i1fcv42P0JTKuK217nOuxDcS07uN1fnQ6Hb6WQHxj/rqGvGPfS9i75SeUchEe1R5LQBg+PmZ0Or27jfrDz+QQf+nY5xK6DLyCLas+wWwJ9EjWfvv2BZZ8MoPwqHbHTVCEqC+dTkdM6z7EtO5D/9G3kHdoN9kHtlKYu//w53rsVWVUV5W5u1murCimojSPotwDpG77heVfPQVAQEgMyV1HMei8O2kV0+mE2+KQJ2w3CUlSWgCHQ6GTU4uaoj98bbC9ia8TdtgrmT090WOYj9EXsyUQ/+AoAkNjqa4spaqyhJHjnySpk/euepvawndvJvvAZpI6n02r2C5UlheRsn4BSV3GYHcoyoqzyc3YTlFuGgf3rCM8pjNW/1B++vRROvWf4LVegzGAuPZncXDXSqqrnRj0zROb04nL5WTHui9YMv9eDD4mhl/2L0y+AeSmb2PFwmdQhx8ad839S4iI6cTq714gJ30b1ZWlRCX2pmPf8QSGxuIfbMNoslJRmseqb/8NQPezbiAivjcR0R0JDI1Db6j7Z8ju5TvPZA1n2KVPuN+rY5StHz0JnUZ7DFGH/2oWBjhdp/d6dDLfZ8UFWWxZ9QkAXQdfw8YVH1NalMWBXSvZuWEhA8+7h+GX/gu9wecU43Nma67fmpYgMCyZwLD6X+arlKIodx8Ze38nI3Udaxa/TG7mLq6bsfSEpy2XezUNSVKEaAFWfvscWfs3ERKRdNRnDnslDnslZcXZHEr7wz384+fO5dZndxBcxzh/l5G6nqX/e4iug66h68AJ7qc5N6QeQ69n6WcPk7r1J1K3/uQevui9W/jpkwepqihyDwsIiaHLgKv449f3sPgfv3/9Tv3Gs2nZu2xf9zld+l/c4G0/3blcTvKzdpK+dx0Hdv7G7j8WUVacTXLXsVww+S2PM11nX/EM9upydDo9vtaaB/8NPO+e406jXfdRwI8MufCBFtdLkThaQHA0o658lk3L5/Lb10/jdFThY7IQZmvHhTe+Q9dBV0sX4UJTdDodwRFJ+AVGsmn5u+h0eroPmdjczRLHIElKC+B0KHR2ObXYmP5YMZf9O5YSGd+D2LaDscX38uinffPKj3DYKwiNbEtweBJBIa0AyNq3GWtwAr5+IY3avq2rPyX74J/4+oVi8DHjdFRh8DGh0xlw2CsAMPiYiYjpTLueF6PT66msKKKitJT8Q8sozNlLQfZuSgrSKS3KwuTrT0zr/viYrJSX5rLuh5cwW4P4+u3JfP/BbYREtkWvN1BemktweBK9R96Mf3A0PkZfrP7hWANbuU+nlxVn8+P8u/AxWenc70r0Bh8qywvwC7IRGdcdH6MvAD2H3UynfleTl7UTpVxkH/iDHb9/gb2qlOqqUvwCI7FXl1FekktJQTq/fPEo8e2GMnrCSziOs/7HtR1OVGJvfv36aTr2rumS2OlQOOu4UVr8Ze+WH1j7w0tk7F2DvbrmYYFhUR3pPOBqOvb9B7aEXgB/W/5GjKagOoYfm0FXc+RR4qI9Jxub3iNvo/fI21BKYa8qw2iyug9wOB0e56PESZLtpmFVVZbw5ZzLyUhdxxV3LiKhw7AT+h47wilnUpqEJCktgMupcDllg2hM3394CwDb133mMfyqe36iVUwXFr03xWO4xWJh4Lx5fPTsSOI7nU/vkbfitFcSFtUBq38EJYXpZO1fz85NCykvyaHvqOkk/e2SkhMx/LKn+PTl8+k94hY6D7gas28gZmswOp2O6spS8g+lkLrtR9b/9B+WL3jUPd6a756reaHTERgSS2BoPNbAVlSW5vPbN7NwuZyYfQNp0/1Cxl7zKrkZ2zi4eyUF2btQSmHxC2X/jqV8+dqVHu3R6fQERyQT0iqZA7tWYDRaMZr92LLyQ49yBh8TnfpdSUhkOyrL8sk/tIusfb9TWpQJgDWg1eHL1OKxBrTCL7AVAcExBIUnEJXYF2tAzc2Nx1v/7dUV+AVFkZG6Fufhm3BdToVLHgPl1Y71n/P12zU3ng695N/Y4ntiS+iF2RLkLtOQ3zu6wztbEhftaYjY+Bj9UAqU/FY1KNluTl3+oV2s/v458rNSKMjZg8vp4LJbvySu7RDZt9I4SVJEi1ZdWUp2+mYO7d9ITvqf5GZsRykX1oAIfP1CqSjNpbw4G6PZj6DwJKwBESiXE6ejirLiQ5QWZWEwGEnseDZOp52qimKK8vZTVV4AwJrFLxAZ25W4dsPI2vc79uoyj+nr9T6krP8fKev/5zHM5XIAEB7TBaPRwv9euYgB59xLdOv+7E9ZRkBwNG26XUBIq/pdT5vQYQT9x97Db98+yW/f1FzbH5M8kLHXzCHM1h5bQm9sCb3pOui6w+0sx+Vy4BdoIyAkhqCwBPcZjSOUUkddjhGV2IeoxD5HlSvMTcVeVYajuoLy0hxKizLJz0whN2sHvUfcQu+R07D4hZGbue3wPTJBFOcfYP+On9nwy+vs3LgQi38ogaHxdOo/gZjkAcS2GYyvNbhe81+XrP3rSd+zmtKiTFLWf0FZcRZjr3lNLjGpp/KSHPdrizWEhA4jmrE1QgjRcA6lbWTFVzPJSd9CaVEmgaHxxLcfRlLnMXTsM55QW7tTql+Sm6YhSUoLYHc4UQ5nczejUVWWF1KUm4reYCQipgtQc69FzsE/ydr/O5VlBSjlxGGvpLK8gIqSHPIP7aQoNxWoOWIfFtWJsKiO6A1GyosPUZC9B4t/OBHxPbFXlZGXlUL6nlXoDT7o9UasgZEEhSfhcjqoLC+gurKM6soS9HoD1sBIDD5mcjO2kXNwM0o5Mfr6Y/AxAwqrf03vQF0GX09EfH+CwltjMBjJz9pBaVEWQeGJhNo6EBSWgFKKNd89zbolL+B0VOEfHENFaS6/fP4gJt9ArAERBIbGE9V6ACGt2uK0V1BVWUJxfhrFuam4XE4MPiaMJj+Su56P01GNwcfEnj+/4d1/9eTcSe/RvndN7zlmvwgSOp971PKtubn45Nch/5CE45ZxuBTBkR3d78NiQgmL6U6vs+/0Os7JtKm8JIcNP7/C+p9eRm8w4h8cTau4Hgy84BFCI9thdzjcdTuVJCy1VZYXkJm6jtQt37kTa79AG4ldzjul9aM+XLqa+iUu2iOx0S6JzYkpL8lh07LX2PDzfwiOaE2nAdcQFtWJ5K7n41PrYamn+n0nkWgakqSIZqVcLr6dO5Hdm75yD4uI7UZFaW7NJUFKYfAxY/EPR6c34ONjwmwNweIfRnK3CwiL6kREbDfCojpiMDTdzbgGnQNYxpBx/8Kp/tqMQm3tjyqr0+kYcN6DdB0ymerKIoIj2uCoLictZSmFOXsoL8mh4NBONi173X0Gx8doISA0jqCwRAw+JpyOairL8g+f/cnEcfj+AYCQyLaNPr/NwemopiB7V829NPkHKMzZQ/qe38jL3I6P0cKA8x6i75i73ffGCE/2qjJyM7aSfeAPsvb/zqG09eRnpQDgHxxNtyGTad9nPGFRneTskxCixdu07A1WLHgInU5Pz+G30O+c+zCarI0yrcY+qCNqSJLSAjicLjhNH3ZVXpLN7k1fodf7cNntP1CYvZsDO3/BPziGwLAEwqI6ER7dFcNxngWiVNM+EEzp/nrAVn1vZjRbwzFbw3E6FTqDhYRO51H7/IRyubBXl+Fjsh5zx1spRWVZHsV5+wkMS8TiH3ZaPAytqqKQLSvncmj/7xRk76IoZ4/7sjkfk5XAkHhsiX3pOeIO4jucjTUgAper5qnWtZ1MbFoKp9NOWVEGZUVZlBVlUlaUid1ejsthx15dSnlxNuUlhyjM2UtJQRpQc/lhWHRnopOH0GvkXdgS+xIU3tqdmDidTXOD8+kcl5ZOYqNdEpv6Sd3yHb/87x66DplCv7EPYvEPAxpvv0Cek9I0JElpAZwOBXptbBAVZXmk715Oq7jeBIbGe3xW08NLCXqD6aj7H458Xpy/j6x9aziwYwkHdy+nsiwXgKCINoRF9SQsqifJ3cf/bbwT60WoKSh9rR8OV8O1TW/ww+UE13G+AI3mUMKia7rm1dqyOWL3ps9Z/+NzBITGo5TC6ajCXlWKo7ocnV6P3mBCbzDictpxOaspKTiIy2nHljSAmOShdBk0hVBbJ4JbtcPXGnr0k729zHdjxaY5uFxODqT8xLbV75KXuZWyogz3E9kBDD6+GM1+6A0mjCYrFv8IrAGRJHe/hOBW7QizdSbU1uHwZYp/aY6el06nuJxuJDbaJbGpn82/vYMtcQCDLnwGnU6n2d9FcWIkSREnJHXz1yz/ouYeg4iYHhiMvlRXFlNdWURFaS5ORxUmSxBdB9+E2RpCdUUx5SVZFGTvpCBrO5Xl+QCEx3SnU//rCAiJx2wNJtTWuTlnSzSCyvJ8CrJTKMhO8Rie2Olc/EPicbnsuBz2mnuEDCZ8raF07D8Rv0BbM7W4+TnsFeRmbCZz70oyU1eStW811ZUlRMT2pG3P8QSEJhAQHItfUBTWABtma4hcqiWEOOM5nXYKc3ZTcGg7obYTf4L8CU9PuiBuEpKktABOuxOla5rrH11OB5UV+fhaQut8EnRYTM1zExI6nY+P0RedzoDJNxCTbxAW/3B8/cI4tH8tm355GXR6zL6B+PqFExzRlqiBQ4iI6UlEXC98rUc/oM9hb0HXeOpr2uq0O3G4ZCexLu16XUtVRSkHd/1MdUURpYUHqKooZN+27+gz5mF6DLujzvFOeT1owNgU5e7hwM4fKSvOpDh3L5Xl+QSExBEYmkRkQn+iWg85qXtilFJkH/idnIMbKcxOoTB3FyX5+ygrygDAaPKjVXw/up01nZg2wwiP6VlnMuJsSZf5yTajXRIb7ZLY1MvA85/ix48n8f371zLi8jfwC4rGbAk57qXiJ0si0TQkSRFuZUWZzHu2q/u92RKCf0gc8e1Hk9z9HwRHtCU0shPhMT3Yv+1bImJ6EtV6CDFtRxDd+iz3TlRyt0sZeP6sRnlquWgZln9xO7s3fYbLWV3n58rpaOIWnZiyogzWfPcoezcvACAgJIHAsNb4B8dSnL+fgzt/ZsPPz2Lxb0XPEffQacANJ1T/mu8eYctvr2PwMRMU3pbgiDbYEgYQGJZESKsOhEV1rfMggRBCiKMFR7Rl1IT3+P79K/jqtb+eSabTG7jyno34BUU36PScLemgagsmv4ItQFOdSdHxV+9Yce3GEBbVjZKCfWxd9TYbl75AUpeLGXrpHM6/4Vv271hE2vZF7N70GX+ueIWI2N4EhrbGaPbHaPLDbAnBbA3BbAnBaPLD4OOL3seEyRxIQGhii++RSVfr6JZTjm4dZe+fX+ByVjPmmvkEhdf0ZlZdVYzeYCLU1gW93tBoX/KnGpsDKT+w7Itb8PHxZeglr9K622VHncVQSpGbsYnta95h5df3ExzRkcj4fvWexr4t39K251UMPP8ZdDoDSjmpqiiiqiKfqvIC0nb8WHOvjsuB2RJMdOuhJzwfWiPbjHZJbLRLYuNd2o7vyT6wDpfLgctpp7qyGJMpwKOMcjnZse6/9Bh2V4NO2yVJSpOQJEW4ma0hXDT1Z9Z89zAHdv5Axp5lhEV3xz84jvysIlK3LCCx0zgSO51PUudxJHUeh1KKg7t+JOX3DygtTMNeVYq9upSqikKqK4vqnI6P0UpYVFfCo3sQHtOT6ORhdV7+JVompRSBoa3JP7SVn+Zfz+ALn69zR7+hZR9YR8rvH5LQ7izoFntC41ZXFpOXuZmU9R+QumUBce3GMPTSVzH5BtZZXqfTERHTk7CLXyYnfQO7N80/oSTFPySeXRvnsWvjvHqVv+SW5QS3Orp7ayGEONO4nHaWfzmN1C0L8A+Kw2D0Ra/3wWj2JySyE/Edz8MvMBqLfwS+fuGE2bo0d5PFSZIkpQVwOF3om6hP7sDwDoy65jMKc1LITF1OXvpGfExWopKGYraEEBHbD8ff2hLVeiRRrUceVZfL5aCqohCnvQKnoxKns5qqsjzyD20hL/NP9u/4jq2r3wDg8ru2YG5JiUqtHlccLjmi8nfn3fgDRbm7WPv9DJZ/eSu///hvIhMG4usXUXOWzRKCX1AMgWHJ+AXFHvfMWl7mn3z/3jiSOl+MzmBEr/fBx2TFx2jFaPKj4NB29m7+H9YAG+k7F3Lb1fOOG5s/l8/mwM7vKS08SHVlIQABIYkMOO852vScUNNDzN/W9erKIory9lCcu4vCnBTyMjdTnLeHwNDWR5U9lqGXvcnBXT+Cu6cuHSZLEGZLKGZrCEZTAHq9DyWF+/l+7gV8OWcoHfvfRJ/Rj9V7Gpoj24x2SWy0S2JzlJT1H5K6ZQGDL3qFpC6XHvcAmEuBq4H3oY7XA6doGJKktAAOhxM9TfvlFBDSloCQttCrjvYcPs1ZWZ5HYfZ2rAE29AYjer0Jp7OK6sqasyjVlUX4WsMJi+qBr5+15pkXyojFvwBfaxZmSxilhTXPcigtOoTBGNSUs3hqDDXLwOFwIs90qpt/cDLDx3/EobSVZOxZSn7WH+Rm/EF1RQHVlYXurnT1BjPBEe0Ii+5FWFRPglt1JDC0NQafv7qxTt3yFS5nNXv+/NTr9Hz9WtGuz2RSVr8AHDs2FaWH+GP584ff6YiI7UdQRHvCo3uhN5hI+f1DKstyqCjLpqL0EGVF6ZQVHcRe9dfZQb+gOEIju9Jt6P3EtTu3Xjf8u1xOKstyKC/JRKczU16STkXpoZrtpapmm6mqKKCqPJ+qinyU6697d9J3/UT3YQ+13N68ZJvRLomNdkls3JRSrP3uXvZu/oT4DhcS3+HiZus8RO64bRqSpIiT9sMH4ygrSjtuOZ3OgF9QLOUlme4bqf2C4giP6U3rbleQ0HEcRnPAcWoRLZHeYCQqaRhRScM8hrtcTspLMijJ20Nx/l4KDm0ma9+v7NrwPlCzzvgHx2O2hmEyB2L0DSSm7RiqyvMpK85AuRwYfHzxMVkxGEw47BVUVxbx5/LnCI9sc9x2+fpF0P/c58nY+ws5B9eQl7GRnINr2b3xw8MldJgsIZgtofhaQwkMbY0tYQj+wQkER3YkOLwjRrOfR50VZTlk7v2F/MxNFOamoJwOFAqUC6WcVJblUVF6CKX+2tMwGC1Y/W2YfIMx+Qbh6xdBUHg7zJYwzNYQfC1h+IckEhiajE8jPTlZCCFaApfLzsFd3wNwYOd3/P7DQ/Qc+YjHAa2mciJnzsXJkySlBXA5nKgmPpNSH8Mv/5hdG94nL2M9eZkb3MP1BhOt4gbQ6+wncDoqyU3/neL8PfgHJxAY1pagsLb4+kV41OVsYRu87vCOptPhxNmymq4ZFr9oLH7RtIo/yz2suqqY4rxdFOXupCR/D9WVhdirap61U11ZSGVpDlUVee7yOp2BgJAkAsPbExTenuCIDoTb2gMFx41NVOvR+AUlEp08irKig5QVH6S0YC8lBalUluVQXZFPdUU+JflHj6vT+2A0BWANsGENiMHXL4IDO7/BXlVKYGgyQREdMfj4otPp0en0oNNhtoZh9bdhCYjC4h+JNSAGk29Qvc+MtLRt5O9km9EuiY12SWxqM3DRLZsozttFxp4lbF31MgdSFpHY5XKikkYQFtUDvcF4/GoaQAs9n93iSJIiTppfYCw9hj8E1JyGzTm4hvysPyjM3krajoXs+eNjug97kKBwueFX1I/JHEh4dG/Co3t7LeN0VFJekkVFSQYlBakU5e6kKHcHuzb8RnVlIRaLhevPnUfqls+Ibncper2Rkvw9FGZvpTA3haLcHRTl7KC8JOOv6fqG4BcUi19QPOGx/QkMTcbXGl6TZOgNgB6lnDiqy7BXl2CvKjn8ANNMyksyycvcSGzbc+l21gMt694qIYRoQXQ6HUHh7QgKb0ds23PZuWEue//8mB1r5+Bj9CM0qkfNpcO2HoTauh11QLShNPQ9LqJukqS0AC67C5T2N4gwW19CI/uwbvGd6PRGWsUNPm37EtcfvoHRZZejW03PiNU/Dqt/HGFR/d1DlVJUlmVTXrQdgE1LH2fr6ndw2MvcCYnF30ZgWHvi2o0jKLw9gWHt8AuMw8fkV+eUTsbpus6fKtlmtEtio10SG++sAQn0GDaT7mc9QkHOVnIOrCIvawN7//gv21e/AoCvXyT+QQn4BcfjH5RAeEw/wqP7nPrEW9KDdFswSVJaqCP3dugNfz1NVbmclBUfoLqqGHtVCfaqYuzVJSjlJDSyO0HhHWouPWlExfk7ObDzawAOpHxDdtpKTJYQwqJ6Ehbl/ei4EKdKp9Nh8Y8kMCgc2MbQSz5k068v4R/SGlvCMEJtPTD5tqDOGYQQQhyXTm8gNLIboZHdgJoDVuUl6RQc+pOi3O2UFqVRnLfz8CVis2nT4zqsgbGYfUMO3w8YQkBIa7k3VoMkSWkBfv70H1RUlBEY2o6A0LaUFaeRc+A3lMtBWHRfImIHU16cRsbeJVRX1nkBPSgXRnMQobZehER2JySiC75+kZit4Zh8gxssefExhpDQcTwVpZkU5m6nuqKAqop8nI5ygsI74xcUf/hLIRiTORijbzA+PhY4fO1+7T+Dj4XQqF6NnlidDN3ho1sOuwOHUzVza0RtR2ITENqRAee/7fGZw67tJ92fzmSb0S6JjXZJbE6O2WLDlmjDljjGPUwpF9tWP8/BXd9SXVmAy1nl/szg40tU0ih0ehNVFblUledSWZ6D01GJNSAav8B4/ILiD/9PwM8/pDlm64wjSUoLYAmIJjSmEyUFO8ncuwSzNZw2PW/CYDCTc/BXdqx7GbMlgti2FxEeMwiTJQSjKRCjyR8foz8ul53C7D/Jy1xHQfYmdm96B0d1ibt+nc6AyTcEs7UVfoFxWAPjsQbGYfGzYTQF4GMOxGj0x8cciOHwmRulFI7qYirLc6mqyKG6ogCd3gcfo5XoNhfh42PFYLTU/PexkJe5how9i6iqyKe0MPXwDdFFOB0Vx5z3fue8QUTskEZdvifjyM2MDocDh0N+OLREYqNNEhftkthol8SmYbXvcxft+9Q8fd7pqDj8yIQCsg+sIGvfEvQGE2ZLOEHhXWllCcPgY6a8JIPykgNk7PmBitIMlHLi69v0PYqdiSRJaQF6DH8WnaHuy1SSukzE5bKj0/l47SXIoDcQFt2PsOiaJ2Ir5aK85CBV5blUVeRRXZFHVWU+lWVZlBcfoDDnTypKs4CjvxD1BjM+Rj8c9lL3JWf1YfCxYA2IxRIQQ0BIW3R6HyrLDlFenEZZ0X4c9tKjxrH4RxNqk0vEhBBCCNGwDD4WLP4WLP5RBIV3om3Pm447jstlp6Ikg4rinaxZfEfjN/IMJ0lKC+ByOMF1rMtUdCfcRbGvJRpfS7TXz53OKqoqcnFUlx7+K8FuL8FRXYKjuhQfkz9mSzhmSzgmSzhm31BcyonTUYHTXo7TUY7D/b+CqopsKkrSKS9NJ+fgbyiXE1+/SPwCkwiLGoCvNRKzNQKzJaLmv29YTVeCCpwavERHf/hBhE67E6cc3dIUiY02SVy0S2KjXRIbrdHha43B11fuX2kKkqSIOhkMZqz+MSc+olluTBZCCCGEEKdGkpQWwGG3g97e3M0QtegOH91y2O04HNq7sf9MJrHRJomLdklstEtio1Eu2SdrCpKktAAuhxOXTnuXPJ3J/joF75AfDo2R2GiTxEW7JDbaJbHRJh9Dc7fgzCBrvBBCCCGEEPUkT5xvGnImpQVwOuy4kFOLWuLk8NEthx2nXXJ9LZHYaJPERbskNtolsdEmvew9NwlZ44UQQgghhKgnp0MOHDcFyQVbAHtVKT26Sq9ZWqLXQ3qWi47JelwuyfW1RGKjTRIX7ZLYaJfERpt27jr2g6hFw9AppaTjbY2qqqqSp5oKIYQQQmiMzWYjNTVV9tMakSQpGldVVUVVVVVzN0MIIYQQQhxmMpkkQWlkkqQIIYQQQgghNEUucBRCCCGEEEJoiiQpQgghhBBCCE2RJEUIIYQQQgihKZKkCCGEEEIIITRFkhQhhBBCCCGEpkiS0gSWL1/OhRdeSHR0NDqdjgULFnh8XlpayrRp04iNjcVisdCxY0dee+01jzJVVVXcdttthIeH4+fnx7hx4zh48KBHmVWrVtGjRw8SEhJ46623Gnu2WoxjLX+73c79999P165d8fPzIzo6mokTJ5KRkeFRx6ks/wEDBnDzzTd7lH3ttdfQ6XS88847HsMnT57MoEGDGmjOW5709HSuueYawsLCsFqt9OjRg/Xr17s/V0oxc+ZMoqOjsVgsDB8+nK1bt3rUkZKSwuDBg4mNjeXxxx93D7/yyis599xzPcp+99136HQ6HnnkEY/hTzzxBNHR0Y0wh2eOOXPmkJSUhK+vL71792bFihXuz7Kysjj33HOJjo7mlltuweVyNWNLTx+Nuf0AJCYmotPpjvp7+umnm2T+Worm/s0B6oyTTqdj/vz5jTbfLZHsn2mcEo1u0aJF6qGHHlKff/65AtSXX37p8fmNN96okpOT1dKlS1Vqaqp64403lMFgUAsWLHCXmTp1qoqJiVFLlixRGzZsUCNGjFDdu3dXDofDXaZDhw7qf//7n/rtt99UcnKy2r9/f1PNoqYda/kXFhaqUaNGqU8++UTt2LFDrVq1SvXv31/17t3bo45TWf4PPPCAat++vUd9l19+uYqLi1NXX321x/DWrVurGTNmNPASaBny8/NVQkKCmjRpklqzZo1KTU1VP/74o9q9e7e7zNNPP60CAgLU559/rjZv3qyuuOIKFRUVpYqLi91lzj77bPXaa6+p33//XfXp00f9+uuvSimlXn/9deXv76/sdru77H333afi4uLU4MGDPdoycuRINWHChEae49PX/PnzldFoVG+99Zbatm2buv3225Wfn597m5g8ebJ65JFH1KZNm9S5556rPvroo2ZuccvX2NuPUkolJCSoxx9/XGVmZnr8lZaWNum8al1z/+YopRSg5s6de1SsKioqGn3+WxLZP9M2SVKaWF0bQefOndXjjz/uMaxXr17q4YcfVkrVfKkZjUY1f/589+fp6elKr9er77//3j0sPj5e7d27V5WWlqo+ffqorVu3Nt6MtFB1Lf+/W7t2rQLcXyKnuvwXL16sAJWRkeEuGxkZqebMmaNiYmLcw9LS0hSglixZ0hCz2uLcf//9asiQIV4/d7lcymazqaeffto9rLKyUgUFBanXX3/dPax3795q9erVqrq6Wo0bN059++23SimlUlJSFKBWrVrlLtuvXz/16quvKpPJpMrKypRSSlVVVSmLxaLeeuuthp7FM0a/fv3U1KlTPYZ16NBBPfDAA0oppS677DI1f/585XQ61S233KJeffXV5mjmaaWxtx+lapKUF198sVHaf7pqjt+c+k5XeJL9M+2Ry700YMiQISxcuJD09HSUUixdupSdO3cyduxYANavX4/dbmfMmDHucaKjo+nSpQsrV650D3v00Ufp2LEjQUFBDBgwgE6dOjX5vJwOioqK0Ol0BAcHA6e+/AcPHozRaOSXX34BYNu2bVRUVHDDDTdQXFzMrl27AFi6dCkmk+mMvdxr4cKF9OnTh/Hjx9OqVSt69uzpcVo8NTWVrKwsjziYzWaGDRvmEYfHH3+c0aNHY7Va0ev17u2oXbt2REdHs3TpUgBKSkrYsGED48ePJzk5md9++w2A1atXU1FRwYgRI5pitk871dXVrF+/3iNOAGPGjHHH6YEHHmD69OmYzWY2btzIxIkTm6Opp5XG3n5E42no3xzRcGT/rHlJkqIB//d//0enTp2IjY3FZDJxzjnnMGfOHIYMGQLUXL9tMpkICQnxGC8yMpKsrCz3+8mTJ5OXl0dOTg6vvPJKk87D6aKyspIHHniACRMmEBgYCJz68vfz86Nv377uJOWXX35hyJAhmM1mBg8e7DG8f//+WK3Wxp1Jjdq7dy+vvfYabdu2ZfHixUydOpXp06fzwQcfALiXdWRkpMd4f4/DeeedR05ODhkZGXz55ZcYDAb3Z8OHD3cv7xUrVtCuXTsiIiIYNmyYRxzi4uJITk5uxLk9feXm5uJ0Oo8Zpz59+pCens6BAwdYuXIl/v7+zdHU00pTbD8A999/P/7+/h5/R7YdceIa4zfniKuuuuqoWO3du7dxZ+g0I/tnzcunuRsgajaC1atXs3DhQhISEli+fDm33HILUVFRjBo1yut4Sil0Op3HMD8/P/z8/Bq7yaclu93OlVdeicvlYs6cOcctfyLLf8SIEXz22WdAzU7w8OHDAdw7x1OmTOGXX345o48ou1wu+vTpw6xZswDo2bMnW7du5bXXXvNYLn9f5nXFwWw2ExERcdQ0RowYwR133IHdbj8qDkd+OH755RdGjhzZkLN2RjpenHx8fLDZbE3drNNWU2w/APfeey+TJk3yGBYTE9MAc3DmaczfHIAXX3zxqH2IuLi4k2/wGUj2z5qXnElpZhUVFcyYMYPZs2dz4YUX0q1bN6ZNm8YVV1zB888/D4DNZqO6upqCggKPcbOzs486KiZOjt1u5/LLLyc1NZUlS5a4j2hBwyz/ESNGsHPnTtLT01m2bBnDhg0D/kpS0tLSSE1NPaMvMYqKijrqFHjHjh1JS0sDcO/Q1j46BSceh7KyMtatW8fSpUs94rBu3Try8/NZtWrVGR2HUxUeHo7BYDilOIkT1xTbD9TEt02bNh5/FovlFFt/5mns35wj9fw9VkajscHm4XQn+2fNT5KUZma327Hb7ej1nqEwGAzubjl79+6N0WhkyZIl7s8zMzPZsmXLGXv/QkM68mOxa9cufvzxR8LCwjw+b4jlP2jQIMxmM3PmzKGiooLevXsDNZe9FBUV8cYbb+Dr68uAAQMabsZamMGDB5OSkuIxbOfOnSQkJACQlJSEzWbziEN1dTXLli2rdxySk5OJi4tj4cKFbNq0yZ2kREVFkZiYyAsvvEBlZaUkKafAZDLRu3dvjzgBLFmyRL6vGlFTbD+iYTTFb444dbJ/pgHNdsv+GaSkpERt3LhRbdy4UQFq9uzZauPGje6ePIYNG6Y6d+6sli5dqvbu3avmzp2rfH191Zw5c9x1TJ06VcXGxqoff/xRbdiwQY0cOfKoLu5E3Y61/O12uxo3bpyKjY1VmzZt8uiqsaqqyl1HQyz/oUOHqoCAAHXOOed4DB81apQKCAhQI0eObLB5bonWrl2rfHx81JNPPql27dql/vvf/yqr1erRPe3TTz+tgoKC1BdffKE2b96srrrqqqO6UD2eiRMnqoCAANWhQweP4TfeeKMKCAhQrVu3brB5OlMd6YL4nXfeUdu2bVN33HGH8vPzU/v27Wvupp22mmL78dYFcVFRUWPNVoukhd8cvHRBLN1Fe5L9M22TJKUJLF26VAFH/V133XVKKaUyMzPVpEmTVHR0tPL19VXt27dXL7zwgnK5XO46Kioq1LRp01RoaKiyWCzqggsuUGlpac00Ry3LsZZ/ampqnZ8BaunSpe46GmL5P/bYYwrw6AJUKaWeeOIJBagnnniiIWa3Rfv6669Vly5dlNlsVh06dFBvvvmmx+cul0s99thjymazKbPZrIYOHao2b958QtOYO3euAo7qIvfDDz9UgJo8efIpz4dQ6tVXX1UJCQnKZDKpXr16qWXLljV3k057jb39JCQk1PldedNNNzX0rLRoWvjN8TaNp556qhHmuOWS/TNt0ymlVMOemzlaZWUl1dXVjT0ZIYQQQgghhMaZTCZ8fX2PWabRe/eqrKwkMDgGe1V+Y09KCCGEEEIIoXE2m43U1NRjJiqNnqRUV1djr8qnz9mfYvDxQ1frBiSd/q/u2XQ6L8Nrla9975LX8l6G62sNx6P+eoxbu806L+PWrh5v81J7WrXrr3t+vU+r7nqOajcnNr7n8q27fK2X9arT8/Vf4+q9tUFX9zL1ury8tMejnd7qxEv8vLTZe/trj1t3e7yW8Qzf3+a57nmofQuf9/Z5Ge6lfP3KKC/DvdVz/PIe80Ld5fX1aIPey7jUKu9Rj5fytev3VmftMn+vy/s0VD2G1z2NUynvMQ8n+tpbG+rxGlx116O8jOtteO16PMp7G16fMn8NP3ratT5TtcvUXRe16/I2bVV3+frU6Tlu3a892+xlPr3V6ap7XO9t8DatE6vT+3AvZVyeMfO2LHB5qbc+bao1rqpHW5W3Ol11t82jvLdpeWm/8jbcyzJSXtvQ8O2pXU+92sPfQ36C9XpZpl7HdR1//r2XOdHXddfpcnop72WZei1fu/7aZZz89Vp5qcdZj2k5vQy3e8bP5aj7M89xak2vjvLluJiUlUp1dXXzJilHGHz88DGeapJyYuW9JSned65PJUk50WmdfJLibQf/6HacaJLiJbk4wSTF63Q1kKR4T8Rqta0ZkxRvyYXnMvJW5vj1NFSSUr966pGk1CNxOJUkxWubNZikeCafJ5g41CdJOdGk4xSSFN0JJyknmow0RZJygnU1epJSn2SkKZOUk6/TawJyUknK8XeqTylJ8bqjXp/5aYQk5USTgnokF97a43W6p5yknHyi0dhJimeCcKKJjJc69SdYXleP8rrjl3fV+l5RtQ5reYzrUabW8NrLWe8ZP4/5qfXaVbuttX40Pdp35LVnlV5JF8RCCCGEEEIITZEkRQghhBBCCKEpkqQIIYQQQgghNEWSFCGEEEIIIYSmSJIihBBCCCGE0BRJUoQQQgghhBCaIkmKEEIIIYQQQlMkSRFCCCGEEEJoiiQpQgghhBBCCE2RJEUIIYQQQgihKZKkCCGEEEIIITRFkhQhhBBCCCGEpkiSIoQQQgghhNAUSVKEEEIIIYQQmiJJihBCCCGEEEJTJEkRQgghhBBCaIokKUIIIYQQQghNkSRFCCGEEEIIoSmSpAghhBBCCCE0RZIUIYQQQgghhKZIkiKEEEIIIYTQFElShBBCCCGEEJoiSYoQQgghhBBCUyRJEUIIIYQQQmiKJClCCCGEEEIITZEkRQghhBBCCKEpkqQIIYQQQgghNEWSFCGEEEIIIYSmSJIihBBCCCGE0BRJUoQQQgghhBCaIkmKEEIIIYQQQlMkSRFCCCGEEEJoiiQpQgghhBBCCE2RJEUIIYQQQgihKZKkCCGEEEIIITTFp6km5HSUAaDT/5UX6fS6v17rvAyvVb7WS+/lvQzX1xqOR/31GLd2m3Vexq1dPd7mpfa0atdf9/x6n1bd9RzVbk5sfM/lW3f5Wi/rVafn67/G1Xtrg67uZep1eXlpj0c7vdWJl/h5abP39tcet+72eC3jGb6/zXPd81D7yIL39nkZ7qV8/cqo447rWc/xy3vMC3WX19ejDXov41KrvEc9XsrXrt9bnbXL/L0u79NQ9Rhe9zROpbzHPJzoa29tqMdrcNVdj/IyrrfhtevxKO9teH3K/DX86GnX+kzVLlN3XdSuy9u0Vd3l61On57h1v/Zss5f59Fanq+5xvbfB27ROrE7vw72UcXnGzNuywOWl3vq0qda4qh5tVd7qdNXdNo/y3qblpf3K23Avy0h5bUPDt6d2PfVqD38P+QnW62WZeh3Xdfz5917mRF/XXafL6aW8l2XqtXzt+muXcfLXa+WlHmc9plXrtcvLPB7rM5fyMm8ew2tel/O3bdqLRk9SlFL4+/vz+0+XN/akhBBCCCGEEBpns9kwmUzHLNPoSYpOp6O0tJQDBw4QGBjY2JMTGlVcXExcXJysB2cwWQcEyHogash6IEDWgzOZyWTC19f3mGWa7HKvwMBAWQGFrAdC1gEByHogash6IEDWA1E3uXFeCCGEEEIIoSmSpAghhBBCCCE0pdGTFLPZzGOPPYbZbG7sSQkNk/VAyDogQNYDUUPWAwGyHohj0ymPvt2EEEIIIYQQonnJ5V5CCCGEEEIITZEkRQghhBBCCKEpkqQIIYQQQgghNEWSFCGEEEIIIYSmNFqSMm7cOOLj4/H19SUqKoprr72WjIwMjzJpaWlceOGF+Pn5ER4ezvTp06murm6sJokmtm/fPiZPnkxSUhIWi4Xk5GQee+yxo2Is68Hp78knn2TQoEFYrVaCg4PrLCPrwZlhzpw5JCUl4evrS+/evVmxYkVzN0k0ouXLl3PhhRcSHR2NTqdjwYIFHp8rpZg5cybR0dFYLBaGDx/O1q1bm6exolE89dRT9O3bl4CAAFq1asXFF19MSkqKRxlZD0RdGi1JGTFiBJ9++ikpKSl8/vnn7Nmzh3/84x/uz51OJ+effz5lZWX8+uuvzJ8/n88//5y77767sZokmtiOHTtwuVy88cYbbN26lRdffJHXX3+dGTNmuMvIenBmqK6uZvz48dx88811fi7rwZnhk08+4Y477uChhx5i48aNnHXWWZx77rmkpaU1d9NEIykrK6N79+785z//qfPzZ599ltmzZ/Of//yHdevWYbPZGD16NCUlJU3cUtFYli1bxq233srq1atZsmQJDoeDMWPGUFZW5i4j64Gok2oiX331ldLpdKq6uloppdSiRYuUXq9X6enp7jLz5s1TZrNZFRUVNVWzRBN79tlnVVJSkvu9rAdnlrlz56qgoKCjhst6cGbo16+fmjp1qsewDh06qAceeKCZWiSaEqC+/PJL93uXy6VsNpt6+umn3cMqKytVUFCQev3115uhhaIpZGdnK0AtW7ZMKSXrgfCuSe5Jyc/P57///S+DBg3CaDQCsGrVKrp06UJ0dLS73NixY6mqqmL9+vVN0SzRDIqKiggNDXW/l/VAgKwHZ4Lq6mrWr1/PmDFjPIaPGTOGlStXNlOrRHNKTU0lKyvLY50wm80MGzZM1onTWFFREYB7X0DWA+FNoyYp999/P35+foSFhZGWlsZXX33l/iwrK4vIyEiP8iEhIZhMJrKyshqzWaKZ7Nmzh1deeYWpU6e6h8l6IEDWgzNBbm4uTqfzqDhHRkZKjM9QR+Iu68SZQynFXXfdxZAhQ+jSpQsg64Hw7oSSlJkzZ6LT6Y759/vvv7vL33vvvWzcuJEffvgBg8HAxIkTUbUecK/T6Y6ahlKqzuFCO050PQDIyMjgnHPOYfz48dx4440en8l60DKdzHpwLLIenBn+Hk+JsZB14swxbdo0/vzzT+bNm3fUZ7IeiL/zOZHC06ZN48orrzxmmcTERPfr8PBwwsPDadeuHR07diQuLo7Vq1czcOBAbDYba9as8Ri3oKAAu91+VDYttOVE14OMjAxGjBjBwIEDefPNNz3KyXrQcp3oenAssh6c/sLDwzEYDEcdGc3OzpYYn6FsNhtQcyQ9KirKPVzWidPTbbfdxsKFC1m+fDmxsbHu4bIeCG9OKEk5knScjCNnUKqqqgAYOHAgTz75JJmZme6V8ocffsBsNtO7d++TmoZoGieyHqSnpzNixAh69+7N3Llz0es9T97JetByncr3wd/JenD6M5lM9O7dmyVLlnDJJZe4hy9ZsoSLLrqoGVsmmktSUhI2m40lS5bQs2dPoObepWXLlvHMM880c+tEQ1FKcdttt/Hll1/yyy+/kJSU5PG5rAfCmxNKUupr7dq1rF27liFDhhASEsLevXt59NFHSU5OZuDAgUDNzZKdOnXi2muv5bnnniM/P5977rmHKVOmEBgY2BjNEk0sIyOD4cOHEx8fz/PPP09OTo77syNHTmQ9ODOkpaWRn59PWloaTqeTTZs2AdCmTRv8/f1lPThD3HXXXVx77bX06dPHfWY1LS3N4z41cXopLS1l9+7d7vepqals2rSJ0NBQ4uPjueOOO5g1axZt27albdu2zJo1C6vVyoQJE5qx1aIh3XrrrXz88cd89dVXBAQEuM+mBgUFYbFY0Ol0sh6IujVGl2F//vmnGjFihAoNDVVms1klJiaqqVOnqoMHD3qU279/vzr//POVxWJRoaGhatq0aaqysrIxmiSawdy5cxVQ519tsh6c/q677ro614OlS5e6y8h6cGZ49dVXVUJCgjKZTKpXr17ubkjF6Wnp0qV1bvvXXXedUqqm+9nHHntM2Ww2ZTab1dChQ9XmzZubt9GiQXnbD5g7d667jKwHoi46pWrdyS6EEEIIIYQQzaxJnpMihBBCCCGEEPUlSYoQQgghhBBCUyRJEUIIIYQQQmiKJClCCCGEEEIITZEkRQghhBBCCKEpkqQIIYQQQgghNEWSFCGEEEIIIYSmSJIihBDijPPOO+8wZsyYBq2zb9++fPHFFw1apxBCnKkkSRFCiGai0+mO+Tdp0qTmbmKDGz58OHfccUeztqGqqopHH32URx55xD3M6XRyyy23EBUVxbnnnktWVpbHOMXFxTz00EN06NABX19fbDYbo0aN4osvvuDIM5EfeeQRHnjgAVwuV5POjxBCnI4kSRFCiGaSmZnp/nvppZcIDAz0GPbyyy83dxPrzW63t5jpff755/j7+3PWWWe5h82bN4+0tDQWL15M7969PRKYwsJCBg0axAcffMCDDz7Ihg0bWL58OVdccQX33XcfRUVFAJx//vkUFRWxePHik58xIYQQgCQpQgjRbGw2m/svKCgInU7nMWz58uX07t0bX19fWrduzb/+9S8cDod7fJ1OxxtvvMEFF1yA1WqlY8eOrFq1it27dzN8+HD8/PwYOHAge/bscY8zc+ZMevTowRtvvEFcXBxWq5Xx48dTWFjo0ba5c+fSsWNHfH196dChA3PmzHF/tm/fPnQ6HZ9++inDhw/H19eXjz76iLy8PK666ipiY2OxWq107dqVefPmucebNGkSy5Yt4+WXX3afLdq3bx/vvfcewcHBHtNfsGABOp3uqHa/++67tG7dGrPZjFKKoqIi/vnPf9KqVSsCAwMZOXIkf/zxxzGX+/z58xk3bpzHsMLCQhISEujSpQtdu3Z1Jx4AM2bMYN++faxZs4brrruOTp060a5dO6ZMmcKmTZvw9/cHwGAwcN5553nMsxBCiJMjSYoQQmjQ4sWLueaaa5g+fTrbtm3jjTfe4L333uPJJ5/0KPfEE08wceJENm3aRIcOHZgwYQI33XQTDz74IL///jsA06ZN8xhn9+7dfPrpp3z99dd8//33bNq0iVtvvdX9+VtvvcVDDz3Ek08+yfbt25k1axaPPPII77//vkc9999/P9OnT2f79u2MHTuWyspKevfuzTfffMOWLVv45z//ybXXXsuaNWsAePnllxk4cCBTpkxxny2Ki4ur9zI50u7PP/+cTZs2ATVnL7Kysli0aBHr16+nV69enH322eTn53utZ8WKFfTp08dj2LXXXsvq1asxm83cfffd7jMpLpeL+fPnc/XVVxMdHX1UXf7+/vj4+Ljf9+vXjxUrVtR7noQQQnihhBBCNLu5c+eqoKAg9/uzzjpLzZo1y6PMhx9+qKKiotzvAfXwww+7369atUoB6p133nEPmzdvnvL19XW/f+yxx5TBYFAHDhxwD/vuu++UXq9XmZmZSiml4uLi1Mcff+wx7SeeeEINHDhQKaVUamqqAtRLL7103Pk677zz1N133+1+P2zYMHX77bcfc96VUurLL79UtX+iHnvsMWU0GlV2drZ72E8//aQCAwNVZWWlx7jJycnqjTfeqLM9BQUFClDLly+v8/PMzEzlcDjc7w8dOqQANXv27GPO5xFfffWV0uv1yul01qu8EEKIuvkcK4ERQgjRPNavX8+6des8zpw4nU4qKyspLy/HarUC0K1bN/fnkZGRAHTt2tVjWGVlJcXFxQQGBgIQHx9PbGysu8zAgQNxuVykpKRgMBg4cOAAkydPZsqUKe4yDoeDoKAgjzb+/WyE0+nk6aef5pNPPiE9PZ2qqiqqqqrw8/M71cUBQEJCAhEREe7369evp7S0lLCwMI9yFRUVHpe4/f0zAF9f3zo/t9lsHu/V4Zvia196diwWiwWXy0VVVRUWi6Ve4wghhDiaJClCCKFBLpeLf/3rX1x66aVHfVZ7B9toNLpfH9mRrmvYsXqcOlJGp9O5y7311lv079/fo5zBYPB4//fk44UXXuDFF1/kpZdeomvXrvj5+XHHHXdQXV3tfUYBvV7vTgaOqOvG+L9Pz+VyERUVxS+//HJU2b/f43JEWFgYOp2OgoKCY7bpiIiICEJCQti+fXu9yufn52O1WiVBEUKIUyRJihBCaFCvXr1ISUmhTZs2DV53WloaGRkZ7nssVq1ahV6vp127dkRGRhITE8PevXu5+uqrT6jeFStWcNFFF3HNNdcANUnErl276Nixo7uMyWTC6XR6jBcREUFJSQllZWXuROTIPSfH0qtXL7KysvDx8SExMbFebTSZTHTq1Ilt27bV6zkper2eK664gg8//JDHHnvsqPtSysrKMJvN7vtStmzZQq9everVFiGEEN7JjfNCCKFBjz76KB988AEzZ85k69atbN++nU8++YSHH374lOv29fXluuuu448//mDFihVMnz6dyy+/3H2p08yZM3nqqad4+eWX2blzJ5s3b2bu3LnMnj37mPW2adOGJUuWsHLlSrZv385NN9101PNGEhMTWbNmDfv27SM3NxeXy0X//v2xWq3MmDGD3bt38/HHH/Pee+8ddz5GjRrFwIEDufjii1m8eDH79u1j5cqVPPzww+5OA+oyduxYfv311+MvqMNmzZpFXFwc/fv354MPPmDbtm3s2rWLd999lx49elBaWuouu2LFigZ/SKQQQpyJJEkRQggNGjt2LN988w1Lliyhb9++DBgwgNmzZ5OQkHDKdbdp04ZLL72U8847jzFjxtClSxePLoZvvPFG3n77bd577z26du3KsGHDeO+990hKSjpmvY888gi9evVi7NixDB8+HJvNxsUXX+xR5p577sFgMNCpUyciIiJIS0sjNDSUjz76iEWLFrm7LZ45c+Zx50On07Fo0SKGDh3KDTfcQLt27bjyyivZt2+f+/6cukyZMoVFixZ5dDN8LCEhIaxevZprrrmGf//73/Ts2ZOzzjqLefPm8dxzz7nv1UlPT2flypVcf/319apXCCGEdzr19wuBhRBCnLZmzpzJggUL6nU51ens8ssvp2fPnjz44IMNVue9995LUVERb775ZoPVKYQQZyo5kyKEEOKM89xzz7kfwthQWrVqxRNPPNGgdQohxJlKbpwXQghxxklISOC2225r0DrvvffeBq1PCCHOZHK5lxBCCCGEEEJT5HIvIYQQQgghhKZIkiKEEEIIIYTQFElShBBCCCGEEJoiSYoQQgghhBBCUyRJEUIIIYQQQmiKJClCCCGEEEIITZEkRQghhBBCCKEpkqQIIYQQQgghNEWSFCGEEEIIIYSm/D/Z6bzRBNX/HwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "# Create latitude array from -90 to 90 degrees\n",
    "latitudes = np.linspace(-90, 90, N_lat)\n",
    "\n",
    "# Create an initial temperature distribution with higher temperatures at the Equator\n",
    "initial_temperature = np.zeros((N_lat, N_lon))\n",
    "\n",
    "# Assuming a simplistic temperature gradient\n",
    "for i, lat in enumerate(latitudes):\n",
    "    initial_temperature[i, :] = max(30 - abs(lat)/90 * 60, -30)  # Temperature decreases from equator to poles\n",
    "\n",
    "# Set up the map projection and the figure\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n",
    "\n",
    "# Set up the longitude and latitude arrays for the grid\n",
    "longitudes = np.linspace(-180, 180, N_lon)\n",
    "latitudes = np.linspace(-90, 90, N_lat)\n",
    "\n",
    "# Create a meshgrid for the map projection\n",
    "lon2d, lat2d = np.meshgrid(longitudes, latitudes)\n",
    "\n",
    "# Plot the data using pcolormesh\n",
    "temperature_plot = ax.pcolormesh(lon2d, lat2d, initial_temperature, \n",
    "                                 transform=ccrs.PlateCarree(), \n",
    "                                 cmap='coolwarm')\n",
    "\n",
    "# Add coastlines for context\n",
    "ax.coastlines()\n",
    "\n",
    "# Add a color bar\n",
    "cbar = plt.colorbar(temperature_plot, orientation='horizontal', pad=0.05)\n",
    "cbar.set_label('Temperature (°C)')\n",
    "\n",
    "# Set map extent (could be global or specific region)\n",
    "ax.set_global()\n",
    "\n",
    "# Add gridlines and features\n",
    "ax.gridlines(draw_labels=True, dms=True, x_inline=False, y_inline=False)\n",
    "ax.add_feature(cfeature.BORDERS)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.quiver.Quiver at 0x172090250>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.quiver(windfield[\"u\"][::10,::10], windfield[\"v\"][::10,::10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = OceanModel(initial_temperature, deltat, windfield, grid, kappa)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 360)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.delta_lon.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.timestep()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x28613fa90>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolor(model.T[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "class Grid:\n",
    "    \"\"\" Creates grid for ocean model on a spherical surface (aqua planet). \"\"\"\n",
    "    def __init__(self, N_lat, N_lon, radius=6371e3):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "        N_lat: Number of latitude grid points\n",
    "        N_lon: Number of longitude grid points\n",
    "        radius: Radius of the planet (default is Earth's radius in meters)\n",
    "        \"\"\"\n",
    "        self.radius = radius\n",
    "        \n",
    "        # Latitude ranges from -90 to 90 degrees\n",
    "        self.lat = np.linspace(-90, 90, N_lat)\n",
    "        \n",
    "        # Longitude ranges from -180 to 180 degrees (or 0 to 360)\n",
    "        self.lon = np.linspace(-180, 180, N_lon, endpoint=False)\n",
    "\n",
    "        # Create 2D arrays of lat and lon values\n",
    "        self.lon_grid, self.lat_grid = np.meshgrid(self.lon, self.lat)\n",
    "        \n",
    "        # Calculate grid spacing in meters\n",
    "        # Circumference at a given latitude is 2 * pi * radius * cos(latitude)\n",
    "        # Grid spacing in longitude direction changes with latitude\n",
    "        \n",
    "        self.delta_lat = np.pi * radius / (N_lat - 1)\n",
    "        self.delta_lon = (2 * np.pi * radius / N_lon) * np.cos(np.deg2rad(self.lat_grid))\n",
    "\n",
    "        self.N_lat = N_lat\n",
    "        self.N_lon = N_lon\n",
    "    \n",
    "    def get_coordinates(self):\n",
    "        \"\"\" Get the spherical coordinates of the grid points. \"\"\"\n",
    "        # Convert degrees to radians for calculations\n",
    "        lat_rad = np.deg2rad(self.lat_grid)\n",
    "        lon_rad = np.deg2rad(self.lon_grid)\n",
    "        \n",
    "        # Convert lat/lon to Cartesian coordinates for a sphere\n",
    "        x = self.radius * np.cos(lat_rad) * np.cos(lon_rad)\n",
    "        y = self.radius * np.cos(lat_rad) * np.sin(lon_rad)\n",
    "        z = self.radius * np.sin(lat_rad)\n",
    "        \n",
    "        return x, y, z\n",
    "    \n",
    "    def get_grid_spacing_meters(self):\n",
    "        \"\"\" Get the grid spacing in meters for both latitude and longitude. \"\"\"\n",
    "        return self.delta_lat, self.delta_lon\n",
    "\n",
    "# Example usage:\n",
    "N_lat = 180  # Number of latitude grid points\n",
    "N_lon = 360  # Number of longitude grid points\n",
    "grid = Grid(N_lat, N_lon)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class grid:   \n",
    "    \"\"\" Creates grid for ocean model\n",
    "    Args:\n",
    "    -------\n",
    "    \n",
    "    N: Number of grid points (NxN)\n",
    "    L: Length of domain\n",
    "    \"\"\"\n",
    "    def __init__(self, N, L):\n",
    "        self.deltax = L / N\n",
    "        self.deltay = L / N\n",
    "        self.xs = np.arange(0-self.deltax/2, L+self.deltax/2, self.deltax) \n",
    "        self.ys = np.arange(-L-self.deltay/2, L+self.deltay/2, self.deltay)   \n",
    "\n",
    "        self.N = N\n",
    "        self.L = L\n",
    "        self.Nx = len(self.xs)\n",
    "        self.Ny = len(self.ys)\n",
    "        self.grid, _= np.meshgrid(self.ys, self.xs, sparse=False, indexing='ij')\n",
    "        \n",
    "class windfield:\n",
    "    \"\"\"Calculates wind velocity\n",
    "    \n",
    "    Args:\n",
    "    ------\n",
    "    Grid: class grid()\n",
    "    option: zero, or single gyre\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, grid, option = \"zero\"):\n",
    "        if option == \"zero\":\n",
    "            self.u = np.zeros((grid.grid.shape))\n",
    "            self.v = np.zeros((grid.grid.shape))\n",
    "        if option == \"single_gyre\":\n",
    "            u, v = PointVortex(grid)\n",
    "            self.u = u\n",
    "            self.v = v\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea48a1471eae41e6958db7f041099976",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='Time Steps', min=1), Output()), _dom_classes=('widget-in…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.update_model(steps)>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "from ipywidgets import interact, IntSlider\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def plot_temperature_distribution(T, grid):\n",
    "    # Create a figure with an axes set with a projection\n",
    "    fig = plt.figure(figsize=(12, 6))\n",
    "    ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n",
    "    ax.add_feature(cfeature.COASTLINE)\n",
    "    ax.add_feature(cfeature.BORDERS, linestyle=':')\n",
    "    \n",
    "    # Set the extent (min longitude, max longitude, min latitude, max latitude)\n",
    "\n",
    "    # Plot the temperature data\n",
    "    mesh = ax.pcolormesh(grid.lon_grid, grid.lat_grid, T, \n",
    "                         cmap='coolwarm', transform=ccrs.PlateCarree())\n",
    "    \n",
    "    # Add a colorbar\n",
    "    cbar = plt.colorbar(mesh, orientation='vertical', pad=0.02, aspect=50)\n",
    "    cbar.set_label('Temperature (°C)')\n",
    "    \n",
    "    # Add titles and labels\n",
    "    plt.title('Ocean Surface Temperature')\n",
    "    ax.set_xlabel('Longitude')\n",
    "    ax.set_ylabel('Latitude')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "def update_model(steps):\n",
    "    for _ in range(steps):\n",
    "        model.timestep()  # Call the timestep method of your model\n",
    "    plot_temperature_distribution(model.T[-1], model.grid)  # Plot the latest temperature distribution\n",
    "\n",
    "# Initialize your model here with the initial conditions\n",
    "# Example:\n",
    "model = OceanModel(initial_temperature, deltat, windfield, grid, kappa)\n",
    "\n",
    "# Create an interactive slider for number of timesteps\n",
    "interact(update_model, steps=IntSlider(min=1, max=100, step=1, value=1, description='Time Steps'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize $10x10$ grid domain with $L=6000000m$ and a windfield, where all velocities are set to zero.\n",
    "This should result in diffusion only.\n",
    "\n",
    "Initialize temperature field with rectangular non-zero box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63c5230caeed453babfc9a01182bcc83",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='Time Steps', min=1), Output()), _dom_classes=('widget-in…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "import numpy as np\n",
    "from ipywidgets import interactive, IntSlider\n",
    "\n",
    "# Assume your OceanModel class definition is here\n",
    "\n",
    "def plot_temperature_distribution(model):\n",
    "    fig = plt.figure(figsize=(10, 5))\n",
    "    ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n",
    "    \n",
    "    # Retrieve the latest temperature data from the model\n",
    "    T = model.T[-1]\n",
    "\n",
    "    # Clear the previous plot to ensure we do not overlay the plots\n",
    "    ax.clear()\n",
    "    \n",
    "    # Plot the temperature distribution\n",
    "    temperature_plot = ax.pcolormesh(model.grid.lon_grid, model.grid.lat_grid, T, \n",
    "                                     transform=ccrs.PlateCarree(), \n",
    "                                     cmap='coolwarm')\n",
    "\n",
    "    # Add coastlines and borders for context\n",
    "    ax.coastlines()\n",
    "    ax.add_feature(cfeature.BORDERS)\n",
    "    \n",
    "    # Add a color bar\n",
    "    cbar = plt.colorbar(temperature_plot, orientation='horizontal', pad=0.05)\n",
    "    cbar.set_label('Temperature (°C)')\n",
    "\n",
    "    # Set map extent (could be global or a specific region)\n",
    "    ax.set_global()\n",
    "    \n",
    "    # Add gridlines\n",
    "    ax.gridlines(draw_labels=True, dms=True, x_inline=False, y_inline=False)\n",
    "    \n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "\n",
    "def timestep_and_plot(steps):\n",
    "    for _ in range(steps):\n",
    "        model.timestep()  # Call the timestep method of your model\n",
    "    plot_temperature_distribution(model)  # Plot the latest temperature distribution\n",
    "\n",
    "# Initialize your model here with the initial conditions\n",
    "# Example:\n",
    "model = OceanModel(initial_temperature, deltat, windfield, grid, kappa)\n",
    "\n",
    "# Create an interactive widget to control the number of timesteps\n",
    "slider = IntSlider(min=1, max=100, step=1, value=1, description='Time Steps')\n",
    "interactive_plot = interactive(timestep_and_plot, steps=slider)\n",
    "display(interactive_plot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_grid = grid(N = 11, L = 6e6)\n",
    "my_wind = windfield(my_grid, option = \"zero\")\n",
    "my_temp = np.zeros((my_grid.grid.shape)) \n",
    "my_temp[9:13, 4:8] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23, 12)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_grid.grid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolor(my_grid.xs, my_grid.ys, my_temp)\n",
    "plt.colorbar(label = \"Temperature in °C\");\n",
    "plt.ylabel(\"Kilometers Latitude\");\n",
    "plt.xlabel(\"Kilometers Longitude\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "deltat = 12*60*60 # 12 Hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_func(steps, temp, deltat, windfield, grid, kappa, anomaly = False):\n",
    "    my_model = OceanModel(T=temp,\n",
    "                      deltat=deltat, \n",
    "                      windfield=windfield, \n",
    "                      grid=grid, \n",
    "                      kappa = kappa)\n",
    "    for step in range(steps):\n",
    "        my_model.timestep()   \n",
    "    plt.figure(figsize=(8,8))\n",
    "    lon, lat = np.meshgrid(grid.xs, grid.ys)\n",
    "    if anomaly == True:\n",
    "        plt.pcolor(my_grid.xs, my_grid.ys, my_model.T[-1] - my_model.T[-2], cmap = plt.cm.RdBu_r,\n",
    "                  vmin = -0.001, vmax = 0.001)\n",
    "    else:\n",
    "        plt.pcolor(my_grid.xs, my_grid.ys, my_model.T[-1], vmin = 0, vmax = 1)\n",
    "    plt.quiver(lon, lat, windfield.u, windfield.v)\n",
    "    plt.colorbar(label = \"Temperature in °C\");\n",
    "    plt.ylabel(\"Kilometers Latitude\");\n",
    "    plt.xlabel(\"Kilometers Longitude\"); \n",
    "    plt.title(\"Time: {} days\".format(int(step/2)))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e79e38c9b21643e4a2ea17d105009806",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=2, description='Timesteps', max=10000, min=1), Checkbox(value=False, des…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(plot_func, \n",
    "         temp = fixed(my_temp),\n",
    "         deltat = fixed(deltat),\n",
    "         windfield = fixed(my_wind),\n",
    "         grid = fixed(my_grid),\n",
    "         kappa = fixed(1e4),\n",
    "         anomaly = widgets.Checkbox(description = \"Anomaly\", value=False),\n",
    "         steps = widgets.IntSlider(description=\"Timesteps\", value = 2, min = 1, max = 10000, step = 1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create single gyre windfield"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diagnose_velocities(stream, G):    \n",
    "    dx = np.zeros((G.grid.shape))\n",
    "    dy = np.zeros((G.grid.shape))\n",
    "    \n",
    "    dx = (stream[:, 1:] - stream[:, 0:-1])/G.deltax\n",
    "    dy = (stream[1:, :] - stream[0:-1, :])/G.deltay\n",
    "    \n",
    "    u = np.zeros((G.grid.shape))\n",
    "    v = np.zeros((G.grid.shape))\n",
    "    \n",
    "    u = 0.5*(dy[:, 1:] + dy[:, 0:-1])\n",
    "    v = -0.5*(dx[1:, :] + dx[0:-1, :])\n",
    "    \n",
    "    return u, v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impose_no_flux(u, v):\n",
    "    u[0, :]  = 0; v[0, :] = 0;\n",
    "    u[-1,:]  = 0; v[-1,:] = 0;\n",
    "    u[:, 0]  = 0; v[:, 0] = 0;\n",
    "    u[:,-1]  = 0; v[:,-1] = 0;\n",
    "    \n",
    "    return u, v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PointVortex(G, omega = 0.6, a = 0.2, x0=0.5, y0 = 0.):\n",
    "    x = np.arange(-G.deltax/G.L, 1 + G.deltax / G.L, G.deltax / G.L).reshape(1, -1)\n",
    "    y = np.arange(-1-G.deltay/G.L, 1+ G.deltay / G.L, G.deltay / G.L).reshape(-1, 1)\n",
    "        \n",
    "    r = np.sqrt((y - y0)**2 + (x - x0)**2)\n",
    "    stream = -omega/4*r**2 \n",
    "    stream[r > a] =  -omega*a**2/4*(1. + 2*np.log(r[r > a]/a))\n",
    "    \n",
    "    u, v =  diagnose_velocities(stream, G)\n",
    "    u, v = impose_no_flux(u, v) \n",
    "    u = u * G.L; v = v * G.L\n",
    "    return u, v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.quiver.Quiver at 0x134f329b0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_wind = windfield(my_grid, option=\"single_gyre\")\n",
    "plt.quiver(my_wind.u, my_wind.v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_temp = np.zeros((my_grid.grid.shape)) \n",
    "my_temp[8:13, :] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x134f30190>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolor(my_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model = OceanModel(T=my_temp,\n",
    "                      deltat=deltat, \n",
    "                      windfield=my_wind, \n",
    "                      grid=my_grid, \n",
    "                      kappa = 1e4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "017043250d56415a8f6b37f4abea224c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='Timesteps', max=1000, min=1), IntSlider(value=10000, des…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(plot_func, \n",
    "         temp = fixed(my_temp),\n",
    "         deltat = fixed(deltat),\n",
    "         windfield = fixed(my_wind),\n",
    "         grid = fixed(my_grid),\n",
    "         kappa = widgets.IntSlider(description=\"kappa\", value = 1e4, min=1e3, max = 1e5, step = 100),\n",
    "         anomaly = widgets.Checkbox(description = \"Anomaly\", value=False),\n",
    "         steps = widgets.IntSlider(description=\"Timesteps\", value = 1, min = 1, max = 1000, step = 1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optional: Create two gyres!"
   ]
  }
 ],
 "metadata": {
  "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": "3.10.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "85c5783586d30ff72e98f5d52ef95e9c172daa6cb2628b0e9aeb01c271dd5883"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}