SIAMFANLCh5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\newcommand{\\calf}{{\\cal F}}\n",
    "\\newcommand{\\calk}{{\\cal K}}\n",
    "\\newcommand{\\calp}{{\\cal P}}\n",
    "\\newcommand{\\call}{{\\cal L}}\n",
    "\\newcommand{\\calg}{{\\cal G}}\n",
    "\\newcommand{\\cals}{{\\cal S}}\n",
    "\\newcommand{\\dnu}{d \\nu}\n",
    "\\newcommand{\\dmu}{d \\mu}\n",
    "\\newcommand{\\dmup}{d \\mu'}\n",
    "\\newcommand{\\mf}{{\\bf F}}\n",
    "\\newcommand{\\md}{{\\bf D}}\n",
    "\\newcommand{\\mP}{{\\bf P}}\n",
    "\\newcommand{\\mU}{{\\bf U}}\n",
    "\\newcommand{\\ms}{{\\bf S}}\n",
    "\\newcommand{\\vu}{{\\bf u}}\n",
    "\\newcommand{\\vx}{{\\bf x}}\n",
    "\\newcommand{\\vw}{{\\bf w}}\n",
    "\\newcommand{\\vy}{{\\bf y}}\n",
    "\\newcommand{\\vf}{{\\bf f}}\n",
    "\\newcommand{\\vs}{{\\bf s}}\n",
    "\\newcommand{\\ve}{{\\bf e}}\n",
    "\\newcommand{\\vc}{{\\bf c}}\n",
    "\\newcommand{\\vq}{{\\bf q}}\n",
    "\\newcommand{\\vd}{{\\bf d}}\n",
    "\\newcommand{\\vb}{{\\bf b}}\n",
    "\\newcommand{\\vg}{{\\bf g}}\n",
    "\\newcommand{\\vz}{{\\bf z}}\n",
    "\\newcommand{\\vr}{{\\bf r}}\n",
    "\\newcommand{\\mg}{{\\bf G}}\n",
    "\\newcommand{\\ml}{{\\bf L}}\n",
    "\\newcommand{\\mg}{{\\bf G}}\n",
    "\\newcommand{\\mv}{{\\bf V}}\n",
    "\\newcommand{\\ma}{{\\bf A}}\n",
    "\\newcommand{\\mi}{{\\bf I}}\n",
    "\\newcommand{\\mm}{{\\bf M}}\n",
    "\\newcommand{\\mb}{{\\bf B}}\n",
    "\\newcommand{\\mt}{{\\bf T}}\n",
    "\\newcommand{\\ball}{{\\cal B}}\n",
    "\\newcommand{\\ptc}{{\\Psi TC}}\n",
    "\\newcommand{\\diag}{\\mbox{diag}}\n",
    "\\newcommand{\\frechet}{{Fr\\'echet\\ }}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "include(\"fanote_init.jl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Files for this Chapter\n",
    "\n",
    "- From the Package repository:\n",
    "  - Case Studies: __/src/TestProblems/CaseStudies__\n",
    "    - Pseudo Arc Length form of the H-equation: __Hequation.jl__\n",
    "    - Continuation Code: __knl_continue.jl__\n",
    "    - Heat Transfer: __CR_Heat.jl__\n",
    "- H-equation: __src/TestProblems/Systems/Hequation.jl__    \n",
    "- From the Notebook repository: __/src/Chapter5__\n",
    "   - Julia codes that generate the plots and the tables\n",
    "\n",
    "# Chapter 5: Case studies\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 5.1: Conductive-Radiative Heat Transfer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we consider a multi-physics problem where conductive\n",
    "and radiative heat transfer are coupled. Our description of the\n",
    "problem is from \n",
    "<cite data-cite=\"ctk:xferheat\"><a href=\"siamfa.html#ctk:xferheat\">(Kel96)</cite>,\n",
    "<cite data-cite=\"ctk:xferheat2\"><a href=\"siamfa.html#ctk:xferheat2\">(BK98)</cite>,\n",
    "<cite data-cite=\"ctk:xferheat3\"><a href=\"siamfa.html#ctk:xferheat3\">(BK99)</cite>.\n",
    "These\n",
    "models have applications to\n",
    "the study of porous materials\n",
    "such as fibers, powders, and foams\n",
    "used in insulations at either very low or very high temperatures,\n",
    "heat treatment of ceramics, and the thermal properties of coated materials,\n",
    "<cite data-cite=\"ozisik\"><a href=\"siamfa.html#ozisik\">(M. 73)</cite>,\n",
    "<cite data-cite=\"siegelhowell92\"><a href=\"siamfa.html#isiegelhowell92\">(SH92)</cite>,\n",
    "<cite data-cite=\"viskanta65\"><a href=\"siamfa.html#viskanta65\">(Vis65)</cite>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 5.1.1: The Equations\n",
    "\n",
    "We begin with the continuous formulation of the\n",
    "normalized and dimensionless equations for the problem\n",
    "<cite data-cite=\"ozisik\"><a href=\"siamfa.html#ozisik\">(M. 73)</cite>,\n",
    "<cite data-cite=\"siegelhowell92\"><a href=\"siamfa.html#isiegelhowell92\">(SH92)</cite>,\n",
    "<cite data-cite=\"cesjrt\"><a href=\"siamfa.html#cesjrt\">(ST91)</cite>,\n",
    "<cite data-cite=\"cesheat2\"><a href=\"siamfa.html#cesheat2\">(Sie95)</cite>.\n",
    "We will focus\n",
    "on one of the simpler problems in <cite data-cite=\"cesjrt\"><a href=\"siamfa.html#cesjrt\">(ST91)</cite>.\n",
    "Our formulation\n",
    "is taken directly from <cite data-cite=\"ctk:xferheat2\"><a href=\"siamfa.html#ctk:xferheat2\">(BK98)</cite>.\n",
    "\n",
    "We will discuss one way to discretize the problem in a later section.\n",
    "We refer the reader to\n",
    "<cite data-cite=\"ozisik\"><a href=\"siamfa.html#ozisik\">(M. 73)</cite>,\n",
    "<cite data-cite=\"siegelhowell92\"><a href=\"siamfa.html#isiegelhowell92\">(SH92)</cite>,\n",
    "<cite data-cite=\"viskanta65\"><a href=\"siamfa.html#viskanta65\">(Vis65)</cite>\n",
    "for the derivation of the\n",
    "equations.\n",
    "\n",
    "The unknowns are\n",
    "the dimensionless intensity $\\psi(x,\\mu)$\n",
    "of radiation at a point $x$ in the direction\n",
    "having cosine $\\mu$ with the positive $x$ axis and the dimensionless\n",
    "temperature $\\Theta(x)$ at $x$.\n",
    "\n",
    "The radiative transport equation is\n",
    "$$\n",
    "\\mu \\dfrac{\\partial \\psi}{\\partial x} (x,\\mu) + \\psi(x,\\mu) =\n",
    "\\dfrac{c(x)}{2} \\int_{-1}^1 \\psi(x, \\mu') \\dmup + (1 - c(x)) \\Theta^4(x),\n",
    "$$\n",
    "for $x \\in (0,\\tau)$ with boundary conditions\n",
    "$$\n",
    "\\psi(0,\\mu)= \\Theta_l^4, \\ \\mu > 0\n",
    "$$\n",
    "and\n",
    "$$\n",
    "\\psi(\\tau, \\mu) = \\Theta_r^4,  \\ \\mu < 0.\n",
    "$$\n",
    "Here\n",
    "$$\n",
    "0 \\le c(x) \\le 1 \\mbox{ for all $x \\in [0,\\tau]$.}\n",
    "$$\n",
    "We assume that $c$ is continuous.\n",
    "\n",
    "$\\psi$ is the\n",
    "__angular flux__.\n",
    "Define the\n",
    "__scalar flux__\n",
    "$$\n",
    "f(x) = \\frac{1}{2} \\int_{-1}^1 \\psi(x, \\mu') \\dmup.\n",
    "$$\n",
    "\n",
    "The temperature $\\Theta$ satisfies the boundary value problem\n",
    "$$\n",
    "\\frac{\\partial^2 \\Theta}{\\partial x^2} = Q(x), x \\in [0,\\tau], \\,\n",
    "\\Theta(0) = \\Theta_l, \\Theta(\\tau) = \\Theta_r,\n",
    "$$\n",
    "and couples to the radiative transport equation by\n",
    "$$\n",
    "Q(x)\n",
    "= \\frac{1}{2 N_c} \\frac{d}{dx} \\int_{-1}^1 \\mu' \\psi(x, \\mu') \\dmup,\n",
    "$$\n",
    "where $N_c$ is the conduction to radiation parameter\n",
    "<cite data-cite=\"ozisik\"><a href=\"siamfa.html#ozisik\">(M. 73)</cite>.\n",
    "\n",
    "The order of integration and differentiation\n",
    "in the definition of $Q$ can be changed \n",
    "<cite data-cite=\"ctk:xferheat\"><a href=\"siamfa.html#ctk:xferheat\">(Kel96)</cite>    \n",
    "and we obtain, using the transport equation \\eqnok{transport} and\n",
    "the definition of $f$,\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\dfrac{d}{dx} \\int_{-1}^1 \\mu' \\psi(x, \\mu') \\dmup\n",
    "& = \\int_{-1}^1 \\mu' \\frac{\\partial}{\\partial x} \\psi(x, \\mu') \\dmup\\\\\n",
    "\\\\\n",
    "& = - 2 (1 - c(x)) f(x) + 2 (1 - c(x)) \\Theta^4(x).\n",
    "\\end{array}\n",
    "$$\n",
    "Hence,\n",
    "$$\n",
    "Q(x) = \\alpha(x) (\\Theta^4(x) - f(x)), 0 < x < \\tau\n",
    "$$\n",
    "where\n",
    "$$\n",
    "\\alpha(x) = (1 - c(x))/N_c.\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 5.1.2: Formulation as a Fixed Point Problem\n",
    "\n",
    "We will formulate the equations as a fixed point problem for $\\Theta$\n",
    "( _ie_ we __expose__ $\\Theta$).\n",
    "We could have equally well used a fixed point problem for $f$. Decisions\n",
    "like this are common for multi-physics problems and chosing which variables\n",
    "to expose can be a subtle problem.\n",
    "\n",
    "In Section __5.1.5__ we will show that the transport equation\n",
    "is equivalent to a linear\n",
    "integral equation for $f$\n",
    "$$\n",
    "f - \\call_1 f = \\call_2(\\Theta^4) + g\n",
    "$$\n",
    "where $\\call_1$ and $\\call_2$ are compact operators on $C[0,\\tau]$ and $g \\in C[0,\\tau]$\n",
    "depends on the boundary data. We can solve the integral equation efficiently with\n",
    "GMRES, for example, and that is what we will do for our computations.\n",
    "\n",
    "The operators $\\call_1$ and $\\call_2$ are related by\n",
    "$$\n",
    "\\call_1 w = \\calk(c w) \\mbox{ and } \\call_2 w = \\calk ((1 - c) w).\n",
    "$$\n",
    "The operator $\\calk$ is defined for $u \\in C[0,\\tau]$ by\n",
    "$$\n",
    "\\calk(u)(x) = \\int_0^\\tau k(x,y) u(y) dy\n",
    "$$\n",
    "where\n",
    "$$\n",
    "k(x,y) = \\frac{1}{2} \\int_0^1 \\exp(|x - y|/\\mu) \\frac{\\dmu}{\\mu}.\n",
    "$$\n",
    "Our discretization will be based on the derivation of the integral equation\n",
    "and not directly on the integral equation itself. In this way one does\n",
    "not have to struggle with a quadrature rule for integrals against $k(x,y)$.\n",
    "\n",
    "So, given $\\Theta$, one can recover $f$ via\n",
    "$$\n",
    "f = (I - \\call_1)^{-1} ( \\call_2 (\\Theta^4) + g).\n",
    "$$\n",
    "\n",
    "Let $D_2^{-1}$ be the solution operator for\n",
    "$$\n",
    "\\frac{\\partial^2 u}{\\partial x^2} = h(x); \\ u(0) = u(\\tau) = 0,\n",
    "$$\n",
    "and\n",
    "$$\n",
    "\\Theta_0(x) = (1 - x) \\Theta_l + x \\Theta_r.\n",
    "$$\n",
    "Then \\eqnok{heat} is equivalent to\n",
    "$$\n",
    "\\Theta = D_2^{-1} Q + \\Theta_0.\n",
    "$$\n",
    "Recall that the discretization of of the second derivative operator is a tridiagonal matrix\n",
    "and hence $D_2^{-1}$ can be applied efficiently.\n",
    "\n",
    "Hence, we can combine the equations above to obtain\n",
    "a fixed point problem for $\\Theta$.\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\Theta & = \\calg(\\Theta) \\equiv D_2^{-1} Q  + \\Theta_0 \\\\\n",
    "\\\\\n",
    "& = D_2^{-1} ( \\alpha (\\Theta^4 - f) )  + \\Theta_0\\\\\n",
    "\\\\\n",
    "& = D_2^{-1} (\\alpha (\\Theta^4 - (I - \\call_1)^{-1} ( \\call_2 (\\Theta^4) + g) ))\n",
    "+ \\Theta_0.\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "We will compare Newton-Krylov methods with Anderson acceleration for\n",
    "this problem. Either approach needs an efficient evaluation of the\n",
    "discretization of the fixed point map $\\calg$. The steps in the\n",
    "evaluation of $\\calg$ are\n",
    "\n",
    "-  1. Given $\\Theta$ solve the transport equation by solving the integral form with an iterative method.\n",
    "-  2. Use the solution $f$ of the transport equation from step 1 to build $Q = \\alpha (\\Theta^4 - f)$.\n",
    "-  3.  Compute $\\calg(\\Theta) = D_2^{-1}(Q) + \\theta_0$ with a fast solver.\n",
    "\n",
    "\n",
    "We have covered step 3 previously and will discuss step 1 in\n",
    "Sections __5.1.3__ and __5.1.4__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 5.1.3: Derivation of the Integral Form of the Transport Equation\n",
    "\n",
    "We begin with a more general form of the transport equation\n",
    "$$\n",
    "\\mu \\frac{\\partial \\psi}{\\partial x} (x,\\mu) + \\psi(x,\\mu) =\n",
    "\\frac{c(x)}{2} \\int_{-1}^1 \\psi(x, \\mu') \\dmup + q(x)\n",
    "$$\n",
    "for $x \\in (0,\\tau)$ with boundary conditions\n",
    "$$\n",
    "\\psi(0, \\mu) = F_l(\\mu), \\mu > 0; \\psi(\\tau, \\mu) = F_r(\\mu),\n",
    "\\mu < 0.\n",
    "$$\n",
    "in the equation\n",
    "$\\tau < \\infty$, $c, q \\in C([0,\\tau])$, and\n",
    "$F_l$ and $F_r$ are given continuous real valued functions of $\\mu$.\n",
    "It is known\n",
    "<cite data-cite=\"busb\"><a href=\"siamfa.html#busb\">(Bus60)</cite>\n",
    "that the boundary value problem\n",
    "has a unique solution\n",
    "if $0 \\le c(x) < 1$.\n",
    "\n",
    "We begin with the continuous form of the source iteration map. Suppose\n",
    "the scalar flux $f$ is known, then\n",
    "we can recover $\\psi$ by integration. For $\\mu > 0$\n",
    "we integrate the transport equation forward in $x$ (__forward sweep__) obtaining\n",
    "$$\n",
    "\\psi(x,\\mu) = \\frac{1}{\\mu} \\int_0^x \\exp({-(x-y)/\\mu}) (c f+q)(y) dy\n",
    "+ \\exp(-x/\\mu) F_l(\\mu), \\, \\mu > 0.\n",
    "$$\n",
    "Similarly, for $\\mu < 0$, we integrate backwards (__backward sweep__) to obtain,\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\psi(x,\\mu)  & = - \\frac{1}{\\mu} \\int_x^\\tau\n",
    "\\exp({-(x-y)/\\mu}) (c f+q)(y) dy + \\exp({(\\tau - x)/\\mu}) F_r(\\mu) \\\\\n",
    "\\\\\n",
    "& = \\frac{1}{|\\mu|} \\int_x^\\tau \\exp({-|x-y|/|\\mu|}) (c f+q)(y) dy + \\exp({-|\\tau - x|/|\\mu|}) F_r(\\mu).\n",
    "\\end{array}\n",
    "$$\n",
    "The two computations of the\n",
    "angular flux\n",
    "via the forward and backward ieeps are a\n",
    "__transport sweep__.\n",
    "\n",
    "Integrating the forwared and backward sweeps in $\\mu$ and adding the\n",
    "results leads to a linear fixed point equation for $f$.\n",
    "$$\n",
    "f = \\cals(f, F_l, F_r, q) \\equiv \\calk (c f) + \\calk (q) + g\n",
    "$$\n",
    "where $\\calk$ is given above and\n",
    "$$\n",
    "g(x) = \\frac{1}{2} \\int_0^1 \\exp(-x/\\nu) F_l(\\nu)  \\dnu\n",
    "+ \\frac{1}{2} \\int_0^1 \\exp(-(\\tau - x)/\\nu) F_r(-\\nu) \\dnu\n",
    "= \\cals(0, F_l, F_r, 0).\n",
    "$$\n",
    "This completes the derivation of the the map from $\\Theta$ to $f$ with\n",
    "$$\n",
    "q = (1 - c) \\Theta^4, \\ F_l = \\Theta_l^4, \\mbox{ and } F_r = \\Theta_r^4.\n",
    "$$\n",
    "The map $\\cals$ is the\n",
    "__source iteration map__.\n",
    "\n",
    "The\n",
    "__source iteration__ method for solving the integral form is simply\n",
    "$$\n",
    "f_{n+1} = \\cals(f_n, F_l, F_r, q)\n",
    "$$\n",
    "which is Picard iteration for the linear integral equation form of\n",
    "the transport equation. However, source iteration does not need\n",
    "to compute $k(x,y)$ or the integral operator. Instead the action of\n",
    "the integral operator on $f$ and $q$ and the construction of\n",
    "$g$ are done by solving two initial value problems for the forward and\n",
    "backward sweeps.\n",
    "    \n",
    "One must formulate the source iteration fixed point problem as a linear equation\n",
    "to use a Krylov method. The way to do this is to solve\n",
    "$$\n",
    "f - \\call_1 f = b\n",
    "$$\n",
    "where the operator-vector product for $\\call_1$ can be expressed as \n",
    "$$\n",
    "\\call_1 f = \\cals(f, 0, 0, 0)\n",
    "\\mbox{ and }\n",
    "b = \\cals(0, F_l, F_r, q).\n",
    "$$\n",
    "Hence an operator-vector product costs one transport sweep. Note that\n",
    "there is no convenient matrix representation of $\\call_1$ and that\n",
    "matrix-free methods such as Krylov methods or source iteration\n",
    "are the only sensible way to solve the problem.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 5.1.4: Discretization\n",
    "\n",
    "We begin with a spatial mesh on $[0,\\tau]$ with mesh width $h=\\tau/(n+1)$ and $x_j = (j-1) h$.\n",
    "We will discretize the map $D_2$ with the usual central difference scheme and let $\\md_2$ be that discretization.\n",
    "\n",
    "For the transport equation we will use the discrete ordinates approach\n",
    "<cite data-cite=\"larnel\"><a href=\"siamfa.html#larnel\">(LN92)</cite>,\n",
    "<cite data-cite=\"scott\"><a href=\"siamfa.html#scott\">(PS83)</cite>,\n",
    " <cite data-cite=\"ctk:abtrans\"><a href=\"siamfa.html#ctk:abtrans\">(KEL95b)</cite>.\n",
    "    \n",
    "We construct our sequence of approximate source iteration maps with the\n",
    "diamond difference discretization. We use a variation on\n",
    "the notation of \\cite{larnel}. We let $\\vf$, $\\vq$, and $\\vc$\n",
    "be the evaluations of\n",
    "$f$, $q$, and $c$ at the spatial grid points.\n",
    "$$\n",
    "f_i = f(x_i), q_i = q(x_i), \\mbox{ and } c_i = c(x_i).\n",
    "$$\n",
    "We let\n",
    "$\\{ \\mu_j \\}_{j=1}^{N_A}$ and\n",
    "$\\{ w_j \\}_{i=1}^{N_A}$ be the nodes and weights for a\n",
    "quadrature rule in angle. The optimal quadrature for this application\n",
    "is a {\\bf double Gauss} rule, which is Gaussian quadratues for each\n",
    "of the intervals $[-1,0)$ and $(0,1]$ separately    \n",
    "<cite data-cite=\"scott\"><a href=\"siamfa.html#scott\">(PS83)</cite>.\n",
    "We use a double 20 point rule for this application and\n",
    "obtained the nodes and weights from the Julia package __QuadGK.jl__\n",
    "<cite data-cite=\"QuadGK\"><a href=\"siamfa.html#QuadGK\">(Joh13)</cite>.\n",
    "One could also use __FastGaussQuadrature.jl__\n",
    "<cite data-cite=\"fastgaussjl\"><a href=\"siamfa.html#fastgaussjl\">(Olv21)</cite>\n",
    "or copy the data from an old table <cite data-cite=\"gausstab\"><a href=\"siamfa.html#gausstab\">(DR56)</cite>,\n",
    "which is what we did in <cite data-cite=\"ctk:abtrans\"><a href=\"siamfa.html#ctk:abtrans\">(KEL95b)</cite>.\n",
    "    \n",
    "We solve the subproblem for $f$ with GMRES and give a tolerance of\n",
    "$tol = 10^{-12}$ to the linear solver. Therefore we expect the\n",
    "nonlinear residual to be accurate to (roughly) twelve figures. In light\n",
    "of this we set the difference increment for the finite-difference\n",
    "Jacobian-vector product to $dx = 10^{-5}$. \n",
    "\n",
    "The diamond difference method first computes\n",
    "approximations $\\psi_i^j$ to $\\psi(x_i, \\mu_j)$ by computing\n",
    "$\\psi_i^j$ for each $i$ with $j$ fixed.\n",
    "The discretized forward integration $(\\mu_j > 0)$ is\n",
    "$$\n",
    "\\begin{array}{l}\n",
    "\\mu_j \\dfrac{\\psi^j_i - \\psi^j_{i-1}}{h}\n",
    "+ \\dfrac{\\psi^j_i + \\psi^j_{i-1}}{2}\\\\\n",
    "\\\\\n",
    "= \\dfrac{c_i f_i + c_{i-1} f_{i-1}}{2} +\n",
    "\\dfrac{q_i + q_{i-1}}{2}, \\, \\mu_j > 0,\n",
    "\\end{array}\n",
    "$$\n",
    "with initial data $\\psi^j_1 = \\psi(0, \\mu_j) = F_l(\\mu_j)$.\n",
    "Similarly the discretized backward integration $(\\mu_j < 0)$ is\n",
    "$$\n",
    "\\begin{array}{l}\n",
    "\\mu_j \\dfrac{\\psi^j_i - \\psi^j_{i+1}}{h}\n",
    "+ \\dfrac{\\psi^j_i + \\psi^j_{i+1}}{2}\\\\\n",
    "\\\\\n",
    "= \\dfrac{c_i f_i + c_{i+1} f_{i+1}}{2} +\n",
    "\\dfrac{q_i + q_{i+1}}{2}, \\, \\mu_j < 0,\n",
    "\\end{array}\n",
    "$$\n",
    "with final data $\\psi^j_{N} = \\psi(\\tau,\\mu_j) = F_r(\\mu_j)$.\n",
    "\n",
    "Following the forward and backward integration we compute the discrete\n",
    "source iteration map\n",
    "$$\n",
    "\\ms (\\vf)_i = \\dfrac{1}{2} \\sum_{j=1}^{N_A} \\psi^j_i w_j.\n",
    "$$\n",
    "Similarly to the derivation of the continous source iteration map,\n",
    "$\\vq$ and the boundary data are\n",
    "built into $\\{\\psi^j_i\\}$ by the way in which the integration is done.\n",
    "Hence we also write the discrete source iteration map as\n",
    "$$\n",
    "\\ms(\\vf, F_l, F_r, \\vq).\n",
    "$$\n",
    "So the discretized linear integral equation form of the transport\n",
    "equation is\n",
    "$$\n",
    "\\vf = \\ms(\\vf, F_l, F_r, \\vq) \\equiv \\ml_1( \\vf ) + \\vb\n",
    "$$\n",
    "where\n",
    "$$\n",
    "\\ml_1 f = \\ms(f, 0, 0, 0) \\mbox{ and }\n",
    "\\vb = \\ms(0, F_l, F_r, \\vq).\n",
    "$$\n",
    "\n",
    "We obtain the discretization of the linear map from $\\Theta$ to $\\vf$\n",
    "as before with the substitution $\\vq = (1 - c) \\Theta^4$ where products\n",
    "are understood to be component wise. We let $\\mt$\n",
    "and $\\mt_0$ be the discretizations\n",
    "of $\\Theta$ and $\\Theta_0$. The discretization of $\\call_2$\n",
    "is\n",
    "$$\n",
    "\\ml_2 (\\mt^4) = \\ms(0, 0, 0, (1 - c) \\mt^4).\n",
    "$$\n",
    "The discretized fixed point problem for $\\Theta$ is\n",
    "$$\n",
    "\\mt = \\mg(\\mt) \\equiv\n",
    "\\md_2^{-1} ( \\alpha \\mt^4 -\n",
    "(I - \\ml_1)^{-1} ( \\ml_2 (\\mt^4) + \\vg) )\n",
    "+ \\mt_0.\n",
    "$$\n",
    "Here\n",
    "$$\n",
    "\\vg = \\ms(0, F_l, F_r, 0).\n",
    "$$\n",
    "\n",
    "The Julia code for the fixed point map is in __CR_Heat.jl__ in the\n",
    "__/src/TestProblems/CaseStudies__ directory\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 5.1.5: Examples for Conductive-Radiative Heat Transfer\n",
    "\n",
    "In this section $c(x) = \\omega$ is a constant. The example in this section is taken from\n",
    "<cite data-cite=\"cesjrt\"><a href=\"siamfa.html#cesjrt\">(ST91)</cite>. Transport\n",
    "theory has a generous supply of benchmark problems and this is one.\n",
    "We begin by comparing results with the tables in\n",
    "<cite data-cite=\"cesjrt\"><a href=\"siamfa.html#cesjrt\">(ST91)</cite>.\n",
    "We use an $N=1001$ point grid so that the tabulated data are at mesh\n",
    "points.  We consider the two problems:\n",
    "\n",
    "- Problem 1: $N_c = .05, \\omega = .9, \\tau=1, \\Theta_l = 1, \\Theta_r = 0$\n",
    "- Problem 2: $N_c = .05, \\omega = .9, \\tau=1, \\Theta_l = 1, \\Theta_r = .5$\n",
    "\n",
    "We solve the problems with Anderson(2).\n",
    "\n",
    "The results agree to all figures reported with column two of Tables\n",
    "2 and 3 in <cite data-cite=\"cesjrt\"><a href=\"siamfa.html#cesjrt\">(ST91)</cite>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  x          Theta1          Theta2 \n",
      " 0.00     1.00000e+00     1.00000e+00 \n",
      " 0.10     9.18027e-01     9.54270e-01 \n",
      " 0.20     8.36956e-01     9.11008e-01 \n",
      " 0.30     7.53557e-01     8.68433e-01 \n",
      " 0.40     6.65558e-01     8.25127e-01 \n",
      " 0.50     5.71475e-01     7.79940e-01 \n",
      " 0.60     4.70505e-01     7.31936e-01 \n",
      " 0.70     3.62437e-01     6.80375e-01 \n",
      " 0.80     2.47544e-01     6.24709e-01 \n",
      " 0.90     1.26449e-01     5.64610e-01 \n",
      " 1.00     0.00000e+00     5.00000e-01 \n"
     ]
    }
   ],
   "source": [
    "heat_tabulate();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We close this section with a comparison of Anderson($m$) for several\n",
    "values of $m$ and Newton-GMRES on three instances of the\n",
    "convective-radiative heat transfer problem. We begin with\n",
    "Problem 1 from the table above and then vary $\\omega$,\n",
    "$\\tau$, and $\\theta_r$. The first problem is easy and\n",
    "Figure __5.1__ shows that Anderson($2$) is clearly\n",
    "a good choice.\n",
    "\n",
    "The fixed point maps are not contractive for the second two problems.\n",
    "For the problem in Figure __5.2__ Anderson($m$) shows irregular convergence,\n",
    "but for $m > 0$ recovers well.  Figure __5.3__ shows that\n",
    "Picard iteration diverges for the last of\n",
    "the problems. Our code __aasol.jl__ halts the iteration with a failure\n",
    "message once the residual norm increases by a factor of $10^4$, as it\n",
    "did for Picard iteration in this example.\n",
    "Notice that Newton-GMRES has a far more regular and predictable\n",
    "convergence behavior for the two non-contractive problems. This regularity\n",
    "is an important advantage and it is not clear that Anderson acceleration is a reasonable\n",
    "alternative. For example, in both Figures __5.2__ and __5.3__ Anderson($10$), needs over twice the storage\n",
    "of the Newton-GMRES iteration.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diverging for m=5 in aasol.jl.\n",
      "thetar = 2.00000e+00, tau=4.00000e+00, omega=5.00000e-01\n",
      "Failure to converge after 50 iterations for m=20 in aasol.jl\n",
      "thetar = 2.00000e+00, tau=4.00000e+00, omega=5.00000e-01\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAHICAYAAABNpu4dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC9J0lEQVR4nOzdd3QU1dvA8e/spldKIJTQe+8gnYBUQQVBRUBEDT10BQEBEaR3UIqIgFIEBASRovReJHSpCaGFTkJCQtp9/8i78yMkgd1kd2c3uZ9z5hwyOzvz7JKdPHvLcxUhhECSJEmSJCmL0mkdgCRJkiRJkpZkMiRJkiRJUpYmkyFJkiRJkrI0mQxJkiRJkpSlyWRIkiRJkqQsTSZDkiRJkiRlaTIZkiRJkiQpS5PJkCRJkiRJWZpMhiRJkiRJytJkMiRJkiRJUpYmkyFJkiRJkrI0u0yGfv75ZxRFSXUbMmRIsmNCQkLMdt3du3ened3Dhw+/8rlPnz7lyy+/pFmzZuTKlQtFURgzZozZYrMV5nidkZGRDBgwgHz58uHi4kLlypVZtWqVZQK2ERl9zUePHqV58+Z4enri4eGBv78/Bw4cyHBcERERDBkyhAIFCuDi4kLNmjU5dOhQhs9rThl577Li75okSSk5aB1ARixZsoTSpUsn25cvXz4A3nrrLQ4dOkTevHnNft3vvvsOf3//ZPvKly//yuc8fPiQhQsXUqlSJd59911+/PFHs8dlC8zxOtu1a8exY8eYOHEiJUuWZMWKFXTs2JHExEQ++ugjC0StvYy85mPHjtGgQQNq1qzJ8uXLEUIwefJkmjRpwq5du6hdu3a6Ynrw4AH+/v64uLgwc+ZM3NzcGDlyJK1bt+bKlStkz549Xec1t4y8d1nxd02SpFQIO7RkyRIBiGPHjln1urt27RKAWLNmjcnPTUxMFImJiUIIIe7fvy8AMXr0aDNHqL2Mvs4///xTAGLFihXJ9jdt2lTky5dPxMfHmzNcm5DR19y8eXPh6+sroqKi1H0RERHCx8dH1KlTJ91xvfXWW6JcuXLJznv06FEBiCVLlqT7vOaUkfcuK/6uSZKUOrvsJjNGWt1kGzdupGLFijg7O1O0aFFmzZrFmDFjUBTFovEYutPMbe7cuWl23SmKgpubG7GxsWa/bloy+jrXr1+Ph4cHHTp0SLa/W7du3L59myNHjqTrvDdu3MDLy4vAwMBUHw8PD0dRFHr16pWu82dERl/zgQMHaNSoEW5ubuo+T09PGjRowMGDB7lz547JMe3cuZM///yT6dOnJztv0aJFAbh27ZrJ57SEjLx3lvpdkyTJ/th1MpSQkEB8fHyy7VW2bt1Ku3btyJkzJ6tXr2by5MmsXLmSpUuXmnTdPn364ODggJeXF82bN2f//v0ZeRmpUhSFRo0avfa4Nm3acOjQIQ4dOsT3338PwPTp09V9hw8fxsnJKdlzhBAp3re0Nms7e/YsZcqUwcEheQ9uxYoV1ccNjH2PAPr164eTkxPjx49P9XFvb2/y5cuX5tgvS75nprzm1MTGxuLs7Jxiv2HfmTNn1H3GvmcLFy6kcOHC+Pv7J3ttERERADg6Or72HAa2+t5l9H2XJCnzsOsxQ2+88UaKfXFxcSlubgajRo0if/78bNu2TU0QWrRoQeHChY26nre3N/3796dRo0bkzJmTK1euMGXKFBo1asSff/5J8+bN0/1aXqbX69Hr9a89rlChQhQqVAiAf//9F4D27dtToECBNJ+zZ8+eFGOe0hIcHGz0+2MODx8+VFsfXpQjRw71cQNj36OjR4+yYcMGvvvuO7y8vNI8rnDhwly4cCHVxyz5npnymlNTtmxZDh8+TGJiIjpd0veb+Ph4tWXD1PcsMTGRbdu28eTJkxSJtEGRIkVeeY4X2ep7l9H3XZKkzMOuk6Fly5ZRpkyZZPvSSoSioqI4fvw4ffv2TXaD9/DwoE2bNvz888+vvV6VKlWoUqWK+nP9+vVp27YtFSpU4MsvvzRrMpSeb8lBQUHkyJHjlYkQQLVq1Th27JhR5zQMSLemV3WzvfiYse/RDz/8gE6n49NPP1X37dy5k7t379KxY0d1X1RUFEKIVM9h6ffM2NecmsDAQD777DP69u3LiBEjSExM5JtvvuH69esAaoIExr1nFy9e5MmTJ3z77be0aNEi2WNz585l6dKl1KxZM83nx8fHJ/sc2vJ7l5HnSpKUedh1MlSmTBmqV69u1LGPHz9GCIGvr2+Kx1LbZ6xs2bLRunVr5s+fT3R0NK6uruk+V0YFBQVRuXLl1x7n4eFh1HGQdnJpKTlz5kz1G/mjR4+A/31rN8WOHTsoV65csv/nMWPGkCtXrmTJ0N27dylevHiq57Dke5bR1/zpp59y//59xo0bxw8//ABA7dq1GTJkCJMmTSJ//vwmxWMYZ1erVq0Un6+zZ89SrFgxSpYsqe6bNWsWe/fuxdnZmU2bNrFy5Upat26tPm6r750lftckSbJPdj1myBTZs2dHURTu3r2b4rGwsLAMndvQmqDlN0khBGfPnjXqj86ePXtwdHQ0ajNnnSZjVKhQgQsXLqRowTCMe3ldCYOXRUdHc+vWLYoVK6bue/ToEYcPH6ZgwYLqvrNnzxIWFpZmd44l3zNzvOahQ4fy4MEDzpw5Q0hICAcPHuTx48e4u7tTrVo1k+KJi4sDSNGdFhQUxIkTJ+jevXuy/adPn2bPnj10796d8PDwFC2ktvremft3TZIk+2XXLUOmcHd3p3r16mzYsIGpU6eqXWWRkZFs3rw53ed9/PgxmzdvpnLlyri4uJgrXJM9evSI6Ohoo8Zy2HI3Wdu2bVm0aBHr1q3jgw8+UPcvXbqUfPnyUatWLZPOl5CQAEBMTIy6b/78+ej1+mSz7GbNmoVOp6Nbt26pnseS75m5XrOzs7P6Bzw0NJTVq1cTEBBgcmulYRzNmTNnaNy4MZDU9dWvXz+KFClC3759kx1/+vRpRo0apQ7MfrFbDmz3vTP375okSXZMw2n96WZMnSHDMcHBweq+v/76S+h0OtGoUSOxfv16sXbtWlGrVi1RqFAhoSjKa6/bsWNHMXToULFmzRqxa9cusXDhQlGqVCnh4OAgduzYoR63e/duodfrxTfffJPs+Vu2bBFr1qwRP/30kwBEhw4dxJo1a8SaNWuS1XIRQgi9Xi8aN25s5DsixNOnT4Wjo6N4++23xZ49e8STJ0+Mfq65GfM603qPhEiq85I9e3axcOFCsXPnThEQECAA8csvvyQ7DhANGzZ8bTwlS5YUTk5OYvHixeKHH34Q2bNnF0OGDBH58uUTmzZtEl988YUAxKBBg8zy+tPD2Nec2vt25swZMWbMGLF582axY8cOMXXqVOHj4yOqV68unj59muz5xr5n1apVEz4+PmLlypViy5YtokmTJiJnzpwiKCgo2XEJCQnCzc1N3Lx5M/0vPoOMee/S+n0z9n2XJClzy1LJkBBCrF+/XlSoUEE4OTmJggULiokTJ4p+/fqJ7Nmzv/a6EyZMEJUrVxbe3t5Cr9eLXLlyibZt24qjR48mO85QnPHlYoOFChUSQKrby3Ea+0frRdOnTxf58uUTgLh48aJJzzUnY15nWu+REEmJXb9+/USePHmEk5OTqFixoli5cmWKYwDx4Ycfvjaew4cPiwoVKghnZ2dRvnx5sWPHDvHw4UPRoEED4ezsLIoUKSImT56sFovUgjGvWYjU37eLFy+KBg0aiBw5cggnJydRvHhxMXLkSBEZGZniGsa+ZyEhIaJ58+bCzc1N5MiRQ3Tq1CnF76jh2r6+via/XnMy5r1L6/fN2PddkqTMTREijekzWURcXByVK1cmf/78bN++XetwJCNt2bKF1q1bc+rUKSpUqKB1OHbBEu/Z2rVrWbRoEdu2bTPL+SRJkrSQZcYMGXz22Wc0bdqUvHnzEhYWxvz587lw4QKzZs3SOjTJBLt27eLDDz+UiZAJLPGenTlzRi1SKEmSZK+yXMvQ+++/z8GDB7l//z6Ojo5UrVqV4cOHp6inIkmSJElS1pDlkiFJkiRJkqQXZZk6Q5IkSZIkSamRyZAkSZIkSVmaTIYkSZIkScrSstxssvRITEzk9u3beHp6ysUbJUkDQgiePn1Kvnz5UlS4tlXyviFJ2jP23iGTISPcvn37tSvBS5JkeTdu3MDPz0/rMIwi7xuSZDted++QyZARPD09U92/efNm6tevb+VoJCnriYiIoECBAml+Fm2RIdYbN27g5eX1ymP37dtH69atX3tOec+RJNMYe++QyZARXm7iVhQFPz8/WrRokWJlb0mSLMeeupsMsXp5eb02GWrRogV+fn7cunWL1KqdyHuOJGXM6+4d9tH5boNmzpwpb0qSJJmFXq9Xq+CnddOW9xxJshyZDKVDkyZNaNeundZhSJKUibRr1461a9eSP3/+VB+T9xxJshyZDKXDzp07SUhI0DoMSZIymXbt2hESEsKuXbtYtmyZun/9+vU8evRIw8gkKXOTY4bSITExkV69erFw4UKtQ7G4hIQE4uLitA5DyuQcHR1lF9D/0+v1NGrUCIBVq1axZcsWEhMTGTBgQLIESZIk85FrkxkhIiICb29vdDodiYmJQNLN+8mTJ7i5uWkcnWUIIQgLC+PJkydahyJlEdmyZSNPnjypjpkxfAbDw8NfOxjZVpgj5tjYWNzc3NSW6FOnTlGxYkVzhilJmZqxn0PZMmSCevXqsXfvXgDi4uIICAjg119/1TgqyzAkQrlz58bNzc2uZvFI9kUIwbNnz7h37x4AefPm1Tgi2+Hk5ET//v2ZPn06AN27d+fQoUPy8yhJZiZbhoxgyCwnTZrE0KFD8fHx4cGDByiKQkhICAULFtQ6RLNKSEjg0qVL5M6dm5w5c2odjpRFPHz4kHv37lGyZMkUXWZZtWUIID4+Hg8PD54/fw7Ab7/9RocOHcwVpiRlasZ+DuUAahO89dZbAOpARiEETZo00TIkizCMEcqsXYCSbTL8vskxask5ODjQuXNn9efAwECePXumYUSSlPnIZMgEBQoUoGrVqiQmJlK6dGkArly5wtWrVzWOzDJkU7xkTfL3LW3ff/+9+u+7d+8yadIkDaORpMxHJkMmevfddwEoXrw4zs7OAHz44YcaRiRJUmbn5OSUrHVo0qRJhISEaBeQJGUyMhkykSEZ+vvvvxkyZAgAx48f58iRIxpGJUlSZrdkyRL1C9jz58/V+48kSRknkyETlS9fnqJFixITE0OVKlXw9vYG4O2339Y4Mkkrjx8/pkuXLnh7e+Pt7U2XLl1kSQLJ7BwcHOjfv7/687p16/jnn380jEiSMg+ZDJlIURS1deiPP/5g2LBhANy7d4+5c+dqGJmklY8++oigoCC2bt3K1q1bCQoKokuXLlqHJWVC48ePTzbTrn///nLAuSSZgUyG0uGdd94BYNOmTQwZMkRtul65cqWWYWV5jRo1IjAwkAEDBpA9e3Z8fX1ZuHAhUVFRdOvWDU9PT4oVK8Zff/1ltmteuHCBrVu38uOPP1K7dm1q167NokWL2Lx5MxcvXjTbdSQJklqH2rRpo/587tw5fvjhBw0jkqTMQSZD6VCnTh18fHx4/Pgx+/btY9++feh0Og4ePMihQ4e0Ds/shBBERUVpsplaBmvp0qX4+Phw9OhRAgMD6dWrFx06dKBOnTr8+++/NG/enC5durxyanLPnj3x8PB45RYaGgrAoUOH8Pb2platWurz33jjDby9vTl48GD63nBJeoXVq1dTpEgR9efRo0dz//59DSOSJPsnk6F0ePHb2YYNG6hRowbdunUDYNiwYSb/Abd1z549e21yYKnN1HoqlSpVYuTIkZQoUYKvvvoKV1dXfHx8CAgIoESJEowaNYqHDx9y+vTpNM8xduxYgoKCXrnly5cPSKrUnTt37hTnyJ07N2FhYaa90ZJkBCcnJ6ZNmwaATqfjyZMnjBw5UuOoJMm+yWQonQzjhjZs2IAQgjFjxuDk5MTevXv5+OOPtQ0uC3tx3Sa9Xk/OnDmpUKGCus/X1xdAXfohNblz56Z48eKv3Bwc/reSTWr1cYQQsm6OZDHvvPMOlSpVUtdKXLRoEf/++6/GUUmS/ZJrk6VT06ZNcXNzIzQ0lKCgIKpUqULOnDm5c+cOv/76K4sWLcLFxUXrMM3Czc2NyMhIza5tCkdHx2Q/K4qSbJ8hQTH8EUlNz549+eWXX155nfPnz1OwYEHy5MnD3bt3Uzx+//59NfGSJHPT6XT4+/tz6tQpICn5DgwMZP/+/TIJl6R0kMlQOrm6utK8eXPWr1/Phg0bqFKlCps2baJ69eoIIWjVqhU7d+7UOkyzUBQFd3d3rcOwmrFjx762houhm6x27dqEh4dz9OhRatasCcCRI0cIDw+nTp06Fo9VyrqmTp3KrFmz1FbIgwcPsmLFCjp16qR1aJJkd2Q3WQa82FUGUK1aNUqUKAHArl275KBGO2VKN1mZMmVo0aIFAQEBHD58mMOHDxMQEEDr1q0pVaqUxq9Eysz0ej1t27YFUMcpfvnll5q14kqSPZPJUAa89dZb6PV6Tp8+zbVr14CkytQGmXERVymlX3/9lQoVKtCsWTOaNWtGxYoVWb58udZhSVnAb7/9pnaL6fV6bt++zXfffadxVJJkfxSR2aY+WUBERATe3t6Eh4fj5eWV7LHGjRuza9cupk+fzsCBAwGoVasWR48eBZLq0BgWdbUXMTExBAcHU6RIkUwz7kmyfa/6vXvVZ9BWWSvm9u3bs27dOvVnJycnzp07R/HixS12TUmyF8Z+DmXLUAa93FUGJCuR36JFCytHJElSVrJ69Wq1dcjZ2ZnY2FgGDRqkcVSSZF9kMpRBhmrU+/fv58GDBwB4eHioa5Vdv36ds2fPahafJEmZm16v57333gOSFnB1cHBg06ZNbN26VePIJMl+yGQogwoVKkSVKlVITExk8+bN6v7169erM7Dk7A5Jkixp1apVatdirly5ABgwYACxsbFahiVJdiPLJENt27Yle/bstG/f3uznTq2rTKfT8c033wBw+vRp9u7da/brSpJkWZa8b5iTXq9n8ODBANy5c4ecOXNy8eJF5syZo3FkkmQfskwy1K9fP5YtW2aRcxuSoe3btydbPmLw4MFq69B77733ykJ/kiTZHkveN8xt7NixODk5Aaj3nW+++UYuCyNJRsgyyZC/vz+enp4WOXeFChUoUqQI0dHRbN++PdljhsJ7Dx484KeffrLI9SVJsgxL3jfMTafTUbJkSQBCQ0MpXbo0T58+Zfjw4RpHJkm2zyaSob1799KmTRvy5cuHoijJupsMvv/+e3XKbbVq1di3b5/1A02DoiipdpUBjB8/Xv334MGDU126QZIk09n7fcMS+vbtq/47Li4OgCVLlqilPiRJSp1NJENRUVFUqlSJuXPnpvr46tWrGTBgACNGjODkyZPUr1+fli1bEhoaqh5TrVo1ypcvn2K7ffu2VV6DIRnatGkT8fHx6v4aNWpQt25dIKnewYABA6wSjyRldpnhvmFun332GTlz5gTg6tWr1KpVC4DAwEDZTS9JryJsDCDWr1+fbF/NmjVFz549k+0rXbq0GDZsmEnn3rVrl3jvvfdee1xMTIwIDw9Xtxs3bghAhIeHp/mcuLg44ePjIwCxa9euZI/NmTNHAOr2119/mRS3tUVHR4vz58+L6OhorUORspBX/d6Fh4e/8jNor/cNS+jVq5d6r8mWLZtwd3cXgFiyZIlV45AkW/C6e4eBTbQMvUpsbCwnTpygWbNmyfY3a9aMgwcPWuSaEyZMwNvbW90KFCjw2uc4ODjQpk0bIGVXWfv27dHp/vdW9+rVi6ioKLPGLEnS/9jLfcMSDOuV6fV6njx5QoUKFQAYNmwYERERmsQkSbbO5pOhBw8ekJCQgK+vb7L9vr6+Js2SaN68OR06dGDLli34+flx7NixNI/96quvCA8PV7cbN24YdY0Xxw2JF1Y5yZMnj1oG3MXFhZCQEMaMGWN07JLtCgkJ4bPPPqNIkSK4urpSrFgxRo8eLeu7aMye7hvm1qhRI5ycnEhISADg8OHDFCpUiLt37/Ltt99qEpMk2TqbT4YMDOXmDYQQKfa9yrZt27h//z7Pnj3j5s2b1KhRI81jnZ2d8fLySrYZo2nTpri5uXH9+nVOnTqV7LG3334bb29vWrVqBcCMGTMICgoyOn7JNv33338kJiayYMECzp07x4wZM5g/f76cwWMj7OG+YW6Ojo74+fkB/yvA6ObmBsDMmTP577//NIlLkmyZzSdDPj4+6PX6FN/m7t27l+Jbn9ZcXV1p3rw5kLKrbOnSpTx58oR169bx/vvvk5CQQEBAgPrtTcq4Ro0aERgYyIABA8iePTu+vr4sXLiQqKgounXrhqenJ8WKFeOvv/4y2zVbtGjBkiVLaNasGUWLFuXtt99myJAh/P7772a7hmQ6e7pvWML06dNZvnw5x48fx83NjQsXLlCpUiXi4+MZMGBAspZrSZLsIBlycnKiWrVq7NixI9n+HTt2qDV8bElaU+xfNGvWLLy9vTl+/HiaM2FsUVRUlMnbizPr4uPjiYqKIjo62qjzpsfSpUvx8fHh6NGjBAYG0qtXLzp06ECdOnX4999/ad68OV26dElWHPNlPXv2xMPD45XbizOSXhYeHk6OHDnSFb9kHvZ23zC3d955h86dO1OwYEFGjhwJwK1bt3B0dGTbtm3Jlg6SJAnbmE329OlTcfLkSXHy5EkBiOnTp4uTJ0+K69evCyGEWLVqlXB0dBSLFy8W58+fFwMGDBDu7u4iJCTEKvEZOxpdCCEePHgg9Hq9AMS1a9dSPH727FnRq1cvsWDBAgEId3d39XXairRm9fDCjDhjt99++019/m+//SYA0bBhw2TnNczCe3kzVcOGDUW9evXUn+Pj44W7u7vo0qWLuu/OnTsCEIcOHUrzPHfv3hWXL19+5RYXF5fqc69cuSK8vLzEokWLTI4/qzN1Nllmum9YUkxMjChevLgARM2aNQUgihUrJmJiYjSNS5KswdjPoU0kQ7t27Ur1j2HXrl3VY+bNmycKFSoknJycRNWqVcWePXusFp+pNzV/f38BiBkzZiTbf//+ffW1/f7776Ju3boCEG3atBGJiYkWiDx97DkZ6t27d7J9BQsWFJMnT1Z/TkxMFIDYuHGjyed/nVu3bonixYuLzz77zOznzgpMTYYy233D3I4ePSqKFSsmvLy8xObNmwUg9Hq9yJUrlwDEhAkTNIlLkqzJrpIhW2fqTW3WrFmp/tEXQojcuXMLQNSvX1+cO3dOODo6CkCsXbvWzFGnX1p/lCIjI03eXmxBiYuLE5GRkeLZs2dGnddUDRs2FP3790+2r1ChQimSUlKpSfOiHj16CHd391duL7fm3bp1S5QsWVJ06dJFJCQkmBy7lLE6Q7ZI65hDQkLUBHHLli3irbfeEoAoX7682ip98+ZNTWKTJGvJNHWG7NE777wDwL59+3jw4EGyx9q1awckTXctXbo0w4YNA5IqxIaHh1s3UBO5u7ubvDk4OKjPd3BwwN3dHVdXV6POq5WxY8cSFBT0yi1fvnzq8bdu3aJRo0ZUrVqVJUuWJKspJUlaKVSokFqNetasWcycORMnJyfOnj1LqVKliIqKYujQoRpHKUm2Qd61LaBQoUJUrlyZxMTEFAMVR4wYASStG7RmzRqGDx9OyZIluXPnDl999ZUW4UovyZ07N8WLF3/lZkjybt++TaNGjShQoABTp07l/v37hIWFyZXCJZvQokULAPbv30/x4sUZMmQIkLQ0kKIo/Prrrxw4cEDLECXJJshkyEIMs8p+/PFHVq5cye7du0lISMDPz09tVZg5cyYuLi7Mnz8fgPnz53Po0CGtQpbSYfv27Vy5coWdO3fi5+dH3rx51U2StGZoeY6KiuLIkSMMHz4cPz8/7ty5Q5UqVYCkVul//vkn2X1KkrIcK3Xb2bX09P1PmzYtxcBOPz8/sW7dOjFgwAABCAcHB3V8Sbdu3dT+/OfPn1vqpRhFrk0maUGOGbIMLy8vAajrq61atUoAwtnZWbi4uKR5n5KkzECOGdLQ77//rjZHv+jWrVu0b9+eihUrAkl1dwzF+aZMmYKPjw9nz55l6tSpVo1XkqTMq1GjRgD8888/ALz//vs0atSI58+fExMTk+J4w31KFg6VshKZDJlZQkIC/fv3T7XCq2Hf6NGjadiwIYDaLZYzZ05mzpwJJA3gvXz5snUCliQpUzN8MQsPD+f+/fsoisKMGTPSPN5wnxowYIDsMpOyDJkMmdm+ffu4efNmmo8LIbhx4wZNmjQB4LfffiMxMRGAjz76iGbNmvH8+XN69uwpS+ZLkpRh9evXp0qVKggh2LRpEwBPnjx55XMM96l9+/ZZIUJJ0p5Mhszszp07Rh1XsGBBPD09uXnzpjqbQ1EUvv/+e1xcXNi5cyfLly+3ZKiSJGURbdu2BWD9+vWA8fcpY4+TJHsnkyEzM3YWUaFChahZsyaQNJvDoFixYowZMwaAQYMGpahTJEmSZCpDfbOtW7dy+/Zto+9TclaklFXIZMjM6tevj5+fH4qipPq4oigUKFCA+vXrU69ePQDOnDlDbGysesygQYOoWLEiDx8+ZPDgwVaJW5KkzKts2bI4OzsTHx/PlClTTLpPSVJWIJMhM9Pr9cyaNQsgxY3G8PPMmTPR6/UMGTIEJycnEhMTk/XNOzo6snDhQhRFYdmyZeosEEmSpPRQFIXKlSsDSbWxXrxPpUYIod6nJCkrkMmQBbRr1461a9eSP3/+ZPv9/PxYu3at2mTt4eFB586dAVi7dm2yY2vVqkWfPn0A6NmzJ9HR0VaIXJKkzOrrr78GIDQ0lJiYGPU+5efnl+LYHDlyqNWrJSkrkMmQhbRr146QkBC1unT27NkJDg5WEyGDDz74AEhKhuLj45M9Nn78ePLnz8+VK1cYN26cdQKXJClTatmyJfnz5ycyMlJtbTbcp9atW6euGZg9e3YePXr0yun3kpTZyGTIgvR6PR07dgTg8ePHREZGpjimcePGZM+enQcPHjBnzpxkj3l5ean7Jk+ezNmzZy0ftCRJmZJOp1Nnla1Zs0bdr9fradeuHePHjweS1k0EmDhxInfv3rV+oJKkAZkMWZiXlxe+vr4AqRZSdHBwUGds/PDDDykeb9u2Le+88w7x8fH06NFDrUkk2Y7ChQujKEqyzbAmlCTZkgYNGgCwfPnyFNWn+/btS9myZYmMjCR37txERkbyzTffaBGmJFmdTIasoGTJkgBcunQp1cd79uxJ7ty51cVdXzZnzhw8PDw4ePAgCxcutFSYUgaMHTuWO3fuqNvIkSO1DkmSUmjTpg2KopCYmMiyZcuSPebo6Mjs2bMB1JIeCxcu5MKFC1aPU5KsTSZDVvC6ZCgwMJC7d+8yefLkVB8vUKAA3333HQBDhw7l9u3blgnUzjVq1IjAwEAGDBhA9uzZ8fX1ZeHChURFRdGtWzc8PT0pVqwYf/31l9mv7enpSZ48edTNw8PD7NeQpIxycXFh9OjRbN++ne7du6d4vEmTJrz33nskJiaSM2dOEhISGDp0qAaRSpJ1yWTICl6XDBmjd+/e1KxZk4iICPr372+u0EwSFRWV5vZyk/urjn15Zlxax6XH0qVL8fHx4ejRowQGBtKrVy86dOhAnTp1+Pfff2nevDldunTh2bNnaZ6jZ8+eeHh4vHILDQ1N9pxJkyaRM2dOKleuzPjx45PVjZIkWzJ69GiaNm2a5uPTpk3D1dWVhw8fotPp2LRpE7t377ZegJKkhVeuaS8JIYQIDw8XgAgPD0/X89evXy8AUb169Vcet2fPHtGqVSvx+PHjVB8PCgoSer1eAGLTpk3pisUY0dHR4vz58yI6OjrZfiDNrVWrVsmOdXNzS/PYhg0bJjvWx8cn1eNM1bBhQ1GvXj315/j4eOHu7i66dOmi7rtz544AxKFDh9I8z927d8Xly5dfucXFxanHT58+XezevVucOnVKLFq0SPj4+IjPPvvM5PizurR+74TI+GdQC/YYs8E333wjAOHu7i4AUa1aNZGQkKB1WJJkMmM/h7JlyApebBkSaSy+mpiYSOPGjdmyZQsTJkxI9ZhKlSoxaNAgAPr06ZPq7LSsrmLFiuq/9Xo9OXPmpEKFCuo+w2D2e/fupXmO3LlzU7x48VduDg4O6vEDBw6kYcOGVKxYkc8//5z58+ezePFiHj58aIFXKEkZN2fOHPLly0fjxo1TffyLL76gcOHCREVF4eTkxIkTJ1i5cqWVo5Qk65HJkBUULVoURVGIiIhI84+wTqdTK8S+6qYzevRoChcuTGhoKKNGjbJEuGmKjIxMc1u3bl2yY+/du5fmsS+P2QkJCUn1uPRwdHRM9rOiKMn2GaqAv2pWXnq6yV70xhtvAHDlypV0vQZJsrTLly9z584d9u/fn+pnwdXVVa0zlJCQAMDw4cNl8Vcp05LJkBW4uLhQqFAh4NXjhgwLtt64cSPNpMnd3V2dgj9r1ixOnDhh5mjT5u7unubm4uJi9LGG4m6vO1YrY8eOJSgo6JVbvnz50nz+yZMnAbnIpWS7vvzySyCpptDmzZtTPeadd96hWbNmJCQk4OLiQmhoqDrbTJIyG5kMWYkxg6i7dOmidr+k1VUG0KJFCzp27EhiYiIBAQEpKldLGWNKN9mhQ4eYMWMGQUFBBAcH89tvv9GjRw/efvttChYsqPErkaTU+fn5kStXLoA0ExxFUZg1axYODg7qBInvvvtOnXYvSZmJTIasxJhkSKfTUb16dQB+++23V55vxowZZMuWjZMnT8pvaxpydnZm9erVNGrUiLJlyzJq1CgCAgLk+ArJ5rVs2RKAgwcPpnlM6dKlGTBgAABOTk5EREQwduxYa4QnSdZlnfHc9s0cs0Jmz54tANG2bdtXHrdq1Sp1NtWNGzdeeeyiRYsEINzc3ERISEi6Y3vZq2b1SJKlyNlk1nXu3Dn1XrNv3740jwsPDxd58uRRj3VwcBAXL160YqSSlH5yNpmNMbbWUIcOHdQBv4ZFXtPy6aefUr9+fZ49e0afPn3SnKkmSZL0srJly+Lt7Q0k1RZKi5eXF5MmTQKSZmjGx8fL5WakTEcmQ1ZiSIauXLmizs5IjU6n49NPPwXg1KlTrzynTqdjwYIFODk58eeffyZbfFGSJOl1mjRpAsB///33yuM6d+5M7dq1SUhIQFEU1q9fz759+6wRoiRZhUyGrKRgwYI4OTnx/Plzbty48cpjDbPKtm3bxuPHj195bJkyZfjqq68A6N+/P0+ePDFLvJIkZX7Tp08H4OLFi69coV6n0zF37lwURVFboIcMGSJbo6VMQyZDVqLX6ylWrBjw+q6ycuXKUb58eeLi4li9evVrz/3VV19RqlQpwsLCZPO1JElGK1SoENWrV0cIwR9//PHKY6tWrUpAQACQlBwdPXr0tRM9JMleyGTIikxZo8xQuM+Y1c+dnZ1ZsGABAAsWLGD//v0ZiFKSpKykbdu2QNIK9a8zfvx4smfPrhZqHDZsGM+fP7dofJJkDTIZsiJTkqH3338fgIcPH3Lx4sXXHt+wYUN1rFGPHj3kQqGSJBmlTp06ABw/fvyVldUBfHx8+Pbbb4GkOkQhISHMnTvX4jFKkqXJZMiKDMnQ5cuXX3ts06ZN1QrGxq4YPWXKFHLlysX58+eZPHlyuuOUJCnraNSoEU5OTgDMmzfvtcf36NGDihUrquOFxo0bx6NHjywaoyRZmkyGrMiUliGAzz//HMDobq8cOXIwc+ZMIOkGZex1JEnK2j766CMAo9Yec3BwYM6cOerPT548Ydy4cRaLTZKsQSZDVmRIhkJCQozqZ69Xrx4ABw4cMPoaHTt2pFmzZjx//pyePXvK2R6SJL1W8+bNATh8+LBRxzdo0ICOHTuqP8+dO5erV69aJDZJsgaZDFmRr68vHh4eJCYmcu3atdce/8Ybb6DT6QgODub06dNGXUNRFH744QdcXV3ZtWsXS5cuzWjYkiRlcrVq1QIgKChIXYfsdaZMmaIuqBwXF6eW+JAkeySTIStSFMWkrjIvLy+1L9+UpKZo0aKMHj0agMGDB3P//v10RJtxCQkJ7N69m5UrV7J79+5XFps0l08++QRFUZg4cWKy/Rs2bEBRFItcc8yYMVSuXNki506LEIJFixZRu3ZtvLy88PDwoFy5cvTv358rV64ki01RFFq0aJHiHJMnT0ZRFBo1apTieEVR0Ol05MuXj06dOqWojdWoUSP1uBe3nj17qsfs2rULf39/cuTIgZubGyVKlKBr165yYWEbVLhwYdzd3YmLi+PXX3816jn58+dPNtt1zZo1HDp0yFIhSpJFyWTIykwdN1S/fn1KlChB2bJlTbrOoEGDqFixIo8ePWLQoEEmx5lRv//+O4ULF8bf35+PPvoIf39/ChcuzO+//27xa7u4uDBp0qTXFqy0V0IIPvroI/r160erVq3Yvn07p0+fZvbs2bi6uqYYv5E3b1527drFzZs3k+1fsmQJBQsWTHH+cuXKcefOHW7evMnq1as5c+aMOrvxRQEBAdy5cyfZZhi4f+7cOVq2bEmNGjXYu3cvZ86cYc6cOTg6OqrTsiXboSgKHh4eAGzatMno5w0cOJASJUqoP8tCjJLdsuwSaZmDORdcHDVqlADE559/bobIXu3IkSNCURQBiG3bthn9vIwu1Lpu3Tr1ui9uiqIIRVHEunXr0nVeY3Tt2lW0bt1alC5dWnzxxRfq/vXr14sXf90PHDgg6tevL1xcXISfn58IDAwUkZGRQoikRXXLly+f4rlz585V9zVr1kwMGzZMLFmyJMXrXLJkiRBCiOvXr4u3335buLu7C09PT9GhQwcRFhamnmP06NGiUqVKYtmyZaJQoULCy8tLfPDBByIiIuKVr3HlypUCEBs3bkz18cTExBTXaN26tRg3blyy1+/j4yN69eolGjZsmOL4FxkWGX7x979hw4aif//+acY4Y8YMUbhw4Ve+jpfJhVq19e2334rPPvtMHDx40KTnbdmyJdnv/9q1ay0UoSSZTi7UaqNMmV6fUTVr1lSX9ujZsyfPnj1L13mEEERFRRm1RURE0K9fv1S/HRr29e/fn4iICKPOl9p5Xkev1/Pdd98xZ86cFK0hAGfOnKF58+a0a9eO06dPs3r1avbv30/fvn2BpC6gc+fO8eDBAwD27NmDj48Pe/bsASA+Pp6DBw/SsGFDPvjgAwYPHqy2pty5c4cPPvgAIQTvvvsujx49Ys+ePezYsYOrV6/ywQcfJIvl6tWrbNiwgc2bN7N582b27NmToovvZStXrqRUqVK8/fbbqT6eWnfgp59+ys8//6z+/NNPP9GpUye1GzYtYWFh/P777+j1evR6/SuPfVGePHm4c+cOe/fuNfo5krZGjhzJjz/+SO3atU16XsuWLZP9Lg4dOlTWOZPsj+XzMvtnzm94R48eFYDImzev0c+Jjo4Wy5cvN/kbmxBCRERECD8/PwGIoUOHGn29F7+hR0ZGpmj9sNZmaK0xVteuXcU777wjhBDijTfeEJ9++qkQInnLUJcuXUT37t2TPW/fvn1Cp9OJ6OhokZiYKHx8fNRvuJUrVxYTJkwQuXPnFkIIcfDgQeHg4CCePn0qhEi9NWX79u1Cr9eL0NBQdd+5c+cEII4ePao+z83NLVlL0BdffCFq1ar1ytdYunRp8fbbbyfb179/f+Hu7i7c3d1F/vz51f2G2GJjY0Xu3LnFnj17RGRkpPD09BSnTp0S/fv3T9EypNPphLu7u3B1dVX/H/r165fseg0bNhSOjo7qNQ3bzz//LIQQIj4+XnzyyScCEHny5BHvvvuumDNnzis/Q7JlyH5dvXpVODk5qb8vM2fO1DokSRJCyJYhm2XoX79z5w5Pnz416jlly5alS5cuRi3N8TJPT0++//57AKZOncqpU6dMPoe9mjRpEkuXLuX8+fPJ9p84cYKff/4ZDw8PdWvevDmJiYkEBwejKAoNGjRg9+7dPHnyhHPnztGzZ08SEhK4cOECu3fvpmrVquoYi9RcuHCBAgUKUKBAAXVf2bJlyZYtGxcuXFD3FS5cGE9PT/XnvHnzcu/ePQB+/fXXZDG+uEr4y60/I0aMICgoiFGjRhEZGZkiHkdHRzp37sySJUtYs2YNJUuWpGLFiqnGXqpUKYKCgjh27Bjjx4+ncuXKjB8/PsVxnTp1IigoKNlmWNpBr9ezZMkSbt68yeTJk8mXLx/jx49XW9Ak27R27VratWvHsmXLTHpe0aJFGTp0qPrz2LFj5aLRkl2RyZCVZcuWjVy5cgHGd5VVq1YNIN2JTJs2bWjfvj0JCQkEBASYPKvLzc2NyMhIo7YtW7YYdc4tW7YYdT43N7f0vGQgqRZK8+bNGT58eLL9iYmJ9OjRI9kf8VOnTnH58mV1Md1GjRqxe/du9u3bR6VKlciWLRsNGjRgz5497N69O9kMrNQIIVLtrnp5v6OjY7LHFUVRBxi//fbbyWKsXr06kJRQ//fff8melytXLooXL07u3LnTjOnTTz9lzZo1zJs3T126JTVOTk4UL16ccuXKMXz4cCpXrkyvXr1SHOft7U3x4sWTbV5eXsmOyZ8/P126dGHevHmcP3+emJgY5s+fn+a1JW2NGDGC9evXp+v/aNiwYWry/+jRI7777jtzhydJFiOTIQ2YOqOsXbt2QNI6Zenti589ezbe3t4cO3bMqJL7L1IUBXd3d6O2Zs2a4efnl+Y0dkVRKFCgAM2aNTPqfBmdDj9x4kQ2bdrEwYMH1X1Vq1bl3LlzKf6QFy9eXB1DYxg3tHbtWjXxadiwIX///bc6XsjAyckpRYJZtmxZQkNDk01JP3/+POHh4ZQpU8ao2D09PZPF5urqCiQV1rx48SIbN2406b0oV64c5cqV4+zZs2rFYWN8/fXXrFy5kn///dek670se/bs5M2bl6ioqAydR7IcQ72hl5NtY7i5uTFjxgz155kzZxISEmKu0CTJomQypAFTkyFD1wNg8h9Ag7x58zJp0iQAhg8f/toFGdNLr9cza9YsIGVXjuHnmTNnmjQYNyMqVKhAp06dki0fMHToUA4dOkSfPn0ICgri8uXL/PHHH+pgc4Dy5cuTM2dOfv31VzUZatSoERs2bCA6OlqtDg5JXV3BwcEEBQXx4MEDnj9/zptvvknFihXp1KkT//77L0ePHuXjjz+mYcOGagtPen344Ye0b9+eDz/8kLFjx3LkyBFCQkLYs2cPq1evfuV7u3PnTu7cuUO2bNmMvl7RokV55513GDVqVLL9z549IywsLNlmKGewYMECevXqxfbt27l69Srnzp1j6NChnDt3jjZt2qTrdUuW98477wDw+PHjdH3xateuHU2aNAGSCjG+3CorSbZKJkMaMDUZcnFxIUeOHACsW7cu3dcNCAigbt26REVF0adPH4vVA2nXrh1r164lf/78yfb7+fmpYxKs6dtvv032WitWrMiePXu4fPky9evXp0qVKnz99dfqwriQlLgZWn/q16+vPs/b25sqVaok6w567733aNGiBf7+/uTKlYuVK1eiKAobNmwge/bsNGjQgDfffJOiRYuyevXqDL8eRVFYvXo1M2fOZMuWLTRp0oRSpUrx6aefUqBAgVeuZefu7m5SImQwePBg/vzzT44cOaLuW7RoEXnz5k22GZZoqFmzJpGRkfTs2ZNy5crRsGFDDh8+zIYNG5K1qkm25cVE9c8//zT5+YqiMGfOHDUhX7lyJUePHjVbfJJkMVYYzG33zD0rZN26dQIQNWvWNPo5jRo1EoAoVKhQhq597tw54ejoKACxZs2aVI/JaJ0hg/j4eLFr1y6xYsUKsWvXLhEfH5+h80mZm5xNZhuyZcsmAPHxxx+n+xyDBw9WZ5bVrVs3We0rSbImOZvMhr3YMiSMbJ1p2bIlQKp1c0xRtmxZdQ2hwMBAi8740Ov1NGrUiI4dO9KoUSOrdY1JkpR+pUuXBkjWCmiqUaNGqRNFDhw4wB9//GGW2CTJUmQypIFixYqhKApPnjxRC/u9TufOnVEUhYSEhAwnRF999RWlSpUiLCyMYcOGZehckiRlLv7+/gBcv3493efw8vJi+vTp6s+DBg0iLi4uw7FJkqXIZEgDrq6u6ppQxo4bypcvH1WqVAFINjMqPVxcXFiwYAGQNND1VWNMJEnKWj755BMAYmJijK6FlppOnTqps9OuXbvGwoULzRGeJFmETIY0Yii+aGwyBFC3bl0AsyQvDRs25PPPPwege/fuPH/+PMPnlCTJ/pUsWZJChQoBcOzYsXSfR1EU5s+fr84iHTFiBOHh4WaJUZLMTSZDGjF1Rhn8r/iiuVZ+nzx5Mr6+vly4cEGddv8iY8czSZI5yN8322Fo0cnIuCGAypUr0717dwDCw8NlIUbJZslkSCPpSYbKli0LwK1bt8yypEH27NnVmkDjx49XC60ZqiKnd2FXSUoPw+/by1W5JesrUqQIkLSgb0Z999136pIzM2bMsFiNM0nKCAetA8iq0rN6fY0aNXBwcCA+Pp5t27apffsZ8f7777N06VL++usvunfvzu7du9Hr9WTLlk1dI8vNzS3DlaAlKS1CCJ49e8a9e/fIli2bnHVoAwzJ0LVr10hMTESnS//35hw5cjBlyhR69uxJXFwcgwcPZs2aNeYKVZLMQhFZoG366dOnNG7cmLi4OBISEujXrx8BAQFGPz8iIgJvb2/Cw8NTrL2UXlevXqV48eK4uLgQFRVl9M2mY8eOrFq1ijFjxjB69GizxBISEkK5cuV49uwZixYt4vPPP0cIQVhYmFxsUbKabNmykSdPnlQTb0t8Bl/HFu8b1vL48WNy5syJEIL//vuPUqVKZeh8CQkJlC9fXm19PnHiBFWrVjVHqJL0SsZ+DrNEMpSQkMDz589xc3Pj2bNnlC9fnmPHjpEzZ06jnm+Jm1p8fDxubm7ExcVx/fp1dXbZ63z//ff06dOHpk2bsn37drPEAjB9+nQGDx6srqqeJ08eIOm9k1NiJUtzdHR8ZYuQFomFLd43rKlGjRocP36cVatW8cEHH2T4fIcPH6Z27dpA0vjHY8eOyRZnyeKM/RxmiW4yvV6vrn4eExNDQkKC5oM1HRwcKFasGP/99x+XLl0yOhkyzCg7fPgwsbGx6sKiGdWvXz9+/fVX/v33XwYMGMCqVauApPdOdltIWZEt3jes6Y033uD48eMcPnzYLMnQG2+8wXvvvce6des4ceIEmzZt4u233zZDpJKUcTYxgHrv3r20adOGfPnyqWs6vez777+nSJEiuLi4UK1aNfbt22fSNZ48eUKlSpXw8/Pjyy+/xMfHx0zRp196pteXL18evV7P06dPzTarDJKSs0WLFqHT6Vi9enW61iWSJGvKqvcNazHMKNu5c6fZzjlv3jz1C1zPnj2Jj48327klKSNsIhmKioqiUqVKzJ07N9XHV69ezYABAxgxYgQnT56kfv36tGzZMtmshGrVqlG+fPkU2+3bt4Gk8QinTp0iODiYFStWcPfuXau8tldJz4wyw+BmgC1btpg1nqpVqzJw4EAAevfuTWRkpFnPL0nmlFXvG9ZiaK0+ffq02e4Fvr6+jBkzBoA7d+4we/Zss5xXkjLMguujpQsg1q9fn2xfzZo1Rc+ePZPtK126tBg2bFi6rtGzZ0/x22+/pfl4TEyMCA8PV7cbN25YZMHFBQsWCEC0bNnSpOf9/vvvYt++fSIhIcGs8QghRGRkpChUqJAAxMCBA81+fklKj9cttpiV7hvWkpCQIBRFETqdTvzzzz9mO29sbKzIkyePAISrq6uIiIgw27kl6WWZZqHW2NhYTpw4QbNmzZLtb9asmdHLUty9e5eIiAggaTDV3r17Xzk7YsKECXh7e6tbgQIF0v8CXiE90+sB2rZtS7169TI03TUt7u7u/PDDDwDMmjWLEydOmP0akmRpmfm+YS06nY7Lly+TkJBA48aNzXZeR0dHli5dCkB0dDSDBg0y27klKb1sPhl68OABCQkJ+Pr6Jtvv6+tLWFiYUee4efMmDRo0oFKlStSrV4++fftSsWLFNI//6quvCA8PV7cbN25k6DWkxZAMBQcHExsba5FrpEfLli3p2LEjiYmJBAQEyH59ye5k5vuGNRUrVswi523WrJk6GeSnn37K8OLTkpRRdjOb7OUpmEIIo6dlVqtWjaCgIKOv5ezsjLOzsynhpUvevHlxd3cnKiqK4OBgk2p59O7dm/Xr1/Ppp58yfvx4s8c2Y8YMtm7dysmTJ5k1axaDBw82+zUkydIy431DCxktvJialStXUrhwYRITE+nUqRN79uwx6/klyRQ23zLk4+ODXq9P8W3u3r17Kb712RtFUdI1iBrg0KFDhIWFsWnTJkuEhq+vL1OnTgVg1KhRBAcHW+Q6kmQJmfm+YU0xMTH4+vri4ODAxYsXzXruAgUK0KNHDyBpZuCBAwfMen5JMoXNJ0NOTk5Uq1aNHTt2JNu/Y8cO6tSpo1FU5pOe6fUA/v7+AFy5csXsMRl069aNRo0a8ezZM3r37p2laqxI9i2z3zesxcXFhSdPniCEYOXKlWY//4wZM/Dw8ACgU6dOZj+/JBnLJpKhyMhIgoKC1Cbp4OBggoKC1CmwgwYN4scff+Snn37iwoULDBw4kNDQUHr27Klh1OaR3pahzp07A0kDEA3TgM1NURQWLFiAs7MzW7duVQsxSpItyMr3DWvy8/MDzFtvyMDZ2ZmZM2cCcP36dRYtWmT2a0iSUawws+21du3aJYAUW9euXdVj5s2bJwoVKiScnJxE1apVxZ49e6wWn7FT89Jj2bJlAhD+/v4mP9fBwUEAYsKECWaP60XffvutAESuXLnEw4cPLXotSUpNap/BrHzfsKYPP/xQACJHjhwWu0bRokUFINzd3UVcXJzFriNlPcZ+DrPE2mQZZck1ho4cOcIbb7xB/vz5TZ5RUbRoUYKDg2nQoIFFBx/GxsZSpUoVzp8/z6effsrixYstdi1JSo09rvNljzGn5tdff1VbouPi4nBwMP+8mxMnTlC9enUAevXqxffff2/2a0hZk7GfQ5voJsvKDGOGbt26ZXKV1zfeeAOAM2fOmD2uFzk5OanN1z/99BO7du2y6PUkSbIdbdu2Vf+9detWi1yjWrVqNG3aFICFCxdy7949i1xHktIikyGN5ciRQ13vyNTB0B06dACSMl9L1wKqU6cOvXr1AqBHjx7ExMRY9HqSJNkGNzc39Rt1auu/mcvq1atxcHAgISGB999/32LXkaTUyGTIBqR3EHWbNm3w9vYmISGBU6dOWSK0ZCZMmEDevHm5fPmyRWobSZJkm8qVKweQbF03c8uePTuBgYEA7Nmzh8OHD1vsWpL0MpkM2YD0Tq93cHBQpwlbo0aHt7e3uijmxIkTOXv2rMWvKUmS9gxLZty/f9+i15k6daraCvXhhx9a9FqS9CKZDNmA9LYMAdSrVw9IKlpmDW3btuWdd94hPj6e7t27k5iYaJXrSpKkHcP4xNOnTxMVFWWx6+h0OubNmwckTbU3rJMoSZYmkyEbkN4FWwEKFSoEJPXlWyMxURSFuXPn4uHhwaFDh1iwYIHFrylJkrb8/PzIly8fiYmJ7N6926LX6ty5s9paPmTIEJ4/f27R60kSyGTIJmSkZah58+YAJCQkGL0ad0b5+fkxYcIEAIYNG8atW7escl1JkrTj6uoKYJVp76tXrwbg2bNn9OnTx+LXkySZDNmA4sWLA/Do0SMePnxo0nN9fHzU1qH0JFPp1atXL2rVqkVERAT9+vWz2nUlSdKGYSFpUxavTa8qVarQrFkzAJYsWcL169ctfk0pa5PJkA1wc3OjQIECQPoSmnbt2gFw/Phxs8b1Knq9noULF+Lg4MDvv/9u0Sm3kiRpr0uXLgBWW6Pwl19+Qa/Xk5iYyAcffGCVa0pZl1GlRMeOHWv0CRVF4euvv053QFlVyZIluXHjBpcuXaJ27domPbdu3brMmDGD/fv3Wyi61FWsWJEvvviCCRMm0LdvXxo3bmzXlXYlSUpbmzZt0Ov13Llzh5s3b6prlllKrly56Nu3L7NmzeLIkSNs27ZNHRYgSeZm1HIcOp3xDUiKopCQkJChoGyNNcrq9+rVi/nz5zN8+HCTa/jcuXOHfPnyARASEqJ2m1lDdHQ0FSpU4OrVq/Tt25c5c+ZY7dpS1mGPS1vYY8yvU6VKFYKCgli7di3vvfeexa8XExNDrly5iIyMJHfu3Ny6dcsiy4FImZdZl+NITEw0estsiZC1ZGQQdd68edUbxPLly80a1+u4urqqM8rmzZsnC6VJUiZmGN+4cOFCq1zPxcWFadOmAXDv3j0mTpxoletKWY8cM2QjMjK9HqB69eoUKVKE/PnzmzMsozRp0oSuXbsihCAgIIC4uDirxyBJkuV5e3sDWPVLz+eff662dn/77bdy3TLJImQyZCNeTIbSUy/o0KFDXLt2jW7dupk7NKNMnToVHx8fzp49y9SpUzWJQZIky/roo48oUaKEVdcO0+l0/PTTTwDExsaqayRKkjkZNWboZXv37mX27NlcuHCB6Ojo5CdUFK5evWq2AG2BNfr+4+LicHNzIz4+nhs3blh8cKIl/PLLL3Tp0gVnZ2fOnDmjFk6TpIyyx/E39hizLWvQoAH79u0D4MiRI9SsWVPjiCR7YNYxQy/av38/TZo0ITw8nAsXLlC6dGny589PaGgoDg4ONGjQIEOBZ1WOjo4ULVoUSH+9oNjYWNauXcvp06fNGZrROnXqRNOmTXn+/Dk9e/a02hRcSZIyv0WLFqEoCpA0zV8uBSSZk8nJ0OjRo+nWrRtbt24FYNy4cezbt49///2XyMhIteaNZLqMDKIGKFq0KB06dGD06NHmDMtoiqIwf/58XF1d2blzJ0uXLtUkDkmSLCcmJobVq1erA5utpVSpUnzyySdA0j3yxx9/tOr1pczN5GTo7NmztG3bVs3QDbPHKlasyNdff21STSIpufSuXm9Qvnx5AI4dO2a2mExVtGhRxowZA8DgwYMtvsq1JEnWNWnSJD788EOGDx+uybWdnZ2BpPvL48ePrR6DlDmZnAw9e/YMDw8PdDodzs7OPHjwQH2sdOnSnD9/3qwBZiUZbRlq3bo1kFR3SMsm5IEDB1KpUiUePXrEoEGDNItDkiTz+/DDD4Gkbvng4GCrXjtXrlyMHDkSgMjISE0SMilzMjkZKliwIHfv3gWgbNmy/Pnnn+pje/bsIWfOnOaLLovJ6PT6jz76CEiqC7V3716zxWUqR0dHtX//l19+Yfv27ZrFIkmSeZUqVQpHR0cAVqxYYfXrDx48mFy5cgGwYMECTp06ZfUYpMzH5GSoUaNG7N69G4CAgAC+//57mjRpQqtWrRg3bhwdO3Y0d4xZhiEZunbtWrpq9eTIkQMPDw8AVq1aZdbYTFWjRg11AdeePXvy7NkzTeORJMl8DPXM/vnnH6tf29XVVR2vJISgR48ecrKGlGEmJ0PffPNNsj9yU6dO5cmTJ9y7d4+RI0cybtw4sweZVeTLl0+dXh8SEpKuc5QuXRpA05Yhg2+//ZYCBQoQHBzMN998o3U4kiSZSfXq1YGkMaRa6NSpE+XKlQOSptmvXLlSkzikzMPkZMjHx0dtwQAYNGgQJ06c4Pjx44wZM0ZtPpVMp9PpMjyI+s033wSSWpe05unpybx58wCYNm0aQUFB2gYkSZJZGMYnPnjwQJPxiTqdjtmzZ6s/DxgwgKdPn1o9DinzkBWobUxGB1F//PHHADx//twmZlq0adOGDh06kJCQQEBAgFy7TpIyAcMirUIIdu7cqUkMjRs3pmXLlgDcv39f9kpIGWLy8r+ffvrpKx9XFIXFixenO6CsLqPJUJkyZShRogSXL1/m0KFDtGrVypzhpcusWbPYvn07x48fZ+7cufTv31/rkCRJygAPDw+KFSvG1atXCQ0N1SyOqVOnsm3bNhITE5k2bRrdunVThwpIkilMbhnauXMnu3btSratXbuWn3/+mQ0bNrBr1y5LxJllZLSbDKBu3bpAUrVwW5A3b14mT54MwIgRIzS9eUqSZB5t2rQB4OTJk5rFULZsWQICAoCkmneBgYFyMLWULiYnQyEhIQQHByfbIiIi+Pvvv8mdOzcbN260RJxZRkan1wNUq1YNgDVr1pglJnP4/PPPqVevHlFRUfTu3VvesCTJzr3xxhuAdVewT82YMWNwc3MD4O+//2bDhg2axiPZJ7ONGWrcuDF9+/aVXSAZZEiGbty4ke7p6IY1zq5cuUJkZKTZYssInU7HwoULcXR05M8//2Tt2rVahyRJUgYYZpSdOHGCR48eaRZHnjx5GDZsmPrzgAEDUiwgLkmvY9YB1GXLluXo0aPmPGWWkzNnTnLkyAEkJTPp0aJFC3W5lB07dpgttowqU6aMWjE2MDDQJgZ4S5KUPkWKFEGn0yGE0LwVetCgQeTNmxeA0NBQJk2apGk8kv0xazK0Z88efHx8zHnKLCmjg6h1Oh1vvfUWYBtT7F/01VdfUapUKe7evZvs25wkSfZFp9Ph6+sLoHnZDHd3d8aPH6/+PGHCBKsvFSLZN5OTobFjx6bYRowYQZs2bRg/frysQG0GGU2GAOrXrw/AgQMHzBKTuTg7O7Nw4UIAFi5cyL59+zSOSJKk9OrTpw8ADx8+1DiSpLIiFStWBJLWTZPrIkqmMHlqvWFF8hc5OztTuHBhxo4dyxdffGGOuLI0cyRDDRo0AGD37t3Ex8fj4GDyf7XFNGjQgICAABYtWkT37t0JCgpSV6KWJMl+GGau7tu3DyGE2j2vBb1ez5QpU2jevDkAGzZsYOvWrbRo0UKzmCT7YXLLUGJiYootOjqaCxcuMHz4cFmB2gzMMb2+atWq6PV6Hj9+zJYtW8wVmtlMmjQJX19f/vvvPyZMmKB1OJIkpcMbb7yBs7MzYWFhnDt3TutwaNasmZoMAfTv35/Y2FgNI5LshaxAbYPMMb3eyckJT09PAH7//XezxGVO2bNnV8vpf/fdd1y4cEHjiCRJMpWLiwteXl4Aave31qZMmYJOl/Sn7dKlS8ycOVPbgCS7oAgjCr6YWiSvYMGC6Q7IFkVERODt7U14eLj6wbekyMhINZF5+PChOrvMVKtWrcLHx4fGjRurNwdbIoSgTZs2/Pnnn9SrV489e/bYZJyS9qz9GTQHe4w5PSpUqMDZs2epVauW5jWHDD7//HN1JQQ3NzcuXbpE/vz5NY5K0oKxn0OjkiGdTmdSX3BmW39Ki5uan58ft27d4vDhw9SqVcsq19TC9evXKVeuHFFRUSxYsIDu3btrHZJkg+wxsbDHmNNj//79JCQkUL9+fZv5MnP79m1KlCih1mrr2LEjK1as0DgqSQvGfg6N+s396aef1G3BggUUKFCAUqVKMWrUKL7//nu+/vprSpYsiZ+fn800ldo7cwyitgeFChVSF1j88ssvuXPnjsYRSZJkinr16tGwYUObSYQA8uXLx5AhQ9SfV65cyd69ezWMSLJ1Rv32fvLJJ3Tt2pWuXbty7do1ypYty9mzZxk9ejQ9evRgzJgxnDt3jrJly2ZonIv0P+ZKhoYMGYKfnx89e/Y0R1gWERgYSPXq1QkPD5cVzCVJMosvvvhCrYME0LdvX+Lj4zWMSLJlJqfyy5Yto3fv3im+Beh0Onr37s3y5cvNFlxWZq5kaOfOndy6dYs//vjDHGFZhF6vZ+HChej1etasWcOmTZu0DkmSJBN8/fXX5M+fn9atW2sdisrDw4Nvv/0WAEVROHPmDD/88IPGUUm2yuRk6OHDh2mu+xIdHS2XWDATw/piR44cYffu3ekeh2WYZnrnzh1u375ttvjMrUqVKmqRtD59+vD06VONI5IkyVgnTpzg9u3b7Nq1i8TERK3DUXXr1o1y5cqpC0N/9dVXzJ8/P0P3VClzMjkZqlq1KmPHjuXBgwfJ9t+/f5+xY8dSpUoVswWXVf3+++/06tULSBpg7O/vT+HChdM1Rb5x48bqv//++2+zxWgJo0ePpkiRIty4cYORI0dqHY4kSUZq1qwZAM+ePbOJekMGDg4OTJkyRf05KiqKXr16ZeieKmVOJidD06ZNIzg4mMKFC/POO+/Qo0cP3nnnHYoUKUJISAjTpk2zRJxZxu+//0779u0JCwtLtv/WrVu0b9/e5A9vjRo11H/beveTu7u72ow9Z84cueivJNmJN954Q/33zp07NYwkJcOMspel954qZU5GTa1/2YULF/jmm2/YvXs3Dx8+JGfOnPj7+zNy5EjKlStniTg1Za0psgkJCRQuXJibN2+m+riiKPj5+REcHIxerzf6vIUKFSI0NJTs2bPz8OFDTUvmG6Nz5878+uuvVKpUiWPHjsmq5pJdTlO3x5jTKzo6Gnd3d4QQtGjRgr/++kvrkADL3VMl+2HWqfUvK1OmDKtWrSIsLIy4uDjCwsJYuXJlpkyErGnfvn1pfmghqUjhjRs3TF7ctFGjRgA8fvyYs2fPZiREq5g+fTo5cuTg1KlTzJgxQ+twJEl6DVdXV4oVKwYk1R2ylXFDlrqnSpmP7RSGkIyusWNqLR7DYore3t5ERkaaHJe15c6dW+1uHTNmDFevXtU4IkmSXsewOHRkZCRnzpzROJoklrqnSpmPUUuZjx07ls8//5x8+fIxduzYVx6rKApff/21WYLLavLmzWvW4wwMFawTExOpWbOmyXFpoWvXrixbtoxdu3bRq1cvtm3bZvPde5KUldWsWZOffvqJbNmyERUVpXU4gOXuqVLmY/RyHIcPH6ZmzZqvrTKqKEqmm7Jo7TFDt27dIrX/lvT2b8fHx6s3qDNnzlC+fHlzhm0xly9fpkKFCjx//pzly5fTuXNnrUOSNGKP42/sMeaMOHHiBNWrVydHjhw8ePDAJr68vO6eCuDr68utW7fkmKFMyqxjhl5sUUhMTHzlltkSIWvS6/XMmjULIMWNxPDzzJkzTf7QOjg4qLPKdu3axf37980QreWVKFGCUaNGATBw4MAU5RwkSbId5cuXx8nJiUePHhEcHKx1OMCr76kGOXLksKmlRCRtyN8AG9OuXTvWrl2bYoVlPz8/1q5dS7t27dJ1XsPU1/79+9vVoOQhQ4ZQvnx5Hjx4kGytIUmSbIuzszMVK1YEYM+ePTx58kTbgP5fWvdUHx8fnJycuHDhAitXrtQoOslWmJwMxcTEEBERkWzfb7/9xrBhw2y+qJ+9aNeuHSEhIWrNnVy5chEcHJzuRAj+N25ICMGOHTvMEqc1ODk5sXDhQhRFYenSpfzzzz9ahyRJUhoMLdCfffYZixcv1jia/zHcU3ft2qXG2KdPH7Xl+YsvvpBV77M4k5OhLl260K9fP/Xn2bNn8+GHHzJ58mSaN2/Oli1bzBpgVqXX69WlNCIjIzPcn12nTh169OgBJPXtP3z4MMMxWkvt2rXp3bs3AD169EhzORhJkrRVvXp1IOlL1+7du7UN5iV6vZ5GjRqp98GtW7cyePBgihUrxu3btxk3bpzGEUpaMjkZOnr0KC1atFB/nj17Np07d+bJkye0a9eOqVOnmjXArCxnzpxAUkGztKqoGit37tzMnz+f8uXLI4SwuxaW7777jvz583P16lV18UVJkmxLgwYN+OSTTwDYu3evTa4S37JlSyDpb9nTp0+ZOXMmADNmzODixYsaRiZpyeRk6P79+2rfa3BwMNeuXSMwMBAvLy8+++wzuyjqZy88PT3V6svmaslp2rQpgF11lQF4eXkxd+5cAKZMmcLp06c1jkiSpJcVL16cH3/8kWzZshEREcHJkye1DimFfPnyUblyZYQQbNu2jdatW/PWW28RFxdHv3790px1JmVuJidDbm5uhIeHA0nVPT08PNSmURcXF7so6mcvFEXBx8cHwCwzqZ49e0auXLmApGTI3j707777Lm3btiU+Pp7u3bvLmYuSZIP0er1agHHXrl0aR5M6Q+uQYVjHzJkzcXJyYvv27WzcuFHL0CSNmJwMVahQgXnz5nHmzBm+//57/P391SmLoaGh5MmTx+xBmoODgwOVK1emcuXKfP7551qHYzRDV5k5WoZOnz7N8OHDAbh+/TpXrlzJ8Dmtbc6cOXh6enLkyBF1gLkkWYq93je09OTJE/XvgK0mQ61atQJg27ZtJCQkULx4cXW26sCBA+W4xKxImOiff/4Rrq6uQqfTCRcXF7F//371sffff1+89957pp7SKnLmzJnu54aHhwtAhIeHmzEi4zRq1EgAYuXKlRk+V0xMjChRooQoV66cmD17tnj48KEZIrS+efPmCUB4eHiI0NBQrcORrECrz6C93je0tGzZMgGon9HY2FitQ0ohLi5OZMuWTQDi4MGDQgghIiMjhZ+fnwDEmDFjNI5QMhdjP4cmtww1btyYCxcusGbNGs6dO6eue2V47IsvvjBDiiYZmLObzNnZmUuXLnH27FkCAwPJkSNHhs+phZ49e1K7dm0iIyPp27ev3XX3SVJmVqNGDUqUKEHJkiWZNm2aTXZnOzg4qLN1DV1l7u7uTJ8+HYCJEyfaTOFIyTrSVXSxUKFCtGvXjqJFiybb36NHD7WejSn27t1LmzZtyJcvH4qisGHDhhTHfP/99xQpUgQXFxeqVatm8irDERERVKtWjXr16rFnzx6TY9SKOZOhzEKn07Fw4UIcHBz4448/WL9+vdYhSRqQ9w3bVLp0aS5dusTFixfp3r07Li4uWoeUKkNX2YvlYNq3b4+/vz8xMTEMGjRIq9AkDaQrGXr+/DkLFiygY8eONG3alMuXLwOwceNGrl27ZvL5oqKiqFSpkjpb6GWrV69mwIABjBgxgpMnT1K/fn1atmxJaGioeky1atUoX758iu327dsAhISEcOLECebPn8/HH3+conCkrTLnmCGDxMREdu/ezdy5czl//rzZzmtN5cuXZ+jQoQD07dtXHdQvZR3yviFlhKFEzL///quuWq8oCnPmzEGv17Nhwwa2bdumZYiSNZna/3b//n1Rvnx5oSiKyJs3r9DpdOLEiRNCCCE++eQT0atXr3T16xkAYv369cn21axZU/Ts2TPZvtKlS4thw4al6xotWrQQx44dS/PxmJgYER4erm43btzQrO9/xowZAhAffvihWc4XFhYmsmfPLhRFEYD45ptvzHJeLURHR4sSJUoIIMO/d5Jte12/v7xv2J74+Hixd+9eMXfuXPH8+XOtw0lV9erVBSB++umnZPsHDhwoAFGyZEmbjV0yjsXGDH355Zc8efKE48ePExoammy8hr+/v9mbkmNjYzlx4gTNmjVLtr9Zs2YcPHjQqHM8fvyY58+fA3Dz5k3Onz+foovvRRMmTMDb21vdChQokP4XkEHm7ibLnTs3Dg4O6v+bvdUbepGLiwsLFiwA4IcffuDAgQMaRyTZiqx+39DaP//8Q7Zs2WjcuDF9+/bl2LFjWoeUKkNX2V9//ZVs/+jRo/H19eXSpUtqUUYpczM5Gdq8eTNjx46latWqKVYB9vPz4+bNm2YLDpKSgISEBHx9fZPt9/X1JSwszKhzXLhwgerVq1OpUiVat27NrFmzXjl4+KuvviI8PFzdbty4kaHXkBHm7iZTFEVdtBXg8OHDdt307+/vT7du3QDo3r07sbGxGkck2YKsft/QWqFChYiMjCQxMRHA5pbmMDAkQ9u3bycuLk7d7+3tzaRJkwAYO3Yst27d0iQ+yXpMToYiIiIoVKhQqo/FxcVZrPz6y4mXECLFvrTUqVOHM2fOcOrUKYKCgnj33XdfebyzszNeXl7JNq1YYgC1IRny8PAgPj7eZm9UxpoyZQq5cuXi/PnzTJ48WetwJBuSVe8bWitWrBjZsmVTkyFbrTdUvXp1fHx8CA8P59ChQ8ke69KlC7Vr1yYqKoovv/xSowglazE5GSpSpEiKXxqDo0ePUqpUqQwH9SIfHx/0en2Kb3P37t1L8a0vM7JEMmSY8Wf4o2DPXWWQ1HpmaMr+9ttv5fpCUpa/b2hNURR1ZQKAAwcOqF2OtkSv16sDqV9eZFyn0zF37lwURWHFihXs3btXixAlKzE5GerUqROTJk1i48aN6rgTRVE4duwYs2bNokuXLmYN0MnJiWrVqqX4g71jxw7q1Klj1mvZIkMyZI7FWg1q1KiBoig8ffoUsP9kCKBjx440b96c2NhYevToIWsPZXFZ/b5hCwzJkKurKzExMRw9elTjiFL38tIcL6patSrdu3cHkmat2uLCs5KZmDoyOzY2VrRo0UIoiiJy5MghFEURuXLlEjqdTrRq1UokJCSYPNr76dOn4uTJk+LkyZMCENOnTxcnT54U169fF0IIsWrVKuHo6CgWL14szp8/LwYMGCDc3d1FSEiIyddKDy0rySYmJgpHR0cBmLXacvny5QUgdDqdcHBwEPfu3TPbubVy7do14erqKgCxePFircORzCi1z6C8b9i2tWvXCkCt9GyrM1cfPHigzq5N7R774MEDkSNHDgGIOXPmaBChlBHGfg5NToaESPoDvXLlStG5c2fRtGlT0bFjR/Hrr7+mKxESQohdu3ap5dtf3Lp27aoeM2/ePFGoUCHh5OQkqlatKvbs2ZOua6WH1je1vHnzCkD8+++/Zjvn559/LgDRqVMnERERYbbzam3KlCkCENmzZxdhYWFahyOZSWqfQXnfsG3Xr19Xv3ABonHjxlqHlKbatWsLQCxcuDDVx3/44Qc1sbt7966Vo5MywiLJ0LNnz0SdOnXEjh07MhScvdH6plahQgUBiO3bt5vtnD/++KMAhL+/v9nOaQvi4uJE5cqVBSA6duyodTiSmWj9GUwPe4zZnBITE0WuXLkEIGbOnCmePn2qdUhp+vbbbwUg3n333VQfj4+PF1WqVBGA+Oyzz6wcnZQRFqkz5OrqypkzZ3BwcDDlaVIGWaIKtWEQ9dGjR21y7aD0cnBwYNGiReh0OlauXJmifogkSdbx4iBqR0dHPDw8NI4obYYp9n///XeqA731ej1z5swB4KeffrLZ8U9S+pk8gLp27dryF8HKLDGjrEyZMnh6ehIVFcXAgQOpUaMGJ0+eNNv5tVS9enX69+8PQK9evYiKitI4IknKmgzJ0PHjxzWO5NUqV65Mnjx5iIyMZP/+/akeU7duXT7++GOEEPTt21ctGyBlDiYnQ9OmTWPBggUsW7aMyMhIS8QkvcQSyZBer6dmzZpAUkG048ePs337drOdX2tjx46lYMGCXL9+ndGjR2sdjiRlSYZk6ODBgwwdOpTevXtrHFHqdDpdmlPsXzRp0iQ8PT05duwYS5YssVZ4khWkq2Xo5s2bdOvWDW9vbzw9PZMVGfP29rZEnFmapVauNxRfzJ07N5A5ptgbeHh48MMPPwAwY8YM/v33X40jkqSsx5AMXbp0icmTJ7N48WKio6M1jip1aS3N8aI8efIwZswYIKni+OPHj60RmmQFJg/+ee+994yu4CqZhyXGDAH079+fwYMHc/fuXcqUKcP+/fuJjo7G1dXVrNfRSqtWrfjggw9YvXo1AQEBHDlyRI53kyQrypcvH/ny5eP27dvkzp2be/fucfDgQZo0aaJ1aCk0bdoUvV7PhQsXCA4OpkiRIqkeFxgYyI8//siFCxcYPXo0s2fPtnKkkkVYZzy3fdN6Vsjy5csFIN58802LnD8xMVH4+fkJQGzbts0i19DKnTt31Don06ZN0zocKZ20/gymhz3GbAlnzpwR4eHhonPnzgIQI0eO1DqkNDVo0EAAYt68ea887u+//1bLBpw6dcpK0UnpYbFV6yXrs1Q3mYGiKDRt2hTIXF1lkNSsPWXKFAC+/vprQkJCtA1IkrKY8uXL4+Xlhb+/P2C765TB/7rKXjVuCKBJkya0b9+exMREAgMDZcX7TEAmQ3bAUt1kAKtXr8bf31+dTprZkiGAzz77jIYNG/Ls2TN69eolb1ySpIFGjRoBSeU8bHWGpyEZ2rlz52vHNk2bNg1XV1f27t3LqlWrrBGeZEEyGbIDlmwZunfvHrt37+bu3bvkzp2bsmXLZrr1dxRFYcGCBTg5ObF161ZWr16tdUiSlGUkJCQwaNAgunbtip+fH3FxcRw4cEDrsFJVvnx58ufPT3R0NHv27HnlsQULFmTEiBEADBkyRF3rUbJPMhmyA5ZYrNWgVatWLF68mLlz5xIWFsaKFSsy5SDjUqVKMXLkSCBp4PijR480jkiSsga9Xs+6devYv38/5cqVI3fu3Bbr8s8oRVGMmlVmMHjwYIoVK8bt27cZN26cpcOTLEgRss/gtSIiIvD29iY8PBwvLy+rX18IgYuLC7GxsVy/fp2CBQtaPYbMIDY2lipVqnD+/Hk+++wzfvzxR61Dkoyk9WcwPewxZkv58ccfcXR0pEGDBhQuXNimZyRv2LCBtm3bUrx4cS5fvvza4zdv3kybNm1wdHTkzJkzlCpVygpRSsYy9nMoW4bsgKIoFh039CIhBBcuXMiU42qcnJxYuHAhAIsXL2b37t3aBiRJWcTnn39O165dKVKkiE0nQpA0ONrR0ZErV64YlQy1bt2at956i7i4OPr165cp751ZQbqSoefPn7NgwQI6duxI06ZN1V+YjRs3cu3aNbMGKCWx5LihkJAQ5s6dy5IlSyhRogRly5blv//+M/t1bEHdunXp2bMnAD169CAmJkbjiCQp6xFC2GzxRU9PTxo0aAC8flaZwcyZM3FycmL79u1s3LjRkuFJFmJyMvTgwQOqV69Or1692LNnDzt37lQHjm3YsIGpU6eaPUjJssnQ0aNHCQwMZN68eWqhscw4q8xgwoQJ5M2bl0uXLvHdd99pHY4kZQkHDx5k1qxZzJgxg3z58qmVnG1Ry5YtAeOToeLFizNkyBAABg4caLOJnpQ2k5OhL7/8kidPnnD8+HFCQ0OTNQn6+/u/dgS+lD6W7CYzrGB/6tQpdfprZk6GsmXLpq5APXHiRM6dO6dxRJKU+XXt2pUBAwYQFhZGWFiYXdQb2r17t9FlAIYPH46fnx8hISFMnjzZkuFJFmByMrR582bGjh1L1apVU/T9+vn5cfPmTbMFJ/2PJVuGChYsSJ48eYiPjydfvnxA0k0gLi7O7NeyFe3atePtt98mLi6O7t27yxWoJcnCDOuUGf5unDhxgvDwcC1DSlPp0qUpXLgwsbGxRidt7u7uTJ8+HUj6kiULvNoXk5OhiIgIChUqlOpjcXFxma5Gja2wZDKkKIq6aOuDBw/IlSsXkZGRHD582OzXshWKojB37lw8PDw4ePCgOrBakiTLqFGjBpC0aGvx4sVJTExk//79GkeVuhen2BvbVQbQvn17GjduTExMDIMGDbJUeJIFmJwMFSlShEOHDqX62NGjR+W0QgsxdJNZqj6Hoavs6NGj6iKK27dvt8i1bEWBAgXUMUNDhw7l9u3bGkckSZmXoWXo+PHjane8PXSVbdmyxegZYoqiMHv2bPR6PevXr2fbtm2WDFEyI5OToU6dOjFp0iQ2btyo/oIoisKxY8eYNWsWXbp0MXuQ0v9ahiw1td7QMnTkyJFMu05Zanr37k3NmjWJiIigX79+WocjSZlWlSpVUBSFGzduULVqVcC2kyF/f3+cnZ25fv06Fy5cMPp55cqVU+8l/fr1IzY21lIhSmZkcjI0dOhQ6tatS9u2bfH19QWgefPmvPHGG9SqVYv+/fubPUjJ8ou1Vq9eHZ1Ox40bN6hYsSKDBg1i7NixFrmWLdHr9SxatAgHBwfWrVsnp8VKkoV4enpSunRpADw8PAA4efIkT5480TCqtLm5uaktWKZ0lQGMHj0aX19fLl26xKxZsywQnWRuJidDjo6ObNmyhRUrVtCqVSvefPNN3nzzTZYvX86mTZvQ6WQdR0uwdDLk4eFBhQoVAAgNDWXatGk0a9bMIteyNRUrVlSnxfbp04eIiAiNI5KkzMnQVRYcHMz777/PoEGD1EWibZEpS3O8yNvbW51RNnbsWG7dumX22CTzSlfmoigKH374IcuXL2f79u2sWLGCjz76SCZCFmSNCtSGcUNHjhyx2DVs1ahRoyhWrBi3bt1SF1+UJMm8DIOojx07xurVq5k6daraw2CLDMnQvn37TP6S1LlzZ2rXrk1kZCRffvmlJcKTzMjk7KV9+/Zs2bJFTkW2Mksu1mpgGDd0+PBhoqOj2bp1KwsWLLDItWyNq6ur+lrnzZuXJRNCSbK0FwdR28OyFcWLF6dEiRLExcXxzz//mPRcnU7H3LlzURSFFStWsHfvXgtFKZmDycnQoUOHaNOmDX5+fnz11VeZdtkGW+Ph4YGTkxNgua4yQzJ07NgxQkJCaNmyJf369csyS1Y0adKErl27IoQgICAgU9dZkiQtVKpUCb1eT1hYGLdv3+bp06fs3LnTpr9cp2eKvUHVqlXp0aMHAIGBgbL0jA0zORm6ceMGmzdvpn79+sycOZNy5cpRp04dFi9erC7LIZmfNRZrLVWqFN7e3kRHRxMdHY2vry+xsbEcP37cItezRVOnTsXHx4czZ84wbdo0rcORpEzFzc2NcuXKAUnd8fnz56dJkyZcvHhR48jS9uLSHOlpzRo3bhw5cuTg9OnTzJ8/39zhSWZicjKk0+lo2bIlq1ev5s6dO8yZM4e4uDgCAgLImzcvXbt2tUScEpYfRK3T6ejatSuBgYF4enpSr149AJstjGYJPj4+zJgxA4BvvvmGK1euaByRJGUuhnFDJ0+epEqVKgBp1q6zBQ0bNsTV1ZXbt29z+vRpk5+fM2dOxo8fD8DXX3/N/fv3zR2iZAYZGvGcLVs2evfuzbFjx9i7dy85cuTgl19+MVds0kssnQwBzJo1i9mzZ1OiRAk1Gdq3b5/FrmeLOnXqRNOmTYmJiaFnz552MbZBkuxFvXr1qFu3Ln5+ftSuXRvApqvdu7i4qIVoTZ1VZhAQEECVKlV48uQJw4cPN2d4kplkePrXjh07+Oijj2jWrBk3b95Uf7kl87NGMvSi+vXrA3DgwAGb7tM3N0VRmD9/Pq6urvzzzz8sX75c65AkKdP45JNP2L9/Pz169FDHKdpyyxBkbNwQJNUzmzt3LgCLFy/m6NGjZotNMo90JUNXr17l66+/pmDBgrRo0YK9e/fSv39/Ll68mKW6VKzNGtPrAZ49e8b+/fspWrQo7u7uhIeHZ7mV3YsWLcqYMWMAGDRokGzaliQLMCRD586ds+n6XoZxQwcPHuTx48fpOkedOnX4+OOPEULQt2/fLPUF0x6YnAw1bNiQkiVLMmXKFGrVqsXmzZsJDQ1lwoQJlChRwhIxSv/PWi1D1atXp379+hw5ckRt6Tt48KBFr2mLBg4cSKVKlXj48CGDBw/WOhxJylQiIyNxdHSkcOHCCCFsurWkcOHClC1bloSEhAwtUzRp0iQ8PT05duwYS5YsMWOEUkaZnAw9ffqUmTNncvv2bdasWUPLli1lsUUrsWYylCdPHh4/fszEiRM5e/YsAQEBFr2mLXJ0dGTRokUoisLy5cuzxFptkmQNo0ePxsvLi8mTJ9vFuCFIPqssvfLkyaO2OH/11VfpbmWSzM/kLObff/8lMDCQHDlyWCIe6RWs1U22YMECbt++TceOHalWrRrlypXLsglvjRo11EUXe/bsabGCl5KUlRQoUAAhBMHBwXzyySfMnTuX999/X+uwXskwbmjr1q0Z6uIKDAykTJky3L9/n9GjR5srPCmDsuZfODtlrZYhV1dXFEWx6DXsybfffkuBAgW4du1alli8VpIsrUOHDty5c4fffvuNZs2a0adPH0qWLKl1WK9Ur149PDw8uHv3LidPnkz3eRwdHZkzZw6QVO0+PdP1JfMzKhkqWrQop06dAqBIkSIULVo0za1YsWIWDTgrs/ZsMgAhBBs2bKBTp0788ccfVruuLfH09GTevHlAUlFGw2dBkqT08fb2Jk+ePFqHYRInJyeaNm0KZKyrDJKq3bdv357ExEQCAwNl+Q4bYFQy1LBhQ7y8vNR/v2pr0KCBRQPOyqyZDA0cOJC8efOydu1a9u3bx4oVKzJ8A7Bnbdq0oUOHDiQkJBAQEEBCQoLWIUlSpnHlyhUWLVpk8xM1MjrF/kXTpk3D1dWVvXv3smrVqgyfT8ogIb1WeHi4AER4eLimcURERAhAACIqKsqi1woICBCAGD58uFi/fr0ARLly5Sx6TVt3+/Zt4e3tLQAxa9YsrcPJUmzlM2gKe4zZmlavXi1atmwp5syZI4YMGSIA0bNnT63DeqUbN24IQCiKIu7fv5/h840bN04AIl++fCIiIsIMEUovM/ZzaPKYoWXLlqU5gPfRo0csW7YsXUmZ9HrWWKzVoFKlSgAEBQVRt25dIKkWyKNHjyx6XVuWN29eJk+eDMDw4cMJDQ3VOCJJsl+hoaH89ddf7N27V603ZOszyvz8/KhYsSJCCLZt25bh8w0ePJhixYpx+/Ztxo0bZ4YIpfQyORnq1q0bV69eTfWx4OBgunXrluGgpNQpimK1rrLKlSsDcOrUKXLlykXp0qWBpGrUWdnnn39OvXr1iIqKok+fPrKvX5LSqUKFCgCcOXNGnV5/+vRpIiMjtQzrtQxdZeldmuNFLi4uzJw5E4AZM2bY9IK1mZ3JydCrbv4xMTHo9foMBSS9mrWm11esWBGAW7ducf/+/Sy5aGtqdDodCxcuxNHRkc2bN7Nu3TqtQ5Iku2RIhi5fvkyOHDkoUKAAiYmJHDt2TOPIXu3FKfbmGDvYunVr3nrrLeLi4ujXr5/8gqURo5Kh0NBQ9u7dy969e4Gk1YYNPxu2bdu2MX36dAoWLGjRgLM6a7UMeXp6Urx4cSCpdSirLtqamjJlyqiLLQYGBvLkyRNtA5IkO5Q3b15y5MhBQkICFy5csJvii7Vr18bb25uHDx+aLXGbOXMmTk5ObN++nY0bN5rlnJJpjEqGlixZQqNGjfD390dRFHr37o2/v3+yrWXLlqxfv57+/ftbOuYszZozygxdZUFBQdSvXx+dTkdiYqJcU4ek6rGlSpUiLCyMYcOGaR2OJNkdRVEoX748AGfPnrWbRVsdHBxo3rw5YJ5ZZQDFixdnyJAhQNJM3ujoaLOcVzKeUcnQ+++/z5o1a1i9ejVCCMaPH89vv/2WbPvjjz+4du0agYGBlo45S9MqGSpSpAiPHz/m8OHDWbYa9YucnZ1ZuHAhkFSxO6t3H0pSeqQ2bujIkSM231VkjqU5XjZ8+HD8/PwICQlRJ2pI1uNgzEFlypShTJkyQFIrUevWrdWxK5J1WWvMEPxvRtmpU6dQFEWtNSUladCgAQEBASxatIju3btz8uRJnJ2dtQ5LkuzGi8nQt99+y19//UXNmjVtvgJ+ixYtADhx4gRhYWFmKSDp7u7O9OnTef/995k4cSIff/wxRYoUyfB5JeOY/BW/a9euMhHSkBYtQxcuXCAmJkbdHx8fb/Fr24tJkybh6+vLhQsXmDRpktbhSJJdeTEZcnZ2pkWLFnax7mWePHmoVq0agFmm2Bu0b98ef39/YmJiGDRokNnOK71euvo7Hj16xNy5c+nTpw+ffvppsu2zzz4zd4zSC6yZDOXPn5+cOXOSkJDAuXPnCAsLo06dOuTNm1dWYP5/2bNnZ/bs2QCMHz+e//77T+OIJMl+lCtXDkiatWpvK7ibsxq1gaIozJkzB71ez4YNG8yaaEmvZnIyFBoaSpkyZfjqq6+YP38+f/75J8uXL+fnn39m48aN7Nq1yxJxSv/Pmt1kiqIkGzeUK1cuzp49y4MHDzhz5ozFr28vOnTowFtvvUVsbCzdu3eXA8wlyUje3t7qDOSzZ89y69YtvvrqK3r06KFxZK9nSIa2bdtm1tbycuXK0a9fPwD69etHbGys2c4tpc3kZGjYsGGUK1eOu3fvIoTgr7/+Iioqijlz5uDi4sKff/5piTil/2ftxVpfLL6o1+upU6cOIOsNvUhRFObNm4e7uzv79u3jp59+0jokSbIbL3aVJSQkMHHiRH766SeePXumcWSvVqNGDXLmzEl4eLjZZ8CNHj0aX19fLl26pBZllCzL5GTo0KFD9OrVCxcXFyCpCKOTkxN9+vThs88+44svvjB7kNL/vJgMWWPGRdeuXfnzzz8ZMWIEAPXr1wdkvaGXFSpUSC2n/8UXXxAWFqZxRJJkH15MhgoUKEC+fPmIj4/nxIkTGkf2anq93uxT7A28vb3VMYhjx47l1q1bZj2/lJLJydDdu3fJmzcvOp0OvV5PRESE+ljDhg1li4GFGZKhmJgYq3xzqlChAq1atcLX1xcgWSVqW5/+am2BgYFUr16dJ0+eMGDAAK3DkSS70Lp1a8aNG0enTp1QFMVu6g2BeZfmeFmXLl2oXbs2UVFRfPnll2Y/v5ScycmQr6+vulhn4cKFOX78uPpYSEgIDg5GzdaX0snd3V1drNUa44ZeVrNmTRwdHbl9+zYhISFWv74t0+v1LFy4EL1ez+rVq2WXsSQZoW7duowYMUL9omUvlagBmjdvjqIonDp1yuytNzqdjrlz56IoCitWrFBXgJAsw+Rk6I033uDkyZMAtGvXjrFjxzJu3DgmT57MsGHDaNy4sdmDlP7Hmou1GuzatYtRo0Zx6NAhXF1dqV69OiC7ylJTpUoVdUps7969bX7RSUmyNYZk6NChQzbf+uzj40OtWrUAy7QOVa1ale7duwPQt29fWdbEgkxOhoYMGaKWUB81ahT+/v6MHj1aHVg9a9YsswcpJWftZGj58uV8++23bN26FYA2bdrQtm1b8ubNa5Xr25vRo0dTpEgRQkNDGTVqlNbhSJLNu379OuvXr+fSpUtUrVoVBwcHwsLCCA0N1Tq017LEFPsXjR8/nhw5cnDmzBnmz59vkWtI6UiGqlWrxnvvvQckddn88ccfPH78mPDwcHbv3i3/QFqBNafXQ1JT8KeffkrNmjWBpHW5fv/9d5o2bWqV69sbd3d3fvjhBwBmzZqVrCtZkqSUhg0bRrt27Vi/fj2urq5UqVKFHDly2EVXvGFpjh07dlhkGnzOnDnVyRlff/019+7dM/s1pHQWXXyZl5cXnp6e5jiVRVy8eJHKlSurm6urKxs2bNA6rHSzdsvQBx98wOLFi3nrrbescr3MoHnz5nTq1InExEQCAgJk87Ydymz3DVtWvXp1qlatSrZs2YCkLqcHDx7QsGFDbQMzQtWqVcmdOzeRkZEWm0DUvXt3KleuzJMnTxg+fLhFrpHVKcKITllTB241aNAg3QFZWmRkJIULF+b69eu4u7sb9ZyIiAi8vb0JDw+3ifW5evfuzQ8//MCoUaP45ptvNIlBCEFwcDAODg5q0TQpuXv37lGmTBkePXrElClT1FWpJdNp/RnMDPcNyXI++eQTli5dypAhQ5gyZYpFrnHgwAHq1auHoigcPnxYbamXXs3Yz6FRLUONGjXC39//tZvhOFv2xx9/0KRJE6NvaLbI0E1mrZYhgNjYWE6dOsWdO3eApFo6xYoVU5eikFLKnTs306ZNA5LG1wUHB2sckZRemeG+Ya9sfRA1WH7cECTNuvv4448RQtC3b19Z6d7MjEqGdu3axc6dO1+7GY4z1d69e2nTpg358uVDUZRUm6K///57ihQpgouLC9WqVUv3TKbffvuNDz74IF3PtRWGbjJrTq3/6KOPqFy5MqtXrwb+V5la1pV6ta5du+Lv7090dDS9evWyixu7vZD3jcwnLi6O58+fA9CnTx8KFixoF/WGmjZtil6v5/z58xYd5zRp0iQ8PT05duwYS5Yssdh1siRhA7Zs2SJGjBgh1q1bJwCxfv36ZI+vWrVKODo6ikWLFonz58+L/v37C3d3d3H9+nX1mKpVq4py5cql2G7duqUeEx4eLnx8fER0dLRJ8YWHhwtAhIeHZ+h1mssvv/wiANGkSROrXXPMmDECEF27dhVCCBEcHCwA4eDgICIjI60Whz26dOmScHZ2FoD45ZdftA7HLqX2GZT3jcylc+fOwtHRUaxevVoIIcQ777wjADF16lSNIzNOvXr1BCC+//57i15n2rRpAhC5cuUSjx49sui1MgNjP4cZSob+++8/sX//frP+MUztplazZk3Rs2fPZPtKly4thg0bZtK5ly1bJjp16vTa42JiYkR4eLi63bhxw6Zualu3bhWAqFSpktWuuWHDhmTXTExMFH5+fgIQO3futFoc9mr8+PECED4+PuLBgwdah2N3XndDk/cN+/fZZ58JQIwcOVIIIcTEiRMFINq3b69xZMb57rvvBCBat25t0evExsaKMmXKCEAEBgZa9FqZgbHJULpmky1btgw/Pz/Kli1LgwYNuHjxIgDvv/8+ixYtSs8p0xQbG8uJEydo1qxZsv3NmjXj4MGDJp3L2KbuCRMm4O3trW4FChQw6TqWZu2p9fC/brHz588TGxuLoijJluaQXs1Qn+vBgwdyILUVyPuG/XlxjTLArpblgP+NG9q5cycxMTEWu46joyNz5swBYN68eZw+fdpi18pKTE6G1qxZwyeffELVqlWZO3dusjEQVatW5bfffjNrgA8ePCAhIUFdG8vA19fXpMUww8PDOXr0qLqw3qt89dVXhIeHq9uNGzdMjtuSrL1YK0DBggXJli0bcXFxXLhwAZCLtprCycmJhQsXoigKP//8c7rG1knGk/cN+2NIhs6ePQskTbfX6/XcunWLmzdvahmaUSpWrEi+fPl49uyZxZfOaNKkCe3btycxMZHAwEA5FtEMTE6GJkyYQLdu3fjjjz/UMuEGZcqU4fz582YL7kWKoiT7WQiRYt+reHt7c/fuXXVdr1dxdnbGy8sr2WZLrL1YKyS9/5UqVQIgKCgI+N+irYcOHZJ1dIxQu3ZtevfuDUCPHj2Ijo7WOKLMT9437IchGbp27RpRUVG4u7tTsWJFwD5ahxRFscqsMoNp06bh6urK3r17WbVqlcWvl9mZnAxduHCBDz/8MNXHcuTIYfauGx8fH/R6fYpvc/fu3UvxrS+reHGxVmtOrzd0lRmSoXLlyvHll1+ybNky+c3ESN999x358+fnypUralVZyfzkfcP+5MqVC19fX4QQnDt3Dki+Tpk9sGYyVLBgQbUA45AhQ3j69KnFr5mZmZwMubm5ER4enupjt27dInv27BkO6kVOTk5Uq1aNHTt2JNu/Y8cO6tSpY9Zr2YsXF2vVYtzQqVOngKRV2idNmkTbtm1xdHS0Whz2zMvLi7lz5wIwefJkdXyEZF7yvmGfDOteGj4X9evXp3r16nZT2LVJkyY4ODhw+fJlLl++bPHrDRkyhKJFi3L79m355SqDTE6G6tatm2KskMHPP/9Mo0aNTA4iMjKSoKAgtcUhODiYoKAgdZG+QYMG8eOPP/LTTz9x4cIFBg4cSGhoKD179jT5WpmFtZfkgOQtQ7IlKP3effdd2rZtS3x8PAEBASQkJGgdkl2S943M5+VxQx9++CHHjh1jwIABGkZlPC8vL3UspSVWsX+Zi4uLujj6jBkz1MlMUjqYOk3t2LFjwtnZWVSvXl1Mnz5d6HQ6MWLECNG6dWvh6uoqzpw5Y+opxa5duwSQYjPUtBFCiHnz5olChQoJJycnUbVqVbFnzx6Tr5NetlgvpHHjxgIQv/76q9Wu+fz5c+Ho6CgAtVZLbGys+Pvvv8WECRNEYmKi1WKxdzdv3hSenp4CEHPnztU6HJuX2mdQ3jcyn8WLF1u9hpq5TZkyRQCiRYsWVrtmq1atBCCaNWsm78MvsWidoZ07d4rSpUsLRVHUrWTJkmLXrl3pOZ3Ns8WbWocOHQQgZs+ebdXrVqxYUQBi48aNQgghoqOjhZOTkwDE5cuXrRqLvZs3b54AhKenp7hx44bW4dg0W/wMvo49xqy1o0ePCkDkzp072f6oqChx+/ZtjaIyzblz5wQgnJ2dRVRUlFWueenSJfU+/HK9razOonWG/P39uXDhApcvX2b//v38999/XLx4kUaNGsnuEyvRopsMUo4bcnFxoUaNGoCcYm+qnj17Urt2bZ4+fUpgYKDW4UiS5sqWLYuiKNy7d4979+4B8OOPP+Lt7c3AgQM1js44ZcqUoVChQjx//pxdu3ZZ5ZolSpRQ65cNHDhQzlRNh3QlQwbFihWjTp06lCxZEoAVK1ZQpkwZswQmvZqWyZBer082cNvQRy6LL5pGp9OxcOFCHBwc2LBhA+vXr9c6JEnSlLu7O0WLFgX+N26oaNGixMfH282MMmtPsTcYPnw4fn5+hISEMHnyZKtdN7MwOhkKDw9n6dKlTJ48mQ0bNiRbMff333+nfPnydO7cWV1kT7IsLVauBwgICCAqKoqZM2eq+2Ql6vQrX748Q4cOBaBv375pztSUpKzi5UrUNWrUQKfTERoayu3bt7UMzWgtW7YEkpIha/WWuLu7M23aNAAmTpxIcHCwVa6baRjT53b58mWRJ08eodPphKIoQqfTCX9/fxEeHi5atWoldDqdyJEjh5gyZYqIiYkxQy+fbbHFvn8tFmtNy6NHj4SiKAIQYWFhWodjd6Kjo0WJEiUEIHr37q11ODbJFj+Dr2OPMduCkydPihMnTohnz56p+wxjFdetW6dhZMaLjIxUx/CcP3/eatdNTEwU/v7+AhBt27a12nVtmVnHDH399ddEREQwZswY/vzzT2bPns1///1HnTp1+Ouvv/jss8+4evUqQ4YMwdnZ2TJZm5SMVt1kqcmePbtaH+TAgQMaR2N/XFxcWLBgAQA//PCDyWtnSVJmUrlyZapWrYqrq6u6z96KL7q7u6tlZqwxxd5AURTmzJmDXq9n/fr1bNu2zWrXtndGJUN79uxh5MiRfP3117Rs2ZI+ffqwZMkSzp8/T8+ePVm4cCHZsmWzcKjSi7RMhubNm0fNmjVZuHChuu/FpTkk0/n7+9OtWzeEEHTv3p3Y2FitQ5Ikm2FYtPXw4cMaR2I8LcYNQdLKAIYJGf369ZP3EiMZlQzdv3+funXrJttn+ONnzGrOkvm9uHK9sPIMvrCwMI4dO8aRI0fUfQMGDODkyZNMnDjRqrFkJlOmTCFXrlycO3eOKVOmaB2OJGlm0aJF9OjRQ13s1tAydPz4cbv5425Ihvbu3Wv1pTLGjBlD7ty5uXTpklqUUXo1o5KhhIQEXFxcku0z/Ozp6Wn+qKTX0mKxVoMPPviA3377ja+//lrdV7JkSXWmmZQ+OXPmVAemf/vtt1y6dEnbgCRJI99//z0LFy7k33//BZKmjnft2pUJEyYQFxencXTGKVGiBMWKFSMuLo5//vnHqtf29vZWZ5SNHTuWW7duWfX69sjB2AMvXryIg8P/DjcsIfDff/+lOLZq1apmCE16FXd3d5ydnXn+/DkPHjzA3d3datcuX768OkZIMq+OHTuybNkytm3bRo8ePdi5c6dJq6xLUmbw8ccf06pVK4oVKwYklaH4+eeftQ0qHVq1asWcOXPYsmUL7777rlWv3aVLF+bPn8/hw4f58ssv+fXXX616fXujCCP6WHQ6Xao3ZCFEsv2GnzPbWksRERF4e3sTHh6Ol5eX1uGo8ufPz+3btzl+/DjVqlXTOhx2797N0qVLqVGjBr1799Y6HLsVHBxMuXLliI6O5qeffqJbt25ah6Q5W/0Mvoo9xiyZ19atW2nZsiV+fn6EhoZa/YvNiRMnqFGjBkII9uzZQ4MGDax6fVtg7OfQqJahJUuWmC0wyXx8fHy4ffu2VVeuNzhy5Ai7d++mbt266vix//77j59//pkbN27IZCgDihQpwtixY/niiy8YPHgwb731Frlz59Y6LEnSlBCCS5cuERQUZDdjVRs2bIirqys3b97k7Nmzag0la6lWrRrdu3dnwYIFBAYGcuLEiWQ9PNILLDzFP1Ow1XohWizWatC3b18BiCFDhqj7zp49KwDh5uYmYmNjrR5TZhIXFycqV64sAPHRRx9pHY7mbPUz+Cr2GLOtSExMFLdv3xbbtm0Tz58/F0II8eTJE7usZ/bWW28JQEycOFGT6z948EBkz55dAGLOnDmaxKAli65NJtkGLafXV6pUCYCgoCB1X5kyZciePTvPnj1Ltl8ynYODA4sWLUKn07FixQq2bt2qdUiSZFVlypShefPm6rhUb29vypYtC8gp9qbImTMn48ePB5JqBt6/f1+TOGydTIbs2IvT663NsGBrUFCQOrVfp9OpJRjkoq0ZV716dfr37w9Ar169iIqK0jgiSbIORVHUSRqGZTnA/oovwv+W5jhw4ABPnjzRJIbu3btTuXJlnjx5wvDhwzWJwdbJZMiOadkyVK5cOfR6PQ8ePODOnTvqfrloq3mNHTuWggULEhISwpgxY7QOR5Ks5uU1ysA+iy8WKVKE0qVLk5CQwI4dOzSJQa/XM3fuXAAWL17M0aNHNYnDlslkyI5pmQy5urpSunRpIHlX2YuLtgorF4PMjDw8PPjhhx8AmD59ulp3RZIyO0MyZFi9Hv7XMnTs2DHi4+M1iSs9DF1l1lya42V169alS5cuCCHo27dvssXWJZkM2TWtVq43SG3cULVq1XB1dSV37tyadN9lRq1ateKDDz4gMTGR7t2729UfAUlKr9RahkqXLo23tzfPnj1Ltt/WvZgMaZmETJo0CU9PT44dO2aXdZssSSZDdszQMqRV0vHiuCEDZ2dn7t27x9mzZ9X4pIybOXMm2bJl48SJE8yZM0frcCTJ4gxjhkJDQwkPDweSxiXWqlULsK9xQ/Xq1cPDw4OwsDBNJ5fkzZuX0aNHAzBs2DAeP36sWSy2RiZDdkzrlesNydCpU6eS7ffw8NAgmswtT5486nplI0eOJCQkRNuAJMnCsmfPTv78+YHkXWVffvklGzdutJtaQ5D0JbFJkyaAdrPKDPr160eZMmW4f/++mhhJMhmyay8mQ1qMzzF0k12+fJnIyMgUj8vuHPP69NNPadCgAc+ePaN3795yTJaU6aU2bqhJkya8/fbb6jABe6H1FHsDR0dHZs+eDcC8efM4ffq0pvHYCpkM2THDzeD58+dWX6wVIHfu3OTNmxchRLL++7i4OPz9/fHy8pI1LcxIp9OxYMECnJyc+Ouvv/jtt9+0DkmSLCq1cUP2yjDF/vDhw5qPp3zzzTd57733SExMJDAwUH6xQiZDds2wWCto31X2Yj+4o6Mj9+7dIzo6moMHD2oSV2ZVunRpRowYASQ1d8s+fykzSysZ2rt3L6NHj+b48eNahJUuBQoUoEKFCggh2L59u9bhMG3aNFxdXdm7dy+rVq3SOhzNyWTIjimKYrPjhgzFFw8cOGDtkDK9oUOHUqZMGe7du8eXX36pdTiSZDGGQdRnz55N1nqxYMECxo4dq3mXk6lspasMoFChQmoBxiFDhqQ61CErkcmQndN6en2dOnVo2rRpigUIZTJkOc7OzixcuBCAH3/8kT179mgckSRZRpkyZdDr9Tx69ChZcVfDjLIjR45oFVq6GJKhrVu3kpCQoHE0SUlQ0aJFuX37NuPGjdM6HE3JZMjOaT29vnXr1mzfvp0+ffok229Iho4fP05MTIwWoWVq9erVo0ePHgD06NFDvsdSpuTi4kKJEiXw8fHh1q1b6v4XkyF7Gu9Su3ZtvLy8ePDggU108bm4uDBz5kwgqajrxYsXtQ1IQzIZsnNad5OlpVixYuTOnZvY2FhOnDihdTiZ0sSJE8mTJw8XL15kwoQJWocjSRZx9OhR7t+/T40aNdR9lStXxsnJiYcPH3L16lUNozONo6MjzZo1A2yjqwySvtC2atWKuLg4+vfvb1fJpTnJZMjOad1NZvDw4UPu3bun/qwoiuwqs7Bs2bKpBRgnTJjA+fPnNY5IkszP09MzxT5nZ2eqVKkC2G9XmZZLc7xIURRmzpyJk5MT27ZtY+PGjVqHpAmZDNk5rbvJIKmSqY+PD9OnT0+2v0WLFrRu3ZrixYtrFFnm995779GmTRvi4uLo3r27XG9IyjLsddxQixYtgKT11e7evatxNElKlCjB4MGDARg4cCDR0dEaR2R9Mhmyc7bQTVa4cGGAZAMcAbp3786mTZto166dBlFlDYqiMG/ePDw8PDhw4ACLFi3SOiRJMqsnT57QunVrSpUqlWzQsSEZsreigXnz5qVq1aoAbNu2TeNo/mfEiBH4+fkREhKiVrvPSmQyZOdsIRnq1KkT4eHhLF26VLMYsrICBQowfvx4IGmpgtu3b2sckSSZj5eXF7t27eLSpUtcuXJF3d+qVSvOnj3LP//8o2F06WMowGgr44YgqW7dtGnTgKRu96y25I9MhuycLYwZ8vT0xMvLK83Hb9y4wbVr16wYUdbTp08fatSoQUREBP3799c6HEkyG51Ox88//8zevXspWLCguj9btmyUK1cOvV6vYXTpYxg3tG3bNptatqhDhw74+/sTExPDoEGDtA7HqmQyZOdsYczQq4wfP56CBQuqLReSZej1ehYtWoRer2ft2rX88ccfWockSWbToUMH6tevj6urq9ahmEWtWrXIkSMHT5484fDhw1qHo1IUhdmzZ6PX61m/fr1NVMq2FpkM2TmtF2s1WLZsGf7+/sybNy/ZfsNirnJGmeVVqlSJIUOGAEktRU+fPtU4IkmyrGPHjtG5c2e7q8Su1+tp3rw5YDuzygzKly9PYGAgkLTkT2xsrMYRWYdMhuzci4u1RkVFaRbHrVu32L17d4qkp06dOgBcvHhR8+n/WcGoUaMoWrQoN2/eZOTIkVqHI0lmERERwZIlS1K0MD9+/Jhff/2V33//XaPI0s+WluZ42ZgxY8idOzcXL15k1qxZWodjFTIZsnMvLtaqZVdZagu2AuTIkYMyZcoAyEVbrcDNzY358+cDMGfOHI4ePapxRJKUcc+ePePTTz/l66+/Tjbtu2bNmgBcvXqV+/fvaxVeujRv3hxFUQgKCkpWXdsWeHt7M2nSJADGjh2bJSZlyGTIztnCYq3wv2To4sWLPHv2LNljhtYhmQxZR9OmTenSpQtCCAICAoiLi9M6JEnKEF9fX3LmzIkQIllx0WzZslG6dGkAu0v8c+XKpVbV3rp1q8bRpPTxxx/zxhtvEBkZaXfdkOkhk6FMwBaSoTx58pA7d24SExM5e/ZsssdkJWrrmzZtGjlz5uT06dMpimFKkr1RFEVdDPrMmTPJHrPX4otg211lOp2OuXPnoigKv/76K3v37tU6JIuSyVAmYAvT6xVFUVuHTp06lewxQzJ07Ngxnj9/bu3QsqRcuXKpSdCYMWPsav0mSUqNIRl6+ctWZkiGduzYYZMtuNWqVSMgIACAwMBAmyoDYG4yGcoEbGV6vWHm2MvjhkqUKMEXX3whizJaWZcuXWjSpAkxMTH07Nkzyy7AKGUOr2sZOnr0qN0tR1OtWjVy5crF06dPbbblfPz48WTPnp3Tp0+zYMECrcOxGJkMZQK20E0GaQ+iVhSFyZMn88EHH6iDvSXLUxSF+fPn4+Liwt9//80vv/yidUiSlG5pJUMVKlTAw8ODggULan4PNJVOp7PJatQv8vHxUWfxjRw50u4GqhtLJkOZgC10kwHJusns7RtaZlW8eHFGjx4NJC3AqPXviCSlV7ly5YCkNRBfbAV3dHTkwYMHnDp1ity5c2sVXrrZejIESetMVq5cmSdPnjB8+HCtw7EImQxlArbSTVayZElcXFyIiopKMUYlPj6evXv3Mm3aNNldY2WDBw+mQoUKPHz4UF2ZWpLsjaenp7oo9Mvjhuy5xblZs2bodDrOnTvH9evXtQ4nVXq9nrlz5wKwePFijh07pnFE5ieToUzAVrrJHBwcKF++PJCyqywhIYFmzZoxZMiQZIstSpbn6OjIokWLUBSFZcuW8ffff2sdkiSlS1pdZQb2OMA3R44c1K5dG7C9atQvqlu3rlqyo2/fvpmu9V8mQ5mArSRDQJozypydnalevTogp9hroVatWvTt2xeAHj16pKgFJUn2IK1k6OnTp9SpU4fs2bPb5e+2YVaZLSdDAJMmTcLT05OjR4/y888/ax2OWclkKBOwlTFDkPYgapD1hrQ2fvx4/Pz8uHbtGt9++63W4UiSyQwtzy93k3l4eBASEkJkZCT//vuvFqFliCEZ+vvvv226/EjevHnVMYjDhg3jyZMn2gZkRjIZygReHDOk9XicN998k+nTp6c6yE4mQ9ry9PRUF9KdMmUKp0+f1jgiSTLNi7WGXrzXKYpi1/WGKlWqRN68eXn27JnNFzfs168fZcqU4f79+2pilBnIZCgTMCRDWi/WClCqVCkGDhyoLsHxIsO+Cxcu8OjRI2uHJgFvv/027733HgkJCQQEBJCQkKB1SJJktFKlSlG9enXeeeedFN1h9pwMKYpiF7PKIGkM4uzZswGYN29emuO37I1MhjIBNzc3dTaFLXSVpcXHx4dSpUoBcp0yLc2ZMwdvb2+OHj3K999/r3U4kmQ0R0dHjh07xrJly3B3d0/2mD0nQ2DbS3O87M0331S/VAUGBmreI2EOMhnKBF5crFXr6fUA169fZ8WKFezcuTPFY4auskOHDlk7LOn/5c2bV12Revjw4dy4cUPjiCQp46pXr46iKISGhnLnzh2twzHZm2++iYODA5cuXbKL5XOmTZuGq6sre/bsYfXq1VqHk2EyGcokbGlG2caNG+nUqRPfffddiscGDhzIkSNHGDNmjPUDk1QBAQHUrVuXyMhI+vTpkym+2UlZR1xcHOfOnUu2z9PTUx1gbY+tQ97e3tSrVw+w/VllAIUKFeKrr74CkmqZRUZGahxRxshkKJOwpWSoRYsWVKlSRf1gv6h8+fLUrFkTR0dHDSKTDHQ6HQsXLsTR0ZFNmzbx+++/ax2SJBnl1q1b5MiRg+rVqxMTE5Pssbfeeou2bduSPXt2jaLLGHvqKgP44osvKFq0KLdv32bcuHFah5MhMhnKJGxpen3JkiX5999/ZeuPjStbtqz6za5v376ZapqslHnly5cPT09P3N3dUxRwnTBhAr///jsNGzbUKLqMMQyi3rVrl13US3JxcWHmzJkATJ8+nYsXL2obUAbIZCiTsKUxQ6+zc+dOunfvzvLly7UOJcv76quvKFWqFGFhYWpiJEm2TFEUjhw5wr1799RuscyiXLlyFChQgJiYGHbv3q11OEZp3bo1LVu2JC4ujv79+9ttl7tMhjIJW+omM4iMjEx16Ydjx46xaNEi1q9fr0FU0otcXFxYsGABAPPnz2f//v0aRyRJr1egQAF0utT/fAkhCA4O5vHjx1aOKuMURbG7rjJFUZg1axZOTk5s27aNP/74Q+uQ0iXLJENTp06lXLlylC9fnl9++UXrcMzOlrrJAGJiYsiTJw9NmzYlODg42WMvFl+0128RmUnDhg35/PPPgaTVqW25Aq61Zfb7hr0TQqRYI+u9996jaNGirFu3TqOoMubFpTns5f5YokQJdRHogQMHEh0drXFEpssSydCZM2dYsWIFJ06c4Pjx4/zwww+ZbnyErXWTubi4UK1aNQC2bduW7LHq1avj5OTEvXv37GIKaVYwefJkfH19uXDhApMnT9Y6HJuQFe4b9mzkyJEUKFCAzZs3J9tvqGVmjzPKABo3boyTkxPXrl3j0qVLWodjtBH/196dhjV1rW8DvzdhHkQGRRAFbBWpqMigAkVRKSrUtseqVYpIrRN/q+JMtc4DB1GPtogVZ61T63RO8dRKQdEeqYCISkFt6wDiiIogKkhY7wfe7BLCJAZ2dvL8risfWNkmdyJZPNlrrb3mz4etrS1u3LiB6OhooeO8No0ohnJycuDl5QV9fX3o6+vDxcUFx48fFzqWUqniMNnAgQMBQOG9rloo0dYcqsHMzAzr168HACxfvlzUEyGVRRP6DTF79OgR8vPzkZiYKNcu9osvGhsb8xPAxTJUBgBGRkZYvXo1gMqJ7Ddv3hQ20GtSiWLo9OnTGDJkCGxsbMBxHI4ePapwTGxsLBwcHPg/pGfOnGnw4zs7O+PkyZMoLCxEYWEhkpKSkJ+fr8RXIDxVGyYDKpfYA5UTpsvKyuTuo33KVM+IESMQEBCAsrIyTJgwQWH4QdVQv6HZBgwYAAC1FkNZWVkoLi5u9lzKIJatOaobMWIEfH198fLlS37YTCxUohgqKSlB9+7dERMTU+P9Bw4cQHh4OObPn48LFy7Ax8cHgwcPRm5uLn+Mm5sbnJ2dFW537tzBO++8g6lTp6J///74xz/+AQ8PD2hrazfXy2sWVc8Mqco4s4uLC1q1aoXi4mKFK05TMaR6OI5DbGwsDA0Ncfr0aWzfvl3oSHWifkOz9evXDwDw+++/4969e3y7tbU12rVrB8YY0tPThYr3RmTzhpKTk0V1MUOO4/DNN99AIpHg8OHDOHHihNCRGo6pGADsyJEjcm09e/ZkkyZNkmvr3Lkzi4iIaNRzfP755yw+Pr7W+1++fMmePn3K3/Ly8hgA9vTp00Y9X3N49uwZA8AAsOLiYqHj8D799FMGgH355Zdy7ffv32cAmKOjI3v58qVA6UhN1q5dywCwli1bsnv37gkdhzHG2NOnT+v8DFK/oZlcXFwYALZ371659mHDhjEALDIyUqBkb6aiooJ16NCBAWBHjx4VOs5rCw8P5/v30tJSQbPU13fIqMSZobqUlZXh/Pnz8Pf3l2v39/d/rc0+Hzx4AAC4evUqUlNT+fksNYmMjISpqSl/a9euXePCNyNDQ0Po6+sDUM2hsupzLVq3bo2CggJcuXKF32SWqIYpU6bAzc0NhYWFCA8PFzpOo1C/oRnqGyoT67yhqkvsxbA1R3WLFy9G69atcfXqVX6He1Wn8sVQQUEBpFIprKys5NqtrKzkTo3W56OPPsI777yD4OBgbN++vc7T3V9++SWePn3K38SwkSXHcSo5b0j2x+jChQu4f/++3H2yvES1aGtrY/PmzZBIJNi/f7/o5i0A1G9oitqKoffeew+zZs3ChAkThIilFFWvN8RUZOpDQ5mamvKbQS9ZsgR37twROFH9VL4YkuE4Tu5nxphCW13Onj2L7OxspKWl8SuZaqOnp4cWLVrI3cRA1ZbXA5VngFxdXQGg1vFjVZ+oq4l69OiB6dOnAwDCwsJENW+hKuo31JuPjw+0tbVx8+ZNXL9+nW/v3r07oqOj+YnIYuTr6wt9fX3k5eUpbEorBiEhIejduzeePXuGOXPmCB2nXipfDFlaWkIikSh8m3vw4IHCtz5Np4rL64G/l9hXv97Q48eP4efnB2tra7x69UqIaKQOixcvhr29PXJzc7Fw4UKh47wW6jc0g7GxMXr37g1A8eyQ2BkYGPCTxMV4dlZLSwsxMTHgOA579ux5rZWcQlD5YkhXVxdubm5ISEiQa09ISICXl5dAqVSTKg6TAfLFUNWzQC1btkRGRgYePHiACxcuCBWP1MLIyAgbN24EAKxfv15UK3Oo39Ac/fv3B6BYDBUXFyMxMVHURZLYtuaozs3NDePHjwdQuRl0eXm5wIlqpxLF0LNnz5CZmYnMzEwAwI0bN5CZmckvgZ0xYwa2bNmCbdu2IScnB9OnT0dubi4mTZokYGrVo6pnhjw9PWFiYoKCggK5okdLS4v/w0RL7FXToEGDEBQUhIqKCowfP16lOjPqNwjw97yhpKQkubk1hw4dgp+fHxYvXixQsjcnK4b+97//4enTpwKnaZwVK1bAzMwMly5d4vdBVEnNsLKtXidPnuSXhVe9jRkzhj9mw4YNzM7Ojunq6jJXV1eWnJzcbPkaujRPaAsXLmQAWFhYmNBRFIwaNYoNGjSInTt3Tq595cqVDAD7+OOPBUpG6nP//n1mZmbGALDo6GhBMtT0GaR+gzDGWGlpKTM0NGQA2KVLl/j2nJwcBoAZGBiwsrIyARO+GUdHRwaAHTx4UOgojRYbG8tfruPBgwfN+twN/RyqRDGk6sTSqX399dcMABs+fLjQURRUVFTU2J6cnMwAsDZt2tR6DBHetm3b+D8s169fb/bnF8tnsCoxZhargQMHMgBs8+bNfJtUKmWmpqYMADt//ryA6d7M9OnTGQA2duxYoaM0Wnl5OX9NqPHjxzfrc6vNdYZIw6nqnCFAcVWPjIeHB3R0dHDv3j2F3e2J6ggNDYWvry9evHiBsLAw0S31JeptzZo1yM3Nxbhx4/g2LS0t9OzZE4B4rzcEyG/NIdbPnUQi4a8Uv2XLFqSlpQmcSBEVQ2pEFZfWV3f79m25DfwMDAz4pfc0b0h1cRyHTZs2QU9PDz///DP27dsndCRCeF26dKnxIpdiv/giAPTp0weGhoa4d+8ePz9OjLy9vTF69GgwxvDFF1+o3CVVqBhSI6o6gVpm+fLlaNeuHf75z3/KtQ8aNAgDBw6kizCquE6dOmHBggUAgPDwcJUuugkB1KMY0tPTg5+fHwDxriqTiYqKgomJCVJTU7Fz506h48ihYkiNVB0mU8XTqa6urtDS0sLjx4/l2hcvXozjx4/zKyeI6po9eza6dOmChw8fYvbs2ULHIYQXHx+PgIAArF27lm+TFUNXrlxBYWGhQMnenJi35qjK2toaixYtAgDMnTtXpf5PqBhSI7IzQ2VlZSp5xeABAwbg4cOH+P7774WOQhpJV1cXmzdvBsdx2L59O5KSkoSORAgAIC8vDz/99BN+/PFHvq1Vq1bYuXMnzp8/DxMTEwHTvRnZvKGUlBSFL5NiM3XqVDg5OeHhw4d8YaQKqBhSI1U3a1XFIQw9PT2Ym5vXev+9e/dea98oIgxPT0+EhYUBACZOnIgXL14InIiQyrMnq1evxvr16+XaQ0JC4OrqColEIlCyN9e+fXs4OzujoqKi1m2NxEJHR4ffvHXDhg24fPmywIkqUTGkRjiOU/l5QzIvX76U+3nmzJmwtrbGhg0bBEpEXsfKlSthY2ODP//8EytWrBA6DiGws7PDzJkz0a1bN6GjNImqq8rEzs/PDx9//DGkUimmTJmiEtM6qBhSM6q8vB6o3I/Mx8cHVlZWcgVR586dAdCKMrEwNTXll8pGRUUhKytL4ESE1Oz58+eIi4tTmT+6jVV13pCqrcRqjDVr1sDAwADJyck4cOCA0HGoGFI3qr683szMDNevX0dRUZHcxn2ybTnOnTtHm7aKxD/+8Q989NFHKC8vx/jx49WigybiVlRUhJ07d8ptLCyRSDBlyhTExMSI+lpm3t7e/LZGYtonsDZ2dnaYN28eAGDWrFmCz3OlYkjNqPowGcdxNe5i7+TkhJYtW+L58+e4ePGiUPHIa/rmm29gYmKC3377Dd9++63QcYiGKy4uRmhoKJYvX44nT54AqJyr6OLiAkDcS+x1dHTg7+8PQPyrymRmzZqFDh06ID8/X/DhdiqG1IyqD5MBf+9if/z4cb6NNm0VJ1tbW0RGRgIAIiIikJ+fL3Aiosnatm0LR0dHMMaQnJzMt6vD9YYA8e9iX52+vj7WrVsHoHLY7Nq1a4JloWJIzaj6MBlQOXlOS0sLv//+O27fvs23e3t7A6BiSGwmTZqE3r17o7i4GFOmTBE6DtFwsl3sExMT+TZ1KYYGDRoEAEhLS8ODBw8ETqMc77//PgICAvDq1StMmzZNsHldVAypGVUfJgMqz155eHgAkB8qq1oMiXmio6aRSCSIi4uDtrY2jhw5giNHjggdiWiwmoqh3r17AwAyMjJQWloqSC5lsLGxgYuLCxhjcn2nmHEch3Xr1kFXVxfHjx+Xu05Uc6JiSM2IoRgCUOO8IQ8PD4SFhSEqKoom44pM165dMWfOHADAF198gaKiIoETEU3l6+sLjuOQk5ODO3fuAAA6dOgAS0tLlJWViX5OoroNlQFAx44dMXPmTACVW/0Ice0yKobUjBjmDAF/n+795ZdfUF5eDqDyopGxsbEIDg4W9QXSNNVXX32Ft99+G3fu3OFXiRDS3MzNzfnNn2VXSOc4jt/BPicnR7BsyiArhn7++WdIpVKB0yjP/PnzYWtrixs3biA6OrrZn5+KITUjhjlDQOVZoJYtW+LJkydIS0sTOg5RAgMDA2zatAkAEBsbi5SUFIETEU3Vv39/AJDbLiYmJgaPHz/GmDFjhIqlFL169YKZmRmePHki+jlQVRkZGWHNmjUAgMjISNy8ebNZn5+KITVTdZhMlefdaGtr47333gMAtRn7JpV/hEJDQ8EYw4QJE1BWViZ0JKKBqs4bkvWDDg4OMDMzEzKWUmhra/PTDNRpqAwAhg8fjn79+uHly5f8sFlzoWJIzciGyVR1s9aqalpiT8Rv9erVsLS0RFZWFlavXi10HKKB3n33Xejo6CA3Nxd//fWX0HGUTp225qiK4zh8/fXXkEgkOHz4cLPuw0bFkJpR9c1aq5IVQ2lpaaLfiZn8zcLCgr92yNKlS/HHH38IG4hoHCMjI3h6egKQX1WmLmRzLi9cuMBPElcXzs7O/CU6pk6d2mxnl6kYUjNi2qzV1tYWXbp0gaGhIbKzs4WOQ5QoKCgI/v7+KC0txcSJE1V6yJaop5qW2KuL1q1b85cnUccz64sXL0br1q1x9epVfof7pkbFkBoSSzEEAD/++CMePXqEd999V+goRIk4jsPGjRthYGCAkydPYufOnUJHIhpGVgydPHlSLS/VUXXjVnVjamqKqKgoAMCSJUua5ewXFUNqSCzL64HKSY26urpCxyBNoEOHDliyZAkAYObMmWpzxVwiDj179sTYsWOxZs0atVqCLiMrhk6cOKGWm1uHhISgd+/eePbsGX8Ns6ZExZAaEsvy+urUscPSdNOnT4eLiwseP36MGTNmCB2HaBAdHR1s3boVISEh0NHRETqO0rm7u8PS0hJFRUU4e/as0HGUTktLCzExMeA4Dnv27MGZM2ea9vma9NGJIMQ0TAYA3377LTp27Ih//etfQkchSqatrY24uDhoaWlhz549dBkFQpRES0uLn0itbqvKZNzc3DB+/HgAlVe2l12gtylQMaSGxDRMBgClpaX4888/kZCQIHQU0gQ8PDwwdepUAJWbupaUlAiciGgKxhguXLiAtWvXquVQkjpuzVHdihUrYGZmhkuXLvEXdW0KVAypIbENk3388cc4evQofvjhB6GjkCaybNkytG/fHjdv3uTnERHS1Bhj8Pf3x8yZM5Gamip0HKXz9/eHlpYWsrKykJeXJ3ScJmFpaYkVK1YAqNzy5+HDh03yPFQMqSGxDZPZ2triww8/RIsWLYSOQpqIsbExYmNjAQBr167FhQsXBE5ENIGWlhY++OADBAQEqOV+hxYWFujduzcA9VxVJjNhwgS4uLigsLCwyfY9pGJIDYltmIxohsDAQIwYMQJSqRTjx4+nCfOkWWzduhXHjh3jiwZ1owlDZRKJBDExMQAq/z+bYj9LKobUkNjODAGVQ3oLFizA8OHDhY5CmtD69ethamqK8+fP45tvvhE6DiGiJ9ua45dffkFpaanAaZqOt7c3Ro8eDcYYvvjiC6VfO4qKITVUdc6QWK78q62tjcjISBw8eBA3btwQOg5pIm3atEF0dDSAyvH/W7duCZyIaIq8vDzk5+cLHUPpXFxc0KZNG5SUlDT58nOhRUVFwcTEBKmpqdixY4dSH5uKITUkK4bEsFmrjKmpKb+XEC2/Vm+ff/45fHx8UFJSgsmTJ4umYCfiNWfOHLRv377ZtnZoTlpaWmq7cWt11tbWWLRoEQAgIiIChYWFSntsKobUUNXNWsU0VCa7ZgYVQ+pNS0sLmzZtgq6uLo4dO0arCEmT69atGwD13KcMUO+tOaqbOnUqnJyc8PDhQ74wUgYqhtSU2JbXA3/vYp+YmKiW1wQhf3NycuJXhTTFZEhCqurfvz8AICMjA48fPxY4jfK99957kEgkuHLlCq5fvy50nCalo6PDn+HLyMhQ2oUYqRhSU2KcRO3q6gpLS0sUFxcjJSVF6DikiUVERCA5OZmfQ0RIU7GxsYGTkxMYYzh16pTQcZTO1NQU3t7eADTj7JCfnx8SExORnJwMbW1tpTwmFUNqSozL67W0tODv7w+Ahso0gZ6eHvr06SN0DKIhZLvYq/tQmbrPG5Lp378/tLSUV8JQMaSmzM3NAVTuaHzq1CnRXNPlgw8+wKBBg/gxfkIIUYbBgwfD398fHh4eQkdpErJiKCEhATt37hRVv68KOEZLOepVVFQEU1NTPH36VBRXST58+DBCQkLk9oCytbXF+vXrMXToUAGTEdI4YvsMAuLMTMTr0KFD+OSTT+QKIOr3G/45pDNDaubw4cMYNmyYwmaY+fn5GDZsGA4fPixQMkIIIU3h8OHDGD58uMKZIOr3G46KITUilUoxbdq0Gq/bImsLDw+nU6eEEKImqN9XDiqG1MiZM2dw+/btWu9njCEvL0/tr1JKCCGagvp95aBiSI3cvXtXqccRQghRbdTvKwcVQ2rE2tpaqccRQghRbdTvKwcVQ2rEx8cHtra24Diuxvs5jkO7du3g4+PTzMkIIYQ0Ber3lYOKITUikUiwfv16AFD4YMh+XrduHSQSSbNnI4QQonzU7ysHFUNqZujQoTh48CDatm0r125ra4uDBw9q9PUmCCFEHVG//+booosNIMaLp0mlUpw5cwZ3796FtbU1fHx86JsBES0xfgbFmJmIG/X7ihr6OVTODmdE5UgkEvj6+godgxBCSDOhfr/xaJiMEEIIIRqNiiFCCCGEaDQqhgghhBCi0agYIoQQQohGo2KIEEIIIRqNiiFCCCGEaDQqhgghhBCi0agYIoQQQohGo2KIEEIIIRqNiiFCCCGEaDTajqMBZNu3FRUVCZyEEM0k++yJaStF6jcIEV5D+w4qhhrg0aNHAIB27doJnIQQzVZcXAxTU1OhYzQI9RuEqI76+g4qhhrA3NwcAJCbmyuajhiorIjbtWuHvLw8Ue2aTbmblxhyM8ZQXFwMGxsboaM0mFj7jdclht+fN6UJrxFQz9fZ0L6DiqEG0NKqnFplamoqyl+QFi1aUO5mRLmbhtgKCrH3G69L1X9/lEETXiOgfq+zIX0HTaAmhBBCiEajYogQQgghGo2KoQbQ09PDokWLoKenJ3SU10K5mxflJlVpyvuqCa9TE14joDmvsyYcE9NaVUIIIYQQJaMzQ4QQQgjRaFQMEUIIIUSjUTFECCGEEI1GxRAhhBBCNBoVQ/WIjY2Fg4MD9PX14ebmhjNnzggdqU4bN25Et27d+ItmeXp64qeffhI6VoPk5+cjODgYFhYWMDQ0hIuLC86fPy90rHoVFxcjPDwcdnZ2MDAwgJeXF9LS0oSOJef06dMYMmQIbGxswHEcjh49yt/36tUrzJ07F127doWRkRFsbGwQEhKCO3fuCBdYDYit73gTkZGR4DgO4eHhQkdRqvLycnz11VdwcHCAgYEBOnTogKVLl6KiokLoaI1GfUHNqBiqw4EDBxAeHo758+fjwoUL8PHxweDBg5Gbmyt0tFrZ2trin//8J9LT05Geno7+/fvjww8/xO+//y50tDo9efIE3t7e0NHRwU8//YTs7GysWbMGLVu2FDpavcaNG4eEhATs3r0bly9fhr+/P/z8/JCfny90NF5JSQm6d++OmJgYhfueP3+OjIwMLFiwABkZGTh8+DCuXbuGDz74QICk6kGMfUdjpaWlIS4uDt26dRM6itJFRUXh22+/RUxMDHJycrBq1SpER0fjm2++ETpao1FfUAtGatWzZ082adIkubbOnTuziIgIgRI1jpmZGduyZYvQMeo0d+5c9u677wod47U9f/6cSSQSFh8fL9fevXt3Nn/+fIFS1Q0AO3LkSJ3HpKamMgDs1q1bzRNKzahL31Gf4uJi1rFjR5aQkMD69u3Lpk2bJnQkpQoMDGRjx46Vaxs6dCgLDg4WKJFyUV/wNzozVIuysjKcP38e/v7+cu3+/v44e/asQKlej1Qqxf79+1FSUgJPT0+h49TpP//5D9zd3TF8+HC0bt0aPXr0wObNm4WOVa/y8nJIpVLo6+vLtRsYGODXX38VKNWbe/r0KTiOE8WZOVWjDn1HQ02ePBmBgYHw8/MTOkqTePfdd5GYmIhr164BAC5evIhff/0VAQEBAidrPprSF9BGrbUoKCiAVCqFlZWVXLuVlRXu3bsnUKqGuXz5Mjw9PfHy5UsYGxvjyJEjeOedd4SOVafr169j48aNmDFjBubNm4fU1FRMnToVenp6CAkJETperUxMTODp6Ylly5bByckJVlZW2LdvH86dO4eOHTsKHa9RXr58iYiICAQFBanVZo3NRcx9x+vYv38/MjIyVG5+nDLNnTsXT58+RefOnSGRSCCVSrFixQqMGjVK6GjNQpP6AiqG6sFxnNzPjDGFNlXj6OiIzMxMFBYW4tChQxgzZgySk5NVuiCqqKiAu7s7Vq5cCQDo0aMHfv/9d2zcuFGliyEA2L17N8aOHYu2bdtCIpHA1dUVQUFByMjIEDraa3v16hVGjhyJiooKxMbGCh1H1MTYdzRUXl4epk2bhhMnTiicFVUnBw4cwHfffYe9e/eiS5cuyMzMRHh4OGxsbDBmzBih4zUpTesLqBiqhaWlJSQSicI3uQcPHih841M1urq6ePvttwEA7u7uSEtLw/r167Fp0yaBk9XO2tpaoVhzcnLCoUOHBErUcG+99RaSk5NRUlKCoqIiWFtb45NPPoGDg4PQ0V7Lq1evMGLECNy4cQNJSUlq/02wqYi572io8+fP48GDB3Bzc+PbpFIpTp8+jZiYGJSWlkIikQiYUDlmz56NiIgIjBw5EgDQtWtX3Lp1C5GRkWpdDGliX0Bzhmqhq6sLNzc3JCQkyLUnJCTAy8tLoFSNwxhDaWmp0DHq5O3tjatXr8q1Xbt2DXZ2dgIlen1GRkawtrbGkydP8PPPP+PDDz8UOlKDyTq/P/74A7/88gssLCyEjiRa6tR31GbAgAG4fPkyMjMz+Zu7uzs+/fRTZGZmqkUhBFSurtLSkv8zKZFIRL20vj6a2hfQmaE6zJgxA6NHj4a7uzs8PT0RFxeH3NxcTJo0SehotZo3bx4GDx6Mdu3aobi4GPv378epU6dw/PhxoaPVafr06fDy8sLKlSsxYsQIpKamIi4uDnFxcUJHq9fPP/8MxhgcHR3x559/Yvbs2XB0dMRnn30mdDTes2fP8Oeff/I/37hxA5mZmTA3N4eNjQ2GDRuGjIwMxMfHQyqV8mc1zM3NoaurK1Rs0RJj3/E6TExM4OzsLNdmZGQECwsLhXYxGzJkCFasWIH27dujS5cuuHDhAtauXYuxY8cKHa3RqC+ohbCL2VTfhg0bmJ2dHdPV1WWurq4sOTlZ6Eh1Gjt2LJ+3VatWbMCAAezEiRNCx2qQH3/8kTk7OzM9PT3WuXNnFhcXJ3SkBjlw4ADr0KED09XVZW3atGGTJ09mhYWFQseSc/LkSQZA4TZmzBh248aNGu8DwE6ePCl0dNESW9/xptRxaX1RURGbNm0aa9++PdPX12cdOnRg8+fPZ6WlpUJHazTqC2rGMcZYcxZfhBBCCCGqhOYMEUIIIUSjUTFECCGEEI1GxRAhhBBCNBoVQ4QQQgjRaFQMEUIIIUSjUTFECCGEEI1GxRAhhBBCNBoVQ0SpduzYAY7jkJ6eDgD473//i8WLFwsbqp4c9vb2CA0NbdY8hDQF2eevptusWbMEzbZ3716sW7euxvs4jhOkn6jr/eI4DqdOnWqy5/b19YWvr2+TPT4AZGdnY/Hixbh586bCfaGhobC3t2/S5xcT2o6DNKn//ve/2LBhg+AFUV05jhw5ohEbERLNsX37dnTu3FmuzcbGRqA0lfbu3YusrCyEh4cr3JeSkgJbW9vmD/X/1fR+AVDYPFpssrOzsWTJEvj6+ioUPgsWLMC0adOECaaCqBgiovT8+XMYGhoq5bF69OihlMchRFU4OzvD3d1d6BgN1rt3b0GfX2zvlzK89dZbQkdQKTRMRppMaGgoNmzYAAByp55lp2wZY4iNjYWLiwsMDAxgZmaGYcOG4fr163KP4+vrC2dnZ5w+fRpeXl4wNDTkN0o8cOAA/P39YW1tDQMDAzg5OSEiIgIlJSUNzlHTMFlubi6Cg4PRunVr6OnpwcnJCWvWrJHbrfrmzZvgOA6rV6/G2rVr4eDgAGNjY3h6euK3336Te7zr169j5MiRsLGxgZ6eHqysrDBgwABkZma+6dtMyGupbUiq+udANoR08uRJhIWFwdLSEhYWFhg6dCju3Lmj8O/37t0LT09PGBsbw9jYGC4uLti6dSuAys/wsWPHcOvWLbnPYF2ZsrKy8OGHH8LMzAz6+vpwcXHBzp075Y45deoUOI7Dvn37MH/+fNjY2KBFixbw8/PD1atXG/8mVdOjRw/4+PgotEulUrRt2xZDhw7l25YsWYJevXrB3NwcLVq0gKurK7Zu3Yr6dr6SvZbqQ3OyfmbHjh18W3p6OkaOHAl7e3sYGBjA3t4eo0aNwq1bt/hjduzYgeHDhwMA+vXrx7/nssepaZjs5cuX+PLLL+Hg4ABdXV20bdsWkydPRmFhodxx9vb2eP/993H8+HG4urrCwMAAnTt3xrZt2+SOe/78OWbNmgUHBwfo6+vD3Nwc7u7u2LdvX53vhRDozBBpMgsWLEBJSQkOHjyIlJQUvt3a2hoAMHHiROzYsQNTp05FVFQUHj9+jKVLl8LLywsXL16ElZUV/2/u3r2L4OBgzJkzBytXroSWVmUd/8cffyAgIADh4eEwMjLClStXEBUVhdTUVCQlJTUoR3UPHz6El5cXysrKsGzZMtjb2yM+Ph6zZs3CX3/9hdjYWLnjN2zYgM6dO/PzIRYsWICAgADcuHEDpqamAICAgABIpVKsWrUK7du3R0FBAc6ePavQyRCiDFKpFOXl5XJt2tqN6+7HjRuHwMBA7N27F3l5eZg9ezaCg4P5zxcALFy4EMuWLcPQoUMxc+ZMmJqaIisri//jHBsbiwkTJuCvv/7CkSNH6n3Oq1evwsvLC61bt8bXX38NCwsLfPfddwgNDcX9+/cxZ84cuePnzZsHb29vbNmyBUVFRZg7dy6GDBmCnJwcSCSSep+vpveL4zj+33722WeYNm0a/vjjD3Ts2JE/5sSJE7hz5w4+++wzvu3mzZuYOHEi2rdvDwD47bffMGXKFOTn52PhwoX1ZmmImzdvwtHRESNHjoS5uTnu3r2LjRs3wsPDA9nZ2bC0tERgYCBWrlyJefPmYcOGDXB1dQVQ+xkhxhg++ugjJCYm4ssvv4SPjw8uXbqERYsWISUlBSkpKdDT0+OPv3jxImbOnImIiAhYWVlhy5Yt+Pzzz/H222+jT58+AIAZM2Zg9+7dWL58OXr06IGSkhJkZWXh0aNHSnkflErQbWKJ2tm+fTsDwNLS0hhjjE2ePJnV9GuWkpLCALA1a9bItefl5TEDAwM2Z84cvq1v374MAEtMTKzzuSsqKtirV69YcnIyA8AuXrzI31dbDsYYs7OzY2PGjOF/joiIYADYuXPn5I4LCwtjHMexq1evMsYYv8Nz165dWXl5OX9camoqA8D27dvHGGOsoKCAAWDr1q2rMz8hb0r2+avp9urVK8YYYwDYokWLFP5t9c+B7LH+7//+T+64VatWMQDs7t27jDHGrl+/ziQSCfv000/rzBYYGMjs7OxqvK96ppEjRzI9PT2Wm5srd9zgwYOZoaEhKywsZIz9vQN7QECA3HHff/89A8BSUlLqzFTX+yWRSPjjCgoKmK6uLps3b57cvx8xYgSzsrLi39vqpFIpe/XqFVu6dCmzsLBgFRUV/H19+/Zlffv25X+WvZbqu8PL+pnt27fX+jrKy8vZs2fPmJGREVu/fj3f/sMPP9S64/yYMWPk/j+OHz/OALBVq1bJHXfgwAEGgMXFxfFtdnZ2TF9fn926dYtve/HiBTM3N2cTJ07k25ydndlHH31Ua25VQsNkRBDx8fHgOA7BwcEoLy/nb23atEH37t0VThWbmZmhf//+Co9z/fp1BAUFoU2bNpBIJNDR0UHfvn0BADk5OY3KlpSUhHfeeQc9e/aUaw8NDQVjTO4bMQAEBgbKffvs1q0bAPDfis3NzfHWW28hOjoaa9euxYULF+SG2whRtl27diEtLU3u1tgzQx988IHcz9V/vxMSEiCVSjF58uQ3C11FUlISBgwYgHbt2sm1h4aG4vnz53JneBuSsT41vV/nzp3j77ewsMCQIUOwc+dO/rP75MkT/Pvf/0ZISIjce5uUlAQ/Pz+YmpryfdLChQvx6NEjPHjwoOFvQh2ePXuGuXPn4u2334a2tja0tbVhbGyMkpKSN+r3AChMGRg+fDiMjIyQmJgo1+7i4sKf/QIAfX19dOrUSe4979mzJ3766SdERETg1KlTePHiRaOyNQcaJiOCuH//PhhjckNhVXXo0EHu55qGtJ49ewYfHx/o6+tj+fLl6NSpEwwNDZGXl4ehQ4c2+oP36NGjGpecylbjVD/Fa2FhIfez7FSy7Pk5jkNiYiKWLl2KVatWYebMmTA3N8enn36KFStWwMTEpFE5CamNk5OT0iYE1/f7/fDhQwBQ6mqwR48e1fiZb+xnsD4Neb/Gjh2LQ4cOISEhAQMHDsS+fftQWloqVzykpqbC398fvr6+2Lx5M2xtbaGrq4ujR49ixYoVSisGgoKCkJiYiAULFsDDwwMtWrQAx3EICAh4o35PW1sbrVq1kmvnOA5t2rSp9z0HKt/3qs//9ddfw9bWFgcOHEBUVBT09fUxcOBAREdHyw03qgIqhoggLC0twXEczpw5IzcOLVO9repkS5mkpCTcuXMHp06d4s8GAXjjeTgWFha4e/euQrts0qilpeVrP6adnR0/mfTatWv4/vvvsXjxYpSVleHbb799o7yEvA49PT2UlpYqtDd2Hofsj+ft27cVzuQ0VlN8Bt/UwIEDYWNjg+3bt2PgwIHYvn07evXqJbf8fv/+/dDR0UF8fDz09fX59qNHj9b7+LLjq//fFBQUyP389OlTxMfHY9GiRYiIiODbS0tL8fjx48a8NACV73l5eTkePnwoVxAxxnDv3j14eHi89mMaGRlhyZIlWLJkCe7fv8+fJRoyZAiuXLnS6KxNgYbJSJOq7Rva+++/D8YY8vPz4e7urnDr2rVrvY8tK5CqF06bNm1qcI6aDBgwANnZ2cjIyJBr37VrFziOQ79+/ep9jLp06tQJX331Fbp27arwHIQ0NXt7e1y6dEmuLSkpCc+ePWvU4/n7+0MikWDjxo11Hlf9rEFdBgwYwH/ZqWrXrl0wNDQUZCm+RCLB6NGjcfToUZw5cwbp6en8qlYZjuOgra0tN2z+4sUL7N69u97Hl52Nrv5/85///EfhORhjCv3eli1bIJVK5dpet98DgO+++06u/dChQygpKeHvbywrKyuEhoZi1KhRuHr1Kp4/f/5Gj6dsdGaINClZURMVFYXBgwdDIpGgW7du8Pb2xoQJE/DZZ58hPT0dffr0gZGREe7evYtff/0VXbt2RVhYWJ2P7eXlBTMzM0yaNAmLFi2Cjo4O9uzZg4sXLzY4h66ursKx06dPx65duxAYGIilS5fCzs4Ox44dQ2xsLMLCwtCpU6fXeg8uXbqEL774AsOHD0fHjh2hq6uLpKQkXLp0Se6bHSHNYfTo0ViwYAEWLlyIvn37Ijs7GzExMfzKx9dlb2+PefPmYdmyZXjx4gVGjRoFU1NTZGdno6CgAEuWLAFQ+Rk8fPgwNm7cCDc3N2hpadU6NLVo0SLEx8ejX79+WLhwIczNzbFnzx4cO3YMq1atanTW2mRlZSmsJgMqV15VPUsyduxYREVFISgoCAYGBvjkk0/kjg8MDMTatWsRFBSECRMm4NGjR1i9enWNZ7+ra9OmDfz8/BAZGQkzMzPY2dkhMTERhw8fljuuRYsW6NOnD6Kjo2FpaQl7e3skJydj69ataNmypdyxzs7OAIC4uDiYmJhAX18fDg4ONQ5xvffeexg4cCDmzp2LoqIieHt786vJevTogdGjR9f7Gqrr1asX3n//fXTr1g1mZmbIycnB7t274enpqbTrxCmNoNO3idqpvpqstLSUjRs3jrVq1YpxHMcAsBs3bvDHb9u2jfXq1YsZGRkxAwMD9tZbb7GQkBCWnp7OH9O3b1/WpUuXGp/v7NmzzNPTkxkaGrJWrVqxcePGsYyMDIXVF3XlqL6KhjHGbt26xYKCgpiFhQXT0dFhjo6OLDo6mkmlUv4Y2SqP6OhohVyosjrm/v37LDQ0lHXu3JkZGRkxY2Nj1q1bN/avf/1LbhUaIW+q+uevJqWlpWzOnDmsXbt2zMDAgPXt25dlZmbWupqs+mPVtupp165dzMPDg+nr6zNjY2PWo0cPuc/g48eP2bBhw1jLli35z6AMaljhdvnyZTZkyBBmamrKdHV1Wffu3RVWVMmy/PDDD3LtDVmBVfU11nbbvHmzwr/x8vJiAGpdPbdt2zbm6OjI9PT0WIcOHVhkZCTbunWrQt9XfTUZY4zdvXuXDRs2jJmbmzNTU1MWHBzM0tPTFV7L7du32ccff8zMzMyYiYkJGzRoEMvKyqqxL1u3bh1zcHBgEolE7nGqryZjrHJF2Ny5c5mdnR3T0dFh1tbWLCwsjD158kTuODs7OxYYGKjw2qu/poiICObu7s7MzMz492P69OmsoKCgxvdOSBxj9VwJihBCCCFEjdGcIUIIIYRoNCqGCCGEEKLRqBgihBBCiEajYogQQgghGo2KIUIIIYRoNCqGCCGEEKLRqBgihBBCiEajYogQQgghGo2KIUIIIYRoNCqGCCGEEKLRqBgihBBCiEajYogQQgghGu3/AZ4Sw6R1p2o9AAAAAElFTkSuQmCC",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vary_xferheat_parms();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 5.2: A Continuation Problem for the $H$-equation\n",
    "\n",
    "In this section we will explore some properties of the solution of\n",
    "$H$-equation as a function of the parameter $c$. As before, we will do the the analysis\n",
    "for the continuous problem, but the results are valid for any discretization\n",
    "with a quadrature rule with positive weights that integrates constants\n",
    "exactly.\n",
    "\n",
    "We have seen other examples\n",
    "of parameter-dependent nonlinear equations in\n",
    "the buckling beam problem from Chapters __2__ and __3__\n",
    "and the cubic polynomial example\n",
    "in Chapter __1__. These examples have\n",
    "pitchfork bifurcations <cite data-cite=\"herb\"><a href=\"siamfa.html#herb\">(Kel87)</cite>\n",
    "and most path following codes\n",
    "<cite data-cite=\"doedel\"><a href=\"siamfa.html#doedell\">(Doe97)</cite>,\n",
    "<cite data-cite=\"govaerts\"><a href=\"siamfa.html#govaerts\">(Gov00)</cite>,\n",
    "<cite data-cite=\"kuznetsov\"><a href=\"siamfa.html#kuznetsov\">(Kuz98)</cite>,\n",
    "<cite data-cite=\"loca\"><a href=\"siamfa.html#loca\">(SBRP<sup>+</sup>02)</cite>,\n",
    "<cite data-cite=\"pitcon\"><a href=\"siamfa.html#pitcon\">(Rhe86)</cite>,\n",
    "<cite data-cite=\"bifkit\"><a href=\"siamfa.html#bifkit\">(Vel20)</cite>,\n",
    "have machinery to detect intersecting solution arcs and\n",
    "follow the different branches. Those methods are beyond\n",
    "the scope of this book. The Julia package __BifurcationKit.jl__\n",
    "<cite data-cite=\"bifkit\"><a href=\"siamfa.html#bifkit\">(Vel20)</cite>\n",
    "is quite complete. Our example of the $H$-equation\n",
    "is the simplest possible case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 5.2.1: Properties of $H$ as a function of $c$\n",
    "\n",
    "\n",
    "\n",
    "Recall that the equation in $C[0,1]$ is\n",
    "$$\n",
    "\\calf(H)(\\mu) =\n",
    "H(\\mu) - \\frac{1}{1 - \\frac{c}{2} \\int_0^1 \\frac{\\mu H(\\mu)}{\\mu+\\nu} \\dnu} = 0.\n",
    "$$\n",
    "We will also refer to the fixed point form of the equation\n",
    "$$\n",
    "H(\\mu) = \\calg(H)(\\mu) = \\left( 1 - \\frac{c}{2}\n",
    "\\int_0^1 \\frac{\\mu H(\\mu)}{\\mu+\\nu} \\dnu \\right)^{-1}.\n",
    "$$\n",
    "\n",
    "We will use an alternative form of the $H$-equation to compute\n",
    "the $L^1$ norm of $H$.\n",
    "$$\n",
    "H(\\mu) = 1 + \\frac{c}{2} H(\\mu) \\int_0^1 \\frac{\\mu}{\\mu + \\nu} H(\\nu) \\dnu.\n",
    "$$\n",
    "\n",
    "Integrating both sides of the alternative formulation yields\n",
    "$$\n",
    "\\| H \\|_1 =\n",
    "1 + \\frac{c}{2} \\int_0^1 \\int_0^1 H(\\mu) H(\\nu) \\frac{\\mu}{\\mu + \\nu} \\dnu.\n",
    "$$\n",
    "Since $\\mu$ and $\\nu$ are variables of integration, we may interchange\n",
    "them to obtain\n",
    "$$\n",
    "\\| H \\|_1 =\n",
    "1 + \\frac{c}{2} \\int_0^1 \\int_0^1 H(\\mu) H(\\nu) \\frac{\\nu}{\\mu + \\nu} \\dnu.\n",
    "$$\n",
    "When we average the two equations we obtain\n",
    "$$\n",
    "\\| H \\|_1 =\n",
    "1 + \\frac{c}{4} \\| H \\|_1^2.\n",
    "$$\n",
    "So\n",
    "$$\n",
    "\\| H \\|_1  = \\frac{1 \\pm \\sqrt{1 - c}}{c/2}.\n",
    "$$\n",
    "\n",
    "As we said in Chapter __2__, the H-equation has two solutions\n",
    "for $0 < c < 1$.  The\n",
    "__lower branch__, where $\\| H \\|_1  = \\frac{1 - \\sqrt{1 - c}}{c/2}$\n",
    "is the solution of physical interest and the one you're likely to find\n",
    "with any iteration. In this section we will explore computation of the\n",
    "__upper branch__, where $\\| H \\|_1  = \\frac{1 + \\sqrt{1 - c}}{c/2}$.\n",
    "\n",
    "The formula for the $L^1$ norm implies that the $H$-equation has no real\n",
    "solutions for $c > 1$. This implies that the Frechet\n",
    "derivative\n",
    "$\\calf'$ of $\\calf$ must be singular. The solution path cannot stop\n",
    "abruptly <cite data-cite=\"crandall\"><a href=\"siamfa.html#crandall\">(CR71)</cite>,\n",
    "so there must be an upper\n",
    "branch. The formula for the $L^1$ norm implies that $\\| H \\|_1 \\to \\infty$ as $c \\to 0$\n",
    "on the upper branch.\n",
    "\n",
    "We will briefly consider the details of the singularity before\n",
    "following the path. We begin by showing that $\\calf'(H)$ is nonsingular\n",
    "for $0 \\le c < 1$ on the lower branch. To simplify the analysis we define\n",
    "the linear operator $\\call$ on $C[0,1]$ by\n",
    "$$\n",
    "(\\call u)(\\mu) = \\int_0^1 \\frac{\\mu u(\\nu)}{\\mu + \\nu} \\dnu\n",
    "$$\n",
    "and express the $H$-equation as\n",
    "$$\n",
    "H = \\frac{1}{1 - (c/2) \\call H}\n",
    "$$\n",
    "where the division is understood to be pointwise. Now\n",
    "note that for $u \\in C[0,1]$,\n",
    "$$\n",
    "\\calf'(H) u = u - \\frac{(c/2) \\call u}{(1 - (c/2) \\call H)^2}\n",
    " = u - (c/2) H^2  \\call u.\n",
    "$$\n",
    "Here we use, as we have before, the simple trick to compute a Frechet\n",
    "derivative\n",
    "$$\n",
    "\\calf'(H)u = \\frac{d}{d \\epsilon} \\calf(H + \\epsilon u)\n",
    "\\bigg|_{\\epsilon = 0 }.\n",
    "$$\n",
    "Since $\\calf'$ is the sum of a compact operator and the identity, it is\n",
    "singular only if $0$ is an eigenvector. So singularity of $\\calf'$ is\n",
    "equivalent to $\\calg'(H)$ having $\\sigma(c) = 1$ as an eigenvalue.\n",
    "    \n",
    "When $c = 1$, the Perron theorem\n",
    "<cite data-cite=\"karlin\"><a href=\"siamfa.html#karlin\">(Kar59)</cite>\n",
    "and the positivity of the\n",
    "$H$-function imply that the largest eigenvalue in absolute value\n",
    "of $\\calg'(H)$ is positive and the corresponding eigenfunction\n",
    "does not change sign, and hence can be taken as\n",
    "non-negative. That eigenvalue is $\\sigma(1)= 1$ and the eigenfunction\n",
    "is $u(\\mu) = \\mu H(\\mu)$. To see this use the formula for the $L^1$ norm and compute,\n",
    "with $\\| H \\|_1 = 2$,\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\calg'(H)(u)(\\mu) & = H^2(\\mu) (1/2) \\int_0^1 \\frac{\\mu \\nu H(\\nu)}{\\mu + \\nu}\n",
    "\\dnu \\\\\n",
    "\\\\\n",
    "& = (\\mu H(\\mu)) H(\\mu) (1/2) \\int_0^1 \\frac{\\nu H(\\nu)}{\\mu + \\nu} \\dnu \\\\\n",
    "\\\\\n",
    "& = u(\\mu) H(\\mu) (1/2) \\int_0^1 H(\\nu) (1 - \\frac{\\mu}{\\mu + \\nu} ) \\dnu \\\\\n",
    "\\\\\n",
    "& = u(\\mu) \\left( H(\\mu) - (1/2) (H \\call H)(\\mu) \\right) = u(\\mu)\n",
    "\\end{array}\n",
    "$$\n",
    "Since $u \\ge 0$, $\\sigma(1) = 1$ is the Perron eigenvalue. Therefore\n",
    "the eigenvalue has multiplicity one.\n",
    "\n",
    "The Perron theory is also applicable if $0 < c < 1$. Let\n",
    "$\\sigma(c) > 0$ be the Perron eigenvalue of\n",
    "$\\calg'(H)$ with eigenfunction $u$. Set $u(\\mu) = \\mu H(\\mu) p(\\mu)$.\n",
    "Then\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\sigma(c) p(\\mu)  & =  H(\\mu) (c/2) \\int_0^1\n",
    "\\frac{H(\\nu) p(\\nu) \\nu}{\\mu + \\nu} \\dnu\n",
    "\\le \\| p \\|_\\infty H(\\mu) (c/2) \\int_0^1 \\frac{H(\\nu) \\nu}{\\mu + \\nu} \\dnu\\\\\n",
    "\\\\\n",
    "& \\le \\| p \\|_\\infty H(\\mu) (c/2)\n",
    "\\int_0^1 H(\\nu) \\left(1 - \\frac{\\mu}{\\mu + \\nu} \\right) \\dnu\n",
    "= \\| p \\|_\\infty H(\\mu) (1 - \\sqrt{1-c} ) - (H(\\mu) - 1)\\\\\n",
    "\\\\\n",
    "& = \\| p \\|_\\infty (1  - H(\\mu) \\sqrt{1-c} )\n",
    "\\le \\| p \\|_\\infty (1 - \\sqrt{1-c})\n",
    "\\end{array}\n",
    "$$\n",
    "Hence, taking the $L^\\infty$ norm of the left side of the above,\n",
    "we have\n",
    "$$\n",
    "\\sigma(c) \\le 1 - \\sqrt{1-c} < 1.\n",
    "$$\n",
    "Hence $\\calf'(H)$ is nonsingular for $0 < c < 1$ on the lower branch.\n",
    "This also proves the norm estimate from Chapter __4__.\n",
    "    \n",
    "For $c=1$, $\\calf'(H)$ is singular with a one-dimensional null space.\n",
    "To complete the analysis of the singularity we need to make the dependence\n",
    "in $c$ explicit and write the equation as\n",
    "$$\n",
    "\\calf(H, c) = 0.\n",
    "$$\n",
    "We let $\\calf'_H$ be the\n",
    "Frechet derivative in $H$ and\n",
    "$\\calf'_c(H,c)$ be the derivative in $c$. At the solution,\n",
    "$$\n",
    "\\calf'_c(H,c) = - H^2 \\frac{1}{2} \\call H.\n",
    "$$\n",
    "So, using the H-equation, \n",
    "$$\n",
    "\\calf'_c(H,1) = - H (H - 1).\n",
    "$$\n",
    "    \n",
    "The singularity at $c=1$ is a\n",
    "__simple fold__ <cite data-cite=\"herb\"><a href=\"siamfa.html#herb\">(Kel87)</cite>.\n",
    "This means that $\\calf'(H)$ has a null space\n",
    "of dimension 1 and that $\\calf'_c(H,1) \\not \\in {\\cal R}(\\calf'_H(H))$.\n",
    "Here $\\cal{R}$ denotes the range of an operator. In this case\n",
    "$$\n",
    "{\\cal R}(\\calf'_H(H)) = \\{ u \\in C[0,1] \\, | \\,\n",
    "\\int_0^1 u(\\nu) \\psi(\\nu) \\dnu = 0 \\},\n",
    "$$\n",
    "where $\\psi$ is the eigenfunction of $\\calf'_H(H)^*$, the adjoint of\n",
    "$\\calf'_H(H)$ corresponding to the zero eigenvalue. The reader can\n",
    "verify that, for $c=1$,\n",
    "$$\n",
    "\\psi(\\mu) = (\\calg'(H)^* \\psi)(\\nu) = \\frac{1}{2} \\int_0^1\n",
    "\\frac{\\nu}{\\nu+\\mu} H^2(\\nu) \\psi(\\nu) \\dnu\n",
    "$$\n",
    "is satisfied by $\\psi = H^{-1}$. Since $H \\ge 1$ and $\\calf'_c(H,1)$ does\n",
    "not change sign, $\\calf'_c(H,1)$ cannot be in the range\n",
    "of $\\calf'_H(H)$. We will show why this fact is important in the next\n",
    "section.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 5.2.2: Parameter Continuation\n",
    "\n",
    "The object of continuation methods is to solve\n",
    "a parameter dependent set of nonlinear equations\n",
    "$$\n",
    "\\mf(\\vx, \\lambda) = 0.\n",
    "$$\n",
    "The output is a\n",
    "__solution arc__\n",
    "or\n",
    "__solution path__ \n",
    "$$\n",
    "\\{ \\vx(\\lambda) \\, | \\, \\mf(\\vx(\\lambda), \\lambda) = 0,\n",
    "\\lambda_0 \\le \\lambda \\le \\lambda_{max} \\}.\n",
    "$$\n",
    "The solution arc for the H-equation is a simple arc\n",
    "with no bifurcations. Because of this we can use a\n",
    "straightforward path following algorithm. The\n",
    "objective of this section is to show how a nonlinear solver\n",
    "can be modified in a simple way to resolve singularities\n",
    "such as the one for the $H$-equation at $c=1$.\n",
    "\n",
    "\n",
    "    \n",
    "One simple approach to computing a solution path would be to\n",
    "identify a solution $\\vx(\\lambda_0)$ for a particular\n",
    "value of $\\lambda$ and then increment $\\lambda$ by a small amount\n",
    "and use Newton's method with an initial iterate from the previous\n",
    "solution to compute the solution for the next step. In the\n",
    "case of the $H$-equation, where the parameter is $c$. One can\n",
    "use $c_0 = 0$ and $H \\equiv 1$ to begin the continuation.\n",
    "\n",
    "A candidate for the algorithm is __natural parameter continuation__\n",
    "where we increment the original parameter in the equation. Note that in\n",
    "the discussion in this section, as is common practice, the parameter is\n",
    "called $\\lambda$ in the context of a general method, but the actual\n",
    "name of the parameter ($c$ in the case of the H-equation) is used when\n",
    "talking about specific examples.\n",
    "    \n",
    "<div>\n",
    "<img src=\"Images/Alg5dot1.png\" width=\"500\"/>\n",
    "</div>    \n",
    "\n",
    "One can solve the nonlinear equation for $\\vx$ with any iterative method.\n",
    "In this section we use only Newton-GMRES with a finite-difference\n",
    "Jacobian-vector product. Our reasons for this are that\n",
    "we can use the same method for all continuation algorithms and that\n",
    "it is the most efficient choice for the H-equation. We will discuss this\n",
    "in more depth later.\n",
    "\n",
    "One other point is that $\\vx(\\lambda)$ is not the best choice\n",
    "for the initial iterate (the predictor) for the\n",
    "nonlinear equation for $\\vx(\\lambda + d_\\lambda)$. We use the\n",
    "linear (secant) predictor \\cite{herb} in our codes\n",
    "$$\n",
    "\\vx_0 (\\lambda + d_\\lambda) = 2 \\vx(\\lambda) - \\vx(\\lambda - d_\\lambda)\n",
    "$$\n",
    "once we have two solutions on the path.\n",
    "\n",
    "Natural parameter continuation is rarely used in practice because it\n",
    "is unable to resolve even the simplest singularities. The reason for this\n",
    "is clear for the $H$-equation. If we set $c_0 = 0$ and start the\n",
    "continuation, the iteration has to fail as soon as $c + d_c > 1$\n",
    "because there are no real solutions for $c > 1$.\n",
    "    \n",
    "The singularity of the $H$-equation is particularly simple. Following\n",
    "<cite data-cite=\"herb\"><a href=\"siamfa.html#herb\">(Kel87)</cite>\n",
    "we consider a solution arc\n",
    "$$\n",
    "\\cals =\n",
    "\\{ \\vx(\\lambda) \\, | \\, \\mf(\\vx(\\lambda), \\lambda) = 0,\n",
    "\\lambda_0 \\le \\lambda \\le \\lambda_{max} \\}.\n",
    "$$\n",
    "We say the path is\n",
    "__regular__\n",
    "if\n",
    "$$\n",
    "Rank [\\mf'_\\vx, \\mf'_\\lambda] = N\n",
    "$$\n",
    "for all points on the path. Here, as in the previous section,\n",
    "$\\mf'_\\vx$ is the Jacobian of $\\mf$ in the $\\vx$ variables and\n",
    "$\\mf'_\\lambda = d \\mf/d\\lambda$.\n",
    "\n",
    "The solution path for the $H$-equations is regular as we can see\n",
    "from the following lemma (Lemma 4.2 from <cite data-cite=\"herb\"><a href=\"siamfa.html#herb\">(Kel87)</cite>).\n",
    "    \n",
    "---    \n",
    "__Lemma:__\n",
    "$Rank [\\mf'_\\vx, \\mf'_\\lambda] = N$ if and only if one of\n",
    "\n",
    "- $\\mf'_\\vx$ is nonsingular.\n",
    "- The null space of $\\mf'_\\vx$ has dimension $1$ and\n",
    "$\\mf'_\\lambda \\not \\in {\\cal R}(\\mf'_\\vx)$.\n",
    "---\n",
    "\n",
    "We showed that (at least for the lower branch up to $c=1$) that\n",
    "the solution path for the $H$-equation is regular. The upper branch\n",
    "is also regular with $\\calf'(H)_H$ nonsingular for the upper branch\n",
    "where $0 < c < 1$. We leave it to the reader to verify that.\n",
    "\n",
    "Not all solution arcs are regular. The pitchfork bifurcation where two\n",
    "solution arcs intersect is not a regular point and the algorithm we\n",
    "use in this section is not sufficient to identify a such\n",
    "a bifurcation and differentiate between stable and unstable branches.\n",
    "\n",
    "One can resolve any singularity on a regular path with an appropriate\n",
    "reparameterization. One clever way is to swap $\\lambda$ with a component\n",
    "of $\\vx$ (see <cite data-cite=\"pitcon\"><a href=\"siamfa.html#pitcon\">(Rhe86)</cite>). In the case of the\n",
    "discretized $H$-equation, making $c$ one of the unknowns and $H(N)$ the\n",
    "parameter will work fine and there will be no singularity. However, this\n",
    "approach is highly problem-dependent.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 5.2.3: Pseudo-Arclength Continuation\n",
    "\n",
    "We will compute regular paths with\n",
    "__pseudo-arclength continuation__ <cite data-cite=\"herb\"><a href=\"siamfa.html#herb\">(Kel87)</cite>.\n",
    "Here we introduce a parameter $s$ for the path, so\n",
    "$$\n",
    "\\cals = \\{ (\\vx(s), \\lambda(s)) \\, | \\, 0 \\le s \\le s_{max} \\}\n",
    "$$\n",
    "and we apply the simple continuation algorithm\n",
    "__ParamCont__ to the path as a function of $s$. In order to do\n",
    "this we need to augment $\\mf$ with a new equation for $\\lambda$.\n",
    "\n",
    "So, given $\\vx(s)$ and $\\lambda(s)$, we solve\n",
    "$$\n",
    "{\\hat \\mf}(\\vx, \\lambda, s) = \\left(\n",
    "\\begin{array}{c}\n",
    "\\mf(\\vx, \\lambda) \\\\\n",
    "N(\\vx, \\lambda, s)\n",
    "\\end{array}\n",
    "\\right)\n",
    "$$\n",
    "for $\\vx(s + ds)$ and $\\lambda(s + ds)$.\n",
    "\n",
    "Here the __normalization equation__\n",
    "$$\n",
    "\\label{eq:pacnorm}\n",
    "N(\\vx, \\lambda, s) \\equiv\n",
    "\\nu {\\dot \\vx}(s)^T (\\vx - \\vx(s))\n",
    "+ {\\dot \\lambda}(s) (\\lambda- \\lambda(s)) - ds = 0\n",
    "$$\n",
    "is intended to let the artificial parameter $s$ play the role\n",
    "of arclength <cite data-cite=\"herb\"><a href=\"siamfa.html#herb\">(Kel87)</cite>.\n",
    "    \n",
    "In the normalization equation $\\dot \\vx$ and $\\dot \\lambda$ are approximations\n",
    "to $d \\vx/ds$ and $d \\lambda/ds$. The approximations need not be\n",
    "highly accurate, only good enough to make sure that the continuation\n",
    "moves in the correct direction along the path and does not reverse\n",
    "itself. After we have two points on the path, we use\n",
    "$$\n",
    "{\\dot \\vx}(s) = \\frac{\\vx(s) - \\vx(s - ds)}{ds} \\mbox{ and }\n",
    "{\\dot \\lambda}(s) = \\frac{\\lambda(s) - \\lambda(s - ds)}{ds}.\n",
    "$$\n",
    "The parameter $\\nu$ in the normalization equation is a scaling parameter designed\n",
    "make $\\lambda$ and $\\vx$ equally important in the\n",
    "continuation <cite data-cite=\"loca\"><a href=\"siamfa.html#loca\">(SBRP<sup>+</sup>02)</cite>. For\n",
    "the $H$-equation, we use $\\nu = 100/N$ so that the scalar product is\n",
    "the product of the composite midpoint approximation of\n",
    "the $L^2$ inner product for functions with a weighting factor that\n",
    "makes the continuation perform better.\n",
    "\n",
    "If the path is regular,\n",
    "then ${\\hat \\mf}'$ is\n",
    "nonsingular. The Jacobian is\n",
    "$$\n",
    "{\\hat \\mf}' = \\left(\n",
    "\\begin{array}{ll}\n",
    "\\mf'_\\vx & \\mf'_\\lambda \\\\\n",
    "{\\dot \\vx}^T & {\\dot \\lambda}\n",
    "\\end{array}\n",
    "\\right)\n",
    "$$\n",
    "Regularity implies that the first $N$ rows of the\n",
    "$(N+1) \\times (N+1)$ matrix ${\\hat \\mf}'$ have full rank ($N$). The\n",
    "last row is non-zero on a regular path and this (see Lemma 4.9 in\n",
    "<cite data-cite=\"herb\"><a href=\"siamfa.html#herb\">(Kel87)</cite>) implies that ${\\hat F}'$ is nonsingular.\n",
    "    \n",
    "The last subtle point is how one solves the linear equation for\n",
    "the Newton step in $(\\vx, \\lambda)$. We will exploit the integral\n",
    "equation structure of the $H$-equation and use {\\bf nsoli.jl} with\n",
    "Newton-GMRES for this. One could also use a direct method and\n",
    "exploit any structure in $\\mf'$, but that is a bit more difficult\n",
    "<cite data-cite=\"herb\"><a href=\"siamfa.html#herb\">(Kel87)</cite>.\n",
    "\n",
    "We apply these ideas to continue the solution of the $H$-equation in\n",
    "$c$. Figure __5.4__ is the result. The horizontal axis is\n",
    "$c$ and the vertical axis is the midpoint rule approximation of the\n",
    "$L^1$ norm of $H$. We used a constant value of $ds = 1/100$ which\n",
    "worked fine until the predictor failed to produce an initial iterate\n",
    "from which {\\bf nsoli.jl} would converge within 20 iterations. While\n",
    "reducing $ds$ or using a higher-order predictor would enable one to\n",
    "move further along the path, the fact that $\\| H \\|_1 \\to \\infty$\n",
    "as $c \\to 0$ on the upper branch implies that you won't get much\n",
    "farther than the results from the figure.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heq_graph_continuation();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 5.3: Projects\n",
    "\n",
    "### Section 5.3.1: More on heat transfer\n",
    "\n",
    "There are more interesting examples of conductive-radiative heat\n",
    "transfer in <cite data-cite=\"cesjrt\"><a href=\"siamfa.html#cesjrt\">(ST91)</cite>. \n",
    "Extend the methods from this chapter to\n",
    "the more general boundary conditions in equations 10a-10b on\n",
    "page 274 of<cite data-cite=\"cesjrt\"><a href=\"siamfa.html#cesjrt\">(ST91)</cite>.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 5.3.2: Advanced Continuation\n",
    "\n",
    "Explore solving the H-equation and the buckling beam equation with\n",
    "a more sophisticated continuation code that the simple one we used\n",
    "in this section. The Julia package __BifurcationKit.jl__\n",
    "<cite data-cite=\"bifkit\"><a href=\"siamfa.html#bifkit\">(Vel20)</cite>\n",
    "is a good place to start."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.11.0",
   "language": "julia",
   "name": "julia-1.11"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}