06_soil_column_FEM_HM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "deletable": false,
        "editable": false
      },
      "source": [
        "<div style=\"display: flex; align-items: center;\">\n",
        "    <img src=\"https://github.com/nagelt/Teaching_Scripts/raw/9d9e29ecca4b04eaf7397938eacbf116d37ddc93/Images/TUBAF_Logo_blau.png\" width=\"500\" height=\"auto\" height=\"auto\" style=\"margin-right: 100px;\" />\n",
        "    <div>\n",
        "        <p><strong>Prof. Dr. Thomas Nagel</strong></p>\n",
        "        <p>Chair of Soil Mechanics and Foundation Engineering<br>Geotechnical Institute<br>Technische Universität Bergakademie Freiberg.</p>\n",
        "        <p><a href=\"https://tu-freiberg.de/en/soilmechanics\">https://tu-freiberg.de/en/soilmechanics</a></p>\n",
        "    </div>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy as sp\n",
        "from pypardiso import spsolve\n",
        "\n",
        "#Some plot settings\n",
        "%run plot_functions/plot_settings.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# HM coupling in 1D -- liquefaction test\n",
        "\n",
        "## Strong form\n",
        "\n",
        "\\begin{align}\n",
        "    0 &= \\partial_z \\sigma_{zz} + \\gamma_\\text{r} = \\partial_z (\\sigma_{zz}' - p) + \\gamma_\\text{r} = \\partial_z \\left[ E_\\text{s} \\partial_z u_z \\right] - \\partial_z p + \\gamma_\\text{r}\\\n",
        "    0 &= n\\beta_p \\dot{p} + \\partial_z (\\dot{u}_z + q_z) = n\\beta_p \\dot{p} + \\partial_z \\dot{u}_z - \\partial_z \\left[ \\frac{k}{\\mu} \\left( \\partial_z p - \\vec{\\gamma}_\\text{w} \\right) \\right]\n",
        "\\end{align}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Weak form\n",
        "\n",
        "The displacements can have (the essential/Dirichlet) boundary conditions in the form:\n",
        "\n",
        "$$\n",
        "    u = \\bar{u}\\ \\forall z \\in \\partial \\Omega_\\mathrm{D}% \\qquad \\text{and} \\qquad - E_\\text{s}A \\frac{\\partial u_z}{\\partial z} = \\bar{F} \\ \\forall z \\in \\partial \\Omega_\\mathrm{N}\n",
        "$$\n",
        "\n",
        "We now introduce a test function $\\delta u$ (virtual displacement) which vanishes where the displacement is given\n",
        "\n",
        "$$\n",
        "    \\delta u = 0\\ \\forall z \\in \\partial \\Omega_\\mathrm{D}\n",
        "$$\n",
        "\n",
        "and construct the weak form of the equilibrium conditions (using integration by parts):\n",
        "\n",
        "\\begin{align}\n",
        "   0 &= \\int \\limits_0^H \\left[\\partial_z \\sigma_{zz} + \\gamma_\\text{r} \\right] \\delta u\\, \\text{d}z\n",
        "   \\\n",
        "   &= \\int \\limits_0^H \\left[\\frac{\\partial}{\\partial z} \\left(\\sigma_{zz} \\delta u \\right) - \\partial_z \\delta u \\ \\sigma_{zz} + \\gamma_\\text{r} \\delta u \\right] \\, \\text{d}z\n",
        "\\end{align}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Integrating the first term yields the natural/Neumann boundary conditions, so that\n",
        "$$\n",
        "\\left[ \\sigma_{zz} \\delta u \\right]_0^H + \\int \\limits_0^H \\gamma_\\text{r} \\delta u \\text{d}z = \\int \\limits_0^H \\partial_z \\delta u\\ (\\sigma_{zz}' - p)  \\, \\text{d}z\n",
        "$$\n",
        "which is recognised as the principal of virtual work: $\\delta W_\\text{ext} = \\delta W_\\text{int}$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": []
      },
      "source": [
        "The pore pressure can have (the essential/Dirichlet) boundary conditions in the form:\n",
        "\n",
        "$$\n",
        "    p = \\bar{p}\\ \\forall z \\in \\partial \\Omega_\\mathrm{D}% \\qquad \\text{and} \\qquad - E_\\text{s}A \\frac{\\partial u_z}{\\partial z} = \\bar{F} \\ \\forall z \\in \\partial \\Omega_\\mathrm{N}\n",
        "$$\n",
        "\n",
        "We now introduce a test function $\\delta p$ which vanishes where the pressure is given\n",
        "\n",
        "$$\n",
        "    \\delta p = 0\\ \\forall z \\in \\partial \\Omega_\\mathrm{D}\n",
        "$$\n",
        "\n",
        "and construct the weak form of the equilibrium conditions (using integration by parts):\n",
        "\n",
        "\\begin{align}\n",
        "   0 &= \\int \\limits_0^H \\left[n\\beta_p \\dot{p} + \\partial_z (\\dot{u}_z + q_z) \\right] \\delta p\\, \\text{d}z\n",
        "   \\\n",
        "   &= \\int \\limits_0^H \\left[ \\partial_z (\\delta p\\, q_z) - q_z \\partial_z \\delta p + \\partial_z \\dot{u}_z \\delta p + n\\beta_p \\dot{p} \\delta p \\right] \\, \\text{d}z\n",
        "\\end{align}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Integrating the first term yields the natural/Neumann boundary conditions, so that\n",
        "$$\n",
        "\\left[ q_z \\delta p \\right]_0^H = \\int \\limits_0^H \\left[ \\partial_z \\delta p\\ q_z - n\\beta_p \\dot{p} \\delta p - \\delta p\\, \\partial_z \\dot{u}_z \\right] \\, \\text{d}z\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Finite elements in 1D\n",
        "\n",
        "We first create an element class. An element knows the number of nodes it has, their IDs in the global node vector, and the coordinates of its nodes. Linear elements have 2 nodes and 2 quadrature points, quadratic elements 3 nodes and 3 quadrature points. The natural coordinates of the element run from -1 to 1, and the quadrature points and weights are directly taken from Numpy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "#element class\n",
        "class line_element():#local coordinates go from -1 to 1\n",
        "    #takes number of nodes, global nodal coordinates, global node ids\n",
        "    def __init__(self, nnodes=2, ncoords=[0.,1.], nids=[0,1]):\n",
        "        self.__nnodes = nnodes\n",
        "        if (len(ncoords) != self.__nnodes):\n",
        "            raise Exception(\"Number of coordinates does not match number \\\n",
        "                            of nodes of element (%i vs of %i)\" %(self.__nnodes,len(ncoords)))\n",
        "        else:\n",
        "            self.__coords = np.array(ncoords)\n",
        "        \n",
        "        self.__natural_coords = (self.__coords-self.__coords[0])/(self.__coords[-1]-self.__coords[0])*2. - 1.\n",
        "        \n",
        "        if (len(nids) != self.__nnodes):\n",
        "            raise Exception(\"Number of node IDs does not match number \\\n",
        "                            of nodes of element (%i vs of %i)\" %(self.__nnodes,len(nids)))\n",
        "        else:\n",
        "            self.__global_ids = np.array(nids)\n",
        "        self.__quad_degree = self.__nnodes\n",
        "        self.__quad_points, self.__quad_weights = np.polynomial.legendre.leggauss(self.__quad_degree)\n",
        "                "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, we wish to generate a one-dimensional mesh by specifying the length of a line, the number of elements into which the mesh is to be split, and the number of nodes per element."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "def number_of_nodes(nelems,nodes_per_elem):\n",
        "    return nelems*nodes_per_elem - (nelems - 1)\n",
        "\n",
        "def generate_node_ids_u(num_iterations,max_order,p_order):\n",
        "    sequence = []\n",
        "    last_element = 0\n",
        "\n",
        "    for i in range(num_iterations):\n",
        "        if i == 0:\n",
        "            #el_tuple = [0, 1, 2]\n",
        "            el_tuple = [x for x in range(max_order)]\n",
        "        elif i == 1:\n",
        "            #el_tuple = [2, 5, 6]\n",
        "            el_tuple = [max_order-1,max_order+p_order]\n",
        "            for j in range(2,max_order):\n",
        "                el_tuple.append(el_tuple[-1]+1)\n",
        "        else:\n",
        "            #a = last_element\n",
        "            #b = a + max_order-1\n",
        "            #c = b + 1\n",
        "            #el_tuple = [a, b, c]\n",
        "            el_tuple = [last_element,last_element + p_order]\n",
        "            for j in range(2,max_order):\n",
        "                el_tuple.append(el_tuple[-1]+1)\n",
        "        \n",
        "        sequence.append(el_tuple)\n",
        "        last_element = el_tuple[-1]\n",
        "    return sequence\n",
        "\n",
        "def generate_node_ids_p(num_iterations,max_order,p_order):\n",
        "    sequence = []\n",
        "    last_element = max_order  # Start with the first element as 3\n",
        "\n",
        "    for i in range(num_iterations):\n",
        "        if i == 0:\n",
        "            #pair = (3, 4)\n",
        "            el_tuple = [x for x in range(max_order,max_order+p_order)]\n",
        "        else:\n",
        "            #a = last_element\n",
        "            #b = a + 3\n",
        "            #pair = (a, b)\n",
        "            el_tuple = [last_element, last_element+max_order]\n",
        "            for j in range(2,p_order):\n",
        "                el_tuple.append(el_tuple[-1]+1)\n",
        "        \n",
        "        sequence.append(el_tuple)\n",
        "        last_element = el_tuple[-1]\n",
        "    return sequence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'\\ndef generate_node_ids_u(num_iterations,max_order,p_order):\\n    sequence = []\\n    last_element = 0\\n\\n    for i in range(num_iterations):\\n        el_tuple = [last_element+x for x in range(max_order)]\\n        sequence.append(el_tuple)\\n        last_element = el_tuple[-1]\\n    return sequence\\n\\ndef generate_node_ids_p(num_iterations,max_order,p_order):\\n    sequence = []\\n    last_element = number_of_nodes(num_iterations,max_order)  # Start with the first element as 3\\n\\n    for i in range(num_iterations):\\n        el_tuple = [last_element+x for x in range(p_order)]\\n        sequence.append(el_tuple)\\n        last_element = el_tuple[-1]\\n    return sequence\\n'"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#use this for consequtive node numbering (global system split in [u,p])\n",
        "'''\n",
        "def generate_node_ids_u(num_iterations,max_order,p_order):\n",
        "    sequence = []\n",
        "    last_element = 0\n",
        "\n",
        "    for i in range(num_iterations):\n",
        "        el_tuple = [last_element+x for x in range(max_order)]\n",
        "        sequence.append(el_tuple)\n",
        "        last_element = el_tuple[-1]\n",
        "    return sequence\n",
        "\n",
        "def generate_node_ids_p(num_iterations,max_order,p_order):\n",
        "    sequence = []\n",
        "    last_element = number_of_nodes(num_iterations,max_order)  # Start with the first element as 3\n",
        "\n",
        "    for i in range(num_iterations):\n",
        "        el_tuple = [last_element+x for x in range(p_order)]\n",
        "        sequence.append(el_tuple)\n",
        "        last_element = el_tuple[-1]\n",
        "    return sequence\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "def generate_mesh(domain_length,nelems,nodes_per_elem_u,nodes_per_elem_p,eltype):\n",
        "    if (eltype=='u'):\n",
        "        nodes_per_elem = nodes_per_elem_u\n",
        "    elif (eltype=='p'):\n",
        "        nodes_per_elem = nodes_per_elem_p\n",
        "    nn = number_of_nodes(nelems,nodes_per_elem)\n",
        "    #coordinate vector of global nodes\n",
        "    global_nodal_coordinates = np.linspace(0.,domain_length,nn)\n",
        "    global_solution = np.array([0.]*nn)\n",
        "    \n",
        "    #generate elements\n",
        "    element_vector = []\n",
        "    if (eltype=='u'):\n",
        "        node_ids = generate_node_ids_u(nelems,nodes_per_elem_u,nodes_per_elem_p)\n",
        "    elif (eltype=='p'):\n",
        "        node_ids = generate_node_ids_p(nelems,nodes_per_elem_u,nodes_per_elem_p)\n",
        "    for i in range(nelems):\n",
        "        node_start = (nodes_per_elem-1)*i\n",
        "        node_start_id = (2*nodes_per_elem-2)*i\n",
        "        element_vector.append(\n",
        "            line_element(nodes_per_elem,\n",
        "                         global_nodal_coordinates[node_start:node_start+nodes_per_elem],\n",
        "                         node_ids[i]))\n",
        "        \n",
        "    return global_nodal_coordinates, element_vector, global_solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's put this to test."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "number_of_elements = 20\n",
        "L = 10.\n",
        "nodes_per_element_u = 3\n",
        "nodes_per_element_p = 2\n",
        "\n",
        "nodes_u,elements_u,solution_u=generate_mesh(L,number_of_elements,nodes_per_element_u,nodes_per_element_p,'u')\n",
        "nodes_p,elements_p,solution_p=generate_mesh(L,number_of_elements,nodes_per_element_u,nodes_per_element_p,'p')\n",
        "solution = np.append(solution_u,solution_p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "def split_sol(n_els, solution,max_order,p_order):\n",
        "    sol_u = np.array([])\n",
        "    sol_p = np.array([])\n",
        "    n_ids_u = np.unique([item for sublist in generate_node_ids_u(n_els,max_order,p_order) for item in sublist])\n",
        "    n_ids_p = np.unique([item for sublist in generate_node_ids_p(n_els,max_order,p_order) for item in sublist])\n",
        "    return solution[n_ids_u], solution[n_ids_p]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.xlabel(r'$z$')\n",
        "plt.ylabel(r'element ID')\n",
        "plt.title(r'Finite element mesh for $u$')\n",
        "for i,elem in enumerate(elements_u):\n",
        "    if (i==0):\n",
        "        plt.plot(elem._line_element__coords,[i]*elem._line_element__nnodes, 'bs-', label=r'elements')\n",
        "    else:\n",
        "        plt.plot(elem._line_element__coords,[i]*elem._line_element__nnodes, 'bs-')\n",
        "    for k in range(elem._line_element__nnodes):\n",
        "        plt.text(elem._line_element__coords[k],i+0.5,str(elem._line_element__global_ids[k]),size=7)\n",
        "plt.plot(nodes_u, [0]*len(nodes_u), 'ro ', label=r'nodes')\n",
        "plt.legend();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.xlabel(r'$z$')\n",
        "plt.ylabel(r'element ID')\n",
        "plt.title(r'Finite element mesh for $p$')\n",
        "for i,elem in enumerate(elements_p):\n",
        "    if (i==0):\n",
        "        plt.plot(elem._line_element__coords,[i]*elem._line_element__nnodes, 'bs-', label=r'elements')\n",
        "    else:\n",
        "        plt.plot(elem._line_element__coords,[i]*elem._line_element__nnodes, 'bs-')\n",
        "    for k in range(elem._line_element__nnodes):\n",
        "        plt.text(elem._line_element__coords[k],i+0.5,str(elem._line_element__global_ids[k]),size=7)\n",
        "plt.plot(nodes_p, [0]*len(nodes_p), 'ro ', label=r'nodes')\n",
        "plt.legend();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "#N\n",
        "def shape_function(element_order,xi):\n",
        "    if (element_order == 1):\n",
        "            return np.array([1])\n",
        "    elif (element_order == 2): #-1,1\n",
        "            return np.array([(1.-xi)/2., (1.+xi)/2.])\n",
        "    elif (element_order == 3): #-1, 0, 1\n",
        "            return np.array([(xi - 1.)*xi/2., (1-xi)*(1+xi), (1+xi)*xi/2.])\n",
        "    elif (element_order == 4): #-1, -1/3, 1/3, 1\n",
        "            return np.array([9/16*(1-xi)*(xi**2 - 1/9),\n",
        "                            27/16*(xi**2-1)*(xi-1/3),\n",
        "                            27/16*(1-xi**2)*(xi+1/3),\n",
        "                            9/16*(xi+1)*(xi**2-1/9)])\n",
        "        \n",
        "#dN_dxi\n",
        "def dshape_function_dxi(element_order,xi):\n",
        "    if (element_order == 1):\n",
        "            return np.array([0])\n",
        "    elif (element_order == 2): #-1,1\n",
        "        return np.array([-0.5, 0.5])  #xi only later for plotting dimensions\n",
        "    elif (element_order == 3):#-1,0,1\n",
        "        return np.array([xi - 0.5,-2.*xi,xi + 0.5])\n",
        "    elif (element_order == 4): #-1, -1/3, 1/3, 1\n",
        "            return np.array([-27*xi**2/16 + 9*xi/8 + 1/16,\n",
        "                            81/16*xi**2 - 9/8 * xi - 27/16, \n",
        "                            -81/16*xi**2 - 9/8 * xi +27/16,\n",
        "                            27*xi**2/16 + 9*xi/8 - 1/16])\n",
        "\n",
        "#dz_dxi\n",
        "def element_jacobian(element,xi):\n",
        "    element_order = element._line_element__nnodes\n",
        "    Jacobian = 0.\n",
        "    Jacobian += dshape_function_dxi(element_order,xi).dot(element._line_element__coords)\n",
        "    return Jacobian\n",
        "\n",
        "#dN_dz\n",
        "def grad_shape_function(element,xi):\n",
        "    element_order = element._line_element__nnodes\n",
        "    Jac = element_jacobian(element,xi)\n",
        "    return dshape_function_dxi(element_order,xi)/Jac"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Discretization of virtual work (weak form)\n",
        "\n",
        "The discretized system is given as\n",
        "\n",
        "\\begin{align}\n",
        "    {\\sigma}_{zz}(z=H)\\delta_{in_\\text{n}} - {\\sigma}_{zz}(z=0) \\delta_{i0} + \\bigcup \\limits_{e=1}^{n_\\text{el}} \\int \\limits_{z_e} \\gamma_\\text{r} N^u_i \\det J \\, \\text{d}\\xi &= \\bigcup \\limits_{e=1}^{n_\\text{el}} \\int \\limits_{z_e} \\nabla N_i^u (\\sigma_{zz}' - p)\\det J  \\, \\text{d}\\xi\n",
        "    \\\n",
        "    {q}_{z}(z=H)\\delta_{in_\\text{n}} - {q}_{z}(z=0) \\delta_{i0} &= \\bigcup \\limits_{e=1}^{n_\\text{el}} \\int \\limits_{z_e} \\nabla N_i^p q_z \\det J  \\, \\text{d}\\xi - \\bigcup \\limits_{e=1}^{n_\\text{el}} \\int \\limits_{z_e} N_i^p \\left[ \\frac{\\partial_z u_z - \\partial_z u_z^\\text{prev}}{\\Delta t} + n\\beta_p\\frac{p - p^\\text{prev}}{\\Delta t} \\right]\\det J  \\, \\text{d}\\xi \n",
        "\\end{align}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The latter equation was modified to yield a symmetric stiffness matrix:\n",
        "\n",
        "\\begin{align}\n",
        "    {\\sigma}_{zz}(z=H)\\delta_{in_\\text{n}} - {\\sigma}_{zz}(z=0) \\delta_{i0} + \\bigcup \\limits_{e=1}^{n_\\text{el}} \\int \\limits_{z_e} \\gamma_\\text{r} N^u_i \\det J \\, \\text{d}\\xi &= \\bigcup \\limits_{e=1}^{n_\\text{el}} \\int \\limits_{z_e} \\nabla N_i^u (\\sigma_{zz}' - p)\\det J  \\, \\text{d}\\xi\n",
        "    \\\n",
        "    {q}_{z}(z=H)\\delta_{in_\\text{n}} \\Delta t - {q}_{z}(z=0) \\delta_{i0} \\Delta t &= \\bigcup \\limits_{e=1}^{n_\\text{el}} \\int \\limits_{z_e} \\nabla N_i^p q_z \\Delta t\\det J  \\, \\text{d}\\xi - \\bigcup \\limits_{e=1}^{n_\\text{el}} \\int \\limits_{z_e} N_i^p \\left[ \\Delta \\partial_z u_z + n\\beta_p\\Delta p  \\right]\\det J  \\, \\text{d}\\xi \n",
        "\\end{align}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "What we require now is the local assembler to calculate the stiffness matrix and the local right-hand side. Local integration is performed by Gauss quadrature:\n",
        "\n",
        "$$\n",
        "    \\int \\limits_{-1}^1 f(\\xi)\\,\\text{d}\\xi \\approx \\sum \\limits_{i=1}^{n_\\text{gp}} f(\\xi_i) w_i \n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Local assember"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "def Stiffness(z):\n",
        "    return 5e6\n",
        "\n",
        "def gamma_r(z):\n",
        "    return 0\n",
        "\n",
        "def gamma_w():\n",
        "    return 0\n",
        "\n",
        "def Conductivity(z):\n",
        "    return 1e-5 / 1e4 # k/mu = k_f/gamma_w\n",
        "\n",
        "def Compressibility(z):\n",
        "    return 5e-10*0.4*0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "def local_assembler_res(elem_u,elem_p,sol_u,sol_p,sol_u_prev,sol_p_prev,dt):\n",
        "    order_u = elem_u._line_element__nnodes\n",
        "    order_p = elem_p._line_element__nnodes\n",
        "    res_loc = np.zeros(order_u + order_p)\n",
        "    z_nodes = elem_u._line_element__coords\n",
        "    for i in range(elem_u._line_element__quad_degree):\n",
        "        #local integration point coordinate\n",
        "        xi = elem_u._line_element__quad_points[i]\n",
        "        #shape function\n",
        "        N_u = shape_function(order_u,xi)\n",
        "        N_p = shape_function(order_p,xi)\n",
        "        #gradient of shape function\n",
        "        dN_u_dX = grad_shape_function(elem_u,xi)\n",
        "        dN_p_dX = grad_shape_function(elem_p,xi)\n",
        "        #determinant of Jacobian\n",
        "        detJ = np.abs(element_jacobian(elem_u,xi))\n",
        "        #integration weight\n",
        "        w = elem_u._line_element__quad_weights[i]        \n",
        "        #global integration point coordinate (for spatially varying properties)\n",
        "        z_glob = np.dot(N_u,z_nodes)\n",
        "        #evaluation of local material/structural properties\n",
        "        E = Stiffness(-np.dot(dN_u_dX,sol_u))\n",
        "        k_over_mu = Conductivity(-np.dot(dN_u_dX,sol_u))\n",
        "        beta = Compressibility(z_glob)\n",
        "        #evaluation of local body force contributions\n",
        "        gr = gamma_r(z_glob)\n",
        "        gw = gamma_w()\n",
        "        \n",
        "        #flux quantities\n",
        "        \n",
        "        q_z = - k_over_mu * (np.dot(dN_p_dX,sol_p) - gw)\n",
        "        #print(q_z)\n",
        "        s_z = E * np.dot(dN_u_dX,sol_u) - np.dot(N_p,sol_p)\n",
        "        delta_e_vol = np.dot(dN_u_dX,(sol_u - sol_u_prev))\n",
        "        delta_p = np.dot(N_p,(sol_p-sol_p_prev))\n",
        "           \n",
        "        res_u = dN_u_dX*s_z - N_u*gr\n",
        "        res_p = dN_p_dX*q_z*dt - N_p * (delta_e_vol + beta * delta_p)\n",
        "        \n",
        "        #assembly of local RHS\n",
        "        res_loc += np.block([res_u, res_p]) * w * detJ\n",
        "    return res_loc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "def local_assembler_res_Jac(elem_u,elem_p,sol_u,sol_p,sol_u_prev,sol_p_prev,dt,fac=1):\n",
        "    order_u = elem_u._line_element__nnodes\n",
        "    order_p = elem_p._line_element__nnodes\n",
        "    res_loc = np.zeros(order_u + order_p)\n",
        "    K_loc = np.zeros((order_u + order_p,order_u + order_p))\n",
        "    z_nodes = elem_u._line_element__coords\n",
        "    for i in range(elem_u._line_element__quad_degree):\n",
        "        #local integration point coordinate\n",
        "        xi = elem_u._line_element__quad_points[i]\n",
        "        #shape function\n",
        "        N_u = shape_function(order_u,xi)\n",
        "        N_p = shape_function(order_p,xi)\n",
        "        #gradient of shape function\n",
        "        dN_u_dX = grad_shape_function(elem_u,xi)\n",
        "        dN_p_dX = grad_shape_function(elem_p,xi)\n",
        "        #determinant of Jacobian\n",
        "        detJ = np.abs(element_jacobian(elem_u,xi))\n",
        "        #integration weight\n",
        "        w = elem_u._line_element__quad_weights[i]        \n",
        "        #global integration point coordinate (for spatially varying properties)\n",
        "        z_glob = np.dot(N_u,z_nodes)\n",
        "        #evaluation of local material/structural properties\n",
        "        E = Stiffness(-np.dot(dN_u_dX,sol_u))\n",
        "        k_over_mu = Conductivity(-np.dot(dN_u_dX,sol_u))\n",
        "        beta = Compressibility(0)\n",
        "        #evaluation of local body force contributions\n",
        "        gr = gamma_r(0)\n",
        "        gw = gamma_w()\n",
        "        \n",
        "        #flux quantities\n",
        "        \n",
        "        q_z = - k_over_mu * (np.dot(dN_p_dX,sol_p) - gw)\n",
        "        s_z = E * np.dot(dN_u_dX,sol_u) - np.dot(N_p,sol_p)\n",
        "        delta_e_vol = np.dot(dN_u_dX,(sol_u - sol_u_prev))\n",
        "        delta_p = np.dot(N_p,(sol_p-sol_p_prev))\n",
        "               \n",
        "        res_u = dN_u_dX*s_z - N_u*gr\n",
        "        res_p = dN_p_dX*q_z*dt - N_p * (delta_e_vol + beta * delta_p)\n",
        "        \n",
        "        #assembly of local Jac\n",
        "        K_loc += np.block([[np.outer(dN_u_dX,dN_u_dX)*E, -np.outer(dN_u_dX,N_p)],\n",
        "                           [-np.outer(N_p,dN_u_dX), -np.outer(dN_p_dX,dN_p_dX)*k_over_mu*dt - np.outer(N_p,N_p)*beta]]) * w * detJ\n",
        "        \n",
        "        #assembly of local RHS\n",
        "        res_loc += np.block([res_u, res_p]) * w * detJ\n",
        "    return K_loc, res_loc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "def local_assembler_Picard(elem_u,elem_p,sol_u,sol_p,sol_u_prev,sol_p_prev,dt,fac=1):\n",
        "    order_u = elem_u._line_element__nnodes\n",
        "    order_p = elem_p._line_element__nnodes\n",
        "    rhs_loc = np.zeros(order_u + order_p)\n",
        "    K_loc = np.zeros((order_u + order_p,order_u + order_p))\n",
        "    M_loc = np.zeros((order_u + order_p,order_u + order_p))\n",
        "    z_nodes = elem_u._line_element__coords\n",
        "    for i in range(elem_u._line_element__quad_degree):\n",
        "        #local integration point coordinate\n",
        "        xi = elem_u._line_element__quad_points[i]\n",
        "        #shape function\n",
        "        N_u = shape_function(order_u,xi)\n",
        "        N_p = shape_function(order_p,xi)\n",
        "        #gradient of shape function\n",
        "        dN_u_dX = grad_shape_function(elem_u,xi)\n",
        "        dN_p_dX = grad_shape_function(elem_p,xi)\n",
        "        #determinant of Jacobian\n",
        "        detJ = np.abs(element_jacobian(elem_u,xi))\n",
        "        #integration weight\n",
        "        w = elem_u._line_element__quad_weights[i]        \n",
        "        #global integration point coordinate (for spatially varying properties)\n",
        "        z_glob = np.dot(N_u,z_nodes)\n",
        "        #evaluation of local material/structural properties\n",
        "        E = Stiffness(0)\n",
        "        k_over_mu = Conductivity(0)\n",
        "        beta = Compressibility(0)\n",
        "        #evaluation of local body force contributions\n",
        "        gr = gamma_r(0)\n",
        "        gw = gamma_w()\n",
        "        \n",
        "        #flux quantities\n",
        "        \n",
        "        q_z = - k_over_mu * (np.dot(dN_p_dX,sol_p) - gw)\n",
        "        s_z = E * np.dot(dN_u_dX,sol_u) - np.dot(N_p,sol_p)\n",
        "        delta_e_vol = np.dot(dN_u_dX,(sol_u - sol_u_prev))\n",
        "        delta_p = np.dot(N_p,(sol_p-sol_p_prev))\n",
        "               \n",
        "        rhs_u = N_u*gr\n",
        "        rhs_p = dN_p_dX*k_over_mu*gw\n",
        "        \n",
        "        #assembly of local Jac\n",
        "        K_loc += np.block([[np.outer(dN_u_dX,dN_u_dX)*E, -np.outer(dN_u_dX,N_p)],\n",
        "                           [np.outer(N_p,dN_u_dX)*0, np.outer(dN_p_dX,dN_p_dX)*k_over_mu]]) * w * detJ\n",
        "        M_loc += np.block([[np.outer(dN_u_dX,dN_u_dX)*0, -np.outer(dN_u_dX,N_p)*0],\n",
        "                           [np.outer(N_p,dN_u_dX), np.outer(N_p,N_p)*beta]]) * w * detJ\n",
        "        \n",
        "        #assembly of local RHS\n",
        "        rhs_loc += np.block([rhs_u, rhs_p]) * w * detJ\n",
        "    return K_loc + M_loc/dt, rhs_loc + M_loc@np.append(sol_u_prev,sol_p_prev)/dt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "epsilon = lambda p: np.maximum(1e-8,1e-8*p)\n",
        "\n",
        "# could use forward differences to safe one set of evaluations\n",
        "def local_assembler_Jac(elem_u,elem_p,sol_u,sol_p,sol_u_prev,sol_p_prev,dt):\n",
        "    #print(\"res\")\n",
        "    order_u = elem_u._line_element__nnodes\n",
        "    order_p = elem_p._line_element__nnodes\n",
        "    Jac = np.zeros((order_u+order_p,order_u+order_p))\n",
        "    r0 = local_assembler_res(elem_u,elem_p,sol_u,sol_p,sol_u_prev,sol_p_prev,dt)\n",
        "    sol = np.append(sol_u,sol_p)\n",
        "    sol_prev = np.append(sol_u_prev,sol_p_prev)\n",
        "    for j in range(len(sol_u)):\n",
        "        #print(\"Jac \", j)\n",
        "        upper_up = np.copy(sol)\n",
        "        eps = np.maximum(1e-8,1e-8*sol[j])\n",
        "        upper_up[j] += eps\n",
        "        Jac[:,j] = (local_assembler_res(elem_u,elem_p,upper_up[0:len(sol_u)],\n",
        "                                        upper_up[len(sol_u):],sol_u_prev,sol_p_prev,dt)\n",
        "                    - r0)/eps\n",
        "        for j in range(len(sol_u),len(sol)):\n",
        "            #print(\"Jac \", j)\n",
        "            upper_up = np.copy(sol)\n",
        "            eps = np.maximum(1e-1,1e-8*sol[j])\n",
        "            upper_up[j] += eps\n",
        "            Jac[:,j] = (local_assembler_res(elem_u,elem_p,upper_up[0:len(sol_u)],\n",
        "                                            upper_up[len(sol_u):],sol_u_prev,sol_p_prev,dt)\n",
        "                        - r0)/eps\n",
        "    return Jac, r0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "elem_u = elements_u[1]\n",
        "elem_p = elements_p[1]\n",
        "\n",
        "IDs_u = elem_u._line_element__global_ids\n",
        "IDs_p = elem_p._line_element__global_ids\n",
        "        #\n",
        "el_sol_u = np.array([solution[IDs_u[i]] for i in range(len(IDs_u))])\n",
        "el_sol_p = np.array([solution[IDs_p[i]] for i in range(len(IDs_p))])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0.])"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "local_assembler_res(elem_u,elem_p,el_sol_u,el_sol_p, el_sol_u*1.1,el_sol_p*1.1,10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5.7098959814013304e+16"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "K,r = local_assembler_Picard(elem_u,elem_p,el_sol_u,el_sol_p, el_sol_u*1.1,el_sol_p*1.1,10)\n",
        "np.linalg.cond(K)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 2.33333333e+07 -2.66666667e+07  3.33333333e+06  8.33333333e-01\n",
            "   1.66666667e-01]\n",
            " [-2.66666667e+07  5.33333333e+07 -2.66666667e+07 -6.66666667e-01\n",
            "   6.66666667e-01]\n",
            " [ 3.33333333e+06 -2.66666667e+07  2.33333333e+07 -1.66666667e-01\n",
            "  -8.33333333e-01]\n",
            " [ 8.33333333e-01 -6.66666667e-01 -1.66666667e-01 -2.00000000e-08\n",
            "   2.00000000e-08]\n",
            " [ 1.66666667e-01  6.66666667e-01 -8.33333333e-01  2.00000000e-08\n",
            "  -2.00000000e-08]]\n",
            "[0. 0. 0. 0. 0.]\n"
          ]
        }
      ],
      "source": [
        "K,r = local_assembler_Jac(elem_u,elem_p,el_sol_u,el_sol_p, el_sol_u*1.1,el_sol_p*1.1,10)\n",
        "print(K)\n",
        "print(r)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 2.33333333e+07 -2.66666667e+07  3.33333333e+06  8.33333333e-01\n",
            "   1.66666667e-01]\n",
            " [-2.66666667e+07  5.33333333e+07 -2.66666667e+07 -6.66666667e-01\n",
            "   6.66666667e-01]\n",
            " [ 3.33333333e+06 -2.66666667e+07  2.33333333e+07 -1.66666667e-01\n",
            "  -8.33333333e-01]\n",
            " [ 8.33333333e-01 -6.66666667e-01 -1.66666667e-01 -2.00000000e-08\n",
            "   2.00000000e-08]\n",
            " [ 1.66666667e-01  6.66666667e-01 -8.33333333e-01  2.00000000e-08\n",
            "  -2.00000000e-08]]\n",
            "[0. 0. 0. 0. 0.]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "5.709895981401257e+16"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "K2,r2 = local_assembler_res_Jac(elem_u,elem_p,el_sol_u,el_sol_p, el_sol_u*1.1,el_sol_p*1.1,10)\n",
        "print(K2)\n",
        "print(r)\n",
        "np.linalg.cond(K2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[-1.59655299e-16 -0.00000000e+00  2.79396772e-16 -2.66453526e-16\n",
            "   0.00000000e+00]\n",
            " [ 1.39698386e-16  1.39698386e-16  1.39698386e-16  3.33066907e-16\n",
            "   3.33066907e-16]\n",
            " [ 2.79396772e-16 -0.00000000e+00 -1.59655299e-16 -0.00000000e+00\n",
            "  -0.00000000e+00]\n",
            " [ 0.00000000e+00 -0.00000000e+00 -0.00000000e+00 -1.65436123e-16\n",
            "  -1.65436123e-16]\n",
            " [ 0.00000000e+00  0.00000000e+00 -0.00000000e+00 -1.65436123e-16\n",
            "  -1.65436123e-16]]\n"
          ]
        }
      ],
      "source": [
        "print((K2-K)/K)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'condition number of Jacobian')"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def Stiffness(z):\n",
        "    return 1e8\n",
        "\n",
        "def gamma_r(z):\n",
        "    return -2e4\n",
        "\n",
        "def gamma_w():\n",
        "    return -1e4\n",
        "\n",
        "def Conductivity(z):\n",
        "    return 1e-12 # k/mu = k_f/gamma_w\n",
        "\n",
        "def Compressibility(z):\n",
        "    return 5e-10*0.4\n",
        "\n",
        "dts = np.logspace(-3,10,14)\n",
        "conds = dts*0\n",
        "for i,dt in enumerate(dts):\n",
        "    K,r = local_assembler_res_Jac(elem_u,elem_p,el_sol_u,el_sol_p, el_sol_u*1.1,el_sol_p*1.1,dt)\n",
        "    conds[i] = np.linalg.cond(K)\n",
        "plt.plot(dts,conds)\n",
        "plt.xscale('log')\n",
        "#plt.yscale('log')\n",
        "plt.xlabel(r'$\\Delta t$ / s')\n",
        "plt.ylabel(r'condition number of Jacobian')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0.]]\n"
          ]
        }
      ],
      "source": [
        "print(K2-K2.T)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Symmetric, but ill-conditioned, analytical Jacobian confirmed numerically (and more stable)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Global assembly\n",
        "\n",
        "Now we can construct the global matrix system $\\mathbf{K}\\mathbf{u} = \\mathbf{f}$ or $\\mathbf{A}\\mathbf{x}=\\mathbf{b}$ (see lecture script)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [],
      "source": [
        "def global_assembler(elements_u,elements_p,solution,solution_prev,dt,f=local_assembler_res_Jac):\n",
        "    K_glob = np.zeros((len(solution),len(solution)))\n",
        "    b_glob = np.zeros(len(solution))\n",
        "    for i,elem in enumerate(elements_u):\n",
        "        #DOF table\n",
        "        IDs_u = elem._line_element__global_ids\n",
        "        IDs_p = elements_p[i]._line_element__global_ids\n",
        "        #\n",
        "        el_sol_u = np.array([solution[IDs_u[i]] for i in range(len(IDs_u))])\n",
        "        el_sol_p = np.array([solution[IDs_p[i]] for i in range(len(IDs_p))])\n",
        "        el_sol_u_prev = np.array([solution_prev[IDs_u[i]] for i in range(len(IDs_u))])\n",
        "        el_sol_p_prev = np.array([solution_prev[IDs_p[i]] for i in range(len(IDs_p))])\n",
        "        K_i, b_i = f(elem,elements_p[i],el_sol_u,el_sol_p,el_sol_u_prev,el_sol_p_prev,dt)\n",
        "        for j in range(len(IDs_u)):\n",
        "            b_glob[IDs_u[j]] += b_i[j]\n",
        "            for k in range(len(IDs_u)):\n",
        "                K_glob[IDs_u[j],IDs_u[k]] += K_i[j,k]\n",
        "            for k in range(len(IDs_p)):\n",
        "                K_glob[IDs_u[j],IDs_p[k]] += K_i[j,k+len(IDs_u)]\n",
        "        for j in range(len(IDs_p)):\n",
        "            b_glob[IDs_p[j]] += b_i[j+len(IDs_u)]\n",
        "            for k in range(len(IDs_u)):\n",
        "                K_glob[IDs_p[j],IDs_u[k]] += K_i[j+len(IDs_u),k]\n",
        "            for k in range(len(IDs_p)):\n",
        "                K_glob[IDs_p[j],IDs_p[k]] += K_i[j+len(IDs_u),k+len(IDs_u)]\n",
        "            \n",
        "    return K_glob, b_glob"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "K, f = global_assembler(elements_u,elements_p,solution,solution*1.1,0.5)\n",
        "plt.imshow(K, cmap='turbo',alpha=np.where(K==0,0.,1.))\n",
        "plt.colorbar()\n",
        "plt.title(r'sparsity pattern')\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Application of boundary conditions\n",
        "\n",
        "Now we apply the natural (Neumann, nodal force) and the essential (Dirichlet, nodal displacement) boundary conditions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [],
      "source": [
        "def apply_Neumann_bc(b_glob,node_id,value):\n",
        "    b_glob[node_id] += value\n",
        "    return b_glob"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A Dirichlet boundary condition is introduced via\n",
        "\n",
        "$b_0 \\leftarrow K_{0,0}(u_0 - \\bar{u})$ so that $\\Delta u_0 = \\bar{u} - u_0$ and matrix condition number is not changed.\n",
        "\n",
        "$b_{i \\neq 0} \\leftarrow b_{i\\neq 0} - K_{i,0} (u_0 - \\bar{u})$\n",
        "\n",
        "$K_{0,i\\neq 0} = K_{i\\neq 0,0} \\leftarrow 0$ \n",
        "\n",
        "This preserves $K_{ij} = K_{ji}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [],
      "source": [
        "#adapted to incremental scheme\n",
        "def apply_Dirichlet_bc(K_glob,b_glob,solution,node_id,value):\n",
        "    c = K_glob[node_id,node_id]\n",
        "    if c == 0:\n",
        "        c = 1\n",
        "    target = solution[node_id] - value\n",
        "    \n",
        "    #for i in range(len(b_glob)):\n",
        "    b_glob[:] -= K_glob[:,node_id] * target\n",
        "    b_glob[node_id] = c*target #no increment\n",
        "    K_glob[node_id,:] = 0.\n",
        "    K_glob[:,node_id] = 0.\n",
        "    \n",
        "    K_glob[node_id,node_id] = c\n",
        "\n",
        "    #solution[node_id] = value\n",
        "    return K_glob, b_glob"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [],
      "source": [
        "def converged(inc_u,inc_p):\n",
        "    n_u = np.linalg.norm(inc_u,np.inf)#np.linalg.norm(inc_u)/len(inc_u)\n",
        "    n_p = np.linalg.norm(inc_p,np.inf)#np.linalg.norm(inc_p)/len(inc_p)\n",
        "    #print(n_u, n_p)\n",
        "    if (n_u <= 1e-8 and n_p <= 1e-2):\n",
        "        return True\n",
        "    else:\n",
        "        return False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [],
      "source": [
        "def time_loop(elements_u,elements_p,solution,dt,t_end,p_top):\n",
        "    #Startwerte\n",
        "    max_iter = 50\n",
        "    y = [solution] #create a list that will hold the solution vectors at all time points\n",
        "    times = np.array([0.])\n",
        "    n_ids_u = np.unique([item for sublist in generate_node_ids_u(len(elements_u),elements_u[0]._line_element__nnodes,elements_p[0]._line_element__nnodes) for item in sublist])\n",
        "    n_ids_p = np.unique([item for sublist in generate_node_ids_p(len(elements_u),elements_u[0]._line_element__nnodes,elements_p[0]._line_element__nnodes) for item in sublist])\n",
        "    #\n",
        "    while times[-1]+dt < t_end + dt/2: #repeat the loop as long as the final time step is below the end point\n",
        "        print(\"Solving time step %i.\" %(len(times)))\n",
        "        times = np.append(times,times[-1]+dt) #here define the next time point as the previous time point plus the time increment dt\n",
        "        y_old = np.copy(y[-1]) #Starting value for recursive update\n",
        "        i = 0\n",
        "        #\n",
        "        #print(\"Solution \", y_old)\n",
        "        K, f = global_assembler(elements_u,elements_p,y_old,y[-1],dt)\n",
        "        #print(\"Residual \", f)\n",
        "        f = apply_Neumann_bc(f,n_ids_u[-1],p_top(times[-1])) # apply top load\n",
        "        K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_u[0], 0.) #Fix u at bottom\n",
        "        K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_p[-1], p_top(times[-1])) #Fix p at top\n",
        "        #inc = sp.linalg.solve(K,-f)\n",
        "        sK = sp.sparse.csr_matrix(K) \n",
        "        inc = spsolve(sK,-f)\n",
        "        #inc = sp.sparse.linalg.bicgstab(K, -f)[0]\n",
        "        #lu, piv = sp.linalg.lu_factor(K)\n",
        "        #inc = sp.linalg.lu_solve((lu, piv), -f)\n",
        "        #print(\"Increment \", inc)\n",
        "        y_old += inc\n",
        "        #print(\"Solution \", y_old)\n",
        "        while i < max_iter and not converged(inc[n_ids_u],inc[n_ids_p]):\n",
        "            K, f = global_assembler(elements_u,elements_p,y_old,y[-1],dt)\n",
        "            #print(\"Residual \",f)\n",
        "            f = apply_Neumann_bc(f,n_ids_u[-1],p_top(times[-1])) #apply top load\n",
        "            K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_u[0], 0.) #Fix u at bottom\n",
        "            K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_p[-1], p_top(times[-1])) #Fix p at top\n",
        "            #print(err)\n",
        "            #inc = sp.linalg.solve(K,-f)\n",
        "            sK = sp.sparse.csr_matrix(K) \n",
        "            inc = spsolve(sK,-f)\n",
        "            #inc = sp.sparse.linalg.bicgstab(K, -f)[0]\n",
        "            #lu, piv = sp.linalg.lu_factor(K)\n",
        "            #inc = sp.linalg.lu_solve((lu, piv), -f)\n",
        "            #print(\"Increment \", inc)\n",
        "            y_old += inc\n",
        "            #print(\"Solution \", y_old)\n",
        "            i +=1\n",
        "\n",
        "        print(\"Performed %i iterations.\" %i)\n",
        "        #print(solution)\n",
        "        y.append(y_old) #append the new found solution to the solution vector\n",
        "         #store how much iterations this time step took to converge\n",
        "    return times, y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Consolidation under self weight"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [],
      "source": [
        "number_of_elements = 20\n",
        "L = 10.\n",
        "nodes_per_element_u = 3\n",
        "nodes_per_element_p = 2\n",
        "\n",
        "nodes_u,elements_u,solution_u=generate_mesh(L,number_of_elements,nodes_per_element_u,nodes_per_element_p,'u')\n",
        "nodes_p,elements_p,solution_p=generate_mesh(L,number_of_elements,nodes_per_element_u,nodes_per_element_p,'p')\n",
        "solution = np.append(solution_u,solution_p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [],
      "source": [
        "def Stiffness(z):\n",
        "    return 1e8\n",
        "\n",
        "def gamma_r(z):\n",
        "    n=0.4\n",
        "    return -(26000.*(1.-n) + 10000.*n) #kg/m³\n",
        "\n",
        "def gamma_w():\n",
        "    return -10000.\n",
        "\n",
        "def Conductivity(z):\n",
        "    return 1e-14/1e-3\n",
        "\n",
        "def Compressibility(z):\n",
        "    return 5e-10*0.4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {},
      "outputs": [],
      "source": [
        "day_in_sec = 86400"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 2.\n",
            "Performed 1 iterations.\n",
            "Solving time step 3.\n",
            "Performed 1 iterations.\n",
            "Solving time step 4.\n",
            "Performed 1 iterations.\n",
            "Solving time step 5.\n",
            "Performed 1 iterations.\n",
            "Solving time step 6.\n",
            "Performed 1 iterations.\n",
            "Solving time step 7.\n",
            "Performed 1 iterations.\n",
            "Solving time step 8.\n",
            "Performed 1 iterations.\n",
            "Solving time step 9.\n",
            "Performed 1 iterations.\n",
            "Solving time step 10.\n",
            "Performed 1 iterations.\n"
          ]
        }
      ],
      "source": [
        "p_top = lambda t: 1e5\n",
        "times, sols = time_loop(elements_u,elements_p,solution,day_in_sec,day_in_sec*10,p_top)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(ncols=2, figsize=(12, 6))\n",
        "for i, t in enumerate(times):\n",
        "    sol_u, sol_p = split_sol(len(elements_u), sols[i],nodes_per_element_u,nodes_per_element_p)\n",
        "    ax[0].plot(nodes_u, sol_u, label=r'$t = %i$ d' % (t/day_in_sec), color='blue', alpha=(i+1)/(len(times)+1), ls='', marker='.')\n",
        "    ax[1].plot(nodes_p, sol_p/1e3, color='blue', alpha=(i+1)/(len(times)+1), ls='', marker='.')\n",
        "\n",
        "ax[0].set_xlabel(r'$z$ / m')\n",
        "ax[1].set_xlabel(r'$z$ / m')\n",
        "ax[0].set_ylabel(r'$u_z$ / m')\n",
        "ax[1].set_ylabel(r'$p$ / kPa')\n",
        "# Add the legend next to the plots\n",
        "handles, labels = ax[0].get_legend_handles_labels()\n",
        "fig.legend(handles, labels, loc='center left', bbox_to_anchor=(1.05, 0.5), ncol=1)\n",
        "\n",
        "fig.tight_layout(rect=[0, 0, 0.85, 1])  # Adjust the layout to make room for the legend"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Condition number bad for:\n",
        "- small $\\Delta t$\n",
        "- low $E$ (but high $E$ makes problem more stable)\n",
        "- low $k$\n",
        "- low $\\beta$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Testing restart / IC\n",
        "\n",
        "Starting the same simulation but with final result."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 2.\n",
            "Performed 1 iterations.\n",
            "Solving time step 3.\n",
            "Performed 1 iterations.\n",
            "Solving time step 4.\n",
            "Performed 1 iterations.\n"
          ]
        }
      ],
      "source": [
        "times, sols = time_loop(elements_u,elements_p,sols[-1],day_in_sec,4*day_in_sec,p_top)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(ncols=2, figsize=(12, 6))\n",
        "for i, t in enumerate(times):\n",
        "    sol_u, sol_p = split_sol(len(elements_u), sols[i],nodes_per_element_u,nodes_per_element_p)\n",
        "    ax[0].plot(nodes_u, sol_u, label=r'$t = %i$ d' % (t/day_in_sec), color='blue', alpha=(i+1)/(len(times)+1), ls='', marker='.')\n",
        "    ax[1].plot(nodes_p, sol_p/1e3, color='blue', alpha=(i+1)/(len(times)+1), ls='', marker='.')\n",
        "\n",
        "ax[0].set_xlabel(r'$z$ / m')\n",
        "ax[1].set_xlabel(r'$z$ / m')\n",
        "ax[0].set_ylabel(r'$u_z$ / m')\n",
        "ax[1].set_ylabel(r'$p$ / kPa')\n",
        "# Add the legend next to the plots\n",
        "handles, labels = ax[0].get_legend_handles_labels()\n",
        "fig.legend(handles, labels, loc='center left', bbox_to_anchor=(1.05, 0.5), ncol=1)\n",
        "\n",
        "fig.tight_layout(rect=[0, 0, 0.85, 1])  # Adjust the layout to make room for the legend"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Consolidation\n",
        "\n",
        "As in Terzaghi scripts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "number_of_elements = 20\n",
        "L = 10\n",
        "nodes_per_element_u = 4\n",
        "nodes_per_element_p = 3\n",
        "\n",
        "nodes_u,elements_u,solution_u=generate_mesh(L,number_of_elements,nodes_per_element_u,nodes_per_element_p,'u')\n",
        "nodes_p,elements_p,solution_p=generate_mesh(L,number_of_elements,nodes_per_element_u,nodes_per_element_p,'p')\n",
        "solution = np.append(solution_u,solution_p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "def Stiffness(z):\n",
        "    return 5e6\n",
        "\n",
        "def gamma_r(z):\n",
        "    return 0\n",
        "\n",
        "def gamma_w():\n",
        "    return 0\n",
        "\n",
        "def Conductivity(z):\n",
        "    return 1e-5 / 1e4 # k/mu = k_f/gamma_w\n",
        "\n",
        "def Compressibility(z):\n",
        "    return 5e-10*0.4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "def time_loop(elements_u,elements_p,solution,dt,t_end,p_top):\n",
        "    #Startwerte\n",
        "    max_iter = 20\n",
        "    y = [solution] #create a list that will hold the solution vectors at all time points\n",
        "    times = np.array([0.])\n",
        "    n_ids_u = np.unique([item for sublist in generate_node_ids_u(len(elements_u),elements_u[0]._line_element__nnodes,elements_p[0]._line_element__nnodes) for item in sublist])\n",
        "    n_ids_p = np.unique([item for sublist in generate_node_ids_p(len(elements_u),elements_u[0]._line_element__nnodes,elements_p[0]._line_element__nnodes) for item in sublist])\n",
        "    #\n",
        "    while times[-1]+dt < t_end + dt/2: #repeat the loop as long as the final time step is below the end point\n",
        "        print(\"Solving time step %i.\" %(len(times)))\n",
        "        times = np.append(times,times[-1]+dt) #here define the next time point as the previous time point plus the time increment dt\n",
        "        y_old = np.copy(y[-1]) #Starting value for recursive update\n",
        "        i = 0\n",
        "        #\n",
        "        #print(\"Solution \", y_old)\n",
        "        K, f = global_assembler(elements_u,elements_p,y_old,y[-1],dt)\n",
        "        #print(\"Residual \", f)\n",
        "        f = apply_Neumann_bc(f,n_ids_u[-1],p_top(times[-1])) # apply top load\n",
        "        #f = apply_Neumann_bc(f,n_ids_p[0],1e-14*dt) #apply top load\n",
        "        #K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_u[-1], -1e-2)\n",
        "        K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_u[0], 0.) #Fix u at bottom\n",
        "        #for k in range(len(n_ids_u)):\n",
        "        #    K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_u[k], nodes_u[k]/100) #Fix p at top\n",
        "        K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_p[-1], 0.) #Fix p at top\n",
        "        #inc = sp.linalg.solve(K,-f)\n",
        "        sK = sp.sparse.csr_matrix(K) \n",
        "        inc = spsolve(sK,-f)\n",
        "        #inc = sp.sparse.linalg.bicg(K, -f)[0]\n",
        "        #lu, piv = sp.linalg.lu_factor(K)\n",
        "        #inc = sp.linalg.lu_solve((lu, piv), -f)\n",
        "        #print(\"Increment \", inc)\n",
        "        y_old += inc\n",
        "        #print(\"Solution \", y_old)\n",
        "        while i < max_iter and not converged(inc[n_ids_u],inc[n_ids_p]):\n",
        "            K, f = global_assembler(elements_u,elements_p,y_old,y[-1],dt)\n",
        "            #print(\"Residual \",f)\n",
        "            f = apply_Neumann_bc(f,n_ids_u[-1],p_top(times[-1])) #apply top load\n",
        "            #f = apply_Neumann_bc(f,n_ids_p[0],1e-14*dt) #apply top load\n",
        "            #K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_u[-1], -1e-2)\n",
        "            K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_u[0], 0.) #Fix u at bottom\n",
        "            #for k in range(len(n_ids_u)):\n",
        "            #    K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_u[k], nodes_u[k]/100) #Fix p at top\n",
        "            K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_p[-1], 0.) #Fix p at top\n",
        "            #print(err)\n",
        "            #inc = sp.linalg.solve(K,-f)\n",
        "            sK = sp.sparse.csr_matrix(K) \n",
        "            inc = spsolve(sK,-f)\n",
        "            #inc = sp.sparse.linalg.bicgstab(K, -f)[0]\n",
        "            #lu, piv = sp.linalg.lu_factor(K)\n",
        "            #inc = sp.linalg.lu_solve((lu, piv), -f)\n",
        "            #print(\"Increment \", inc)\n",
        "            y_old += inc\n",
        "            #print(\"Solution \", y_old)\n",
        "            i +=1\n",
        "\n",
        "        print(\"Performed %i iterations.\" %i)\n",
        "        #print(solution)\n",
        "        y.append(y_old) #append the new found solution to the solution vector\n",
        "         #store how much iterations this time step took to converge\n",
        "    return times, y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 1.\n",
            "Performed 1 iterations.\n"
          ]
        }
      ],
      "source": [
        "day_in_sec = 86400\n",
        "p_top = lambda t: 2e5\n",
        "times, sols = time_loop(elements_u,elements_p,solution,10,10,p_top)\n",
        "dts = np.logspace(1,5,10)\n",
        "for dt in dts:\n",
        "    times_i, sols_i = time_loop(elements_u,elements_p,sols[-1],dt,dt,p_top)\n",
        "    times = np.append(times,times_i+times[-1],0)\n",
        "    sols = np.append(sols,sols_i,0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(ncols=2, figsize=(12, 6))\n",
        "ttimes = [0,1,2,3,-1]\n",
        "for i,t in enumerate(times):\n",
        "    #t = times[i]\n",
        "    sol_u, sol_p = split_sol(len(elements_u), sols[i],nodes_per_element_u,nodes_per_element_p)\n",
        "    ax[0].plot(nodes_u, sol_u, label=r'$t = %i$ d' % (t/day_in_sec), ls='', marker='.')#,alpha=(t/times[-1]),color='blue')\n",
        "    ax[1].plot(nodes_p, sol_p/1e3, ls='', marker='.')#,alpha=(t/times[-1]),color='blue')\n",
        "\n",
        "ax[0].set_xlabel(r'$z$ / m')\n",
        "ax[1].set_xlabel(r'$z$ / m')\n",
        "ax[0].set_ylabel(r'$u_z$ / m')\n",
        "ax[1].set_ylabel(r'$p$ / kPa')\n",
        "# Add the legend next to the plots\n",
        "handles, labels = ax[0].get_legend_handles_labels()\n",
        "fig.legend(handles, labels, loc='center left', bbox_to_anchor=(1.05, 0.5), ncol=1)\n",
        "\n",
        "fig.tight_layout(rect=[0, 0, 0.85, 1])  # Adjust the layout to make room for the legend"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Pressure drawdown"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "number_of_elements = 20\n",
        "L = 10.\n",
        "nodes_per_element_u = 4\n",
        "nodes_per_element_p = 3\n",
        "\n",
        "nodes_u,elements_u,solution_u=generate_mesh(L,number_of_elements,nodes_per_element_u,nodes_per_element_p,'u')\n",
        "nodes_p,elements_p,solution_p=generate_mesh(L,number_of_elements,nodes_per_element_u,nodes_per_element_p,'p')\n",
        "solution = np.append(solution_u,solution_p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "def time_loop(elements_u,elements_p,solution,dt,t_end,p_top,debug=False):\n",
        "    #Startwerte\n",
        "    max_iter = 50\n",
        "    y = [solution] #create a list that will hold the solution vectors at all time points\n",
        "    times = np.array([0.])\n",
        "    n_ids_u = np.unique([item for sublist in generate_node_ids_u(len(elements_u),elements_u[0]._line_element__nnodes,elements_p[0]._line_element__nnodes) for item in sublist])\n",
        "    n_ids_p = np.unique([item for sublist in generate_node_ids_p(len(elements_u),elements_u[0]._line_element__nnodes,elements_p[0]._line_element__nnodes) for item in sublist])\n",
        "    #\n",
        "    while times[-1]+dt < t_end + dt/2: #repeat the loop as long as the final time step is below the end point\n",
        "        print(\"Solving time step %i.\" %(len(times)))\n",
        "        times = np.append(times,times[-1]+dt) #here define the next time point as the previous time point plus the time increment dt\n",
        "        y_old = np.copy(y[-1]) #Starting value for recursive update\n",
        "        i = 0\n",
        "        #\n",
        "        #print(\"Solution \", y_old)\n",
        "        K, f = global_assembler(elements_u,elements_p,y_old,y[-1],dt)\n",
        "        #print(\"Residual \", f)\n",
        "        f = apply_Neumann_bc(f,n_ids_u[-1],p_top(times[-1])) # apply top load\n",
        "        K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_u[0], 0.) #Fix u at bottom\n",
        "        K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_p[-1], p_top(times[-1])) #Fix p at top\n",
        "        #Dinv = np.diag(1/np.diag(K))\n",
        "        #inc = sp.linalg.solve(Dinv@K,-Dinv@f)\n",
        "        sK = sp.sparse.csr_matrix(K) \n",
        "        inc = spsolve(sK,-f)\n",
        "        #inc = sp.sparse.linalg.bicgstab(K, -f)[0]\n",
        "        #print(\"Increment \", inc)\n",
        "        y_old += inc\n",
        "        #print(\"Solution \", y_old)\n",
        "        while i < max_iter and not converged(inc[n_ids_u],inc[n_ids_p]):\n",
        "            K, f = global_assembler(elements_u,elements_p,y_old,y[-1],dt)\n",
        "            #print(\"Residual \",f)\n",
        "            f = apply_Neumann_bc(f,n_ids_u[-1],p_top(times[-1])) #apply top load\n",
        "            K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_u[0], 0.) #Fix u at bottom\n",
        "            K, f = apply_Dirichlet_bc(K, f, y_old, n_ids_p[-1], p_top(times[-1])) #Fix p at top\n",
        "            #print(err)\n",
        "            #inc = sp.linalg.solve(K,-f)\n",
        "            sK = sp.sparse.csr_matrix(K) \n",
        "            inc = spsolve(sK,-f)\n",
        "            #inc = sp.sparse.linalg.bicgstab(K, -f)[0]\n",
        "            #print(\"Increment \", inc)\n",
        "            y_old += inc\n",
        "            #print(\"Solution \", y_old)\n",
        "            i +=1\n",
        "\n",
        "        print(\"Performed %i iterations.\" %i)\n",
        "        #print(solution)\n",
        "        y.append(y_old) #append the new found solution to the solution vector\n",
        "         #store how much iterations this time step took to converge\n",
        "    return times, y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "def Stiffness(z):\n",
        "    #return 1e8 * np.heaviside(z,0) + 100*np.heaviside(-z,0)\n",
        "    return 1e8\n",
        "\n",
        "def gamma_r(z):\n",
        "    n=0.4\n",
        "    return -(26000.*(1.-n) + 10000.*n) #kg/m³\n",
        "\n",
        "def gamma_w():\n",
        "    return -10000.\n",
        "\n",
        "def Conductivity(z):\n",
        "    #return 1e-17/1e-3 * np.heaviside(z,0) + 1e-12/1e-3*np.heaviside(-z,0)\n",
        "    return 1e-12/1e-3\n",
        "\n",
        "def Compressibility(z):\n",
        "    return 5e-10*0.4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Solving time step 1.\n",
            "Performed 1 iterations.\n",
            "Solving time step 2.\n",
            "Performed 1 iterations.\n",
            "Solving time step 3.\n",
            "Performed 1 iterations.\n",
            "Solving time step 4.\n",
            "Performed 0 iterations.\n",
            "Solving time step 5.\n",
            "Performed 0 iterations.\n",
            "Solving time step 6.\n",
            "Performed 0 iterations.\n",
            "Solving time step 7.\n",
            "Performed 0 iterations.\n",
            "Solving time step 8.\n",
            "Performed 0 iterations.\n",
            "Solving time step 9.\n",
            "Performed 0 iterations.\n",
            "Solving time step 10.\n",
            "Performed 0 iterations.\n"
          ]
        }
      ],
      "source": [
        "day_in_sec = 86400\n",
        "p_top = lambda t: 1e5\n",
        "times, sols = time_loop(elements_u,elements_p,solution,day_in_sec,10*day_in_sec,p_top)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "drawdown = 1e5\n",
        "duration = 10\n",
        "p_drawdown = lambda t: 1e5 - drawdown/duration*np.minimum(t,duration)\n",
        "\n",
        "def Stiffness(z):\n",
        "    return 1e8 * np.heaviside(z,0) + 1e2*np.heaviside(-z,0)\n",
        "    return 1e8\n",
        "\n",
        "def gamma_r(z):\n",
        "    n=0.4\n",
        "    return -(26000.*(1.-n) + 10000.*n) #kg/m³\n",
        "\n",
        "def gamma_w():\n",
        "    return -10000.\n",
        "\n",
        "def Conductivity(z):\n",
        "    #return 1e-17/1e-3 * np.heaviside(z,0) + 1e-12/1e-3*np.heaviside(-z,0)\n",
        "    return 1e-19/1e-3\n",
        "\n",
        "def Compressibility(z):\n",
        "    return 5e-8*0.4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Solving time step 1.\n",
            "Performed 4 iterations.\n",
            "Solving time step 2.\n",
            "Performed 3 iterations.\n",
            "Solving time step 3.\n",
            "Performed 3 iterations.\n",
            "Solving time step 4.\n",
            "Performed 3 iterations.\n",
            "Solving time step 5.\n",
            "Performed 2 iterations.\n",
            "Solving time step 6.\n",
            "Performed 0 iterations.\n",
            "Solving time step 7.\n",
            "Performed 0 iterations.\n",
            "Solving time step 8.\n",
            "Performed 0 iterations.\n",
            "Solving time step 9.\n",
            "Performed 0 iterations.\n",
            "Solving time step 10.\n",
            "Performed 0 iterations.\n"
          ]
        }
      ],
      "source": [
        "times, sols_dd = time_loop(elements_u,elements_p,sols[-1],duration/5,duration*2,p_drawdown)\n",
        "#dts = np.logspace(4,6,2)\n",
        "#for dt in dts:\n",
        "#    times_i, sols_i = time_loop(elements_u,elements_p,sols_dd[-1],dt,dt,p_drawdown)\n",
        "#    times = np.append(times,times_i+times[-1],0)\n",
        "#    sols_dd = np.append(sols_dd,sols_i,0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "data": {
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LbDbj448/xmeffYbY2Fi4u7vjzjvvxNy5c0snsa7Knj178M9//hO7d++GwWBAhw4dMHPmTEyfPh0KhaLa1/3yyy/4+OOPsXv3bmRkZECn0yEyMhIDBw7EP//5z1qNPTc3Fz4+PsjJyYG3t3etXktEROQIly4BLVpUXBYRAcTGOmc85DyOPi8pKSnB//3f/2Hp0qXYsmULBgwYUO26ZrO0vHNxkaCppKSsYsnVFfD0lPmZrl2PiIiIGqebeb3EbDZXaldMRETUEDSZ/70KCwtx9913Y8GCBfjnP/+JzMxMbN68GcePH8ett96Ko0eP1nhbZrMZEydOxAsvvIBZs2YhLS0Ne/bsQV5eHu644w5s3bq1ytetXr0a/fv3h6urKw4dOoTLly9j0qRJmDVrFh599NFq9/faa69h7NixGDx4MA4fPoycnBz88MMPSEtLw7/+9a9avxdERETOZi2CGTIEGD0aiI5moESOsWTJEnzyySel8xQcPny42nWVSgmNrNdzXFwAHx/A11ceZ2cDqalAcrLcUlIAg6HOD4GIiIiagKoCpd27d8NkMjlhNERERDXXZCqVXnnlFcyfPx8ff/wxnnrqqdLl8fHxaN++PTp06ICjR4/WaGLpTz75BE8++SRefPFFLFiwoHR5VlYW2rZtCzc3N8TFxcHLy6v0udTUVERERMDX1xdnzpyBVqstfW7atGlYvnw5Vq5ciUmTJlXY1/LlyzFt2jSsWrUK48ePr/DcL7/8gieeeAKnT5+u1XvBSiUiInImg0Fajbm4AMXFzh4NOZujz0tMJhPGjRuHTZs2AQCCgoLwxx9/IDIystbbKiwE4uPl36yvL6BQyDxMISGsWCIiImqMnHm9ZNmyZXj88ccxZcoUfP7559ftZkNERORMTeLP4YKCAixevBgqlQpTp06t8Fzbtm0xZMgQnDx5Ehs3bqzR9hYuXAgAmDFjRoXlOp0OEydORGpqKpYtW1bhuU8//RR5eXmYMmVKhUAJAGbNmgUAmD9/foXlxcXFePXVV9GxY8dKgRIADBo0qNaBEhERkbNZq5Sq+K+NyG5ubm744Ycf0K9fPwDywZ6hQ4fi4sWLtd6Wq6vMr+TnJwGowQDk5DAMJSIiIsdKTk7GrFmzUFJSgmXLluG1115z9pCIiIiq1SRCpe3bt0Ov16Nbt27w9PSs9Hz//v0BAOvWrbvhtk6cOIGEhAQEBgZW+YnX6rZlfWy9wFFejx49oNFocOjQoQoXPLZu3YrLly/jrrvuuuG4iIiIGoo9e6Ti47vvnD0SaoxMJsBiccfq1evRpUsXAEBSUhKioqKQmZlZq225uAAqlfx79faWuZWKiyVYYhs8IiIicpSWLVviyy+/LK1Oeuedd/DBBx84eVRERERVaxKhknW+pLCwsCqfDw8Pr7Ceo7dVUlKCkydPVvs6V1dXtGrVqtLrdu7cCQAIDQ3F119/jd69e8PDwwOenp7o3bs3li5desPxEhER1Sf33gtYLEDv3s4eCTVGWVnA4cPArl1AUpIO3367pfTc7OTJkxg5ciTy8/NrvD2lEvD3l2DJaJSKpbZtAbUayMuT/RUVla1vNkuoZTY7+siIiIiosbv33nuxePHi0sfPPfccVq5c6cQRERERVa1JhEqXL18GAPj5+VX5vE6nq7CeI7aVlpYG89UrCtnZ2TBc/ThrbcYQe3XG8ujoaLz44ov429/+hsuXL+P48eNo3rw5Hn300dLWeddjNBqRm5tb4UZEROQMa9bI/fbtTh0GNUImExAXB1y+LJVE584B2dkh2LhxG5o1awYA2Lt3LyZMmACj0Vjj7VrnUGrZUu49PKRqyTrHUk6O3PLzgZQUIDlZ7lnJRERERLX1xBNP4K9//Wvp44cffhjbtm1z4oiIiIgqaxKhUkFBAQBApVJV+bxara6wniO2VX7d8tutzRhycnIAAAkJCVi+fDlGjhwJLy8vhIWF4ZtvvkGzZs2wZMkSbN269bpjnjdvHnx8fEpv1qooIiKim2n2bKCkBGjfXi7UEzlSURFQUFAW/ri4AGfPAipVODZt2gJfX18Acl6VkZFRq20rlYCbm9xbublJsOTtLYFWQgKQmSlVTEVFQEYGK5aIiIio9v7+979j+vTpAACTyYTx48fj4MGDTh4VERFRmSYRKrm7uwMAisr3JynH+mlV63qO2Fb5dctv15YxBAUFYejQoRWWqdVq3H///QCAr7/++rpjnjNnDnJyckpvFy5cuO76REREdcHazeP33507DmqcVCrA3V2qhtzdJewJDgZycwGttiu+/vpH9O3bF3/88QeaN2/usP2q1YCPj4RMLi6yP7NZgqWSEofthoiIiJoIhUKB//znPxg3bhwAID8/HyNGjCjtZkNERORsTSJUsrY8qW5y5qysLABASEiIw7YVFBQE5dWPs/r6+kJz9SPZtRmDtVVe69atq3yNdX6ms2fPXnfMarUa3t7eFW5EREQ300cfSTVH8+ZAUJCzR0ONkZsbEBEBeHpKsOPjA9xxB9Cpkyxr3bofli37A+7uwaWvMZkAvV7u7eHiIiGWRiPjyM52zHaJiIioaXJ1dcU333yD/v37A5CuNvyAMBER1RdNIlTq3r07ACAxMbHK5xMSEgAAXbt2rZNtubi44JZbbqn2dcXFxaUnB+Vf16lTJwDVVzdZKRSKG46biIjIWWJigOeek683b3bqUKiR0+mA7t2BHj3kXqeTkKdFCyA8HHBzU+D8eZlvKSUF2LOnEB98sA5HjgBXP99jE6US8PeXqiWlEggMlCopvV5a4t3gVI6IiIioEq1Wi3Xr1mHQoEHYvn07Bg8e7OwhERERAWgiodKgQYPg4eGBY8eOQa/XV3p+586dAICxY8fecFudO3dGeHg40tLSEBcXV+NtjRkzBgCwa9euSq85ePAgDAYDbrvtNrRo0aJ0eVRUFADg/PnzMFfRlP/cuXMAysInIiKi+iYyEpgxo2xumfHjnTseavzc3AAPD7kvT6sF2rQBWrcGCguBTZtyMGvWCLz++jisXv0F4uLsqyzSaGQup5Yt5RYYKKGWUikt+bKzgeJiWddsln1xziUiIiK6Hp1Oh59//hm9evVy9lCIiIhKNYlQyd3dHU8//TSMRiOWL19e4bmEhAT89NNP6Ny5M0aOHFm6/Pjx4+jXrx9ee+21Stt76aWXAADR0dEVlmdnZ+O7775DUFAQpk2bVuG5WbNmwcvLCytWrIDBYKjw3JIlSwAAL7/8coXlvXr1Qt++fZGdnY01a9ZUeK6oqAgrV64EADz22GM3eAeIiIhuvpgY4NrPX8TFyXIiZ/HyAlq1Ag4c+AZ//vkbAGD+/EexZcs6uyuKlEoJs652QIarK+DrK634zGaphkpLAy5eBJKTpVrqmtNCIiIiogqu7U5jsViwevVqlHDyRiIicpImESoBwNy5c9GnTx+8+uqrWLVqFQoLC3Hs2DFMmDAB7u7u+Oqrr+Di4lK6/pIlS7Br1y7MmzcPGRkZFbY1Y8YMjB8/Hu+//z6WLFkCvV6P+Ph4TJw4EXl5eVixYgW8vLwqvCYoKAhLly7FxYsX8cADD+D8+fPIy8vDwoULsWzZMkydOhWTJk2qNO5ly5YhODgYTz/9NLZu3Qqj0Yhz587hgQceQEpKCv71r3+hd+/edfOmERER2WHt2qqXr1t3c8dBdC21Gpg2bSbGjXsaAGA2l+CttyZi69bfYLE4fn8qFeDnJxVUV64AqakSMhmNQEYGK5aIiIioZsxmM5577jlMmDABzz77LCx1ceJCRER0A00mVNJqtdi+fTteeOEFzJkzBzqdDkOHDkXnzp1x+PBhdOvWrcL699xzD3Q6He655x74+flVeE6pVOK7777DwoULsXjxYgQEBKBXr15wd3fH3r17MWzYsCrHMGHCBPzxxx8wGo3o1q0bgoOD8fXXX+OTTz7B0qVLq3xNZGQkDh8+jHvuuQfTp0+Hl5cXbr/9dhQUFGDLli1VVlIRERHVB+PGVb28Bt1mieqUmxsQGanAnDkfYMiQBwAAJpMRDz88FuvWHUZubt3t18NDqpdMJqlSys0F+EFjIiIiqonDhw/jP//5DwBg8eLFePvtt508IiIiaooUFn6socnJzc2Fj48PcnJy4O3t7ezhEBFRI/Xbb8Bdd1VcFhEBxMY6ZThUTznzvMRkAvR6EyZPHofNmzcBAPz9A7F8+R/o1KkdmjWTuZhMJqCoSCqOrp2rqTbMZml5V1Qk1VIZGYDFAjRvLm351GoHHRgRERHZpCFcL/niiy8wderU0scxMTF4/PHHnTgiIiJqahgqNUEN4SSJiIgaPp0OyM4GHntMWn6NHQtMn+7sUVF9Ux/OSwoKCjBs2DDs3LkTANC6dSi++uoP6HQtYbFINZHJBLi7SzCq09m+L4NBwiRrSOXjI5VKRUVllUz2BFdERERku/pwXlITCxYsKJ2XW6lU4n//+x/Gsh0AERHdJAyVmqCGcpJEREQNl7VKycMDyM939mioPqsv5yXZ2dkYOHAgjh07BgDo1KkTVq/+HceP+yMvD2jZElAoJATq3t3+iqWSEsDFBVBebUZdVATo9UBxsVQseXjI/q5dj4iIiOpOfTkvuRGLxYIXX3wR7733HgBAo9Fg27Zt6N+/v5NHRkRETQH/PCUiIiKHu+ceuY+JceowiGrM19cXmzdvRnh4OACgZcuW8PVVw98faN0aKCyUiqVLlwCj0b59KZUSSpUPilQqqYDy8pKqqEuXgLg4IDlZWuYZDPbtk4iIiBoPhUKBBQsW4MEHHwQAGAwGjBkzBidOnHDyyIiIqClgqEREREQO9fPP0vbOwwOYPNnZo6GmxmiUf3+2BD8hISHYtm0bnnzySaxfvx5+fp7w9JTwJyREKowMBiApCcjJcfjQAQAaDeDrK+PPywMKCuR40tNl/0RERESAtL37/PPPMWzYMABSdT18+HCcP3/eySMjIqLGjqESEREROdS998r90qXOHQc1PSkpwObNwP/+B2zZIo9rKzw8HIsXL4ZKpYKbm8yh5Okp4U5oKDBwoFQTJScDCQmy3MpkkhZ2JpN9x2E2S8u7kBCZx8liAdLSKu6LiIiISKVSYdWqVejZsycAwM3NDUZ7S6qJiIhugKESEREROUz5KqX77nP2aKgpMRqB/fuB8+clgDl9Gti92/5WdTodEBh4ET///E9062ZBs2bSDi8sTJ5PTJR9Xr4MHDkCHDgg91lZtu/TxUXa4RmNgFYr1UsajQRWWVn2h1ZERETUeHh6emLDhg2YMGECdu3ahcjISGcPiYiIGjlXZw+AiIiIGg9WKZGzFBZKu7i2bYH8fODCBQl3IiKAzp0lqLFFbGwshg4diqSkJJSUFOJf//oXAKkgCg+XNnjJycCJE7KP0FDZf1wc0L27zJ1UW0ol4O8PZGTIcWm1QMuWsv38fAluVSqpoLIel9kMlJTIYyU/NkZERNSkBAYG4ocffnD2MIiIqIngn5xERERkt5gYoHdvVimR82i10pYuPR3w9gb8/IAWLYArV4B9+6SSyGKp/XaPHDlSOjfB22+/jffee6/C8z4+EiRpNBLmpKRIwJOfDxQV2X48Go20v2vZUu41GgmodDo5vpISIDNT9lNQIPtNTpZ7g8H2/RIREVHjYDQaER0dDYstJ0BERETXwUolIiIisktkpFRlWHl4OG8s1HSp1UDPntICLyNDKn169gR8faVF3enTErqEh0vgBEh7OWslkFpd9Xbvu+8+pKam4umnnwYAvPDCC/D398fUqVMr7Lt5c6laslikHZ6Xl4Q97u6AQmHbMSmVVVcdqdVSqVRYKKHSlSuynp+fBEoZGRJEsWKJiIioacrNzcX48ePx888/IyEhAe+8846zh0RERI0IQyUiIiKyWUxMxUAJAFJTZfn06c4ZEzVdISFAVFTloKhTJ6n4iY8Hjh2T8MXdHfjzT2mZ5+UlAVRISNXbfeqpp5CRkYG5c+cCAB577DH4+vpi3LhxAKSCKCJCfhYKCoD27aWaKDUVyM0FgoOlVZ0jKRRyDC4ush+LRfalVkuFVEkJQyUiIqKmateuXdi+fTsA4N1330WzZs3w3HPPOXdQRETUaPBPTSIiIrLZ2rVVL1+37uaOg8hKrZbqpGsrj7y9gVtvBW65RSqKvv0WOHVKAqXsbKlwMhqr3+5f//pX/N///R8AoKSkBJMmTcJvv/1W+rxOJ3Mo9eghAdUttwBhYRLsJCXJzdqWzmQC9Hq5t5e1JZ61/V56ugRbJSX2b5uIiIgapuHDh2Px4sWlj2fPno1vvvnGiSMiIqLGhKESERER2exqoUYlY8fe3HEQ1VRAgAQ+3t6Aq6u0xjObJWgqLKz+dQqFAosWLcKDDz4IQOYpGDNmDA4fPly6jpubtH90c5PH7u4SLLVqJdVD8fFSHbV/P3DgAHDkCJCVZd/xKJXS6k+rleMJCJDHeXlyTMXF9m2fiIiIGqYnnngCf/3rX0sfT506Fdu2bXPiiIiIqLFgqEREREQ2KzetTKmICLa+o/rN3R0IDZXwxdsbOHsWSEmRgOd6c1krlUosXboUo0aNAgDk5eUhKioKly5duu7+vL3l5yIgADh5EjhzRqqUcnOlZZ69FUsajbTua9kSaN0aaNZM9llSIseUlyfBGSD3JlPZYyIiImq8/v73v2P61RNzk8mE8ePH48CBA04eFRERNXQMlYiIiMhmXbvKfefOwOjRQHQ0EBvr3DER3YhaLS3q/PwAlQq47Tagd29pUbd/P5CRUf1r3dzc8N1336F///4AgClTpqBZs2Y33KdCIfMq+ftL+FNQIKHS5cvXb7tXU0qlVEhZ51FSq+X4PD2lSiozU47r0iUgOVlCNGs7PiIiImqcFAoF/vOf/+Cee+4BAOTn52PkyJGI5Qk7ERHZQWGxXO/zmNQY5ebmwsfHBzk5OfD29nb2cIiIqIH6+WdgyBC5kF1U5OzRUEPlzPMSo1Fa3mm1EsLk5QEJCVLd4+sLtG0rcy5dux4AZGdn44cffsBjjz0GhUJRo/2ZTNLyLj9f2uSdOycVQ926SdDk5VU3x2mxyD7PnZOfVX9/WaZSSYWTkh8zIyIiAtB4r5cUFhYiKioKO3bsAACEhYVh165dNfpgDBER0bVcnT0AIiIiapjGjJH7//7XueMgspVaXRYSARLqdOsmFT0JCcDBgxK4pKZKsOTlJRVOISGAr68vHn/88Vrtz81N2uDFxUmlUlgY0KKFBD3nz0vQFBws4ZUjKRTSIs/dXbZtnTvKZJIWeQyViIiIGjetVou1a9diwIABOHHiBEJDQ6F19AkHERE1GfwTkoiIiGrt8cflwnRICPDww84eDZFj+fsDPXoAbdoAe/YAhw6VtZDbv79yuzpr4f+ePXswadIkGK/Tz06nA7p3l+137w40by77ad0aKC6WMCs5WQIfkwnQ6+2fcwkAXFwkWHJxKau+MhikOqu42P7tExERUf2m0+mwefNmPP3009i0aRN8fHycPSQiImqgWKlEREREtZKbC3z+uXx98qRzx0JUVxQKaYEXEiLVQzk5Eu7k5UnQU77CCQC2bt2Kv/zlLygoKIDZbMbKlSvh4uJS5bbd3ORWnpeXzH+UnS2VUQcOyD5VKnkuIkICKVsplRKWZWRIQBYYKNstLpZ2fxqNVEqxaomIiKjxatGiBT766CNnD4OIiBo4/tlIREREtdK1q8zHMmmSfRe5iepCYaGEMtYWb/bQagEfHwmY2rSRACYvT8LUtLSy9RQKBTw9PUsrln744Qc88cQTqO3UpQqF/Ey1aSPhbUqKhEyXLgGxsfZXLGk0EpK1bCn33t6An5+ES9ZKLL1efr7NZtmf2WzfPomIiKh+u3z5MhYuXFjr8xYiImq6WKlERERENRITA3z6KZCUJNUTK1c6e0REFSUmAlu3AunpUokzdKjMW2QrtVrmUNq/X8Kkjh2Bzp0leDl5UkKZiAi579u3L779djXGjx+D4uJixMTEICAgAG+//Xat91tcLAHQLbcA+flSXXT2LNCqlYRB9lAqK1cjaTRyrAUFEsZlZ0trPKVSftb9/WUdIiIialzi4uIQFRWFhIQEGI1GvP76684eEhERNQAMlYiIiOiGIiOBuLiyx6xQovqmsBDYtk2CJb1eqpWKi4FHHpGKI1uFhABRUbJ9rbas7V1WFhAfL/MtBQbKcxbLcMyY8QU++eRBWCwWzJs3D/7+/njhhRdqtU+VCnB3l0DJ11eqiFQq4MoVCXuCg+V5R1IopP2dWi3HlZcnj63TQ4WEsDUeERFRY7N//34kJCQAAN544w0EBwfj8ccfd/KoiIiovuOfhkRERHRdMTEVAyVALm7HxDhnPERVycuTOYg6dADatpVle/cCR44AJSX2bVutlnCn/DxKOh1w++2yv/R0qdw7cwYYMWIypkz5uHS9F198EcuWLavV/tzcpALK01Pa4Pn5AXfeKcvMZgnOLlyQsMnKZJIwzd4WeRaLBFjNmsl9cbEcn8Fg33aJiIio/pk8eTLmz59f+njmzJlYu3atE0dEREQNAUMlIiIiuq7q/q5ct+7mjoPoery8ZP6jK1ckhAkKAlq3BhISgM2bgfPnJTBxJIVCwpcuXSQAUigk8Bky5ElMmPBW6XqPP/54rS/Q6HRA9+5Ajx5yr9PJPsLDgRYtpHIqLg64fFlCnyNHgAMH5D4ry/ZjcnGRMMlkkkolNzfA1VVCu9xc+wM6IiIiql9efPFFPP/88wAAs9mM+++/H3/88YeTR0VERPUZQyUiIiK6rnHjql4+duzNHQfR9Wi1MoeSTifBUmAg8Oij8u9Up5OqpZ9/BtLSyl5TWCht8goL7du3hwfQpo2EWe7uwKlTQK9eb+CRR54BAJSUlGDy5MlISUmp1Xbd3MqCHSuFQqqmIiLkGFNTgV9+AS5elGAtP1/CJlsrlpRKmUNJpZL3xdNTKr98fGSbmZmyD7NZ1jebZbn1MRERETUsCoUCCxYswIMPPggAMBgMGDNmDE6cOOHkkRERUX2lsFgc/ZlNqu9yc3Ph4+ODnJwceHt7O3s4RERUz126JJUR5UVEALGxzhkPNS6OPi8pLJTQw9Oz4lxK6enA0aMSioSEAN7ewJ490jLPx0cCqbAw2/ebkgLs3y8VPSoVEBAAWCxmLFw4FVu3foNly5bhoYcesvv4rpWTA/z0k3yt0UiwZLFIhZOHh+3bNZulKsnFpWwuJYtF3t+CAnmsVMrXJpMcs7+/jIGIiKihasrXS4qKijBmzBhs3boVANCiRQvs2rULrVu3dvLIiIiovmGlEhEREV1Xz55yP2QIMHo0EB3NQKk+MxgMeP3116FSqTBt2rRq12vTpg0UCsUNb+fOnavwutquf7NptVLBUz5QAiTkuftuoHdvqVZavBg4flyqmLKygG3b7KtYCgkBoqKAUaOkOmrQIOD225WYO/dzfPDBDnTr9lBpGGM0AtnZcm8vd3dp8+flJVVM8fESnNk7t5JSKRVSynJ/LSgUsj8/PwmRkpOlUkqplPmdMjJYsURERNRQqVQqrFq1Cj2vnvxfvHgRUVFRyM7Odu7AiIio3nF19gCIiIio/vr5Z6lUUqvlojvVb7/88gtmzpyJ9PR0mGqQKjRr1gw+Pj5VPnfx4kW4ubkhODi40nPh4eFwK9+TrZzqltcXrVpJWLJnj4Q6Z85ISJKVJRVO14ZRtaFWy83Kxwfo1csNYWF9kJAglUwuLhLE5OWZoNO5oWdPCaRs5eYmlYNxcVI11LatBEwXL8ocSMHBFcfkCEqlvE8eHlLNVFAggVNxsTxW8mNrREREDZKnpyc2bNiAfv36ITY2FkOGDGlyFVtERHRjDJWIiIioWvfcI/fR0U4dBtXAN998gyeeeAJvv/02tFotHn300Ru+Zt68eVVWMxUVFaFVq1Z4+OGHoa0iZfn555/Rpk0bB4zaOXx8JHxJS5PKmpMnZZ6i9HSpaFIoHLcvhQIICpLtJiQAP/wA7Ny5HEeO/BuvvPILAH9ERdkX/Oh0QPfuUi2kUknQlJMjc0vFx8vzgYGAq6tUMJVfz1YuLtLqrqhIAqb0dFmWny+tBV1cbN82EREROU9gYCC2bt2K//3vf3juueegcOSJERERNQr8HCERERFVadEiuUCs0wEPP+zs0dCNhIWF4c8//8STTz5Zoz/+x4wZg7BqJhFauXIl0tPT8eSTTzp6mPWCVitzKAUGSjByxx3A4MHAiRNSkXf5suP3qVRKsBQX9zl+/HEakpOPY8GCkYiLyy9ti2cPNzepHLIGRT4+QGSkVCrl5EjLythY4NAh4MAB4MgRqc6y53j8/SWcKi6W4Cw0VOZdysyU3x1shUdERNQwtWnTBrNnz2agREREVWKlEhEREVXp5Zfl/rffnDsOqpnevXvXav2PPvrous+NHj262tCpMQgLA6ZMkfDD01OCpsxM4OhRYMcOCWO6dZNwprAQyMuTtnL2tMfTaoE77hiEVatCkJ2dgvPn9+E///kLbr31R3Trpoafn+OOD5AqKX9/qcK6dAnYtUta/oWGyvHExUmFk60VSxqNtO4rKZHKJKVSQqXCQmmJZzDIHExarSwvvx4RERE1LAcPHsSWLVvw2muvOXsoRETkZAyViIiIqJKpU6VNVkQE0KWLs0dDN9OePXtw4MAB/PTTT9WuEx0djc2bNyM+Ph7FxcUIDw/HPffcgxdeeAG+vr43b7B20morhkR+fsCgQRLAHDsGbN0qgUtysgQlPj5S4WRr1qZWA6NHh8Fg2Io5cwZAr89CfPxPePvth/DqqysRGOiC8HAJuRzJxUWOzd9ffq6zsyVwMhqlfZ09bfCUyoohkUIhQZJGI8GSXi8VUUajPKdSyTg0GrsPi4iIiG6Sbdu2Yfz48cjPz4e7uzuee+45Zw+JiIiciJ8TJCIiogo+/RT44gv5+uBB546Fbr4PP/wQnTp1wt13313tOn/88QcWL16MK1eu4Ny5c5g+fTrmz5+P2267DcnJydfdvtFoRG5uboVbfdO8OTBsGNCpk7TD27tXwpf0dHlcWGj7tkNCgBkzbsHq1Rvg7u4OAPjppx+wfPkTKCiw4MAB4PRpCWGMRgmAjEb7j0mlklBMo5FWdfn50hYvJUUqihxNqZRwzNdXqqIyM+U48vOBjAy2xiMiImpI4uPjkZ+fDwCYPXs2vvnmGyePiIiInImhEhEREZWKjASeeKLs8e23O28sdPOlpKTghx9+wNNPP13tOj/++CO2bduGPn36QKPRIDAwEM888wxef/11JCYm4tFHH73uPubNmwcfH5/SW6tWrRx9GA6hVMqcS61aAR06SLVNaiqQkCAhiT3UamDYsD5YvXo13K6WCa1YEYO1a19HZKSELhs3Sri7fj2wZYuEP/Zwc5PKQ09PCcgiI4E+faQtXXy8VGeZTPbtozpqtbQTdHWVfWRkOCYoIyIioptj1qxZmDt3bunjqVOnYtu2bU4cERERORNDJSIiIgIAxMTIHCvlxcXJcmoaPv30U7i7u+Phhx+udp1Ro0ZBrVZXWv7UU08BkPYo16tWmjNnDnJyckpvFy5csH/g5eTnAxcvyr29vLykbZyrK9C+PVBcLBU3u3dLCGOvqKgorFixonQS7HfemYeVK/+N7t2BtDQgMbHsft8++4MYnU7mUOrRQ+5DQyVoCgkBcnPl5z01VaqITCZpXWdv0OTiIlVSxcUSaLm6SmCXmyvhHCuWiIiIGoa5c+dixowZAACTyYTx48fjwIEDTh4VERE5A0MlIiIiAgCsXVv18nXrbu44yDmKioqwZMkSPPLII/Dw8Kj16/39/UvnUzp16lS166nVanh7e1e4OcqpU8D77wPvvgssWiSP7aHVyhxKOp20oevaFXjqKWnptnMn8OuvUsFkVVgooUxt2uNNmjQJixcvLn385ptvIjn5Mjw8gN69gYAACZOOHpWwzF5uboCHR9k8SgqFBGeRkXKfng4cOAD89huwfz9w5EjFY6wtpVLmUFKp5H3x8pIgy9tbKqYyMyW8slhkfWugxbCJiIioflEoFPjPf/6Dv/zlLwCA/Px8jBw5ErGxsU4eGRER3Wyuzh4AERER1Q+tW1e9fOzYmzsOco7vvvsOqamppRVHtjBfTQKslTc3U34+sGqVVNvk5wNXrgAlJcALL0iFjK3CwoApU2Sbnp4SNAHA5csS9Pz0k/zseHpK0JSTI3MXDR0qr62JJ554AhkZGVi0aBE2btyINm2a4cwZCbKCgiRUKiiQY8vNBdq2lXDGkVxcpEWdlxfwyy8Sjvn7S+ADSGWTNYiqLY1GqqFKSmQ/SmXZ8oICCZsMBnlOr5ewSaWS/Ws0Djk8IiIicgAXFxd8/fXXGDZsGHbs2IG0tDRERUVh165daNasmbOHR0RENwkrlYiIiAgAsHRp5WUREcD06Td/LHTzffTRRxgxYgQiIiKqXefZZ5+ttjVeamoqcnNzAQAdOnSokzFeT06OhDCdOsm/W7MZ2LVLbiUl9m1bq5X5layBEgA0awYMGyat5C5cAD76CPjzTwlCsrKAbdtqV7H0+uuv48SJE+jVqxfUaqBnT6mIysiQsGfiRJnjzGQCDh6UKiyDoez1RqMcv70t8iwWCcg6dpRAp7BQ5lzKybFvu0qlhFLKcn99KBRSNWVtMXj+vISBLi4SLGVksGKJiIiovtFoNFi3bh26dOkCAEhMTMSIESNQVFTk5JEREdHNwkolIiIiwrPPygXqZs2At96SlndjxzJQair27t2Lffv2YdOmTdddLycnB1u2bEFubm6ltnUff/wxAODuu+9Gy5Yt62ys1fHxkRAmJQVo2VIqXry9gZMnpeqmd28JmxxJoZBqJLUa2LNHAp/Tp6VlXXa2VDeVD6Kuvy1FhU/4hoQAw4ZZkJiYgvDw5rBOY+XnJ1VS1vmWWrSQqp7Dh2WOIi8vCaRCQmw7JpUKcHeXsQcGylxIhYVAcrL8jggOtr1iqTpKpezT3V0CQL1elpWUyE3Jj8ERERHVK76+vti8eTP69u2LCxcu4Omnn4ZKpXL2sIiI6Cbhn2hERERNXFERcDUPwMGDEiStX89AqSn56KOPEBkZiaioqOuup1AokJqaijFjxmDPnj3Q6/VIT0/HokWL8M477yA0NBSff/75TRp1RZ6ewIQJEoRcviyhyosvAlOnypxIW7dKe7yUlLLX5OfLPEX5+fbtW6cD2rWTfXt6AseOSQiTnW37Ns1mM159dTYGDOiGc+fOlC5XKOTY7rhD2u6dOwd8/bWETNa5n/bvt71iyc1NwjdPT2m1FxgIDBoEhIZK2BMbW9ZaEJAgTa+Xe3u4uEgAp1JJ9ZJeL63x9Hr7K82IiIjI8Zo3b44tW7Zg7dq1eOyxx5w9HCIiuokUFot1WlxqKnJzc+Hj44OcnByHTo5NREQN0x13APv2Af37Azt2OHs0ZI/rzWW0dOlSTJs2rdLyK1euoHXr1liwYAGeeeaZ624/NzcXa9aswapVq3Dw4EGkpqZCpVIhIiICY8eOxfPPPw9fX99ajdnR5yX5+WXzGpWfS+niRZnzKD0dCA+XAGbbNglhfH0lkOrY0fb9JibK9nJyJBgJCZEAyN8f6NZN7mvjnXfewZw5cwAArVq1ws6dO9GqVatK66WmAl98IW3iPD1lP8XFwKhRcly2MpnK5jayViaZzfL+ZWTIsbm5ydeFhVJlFBEh76utDAbZnnW/Hh4SKFksEji5u8t+iYiI6gqvlxAREd0YQ6UmiCdJRERkdf68VCAolXJhmF0r6Ga7meclFotU2vz6K/Djj9K27rbbpPImMFDaQJYPomqrsFBCLU9PCUFSU4GjRyW4atkS6NJFnissLGtVV117vOzsbNx11104evQoAJmnaseOHQgICKiwntEIbNkix1BcLPM7BQYCDz4IBAXZfizXU1wsId3u3RI+tWkjyzw9ge7d7WuPZzZLkOTiIr+XLBapWCoslEDJ3b3sPbt2XSIiInvxeon9Vq5cicTExNIPxxARUePDOZWIiIiasB495P6llxgoUeOnUEibOrUa+OMPCSpOnJAQJj1dqozsCZW02oohUVAQMGSIhLcnTgCbNwMaDZCUJEGJjw8wdKjMy3QtX19fbNmyBf3790dcXBxOnz6NkSNH4ueff4aXl1fpemq1zKG0f78EVbfcIvMu/fmnzLkUHl7zeZ1qytVV5o3y95eqosxMCXWMRnlsT6ikVFYMiBQKqVjSaqUVXn6+fN9cXORra1WTv7+8t0REROQ8H3zwAZ577jkAQEBAAKaznzYRUaPEz/QRERE1Ud98Ixed3d2Bd95x9miIbh5/fwmXgoMl2Dl2TIKfK1ekMsaRFAqpBhw+XNrDbdwI7NkjFTYZGdIyr7Cw6tcGBwdj69atCAkJAQDs378ff/nLX2C8ZsKkkBAgKkpa3k2YIF936CBzIu3bB8TFlc15ZDRK5ZStcy5ZqVTSXs/dXQKmnBzZ7pUrEvQ4mlIplV06nbynSUny+8vVVfaXkSGVS0REROQ8pnKTLM6aNQtr16514miIiKiusP1dE8RybiIiiokBZs6UC+ibN8tFaCJncNZ5yalTwOrVQFaWVLi0bi0t3AICgH79gBYtHL/P1FTgs89kP9nZ8vOn0QDPPXf9VnUnTpzAgAEDkJWVBQCYMGECvv32W7i4uFx3f2YzkJwsAYxCIVVNly5JhY+Xl1Q4Xc2rbJKVJYFVQYFUEgUFScBTXCzVUoGBUlHkaCaTzGFlNst76OoqoVPr1vZVSREREfF6if1efPFF/Pvf/wYAaDQabN26FXfeeaeTR0VERI7ESiUiIqImJjISmDGjrCLj6aedOx4iZ+jYUeZQmj0bePVV+Zn4y18kBFm7FtiwQUITQEKYixfl3h5eXlIlpVJJpVRhobTd279fAqfq3HLLLdiwYQPc3d0BAKtWrcKsWbNwo8+GWYOW3r2lwmfbNuDQIVmelSX7tadiSaeTOZR69ABuvVXmVoqIkHApK0vmr0pPl981JpO0ryv3AWabubhISzy1WsKs3FzZdmEhq5WIiBq6HTt2YPz48QgPD4dWq0WrVq0wdOhQrF+/vtrXfPnll+jduzc8PT2h0+kwbNgw/PLLLzdx1FTe/Pnz8dBDDwEADAYDxo4dixMnTjh5VERE5EgMlYiIiJqQmBipLCgvLk6WEzU1np5SkWSdRykkpKx9XFYWsHIlsHw5sGAB8P77wKJFUuFkK61W5lDS6SQIufVWqRjUaIDffpN5nnJzq35tnz59sHr1arhdLcX5+eefkZ6eXqP9urnJcYaEyO3KFdnPlSvVt96rKTc3CXisFUJKpVR7RUZKa8HUVODAAWDHDrk/cqQsrLOVUlkWzlks0sawZcuy+Z0KChzfxpCIiOrexx9/jAEDBiA2NhZffvklMjIysH37dvj4+GDs2LF45plnKr1m9uzZmDJlCoYPH47k5GScOHECAQEBuPvuu7F06VInHAUplUp8/vnniLraCiE7OxtRUVFISkpy8siIiMhR2P6uCWI5NxFR0zV6tFRgVLX8Oh8AJaoz9fW8pKRE5iOaPx/IywM6dy4LMJ59tiyIskVhoVQ9eXpK0AQAFy4Ax49LIBIWVra/vDypcLKu9+2332LevHnYtGlT6VxLNWE0Alu2SNs9d3fgzz+l4mf4cKnauloE5XD5+cD27TLnkb+/tKoLDJQKJ3tb1ZnN8n1ycZGgyWyW96+wsGI107XrERFR/WMymRAQEIDc3Fzs27cPPXv2LH2usLAQLVu2RGZmJk6fPo327dsDADZt2oSRI0fi3nvvxffff19hW507d8aFCxdw+vRphIaG1ngc9fW8pCHKz8/H4MGDsX//fgBA+/bt8ccffyAgIMDJIyMiInvxzyoiIqImJCKi6uVjx97ccRDVdy4u0jouNBTo0EEqblJTpbIvO9u+bWu1EqxYgyIAaNVKAp6uXWUepGXLJNBauhRYsULmEAKASZMm4cCBA7UKlAAJV3r2BHx9JWDq3l1+7k0maYN39qxU+jiaQiEVS+3bS2VRQQGQkCBhmb2USgmmrEGRUilBnZ+fhFe5uUBKCnD+vLynKSmAwWD/fomIyPGysrKQe7Vct3PnzhWe02q1aNu2LQDgyJEjpcsXLFgAAJgxY0aF9d3c3PDoo4/CYDDgww8/rMNR0/V4enpiw4YNiIyMBACcOXMGEydOvGH7XiIiqv8YKhERETUhS5ZUXhYRAUyffvPHQlQXYmKk8s4RLR19fKSVm6urVA4ZjVJx89NPElQ4mlIpcy3ddRdw6ZLMSZSSAsTHA1u3lrWqc3V1rfA6o9GIXbt23XD7ISHS2m/UKLnv0gXo1QsID5fAbO9eIClJqnqMRgnP7JlzCZAgyd1dthMcLOGWi4tUZqWkAMXF9m2/Ki4ugLe33DIzZV4nk0nev4wMzrtERFQfBQUFoVWrVgCAkydPVnjOYDAgPj4eAEo/VJGbm4vffvsNCoUCffr0qbS9/v37AwDWrVtXl8OmGwgMDMTWrVsREhICnU6Hf/7zn1AoFM4eFhER2YmhEhERURMxdap8Sr9FCyA6Wi68R0fLhWuixiAyEpgxQ1o8zpghj+3h6SlzLAUGSsDSowfw8stS7fPjj8C6dRJYOJrRKC3vBgwAgoIAvV7mI7p6Pa2CvLw8jBo1CoMGDarRpORqtYxfrZbHSqVUSd1xB9C8uYRKGzfKfFIbNkjLvJQU24/FzU2Ca09PqRxq1gwYPFjmQMrJkd8/aWl1E/QolRJoBQbKY4NB5nMymRy/LyIist+XX36J4OBgPProo9i9ezcKCwsRGxuLhx56CJmZmejZsyf69esHADh+/DjMZjMCAwPhWUVP2vDwcABAfHw89Hr9TT0OqqhNmzbYvHkzduzYgb59+zp7OERE5ACuN16FiIiIGrrcXGmhpVDIvC06HauTqHGJiZHWdOXFxclye/6td+wocyjl5EjlkvW6VWIisHs38N130h6vVy9Zfu16tvDykm1kZUn4YjTK3ERHj0rlTdeuEgwBwAcffICff/4ZADB27Fj88ssv6NGjR6336eYGtG0rlVlffglcvCgBUE6OPB8VVRZE1ZZOJ+32ioqkcsk6l5KvrwRKaWlyXMHBEgKZTBXXs5WLi2ynqEje06wsqcLKySmbz4ofliYiqj8GDBiAffv24YUXXqgQPgQGBuLJJ5/E22+/DRcXFwDA5cuXAQB+fn5Vbkun0wEALBYLrly5UhoyXctoNMJYrizX2oKPHKtr167OHgIRETkQK5WIiIiagFtuASwW4MEH5QIvUWOzdm3Vy595Rlq72cPTUyr8ygdFYWHA/fdLNVFSEvDvfwMvvAAsXAgsWgScOmX7/rRaYOhQ+Vm9ckX2/fTTwKBBMifRtm0yD1JhIfDKK69gzJgxAGRC7BEjRuD06dM279tikbDn9ttlHPn5wMmTEvzYw80N8PCoGBS5uEhwFREhz/35p1SA/f47cOSIhED2UCoBf38JlgwGCeoiIiS40uslyCo/x5LZLIEW2+MRETnH+vXrceuttyIhIQF//PEH8vLycOrUKUyZMgVGo7FCxVFBQQEAQKVSVbktdblPQljXrcq8efPg4+NTerO24KO6ZbFY8Morr+D999939lCIiMgGCgtnyGtycnNz4ePjg5ycHHh7ezt7OEREVMe++QZ44AFAoymbk4WovnDUeUlMjLS8q06vXsCOHRIwOFpmJjBnjrRyCwyUfbRvDzz3nH0VS4WFEupYq2oACTwSEyXoKS6WOZjM5kLce+9wxMb+DgAICWmFvXt32nRhzGiUlnfZ2dJ6LyFBxtGlC9C6tYRptlYsXY/JBOzZI3MtqVRybC1bSls+eyuWzGapUHJxkaAJkMd6vRyvq6s8l5dXVk3l7y+/M4mI6OZISkpCp06doFQqERcXh+Dg4NLnLBYLevbsiXPnzuHQoUNo3bo1Vq1ahXvvvRcdOnTAqSo+yVFYWAh3d3cA0gKvNpVKrVq14vWSOmSxWPDII49g+fLlAICvvvoKDzzwgJNHRUREtcFKJSIiokZu6lS5X73aueMgqkvTp0sVSnkREVLho1QC+/ZJSDBlStnzMTEyt1hMjH37LiyU9mpDhkjljV4vrfGOH7dvu1qthFTWQAmQY2nbFhgxQgKl48eBJUu0GD58HVq37g4ASEm5gKFDhyHdhgmf1GqgZ0+pVsrIkGBn8mSgc2d5vHevhFolJbK+0SgBVLnrcTYpKpLwp2NHmeNJq5X2hefO2T8HklIpwZSy3F8+Li6At7ccp8Ui+0lPl/WKiuRYWbFERHTzrFy5EgUFBRg8eHCFQAkAFAoFJk+ejIyMDLz55psAgGbNmgEAMjMzq9xe1tVyV4VCUWl75anVanh7e1e4Ud1SKBRo06ZN6eNp06Zh69atzhsQERHVGkMlIiKiRiomRuYoMZmAyEi5CE3UmMXGAtHREhRFR8vjjz6SAOTuuyU8+PJLqUQJCJDKpg0b5D4y0vb9+vhIOJGaKoFIYKC0rtu9G1izxv72e1Vxc5O2ln37SkBiNvvgnns2IyBAkrUzZ05j5MiRyMvLq/W2Q0JkDqVRo+S+eXNpwXfHHRIyXbgg4dLhw8DmzfIebtkCpKTYfjwqlbSly86Wr728ZF8Gg4RLqal1E/K4ucm+1GrZb16e/HsxGsuCMyIiqnuJiYkAgJCQkCqfb968OQDg4MGDAIAuXbpAqVQiLS2tQls8q4SEBABA27Zt4eHhURdDJjvMnTsXM2fOBACYTCaMHz8e+/fvd/KoiIiophgqERERNUKRkXKh3Hoxu7jYueMhulmmTwfWr5f78n76CcjJkeolk0kqUcqLi7O9YsnTE5gwQcKky5elTdzrrwP33iuhyA8/yDxI1nwnPx+4eFHu7RUUJOFSQADg4RGMvn23wcNDLrzt378ff/nLXyq09akptVqCsvKt7lxdgfBwCZc8PYGNGyVYcnOTMGj/ftsrltzc5Hvj6Qnk5krQc/vtQKdOgJ+fVBHFxkqrQWvzbpNJqsLsrWRycZH9ajRyy8mR7RoMZfsiIqK6FRAQAAC4dOlSlc9bl7td7Ynq7e2NgQMHwmKxYPfu3ZXW37lzJwBg7NixdTFcspNCocDixYsxfvx4AIBer8fIkSNx9uxZJ4+MiIhqgnMqNUGcU4mIqHGrbm6Z6OjKF9qJnM0Z5yUDBsj8StcaPVoCKVvl50sg4eNTNpeSxQKcPi2VPQaDtFw7e1bW9fWVMKpjR9v3CUg7um3bZN8AYDKdxNtv3wm9PgsDBw7G+vVr4OXlZd9OrpGdDaxaJcdkMkkopFZLkObra/t2TaayeY3Kz6VkMklInp1dVlV05QpQUCAVThERUh1mK4NBgsaiItmvVivfO4VCtl++BSERETnevn37cMcdd8DT0xPx8fEICgoqfc5isaBXr144cOAAXn31VcybNw8AsHnzZowYMQL33Xcfvvvuu9L1TSYTunTpgqSkJJw+fRqhoaE1Hgevl9xcBoMBUVFR+P13mReyTZs22LVrV7UVa0REVD+wUomIiKiRWbu26uXr1t3ccRDVV+XnVSrv8GEJFWzl6Slt4qyBEiChRMeOwIMPStXNt98CO3dKK7e0NJnrzN6KpbAwOaZp02QOqTlzOuOLLzbirrsewsyZG5CQ4FV6XIWFEs4UFtq3T+t8T15ecsxpaRLyJCVJQGMrNzfAw6NioGRd3qKFzCdlsQC7dkl1mUol719cnH0VSxqNtP1r2VLa/fn7S4WUdfuZmWX/Nsxm2RfnXCIicpxevXrh+eefR35+PkaNGoXdu3dDr9fjzJkzmDJlCg4cOIBbbrkFr7zySulrhg8fjmeffRbff/893nrrLeTk5ODSpUt45JFHcPbsWSxevLhWgRLdfBqNBmvXrkWXLl0AAOfOncOIESOQY/2kDBER1UusVGqC+MkbIqLGjZVK1JA467wkMlKCiGspFMD48dKyztEuXgTmz5dwIisLUColnHnrLQlMHK24WKqizpyRfXl6yjHn5ko11dChEkjZKiVFWt7l5cm2Q0Olcqi4WI4nNFRa5jmaXg/8/rsEOxZLWVVTnz4SSDlacbEESyZT2XxLZrPs199fAikiInKMNWvWYMmSJThw4ACysrKg1WrRvn17jB8/Hs8++2yV8yOtWLECH3/8MU6cOAFXV1f06tULc+bMweDBg2u9f14vcY5Lly6hb9++SEpKAgA88MAD+Oqrr5w8KiIiqg5DpSaIJ0lERI1bnz7Anj0Vl0VEyHwkRPWNM89LYmKkgm/sWAlcx4wBNmyQoMLVFXjjDWDuXFlv7Vpg3Dj7gtn8fGDRIpkfyN8fOHBA9jVxInD33VIpUxcMBuDgQeDTTy8jMXEzJkyYhqIiaRc3ZYp9rd2MRql60mqlLV1JCXDhgtyUSgmWmjeXQKb8evYwmYAjRyTMUiqlOkqjAfr1k/fQxcW+7VfHYJBQzmCQUE6hkOMJCZFxEBFRw8frJc5z5swZ9OvXD8HBwdi8eTNatWrl7CEREVE1GCo1QTxJIiJqvI4fB7p2lYuqH38sF8itF8yJ6qP6dl5iMAA9egAnT1b9vL0B7alT0vIuK0tCnX79pHVcWhoQHg707m3fnETV2bcvAcOHD0VWVgKGDv0vbrvtMWg0wFNPSRs7RysqAs6dk2oma/s4pVLa5fXsaX+AlpUlAU9BgQRKfn5SUaRQyPH4+cnXjmQyAcnJsl2jUfYHSNWbSuXYfRERkXPUt/OSpubo0aNo1aoV/Pz8nD0UIiK6DoZKTRBPkoiIGi9vb/n0/oIFwIsvOns0RDdWX89LEhKAbt2qnu/I3laS+flATo5Uu3h6SrVSbKxUGBYUAJ07S7BVUlJxPXssWLAIL788GwCgUCgxePD3CA8fj8mTZV9eXvZtvzqZmcBXX0m41Ly5VIA1bw5ERTmmYqmoqKz9XXGxzBeVlSXLgoPld+K169nKbJbjKCqSICsjQ753wcHy/tlT8UVERPVDfT0vacrMZjOULAkmIqpXGCo1QTxJIiJqnF58Efj3v6WtVnq6s0dDVDP1+bxk9Gip9rvWyJFVL7dXcbFUGx48KOFFSooEL/7+wIQJQMeOtm/bYrHgsceex9KliwAArq4qLFy4CRERg1FYCLRtC3TqZH/Qc63sbHmv1GoJYXJyJIh5+OG6a/dnNAKXL5fNg5SbK/t0d5dKM53O9m0bDHIc1pBKp5Pwz2CQClEPD8e/h0REdPPU5/OSpkiv12PixIm45557MJ2tF4iI6g1G/URERI2AwQC89558feiQc8dC1FiMG1f18o0bJVhyNFdX4NZbgXvukXmCTp+WcCQ2Fli1quqqqZpSKBT473//jQcemAIAKC4uwhtvjENAwAHccovsb9Mm2WdJicx/lJoq9/bQaqWKp6AAaN1aHisUwIkTclwmk33br4paLXM5tWghFWfx8YBeX9Yyz559ajQShrVsKffu7nJ8Op2ESrm5EqSZTFLZZL0nIiKi2iksLMTdd9+NjRs3YtasWVizZo2zh0RERFcxVCIiImoEunaVT+KPHCkXbonIftOnS2VLee7uEops2iQh0MsvO36/RqPMqzR4sFQp5eUBe/dK4GMPpVKJZcs+w5gxYwAA+fn5GD16BIDTGDFCgpgTJ4Bly4BFi4Dly4EVK4DERNv3qVbLHEq+vtIKLzQUePBBoH174MoVOa7z5+smeHFzk/cvPFx+P+blARcu2B+UKZWy7fKdeFxdpU2hj4/s6/Jl4OxZObaUFAn+iYiIqOa0Wi369+8PQFrg3X///fj999+dPCoiIgKaWKhkMBjw1ltvoX379tBoNAgJCcGUKVOQaMNfymazGR9++CG6desGd3d3BAQE4C9/+QuOHDly3dft2bMHo0ePhr+/Pzw8PHD77bcjOjoaNe1CuH//fri6ukKhUODcuXO1HjcRETUuMTFywTY2Vi5y1kVLLqKmLDZW5lAaPVru9XoJCG67TSp6FiyQ6psvvpCfx9Gj5d4ePj4SwqSnA23aSDDi6Qn8/rv8jGdl2b5tNzc3fPvttxgwYAAAID09HcOGDcOVK+dx663AgAEShpw4AaSlSQizbZt9QUxIiMyhNGqU3LdoIeH3HXfIfESJiRIuXbki7212tgRr9lKpJAQ0m4FmzaSSqLhYqrLS0yX8cTSVSr5/JpP8WykslDAtLY0VS0RERLU1f/58PPjggwAAo9GIsWPH4vjx404eFRERNZk5lQoLCzFkyBAcO3YMn3/+OUaNGoXY2FhMnToV586dw2+//YZu3brVaFtmsxkTJ07E2rVr8eGHH+Lhhx9GSkoKZs2ahR07dmD9+vUYNmxYpdetXr0aEydOxOjRo/HBBx/Az88Pn3zyCV599VVMnToVS5cuve5+i4qKcNttt+HkyZMAgMTERLRp06bW7wV7BBMRNQ6RkdLKySooSC7KEjUkDfm85NIlCXUvXar8XESEBFK2OnUKWL1aAiSdDhg/XgKL3bul4qZjR6BXLwlNAGmNl5MjgYan5423n5OTg0GDBuHw4cMAgPbt22PPnj0oKvLF8uWy3StXJBCxWIAXXpCKn7pQUCDB0unTEvh4eUnY1LOn/fMuWVveFRTIMbVpI8FSVpZUFwUHy3sGSBBknSvJzc32fZpMQHKytMorKpLvjcEg/ya8vaXSjYiI6qeGfF7SWBUVFWHs2LHYsmULAKB58+bYtWsXQkNDnTwyIqKmq8mESq+88grmz5+Pjz/+GE899VTp8vj4eLRv3x4dOnTA0aNH4eLicsNtffLJJ3jyySfx4osvYsGCBaXLs7Ky0LZtW7i5uSEuLg5eXl6lz6WmpiIiIgK+vr44c+YMtFpt6XPTpk3D8uXLsXLlSkyaNKna/b722mv49ttvodfrceXKFYZKRERNWEwMMGNG5eXR0dKyi6ihaAznJS+8UDanWXn2/jxWFRSZzVJFtH+/fN29u7SYW7dOKnx8fYEJEyR0upHU1FT0798fsbGxePrpp/HBBx/AaFRixQoJXUJCJNzKzwd69wY6dAA6d5awxNGMRuB//5MqKY1Ggp+ICJnXSq22b9tVhUVFRRKa5eZKpZlKJW3qrOFTRISEebYwm2VbRUWybb1eqtp8fGT/Hh72HxMREdWNxnBe0hjl5+dj8ODB2L9/PwD5MMwff/yBgIAAJ4+MiKhpahLt7woKCrB48WKoVCpMnTq1wnNt27bFkCFDcPLkSWzcuLFG21u4cCEAYMY1V/N0Oh0mTpyI1NRULFu2rMJzn376KfLy8jBlypQKgRIAzJo1C4CU9VbnwIEDWLhwIf773/9CUxd/yRMRUYOydm3Vy9etu7njICLgzJmql7/6qgQLtvL0lFZx5SuPlEqZQ+2hhyTg2bkTePNNGUNwsLR1W71agqAbCQoKwtatW7Fw4UJ8+OGHUCqV0GqBoUMlULlyBQgLA557TqqGkpNlLqk//5TQB5D2bqmp9s9TVFgowUuvXlKF6e4OHDsGHD9u33sIlAU55auPVCqgVSs5PpMJ+OMPID5eAq38fKluMpls259SKS0LVSo5Lo1G2v0FBEh1VG6uhHblt282y2O2yCMiIqrM09MTGzZsQGRkJADgzJkzGD16NPR6vZNHRkTUNDWJUGn79u3Q6/Xo1q0bPKvoB2Kd+G9dDa7EnThxAgkJCQgMDCz9z6wm27I+7tevX6XX9OjRAxqNBocOHcLFixcrPV9UVIRp06bhsccew6BBg244RiIiavzGjat6+dixN3ccRFT9z2NmplSqzJzp+H2q1UDfvsCIERJi6PVSVeThIYFFTk7NttOmTRu88MILUJTryRYWBkyZAkybJvdt2wLt2sm+wsNlP5s2yRxPX3wBLF8OrFghLexspdVK27vUVPlap5P95ubKfEvnzkno5Gju7kDLlrJvrVaCNJNJ9mtPmKXRSKVXy5Zyr9HInE7e3lJNBkhlWU6OfO9SUiS0S0mRVnlERERUUWBgILZu3YqQq71x9+7di3/+859OHhURUdPUJEKlo0ePAgDCwsKqfD78aoN463qO3lZJSUnpPEhVvc7V1RWtWrWqdgx///vfkZeXd91KpusxGo3Izc2tcCMiooYtObnysogItr4jcobp0+Xnr7ywMKBPH6k8iY6WEGjJEnkuJgYYPVru7dWihbTACwiQfe3eDVy+bHuVDSAV8n/964sICLCgfIG9SgV06wYMHy4hzJdfAvv2SWCSlQVs22Z7xZJaLdVQvr5ARoaESsOHAwMGAM2bA+fPS7iUkiJzPAHSMi87W+7toVLJnHReXnK7dEnGkJNjX+WQUinVUcpr/uJyc5Pj8/aW71N8vASQarUEWRkZrFgiIiKqSps2bbB582Z4e3tjzJgx+Otf/+rsIRERNUmuzh7AzXD58mUAgJ+fX5XP6642TLeu54htpaWlwWw2Q6lUIjs7G4arHzms7RgOHjyI+fPnY8OGDRXmaKqNefPm4e9//7tNryUiovrHYAD+8Q/5+h//kAutY8cyUCJypthYCYnWrav485iVBdx6K5CUBMyaBTzxRFkosmEDMH++vNZWnp4yh9Lq1bKvgACgWTNg40agfXvgjjsqts+7ke3bt2PcuHHIz8+Hi4sL3n333UrreHgAnTpJoGUwSIWSRiPhTn4+cE2n5xoLCQGioiSY0mrL5h1q21b2lZAgbf6Sk2UMcXFAXp4EQT17yutt4eYmoWBcnMyp1KGDhFvZ2bL94GCZD6lcMZdDqNWyzbQ0CZFycyXgKiqSqqxrwygiIiICunbtit27d6Ndu3ZwdW0SlzWJiOqdJvHbt6CgAACgUqmqfF599S9W63qO2JZ1XU9Pzwrbrc0YioqK8Mgjj+Dhhx/GsGHDbji26syZMwfPP/986ePc3NzSyigiImp4unSRi9IjRgBvvOHs0RCR1fTplcNdnU5at+3bBwwcWLm1WVychFH2hMIdOwLPPiuVNT4+0tLt1CnZZ1ycVBfddpsEFvn5ZetVFTZlZGSUzk8wf/58+Pv74+WXX660npeXhDhZWRIoHT0q8wWdPi3btTVYUqvLwqTyNBoJslq1kmP73//k92DnzhL+7N8vgVRVr60JnU4qvoqK5H1yc5OvU1OBixeleig4WI7NZKq4nj1cXaViyWiUYCkzU47VZLJ/20RERI1Vp06dKi0rKiqq9pobERE5VpMIldzd3QHIfzBVMV7tmWFdzxHbKr9u+e3WZgxvvfUWMjMz8d57791wXNejVqsrhF1ERNRwffONXCRWqaQSgYgahl69gLvvluqka61ZY3+loadnxZCoc2eZB+nwYeDIEQli/P0l/MnJkUqcCRMkkCrvvvvuQ2ZmJmbNmgUAeOWVV+Dv74/HHnuswnpaLTB0qLS8y8mRiqj27SVk2rRJ9t2+veODES8v2fahQxLEJCVJ1VJmplQ42XPK6+ZWcbwqlcyJ5O8vLQWTkqSCKDdX7t3dpcLpasMBmyiVsv2MDAmqgoIkVNLr5fg8PGQcREREVL2kpCQMHz4cb7zxBh588EFnD4eIqNFrEk0VmjVrBgDIzMys8vmsrCwAKJ3szxHbCgoKgvJqzwpfX19oNJpajeHQoUN499138emnn8LHx+eG4yIioqZh6lS5X7PGqcMgIhuMG1f18o0bgSlTHL8/NzcJsx54QMKKmBhg505Znp4uLfPy8yu/bubMmfjXv/5V+njGjBlYvXp1pfXCwmTc06YBDz8M3HWXVFC2awecPSvhUny8VOAUFkrVj61zLpWn1co8S76+QGCghD0pKRL8FBfbv/2q9hcWJpVZsbFyKyyUAC0uzr75qwAJkUJCJMBq2VK+V76+0hovJ0du1uMym2V/nHOJiIhIXL58GX379sXp06cxbdo0bN261dlDIiJq9JpEqNS9e3cAQGJiYpXPJyQkAJC+rHWxLRcXF9xyyy3Vvq64uBgXLlyo8Lp169ahuLgYY8aMgUKhqHBLSkoCAISFhZUuO3fu3A3HTkREDdudd8rFxE6d5MItETUs06dLZUt51jZxX34pFSnvv+/4/Xp6Smu3Vq0kjElMlEAkOVkCi6rMmTMHs2fPBgCYzWZMnjwZP//8c6X1tFoJdqzH4eYG3HKL/I4KCZGKoi++AD76CFi+HFixQvZvD7Va5lCyVgjdfjsweLCEVnv3Srs667xVjqRWS1VRWJj8Ls7Lk31d29LQFkqlvHfWeZTc3CRY8vaWqqisLJl76eJF+b6lpDhmv0RERA1dcHAwxowZA0Cur40fPx779+938qiIiBq3JhEqDRo0CB4eHjh27Fhpj/jydu7cCQAYO3bsDbfVuXNnhIeHIy0tDXFxcTXelvU/uF27dlV6zcGDB2EwGHDbbbehRYsWAIA333wTFoulyltoaCgACaisy9q0aXPDsRMRUcP1xhvAH3/I14cPO3csRGS72FggOhoYPVruCwok2ImMlKDi+eclBNqyRSqLRo+We3v5+Eig5O0NtG1bFlAcOCDt3K6lUCiwcOFCTL1aHllUVIR77rmnxhdptFoJfvr3B06elLZ7mZmyz23b7K9YCgmROZRGjQJGjpT2e3fcIaFPbKzMsZSeLusajTLvUrku1TZRqaQdncUi+7duOylJjq2ugiw/P2m1d+WKBGdms+w3I4MVS0RERAqFAosXL8b48eMBAHq9HiNHjkRsbKyTR0ZE1Hg1iVDJ3d0dTz/9NIxGI5YvX17huYSEBPz000/o3LkzRo4cWbr8+PHj6NevH1577bVK23vppZcAANHR0RWWZ2dn47vvvkNQUBCmTZtW4blZs2bBy8sLK1asgOGajxUuWbIEAKqcBJmIiCgyEijXiQqdOztvLERkv+nTgfXry+ZR8vaWdnHHjkmAoNcDw4cDM2bIHEwzZsjvAXt4esocSoGBEkj07Svbzc4Gvv5a2uJdG7oolUr897//Lf2wVH5+PkaOHIns7Owa79fFRdq59e4tX2dkAMePS0BiL7Vaqnms8yip1UCHDkCPHvL1iRPA1q3A//4n7+OWLVLhYys3N6k08/SU71Hr1sCgQVIxlZIirf7y8uw/rqpYAy1fXwkfDYayuZ2IiIiaOhcXF3z11VcYMGAAACA9PR1RUVFIsec/fiIiqlaTCJUAYO7cuejTpw9effVVrFq1CoWFhTh27BgmTJgAd3d3fPXVV3BxcSldf8mSJdi1axfmzZuHjIyMCtuaMWMGxo8fj/fffx9LliyBXq9HfHw8Jk6ciLy8PKxYsQJeXl4VXhMUFISlS5fi4sWLeOCBB3D+/Hnk5eVh4cKFWLZsGaZOnYpJkybdlPeCiIgajpgYmbOjvLg4x1QuEFH90qWLhC4zZlR+zhE/9x07As8+C8yeDTz3nFRBPfCAhDB//ikt+I4elaAiP9/a2s0V3377LQYOHAilUol3330Xvr6+Nd6nl5dUSRUUSDDm4SHVNbt2yb6Kiuw7pqp4egLdugHt20uAdeyYHE9amlQw2VOxpNNJK8EePeQ+KAho0UKqv1xdgfPnpb1fYaGEP3q9/XMuARLIqVTSHs/aEs9kkiq3ungPiYiIGhqNRoO1a9eiS5cuAKS7z4gRI5BTXa9fIiKymcJiqYtGDfWTwWDAu+++i6+++grnz5+Hj48Phg4dirfeegvh4eEV1v3pp58wceJEDBw4EKtXr4ZCoajwfElJCT7++GN89tlniI2Nhbu7O+68807MnTsXt956a7Vj2LNnD/7xj39g165dMBqN6NChA2bOnIkZM2ZU2kd5v/76KwYNGlTlc1OnTsWyZctq/D7k5ubCx8cHOTk58Pb2rvHriIjo5hs8GPjll8rLR4+WSgeiho7nJZWNHi2VNdcKDgYuX66bfRYUSODy559SAXP5sgQZvr5S4dSiRS727NmDYcOG1XrbiYnS8i4nRwKmQYMkEDlzRkKSjh2lAkjp4I+7ZWfL+6hQSHu6wkI5pocfBgICHLsvq/x8ee9SU2WfGo0cc0RE2RxQtjIYJHQsKpKAycdHwqWiIqmi8vSUYIuIiGzH85KG79KlS+jbt2/pfOR33XUXNm3aBI1G4+SRERE1Hk0qVCLBkyQiooZDq616Mvbo6LLWWUQNGc9LKouJqbpaCZDQ4LXXgL//vW72feEC8PrrQHIy0LKltJFr00YqnDw9K69vsViu+8Eoq8JCCVw8PeX3GiC/206elNDJ3V0qtQICpIWcl1fZerYyGqXlXXa2zLV05owsu+MOoF07mWPK0UEWICHP779LpZf1PWvRArj9dgl/7GE2S5Dk4lI29qIiqYgqLpbvl4eHBGnXrkdERDfG85LG4ezZs+jXrx/Sr06wuGTJEsyo7uSKiIhqjX9iEBER1VNPPSUXXa+9XhsRwUCJqmcwGPD6669DpVJVmuOxvF9//RUKhaLaW5s2bap97enTp3H//fcjKCgIWq0WnTt3xrvvvguTI/p8EaZPl5/z8sLCZD614mLgrbckhFm9uuz5mBipcLK3RZ5SKS3d+vWTapiMDODQIWnrdq2vvvoKw4cPrzRfaFW0WpnPqXxQpNFI0DJsmLR0W7dOArP//AdYsULCJnuo1UDPnlJtlZ0t7+kDD0hYFh8vlVmpqWXrG42ynj3t8QCpwnJzA265Rd5LQObMSkkB7P04n1Ip2y4fFKlUUgXl5SX7ts7vlJwsX9fg20NERNSotGvXDhs2bICHhwf++te/Yjr/eCIicig2SCAiIqqHUlOBTz6Rr5OTpYXTunXA2LEMlKh6v/zyC2bOnIn09PQaBTyurq5o27Ztlc+1aNGiyuW7d+/GkCFD0L17d/z2228IDQ3F999/j5kzZ+Lnn3/Ghg0b4GZvOQYhNlYComt/7hMSgL59gStXpC1dQICEM8nJ8vyGDcD8+fJ6W/j4SAiTni5zEp04IRVGW7dKwNSjhwRD0dHRmDlzJgBg8uTJ+P777+FqY+81b28Jl/bulSolk0mOz2CQ47anYikkBIiKkkoprVaCJkAqhxISpN1fcrJU95w5U1Yl1bOnvNYWKpWEfrm58l4aDLLvzEwJrIKD5ZgdTaORwCkrS/at0chxA3IsrFgiIqKmpFevXjh16hRatWrl7KEQETU6bH/XBLGcm4io/gsIkAu4jz9uf+UBNQ3ffPMNnnjiCbz99tvQarV49NFHrzvv4q+//opp06bh3LlzNd6HwWBAhw4dkJ6ejvj4eAQHB5c+9+abb+Lvf/873nnnHbzyyis13ibPS2yzaRNw333S9qwq9rTIPHVKqqCysqQCZtw4CXoOHpTnb7sNKCzci6iou6G/OoApUx7F8uX/rVErvKqkpgLLl0tlj14PxMUBaWnAI49I1ZQ1DHK0rCw53l9+karQzp1l7idfXwmjbN1vVpYcQ0GBBEwREXJ/5YoEV+7uEi65u8v6JlPZXEn2ZLImk4RkarUEWHq9LIuMrLp9IRERVcTzksYvJycHPj4+zh4GEVGDxs+rERER1TMvvyyBkpcXAyWqubCwMPz555948sknbb6wfyPfffcdkpKSMG7cuAqBEoDSqpX33nsPJSUldbJ/KjNihFQQtW9f9fPr1tm+7Y4dZQ6l2bPl/pZbgFtvBR56COjQAdi3D4iPvwNz5vwPLi6SgKxY8Tkef7zmYeK1vLykSuryZQk/AgMlCElLkwDt9GmZI8jRdDo5Xp1OqokSEyWEyc4uq/Kxdbvdu0tlV/fu8litBlq3ljmqzGbZ14ULEjQdOQIcOCD3WVm279fFRYIpo7GsMkujkXApO1uOjYiIqKn67bffEB4ejjVr1jh7KEREDRpDJSIionokNxdYuFC+PnLEqUOhBqZ3795o3rx5ne5j3dWkol+/fpWeCwkJQdu2bZGamoo9e/bU6TiozAsvVL3c3nl0PD2lRVz56haNBujfH5g8Wdq37dkzFF26fAVAQszPP1+Af/xjvk3702qBoUMlfLlyBfD3B6ZMAe65R0KYEyeAzZuBpKSyeYkKC6XCyZ7wB5BqoZYtAT8/uSUmApcuSaBlNtu+XTc3aat3beWRhwfQtq28v7m5wK+/ynF5eEhQGBdne/ijVMp7p1LJ++LuLvNx6XTyvmVnyz6tAZ3ZLPuy5ziJiIgagmPHjiEqKgqZmZm4//778fvvvzt7SEREDRZDJSIionoiJkY+nW+xAJMmAeHhzh4RNXaFhYV4+eWX0a5dO2i1Wvj6+qJ///6Ijo6GuYqrzEePHgUgVVFVCb/6j9a6HtW96dOltdq1fvpJQqAvvnD8Pn18ZM6hli2Brl3vQ48en5Y+97e/vYL//ve/Nm03LEyCpGnT5D4sTCptuncHhg+XwGffPjm2/fuBFSukZd6KFRIE2UqtluPR6aTS57bbgDvvlGBp714JueqiYbivr1QuaTTSeu/SJdlPfr60wrOVRiNzKLVsKfcajYRMOp1UhJlMMr9Tejpw8aK0y0tJsT+IJCIiqs9uueUW3HfffQAAo9GIsWPH4vjx404eFRFRw8RQiYiIqB6IjARmzCi7kGidu4SoLqWmpiInJwfr169HTk4ODh8+jM6dO2PmzJkYM2YMTNeUS1y+fBkA4OfnV+X2dDpdhfWqYjQakZubW+FG9omNlTmURo+W+4sXpQrGaASmTpUw5vBhWTcmRtazt7Wmj4/sQ6cD7r57BiIj3y59bubMmVi1apVN29VqJVzXaisu9/QE+vQBBg0Ciotl/IcPS0iSlQVs22ZfxVJIiMyhNGqU3Pr1k6DJ21vmXDp0SKp8AHlfs7Pl3l4aDdC8uexHo5GKpYwMmYvJHkqlVEgpr/lrT6ORfw9arQRJqakSZBmNsl9WLBERUWOlVCrx+eefY/jw4QBkbqXhw4cjKSnJySMjImp4GCoRERE5WUyMtDsqLy6O8ylR3erSpQt+/fVXLFmyBO3bt4dKpUJYWBiWLFmCQYMGYePGjViwYEGF1xRcvdKtUqmq3KZara6wXlXmzZsHHx+f0lurVq0cdERN2/TpwPr1ct+8uVSf/PprWehy222Aq6uE1xs2yH1kpO378/QEJkyQAKi4GJgy5VWMHfs8AMBsNuORRx5HenqOYw6unIAA4PbbgaAgCUZiY8sCkfx8+7atVkv10NV/xnB3Bzp3lvmkFAppSbp9O7B2rbyHW7ZIMGMPNzepNLPut317uaWmAvHxMheSoykUsi8PDwkHjUapUsrPr5t5q4iIiOoLNzc3fP/99+jVqxcA4NKlS4iKikJ6erqTR0ZE1LAwVCIiInKytWurXn51+hqiOuHv74+BAwdW+dxTTz0FAPj8888rLHd3dwcAFFXTm8t4tXTDul5V5syZg5ycnNLbhQsXaj12qpmBA2X+nA8/lIqVawMDe8Prjh2BZ58FZs8GZs9WYM2ahZgyZRq8vHwxc+YG/PijD/78U6pf8vOlgsre4AeQoKx1awm0goPlOM6fBy5ckIDL0Xx8JJRr21Yqlo4ckaqo9HRpw2dvxZJOJy3+evSQ6qguXaT1n0IBnDsnx2bdh8kkQZOtcy5ZubhIsOTiIlVSxcWyj9xc+7dNRERUn3l6emLDhg1o164dAODMmTMYPXo09HXxSQ4iokbK1dkDICIioqqNHevsEVBTFXF1kp74+HgUFRWVViY1a9YMcXFxyMzMrPJ1WVlZAICQkJBqt61Wq0srmujm+L//k6qaDRsqP7dunVQ32crTU25Cgc8/j8HcuW8gKKgt9uyRaqlNm2S+IIVCKnImTJBAylZaLTB0qLS8y8kBevcG2rSReZUuXgRuuUUeKxS276MqXl5As2Zyy8oqC2Hy88uqm2zl5iY3K3d3mVcvN1fmdIqLk+PJyZE2qe7uUuF0teNkrSmVgL+/VHgVFcnX1nApO1vmYPL0lNCJiIiosQkICMCWLVvQt29fpKSkYO/evbjvvvuwdu1auJX/D5mIiKrESiUiIiInys0FNm6svDwiwr4LvUT2MJebWEVR7sp89+7dAQCJiYlVvi4hIQEA0LVr17obHNlk3Liql2/cCHz0keP24+rqirZt28LLS4KfESOkumf//lxkZEg10erV9lcshYUBU6YA06YBjzwCjBwJDB8ubfEOHAC2bgWsU3sVFko7OXvmXAIkzPL2lsqr8HCZi6iwEDh+XNrgWSz2bb8q3t7y/4G/P3DyJHD6tAQ/ubkSNNlTVaTRyFxSLVvKvZeXhFTe3lLVlpkJ5OXJ8ZrNsi/OuURERI1FmzZtsHnzZvj4+AAANm/ejN9++83JoyIiahgYKhERETlRRIRciJw0CYiOBkaPlvvYWGePzPkc2S6LKuvZsyc+/fTTKp+LvfoPsG3bthU+rTlmzBgAwK5duyq95vLly4iPj0dQUBB69+5dByMme0yfLr9vynNxkZDgmWckUPj5Z8fv183NgrNnX8bJkz1hMKQhLU3ax126ZP+2tVppgafVymMPD+COO4AhQ6RyaMcOYOVKYMkSYPlyYMUKqWaylVot7el8faWap3174P77Jcg6c0bCrGqK+OyiUMj3x99fAiC9XkKly5ftb72nVEqFlLLcX4VqtYRLnp6y/UuXgIQEmacrJUXmXyIiImoMunbtinXr1sHHxwffffcdhgwZ4uwhERE1CAyViIiInOTFF4G0NLlwt3KlXPRdv54VSgBw6hSwaBHw/vtyf+qUs0fU+KSlpWHNmjVVPvfxxx8DAKZNm1Zh+cSJExEaGoq1a9ciNTW1wnPR0dEAgOeffx4u7JlVL8XGVgyvi4ulikink/B2yBCpWDl/XuZaGj3avjmXLBYLPv54Lg4cWIDMzLPYsWME3NxyUVIiLfH27JHWa46m0wF33SXzIO3eLfMe6fXSRm7bNvsqlkJCgKgoYNQouQ8LAzp1Am6/XcKZY8eAo0fl/TQaJXyyN/gBpB2dlxfg6irt94qK5JguXKib4F2hkLBOpwMKCuQ4CgslzMrIYMUSERE1HgMGDMC5c+dw7733OnsoREQNBkMlIiIiJ0hNBd57T74+etS5Y6lv8vOBVavkwnZwMJCe7ph2WVSRQqHAli1bMHPmTJw5cwZGoxEJCQmYPn06duzYgaioKLz88ssVXqPRaPD111+jpKQEEyZMwOnTp1FYWIgvvvgCb7/9NoYMGYLnn3/eSUdENXFteH3rrVJd8/XX0g7t8mUgNBSYMUPmYJoxA4iMtG1fCoUCTzzxGIKDWwAALl48iP37x2HOHAP69JEA5ssvpX1cXYQUXl5S2dO9uwQiaWlSUXR16i+bqdVSrVR+HiUvL9nPLbdIiLRlC/DZZ8CaNfJ1Sop9+3Rzk0ozT085lvBwYMAA+Z4lJQHnztVNBZHZLPsOCZF9lZTI+2hvK0EiIqL6xNfXt9KyS44oqyYiaqQYKhERETlBx47S9u6xx+TiIJXJyZELv+np8nXLlnIROCfH2SOr/xQKBRQKBR555BEAwPLly0uXLVu2rMK6v//+OxYuXIjTp0+jX79+8PDwwO23346zZ88iOjoaGzduhEqlqrSPvn374tChQ2jevDnuvPNO6HQ6vPPOO3jzzTexceNGTm7cQE2eLEHB8OGVn4uLs71iKTQ0FNu3b4VO5wcASEz8FW+/PRm33VaMBx8E2rSRNnXffCMt1hzZ9tLLC/DxkWCkQwdp91dQAOzcKcdUF0FWQADQtatU9Fy6JAFMbKxUZdlbsaTTSXDVo4fcN28ulVKtW0vVWXy8vHcmk9z0evvmXALkPVOppDLK3V2CNJVKvj85ObJfIiKixubTTz9FeHg4tm7d6uyhEBHVSwqLpS6mlKX6LDc3Fz4+PsjJyYG3t7ezh0NE1KTExAD/+Ie0LPLxkZZCVMZkkuqIzz6T+VFuv10ukgYGAs8+K5/Sp8aF5yX1y+jR8jN4rXbtJOy11b59+zB48GDo9XoAwCOPPILPPvsMCoUC6ellberi4uR3Y4sWwIQJEsDbIzFRWt7l5Mh277xTApFz5yR06tJF9gVIqJaXJ8ut8zTZIjtb3kNfXwmXkpNln5MmyfEoFPYdU1UsFgnf09Kk8iw3V8IfT0+pcNLpbN+2wSAt74qKZJv+/nIMer1ULmk08vvaOi+T2SzLXVwqztVERNQQ8LyENmzYgNGjRwMAPDw88Msvv6Bnz55OHhURUf3CUKkJ4kkSEZFzREbKBVOr1q2lbRGJnBxg40a5+NqmDbBvn1wk1emA8ePtv7hM9RPPS+qXmBhpeVcVd3dpRRkVZdu2f/rpJ4wcORKmq+UzL730EubPnw9Afu7//nfg5EkJKSwWaSX3yiv2h8mFhbJ9T8+ysCg7W9rvXbki1UW+vhJqWcOnoUOlCsgW1vZ32dlAUJAE40ajhDu+vlKdGhBg3zFdb9+//ipVUr6+EuqEhEibQ3uKCKsLigoLpfrLYpH3VqmUUKt8AKXR2HtUREQ3D89LqKSkBBMnTsTq1asBAAEBAdi5cyfatWvn5JEREdUfDJWaIJ4kERHdfNVdqI2OLpvbpCmytlDKzpZKBXd3YMQICZKsz/n4sEKpMeN5Sf1zbQDeqpWEB+np8rhZM2DvXgnGY2KAtWuBceNq9rvshx9+wKRJk2C+2nvunXfewSuvvIKLF4H335d51PLygLNnZX9PPy2/E8rPX+RIly9LmLR+PeDqKm3lrGH2lCm2VyylpMh2rZVPPXvKfXy8bN/HB2jbFrD+kzca5T3Wau07Vr0eOHBAtpOfLwGPwQAMGSLft7pgsUiwpNfLfIFKJeDnJ8ejUkmoxYolImooeF5CAGAwGBAVFYXff/8dANCmTRvs2rULISEhTh4ZEVH9wFCpCeJJEhHRzVddS6nRo+ViZlN06hTwww/SUisnBxg1Cnj0UbkISU0Hz0vqp5gYYN06YOzYsrDo+++Bhx+WkAKQypfyc/ZERMj8QTfedgxmXE3ZQ0NDcfz4cSgUXli0SIKkli2B8+dl2506Sdjco4dULrm4OPY4AalWWrRIjkuplAofNzeZ8y4w0PbtVhcUZWZKuKTXSyWTVgscP14xgLL1mpXJBBw5IoGSr6+EPAYDEBoqQVazZva19rseo7Hs+69SSYVSSYl8PznVGhE1FDwvIavs7GwMHDgQx44dAwB07doVv//+O3x8fJw8MiIi5+NnxoiIiG6CLl2qXj527M0dR32Rnw+sWgUcPiyfcPf0lIvJRUXOHhkRARIkrV9fsfrovvskJHnpJXlcPlACpLopJqYm256OefPmoWPHjvjjjz/g5eUFT0+ZQykwUKqHgoOBZ56RYCciAti1C/jmm4oVVPn50louP9++Y/X2llZ3gYHSlu7sWZn3LiVF2r7ZSq2WYOfayiM/PwnJ2reX0GflSgnXfX2lanP/fglobOHmJu+Xp6fMq+TnBwwYIMtKSoCEBJnjyfq71mSScOva76Wt+9bpyo43NVX+vfAjjERE1BD5+vpi8+bNaNOmDQDg2LFjGDduHAzWT9cQETVhrFRqgvjJGyKim0+jqXyRsKaf6m+MLl6U+VPS0yVw8/KSC8mzZwMtWjh7dHQz8bykYbK3+tJisaCgoAAeHh4VllfX9jIrS1pknjsngVNgIPD77xLC+PpKIGXPvGuJicC2bbJvd3dp+VdUJF936SKP60JGBvDllxLqqNUSygDyPvr62r5dk6lsXiNrlZDFIu9XaqoETEqlPDYa5TgjIsr2byuDQY7JGlppNNJWUK0GPDzqptKMiMiReF5C1zp79iz69euH9Kt9gCdMmIBvv/0WLvxPjYiaMFYqERER1bE77pCLduHhMofS6NFy31QDJUAuFmdlyYVPLy/55LxOJxeSiaj+Gzeu6uWbNgEffXTj1ysUikqBUlFRES5ePIMWLSrPo6bTASNHyn4LCoAFC4CDB+V3Rno6sHq1fRVLYWEyh9K0adKGc+xYYNgw2f6ePcDPP5fNKVVYWFaFYy9PT9m3n5+0pfvzTwnY8/Ls266bm4Q45dvOKRTyPkZGSmB17Bhw+rRUY+XlSRWYvRVLGo207mvZUlruhYTI73iTSdr+6fVllUtmsyy3pxqMiIiorrVr1w4bNmyAu7s7AGDbtm04e/ask0dFRORcrFRqgvjJGyKim+f774GJE+XT2QUFnC/I6vBh4H//k0/LG41yoXP8ePsqDahh4nlJwxUZWbEdnYuL/EwDUvmyYoX8XNeEXq/Hvffei/3792PHjh3oeJ1fBsnJwN/+Vhbq6HTyu/Wll+qm0jEtDTh6VIJwhQK4dEl+b/n4AEOHSihkj5QUaXmXlychUECAVBF5ewNt29ZN2K7XS1hWXFxxjqyBAyWMcjSLRb5fBQXy2Pp/orWayt9fAikiImfjeQlVZ/PmzZg5cybWrVuHbt26OXs4REROxVCpCeJJEhHRzWFtnVRSIvMH1fTiamOn1wNffy0BUvfuVbe6oqaD5yUNW0wMsG6dVPZMny6B8dCh0gINkIqYTZuA48eBtWul0qj8PE1Wzz//PN5//30AQIsWLbBz506EhoZWuc/8fGDRIgl7lErZtqcn8OKLQO/e0m7N0SwWqS796CMJl9q3l30HBkqFk1Zr3/aNRgldtFppFZedDcTHS9AUECCVru7uldezlckEHDki76W7u7T/A4Bbb5VWf1c/jO1w1qqopCQJtPz9ZZlKJVVNSvbRICIn43kJXY/BYICGn4IgImKo1BTxJImI6OYIC5P5P/r3B3bscPZo6o+ffgIuXAAeeMC+i6LUOPC8pHHatEmqNKtqSVfVfHK5ubkYNGgQDh06BEBazezYsQNBQUFVbv/UKWl5l5Ul7dXati0LSO64A2jXTqqKHCk1FVi6VL5OTy+rsnnhBaBZM8fuC5AgKzVVAh+jUcKyK1ckVPLyAnr2lCDGVllZUmlWUCDvW0iI7MdgkCqp4OC6qa41mSRUMpvL5nZSKoE2bSq26yMicgael1BtWCwWnDp1Cp06dXL2UIiIbip+FoyIiMjBYmLkgua5c9LOh4FSmZQU4OxZqSZgoETUeI0YIRUpU6dWfi4uTn5Pluft7Y1NmzahXbt2AGRS7BEjRiA3N7fK7XfsCDz7LDB7toQ6Dz0ETJ4sQcjPP0vr0QsXZN38fODiRfvmXAIkyNHp5OsOHeQ+NxfYtUtCEkd/VE+hkOPp1Qto3lz+Lzl0SIKYrCxpmWc02r59nU6qRXv0kPuWLSWca9FCgqu4OPmdXVIiQZBeb/+cS4C0vtNqJUDy9JRQq7BQtm9tn0hERFTfFRcXY9asWbjtttvw+++/O3s4REQ3FSuVmiB+8oaIqO5cO8dIy5ZlFzabOotFLvQqlcCECY6vIqCGiecljdvo0cCGDZWXh4UBCQmVlyclJaF///5ITk4GANx1113YtGlTrVrNXL4sQc/ly/J75sIFqSry9ZXfPfbM3ZaYCGzbVta2s29fCc+Sk2X73boB1RRX2SU7W9oMmkyyP4tFgvnJk8uCLkcym4HMTGkxmJMj+3R1lRAoIsL+fRoM0iLRWu3l4SGBktksgZO7O1vhEZFz8LyEaurjjz/G//3f/wEAfHx8sGPHDnTp0sXJoyIiujl4qk5EROQgMTEVAyVALjRe+4n8purPP6Vl1J13MlAiairGjat6eWKiBAe//VZxeWhoKLZu3Qp/f38AwK+//or7778fxcXFNd5ns2Yyh13//sD27cCePVIFk5IiLfPsqVgKC5M5lKZNk/tbbgH69AEGD5YKnN9+A/74QyqYCgulfV1hoe37s9JqJbRyc5MxFBZKtdKpU3LvaEqlzOXUpo1sPzlZwqWUFPl/zt6KJY1G2u21bCn3vr6An5+ESwaDBFoFBRKemc2yP7PZEUdGRETkGDNnzsSIESMAADk5ORgwYDgGD07i335E1CSwUqkJ4idviIjqRnWfyB89Gli//uaPp77Iz5cLq5s2SYXA4MHOHhHVJzwvafyureBs3VqCg9RUeRwRARw/LkGD1b59+zB48GDo9XoAwLRp0/D5559DUYtE+uJF4L335OsrV2SfGg3wz39KWFIXkpOBY8ckNLt0SSp7/P2BoUMlDLJHSoq0vMvLk1Z87dtLWJabK/sID5dQxpH0euDAAQm1cnOlYqqoCBgypG4qsgAJj6wt8YqKpMWfQiEVTf7+Ff+dEBE5Gs9LqDb0ej0GDx6Mffv2XV3SHsAfiIgIqDR/JBFRY8JKJSIiIgdp377q5WPH3txx1CenTgGLFgGvvSYto3x9nT0iIrrZYmOB6GgJ2KOjZf6hK1eA5cul8iYuTqqWpk6Vys7Ro4GjR3thzZo1UKlUAID4+HgUFBTUar8+PtKmraREKopcXCSQ2bxZfjfVxUfrWrYEBg6USpsLF6QF3+nTsk97K5ZCQoCoKGDUKLlv3x647Tagc2cJYQ4cAM6cKQtisrPtm3MJkCDH3V3CJX9/Ca20WgnMzp+3f/tVUSoljPP1le9XZqbsR6+XlnmsWCKihsb6f9uNKlictV5DGKMzj+V69HoPFBRsgIRJAHAGwCjExelZsUREjRorlZogfvKGiMjxiorkYtu1HZoiItBkP6WWny+B0oULMi+HVgt06gQ8+6xcMCQCeF5CwL33Slu6a/8qiYgA3nlnFVasWIFvvvkGWq221ts+dUq2nZUlAVNUlHwdGyshSd++QKtWDjqQq1JTJTDz95dAJClJfh8+9RRw++110/7TbJagJylJ9p+RIXMu+fgAPXtKIGWrrCwJ/goKJGCKiJCA7soV+b9Pp5OqJVdXWd9kKpsryc3N9v2aTFL55eoqoZLBIMfZrh2rlYio7jj6vOTaat3q/jZw1noNYYzOPBZAQqe1a6Wl7/TpwEcfAe+/L3/jlP3tlwSgL4BLVx+PwMiRa7Fhgx3/ERIR1WMMlZogXrwhInK8tm1l0vnbbgNmzZIJ1ceOlT88mipr66n0dLkA2bGjXIScPRto0cLZo6P6guclBAAffiiB87Wio4HHH7fUqu3dtfLzZT4gH5+yQPvKFWDXLmkp17q1hEsqVeX1bFFYCKxYIWFMSAhw7pwsu+UWmaeoa1f7Qp7r0euBL7+UcCkgQAKZ0FBg+HAJmWxVVVBksUholpYmXwcESJVRQkLFAEqns22fZrN8f4qK5EMJ2dly8S4wULbt4SH7IyJyJEeel8TEADNmVF7u6Sm/x6wKCqqe78/R63l4VFzPuu7VTrPXXbcm61ks8v/djdZTKKofo7u7fHDAYpGbwSC3a2k0sq6Li2xPqZT1srMrr+vqKuuYzWVz9VXFxaVsnD4+MkfjwYPVz8WoUMh8gB06ADt3AsBxAHcCyAEA9OgxBXv3LoOS/1kRUSPEUKkJ4sUbIiLHeuMN4F//kgt2Vf3R01Tl5wNz5gAnTwL9+8t8HIGBrFSiinheQkD1c9IBQJ8+0j7O+s8jNTUVhw8fRlRUlN37TUgAdu+WtnEXL8rvpsBAYMIECcJtlZgoLT+tIdXQodLO7ehRCWGCgoBu3cpaghYWls2VZENBVqnsbHkfvbwk1LpyRap8HnrI/jmdqlNSIh8euHwZOHtWgqdWreTYPT2B7t1tr1gyGKTqyhpo+fnJcusFS3d3eb+sFwtLSuSiIK/fEZGtHHlecr3/2+jmsgZPSqV8QMGeK6GhoVKRPHBg2bKyyqffAQwDYIRCEYnk5F1o3jzAvsETEdVDPN0mIiKyw/nzwNtvy9f79zt3LPVRYKBcyCwokK/Hj2egRESVjRtX/XO7d0swExQEREcn4c4778SYMWMwe/Y2u+dCCA8HxoyRipvz56WF3MmTwPffV//J5JoICwOmTAGmTZP7sDCp2LnrLqBfPwmRtm0D9u0D/vxTKpuWL5f7xETb96vVSqCUnQ0EB0to5eUlLX2OHrXvmKrj4iL7at1aHptMEmapVPK7v6jI9m1rNFLV1bKl3Gu1cvP3l+f0evneZWdLVVNystzzAx5EVB9U939bdHRZJY7FIo+dsZ4z932zj8VsljCpqAhYsuTG20xKArp0qXq9Ll0qBkpA+fkjB+CJJ1ZCo+kNi2UnOndmoEREjRMrlZogfiKYiMh+1t7a27bJHydPPgksXuzsUdUvmzbJhcUxY+QCn70tpahx4nkJWVU3v8EjjwBff20NJ14BMP/qGh4AtgPoZdf8dRcvytwIAQFScZOYKGHFyy9LCFQXVS8Wi1RJHToEbN8uAUmXLlLFpNNJEGVrxVJKinzIwVr51LOnVApZ29I1ayYhl1otVUyFhbIve9rjARImHTkilUUmk7yXfn7A4MEyjrpQUiLHef68fO3vL/cqlYRQrFgiotrinEr1b4zOOpbq2hdGR9+4xbnZbEZIiBKpqVK9e/789dcnImpoGCo1Qbx4Q0Rkn2v/CFEq5SIWlUlIkHZVUVEy3xRRdXheQuXFxFQ/J92GDcAjjxQjLe0+AGuuLvUDsANApxpd5KlKfj6waJGEIC1bSqhknQOpWTNpv1dXreMuXQL+/W8JYbRaCbYsFgnSAgNt325VYZHFIoHTuXPyaW21WvZfUFAWPtk711NWlvz/WFAg+/PyksDM11cqzWxtg3c9JpN8orykRD6Jbm1/FxpaN/sjosatLs5Lrvd/W31YryGM0VnHUptA61pFRfLhCr0e6N69ENHRJ9CzZ8+avZiIqJ5jqNQE8eINEZHt7PnEWlNhNALffCMXEEeOdPZoqL7jeQnV1t13G7B9+0gAv1xd0gLAToweHYr1623b5qlTwOrVEorodNKqMzBQWu9duCBhS58+EjLl55fNlWRv9WVhobS8S02VUOTsWQmWnnmm7gL5khK5IPa//8mFrg4dZJlOJx8EcETFknUOJFdXeU/T0mQfAQFyUyorrmdP+GM2S1hWVCTbzciQ/YaGSqjFaiUiqg2el9C1ahNoXctgALy8slFcPBYuLoewa9d29OrVq24GSkR0EzFUaoJ4kkREZLvqJtwdPRo2X8xsbH77TS6MTp7Mdnd0YzwvodqScD8XwGAAB68ujcTChX/ghReCbN5udWHRhQsSLqWnS/iRlCQXiXx9gQkTgI4d7TgYSGXUtm2ybze3srZtgYFA167yKWdHy84G1qyRYEevlwnM1Wrg/vvluBzNbJb3Lz1dKonc3OTrwkLA3V0++a3T2b59g0HCJGtI5e4u+7RYpFrL3V2OkYjoRnheQo725JMv45NPFgAANJoAHD26E+3atXPyqIiI7MNQqQniSRIRke1YqXR9KSny6fc776x+clui8nheQraQdjRpAO4EcAYA0LbtrTh06Nc6+XdksQCHDwP/+pdU3rRvL+FISAjw7LOOqVjKz5ftaLXA5cvAsWMSNLVuLa34PDxkPetcSbbOuQRIRemWLRIueXkBJ09K6DJkiIRkdfWjaDLJHFZ79kgLvjZtZJmnJ9C9u/0VSyUlZe3vLBZpw1dYKMfm7i7v2bXrERGVx/MScjSDwYB+/Ybj0KHfAADu7qHo02cXJk1qzr8fiajBYqjUBPEkiYjIdlVVKtkzQXxjkZ8PZGYCP/8sFyPHj+enwqlmeF5CtoqJAb799jx27uwHgyEZAHDbbQOxc+dmaDQah+/v4kWZ/8hikXZ11nmQ/vlPmYTb0SwWmf/oxAmpwNFqpUoqP1+qqYYOtW+up5QUYP/+spAqIkKqlvR6aV8aHi7zITmaXg/s3CnHVFIiwY5KBfTvL8GZo5nNsk+DQb5nRqMsV6kAf/+6OUYiarh4XkJ1IScnB926DUBS0rGrS7oC+A0REb5N/u9IImqY+NksIiKiGvr+ewmUlErggw8kYIqOZqB06pRMcv/aa1Kl1Lw5AyUiqnvTpwM//dQahw5thVrtDwA4dKgDUlLsKHe5Dh8faUVnsUjlkNksVUs//QScP+/4/SkUEhqNGCH369dLGz6LRVq9bdsmVTi2CgmROZRGjZL7Tp2AHj2kCisnB9i3D4iPLwtisrPLAhl7qFTS6s7DQ+ZXysmR9zE1VYImR1MqJTTz8ZF9WdvkFRTI12az4/dJRERUno+PD556ahOA0KtLjgEYh7g4A2JinDgwIiIbsVKpCeInb4iIas9gkAtgZjPw9dcyXxDJJ+YXLZJP8KemyuToXbs6ph0UNQ08LyFHOHDgAB56aAPOnPkbXF0VyMiomxZup04Bq1dLCKLTAYMGAWlp8jswNBTo169u5iRKTQU++0zCkNxcWabVyu/awEDH76+kBEhOlrAsI0MqUVUqeU979pRAyh5ZWUBcnAQ7Wq0cg7Vyyc9PHru4OOZYrEwmOSYXFwnHjEYJ6CIjWa1ERGV4XkJ1RTpenAXQD0D61aXjMWrUd/jxRwf/p0dEVMdcnT0AIiKihqBlSwmURoxgoFSe9VPfubnyKfAOHeTiZ04OQyUiunl69OiB06d7oG9fqeYJDpbfQyqVY/fTsaMEOTk58jvP+nsuIQHYtQtYuVLmk+vRQ0KMa9ezlZeXtGrLypJjO3pU2tbt3w/06iUVP47k4iIhmZ8f8OWXwIUL0hIvO1uej4oC1Grbt6/TyRxKRUXyPXJzk/9jMzKA9HTZT2Cg7L+4uOJ69hyTSiXb8vKS7ZaUyPeouFg+OMIqWyIiqivjxgEbNrQDsAHAYAB6AKuRk/M0LJb/QMH/hIioAWGlUhPET94QEdVcTAzwxhsSlPj6ygU9KpOfDzz1lHxKf+BAuSAYGMhKJao5npeQo3XoAJw5A7i7H8G8eb/jmWeeuSn7LSmRsOfgQeDKFblZW71NmCCBlD0SE6XlnTWouvXWstZxLVpImOXl5ZhjscrOlravHh5SrZSeLqHMlCkSOtWF4mL5PzcrS9r75eZKIOTuLvM+6XS2b9tgKGt/p1KVtTMsKJDnPTykcoqImi6el1BdioyUSl1gC4DRAIrh4hKFrKy18PKy49MaREQ3GUOlJognSURENVN20i/atJGLelTm2DHghx8kXFIq5WLf+PH2XzylpoPnJVQX/P1/R2bmGAC5ePDBT5GdPRPjxsk8THUtNRV49VXg3DmZY06lAtq2dUzYXlgov289PSX8sFikiuj4cXkuPFzmRrJYpJLJy8u+kMRoBLZskXApKEiOqaBA5pRq3lz25+5u3zFVJz8f2L5dQiB/f6lSCgiQCid7KpbMZgkAXVzk/y3rMr1eQicXFwmX1Oqq1yWixo3nJVTXYmKAdeuA4OCv8M03W1BQ8BmCgtxw5YqzR0ZEVHMMlZogniQREd1YTAwwY0bl5dHRN+eiZEOQkgKsXSufju/WzXFtnqhp4XkJ1YWFCxfipZdeuvpIAeBbAPchIgKIja3bfV+8CLz/vvwuTEmROZeKi4G//lXmI6oLJSXyIYhTp4DLl6VKSqORoH/oUCAszPZtp6RImz1rSNWjh4QsiYkSOjVvLh+6sCfoqYpeDxw4IHP15ebKY7NZjseeaqXrKS6W/VjndzIYJKBTqSTY4txLRI0fz0voZtPp5MMb7dsDp087ezRERDXDz1sRERFVYe3aqpevW3dzx1FfFRTIp9eDg4E+feTiaYsWDJSIqH548cUXMXSoNVSyAHgQwFbExcmHBuqSj4+0S83OlopXnU4qX3bsANaskZDG0Vxc5GLUoEESasXGSrgUHw9s3SpVTLYKCZE5lEaNkvvmzeV3f69eUql05QqwZw9w/ryEPkajHLvRaN8xqVRSBVVUBDRrJpVDSqXsJyVFAiBHc3WV75+3d9n8TsXF8v5lZMjxEREROZL1gyBnzgC9eiVj9+7dzh4SEdENMVQiIiKqQvm2d+WNHXtzx1Efmc1ykRKQC4xsCURE9ZGb27sAHr36yATgLwD21PmHAzw9ZQ6lwEAJdsLDgb/9TVqDFhUB//sf8OOP0ibPKj9fwqD8fPv2bTRKIDJggLSK0+tljqdz5+zbrlotQZm63HQPSiXQqhVwxx0SPCUmyvxLK1fK/ZYt9gVobm4yh5Knp1QqNWsGDB4sH2DIzpbgLD29LOgxmeR4TSZ7jrTs2Nzd5T00m6ViKSvLMdsmIiIqT6UCLl0ClMpT2L+/LwYOHI5jx445e1hERNfl6uwBEBER1TcffSSfFLtWRETTbn2Xny8t7k6dkguF99xTd3NpEBHZ6557FNi4cQmATABrABQAGIUePX4H0LlO992xo8yhdG1b0NBQICEB2LdP5qNr00bCmu3bJSjx9ZVAytZ56by8ZH9ZWRL4GAzyu/vQIVnWtavjK0qt4U9AAPDll3JhLCREgiBAPnygtnHucZ1O5lAqKpKLbtYWezqdtBVMTQUyM+W5tDSponV3l/HY0yLPxUW2WVQkIV1mpoRLOTll81kpFLZvn4iIqDydDhg58p/48ccLMJmAfv2G4/jxXWjTpo2zh0ZEVCXOqdQEsUcwEVH1zp+Xi3wWC7B5szxet04qlJpyoHTqFLBqlXza/coV4NFHgb/8xdmjosaA5yVUlyIjgbg4A4CRAH4BAHh5NcexYzv/n73zDI+q3NrwPek9BBJa6IQmvUmXJkXpRcFCEYUDylFR0WP5LNjbAT16FMKRpmIDMYL0LgiC9A4SOiSkZ5JJnfl+LIYkJKGlzE6y7uuaa8je7+y9ZkJmv/t91nqWQxdqbDaptLFb4rm4SK+iy5elwumpp25f/AkPh9WrswStu++W4+/fLyJTSIiIVm5uYutm75Xk6Vmw9xQXJxVK7u4iwsTFSV+i0aOluqgoSEuDc+dEpMvIkN5RaWny2bVoUbA+TykpYnlnF7QCAkRYslikksnbO6vHktUq79XZWat3FaWko/MSxVGYzWbatu3JkSN/AlCpUn2ef/531q0LYtCgsn0vqiiK8VBRqQyikyRFUZT88fSUhaTHHiv6vhslBbMZZswQayZ7f4k2bQq26KkodnReohQ1oaGwaFECu3f3IDLyLwAqVqzHqVN78SyoklJAzp4VazyLRQSJ4GARKKZMKZgQY7HId7e9qgZE9Dh2TJqAOzlJBc6xY1JR5O8PvXqJKHO7pKaK5V1cHFSsKP2cLBZo3hxq1JBju7nd/vHzIylJxLm0NBHrXF3l0bGjCD8FIS+xKDNTzpmaKmKdi4t8hnbxqUKFLLFJUZSSh85LFEcSFRVF06aduXTJbptxJ7AW8CEkRBJSFEVRjIDmUSmKoijKFRo2FEGpTh0VlLITHy+2SWazLNC1by8/x8c7OjJFUZQbM348rFjhx4EDy6lUqT5gIjLyGdatc6ygBFL9UreuVCf5+MDOnVKtVFDxxdNTjpldM3N2lgqle+4R4WPhQti+Xc4VGyvVTRbL7Z/T3R3athULv+hosd974AE5Z1SUnOvUKRFlQESZuDh5Lgh2IcfXF8qXl3NHR2clQRQEJycRqLJXHzk7iyBXrpyIWOHhci67XV50dFafJ0VRFEW5FQIDA9m2bSXu7lWvbPkTGA6kc+KE3qMqimIcVFRSFEVRyjyhodLn4uhRWRT6+29HR2Qs/P2zmsjXqiX9lAICZLuiKEpJISgoiD//XM3IkYuAiQwYILZpjsTHR3ooVa4sokirVlC/PixdKmJFUeDhIeeoVk3Oe/q0iEoXL8p3fUGoUkV6KPXrJ8/BwXKedu2galWxlN2+HfbsEYvZZcukuunixds/p72nk4+PiDkNGoi4ZbFIRvfly0Uj8ri6yu/M3V3mDgkJIpilpmYJZ4qiKIpyq9SsWZM771wB2G+2VgLjACthYY6LS1EUJTsujg5AURRFURyJ9NvI+rlSJcfFYlTi4+VzcXGRBcegIBg6VK3vFEUpedSoUYOFC2vg5wezZkGHDiLe1KjhuJgaNRI7UXsPJGdn2LABli8XgaRLl8K3jfP1FUEpNlaEoL17pRpn/36xNy2I45O7uzyy4+IiFVnBwZLAsXQppKdD48ZSrbRjh4hQ177uZgkIkB5Kdgs6V1cRdqKiRFSKiRFLvnLlpHop+7iC4Ows18LUVDCZ5DxublnWeCZTwY6vKIqilE1GjWrK5s1hQG8gFfgaqMzAgR86NjBFUZQraE+lMoh6BCuKogihoTBhQu7ts2ZpI1Q7qanw3XeyENejR1bvDRWUlMJC5yWKoxgyBJYsASenH3n44VXMmTMTJyfjGDkcOSK9gtzdoWdPEWTM5izxqaDfw+HhYnkXHy8iUuPG8h2flCRVqY0bg5dXobyVHMTFwU8/yfUlPV2EHQ8PqdgqV67wz5eeDhER8j5TUrKqsby8pMIpIKBgx09JEcu7tDR5L56eUhnl5CSWsdpfSVFKFjovUYyCJD8uAYYBVuA5rNYPMGnGgqIoBkBFpTKITpIURVGE/v3Feiev7b/+WvzxGJGVK+HcORgxQoUkpWjQeYniSOrWDeXkyX8ANnr2nIK7+8cMHmwyTGJBYiKsWyf2o/7+UlmbmCjiy7BhUuVUECwWEVl8fLLEkJMn4dAhEWNCQqTfoNUq5/X1zdmn6XZITZVrS1ycCC6HDokYM2CAVGbdbrXSjUhIgPXrpZooKEjOWb68VDgVtGLJapXKKGdnEZMyM0Wcs1cseXtLBdO14xRFMR46L1GMRGgo/Oc/szh6NIG0tOeoVg3OnnV0VIqiKCoqlUl0kqQoiiLUri1Nw69FK5WEw4dlAa5PH7EtUpSiQOcliiNZvHgxw4bdh2QAA7wNvERIiPTjMQI2G2zbBu++K0JPhw5ZwshTTxWN4J+RAceOiVVdZCRcuiQCUEAA9Ool18+CcPGiWN4lJkr8NWqIwJWRIT2YatQQMaYwSUqCnTtF0ElMlJ8B7r67aCqkQN6P2Sy/N6tVqpqsVhGYKlTQKiZFMSI6L1GMSFqaJEIkJUkyxO7djo5IUZSyjuZHKYqiKGWSf/0rb0EpJEQFJbNZBKU1ayQLXgWlkkVKSgovv/wybm5ujB07Nt9xZ8+eZdq0aXTo0IFy5crh5uZG1apVGTZsGJs2bcr3dSaT6bqPU3n9YSmKQRk6dCijR8/KtuVlYCYnTkh2sBEwmURkqVFDhIhjx+Q5NlYs3YoCFxe44w7o3l2qVY8fFyHo5EmxzLNYCnb8KlUkYaFfP+jbF1q2hHbtoHp1Od/27VKdZbNJtU9cnDwXBDc3sbxLT5c+ga6ucvzTp8Uez2q98TFuFRcXEax8faW30+XLUqlkt8wrinMqSmlg9+7djBo1iho1auDu7k5QUBDt2rXjueeeIzExMdf4r7/+mvbt2+Pj40NAQAC9e/dm/fr1DohcUYoGNzfpE+jqCnv2QMeOf/Dnn386OixFUcowKiopiqIoZY6NG+H99+Xfu3ZJZVL//vJslMx0R3H4MEyfDi+8AJs2ycKlUnJYv349zZo144svviA9PT3fcXFxcdStW5d3332XBx98kEOHDhEdHc28efPYv38/Xbt25ZNPPsn39XXq1KFBgwZ5PlwL6iOlKMVMdPSjwAfZtkwCfiQszEEB5YG/P1SuLJVCNhv8/rsIJP7+RXvetDTpt9S1q1RGmc2SHX3pUsGP7e4ugovd7s7ZWSqg2rWDwEC5Hv/2G/zwg1jVrlwpwtbt4uoqiSM+PlKpVLWq9AqsUkUqv44fl+ei8PFwdhYLvAoVskSluDipZFIUJSdffPEFHTp0oEGDBmzZsoWEhARWr16Ns7MzH3/8MdHR0TnGT5kyhVGjRtG3b1/OnTvHgQMHCAwMpGfPnsyZM8dB70JRCh8PD0mKNJmW8scfPenatR/Hjh1zdFiKopRR1P6uDKLl3IqilGUSEsQ6IDMT3ntPxBNFMJthxgzYv18WuipUgJo1i85eSSlcFi5cyKRJk3jnnXfw9PRk3LhxjBkzhrlz5+YaGxUVRVBQEO+99x4vXPNHcPDgQZo3b46TkxN///031atXz7HfZDIRHh5OrVq1CiVunZcojiY0FCZMAHiBLHHJlaeeWsqMGb0dF9g1HD4MixdLVc2FC9C+PUyeXLQWahYLLFggVVFVqkiVVHy8WPA1by79lpydi+bcMTHw9dciYFWtKqKQvcKpIH2X0tNFLHNzy+qllJ4uNn9xcbK9cmWpLspr7O1gtYoglpYmv6/oaBGvKlfO6melKAps2LCBHj168NFHH/HMM8/k2Hfs2DEGDhzI2rVrCQ4OBmD58uXce++9DB8+nB9//PHq2PT0dBo3bszZs2c5cuQINWvWvOkYdF6iGBmbzUb79r358881AAQE1OTAga1UrVrVwZEpilLW0EolRVEUpUxRvboISj17qqB0LfHx0vg1PR3q1YP69YvWXkkpXGrXrs2hQ4d4/PHHMZlM1x3r6upK69atuf/++3Pta9y4MXXq1CE9PV2tY5QywfjxUsEC7wGPXtmazmefDWHbtm2OC+waGjUSkf+FF+CTT0T4X7pUvrOLCk9P6aEUECBiVo0aEkOzZnDkCKxaJWJMUeDkJO+xVSsRdOLj5ZwxMQU7rqurVA1lF4lcXSE4WOxeXV3hzBmxF9q2Tfow7dkj18OCvhc3N6lSCgiQc3l4SEJHTEzB7f0UpTTw3HPP4efnx+TJk3Ptq1+/PkeOHLkqKAF8+OGHAEyQzICruLq6Mm7cOFJSUvj000+LNmhFKUZMJhOrVv1ErVrNAYiNPU3HjvcQrzdsiqIUMyoqKYqiKGWC0FCx8ElIgIoVpV+QkhNXV+n3YK9SOndOFr6K2l5JKRzat29/01mK/v7+7Ny5k9q1a+e7X1HKEsePw6xZJu6990uaNBkKQGZmMj17DiclJcXB0WXh4yPiR3AwDBgglTXLl0uyRFFRuzaMGgVjx8pzSAg0aSJik6enWMpu3y5iCUh1U2RkwfsueXpKtVBysohZ9mqeAwfg77+LRkzz8IBataQy6u+/4cQJOX9srPy7IOf08JBKq2rV5NnLS95fQIBUeyUkZFniWa1yLu25pJQlDh06xF9//UXHjh1xc3O74fiEhAQ2btyIyWSiQ4cOufZ37twZgDAjeZkqSiHg7+/P1q3LKV++FgCnT++jadNB3HNPimH6QSqKUvpRUUlRFEUp9dSrJ9ZG9r6+3t6OjceI2Gzwxx/Qpo00TI+IkP4ZQ4eq9V1ZIyMjgxMnTuDs7EyXLl3yHDNr1ixatWqFv78/3t7eNG3alP/7v/8jLi6ueINVlEJk/HhYtsyFHTu+4c47ewB+JCd/y1tvFaG/XAEIDIR77xVbtTVriqYXkB1PT7kmZLdp8/ODbt2gbVuxqFuxQgSm+fNh3jyxzQsPv/1zurvLscuVk0qemjXhwQelivbCBRGyzp7NEl5SU0WUKYyKHzc3Sa6oXVsEu8REOVdBhTInJ0ngcMp2F+7iIskb/v7yO7x4UWwGz5yRfxtI01SUImXLli0A1KxZk1WrVtG9e3f8/Pzw8vKiRYsWfPzxx2Rka0S2f/9+rFYrQUFB+OQxWa1Tpw4Af//9N0lJScXzJhSlmKhSpQp//LESD49AAM6e3ciKFQ8xYUIm9eo5ODhFUcoEKiopiqIopZrQUMkuzk54OJrFdQ07d8oi3ZgxMHUqTJkiFkeNGjk6MqW4+fXXX4mLi2PUqFH5VjL9/vvvfP7550RERHDq1CnGjx/PBx98QKtWrTh37tx1j5+amkpCQkKOh6IYCQ8PD9asWcIXX/wO3MXbb8N//+voqPKmalXpMXTypAg6jqBWLejbV0SYb76R64mnp1T3rF5dMCHG3kOpXz95rlZNxKV27aTq+ORJ+PNP6QW4ciUsWybPFy8W7D25uUkCis0mMTg5SQXR6dNZ/ZAKGzc3EZbS0yEpST63mBiIitKKJaVscPz4cQBWrlzJ/fffz/jx4zl9+jQnTpygQ4cOPPfccwwaNIjMK6WZly5dAqB8+fJ5Hi8gIACQHjQRERH5nlfnJUpJpX79+jzzzG+APWNyMfAEJ07Y9F5XUZQip0yJSikpKUybNo0GDRrg4eFBlSpVGDVqFOG3kUJntVr59NNPad68OV5eXgQGBjJkyBD27Nlz3ddt27aN/v37U6FCBby9vWndujWzZs3ClsedidVq5ZdffuHBBx+kdu3auLu74+PjQ8uWLZk2bRpms/mW41YURSlrLFyY93Z1wsji/HnYsUOqlIKDs+yVtEKp7JGUlMRzzz1H7dq1+eSTT/Ics3TpUlavXk2HDh3w8PAgKCiIJ598kpdffpnw8HDGjRt33XO8++67+Pv7X31Ur169KN6KohQIX19fJk5syuzZ8vMTT8CSJZCWlubQuPKiVi3o0QMOHZIeQI7A3R0aNBABJiBAxJ7oaDh1SmzdCnrscuXk2Y6bm1QstW0rlT9hYdL3yN1dqpV27ChYxZKrq9j8+fhIz6Nq1aB7dxHOIiIkWSV7+wq7EFRQS77MTDCZRCz09RUxKyJCYijKSjRFMQL2njDh4eF8+OGHPPjggwQEBFC1alW++OILWrZsyW+//cb//vc/AJKTkwHytcpzz/alYR+bFzovUUoye/e2RcQklytbZgLT9F5XUZQip8yIShaLhZ49e/Lhhx/y1ltvERMTw4oVK9i/fz8tW7Zk7969N30sq9XK/fffz7PPPsvEiRO5fPky27ZtIzExkXbt2rFq1ao8X7d48WI6d+6Mi4sLu3bt4tKlS4wYMYKJEyfmuQDzwgsvMHjwYC5cuMB3331HTEwMBw4coFevXrz22mu0adOGy5cv3/ZnoiiKUtpJS8s/c3vgwOKNxagkJ0smeXCwiEpK2SUzM5MxY8ZgNptZsWIFfn5+eY7r169fjoUaO0888QQAq1evvm610osvvkh8fPzVx9mzZwvnDShKEfDoozBtGoCNIUNepn37XlgK6oFWBDRoAJ06wa5dIq6YzZIwUJw5aL6+ci2x28YlJEiVzebNcOSIXJMLGy8vqFtXqpYCAqQXYHKyCFoF/TUFBECLFnJtbNFC7P+qVpXzubvLuU6elM95zx6p0NqzRyq0bhdnZxHMUlLkHB4eImxZLHLcwrD2U5SSwEMPPZRr25gxYwD49ttvAfDy8gLyF/tTs/3B2Mfmhc5LlJLMoEEAvYG5V7a4ACF6r6soSpFTZkSl119/na1bt/Lee+9x33334eXlRfPmzVm0aBFms5mHHnroahn1jZg5cyaLFi3i6aefZtKkSXh7exMSEsKPP/6It7c3o0aNItHeuOMKkZGRjB07lqpVq7Jw4UJq1qyJr68vzz//PKNHj2bu3Ll8//33OV5jsVioVKkSYWFhtGvXDm9vb2rVqsUHH3zAiBEjOHr0KK+++mqhfUaKoiiljWrVxDLG1TXn9pAQ6Z1RljGbZUFs6VLJfu7VS7KjlbKJ1WplwoQJbN26lTVr1lC/fv1bPkaFChUoV64cAIcPH853nLu7O35+fjkeimJk/u//oGXLF4F32L17E61ajeDeezMMZy3TvDm0bg2LF8PLL8P06TBjBlznz7FQ8fSUa0lAgIgirVrB5MkiMB08KNeb3buzhC6LBSIjCy7+eHpCpUrS46lSJbG+u3hRrnEFFbJcXcUGL/s8wt0datSQCrH0dBHN/v5bBCCzWaqYbrdiyclJRDk3N/lcPDzE7i8wUASnhASpxLIf32qVf6s9nlIasNvYBQYG5ikC2S15jx07BkDlypUBiImJyfN4sVcUXpPJRKVKlfI9r85LlJLM+PFybwsPAf8BlgEP8dFHDg1LUZQyQJkQlZKTk/n8889xc3O7mt1ip27dutx9990cPHiQ33777aaO99GVb+cJEybk2B4QEMD9999PZGQkc+fOzbHvyy+/JDExkVGjRuGZvcMtMHHiRAA++OCDHNtr1qzJI488kuekZuCVtIOVK1feVMyKoihljXbt4PJl6U+QlgazZkH//vJ8xbK9zHL4sCw0/utf0ki9Vi3J9lbKJvYKpdWrV7Np0yaaNm1628eyXlnZNKlCqZQyZs0ajouLeIIeOfIry5c/yoQJVsM1w27cWCpn9u8XISQqSkSm4qpYql0bRo2CsWPluVkzEbr694eGDeHsWVi+HH78UfpUzZsn16HbcCO/iru72OAFBIi40qoV9O4t4sv27dIH6SZzB28Jb29JXvH1FfEnIkIEnoSEgolZHh5iI1itmjx7eIig5O8vNoA2m7y3yEgRzs6dExEtJaWw3pmiOIY77rgDuLHNqH2O0bRpU5ycnLh8+TJJSUm5xp08eRKQNR9vb+9c+xWltHD8uP1edzJffNEbDw84dgy6dHF0ZIqilGbKhKi0bt06kpKSaN68OT55NIjo3LkzAGE3YTp64MABTp48SVBQEPXyuIvM71j2nzt16pTrNW3atMHDw4Ndu3Zx/vz5q9unTp3Ku+++m2cc/v7+N4xVURSlrPLEE9K428UFrvTwZfx4+PVXrVAym2HRIulzkZwsi2JbtxavRZJiHNLT0xk5ciRbt25l8+bNhEiqIwArVqxgxYoVOcY/9dRTjB49Os9jRUZGXm1u3bBhw6ILWlEcQJs2bXjyyV8Ae++O+cCzhmuGHR8vwkNIiHzP+/qKbVr2/j9FjaenWMVlz6Nzd4c77oB+/aBJE/j9d7HqM5vFqm716oJVLFWpAn36yPH79hXLunbtxK7u9GkRly5eFEEmNVVEmcKwknNzE+s9Pz+xqbtwQd5PQkLBqoecnEQUdLrmbt3VVcQzb28RlSIjRTBLSZHzasWSUpK5++67cXJyIiEhIc/qo1OnTgFZ4pOfnx9du3bFZrPxxx9/5Bq/ZcsWICshV1FKM/Z73YkT5Vrk4iLX2g4d5rNv3z5Hh6coSimkTIhK9n5J9nLpa6lTp06OcYV9rMzMTA4ePJjv61xcXK42g7zZ3k5HjhwBoFu3bjc1XlEUpSwQGgotW0r2s8kE+/ZJhq+SRXy8VHCZzbLo17p18S84KsYgNTWVoUOHsn//fjZt2kTNmjVz7P/uu+/47rvvcmyLj49n5cqVV8Wj7Hz22WcA9OzZk2rVqhVd4IriII4e7QF8R9Yt1AzgHUM1w/b3F9HB01Mef/whQoRR8tGcncXerVo1uf6YzZCUJCJPQZMb3N1FULO3fHN1lf5Hd94pn8nRo7BiBfz0EyxbBitXitBUEFxdRcDz9ZX31rChVGfFxooNXlxcwY5/vfN6e0P58pCRIaJSfLz8W1FKKlWqVGHkyJEAuZxfAObPnw/AY489dnXb888/D8CsWbNyjE1PT2fOnDl4eHjw5JNPFlHEimJMAgJg/34b8B7bto2hU6e+V0VZRVGUwqJMiEqXrqSp2z16ryUgICDHuMI41uXLl69awMTFxZFyxY+gMGKw2WwsWLAAd3d3XnjhhRuOT01NJSEhIcdDURSltFGvHkyYIE2yQfoPNGrk0JAMia+vZDbHxIjt3fnzcuNhlAVHpXhISUlhwIABLF26lKpVq/Lss88ycuTIHI+NGzfmep3JZCIyMpIBAwawbds2kpKSiIqKYsaMGbz33nvUrFmTr776ygHvSFGKHmmGPQTIXpr0Cv7+XzomoDzw8YFhw6R6xt9fkge8vY0lNvj6Smzp6dIv6NQpSXa4xiG80PDwkPlAkyYiLB08KGLPpUuwY0fBK5YCAqBFC6mOat1ahKWQEHk/589LvyW7M1d6uvz7dnsu2XF2liopk0mqpKxW+R3Hx6sNnlKymTFjBg0aNOD111/n+++/x2KxcPHiRSZPnsxff/3F+PHjGTFixNXxffv25amnnuLHH39k2rRpxMfHc+HCBR555BGOHTvG559/nitpRlHKArVrp9Gw4RIAzOaLtG/fh6ioKMcGpShKqaJMiErJyckAuLm55bnf/Uo6m31cYRwr+9jsxy2MGGbOnMnevXv56KOPaNCgwQ3Hv/vuu/j7+1992KuiFEVRSguhoZIRnJ3LlzGUJZFR2L0b6teXnhPR0bLgOHSoLEQqJR+TyYTJZOKRRx4BYN68eVe3Zc/6PXLkCKtXrwZg/fr1fP/997keeWU0fvLJJ8ybN49y5coxfPhwAgICqFWrFnPnzuVf//oXe/bsoUaNGsXxVhWl2Mlqhj0OyOqF+s03j/PDDz84KqxcNGoETz0Fzz0Hn30mMYeFGcfm1NMTevUSMSY1VSqM69YVgaegYsv1cHGRa16LFiLKxMWJyFQY1UT2yiFXV/nZzQ2qV5ceUyaTCGf79ok1786dkgATG3v753NykoovNzf5DMuXl8/Q3R0SE+XYRflZKkpRERQUxPbt25k0aRKvvPIK5cqVo1GjRhw8eJCFCxfmqkgCEaLmz5/PsmXLqFq1Ko0aNSIiIoI1a9Ywbtw4B7wLRXE87u7ubN68lMqVZc0wIuIYXbr0y7P/mKIoyu3g4ugAigOvK93H82v4mHolPc3rJrqU3+yxso/NftyCxrB161aeffZZnn76aSZPnnzDeAFefPFFnnnmmas/JyQkqLCkKEqp4pdf8t4eFqY9lLJz6JBkaI8YATVqSEazv78KSqUJm812U+NatGhx02Oz4+fnx+jRo/Ptq6QopZ3jxyVhISxsKmlpUaxa9QFgY9SofzFo0KAcCWaOxMcn67t9wABYvFh6LQwZYgxb2Nq1YdQoEbp8fKTH3+bNsGmTNBbPJw+vQHh6SpVUXJxUSB07JoLM/v1y/ho1RGwqTLy8oE4dSeLYsEF6LVWtCmlpkgzTokWWEHWreHhIL6nMTInb3n/JXg0VFyefo4+P7Ldac49VFCPi7+/P+++/z/vvv3/Trxk1ahSjRo0qwqgUpeQRGBjItm0radKkI2bzBY4c+ZOWLYcTEhLGkCGuep+sKEqBKBPTycqVKwPk2ewRIPZKmliVKlUK7VgVK1bE6cpsvVy5cnhcuXsrSAy7d++mf//+PProo/z73/++Yax23N3d8fPzy/FQFEUpTWzdmvd27cubRUSELNY1bizN0n18IDhYBSVFUZRbxd4Me8WK9xg16lGgHmlp6xkyxBiC0rV4e8v1MCUFli41TgWLp6dUDnl6StVN164iMm3YUHBLurxwd4e2baXvUkyMCFsPPCCiz9mzsH279Fi6Db39hnh4yHusVUsEpYQEscYrqFWdk5OIUtlFIldXeY9+fiIixcRI9fb583DunLxHtchTFEUpG9SsWZOtW1fg6ipe58ePr2D58nFMmGClXj0HB6coSommTIhKLVq0ACA8PDzP/SdPngSgWbNmRXIsZ2dnmjRpku/rMjIyOHv27HVj2L59Oz169GDixIl8+umnmEymG8aqKIpSFujSJW8LmZAQrVKyk5wMy5dLj40uXRwdjaIoSunAZDIxZ85M9u//AyenmixfDlecJw2Hv79ULMXFyfUgM9PREeUmIAC6dxdBaf16sFgK/xxVqkCfPtCvnzxXry6i0p13yvmPHhV7OnseYGqqfGYFFbnc3ETcs8dgP/bp03KuohCy3N3FFs/LS/pHRUbKeVJTpXLqSvtfRVEUpZTTtGlTnnoqDLAnv3wNvMCJE2oXryjK7VMmRKXu3bvj7e3Nvn378vQP3bJlCwADbyKlvXHjxtSpU4fLly9z4toGHtc51oABAwCxr7uWv/76i5SUFFq1akVwcHCu/Zs2baJXr15MmTKFd9555+r2hIQEPvroIxISEm4Yt6IoSmlk/Hj4/XfpkxAfD7NmQf/+8nz8uKOjczxms2Rf//yz9HTo21ctbxRFUQoTSR6rwJ498j07dy68+GI6Fy9edHRouQgMhHvvlUqV1auLRsgoKH5+IixlZoqwlJQk4lJkZOGJTO7uUsmT3anQw0N6UbVuLXOKfftg7Vqx1122DFaulM/tdnF1lWQXHx95TzVqyPsMCJDj/v239EIqCuwWeOXKiaCUkiLnMqKwqCiKohQNhw/fBXxH1jLwR8AMwsIcF5OiKCWbMrG05OXlxeTJk0lNTWXevHk59p08eZI1a9bQuHFj7r333qvb9+/fT6dOnXjppZdyHW/q1KkAuZpExsXF8cMPP1CxYkXGjh2bY9/EiRPx9fVlwYIFpFzjNzBz5kwAnn/++VznWr16Nffccw+vvvoqr776ao59MTExTJ06NV9LPUVRlNJIaKgIRyNHwuzZsoi3b58sRNktibRCCQ4fhhkzpFH7999LJvZNtA5UFEVRboOmTaUCCCy8994wmje/i4iICEeHlYuqVaVCJzxcbObMZrFFM5sdHVkWPj4iuJhMsHChXPfnzYMFCyTuosTXF1q2FAFo927Ys0eqfS9fhh07ClaxFBAgPZTatJHnihXFhrZuXRGyzpyR92exZPVFKgyrQmdnEZacnGSulJGRZcGXT7tfRVEUpZQxaBDAYOCLK1sCgU5qF68oym1jst1Ol+YSiMVioWfPnhw4cIA5c+Zw7733cvz4ccaMGcPJkyfZtGkTzZs3vzp+8uTJfP755wBERUVRoUKFq/usViv33XcfYWFhfPbZZzz88MNcunSJSZMmsWHDBpYuXUrv3r1zxbBo0SJGjBjBwIEDmTFjBgEBAcycOZPnn3+e0aNHM3fu3Bzjf/vtN4YOHYqfnx89evTIdbykpCSWLl1KeHg4tWrVuunPIiEhAX9/f+Lj47W/kqIoJYp69aSxdXYWLYKhQx0Tj1Exm0VQOnpUFqW8vaFhQ3jqKe2hpBgPnZcopYkuXR7j99//B0BgYAtatdrA8OH+hkt2OHoUvvlGbNH8/KSKZdgwqdYxCrGx8PrrYj/XoYNUJAcEwKhR0oepKImLk/5T9jhSUkSUGTVKxKCiwGyW/ocREWKJ5+EhtoUhIfK+C0JKiljepaWJwOTvL+JSenqWNZ+Li4y1WqWKydlZq5uVsonOS5TSSta99GdAb6A+LVpIEoWiKMqtUmamiZ6enqxbt45nn32WF198kYCAAHr16kXjxo3ZvXt3DkEJYPDgwQQEBDB48GDKly+fY5+TkxM//PADH330EZ9//jmBgYHceeedeHl5sX379jwFJYBhw4bx+++/k5qaSvPmzalUqRLffvstX3zxBXPmzMk1ftasWaSmpnL58mW+//77XI+l9jsdRVGUMkBoaG5BCWSRRMlJfLxknicnQ82a0Ly5LIrFxzs6MkVRlNLNt9++hr9/dQCiovawatUAJkywGK4ZdnCwXD9PnBABISoKFi82VsVSerr0H6pUSSp4ypWT61hxxOjpKWKb1SqVvs7OEs/+/RJLRkbhn9PHR3o8WSwiKsXHS/XS0aMFr1jy8JDPslo1ebbb4fn7y+8/NlYs8ZKTxY7v3Dl5vsZgQ1EURSnBHD9ut4ufzOef18fVVSpy+/d3dGSKopREykylkpKFZt4oilIS6d9f+hrktf3XX4s/HiNz6RI8/rhkJHftKgJTUJBWKinGROclSmnjzTeP8uqrnYGoK1v6A4uZNcvVMBVL58/D9OlynYiMlB4/VitMmSKCkxGwWMTy7vJlET2io6Xn0aOPFn2lEoiosmOHiC2+vmJZl54un52zs3xmwcFSzZOaKvF6eubs1XSrJCXBzp1yrU5Olt9NUhJ06yaCk8lUWO8uJxaLiHWXLsk5KlQQQcnNTUQorVgqm/To0YN169Y5OoxiR+clSlnhwgW5tlitVlq0eIXvvhtDgwYNHB2WoiglBBdHB6AoiqIoN0N+opL6QOckIwN+/x3uvFMWiSIiRFAaOlQFJUVRlOJg+/YGwAqgG2AGlgKP8ssvcxk/3hir8/7+UqkSFSXWajt3Si8hf39HR5aFpyf06gWrV4vQkZoq17HiEjiqVJH+U9eKRdWqwenTcPKkVPR4eEhFkdks4lPbtvLa28HNTfofJiTI7yclRc4dEyPvv1IlqaAqbDw9RSiLjJSfExIklrQ0qWRSUan0kpSURGxsLFarNcd2m83Gxo0bHRSVoijFQdWqsHVrKu3bj2bPnh/o0OFbDhzYStWqVR0dmqIoJQCtVCqDaOaNoiglkaAgWfzKTkiIlPErWaxZA3//LSKSp6fY5/j7q6CkGBedlyiljdBQmDABYD3QF0gDoHv3p1i7djqmoio3uUUOHxbLu5gYqQKqWVPiNkqlkh17FQ3AH39I/5+uXbN6ADkyrmPHpPdSWho0biwiULlyIkbdbsVSbKzYEiYni8AUEiLPly7J5+DlBZUryzU+PT2rT5Kra8Hej9WaZXlns0lfKQ8PqFu3eCrDlOJlzZo1vPDCC+zZs+e64zIzM4snIAOh8xKlLJGQkEDz5ndx6tReAGrUaMrevZsoV66cYwNTFMXwqKhUBtFJkqIoJY26dSUjuHx5eO89CAuTCiWj2AgZhb17YcsWyew2Wv8ORckPnZcopZGsZtg/A8MBqQJ48803eeWVVxwYWU7MZkk+8PWFjRtFuBg0CCpWdHRkeRMXBxs2SHVVly6Or6CJi4NFi6SKKC1NPkc3NxgwQMSl2yU/schuUZeaKsJPQoKMtYtPAQEFez8pKSIwpqXJZ+vhIc+uriLmFVS4UozBtm3b6NKlC+XLl6dhw4Zs3ryZrl27AmCxWDh48CDJycncddddrF+/3sHRFj86L1HKGhcvXqRJk07ExIQD0LLlXWzduhIPDw8HR6YoipFRUakMopMkRVFKEu3awZ9/SpZsXJwssChZ2BcEExNh/Xrp+dChg6OjUpSbR+clSmklNFSSIMqX/4r58x8FwMWlA0lJG3Az4MUsPV3ijY+HIUMKLlAUFZcvw+bNYjHXvn3R9Rm6GVJTYeVKmZ84OcGhQ9KPaMwYCAwsmnPabPIZbNwoc4Bq1eQz8PeXOUBhVCxlZoodnpOTCExJSWKv6+4u4pKzc+5xSslh0KBBuLi48P333+Pi4oKzs3OOiqSYmBjuv/9+RowYwfgymMGl8xKlLHL8+HGaN++ExXIZgCZNhlCjxo8MHuysiZyKouSJikplEJ0kKYpSEggNhVdeEX9/FxfJnNWvrJwcPiwZ0hER0tehd2+YONGxC2yKcqvovEQpC3z00Ue8+OIaMjIWUaeON3//7eiI8iY1FX7+WYQEI/fiu3ABtm6FWrWgTRvHxnLxIuzYIckdbm4yV/HwgNq1pQF6UVyTk5Ik4SYzU2zyMjJE3OnRQ6qlioKUFDmv1SrvKSVFhEg3NxHSNKG95FCpUiU2btxIw4YNAXKJSgCHDh1izJgx7NixwxEhOhSdlyhllZ07d9K+fTcyM5OubJkAfElIiEkt5xVFyYXmFCmKoiiGo1496etgbxhdtaoKStdiNmcJSjEx0tshMlIWfBRFURRj8dxzz5GQsAwPD29OnoS2bR0dUd64u4t1m8kkVUsWi6MjypuqVeUzDA+H/fsdG0uVKtJDqV8/sebt1UvEpJMnYc8eEV8KGzc3EfycneX8Vquc58wZ6clUFGmjHh5iQ+zpKUJaRIRsT02VxB+rtfDPqRQN8fHx1KlT5+rPLi4uJCcn5xhTq1YtDh8+XNyhKYriQNq0acPkyYsBe9PCWcAbnDghCZ+KoijZUVFJURRFMRShofY+FFmcOaMT2WuJj5eFI/saQKdOkiUdH+/YuJTbp0ePHo4OQVGUIsTT05mICLEn27kT7r47ml27djk6rFx4e4s4kpoKy5ZJNYoRqVlT7N6OHIGjR7OSKxwhhLm7Sw8ld3exgqtTR2JLTZXfdXYBJi5OnguCq6v0UPLxkXlAzZrQrZtY4F24IPOohISCnSMvTKYsCzx/fxGyUlMl0eWaQhfFwFStWpXz589f/blatWq5KpK2bNliSJtORVGKlhMnegPzsm15A5hPWJiDAlIUxbC43HiIoiiKohQfn36a9/awMNTPORv+/rJgFB4uPZSioyEoSLYrxiUpKYnY2Fis16R022w2Nm7c6KCoFEUpLvz84NQpqFbtHGvX9qZDh0v89dcmmjRp4ujQcuDvLxVLS5bAb7+JYGE2y3YjWeLVqyeixpo1klTh6iox9uol9nOOpFw5seY7flzsao8ckcri5GSxqGvbVqqMbpeAABGu0tKkcsneSykwUESss2elqqhiRfmdpafnHns7ODuLsJSWJsePihKxKSFB/n8XtKeTUvQ0bdqU//u//2PevHk4OzvTunVrxo8fz7///W/q1avH3r17ee6557jjjjscHaqiKMXMoEGwbNmDQATwDNAC6M3AgQ4NS1EUA6I9lcog6hGsKIpRWbwYhg3Le9+sWSoqZefAAfjhB8l4dnOTxaWhQ6FRI0dHpuTFmjVreOGFF9izZ891x13b06AsoPMSpSwyaNBYwsIkE9jHpwovvbSFLVtqM2iQsa51Fy/CzJmSwBAYKNeaYcOMda2xWOCtt+D0aRFqUlIkzlGjRPQwAmfPwjffiKDUrJk8lysntnnu7kVzzqQkqdyy91xKSBCLOi8vqXIKCLj9Y6ekSDKLXaTy85NzZGRkVTI5Oxfee1EKlzlz5vDoo4/SqVMnNm/ezPbt2+nYsWOucYsXL2bQoEEOiNCx6LxEKevUq2d3DpkN3A/40bkzbN7s2LgURTEWKiqVQXSSpCiKEVm5Evr2lX+XLy/ZvHZCQtDmoNk4exaWLoUmTaBlS8nONlr2uJLFtm3b6NKlC+XLl6dhw4Zs3ryZrl27AmCxWDh48CDJycncddddrF+/3sHRFj86L1HKIomJibRp05Njx+yWU3WB34HKhrrmmc3w2muwezc0aCCCQVAQPPWUca45kZEwb55YwCYlSYWSxQJjx0qsRiAuTqq+7DZxwcFSYdWvn4hLRUlMDKxfL6JS5coi9tirnApSVWS1yntxdhbLP8iywrNaRdDz8pLt145THEtaWhrh4eF4eHhQs2ZNAL7//nteffVVTp06RY0aNZg6dSoTJkxwcKSOQecliiLW82FhUvn77LOSNPDAA/Dtt46OTFEUo6CiUhlEJ0mKohiNP/+E9u2lsfTUqfDBB1kT2YEDjZW17WhiY2HRIlkYuvdeXaApCQwaNAgXFxe+//57XFxccHZ2zlGRFBMTw/3338+IESMYXwb/s+u8RCmrREVFERJyF/Hxh69saQ5sAMoZpjr3/HmYPl0EgdOnpVrJwwOmTBFhxAhYLLBggVTOJCZK4kWrVjBxonEqlVJTJXkmNlaEr3PnoGlTqTAuqkolO0lJYG+XYzZLLC4u0LNn0QiDNpv8TpKTpaIpJUX+/7i5QYUK8v9HUYyMzksUJScnT0qSp82WwB13TGLp0reo7WiPWUVRHE6xLEVt2rSpOE6jKIqilDBCQ6FrV2jXThYhHntMBCWQxbRffzXGopoRMJvFhmDRIlkE6t1bBaWSwrZt23j77bdxccm7lWX58uX59NNPmTVrVjFHpiiKIwkMDKR161VAjStb9gIDgGTDNMT295dKmtRUEZEOHRLBwEj9+zw9JZO6QgWJKyhIrpMZGY6OLAt3d7HmCwgQq7hKlURcSUkp+nO7uYkdnc0mCSkg5z1zRkS4wsZkkgqlcuVEWIqLk/8zCQki/F3TUlApRjIzM9mzZw979uwpk3a7iqLcPGazJJaYzVCnDixaFAF049Chb2nfvg+XL192dIiKojiYYqlUujYjV3EsmnmjKIoRyPJqFry8JJtWyc3hw/Djj/DXX5Lt+9JL0vxbKRl4eHiQkJCAm5sbAO7u7sTGxuJl9wUCkpOTqVixImaz2VFhOgydlyhlmdBQmDDhKNAZiLqytR///e/PTJpUAG+yQuTwYel5GBsri0sVK8LIkXDHHY6OLCcWi8Tn5gbbtomo1L17lgWbEUhNlTjd3MTiMCFBeiwVtUgXGytzruRk+TyqVxdhKSlJBLgqVSSmwiQ9XSqy3Nzkfdt7O9WrJyKXUrz8/PPP/OMf/yA6OhqAoKAgvvzySwYPHuzYwAxIUcxLzOabs6t21LiSEKO+l+I79+HDksgYFycJAsOGQVBQFM2adebixaMANGjQlp071+FjFC9cRVGKnQKLSmlpaaxevZqjR4+SlJREXod74403VFQyELp4oyiKo5FFtNzbjWL3YyTMZpgxA3btkuzfChWgZk1j9bNQrk+dOnVYu3btVZuIunXr8tVXX13tqwSwevVqRowYQUz2ZmJlBJ2XKGUdSbL4C+gGiLB8//0Ps3DhPJwMUpKafRFq1y44eBD69JHsZSNisUgfIScnEZaK2mLudsjMhP37pVqoeXOpYCpK0tMhLU1EHnsvpYQEuHRJxJ4KFaTKKzMz97jbwWqFixflWJ6eci77eTw8RFjKp4BXKWR27dpFu3btcq3JuLi4sG3bNlq1auWgyIxJYc9L8lqgb9TIOONKQoz6Xorv3HFx8PHH0q+wWjWpMLX3UoyOPk2TJh0xmy8A0KlTb9at+/Vq4pyiKGWLAolKBw8e5N577+XcuXN5iklXT2IyqahkIHTxRlEUR9O7N6xenXt7//5ieadkcf48PP+8LJA1ayZC0qVLxupnoVyfQYMG4evry7x583B2dub+++9nz549/Pvf/6ZevXrs3buX5557jho1avD77787OtxiR+cliiLJFl99tZ7t2+/BZkvFyWksiYmheHkZb9XdZoM1a+Dvv+W6Xa2aoyPKG7MZ1q2Typxu3YwpYGRmwr59EmtxCEt5YbXKouHlyxJHYqJ8Vl5e0kMjIOD2j52SIse2i1QVKkiCTFKSvHe7uOTkJHFkZkpFtkG01FLDAw88QFhYGK+88gq9e/cGYOXKlbz99tsMGjSIb7/91sERGovCnJfYk8O2bQNfX4iJkec+fXL2F0tJkb5riYlQvnzxjXPkuUvye+nb99aOZ18uTUmBFSvk/4V9nJ+fHM/TU74fQZ5TU+G332RsQIB8l9qP6e4u35n2HnYrV8q4ChWyBKNBg+TYrq7ySE+XquOEBBGJLl6U79977pHzWSwSX1SUXDv9/OR722SS7+annxanjGPHDtC6dRfS0uIAaNHiIcqXn8+QIU5MnoyiKGWIAk2tJ0+eTHx8PBMnTqRhw4Z5XnBtNhuPPvpoQU6jKIqilCJiY2Ht2rz3DRxYvLGUBC5ckMm/r68ISufOyY2AkfpZKNdn8ODBPProo5w+fZrNmzfz7LPP0rFjRwYNGpRj3H/+8x8HRagoiqMZPx7Gj+/OkiXf8eijW4mJeZ9KlUxF0vOmoJhM0LOnLHgtXy4LVxUrOjqq3Pj4wF13wYYNsGULdOliPLHC2VkSRvbtk0fz5nK9L06cnGRe4e0t87OoqKyFSasVWrW6/YolDw+x1rtWLHJ3lwXMpCT5f2Rf0ExPzxKfrl0sVm6fzZs38+GHH/L4449f3da6dWv8/Px47733HBhZ6Sc+Xv6W6teXv4HAQBESKlfO+b0ZGSl/F1WqyP/94hrnyHOX5PdSqZKMs4tFkZHyPVmvnoyvUEFEoIoVZaydy5flOMHBWcezVwIFBmYdz2aTY5pMUKOGjPX3l3MHBclxnZxk/+XLcr2rVUu+PwMDZZvJJGNSUuReMiICTp+Wa0xsrMQbGyvfu9WqyTk8POR7PyYmS3w6flxi2rZNrlOVKzfhv/8NY/z4XthsqezZ8w1QmXXrPuL99+HsWRRFKSMUSFTavn07S5cupUePHtcdN27cuIKcRlEURSklxMbKxNpqlQl3amrWvpAQtb67llOnYOdOuO8+maBfuiST+6FD1fquJPHQQw/RsWNHPK6skLVr145vv/2WV199lVOnTlGjRg2mTp2aS2RSFKXsMXjwYAYPHky1alKpWrGiLCwZDScnyZYOC4OlS2HIkIJVtBQV5cpBp06webMsiHXokJUJbhTswtLevfJwhLAEsojp5yeLocnJshB+9KjMO6pVu/2eS05OeYt5np6ygGk2Q3i4LGxWqCBVTdHRsohrNBGwpBIZGcnQoUNzbR8+fDjPPPOMAyIqO/j7y/dQVBRUrSrJYXfcIbac2efyZrP8/UdFyb1ScY1z5LlL23vZt0/GVa4s4xo3hh49co/bs0fGVayY/zj72EOHZGxwsIxt0iTvY+7aJeOCguT+ulkzqSS+dlxsrIyrVi3rvQwalPvcHh5ZvRSbN5d7z+BguR89cwas1i60b/8df/wxDLACHwOVOXfuOT77DK1YUpQyQoHs74KDgzl27Bje2mmzRKE2M4qiOAK7oJSeLqXzO3aI3U9YmFQoqaCUk8hIWLJEstP69JFs3ptt6KooJQmdlyhK3gQEyMJ+cPABPvnkKMOGDXN0SLlITYWffxYhwMgJDxcuwNatksnduLFYFPn6irBhFDIyZFEyOVl6XDg7S3zF1Q8qPV0WO81mWQS/fFmSgIKDRdzx9RXRx37rn1ePpts9ryxSymcA8t5r1y7YcZUsnJ2d821HcL1906ZN49VXXy3K0AxJUfRUsi/QBwTId2V+/W4cMa4kxKjvpfjPnb2X4rXXdqtVKoG3bAkFsjdK/o1eve5h1aq8j6koSumiQKLSSy+9RKdOnejXr991x40bN46vvvrqdk+jFDK6eKMoSnESGgo//iiWKlYrtG4t1TdK/sTHSxPVcuVEcDNiHwjlxmRmZrJ//34AmjZtirOzs4MjMiY6L1GUvElLA1/fP0hL6wcksnz5r/Tt29fRYeUiKUkWqZydpWIpM9OYSRCnT0tVVWSkxOXvD716iXhhFDIyZL60Z49YGAUFQdu2UrVTHMTGwokTImzZeyr5+8vvMyZGbJTc3UVkunxZfi5o7yWrVXp7pKWJiBQVJcevXl2ELJ0DFZzbFZWut680UxTzkust0BthXEmIUd9L8Z/7enz2GfzznwBvAf8H3Acs4D//cddKJUUpIxRIVMrMzOSFF16gVq1a9O/fnypVquCeRypVWZ2MGBVdvFEUpbioV08WJ+y4usqigZI3ZrNY3G3cKNnJQ4dqT4GSys8//8w//vEPoqOjAQgKCuLLL79k8ODBjg3MgOi8RFHy5/HHn+SLL6TfmpOTJ1OnruHAgY4MGmSsCt/4eBGW4uKkD0NioiRGDBuWfxZ0cWOxwEcfwZEjYg0EIoSMGmWciiV7Y/Z9+7KEr4oVsxqzFwfXq0BKSpK+HH/+KYJSzZqy3ccHWrS4/cqilBSxvLOf19dXBLbMTJkHeXurFV5BcHJy4vTp01y79GOz2ahTpw6nTp3Kd19ZXMfReYmi3BzVq8O5czbgJ2Ao4EyPHvn3T1YUpXRRIFEJ4JNPPuHNN98kNjb2uuPK4mTEqOgkSVGU4iA0FCZMyL191ixjLYQZhcOH4Ycf4K+/JCv3pZfEJlApeezatYt27drlmvu4uLiwbds2WrVq5aDIjInOSxQlfzIzMxk0aCTLlv10ZUs5YBPQlJCQrAbaRuDUKXj6aRECunQRy7mgIHjqKWNULEVGwrx5WT17goOlSmbsWInTCMTFwbJlIiadOydCWEAADB4sIp0RSEqCP/6Qz85iEcHH1VXmLAVxxbda5f+Os3OWgGSxSNWUzSbCn5eX9MTKa6ySP05OTphus5lYWVzH0XmJotw8n30mdvadO8Prr8v39ejRcr1VFKV0U6Ap2EcffcSUKVOIjY0lICCAGjVq5HpUr169sGJVFEVRShALF+a9PSyseOMoCZjN8NNP0mTV3V0Wt1aulO1KyePDDz/Ezc2Nt99+mx07drBjxw7eeustXF1d+eijjxwdnqIoJQhnZ2cWLfqaOnXuvrIlDugDnOTECUngMAqurtKg3M1NrOaCg8VOLT7e0ZEJvr4i1ri4iECze7eIE0YQvOx4ekqcMTFQtaqITNHRxrKAc3OTz9HVVWI9e1bEHze3gh3XyUmOmV0k8vSE8uXl2WKRzyU2Vuzyzp2T55SUgp23rGCz2W75oSiKciMmT4ZVq+DVV2HbNtk2f/4J6tTpyYULFxwbnKIoRUqBRKUvvviC0aNHExERQVRUFOHh4bkeeZVSK4qiKKWbo0dh/fq89w0cWLyxlATi4sTqxsVF7GPq1TPWQpxya2zevJkPP/yQF198kdatW9O6dWteeukl3n//fTZt2uTo8BRFKWG4u7tTr97PwJ1XtlwEegOXDJWo4e8P1arJ88WLsH27VNn4+zs6MsHTU3ooBQTIv6tVE1EkMdHRkWXh7i49lMqVk7iaNJF+RUePSmWOEXB1lZh8fKRSqFo18PMT8asoMJmkAqp8eZknnT0rFnxOTllVZ1Zr0Zy7tGAymbBarbf8UBRFuRXuvBPef38v0JHw8HW0b9+XuLg4R4elKEoRUSBR6dKlS3z88ccEBgZed9xrr71WkNMoiqIoJYjdu+GOO+Tf1/YoCAlR67u8OHxYMm19fWVB6dw5Yy3EKbdGZGQkQ4cOzbV9+PDhREZGOiAiRVFKOsOG+QDLAHuDor+BPtx9d5zDYroWHx/poVS3rogiaWlQp46xKoFq15YeSmPHwv/9HzRtClu2SCKHUahSRXoo9esniTg9e0qVzoEDxhFPAgIkCaZNG+jWDRo2FHHn4sWiO6eTk9jfeXnJ/6nkZPk/lpxsHMHNqNSoUaNYX6coStll1KiKBASIF+rZs/vp3n0QKVpSqiilkgKJSk2aNCE5OfmG4x555JGCnEZRFEUxOKGh0L8/PPsstG4tix7DhsmN/qxZsm/WLGP1fjAKf/0ln8v48VKhdOmS2N8NHWqshTjl5snMzKRy5cq5tleuXPm6vQmmTZtWlGEpilKCGT8eQkICgVWAfaF3H3PmDCAjI8OBkeWkUSPpofTmm9IbMDZWkk2MhKenXGe9vaFDB0ng2LzZeBVL5crJs4+PVCzFx0sSilFMQFxds/oplS8vdn0xMdJLq6hwdpbfn/3cyckiuCUlqbB0PcLDw4v1dYqilF2qVKnC9u2r8PCQZoV79mxi0KAHy2R/NkUp7ZhsBfCmW7t2LQsWLOB///sfzs7O+Y5zdnbWLxADoY0nFUUpTOrVgxMncm4bOxbmzHFIOCWKgwdh40axCmjTRnooxcfLApcKSiWX6817bndfaUbnJYpy84SGwrffHmPLls6kp1/GZPqUEyf+SZ06jo4sb/78E3bulGoWexWz0UhLE8vejAzo3l0qYYxIVJTMGypXhgYNHB1N3sTFwfnzIohVrSrWdYVNSopURaWlSR8nb28RlKxWEZy8vYvmvKWJTZs2cdddd91wXJ06dTh58mQxRGQsdF6iKAVn586dtG/fjczMJADGjZvA7NlfYtIvaEUpNRSo5eeWLVu4cOEC1apVo0+fPlSpUgXPa72OFEVRlFJLaGhuQQmgY8fij6WkYBeOoqPh99+hWTMRlECEJBWTSj42m42zZ8/m6ilp//l6+xRFUa7H+PEwfnx9du1awcSJR9ix40Hq14fLl8WSzGjceaeIABs3StVN3bqOjig3bm5w110iLG3aJMKSu7ujo8pNYKCISUeOSKWOEYXEcuVE0Dl3TiqqgoMLX+Dx8BCLwMxMqVxycpJzWSxSuZSSIsKSh4dszz5OEbp3737DJJb4+HhOnTpVPAEpilLqaNOmDb/9tpg+ffoBGXz11SzOn69CRsbrDB4Mkyc7OkJFUQpKgSqVnG5yZmYymcpk5q1R0cwbRVEKi/79YdmyvLf/+mvxx2N0Dh+GRYvgzBmxuRs0CMaN04za0oaTk9NtZ+GVxfmSzksU5fbp0kUSFNzcxL7Nzc3REeXGZoM1a+Dvv2V+UK2aoyPKG7MZ1q2TSqVu3cClQOmXRce5c5LQU7cuVK/u6GjyJiFB4vT1hUqVID1d/m+6uhbtea3WLEu89HRITZXtbm5QoYIITYrMU2JjY/HPp3nnxo0bGTNmDGfPntV5ic5LFKVAfPvttzz00EPZtvwXmES1anD2rKOiUhSlMCjwVHnODfyNbDYbjz76aEFPoyiKohiQhIS8tw8cWLxxlATM5ixBKSZGhKSLF6UPgFYnlT5uJ2dH7SAURblVNm+Gxo3h0CHw81vCK68c5pVXXnR0WDkwmaBnT1ngX75cEioqVnR0VLnx8ZGKpQ0bYMsW6NxZKlyMRrVqIpj8/bdU4ZQrJ7ZvRqqu8vMTwevAAemp5ecn1UMhIUVbUefkJL9Hd3cR3pKSRNhKT5f9VapoxZKd3r17s2bNGnx9fa9uS09P58UXX2TGjBlYrVadlyiKUmAefPBBfvopgp9/fubKlieA9pw715LPPtOKJUUpyRRYVBozZswNx4wbN66gp1EURVEMRv/+sph1LSEhYs+j5CQ+XppXx8VJQ+u6dcWuKD5eRaXSxu1WaF+vP6WiKEp+HDwIAQFziIt7jP/7Pyvly/vz+OOPOzqsHDg5QZ8+UsW8dCkMGSJVK0brI1iunIhJmzbB9u3QooUkhfj6inBjFGrXlt5F334rc4qqVaFtWxFNjIKHhwiJ0dFS9WW1itDTokXRVyzZz+/jI3Z4qany0GqlLFq0aME999zDqlWr8PLyYt++fTz88MMcPHiQwMBAZs+ezaBBgxwdpqIopYCEhCnAJeAD4E2gBQBhYSoqKUpJpkB5On/88cdNjQsPDy/IaRRFURSD0bq12N45OcGuXTBrlohMs2bB8eOOjs6YWK1ZlUl16si/AwJkMU0pXdSoUaNYX6coivJ//xcLWAF44onJjB+/kP79pfehUXB1hX79xF5u5kx4/32YPh1mzBB7WKMQGAgdOkiFzdtvw7x5sGABGOmW1i7WmExie3jhAuzYkWX3ZgTS0iS++vUhI0NiS0qS7UWNs7NY3mVkiCDo4iKxxMeLSGi1Fn0MRmbMmDF8+eWX1KlTh/79+/Pee+/Rrl07Dhw4wD333MP+/fsZMGAAr732mqNDVRSlFDB4MMB7wHrgZUCqINXdRFFKNgXqqaSUTNQjWFGU2yE0FH75BbZuhdhYWRw6dUqyY5XrEx8PP/8stneJifIICIChQ6FRI0dHpyiOReclilI4vPDCS3zwwbtXfnIBfgX6EhJirISPiAh44glZ3O/aVX4OCoKnnjJOxZLFImLXgQNynXZ1lev2qFHGqFiKi5PknoAASVKJjZUqq+HD5dkIpKfDnj3ye3ZxkSqlSpWgV6/iqVRKSRHhLS1NBKby5aW/V3Ky7Pfykt+lySQiU2amiFFlyR7ParUycuRIfvrpJ7y8vPjwww+ZNGmSo8NyODovUZTCp3p16bWXnSefhOnTrTiVpS9eRSlFGLT9qKIoimIk6tWTxYDsJCSohcjNkJgoYpy7O0yaJIsWRrP7UYqWTZs2cdddd91wXJ06dTh58mQxRKQoSmnkvffeZsuWaLZsmQVkAEOBNZw40ZHQUONY02ZkiE3b5cvSFygkBKKijGUHm5goQkjz5nDpkogh9ioXI4hKnp5SgRMVBZUrixVeZqaIJkbB1VV+tydOiJBTo4bEHR0tMRc1Hh7y/+xascjDQ+JJShLx0Nk5q4LKza1sWeQ5OTnx7bffkpqaitVqZeLEiTn2Ozs735adr6IoyrWcPQuffSaWd/XqwX//C59+uprFi//Frl0rCAoKcnSIiqLcIioqKYqiKNclNDS3oARiBWOUBSqjYTbL4pOzM6xZI88DB2YtRBll0UwpHrp3737DRZn4+HhOnTpVKOdLSUnhzTff5MMPP+TBBx9k7ty51x2/fPlyPvzwQ/766y9sNhvNmjXj6aefZvjw4fm+5sKFC7z66qssX76c6OhoqlWrxn333cfLL7+Mj/4HVxSHYDKZ8Pf/LxAL/AhYgH7AJsLCmhrmmu3vLyJNWppU8G7ZAnfeaSw7WF9fiSc2Vuzw9u2Dxo2Nc/12d5ceSjt2SNVSkyYyxzhyBFq1kv1GICBAeijZBZvERKmscnaW6rSixskpd+WRk5P8Hj09JZ4zZ0R4qlBB4oyOFjGqNCXOz58//7r7+/fvz0svvcSIESPo379/MUWlKEpZY/Lk7D2Uwvjvf4dz7lw6nTr1Y9eudXoPoSglDBWVFEVRlOvy1Vd5bw8LU1EpLw4fhkWLJAP7wgXpP/XEE+Dt7ejIFEdhs9mIj4/HP58V040bNzJmzBhMJlOBz7V+/Xr+8Y9/EBUVRXp6+g3Hz5gxgylTpjBhwgQWLlyIi4sLb7/9Nvfddx+vvfYar7/+eq7XHD9+nE6dOlG+fHmWLFlCkyZNWLt2LaNGjWL58uVs3Lgx3/eqKErRMniwM7/9tgCIA1Zfee5Nhw5bgDoOjCwLHx8YNgwWLxbrsYsXoWJFsSMzCp6eYtO2erUkidSuLSJTTAwEBzs6OqFKFejTR6pt7Ekru3fD3r3QsmXxWMzdDK6uWbGULy8CTmSkiDYVKjguLmdnmZt5eor9XVKSbMvMlEdpEpXGjh17wzmGzWbjp59+YtGiRVd/Lox5iaIoSl688EIL5s+viNl8nuPHd9Cnz3DWrw/Dzc3N0aEpinKTaE+lMoh6BCuKcrP8978iiOTFrFkqKl2L2Sw9GC5dkuzmy5cl+/pf/zJOdrNS/Dg5OdG2bVvWrFmDr6/v1e3p6em8+OKLzJgxA6vVislkKpDNzMKFC5k0aRLvvPMOnp6ejBs3jjFjxuRbqXTgwAFatGhBq1at2L59e47Fo+7du7Nhwwb++OMP2rdvn+N1HTp0YPv27ezdu5emTZte3T537lweeeQRJk6cyBdffHHTceu8RFEKF7GsNQM9gT8BqFz5Ds6e3YuLi3FyCu1VvfHxsHEjNGgAPXo4OqqcWCwSp7e3VCudPw9duogIZkQsFhGW3N2lQsjZ2dER5U1EhFj3BQc7tgeU1SqiZlqaiEjR0WJ7WLOmVKuVFmHJycmJ11577ZZeY7PZePPNN8uk/Z3OSxSleDhw4ACtWnUhPT0OgPvue5jvvpunPZYUpYSgolIZRCdJiqJcj9BQ6QEUFyeWNCA3/HFxWWOM1vTbKJw/Dx9+KAslJhPUry+LZVOmGCezWSl+nJycGD9+PAcPHmTVqlV4eXmxb98+Hn74YQ4ePEhgYCCzZ89m0KBBWK3W2z7Ptm3bqFGjBlWrVr0q8FxPVBo3bhxz5sxh1qxZjL9GIf7uu+944IEHGDp06NWsZZD+UF27dqVjx45ssX9BXCE1NZWKFStisVi4dOkS5cuXv6m4dV6iKIVPaCj89FM0f/55F3FxZ4AlLFrUk6FDHR1Z3hw7JnaxLVtChw6OjiZvrFaZF0VFQdeuUnVjRMxm2LNHRJGmTY0rjFy4IAk41auDI7/6U1JETLJb9Hl5SaUSSBWTl5fM6UoyTk5OtzW/uN3XlXR0XqIoxcfvv//OXXf1wmZLAeDpp59l+vSPHByVoig3wy1NMUNCQnj++efZunVrUcWjKIqiOJB69WDCBFi2LEtQWrRIbvpnzYL+/eVZBaW8cXOTRZKICPkso6Oln4A6gd0eFotY5Fgsjo6kYIwZM4Yvv/ySOnXq0L9/f9577z3atWvHgQMHuOeee9i/fz8DBgy45Szia2nfvj1Vq1a96fG//vorAJ06dcq1r3PnzgD89ttvZGRkXN0eFhaW72vc3d1p27Yt6enpLF++/JZiVxSlcBk/HlaurMCBA6t48sn1QE+GD4dt2xwdWd7Urw+dO0uVze7djo4mb5ycRPDy94fNmyEhwdER5Y2Pj/RYio+HQ4fAqCmkVarIZ3nunAhhjsLDQ2KpVk2eAwLEls/TU+YfMTEiPIEIi+np8lySWLBgwW29bv369YUciaIoSk46d+7Mzz9/h315esaMjxky5CPuvhs++8yxsSmKcn1uSVQKDg5m+vTpdOnShapVq/L444+zZs2aMlkSrSiKUtoIDYUTJ3Jvj46W5/Hj4ddf1fIuP5KTYe1asZtp104Wm4KCYOhQtb67HcLDYfZsmDcPFiyQn0sqc+bMwWQyMXfuXAIDA3nppZdwdnbm888/Z+nSpVS84qNUUFHpVjh//jxRUVEA1K5dO9f+4OBg3NzcSElJ4dixY1e37927N9/XANSpUyfHOEVRHEtwcDCffNKGJ58UcaFTJzh5EkNWHzRrBm3awB9/iBhiRFxcRPzy9IRNm+Tab0TKlYPGjWUOl+0r3FCYTFLF7eMDZ8+KCJaUJKJNcePkJH2f7FVdJpNYHgYEyPbERLHJO31aRLCLF7OEppLAQw89dFuv69q1ayFHoiiKkptBgwYRGjrz6s9Llkxl7doF/POfUs2qKIoxuSVRaePGjVy8eJGZM2fSsmVL5syZQ58+fQgKCmL06NEsWbIES0lPJ1YURSmjLFyY9/YrhQnKdUhOFsvAlBSYNEl6KE2ZAk89BY0aOTq6kkdiIsydC9u3y8JObKw0Sy/pUwwnJye+/fZbBgwYQPfu3Zk4cWKO/c7F2Pzi0qVLAHh4eOBp7/CeDZPJRLkrjS7sY7P/Oz9ru4CAgFyvuZbU1FQSEhJyPBRFKVo++QTuuw+sVhshIdMYOXK0IYWlO++UKpuNG+Hvvx0dTd64uUlfJScnEZZSUx0dUd5UqCB9qi5eFCExNVWsjI0Ur8kkFUJpabBypVTJ79kj130j4Ows1nz+/hJTdLSIXmlp8m8D/gkpiqKUSB577DH69Xsz25YJwEXOndOKJUUxKrfcqTUwMJDHHnuMxx57DLPZzNKlS/n5559ZsmQJX3/9NV5eXvTp04chQ4bQv3//qwsSiqIoinH58UfIz+Fi4MDijaWkYG8w7uoqFUppaTB4cFbDaa1Ouj3OnpVFuvBwuOMOyU5LTRVLQbNZssONzvz586+7v3///rz00kuMGDGC/v37F1NUOUm+kl7v5uaW7xh3d/ccY2/mdXm95lreffdd3njjjVsLWFGUAvPDD1C9+gucO/chP/4IFSoE8N//forJYA1junSR7/3Vq8HdXa6r8fGysG+Ua6unJ9x1l8ydNm8WMSwlRfoYGek6VbkyZGRIksb69WL15usLbduK1ZsRyMyUzy49PcsG78QJqfx2dXVoaFcxmeT36uMjFX/u7pLokplp3J5ViqIoJQ2L5WXgEvA/4AdALlRhYTB5sgMDUxQlT25ZVMqOj48PI0eOZOTIkaSlpbFq1SoWL158VWhydXWla9euDB06lMGDB1O5cuXCiltRFEUpJP7xD+mTBGL1kZSUtS8kRO3u8uLwYek1FRkpGcCtWsHjj2vvpNvBYpHKJKsVjh6Vz7RKFVnwSk6WhcWLF8WCxiiLiTdi7NixN1yktdls/PTTTyxatOjqz8W5sOvl5QVAWlpavmNSr6Sz28fezOvyes21vPjiizzzzDNXf05ISKC6elsoSrHw+eedGTTo30AmX375GefOBWGzvcqgQca53ptM0LOnfP9/9ZVcCzIyRFwaNsw4FcA+PiKAff89rFghlrcBAdCrF+TjEOoQgoKkL1B4uFSBxcXBjh3Qp4+II44mLU1EpQYN4PJl+X07Ocl2o4hKzs5SoZaWltVryc1NtiuKoiiFw5AhJtat+wR4HLjj6nZNclUUY1IgUSk7bm5u9O/fn/79+2O1Wtm0aROLFy/ml19+4fHHH2fy5Mm0a9eOLfbO74qiKIrDCA0Vu7bdu+HCBVnACQ2FRx+V57AwmbwZZYHJSJjNIihdvChWKAkJIsTpwsKtEx4uC3HHjskiUocO0LevZFbXri1Z6hERWYt0Rsr+vhGvvvrqLY232Wy8+eabNx5YSNgTfVJSUrBYLLks8Gw2G3FxcQBUyZbOXrlyZQ4cOEBMTEyex4294llU5Top8O7u7lcrmhRFKV4GDhzI3Ln/Y+zYsQAsXfoaUJ5lyybzwQdw/LhDw7uKk5P0LvrmG4iKkqqgqChYvFisZY2SZODuLnOAiAi5Rtlscu0aNco41yyLBby8oE6drMSNxETZboSvYjc3iS8xUSz7jhyRCiAjzaucnCS26OgsQalCBa1SUhRFKUwmT4b333fm3Lk7cmyPiZHENb1/UBRjUWiiUnacnJzo1q0b3bp149NPP2Xnzp0sXryYJUuWFMXpFEVRlFugXj2xFcnO4cOSIQoiJKmYlD/x8bJ4FBOT1VchLk62G2WRqyRgscDy5ZIt7ecndjypqVnVXrVry6Kc2Syfq1EW526W11577ZZfM23atCKIJG+Cg4MJDAwkKiqK8PBw7rgj583b+fPnSUtLw9PTk3r16l3d3qJFC9asWUN4eHiexz158iQAzZo1K7rgFUUpEGPGjGHZshh+/NFeMfhPoDwnTjxIaKhx5gBJSVCpkogLJ07IPCUmxljX28REEUDatoXTpyXW+Hhj2bV6eso1NiND/r13r1QsGSU+V1epjD9xQhJMQkLAxUWSnmrVMo5w4+Ehgpxd8DJKXIqiKKWJs2elh1JYGAQHS5/d116bwf/+N4e9ezdqixVFMRDFMhVq06YN77zzDocOHSqO0ymKoij5EBqaW1AC6WGj3BwZGXD+vFQpNWggmdMBAWp9d6skJMCBA/K5tWkDzZrJNns/BZAFr6Ag4yx83SwLFiy4rdetz6+xWRExYMAAALZu3Zprn72y/J577sHFxeWmXpOWlsaOHTtwdXXlnnvuKYqQFUUpJJKTpwAvZdsyBviNsDAHBZQH/v4QGCi2d+npMlfx8THW9dbXV+KxWGTx6/hxEZqMInqBVCO1bStzFW9vqQb29BQrN6MQECA9lNq0kVibNZNEkzNnpPrLKDg5iQhW0gSlkJAQnn/++Tyv3YqiKEZj8mRYtQrmzIF77/0ImMKZM/vo2nUgFovF0eEpinKFEjYdUhRFUQpCfoUQRlpEMjKRkbBmjVjytG8vGdNBQTB0qLEWkEoCZ8+KQGdven3xorGasBeEhx566LZe17Vr10KO5Po8++yzODs7M3v2bGzXrNrNutJoberUqTm233XXXbRv356tW7dy8ODBHPu+++47EhISePTRRylfvnzRBq8oSoEYNAjgLeAfV7ZkAMO54w7jWJX7+EgPpSpVxGrM21uuE25ujo4sC09PsWcNCJAKloYNpbrqzBlHR5aTKlWkh1L//vDYY1CzJuzbJ5VBRsHVVX7Hrq5SFVSzpsR37pyxhKWSSHBwMNOnT6dLly5UrVqVxx9/nDVr1pCZmeno0BRFUa7LjBmD8PAIAmDfvs0MHvygfncpikEw2a5dRVBKPQkJCfj7+xMfH4+fn5+jw1EUpRhIS5OqmlOn8t4/a5Zx7G6Myvnz8NtvsrDVr59kTcfHlx4hpDg5fx62bpVFuJMnsz5HozU3L8nMnTuXRx55hDFjxjB37tx8x82YMYMpU6YwceJE3njjDVxcXHjnnXf4+OOPee2113j99ddzveb48eN06tSJChUq8PXXX9O4cWPWrl3LqFGjqF69Ops2bcL/FkoJdF6iKI5B7HAzgQeAHwHw929EdPR+nA3U0MZslutERob0KwoKEnHEQCFisWTZtZ48CYcOSdWNUa9p6emwZ498pi1biohjRBITJQmlXDmoWtXR0ZRsoqKiWLJkCT///DPr1q0jLS0Nf39/+vfvz9ChQ+nTp0+u/oplFZ2XKIqx+Ouvv2jXrhuZmWJpMW7cBGbP/hKTyeTgyBSlbKOiUhlEJ0mKUjYIDYVffpFFjXffFWHJbieWPYM2JMQ4jbmNiNksNm3bt0uT6759JYtWuT0SEmDtWrHf6dAh50KcrmUUnOvdXM2ZM4exY8fm2v7bb7/x4Ycf8tdff2Gz2WjWrBlTpkxh+PDh+R7r/PnzvPbaa/z2229ER0cTHBzM/fffzyuvvILPLaqsOi9RFMcRGgo//5zKsWMD+Pvvv4FVTJ1alw8+cHRkeXPxIvz6K1SvLpU3RrUh27VLxKX27aFaNUdHkzepqbB7t3yGLVsad24TFyfJKIGBUgWmFByz2czSpUv5+eefWb58OWazGS8vL/r06cOQIUPo379/me5bovMSRTEeq1evpk+ffths6QC88sprvPnm644NSlHKOCoqlUF0kqQopR/JPs65rXFjEUdAFpHCwmDgQK1Quh6HD8PMmfK5BQfD1KnS3Fq5PdLTRVAymaBnT2nErSg6L1EUx2M2m1m1ysywYZUBmD4dnn7asTHlx+nTsHw51K8PPXo4Opq8sdkkGeX8eejSBSpWdHREeWOxiLDk7i49jYxU/ZWd6Gi4dElEpcBAR0dTukhLS2PVqlUsXryYpUuXEhUVhaurK127dmXo0KEMHjyYypUrOzrMYkXnJYpiTBYuXMiDDz549ef77/8v0dGTGDxY+jApilK8qKhUBtFJkqKUbkJDYcKE3NvV4u7WMJvhxRfh4EER6Tw8ZFHoqafU7u5WsVikQungQXm++279DJUsdF6iKMZh/nwYM0b+vXBhBvfem2zIv8tjx6THYYsW0LGjo6PJG6sVtmyBqCjo2hWM2mrObBYrPF9faNrUuNVfkZFw+bLY4Pn4SAW+m5txK6xKIlarlU2bNrF48WJ++eUXzp49i5OTE+3atWPLFuP0WytqdF6iKMbFbt0tmBD73mFUqyZ2qYqiFB8GnTIqiqIot8u8eXlvDwsr3jhKOuvWSaVSo0bQrJlY7cTGSl8H5eYJD4cFC+DDD+G776RZuApKiqIoxmT0aPm+hhQeeGAEnTrdQ3JysqPDykX9+lIBtGePWM0ZEScnsXn194fNmyWpwoj4+EgVdny89IJKSRHLudRUR0eWk4oVRZg7fBh+/x127pTff2ysoyMrPTg5OdGtWzc+/fRTTp8+zZ9//snzzz9PrH7IiqIYhKeffpoePV648pMN+CeQzLlz8NlnDgxMUcoghS4qNWrUKM+mzoqiKErRM368ZMXmxcCBxRtLScVqFYu2EyegQQPJgLVY4Nw5CAiQxSHl5rBYpKn66dOySOXjA3v3ynZFURTFmDz3HNSv/wiwmAMHtnLPPcNJT093dFi5aNoU2raFbdtEDDEiLi7QubP0DNy0SWzcIiONdx0sV05sko8dg6++gmXLYOVK6WFlJCpUgJgYOHVKPluzWeZrBvzvWSpo06YN77zzDoeM+gemKEoZ5V1gLFANWAN4AZpEqyjFTaGLSkePHuX9999n/PjxXOusd/LkSV588cXCPqWiKEqZJTQU+veH//5XfOZnz5Z+NQEBOceFhKj13c2Qng6//QbHj8OAAeLNHBQkPv5BQTB0qFbZ3AqJifLZmc1QuTI0by6Z0GazoyMrWkJCQnj++efZunWro0NRFEW5LRYunIqLiy8AmzYtp337sfTrZyU01MGBXUPbtiIubdwI+/ZJDyOjXWPc3KSqKjISpk2D//1PKnjDwx0dWU58fOSzO3dOqpTi4mDHDmNVLKWnS5wVK4pA5+EByclihacoiqKUDYYMMQGzgD+BO65u1yRaRSleisT+bvbs2axdu5aRI0eSkZFxdfvly5f54IMPiuKUiqIoZY569aR30rJl8MQTsljh6yuLATEx0kOpf395Pn7c0dEaG3um63ffSVbugAHy+TZqJD2UpkyR50aNHB1pycLZWf5fJiVBcLAITP7+pV+YCw4OZvr06XTp0oWqVavy+OOPs2bNGjIzMx0dmqIoyk3RqlUr1qz5FZPJHYBdu77lt9+eYsIEG/XqOTi4a+jcWYSbV16BN96AGTPEIs1oJCWJUBMbK72BVq82VsWSxSLX7TvuyLLqS0w0VoxubuDtLWISwNGjUrHk5ubYuBRFUZTiY/JkqFbNFaiSY3t6upUEo3rNKkoppEhEpXr16rF582YOHDjAwIEDsRhpJqooilIKCA0VEeRaPv5YGhiDVCb9+qtWKN2Iw4fhvffgySfh55/F/iU4OGu/j4/8XNqFkMImPR3++gtatpRHVJRU0PXqJTZApZmNGzdy8eJFZs6cScuWLZkzZw59+vQhKCiI0aNHs2TJEp0bKYpieLp27cqkST8Azle2fAa8wYkTGKpiKSkJLlyQSu3YWDhzBhYvNlbFUmKi2MC2bw82m8QcG2usGD09JTnJahU7vCNH5FpupGu2q6tU3/v5ybzM11cFJUVRlLLI2bPwn//IveWIEQDpPPPMWFq37onZSBdXRSnFFImoBJKlu3HjRiIjI+nduzeJiYlFdSpFUZQyR36LOeojfGuYzTB3LmzfLgsolSvD+vXGWuQpiVit8McfYklz330wbhyMHQujRkHt2o6OrngIDAzkscceY9myZVy+fJlvvvmGXr16sWTJEoYOHUpQUBDDhg3j66+/Ji4uztHhKoqi5Mnp0wOBr7JteQP4j6HmG/Hx8ujYUZIX4uKk6jg+3tGRZeHrK5W6CQlQq5YshsXESNWNUXB3FzvBcuVEvKleXcQboyV9BwRAixbQrh3cc498rqdPgxYDK4qilC0mT4ZVq8RtpGnTicACTpzYSZ8+w0hTX1RFKXKKTFQCWVBZv349Tk5OdO3alcjIyKI8naIoSpmgSxfxuM8L9RG+NXbvlmqa4GBZnKhTRzKHjbQQVRLZtUusfTp2lAUpT0/pSWWkbOfixMfHh5EjR/L9998TFRVFWFgY999/P5s3b2b06NFUqlSJ3r178+WXX3Lp0iVHh6soinKVQYMARgP/zrb1SSpV+sYxAeWBv78IIRcvynXcbJZ/OxXpne6t4ekp2dQBAZJw0awZ1KgBhw45OrKcVKkCffpAv37wyCPQsKFUdMfGOjqynLi6iiDn5SUiXWamCEtWq6MjUxRFURzBt99OwdW1HABbt67iwQcfwaoXBUUpUkw2m81WmAecO3cuffr0oUqVLG/LlJQUhg0bxpYtW0hMTNSeAg4mISEBf39/4uPj8fPzc3Q4iqLcgNBQ+OUXaNAAPvtMmhE7O0P58rJwbyckRHsn3Sw2mwhzv/8OBw5IBnH16tKPKihI+iep3d2tYbGIvc/582LNeOedULOmo6MyNlarlU2bNrF48WJ++eUXzp49i5OTE+3atWPLli2ODq/Y0HmJohibevXslruvAG8D4OXVgcTE33EyiHJz+LBY3sXGZokN1arB0KHGSmiwWET08vGROdz27TK/a9bM0ZHljdUq86T4eKkO8vV1dER5k5IC4eHyu65ZU6wQFeV20XmJopRMNm/eTNeuvbHZUgB48skpzJjxMSa9KChKkVDoolJ+ZGRkMH78eH766Se1wnMwOklSlJJD1kJOzm0HDoiHfGioWN4NHKi9k26E2SyLIt7e8Oef8rm2by8LEPaFqIAAWYBq1MjR0ZYswsOl4fipU9I7afhw6N3b0VGVPHbu3MnixYtZsmQJh4yWvl6E6LxEUYyPJLjYiI+fxJYtx7HZlnD33b6sXu3oyLKwX+f9/UUMWbxYxKXBg43bd+f4cdizB5o2laogI5KZCXv3iiDWsqV8pkYkOVnmIb6+IijqGuL1SUlJ4cSJE0RGRlKhQgUaNGiAh4eHo8MyBDovUZSSyy+//MLgwUMBqVL64IMPmDp1qmODUpRSym2LSrt27aJJkya4GXWGruSLTpIUpWQQGgoTJuTePmuWCki3yuHDsGiRZAVHRIho9PDDULeu7M++EKUVSreGxQILFkhj9Ph4qQJr1kz6JxkpO1wxLjovUZSSQ2ZmJhcuZFC7tjuZmfDQQ/D1146OKm+io2HJEqhQAQYMkCpvI3LwoNjgtW4t9n1GJD1dxK+MDBGWjKo9JCZKv6py5aBqVUdHY0xsNhtvvvkmH3/8cY5m9h4eHvTq1Yvnn3+ejh07OjBCx6PzEkUp2cyePZvx2RZMHn54HhcvjmbwYOnDpChK4XDbfgVt2rTBx8eHpk2b8vDDD/PRRx+xevVq7ZukKIpSCKSliQVbXhipOXZJwGwWQencOYiMlMbYKSlQqVLWGB8f6aukgtKtk5iYJShVrAht2si/s61TKIqiKKUEZ2dnqld3Z/9+qQT55ht48slYwsPDHR1aLipUgHvvlWv/ypXG7bfTuLFYGO/aJXMVI+LqKgkjJhPs2ycikxHx9RUxKTZWkojS0yEpybjxOoI33niD119/ncTERJo2bcqQIUPo06cPPj4+hIWF0aVLF8aPH69N7hVFKbE89thjvPXWW1d//vrrcaxd+xv//KdY3iuKUjjcdqXSsGHD2LNnT44bCLtPZaVKlWjevDnNmzenRYsWNG/enEbqJWQYNPNGUYyJvXeS1Xr9xQ+tVLo1zp+H114TocPPT6qTYmJgyhQRkpSCcfAgfPKJLDi1bSuLOAEBWqmk3Dw6L1GUksnGjdCt20WgD+XLmzlwYEuOvrpG4fRpWL5c7IN79DCmLZrNJta8585B5845E1+MhMUCu3eDu7vY9aWny7Xe3d3RkeUkOhqOHhVByc1NLPtCQmR+UtapWbMm58+f54cffmDo0KE59q1evZpXX32V7du306tXL5YvX26YvmnFic5LFKXkY7PZ6NLlSbZs+ezKljuBPwAn/vMfrVhSlMKgwD2V4uPj2b17N7t27WL37t38+eefHL/SKd5kMmGz2XByciIjI6NQAlYKjk6SFMV45NU7qU8f+PvvnNtDQsR/X7k5bDZYvx6mTwcXF+mhdPEiBAVJJZhWJhWMw4elv5enp4h3CQliIdirF9Su7ejolJKCzksUpeTSuvVAdu36FYAaNZqyZ89GAgy4cn/8uPT+a94cOnVydDR5Y7XCli3Sm/DOOyVZw9fXeAkaZjOsWSP9iwIDJWGnbVswkp6Yng7r1sGFCzIfsdlkzteihXyuZRl3d3c6derEunXr8txvs9kYN24c8+fP5z//+Q+PP/54MUfoeHReoiilg549M1m37gHgAvArIPOTXr1g1SpHRqYopYMCp534+/vTrVs3nnnmGRYsWMDRo0fZt28fAwYMAODhhx+mT58+BQ5UURSltBIamltQAhg2TBZBZs2C/v3lWQWlmyc1FZYtk0zVsWPFlu3yZRGUhg5VQel2sFjERig5WZp2Hzggtj39+8Po0fI5jxqlgpKiKEpZISzsCwICagJw5sx+7rqrP599lkz//jK/MQr16kGXLnLt+v13SYQwmk2rkxN07CjX2HfegZkzpWeh0ZwFXV3FRjgyUj7D2FjYsUPmXUYhLU36PgUHS6W6i4t8ruroBhUrViQwMDDf/SaTiS+//JKgoCBmz55djJEpiqIULkOGOAMLgNXYBSWAgQMdFZGilC4KXKl0PZ5++mlWrlzJrl278DRailUZRjNvFMU4vPIKvP123vv694dffy3eeEo6ZrMsHmRkwObNssDRu7d4J9v3+furoHQ7hIdLlndcnCwgVasGffuKnaCiFASdlyhKyeb48eM0b94Zi8XeW/ceYAngZrgK6x9+EKGmQgWp/h42DIzk0m6xwJw58NdfUgFUvrxY4RnJUjYuTpJ2XF2lEsjbW+zl+vWDcuUcHZ2Qng579kjfx9RUuHQJ6tQR0a6sVyo9/PDDrF69mtOnT+Ph4ZHvuPvuu49ly5aRnJxcjNEZA52XKErpoXr13P0KZ882M2xYBuWMctFSlBJKkRrkvv/++0RHR/Phhx8W5WlumpSUFKZNm0aDBg3w8PCgSpUqjBo16rYay1qtVj799FOaN2+Ol5cXgYGBDBkyhD179lz3ddu2baN///5UqFABb29vWrduzaxZs7iethcfH8+zzz5L7dq18fDwoEaNGjzxxBNcvnz5luNWFMWxhIaKWPTssyJs5CcogWbQ3CqHD8OMGdI/6cknJYP2vvuymnH6+EjGqgpKt47FIoJSdLRY3J09Kws1Vas6OrKSQ0pKCgcOHGDdunXs3buXlJQUR4ekKIpSKNSrV48tW1bg5GRffF0OjAWsnDhhnIolsxmOHZMKloQEqRJfvNhYFUuJidIHqEMHiTM+Xq69RorR01Ns+VJSRPAKD5f4jCJ6gQhHISFZ9oEVKkjfJ3Xkh1deeYXk5GQmTZp03XHx8fH4+/sXU1SKoihFw9mz8J//iOWdrK9E8dhjPenadSAWi8XR4SlKiaZIRSV3d3fuvPNOvvvuu6I8zU1hsVjo2bMnH374IW+99RYxMTGsWLGC/fv307JlS/bu3XvTx7Jardx///08++yzTJw4kcuXL7Nt2zYSExNp164dq/Ix51y8eDGdO3fGxcWFXbt2cenSJUaMGMHEiRMZN25cnq+5fPkybdu25dtvv2X27NnExsby7bffsnTpUlq1asW5ayV3RVEMS716MGGCZHf++9+yaBAUJH2TQkJyjg0JgfHjHRNnScRshp9+gv37ISZGLGTMZnlWCk5iolQnmc2yiNSuHTg7G2uRy6jYbDamTZtGpUqVaN68Ob169aJVq1ZUqFCBwYMHs3XrVkeHqCiKUmBatmzJnXf+CtgrHxYCTwE2wsIcF1d24uOlyubOO6XaNjERTp+W7UbB11cqqqOjxUr28mVJknF3d3RkWbi7Sw+lcuWkD1S9etJbKSLC0ZHlJCBAeii1bSt9SitWlN+3kWz6HMGYMWOoX78+8+fPp3v37mzbti3XmI0bN7JhwwaGDh3qgAgVRVEKl8mTpYfSkiU2KlceCvzJvn2bGTz4QTIzMx0dnqKUWG57ue3ll19m0aJFnDx58rrjYmNjOX369O2eptB4/fXX2bp1K++99x733XcfXl5eNG/enEWLFmE2m3nooYdu+stk5syZLFq0iKeffppJkybh7e1NSEgIP/74I97e3owaNYrExMQcr4mMjGTs2LFUrVqVhQsXUrNmTXx9fXn++ecZPXo0c+fO5fvvv891rsmTJ3P8+HFmz55Nz5498fT0pHPnzsybN49z584xXledFaVE8OWXefdNevttsePQ3kkF48IF8fPPyICGDaUJd0KCsRaKSjJeXtI4/MwZqFVL+hKojeDN8cYbb/D666+TmJhI06ZNGTJkCH369MHHx4ewsDC6dOnC+PHjSdNGD4qilHDGjbsL+BFwvrLlM+ANw1Re+/uLEHL+vFQxZ2SIYGMkkcHTU7KpAwJkDtOqlcxrdu0CI617VakiQk2/fvDggxLnyZNw8aKjI8uJq6vY87m7Q82a0lvp9Gmxxyur7Nixg927d2Oz2di4cSOdOnWiZs2aDB06lLFjx9KtWzd69uzJgAED+OijjxwdrqIoSqFhMplYunQ6zs5yE7tq1RImTHj8us5RiqLkz233VHJycsJkMgHg5+dHixYtaNmyJS1btqRJkyYEBgaydOlSnnjiCerUqcOJvFZTi4nk5GQqVqxIeno60dHR+FyzCta3b19WrlxJWFgYVz22AwAA4IBJREFUAwYMuOHx6taty8mTJzl27Bj16tXLsW/ixInMnDmTTz/9lH/+859Xt0+bNo3XXnuNl156ibev8bvatm0bHTp0oFWrVvz1119Xt58+fZratWsTHBzMmTNnrn7edho2bMjRo0fZt28fTZs2venPQz2CFaVoCQ2FX36BQYOk2mj4cFi0KO+x2jep4Jw+DUuXwrp1Ym9Sv774JgcFwVNPqfBRUDIzYcsWOHhQxCSrVRbmevWSLGrl+tSsWZPz58/zww8/5Mr4Xb16Na+++irbt2+nV69eLF++HKcyWF6n8xJFKT3UqwcnTswHxlzZ8jCZmfMM8912+LBY3sXGSs+igABJnBgyRPoXGQWLRaqBfXyksn3TJrGa69gRrrklNAzHj0uSzx13yBzMiGRkiPjl5CRJMi4ujo6o+ImKimL37t1XH7t27eLEiRO5FlVr165Ny5YtadWq1dXnSpUqOSjq4kXnJYpSulm9ejV9+vTDZpMMg5dffpW33nrDwVEpSsnjtqdR8+fPZ8+ePezevZs9e/awceNGNm7cmEv4AJgwYUKBgiwo69atIykpibZt2+YSlAA6d+5806LSgQMHOHnyJEFBQbkEJfuxZs6cSVhYWA5RKeyK70OnTp1yvaZNmzZ4eHiwa9cuzp8/T3BwMAC//vorNpuNDh065Pm5du7cmaNHjxIWFnZLolJh8847sGQJDB4ML73ksDAUxRDIYor8e9kysbu7HkbJ3i1JmM2SvevrK0LH7t0ibrz6qohLly7JYsbQoSooFRS7oBQVJc3MfX2zFrmM1DvByFy6dIm77rorTwuZXr16cffddzNu3Djmz5/Pl19+yeOPP+6AKBVFUQqH48chNHQ0M2bEcPRoOJmZ06lb14nbaGFbJDRqJAkn8fGSIOHqKvcxv/4q8wZfX0dHKHh6Zl1nPT2lx9KWLbBzp9i5GZGQEKkAOnxYxJqAAEdHlBsXFxGTwsOzqq8NoncWG4GBgfTq1YtevXpd3WY2m9m7d+9VkWn37t0cOnSI8PBwFi9efHUtonLlypw/f95RoSuKohQKvXr14ttv5/PAAw8A8Pbb0wgOrnzDXnOKouTktkWlhx9+mIcffvjqz2fOnLma7bJ//37OnTtHUFAQQ4YM4dFHHy2UYG8Xe7+k2vmkVNepUyfHuMI+VmZmJgcPHsz3dS4uLlSvXp3jx4+zd+/eq6JSYcZdVAQFyWIjiPXU9Oni/a0oZZHQ0Lwt7oKDZXvTpjn3a9+kW+fwYan6ioyU75569SS7uEUL2V+3btZCkQpKt4/FIn0nDhyQnhNdumRlHauYdGtUrFiRwMDAfPebTCa+/PJLli9fzuzZs1VUUhSlxDN+PIwf/zSpqTYCAkycOgUtW0oSiBHw8ck5RxgwAH7+GcLCZE7h5eW42PKjShXpBbV9O7i5QfPmjo4oNyaTWPUdOCCP5s2lGsxouLmJFZ5dWKpZ07jVX8WFj48PnTp1ypEAm56ezp49e9i3b99VsWn//v0OjFJRFKXwGDlyJBERETz99NMAPP74EwQFBTF8+HDHBqYoJYhCy8upUaMGgwYN4vXXX2fRokVs376dpUuXOlxQAskSBiifj6dBwJU0Kvu4wjjW5cuXsVqtAMTFxZGSknLLMRRW3KmpqSQkJOR4FAbvvJMlKNmJisq5SP7ZZ3D33fKsKKWdd9/Ne3vLluDhoX2TCorZLILSyZNSjRQdLX0QQkKyxvj4iIingtLtEx4O8+bBtGliEVSjhnFtbEoCXbt2ZePGjVfnAXnh7u5Oly5dOHLkSDFGpiiKUrS4u5uIiZFqoD17oGPHw2zdutXRYeXCy0uEpfR0qXg2aou7GjVkTnnsGBj1cuHkBI0bSx+j/fvFNteIeHiImJScDGfPyu88Kals91rKzu7du3nhhRcYNGgQjz76KJ999hlbt24ttHWEW+XMmTP4+flhMpnYsGFDvuO+/vpr2rdvj4+PDwEBAfTu3Zv169cXX6CKopQonnrqKaZO/deVn2zcd99DtG69QdcvFeUmKRPF3slXZrNubm557nd3d88xrjCOlX1s9uPeSgyFFfe7776Lv7//1Uf16tWvO/5mWbIk7+2zZ8sNhZMT/POfsHatPGc/bWioLKyHhhZKKIpS7GT/P/yPf8iCSX7WLtkt7saPF4sVrVC6daKiZIEiLg4CA6FbN/HGj493dGSlB4sFVqyQbHJXV/mcd+2S7crt8corr5CcnHxDO4X4+Hj8/f2LKSpFUZTiwcND+hw6Oe3gjz+60K3bvQ51WcgPPz8RlhIT4bffZH5hREJCpGfR/v2SZGNEnJ2lOt/NDfbulXlaXJwkAhkJLy+5Pz1/HtasEWvBPXuk31ZZ5OzZs7z33ns0adKENm3aMGPGDCIiInKMycuSvzh47LHHSExMvO6YKVOmMGrUKPr27cu5c+c4cOAAgYGB9OzZkzlz5hRTpIqilDTef/8dHnrokSs/pbFr1/9yrV8qipI3ZUJU8rriYZCWT9pZ6pUZrtdNeB3c7LGyj81+3FuJobDifvHFF4mPj7/6OHv27HXH3yyDB+e93cUFbDZ5ZOfcOenJUa+e9Jmx95vJozWVohiaa/8Pz5oliw916kgWaXbU4q5wiIwUgTolRbJf69SBiAjx69d1+MIjJkYWVDw8xLamXj1ZDDKbHR1ZyWXMmDHUr1+f+fPn0717d7Zt25ZrzMaNG9mwYUOefZcURVFKOhUrwt13vw9Ek54eT8eOffj7778dHVYuKlSAfv1kzrFqFVwxnTAcjRvL/HLXLql6j4w0XvKHqys0ayZV5bNni7XgypVw8aKjI8uJh4dUK0VESLWS2SxW1WWlYikxMZGvvvqKHj16ULt2bV5++WUOHTpEhQoV8nVLKW5mzZrFzp07adiwYb5jli9fzowZMxg+fDivv/465cqVIzg4mHnz5hESEsLjjz/O6dOnizFqRVFKCiaTibZtZwH9gMcAEaHPnVPHJUW5EWVCVKpcuTIAMTExee6PvZKOVKVKlUI7VsWKFXG60vWzXLlyeHh43HIMhRW3u7s7fn5+OR6FwUsvSRZ7dvz94f/+T3y/82Lx4tw9Z06cyFmxpFVMipGZPj3vvkn//jf8/TecPq0Wd4WF2SyWJJs2ie2dv7987zRsKPZ3QUHSVFut7gqH1FSpUHJxgXLlZEHo4kXtT1VQduzYwe7du7HZbGzcuJFOnTpRs2ZNhg4dytixY+nWrRs9e/ZkwIABfPTRR44OV1EUpUhYvHged9zRHoDk5AjuvLMXH354wXBz/sqVoW9f6bWzfr1ULp0/b7zkihYtpCLo44/h889hwYL8K+Ydib1HY3y8JK7s2GGsiqW0NJn31Kol4pLNJs9GtUAsDDIzM1m2bBkjR46kcuXKjB8/ng0bNuDh4cHIkSNZunQpFy9epGnTpo4OlTNnzjB16lSmT59OpUqV8h334YcfAjBhwoQc211dXRk3bhwpKSl8+umnRRrrjbBYbk4AdtS4khCjvpeSPe5Wx94MhXW8X391ARYBswCXq9vDwgp2XEUp7bjceEjJp8WVDu7h+cy0T17xDmjWrFmRHMvZ2ZkmTZqwc+dOwsPDadSoUY7XZGRkXK0eyv66woy7qLh8WXorhYWJxdcLL4jH9+7dedvjOTtDZmbu7RMnygL8uXOyWAxSBfLBB7oorziO0FD45RcYNEgqkEaPlklLXqxbB1OmyL+lQXXxxVkaOXwYvvkG9u2Tnx96SAQku1d/fLyKHYVJcrKIdzabVN9t355VCdarF3h6OjrCkktkZCS7d++++ti1axcnTpzIVTW8Z88eRo0aRatWrWjZsiWtWrW67gKKoihKScLb25vNm5fRvPldnDt3kJiYcJ5/vg+wiWXLAgw1569RA3r2FKHmu+9kvlGunDguXHMb5zBSUrLmpHFx8rx6NYwaZZxrtsUi4kyrVnKPl5QkAo7FAtkc4x2Km5vY4JnNUgl/+rRYHuXjPl+i2bFjBwsWLOD7778nKioKm82Gs7Mzffr04aGHHmLw4MF4e3s7OswcPPbYY3Tq1IkxY8bka2GXkJDAxo0bMZlMdOjQIdf+zp07AxAWFsbHH39cpPHmR3i4/H3a71969YLatY0zriTEqO/FmDEWxXsBuU4kJoKvb/7XtFs53o3o1w/Wrs19YQoIOIPZXB4fXXRQlDwx2WzXmpSVPpKTk6lYsSIZGRlER0fnmizdc889rFixgrCwMAYMGHDD49WtW5eTJ09y/PhxQrJ3iAcmTZrEl19+yaeffso///nPq9unTZvGa6+9xssvv8xbb72V4zXbt2+nffv2tGrVir/++uvq9tOnT1O7dm2Cg4M5c+ZMLg/jRo0aceTIEfbt23dLmUQJCQn4+/sTHx9faFVL2cnMlAXJbt1yWgc4O0vm37Jlt3a8WbOyFuizL/Lror1SlNSrl3dFUn7CaPb/p0rBSEyUiqTDh8Uyx9tbMkifekpFpMLEPlkHyRw2meCuu+QztlhkgcXHxziLU6UJs9nM3r17r4pMu3fv5tChQ6RfuWjar/eVK1fm/Pnzjgy1WCjqeYmiKMbhwoUL1KvXieTkU1e2dABWA96GmkuZzfDii3DwoNi4gVRIG2UuEhkJ8+ZJTOHhsqhWoYIk6gUFOTo6ITVVLO/i4kS42bVL3CzGjxfbOaMQGytz/uRkefj7Q5Mm0merNPDWW2/xzTffcOzYMexLP+3ateOhhx5ixIgRBOXzH6Z79+5s2rSJzLxufIqB0NBQnnvuOQ4cOED16tXp1q0bGzduZP369XTr1u3quC1bttC5c2cqVqyYqwcUyHdOcHAwJpOJxMTEPIWz1NTUHG0MEhISqF69eqHMSywWEajPnpX7mogI+T82fHjOObbFAj/9JH/LlSoV3zhHnlvfS8l6L/fdlzXOZMoaFxcnVb6XLmUlYHh6ZrXDsNlyj42MhPLlYcQIude392R3cpJK4bVrISFBrrd33SVif0aGrMNkZso1etEi+f6uUkWOW6kSjBsn15tryUukysiQCtroaLkGjBwp14As9gF96dixKevX/5pvr3tFKcuUiUolLy8vJk+ezPvvv8+8efN4/PHHr+47efIka9asoXHjxtx7771Xt+/fv5+JEyfStWtX3nnnnRzHmzp1KpMmTWLWrFl88MEHV7fHxcXxww8/ULFiRcaOHZvjNRMnTuSjjz5iwYIFvPLKK1ft8ABmzpwJwPPPP5/jNTVr1uT+++/n+++/Z/ny5Tni27RpE0eOHKFv376GKE3PjrOz+HyPGCHVGwkJcqFo3Bg6d87qi2LHwwOWLoUnn4RDh3Ifb8IEmDw56wICeVcxqeCk3C7X/t+ZNi1vQWnCBJg5M7fgpH2TCo/4eJkgHj4sn3OjRpLleumS7DPCQk5pwJ7ZdemS3CjceSfcf3/WJNvTU8WkosTHx4dOnTrRqVOnq9vS09M5cODAVZFp165d7N+/34FRKoqiFD5Vq1blzjtXs2FDJyAS+AMYDvxCWJibYeZT8fFSTdO0qVwrq1eXxSujzEV8fWWRLyICataELVtkgczJQOb27u7Qtq0kriQmyr2gpyecOiVWxkYhIEDsBNPSpEIpIkIqq2rWlHvYks6rr76KyWSicuXKTJo0iQceeIC6des6OqzrcvbsWZ577jk++ugjqlevft2xl67YnOTXAyogIAAAm81GREQEderUyTXm3Xff5Y033ihg1HmTmCjfG0lJMv9OS5OKuPLlcwqX8fHi0ODvn2XBWBzjHHlufS8l/73s2SPjkpJk3KlTIizl9V7275exKSlZx/T3zzk2LQ02bpTjBQTIdXf/fujaNWcFaXy8OCSVLy8WtfZze3tD1apynbY/oqMl6T0+XtYqGzeWNciEBBG8XF3ldVOnwh9/SEsDP790DhwYTHr6RbZuvchDD43j++/nX21xoiiKUCZEJYDXXnuNTZs28a9//YtKlSpx7733cvz4ccaMGYOXlxfffPMNzs7OV8fPnDmTrVu3snXrVp599lkqVKhwdd+ECRNYvXo106dPp27dujz88MNcunSJSZMmkZiYyNKlS/H19c1x/ooVKzJnzhxGjBjBgw8+yIwZMwgICGDmzJnMnTuXMWPGMGLEiFxx/+c//2HXrl089thjfP3113Ts2JG//vqL0aNHU61aNWbNmlV0H1oB8PGRSiVfX8kaSE+XL/cqVeTmLDxcbny8vOQm7a+/oGXLvEUlN7e8fa1PnJAbks8/l6w8+yK/2uYpt0J2gWjZMhGO8uPCBXk+flyEKLvto1EWQEoiZrNM8Pz85Hth2zbJfGrZUr430tLkxj4oSCadSsGxWERQOndOPvvUVOP1iiiLuLq60rJlS1q2bHl1WxkoJlcUpQzy4IMhbNiwEugKJAArgDEMGPAtYLrua4sLu+VdVJRUAP31lwgPRpmLeHqK1c/q1bJg1qqVbPvzT+jRwzj2clWqQJ8+Mvfw9JT7wsOHZRHPSLqGq6s8AIKDJZHxzBmplC8NSTY2m41Lly6xcuVKgoKCCAgIyFeEMQLjx4+nbdu2jL+Jm6zkK6UF+VURuGf7Y0jOWYZwlRdffJFnnnnm6s/2SqXCwC4Ap6ZmVXM0bAhDhuSuDklJyVn1URzjHHlufS83917s1Ti3c8zsVaHZj5d93ODBOat78jve4ME5j5ecLGNjY7Mqmho0kERdLy+5p4esqqbU1KyKogsXoH59sZ1zdwerVR4RESIWBQbKe0xLk2tc+/byOhcXEYbS0mS8/dynT0siQJs2ss5oNss5YmJyi1SXLknVVb16cn339ZUYQ0JkzTI+HqxWV3r0mM+//90Lmy2Fn376hilTKjJjxse5HKQUpSxTJuzv7KSkpPD+++/zzTffcObMGfz9/enVqxfTpk3LlbGyZs0a7r//frp27crixYtzfXFkZmby2Wef8b///Y/jx4/j5eVFly5deO2113IsCF3Ltm3bePPNN9m6dSupqak0bNiQf/zjH0yYMCHfL6e4uDimTZvG4sWLuXjxIhUrVmTAgAG8/vrrVKxY8ZY/h+KwmbFa5Ybh4EH5IrdY5Ava2VmsGk6elIuKk5NcoF57TS5cvXvnLDktV05KxUeOVNs8pfAJDc1bRFKLu+Lh8GGpSrp0SSaL9erJAkn79iL0LV4sE7+AAOmnZJQ+BiWdyEj4z39kwhwQIBPw6GgYO9Y4ljlK2ULt7xSl7CFJPZuB3kAK8BEWy7OGskU7fFjmIjEx8qhRAx57TBLijEJ2u1qrVVwiPDwkuc8ukhiNc+dknlenjnymRsRqlUXK1FTp0WEUke522LFjB/Pnz7/aS8lkMuHi4kLfvn156KGHGDhwYA4XFTuOsr+bPXs2Tz/9NAcOHKBWrVpXt+dnf7do0SKGDx9Ow4YNOXz4cK7jWSwWvK6smP/99995VipdS2HPS0pTvxujjysJMep7yX+s3S7SLqRdvCj3q3n1C7yZ40VEwOzZksDq4SHrPNe7781+TU1NhX//O4w33xwCWAH44IMPmDp1at5vUFHKIGVKVFKE4lq8SUmRxeKkJJmYu7hItt8nn8h2e5msn58sGPv4wJIlsHev3Gz4+MhN29ixkmWQV0X6wIGwZs213qdZeHhItUP2uXBISFYVk4pNZYfsv+saNcRSMS+LO4D+/SVD5lqLO61+KzzMZpg+XRZsUlPlu6BFC3jllSxbGXsVk7+/MaxmSgv79sFnn8l38p13ymQ7v8m6ohQHKiopStkkNBRCQ5dy/Phl4uIewdtbxBsjtS2wz0V8fWHzZrHZGTRIMqONSHw8rF8viXldusgCmhE5dUoeDRrIwqERycyURUurVRYqjSrS3SwZGRksX76cr7/+ml9//ZWUlBRMJhM+Pj4MGTKEBx98kLvvvvuqvZMjRKVz587RuHFj3n77bSZPnpxjX1H2VLqWopiX3Gy/UkeNKwkx6nsxZoyF/V5uRai60fFuRaTKi5QUePnl2fz731mLhfPmzWP06NE3frGilAFUVCqDFOfijdUqE3JnZ1k0PnsWvvwSjh4VIcjZWRqhjhsn3qXvvy8LzPXqyUKnlxc88oj8+5VXcopH5ctLBtnChbdWbQJycUpLk4uMnWtFAxWcSg/X9kC6EfaKJLW4Kzr++gtefVUyP+vUkUyhy5dhyhSxHVEKH5sNdu+W71ofH7F1SUi48WRdUYoaFZUURalZU65LFSpIEpoRyciAX38V4WvIELkXMSJRUbBpkwhfHTtmWRAZjePHJXHwjjuMWymdkSELnCaTWOG5lJLmAQkJCfz4448sWLCAzZs3Y7PZMJlMVKxYkZEjR/Lggw/y/PPPF7uoNHfuXB555JGbHr9+/XpatWpFQEAANpstT9Ho999/p0uXLoSEhHD8JjMEdV6iKI7lVoSqG3ErIlVeZGbCE0+8zcyZr1zZ4syyZWE5et4rSllFRaUyiCMnSRER8OmnssDv5SUiUYUKsmgfFCSL+fv3y3Y/P2jWTKzx4uPhzTelquH8efF2rVtXmum1aSN+rHlVlfTvf2u2eZUqiYi0fLkIYNcez44KTsYk++9lzBh46SWYOTPvfjHjxsH//pdbcNKKpMIne8WRk5M0wNy7Vxo3+/nJ78DeN+mpp7QqqTCxWKRBsKenNFKNiIDWrWUiXZiTdUUpCLp4oygKyD1BTAwEBS1j+vQ4HnroIUeHlIvUVHFWSEkRp4Vr2ugahkuX4Pffs3pMGFFYstnk3i4qSnruBgQ4OqK8SUuTRUlXV0l8ysiQarqSXrlk5+zZs3z99dcsWLCAI0eOAOSw5S9u+7v8yK9SCaBHjx6sX7+e1atXc/fdd+fY9/777/Ovf/2LZ555ho8//vimzqXzEkUpXRT0vtdmszFy5D/54YfPATCZvGjWbC1jxrRnypRCDlZRShAqKpVBHDlJSkyUnkp//y22dOnpYkV2zz1yQ7FsmYhGVqvcVFarJpVK9erB/Plyg+TtLa83maBDB8kSdHGBr7+GY8dEiHr3Xem/kl/PnEaN5CbmVujQQSqpxo27vgihglPRkt/ne6vVSP37S7ap/ZhakVQ02PsmxcbKTXnFitJfrWNHWUz4+Wftm1RU2LOyoqKkj1KjRtJg1aiWPUrZRRdvFEUBmSf4+n5DWtoYAJYu/YV+/fo5OKrcJCfL/AWkYil7g3MjceYMbN8uyXfNmzs6mryxWuHAAUk+athQhBpPT+P1MEpJkYQou4W7t7fcAxpVCLtddu3axYIFC/juu++IiIjAZDKVCFFpxYoV3HPPPdx333388MMPV7enp6fTtGlTTp8+zZEjR6hZs+ZNnUvnJYqiXEtmZiZ9+z7AmjU/XtnyHvACVapI1a2ilEWcHB2AUrZwdhahqGVL6eXRvLl4fptMciPh7S02eS4uklnXpIlkFWzfLjdsJ0/Ctm0iRk2YAA8/LBljixfLjWi7dnITMn++ZCKMHy8T/uyEhJBvNsELL8iCd1788QfcdVdu4eLECbj7blk8r1dP4lq2TJ7r1cs5NjRUxIzQ0Nv6+Mo8136+1apJI2IPj7wFpYYN4e238z7WwIFZ/x4/XgQmFZQKF7NZBKUzZ0TUOHhQ/PMHDRKB4447pDJpyhR5VkGp8LBYRFC6cEFsBePjZUFE74sVRVEUo+LmBo888geQCWQycOBwNm/e7OiwcuHlBQMGyP3I0qVyD2JEatSQe65jx6RXqMUi87Hs9t+OxskJGjeWmL76SsS6lSul74WRcHaWKrWYGBEVExPl3iM93dGRFS6tWrVi+vTpnD9/nmXLljFy5EhHh3RT9O3bl6eeeooff/yRadOmER8fz4ULF3jkkUc4duwYn3/++U0LSoqiKHnh7OxM794LgLuBz4EXALleTZ/uyMgUxXGoqKQUKx4eIgz4+cmNo48PtGgh1hEXLoiQNGiQiAGNGkkVUqdOMub0aRnfvLnsDw+X191xh1g5dewoGfg2m9hqffWVCEFz58oxa9eW57Cw/MWm996D11/PO/YuXfK35Vq7VuLIS3Bq2lQuMrVq5S84XU9sKs1CVH7vLa/toaG5P9/z52HjRrnJy4uQELHAy+t3rQJS0XPhgmR1RkXJ33uXLpLRmf335eMjwrBa3hUuiYli4RkTI59tp06y6JWXFaSiKIqiGIX//vdTBg+WhWyrNYWePQewd+9eB0eVGz8/EZYSE+G33yAuTualRrvOhoSIaLN2Lfz73+IYsWCB3EcZhYwMSSq0WORzvHxZ7uXym987grQ0ucesV0+SIAMCRFwyqqBYUJycnLjnnnv45ptvHBrHqVOnMJlMmEwmNm7cCED37t0xmUy5qpVmzJjB/PnzWbZsGVWrVqVRo0ZERESwZs0axo0b54DoFUUpbfz6qzuwEng8x/YlSxwRjaI4HrW/K4M4upw7JUWsA5KSZKHZ01My/uLj4ccfZV9AgNyYBQbCffeJx/rChfL6iAh5rc0G//ynCFEzZsjrAgPlRsTbG7p3lxum5csliywkRCb+NWrA00/LQutnn2XZnk2enBVjfn128rPTa9VKMgCTk2/ts/D1lc8je5Zbdju9G/X7uZ7V3u3uK2xu1q7O/t6u3e7kJI+MjLyPX7++VLpMmpR736xZWedUi7uiJXvfJHd32LVLqgo3bpS/x2bN5G9a+yYVD7t3wxdfSNXnnXfK92ZAAIwapf2TFOPh6HmJoijGIi0tjZ49B/L77ysBcHOrRMeOv/PggyGGm8NduiT9O//+W+Y4AQEwbJixqq8tFnjnHXF8aNtWxBojzQni4iTpzs9PEmLS08XJYtAgeTYC6enSm9Jslpji4rKSI0tLbyUlC52XKIqSF9OnwzPP5N4+dOgOvv66CZ5GuKgqSjGiolIZxAiTJKsVMjPFSsBqlcXojAzYsAG2bJGFUH9/ERqqVZOx69eLjZaHh2Tflysn1meVK4uQtHChTPDLlROBqFcvuTF58005j9ksYlRysogK5ctLA9v4eHnNtTeA+YkQtyo4DRgAW7dCdHThfHZNm0Lv3mLxd/ly7jiuF+ON9hW2SJXfud5/H/71r1t73+7ueWcs2oWjGwlwStGRvW9Serr8TVaoIDfanp5iLah9k4qHzEzYuVO+K/39xW7QLvb16iUVm4piNIwwL1EUxVgkJSVx5513c+jQtitbagFbCAmpaqj5ndksLge7dslc1MND+kcaKYEmMlIqlMxmedSqJUltY8eKEOZoUlPF8i4uTuYrO3fKnPEf/5DEJKMQGyv3GsnJkhBZGnsqKYLOSxRFyY+qVa+1aF0EPMjdd9/D8uU/4eLi4qDIFKX4UVGpDGLESVJmplhl7d4tN2CXLkmfJT8/sW06fVpElBMnRABychK7u969ZUF78WK5OapdW/ZVqSLZd5mZUsUUFSUWW0ePynGbNpV+IykpUKeOVDDVri1ZB/YbwOyVF9feFBaW4HTHHXDoUO7tTk4ithUm5cuLMJOXR/nAgSJ8RUVlbateXSpNPD2hdeucNhnXE6mCgqSCbO1aqVIpDPr3F2HiZiq3tBqpeDGb5W8sPFwWBew2lu++K+KSfUx+f0tKwbFYxH7H1VW+Q+PiJBO5enXZZzbL566JU4pRMeK8RFEUxzNjRgxTpnQFDlzZ0gTYyKxZ5Q0zzzt/XjKXXVwkkaNCBbneTpki9x5GwGIRy7voaEmwO31aXBYmTjTO3ODiRbG8S0yUz9LdXcSvZs3kvsgopKfLfaObm1YolWZ0XqIoyvWYPl0s73r3juX112uTkREPwCOPjOd//5uJyWRybICKUkyoqFQGMeokKTUV1q2TReny5eXmwtcXunaV5x9/lMXS9HQRLry8RGxIS4PPP5ftKSlyLDc3sbirXFlEp++/lyy9ihVhxAgRq95+WyqYkpLkuImJ8PDD0KaN3HytWJFV+XQrNha3Ijg9/3zeYtOsWfKc176BA+WmKy9xyGQSW0Aj4+QkWX15VW6NHi3i4bWojZ1x2bkTpk2TBYDAQMncMZuNtZhSmgkPF4H80iWpXGzWTL6vypd3dGRKSWDDhg107979huO6du3Khg0bAJg7dy6PPPLITY29WYw6L1EUxbH07w/Lll0AOgGnrmztSr9+61m61BgLNvbkmqgomQvt2iW9Xz/4wFiJNPb5Qmys3BM1aAAjR8o9kVFITZV7ME9Puafbu1fmM40byz2OohQXOi9RFOVmWbNmDb163QtIT4uXXvo/3n57mmODUpRiQuvyFMPg6iqZ9dHRUtUQFCQL1AkJIvwEBooIlJ6e5VtusYjoExwsN0nVqsGBAzJ+82Zo0kReU7682OuVLy+2FP7+cuyoKKkW+vtv+dndHVatEnHLahXh58wZsfay92GC61dejB+ft9hhr1i6VhD54IPcYtP19v3yS/6VTzNnynNe+774Qt7v//1f7n2+viKqXYuXl4hUFkvuffbMxrz2VaoEdetK9dO1fPll/nZ18+bJa/L7PCD/z1cperL/v09MhD//lL8dLy+xJ6lfH86dk79Nf39HR1v6sVhkgSg8XKxYLBZZkDFK1rFScmjQoEGe261WK8ePH6fRNVkVnp6e1KhRI8/X5LddURTlVhk0CJYtqwqsRoQlM/Ai7dsbR2Hw8ZFkjsWL5V6kQQO5Zzl9WsQQo1C7trg4mM1yv7NtG2zaJD1ojWIx5+4uD/u/GzeW+7qjR+VzVWFJURRFMRp33303CxfO54EHHgDgnXfeJDi4Mo8//riDI1OUokcrlcogRs28SU+XBWlnZxGS0tNFAAoOliz848elQarFIjc/DRtKxUtQkLxm7VoRpCpUkOqm1FS5Cdm8WW5MmjSR/fbGtKdOZd0AZu/1cvJkziomi0WeH3lEbOCSkmDp0turYsqP61Xf3KrV3u3su52KqRvtu5k+R/m9N61GMh72vkkXLsjfZ506crPfrp38jeT1t6QULRER0p8sLU0sP4ODpVrJKD0SFONjr1TKbyq4ZMkShgwZwv79+2nSpAkglUpz58695Wqk62HUeYmiKI4nay65B7AAHXB2lvlIxYoODS0H2RNv9uyBffvEpjskxNGR5U1KiiTRmUwiLHl4ODqivImIkDlo9eqSsKYoxYHOSxRFuVU+/fRTnnrqqSs/mfjxxx8YPny4Q2NSlKJGRaUyiFEnSVarWLqlpUmm/eXLUiVTu7ZUMa1aJeJSUJDsr1BBFlLPnJEbjshIOUZgIHTunLXvk0+kSsnFRSpynJxg0iQ5zuXLclNatWrWImx2G4vgYDh2TASmjh1l24YN0qspJEQW02vUuPkqpsLmdsSo/PYVpkilfY5KF2azWNwdOSJ/RxaLiEZvvCF/U/Yx2jep+EhNlZ5lixbJ76BZM/n+tIvmWq2k3AyHDh1i2rRpfPfdd3nu79GjBwDr1q27uk1FJUVRipvsc8nvv5dEMjc3mXcYUQyx2STGEyfg3nvlXsGIJCWJsOThAd26GbdH0Pnzcm9Rp45xP0uldKHzEkVRbocXXniJDz54FwCTyY21a1fclNW4opRUVFQqgxh5kpSSItVE9gaoXl4iCGVmStVRZqYsXptMsmjaoYO8bv58OHtWhCRXVxGJ+vQRkWnBAhGPXFxkUdzVFe67LyuT0L4Q3quXCFggGXF5VV4cPw7vvCPHsttNmc3SC6hlS9m2cmXhVjEVJ4UpUiklF7tA5Ocnfztr1sAPP0iPsrp1RTSKiNC+ScWNxSK2g+np8t2VmSnfdbt25f09pigF4cCBAzRt2pTFixczZMiQq9tVVFIUxdE0bw779tlwdX2fqVOTDdm7wGqF5ctFEBk0SKyhjUh8PKxfL/ctXbpIIp0ROXVKHvXry9xHUYoSnZcoinI72Gw2Ro16lG++mQOAi4sfTz21kT//bMGQIbJ+oiilCRWVyiBGnyRZrbJY6uwsVUWpqSI0HTki+9zcxKLO2xt69pQxy5ZlVTfFx4voNGaM2GL8P3v3HV91df8P/HXvzb3Ze7O3LNkgCDJEUIYg4qhaFQdoW6tiW5S237ba/hy4altbhao4cNUFggIqiEwF2dOwA4Hsndybm9z7++PF5WbcQAJJ7k3u6/l45JHkcz/3k3NHknPO+7zfx7UxrWuSvHdvBn0WL2bAqlcvBrFiY6uu8PeUeVE9iyklhe284gpOsq9axQnfjh05Ady+PfC733kni0nkQuzbB3z0EX9viopYZrJ7d+DHH/l72aaNe9+khx7S+7ipuP6OHTnCYPeIEQxaBwe7g9thYcpQkoZz//33Y/ny5Th06BBMlWYZFy5ciOeeew4jRozAihUrkJaWhrCwMAwYMACzZs3CjTfeWO+f5ev9EhHxPRERc1BY+CwA4O9//3ulkjO+o7wc+PxzICcHmDaNe7v6oqws7q+UmMjKDL66d1FKCoN0vXpxTFdayn6Pax8mkYaifomIXKjy8nKMGzcN3367FIAFwIcApgLgIvi0NG+2TqRhGb3dAJHqjEZmExnPvDsDA1nSLiGBg56TJzko69CB5Z7y8zmZmp/Pc8xmBp927+bkd4cOwE03cTB3883A8OHA4MH8g96qFa+Xns5MqKwsdztMJl6r8oo912a88fG8T/v2wIMPArfeClx3HTM5OnZkIKygAFi/HnjzTe7rtHIl8NxzwIsvMjC1b5/7ukVFbEdRUeM/vyK1ycgA/vEPvl/z8vg+LitjBtq99/J97ypBef31Cig1ldJSZkDu2sVMJbOZJWtcgoPdZUFFGkJeXh7eeecd/PKXv6wSUHLZt28f4uLi8O2336KwsBBr165FWFgYbrrpJszytNFeNTabDQUFBVU+RETq429/a3P264cffhhvv/22F1vjWUAAy9+FhTG4VFjo7RZ5FhfHYNKpU8CWLay8kJHB/ocv6dKFga+1a1n+d9ky9o9OnfJ2y0RERCggIADLln2ANm0mAFgBV0AJ4P+rF1/0WtNEGpwylfxQc1x543CwvN3p0xyghYRwhZrFwuykEydYDqqggOXqxo9nMCotjSX18vKYZREezoBSVBTL4uXm8us9e5hhdMUVHLBYLMAPP9ReUup8WUxt2rBEg9nMtpw8ycyo4mIGsqxW1gR/9FGuXvz44+ZbMk+ap8rv4aIiBix+/JHZdp07uzdDPn3aXeZOmXbecegQ8PTTDBpdcgn/jqWnAzNmuPeCE2lIzz//PP7v//4PJ06cQEy1pfUnTpxAWloahgwZUuV4eXk5evbsiZSUFCxatAi33nprrdf/y1/+gscff7zG8ebULxER7/vDH/6EJ5/865nvTFiy5FNce+21Xm2TJyUlwKef8utx4zjm8MW+1PHjDH6dPs1xli+W1bVagfnz2Q8aMIBjq6golj1XxpI0lOY4XyIivmXkSC6C8HR8zZqmb49IY1CmkjQLFRUMLLVpwwlVp5MDtKgoHnMFk4YPZ3k7q5UDoN69mRG0cycHHZmZwObNzIIaN473KSjgebNnA4MGMQD073/zPLOZpfe++qrqar2wME6yVx4MVs5iOn2amVCzZgETJvCjY0dePy6OQaudO/lz/vQnYNs2tunkSe7lVDljSVlM0tD27QOefx744x+Be+7h+zAjAxg1iitVXYPyEyf4OxIZye89ve+lcR05wgB3SAhLdIaHc4WTL05GScvgcDjw8ssv49Zbb60RUAKANm3a1AgoAVyVd9999wEAXn/99XP+jLlz5yI/P//sR2pqasM0XkT8yt/+9jjuu++XZ76rwNSpN+Hyy9diwQKvNquGkBDg2muBY8eAX/+alQuqVy3wBfHxXHB3+DBL9+Xm1hwDeZvVyr6pq4RQeDgzwHypjSIiIpW2pK1kMSZPLmvqpog0mgBvN0CkLkwmBmLKytyr9CsqOOAxmVgOLySEtwcHM4BUVsaspoQElkrIyeFApKiIt3fsyD2Uqu9FEhnJje/Ly7liz+lkYCkrC2jblueUlnIAEx5eteRUjx7cZ6Z6NkdkpLt8X5sz1Tq6duW+KAcOcBL/+HG2+fBhntO7N3/GmjX87CmLSZkjci6e3h+HDnEy49gxrkKtqOD7ecoUvp+johjYVJk773D9bQkKAvbuZYZmt27MsFy1in/7oqMZFFe5O2kMS5cuxZEjR/DrX/+63vft0qULAJbHO5fAwEAEakm5iFwkg8GAf//7n0hPz8Fnn70Pp9OKjRsnY+PGNZg3rx9SUrzdQjejkf2ynBz+j6+oYH/Ll/anLCzkmKR3by7Ea9WK/ciiIt/pcwQHs19bUcH2btsG9OnjO+0TEREBuGj92WddJVqdAP4C4Als2HALHI53YDQqx0OaPwWVpFkwGrlKPzubgSFXtk9REQc/4eHcVDYkhCv7IyMZDAoO5m15eQwi7d3LgNKePQzOhIdX3b8J4ER727ZcnRcZyXOtVm5g27kzz//xx9pL44WF1RwcurKYqk/Wt20LfP89g01JSdyANjGR7d64kXXCXSXzjh7lAG/2bJbOO3BAZfOkdvv2ud8fwcEccJeVMZiUlsb3SocO/D05fZoZe+HhtQdGpfEdOcIVwadO8W9C797ci8EVzHaVIKwcBBdpaP/4xz9wxRVXoG/fvvW+r8PhAMCJXhGRpmA0GjF+/Jv47LM8AMsBFAC4BgcPrsOCBV0wc6Z32+eSn89+2OWXs79fXMwAk2tvWF8QHs6+X24uF7Bs2wZceqnvtA/guG/wYFaUMBi4/2dwML8WERHxJWlp3EPpvfcOYvPmeQCAzz57Dw8/nIiXXnpBYyZp9hRUkmYjKIilDioqmJ1kNHJAVlLCgExaGlesde4MhIZyhX9SEgceGzdyhX/nzkDfvrzfmjUs7xUeziDV4MG8fnAwA0VffcWB3qWXAqNHc7CyZw/wxRdsQ48enPj96itmPJ1vkre2yXpXsCkrC+jUicGmHj3Ytv37eV27nbfv2QO8+y7vu3Ej29GxI8vjffwx8PDDVfd4UmCg5attf68PPmDWm9HILLidO4GZM/l+q6hggNbp5PssPt5d4g7wHBiVxlVaCqxcyYkmm42B7LIy/m1yCQ5WMEka1969e/HNN9/gww8/rPWcpKQkvPbaa5g0aVKN21LOpAV079690dooIlLdsmUWAB8BGAdgI4B0ALdh8eJNmDnTNyZsIiO5CCwri333jRu5uMeXtmupPgZq147tKyjwrf5HcjL3UHKVvNu7l/3cfv1YpUJERMRXzJ4NzJ7dFUuWfICpU6cBcOCf//w72rRJxpw5c7zdPJGLom6XNCtGY9WsotBQfm+1MkspLIzHEhKYoZGWxolzV2ZTdDRL5bVpA+zaxRWCNhsn2MvLgUmTuAKuttJ4ERFctVdRwWyC8nIGq06eBM5U/am1NB7gebK+tmBTVBSDYpVL5nXrBtx0E4MF333Hx3TsGCefDx3i4+7enddav57HY2JUNq+lqpyNFBXFrJbAQO7Bs24d36/x8dzLy24H+vdntssNN6jEna85fZp/WwICOMkUF8fMRF8qOSMt3z//+U+0bt0a0zwXAQcApKen4/PPP68RVLLb7Xj11VcBADNmzGjMZoqIVDF1KrBsWSiApQBGgdlKbyMpyTcCSkDVqgW5uVzkFh7OYMjw4d5unVvlMVBICPsm69dz383YWG+3zi0w0L0HaJ8+wPbtwO7d/FoVhURExNdMmTIF//3vfNx7770AgEcffRSJiYm48847vdwykQtncDqdTm83QppWQUEBIiMjkZ+fjwhfWh53gRwOBlZycjhgCwhwZzXl5ACrVzPQ06ULA0hhYZy0XbmSQaaiImY15ecDN9/M4IvRyHNLSzmh6xq0lJYCb7/NwWBMDFfG2e3AsGEMAJnNzCYqKvJcGq++9u1zDz6jo91ZTEVF3ODXVTbv4EE+5quvZoDsk094TlQU25yQANxzD8toZWUBy5fz8VYvm6dgk2+qLRvp739nQDMkhOUQjUZgzBiWS1y3jgHVDh3c2UiV6/brtfYdR46wDOb69fyd7NqVQevo6LplQYo0hPz8fLRu3Rpz587FH/7wh1rPMxgMMBgM+MMf/oB77rkHrVq1wsGDBzF37lwsWbIEd911F1577bV6lXNoaf0SEWl6XbuyPwykAXAAaAODgeOAUaO827bKKve/jh7lIrHLLgMGDvR2yzyrqGAbCwrYx/TVP9H5+cCOHRyf9eqlcnhycdQvEZHG8uSTT1Yaa5mwdOlijxUgRJoDBZX8UEvrJNntnDQ3m1nWzuFgBk+7dszU2biRJfIAZjGZTFzFtm4dMzwSEpjRZLMx8BQRwfMOH3ZnHLlK4wHufU9cA8KrrmLQad8+4MMPGcTp2pWDsNatL35SuLbJ/9oCTidPAs89x8fhcDDIlJYGjBjBoIMryNaqFa+dnAz84hd87lau9BxsOlc7pHFVz0aaMoWv7Y8/Aq+9xmBiYCADSw4H8Mc/ssxjbe8P8T5XNqPZzMB0WhqDf5GRwLff1r5fm0hjevHFFzF37lykpqYiPj6+1vMOHDiADz74AMuWLcOBAwdQVFSE6OhoDBgwAPfeey9uvPHGev/sltYvERHvWLAAWLKEfaUDB4Dnn2dwYefOCnTrVgGLxeLtJtawZQszzEeO5F6Kvshu5/ihrIyBpdBQb7fIs5wcVqJITAQuuUSBJblw6peISGNxOp345S8fxCuv/AsAYDQGY/36VRg6dKiXWyZSfwoq+aGW1klyOLiqv6yMwaT0dE7WduvGIMr27ZzAdTiYkRQdDYwfz4nbzZurBo4iIjjJu3Ilszx69eLtUVHMAqqcsVS9NF5GBgezAFfzFRczUHXvvSw7ZrGcuzTehThXBourbF7lLJVTp4Bnn+W5DgcHX6dPM9Nq61a2OTmZnxMT2fZWrXjOl1+6AxsKODWO6s9jURHwwgt8DUNCWOLQaOSK24gIBkwdDg6cXaXslI3k21xB6dRU/v7168eyha1b83ZPf1tEWrqW1i8REd/w858DixaVwWC4A9de68Qnn7wLk8nk7WbVsG4dy+CNH+8up+1rrFYGlgAGloKCvNue2mRkcCzXpo3vPpfi+9QvEZHGVFFRgWnTbsXnn3MfW4slBrNnr8OGDT0wbRr3YRJpDhRU8kMtsZNktTIjp6yMAaWAAGYkRUQwS+mnn9yZAa69l1q14jnVJ93z8oAPPuA1nU6uxjObgeuuY0ClNpVL4yUl8WeWlzMrKjiYq+UOH2YQoLGzEOpSNq9ywGnyZODf/3YHm/LzGaS44goG0lzZTUlJvEZCAnD33fz+1CkG4VzBt/oEnPwx6HGuzLOPP+ZrYzJxH6TiYr4XXUHIoCC+px57jM+xspGal9JS4I03ONkBMJuxd2/+LimAJP6sJfZLRMQ3JCRcj8zMTwEA9957H+bP/0+9ynM2BacT+OYblu+bOJHVFnxRcTGwahX7o6NHc3zki06eBFJSOM5KSqpZzlzkfNQvEZHGZrPZMGLERGzZsgpAEoAVAPoA4ELvtDRvtk6kbhRU8kMttZPkcHCS1mRiNkdhIQNDJpM7cyg0lEGU3Fzg+HF+dgWZunThxLzNBqxYwdtMJmD/ft5v2jSeExDgeb8loGZpvHHj+A/hwAHg9dcZMEhM5OCxUydmAgUHN3wGE1C/snlt23oONj3wAL/++9/ZNqeT10xPZ8DJYmHAqbgYiIvjz4yLA269lYGn9HSW8yop4fGbbnIHPaqXdavr3k7NIRB1vsCR6zFfcw2fl+PHWcouPZ3vx7w8vjY33sjVq2VlLKl48qSykZqzrVuBf/6Twe5u3fg7lZ4OzJjB11XEX7XUfomIeN+yZcswefJUABUAgLlz/4gnn/yrdxvlgcPBPU9PnGD5vqQkb7fIs/x89v0jI1nloaSkYccvDeXoUZaKLijgeKV6OXORc1G/RESaQkFBAfr0mYljx54GUHXF+QsvKGNJfJ+CSn7InzpJRUXMDjIYOGlbWsqBRVwcJ+uPH+eGrq5j/fpx1d2pU+7SeCEhzGqy2RhkCg5m6aqiIs8DlNpK4735Jq9VWMjr5+QwoBMbC+zZw/s01T4qngIR9c1uevBBZjM9/zyvYzC4A05XXcXB8YoVDDhFRTFQEhkJXHstn4flyzkQbd2aWWYJCcAvf8mf88UX/LnVg03nCkTV9rgu5rYLuY+nPZDatOFr/u9/870QGsoAUXAwV3o6HMDatTwvPp7vx5wcdiIKCpSN1BxVDhQ7nQwoHTvG8jbh4VyFfOoUX9OL3XdNpLnzp36JiDS9RYsW4ec///nZ71988UU8/PDD3mtQLcrLgc8/Zx9w3DiOO3xx0VB2NveRPXqU45ioKN/bB9JmY3b48eOsGlFWVrOcuUht1C8RkaYyciTngjwdX7Om6dsjUh8B3m6ASGMKDOSH3c4gQEiIO8soIIB7JuXmcvLXZuNkvdnMINHVV1fNRrLZmHH0+ee8Xs+eDBxs3lx1gBIcXHOCODycg8LcXAaonE7+DLMZeOcd/pxOnXi9lSuBO+5wX6MxspjCwmoOUHv0YAZM9UBJWBiDN5984t635/rr2R6DgYEQV8DJagUGDmT78/O5B1BMDDPHCgo4iR4Tw2BSbi6DK5mZ/D4tjYE3195O0dG8z86dDMwEBgLLlvF1TE5mCbGMDGZ5REVxwn75cncQa9o0lhYzmVgCo7ZgVG2BqnMFsHbu5G3Z2XwMY8e6g2NvvcXPERE874cfGDiyWtmO+Hg+pz178nFPnsz3hNXK5zEszB24i4zkdT29LuK7Kmcs2mwMWLdpwz0ILr+ct6Wn8z0+bpwCSiIiIo3ptttuQ3Z2Nh566CEAwOzZs7FwYQx+9as7MHOmlxtXSUAAy9+9/DLw8MPsAyYm1lxE5W0hIeyPuxZIORzs2/jSIpnSUo5VOnTg+KV1a46nSksVVBIREd8xbVr1oFI5gAW49tqZ0JS9+Dq9Q6VFM5k4CV9czBVqBQWc4A0Odg+I4uM5uW+zcXDUoYM7GFV50BEYyOyCpCSem5ZWtRTeuQYowcGcPHZNJsfG8vvQUAYeXOX68vOBTZtY6qxvX7a3ejm9xlwF6CnYBNQ/4OS6PTbWHXCy2YBLLmEWE8AMMtdtqakM3EycyOtER/O5LSxk4MhsZtAlN5cDxIICrubcu5eBpKAgdxm+qCge37mTwRzAfVtsLLBjBwNXV1/N533lSr4PYmKYMbZrF3DllawZX1jI47t3A9u28Xp2e9WflZfHANSYMfxZruchPJy3FxbyttatOeiunPHVoQMDSud7Hmt7XcT3lJbyd/bUKX6dmsrf2VtvZaAR4KRL9WxGERERaTwPPvggMjKy8f/+3xMAgB077sasWdGYN+9apKR4uXGV2O3sb5eUcJGS08n+YeXSx95WWMjPAweyn+Pao7aoyHf6NcHB7IuXl7M8+vbtwKWX+k77REREAFanefZZzh8ApQBuAbAYu3ZthdM53+f2gRSpTEEladGMRgYSAAYlHA4eMxq5P9LBgxwYtWnDSf+SEgY7EhM5ECkrYyky10a0wcGc8M/LY1Bqzx4GPw4eBLp352Cvtv2WOnasOZlcWsogV24uAy6HDzOgkpvLYMn333Og1r07j3lzFWBDB5wq35aQ4N7bqW1bd+ClqIjZRpMm8T7HjlUNyvTtC/zqVzx25Ii7dFxxMYN3Q4dyILlzJ/ewCQzkc56ZyWs4HHxtO3ViYCoqigN4o5FZWO3bu4/n5fE1Mpv5uicm8rFUVPA+N97I84qLq7axc2cGCc/3fNT2PErzkpPDIGN5OYNIl1/uDlq7eMpmFBERkcbVrt1fAGQDeBncY+kmHDy4AgsWjPSZjKX8fPYlR4wAfvqJ/c+AAB73lb5h5QoMyckM2HTr5jvtA9jnHzyYFSWMRva7g4PZP1OmkoiI+JK0NODFF4G33tqO7du/AAC89dZ/0bp1kk/uAynioqCStHhBQRzwVFRwUFFUxAFaeDhrbFcOADkcDEbs38/AQGgoJ4a7dGGwp/IApbCQezB16sRJ4y1beP2MDE4ge9pvqfpkcvUMplat+H2HDsy2+eEHtunAAQ4os7J4XocOvH9jlMa7EPUNOJ3rtroGoly3RUYy8JaY6A7mFBYyINSnD+/XpQtvi4lhoKdHD2DUKN62YwdvS07mbZdeyn2f0tN5vHVr930uv5z3Wb++6vXi4xlQutjAkTKSmpfKv39BQQx6bt7MAHZICIOJrjJ3el1FRES8a8kSA4B/AMgB8B4AK4A/Y/HiVZg50zdWAkdGsk+ZlcX+69q1zFYKDfV2y9wqj1/y8xlQiovjpFjnzt5unVvlcuZmMxf97NwJ9O/PfpuIiIivmD0bmD17GN5//y3ccsstAICnnvobWrVKxAMPPODl1ol4ZnA6nU5vN0KaljaeZGApN5eTvwEBzG6JjeUAw24HNmzg5rPh4RyEJCUxgOTKWKqejeR08vz//Y8Bq86dmekSF1e3DWFLS2uWwyotBd5+m1kwISEMLDmdXLmYmMjr79vH85qiNF5TKiqqPfBS22379jGYk5vLSfzrr6+6b1J9b7vQ652v/dIyVN43KSCA2XYWC0tkRkRwEqipylaKNHfql4hIU1iwAJg1CwDKAFwHoBjAEowdG4mvv/Zmy6qq3M8MCODYYPBg4JpruIDNV1Qevxw4wL1Dhw5l1QFfVFbGctYAA0sWi3fbI75L/RIR8aZ//OMfZ/eBBAz48MMPcOONN3q1TSKeKKjkh9RJYvbPkSMcrEVEcMAWGMgVbaWlzDoKDWWAKC+PwZyrr3bvieJJXh7w+ee8dkGBO2D1858zYwWovTRebSpPXEdGcm+eoCCW2/vgA2ZJtG3LUg7t2wP33ls1KOULWUxN6UKCUee67UKvJy2bK+CbmckMyAMH+Dv+yCMMKrnO0b5JInWjfomINJWuXdmPBkoAGADwn/SzzwK//a0XG1ZN5X5mTg7wxRfMXBo7lgvLfI3TyWzt1FRg+HAuyPNFpaUMLFksXDAYoLot4oH6JSLibY8++nvMm/cUAMBgsOCbb5ZjjGsjbxEfoW6U+CVXKbyEBJauKyvjYKiigoOMkBAGZGJjOfiwWoHjx1l2LiyM2Uye9luKjGRwqVMnltCzWoFdu/i9ycRBjCvQU700niee9mECGJDq0MG9f1BGBkvlJSVxD6LycmDjRv/LlDhX+bgLue1CryctW14eV+O6/gb068fj1UtbKpgkIiLiW1JSmLG0ZEkIpkxhqbmbbgJ+9zsgOroA06aVIyYmxtvNrNLPDAsDrroKWLmSi8tGjPBu2zwxGIBBg9g32rCBZaZd+9r6kuBglsfevh3YvZtf+1L2l4iICAA8/fT/w8mTp7Fo0RtwOsswbtxUbN68Bv379/d200TOUqaSH9LKG2YTnTrFgY/ZzKCMxcLVi4GBzGA6eJABp5AQBnBKShjcMRgYGHLtm+LabwngNV37LYWHA337MsBz5Ajw44+8dteuLGkXFVW30nieuDIlXBvknjjBAdHQodzLZ9Uq/tzOnfkYW7dmcMqfs5hELpTr9yUsjHskbd3KDMLAQE5GZGXxb0Dl3zERqTv1S0TEm/7+d2D27AwA16Bnz0D88MPXCPWlTYzO2L0b+O47YMgQBnB8UUUF21hQwAoLvvonPT+f+6pGR3NBni9mf4n3qF8iIr6gvLwc48ZNw7ffLgUABAcnYvfuDejUqZOXWyZCCir5IXWSyGplcKesjKUPzGZmE4WHu/dWqp6NlJEBfPst7+sK2ISFnXu/JYAT0e+8w4FWSAgDSgAwebL76/qqXhrPlY106hTw73/zsZSUuD9uuw3o1YuZTd98439ZTCIXwvV7lprKCZJu3TiRExYGrF+v3yORhqB+iYh4k9PpRLt2o3HixHcAgBEjrsY33yyBxQc33dmyhdUJRo5kMMQX2e0cL9lsDCz5YHwOAMsK7trFvWo7dOD4rq4lyqVlU79ERHxFSUkJBg++Cnv3bgQwBGPHLkVZWTymTQNmz/Z268TfKajkh9RJcnM4GOgxmZjpUzkDKTi46m0AAzIbNzJo5HDwHLOZKwbPNWCy2YAVK7gHi2s/p6goYPp0TkTb7fXba8nF074tlbOYEhOBQ4d4fMAABpfWreNqvC5deG58vDvDQhlMIm6lpcB//sNSlhYLf0+7dwfuv9/9+6J9k0QunvolIuJtO3fuxKBBI2G35wMApk69GR9/vAgmk8nLLatp/Xpm2YwbxwoIvshqBVav5tdDh7IP5Yvji4wMYO1aZp2HhdW9RLm0bOqXiIgvycnJwbRp/4fvvnsGgHsPhuRkIC3Ne+0S0Z5K4teMxqp1tMPDmbWUlcXJ4qAgTibHxrq/joxkoAkAjh5lWQe7/dw/JzCQAxRXabz+/RnwOXEC2LOHGVNmM69Vn4GMp31bgoM5yPzqKwax2rZ1Z1EcPMga4gYDf3ZZGfeK6tWLQbEfflDmhfin6gHVzExO2mzbxt9HV2nM9HT+bXD97vna5IiIiIjUX58+fbBq1VKMGjUeDkcpFi/+AL/4RTReffXfMPhYbbTLL2fQ5ptvWO46IoJ9d1/a6zMoiNlU774LPPEExz0xMb43voiMZCb6sWPAJZdw38zNmy+8RLmIiEhDi4mJwXXXvYzvvqt6/NQp4MUXlbEk3qOgkkg1gYEcoJWUMPhSUcGgT3IyAz9durj3W7rkEmY1nTzJSenERJ5fvWwewPtffXXVjKS8PA62TpxgxlB2NjOZJky4uIFMx47MPqqeRdG6NSfHc3MZKEtJ4fFDh5jBZLMBnTqxHMSXXwJ33aV9mKTlq1xK0mjk74nJxPd5796A08nfx1OnWHvflyZtREREpGGMGDECn3/+ESZNmgqgHAsWvIITJ+JgNP4VU6cCM2d6u4VkMLCsXEoK8NhjQKtW7LtMnw706OHt1rkZjRwvZWUxyOR0sr/lS3tQlpZyzHbJJRwfxcdzvFNaqqCSiIj4jk8/rX4kF8C/8emnczF7ttHDPUQan4JKItVUVPBzUhLL3dls7jJ5RiMnlfv1qxo4ys5m+YSTJ3kfgMGmLl14vktgYM0BSmQkB4L5+cDp08yM6NKFg0KTyfMeTXVxviym3FzuCzVuHI8fOsQsLauVg6mNG/kcXHIJ27B5M1fyKYtJWpLSUveeSeXlzNw7cQL41a/4+9GrF29PT+fvsuv3RURERFqeiRMn4p13FuLnP/85AODLL/8GIBbLlj2MefPcC7K8raSEfRPX4jenE/jkE+Chh3xn8UthIcdLQ4dyAU9xMcc2roxvXxAczAVzeXn8vGcPSx37SvtEREQAYNo0lmulEwCuAbAHTmcWnM4XfC6rWvyDgkoi1ZhMDBaVlXFwkZHhzj5yZR6ZzVWzkFzl8VzBmtatOUF98CADUJXPrazyQCYhgT8jLIwZEfn5bMeJExw4NlSNb09ZTKWlbHNuLtC+PUtAtGrFMhW7d7O8Rmkp75uRwcDTzJnKYpLmpfr71OnkfklbtvB3LTqa+6OVlTF4ajDUnvUnIiIiLdNtt92GpUtz8P77D545MhtADA4evAMLFvhGxlJ+Pvs0I0Yw0JWXx/FGfr7vBJXCw9mfco0vvv+eZbnPtQ9tU6teorxNGz5/xcXKVBIREd8xezbw7LOcKwR2AdgPAFi37u949tlkzJkzx5vNEz+loJJINUYjg0TZ2QyexMVxUFFSwoylsDBORldUMABVeU+m6GgOngoLeW5ZWdVgVHWVBzLZ2QwsDR7M6xw6BCxZwkFNly68vaFqfFfPYqqcwZSezjJ+rmyk9HROvFssfD6sVrYjNpal9MrKuE9TaannLCYFnMQXVC5xFxYGdOvG93JeHvciCAri+9lTiTvtnSQiIuJfCgt/DSAbwONnjqwAcDuWLDH4RFApMhKIimKVgy5dgDVrODapbczhDZXHF/n5LCkcHg789BMX3fmKyiXKg4IYpNuzB+jbl31EERERX5CWxj2UPvtsApKT5+ODD+4BADz66KNISEjAjBkzvNtA8TsGp9Pp9HYjpGkVFBQgMjIS+fn5iFBPuVaukneuwJHVymyF8nLAbudni8WdpWS3M7hSVMQVeIcOMdNh6FCuyjOZeI6n/ZY8lbjLywM++4ztKCri/QwG4Oc/Z9CntvtdjNLSmhkZpaXA229zlWFyMjOnzGZuvpuZ6Q58JSWxja1aAbNm8XmpPJGvsnniLa73cHo6f4cOHGB5yttuA/r04Xtb71MR71G/RER8zYIFwKxZTgAPArADeBmACfPn+0amEgDs28eSd7m57NcEB7N079SpvpVlU3l8kZYGbN3KAJMv7f9UWUUFsHMnxzf9+/tWZpU0DfVLRKQ5ePLJJ/GHP/zhzHcmLF26GJMmTfJqm8S/KKjkh9RJunBlZVxdZ7MxsGO3M0CUnMzAU24uS96VlHBwFxfHcw0GBp4yMty3Vd9vqTqbDVixgsGlmBj+3LIyYOBABqnMZq6ic2UBNURpvNrUFhzKyABee42DWJuNQab0dGD0aLZ582YOzDp35nMTHV11c15lMUlD81Tibvdu4JVX+DsaFMT3sNHISaH4ePf9VOJOxDvULxERX9S1K3DwoAOA4cwHcOONwIcferVZVRQVufvnZWXcyDsmBrj2Wu6V6ov27WPfbMAAjhF8UXk5Fwva7QwsBQV5u0XSlNQvEZHmwOl04oEHHsK///1PAIDRGIz161dh6NChXm6Z+AsFlfyQOkkXzm4HUlP52enkAMPpZP1tV+ZR9Wyk8nLuUbR5M2/v3Nk9eX2u/ZYAluJy1fgOD+fgy+kEDh8G1q1jsKp7d14vKqphSuPVpi5ZTKdO8farrwaOHwfee4/tsViYcWW3M9OqWzcGy775xnN2iIJNciEqBz+Dg/k+s9v5XtuyhZMrPXsyGFo9wCki3qN+iYj4qgULmJV/2WXAn//M/uwdd6Tgj38Eunbt6u3m1ZCeDixezL1SJ0yoWqbbl2zfzjJzrooOvqisDNi2jV/378/xjPgH9UtEpLlwOBy47rpb8PnnXPFiscRg+/Z16OGr6cDSoiio5IfUSbpwDgcDJzYbv3btx9Kly7kHbcXFwLff8n5mM4MlBgMwaND5Syp4KnGXk8OAjd3O781mTpjfdJM7+6mhS+PVprYsJlfAKTOTz9Hhw3yOBg3ic7d2LbOY2rVj9lbr1sA997A2vEqRSX2VlgJvvcVgptHI92V4OEvc9ezJ95PeVyK+Sf0SEWkO1q4FRo7cBuAaREcHY9eu9WjdurW3m1VDaiqwbBnHJ2PHcszha5xOLpxLTQWGD2cZbV9ktbJcn8XCxYC+mv0lDUv9EhFpTmw2G664YhI2b/4GABAe3hYHDnyP5MYqZSRyhoJKfkidpItjtQLZ2Vy95nBwkBEezsBJbYEl135L+fnMXDp1iqUprrrqwup0u0rj5eYyW+rAAe7ZNHo00KkT2/Xjj01TGg+ovXSYp4BT+/YsEfjaawx22e1sZ34+MGYMH4vdzgn/ggKWJ1PJPKms+nsgO5srSRct4u9TVBTfbxUVDFSqxF3z1VTBcfEu9UtEpDlwOp3o1WsU9u1bCwBo1aondu1ai5iYGC+3rKaDB4GVK7l35IgR3m6NZw4HsGEDM8hHjeJ+rL6ouJj9zLAw4JJL2DdRv6RlU79ERJqbgoIC9O07GkePbgNwO6ZPfw0ZGWZMmwbMnu3t1klLpaCSH1In6eI5HJywNpn4uaCAx117tbhuqxxkqrzfEsDJ75AQTnhHRrr3ZzpXObzKqpfGu+QSXiMtjQEss5nlv7KzG7803rnUpWze8ePuFYCffsqvTSYG7kpLgenTGSwrKGAZs+Jiz9kmCji1fK5AZUYG3+/t2/P1NhqBHTv4uVMn/n6oxF3zduoU8P33/PsREdH4wXHxHvVLRKS5OHXqFC69dASysw8DALp3vwybN3+NsLAwL7espj17gDVrgCFDWCnAF1VUAN99xz7+ZZexH+eL/fiCApbtPnGCCwObYtGeeI/6JSLSHKWnp+OOOxZi5crfAXBPRiYnc55QpKEpqOSH1ElqeA4HBxtFRQyEGAwMjMTGVt3YtfJ+SyYTS8MdPsw/8GFhHKR06eIuYXc+nlbxp6UB777LshKBgQxeGY3AtGkMLtV2v6bmKYspKYnBppwcPgdHj7LMxIgR7j2YiouBxES2PyGBezQlJfG5/Prr2subKeDU/FR+zYKC+N6eP5+lUkJC+Fq3awfMmsXPR4+qxF1z5OnvUUYG8NFHwMmTwKWX8u+mN4Pj0rjULxGR5uTw4cPo02c4iotPAwCGDx+Pb75ZgkAf/Af1449coDFyJNChg7uP5EsxMLudZb2//x5o1YoL7nytD2ezsV+yZw+fx8BAjlXUL2mZ1C8RkebqxReBRx6pefyFF5SxJA1PVYFFGoBrVV16OifBo6I4CZqdzVUBrowls7lqJlJMDDeptVoZBCos5Iq9gQPrlrEUGFhzIBMbywylrCwGrlJSOFl76BAzOKzWqhlO3lpl17Ejs0iqZzGNG+cODHTs6B5Upqcz0yskhM9Rbi4znL77jgGHdetYWrBNG56bnw/cfTefj9r2fXJRwMn3HDnCEo9paXy9O3RgsPbYMX6dnMz3fmYm3xMGQ+3vKfFdlTMug4MZHHQ4GFTKzOTfspgYHsvO5u+qJm9ERMSbOnXqhA0bVmDw4FEoK8vD+vUrcdlld6B163dx3XUmzJzp7Ra6DRzIvv+HH3IBXEAAxynTpwO+sod3eTn7ASUlXFjmdLLf7kvZ5qWl/NynD4NgcXFsq/olIiLiSz79tPqRfQDewaef/g2zZ/vgJovSrCmoJNJAXPsrxcVxgFFRwUFRRUXtey2VlfH2Hj14n9OnGQRq144r9S5EYCADRa6J2sGDWSLMbuexXbvYzs6dGXjavNl7q+yCg2sOFmsLDEREcOWiq2ReRQUf1/TpzGbYuZPnlpfzed25E/jsM2Zqff89H3/btsxmWbIEuPde3naugJOCTY2vejZSfj6DhW+/zd+HyEhmpxmNfF9UVDBrLTjYXeKu8mpbT+8p8U02G//+uFLx9+zhnmoTJrBMT0UF3w+uIFNUlF5bERHxDX369MGqVUsxcuQ4OByl2LHjQ+zYEY0vvvgP5s0zICXF2y1069MH+O9/uTBn2DD2/z/5BHjoId/IWHIFlIYNYwUHV/CrqMh3/u8HB7OvmpfHSgnql4iIiC+aNg1Yu9b13UYAkwHkwGIxAXjCa+2SlklBJZEGYjIxWFNW5s5aCgzkhGhtLBZ3Ga+oKN7PYnHvF5OczAwMV8m8uu63lJzMQFH1klJHjwJbt3Ky9tAh/rzKq+x8oSwe4DkwEBzszmJKT2cwYdw4Pm+BgQySuQJOaWlA167AmDEMTGzYwOc5K4uPLzWVg+jYWGD9ej6/bdowmPH558A99/A1UHZTw6jtuTp8mM/3qVMMwHbuzNe1qIgfvXq5fwdcr/n48TXfA3r+m6fSUv5Nslr5O9azJ/9edujA3+shQxh0cu0LN3iwVgOLiIjvGD58OH71q4/xz39OAVAO4FUAcTh48G9YsAA+k7FUUMCsX6ORi6m6dmWfOT/fN4JK4eHsB+Tmsg+wYQPHKr7Uv6u8aE/9EhER8VWzZwPPPss5FiAVQC4A4Jtv/oqXX07Er371K282T1oYBZVEGojRyCBFdjaDFAkJDAIVFLgzbhwODpJMJp5vNnMPpYMHeV50NL83mxkY+fFHTsZbLMyqqc9+S55K4yUnc+I2I4M/PyXFna0THAz89JP3y+KdS21ZTNUDTjEx7iBQcjKwe7c74HT8OI8PG8ZzS0p4/9OnGVQ7dozP/969/L5VK/5Dzs4Gbr2Vr3F6Ovd3upCAU0sNRtX2uCpngoWGsgxLWBjf359+yuMxMQwsBAcD11zD59hu52tmMFTNSIqPV4m7lqCiggGlwkJmqLVrx3J3lVf91hYcFxER8RWHD08A8BaA2wA4AYQAYFa8rwSVIiPZj7Lb+f93wwYu3IiM9HbLqHI/Pj8f6NePx7ZvBy6/vPaKD01N/RIREWkO0tK4t9Jnn92E2Nh0fPrpgwCABx74NRISEnDjjTd6uYXSUhicTqfT242QpqWNJxtX9cBRcTEDF04ng012O4NEsbGcTAV4rHo2ks0GrF7NCfWYGB6Pj+dAq64ZS55U3sMkNJSTuSUlwJo1bEfXrvwcE9P8Np8tLfUcbKitxF1pKcus5eYCSUkMOIWEAJddxoBHaCjvX1DAoNLo0XzN1qzh65OUxNtiY1mGLzqaAbuNG/mcxsQws8YVcLrQvZ0uNEh1oQGs+raj+uMaO5bv1fR04N13+dkVuAsN5eMOCuJ+WO3a8dyyMp43Ywa/P99zJc2X1cpAb2kpf2cOH/btYLY0PvVLRKS5WrAAmDULAF4GYAYwCwAwf77vBJUAYN8+lrzLyuJYoE8f4IEHWF7aV1Tux+fns5pA27bsGxi0DYQ0IfVLRKQlefTR32PevKcAAAaDBd98sxxjxozxcqukJVBQyQ+pk9T0SkuZjWS3A4mJ7gBScnLtq++Ki4EtWzj5npfHSVen013y7WJUL3OXl8cNfB0OTvhWVPC8n/0MaN3a832am/oEnJKS3MGm5GQOviMjGTjKyADeeYeDcIOBr0tGBjByJF/LNWv42kVH83mNjOQeMYGBwKpVDDYlJfHasbF8jiMjuZpkzRq2sXIQ5VzBlQu9zfV81CVAVFs7Ro7kezknB3jvPU5SREQAJ04w6DlyJH/GunV8vK7AaEkJJ1/Cwmo+x9HRVTdlru01k+bH9fejrIwZkiYT0Ls3X9vm/rdFLp76JSLSnHXtyn5+Zb16cQGFLykqYj/OYgFWruS44vrruaDKF6WmAps28fnt18/brRF/on6JiLQkTqcTd9xxL95553UAgMkUji1bvkM//XOVi6Sgkh9SJ6np2e3MgikrYyDCVQqvTZvas47sdpZ9KCpiECk1lZOvl1zCkmyxsUB5ef33W/LEZgNWrGAQJCqKk74VFRzAxcdzAvjIEbalJWYTeApe1CW7qXowJD8feO01PocBAcxiSk9nUMlq5f5BYWF8PktK+HxXzn4qLmbwxfU6jBzpznpKSGAAJyoKmDKF11i2jIGhpCR36bAbbmBw65NP2J6kJLYhKgq4+WY+vuPHmQXnCiqNGcOModJSBhddAa+TJ5lZNGoU8OWX/PkREQyiBQfzeGkpM47i4vg8ubLz7ryTz4/rep4CR8pG8g+u7Mjjx/k7MXgw3/cX8zdLWhb1S0SkuVuwgCXvJkzgfgZlZcCll67EP/8ZiFGjRnm7eTUUFrKvGBQEXHed7y7qOHSI+8H27g306OHt1oi/UL9ERFqa8vJyjBs3Dd9+uxQAEBychN2716NTp05ebpk0Zwoq+SF1kpqew8GJVZuNwZr8fE7Od+ly7jrhublc+VhSwlWEHTsykJSdzSBFURHPCwmp335LnlQuixcezr1vLBb3xH9pKdCpEx9LQkLzK413IS6mnF71IApQ9ba0NN73xhsZrFm0iIEfs5lBmYwM1pFfs4bnBQTw+tnZLC9XXg58/TVfK7OZkxf5+Zysdzrd97NYqt5WOYAVHc32uAJHpaU171dczHZs3crXPSyM1y8sBO64g8GkDz9kIOxCAkfKRmrZbDYGJA8cYEDdYAC6dePeWS3974fUnfolItKScHHOh3A6f46AgGD88MO36N+/v7ebVUNODss9x8QA117LvqYv2rePWV8DBgCdO3u7NeIPGqNfUtfMfG+d1xzaqMfim230x8fSXB9zSUkJBg++Cnv3bgQAxMZ2wdGj2xAWFnbuByFSCx/tOoq0LEYjMz+yszlRHx3NSfuCAnct88r7MLlERzNbqHo2Umgo8M037r2AbDYGny5mv6XaNp81m4E9exhIKC7m7RkZQPfu7qBYSy1fFRzsOdDRsSODJtWDIZU3Gk5P5+s3bpz79sq3xcTw+7g4vp6tW7szhIqLmZE2ZgzfM5WDVJ06AVOn8nolJe73QFoaS63ccIO7jGF2NkvUpaezdMikSQwGHTrEnxsczPMyM7mPVGgof3ZeHtuTlcV2Tp/O1z83l9c7dYo17tu04TXGj6/9Mdf2XJ3vOZaWISsL2LmTr3GnTnyPZWfz70VL+lshIiLiEhXlxKhRb+Pbb+0oL7dj+PCrsX37OnTr1s3bTasiJgaYPJkZVitWMMvqXIvdvKVHD441tm7leKhtW2+3SKR+qi/erK3qh7fOaw5t1GPxzTb642Px9cfscHBLgu+/d1fCcZ0XEhKCtWuXolevETh9eh+ys3+JX/0qDEeOANOmMdNapD6UqeSHtCLYexwOd/DI4WD2RmkpS90BHCjFxjKb5Fxc+y0B/Idis3F14dixnLhvSJVL48XFMSBRUQH07MkMKYOB/7TKy1tmabwLca7sm/pmPzX0nkrnyqY6V2aRMo6kvk6e5OriHTsYPG/blgHpqCj/yHSUulO/RERampKSEgwdOh67dq0HAERFtcfu3evR2rVZqQ9JTWVJ5c6dgauuYt/e1zid7lK6I0awL+ppb1CRhtCQ/RLXWHr/fvaHc3L4vh0+nGN/l7IyYP16vq9jYpruPG/+bD0WPRZfaeO5zqu8aLusDNiwgee5qr6Eh7Oyi8VS9f9nbdccNoznOp38qH7NvDz3zw4MdFf7sNurnpeVxfm4IUM4v1heznk6q5X/L4uLOe4uKeFi4GnTONcIAKmpqXjggU1YsuTGKq+Tq6KOSF0pU0mkCRmN7hWARiMHRBkZ7lJkZWVcxZ+cfO6VghYL/4EUFTFL5dgx/vM4fpwTt+Hh/KfTEPstBQYyULR5M/9ptm/P76OigKNHuU+Qq/RZVhb/kU2c6J4wbqlZTOdyruyb+mY/nSvT50JuO1821YW043yPWfyD63c9IIB/G7Ky+PeiRw/gxx/5ty0qin8//OVvgYiI+KeQkBB8991S9Os3CseO7URe3jEMGTIeO3d+h1jXrI6PaNuWwaSVK7mwrX9/9yIiX6mIYzAAgwZxfPPpp+xvVFRoT07xfaWl7klgk4mfXQtLK4/RS0vdcwIWS9Od582frceix+IrbazLeQYDx7slJQwSBQbyc34+5+JcgaLK13Ttj+66ZkEB/48FB7sDUDYbjyUl8ZoWC+fX7Hb+T3Y6uSC9sJDHIyI4VxgdzWMWC9thMnEcXlQEHD7MyjcBAZxvPH6ce3XHxfEjPr4tRo1qiyVLqr5Op04BL76ojCWpOwWVRLzI6eQ/isBA/jMxGnmsouLcQSWzmaXnDh7kP7927TggdAWWXP907PaG2W+pttJ4ycksk9a+PduRlcXSFMnJ3DfFamVGVV1Sg6X2wMyFBKnOdduFBogUOJLauFLx09MZfL7kEq6uiovj7XFx/hdcFhER/xYVFYWNG5fj0ktHIDv7MNLS9qJ370no2/drTJ8ehpkzvd1Cty5dOBb54APu8xkWxomw6dO5OMQXGI1A377AJ59wkmz4cPY5vvrKnXEv4muCgzkOrqjg/rQZGRw/DxpUtU9ss3EBVl5e057nzZ+tx6LH4ittrM95WVlVz2vVqn6PecCAmtdMT696Xvfuns/Lza15vUsvrXleq1bu84KCuF/6wIGcl8vIYKWht95CNUsBrMOnnz6F2bN9MGVZfJIPVk0W8R8mkztNNiyMk/wlJQwKnY9rv6VBg/g5KQno0IGfDxzgR1kZV0McPOgusXehAgM5uKz8D8vVSXb944qLY8CiooLBpHfeAX76ifuo5OVx0tlmu7h2SMMIDgbi4zUAl4tns3Hl04ED7OiWlbnLYbp4+vshIiLS0iUnJ+OHH75CaGgSAOD06e+xYsX1mDXLhq5dvdy4atq35z6bP/3ERW5ZWQzgFBV5u2VuJSXsvyYnszSzazW5L7VRpDJX1Y+oqHNn7XvrvObQRj0W32yjPz6W5vqYhwxxL/weNowBq/HjK9/rDQDXAXgGYWHzaj4YkVpoTyU/pL0LfIvVyj/2ZWVMT7VYuBIvNJRZRpX3YarL5rnFxcAPP/A+JSX8HBAAjBnTOCUsatssMDUV+PBDnmMy8QMAbr6ZKyYA/yyNJ9KSOBzA3r38XY+MdJffzMkBJk1iJ1bkfNQvEZGW7s9/3oUnnhgJIO/MkVsAvIv58+EzGUsnT7LsTWkpxyYdOnCRyOzZXA3tC1x7g2ZkcCFLTg4X2N19txZKScNpjH5JXce93jqvObRRj8U32+iPj6UlPOa8PC4Iz8sDGFS6++xtr7/+Ou66665zPzARqPydiNcFBTEIUzlwVFLC4FBhoXvVv8XCjfWCgs59PYvFnfWUlMRVfDYbU1zbtmWwqqH2WwJqL42XkAB07coBX1AQcOgQj+/YASQmMjvr0CE+TpXGE2keKndO8/NZr7mwkJmKgYGs8ZyRwWCSJndEREToxx8vBbAMwFUAnGBQCViyxHeCSpGR/P9tt/PrH37gaubISG+3zK3y3qCu/S3Cw7mATsSXBQbWbRGlt87z5s/WY/HNn63HcvHnefNnn++84GDgzTeB994Ddu26C2Vl6UhJmQsAuPvumYiPj8fkyZPP/4PErylTyQ9pRXDzYLMBKSn8nJDgDiwlJ58/Yyk3lyXvSkqY7dS6NTOiSksbfr+lc6mexdSvHwd9qanA11+zfe3b87G1bg1ccw3/8SmDScT3uH6fMzL4O92uHVPoO3dmgMlTxqJIXahfIiIt3YIFwKxZALAcQAiAkQCA//wHuP9+Lzasmn37WPIuJ4el8Dp3Zrt97X+6awN0hwPYtImLWkaOdFdGELkY6peIiD+oPF8XGurEm28+jM8++wcAwGgMxrp132DYsGFebqX4Mr/ZU2nTpk2YPHkyYmNjERoaioEDB2L+/Pm40Jja/v378bOf/QwJCQkIDg5Gr1698Mwzz8B+jo1rrFYrnnjiCVxyySUICgpCcnIybr/9dhw5csTj+ampqXjiiScwbNgwREVFwWKxoFWrVpg+fTq+++67C2q3NB9GI4M+rqwjh4PZRRUV579v9f2WWrUCOnVicKryfkv5+Q2z31JtXFlMkybxc7t2bMsll/Bzt258PEVFwNatwM6dwP79wPLlwLJlwIoV/EcnIt7l2jdp715ONBUU8Fi3bvw7Vf133dcmn6R2M2bMgMFgqPXjL3/5i8f7vfPOOxg6dCjCwsIQHR2N8ePHY/Xq1U3beBGRZmLmTC7kAq6BK6AEAL//vbda5FmPHsBDDwGPPAL84x9A377AF1+wHJ4vce0NmpgIjBjB8j0bN9ZtX1oRERGpOoafMMGAjz56EWPG3AwAcDhKMXr0ZOzbt8/LrRRf5hdBpU8++QQjRoxAQEAAtm7ditOnT+Pmm2/G/fffj7vvvvv8F6hm48aNGDhwIFJTU7FmzRpkZ2djzpw5+POf/4xJkyZ5DCyVlpZi7NixePbZZ/G3v/0NOTk5WL58OXbt2oX+/ftjx44dVc7Py8tD586d8dRTT+HWW2/F3r17kZ2djTfffBO7du3CqFGj8NJLL13wcyK+z2Rilo7Fws+5uVyVZzDU7f5mM0vdVS5vFxLCEnpt2/JaBQUM2thsjfMYALY9KqpqxlFwMNthMLCOa3Q0M5Xy84GPPwa2bGGb0tJYeqNy+2w2Dhwbs80i4uZ0Mmty+3b+PencmZt9Op38O+Li6XddmofY2FhccsklHj/i4uJqnD979mzcfvvtuOaaa3DixAns3r0bcXFxGDt2LN544w0vPAIREd+XkgLMnw9MnszP8fHs30dFvYiFCxd6u3lnhYWxXx4VBUycyAzkzz9nP90XxcYCl18OpKdzxbXqsIiIiNRN5TG8yWTEF1+8iUsvHQsAKCvLwWWXXY0TJ054t5His1p8+buMjAx06dIFUVFROHDgAIIrbfIwY8YMvPnmm3j//fdx88031+l6VqsV3bt3R1ZWFg4dOoTExMSzt/3lL3/B448/jqeffhqPPvpolfs9+uijmDdvHv71r3/hV7/61dnjhw4dwiWXXILu3btjx44dMJ3J2c/KykJ8fLzHa+3Zswd9+/aF0WjEoUOH0LZt23o9J0rnbj6sVq4MLCtjAMZsdu9bYrFwNV7lvZjOx27nxHBREQeMR4/y/n36MNAUFNRwey2dT/XSeIMHM9j00UfufaVce0tNmcKJ7JISBpxUYkukcblKUFqtwPHjnEg6dIh/I5KS3PsmXX21gkjN3YwZM9ChQ4daM5Kq+/LLLzFx4kTccMMN+N///nf2uN1uR69evZCamor9+/ejffv2dW6D+iUi4o9sNifCw/8Iu/1JACZ89tnHmDp1qrebVUNpKUviOZ3A9ddzkZovSk1lKbwuXYD+/b3dGmnO1C8REX9WUFCAXr3G4MSJrQCAtm374ciRLWfnq0VcWnym0iuvvILCwkLcfvvtVQJKAHD/mQLW8+bNq/P1PvzwQxw7dgxTp06tElACgPvuuw8A8MILL6CiUo2ykpISvPzyy7BYLLjzzjur3Kdz58646qqrsGfPHnzxxRdnj5vNZgwcOBA33XRTjTb06tULnTp1gt1uV6mZFi4oiEGTNm1YOi45mcGe/HwgK4uZPCdOMEBjtZ7/emYzB1phYQzQdOgAjBrFjKZdu1jeYt06Bp5ycxv3sXkqlxUczBJ9AQEMIkVEsExeUREzlt55h+XxXJlbmzcrY0mkoZ06xRXJr74KvPYaMwOHDQOmTgViYhjojopiUFcBJf/z7LPPAgBmcXOQs8xmM+6++25YrVb84x//8EbTRESaFYsFmDmz8Mx3FZg27WZ8++233mySR8HBXOBVUcH+ga/2vdu2BQYOZGnvvXu93RoREZHmKSIiAlu2fIGoqM4AQpGa+gxWrVJASWpq8UGlJUuWAACGDx9e47ZBgwYhKCgIW7duxcmTJy/6esnJyejcuTMyMjKwadOms8dXrVqF4uJi9O3bF2FhYTXuN2LEiCrXBoDIyEhs2bIFHTt29NiOyMjIOrVXmj+jkcEgo5EfkZFcIZiWxg10LRZmF2Vn162OePX9lpKTGbix25kBVFgIHD7MjXoba68ll+rlsgIDOVEdFcV9W1q14iB25Eige3cGmywWBtKyshhgOnHCvc+USuOJXJziYk4Ybd3KvzsREQxYm83aN0m4am3NmjUwGAweN2311J8RERHPDAYD/vnPv+PGG28DADidNowbNwVbt271cstqCg9nn7yoiIvQysu93SLPOnUCevcG9uxhhnVpKbOrK5frFRERkXNLTEzEli0rMHbstwDGY/x4YM4cLjatR16GtHAB3m5AY6qoqMCePXsAwGNwJiAgAG3btkVKSgp27NiB1q1bn/earr2Pagv2dOrUCYcOHcKOHTvOBp7qcp/K551PeXk5Dh48CJPJhCuuuOK859tsNtgqzbIXFBTU6eeI77JYGFhyBYJcZesqKupWBs9srlrerqyMn3v25ARyejrw00/c/LZ9ewZz7PamKY3nmrguLeXKSFfAKTGRWVZ5ecy0OnKEZf8OHQJOnuRjP36cZQJdWRSa9BapnavEXXAwf6dPngR272b5mG7duOLXbmfAurSUv4uuD2lZtm/fjilTpmDLli3Izs5GXFwcrrjiCvz2t7/FoEGDzp63a9cuOBwOJCQkeFwk4+rPHDp0CMXFxQgNDW2yxyAi0hwZjUYsWvQGsrLysHr1MpSXF2LEiGuwffs6dOvWzdvNqyI6mvtBLVkCLF/ORV+FhVzw5uFfgtf06MExy/LlbJ/JxDaOG8e9XEVEROT8OnfujK+/Bq64ghWNzhSswKZNwPPPc95Q/FuLzlTKy8uD9UxNsJiYGI/nREdHAwBOnz5dp2u6zqvP9S7kPufy+eefIy8vD7fffnutgarKnnrqKURGRp79qO8eTOJ7TCZ3wCUggFk7NlvdAkqeuIJU+fm8bkQEN+gtKeGmwikpzFzYsqVpSuNVz2ByHXNlMZWWAl27Arfeyn9wrVoBO3YABw4Ap0/z6y++YLaTi7KYRNxOnQJWrACWLQPef5+fDx9mIKl/f/4tsdu5ujc8nH8XpOVau3Ytpk6dil27diE/Px/vvPMOdu/ejWHDhuHNN988e15d+zNOpxPp5xhl2Gw2FBQUVPkQEfFXZrMZS5d+iL59me1ZWpqJfv3GYezYE1iwwMuNqyYxEbjmGpagfvBB4IUXgL//nRUOfEnXru7FMkFBHLt89ZUylkREROrr2mtdXzkBPAXg/5CRoYwlaQaZStOmTcO+evZS33rrLQwZMgQlJSVnj1ksFo/nBp6Zta587rm4zqvP9S7kPrUpLi7Gb3/7W3Ts2BEvvfRSndo8d+5cPPLII2e/LygoUGCpmTMagdhYZhCUlfFr115L4eH82uFg9o7JdP5gk2uvpYMHgYICBpW6dOHntDRgwwZmMLVvzxV/Bw+ydF5jZix5UlsWU1wcEB/PfZjKylgW8Ngxtjsujs/D0aPcYDgiomYWU+WMDWVhSEtns3Ey6MQJlq85fZqB2dtu4yrk2Fjerr2T/MPs2bPxf//3f+jcufPZY2PGjMHixYvRvXt3zJo1CyNHjkTHjh3r3J8Bzt2neeqpp/D444830CMQEWn+QkJC8O23n6Nfv1E4dmwnSkuPY9Wq8Vi1ai3mzYtFSoq3W+gWHc3xwPHjXJRWUQF88gnw0EO+k7FUVMR2Go0cE3TsyHFSUZEWyoiIiNTHp5+6vpoNwDUPnYjFix/AnDneaZP4Bp8PKh05cgQHDhyo131cExkhISFnj5W56ntV4yoLV/nccwkJCUFRUVG9ruf6+mLbUFFRgTvvvBNFRUVYu3YtIiIi6tTmwMDAKhM90jIEBTEw4gocOZ0c4OXl8fvSUne5uthYnn8urr2Wqpe4i4nh/cvLORgDmMFkszV9UAnwXH4rOJjBtNxcICGB+8IMHMiPnBxmLeXm8jEeP87J8mnTOGF+6hQn0AsLeQ2VzZOWpHrA1Onk5MqOHfw+Joa/J1YrS0cCtQdvpWXq27evx+OdO3fGNddcg6VLl2LRokX44x//WOf+DHDuPo0Wu4iI1BQVFYUHHliB3/1uBIBDAPYBuAMHDy7DggXAzJlebuAZ+fkM1gwcyJK5iYnsZ+fn+05QKTycJe/KyzlW2rSJ+8n6SvtERESai2nT+H8U6Fzp6IPo0CEBwE3eaZT4BJ8vf7d9+3Y4nc56fYwePRoAO+ZBZ2bScyrXwaok90wdr+Q6ziInJSXV+3oXcp/qHA4HZs2ahQ0bNuDrr7/2uRrb4h1GIwM7RiMDSVFRDB6dOMFsncBABomys5m5dD5mMxAaWjVYZLFwUBYYCCQlcaK5qIiDyMJCnmO3M5BjtzfKwzyvyqXxXNkVQ4Zwcrx1a2ZhDB7MTKvISO7DtG4dsHYtsHgxn6+oKAbkNm92l8hTyTxpziqXuFu+HNi2Dfj+e2btRUYya69NG/7uVi9x56kEpfifLl26AMDZjPG69mcMBgMSExNrvW5gYCAiIiKqfIiICPDtt0kAVgJIBtAawHMAuI+Rr4iMZB/BbufYYNcu9pUjI73dMrfgYO6hFBvLdsXFMauqLuMhERERcZszh4u3gV8D+P2Zo068997tWLVqlfcaJl7n85lKF8NkMqF3797YsmULjhw5gh49elS5vby8HKmpqQCAPn361Oma/fr1w8GDB3HkyBGPtx8+fLjG9fr16wcA9bpPZRUVFZgxYwbWrFmD77777uwkj4gnQUEcSNntLGUXHMzAUkXFhe25VLk0XkkJS8y1bs1rHj/Oz67tMEJCeO6ZbTWaVG3ZFa4sprw8/iO0Wtn+AQNY2i89neekpHB/qpwclgMzm7mHlDKYpDlylbjLyeHv/ZYtwJ49wMSJQO/ewKWXqsSdnJ/jzOyb4Uwa26WXXgqj0YjMzEwUFxcjNDS0yvmu/kznzp1r3CYiIuc3dSqwbFknMLAUDqA9AGDkSG+2qqqwMGD6dJa8y8119/3T0gBfWvfYsSNw++1cDGc2Axs3AmvWAFdeef4KDiIiIuKWns49lD777G8oLT2N7dtfh9NZhvHjr8PmzWvQv39/bzdRvMDnM5Uu1rVndhTbsGFDjdt+/PFHWK1WDBgwAK1bt77o650+fRqHDh1CQkIChg4devb4mDFjEBoaip07d6K4uLjG/davXw8AmDJlSo3b7HY7fvazn2HDhg1Yu3ZtlYDS8uXLsXz58jq1W/yHycTgTlAQv87M5ATzhQSUXFyl8QYN4udWrYAOHRicOXoUOHyYwZesLAafvJmxVD27wlMW02WX8TFceinQvz9LgMXFMThmtXLF5ZtvcnNfp5P/QH/4oWrGkrKYxFd4ei/m5DBQmpXFCZ927RgU7dCBk0GuIOykSfysgKl/2rBhA5KSks5mGFWXcmYTj+7duwMAIiIiMGrUKDidTmzcuLHG+efqz4iIyPnNnMkgDdAbroASAPzhD0BJiQNOp9NbTauiRw/uoTR7NvD888CoUcCqVSyx60uCg7nvalQUA3MOB/Ddd94bq4iIiDRXc+YAGzYYsHnzqxg9mnPjFRWFuPzyCWcXF4p/afFBpfvvvx/h4eF4++23YbVaq9z26quvAgDmeNhZ7NFHH8WIESOwZ8+eKsdvuukmtG/fHosXL0ZGRkaV2+bPnw8AeOSRR2Aymc4eDwkJwQMPPACbzYY333yzyn0OHz6Mr7/+Gr169cLEiROr3Gaz2XD99ddj165d+O6779C+ffsqt7///vt4//336/I0iB8xGlnqITCQmTexsZxEzstzD6AcDn5dnxIQnkrjmc0MyHTtygBWcTFLy7n2XgK8XxoPqH0C3RVwSkjgfjK9ewN33AH06cPAXGQkA1G5ucDWrfw4eZKPcflylhVbsYJlxkS8oXKJuxUrgP37GQzdtYv7CBgMXKkbGMi/BSpxJ5WVlZUhPT0dK1asqHHbTz/9hJUrV8JsNuO22247e9zVZ3L1eVzsdjveeOMNBAUF4cEHH2zchouItGApKcD8+cDkyfw8cCD/xkZEzMDcuX/0dvPOCgvjArPwcGDMGC5g8eV+cWgoA0ulpSyDXVHh7RaJiIg0PwEBAVi27H306HE5AMBqTcegQeORnp7u5ZZJUzM4fWW5UyP6+OOPcfPNN2PKlCn4+9//jujoaLz66quYM2cO7rjjDixcuLDK+VlZWYiPjwcAPPDAA/jnP/9Z5fYNGzbgqquuwsCBA7FgwQK0b98e//vf/zBr1ixcccUV+OKLL2CuPPMOoLS0FGPHjsXu3bvxxhtvYOLEiUhJScGdd96Jw4cP47vvvquyWbbVasWUKVPw1VdfYcyYMUhgAcsqvv/+e4waNapG+8+noKAAkZGRyM/P1z4GLZjDwcGSK75ZUMDAjtHIwZTdzv2SYmMvvASE3Q5s386yElFRDLjYbCyRFxPDoNbJkyyb583SeOdjs1Utm2ezcVCcl8fn5/hxPpY+ffg8/vADz2/ThucmJgLXXed+HqtfT6QxuN6nubn8Xf7pJ75PR48GOnXi34Aff1QJR6ndmjVrMHr0aMTExOD555/HhAkTEBkZiY0bN+JXv/oVfvrpJ7z66qu45557qtzv4YcfxksvvYTHH38cDz30EIqLizFnzhy8++67+O9//4u77767Xu1Qv0REpHZOpxNhYdNQUrIYAPD888/jkUce8XKraiovB5YuZZb0tGnsQ/uinByWwYuPBy6//OKqOUjLpH6JiMj55eTkoFevK3D69F4AQN++12D79i+93CppSn4RVAKATZs24a9//Ss2bNgAm82G7t2747777sOsWbPO7hXg4nQ6ce2112LTpk346KOPMHr06BrX279/P/785z9j1apVKCwsRKdOnXDHHXfgN7/5TY2AkovVasUzzzyDRYsW4fjx44iMjMS4cePwxBNPoFOnTlXO3b59e51qUt55550KKkmdFRWxVF1FBQdSrsBScvKFD6hyc937LYWEMKBkMjGYtHMnz+nQgUGWsDCWz6vlV8SnnDrFPWeqT8hnZwMff8znzW5n4CkvDxg+nKs1bTY+HxUVzHSqPpGvgJNcqOrvnRMngA8+4G0mE4+ZTMBNNzHI6+k+IpU5nU6sW7cOH3zwAVavXo0jR46gvLwcSUlJuOKKKzB79mwMGjTI433ffvtt/Otf/8Lu3bsREBCAIUOGYO7cubjyyivr3Q71S0REzu0///kPfvnLX579/o033sCMGTO816BalJUBn33GccG0aewL+6L0dGYrtWkDDBnCzG4RF/VLRETq5sSJE+jR43IUFQUAWIGNG7ui0m4w0sL5TVBJ3NRJ8l92O2ud2+0cPLkmmdu0ubhAj93OQaTF4r5OURHw7bfu0hJmMye8R4xg+YnmwNOEfOUspoQEDkoDA7nfVEEB8NVXLP8XFcX7JiQAEycyiJeXB2zZUnvmiAIAUhtXkDMvj79TrVrxd3j7dgaEu3Z1v++uvlrvH2le1C8RETm/v/zlr3j88T+d+c6Ezz77GFOnTvVqmzwpLQU++YT7kl5/PRed+aLUVGDTJlZS6N7d3T+vXCpY/JP6JSIidXfgwAE8+GAEVq5MhsEAzJ3LfRanTeM+TNJyKajkh9RJ8l8OByenbTZ+nZfHgV7Xrg2fPeQqjVdYyGDS8eP8GYMHc0I8ONhzMKo5qC2LKS+Pe9uEhnLiPycHOH0a6N+fj3HrVpYGadOGKzgTE1kvPzCw9muKf/EUWLRamR2XmsrfpdxcvjemT+ft5wpUijQH6peIiJyf0+nEr3/9MF5++R8AAIMhEKtWLfdYVcPbCgsZWAoKAq66in2ZyEhWLfAlhw8DX37Jkn2u/VTHjeN+lOK/1C8REam/gQM550UOAAYkJBigrZZaLgWV/JA6Sf7NamUJt7IyZjqYzZy8Dgtj4MO1D1ND1BevXBovOJhBFLudE+cVFQzCOJ2+vd9SbeqSxZSRwcyRsWOBzExg8WL3c1xSwsySkSOBuDhg2za+Jm3a8P6VM06UweQfKgcWw8KAbt34e3jkCLB2Ld9TSUn8fSkoACZN4vtE7w9p7tQvERGpG4fDgVtuuRMffvgOAMBoDMfll6/GHXcMxMyZXm5cNbm5wL/+Bezdy/5LbCwXxPTo4e2WuZWWAvPmcV/Kfv246C46Grj9dmUs+TP1S0RE6m/ePODRRwHABuAOAAMAPIpnnlHGUksV4O0GiEjTCgpiJoMreGQwsFRdZiY/BwbyIzaW516M6GgO0CpnIzmdDGqtWcOJ8fh4oLiY5zeX/ZYA9/NU/djgwQwMZGdzwn/wYGYuBQRw1aMr4HTyJDO22rfn6sjUVJ7n2vPq5EmgbVu+Rvv3MxinknktQ20ByR9+ANLS+Duwbx+wezeDjh06MABps3EFrStY6Zrs8PReFBERkZbHaDTinXdeR2ZmLlavXgaHoxDr1l2DdevWYd68S5CS4u0WupnN7jFGcDADNp98Ajz0kO9kLBUWcrFO9+5c3NO2LftcRUUKKomIiNTHp58CQDmASQC+AfAhgEQsXjxDQaUWqgFyEUSkuTEaOdAzGhlUCg1lWbaSEmYyFRYyKOJwXPzPMpt5fVewyGDgIC0ujtlJJhN/7sGD/JkAAyjFxfzc3CQnM8No0iR+dgWAXAGnqCg+zvh4ZjB1787jAwcykOfaKycsjOd9/jnLCKalMZvp00+BlBTeduQIsHw5S+6tWMHBcGU2G4NYNlsTPwlSq1On+Fq5XrNjx1gicetW4Mcf+btXVgZ06sTMvksv5Xtk5Ej3e8cVrFQgSURExP+YzWZcf/2HAEacOZIF4BEcPOjEggVebFg1+flcKDV0KBfTlJayNHR+vrdb5hYezgU7ZjMQEcG+GOA7QS9/tWrVKtxzzz3o1q0bgoKCEBISgp49e+J3v/sdMjMza73fO++8g6FDhyIsLAzR0dEYP348Vq9e3YQtFxHxX9OmAcxdGVvp6L3o1m2ZdxokjU5BJRFBRQU/JyczO6m8nIO+xgrqWCxcFVhe7i7nFRzMyfUff2Spr82bGUzJzW2cNjSmwEBO/Fef9D9fwCk+ns95ly7ADTcAgwZxxeSAAcxWad3aXVLwxx+Bd99ldktODkuLLFsGnDjBgNzJk1WDFwo4NS1Pz68rG+nUKb73t28H3nkH2LmTgcR27fi+6dSJmW0JCe5VsrW9d0RERMT/LF8eAuBzAH0BDAPwDgADlizxarOqiIxkv6aoiJn5Bw+ySkFkpLdb5hYczD2UYmK4CK51awaUXFUUpOm9/PLLGDt2LLZs2YJXXnkFmZmZSElJwYwZM/DSSy/h0ksvxU8//VTjfrNnz8btt9+Oa665BidOnMDu3bsRFxeHsWPH4o033vDCIxER8S9z5nAOA3gMwINnjlbgzTdvxMaNG73XMGk02lPJD6lGsFTncHCiu6yMg6u8PAY3kpK4ai8wkOc01n5Lrj2VjEaWxcvO5uAOYBsGDGg+ZfEuRvWyaLXt0XT11Tz22Wd87pxODtIzMoAhQxi027KF909K4mA+Lg645hoO5HNz3Vkx9Smpp1J7VdX2fFTfG6l7dwZrU1OBlSt5LDiY72mHgwHEhISq9/P0uoi0VOqXiIjUz4IFwKxZAJABIPTMB/DII8Dzz3uxYdXs28eSd7m5HFtERgLXXss+ji8pLXWXvNuyhf3sMWN8KwDmL5577jn84Q9/wMGDB9G2bdsqtz322GN45plncM011+DLL788e/zLL7/ExIkTccMNN+B///vf2eN2ux29evVCamoq9u/fj/bt29epDeqXiIhcuHnzgM8+cyAv71bs2/cBAMBsjsGOHevQw5c2VpSLpqCSH1InSTyxWhnMce1/FB3NwV9ZGYMWZWX83mJpmP2WAPf1XfstFRdzIBcYyK9zcxl0GjnSvb9Q9fu0dLUFGmoLOI0Zw8ylpUvdWS6Fhdy36bLLqgacEhI4gI6N5SpNV8Bp504er/zzzhfwaMkBJ0+PrbbnIy+PJQvT03n+yZNc+TpsGJ/f7duZpdSuHfcYcAUJK++t1FKfR5HaqF8iIlJ/XbtygVZlBgOwcWMR+vUzI9BHOhJFRSx5FxkJ/PQTsGkTcMUVLPHri+x24NtvOTa68kr246TpvPPOO/j666+xcOHCGrdt2LABw4cPR0BAAKxWK0wmEwDgyiuvxOrVq7Fy5UqMGzeuyn2efvppzJ07F4888gier2PEVf0SEZGLZ7PZMGLEJGzZ8g0AICysLfbt24A2bdp4uWXSUBRU8kPqJEltPGUjlZQAhw4xkBMfz9stFk6gN0TGUmV2Oyfdi4o42Z6ZyZ/XqhV/psHgLsvnym6Kjm7YNviiumTEnC/gFBkJjB7NoNGyZbyWwcDnOiuL97VYeL3iYr7WhYXMGBs+nPs5lZQw8ykvj5lPEyawPRcTcLrQ2xpafZ7jmBjuZZWRwUy+1FQGOC+9lM/Rxo18fqKieNxmA66/nu9VZSOJ1KR+iYjIhVmwAFiyBJgyBdiwAVi4MBvAREya1A6LF79/dtLdl2zYwP7+VVcB3bp5uzWe2WzAqlVcWHfllQ2zmE4u3p49e9C7d2+YzWaUlJQgICAABQUFiI6OhtPpREFBAcKqbYi1bt06XHHFFejSpQtSUlLq9HPULxERaRgFBQXo23c0jh7dBgBISuqJPXvWIsZVmkiaNQWV/JA6SVIfdjsnzcvLGeAxmxlMatu2cTKFPJXFCw9n5seGDQyCJCUxIBIbC/Tr5x8ZS7VpiIBTVBQH9q4sm7AwvsaugFPPniyXFxbGwFNZGVecXn45r/3jj2xDQgKPx8RwAB4Wxuy3HTt4e2Qky/O5gijnCrCc67aGDlLV9rOKi4EvvuBjiIhg1lFQENC5M9+LERF8PlyZfBMmMBi3cSOfu+olC5WNJOKZ+iUiIhfP4XAgJmYo8vM3AwDuvnsm/vvfV2EwGLzcsppWrWLW0oQJ3G/JF5WUsJ0WCxdmWSzebpF8/PHHuOGGGzBmzBisWrUKALB+/XqMGDECCQkJSE9Pr3GftLQ0tG7dGgaDAYWFhQj1kHpms9lgq7QRakFBAdq2bat+iYhIA0hPT0fPnsORk3MIADBhwix88cWrXm6VNIQAbzdARHybycSJb4OBg6nsbH5fXt44wZzoaAaKqpe4i4pi9kdcHCfki4sZBGnbFkhMZPv8rTQewNfCU2AiOZmBjOrBi8BABk02b+ZrGRXF70NC+Fq7SrjFxvL57NWLA2lXllhUFHD6NAN73bszEFNezuOuLLcjR4Bdu6pmPkVFAfv3A3v3MhgVEMDbSkr4s06cYHBn1CgGtNav57Xj4xnULCwExo5l0GrbNl4zPBwYOJCZbAYD7791KwM6YWHciys5mUGftDTelp/P56NPHwa/iouB1av5mMPDGdDcv5/3tVq5kjYykl+7SjTGxjKwZLNxQ+fcXD6+Sy7h8ztsWM3nt/JrVNtrJiIiInKhjEYjPvjgr5gw4Vo4nXa8/voCxMbGYt68p7zdtBpGj2Y/avlyZln5YtZ2SAhLcK9ezX7pyJHs54r3vPXWWzAYDPjTn/509tjp06cBoNZV79Fnylo4nU6kp6ejU6dONc556qmn8PjjjzdCi0VEJDExET/8sAK9e18Oq7UXvvzyWezdy8XL0rwpU8kPaUWw1Ffl/ZYCAtxBpsBATt4DNcvmNbTKpfEiIxkkKC8HOnRgrXOTieXyrFb/Ko13oeqbtVPfzKdx44CCAu7tFBnJ901JCTOfRo3i++Wbb3itgAC+brm5wNChvG39et5WOTNq0CAGpVxBqrw8vvauzZ4rB7DqctuQISz5+P33DDCFhvJnFxUB48fz569bx/u1asW2u7KOcnL8d48pkYamfomISMN5//33ccsttwLgMP/ZZ5/Fb3/7W+82yoOKCvYTMzOBadO4aMcX5eQAa9ZwoVP//u6FTa69S6VpLF++HBMmTMBvfvMbPPfcc2ePv/3227jjjjvQp08f7Nixo8b9HA7H2TKQu3btQu/evWuco0wlEZHG99NPP+Hee9tj7dpAGAzA//0fsHIl+wBz5ni7dXIhFFTyQ5q8kQtRfb8lm42T71YrJ/0BBgBiYxuv7rin0nhBQcyc+f57tqV1a2amuDKe/CVjqSHVFhBpqFJ7V1/N+53vttxcDuBdexcNHMgyJFFR/PmlpTxn3Di+5itW8HUPDOR7MieH1zMY2FmJjmbgyG5nkOraa/lYamvH+faLUuBIpGGoXyIi0rD+85//4Je//OXZ719//XXcddddXmyRZ2VlwOLFDNSMG8djkZHuRWu+Ij0d+PhjZs/HxLCN48YBHTt6u2X+ISUlBSNGjMAVV1yBDz74oMpeYa6SeN27d8e+fftq3Le0tBQhISEAgEOHDnnMVKpO/RIRkcbTowerw5ANQCASEvi/VpqXRsopEJGWxmh076cEcBI9MpKT7dnZnKh3ZTQ5HI3TBlegaNAgfo6O5oR+QgLL4rVuzUn+wkKWYMvLc9/XbueA1W5vnLa1JIGB7sBNXY67Su1NmsTPrqCLq9ReVFTNUnB1uS06mkGjmBiWlEtKYtCysJDvw+JivvZJSfyZycl8D7oCTq5jSUkskVhSwgBTXh7fu65gUG3tONdjO9fzISIiIuJNv/jFL/DXv/717Pd3330vfvnLzzB5MrBggRcbVo3FAkyezGylhx4C5s0D/v53wENswKsiItj/PH7cnV3/1Vfsb0rjOnbsGMaNG4cRI0bg3XffrRJQAoCkpCQAQE5Ojsf75+bmAgAMBgMSExMbt7EiInJe7jUupwEMBfAvZGSwDyDNizKV/JBW3khDsdu5F47RyAFWeTm/7tKlaTOEKpfGi4hgm8rLue9NeDjblJGh0njecq6Mnvredq7MoQu97XztEJHGpX6JiEjDczqdePDB2fjXv146c8QCYDmAMejSBUhJ8WLjKikqAp55BvjhB2aoR0dzQdBDD/lOxlJGBvDmm1ycZLMBbdrw2IwZbLM0joMHD2Ls2LEYOXIkFi5cWCOgBLAPER0dDafTicLCQoSGhla5fd26dbjiiivQpUsXpNTxTa9+iYhI4xk2DNi0qQBAPwBHABgAvI/LL78J69d7tWlST8pUEpELZjJxhaHDwWCOw8EAT2EhS+UB7mONlb0EMIDVpQsHnkVF3P/myit5DGBpvH37WGLDVUJPGUtN51wZPfW97VyZQxd62/naISIiItLcGAwGvPTSCxg48OdnjpQBeAMA+8K+krGUn8+FPSNG8PuCAu5jmZ/v3XZVFh7OLPeKCqBtW5be9sUyfS3J3r17MXLkSIwfPx5vvvlmlYDSc889h9TUVABAREQERo0aBafTiY0bN9a4zvozM5RTpkxpmoaLiMg5TZsGABEAbj1zxAngdvTqtcprbZILo6CSiFwwo5HlyCwWZgHFxLhri+fk8CMtjZlDp07xnMbiqTReWBhXOrpK41mtHKgePsy2uag0XvPSkEEqERERkZbKaDQiIeF1AJMB3A3g9bO3LVnirVZVFRnpLkPcpQtw8iSDSuHh3m6ZW3Aw91CKjuaeD9HR/D442Nsta5l27NiB0aNHY/r06Zg/fz6MxqrTVr/73e9w6NChs9/PObPD+/z586ucZ7fb8cYbbyAoKAgPPvhg4zdcRETOa84cbmMA/BXAPWeOluH116/Dtm3bvNcwqbcAbzdARJq3oCBmfVRUMHPJaAScTmYMHTnCMnTx8cwSys7mucZGCmebzTXL7lksHKy6Mphc5fpOnWKAyWDgwNVmU2k8EREREWlZpk0z48svPwLL3xnOHveVxI2wMGD6dOCTT1hRYMAABmu+/x646ir21X1Bx47A7bdzTBEWpoBSY9myZQvGjx8Pm82GzMxM3HLLLee9zzXXXIOHHnoIL730Ep544gk89NBDKC4uxpw5c/DTTz/hv//9L9q3b98ErRcRkbpITwfmzTPgs89eQUZGBg4d+hwVFYW4/PIJ2L17PTp37uztJkodaE8lP6QawdIU7Hbg2DEGlZxOBntMJtYgb8r9lgB3ybuSEnfgKCiI/8i+/57Hz+zxirg4ZjqZzXwMZWUMTDV1m0VE/IX6JSIijatrV/aFK3vuuSO4885wxMXFeadR1RQVseRdZCT76CtXAr16ASNHertl0pQefvhhvPTSS+c9b/Xq1Rg9enSVY2+//Tb+9a9/Yffu3QgICMCQIUMwd+5cXHnllfVqg/olIiJNp6SkBIMHj8fevSxXGh3dGfv2rUdiYqKXWybno6CSH1InSZqCw8FsoLIyfp+Tw7JjnTu7V/Y5HFUznBqTpwBRcTGwaRNXQFqtDC7ZbMAVVzD4dORI1UCUMphERBqe+iUiIo1vwQKWvAsLA95/fxeAq9GtWxts2fINwn2p1twZe/cC337L0tZDhni7NeJP1C8REWlaOTk56N37Cpw6tRcA0KHDAOzc+a1P9k/ETXsqiUijqLzfEsCaqQkJ7lWIxcUMOjXFfksAA0mhoVUzjiwWICKCQaXkZAaPIiOBjAzgm2+491JAANt88KD2XBIRERGR5mnmTODzz4FFixyIi7sFwCn89NNmjBs3DTabzdvNq6FnT2DYMGDLFmDnTm+3RkRERBpLTEwMfvhhBcLC2gIAjh7dikcffdnLrZLzUVBJRBqNa7+lNm2A1q0ZVIqMZMbQ4cNAXh6zllz7LTkcTds+s5kZSGFh3F8pPh4YPpwlQoKD+ZGVxdtOnmQgzMVu5/cKNImIiIhIc2E0GrF69fuwWJiC//333+D6629FRUWFl1tWU//+LEu9bh3w00/ebo2IiIg0ljZt2uCHH1bAbI4B8Gv85z9zcPKkt1sl56Kgkog0KqORwRtXeTuLhYElo5F7LRUUMBuorIyl8JpadDQHq4MG8XN0NDOWkpOB8HAGmmw2tu/oUe4TdewYsG0bV05u3849m0REREREmoPevXvj22+XwWQKAQB88cUnuOuu++CLlfEvvxzo3p1VBI4d83ZrREREpLH06NEDhw/vwIABLwEwol074Mknmbk8b563WyfVaU8lP6QaweJtrv2WSkr4dWEhAzidOzPA5Atyc1nyzrWnUqdODIRlZgJbt7JcX6tWbH9sLANSlUvriYhI3ahfIiLiHStWrMCECdfC6WTq/W9/+yieffZpL7eqJocDWLECOH4cmDKF44b8fC5UCwvzduukpVG/RETE+9q35/99ygcQiYQEID3di42SKhRU8kPqJIkvsFpZ8q6sjBlLgYH8CA3l54oKwGRyZzh5g93O9lks7oBRcTGwYQO/ttn4OGw2YMQIBpnMZs/3ExERz9QvERHxng8++AA/+9ktADgtMH36PFitv8PUqdyHyVdUVABLl3J/pZIS9rejooDp04EePbzdOmlJ1C8REfG+efOARx8FgC0AJgF4GsBdeOYZYM4crzZNzlBQyQ+pkyS+wuFwB48ABmzy8lgSLzCQexrFxnJvJl9ht7PkXVERV0eePs3H0a4dH0d5OfdhMhq5crJLF5bUExERz9QvERHxrldeeQW/+MUvKh15DcDd6NIFSEnxVqtqyskBfvlLfh45kn3u+HjgoYeUsSQNR/0SERHvGzYM2LTpCIA+AIoAmAB8hssvn4z1673bNiHtqSQiXlN5vyWjkVlKFRXM8rFaOWDMzGTQxleYzQwUhYWxbF98PDB8ONC7N5CQwH2Xjh9n2w8dAnbs4ONxsdsZPLPbvfYQRERERETOuv/++zF16t8qHTkBgKWgFyzwTps8KS0FkpKAuDgGuxISWLI6P9/bLRMREZGGNG0aAHQAcPeZIxUAbkLfvhu81SSpRkElEfEZFRUshZecDEREMNCUkcGsIF8SHc09lAYN4ufoaLY1JIRlOHr1AhIT+f3Ro8Du3UBaGnDiBLBtG7BlC7OdcnO9+jBERERERAAAdvvvATwC4B8A/nT2+JIl3mpRTZGRDChFR3PR1tq13F8pMtLbLRMREZGGNGcOkJBgAPAigJvPHC3Ff/87GXv37vViy8RFQSUR8RkmE/chKi3l56AgZgSVljIAY7cza8n12ZvMZmZWVd4zyWJhIKm4mLeFhwNduzLAlJcHrFsH7NvHQXBWFld/Vs5YUhaTiIiIiHjDddcZADwP4NdVjk+Z4pXmeBQWxj2UWrViiezgYAaYAgO93TIRERFpaOnpwDPPGDF06Jto2/YqAIDdnovLLrsaqampXm6daE8lP6QaweLLrFYgO5uBF4uFA8aAAJaaKyrixrxmMwePvrbfEsDg18GDbGdIiHtPpeJiBpWMRsBm4+MsLWXpvKQkHvN0PxGRlk79EhER39C1K/ujlU2d+i2efjoJ3bt3906jPCgqYsm7sjLg669Z5WDiRPazRS6W+iUiIr6nsLAQffuOxpEjWwEAyck9sXv3WsTExHi5Zf5LQSU/pE6S+DqHg6XwTCb34NDhYCm53Fx3ICkykisVfW0Aabe7g2KuTCa7nSXviopYIu/0aR5v2xYoL+cA3mDgoNhqZZZTv368v6friYi0FOqXiIj4jgULWPKud2/g6ac/BfAzREYmYteu9Wjbtq23m1fDiRPA0qVAp07AuHHsT4tcDPVLRER8U3p6Onr2HI6cnEMAgJ49h2Pz5pUICQnxcsv8k49NxYqIMEhkNlcNFlVUcJDYqhUzeZxOIDOTmT2+xlNpPLOZ2UdhYUBBAevBDx3KAXvr1nxsRiMfU34+cPw4kJPDj+3btQ+TiIiIiDS+mTOBzz8H/va3CnTp8iSAMuTnp2LIkPHIysrydvNqaNMGGD8eOHSIeyyJiIhIy5SYmIgffliB4OBEAMDevevx6qtfeLlV/ktBJRFpFlz7LVmtrJ8eFMSP4mIGYcrLfWe/pdpERzP7aNAgfnaVt4uKAtq14+f4eHf7jx8HvvkGOHKEAaeCAu3DJCIiIiKNz2QyYd26pYiP7wIAOH16Py6/fAIKCwu93LKaOnUCRo0Cdu8GfvjB260RERGRxtK5c2esX/8lAgKiAbyG3/zmBi2+9hIFlUSkWTAauYeSxcK9iIKDgY4dGZipqABOnWLAJTWVX1ut3m6xZ+fLYrLbGWC68koOkM1mBs9ycoC8PK7CPH2a5+XmKotJRERERBoHVwR/hbCwVgCAlJQtuOqq62Cz2bzcspp69gSGDWO/eOdOb7dGREREGkv//v2RmXkYPXrcDacTSEjglhHStBRUEpFmIyiIew61acPPQUEMMkVFATYbUFjIgFN+PpCd7bsZS554ymKKiGBpvPBwPt6AAAbTcnKAPXuA1asZRLNYuFeTsphEREREpCF16NABmzathMXCFPsffliF66+/FRUVFV5uWU39+/Nj3Trgp5/YPz55kp9FRESk5YiKisLevaz2U17OxdsDB57EvHnebpn/MDidTqe3GyFNSxtPSktjt3OT3sBABpdcgZSuXZn9AzDAVFHBMnrGZhROz81lsKikhHtJdenCYFN6OrB+PR+PyeR+fJdfDiQmukvlVb6fq9yeiIgvUb9ERMT3bdq0CSNGjEVFBTc0HTr0HsTELMB11xkwc6aXG1fN6tX8KC7mvqVRUcD06UCPHt5umTQH6peIiDQvBgMAvAvgbgBvIiHhZqSne7dN/qAZTa2KiHjm2m/JZmMmT2Cge7+lvDyuTjx1ioEnXy6N54mnDCaTiasxOnTgIDkujkElgwHIyGA9+TVruDIzMFBZTCIiIiJycYYOHYovvvgUBgNrOG/a9Bq++OL3mDWLC7l8ycCBwLFjwLZtXFyVlQV88okylkRERFoaZiZtAHAbABuA25GR8Y0ylpqAgkoi0uxV328pJKTqfkuHD7NkXGAg66w2t9J459qHKTycqb6ufZh69eJzYbPxsaenM7B29CgDTtqLSUREREQuxPjx4zFz5jsADAAsAIYA4OKlBQu82bKqCgvZH27XjvuRRkezv5uf7+2WiYiISEP69FMAGAbgnjNH7ACmYdGirV5rk79QUElEWoTa9luKiOBnk4kl4RwOBpZ8sAx8vXnKYjKbWf6uUyd+HxcHOJ3cjykzk3sxrVoFHD/O5yQ/X1lMIiIiIlI3J0/eBGA+gC8BTDt7fMkSb7WopshIICaG4wCTiXssBQXxuIiIiHhfQ807TZkCcLHLKwCmnDlaiL17J+DQoUMXd3E5JwWVRKTFMBoZVKm8Z5LJxGyeoCDe5iqHV1bGYAvAQJPd3ryyl1zqksXUti0wejRw6aVAQgLPMZmYsZWXx6BSaiqfl+xsZTGJiIiIiGdTpwLAvQCurHJ8yhRPZ3tHWBj3UEpMZHApOprHuOeCSNOr6+Spt85rDm3UY2ne5zWHNuoxN90161M9p7brlZXxftOncxEJEADgPQDDAQDl5RkYPPhqpGtzpUZjcDpd06riL7TxpPgbq5XBkrIyZuwEBfG4ycQPV5DJYmGpDNftzZ3d7n5crqCT3c5/2kVFDEadOsXnoEMHBqAOHGAWV0IC7xsbCwwYwPt7up6IyMVSv0REpHnp2pWLkirr2PHf+O9/u+PKK6/0fCcvKCpiVr7JBKxcyT7stGksiS1Sm4bul+TmAvv3AyUlLNPepQsDnZ7OO3iw6c/z5s/WY9FjaannNfXPrjyzX9t51RdWVD+vc+eqP7fyNXNyWE62pITzSJ07c9EG4L6uwcBrHjrEIFBQECvoREZyAbfrw2YDdu7k/+iICJasjYgA+vfnfUwmztu5rudqo9kMtGrFz64gk8HAc48cAT74ANi8GejYMQfr1o1Ebu4eAECHDv2xY8e3Gmc2AgWV/JAmb8QfORwMlphMzGSqqGA5vNRUfh0by88WC8vnGVtwHmdtnYycHJYHCQjg82W18h/9wIH8x33qFI9HRNTs6CjgJI1B76vGt2rVKixatAhr167F8ePHYTQa0aFDB0yaNAlz5sxBfHx8lfMXLlyIu+66q9brjRo1Ct9++2292qB+iYhI87NgAUvejRnjxO9+9wQcjr8gICAMGzeuxqBBg7zdvBpyc7nvQmQks6rUr5DaNGS/xLWgb/9+IDiYk6fBwUD37lXfg3Y7zyktZbWJpjrPmz9bj0WPxVfaqMfctI+lpITbMkRE8LjNxnmnXr04P+VSUQH89BPnpcLDOV8VHMytH6KiGNwKDeXcXeU5LqMRyMs7gVtuuRzFxakAgEGDxmLdumUI1KqSBhXg7QaIiDQFo7FmWbywMP5Tcji40sFk4j+uioqWHVRy7cVUfbI+PJwBtaIi/pPOzOTnoCCuJCko4DlpaUBGBvdyiozkP27XqpXzrdYRqa62wFF9VoHJhXn55ZfxwAMPoE+fPnjllVcwePBgFBQUYNGiRfjjH/+It99+G9999x26detW5X7BwcFo166dx2vWdlxERFqWmTP5UVHhwJdfbsXXXwPl5UW44ooJ2LZtLbp37+7tJlYRHQ1MngwsXgwsXw5MmtSy+/viG8rK2Jft1In93LIyTrQmJXEy1KW4mAv42rRhn7ipzvPmz9Zj0WPxlTY29mMODOS5BQWcb/F0zbZt3dc833muKjIXej2ns/bH4jqvclZTSQlw+jTQrh3PtdmqnutKVSkqAk6eZDZRYCAr4RQWsi3h4e45ufJy9/lRURz3t2kD9OzpXgBeXs52BwSwio7Fwswou53Xq/58V5/jcjrb4H//W4EpU0agvDwHW7ZswNdf78SkSYMhDUdBJRHxWyYTJ6vLyvjPKyeHx4qL+U/PYKia3dSSmM01V6C49mI6eJD/wKOj+b3FwkBS27YMwBUWAllZzPI6dYqrRyoqgPh4BpvKyxlwqlxyT9km4kn1wFGnTvxcWAj8+CNvb9+eHc6DB9lR1Huo4ZSWlsJisWDp0qVo27YtACA8PBxz5sxBTk4OnnnmGTz00EP48ssvq9xvyJAh9c5GEhGRlslkMmHJkvcxfPg12LbtO1itWRg6dDx27lzncwsNEhKACROApUuBr78Gxo3TPkvSuCwW9m2Livi5uBiIi2OVjMp92qAgHneVKG+q87z5s/VY9Fh8pY1N8ZiLivh9TEzVcwMD3ee5/lbU5TzXz77Q69X2WKqfB7i3iSgq4qJs1zYJnq6ZkMDzLBaO8WNj3RlJla9Xed4pPJzfVw8UhYYCrVvzepGR3A88PJz396T6HNf48T3w/vtL8bOf3YTy8g8wZcpglJbWfn+pvxY2TSoiUndGI//JWSwMliQkcPWFw8EgSkqKO3BitXq7tU3DtcJj0CB+jo52D4aKi9lJCQjgP/1LL+U/+cBA/pO3WpnqfOAA05mPH+dzuGED8P33njdgrM/GkNKyuNLj09L4Hti/H/jqK75/jh9nkDc6mu+vqCh2SsvKvN3qliUpKQm33HLL2YBSZVPO7Lj+9ddfo6KioqmbJiIizUhwcDBWr16Czp37AwDy81Nx2WXjkZmZ6eWW1dSmDTB+PLPsv/vO262Rls61aC8sjJOnYWH8vrbFfU19XnNoox6Lb7bRHx+LPz7mxrqmp3mnC/25tTGZgGnThmHfvoNITr787FYO0nC0p5If0t4FIlV52m/p6FGuhAgMZDpvVBQDKC0tY6muaitF5qoTXrlkntnMDZxLS4Ft29yZXyUlvM/AgfzeauXzXFt5M2U4tRyVX8uAAGYipaUBmzbxvRAWxt8tqxUYNoydvR073O+rvDyeo0ylprNnzx707t0bZrMZJSUlCAgIAMA9lRYuXNigmUrql4iItAwZGRm49NIRyMhIAQB07ToIP/64CuHh4V5uWU379gGrV3NCq2dPID+fi6TCwrzdMvG2xuiX1HVc463zmkMb9Vh8s43++Fj88TE31jXr4mKv53QC6emcoyoq4lzDXXftwcaNvXDddcDcuRffRn+loJIf0uSNyLnZ7cCJE0zfLSvjPx6rFejcmf+AjMaagSh/UN+9b4qLgS1b+Dw6nQwkZGcD3brxOdu/n/V4Y2P5OSoKGDyYg/n8/Nr301GwqXlxvT9ycvh7ExPD1zgggJmAAN8D1QNH2lPJuz7++GPccMMNGDNmDFatWnX2+MKFC/Hcc89hxIgRWLFiBdLS0hAWFoYBAwZg1qxZuPHGG+v9s9QvERFpOY4ePYo+fUagsPAkAKBDhyvRvfsyXH99EGbO9HLjqtm2Dfj0U/ZPg4PZF50+HejRw9stE29Sv0REpOXJzgaSkx2w2x8D8AKATwFce3YbB6k/P5kKFRGpO5OJAQurlZlKwcEMJpWVcWI8O5tZFidO+FdpPLOZdW2rB3JqS112lc0rKXGXzevcmWXzOnTgc9q6NYNOrgDDgQPAzp3AqlUsS2K3c1PIPXv4/OfmMjNqyxaV0/M11Z97p5Ovz6ZNDCAWFrKz5ton6ZJLmLUWGek5nb0uKfHSeN566y0YDAb86U9/qnHbvn37EBcXh2+//RaFhYVYu3YtwsLCcNNNN2HWrFnnvbbNZkNBQUGVDxERaRk6dOiATZtWIjAwBgBw9OgqLF/+C8yaxVXCvqRrV/Yz9+1j/z8rC/jkEy4oExERkZYjNhaYMOF9AM8CqABwE4ANyMwEnnrKu21rrpSp5Ie08kbk/KxWBo9cGTGuvZeKi1myrazMPckdGAgkJ/tPxlJ91LVsXl4eb+/RgwGG77/n8wrwtSgqYhAiLY33jY3lNV3ZTSEh589sUYbTxTtftlpBAYNJiYkMFBYVMaCUmMjXwmRicGnQIPdGnHpdfM/y5csxYcIE/OY3v8Fzzz1X5bYTJ04gLS0NQ4YMqXK8vLwcPXv2REpKChYtWoRbb7211uv/5S9/weOPP17juPolIiItx9y53+Ppp8cCCAewAkAfAMD8+fCZjKWTJ4EXX2T/PjcX6NSJfZLZs7nwSfyT5ktERFqmIUMc2Lz55wDeO3MkGsA6XHZZT2za5MWGNVOaAhUR8SAoiIGiNm34OSiIQaOgIE6GR0Zy0FlaylJt5eXebrFvqi3bxNOmi926MXspMRFo147Pc+vWDBx16gQkJQEGA/fgsdv5ceQIs5h27mRd/EOH+LqkpfFYcbE7Y+ZCM5z8MfvJ02Ou/hxmZzOAd/o0sH49g0e5ucw4O3SIr1v37vwwmxlkys9nwM9icV+3tgw48Y6UlBTceeedmD59Op555pkat7dp06ZGQAkAAgICcN999wEAXn/99XP+jLlz5yI/P//sR6qrDqKIiLQYu3ZdBmApgPVwBZQAYMkSb7WopshI9lcsFvYvN23iApjISG+3TERERBratGlGAAsBXHXmSC6AqzFqlMajFyLA2w0QEfFVRmPN7COTiRk0ZWWcHM/O5nHXfjDBwQxi+Nt+S+diNnsOGLgCTtWzVFwBJ1fmS2SkOwCVleXObsrNZcCvc2ceczp5TlmZO+AUEsLrHTzI4zExfM3y84EBA3h7URHP9ZThdK7sp3Nl2DSH7Jv67JEVGgrs3s3yj4GB7sBR9+4MqBYVMegXHs7gUXExA4ShoVVfy+ol7sS3HDt2DOPGjcOIESPw7rvvwmQy1ev+Xbp0AcDyeOcSGBiIQFcqooiItEhTpwLLlo2ucXzKlKZvS23CwriH0iefuPZa4DGrlZ9FRESk5Zg7F3jxRQsyMz8BMAbAjwBO4M03r8ajj65DTEyMl1vYvKj8nR9SOrfIxaleGi86GnA4eNz14dqXKTaW2U1Sf56CHnUtp5eby+e9Z08e27KFwRCjkdlleXlAr1687v79PBYT4w6G9O3L4MjevYDNxtsKCxng6t+f16wt2HQxZfgu5LYLuU9tbbTZgB9/ZAAoKAjIzOR7uVUrPk/h4TzfaOR1L7+cx3bsqFrKMCyMAUPXz2wOQTZ/d/DgQYwdOxYjR47EwoUL6x1QAoBPP/0U119/PVq3bo0TJ07U+X7ql4iItExdu7K/4VaO4OD78Y9/DMW9997rrWbVUFTkzqb+5hv2+a6/XhlL/kr9EhGRlu2pp4CPPsrAvn3DUVrKjkqPHpdjy5avEBIS4uXWNR/KVBIRqSdXabzq2UhBQcDhwxyIBgUxQOFwsISbMpbqz1OGU12zm8LD+X1UlLuMXuWgR6tWDCqVlLBUXmIiXyOLhZMKubnMwElNZYDEVW7v5ElmRJ04wQBMVBSQns4Mqr59eY09exhYjI5mZs++fQxEBQby2hcSjKrttvrcp2NHPpaiImDbNvc+VidOAMeOsfygzQb89BPPs9ncWXkJCe7nIDqa942JYQCu+nPvKRuptmw18Q179+7FVVddhUmTJuHVV1+FsdIfrOeeew4333wz2rZtCwBISkrCa6+9hkmTJtW4TkpKCgCge/fuTdNwERHxaSkpwIIFLHk3aVI5fv3rm1Ba+ilmznwDMTExuP76673dRADsu7gykyZNAj79lG2+/nr3HpAiIiLSMsydC8ydm4DDh1egV6/LYbWmY9++DZg48WZ8/fWnCAhQuKQulKnkh7TyRqRx2O2coA8M5IR8SQmDC506caWj0cggk0rjNZ76ZuZUz3CqnGUDAFu3MhAUHs4AUVAQ0KED92sKDmY2U2kpgyk9e/K13b2b1zCbef2iIndW1IEDPD8yksdDQoDevblX1P79bF9EBAOTYWG8n8HAjKniYt6voID3696d13NlV7mO9+jBwNfu3bwtJMTd9u7d2aZ9+/h4Q0LY5tJSlgMMDubPslqBuDh3kKhfv3NnZ53ruRfftmPHDowbNw4333wz/vGPf8BgMFS53WAwYPXq1Rg9evTZ7++77z688sorVc6z2+3o3r07Dh8+jLfffhs///nP69wG9UtERFo+p9OJ2bN/i5deegEAYDBY8NVXX2Ds2LFebllNRUUsiWexANOmsW8v/kP9EhER/7Ft2zYMHjwKFRWFANpi5851uPTSdt5uVrOg0JuISANxlbyz2Tg573Dwe7udk/oAJ+QrKlQar7Fc7P5N1bNsunVzB1ISEtx7O+XkVA1EJSUxOATwdc/PZ6AnNxeIj+e+T1YrcPw4vzebeZ3CQr5vnE7eHh7O20JDeVtxMW/LzeX55eW8PSfHnSEVHs73mNnM84qKGLB0BZuCgng9qxVo08a9Erdy+2NimH1nNgN9+vAxFxVVfT5qew7P99yL79qyZQvGjx8Pm82GzMxM3HLLLXW63/z58xEfH4977rkHrVq1wsGDBzF37lwcPnwYd911F2677bZGbrmIiDQ3BoMBL7zwLDIzs/Huu2/C6SzD1Vdfh40bV2Hw4MHebl4VYWHAtdcyY2npUu4DpT6OiIhIy9O/f3+sXPkZJk78HWy2xRgwoA2KiznnIeemTCU/pJU3Io2n+n5LsbFc3VhcDBw9ytujohgoCAnhRL4ylryvvvsSXUipuvNlRdX3tl69WGqvvtdzBZ+UdSQPP/wwXnrppfOeVzlT6cCBA/jggw+wbNkyHDhwAEVFRYiOjsaAAQNw77334sYbb6x3O9QvERHxH+Xl5Zg4cTq++moJACAwMBbbt6/zydKpGRnA4sVcPDRpkvrs/kL9EhER/+NwOJCYaERWFheJl5R4u0W+T0ElP6ROkkjj8lTizlUaD+Bkvd3O87p04aR+bfcT31XfQBRwYcGoc912odc7X/tFmpL6JSIi/qW0tBQjRkzA1q1rAAAREW2wa9d6tGvne+VmTp4EPv+c5ayHDWNme2SkO/NbWh71S0RE/FdICLcHiImx46abtmDr1mG47jruwyRVKajkh9RJEml6Dgdw6hQn8YODOeFfUcFSaIGBDCIVFlbNcFJpvJbpQoJR57rtQq8n4ivULxER8T/5+fkYOHAMDh3aBgCIiLgEQ4asxU03xWPmTC83rprDh4GFCxlgio5mFvj06dzHUloe9UtERPxXWRkQElKMioobAHwD4AsAVyE+nhnM4qa18CIiTcBoZKDIYuGqh4gId+aIw8HSeBkZgMHAf2LZ2TwuLY9rzyRPQZ4Lue1CryciIiLiLZGRkdiwYTkSEroCAAoKDuDrrx/GrFlA165eblw1CQncL/PIEfbTs7KATz5hmWERERFpOSwWYOTIfwJYDsAOYBqAH5GZCTz1lHfb5msUVBIRaSJBQdxDqU0bfg4K4j+s8HBmL4WGMuBUWsqBa3k57+dwuMvliYiIiIi0BAkJCZg9+ysArQEMBsB9/g4eBBYs8GbLqsrP5wKdvn3ZJ2/dmlUH8vO93TIRERFpaEVFvwUwxfUdgIkADmHxYu+1yRcpqCQi0oSMRg5KK++ZZDKxbqvZzAymigoGlPLyuBLyxAl+nDoFWK1ea7qIiIiISINat649gNVgiZm4s8eXLPFWi2qKjGTJO6eTeyu5yuBFRnq7ZSIiItLQpk0LAPA+gOFnjmQAGI8rr0z3XqN8kIJKIiJeVrk0XlkZEBPD0njBwcDp0yyLV14OFBerLJ6IiIiItBxTpwJAVwDhVY6PHm2Fr2z/HBbGPZTi44H0dH6+/noeFxERkZZl7lwgPj4YwBIAvc4cPYx3352AgoICL7bMtxicvtJTkyajjSdFfJPDwSwlk4mBJrudGUoAvy4rY3CpSxf3ILb6fUREmhv1S0RE/FvXrix555YLg2EyZs8ejuefn+etZtVQVMSSd5GRCii1ZOqXiIgIwD2UPvzwBPbuvRxlZakAgIEDr8T69V8gMDDQy63zPk1Bioj4iOql8UwmZi8BLItnNgOBge6MpZwclt9QaTwRERERaa5SUoD584HJk4F//9sOi2UMnM4NeOGFZ/HMM894u3lnhYVxPyUFlERERFq+uXOBbdvaYPv2FTCbYwAAP/64CjfccAccKiGkoJKIiK+qXBavtJQD2I4dgbg4ICCAAaWMDGYqlZSoNJ6IiIiINE8zZwKffw784hdm/POfvzx7/LHHHsOCBQu82DIRERHxZz169MCaNUthMAQDAJYuPYmMjGIvt8r7/CaotGnTJkyePBmxsbEIDQ3FwIEDMX/+/Auu07x//3787Gc/Q0JCAoKDg9GrVy8888wzsNvttd7HarXiiSeewCWXXIKgoCAkJyfj9ttvx5EjR+r8c6dOnQqDwYAZM2ZcULtFpHkJCgKSk4E2bfg5KIgZTCEhDDJFRzOoZLMBWVnMYnL9WXM4WDZPgSYRERERaS5mzZqFJ598stL39+Pjjz/2YotERETEnw0bNgyff/4/BAdPB/AV2rcPP+99Wjq/CCp98sknGDFiBAICArB161acPn0aN998M+6//37cfffd9b7exo0bMXDgQKSmpmLNmjXIzs7GnDlz8Oc//xmTJk3yGFgqLS3F2LFj8eyzz+Jvf/sbcnJysHz5cuzatQv9+/fHjh07zvtz33nnHSxZsqTe7RWR5q16WTzAXRrPYGBd94AAfl9SwrJ42dkqjSciIiIizdNjjz2Ghx565Mx3Dtx44634+uuvvdomERER8V+TJk1CSclHiIgIRlkZt6nwZy0+qJSRkYEZM2agVatWeO+999C+fXuEh4djzpw5uOOOO7Bw4UJ88MEHdb6e1WrFLbfcAoPBgE8++QQ9evRASEgI7rzzTjz22GP46quv8MILL9S431/+8hds2LABTz/9NG688UaEhISgb9+++Pjjj1FUVITbbrsNFRUVtf7c06dP46GHHsKwYcMu6HkQkZalemm88HB3aTyzGUhLA9LTgfJybiqs0ngiIiIi0lwYDAa8+OJzuO22GQAAp7MM11xzHX744QfvNkxERET8Wn4+5+IKC4GkpEzce+9qXHYZ8NRT3m5Z02rxQaVXXnkFhYWFuP322xEcHFzltvvvvx8AMG/evDpf78MPP8SxY8cwdepUJCYmVrntvvvuAwC88MILVQJEJSUlePnll2GxWHDnnXdWuU/nzp1x1VVXYc+ePfjiiy9q/bn3338/+vbti5kzZ9a5rSLSstVWGi84mKXxYmN5nt3OvZcKC92BJZXGExERERFfZjAYsHDhAowfPxUAUFFRjJEjJ+KJJ/Zh8mRAWy2JiIiINxQWAkbjUaSnD8drr03EDz+sx+9/DyQkeLtlTafFB5Vc5eKGDx9e47ZBgwYhKCgIW7duxcmTJy/6esnJyejcuTMyMjKwadOms8dXrVqF4uJi9O3bF2FhYTXuN2LEiCrXrm7RokX46quv8N///hcGg6FO7RQR/3Cu0ngAM5gsFu7BZLMxYykjAzh2TKXxRERERMS3BQQEYPHi9zFw4CgAgM2WjT//+QUsWwbMmgV07erlBoqIiIjfsViAAQNeBJACwApgMoA9yMz0n4ylFh1UqqiowJ49ewAAHTt2rHF7QEAA2rZtCwB12tOo8nmergcAnTp1qnG9C7mPS3p6Oh588EE89dRTZ88TETmX6qXxQkOB9u2B+HgGlzIyGFyyWoG8PCAzs2rGkrKYRERERMRXBAUFYdWqJYiPHwDg5wD+ffa2gweVsSQiIiLe8CyAcWe+zgNwDYBULF7stQY1qRYdVMrLy4P1zBL8mJgYj+dER0cD4J5FdeE6rz7Xu5D7uPziF79Az5498cADD9SpfZ7YbDYUFBRU+RCRls1TaTyDgYGm0FAgMZHHAO69lJPDTCarldlLymISEREREV8RERGB/v1XAXgTgLnKbbUU/BARERFpNNdfbwHwMYCBZ46cAHA1xo3L8V6jmlCAtxtwPtOmTcO+ffvqdZ+33noLQ4YMQUlJydljFlctqGoCAwMBoMq55+I6rz7Xu5D7AMB7772HL7/8Ejt27IDReOHxv6eeegqPP/74Bd9fRJono7FqWTzAXRqvrIx7Lzmd/GwwuLOWnE4gOprnZGczKHURf4JERERERC7aDTdEYuXK6kdPYsKEWABBXmiRiIiI+Ku5c4EXXwxHZuYXAIYDOAhgHz76aDLmzv0aISEhXm5h4/L5oNKRI0dw4MCBet3HFZyp/OKVlZV5PNdms9U491xCQkJQVFRUr+u5vq7PfdLT0/HrX/8af/3rX9GtW7c6ta02c+fOxSOPPHL2+4KCgrNl/0TEv7hK42VnszReYCC/Dwri95mZDDwVFjLQZLUCcXE8z+EAKip4u4JMIiIiItKUZs4E5s1jyTvaB2A8Fi8eiFmzPkJAgM9Pb4iIiEgLkpEBPPVUAj74YAX27Lkc5eXp2L9/IyZOvBlff/1pi+6b+Py04Pbt2+F0Ouv1MXr0aABAVFQUgs7Ud8rJ8Zx6lpubCwBITk6uU3uSkpLqfb0Luc8vf/lLdO7cGbNnz65Tu84lMDAQERERVT5ExH95Ko0HMHAUHc3MpZAQZirZ7cxgysgAjh1TWTwRERER8Z6UFGD+fGDixDKYzRMAnMDKlYsxY8ZMOJ1ObzdPRERE/MzcucD27Z2wefNymEzhAIA1a5bizjtntei+ic8HlS6GyWRC7969ATDjqbry8nKkpqYCAPr06VOna/br16/W6wHA4cOHa1zvQu7zySef4IcffkBAQAAMBsPZj7vuugsA8Oabb549NmPGjDq1XUTExWgEzOaqGUeuLCaLhVlJsbFAly5AWBgzmFzZTTk5wOnTPMfF4WAAqvIxEREREZGGNnMmsGyZBV988V8YDNxfadGihXjkkd+16MkbERER8V39+vXDV18thsHA7W/eey8AVmvLnSRr0UElALj22msBABs2bKhx248//gir1YoBAwagdevWF32906dP49ChQ0hISMDQoUPPHh8zZgxCQ0Oxc+dOFBcX17jf+vXrAQBTpkw5e6y2LKw33ngDAHDnnXeePbZw4cI6tV1E5HyqZzGFhDDIFBICJCXxs8kEZGUx0FRczI9Tp5TFJCIiIiJN56qrrsKHH74L17TG3//+PObNm+fdRomIiIjfGjNmDD78cBFCQv4Ep/NVxMaavN2kRtPig0r3338/wsPD8fbbb8Nababz1VdfBQDMmTOnxv0effRRjBgxAnv27Kly/KabbkL79u2xePFiZGRkVLlt/vz5AIBHHnkEJpP7TRMSEoIHHngANpsNb775ZpX7HD58GF9//TV69eqFiRMnXvgDFRFpINWzmEwmBpbsdgadgoO5z1JQEANKhw6xPB7AgFJ2trKYRERERKTx3XDDDZg//5Wz3z/22GNYsGCBF1skIiIi/uyGG25AcfHjCAkxoLSUVYBaohYfVEpISMAbb7yBkydP4tZbb8Xx48dRWFiI5557DgsXLsSdd96Jm2++ucp9srKyMG/ePKxfvx6vvPJKlduCgoLw7rvvoqKiAtOnT8f+/ftRWlqKt956C08++SSuuuoqPPLIIzXa8ec//xnDhg3DY489ho8//hilpaXYuXMnpk+fjpCQECxatKhKIEpExFdULotXWsrPSUlAZCQQFcW9mMLCAJuNt2dlAUVFDCJZrcpiEhEREZHGM3PmTDz11FNnv58163589NFHXmyRiIiI+LvcXCAggFtItG27E6tXr/Z2kxqUweknRYc3bdqEv/71r9iwYQNsNhu6d++O++67D7NmzYLBYKhyrtPpxLXXXotNmzbho48+wujRo2tcb//+/fjzn/+MVatWobCwEJ06dcIdd9yB3/zmNzCbzR7bYLVa8cwzz2DRokU4fvw4IiMjMW7cODzxxBPo1KnTOds/evRorFmzxuNtR44cQYcOHer0PABAQUEBIiMjkZ+fj4iIiDrfT0T8m8MBVFQwc8mVxeRwMFhUVsbMpbw8Ho+J4efsbH6OimLQyWJhWT1ji1/SICJ1pX6JiIhcLKfTid/8Zg5efPE5AIDBYMFDDy1DSspVmDqV+zCJ1IX6JSIi0lBycoDY2G8BTIXB4EDPnmtw220DMHeulxvWAPwmqCRu6iSJSENylbwrK2PQKDaW2UtFRcCRIwwguQJRDgfQqRNvBzwHqkTEv6hfIiIiDcHpdOKOO+7BO++8cebI4wD+BADo0gVISfFa06QZUb9EREQa0sSJN+PLLz88810CgA2Ij++MarvqNDuawhMRkYsSFMTsozZt+DkoCDAYgNBQBpiCg/lhtXJvpbw8fuTksCyeSuOJiIiIyMUyGAx444356NVrGoDn4QooAcDBg4C2WhIREZGmNnToQgAjznyXAWA8MjPTUalyb7OkoJKIiFw0oxEwm6tmG7n2YgoKApxOIC4O6NqVezEBwMmTQEYGy+Ll5gLp6cxccnE4GISqfExEREREpDYBAQFo3/4jADX3OV6ypOnbIyIiIv5t2bJgAEsA9Dpz5DCACfj44wLvNaoBKKgkIiKNpnoWU3Awj4WGAmFhQHw8S+YZDEBmJoNMRUVAQQGzl5TFJCIiIiL1cd11nqY5NmL06GZeZ0ZERESaneuuA4BoAMsBtD1zdBtOn74eNpvNW826aAoqiYhIo/KUxWQyMZjkcAAhIfyIjeXn0lLg6FHg9GmgvJwBpsxMd8aSMphEREREpDYzZ3IPJbelAK7Ef/4zAQUFzXtVsIiIiDQvc+dyQTXQBsAKADEAgJMnv8ENN9wBRzOd3FJQSUREmpyrNJ7FwiCSxcJMpogIICqKwaXoaJ5bUcHSeFlZ3IcpNVUZTCIiIiJSu5QUYP58YMIEG0JCHgRgxaFDWzFmzFRY1YEUERGRJpSRATz5JHDZZT1w//3LYDAEAwCWLv0Qv/rVw95t3AUyOJ1Op7cbIU2roKAAkZGRyM/PR0REhLebIyJ+zOFg0MhkcmcyORwMGJWVsVxecTHL40VFcR8mm41Bp4oKltBr2xYICKj9eiLi29QvERGRxrRv3z70738FbLZsAMD48VOxbNlHCHB1IEUqUb9EREQa2xdffIFJk6YAcMBkehnl5b/wdpPqTVNuIiLiNZ5K41XPYgoKAlq1AsLDGURKTGRQyWwGsrO54iM3l18fP157FpPK5omIiIj4nx49emDt2i9hMoUCAFauXIwZM2ZC62tFRETEGyZOnIg333wdISEfoqLiF2iOaxgUVBIREZ8TFMRyeG3a8HNQkHsfpvJyfh8YyABTVBQzmU6dYom8khJ+PnmS2U4AA0ynTqlsnoiIiIg/Gjx4MFasWAyDwQIAWLRoIR555HcKLImIiIhX3HHHHSguvgGBgUBhIdC6tbdbVD8KKomIiE+qnsXkaR+m+HhmLYWG8iMpidlMwcFAXh6DS5mZwNGjQH4+71NWxqymyhlLymISERERadnGjh2Ljz56D65pkL///Xk888wz3m2UiIiI+LWCAi6iTksD2rf/COvXr/d2k+pEQSUREWk2PGUwAe4sJrudGUxmM7OYYmP5tc3GgFFBAQNSOTlAURGznpTFJCIiIuIfrr/+eixY8OrZ7+fOnYvbb5+PyZOBBQu82DARERHxSxYLA0rAf3D8+E0YOfJaXHrpHjz1lLdbdm4KKomISLNSl32YLBYgLo4BpogIfu3KaHI4GIQqLWXG0sGD/AwAxcXKYhIRERFpye699148/fTTZ74z4513orBsGTBrFtC1q1ebJiIiIn4oLs6Byy77FIATDkcudu++Br//fSoSErzdstopqCQiIi1CbVlMroBTYCBQUQFERwOdOgEJCSyVZzDwNpuNpfFOn2ZgqbiYmU1paZ6zmBRsEhEREWmeHn30UUyc+H8APgdw09njBw8qY0lERESaltFoxDXXfAxg4JkjJwBcjczMHJ/NWFJQSUREWgxPWUyA54CTwcC9l6KimNkUFcXgUlQUr1FSwr2YTp9mMCknBzh5koGn0tJzl8xTwElERETEtxkMTwC4usbxJUuavi0iIiLi3778MhzAFwC6nDmyD8BkfPJJifcadQ4KKomIiF+oS9m8kBCgdWsGlqKiWC4vPp5BKLMZyM0FMjKAlBQgM5OZTwUFQHq6O4CkPZpEREREfN/UqZ6OvoFBg/Y2dVNERETEz113HQAkAFgJIOnM0Y3IyroJdrvdW82qlYJKIiLi12orm2cy8WuHgxlNgYFAUhLL55nNLJ3ncDCwlJnJwFJODnD8OANNZjNL6mmPJhERERHfM3Mm0MW1GBhOAE8BuBvPPz8ex44d817DRERExO/MnctFzUBHAF8CCAcAHD26DHfeOQtOp9OLratJQSUREfF7dclisliAuDgGmCIiGHSKiGB2U1wcvwa4F1NFBVBYyBJ6mZnMcCopYbDp5EmVzRMRERHxBSkpwPz5wIQJNiQkfAIAKCw8iSFDxiEjI8PLrRMRERF/kpEBPPkkcNll/XDPPYthMFgAAO+9txC///3/ebl1VSmoJCIiUgtPWUzVg02uDKbQUJbMS0hgoCksjIGnkBDu31RUxD2a0tPdwaZjxxiEKiv7/+3de3BU9f3/8deSkBvJxvANl0CRAkJALJdpoXIphGsBBSrUVkUmtCW/doS2M9ws+v1V5Gu/tQJqZ2CoigqOpQxQaENNsVBiK3gtI3fRQMJNRCAJScjmQsj5/nFcYsjC7mbP7jlLno+ZjOR8PrvnfT5Czgvee84xt/GcJkSL6upqLV26VJmZmUpISFBGRoZmzpypoqIiu0sDACAoOTlSXl6CDh3KU8eOmZKk8+cLNHToBJWXl9tcHQJFNgEA3AoWL5bee09as2aUNm78oySXJLeefXac3aU1QlMJAICb8HUV041umedtOMXHS3V15tVLt98utW1r3jbP+4ym5GTz11VVUlmZeSXTsWPSuXNmg6mkxGwuVVWZ71NVdeOGE80mRFpVVZXGjBmjZcuW6amnnlJJSYm2b9+ugwcPauDAgdq/f7/dJQIAELR27drp/ff/Ibf7a5Kk48c/0qhRU1RVVWVzZfCHbAIAuBV9//vf1yuvvKyEhH+rtnak0tPtrqiBy3DaDfkQduXl5UpNTVV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            "text/plain": [
              "<Figure size 1800x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(ncols=3, figsize=(18, 6))\n",
        "for i, t in enumerate(times):\n",
        "    sol_u, sol_p = split_sol(len(elements_u), sols_dd[i],nodes_per_element_u,nodes_per_element_p)\n",
        "    ax[0].plot(nodes_u, sol_u, color='blue', alpha=(i+1)/(len(times)+1), marker='.',lw=1)\n",
        "    ax[1].plot(nodes_p, sol_p/1e3, color='blue', alpha=(i+1)/(len(times)+1), marker='.',lw=1)\n",
        "    ax[2].plot(nodes_p, sol_p/1e3- (p_drawdown(t)/1e3 - gamma_w()/1e3*(10-nodes_p)), color='blue', alpha=(i+1)/(len(times)+1), marker='.',lw=1, label=r'$t = %i$ d' % (t/day_in_sec))\n",
        "\n",
        "ax[1].plot(nodes_p, p_drawdown(t)/1e3-(gamma_r(0))/1e3*(10-nodes_p),color='black',ls='--',label=r'$\\gamma_\\mathrm{r} z$')\n",
        "ax[2].plot(nodes_p, -(gamma_r(0)-gamma_w())/1e3*(10-nodes_p),color='black',ls='--',label=r'$\\gamma_\\mathrm{r} z$, $(\\gamma_\\mathrm{r}-\\gamma_\\mathrm{w}) z$')\n",
        "\n",
        "ax[0].set_xlabel(r'$z$ / m')\n",
        "ax[1].set_xlabel(r'$z$ / m')\n",
        "ax[2].set_xlabel(r'$z$ / m')\n",
        "ax[0].set_ylabel(r'$u_z$ / m')\n",
        "ax[1].set_ylabel(r'$p$ / kPa')\n",
        "ax[2].set_ylabel(r'$\\Delta p$ / kPa')\n",
        "#ax[2].set_ylim(0,50)\n",
        "# Add the legend next to the plots\n",
        "handles, labels = ax[2].get_legend_handles_labels()\n",
        "#fig.legend(handles, labels, loc='center left', bbox_to_anchor=(1.05, 0.5), ncol=1)\n",
        "\n",
        "fig.tight_layout(rect=[0, 0, 0.95, 1])  # Adjust the layout to make room for the legend"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "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": "3.11.4"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "state": {},
        "version_major": 2,
        "version_minor": 0
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}