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author | adityacp | 2015-12-21 15:33:11 +0530 |
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committer | adityacp | 2015-12-21 15:33:11 +0530 |
commit | 970416b7c74755e3d0b8d6709140c65bc0522194 (patch) | |
tree | 8de768654c45e3dce0a7e96acbd706e9e73d1d73 | |
parent | d165cf0241bb925711c63e1c0a12ca8807db8b12 (diff) | |
parent | e0bf3e288f8add4cc8f243f7ed068c3df81e6882 (diff) | |
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diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal deleted file mode 160000 -Subproject bcc911d818cd033b4fab471e763f5fda73ffbb0 diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-2-Electrostatic-Potential-and-Capacitance.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-2-Electrostatic-Potential-and-Capacitance.ipynb new file mode 100644 index 0000000..b46447e --- /dev/null +++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-2-Electrostatic-Potential-and-Capacitance.ipynb @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 2 \n", + "# ELECTROSTATIC POTENTIAL AND CAPACITANCE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 2.1; Page No 55" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Potential is: 89877.4243799 Volts\n", + "Work done is: 0.00017975484876 Joules\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "import math\n", + "# Given Data\n", + "def pot(Q,r1):\n", + " e=1e-7\n", + " Q = Q*e # value of charge at p\n", + " r1 = r1/100.0 # distance from point p\n", + " eps = 8.854e-12 \n", + " q1 = 2e-9 # value of charge at infinity\n", + "# Formula to calculate potential V = (1/4(*pi*eps))*(Q/r)\n", + " v = (1/(4*math.pi*eps))*(Q/r1)\n", + " print \"Potential is: \",v,\"Volts\"\n", + "# Formula to calculate work done W = qV\n", + " w = q1*v\n", + " print \"Work done is: \",w,\"Joules\"\n", + "\n", + "interact(pot,Q=(1,9),r1=(1,9))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 2.5; Page No 65" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Electrosatic Potential Energy is: -1.24829756083 Joules\n", + "Work done is: 1.24829756083 Joules\n", + "Net Electrostatic Energy is: -1.24829756083 Joules\n" + ] + } + ], + "source": [ + "import math\n", + "from ipywidgets import interact\n", + "#Give Data\n", + "\n", + "def potential(q1,q2):\n", + " e=1e-6\n", + " q1 = q1*e # value of charge 1\n", + " q2 = q2*e # value of charge 2\n", + " r1 = 0.09 # distance between charges\n", + " r2 = -0.09\n", + " r = r1 - r2 \n", + " eps = 8.854e-12\n", + "# Formula to calculate electrostaic potential energy U = (1/(4*pi*eps))*((q1*q2)/r)\n", + " u = (1/(4*math.pi*eps))*((q1*q2)/r)\n", + " print \"Electrosatic Potential Energy is: \",u,\"Joules\"\n", + "# Formula to claculate work done W = U2 - U1\n", + " u1 = u\n", + " u2 = 0\n", + " w = u2-u1\n", + " print \"Work done is: \",w,\"Joules\"\n", + "# Formula to calculate net electrostatic energy (q1*(V)*r1) + (q2*(V)*r2) + ((q1q2)/4*pi*eps*r12)\n", + " A = 9e+5\n", + " e = ((A*q1)/r1)+((A*q2)/r1)+u\n", + " print \"Net Electrostatic Energy is: \",e,\"Joules\"\n", + "interact(potential,q1=(1,9),q2=(-9,-1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 2.9; Page No 79" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Charge on capacitors C1,C2,C3 is: 0.000833333333333 Coloumb\n", + "Charge on capacitor C4 is: 0.0025 Coloumb\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "# Given Data\n", + "\n", + "def capacitor(c1,c2,c3,c4):\n", + " e=1e-6\n", + " c1 = c1 * e # value of capacitors C1,C2,C3,C4\n", + " c2 = c2 * e\n", + " c3 = c3 * e\n", + " c4 = c4 * e\n", + "# Formula to calculate capacitance when connected in parallel is (1/c) = (1/c1)+(1/c2)+(1/c3)\n", + " c = (1/c1)+(1/c2)+(1/c3)\n", + " c = 1/c \n", + "# Formula to calculate capacitance when connected in series is c = c1 + c2 + c3\n", + "# Calculate equivalent capacitance \n", + " c_new = c + c4\n", + "# Formula to calculate potential difference is V = Q/C\n", + "# the potential difference across AB is Q/C1\n", + "# the potential difference across BC is Q/C2\n", + "# the potential difference across CD is Q/C3\n", + " v = 500\n", + "# Charge on each capacitor is \n", + " q = v * c\n", + " q1 = v * c4\n", + " print \"Charge on capacitors C1,C2,C3 is: \",q,\"Coloumb\"\n", + " print \"Charge on capacitor C4 is: \",q1,\"Coloumb\"\n", + "\n", + "interact(capacitor,c1=(1,9),c2=(1,9),c3=(1,9),c4=(1,9))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 2.10; Page No 82" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Charge is: 5.4e-08 Coloumb\n", + "Energy stored by capacitor is: 2.7e-06 Joules\n", + "Electrostatic Energy is: 1.35e-06 Joules\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "# Given Data\n", + "\n", + "def energy(c):\n", + " e = 1e-12\n", + " c = c * e # value of capacitor \n", + " v = 100 # voltage of battery\n", + "# Formula to find charge is Q = C*V\n", + " q = c * v\n", + " print \"Charge is: \",q,\"Coloumb\"\n", + "# Formula to calculate energy stored by capacitor (1/2) C*V^2 = (1/2) Q*V\n", + " e = (q * v)/2\n", + " print \"Energy stored by capacitor is: \",e,\"Joules\"\n", + "# Electrostatic Energy\n", + "# By charge conservation, Q′ = Q/2. This implies V′ = V/2\n", + " e1 = (q * v)/4\n", + " print \"Electrostatic Energy is: \",e1,\"Joules\"\n", + "interact(energy,c=(100,1000,10))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-3-Current-Electricity.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-3-Current-Electricity.ipynb new file mode 100644 index 0000000..1b4647a --- /dev/null +++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-3-Current-Electricity.ipynb @@ -0,0 +1,200 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 3\n", + "# CURRENT ELECTRICITY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 3.1; Page No 99" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drift Speed is: 0.00110243055556 m s^-1\n" + ] + } + ], + "source": [ + "# Given Data\n", + "e = 1.6e-19 # charge of electron\n", + "A = 1.0e-7 # cross-sectional area\n", + "I = 1.5 # current in copperwire\n", + "# Formula to calculate Drift Speed is v(d)=(I/neA)\n", + "n = (6.0e23/63.5)*9.0e6\n", + "# Calculate Drift Speed\n", + "v = 1.5/(n*e*A)\n", + "print \"Drift Speed is: \",v,\"m s^-1\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 3.3; Page No 105" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The steady temperature of the heating element is: 847.248418092 degree celsius\n" + ] + } + ], + "source": [ + "# Given Data\n", + "V = 230 # supply voltage\n", + "I = 2.68 # current in toaster\n", + "r1 = 75.3 # resistance at room temperature\n", + "t1 = 27 # room temperature\n", + "alpha = 1.70e-4 # Temperature coefficient\n", + "# Formula to calculate Resistance is R=V/I\n", + "r2 = V/I\n", + "r2 = round(r2,1)\n", + "t = (r2 - r1)/(r1 * alpha)\n", + "t2 = t + t1\n", + "print \"The steady temperature of the heating element is:\",t2,\"degree celsius\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 3.4; Page No 105" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperature of the bath is: 345.652173913 degree celsius\n" + ] + } + ], + "source": [ + "# Given Data\n", + "r0 = 5 # resistance at ice point\n", + "r100 = 5.23 # resistance at steam point\n", + "rt = 5.795 # resistance at hot bath\n", + "# Formula to calculate temperature is t=((rt - r0)/(r100 - r0)* 100)\n", + "t = ((rt - r0)/(r100 - r0))*100\n", + "print \"Temperature of the bath is:\",t,\"degree celsius\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 3.5; Page No 112" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Equivalent Resistance of the network is: 7 ohm\n", + "Voltage drop across AB is: 14 volts\n", + "Voltage drop across BC is: 2 volts\n", + "Voltage drop across CD is: 8 volts\n" + ] + } + ], + "source": [ + "\n", + "# Given Data\n", + "\n", + "V = 16 # voltage of battery\n", + "r = 1 # internal resistance\n", + "# Equivalent resistance for two 4ohm resistance in parallel\n", + "r1 = (4 * 4)/(4 + 4)\n", + "# Equivalent resistance for 12ohm and 6ohm resistance in parallel\n", + "r2 = (12 * 6)/(12 + 6)\n", + "# Equivalent resistance for r1, r2 and 1ohm resistance in series\n", + "r3 = 1\n", + "R = r1 + r2 + r3\n", + "print \"Equivalent Resistance of the network is: \",R,\"ohm\"\n", + "# Formula to calculate current is I = V/(R+r)\n", + "I = V/(R + r)\n", + "# I1 is the current in one of the 4 ohm resistors and I2 the current in the other 4 ohm resistor\n", + "# I1 * 4 = I2 * 4 i.e. I1 = I2\n", + "# Here I1 + I2 = I\n", + "# Therefore I1 = I2 = 1A\n", + "I1 = 1\n", + "I2 = 1\n", + "# I3 is the current in the 12 ohm resistor, and I4 in the 6 ohm resistor\n", + "# I3 * 12 = I4 * 6 i.e. I4 = 2 * I3\n", + "# Here I3 + I4 = 2A\n", + "# Therefore I3 = (2/3)A and I4 = (4/3)A\n", + "I3 = 2/3\n", + "I4 = 4/3\n", + "# voltage drop across AB is Vab = I * R\n", + "Vab = I * R\n", + "print \"Voltage drop across AB is: \",Vab,\"volts\"\n", + "# voltage drop across BC is Vbc = I * r3\n", + "Vbc = I * r3\n", + "print \"Voltage drop across BC is: \",Vbc,\"volts\"\n", + "# voltage drop across CD is Vcd = I * r2\n", + "Vcd = I * r2\n", + "print \"Voltage drop across CD is: \",Vcd,\"volts\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-4-Moving-Charges-and-Magnetism.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-4-Moving-Charges-and-Magnetism.ipynb new file mode 100644 index 0000000..15b9ae8 --- /dev/null +++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Chapter-4-Moving-Charges-and-Magnetism.ipynb @@ -0,0 +1,324 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 4\n", + "# MOVING CHARGES AND MAGNETISM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.1; Page No 136" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "magnitude of magnetic field is: 0.784 Tesla\n" + ] + } + ], + "source": [ + "# Given Data\n", + "from ipywidgets import interact\n", + "def magnetic(m,I,l):\n", + " # m = mass of wire\n", + " g = 9.8 # gravity\n", + " # I = current in wire\n", + " # l = length of wire\n", + "# Formula to calculate magnitude of magnetic field B = m*g/I*l\n", + " B = (m * g)/(I * l)\n", + " print \"magnitude of magnetic field is: \",B,\"Tesla\"\n", + "interact(magnetic,m=(0.1,1),I=(1,2,0.1),l=(1,10,0.5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.3; Page No 139" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Radius of the path of an electron is: 0.28125 meters\n", + "Frequency is: 16976527.2631 Hz\n", + "Energy is: 4.05e-16 Joules\n", + "Energy is: 2.53125 Kev\n" + ] + } + ], + "source": [ + "import math as mp\n", + "# Given Data\n", + "m = 9e-31 # mass of electron\n", + "v = 3e+7 # speed of electron\n", + "q = 1.6e-19 # charge on electron\n", + "B = 6e-4 # magnetic field\n", + "Kev = 1.6e-19 # 1 eV = 1.6 × 10^–19 J\n", + "# Radius of the path of an electron is r = m*v/q*B\n", + "r = (m * v)/(q * B)\n", + "print\"Radius of the path of an electron is: \",r,\"meters\"\n", + "# Frequency is f = v/(2*pi*r)\n", + "f = (v/(2 * mp.pi * r))\n", + "print \"Frequency is: \",f,\"Hz\"\n", + "# Energy is (1/2)*m*v*v\n", + "E = (m*v*v)/2\n", + "print \"Energy is: \",E,\"Joules\"\n", + "E1 = (E / Kev)/1000\n", + "print \"Energy is: \",E1,\"Kev\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.4; Page No 142" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "kinetic energy (in MeV) of the proton beam produced by the accelerator is: 7.10611516878 Mev\n" + ] + } + ], + "source": [ + "import math\n", + "# Given Data\n", + "f = 10e6 # frequency of cyclotron\n", + "r = 0.6 # radius of \"dees\"\n", + "Mp = 1.67e-27 # mass of proton\n", + "Mev = 1.67e-13 # 1 MeV = 1.6 × 10^–13 J\n", + "q = 1.6e-19 # charge of electron\n", + "# Magnetic field is calculated by B = (2*pi*m*f)/q\n", + "B = (2 * math.pi * Mp * f)/q\n", + "# Final velocity of protons is v = r * 2*pi * v\n", + "v = r * 2 * math.pi * f\n", + "# Energy of protons \n", + "E1 = (Mp * v * v)/2\n", + "E2 = (E1 / Mev)\n", + "print \"kinetic energy (in MeV) of the proton beam produced by the accelerator is: \",E2,\"Mev\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.7; Page No 147" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Magnetic field at the centre of the coil is: 0.0009375 Tesla\n" + ] + } + ], + "source": [ + "import math\n", + "from ipywidgets import interact\n", + "# Given Data\n", + "def field(N,I,R):\n", + " u0 = 12.5e-7\n", + " # N = No of turn of coil\n", + " # I = current carried by coil\n", + " R = R/100.0 # R = radius of coil\n", + "# Magnetic field at the centre of the coil is B = (u0*N*I)/(2*R)\n", + " B = (u0 * N * I)/(2 * R)\n", + " print \"Magnetic field at the centre of the coil is: \",B,\"Tesla\"\n", + "interact(field,N=(100,500,10),I=(1,2,0.1),R=(10,50,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.9; Page No 154" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Magnetic field inside the solenoid is: 0.00098 Tesla\n" + ] + } + ], + "source": [ + "from ipywidgets import interact\n", + "# Given Data\n", + " # l = length of solenoid\n", + " # N = No of turns\n", + " # I = Current in coil\n", + "def solenoid(l,N,I): \n", + " u0 = 12.5e-7\n", + "# number of turns per unit length is n = N/l\n", + " n = N/l\n", + "# magnitude of the magnetic field inside the solenoid is B = u0 *n *I\n", + " B = u0 * n * I\n", + " print\"Magnetic field inside the solenoid is:\",B,\"Tesla\"\n", + "interact(solenoid,l=(0.1,1,0.1),N=(100,500,10),I=(1,2,0.1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.11; Page No 159" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final torque is: 1104.46616728 N-cm\n", + "angular speed acquired by the coil is: 148.62477366 s^-1\n" + ] + } + ], + "source": [ + "import math\n", + "# Given Data\n", + "def torque(N,I,R):\n", + " u0 = 12.5e-7\n", + "#I = 3.2 # current in the coil\n", + "#R = 0.1 # radius of coil\n", + "#N = 100 # no of turns of coil\n", + " i = 0.1 # moment of inertia\n", + "# Magnetic field of the coil is B = (u0*N*I)/(2*R)\n", + " B = (u0 * N * I)/(2 * R)\n", + " B = B*1000\n", + "# Magnetic Moment is m = N*I*A\n", + " m = N*I*math.pi*R*R\n", + "# torque is given by τ = m*B*sinθ\n", + "# Initially, θ = 0. Thus, initial torque τi = 0. Finally, θ = π/2 (or 90º)\n", + "# Therefore sin(90)=1\n", + " t = m*B\n", + " print\"Final torque is: \",t,\"N-cm\" \n", + "# angular speed acquired by the coil when it has rotated by 90º is w = (2*m*B/i)^1/2\n", + " w = math.sqrt((2*m*B)/i)\n", + " print\"angular speed acquired by the coil is: \",w,\"s^-1\"\n", + "interact(torque,N=(100,500,10),I=(1,4,0.1),R=(0.1,2,0.1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 4.13; Page No 165" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current for series equivalent resistance is: 0.047619047619 Amp\n", + "Current for galvanometer converted to an ammeter is: 0.993379675604 Amp\n", + "Current for ideal ammeter with zero resistance 1.0 Amp\n" + ] + } + ], + "source": [ + "# Given Data\n", + "Rg = 60 # Galvanometer Resistance\n", + "Rs = 0.02 # Shunt Resistance\n", + "V = 3.0 # voltage of cell\n", + "R = 3\n", + "# Total Resistance in the circuit \n", + "R1 = Rg + R\n", + "# current for series equivalent resistance\n", + "I1 = V / R1\n", + "print \"Current for series equivalent resistance is: \",I1,\"Amp\"\n", + "# Resistance of the galvanometer converted to an ammeter is R = (Rg * Rs)/(Rg + Rs)\n", + "Rgs = (Rg * Rs)/(Rg + Rs)\n", + "R2 = Rgs + R\n", + "# Current for galvanometer converted to an ammeter \n", + "I2 = V / R2 \n", + "print \"Current for galvanometer converted to an ammeter is: \",I2,\"Amp\"\n", + "# For the ideal ammeter with zero resistance\n", + "I3 = V/R\n", + "print \"Current for ideal ammeter with zero resistance\",I3,\"Amp\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Right_Hand_Rule.ipynb b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Right_Hand_Rule.ipynb new file mode 100644 index 0000000..beb92fc --- /dev/null +++ b/Physics_Textook_Part-I_for_class_XII_by_NCERT_by_Chief_Editor_-_Shveta_Uppal/Right_Hand_Rule.ipynb @@ -0,0 +1,82 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Right Hand Thumb Rule" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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duTNi4hs10pYlbDQib2DbNsyLgQMRy1+0qP73Zca95s9HJv+CBUStWmnvQ2bs\nLTJpEmptjR+vnDjHjAz9qVOxNidOVFdavv0WZW+khNdNm/AsaxQbi/knvcPBg1BYXV3BG8yTTpnR\njzt2EP311zuWR2A0Mk+bBnzREqaxRo8fA5bp0kUbFtm2rQmLkxwzP/+M354/B6RUqRLzv//KX3/3\nLszTevUyBgZ68QLRGxIEFBqq73op7K5CBVSdXLtWG4xmMMDU7tcPprurK6KgbMVzfXzgLO7ZE7Cb\niwsiHrZvV49jT0pSDkE0GDDOhw8jkmv0aOC95crBnHZwgPO4Uyc41L7/Hn1y9Cicos+eZWxFVWaY\n6adPI7CACBi+JRmNwKO9vDCfDh9GeO706eh3Kaosd27m+vWB/375JXwv69cjeiQw0DbYx2iEc3TV\nKqwNR0fEqQsCPnPkACRnq8PdywulxFu2RJn0KVO0h3MzYzxHjkSgQe/etlfjFEXmPXswF5o00Z+H\nEBmJyCfJOazmE/HxQWn46tWtl5qXKCoKEVWZM5t8L3p8B8xYFz17Mn/8sXxFU1F8B6Ghr75iCgwE\ndODkpH7Nvn2wHPr0gfarpA0YDNAeZs0ymWCVK6OuR9WqyKQcPhxSfMyYtFl9CQlE8+YhrX32bGhR\n6dkpKiKC6IcfAOH06AFNQylFPjERFSMnTIAWFhKCYnerV8Nc79FDWw2hR49g8axdC82/Vy9AWlrS\n883JYIBGf+QIDlFEvZ3PPkOtmlKl9Gu4RiPgh3v3UOZAqjP05AnaKu3UJe1MJhVp07pLGDMsQam0\nREQEjqQkfJ+cjL+Tkkx/E+FvqdZQliwoN3HwIGCipCTMlZEjAZnkzQuIKG9eQEZa+yAiApmlljWV\nHj/Gc1q2xD2lrOtPPtFX+kHSaEePNpXIyJQJY9+/P7R6WwsdenigVtf27bBC+vZFX2jJ8A0PB2Qk\nZfdOmoR31VtTyWBA3Z5Zs2AFLVyovVYPEepEDR2K8V2zRhn+4ZT6Q3/8gSoDU6datw5++43ou++w\n7s6cAW/p1w/t1JOVrFTR9J0rMdGrF9Ovv6p3kNEIk2n7dkBBcvi9OT17BoaXIwcmi7kpGhkJvPDy\nZfwmhxeePw/cztGRaPly20xZiRITwcBPnwaGO3MmFo8aDR6MyVegAEzyHTvw+d13YI5KFB8PzHL9\nekA2ffpgMlaqpK/t4eEwxw8fRhE7FxdAd+3aoayBnsWbnAyGf/Om6bh9G33SqhUUAYnxly1rG54s\n0YYNMP2fa0pNAAAgAElEQVSjokxYfr58phITJUvit2zZsKCzZTP9nSOHqRCd5D+4cAFwVqZMmIuZ\nM5vaGBVlKi9hNIJRhIWZSl5IZS8KFwY81LixevsjIyEQPTzQR1Itphw5IBQaN8bz69VTL58eEAB4\nzNcXDG3yZBSeu3cP8FGbNqjvZEtVWoMB82PzZnz27g0lpXlzdYGYnIy1vGsX4NGpU8H09CpbCQlY\nX1u2oO9nz9buKzEaAfnOmIEaSDNnKtcACwjAugwMxNq0BklHR5ug45cvoXBmyoQ5qafqqLWaRe9c\niQktJnBwMGCMZs0Aq6jR3r0wXRcvTmv2nTyJiIBhw0xwyo0bMHOZ8d3o0Shjcfiw+rOUSNqeskQJ\n5i++0JcHsHs3THkJ0mrbVluKv7s7zO8CBZBEt2eP7ZFEiYmItmjXDvCG3jDWkBDmQ4cAezRoAEil\nfHlsTfnDD4BZ9GwlqoeiojBvMiqngxmQwtSpiA7JkgVja0kJCYh6unULUTcbNwJOGTMGkXBffmn7\n80URuSWHDyOLu317wBvFiiHjfckSRBxJ21iaU3Q08gZatDB9FxwMGK9XL3xfowYSFz09bYOmwsKQ\ndVylCna8W7pUWwayKCLJrUEDwCG//mrbnA0NRaSfgwO2d5XrB2sUFIQyEB9/jF0H1dq7cSPmQd68\ngCalpDBruUbmWclTpujfeGr/foS1SpFF9DaEjxJRdiK6SkTuRORBRG5WzlPtgOvXgT1PmKAeyhUf\nj9Ct0qXTZuUlJCD0rlgx1LqRSBSB/WXODF9F6dJYGHpxe0s6exZhsDVrqk8sS1q/HriuJASI0G5r\nWGZyMgRHw4bAMt3c9Cd5WSM9jNTfHwlggwczV6wIHPyzz7B71d9/v5p4+NdBwcHA/G1JxMpoEkVg\n99u3Y9vF2rXBCD/9FELr1ClTfLooWmewycmYp6NGoaZX2bLwX/37r36hIIrIfu/VC3h5nz7A8bXc\n59w5zOGPPkLYsh5mLtHjxxC4zs7oFz15EQcPYq0NH66+w9vWrab1mSsXjgIFlK8JCkIJkEqVrJep\nsUYhIVCi6td/SwQBg8nnSvnMTERXiKiOzDmKL751Kyb07t3qnfT4MRj6V1+lTTp6+BC/deqUNjb/\nzz/hQCYC85VzAOohLy9oFi4u+iehhwecfB9+iFoxvXqhoFj//tAELGOgw8Jg9Tg7Q5vavfvV71tr\nTnFx0HrHjQPjd3RE+vzPP0OA/5dtsZOJYmMhAKZOhRWWOzc0/oULwdjVYulFEedNmQKH7KZNtrcl\nOBiWSunSqEO1fbu2eXHtGizbokXh/LbFQjh/HgmjnTppL+bGDGd/377gCZ98grW3YgWEhCVvGTMG\nyZFE+Fy9Wv3+ooh+cHSEQqH33YKC3iJB8P+GEOUioutEVFvmN9kXNY8k0hKls2sXOnXlytRah2TC\nFSyIAbLUSIxGU0VTSRBUq6b+PDmKi4Mm7uCAia8n5fz+fQiAQoVwrVoE0KNHgLak6Iv0JO3opYcP\nUfqjZUswmIYNASdoYTDWyGhEduru3ciqfZ+TyZiRvJdRWerMuNeBA9D2K1YEdNOnDyBUawmT5mTr\nuFre49AhJGC6uCBpS8uzr12DhVCiBNayXuXCYABc5eQEwaKnX80LV2bPDv6wfn3qcyIjobhlyoTP\nunW177UcGIhkt2rVrFdRtkZvjSAgokwp0FAUES20ck6aF4yNhVbfoIG6PyApCRK1bNm0zDAyEmZU\nxYrywsRgMIUBZsoEgeDqilosekgUoSmULAlm7uur/dqAAGRfOjkB81UTAB4eqLlToQJwyYwqC6xE\nogi8e8YM9GWZMmjzvn22lXswGPAeGzfCF9OoEXDW4sXhR5k5M2Nx/beRRo2ClersDP/MlCko5ufp\nmTFWlo8PNFxXV5T7+PxzhCCnZz4ZjSjgpsUCvnoVa9zBAe+mpdjb+fMQIuXKAWvXWwYjJASQZZEi\nQBq0KBvh4egfSRi4uMj3/5o1CGn29ka2sIMD+ldLG6VSNwULQsHS+l5vjSD4f0OI8hLRaSKqKPNb\nqpfz9wem3ru3ujMlJATOrzZt5B2Ou3dD2srFkvv5wfFcuzacPLYynidPsIjKlQMGrpWiosBYCxRA\nwSo1h6m7O5yBhQohXl4Nu0wviSIW68SJMOlLlEAtpUuX9C/A+Hgs4vnzMVYffggnZ48esH7+/tu2\nUhrvOklW0r598LF07gwhXLs2FJhx42AJp7cOVUQEhEz37sgt6dQJzOz5c333CQpCOYxSpeAk1hIE\n8OQJsPjWrQGxqD1TFCFsunUD5GPLPgZXrgAmbt5cWw6ExOSzZcN6nTEjLb8QxdRK68OHGKNBg6Ds\naaEnTwCDN2umbe8SrYLgjQofFQRhBhHFMvOPFt+zm5sbEaG8g7t7U+rYsSlNmqQcenbnDlHHjqZd\nf/SEmx04gHyA0aMRRqf1Wk9PZMJmz46QsxUrkA8wYABKQmjZvSspCeGcc+cibnruXOWQvZs3sb3l\nv/8i7GzIkIzd3tKSHj8m2roV5SaSkxEG2Lkz9pzVGhsvbWx/9SryNG7dQmx3w4YIfWvQQFu+SHoo\nMRFhdyEhpk/pb0FANrJ5lrD5Z7FiCLPMnBnhfuaf+fKhX/LkQXy/5aeTEz6lUNH8+TN2c3gihCXe\nuIHQZ+nIkQNhk/XrI0y0ShXb9jlOTET2744dCBeuWxe5Kp06acs2Z8ZcXbECY9+1K3ItqlRRvu75\nc+w4tnUrQjMnTFDOsBdFhJFPmYIw2u+/Vw+hNSejEWUjJk9GyOqYMdb5gNGI9nfpgpyDAQMwj7Zt\nU97v3GBAXsOqVciZaN9eW7sWLyb68UfkNXTubPrt7NmzdPbs2f//P3v2bOI3PY9AEISCRJTMzJGC\nIOQkouNEtIiZ/7Q4j5mZLl3CZPv5Z9QjUaK9ezEgy5ern2tOyclIXPH0RJyxnu0cQ0NREuDrrzH5\nBgzAQtuwAcJBC509izT1MmWwGXa1atbPffoUEzQ0FBNo4MCM3VjcnCIiUD5gyxYkNfXogeSgTz7R\nzsT8/bGx/V9/IVeiXDkwgWrVwEy0Jn9pIWbTJvC+vqZD+j8yEr87OGBbU+lT+rt4cbyXeb0g88+s\nWU11hozGtJ+xsUgS+uMPCLWKFcGco6IgpG/dMm1PGR9vyiMoXBgJSwUKoARI6dJIwEuvYGfGuF2+\nDOF76hRyGFxdoWy4utqWAxMXB2Hw+++Yu66umPctW2qrN/T8ORjg2rXIUejSBfdQmlN+fkjg3LsX\neT5jxypvvxkbCyEwdy7Gr3Bh05akI0fimUrk5YXEOmYoddYSypKSMC8EwXTupk1QzAYMUH6nS5dQ\nYuLzz5FMqmUd37yJvJ9atZAfIbd+3oo8AiKqQkQ3iegWEd0homlWzuOjR4GPmYd0ypEoAhapWFG/\nczQoCFh069a2hYV+9x1Mw6xZ4SBdvlw7RBIYCBjE2RlmvhI2GRKC0hMODnDAvqotGI1G7LPQty+g\nmi5dEJuuFSKTokrmzEGJhAIFAC1s3ZoxW1AyI5Li7l3kQcydi5T7GjWAnbdqBdO7SxeMzfLlcIbe\nvAmYSY+z2WCAoz88HOa9nx8iv548gYkeGIh7Bgcz//ILxjF3bjgOFy5UvndcHHDjS5cw9qtWwS/S\nti3yKXLkQBmCBg0AjSxahCg2P7/0OcyfPQPu3KULxqZyZZRUOHPGNhg0NBSlROrVQ2jlzJnat15N\nSkKETIUKgLUOHlRfO0+ewJndpAki0NTaPH8+/HwSlp8lC2AzLWQ0Ms+bx/8PAf3kE/Tb7NnKPhNP\nT/Rrjx7qUG14uKmMjdYSNdHRKElStiygYUuitxEaskaCILCTE9OBA8rVAA0GpFrfvInSBnpKI1y8\niNT3gQORNajXZA4OBnyTkID/c+VCAa+SJZWvMxggzefPxwbb06ZZ1/7i42HhLFmCts6cmTFVJC0p\nIgIZoKtXQ4MaOxYQm5ayAMwoAbFrF44sWdCnDRtC609P6Y24OGjS16/j8PeHJuXigizjChVwSFnH\nWjZp9/ICxCEVgwsLQ5a09HfZsoCwmAH3Zctm+pRgvuRk0xEVhTE1p/z50cb8+XHky4fPkiUBpUjF\n5YoVk8+SFkVYL15e0IZv3kQm8Z07eGbVqoAlqlYlqlkTf+ut1Go0Ako6dgyb0t+/Dyuzc2eUBhFF\nWBQPH+Jo2hSWjjW6cweZuL//Dqt68GAUKlSzEkQRZWTmzUObpk1DkUOleXP3LrLofXxQPfjzz+W1\nb2b8dvIkxujDD9FOPZnSffsCmpLYpiCgiF/NmtaviY/HGjp9GlZ1jRrWz2XG2tu5E+jHoEH6qiu7\nuaFYn3TNW2ERaD2ISDVsKiYGGlSrVvqcpKIIDc7JyfrGMlqoXDloCzlyQBPMksW0k5ASde2K2O37\n95XbuG8fnKcdO+or4KWHbt9GxES+fNBM/vlHu8Z56xbi0T/+GI7AKVOgodiqsYoiNP1Nm5AfUaUK\nnHG1amEznvXrodnrzby0pPv3EYK8dCm02QMH4LS+excOvOhofRE40lg5O8MqyZIFmvH163B479qF\nHJTvv4c22aMHolxKlULoYYECsJ66dtX2vOfPcd+lS6EZNmyI5zZqBCf+/v36HbrMsBZ+/BFWiKRB\n58yJ6Bitc5sZQRibNsHB2aYNrCMtDmJpT+969bCmd+1SnkuiiPVbrhxClq3xixcvEH2WPTvmuoMD\nLEWtoa8REbhe6pMOHbRdxwxne8GCeJ7aurh/H5ZEr17aLf5Hj2ANjxhh6mN626KGFBupklD24gXM\nyX799Jm0SUmYDJ9/rrwxhBLFxmLjb0FAxMYff2ASak3+ePlSeVI8egSoqkIF7Vtx6tkwXVpA/fvD\nnJ8zR/uerCEhmNTVqmFBTJgAhmfrxvaWFTFLlQIzW7MG900v0/8vyWBApFmRIvIlJuRIFDEfbt7U\nt+2qJUVEANKbPRtMNF8+U19u3qx/r+gNGwB5SswvUyYIGL3j7O4OKCd/fkBfT5+qXyNtOlOjBiKA\nzp1TPj8pCTBRwYJQTOSEzsGD6AtmMNzGjXFvrXDMqlWAfx0dMb7Tp2tXFh4/Br/RAhXFxgKWrVhR\ne9mZhATAoCVKAJZ9bwSBtzfinGfM0Dcxw8NR1qBNG9tLGnh4YJB69sz4UM3YWEwwKfFMq4C7ehXa\n159/Kp9nMEBoffIJtO3t27U9w2CAn0bKbO7RAwvVlk3Eg4PBmEaNgkVWsiQE0ubN2rFlO6mTVHZ6\nwwYoK/nyweqYMAHWhJakxqtXTVsmSmWTS5eGsNG7K52/P+pKOTjA8tFSftloxBx1cYFlLLdvuDmF\nhKD0RZEimOdKvMFohK+kYkX57WgtKTkZVsfVqxCqLVpAmKiVVJcoPh4KaPny6u/BjES3ggWxI6BW\n2rsX/O29EASPH8MEX7FCewcxQ/uvUAEMyNbEm61b4dTZuDHjM1xPnIBE79pVe/y3jw+YctGiaJO1\nyZyQgEn/8ccQGIcPp22/n19aTSogAFBBsWKAZ1avtq0Q3KNHKCQnJYh16oQJnlE1jzKaEhJgcXp5\nIe7b0xMQ2s2b0LguXUK9nEuXoPE+eIB3efECCkZS0puXAZ2cjPa6uaEeTZ48gNvWrVPO1bh+HbDT\nDz+YAgFGjABDb98ejFpPGYSoKGjv1avDcrl4Uf2a+HjAYI6OcGyrJZNeugSIpXVrdQvExwcF2xo2\n1GatSGQwIFChbVvUDtNKGzaAwWtxWN++jbpII0fqC9Z45wXB/ftgSmvXausUiS5fhkajV3hIlJyM\nJJ3SpTNuD2KJQkOBCZYurT3xLCoKJnCBAsCiraXjJyUBYmnTBovi3Dl5BhUSgkXWuTP+v3LFlEQ0\nZgwmpB4SRTDIefOgARUpAm3oyBF95TUyigwGRHlcvQqtadkyaMbjx6Nv6teHklCkCDBxCQJo0QKJ\nWtImP9WqQSDWrQsGUK8erKuyZaGcODqafEVNm8LiqVQJf3fpggQpNzdg7QcPQrBkZLkIPRQejr7o\n2hVW3mefYa7IMdnAwLTMPiEB79CiBZIZp0/Xl8CWlASfj4sLxkBLkbWwMMCYjo6AapS0+KQk+CYc\nHOCbUWKkRiMEXcGCYNR6hPiJE3j/FSu0X3fjhikRU00pDQ8HrNS4sbbqyszvuCDw8MBC1Vvk6sgR\nLFRbncKhoVgkrq7przpqSfv3451Gj9bmHBJFaNLNmgF3tbbwpF2aypZFlqSSGW40YpJlzYqjalXA\nNUuX6mdSPj5YfJUqYYF//z00SFsgJL1kMCC08OhRtH3wYLy7szPeq1AhZKZ36ACNduFCOPKOHoVW\n6ukJCyg2NmO0+YQE3O/2bcBoO3bAtzJjBjTxbt0Az+XODUZcrRqCAsaNg/X299+AIP4LyyI2FvOl\nWze0pXlzVIrVOv737pl2F/vyS5QQ19ruxERYmsWKYWy0KB0eHpizNWpAaVEiLy8w0s8/V9/l8M4d\njMmgQfosXy8vXNevn3ZFJyQEVRLatFHnK0YjBK2zs7bw+HdWENy5g4W8Y4d6J5jTtm3Qyi5f1ned\nRB4ecLhpkdx6KDgYGre17ebk6OFDaF/VqilP/tOn4QSrVg0p92oLctIkU4VEIiwwPYXEwsPBuJo0\ngfY1ZAgij14l83/5Ej6LefOQW1GpEiK3XFyA444aBY3x5EksUlv3XPgvSBTBFK5dQ+mTxYsR0dS0\nKfrTwQFjMnw4GOb586/WioiLQwTU8OEQCt27QyhpGc+oKLSxYkUUkdND8fGAjJydwYjV6nKJIqDa\nIkVwvtLeBqII66NgQfjelOZ3QgL6v2RJfTlJMTGw+mrX1m4ZGQzgLaVLa3MM792Ld9i8Wfm8d1IQ\n3L8PWOfAAfWOMqfly1EsTm/lPokOHQJD1eOsYQaDUmI8R48Cpvn2W2375iYkoJ6MgwPwemsC6f59\n7AlcqhQiVtQWbng4YAEi7LeQOzeOzJnVtSFRBA7bpw8spc6dYd28igif0FD06dy50JiLFweDatYM\n0M7u3Qhjzeg9iN8EEkWEgZ48CSY5cCBgrJIlAVf17w+Y9NatV1PWOzQUkEf16mDQM2dqj/qxVRGI\njobV5OAAGEhtz4HwcFjUrq5QFJUUHy8v+KkaN1Z/j127wHTXrtUXjbdwIda3nr2IN20C3KUFtbh7\nF1bXhAnW+/idEwTe3lj4euAgUQTj/Phj/ZENEv3yC4SPHksiKQlmfcmS8s9NSMDvxYtrdy6dOwd4\np0MH6xE1MTHQ6qVy2mrab0iIKTKpWzdooGfPInTxxAlkmFqbYJGReEbVqujfH37I+KJwISHQSMeM\nAf6eOze00okTmXfuRLDAm+aI/a8pORk+mF9+QaihtOF906bQeE+ezHhfjLs7GK6jIzTfQ4cypgS1\nNfL2xnNcXNTzCZhhJVesCGVBKVTWYEAfFSyoHvTx4AGszd699SkaBw+in7RmMDNDsSpSBOtR7V1D\nQiDMOnaUh5S1CoK3JrO4dGmmsWNRG0QLMRNNnIhMzGXL9GfgiiKyGvfsQW0crbWCXrxA1m/OnMj2\nsyyK9fAhah+VKIHCckpFs4hQJ2XSJBTpmjaNqEOHtOcwE+3bh6J2TZqgIJVSVnVUFDIwN24kat0a\nRbVKldL2fnfvoljYrl3IOB06FAXMbCleZknx8ajPc/w46tZ4e6NIWtOmeK9atfRnzCqRwYC9Zf39\nUfMmLAx1m6RD+j9PHrTFfON66e/ixbHvdfbsyMLOnj3131WqIFPZ0RF1jCw/pcJzGdF/EoWHY764\nu6N4oqcnsoBdXXFUqZIxRe6k/a6XL0c/jRiBmjr58qX/3nJ09iwKvzk6ot6Y0ibyiYkoxPjTT8ja\nHj0aa87FJW3tprt3sc95rlyoeSTtIWxJsbGoXBAWhjWgVjVAolu3kKU9ZAhqg2npez8/ZEvnyYPC\nckrzPikJ9759G3WfihUz/fbObV4/bx7TtGnazueUjbePH0dxLQeH1L9fvYoSBSNGyF+flIQJ7eWF\nCpuOjtqee/UqClj164dUb/O0eGYUoJo4EcWvhgxRnxAXL+Je9etDmMmVeHj0CIW3AgJQEkJpw/Pk\nZBT4mjsXAmDWLCwONWJGevySJShtMGoUUu31lPCwRi9eoALloUN4Ro0aKEXQuDH+Ti/jDwkBI3zy\nBCUInj0zfT5/jkqgTZuiIJyDQ+qjQAF85s0Lxi5tXm++iX2WLFAaEhNRXiQxMe3fwcE4QkLSfoaF\noQ+cncFYzI/SpVHiIr3F+MLD0bd//40jLg7FyurWxYb0GVGo8OpVCIQ//0TRxVGjUGgvo8lgQImH\niROx0fvUqRC41mjMGLQrUyYw/+RkzPn791OfFx8PYfHPP0S7d1uvhMoMxjxnDoTgp59qa3dgIIRB\npUpYg0ptligmBn2ZlIQ2KZVMYUZhvVWrIPylkhfvXIkJPdjc5MmALOSgigsXYKodOSJ/fVQU8zff\nwNTSYwJu3Qrs9ODBtL/FxwPD/fJL9WgF6fzx42EeWvOHJCcjEqd2bcAySiFxogjnUpkywE/lilPJ\nkVQIrHp14NC//ZYx2P/jx8BP69cHxt+1K/wv6YnEioyEY3rNGjiImzdHcEDevHhO//4I19ywAU70\nN8lxHBsLB+GRI8Dhv/0W+RXduiGEtVQpQILTpyM5Kr0bz3h5wWHavDn6p2tXQBcZUbwwMBD93LYt\nNhBSi+Rhht+nbVttyVUS+fujTypUwNxWgqbatDEVm8ueXdnXt2WLKXRUiY4eBR/RmjXOjHH++muM\nq1Ynf3IynPVVqmjbzGrvXgSSSA56etd8BFpIFBFTX6WKvBA4dw6DfOyY/PVhYYgLHzJEO+YpinBo\nlSwpXy/I1xfMumtXbQvN3R1RN199ZR1z9/SE87p5c3VH1/XruF/Vqogc0kJxcWAUzs649siR9Ef+\nhIQgeqduXUR9jRsHP0RGMeP163HvAQMQMnrsWPqrc74JlJyMebV7Nxy0nTpBoOfMCWx4xAgwNi8v\n29715UskkrVsCaEgOfvT61eIi0OOhLMzGJNSGGlCgil2f/hw7ZVpRRFOYSnKLX9+CIaWLVNvRvPs\nGforc2ZElI0cqfx+np7wMYwYoeygvn0b7zdrlva+Nxrh86pUSXtEkSiif4oVQ76JGl29CiVyxYr3\nVBAsXYrEHrmJdOoUJtrJk/LXvngBh+S4cfrinnv3BlOWK+x17hwG5Pvv5e8ZG4uU9uRk/L5yJdpo\nLSU+OZl5wQKcs2aNcjsjIjDhCxWCVm9NsC1aBC2FGQty5UpMuD59EPefHkpIgIbSsSM0/6+/hiZl\n36w+/RQdjZyHH34A8y5aFBZQhw6wti5d0l9KOiQEmnCvXgggGDlSu/VojRITcc+yZWGZHTlifd6G\nhMAJLSV+aRVGy5eDyZvXQrK0RCZPRla0tO93lSrKUYQxMXj/WrWUdw8LCsL6/+477UqNKMJJXby4\nNoRAot27kQOhJdn06VP05XsnCH75BTG4crXBz5yBxm4tQsffH9EWM2dqFwLh4Qhb7NAhLYQkipic\nTk7KWviKFShWN2AAYKPq1VGCQY7u3YN2/tlnyuUYRBFmfrFiCDFUiqn29ISmlDMnJrKzMyZaeje5\nf/QIMf316yN65bffbK/nFBUFeOx1ZCG/TSSKiCb74w/0ffv2EL4dOiC668kTfffz9sZ6KF4cyVqr\nVqUvZ8FgQKTX0KHq5z58iHa7ulqHcM3JaISGLQmCqlXTQphJSaYIPvNcAiWYSBSxh8FHHylnO8fF\nYf22bq0PXtu+HTxCrZCeOZ07B0hq505t579XgmD3bmje0mQPCTHFv1+9io47c0b+2mfPYGovXqz4\niFTk5wdzd/TotJq2wQDztls35YqmRiPaTARh0KyZPP4ubVrt4KAe4vb0KfDQSpWAlyuRwYBSCeYl\nhrUmtFm736FDqBnj6IgwVlsqukolKRYuhBDJnRt9bS9Cp59evACz6dMHIdClS2NuHjum3ddjMOD8\nLl1QZmT4cOQq/Bd08iTa3K2beinta9dQIVWqe1SlinoJmPv3ASUNHqzcH/v2mSx1a5ScjPDd+vX1\n+bpOnoTVsX+/9mtu3YIFuHq1+rnvjSA4fRqMxxw769YNA7dnD6CRw4flr/X3hyNOT70ib29c8+OP\naX+LiwMM0qKFugZ84AAcVxIjzpwZdUfMKTzcZMYqZRuKIjQ2qd67FhO1fXvTsyVz+pdf1K+zpJAQ\nmPElSsAXsnmzfu09Ph6LrW9fMKyPP4ZZfuTIq9t97X0jUQSmvWQJcjHy50d///mndgjp5UvMr2LF\n4J86fPjVlwyJjYVS4eioXvtn9mwofKIIpUlL9nBUFPwu9eopQ0Du7rCYlWL7jUY4+itX1r4ZPTMs\n8MKF9SWsenmZqr8q9cl7IQhu38YEOX069fdOTiZN+9tv5TvoxQvAQYsWWe9ES3r0yHq10+BgaAO9\neqkz4oQEUymHrFmRKNOnT2rz/eJFfK/m2AoKggCoWVPbhjUBAfBrEMHkbdMGzx4/Xl/SnJ8fnF51\n64Kh6PUnJCdD0+zbF0ypWTMIZL0QxqsgoxEC7t49mOKHD0Op2L4dzGjNGhSrW7IEc2HxYkCBa9dC\nEO7cieix48fRp48fQzF4kxzXAQHIUK5fH1r0wIHQTrX4bxITwbRq1gT2v3r1qxfYN2/iec2ba58j\nXl6Y223bKmfIG42AgIoWRVShNXr+HBbv0KHWhYuUUVyypD6L+O5d/UU0Jf+Em5v1uaVVELw1eQSW\n7XzxgqhOHSSWdOpk+j4gAAkjSUnStdj6zjwRKywMSVAdO2KDei107x6ScebMIfrmm9S/PX2KeOwv\nvyRasEA5PyA4GO318MCm9i1apE7AYcbWlUuWIGlFLoFMogMHkNA1aBC2rVSKuU9MRF8tXoxtA6dO\ntZ44o0RPniBeee9ebOj93XfaNz1nxubpv/+OuGgXF6IePbCBvS0bp9tCzMgfePLEdCQmIvnq5UuM\nTyzVbKYAACAASURBVGgoYvednJBD4uSEMTXfplJKHHNwwHxKSDAdUh5BQgLuc/s25qvRiMTGQoVw\nz48/Rq5CmTKmI2/e/6YfzMnHB+OxcyfeydUVc0QtT4QZ23j++CPWW8OGyB9Ib96DNTIYMIe3bCH6\n9lvksqjl4iQnI+fg4EHM2erVrZ/711/YVvX774l69ZI/JyoKa7JwYWwpKW1XakmbNxPNmIE8pjJl\ntL2flxeSNEeOxLrSQuHhyAmqVQv8wjIx8a3IIyCij4joNBF5EpEHEY22cl4qKZeQgFr6M2emlYCb\nNsESyJoVuPegQanTzCMjAWF89512De3OHeum24MHcBJpKWt9+zYglGnT5E3quDjsKVCzprJDODra\nVEtISw33K1fgBG7b1rozWo3u3kXbHBwQLqunnERsLDSdSpVghs+dCy35VZIoog/37YP53LkznIgf\nfAArsn59WEazZ8O5/vffwF4DA23buF0LxcRAS7x8GdDghg2IZpHaljMnoMyGDTG+v/wCLd1SmzUY\nYIHNno0ot4ykO3cQPp0vH0KerZUrt6R79xAV5uQES+lV1nvy8MBc6t4d0XFaaOdOQEW//aZ83sOH\ngFyUAkfi4xGimjkzouKshZiuXw+r++FDbW1kRrh52bKwOLVSRATm85AhafkKvQ3QEBEVJqJqKX/n\nJqKHRFRe5rz/v5goYpF06pT2pY1GDE7WrAirs5wkiYlIcpk8WbsQePAAJuO+ffK/FSsGPFKN/voL\nOKS1qqm+vhAAPXooxy4/eIAY5wkT1HdFS0xEXoWTk/YoA0vy9wd88/nnMHn1RP94e6OdDg6IAjl5\n8tXAI6IIBrtzJ/BkV1c8s0gRCL/p09HvN25oZxyvg4xGQG6nTwN+mj4dQiF3bjCnpk0hxKR9sTNn\nhrMzvRQQkHYuRUSAGZUrBx/V+vXqRd+YwaQ7d0bf//yzbdFejx4p7+HNDEEzdCggGC1Ja8zws5Uv\nDxhMyTn8/DnW4sCB1qGymBjsNSEIcFI3agSIzFKJ+O038Ag9+4wHBqKdCxdqvyYqCnPlm29S88W3\nQhCkaQzRASJqIfP9/19s2TJMTLkNWKZPh6NGLsJAEiBffKGeLPbFF8hOffoUPgG5LEM9QmDnTjBj\na/jjhQvK+QYSHTgARrBunfozb99GXkS7dvr3qGU2VX4sUAAb0esRAJcvQ1A7OMDysnU/aCXy9jbF\nvH/0EbDbjh1RpfLIEfkw4reVDAYwsRkzoOQIAv/fyV+pEqK1bA3PZcaaKlAAUXCWFqMowloaPhxW\n8dKl2hMj27eHD2rbNn0KgBSls3Kl+nX79mFtLVum7RlRUXjPxo2V/QbR0ZhT7dpZt25mz06dv5A9\nu3z278aNUCb1ZE4HBCBoQi4oRanNjRvDLyoJg7dOEBBRCSJ6RkS5ZX5jZpip1rJpt28H7GJt554F\nCxCnb20HL4mSkiDhc+SAqS4nlR8+hBBQS0NnBtMuWtT6Jhu//w7pr1R21mCAkCteXF37Ma+oqHeH\nJen6desgmHr21Be2eeECIqaqVEEUk1pf66GwMIQJ9++PcS5UCFDE2rVgXm+SI/ZVUng4+jhXLjCe\nzp2xJj74AI77KVNgUejN2Pb1xbWOjoA6jx5Na3G7uyNevlAhOMi1CISLF5GH0LChvuS0hw8B4bZu\nrS7UfX0hdLp00dYmoxGQWoUKyhBsUhKcze3ayQsNX19T5F+mTMprePNmOHb1bIHp6wuLZ9Uq7dfE\nxAC2GjgQ7/lWCYIUWOg6EXWw8jtPmODGefO6cY8ebnzGIing2jVgZNbihnfsABPVEtJlvkl35szA\n4c0zlZ8+RWKXGtbIjMVSooR1XH7ZMvXswvBwLIamTdW3pwsLgwbWu7dtewBfvQrctVEjfVFAV65A\ne3JxAYSQURh7UBAgEldXlD8YPhxaoqfn+8P45choRFkDQTBlx8bHQwBMnw4BmT8/5sHBg/rgmbg4\nKBDVqwOr/vXXtELlzh0wXScnRN2pMV+DAQLbyQljqDXOPikJWH2hQsDilSghAbuCVaumfe7/9JN6\n2Qapdlnt2vKwYp064BMNGwKGVBLAK1dCy1fLiTCnp0/BI7TwG2bmM2fO8OTJbvzRR25cu7bb2yMI\niCgLER0jojEK53DHjvKhoOHhkJrW6n3/+y86X+teu4sWmQpU5coF7VbKSAwJweJYvlz9Pj/+CE1f\nrp6IVJ+oTBnlSevjA3/A7NnqYX23b0NojR2rnxHHxKBvpXIUWpmsuzsmf/HiYNgZUTfI1xcLtGFD\n065Ye/bY8wnkSAny8/dHAEOTJqZ+3LtXuxNXFGHhKQn4u3dRj6dYMQRpqOUUhIZCEDg56Ztnly+D\n4Y4dqzzHRBHrrnBh9YRKifbsgRVkrf6YdN+RI6FsWvpSLl4EPJWUBD9Yly7Ka3X2bEC21rK0RTGt\n/+LRIzxbzk9pjSIiEJTxNgmCLUT0o8o5XKNG2g4SRZjGI0bId0ZwMDB+rTuaiaLJ1OvQIbVWHBeH\nSKWJE9Xvs2gRGLic5DcasRiqV1fW8G/fBvatBSP8/Xf1dHlrdPIkBEjPntojgYKCoH3VrAmzNb0V\nSZOTMUZt2jA3aAD458iRV7PLmTUSRbzX9etw7G/ejICDSZPgW2rXDsy0fn0wpZo1saArVwbE0KYN\ntNEmTeBj6tUL83LKFMyHbdtM22W+qqgkaxQUhAikFi0AGwwapA+muXAB8FPp0qjOaelju3wZsFSt\nWtoY8M2b6Kc2bbQnXkVGol8bNVK3jI8dg7DRuh4uXAC2rsQnRBHO6YYNrUOe8fHwU02caF3IiSL8\njw0bygvljRsBc1n28fXrWONaogTN6a0QBETUgIiMRHSLiNyJ6CYRtZY5TxZeWbkSDFXO9DUYMOm1\nMG6JfvoJsJBlhq/BAOdn9+7qWs/q1WCschNc2pe0cWPl6BUpW1oppV2639SpeJ7etP/ISEQYFC+u\nbVs8ZmhjixfDCTxhQvoclMywltzcoFHWrw/mqyUyJT0UG4u+2rkTjmUpXDdPHjCz6tUxb3r2hAa6\nYAHgkQMH4KO6cAGM799/wdBu34Zm7OmJeXPmDM7dvBmW49y56KtRo8D8nJ3hg3JxQRLdgAEQ9gcO\nwBp61ZBXYCDaVLw4otj0ZIKfOQMGVq4cICfztooitPzixRF2KlnR1igpCWPv5AThooWMRljSzs5p\n16gl3b8Pa37ePG19eu0a2qIkDIxGYO9NmlhXUqKjMYeUIn6MRsy7kSPTMvzERAjdUaPStvvPP2G1\n64lAeisEgdbDMo+AGROhYEHr8eizZoHhaq10efQoHKRyUM24ccDo1TTUbdugxctFyRiNCMNs3VrZ\nPP/jD0xIa7WRJJLKWfTsqb+O/7VrgNOmTtXOzI8cAZTVrp2+uGg5On/eVOZg+HDtsJ1ekrZxXLMG\nVkbFimBkFStCsE+ZAkjj8mX1vZkzkhITMW9PnAB2Pn8+tONChTCnXV1hiUjbcb6KbSCTk8HMW7XC\nM8eP1xYYIIrImK5cGWvCcuxiYyFgmzTBe6lZP//+y/93tpYvD2f0rFnKGb67d6PN1kKxJQoMhDAY\nN05bKYzr17H2lOr+GI2Ak5SES0AABKKSIicxfGtwd6VK8mjAhg3wO2qNBHynBUF8PCAEa36B48fB\n1LWGEN6/D1zRvIa5ROvWIYZerfLikSO4h1xNIFFkHjYMZq2SENi4EeeoFcsKDcX79+ihD5eXqqI6\nOmIxyZFUElsiqSRF1aqATNJD//wDeKJkSTDgjIwqYsb9jh4FU2vcGNZd+fIQwKtXQ3n4r2EZvRQY\niLk0Zw4EvbMzIJlOnTB2d+5kfH2fJ08gFAsWRFKSlg1QkpNhkTs6AsrYuBFzPzAQSsqzZxAy1aur\nw1BXr5r8clKQRt++ytfcugVGOneuMlMOCwOk27evNqVQizDQQrduoW+UBFpoKJQruXBwHx9Yynv2\npP1tzhwogFr8Pe+0IJg4ERu3yNGLF5ggp06pdxIznJAVK8JEtqQrVzCYaqbYjRvAia9eTfubKIIx\n1amjrH1v2oQwU7VEGh8fPOu77/QxhPBwaFs1aijXamndGiFzogiT3dER5nh68PoLF1A+u0SJjI0q\nMhqxcBcsgAb6wQeAW+bPhzKQnrLJbxL5+8PaHDgQQqFgQfjGVq60PVNcjoKDYYkUKADYQosiFRIC\nqFCKo8+ZE0y9USPMoU2bwFinTVOGoHr0QIKWFKShxcoNCoKfZvRo5bUQE4N53b69Nhjsxg1YZX/+\nqX6uEv31F5RDpTyahw/RP3L86uZNrBvLEtiiCEHQvbs67PXOCoKrV2FCyzmMRBGakx6/QL9+JsZn\nTs+fw7xT0wwCA3GetfC2uXOhTStNbK1CwMMD0NPSpcrnWdLt29DCR45UZuju7qb9CT75BO1Ww2KV\n6MYN4O0uLvJhiLZQdDQsQanCbPnyKH539Oj7E1nk6wshPWAABHuFCoD5rl3LGB/D8+eALPLnB6yi\ntmNYfDwYnnlJ819/Nf0eGIh1Wb689bDkgABclyMHFIY+fbT5i8LDIXR69lRWMBIT8S6ff65tHl68\nCCUoPfOfGWGflSsrz83TpyEM5CDXvXthFVqOQVwcfFtq5fPfSUEQHw/t3Rr2tmULsDWt2uvmzZic\nlhBFcjI0zGnTlK+Pj4eDcfZs+d/XrwcUohTlsHmzNiFw6xa0QT17pDKDQUoludWoVStT1mrWrNq2\nxZOj4GDUeC9UCJhmegWAVKa6a1fkE7Rpg76171EATfjKFWjyZcqAaYweDR9TeneCCwiAkC1RAqGo\nSve7dAmMXML7581Lfb4owufh7IwMejkNfsECOEljYqDt1qihLScgLg4RRW3aKMMlSUmA2778Ulvf\n7NuHtanm+FYiUYSy+fXXykJ682YoXnJw6ZQpsHQt2+zrCwhcCbJ9JwXBvHkYRLkO9fVNuy+BEt27\nBwYph8cvWgTNQclJJ4rAzrt2lW/PmTNojxqDHzdO/Zzbt8FUrflErNGaNdZ9H5Z05YpJCGTJgsiW\nzz7T9zyDAVi8oyOYUXqgGalMdZ8+KIAmlanWU+xOotBQCMTp0wEb9utne7veZBJFRDDNnQsosmxZ\nvHN6GBkzsP9mzWAlKmHew4Zh/uzdi7lTq1baZEkfH0QrtWmjPJaiCMu3TRt5yNWSpCzg/v2VfU8J\nCYCJevTQ5oRfsQJRUko7/alRXByE2k8/KZ/Xvz/aZclPDAYoaXKO5X/+wXqzFjTzzgmCBw9gPsmF\nZYoi8L85c+Q7w5ISEyGhzc1Xif7+G04aNYazdq115++jR2irtf2R9ZBU+VQtlNScjEaThqilyuf9\n+xACWbJAAC5ejH7Qw8gvXkQcfePG6YsCCg2FxujsjEWxbJn+ukHPnjFv3YqFVb48QkObN4eFd+RI\n+hb120QeHqY9gFu1AoO21T8jbRQv7WctlyMTG2sKvxRFOEGlmkHmFkBSEkJqixdXzzs4eFB5r3Fz\nMhoREt20qbJlEBcHwSaVYVCjCROQOJkeP5m3N5Q5a9vlMqPNVavK7zwWGoowcTk+sHGj9TD6d04Q\ntG6NBB852r4dC13rJJ85E2GQlpI3NBQY/IkTytdfuwYpLFc3JCwMmpieDSaskYcHhIBamJw5JSaC\nAX76qTbteccOLLQ1a2zDl6OjEQLarh1gK1sx6tu3sTDz5QOjUdoj1pISEjBm334LvNzREX+vWgW/\nhx6IJCoKwQGnTgFqXLQIcMW4cXjHli3BRBo0QNmBTz7BM6tVg2LQrh3w6uHDTclk27eDkT179up3\n9JKjuDgIxkaNMJ8WLbKtECEz+mf8eIRlrlunPt7PnqG/WrZMi3MfOQLm+Msvyvc5exZjqiWz1mCA\npe7qquwYjo4GTCRXyt6SjEbMySFD1M9VouPHMT+USkw8eoR3lfOl3LoFYSBXGLBLF8w5S3rnBEG5\ncvJYc0QEcDwt8AczoCNHx7SWhSgiEmnsWOXro6JQskKurLPBgMmSETXivbygnevJFk5MhGU0cKC6\noy0pCYyqdGnbfQFnzsAJ3bevbXH4UmXLpk0xhnPnpvWnJCbCmW7JKIKC4Ihr1w4af/36sAivXdPG\nbCMikM+wYgX6q3ZtMPOcOTG+jRsDpx4/HvHcu3ejyuexY3Du/fMPIAt3dwjsmzfBsA4ehABZsQJQ\n5vjx0CgbN8Y75sgBwfHFF5gnv/4Ka+pVJ9JJdP8+4KL8+WEt6NlS0Zzu3IGzslUr+TIq5pScjHo9\nH32U1gLw8QGENGCAsiJ34waEmJaaO8nJgGzVav+EhMBq1qK0RUYCItq0Sf1cJZo6FXCXkuCToDW5\nNbV8OfrL8r0iIrAWLYNW3jlBYM0hMmYMzEEtlJgI00suVHTjRnj31cLLevcG45CjefPgZE5vAtDL\nl5igeqoOJiaiLEaHDurO2chIaGg9ethWnz862lRj5sgR/dczg/k1bAjn/u+/yzMBUUQbicC0IyMx\nTq6usBzGj4eprBZqmJCA63/8EYLSxQWhpvXqQXCvXo32hIa++szemBgw0X37AIF99x3w45w5wViH\nD8f8fPDg1VoPgYGmyKARI7TlDlhSUhICJQoWlBfWlnT0KCyARYtSv1t0NAR6ixbKcOSDB1BctAgD\nyTHcv7/yenz0CG36+2/1e969i3fVU55Drl116qhvPDNiBOa+JYkiBEnbtml/u3oVSq65T+idEwRy\nJCVtqIW3STRzJjrQcsL6+2MSqpVp2LIFmLNcKNj589BY/P21tcUaxcYiEmnyZO3XJCZi0rdvry4E\n/P0hDIcOtS2q5PJlmKd9+thmBXh4oJ3OzmDqSot08WLElBNhsebNi2t37lTGgBMSoHnOnQvIMHdu\naFETJ8Lh/ujR64FolCguDo7YpUuhzbq4gEn36gUt8OHDVyOkXrxAvxQogE89lTElunkTUNEXX2gr\nGV2/flrN2mCAhVKhgnKpZm9v9I0WYZCQgOCSESOU++7cOfj05JJBLWnHDsz/9GSiP35sPVBFothY\nWCBysPDTp1gT1aqlvceSJYAmpbX9zgsCUYTDd80a651pTg8eYALKmbFffomkKSXy84MGKycsgoNh\n9qY3AcVgAKPr1Uv7ok9KQoy2FiFw5w4cdGob4MiR0YjwviJFsJm7XvLzA4Tk6Ahmp2Z5rVplSjCS\nIpmUYrr9/OCUHDzYxPjHj4fF8ibvSqZEgYFgBP37A1YqWRICfP/+9Nd5sqTgYEBrBQsC1tJr1SYm\nwhlfo4a6YzcpyboSsmwZ3lVJKXv4EPNQy657ERHWyzWY05YtEEJalMqJE2G5pUcwb9qEdilBgpIv\nUs5ac3HBusiRAyiA5DcwGgErSSHt77wgOHAAmq2WCSuKCBmTS8Q6eBDOXSXGJIrQdmbNkv+tbVvg\nwOmlmTMxiFrj7qVd1wYPVr/m/HlMKj2OZ4mCg9F/DRqo48GWZDSCQRcqhMmpxpRv3IAgJEIkkyAg\nYzVTJmQMm9O9e/iudm1otH36gEm+iozihAT4Je7dg+Z+6hQY3rlz8E9duwbm5emJzO2IiIzV4EUR\n1tQPPwAay50bSXXr1mVsFNTdu/BnVK8OphwUpM9yPHUKTHrOHNutrsOHoaErBQzcvo1ztBRMlMo1\nqDmb3dzg81Brd0ICmLgt1X4lEkX4IydNUj5Psmot29S9u0lJypQJwlOab/7+6JsrV95xQWAwILFM\nKz596BAgHUtmGRUFDfn0aeXr//gDz5MLH1u3DlheessmbNyIZ+hhYjNmAG9Uqzly+jQmilLomjW6\ncAF9NHGi/nd88ABW1KefKm/TZzRCIDdqhGctXmwyvRMTgd0/ewZh7e0NZliuHKywkSPBfNLT/6II\np+mpU/AXjB4NLatSJfRbzpxIsHNywnPr1UMuQrNmeL+6daEJV6mCeda2LXwQH3wAJaNZM/iWJk+G\ntv333+qllNUoJgZJgl26ADJr2xaMSW0fa639sWxZamGcKxf6W0umbUAAxrJVK9vyPpixZtWEweXL\nsGDOn1e/n1TGWSknISkJyo6WvYKvX4dilR4o+PlzvKNSnyYnA+qxdGj/8INp21Jn57RlY3bvRtDD\nOy0INm/GgGnRuOLjgenJhYR++616clFICLRZuaikwEBMBrUicWrk7o5JqgWjlOi33/BeagxFSjhR\nq2ZqSaKI7F0nJ/1QUFISFpMEMyhpWFIt+6+/hsCVY+jJydD0W7dGTPy4cQivs0XjFkUsmvXrETVV\nqxaijpycoAkPHgzL8fBhaPi+vnBm6n2WKMIq8PTE3Nu4EcEEQ4bAH5UvH7TUL76AJnrwoO1lqKOi\nEB7ati2EQq9eeGZ6LZJZs1LDc/nyaYekkpOh7RYvri0hTI4OHlQXBqdOgRFqyZc5eBBzSCl01tcX\n613LvgqzZqlHAKnRpk2wvpSsLslJbe5/OXYMQqBzZ/Sx3Li80xZBQgLwMa07EC1eDEeqJT14AA1c\nDRMcPhwaohx17qxehkKNwsLA0PVANidOYIGoFcOTiuap5UVYkuS4q1xZ3x6rzGCyXbtCG1QqD+Dr\nC0uqWDEIdjlh4eMDq6doUVgVtu5X4O2NUhd9+mDRFCmCZ2/YAAGvt4x3RpAoom/37MEcatMGY9qm\nDXwCe/em1e7DwgBFKW0jGhICJaFiRTgTt22z3VpKTkb0mmQZdOigP8DgwAG0Rc/uWpbXq2nNv/wC\nfF+LL2jGDAhiJUj5yBHMEzVrJikJlqBc9VCtJIqA+r7/Xvm8KVMABUpkMJgqEgwYIJ9DwPwOC4JN\nm6A9aqHQUETyyFVo7NRJvfOZsbDkUtb371f3LaiR0QiN0JqgkaP797Gw1MzhGzewgPSGd8bHI5+i\nSRP9WPuff+KZq1db15JiYuALKVAAn3IRWH5+CAlu1QrQj9KeznIkitDmJ0yAElCoEBbRmjWvLvom\nI0iyVpYvR3hv7tyo85MvH7abzJpVe+kPoxFj37QpmNrSpbbBRteuAYOuXh3PbdhQv5/oxg0IX62B\nHZa0fz9gN6XaUsOGwSJS8xkaDOgTa/XBJJo6VT3aiBloQNWq2iMX5ejpU6wbpSqlsbFQGOW21AwL\nQ87R5ctpf3snBYHBgMgJrclj06bJ5xhIuLetSTxRUYhnVsLco6MxOEpVB1evhlDT6hyOjYWWLlca\nw5z8/OBg0lJozpzCwgCPdO2qL53eaIRzsGhR5Vo0//6Lxdq9u/yiDgszlUGePFm/IHr2DJFNlSrB\napwyBQLhTWX8ahQVBehSqgElFQPUW3322jVTtdalS/WXStiwAUzKaATkZ4uC8fgxGNns2baNx5Il\nEEbW/GFJSWDwas5XZkAsRYool6pPSICVoWUNjRmDaK700A8/QDlVor/+Qh9a2+KyXr20fftOCoLd\nuxECqoVCQsBQLAtuiSJghvRkCM6YASxZiSZMgIPQGj15ArxbTz35QYPki1KZU0ICJsSCBdrvywzH\nVa1aiGTQE+0RGQnI4NNPrWepiqKpGJ0cRBAfj4Xu6IhkPT0OuIQE+BYaNkR/Dh0K2PBNyxNID125\nAj+GFCGSNy/gAD1lOJgx1zp2hCWbnjpYFy7AAb5ihb7rgoIAVw0bpj88VSry2K2b9fkfHAwntZZN\nZU6cgPNbSZO/cAHKjZpCEhYG4ZieGlvx8VBezp1TPm/kSPm1bTQCprKEmN85QSCKiJCxVvffkqZM\nAeO0pKNHoVXbmv0bFAQBo4R/S84da8k5ooiFZK12khzt2IEoADXzftgwLHY9WldEBGrmaIHKzMnf\nHzDSsGHWrZqYGEQ9VK0qL/QuXIAm1KGDcmSRJUVHIza8WLH/kXfd4VFU3fssUgIoLQmCgNK7FAEV\n5AOkCaIC0gQBaYKIAiJFaQFEQJSqAtK79CZVOqGXoLRASMCQhEAq6cmWub8/XsZsdueW2YTvU37v\n8+yDZvrMvae+51x4GTt3qnlWmgaP0tPWCk8avIKsq1cRIipXDgSB6dMRNqpXD0aNGSt/504IndKl\nkUP7/XcIsQcP1BXonTvIH0ycaG6sJSTAy1EJu7giLQ0ywJVG7Ixz5yCUVRoV+vnBOxXhk0/Uegwt\nWIA5nR3vc906GGOibxAUBNliRBk+fhzf1TnS8a9RBES0jIgeEtEVwT7M3x/hGBUBnpGBF+IqrDUN\nnHPRAtUyfPqpuJeQpiG+LrKWfvkFA1pVGemViDLq3ooVsPbMFBulpyOMZHZi3ruH7yFSHno+o29f\n9zCc3Y5w0vPPQzCpIjYWbA1fX1AnVRcOiYlBG+AaNSDAZDmWpCRQBDdvhrAcPBiMkypVwHIqVgwe\niI8P7qV4cYQuatWCYhs1Ct9Zbzan8q0fPsQ533kHBovrMXfvZo0DOxzIywwciPG+cqX6mEpOzqza\nzp8fXsYzz5irh3n4EFboJ5+YM6wePULM36zhwRgUeM2a4pYQEyYYdxBwRXIylOmBA/x94uPhFcjI\nKTYb7mvXLvF+IjgckE+y+oRBg9CaxAidOmVVlP8mRdCYiOrIFMHw4WAHqMIoiXv8OISAp2GD27cx\nUUVsgg0bIBB4EyM8HMLj2jW1azocsLplfYeuXsWANUNBdTjganfqZG4ih4YiVimKVV+6hEFt1NAr\nPBzx3GbN1MNASUnwoIoWRVhExphiDM93+DDyMIULg1Z57Jiawtu4EV5Shw5Q/PPng1J67RrCCTEx\n+PfhQ1jSkZHIzVy6hDYW06YhP9W0KSzvVq0g4BcuFCc9U1IQk2/QAEJq+nS1mgN/f1Cqa9SAYlV5\nxqioTGWgKwSzS18mJMCQ6NzZnFcSFoY83bp15q7HGJRrqVL8kE1GBuagLJfGGHIdFSqI84VbtsAI\nkMmNffvw/rMTlvT3x7wQvcuICMwDo3EUHIw8nx7y+tcoAtwrvSRTBIULZ6WHDR1qntr4zjueMxcY\nQ1JItOaB1Yrwjcja7N3bnCW0fDm8B9HgstkQIlixQv28jCEf0KSJOebT3bsIT4gW2dAZS0Zhr49H\nBAAAIABJREFUPL318NSp6lXh27aBK96rl9qqZHY7BEyrVrA858/PXm+YnEBsLMJ7vXrBEKhZE4nN\nEyf47+HCBSi9IkVQ0CdbwEjToKxq1kQuTZS417F4MZTBM89AYXlS6JaeDq/ggw/MGRRXr8KbkhV0\nGuGTT/BueNDDsyoyolMndGTlQdMwvzZvFp9H3y+7C9+3bi1XYl9/DS/VCJ9+mukxPHWKwLkT3927\n+MhmLJAbNyCcPGUK3b2LcICIq7xsGTjKPOhl8aqhG72YTRb+mD0bVpmZ0M6qVbDizDBzIiIQFpk/\nn7/PxYt4RqOk8LJliKOeOqV2vZAQuPjVqqkVxNlsKKyqUgXW8cGD/0zGkN2OEM+ECfhuZcvC2+GN\nrbg4dOz08UG/Jplws9vRO6d1awgL0YpdDgc8n1KlIFzKlvUs6ZmeDktW1sbdFUeO4DiVgjBnJCYi\nHGZEp9Tx449Zufc8hIfjHYi8oX37MA5lim7bNiiD7Iy7o0cR4hVdKzYWzCejPkQREZBV4eFPoSLo\n2dOP+fnh99FHR9lnn6m+VmDUKONeQaoYMYIfl2MMQqh8ebE38PbbjM2dq37NgQOZ9Dnv3kW4ysxE\n0nMOZiZ8WhrCFaLye5ES+OUXWJxGC3S7Qq9M9vbGv7IksM0GwVe5MthDhw7lfJ+fkBBM0B07oETn\nz0cfmJEjIdBnz4bFf/QowlZmeg2dO4ekZdGiWATHtV2Ajvh4XKtYMVjEMj5/fDwUR4UK4hj3/fuZ\n11y/HmNDRK0UXa96dfmSjK6YPRtMN7PFar//Dk+RZ1ilp2NOqjCkvvkGpAYeNA1jy6iFvTMcDoSH\nRApKBk2DwSWjrg4bBk/RGUePHmV+fn6sYUM/Vq+e39OnCHTt6HAgNHHxotI7ZYwhKVSkiOdMkYQE\nOVNo5UpYNjwcPw5rS9WLOX8eGl9kseu9yc1QRVX7obteR0bdCw6G9W7kFi9YoN4G4P59TLiPP1Zb\na/f4cYSAmjSB8MoJBRAfjwTi5Ml4v0WLQgk2aYICwJ49kVwfOxZhvqlTEars0gX0xUqVUAzWtCkS\nxz/+iNyN7N7CwlA/4eMDFhWvgjg6GoZN0aLwFEQWP2P4JiVK4BiVMKC/P/ZX6eHjCr3BmyyM4gzX\njplmMHq0eJWxLVtg7css+YQEhKlEzLVjxyB7ZJXa69YZdzMwg+3bwSASjZk7d2AsGX3/6GjIm3+b\nIihLRFcF2/9+wGPHEAM1M+FXr4Y1zoPNJk5azpmDIise7HZocF74Qu9+unq10u0yxjAx1qwR77N7\nNwSVmRYCKiskuWLWLHExT2IirCCjtVZ//BEuvKhqUoe/P4SISufKhw+RbyldGpM9uwogNhbeWvfu\nmUJ8zBhMSLNrJuuIiIBQ6N8fAuT553H+LVvEIcrkZJADypdHi3ReyCIiAu+gbFnGXbhJR1QUzvX2\n22oJYb2NiSqpwRmXL2M+qDK6GMOz6B0zzUBG59YteZX1C6ZPl3ct+OADeTsYqxXjMjt1BQ4Hwswy\nZdy5M9+os1r/RYqAiNYT0X0iyiCie0TU12Cfvx9u1ChzK3cxhhcq6l2+fz8GrhEcDghOUTXznj1w\nbXk4eRIus6rre+IEhIBIwNvtEL4qbXh1nDsHy9zM4iMHDsAz4SVpHQ5YvR9/7C6MN2+GZSuz7DUN\nyrZ4cblAcziQ8NfXJc5Ot01Nw7fp1Qusoh494FV4smAPY/BO3n2Xn5i+cwcCqVcv3P+4cWIvNTU1\nM0T22Wf84qcDB6Bo6tYFK+nGDePQlKYhRFeihFp1/po1YPaYbSnBGIRllSryzrjO0DtmyjwcV0yY\ngBAYD+fO4TlEVf6M4boy5bdlC7w+GSZOlId1ZZg/33iVMmecPi1mKf5rFIHSTT4uKHM4IJTMUNzu\n3YPFIHKJe/Xia9UTJ+CBiNChg7jxVLdu5nIDrVqhO6YI69cbl5TzYLejqtNMc7voaIRERInayZNR\nVewa8rp5EyEOWQgvIwNx8VdekSdBg4LA8GjYUL6anAjp6ZkLg1SpAo/H03bJjMEC/s9/wL7Jk0eN\nwnvrFsJL+ipkovcUFYV3pOdMjMKLevhTX6xE70tk1F13zx58G5XizO++w/j3ZI2Hnj3Nt1748ksx\ng8cICQnwtkRdgD/6SC0cOnu2uJbCagVNW9Zx+K+/IHeysxZ1TAwMFBnjrX59fk7nqVMEBQqgWMfb\nG+Eh1XDIjz+KKwOTk/GyeVbyoEHiBOn9+5iAPMs0LAyTXXWVrDNnEEoRJUhtNiRGzXQVXb4cAttM\nCKVnT3Hx3LVrcIFdQyfJyRCysq6M+jrL3bvLJ8zGjRBeCxZ4ztPWNCjCcuUgGFRrCtLTEeZYsgTF\nOn37IrHYsSP7m4Ov/7y8IHCCgtTGaFwcCtbKlIFXJVIIt26hBUfVqsbMq6QkFIY51wWsX298rkuX\nINBkBoqmIZz44Yfmw2+PHmEsm2ljHhYGAWp2DeU5c0AP5+H0aXx3Wa4gLg7yQNR6YtIkNQVnNhxs\nhK5djUOuzpg1i99O/6lTBPnzs797rRChaEcFr74qrkJcvx5dLo2QkQHFI0oST5vGX8yeMbj/ZlzE\nbt3khXOrVsECVZ2YSUmY9Gb6wu/Zg/CUzJ12beGsaRAaffqI7091ic20NPCiK1QwF3N2xYkTGAv1\n6smpqMnJsJYHDMhcWL5GDXiO8+cjvLNmDcZO+/a4t3z52N9N4V57DUInXz7ElL/+Guv6it6HzQZF\nU6IErivi82/ejP2++MI99LJhAzwTfTEZEUvrr7/wXLJWJxkZ8CZVCrRcoa/lbSYcOX48ch9mkJ6O\nvI7IU2zYUK2J3EcfQTnzoBd0yWjgW7fCg80O9u+HxS+CbowaheGeOkXg5ZU50Vq3zrQKw8P5Md2H\nD6HdRYKmVy++1t6xQxwP1BvY8RJc6elwq1WqYBlDQrVECbl1/Pvv5pJqEyaIqXGuSEiAheoJhXDt\nWnhuotiw1YokV7t2YhbV7duIf3bu7Pm6w8HBCN29+CLujedNaBqs7P79Manat4c3eeaMWpz7zBnk\noogyFW5aGr7TmDFQDBUrwrq+fJmvFOLjIeCfew7K+9tvkRPZuBE5CP246Gg8FxFYMQMHonHfpk0I\n5+XNi2N9fcUtVaKi4F3KjI/r1+GRmeX7MwZWlchYckViIuZBQIC563zzjfg6W7Zgvspw+jS+lUhx\n9+wpp5Kmp2MseUo2YAweTPPm8nBj69bGYd+nThHUr4+7LV8+azKpbl1+BeXKlWBK8JCaCoYILwb3\nxRfi8Mbly7AGecLlt99Q2KSK8ePBDc5JPHgAAaVSkatj7Fjjhn0yqLTPcDjgLbRtK1YCR4/Ctf7p\nJ88YQTYb4tsvvQSKJS9HFBuL/apUgUCcMSN7DenCw43Hg6ahUnj0aLB8OncWs52WL2d/h3jy5YNX\nkjeveyL15Zcz98uTB/+uW5cZNjx7Fkp9+nT+tW7fhuCVEQ/mzYO3YzaZrgt2Mx7dwoXwFs3g4UMI\nXt5CQ3Y7qL2yvJWm4b2KDKHVq2EsyNCjh7nWOPr1nTF0KJSpCGvWGDfQe+oUQcuWCAs5h2ni4iDI\neRZ/ly5i2tj+/XxBbbPB/RNp88mTxTH03r3V+fp2O+Lt2V320hWTJslbZjtD727oSfK0Sxd5om/a\nNISERMn7nTs9bz3AGFoX1K8PS4qXgE5LgwIoXhzx3pMn/3tVyJoG6m+dOghVHThgfO0ff8wMOeXO\njRoDV4SHs7+9ZSLjDphRUVC8AwfyjZbTp/HdRVa4wwEig9mCMcbg1TRrpv6OrVZU8pqpF2IMHv73\n3/O3z5ih1k100SJx0jg+HvkYGcNp0yZ+6NkIU6a4520OHuSzGp3vp2BB91DuU6cIGjdGjNcZO3di\nYBpB02DhidYn/eILfu+gM2dgFYggijenp0ORqDZV27tXHgs0i/R0WGJmuOAffggX2yz27pU37zp9\nGoJXlAhctQoMENFyjDxYrfiePj7w5IyEjqZhcpYrB4tOpdL5ScHhQMincmUISVdKp8OBMWaxQNiX\nK2fs/U6cCG8hb14+NTQxEWHOPn34CdPNm2GMiOL5d+9iXJtN5tpsoFCb6TT73XdiWqgRzp0TJ4X1\n9jSyRL6eBxDt99ZbYlo6Y3jvzz2nHtrcssVdcWRkwNOR5VmaNnX36p46RfDqq+4rgo0YIXaZZOyS\n6tX5CdTJk9E+gIfwcLAbeANl925zYaF+/cy7kDKsXq22rKGO69chqM1y81NSMPlEZfXx8QiJiOLV\nc+cijGFmXQIdgYFIzL39Nl9InTuHGHGdOp57G08CNhvowtWrYxw4e2O3b0PAb9mC1h1GFcKpqQiB\nrV2LcKSvr/FyocnJ8JJ69OCHd6ZPl4dkxo9HjNws9u1DPkOV8RcTAwFodhnI1q3FhICGDdXqb2RE\nk8WL1ZbNbd9efbXA+HhEOVwNKll0gzF8u88/z/q3p04RFCjgnrRr3Fitw6IRIiJQk8CzHBo1EtMz\nV64UJ2CHDQMnWQVpabAasrPuqSs0DYwXM9S9Ll3EbAke5s4V5xQ0DeceMoS/z6JFmKAihhYPv/4K\nK2/JEmMvIDUV3+Odd9Ch1dNFiZ40UlJg3JQokZXj7xzz1iuEu3XLmrh1fqagIHz78ePdjaHUVORe\neAuypKcjXyLqoJmYiLljdoU0xiCkVRl/jEExml1t75tv3AWiM+bNE68eqKNTJ1S68xAWBhKCzOCc\nN89czq1RI/c1Etaska9tfvkyciDOeOoUgasFkp6OBJqnBRubNvEtn4QEsH1E5+7Rg1/0pWkYQKps\noQMHjNkMGzeK45Q//cRvaX3xIp5PlXN/4wbYJmYqQRmDUPDxERf5bd8OAcDLC+zdK65e5iE9HdTc\n8uX5se2LFxFr/uADfhLxn4aTJzGhP/jAOFejL/1ZvDjfC3v0CB7pgAHuYyA9Xbze9rFjCBGJPMPF\ni9F7yWxeZfNmtcpcHQEBCNGaUd43bmD+8cZ+ZCQ8T1nfpXHjICGNmijqKF9eHnoNCMAYVMWUKTAI\nnHHvHr636H3rcsc5L6aqCHKRIiwWS0GLxfKM6v45jXz5sv7/zZtE5coR5c/v2fkuXSKqV894W0AA\nUaFC4nOfPUvUqJHxtr/+InI4iCpXVruXvXuJ3n7b/e/79hG99BL/uJ07iSpVMt62ZQtRjRpEuRS/\n8OLFRG3aEBUokPXvw4cTHT7MP+6XX4hatODfh8NBNGYM0ejRRF5e7tsDA4k++oho82aiF19Uu1ci\notBQoiZNiMLC8C3r1s263W4nmjIF73XiRKJffyUqVkz9/DzY7UTBwRgjly8T/fEH0Z9/4hcRgZRt\ndvHGGzhvyZJEtWoR7dmTdbvFQjR4ML5x375E33/vft3ChYn27ycKCiLq3x/fQUe+fERNm/Kv37Qp\nUcuWeG889OuH+zh0yNyzdeiA+XH5str+desSPfMM0Zkz6teoVg3z98IF4+0lShCVLk106pT4PBYL\n/u3Rg+j33433+c9/iE6eFJ/n5ZcxNmJixPvpaN6c6Nq1rH8rXZood26iu3fF96tyP4bgaQgiykVE\nPYhoDxFFEVHY439vENH3RFRRRdPkxI+I3FbDWr1aLT7HQ8uWiOMbYdYscRGYXp/AszhWrUIoRBWV\nK7tbtJoGHjnP0k5KQiyRZ7VVq6ZeQJaRgbiya/vjR4/EVZZpabDkRUU869bxK5pjY8HXNrugzuXL\nyAV8/73xeUNDwZ5p3Vo9WW8Eux3fZfFiMItefRVFWm3bIs9QuzZqJmrVgtVapw4KEFu3RhHZ1q3Z\n45AzBu/gP/8B+8voWe/dQxioe3djby45Ge+iZ09ztM/oaDyXaDGcNWvMMWJ0TJuG6mxVjB/v3m5Z\n5RiRN/3VV6ivEaF1a/Y3G6tAAeMGcEuWqOVLWrVST5Try4i65lLef1++otvMmVkp6JTd0BARHSei\nCURUi4hyOf29GBF1IqKtRNRT5SLZ/RGRWzOykSPFi1iLoGlgBPAYRR9+CB43D7t2YZDw0L+/eM1i\nZ9y5AzfcdZJfvYoELM8V3LmTvwjOrVsQ0Kphoe3b4ea7YuFC8N15WLgQRWE82O1oh2C0Jqy+TKar\nCyzDlStgFfHaHF+9ivc5f77ndND4eBgD5cohnq73qfH3lyfSIyIwPvz88G5atMC/v//u+f1ERkIJ\n9ehhHM5ITcU9dutmvD0lBfdhtofP5Mli6nFaGsKCKp1lnREdjW+kGqo7d85caIUxsP6MxrSOffvE\n2xlDmIUIq7flzWtseAYGIlEvw6RJCDWponp1GDzO+P57ce6DMdQ+OJNUckIR5JEerLBPTvyIyC1+\n/NZb5hKhzrh7F9Y2D9Wqia3cCRPEPdDr1FFvirZmjXGL61mzxHznQYP4awbPnGmu2dd77xkrvvr1\nxUygTp1Q7crDhg3gPxsJwE2b4CmYif1ev45EKq9xnr8/4qierIPLGHI6Q4aAqdK9u/mWyEZISYHV\nWK0aZlvZsrC0K1SAQuP1iHFFairGSaNGxh6aw4HtnTsbGwCJifA8zSRq9dYFolYKX36JymmzeP99\neZt1HQ4HvjtvwR4jpKfDqua1SElMBO9elCfw9cU7K1GCX1GtaShqldXd/PYbn+puhJ493VlCJ07I\nW1borCN9XmVbEbCsgrjoY8/gFf2nclxO/ehx91Fn1Kqlnox1xd69/AZVKSlQEiKKW/v2fIs0NRVJ\nbFWKHM+z6d9fPGmbN+cnSAcMwDOqIC4OFoSrpfvHH2BE8AR1YCAsJlkZ/q5d7n+32cBMMfIUeAgM\nxHfhCY9duzBxzZxTh8MBxd66Nay27ISTeNA0fFPnBnX58sn7/Lje5/jxSCQbUWT15SKHDjX+LufO\nQVGaCVd17ixu+37rFs5pZtlYxhBuM9P2xBN6dd26YuZfgwbifv+ahrBpgQLiwrEGDeCBiHD7NowA\nVcye7c6yi48Hu1DmWVaokNmSIscUARF98zg/cIyIjj7+HVE5eU79yGk9AsbwIry8zDNcdCxcyO9J\ncu2a3A19+WW+EL5yBeEQVbRsacxprlKFX2WclgYhYjT5NA0Vj6qVwbt2GdcaTJsmXtpzxgz+4tmM\nifs8LV1qrso0IQFeCy+XcOAAQg1mmurpiI9HvqFJE3ON0TzF9OmZ1cJ583rWQ+mnn5CfMLJm4+PB\neONV106caG5hokOH5AtBde9ubl0MxpDH8fVVD1+uWMGnvPLg44McFA+ffaZW+d+li3j9hh495H2H\nbDZ8d5UV4hhDiMdoLYKiReU08w8+yKT/qioCFU5JVyKqwBhrxhh78/GvuYl8dI4jLg6MHleGiwz9\n+uG44cPBIqlcGewTZ4SFIUPPA2NEd+4QlS9vvP32bXW2EGNgm9Sujf//80+iUqXAFAoKIho4kGjl\nSvfjAgOJKlRwZ1IR4XmefZbIx0ftHs6cIWrY0P3vx44R1anDP27HDqL27fnb9+8Hmyhv3qx/T08n\nmjyZaNq0TFaGDN9+S1S0KFGfPu7boqKIevcm2rCB6NVX1c6n49o1ogYNwHg6dIjo+efNHe8Jxowh\nev99sLm8vIjGjSOy2cyd49NPMca+/NJ9W5EiYJsdOgRmmyvGj8c7W7tW7VrNmxO98ALGJg916+J7\nm8GLL2KMBgSo7V+rljuTRoZixcDwOn7ceHuFCtguQ968mNc8VK6M+SpC7txgOapcjwgy6Nw5979X\nqEAUEiI+1tvbXa7JoKIIrhFREXOnfbIID4fANIvmzTEBMzKIUlKIEhNB0XM9t0gRxMRgYBQubLw9\nKIhPpXTFw4dEmoaJRgTlEhdHdO8elMSlS8bC8to1UNKMcOUKJo0qzpxxp8FqGgahkYIgInrwAPTd\nN9/kn3fPHmNK7J49UBC8c7siJIRo6VIoDiMMG0bUqxcol2bw8CGOnTCBaO5cojx5zB3vKSwWKPfe\nvSGgkpKIJk0yf44lSyB8t2xx3166NFGnTsbnzZOHaN48KFemQHW1WCB8jh3j79OoEdHp06p3n4nW\nrYn8/dX2rVYNwlhVadrtmcKwfXvMKVdUrKgmmF980fh4HZUrixWF834yhaGjdGljOnL58jBERShT\nBgatGagogulEdNlisRywWCy79J+5y+QsIiI8UwTdumXWBnh5YUK4WqxhYXiRPIi8ASIMCFVFcPcu\nhLYu7J97DpzvZx5Xa1SoACHniqtX+YrA2cOQwW4nuniR6LXXsv49MBDWWvHixsedOwduteu7cz7v\n778TtW3rvu2334hef13t/oiIRo2C5asrS2fs2oX7nzxZ/XxEmFyffgoPondv9+03bxItWmTunGaQ\nNy/RihXwuH74gWjZMqLz582do3BheEFjxsDCd0Xv3kTXrxtz6Rs1wjdS5fLXrw+jhId69TBmUlLU\nzud8nJHVa4T8+TEvVS3qgIBM5Z6cTPTWW0RpaVn3MaMIRIK1fHk1pVqhAowoFRQogF9sbNa/V6wI\n+SfCk1IEq4joOyKaQUSznH7/Mzx6pB5+OX8+s5AjTx4UNhFB43bt6r5/RITYI4iOJqpShb89Pl5d\nSUVHExUsmPVvX34JxZAnD6w+o4Kw5GRYSEZISlJXBEFBKMoqWjTr30+f5hfLEUHAPPccf7vuLRgJ\n71On1K33a9fwrUeMcN9mtxMtWABvwShEmJjIDwls3gzB5efnvi00FELD6Jw2GzyTOXOIOnZESKRm\nTbyrzz6DYHYVNjL4+hLNng0DwGo1d2yDBvCuli9335YvH8a6kSdlsWDsb9yodp369aFwefDygkHD\nK+DioVw5saXtiho18N1UcPhw5rfIlQtKylWA6te328XnKlNGfJ+FC4tDZzqMBLsIpUsjQuEMX1/3\nv7niSSmCVMbYfMbYUcbYcf1n7jJ8WCyWNhaL5abFYgmyWCxjVI5JTER4RwVjxiBcouOTT/Dv5MnG\nYZf4eHfB6HptTeNvj45GVaMKYmMRz3NGuXK4/vPPEzVubHxcUBD/Gn/8oV5BGx5u/B7Dw8Xhpb/+\nIipblr/93j2i1FT3vz98CKVcvbra/f3+O/Y1qki+fBkTm1che+AA0Xffuf89LY1o/nyEZ1zPa7VC\noI8Y4e4pMEbUsydi+vv3E3XpotGXX16iCRMu0fTpGlWtSrR1K6y+3r0x5kJDEeqThTO6d8fkXb1a\nvB/v2M2bjbcNGABFaiQ4unUj2rRJzZKtXh2CJTGRv0/LlurWuo6XXjIXyy5dWl3A1a6Nb1WyJIyP\ne/dgTTsjXz6M47g4+XVz5+ZvL1SIKCFBfk9FiuB7qOLVV90Vx3PPwRAUoXhx4zkjgooi8LdYLNMt\nFktDi8Xyiv4zdxljWCyWXET0ExG9RUQ1iKi7xWKpytt/2zaEG2bORLKyVi25e5uRkfWlFCoEa5sX\n305LE7eWSE52t+Jdtz/7rPiedMTEuCsCIlg+7drxj0tI4CuCpCSxte6MR4+Mld6DB+JnkCmCBw/c\ncy9EmJCNGqm3vTh/np8APnaMqFkz/rF79xq/w8uXMSaMzrtrF97fsGHu20aNItq9G/+d8ugynf2+\nHhUY1ITy9GtCW4fXozffuEybN0O4rlmDsE/16vCK8uYl+vpr/r1aLESff66ewHVGkyYQ9EZx4/z5\nce2rV9231aoFb0YWbyaCEHzvPXF8O18+cfsDI5QsiTmg6gk9+6x6+Ontt2HsVahgbJToSE2Ve3HP\nPSdOVBcuLFaSOswqgqgod6H/7LNyReDlJU8ou0Kg5/6G3sXFObLLiCgnmEOvEtFtxlgoEZHFYtlA\nRO2J6KbRzqmpYEPorlxcnHH4wRnp6e7sGouFb/VbrcZsHB0pKXJFoCqIY2KM2T2lS4tDM4mJfEUg\n2uaK+HgMTlfIlImKIihRwv3vUVHmEtnnz/MTqceOoc+OETQNzBmjXjkBAe59iXQsXgymlitWrEA4\nCJ6gRs9c6Edz2R9/W1Ed/viDhvXtR/MCLlHjxrno9Gn0fNEFUP78sMBFqFcPXgRj6mwqIuST3n+f\n6MQJ49zVSy/he7nCYiG6fx9jQAWRkRgXPBQsqN5LR0fu3Aiz3r8vHk86ChQQC3Uj1KghFpxeXpAR\nIuTJIw4fFSgAr89mE5MOihRR8xycr+vqTaoogrx51SMmOqS2mRNl9M0nQB8tRahR0BH++G+G6NED\noRMiWJXduskpf+npWT0Cux0vlyfsMzLkikBkLSclqXsEzzxjvK9MECcm8llLOeERyJRJ6dLGnowO\nnkeQlqYeQ9fvgZcLslr5zKNbtzA5dQveGZcvE73C8WdjY40T3C+/TNSlCyayl9dlGsCCskycXETU\n4M8guvzYPW3YkKhVK0zkPHmQTxBRcYngzufNK08E8o7lhVjKluVvy59f/XvkyiUOiRYsaD5ZTIT5\nJhPE2blGZKRYiKu8g9y5xeewWBBhkN1bgQLqsoEI48HVWypYUKyQecfJIFUEFotlmsViKeL0/0Ut\nFstUc5fJPiZNmkRTpkyi11+fRM88c4yeeQYxQBm8vbNqabsdYQGe1SVTBPnziwVt6dKZrB8Z7HZj\nC6dECXF4qkYNPmOnShX1+oqyZZEIdIVMEQQHiwd9wYLGA97IwuEhTx4knXkx7Dt33N+d3U70009g\nQd29a8xt9/Iy9iJPn4a3YERnrF8fieC4OKJVq8TxYh2tWkFZ58pFNEuRWtGsGfIoZiH6Xi+8wBcK\n/wRFkJamPl498QjsdvH3UvEIcueWj9uTJ/lzUkd6ujmPIG9e9+syJg6tHjt2jGbNmkRJSZNokgle\nskq0ti1j7O/IFmMsnogMGOIeIYKInJsPl378NzdMmoQHW716Enl7N6MiRfjMGWdER7u34L1wIevf\nnOHry99GhMkgcoFjYtQHK28QxsaKB8zt23z3MChIfUJ262bMnCpWTDw5ChcW358ednCFGUWQPz+U\nOM9CNioGatcOiV7dYjJKAj77rHteKTAQLbiJkEjmwWIh6ty5Lp2pWZmcZaJGRIfKVqb5Or1TAAAg\nAElEQVS6TjGnPXuIpk6FV2IUJnNFRgaOEVGTeRApgrAwviAUeXWukCmCfPmMvUAZUlPVFYHdbr6I\n1G4XG2a+vnLDLXduPpWaCMLZKATtCpmR6YqSJd33d41wuKJZs2Y0diwM5pxWBM9YLJa/b8diseQn\nIhOPI8QFIqposVheslgseYnoAyKS1ii88op67/rnnsvqSlksYsvCZjNO/Ny4gYm9YQMYJyVKGPf9\nfvZZueumg6cIZLFEUdLJ29scRc0IhQsbC3Ln7aL7e+EFuOSuKFRI3VsiEldRGm2bPh3X1r0pI0XQ\ntGlWWmlEBBKu+jc7dkyc0MuVKxcNWr6chtepQ1sLFKCtBQrQsNq1acy25ZQrVy5iDLRNHx9Usrds\nqfasZ88SVa0qZqzxwBg/VyaiAp8/L6ZKO6NkSXFu7O5dz9ZiSE1VX1MkOpofEuXB4RB7BIGBxnky\nZyQni8eEzYZxLRvbZhVBUJD7OVUUZ2KieQaXiiJYR0SHLRZLf4vF0p+IDhJqC7INxpiDiD4jot+J\n6DoRbWCMSZnC77yj3k7AKLlSoADfai5UyFgR+PuDdx4QAEEbG2tcOKZC79LBUwQyQfukFYGMhyy7\nv5IljRVJuXLyxUCcUakSnzf++uvug/2VVxBO+vhjKHyjMEvjxihi0r+Rvz+UQKFCsHodDuP6AmfU\nqFuX5l66RGVPnKCyJ07QvIAAqvHYG2AM51qwwFy18sGD6krDGQ8eEG3fbjwfRBXid+5g7Kl41YyB\nhSXyVoKDoZzNwOHAWFJVBFFRYsvcCMWLi0O5RhRuV0RHw3PgQWal6zCrCIyo7DKyin6cTLm5QiVZ\n/B0RTSWiao9/3zDGZpq7jPD8+xljVRhjlRhjM1SOKVBAPbbp6hEQ4UXyPAKekPv4Y7BN9PjcK68Y\nJ6pVsvo6unUDbdAVMprZk1YEspL6unXFiqJkSWOPoH59UB2NthnhvfcQkzeyNNu1A93SVRnoFeMr\nV+I7ulbEFiqEau1vvsH/f/AB9tuxg+iLLzBe5s9HwlmEXLlyUb169ahevXqUyylomysX+vmYCbuE\nhaG4y6iXEhFqQ3jUzZ9/Ri2B0fX++ANj1GicHjqEYjQVhpKu1EWhn5AQd56+DHfvIg6uSif2RBEc\nO8b3lux2zFWZ0IyJESuC5GTxim86bDbxioOu8Pd3N1hVPAIeCUQE7iewWDKHyGNhPfLx74DRPv9N\neHurM2OMLPzXX+cLUp4iyJULHHF94H78sfHx1avLC1R0lCplbGWVLSvOU9Sqxb9GpUp8a/3RI6IZ\nCqpWVklZqRLoijxUrYpruSYpc+dGvyfV5Q3ffReT7MAB920+PmgeOGGC8bG9e4PpM9PAZPnmGygR\nfQnOfPnA+vjhB4yV5cvdQ0hPCjYblFH//sYV68nJeA6jytXUVCwV+sUXxueeMQPLgBrh0CF1D+SP\nP8B6Es324GDziuDSJT6DywhmFUFamphiHhcHJSBTRDKPICJCHErVERys7v0EBWUui+qMuDhxCxwi\nzL2c9AiOWiyWzy0WS5ZovMViyWuxWJpbLJZVRMQZZk8W3t7q5ey+vu69WJKS+IKufHm+RV++PAqL\nNA3cbSMULKjeWIqHUqXE644WLcrv2liyJL8dQIECyHPImAu1ayMnwlNGTZpAEfBiwsWKYSAaMXBa\nt+av/+qKXLlQCzB5svG1vvgCwprXB2fpUliEru/S1xeexkcfYZK7om9feBQjRxJ17py1Ml0Vmobn\nlDGGJkyAsaK3PnHFsGGoSejSxX3bnTtQIkYhykOHMEeMiuOIMI7feUd8bzp0RcBDXBzuwcya00QY\nw6qKgDF8KzPJ9NBQCE1e7D48HGNZhpgYsQK6d0/N0r93T/0d6YbmmjVZ/x4SIleGjx7xe5HxIFIE\nbYjIQUS/WiyW+xaL5YbFYrlLRLeJqDsRzWWMrTR3uZyBUQ8OHsqWhVb988/M4hlRabuvr3jij3nc\nBIPHB65USa0ToQg1a6KSkSdoa9fm9zZp0ICvJPPmRXjGqD2xM154AcqGd56yZTG5RNWL7doZ8/jb\ntsWEVqXRde6MfX/7zX1bwYLo08Pz7goVwkTq0sX9m7ZsSTRoEPomGaFNGyiRhg3Re+jjjyHUz5/n\nM58ePYLSmTcPXtHo0XwX3W6HErh2Da0ljKzSzZuhcOfNMz5HzZrGLCerFSHHuXP5FmiDBurhq5s3\nxVXcu3bBQzfbwfXSJRTSqSA0FOPATLPJu3cz646McOWKWmw/OFhc8BYaqqYIZA0tdezenTn3goKy\njlHZMxHBiDPLrlJdGCYPEZUkoiIq++f0j1wWprFaGcuTR7wY9927WG+1dGnGLBbGcuVibNEibPv+\ne8aGDzc+7uJFrH4mQtmy/KXr/P0Ze/118fEqeP55xsLCjLdFRWHRF6PFQtLTsUIab9GecePU1q4d\nPVq8HGePHlhghoeAAKykZYR+/bAymyr8/bFcoKcr0m3ciPfpugasKlJSGNu2jbFPP8XYKF4ciw/V\nro21hDt1YqxMGSx9+OqrWNDn9Gn+Yi4hIRgjrVvzVwvbvp2xhg0ZO3/e/P3OnIm1lj1dI9kZoaGM\nFSuGlfd4eOcdxtauNXdehwPvUHW1tA0bsDKgGSxciHWXeRg+HAssyfCf/2CBHh6GDuUvG+sMldXh\n7HasN66vYpcrF2N9+2ZuF8keHZ06ZS7nSjm5VOX/+ueqCBjDy+IJSsaw1GKuXJkvtGDBzOXbNm9m\nrGNH4+MePcLSdKJJ1Lw5f0nEBw8Y8/bmH6uKFi3Ey02WLMnYX38Zb6tXj7FTp4y37d3L2Jtvyq9/\n9CjWLOZh61asGsaDpmFpSaNBGxmJdxQUZHxsYCAmsPM3WL4ci4R7uozkli1QBpcueXa8MxISGLtx\nA4rlzBkI/ZAQ+Wpbdjuew8eHsTlzjPfXNBgqL7zA2IUL5u9txQq8J9HcMIPJk6EAeUhIwPKJZlda\nO34cK/2pYvhwrO5mBp06iRXUm29iEXsRNA1Gl2hVsMGDjZdkdUZiIua0bIzYbIx99RXmXsmSWP+8\nZ8/MbXnzGq/654yaNTNXUHzqFUGPHnxhp+PHHyHUibAIty5YAgIY69aNf5yvL2MREfztEydiQXIj\naJrY0lPFr7+KBcGQIbBSjTBqFGMLFhhvi4/HItquaxS7wmrFO+O9h7Q0vCeRdRIVxVeo333H2Lvv\nGm+Li4OQmDYt69+nT8cgj48X3zsP27fDYuetN/2kcOcOYxMmwGvo3RtGihGsVnixtWoZr0ksw4oV\nWEfaU8/JFQ4HLNCLF/n7rFvHWLt25s89cKA5wd6wIYwTVWgaxifvPWoaPB3ZPA0NhUAWoVQpGAIi\nnDiBsaeKL76AZ+eM69fh6YngcGAZX32NZVVFoEjc+uchb1750nVDhmQmxF5/PZP1ULUq4pq86tl3\n3hH3PS9Ths+asVjAjvFkxSZnfPCBcfsHHZUqGcfgidDvn9drHv1yjFe2ckaePOjtxGuN7OWFpOrC\nhfxz+PrymSbDhiGWuWeP+7aiRdEeYskSJHx1jBkDymPXrp4VL3XogHj7+PHIPcgWCenWDfmFn35C\nB09RZa0zrFY829q1yEW8+mpmnmPVKuP1IiIicE9hYcgzqMSSnbFyJZ7r8GHxehmuWLWKXwB59ChY\ndKKE7m+/oXDODKxWtOzu3l1t/7g43KOZpUhv3kQOifceQ0IgE2RV35cvixslRkZCjsji9pcuieez\nKxIS0ErGGefPy9lAd+6gvYmZnkZEpLR4/edEVFRFqzypHxl4BHPmiF1WHampjOXODSvdGbVr8xc7\nHzmSsW++4Z/z6lV+/JsxxB2HDpXfW3YQHIy4uZGrmZYGaz4y0vjYbduwWLsMf/yBEAXPFb1zB1YV\nLx8hw6lTCNfcumW8PSgI1tjWrZl/czjw/mWwWvnb0tIY+/prWIwrVvC9lr/+wqLk/foxVqECwlmD\nBsGq/+QTxr78Epb+1KkYM++9x1jlylikvFIlhDM2bBAvWJ6SgvBLsWKMzZ4tznvx4KknsGQJ7jM2\n1n2bpsHSX7+ef/yZMwhDyUIVrti5E3F3VSxejAXkzWDRInwnHn78kbE+feTnGTOGsW+/5W/fuVNu\npTPG2IcfMrZsmXw/xjDGixRBmNkZn3wCuSfCqlVZ3xXlVGiIUEwWTESbCEwii8qJc/JnpAgOH2as\ncWPxS9HRo4f7R+/fn7Gffzbef9s2xtq25Z/PbmesUCHGoqONt586xVjdusbbQkORKMyJRF7Vqvzw\n0Ycf8p8vIwNx6uBg+TWaN2dszRr+9nffxUT1FEuWQMg+fGi8/dIlxho1QozfDLp0gTIWKamAAMRg\n+/dXO2dYGGP790Pw/vwzYvmTJyMUN2MG7vH6dSTsZXA4EL8uUwb3eueO2j04IzUV916zpjyB6IpD\nh6CEeXmaX3+FsSRSTG+/jYSsWQwaZO645s2zGgMqGDpUrMTefhskAhnq1gVZgYdx42AMyFC1KmN/\n/infjzHsZ2RovvIK8lEifPwxY/PmZf5/jikCnIsshMVjNjxWCtOIqILKsTnxM1IEMTEQxioCdd8+\n9wTpwoVZs/HOiIxkrGhRcWKnZUvGdu823paRgeR0QoL7Nk0Dk+nGDfl9yzByJGN+fsbbduxgrGlT\n/rFDh4pZQTp278Zk4L3ns2chyEWsEhnGj2fstdf4QvviRQjMKVNwH+vX85P1OmJjYQBUqiTOJVmt\n5oVodmC3495few0JQZGQEeHWLeQSevTIjAerIjAQDBZezD0xER6G6L1duIB9VJSeM27fhhFiNDeM\ncP8+rGMz4yspSWyopaYiwR0XJz7PgwdIFIu8y759xaQOxjAWW7VS9/Z++gleqOs9Fygg9i4Zg8LR\nE8WM5bAiwPmoNhHNJSwas5CILhPRTNXjs/MzUgSMMdasmZpVGx2NgeEs2C9dYqxzZ/4x5cphwvDw\nww/uyUxnfPwxY7/9ZrxtyBBxomzTJjWq46lTjHXtaiyk09IgwHnJsj//hNcjm8gOB2M1aojpc++/\nL3afZdA0xnr1YqxDB/5kiYyEJ9WmDUIvvr7yScEYLMkSJaA0VfbPCaxbB0Njzx6EsRISIDhHjYLw\nrFcPykzGIOFhwwYI00WLzHuW9+8zVq0a2Es8jBjBN5J0tG/P2Pz55q7NGDxzEaXTFfPmYWyYwbp1\nsPh52LdPLZqwdi3GJA/JyYw9+6ycMbVpk/h+XNGtG7xOZxw5In8PUVGQc85zKCdDQ8OI6BIRHSCi\nLkSU5/HfcxFRiMpFsvvjKYKuXRlbuVL8cnS0apVVU9pssPp5rJjRo8WT5dQpMf1t/nx+jPLgQViE\nPEyfDkUig8PBWPnyyHXY7e6C5bPPQEXjoV07WB8ybNwIt9RuN94eHKzGwBAhIwPP3K0bXzklJmKg\nE6FWQoUDzhgmSOfOjDVogJCiM0RW2uzZjM2di29tJg/y+uvISxUsCKWlU5i/+oqxa9fUz+OKxESE\nIipUyDqWVXHnDmMVK2YNHbgiIADeAi9UxxjeYYsW5r3AkBCME1XWl8MBr+nECXPXaddOHM4cNUot\nNNWrl3i/nTvVqNh9+yInoQJNY+yNN9y91BEjUJ8iwo4d7p5ETiqCyUT0EmdbNZWLZPfHUwQLFjD2\n0Ufil6NjyBB3wdGli7vm1bF+PQpleLDbMWF4sd2ICCgaI6FmtYqVUHg4tqsIn6lTIXiKFmXsl1+y\nbrtzBwlOHlX04kVYqDJLWdMQpxcVkI0eLbciZUhLA/e7eXN4cY0bZ+V5jx2LQkJdsHp5GSc6ec+w\nezcUZ9u2jF25AsuxVCn+d9i0CQm6evWgeGrVwkSbOBHv/bvvoCzmz4d1Pnw4SAlFimTe4zPP4Jqq\noRAe9u5l7MUXkRPw5Fx//olnFSn+qCh4wjxaMmMQ/hUrynnzRhgwQK2YUceuXeKwpBFiYhDO4Y35\nlBTMFVmdRXo6vptov48/lheSaRoID6rhx4sX8X5dn7lKFTGNlzHIQlcvLcdDQ//LH08RBAZicqgM\nlN9+c9fey5bx6wni4hBHFAnjfv1gMfLwxhv8PMLIkUg28tCmjbxac+VKCHpd4Bidr2tXCCse3ntP\nbCHquHgRIRaeG5yQgAF89qz8XCLY7WCDeXvDqi5fPtPTmTMHVr2PT2axYMGC5mLkGRl43uLFcQ2L\nBWNIVDDEGATD+fPwEidOBOto5EjGhg2DkTFpEhTD7t1QMl5euP9KlTyve2AMgq1XLwjogwc9O8eJ\nE3jeDRv4+6SlQdmPGyc+17hx4pAqD7du4fwxMerHNGqkltB1xuLF4ODzsHq1mAiiY8sWGCQ86AWT\nMqbWH39gXqhi3DgYVc7Q2XOiUKLNhvHsGgr+f6EINA3CSYVxkZSEeJ6z0AgLw8vjhTyaN4f7x8OO\nHeLBMncu32M5fRpCgvdxN2xAQpqHjAywPnLnxle0WIzzDhcuINHKS3gFBGCQqXgf/frBReVh82bQ\nJ5OT+fvY7WDeiLB7d6blnz9/Zrm8M9LTM6ukK1SQsylcERgI5amX8Zctmz2B7YoOHdQUDA+aBiFY\nogQ8DdE7FWHnTihOUXJd05B07tJFLGyuXsW5RMWWvPO3bIm8mir8/fFdeXPTCHY7jjl5kr9Ps2Zq\nDLT33hOHnS9eBAVbZoTOncsndBihWjV3Y2rOHHhTIhw+bNwJ4P+FImAM7tnq1eKXpKN5c3eX9qOP\n+Hz7uXPF1MKUFMRKeZM9PByhAqNJrGkINfCSsGlpsFx49D7GEBZ56y1Yn0Rg1Rihe3fxOxoxQs1l\nf/AAg1+UyP7oI/GgffAA1i0v0WizIbTi3B6kWDFxLH/LFli8Eyeqs1hmzIAiyJULZfv6tebOFcfH\nVREX57liOXsWjK9u3cwrOB0ZGfiuZcvK22pMmoSqV1HM32aDcORVrIuwdq2ciuqK9u0ze4OpYts2\n5N54wjk4GCQDWd2D3stLVH0/dKicNqppEOwixeSMwEB4Ga7K+IMP5MbTZ58ZEzaeOkXAe5lr14pj\n+c5YvFitCE3HnTsQxiL6WK9e4jihaEAvWCB2sydOlHPcNQ1ClQhhEyOcPQurnxdbjoiApafCc161\nCoObZ6EmJsIqE/G+795FIZJRnYOmMfb772BktW+PiUGEa4q8lvv3Gfv8c7jhvHCcM1auRK3FlCl4\npkOH4FH16gUh8PbbUJ4BAZ6zjZKSEE5SZQcFBWE8lCqF+gpPissYw7ht0AA1HqJQjKahcLJDB74x\npOPLL6EIzDKdYmPh1fCKN41w8CCMJLPvvVEjcfuQr79Wa2sxb15mfx8jpKdjvsgiERcvIrSpmuPY\nuBHv2RkqystuR4Jc76XmjKdOERQtavwyHj0Ck0QlgaYnYc0MsMaNxckzf39wd3kf+/Bh0C+Ntick\nwPrlJaRiYmANh4bK7/Ozz8Az5imtvn3dB5kzFi+G8FBxxXv2FLOazp6FhS5qEBcSgtCJitV3/z6s\nomrV5Mpq3z4k1tq25Vcsy5CcDANjyBB8Oy8vXLtbNySJ161D6G7jRgierVsxRpYuxTtu2xaKLn9+\nFKzJEtoPHmTmRaZN87xSmzHcj68vwgkiAWS3IxFep46c7bVxI7w4M/F9Hf37Y2yqIiMD308UkjXC\nqVO4R974ffhQTNDQYbczVr26uIZiwwZxSFjHsGHmwkJGmDCB3ylZx549fCPwqVMEDRvyOwXKSuGd\n0by5uaZjK1aIm2rp7h+P4qZpECautEUd48eL4+6jRqlPpJYt3ZlDOh48gBXDK2TTNMRPVdrpJibC\n8t60ib/PihVypkRwMPIXvAZ+rli9Gs/wyy9ihZWRgXi0tzfeX3YZO+npSPqtWYPzDR6MJHznzmA5\ndewI7+Xzz2Fx7tyJZxPdo8MBbviHHyK8OHw4vwBKBTExSJJWqiTvWpqSgvtt2VL+bq5dwzv3hK66\nYwe8CDOdSWfMwHwzWx/RqxefAcgYErBDhsjPs2WLOLzEGMK969aJz2OzIYeXnWJFhwPGEq9JoY72\n7Y3n0Nq1/wJFQESdiegaYfGbVyT7sh9+4Fuhy5ejqEkFy5eb62uenAyrXWTdzpolLvZYuJBfmKLT\nTHmWil7lLHPdGUPvl9Kl+R7PnDmop+AN8tu3cbxKz5oLF+TdR1Vw9y4UZb9+arz0oCBY3HXqyLvP\nRkaigKlFC8RPczIZ7CnCw+FVlC+POpS5c7OnABwOCIHixaGIZF1lo6LQyfPDD+Wx8kePoFhWrTJ/\nX7duYXyYYZGFhkJ5qxSJOmP/ftwn73mio+FZyzq6ahoSrtu38/cJCYGikI3VXbvwjrODgwf5rWp0\nhIdDPrky5y5exFz+NyiCKkRUiYiOqCiCO3cw2I2srNhYDG49fhkQwA8h6KEkVf45Y1BAosrZ6GhY\nCa5NonQkJ4MOahTDYwyWnMjq/+YbuXuoQ+SOWq0IsYjqAZYuhWCWCRTGYIHVrGnuXRohKQlhl7p1\n5e18GctsM1GqFJLTvPeu49o1KOpixVDUJds/JxETA092yhQk0YsVQ0jm/Pns95sKCEANyeuvq1ns\nBw8if/Ptt/JYf0ICPAZZEZMRkpIQXjGT7NU0eFpmqo4Zg+VdvTq8Dx7GjkXbaxkOHUKYV/RuPvkE\n55OhaVP1KAUP/fuLvRzGIBsGDXL/e7duMFD/8Yrg7xsgOqqiCBgDQ+b4cen7Yz//DJedhy+/lHfx\nc8aFC0i8iZJ3Q4aIV936/nv+Yjh6/JKXC4iNhZupMtnDw2GJ8dzJ69fh6vMqXDUNAqtjR7XE4MiR\nSNJlJ66tX3fuXCj7PXvUjklMRKjGxwe1EjIL984dxOKLFMH3UlE6svMFBCAsuHcvQmXLl+M5unZF\nzLpQIVBcR4/Gc2X3PTEGRTZ6NN7V0qXy75SaCpZL6dJIxMsQHw+r95NPzCeHNQ3P3revOUU3ezas\ncbO9ixYswPvlXevePXhevEWcnNGhg1jw6jlGGSVY740lIpnIEBSEcS2qkbFaEd1wZYWFhGQWkj6V\nikAWgtGhJ2F54ZazZ0GrM8NRbtpUXLYeFoZBwqMepqZiIp45Y7x9zBix1b9kSVavR4Rly2Bd8wbi\nihVi5k96Oq6lYp05HPgm77zjOcvFGSdPohZh5Ej12P6NG1BeOhNJRgaIjMT7btoUz/nDDwhRmUWb\nNqBFvvEGjJROneChTJ+OfEZgoOf9hIxw/z68x6JFYamrJG8vXsxMdKt4btHRGDvDh3vmscyfj8Sl\nmfYTp0+Lq/R5iI3FcaIYeqdOagnbvXvhCYuMiWHDxPk8HT16iItFVTBokJyeuny5cdJ64sTMY/8R\nioCIDhLRFaff1cf/vuu0j5Ii8PPzY6NG+bF8+fzYrl1HlV6kSJA1bGiutfGBA3BBRRN78GBYqDws\nXQrhYzTB4uNBleTxxh0OcL1F/Y90aBoEkyic1bu3uB/7/ftQXCKXW4fViri9WSuQh+honKtUKRST\nqZ7zzBkopJIlIdxlFccZGQjbDBgA66tePQjx/2Y3UhWEhSH+X7QohJHKcp2JiUi8+vqqhygePECo\nb+xYz77jjz/CCzJTcBYdDevZLEuIMShdUQfdvXvVOuPqTCVek0jG8G6KFpUzrO7dQ/jP7NKdztDz\ngqJ6FrsdhI0jR7L+fePGo8zLy4+NHOnH/Pz8/hmKQOkGTHgEjKE4SqXr4R9/QJDxrNRNm2DJqULT\nIChEiaTQULHraLMhBskLffz6KyYiz5K/cAGcbBVLMDQUioWXK0lKgqUoKjQ7exYFZCpLBCYnIyQw\neHDOeAaMwTuoVQtWj6gTrCsuX0aVrK8vYvMqyVibDcyuwYMhbP8JuHIFhkXRoghnqhAGrFZ4RSVK\nwDJVFcr+/gixiDrqirBoERguZjwrux1elch44mHrVgh5nrJPS8N2WYtoxhBpaNNGrPwmTUIdggxj\nx4IMkB2MHSuvd1q/HvLL9Z4HD87aouLfpgjqSfb5+8GOHIGwVLFYPviAL7htNoQSzp+Xn0fH1q1w\ne0XX9vMTD+zff4dwM7JSNA2WtWgyTpgAIafy/Js2wULjCcKgIAgMkdV/5AgEqqvlYYSEBLCS3n0X\nsXC9UCs7sNkQc/fxQWLMDMPmxg2EmAoXRs5jx47sxW3/G4iPB8usfn0o8lmz1CqdNQ206EqV3Dvt\nimC1gsL8/POeNZJjDKHI0qXNsX00DV57//7mv8n9+7hfUdX19OlqTMKHDxFPFxkaOoVW9h30Ffuy\nU5keFYXQnChMpreGd6XT//UXru88R/7xioCIOhBRGBGlEVEkEe0T7Pv3g+l8dxl1kDG4m7Vq8cM5\nN2+aG4QOB+LrotxCTAzilqI2DF27ujeW0nH3LgYmLzyRloZnUl0V7KuvsCwgL/Z58SIEvUhgHz2K\nicCrhXBGRgZyBrVro3VD8eI5sw7A/ft4Z0WLIkyikvzT8egRciyNG+NZhw5Fgi0nwlg5AeeagsKF\nUZ+wd69aDstuh4J74w0IEJVksI7gYCSF33pLzdswwtq1COGZKd7TNMTaX31VjZ3memzbtuL4+cmT\nGHcq3tDo0WKSh6bBU1KJQnz4oWcsK2cMGSJf5jYpKXORJmcMHOjedv4frwjM/Fx7Df38M6xOGTQN\nk0MUzpFh/37z7JKlS0Hp4ymghw9h0fAKf+bMQfyeF2K5cQOCmUdHdYbDATZEv358wXfsmJzzfewY\nrqnSAdNqzeyKmj9/9hatcUVEBDyuYsWgcFTWL3ZGcDDiymXLwksYPBhutqwtcU5C0/ANFyyAUVCx\nIsKOZmoKoqJg9b70EgTqjh3qiWm7HQaNjw/aKXiS0HY4EMKoXNn8Wsl+fjBmPKEd//IL8ns8Ay4m\nBiEqFe9m2zbUc4iU0YYNuFdZuPPyZXjXZhWbM27dwjfxpK7k1i3QfV3Dxk+1IkhNxUtX6Y2zY4f5\nnubO0HvemIHDgcEqqphduxa0NiNL3W5HEZSIr7x0KY5XYWckJcFCF1UN794N5Wd7aegAACAASURB\nVCTyZI4fRyxVVlU5ZUrWdQPy5cuZRm7OiI/HtylRAnUemzZlfRcPHoC5wbOqHQ54Q7NmQVH6+CCM\n1rs3vltAACZVdr0Gmw0JxJMnsRZA166wVl96Ccp+5Ur1uLqmISHesydYcX37yquIXY/fvh1hhXff\nRQ7CEyQmomK4SRPz3VVnzkRi1pNajj17QATghU00DfclakOtIyIC30EUXkpKQshLZWGcNm3UF5/h\noWNH9cWWnKFpuL4RU+mpVgSMofd79+5qL6lOHc9YCYwhrFG+vHyNXFdcvoyBxhvwmobGZjNnGm9/\n+BCDkNdATdPAmhg9Wk1YhYaCUbNsGX+fHTvg5ouSw1euIAn3xRcQctu2ube1mDULK5o5rxuQN69n\nvWpkSE1Fkr1lSwjHnj0hMObNQ2vuN99Us9I0DR7WokVw8Zs3x/m8vGCxN22Kv48Zg+ebMQMW+bRp\n8HimTkUOY/x4JGkbN4ZlmicPhNdrryEJvWKFZ1RVxhDOqlABjCiz1vThw7iHWrXAjvFUwYWEQJEM\nHCiv23CGpoHF166dZ95XQAC8Vh79mjF4VPXry+/L4UABqGzN7mHD8L1l2LULQtzM+3CFvz/Giydr\nf+/YAeKHkZf01CuCxEQIGhWq365diLuaqRtwxs6dYPuYTWpNm4YBwpt0kZEQEry4rh7r5AmOxER4\nO6pMj1u3QNUTtRI+fBjXFLGJ9PbXDRui0V3+/HxandWKfE7Hjkh+epqQVEFkJGK5DRtmeiR58kCR\ny9oL8JCcjPd2+DCS39OmZRICRo+GoPjqKzBKxo5FWG/1aoTS7tzJnnBwhaaZC+NoGgRMixZQINlZ\nJ5kxjNPnn4fla0aRpKcjjNeggWfLmd67B6NIRPc+cQK5A5WE9bx5UIqicM9vv8Frk7UlSUzEnFLJ\nn/FgtYIA40kIOzUVYU5eju+pVwSMYVKqaGxNgxtrtr+58/GtW4tX+jJCejq8EdF1jxzB5OJVFc+a\nBSuHZylERMCSUOWKh4Rg4Iie5fp17DNpEn/C22x6LxMsjtOjh/zax44h/NKnT86HipyhaVhdTg9N\n6b/PPkMSNrtN6J4UEhJypgAtJgYKqUYNjL9ffskeWyopCVXGZcqYXz84JgZkhU6dPKuqjo9HCFRU\noHXzJowX1UT59u1iAzIyEiFHf3/5uT7/PPtLtH77LZSYJ16anx9YhDz8v1AEjx5BiMq68zGWGaqJ\ni5Pva4QbN2AFm7UsAwPlid3vvkPCz6i8XmdYdO/OFxJXrsBtVmm/wRiUTsWK4t7skZGw4Hr1Mmb9\nLFqUuTqargxkC6AwBqHy7bdI9n79dfb7FBkhNDTTG6hSBRTFgQMRtmnaFMtb1q+PSfTLLxBunq4k\n5gk0DUVhu3cjnNSpEyz2AgXMsaGc4XDAKv3gAzCPPvwQije7OY6jR6G8+/UzXyR18ybG2Zgxnim4\nuDhY7kYMGR0PH8LjE4U8zcDhwDyXVfUyhjBViRLZG8O3boFY4cl3v3EDVGFRm/qnThHwwjo//YT4\nsMqAHzgwe8VCU6fCMzA7ufTELo9GqWlIWPKKSFJTQQ8U1SccPAhFp8IkYgyeRIsWCG/w3m1KCnIB\ndeu6r5S2dSsmTMWKmSukEYlXVHPGX3+hotfbG55HdioxXWG3IxHM86LS0yH8f/oJ1lzDhsgHeHvj\nPffvjxDTmjXIN5w5gwkbHS1nj2ga3ltICCzKDRvgfY0cCa+pWzcYBr6+GEujR8Obu3HDfCGew4Fk\n55gxsChffhn37amx44zkZFi7pUqp935yxrZtyIGJGhyK8PAhCA6iVhcpKVAUKqvrqeKnn3BOmQeV\nng6ml9EyqqrQqfBm+p7psNlgqC1cKN7vqVMEvFa4VisobLy1CpwRFaVOuzSCzYaPr8rh16E34hJZ\nGY8eoQCG92FjY5GnEC00v3kzJq5qFW5MDJKibdvyBbGmYXL4+IDpxENUFCattzeEkaoFGBwMpo6v\nL0J9ZltFOxy4/ylTzK0G5gpNQ2L/2DF8g2++geBu0waCoVIlPNszz8DiLl8e/1+oECz5vHkzE+Nv\nvon4csOGyE0NHYrk8urVCAVGRHhuqaemIn49YAC84Ro1kJsICMiZugi9MK1DB3wXs0olNRWU3HLl\nzLWgdkZ4OMb6+PHi0GSfPiAH5FQ9yOHDsPBVkvnDh2e/pcrq1WjY6EnucupUcUt5HU+dInjxRb5F\nvWMHJoSKRbV0KSwVTwWGXmVolvnx6BEGt0iD376NgchLqN69C0EvSpqtWoWkLK+7qCusVlh+VaqI\ni4IuX4bC7dtXvJD6rVsQgM2aqXsHjEF5jRwJy/zjj9UrY+12xIZHjEAvKF9fhEXWrHkyeQiHA8oq\nMhIeQnw8wl3p6biXnC5SczgQ+vvxR+Q4ChVCvmvWLPN9+2U4dQrfrk4dtXoRV1y7hqRnt26ee3h3\n7yJMJgpb2mwIgXXubL5bKQ/BwfCoVZK+O3ciL5edkFBQEAwJs3UwjCEU7uOjFqZ+6hRB+/agzRlB\n0yCgZG4SY5isDRvCyvUUM2YgrCJSJjEx7oLw9m0MNlG7hrNn8ZF5a7xevgxBIJqoa9dCoZjhiS9e\njHsT0WSTkpAzqF5dnA+w2/EtvL1hrcqavzkjMhLWTpkyKMpbtcpcZfJffyHu37EjlEqjRmhlsGJF\nzncDfRKw26EE58yBVe7tDcHYvz9qJbKziA0Pt28jT1GmDN63J62nFy/GuF22zHNleP48PFQRH99m\nQ76sdeucqVjXr+vlZbyGtivu3cM8UelswIPVirCOSrWyK9LTkd9SaT7J2FOoCK5fh2DgTQS92lal\nM2NgICaYqJ/HrFmoKjaC3Q6LRFQxu3EjJrArd16nZ4pYC7/9BkHO2+f4cVi+IutlwwacQ1QgZnTe\nli3B9xa5q1u24PrjxoktsvBwWOelSkE5mREQNhssr7fewnedOBHxcDNCKiMDCnXePHyvsmXRoqJN\nG+QlDhzAGPCUVpxdJCfj/pYsQfioWTPkKKpUQT5r3Tq18ewpgoMRrtTXSvaEw/7XX3ifPXrwl0FV\nwa+/4juLKJQ2G67TqpVn9+qK8+dRW0MERSAbnzYbvo+njfl0jBsnb3LHw4gRMApUjrVan0JFwBhc\n4wED+A8+caL6kpW6Vc97oUeOgOPPKwgLC4OgPXIEgtHIwxg1CvFi18TTggUoABG5z4sWYcDzoLeF\nEBV/bd4Mi9iMmx8ejntu0kTset6/D4u1enV5LPjUKeRW3nhDjVnkitu3kQOoXh2U1eHDcU5PrPvI\nSIQSx4xBjPnFFzMXp2/fHuGpxYvxXm/cwP6ehh9SUiBs/f1hzc+bh7j3++8jyZ4/PxLxvXvD2/39\n9ydLq2Uss7agY0cogIkTPbumw4Ex7+0Ng8hTeqrDgXfy0kti9p/NBqOiZcvsK4HwcCSiCxSABLRY\nkBOSYcoU9QWbeDh+HHLDk8rqXbvUQ1LXrmFMP5WK4NEjxL95bllaGuLYKoUZetZdRDsbNw4WKe/D\nHzgAvnqePEjcucJuRyXl4MHu2zZskE8eUSyeMQgrX18oBR6OHcO98Ra1N4LdDquneHGwg3jQNHg+\nJUrAUhHxxO12WL5t2iC264lCYAyJ/kmTkBMqVQqW9MGD2QsTpKQgjLZlC567Tx8IncqV8X5z54ay\nKFkSyqhRIwhz3YJ/7TUoutq1cV8dOyKWny8fvJCGDbH/kCEQmhs2eMYSyg6sVrCT6teHEvr5Z/n4\n4iEwEM/dqJG59uCuSE7Ge3njDbEySk5GS4xBg9RqEaxWcTgyNhZel05/fu45ec3AihXw8LMTmouK\nArFBtO4BD6Gh6iGppCTkQVeufEoVAWNwIUVNoI4dg9WokqwKDBRTLq1WTGKjYha7Hd6JXsHq5WXM\nBU5IgPBQiT86QzUZrYeaRJ5BUBCE2hdfmAuDnD0LdszAgeKJFR0Nl71DByhhkduanIz4d6lSmUuP\nehpTvnEDLToaNmTs2WdhLc6Ygf47ORnu0TTcd1gYknsnTqCS88gR/Pfp07jm5cvYHhQEts0/obvp\n1auofG7bFopr507PLdrUVIRMvb0Rx8+OZfznn1Akn30m9rgiI6G8+vRRq9JOT0e/IRmldM6czKr4\nPHnEXoYeis1O6EvvH6ZSAOsKqxXv6rvv5PtqGgrM9MjJU6sINA0TXtRAbfx49OFRwdKlYDrwBsLd\nuxgErsnb8+dBF8ybl/3dS4eXwAkJgdDbtk3tnnRKrCq/2N8fykDEaY6LQyLu3XfNdUhMSIACUem3\npK/i1rKlnLWUng4PoWJFWIS//ZY9wfLoEYTc55/DJS5WDNbm/PmwonJireB/C8LCoCBr1YJRNHq0\nZ+wUHZqGsfXii0gqiwqYVM41fz7yAatWiZXljRvwqCZPVlOqKSlIInfqJFYa69fDEAkJQVHhW2/x\n9w0OhkdttteYK77+GvPPEy9w6lQYWSrz4/vvoTh1D/mpVQSMweJq25bfHjopCQJm0yb5i9M0sBAG\nDuTvs3s3JoFrXC8sDB+4UCG8yRdf5J/j0iUoFNV4fWgoXFEVK4AxhDbKlEGsmTdprFasdFWvXtbO\nrSqTbO9eTMqePcVVuFYrJrqvL6w9WTzTbkeo5N138c2+/TZnWkKHhyNB/cknmBgFCkAw9u8PRtOF\nCzlHPcwO9CrjAwdQeMYjKMhw7x5CAW++iYT4gAHwErPLkjp3Dh7XK6+oV67zEBWF5Gz9+vIeYUeP\nwrhZuVLt3ElJ8Hg+/FAsbHfvhnJUUYyxsTAqzHrzrti+HXPTk1zM6tUwwlQaNh4+7N6u5qlWBIxB\n4DVuzA8BnD+PgaTCtU1IgBASWdQTJ+J6RpaG1QreM5G4qdqJE7CERK1vnREeDs9gyhS1/e/dQ4x6\n2DCxAFi9Gvfx88+wpMuWVVNQycnIBRQvLrfmoqNRKV28OBLfsgSfpkHoDBoEQda2LZLdOSWs09Mh\n/BcsQLuEWrXgiZQtC5d94EAo3S1bQN/MyUrn5GQYLadPQyjMmQNB3bAhitOKF4cAHzJEvXlZRgb2\nHTkS39zbG8+wdWvO0Crv3IHSf+EFxMezq1AOHsS5Ro8WW+uaBm//P/9RN5ri4/EuBwwQhwQ3bcK7\nvnhRfs6kJJxTJYksgt4HyZPiunPnYFCpKK1795Crc20+99QrAocDFoDIYv72W+yjEi++dAluJS8O\n6HCAVTJwIF8A6h9O1CN+714MDJX+SIyBnVO9urjK0hnx8eino1L4Vbcu2Bq5c8NVVhW6Fy/i2H79\n5IP06lWEaEqUQLhCpaYgJQUFYc2bQ2H5+SEen9PLTGZkwLvctw9KccQIfOOaNWG15suHb1WpEv6/\nRQuEHfr1Q9z9k08gfPQKV73IadAgKJkKFZC78PLKXEDmvfdw7MKFsLBVk482G3IQS5fiHgsVwvn8\n/CBkcioncvMmxk7Ropg/2VlohTE830cfqa2eFhuL9/Pqq+o5stBQeCsTJ4qV1apVGIMq8y41FYq5\nf//sKcCYGIwdUUU+D/fvw3MRLSOrIykJY9OIufjUKwLGkJz18eF/XLsdloVq4caKFfAMeOGMxERY\nX6I2zjt2wPIRrWq2aROooapx24cPkQAaOFAtxpieDopljRqZq0fFx7sns48dy2ROeHmpex6M4T4W\nLIDi+/hj+VKHf/6JilNfX1hZqq0k7txByKRBAwinHj3AVMpJi90ImgaBcP8+SAVnzyJ8s2kTchuL\nF+P5Fy9Gbmj1asSeN21C+OHECSjbR488SxrHxqLHz7hxUIjPPYfK9CFDIFhyukleQADGmK8vYvLZ\nbQaoaXgnzz+PsSgzAM6ehbIcPly9dffFi5nrOove8cKFEKoqyd6MDDBuunfPnnJNT4fs4S1JK0Jq\nKphoKt6I3Y5wG6+24P+FImAMmr5GDX7oISxM3PPfFV9+Ce3Ksz6Dg+E5iHobrVwJa1CUVFu3DpPk\n/Hm1+0pMBPWyTRv1hVb0as9ff4WXUKpU1klWtWpmfxydTy2iohohLg7vrFgxDFxZUjYwELx5b2+E\n00RFfa4ID0eY6e23IRhbtQIt9vLl/11RWHaRmor7X78+s8agUyc8X/Pm+NuePU+mS6um4Xu3bZsp\nUM1UgfMQHAzCQJ068hXUHI7MnJKZfvw7dmBsywgY8+cj/KfSjsNmg0fXvn32vE9NQ66iUyfzHoXd\njnsYMUJuQGga8nAtW/Lv9x+vCIhoJhEFEtEfRLSViAoJ9hW+jOHDxclenUuvInTsdkyMIUP4+5w8\niUEoWi1p9mz5gii7dsnrAJxhs+E5a9dWT6gGBOBec+dGwtR5Kbx9+8AyGDIEg0nny6uym5wREgKL\nsnRpKDmZ5xISgvYTPj4QeOvWmYttJyUhHj5iBDjhRYqgZmPGDMTic3JBGE9hs6HB3KVLEObLliHc\n8vnnaEJYoQLed40aeHd+fvB2rl9/sjUG9+5BaZcvjzj4okU5k4tJTITiql0bOTzZM9y8ibxbly7q\nBoGmYW698ILYiLLZMK5ff12N5ZSeDuH90UfZfxd+fghvmS180wV7s2Zq96CvOSHyjv8NiqAlEeV6\n/N8ziGi6YF/hC0lIgDBYsQKTyM/PfZ9585AgVCmiefQIbAHRgjJ79kC5iDqZzpqFUJOoTcDhw1AG\nqq1+NQ15kTJl1NpHREai/75u9efPL65qPH4cCeqOHSHEzOLUKVhC5crBJZcJ9/R0CL9WreAlfPaZ\nubYYOh48QKJ36FBYos8+i1j+F1/AQ/vjj5xXDj17QtC88gryCpUrw/osWRJeZe7cGCO1a4Oi2KcP\n8gNz54IuGxiY83kPHtLS4Bm2agXvbfBgWOs5UetgtSJMVqIEelHJBK/VisI9s/UIeq+rbt3E/fsT\nEmDMtWqlFkJMSsK+HTtmP9G+ZAnyHJ5UDk+fjlbiKve8YwcMCdk6Bv94RZDlJog6ENEawXbpi7l+\nHQyMAgUwAV1daU1DSKJ7d7XBHxIC5SLq9Ll2LQSy6GPMnImEkUgZnD0L4bFhg/y+dGzcCAEjS0TN\nmIHwT6FCmesGFCsmttbS0tCDpnFj3L8nk+PUKYRwzIQc/voLSvz116GIv/oKXpcnCbv4eLBOZsxA\nErdaNTx/rVoYB7owvnLF8xXLzp+H93HxIs5z8yYs2/BwtbULnjQSEzF+e/dGjqVlS4SgcqJPD2OY\nRzt2YJ60aKHWMfbSJSjqt94ytxjLlSsIZcpIEKGhEKaDBmVVstHRxiykmBjE4/v3z/732rAB491M\n110dq1YhR6LSW2r/fpAYVAymf5si2EVEPQTbpQ+8Zk1mcddzzxm3RkhNhfunmhTVF8wW0djmzpWv\nxfrDDwh/iLjT+vKQ48apC74//4SSGTyY70o6HPAKzp0DHXPYMHgF9evLk9WBgYiXvvQSFI4nAvny\n5cwk5IoVasc4HFCOX38NxlSJEkhI796dPYstNRXCe8kSvOe2baEgChRAIrpOHRTuDBtmrnPrPwn3\n7oEB9dZb8IpatwabxNM1m42gaWBxvfcePKF9++TGVWwsvLNKleTUY9drLVmCEKJoHW3GMGZeeAGh\nI+fzX7kCD9V1sfrwcIyvUaOy7xn99hu8P0/Gzc6d6O2lksz298dcOnlS7dz/CEVARAeJ6IrT7+rj\nf9912mccEW2VnIf5+fn9/Tvq0k8hORnWnq4IiBAWMEJkJGKjqisnnTiBFy/iAc+ejQEucokXL4ZA\n47WXZgzsIL2PjWofGH1Bm/r11Sl3zonkKVPk4YnjxxHzfOUVcQttEQIDxc8uQlAQchmNG8OqzWlo\nGizGCxdgQf/wQ/baCfw3ERWFnM4XX2S2re7VC0o/p9dm1jTktV5/HWGwjRvlSfr0dHiEPj6g25ph\nOz16BMOtZk3x99A0GGTFi4Oe7Qw9qezqOf/5J96Xc87MUxw5Ytx9QAV79+JYFdKIXpQqIr4cPXo0\ni6z8RygC6cWJ+hDRKSLKJ9lP+pKiojIbpVks4IDzLNhbtyCUd++WnpYxlqntRW0TZs+G5SxrL+3j\nI246lZ4OV75uXXUrTi/CKV5c/ZkYw/m7doUS2bNHbBXpDebKl0dCTZXtlNP4b8XU/4nQNISeVq+G\nh1S1KkJ+bdogCX3mzJMJR9ntCCm9/DK8pk2b5ApAX+msfHmECM2uCrhzJ4gHEyaImWjx8TCE6tXL\nStnWNLRmKF3afaxu2YJ5uH69+/mSk80xtPz9EasX9fri4cABCHaVAtNr1yCzzBI5/vGKgIjaENF1\nIvJW2Ff5wR0OhIWIxA2ezp6VW/rOWLMGE05kmfzyCyiaokF/9iw+qKgbqJ4QrlIFHokq/P0RpurX\nT52nr2lQHlWqIGHm3HrCCOnpYL+UKQPr/MiR7LvVmoaJn93iJVecPInz3r7976OXpqcjNLlyJaz9\nFi0gvBo1Ar1w3jxsf5LPFR+PsFKVKiiQ27tXjdJ49CjGUq1a5lc6u38fz1exotz7vHgRimbIkKyh\n0UePwCR89dWshAeHA+GhMmWMq4vDwmCAGTWZNML+/fJ1QXg4fBjfU9b1lDGEm0qXVmuZ44p/gyK4\nTUShRBTw+LdAsK/pFxAejg++bh1/n927Ee5QtVZWr5ZXJ65Zg31EbZaDgjBRRo4UW3AHDsATmTpV\nfcInJiJnULq0uUXHrVYwOIoXR7WsrEAsIwOFVFWqINm2Y4fnVZjx8YhlP/cc4vaLFolzLqpYvRrK\n+6WXEDp8+WV4QH5+YNCcPw9B8b9SEmlp8E7378czf/UVhFrNmpm00u7dEb7Yuzd7ax2rQtNgofbp\nA/JFly5qrY81Dff4xhsQ4iphI2c4HAhX+voiNyRKaOv7+vjgOs44cwb5gE8/zZpPSkoCK6hRI2NG\nj55fmDlT7R1v2eL5SmXHjkFGqNDGL12CDBC1vxFBVRFYsO8/GxaLhXlyn1evErVoQbRpE1GzZsb7\nrF9PNGoU0eHDRFWrys+5eTPR558T7d5NVL++8T67dxMNGkS0aBHRu+8a7xMbS9SjB5HNRrRxI5Gv\nr/F+4eHYL18+ojVriEqUkN8jEdGRI0QDBhA1aUI0Zw5R0aJqx8XHE337LdG2bUQ9exING0bk7c3f\n3+Eg2r6daPp0oly58NzduxMVLKh2PWckJBDt30+0cyfRvn1ElSsTvfce0VtvEdWpQ5Q7t/lz6khJ\nIbp1iygwkOjGDaKICIyPiAh8i+LFiUqVInrhBfxbuTKRxUL07LN4Fud/n30W96JpeH7Xn6bhWeLi\n8D5d/7VYiM6exXXLlCEqW5aoXDn8W6ECUZUqRNWqEXl5ef68ZhEfj/G1ZAlRejrRwIFEH32E9yKC\npmGsTJuGsTx2LFGXLua+1cWLRD//THT9OtHSpUS1avH3DQ4m6t+fyG4nWrWKqGJF/N3hIJo5k2ju\nXKJffiHq0CHzmGvXiCZOJCpWDNfJly/rOdevJxo+nGjZMv58dcbKlXjOvXsxLs1g716iPn2ItmzB\n3BTh/Hncz6JFRB07mruODovFQowxi3RHFW3xv/7RY4/A4TBvER09ikSuKKatL/iut2OQYedOeeb+\n3Dl5+bvdjqKqF18UJ5psNsRKzVRIMwYraMgQxE/Xrzf37u7cgWdQrBjK5GWdE3Umybvv4pjPP5e3\nohYhIwNhhbFjwezQY+HTp8MKy8maAKsV+ZIzZ2DpzZ+P8MDnn4Ou2LUr4txNmsCDrFIFVnvFivjv\n6tUz4+f16qENRsuWOG7QIFi4M2eC/bJ1K+7/3r3/fbgqNhahp/feg0fWvTvmi8o4SU3FsVWrIgTj\nyToHd+7gmi+8APKG6H04HEgIe3sjH+e8b0QEWHmuq+ppGmpZfHzAWHN9LrsdhXVly6qzfRYswHxV\nlRXO0JveiQpRdZw8CRnjySI2OiIj/wWhITM/XRF8+SV/AXsRdu2S825XrkR8/9YttXPu349JL2ob\nHBqKOOnAgeIk5/bt+OiLF4uvefgwlNrYsea44KdPI/b5xhuZJf/jxrnT6XjPMGQI6JXDhqnxnEND\nUWFasiQm5/r12a/W1Nkxw4fjWQoWRGOwSZOQlAwOVld0sbE5x6X/tyEyEsKxVSuE4jp0QDhTNacU\nEoKQpo8PlKMnOaLYWFSEFyuGvkayGpNbtzB2GzfOytHX10lo1859ne3YWCSR69QxFtqRkVAenTur\ntYe2WhFuql/fs/UYli3DfJDl4BgDHbdVK8/XQNA0EGdq1XpKFcG9e/K4Pw9btiAuJ7JSly9HbF1V\n2588ifjdkiX8fRIT0RTqzTfFPcVv3gS9TmZVRUXB0qxSJdOymD5dnkiy22F1lSiB4728UE+gOtgi\nIjB5ixZF8lKlYZ7VivfeogVi/4MGIS6a3ZbGjCEhuGcPlNl772FcFCqERl9Dh8ICDAgwFjLt2uG7\nifpF/ROgaUhgHjiAdgIDB8LyNoOMDCQkJ00C26twYVjhW7aoU5QdDsT/27WDRT5ypLipIg+Jiahx\n0KmksjxUSgryOa1aITnuPG4iIvDda9Rw96ZPnIDVPmyYsQFy8CCE8sSJal5ZXBw8vLZtPaPk/vgj\n7kfFyNQb9am2qneFwwEv5+WX8Y6eSkXAGASQr697320VrFsHN1T0Qf6vvfMOj6ra2vja9F6SUKUJ\nCEpHqoB0QfpFwQZeRBREkCsi5aIYFEQ+UBFRBAtdQLEhiAqKoNIMVUCq9BIICSGkJ3P298cv584k\nTDkzCSbAeZ/nPDOEM3P2nLP32nuv911rLVtGJ7ESJak1K5Tq1dn+ezJwZg3g22/3nYTLKlasoMM8\n8QRS2aJFra1srlwhItmMtyhe3PeAdMXFi6y+ypVjRfXNN9YG07FjTFj16/MMRo5kAGcl+RkRQb+Y\nPh33TN++THbmzmTQINpQoYL+X7qN7t2zhpgOFMnJxH/8+ita96lTcUc1a8YzLVPGWavg/fd9GxOH\ng747fTqGq2hRdlAvvsi98WdnduwYcSY9e/Idn3wSWKW3y5f5nlKl6K++To/r7QAAIABJREFUYjRM\nqXKlSixajh4lCvnXX9m9mLLZV15J/3vi4/mdnuoCp6biYi1f3rr9OHSImInnn/ffledwEKzWtq21\nXcSbb/Kb/ZXamkhM5H61bs3kpbX1ieCGJIs3boSQWrdOpH59/75r/nzIrWnTIOTc4auvRJ55xhqh\nIyJy6ZJIr14ilSrx/Z5Ivi+/FBk6VOTVV/l+5ZvC8YqLFyG4L1+GnOveHeLWG86cgZR0OJx/K15c\n5PRpkaJFrV87ORnifOZMfv/w4SJPPilSooTvzx44ILJ8uciyZZB+Tz0l0rKlSIsWInnzWm+DFRgG\nhPCRI85j1iza74rGjSFuy5blqFyZthQuLFKoEK+u7/PmTU8Wu75PTRWJjRWJjubZREc7j8uX+WxY\nmMipUyIREVyvUiWuX7EiBGitWvRPb0S9CJ8PC4NYDAvj2idOIJLo0AGRhK/vcEV0NP1+0SKe08MP\nQxo3bux/f710CfJ2zhyRbt1E/vtf34KMPXsQKERH07fuuQehg2Fw3+LieD9jBgSviS1bRAYOxB68\n99614otTpxCFREaKLFliTXSxfj3Ch0mTIM/9QXIy7Tl+XGTVKu/PwDBExo4V+e47kR9/pA/4i+ho\nCPJSpSD9TRtklSy+IScCEZRA8+czqE3lgFUsWsSNX7NGpGFD9+f8/DOd4OOPUa74QmKiyL//jSLh\nzTdFypRxf96RIyJ9+ojUri3y4YcoUALF0qUM0tRU/p0rF53gscc8f+boUZGRI1F4GAaGaedOkZAQ\nkQkTRJ5++lpVhS9s2yby7rsM/PLlmRBatfJtOLQW2b2bAfD11yJ//43x6tJF5P77RSpU8K8dVqA1\nv8805Pffz/Vq1xYJD3ceItyr+HiMT8bXsmWZVHPndh65cvFatSoKoRIlOEqWTP9avjwDtlIlkXLl\nrClstBY5dw7F0759qI7++IPn17ixSNOmHE2aoHryBwkJGL2FCzFEHTvSl7t0EcmXz/97fOoU3zVj\nBgu2sWO5J95w7hyKno8/ZqH09NPcSxEWCgsX0s/z5GEx9e67/F98PP126VImgAcfTP+9WvPZ0aNF\nXn6ZBYv5vZ5gGKigPviABcu993o+NyFBpGDB9H+7ckXkgQdEihWjXRn/3xVJSUx8J0+KfPopyiZ/\nceYMz6p9e+55rlzO/7spVUMZMWcOWymrqRVcYeqAvfni/viDrbnVuqkOB1vgChW8KwPi43FT1Kxp\njTzyhBUrcBvUqYPvP3duXB6ffOK/H377dtwkFSoEnpb4/Hm2t3fdBak9ZYo1ctn18wsXkiQuKIjf\nNWkSZHpWFWIxDEjE2bOvf3GbQJCUhCtk9WqURqabqFgx+mvbthD9ixbBKwXKt4SH00/MamcPPwyJ\nHGjdA8PAdfPgg04S2Eqq9IsXEYGULImrx/X6hgH3VaGCM2FiiRLOwMMNG3DbPPLItZXeDAMOr0sX\nSFOr4ywigjxN997rO/uumcPINa7o9Gmu9+yzvl1Jly5xnQcfDMzlZrahShVcgbd0YZpZs3gYgSTV\nWrMG4sqbv/DAAWSLr71m3Z+9ahX+0NmzvX/ms8+4/ltvZQ2BqjUcRLNm5JkPJJ3ztm0EpJUrR9qC\nQAyDYRCgM3gwA7xrVxQ/VslJrRlEW7YwsXTuDJdxxx34mD/6CD/z9Q6uykrEx2NY9u/HQK1YAQE8\nciTKlaZNmczz5uX3duqEX3ruXMheK8XLvcHhwGi88YazVnLfvvASmSl6k5AAMd+wIc9n1ixrEeJR\nUUxoQUHwHxmN7t69LHLq1sXg//wz1mr+fBYM/fsjEDBTqqSkwNN17IhhNCvveaoz7g6bNvGdY8f6\nTtXx9deMXVfhSlgY6SZmzPDdN48cYRIbPTrwsf/dd9gZb2knbpmJQGsM6R13BEb6bdjACtbbzTx/\nntQNjz9ufaV85Agr2gEDvEsVjx1DGteuXWCyNHdwODCWpUsjNQ0kN/qff2J0S5ZETx+ISkRrjP+i\nRZBYxYqxGl+61H/1RWoqq7rZs0lEVqUKBrNtW9Qh8+YRhZkVhdtN9O5NVPKdd2LoWrRAAdWjB/fm\n4YdZzfXqhaKmc2f+v00b8kXddRfGPX9+EiKWKcN33XMPhmz4cFb9y5ezMz1zJutiCxwOjOmsWbTR\nLAA0bBgqpMzKeQ8fdsbAdO7MosqKQYuKYnIPDmZXnDEV9alTxK80b056C1eDvGkTRjYkBGPtqghL\nTeVZmSIIEYyylcWCw0G/Kl0aqbk3GAa71Iw5jMxFnbusxxlhqg291Tvx1YaZM1ms+YpsvqUmAq1x\nQ9SvH9hkYNY+nTnT8zlxcQyoe++1vjqLjUWq98gj3iWpqams1EqVYoWWVStdM/VvcDBqiUCkb2fP\nkvogOJiV66ZNgbcvMpIVXbduqFl69MAVFOhq9/x54jimTXNmqSxQgACv/v1xTyxZwu4kIsL/dkdE\nMFHv28dq79dfMaLffIPLYtkyVvZff80u8PvvkSb+8gvX3LsX4x4Xd/13L3FxPJuPPnIa/mrVMLaL\nF2dNGurYWJ5X69YYzVGjrMfdnDxJXyxZEmVPxpz94eHIfoOC6G8Z+8SmTYzvdu3cq44cDj7nWoDJ\nSuDWiRMsJlq18u1ivnqVBY1rDiPDQOJaqZK1HfjixSwEApUup6Tgdqpd25pL/JabCLQm8vCOO/wr\neGHi+HFWcCNHel7ZOBysRKpXtx5rYBhs70NCCBgzDFbb7tLf7tyJERsxwj9Jpy+cOMEKtXRpVlSB\nrAavXnUmIKtTh+hbq0FI7hAdjZHu1QuD1bQproINGzIXNZyY6KwBPH48A7dRI3zLxYoRGdy3b+DB\nOjkBptGfOZPnWrs2hu/uu7mHS5ZYL2XqC4aBu9DVzffll9af0e7dTNJBQUwcGSekyEgMeFCQ+35/\n5Ag7r27dmHjdTah//olxbdGCrKy5c3O+r9+1YAHjcupU3zuxffsYm+PGOXedsbH0r+bNfY/X1FQm\nwKpVA691cfky4+X++60v6m7KieDgQd/Rt++8E3gIeFQU2/oHH/TuzvnkE1wF/gT3/PUXUY69esFp\n5MnjPvNpfDy+zlKlCAAzDDrhgAGeJ6gZMyD6fPk1//wTQtgsNBOIwTVTSTz8MH7mgQP5HZlZ8SYm\n8p3//S+Rm0WLYnBmzMAdlFXukshIjNrSpYHxJ/8kDIO4kI0b6fMvvIBxq1aNia1RI4zz3LnsaLOi\n5rDrtXfuxOhVq4aLbPJk65NLaiq+++7d2WlPnep+0eBwYFyfeupat+j586x8g4PhqtzxS1evshoP\nCcHN4nAQl/H4495rIEdE4KKsU8d3PzAMxmFISHrRyKFD8BejRvl2R16+jPFu3z7w3e/+/SxyQ0P9\nSzV+U04EJ04wo/pKEztvHv6zQAZ7YiLunP/8x/t5W7ZALI0fb91QJSaycjG3r3fd5dm4794NL9G6\nNR0ud24mOXfYuZPtbe3a1la6v/2GX7diRVaVgSoWLlxgkFetygT39ttZE5x16RKul6efZvAULcrr\n+PFMvoFwHjkNSUnOQLKlS3FvjRiBgerblx1MyZL0l4EDuc/ffIN4IatrL2uNwdu1i3tcvTrPdOxY\neBerk3x4OEa7cmUm9GXLfE9QGQ38lSu4MYOC2J1nVANpzZiZP5+UMC+8YH33bAaqdevG6tyXAY+J\nITCxTp30QV4mHzBnju97c/AgpPCIEYHXivjqq2snIquwOhHccHEEZ86gl33iCTIAesIXX4gMG4Y+\nvUUL/65nGOiTfWn8L14k1iBXLvTCnjKImjh0SKROHafuP08ekbfeEhkxwv35Dgca6gUL+HfBgmRo\nvP32a8/Vmqydo0eL3HEHsQy1anlvT1gYWUM3bSKj6rBh1rOUusIwRH79lXauXImWvV8/tNT+BKl5\nQmQkmvmtW4lZ2LaNdpoBNLVrc1+rVEmvoc4spk4lIKhAAe6962vhwtzz1FQOM5DMPAoWpK+6BpO5\nBpiFhBDLUaECR8WK6d9Xrco5mQ06FCFeJjiYrJ6umvb4eJENG4gdOHNGZNcukYceQvt/993Wrq01\n3zFnjsjatcTIDBniOTOvJ1y5IjJ7Nm2pVEnktdd4nhnx22/EweTNS7Bas2bWvv/kSfr3iRNkJ23Z\n0vv5YWFk323ThusUKoTm/8UXuZ8rVnCPvOHbb4l3ePRRMqb6C8MQmTiRcfXll4wrf3FTB5SdP89k\n8PDDIqGhnjvsjz/y8P/v/64NNMkqpKY6A1o+/9x7x/zjDzrxmTNMIomJ/P3LLzGaGZGYyKCIiHD+\nrXp1kcOHPf/m5GQG1JQpDMhnnvEdYPTXX9yj1atFRo0iIM3dILSC+HgiKT/9lAjwLl1EHn+c5+Ut\nsMYfGAb3YO9eBuy+fRxRUUx+deowOdSsiUGtUoWB7C/WrCE4KjGRwCHX11y5CCzLk4cApTx50h8h\nIRhJM6gs41GsmO/ApqxCqVK0NSWFCSElhXty+LBIo0ak+e7ShahcqxPP8eM84927RQ4eJMirf3+i\n1P3BxYvO1NFduhB9XLu2++uNGcMYmjpV5JFHrLU1NZWg09dfZ+yNHu09SC45WWTyZNrjms76+HHs\nzW23EcjqLYI+NZW010uWkGL+nnt8tzMjLl8m+O30aSYdTwGqvnDTB5SFh7Nle+MN79uzHTuQer35\n5vVVbnz9Ne2ZMsXpKkpK8p5ZMSYG6WuVKqhcMm6Do6LwszZqxHa9RAlcSvXr++ZALl0iA2jJksgU\nrQR2nThBUE9wMIqeH3/MXHxDRASyvAEDIGr/9S/cdlZyIgWCy5chUefOZevfuTPb8vz5kes1b47b\nb/x468W/b2QkJ6N2atDA6Y4U0TpXLirg+asii4rCHdKqFa6KZ59F9hrIuDpxgn5ZsiQJ6HzJk+fP\nR7bpT9bY7dsZO+3aXatScofff2dsueafMgw4weBgeDhfv/XCBdyYHToEHgS5fTs84oQJgbsBDYN7\nKjcjR5ARly4RPPXvf3tP83zqFH72oUOvT01X1+u0a8dAOXYMkq1+fd/GNDYWf2jZssgRvXW2pCSn\nDvuFF3xHx4aHYxT9mRBiYyEo69eHoJoxI3MKIa15VosX4/8uXpzYif/7P4jw6y2tdDj43b/9RkzD\nq69eW+T8RodhYLxWrYJ0b9NG6yJF6PcdOiCrzZsX/70/iqKrV+EmevdmMu/bF54mUKHB5s0seqpW\nJZjqeiT8O3+ecq0VK9Ln3PWvo0e5frduKOHy5cMamso+rRk7PXowDqwofTZvZtH50kuBCRwMg4VT\nSIi1spTm2M5o+0xu4/HHb5GJQGuMVvfurP68RTReucI5XbsGpqe3CocDQ12sGJ2rUCFWFFawZQsT\nW+vWvkPiw8PRiLdtS9CQL1LOdUIYP56B4AtmmP6jj7JrGTgQjXxmo6ATE9ltDBvGsytblliLOXPY\n6dxIEcPZgYQEVo3z5hF93L49xiM4mEVRaCj311wkXLqktVLsDKxEEYeHE49gxnv06oVyJtCUHHFx\nfL5hQ1RIb7+d+YWFOyQmQqoHB2PkvbV3w4Zrd0njxjn//4sv2EWOH+970nM4IPs7dQq8kExMDOOs\nXj1rsRk//YQia8KE9Ivb3bvZBQ8axH3P8ROBiLwmIntEZJeI/CAiZb2c6/WmpKQgQWvUyLuiJDmZ\nB1u7tvVAmEAQG4vqwexkRYs608L6Qmoqq4JSpTCUvgburl0M2MqVMQy+djzh4bivgoNx1WzcaM3w\nhoczwdWty7VeftnadtsKjh+n7f/+N6u4cuUYFPPn07G97fa8Yc8edgEnT17fneD1QGIiCqHVq1GL\nPfcci5hOnZz1l/v1wwD98AMra2/PcdUqz+oww0DePH06O7XixdHHL12aOYN95AjyyuBgJvzvv8+6\nVCquMAyUNaZ67cgR35+JimK3a47RsmW55xcvsmCqXt1aTYCzZ9lxtWwZWPyS1vTTli2xYb5cX67V\nCtetc/7dtRrbkiXOv98IE0ERl/fPicgHXs71eTMNg+IbNWowgLxh7lwMrb9FPqzirbeQexYpwkpD\nBOPpz0r30iVcWaVL015fW83ff2cnUbMm20or7qjZs7lfd9+Ny8TKdt+UGT7/PG275x5We+5kfoHA\nMHCrffIJK7RatdhVNWvGxDh/PhG7Vgz77Nm077bbcItUqkRkeL9+LAhmzULiuG4dv+n06etbucww\nMMbnznG9NWuYAF9/Hbfdgw9iENq2ZTdZrRq72GefZRW9ciWutKyQj548yb3s35+VZb16yHXXrMlc\nTMKVK/SH1q0pKDN6tHdNvyuOHMFFaiVXkYmNG3HHtmmT3jB6gmEQHV22LIY3OJh7vWIF7S5dmjZb\nyYu1ciW7hldfDWyhYaaKCAmxVpz+zBnua4cO6SWzly8zcderdy13aHUiyBGqIaXUOBGpqLUe5uH/\ntdV2Ll+OHHPhQlQInrB1KzK5J59EeeRNdhgZiZTUanrmq1dJ7XzuHLnwf/2VVMtt2pB62p+02bt3\no0AID6edXbt6VktoTY2G8eNRhwwejOrBmzrFMCgUP2OGyLFjpB8eOJB8/CZ++gkFTsY86SkpFJvf\nsIH0wY0bIx/s3dtavneriI1F2rh9u/OIjUV9ZVXlkpzMszh5EiXQqVPOwvWRkaTQNo+8ealDcfz4\ntbJR87V8eRQvZh0C11cRVDoxMRxXrzrf58tHPzh7lhTUZcvy6nqYElJ/CsB7g9bIJnfuROK5fj0S\n1vbtSfvdvr1ItWqBS1UdDvrIwoUordq1Iz16166+01hrjbpsxgyRzZtJPz1mjO+6Ftu3i7z0Emnd\nJ05EruxLhbV3L0qcuDhSTDdpQt+dOpW/JSWhFPKUmt5EXBxtXLMGZZAvKao7XLzIOIuIQHHoyyZ8\n/TXqp3btGN/mb/39d9Ra/fqRZjujMu+GUA2JyGQROSUif4pIsJfz/jfD+apvqjWr43Ll2DZ7W4Wf\nP88KsWtX7xF/r7/ObGulPKMnpKSwUwgOxjXjj7vDMFAl3XUX7fWVaMowyEzYtCnb37lzrSVj27cP\nF0RwMKu55ctZwRYvzgrKW76auDi25489hrqpVStcGlmR48bT9a4HDIM+duIEQUQ7dtCffvoJ98qK\nFeyelixhNb9wIYTk0qXcrxUrSMGwZg2f+/NPXF+RkYG7uPxFRATXnziRvh0Swq7o6afZWezZk3kX\njcNBPwwNZaw1acIOy+rOMDGRAKkGDdjFfvCBtWe6bx/E9W238Rkru6OzZyGPS5eG+zB313Fx7AxD\nQkifYoXg/eUXxtSIEYFzJj/8wD37739994mrV2l7tWrpXVXJyZDSZcp492xITnANici6NCNvHnvT\nXntkOG+siEz08j1aawZpo0YYVF9ulpMncXn06+d9u5+czNaucuX0kkLXh2yGmQcHY9wyM4iOH0eJ\n0KGD//luUlMxPhUrUj7QW/1lrWn3xo0Yg7JlmYCs+HwTEjBsHTpAeufNi4urXDlripPERHzbTzxB\nPvi6dfG7rluXtdlBHQ7acz38zjcCEhJwMS1ZgkHr1Yu+VawYBPK4cSwg/KkJ4Q2pqRjC4cNxJ9Wq\nxUTgT2nFAwfoC/ffz2LDatbS7duRIZtScCsuvJgYZ5Ty2LHOvu9wkD66ShXqaPuqO6A19mDIEK4f\nqEs5Ph630113kVbbF7ZscSYOdHWXHTrExNuli++o6hwxEVg9RKSiiOz18v86NDRUh4aG6uefD9VV\nq/6in3rK92ogLg41Sps2vomcb79ldn3tNYx14cLpc41rjQ+zWTM6sJXO4wmGgSSvWjVINH/zIiUk\nMBgqVKCTWBmIf/4JGWsOCl91Y0307q3TqSvy5/edqtcVZl2BiRPR8RctSgd+553M1xQID2eCK1SI\nHVufPhjE+fNZrV64cOMrkBISGPg//MAK+KWXMPjVq0Ma166NpHPiRHYjmSlW4w6xsZC8Tz/Nivru\nu9kh++LhXHH1KpxPixY8r7FjrfV5w9B67VoWJBUqsAC0wh+kpLALLlcODsR17P/2G0a0SRPeu8OF\nC4z9N95wFpASIbdToLuAbdv4nkce8Z1vKDkZO1SmTPr0+IbBbtTcwbjr27/88sv/bGVoaGjOnwhE\npLrL++dE5HMv56b7sTExGNB27XyrakxtbunSvqVdZ85A1BUrxgo4JOTalUdKCoOucmXP2RCtIjER\npUZwMOSrVWWRiStXWOWXLg3RuHOn78+cOoWxLFOGCdJXPpigIAxO7twY8Tx5cBXVr8+1/a1TEBkJ\nmT1oEGqnMmWYbKZPx3gHQlReuYILZ9kydnf9++MWMxU21arxWx97TOsxY8ic+tVX7AAPHGDg/1Nu\nGxPJySwmdu7E0C5ciCtz1CgI0xYtWHWbpHGHDpCbkydz//bvvz5tNrPjTpvGNYsUYXfx5pvWSV+t\nmYx+/ZXnXKIEO9iVK621OSWFHWmDBuw6FizwjyBPSMATsH27829HjzJGKlViB+VtsvzwQ8a/WfFP\nhP4TCJKSUNiVKUOOIl/Yu5fJdvDg9IvNU6foz82a+bcDszoRZBtZrJT6QkRqiIghIidF5Bmt9XkP\n5+qM7XQ4qIX67bekNKhZ0/v1Nm8mLP3hh0m/4KlI+vvvO2v65stHyPvEideeFxYG2XP77aR0CKTg\ntImLF0lT8e23vA4a5F/d4Lg4kY8+Ir9QvXqQaL4IrORk8gLNnQuJ9sQTEHUZSauwMIjy2293FsR2\nOCCpPvuM9Bj33cf9795dpEED66Sj1pC2mzdzbNpEPqYGDWj/G29kPg1DfDzE7JkzzsP8d2Qk9z4q\nipD+QoXIYRQURJqK6Gh+c4ECPA/X98HB5McxDPeEcXKykyiOjU3/WqUKxdZDQkRKlyZ9QJkyzvdV\nqkAiV6lCSoPrnYri9GnEE2vWQCbnz08t586dISeLFbP2PVqTB2r5ctIiBAfTrx59FBLcFy5cEJk3\nj7Zcvsz47tIlc/mjzp+HDP75Z1KnjBzpO9VJXBz93UztEhREPy1c2L9r//knpHmFCoxPbwKK1FSR\nadMgzd94AxugFPd0wQLI6eef59WT7cqIiAiR0qVv4lxDrli0CMP37ruoVbwhMhJVTFQURqxSpfT/\nrzW5UhwOjqQkHsbatRT0zojkZDrZrFkU3H7mmcx12n37RMaNwzCHhtJWf5QjSUkoN6ZOpeB2r14i\nPXv6/o7Dh1Ez7d+P4ezfH/WPlQR0qakY8JUrmZATEpgQevQILL9QbCzGZN8+inr/UzAMjHRUFIep\n9ElM5L4mJqZ/X7Ag55gF6zO+monpihZlIi1aNP37EiWyNkGeVTgc3Nvff+e5/f47v6dLF1Q0nTuz\nGLA6mRsGaqTPP+coWJDF1sMPi9x1l+/P6zTV0AcfZC5pXUZERJA/a948jPG4cdby9Xz/Pf2uXDny\nGuXKJfLJJywircK0Cz//zLUHDkx/Px0O8nodOUKepp07UcXVqYO60LRLZ8+i/Dt/nsmgXj1r14+M\nxB6FhYls3XoDqIasHuIjjmDbNrZ8Y8b41vOaUYDVqrkvT3n8OFv12bMhxfLnxyXizS++fz9b+ZYt\nrfvevWHTJraiNWqwnfTX55uSwudatMCFNW2atYjSxES4iz59cI89+CBko1V3jWHgapk+Hb1z0aJo\n4GfO/GdSSdhID4cDXuvzz1GoDB6MW69mTVw28+YRFOjvc0lKwnc/fDjjrnVrvn/PHuvfdekSqUtq\n1oTnmDUra6KNIyNpS1AQfc8qUX7oEO7mO+5Acac1LtTmzf27P2FhiCO6dfMsrLh4EQGGWVdZhPaa\n7i8zv1Hbtrg6rboADQOeqFw53FGxsdZdQ9lu5C010kJAWUQEJG7bttby1W/dSiTik086Caj333cv\nd9ywgXMHDPDcWR0OCJxOna6tpxoITKKscWP88atXB2ZIw8LIOVKiBKoHX0ojE5cvI7Vr0wYO46WX\nUDn545eOjERK+dRT3L8yZYgY/ugj//zNNnwjJoZnvXAhtTRat2YyNxVmoaEodAJNhHbpEkSlmSuq\neXM4or17rffLxEQWX2beoiFD4BGyYoFw8SLcnRkoZjXK98IFJrR69SCHMy56vElK9+1jwTV9OhNr\nwYIcVsrNjhhB2g+zrOb69fz977/hZho18q+eyrlz3Nc770wvL7/lJgKteWgvv4zG2JfWXmsGz1NP\nkenv7bd5MJ07uz/36lVWGF26sEr2hLNnMby33YbyILOd3FQYNWxIZ/3ss8ASWp0/z+qiQwc0/vPn\nW4ue1Bop7qxZEFVBQaiPvvnG/yjc48dZ6Tz2GAP2vvsg9d59l0Lg/kbMzpvHrmXMGAi+9etp680q\nJ01OZgL94QdUV0OHQuSWL49yqn59/jZtGlLdzER7JyUhPZ4wAaNftSopST75xL/CQGaiuWeeYUHR\npk3m8hZlxKlTGNWSJVE2WV1gxMQwcQQFMXEGMkFWqIDHwJVU/uIL75+Jj6e/lirFbipXLsZBaqoz\nzmj6dOuRyobBOChVisVaRnn2LTkRmFizhpXQa69ZM5rLlztTQRQq5D1UfcMGtrO9enkPlvr9d9j/\nVq2slcPzlQTOMNgVNG/O9RcsCEw1kpzMqqx7d+fg8afU5OnTTAqmuqpvXzq/FddTxt9z4ABG4emn\n2U4XKoQ7a+RIJjxfg+HYMc6bMoWdXevWGMUCBdBq9+zJSm3CBBLarVqFuig8POdNFvHxTGJhYUyy\n772HwXjkEe6JmSajSxcmc9Pl9uOPrH6zIkBs927UQfffj1uvcWNiEdat82/SN0tdjh+vdceO9NfJ\nk60VW/eE5GT6SJUquG+qVcMAFyyI0sqqnDspiXtbtiyLkMzsTBcudE4C+fIRI+ANv/yC7Pehh+iD\nhw+j+Fu9GpVb27bW8iSZMOs5N2/u2cZYnQhueLLYE86ehWxNSSEMPCMx7IohQyg2kZLCv8uWRSXg\niZ1PSoKIevddwrqHD3dPyDockFUTJlBpbNgw9+qJo0cpXtG1K+eLIEGuAAAdxElEQVR6CzfXWuSX\nXyi0cewYpPJDDwVWeOXcOcjlefNQigwbBrnsq5CNiYgIlE5btkAU1qsH6dilC8off8nQq1dJHbB1\nq8iePYTeB0KoxseL/P23M43E2bPOdB/ma40a3HdTJVSyZPr35ctDhLpTDBUo4GyXqRoyD7ObxsZy\nxMVd+75gQYoBRUQ4j9RUCsiUKoVipXRp+mzlyrxWqkSbsirtRGwsZOiWLSi2Dhzguzt0QBjRrh33\nwiq05vu++AIlmVIQvw89ZL3amS80aEC/MJE/P2S3FWI5JUVk8WJEIiKQub5SSXiCYaACmjABRdWJ\nE4hMPCmLoqJQ+1y8iB3o2ZO/x8dTie3bb1EEPfWUtf6ekoJC8K23IMH/8x/PtuqmrVCWkkK5uvbt\nfX/O4RCZPh1J1uzZnquUPfYYyoWICGf5wdtvR0XjTfVy6BCVma5cQYbpqUNGRSEJmzdP5NlnnVWS\nmjWjXS1b8h0zZzK59OrFBOOuJKUrtmzhut99h/Rz+HCMhb/QmnxIq1bRxrp1UUn06eO7/KaJxES+\nY80alBcxMUgQe/bk95Uu7X+7rieSkpAoXr7slI+6vubOzaThTjGUmIhhMwwGrnkoxWulSs78VIUL\n8+r6vlQp56t5FCmSNcbSHVJSMPQHDtDPt2xBKdagAQuQFi14tSLxdEVyMmNxwwZULUWKMMb69PGv\n2pkvJCaKLFtG5bDjx+mvBQvyO+rX9/7ZlBSUha+/TrW60FAUdYFi1y7GfO7cKJ3KlUM6/f77yGRd\noTVV3F58kQlx8mSnFPfHH7EFTZpgn6ze+y1bUBJVrIjt8FVJ8IbINWT1EBfX0LFjKGFGjrTuU962\nja3kuHHeIxMNA1JswQK2tDVqeI4+dP3MwoWc++ST3kO+T5yAPyhdGmInb158gq6+3KgoZ1j84MHW\nSK8jR5zVnvr3x/0RKEzl0COP4Prp3Bk+wV9Fx9GjuJCeeQZy8a678F9/9tnNUXw+pyIyEq7k7bcR\nNzRogPukZk186W+/TaR3oBlGz5yB7P/Xv+gfzZrhovInyMmfa40fj/+7SxdcvlWrorZZuND7Z5OS\naGeVKoxlX+PYF8LDIbe7dsWd6eqKc+c2O3wYF17DhnBzw4bx2Tp1cEPnzetfcaTLl3l+5crhyrbq\nypWbmSOIjMT/27Spdb/jlSv4VatUcTL0Fy/iO/aEL7/E5zxihO9iNtHR5FAJDqbylreB9scfdAQR\nfIytWl3r4710ibKRpUszeVhJeBcVBVFYoQI5Zz77LHMpi2Nj6XT/+hf+4k6d8K/6m0guNZUozzff\npF0lSqBuGD2aSXf//sAI8N9+4/mtXYva4p+ODs5qdOnCYsIXZ5OQgGLlq68oxDJoEMkIy5RhTLRs\nSV//8EMWQZlJ0BcfT16c116DjA4KQvm1eHHgCiRvSEmBy+ndm0nmuefS1w758UdIUU+4ehUivVcv\nSFhPJUmtcioJCaiJzIqAVqL/16zh/Lff5ve0a+dUCJlFcMLCrF3fMFAhlSvHAtHf7AM39URg3qC3\n3sJQfvON9Rvz3XcQb8OHM5koRWZJT4iKYlVy223WUkocPsz3VquGusjd+e+9l15DLMJKwZ0xvHwZ\nMrRMGYyoFTVUcjL3pF077s+YMZkvInP1KqTw44/Tye++G9XFrl3+K6NSU9m1fPQRO4+qVSEn27Zl\ncvj8cyZ4X9+7di2qr/btmeDz5UMB1qEDBPQbb6ByWbMGIvTChZxHErvi9tvpj4UKkd7k3nv5DaNH\nc59atmSHlj8/K/wePSBK586FiDxzJvMqtcRE1EITJ6LwKVwYMnLaNIzq9Srwc/gw+v/y5ZkAPvzQ\nv0qC4eFMECEhKMm2br32HFOS3bo1fc8bDIM0F5Ura/3AA/6RuNHREP8mvv46vVR00iRr37N/P2Oi\nYUP3v8cKrE4ENxxHkBFbt+Ljf+wxIoytRLJevkwtgvXrMcMVKkAcekvrsGkTPr1SpfAH+kppsW4d\n7SlVitcWLZz/9/nnkGr8NojNAwfw+738Mv75jKRgQgJ+2OnTae/48SKdOvkmlw4fplbAwoVELpo1\nCvxJYZERrtHEK1eSy75SJdrToQOpBfxFZCREcVgYpOOuXXAw/pDgKSnUGzh2jHt6+jR+/vPnna8x\nMUSY3nMPhGmJEk6i2HxfogREad68TnI4f/7073Pnpu9kJIoNg/uTmAg57O4wDIhFs/5BRASvR47w\nWRNKES3fpImzRkHFitzrrCKNIyO539u2MZY2bRK5807I4nbtRFq1IhL6eiAmBk7pgw/o/48/Tn2Q\n2rWtf8fhw5Cmn3/OuHnhBZE77kh/jtbwaJMnw8W99JL7MWZi/Xq4soMHRd5+m9oUgSAqinG6ciXj\nf9UqOLzDh73XaYiLE5k0iYjmiRPJWBBompGblix2h6gojPTevSiEGjbE8IWEiHTrdu35CQkQR+Hh\n/DtPHgjcKVO8tyM1lUlg0iQM6ssvezdUqakQVRMnQsy9/jpErDtoTQd89VXa9eqrkG4Z1QCpqeRx\nWbaMjvrcc4SxFyuGImLBAjpexo6WlOTMLZQvH0R0v3500MyQelpjsNeu5fjtNybJzp2ZGJo3t54b\n5Z9AYiL39+JFDHB0NAuDjK9BQdxfkyQ2iWLztUgRZ4oJV6I4Vy4M6enTkMPuDpPgCwlhoRASwjF5\nMnl6Chbk2c+ahRolq5CUhOpm2zbnceECIodmzSBRW7TwXRQmM4iPxygvX04xm8ceQ6XUo4fvIjYm\nDIO+9v77PKsOHVC8ZRQkOBz0+cmTef/yyyIPPODZqG7ezDmnT/OZvn0DU60ZBuN+3Die4+TJPPeO\nHXnviazWmgXip58y+U6fnvkCTzctWewJhkEAV6lSbJcLFMAX7W57eegQW75ChfDRmzEEM2ZY21qf\nPYuftFcvto++PpOQwHeXKYN22VvGTsOAw+jfn1gIT6l3DQMfed++kMQjRqBRzpsX/6o3F8ixY6QS\nrlULl8r48VlH9iUlEWsxfjy8QseOuGomTsTXbDWI7VbEjBn4gk0OK1AYBiKDVatwKz76KK7HRo2c\nJSk//hjeKRBuxl8kJdGWfv0QDnTsiMvOX393VBTjoXp13CUff+ye/4iLI2V3jRrOrKfexsOOHRC5\nlSvTrsy4v7ZsIcX144/7J9rYuxdXbt26jJ+sgtzsHIEnnDiBv9nMnf/cc97PT0jAHz1+PB2nR4/0\n/j1v2LiRwdW0qWdSyhUxMSgITOLJVzDL9u0EjAQHk7bCU9DMqVP4yl1rBgwZ4nuCMusPv/givtkG\nDVD6+OMP9YVLl8jTNGYM9YMLFeJ+jRrFAM1MXYezZ28OkthEaqp/hjkmhsCtzz/H4A8cSH8pXpwJ\npVMn7vOCBZyXlUWBfCE6GqHBo4/ik2/ZEm4sEMXYzp3OdNb9+hGp7K5vh4dDqJYqxTjesMH7GNi5\nE2Ve+fK0LTO1ms+cYfF2220Q6Z4mnuRkRChz5sD/DB3KmChenDZkNQdzy04Ec+c6FTkivLe62k1M\nRB0RHEzSOSsGxuHgwVesSLI2XxHCWiMXfeklrjNggO8iHceOMaGVLImB37372nOefda5szGPO+6w\n1h6tMUA//wyJXrYsK8gJExgsWZksLi6OATppEr8lOJjrdevG9b75honNyjXnzGEVly8f4fpt2nA/\nJ07E+K1fTxLAqKgbL+Gdw4EU2TXSeNw40iG0bMnuslAhVpC9e0Moz51L7p7MpJbIDMxUJPfdx2Ks\na1faFMhkf/Ei6p8GDSBMJ0/2PIns3YvaqkQJ5MrexpNZta9zZ4z2nDmZU1UlJLC7Dg5mMekrx9jZ\ns4zT/Pmd4zV37syVwfUGqxPBTcERuOLDDznOn8cHbEYL//WXtbS4IpA5r75K4fiZM92noM6I+HgC\nQ2bMIEpwyBDfwVjR0SLvvUcQWfv2EEveUs1GRsINTJ2Kj3/4cMjEfPnwke7aBalYuTK+36go/MB1\n65Lf/IEHrJHpDgfE4ddfcxgGBHPv3pCsWenz1xqf7I4dHDt38qo1z8GKvzolhe84cYKAo+PHeR8R\nAXkcHo5fv0wZZ8H4O+/E/1uiBEfx4unfFy2aniTOk8d/LiU1lX5hksTm+4QE2hYZCUkcGZn+fe7c\nPMvixXmet93mfK1cmaN6dX7L9QpCs4KEBCJ7167lnm/cCCfXqxc1KooU8e/7UlIIRlywAL6sRw9S\nOLdte62vPilJ5KuvIJpTU4lmHzoUrsUddBph/MYb8ENjx0JOByqaMAzG4hdf0Lbp0+EdraB3b7gL\nreljK1fCp10P3FJksSdozYB75x0mh5EjrRd20JoH9MILEL1vveU70lcEo/PWW6gOhgwhqtBXqH5s\nrMicOXzugQeIQmzd2vMgT0khLP299yBqBw/mcBdVnJTEuR9/zPdVqEAEZNu21pQIWkPCf/UVqS32\n7EFNcv/9HJUr+/4Of6E1Kh+rqS6sID6eyTE8nOPKFVJNREfzPjo6/fvq1SEPk5O5h4bhnBTy54eY\nvHQpvVrIfF+1Kjn/HQ5IwkKF0r/Wrcs1goOvPcxiNeXLZ07ZdT1g9gVTGLBlCwuXTp04mjb1X91i\nGCw6Vq5E4FGtGsa/b18mwuXLMbIhIUzgSrFQuHiR+zh0KNHr3tLBfPYZYyU5mUJTffoErsLRmt8+\nbhzPZ9o066qiI0ewB3v2YJeSkxnvZtqL64Fbjiz2hZMnnYXU//jD+ucSEtiWBgU5c3xbwYkT+O2D\ngkgBbCXbYnw8+umaNSHDFi/2HRC2dy9+xhIlcBt8/71nP/OpU2Q2bNQId8xzz3n2t3rChQu0q18/\nNNt33kmZzbVrrdWT9ReZTeedVUhNxYUQFYXL5vRpUv+eP889iYiAD4mKIvYjMfHGc0dlhBnv8c47\nuD27doWoHToUbXygGUQNA1J15EiCH2vXhjtzF+vy/fe4/lxdngUL+nanhoc701Lfdx+J8zL7PMLC\niFmpUQM/v9Xvi4rit5rBpgkJuC6DgvxP1ugv5FblCLzBLNxQtizKCX98qadPE9TTpg3BKFZJnaNH\nSdscEkKkoZXB43CQkbBDB4isKVN8d5joaPyxjRszuMaP9076HjrE4LvzTvzsU6YwKfgTcOVwMDgm\nTSLopnBhiOAxYwjc8ycgyB2Sk/E1ly3LfR88GNXI6tX8tusV3HSrIjYW//nkyfjQixUjNcjgwUz+\nmckempqKoGLUKPicO+9kgeSpPoZhEBU9dKgzGCt3bhZy3jKh7tjBeCtRgnZbrb/hDfv2scgqX54x\nZrXfJSYy5mvU4Hdk5Dj8TeMeCKxOBDe1a8gToqNJPrV8OTEBgwZZ3ypu24Z7KSICf2PPntb8tIcO\n4f5ZtIgEcWY5PF/Yswfe4eBB51b47ru9f2bvXrKpfvopmv6BA9kOuwsM0praqt99R7bPS5coNdmr\nF9yIP6UmExK4Pxs3kogsLEykVi34hZo1cR1UqOCfX9swcOEcOpT+OHiQwKHjx50ZOk3/ufm+QgX/\nS2Xu3EnitNBQfMjXu16wLxgGz+TcOY7ERNwJmUV8PH1rxw4C+bZvJxCvd29cMPfeSzCZJ5+7FVy5\nQnK11avx/Zcvj1uye3eCxtz1g1On6LeLFuH7HzCA8TpzJhzP7t3XtikhgYynP/9MbMLw4WTyDCSw\n0RV79qD7/+03XEpPPWWtbrFhEOA2fjy/c+pU/4LkshI3DEeglBolItNFJERrHeXhnCydCEzs2QPJ\nmpREJr8mTax9Tms69rhxGNdp03wXizdx4gRcwJIlcAGjR3tPO20iPBze4cMP8SE/8wx1Yb11zORk\nDPynn5IZtHVrPtOtm+dAuL//dkYM794NiW1yFv767BMTiVrdvZtI623bIF2bNWNSaNaMYCarxdEz\nIiGBIvQnT7o/kpLgXzIWhy9ThkmiSBGuXbw4R7Fi+KsHDGACKFUKIr9798yTslrD7SQk0CYzy6l5\nREY63zscZL49d47nXqwYRrR8eSbWt96yfl3D4F789Rffefw43MeRI4gnGjfmaNSIyHOrQV2efuPB\ngyI//IDxDwtjQunenT7nKRX86dMY8i++4JlUqcIzaN6c+x4RQYDiihVwCCb27ycd9JIl/IYhQ7hW\nZsUMO3eyQNy6lfE5ZIj1wvUbNvAZEbiNtm0z15bM4oaYCJRSFUTkYxGpKSKN/umJQISBsngxCoCy\nZYn+tWrwHA6M7IQJ1BJo0GCDDBnS1tJnL14kcnTOHBQPw4ZhGK1c88cf+dymTUQHDx7MIPaGyEiR\nb76BmNq8eYP06NFWHnqIaxco4P4zly6RAmDrVj5XrpyTGGzd2v8aCFpjlLZtc6Y1iI5mwqpbl6Ne\nPV6rVhX57bcN0jYTI0lrVqUXLnC/L1xwvk9JYdK7coUjJobXyEgmMFfkz49xyp8fQ+lKFl++vEGK\nFm2bjjQODmaCio9Pf+TKxT1r1QqDHBSU/ggO5rVcOSah8uV5b4U0vnKFFf2xYyjmwsIwlAcPooQq\nV26DtGrVVmrXJnVzvXpZQ0afPctK3FyN587NDrBjRyJ+PRnQ48edxv/IEXagffqI5MmzQTp1auvx\nenFxfOajj/itTz7Jjt6KkMMbtEYB9cknLFrGjmXn7mlHuWFD+r65YwfpKP74g0nkoYcCi0rOatwQ\nZLGIrBCRuiJyXESCvJyXJf4yb7hyxVn0esIE/0jKhAR03kWLhupu3fBtWkVMDARxlSr41xcvth7Y\ncvIkbW3XDgJ45kxrGSFffDFUz5nD5ypVomLS4sXeeYjUVH7XpEkkQytSBA5j1iyIv0CznCYnE+ex\nbBm8Ro8ecBaFC2tdvnyoHj0a/mLFCuInrndk8rJlRKUXLEgbnnqKCO6//iL4bts2tPobNuBPHzgw\nVP/+O/zK1q0IEXbsgMT/+2/I5CtXMhf0lprK9+zYQXDexx/TVx9+mCjW4GCeR716ZIqdMoVzNm92\nclKhoaFZcn/CwwkEHDYMP39QEEnePvgA3sYTgepwcO9CQ+HaSpWCp8tYB9tdO1NTIXsHDOB6Dz0E\nWZ0VgYQpKQS+NWkCEb5ggbXAO7Ode/fCj2VFUNr1gFjkCLIodZX/UEr1FJHTWuu9KjvF0GkoVoxc\nQ888g2+vRg1iCZ580refuEABVvTnz7OK69OHLXxoKLp7byhalJXHk0/ixpk1i63l4MG0xRuPUKkS\nFY4cDnTXixaJvPKKSJs2VGfr3t39qq9wYba7Q4awQl69mm33s88ile3Rg6NmTadLJHdu3DlNm5KP\n5epVtsFmoY6jR/m/e+/laN7c2nY6b17uVa1aJAIzEROD3LdWLdwan37KyvHYMRLDVa/OyrZUKWcy\nNvOwygvExJDsrGhR51GwIH1hzBjujy8t/Pr11t2CIuxEzLgC0z1kuoVc3UNa47o8d44dTIkS7FTL\nl2d3Uq4c/FTVqrhLQkKyPqbAMNhRbNrkPC5dQj7crBkumQYNPI+P6Giklt99h7soOBgX0dCh7MK9\nJc7TGpfikiXs1suXF+nfH397ZvPviNB/P/4Y7qFiRcZ8jx7WOaHISHbjP/1EX1m8OLAqgTkF13Ui\nUEqtE5Eyrn8SES0iL4vIeBG5L8P/ZTsqVaLzbd8uMmoUnX/BAmufzZMHYzpoEJroRx/FmP3nP74/\nmzs3A7tnTwzfe+9hBL/4gi22r8/edx/H1atsud9/H0O2axcd3RPKlKG9gwbhv16/niyJHTticHfv\ndj84ihZ1ThivvMKg37wZLuKVV/hc797cy0BQrBjtfuKJ9H83DNwuR4/iWz50CLfE6dMcZ89ivCtW\npB29e3u+xt9/Qyxeveo84uKc+vBNm5go8uVjwnI98uXDsG3dyu92ODgMg9fixTHiGQPKDIMJ8t57\nne4h0yVkvlatiqEfNAgDWLZs5nz3gWDJEvpt8eK4slq2RANfq5Y1l8dLL7Goufde3KYTJ/rnvuna\nlUmoXz+er9VgUCuIjGSR06EDpG7Tpv59futWXEjjx+OivV7ZWf9JZAtHoJSqIyI/iUi8MAFUEJGz\nItJUa33Rzfk5X9pkw4YNGzkQOqeTxf9rhFLHReRurfXl7G6LDRs2bNxqyAG8tojgLsoRriEbNmzY\nuNWQI3YENmzYsGEj+5BTdgSWoZQapZQylFI+UrllD5RSryml9iildimlflBKZYHGIWuhlJqmlDqg\nlNqtlPpSKRVgSNf1hVKqj1Jqn1LKoZTyEU/9z0Mpdb9S6qBS6rBSamx2t8cdlFKfKKUuKKX+zO62\neINSqoJSar1Sar9Saq9SakR2tykjlFL5lVLb0sb2XqVUaHa3yRuUUrmUUjuVUt/6OveGmgjSAtDu\nE5GT2d0WL5imta6vtW4oIt+JSE7sLGtFpLbWuoGIHBGR/2Zzezxhr4j0FpGN2d2QjFBK5RKR90Sk\ns4jUFpFHlVJ3Zm+r3GK+0MacjlQReUFrXVtE7hGRYTntfmqtk0SkXdrYbiAiXZRSfmqO/lH8R0T+\nsnLiDTURiMgMERmd3Y3wBq11rMs/C4uIkV1t8QSt9U9aa7NdWwXVVo6D1vqQ1vqI5Ez+qKmIHNFa\nn9Rap4jIchHplc1tugZa699FJMeLMLTW4Vrr3WnvY0XkgIhkYSLyrIHWOj7tbX5Bfp8jfetpi+au\nQuYGn7hhJgLXALTsbosvKKUmK6VOichjIvJKdrfHB54Uke+zuxE3IG4TkdMu/z4jOdBw3YhQSlUR\nVtzbsrcl1yLN3bJLRMJFZJ3WOiy72+QB5qLZ0kSVbZHF7nCjBKB5aedLWutVWuuXReTlNL/xcyIy\nMae1Me2cl0QkRWu99J9u3/8aZaGdNm4dKKWKiMgXIvKfDLvrHIG0nXTDNF7tG6VULa21JffLPwWl\nVDcRuaC13q2UaisWbGWOmgi01ve5+3taAFoVEdmjyEdRQUR2KKXcBqBdb3hqpxssFZE1kg0Tga82\nKqWeELaO7f+RBnmAH/cyp+GsiLjm0zSDIm0ECKVUHmESWKy1Xpnd7fEGrXWMUuoXEblfLPrh/0G0\nFJGeSqmuIlJQRIoqpRZprf/t6QM3hGtIa71Pa11Wa11Va327sA1vmB2TgC8opVyTSv9L8HXmKCil\n7he2jT3TCLAbATmNJwgTkepKqcpKqXwi8oiI+FRnZBOU5Lz75w7zROQvrfXM7G6IOyilQpRSxdPe\nFxQ8FAezt1XXQms9XmtdSWtdVeiX671NAiI3yETgBjk5AG2qUupPpdRuEekoMPc5DbNEpIiIrEuT\nl83O7ga5g1LqX0qp0yLSXERWK6VyDJehtXaIyHBBgbVfRJZrrXPipL9URDaLSA2l1Cml1MDsbpM7\nKKVaikg/EWmfJs/cmbZgyUkoJyK/pI3tbSLyo9Z6TTa3KUtgB5TZsGHDxi2OG3VHYMOGDRs2sgj2\nRGDDhg0btzjsicCGDRs2bnHYE4ENGzZs3OKwJwIbNmzYuMVhTwQ2bNiwcYvDnghs2LBh4xaHPRHY\nsGEBSqkCSqkNaSlOMvM9eZVSG9PSWNuwkSNgd0YbNqzhSRH5UmcyAjMtZfVPQui/DRs5AvZEYOOW\nhlKqcVpFuXxKqcJpFdFquTm1n4isdPnc2LRUIruUUlPS/vaLUuptpVRYWqWtxmkV4A4ppSa5fNfK\ntO+zYSNHIEdlH7Vh45+G1nq7UmqliLwuZGpcnDGtsFIqr4jcrrU+lfbv+0Wkh4g00VonKaVKuJye\npLVuklZqcaWINBSRaBH5Wyn1ttb6sojsE5Em1/3H2bBhEfZEYMOGyCQhm2iCUD8iI0IEY26io4jM\nNzO3aq1d/8/MQLpXRPaZGXKVUn+LSEURuay1NpRSSUqpwlrruKz9KTZs+A/bNWTDBoa+iIgUFZEC\nbv4/QdgtWIGZ1ttweS9CxlzXhVd+EUn0r5k2bFwf2BOBDRsic4QqeJ+KyLSM/5m24s+VVndARGSd\niAxMy0kvSqmS/lxMKRUkIpfSUlnbsJHtsCcCG7c0lFKPi0iy1nq5iPyfiDROK++XEWtFpJWIiNb6\nR8EFtF0ptVNERqWd401R5Pp/7UTku0w23YaNLINdj8CGDQtQSjUUkee11gOy4Lu+FJGxWuujmW+Z\nDRuZh70jsGHDArTWu4TqVJkOKBORr+1JwEZOgr0jsGHDho1bHPaOwIYNGzZucdgTgQ0bNmzc4rAn\nAhs2bNi4xWFPBDZs2LBxi8OeCGzYsGHjFsf/AyogHj25QzyWAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x3de3048>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Right Hand Thumb Rule\n", + "# Reference: University of Redlands:Computational Tutorials\n", + "%matplotlib inline\n", + "from pylab import *\n", + "# Set limits and number of points in grid\n", + "# value of xmax and ymax can be changed to change the direction of magnetic field lines and can have any value except zero\n", + "xmax = 4\n", + "xmin = -xmax\n", + "NX = 10\n", + "ymax = 4\n", + "ymin = -ymax\n", + "NY = 10\n", + "# Make grid and calculate vector components\n", + "x = linspace(xmin, xmax, NX)\n", + "y = linspace(ymin, ymax, NY)\n", + "X, Y = meshgrid(x, y)\n", + "S2 = X**2 + Y**2 # This is the radius squared\n", + "Bx , By= -Y/S2 , +X/S2\n", + "if(xmax>0 or ymax>0):\n", + " plot(0,0,'ro') # Red Dot to represent direction of current is out of the plane\n", + " QP = streamplot(X,Y,Bx,By,density=1.6)\n", + "else:\n", + "\tplot(0,0,'go') # Green Dot to represent direction of current is into the plane\n", + "\tQP = streamplot(-X,-Y,Bx,By,density=1.6)\n", + "# Set the left, right, bottom, top limits of axes\n", + "title('Magnetic Field for current')\n", + "xlabel('x (cm)'), ylabel('y (cm)')\n", + "show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} |