(* Content-type: application/mathematica *) (*** Wolfram Notebook File ***) (* http://www.wolfram.com/nb *) (* CreatedBy='Mathematica 6.0' *) (*CacheID: 234*) (* Internal cache information: NotebookFileLineBreakTest NotebookFileLineBreakTest NotebookDataPosition[ 145, 7] NotebookDataLength[ 2555926, 64678] NotebookOptionsPosition[ 2535732, 63964] NotebookOutlinePosition[ 2536220, 63983] CellTagsIndexPosition[ 2536177, 63980] WindowFrame->Normal*) (* Beginning of Notebook Content *) Notebook[{ Cell[CellGroupData[{ Cell[TextData[{ StyleBox["Mathematica", FontSlant->"Italic"], " Tutorial" }], "Title", CellChangeTimes->{{3.397997955140319*^9, 3.397997955551935*^9}}], Cell[TextData[{ "Robert J. Frey, Research Professor\n", ButtonBox["http://www.ams.sunysb.edu/~frey/", BaseStyle->"Hyperlink", ButtonData:>{ URL["http://www.ams.sunysb.edu/~frey/"], None}], "\nStony Brook University, Applied Mathematics and Statistics\nJanuary 26, \ 2005" }], "Subsubsection"], Cell[TextData[{ "Revised 04 Sep 2007 - Updated to ", StyleBox["V6", FontSlant->"Italic"], ". Miscellaneous improvements and additions made." }], "Subsubsection", CellChangeTimes->{{3.39799796400347*^9, 3.3979980069495363`*^9}}], Cell[TextData[{ "Revised 05 Sep 2009 - Updated to ", StyleBox["V7", FontSlant->"Italic"], ". Miscellaneous improvements and additions made." }], "Subsubsection", CellChangeTimes->{{3.39799796400347*^9, 3.3979980069495363`*^9}, { 3.4611146848737392`*^9, 3.4611146925618553`*^9}}], Cell[TextData[{ "1 - Overview\n\n", StyleBox["Mathematica", FontSlant->"Italic"], " comes bundled with an excellent Help Browser. This brief tutorial has two \ rather straightforward objectives. \n\nFirst, I want to get you quickly up to \ a level where you can begin to use ", StyleBox["Mathematica", FontSlant->"Italic"], "'s built in help tools productively. In fact, it would be a good idea to \ have the help browser open as you work through these examples.\n\nSecond, I \ want to illustrate some of ", StyleBox["Mathematica", FontSlant->"Italic"], "'s remarkable capabilities to motivate you to spend the time to learn to \ use this tool effectively. It's remarkable what can be accomplished in one or \ two lines of (quite readable) code.\n\nProbably the best book on Mathematica \ is the ", StyleBox["Mathematica Book", FontSlant->"Italic", FontColor->RGBColor[1, 0, 0]], " itself. An electronic copy is bundled with ", StyleBox["Mathematica", FontSlant->"Italic"], ". Also very useful for learning how to program in ", StyleBox["Mathematica", FontSlant->"Italic"], " is Romans Maeder's ", StyleBox["Programming in Mathematica", FontSlant->"Italic", FontColor->RGBColor[1, 0, 0]], ".\n\nI want to emphasize again that this tutorial is neither self-contained \ nor complete. You need to be stepping through it by executing its code \ rather than passively reading it. This is an active document and it's a good \ idea to make small changes as you go along to get an idea of what's \ happening. Finally, keep the help browser open on your desktop and use it to \ read deeper into these topics as you work.\n\nThere are also two useful \ tutorials bundled with ", StyleBox["Mathematica", FontSlant->"Italic"], ": An interactive ten minute tour can be found (from the top-line menu) at \ \"Help > Tutorial\". A quick tour of ", StyleBox["Mathematica", FontSlant->"Italic"], " can be found at \"Help > Help Browser > Tour\".\n" }], "Section", CellChangeTimes->{{3.461115307515107*^9, 3.46111538086429*^9}}], Cell[TextData[{ "2 - Using ", StyleBox["Mathematica", FontSlant->"Italic"], " To Compute Interactively\n" }], "Section", CellChangeTimes->{3.461115345702503*^9}], Cell[CellGroupData[{ Cell["\<\ 2.1 - First, try entering a simple computation: \"2 * 3\". Pressing \ will only put in a new line. You press on the keyboard to \ execute an expression. The first time you submit an expression to the kernel \ for evaluation there is a lag while the kernel starts up. After this initial \ delay, things work much faster.\ \>", "Section"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"2", " ", "*", " ", "3"}]], "Input"], Cell[BoxData["6"], "Output", CellChangeTimes->{3.397996148000483*^9, 3.39799753938054*^9, 3.397997945936511*^9, 3.461114700161231*^9, 3.461116310503696*^9, 3.4611163810093107`*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[TextData[{ "2.2 - ", StyleBox["Mathematica", FontSlant->"Italic"], " also follows the usual conventions regarding multiplication: try \"2 \ 3 \"." }], "Section"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"2", " ", "3"}]], "Input"], Cell[BoxData["6"], "Output", CellChangeTimes->{3.397996148084764*^9, 3.397997539438155*^9, 3.3979979460304947`*^9, 3.4611147003263397`*^9, 3.461116310615776*^9, 3.4611163811268053`*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ 2.3 - Other arithmetic operations work more or less as expected. Note that \ there is a difference between the integer \"2\" and the real number (with \ decimal point) \"2.\".\ \>", "Section"], Cell[CellGroupData[{ Cell["\<\ An expression involving only integer frequently results in a rational number.\ \ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"2", "/", "3"}]], "Input"], Cell[BoxData[ FractionBox["2", "3"]], "Output", CellChangeTimes->{3.397996148119014*^9, 3.3979975394756536`*^9, 3.3979979460650253`*^9, 3.461114700378286*^9, 3.461116310719116*^9, 3.461116381183455*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"2.", "/", "3"}]], "Input"], Cell[BoxData["0.6666666666666666`"], "Output", CellChangeTimes->{3.397996148187654*^9, 3.3979975395420723`*^9, 3.397997946131371*^9, 3.461114700433083*^9, 3.461116310801609*^9, 3.461116381217577*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ Mathematica handles arithmatic involving rational numbers exactly.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{"2", "/", "3"}], ")"}], " ", "/", " ", RowBox[{"(", RowBox[{"3", "/", "10"}], ")"}]}]], "Input"], Cell[BoxData[ FractionBox["20", "9"]], "Output", CellChangeTimes->{3.397996148221632*^9, 3.397997539574922*^9, 3.397997946164837*^9, 3.461114700483016*^9, 3.4611163108521843`*^9, 3.461116381263732*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ If any of the arguments are real numbers this typically coerces the result to \ a real number.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{"2", "/", "3"}], ")"}], " ", "/", " ", RowBox[{"(", RowBox[{"3", "/", "10."}], ")"}]}]], "Input"], Cell[BoxData["2.222222222222222`"], "Output", CellChangeTimes->{3.397996148254731*^9, 3.397997539608289*^9, 3.397997946198346*^9, 3.461114700529023*^9, 3.461116310906226*^9, 3.4611163813182173`*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"2", "^", "3"}]], "Input"], Cell[BoxData["8"], "Output", CellChangeTimes->{3.3979961482872133`*^9, 3.397997539641972*^9, 3.3979979462316713`*^9, 3.4611147005832977`*^9, 3.461116310952744*^9, 3.461116381364725*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"2", "-", "3."}]], "Input"], Cell[BoxData[ RowBox[{"-", "1.`"}]], "Output", CellChangeTimes->{3.397996148321288*^9, 3.397997539675558*^9, 3.397997946264697*^9, 3.461114700629134*^9, 3.461116311006721*^9, 3.4611163814189997`*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{"-", "2"}], ")"}], "3"}]], "Input"], Cell[BoxData[ RowBox[{"-", "6"}]], "Output", CellChangeTimes->{3.3979961483546124`*^9, 3.397997539708556*^9, 3.3979979462979097`*^9, 3.461114700682756*^9, 3.461116311052994*^9, 3.461116381463882*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[TextData[{ StyleBox["Mathematica", FontSlant->"Italic"], " will handle computations that other systems won't." }], "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"2", "^", "1000"}]], "Input"], Cell[BoxData[\ "10715086071862673209484250490600018105614048117055336074437503883703510511249\ 361224931983788156958581275946729175531468251871452856923140435984577574698574\ 803934567774824230985421074605062371141877954182153046474983581941267398767559\ 165543946077062914571196477686542167660429831652624386837205668069376"], \ "Output", CellChangeTimes->{3.39799614842192*^9, 3.397997539775659*^9, 3.3979979463814707`*^9, 3.4611147007289886`*^9, 3.461116311136301*^9, 3.46111638156464*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Integer arguments vs. ...", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{"2", "^", "1000"}], ")"}], "-", RowBox[{"(", RowBox[{"2", "^", "999"}], ")"}]}]], "Input"], Cell[BoxData[\ "53575430359313366047421252453000090528070240585276680372187519418517552556246\ 806124659918940784792906379733645877657341259357264284615702179922887873492874\ 019672838874121154927105373025311855709389770910765232374917909706336993837795\ 82771973038531457285598238843271083830214915826312193418602834034688"], \ "Output", CellChangeTimes->{3.397996148455542*^9, 3.397997539809442*^9, 3.397997946415082*^9, 3.461114700783662*^9, 3.4611163111905127`*^9, 3.461116381615439*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Real arguments.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{"2.", "^", "1000"}], ")"}], "-", RowBox[{"(", RowBox[{"2.", "^", "999"}], ")"}]}]], "Input"], Cell[BoxData["5.357543035931337`*^300"], "Output", CellChangeTimes->{3.397996148487996*^9, 3.397997539841774*^9, 3.3979979464490232`*^9, 3.4611147008290577`*^9, 3.461116311236699*^9, 3.461116381664379*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["What's the difference here?", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"2", "^", RowBox[{"(", RowBox[{ RowBox[{"-", "1"}], "/", "3"}], ")"}]}]], "Input"], Cell[BoxData[ FractionBox["1", SuperscriptBox["2", RowBox[{"1", "/", "3"}]]]], "Output", CellChangeTimes->{3.397996148521064*^9, 3.397997539875771*^9, 3.3979979464825487`*^9, 3.46111470088522*^9, 3.4611163112912292`*^9, 3.4611163817197742`*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"2", "^", RowBox[{"(", RowBox[{ RowBox[{"-", "1."}], "/", "3"}], ")"}]}]], "Input"], Cell[BoxData["0.7937005259840998`"], "Output", CellChangeTimes->{3.3979961485546503`*^9, 3.397997539911234*^9, 3.397997946516137*^9, 3.4611147009295797`*^9, 3.461116311336905*^9, 3.461116381765299*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Euler's Formula!", "Subsection", CellChangeTimes->{{3.397997856943581*^9, 3.3979978625166693`*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"1", "+", SuperscriptBox["\[ExponentialE]", RowBox[{"\[ImaginaryI]", " ", "\[Pi]"}]]}]], "Input", CellChangeTimes->{{3.39799777855858*^9, 3.397997789095104*^9}}], Cell[BoxData["0"], "Output", CellChangeTimes->{3.3979977897765093`*^9, 3.3979979465829277`*^9, 3.461114700984473*^9, 3.461116311423662*^9, 3.461116381866336*^9}] }, Open ]] }, Open ]] }, Open ]], Cell["\<\ 3 - Simple Symbolic Operations \ \>", "Section", CellChangeTimes->{3.4611153992401333`*^9}], Cell[CellGroupData[{ Cell[TextData[{ "3.1 - ", StyleBox["Mathematica, ", FontSlant->"Italic"], "as is the case with other programming languages, has symbols. In ", StyleBox["Mathematica", FontSlant->"Italic"], ", however, a symbol does not have to have a value to be used in a \ computation." }], "Section"], Cell[CellGroupData[{ Cell["A symbol without a value just returns itself. ", "Subsection"], Cell[CellGroupData[{ Cell[BoxData["x"], "Input"], Cell[BoxData["x"], "Output", CellChangeTimes->{3.397996148588698*^9, 3.397997539942823*^9, 3.397997946615932*^9, 3.461114701030792*^9, 3.4611163114708843`*^9, 3.4611163819196167`*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ Mathematica tries to do reasonable things with symbolic expressions.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"x", " ", "x"}]], "Input"], Cell[BoxData[ SuperscriptBox["x", "2"]], "Output", CellChangeTimes->{3.39799614862131*^9, 3.397997539976454*^9, 3.397997946649753*^9, 3.46111470108493*^9, 3.461116311504076*^9, 3.461116381965288*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Sin", "[", "x", "]"}]], "Input"], Cell[BoxData[ RowBox[{"Sin", "[", "x", "]"}]], "Output", CellChangeTimes->{3.397996148655876*^9, 3.3979975400097218`*^9, 3.397997946682974*^9, 3.4611147011308813`*^9, 3.461116311554389*^9, 3.4611163820153913`*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Simplify", "[", RowBox[{ SuperscriptBox[ RowBox[{"Sin", "[", "x", "]"}], "2"], "+", SuperscriptBox[ RowBox[{"Cos", "[", "x", "]"}], "2"]}], "]"}]], "Input"], Cell[BoxData["1"], "Output", CellChangeTimes->{3.3979961487862*^9, 3.3979975400772667`*^9, 3.397997946717081*^9, 3.461114701235948*^9, 3.461116311652266*^9, 3.461116382066578*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ FractionBox["1", RowBox[{"1", "/", "x"}]]], "Input"], Cell[BoxData["x"], "Output", CellChangeTimes->{3.397996148823265*^9, 3.3979975401101522`*^9, 3.397997946750205*^9, 3.4611147014695683`*^9, 3.461116311709139*^9, 3.461116382163453*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Exp", "[", RowBox[{"Log", "[", "x", "]"}], "]"}]], "Input"], Cell[BoxData["x"], "Output", CellChangeTimes->{3.397996148859727*^9, 3.3979975401429033`*^9, 3.3979979467836447`*^9, 3.4611147015696707`*^9, 3.461116311755018*^9, 3.4611163822000093`*^9}] }, Open ]] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ 3.2 - If you assign a value to a symbol then use the symbol in the \ expression, Mathematica will repeated evaluate the expression until resolves \ all of the values it can.\ \>", "Section"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"x", "+", "y"}]], "Input"], Cell[BoxData[ RowBox[{"x", "+", "y"}]], "Output", CellChangeTimes->{3.3979961488888597`*^9, 3.3979975401764297`*^9, 3.3979979468164673`*^9, 3.461114701615327*^9, 3.4611163118096523`*^9, 3.461116382254278*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"x", "=", "10"}]], "Input"], Cell[BoxData["10"], "Output", CellChangeTimes->{3.397996148923127*^9, 3.3979975402099543`*^9, 3.3979979468501453`*^9, 3.4611147016703367`*^9, 3.461116311856021*^9, 3.461116382299947*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"x", "+", "y"}]], "Input"], Cell[BoxData[ RowBox[{"10", "+", "y"}]], "Output", CellChangeTimes->{3.3979961489562893`*^9, 3.397997540243723*^9, 3.397997946883608*^9, 3.461114701716105*^9, 3.461116311910108*^9, 3.4611163823548117`*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"y", "=", "20"}]], "Input"], Cell[BoxData["20"], "Output", CellChangeTimes->{3.397996148989573*^9, 3.397997540277981*^9, 3.397997946917087*^9, 3.461114701770402*^9, 3.4611163119556513`*^9, 3.4611163824007177`*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"x", "+", "y"}]], "Input"], Cell[BoxData["30"], "Output", CellChangeTimes->{3.397996149023506*^9, 3.397997540310177*^9, 3.3979979469507923`*^9, 3.461114701870491*^9, 3.461116312011363*^9, 3.461116382455035*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Sin", "[", "x", "]"}]], "Input"], Cell[BoxData[ RowBox[{"Sin", "[", "10", "]"}]], "Output", CellChangeTimes->{3.3979961490879393`*^9, 3.3979975403439627`*^9, 3.397997947014661*^9, 3.461114701972192*^9, 3.4611163120562363`*^9, 3.4611163825004873`*^9}] }, Open ]], Cell[CellGroupData[{ Cell[TextData[{ StyleBox["Mathematica", FontSlant->"Italic"], "'s use of exact results is whenever possible is an extension of its symbol \ manipulation capabilities. This means improved numerical stability. Compare \ two similar computations, one using a rational argument and another a real \ argument." }], "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"Exp", "[", RowBox[{"Log", "[", RowBox[{"1", "/", "10"}], "]"}], "]"}], "-", RowBox[{"1", "/", "10"}]}]], "Input"], Cell[BoxData["0"], "Output", CellChangeTimes->{3.3979961491239147`*^9, 3.3979975403773003`*^9, 3.397997947050832*^9, 3.4611147020877123`*^9, 3.461116312111158*^9, 3.461116382554902*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"Exp", "[", RowBox[{"Log", "[", RowBox[{"1", "/", "10."}], "]"}], "]"}], "-", RowBox[{"1", "/", "10."}]}]], "Input"], Cell[BoxData["1.3877787807814457`*^-17"], "Output", CellChangeTimes->{3.3979961491569138`*^9, 3.397997540410594*^9, 3.397997947084297*^9, 3.461114702133915*^9, 3.4611163121562653`*^9, 3.461116382600663*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ You can coerce a result to a real number with the N[ ] function; remember at \ this point x = 10.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"N", "[", RowBox[{"Sin", "[", "x", "]"}], "]"}]], "Input"], Cell[BoxData[ RowBox[{"-", "0.5440211108893699`"}]], "Output", CellChangeTimes->{3.397996149190624*^9, 3.397997540470085*^9, 3.3979979471187267`*^9, 3.461114702188703*^9, 3.4611163122069187`*^9, 3.4611163826512623`*^9}] }, Open ]] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ 3.3 - Symbolic values are used naturally in many different built-in functions.\ \>", "Section"], Cell[CellGroupData[{ Cell[TextData[{ "Symbolic differentiaton. The D[f[x], x ] function performs symbolic \ differentiation of f[ ] with respect to x. It can also be typeset as ", Cell[BoxData[ FormBox[ RowBox[{ SubscriptBox["\[PartialD]", "x"], " ", RowBox[{"f", "[", "x", "]"}]}], TraditionalForm]]], "." }], "Subsection"], Cell[CellGroupData[{ Cell[BoxData[{ RowBox[{"D", "[", RowBox[{ RowBox[{ RowBox[{"Sin", "[", "z", "]"}], RowBox[{"Cos", "[", "z", "]"}]}], ",", "z"}], "]"}], "\[IndentingNewLine]", RowBox[{ SubscriptBox["\[PartialD]", "z"], " ", RowBox[{"(", RowBox[{ RowBox[{"Sin", "[", "z", "]"}], RowBox[{"Cos", "[", "z", "]"}]}], ")"}]}]}], "Input"], Cell[BoxData[ RowBox[{ SuperscriptBox[ RowBox[{"Cos", "[", "z", "]"}], "2"], "-", SuperscriptBox[ RowBox[{"Sin", "[", "z", "]"}], "2"]}]], "Output", CellChangeTimes->{3.397996149275002*^9, 3.397997540562025*^9, 3.3979979472023773`*^9, 3.461114702322234*^9, 3.461116312324133*^9, 3.461116382768832*^9}], Cell[BoxData[ RowBox[{ SuperscriptBox[ RowBox[{"Cos", "[", "z", "]"}], "2"], "-", SuperscriptBox[ RowBox[{"Sin", "[", "z", "]"}], "2"]}]], "Output", CellChangeTimes->{3.397996149275002*^9, 3.397997540562025*^9, 3.3979979472023773`*^9, 3.461114702322234*^9, 3.461116312324133*^9, 3.461116382773059*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Symbolic integration...again note the typeset version", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[{ RowBox[{"Integrate", "[", RowBox[{ RowBox[{ SqrtBox["z"], RowBox[{"Log", "[", "z", "]"}]}], ",", RowBox[{"{", RowBox[{"z", ",", "1", ",", "2"}], "}"}]}], "]"}], "\[IndentingNewLine]", RowBox[{ SubsuperscriptBox["\[Integral]", "1", "2"], RowBox[{ SqrtBox["z"], RowBox[{"Log", "[", "z", "]"}], RowBox[{"\[DifferentialD]", "z"}]}]}]}], "Input"], Cell[BoxData[ RowBox[{ FractionBox["4", "9"], " ", RowBox[{"(", RowBox[{"1", "+", RowBox[{ SqrtBox["2"], " ", RowBox[{"(", RowBox[{ RowBox[{"-", "2"}], "+", RowBox[{"Log", "[", "8", "]"}]}], ")"}]}]}], ")"}]}]], "Output", CellChangeTimes->{3.397996150377531*^9, 3.397997541024741*^9, 3.3979979475145073`*^9, 3.461114703016856*^9, 3.461116312673211*^9, 3.4611163830968*^9}], Cell[BoxData[ RowBox[{ FractionBox["4", "9"], " ", RowBox[{"(", RowBox[{"1", "+", RowBox[{ SqrtBox["2"], " ", RowBox[{"(", RowBox[{ RowBox[{"-", "2"}], "+", RowBox[{"Log", "[", "8", "]"}]}], ")"}]}]}], ")"}]}]], "Output", CellChangeTimes->{3.397996150377531*^9, 3.397997541024741*^9, 3.3979979475145073`*^9, 3.461114703016856*^9, 3.461116312673211*^9, 3.461116383147441*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[TextData[{ "While ", StyleBox["Mathematica", FontSlant->"Italic"], " will tend to hold exact arguments (e.g., Log[8] above) in a form that will \ permit simplification in subsequent computation, sometimes you want to coerce \ a result to real. \n\nNote the special variable \"%\" refers to the most \ recently returned result." }], "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"N", "[", "%", "]"}]], "Input"], Cell[BoxData["0.49437658029308945`"], "Output", CellChangeTimes->{3.397996150545001*^9, 3.397997541113287*^9, 3.397997947602275*^9, 3.461114703124614*^9, 3.4611163128397017`*^9, 3.461116383267077*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ Symbols are evaluated where possible so you must be careful if a function is \ expecting a symbol argument. Again, recall that we've given x a numeric \ value, so the following doesn't work.\ \>", "Subsection", CellChangeTimes->{{3.39799810138724*^9, 3.3979981103586407`*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"D", "[", RowBox[{ RowBox[{ RowBox[{"Sin", "[", "z", "]"}], RowBox[{"Cos", "[", "z", "]"}]}], ",", "z"}], "]"}]], "Input", CellChangeTimes->{{3.4611163498622227`*^9, 3.46111635428522*^9}}], Cell[BoxData[ RowBox[{ SuperscriptBox[ RowBox[{"Cos", "[", "z", "]"}], "2"], "-", SuperscriptBox[ RowBox[{"Sin", "[", "z", "]"}], "2"]}]], "Output", CellChangeTimes->{ 3.3979961508214607`*^9, 3.3979975413432283`*^9, 3.397997947814005*^9, 3.461114703271617*^9, 3.4611163129690857`*^9, {3.4611163552448683`*^9, 3.46111638330381*^9}}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ Another example of the difference in function results between an integer and \ real argument.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ SqrtBox[ RowBox[{"Sin", "[", RowBox[{ RowBox[{"-", "\[Pi]"}], "/", "2"}], "]"}]]], "Input"], Cell[BoxData["\[ImaginaryI]"], "Output", CellChangeTimes->{3.3979961509120407`*^9, 3.397997541380237*^9, 3.39799794783694*^9, 3.461114703324544*^9, 3.461116313009942*^9, 3.461116383358485*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ SqrtBox[ RowBox[{"Sin", "[", RowBox[{ RowBox[{"-", "\[Pi]"}], "/", "2."}], "]"}]]], "Input"], Cell[BoxData[ RowBox[{"0.`", "\[InvisibleSpace]", "+", RowBox[{"1.`", " ", "\[ImaginaryI]"}]}]], "Output", CellChangeTimes->{3.3979961511125813`*^9, 3.397997541413582*^9, 3.397997947872403*^9, 3.461114703372911*^9, 3.46111631306448*^9, 3.4611163834039*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[{ RowBox[{"Exp", "[", RowBox[{"4", " ", "\[ImaginaryI]"}], "]"}], "\[IndentingNewLine]", RowBox[{"Exp", "[", RowBox[{"4.", " ", "\[ImaginaryI]"}], "]"}]}], "Input"], Cell[BoxData[ SuperscriptBox["\[ExponentialE]", RowBox[{"4", " ", "\[ImaginaryI]"}]]], "Output", CellChangeTimes->{3.397996151163184*^9, 3.397997541447474*^9, 3.397997947899823*^9, 3.461114703610248*^9, 3.4611163131101913`*^9, 3.461116383458189*^9}], Cell[BoxData[ RowBox[{ RowBox[{"-", "0.6536436208636119`"}], "-", RowBox[{"0.7568024953079282`", " ", "\[ImaginaryI]"}]}]], "Output", CellChangeTimes->{3.397996151163184*^9, 3.397997541447474*^9, 3.397997947899823*^9, 3.461114703610248*^9, 3.4611163131101913`*^9, 3.461116383462433*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ The table function is an iterator that generates a list of results. They'll \ be more about lists later, but for now just think of a simple list being a \ vector.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Table", "[", RowBox[{ SuperscriptBox["z", "k"], ",", RowBox[{"{", RowBox[{"k", ",", "0", ",", "10"}], "}"}]}], "]"}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{"1", ",", "z", ",", SuperscriptBox["z", "2"], ",", SuperscriptBox["z", "3"], ",", SuperscriptBox["z", "4"], ",", SuperscriptBox["z", "5"], ",", SuperscriptBox["z", "6"], ",", SuperscriptBox["z", "7"], ",", SuperscriptBox["z", "8"], ",", SuperscriptBox["z", "9"], ",", SuperscriptBox["z", "10"]}], "}"}]], "Output", CellChangeTimes->{3.397996151264008*^9, 3.397997541531454*^9, 3.397997947937406*^9, 3.461114703711733*^9, 3.461116313165276*^9, 3.461116383503957*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["The operation \"Plus@@\" adds up the elements of a list.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Plus", "@@", RowBox[{"{", RowBox[{"1", ",", "2", ",", "3", ",", "4"}], "}"}]}]], "Input"], Cell[BoxData["10"], "Output", CellChangeTimes->{3.397996151380454*^9, 3.3979975415649843`*^9, 3.3979979479708357`*^9, 3.4611147037781267`*^9, 3.46111631321076*^9, 3.46111638355431*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ We can combine these elements together to create a rational polynomial.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"rf", "=", RowBox[{ RowBox[{"Plus", "@@", RowBox[{"Table", "[", RowBox[{ RowBox[{"k", " ", SuperscriptBox["z", "k"]}], ",", RowBox[{"{", RowBox[{"k", ",", "0", ",", "2"}], "}"}]}], "]"}]}], "/", RowBox[{"Plus", "@@", RowBox[{"Table", "[", RowBox[{ SuperscriptBox["z", "k"], ",", RowBox[{"{", RowBox[{"k", ",", "0", ",", "10"}], "}"}]}], "]"}]}]}]}]], "Input"], Cell[BoxData[ FractionBox[ RowBox[{"z", "+", RowBox[{"2", " ", SuperscriptBox["z", "2"]}]}], RowBox[{"1", "+", "z", "+", SuperscriptBox["z", "2"], "+", SuperscriptBox["z", "3"], "+", SuperscriptBox["z", "4"], "+", SuperscriptBox["z", "5"], "+", SuperscriptBox["z", "6"], "+", SuperscriptBox["z", "7"], "+", SuperscriptBox["z", "8"], "+", SuperscriptBox["z", "9"], "+", SuperscriptBox["z", "10"]}]]], "Output", CellChangeTimes->{3.3979961514810123`*^9, 3.3979975415984783`*^9, 3.397997948054455*^9, 3.461114703824573*^9, 3.4611163132653437`*^9, 3.461116383608816*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["We can differentiate it once. (Try doing this \"by hand\"!)", \ "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"D", "[", RowBox[{"rf", ",", "z"}], "]"}]], "Input"], Cell[BoxData[ RowBox[{ RowBox[{"-", FractionBox[ RowBox[{ RowBox[{"(", RowBox[{"z", "+", RowBox[{"2", " ", SuperscriptBox["z", "2"]}]}], ")"}], " ", RowBox[{"(", RowBox[{"1", "+", RowBox[{"2", " ", "z"}], "+", RowBox[{"3", " ", SuperscriptBox["z", "2"]}], "+", RowBox[{"4", " ", SuperscriptBox["z", "3"]}], "+", RowBox[{"5", " ", SuperscriptBox["z", "4"]}], "+", RowBox[{"6", " ", SuperscriptBox["z", "5"]}], "+", RowBox[{"7", " ", SuperscriptBox["z", "6"]}], "+", RowBox[{"8", " ", SuperscriptBox["z", "7"]}], "+", RowBox[{"9", " ", SuperscriptBox["z", "8"]}], "+", RowBox[{"10", " ", SuperscriptBox["z", "9"]}]}], ")"}]}], SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", "z", "+", SuperscriptBox["z", "2"], "+", SuperscriptBox["z", "3"], "+", SuperscriptBox["z", "4"], "+", SuperscriptBox["z", "5"], "+", SuperscriptBox["z", "6"], "+", SuperscriptBox["z", "7"], "+", SuperscriptBox["z", "8"], "+", SuperscriptBox["z", "9"], "+", SuperscriptBox["z", "10"]}], ")"}], "2"]]}], "+", FractionBox[ RowBox[{"1", "+", RowBox[{"4", " ", "z"}]}], RowBox[{"1", "+", "z", "+", SuperscriptBox["z", "2"], "+", SuperscriptBox["z", "3"], "+", SuperscriptBox["z", "4"], "+", SuperscriptBox["z", "5"], "+", SuperscriptBox["z", "6"], "+", SuperscriptBox["z", "7"], "+", SuperscriptBox["z", "8"], "+", SuperscriptBox["z", "9"], "+", SuperscriptBox["z", "10"]}]]}]], "Output", CellChangeTimes->{3.397996151581044*^9, 3.397997541632456*^9, 3.397997948089136*^9, 3.461114703880494*^9, 3.4611163133113813`*^9, 3.461116383655158*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Or twice. ", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"D", "[", RowBox[{"rf", ",", RowBox[{"{", RowBox[{"z", ",", "2"}], "}"}]}], "]"}]], "Input"], Cell[BoxData[ RowBox[{ RowBox[{"-", FractionBox[ RowBox[{"2", " ", RowBox[{"(", RowBox[{"1", "+", RowBox[{"4", " ", "z"}]}], ")"}], " ", RowBox[{"(", RowBox[{"1", "+", RowBox[{"2", " ", "z"}], "+", RowBox[{"3", " ", SuperscriptBox["z", "2"]}], "+", RowBox[{"4", " ", SuperscriptBox["z", "3"]}], "+", RowBox[{"5", " ", SuperscriptBox["z", "4"]}], "+", RowBox[{"6", " ", SuperscriptBox["z", "5"]}], "+", RowBox[{"7", " ", SuperscriptBox["z", "6"]}], "+", RowBox[{"8", " ", SuperscriptBox["z", "7"]}], "+", RowBox[{"9", " ", SuperscriptBox["z", "8"]}], "+", RowBox[{"10", " ", SuperscriptBox["z", "9"]}]}], ")"}]}], SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", "z", "+", SuperscriptBox["z", "2"], "+", SuperscriptBox["z", "3"], "+", SuperscriptBox["z", "4"], "+", SuperscriptBox["z", "5"], "+", SuperscriptBox["z", "6"], "+", SuperscriptBox["z", "7"], "+", SuperscriptBox["z", "8"], "+", SuperscriptBox["z", "9"], "+", SuperscriptBox["z", "10"]}], ")"}], "2"]]}], "+", FractionBox["4", RowBox[{"1", "+", "z", "+", SuperscriptBox["z", "2"], "+", SuperscriptBox["z", "3"], "+", SuperscriptBox["z", "4"], "+", SuperscriptBox["z", "5"], "+", SuperscriptBox["z", "6"], "+", SuperscriptBox["z", "7"], "+", SuperscriptBox["z", "8"], "+", SuperscriptBox["z", "9"], "+", SuperscriptBox["z", "10"]}]], "+", RowBox[{ RowBox[{"(", RowBox[{"z", "+", RowBox[{"2", " ", SuperscriptBox["z", "2"]}]}], ")"}], " ", RowBox[{"(", RowBox[{ FractionBox[ RowBox[{"2", " ", SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", RowBox[{"2", " ", "z"}], "+", RowBox[{"3", " ", SuperscriptBox["z", "2"]}], "+", RowBox[{"4", " ", SuperscriptBox["z", "3"]}], "+", RowBox[{"5", " ", SuperscriptBox["z", "4"]}], "+", RowBox[{"6", " ", SuperscriptBox["z", "5"]}], "+", RowBox[{"7", " ", SuperscriptBox["z", "6"]}], "+", RowBox[{"8", " ", SuperscriptBox["z", "7"]}], "+", RowBox[{"9", " ", SuperscriptBox["z", "8"]}], "+", RowBox[{"10", " ", SuperscriptBox["z", "9"]}]}], ")"}], "2"]}], SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", "z", "+", SuperscriptBox["z", "2"], "+", SuperscriptBox["z", "3"], "+", SuperscriptBox["z", "4"], "+", SuperscriptBox["z", "5"], "+", SuperscriptBox["z", "6"], "+", SuperscriptBox["z", "7"], "+", SuperscriptBox["z", "8"], "+", SuperscriptBox["z", "9"], "+", SuperscriptBox["z", "10"]}], ")"}], "3"]], "-", FractionBox[ RowBox[{"2", "+", RowBox[{"6", " ", "z"}], "+", RowBox[{"12", " ", SuperscriptBox["z", "2"]}], "+", RowBox[{"20", " ", SuperscriptBox["z", "3"]}], "+", RowBox[{"30", " ", SuperscriptBox["z", "4"]}], "+", RowBox[{"42", " ", SuperscriptBox["z", "5"]}], "+", RowBox[{"56", " ", SuperscriptBox["z", "6"]}], "+", RowBox[{"72", " ", SuperscriptBox["z", "7"]}], "+", RowBox[{"90", " ", SuperscriptBox["z", "8"]}]}], SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", "z", "+", SuperscriptBox["z", "2"], "+", SuperscriptBox["z", "3"], "+", SuperscriptBox["z", "4"], "+", SuperscriptBox["z", "5"], "+", SuperscriptBox["z", "6"], "+", SuperscriptBox["z", "7"], "+", SuperscriptBox["z", "8"], "+", SuperscriptBox["z", "9"], "+", SuperscriptBox["z", "10"]}], ")"}], "2"]]}], ")"}]}]}]], "Output", CellChangeTimes->{3.397996151615088*^9, 3.397997541668002*^9, 3.397997948123809*^9, 3.461114703927557*^9, 3.461116313368497*^9, 3.461116383711787*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[TextData[{ "Sometimes it a good idea to ask ", StyleBox["Mathematica", FontSlant->"Italic"], " to work a little harder to simplify a result. Again, the \"%\" argument \ means the result of the most recent computation. ", StyleBox["That's the most recent computation executed by the kernel, not \ necessarily the computation immediately above in the notebook.", FontSlant->"Italic"] }], "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Simplify", "[", "%", "]"}]], "Input"], Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{"2", " ", RowBox[{"(", RowBox[{"1", "-", RowBox[{"3", " ", "z"}], "-", RowBox[{"12", " ", SuperscriptBox["z", "2"]}], "-", RowBox[{"26", " ", SuperscriptBox["z", "3"]}], "-", RowBox[{"45", " ", SuperscriptBox["z", "4"]}], "-", RowBox[{"69", " ", SuperscriptBox["z", "5"]}], "-", RowBox[{"98", " ", SuperscriptBox["z", "6"]}], "-", RowBox[{"132", " ", SuperscriptBox["z", "7"]}], "-", RowBox[{"171", " ", SuperscriptBox["z", "8"]}], "-", RowBox[{"215", " ", SuperscriptBox["z", "9"]}], "-", RowBox[{"198", " ", SuperscriptBox["z", "10"]}], "-", RowBox[{"45", " ", SuperscriptBox["z", "11"]}], "+", RowBox[{"80", " ", SuperscriptBox["z", "12"]}], "+", RowBox[{"177", " ", SuperscriptBox["z", "13"]}], "+", RowBox[{"246", " ", SuperscriptBox["z", "14"]}], "+", RowBox[{"287", " ", SuperscriptBox["z", "15"]}], "+", RowBox[{"300", " ", SuperscriptBox["z", "16"]}], "+", RowBox[{"285", " ", SuperscriptBox["z", "17"]}], "+", RowBox[{"242", " ", SuperscriptBox["z", "18"]}], "+", RowBox[{"171", " ", SuperscriptBox["z", "19"]}], "+", RowBox[{"72", " ", SuperscriptBox["z", "20"]}]}], ")"}]}], ")"}], "/", SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", "z", "+", SuperscriptBox["z", "2"], "+", SuperscriptBox["z", "3"], "+", SuperscriptBox["z", "4"], "+", SuperscriptBox["z", "5"], "+", SuperscriptBox["z", "6"], "+", SuperscriptBox["z", "7"], "+", SuperscriptBox["z", "8"], "+", SuperscriptBox["z", "9"], "+", SuperscriptBox["z", "10"]}], ")"}], "3"]}]], "Output", CellChangeTimes->{3.39799615166498*^9, 3.397997541699242*^9, 3.3979979481557198`*^9, 3.4611147039768953`*^9, 3.461116313412093*^9, 3.461116383756138*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Symbolic integration can produce a symbolic result.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Integrate", "[", RowBox[{"rf", ",", "z"}], "]"}]], "Input"], Cell[BoxData[ RowBox[{"RootSum", "[", RowBox[{ RowBox[{ RowBox[{"1", "+", "#1", "+", SuperscriptBox["#1", "2"], "+", SuperscriptBox["#1", "3"], "+", SuperscriptBox["#1", "4"], "+", SuperscriptBox["#1", "5"], "+", SuperscriptBox["#1", "6"], "+", SuperscriptBox["#1", "7"], "+", SuperscriptBox["#1", "8"], "+", SuperscriptBox["#1", "9"], "+", SuperscriptBox["#1", "10"]}], "&"}], ",", RowBox[{ FractionBox[ RowBox[{ RowBox[{ RowBox[{"Log", "[", RowBox[{"z", "-", "#1"}], "]"}], " ", "#1"}], "+", RowBox[{"2", " ", RowBox[{"Log", "[", RowBox[{"z", "-", "#1"}], "]"}], " ", SuperscriptBox["#1", "2"]}]}], RowBox[{"1", "+", RowBox[{"2", " ", "#1"}], "+", RowBox[{"3", " ", SuperscriptBox["#1", "2"]}], "+", RowBox[{"4", " ", SuperscriptBox["#1", "3"]}], "+", RowBox[{"5", " ", SuperscriptBox["#1", "4"]}], "+", RowBox[{"6", " ", SuperscriptBox["#1", "5"]}], "+", RowBox[{"7", " ", SuperscriptBox["#1", "6"]}], "+", RowBox[{"8", " ", SuperscriptBox["#1", "7"]}], "+", RowBox[{"9", " ", SuperscriptBox["#1", "8"]}], "+", RowBox[{"10", " ", SuperscriptBox["#1", "9"]}]}]], "&"}]}], "]"}]], "Output", CellChangeTimes->{3.397996151715263*^9, 3.397997541732622*^9, 3.397997948188587*^9, 3.461114704024797*^9, 3.461116313466754*^9, 3.4611163838106422`*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ Symbolic integration can also be definite as well as indefinite\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Integrate", "[", RowBox[{"rf", ",", RowBox[{"{", RowBox[{"z", ",", RowBox[{"-", "2"}], ",", "2"}], "}"}]}], "]"}]], "Input"], Cell[BoxData[ RowBox[{ RowBox[{"-", RowBox[{"RootSum", "[", RowBox[{ RowBox[{ RowBox[{"1", "+", "#1", "+", SuperscriptBox["#1", "2"], "+", SuperscriptBox["#1", "3"], "+", SuperscriptBox["#1", "4"], "+", SuperscriptBox["#1", "5"], "+", SuperscriptBox["#1", "6"], "+", SuperscriptBox["#1", "7"], "+", SuperscriptBox["#1", "8"], "+", SuperscriptBox["#1", "9"], "+", SuperscriptBox["#1", "10"]}], "&"}], ",", RowBox[{ FractionBox[ RowBox[{ RowBox[{ RowBox[{"Log", "[", RowBox[{ RowBox[{"-", "2"}], "-", "#1"}], "]"}], " ", "#1"}], "+", RowBox[{"2", " ", RowBox[{"Log", "[", RowBox[{ RowBox[{"-", "2"}], "-", "#1"}], "]"}], " ", SuperscriptBox["#1", "2"]}]}], RowBox[{"1", "+", RowBox[{"2", " ", "#1"}], "+", RowBox[{"3", " ", SuperscriptBox["#1", "2"]}], "+", RowBox[{"4", " ", SuperscriptBox["#1", "3"]}], "+", RowBox[{"5", " ", SuperscriptBox["#1", "4"]}], "+", RowBox[{"6", " ", SuperscriptBox["#1", "5"]}], "+", RowBox[{"7", " ", SuperscriptBox["#1", "6"]}], "+", RowBox[{"8", " ", SuperscriptBox["#1", "7"]}], "+", RowBox[{"9", " ", SuperscriptBox["#1", "8"]}], "+", RowBox[{"10", " ", SuperscriptBox["#1", "9"]}]}]], "&"}]}], "]"}]}], "+", RowBox[{"RootSum", "[", RowBox[{ RowBox[{ RowBox[{"1", "+", "#1", "+", SuperscriptBox["#1", "2"], "+", SuperscriptBox["#1", "3"], "+", SuperscriptBox["#1", "4"], "+", SuperscriptBox["#1", "5"], "+", SuperscriptBox["#1", "6"], "+", SuperscriptBox["#1", "7"], "+", SuperscriptBox["#1", "8"], "+", SuperscriptBox["#1", "9"], "+", SuperscriptBox["#1", "10"]}], "&"}], ",", RowBox[{ FractionBox[ RowBox[{ RowBox[{ RowBox[{"Log", "[", RowBox[{"2", "-", "#1"}], "]"}], " ", "#1"}], "+", RowBox[{"2", " ", RowBox[{"Log", "[", RowBox[{"2", "-", "#1"}], "]"}], " ", SuperscriptBox["#1", "2"]}]}], RowBox[{"1", "+", RowBox[{"2", " ", "#1"}], "+", RowBox[{"3", " ", SuperscriptBox["#1", "2"]}], "+", RowBox[{"4", " ", SuperscriptBox["#1", "3"]}], "+", RowBox[{"5", " ", SuperscriptBox["#1", "4"]}], "+", RowBox[{"6", " ", SuperscriptBox["#1", "5"]}], "+", RowBox[{"7", " ", SuperscriptBox["#1", "6"]}], "+", RowBox[{"8", " ", SuperscriptBox["#1", "7"]}], "+", RowBox[{"9", " ", SuperscriptBox["#1", "8"]}], "+", RowBox[{"10", " ", SuperscriptBox["#1", "9"]}]}]], "&"}]}], "]"}]}]], "Output", CellChangeTimes->{3.397996152297821*^9, 3.397997542281336*^9, 3.397997948734397*^9, 3.461114704749753*^9, 3.461116314177059*^9, 3.4611163845312233`*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Coerce to real.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"N", "[", "%", "]"}]], "Input"], Cell[BoxData[ RowBox[{"0.9045728088666449`", "\[InvisibleSpace]", "+", RowBox[{"8.326672684688674`*^-17", " ", "\[ImaginaryI]"}]}]], "Output", CellChangeTimes->{3.397996152333256*^9, 3.397997542316815*^9, 3.3979979487725887`*^9, 3.461114705031926*^9, 3.461116314293872*^9, 3.461116384721487*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Chop gets rid of \"stuff\" that is \"near\" zero.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Chop", "[", "%", "]"}]], "Input"], Cell[BoxData["0.9045728088666449`"], "Output", CellChangeTimes->{3.397996152383974*^9, 3.3979975423506308`*^9, 3.397997948806415*^9, 3.461114705132085*^9, 3.461116314334279*^9, 3.461116384757889*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ Numerical integration is also an option. Although this example was able to be \ solved symbolically, some problems can't be solved in closed form but are \ perfectly amendable to numerical approximation.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"NIntegrate", "[", RowBox[{"rf", ",", RowBox[{"{", RowBox[{"z", ",", RowBox[{"-", "2"}], ",", "2"}], "}"}]}], "]"}]], "Input"], Cell[BoxData["0.904572808866646`"], "Output", CellChangeTimes->{3.397996152513545*^9, 3.3979975423977013`*^9, 3.397997948840211*^9, 3.461114705177986*^9, 3.461116314367777*^9, 3.461116384813591*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["We can plot our rational polynomial. ", "Subsection", CellChangeTimes->{3.3979982239927177`*^9}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Plot", "[", RowBox[{"rf", ",", RowBox[{"{", RowBox[{"z", ",", RowBox[{"-", "2"}], ",", "2"}], "}"}]}], "]"}]], "Input"], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwtmnk01P/3xxVKKsr6kZKtRFRCSbgiLcrWolKylEqhzRZZEkUhUrZsIQmJ 7OvFjH3fdzOMLcsY+85vvuf8/pk5jzN3e937fL3er/c5I2Ty9IrpZgYGhgr6 x/++jwDP6MbGBjr/sI4vXHsDT5dEPJaWN5CB6VDIAy5XSE6VPjgzt4E9pkdc JQ+7wvFDmiZDYxtokqTLyHfLFU7uduusbttAtb/21+0zXEGFMlUZ/GcD76zc yPho+xauvK9JkDHcwEbvKZlHXO7gf6bropT+BiZb8xVbHXWH5pWR4YPXNzD6 V6V8nIY76D1jEt1zaQMLmFva3N64w+1bp0MZTm4gJAbl8U+5w/3Dv7xq2DZQ aw29F9vegW2dm8XDgnV83xntc7HUA7zGbNpms9YxmPIf5ek/D4jaanbGNXUd F1Uf7qnd4QnVoMkdFreO/CPyuTLXPEE4mTu/8fM6Hiuh/E0a9IRa39gdyg/X sZZL5H4+50c4eKU8gWvXOrLIpsucjPAGRcsc7ijWdUydsbkjVukNuh8SnY8y ryOXp66iwZw3vC72vaqxvIZPFE15Qi/7QOPxWyvOA2v4q9Z1n9W6DzhyjmqM Za3hVcKuTQzyvtDcvH20yHgNj3zbsVu8zA/e3E8vSLizhnm2cyZI8oMjs3f9 v95Yw2+je0ocFv3Ag/OvopnmGo57/Cx4KPEZlK7c/LTr1BrGXlUt4vT9DLF1 MbKGu9YwNf7kiwUTf7CrVHJeLVjFVufEVsqhr3BAf/jqUPYq3vfT6iSc+wqN /3wP1aetour3ezua7n8FyW0DDdHxq/hu88JZl+9foe/8B9FLAau4id9CfP++ ANAoaa0ItljFgGP6Xdn7AmFf4VOuk3tXsSZk95Xec8FQzSFnpcC7islLXgJB j4PB3nS5SYljFesFRG998AmGVlZ3/7Msq6jAFfWdry0YfK+HcFyZXUGLtucP qY9DgHGMuMuiegVVxnKtKsO+wT/uPTujX68go5fEf2oy4RD0iGQea7uCPDJc Vm13w+Fcbkz1rxcrSFM2YPz7IRyijI56JT9aQd4Tkb9PU8LhTsLZ7QXXVnDw 5gGGP4ERUKfydFuH5Ap+iBQPU3sfCelPiMzsPcv4mkWticQfBfKCltKxbct4 ON9yo0k+CnKaeQ2UGpfRTXBT2cr1KChUepL2pGwZI7fw5g37RkElO8e98pRl pGwrsFHcFg3kVMPCN++WMThKokiOKQZYV5ftZ48u46289z4OB2PBOzkm9qPE Mu6weq+bqRELu0y1GoUPLOMzTm5muaexwF37XVx3zzIyoZvGYlYsCEZeaP/N tIwJh8wGErV/gtzZALmHHUvIXOuUdO5DHBh5SdM6XJew9DvzfSX5BLBP+/yu xnEJ3wpuUi4wSYCv3TN7i+yWcMiiOdvDOwEqJTMuxlku4ZETsUWrlASQqVGI ttFfQlehBrtnXxJhC9tZPc7jS/gl3OAOM1MSJH7Sy7vct4iHr5DUd/glQ2lm 5hWV7kU0fyXfYZuQDH2k//7JtC2iRf+3zRylycBzrIubv2YRexVl7ZtWksG5 3vDpv6xFXDFZa/lilgJXdpsJv/NdxGDZkpsmWn9h8bODB8Iiup1QGW+DNBje WzR6TmERRXQ5Jr7cS4PW2C2atbKLeOacyK3A92mQmuO3u0d8Eb9UZaZa1qeB Zf/PkGXORXzIoWlva5oOFOnmRLmRBYypTvG0DsuAxlw+9vz+Bcz4u3q9riQD CtUNn5/tWcDULQYCdtQMCL81Kne1cQE/WfDIZqtkws03DPgsbwHf5wulbv+X CbX1kg0JvguocifH2uNiNuTpv5CR+biArt5GZUK22ZAwkPk1x30BxVVS57l+ ZIPHoqp+hf0Ckvv027dvzgE1wVuUofsLyOZlbqRTmANZT93nhE4t4IZswmmS Vh5Es/XyBfXN4zKPSFduNsLn27D1VPc8EgVe5DHPILjGRc52tM7jo8iPpYY8 hWCieq+Ov3oePTPlVQg6hSBsM+IWkTGPAWM7ryqVF0JUz/Rk7Md5bM166nS/ uAg+S1zrOf9uHn3Z/a9jfxG42qZXjrjMY1nVqY+3GIvBZLfdDwmbefwYXMOi pV4MQupr+klG80g+rX9avboYIhO3lqbLzaOqy4eM3iEC+C49StU7No/SZvb7 zbcTweVcZeSCxDyaHaKKnDlGBCOyl8MpwXl05dCQJNoTYT8Xh3Q+6zxed3WM UeIsgXCHvaFE0hyKW8yqvL5SCqEa0i+aPOdwt3LtbzW2ChDdrlFZ7zaHPWmZ G3mnKiChykS41nkO5Xc6cbubVkDO5S+N5dZzmBdwPIqxoAI6tBaOFxjPYT63 yUz5i0rguZo/E3dqDkVDLoaNjVXBp9sXrZ3+zeLzP9pNhoJ1wLPXpMZhYBb9 dbpv5+nVQXi3/YFXpFmsCszb88q7DpIMEltetszi7+nzDtyrdVBtyH7CrGgW r71wIM5a1gPL/Zb5q8GzyKiS/EBZvwFczY3txC/OovmA9lDIzSbo9bkbmqM2 i8n/pbEfd2wChZTbhZeUZ/HSVqrkjqgmmJ67zmIpM4vGAY/9P4w3gbGzRtDf fbO43URqX7ZbM6h8lsk6PT2DJvnuvcLFLbCeybyo+W0GvQlaB/a/bAf9zs17 SV9n0LLWjvQjsh0yVjfgme8MNo4SWOxq28HyzPL7z+4zODx6RntDogN6Kqm8 bU9nUDX1a17YcAfk9bSdNDo7g3ryMeHHn3aBA2O87YuJaVyseDj7L7MXLIPv bjEamUZNvtYY3bFeMDnK+VWTMo3vHx7uWhYggcbt138PdUzjvWGL7bvfk+C/ VK2JXuI0Vp+SYhGRIkOayYzJpdBpNGN46XpxSx9QixS1RC9No5fGQwtabT8Y u9SJtsZNYUi69C2J2CHQ64hQ7omeQszxm1UsGQKN489uDoRPIe3G4ojewBDI Duzymv4yhcC5Ph8mPAwsF67MsL2Zwh1eY+n3IoYhib2l6PytKWyV2nRnb/AI rIR33s3eNoUirFJH9r8fBdpCvF0h0xQOasnLOv0YhQEdh89lGzQ0f3BwvIww CrWb95a2zNGwcElPmpdhDKIeGEhN99HwiuzP3ne2Y3DhCHlFIoeGJY+5r6ca jcPX/MGg0Mc0tC9nj1HhosJduxCnk6Y09N/3hWXmKBUOyWjfbzSk4SvL5YvG l6iQE5d1lOU6DY+2rVg9eUOFXv+PpS+Bhh0XLkrvHKXCTy2VRDYFGopsj1a0 ZpyEZ9vm/H7J0lDzWtVT772TsNn5rgFJnIanApeDyjUnQeyx9KwGFw1tbw9F qCZOAk10qGOQjYbfvA3fMhEmIZsUgi7baLgltPP5k45JCIxn4bFYm8Snsnx+ fptpQHZJUjvbOImHjU5Us0rS4MU8L7t92SSa12sXTcjRgMn8TeefvEk8kOJ3 MBxoIH7j2nP+n5P40l9n4P4Vur3Ucvi0wyTes+yWt7em20ffe3zo+SQ+8iD9 ynKkQcB/NXJ3H0xiaGA9X6E7DXIZI6srdCZx3kN+QewrDbRebQvaUJ/EL10K Fu9D6fVQX9yTOz2JyqX3Zn5H0+N1qi9HHphEY/+oB1op9Hjaf0pa90yipjbR tiyTBodK/vPbsWsSk1fO9DEX0EAzeUzMbpmK2aOmUg0V9HgHrs/8nqRikvgL XYM6er3fCgooA1R8cQd1fjfT473zu65dR0X25r0+gb30eKvLgu5EKvIYOUpK U2iQ8/z+eE42FU9YNz3wHqbB5eGaTFoSFZ9rqi0kjNGAdOfk24MxVPyhsfHN a5IevzFS604wFTl+avMenaHXf4F1z2cfKl6zDT7qP0/Pl/9ysOwtFVVX7//J WqLnk+lJXrOj4nm1e6YRq/R8cedey1hScT/XS9HzG/R8Asnnze5RcUXiB+HP pim6Xvg4I25S8XIBCHQwTsHzbW97mzWp2KxdxElgngIm5/FfrGpUDCnvf2+x dQoCZq9bq8hTsZwUZNLOMgWHHqOKjRQVnaq5fBlZpyCHdGhHojAVz6gRGSbp /Fpx0++Q/6i4SyJewm/7FCgFd2h6slHR82ZA6Qqd1+dSqLZMVCwmtDUc2TEF hVc+fHqwPIH3nlMvS9DZ9Y/Jseu0CTTg9jxNpduf3XG6QW1oAgsVXWPs6bzF jPPF8e4JLNrS41dHz1dWMsYh1DiBZy2Kdk1smwIPYWIqe/kEnhSwk26i16vh HHptPX8Cjw2bbnWlr2dHt9XceOoEtqZt+bVEX2+tvGZA168J7BAbOarANAW+ Xw+crIyYQKGi6Cy1zVOgO73WlvV1Anl/tpvwMEwBh3ar3c+PE8i15YDu3zUa NCck8QW8mcDqnft/8a7Q58PyPsfNdgJ5fDT9zy3S4Iap4e2XFnSWEzwNczTo FNgVpnNrAjcWq5x9qDQIdRhRAu0JFFkteTM4SoO77YW9UuoT+Hfdf/tWul76 /J4LbpeeQPnvlyPi6fqKol4sWjo4gXknlUSlOmlw/5KwycjeCTS8uHTQoYUG I0xN0SUsE8jv5p5lX0UDqq2smAt5HNlNtfbsSqdBcvOOcsvWcXwkG+Q5n0SD 59KDjwyqxzGg374rPo4Gs6Nf4xWyxjH46yN3p280WDFYkJr7NI7+q+mrXU40 yMupq6W4j6PWj3yH07Y0cOKNe9roMI6ZR+zqnjylweaGmyl/Ho7jLXXLzyqG NGA5myP7GMZRpW23TKkiDSojP7fclBvHFLGHC5kyNPBae2xz/vA46rAlzDlK 0IA9kz9LlHcc2R63W3rw0oBHwvE0iTqGgUI/3S6QJkFkl4rqtfAxNKs7MUI8 Ngk+x/rsH/uPoUqnkXAi7yQs6rimuniMYWZQW4byOhWq/Yiiv1+MYaGExb4n lVSw4ry4dcv5MTx+de0RsyEVSnmuVGdOjmLb7z/vHthPwON99/X4z4zi032F v1jfjkGzEvMn6ROjWNHKGjFoPAbKd2PLzh8exaThewZfVcaAM2JE3op7FA3W tLns1kYhT9CCv+bfP0wzfMFibj0KbKI2ZKfP/5BdP5SX0/Af/JXweNxPGUHh Em6hZcFhIJWkCJ44MYR1RbqHzFO74fXVpbPykkPIKkpOj7Xuhv/6VMwUhIeQ c+ZOG6t8N+is1qUosw1h+YMnz3Tzu6DwOFX1/NAg8qUJpPRXdEJkhITpzYBB zIhV6r031g4mr6J/2c8P4PavshWjuvTn/5axmtfjA7hqdyB7nq8FQr4cn3bq H0BVVQkZuf5maPxTpPC2dgBv8Lc+13jZDGpDpKqPsQPocfXx287AJhC9upca qjeApi8+MSSPNsCg5NfjmEFB/poa6banNSDh9IM6kkBBk9ISvlSmGrCsS4/n +E7Bf1uXru0MqYbF563CDz5S8GAJq5BSaRWwZvJy7jSiIOfSW0Ev0Uo4diZk Rn8bBS9dM16cWiwFh+sRafO3+9E8Zf/pNoYiKIz980xQtx9PREef8vhRCEyL KKlxrh99qBnSmRqF4BNCjgk71o+MAQnX90khRJKEvp5l7sfa0ozvZx7lQalZ jPXnpD6Uidi6YrkzE3Y7/TpxZFMfbhamsVYJx8O7jEPluxbI6PE+cHPK1l+w TP15a2acjCoZIetK1J/Qbxj7OqudjF9Ge35oFP6Av2eii1WTydhwnLVzn913 uMIcpnXDkIzaf+RiVc0/gxifUA2DAwknhOvHE+954K4P+2klD0ioH/7T89Um L1xc3sf58QoJ/XNJlmtBn7C8e88tbgkSrmTFCX/T96ff07kGxTt6cTzb6LWB bgjGi7GsXTnZizwWVroiLdEoeWJS8sdMN+ZWvAqeYkrGB0ecnbN7upFJWftC 48NkjDjI3lhT1o3EPJ3XzRXJyMF71Hb+WzfeechzAz6m4MKCZdH5s92o1qbO nceYisXZ1OujX7pwrdV8NbcqHVdTnOLWnbqw+sxo54RgBsrFs61wmHWhzs3J tgvWGRgXciTytFIXVumM87zmz0RvB8tRr8FO9Ewv3zlokIU3lahOR090okHg hxOSuTn4Wc6pQU2wE9V391uJM+ZilRSb6E3WTvQ54+p4QyMXlQWOVLj0duDZ sO2d51tzUWTDgrPxXQfSxJbk3w/kIbVw4qdVWzvqrBubjXcWoFi247JHUTua iNxjP8aDaJSyUzMsoR2f5mvIJuogNn2Xmi5xbsc710JGtAmIWa4WiryH2vGl Fyj/0i7Et2cn6rPt2jD0YJb0Tb4iLL4wri17rw3Dqc9lLI8VIYPmWG2SZhtu 1LwsETtfhK+v/6uOFm5D5zJpt3cvi9DmwWC5d3Ur/iC1NiyWFWHa44HzLJmt KPqULVe6uwinLSmlrt9b8daDWyy7aEX41LaPaGvTinfTOLLzeYrp95yeQmPB VjSosG6nGBZjrFc3dLO24odf8ee1nhfjgG9Xgd5cC25qKaEauhajcXBH3qXK FvTLDqx9FV2M+vEtWXJWLRj7beOUT18xBiU1n0y+24K8Z/Jr5GnF2Pq3KUPi Ygv+oh6x8Fovxqu5DWn7BVrQirY7++geAl6urkneVt6M6fKMo+aaBPxQX33U 7W8zRop9Unlwi4DlzVVJ66HNyK/na8NqSkD1norEmefNyGNe+/iaAwHd+soP W95pRtb4xbBldwIWD5bFj5xrxu7GFyGXfQmoTC2J6+FvRvkfRzpmown4epoo dnNLM551ve176TcBc+YJsY20JvyRnLSslUHAkxtFMWUlTfjX/FvjnTIC2jAW iagmN6HyZoH0h3UETNtaGJUX0oTSS0Gygm0EPLarIDLlaRNqCp+fDRkkoCVX /n5J/SbMljX+ZTBOwMT/8sJjzzYh2ezMYO00AcWFckK/8TUhcO/vyl8n4KMD 2fw8TE1YpbuJVZGJiLHiWSG+1Ebkgpe/zLYRUfh4RpA7oRF/jFeLNHMQ0ehE Oi9DUiM6Wwa+3M1LxAiFtAD7oEYcPpC5b2MPEXuVU7nnXOn27aF3wgSIuFft 75enFo24f1f243EhIuqfT+EcvdGIqnteu46LEjHoUvLn+6qN+IZVrzdMjIit 2n92kyQbsTgnI4tBgojc15J8b/E2onn1zsvckkT8e/BjjjpjI4avpRFQiog6 Sw8HpCcb0JHbxfHgUSJOVJ1lE+hqQEN3ctzFY0T8EC4kz1rWgJUB563FpYko 9nzdeP5vA8qZH+QopTNRretjf3gDVmyiJu0/TkRjnqz02g8NyHeE3eEEnddH vpBybBqQFrX8g5XO33Kfb/tp0oAMVEHDULq/vI+WjL9WA/59Nd0yRs/XYnTY wFmhAXu8SaeW6PW8kGF5/+RgA77XuZlDPEJE9i2DyTc4GjC0tPqdFr3+xPai TrX1eixpTqwNOEzEiwnhTMdG65FB5VFRmDgRhxwdjuxtrUeHJEufe/T+vNW5 eZOluB5PxB01HaH3T1BEznX2dz22Bu6xlhQmYv7c7kRycD0qVoVPHN1P73c5 taXavR71rQ6vT/MTcSGkaiPreT2W/Traav0fEb9YxIn/MKhHn4d7fmRxEbGW w8TRUa4eL4/dHXTaQcQng8o/zYTq8aFEQPpmFiKyZPE3XN9Zj9aDlgrnGYno nhN/8U5lHYrkF+znXCJgoeH9MNmoOkwM4iCFzBBwmUlgaod9HZ5+WrRrYIKu T22/oHzxOrweNeKR20fAuFmN8a+b6tCZV+mIRhcB+4OZVCw7apEjJ4bxWzMB 9QZshwU8azH13/3Vt6UEhFd3T7iM1GBIa9x1u58EtBf478PNwhrU2x+YsC+C vj8IDT3HgmrwTizz73cBBDzEpu7ed74G9Zv+exbjRsBd0YebVX9WI48tqxjX XQJqXBgU43euRuXhCZbuq/T9PBHuMKNXjS5subvNLhJw8SSHSMyWatQ2LL3e JEPAvurF58wPqjC+x7NtgpmAf+dL2MtFK/G93oTK/Yhi9ImPKlZeqkCpzhMe j3yL8cldZ+v0mgo0M2YTOfymGEVL5buibCpwMxxWHzcuxoCvCbGO5eW4MW/V dl+wGO1l/ZRkzMvQXn+J575fEeqNWNB+qZThytUt42ZORSgTqhEtyF2Gll6/ u6SfFOE4I9M2toJSlN7vvcpytgjvNtk0j7CXIrWRTO6cLkTVF3eehKcS8dNV XR5V9UIUOHhKgNuDiDkjhH/uRwpxpYO74eMdIo6wdYR/4i3EtDN1J14xE3Em 7RaMNSAe3K26+doNAh547n3sAAcia7JY8LaVInR8c/bTB8d8/NE16P3PrQCT 0r3umTHmYOdrnogwmQI0k9c9rFqdjWwC55N1+/Mx2FmJWPwlG22M4hqzIR93 q3HP5Ilm47nBx7wflnPx3z+7rZEqWThCnYyUeJaNlv6VgraPMlBi83Lqk9tp 2GBoTOBz+IsGunvTuu7FYKeWoIX522gcn5k7ZU2MxhohWa6NqSh8HVCHbAei 8UPd4mteoygM7XKtOjP0HdmGpF51K3zHtKrGEu2GcGy+Si0NaAnDP7PmIiOd X5BCYHtRYPYFXZoPNfMTNHF9VotVKk8Ts1kdWoqto4EmKp+eIRoDcv2bBN7c RGCvG4+Q318EIrTcJYXiCniQmHVSrKsaLkZ31GjdbIQGB4n/CgKageae9KDE ohEsLss2jPxuhqBHbzdOv22E4DWr3dtKmmFESkpaPKkRIsupqeIzzeCR5fKF kakJqMuHb4xotUB5jdjtrD9NcMNxteoMUyucX7QeEdraAnGhc8rfTNtAXZOD cS69DZC9+FXIagf4GG+bWqpog9nEAcIUWye0WzOQ1nvaINZla8opoU54HE7N YdnSDkk6hbzf1DvBZ7LyxV69dnBsyvr03Ztu7/e2X22+HV5dPrMUt6cLHrfO Fn0+0Qkab3f0MxzqhtTRsT+BGp2gxS81C/T7+Op6f1jo3U6QvPe4yvxCN/iI NbyKfdcJ1zijLT89otvb/ZbOae0Ehe2C7PM/6fZ7Hnzvs+kCrdffmOREeuCT YZvLscxuUGb9/bGHtRfejSvCUHU3DG2el2ni6wWnV1Fr3/q7wV06WTT5UC9Y +lvYb2XrAeFdpGf7z/WCVhmjVe/9HnhbJ5VS79wLbEePPfTi6IWrgVITptRe 2JL39YAq3e/IDZ9zhJVeWL+wQllQ6oX6H1JyW7aRgGpSanTPrBfksggPFUVI UBtwR1+hsBdGbk8qPdQjgc+6h+aIBQn4f1DqIzJJ8O4jdXv4WxJojudJaxBI 4PTftcqrwSSwmV97315LAsvj+88jkQQiVqt/IgdJIJaqL1i1kwz3+7X4qCxk oL/T8kXdJ8O7+k2q1sfJ8Pa3yNLrp2Rg3mly9f5JMmy81Gu/YU+G8Frz0LOK ZFhgyAvY6Uv3u9UT265OhuE97zlf5ZJBbuapvuZNMpRp7dupw9EHJ77NGRvS /biO9PG82NsHidtTxPmcyGC084fgl4N9YLmwxbrkDRmWqyRl2xX6gNHGwmrO gwySFxVvG93rA/Xe44f/BpDB7hCDqatFHwwlG4luBJOhZCvRMsa2D86Gp91V DiPD3dJLriMf+6DnHcnGP5oMvmq3fz1L64NUxdxbgX/I0C28P/VzQR+4/eN1 sf5LBvHNlLy08j7oSov8pZ5OhuLCx/WL3X1QXPlzJDeHDHNK9gsuzP3AOMhx eYNIBtV9ypui2fthOae20LyMDD6rm7aX8PWDj9qfjaoKMhzK9RTYdqQfAgkT hYa1ZNCXD1b30+uHKtb44K5WMsTyGminGvWDp5/yz9Z2MszMC95qedwPoUlG lwidZPBKjzPnc+6HXN/T8LyXDB1fzG1Oe/ZD/i+xt8fJZDhodczFwL8fXN+x nBvsIwMez/L//rMfwE3g+P5BMuzgeB1GSOkH6++aOrFDZLg5BT8Hc/thbtOD NoERMkz/KcsRr++H8SfG2wdGyaD86SPxUmc/nPqwM+nYOBk+WmrXWgz0A4bJ /bCcIIOoVFtfyiK9vgd/HQsmyfBsx7exps0U0Kn5eb2eRob8sbtzczsocIj7 9tOmKTLQX9I2eHkp0MdvUlo2TQa9+CEWBSEKELc91fs9Q4Yoz3iOO4cpcD9A h99tlgyTjyz3OslR4I3m9K7Lc2RQvHD8YCRQYKzpjgzzPBk8xOaPFl+kwFXJ DKc/dG7eknNq4CoFFFbPTGoskEFoyFFty10KlNQbv2mjs2XJGc1DjygQcu+r 9PVFMuTEMN/QeEGBnyT7VQKdt7pVGJm/pgDpQF2vyBIZrt3zfuzzjgKDuy1b rOgcqaprlexLge8FV3oy6DwhxO3UGEIBQmXgzD86yzt8f14UQ4EvO8+57Vwm g2uzlGlyEgWAyzBVmM7VUjk3I7IocILf1Umczjzvz132Kaav54U5RYjORuRG cKym9y9/ibSdzvGnDGXMWykwIyH6Ypgef/bz2MHbZAowZTEFpdFZedx2j8Yo BY6bNWm/pLOHOhPbqVkKnPQYDvxf/Y3hvpsOrVPgPDnTsoS+vr2Le+d4WAbg 6HJn4006P9D9NcLMMQDKN7cSeuj9SY6X657lH4ChFE7la3ReZiyuoxwYAM6j C6p59P6eNdAiNB4dALXoinpuOn/K6MwoOjUAp8oJQ0b0+XSyP4xPVhuAJ3bg Fkafn6jZTFiE5gAc/JcZV0Wfbxb/DndH4wEQ3STrvkrXw2brIDvzJwPg09uq uEHXy+VaUfPb1gOgq59uP0PXE9lF6eopzwGYkO7xTKbrTaKz4twh/wHId+y4 /YquRysZPQXesAEYSbdNlxkjA8uQpdBc8gDkDLpudqTrWfpS5GRy+wCwVjIJ mvST4XWMJCWifwD2/ndJI5O+X0rXslp9xgfArCiReZ2+n/STG/LNGQZhukj2 pFEXfX7cjF6HDg2Cr1JMkW0TfX6Wn5x5jw+CYeGu5FsN9PmV87/cojgImoua qlJ19HnZy+oPaA8Cb8XDu78qydBEMj0UaTsIvwl1jXOFZBA4Nc3/6c0gqFHN FU0LyPDosxO708dB+Ptb/EEp/VxcPRs4fztiEL5IcW+/m0HvX3w5kbdsEExf L+2hxJPB2uqw8SeeIcgIjO+44EfPzzqZpyc0BJ/P1ZM+eNP7G/H3PwHJIdh2 w849z5Mer/JUfeKZIVDefb6RSj8/XYUuQKX5ELyVvvjS9zn9fKq9v4+ZMART XZTQek0yHLt/6FV1zRAEmulJ7r5IhoalsWb/9iFQy3TZpHqWfj4feOklTB2C ko53SVYKZAhxeLMCfMPwp5xqv/8g/fw6FN5h/3QYRJv7/0rNkeBCgbGcqv0w 4AFmXuI/Evy7esBvm/swDBOLyxR7SSDpnHghKGQYIhmlPrWUkuBvS05meskw 2BlZa8cFkKDAte0rjX8Eis5dbaEdI0Fb964rD8pHgBRDNefR7QWNj6btVo0j cOgqB6eiWi/kncq5+7Z7BJTFLHdeluuFyIB7TyJpI6Clm+p3mP48fqST4db5 3z9gnN0/Pk7qgWXC7Uwts3+wzybYNMSsB/YmxO49yToKJt/yr2VbdoPRK8Xh rZfHICi0uMf+YicQDp/8QGCmgjWl5luURAsc3JY3EnOBBrUMbf5JT+thuuzI 4yhNGlxu0XLafrceCt59H4u4QoNN55hqn1+uBz2m99SQOzQIbDkwqy9eD+4b urO+z2hgPbH9plpZHQzMDW+8DqLBaPWVfNG0Wojq5+S9PkKDVxKVHGth1bAv 74k6s8cUJA0z1XFllQMX3/KypfcUFDW+r1gLLIcdNh7J7Z+noJ+nIpbJrhxW j/7gTwybgr5HAkrm8uXQGd07dSV1CrY67xKKySmDgI+64ZG9U/Bo51VhJJYC 2+2TC6flpuFIzapp+SQRtmSVJP5QmIZi2yyx101EWOO6ZsKuMg1Vh88bamUS Ybz2WU2/xjQ49ngLaTkToVI1PtrTaBrC9DpscnYR4b3EPp22j9Ow2v786JlT BGBY3vzrRf80uIVUv6mILAIjSseM8/A05CgKdJM8i6CwOlnZe3wafLT+5bK/ LAKXiLvNP+en6ftxX3euehFsnM3Z6GadgTxvDvWz44Ww/um53nmZGVAOHb3A qlwIqwfIjHvdZmDosAnnzpF8uMOeqSXuOQPB88tLpoX5kLfoHXzCZwbI9bfV qUH58Lrq9FHdoBmQqop09dLIh5VngfrvEmdA8+rR5pU/ebCcq5VCa54Bew7t 3e/f5sKCbv7dkgOz4CSRWq9zORtcO/70KB+eBSG911n2YtnAZhx1J+vYLPix iA+0bs6Gg8/e6yecnoWRldozB3Ky4JqPrp6f7izcuJAWz3U4C5IrBzUNHGcB Jfs9OXgywUyNTWmueRbefhtjlWBPh9lKhjzLzlnwkr9tdpqWBi5XZhRGSLNw Tul4sV1DGgQZt8t3jc7CSZn+IJsvaVDqHCVbxDAH7/V+XXu1Nw2E805IekvO wWWbX6uWsqnQJWvIf8BtDjKkh1fyXVPgwZIcu7vnHAiIlVWZmqUALX8H46DP HDzX2st8XCcFmM7njMYEzwGTRtXaCYEUOHKLO0f0zxz0tmfsouYmg4tj1U3R zjm4trezunHlDxwsPRkgcmwe5GMLBgvlfkPyB7YPb+XmwS45MMxp+29Q0B50 pCjMQ1HPq5P2fYmg3f7ZNFp9HnooG0Rxn0SwG52QFbk9D3WZRSrGowlQxRbT JPxuHgrfqzqN/o6HZzd27Rbunofobwp9pw3jwCTuaMiBvnnQUXtIslKOg+tL WiLiQ/Ogq6d/rGtfHCh885Y7RpuHZ+sHuo/0/ARmEqu+MtMCSPQFUsYMfkLI A+ZofckFKKPo53o+jAWi9bKs/+sFICz0a1cEx0BGKV9BwJsFUJori1FyioFf vKfOh7xbAOqDtMvtJjHgk21767vvApy2sy9xloyBW2uzjn9iFoA9MS7Dryga qG6TpVXVC1DUXXbq63QU8H0ZuMm0bxHidxN7CZbfQf7tfh4r4UVYGpIwTL7+ HfRe6DdRxBbBeyKhrFjxO3zWqdckHKfbZ6L5ze3fYcfOPFXXC4vwzOVap0p8 JKy7+0syWC3CFSmxxISeCBiwVWVYrVwEl3g+Y3mRMNj80DH/Sf0itJhZmmUv hYKgXpZ9V8sivGhz2POqPhQMZI/M5ZAX4dRp04W/TqHQSuMbfTW/CE0XhpW3 9H6DCrOppgWhJdjXV9XU8TMEkm5H/pyxW4JBcp5XqWkQyLaJLG9xWgJ2Rz2x VPUgyL3y8/Ket0vA1upWU30gCMovJk2peC9Bp2mt/LuhQKCczDvt830J9l7k c2N9HAj/cbXXHapcouvvU4fQqwBwrWZfNOBfhq8sgt8G0r8AywV/jeeCy9Dx sujcSsgX+ETgCXM7sAzvTj1zO+nyBUJz9qkmHF2G9Eqd7v2XvkBm3GGvRbVl kNvlzx/f7w/jbucF/c2XgbRPt8DmP3+4oeR8oTx/GV6+mZv1CfOD7rkh9uLi Zbjw85Od6Xs/MEnSasstW4bVzdy8+s/9wHy/wIM/DcvAyYlLoep+4Lop/23A 4DKUbZlKc5v0haSS5YL7O1fgTJ5KceYFX2DSspHbZLACG8fCj0ru94EPW3pX l41XAMMVOoqXvIEd1YmzD1bghc0I5WOzN+w5xn115NkKzPt8W0/94A1HOdKe 1bmtgEdNo8+1JS/Qb51KCEuk+y+l+oWQPkLKXQthhdUVqN1tOGhW6wkm6bR9 kZtWweNZWQF/oidw7bDi27J1FbbpyJzc8PQEm2z7XY27V0Hfabz51jlPUODy WDcTW4X8/H6v60UeUFwR1Rl8ZRWo6VyZBwvfQ7Ns++eluFXYpHTrljrJHdw/ 3vYxTFoFhcziU5HF7nCiv9ezJHUVsq1M2UVj3SHo06CLX8EqMDu1Rf6wcAeD 0WlL8eZVEHB6GuG57gZDETsv3dpYBcJDJ8NIMTdYZFVjzL6+BoK2ZT42X12B g8r2pVh/Dab14114nV3hcEOnaLXhGuyjek+2PHKFu4EvzpHM1kCJfyUsXtEV CCJRnsxOa7B05EJB0uAb8FHctOtKLP13TYP2dcU3cMAS947Nr4Hpos8d4w1n UNb9+Ht2ZQ1KWOJYHnc4w03ZG8rrDOtgn895MCbVGT4sUw13b18Hq8O6kQmP nIH2TiD65P51qOvx+4+hxQnyIhzF3c6vQw2V9sktwxGuNJw+sS9oHfg/jDOe /uYArKe3F+WFrYO7fOJfWVsHKIrpvHQneh0cK9q4TK46wDG7V8bfktZBhVCX qrfDAdj2Z3rxlawDOfxMbriLPVSYH6dwT6+D06spzwcvXoEKi7gv2+UNGEsr 2dpkZwuLzxf3JOlugPXKJfcUfVv401X2Q/PGBvzQIUblKNqCwJ8HuV4mG2Br sSn70iZbWL0eM8T6agOW+KYumnjZQGb0fqWtsRsQH8DMKhFvDZY7J8tiEzZA YY6t5YuPNRy0LbhyLmUDWh4/vnPwpTV80TB45J63ARe58tPyFK3hxVSIP2PT BiS47pJObrACiduPBaLbN4AaqP5GI9MK+oinfqn2bkAjT0zhtjAr0AlqL3jz bwOah0Wk5h9ZAcvmuItCkxugyqYxJqxtBfjEtrlwdgPe8EblvJSzApuWc4ZG yxvQr+l9f4TfCv7//8cQxlXy12WzFfwfqM/alg== "]]}}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, 0}, PlotRange->{{-2, 2}, {-0.15778237351183647`, 1.0654743382440164`}}, PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{3.397996152681655*^9, 3.3979975424471903`*^9, 3.397997948876378*^9, 3.4611147053156633`*^9, 3.461116314454413*^9, 3.461116384861217*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Its integral.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Plot", "[", RowBox[{ RowBox[{"NIntegrate", "[", RowBox[{"rf", ",", RowBox[{"{", RowBox[{"z", ",", RowBox[{"-", "2"}], ",", "i"}], "}"}]}], "]"}], ",", RowBox[{"{", RowBox[{"i", ",", RowBox[{"-", "2"}], ",", "2"}], "}"}]}], "]"}]], "Input"], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwt2nk0VO//AHAlSypJKQlJlkISsoW3SikiSyTK9lG2kmQpZN+37ElCCCF7 WZIn+5IlS3YzY42ZsW9jbL/7Pef3z8x5nXvn3vfzvu/389znnDlj9lz78V4a Gpp47ON/36JwnLi7u4s2UviKJ9IYFZ9vnA3coO6iHOLlvR5ZU4oFxZcElld3 0cfS8qDI72uK4ufUzaZIu2i/9NzwuW56kD7iO9jat4vC7arjTh86AUrjiy3v 83eR0ztTngofQdAOaMuRMN5Fh0Lz8/tdZSD66tDtCwa7iH1yMlVWUxZ6Nqf/ Cejuoov+18Ry+OVAz24fH4faLpJZ6JRv6L4Chg+uJNJI7yKF3jdJDAoA5sJf QtuYd9FkWflEGO91cO7wfWZRtYM6OvXyEz6oQijJqW+lbAflKTXHLvCqQSqD 1VXv4h3UGZDwlj5HDVpBne1j1g4aqLLwc/55B3gL2H52Re0gL6UXlm9JGtAe kXFQ0WIHyanmtPd+1wIB7aacYyw7SFWVptwiSA/kbSvYUpl2ULC+5F2tQT3Q Cs71uEi3g16+K5IpEL4PbjUROqrUbXQ98NSezo770CX+YNNjYhtV6w5FF3M9 gDdHiaqksm3kSqvzJfuPIfT0HCBWm24jXT3TibBWE/Ay/1aV83AbVVwQPjGx YQKiK0bRsfe30bWPn/e+FjSFwKNF8lbq2yjgfW+giY8pKGjrv2WR3UZJG2Mc lxTNIKMjXdKYZRvtiSa2iv36D161KHhsVW2hxctsw1GUJ8Bv8E9nqnwL3WlN iA0StYCumYhzf0q2UI04+LeaW4DI/onOtOwtpJujMy/TZQGjKsF8anFbKP3C PygvsATV+t7m98+2kLj4sYR6N2vg+vX8mDTnFjJ+wJr05Z4ttLJedpA7sYXl 70PacLQtuDymdiuwbiGDijRaq25b6GXyi1Zm3EL28qOMETrPIUI3gVV7ZROd ublsfVTEDmhJdSzPWjfRMemSo26yL2CGjeNQmtsmCq0I/l70+iXEW+KfZjhv It6mtZcOyS/h5o/01i/2m8ijsKnwaf1LSDW5GFpguYmav3gz72V1gIc5ygeq 7m2iu9TWQy1fHaBD6fn+AZFN1ERTrHB6wRG+2dTRHR6hIp7NxHSv1Fcgw2N7 KaOPirpvZCemdb+Cip4TjxS6qOgQq6PQMbrX8EvBpsSmETve0CIzbvkaWg6z /tdUSEVBs56ltyRdgFBs/MvLn4rG5F/GFQ66AtMW1WXlIhVdf36yRs7aA8IK 0jNChKiozUF+5l6GB7A81uji5aeijQEa684xD2Br/3Rei4OKJky2eV1EPYEn 5Vb/131UlBbhmk3N9oTLynGXLQY20Cv1F6/qvnqBSeilhQHvDRRBd/OexIgP uJRE+be92UDnG6+MZ+36QOzwMmf1qw10qv7tjXu8vtAi8v12lu0GUsOfpKpb +oJEm1yak8EGOtDW6dW35gv0zMp6R8U30MR7mvvx3P6Q+1av8s4oBWkqBz9N /RAIDaWl2krDFFTrfFw4tj4QRvHsMxJ9FOSvdeNvy3wgHBcbYjvVRkGiZsH1 NDeCwOOP8fOZMgoSrOCSSl8KAu0jVrz+ERSkSNOpfdkwBChRroEIKEitkkeI 9XE4/OOsJt6Uo6CSmrTfmYnh0JtBr94uSUFMgi1vk3vCobgi8sjIeQraIcm4 ld14C7ZjmQnUoxT0eM+5SO3jETB+qSf38vQ68p2I+e8gQyS0/xHpzIlYRzY6 56w/skZDpYG9hETIOpIwo2xpX46GnInS2Aq/deTq0Cqsoh8NgZRrBs0u60jZ 9Nyd2aRouM7zYHzKfB3pDiTrKV6MgbLnfqtnZNfRKhDI5g9jIY0ZdzJ+dA1t feFpDyC/gyhDYJAdXkOODPfsb52IB++slJWB3jV0mD/2rt61eDC79l/HqdY1 5O7KyPrwfTzwOk37Jn9fQ++mBr151N5D6sjSfEYIdr2uLIcTVQmQksvQ8O3y Glq5rm+mM/URIjYsi/XE1hDrSa309VNJ4HmzJWVdaA1FTFm/2NFKAhNCqKss zxo62p9eJIWS4PQx1ks/mdZQmf8xNYukZEhy5Uysw6+iEmfHRvmyFEhUvWTf HbSKLJVfTyWWpALfAdWWP76riCuMJoKjJxVyfpvxtnusIjobbXz7cipU3Inp anJcRbduz28OS6TBgMa6eJXpKtowo5xP+J4Gx3V+LmfJrqLNhd5nSw3p8Nbw tqP7zApKsHSoZD6eCcc5zdpcJ1YQvfcA4YJCJiQNu/C/xq+gQHXJB5HmmZD3 KPfvy78riMywfl6wJBNajQ9LWVWvoKtdhgUf9LKA0fzvms77FcTLOF3P9uUL eD81fXX+9gra01jqhwvIBVy4UWLF9RUUfuohfVJlLsgVGv5SU1xBWaIUlerF XFha1WW0lVhB7rK3py8/+gqmHqrxRVzYcTWBBpJMHihFSZRdWVpGxKk++hN7 C2CnlI6i/mEZsZMFI3LWC8FgcC8nPnYZ/eq+TdllL4LvW7tgF7GM/M1/W0XJ FYHtVWpAlN8yQqzPaV+6F8FIy9yJvufY7+Pu6onuL4bKkT5pE+Vl9DU/WnRa qARcabOd7WeX0CkbZZVrqd/B9r0Rvcn0EhLdrH6k1/wdzC4ejVUfX0IRUbat OQvfQdXQrejcwBLq0JnZPqVUCuzFGrO4uiW0ajxfxDFWCiVmy2ZqiUvoOF/k /X7xcpirltfgU1tCNey/qS/3V8LY/cXhIzeX0NAuB/sd2Ur4O/vZZldpCS2W Bu5/blUJlScPBw1KLaGou9ZWcS2VEPJitDaCdwkZljbOvIj4CefO+Mtubyyi 7za95eFCCEw9O/h6sxZRiSHReeBONegNJCuOpC2iOz/4VY5ZVYOquJ3+RNIi mr3H/cPRrxokJ1hCl2IWkXM5d3FuVTUw3tJeZvZaRKyFvxNJ4jWQd/hvtcqD RWTdqLSez1sLm0mDRuX7FxF/btMrsdP1EPtzMj7RegFVcTJE7AtoBqNXCe7S jxcQRYphKCu/Gc5J3DXvMl5Ad/jVdOz7m6Eiq+wio+4CeixKp/dSqAVw0SEN L2EBcXoHb/P+aQFB60srqscW0LbcM9GRM61gf4GatOQ6jwRTD5i+GWqHCvy5 g7m8c+gO94l8TuUucJPf8zWBfQ6RUGbj2wddoPB+QD2IeQ6l39IPon3eBb+0 g98+oc6iKAGLBdL7LmisJ7Ge6ZpFyo2LmWXzXdCTk3cyzmsWlTvl1+370A1z zpKCngQyYnr6gBIw1wNnWZSu3UsiIaVNs383XfsgXGzUxTqahJw/Hon2jegD iqZ3sWcgCfkIpNIWfu6D1sg6vq/2JJT3levu344+cDh6m4FehYS8J/l9bpzt h4bj2q2l80RktfKcHNfQD9Zc5nqnrhLR8H9VKtXbA9CjQPf2khQRpUyq4l6z DIKiUUajijAR2ce/bzl1dhCOJk/LOLARUa4u26VTtwahkufZqbaZGbTzp/EK XeQgMPM5EdyjZhCN/BbnC+4hKBIKtB4bn0b2743TBASH4XSnSbnwwDTyzF8y eyc1DKHOsoyO7dPIIjNIauHGMDyuI35mKJ9GHMqBtAbmw8BupD4qEj6NxhJD Sw+lDMObSFZ9Z5lpJM+epLXMOgJEaVLGrwvTKLfVs3WcZwT0cLWr+89OIyZ9 XsVK0REQE3aMTjw0jQKeLjHzq47AWF1fe/XYP5R9fwD1eozALUrijYNh/5DB 1XmXqqkR+JbkGKPr/Q/1vryhLbY8Arw3NMaTnP+hxg1Gh+DdEdiM3PW8ZPYP /fc6oGz9BA7yhM0q9aT/IaKX8PVSFRwcMxaU+DQ6hW62Cat1p+IAX1/IIyU1 hZirYiN6VPDgprOhLCMyheqV61mqNfHAPqpkJcc7hSytcOoJD/CgudVRqMg8 hZ72nZI4aoOHX+Jz11SmJpHUzjdunjA8pCQLPdaPm0SyD356FrXhQf6CfZBB 6CRiNEi+8OIvHvoryr8+9J5ENIWhjlwjeGDpvbVmYjuJmlzcjgAZD14HLQKs bk4i+Rz7Qz37CGD2Ou2Ly9oEytU5bvSWhwA79KQ2N/IESp/UY8OfJUBCjPiS +9gEisu5cUxAkABd+dVyPu0TyDm3kzHhAgGuT+F/h2RMoJdHA3M45AjAp8M5 l6g3gYbh/aifFgF+Ef5jTb4zgVLlVf6q3yPAQ9scqU/XJpDSrYbIQ/cJEBMk 5/FZdALNxZ/1d3hIgH2/9Fny6CdQCnfU9dEnBJgUiRVH38fR7ZTqewouBBBy /zw3nTOO0u5LtH93I4Btx7ds1k/jKKWxS07QgwCUF728T0LGkVeSJfusDwGY Sk8cPWQyju74LbIwhxHgLuO5DindcSRfXHNX6S12/wcyISaq46j9sF+fVSQB uLb0aUskx5F6azc5NZYAYlcTlg32jyNVeTvDvI8EcIzKzvfdGUMGbz4wJyYT oGK8wiZveQwtvsg18fqE5cN/aHwPbgylK706KPGZAHq/OXuyisZQ5bBZvkQu AVx1k0vWDMeQS3C2b3EZlp+MfDserTEUqOVzgacCGz8FiajeHENSzbk8fj8I EJ5ASP8oNoY0d2NYZKoIkII/E6tMN4aE6L4pXa/F8iMmrmVLHUXin049eVmH 5cf72qH4+VEk8nREKLGeAMV8//mRBkbR5ummyr5GAjRYpTtG5Y2ib3nL5ym/ CXDgR8mlyrRR5NCy+2KulQCaB+tnJ+NH0Q2Nxq8jbQQYyJt8LOszio7Wugtl dhCAm2btzH+vRtGvbFNPrz9Y/WjR40KfjSLR1snle50EIC8L6BHuj6LLbNGP /nVh+bshzcqkPor+K4i2zOjG8hen0i5xbRTdOTxZYdSD1ZuM5c0AkVE0HLvX +MdfAhxx/yIlumcUuSgdb+TvJ4D/93NNLOsE1Ox29HwmZupc5oNlMgFRbJ3W eQcIMGac4VbWT0DsHF3U/YNYvuP5mT+0E9ChFEclR8wtf9KT39QRkJBVz9Eh zEVX02quFRDQaNW6b9wQAQRceO/xZxBQnJ+GBBlzQtGnSYZEArpK8Y2QHyYA M4nHmRhJQPH3h9MDMHufTWFsCyCglG9Rr9owrxmeTsh/Q0DPF8aOMo8QwDom STjqJQGJfd3ncRszrpXrp4MVAXHriNR4YNam+6hx35iAftOHTBZgblDgJMjq EpDdZ43FYcxyTh9ecKoRkPmvT7O0OALk5XHQ7ioR0Pf4nDF+zLz/3seMShHQ GEdE/zXMcadPCtSJEFDa4PM+A8xM+vGlGbwENM5s+e8ZZveIE7eD2AmoPyyK xQ3zclPcoA0zAZ1qpjP0w2yx5/hTjX0ERNPS0x6E+ftLoU3TFTyauXDY4X9O mjjHLUjAoy7eam1fzP66glfJv/Go779lexfMtg385oWleDR9OWfYBrOeNF+A UxoeRZsT3+ljVszizb7yFo8Iivm5SpgFT55po3HFI+WYFV4+zCzBpxfqn+BR YWHh3v+Nl0LlOhqijUfFRoM6I1g+CDacUpqKeFQwaMRdjLlpmOMBmxAeCQ8K W/tiLlA/6TbIhkfnfYVAC3N81Ynk5D14lPtc6fNJzJYpxybPD+DQjUAp5Y/Y 89I8cpRxvg6H9LY7QvUxy3gfES4pwCFlWPY7jHm/OfMLxUAcmmfn/WKH1cNS z8EYWgccaj3VMsaBefDGgdImYxwq/ylJ/oXVU7Yg47a2NA6FlJuF0WCOiqfn YT+LQxSTz5IJWH267Ke7PsKMQ5uKX1ouYlYl7QmymBpBqnMtoxpYPZPyNo+5 xY6g0+K+0Y96CdBzmip91WsE0d6mBPRh/VAZQTGgfzaCHA1cstQxh9qvpkQo jyDt14yholj/iEjNi3xeHkbmHpyFtVj/PRH18CgfGUaRXf07RzAnCxzuamsc Rml7k9geYv3KeuKi89qHYVQE6nXj7QRYX7etVlEeRrKsjy/WYPNBTfmcLjFm CDH9/ehQi80nW4XuWTvuQ0hW+NHPLmy+uZzNvMlqNYSKPi+b42oIkJUgmnJF YQgJrpitEn8RIMzVlhg6OYgyyHdPDVYSQF9hzv2i1CC6GK9lzV6C5eeye+d1 nkE08a95fLGIAL8vMPPpMw2iC3S5d5oKsXrhFm32xA0gGVEOlqf5BDi7++xo l/8AEqt+fi8imwBzv2YzHfr60avnwq90Ugjgozz7p/xVH2qgva1EE4zFf4t8 V/K/PhQkJn7rbSABaNRJ7XnqfeiyksB/nAEEcNOdaU3j7UObtZcXxXwJ4PRk simstRfJyDDfVHbH6iVw5JcpTy96ftvMh8OeAHda2wr2N/Wgx57Wf8r1CcB2 Ly/iwYkuJDftUngfWx+LBEIqbtB2ISZWr6SaM1h9bVhMXJrvRAcv/Xx6AVtP g5POyDA1dqK2Vwdr9nJh8910DL7CqRM93cgezz1OgKk3rqKcvX9QefmfaFom rJ/uRsb/PN+B0met+ipH8ZC1okqO3dOByqPHt5NweBh7v0/JdqAdVWSQhb2H 8KA34fyPO6gd7dIb16th6z+8NpLynG5DiT/3bJKbsPU/TbjnWmYrajWpqnle gIeitfrDTXwtKDvSq7nUEw/h2ak1ihvN6I7dplr4GzzYGHk4fmtrxurlkYmF Cx74GmSGUp2akciFxzZnHPAQF5uT8aapCR1XFFb6boEHF8lIBYmnjUjDZfz5 07t4uGb/0CapuA49yaUV1D2DB24BWW62wDrkW0pXHsWNh80Bts6Qh3Votbnh Qs8pPJRc7ZB6TVeHqpQXpsyO40HgyLW99+7XIqeywC8FB/DAVCD4fv9mNTrV rzfEto6Dz0OTYTO+VUhtaq/4rU4cDLodT/4oUYWMsnbyZDtwwMytUqA19hNJ GT+NuNiGAyeTrK5y+IkOSgcknmvGwc1J6xPB1B+IddPig2Y1Dqbn5lOE7MoR /6UUQfciHHBG8BThuMtRz+IsT3kBDjQvadVGtZUh7t/NREoeDspeFk1ShcrQ M7szTAE5OAiiOAj9nvyOXH9qC+PScSC0l1psY1iCVjlF3d/E48AoTaj+NFMJ 2vXedGF8h4MoZcPe7rJitPE+LTc+FgdU/0rKlePFqDDFr6EpCge/D3goHOws RKPHpl0sw3BA8zVf45dHIfpexF7PFYoDSQ2CsYNoIdb/D1/2B+MgMeKq93BI AfLS5T9qFIiDfiHG8AdheehMscJutg8OlJ5ZFVQTv6L3X9H+UG8cZOW3dJ2/ 9RXBpsdney8cvJYMO0Hdm4uqu+5kanrggEOR9VPCqy/orsayjaYrDrw9X9bu 7c1CRsVsCUYuOCDV9ExaS2Qh3UCCr/1rHFSqvBO6MpeBil76fM5xxsEjLc6S of/S0S+/nvT/HHDY+8CqrGNdGppw6zuS/hIHbnEdiJk/DdExMwsR7bH4h7x/ X536hEqqGZ4HvMCBiPtDraGbn5Bh5aXPODsc/OCR6nPITEGSex69v4K55HdX /d3OJCRWebmd/jkOelj8O72jPiImbRZ2R1scLOvKjnzTSUTBL26KzzzDgTgh eeVU73vkdPHPzNhTHGjx69DcfRePZC6fiLDEbGdNf9Bb/x3Si05hX7HBQf7K 07PTgzGot/nIGCfmdlmei6cSo5GYVrFwhTUO5ty75TQeRaGJcCarR5gP1fnf 9OKORNWaZkn0mPVZHdIt0VuUdmGn45sVDt4lDv5ovxqG7vlx7LXB3CtwtVuy LhgFe5JlBDAfK8wkJtwMRMssqa/+WeJA5wrz3j3Nfkh+0bQ+D3NUvcNJCzUf tJSudtYN85+7Q2JtbZ4o65Vz3F3M3CU+EU9S3FC/1xrvecwfmjSR+GdntJnU 1cqImX2Ea27niz1ydGaOn7PAgfD4X9m4RzYo1fVz4CBmgYN270QtTFHPREhq K2bPnnM9p2rVkeq3vJk6zCKHR6zpLkiCctmyaS3m3kXvZBaKPkx7yR5uwrxw V3Dpuf0T0Hl+f7ULs2v0zRYbSTtIyuBin8C84/76Q/JFR/hzXNd183/Xt859 2i3kAhPOiPckFh+tHl6BQcADmlRPsCpgruUJCvjI4g1PBg6rW2D2jeO1kw73 hQgp3b53mG8erNTvPBAArIqhJe2YGbx1r9oEBUFBjPb0ASyfTetz5+kYQoEz S9HpLubgZ4Gsyb7hsCcfb/oec8ZixBtOrwhYzvqZN4PZwkLoyfedKLhlKuLy EXt+t3idDXwUYrA8fNilwZ7/uZFaDU23WHCZElmxxkzUfiRN3HgH3WNsMtpY /bQcyhYulXkPZBqe8A7MOU1rp32dE8BcM81EB6u/p4oRjNyriWAZJhRoi9Wn +sbwFlEiCR6tZYzTYfUrWnJ+sdQ+GRwDwprSMFNiRsnnKCkwZXnnyjJW7+VM rn9rHNPAZnaXYxTrn5QLbN35vOmw0XrsexXWX/6a+X8SO9IhSaPhdLojDu7F jf92Op8Bt32Xn/lj/bhw5k6N0EgWTJ9fNfyC9XOf8hQ6EfwF/D2Ku+rdcFBl 4flzn3Q2qMgtSxLf4CDka0kZLiIHdoxzmlU9sfqQ5cqPUs6DQo2qSBs/HBx8 WJrrsZgHfOlhNY3+WL+5a2U/TcoH3ZDrG8LY/GMyVqN91q0Aour6fA+GYOM7 t66+/bcQvKXSrEQjcdBcbKxcGFQCL3jpF9eTcCBfL1IrSCiBOZUro39TcFDQ u3E1SeobSMSm7qvE9s9xG9EQMv4N7jRszaVk4MAcmuQeK5TC3or/Ytuw+Xi3 RezSycVy0D6gw1fyCwf2w9v5b1UqwK26+NHxWhxMzbaI0idVwNhQ9KxnPQ5a WR6LLKv+AKkGFfbnLThI0Hsv2JZeCf4hfr/ze3BweWwPt5c+gpoTjex1RCz+ ivuLYl8QDF4l+bvMYvmI+lpH2EBgfntEXWYBBwc+5V5OUf8FMq1SxOZVHGxp 6g/vzv2CNQlr5xt78DBSkH8eXaqBmd+3UpJO4mEw0eDoCYMauPl5e99vTjz0 BtJt23rXwE6yUsfOaTy0mxj+4e6ugbqGd3FuAnioYmFwdneohQWmpe0/EnhI tjOqUyitg5rUQq8RbL1NfLg/LxZfB6GNZ+08dfAQf6vk3SxDPUhxaDYL3cdD BA+TzUf9elhkPFkZa4QHzz/fjmxv1AOdjHta3TM8mIodMq6UbwTPZi0z/zA8 nF34sSFX0wza/Td03nXhwa3tuu/KbDNwC7KYBvVh8Wb/Zs472QI0xPDjvsN4 CH48dPbMixbY6uZziJ7Cw8IgVZ2B5zeIj9T3ndzE4q+XTe1+0wo9YrEMY6wE MPhQqvpUtgO+z23eqFYiQMkr+Mv/uAPuz4qp117H9lt6jcb4iA4w25EMbb5J gGqWPgft6Q5Q/taSO3aHAMeuu95NVPsDI7rEeyoPsP10WcJrb+of+PCslm0A e1+7nTbQpqHfBXxNXbNHvxBgwS/vSf2zLlgUX/wahO2/4y19dq/4dIFUdtZd 2gICTF+4cOl8XhfkcbeL7S0lQGCZZwztvm74Lq8jLYrtn5vaBA3L8ruBz9S4 QGGMACoUx+kzDH9he2leROT0KNxQZ6Vd/dYHuK0fyg1poxBuun9xo7kPftK/ FOnPHIV+Rxr8zkgf4KXuLpJzRsE6aa6Ckb4fygo6GE+XYOfPt9hz6vWDKku0 0Oc67PxIn7Hra9jxRwZM0lPY+b0r1VFSg3BdoYjBWHgM3hr3eYqVDsPmt4Ac 3p9j0KjBdUiTdRRqiP0jqccmYPYMm3tXwji87WmQ57OaBEcHYdO3x6eAS91c 52/pFPQNs2g/aZqGij39bk6Hp0E15HG/Q9c0eN8wXpY5Ng2VshVGPsPTwHtn Xm6DfRpS4v6zSVmYhuNjHy6+5J0GS83vvoPsM+DPG6Z15/I0UGsNSzWsZsCV PjwpwmAaOHMyOKWZiDDwZqhfLW0aTF7L/2O4Q4KFL2ov1kRmQMi86e+cHgnE RW9zPb80A8sa9+r+mpJg3/pdx4nL2HX5nn5KcyZB0jqRp15xBnI7PhhCGglU +opFjDRnYIN/s8OJSoKbHqY2s/YzENVVXjaZRQZ2n97bjt9m4OHPG5mtxWQo 2qZl8yyfAf6sztjiKjKsCNgaBvycgbI3M/ZePWRokg/RC6mfAdy5kxe4aGbB +i/HguVfLD6PV5/u3Z8FM95EXpeVGagVlg6upZuDhYaZI0sXiWB08V7a4uE5 OMQa9UxDgggU8ReVpznmoIFrKz9TiggicjlzrqJzQCjmIWoqECHm1mkdyftz gK2K8u6qRHjymIHzc9YcxC1dK1E2JwJjct9Xf7V5+I+8xOIXS4TU1JWGEt15 eHLtn4JHPBHkM44QxoznYVO687HTByLYfVVjBYd5+EAIyzb8hOX5B3JaT5yH oQ1a/r1fiZDdnwmWc/PAWSczgGqJILC/cjr91gL88546wjBPhKVGUetU9QUQ jaYbiV0kQpX/J1Ky9gL4vq6bPrNCBL19AXMJDxfg4T3GpksbRPDb1VqJsFsA 5qGCDjFaEkys/tt1i1+A9nRlo6/HSZA6dvSE7vQCWCVa/su5QgLbT/7x2rML wBb7a2hSgQRyJhsnNZcWIDLJ5waXEgm6RnCcalsLIDLk7emrTII9A194r7Is gtaUX6OkOglMOkD0gswipInGyHAbkYCr0uYGXeAiROkfWjzjToJjJ6lU27BF IHAr/pb2JMFBp8CC/qhF6KwamFf1JsHWxc+ncj8uwjHFaF4rfxIMpuEWtYsX Qe2IgKNfOAniQrSSUnCLcFDtEZNSEgmYDaXXr1xeghaGMwvtP0hAX1af+1lu Ceyf7GWO/kmC7WP3zA4rLQG9jmvTPUQCcrtd25jqEpTSenF11pCg5Vp2WpDJ EjSFTbz92kyCACEuzb6QJfBd/NOz3ksCGureL/ZjS1B8qLhofB4b3/jAsse/ JUj7yLRPZZEEv1oLFMPIS3CLbBKQtUQCz2Sjnsy1JXg2wibyZJUEu8oVu8NM y0CoRvytWN3vvH2hpyKxDAU9pe+U6ciwxU+g5fRdhhS90POr7GR4eLhU43zQ MravSg3l5SBDJSXsvVT4MtyN3GuicYoMbr+vXNSKXwbx5pPGn7jIsGn3zsA/ dxk68AlLkrxkoP7QKFzoWYaHk3xMx4TJsK7106iefwX85ZKCq66QwXsgf0RR eAWMaYRQlzwZmE1TH5aJrYCMsqPppAIZBOwCDHKurEDG45F1OiUy3AvX0ovU WgE9dZ9MSWUyFLRMqj96swIup9L/3b5DBqvrzAqrPSugoW8tYGaI9WkLTaXt 4Apk9jUeufWQDJ7ay3LT+BVw/G6RI/KIDPGm/TJDxBWQuDi3sGhEhgaPVMlq mlW4bsix8dSMDLyVUiJhIquw9+9unIAVGYYkjU/x+67CnUN8piWOZHiycfmw X9AqmFITjtk5kWHh50HayfBVCD9nXibkTIZ9KhXE9PerICNO15f4igyiD9gq +PJXQfBNVYKDKxbPm9/6fIOrcOHkj4BlT2y8DdJxZ8XW4EnOy2WZEGx8wczB PpfXIOmBptdfzHJ3J9+My62BvGyhyItQMtztj3qcdmMNDv0SGf0cRoZXxFnJ s4Zr8Pjw1MS+CDL8Zk7v5vVfAybFeLuEGDLY3Wc5wju8BsdvhzOJfCSDWdbF BP7RNXAYyyB+xqy7oXH2/NQaSKoMCpxOwu73Ieyy2AL2eyJH6eFkMtDhmQwU 961Ddovv3ZkUMiQ8oUszEFkHXaOrPB7pZKhzpEpGu61DwkEC45UcMnxvOFkV 54V5xP9QKuYvJ2RVEvzXgYXTPpAxlwzh5c4PPkWsQ8rToxPdmB9sr7zJT18H E0tpFfM8Msz5zjf8bl0HErOt8dNCMpyMmdDfx0WB4tnJUu5SMsj4nD7uwEsB XgZL2TeY9ewNuscFKXC8eVt8CHOU5h/1WnEKfF2Oj4wtI8PBQ5XXvG9R4JnU D9Z9FWTY8YsWoXGgwIijQldTJRnbR1yj2WqhgKG4nNNaNRn2Wrz5afOHAgVx FS9u1JCBR6/MZegvBQ7Y121FY34kKbpaQaDAu2aOuou1ZOhdOEl8vUaBNwz8 QmZ1ZGi2WuxeP7MBVe3040UNZMgzTMlcfrUBcS2mmoy/ySDZd5ZK774BgUMt R25i/qGdeYfDZwMUq+m9fTA33c5bVArbALeHVXbbmMelK6+Ef9qAb/Ju0bOt 2Dp1rL/jXMsGlLMmaaB2rJ9aD1MenaKCdNKCu3AXGRhvRau+4KFC5kIMyRjz 29rjH335qYDP4joWgzmxgutazkUqGBzUT9jEXJolHEq5TgWa2RK/xm4ykH1V eKKfUiE30vmczl8y3FfwuNX0kwoJYd/p5fvJMLw6dbimhgoEAdkqC8xmeRp9 Pxqp2HtClWI05qenuZ/kd2Ln+2x+mMHsveenT9wkFWSKNTuiB7B81FOrzA9t Qr1noj5uEOsXDafLex5tgsWNnkH1ETIE0+O2qKabEBqTdeUl5sPoRt3Kk01Y qWW1jcfMIcamM223CQHmqbfGMF9kLbHr8N0EljrboZc4Mhj0LuZ8zN0En6sp ueF4MhQaPeOV29qEHeFH0emjWLzfFrhS9mzBcNoZzUbMxw46nKRn2ALPqWrc DGancheWriNbYFIUai86hvXDscAdK8EtILZ6uH7DXNOcOvheewuC86XVfoyT oUeyP2ojawt4z3FFZk6SwS/EMNw4bwvCfnjsrccsNYYLqi/ewvYduupjmOPf TnpGVm1BttRlO84prJ6IS7bne7Yg/sWPhXDMU8mH1B7sbkHoH8sDz/6RgcJ0 nbZcdxtadA0OHJ4hA+scc0yNwTYMqfibC2AW7hzkazXehkfu8Z8UMBu9s7+J t9oGuwzaJhvMtWdTg+jct8GWdrW5AXO4/B4W7YxtePLyY7UzkQz8toiTtLYN HCPf7RpIZFDUCvm6srkN05HJwYOY9SXvK+7Q7IDbaH7oHOZg6pzxkQM7IPyy /fZxMjb/+XOnSZ/eAaUrHDnmmCuT35z3VdkBnXRvvm3M2p1XpLjid0CEaaOS a44MTFcOVFd+3IGS6ZETlzBXpw+qPUzbAc447wfKmMVevTb9kLcDuUcSI60x M58uDT1ZvwP/VCj93zA3PxUfZ1vaAQPb6TGVeTIoMZ6PYL6zi73vv24zWMDy 9YLCkae1C8tcb67ZYM4favysfn8X9j4xTXXFzJ3/5Eeo2S7wVCmfTcS8pZs+ xfR6Fybq8kWGMZemnVZgyNiFhW4NZ/1FMtgemm/MyNmFCDbaLAvMAs5V2jcL dyE41bTeCXOM6iNLv8pdkDgQ3BCD2X4xIZq2exfM2ZwmOzALGVpzp/XvQlOP jTsO82id7JdruF2IjiXsm8WsGd9f5TWzC8/f5A3uX8L6d2/W7TPzu9j8JSfE jhnZOPf8WtkFPqavFgKYnf7eNDahYscZuKMlMf///y3h18HM3GuY/w/igR5F "]]}}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, 0}, PlotRange->{{-2, 2}, {0., 0.9045728084678544}}, PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{3.397996154155615*^9, 3.397997543864972*^9, 3.397997950281551*^9, 3.4611147068055687`*^9, 3.4611163159760847`*^9, 3.461116386374875*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ And its derivatives. Certain functions \"hold\" their arguments, i.e., they pass them in unchanged \ and then evaluate them later. For a function such as Plot[ ] this makes \ sense, but sometimes we want to evaluate the argument first and then pass it \ into the function. With a symbolic derivative of the symbol rf, we want to pass in the actual \ expression representing the derivative, not the unevaluated D[rf, z]. We do \ this by forcing an evaluation with Evaluate[ ]. If we hadn't used the \ Evaluate[ ], then the the D[rf, z] would have been passed into Plot[ ] a \ value of z would have been substituted in and then an attempt made to \ evaluate the expression. Not what we want to do. Here are plots of the first and second derivatives.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Plot", "[", RowBox[{ RowBox[{"Evaluate", "[", RowBox[{ SubscriptBox["\[PartialD]", "z"], "rf"}], "]"}], ",", RowBox[{"{", RowBox[{"z", ",", RowBox[{"-", "2"}], ",", "2"}], "}"}]}], "]"}]], "Input"], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwtm3c8lf/7x8k6lJHStBIqlSiVEtephJQ+kpAVijSUlBJCkqaRUQjJikgy MpLrmCF77z2OcY5zjn2s3/k+Hr+/7sfz8R73NV739X5ff9w7rO7pWa9hY2Pb yMHG9r+nPGwaW11dxdeBkq6JzC9wb2HnqwXmKp4faUgibIyG1HRF2amZVSS6 Kbj47Y+Gg7t1rIbHV3F66QhBxDIajq73aq9sWcWJQF0rnopoIA7QK0J/rOJz Ha7Z0pgY0HtZlXTo6ipyZsja5znFQeDJjrP7jVfxe++xqvCPcdC4SB6RvbyK NF1lzZRfcWBgzym97dwqHvM5QraeiQOTKyrhbEdX0eKaiZmiYzxc35v4rkpg FZ2vdO5/6fYVHtd42d3IX8H11daMwtxEeDf+qGU6ewV1xH5IJAwkQjTPzZOe 6Su4FFm9u2ndN6gEHZGIhBWs/XBujmDxDaRSRf7UB6wg/6RF3zBvElT7x69T u7GCtkZlH5i3k0FWryxpo9AKOl7ZwpVl9wNO3M0VieZbwXNrm4Zzon7AxTfJ 7ge4VlDr5xfVLY0/wLXQ/5I2cxkHP4atnRFLhfqDVxbdB5fRecju0OLLVHi6 YUx7PHsZd1JWqHLWP6Gxce1YgeUyvnouNN2gkQ7PrmfmJ5my5mc4H0i8ng7y 0+aBwYbLGM6TbfrTMx1ebUg7cVOHNX9RiWCK6aCqZ+QndGwZmc/C98uoZEB8 TazSVaFlZL8f7d11IhOcKlTdl/KX8NvO7xp/b2eBjPHIpeGcJbQ/oW5T4J8F 9aP+u2szllDkYEfHYmYW7OMdrIv5toTUS1qph9izoU/zjfS5D0uYvLTknReW DdolzeWhdkvoKqX7MLo9B8RI9zYeFV3CbM+8Ww7v86BS+PDD45uX8D4jJ9M3 Pw+crZkNqsJL6FEtZbEwngfNfC8C1QlLWB4pKZWn+Qf8L4cJ600v4nLsnmIe jnzgGC8WsqtcxG9qxulqrxFGRbbxx7gu4ogtaFy6UwAhtj134h8vou6ug+us XhWAxu/YykSHRVwjl3I3OrYAoi0OvEu1XURxDXevnK4CME1SX5uvv4ilszKu 5/QKoYZ4j7dt3yLyCHPZXD5TBJm3i7kEu5gYu2tDZ9/lElCWvKsY38JEG/a5 niGXEsht3GymWs/EydDx9UrRJUBSvZ1x+y8Tr638uPtzsgQqBIWvlf1kor2c qNlJ31LoTb9KeubNxGcuftvOtP8FK1veiS3PmPhI4k7FUa4yGBJN35zqwsRN vNIc7gplMPaC+163PRPZk6+28r8qgxnD72IqJky8xyZBJqqUA98S03n6ABOn Z50cotIqwCc1Nv6tHBOrK+LcDg5UgJD1hXopGSbKPLCblN74D0Sqv+y5uI2J KbvMDsg//geSUVqt3zmZqHWav5t8shIOq384fKNtAS9YhrjkjlWBxTtFWpvn ArouC0Vyl9WCc0aAd9XTBZQ/JtS9aagWgjunRAucFrDgn/lvszV1ULHv19mE uwu4L0dk1V+1Dg5VHY95ZLyAHWuPpoX+qgNuAXWDDQcXMGXZs+FDej1IHomb 4N6/gLqef+zPNNaDijn3c+auBVy8tr5RcaYe7FPKfvSJLeCSqXtzydEGaLug w5vKu4AH/vBbkrEBkv0M8s73zaPmMU3Ll52NUJqVpUfsnMcK2WdpOuxN0Nez ZfRQyzy+zn7ffUW2CTYpdIhsr5rH4a5x6pn7TeBee/XeaPY8avQOgStfM+it vynl7T+PnjK+b3q0W2A+wOUVwjyey1z0TeBphxHRgjGN4/PopneeonS8HZrj uXWqleax5+5WQTm7dkjPfb++a888LvtvENJrbIe7/V/DmBvmsZnb7+XPrx0w oNiYfJg8h07bPXMeX+uC+t9bBf/0z2H1LE1hMbwLSGeu3lfvmsNTKn5zM81d EHll7PCl+jlco8yre/l8Nxg9Y0P7vDksOb4c3nyiB6pr99Ul+c+hCSnfpD+s F/KMHQ4dejuHiWe+Xwz73QtJg1nBuS/msN7jJv+Hzl54NX/KuNx5DrOEYhaN JfrgtOSVgeHrc6icnjeyJ6EPsu+9mNlxbA7lP6dZGP3thxiB7q0hfbOY8i1A l4s4BAEmwHOscxZ9NwcYvL45BJ4JUdNtzbM4PCmRZhE4BFanrtVsr5xFaUvF 6rXkIZB6RPb6/GsW7+wzfHw9eBiiuxiT8W9ncYF9vVQCGxkC5PS7NL1nUex6 qf63A2TwfJxZQfaYReGj+JX3Khms1jvFyT2aRaLy0QP9+WTYcWbZOMViFutt xw3XPR+FqGSe0szDs6gmcupUvdg4+C/YphsozKLx4dZZut44eGhURM3Jsezl K+l1ejUOFr3vXI5JzmK4VmVb/vQ4SGwUVvzDN4ttTyznTtRPQKSLaHhxzwy6 3hq96vOFCuHaig4Nr2cwYONIT4c2HaTXalfUes3gY/M/Cgdv0SHpn5VUtfsM jkrLhf55TYfc80H1ZY4zmFrr5/mwnM7S69zBfEsW3+s7Y3uOAZsu/ZlKODaD hJwXM/5XpsDP5Kyj2+g0ukQmBs58noFNolZVLoPTeDn+yUdi6QxEdjrLPOmZ xk38t9aWTMxAilly04OmaRyeybZyPj4LlVcFj9wsmMbb3+f3fmydBcL1ptlL odPYyUPYGSQxD553LJ32nJ1G+dkB/p4WJnT7mofnnmbxkdE6Pt5FOP7ThHRO bRrtNZ73Pzy+CIyZy4S7h6bRNLxtkT1yESzdtUPSxKYxIDNAhn5jCYgBh7JV GFMobrNZM5h7BVayuOZ1Pk3hLtfEQocMNqJx+xrRnuApPNtW/OxALRvx19Iq 2PtP4d7TqnuXxtmId08yXwa8mMKgPMr6wp3sxK4K6uaWe1N4IJAzgxzITszr ajlqoT6FSecXJeoc1xBdOL49dqAwUD+RN1pOmZN4N9Sc24LMQOVdIXZ8epxE qwMbgnUGGHhpbIbSfJuTqG3imra7jYFBHp3+Cp85iVvSL1C6ixn45rBKUD4n FzHDasrqXDgD2a2NlunlXMSE+a905Y8MzHOb3z/ex0X85GvqIRvAQL7m84ey F7iInrklEeyvGMi/zSeydDc38aJwSGvWA9b4ol/OMy9uIrXgxAXpcwycVYT6 AgUeYr8hvXO9BgMHXwTZipzhITZR4m6vEhkY7ZzmIHaFh5i3VfB1+xEGLn1z UDzlwUN8e7+vyF+KgQePd5yOquAhuvF80HMTY+AHK1OfK508RIcI7b7bWxhY ltJlmEnhIRqXp69oCDBQ01tZ+Jwggbh7h/ex5QU6Fp7Je56sQyCKZh0vG5um Y+trXsujpgSioM6kQeskHb2+ZvtcvEUgzjoZPUwfouNvBXfqnhcE4pgAP8eX Xjoq1IXIDrwnELtiC977dtBRJPfZrx2RBGJxrdyPm3V0TCQYF0tnEojZNj2q hpV0jP4lfmAQCcSkpcBK9b903PKv6Z94BYEYGaBlcrCQjv1+L9bWNxCIAbuX RyX+0NHJz91ssZNA9M7/6cSfTcehopgDIUMEorO+Dc9iGh33FsRR4ygEoqVH jXRzAh1X1Hv7eJcIRIO2z2pdMXR07RAOPrGGl6h90N5oMJKOpSqnyR08vER4 S3QYD6Uj6UTDnaF1vESlQaF3jCCWPfU/MwzW8xL3qPbFLfjR0YUjXm+/CC9R /MNPZHtLx3sfP7XbbOElbph81sbjTUf52/Q+tu28RIKW3pTAMzrOPihfnRfl JS5HSfFvcqWj9TXjqvPivETGAkNW7DEd2UZPDS+weFiviCjtQEfGq27yMos7 kgKN99qx4sse/siAxbWc1x8etKXjtqOXldjFeInFZkq+x67R8URYQsXiNl5i zi/OBKI5Has49YdPs+xJEWwq0LxCx6Alf+WWjbzEGNu4jgv6dBzVd3icLsRL DClwnLn8Hx2jdpw2b17LS3y3TUPQTJsVT+rwd1VuXuKzB5v2XD9DR01Flf0j qwTio8rhU7eJdEzofpJYO08g3pLJMnVQoWOBVMTsHI1AvOr28tGTI3QMFC+e NCYTiPothv4einQsig55PNdNIJ5V2P3t5T46Uhsynf41Eohqr+eLfHfRkf2r 4HBjOYF4qL+sK1iKjp47TX6vz2fpUSV0LlyMjuaGj5fdfxKIYkE318duoePH 0eZcsVgCUZhybG/SBjpyHBlbOxpMIC5Gtpvn8NLxdZrlKttjApE2982JxElH NZoJQduGQBzUdQn4u0rDtk/rHuToE4jVa0RLm2Zo+HxfVh7nAQKxyGSip5NG Q35NulXXNpY+M/IWBsZpmFigzGziIhCjbcz2M/po6BrbLr6jjYf4EfdrLnTS cCTneZ99AQ/x3ZYVC7ZWGp5J+8bWkcBDdKyIDBKopuFn0WTrhYc8RC353kW5 XBqqUburYQ0PUfVlqsjBTBoGqsYLnR7gJh7s9ThwLJWGT3PCPY2KuInbA3Zc 04yn4bSxvGKWB6s+zFhVXAug4cax1edKNC7iwIVDg7d8aPhuaTogh1V/Wr9y rNx/RUOL2APqOtFcxMIrsYoebjScPJtxLop14gb/GQoJv0VDP86i/rkvnERz pzC3o9Y0NPh2ioPNnpO4+9B/1+uv0jCJx2wj9QQnMTch+wDhMg13XW4wcm/g IHYHvi19ACz/+P3ygqbWEHfdUpzW3kjDJ6Pt0mNs7ESa9HDbkAANDwg50eb/ sBFzesLQg5eG5OqHE7zObMSP3wib7JYnUet1ym7X7lXo9Ug5rV4/iQGbRk77 hS+Dw35mJMNlEp0sqj02F88DZ8y1W7vvT2KYYahX9o55+LCl6rC5zSTS1IR1 W57OwW+OqMpy3Un0uvzPO0NxFjjbzzCjZCZRX7jOivP1FHzwfn/5vxoq7jvQ MPBmZRxye3avS5aiIu37huG3XYXgeoL9e9gWKhId+xSupOWCamibzmsBKpK4 cxo8Lv4Akt4bPxsmBdm6gbc1MRT/lowL76inoKRg48hXKMfGpJStH55RsDVU fpVDpQ8/EF7mej2mYFnk2xOuQf1oaH3V5IEdBU/cE9/fMjGA7eJCEbpXKKjE funTROgw9r2/L7lWkYK6zdsvxA+MI/Wx0i6P3gk8D46z+humMLVxXdnd5glU cjY0kU2fwvuKQ7ZmlROYRsrwGb44jdNjwd+OZ09g0PP+zM3vZnDRbG7/jN8E Rv0RfunFmEOCeq7SLZhA9eP99y54LmFFVECT0eEJrKWRN8sKLOO75VuPNPdO INdmjt3vQ5ZRMGt7tvTmCeyRuRchlLiCm+SeqvRQx9HJg2OXtCkbaacQ8ZR+ 5Dia36outtJYQ/JV6HO+FTiO5/U0OEa91pDmdT3TPV6NY1TE2w7XwjWkyvfF 0t8dxlFsr+398uMcpIcbzvJwa46jdNepv70SnKTuQ2MgemIcFQ5Zx/wx4CRp 6b91Oqg4jv6DR1ai33GSxIKrRs23j+MRM8tX3jOcpNJNepVZk2O4TXna5+dv LpLC0SnO6sEx/Ohsd6F+jIsUZhikOtg2hsy0f2FLW7hJ90KaU4SKx1DDfHLl ngM3acs2k/c3Q8bwc/nk9aGtPCTP40vl7j5jmGmRaW59koc0YRyx5oPnGPo/ ydszc4OHVPCp50HhHdb61OpvWmk8pFti1w22nxxD09+3BPtVCKRGVS4/xSNj 2D5vWbvGlEBSM4//q7l3DJ2T5jtUXQikDZ/Jyg9FxrDSWnETbxaB5Iav77/h G0O9Riev8AYCidwj9y1qdRR/nUz5rTtJIOVJ2m2vGh3FldTWp1LSvCTZkwL6 A92jWBniEnlalZfkb/nj3ULDKN78+Cjt3WVeknU0fVkmfxSXD/urRz7nJQlI P+p1CxjFD6eKcKaJl+Skvnlr8MtRfD4QS35H5iX1X8++mOQ6inI+w8MmTF5S VhyzsMVmFC18zCoTt/ORpEo/LVJMRpHauG16314+0rvhE0qcF0exq/bZfdox PpLFLvc4BZVRnP9tu/3kZT5ShaZkt4bCKC4U6n2tt+QjKdkWbDKTGcUfR7g0 v9vxkSJfWf33YNsotuUvKTQ48ZF4EzlevRYcxae3aiM0nvORHpbHkj5zjuLn B91/eX34SN2jZxYyF8i4WW5xTPwDHylN7tWt/gEycvfuXq8Vz0eSqLPI2dtG xlTb7c3E7yz7Hh8jOFaTkdNn09OwdD7SvJiwYX4RGYcvNskY5vCRrIvH4nhy yFj6oHnSKZ+PVH+raFo3hYw17JJM9iI+ktr68NNhMWR80cp5bbqUj5SU9TBg IISM2+bHNfUq+EhbzHX69vmS0SZta4lYFR/Ji1NW4dFzMq5Pdp4xquEj0b+t uKETGTWT7RlcdXwk84stVYS7ZEw6/KVBtp4Vn7kfonrXyOg7UJLyh8VHI1/d /mRERpcXbh8rWRyrbpk7qENGtynpz5dZLDR+jFf+NBk7g6IbjVn7PX0vbPRY mYxHAjXVu1nvGzs6Hk/aT8br6TFzfSx7DLqLZnh3ktG+ymLmxj8+UpFXuPql LWSkdIqfdCjjIynsdQwM5ydjbvTFjjUlfKTwOp3+oTWs+NRKFGwvYOXDSVbx wPwIVuecnsjP4yM9El91d6KM4JAzRY+axUfqL26pLugfwe5P8SNJaXyk/26n iq1tHcGf246GLiTzkfLWv76jXzWCS51v9dpY+Qk2P843kjWCJU/MHxqE8pHW cG24ovB9BGeDPkXOvecj3U0a//okegQPxNAvHXzDR9KaDz+zzmcEX8kOZLg8 4SNlRjoGXfYcwUOa/Gy+9iw9nrkwEPl4BO0+6YicvMFHWny/6qFoNYKdHqVJ /pf4SCl7rfIMjo7ghtrXQ1y7+Ujb64+vjdo3gqE2LcsXRPlIr5w2GI/uGEGZ 1AOeGkJ8JMuS4jmXdSM4LXn3jOYcL2nj1V2HvvQN47yaXf/vAl7SMy42z7Hm Yezu350Ym85Loia11h6qHEZFueqzWnG8pL/zr++W/hrGo+JvGCUveUnOAROJ 42+HsZ87r7DwLC+pp+Sn5JEjw/jJKOc6vZBAcr20oK68bxhVeEeVLdMIpC19 xJvHpYYxYt9mza9fCCTdpZqfagLDmDoU3prjTiCRDlJPaQ4PoRD/Ew/h4wRS 1Gc5a6MPQ5ixo0JTP4GHZPUkJtF5dhCNhWU5vey5SUP7gg/irwHkjHzPFVrH QVrvlnhEnr0Pzbqar8+sX8J9Ryb3xU11Yhin/Nh5CQY+V6fU5ji14IXyBs69 5/uwUGviP6VrLSiyf/mbKU8fsumMV6fotOBQonR5TmEvul4erYyRasHffoF3 Z4/14iOboTKfymZscTcqY+h3o+2rLpKlZDPWFGTInd/Vjucrq1J5yxqx+Si9 Qom/HkX0U/yvbK5H6ZLz/1lQ4zFN9m3uGY56/NBGznbwiUHdhRuDipN1+KtX aX2/fBS+idyhzPe3DkNaKtb+oAfgCjmoJ/dRHeqOqaWKZLnB8FMXedHmWiRQ zl+kcKbA3f/eh/zZU4Mf9CxutMkWQ9psiWCZdAW+OyT34nRzE/h+iy5UWyjH sJjkduaRZrht7u6YWVWORkRTgysfm0G6VLkj+lE56nHfJ601bIEPwUnxT8vK 8JL0RwuN+lZwVnqveujOXxS/FdwqnN0BpxxMb0emF6O6jctNr6heEJc9Ji7y qhiNExzd9P71wmKbSN1b02K8/XNYdnqmFzJO1hx5wlWMMSaKB9O0+0B2/ak1 +oZFyP7k+cGtjD7gS90VyrtYgLu9C9sDlAcgrmPIZ9QrH6XXMyqVYoeg3XXT 54hD+Th++t/GldIhEBDXTL3Y/wfz2KnRUaND8MgioT4H/uB3R6GA+/LDoDF0 a/Mb5m+U0Pz3+9SvYSBTJ6Pk7HPwpp+0anT+CMitYabfNslAol/o3OW0UTCP kSuR4MtAjcYcglbtKASomzQ3ZKdjXZi0xQbqKDC98+ZVNqXj2tS2vsN7xuDf WnfVdXU/0UKgUOFbxBi0yhF8r/ikYNri26X/3MfB7KJoRse1WBQv4zrvL0OB iamZY47FMai2sV53vxoFXD/UoIBMDPZ/dBCLM6BAeIfnv5PDX7D4jIKAzEsK ZPyrL/mvLhLZTkTq2AxT4Mf0nZ3k9iC8FqCuTo2gQvUxyQPbwwNR0lC/9GgG FahuDccvmAXgzUvKQqYVVDASfhhri34YzHBdVJqlwiUVgTXs5S/wwb/5A7vP T4JH4+7G7UU6uHckV2gjfRL2CXbd4tqvBGeFeQgv1tCgme75WWjeCAoqw27b raOBS6BGxW0le+AM9jDnlqRBkeTrlxFCnlDqcGbKSIMGN27I2fxaCYBz1zJa PfxokMPn0lToGANWW3YFdmygQ9R+kYYfUrEQ78f/ZLcoHbx1f9SG18TC75WN Tlel6aD/YeDfoz3xYF7sXfBWiQ60HecL5boSIJ0y86zrEh1kj4n9CFBPgcN5 a3KsfemwzjQr2Z2eAjpp8wr6wXSYcrv47U7kD/geJVi4L5wOFv2FejtdUyHR JupAeCId5HfP6Sw3/YTSTzrKD4voUJ5+Vf3n6wyQG2teJUzR4UTJvqJdvRkw 6dnJGzpPh9TmhZORRzLBaxvH/g0rdPiwEAhvBzIhXDs9v4TAgOtQdtxaNQsy Wz4LSosxoFU3OKcjMAu0/b73c0ox4JyVlbLeWBYUXOqsrpFlwMEXS4fhYzYc F4xg7FRgwGqFguJWeg58yEm6UXKSAQ6dyz/8NHPhBRRyzJ1hwDClQp47Mhe8 ZhayhbUZUClkvW9K+zdsFqi6S9BjQJhB6K6q2Dx4OLSGQLFggICtTfzpxTzo 6dhpd+86A549OSSTe/EPfNKzGe+4wQDb8Cqpryt/oG/lv0MP7jLgcD+7+DMj BD6zbFqYMwPkcw3pCokIPN4rT9WeMkA24Htx7wLCcxGP+Wp3Bqz9knw4SocE ulWHM395MYDgYygx70iCkP235vhfMoDzyRpe3UgSBF5vl9V7zYAlXaPOVSoJ dvbd1fniw4D5ExylhpsLoMshUD3ZjwHTu1N+/IACYD6Y3h37ngETbJzPr/oX gJb7nRzjYAaQJ1LuZGUXwPjkc+utHxkw2HrFQLCvAP6LaF0pDmFAV+qPPahY COSvxxf6PzGgPdx4w2ZjVh/jo2V8OYIBza+4lu96FsKWgqCEjEgG1D9MHS79 VggVgRKDbFEMqLYwqRVvKARz09F1x78woOI8d+6jxULIfsyQsohmQKnyz5jq nUWgeFZF9kEMA/KFeB67PSwC/3yf6atxDMhd+mnRHF4EwjoheCyeAb/Iptry JUVQfmPwMdtXBqQ18ih5U4ogJsRBLIPFKaQ0sW6RYohwOJ2qn8CAb8lmPEfU isEswFChn8XxIQS6j00xZET+iDBNZEC0V3r7kG8xHD97fr6IxZ/tzYtVs4qh 7YI8ccs3BoSb8qYE9xSDg5OB4xUWh2hlfKTwlEDYp5KPL1kcpHT12RmFEtj9 yfVrNIv9JfluRxiVwBpb19hvLH63LlN/xqME+tlLfT+z+NX8VTWdxBIQcTK2 ecZir0G+3XF1JXBm7PheXRZ71GauX14oAbvXtl1rWeyaZ7GoL1UK30MGXTNY 9jklrB1K1i4F5bupvNosfhj0q5rzQSlYPKx6Xsnyz97DMtv0Uym8kFMdV2Hx nTvrojOKSuGQLDsxhBUfW6Ost+smSuHExu2efax4Xle3cry+8S8057z7uZnF lgr8V/NO/IWOGcPq46z4m4tma220/guEQrdW7VgGGBOuHbzj8xdOKXHUarLy ZTDNL1qc+RcW3EcyFFn51OvN5hLt/gsf5va/JLDyfaHy2uQD7jIY5G/TqPzM AO1sgbZ/8mWQbDg+5crSyyn/68ku7mUg9E53UxxLX2qugh8avpZBkFHiW9Ew Bhy3zXXfW1sGIxKOVA+WHg8ShS51SJYDn9wDVyGWfnfSfi8cLyyHj7IOudos /btWnfaappRDVE8S/+Iblj6//RNI2VoB55T+LAS9YsAb646dO+5XQK2LR/iL 5wygtTN1eCT/QSCPHr+x0//s82wlnfsHz5cEdhs6MiA2mO+a8+N/4Dba26Hq wABD3W1OlOp/8M1mTKDsNkuvJceiG55Wgr0ZxtSYMWBLTME+n8RK4Lc//2qr MaveeJzN0miqhK7is9w6BgyQUblSmbOvCsh7q6vtL7D0keo0+7mjCiRkMwKT VFnx/ZSlfedYDaSOaS1TNjMgwwmaZKxroLVJ+lmdMKv+GPy92uNfAx+7Mj5H 8jOgQKjloR65BjYMWH+fWcOAjadd/gs/Vwsfdr8Q+DhBB5fssCeezFqoqLtE +PqbDru/dqtdFaqDtvXCWtKZdGgMluI8IVsHt35zybxNYdXvB998Zy7WgdtF uyyRL3To358bY5tYB6n6XeriL+hwNqat6oJRPRC2BcCyFuv8eJFiU2JXD5xa quHHTtIhxPb5qsrzeti2zLhpcYwO5P37Ffek1EOYyOoVuz10eJXtEcTB2QCl mcMfvQh0KKvaZZL9owEW5QQZCYU0sE9dmpIvaYB1T8yC3uXQYEtg3bu49gY4 mvPih3EqjaVfl/wArkYQi1zQ+xVBA8JA9Q47k0YI8/tjefsxDTTnHck7eJqg y1FMp0qWBtR27Wchok3QP0puNhKlwYc/EtsEDzbB9Vfpw9XraTDsWX5uybQJ zl7bcGxmfhK8+cV+NKc1weaUNvYvXyahVKrY8a15M/jm/PSnjlHhjI4wx0xm C0g7mZQ9NqOAryUvfaG8BdL4PF4vqlOg1ZGtZ6WrBcojjpwz3keBW5HUXAJ3 KxhfJ/YlMCfAd7LCQdSgFfgkFxreBE9A6/vn/adnW8FtpWKDTfE43GqeLgg4 0g57FfdGR3OPQfrY+I+P2u1QfM1x1H90FJZW+iPCzduhp8ds1rhyFHx31T2J 926Hu5ycXp7vRyHd6btibnM7XJ4Quyu5jTV/m82Xvkcd0Dj/YUeUNBn8rrZ4 KGR1Qp5iedWq+DB4T5yA4cpOWL9y9WUccwjcnkQvf+rvhN4w+j2p5iG4G2jn zCPQBUVpHlLR74bgwl+Oh93Xu0BffeTh77lBEDigcOOdcDc8rZh5dat0AHxX XumQ7XpgY9SJK84affD3ghi/rnAfdB6tPRKR1gKUHSJu9WEDQGxzHjh89D04 Ptxr6bdpGJI3l969wdOMm/gm8wx2DENrUfmufNtmzP6ctkV83zCkUjiZfeXN uFRxrDb55DD0Jm/0f/u2BT13aEHFnWEonjhJJq5rQ9/q62JcRcOwtBTaxLWm E+N3R7Y53xuBlWIV+z/tvaiVb3n4lPMI1Cb33Bjn7sPRSzLveV+MQK9O76Wx g324zz1ZKyRsBH5F0iyN3vRhWlNuVmbJCCgcZu/vP9KP+Z4twbTtZOgfl62R 8B7Alk4hPZsyMvCRbf41zQ+h9lvr1of1ZFgXS9pnt30Y847lmj/vJINc255H FarDGPXh2u0oGhkm5ZwWG58No63uL6/2LaPAHkeLFeMZQWaRSdaFm6PwK8W0 S4KbjHccUlXNHoyC1LLNRL80GbsluYpvPx0FWQWKnjWrby98mlL7ipV3vXOy KfHuZHx7lH2sMGcU2r9uiFo/Q0bRpHjRo3xjcGrPkIJqwyj6XlmMPrNxDA5o O7yupIwiG0F3j744a/zXhhURwhgOXl84fP/gGBhsurWjWWUMv4uf/y/JeAx2 /fJjcESNoVoA/ZlE0hjofVPb02s8jqlEDYJ85hgEH7L2mbIfR6nJMN8TyGKP JKs/3uPIfV7905UG1n6D3akPfo5jDdfHjMDFMVh/8ew9G44JtHhyYoTn/DgM rpFr1AidQLnrZU1Ug3E4ZZUWsDN5Aqcu6Bc3WY4Du/G/wK/5E+gtfedLzONx uODqJxjXP4HJNZ9MIGYcrq/VvfxQloKPcndpy6aMwxpnhtvrIxSEuHRl/pxx iO7Q9zujQcF6538iHdXjEOny2DrtOgUXZBZrHjHHYYvvjNPVCAoWCr3MN+Oa AO3XbFeSv1Hw3aLwd3WhCai6NGYZlUVByTq5N8KyE1B755+qWy0Fx37/clpQ mIABupeabScF0+NP3ehVmQCxTRFFCyMU1HA1Vk+5OAHrd/2r3bBCQaEbwweD TSdAKaIwNJxAxbaLDjtcb0xA8Jpm2yphKt7Z9Wbl7NMJeGw/m7FLlooB9TnZ QwkTkDrhU1akRUXTP2e+VqZPwMndOo7vdakok1AXnJ4/AWVXDT8RjKiY/XTU 4VnjBOTuHz46bU1FT1tHS9ueCXhaoCJ78w4Vz11i1/1vbALeWDo+feVAxe7d W/eLsbHqltz2u3+fUjFhQ9x2zrUUKHsf5T/iScX7Kwp84yKs8fiD8ukvqXh8 NG++TpICMjkR/nLvqMjZqDWSvZcC3BfzOS74U7E6v7Hp8xEKVPS5NIkFUfFj okWx90kKBFkUXQ77SEWLoIk0u/MUcCt41lwURkU5d6cv+oYUqDOOTAqLoOL0 TU5/FSsK0A/MyElEUfGPvr+blB0F/HZeTL4YTUVvELXjdWL1ccI2rw/GUlFX LsGE5kkBRkuFWGEcFbeJKGm3+FCg4NhUKdtXKg6sonJ+CAU2FewZpLI4eezc rrgYCrx2fd38LoGKj5paRN6lUID2fY6tg8VE0jXOBzkUKLkuVtbGYt6kScaV YgpQOS0DXrO4Ptilj1hDAeLvnr9jrP3CPXhqd7VToL7Hpn45noo2twPzBYYo 4KVpJ1bEskfBQOL7zCQFnnqt26TCspdJTPrUyaTA6CamnA3Ln6K9R98UcVEh w9ejUp3lr/kB/Ri6IBW2DIeOrYmk4vzB+3kS26hQu2ZY0v0TSw9HfJt0pKlw +evd6pwQKu47nkR1kafCjWnTS+nBVCxVLeP5pkwFNpNpoTsBrHifHJJsPUWF CG+fm4O+VAzSkrikZEgFhs4DZxFWPuXPn7hjZUmF7FPfl6pZ+S7778oL/9tU eHs55/tZNyouGwRmTbhT4eX7jSpeD6kYYpxau+0NFSwM942q36PiQfOqUa0g KuQzwm+W3WT5b80jGpdAhfu6Mn5bzanIdlP6cEMaFayHlfnJhlQMu3PyAvsf KgybBAi6XGTp5YGLh1kdFdzrFExpp6l41HNyUIRJBWEfNXNplv7rX6xbOc05 CeLspSd3irO+j9d7NjsITMKvt7qHxkSoGOV/7Wy11CRwhz0XruekIuFzy3fv c5PQOUeI3tRLwejo6dKMy5PgtC2yzqqZgifi1/f2X52ER2xBhc6VFLT/fk4Y Hk7C5mbzSsFsCrb9xkdz4ZOwdu3D+o53FPzW+hVsqaz16/a639hPwTOdxUYf WPcETzlRjURJCtImacpW7DRobJt8mb+Bgm+m+PX819JAlFr0wHphAvOZml4T EjQQK+TJTiucQFnePHKsFg2UOjl8c3QmkPFX/la0Dg0OSs2d/63Gmu/9Zfyz Hg3q+N2bQg9MoAHnS2qYKQ02dFwWnxSawBerF6f97WlQS8j24qkfx8GZkVXX EBqEXFfJNrgwjtH9GzZfJtNA3cHDNfTgGIrl3T7D9Yp1z3usXJFWMIJszDWJ Dv2svu4U2fmGfD9aDLRNuY+w+hIaqWj/tn4kVaaq+UwwQDwp6doyVz96fDZv /DrLAGbfi/9yuvpwVT13tZNvCvhiVE4l+PThit99A81DU6B36ljN88leXJLp 5RD1mgL9fyfXHXHqQVPBrAt7Xk9BAfcr5Wi9Hsyb9wk94jsF87MxMnv296Dr P5UDF0OmQERWb/5lfzcu2n809k6egrtnIsD3Qjcyf1/4SWucgp27Lgv27u3C uYt/zEtkpll9l7bQ06V29Gz70aW2dxpELpvI/2xpRwHLaNNshWlQc7Q9y5ve jrL2L42TVKYh861ApcytdtT3vWjw/uI0vJef2SHc3oapFUM6Zk+nIUZUvKGE 1Io3TwuozjROg3CYi11rYjNOV7Dl3W2fhu5L37liXzejh97UcXLPNGgdEnof dLMZQyxblTvGpuET2fPO4J5mLHWPVipgmwFBg5MeaslNKJV3ZJ/PvhkoP/zV oTGrETuUrm6X8ZoB9bDipv1j9WizcFjwxesZ+HX9TRpHTT3S/qzjGPKdAdKe 728X0+uRUzN3LDZ0BuyOOg3outWj/BWRXOkfM8D5SHbMb2M9ejz9ZyTdPgM2 1uSr5zXrULb06IedCrOwt7HjxKE3NZj6RuDN88OzsE246Tq3aQ0e/2/o6cDx WXAxuvZ2s3wN/tcaYB1zZpbVr3AzGfXV6DRGUdppMguRK3YGrySq8Z9AbIOU 9yxIba3hXCqqRHtDofVSnbMgKGRT8lC5Aq0SDoTJ9LHedyLgpNLGCry8cGHn nuFZeCxGubeTVo7HP/kcVqDNwvPMkh3+CeXI1cNnrMY5x7qP3PeN2F6OYTZc Mcb75uDnXQNJc74yLHZkKgW6zsG9U2a8O3lL8Vfp1vwPz+bAwf/isvNYCSZu PqYZ5j0H2SMvQ5f+laBvzuMrX/zn4K7YzFpXvxK8sjz99EfsHGxZMoyz2VKC VK/J0n+Vc2BebPJfgWIxbg0aNOIUm4dg6pOrGs8KUfm5xKaHUvNguVimrWNd iAYOxg0Du+YhNkjV1O1sIQbo1uoUHZwHN4mXAubChbiOP++Up9Y8vK2q9+KP L8CVF4H72B7Og8k1rcnFRhIOPj7FtlQxD+FGxxyVPufjmhtP/9yunQfVU455 s/b5KGmQ7dzRNA/S3vkNq6fy0UxJfia3dx789PS7Bkf+YDNt69iT2XnwlEq8 Oqn0B8tv0hvmdixA0NLchx8dvzHFJOrrlNMCFO4Z9VK7koNKLTuZ3G4LkHLp Znbs4Rz8rff1/LbnC7Cx6r6emnAOlp1NoRN9FmD7aa5wvspsHDiap+L7ZQGY K67FqyezccvG1prdFQsg0mJlL3E0Cz0rBefNtjNhI2/irKl6JhK0ArXvSzJB 2PigdZJsJvoVbYrwkmGCncunAWneTAzPFTuVdIAJln5LW0OqMzArYe+7+dNM 0F+2K3IyycAJL03JwDtM8Ny+P5vqmo6Gqu5aZX+YkGF2YeO2rp/YOTMsWFjI hM3uH2Wain+iVcqFlt9/mfBuIY2UlvwT70iI2/yoYwLnA+uaRtef6Mn+5/mH ISZM0JkLKeI/MaWEmX+dfxGsGyN+vLdNRc4Ljw6zmy1Cv90u/4hdKfiGu3uJ abkID/e9jGVb/Y6CeKZ42mYRcJrzdkTzd9ymIHKJbL8In0tfFXh6f8cDwhn2 NV6LUDM/EDhFTkbjZnpSRPIiDIneUd6VnYQ/ze2kji8tgmkOm0b200S0yqSJ RbEvgUB1pGOLUSJuXPdwKzfPEgwrMOGAUiI+ynEWql+/BIc5rYkvxxPw+MZX Kzd3LYGzvPaFGtMELCyPbg/VWwLX67+0Hp79io1KrQELCUsgHTXzo04rDl+8 NfG9mrIE97amq1vKxeGR/u7XJelLQOzepCWxLg5D/IY83ucvQbvgI0eZ2lg0 G2Pc3dO4BB/pim56xrE4/Jn/3JXVJejZG2lAfxSD83ynOXIuL0O8ljrNtuYL ClMFggqNl6G+c4BKyfqCe+vapSuvLoPMk7gtQVFf0Pyjg0bPzWUQNWOTO+3w BYt2Rr/mcluGK318i7Kbv6DvCXYhvfhlkHpUP9dhE4Uyd1F0fJa1f3cV4emx SFS7+Pb79OIydG70TBUXjkQjJUO1FbYVmHl7x3VuLALfMKlX169dAfb/ok/o REQgzVs85qjECrzRPvPJgzMC8z4/3eOluQKhJk6qhZ2fUK9O5YhYyAoQ3b04 Y9NCkU9lbUFexArkbckPIvmEYkFs+znTmBW4sxp5atPNUFRwemL5KWUFrP01 PG9LhqKARNa7rSUrcLl80D39fQiW3zk4IMJYAX/H5Z5bbh+RSNjjL3B+Fb5t J6ZJugXj/P35bSkXV4HnBehlXQ3GHx1/43QMV+HW7i0erieDUfyHze93Vquw weuc4XuuYFy6HDvM92QVer/vqur0C8KsGAlVnvhV0DEtVf6dHIh3+Sf/xiet AvWX5KUt7wNR9nG+nsbPVagYFnX57BiIQdpmti/yVuHz9qODchCIDvSwQI6G Vai+414i3xiAcia3xGNaV0H2rtZ0Y04A9hUfSzzVvQpifbGiMZ8DUDekNf/Z 6CpUiekY/bodgIQ1CWd3TK6CimbhOPNiAOLtx42k6VXgUhR9YqMcwLr/aly1 YK6CkbxLwbx4AP7//xLQZnTT+ydXAP4fhlpNbA== "]]}}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, 0}, PlotRange->{{-2, 2}, {-3.3529292903410215`, 3.1401327317154193`}}, PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{3.39799615426895*^9, 3.397997543923109*^9, 3.3979979503468237`*^9, 3.461114706966104*^9, 3.461116316244*^9, 3.461116386479759*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Plot", "[", RowBox[{ RowBox[{"Evaluate", "[", RowBox[{ SubscriptBox["\[PartialD]", RowBox[{"{", RowBox[{"z", ",", "2"}], "}"}]], "rf"}], "]"}], ",", RowBox[{"{", RowBox[{"z", ",", RowBox[{"-", "2"}], ",", "2"}], "}"}]}], "]"}]], "Input"], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwVVnk0lev3l1nJTMZwvEqFMjQQnqdCyVAhlboZ0kVliKhQ5NsgqStUSG4h KSqFBmQfJck8T1HIEOly3vdwBsP5nd8fz9rrs/Zn78/a01qPtneQ83FBAQGB 3/z3/9YQKU3weDyoWPvqwTNaFQri6MRxuDyYrbcMV91ehQqLjFZRMzxg6/cI 0r2qkLGeo/fobx7YVNhVP39UhTbLXu6t7+JBgWebkCbxGeGfjNq0lzy4PJh2 rMikGjlfa8g38eDBnTUlrCcxNSh52zc7A3ce1KD625lPalD73K+xVft5oJsd 96u7sQa5BQsTqvY8+BBjUKu68is6fGhrhsBmHjQP6McPwlfks+5pQoMUD/Bo WOWUTB0623Q5wLdiEcylX7r+N9mAEn6HdzHfLUKT4aWQaO1GlCXmvy22aBGU 6radCHNrRPXIUfFB3iLcUE/VT65sRLRCxQ+tSYvg9fK8zfOMJtSYmCtp5bsI DntmVtwqakbDBanhdV6LsE4lOmBDSzPi1MQPHDyyCM23JVdLTjUjYklQcei+ RXC+HJZ7el0Liji9+cjTrYuwwXXeWPBJC1rlXJOvILMIgz8Cp588bUUWgaWK WUsXIet5tohaXSvaF18QvV5kEYoD319ommxFUR8TXXZzF6CpRaFicUMbajU+ NBc9vADxldrdV8rb0AX5id2/3y1AcI2CQ8lAO2pvXzZR6bUAX6lGgw6vLnTJ p6Qi/8gCsMOKWXC9CxkyjybfObAAzjzuFPNVF4qTf23h77gAUtlnypFQN7J0 PviPjNkCHF+rjvyfdqPcphxTD5kFkAup6E0W7kUuHk4SdssWYF2KYqq/US8S mGL1G4suwPDH2EPJR3uRu5T9NdH5edjCs0sYed+LpB0ZPc/H5mGHyoXz9858 Q+dqLaPnK+bhqOaAbz23D+m6j7mMvp+HDfvexpka9KPW8US95uJ50HOn/5zz 6Ef6EsMt2c/mIZVTsTu/uh8N7own7O/OQ4O/vm1W6ne0+3Pn17SAeTAVuNnc vHoAadCDFDar87HOcuaQ2BCql9t4xnzFPDh7h600XT2EIo5z2yzl5uHWt8n1 dNsh1Ln0SrK1+Dw4ypb9iL86hBL3p8s5M+cgPWDzLZb4TyT0u0omoH4OMm+a mKWrDKPXlvHBwV/moPZur2abxTDyTNzTHPpxDuLOZ1U4eQ6j8o29/0S8m4Py +XoLmbxhFB4zJRWXMwczT4u068xH0Lii6vLsqDmYkOrwvh8wilL9fpzKPTsH xwQKJ/CdUWRbllP/NGQOop7+kDT+MIqyPNcnFPrNwRvDgEuc5WPoSL71sgrX Oag+MLfCsGgMNeEgiR79OfBtzI58LzmOSk5WiUj3c+FNGRrmCEyiLVqBRrld XBir6WlN3zyJSttX/GXZygXs9FvrReAkolueLD75hQtnZRqr1n+fRLXScsdq XvH594yEyz7+QQNFHvRLV7lAX6fo0JU1hbz9JCaVL3FB+HqZqPLgFBpRL1pR GMmFjALxoT/S02jiimjQ92B+vE+ix7k902jmwHONrYe5ENy3Zce7lmm0dJ4b wVzPBZfE6F9//2Sgm4U5uTfWcuHK7T3jw6Ikkjnu1ErT5UJsW9H487UkUmx8 tGafKhfEDrDO6ISSSOvhru7nwlzYHmnb8kKMQhut72707eHAvMvs8DfERJ4J RtM9sRxYPC+LYxmzKKI46WrDBQ7sNXlm+0Cdhe70UeqV5zhwcGT0/vJdLFSr /8YuL5ADw+MzndP/spBJg3l2uDsHliWe39vuwkaiUtZu8sYckD6qsPppPQdp bXo8KWrAgUyj/poTCxy09ajo/7irORAqfaE1wZCLgl/UvBzU4ICc/VXJmdtc 1OPkKFEowQFtzfRfJ/6aQwX/uJU7DLIhWXXg8l2xBVT99q0z7mPDlkGTle+t FtDgD+Vxky42+EgO++wKX0BKG74pqjWw4edcjmnh6AKKbvYIGn/HBmaa7puy +kXkLOtPu5rIhgYzLQ+rAwKYnRQZB4gNL0Yklrn7CuIx9coJW3M2nCrQ+nPh piDuzBV1bDRlw5E9esvJIkFcVHpbtn8NG04of/RrWCKEA4eepHPl2SDjYHTQ 65EQ/mnUXrDxFwsSbUPmr0wL49YyFekPQyxQG0veNKsmguk2Hqet+1nQofXo JH2nCM48NLHRpZUFrMtG248/FMEHLwlAcDkLeEP/Lhd0E8WNzfot+YksMJBq apBvE8Pl7iEmJjdYEEpLOR0oLI7zh9/eKb3CgmcJdaIGm8RxHHu7+9cIFijW jl7sThfHO7QO/Rz1YUGAXOIAx08Cvwu6MqNtxoJ7RYN//6e8DGdLfVdJHZyF 0Ql27/M6KZx0GImZ9c2CRcq7kf9JSOPYvIfMns5ZMLfg3a/bKY29tx9rUquf hfQoHrz6LI1p4b8u//tmFurHx2khx2RwVj85lXtjFn60XWhIC5LFDwvEqks2 zgJz+1DJo2J5nMjxK3LbMAvdNNsIRq88jrGtfchaOwsfd4wdurBEAXsOJESa ac3CPptHNLs9ClhTQc7ow9JZaA7SlnnxRwFnRqpnVP2YgeSULUXXNyrhjN1G IW3XZ6BMX0sjYEoZE8t21zZfnoErb5NzS1VUcH6dN60xegbslppfNbZWwaUO Ka01YTPw0Wr6c2aaCu5xYhlXeM2A0XU7VzdbVazk8oHKM5sBi7rcz8m5avif w3ZhF8eZMJCrkLAtciVWUvduiBxmwgaTogzlpytxZl+E7vkfTEiszFnN6lyJ X/xV0BHawYQhEfmmT8aauN5DepN/JRP+qKWW+U1oYnGfjlmXNCZUr6NZyhZo 4dhTXufW2PH9v7XLtknR8PdbRzNKdzDhxxqfg7a6NGz+6jDd3ooJMsO0HZoW NEzO7BcPNGFCiMvzD9b+NOwVvTv1tQYT3M+XxahW0nD5o53lO5T5+stB42In DStXWQ+0yzEBWX7WzPpNw83iSI8lxgQrV3sXLUUdjJNM3m0lKVhZZIEf++jg jOINffWTFOx3cDcqP6uD2Z0GAkfHKPj1OEUoKl4HF6rp7Yrpo6CDOHV8/IUO 1nys3l1VTUER95ubBqWDI76ozO+vpMAtVG9WQ4jAneNKWmNlFNwSrRQrkCPw LUNZP4lXfL739iSvDQSe2CuVkJ7P15fy8r9kRWDb0GWF63IpEKyRDNJ0IPDi WxG2430KBGJIserjBHbvFVT/cYcCSs73dPVpAr+Z56HgRAqEz1SVmF4gsJzm wrElNyjwUzC/IxlH4MBt3GtJVyg4fvRDhV0SgWuPsfJ1YihQkxVtHrtP4FVX mU3FERSIn725fyiH4O87g7IJo6D1fmKt6XMC99f+t6IriAL7DMW03mICb/nz e6vfCQo8NzFVmsoInCI97sHxoWDz28GU5R8JPG00+r94DwreB8Tg218I7OD6 84maOwVh9JDt++sJnBc+UFfgSsElqZCXrs0EFk7rn7LcQ8E/Uf434tsI7FnW K99kR4FKZ8pXRgeBy/u7NntaU5D1ONvhSheBlQU6DjOsKIgoGBhF3QQ+Q2uN jjWj4ET/X+7qfNxs3ZQtb0pBn6juIUU+X9+3/kuOIQVRjgFXdPn54q5//b1x DQUFOR2nd7YSeDi/WvqLDgXGJ3MehTUSGDd+Mjm4koJoV8nrz74SOGOafmBc mYL+mvCbA58IzJariIyQ59cz0iYi94HArhvL/l0mRUG47bS5WQmBCw+8+5Qh TkHvp2bfvQUElowoGTMQogAcFUr3ZRHYL+P1MlggIWn5EefN9whcVfFy/V42 CVPz763n4gmsNVjgMkiSYK60Ku0ef56RQs/OhvwhodKBay8axJ9n2lFRz18k eDoFbDrgSWDv9fJ3HH+SUCwdKXhvL4HdPn/R2fqdhJKqc6/piMC7D0e91ush wdjv2rYmAwJbMTZsU2on4WCUcM4HVQIbXRtpEmoiIX2Q9+WqKL+/RU5/vlfx 899SmE7p1cGSdkJR9UCCwvHNJU0fdTDv+9ulpaUkSKbMVzQ/1cGjS7X17hTy 9VVFZ2XO6ODehx1vYvNJiKIxqm0O6OCGTfE2wbkklE5J6Oqb6eBib8rbPoME uWTHCjabhvPYTxhb7pEQSHuY3M6/1/u3jsSsSiIh827/OsciGo4t/fxgSRwJ Qx7+ozxfGg7fG6E/FUvCi3eWhesxDZ8YNSzru0CCwK68m2NKNLxPLrX7bSgJ NaST4DfQxtZ5Dr6PA0lwLzvuJ5mkjbdYLZlN8ifh8pH0S6+8tbHWiRPygR4k xD4883E7Twv/V2nhRNjz672xJJpFaOGhA4w+WVsSLL4cu3uuXRN3/Hl8kodJ 2NJyse7+YU1criJ9vXcTCRn5bi7xHivxjdODnxJpJOw1nLCXtlTHF8XuOl/U IEHmYZDnqXw1HPJg9+BJZRJ2lcb0aSipYfevRYu2UiS4nnu61GNABetpXzVb 4DBAPTnU+tbOFVj9rXnNBJMBMSOeppX3lLC045Rb9xQDHu6om5QZVsSz5w6e KRphgIWREjEdrICrmte+9G9hQErcxGftw7LYK6aJ6MxjwIYnD3Rs+oSxW8+/ Vv3ZfDy6S5spIoR3GwcfHM5kwK5zusubVi/BpsMyCWQKAwT2mr7RyOMi8V3O lNQlvp6YTcRhixH0QrqjcuchBiReC71R4sqGbL/H35xcGUCXnXXFs/x/d2XY zP49DPglsurYmjYB+qVQpTU+Ngxg3z+SWZYqQnftOpAYY8SA1PyOe9fOStPn MnuPvpdgQHdLcJrPJlX6NOvZObowP96p4DORrEYf3huZ9IU3DaZtMp9zSHV6 o6B6dcfMNHjWGISmvNekZ/39lwE5OA3bq9cSQa9p9F2GA3NrS6dBjLM+TveW Hv31nuoTNunT4ENJtJ3624D+f9Tpvaw= "]], LineBox[CompressedData[" 1:eJwVV3k8VO8XHorKbiKRZaYoFVHZk3MrpVSKSiJSSYslZCm7siRC2tBC2Slb dnIv2ULZsoSvZTZrzIxtjGV+8/vrfp7Pvee9z3PO8573vMQb981u8eJwOA4P Dvf/Z2C/qvhS/ARqN7up09FeFdsqY/XibtwEeuPRmy7mGVXsnpyd+bYjE6it 3Ae+xpFd2J/DfNH7tSbQBqmWhC6dXZiBTVqD0d4J9Nbl2p28L3ZimxPHdNwl J9D1b5+aDxgqYZUEp22/xsfRJPaioX/FdkxE0XPYP3YcdaNqB+t2bcMeGkpJ vw4bRxMnKFN6OTIYya7UNNt3HK2a5RSdfiaNlaSya3rsx9GC7gzzstNSmO2u gFT1Q+Oo2Fh2iOccHivY8/QeiTyGunducXxZw4cptNuW7f07hvrnuhgoUdZh kV66Gz1+j6Glhi7aokK82K3aidQNZWOo3Wt3oc7/VtCtNmdHVKLGUBb1tONG 3mk0eP1Odc8nY+jY+h9/X5qNooysNX/04Rj6dFYpfnflMNq0mCtrdnMMxaWF +nS2VaF+L/AWXjpjaKDWveieagpMaE+mYarc77XiD3VkT4D54I/5TTvGUMqk ikiXAR3U93q8fC/M5SO+uebH1DyQant+V5NG0Wk/SyzeGoecZL0/LvR8FG3T P2EitU0AKfro8erS41H0EaGvl6AniGw/bkL+6DWK2jRXdPVfEEKWX3AC998Y Ra01j5e89xJBcvbeqDTXHkUxFbOeUUQc2dahJ5ikMor+OfnnzVyFOPL04WbL ceIoGuWaoBOrgUeu19Uu+giNoqe/ft36QX4zInFt18FPIzT0Pf7yne/dEkgQ H+7xRDcN1ZIRwsuBJDKd3dt2sIWGmknGCZ1MlUQaWOHO9cU0dKCxxkzOYQvi HTuVORlBQw8ejz4W+58UQtOpY2kE0VCS3v0Yac2tyIWhD0b+njQUGfqakPZs K6Kico4qdp2GWlxtL51Wk0aG6vIJWlo09J5b8zrOTRnE98KSoY4KDX2QBJPp 6TLI1hHkrt52GvpWscLx57gMcn6lNd9AhIZq7DmVFHl3G4IdmD5qRKOiz/oL 7HMuyyJXMc3bpwaoaKHOt8XiaFmEddYv4nQHFa1lNo/erJdF1O8K/TlXRUV1 5oQ4qJockpS455bFGyra6X5GFz8rh+iruoVbRlJRp0pLaCXII73lZV+vPqai UyHn06+clUfEuk8u2Dpz35u4aU98lkeChG6H3T1BRa/o+jQHIgqIfEJOtoM+ FZ2Xep2ldksBKd+10Op0gIq6LWz40/5UAWEeDdnqJk9FPxw+t473lwJy41Fy pvcCBd116r1zvSIBWeOf/OU7RUEfPHYrVz5IQBJeHWD6kyiotk37iuQRAtKR W6335DcFtdWlRsdbERDnw5uuhdRS0LxcB+rBOwREoPn8k7ByCtqZuuPQdXcC cow21ByRRkGP/jiS4RtBQIYf7KI/f09B34a1Rju/ISC+PPclYmIpqPHFcanl JAJSKLt29ZU/BVXWdLoxWUhAFC/ITr83p6AxkiSKZS8BwYZv4hPPcPkFfOe9 OExArjpna306SkElr8m0zI8SkFfhegGp+yjoo1viM7oLBERd6nFyuiIFFfUi BA6tEJCWlJ8NmTIU9Pe1/L0a64jIesxCLIefG09wW6OLEJGks0kaeStkVPVU pcpVCSKi3z9qUcAko7yDXU98pYlI7x01v8IxMhqsGfvwgjwRcV/w/FQ8SEbX b2I9Im0nImLBVXWlf8goEzVh7NlFRL6I80+UN5HR4um9R/ftJSJUldcH0GIy +lIpk3TvABHZ4586PZZNRm+K27HGNYmIc2tRFv4TGc33+CEnr0tEvhHq7fXf kFFvJZO5jfpEhOXavd0+gowS84hbvhhw+f2gDUYHklFX1Y1bNh0hIkESiwll HmQ0Ri0qgHiMiNTf2nCZfI+MtnRt7VkwJCICJVKbhW3JqNPycvyzE0Tk3Ebl Vq1LZJTjF2MwaEREXl3RibA1JqNRb4w1Zk9y9WadNHoGZDTzSBu56RQRkVux WFeoQUbZ+aK59sZE5MbZu+h/u8nol5+XGA1cnPbxkc8GBTJa/caOM8PFkzPh 2vslyCjld/iuYS5WP5Iwa7mJjB4wKsqP52KP2Kzc4DUS2mAXPSTHxeXkcoec WRI68Dh6yon7vzWN5l29YyQ08TReJILL51hoP5lnkIT+wrk88OTyfdozmbi3 k4Q65+NP7OfqaVFesbrUSEIVLCMzK7l6xb2FtgZ8J6Hu/75USHPzYd4s+yej gIRqHY17dYqbrwRZ1ZiOdBIqRe0/cRaIyJDT4TMr70moYkvwkNJhInJHzKb2 XBgJ5X+v898VHSLy9bpz4CNfEootvztSwq0Xs8BfP9mVhKbgkIh/3Hr6XEos XLAioR+E3V+NqRARLC3XhWBKQgNeZu3J3c31GwtVMT5BQudG314w20lEohKG Uz6ok9CxqMmu/Qpc/w0RXxvykdDMkkKKoyjXL+oHTJ3ZI+hGBPVWE+T65fFR 4biZEVTTLUW+i5/rD8WbIZN/R9CEkAifllUCUn83xSM2ZwRdPrgmHTBBQAQr CvdXJo+gpc5JLU5UAnJeqO4fNW4Evb6Zb82Qu3/+5lBv6T4ZQUlydia5XQRk anan+fDlEfS1aODKE5SAiPtnau3jGUHvGc9Gp0YTkNBi5UaxxWF0LLRiR1M4 AWFPp1+ZnRpG54SLHow8ISCka2m+pb3DqO/+j2njXgSk4EhyzdG8YZTZUxV4 9xoBMeP7YHL52jDaQN1odk+FgOySJv7C+Qyh4s9JMw6BCoiK1oxK6uwAqtxn uH8mXw55YvivrexhDyqt3Hm96J00InkxJ+aKVAeq/XyzfdELSaRgoU60UbEJ Rab0un/9EkdS+6nPx4Or0JUXnz/0uAoi1qayhf03U9BzlnX7yzrWIWUCPl01 HslwcJIx0/2NBZokHvkgCxQU9+lEGSwPww56xZJezU+of9Q3I/e8Dz2V/PeX iUUHhP57v/4L/yJ6/Cx+3XxRD9wXHhlYfrwOi77WE6heMgAm8h3hLyoEsNAp faC1DEDNtSrO178CmP+jz6vvSAMgXFRBqFgUwJxfOnlvEPkPXEVydqQdFMRM Gta5D9r9B3+1YWdxliAmoqZ+OxI/CPtPCRa7vhbCotaenh1zGgICsaLnq6UI FhoxLfjxyRDwJSrM7/UUwfy3Xmy6ED8ERLa3RtQLEcz5gIIRWjsE4bphu1cb RLBd3ywJzcLDMNV5VNjroCi2YZQu/dluGA64Jjsf5BXDGkzkhM/jR6BoiVmn WCeGSewb2eImOwL6cgvE/1rFMFvhVMKrnSPQHeZkF9QnhrGbVTR69UbgrOzB sMgZMUzllL6V7c0RuCTvHZO6VRyLOWaV6VI4Auc2n7qfbC+ODWxX+BZbNQK8 yzyfWPfFsd285MrCxhGQP2u6W/OROFaD3WtjDYzAjLb1ppsR4tj8Ye/FQD4S rN96MCg8Rxyz1Ik//sKcBJN3lP9UzIhjaVLW577ZkoA/6/nD64vi2OwC4UrX PRKETkh5jK+JY5FFGY7SASSoPqW0N1EYj6EHSl9+SifBe/KqQ9xuPCaE9/3w I58ESvJPCo3U8ZgFA9KpFSRwLMgr6dbCY8zchvLdbSSoL5N9F3sMjymq9ozk s0hweyTt6UMrPOYi9G6yk5cMCrXnLGWv47Hvkzbz80JkSJWd1/9gj8fMs2gb 9YhkYF00HtRwxWOfw7PwV/eSYfuALcPYE4/N3HGW9dckg5DuqSp9Hzz2dNeC Ws0pMixGbtlXEIzH/vCX61IukGHN2lBJMxyPEWl+x/htyKB6etA/9jkeK0/h u2zsRoaGhqu8/a/x2Ibgn7aOvmRwCp4PrIvHYxdvPr8XFUqGqAYHwfAPeOwf UdK/I4EM9d4vp+NT8JiOzyfX6hQyePxIte5Ox2OP/6jeysshQ+VUi9RUFh5r US23SCzl6nPW/9X3FY9tCTtxJqqGDC1Nlvs+5+Ex2+EO8GshQ65o/eSRb3gs S/faQcduMtRaWxSXFeExgykvGeMJMvS8+KytXs7Vd3y9iO4cGbpT7Uz3VeKx jo8xPMpr3PgwB4N1VXhMliU7v2UjBVL81uXnoHjM3jRzjA9PgSXpdiv1ajyW l6U5MLeNAp5buhlhNXiMva6mlaxEgZd6njuLfuAxQ2uTHx1qFJA64/u1pBaP RRf3FVfrUqB8k8Kh6Do81id6OyvvGAVa1PiDD9Vz63l39kPiWQrcDD13uJKL nWsCXkRdpsA31ktZ8QY8VrpNKMTvOgWyiqZ6dLmY1yPuoaMDBdgWLCUtLj7z W9HRyoMCX2WHv/Nx8Ztd+deMAyhQhre+nMFdbzjw8AXdcAqcsP+eJcfFe/p+ nlB+SQH7lHGXW1w+7gfN9aQ+UKBb8N7NAC7fqkiSKn86BS6SHfQduHo20pyJ 83kUuN9dX6zM1WsGyxKUcgpcsuCNKsHw2Pu4sI2dtRRoFrTyl+Lmi8bYvFL9 mwL/1BsNT37HY/tPJ83k9VLgvBRP4qkKPOabokJOJFEg9dXj09vK8Fj9aml3 1BQFXAJebfpejMfELh9v8lugwC7Vc/H7CvGYZV77d0ccFd7PJlXcz8djKZts 8q0EqCD69cKBwBw8Nn1jIsVYggr3DfDYtWyufyTXRSorU4FApahFcP3V4hwd IHWACjuaqO+6k7j+adz2gF+fCquHeg7Q33P94q1hSTlHBfsiGdzzV3hsrhM7 23mFCrIu0Cgaw/WP6tkjNTepYK3GN3QtAo91Dt1STvKiwjxPza7bQXhMXpe5 LTqICn452+fkfPHYnVh/Uf8IKrhG8xp+4u6vFcO3C1aJVNCRzMkVcsBjJz7u mDDOpAJpdff9OTs8FrOY+5/uNyoM+MQspdtw/ZDVWCvVQIVjpA21rqZcP6y7 VMrfToUX2t/zw4y5frg6kj3fR4XyxLy6O9x+cEaUHds5TQVjL9GmCA085uG+ 93r0FhqUznh7mEpw9QrMVJoTacCbyDDhCHDjEwu2yqvQ4NWdqW9OPFx+Tbpt X47QQHK89l3EP3HsMfEkNDnSYKFsS1ljjTimWCL47oUXDRb9g4q9SsSxujOt CxaPaeB3RNSZli2ObXxonjP6lgbq+3b+4n0ljkX9tpPj+0GDNbE/W7Wui2Pq dsqPWn7RIGloa+rIBXGsfWnyz8teGnTcWsk+c0Ick1B6ELl9mgb+94e1r+8R xxJ8gpZBehSMOyN0Qrj9PE3541/v+6OAuxrd3ugkhp2suq551HsUVi4+XnO7 KoaNX1B6sSlkFBICHggMGnP7f8CXk3EJo3Bp0xePxZ1iWEFXeUlR3SisbSu/ 5l0qilU97nlN3zYGknZayy+555Xt1neMkp1j4JCtZad3RgTjybE5G7B/DKje 7dg9oghm+Je6XsRoDNRQYyXHJmGsSW3uwV63MXBiRofwSwljPQNiZvaNY8CL jG248E4QM4641eveMQa3c2ukjZ0EsUrdcpsnA2Pgp1p7jmYgiCW9uemQRB8D R6md4eIjAtid88XBfVvHoeELFJwmCmDsH1YlJnfHIamk5KpC7EZMNjtNVltg AkZER9Wf6PNhto/0RzecmQSNUJXjTTPL6B67xq5p80lQDG7798FpGZ01uVjb dX0S8txdDphNstFQRcdPyV6TcJV9fJsDbQn90vrOCpInQcx+k1Ft/yK6pLTc 6snmxguryh6pn0VjO8pKqRlTkGHjstJqP4pe/X48veXbFGy8CNSQOzRUKaP9 9beqKWjTy8kcvEdFS/3G3YL+TAG9zKA11pWMDipLq8rh/sGwaN6Eq+gQuifg 4aeLl/9BoEPU11TXVvTHXu1nP/imAZPRKletaQIbtYvJDNFpQNp3Be3m+Q2s A66VCjLTYBtaWYZ9aAMVvexpn33TQJfEveRv74RXJxUuaFyehvNHtt18JtMH 9rc2yKZmcPFawm26NBlwdxU1OwumAZc9TozrIkOC4xETnu/TMNbjHkmNpsDv Bz6B1u3TQJgwsrmNo4H24xmKJHsaer9dkKhtG4ONiT1fQ0/PQNwGl24e1Wn4 /HmuvvDSDOTlPFtkceP008SHSddmAGk9Ie7hMQMuX0/jwX0GAiM2KC5b0eFv Beq5+H4GUnxLQnxeMyCrNx3uTM+AsunrjBXyLBwfqLV4w5qB4FCh0xZ75oA+ Q9e5wUMHHUF/w+n7c/BsVtgsRpAOr5wzjzey5qCKbRQ8pUAH5Qenb6vxLMDO TZVjKSfpQKE12JZNLQKzYd+9z2fpMFwyS4pQYkFV6KfJRDM6FMLyYKI1C8zX h00nXKVD5J5Z/ufNLAjhmM7FuNBB5vxi4eOPS3C+qtYjyoMO9AmDR7fbl0DW T3sxwpsOHXyyXdHr2FDIlmWHBtNh7X3V7y+32ECZH+X4xtGBN9nYfAdxGfIK LYO8P9BBxDdMX91kGXwf/OJ9+JkOA4X/9IK9l0GCUcD34AsdGvdUf1rXvgzH pvwE72J0cHa8L+risQKfSZulLo1x+dyXIEa9XwXnT6FxZv/oEPjl0EbzqlXQ s12SPs+kw9Qekfj7Q6vQ8d+g7OkVOhjIh00mKKwBz9/M7UfEGFA5fyN0/O0a tLyVTTGQZIChD7njUuEaxJlHK+nLMEAgSyVCpm0N1P+4K2srMqDglPlgHh8H bFthn6oOA8Zs9onuvcsBlaiC3D2HGRDzrjvZPZADrDNK+5WPMuCe7wVV+bcc iG4S0NhxhgFam5clX9dwwDLcr4hgyoC5ANTTrocDO0/SteTNGWCcaN/6dpID VXVdutK2DHCT1DoqtQmHPAs+WbHlFgPAXJh9Co9DzI9V6EvcY8DmX5ur1bfh kGksCUTdGaDR+jdBVgWHyFU6HOd7yoDsN3GPfY1xiIQ0m+38nAHLle7pdFMc IuT5NK83lgH+viNvxK7gkBW11G1fPjBgO/omy9weh8xFHmyXTGYA2yn7kL8j Dpkcrw4NyGAAn1mby0U3HNKXPMgw+8aA86LxmaJ+OKQD55ReWcoAxINIWAjC IT+tl6/urGLAniAHQlQoDsHKw/ExPxggMuh6q/cZDimV2tq41MiARuuSrK4o HJLnnuZ38zcDbnw+9CU0Foekt2sc/NXJgL9T93dOvcYhH/f9GNP6ywCOqEo3 fzwOeRNh+jFpkAEJ4ckPe97hkKixoQsCFAYwMctmm484JOS48yb3cQZoOshG fkjCIb6fV6r+m+bWZ8vMw1efcYg755m70RwDxPsFzhtyL72OV6X35C8x4L8D ra1fU3HIzbL0IRkOAzJZpcXtaTjEcovW6+D1TMCeW/zIS8chZg9qjac3MSF5 58nC0xk4xLjNDGchygQv2H4ymYuPqI4UVUswYbv2w8OlXKzz7L7DXhkmFOF/ mkVwsfroKuG1AhO+3n54Qo6LlQ0ju9cUmSDiXNftzF1f4ZNM5J09TG4fq6kO 5P5/y1rGkQ41JnTKna67wOUnYqW9eEiTCbb71z6OJeMQ/tK6L6l6TJBbf1AI 4epblbh4QxRhgqs786clV/+cK0nq0XEmtOROv9Ll5mfqt8svkjET1K/47P6P mz/KXs7jM+eZsKs35NgJbn77nz7XKb7EhPO/n3/0eINDmo5mJYfbMsH047F1 e2JwSE2izpXZW0zgKD0LyovEIWUr9SLWDkzQ92PsxoXjkIxi8iN1TyZ8bD8x sBqIQ5I2u6kl+DBhlmJo/tUXh8S54KjrgphQrOjVp/wQh4TtkTvfE8EEUUWn vlBnHOIfls135AUTJm0msXt3cYgnRbci6w0T8DIbwuTscIj9R/Od/p+YkFV9 NYBpgUMM8TEriiVMKMPexzghOET/vnxBVCUTmJqm4/26OORgy5fbrGomxBU1 pckfxCHbQ392NLcwYfwrP6+iEg7BsXkz3UhMyHztQUI24BBb8t/ZgFEmuKsq DfdyOIC15Bk8n2LCl/styv/oHAhMtPmTvsCE2MNJrqYdHOAYlnMGBGaB8lGp QymWA2vRruZGB2fhgcQ4ZYifAzaPTn66qDMLZ472sZ7MrUHVDYWp64dn4dsv Oa35kTXw1/wV5Gs0C/GRv6aPVq7Bap/y13yrWdD7XXs01XkNVpSG18kGzwJG oaEmzatwVbTEZHf4LKjCv+sHilehkvU8XitqFmwzcdcZSavg23xIzTRuFi6N LlWv91yFZZe3lqFfZuGwybGSt/KrwK4wyaf/mYUocref3L0VWDT9blOnNAdZ m0oNq2bY8Phv7n8Ge+dAtX9x2buLDSLXP18tVZ8D2YS1gF0VbNjpEmaZfWgO gsMj6GahbLgYZWr+wnQOzmUGszfIsiGviXrW2m8OyhQthMOOL8HdYyKH5//M QbHgoa9HXy9Cv8a1bUrB89DWXqew3nAO7Jc0RUPC50Fs/sJU/v/Pt+9C66hR 83CgyfCys/gcrDcqn0iJnwc++a3rJYZmYd8VyXLF3HkQ+iZiJu0zC4F+zRaK ffMQ8jz4kn4xE3bWa7/Zob7AnW9EvPfoMSDvmcizJ5oLYOnf3y2ygwF656h+ ZL0F+DJDddggxIBzvbG3ko8vwG/c1i+ag3R4OPFPY4fVAnhfP2Aa/oQOzSIp ndtDF+DT/uOvjqRwz+/LYuLbBxZA11DCbhNnCm5kqCUojSxA9OGy4bHeKbi0 ZLJjN20Bvr4+NDldMAV6755rqtMXIMz4Xb3z7SngGxKwNFi/CJ2mk/sNOiYh wZ4v2VJlES5vWybI505ArQdb46XvIuA/JRi2hoxBcb101ZugRZh5pHU+0G4M MqV0jRJCF6FsMt3t8rExiCrzuvIpZhH2zu6o8+IZgyurc365KYtwTeo5X0DA KEwHz9Q3tyxCm9+W1HjuHC79imKxXo4FBevWKz5MoYDOE4Ut7ttZMJKhuN02 hALmbpad5F0s+C0cteJqT4HY821nfxxgAXJ1qA6/mwJCwpVHH59kQeNFsuTx PO49P+SlCs6dBW6BIyLn6klA8TqKW2ligaRCYOqa6Ajw3vb77tDGAnlJ07HB +WEgmJd693exYHe3UgSlfxisNfbNlw+zwC6769K1jGHopktPPFpgwVPHvkqR Y8Pw8y6jc5G4BEdtLrLs1gYhxyopffbhEnRaa4sxtg+ARs8ONr//EsT3dfmY sfuhwiz9jMyTJdhkVOQx1d4PjadyGMjzJfB9sQP/M6gfyNqVh6I+LUGv3lmS HKUPtkr0tio3LcGnpbffVwv+wuMWUZb1NjYcOPxQos+rBzaefGnsSmBDx/4v De2XeiD6x5YPwUps+K1J28E+2APvy+WOZquxYYm0Ruqnd0NJxt5I1jE25M86 dg04dsNUsBHhpSMbvJb/G0p06oLLhwNONn5nw0AnQzsqphMG5mmiNTVseFqW caHYvRNu5Jj0VDSw4cH+s6r8VzrBUUHePredDXqnP52S3d4Jj3m+P3lDZYPW jnc+ssUdkFPHrrITXgb/bXkCCLUd1pt4avJYL4O93bHlEps2eMY/uMK+vgyG Ys4nPI3aQBQ9Xjtnvwxxj1lK5uptIKMueWHMZRl+ZU6OP+JtAzV8oUtr8DKc yaYJxZ9vBctuRvaHL8vwhpjg/2/2F+TbOG3XW1kGW/2LqdZ3muFGEV0uiWcF 2hdemp42aAYJIXdp/g0rcHKy+aKPRDN4lnmLdYivgGqk4M+k6ibQk3i6dnfX CgRudrBbkG+Cmp+f++LNVqAyU5flQW2EPxq9sUsZK6A8abOsmlgPIRFWUddy VkBU4Qo+27cetEiD4XXfVkCxTLbaxrIe4qKpgS+qVoDf82L0nS31YD3BdN79 ZwXoc0FZKzF1QEsUPn2FswIxnipGg9G1wBI4tq7s0irIkfzeWBbUAH5a5FWN 5SoUS6yPor+tgb3tfYot11bhDhg++epXAzZv3U4M3V0FoY07At6dqoEfOz6H 8/mvwrMcB68j5GqI0ucRM0tbhTo7lVPBctWg5IzKTi5w504aHeu1QMHANOLr 3PIqhJy9vX9GBQULjcsGa7g1cNvpeeoEDwrP2NPXxAXXIP8JOlWYVQX0UPlk be6cahNmFRKOq4LKRL/dwUZrIGPgMf29pBLM2g9pycWtweENhS8HTctB4JBg deWHNRDeJmkku7scqlP6Tl9NXoOPiQ/T3uPKQf3ho+vvctZAu+VC5s28MhBR KImUrluDE4vKGWv4MvjpeIAsyVwD9ycXFQUoJYBs3B0jcoYDaXnmTekFRcBy ZcnkmHLgiJrd7vsviiC3vyH17GUOPE0QfuLoUgTyufYVkTc4kFQoVrFBrQhW LqXQBB5x4Mz6Q67FOYVQkqxweEMad05Wr/PJKPkGzsIzDWnZHChF7qmmxX+D nV5VZifyOaDfGu/yx+cbvDK2vhNSyYHXVXy3qpFv4MZIeLmukwPCroe0/rQU wB6re/LJvRy4Lh2WdDavAEZqdTOPDnJgaxxBbv5lAZyP660KGudAw+/3Ur+s CmAjb8Yp4gwHDO1ZyytQAKiD1x9sjgNatBfoFcUC8Ow6cc2WzYEfnIkt5I0F sA+2THC4574Yjr8k7l8+/A/DUxmb "]]}}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, 0}, PlotRange->{{-2, 2}, {-14.700049014057818`, 10.422362532545083`}}, PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{3.3979961542924337`*^9, 3.3979975439569407`*^9, 3.3979979503800097`*^9, 3.461114707003715*^9, 3.461116316344266*^9, 3.461116386514349*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[TextData[{ "Note in the plot above ", StyleBox["Mathematica", FontSlant->"Italic"], " has attempted to show what it thought was the \"interesting\" parts of the \ plot. We can override this with the appropriate Plot[] option." }], "Subsection", CellChangeTimes->{{3.397997440387521*^9, 3.397997496328631*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Plot", "[", RowBox[{ RowBox[{"Evaluate", "[", RowBox[{ SubscriptBox["\[PartialD]", RowBox[{"{", RowBox[{"z", ",", "2"}], "}"}]], "rf"}], "]"}], ",", RowBox[{"{", RowBox[{"z", ",", RowBox[{"-", "2"}], ",", "2"}], "}"}], ",", RowBox[{"PlotRange", "\[Rule]", "All"}]}], "]"}]], "Input", CellChangeTimes->{{3.397997508255364*^9, 3.3979975132532663`*^9}}], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwtW3lYzN/3H1RCWkar9poIRdEunVsSRSEJoUJ2lVKhXZIlKoVPEiLSJilp 1X2XNpVWLRItM9O+zNI6LfOb7/P8/prn9bzvWV7nvO695/4xyqfdbc8uJZFI O5aRSP/73QySQ1wuFxdv/PwyVaUM3GdV781yuHiqdofPWrMyyMzWXsee5OIZ jd9LiVNlsFXd+nTfMBfvKras+PimDPTFQjtq27g43bl5mSKlHBCVWf38ExeH 9jw/k72tAmzv/kzb5sTFTzfkTH8IroIY0z+Wmg5cXAW1j199qIJfcwP96w5z sVrivYH2uiqwv8pHWbuXi78Fa1avVfgBx49tjyfpc3FDt8aDHvwDXDalPPwp zMWoz7tkXLQGrteHup4vXsRGIp/sxkZ+wsNhn7aJvEVcv/mWZ5ByHbxdftE0 JHsRS9aYXvK2r4NasJZ4mbyIw+ViNWJK6kAlU+JbU/QiPvXp5q6P8fVQF5Uk ZHJ+Ee/bPykVkd0AtPRYn5pTi3iTTJCrVmMDzFY96D56YhE3PBZaLzTeAJQl 7l+uHVzEtqHeSR6bGsHXQ/9EyvZFrGU3v3Xph0ZYZ1uVJi66iHu63BgfUprA 2K1A4u3KRfz2YyK/bE0THHyQHrSFfxF/ccsPqB9pAv/SqENWnAVc3yhevKjV DE1bj80F0RbwgxLl9jtFzRCwZshqOG8BX60S35fT/Qt+/Vo1VHJqAf9g12m2 nGqDWy45xWknFvCM95dpfL8NNk84xjw9soBtuZzxic9tcG9NlvFF6wUsnOhV BMvaYYft0UhRwwV8dqMcXExph6T6dzpOoguY7FncEcPXAYecbFZYrlrAm55I xF7U7gDS+PTfrQILmFYacizGsQMchPfeFZifxwZcy4f0/A4QsWb+/tg/j3fK BNz8z+sP3KjeETRfPI8dFbvP13I6Qc2h/1Bf/jzWOph7T0fzLzQNRqk3fJnH 6g4Edc7pL2isoDUmps7j2Nliq7SKv9Cz+wFl77N5/POihsXb2H9gVd7647nr PNYhPWpoWN8N8oS7uL4cD6uunuhd3gu1ZF0vI6l5bHvaW0FnfS/4nuU07yDP 44g/I1sIi15oXXknxlxwHluLFXY9COuFqMNxZNuJORznqh8xLUiFZcNloq61 c/jVo22GcTI0yNrx4OrVyjlc/axDsdmYBs5R+xuulc7hezffFts406BItyPS N28OF83XGosm08AneFz43rs5PJmSrVxjRIdBibWrE/3n8JBwy+kXrn0Qe6Hr StL1OXyGlDmEnvaBReG72hTPOeyf0iW09VsfvHXe8jDzwhz+utn11uzqfjiR Zr6q2G4OVxyZk9qc3Q/1yH3Fb405fL4u0S9faBByLpfxi/zl4K+FQJsljYCB kpt2UhsH91f9borTH4GCX1IndzRxMLIZVspwGwFix+Uvlys5+LpoXdmWfyNQ LUI+U/WZt/4/bb7C0lHoznYiboVxMLFJYl/b23E4fWHFiPQtDua7Xygg3TMO dLlsqUw/Do5PF+wdFWHA0B0B939XefYuUU439jNg8shH+e3HOfhqp8HOvEYG rJzn+E5s4eBDUUED56hMeJT5Lil8Iwffebx/kCbAAtGzNk0qahwc0pw9+HEj CyTq3mw4uJaDlx+Z9lK9xgKlhD3tH/k42MzPojFjORt0zZ/pnv89i+cPTdH+ wAQ4P9Rm/A6ZxYs3xVAIcwp8v0SH/QyYxQe2pVq8lJuGp51suZIbs/gove/F 6j3TUK3x1TLZbRbTBidbGa+nYdtPo0Qfh1m8KurmgV+HZkBA2Nx+zdZZLOIo vj6ldhaU9N6PCGjO4lfaf6suLczCdkeB25z1s/iaSEDTw80cuJpR9alHfhaT 94YJTT7mwG8b6xWZK2axsmLcwKWTc5AeaV+0r2cGx6ztDn22fAEqcnNtUecM NujZppBvsgA9XdKD29pmsIsQzWWPzwJIav2RkP05g6lz73Qy+xYgqMHJfTBv Bk88V/taWLsItmIXVcKiZvBPQyUnkyMkNBPtdw/DDM6gr1jlcH4p6pcrGbIw msFX0pVGAx4tRa1JAtZ1OjP4xH711azspSi74LHY3w0z+JJ06YWfS5Yht94P cZw1M1h0n/bRU2+WIar2r3TdgWkcZeE5f4fBh5oKZUS+9U5j2f4YvSlZfkTs cvIw/zuNW5TeXCZ286NXx4Z0DzVN4+lQbbOzCfzo6C0Svlo0jbm9r1cvtRdA dQ0ajWlR01hTuP7nmublqMjBc9u28Gl8TeWJhxufIEqj5T4tuDONUx/WCGjq CaJ7M2YOP3ynsUR1X2B7nCDaqXSM2ucyjV3JUd2zF1agPPc7k8qG0/i/7J5z Y9KrUKLwP5nYnincNzTT8bFGGEUfh+WGnVPY+Eke/fYKERSSnDDxu3UKGxlz X9TsFkGnzc7Uy9ZO4Th/Lv5cLoJUfAZCX3+dwrWDgyqeZ0TR27+s8aTwKdzV HPDzubsYSkhfXpGjO4UnzHpz3nxZg6JmL2Tba03hdhULX2bHGhRsUZ0wvXEK l+7sPxawRBw5dz/0M1Sawgd3vVGx3C+OFMXJ2t9WTuEGd2XRjFFx9MpPLr6s axLHPDHIvq8rieKttD2b70/iQg0leddxaURZZVXdEDqJ7+TGJBXIyKC0mtMq dUGT2HKlUdhWcxlUsO9JU5X3JC41YZS/ei6DfttMby0+NYm171va2VusRZKH vrGTDSexcU1SeUySLIo8bukdODiBu5PEH5r6KSBJudM//WgTWGtbdrx0igJ6 1emrdrNrAkeVvFs/3aqAMk6mt1xrmcC9/Gvqv29VRLVOInoXSybwqGxs4YUh RSTo0jJ16PkErtikskMsXQmFXDl1Y4Ml7/uwcqGpsAr6F+EYX7BzAndtcDlq oaaCjD4fJ/aaTGBRmspORWMVxJo8LOi2bQJ7Hvr4zfyiCjoVZBWbJT+BHW4W Bq8tUUFFb3YX7ZTmxV+N5QNbVZB0mXn3L/IEhh3lim+HVVCDIKhPL5/AJnZ7 DylJqCIUvS1vO4uNFbKN0XsXVRT/RauzdoSND+9z0C66ropmWjVJjv1sPPD+ yTL/B6ooU1Z9T3AnG7dQrpwdzFBFiu/l2ssq2Dib88denq2KfCtl5g+XsLH9 NfUp+WUU1DooqdRfyMYRAiXL08kUFLFZ7MKKz7z1p82iT2lR0NAB4Ydxabz4 wqcu3jKhIItrqzI3JbHx0iohd8V9FLSYyz9j/YKNScGs5RVnKcihY6lc11M2 ZpPPe1R4UNDXeS5cjWJjPq+yHJ0ACiIrLpxZEs7GF8SNngrdoyA3U87d6Dts fNbxW7FlNAVVn5lOUw1mY1kxgYb+FxS0Lmyi/osvGwtef3S49x2Fp3cme5c3 Gze9iKrW+UhBf6vHpNrc2XhvvMTzji8UZDA6vP3CJTZ21puQqS+koCcig06z Lmysn9vzZHUpBTG0+24/cGLjfNdg9LiSgvbZUT/IOrCxN+FpdriWgpJ9umvS 7dj4lrDnJ7sGCuJ7/nd8x342jvS/GP6gmYKcCzvW1FuysUzrkx/MFgoq+tum 72zOxm/fJ+6700ZB0qSW40wTNvZN7+6DdgryUmkKCjFk40t/TzrI8XCDeX3i Gh027hRQOybBW69xvrby3WY29rd2vaPG83fv/o9h3Q1snP6uxWN3EwXR0ipE KlXZeOvld2+86ygI1X3fdlSBjYPshO6n/qCgeAZxZFCajf9W+Tzq/k5BM+Ri P981PD70Zn7yNwqy0y18vUqYjX0sGEaGORSUeSTve7wgG3d8bzh/IJ2ChHxz +jWXsTG2Fi84+JaCLsRnrcILLBy9+oSt/n8UVFb8acuBGRYen883n3tAQUo9 6Yd6WCxsJLnu+X+8fvotS73uOcrCJfs4ewXcef187ijgPMDCzjauekecKej0 ljVPraks/EXEb+l/ByjIvrxSdfs/Fs4pu5FFAAVZHffPUv/Nwlsv3DWt16Qg E6aWqeQvFj7qz/fu21oK0r5Lr19Wz8JxPdzKMAFefbNtRv+V8fxHiDOedKgi Ictl/rWYhcXP6ufUl6oi7r/clQUFLCz0ZL64IUUV9a1UVn+ayYu/VmBK1EsV dSS0fA1JY2F/FWbFriOq6Kfeg11Xk1i4YHyFmoahKvpymn16bzwLk2Osi2dm VFDyzAemwX8s7KaSEPOLt19fRJwIXhfNwq+e/d1kna2CQgrKXy65x8K9Thf7 uOdVkM8BX43xEBbOyNuRuQWpoEt9mws7A1iYtCf5Ub+kCjpIjm3PvcbCVSyb pX+wMjJP3nf+vRsLOxSevSAUrYwMTJZMRV9k4dATcbc+n1ZGSpcurXFzYuGQ BK9SM64SGisxtqHs5fENXxI0TVFCvUeYnWIWLGxceebZjV+KqGX0/WUuYmGD xsCaF8cVUZGMyP0OPRaOT7M/9MBJAYV79HyPUmHhA5uH9orskEOBy5/ZBsqz sGiCu/OVNFnk+dKq57I0C+8pCO6Ul5RFDj+yFy2EWdjuRspKp24ZpK4cZrgw y8RyMdfMI3ZLIblco6qhCSYOpjvrlPwniUSsx+3bx5k4YWfNiChNAk3dOOqV TWdiY21JCuOqOCpr2PjpYiMTP7k3VK58XAydCq6ntCYzsdaHl6q7OvmQ/e/X Jn8Tebhvj/IE/zJktfXqUdorJt5zQ211/folSIcm+pD1hIlJB3S+yidzQHCP LVv4Fi/e8l2+x43pkCHSUrL7GBNH3b0WnmM3gxMvvP9jY8fEhNiUHZrizd0l 3pOH9zPxAP+6MxuaScSta5IbXHYx8cyLE68KY/kJu7YjUcHaTByb1vLf3esi xNyrDsf8FUzc3nj1uYveWoIxnXqD4OPZ26SXU2JkCdoBv+hKLgPrNIuWv2PJ EXVL5SpaJhnYuUrz2pN8ReLtuZOarB4GNqvYSHHPUiH2bO6e21jAwMtnt9xT i1Annn6jx8ZfYuCsI0R1/cGthOONuED9swxc9wdMQgq2Eurb9rs0OTHwCbnd y8ZVthEFyXlbBA8zcObLY5mVrG3Ev5jwimvAwD/arixReaRLrL+kPWElzsDi o/cCP70xIBiUvt90YQZOS3t0ei/JkMjvisPBKxj4DjpBdJ40JP5LFZR0XRjH e1v7+D5JGBHdwRk7zZvGcXSejCXhs53wnJIS8a0cx5FhfXpfa7YTfFdudXwq GscF/11wM1M0JjYcsfOQ/cDDuuUJjBJjwlOT84rlN4751hme9ZzYQfAlnrmk 7jGOayZlxvqNTYhn0j91Hc+N45FPU/df3TYhCpcl1P44MI7V9+hGJ68Cwubm iljurnFMTfV1H9wHRPeY5xnd7ePYQNnPwuIhEHwduzgJauO4L+9Mf+xSRDzb /6m8de04blxTIXNUERHq5dKPhUTHccl82qL4dkRYZw6vv8EZwzXmilY33RHR rXaY/XF8DL/t9KyTuYcIzxfFxVTaGO7XfW+W+prnL+zx4f31Y/hp36BEZA3P 3zxH6U7ZGC49kcz+24WIAg+XkYL8MWx2dgkhxkbEvv6fuYyMMbze+8RmdX5T ouuE/u1173hYLV5ZUdKU8GxKsDnxfAznUF6mT6iZEnx7Vq6NjhjDaqpJ5A86 psSzb9folbfH8I7bVZU6Zqa8fv/NXLgxhq1ztD3ibUx5/bbw3+Y2httlXemd x0yJfQqZuy+eGcNu555pTJ4x5fVfZs3ro2PYy94/mHbFlPBYcfvfL+sxXDuQ vS/VixcvaCRl5c4xfGLgxWYLP168icPeyGAMa04cmMwN5sW7hJGP5hj+tCr6 DTeUF69LXShdZYw34z49K3/PlPA3XvIxTnoMZ+n4es7eNyV2PP9tfV94DO9x Dx5temBKLE5+HrvON4aFKso8U3mYsH0QeY4ziqU3ckqDeetDPp3WOswYxZVc 6e/2d00Jc6HtjTv7RnFdHJ+/Ji+ewMU1nls7R3HpvfVrBXj5VJYPk5WbRrHV H4HRHl9T4p5KWbZI1Sge+FB7rpTHxyoo3m7x2yh2mPwhm+JqSgh1ek2OZI/i 7YcYJf+dNSXqDKyf/UkZxWYOO7WiTpgSUU/V9Ktfj+KEI2aOT2xNiYOshba8 p6N44bzVmaTdpsSvtAyZZ7dGscHmY8Pzm3n1EbxbEHp9FD/sDNCzUDYljpx1 On7NdRSrXPTrTyKbEh0Koi8PHBvFOyUf78lnISLeb2AH7B/FpjvSE6/2IMKx nfinuWsU/5ISyreqR0TPYw+lVdqjeHC17e1ryYh4O2ZZMrtuFN8TfpBeFYMI l70qpwfkRrFg14iPdSAiBviaE8sFR/FFh5xa9gFEjF3XWR/cPYIPHOiv1O4A IvOXUJVb6wguDZTHr98B4aFNv3CydgRLt4ShZFcgJoaephrljWBJgVJG8pwJ MXdyWnMycgTvqyhrsRE0IQTNC3QuwQiuVWq6GDu8nahOiG45qjuC6c4Sl+MS txMPFy757N40glNkpyjYYTshkiubR5Eawb2OnK6YCiNCcmPA9q6xYfyIKXLe 6akhoSqKzOxeDePzF4vTV0jpExFaPb6XYoaxlrB90JliPWLmQEh28L1hrE5t lHQ4q0fUPi6jfPQcxneHt0zvytIlvNZYLhfYPYwbHjkWb7LUISokbWtzx4ew RXn8h33XtAktfTZfHW0IRzWu/NEjrU3EHXmyg/Z7CC+5Y/AzsViLcI9tzRAt G8KnT849rV2pRUivPf74YiwP33zWwtqnSVySd7GXNR3CzvIv+at61hO/dvBH ausN4Uqp2rgWg/WEiWNS5e5NQ/jskbJ1Sx+vI9a8HjDwkhjCfP/ds+80VyOK lFxlfw4O4gTOtHlgoQohTPHpDowexJ50/VDDFlnihrmUzNO7g/j1EG3EKGMt 0euSdzDNfxAXs7k5ex/IELnvOaVt5wZxVmuyff5eKcJ5fdB7re2DWHQg7Y7P BJnI2njvUi91AHs1S16JKeUnFBud8zf9HsCBn66aqNGWEQ+vGwp61w3gPPOr +iJCS4mzZUPvl+cPYJenXkLNf+extKN1j0bEAJ6h770iuHQMh/Kt0/K5PYAH +L7/jrHtx8zUxUB8YwDfY6s931DUjaunP8nZnhnApKQwv+aGYhzwmHz0usEA Dta7FNlWQoMh/eEkQpO3Xu/59qa0IbD/931yheoApg1rCLeYMEBrk3dM/Gpe PmJrSr+PTEJvWVtdSW8/HgtwIJ6fJKE9M/G7hB714wZjCxsp2ZUo55X3k8Mh /fimUke7ktEqpLLLhvrqej92rCls+XNICM095gZrn+7HJ3V35cZfF0YZm04X 2ev3Y0LDtq0fiSHZJqNVCRr9+NeeX88mCsXQvRtrHAaV+3GER5xBtA4ZnSov m/YT6sd7P36UfqmwBok7rd/2pqcPx5OPXPjWKo5u8ZNChlr7sN5aIbI8SKCx tPaGbbV92FYiVmjPewlUOXPfreJrH+6sKrWVvyyJfKNHUobD+/C2XZE7o/9K oT6D8hmdW32418g9SkZXGh3qerk70KcPo66PcUkPpJGGxn666Kk+fPREY97Y FhnUVf5ZSU+vD1/yrFnGPbMW+R+aNTfQ6MPXEmD4w4e1SLoHXTRS6cP/UQqv /Bhciw7M1382Ee7DOhstEx5elEXE1jGz3X10/OBP1rmMI3K8W1r3vGUnHX8x yJ7+GimHZqwDwvc20XEZq6b/TIUc0roo9Gt/MR0bTAhx8RZ5lPB649mjz+i4 2WufIZktj4w1Pe87PKRj1yIHqFdSQO0F+R9PhNDxyJ0DH45ZKyDR1j1Tzm68 7zae+kNvFdAtofN3L1rQ8TFDv5pgpIgU4jLSLhvT8aTU09QtZxVRwfqpetet dOw5tfxX4z1FxDK7I+2pQMcvd+xftvSnIjp9MzHFd4qG11vGu1Xw5tNFgeGf /iM0fC3Es0B9mxKKe7KVFdhLw/qOjfMSpkqo6VOJ0e06GnY2pEc+P66E3Has cLpTRsOZny7Tt11QQitrDty+W0DDze9Vt5/yUkI7+7pqwpNo2Oy7abJ/uBLq vrae8Siehv+7Wx/p9kwJ+S9xF4+KpmEru0GpuQQl9EVu8cSTQBpW13U9PfxF CVEOyY3F29NwlEQvzaFdiTcQnCG/3sfLL+jbUrtuJXTCLU3vjRkNSzitrZ3s V0JP7hsFvd9MwzfPio0bTikhLamQxA8UGha5rhTcNa+Eat/9qExZS8N1Tp83 6SxTRnzEUdEMAZ69kuciQ1gZJVgn6GTOU7GmZZHGCXFlZPyn/2gWi4qX/mu5 7S+jjNovbAn4MkDFobrRNw4pKCOvKZ83X/9RMd+KmZu9KspINLS4PO8XlffC sWFuXK+M0sUEhgqqqfjr2CazzZuUEV3j6Vb8lYpj1FJ6L21VRhsD348NpFHx GTGXmUFdZeRWn5NKfkPFn72/yysYKqNspYpzxs+o2FfNZkLQWBnNeLSqnAun YuVMZcl0E15+3/v+RQZTsYemoOQKU2V0S3w6Lt+biqO2RAQp71RGFWeXH6Fe ouLaFum2KXNltDJXas1qZyp2nZt7/sBCGe0XVK/XO0zF3IAok3+7ldGTYwbh zlZUHPHMSoe9h8c3dc/uB0DFKaYN1GpLZSQ/f3TZFx0q5nwW+XTOShmdtr6I /26g4vQfh5mVPJz06qbfckUqLnnmwh3n4eHx+/ra4lRMq7u/vpuHtUzj2A4r qHjr7pzPz3nYOzr1U+hiL650ieyS5+ECasHlDHYv7gyJHHHlxVvUqVnfPtCL X+8lC4fz8tkZ9oe65F8v/km6es2Hl++9tuHXm5p7sdtnsoU2j0+t+vzxw1W9 WNHhYUoRj6+Yr5B00Lde7DWaXijDq4d9jdyv5KxerGcW+8SSV684Oc2opg+9 WIr+x8IalFGX64598/G9mFIb2qW2QxldEHUs23+3FwvEG/w9ZqCMPp5yC77p 34uJuRemubx+sbICjRM9evE7Egof5fXT7/DrL1PHe/HL1V5PBjSUEZH06arS wV4cFJO68dMGnt5msIaVRS+e6P/vkO06ZRQR1/3upVYvHogYbtFW5OmvS/mp OX8vTsn9QrsiwtOL1taDbpweLIiw75ZVPL2EmK2OHe/Bup7vFFoEePqgnLkz /LsHx90J96tdUEIVF995R2f04LltizJBQ0poVeEX7aLEHpznllDrSldCB4TK R+mxPfjUGv5Fc97++Z1BP2t4uwf3yrvYfGpRQiPsdfbdR3rwU5Hg+dtYCYkF puhtXtKDL1mxI99HKqGwr+pVotPdeCCsULX6vhLijH04xh7pxhOrc6713Oa9 b52S/PPau7G/9qukwetKKMs0sdQssxuz2oqDLzopIVv+lzZHnLpxJV3Q9pKG Elovo/yT5NeFxR71jl8OVkQaeuMa79mdWL3DXHv8szy6bT7akH+jDcuoN5/K eSGDJOwyoo5JNWH9R2vO5TyWQFlT5SJVlGqMRoxaf/4UQ+//0B8Nhhbj+cdv X7Z5rEInD8p9+XPmHd7vUK6d37QM5a/0ayn1ToRtw8zx1uwZ0O1donDrKAbK ZoMIk7luUGUUzhqV/oCKmx3j8o86sGXi7582R5sgbDSeL11gGu+yJi+bzGkD 99U9nXMhy4hIp7ZgrdxOsFFouv+4cCURNmIMfbWdUOpUzP34eyURePPtwove TlidU6hUOL2ScItx9V0u/Bc8hDNUk7atImwql3n9c/kLv/Vh3dfUVYTwFq3z D8n/QNty1VePp0JExOI96wHXLlBSLmz76CBMhIWPrXp1uwv4XytObvIRJgKl 7aoPPe8CZY6vTsRjYcJtq+JuXNYF9w3vblioFCbWZzso1azuhpFms9XXt4kQ y/sZMm9dumGrR6LbtqWiRKWN/OoD5B7ImWWVU8pFCfHNPZKecj1gLD+l/Lde lHBe/V7pyboeaL3r6nKrQ5Tg1GjotBv1gLXctrsPx0UJDUvj485neuCwgm/U e2kxImrn8ZSrX3pg/xpL98RzYkSnimJ2dHEPLJ1b8mbGXYzYsJRa9KWqBxSs D27QvSlGlBKXGmY6e2Bc/+SKM+FixOQO3+lg/l7gk952636GGOFg8HzXY/te GL6g/qtwXIxIkjq5P9u5FwRSH904NS1GsKeUjrVc6oWwISnvwUUx4mFO8hWZ oF4osVTb9Ho1mcBb82LefOiFeOrC5dgNZEKI7P/y++deUFO4/WW3Fpk4yoQP 9MJeuJKVmduqRyZYnyoLNjT0QkW+3IvonWSCotnW83mmF873JN27cZxMXBV6 Mdy8lAqKZfsd5E6RiW/DjpOTQlR4Lzdp/PIcmbBP7RM0UqbCjJ3VPx0PMvH2 fir5xCYqqHQ6M618yMT4BTe5QF0qCBlaFhv7kYl766e2lFpSYfqh5OasUDLx S6DAkHaICosnzdV075MJ5b6AnQKOVNDc+y8w+hGZKHjHf8TKkwqVlSeW/nlK JpaH/nC+4k8F19DJ4PLnZMLuzKNLEWFUiKi8vOr+SzIxqiwR2BRHhQrfmLHn 78iEgd8bj5J3VPD+/v5k6wcyEfJL82xmBhWKRmqlRlLJRK1mwdHXeTx+bsY/ Oz6SCcm7FvsiSqlQW+2w+W0mmXDuboKAWip8EqkYNs0mE6mGTtuutFKh7OTR r/k5ZMJk5PpaqyEqtD1+q69VwOO3i0/YcIIKre9dDm4uIhNNr6KWqC/y7O9e NllWTCbkZuQmJQVp8C5g2ecMTCbOHUwZ4CfTYFam8bhWCZnITNXtnJClgY9k K/NuKZngLCutp6rRIMbIZ13OdzJhftLme9MWGkjt8/+YW0YmIr92fC0xpEHB CsXtkeVkokPkfGrmThrUbhEI3V7B6+dF9svX1jQ4E7Z/RxEPu5UGPY44QoPs mRg5sUoykScrdCfgFA1Sc0baDHl4qXfsjSuXacA5OqOmx8P76ihXjnvT4KNc 9zd+Hn62/rOTVRAN8sknjyTz/HUH7zhkeJ8GFue+pcrz8MaOHxbqMTQ4927w 6llePl7b7I2kXtKgddWlM0G8fIsf9moKfKCBHfWy8WUeH8E+N+XJTBq4t1Z8 VefxtYU5cVoBDQ4fXRqRS5CJ+Ni7gs1lNKhZdTxQilevPuaa+ZI6GoxqVZnv +UYmtPcmjGe20+CA1JLXloVkwv+dBvV1Lw3ePwnZK5tPJioW8lojRmhwNejJ im9fyYTokV3VAVM0WK+5//nmL2TCIbPx2xUSHeLZCYXun8nEuxWOn4+vpIPI x0NbgzPIxNjpoXdW4nRwNyETTmk8/Ugse6iuTgclOm1LOE9ftW6RQVJb6aBa TX/RmsDTT5XsNQFjOixsb9vKiOfpxVfHgbafDudy1pIePSETE82EdfMxOshd hSqRKJ5+NK1NS8/Q4eQW/i6ncDLR3HVWPeE6HSaXlK4/f4tMKBiyZCNv0SEg Q2VC3p9MXIgOFAkMp4NH5FLzN7z9NW/+39Tx13QwkMj4JHSZTFi8Uh2ySqFD 78IG9wkXMhE1/emvYTYdOv2iZj848vSQWlUmVUmHnb3LyzwO8vSw7HCeQCMd Hut/+3zXiqeHEz1pkx10KHidWX6Bdx7sE+FEN4/Rweq6SHW4Dpnw9tp0KlKy D/LGfb0PivP4rhwvslfug6WvmTbclTz711nSChp98OTCSLbrEl5+1YYN6aZ9 IDFY9iJ8VIwIUd4D1Vf6YCpfMr+qVIyg5K568fh6H0wH3vp6PVeMKN9XP3U0 pA8CTEXc+tLECMEb9hn9//WB1uZ1P5c+ESMi6lzk+b/3waLoL2m9U2KElov6 zdqffZDQJf2+55AY0Tg7/CumvQ+azs6n7bMQI8TVrj1UGeuDQPdu/VMbxYg4 v1tzINMPVs3hBnd453mS+qvfvu79QDoR2VjlKkrsKT6la+bbD/N2IYueJ0SJ wUNqj1fc6Ye4oGsr/1nxzv+g9D2xcf1weEW69/Q6USKrpSA3p7wfFmULnHzz RIjikLanDNkBkHDRm4vh3VfO0i+YuesG4HKanovRPmFiSYajdZD2ANB9G4lL ysKE+W86n/DuAdiCrdSuVK8mqrdMXNvkOQCurMg7AlKribZOUdtzVQOwFA0s P/RiFWEVfrbdq2kAzn8qlbFyXUUUGRY43u4cgADNsv19JquIhGdnLicwBuCK 1Lr7Yj0riQsHvoZ2SA9CZTpk7VVeSXC+H8+1uTgICbm5JxSjBQm5tCQ5/ZVD 0CPSr3XbmJ9wvmncv3zfMOiEaeyqHp/DG12qWsbsh4ES2jD60nUOs23sylpO DUOm19WttsMcHEa58ibx+jCc4OySvdw3i9PrXxyHxGEQPbdid9mfaTyrNlfv w+HZr9aUM61g4+im/Dx68ggkO16drz/Xj0982/WhNnsEBO2AfudCH1ZLbnya XTwCDUYZKf8u0XFewKDnrV8jwMg3qY/2oOJ/6jKa8qRR6BbJHPIQ6cIbg268 sTsyCsGXIz6+96jH3zfpP/jOPwbEWr0CzdJqcNxil8gUGQPUuP7WhiV1MLPV o0hx7Rg4hxXlEy8bQMMobcxv8xgwJEgxAo3N8GSP4iGdI2NwwFT2zIO1HXDu 7HK598k8vBh3niFDBdJFim5z1hiQ0gaVY1uoEHfF1GbJtzEYaPN6SI+kQd01 v+CTjWOgNLTb8TypD/RDxmkSnDFozz4kXtYwAIKv2z6G7R2H2OVXW5dojsHb txMVXw6PQ2bGg+kZnp1xklh3r9M4oHoLMW/vcbj6cS8ZvMYhOHw5Ze44A34X Yp/p+HF45597x+8pE1LbP8CFsXFQP/g0eZ7Khl2dZUefzYxDaJjQ3qMbJ4Ax zjA4vYQBBqsCzcfcJ+ABe7Vt1CoGPHFL2VU1MwHFnN2hI4oMUL+29/yWJVOw bkXRwLs9DKD1VTrnj0wDq3LzpbfWDOjOZfeGq81Acdib4de2DPgCc/9en5wB e767Y3EnGPBwI1vgUc0M3OEenIi6yoC1B6a/hLyahQPFZd4R3gxgDJncPN84 C3IB+tPhvgxo4pdriVzGgS8cOU5YKAMW44vr0s9ygDbZz/WPZcDSRCt7VeU5 yPzicMv3JQOE/e8aa9nMgf+1n0tvvGVA55dRo1DfORBnZvFfS2dA1caSN8sa 52DnSMCqiwQD3K64i1z1noe3vWukDg/w8nEXV46IXwC3N2GxtqMMCE7fLmhf vABGzrMyB1gMGNko/Ny9awGa/v6T2zvPABOFu8Nxiouw5HeKiqkoE4omT4cN /rcItf/JvTORYIK5H7Xp8JdFiLWPVDNey4SVqRrhaxsWQeuXl7o+hQlZlvb/ Mvm54FwPmzUNmDDguFlk00UuaERkfdq4gwlRL1oTvYK5MLNPTVvdjAmX/A9p KvzHhcjqlTqq+5igt2ZO4mkpFxzuB+QoHWTCRBD2cWnjwro9DD0FeyZYvT5X /98wF4rLWwxlnJngKaFnJrWChB6E7imUPMsEsF/NsSSTkP3OQmPxS0xY83NN iZYsCY0RCSDixQSd+t9xchokJF90eRf/PSakPYsN8bciIXEZDsftERPmirw+ MA6SkJDPvcz2aCYE+vc8Ez1GQvNb3sumv2SCCn6Wan+OhCYebmuUSGQCxzVt e+AVEhoeLAkLSmYCv23DVTtPEupI/Me0zWbCAZHnKSIBJNREcv1QlMcE5K2s NHWLhH6cnDuxrpgJG29dVooIIyGi4D456jsThP95nG1/QEJ5UtJVs1VMqDqZ m9oSQUKZXkkBZ+qYcPrt9vSwaBL60Kiz7WczE36PuK8beUpCrzZ/H9D7zQSu iEarwHMSehZ+8FXCPybE3U+80faChCIGug6tpDGBRTjUOL4ioTu73FZ4DTJB 97Lcw5cJJOT/dr747xivP5LjN568JSEv7gOv3RNMEPuz8oA579F75YTMxs+z TPi7tb7+43sSOpP/oWstlwkpM3lfG5NIyEFS72koHwuIR0e/Z34gIdtrZVZj K1iQuG7Pl73JJGTVYEs6KsKC66CyJ5GHTTV7ckrEWaCif2NHHg8bPHC/vGkt C3LIP2zDeVirf0HpqSILPp6/YSHPw+rmD1sXKSwQditvdeP5V3yz9uGFjSze OVZaEsyLL7mYbNq0hQXN8nvLD/HyEz6uP71dlwXO2ouvBhJJSCCvPP29EQvk +bYJIR6/BXG70yKIBR5erB8OPP4THr1SN3exoPbT2BNDXn1G6q7+7LVigdYx vw1/efWjbeKG7DvAgvXtd3Za8Or7594jg6+HWXCg7tEr72ckVG2WmnjfmQUH X+1ctjGKhEpfGxxjn2UBV+3BrcyHJJQ/XyF88jILjAOYG0j3SSj5K/Wmlg8L XjVadC4Ek1DCGs8tcX4sYNPM7T/6k1DsVRJ92S0WfKVc71C/QUJ3N8ofaAtn gQjFtSPMjYQC76bxmz5mwbDjMHHpIgn50AwLU5+xgLx2+V15FxI698p+XeAb FqSWnAhiHSUhc3LUPCWXBflEfJQrIiFjd4WsiCIWsHQPDv4xJKFttennZ0pY EJtTnaSwjYRUwn401dSyYPCjwFKKGgmROEtTPHtZkPLUuxctJyFn6m92UD8L vDTVutu5XCBqM00ejbAg3b1WfZTBheDXjr8+TLEgekeCx8EmLnDNC7idK9lA e6XWpBbNhcVID/vd29hwTXyQ1iXABcebe97YGbBhn1nHzO2JRSg+rThyagcb sn/K6032LEKg7s9b/rvZ8PzhzzGzokVY6FD/+Pk4G4zqyszeuy3CvFr3MrlQ NhC0PmxTswAnRHJtNtxngyaMntr6dQGKZh4914tgg3MK6RQzYQH8a7ZvORjL hsP9syV8Pgswd/U/h7B0Nuyw2Zn7n8ICcAptPjN+sSGC2hogf2kepg9+cyxX m4DUFXnmxeMcCPn96a/JpgnQ/DM959vCAeFTb0/kaU2AXNxi0PpCDqy7etch bfsEhN4PZ9iGccAu4qD944MTsD8llLNcjgOZ1XTrkwETkE85uvrurlm4uFN4 x+SvCfi6avtHs6fT8EfHSVYtdBIaGssV+cwn4Nysrsid+5MgOnlo5PP/7rdv QsvoEZOwtdr8iJvYBPDtLhh693wS+BWk+cS72LD5mEQB5dMkCGUL28r4sSE4 oOYopWMS7jwKPWz8lQXrKvSfqWpN8eYbYd+NRkzIfCD84LbuFDgE/mkVVmWC 0X56ANVoCtLH6ZeXCzFhf3v02cRdU1BHkk7X/ceAG0OjOqrHp8D31NaD928z oEb4XbNK2BS80d71xPQd7/4+Iiqm0jkFhubiLiu4I3A6eUucWs8URO7I7x5o H4HDszaqG/qm4OPT7cNjWSNg9OKRrhZjCu5avahwOz8C/F0rHUz4pqH54LC2 SdMwxJ3jT3TQmIYjsnNKCp+GoMyboxPjPw3kN3Hm9XcG4GuFTPGzW9MwflPv QLDLAKRIGe6OC5uG/OEPnkd2DkBE/vVjb6KmYRNbtfz6kgE4tjAR8OndNDhJ PeIPCuqHsdDxipraaWgIkHz/nDeHyzyhHeWTn4GsZXyUG+9oYHBbUdJLZQZ6 kikqzndoYO/p0ExdPwN1qyPmPc7RIPpAg/X3rTOATnSVkzfQQGh1kVnInhmo sqNK7MrkvfPvxGiQvGbAM7hHeH9FL9Cum5Hmq2dAQjH4/aJIDyw9H/DtcsMM KEgcHPg32Q1K9nm+f1pmYEOrWjjtTzec1Nk8WdA9Ay5pLYedkruhlSEzdHNq Bu5d6SgS3tkNPy4ym6eVZ8HM0W7GZfEfZBxP+MC+MQvNJ/VFmSqdoNOmyhEI nIXnHS1+tpw/UGj7Yd/a27OwYneO90jjH6iyzGCiR7Pg/1iV/OPWH6DqF22P eDML7UbWvfK0DpAWb69Xr56FN7P/fVvI+g0htSIzJ2U5sHXHDfGO620guCfG ykOJA03a6ZWNh9sg8rvky1A1DtTp9qlytrVBfIG8WdoWDsz2Lvb+YbRCbvKm hzM7OfCZfaWl80orjITuVoq5woHrc3+7Xru2wJEdQXuqvnGgs5mpHxHVDJ2T fSKlpRy4l5986KtXM5zOsGkrrOTANW1rTYFjzXBFUeHcp0YOGO19Yymn0gwh S77dfkbngJ7qCz+5r02QUc4pdlk9B4GymSsRvRH4bHx0l5ycg3MuO+dyHRvg gcC/ec6pOTAXdbPw2d0AInhX2cS5OYgNmVGz12qAtVoShwauzsHPlOHBm0sb YAv5y9X60DnYl9Yn9PxAPTi0MtNeps/BM+W4wFH2T/js6KpiND8HzsZ2709e qIHTOQz5hCXz0DgVc3CvSQ2IC3nJCCyfhz3DNXZ+4jXgk+8r2iQ2D5oPV/1I KKkGI/F7ixfXz0PwmssuUwrVUPrjbcdz23koSjGc8aZXwS+d9ujZ5HlQH3ac 03xdAXfCj0c4ZcyDiOIxcpp/Bej1/rtfnj0PlHy5EkeHCoiNpAc/Lp4HAR+7 yAuSFXByiOW24dc8MCZupc5HlUPf69V7j3HnIcpHY/e/yDKYWblzWf7hBZDv DXjmkFUK5DHhJ6UOC/BVnC+C8V8pbGrsoNQ6LcAFML/9MaAUHP/ztOi6uABC gqpBLyxL4bvq2/v8gQvwIOPydVNqCUQYLxG1TVqAchcNy1D5ElBzw3LDU7y5 s49BtB/FYHIw/OPE3ALcsT6vPa6B4ajOEZNF0iJ4rvOxtFiC4QFnzEls1SJ8 vo1HvqQWAyNMIVGfN6c63j1+5z6pGIpeB2wI3b0Ia028x77lFoFt43Y9+dhF 2LH8S8y/gwWwcvuqkqKXi7BaVmK33IYCKHnXsfdE4iK8en0jKZ5UAFo3bp56 kbEI+rWHUs5k5oOwYu5DmfJFsJhWT14k58OPK1upEqxF8LptR1lJywUkuCFK eB8XkjLtqz9k5cCMx8zajINcMN3issH9cQ58+lP53voIF+7Frb595WoOKHw6 V/jwNBcSvogWLt+SA/OH3/WtvMmFfXzbPb5mfIHcRMUdy5N4c7JWuV9ybja4 rR6vTErjQh66pJn0PBvWXS+2tfjMBeP651d/+WXDE6uTF+4UceFpMf/ZEpQN nsy4mGXNXFjtsV3vV20WbDx+SSGxnQunZO4mWGdmQU+ZYYrZPy5IxyrJT8Zk wYHY9uJbg1yorIuX+nk8CwSXJlsqj3PB/NzM3DxkAb58/RcxwQW9vsf4GCUL fFosnJw5XPjOHZKkCmbB//9/B0RJArmxo5/h/wAkgaVp "]]}}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, 0}, PlotRange->{All, All}, PlotRangeClipping->True, PlotRangePadding->{Automatic, Automatic}]], "Output", CellChangeTimes->{3.3979975138554897`*^9, 3.397997543991941*^9, 3.39799795041635*^9, 3.461114707032436*^9, 3.461116316370029*^9, 3.461116386547621*^9}] }, Open ]] }, Open ]] }, Open ]], Cell["\<\ 4 - Lists \ \>", "Section", CellChangeTimes->{3.461115415789877*^9}], Cell[CellGroupData[{ Cell["4.1 - Vectors", "Section"], Cell[CellGroupData[{ Cell["Here's a simple numeric vector.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"v", "=", RowBox[{"{", RowBox[{ "1", ",", "2", ",", "3", ",", "4", ",", "5", ",", "6", ",", "7", ",", "8", ",", "9"}], "}"}]}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{ "1", ",", "2", ",", "3", ",", "4", ",", "5", ",", "6", ",", "7", ",", "8", ",", "9"}], "}"}]], "Output", CellChangeTimes->{3.3979961543213053`*^9, 3.397997544020698*^9, 3.397997950443*^9, 3.4611147070507793`*^9, 3.4611163164169703`*^9, 3.4611163865769176`*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Indexing.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"v", "\[LeftDoubleBracket]", "3", "\[RightDoubleBracket]"}]], "Input"], Cell[BoxData["3"], "Output", CellChangeTimes->{3.3979961543729277`*^9, 3.3979975440555487`*^9, 3.3979979504786243`*^9, 3.461114707101884*^9, 3.461116316474883*^9, 3.461116386684828*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[TextData[{ StyleBox["Mathematica", FontSlant->"Italic"], " interprets this as squaring each element of the matrix and not as \ multiplying the matrix times itself." }], "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ SuperscriptBox["v", "2"]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{ "1", ",", "4", ",", "9", ",", "16", ",", "25", ",", "36", ",", "49", ",", "64", ",", "81"}], "}"}]], "Output", CellChangeTimes->{3.397996154406694*^9, 3.3979975440897408`*^9, 3.397997950511565*^9, 3.461114707155407*^9, 3.46111631652555*^9, 3.461116386785347*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Most \"scalar\" functions are applied this way, elementwise.", \ "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Sin", "[", "v", "]"}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"Sin", "[", "1", "]"}], ",", RowBox[{"Sin", "[", "2", "]"}], ",", RowBox[{"Sin", "[", "3", "]"}], ",", RowBox[{"Sin", "[", "4", "]"}], ",", RowBox[{"Sin", "[", "5", "]"}], ",", RowBox[{"Sin", "[", "6", "]"}], ",", RowBox[{"Sin", "[", "7", "]"}], ",", RowBox[{"Sin", "[", "8", "]"}], ",", RowBox[{"Sin", "[", "9", "]"}]}], "}"}]], "Output", CellChangeTimes->{3.3979961544568787`*^9, 3.397997544122761*^9, 3.397997950546001*^9, 3.461114707200461*^9, 3.461116316570705*^9, 3.461116386886203*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Conforming list work as one might expect.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[{ RowBox[{ RowBox[{"w", "=", SuperscriptBox["v", "2"]}], ";"}], "\[IndentingNewLine]", RowBox[{"v", "+", "w"}]}], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{ "2", ",", "6", ",", "12", ",", "20", ",", "30", ",", "42", ",", "56", ",", "72", ",", "90"}], "}"}]], "Output", CellChangeTimes->{3.397996154507258*^9, 3.397997544155471*^9, 3.39799795057908*^9, 3.4611147073060083`*^9, 3.4611163166249447`*^9, 3.461116386932263*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"w", "-", "v"}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{ "0", ",", "2", ",", "6", ",", "12", ",", "20", ",", "30", ",", "42", ",", "56", ",", "72"}], "}"}]], "Output", CellChangeTimes->{3.397996154556836*^9, 3.397997544190277*^9, 3.397997950612227*^9, 3.461114707406878*^9, 3.461116316671403*^9, 3.4611163869866734`*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"3", " ", "v"}], "+", "10"}], ")"}], "+", "w"}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{ "14", ",", "20", ",", "28", ",", "38", ",", "50", ",", "64", ",", "80", ",", "98", ",", "118"}], "}"}]], "Output", CellChangeTimes->{3.3979961546069393`*^9, 3.397997544223322*^9, 3.397997950645834*^9, 3.461114707507044*^9, 3.4611163167260227`*^9, 3.461116387031993*^9}] }, Open ]] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["4.2 - Matrices", "Section"], Cell[CellGroupData[{ Cell["A matrix is a list of lists.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"q", "=", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"1", ",", "2"}], "}"}], ",", RowBox[{"{", RowBox[{"3", ",", "4"}], "}"}]}], "}"}]}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"1", ",", "2"}], "}"}], ",", RowBox[{"{", RowBox[{"3", ",", "4"}], "}"}]}], "}"}]], "Output", CellChangeTimes->{3.3979961546572447`*^9, 3.397997544256115*^9, 3.397997950679517*^9, 3.461114707607214*^9, 3.4611163167720003`*^9, 3.461116387086966*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["You can display a matrix in a more human readable form.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"MatrixForm", "[", "q", "]"}]], "Input"], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"1", "2"}, {"3", "4"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.397996154708132*^9, 3.397997544290803*^9, 3.3979979507133083`*^9, 3.461114707654255*^9, 3.461116316876649*^9, 3.4611163871335077`*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ There are a number of functions for computing arrays. Here we use a \"pure \ function\". This is very similar to lambda expressions in LISP.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"n", "=", RowBox[{"Array", "[", RowBox[{ RowBox[{ RowBox[{"#1", "-", "#2"}], "&"}], ",", RowBox[{"{", RowBox[{"5", ",", "5"}], "}"}]}], "]"}]}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0", ",", RowBox[{"-", "1"}], ",", RowBox[{"-", "2"}], ",", RowBox[{"-", "3"}], ",", RowBox[{"-", "4"}]}], "}"}], ",", RowBox[{"{", RowBox[{"1", ",", "0", ",", RowBox[{"-", "1"}], ",", RowBox[{"-", "2"}], ",", RowBox[{"-", "3"}]}], "}"}], ",", RowBox[{"{", RowBox[{"2", ",", "1", ",", "0", ",", RowBox[{"-", "1"}], ",", RowBox[{"-", "2"}]}], "}"}], ",", RowBox[{"{", RowBox[{"3", ",", "2", ",", "1", ",", "0", ",", RowBox[{"-", "1"}]}], "}"}], ",", RowBox[{"{", RowBox[{"4", ",", "3", ",", "2", ",", "1", ",", "0"}], "}"}]}], "}"}]], "Output", CellChangeTimes->{3.39799615475803*^9, 3.397997544324448*^9, 3.3979979507476673`*^9, 3.461114707703583*^9, 3.461116317077312*^9, 3.461116387271143*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"MatrixForm", "[", "n", "]"}]], "Input"], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", RowBox[{"-", "1"}], RowBox[{"-", "2"}], RowBox[{"-", "3"}], RowBox[{"-", "4"}]}, {"1", "0", RowBox[{"-", "1"}], RowBox[{"-", "2"}], RowBox[{"-", "3"}]}, {"2", "1", "0", RowBox[{"-", "1"}], RowBox[{"-", "2"}]}, {"3", "2", "1", "0", RowBox[{"-", "1"}]}, {"4", "3", "2", "1", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.3979961548591557`*^9, 3.3979975443570004`*^9, 3.3979979507802067`*^9, 3.461114707758628*^9, 3.461116317237556*^9, 3.461116387372115*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["There are functions that are specialized to matrices.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"MatrixForm", "[", RowBox[{"Transpose", "[", "n", "]"}], "]"}]], "Input"], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "1", "2", "3", "4"}, { RowBox[{"-", "1"}], "0", "1", "2", "3"}, { RowBox[{"-", "2"}], RowBox[{"-", "1"}], "0", "1", "2"}, { RowBox[{"-", "3"}], RowBox[{"-", "2"}], RowBox[{"-", "1"}], "0", "1"}, { RowBox[{"-", "4"}], RowBox[{"-", "3"}], RowBox[{"-", "2"}], RowBox[{"-", "1"}], "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.397996154942546*^9, 3.397997544390565*^9, 3.3979979508137407`*^9, 3.4611147078050957`*^9, 3.461116317294841*^9, 3.461116387472*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"MatrixForm", "[", RowBox[{"n", ".", RowBox[{"Reverse", "[", RowBox[{"IdentityMatrix", "[", "5", "]"}], "]"}]}], "]"}]], "Input"], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ { RowBox[{"-", "4"}], RowBox[{"-", "3"}], RowBox[{"-", "2"}], RowBox[{"-", "1"}], "0"}, { RowBox[{"-", "3"}], RowBox[{"-", "2"}], RowBox[{"-", "1"}], "0", "1"}, { RowBox[{"-", "2"}], RowBox[{"-", "1"}], "0", "1", "2"}, { RowBox[{"-", "1"}], "0", "1", "2", "3"}, {"0", "1", "2", "3", "4"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.397996155042412*^9, 3.397997544424552*^9, 3.397997950847307*^9, 3.461114707854457*^9, 3.461116317344994*^9, 3.4611163875806923`*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Again, a scalar operation is interpreted elementwise.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"MatrixForm", "[", SuperscriptBox["n", "2"], "]"}]], "Input"], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "1", "4", "9", "16"}, {"1", "0", "1", "4", "9"}, {"4", "1", "0", "1", "4"}, {"9", "4", "1", "0", "1"}, {"16", "9", "4", "1", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.3979961550931*^9, 3.3979975444580173`*^9, 3.3979979508808413`*^9, 3.461114708059229*^9, 3.461116317390996*^9, 3.461116387638929*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ The period \".\" is used for matrix multiplication. Here's a reasonable \ notion of squaring a matrix.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"MatrixForm", "[", RowBox[{ RowBox[{"Transpose", "[", "n", "]"}], ".", "n"}], "]"}]], "Input"], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"30", "20", "10", "0", RowBox[{"-", "10"}]}, {"20", "15", "10", "5", "0"}, {"10", "10", "10", "10", "10"}, {"0", "5", "10", "15", "20"}, { RowBox[{"-", "10"}], "0", "10", "20", "30"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.3979961551262207`*^9, 3.3979975444917583`*^9, 3.397997950914507*^9, 3.461114708159484*^9, 3.461116317445603*^9, 3.461116387685348*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["You can also multiply a matrix and vector.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"MatrixForm", "[", RowBox[{"n", ".", RowBox[{"{", RowBox[{"0", ",", "1", ",", "2", ",", "3", ",", "4"}], "}"}]}], "]"}]], "Input"], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", TagBox[GridBox[{ { RowBox[{"-", "30"}]}, { RowBox[{"-", "20"}]}, { RowBox[{"-", "10"}]}, {"0"}, {"10"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.5599999999999999]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], Column], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.397996155176227*^9, 3.397997544524432*^9, 3.39799795094746*^9, 3.461114708260028*^9, 3.461116317491519*^9, 3.461116387788959*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ There are lots of linear algebra tools. We can generate a random positive \ semi-definite matrix by multiplying the transpose of a 100 \[Times] 70 random \ matrix with itself. We can then plot the spectrum of that result with a \ single line of code. Note that the final 30 eigenvalues are zero as expected.\ \ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[{ RowBox[{ RowBox[{"m", "=", RowBox[{ RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"Transpose", "[", "#1", "]"}], ".", "#1"}], "&"}], ")"}], "[", RowBox[{"Table", "[", RowBox[{ RowBox[{"RandomReal", "[", RowBox[{"{", RowBox[{ RowBox[{"-", "1"}], ",", "1"}], "}"}], "]"}], ",", RowBox[{"{", RowBox[{"i", ",", "1", ",", "70"}], "}"}], ",", RowBox[{"{", RowBox[{"j", ",", "1", ",", "100"}], "}"}]}], "]"}], "]"}]}], ";"}], "\n", RowBox[{"ListPlot", "[", RowBox[{ RowBox[{"Chop", "[", RowBox[{"Eigenvalues", "[", "m", "]"}], "]"}], ",", RowBox[{"PlotRange", "\[Rule]", "All"}]}], "]"}]}], "Input"], Cell[BoxData[ GraphicsBox[ {Hue[0.67, 0.6, 0.6], PointBox[CompressedData[" 1:eJxd03tIk1EYx/GXlaFRm5GXaS2ciYWamk6nedlPp3O6qdPpMoxIu5NYeOmG xau1LCspHJlYSVFod6T+qMR6wQq6MDJaRUR3pbCbtZJY0oU9b7Bz4HD4nO95 /jzKynXFKyQcxy3/u/+d7vVF0/nEaLDrykEXKE79ftQwuIjsjY7igvB3k8vI vqiTVtSVTF9I9sP+BkvMdnspWY7qs/KvNbtKyDPRoe4tXcObySE4oMrZrfQT HYqwIXxak1hEDsNQf7R9tb/ocEg7LV3n20zkuZg0cv+0vFZ0BB77OK/0DuST o1B/cyiOHzeSo6F523NkeJnoWCxPdgbJFxvI87HbfLLqcVMeOQ575Q2KJ8Gi 43Hyoct8+EcOWYXNn80SbY3oBBy1bJ3Nt+rIiUiHK601JpushtHx1FIxqiUn 4XfB7a+/WkUnY+mUwBN6megFqO6pONb8JoOcAn9dZFRjv+hUvDYej/BKEJ0G 6/WoiD4ryOnoVEy8c+eQhqzBNds+nzpXOhl42VZ5RqlJc5sH2q0X/EfepFDP QLercM88ZzL1DOhlrhcrdyRRz0SpNLDVaVdTz8Tb7pZLhy4nUtfiUcm5doUt gboW6/2Cw7cXkrkszFBcy5PuUlHPgvepac2njPHUs/FgQ8rBmo1x1LNhe/Wp 1vF+PnUdbIqmdfW+ZF4H2f1hv9jsWOo5eF7bPaHxZTT1HCQ2Bg0EtMyjrser kVnjMu8o6nrc+nbM8awsknoueJNP5pzcudRz0au3daRuCaeeh+BvQYEtz8Oo 5+HGktpLT3eEUjegI/CzdmxQSd2A+HF5fER+CHUjNjm2hcSMzaRuxIAktGq4 i8zlY+/VrC7n5RnU87Fls04l7ZNTL8CH5p+ShtAA6gVI+f5uqqxyOvVC3Eub dbXc7ku9EIMBCxrVIzLqJrR9bOq3Dk52Gyb0SZVxVf4+9N6Enc1JDZKpXm4L JvRkjkrvVktovgjlq2bzvtFjGvd8ES56jR1Xq364zRchcm39Vod11G1B/Mfi KvY0GPOMBcac2dNgzDMWGHMlngZjnrHAmCv1NBjzjAXGnIWZZ8wzFhhzCz0N xjxjgTFX5mkw5hkLjLlF//0HzhM4EA== "]]}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, Automatic}, PlotRangeClipping->True]], "Output", CellChangeTimes->{3.39799615536712*^9, 3.39799754455875*^9, 3.397997950981208*^9, 3.461114708361083*^9, 3.461116317540921*^9, 3.4611163878894587`*^9}] }, Open ]] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["4.3 - More General Arrays", "Section"], Cell[CellGroupData[{ Cell[TextData[{ StyleBox["Mathematica", FontSlant->"Italic"], " is very LISP-like in its handling of lists. For example, we might store \ data on a person's name, address and personal data in a list. The name is \ itself a list containing the first name and surname. The address is a list \ containing the street, city, state and zip. The personal data contains the \ date of birth and height. The date of birth contains the year, month and day. \ The height contains the number of feet and inches." }], "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"a", "=", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\<1400 Smith St.\>\"", ",", "\"\\"", ",", "\"\\"", ",", "11215"}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"1965", ",", "03", ",", "21"}], "}"}], ",", RowBox[{"{", RowBox[{"5", ",", "11"}], "}"}]}], "}"}]}], "}"}]}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"\<\"Joe\"\>", ",", "\<\"Smith\"\>"}], "}"}], ",", RowBox[{"{", RowBox[{"\<\"1400 Smith St.\"\>", ",", "\<\"Brooklyn\"\>", ",", "\<\"NY\"\>", ",", "11215"}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"1965", ",", "3", ",", "21"}], "}"}], ",", RowBox[{"{", RowBox[{"5", ",", "11"}], "}"}]}], "}"}]}], "}"}]], "Output", CellChangeTimes->{3.397996155391244*^9, 3.397997544589188*^9, 3.397997951012148*^9, 3.461114708388406*^9, 3.461116317571237*^9, 3.461116387972744*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["The person's name is the first element.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"a", "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{"\<\"Joe\"\>", ",", "\<\"Smith\"\>"}], "}"}]], "Output", CellChangeTimes->{3.39799615542662*^9, 3.397997544624105*^9, 3.3979979510470753`*^9, 3.461114708441053*^9, 3.46111631762877*^9, 3.461116388018716*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ The person's surname is the first element's second part. This can be reached \ in more than one way.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"a", "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[LeftDoubleBracket]", "2", "\[RightDoubleBracket]"}]], "Input"], Cell[BoxData["\<\"Smith\"\>"], "Output", CellChangeTimes->{3.397996155526655*^9, 3.397997544657165*^9, 3.397997951080387*^9, 3.461114708489848*^9, 3.461116317728698*^9, 3.46111638807329*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"a", "\[LeftDoubleBracket]", RowBox[{"1", ",", "2"}], "\[RightDoubleBracket]"}]], "Input"], Cell[BoxData["\<\"Smith\"\>"], "Output", CellChangeTimes->{3.3979961556271687`*^9, 3.397997544691524*^9, 3.397997951114048*^9, 3.461114708537249*^9, 3.461116317828579*^9, 3.461116388118786*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ The month of birth is the third element's first element's second element.\ \>", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"a", "\[LeftDoubleBracket]", RowBox[{"3", ",", "1", ",", "2"}], "\[RightDoubleBracket]"}]], "Input"], Cell[BoxData["3"], "Output", CellChangeTimes->{3.397996155677059*^9, 3.3979975447240763`*^9, 3.397997951148128*^9, 3.461114708643257*^9, 3.461116317929723*^9, 3.46111638817292*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["There are more complex forms of indexing.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"a", "\[LeftDoubleBracket]", RowBox[{"{", RowBox[{"1", ",", "2"}], "}"}], "\[RightDoubleBracket]"}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"\<\"Joe\"\>", ",", "\<\"Smith\"\>"}], "}"}], ",", RowBox[{"{", RowBox[{"\<\"1400 Smith St.\"\>", ",", "\<\"Brooklyn\"\>", ",", "\<\"NY\"\>", ",", "11215"}], "}"}]}], "}"}]], "Output", CellChangeTimes->{3.397996155710594*^9, 3.397997544758006*^9, 3.3979979511817017`*^9, 3.4611147087448883`*^9, 3.4611163179752407`*^9, 3.461116388219616*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["There are also a number of useful functions.", "Subsection"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"First", "[", "a", "]"}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{"\<\"Joe\"\>", ",", "\<\"Smith\"\>"}], "}"}]], "Output", CellChangeTimes->{3.397996155761002*^9, 3.397997544791452*^9, 3.397997951214855*^9, 3.461114708843936*^9, 3.461116318029735*^9, 3.461116388274125*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Last", "[", "a", "]"}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"1965", ",", "3", ",", "21"}], "}"}], ",", RowBox[{"{", RowBox[{"5", ",", "11"}], "}"}]}], "}"}]], "Output", CellChangeTimes->{3.397996155810967*^9, 3.397997544824844*^9, 3.397997951247901*^9, 3.461114708890937*^9, 3.4611163180760803`*^9, 3.461116388374539*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Prepend", "[", RowBox[{"a", ",", "10"}], "]"}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{"10", ",", RowBox[{"{", RowBox[{"\<\"Joe\"\>", ",", "\<\"Smith\"\>"}], "}"}], ",", RowBox[{"{", RowBox[{"\<\"1400 Smith St.\"\>", ",", "\<\"Brooklyn\"\>", ",", "\<\"NY\"\>", ",", "11215"}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"1965", ",", "3", ",", "21"}], "}"}], ",", RowBox[{"{", RowBox[{"5", ",", "11"}], "}"}]}], "}"}]}], "}"}]], "Output", CellChangeTimes->{3.397996155861508*^9, 3.397997544858726*^9, 3.397997951282222*^9, 3.461114708986726*^9, 3.461116318130158*^9, 3.461116388474289*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Append", "[", RowBox[{"a", ",", "10"}], "]"}]], "Input"], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"\<\"Joe\"\>", ",", "\<\"Smith\"\>"}], "}"}], ",", RowBox[{"{", RowBox[{"\<\"1400 Smith St.\"\>", ",", "\<\"Brooklyn\"\>", ",", "\<\"NY\"\>", ",", "11215"}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"1965", ",", "3", ",", "21"}], "}"}], ",", RowBox[{"{", RowBox[{"5", ",", "11"}], "}"}]}], "}"}], ",", "10"}], "}"}]], "Output",\ CellChangeTimes->{3.397996155911807*^9, 3.397997544891934*^9, 3.397997951314666*^9, 3.461114709045722*^9, 3.461116318256057*^9, 3.4611163886345577`*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Length", "[", "a", "]"}]], "Input"], Cell[BoxData["3"], "Output", CellChangeTimes->{3.397996155961998*^9, 3.3979975449567347`*^9, 3.397997951370475*^9, 3.461114709090579*^9, 3.4611163183138847`*^9, 3.4611163886917133`*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Length", "[", RowBox[{"a", "\[LeftDoubleBracket]", RowBox[{"3", ",", "2"}], "\[RightDoubleBracket]"}], "]"}]], "Input"], Cell[BoxData["2"], "Output", CellChangeTimes->{3.397996156011716*^9, 3.3979975449917173`*^9, 3.3979979513982277`*^9, 3.4611147091460133`*^9, 3.461116318363772*^9, 3.4611163887416487`*^9}] }, Open ]] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["5. Simple Graphics", "Section"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Plot", "[", RowBox[{ RowBox[{"Sin", "[", "x", "]"}], ",", RowBox[{"{", RowBox[{"x", ",", "0", ",", RowBox[{"2", " ", "\[Pi]"}]}], "}"}]}], "]"}]], "Input"], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwlmXk4lN/7x61ZEjPPjJAU7SsppYXuUwlFSokWIWkhSURIKpWQZK9kS4X4 RHZJTpSikKiEopAsYx7rmJksv/O9fn+d63Vd82zv9/u+73Ou0bA/u/e4mIiI SJaoiMj/VnEsW9ux9upmXun/rwt7SjfNibSFu/mNw7PJKnyu5dAUcRbORecZ fCHrH/YpfkHEZbjytMwnmazNhtfLn0TcAbWrSdX7yPrVNmJxfEQirNs+w2J/ eCLsdVZpvheRBWux8JX37SwIH7LOjYnAsF7xqYGaH4ZNFTtlt7rXwY2SV9tl t9eBq4/Z0VzX71B44Y1OSVMjnCk6Hio2/xcsypjzyZjTCl+6d8ik2ndCZ1fT wQrpDgjW4okUHOwGDal00Z3MLji3vDL69RMOCNzfr83gd0Pn8c4rSQUcOOdZ NH+teA9YJok4X3nPgedXZqgVzeiBjYobtqIeDtTb6Sx6Oq8HxKaecvGKftja M3OLrmkPRNTfMsK5/WB/MpktmtADOd67Ba/KuGDW1Oihq9cLwmOnM3+6DEDG zO7au/Z9YMcc9irxHICSqcNzXzj1QUWpz7YHfgMQ4RBZ0+DWB2Eqwd8PhA5A kYnnplH/Plj8KU3sy7MBWGcWvKg7uQ8sNnZZfuAMQPaxhbdCfvdBJuPoVIHT IKjEWJwpP8gBdml3VbTbIKSUiVVNP8oB79OuUed9BsHkR7Oh2SkOGLy7vHR1 8CC4N4gnl3pyoOViwr5nTwfhTo7bE9NIDkj/bUl91D0IHw3k7WZ/5ID9K8s9 YSeGIGA/5vmt7YfVBcqUytkhcOPND3bX6wexrOaGhxeGwHveqmrbbf2QnGRj lRs4BJoP7SzmmvdD+7UTNt/Sh0A3OXzzRmei005P59n0ELxe71vzM4lwY3Rg 2oVhYJze2F8rzoXVdVY7ta8Mw5HOxpx8GS6IVanIFQcOQ6BDISdSgQvJxfF3 Pt4fhp7D4QXrVLnQHv8khvtyGA7k3TqvuJoL9g75j3VERuDXG9dV62y4cGzw y+vXgSPg6O/YJsjigp1PmFVR2Ai05sw+5pPHBWtxU27WvRHw1K3ZMVzEBUvF N6qJaSMQGle0qZb4aLwh2/NS5Qi0Gc2PXNLABc2rt1dslB4FmYnPGi4jXFgm u+ONNmMU1GKdLc7zubA4UuLQUuVReFu0tMltnAvqTy7eVF4yCrdB6esBCRpY VY7tPKNR2Bzme7GSRQOfYXgv9+YorMuIy1yrTcPIfVGtjDuj0PYxN+y1Dg2D 815VJN8dBX9RYf629TT06ugMh6eOQlHd/XergYafB+aZub4fBdFg/e5cUxrK kybFV0rxgMtRPr/lBA2lS4tjFyjwoMrjy4I9jjS8zPHQnq3Eg/6ygulWzjTk veXYTF/MA1py0GuXGw2p3c0vegx5cG9+TsmPSzSErCo8mxLAg8NaZ02uR9Lw YXVjl0coDwZ3JC+XiaFBeu3Yke0xPDDmiNwIuEfD9Q26pp1PyP1ZHUlO8TRc 3Fq4ZF4FD0avKJz5nkJDkUFj4mA1D/a2npim8ZQGnuHYzLIvPJghfYRtn0GD m4mupF0nD5pviIbUZNHgZFH4O158DN7tjGjZUURDmmXjgTPTx2BlzMbplsU0 dB0Y+6THGoNSkVMZh0poOHpEt7Rl3hjUR5zctOs1DQdOFMaqbB0DB92RkMb3 NBh5Fe6LvjIGOnPFo658Je/r0/jBIXAMlp/aGMNqJPr5jm3RCRuDwkg1scTv NGy+qruqIXEMnvu4Kia20LAuuFCOicfg9VJndYPfNJwPabz2690YPLmqciK2 nYac0DFhVu0YrIrOpXo6aFgZqdtj1joGnFLXxLNdNCyIK6wImRiDvHWVfhp9 NNgnNOpZS/LhWtPN4Y0cGpKSxnKXz+DDzIHuatN+GmY/0U3+MJsPOVtD/jtI 03Ao1UoldgEf1J3NcywGaLj39EKY4wo+bOx8pWo8SPKVWegnrceHptspysrD NOx53jjauI0PA5p5GSOEQ3PGnFNN+DA8ceth1QgNsoW6hw0P86FWUJdnzaPB +IVVveIxPkyf1fp51hgNAS8v7PjjRN5n9+iez4TFXhfqXvfhg/nX6POLBTSk z6BSJvz5ECLxe88XwnsPO7Mv3OLDz7RGHS8hDcK0d/4DkXwIrzRRZ/2j4RFP fdAxjg9aXCbrCWFTg4u2HY/5ELBEdIbmOMl/+Nca62d8yJcdnpFJ2GBlcPru Uj5MpJkviJyggePTqVz1jg9mGn4b+ISjKjff3PqJD+MvVuy3mKRBb+b90ZeN fLCK23YhlfCfY8PH1v7iwzrPjMRBwqHZu+ozu/kw5b27dvUU8XMqFS0Z5MMz GzXx04RbTcWyHgr40GAruTmWcECstZqqmACe6Av9XhPW7C64FSUrgDG9wYqf hL+tZQpnsASwbXE7NUTY79rpUzdVBZC+8/3xCcKLP1d8E1kggBHv+NIpwnVz 1Lf7rBBAtJyDmoCwl7NP7rCOADJaZ/v3EFYv/qJxRl8A43uqOXWEK6W0wrq2 C4Bn7H0ki7Dr/qBJWzMBZK1a+eU6YeVHHc5NlgJo4w3vMSf8ekC/Za8tYfkf DWzCjpvv7ag+KYBHrjzrT+T7qZChwu2uAnhxz5JzhXBxk+ki7CWAzYGU/1LC 9otTo9ZfFcCPt1vnVBF9ZT1ExXOCBFB+QKzMlnBO+eFzyyMEkFDv4tRP/DnE KGh7HCuAO9vTZrkRFrNhmM15JID5Oz5/7if+ZmQ4ldzNEICJQD7MjrCF4O0y Zp4AdvEC938g+Rg3nHs/uEQArxwc5y0nbNre4HGpRgDnX1ENn/kkD1qanbyv 5Ptv6BUqEY67FLjXtVUAyDf0kQXJY7+yvtYxWgCXDwdG544Sf3endBszhbDy ocsfySHib7yIVbmKEDLG7JkSpD5aew9VbJonhKM5F40EpJ60AhSSNdcIgdYx bqgk9VZX4nWYvV8ItSaqbuPdxB/ZhqrbR4SgLk2P5fwl/hxYuV7qhBC09K8H 25F6dh3+zRZ6CkHc4lxjEql35jKT2rZ7QtgdZala8JP0J6lEZmWSEPrOuGmh H0TvP0MWz9OEkBDpe7G8mQaHh7EtV4qEkBNeX5JF+k+Rcm+3xnchHD6qRa/4 TMNtnv4K2V9CYFgeqvH6RPz5En526K8QFsdEdJXU0DA9bAOvfEwI3KDWjys/ 0GAjFSTuoPQPOOd0XXLKaVjz54eh6dx/4JlOab8m/VD6zapgncX/oDYhcvW7 UuKn33eGpO4/aBnxHC4m/VSSt3hOiuU/kBos9THKoeG/znfru6P/QZHwuGJ/ Ig1Xy2f51sX/g89+mSKXSX+3THLBRU/+QY3shjUyD2gQsZ5pGJT/D8pPXHKd RubDvobj+5Z9/QcfB+SOPL5F+kGZhIszexySeOWhLh6k3yRufTQQMQ7vAuU2 bdlGw83Kup/+D8ahSNVjYRSZb+8GbZQVH5Pfb1+l3LqJhu0GF29vyB+HuVc7 ZlmR+bi1J/eCf+M4/LyenftzAan/NQtN2bMnICNoTHMzmber3kuN6j6ZgKsx HTkWJVw4OxCt9eHZBKDnN3qCC7mQqbLAybpgAt5aXd9dlMOFlc6o7cr7CZCR nZ/FTyPzneldVdUzAQW3t3cqRHNhweHe+MOak+CccdTx/mkuKNHVhpcLJ6Fc st13PsWFe+H4aA+ehNCrxko2clxQ1snx3Vc5CS9Tl9qHTSPsfTdncdMknO6o Uvgh7IdZ4vZz6oSTsFJLgq/Q0Q9zlMZG1TdPwWj78Nm+7H5YBBpPyt9OQYrX GPPSjn6IfV8sKu4ogg6zfy10t+PAwK/n3rSzCLJ/HpemSfZ/RsKUwRZXEXQi PfZghzkHRlZE/s7zEkEL1I7uWr+NA2YRp8tOBIogReekZ2ELOSB2ZPbVD6ki qLg+oH28uw9OD10SiegSQb6NIiJ1ZH+qp7ZlSt1BFNXdDDxYc6gXjK2/uNmf EkXf2950aZj3gsWDk12PnEXRIcEN0bNGvXBaJbRmkYcoavsX2zu0phdiFVse rAgQRZ+Kjn9/KNcL/Bke69eniSLfUsOohJIeyJtMO7ebI4puRhgGLlbqgeW/ GJ2XzouhQ7rDnWqFf+FZGnUq300cyfGkPrm2dYJv65GG5QES6Ofe40uvufyG c3rrTRKiJJFvnVHUGr8WECYa2PeFTUPNXaYPKzW/QJ1rrN2FZCmkJue9xO3L e9hgNG/y50Np9Pmb3IlJkedQtX1DGoqTQRvqbOZkhWTiJ9n3N7RnyKLPLHW9 JxcqseL8FU42T6cji8U5Cwz/NOB6UWl/lRI5JPEh8alOcwvOl3ZPqauagaK8 V+W/ifmNVygynOwr5NGKsEcHrF92YjqUx17zTQGtus3sWFT1F3MG5pSvbFZA holviqsa/uLevUZnl7QqoLf0vmcnWv/izpn3qtS6CI/f+R46/Bc3J2zwk+Yp oI0RppN31bqxvVXpMFeWgdSX2W+95dqNd50UPfJ2GQNti3do7GD24Pk3b2qd dWIg6SpL/4Ytvdjtm46fvTMDrSsQj325sxe/Xthevd+FgZa0njgXv68X27zV c9JzY6DwllZLs+O9OFZ06LGMDwOd7vtc5hDYiylf61mPgxjIyyzM0L6mF4ud 05ZsSmMgx6PjdeF7+7D569Z91ekMtDJI8c+zQ304SSEkGf/HQL4xel5v7Pvw 5swuSHnOQD+yJdktbn3Yp++Bj3sRAwX9ZXFeRvThoePTBmZUMlDdp+ILWz/3 4fZDLc1b/zKQ9vYtsy13cLCcvlWqXQ8DJbxq+aRnzsFr5za4+/UxEFdeIUb1 IAcHdnyQK6YZaJalm3nZKQ7Wci7erM1nIKfCaUbPbnKw76X7yXNlmCjdLtJE 8i0HKyZZOY0vY6K90iEaqev68Wb/hnWzVjJRycl6+RD9fnzKYbf4ei0mMlwS XH3KoB+XLDGKc1/DRHq9Sdkye/uxffa62t5NTPSmTru/27kfZ5Yrrm4yZSIN 9eSbqg/7sfGfBkG+CxN1zjfNNhDn4kvLQy9muTKRUUnU8EtpLs45ZzyR5sZE l8LVO5fLc7HqZIlInCcTvW8r+DCqzMXcmSlS/n5MNBmmE6StycVRxl6KZqFM VL9S+j8jKy7+lTFbu+sZE9UkKnD+POJixaFvOW1ZTLT6uWGgaxoX71wfrtOU zUSp579mDP/HxblvJddX5zNR5p/o6x35XBzwk7s55xUTLWeY2Di+J0dZhTJT vxomEuRWVcj1cLGX2/FTM/uZaF+yvW3GIhoHmnBPbqSZaEvVqmkRy2h8d8GF kzaDTHSSI/b6rCaNC74FnkgZZaLgPqNTjHU0Ht74n8O6SSbqcB917jKgsYv4 sJ2lAoVCHE2tO+1ofOnHRTsfJoUWjc3M2+RA45B8CbsEFoUM/x6VDz5J44yT M227lChE8bLfTXehcffHDUc81SnUb3JoxSMfGh+LunIwRptCDDmQ1IigsdsZ mYPFayjUc1dRpTuKxv6GEQda11Jo/8RFjZS7NE7iP7JatJFCkbnf5KTiadxq /X5/wVYKzQ9xXG+dSuP+tXv2NxtQKNF/b0vzUxqPyzdZTBpSaNPFj857/qOx alnvPkMTCp39lnBAPZvGBxbK7/22j0Ka7gVoXzGNT03GmAv3Uyg4hG8QVELj C41zzeccoNBSyXOrC0ppHBOkveeENYWWPw96N1JO44Z+CzOeA4UcZULKJT7S 2Kzgwc6ZnhTaqDp862wTud+w/ZSiF4W2yj3Yw2+m8bVVy/IUfSikN9qi7v2D xkXpRWqKfhRyM01vt2+j8bzErwOsAAotfOL++1snjfV+xD1hBRK9xOv4c7to bKXicIgVTCG7rePKR/8SvSOH3lChFHp5/4bbxx4a824q3GXGUIiFWf6IS2NG xTcT5j1y/ab7jTtoGi8XSxBhxhI/ah6vMRmgsZ3vCidGAoUeq+yeXDtEY58X w3MYSRTyMHR3njtM42hecYNCMmGnJb9ERmj8wXWHvkIKhRIcTjRmjNK48xlj SD6NQhZx1w958Gg81duYIp9O9A21aV83RmOd4ycY8pkUmm0eMPWQT/RJXlkx 4zl5vod3lImAxo5tI94zcihkJHRb2U844eC1DrkCCp181eSk/I/oE7PznlwR hYa3jMsnE25oYO6SK6YQO/9OwfxxGnMZTaJyJRRyKXA/Gk9Y2iypYHophdZ2 BikoTBA9b508Pf01hTa35L/2Iqxfqak+vZxC4ro/zzcTPiDJ+yL7lkKp4X+X 60yS/G19FST7jkIT10r/3CAccvn6ZtlKCkU82ZVM9ts4tcRkWOYDhf4L8T/K nKJxuYBKk6mm0N5a0/kmhH+sa7aWqaXQnsn4v76Eee4PmTJ1FNqQ5ZiZQpiZ feqddD2FzriRaUR4BVfrovQXCil9N97WTthw+ZiW9DcKrTm7izlC2O5UaafU dwqFVWT+JucPfPHJjftSzRSqPemUR84fOKbd1EzqB8nD78tB5PyBn89li0u1 kvosGbDrI/zBuqVw2i/yPP/XG78S/nM/2XlaO8lD8cjMQsIijY4a0zopVMWJ Gg0jPIut/U2yi1wfn/7NnrCOOT9YsptCt823FK8gvDsUg2Qvuf7WiSQu0cPp Y8CIBIfoU7s0KI3wdWmzpxJcCi2OjTt/iHDCdkUbiQEKMV3q7SUIv/D/QUkM Uehc5a99KUR/7riTrziPQqFifzfXE/8OHjBZKhij0Gl7kXgbwm9zl3/jCijU WFGyq4P4f9+Jo9U8QSGR+pjUr0IaS7yr/vFpiuRnzeiebYRdNJ4FVYiykIaM 166nJE8G3890PJdkIZ/3V5ANyV/WGrOwFCkWslhoqfuM5HPWHU39OBkWsmvn Xh0h+R3YTsfcnMFC01PVWM4k3w9yXXfaKBLOebVsapDG0xTMx/YpsdBBX+5b BmFXJ+3HO1RYaOynV94sUk+GGkMTOmos9OxiYJQSqb+hUPfs6QtZyLPA1zaf 1OeR3n02ootZ6D3XMiOom8aV23Wmjy1hoYTiGDNLUs/x4yMO7SvI9cKzcd9J /Rs7eSq/0GGhrBvyNldJf8itsKzIXMdC/Z23zyi30niOhq7b4/UsFOo18i2V 9JPhxrGPd/RYSH+ouSir6X/6e18+YcBChT0r3xxroLHMw4MrrA1ZqPujMOPV ZxqfH9/QZG7MQldEUwYV6mi8I1e4Wt+UhUxq10bHV9N4RN23i2XBQrNseMZH Kmhs62sdKWPJQnkeDgt83pB8NeqhKSui5x41uztlNE4Mnbjfe5iFdu02Fia/ ovHOcb9dZcdYaMmf7+9c8mn8sPFqnos7C8XMuqN9/xGNuypWtYV6sJAQFRRK PyT1kNcmk3WBhWaAwe6zCWQehenb0hdZ6AYnIF/jPo2rdwhkXK+z0AabBnfV UBrzX7ranosm9zfXdDzmSePN6XODw++ykFbNoWMG7qTf3qvNy75P/K1rvjjb lcbyHitkh+JZyCvo1PQcRxrP1+zOc0thoUt9pqVu1qQ/JdnKni9kIba4qLEJ onFUqPzaqBcs1GxWfzdBj8ZNvq9s816yUE3jccWe9TR2OKiaP4JZaCpzWsIx bRp7U422HpUsFLBaXZzWoPHj62b5nk0s1MWxZduJ0lh4St/O+x8L1dcER+S+ IPP95TLkMcFCb7LTryIyzx/MUFY/N8VCzmckOyqec7FezlDbKXE2avD7KlKc ysV+/1JtD0xnI84lzUqjaC4WD6VsdVXZ6FmWSYDQhYvlcv8eGd3IRmX/7V9Y MZuLrSW/6g/qsdG26jnJ9kpc/MyqXK1/MxvVUPxCPpOLzcbjfnZuZaPYiFV5 8lJcHLZ975EvO9koNcGD3T7Qj9nfS6xzD7FRd+XCtMVv+rHaRPjhcz6E/8aq rLcn+6P2XS/u+rJRe22ulsPhfnz+vYxSqR8bFQzUf7hp0Y8/hF+tl73GRvIN rHv5hv3Yc9G5HY9vsdHPa0XvMpb147rde3Qb49joqoS5vsQgB/sny7P0MRs1 FynuP3+Bg7uMbn2QllBE1rb9az6d7sP2JnbqC6cpois7fLSVjvXhn2ZrPbdI K6Jxwe9QK7Jf/bK/TcNHThF1aYm5lxn34fJja7z72IrojxJauHFhH46/3LK4 dqEiMh9xsSn70YstCpffiDRSRA/rDo+tN+zFbxZVw5xbiqjx2FsNe/Ee7CSd IqEhMxN5bpfdWWPbhQuTFui/CZ+JpF6tdCrR6MC1m/Qq9jKVUIBbccWbu22Y 7/Rgn12oEtIqtCh+H/kdV+5J3D9jpjKaaP3+Xj20Dttv0EtNjVJGcZ7WaWpx Zdixv2b0R4wyKpG001SzLMOuD20NqPvKqLrn42klRhm+LHPtt2+8MmJN22Q/ fu01jmuqUt2booyCz79dpNhYir95W4WNFyqjhu6ZntHuxXhnsZu3eYsyqndO mPErJRubu0hUBvxURhu/N7a0qWTjA/NiZpa0KaP51766N4c8xyeDX+Qu6lRG x/ypQ1f/ZOLrh0S4/zjK6L2P5kCGbzouFd62T5lURh5PoxdFPn2IKzLnZreI qCCe01lT9p0kXG2fPcUQV0EtYXnV6j0JuOXDl7iLUiros9H9+FG/WMyPVW3c w1BBv7clFm9LD8dTZs8WBlAqyGHRzk2M3lA8TRzOv2SroNXD9tF3Lgdj9ml7 5iIVFVT39e/JC+lXsercEdvDqipoAdU2r+KyD57XcCMzTE0FvUjxsI9MP4eX 3VSaqJirgtbHnblclH4Ua296avJPQwUt1L79v/+3Sv8Ph2LZdw== "]]}}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, 0}, PlotRange-> NCache[{{0, 2 Pi}, {-0.9999998592131705, 0.9999998782112116}}, {{ 0, 6.283185307179586}, {-0.9999998592131705, 0.9999998782112116}}], PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{3.3979961560993967`*^9, 3.397997545027596*^9, 3.397997951438455*^9, 3.46111470919293*^9, 3.4611163184126167`*^9, 3.461116388789583*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"ListPlot", "[", RowBox[{"Table", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"x", " ", "\[Pi]"}], ",", RowBox[{"Sin", "[", RowBox[{"x", " ", "\[Pi]"}], "]"}]}], "}"}], ",", RowBox[{"{", RowBox[{"x", ",", "0", ",", "2", ",", "0.25`"}], "}"}]}], "]"}], "]"}]], "Input"], Cell[BoxData[ GraphicsBox[ {Hue[0.67, 0.6, 0.6], PointBox[{{0., 0.}, {0.7853981633974483, 0.7071067811865475}, { 1.5707963267948966`, 1.}, {2.356194490192345, 0.7071067811865476}, { 3.141592653589793, 1.2246467991473532`*^-16}, { 3.9269908169872414`, -0.7071067811865475}, {4.71238898038469, -1.}, { 5.497787143782138, -0.7071067811865477}, { 6.283185307179586, -2.4492935982947064`*^-16}}]}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, PlotRange->Automatic, PlotRangeClipping->True]], "Output", CellChangeTimes->{3.397996156148087*^9, 3.397997545057892*^9, 3.3979979514635763`*^9, 3.461114709279867*^9, 3.4611163184411*^9, 3.461116388875846*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"ListPlot", "[", RowBox[{ RowBox[{"Table", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"x", " ", "\[Pi]"}], ",", RowBox[{"Sin", "[", RowBox[{"x", " ", "\[Pi]"}], "]"}]}], "}"}], ",", RowBox[{"{", RowBox[{"x", ",", "0", ",", "2", ",", "0.25`"}], "}"}]}], "]"}], ",", RowBox[{"Joined", "\[Rule]", "True"}]}], "]"}]], "Input"], Cell[BoxData[ GraphicsBox[ {Hue[0.67, 0.6, 0.6], LineBox[{{0., 0.}, {0.7853981633974483, 0.7071067811865475}, { 1.5707963267948966`, 1.}, {2.356194490192345, 0.7071067811865476}, { 3.141592653589793, 1.2246467991473532`*^-16}, { 3.9269908169872414`, -0.7071067811865475}, {4.71238898038469, -1.}, { 5.497787143782138, -0.7071067811865477}, { 6.283185307179586, -2.4492935982947064`*^-16}}]}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, PlotRange->Automatic, PlotRangeClipping->True]], "Output", CellChangeTimes->{3.397996156180436*^9, 3.397997545090662*^9, 3.39799795148626*^9, 3.461114709363072*^9, 3.4611163184742403`*^9, 3.461116388959148*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Plot3D", "[", RowBox[{ RowBox[{ RowBox[{"Sin", "[", "x", "]"}], " ", RowBox[{"Cos", "[", "y", "]"}]}], ",", RowBox[{"{", RowBox[{"x", ",", RowBox[{"-", "\[Pi]"}], ",", "\[Pi]"}], "}"}], ",", RowBox[{"{", RowBox[{"y", ",", RowBox[{"-", "\[Pi]"}], ",", "\[Pi]"}], "}"}]}], "]"}]], "Input"], Cell[BoxData[ Graphics3DBox[GraphicsComplex3DBox[CompressedData[" 1:eJx1XXecVTXTXsrSqx8qqHRBepNXUJfNAZUmICK9KEhvUkWKSBFQkCZFkCJN RAQVG6yAm0sHWXoRcKVtE7gsrLDS4Tu5k2funpHDP/fHQ0gmk8m0THJKvj2g RffMERERE3JERGRxf3fNyF74TsmcAfyebLDi62bFvoielLfi839MiWQ8X4t5 W7Zs/1O98kmz9j9dy8S4U6Ll2W1ZL6gcjyT+W+zXuxr44qJ3Sy+qcUVRP+mM bx75ZupCF6f2Qcbr6CKNw/3EM962S9/8AXfcCPsHOP5u2yvRXtv+lehfW3qU oMe2v6sE/drO1xHztf1EOoI/oNsR/IzF38HXtEM1TsWWWsh8xu+oD6YP2571 JPMZ+NINq6p9MCWZ+Qy85qv9x33/ZJD5DPxIsNjrBgefgV+61HT1aO4nnnH9 bZcrW91xwVfgks+iPfNZ9M98FvQwnwX9zGcxX+az4A/zWfCT+Qz+le0/Y9er u2Yxn4EfzZX4elTzQ8xn/PY4GohZWOMM8xn48Um/d/p0SALzGXj3zR2jDA4+ A7+Yo9+9BdxPPOPjhlV76UV3XPAVuOSzaM98Fv0znwU9zGdBP/NZzJf5LPjD fBb8ZD6DT7mSP1t/YP9I5jPwyY0WbeyyIYbpAP5hizfa7nh5J/MZv9P7q06x 2/cyn4GfGzGpssHBZ+AJVRYd3s79xDO+tl/52p3dcb18vasln0V75rPon/ks 6GE+C/qZz2K+zGfBH+az4KfQG+k6+/J/Kxxxca/eSNcJU+5e6Ob249Ub6Xr4 Y9ef2+2O69Ub6Tq9WfO0LS6dXr2Rrku1MnoM8woyvvhc1A+7uJ94xp9/f3LP ru64Xr2R/h8+i/ZYRyX6V169wfSAv0rQr7x6g+ervHqD+aO8eoP5Ge2V56Bu k/ev4U1cOffKc1A37tMx3ewLrzwH9bBrzc8scveRV57d9usHPzrL3XdeeQ7q Ly5mvR7ep0H+LVjS2J0z2ivPQX1779fJRg94+Rr0kWdur7zyzP0rrzwzPcor z0y/8sozz1d55Zn5o7zyzPwUfI7XxSvOj9Ku3vbyOV43ml//kNHzXj7H60NJ Xccbu+Dlc7xe0LxdirEjXj7H6ylpuTaE7U6Q8eqFc90Zzf3E82+WrcZPOCn5 6sNnbi/4zP0LPjM9gs9Mv+Azz1fwmfkj+Mz8jJZ+UtVXyQ+Rdr1kK/JbpB2K LE1+jtSbJD9XtNzn1T7I6EeF5TLzVvK7JB/7diM/LcLnD/gs2jOfRf/MZ0EP 81nQz3wW82U+C/4wnwU/pTwrH3lWPvKsfORZ+ciz8pFn5SPPykeelfy7aC/l WfnIs/KRZ+Ujz8pHnpWPPCspz6DLRz8rH/2sfPSz8tHPykc/Kx/9rHz0sw+f ub3Uz8pHPysf/ax89LPy0c/KRz8rqZ8xvo+/oXz8DeXjbygff0P5+BvKx99Q Pv7Gf/gs2kt/Q/n4G8rH31A+/oby8TeUj7+hHu5v3FU+/rPy8Z+Vj/+sfPxn 5eM/Kx//Wfn4zz7yzO2l/6x8/Gfl4z8rH/9Z+fjPysd/VtJ/Rn8+8aDjEw86 PvGg4xMPOj7xoOMTDzo+8aAj+Szay3jQ8YkHHZ940PGJBx2feNDxiQcdGQ/i //nkNxyf/Ibjk99wfPIbjk9+w/HJbzg++Y3/8Fm0l/kNxye/4fjkNxyf/Ibj k99wfPIbjsxv4N998nWOT77O8cnXOT75OscnX+f45Oscn3zdf/gs2st8neOT r3N88nWOT77O8cnXOT75OvAz9qtmn5fr3Tp7oEfc5kav6CyBOfN/+Gs368k4 Dfzi1QIPwno4Tglc234c0Q/wwNONz/YpWS4iUCL4oI6xF2gHvHKp3D8Y/UD0 J2ngi4/Myhv2H+IYr3pzSb4ubO/ilMAtH5KU6AfzccS4TL+gk+knvt3Ssz4o m3R0f59Y0A+8a/yHh5rsGh8L+oE3in6uhMFBP/BJO3YOPOT2A/oFHg36RT/R oF+MGw36BZ3ArZ5P0wMfGxLo7s4L9APvuStWG70H+oHHv1P/qSjWw3GM/3T1 kU/fZjsYpwSOdVSiH6y7I8ZVoF/QCTwAusad6NV1D/tLcUzvwuK7J+wM40rg 3L/oR/avfPpXPv0rn/6V7N/OS/nwX/nwX/nwX/nwX/nwX/nwX/nwX0n+W7lS PvKvfORf+ci/8pF/5SP/ykf+lY/8Kyn/dl87PvrH8dE/jo/+cXz0j+Ojfxwf /eP46B9H6h/8Px/97PjoZ8dHPztSP+P3V7VtyDbXf4b9BZ5fV699v+Ni8J/x NoVLKROXQW8C/0ifPGT8FrTHb67fPm9n4jW0xzgrSkdc3nw0yPoO/TRePeOI sctoj36av/TiWeP/oD36mXXngfvnCusd9PNS7OW+hh60Rz96+t37W137jvbo Z2aJdycYerCO6Cc5OOKjsD+ZpkT/vO7of/+anM8aPxPtRf+8H8R8uT36Gf3E mOnGH0N7MV+WS8F/bi/my+0F/1lOhDyw/yDkgeNl9J92/9PKJ385JvRepsC8 Hhufe37rFvbT0L7Z9Lv/N3hGgvLqw0yBIUeynXjy3An23/jfI/P0rdLtb5YH /L7b4cxPWzmfE8/9rc9f/NQfvxwTejJTYGj8ueBzW7ewP4b2e+Z9/MWgGQna qz8zBSL+Lnb5iXMn2E9D+wUJJeq69PB6gZ5NTZ5pZ/xq8Af4/PxffhJbeDfz B/P73/LW8U+2X83joX1M+tVrLv08HtqXbTl8m0s/+59o7/U/4zg/8cbW+/MK u/2DP2gfOe3cI78V3s38Ee2ZP2i/NM+cQYaf4A/aNz++90nDT8iPmJf2+u23 9J0Wj3TaWmpkrNf+puspZb8vUZzHjWd/KSHYf3ug1Ei2L2j/bP8vu9d2x/X6 4be05APanztF9Hj9c+4/2mvfuT3HOYJ+tkdovzLi9bxGzr3+AM+L9Qx+s4by sRx/Me5kzr7jhMtn/B3zXnHhxKMfZMh/4t93Fw3JlfbyJ0lXDeWHzzA/0P7h chLUBXtOn1PL5Sf0A9o3qpXnxQzxLOPNrnVtYPY72mPcLoXm9syY/0T7Nzf0 yBpw5d/LtyTd8rPUQotdu+CVn6A+kK3nacNP9o9t+wn/PDc3Q9wUnnfueUfM fkR7zG/Za5VzLsqQt0f7Pz4N6R/mI9o/USJqUTjOjQ/z88GojifC8s/ti25M +drYBfZjvHSKPFuSrlxl/D2jx9Be0Cnyb0m62CcDJxs9CT6jfZ5S5TKvezIc t6P9W/+mfn8yrDe4/e/JMzpntNeiH5E3jtM3Hxk77M0+qVq0V2jvzb/F6YGf vlDPtBf8V5L/aH9v8utLMuhPbg/+Q6+KdRF5Yx5XifboR+STeV6S/0ryH+03 fL+retWw3XEEH4R+jlOWzyI/H6dsP2zPoEet/Ev+K8l/tLf7S/JfSf6jvXe/ JPF8rdyK/HycsuvC/g9+rT6R/FeS/2hv9ZXkv5L8F3Ry3QxwK8/aazeDyuoH kZ9PUna/aO+6BNXq/albn3LtuDf/lqTsfmQ+Yx5W77G9YD6QHuD5oH+rP0Ue PklZPaO8/A+qT06Rn+PNs7n8Jj3G/MSvtRfMH+BWr2phTxXsqTffm6as3hZy mK6s/ZL2QvnYUyXtKdpbe8T8Ee15/mhv7Z2Qt3Rl7bu0p5gX8wFyJP0KyB38 K+FXKOlXoL31Z0R+79Z/+ID20r8S/Ud75YTbi3wd0x/t1dt3lfX3pH+FebH+ hH6y/ifnM4Fb/439EIxj4wvmD9pb/5DtFtrbuEb6n47kD/So9bdZf+LX+u3c Drj1e5kOjGvjPuYb/t361Uwffm28qb35jUyOjUe0iAcdxIPQq8Ctny/jUwfx KfiM9jaOkHGogzgUfEZ7G6cwn9HexptKxHGgk+WP8zoUH8m40vHGlZmYPzb+ kvGjg/gRfEZ7G98x3Whv8wAyn+D45BMc5BNE3gDjclwBPGEa2ReRN3CQN8B6 of0vedLzZrAvHL8iP4D1Qvvi3Xu8m0HfcnvkAUS873jj/XQlxpXxPvrh9RLz knE96OT1Qvu/Jsdnrxq2U47gg4zfwX/G+Tyi3YRX54yKE/ntnAGLcxyKf7fj Kq+9iAzkfHpr7MFb5+R6BeR6oX3J6491OnjrglyvgFwvtM+2pGpzw3/YC7S3 66W99iIysGJNjkddeuS6BOS6sP18Pfn+gVsX5LoE5LoIeiQ/wTfhP2QKDFyS VtLwx2vHMwWeqLDt7AGXTq/fG9Q/rXx/Spj+JMatn6C9/mdQDyq6Zq/p32sv gtr6Ocrrh8frRis3xhj6vePG6zU3Qusixo3XFZ6u1drQ4/WTuR/hD8fr+IUN Npn19dITr298cfWBoVPmKY6WJbmVcXvRBxnlPxyvbilN+0j6JRZX0k7bfpS0 T3Zc4bfHK0uniHfilZ2XiGviFfjp9evileWbiEfileWziDu4HxGXBZVdXxEH BZWVE5FPyORYuWI7wufslL9S3rg+k2Plk/U5cJt/E3FEpIP96M1LuPE97SMe F7jdj8LP5360N28Q6Vj9oL35gUjH6hmpr6DHWG8K+2LPFzbrcm+0SF434b5O O3Rmw63U5eqxKuerLR2Yzeq3G7po8RWz76cu12gPfGPRajXHr51s54O85g09 6WKtu3fdftCe6L2mhhxp/Nodtx/0//fBd7NPu3xblZvYafmtDO2BT94yauyo tZMV+gce3PhPrpthepSgX/av0b/tB/1r248cV4txtZgXjyvaa0GnlnQKvoHP jqAT9DuC/5KfGvxE/wJXAgf/5foqrK+YrxLzZVysuxbrosR80V4JOVFCTpRY d8kH0Cn5BvnRQp4V5FnICXD93IDmJWpE5Qjgt3+Jp2/MG7VZ2b87ApftHZ/2 wNlPgt66sv7Jm7rUlFi0g9y2+v3p1Yb/3jqEW/rDXwdUu9o2qg7aQx6KtElK +djlp7cOJ01dKR6YuaXUlGi0x7pXf26mE3FluRL9K9k/+Jnjxaqh9YX9FvSj /4Cgn/1YQf9vaC/o12JcLceFHrXzihXz1T7z1WK+AcEf8D8g+KMF/UrSL/ij xLoosS58fgq+CXlwfORBCXlg+czd6M+RRy4v5vni38vNeL7PptVh+cQ4Fud1 x/+z/cSK9gGf9hg3VtDj+NDjSHrE+irBf6yXFnKihJwEBH/U/sXZftl0L3xv rkulzMUqJR5Tx9ZEtnj/ZGae1+DMg0tUjEpmHO2PX/75vWfqJaqYITlPj+jz gP3q5MmVyjYcf1Ht9PG3j4b6+YfX54n3rrxZs99lxtF+1NRqP0ZFXlTbQ+0u cPvPNo0dUcGlBzjaf/VV8qJhORLVlhDO9V+BZ9JH5y3X5ATjaJ95+LufVnDn S15bTBTaNxmQJf/BZzds3kftWR6fpX402qOf388XODU35WduDzwP9a9/Jzq5 n4VEP+Nov+fVimfK1kvUfxAfGK8wfOiRBuMv6j0+8cIm4j/3X6KU3ujyk3G0 //PZ0lnqRF7Ux2kdWf4H0voyjvYTP3/t2fdyJOoDJCfc/rscy09l6XqCcbRv T/KjIVeIRxJXT9j9d6/9LD/AU4903f1nzniWH+CNT6YMdNqeZ/kBvnjZ/zU1 +QrICfA+taKPbch0nuUBeNOn/8qXZeOfvO7A2x+Y80vPJvsV1gt4crlMS5N7 7ed1AT58+Cvpp3LG87oA/3b66lounbwuwL8gOpn/wAtvrpc3JtN55jPwB8ty nnHpZH4Cz1mv36neTfYzPxEHv5N77K64n39hfgJvfSQp7wdZtjM/gfdJ6HEn 8PMe5ifwDdfefsXUM4OfwLcsfmeNaQ9+Ao9tfvvLMb9vY74BP3ihWJ29P//C fAO+MHPN90Zn2c58A76N+me+AY8hephvwHsR/cw34AWzz3p+7O/btFePpeu7 TWbPPuDS49VX6brW8mE7x7vtvfoqXe/6tf+r293+vfoqXec+POSkqav36qV0 faD3u3e2ue29+z1dT71ZPed+d128+zpd6xOnq7vjKu++TteHqR/l3dfpOg+N q7z7N11vJzqVVx6COvnSBzf7uHLilYegrnToSGJWV6688hDUc8/U32Dk0CsP QT304KasJs8G/Qa86Ox+nx1JXRvlXfegvttt1ZFe7j7yrntQ7391VSF3XOVd 96D+o229Me64yrvuPK6YV7ze+FLX/kafeOcVr3voa6OMXvLOK17PaDi6i9Fv 3nmF8xLQA8B/m3z8rNHP3vnG67mFTpf5LOVnMd94vZvai/nG656/T0kydsc7 33ida+mUAi49Yr7h/Ab2O/CfaL7MB+QTDmRftqO8a3fAB+B/ZyW9DT4A71qG 9D/4IPMe2NfA88aSPQJ/gJ+44bV3wE8PCtlH5g/wRGrP/AH+CPXP/AH+5kiy ++CPzKtgvwNPpvky34DvIv5I+VE+8qO616uwxfVbpPyoPDmbTHL9Fik/nIfx 6sl4lYPWV3v1ZLzqSvIg5U35yJvykTflI2+q6hdLyxm/S8ibevrf2c8av0vI G+d/vHo1Xk2j/aK8ejVedaP9JeVTSflEvqh0ly9TLvT6j/5RBc8VPxTv2k2h f9SqLEWL1W37H/2joH+8fA6qY6Q3BJ+DajfpGcHnoEolvaSFvlJb95bQKb3+ o6/UodGZ7hs/ROgrlbr+dKLxQ4S+Ap2Cn0E1m/Sq4GdQlSM9LPgZVPGkt4Uf nq587Jea+Ump5WNdeyrsl/KxX8rHfinYL6//nK42kZ2Sdk352DXVZ9XA3C49 0q4pH7umfOyagl3z+p/pqibZayFvd5WP/6MKkT8g/R/l4/8oH/9Hwf8Rfo7y 8XPUVvKLpJ+jfPwc5ePnKPg5wn92HiE/UPrPTublIb9R+s9OEfIzpf/swH+G /gFebl30xMOpazcLf9jpRX6y9IedN8ivlv6w04/8cOkPO/CHRbzpyHgT+McU d8i40vmL4hcZV3LeWMR3jozvgPvEa46M14B/Q3GljMuc0RSfyriM888iPnJk fIR4fz3FUzLudhB3gw/AS1F8x3yQ548iXnYQL4u42EFcLOJfxyf+dRD/injW QTwL/gB/iuJ65o88fxRxqIM4FPzB79H6XXdVjDrO/EH+9W5KYq0cD8Jy8mbs b2snJGQLDF4e+5s55wV/kNdvZM/TwR/kRUpPHHOsQmIS8+eDBxUXPfZ9joDk z04b/0r52WfplPLzqKWzT7Dr/OwPwvLTydKZkvOF7uacF/xZaelsaM/TwR/Q eS+lwHsVE5OYP9OerHB8Xo/Mga7D6v04dfIB5g9+YxqaerMzzJ9iUef2dW6S JVCj0BK9dXsS86e7Paf6zdYnQB9+23H+jvI1sga+m1tr+e99Uphv6cEhdfOt zBqIHdN5VVLBBNaTUy09mVKndpsy+QDzAfR0PPVCYVMvCj4UtfT8O/7fES49 zAfQs9nWIUBPrrX0HBz2cYJLD/MH9BR4dWOV5IJhPTO/xsLUlC/u6ao13u1g 7q2Ajmds3rXv3pqdvhgcx/zBb+PBMw6Xd+UN/EH+9tUylS4Eup1i/vQ7tSk5 cmFE4OWaaYPbj45nPnxmx52Wf1d/c58XfMC4uS6dHbZ4cBzzYb0dd1vD6R+7 4zIfMO7WeWuPuOMyHzDu3V1xv7jj8nxpX/yrn8+bUmTw4z+w/4C88eIEc29o Pc+T5OqmPpXYve+bmX9k/yEcNxSoo0t9Eov5tl4Xm3979tv6kQG3r7XJ/CPP t5Add2/M4X8GPv4D+wkr7bg/rTD3Qdazn/A/O27b7DnmuOOyn7CDx126ulmx 6dGYbys77s1abfe64/J8KZ+ZqutkLlKuYf59PF/i21U9+K+PDvSre4Ln+3Of eW9fzJumHytXtExHl2+Y58uoH77fduBWl8+YV1nb/2Mzi1eqn38fz6u07f9u g5Il3P55Xj/Z/hfMqHu+gysPmNdLtv8Fn5yY6PbP/gzReVY3yPv2nkpTw3EK 0XNOrxoxbKyRZ9DfLbQvEnSOiPfa73XlH/RPsOeiE20dJugvZft/v1WlhhWn huOIeuh/zVO3zP4F/W/b/isVLVvE7Z/pH49zV1s/yfFpyG9Zq2/8vVp74lN7 jtTscs/xRl957fgOva/k4lx9zwXDcmbruVDHCz2MXxk3ZbbxsoxDTxQkemQc erUg0bM91E8Sz3e4pWfZhu3r+pwL8nxvFvTQw357wNLzydvth2SMg3bb8WcR znyYYs/rmiWPGOTaKe310w6pdf2H3jH6H3woYOv4llo+QN8Wt+dvmuhnvtQm /quHx+kxUTKuxK+MK6daOstduHnItVPMnyaWzmFHryw39hT8KWzpXGL5Az0M OhvTujPfHEvnFZIT5huda1xUM44l7p7m2gvwbY+lK2l78dumvg58Iz2Wov58 P88b21x7Ab59bOsjxlv5h77qRfKsniF5Zn42JPlXy0j+eb9ctPQkPF911Ceu PQV/8Evrcob5842l5+yFrnldepg/Uyw94+x+gR7rY+nJTPuX+QZ6VtB+Z/78 G7Jr19XOyguKm3ud4A/O3ZYerv3nUteOgD/HLF2tnh3Sx/WXmD/17fndTNI/ zJ/1pK/UdNJXzIcbdty2Jy9VN+9+YP497bhfnUm8vsS1m+ADfs+H8jPHmQ8N 7LgtSa8yHzbYcXORHub5Dk+r9KDVkTsq36SkVwa4dgTzxbnh8ImnK2537Qjm 25bsgipRO2QX2F7v5nxSta9dO8L2qzbZHVWN7A7Pd6Qdt1j3Q8Xece0m5nvB jpvW75mvtrl2E/NsZ8eNIjvI9jqcx1r6oms32X49b8ddR3aW53vv9uDT+8dm crbm/SxzA9e+YL6/hPiTyWl2J+nnvq59wXz7k913su0O2X2ebxl7HhpHfgLv 8/u2/4PrFmV7xbWPmBf6b/TOmEi3f57XO7b/5uTP8LzK2v5bk//D9jd69Gu/ /fN4pHOl4LKhrv1i+kluszqFyB9j+r8j/805Rv4b0496nID1P0G/sv0P7tz3 WoWpYb8a/W8n/5PpR/8/kb/K9Pe0/W+x/iToH0N+vjM+ifQS6K9lz2Mj/g75 20x/6umhXzbdk93J0+btZGMvQP8qUa8L/Qz9J+PQj2w8KOOssZYeGWfVxvk2 xSk83yuWnq6XJ0916eH5CnrkegWwXqCzjsV7Ep8Zn279+RuXQ/480z/Rnvc9 2vV+XIeFP24W6xXAeqF9fXuehfbgC8ZNIXqYXxi3I8U1WuyXgM9+CWC/QM// bPFoknPGy1s/vBD54Tzfu7b/WNovjC/w+vPMh5ftvMosGDvw8ldfbhb7LuCz 7wLYd9D/oLM+0c846BxMcQrzB3SuJz4wvsAb72ihPzX0J/iQbPH9pN8Y/9r6 7dPJb5f6UEMfgk708yb1zzj6+ZDiDmnXtI9d07BroKebxdeR3WG8rPXD08gP Z7/ouu2/DdkvxitZfz4r+fPS3mnYO6xvbns+tWxDz59Sv/oySthBDTuI+YLO r4h+xkFnH4pH2C+67uUD45UtndUorpH+koa/hHklWvwE+S2Ml7P+/0Dy/3k/ Rtp5Nd0QE9Vx4Y9Rwv/R8H/QPp/1t9EedCbY9h8TPUx/eTuuorhGxjsK8Y6g U4FOMS+FeQl6lKTfxjsK8Y6Yr5L0WzoV6BTzUpiXiDeVT7ypEG8K+VSQTyHP CvIs5FNBPoU8K8izkE8F+RRxq/KJWxXiViGfCvIp5FlBnoV8KsinkGf4q0rk HxTyD0LPKOgZoZcU9JLIJyjkE4SeUdAzQi8p6CWRF1I+eSGFvJCwFwr2QtgX B/ZF2AsFeyHsiwP7IvJFaK+FfXFgX0QeSSGPJOyFgr0Q9sWBfRH2AnyQ9sWB fRH5Rgf5RuEnOPAThF/hwK8Q/oAj/QebP3SQPxT+hiP9B4wLP0H4FQ78CuHv BXz8vUDTpzblz5hvt/5eAP6e8G8D0r9FfZTMz68S740IfzjQaMdHDX9PTZH5 fH5vBPEL6OlM/h7jb3nzydKvDsCvhvxgvoWO7+mVMW9v/ckA/Em0v2HnG+zc e1lGfxt0yjy/mC+3/97O99H8zzzYk5oizwX4vRHETZctPbmI/4x39ub5pZ8f gJ8PeUA+fyStO+P2N7CPzhdYTsC3Bj8m3LtT4Ijy+idZAn3XdyvRec45Xkfg +4ZcyXwxIoVx5Gmz2HgNcs7vm/UeseJ2gSMs56j7alB6yOCm337L+x3tT63P tuitOeeYn8B3TH1m04WIFMZtnBjY+UfuLhnzw8gnP5dz1MBR5Xcor/2KCFzP /cHhnB8e4X0KHPrHe85+V++k81CeF9rvL3fvyojyO0Sdw139Yq7DjRKqzOV5 8XtoA2crd1ztPR+/q3vSOa84L7ul800f/Hbw2rBY73nWLd26cd8tF11cnGvr HnROzfSgfd6JFUu26NH6Re+5YboeT+fd2nv+dUt3pv6jveeJ6XrwkhVZk6vM jfLWSd7S2YnOaHGurafROb7yng+m63Q69+d9jd9LlFeReRs9zeZtvHWPaXpq zFcDc7n8RPsvKU+lEyhPJfNjemHfMh+4+kd76yTTdM+hf73gygn3j1+bB5P5 NI18mlee09h/FnUa+meqx+B9fRj6jPJXnC+KsfOdOSFzLZMv8tZPpul2nT7N ktuVW7Rfaud7mvKEjPez8+1TetIuo2+99ZZpulm/Dpved/cF+t9h52fzkIw3 sPN9f37/SUb/i7oOXYTqYZTIT2rkJ73n1+H3Rrx1Gkn6ycENX3H1icwPa5kf Rvthf8+NdPUS9+NQ/lbb/C3jM23e+wvKe2uv/CfpFln+fdTVe9z/WHtPpSzl h2VeWqN+TOQ/NfKf3vPx8Lsl3jqQJH24fYFpRn+KPLOWeWa0L33p2mSjhznP Zudr8+qMf2Hnu5fOHcS+S9KH3ug88K6r59H/eDvfppS3Z7ywnS/q0EQ+H+OK eqc43aBY9JIO3wflOYiS5yA5xDmIty4lTg/YvOkV0484N+H3T7z1UXE69/Kn nj57KUmuu8K6CzlBnl+L/D/kR9RHxelBRI88T1HyPEXMS9RNxelXiD/y/IXf S/HWU8VptbfiHndecn0V1lfIA85Z2N5R/5vVBVs/6a0jilP1qH/GX7L5+bG0 3+X6Krm+PWyefwDpGbm+ymd9lVxfe66ncK4Hu5lm6UddpbceKU7lp3WX531K nvehvVxHew6olpBdkOuofNZRyXUcj/vT4twQ91lP0L4TdW5Jqgzta7FfktRR 0g+M/2LPERaRXRB1a0mqA+kx5fVDgirnuFVTmn37bZS3zidJjSD9KerZklRR 0sPynFd9bs95vfVaaaol2RGeF/COZKekfVTSPqL+TfohaD+I7LKoa0pX8EO8 9Vppaib5A9LfUPA3hH+lfPwrJf0r1K3Npzp/UUd0S0n/Cu2l34j20r9Ce/iN wr9S0r9C+zfoHoT0JxX8Sey7X638xlHdhazrcDaS/8zxPu6dFSV/ldvbuhrn JtWxyPoZpwDFOxwXo58d5Cdz/5iHrZOR9TZOMsVf0j93pH+OursTdJ+F9yl+ 36D6Fva7+tr53rD1JKAD/adRXMDti9v51qQ6Ilmf47xM8Szzn98vpbiD+8ev rVNiHOc7qOcR92KcU3SfSIv6JQf1S/JcSYu6JuDbKW6SdWKOrBND++MUf8m4 20HcLeJ0B3E61gX9nKO4j/t/3N7H7RkM1YnJ+jQnP8XpStRHOaiPAi7fb8G6 AI+j+FTWmzmy3gzte1GcK+NuB3G3iNMdxOlYL/RTl+Jo7h/3j+9Q/Z6sc3Ma Ux5G1gE6qAMU9X6gR3nrBLI7xa6l9Tf2SK4v8jBYL+BnPh1QyLQH/4FHlB94 /8yl8L5GvSLuLYq6PsgD+1Ho5xz1z/yX7+eAn8CfIvqZP8CLvD5mhfFPxL0/ B/f+RH7DQX5D5EMcmQ9BP7jfJ+pUA7JOFXWVTxI9kv8BH/4HfPgfAP9hv4Bn Jf5LfgZ8+Bnw4WcA/BT3C/RlqvOX9wv0XroXIOq0g/orur8g6/x1PrrvIOrJ 43Upupch74noSnSPQ/Qfr3PRPRR5L0N3pXsr3H+E/dOR7tFwP8C70D0geV/S WUv3FuV9SWcw3XNkOwG8Id3HlPcWnUt0f1PErZFOObqvKu/nOjvofquI+yKd RLovLO+fOkfofjHLIeoVdZ87f2S6Mov9IlvXoa9nNvegZ7E/g/qQSc9Hvvlv 6iz2N2wdo4pevmDn9dRZ7I/ltHmnIg+yT76wYslm4KVtngq4qFfRqFcR42qM C/0/zLbPSnUmjNt6Ej2G6GG8sK2r/IHqKjlOR/3ks1lC82X6s1v6N1Wo/ejG IROZfrs/GBd1Lxp1L4I/WvLnI5univwp7js9ZCLXmaFuEzj0CeZ7b2KIP6xn 2tv+1xF/GMd8y1H9KvurNe18L9G6izqEdN0l6Wz14IolTI+tO/oPbtsrn/aM i3pahXpaIYcKcijWS/msl8J6CXlQPvKgIA+C/8qH/4yLulyFulyxXxT2i5Af 5SM/jIv1Uj7rpbBeQh6UjzwoyIPYj8pnPzrAcf8c63N5/pXi2zuG7+0Drz3t xx+a/bAwmu+pWrxVj56ddp4Mv08IvEbi27O3dgzfb5T3MnEvTrRnfQr855cX J7v9sx4UdPJ5D+xq2/q1p5Y7PScaeWHgYxtO2zJ/Mb/vyXiL+x0mf97qHNMP vPn2qCqd6hxh+uX9UcRFwC/X+inZ7Z/pBz69zuA0t3+mH/hvq/J1eLPOEfaH BP383gX8gusrU65euDYpmu97W7z9hlEflnv5D77nDLzz7DEdDI75Ar/3+sml 7319mOcL/GjjtA9H/rKd75WJfjj+FOPyfIG/0+58geFfH+b5Ai98ZtOuUb9s 5/WCXzO9WkronROc32Sony+RLX1SLJ+XML4u90dLp8ZivmF87KlJLo75hvFq n0VyP2F/yuKQEyX6ieZ42ztuNOYr6OTvH8Gel3rx6bqj3fliXsBbLb/YbYTL H8yLzxNKjqlYweUn5gX8s9vjs1Rg/of9r5S/gpNNXIB1BH7ulbWtRvH5UUSE 9Hew74AfntHunrsufA+Tz01mnbvuriPLg6CH790J+tnvAd50RO+7Jo8EeWD6 F0xdas4LIP9cZxpM/fTStTA/4QftTW8dzLBfwvjRveXLu/sFfAa+tWWboQvc fQc+Az/bMTj588Xhd+qBF7qzr6uJC8Bn4NVfv/eIiePAZ+CfdZ9VMhfnoyIi 5L1w8Bn4BzknaqNPwGfgQx+pm8elM3yf0+Id+u3s9jnrq7uMRw8eOdnEC+Az 8HfGT33f5N/AZ+Ct161sYc53wGfBN+Yz7GfS980HZNSrwEvNKFKh+viFzGfg wy+XrLLrZPj7ocBrZCr9iYkXwGfgE04eb2dw8Bn4yutPPm3ia/AZ+Ov/bjtv 7AL4Ku/Zg8+iPfMZeL+8wS+NnQKfBT3MZ0E/8xn4qyeivjP5TPBZ8I35LPjG fIYdLmG/ewsc/kWC4D/aV7HfzwWO9iXEuqD9w7+fGxP18O/nxkQ9/Pu5MVGZ PN/PDbKf24f4yeuF9n26ZfyeWjiv/JpYR7SPEH/kuwhYX9E/38MX/fO6o/3D v8/L8+V1F/wR3+eNicpqv88LHOO+R/LP8iDWl3GxvuJ7vry+/E6XWF/+Xiff H7V6D+sIvA3pDV4v4INIz/C6AB9JeonXRb4/Af4Ajyf9yXwArknf8nyB7ya9 zfMV9PO8kJ+oQvqf7T7wN4V9BP6JsI98n4LsF88X+G6yd3xOIPpheRDj8nz5 fI/sOM8XeBGy++w/w//vt3ds1Yx+Tvi+w8FiGf2cMF4tV0Y/J8M7Hycz+jkZ 8LkZ/RyBs58j+mE/R4zLfo6gk/UY8hAlya/jd5qAv0t+IL9DBLyb8Ff5fo/w V4F3mjG+qsn3Yh2Bd21T5dgIPseJiJD5G+gH4KfJv+V3LoBHtgj5w+F3HLz0 sN8r6Ge/F/iKUg+WmXokyAPnd+fkK2H8Mcg/8Afkz/N3mpC/0RQXsPwAb2/j GvAZ+EyKL5jPwK9QPMJ8Bp7pYGysyceCz8BLNFsy3eTnwWfgRbetmh6ua4qI kO/KgM/AW1DcxHwG3pLiLOYz1wtQXMZ8Bp6/ft9Ek7cEn4HXe7/KTVPHBT4D T8y6SRt/DHwWfGM+I25OtXEu+An8F4o3mZ/Azy1cWt34A/yOFfLDQ1tGhP2H cP7++Vwt4835CPgJ/FkRF8v3eMBP0Z75Cbw1xd3hd0m89DA/Bf3MT+DVgkN7 GL8L/BT8YX4Cf4HyALG4P8/nDK/Vr2nqhXBvHHijXh/PNecLuF8N/OqTmwaZ 8x3cQwY+7mx6EXNujvu9wI+n3OnaoYzrB9B9SMabh77fukal2Lpz4BWCJ5aa 8zXcAwReddnhNuZ8AfflgP96Pn2uOX/BvTLgC56/Pt+cx+G+FvCN6w+d6Vgm XuMeFPCph35fum/7Go376ojrH0nKvMScT+E+OfBPV64tas6ncO8a+NTo5teS fj2vcJ+Z3ydolruBqZe2dRiM/xX6nuB6vmcIfOCl/t1N3RHuAfI90Sldq5i6 I9xnAz5k+YpXkn89r3GOCDw1IbaVqcPH/S7gIwoufG7vy+v5Pjzi/ZqXPh1q 9DnujQO/2uGxAaae2dZ5MJ6pb5eFL+ffx/cDgX/4Vu8+Rt/i/h7wpNS0H8w9 KdxbAz40KjFg7g3hXjri9BOdy6adfDBX4b438K3N+4e+g4l7gMBLdKz8+YkH c/ncEfjZ1I2h71ri3QquQ7f3l/GOA/ACyakjTN0g3jXgezNZ86YaOXzU7gvg +SbP3mvkEPfngZdf0LqrkUPcSwcefbvjT+6+0Li/DdwJ+clrNO7lAv927Lhk c86Oe6rAq9Rps87s0xF2XwBv33z/FbNPcR8S+OnrLc+bfYp7hsDv7qtRtZO7 T3EfD/jwB6F9wfIHPxD3lMva/QK8ZdKH0eZ8E+8gAJ+euni7kVu8IwB8Z8O6 o4zc4n4+8HwrP7xo6vZt3Q/jk0Pfh12vsV+AF7+YmtXUreH+KvDuR1seM/sU +wX4wO/n7HPH5XuVwBfUq3XL3AfBfgH+Sc8Wm939wu9WwG98zd47xvkE8KZv zH3K+Ld4HwH4wPU9Tpv7Pnh3APjOY+3nmfsv2EfAF+/u94jxY3FPld/PeK1e SXN/x9aXM95v81+bzT1BnE+wH1j351XmXjDeZQA+c35w0zV3v+C9A+DNi+wu bvYX7sECb5t2res/7n7E/VLgXTc8GtpfqLMXesbWR4bfCbidGlPP3De5buu6 gJ8ifcjveAMv8cfmgLkfh/sA0BsryzXaM+jxH3APNiD0idXb4fqpMo2y7Jk8 +YCqb+v8gDdrlflkZ/7O7w7WY3OuNkg350SobxN6j+tggCd+tPKMuceBc3rg I7M8s7hrhu/PQi9F/fSW5/u/Ql/Ffiz03tmRn4f0HupigT81ccAg8/1f1EcC L3n7Qej7v6irE/ozurvQk3cLp3i+/wu8VvSB0Pd/cT8Beb9D8ztdNedEuI8B fNqQY1PMPUe8pwP8A5JPfpcH+OZqHSqb+32o2+a679Mxj5l7LqjbBn7K7hfU +wKfM3vdTnOvZ5atfwX+UuWOT5nzKYsrgSvUgwKfS/0ovKcgxlWWTiXoVJAH MS+FuijBB4V6GsE3ri8RfOZ3sJEP/I70EuppGJ94vMCqZe5+GWf3HedX/8xT 3ty/Qx0z8G+tXi1p37MA/j3hyvbD+DbqR6GOh8+9aVx+51zQyfdwhF7V0yyd wPdZOu07JqzPP7b02HemWD/PtOPiPg/0YYyVT9zDEXpS2/csWA+vsfKGOmDg kKuRVn6AV7Dyg/pRoc/x3hPr5x+sPKBuBjjWHfeIoFdLPPpzotEPuOcj9G3s FKG3S+wYENLbqCMHPqz+uwOMfkCdMfAaHVuG9APqVoX+Z/0A/Lmuvx00OOqo uD7rwdGEjPoB8XKs1c+4vwTc6kN+3wr4x1Zv23o7xlNJr/I9B+DVrD7HPQfg haxf+oJdR+DQ86iPB/4t6Xmuqxa4rm31g+hH23GVGFcvs/oBeBWiU6OOEPgV ay9QRyj4oFF/JvimoR8En/l9N8TRs639hX4AXoTsqULdP/Br1i6j7h/4DRun FLL6Afh1a6/HWP0A/Cr1w3V+Ylyu8xN0Ii4LiLhM4R0Hfhfz0qjh5l4M3kcQ cR/8OvZDLlEcpFC/wvEgxY/8TgHaB443PW3qTvGOgIi/EOdyHLq1dv0bpo4U 96VFXKxRT8/3zwoWfdH4t6i3QPs0iq817lej/XszFkwy94xwrxj4TIpDOd4X 5/6cP0H7c5Rv4fow4HMe/C/XuCzhfCBwRXkbvncHvE7Dg4VN3RfyLcDzUP6H 32uD//M3xX2c/0f7JyhPxfcBgH/X49a7pp6Z38O1eDHKd3F9PPCkRoUKmLos 5FWA3z8Qyptx3Tzo6f2gWicTh4I/wBMo78f8Af7WjlzfmPdgwR/gCyh/yPwB 3nT3Oy+Yd3f5xMTi3nxR+B2jdw7f+MvUh4M/wNtRvpT5I9ozf4C3pLwr84fH ffa1J807//DrxLyYD/D3brw31vPdT+C/Dp8X8leBwz/55n/9ah5kesLvzJ0s GO/5Hijw7V0mhvxY4Ohn0ZbDL5k6Z/BJvuedQ/i9S3StkN8LHP0k52/Xa1+Y HiXo4e+Hin4YF/3weYHgT/R/8gwPDof8Z+Dop3Hff9429+OkX235yf624KfC vUfg79v4Ee9rwL8qMvrS/5m6cdxLBP6EjaPBH+BtX1qTZ4y7v1D3z3SufeZZ E1fi/jzaF9z1RV9z7w/6GjjuCaJeH3irsWUi+7v6FvMC/s2VlKVGz+D9FOC9 KU7nekGcw3aiPAa/YwK8is2r4L0P4POqZG9i7ofifQT0P5vyDwr1W2hfkPI2 Cvf/gZ+3eRLcqwe+PClqn7E7eI9GtNcvi/PcbPY8F+/jiDyMzuZzzmvzOVrk czTeZ0H7LPacF+9oCP4ovJch2vN7GSJPpT72OW+1+S4l8l3qZXHeauer8F4S 8LL23BzvJXH+jfjJ9TbAG9N5vcY9fPj5jUiu+Hwc7avbugXYcbTPTXLL549o /6GtZ8D7PiJfpPHeCugfaM+v8d4K+vmE5I3Pr4GvoLoIvoeG/vPTfuRzbUEP v1eC9v9H+5rPN8V8+X0QtJ9h5Zy/02PxN6iehPMPXG9O+5H5D7wv1aXwPX/g 7/+Yesm8bw/+A3+B6luQ7+U4S5E+YT2M9rOpDofv0XH+kPQS8xN4Farn4ftp wNuvrZFg3rEHP4EXoLogvrcm8m/MH+B/UV0T8wf4EmvXwB/gL1N9FPMHeBTp ebZX8vsP0MPA44WdAn6C6sGYP6I98wf4aaorY/4An072lPWwmFf4O0w2jsuc 87mOGe0+v+c65eXEjHYfcUdv6xfB7qP9jeWfbsto94G/cWNe8Yx2H/3kI/+E +SS/jwG+AW95KHFARruPfoYLP0rQw/Zd9MO46IfP1wR/2L4D7zU36WBGu49+ OpAfyH6X4CfbfcFPjXcB+J0Ae18P7wIAH0p+rII/APs8iuIFhXv7wCtR3MH3 kIGvI3+b78Wh/+v2vAb+ANt/io/4vInjR3vehHkBn0fxBb+/BjzOxlOw+ziv 3GTjMrwLBnyCPWfEe1vAf6f4S8Huo/85Nj6C3Uf7f2ycBbsPfLE9N4TdB16A 4k1+J53HtfEp3lUBvt/e20I9gDiHjYb/IMbFfJWYL8Z1xLisl/iclM6FOR4B /lTJU6H7O9DbwM/a83F+x8Tit79q8IK5jwO7CTzOnpvjvQ/g85Zlr27u3UAv cRxK5+kcjwC/fufLQeZ+DfS26J+/zwt8QqVyp8z34GAHBf18X1fQw++YIx58 a9Bjmcx9IvQPvHGpZRvMd+jwLgbnRXdVaG7u40BfAe9MdofvtQK/VHjp56Z/ vm9p8XFkN5lv8NPOvvbHVEMnxgW+u+7ZSuZ+EMYFvqpRwjeGb5iv6Ie/Rwy8 928Pppp1BD3AgwMLtzXy4P1+dEzUw78fHRP18O9Hx0Q9/PvRjIvvR3M/4vvR PC7LLfycS0QnryPwnjQvppP9astPyBvwr4hvvO7AdxKfmU7RD/MHfkUKrS/P F/hqkhP+jjP0T1OSK37vA/gFylcwH4B3IfkM37+1uEP5Fp4v9MB8u+8wLt93 p/3C4wJPp33H8xX9sDzzu2ikB5ge4EVJn/D75uK8zOqBtYzT93NnKZlPsOdK OLfS4twq9kZIXr7fDHx1aoELLRe8v7mAiN/t+Ve0zc9rkZ/Hu+GMzw5913iW Xin8HJvHRp5cizy5pUdvBp5We+mgpgumRhUWfoXNt0fbuhfOwwSJD3zuj7j7 DUvPAcI5/9CMcFXN9sN+ncVX235gr3+0fF5m++E8th0XeUJxPyUW97fFuSrb HeAddx+tHnkl3I+4D1JH5jfs+Wb0TtF+8Zb/Hf+1fVQd9CPqSKPxzq84x1Gg B/i4Bc6ebFdmcZ2qqNuss0r4XfY8henhdZ93OKljq6g6sO9ivgrfQRH0/wb7 Lu5BxKK+CHhdSyf6F/Rr9C/o4f5F/WE06jSAF7Xrgvy5GFfb9gHR3tYJnGS7 GWm/54LvzYjzd/WRzfMDv2Hz/PgOjTjvxnloQJyP27zBISXO3+27geH35J62 5yZ45x34HvvdkO7i3NyeX8dCbwO/SXUL/G6+OE+PxnvxwKt9HqqLUHhXPZyH LBH6Tgfy2+DzVqrH4O+F8D1vqt/gd+eBj7ZxKO4lAm9DdSPIp/F57ldUl8Lv OwMfRnUs/L6/yPshLmB8L9XbsBzCHzhEdT78LjbwbPtDdUH83QXgn1OdksI7 tsAbUl0T2xXgU+33IyDnsPuTLvwbuh8RKfJaD7ZQXgvfvQDe3d5TqC/yTplt 3gnvjAN/0tbz47tEnCex5+P4DoQ4d+bvFYnzWY3v9wD/0dYt4LsRwFE/gO8l AF9r6wS6i/Nce67Kcgt8N9Wz8fcVxDlvNO7xAh/bJVQvx+8wYr+n2fMXfFcG +Eyq00Oeme3FLar34+8HAI+i+kD+HhJ/j8ier0E+gQ+jukTVyMon8NNUD8nv BXNdNNVPshxCjrZS3aaqYuUQ+PtUR8rvKQOPp7pT1JFqUUeKelfWe7beFe34 HO2IfUcC71xjH5WiejN+Twf7t619xw/v0qJ9G6qLQ3yqRV6a772LvDS/6815 Xar300n2HWrga6g+kN8RFnl1xNdK5NUV3sMFbusbFd6ZBZ6P6iHRXov2Gu8I YN8VsHnsj33uK00QeFWL411yUd/I70ELPti60HC+2vajsvncD8opcEsnv0cs 6jxVfZHPz2Lz+XhfG3iEzatDTiDPg6mOl+UBeCGqK+Z350W+AucUWuQrFOQB OOQZ76EDL0d11DhnUSLfwvfwRb6F33EGjn0xw8oD8DiqG+fvBIj2oFMJOv9z zrjzxx6hc8as4pxuVZOtoXO6j0R+svXs3qH8ZITI7zWtvCOU38sqzh9tP7G0 Tl9HAad3A96Nkud6lh7rt4fzmbb/2I9EPtDSE439z/mNzqcvv70h/B4I8AIl I17XbRJZjoG/+92w/1Vw9QbkCTi+uwf9C/ztLG3qdt0Qfj8E+OdlX3GVTfj7 9ZxXsXoM84T/VrlwjrJG3kA/8Inb3mmvK4bfZeL3AguXf9q804X5i37YrgCv 1r/iZ24/TCd/v8K+fwV64NcNGDntPWOPQA/w11YOWVA+w3d2gDc82L+nsUf8 bqrF69v3vtAPxml5ee/ZM2Nmsj0D/k3pQ6dPj5kZzmvY9cH3tsBH6Ad89wr9 Az+ejdYFfAO+WPebYdYF/QLPFXrP8DiPCxzfsQKdwOt+c2CO2z/vZ+5n1w/P BNqEv18MvGmH/rkrZfiuH/ycEV1uxZn1Av1cp30qy1WzXqCf66Lt+7fgP99D pH6Yn8DLTApUDFQMvyMKvNpH1VMzfp8RflEz8f0m4BfPzyxu/GfQKb67xPTw Ocvzg/Ib/xn0cD3e8WltK2bgA/7fA4fqotE/10U3zdzltisP6Ad4s+PZ37rl yg/WC/yW37WBvOS18RTmy/EI9ROu/7Z47WahcXk8GXfwuw+W3sM0L+4XeC7i A/fP77SJ71UBv058Zr5Jfwb7FnLxAq0jzwt4RVr3cJ2HxT+w8gY5AL5SfF8M +BySQ6ZT9MP8xX5qTXLOegp4XtoXTCfw+rSPmE6Zj8U6An+M9inLN9cD0L5m +oGfIj3A9Ms8rYyP5Pf12A7Qd97D3xm0+N/2HVdvXVFMVFd6/yrDOxre/K33 vYOYKLxj7L2fHxOF7+t573XHRCU89Hv3MVEP/959TNTDv3cfzgN77yXGRD38 e/cxUfjePcf/tr+2T4f0No/L46SG9DzzB3LdQnznlL+3SXaE+wHejuwOj8fn g2SneF0wj83i+7PAa5Ad5HGBV7P2F/wE/gjZWd6/wKeQXWZ6RD9MD58rkT/A 9PA7heQ/MD0yn4x1Bz6W/BPev8AfJX+G6QR+k/wfplPmmSHnoLd2TJ6ZlU8f 5H3C9YqL455pvekg0wP8taKZ21Y6fZDlFbj8XgbwpZPXd2qz6SCPC36Mm56U tfYLW7lfXrfaU8e6/bPe4fd3n72+r9WmMJ3A+8wdduhccAvTCbz0tUY/PvfC VuYb6+9/xt2r6NLP74Fa/IlXKzd358vrwX7Oo+2KJwS3MP1Yt78u7wy9Wwv6 w/v+5vDypyfwu7VhvNef5Vyczz8sXu4bHXqHlu9lcfuBpj2/Nyv6j+bvVVt7 sW7ji/0TXTpBD/BO9R+fZPgPeoDXnDP1f+66s/4BPnZRaF1Y/wCvUDrEB94v wBPKnM7i9s/7ge1XwmcxRq7AT/YjXhvX5nl33UE/6Jq87soZQyfoAf5r7zx/ GnkDPcDXb4r8xsgn5BC4/O4S8N15H/+/Ki497N/beewlnMcF/g31z+MCjyV6 eFzgclzgY2lePC77BcQHXi/gLYhvPH/+7iXxmekEXpjWhekEbteR6eY8P607 rxePS3LC6wV8KckV0w+9Mmj/ni4Z5T/DuwfvZZT/MF77VEb5B35oytZARvkP t3dOZZR/0T/LP+xCW9qnrE+Al6F9zXID/E/SA6xPgJcivcH6BPgHpGdYPoBr 0kusT4DHkx4L11VYfALpvfA9bfv/viY9yfTw91pJ3zI9wIOkn1kPA5d6GHhN 0vMsN9Bvv9t8PvoBjvfuZN1psq07lfdSE+29VPSDcfDOIfY/+HDE1tWAD2w3 bP0G+se/X7gY98e8pWF7ClxHdcoSyHWJ9aasiwB9wJ9YtOtFtx/mA/Deb6z/ Tue6xPIq6xm837117eaaPM0NPegH+OlJH+Uz/WO+4FvKianfGBzzB17iQcGg 6Qftsf7KCc2L/z/w1duajM7YD9ffVsja1vAB6wW8baEWReYvle+4urrqoe+4 xkThHVfMB3LXjvphPgC347Kdk/UAnN+3+Bqin/nG3/Ok+fL+l/UALK+2v6LE N15HlkPiM88X/DhL68L7FvgLtI68XpDDXiQPvE7AHyP54X6Ax5Ic8njA/ya5 5fUDn4qR3Y8F34FXvPHZqK1D3mU7wn4r+Rv8Xj3oxf5C//ydK9KrsegHeB/S 29wP5oH9/v995kGD "], {{ {EdgeForm[None], GraphicsGroup3DBox[{Polygon3DBox[CompressedData[" 1:eJw1m3ccV9P/xz93nitRSEVR9qgkkcqorKIie4ZSGUVShJRK1teKrKxI9h7R MEIImdkjaZIkM9vv9fQ6vz/O4/267/u++5z3vpv0HXzQaWmtVjukqNUy0XVE 1xGtp41jNIYIT5XAmXmtNjap1fqJDtG4XPh00XM0LhY+BaxjL5f8d6Lna3u8 +MNER2lcKXyG6Ik650jJzNI5h2p7tPh9RE8Sf5T4L4h/rrYvFX+Q6HkaufC7 2n+eznuVZFaJPljWamcJnyG8k/bvrLGWtteU/Jqia4iurVFXuK7oCu1vq7GB tjfVubYWPV/X2lG8HRIfUy/KDxF/pXjtNDbU9nBtNxe9S8edLZlU/DeF1+Ua 4p+p/VuK1zHxuztR97ax6Gu6t7uFB8MTPkDH9BG+V/K3lMaHiz9G/DuEF4q/ v0ZnjfV1rrHiTxZ/kbYryT0q3pW6Zlcd+6D4N4rXSvse5roaS8V/QPwbxB+a +Vu8rf2Pa18DjSnavlD7bhX9nXcoXv3E2+dJ/ibRzyU/WvuuF/5F9DTxL+RZ xD9VeJzwHOEjdR8DdezeosfzjYWPEz1cY7jwYaK9JN9X8vdJ/mbd2/HCh+mc pwhvItyU/ZI7Q/IHcD7mkPARordL5iTJHCv5U7U9QvyjRSeJf6L4vcU/WdsX in+S6EhtTxD/R9E7xVsj8Vzhm+8Rv/s2ortqrCu8OPE343ttK7q1Rh7viXs7 W/e8lXhbJF4XSxMfz7EtRFtp6FZqX4u21FhPuJ2OWSi8qUaS+3z/6uCDUu/b jGuIqVuspdCIT9C+HyT3uPDV4m0n3F74CvF/F+2j4w4W/U3jOOGDRFeLHpv4 nf4h2j/xe/lR9BiNA4Vb61xdRK9Lzesd+Tvlpu+Lts99vmd1zWWi+ySe6z+L nsC34LpcJ/F8hdcv8i/RMc8xT3T+tTSaiv+itnvqxTQR7lH6PfI+7878nnaP 72qVaKfE+wZJblPR1/Uythevs0ZjbW+ge9tSdDK6ge8h/GDmdcv6fUh4G402 wheLd4bOsw1zXuf5gbmZWI51vFdcy8sTX4PztxFtrVEHvaHjGwvPFP5J53xC 9Bpd/wXxNtJ4iWuI/yzvRrJf6lobir+f6Ezml8Yz2nd66WuiYw5GD/C9RK/S 9lzx/9Gxv2a+zi06/5+Z39ldwpcJv8z7lsxvmc/3U2pdzDw9lmcUvkz4LeGx Oibo/B+KN0a4FP5AuLvGQMk8Ipl9hfvz3oS7CfcTfkC4h/Ag4UeFD9b76i18 h+7zqMIy9wgfJJmhwk9L5uzE6/1UbfeQzGGit0pmX133X+FXM+s37MWTzDfx B0u+u+hRGqcKdxM9WCMRnivZXtgDvrfoz+gO4W2REV2l8+yosYnOdai2f2De 6pgdRC9Fb8fvfonwPhq7avyi7Sa6pz1FO6BvS3/HScL7514ju4kuL73WbhO/ o87TQfgqdJr2/yy8C7YhsV4axprIPf/bie7NecWbqHOcoO1hfF/R3cXvJv7l QfpG5zxc+E7uHZshPFP4GR1zDe9KdJrGeOGXRGdoXC38sujOGpOYw6J75V7j bUXnaPt28d8VbatxIzpZ9EWNicJviO4iub+EX9Q1dxf+W3i28J66tx7CW0pm d947z847FH934cbi75hb16AH2uTWNTOFdy6sQ9Al4yW3h3B78boyj2s+787a 94fo85LfK/Ncukv7d8v8rm4V3lv4WPSA8C46fm/h5jpfl8xzabL4HTW+E27F 2iutl7pJ9urS+qqr8Ia5128d0Sa59ftaolvoPE0kc4bOsbHkthV+R3Tj3HoN /bNZ4TmDztg2s845V/L76Rzf1KxrNpL80ppt/SbMlahzWmfWLWPQ4+J/G/XP lhpNhYeh5wrf+zzR60rryP2FLyps38/CkAjfr+tdit+EryL8hfhjC9vfX0Uv FL+O+J+L31h4MWuTb4qeTWybR2r7ZtEvxLtB1zpS+AAde2lhO/8X98J6Fu4l PDzz3PuAtZzZVn4ovKdGV51zO2234B2JjhBv+8zvcKxwo8LP+Kpoc/HXFx4g /s4aHXRse3RrZv9rqHjbFl5Dn4iuwNZgR0U30/3sJ35n8bcpbdP2FW6gfQuZ JzpHT+HTsR2i16JfxO8kmT21fTK2XHT93LYbA9swt13ORDcsfP9viK6X2T73 x5cSroRPFm4grCVaO1F4o8Lf/C3RZuI3EO6HL1dY93zIeQo/7wuib+gam+ta /0j23dx+I77l3Nx+Arb+ndw2Bf/zzdx+Bbb+bey6cCX6T24f5YK4vgdFO87a HZD4W87IvNa2wtcurZe+0D08JX5H4fHoPOH9sSPCG0hmdVzLu/AehW8Wf0zp ddpQ9C1sJ/qBbye8j/ANwleW1p176MWcIP6lvEPeg0avxPtaSyZL7Hs3F797 1MkXlNbVn4k/J/M3naT9b2R+ji66/z2JJYSnMNeETxC+X7hvYp3DMc9k1tvb SP6nxPqH9X9R6eu01r09IZm2wv/TsQdI5ifhdtgDyfwo2kYym2lfz8Tb70h+ NDpWvM9K+8g7Saaltqck3l4gmesk8zE2S+N28dbW2EH8Tugh8a5PbGd5L4Mz 695PxD89s+79VPjaxPb3Em1/JP4VXF/8r0r73oloG40isV89TzIXSeZ1yZyc 2Ra8J7xU+E7hxcIflL6XHXXPO2j7vsRxwWSNeonvY5HkbxGdT9wkfK/wUuHW Gnfjr2p8XNrP/1fXfSXze8Xvm196/846//niPyL+tzpuUenrdBP/Xvy1xDbp Q/EbsgZF99ZohF8leoGOfUr7v8emaLtKHF9sJfxPtDudJXMoekwybbUYX9L2 5uIfWtkfOkT48Mp+z2HCOwi/gI4W/lbjOZ1zc93PCuFZ6BPhQ4WbobtFv9aY LryZ+EdU9quOYO5ozMEPFP9I4g7h73g+yUzkG4i/StuviL+V8P7iL2dtoJ8q +4K9sMuV5/e26LDK72uE5LtWfu/nCLevPO9bSmanyu96a+EOldfVUMl0qjxv zhI+qLLfv79kDhBeIdwNXV45BjhaMrtXnnNnCu9Sea4ME14muad1z5sGx4P4 rp+U9nnwwxeKDi0cu30t+rC2zxXug89TOKbooGOfEn+M+KcV1mGLEsc7fB90 9XaaJ78k1k2s5058K9Hb9R2P1v100v7rea8af0abflJi3wPbwzdEh7cixi8c 4y8UnagxQ3K76h6O1XkmaP8SHdu6si5oKrxdZT24ofBWldfkMZL/Wdtv4isK b1tZlx0r/L/SfsjG6DONtyXTQvwNJNOE+Se8qXBLdJNwE+FNmG/oDeFWwkcJ byy8FT6ccDPhrZlLwp+IXqnzv6tnb6/z101sZ+eLfy3rPvU3JDa/Ivc8Ih6/ THh27nt+KrdOR7c/qf3bB/u3F+IDpvbHpom/t/BB+KscH+0m+v9T8d7AZ9D2 1pI5jDWInY5+L3r4+5ptbuuoT+F/T0yt8zYSHogvKrl3seni3yD+p8KV6HO5 18KDos/nXjuPiM4TnU2sIblrJfcB5le0vc71rvjvaDyGfhH/99Lvu3nNuZOX c3+L6aK3id9Q/JPEv0n4c+E1Rafp2DeF/9Cx16f2D5/LrOOJC9DzjwTjSySz j/YdJ949xDjMRWKNOF95V7/ntj3MWXxg5in+2IfEXInjDvIL+xT2hy8L9i// Pz7eVGMn4W+w14njF/I4awfHQTeVjglOSxwX4w/2SByjMteZ80/rupelvvef 8B1y+0WPid8q2O88u3T8gp3dVfta5P6ev4g+HPwdz8f2aiyKvspF+GfCj2eO obsljqOJd/dPHH9emPr7f4v/gL0Xnso3ye3b3J85tsKO85ysA+6dOPvJ3Dp2 CnMn8bOx7wrJvMo3kszlWfzWwpN1T1NrjnOvFv8tYZTI+Mzf9F/JPJRbV68W 7/7cuvom0Qd07POS+VHHXpoZ/yr5e3PrtxtEb8wdX3+n/VMkP00yKyV/c+4Y /Hvxb0LH1Zxz+J+2Zwmv1nnmlfaZ99a7fKt0XmgH4WGZbdqXkjkzc2y1QPiW xD459vi63Hr1W+3/LHM+aaR4o3Welcw1nWdy7vzAz+i5xGuP936r7ucxKPFv 9LE5ZlJu/fwjel/jNL6j9l8kPJ33IDxC+D7hZcLnlI5NWupa94m/BfeQWj+1 jt/5gcx+LPmSUaVjqE6SfxRbJDxOMnXFX1GzT47+Y04yNwvxl9Qcs9wp/kbC Z4n/sHAL4QtS60XmD/Nol8TxUTON3RLHVqzzChtcs898YeZv9IPkJ2j7npr9 hNHiPyq8Qvw6hfOWl+ueO5X+dl/gu5aWfx9bltrfYG6N07FPi65KrY+Z/6wD cqe85++E78GnED5P+PHcPsMdzKPcdv8vfOrK33643k9/8c8S/1DR0zRGCh9L 7KXjpwrPqJk+FXG91HPyNfQr60T4yZpt1/3oitTzg3nyXuac98TE9vXownmY 20rnlchLrFU4H3q88NW6n4Mz5xumpdY350Wd0yg4T35v6fwguYtlkt1Rco+I /5C266deU68L3ymZF5jPouulXsvoeubd84nzTfiazHFizIm5n29+nO/TEuen HgvOOfNd8AEfjX7gVbmvuYbufUHpZ8G/XZ76O67MnJ8lH3ldcI7kgsT5+sbB dYT7Jf88axUdVTjewL8mfzVE22fyriRbiX9jYrnL0Ae8F+EXSufBVkh2bur9 5MFOyZxLmSfezNL56mWSOb3wu+W6ZeraxNia6wzXaYwQDuJfw73WXNOYkNhn ulLnnCP6Z+oc0tDEz3ZI5vucTp6nsD0ar3s+RniA8LXC6wfnrqeUzvHx7tEV PQvniK7Q/iT1O+L99MycY3tMvHWCc1DMGeoz5E+wO9iuM6P9ahCcM58sfv/C x3KtdVPP/3dq1p34WuQTryusW0bpuKsK673zgtcg3xif9trCccRI4c4xfiGO GR/zGKy5C7SO1iYeDdabnGeJ+LvGWIOY4zbRexJ/j1GZ58byOGebpp5DF+ET iP4t2iHq6nlRDxDj4FffUTi+GCN8c+E45XzhWwrrk9HCdxbWD2OF7y+clx6H ziycs71A+G/iUuwXdrlw7HZucKxFzPVl5jwb902u5q/U6wmbRR6W94etwT7c FW0E9oM1EXS+S3LnXr7KHBNy/oXCbYPj/TdLx5GTEs915vUdcW53j7HnDTXH lsR2/+p87YL15Kc69hnRZxOvbe6Pb/FVahuDDlyZ2u/E/8x0P3VTH4M8uUW+ A+uDuX9znP+sE/jkoD4qfSxxYk/simTurrkmRex5l/Cjwfkr9DN+Lf7t4sy5 bHQGNprvhgzP0CrG2nzjNVK/W663JLWe/zpzPpTc1A+67rjc9ahPM6/FW+N6 fKz0ul8gmSz1umXN9iucx2b+f5zYd/24Zl34atSH6L+3og5Ef78cdfj1PLNo KXp3YR2ILnyo8DtDjz5Y2Je4k7mX2e+uK7pNsB/+U4xN58a1dov2zUe3i36Z 2Of/UttPFNYhdwff40fxPkenzis2RTeIt3ZqH+s+8h3oY2JiXWO2eL+KXpfZ By9EJ2i8z7cWXV3aB0fmycLPd0+IPnviY2blfl58+8cLX+d7yb/H90zs01+j c72NPhedl9s33l70E+3/LLG//l/8kfhdzCgsf3/ws86Pz9uB3IfwAt61jl8g 3hzR1zVS1nvm3NiSxHWyv0rX1p4rHJPwrVIC0eC83Czx/yldO3oem5lanoNn Fn73D/Buc79/4qw+qeXJDabBNcDZku2b+pwh5vC+TVz/zYNrRC9LhqI48uSZ 70KPiDaonNv7JnHNqAzOOb8i+emF/V7mw+DU5yEvfXpqmXWEz0mdc24kfHDq /On36NbccwNfd2Hub4dvvCDacXzFl1OvV2roz6ae+9Tcqbv+m7hO83zq2vpg fObgeunfos+lXjvU019JvZ6oy18VXL/9WfSz3L4oNRryrL8mtjfEOn8ntivU df9KvN5uCa7bp3onn+e2g9RBsFd8YGztgMLnnCjZIrU/hC/0ROn7/ApdXvpZ vhROU9tdbO6HMV6jljG48PPh/0wvff9Lxbs1uB8h0z08Xfr9LCGnG+c9cztP 7Tfw/HcV9rEna/9OMRYgJvggty3uKPpxbn+MWk+7zHEo8Sg6fMPUsQP6DIxO ezW1jzFc8otyrwV8ztcks07qOTE/+lfEEV/m1ie3MR8y60B04fupdeBo8b/I fZ/Xir6Z2s8ZEW0BdpMcJnF/49SxIjkEMDnSG4Lr538F59F/T+x3Tgiusf8m +mlu/4W62ye5fRXqYh/l9mGoN1EbJhaeGWPVhqljkDaZcxRXx9gRPvHjIalz AQ3whVKv//U1Fudem8SST0afDd/tQJ3nDNYpeYBgv3tusJ3B3pB/7Fg6/0ke lJ4I+NggaunU1JlqrCHWUr2ar899zBPeK3XctTLKHJ66VnFwcFzRK1j+iNT5 C+Iz5LeLuuvIqL86xOf5SvwDS8d6i0vnZo5OnZ85qnTNnzotNrN3arvJtcDk S3ukzi/zPDOCaxKHiOal6zuDS8d1xHeTtO+g4PM3jNfslboGjY/AefATLpVM ffQfuS+N9dANwe/nsNS9CMR+xIBTMuflybVTp6tXOlYln9Yq1sWoqdYvnc8n r9+ycJzVO/iaPeJ1dyid63699P20iM/VUXLbpM6/rVc6d0Nerl3petY7Ze2/ +hfy+DZJYVuPzd++cIx5nOTXijEssSx1Duodj+je9ipc7xggmT0K1zj6C+9a uJZxAuu6cN6+D9cV7izcV/gSjZJYUHQ38buK34/5XLjX4njhU0vnU6gPNihd c6H2Qr2BusO94g8tnWchN7tGjM2J0f+MeRvyNw0z9xcM0nEDStt3ehNejHb0 BfRzZl8Un/Sl3DYVe89cqi/+urqvo0vX/enJOibaR+xk39K2CZv2Vu5+FJ7t T2y5+M8SK0Rbic3sU/p+GhbO9eET4dv2K22n8phTZZ1iSztkrmVPxC+O+hk9 fXzhvoKHiGWiP4ZfNrBw3Z9+sVMLr+up4t8XdTK6+fEY6xHznaV7Had7PjF3 nhZbTz7ygajb0fGPlvaRnxXdrnRd5ezC+Xzufyp+fW49j20m/4//eU/hedwF /Zl6HqLDyRm2z/zdbxT/+NK2mN6T3qVzGCeJpqVzGxsQz5XOq9GvFErnW4aJ ts08ryaknjOskbNETyidJzuNWKx0Ho5+ENYaOpM8HvOHeX6e6PLcOcBmos/k 9sNm5q5TsNYfIXcS/cK7c+f8sSP4qtQ+iEfuFd4vsw5/KHV9hBjzYfJP0Q49 Krpm6ZzPuaXrHfgY+InoDXI+jQvXWfA38H/fy91Xh34h/mgV7d2UiKmdkcNp kzoPsHnh3oz3ickK97FcqHMfl7qeQM2UWiPrdFbmXgJ6kugraFY6b0/+npol 6/rlzDUGag2vsR4L541fFL0yuA78neg1wbXiVaIt0C+JazRbFF6zH4iuXzjv /VLhecP8YR2wPlkX1NbprWGtUYNuUjiPNFe0eWH9+Z5os8I5sXcLvy/e2xxi 7tT+ILXdkNmvJ2e+dmZfmLw3tabPhOuQxyrcC3Sx7vmrYF1AHYpaI7prOvFF Yb33sejWhXtUPhI9CL0gfDJxYeocAL00fcU/hblHXrnweztRuHvhuttJ5JIL 12FPIbZInVuiP+f14F69gcF9ivAHCx9ZuFY7KLgPj2udKjwjda6CvBl1R+qP H+v4k8mzMOeD66nUVd8nT1m43vFpYZ+e9U4ubrvC65T12rZwzvxzvlXpmjW1 66aFbeKb5G80hqNLdf63g/VNb3KBpXtEZ5fuB+XehgTXaajXvB3l8D1nizcp WPe8E9yb2Ff81zP3ZfapuZdp/ejDEL+sW7qeTJ15de4cPrn8H3L7PtQKftXY InXenvrz5qmPWb90/ZwaOz1O8KkFlKXzqGei40vXuC+KvD1S66A1C+cTyCuQ a2mbOme4Stubpq7p4Lc0i35OndL9KiPwGUrX1i/m3eiY7VLnBIrC65i8C/Xh 7VPXgOkvBZPHIK/ZJXVuc7fSa536+R86ZqvUtRX6Orgu9YXdS5+DevJ3uesL 1Bmoa20S/cPuEfOc9LzynukX2zfYt+sajLkudWfiIvw2ekvw3XaOPt6+pfNZ n5f23dpHf2/P4B6wLsG1lF1S11M6B2Py4Q2iPHFyl9I+JrVuake7pa4fIU8f 0u7BPmCH6PvVK5yvIvdJHyc+Fj7fbvgcqfsAtyhd97wq0m6pa6D4SvultmH4 mHtFP3Pt0t+NegJ+b6fo3+L70V/VIz7TPvFZ8OnaRb93n2B56ho8O/e/R3x2 5H+JMvQM7YVM5TrH2cLXB/fn/kRsqLFx5p6ZecIDMvdvv0t8J/wNtkC4r/By 4Y+C+4B/En4/uJ97ZeE+Z/LnDYN7d8mrrxds08n3NsNHzd1znEr+4+De4p8L 9/SS/1xXvC+C+5P+FP9D4UGZe3rnB/co/y38S3D8gL76OriHDN9vZXCPI3pv RXDPGb7iB8H95d8X7kMmt15PvOUaHXL37H0Z3FP1j2QWBPde00D0bXBPG34m /Rvos92IeYN7qv4o7OeQn99YvM+Ce69/Q49KbkDuPPZpwr1zx8L4NsS5s0RP F//wzPHvqcKH5M6ZDxE+InO+62ThXXPnivsIr5M5B3KCcKPMOY2TqI/nrmOe Itw1dy56qPCxmePigcL75a41DBI+MHeO+hOul7n37PvgnlHsBb0B9PlvRMwY 3Hu6unDNBp+gxB8L7jfFHtHv+gtxTWWfjT7550r3utMLWj/YDyR311z4x+A+ VOxRncr1P/K6fYXXzZzz+Sa4nxJfnR4kdHnH+D7IxdPXeW7hfhvmIXVw+kcf D66b09P5ZHCuntwE/eb0E9J3VSdYP6In6Tf4/3VLXwr9afS1Tihdm0eXPSH5 K0r3QtCXgu+CD0N+bLPIZz/vlflMfyi+Cjk06szU+8in0DvP92Ee0htLLZuc Cz1uN5buj6WHhG/Fe6OXFj+HHAV1A+YI64v6FfUC8jL03V9QuCdwdfQJif/p p6OXklr1msH9sfRWjSttC7GJ/FtB3p48bjca5SrbS+zr0uA+TuIpcibkZbtU zgMv494r9/xg/zsLLwmOH4inFgf73MR0C4PjImLeRcE+NHHlsuCeVGI0/ukg LtgguBZAHn1v1kGML7AXE+J3+a+vJnc/Onmbpwr3MODXUb/neenbpW5L7Emv HT2ZzJm6wbWtr8Xbt3IvB7n/vSr7EtTudo3riXlCHy49ltRd1gruq+Qb8a3G xfdJj0o/HdM4c/6wv/AGmXODs4L7JvFh3hI+Wnix8Jzgnmb8oteCe5Hxf7AL 9H90F+8NdLX488V/U/go4UXCLwb3vOKLvhLcn4p/+Gpw7yZ+I7aSGiE9Ng8F x52NdF/PB/c74t/eXji3cGBwXE8dYv/gejEx9+aSzyvbAnwqbBz9Fj2Dcxf0 5jfV/jGFbRzHkic5MJ6THA61hAOCa83kITaS/D/xWfAz/1c4t4DMCxqbZ+7d fSm4vxbf++Xg/lT8efpAlupZtqkcO9Ansn1lv5HemZaVe5LpcfordyxPL0YL 8bfMrR9W5s510IOzdeX+SfzuVpV9M/pi9wv2o+izoVdq6+jT/CjaMrdu+S13 HYE4lHh008L+G8d2KewD8K5mB/ccEx8Rp+NXkuduUdgf4FvT1019aG7pPnDq TO+V7v0gJ7Rh5T4r4qc2wv/GuYS//UDuugL9O+RP6DHZonIPyRLxthQ+UWOz zP9H0DOAr0jsSR7jv34Q7T9H4/bc9Y7hwgMz12XOEu6fud56nvDU3Dn/s4Wv z51TPbfyfVDjOF64fuZ8/uHoWOHDxKsq9wdQWwyVewKoP47gXnPXLOpW7i+h 3jdSeFru3P6alXs7qPcNEz4ucx74DOEjM+eTBwhvlPlfkqMqx9jtJb9G5R4L 6p703pOLfkV0VGV/GnsxtrKvPFV4dGV/F9txfmX/Hv1/pvDxmWvKa1XuiaHm SO4Pf5C6xrqVe6SIf48R7pe5xk2fP7W3TcT/Nfj/CmKr3pV7gncR/j34nwHi rD/i2iemo7+C/FNzyU4P7mMmnp0W3Ov8X1xcuB6HD0MsTNzcTvLdS9fke1Xu 2/xGuKfw+Nz/htWVfFm574R6aP3KfTzUT/mXZ5XwgZVzPtSymwbnhag7Nwnu LeQ/rA15D5X7fqi3FpX9RuK1Z4J7xImXqSnTv9C9cg8Pten64u9Zuh7YI/o+ nJ9/wkYUjuPw3wYV7ulFf5Jv4V+AIaX7VqkLc688L31B/KNDrva//GnpHM4y yQwv3TNKPxJ9p+Rq+I9gjeBaKjVT8vMNK/eoEaczx6kd0pdEjmghMXjp3BG9 6ANL1w6pO9LfxL949I7Q00itlronuX18Pd4V/67dG5ybWafyPylcl+vz/w/5 kxm5c1D0t1eSnRKcB1q78j9I1LVZV+RhiTHoEX0wOIe0XvQ9safk2+jNpYaO f0YtfqPU9o86IzUL6nj0gOIPYPvJ1WI36ZOZG/USPat3BP/3mVTWTegoYgpq o+Q2yXFSq3445oc+j/Eafa3kq6mFUTejb4S5R/8AuVnqcdT60D3UU+nfOaVw vzr2DpvAf1zU0dAr9OHTj887oVZCbYf4d+PU6x3dio6l5kj/Bjk0cmn0xdIH Q88t9V1qw/S70RNMjxA9w8N1rotrjneIh6gfUVO7Pbj2Rc2ZNY0Pz/+v5BPG 1dxvQrxC3Zp6ED4sNU7+AyDXTd85sQ91K+pl9LlQR6NWRn8Kuhb9wL+z1OnR UaNinoG+duwO/ycy3/gHgjiAnl3+PyA//N8/CKVzv1w30bmvKdwvRCxDPoE+ 8rpxrdFbRU/31YX7B+jreC767fQqX1G4r+nvmDegpxyf/+LC/6US+5Cj5lv8 XToWZh2Pq5wTIDfIHCAnwD8D2NxLCvdaEN+9EvUwz4r+Rrfwryd9UNQ1+a+U HDU12hAcPxFj0qtFPWtAzb025DbpURxZ2tY0T9xDTi2MuiV9Mdgu4g7+LaY2 Sl2S+iS6nPiU/5vpn6b/id7vm4PrZvRtYWeI7/hfmd56emPQoeSC+EcCH4Dc EzmoYXGNUK+kbkmunppxEVwH2Ta1nicnz/8gWXD9i3oodVFiNWIu/rEmJqNH hNoftRV8mTFV7NNIbKfosSdXuiPxfeF/CojZh0TdyH+IJ0d9Rf/z8KgD6Wem Vx59hd7iefl/d3WwD8s98y/LM6nrsfzvjG55tebeAfxJapvUvUcV7n8mJ3B+ 4bwfcf24wrlBYmH6Fshvk+emd591x/qjtk7tm/4IetO5Fr4uOSz8E/q0qcNu kNrn459Z6h3UPXg2npF/woiDeZ/8Z0MPBvUU6irUWJukjlXwC9Cl/DNEzoP/ UekBoPa2uOY+8IFRn7PO+B+cHDi5cPwmbAT/nOHXMyf5B2hknHv0jZPLW1pz 3zh10kapfS/8Gvqo6afGb6JPYnbUJ/y7/Gdw3wW5enL21J+oQ9EnQI2emiz9 ZfR4/GcTok9ETzW91cQkxCb04fNPOjkzaoHEe8RBO0VbM6vmfhbyP9T26Sn7 PyeP1lM= "]], Polygon3DBox[CompressedData[" 1:eJw1nAf8V+P7xs85zxlfRBGSRDu7IVEqLTSkITNaKokyykhDS4WyChll7xlC SGWk/LLJTjaJ7JTof727zv/16ry+z3We55zPGc9z39d93fep9sCze41Ioig6 Oo0i/YvGZFF0if6+EaKoZUUUnVxE0Y/CddVZV/hv/W2gA/7Uvv3Vflvtq9Te Jo6ij3TM2dp3hdqp9i/WdoKO/1b9NbV/Tx2/SmMK9V+vbaX6N+j3blb/p2p/ rLF18ijaLHyYxjfV+LP09zKNDep/QdvxGvO1+nfT/hrqP0x4X+3/V/hU4UTn 2194Rx1zufa9qPYEjQ+x7+kYje+qfTdo30SN/VnbTLXf1L73OJd+P9LY1zT2 EJ1vf+FY+Fid61Dhahq/vYbsrv7nNX539f8t3ET9B6r/RO37TnhP4b2E12r8 NOHXdP531He98ELhqTp+G/WfpH1rhWtrfB3h+uqvJ3yv+msJ1xKuKXyncE3h vfV7W4Q7a/zBwj+of6rwCp3/bZ3rb+FbhD8T/kT4deH6wmOFlwi/L3yQ8GXC r/AsMz8fntMbwquFLxZ+Xvh/jNfv5rr/2dr+p32naN969T+u/Q31+ztrvHZF Owsv5Nnqus7W2HO07wmNf0b9n6p9k/p31fiawtWEbxPeQ3iu7ud47k9jf9Hx n2n/djr+Fm3val8L7dtbfztqTMvMz5HnmdGvsRP0e/PU3ob3p22Y8DThRO1F 2n4WrqW/v+qYBmq/w/xQe7L2vaRzVxXmlVfVuZ4RniF8P+uA+9b1fahx1wtv r3Ou0b6V5fsZw/zW+OO0fcn969zVNf499TcVnq7+l9WXCzdSeycdP0NjPlD7 OvVXEl6t831Vrrel2v+6xo/RmDnqqwBr+1JjKgvfoe1j3rHG76fxk9R+UeMv 1Phr1VcIv6ptH+3bUX9jHbebrme9xl+p8W9p3/vqG6XxV2t8LrxM23nCVwpn zAdte2j8rho/T8fvruMX6e9G4YEac6jGHqmHtZva16l9DM9H41uq/yp+X+ff p7y+R3RcPfVP1L6nhH9RfyXh29X/ivAV6u8t/GLwev9T51ylc74s/K/wUOF2 whcUtheT1D5c44fo95sL19O4pazX8n0+Jlxf/R8ItxC+kvtT/1zhJcKXM6/U f6fwMuEZwdczSmNSnW+szn+Y8Kfav63wTdreSfzceH4Vwh+ob5LGPy38q/Zv j33U+UYK91LfX+obqS3R2It0vhbqv0vj+qj/P20dNGYMc07936jdRf336/iV PD/h4/l94c6sFx23QmP3Et6DuRs8X6tp26Ljn1F/ovHPBduf37XvPf3mbixG 9S3UFtR/ivoXqP0URl54F/X/q7GPYzPVPr/w+p6oY9tgj/R752loTx33p/oe E35PeJZwP9a79m3W+Pk6frPaPfQ+ajI/dfyx6t9T42tofP3g+Xm7xvUW/kdb W+2brH2LeN/aX1njb9HfbsIbtLVSf2Od5xSd7zfWt9qXFp4vG1lDGj9F+AXh v4SrCHfS71fX2Dka2134Zu0/mn58iM7XVeM7YgeEd9DYiwrbl2ka30HjDyrn 71Mau5/w08KfCN8ovItwT43vqeOfxGap70Ftb6h9TfDv1xOuK7xfsD3dW3hf 4cbB6+Fj4fbC1+kcy4Wv0dZf+EP9/pk6fnlp30cl9icPCL8ufHXw82lazueW wfOhhvAuwrWC7Xl/3dP+up89hV8QbqBtB53rH2076/gGpf2/X/21hXcX3ll4 r2D7dWBprw4Ovr66wnWE9w22X/OF3xeeHfz8j9Hv7aHfu1XX34v5rf5X1T8z eD3cIrwY/8I6Eh5d2H5hx45i/mVbp2G0i/Cz2K/C6+1Sna+9OnYU3ij8sK7/ b7VfKv3F0MT+pbd+fy/6Nf447Edpr4Yktt/9hC8SPg67JdxdWzf13YMNUF9D bfuo3SjYPkwrvP62JPZPJ+r8tXX+p3X+E4Ublf7yCY3fW/h49ddS/ybhE4Rb atxpwn9oe1/tJ/AJau+t9iXqf1z42Ni+Z7xwF+H2iTlRntumYdtux7/ybHPb 22ba10h93+j3J0W2zSOFF+Ren09j44U36zrehq+V1/yTxs8Aq/9d/da92veS +pZjEzX2UeEP1f5A+xarfZe2t4RXCj/JdWh7E76CjVA71zGr1H4Zzqb2Q/gw 9b8v/ILa6/R7V0T2VRfoepbk5naXYZPVrsd6Fb5NY0ervw72k3MJXyS8q/B/ wmdr/D0a/7C2MzmWOYx90t/1+B5tO+mYbbTvILXr6lpO1/GZ/r6HrcYnqf2j rufyyNzyFPVX0r5P4Kbabla7uo6PuVaNeUi/tXthrgDneUR4u8K2tJfwtXAt bfWFd9SxA+CXugb9RPSd9r2u9vaFfddJ2neDxlYqPBdOZM0LVxbeEHvN3yy8 UMd8rvZq7XtF7arq/0ftodgX9T+lfavV/pRnDncpzH8HBz/fStpXSf0/ad/b PA9t2wn/KPyW2hXathX+QfhNOHdhX88cul/nL4Sbqq+6zjeY5yfcSHg34UHw s8JcYCI2kflYmMuPFb4bLoyN411o/ECND9gQ4WrCpwnXKMzVx8DpWKvCk7h/ 9Z+v/r/08G5S3yca85H6ahfmI2eq/0L1z+Odc//wSOGB+jsx9r7eGvOtjp8c mbuMUn+VwnyzS2lP79X+v4TP0HYkfD/zuzhc/X3UvybzvRBLnCs8W8cPEf5I /WdV+H0N17FHCQ8r72c07zN4/Hc6fkpkrsX98L4Hq7+Vxp9R4fl0qfAJGn+O 8I3CI7hf9Y+o8Pybov4h5f1tK9xXuKnwUOEvdP5xkWOn84T7Y0fUP0r7jg2e LxcI99b+4RW2k9jLc3gG2Gg4nto9y/vZXvs+w7bAqYPf7wy1+wbbkwo4m3BN 4SHCv+n3Z7G+dd5V+Orca/m0YD7zeeZ3C7fm/r4XvlR4OVyJuaFtuc73iH5/ jPobCi8TflD4YuFfNf7ayFyd942fwl+9GNs+vZA71piuMQeq3U3HnRv7HXF/ x6W+/wuFn9VxJ3He2M/4GPV3z7wW/xccr8GD4cMrNOZatfclJlJ7gY4bq/7r hE/X+I/L58l6maD+fsHz6wTslPBwjekGR1D/m/Av7R8Hn9U1nort0m/8ztzX mJ15FrE505e6nvHqX6J9Kwu/n02xf/NWHfufxuhf9BXvSOecn9r2YoO53j+F 6+vYE7VvP+JZbR+r/Vlke7ZT7v6/Sn+zo3AdYqnS32zR3/2Eb8MGJl6va1nv On6Axv5InKr9R+GD9fcy4R1iH4OhI64mvr5H+6aq/Vtu/76BGJv1pjHPwbeC /fFzOv95iTngZfCZwvfyVfm816e+vuN1vn3Uvqewb/qinB+76PwN1b9JeILw xtS4j8YfmNp/Mb8+0jGFfvNeHb9C7S/L+fa7/taDU2rMFYnt2zrsncYM1Ln3 5R4ij/kjtX3/FnumfaeofWXqWK2x+it0by+ltsXY5FvKGBcuAufYGvtmHsvz +ZyYAL4AX2S+YZ+FmwjPF75KuF3u/h2wY7ref4W/xr/ofH3UnqCtl/ARiflq Zf1mX7iu+k8WfiO178EHZanX7y/CtYX7qb+1tgM1PoU3VNhf/gofUf/g3BwJ rgSHJPYNmcc/qjEzU9v379Wuqv6+Gv+08KDEnHF6Yf/xs/p3Fx6k/ndS+yJ8 UmW1F6XmBnAE+MIjOv+L8P9gPgUnJlaE027lyqyJxBzkD52vWeH5xzw8UuP3 0XHT1N9Y/cPVf37uudohMX9qUvh98l73qTCHhksTM6HVwJHRPojZ4c7wj22F n8WH6/hF2NPEHPxy+C/xQeI5/X3umAutgJiWWAyOj3YCZ4b749+IvfFJD+Z+ n/V0fVV03f1L//S38ADmT+6YiNidGIFYCf9OrA9neZx3VzhWqa/7aYC+gw8v 53Od1PanamLO92fuGHhiYk5LbLwlWMf7RuNXpub4aBXEKHB/YoSLE3N8YocH Cz/7r0t7Bj/Bvq3Rvh3KmINYtFPkWGRj7vh5k/ZVrXDMQGzTLnIs0UzHzBZu K3yA8PM637s636rI/AzOTWx+aGQuvil3vIMRrFZhPoCWcw32Pbd/JBbsKXwN vii3LcTmYfvwN1XUfk39v5Z8s5LwUjhR7hgGLaZJ5NjmkMLrm3XeucLPmeeN DVyn9tTctgkbhS3Ff1VObGN+wdYKb594Tq8THpP73fCOtlX7Xm2D4JbCG4hf hK8WPiNYXyAuJz4/Lzaf6a7tycR62Qm6vqO1DVa7s/av1/FdM5+vk/DPws2F uwu3hgvBnXTsYaydyDFsz8z8uavav6p/D93TEbFtwITEcxm9ZF1kjeYIje+r 9hFqr9P4jpn9WUe4tPAhwj2E2wh/V3h+wYcbaotTa1ZoV/i4oZn5KHyyibbt 1H4oWN9sJzw+saaItjgaTqHxbbSdoHZ7jfuhXKvEq99H1kBn546vWuhca9Xf OrNe2E593wuP0/gG8N3IMe7DwXp1Bx1zifqmaDsAvhxZI7q49MdDg/UwNF+0 X2Km0ZljJmInYponc2uiaKNodMMyH4MmhGbMuSaV8c95wfr15DL+Ghmsv44v +dEI4Q85V25979xg/ks8AZ/eV1ua+hrgf6cHXxvxGPETNhIfyhpkLbZQ/zeF 1xhr7SDhrwrrsuiznfHXie8BPX5I8L010fij1H+o8NfCl2tMM+Z25Bh3gbbf 1e6uY2awTgvPbezQyRXmMHAZrvEmtXfNzCV2hesI/6u/dXTsl9pew1drW6P2 F9pWpH4GaKCzIz+bt3L7TjhGc7Uv0PFfwFfUv1x4Nf4osSaLNttKuJnww8I3 Zn6GxBI3Rn62r+XmOmj8TXO/A7Rwcgy8G94RuQZiEN7d58I/CD8gPFO4EnoI /lHnuFrX2039nYW7Mucyx+/4ngOF6+DrU/N3YrwP4Bba6ib2SWgA72NjY2sQ LdXuW/j58pz3rbA+ha9vrX37ERultjWv6JgH4B7q/1L9t2vfVPV/JvyV8B3C 0zLnFNDEyQGcj3aV25fj0+ECq3NzDzhIm9zxI/FfM207qF1N2+GxOcRobF5q fRUffw58JnW8DYc6W3gN/gk+gz3W732Vm+vAadqV8Sv8+nNty1LrBfD/D7Ut QTvQ8asSxxjjMscYxBrEBM/r+Il6Lj8JPyZ8tfrvDtZKDoPzJ+Ze6EFfRdao b9BWEcwHF2h8c/1G/9g+F40N374n/jByDqVIrZfBcQbCh1PHP3DAAdhm/G5s TtYfXhecz2ktfBE2Xv2NY3NoEmdcM/r7WcH3Mq6MD4cH3+up6Gga95K2N5lr Gv+cxj+vMcep3Si1/2zP/ea2sbNLf4rtJX/wiMbPE84yz3nmfmV+Bz27sL79 eamvzNP+x/GFwfrcT3AEHf+S9p2k9s9wQLV/g19W2Keg0R4X2dd8hAaj8Q8J H6X2yty5hznYmNw2Y2bpv7Elk/W7d8bOAX4S7P+nl/wIPoCmxdo8J1jr+qb0 /y1KPoHNmlb6Z2wZPm1WyR/wdSty//bZwfr0sty5IXzqM7l9HBr7KZF930/C 8xL7rCsL55TILeFjri1K3VvHnxk7XrwBTVd9a4PzGXcUzs99VupTG4K5xaux NcetujdzMXY8iD4yRu2TguP1OZmf3bpgPRqdHr1+fOz4Hb1rnNqnlnoAPv6O xD4N339l7liHmIdY6KoSY4Pqqz2t5CfkxGrm9tlzE3MCfPmt2p5Q+/dgPf5L jXki9r7uuX3sjSWfa1Pau8fVf5fwdpl96pzEPgJfCwdAoz8mMjdYW/JBchT4 5nq58zVtsBMVzjkMSq35kIsgRzEgdUxN7gLdH/2/hvD0YI0Pre8s7btN7T+C 8wnM2bt13Dxtz8fmdMxxcg4nptZkyEWgEXdPzafQjv/WuJd1rj1j55jIyTyY mEORq4GTwc3gWDfze9jf1PNhB7hmsL4Nx1uTWh9dEptzw5HJsZJrxaeP1Pla p9Z/iOnRkNpqOz22poemMLXUOy4MtuVoJGgl2Ihb1e6VWq9Bk0Tz6pRaf0IT RTPapDGPJuZ0/TNr2o8InxFZ6ybf/ZDGzhWO1f5N/Xcn5nDHCt+QWm+FwxJj v6P3dZ/wrdgXtTNi9sSa+QWZbTq+ZVKwrf8xON9HTLRax6cV1l3QX0Zp7JbC /PVc4TOF387tu88Pzq+gUaNVkyO7Ha6V+7dHBev9aCZoJ3DQ69Ver/YC+IR+ c7LG/Fc4jww/PkPnn61rWBA7hmON/hSc/8Gm3pLafy6MHROgCZB3JP9YTfum BHMIfP/YYG4B/75PYwZq34mZOQBcYVwwN5hW6mEXBftecmZ9Uq95cmlTMnNB coNtdT/94MyRbSS28uvEa2tnfExpI9D20FSxHWhwaPtormhzaE5om2i2aFF3 pp6HzEdiD2w864D1QKxzX+5nu6VcX3fl1qr+La8PG8M8Yj5hex7IrSUSg7A+ t5T2Cpv/Ye6YFb+H/yOWvTH1e+f9ExujgeMn8ZdoJ2iYzFvmL9omGinaKhow 2ikaIVohGjvaKBoiudFZsbVFbCzvkfdJbH5HbluwuVzfaNTwEPgI2gMaJTwF voL2QQ6AuIb4Bq0DDRi/j/8n9kbDhZfBz9B2k8LaODEKehAaNjwAPoD2gYaN X8e/o5WlhbV1OBN6B5ov2i8xP7E/mjy5D2J4Ynk0TbRNYlhi2f9yx+JwfPSU ebltz8bSXqG5w6vgV2hVaJDYDewHWi/+5JnYaxB/vD53LoaY+vHM/n5RbBu5 NnfNCHET8VOF8Fhtx8SOAUeqfYm2nrFjhMq59RlyR9g44tmHM+eCiDmIn1dl PhcxGbnEwwvHUi3x0WrvXzj26qLxBxTOMRKHsW+b3DUr8C74V4Y+xfMJroG4 BH0gs9aA5oCeQc0NtTesx87wOeEVwTnkLsL741PhAsLN4CuZY1liWmoBfs+t daORLsysV6L9krMldwtHhCuiIaIXfKbtsdg5+cPRGzJrdnCsj4Xj3L4TH/pF bk2ZWqDzY2vN8Cm061cj50Lxv4tj+xD8Lfziqdg261vhm3Lbur9Kf3NPZq6P jUaPQk+Fa2DD0J/IuaMjoydvEP4rd/ttOK7am3Nrx8Tk6JfoM+TCyNnOL/UV cl/U4FCL82Tm3B+cHD3jucy5KDg7+tsbmbk1HJtaHXIa5DXJaZHrIEdBXh5N Bm1mvo6ZHtsekh96WtvM2P6efBOcAG4wRLht5nvm3snT4HvJcZLr7Kd9LYiV M9dbUEvWTn2P6hxTY/t78knNsJfBNQPtM9szuMZV2s7JbFOwLdQAkX+Ef5Jb JeYmPsQnYl+ODvaV2E+ulWs+O3NNFbVVI4Q7oYXAcWP7aPT6hbl/i99E76Pm DI0cfv668AFoPHBd4YN1bGPsIToLMQznR9fUdhbrOTP/p+ahR8n3yVH1EB4Z O3eF/Scvy5pkbc4tORGcn9oW5sezqWskqNV49f9j7shra6L6NwsPiF0/9Wpw /RjrC79HjR21doOY/+pvIrxEY45Uf6vMNW3Utp2q/kMz67HEjHUi62mvB9e7 oOeg66wMrpeDn8BTTqqwBoU+id6H/787dg0G/AO+cG/sGi7iffj+rbFrfog3 yK/Atc8q+Qw1iW8GzxFqFY/XNjY2ZyK/iv9jLjInB2deb3A7cji9hftm5i7k kMi3soZZy3A28rdocHANcorotbwj3hUxPvoj7486G3LE5IqpEfuk1BPgFvgv 5ipzdlBmew83JYfXJ3O9G7m6wSXfZ44x14hZyTfzznn3cEryx+iJxA4jS/46 nWuMrSFUVfsT7eugdm/tmyz8irabY+dU0U+XYoNia0BoQeiRN8au+SJ+Wqzt utg5ZvTnYcF1d8RYxFqvapsbO8Yl1iWnTm6dHD+5fnKM8F74L7mt97Tvgdg1 H4flnjPMHWp6iG8/E+7CvQjPyK2JUnvFNXFt2ABsATVNNXLbDGwHNWJ1hVtp /DvB8eZY4uHcud6+ZfzDHGYuoxnOLvkLOgo2Flu7tcYnOH6h9ocaN3QEbF4o z/9WcE1YY+GjK8xD4aMdcs8/6kipJ22UuwaEWpD/sZ50vva5x3bXMzqWeCdz reI09U/I7P+oW2RfE43dQ+1ddH2thIcJ/5L53BPK+9uYufaM+Y5t7JF5bbHG DtD4bhXmtfDbI3L7y6NCtLWGdT/hBrlrz6hB66BjL8q8Vlmz5FfxsdgCbMI+ uTksXJbfbCbcMfe54dCzWAvCbWJrKOQPyPkRF+Oz8F2jtO/I2JpJUdpr6t64 hobMJ2xg7Bpa8jNLeSdxWYOp9orMtR4tWJPaeYG2TrE1KvI/J5c5kYPh5MIX 845ia77blc8TnQa95kzhobyz2JrOqWqP4B5jazin5X7mPHs0IPJndYJ1Y/Tj c1m7PIPYmhj5lXO0tY+tiZFPa1q49hkN5KDCx++eegxjDy5ci4yG07xwzuF7 9ddMnIugBpBaQGKmq3i/aDLCjwjflFlDoLYNDggXRKOg9hJOAbfoJPx1sIZx ZeaYi9o2bCq2FR/6cXB8yPwh5qLWChuMLUYjoHYPzgJ36Sn8i8Y/SnyceX79 QIxEvJ85J/edxtdInKujpovaLjSeK9TfkfhE+E7h6cJHCH8RHANdllmTonYM zgp37cr5gjWrazOvt++F7y3nG/f3lXAVjW/FXCiszbZFgyh8f3AF3sHBuc9H nEW81bbkb7TRONbkfv7kfuB888v4h3MTQ3+QuwZzY7BGQW3m5sy1uteV18/9 UbfLvkNz55yJM4k3yUWRwybOJt4md0SN65/B64faV/g7XJGaB2r1jihcW9xG +MjCz5fcJzb0scLPj7pjxrTQsdUz54K/ZA0z93NrKYvK+cL7Q1dhX7fcNcnU JrMGWAvkSMmV4kMeLWyPyR2dhI9Qf6PCtdLUsDUu7F/IDVDDNik3v0h0/ne0 75/cNcbUWJDjv79wzd+6Ur+lFhCNEa0RTe0u9X+aW59Gw++cuwaXGJ2aWmpz qemntp81ylr9Ore+TA1mz9z8B/2Z+UK8Q00vevH8kg9hz28NjleoZaYGn1p8 bAi2hBo+8hzcUxXWcuFvGVqx5gvXEJMnIsbZPncNM3kyYoxKuWvWqV3/Wjgt n+e3On/1xLnodzPHOmjOtTPHP3cFxyfU8sMPqXOl3rU6viJ3repROr5X4Zwv OUlitEcKxzvUsTKmWm5+Rh0t9bS1c/sz8n68w51z883DhTcJ71Ve7x3BOTL4 /G65f7tbGT/xTQbfZmCzsd3o7+Qb0IDQgooK52Nuj6zH880C+RjyJXzLgCZL 7TCcbUW5fqhlpkbyhNzfjPDtCDEosWit3Nfeu+TDfPPAtw/YcGw5+aRtIteU DSj90YfB3zSgtTyaee5Ro4mevZNw3dg5J+ojqJcgB6VLjHYs7ek0bLL6u+au WaF2Bf58X7k+0QnRC4/N7cPx5dSYos9zDs5FTp/6jF65x6Ix3pE5h0MuB82n ceaaYGqTF5fxJvPn0eAaY2rjqcm9P3UM36CMlx8IzsERXzM/nwzOB1B7yvp6 InVMS20/NbbUnj9TxpsV2M/E+bHrMsf/9wXXrPItCTUX1IUS4xPr880GOULq KfbPPN++UX+1xLUH1Byj0VJjTS0y62F+cI6Z+Jp8y+bEz+ipzDXB1GYvixxv ksMnl0+8/XDhmlpqa8nxE39SM0DtADbwIfX3rbCmt7CcT+Q0qujvs7FzHeML 3xtzjrnH90fYKq5ng9od0TvVPry0n9hT2vdrzLvw98K12+1Kf4H/oI1NIXe1 J2s4cU3B5WqfXbgWFc0R7ZF8HrYQvvFranvROrEmj1ZPvDM0cgyBLsAz5FlS w0Mtz1Y9ODKnJ66jRou8IPlB6hf4PgXbyvv/Se12hXONrUt7NKxwbgNNl3gV /eUwtXvomPHwkcLxChosWiw1ReQt8ZHtSn7UnHcdvNbG5c7tNi/5EjkpclPw zbMyr1Hm8gHBa7cLHCZxDvFzuJ/Gt4p8Deg/6CV810JNBfnFC3PnnhuV/I/3 Sa6Kc7QuXJPEd1lwIvKTxKf/pH4HLdW/V2otCXv1YLC+PiRyDeAxqW04c3fv YNvONaM7850D9zIldyzUoYwfyM/2U3uY9nXk+RfmNvgofBUxO7H77on5Gza8 ejkfqDWZG+xr8bnUYY0rzIVYc6w9OBJrBZ+L74Uj4cvg5HAnavCo/canUk9C /pq5jWaN/zujMDcnh0Uui/wjtWhwUuICOChctGFkvsr1UCuGP8evP1Xaqzna /0VhDgAXwOZh+85C04ycAyEXAqeCixDTwLUWCe+SOEePP78097dWuyX2/+gF AyLHqOgiwwvPd2J7chEjmKORcxSbSj1wV7W7avzo3JwJ7gSHgcvALzg3OSxq bbbmayPH2Ogi15RrhzXUJLhmkLoH6h+oFRtbmCvjk/BN1CyRlyE/Q20UHJr3 e0kwtz4Xnh075qA+jZiBtcw1oleext/YMQj1dOj75AipJ53D+6jwtzPU3A/X uZdkroVDM0Y7XkyeLbK+coiOfy2zNoJGjVZNPTa+DJ82HLuZuRaLGixqsYh3 4ArkqalFIx6iNo+aO2rvFmeuxUNzR3t/K3PtGDVf1H7BsamlgJPCTWcWznWi 2aHdofnwPlljrDV8PGuzYbDv5/sr6qOJmdGBXsg8/8gBkAvAXj+XWlNEW6Re FV+IT+yX+Hst6m2J0dH5iLmIvWpFjrfIx7ZPHaNvjdVz5/L3isxfmbPb6Bwf l/6EGpJhiZ8XXODNzNoRGj9aPxyWc9cI5rZwRNZG7WDuyPcQcAM4AjyV/C/f tqK5oL28nLkWFI0frZ94lFpBalapXeV7N2r/0SDQJZdlrmUlB0Eugnww3IA1 hx+cmVvLQNNAn5iV+1s55gTx9D+ZYyFqNsgfofdw/mXBWiPfWD2QeI3y7RX5 sONTa15oX1cXtl/kMMhlXJ9bq8KXoBWSPx2auuacbyfQVNFWOeYqtTuktn1g 6uHblvrLS8HaLO+E3DE5c97VnNzf3p0cWV+gfoE1wNwfkTmfd07qGnK+RSG/ S46bnNIthfOz5MjJQd2g9ozCuWZyxuSOyb/2S61poW1R04ytxf4zR88rrM+R kyQ3SfxwT2qNHa0dfQjb/nywdgyfeyy1xo/Wvykzd4XDkm9Ff4JLPRusZZIv pmYenzKrcD729NQ1+dSyX1FYi0XjRuu+vvQF+ITmwfXF+E7iJ+qHyXdT08f3 CXML609oS0uDtVW+z6N2EQ0MHfXcwt+ekEMll0r9CesbTWVXdKrMtbXkhMgN EVPjby4OjrWHFM7Fk/Mn9/9vZi4OPyKfeXHhWBxNEm2S/O/yyN8A3af228Hf dqC3obstKBz74pPxzXxvzfcRaIYXp86HL40crxO3Dy4cO1ATQG0Aa6ZKavvK WvovsxZBTRX5UzQJuDsxPLE833ydmTi/wLdggwrXGlCTQG3CaYW1Z2oQqEWg HuDFyPoEOgXfI/M9AvEGcQf1YnwDTI6dXPvRhWup+pTx1ITCsSgxIrEi34Md nbjGmHosvn+dFFkzHZ86pwDfQrNCu0IDpzaGmIvYi3qwTolrHqjHQO87NXGN 1yq1x6X+dpRv2qiHQSMlFzEoWDvl+9vJkTXVS+CGhWMhanCoxYGTkztoGszV qQ9gbvENzGno8IX5DzUz1M4Qc/C8DgmORdDzsD3Lg3Np/QvX6lBzQ+3N1u8n Iuc0BqfOUTCfiEmJTcmnU/u8VW9S/4DC10NNDrU5fA/ZI3GNBvH4wMK1OtQs Ubs0tPDzoqaF2pb1wbVOcJ6bU39T+V/ib+741hIfl6f2v/i+HoW/1eOZop8S TyxNncPaVMZnnO/vYO2VnP1A4R7CrxXOEWJL0UDRQokxeb7ExMTGW79Pj5wz OEXtb3Ln5ohBiEX43hxfzjd41NeQTySW4RsS4i/qIagdR39Dh6NeA67ANygn q7225Mroe+OD4/s+JaeZmLuejvWNplgjdT0Z/gi9EN2weupaWWpm4b/kIMlF 7pFYP+SbCP7fBTQL6oPQH+Gud+mYN3JrcjXK+UjtIfVz+Evie+J8YkRix0uD +St6P/Oe+U/ugPiUb3KwGXfDTQvnjsnhkMshh8N8OjAYUx9XO7K+smfJr+Dv a1NrtbcH14ei56DroCGwHicE8zf+P4Yuib9Bpl4TTbRm+QypXeT7FPgKegj1 2dQT4+/RC9ANaqXmmnzjT/yEZj4hsoaPlo/+yrNDc6I2sl7q2In/w4L4kZp6 6rep2SS+o35478h6BboF34NTa4Zeim66OrGWimaITaMetm/qmi6+9eH/L6B2 Hf0VHfba0v+jSaJNPlk4fuUdTijjD7Tj/1LnEsmJT42cIyBXwP+XwbcB5DD5 TnRK6nfN/3FBvQ/f5FA/y/8xQH0g//8B32eR0xlNfFBY+yQmJDYkX8e3juRI qcNAeyb3TU3QmsSaEPYdzQnt6Y+Sv1OzS+3u9NSxNTWy1Iux5vi+nPof1uI3 5dxjDo4O/v6I+j30TD5a+b3kq9SAUh+J8I+Wh6ZHvRHf01AfhD7L9zfXl/5t Vrk++YaGuUE8SVwJZ66aOmcAl/4/dol9tw== "]], Polygon3DBox[CompressedData[" 1:eJwt13m8zmUax/FnznOe55hmaipaUGjSnjAtKmEKqUhlrFk7dopIhA6Rg3Ci SIpKiCxZk1RDoqZBirJOJdmipGUiknpf/fzxff3u63N/r/v+nd9zL9c5L797 g245qVRqA2WoYV4qdWY6lboX7EpviG/LplKl/pRKTcxNpU5lqob96vkw36c8 E+UViX+hh7BN2DhsuPhtOh37QO5eY6TkvoudjX2EfY3lYq2oCva03Npsxc3Z FLsYG4rdytcHa41di03AbuarZKxN+Gz9u7Gv9NUUt+G7TvsZrA5fZb5H8YZ8 F5n3VX2nipvwXaRdGD59D2JHeftgG7GxcoeJl9Gp2Bq5O/mO8XWnG7VLYlP0 5YqX852mvRbbre841tUcd2IzjTeLfhL3xevqL8c3S9+fxY3w0tp9eKrq64yt MV4ZLI+vhHcpJW7Mdx42ICeZP94jRYOxndhU4ZN86+Od9H/kuRJfRe/hJcXr jXdAX9ZY2+Uu0N6v/1t9dcXN8Mu1R2K36+uPrZJ7Fvah3H1YDs+H2PlYaawW 1pNvN40Nj9zZ3mWP+CO+8nyn4aWxMuLm8q/AiviqYA/wHccLsM+wSdjj4hZ8 /8CexKpjf+MbQHear7zxFug7Wbya91ztM7Hq+u7FWsq9Ehsrt4bcy3l2Uyus B18R35JYa/QL9hK2AjsgLpdN1mKsybf1nSfuIHeE/vv4mtME4/fAnoi1I25H z2GLsomvm7gVPYs9wNeTemjfE7+BZxdxZ7pfuzm2zrOb+D5abIxRxnjNcyD+ mP5D3mWZd5qo/0HqFWPqy9e3wXMADdX+ge91vqf0vyjepT05nezp2NufGHMG 1hfb6/3my/sMm4sNwL6K74o9TI+Iv5EzT99o4/XHBmJf5yT+GvLOxk/RPl1e cRrB0z2TfN/4zvfTi9gq3mexAvEGY1QSl4k9J7cKdoyu57tf7mi+B8T30KS8 ZO/EHlotL6Wvjucj+AjPw/hyeFLsc2wM9hu2FpsZ3yreB9uKTZa72pyDsFHY Eexdvhf5esfc2DZsCt9avv9r/0gH08k5OMzzzWzym/f2bh3pBXl9qJe+7bzT Y9/FnsSKsKOx98wx1RydqCN113d3nI2en8e+Dp92G+17Tijarxin0HhzPYfq f4bvpDgzaId4GD2r/Rf6nzmWyHmHiovb5/xx5KZKiFdSCawjVgw7Q1yoc1yc E/QJNh8bgj0R3zjezWMW1o6Gxpnkb+1Ek3m+Ci82J5us61jfw0/smWvk7uGb F2slm+zbythubC5WQIXi773LEn3j5B6KvYH15xmJDRYfoA5Yd6wg1qv4G2qP 9cOeNv8K438d74j1xZ7ClmNF5rhBXJ96YLfzfE9d+QbGPsX+wzeGr474buqH NcokZ/ZdfK95v8Wx5mKtxJ0S65OaeJfqfFOxyeKFtDq+BTYdmy1eRhuxRZnk Xj3DeJ2N1YW+jDOAqmHPiesb7+rY91ijWF9YH6wVdi/WAFuC9cSaYbOwxcZf RZ+a4w2sLeVTt7xkLa3xnELjeV6mlXwv6Z+BzRUvp83Yq9hWqmrsMbHP5f7V dxkZ526c69SF7+Y456IeELegh7HG2N5Yp+nk/Is9EXtjD9Y6nezf2MdLsWfk tsxNzsQ4G+MsnY29Ln6XPo9c7AtqGmcVNhxbIPd5vg65yX6LOeLM7YTdyjfH O7fz7nWxHdQsnZzPMUecwztokfYTUYPo+6d4O1+TdLKeY13Pwzobry42L34j 4zXg6YLVw+ZHPYTdhY2LtSGvLQ2S2yLWI1Yr7g++Fnw3xe+B3YhNwRphN8Qa xW7CpmKNsWpxFmC3YLOxttht2FNYQ+O3p0fN0TL2AVabbzpfS75aUUed2Ftx Ny7VVzLuUO1f6ai4nNzR8TvIbRtrImozbBlfVex4jE8nYddg+Vh1vuf57oh9 nElqhKgVNkT9R4/Fu/Ee4pskflPfvriP6ce4p6O+wnaJb6Fv4o6PswD7VPxD rOmoc7Ah5iiIOzXuYP3j437TVzab1IpRM67jy8ZaiLWM19DeRD/pOxlbL/8C OT3lniOtrHgjXRZnCVYeu0C8gS7EemHnYuXEn9ClcZZg52PlM8k58cd5YY56 9KQ5bjbv/qiHxK/o2ypejOfypmmO3M3YQ9iDfF8Y7+X4Zlh9+j6drL9Yh1+K X+M7Rd7JtEjuF9lkb8YencE3nablJef9hHTye8bveoaD+wjfc3I+F2/Rd5Xc Y9g0bBe2HbsW+wkbj22J+x6rHPUE3W2OCsZfgpUQT9b/Qtyf6eQeruF5IV4v 6oy4G8UXib+LOxlbj32IVcTWYmW128W76Sstrneijoj64Dt9t4trnag39mD7 sNrZ5FyM8/Hf3mUmveRvXYh9jL0V64rmYEuxbdg7UZvF989Lzrs491bGN418 7FDUqOb4Of5vwQ7H34pXMN+oWM9Rd2AN4rfjOxi1uL5/ZZNzO87vBVELxXkp bx0+DRsi3sZ3tfh9vlKxn3KSeifqns1UERuEXYpdLO4nv3/UVrEOsJHYj3G+ Y+9jhVFnifvy9Usn3yW+T9WoN+IMx/6LDcUGRu2Mr8SuiLoz9rT4YJyB2Aq+ gXz9xT/HPRw1OlaEDREfjhoxaktsFPao+O/yV6STdRDroUi8M5PUI8fzkpo0 atNv43zC3pY7QG4/8Sa6SntE/G36isldF3sr9nKsJSyNfYxViHfApmEZrFeM HbVqTlIHRj0Y52eco/ne5Q4ay7M/k9S0Geu9t3Yfupj3vVjbPPn8Y8XN8YHa bbDWNFHuB1g5vg7mONP854j3ZZKaO7dYUjNH7byQ9zs5B6lm1BvG24ZfzfeY 3CuxipmkHoi6oBlf7bjP5JXifSOd/C8Z/1MOEX/JV6A9PZvUd1HnDYr1FndP 1OP8Z4m34JW0B2OXmeOSWMt0PTYaux67UnxtfAfjl6X92Enx/yv+kPGaYI1p fPyfFv8nyr0xajN9E8S7Msn/E7/p7xrnbZwzsUfi3Iw73XiF8RvRTDnDsR38 12WTez7u+5rYTfS4vFfpqPYRui3WM1/7THI/xz3dNGoxni3Z5HyMc3KX8W4Q n0NvaV8S7xz1s/h3dCsxsQ== "]], Polygon3DBox[{{1578, 968, 671, 672, 969, 1579}, {1622, 1016, 755, 756, 1017, 1623}}]}]}, {}, {}, {}, {}}, { {GrayLevel[0], Line3DBox[CompressedData[" 1:eJwl0k1LlFEYh/F7HMdxfEvEFm4KaxEoCoEgikqTgVJCYZlZi0AJcSBFpNSd row+QK0CEWrbJpeCoAS1q3xLM9Mv4LZlv0Mw91zX/z4vzznPTPP4zPB0JiLe qYdlEcmH8SppyEXUy014GW/qX+Ht/BZv5b18KB8xIA/xfr0naQ8s4ThOqEW+ gK/wtrnL/G1ay1/zD/yefe7K63xNbxv/VkR8c5ZH+j/kB9iostmIHbkcd3HH nBzf43u8gu/zzfKIA/mA5/V+YiUeYgGr1BGvxl94ZG4NP+bHvJb/5p/tcyKf 8Dq9P/hF71RvzHlO5RG8qDac/bv8EbfSvfRW+Sc+4I435PfpHfI+/oYvGV/B eXypFvhTnErr8TEOmlvkd3jR2h65h/fxbt7Gm41fwknvrcvZRvUvyPfxXH6B dfIc1mKn3iyvSfdynw65Or0Pfp1X8efGC1jCKVWZ9kePjWfoUdFm7gTPpd/E 2hYZIuPrGs+m+xj3ia96Z5n//7d/qvpACg== "]]}, { Line3DBox[{1298, 1626, 783, 1297, 1627, 1789, 2144, 1470, 1788, 1628, 1791, 2145, 1471, 1790, 1630, 1793, 2146, 1472, 1792, 1928, 1975, 1473, 1299, 1976, 1474, 1300, 1977, 1475, 1301, 1978, 1876, 2018, 1302, 1633, 1795, 2147, 1476, 1794, 1634, 1797, 2148, 1477, 1796, 1636, 1799, 2149, 1478, 1798, 1929, 1979, 1479, 1303, 1980, 1480, 1304, 1930, 1981, 1481, 1639}], Line3DBox[{1305, 1538, 1641, 1877, 2073, 1640, 1629, 1643, 796, 1642, 1631, 1800, 2150, 1482, 1644, 1632, 1982, 1483, 1306, 1983, 1484, 1307, 1984, 1485, 1308, 2074, 1539, 1486, 1309, 2076, 1540, 1647, 1878, 2075, 1646, 1635, 1649, 802, 1648, 1637, 1801, 2151, 1487, 1650, 1638, 1985, 1488, 1310, 1986, 1489, 1311, 1987, 1490, 1312, 2077, 1541, 1491, 1313}], Line3DBox[{1314, 1866, 1542, 1653, 1879, 2078, 1652, 1543, 1655, 1880, 2079, 1654, 1645, 809, 1315, 1988, 1492, 1316, 1989, 1493, 1317, 2080, 1544, 1494, 1318, 2081, 1545, 1657, 1836, 1837, 1656, 2138, 1848, 1546, 1659, 1881, 2082, 1658, 1547, 1661, 1882, 2083, 1660, 1651, 1883, 2137, 1319, 1990, 1495, 1320, 1991, 1496, 1321, 2084, 1548, 1497, 1322, 2085, 1549, 1662, 1855, 1856, 1854}], Line3DBox[{1323, 1867, 1550, 1664, 1838, 2086, 1966, 1663, 1551, 1884, 2087, 1324, 1885, 2019, 1325, 820, 1326, 1992, 1498, 1327, 2088, 1552, 1667, 1499, 1666, 1970, 2174, 1553, 1670, 1839, 1840, 1669, 1971, 2175, 1554, 1673, 1841, 2089, 1967, 1672, 1555, 1886, 2090, 1328, 1887, 2020, 1329, 1888, 2021, 1330, 1993, 1500, 1331, 2091, 1556, 1676, 1501, 1675, 1972, 2176, 1557, 1678, 1858, 1859, 1857}], Line3DBox[{1345, 1869, 1573, 1679, 1572, 2099, 1344, 1571, 1677, 1570, 2098, 1343, 1684, 2026, 1932, 1342, 2025, 1892, 1341, 2024, 1891, 1340, 2097, 1569, 1568, 1339, 2096, 1567, 1674, 1566, 1850, 2177, 1338, 1849, 1565, 1671, 1564, 2095, 1337, 1563, 1668, 1562, 2094, 1336, 1680, 1071, 1335, 2023, 1890, 1334, 2022, 1889, 1333, 2093, 1561, 1560, 1332, 2092, 1559, 1665, 1558, 1874, 1868}], Line3DBox[{1347, 1574, 1575, 2100, 1346, 1893, 2027, 1348, 1894, 2028, 1349, 1689, 2029, 1934, 1350, 1931, 2152, 1802, 1681, 1351, 1077, 1803, 1682, 1352, 2101, 1576, 1683, 1577, 1353, 2103, 1578, 1579, 2102, 1354, 1895, 2030, 1355, 1896, 2031, 1356, 1693, 2032, 1938, 1357, 1933, 2153, 1804, 1685, 1358, 2139, 1686, 1805, 1687, 1359, 2104, 1580, 1688, 1581, 1360}], Line3DBox[{1362, 1897, 2033, 1361, 1898, 2034, 1363, 1697, 2035, 1942, 1364, 1690, 1806, 2036, 1935, 1365, 1936, 2154, 1807, 1691, 1366, 1937, 2155, 1808, 1692, 1367, 847, 1368, 1994, 1899, 2037, 1369, 1900, 2038, 1370, 1703, 2039, 1947, 1371, 1694, 1809, 2040, 1939, 1372, 1940, 2156, 1810, 1695, 1373, 1941, 2157, 1811, 1696, 1374, 1901, 2041, 1375}], Line3DBox[{1379, 1995, 1502, 1377, 1710, 1818, 2160, 1503, 1711, 1699, 1819, 2161, 1504, 1713, 1701, 1820, 2162, 1505, 1715, 1946, 1996, 1506, 1384, 1997, 1507, 1386, 1998, 1508, 1388, 1999, 861, 1390, 1718, 1821, 2163, 1509, 1719, 1705, 1822, 2164, 1510, 1721, 1707, 1823, 2165, 1511, 1723, 1951, 2000, 1512, 1395, 2001, 1513, 1397, 2002, 1514, 1399}], Line3DBox[{1398, 2053, 1906, 1396, 2052, 1905, 1394, 1708, 1817, 2159, 1950, 1393, 1949, 2051, 1816, 1706, 1392, 1948, 2050, 1815, 1704, 1391, 1953, 2049, 1717, 1389, 2048, 860, 1387, 2047, 1904, 1385, 2046, 1903, 1383, 1702, 1814, 2158, 1945, 1382, 1944, 2045, 1813, 1700, 1381, 1943, 2044, 1812, 1698, 1380, 1952, 2043, 1709, 1376, 2042, 1902, 1378}], Line3DBox[{1400, 1582, 1726, 1907, 2105, 1725, 1712, 1728, 2140, 1515, 1727, 1714, 1824, 2166, 1516, 1729, 1716, 2003, 1517, 1401, 2004, 1518, 1402, 2005, 1519, 1403, 2106, 1583, 1520, 1404, 2108, 1584, 1732, 1908, 2107, 1731, 1720, 1734, 874, 1733, 1722, 1825, 2167, 1521, 1735, 1724, 2006, 1522, 1405, 2007, 1523, 1406, 2008, 1524, 1407, 2109, 1585, 1525, 1408}], Line3DBox[{1409, 1870, 1586, 1738, 1909, 2110, 1737, 1587, 1740, 1910, 2111, 1739, 1730, 2009, 1526, 1410, 2010, 1527, 1411, 2011, 1528, 1412, 2112, 1588, 1529, 1413, 2113, 1589, 1742, 1842, 1843, 1741, 2141, 1851, 1590, 1744, 1911, 2114, 1743, 1591, 1746, 1912, 2115, 1745, 1736, 886, 1414, 2012, 1530, 1415, 2013, 1531, 1416, 2116, 1592, 1532, 1417, 2117, 1593, 1747, 1861, 1862, 1860}], Line3DBox[{1418, 1871, 1594, 1749, 1844, 2118, 1968, 1748, 1595, 1913, 2119, 1419, 1914, 2054, 1420, 2014, 1533, 1421, 2015, 1534, 1422, 2120, 1596, 1752, 1535, 1751, 1973, 2178, 1597, 1755, 1845, 1846, 1754, 1290, 1598, 1758, 1847, 2121, 1969, 1757, 1599, 1915, 2122, 1423, 1916, 2055, 1424, 897, 1425, 2016, 1536, 1426, 2123, 1600, 1761, 1537, 1760, 1974, 2179, 1601, 1763, 1864, 1865, 1863}], Line3DBox[{1440, 1873, 1617, 1764, 1616, 2131, 1439, 1615, 1762, 1614, 2130, 1438, 1770, 1181, 1437, 2059, 1920, 1436, 2058, 1919, 1435, 2129, 1613, 1612, 1434, 2128, 1611, 1759, 1610, 1853, 2180, 1433, 1852, 1609, 1756, 1608, 2127, 1432, 1607, 1753, 1606, 2126, 1431, 1766, 1765, 2142, 1430, 2057, 1918, 1429, 2056, 1917, 1428, 2125, 1605, 1604, 1427, 2124, 1603, 1750, 1602, 1875, 1872}], Line3DBox[{1442, 1618, 1619, 2132, 1441, 1921, 2060, 1443, 1922, 2061, 1444, 1774, 2062, 1956, 1445, 1954, 2168, 1826, 1767, 1446, 1177, 1827, 1768, 1447, 2133, 1620, 1769, 1621, 1448, 2135, 1622, 1623, 2134, 1449, 1923, 2063, 1450, 1924, 2064, 1451, 1778, 2065, 1960, 1452, 1955, 2169, 1828, 1771, 1453, 1187, 1829, 1772, 1454, 2136, 1624, 1773, 1625, 1455}], Line3DBox[{1469, 1787, 1223, 1468, 1781, 1835, 2173, 1963, 1467, 1780, 1834, 2172, 1962, 1466, 1961, 2072, 1833, 1779, 1465, 1965, 2071, 1786, 1464, 2070, 1927, 1463, 2069, 1926, 2017, 1462, 919, 1461, 1777, 1832, 2171, 1959, 1460, 1776, 1831, 2170, 1958, 1459, 1957, 2068, 1830, 1775, 1458, 1964, 2067, 1785, 1457, 2066, 1925, 1456, 2143, 1783, 1782, 1784}]}, { Line3DBox[{607, 1022, 1227, 784, 2144, 608, 1024, 1045, 796, 632, 934, 1054, 2079, 808, 646, 946, 2087, 818, 658, 953, 2093, 828, 668, 2027, 836, 677, 2034, 844, 688, 2043, 1128, 1129, 855, 2160, 701, 1130, 1145, 868, 2140, 716, 982, 1154, 2111, 880, 730, 994, 2119, 890, 742, 1001, 2125, 900, 752, 2060, 908, 761, 2066, 916, 772}], Line3DBox[{611, 1029, 1230, 1030, 1975, 612, 799, 1983, 635, 811, 1989, 649, 821, 1992, 661, 1071, 1072, 1235, 2152, 1074, 1092, 1075, 1239, 2154, 1095, 1114, 1096, 1243, 2158, 1117, 1134, 1118, 1996, 704, 871, 2004, 719, 883, 2011, 733, 893, 2015, 745, 1171, 2142, 1172, 1245, 2168, 1174, 1192, 1175, 1249, 2170, 1195, 1214, 1196, 1216}], Line3DBox[{613, 787, 1976, 614, 800, 1984, 636, 935, 2080, 936, 650, 947, 2088, 948, 1063, 954, 2094, 1073, 955, 1236, 1077, 1094, 1078, 1240, 2155, 1098, 1116, 1099, 691, 2046, 858, 1997, 705, 872, 2005, 720, 983, 2112, 984, 734, 995, 2120, 996, 1163, 1002, 2126, 1173, 1003, 1246, 1177, 1194, 1178, 1250, 2171, 1198, 1215, 1199, 775}], Line3DBox[{615, 788, 1977, 616, 926, 2074, 927, 637, 937, 2081, 938, 1056, 1261, 1262, 1062, 2174, 1264, 1263, 1065, 956, 2095, 1076, 957, 1080, 966, 2101, 1097, 967, 680, 847, 692, 2047, 859, 1998, 706, 974, 2106, 975, 721, 985, 2113, 986, 1156, 1281, 1282, 1162, 2178, 1284, 1283, 1165, 1004, 2127, 1176, 1005, 1180, 1014, 2133, 1197, 1015, 764, 919, 776}], Line3DBox[{617, 789, 1978, 619, 928, 2076, 930, 1049, 939, 1055, 1258, 2138, 1257, 1058, 1265, 1266, 1064, 2175, 1270, 1269, 1067, 1275, 2177, 1276, 1079, 959, 671, 968, 2103, 970, 681, 848, 1994, 693, 860, 1999, 707, 976, 2108, 978, 1149, 987, 1155, 1278, 2141, 1277, 1158, 1285, 1286, 1164, 1290, 1289, 1167, 1295, 2180, 1296, 1179, 1007, 755, 1016, 2135, 1018, 765, 920, 2017, 777}], Line3DBox[{621, 1032, 1231, 791, 2147, 622, 1034, 1050, 802, 639, 941, 1059, 2083, 813, 652, 949, 2090, 823, 663, 960, 2097, 832, 673, 2030, 840, 683, 2038, 850, 695, 2049, 1136, 1137, 862, 2163, 709, 1138, 1150, 874, 723, 989, 1159, 2115, 885, 736, 997, 2122, 895, 747, 1008, 2129, 904, 757, 2063, 912, 767, 2070, 922, 779}], Line3DBox[{625, 1039, 1234, 1040, 1979, 626, 805, 1986, 642, 816, 1991, 655, 826, 1993, 666, 2026, 1081, 1082, 1237, 2153, 1084, 1102, 1085, 1241, 2156, 1105, 1123, 1106, 1244, 2159, 1126, 1142, 1127, 2000, 712, 877, 2007, 726, 888, 2013, 739, 898, 2016, 750, 1181, 1182, 1247, 2169, 1184, 1202, 1185, 1251, 2172, 1205, 1220, 1206, 1222}], Line3DBox[{627, 794, 1980, 628, 806, 1987, 643, 942, 2084, 943, 656, 950, 2091, 951, 1069, 961, 2098, 1083, 962, 1238, 1087, 2139, 1104, 1088, 1242, 2157, 1108, 1125, 1109, 698, 2052, 865, 2001, 713, 878, 2008, 727, 990, 2116, 991, 740, 998, 2123, 999, 1169, 1009, 2130, 1183, 1010, 1248, 1187, 1204, 1188, 1252, 2173, 1208, 1221, 1209, 782}], Line3DBox[{629, 1042, 1043, 1981, 630, 931, 2077, 932, 644, 944, 2085, 945, 1060, 1271, 1272, 1068, 2176, 1274, 1273, 1070, 963, 2099, 1086, 964, 1089, 971, 2104, 1107, 972, 686, 2041, 853, 699, 2053, 866, 2002, 714, 979, 2109, 980, 728, 992, 2117, 993, 1160, 1291, 1292, 1168, 2179, 1294, 1293, 1170, 1011, 2131, 1186, 1012, 1189, 1019, 2136, 1207, 1020, 770, 1223, 1224, 1225}], Line3DBox[{771, 915, 2143, 1210, 760, 907, 2132, 1013, 751, 899, 2124, 1000, 741, 889, 1255, 2118, 1161, 1280, 1279, 729, 879, 2110, 1153, 981, 715, 867, 2105, 1144, 973, 700, 1995, 854, 2042, 687, 843, 2033, 676, 835, 2100, 965, 667, 827, 2092, 952, 657, 817, 1253, 2086, 1061, 1260, 1259, 645, 807, 2078, 1053, 933, 631, 795, 2073, 1044, 925, 606, 783, 1021, 1226}], Line3DBox[{773, 917, 1212, 1211, 2067, 762, 909, 2061, 753, 901, 2056, 743, 891, 2054, 731, 2009, 881, 1147, 717, 2166, 869, 1146, 1132, 702, 2161, 856, 1131, 1112, 2044, 689, 845, 1111, 1110, 2035, 678, 837, 2028, 669, 829, 2022, 659, 819, 2019, 647, 809, 1047, 633, 2150, 797, 1046, 1027, 609, 2145, 785, 1228, 1023, 1025}], Line3DBox[{774, 918, 1213, 1193, 2068, 763, 910, 1191, 1190, 2062, 754, 902, 2057, 744, 2014, 892, 732, 2010, 882, 718, 2003, 870, 1135, 703, 2162, 857, 1133, 1115, 2045, 690, 846, 1113, 1093, 2036, 679, 838, 1091, 1090, 2029, 670, 830, 2023, 660, 820, 648, 1988, 810, 634, 1982, 798, 1031, 610, 2146, 786, 1229, 1026, 1028}], Line3DBox[{778, 921, 2069, 766, 911, 2134, 1017, 756, 903, 2128, 1006, 746, 894, 1256, 2121, 1166, 1288, 1287, 735, 884, 2114, 1157, 988, 722, 873, 2107, 1148, 977, 708, 861, 2048, 694, 849, 2037, 682, 839, 2102, 969, 672, 831, 2096, 958, 662, 822, 1254, 2089, 1066, 1268, 1267, 651, 812, 2082, 1057, 940, 638, 801, 2075, 1048, 929, 620, 790, 2018, 618}], Line3DBox[{780, 923, 1218, 1217, 2071, 768, 913, 2064, 758, 905, 2058, 748, 896, 2055, 737, 886, 1152, 724, 2167, 875, 1151, 1140, 710, 2164, 863, 1139, 1121, 2050, 696, 851, 1120, 1119, 2039, 684, 841, 2031, 674, 833, 2024, 664, 824, 2020, 653, 814, 2137, 1052, 640, 2151, 803, 1051, 1037, 623, 2148, 792, 1232, 1033, 1035}], Line3DBox[{781, 924, 1219, 1203, 2072, 769, 914, 1201, 1200, 2065, 759, 906, 2059, 749, 897, 738, 2012, 887, 725, 2006, 876, 1143, 711, 2165, 864, 1141, 1124, 2051, 697, 852, 1122, 1103, 2040, 685, 842, 1101, 1100, 2032, 675, 834, 2025, 665, 825, 2021, 654, 1990, 815, 641, 1985, 804, 1041, 624, 2149, 793, 1233, 1036, 1038}]}, {}, {}}}, VertexNormals->CompressedData[" 1:eJytvXdcz9/3AJ4d2XuTFRmFilKv642yV2RFRraMKOJNVstWISNRRiIrs+R1 k5VR0TIibUllz8Lv3tfr3Nfn7f6c7/vd4/d7/+Phvq9zz/Occ8+655yX7rSF 1jPKamlpuWlraZVjfw42XT0z4GAu9WrVuc0CnX4WwxSqv5NH12pOmzsvh1Z3 HRYYN3e9wj+yuEmrWy9JN8v4qG5F6dR+ZdioscW+ivWuQ+3c9F6TW3X9ZiyP vUcbB7W18gzxV2yyKNNlnvkbEtp1XfzK2HukukX/PA+2rrvc/sdctm5/P797 96J0Mrgg9+gYBqf6Pa/6HM6pqW82s3MJmaJvHcvOHf7O9pMuO3ezmRovLfV/ a6pZqP8u7VeK/RJ8pYAv4aMU+Ej4KwX+0vcqxfdK9FEK+kj0vPpv9Dw0ufDJ oY9ZtGrY11W110cTvc3zls/emUe0mg96Gl09ja5t5zbjdLsEMtrU2LYkKp8Y H/B0fWRxm96Zf/fbY7fH5MCCkmFjiwsI3Z4y56nFbVJhRccrfN13+C0Tvt5c q+yt69XTSIbbqtRTDE7DFXMMORztyaFVD3/MItuPrBhQi53bTueeHT93xnUV nho6n7+ixlPaT8V+CT4V8CV8qMBHwp8K/KXvpeJ7JfpQQR+M/ph8YvQc+T1n +qe2SfTq8nkjXE69IBUjjj01yX9F0hx9yx6pE0ldzuTa1h+QRarZD/F7Opit PzEIPlYnkjgntiF8PWSMzn6+njitsPPntknEabzeoWUMTtmpG/M4nJzQKjac PjffVbTm9E9+5jSLnzulvwpPDZ1/AZ7Sfir2S/CpgC/hQwU+Ev5U4C99LxXf i9Efk3/svmPyidEzPjpuQdtJQXT86YZ3m2nnEs99FXTfdn5JVt27/1RvUhDZ /Utxga9nxL3uxtd9LM9aHmXf+zjsSw0OJzDJ5x6HExA+scJjJm+xftdPP2Ln dthTaSs/d01jFZ4aOrv3VuMp7adivwSfCvgSPlTgI+FPBf4Y/TH5x/QPpj+x +47JJ0bPgLb3rPQZ/huHzzjI17PKJPTj6x+Th+aHMPx7JBt/qMfg5N4a+ZDD sYk48ZadS12OtHHkdL6w7KEzP3dGDxWeVNC50Uo1ntJ+KvZL8KmAL+FDBT4Y /TH5x/QPpv8xe4TpT+y+Y/KJ0XPBg4+fGRzq0zPr01IGZ215h88cTrVt0b1v sPtYd4xWzEl2rknjEHN+7vY4FZ4aOnsBntJ+KvZL8KmAj9Efk39M/2D6H7O/ mH3H7BGmP7H7jsknRk/3SfMIO5d+P1FSuyY7d72Xfj9+rrm9Ck8NnWe/VeMp 7adiP0Z/TP4x/YPpf8z+Yv4P5i9h9h2zR5j+xO47Jp8YPe/3UvtLYh3+Lu/X Evsl+FoCvoSPlsBHwl9L4C99r5b4Xok+WoI+Ej21/s3/xPiF8ReTB0x+MHnD 5PM/yjNB5Jkg8kwQeSaIPBNEngkiz4p/8+cx/YPpK0y/YfoQ058Y/f+jfiaI fiaIfiaIfiaIfiaIfiaIflb8W3yE2VPM/mL2GrPvGP0x+f+P/gZB/A2C+BsE 8TcI4m8QxN8giL+h+Ld4E/MPMX8S8z8x+mPyj+mf/+g/E8R/Joj/TBD/mSD+ M0H8Z4L4z4p/i9+xeAeLjzD6Y/KP6R9M///HeJAg8SBB4kGCxIMEiQcJEg8S JB5U/Fs+BIvfMfpj8o/pH0z/Y/b3P+Y3CJLfIEh+gyD5DYLkNwiS3yBIfkNR yvwSlg/B4ncs3sTiI8yf/1f/8z/ad8weYfoTu++YfGL01OQ/v9k/V3I79L1N R7tQdp8SXEYX8fsS2/dAKrcffddtDef37LnHzrw/rFOxLsGhAs7xiIehKUyO 0+9fDXnG5Oj1114buJxMWqj4FsrkePi1nx+Yv0DKaNt35nIyurLleu43NMu2 0ubrxve7jOPr5c4ses3gkMt3FyifMDhvmi4O5HCkdSrWJThUwJHOpeJcCU8q 8HSpPC7cI2Szom/kMjdXxp/efnVzOF/C2qwdMaZ4vaLPNpf5nJ/tFx99v57R f9CFIT/YutJYZ9J3rud+tJlnxvkSvsSkgWfIZqXi+ObGfzM43e3C6nE+SutU rEtwqIAjnUvFuRKeVOBZY+fjz9xuNQ7Ia8XpH5JgOEjlr+6/UpXf85vGiuWc zilzWutyOty3iN7F9UvAOI8tXB8E678ZzNdTNi0z5vqo+Yk9PTj9LT/OnMbh SOtUrEtwqIAjnUvFuRKeVOCZMv1+Fa7H/epWa3KCyaGVdvUILlfV+m3V43ao z95FS4PZ+re6w8//YZ2KdQkOFXCwdQwOdi6GJ/ZdGB0wumF0xviC8RHjOyYn mFxhcojJLSbn2L3A7hF277B7it1rTA9gegPTM5hewvQYpvcwPYnpVUwPY3ob 0/OK/Zs2cb/ZYuedsE7Mb/UxuTxGFb/UueIZMCaX7r3VvseXrksUnZ8mDTn9 CrcXGP4fax0YwOPJMPMS+oX5cdbBQ8v3YH5XhWf7lkxc/oQODZjrQLN3K27H pFg2sS1E+YjRza3foWPDmm9VdLHifwYofIp/sf9wvYfJj3dobPSk5U9I++CV SUqGz3adFj8aM3ww/YDd6+cm6vh2QJw63nWsoI5zJfhKAR+7X5h+kL5XKb4X k3Psnkr0Vwr6S/JAEXlQCnlwqbmgxOpnBq2YNnGHzuF7JN3b/YzFJsbflO3Z zxel0+6HPo1emhBNHhdmLGhc9TWp5fprt8fpJ9RlR8sCr9NPyPMX+c8Cx+Wj 61Z9Ttx3TIimebXeXk9blE7cwr0bcTjRF12fV2f+7vdx+ou5P3u84sYO3J+c METfpP/PDGJoHPy48uF79KzDsyMcn/m1G1Tg/97D/nXBEgbvw+eXzhzOuLJD Vnmy887ZhfZl59L+KaPj+LnG55b8xdcdayZV4uuD36x5zdejZ18wcmLf8/XR nEMMHk0+t6k2h6MbrM4rPZiozjON3KDOL12Zt6h/z5xk6mCvE2WWk0wqba2f NL5MARkzcsyVepvv0nfrFuo22XyXtM11uhvmV0Teab8wqcTw1ou4Gsu/46L7 sdkcf4wOo9JVfNT4n6vuqvm4f/6JqvUZ3G1r7J40YH9emLbvA4ffqob/BYYP eTFthZcpw+t66pJijo+0n4r9ewv1pmszvtZ0f/M3w4e2r5O3heNDC/TmLmZ0 eNpu83BOh1o5y8ZzfKTvouK7Npxw91JmeykCfWbOtWP4RnW+/pjLv+6IWeUa sXMt7Ro/aMz+XLVgWCDf3+TIkpY020tpc8eoCb83/q9X6PLvKt+uWwKjPz1U UMGA08H+uaM1P9dwsOpeKAUdmv9S34u6zpGjGXy6bXplL04HxSs1PhJ8KuBL +6nYL+FPBf6oPPz+XVR818zj6jjtKsRhY6uo46/V25+3qML47jq4ex/O9wmm 3rs5nTE7fql250LOv/ZRwxqzP8ms7Wq56mSrjsM/TVfHzbWmqOPl52dU+keT R+pXTa1/9mj9vZzjPfXMguHsXpC398qY/F/yfGffjvdcHmavLbODy8PyUyOX cDwxuxk7r8xqjp/R1RcjmfxTM6vwUI4npiddbnwcx/na77nLJWfG55QbxV04 Ppg/gMkD5lfsNY1qy+/zvWeTJ/P7/ThDrWcwP6q621dP/p0uA+1O8O/+8sJ/ Df9ezA/E/B/svmB+EaZ/MD/k1wDzYVxu+mbWmcXl6FIlq00cT8xPwPwWTG9g 52L3C/suTN7+o3+rWcfuKcZHTL9hcoLZC4xuGJ0xOJj8Y3hi9wujAyaHmNxi fMH0CcZ3TF9hcoXda0yeMf2A3RfMvmP3EdN7mB7A6IbpGWwd02OYvcD0KmZ3 ML2N2S/MXmDyhtkjTE4we4fRAfMTMLuM+SGY3cfkAfMrsHuK+S2Y/GN+Eebv YX4jJreY/4bFF5h/iOkrzP/E9B7mt2N+Lxb3YX41Gm8i8QgWH2H3AovXMD2D xYlYnILFm1jciskJFkdjcRYWv2Px3X/MJ/xr/IjFrdj9xeJWTO9h8TJmR7A4 HcsnYHoJyydg9xfLY2Byi+VPSpvPwdaxczF5wL4Lu6f/UZ9r9CHGL8xPwOQB s5v/MW7V2BdMnjE9htlfzA6i/gOizzH9g9kjzF5jfg5mNzF7gek3zF5gdhbz Q6R1LbEuwdEScKRztcS5GJ7Yd2F0wOiG0RnjC8ZfTE4wucL8YUw+MbuAyT92 j7D7iN1rTD9gega7d5gey+77Sds7NZH4+T+bF+wbThbXaGTr3buI1Fq0sNJx 9vejidcUvuz/Vxq04NN2tm66ofDCp1aRtP20pz/etYok1981eLLQ/w1pdbrV araf5pJakQwe9Rw0rDuH8z3tyCYf9vc2izp8PMr+/7Maps34eiMzwzmH2N+P LzezZfBpaELrSny9dVZtpyIGt3G1dp3es3NSwga+V8G3zUpn+8nK7A2POb4L k8pZ8v0S/lTgL8EnAj4GRzqXaM79/buI+K7/iCcV8CW6UUE3jA4S/amgv0RP IuiJ0Rnbj8HH+Ijh/x+/F5MTjJ4a+mNyiPEXkwdMzr0W9Xsa+XcudRh70caz 3zrycuC+L3E3cuV1Wsp1DRzMH5vjv7JgO6NX7S9NqpxieJ5bZz+Q44P5Y2EJ gZ6MniT583nyjdH1aedcyumD+VHVTTovZHwnO0Nn+5xk32vrtl7nD/A1flSz nrctGf1p/5NaCxh8Mr5IzS/Mj5LwJwj+CgR/IvCXzqXiXOy7MP9T+l4qvre0 +zH6Y3hi34XxvbT01G1wq6v11lw66+HGpk9a2SsOv7VITQ3LldeVpVzH4Pz/ Dh+TK0w+MTnH7suWCxlF+vrZ9LJTh3c1ekaR3NCslID1eSSptn/rgsaZ9M1m hV7sUm/FpEp6bhHT8kni8epNtm1Mp14W6V4p5R4Qx9xjOrbd8klq9BmD51MS aYV3Dz9u9U8h/WoEbe84qoDUVBSq7PIBsMtKsMs5vl4JM4+lEKu5Fca5FOxR LPVuRBwLWZyl//f6DlEJpPo7/z3b7FNIe8X11PnLC0jvmivXM3zI8ICny+8z fE5tn9SH45Pa4E392TbppLOpbpDjr3hi3nnmFzI6nwQ8yHfO9MkhVpuu+Tf8 uUax1Hj3uzFBL8kQm9ZNOupnkw8PP0ZUZ9/rVHYE5d8r6qy2lK8QO890yhVR ZyXgDFPDUQo4ov7N98RGv51vAiJF/dtggF+ihk+dAb4C8B+lxl95GvDvfP+4 K6MnuXkx80tyuQf04tCCzAmMnm8Wk2VpUxKJ1dBYb0ZP2jPSzoHT09njoien 50HJXzKPuDBnxrEUqtuwYRajp7JXwvT1nJ6NFmeS9lEJ9Mq96l8ZPemcBS4/ OT2Tgb8Fav4qJwJ/O+fZh82ySaf7O1+/tvhXPD2RWtCiN6Pn+reHF1YIzqG5 UysOKv68RvnI9plfzraXZCvIzzm1/NAckJ+Ng/zmPExIo+9mOb/a8DOerNEu 2tY8I5+crznnZsrnFPoyr8vhNspnxKf+ZPseBvnEdVJidGXLGNq61cDKi8dk kjNT+u4Zu/4VMRrRqW1H61WKy+9dHXVmZhGjD4tqtF/yijyx3br1bOUY8rDJ vpfdm2QSq7E/A8ucZftvWGZ0vZVCUoY0qNdg5TOi/bVgdsLgfJLUbbTlydA0 smLwnq0TX8WTFlUs8ncV5hOdDYNSGJ6kXpUBZz1/xtN4vcr7OJ7m4UrXR59T yA7/3qmtlc8omZPuyPE8Zf3QTccyhkydez/FcUwmPd30fhDH07FuROt/4Em7 A55m3l8enK4cQ6+u/9bIqEkmPbh4y26O51LLkg4MT2pn7fCM4UmNzQIncDxv z+vwJDQ0jdqc/hxt9yqeDs2zeMHxNKP7PnVuG0MnJi1dW3IrmVyu7u/su6KQ WAaf9t/jxORs63rjdVkZxCHhwJj+JvnkSebKDrlDztAeMR+1i6vkkHkdJnTz qZBHhkXcjdG1t1fk1NptR3Vyyc6eASbRXV+SmsNyZrwccoZMquSS/p3tt723 1JrvP93+5AhDxygyreqXoHdBLC7o9/xMBSYnpOWURV3axpC3VkEti28l01mP V57h+IR7/jTf6xRFau+9m7w2K4MWDArx5vhoA3w7NXw6AeBTn6e3/4EP3QH4 ZAD+Jmr8qQPgf//YozADxyjadHNIufdBGbRx28lLOT4et/p9N2T4nLJM68/p 4zju7DiOz9GAH3FdGf5LooblcvxPB3x04vsrJEQOymP4rBr1+R3/3hdhF3tz +KlfTtq0sbdXxk89M4jTJ8Fvw0COT+2ZbT1fMXy6+Sy/w/c3GJ04he/vblzZ uSvjy4yIyi+/MzrEtvDx5efe65ro0I3hOam3vzs7l5pFLj7Nz60CcLqr4dD6 ACck9uTof5xLxbnVAM+VajxpOuDZxVnvOpfna4b14yYxea5fUXmKy8nArMsl XP5f3nM24fJfv/n78Vyu8srsfnCG3ZcHF83u8fsyKLrRNS6Ho78NXdDJepXy 892Hevx+6a7Y05/Lrei/OJD9vMDQ5lSk6L9ob25tcIrJZ8unl/fYMvl82uZq OD/30oyy57sxee52u+XC+kyezR5bqO5dq+ddtoYx+bcc9mw3O5eGHN54l5/r czXVgZ/7RX0ubQnnzjUpasH1581BUzO4fnarERPO9Yn7pWMFTC8RI4ctiYuY nh/2ZIQu10vO50fsZfqNZNwbH7WV2QvdyqmdFjD9dqxHqir+HQPx736Ifyck Gwxh8KniSqcT1Rj8w/ZGQRy+6OupOHysjy/T56KvR+y3VO+nR2B/bm73kczu UE/31+cWMj2Zonf3K7c7uo6vnjP7RXt+fDmV4UPDk9tW4fiUW3O4xT/jeoGP +N4o9fdS8b2DBx4Ywe2OyCe8m3WozlhmdxLqqvS2Zt0R9HZHpUr/a9YHJ6n1 f8OuLUL+lK9Q1FbZI01+IxrsUZMElb3TrK80Utu72N/7y7Tg7+j+XhL86wA/ xU9l9zXrHr5qu2+3+kTwn/CUv3cRfC9GH0l+NPSMbLZ1DLezOct2beN29m6e YVXut4xyT/NlfgvJLh58eQuzs10anVByOyvJj4Zf7YG/xsDfy8DfVyAPayV5 kOSNIvJmjsgbEfJ2VKEfwf2ull93/0xifleVoU5Z3E8oNJ2fyvwE2sS2vw3D n9yJnrWX4y/Jm0b+V8F9SYP70hLui7hfBtL9kuRTcx9d+/UencDs5tPW0e+8 mN3c5lRvMrebg4ID/Lnd7L2q0TduN19fbKWy76vOVY2rwuzmvTF9w7nd/OCc Hc3tpqR/qNA/BqA3+kl6IxL0TGdJz+iDXqoFeikV9FLKug9RDE96MHFHuAfz Q25vN5zL8Zw+6qPNY+aH7O/a0ZDhSZ5UOKyy7/HhVbZXZX6IIuCWB8OTnDCY f5/jKekrIvTVB9Cr8aBXB4NeHQx6OFPSwwagty9KeluyX1TYr7DihIh9zJ6u 3fm1PrPv9JJjN5V9l+wXRewXRewXFfYrHuzUBLBTvcBOSXaNCLsW/kbX1p/5 G0cPkkvMvhOPvF4q+y7ZNYLYNYLYNSLsWgjY60Vgr8+AvZb8Hyr8n7vgDzQH f6AR+AOS/0MR/4ci/g8V/o/k5xDh55wAv2gG+EXjwC+S/ByC+DkE8XOI8HNu gh9oB37gEPADl4DfOA38RiPwGw3Bz6TgZx4AP1Pyn6nwn0WflI6bR2JXm1Pm ok8qAfzkDeAnNwc/2QD86lTJr44FPzwB/HBL8MMlf5gIf1iKN6mINztC3BEo xR21IH5RQvwyF+IXKa6kIq6U4jsq4jspXjNH4jUi4rUnEFcaQ1xpAXFlQ4hP a0rxqRSXafLPUnxERHwk4qkCKZ6S4m6liLu7QXzXBuI7C4jvHPRV9v3/lW+R 4mWliJeluFgTz0rxby8k/tXE0VI8qxDx7HOI6wdBXL8M4vowF5V9V3SW8lpS HKoQcejer9bHbNdl0yrr9hwlzZTkWft2+8tZ5ZFHH8w2L+ybQR8v9Mslo++Q Tjq95yw2ZPLp1n3XBOdk6mu7a9fk17GkskU9w9VGhaSMP1W9Uw+S3qmXZ5K6 Q2kGqZiY6DrcN4jY767tZrw3n7zd4uvJ5aRX3ZzgJT/WKI7lG3z/Zx/WDoj3 qwN9snYc+9VmVDZZ/2LcwxemUXRZ/M8P/lPzSLmmtgUMT9LyxRRthid1WzGy B8czvLhPoa1zMtFzXViT4UkbNTz2zZXhOS3b6zLHczi8+xPAc0Vx3XNDaAb9 tOXEpRG+QdTsc5UbHE+PKwOqaHVMp6085mUfyIgj7vVXXti1Pp90TS1vE9ny KR22vM4mZcunRKFfddHVlvkk5OaIIfsy4mhSSfI39u/IlMQKy/n+X+eo6t2/ L7z768G7f0y84dKlt2LJDBfPQVmjU8mh7/6TG31+TRZ0vBHC6646pbcI5/Ud mdadFKq+S7dahMONX3gvbj87Z7Vrm0gOv8aM87WuMjxavrGPY3/Skk5qfMre ezuV7SPVTard5N/xauGkxXx/7Yrl/4kPFfjs+dGxutOtWOrsuOgSw4denvNr PscH6380WXnbfQKjb0mrst8nMnqP0Y6szeWh/EAbv16MH4NXfrVbxPiz510F Xc6XZ7Gd+5s2U1KdclrtJ67LJrcic0Zweftcv3uLpt+CaLfhBW7llNnk3NNj 0/eNySNTpx2M5vVtI14u6czrKcq1cz2l6gMdXKRjy+Tvbej7IMZn2rxbh4H8 3CXRm5zNmby2GPv4J5ffEe4frfm5nfqUzOzF5FurjOMPLu+fNo2Yy88d88ut jO63IBKz89p6di5dq3w8lZ+L9c+abAlcEaoTTR6ertsp1TSF1PTJ+5h4vJAM C2u7a367UPJ+iF6chV4mMYuwqGjM9HbPgvCIWua+NPDvfQOK6+aQcl4Ni0tq 5pEhH1X9xXQj9BfHQ39xnfiwVo3Y/vz1tB3ff/SWVYMfbH9d5aFn7Fz6asmV m09MU+h9zyFGSezcwUO/pS5oF0pvK3YtZOfS+pn6VvxcX+dXe2qa+5I+WxJ0 GRza5NXgZhyOK/Q174E+60w41zNwXd/GbH/HxV5D+f6DM+p84njOSG9s9OPK M9J43OS4u1sekOvUeoKX3WuSVNfq6RW9RFK9T3yXnPQ00q1x7LeNY/PJsKu9 9Tm/Ohw4psv5tbZCS1Xdy/hLS2sxOlP9FMMxnL+2WwO9OJ2vrvk7kMGnO2Pd g+5seUBzl3ycy+EPizBzi9RLpLMvpU3LTk+jFgtP1N/E4GN994Er2xgw+SG+ 22zHcj4+OO64icO/5p05yfNuFnlX7caUkBnXyNUB3k+H3sgjD9cOcOT3K2DX dCd+v/4qHzaey3PL8ea/mPyTvBbOJzLZfYxQLjrA5f9R//Oq+kYjqG/8CfWN QVmzNzH4dG5Br/LHZlyjJS6rbnL42LwF755/zVrG7tcAvc0KBp8uHlLdm8M3 3r6qhMPvDnWevwC+qZ/DLK4n+6j6T0cpnKL1GnB7enbNnWtMX5FFKXY/+zO9 uuGTdRTXV++88ssW/0omtFbLdRU3XiOHbAbecIooJIHjw1T1sSZSfezbah/0 247KpsudWlxLM40iNV6bpXK9is0zwdZtZ31sxfQ8NV/kedOK6c9qfhPWqPRn So3ZJb+S6aWTrxszfGirGeU9OD4vgxX1OT4GUl1xNuj5v0HPLwU9L/T/Gkn/ j+qw5zLX/3X+HpJjwfSNU1alefy+j9It94jZKXKkT/fIiUw/dd3zzprrh74V A1R06AfndoNzBf62gL8O4I99r5gDow1xpYjT3wA91wI9awI9dUeGN2R6kB79 Xq8Sw5NMHtHIlOMZta92F6Y36SDHqiYMT3LE0a0pxzNg2CwVfQYBvwSeYcD3 9cB3L+C7kJOFkpx8du7uzO3F0fPfXgUwezE91c1Dpf/BXjSX7EXk+mgrf2Yv 9C1fb+X2YlPtH8F8/95+m1TybwLyWQLy6QfyPBjk2RHkGZP//YqSEA53Wej4 JH6OY+/OWzj88u/UeJSfpcarAuBj6jdtIse7u2urCfw79EJHB/L9h3fFl/wD HyLw0YP7WwD3NxzuL3bf223SnzKJ0f2sW7dwzofJZ1+84X7CkvYHd3M+GQ/c 243zraipogXnlynYkV9aajvyGeyIu6R/4kD/YPpq+MyRuexc6lWr9TTup2x6 PKQd53vHsJSlCibHIa1XJHC5Nj3VaSQ/d+zzux4WzG6a/MxIYOeSjPFX1vNz zUGvdgS9OgH06nBED/ubrJoYwuzIxla9JjD7RTet3VeF25FHPdyLpjM74mq7 +zm3I318HpfndmSAZBcOgF3A7Egs2J3ekt0ZOG61kp1LOudkfGD2i+gu/zWQ n3vAdZzRTGY3Q4dWWs3tZmbEzX783OQ4tR18DXbwCNjBQZLdjINzl4CdPQh2 tjzYWd2ku11LmH15fSSr/z1mXxY4003cvnhvH1kQweyLvfnS68x+0SkdVqvs F2b3ncFPuA1+wjrwE9o4dTzA4BOdelWacvsY12mqP4fva2i1i9vHxmHuX5j9 Im/XTlLZr2mIP1Ongdr/6Qr+z3nwfxpebtKW25ewnz6Ox5l9efL1UzG3L5g/ 5gP+22Lw38LBf7O4lKfy9/4Cf68D+HtPqgRZc/v45U6Vetw+huln5nL4mP8Z Df7qNPBXD4O/GvTr7HAOvzf4t/oAPyu2jNZB3xz6danDuaZ262hfN7OzPQJf Ehfwt9Mlf3vJ4W4J35m9GLcgcF9lZi++WVY5w+3FlDeJqviil1Svi9kLEYeK OEvEoSLu6CvFHS4Qp+QmqOOUGRCnvHg7tgezX+RUsYMRw4cMHXvTkeMTbDFF FUcYQ7zzF+Aj8YsIfj0GOn8AOp8DOk8Hf16p9ueJK/jz0wH/3D3rfimdgyMv AP4Sv6jg1zTIM7yE/SLPIPA5I8nPBohr6qnjGuoGcY10XwhyX4i4L94g5w0l OXcDP7yJ2g8n1uCHt4T7UhHuywO4LzPBn89R+/OkBfjzdvBdAxOaL1sx71yk 1mr1d0n3jiL3jgp8tkn3fSrgXwvilAHqOIWKOKU50OEl6I2FQId+EO98Usc7 VMQ7kv4kQn8uBP12AvRbNui35uC331T77aQB+O2SPqRCH14G+KtAP/cD+J0h 7nirjjuoiDsku0YQu0aEXbMAu3ME7I4Z2J2h4IfPUvvhRAF++EywX6vAfm0G +3UZ/Pltan+e5IE/L9k7KuzdNnj/qu7jXJHzdyO8f0l2kAo7uBrw7yrZ5QSI R6qp4xEq4hEjoMNxsO9TgA7TIK6pr45r6E2IayR/iQh/KRD8lungtywEvyUQ /P8Zav+fCP/fB77L2Mvj3VV2H8W7nuT/UOH/9AI/0wj2zwE/8xvgs0/y36Ig rilSxzVUxDVSvKNZl/DUxCnSd9GFf8bHXOAjxTvY95qL75Xw1MRf0ndp6CzF mxSJNzX8leSTCvmU5JkKeZbkkwr5lOSZbv6zfJoL+ZTiVoLErUTErZJ8auJl SZ4191GSTyLkU5JnIuRZyj9o9ICkZ6jQM5JeokIvSfkEIvIJkp7R5DckvUSE XpLyQhTJC2n0rWQvqLAXkn3R6HPJXlBhLyT7QoV9kfJFpPmf7Yu5sC9SHomI PJJkLzR5Lcm+EGFfJHuhyY9J9oUI+yLlG6nIN0p+AhV+guRX0HN/9gc07w5S /pCs/rO/YS78DclPIMJPkPwKjZ8j+XtE+Ht7Ou3syeVs+c5zCUuYHNWokjST y4nk7xHh790o8gnLYvz2GGEw4irj/6Ftf7XlfJfy8wqRn5f8Q03+uW1WSnwM k4OjD0aN/8nkYvjbrap7JOXzNXnyl+DvRYO/Nxj8vWjIJ9dU55NJQ8gnS341 EX51iEU7P34fmj14sXkxux/nM3Lb8e+V/Ekq/MnXq9a7cn1xPcTsKNcfhub2 2vx7pTy/UuT5Jf+TCv9z1PwzxVwf7ez+4TXXT4MVhQP590rvApr3i5VAfwfw tz8D/c0gz79Kneen2pDnl/x8Kvz8bOC77rLf/Xx/eF8oXqt6X6DifWHPOHdL /j5ddsnRz9UsY4hB/bK5/P13zsgOS1spn9H0D6bNn3xOIVdX3HLh78Vrtx7W df8ZT812BzokJqQRz4SRY/n7ssJxe+xRJvcZztuU/B5UNv3rOJdDxzplXfi7 cu6MY0b8nXnYpOXPOHzxHlc4/9hw4nRa4ycPOtiA8PfpFgOyp/P36sDOlnP5 ufTZw/GeP+OJ96SiX/x9W++yyRB+7q4NDfW5/RvUrrI3vwcZ2jaUn2tddZ37 Zv8U+m3J4LMvpiSSz7sHHeB1AoHjnE2Tyj2gLStcrrx9YzoZ+3N8PK8rEP0s wr8V/Sx/wXtoLryHzob30BmnPPIYfOJ2LXIOr0dIfrflOoe/GuZerq5+bbJ3 l4uRYu7l1eTFd3kdw+3cJkd5XQPZV/8rP9cc3nlt4J03HN5569ZZWdulYINi w893HrOY3N1696Epl7caN7buZ+vKiI4tmnN5PNmdTOXrSninPgzv1F7wTm0A /TtHj0za0aTXpUhRZ2IC790T4b07Dt67qwL862r49ATAnw5zU03C7jXh3yXm pjYAPNer8aS3Ac/z8I6/Bt7xL8M7vhe8+4fAu/9iePdfAHkVY8irZEJeJWvR SzOuT6sM7zOOy1XB5fB0zt/oPZHXeV1LprP/XF7nMmvv1i+cnoMgT2Ug5ak6 hRs5czvk3enQK26X9n0a/ze/jzbBVdvyetPsn1sr8PrTnvluOzkfa0AerHjm 73mwj3/r1+F+z/c2Vpe5PdQbWk+lH9Kgf0r4z5bQP/UI6jG2S/UYIyB/9QPy V18gfzXecnJaMJPn1Qdi3Lk8n8w/co9/79F6tBv7XhK6o2cclx93rYQ4/r3x kCdsB3nCzZAnDGpvO5DrnyF3yo3l+ufZ8WKVXRtYVjua1808Dm02lN0LGrbj L0f+vQ0gD9lAykPqFZr25/rQ8pbZNK4Pmx/+0IZ/7xqoh3ko1cNI+UlNPvDd zoN/nEvQqKn7SP7v3822rMfhLRxaYxWHI+WHNXnXmOqd83mdjU3YqS287kZ3 21qVfrgH+VtjKX+7FfLed9V5b9IC8t4OGZViuN77VmlhB17funrA4zCul9pA ftgX8sOTID88Z5jfUW43q3chOXOY3dSLWNuW2xEp/6nJxxZ/OFzyp3kR5lFj G3E5uHno61ouFy/n6nvx75XyzJp8uBFV6HJ9uH5C+f5cH357UXY5/147yKtv l/Lq3+HdIUH97kAD4d3BTm/TTq6Hv5Wb/5rr4ZByUyP5946BvH0lyNsvgbz9 WYuKA7jdbHWgdsd5zG5aZS2Ywb9XyucTce7Ji/NGcT1iklbfkN/vLy+WUX6v pXcQKt5B5l4L/eOcEIuBvc/z+tqhy60vGDA4axr6uXE40ruJ5p0iaffUz7zu Vv92XjGvw730vHAJ1zMS36nguyQnVMiJlP/XrBsDPiPV+NB1gI/0nkLEe4rN 44EqPlpIeF4E+nRT04d+AvpI7y8aOmwNPLaL6/GmZ3v4Mz1KW475cop/l8Rf ouHv7/Kg4csZ4GNZ4OMA4OMWgN9Cgi/uez/pvkv8pYK/+0HP9Ac9kwZ6RuIv RfhLBX+ldz0q3vVmw71L76y+d+3h3iUA37sA3y8D39+Dfv4m6WeJj0TwsR3Y hS1gF/YLu/A7HwnCRyL4KL0bEvFuOAnu3S+4d8fg3nWFe+0J9/o73OsuoB9i JP1gA3bBFezCKbAL80CPVdBW67E1oMdEPXPLRisGKJxOa/IA10F/jpP0ZxXQ wx8kPZwO9rcy2N9CsL8DwI6kSXbkENipU2Cn3MBOSfbRXNhHyQ+hwg8ZBXa5 EOxyD7DLkh9iLvwQCv5ADvgDM8EfkPwNIvwNyb+iiH9FhX91Fer8a0KdfxHU +Uv+lbnwryS/kQq/UfKviPCvJL/RXPiNkn9FhH81APoglkAfxHzog5D8SSL8 yX7gb/yU/I29iP8cBf7qDclfrQd1LDpQx5IPdSzjId7ZC/HOQIh3ZoOfvBL8 5BTwkxuBv1FP8jcKIf66IcVfkn9uLvzzatDPUgn6WR5AP8sHqG+pDPUtt6G+ pY8Up2hDnHIY4oLmEBeMg7ggEuqIHkAd0VSoI9KHeDYE4tmhEM+OgrjjA8Qd XyDu6Al1SlZSnZKIrz2l+Hoz9BO9lPqJpPolTb3Q2ArJf5y7Egtx0zYpbpLq xDTvOMMg/moM8ddBiL+kuJuKuFuK06mI0xdD3JcqxX2VoE6sBtSJrYc6seMQ pw+COD0M4nSpPkpTrzXb/snwP81v2Q7xaU8pPpXqzYioN3OAODcN4txIiHOl uJuIuFuK04mI0/dCHP198e9x9BOo37sL9XsdoX5vH+Rhdkt5GKkOUBOnS/V+ VNT7xeolNuJ68/17atSd3ft9bbYE8fuuNzbuj3N1LC4Hr+Bx7Iw1xeHd2P5l k0Nn8f2h+1MecX/AsXXVxvuZXmmzco3Knkp9i0rRtyjV9RFR12cE8Gep4ZMV AL/Bgnkq+veR8kKPAf93avzJXsD/7ZFmLlzPfmtpG8HwIc/eb1D5J1Lfn0L0 /Un5DU39pJQP0eTBpP4+hahHlepUNetFgE8x4JMG+Ej0Jwj9CUJ/IugfAvR3 Bvq3BfpL9KQIPSlCTyro+RTq/C2kOv/Z0BewG/oCnkJfgBf0L9yV+heGQL+D udTv8Bn6MhpBX0YM9GWEQB9HM+jjqAx9HLbQh5IBfSidoQ9FCX0rGdC3cgf6 VtKkPho36KMxlvqABkIfUDj0LU6R+hZ7Q5/jNuhz7A19jhuhH7OV1I95Efo3 s6F/0xf6N79Av2pf6FftAf2qBtDfGg39rRegvzUb+oXLQb9wX+gXToL+Yg/o L14E/cW/GjYs/zgohOgsPXbn1aUU0iTp4/p1LwrJkbY3VrJ1qhflOiXvUgrN iOgRw9cfvzvU5h5bd+jl/Yjtp3UzbDfz9ZLw4fvZOnnqNmsB20/WxAQ84Osb wd/oauzQqdjuQqQb+BvDwM6KdVews1K9ChH1KtK5RJzbE+pM2kGdSWuoM7GO UOOTAPisA3zKQl1lhrquksRBXWUJfG9z9feSTPheZ8Df6eupIrd1lzR5Nu0e L1V9VWJd+GNS3QsVdS8Sfaigjy34mbXbacVwOI3BzxwzebKqv6wWrAv43kAf d6jn2QL0qfteTZ8ZwJd6QJ+OUL96X12/SmtD/WrPRmq+l1fznTYFvi8CfKZl zYr5zvgi/N4n4FeLdSvwq6X95sh+c7Ffqqelop5WkkMq5FDiF0X4RQW/JHmg iDzQdX+mvzlCf42/LdXlElGXK90XkvFn+TFH5EcDX+IXQfhFBL8keSCIPBAh D9J9NEfuo7m4j9gcA8x+Yf2zWP9pafsNsf4drP8Fm7eA9S9jfb5YnyzWN4r1 T2F9Rlifzn/sW9G8i2H96VhfNta/jPX/Yv1xWF8Y1j+F9R9h73TYvAKs7x7r N8f6srF+SazvD+t3w/rCsPkb2LwFbG4A1i+P+V2Yv4T5OVgfLtY3ivU/Yn1/ mL+E+TlYXIDNJ8HmmWDzK7A5DNg7F+bHYv4n1g+O9TVjfbhYPymWL8L8T8xv xPrasLku2BwYbB4I9r6JvQ9i71nYnAGsXx7rE8f6mrE8IZZnw/JCWL8k1idY 2rk3pZ2rg72DY+/I2DtsaedXlHb+Q2nnIZS23x/LV2P5XixfWto+39L2yf5/ 7BvVzEHC5qVgc0Ww+RvY3AmsXxvra8b6f/9j36umHgabe4PNgcHmpWBzRbC+ e6wPHevXxvqasfocbN4RNs8Hm2+DzYHB5i1g8wSw/nqsDx2bi4XNa8LmEWHz ebB8EZbnwfI22LwObO4ENlcBmzOA5Xmw/AyWb8TmhmFzxrD5V9h8J6x+Bsu/ YXkzbJ4MNv8Em+OBzanA3qGwvBmW78L64rF5a9jcMKyeCqs7wupYSjunCJsb g80/wd4lsfc77D3o3+YtyPnthe0aHMrtmE5r6VY/3P15HEk2zlw+kfetO2QU 1PkYS51uH2lLHJPJTN/armeNCsnX5rWdKm28RgZ6WJX98SuZlJ8ZPZG/d2D9 yLen3fLZvj+LrLr+/oylaRT7e+uJQR/zNH2RQ6Avcgn0RV4ILKz2smM6uTMz P6Tb8ziqq/V6EMenat9at+t/jCWbh6ZnKxyTadVverYcH6fE7RPK8/rI2T+7 MXxoC8cyqvcmrL+sQ0qP8Qwf6je72TUr0yh6vHikK8cH64dyrfH1bcS4O/Su 9c95O/pkkHvekSuuGuYTK6SvfOWaZTHP6ySQE+Wq/xzy6zlxvxUybG1MPtrn KNVjEFGPMer1yveR4+6QGuHehJ1Lq97+4MLPxfoBP7jYJD+rk0BdXKjv0F/P aazzraH8XKwfTar7peK9yWtanby8Qb7Uz+3igkt1c8gjl2aLjWvlof3jUr0H EfUeS12uLHk3yJfMbLNrCYNDh1u0cuBwsD4+qR6YinrgmC91wy++C6Lfr/pO H6jMJvSX2d4ZY/LQvu/VJ4KPs/3EcVQ9H7afOtRctYTvx/rsRP+yG/SpuUCf 2pm8B4/YvSBvxhzZ2pXJ4VBnrzVcDp8N0F1Sl8nhjPnL4yyYHMYsdzrP5bD+ ZqtUdi9oONHdX8zkUDmm0j4uh1gf/RPTVVFcDnvHmVv3Y+f+uOpkxeUQ62u+ YR/ch90L+uD42h2G7J6eNPceqcKn48ihDdg9nf353BaGD0l8tsGb4/O2681r 7F4QC90nsbw+6KS91WmOD9YX2exUkQe/p3WmzJrG7+mCcYdmc3zeQF9eH+jL C4a+vJrIXIvabVbHX2Fy+yP8rg6X210/QkO53A5D5iEENopfnMbktonx6k7s vtBh23Y7cLnF+pSl+h8q6n8Ma5Z/eJXd06wWdi982T2da5h9nJ8bgPSxLq56 cjW/p72OG0bze9q4z4V5/Fysv1KqGyeibrwDMr/iyaQhd18y+Z9e7kV/Lv9f F03cweUf6zuW6o6oqDta1PCTzVt2H0ceUg7h93GK8dStHA7WryrVmRNRZ471 Bb8+3PXxWXZfFNvPzOD3papd3f38vmBzD24ev3GH7afhbbv58vvovGDMUr4f 6zPF+guqH1hxk/PJpb07YXwjl+9H3eD8EvrwZ8Dv9WlFZavvYfupf6Mqf1HG Z9ufX6L5/oKdfxc/Nk2hAZUu+Z/SiWb+flvVOwLWX7Cj6YuK/P7srrhHj9uZ +C+THfg90lq3piy/P5U7jm/N7YxLM0Mlv0ftqgR14O8HXgd8XE/oRNP5xY/s OHyszi3C+UpVA6YvGq1e6MPvax3lbAsOf0jlK9fMmb7I19nwoyG7r69XWe7j 8M0dkv/YH4H1L8xc8Hs9ragjHbDgmKq+zlKqj3VNOffHOkmsvu7+89/rSTS/ Z1r+7m/1IaJe4uajpKlPGX2sH5434/Q/VudaZU6flOkvGzI7Te61HLiMfS+Z 5p81nX+vVBdERF1QYZc2rZieJa+UFyZxvjRdH23C6VaQ1b8p9wOMrV685/q4 KDx8xsT/o357cXroeO4fFA5utL8eO3eEaZwnP3fx0OJtDE8612V7U8ZHklrB qQXHU1qnmvXf4VABRzqXinMFniZqPKnAs+j376LN4Luw+ihBt/tqulFBN4nO VNBZqpMhok6GBvz1jd+vM4XrpinZ/Tqws5I3vy9rzE615Xo5uerPKdy/2Db1 gc/V/6OeWaxXU6/TdrAu4CSp4VABR5wbpj6XinOxeh6sH2cVwM8F+NsB/izA 54VU/xkB596E7w2Ec5VAt7+AbseBblg/zhPgoynwsRD4OAfkofwQtTyMBHmY C/IzDuQqDeQHqyNNBXkolOT8PvA9Q7ov7/tO+mM/Edbvo9P3z/Xnfq7WqvrV wVI9ueGpXn+sN8bqV5eHlftj/dWEuk4qPEdI9VQfQD+fUetnOhH081jQh4mS PpTq6Kioo7sHerW+pFePgj7fodbn9AHoc6zfoQro+Q/6Kj1Pl4Ge7wx6fg/o +Xmg56V1ItZ1AM47NRwi4EjnEnHuYcBzJ9gdgef937+LiO/C6gkF3ZLUdCOC bhKdiaCzVFem8fPfgj3dDvZ0AtjTWmCX56vtMhV2Gav/D4P1phC/iHUBZybY 90sARzqXiHOx+jesbxGb54DNScD6Fpf8Hj8SET9icxWweQJYfzfWN43V1Yv4 eoA6vqYVIL7G+qyx/mJsviL2+wVYPRs2rx7rB8TmsWP9gNhcYqwfAZuvi9XP Y/Njsfp57HccsLo+bG4/1heJzanG+mWw+cxY/wU2ZxjrI8DqRbHfrShtHSM2 h7y0/V/YHO/S9g1hc61L25+CzV8tbd11aetysf5HbB4I1p+I9Ulh9f9Yv39p 6/axumJsHsszyGMYQx7jNOQxsHkm2NwPbN5CecjbnIW8TRTkbbB5BVhfPzY/ p7RzcpzgHVP054p3zDe/53OoyOdgc1qweRqlnZsxAd5bg6GfRdTdSfkuIvJd 2NyG0s5Nwn6XBOvHx36PA5s7hP1uBTbnp7RzV7C561g/GjZvHJtbgs3lRueE IL+zg9WZY7//gvXjY797gvXjY/P8sT4+bF491r+GzWnH+tew3xvC6u2x38HB 5hJgv/uA9ZNiv2uA9S1ic/uxPj6sLwP7faXS9hdgv/dR2j5o7HcxSttXi/0e RGn7OrE556XtPypt/8soaT7A13+ZD4DN3cL697H+Yqw/Dpurg/WvYf072Py0 GRCXvYF3vZYQl2HzwbC5W9h8pNcQZ/WBd8xKEGdh83+wuTqlna+C/Y4YNtcO mzeF9TFhvzuGzXXBflcLm5eC/c4UNp8E+50UrA8a+x0QrL8Y+10MrJ8Xm/eC 9Sljc0WwuRNYnyzWR4nNb8HmkGBzKrA+ZazfFuvHxObeYHNjsDknWH831h+N 9fNi84KweTvYvBqsLx7rK8f6srG5PVi/OTanBZvjgfUdY32p2LwdbK4LNvcD 6yvH+pex/lbpvYyI97KmfdPCXl5KIcHZkwcmBIWQgvObi3ifi/SupMkbS+9W Grv88WRLVR7A/vnxKmfHndbIj/T+pclvS/l5KvLzo7W0m/L+pec/zj14GBRC X8cNPM7xkfLYmncuKU+u8TeOuh9T+Sc+BxWKs+Oo5l5I+XbNe5ygw0E1Haig gx3g46nGh7wCfMS6K+CZD+s2sJ4C+wX+bQG+NcDPB/jtYH0w0F+sY7+rIr2r UvGuem3Fp8MMDjWcm9T+BYP//XDbXA4H+x0W6X1T4zf2D1bxUbHtS8Zv+7Hf T5HecYh4xxnao0V13jeVrWh/Jo1910GaGcjxwX5vRXpP0cwHLnZV8VHx/hjH 63/7pe8l4nsl/DX7sd91HQt4blXjSQ8AnhL+VOAv4fM/eiK/c3oX8Hw2R43n N8BTOpeIc6X9VOzPgXqY1dI8f+n9nYj39xqQ518C7wXhkOcX792vpHeKcHjv aAzvHbXhvaMsvKdoq9/fqXh/94Z3kz3wviPe63sh896l92uNHx4MdQsRULfg BHUL0nu6Jh6JhboIAnUROlAXgdVRYL+HEgL1GyOgfmMq1G9gdSB17dR1I/ZQ N/IN6kawupSFUMdiCnUsTaGOBaufCYB6m4ZQbzMc6m1EnY+ZVOfTBOqCqkJd 0EKoC8LqlB5DXVNPqGv6CXVNQq5WSXIl6ucj4wJqjPzxv98V8hb9Cye75J+o ckDTv4CdOxXyThtefv94ssoBTZ2/qOffJc0bvwLvxdfhnVq8F6/8/d2ZiHdn 6X1W8y7/5Pf3XCrec6X3YiLei6X3ZSLel6V3Vc0cjylQz7ZYqmeT3nk1c9VC oF6uWKqXw+rurkGd3gyo0xsBdXrY7xSch/rAXVAf+BjqA7E6w/dQl7gM6hLj oC4Rq4dcDvWTx6B+0gPqJ9tD3aYv1G2egLpNUUc6R6ojvQV1p8uh7jQG6k4r QR3pFnUdKRF1pFV/r3elM6DedZ80f+M5xHF9oN7shVRv1luaqyP0Ujuoi/sG dXF+UBfXFJk3LuWlichLv4d6v0lQ75cE9X63oT5QCfWBoVAfuAjymcelecKp kE82gnyyyKt/gPpGW6hvvAP1jeehHvIZ1EMOgXpIaT8R+zdAHjvxhF9Kv43n NPMERD5ZxBEin7wQ1l8022/P94u+ntTf6xupqG+U6EAFHQScdDUcTX+QlFfX 9AcJPBPUeGr6rKU6TyLqPMX83mltTivnuf9vnr+Ys+2yY+Gpue7/m7NtD3W8 VaGOtxrU8W6FuuJbUFd8H+qK2yJz46V8Ba0I+Yr6IM8eUl30JaijjoI66pZQ R30JmQst5VuIyLc0gXuxQKoDXwx14z9bquvGk6BuvDnsnw/3SOxvDHi6S/fu 5Ksvqnc0f8inUcin9c3KUb3TWUv5w78MAlX5t32Qn7wG++Pbb1Pl90ZAfi8A 9ktwNHm/vjZl/jE3IFjjt0v4UPpn+DTgz/gQgU/fNcNX1DmTSGz/XjXQ+30i mZPr+fDijQKypdmsyY8OML9n1ZNXlz3vkZw+zaynLc0nCbR36pNJ2cTN+6tR RPIVktKk68jVC//XX7BruP6Ha28DIkV/QetZaQa1zyTSiZ0NDzD49HmScTGH /8O6sDjlQDpdT+Pywj3v0Wd+ldZw+EKPaUl67IrX6fD6Y9cq9mxJrWBhkElW hE20Uo7OJ32aBbT2nXuXjK7w7XvU1HRSUNR4o/bpfOI9vkNKhRFPyKN495Zz 2j0htn7Xk3dszydjij5e/gccKuDUale7pffcu7SKvo7BtanptEv/yio4ijam 9RkcOjz00wsGh5bR7ZzC4bjU3Be7z8Wf1o5fmnCwTg5x7qrrkVkjjwyokxTj N+0SSdq0u/pOrWxi4h9huDwuj7yZPnPcYRd/ktL++kO2ny492cON79+qU+bQ zmmXqOfYSWZsP+3TbEM9vt8pZGJwzAJ/WjGxSvunDH61tsFG59h+tylft7N1 cmXLu2FsnTYu2VObr2P9jOL3TdZKdeOdDEPuM77T2bfO/trG+FLZwr+E88Xj 5mkfxnda4fniNxcZX2KTE1R8H92pqMLTSdn0nsWmupeTr9Cm/sN7cL6Lcy9W 9imkbwPMxbll6vRcx/hOZjlNt9nO5Mqjy6B2lxj8YcXGBlyuptacXXSJyZVL 56sqvj+366uSqwEX36nk6raOgUquqhg1sm8wdq3y3Nw3weaMX1+emXpyfv01 NLnFDsYvy9MPdjG+08XGPy5xfgXGDdpVkfFrTW6rqbMZv34oBqv4HrTHbto/ 4BAB57jVwKY+TH5GJGXe5fIza/trFZyKfhNU8nMgykclP0vun1fBMUD8+d36 STlMHsjmErcTnL8p2SbHOH/3HU0r2sP462eQkrmD8Td55KvBnL9bWh9cxOSB BQEPj3P5OTxl6FG+33apzh0uP++L96rkp0nSJZX8LEf8+fl3vjePYvJwdeI5 LifU48FuEy4PB9v4abN1uqTB3uFcfqYEb6rL18U9rXkqxrqy2ZRe4p5mS/GU kJOZAMcJ4EwGOF3hXCWc6w7nYnhi3xUDdBgOdDgCdPAHuu2S6PYQ6LxJorMV MpdYG/gYIPExCPg+DPg+G/heA+TtLMjJV5CTg4hcmYIc9gU5dAI5PAryBnCo gCPk/C+Q81sg58PhXoyV7kVxbfU9mgr3yAvuUeGZz3/Ox8I9vSvdUze41yXP 1Pc6Du51e9AD00AP6IAeGJ2v9Ofww6U8rYiPVkrxkZCrKxAfCbkSfXxTwQ8R 80xEfZHwu0R9UQOonzki5W/F3AMRZ4m5B5jeE+tZsRcqDf/xP70k+o6Xgf8j /949+HXm4t1zErzryXngRPiujRDHOUq/dy/iOEGHfqC3I0FvNwK9bQx6vhLo +aqg57chdqH7DLUdSQY7sgzsiGx3jMHunJLs1FKwU5hdqwx2UEeygxTsrx/Y zb/Bbm4DO/tYsrO9JLtcCHZ5PNhfgEMEnBLwB1zBH3gO/kAz8B/Gg//wAvyH t7+y/phPFv7JZvBPHoF/sgn8mZrgz+SCP2MB/s9Y8H/mgv8zxTS0Foe/Tsoz Nx7Yo02zvHQapz2xUcfz90lt90ddrI3yiV9N17DA2HRyZFKvtRXO3Cd2+rFR +y3zyYNh3cax/aSCn1aLDufv04krvhrw/Q9Bft74n11h4BkcKfpbx55ouZrB odrVli+vdOY+LTlrQTmcjjbzrsQ1TKJrzBdP9GuSRMyvtG0z/wSzp8fnVi5i +z5P/L4imp3z4k050pLBnx52Oc/W/z7ZWtXJpmNSOtm0dtucm33yycwBHQ73 CU0k2053GTCXfe/3lTZB028WkO4de9eNb5hEBuvNr7GrSRItuv91GIdvZ5dc +Q37ng3B3Rox+PTq7e0OHP71+qYFE/zvU4/AxxcZfHrOfqw5hx/ncnskg08r +HTymcf41X9zz2UcvunIoj++u+U0Gu7wxWatot6olzUGdMkknXwqO8yyySfn O/rX+2qzVnnmTdLZ/my9YZOSUL7evWyZgD+9m+jp71btP6veTxvA/rENB6vg 11HDp50B/sHonv59GR1mKqk5w5OMHbRxKMdz4+u32RMZ3UzO/FrH6TbgV7de /LtyH5Qp/47RufaKvjnXGJ2XbVs0j9Phy5rhUx8wvng03n1xJ+OLlWEXFV8G 7NpbxODTLy+uvZ/D6FC2Z7ALh99u16KMSYxuuY336XK6zWo8bzaHf6KaX7m3 jM6vDHT6czqPjsiy4PA/HV2hzeCTmI77yzG+06u6tt8cGPzbTRZncHkrPNZw RUX27wZb3/HmcrL/aKUILp8P2k9p3p7JZ4jZvLJc3k6tia8bxOTKTzt1bXn2 HW9uj/Pl+4VeHZysPbcLk0OhV60Dq3pwuXXadKWhPpPbJgMGleFwRsD6Olhv CusnAf4OgP8W4O8BfLIAnxOAj/nv52r08E34riL4rqHwXQVAhwcSHfYC3QqA btZAtzZA5xyg82ygc1/gy1vgS3ngSwHw0UfiYwrwvT7wfSnw3V2Sk4EgJ3tB ruxBrsaDXN3Z5VfrT++8JQ985v1D/qmQ//uZL+r+Q/6pkP+r/Y/7/+m9L+zF I9X+MLgvQv4Xxm9Vwa8r3a9EuKfacE8t4Z7ehnvtDvc6DO61I+gBN9ADStAD 5qA3ekt6wwH0zE7QM19Bz8wGvbQN9NJG0EuTQY99AD2WDnrMCPSei6T3hJ7U kfRkAujbL7vU+tYW9K3Qz8ck/Szs+Fu1HjYXergZ6PlroOdrgZ7vivj/ncGf OWp0sWS/4wWNP9MQ6k7rS3WntWC9hbT+CPwQ+212fgyOuTv4IQORuhoRL5eR 4uX2FWbvZfJK8+cNXfuefa/v86NrOT1LKp7I2sH4NGDHX0YPGd/azuqmomcR 2FOhn2+CfO65alDC5X5dUOYafg/GVG48msNp3+SMIeM3uRXhkMrvjY7tZNV9 tAO76SXJeUDvzIPtGD+yHr4p35zBu1XHuGAkg1PngMU+rhcOxH18wOnt3aru a75uVT9tH+df+eUtFnJ+LpgS2JDTP+TYukA9tv/0ysaH+P5Jo6Mb8fWZndw3 cPnLmTdpK5fHr587qL7rdEFzQy6v38wuenD5NettZsPxt6y7xpzf8/l/dXvJ 9lPzij9UchtsNDaOf2/PTy09mV6h3RvcXsf3i/lmaZD3k+e4irycmAdyCOAM ADhGAMcMznWRzn0F/rxs104A/jq91PibA/628L1vpe8dCX57tKQfdgHdzgHd JgLdjIDOlYDO84HObYAv/sAXH+DLGeDjC+DjbeCjIchDlCQP/iA/y0B+bEB+ KlVSy2EfSQ71QW6fgtz6gNyagN2X450+kB97ltOlywO7/8UL5uBvyP5h+u/3 S8PHKNCrcl3KfdDbcr1Bj9/vu4bv/w/3nY9M "]], Axes->True, BoxRatios->{1, 1, 0.4}, Method->{"RotationControl" -> "Globe"}, PlotRange-> NCache[{{-Pi, Pi}, {-Pi, Pi}, {-0.9999999999999748, 0.9999999999999748}}, {{-3.141592653589793, 3.141592653589793}, {-3.141592653589793, 3.141592653589793}, {-0.9999999999999748, 0.9999999999999748}}], PlotRangePadding->{ Scaled[0.02], Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{3.3979961563478928`*^9, 3.3979975452676983`*^9, 3.3979979516284313`*^9, 3.461114710784205*^9, 3.461116319398665*^9, 3.461116389863666*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"ContourPlot", "[", RowBox[{ RowBox[{ RowBox[{"Sin", "[", "x", "]"}], " ", RowBox[{"Cos", "[", "y", "]"}]}], ",", RowBox[{"{", RowBox[{"x", ",", RowBox[{"-", "\[Pi]"}], ",", "\[Pi]"}], "}"}], ",", RowBox[{"{", RowBox[{"y", ",", RowBox[{"-", "\[Pi]"}], ",", "\[Pi]"}], "}"}]}], "]"}]], "Input"], Cell[BoxData[ GraphicsBox[GraphicsComplexBox[CompressedData[" 1:eJyMvWd4lkUTNpxKAFGxYO+KgoIKVgSdVVHErgh2RX2siAVE7JQHG4IK9oa9 oIgFG4ruqqCPGlCQICVoQgihBEgCAQIhfLxv5pzr2PPifv3y5z4m23dnp89e +1596/nX5mRlZX21e1bW//ndpX3XCzbs2yzgt+qrwpv/Gp5v8P+69nxwwqps g93r33Tee2K9j+vX+ri8kuBig7P0j8qF2gv1T+X1QvNzNH9H62LY1sm/WC9g rDeuV+vj8kqCiw3GeqlcqL1Q/1ReLzQ/R/N2dG4Mp9YFmH+x3rh+rY/LKwku NhjrpXKh9kL9U3m90Lwczd8RXjKcOjdeF2D+xXrj/1cSXGww1kvlQu2F+qfy eqH5OZq/wVhvpnvIeMrnyOvk3xifa229gGN8tnKh9kL9Unm90Pwczd/gGJ/T dIbvIeMpnyOvc8u/xQbH51tJ51tJ51tJ51tJ51tJ51tJ51tJ55umo0xn+B4y nvI5bnmdyW+83mJabzGtt5jWW0zrLab1FtN6i2m9aT7BdJTpDN9DxtMtn2Oy Tv7Deqm9wVgvjW8w1kvzNxjrJTgDX2C6yXSF7x3jJZ8br8vK6dfOV+h8hc5X 6HyFzlfofFN8nvkg8wmmo0xntnwPEzzlc9zyOpNfur+03kpabyWtt5LWm5Zj mM8zH2Q+wXR0y3QmuYeMp1s+x2Sd/Ev0WYg+23pJLmM4JbcwX2e+x3yB6SbT Fb53jJd8brwuGl9ofo7mbzDx35QcynIayzHM55kPbplPJHSU6cyW72Fl6tx4 XTQ/R/M3mOSrlJzNcijLaTE+56f4/Jb5YMInmI5umc5UpvCSz43XRfO3X5Kf U3oEy9ksh2K9GeSWDHw+4YPMJ7ZMRysz3bsUnvI58jr5N6//I71v7FUQihta 3n2Kz9V7WugJFsBa3xEcPhv+wqh922RZP4Abxy/3gNE/wUL1hfpzgDE+wXoe dTZvwBgfMMYnWKi+UH8OMMYnWO97tY0PGOMDxvgEC9UX6s8BxvgE2z6jf4IF MNoTbPtO7YXaC7UXWr/Q+oXWL7R+ofULrV9o/ULrFzp/ofMXOn+h8xc6f6Hz Fzp/ofMXwn9H+O8I/x3hvyP8d4T/jvDfEf7z/XV0fx3dX0f319rH+9gsMF3g e87lfO9jOprcE6YTKI/5aoLXfK9RHvOlNB4yXjNe0vwy4jHKUZ/3nfEO5bSP VD+hu+AvvM9sp+F9ZrsG739MX4uF6TnL3SiP718ip/L+xfQ6P4UXJPekzp31 OOYPrPcwv2A9gfkHyrH/JJek8JL1ZuYfrGcyP2G9jPkLy0HMX1Ae09da4xMx v6hM/T+m98Wp/+M3licTfoR+gb9x/9kGM12L5axqWk857X9lxn3NxKdjeTHN R2P5sZz5nmc+EMuHab4Qy4tpuh3Lj+VMZ1NyToynabknxpu0XBLrQxnlBuLb tRn5bKz/pPlgrDek+dT/m44UpugIrT91T2n9KTmM5ZBYv0vkEuovo1xB60/x /Vi/SfgK6VkpvkJ6p80f643Pofhf1lWZWteW9fhETorXmZ+BDyb2B+ZzbE/4 f+NVMeNdyt7A+45yom82X+bDrP9nusdEj22+bB9gPGc9keU9lBP/EKaDmehm 3E9xim6inPif3TvcQ/w/7j/9/5ju5qf+z/JfrCem+VAmvQfrzaSXYL2Z+ECm fc4kZ2OemeRgrDfTOkm+TcllrFey3EDybIpPk/xq+8N6WqyfV6fuAes1rN/z /rG+z3oByZmpX5ZD2W/I8j3Z9VLyfEw3a1Pye9y+MiWfU/uUPE7jp/Q+lPM5 x/Q3oXsEp+QMvteM9yzPsNzFcgnTMaYLfG+oPLDcyXIc022mi0xnAMf8i+Vs lmOZbzEfYDrL/VVSfYNTchfTOaYDTL9YDmU5jek800mmI4BZb+N71biOSf6C Swce9smwBr/+mfeve+22JoqHaz3KATf2WyRc3jjuKkH7dhWbuo1cvl5QDhjt qVx4fC3HeGjvqT8rp/kIlQu1t/nreA7lgDOsz9pngIX2T2h+QvsptD6h+Qnt t9D6hOYrtB6h8+X9lm3Ove6Sjl2aBvp1//J/+wU+4Z7g/zjPGO/qPMqxv8xX UI79pPZWjvUxncR8afxA4xt+U38pOw7Nz9P8uL7tJ80/0PztftB+pOw0tL+O 9tfq8znx//n+8zmz34D/n+ncaT2e1s/7LXQ+zPdCzFfzyc6W2MtYDor5q8mf Vs5xB8T3U3JSTD9MHzG5INZrjY9k1HtJbjC/CPFd8vskdr6Yfuan7HjsX2O9 l+SsQPzBMb9h/wvbXePyctqXQoZN3mG7LfAJdBO/M/Y9YtB9c3JSfgnSPwLo I+qzHZvkaus3pg91PpazTD9SfFgiVN/kAtJjyH5c5/k3pisGC9UTmpfRT+xP 4/+rUvPmceJ6VcYPAZP+beMD/u76gY8t3RpyTFXqHNhPBrra2L409Ut2AcL7 cuODqL9o3op+27yd5wAznrHfD/ME34vvXyHdz5SfyNYb8+kClwmvyV4nGcax X/ZrUP8mPwDv2K9GdkKTD1Cf/TCkx9o9iflTXQoPYrxcIlTf5GCyGwjhfwqv t4xnpXTudh9Sv3QfBPhB8lCI8TYr8D2K722VnTv2EzCvk+eBevhlusN+YNwP xmvAfG7sR8Q6UZ9/2c+N9YHOEh6n8JDshrYutMc4TLf1/4H23+gtYNQDPYX8 i3Laf7sHgHW/WF9w3D/4N+rH8t0Sk+ex/7E8t4TppcG6HpP/4n1fZeWoj/lx fxgH5QSDvhNfq8K52/2l+QnRd6ILVbhvrK84qm/6AvYnljeX8H0xGOtHfewP YJSjPp2f9Yd6KGcY/WK/WF/h/4NuY//w/0x4i3LAjOf4xflk+j/wFP/newG8 RDnOAeMDxvg4P9TH+QJGOdN57h/lkMtpX0w+w7igKzH+5dp6+H7G/twqu7/o F+tGPewT6FOsrxeSPl9u9x90isrtPsfyH/T1ypRclIku4Bfl+I3lmSLya2an +EqmewU8RjnwHOXA99i+V2vjM10hOcv8CPg/3zPiO0aHYSdguhzLVyZvmV2B 6VAsTxVS/4UZ8TYTnuPcUc58gul+PL9Co3/4jeWs9P9xf7g/lOP+8HyBp5gf 09/Yv1Zlv7H8sEpieWCV0P0x/THWD8vJ3lNI9iW7T6ZvAsZ9QXu6X6bXsl6Q iS8C71AOPI/xrsjuDX5ZrsrEV/CLcvwSf6A4ylobn/l0LA8k/h3UZ75Fcpf9 HzDPg/iTI/5k82H5CzDJXdyvi+WJZi7WJ5oFpsMxvuYbnrJ+zn4Xtr/h3uH+ sh6P/uP7mtjZyG+dyiPhOEG298T1C7mc4ryLZct6XL7REdbDeH/YTkf2qlTc Mds/UD8DXpi/B/vHv7E9q97kB9DHeN9rSU82P5DtP8UFEFxk/ibsB/qP6WcS b8VyKuqTv9TsVhx/hv7IXxJYjsL/Y75Sb3aJ2H+T5JGRXydlR0F95jOw28V8 sTZDnESSJ0B+vJReGs8vn/SXbLMnoR7brbAu/Mb1K1N2IdTDfYjlrUrT27E+ thPE7eupPDulZ8dyS3bKrhfbS+ttnnxOcT+JfYvlO4JTdhTqL2X3oPmk7GGx nJjAMV0pTt0r8lf6+Nw5zrw4JXfGdrtE/ov7K2RYYtjic2g/krwcts+hnPl4 bCcpJftZMdkZOB8wsaexP5fyvmxdmFdcXsj5cik7XJyXUU5wvsmnHNeC+VP9 lF0b48Z0t4joHvsJON+N7Ym8Ds57Y/tiscT8KrELbtkvwHkpbJ8sJvtksfFF 4BHzgXi9STwZ223iedZmuJfltF+VtD+Vts6Ybi1J/cb1a00Oj+2aqGfxlZSX VE77V0n7VZnSs3H/Yz3E6BvFEyb5c0yH2D7L64j7ryQ9zOgbxbUleWVxedru Gfdj/MjWw36ReB+NH9l62B6M+sR/bJ5sd47r11N5dspOG6/T4rkdyyEkPzjm 2yQ/mLwe61uVDKfs8tRfyo5O80nZm2M7QgpO+b3ie27x9i7Gv/yUHQLrY32J 6SbTkRiuTPkhYn9jbcrejXLW67B+1sdQP9Yrk3w/0DGyC9r+xH522IOaZeBj hVye8uOQf9Tg2B6Uirczvx7pAaY/kP5JeVP1rLeaHkB2LqFztH7IzshxU+Y/ ZPuY2n8MTwDHclQhjVtEeF4oZJ+icWsJj5K8DqbbgMn/APymcRK9jey8dr7x uEVCej3lV5aSv8Xi4iAXeJYTSA83vCT7dyC5y2DS+8wfQnYTH8d1JP5kwIOG vVA2rKxJ2K/5fmdPLkj0efafkJ0tFY8V6x+TKG9iikf/sBvG5UWkT0wX1Gc7 H9ZHdvRUPBf6i+NX6vyjVw2cf9YvBQ79Uzy5Jz3W9gNwTM/MLmvxYWRHpP1e ksFua3Jbyi7FfoBYrjZ/MMlXdl+J7pZLvD8mL5ldl+ziQnxByO4nhF+i+wt8 MjyP7UdLOD6Q5IPpwBchfNH6U4Aftk70R/hB660TxXfH8yN/VSC7vZA/zGCy e2Wws5tewHEwAfuFX/LfWtwayZkUh1Zq5wCY/eIch0Z2lVScOc3PYX6Z7FSc 78f+MPYbkT84lZfCegbn6bC/i+3u7IfjuGqUo98t+xGSOOB4vuVGjyiuxuwQ /6a3cznbGwADz/5NLuNytGe6HdMh4+8Gs5+L/B0+xnOTHwzOYOc3/Z/8TRy/ Y/Iz6pO/ieN1DI8ZBl5RfJLEdHQS0ckkTpnvEcnxKf039tvkBqITtg6sn/z5 JMek9dqY3pcL8ZkMfqVV5G+rJn0h8XOR/inx/PID+5vIHyXsTwJMcWtmN/s3 OxKXsz4EGPfj3/QELkd7liNi/mfyH+UPlXLcuMmHMX9Mxz1i/8nfY+XkLzF9 ivxFFkdM/gmWI1NxOKBrsZ+1zsd4YnJDILkhFZcCvKH+DAb/eOqEnn/t9FFT G5/iN8m+X01+4Wq+N+SXr07RYbajkp3J/s9xLqznx3FgiZ4c251zXTxeXYqu b5n+FqXoMMfJxXYCpr9FKTzFPsX5M1Vm/4zpZTHx1SQOi/lg7Cevy0BPi4lO JXFJTGeoP+HxYr01i+xrSZxibLcyukdxFWm+ynZtsvt50qNIDk7TIbZbkJ/L xePVpfg00x/mqyzHMh1j+sN+acCQ4/Q+kj5r8rHJ7wPe/WbJNxvzA8njlN9q +h3ZoU2eN3m98f9zJEP/Jh/F+uccH+uD1t7kH9TX/hzV9zSeyf+0H0av2E8P ekv0jPIjTP+x/lEvA+wxLs8H8jj6A32n+oQXzUIcL8N8PnlfNZbDitnfmYp7 Iv9WKq6D5VrQG9wTwHHcVyJnxPb6fLKfF0sMV6bipoAHmB+Xkz7gKK7Ik7zk SP9l/VVYjwIeYP6Agd84d9IPzd5D/NLOl/Rh2FMM/0mfZfuCkD3BYMq/MDkF /WEecVzdKuNnsRwHelpj/DTWgxK5r9Pdj2zT5CXQyxqOA+a4Vhsn1scTvQjj X3Fsz7b33LTJ9PJYb0rkRIyP+qR/m/zFcbIcd8x4Cpjuq+1fBj8BySfFJF8U e76nfC/YL81+gljeSOzrgIneGF0DnQEeAy6cvWL3kNPUnfvz/OffGtDU6Cba oT/ON0O5tg/a3pF9wcX0pp7oS73H+LM+L2z16GKbT1BYz6vEHz15/om9/txg 94Hjz2M/1hyr3+fX+Strds53gHHuHH+O9uAnqI914vyxTswX+4X9xj5i/nwv 4/zSAqMn2K9MdAT1dT1WH3iK+ijHvY/l+GYk9zUzvSKWp80fm/JjsN0f82f7 OeBYDmqWsoOyHZ7iEc2PwX4Aitt2sf5h/jyTEwHHccdrjU8AJv3ekz6emhfz nViOTfgQrxtyV7zfk1J2ZLY7sh+Ix2U/CcmhwuuieBizb7C+H9sDVhm/RHvA vO88Dp8b83GeN/Nx7APo0VXLN9W27ZgXlP7rusqEYONnWt8RrPprjaA9wcaP 0J7gcGHL/ep7nwl7aZkQrPhWYeMTrPe7wsYn2Gt/WK8DjHsVy+PNTN4g+hFi eSLhi7EdrFlK/oA8R/HsPJ6dU5y3a/40G59gs3dTfpLxW4ojZ/sGx0+aHEfx xNY/+4XI3mww2aOM/rD9G/oK3R+Td9i+zOWgP5AHqFyof4Pp/lk5+mM7y5bX WW2/je1XGMx6ONklTf5D/ZN7Xdf098HZBv/bubEfNOZPS9gukfJrkX3YYKKv nuirsFyN8hi/JzG9lliemSTUP8Uzmf3a7NvUP8l7mdfNfivApBfZeOzH5HMj v4rJoZQXQHk6a4XwXQh/hfCd8h7WCuG3EL4K4bf8/7uH1YaHwF/A/7ZO1MMv 8Bfwv9ER9tP/27mw35XoYUqvI3+gyVs6z3DWEV1znr8ux2DYUwA37uNShu2e Q14GrP3ZPkIeJljPY4VgfMAYHzDGJ9joBMYHjPEBY3zA4GOxfamQ4hEKTZ8G HyKY7F+F5J+29kLtja9jH0jeNZj4rbD8Hb+XUWF2M5KXLZ6E5WW0x/5jXvjF /yEHASb7qeEH2mE+qA85CDDZSwP0Db0vGeP4KT6d5OZq4o8l0Oszxl1SfL2L 5fbEDwS5huz/geRgmwfFX1KcZ1XKbkH2d847MLkPeI9f/D/msyXEj5J4bvQH uQ/94L6gv5juldD8Cg2O3+sqtXsEekTldk8Ak//B4upAt7bsnyg0PAOdoPHJ /1BocjDq03iGl6iH/oHXwH/A5F8y+oT2+MX/gf+AyZ8kwH89R9ObY72xBHYt 05vZjsv8juJGKV+wkt9bIP9tpfFPuj9C98fmE9e3+2Pz4/cYOI+C46r5/QWO eyU/Ryq+NaZzFYbnwHvcA9wf2HnI32lxhugP7dAffvF/3B/0R/YTfsfE+DeV 03uKZm/h/EDbR86Po/gU0hcsv9Ngipezfiif1ewKFB9icBy/kcRzxnHBlp9t 8QEEk3xo+eHmT6T4g1T8FeVfUt6dxReRnJv48zleEzDle5r+Cxh6J/ljAuvD BAv7bwBDbuP4RI4/JD0gFQ8ImPK9TU4ETPMXmq/Q/ITsuyZHnv7AC0ctHrMR cqEAZr0RfCbWI7NT95LK+b0fjg9K8bFYz8xO3VMq53xcu4+QK5huA4ZdCHQG dh7m25BbYzvMHB/LLQndB4z+QXfQP/NxyL3oHzDhoxD+Cfk7HeGfyZmkTzj4 iVEO+zH2BfUoLi3E+JofiB4afWO9Ka5fS3KK5WubXZrtFixnkV/FYPA31huZ z5AfyFH7lPzK9hyy31B+ZmVKXmX7DdlrUvmWHL/McePkV6N4mBW6ztXspzW7 MccJk5/QyqGHoT+Kn+O4eMpHtfgdo0sxvypK4RHK4/y9IqPDOH8qJ7tMMdHR IpNfY/qZxPlSvE3KHkN+9pSeH49bS3J9Pcm5lSn7DMdpkp/QYOAT2zU4jo38 mo7Hw7zofpj8x/HUnJdAflqKt1nhlX+YfMrx1xyHTn5nKwf+oT/Mj+J1Atup KT5I+NwJJjtdMdc3eY7fcaC8U7ND8nsKsb+S88Itj5Hy7pL6wKs4bqfWx/3z +9CV1H8l9Z98pzPGuxL7jeMySo2/o5zzerH+OM6omemLcZ7NJG5vdlned5TH cRpJ3gnkftofof0W2l+KU0zy4Mn/nspTBIz9i+9die0T9g/l+D9+OW8Z62d/ F/h1bN/PNTsM+6NQP7bn55r8E9t/kvwAyIMoj/lwltn9UB7TlWQekB+hXwOO 40aqiX+W2/8hF0FeYnkT9SEHoj72H340/CIOB/uO/8O/hnLsN+LeYjmh3OxU 7L9D/Zjulps8iPrYX8AcN4v9RTnHTWL/yN5teiH2CXIk9o/ladTH/1Ef+4d9 wT6RfGh+SfyinN4hM7ziuCTgHeV5pt6VpXgH+43f0bU42xDfwyyKm2Y51uJ6 Zct8P4n7xf5T3GcqD4fitE1OYLkB5RS/xfff/FAMc1wJ5wWyHBvrSWvJHrye 5Eijb/beHPuBqD+OI7BfjJfp/2hP5UL1hPthP2omv+u/+KmsPLYXm12W7Uyp d4voXDzxr4x+Q6bzrEcRvzO/Ao0jmX45bgL/J/mA86CE9JqUX5rkh4x+PuqP 40hS55oJD6ie/Nv/M/nFM/nRM/kh2U8Z20PNzs52w5Sdifg3+5kz+onjuGTz lwXyj8GubnYb8t+ZHgZ5B3YNyNPgm4Dj+MV6s1OgHHwCMMXLBoqXDRTvFog/ BOIPqXpkjzB+ADqL8phvJ/ky8BegHmDUBx2D3AL5P6bjJRb3A7yP82KXmj0J MOrjHqB/2GuwXpwj5AqcG8EmD0FuBz3CeQIvAYNvx37UCpNr0R7ni/aA0R77 Db6FdcZyT5JvEvsNSwxG/ZhvVFgcbMwXS0yuwn6jPPbLlBiM+jHdSeQtyGHY X4JNXsL+xnnpS40PAIbcg/1Fe+wv2mM/0R4w2pO/1+rhHqOc7XeIUwUMvAIM vYniNe0ecFwpyWcUz1WdirthOw7uK+W/UN58+t0K0hMD6eN2H2m81HdwOW5j y3nd6XcjaH8D7WfKzkX5hRnfGYjvRXWqPcopXyll16B8xH/Jc0/eyeH2sV3R 8pkC2XPMLsH/hzzNcbNshyT/oOWvxP6tWqH6Kb9nJjsT+RPNrsJyLcOwl/A6 +Bf14rzjai5PyUkMx3bAapZf6XtM9dQ+y+zDlI9s58Nxz7Gf2/LvMtqpSe+W +J7Wmjwc+61T3xlIxQ3HfuyqVBwo5Vun/HaUv5iy82N9HAcd+zsTP1qsP6ft qljPluO1U/I9yfEWP2H+LNRjezPFSxgdI/s30b1C1kNTdm2aD51jEcl/6XeB +Jfs5/T/5LvhHLe2ZTtqIce9kXyZfqeH/QbxespTfgzKz7H94vYox/rZDs0w +6lRnumdMG4f+10svzhF93CfmU5SHIXp2US/JKYTaT8OxTsJ0TOhemY35f9T XBS9o12d0sMy2fUpnkqI3tl+MJ2M4wdqGWb9j75XV0/zsvdNzC/IeSNx3I/l u2f025FdkvCoNpV3Ed8jy5e3OBz2y3A+BfAKegbkFnovIOXnZDtZfI+S9944 fy6TX4ntYpA3OV6GYHp/bZLZ+WO+ZHmuJr/FfKaY9qnY9NtYP074Jum7nuJP U3hB+q7pVxQPa/o2v5tM9vDUu5VkHze9Bvt46tJNY+Y9UWB5d4SvZmfH/Anf 9JxXC8cPox3kQlqf2etBxwDDD8x+PsDgqzH/LbH4CeANyqHvoBx6CeL/sB+A Sf4Rjlshec/oQ4zHq00PJHnN9onrAcb62S8JGOuP70+Jj+MpSoTjaPGL+MU4 vrba7AGITwQcx/cm34mmeBzTx/mXx8H4gPkX48fxjdXkj0ji66FPs/2B7Rms PwOmfCxP+Vic7yWU72X8Is4rWm1+KNxDlMd2sBqTl+M8o9Xml0J7lKM9ykG3 oFd3PLzrB90nJ/caMPJiITeCT0E/3nKeS34g+hnIPmBxjBTvZvoy570TPwvE fwLxZ0/82BM/Jrvjek/5AwaDrpI8RHpppdEntAeM9iS/kJxVSf7yYqNPgOO4 ErzTmeRpxvZ3e+/b9AnQs7g8+e4i5X2k3p1kvzjKs+gPfJrmR/6BYqHxhfNO aHy7Lxwvgn0l/Y7e5bRy+l5uLcP0fVzTf4XyW2AvA98yvsx2gdgfX2n3mPJh YL8zvk32CJJ3k++bkH2c5KkkToLur6P76+j+ulh/TeIC4nK7vxm/60Z2RJsf 5AiyJxrMcRpE7+g7zcn3EThvmO0wGeR0o1vAK8ovMnpA8SmUBz0ndf8p3oXu aaXBsZwyjt5rmER4NMnHfN3qm/5F3+1gfiNsN8J5o318/sn94rxrtjuxXgOY 5WbKH3NxXlAiN8d55HNS9wX1IU/E76EkMPgZ3kPAecR0ptJgnAfa4zzovTWD MS/Uh10l9usncRw4x5juF1Je/3TP8TDoD3Zx9Id5xHhQYDDqo784Hzt5BxR+ WPTL7/eifmw3rrN50vu6du9i+0mdyV3xezSlZN8stfqQp6h+6h1Gqp9675fm IzQfofU6Wq+j9TrKb099l5P20+pTvr/NN5b/JhHdKDQY+BHLq4UsD5CdKbHD IS4I7WO/7hLTiwHHcTQVVs55W3GcynRbF61HaD1C60nlrWB89EfzFZqv0HyF 8D31riDhuxD+kv2vTgh/hfBRCB+F8JHe7V1L9r46IfwSel/C+ovl/UnEN4ye OKInTJ/M3kHtKX94us2L6I0jeuOI3jiiXy6TXx2w6iH2C3oTv8eQ5Mlgf9Ce 3tWxOEHA0C8xD+gB9C6X+QH43jIdZL9I47zWWX3gZex3SPJkcJ70PhZ9r7Oa 3l+utnhK5vuxHdXmY3gE/szvkNF6hfbV4uigJ+J8wG/RH/gx+ELsn7fvZKTy hCk/yuphHlyP8oHtXCE/xO/xlFB8aInE+mxlKm8zni/oWradF8bHeVJ+s9kx KK/E8AJ2IowT278sTt/oKMoxHtX3sZxXZnaFON+l1vgRymO+UG32RNhrIB8B Bj1BPX4XE3pMXD/JxwJ9iPOR098FgH4a562VmR6OcqwD5bHeXU71i2ydsX8v WRfp0aaPczwv+mc+EeuV0K/X2Ts6lIdoeiHKYz2wnOoXmT0NMOxamD/pcab/ Y/6xPsd5ntZ/oP5NLqX6QvXJDjjd9CD0B/k67m8F3iE0PYXOS+i8SM5I3r2m +mQXnG56A/qD/I7+AGM+sZw+iejGJKOTWt/oBMaH/RbjE76nvktM+J6SW2P9 DXxkndWP5Sz4h80uY/ID5hv7IYvMvhzLiWtTeiKNb/2Cn9D6hNZH80i+mwl+ AfkNMNrHccAVKTpMeXvE9xK6Tvn0lD+ffAcN/CKOfy+hfa20cvQbz9fyrGif knr0PoDhbRznVGvrgf0/1utWS8xHV9v4sR2yzPwHsZy8WuJ9XG37T/Iu2anL fBx/mdA17Be/z4X24EcxHUziOWM6nGV8g+35TAdBR/mdZfw/rp/k84I/xfn+ 6e/EYJ4kH9p7BCgHf0J5HA+ZfFc8fm+n2mDmm+BP8bpKPOdLoH/WM0g+dCQf utjumJwDyuN4zOQ7z7F/Kckj4H0Ff8J8wI8w/zgfJpf1BUf6giN9IfWuJsn/ juR/w5/4Xc4Vtt+AMR7J5+ZvpvMyPRT4hXLwF5QDjt8rtXfxLB4X+4D5w44G OPa/2Dt5Fr8b52tVmz8XMNnpTP5G/yxvx3DynUL0x/Iw1U/JlbgXLIeCnsT5 ExUmN2SKQwacSS6M5dzsFB2J45izUvcW8wHe/lvcLuBMdIDnF9vFKlJyKOgA 2qM+04F4PsUmz8GuynoTzg/lzBdjv1ultWd+GsPJdyXRH/M7qp/SG0B3WM/A fsX5ExX/GmcOOJPcHesx2Sk+EcepZ6XoItph//8tLhtwJjrL8+P3aVjOB52N +VBeis4CJv9Oys7C73jS++KO47jpfQ7Ll6Q4A4s/AEzjOxrfxXaU5LvoKKf5 cBy6rYfs5YHs5SH2+5h9zOIgyV5P75Ul8SyQI+P807UpOznpi0a3Gun4mlT+ KH6Bz4Ab9xnvFS/wJI+T/8v0a5Pj9N1pgznvMs4rKvNtelz3yeC/Ed+wgN+d o3jVQvJ3sT9rkrXHvlN702PInkzf75hO9MX8CwaTfdb2AXRzy+/JJu8ZkP3Y 7LXAu/j9vrUpvwLJX7bPOG/e9zgvuNRgnPdb/xl45Mt97LuFqfg9kldt3jhv zp+N7UQJjPPGeGSfTcV1sB0R58/+M2rvqL3dY8Dwt5F+4knfoO/j1Brfh1wO ehnH4Zgf3vgAykEv4/dHpts4kOvYX8P2cfIrmvwGOzDoFfYP8TOQs/i9JJQD Rj/gz5AHsV/0XrH5wUGnUA9xTYAzvfsJPMj6Z8Uvl3XLDju+8P63K7bPDXjH nvPVMH/Ip5g/YMwf8kJszy8xvTi2/+QH1lsRdwU407uhmH/jelcK5q/30fRu 8AeG2b8KGPo3YLSL467LKA47sXOyXhnLpcn7kZDvgJegY/Arcj4d5WtYvBDp 7RZXFueZrja+A76OeEmWW+N3Ukp9zOfnpOQY4DfoPPBL4x0kjiudIrH+U0Ty I+y5lcY3cN7oDzD6i/Uh+36V2RVi/Wi64RXOOY4PK6Z3y0osnhFwpndywc9w nxrvy2LBfQLexPrXdLO3YD5xvFmx2XVQD/cDcKZ3czEf3A/MB/cjtisl78nH /tEKu0dxPH8ZxfcndnK2S7DeFtv3840ugw8D/znfkd+DRHvgK+oD/+O86sSv AvxH/BLrYegP9YH/qM9yOcV3uNj/neuIP9g9Rj2si/MnOR4fdCu2rybfn0E5 ++XYr8d+P4o3xj31cXn6O5ux/aKQ7J7lKbyK47ITuzHWCTkSMPuPOc+a+Rhg eufS+GPMz9J+PX4/jvLMUnnHfO8Bx/cisevGfoe0XZj8qJx/mbLjxH6btN2H 4vct/jm2kyd6f2xXSNsJKF/A6CLwgL7fZvIc5Ed+tyfme1UGoz7ohNIvPa8k bp7fsUH7+B2VpD76U/rs0F+cN2zxtZYfGOszxabvMD9FOe5PHBec/g5AHK9b mOK3KAc9QH+YD+JEaf6p70DTfFN+cvazsx8+Xhfy+Wx9Quujd0SKhdaX8rOw n4b9OKDzGJ/2w9bD7ySwnAiY3smV2K+P+Ke0H5/8Qpw3mno3gPkw4Fj+Tfw2 sX8t7fehuAbOF0zZbWM/atrOi/nG+sYaifWqGtLTE7tfbGdM2wnj+O5qkzso P8LicPmdrliurUrlCUEOAH34/eZHPi3crsHiTvndKrQHfeC8G/QH+oD+6P1+ 05dj/lztYzydQnH86e96oD7xT5M3Ynug+ZssX4fiiygOuoH0L/PvBJJnQiw3 2DtnJtdxHkP8/ykGI76Z49bjdyqmGBzXt3gPyweieACK27b4CsuDpHgGi0Mm /c/i4+J5JXkVWE/sPyqz+GLyVwbyP1oeCucHEmz8EvXJHmIw6HycDzOOvl8x ieBxQuVC4xmM+QEmexjJQWZfMbmC/NlEF81+Y3SF/NeGb7E/yeKVhOKPUu8M kH1IyD6UyqMl+5CQfcjkMNjd6HyEzkNov4X22+g2YNpvo8ecbxLr4WavNv8p 8jkpPt7kV7LXeLLXAL8D2S+tPLbrl5m9h+1gFF8fKL4+kL80kP3SyjEe/MKo H8dfJ/Z7fmeR36+kPGd6jz2J2yH93PCPYdAJ0p89xTv72G9ezvFIbC/xZC+h PNwpnI9i8gfZR+zeU3wWxxexfcWTfcWTfYXzW+jd0kmUt2r2HtB7evdqnJB/ hN5pGifkzyC9tsz6Y/8Byile3v6P+kS/TD4CTPTK5BuKtyE/TGKnJ/spvwvq YvzCO0Jmr+T7bfEFZH+3fBmyH9O7bs1MTqT8GYPJnmsw2f8tD5D9cLh3bH9g Oyu/Lxm/b2Zyvp0r+sc5oj/c3zieukKof7b/Gj1CfDtg0Bt8nxUwvssa500n cjXBlDezhPTYMvt/LJ/Yu1DmZwEecB4O9wP/XGyHTegU5WlYPCf70TgPIfZ3 J3jKeQkUj2n6KPAFMNmZUn7Z2O4+nfTThK5DvuO8IdJTU37G2K45nfTPhE5Q /0Y34viLJRRvYTDlHS0hu0Oyz/H3r5N4N9SP88+TPCbGC+Axzpf5DucBxd+J T/ygnBfCdk2K67Jy9Id7CPqBewsY9Az5fIDxXWSyHwnJt0LyrZ0zwfR9lUR+ pXKhcqH5B5p/oPkHmj/TF0f0xRF9cURfbD9Y/mF6S/lGgeVHyocyPKZ8OSH5 3+gk519ifhR/5GP8n+Mxf/ALinez+Lo4f7jW6Bj0gDi+KvGbIe4U9UlO9uxn 4DwAjrvE+Pg/xo/jd1YZjPFRH+PH77Y0GMxx/Bw3memdRIqPxHst5o9HPejt 9L0CT/GDZg+guDYrj/1gFu9o8nj8vcLkXWqUx3S93uRc1nOhd6EcclSsLyff 3YP8FL9zvtbkNMhLKIc8GeeVrLJzjv0/qy1eJrY3N5i+Gvt/VhvfjPNM5tgv fffBx+9nzrG4RorXN3sGxUFaeewXTL6jTt/BpHcGSq085ovJdyL4nR6cD8pZ Do7tDpUm38bvzK81ORnng3KcTxwvvsruNfYZ5Tif2N4Hf3e9/T+OX7fvwNi+ 0/ePzJ6G/+MXeIh1Yd2A43z55Dt6MbzU7GFx3N8UypNOvpsXw0vN3oT2HD+N fuLvb60zGHIC+BnKAaM8lhvXmpyI+nF8TJ2tC+WxXmr9UZ7LFKH6JrdB7qLx KL+7wOKuQC9pPSYXxd+vsv2wctglSG5KwcBj2l+h/RXaP6H9E9o/kkORh2P7 I7Q/QusXWr/Q+s3fQ3Jc6h1byFn83S7yu1p5rF8l72vHfCaJQwLMcUgx3Uvi LOJ38krIn1jEdmGLMyc5hOKqV6XiaCA/xnHsyXfjOK8t/r5fOs8jk90f9TmO AXp2HA9fkIoDoPFTcVKAOU4K+8txI4A5bgT7mSlPEfvLfsj43dwSz/nscVx+ ocGcpxfnbaTzULC/7AdBfY7zwP7G8eQFqbgKzuOg7424+J4lcQfEP/g7d0an cW6Qz4HPsT+ywezfKI/9hQ1Mr4Tolcn/OE+Wq9Af++9QjvON+VaRwdh/wFh/ LF/PIT5XZDDxVTtvfieR4yU53g/l0MM5njH2ByXfEWe7EtsJoNfRdw8Nhl0d cjjJF4YP6Aft4/gQ+06r0c3YzjuJ8jLsu6lm9yD51/J84/eSkzxf3EvgJ8cL wO5B8mzqu2WoH/uzC1JxRrHdeBLljRSm4tzi9xKTd71jOcTeA0y9X4T/N8p9 y83f+9DU+aee+HXir8V+4H7GcUI1lOe/OvXOZRw3kLxTGMfhJe9AkrwlJG8Z /QY/j/Ma1hkc1zd5S0jeEpKfhOQno/9xHvAcqw84w/w8zc/T/IzfxnkRc0w+ jPUjq0950TYfT/MxfkrzcTQfl2E+QvMRmo9Q/0L9C/VP30c1edDKYadBf7Cj oD7gDPU91fdU3/xw1H+g/gP1H6j/QP3befK7K1vOU0zeDcB5cZw67ueW8xST PH+0RznoLewdJJ8aX2V9DXpt/H5b8q4t9LM4LhB6teW3mR4bv3OQ2B0wHvRa 6Lkox/gop3xMoz+xXp58hxPjs10Fcs34E3seeu15iT0O9DjWnxI+gfmiPOYD 60z+JVhi2NZn+4ty0FXAkJ/i9sm7eaBfkB/4HTfcJ/pOiuE36tF+2/0GX4/p YfJdJsgdcX/JO32st/O7cayXx/S+afhhQ8v91u6R+ONZT4rtt81MPsQ9JHuk 2bNQL7bn4l5X2L0n+6T51znul79jxHGlkD8zvSOMevg//6Ic58Hf8eE4NYyX 6d1g9Ac+jPI4/j2xS0PfAp2g93nNLhzbdZI8Xf4OEseZAMY95zxO/s5P7Ac0 vZficO27WymY9GDzc8RyvX3Xi7/zZXKr5fnoX/ydKs7PQHyn2efNfge8w//j 747iO0ycn4HvPpj93ux7HBcOOsNx4v/ybnUKLwCjHPjH331if16Gd6kl1rOS X+Jnpg8D/+h9Z/M3AP8475u/o8VxS7E9Z43FLXGeBPsdyE5j717Td95SMNlB TG7C+uk7co5hej+B7EYrzF+PewR7Snwvk3x9lMf7lrwvAD9r7A9P8jpQHvu3 k7wn/g4s54Pzd045fx3+1Dj+LMkTQXkcb5bkiwDvyf6Tivsk+cqT/ScVR0ny FvmJ0nGiJP+Z3yFT3CXJg+a3RL+QZ4lvpvgo5BjYWzmfG+Wwl3I55BaU477H cWGrjO4AhryGdcPPFb97X2VyLfws/I5tbDdOYMi1HNcDuQHjYx8xHmDMh/PE YjrWLGWXYHsWvysLOwnnQZL+bPJG7IdM3ulEe/6ONOfL8HePUQ7+AroE/gA4 9l+uNrmN409h5+E8TrKPpOLCOO+N7CcSr6ea8iWSeH+Ux/kMSXx9/A7NGs/8 BuVYP8opXy6QnBPie2l2d7KfWb6pvVsb288sn9TeKWX8Ax8HXmCeMf9O4l1R H/uI+jE/Tt45AB5DjuJ4U5SDz6Ec94njYeM4XcQRjDM+ivLYzz8ulcfFeRs4 T/7uJvB5TfZ2fzx1VVa4r2rTmXfNTORl1I/l3OxQdmDXd66Yv8mjPvCBvzuJ +o3/rxbUB56wnQv1G9dXY/XjeLApxv84/gvl/A5tpu+MAt+x/sb/V9k9Rv1Y X6gSrB/1sd74eyzJd6OwftSnPBLKj60SrB/1sd7Yv5q8k4z1opzfxQVfi+1z ReTPKzL9GnR5y/7YZqY/Ir4o9u81A5+w+CHwF4wH+s7+YNyLTO8mcDwN539z fA6/o8DxMJw/y+8nYN5YD+ZH3xcIZO9J6Z0s98f0bZL5CeP45gbT+yG3xPks q+i7GtX0vnyD+ZnQHjDs0cgnjN9ZrTH+BRj1G/drlYf9Gu3jd/BqjN8BRn20 x3ixvlFpMMab0/26cc+sTeKUUI7+AaN/1I/tOfh+wzjTowHHcSKTjP5zXDdg 0H/+TjHoVgxXC9vvABM9djGdtPeohO1vVG7riP2e40wvgZyHcsCQi+K4ogSG nAEY/DHmhwk/AT3EOwLA9/gdjxp7ZyC236wweoq8/9jessLoIcrj+IBaie/h GoPj+AWzE5icgPqAqb6P7QqIm1pjMOyNsb+1luJ41hiM+vH3S2rN3o75xHGZ a+ld71qTM9B/HAeafP8VdIH4ssHQR2P7bjV9VyyBUR/9g66QXOHi8RL8RX3i wy4eL8l7juNJawyfQd9QDjqAco7LbMTbZUa/OG6y+aTCH1u3z0nRJ4yD9kyP UI72KIccEtObSQZD34vpyyTKIzH+YDDkv5hOj2N+YfIx6sd0dpyP7b1r7D4B hn8OMO4Pldt9wH0BDP4JGPcDcBx/sMb4FGDO8wL+AyZ+Sd+1GCfEL+k7BuPk sCtWF10+wfJNws2XfL5w7YD5pq8c98oxFy69cq7J9+92lroBXy83vfW3IR9e 2KV/8l2h2ztd3qyhKHnX7rd5ucM/6p98Z/DaB4bt1e2PJG8HfOl3N/TZ/v8k 32kmWACDLxHsh7486tMFt9UbnSJYAKM9YMzjyfzsur/rV/vpOt84T6vS33fu kdfmNKw2/wrqX6nrieNAsx3qr2+4Z1yr/vPNftxp6mdjnu+ffB8M+9Zr2MQz Tz52jtf9M3uv7rfhO9qdrvWh9wHW8zJ81/M1fQl2QIzXR88b/eA8AdN+CK3X 7KOtPylYu1fz5WZ/JlgAY/8J9uO/XVO7w5/J95MIFsBoDxjn99oFD3+0c7eN QvhmfqrPml1w8upTNtq8UZ/w0fxGqF+v54d9w/kBxvld+cNnJ8xale/+mnSm K5kF+3czh/MDjPN7on7c0pr1yT4DxvmhPs4PMMZ9XOsf+e3HzY45Z76/9bLH rnvxlfzQr9U2/Xe8K9/dovAXV5XuUnxOTth93p9X33b7Rr/dJSvvuuel5f69 +3vOPfPgjf7R9o8tv++0ZbKHlj/dq+kXe3yT7VB+4l/7bLXbpnpfvaJDlxl/ 1Pouw/pv+O/M5Hfl0OtfKe5aKyhfXbTjh/1urReU33bprV+fPq/ev95q/053 bqz0f1e80PyOr2r9Hpt6Drxox+U+NOyy4oMbauU1LT+/34pXhj5WLyi/58hr 7vliVk64/olW/V5aXOq/ueDjh3Yd3OCfPebnI4YtLfXv/N/9nW+/9yz47/U7 tF8i12n9Afmz953/4ypB/bdO2eOZcTfluze1fuM880OLPz8du3+fN7Sf5f7q dzrN7tPqNV9y6oM/fd1nrjTMaCzHeXTS8iN0/8968Ke+BfsXC/b/HIU/1/1v NeizUx6asVpa6v4/Utxx2thda2W47j/Ksf8ox/5Ln143H9A/2VfsM/Yf5fg/ fm/V/f/4+g1TXlmQ7ebr/p//4S1Hlxdnu+91/1HeQ/cf5dj/E375buDMd/Mc 9r/jled3u2Qz/K7u45u6r9h/1Mf+oz7q4Rf7/cCpZW+9XdrUYb8Lr3plytmz mxr+D9Lyv+evOfTjzfp2ztNvvN+wLjfMuPumgy8++A8PeOuvTjhu5vHT/YrS YeV7XGPlgnJt7wDv3Ku+0K1v4lZq/Thez+KZDUZcYSa4WvO3AK8k+CS1wwA+ fsuwUH2h/oTGo7y6XJcJhh0kE/x+/4cOy6tpEi75YubhN+9R4Vvofr7TbuPX d29Y5Kfrfl/4n4feqXlzIcoF5djPtxXGfl+k9XF+Cgv1JzQeygXl1J/QeBRv WSd0PkLnI3QeQvsvtN9C+0nfw6qTsbp/QzZtc1rJJbmO8NERvqJcUE746Ahf 3bo7Tvu1730FAb/Ll04ccOjdBWH3Db3OeOuIgpD3+Z1tb/0rN7Rru6LlU7cU hGOWbrvw0z9yw9iHH63d0LUgnLE+f8I7b9T5O8tvb/PSptxQfuXQ2nNeX2fw +SVHfXD4weVyiLZ/aKvpYd9TygXttb6gvvZnsM7L5gn+/PyFWXMeG5UbWlRe 27rvvkleP373z3ru3L0OyAnZDd1vPPO0LPv9duqOv93XKit80G2vi346Lnnf 6gXt74i8u785NqfSzhlxkgdof4f+X/pRLejvi++u2H7jLjWC/vaaMGf4wDMS O1XR7z3O2Ouzv+k7K+V+WeWx/0ytSL4zAPjzxv78vF/PWHfjtCSe/YBOt36/ 6o5qP6OxP5PTJ2S1n/n54pkmV+r6HNqDv6O9zs/sWjkjv3th4bAkbwzn+dhe 3T8a/OhCk39nTrp65K7XlXndH8sr1vP0S3u9v6z296ke54n2kK/QXs/L8lIV n9wKbX/ZK613nz8f7xcU+h+m3LP1sJuSOHLg4+QTeh817OjfBPio7UXrWxwt rUeAn4CBn79qf8BPXY/ofCyOldaH/g0Gfmb32nb5tycuEuAn8Ah4NWvCZ9vc sXaZZCkeHaZ4BfzU8zP5cmPPxv6An3H8brkUaX/oB7+KT4L+gJ8Wz6D4CTjO u7f4UcpfKTb6FedZ1YneB6/3weTTh58c0X/I9VlO74PZXb9Q/IWcDPxFe/T/ kLYH/qI98BftiR6Z3ajs/mVVd5yW54C/uHfAX6VHjuiR5XuiPfAX7YG/aE/0 k+mrA36DzgO/AWt7h/bAZ/ChK+a9eejZVyffiwBdOSY/f86YHvkB+Gp5PG+6 My4ZlJwrzhn1nR/7ze0PWn6x9Xfhs53GPXxtEp9FsG926cGvTate6i99588X hp65iOL7yr3io50z6g85+fcP5z6efF8B+6j3wehaC6mbU9bvb9920+VN25yf fC8B/aH87A/bnX/7Wcn3EbA/wFOsR+vLHbofKMf6UI76Ol8rJ9jrfrgrdP1x /GC5V35heIr6WD/qY/3Kr4wu6/k4rB/3AP2hHOtHuZ4nvffRzO4nzhny2c2F n1/b3id5X6dv/cjAhy9MvguI///aveNTY4YY7ADT/Q10HwPd90D318Yh/hRi O1Sx7B/zP8nA/4T4mRC/FOJ/di+IvoTYzlUsRL/MDkb75Wi/HO2X0/22/7e6 tGOHMYeV+3ub3/rRMX0LQqO+8qt/f/a8d+VegwXwMdWjwmmP/iq5s4vW9dks n/V4du3PO8zKdWjfpt3qbdr4JuGtx5bMWj08JwwtGHzwizv9KYCzvrrvoLw2 m+WqQxf+/XrFRv/xj6Nuv+DzOv/HA+X5Sydt8lMem3FXp11r/BGHXvTYws3y TJPr7pj1yofVvtX6Zje2OyMrXDVzzwnFFRWC9oOfXXP0G4+vE7Q/qmLuc1ds pt9or+0c2sdxmnWe4X/7jeOYMsKSCY7vQRqO45jS8I66jqa6L9i/3vPeeKPF wzXYD4P/2H34AX9+ssIPLljx5MVrq7y2E7TH/qG+9mfwAe/s+9hDA7Id2s9c lb1ocf+8MG3Ek/tce/lif9097z50wFbZ4auBeSeW7bTE71DedGL3rxbIVC3/ z7WdXvqwfZV8qeX523T+33Wb+dNZ55V/Ubt4muFZseLd6nk3HXvpZnnqh34X d7j316le8Sv83vb8s4478zev+Oe7a/ujFR9PVxj4Cjx+suu1tw4+tVy+0/4U 32Wm9qfzcWdveT5C8xXcl6LG9jYOjWu/ND/BfflD2+t4DuORvir3Lj/3juXf Nwl7PjZ/+GHXL5XBep/+ePCm2l0vWyq4T8ce8tYvF1y2WggfBPfpt41/jWo/ LTl34EFvvU9oT/ggR+t9QnvFP4f2dH9SeE33g+JU61K/hO+Ce4t7TPQC8zV4 huL7RW2nPnb2O1lO6Qf6cUQvsF8GA9/R/k/F9/Jm+7R4o3muA74fvG3usJOK cxzwHeXXKr6jnPRVR/qsU/zwSk8d0VdH9NeNUnyufObQ13ucYvRblP662jFf t3z1iCbhxyuuL112enb4sduBM5u8lxOmnjjs6N6SHfa8/cw9FvT7y0/WcoUF cM9nBmbNqFgiqK/9OfSn+OIVX3yPxvq+/Yeb7rx5+Eq/u/bfo1P/346/bKVX /HSor/NxqI/+UV/x2yt+e+UnbrrCWJ/WF6xP+xMaH7AA1vkK6tP4ovzFK38R 3Q+v++Fo/xzWh/pYH+rT/rkhuj7lhw7rA9zzizGbuuRlh1ELp8+tua5JOHO3 F/q93yI/DH6la/9+x+eF80b2bjv5/9iPb/v+fz+9mxdebdOlvPSXnDBhwdrZ /Z/JC23vzW5+0eAGv+G3sfmdN5e3/a5NtxbPN/hzR/7c8M34vHDhua81nL17 dujT/LKakkU54dHlp4+9r3OVPKnjdThj3EMn9ywRjLfHoMc/KG63RDDe4u32 mXRt7yXyqY5X8M9DY8b8uEowXsHLV1619INVgvEGTez2wUVbV8nNOt4FL7xx YN9tC8xOgviEeeubv9Gsd27Q+xf0/lncJMr1fgvK9xxz+OiTlyf5zMs7n930 nxkLLZ9jWSNs/mKtb/7hzqPe/OewLsssvvuc5z+4vLJplT/7tBsqnyxb7HV9 vt+wG4/LXlbqdX1+r0E1PTcWlnrdHz+5pvmhMxaXet0f/3zbDsv37LjA/Kc/ vPKfrW7pu9z/etqll608P/l+xsret7R+cM9s10PH0/MTjKfnJxhPz99N0fH0 /N1oHQ/+2Mk6np6v79p19G8nz8a7BaV+9f8W/u+nggqv+OaO1XLEga+Jy4OW C/BF+xPgZ+8TGv67/+gyAX7q/AT4qfMT4KeuT4Cfuj7pqfj59fDXHr6xcrEA H6/U/oGPut8CfNT9FuAj+gc+ov+hio+faf/AP10v6H9QecfiY3S/ROUpQTnw D/GYim8Wn6n4aH584J/59RX/EB9xpuJfk90P3nXnJTkO+Kf3ywH/9H454J/e Twf80/tpeTfv5feZ/upP2e4XxT+MV634l6/jAf8wHvAP4wH/MB7wT+mP5Vm8 q+MB/5S+WJ6D3mfQO6f0zvIQUJ6/Zty4Z0/PDWQ/gH4aLmjV479Txiw0f/41 +f3vuHWneebnOLP4sjGjLzf9Ncw8o0/FYYW/WH3Y9X5TfVLbm/3i7/rvfzv+ 3cLU9yVQX/sXsodAPw4/Xn/rqu7rc82+8USPQSt/TfSvoOszv6vqj2ZnRvyG 7mc4rOj38jdH5lo5/M1PjB9b+1bX5HsR3T8qu3TD6Nyw5uomP68pMDuH7d+r Z0087+obcsI7V06fePl5Zj8w+/CAW57dpWfrHOjXlO9QJIpPaE/ltYL5wM6A +Vz69NA3Nu5t9ivbL8xH12f6O+wTmA/+PyK74pYr52cFxWeLd8Z+3L6id4fn Kjb52XVH9LmkQ5b93/ICb560ddkXSXwF6mN/UA49fqSOp/fV4pmxXrQffN0+ M04srbY4AMv7ivqrt/pYP8rn6nwxL8z3/uW/vbjDyFqbH+D7dTzUx3goR/9x ++zQ9YS+V3/xcJXhxePrv1jxfI3Zy8Ki+w9p/p9FS239J2t92HtHan2Mh/qw R8R58AVh4G5PNF2UM99f1nj+hmfYJ9jnbznhqtaXXGn2M0/2M7PLX6v94X6i Pu4nymsb8d/eycV5Ybye86dcftKDZo+z9WA82Ekwf9irMI9L75ra89gHCyy+ Re+7waBHqA/6AljpkcW3KD0zGPYa1KfxpU2b3d97MNlvof2WJko/yd4I+6bQ /AT0EvVpv4XmL7T/QvsvZyh9JHso7KdC68N8Ha0H+w/7Wuo7xSrvyOFKHzme 54D1N95z8QC861zpO68dUrqp8yJR/DC8xv4oPsubSh/jfK5yr/gvav+jfK8i UXkP7am8VjAf0McTdD56P2DHtf3CfHR9Zl8EfdT7a3R4lwu//vWYR6tE5U3L h8B+hD4HDbzyshoheiNEb4yuoD7RH1vXzjqeytOW74B+0Z7olRC9sngo1Cf6 lawv8gfVm32B6L0Qvad84jrDB7ZXoD7WF/ubag0mei9E7ynfuM7Om+0dqI/1 rRm59f77/J2VfN+jkf+Z3Vr5peHBWq0PvEF9zAf1lf8aX8X+wb/UV+kv2fvh 3zE6pPKPI/nI7jHKVf4wPMX+YbxeSn/RnvxDTuUls/uDvsKvQPKeI/nNgb6i PugrYPKHhbNG9T5uYrsqf+qu23Su7NckkPwayF8W+i3c4csxW2U71Cd51vjc KX3P+aXNZfYdMAd4/OcXzZs9075T5sfO/7Ds0SPtu0TsjxbMb+SYj3+56y7T x6DfCfmrBfN7UuuTvmfjqP9fKB7A/MU6X/P3Asb80e59nT/BJmeQ/SNoe8Nz socEsr8Ess94srcEssfQ+2XJ+zKZYOhLgBHfDfhkghGHTO0pvimB6d1ryxfI BGNfEJ9B9l7jWygne64n+7En+zHwzfoh+xvO1+gK2eOE7H9C9kEhe5+QPVDo fFIwnYfGLxUR/bb9l5MJ5nfGGSZ/lJB/yezv5/064vbDHslx5M9yPa7+4sCp Qy3POihsec77jmnR9/an/xC2d/VquKT7fudA3ygVwLALTV978YyWJ1X7NV0v nfnSZ7PpfchSP/fS9lfOXF1jdgPUv3vyV3Ou36qJ2ZdMn9b6FJ/iKb7Ewz6H 895H5w/Y7HWN/Zl9ROdjeSSHd9h5Wc3gLLde5x+/v1jqdb2WF4L6mH/83bzN fELr035zfIKj+AP3c8vWua+MSb73cOrt93W9e+tFQrDde63vCDa9F+0JtnuN 9nFeRPL+GsfzcTnHv3A50SOheEsh+pT6jgDFXwrRJxGKr6mmcppfin5KHK+j 8ZwJPaX+PfVP7yan6SX1L9R/Kh6I7zvdT0f0NhX/yOUcz4DyXbdc7qnc7E5M 76h9oPYot/d7Plz+1EvtW+WY3REwxRMEvU9kH7F4gqD3U+I8x839bwwLjj6i 0uyYgCleQtB/rF9YvIQo/TJ94O99Ws3/qmKBv7L7hfMmNDT40oUNe2x9Q54D jPLpLTtMPemUVYLyGQrvceTGUztOT94jm7j1qltunpkf/nprxGU/P2jnaftH /MITf0jhF9m7Pdm7PflnHPlPnOaTWDwS/AzLFm/1n/uPrkQ+Dr331iAo1/wN i+dB+787nrjh0h1znOaD0HttDYLyUyb8Mb7+3gL7/a1F6xe/n5If/vmpZGSP UQVu9oBbx74/NT8c+NSPe15ZV+B6hVHLh76evCNK8nUgeTmQ/u1J//akD5s8 BP8H2Tcd2Tcd2RvtvRfYr8ne68i+6tTf79U/GjSewAFW/6xX/2yofuDTiZNq Vvrbuj21699VOUH9uV79uUH9vQ71Lxn5V+szW2e7W7S++ocd6h973RvrVx+Y FSqyv3nx8JG13o88eUKLNdUCWP3pQf3piHcRwMfUz1i42w854ah5RSs6nbfA r9j+05xPX2/wTxf98/cnE0v9Ez9O/nbIoCUiWn5St1lvd31jlaC8tvjRQ1/8 IT+s6L1k71VdfvG7DpnRvtmGuVKjsPqPvcZDCPZH4y1E4zW8xmtIne7P2Gd/ XHrH5MWyve6Pxn8I9kfjP+RC3Z8JWl/jRRzqc7wA6++ddP+6XzR1659MH07i B04kfX38Nld1er662h/Y5+eVuV/Wp+ILWF/HeaB/jjdg/fz7t5865rC2WQ79 a3xR0Pgih/MDjPM744vHZ48bmedwfhOOGVH8+IQ8h/M7TctxfijHeTV77pT9 2j1e4JT+BaKPkoFeeqKXgAPoJ9pPj2HQYyF6LH9lL/j5+f55oXSDHFyzGb/z Pnykw/CJC4RgXzy55XsHXLbYa303P4aDwoL2xTHstT+H9oCLchZe/+CfSR4L +bMC+ZsC81/yvwr5R5nfio5n8XErnt3QbLd3F/rnFz6xYaslTTUebq7XfJ6g 5+X1vALl+wQ6z3B5tynTj1yX657T/jSfCPlHQfEB+BI0/8hr/lFQ/PGKP+GU PWTtgOnz/RPdL3um8Pr8UPPkNh3fOr/EVx2655hfu+QFzW9CvlJQfBTrvzEf yqH/oxvx152u/T/Z2K/T31D8SOh2/tb5Dv1rvhXyscLoqyp/P+KjFf6Xr9/+ 4p23s4LmayEfK3y2Y/feh965WQ/Qcr1fQe+XJ/uep3wxej+xzhO9QP6e1df7 7lFO9j9P9MQTPbH+kA+I/kDPtb6gPtkLPeWz0fuNdZ7oEdZr9XV9DuVkT/RE rzzRK+sP81f+5DB/zc8Lmp/nyf+t+YHV9nvb6pv3azK+yreYN2T1tL6b9c38 3yZVbbPC/9Fl3/Ob/1nlNR9Q0B/5y73mGzj0V/Lp1iev+zXLob+JfbZrLWM2 67PaH/DOKZ4r/oVX1700+4eny7zym6D8xndtrCf66/U+yBitr/xMlJ95zW8M mt/o9X6GK90Bl4wb+DLyJf1GzZfEfQc/1XxJ0XxJr/RCumh7zcd0yMdU/uzA nylfU4h/C+hPp8b+hOYjNB+h/E4h/i+gP721P5qf0PwE9EnzTQX0SeUNAX3S /FQBfVL5xM4B5wL6pOchoE/oH/TJ+lf6pPmyovzVqXwkoE+KHwL6pPghmo+L fF0ZpfTp1YVdOo9dVyWaz4t8XQF9ek3LKV9YSL5L+SOIngnRDyH6IUSvUv4J ol9C+clC8mXKX0H0SYgeCNEDIfqT8l8QPUJ+b9D8aqH4Laf5al7z1dztSj9G S+7cHfpkuQFKPz55/IhD7vwqy2k+t6A/ivdy6Ae/oB/o72ulHx9rf6AfwBPQ D+Vf7mKlH30b5Xl3quKR8lMH+oH6qi+I6guO5AFH8gDy2UXz2SFPiMoTjuxB 5icm2Oh9hnzX1PeACLbzY/vGOcOf2vHTvrnh/vanT7+pOD/0X3ffkU8+nhtG PbHTfsukSWgxdvAnf3Uqt3Eonx/lZtc64LEfGg6YvkjQ39Mz9hg07qxFgv4o 3z90rznuXnk9eVed5K/Q/+e1m3bbKid0uO+Whvnv5obD9fcO/f/Z7Y69ZI8P N/meE2csOfWUnHDQDa9+tfuiBt/hjCW/3HBAki809/uv3pkkWeHpZzucN+TS SkF/r/7Y68IBi8sF/S1of+7Zc5+tEfTXrGbxyrmXrxL0B30B/d0/7JXDevbN Dwf5/01785Tk3b3Sne6afs+q+lR+AfTi1xvfa0jlD8zvelTrDm/PlzbaH+wm 6G/L+QWb6ZX2x/kCd//4+vLqc3LoPaCs8MCsnS69f3K1zytsNWTpnzU2D9gb 7vmywxv/3afGP/DOoy+2Ps38uTY+2rd7vN9BLW7bZHoa7BFor+9vWDxH5y7N Tii+s8Hs2GMa99+PbjwX/5T+vqL/L523pvlHX1X7eY37gvc9LF6jds9BN+02 dRXs0F7P1aEfPVen5+x1X12x9gf9AvcG+A4Y78UgHgP4jnLgK2C8F4P61L/d t7n1v+88rN1cwX07svLmUUNa/iU0vtB4gvs1W9vjfh2r7Wk+htfAc+C97o/g /mz9+5Sx0/stk9Z6f8Y83Kft6WuW2bnqeQva6fmInp/o+QnuD/rbSu8P+gN+ oj/cHz1nsxvpezSi5yQoB76jnO5byr7A9gO6Tyl7AdsD7tL7E793lRX0/rsC vT9oh/tz6XmH/HfidlluqN6f2P9RLWjfVu9P/H5Wg6A97g/oJu4P/CLAa+C5 0je7Bwv0/ug+O30PyezkuD/oT+mrQ39Kfx36Uzrn0B/xB0fvKzniD+aXevjt q/4ZPtDs0u4hhW+btP8rj8yyeFnL5wQ923j8hnYXV1r8bOo9CPJf2XdGJ0xo 2Hr6hkWe/Fnmf/tUyzG/x7rs+O5/exTZuT2l8C06P9jFMR/A/D6DltO7uMn7 C7pee9eL4uPtO6ftBk9uWXBNriN/on1nE+WggwrD/+jJ/+hQruOZ/x3zBR7r eu2dMvSv+wX/oCf/oNP9xnrMf/zGRz/2WD8xO4z47e8xo9pt9GvPG3/TYx2y wycD2q7vnbXRz6nNzsl7bbmM1PIHb77wHf/9SvlUy388dccfz2q30t5BBB0H n7hh90FlFb2SdxSBpygHvdDztPfx9Lz9L9o/3q9Tfmx84zrtH+XoH/cC/Ss+ 2/t4iu+oZ3KD3k+jg0o/QY+90k/QY6/0GvTVK70GffXKH7zyB6/8wSt/8Mp/ nPIfr/zHKf/BOgPTXeW/oO+Qt5zKR5C3HOaHcpXHID85zE/lQ4f5qXzoMD+U Y34onzxwnwPe+zrfHf7ZkWOfP7Ag3Lvjml+2ejvfPVj9548zhzcJWh603Gl5 0HJXt6HbOx036/sVA8474r7N8vhp9a+1m/x1tf/46X7hylvyw+rxq45/oWuW A0z5+0Hpped3qFGu9Jneqa70Gp8VlB8YHmr8mNm1QH8g753844gxxS/P9zov k6PIf0fvQmY7jR8Lyr8MLzU+zuK+QN8wnq7bYTx6/yCQfz6wvc5lsKc1Xxm+ qhlh8cpmh8N6Dxz30Ourzq1N2c8y2bfQn8Yv2jsLWA/6G73i5Z1qm+eEXXov +2R+RbnfvucvFbnv5YZd17+8ZpfLKv0Ot1Z833qXjf6l7Yds9fX5Nb7/B+Uv VIzd5HtN3aftDTcv87Mb6ZF/6/Gxza7dudY/1EiP/KCFV4x/5uRav1tjP7KT 9r9z46+g/z2Xvbboj0NqBf1/O/6NbbZ9sUbQv9JDh/7XNNJDh/6xvxof6ku7 3/TZzjfnhj51D+70yd5zbb80ntXPG3H9oMf/WiQ3avncVxd8dMnm+zB72936 9vjkd6/6ltnR19zYYv/84+f5mVoO/xrkVy0XlN8zq/v//s4vtrgc7d+hf/jj 0B7366RGPBbcL8D0vovgfvG77SjH/YrLK73Gp4vqK0bXCf9Ex/U6L9MjDoj9 1fRuarbTeHlRfcroOuGj4P6gf3r/Rig+R+j+m57VfHx2Qe5m+ZTuq8kjKAd+ K1474Lf+322v+P1st4JXulRvkn6K3z/WbeheemqOU37rld+K8luv/FaA3+h/ V8Vv9L+X4jf6/07xG/0Dv5Xfi/J7p/ze7GeaH2LxrICBz8v69u+Q/3e+A77u PqLfnos203fgJ2DgJ/w8wM/dtLz9wiUtyu751ewZCpv9gvJxwyMXdy/s+U6h 7zP/x9y8EUncC94bAMy/jfc/8WtSfeHyVD//d58TPwXy8a1+XG75+hT/bt9b umD2qm9aNKyw/hr5ynJP8el4h116xPUF9fcueq/g/UH/mBzY4bRmgz75429/ zu3jr1laUeLv2P6cHZp0zDc5dPu8aw4o75bvztVy7D/0Zew/YMqn9/k3/LT9 8j9+8XN+Gdtt1InP+aLHB+x/1j0Fpm9T/rx//qed/1s5rMCt0/o0ntB4Qu8J CM77P/fnDPv6o2Kh8YTeC5DBzw1a+8yFBe5qrU/nJXSeQucldJ4Z34VQfBHC PyF8yphXj/qUP4bvMoW8Y7q1vuvqhM820q9sR/ld+I6YUH1BfeAH9ADgx+Qn L/oma3GeA36gHPiBcrqfju6no/dJHPBj2JzPOty1qqk7/s+3juq4VXbo8UV+ 0ZHFOeGLCSccP7p9lQDetW3xxNGDCux7OPu1fP6zD0dlh7t6VZ0ztHPynZjj D980qMfQrDCn/8cPHH3iCkE56DbKP1pzVuXwV3PD+tOrW3U5pcG/OWf2q983 WyQbFAY9f7HHptH9t13idT6+1by6+l13XmLyystarvN3O2n5qJJT8ha8UuIn XvDtsvyjFvrVn3X8c+j6PPelwliv1hesV/sXrG/IJedst/CF5Ds3X9U9+t45 Ui2zdX2DtRzr+1LLMf+OFw27da+/cxzmr/tpcZoox/xRPlrnv/SvC+ZM3DHP Yf6Amzz021NDbi4wP36zjS/sUdG3IGzae9J+ez3SxJ25y8ErJ9xTYPGdWt8R fQ9K3y3+jezL9F1Qiz/1FF/qKb7V5JC5MYz4W6H4WqH4XpNTABO+BoUFsNIf Ab+5Z9zzH7ycrDfQ/Qy7fDcuf+5TCUzvoYZdXp73xh4fr7Pyoy8+8asXutf7 K6Z+9eq3JQk/sv4a6xtM76diPCtfn9Wt6rL1tYL+9J1W+5WZV7+QPSjhh+y/ p/dfU/756gdbb1s7eZ21Rz20c9o/+KH+H+/For3xW/a/0/uyKf+6zt/ao3+M X6X9Y3zs74wRvzftdE2N1/0xWN8DtveCd9XzA3/FfqK+9mewvjds7wnvpOeF 9vhe34073FH+3s5l9v3HvgorfwzrlT8SvwMsgIGfs7U+8TvAAfyP+LVQe6H+ hfi1kHwitD9C7y3LzorfKKf9F5JfhPZL6H1m2U3xG+V0HkL4J4R/Qu9Ji+KH ldP9SMW/0X0RwjchfBN6nxrzEZqP1eN4OLofQvQB6zdY6YG9t0z0xxE9QH8G K32y95iJ3tj3Jk9oU7Hbdpfl2fcjj1cYdspJPf/Tad+6Jm5tzxlX//aKxfc7 ktcd0VNH9Na+i/rZRZMvvPrrjaLvh3t9P1wOa375vX0/WeoLtz3tnNmDN4ry d6/83b4rNEHb6/vkTt8nlxFLb659dUCO+03bq7zgVF6QI7aaV9v7D4tnxvcG wnPjJnw5c1bT0PLkk27/35/54dC5867Y/8Gm4ZIdHjzvrcn54Zap7pSjnywI K5uf0bHb1PywbUPF0z/UFQTip4H4aSB+GsgeFlQ+szwV5ddB+XXot6z08bkz c6y83fIB4+8cmBOGHjGt+xn75pn8dFvrwp7r5+cElQeNL6o8IOjv+SaPtvrv kUus/JNNxz63/ftLBf1BHkF/9D4l1uewPvX/B/X/B40vEI0vCCrveJV3vI5v fP3jxvH9RYv+ahtyFnqVn5zKT17Xn9jZG9fvLtb62D97N0T3T+U1wf6Z3V73 T9ubfPbGXiV1uS2XCPYP9VUeFJUHBfuHcp2/6PxNnntN+9P4qqDxVaLxW6Lx W6Lyrld512F/IOfo+Xg9H4f9QX3sj/kZdH9Qn/z9sMc62GP1PlheBvypeh+c 3gen98HpfXB6H5zeB6f3Ad8vsPhw+p5B0Hfb7Vf1kaD6COMX5FGLDyV7RFB7 hVd7hX3/76D7u3z+6bgkz/PbRvoVHrzs5ym+peUPBbJnWL6Gfk8hkH0jqD3Q YTzQowN1PPiJlF4GpZcmT2o+heVN0H4G2s+g9qDEjqp2T9ih1P4D+1KgfAbz KyFf5eipR+500zFVZqfAfql+Gf4e8nTlklPKrNzsWjo+7FIYX+1bsD8Fypcw PxzGf+Cntt1PbZLowdg/jP96i73nDD43z8rJnmZ+SvXXBnqPzpN/OJB/OJD9 zPyKKKf36Tz5iwP5i+2dE/2eCOzlAfZy0JdzT275173nmvxq8jN9r8TkZcCI J0N/oC/oj+QPk6fRXvfTzhnz1e+nIJ7E4lGwvxaP0nhe1p7kP5s/+pvfiD+C +vT9FsS/WJwA9h/jHdWIn9ae5ENbH/ojedj8rmVfN8z649TpHvIK7Ofws8Au DT/pbK2v9MHs7bAroz7J80L2fsvX0u/jCNn/hez/Avql/gNb949+r/v2K0ry 0nV+AvpF/cFfYPlg+j0fIf+BkP9AQM/Uv2Hy1U86PuiZ7qeAnqE/0Cf1x4j6 Y1SerhT1v8C/I5SvZX5u5Lvp+ZtdFPuh9lFR/LJy0Cf1NyV2cx1f/Uvw/wjl g1kcAMYHfUL/9h11HR/0CeXkzxK6P0LvwwrFfwnFfwn5r4Tuh9B7sULxYELx YKbv6PeqUvZOjn8l/d/K0R73mfqzX45fJX3eytEe9An7jPnq97kQr+MpXsfi fUCf7DvBsf3F/JToT/mbnSvWg3LlH0LxRRYfpPzT2pN9xvxG6A/0Bn4q0BuV RxzoDeQz0BvU1/c67D01fS/E3i/T95Ts/SB9z8ne8yF9y5M+5Umf8qQvmRwA fYvsr+hfUE72WFn7SOmtp5w9X6DvfHrmPS1mDilwve+8Y/zPLZuGP2aXly4c XOB6/Tzzil9WFYTagRO3Wz+v3t9yca/dTl2QHTovmPZs/fW1Alj1u6D6nck5 +r0tr/qjqP5odAblxzX25z84+8uKmQ2Vfk3jeAJY6aXlMa1pnL8fPvGZgrr9 i+HPtXz19SpPj9ByhYPCou1lOGBdn45n+of6z0Xnh/mYPoFyxPOo/1rUfx3U fy3qPxeUq/9cUK77if11ut/YX/jzLE9Xz89DX9DvNQnkd/U3COR3jQ8PGh8e NP5cAIPvX9K39ZKjplTbvQb8FvQDHQf6gfqzRL/TZb8YX/1hgvE1v0U0f0YA Y/w+d35yZW/Jsvf1AcMvp9/f8OetHzCkd9Vy+34y/DL6vQ8/bPyiI8rezrbv SKO9fl/G9dD2+D4y2uv3bBzax9/dtveffPfOrRuOOqTW3/pD/aoLetT46s+v OWL8qlV+/oZ3ZY82NSYPoX7FXte8H3bZKKgvbx0w4LRzGwT16f09Qf/fP/fY Z69XbZIq7f/gw247cvnWiR8c9dE/6qN/1N/pwKo9cn/ND/gu5Zrit694ZEp+ GHr8mGUvP17gcvaZtnjmtPzw0v/uuviMrZviPSN+L9Li3UkfDaSP4vsFRg/Z 3s32bbZHs/2Z3jc2eRn7S/n6nt43tv5Rn/L5Pb0PZ/RV5S28r2ffv6D33Ezu UfkQ7//Z9zDw3ibiQfS9PIsPof6F2gvFC5k/SvUNofgfi49AOb1/bfwc+EPv KTh6/9r6RX16b4HfJzU9UfVvxidH74Wa3Kb2AUf440Zte8dFN47JDxdL7r3b XN7ELd9t37+XjbDvkzt6z9S+Q7xivy8LJveux3ewBTDKTzpm+msdy2qtHDC9 58DvbXt6z8HT+9r+7RcfmfxWLfSiSX7f6uI/tyta7jvt98k9w+dM8Lc22W// 0W/hHCb5P6rO/OeLcdnudC2n+QnNT2h9QusTeu+C30N39N6Fo/fP3cwr7zr8 zqL8gO8Nlxzy4NJ+s/JDm68fm3XlrKZmP7r3pKL3997Q1GG9sFdhvU3euiXr sP82dVgvyrFelHc9+9UlGx6bZn7H37oefFj7VdP8h9VfPtV3SF64ZXzWNi9f NcPu5bTh21b2v3e63+XiuqYXH58bjjz2/Wc6dyqwOOHCR84b8r+OBW68tj9j 52Ezfi5qYnjpO/8wa0RNE7eztkc70BuGY7tMGoY8B3hngmEnBfwLwbBbUjnp BUuE+heaj8sEww6ZCab8eE/58X6q7nf2uYe0f3R0hcd5YV19G8/H5FqtL6iP /c5RGOeF9tSf/Krn/8Hyw3OOGbpAtH/bB5qPUH+C8x+r7XH+aE/zETrvFEzn K3S+QucndH5C55GC6X0DofcNhPCd74vT+2H9an1BfcJ3R/fFvfvUPz0GDCqA fBC+vqbZtD0fKAh/3rbm1hdeLwilUt0sVOWaf+baArl8p+W54cFux9551TZN w9iBk9Z9u1V22Paha+sK+zUJSo980VlHlj+8XV7oIis3PHXlej/z/F33PqlJ nn3HHeWrd3th3NUL11r5/M/OaLt88kJ7F2jaszsWP/XnQnlIx/vnpm2/vKdd lWA8fEee+hP0B/pI87Fysmdj/Q7rv+njVw867qe8cNHBvSfvOiY37HLSoCde KDK7clB5Mai8GPT76WGfZwb92mrrvLDk9cpxnXbMC1MeuPCMR4blhIt6j/3Y r0/qwY6p8m74umbUp3m3lsrFOl7OuB12eLiD2X2Cyq+C8fR77rKvjjep3RWv NDlmoWC87CP6PL7i5YX4rrt9Twzjnfv8yN/aTMsNN7d5YPjS6yyfJBz83ojj Zl7a4PftMXe/088pF5RDXz5Ey/dpLPeTOzTfq8X8VV77c4A7nrPuhtuOrPTf HLFh/V03VZk8ru+l+kcGftYkd+RMP2LwR02POq7Sn7lrk+P+2THHTdL6Fnel 9Q8a03fVJU82ccO1PvCtxf0yaeqJCz3wreWNH8xceMtC32zXq8d82bDaPzvs 4P1WbVWm9Zf6v6/c/qBne2yuP+vOuzsfVmX6peKXP+XY2ln97i7yeh6+ofmT 8tC2i3x243lYXg7wD+MD/zB+mxMPbC6nbJTndHw9B1ei46///pHnLm9u7/x4 vU+um46v+Oc26fiKf/adM20fqH04TdsTLC8WXf5Q7rErfZdzH/lrm5F/+Nab Vo09+6IV/v22c4de1eUnP3BjxW6nuhX+nhOf2O2Quydjf4T2R7A/BMunX47r t75HthPt/5z2P4+teCzbfaz9f9N++NQlo7Ld7do/6AveMQR9aXfxdyWvdPoe /QXtT+i8hc7b5COUg76gHPQF44G+tNHxdH9E90fofIXO1+Qvmo+Vg37o+Qno B+4R6Ifqq3ZPFT8E9OOsiZ/uXzhgqYB+4N6Dfqi+LqAfiq+i+Gp5V6o/C8ZD P3of5BulHxhvU8dG+oF6oB8YD/RD77vZo47e9rnRXzck+WBNPt3Qd7/Fq0Tp haD+W2qfQn3ME/X5PUDmz0qfvNIns88rfdL3kpL3/H4m/gx6hfawryv9kyOU fnV+6qdZ31Um9gh9D989pvSr9sxdtr9lhxwH+nWc1gf90vf6HegX6hO/dMQv HehXzWMTH2u4Is+Bfim9d6BfsA+Bfil/dKBfyk+c8hPLGyT+6Yh/uoOUfmF8 0C/lbw70C+ODfmF80C/lnw70C+NrPKrZJ0j+QP+B+oe84UDPWu7f4+6LWxa4 A5We3T98py5HfVfgQM+yOr30xg1zC7BfQvsFecKBfqE/0C/0B/qVrf2BPmn9 cPSK5ge/8XFWuKtjp2l7L84PmicXLnjquaL+hfkB9AX1f7q/3wWfLqiSu7W+ 5u0J6r/T8sh9jxyYFUpeen7ljcNzwg+/bnr4oYOzTH5YeOw+1z3es1pQ/sHk R55fsqLa6EhZY7lf0HX86FOuWOrfayw3Plj2WtGwg6dV+AHtD96qqsUKs6t1 OGjP3Zq+u9wrPfBKD7zSA2v/buP8HPqf3Dg/w9N/Jm8z4bZDcx36h12to/av 9M2hf6Vv1v6cVnNbfVJe4Wf88cRXEzr8Y/P7+vXlK04tLPZT3p26zRNtct1M LUf/32s5zmPjzAsPL542R3AeB077T23ncXNliu7/ei3H/u+j5bq+oOsTXZ/R X91fQbnur9FLPR+v5+P0fEwOKNf9/+b1u8/zYxK75Zllk87q9FW2U3nOqzzn VJ6z9oofDv0rftg9xf6jf+wP+sf+o3/sP9pj/xs6dpw/uXO+za/0ruvu7PdI vpus+49y9F+i5ds9PrTJwAsLQtkrM3765dF86Oumd888rc3M2y8scPuUtaub trmc8uE95WF6em/C0XsRPJ6j8RyN54YMOXTNa5vhvj/3y9l2RH7Y9s7Rh3Tt U+BWjn7tpSmXNoH9AP6soPQ7KP0OpH8F5QeCcvz/8m32PnPSVxv9P61kv28/ yAmnP/DCUYvHbDS9FuXll+34v3WzlwjKSZ7x9P1sT/KNL6tYvP+tl6zxW136 dMO7y8r8kq41VUWj1/ivL3tt5Me5C/2YZ9q4E28oUfpf5q8pGPFDz54lvv3Q vU7bbnSZJ3nG0/e1Pck3/qx+rw57eMkGwXjdWj375T6fbBCM90nBcXnLCvId xpNDXi97u0W+O1THQ7we5MtrBuz59sP3FNh7FO0/Hb7+2aEFJh9SuZD9QVSf ENUnhOwv+t6v5dML2WMgL3iSFzzJH6a/U/59qpzf4+Vy0ofx/Wv7fjbpxw7n 23OnVy/Y7qA8h/Nd2qr48G33zXM436uWb6pt2zHP4XxHjuiSM/B4yA+mD+N7 3fa9bdKPHc4X4+F8MR7OF+PhfDEe2Q8c2Q9QbvZB/T6kw/chtb5DfeRXdphc PmfNoqbu8tfC+y1qc90T1a1G1N3e1JG8mLLnkTzof96yvc3fnz353KcfXe2H azxsq0GfnfLQjNXwZ/prT3cX7/Xjaq/yq1f51exvaP+5xsOiPeJh0R7+XbTf /sE+/+ReavYu1X+n+7M39i/ZeNNif+ekXrNfnGX2LD9o2Atlw8qauPO1fAdt j3mg/ZT2BxV3Lsh1aI9ytJ+s5UoPA9HDAHqI93F1/4Puf9D9D7/Xdtj19tez zV7U+M5ddhh0atlbb5c2DWr/DWr/DSpfBZWvgspXQeWr8Pxbrw55NJH/gsZD B42Htvea3x8y/7qX3ssLF+53Se9Pn9nkb+156cxeOfnh5F7XNf19cHY446KG ys8OzQ2njv1nx33OzQ7Tpk3sd0jz3KD4jfsUFL9xn0Lr+adVtslZYfaoRj/n cnlA16P2bsF6VJ8WrEflT1H5M2Rv1/3tskNN/lT7T4lovLe9Z431zDnm0NO6 jq8RrKdxvSsE6zlt29Ebb+62Un7X9Sh9wH0NSh9wX2EfD7CP79yrvtCtbxJW lg4r3+Oa3PBdo70wwL7+7Gvv7NnuxTyzb23V8c65XfZeYPYnvffh3QuPWr/6 Z9iPyoRg/2hOj/HZ960yujRcYch5O+zYrOKy7avs/aUvP3zx80HlpV7X79++ 4s1Nox4oMblnR62P+BrU1/OX/6+uaw/qusriIAq4o2XraouP1FozrNG2UJPU c2vUNk3NRw97oKlrq0hqr0lrRBPN12ibPaitNiPRhBqcam3VutcH9hBLVMiI DA0TW1H8ha9AXGf8fA7Dcfzz/u77e8/n3Hs+59z7Y/mSyJfXl22vf7fjgj6o 9lfdNupUxdYDHuc/j/Ofx/z0nHnvo/651s/ofWuPfVk6oD7Olw7nS4/vpeck 4oH7F/GQnN17b2qrd4R4gP9HiAfwJUI8gC8R4oHxXvRDnak4smLK/UX+jwlJ a5unH1O9Qfll/5TfR9A/5TcZ/cN+EvBBQvkFHySUX/ZPP1L04Qv977n21KFe t0er3mE8BfIF+eAhC4V4xv0c9UdiPYV4xnoK8dxr5JVvJew4JMTztTMS58uY CiGecR4R4hnnEY3fwPcSfC/heIhH3B9SfyfHQzxyPMQjx3MH8NgZ4yEeOR7i keMhvsjHEF9ME1/AixBfCxumPfDG8o5p4uvyLF9W8Of699ShXxzxBf2i536W J75YnvhieeKL5xria2e7qa03TWvsYN962LeO+KKdQHyxPvHF+sQX7GlHfLG+ 8fe5r/ttLK2rf8/H7mfO7Gf6HtOZZbGh8ulYR/xwX6R8n0Y+5Zv5xn5wxn7Q d0JQP1D+UT/03vHx25n171vpfQ+MJ1A+WR72VoC9pfZFSq/9fx2V2zjML++T eUfrGN0nGYdg4uc13v7O/p9HZ64u8J1gf02rSijtv7pAaK/B36S8G+xBYf88 d49B/4Ovbl/1TbcKxTXszwD7U8d7W+ep7xWOjQ49Ngze1zM2Rvdp8OXku0Jc cdNxd91ay30jQF70XEc7CfISYE8L++P42N/SL/Z/d8XUCtUj4NfIb4XEjFkD 18SfVNxBvnX+kG/KZwDfF8D3KS8D/AQTz+NNPE8AvyjgF5VXYH0T3+NNfE+w 511nzrcL034cuHh0lW9bvGBd2ugTfuTTRXk9TkQ5pmkvr5mxJHXJplL6Z/We 8JNZbvAD6RrPqO9OgR/xlJ+Nw9bvHXe+PuVnNdqj/LC9lB+yug0dp/GQ+s4U +BhP+/xztGfGJ2Z8Av4ngP9RO/xQ8eKFbxbvF+KBep39mnhsjd828xEzHzHz EeIB/JTaoeXon3hg/7QHTTy6xm+b+QvxA/5M55fSYZTPbnxMegI/3IfA13r4 m4T44b5B/HD8xA/OMwI+UNif8hPobzHww/6IH/jXhPhhf8QP+yN+cP4S4gf+ PuUtcb6TjLG9m3RNjArLJjQbVbHkhJj4OzHxdwJ/o8DfqDwczpeS+Hqrt989 flzYnonPExOfJ4uAn9YZ56oW/ForxA/T5v1YZ96Pdd3yZl+9bkaMxis9BTxR Dogn8L3O6GNn9LEz+tgRT2yPeGJ7xj8QzH4cDH8fzH4b9v14slteu7hw/4T5 2ZGsck/7AfEVvnDG5K6ju+6U+5CP+B1B/IaHv03joUaU9ci5setB/3W/sT0y em738L95+N/0/aXnn0jq3vf5t/yxmy7LXv9Rue89/YsmvaP+Q3+hxk+RL9iK 9sgXwH+o7zG9j/bIF3RDe8b/K8YeEOOfFXPeF36fRi+/u6butJ5HaF+5Xfg+ zEd8iyC+yfH7kP/g97ke/Ae/D+If9P9fyH/w+ywF/2H8UcH4m4K53+vN/V5v 7u96c3/3ov8nsPFQYt4/SzDxUIZf84Zf872wrgkN05LQsP1LxkfZ99JsPJT9 fy0bD8X5GP4mGP4mGP6G/k4dj+GPxPBHYvgj+j+94S9tfISY+AcxfJUYvkoM XyWGrxLDV4nhq8TwVWL4KjHrLWa9xazfRf8vY+OhIH/B3B/n/QLeTxdzP533 C8Tw7w73G4K53xB4vwH2sTf8jjf8DuMZg/EvBuNfDIafDYafDZXLr2mSm1fJ eFX9fynyacCzLG3IpznDpznDpznDpznDp9FfGQyfGwyfG9r/rWbEzk+4Pk3D +KtOje4yQu8DKd8zdUGbjptzGwWjz0LzT/sl7+lbKEfBJ5l4tLAnOenwMNH7 PoHnBLZn3o/VeCOmjX3vjX3vTfygn/a4bPx5mb7P6o097o097k38oKc9yfqG z/GGz/GGz/GGz/GGzyE/4w0/48c15GcYPywmXkdMvI4Yvscbvscbvscbvscb vofzdYa/cr0b8lfO8FfO8FeM9xHyx6sv7MdC/hj7sRj/lRj/lRAvc1CfeMH5 QIw/Sow/Ssz5RSZAvqm3Kd/ZL2XvLN9zWMx5RppBvrNvOLt+Rs0vYuJdZTfk m+1RvleiPbN/6L3s5+5O+nujOtXX3B/0XiTzzXvQF/mfDJ/tDJ/tTHy1mw58 6L3ShvyxM/yx1afO6FNn+H9n+H9n+H9n+H9n+H8HfaT/a2X4eGf4eGf4eGf4 eGf4eAd9zPstbkFlWfnWlTFh6KqS3zsuilV9dFfO8ndaHIsNqyYcn71rY4zy N9RLL9RMzrnubEyorZuZ2+oJfV88SN2Sz76vOihsj/psKNrb2mnwkKyXD4pp T9he65an3VcZcQ7x+MH59zdMnxfnSsuuPhOJxIeVR9NShp1P91ifXFT7Y3wY V/W/3MK5cW7Kjq+y35gUHz4ccGWbgefrV6Rua588Jz4MbDav4L36+5kan9Jh TOLEtW82Ctm3zbr8g556n1LjUZi/8kK++qdov/zSK/XTLq1+1d9hL3rYi+Dp j3rgQ8uRvwQ+PMan/i3aM4fQPs8LsH897F/H9oFnrU8+FHjWc2b/bROyBi0v 91gfP7zbXdt3fb5H94lhl2W8dc9rhR7ro3w614f1IS9uJOoTRw+hPuRF+XDK M+//UP6YJh9IHofyiPY1ng7tC+WR/LWZn9RAHtke5RHz1Xg7zFcoj6Y9zlco P9RvlB+sj0A+VP9RfiAfAvnSdaT8QL503cDXOfB1Hnydw/lB61N+cH5wlB/m U37YPn9n++AfHdvHeUfLUX7YvsG7o/wA33pOAL4d5Yd8scE39Y1jfcoP9I2j /LA+9IHez4E+CNAHDvogQB846IMAfeCgDwL0gTP2lrnXdrG9ZPoPpv9g+g+m /2D6t/rNGf3mjH5zRr85o99c0ebuqcvn8f+AmobiC2l9/8bYqxf9X4HNt/9v ZfON/Yn/Y67/fwNzX0eM/XpRfeNfd8a/7l4tuXPBPbubhCPt9m7InBjvTHyb N/Fs3sQjOhNv6OZ2mr5iYOQ3H3mw5td2I4/4D265Mi5veJ0wTTyCz/TgM/Wd PeIEfKsH36rvmGWg/Xb3zktIat7I5aJ9ptk+/AeO7fPdM9X3yGf7zMf9Fb5n 6sx9dmfus7sNN3/50dH6/9O07zsF835TwHg5/oD5cPx632VrzFPVt2yODpGk qpLtC2r9iqXH9iz8ITr0Of1gi+531njXp8uKA/nRel9lC8r365y5o+uGE8Ly 1Y/ckPnF7yeF5emvq943YnfcuhPq72Y6+cjsfcvalfqTaXFfT+xS7HFf0p1G 2rzvJeb9LsF6c/0F8sD113j7USv7pDdNr5QqzO+nGSPO5idUSj/Mb/yWJwrG TavUeHmWd5gfy0cwP5bn/Po+26Po6JJafS+Dac6P9z85P6YZP4X+NH6NaeZj fTT+Cd9f38GlfrPnOrPvQx/m27Thn+r3YaP3PeWUacYXEgdmPGLPHaY/sfuK iVd0tS9M/ClufBz55/D2gVU7n3wyTs+vc4qj/7ko0jjc3LH5oUGfnPNjHmja fMtnZcJ0asE1g/sNKfGH0/2OhC1VPuVCvj/b+9lNOUsj/u7KQ3s3tizyLeI+ nJdbfcQv/uinRSP3NnEsj/Ydyz/Wvct/56bHOpZn/8gXtC9Mp6H/v6RNSHss RDmMz2N8MgL9j8rZe9PO8mi3CP2zPPtnefbP8vgeur++PueXeR/3jXPDd7w4 ZMtn6mcOeZ1XFnWIqWNciDBt1k/3hz2DJz2UvLhayhDvSb8Hy+1G/gHEezJ/ zIG5TV7K2K1xMQV5y1tOWhur8dCPIJ9+euYzjfkFzC/g+wjTxMOBaa2vuDH/ nOKF6cp1JQen9P/Gt0/f0absxf2e3+MqpNk+1k+w/sI02x92ePpr22ZF9H4M 02yf3xf+lAB/pMBfKUxbu4hybPBg7mMWmv31e7lEfdT73pu04pd+m0uUF5Mv Bt/Cc80l0p7nKJOv44M8B8hzAB6EaeAtAG8CvArT9KfNadu2ZObMs5QrYZrn 90n/btXxuYejwivx22KLcup8y6TIymV/igrzG41PHzBL3/sJKbfckzhz8jn/ 6XUDOja/Nkrtw8mo/6/hm1d0yvxNWH/NfZ0T1+br+z3QSxFhfRPv4028jY4v pe3D/6hbcFxexfj+EMl66Ls2EVmI8fFeEse3Kb7u0dJTx9X+eBj138D4WH8V xsf6HB/rm/hCZ+LzrL6w6+HNetj1dGY9Vd/MTvp5V7Gr1jggpu/79sVmS8+v O+V47oaJy0o2V6lcM59yzHymDf7ClrS+PafXnfCvjN08+v3Scz7u28VFCwtP +tTpPTPHbz/n044MenzegFphfuLi2l1THq8R5ht9IUZfyFa0/+aZ/KFDHo0I 28+vGN5i+TMRmYL2mc/2mW/ilYKJRwomXlBMPKCY9yOCef/dm/dAaWeGQQe6 f1xSWeXNe/DevBdKXkNY3ryf78x7rLQzw2+33vB785wo/R8UljfvtZLXkGqU /z8J+Sha "], {{{}, {RGBColor[0.33397672886243995`, 0.1279285534584573, 0.5864250111696041], EdgeForm[None], GraphicsGroupBox[{PolygonBox[CompressedData[" 1:eJxNl1uMldUVx/dhLpRzznjOd84M5ztHq/EKaVA6Fjxcak3EJm1HEgUv1QfR PlgaRTEtDDAQwSdKoa3IYKNNimlMTEzaJzXeTZyYCBHkAawIMzXGguKMRpvB tIPt/zf/RdqHL3vttddee93X+i7+2YMrHpiRUlpTSKlDa60rpbkzU3pf+9lC PKmvJfxvm4ZX1lL6tJLS8s6UxkSzQPSfl1Ma1T7v8V14HC/5rK07o4IfL6a0 V9+g9i9rX9Ha2zRc1/rX7pROis8R8ensM5+l9ZQ2i8dXwo8J/5juT5yX0m6t 4+cZPiQ+y0Q7KprnRfMryfZLfXu0v0h8dui81pAOwjX1dem9n7bM83atK1uW f4XWn7Qsw4+1tqt+c4t0/EHL/K/V+rZ4NHQ2mrzmVdthh77Lq6bfLPnmdVkX bPZ6yTZkfS3gRaK9Qt83on9Lel4n2vdk2yd0d6No/iynjOUpPSp4VGum/WF9 h3R3f8U+4F38APy3GbbVyYrvwht5HhD9WtnnY3QR/BfxX9Ll/VTBfhvUOlC0 bJ8JHqmYD7Jlwr2i70ad5/LTG4K/ykz7RtAjD3bANud8c5feqjas11Hp9YLW 47Lhc1rXiW69vnHZZl/Z8J/KjpFrhdsonqu1dpSt7z69e0a4P/Sm9LVoKsKv 1flw2f7G18uFr5cdXxuaAWv9hdZTknOqw2/wNna4R2ed2n+3ZhmeKlue5RGj 8wuWa1/giR1iCH3wNbzgw/k0LNyMsnnWZP/Pm+b5ac0yI+87ounU2TLBm3X3 oWbo0rQOxO35ySt6Ebvdkn1Lj9/9lwJno+CH684FYOKfXFva43yD91Twv6XP stcb1mVd2PlL3b1N9CtE/3LT9H+sOTau73F8vKSYu098X8ydZ+ORd8TSN2XH Dzn8a3390mtJy/5drPUR0S3ucE1ZKfgy8bygy3nR3+PceGu2ZR/Uu7/RnYbw O7SeUh2oCC6Jpt5lPPVkdcvv/rzl+8j2pGz5ZsWxTpyjD/lCniDrhoJj+wvd u3imY32T9vfpWyX6e6X7t+CpdVy4q/VpO00HPXfxC2cL9Z3tNf7Kut+FFzpe Ix1miW61+NzS8nu3Iqf2RyTPlVq3FW0X6Ccy59UTcvZcnb0ruCAeZ1v29SPi X9b+qPBX6fwEPut27rwaMf0atVO+uZ/81DqADuSp1qUt3/m+1tOhFzVnYdOx cg1xp9jokJ1uyExDvOto2mbYZ43g34tuvvg8qvVwr+P6ZtHfIWb/FP5OrX/X 27t18cPc8iEntqpK/n58JB2Pa3+g4FweE59/i+aUzl8qOX4qkZvk1ZbIc/L9 Or11tNc8PxT9+ZJjRHcuaLqOHyu4T+1suffs0rq94PqB/FnTb9S0dve5Vi3M LDsyjEUuDxUcP+zBIwP8yfmDyfWgEjVhEf7QWVvrsPx5smQ7/kj7TwTPrhn3 SeDJLeoP+fhD0Xws+AZ8pG9M8JKm6x54ah82o1/ujXiBZnHwOBl86JX0Fmos d9pRM6nbc6qOTWp/O/oRtYf+QP0hV+ZE30HfdsDgF0XPKBKbJd/5xyz3GOB2 0EBPXB/S19/hHCe/6b/U7BlRtyeLhokb+sP+6Fnc4S45CF/wyAbuYOC3Fg0T B+g4bfMO9yJq9I1x/m7QoDt6k1/0v5GAyVHw+Be9R6Kv8ebb8e4rkTvkUDm3 fF/T18uuafQuag14ZGPeQFdqdbvguCfmyRV6PD1/IuYn8u53EbfEL7nAu8wP 1PZWzCRbW657D7dcb6g78D9TdP9ATmo29Y06R31kZS44FvFKrHZkttfc3H4v hh+Zp2aWnC8P5cav1bogc16QH1dlvvNg7rqIL+nD3OuOu+iNr9FvWeYYp5ZQ 42+KOEAn6OlZz9Rd5zcoL043TDMq204WbNNVYYeu4P+fquP925Lhe5lzb11u vtzFpvieGCAfyRPyEDmIj6nIf2oYtYwcmYw98P0Rs7zNjDMS89Jg0FM3zgY9 MTMVPQQ8fYd7k1Ev+6NmTkSNxV/oib70N+KG2GiH3SaCZiRiBF121dyj6dXo ODtiZ0HwLEWMLYzedHWcAff3+i486C/kDLmwf5ZnQfSi39Ir50XMEC/EyXdk 02dF35m53wMzO1Cns5gz6XV7itYfe5wNOzAPMH+g55yYY8mxoaDBbrxPfSHX 6Iv0xw+07sxd43bl9h21D39dlHv2vTC3/4GRf33uXjkY9/aGr/Edb8B/OP4t 4EONZE+d3B5nGyMmpkJ+5qTRmH+po5MRG8QPZ+CRl9yjdlHHzkQOkr+TATNz 8e/AnfG657VlNfPE3vBlnnr2/2ZuVmZAahf+OlfrthX/V6+2BkzvRGdsRh3l XfyCLyZjhoEn/LZE72buQRf8szt8xxyxLWJjKGZQ7INvHwsabI6914SvN4Uf mT+4/1TTuqEj/1VZ/BN8Fj4fCtvy70L8wYs4Ip6IN/rKjuA5EPWbHBqP2ZzY WxLzK/IR56cj/qE/HTMMMxNvExf0lEb8d+CfyfAXvYu+Rd27rc8z6+19/meB 1/QsVHCdPZdT45GbvDMQsoGfiHf5x9of/1noOj2rJMPbQ3f+R/aWbSNmUGon /ro7NzxXuTbWNM1HNc/4w0HP3Et+YqtjTevBzMU5vQB5mdvP1f4PCp6LqZlH ar5/sNf/kNRd/r2w8UD4aFHMA/iBnEBmYuBEwT6iH62vebYYFp9L+tznLu3z fLc6dOL9w+Fb3qb3nIhcGwqeyPZ+4Js9nuGZ5XdWHRt7Gt7XQo/OsCn2HKt6 nhgXzdNV+/HphnNiU8T8gchDZK7E/8f8yEvwh4LmWMReV+Z/m3nyw5zMdlyV O26gfydoD4TMZ6r+VynlgQv8fwFptVE2 "]], PolygonBox[CompressedData[" 1:eJwllUtsVXUQxv/t7UN6b2l7bss954Aa1ISqIDRSoFRoYiE+IolQKeoCERPB qFBiaiWhQYkxAQGtgCbggiaS+Ii6wiit6MK4cIG6aBdK7xUN0mILpppAVNTf l28xycyZb+Y/7zN38/Z128pDCC9DFVAxDmEgG0J3bQgjaQj5JIQh5L5MCHc2 hNCEPIzcAF1C7kGuy4VwtSaEJ7Adj0L4uyqEP/n2CPZjfPu+3D5L8Cl0COxh aDb8+voQdlWG8M2MEPbD98Z+K4/9C/huwt84tk8hf448C3kC+QJ0D/JkIYRP CLyIfiXv/UFMX6I7DRXQ/wy+tSyEaWIabQxhKd/G0K3gjQzYV3hzI3lPzYSf HcK5yNgx/LXjb0MTOVaH0A9mEfbvg+mtA499C/IqfGTKHdO9+G7D5iy2JagD fj75fESu54nxdvhlfDtZYcxy+MXYLM04pmXwE7zfX+YalvDfiv/JMucUo9+H TYHeJNBB+F7iX4O/X6nfCfjJPHUl1ueI8T2YCuw/4P27wQ+Bv4SP4zm/obcU k2L7l29bU/dQvbwCbYavV38znoFG+Fv51pmxz9vgN6jmOfdQvXyHfhwFWwPt QF/E5kjOPVAvNvItC7aOeF7Edndqfpx8R9BvSR2Lat5N7W+GOqudk3JTDspF Na0kt73IUaVr8ir8AMCE3DvI6WvwPbFj6SHmO8BHxHMq65yU2+nEs/WPcoQP xPAt+hFoPnJf4llcw3sT5HaSN9p56yz6VnRl4L+DPwPNQx6AFoI/hr+D8B28 eaXMmGbkx4hnusY1UC0+xF8L/nap5tRvG9+qhSXniibPnGZPM6fZO4L8cZVn VrP7LPiqrG1k+xb+bsFfF/5K6PdQg/5aY4SdKlinndHujEaulTDX1Aswe9C/ BM2EvwhmFrk8iPwb/CA5jGad03H4EwXPnnqu3p/D5o2sd167vyBxLVvwkUM+ wBtxrXum3q2HfqdeRWJYC7+QeDLI10FPYlus99vKSbnlC969QfSX0Wdj124E +xr43eS7s9Y3RrdmSeJeace0a32xb5d2ajG92R57NnTDdiTuoXqpGdAs3JX6 Fmhntbu6Obo9ukm6TW9Hnp0p5GF0j6a+fSXw3fAPJNZpZ7Q7zby5NeMa/YS8 CPsK8OXQpsQkXjVQLQ4XfBt3gj9U8E3TbfsCmsbX1XrX9hnkIfx1RZ69r5Bf A3uAby1Z36hO8CvB7814BxrQz0mMVY/V6/a8Z0892k/8czRT1LIIpg3sZ8hP Zz2zmt3lfCtlPTOaneuhtoy/taPbkriXynlT7JgVu3bkdXQrUt9u7fhNzHtz 7Nt8Hsxq9IPEeyZ4p7XbqxLrFJNii4h5H/g3VXN0v0TmdXN0e/q0zxnPxF+p c1Auqkl95Juv2y/Mu+R+Az6P1jiH/3j7YuRZ083U7VyHj8s53wDdAv3D9C+b S/zX2M1PkY+Vu0anYu+Qdkk7k8PXmUbfOuWk3HSjdauVwxTv34fPCznv+POR b7Bu8Qy+LYk845p1Ye5P/Y/Wv1o3ZV6DZ06zp5v8cOqd0m4p5q7UN0y3TP9w /csX5B27dvAh9MOxZ0k5KBfNnGZPNbkR+cfEsUTof4Bf22BeM/w48v8bLyY3 "]]}]}, {RGBColor[0.4150981865873199, 0.26897686037537194`, 0.7004510335088123], EdgeForm[None], GraphicsGroupBox[{PolygonBox[CompressedData[" 1:eJxFmguwVlUVxw/B5b7v/d7fd85FBStRFCxUhCQUIvMmA4Ii5KSATZhmXrSM yvAtegUSewgioJiGoiLVyAiYPURHkxAJtSSghyAoD81H6mT2//FfTTN87H32 WXvttdfzv/e5/c7vmnDJx5IkWa//eqp9syVJrm9Pkr/p4cGmJHmvLUnmavxa jW3Vu1b1xzYkyTj9UvV3NZjmP6K/T/T/Un+52tPKSbJBY8PV7tO8dzS+Q88H 1L6h32D1HxLd+23/X+f94POoeE6qT5JFkmlhk+dM1Vo71e7Sb2NPv98Z/ZOy JDmjl2QuJMkVTab5ntqb9O7GnpbzcMmxRP3PBu3LkmmW+G8OmZDn5Mz7G6b2 9ibLyfqj2ryvoaIpNko/WnuT5t4uHrdJL109kuTpRuutKzHtqNADe2R/c0TT VvM67Wrz7ebza+hzSXKh3j+kdbMG06PzmZJ5uGSdoXaEBLtH47NFd4HW/Zp+ C9S/V2PvtvkdOn439Py/54vFZ3qL50DPng7Evs7XWnubpQPJ8ZnM9h2q9oup x6sF7+mG2Bc6Refj1H9I8pwr2b6s3xtN3g97oS3oN0LjTTXbo1nt+fpdKLm+ onas5m7VmqfXyd+aTA+PM/U8TTSPS85Xq0nSqbHdVespHzynVJJksuRurdlf 0C8+szO1He4sJUmDZO8Q/SbJs62X/e9u2WuJ9vORaLdWbC/sh59D26fd8o3q 5Wf6g/JJsl08Z2itcWXTvqp1+kn+vq32rV/peaTasngfL/qC1p4p+p1F2Vzj owvWGf6K3t7W+AC1iyXDP1scb+iQeLopfPVo0X5Cujhcvx0l63mP+KzUmkc0 +pl4vKbdfswep2jOuZp7nGTYI5mLYtTY7H01RNsR/b3S6QTpbZ/ax1L70mLx XyX+D+t3sXidIpkPFX1OMrWI50/E80HJkmv2eLvaSdrDGZL7A/HZpHmT9Hul 0fvCZ96M+FgQMYIvoXf0iE32t9g/8dl3wocPK9l/ryt4r+z5yvC3leFzI9We rN/rPS0rMmPXWZr/SLv1Br99wf9GyXiK6K6TD7zQ4Dmr1L9Oez9UNNeq7RSP jexbc48sWUZsfpH2eaF+F0n+AbLrCq1xlNohBfsIfk28k/vID2eX7Y8XKI6m p/bnY0X7pwbvAVsT4/dHTiD/PN/T+e2JevfP0/hA9U8Q/aA664X4v6GHdbIq bPRI5v1NULtaY4/oNydxy/OH4nW69veL3l4HfscFT2xCH7v8trd1ij5fEK9n NLadONW6yzR+VGLdPxS2QPYVIf+IsAWyrcs81kv+8rOS+d5bsn1WxdzBrfYJ /ADfxYeJk2F1jqPuHs4J5APkGCzdXSZ9fatieZEbHsQb9F+Qjn+b2X6/U5sX n27p5g+J9z898h657dqIl4Gi6R96Ji9CMzJ8CT/CN96XTz+u59/InnfI/vdp 7nOi/0i5+vtqD5V+btHagzT3zIJ9Hxr8/2uZ89LRNY/dH3OHpfaRPhqf3Opx 5j2bOSduyJwnqGnUvoEt5j9acxdr7JjokyvwW/IF+mNf1K2TUtetQ2rOB+QF 8h6+uTxqR8+82xNT+yX+OTV1vHeKzwN1rl3j1P95nX3poK+K/1miXyr62yuW ZWDIMzxq/eV6P1+8Sur/U+uMytl3+kmXD2p8sugfiPfMP05zpzZ4fG9P2wBb 4APTtcY0vTtRe1lZb3+gviyWT9Vkj76SLa24Tk7VOuP1/t+iGxI1Dj1Qa6GH dnHIh5zLlYMHVb2nKTnLh5yfV399xTpbIPmf0vrrG2wL5tEnx27XHsZLt/8o ONYn9LbPfLtg23XKjsvl9ys0tkx8pmje0Drn0BUdpv+U3t+R856+rvZbev6h xr9ZskzI1k+095QcWx8Tn8VFY5aZGrtV73qJfploZ+u3H/4aP6FinvXa052S Z7fW/mvFsj8Z8ndIp3eKPlP7QebxmaI9MXPNpfZSR8kNH+Qcu+Sdg3m46H2x vw0F46Gmqn0IX/oUfPT8G9G2VK1TdFvpsC8Rk8QjtRz5wVEDZLcO5KnzPp4K nRflV4P1rlQ2JiJvkl8mSBcT5AM/lfzrqEHg05x9iJxDHtsheT4p2QqSYXjJ frJdtNfkXIfH5GxjbL20ZBxFnkXXW5vdvziwLnKN0nipYJ/EN88NG+FHdRXz XF2x/dkD8l8mvoM0b1vOdgAr4ycTc8ZTkzLXPtam5j3d2/UHHQ1sdg14WM/V DufhcodxMnzmxnqsy55md3hdfOF+9fuL196idUHsXyG6Y+udZ/HFRXr3Xcmy p2Y7HNZoW5yaMz4Bp0zPLNsRojmQ2geK2u8hag9tdI5d1Go990mcj/BhcNEC 7f1i7f20qnkfEvwXSvbbGlyLkQv5yEdDU+v9eLX1ZWOghsDs7AX6Ja2OY3JO l/jeLJ6XqP1jm3ki94eZZXtRtpoY+eqA9tKzYNwIfiQ3MpdYAF8fEnuZL7pp WmeIeBymPT+j8R9VnV/QN340Ts9d4nOZ+KyJ3Ihfgqepq9SGwYFtO/V7vbdx 7Uit06a5L4n/M/K7wzs8v6/auRXHNfGNLMiErrKI06vAkqLp1jqnFuwLYyPf rSzZbx5Wu0DjNzXYFl0Fn6PWaN5srftL8b2h6tr4y9gvtN1Bj45vDLu0RPwu zrmGUcuOUftsq3XCHvHB/0StBE9TN5cWnKfJ19TuurzjFVwwPjPem5jZn/Cr /qFjsADxAC4Ak/O8pWA9PFk07gA/kX9mlRyLuyTbjAbrdUMPv/9z0IBNsAt5 plN7mUjeo1ZE3BF/j2mtdfp1t/ocxBnoD/XOzS9EnO4oum4PyNtm2G6L1t/Z 6Dq/IzHu5ZlzAliYfIF8V5Vce65We6nGVtf5PAIeAE8w/96ceVyecz47vt50 5HRicqPmbqp4X7dIH2tEc5r2pX8H8zR98svVmTHTedLn/tQY+bXAnOBN8vXu 1Lp9JXVtoEZQf56v+D15/Ruaf7/Wv7ZovAhuBI9eWvMe79G812qWf5faPVWf D2aK5+Y2y0T8/CVnuch/3UX7EjUB3AP+GS36wc3OR2CMSQ3Oc2DgtRobj9/W G4fwntz1+dQ45siabUstwb6cZ86qs/6vK7oWHFG1L+KTx5V8ViEncS4CFxCr +PAbZecMcl4ptX5zqeMYGuQ5u+CctbTisx21CowEXqLlvIbe0f+Ums/WmwPP I+eawORgWPwVX8Xv3tK736u/I2fMskA2vK8YObzD5+OX630Wfqpof6Dmc24D 6zCHWsGZBbwEbVfwHxpn+eGZazJrweeV8IFdas/OfE47J/O5DmzSnDft2yEb 53L2Ra3+e8GYB+zTP+/45qzAnvBvfBu7gZmx3f68faMs+nFNzlfkbWyFzaDn TMoZlXMUeIjYm1T2GDTYdFQhcG742PNt9ntyKzmWfb0Vz/TJBzNCHmoC9YOc tjHiG11R/+izZ2otemR8S5v1yxzO28QR8XR81KYhqXEYGGFN5hqAzgcVHM/U U+K5f+rzRQ+NVwPvvqV5R+Vtn00l1zJ0AjaaWrPOLxDtqznnD/IcuANcBE6+ LfO9w48zn6s5nxK31E/OYNTQW8XnRMn5hbxrAn329kBgK3x1m2jma/xvNeNS 8Dfxhc2wHWt/p+w60V71+YfxNPW5l9w6Rv0388533AGgzynB57Wq7d1HNJfG HUSX5n0u75ifp/5dqc+ly/DDBucYsMK61Oexx9VeUTPOH5q3nbfW/9+++Dp6 vCTyFXmrV9n9S+LcOi5iZG7BuHCFeH46fAwMTk4nt7+U+U4KH5whmj9lPpu9 GDgCntRKzo2cU2cFXgQ3LsSeZdeOztS5+6zI89NS1/3xect8ZvgnNXtsyDY5 7kyYQ34iT2Ej7ljo7wl64p08RQ2jln0kns1V19lflK1Dcig5p7NsfPNkxe9v aXasro24xJewP+PEOPOhG1LxvSn3dWDYU/LGoN3STUvBZynuL7kfA9OwJ2oh NfH3eWMr5MCO+Nqa8KVMG3pCfN4mZ7T6LmWh3iUFxwixckzEC2M3t7o2U5fJ J8zF/65s8v1O/7gbAi9/NfPdKDgBvABf+HOXWtb8Mb0s347weXz/i5n3yN0k 94ojAmtdGbJRPzgrbYgzOHqaHzrsH3dM3BuAC8EuP676zolx7og4a64NPbfH fe9ntL/9ZdvyzbL3Bl/q4rqa572XM48rg8/BO6fYy19rttWR0uEPUp+7b01d R9AVuW9Aq9dl/cmR28nxZwbuAn+BJbkTAPeSg4ihsVXnaHI19fyFgnVyleiH 1Ozb+Dg89sVdI/UVPXH2eq5inLG54ry8JvyW/Lo5cuy+ovEl5xLuDsDkYPON oQf87+BdY7PlAD+f2uy4B3Ny3wMmzKfGPiOkq2rFuDur2O7cTXMWmlf2c++K 74a4U6gUfJ4jh8+OerEu9EZ+JT9jL2SgrhN7y7T/o7Xu3Znz09kRX69HfiPP YUf4YJsTUp8XuZO7M7M97sp8pzcx4mJb4KKfaPz9nMc4J+GPyI+twVhgrXur rilgLerK3tDhx7W/myvG7vMqvmdBfurMqzXnNHAr+Zu7MPyfWMyijpyV2YYD RTMic73jPnh+i+P6f/fftOQx6i11F1xHHuKsRS7i/cig4dwBNuC7yrSS45Fv F5wNb2/13Ss+y50PspK/mUs+BJ9Sz6hl0C2Nes398fVxh4xfIQe2n9Nq/8B2 4JN8fDsAe24ObPClzHx3F4yDyN3IRn1HfvbxYskyg2vQ96a4u8Yf8Utik9zM +PaIR3IEsUncPxs+Sc7cGd+b8FPGwZbgWc7vYMla+Co+u6RiXHKH2mVF75d7 Neo/fjkt7sYWh08ujPM1sqIT4rorsA17AavQbok+NYhzBDXlqyXnwG0F23lL 7J0z+sfj+0WtbPpq2Xert8X96o6Kz3jbKs7J3aET+tgA/a8W7VWtvm+nRT/k hW7pv6L+vMz6ZC4xAs3VQd9DueB6Li07vP45UQe5//sw7oE5f3BXS/zhv2Pj myP3zSPivpraTgv24HvQe/E9EcwzIPyeu2Pw9zTFx6qizwV8C6JmIgO2Io/e HPtqlEzz1G/o8B0tdfq5sAW+3id8fmH4Pfdx4ILL8rbVwqAhRqHD9/CDweEb +Dlxge3BzqyF3RhbFHOXthojgg/BC/TBz+hpcuiKb1D4O/kWvXJngm6hXRp3 JcizKGKQOZPCn8F9e0Nv7PG+uIvmGyR5aI101SOwLneQLWX7c3PZfPtGPskV fKbjbpu7Qu4MH46cie2olUem7p8sfuvrnRMZRz/oBt1RL4YGDmcuNOTHD0uu W9xZrq25Jj5ac25eH9/J8DtqMLE5N74BnFT02XFonB95vzq+DfH7RMi2NuoH dYOx9SEbtMyBNzyGhWzc/4F1qWunpR4frX3dUXPNGpM3ZswHbqRmglXAi7XU 9eaEvOMDjJKP2Fkda82J2CGGWmrGAwNT75m9P6b2B3r3F2JR7cupc/dWtQcC Y+yP+7OOZu9pbeaYeTTz+ZYaXVD7ndQ193LxnFUz/bC842FQnMd3xXl/N7GT +RvMymgHx7ck9Hxs2A8Mc3jo9pr4dvvNsr8njIpv6OybPbIH8lM5+p1Rf8EA 6IXxl+Lsxh0NPsJdNf4AhiGH9Ys8lpWM2fi7g+XxzR67k7efibzEGRvcvT3n fH0QL+r5oqq/u5xcdu1HBvJM3+BPzSbO+kaf9egTd/8FT5yDUA== "]], PolygonBox[CompressedData[" 1:eJwll3uQ1WUZx9+zy17wnBPn/M7Zc1sF0QpEl3KRBUUBgcwVhsVFZXMiXGcQ JMYFKaG8gFwM9pLohBEgsDSUYQHVjCNolnGZMEKlEUyUlWm4iApSzpROSJ8v 3z/emef9Pdf3uf/639vW/EBZCOE0pxdnRi6Ee6tDOFAVwms1IdQVQ3jskhAG lIdwDfA68IOgewaaZ4G3lkIYkgxhKGdkBn7uX0D7G3hmAs+EZwyC7+dbQxTC xHwI+2MhtMHfBLwoi8xKDvhZ8A9Moxf8p/DfUwihDJ4p0N7F+Q76hoKvrcAG 7KvEvh3Q3A/9rHgIO4EnoG8LvD/l23jghdgwEv3n+LYI+AL8dyJrF/y74c8g /x7u0zjTkT8fnhS2pJC3APgQPC287W7OWOy7FZtnxyyjEbgR/AHoH0b+bcAr sWE09g2D/yngyzg/A/cIPBdSIfyQ++XgC+AfBm6Ap5UANEIzDPgRvn0ZfC34 R4GHcVrRtQr+BLZ/jM5maBu49yBvMDYeRH89J8FbekHTEbMPBsE7kPMc8BN8 Kwc3HR090G7m233A5/DB/6oc00+AP4GmCX0T5XP8kYS/pdpv/gx9l6KvCd5x 3GuBz+CTS+FdAU0H9N9M++1/IaZbie047tfznq+how/yR3Gv4/478KvBvw/P KXi344NtyLoJm54t9xtHAh9N+a3S+SFvH1b0W46gYzhwnjd/FHfOKndbOLcD 70Tfy+hrlH+qTHMb8ODIufcO3w6CjxccO+XQf9A1Dprh5fbZN4CvTzsWV0Nf Bf1/oZkVc00k4J1TdG73hmYu8C2RfbGc0y5b4J/Mez/kvW/y3huKjpVyYgTw tdDPhXYOZx701xRcO4phBbz/5mTLnfMF6LugfwvaZuRdi7w6fPYe+b0B/Hnu I7ingX/F6VXjmlHtKIblkWOq2E7inEP2Mb5Ngv92zqGsbZAtD3LqwW1E3hJi sx4buoF3wfMG9xuhb4W+C3vreftv0dcJfAqeb4ObyunJugZVizMTxAN4RtG1 3Yq8+4C3ZZ0r57m3If90ZN6v9g7hLPgXyYFR1N7NnNEZ55xybzDyniy6ZlW7 qpEsvD/HhmbseQF7NgFPjkxbx/kx9NMK7gX7kDFAvsVH9cS2A/ntyF8X2dYZ nJeg34T+q5OuSdXmnozf/jdyfiO5cAD7e1W6x7wF75XIG1DpHL4C+E74zyQc ozuAN3IGJd0D1Av61JILSdeQaulu8P9K2IffAq6uce4tgmYp+DroDwBv5Vuc evgl3/ajayr8y1KuIdXSBb5tB16Bjjr8sQ19y4Gf5s1rysxTyjpmil1TnxDW IrsLnbmkc6gTeBU0Zyrc4x7ifYOg2Qd+Nfp34vuH4H8efCP8a8B9Ac9C8JOw Zwn6F2eci32J53lwj+PT5cjqyxuXqNfyhsvA9eMcRn7Etx3YS4hCGrgyZ11N 3CuAVyFjMLHrj45ngNs57wbb3AF8Ep5jqi/knwJuKLpXzIZmKPBN2HMltE8T o+/xtinwtAX7vAX465FzUzXbCv1r+Osw/ppAPKZD/x42nKh2z9oAbT/iMxr7 fsR9LPdj+GNl3DPsfeAc324udw+8FXndyNtSZhsHIu/lgmeXespLwA+CfxR8 B/jj2DoK+VOQ/yLy25C1uGjfqUerVzeX/Dbl2GTgU8hYi7zXkXcSeHjas0Yz dmHWM+/i7OPsAX4eeS0Jz0DNwvnwRHHHVLHdAv6jctP8GvgnJftePpQvF2bc e/bA/3nJPVW99Qjn78Brc94VVmueqPcW3CuPqoax7Z+Re89XiH8P8vdCvxva vZwF4FZDs6nMM6sf9sci7yKaIQPVn9GJKeEI9t0A3JBzbfxR8Qb+fsGz/AP0 DUHWPGxaCj6Dvv3IuoP77Cr3VPXWCnK4HYF/0kzjbVNq3Gu109wFnAa/HvxV 4MdQb/OyziXl9JjIPUG94ffIf4LcPooNncE50wM8IeVcVkxngZ+ATbfE3YMy yF+WN+8K7gtqHCPFSj2om/et4T3jK8yzFjgP/roq97xmZPUteNfYp5oCviTv t6pnq3cfLbj3KUd7gD/g/KDMOXMauCvn2aUc7AQu6VT7DUXN6rx3FdXgu7xl M/fjih34zdxbC54d2vGuQt+cgmMtmrnAm/HXq5XOqc7IO6Z2zUbe9yrvi6e9 62hmN0B/KHLvUI9Qr4hrJ61yTxsNbT3yHkP3P9D5Rs47hnYNzaSxae+I2hWV 09dxj/LONdWsavfJyLxvc16HP5P37FFOKDd25Fx76pnqnepR6lXq+XszznHl +kTs/yv2Z4rONfWwbNE7iHYR9fwN4L+bcm4qBx9H1wnuY6B/hftneee8cl89 97mMdwbtDivJ73eQtz7nWtJM1myeH7n2LtYMuIM5v0U95s2cZ4JmQzs2tyH/ gbzhafI5tj1V9GzUjrFS81j9MOadfjtwn7xzUT3wS3nv+Nr1FYNEjWeyZrNi +gts7UbG+IR3Uu2mrxQ9S9UD/6DejsxlMc/0ncA3lly72kFGlDxDNEu0g59J e+fW7q0cL6DvcvA7ks7JAu/ry/0F9X7eOFX7KfdtSeeEcuPPJeeWdnLt5vtL jpVqSLWkmafZp50vhS8Pl9w7tfO/DVwDvjvpHfks+henPNs0AzUL16XcKzST NZu1c2j36OLem3uu1r1CMvK1/sfRv456iHrJ53nvYsqBk8j6lDfvinuH1S6r HVm7sny6Ef/tyTl26om7gYtF5454SurPede+Zlon8oZkvQvIJ/JN/1r7Sj68 otY7l3Yv+VC+DJylSf9z6d+rutZv0Y6qXVU70sVdKeleOaDgfwnFNJb2jqJd RTuAdoEjReeu/gH1L6gdRLuIdMTgP1H0rFbOHQc+m/GuqZ1Iu5FmqmardqqP wf0fRlzwzA== "]]}]}, {RGBColor[0.4962196443121998, 0.4100251672922865, 0.8144770558480205], EdgeForm[None], GraphicsGroupBox[{PolygonBox[CompressedData[" 1:eJxFmgmUlNWVxz9ouqmmu+mq6qqvu6qJhhgRgoCJLNEcEAxiZHBDQcGMgoA5 kgGiYAbRxCgwsrmwxbAJKJKw44wicKIjiGZEjIbMmJjEgUQWgSgiOhEzceb/ 4385Oae/816977777rv7vV+3v3XC4PEtkyQpVSSJ/pJe5SR5uzZJempcVpUk 5+tZrhfTikkyU4A/S5NkQkOS/LAySTYVkqSj3j/Ee72blUmSP1X696jmJLml bZI8pXF8TZKM03Oh1v9Z++8S3D0a12jvMcG/qPWxwnmX5j8SzqWtk+RuzZcK 50uCHSC41wWzXjS8qrXnc0lyb1OSlIXzCdG5WPPLNN+j+e8Ef1J7jwt+gdbm 6xmrebvqJJkoPL1bCa/Wztb8beHarzHR003rFfH+Da3/q2j4puZ7Ne8v+N0a dwj0F6Lvj4K7qTFJ3q80XYv0boZomw8PRNt1Wd1f+6/UWmc939L8fI2vaN9R 4fxMezpqf1/RVBZ/ltTrDnr6Cd9wwd0p+Js0fk1nXac97QQzUTjnaP0erdcK rovgRwnPdvFxm+78FcHcrGeweL5SY2/hHyL8D2j+YNY8nae9HzRYZvVa76l7 9dBzqfCUNC4Q/dfqjkf0/kqd0Ve0XlNjHjTo2Z1Pkn16t0t01Qh/Qfi7CM/A SvOnVs8rwR/49ETWfNxS9D2QydOCeVXzG+G78D8iuLWaDxa+DcJ1EbRp7S/6 3Sz4T8XPDaUkGSad3Fgy7B7t26F3u/X7lO5+XDz4pZ4PtPawaHxRMAcEc0S/ u0s3/kHjWVq/XjDP6z6p9mW0798rLLP2Om8/OiyYRXq3Wu/WCva48EzSM1C4 umitayvr31ytPaP59Arr0T9pnKz1LaL/du4WPKgQXy4Q3JW6wxU6d5louVjz szS/T/NOmn/SJklGal6r+Qua99L8cJN19D2No0THIc0HiK6twnm5nnd03mqd 0QsZSZ4r9BzU/HHBrshaj1/W2rJ62+n7ouEJ4bpWeJ7UOFPvntF6pejdpPkn mu/X3iclpy26x/tho9gH95qeta7/NPZxL2SNzWEbIypsK/ABPZmhM87X+kyN /5g1DWfprP2VYXPYkWj8c9jCEvH9rxq3lmzT6GIH/R6s9cUaZ2v90qz9Q054 qpt9F+7UX3pyk/Rwhtamir4NgvlcfuD74s8CnVEueg2d+aiF5F1vPC/pdyO6 oKeIXgInXd2scRd2o+cq0blNON4V7Hc1Xxl8ht/4ROadE/uVw+E7joqmZcJ5 RONI0SZzTPqK9neEb221z1jdxjr6btwfnzBLcOcK32atv6m1HcKxM2iDnql6 HmttWQ7QvK6V+Xp5tdfmFP1+tsaeWu9fbRvoJ3yfVtlHXSF6Rmr9W9hWtfUE X4ev7affvxX8evG8VuMrJcuLuyAz8P06Y5w3tzY98OfaRtviNRqHNVrfu0t/ dmasM5w7IXwlenRUZ47W3mmV3sPeVqllclG15TJMMtqh8QLJa5R4tbjKvwdV 2jfhl65uNJ6rND5d5XtNJpa0Mu86irbhQQ90fS6afyCY9TpvT2vbDndB/juC z//SaJ5gQ/jYo3EWvOkb/DlHtL2lcYRom521Xlyv8b42poN3rwp/t0rDZ5sc 9+ry9vXgGZr1Xb8e9+WcaeFvmyS/IZp/Uzo2Tnt+pndzpMtvyD9M1vwxyeUl ze/QvI3o3av53Zp30Pq7mk/V/BbNf6f5D/HDmt8VtoBNPBz+Fr87LHzwQxp+ nrPvwofN0/xSzbdJLi2abcvY9Iac9/TW3qnC+wX55PtL1h/u+ql4Mi1sMKN7 rNe7GwSzVuPMnH07Pr5n8L6ldONerXfR+ifa11Rpv3q0hc9f1da5wXLxfGcb x0d84TDxrY/W59Y7xrWrsJ/+UM/vdZc+Wtuesc3jr66uts+CLt6xvipr2Eka /1CwjRwSvxcK5wI9Q0PeyIvYsDn8JP5yp+R7TviUtzUfUe0Y+Lnuci763GSY G6oNR3zHRw2qsJ9i3jZn3oMT/i/Su52h809mfD668bHm3w4bx88cj3hEHBus 9Zz21WYc38mXiPfw5IjuNbzeMsrjQ4r2qf+nc7dX+W7ca3Tq+NhDNG+ssr8g p/lYex+PnKQiday8Xfq2JeOcjd/kDtwJ3wW/yUOwZ2yV+3AXfBr+jLxilWir 17vN4vN+4d5XbxlzV3jOfd8KG+tU4ZzilgrnXdc1eX69xo9CRu2F+1j40Ad1 1nekI/NiHX+Crz/RwrDzA/8JjY/GOvozL3To7oh55KdTmhwbViiOXFE0P8br 7k+J1pqs9fjH0uk1Wh+hu6xotl8Zo/ko4Rut5xL9Hlrr39C2I/wasoDmUQED H8ALTvQYnPiu1aJjrea3hg8ABn8Lf+An/odYyD3JnYmTi2LvAZ1bX2V9gr/w GTvKV1nW0LMz/B7+EtyrAv/AeusVtoj+QHM74RuamvYPc87LciFffNLiOBc+ Lwz+41fhM3fCt40IOeKHyVGpJTirX1vjOhk5cKalR2wG3SL3JXe8UDb6H0X7 0lyTZb0w7LRrvfNhclV86hN1SfI3nbNJuAtZ+7V2Gdcj5DfIB/4jH2JGh9DP m1PrXcu83/Vvax+FPWPXlU3Okc8N+MZmn5U2O5aTbxIP5uS976mC81bqEXLT N1LnwI/o/TjRd33kxNAN/S+Kn9kq3xH5stY18vyWdZYfORN8nRu8Paa9R2uc Q4xJrVMNwl+sct0DTmTeJ+SObX057Ovaet+Rd8gYWeB75wV+5AgM/hafTTz5 a9h75xrjgs5ZkRcWisbNOu8npqb9q3nHkC/HOnLZqOcF/K/kMjHj3BHcnwX+ fa0dB1kndyTXoe68ps53oz75r6LvOKbJ8RCc1HHcBb1FP6lzqHeOa2+P1Hl8 T43Pam83zb+ueW3R8bqPYI4XrTtF7fuwaH09obGUWofKGucW7XNrNG/S06z5 FwVfmbquqErt99EVcs45qWvjh1LnCJPDJ8ILfDZxs22ja9R6jUvCdrFnZMJ9 sBFkhV4irz9IrwfKBt4T/OGs86TPGp0/dgj8r4nvWeGsotZstBz7aVyp9aPU 6qlzqxfjrLnhG5H7rtS198sap+luf9LZ0zWeV7CezxSOncihyrpNzMK/4rOw b/iGT5ylPa2Ec7bGN8qW35saF2rfOeQ2gjnQ4FrqEuG8rGz/NUDj3qL9Rlet Tyk67v1e84PyvYsFc0jjeP1+Vmc/KzpvpF7QfIXmv0rdC9ir8VTkZ3XSk0mi v6T79daZLXLOxc/T+lM6r4toW61xdJP1qpfwjS/b9iaU7T/QgfqIy8Rk8vBB Rddk9wv+4kb7z280Oj4uDj9GP2BA1G6n40/wmRi7/EztHzH5e4nXqd84A7/6 UcQw6p6u4euWhp9kL/axLPzniYAHP+efDJj5cS6xDz+JniMvcrTR4Q+7RL+H uoZaf1PYKfY0POIXOcXwiAuH6g2Dj0U/8Rf4CmIOsY24Rp9nX8zJZR6Pe5HT LQ3aWPs47nwy5qzTnzlYb/zYAjGD2PSo5DpbMng4dW5L3wldAif85BzqpO11 tk9kwl7s6NK8/dxE6cK/RR2v18ms1L2rAXnHxP2Rn2A33Au7+1KzcbZv9r25 P37nDN3wH5ksjZwP2g8F/Y82ueeys+z43BwxtJ/k0Cn6aOBiD36MeoX+G3Y0 JI1+SKNja7uIucgCOXCfu1L70k7N1uVs+AJ6YAuij3Nj6lrn45zjHHEgaXZN 1LfOddEvRd/Vmr9edq6Gvr9KvlNtnUAffl40HvAtrXY/BzjyHHwK/oS+34Tw cRVxFjH2tbLt+tfCcSrnu+wo+t77oxdHXwNatgp2R9k9qt1F95OeqXa/gVzo 6aiXn2s2bfSosF3uTOy7o2DZvhs50pKg7b5C2EHOfbWJkVe/mTrfoPdGP4ae 3Zkck9hLDJ2Qdz51SdH5LnkvfaQeBcf07hq3Cs9zWn8uYj7r9L6I7fRCiZvk IMcCJ/4Iv7Sg7LgwLvhGPYcsqH1bFx1PLhLNBzOmE5zAjg/4a6LniayXly0j ZIVMbm1jX3+/dGi9mPLdrHMFcoZ1ku+JnO2bXI/+x67oORxL7ec25O2z51e5 r7G9bJ3ZVjad0EiOh03RI8OusEHsj/3kSPQ9jkR9cTLsnVg/rM7xHl9ZG/0Q emZHAm+P6OPRz6N3tKON5YHMJkXdN6bg+e6ca2lyBHSgp/Y8L/ianGMMdD2Y Rg8v49/IGRjyqELwhHzqt2XT9puy9YzYTe43Lufe2L3ocKN5e0j8nF3tfgE1 MXXh2CrXhi0Ed67u2bPBdeHFVc4RqPO591vCP1Z4plMnZt1f2RU9FuoKfMBP Ss6/kdEDJde0A6MXR//pe9GDwhcujNjK+ZxLLUVPB96uy9s/4SvxUQNLziOv KLlvSU0L7iXRV6e/Tq+QHI9cj3x2Z8T9y8PP4s/vTd3bm5xa/7BR+oHYK3ke v7tGDCMvZERfgSlWu59Mv2Z+rCO7wTm/byzajx4Mf7tcNA0SzFW50JEay3tK zrUF3yu26h5fqrNdkRcsyPg7Bb6HviW6ejR64OjbPVH3TYka80TEXGDhA3x+ Oepo6mlqC2IAfufT6Ff/JeL/2XWOUxfU+lziKX1JehD0SOj70vehz1QuuR4r lQy7KOCXp57fkHf+Ra1DnjW5aDlP11lD847XS1LXisTzj4ruW/6t1jbXUDTt g8SbZYJ7jH5B6vqiNmyEOMM6uGqjF8U6eSixiji1LnqD47T3x2XzdmHZNC8M 3p5ocCz7UGOu2t9W6JtPbTbtP03tQ4jd6OeFqd//qNF1B3Ud9Tg6ODB6zcCe Ez4HOODvLDrOPx6xnv7H7LA7ckN6HORTxL2F4Q+hBb9HfKF3sz36FeR0xDDi F/1Z+rQPtLYdEZuJy0Nau6dJP5O6gTgETnohz8W3BHL/LTG/rWz+jynbTsiT yev4tkMPjZ5PqWgeXJb39xviyAPSqY4Fw6+UvDoVvG+FxhlBG/EXf4pfpZ6l t7wm1uEZNo+9o9fkG+Qkg0Km6Dyy5hsHPmV4wfDDCt7TP/QcPSI/nBn1eI/A Q++Q3hb8gy/IGrkTL+hT4K/W5UwX3xPovRWj/wbt7wSda2IOv7GV7tV/7/9h r+Qzp0TTF/T+POnSCzl/e+EbIHlgU8DgR4FZEN9lsHlwTEntn/HTS6PHTZ01 suBakR4qMqYPTg/87UrHLVQW30uPe0jwG74Dmw29AP72kuvQ34i2manjyIzU cruttWXWudrf/rDloSXX7//Z4JzodM+upWs46OZs6hjORMfIUYjV5HjcFf5w r67V1iH050jGv0/3DzM+j7Pal2yzv2rwPmhjL7wpBY+RRzH8LXEU2shDuuTd Z6Pf9p7wLJEOHy6590HfI1/w9xm+05CLUM/wTepMH5yxg2A2Nlsf6GFT75Cz km/xjY0a56qi1xpjnbqOWP6LBvd3yJPonZ8QrgbR3bbBtkZ8JPeiF4md9Ynv HsgM3kHLtqCHeoRahF7MH1Pza0XevedesX4g43V42LvO3znYszzv/uw7qXuO 8OSmgvNp8mryt4fEl87izxyN1aInr72ZRvsZ5vgp+g25mFMDUQtBP72UMbXu eVA3U49hc9tLXt9Wco1NrvQatU7qPsYHEX/pH5FXIj/iJrbxcNREtzbap+Bb HtHa++TAgv8fre3J2/9NLLoWonY408fjLovDpoHHrvn2wzfOycEr8k5yTmrZ jdErpnakhiRnIg50DJ8wO+e8mG/j5CZVYTt8M4bmpXwPKnj9ZN4+5mh8j+5W Mp/3NnjtWKxju9gjPoRc7WjEbvroY2stR+o0aln0GbqYI+uDDY4jd+acj6NX 3IXvx3xH/iB1HXMqanx6u/Rjv536uzJ+/CsF+wr+RwKbHRkw9IMPRrzHBukF YBvrGpyX4CuIp/CSfJe+Fg/1Grz9Wt45ET0z4gPfyNE/fAlxDH9CXQf897Pe S95IbkR8IyYRj9An8uPPoi9GLUQ/Y0XEkS8W3PdN4xsZ9z2c8VnIkv4//ge/ RS+Au7ye+tvfntQ9PHp51HKM1Cr4VXh/IOyIOg1ZfUfwpYJxNhUcf6ZFjUAu OCn6ftwRP8M9Nytedpcdbipb9+jJk1Pyjev8On9PIPdBn9AleI/ucpeLmlwX 1Em+q8qGf1LjyrJzNnK3EQXvvaVgfwZe8nlyyjuitoVu6G/MuydIb3Bj2X0A fAT+AX9AzcNdyImwZ/SQb3/Qz56z865nXte5GwuOn5fnXPO9EjKCx8ui5zI3 +gP0Cb4a+pBpdp1Mzko/7H9l27fR98v6fxSI42s0lgv+Zsa3ux/E/8bwPzK5 gmNktmCb3Ro9KL5j0OPAR8MbfPrpbzRtjYc4ToynRsHPU/vg+/B7yIn8Cj++ L7X+/LfGvjnrQk3RvXlyOXpQ1NzQTC45LnQGPK0id6OXxv9E0LtEvnfHCD33 6w7t9e4bOdPCXYj1Zxfs63+Scy/zQPgl6EIGfN/i6RZxnz2liFkzm33mn1PH AnQbP8v5nMU9qVPpiRyIuvd0vpWaN+QG8Of/ATc24lM= "]], PolygonBox[CompressedData[" 1:eJwtmHu41WMWx986tc85nXPq/Pb+7d8+ex8k84jo4hkpRakeSqY0olIhCTPy KLoQUSoNdU5DoxjdS+VWKYMuMxk1LhMljGFCU7pfkFzmEcPM59u3P95nr/Wu 9a613vVb77rsJjcM7z2sbgihb50Q6vFbSEI4qSSEl9nMAf82F8LE0hAeLw6h NhtC18oQ7gP/Gfwr8C2sHkUhDGTvYni7sa4H3ga9cRTCkuoQBjUMoRHCFwM3 jMy7sUEI7atC2IWOQ+hbhb5VGfigXwO9XqMQpsJ/KfIGg69KYUMcQk21ad9y ZgrwtZwfU9883eG9nHUj8CH434V/BPQH6nuvF7Q7sX8kZ0ex7oT2JesY8NXw L0P388gsg+dp8BXASwshtKgIoSky7sfe8+FvBf/MshB2g5/FmR26K/w7Mr6z 7v4Z+Bvof4697fjnVnxbg9ynOP8k559lbynw54nvUoF/DgMvRGdv/LUM+gLg L/HvFHzzEzKPAL8Ez0vwP8HedHS9mQ9hHLrKOb8sHUIanadzdgb0WuiPQx8D /Rn2BkPflPe30J3fAh6LvNHAY+C/B/jerGH5aHRiH8lXL+K/Ydg/Bnxn8Jm7 gR9kTUb2ZH0P4IcSw63hPxl/TwOfCj4NnqnAPZAxGdph/Dmh0jIl+wXutwj4 ClYN+BzwB4FvQEYN53FJGAw8iL2u0LuxWoI3Z10M3IFvtJpv1Ra8F/iLyJ+C 79fgw5gzO/DB6mqf0dlx0K9l/wd8cjb4E+iYj31pzrQEPwj+WeI76C6Nkf8o 8puBdy61jbK1gL9mILuW++WBL2ftBO4E/Ux4N4CfBN5HNoJfzfe5JWWbZNss dMxA1yzFILb0hH9IsWOmB/AgvtEk+AeBt4D+Pvwfw3sEfCvwWcj7Evh2/NMM uAk62qFrNPfrgo6+lbZFNsv2pqyOwMuhT+Kul0A/v9RndPYidL5a5G80FLwN OlqgbxMx8QK0EayWxE8aein0rvhkTznvmb0f9faRN6fUOSTP2Y7s9dN7V/xC 28d9ZsE/AP49wPv0jckFs7jjfdj/qvTX894xzr6OjL8haws2dYd2AP6boc2G f1xsndKtvf8Cj2c9o/uhbxX8TdFxd8pv4mfew0j2WumtwDMB3kPyEfAuAuwB vkdJzncLnNmIf4rBo1L74DxseZf7/ppY6In/5vH+53A+hp5llcP7Ad+oGngi +m/Pek80xaRi82ClY+E32PsT+ORq61ZOmFRtH8vXklkG/dZK2yp7ZgO/iU2X Yf8nbC0GD5xpxt2ak4/qAD+cOBb1Zh4B3o49X3F2FOsm/LURH2xFfgr7DoH/ B3opcDPoR5G9WN8AeCn21YF3H/TvwTuz8tC+Sfvs7eDXx85BykXi+RR8MD75 jrPl+GcU+hch84G61vEF9D9k/bbLsW868FcZ6+7M3kXKj+BfcHaj8g+6GoJn oF0Fz3mxc5py28nsHQM/I2O4PfQIfAtrP/BI1o3AbbHnFew5rBjBntfZ25ny G/wLstpw/nTODwUfAG1g7Lf2Fjr+Cn0q61HB7BVxfhn0N1KWWQ5tJfgm8P6s 5ZFrmGqZbD4KbTnrTWg/cn429/kA/MgJ+zbpW4AfBO7O9+sZuWapdsmHzSLb IFtkYx38tRYbuqT8Rocj7/nYuawEnhXAnYmZ/sTLPuQNgbcCnnPhreRMK97C EeRF4NXKSdBPgf4r4DT0vtAf5ht8g7z+vNHl4MszxpVz3oN/Sca5+xn4p8WO ScWm3ojeyv+Q37TINfl4bYa+q75z5OjYOV/nX1aPAX1OxnTViFrVFtZ34GpG 5quesToW2cYbobUEv77INV21fR5rL/B89vrqrui4El17kTdPsRv5ro3Ya8J9 zqhybO9GflPgC7C5N7avx19XIesI9pwJ70zO9+H8+sjvUTnvXOgvZ12r1AO8 BHxH1m+9Ehlvw/8654tSzgHKBZ9yZk+Je5yF4B3g71PsGj4MvAH670g5J21O +w3oLcgn8t121Qnw6fCM4G4rs85t0indb0V+24opxdYC8lOOu2S583zgi5H5 KLRfyCcZx5xi7yTsa5jzGxFei7yR6BoU+23rDekt3cWambI9+lbzwNeAd1U/ Ftkm2XYZPv200j2aerXxyt+VztHK1bOwpze2PKY7gJ+Cje05P77K8OnIHwe8 CJsLZY6hhcAVVZb1If4ZhC9TOftaOUy57IByMPzbwPcBP8s37kfs1lX/g74k 72+rmFfsr8g7tlUz30lcc1R7hqBjP/aOzdoXyrH3APdG37PQN8B/BXB3zh9G 3iF9L/XDeb81xahiNYXMkhLnjGnQ89A3lrmmqbY1ytn3HcDHqHdA/4oT/jxF 74M1F7gF697Ib0xvrR1rCvilnLmu2DmgAO22xL2fevDG6B/IHW4r9pvVW74L /oeKHWOD4Z9S5VrxPf1+J+Q9pZqQMr/eei/4b4a/J3tnQitLnKuHc+YVzt4f u7c4yvnx6sezng0U04pt1XjVevns99B/AN8Evg58PnhddG4udo1RrVGNVf5Q TlZuFo941SPsxXetY+d+3Ul3q4yd69VTqLeQz+Q79UxXVjomhatneA3b+8fO 7ZpJNJtUxa5l8rl8Pxb+5in7XL7/IedY2IBP+3H+j9C7pRzzin3NCJoVlPOV +7/N+LxmIs1Gk/gGa1M+0xj6hbH51ROpN1oY2bfyuXx/fGZIOYb3498Rkd+q 3pze3nr2hpU5h/8d/+7Hvj9h2zb805b30Robz6nnHPWK6lnBtG18o0cyznHK dT+xdzO0o1nPbpopNFvsx8ezy/3N9e0P5PyWpUO6VnCmFbJa0/OsBF6jHhu8 E/g64JsKlq076+7q6dTbSeYB4LlVrm09lD+An06sWzPOdfQrmzl/SZlz+tvA T8JzBXhGPNynJOte9WvirVj1r2DdiiHF0hbwXhXukXeDfwjeR/WLvX8VrFO6 xfMO+PMF30UxqdhMsGFRhXtg9cJx1rNDuWo48BeJZ2HNsEPgbcJaV+EZRLPI qeDVJd47DbhR7LeqGVKzpGJIsZTmfkcjzyiaVVSjO+Xck6g3uQh6jP+ehN68 wj3r+/BvT1wL96NjB/C/E8OKKcWWeiT1Snp/F2Lv7MS18HH25ibOQcpFa2Wj 4jnv2XKpZkjgBWnPOqpRqlV/Zq0r8YygWUE1ULVQM8oTiWvq3hP8n4C/zTqI vH+oRwfenBjWTKbZbDj3qct9PucO5/MtstWeZeXzHPCCxLOPekT1inMS2643 pbc1k/OnVbgGqhbOAM/X995jBc9kms1Uw9vnXENUS+TDxdDmYu/QcvcUK5HX L+1Z7/jMlbgHVy+uGUCzQMese73V0NdAvybx7KwZ7ljknkW9i3J8fdES+1L2 KpesTnxWPY56pYmVJ3xfYt/mTsSTYqYK+KOCY1X/Iei/hDLW+gbuMdVr5rJ+ C4rpKuAlsWub3uBS5eesc7neaDnwSPWPdeyjUepnNbMVe6bVbNsc/NJiz7ya fV+LPRtrZtLsNDnn2qQe5nc5f2N9a/lQvqyN/d+I/hPSf0MdYve+0jEg65lH vYlqjmrPw2nPqurJ3kXWwJz/G1HOGgC8G5s03ug/hD3A72Xcq2mmvKTgmFZs q+ap9r2fcS+r/2SWQP8IPJvyTLYV/IOMe1nF3DDwXcTDDPLJUOT/k/PDcp4l NWMMB/4u41p73CfQ26bNK5m38D5OjT1ba6ZvolqT9n8Z6hFruE/T2P/t6A2c EfuNKLbOwf6r0u5Z1LtoRisCz7G6FLmHuAz51Yn/+1FMPBf5Pyb919QE+gTg Et7IkCLnxFLgCyLT1EOpl6oA39DAM4ZmDf2npv/W9B+a/ktTj6JeRTHVpso1 XLVcNUW1RTOsZtke4POB12LTTeX+T+njyP9x6b8u+VC+7BZ51mgDPh388shn NcNolukROXcqp8+GXi9x76QcVBM5BygXyOZ2wO0i53b1fPdV2WfynXr6hXyb jQXrks82AD+Xd65Uj7cM+NvIva56rAL4mrxnLd1hXd4zqGZR+fCXyJ6Ydy4+ Gfok4FrW2eX+j2yr7Ivc26iHHos9NXn3ruKZlnePqV5zrnpA4K+z/i9PNe0e zh6sMm0nOmdh/4S8delNdkH//wHUwfE5 "]]}]}, {RGBColor[0.5737613661287864, 0.5435370519035628, 0.9097253540741593], EdgeForm[None], GraphicsGroupBox[{PolygonBox[CompressedData[" 1:eJxFmnn811P2xz/fpe/323f97CsqTMn6s0xlaRRhfil+lrGXLRSjUPZ9RIgi S99iEJWUSvN7GH5omRgilW1kTChmkgapfk2S5fd6ep3fY/74PO793Pe95557 7rnnvM65t8s5I44fXp1IJGpq9VPZp5BIXKPyuXIi8VGHRGJVx0Rivv4v0/cd mhKJb1UfUZdIDK11+zr1maOBd4pIRWNnqr1TxuP+FmNn6Hv35kTin6rf1ZBI dFZ9nNoWtSQSK9Vnnto31ScSezWKRlUicZDqndRnomjvnfMc1yUTid2bTedw 9dkqOm9p7EMaO7jN31ZXu/1ttT8pGln97hM/E/SbXWM68Pmh+CtrDaenEomv 9H+lvg8XzSHZROJN1qryC9Fb1+xve9V6zazlyEoisUX/92WN+u2g9nNEZ33W fRar/rrkUNZaXkm673K1N6mcpvmn6rer6pfUmoc9RXO56teofjxyLyYSh4j/ XUXjSvW9Uu3d1P6o2o+W/B9RuSLltd5VSiTeUf1z1Z/QfnXQeg/QvLNF728F 199NWg7wgyzGqP0K9Z8tOlMlq4Wa635976G1Dmoz3ZfVtli/gZr3Ce3RItX7 qv5hB3+jnbELVD9Q9VGi+ank257z/kCHvaYcHDRXtUq+jV7LXeL5YK0rLZmd q/bZorNfrb+f1+j9ZT8ZC73hWsPbGndFPpE4Sf3PU/v94uUrjV+r8nHtwxXi 4Rvx8Kh46KkxvfQ7RnSWqu/Z+r2sb+u1zo6iv1W0XteYevGwi2Q4m7HiYZbm OVW8nSN+p0s+96RNc676NNa7D/t2ifq/q/bH1T4kaR3sJ15uVPt5+v6K6sep /Wy1d1fbHpLVLI19Rv/PVftklV20lgObzSvyQVY94oz0iHb4v73a/6n/IFpH qPxCNM6THN4VT19rvnb9VmgtSZUnqq0Xuir+RoSeoWNb1Xa36ifp95BkNlm/ xerzhfovqPGeDhbNTeK1tdZy66N5T9H3AWobrjaJJHGwymG1Pievap0viZ9O sZ9L1T6oynsyIubdRXJ+njmln806O+OlT40qf6vyIv2Gqe+fRWeJ1rlYfS+U nIbpN0nt44POjqLTO2c+x6pMa/1D6zzmU+3nRawXuajvvfrdV+UzgP3izCIf ZNOg9qqc+y8Uzy+lTIP5kQn8n1HlPeTMY4vmZz3XyVmf45s6+Cwfm7Xd+lH7 2J7xXBNV/kvfnhXNKRq7otZ2E5v5y6ztUGvG+8BZQubLJedl+vVutp5OabKu Tsi4z70q35T8N6o+RXSHsB/i42P1aWm0fcKGHaO2F+t8DjN5l5zL3dBJjTlG 627Je9zv0u776+i/rTHxs/FfLp4PUL9nVR+mPo/VeC/Zx71brYfoY0/q+u2u 7521F530e1+8P6M1PqfvT0uuczTXA5p3jOh1VdlBdGo72AZhM8eJ5oUaM0y/ c0X3EZ2lpaI3UvOeU+/6GeozQLpSp3G9JIcTNOdT0plxonFV0npxqMq/69vO ovmZytGS1W0ae5346CzandrstwaKzhC17y/6rXmfX87xaM1dLfr7sifi+xuN +5+S9/l60bwDGyi6f1D/oRo7W2t8UHJqSdnX7Rl60lG0bhHfl2m909R/gug8 p3lG15g3+MEPDgn7Bl+XNVo3/iG6W9Tnr+ia6Jwomndlff7QC84pukmd84X/ vKqjfejXGvuDxq5R//5J2+N67GrBdmxO0Wf9V0Gnu2S4T5vP1jrxe77ofKkx ZxXM61rx/5XaL+jo87JR9Kv1f61o/qfo91Lf7TnvE3v0ct589g1bMa3F5xd/ cWi0M+8CyWtP7c0B6j+m2XJH5rPUvqPaK3nvA+2c5Te0B1ep/rrKa9TnJ9He lLPPp/1bjTmoYJt0X9l2Fnt7UtJ+6SD1maY+W8X3Dhr7bdYyODT42TNwzpt5 n0HWwLmD3z7RZ7HWMiLwSYP2NCU6G0RnsPi4XPUzVe4neR6h8ceq/wWqD+ho m/ym2kri5R/VPtPUoU9ZjvqJedvWgwu2aZmk9Qn73TFpG/58xe2nZN3WGO3/ zNtWf6nyL+LjY/HzqYSxSrR6Sq9+VH3HJuv+5irbGOaFr0UF245v1eeyjHX9 Gs3TM+NzerTq54ruINHvq75LSvYJc9O2hengk328Xb9bNXcpZ9xWVvmrgu1o MmsfyVm7WmPXJ+2/8GOz1X6j5DpebaNypnG5yjlpz8Wccyteb5vonK36NtGZ rO9HFex3TlX7VH3vIJ5eE+9v6Neg+q4aOyr0E7vSJ2fd7KvyP/Q7WvV9VbZJ r9rBm0XbRGzjG1r3vWrvIzrPqj5W9XrJoVb1eeqTwkdo7Pna64vb7DMvKxlT ga2q2tyODpwlGZ+pXx/NcXaj/1Mf0mzbh91jr5pVXlRjW0g7Nuh31R4LXvqF 5j1Z348p2m/hv07X2j9qNc1MjXEn+LNOPPRJ2wbeWPIc0K3XvBfqf5P+/yVp zIodLpaNg8Gi3+v7MunLyEZjyjfDRmFzqjR3F847Zz9owudJGrNVY3bSnDun fQ4OU9+BDV4z632x0X361Hid8FClcon24dZGY+8V2rsW1lXy/KNiXuYfGfV1 Wu9o1XercXlLjH1TYxs19pSS/RLtYGloj44+X0suvVSu1tq7Z+0vd88aR93e Zr+Nneit8/t6rX/gcPzd9RXXr2UssYZ+P1Qbo9POd/aNfUUmv25yn6Oa3MYe 4DOWpo0rLkXf1XZb2MDv8saGE0V/m/biRMnuDLVtSFmOea2rk8YOU/3ivDEH Nqk96fM3Juik1Wew+hxZcqxxa7SDV+ABHdipYDwGLvtO/Ufiw8RPncaN1lr6 JLy3I6P/pJJtabvKYeBgdDZtO3d76Pl3jW4/J7AymBk83kt6sEjfRkjH0ppv ovqerrl2DLt9u9YxSuMubzZeYs5RUR+rvdhZc7wveV4R7cgWbDUy+iBf9mMH 1Z+UrObq2/Hai4c1xyzVH0/ZTp0XPhr7fUet8T92G11ljQ/VG1PgP4hrZ0ds S6zAGPo/nzMvS0Szt/hfqPY/qa1/vXESGOmOvPHHGJWT824/OW3MDeYD732s eacQD4QvfrCD/TTxH3gKva0rur4paZ3/PHR6jX6rG617xElD4uy/0Gzcz9q6 ZY0Pu6pMSDY/aewL2J6s17Y5/PnMkAl6jo4TX1zb5LFPERM3+Iwgx7Hau9X6 frPWdE+d8QxY4S3Ne3PQurvkmG1syecf2fZTmS5bhvjPh1XvKp/aXrbtIibE xmETqgJ/9is4rlmGbFuNc0ZKRu0dnA8YF3YN+9ZXa9mYcYzwL9VvEn+faexf c8Y8YHr8UG3WuAX8cl3KOYCb1XZYxXKuTRubg03BpTvVOyYHu4NnprY4Hnmx 3nxwftrKlnfHjL9NjrkW1jpWwu6BN2gHT9MGz+j9DgXvAdhqacRj4I1VWfsd /M9Vkv8V+o0EO2kf5+s3TnwcnzZ+vCHr3EB7jX0E8TBxPusA04Ptp1Ybf+xS Y5xZXXT9K8mhS9Z9OmetW8QQ2Mn/Lprmf2meHhXr7Uvqf23I7SnJ+LQm52s2 6neleNwM7gWvVzz2OI1NhL/Ab/yhaH82KuU4gnbiXPBIKTDJ5znLB9+1Let4 kjzBL1q8/xuqvP/IHztOLIisBka+hnj0zKxjLvaPvVuh/y3otuj8pmj8v3PK Z25i0Nla7//UNxS8x/PFywsl2+7nwSFF91uXc46KvePsYNsWNtqvka8itzUx 9KM9dHV7tX03NgpcD9/YE3w57ccWneMgNmBfyW+wl2AjYtaJEbcWGr2/fAPT sT5w3X0V52rGFxx3EX99rT3qW7EsumccPxGDEX+tyZsm9hi/APa7I3J05JvQ JfBIU2CSqytuvzjjmIBYA35mx1zEfDuW3U4s8nKzcxHE3Ng34gzitK86OofC /+l1zsWRkwOjkz8kbjmrzdid3BKxDmeDc/FK+GNsFHq0MM5XVv0zbY5fvy/Y Jm5XeXKt8x3Y3dl54+nWrO0gPoL8APgCHQNXjK6zbizGzpNjEp+/yhlvM5ZY 7o6Uy7uzXmt77EtT1vzvlfN68KfEU4/mXcfHQo/4Ed9aLDjWI+bDlqMf6MaD FedLf6/vn7TaT7Bu4jP6QxN8ghxofzpvmheITp+sc0rklsAvhwSGIb+1pNZn DXzPXl+asd2aH3o4u9n7MarWZw3dRW+3RBvzgmW6B555IGe7Mill281+sBfY uUlh6y7p6LiJXOeoyL1CB9wD/nkmZ//5SuwpOcAxEUdgU/YK/7utg/E2eoa/ xu/j/08kJyBeL5feDW9zTEjeFywHjgMbHFrx2rtmbG+QBXLAXxHDgAuwoZy5 dUnnV8D/5FimdzB+Ajt9IPor2xyXkqfs3eBcJbF+l1rjNvoTuxKT7Jx37nmq 1lguW08y4qG32o9WfZX2a2Kz9RM9BcszlpgXfp+o81l8R/Osr7EvHpD1XIs1 dkbJ7WMDa4N7wTDHRC5nVth74iBs/uVZnz/yJfg8bDi2nJwQcRl7iQ3DFmAH OLvIHduzJbAj9KG3InJZL6fND3yhE9gV7CwYbWTgtOqsMc6QtLHcwZEzvFxy GKf/10k+P6g+E/yg+ryccRo5VHwIetUt7h3wxeAi8o5Dg06fitu/0bqmNzv3 zfdX086vH6f5P2u2rYBWp7CZ0OkTtgHfsSjj/q9GjEUMwnm8IeeczaWS1WlZ 71GPjGNcbDw+49y018haT25yfvmL0C/k8/85TcqGguNG9Pmqgm0huVmwyxFZ txNfktegTk75g5Ll9k7a+7wl9gKMix2bId7mZzwXewJeJhYGp9a3+IxwHnbX ufhzizEWMTF6R26tCtzUwZgHn1AVNg1cD75nLHcO/ZuM7esih4d81pWdw12r 8rOUMeky1ZeXHf88XHTcdlnEkvCF/sMbOQZsLvZ2Vd7zPpZ2fM+60MNR9W6H twMjjiCeICdHP/o8lHZcRU4Ovwj2wW9yjmYExpgaZ5mzie9B7sg8kXWOaq7m 3ylredyvsrdkdUir/QH5D/LJ5ImgMT3ocEbxicxRKNjGbU87Z98j8vnEXtPi roc80p9azEvPwLvkW6uDJnYG3UWHh8aeTIi1g/FnBMabV+895C6EHA1y7Jyy ztbFvrA/tCPbUtZ6vjbtXDtYEpx2s+TYWXt0U9F4mfNI/oJcDPI7HXuYNR/z Ms4xEx+BD7/P+b7oVpUPZo1BDk6Z99lxPu8PeRbJz5Sdh7pBc11dsG2ZkLR9 oY5t555nUNz1wBdndbfQ05NCV9EjsCz2J523DbxT61qWt61cqvL8tH0xPnlc 1nh8Q9I+G99NXnZInNm6rPW6NmSFn8ef0i+VNfboljEee7/BMS15narI+bDn +E18GzoCP+hJQ+ABeCHmxn6io2CBx8IP4nvBgvhf9J17CzDJXTm3kwNGBp0j d5eIvAnxEflCcgL4XPzkxsCif88Zkw/TXgyuGEPNyBtHrImYE2wF3gJrzQns hT8A14JvT1P/aWXL/p2MzwZn5AbJcHTFuTtyeNgSbAo6Q+xMHgfdgJdNwQ+x CnEBcePHOeeaB6UcL1NHDg+k7G9fk24cUXYs+7bmvSRl23tDxIDgEjAJdxLg i6qs7y2nxlq6qU/XVusyGAo+5ocNIZeNTeMbfdD1+UXzvEDl02nnF14r+fvm OA/VsdfvRnwAlsWPvtri9dOHnDRngdiS3Ax9wR57iWaVaJ4pmvvnff83P+X7 bnARefCLtd4q/V+cNH3kTx5geNnxxHsZ36thb8h3np32feFE0bw65Zj9j0Xr N3pOznVW3hjz+qTvKtBncOLmWDt7Qdkt6pPKjqt6pJw7Jof8WdE5s3WRc/gs 2nvlfW+AHOATW0Q7Orln5K/IY4GxyTlgn9fEXcKQlG0qOJ2ztqJsf7Iy49wR 7dDZ3uj/yyNfvTzubZ+LHDi58NMKxpenF3yO0Wvkf1zBd4fHB35e3GbdH5Dz fdLAnPs+Hv05c8ifs3tbg/M210XOlbxEt7C91NFL8B84EJxGCS7cL2J6xhLX kxe5Lehwh7A87vWIOfEn3EEx9v2gQ+52SZPpEwvQvj3eI/D2AJsLnaUhB+JS 4tNOBe8NeSHwDDHlF5Gnui1y8vgUci2cHfYY+t0jb0nf9dH/k5zrZ6Zsc6DJ +V8Y+4gu0rYu2ok1GQ92QgbgduTAfhHHE08NT5sn7mxZ/7JoX563LZ1Tsu3G ZoJnyQMsizWiU9tDB7D50MTuE+uhG+gN96TIhJw+6+saccth0qtPNd9TSeM+ 6uRdJgW24A6B+8qVsXfklrAv2JZd8sZc3OcPzdlvgw1PD594gGxOzwbf++CX T8vZh/XOum1UtCfLzselyr7rxg5y1sjhjI/7bnw4NgQ/vrzBWBwcPrPF95vn RZ6JvSYvAQ4aFXbshKT9IZile9k5uN1VPhI5T3KfEyNGI1arlB2T71AO29Zi DEC8w10yMQ95X2JUbAf2aXFgFficF2f88XiDwVsM7Ar98ZU/lRxDJspe06ux Luwj//ERPRp850KOFB/YP+z/HhEzb8n4Hhf7+k3GfftH/3ncK7cZv6wM20nM wHsKYjDszuFlY/1+ZefJLoo1DspY3k3a91/GvTb321+q3xqt6XOVX5TdZ1Dg aGiC1/+35LhiS8nxMvlV/DY4DvmQf3+s4pzFlIp5w77DHzyDjZkDOTIGfRoT b0J4G8KdJ3EicTtvrcAO3ElOrTimnVbxOvEdrLFv3F2i8+gHekL81i/vmPTw vO/hqf+cU6izvqKTnBH0nDwaMTNxwb1JY/z6iBGwwdhosMa2nHUBf/Jjzj62 f9yz3V1vHP9pyvMcFvf83B8whvM2IPAJdJmLmB3avYM+74m4U2PcaynHBOTX iXOIdw4Rzd/kjXO4A9ncYHkhK+6p0Cd0g/t9sCXxBbbyocAhI2Iv2KNnc9bh P+Zs+38Kfz0y6TiSN18/pnzv1VV7XZP3Wu5MOTfcI2wsd7zkIFgLtpd2cqbc dU+L/PATcffN2rjDIcdBfgPe2C/2fnXJ8f8alatb7asWRnxMnIwsuM/Gn3BP wriZMZY5e8a8zEdeGp0m5iCvSrxDuU/kWCn5D1a4Pe6byLHAI7yC7RZFHZ7x UewTPvrKJvtN8Nm9Oct2Qtz9IQfiUOafFnTWhM8FI55dsG09R+X2lHlhQfgO 4mT8B/cM7zU4vwEOJ84kh0DbO9HOGyywBm8ZyKdwXnbV/kzP25c/mbfPRO/R xbaMc/hLCs4LQpe1gNdr2nxfTi6L+BQ/CPbvH/MOTLkkZ7w+8i+cybdT9j8b yAEX471gyjiTPDhYE3pHBZ2tacf1TxdMn3biso1FvwP4RuXwjONHcquPxLu+ t8rOx+MPsN1dMo6nPhSdjUnnXsjpcr/3UqPjBe5VueMBc32ZdNxL3ov7bO61 W1PeQ94PsY/4nwVxjzAzZV0+oeIYBX//YOB2bCj2E/9PO1iOOxUw9MiSc/as Bbk8mvJevV70vQK5p01Fyxz8DB9ghA8ihwa9RTEXeLkq7hApq6M/dw/tcZ9w UfCDjebNIv7kt5rz6LIx0oKkcTx4vilvPEfcjny4o4cP3hPslnG+iLzRrRHD JvLOdZA3/DrGDY6xzfFOizuQU/P2w9wh8/aQmJB4cEnO8xJPgJHAT9gKMA59 wA2cX94tYasPSjmeI24mFud+BzlfnXf9KpVDQ7fIB/Pe8ed3j0nnrjpFPDg4 5bNE7p/Yib19MOX7ObDGPinH9sSP08uWPzlWMA+5NvzK3nnHs+fH2l9O+o7h tYLPCnJjL7ZHXv1I0Xkv6fwFb0w4v9wXgh+4vyX+IfZBD9CHx1J++4WN/45c X9G2f23oJPaFXBT+ivMLb7zZxG4eE7rBvkBzZtL3B7MKxm6dk/8+951j7Pyk 37TwtoU1XRDrGpL3nnI3Tv4WOWAD2T/2CczE/SV5YXLCE+Mt3x5a79jIC8/K 2LZhq7FvlKujju3+JHL1vEPlvdxjedsfMCt4FdyajXsK8inkVT7NO/Z/Ic4X eg7OQS9Oibd33CGDI+a1/fs7dXAFOW9yhWDWLinnf3grxxzMjd3DN3aO9293 VLyvYyt+H701xmL/MsFnLuUcI2+c10Z+b73KfXTmV2Lf0sbkvF3D5yKTNXFX 0R53u5PLjh9XRg6HPCD28fcqb6m4/60V8wQd8jk3xFulG4uWI/SI7b5P2Z5w b3lP3vdr4/O+F8c3c3/LHq6MfYRe5+CNHC39wfbX5Oxbr438K3Ij74R9w34S 48yNd0d9s47LPoq76S4lx2jfipdzK5bbV3nf25EvPiLve3neoRLTES9+FDFj v7gHP1zlNXnflV6btz1bEDZtv4Jx2P4Fz78pcA62/YWw81PSjgFWxZuvzdHn 3cBL5ImJ7Yjx8DXkHtbEO4H3ox3e8BsvBs058baKN1bI4MW4x1wd7wzoz3ni HTzx6J0V2wPeTP0f5BWRSQ== "]], PolygonBox[CompressedData[" 1:eJwlmHm4zmUax39ne9/jOOd49/e856SQFJGZaSEyEbq6BjVyspYtigrFcaxN G2ULmbJWiBQ6WuZCU9GiC9lL5YpqLI0wyTZCqPl85/vH7/rdz3Ovz/Pcz708 tfsOuXNwdhAEw7KCIJf/k2VBsLc4CD4MB0GLdBCMiAfB/dWC4CjjfckgOJsI gkuAP4V4AvDjmSAYDLy/MAhuKQ2CvJIgqChAHl8ucP9YEKyFv3eNIFiA7GtS QbAM/rfQ1RB4diQI3gbeBc1c9L3M9y3wd3ng0N2N8RDGD/N1B16FDW/D/zk8 r8BbCc194A4x9z24q0vNW4VNtcFdjw0fYEvP/CDoh74rseHjIuu8Q/Yjczn8 V4O/FvzfkNkK3EfMPQtuOt864N3InIG8gYzHMQ5BnwP90ojX/iH66oJ/i/Xu AD+Y9S5GV3vsOQy+CBvWQFsXntdkPzSzkHU5+xcOYWtOEJyPBkE59u6Hdh9f J+BP4MnC3sPIb4z8SmyeiS01sXE4cIC855H3IfKmIK8XOjtxflOh/5jxIfhb c7b9kXeBcQp9HbC9PV85vPdBfx/0xdCvwPaT6LwcW/6NU3yOPa3guQfa0ahq CXwB/lLsfRj6M9AXQTMT+R8h/8YS65TuQ6y5HWuvjo6N2DeP7znOZ23EOO2J 9iYKz/gC02wEv4E1LWZ9HyBzPfAu5DUCfxL8D+JnXI3xEcb/Yvwz8ldj61ls PAZ8a8ZnLZvvZTyCbyjrqWQ8CrgEnZdg/9+xsQb7OUFzrG0c+InAVazpFPBJ vv8wboiOYdBWIeNq4BA2LQduCk8u8HOsJ8T5hPkqkT0j6bX2Af8MuHnMvQt/ PfAvlppGtO05rz+xltPYu4S9GAjNN8CTOY8cfGc2658EfBM2NAf3NWvcwF5P QWc54zXYMAhbGyBvB/zLmCtD3hh03oTsFnxXpP0J1p09A+8qFjsh2zSrgSci 40v5A3tyJfgV2PA148ewvwp4ZMp7N5JvbMp3Xndfe3AvvBOwMRt7dyK/DbrG M96HP7XF/nHAU6HZlW+Z2+BfhL0/or9mdc5ZZ8G3DvtnI7MU+HTcez0JmrGs txMyVmZ7j8uBb0be08iqg70/QLsx7bMsxSc+A/40YjiBPSex9SxzfQrs83eh f3faseE65vYA70FeHfZiMvSjObsxScNbFBORtY89mIV9XeCZD7ycuePQF0Dz M/KPIvNMvu/0IninMZ7C+DJ42iL7jbTviniqgB+Cvwp4PPQVokfGHPz9bvZr KvDOiG3LA3+Y8VJ0PpfrGDCI/emLzFeQv5BvCbpK2ZNl4PpDfxXyl6a8d6+C XwYcUvxkrxohYzO8ATKqsK2CcRrcE8xtgbYv/O2xZxzjbYxvU0xCfxvoD0D/ JDLfwV+ijN9jPFH3B7gH9hVB24UzuwLeMbpz+b5zunuv8y3Od4wah/wtKceS L5jbqruH/YuLHHMD9P0VmwayPw/wdQT+FZ4X4G0J+hzwKXQMyPWajkDfGJr7 ix3DFMv24DNN8n3Hv8U/jrOf26C/FBm/Qr8D/A35jmG7447Zit1aY2vWc13C vj+DueuBJ0Ud62XDBPRHEj6rzeCjwMfjjsVDGJ8AXsEe9WWvzkD/JvAG9qcS 2ceZ242sXgn7ltbQG/gW9uA8uBboaAPcOmVYPiJf+Rz+8fDXhX4A+g/Ckx12 TFRsHFLq2NuS/R4MXAHPs+DzWN9i4Mvijg3iuQT6HdC04+71hX478EvYeBvj ImzogH17kH8mZJ/fm/Ed0l1Ko2Md8t5IOn83xMa10N+Z9lnJh+XLa6LOpaIR bYy55iHXBKoNdGd0d47Cswx5FVH71mzs+Qf4HmnneuUA5YJB4E+F7VOjlb/j 3nvJPAd+YNS5oTtrWKZcr/Xneg1aSy/WkMVaLqLjALTXph175TPynXoJx75+ yCiHtyXfMHSPhKYP+G4J56Js5rrLHvSPAF4EzzMJ52Tl5jeR/xryv+F7FNzj 0AwFVyJ/4bw7Q5MBrgU+D1w99GWzP9sYjwhZ5mDodzIeHbLPXsX4B8bjQuZp wviRuGVvY24V8l4tc62hGHQv8FjlV/BLwE8Cfz7pXKX7dwH4nqhrpb/wNWb8 LvvTr9A5fB53ay/8TYHfB3+M9ddmz+cGjm91gHtFnXu7sN+vsd/9oq4NK/ju Qd4+dF4MuWZU7diF8QDGT4B/ANmz0HejfIv92g7vDObOYe92aFYrn8Zda+kM C4G/Ye50yDWear1jjKNh1wCqBU6i8/ewY6Ji44vI/zPyV+JP14P7OOlathz8 OfQtR8aJPMc8xb710FcU+o6+wfj2pGOpYqBiYa5ybI7PRGdTH/7t2JbPeBPw H6FvF3ZM+X9sidtXHgSflXQNp1pOPtgIWY9AM5a1P43MH5OukVUrb2U/fwNX Le7YqjXGuJ9bk86tA5VT4H89al87B/8cxnNjhuVz8r16yom5rik/k318pxkX MN4CvASZtdB1FTyvAhfHHet+weYT7G3HmO+ifFK+2YC520PO4crlLylnVnON rlp9c8qxXDWEaonHSpzbT+FDzeDfxFxD5Mc559X454a4Y5NyrnLvGWT0LnCO Vq6+lrl+OfZ5+X4v1j887Jiu2N4U/lCec8524PXoG4G+hezJC1HnTOVO1VT/ zThmK3aH4Z8M/peYY/VBZByFdmPUtVU75lqwlnxs2Mj4KflY2j4r31WNVZ/9 2oi+MehbCs38qGOGYod8anLEd1p3WzbWjjrn6e7JR0dHnYOUi+RDJ6CfiY1N Cn0n+ii3y0eRvZ89iGPPLPasbQ3XQMvATYbmSI5rAtUGM8A3ruGc0y3qGkR3 tSbrXc54QdS2iieGvHjctZbuaE3WMxb6qWHHWMXaZ1KuhVVjqdaaxh58Us09 kHqhKvRVw+4ZyJwI/cGYY1tL5duEY4pii+7EH4BXQR+LeE8/iDpGKFboTHsC z4z67L7HviJ0t4O2POwaI4J952OuVRQDStC/OmXdnaF5F7hVzHdJPah60TnI Wwx+J3PF4O9O+e43RV5vaMPYeCPxbAX4fOAuzC3IcY0/H9qz8G+sbp+Ub7ZN ubZRjt4PrqP2u9g19CjGl8qHoX8f+uvwjS/ivlu7mGus2ihmeBz4CPh1yNuc Y5+V725Kee3yKflW54h7B9V0qu268DUpdgwKge+QdK2mnNIJ2mMl7tUWwn8C +HiJ4T74RAfVD9gTz/GaamPPl3HXCrK5c8Y9h3oP9Rg7sXVrqXOlaoJtwN3R 36LYd/IuZHfNOJbcwbgO9Ddgz82FjrmKvbP5mhW651fvf3vUtYVqjvnwt2DP I7nu2aqiztnK3YopA6LOKcot8rmejPenHJuy4FmIvOZRxxbFsCegPaiaNccx 9WFwzdPubdXTNQNekPJd1xl3lT2s50rWc4A9fBxcVsK5QjWoatHKiN8W1jOu z7hrwrFLPixfrqH6JeI7oLugHky9mOZWlHnPtfddVbNi208p95Lq6SaC/xT+ /rmOiYqNR9jjh9A1EBsPA4+K2XfVw6uXn4+M4zmOCS8DF0fdG6rnUu91OOJe S282eruZnXTs0B2YpViQcq+gnuwp8OvZk+fR1R/+V9Q7MJ6W5xiiWNIq4bcA 5fyWwA+hrxJZDeC/A/xBxkGOfUK+0Zjx3izvqfa2bsa5UW8IF8CtS/ptQ3dG d+dH1rgvy2s+BFzA3LQiy5iMb65MOZas1Z1mL1qX2NcUAxULL0u7NlFPfSnw LtUf2b4juivVy7yXklkI3AP9s3Ltc92BOyZcS+sNSG9BTXX/wn5TWpl0z6Te ST48J+OYodihGK1YPSvit6va6sGxd3jSvdzT6uGBP4kZp5pnDbrqZZzb1fN+ ga1tmJsHfxSatsC/sb4mBd7TiyV+w9FbjmLUb0nXeKr1lMOT8A9NOjfpzUpv V3qj0luVctwKbH8r6V5PPvZ20jWVaiv12GXqb1Ku3fWmprc1+YB8QT42Hdtb Je27utO627eW+u1IMaQt8J+Tjr16A9NbWG7KuVU9m3q3RyPu1d6BfhH7cyDu Xn063yPY0ijp2Ksa9Rr12kmftd40pgDfHzNvU+irUo6Zip16I9JbUT++X4HP 8/VXr5V2rV4Le6emHIMVi/Xmp7e/Wgn3su/zLQDukXBtpjc2vbWNTPssVIOO Ai5LOXfqDUhvQYNSrr2GI395zDIkSzVRfb1Pxd1LFej8lctjhmWjbP0OmVng qzP3APgH+QoLfeZfsldfRYzTnHDDMo5NunNDgeeW+u3oGPdjTqljkmKTcvZw bPtMbzSFfnPU26PewPQWprlNGfukfFM9pHrJf2bci6rmfy/jNWltqoFVC+uN Q28dylEV4Erhbxb2m6PeHrun3NuqR+gGXJzwWSlnK3dvSLtXVIxRrFlY5rdf vQH0BF6edu+nmKPYsyDm2K47uk+2l/itVHf6TIl7GPUyerP4PeM3Fb2tyKZM 0meus9eby3S9vWTcWzRg7tmM35T1tqx+9GLKb656e9UdvDzlHk69nGrmn7B/ csa8ejO9M+aYp9inM9RZyufke4oZX6H/f042PS4= "]]}]}, {RGBColor[0.6334044084039064, 0.6393668249866483, 0.911085031734951], EdgeForm[None], GraphicsGroupBox[{PolygonBox[CompressedData[" 1:eJxFm3mYlcWxxr/Z9zPnzDdzzswZNxhccpWIILh7o4Ibm4i4LxERURY1rgTk Ek3UJ2pcUHHHKz5REXFhURQ0ap6E3agxCBqNwRA3QL0iINt9f/OWT/74nu7p U91dXV1d9VZ1T5fhl548rjRJkqvKk6RM5bL6JHlM9QPU+HZDkvTOJkk/1c9U +301STK1xm19on1/lT30dVX9741J0k31D1VWZk13ckmS7Gg0zXbaM0nymWi3 aK6OrOmZqyrrPkeo/axS1+n7twbPwfh3Z0wHDWV18DClKkkWie5X5earKto3 a779VN+kcp/U876b0zo1zlrVj9E4la1ae22SnNaeJHvru6AySYY0J8kwta9R +5lqe7stSSZojotbkmRUhcbSXLuqfKjo+oMqr5F8vq9Oko367smYN/jdV2PU iWabeFukMZfr9yV587SvaB7VGv/d4Pq+4ukJ8Veqtv1Tt+0X7SfpG6Jvjni+ qS5JRqrPqWWWKfI5VX3Gi4+syuWa56SQyUGiZ4O/1Vej3w5S+x1qf03817e7 Xqfy9UKSZMTreaqP09wfi36FeBmccf0LjXOX2u9U/Q7RHtDo+vdqf11j3aD1 DdXXW/1vVFkl2kVqvyB+y1a5/Vj1Ganvn/rWqe9loh8hmtNT/z1C7d1V5mut c+jbjRXuc5jaV6fu+xfxtkO8f6J6H7Xf3yR5a31P5rz/fUKfPky99g/U/o3k 8qhkt0FlTuv6Ru33qe8vRLNB9VU56y56+bzGOiPj9krVh5W5HV0dLd4+jXn/ LlkcrL8Hax0rxOvl4qlR9JeL57GqX6LyO+3LVar/n8rPim7/t8qv9bVJJvPF e0mVaX4r+mc15rcaa5bKq0Vzv+a5U3P/SXy/Lv15U/rzguoXai199fs86es0 5kzN8+kZy+I1/b1D472q8kXRPMQeapxsxnrHuu5X/7017wNF8wE/R4v2nBbr zQjJ5CHRPxiyWJL67Pwk9VpZM30W6u/tmmuqxp8hGY4T/WkVlv2Y0L3pJS5v Fx9FtT0ef/cseI2sdSpylDyP0foWxJivqMxo3Y36LhHdJvH5tHRjm87lgWrr EA+rE8sGurNUv1487VdjnWxU/z+IvlXjb9O6ykSzVeWeBZ+PCdimaP+N1tuQ 91z3qv6KZPGKeJ2rMW7XHn2vT92S+dgf8blQf/y7xe2Xi75Oc7youeopRVMq mvmiGaZ1zlF7tdrPEN1X4u2udp9pzvau+r2L5nhbNPuK5mN9Q9VWofJcyWC4 ZL213GdlRIPP1QmiP1PlR6J5QXNs1pjl7KPadxXtq5LTfar/WeM/ot/XFb1n 36hsFQ8z0XH9fmijaYr6+wmtZQHnWjK4WrQrK62TBdHP0O/Ha1/e0N6+rq9D axupNRyveffX+Cv026+kk8tUrtJvb2ic2ZLVN/r9JvFzZOLfoPlLq/UbPf9K 9Q7Je5dyz31FlflfWGq7iw6/kJjPQ/RdofZr1G+jflO35GzxWCz3Pi1ObRP2 Uvm/ojm30uMy5+Q6r7lNvLXWWi+fzNhmo8PzUo/zcpPl2l2/zxL9oxqna7V/ W6JvZ43P/2PttlN/0jo25T3PQvX9h/awt2jm6+9hGv+Uetv0p6Svw1S/pcnn hrmZFzs9PWNdvKTN9gS7wpnj7HG2Z2vd7zTYr8EvfTkz2Tib0LylMZ/KeGzO Ku2c8ZXqNy9jX/pXfS+ovj6xf8bH42cZ76kYc7va7ld9QIltP3XsP+UD0c4Y jEX/neL5UP3+W52VMunWHNnKXtKjQeL5SH0ni99/iGaHaD4QzQTxN0nj7pbY p7LHV6kcrzGv1Xe1ZPJUufd5lmjebfDawSh1ks0C0QxV+9x6n6kO0dViB1Wv 4dwVvEfztL7vNO8e4vdzzXtXhftAz/c8+5S4ZBzOKH6TccASifRhosoS7I/G fUntDUXPv1DfcNGPVfmHjM8GNu/SsHtbwgbiu2kbFzR71LoPfgcduyxjPWPd rH+IaH4Q77/MGJ9MyLiOLZiY8d/Ub1D564z1eVq56/uVGnuAO/qqfpfKKeyX 6ndm/Dd1aH8TfR8sd/vZ+PgG85Mr8R7AU4GzFvsCb9+Jt9mq/7d426r63Iz5 fbLa7c+Vea/QD3SjImN5IasutZYVMlgVOvmNxqmvtTzZW8aYE+Ow7rmxXmzI NcHDjzxhTzbWmB6+4P3y4B/8hSz6hUzuzVh/KO8J+TynPrdpT5cmLn8X9aO1 x+2qT26zn0Q2U9V+XKmxKZhnQurxtjXZV5xcb9/EWR+qb7zqnzVYvmcHnqUv 2BVsAC44Q3/3l56cpvIQdKlo+4CdWFywnVqksrXUeoh+Tol9ZNxD1D5M3yca c7nGW6bvJX2LNFdGtJdI52dnLeOLVO8lezVZ/crR5yrXu8V6LlY5QOUA8bBT 5avq936DbceE8KX4VM45tvzmsOcft7j+asH+8PtK470H2rxXQzTX7PBN+Khn srY9I5tt9zhvnLV29Rsn3i8qtT8BT2Pn8J/YXGjqU/N5Z2rcgP0GSzD/qFLz M002/nD1nSf9OV7lCfXGA7/TmANUv03lcVrjBrXNEi/HqP6p6t1Sz7W23vMN q7Udx4bvJZoVqu+p8n21L1d9utpXV7r9cdWX1jt2Ah/iZ36utoc17k/U51ns OrhLv78IRhRvz0gWG1SfqXK9dGCdvomN9sFXaowFiX08Ngd5Xi8536r2/5Ef vr3e+opdQ9+GhX9p0lyvYAO1lmzRZ7Q/sUGdbe4v9fXROIMl86NU3lRhjAef /YvG/8QBx9VbdsjtxKhju//V4N/Qk/n1ngtdaC8aW4IxVxR8zt8qOC5YFPgf rPBGveWyq+j/qHqauu2P0d6taDnicymXRP2jRv+NbHfRuL20jt3Vd1lqDH+A yp41br9A40zLOc4i3kKGS/XtxdnTOIc32vcsiXZiinNU9gFzqL1vavt9vMrW 1La1TeULOeML8CD6iW7iK49UeTi2T3xOEs0t2FfRnJuzTj7RalpiQ/p/UOs+ 0OP7N0b7lU2W9bQ2x1fEVrerHCNeDma/9Pf8Cp9zzvhJ6r+4wjHT8AqfZ84y bUtiXTubfHZPAeNFvEVshQ7O0/eO6t/iszXWH7PWY9YC1sJ/48d3pJbdkpgT u0UMie1ZFvwMLbVOvxhjol+D9B2deAzqt5XaTmOvse0vhb3SdiQ/azHtwKzj NGKG81osg4vDPn2U2nddGBgNPAZW7Jp3vYvKa8vMRwtxfpPPySrJ8/d1jvHx M9gA9Bw7kGQcN5OLQF+aI77ord/n6vcu+u2OJmM8sF6NZNVbZ/6mnLEOtnBi 1v1aqm3zm/KuPyqa6Vp3XvXLE8erxK0HtNhXIgN0BfyLb8NWlmGjsZ+ivT5n md2otscr3Q4+xI5uqrFc8SdgN+TTtcz7xB5hg6+PeBz8+0O1sfQzKntWetxS 1Z8vs00BW94T/nFxxNroCrSzy0y/m74TS4wDsY/g38GRn8E/Icc9yoyrBqq+ W8EYePeCZTkv5Ek5N/QEm4htPK3Ztp8cAfkBfD7YAr9/T85xHbEv8T7YFPwJ Vn0ysG4v7U/PWtvNcbXGI2CnAY32r4Uyx5Tg48foW+fcTh99J6fGOtubHNMQ e2DLKB+ucb1Hi2Wyf4vtNLE6dmZknDfOGjKmD3J+Nmv/gp85r854nnX/VP1P EE13lWuanSdYL5qeLdaHEtX7VJoG3cqn9vMFlaeUeN+/Trx21oL9ubLF53VM 1jEUMQ9xyBTOVpV1olQ0r7E/OesN43DeOMfkgTjLz2c99vvia6Ta2vRbL7Wf G3kA8gGDMtaBvRLnZLDv6Nj4gmV1bcG4DH3GF4/KGcPPaLX+oBe9Az9AsyHy PetrbJdYA2s5Jm+/hf/KiJ8f9J2nvl9nvQfI/ZycdXxIyAp/BAakDzHK/ZG3 Iu4ltn244JwBPvfHOan/KzGGQ/dHSc7rm932tOZK8z6/j2mu8TXuw2/EEvje d7M+l2CYgwIP/xB7hD6CL3/044tCT4hViSexYaXFsIcF6yW6jE7MyHoeeEEv 0c8dTfbf44MHfMEbYXsZ47uQJ3L9NuqLYo+Yd2bQk7c8Wjp8lL4R0s1Nzead XMjsnG0qOYDl4mEfreHdguNh+N1L8lgo+ve1hjfUdn6l65zhBc3W/ZdS5x2I 37sWnTtYGTTE8WAm9phYY7/IAYEN8P/ZiI+QBXKY1WRfS45hH9GeH7a6F/6z ylj9q8A1yOTq1Hq5SrxckxrffNLs/CoYHCy/sei8xvdF5/Tw+eT6iD1urTH2 vbbOmJ5cEtjnuMA/YzVeXvyPV7+u4m2L6E6Xf9lT9W2qn9VmnVofOkZMsDx8 SrbaGHQ4Op333/fjI8Tn5xr3uqz3lrUQF6+PveZcYKehwV/w+4SgGQteq7Id /lzrGad1fUFOUfNeWeM81W7EUyXOLzH/bcEDcThrWqB5bxVhOYmUvPO6PRqN XbAF6PJzWecnwOvk2k8vMx5h3Ef197Qq+zhwHr4Hm3B3jX+jvbLoszGn4Hzu vTXGHG9p7ds0zsFgLf3WXe1bJcuqouMd4m/iju6Rx5ucs78CCzMnMmQt6FjP wH7k1NCJGRpvRMGy25JaTlNCVvfGPcbkxOV9UScnxG/EFPj8qcHnrSE3dIPy 1pAh+afbIwc1tWD7200y7EZujBxZiW3PlZFbu7Bgf7qpxfcBnfmxEtsY5IWs 8C2sh7XA1+NV5q136Cnj0DY92skHdOYFyp2vx+YNaXJ8gP3BTm1vtc/boXIP 0e6ubwexJz691Dki/MDEamOoda2Oo9arfDnmxI7hB4i7iBfe1lyPieZb0Wxq NcbarLJfszFH32br7Ixq6+0JrY4LiA/m5GwrsBnI4+lq7+ttBdcnZY2XsZVg Y/Aj2AUcAiaZWe1+I8M/4iexZeg8sRx3TBWRczgi7xzxIs25Iuv7mPcKjvWx I9gQ8Do8XpezXYGefPIugZ/glbwefx/XbEwGL78S/V9bfR/znsqZjd4P9mJt g+Ml8Gt14MYqlQ8QJ6Nz4uWmZmOZsZyDvNu3qjwz4g7iD3DaE4HVyI2AHfFx hwXWA6eNbnbO48TUOZdC4ELu8W6ptJ3DL4EhOM/7ZpyTIw76vWhurfSZ/kWb Y9Qr2uxP8NP4kU9z1pdekkGp+pZk/nOXRlmu9hk5Yyzy99tUXyqZdNM4E+uM h8HI3K+Ql3xA9FtVX6LfOtoco78eeXv0b0K1cTz5iZsqjSvJo/bIOLeCTj0Z ejWo3fT759z2VMgHn3N++J1n8m6/SPN2yLZ0E59/KTg/Qp6kX2ocAfYCSwxp t714s8V5E/InL2SdsyB3MTNrHDow8DC4fkRgZu7LwElHlThfBDYnXubu6+ZK 4/SdoSfcaSLn+RFH4w/J63G+sBlgiAXh64mXDip1ruiliJvA0cQqbybGstTB 2Jx7+nL2uZcCf47Jua1XtPeJ+1nyUuQ+n4+8KFiePFNfjXlprfE09z3cm20K HZobOUl0ivuKXoG994k4gL7g3YODn8UZ84c9JGcMnilE7grsSbzWJWeMClb9 WbPP/lEqX67wnSu45eTI/YEtyR/iI+D35pz3rlb0SbNtRKvW/des5x/dZnt7 Y51tLv6NsRjninbH2OdJB36tcQ5U3wqNUd7semWz83SXBE4mD3pN5EWPjTN4 eOLYG33gTpCcBpiG2IFcETiKfNGSrDEO97TcoaCH4CRK6PmN/Ch2klwx/ogc ID6I+6u2uLNAH14OmXBu4R9/jM0m70VstjhrDEWcwd37/nH/vqzWGIv8M7H7 /MCQXaKdPhc0Oea8udnzwQc+kRwfubYNal9ZcI5+bezR/NBP8gSLI1cwJXJ7 +Nwxtb435W5mYda+lFwj/pR4nL+5e6Y+vsl7/nKM+YrmqlD7S1njC+pg/zez lg05DHwyGI57AeL8uhiTvDJ/Dw+/3xryBG+CDzlr3H/ODHnOzdlu9NeYl7Y7 tzIstVyps8/cv5GXA9evrnVOBz8OBuRvMM3Q1PEr93jo/prQ/6VxJw4mf6vW NhO8O6nRuUX0ZGCr6wN4L1Bruk5MXOX7P2I65uCOjfs1ytXBA5iUPuTF2M9l 0Zd4cUXEv7xpgJ+1uXhTUOJ3Bb2izpiMtyrWQk6I3BDj94784UNtvrcAvz+S eL5loVdghMcDJywPHvj9F+2OLc9KfQ5XBc+8Y6Cd+bkXnh5j0m958H9Do3UC fcCeLMrY3l3Zblw+PDDeqDrr3S2pMU1tu99fcPaxAfgZ8D3YHmyOf8I3vdXq +xDuRblrRf7ct4K5iBG4hyL2JY4lhh0Ydez/izm/Gzmp2THW/NAl8sDcO3Ku ezYaW/+YE6Q8L2d7iQ3rG7EMuVowPlie+By7g65OjnEmpsYX5PaOjVwlOUvk jeyQ2/Sosw8vtlom81TO1jcSnJTz2wHq+CniwQujTgmWAkcRtxO/X5zz/m8L Hegf987cP+Ov4K3TZ9W4L/mBZ9X356rPanVekDzIvIhdaAd7bGkxvvlB5Ya4 m+aO+pPITRPDfZU1RgIrnZ1aT9Aj8A85TWxve8H4/7/U962sMc2h7bY9S8L+ LMsaDx3SbnuOLefehPxy95AbeogOov/oG/kj8v+TUudTDmy3b8TvEKMwz+nR Z0TqHNZV7cYW2PS3C75XQJZPYY+L9vW5ovEAWAAfclH4EtZCTg/cwD0R/ha/ S67mxMDr3EOBu2nHF/PWg99uJqYr2BevLjjW6ryfK9gWHxbjI7PDAqsgD7Ah efVvss6xkGspFu3DdlH5ZYOxKNjklaz55o0POXnee5Gj596NeB458n6GWOWZ xDYTe4lP5I6cmOrPBeMf3oSBIfCNxDzYPfDeHtEHe3hdo88gOQP0j7wB91Hk HLuGv+2I87g0awwMFv5N3I3C9/mp7QN2gr1nze1q+3lqv4n/J17mvo994s6P +lFNzhf0j/iI9x3FesfUSyPHjh2emzXf3K1hb8Cv6NbNkbfd3OwYqnfEgNel vofuKfpTU/sU/Ay5Uuwb+ga+A+dh61pyvtvnjQ8xLTbi6YLjGM4jciFXhy52 2s96r6cQ57Et1gUGAgu9l/XZgH7X1HdO+HHs26ctruPbuWO8M+6FF8YdLj51 Tcbv58idc+Y63ytxjvKO3YjhprY49r9P5Rl5xxpnqvxnzncf/fLGAeABdINY jdwOvps3A6fUOmeLLZ0Z+OSRFse901rsAy8I+TMG2IIzvqEQ93ZZj0F9fuSq RwcNMQRvozgvF6W+Bx4mvi7LOZ5fq/F3tDgfQl7kxpzrO9X2UYvzS+SZyKvd GH58ct4xCLEIZxk7wHkeFW8beD/IezPearCX43P2VcQZlzX4PRh3wtsjBiRG xI4wzgVpvAGMd4zoI3qJHeBeaGxgP2JQ7Bd+lbhqTGCtP+cc5x6usQsF51OS ou9FuB/ZqHJQ6juw0ZpzcOr3RmPafRfFndQnbc5Rkmd8XuXoTLxZLPU7B95Z jo27FGIAcnS8X+RO8g3x/WWr7ze4E34n3oz0C+yAXcF3VMe7zbtDF/YJn3ha 6v3mnWBH3jkWci2se3zkobiz5b0Htn6e1ru3aLrnbUs4a9gIzhv1OVnb3Smh P9h06i/z1jLvvj/NW35jQ2d4G8r90cqcYx5iImzv4Gb7fd6NvttqjPFOqzFU Z34ssOhVgT/pd2n0Jabib+4piJUvjTgLO4G9wH6CO7bG2Xw73u4Qd2ADuZsn LuUNxcqor25wXEQdvI+dZE+wtdhcbCaxwI6oE4PuDDtMzqQq4vq/xTjgEsZf FTaXXAY5jQERf62OGIy8x8qwye8Fn8TJlO9FvIcf+Sp8CTrwbtCQW2VMcpFV wQeyWhA5HGwC9qY8Y/kkYX+QGzqILhLD72zw/RBYAb9AfN75LjTjOjhte9D8 aHOYC13lXHI+wa7sHTlTMMCDcWax/+hkRdBD25QxpuVMZqMvORJya+RaKXeL elOMz9lHDvjKXOAO6pwDyi8jJ35mxu847w7/fkbwgK/F5z4TeOD0aCfncFLc d1AOjvzDj/2xdYOi/YvIJeKnwba82yPH3qvgPBM5O3JNzAkf5GuY88OYl3v4 j8Lv83ab38APvPlkL/YN+o+iHcw8KObtknE8gWyJz4nTSyI//1nk6Cn5mz3F 5lHH3qEja0NPoPk83tZ8FXJDnp/HOPStb/QevBZ7sUfUt6fOHZKLJa/IfRj4 ipzi05FX7Bb5UfbuoNR1cmAbWywb8sqcGc4ReohPIT+Fvet8jxF5Ku67ydmR 40HvdsZ75fLQYfDWqMhHkIsgPugZ76rJT9BO/ggMzrtH7MAR8aYO/wjW5h4H vI2PPTLeNnTmnxqtAz0i7mAcxjg03pjgHwfF2zzaDos3oOjEIUHDWLyX4K6B krl5p3RYjANfR0Y7tOQa8SNgyv6Bn8EwzDUweObdOhgeX1UIOsYkBzkm8hnc w5IfITfCnerrUSfOIN4gTiNfQjs5loExVyGwOGN02uDGuOsq8e8DA8/D86Cg J945Ou7CyEORd+RuGvzVP/jHp8IvessbcNaHnMD48Akv3J9zj94v7OrMsLFg uhtiXni4IcYkvpwU+Jbvutjf6+Id0Kw4L+gxegsuo517AfL0EyJXvz5wMviZ N4b4YXJH5GhLQ8fWRM6WOrSTgh5bXxI2jfd674RN5hyPCbtKzigJe9uZ/4r3 Ffg3/C8+jncbnKGvWz0GcuA+FZ/QmRMv9xlkLdhE8D2+GTvd+S6y0XZjdNSJ acn74q+hOyL2Cj3piNifXArxP3Xyn+At/DqxDnlWfDp/s741mf/4jTUhh4GB M9mXkvgdOTDe4hifvM3iyDNsSn1Hz109cdnmuOflrRkYnlgHvUD/8dvsXdew d7zv7ghfAMaCZmBqjAXWOim1XLqE34GHjlgXb7uJtWfF2rvFOORfyMPwnmvP aO/kucpjwgP2uSP+H+eQsPfoFXhyc8icWJz7AM4vbVuinX3ZEriOsfcMm0hc xRnET2H/tocNZG+7hM87LrUO8mYc7I1s8LnkunplnIvCZh4QPu6UdrcPbbd+ ro8zAD/4V3wrb+rJ0ZP3w9/Sjl+Abl2cC+LIdaHb62Ic6siNPUCe6DV6ho7x VpTYFfkiM84hcsMPEftMDTwwJHwBNr1H+FPksjl4Yx09wv6jR8gE3PWHOI/k 4brG/nKW++adUzs6bxuFvcaG/z8VNQ9w "]], PolygonBox[CompressedData[" 1:eJwtmHeU1NUVx9/2yjKzMzsz+1uIUmyxAgqcxERAVBCUbkEEBFwXD6AoqEgR 1LhG7FhAUYgSlS5dBIKAiaACQeSo4IklipogC0TAAks+X7/7xzvz7tz7bnv3 3fJrNuSW3qOyQwijskLI5bdLIoSr80K4pSyE4VUh3F4ZQqw0hNE5IXxSEcJJ 4NsUhdCaVZUOYXF5CEcKQ7gTms3QXgt+Abz+xPnRnM8HjhXAH57D2D/GmScQ 2LpxCNPAP8t/Y4DfYU1n/wRreLZpnmR/SSqEP8J/T3EIPTIhpPnveRT9rlEI 7Th/Mf9tA7ed1Zl9AfgHci1Tsjeh8wb2I8FngR8LPB54KbaOYX9dHL2Q9SVr N/T10MwtCaEt9J+i637gWcBfgV8JfB02HgM+Brwd+tIohFXYfi8y3wS/CX98 kh/CcexdB/5k+K/HHyfjj27o2wl7Liy0TV+Cmx4LoRBfFrHqwXWB3174ZSN/ FbiR8OjO+ec4P47zZ0NzGmcLoN+ArDehmYasB5E5PBnCj+j7CvqVcv4t9OnG fzeA+4j1FvTH0H9+qW2W7SXg24J7lVUNfj78vsY3dfBfyPk+yGxTZh/Pg/4U 9NvG+d3Q/x36t+SzPPu0Bn/m8t+38C7gTCfOruO/z6Hvx/k6+OVB37jAOk9i /xL6dAf/KPJuBd+U/17Bl5XQNFGs8V8XcDPQqTX7DvCPoD8C/ht4dwSuAq5k 3QOvw6zH2J+Mz35gPwgeI5D1MPqOxNa52PdV8JlO2PI8NBeW+o6/x3fPwa8O /fuj/w3o/xE2jy22T4+Cn4TMx5B9mHUruL8lfd/y8Qrg+ei4r8g+nIeszfx3 Pve1DPo35B9k/gh+Oe8pJ/Kd6G7uIJ4PA8dZq9HnIs7H2F+EDicaOQb2wa8j cC66DQI+CDwInofQV494IPv+xEdv5J0JzbfAt8O/K/y6sF7E1g6czy7zHZ2e 8BvVW52Fz/sDX8G6Hn9NRacR2HYH+GllfnPV4A7hg5fwRV/kfx/zm9LbGsEK Gf8n3C3A2RnHqGJV72Et569Bh584fxD57wNXKQbg/T76Rez347++xbbhWmzZ CL9s+PUDPxJ/1IJfVeScdBu8huDfEdn2waecfyHtXPJ78ssz6D613Gf7sl6r dE5TbpsHz8ugv1HvK9s2yta7kVGCv89Eh23IvpfzvTnbGH0aoU8dPnkQfWtZ hZxdDs2T2FaLv2rAXQhNG2gf5cwHnH0N/Z5CViv494b/AN0JsfAzNEvK7SP5 6j3lFGivh2c75Ut0+A5drue/avR7ABn/4uxw4NuB5yL/K+A28KgHPgX+V8B/ I/DHDfHYHX3Ws/awH8paxdmB8G9f4DcxWPmVVYZ+Izhfw/m3y52LlJOOYUtP 8AOhbcuZ7gn7VL6VDjXwy9F5cEOguYr9Puw5xNle8Pwv+1/gdzbwWaw0d3MS 64Ii5wTlhvasXgXOoSXYOx5/tiOUXkaficg6A557sx3ziv2vsScL+tPx2VZo c8FPzrUO0uVU1hfZfjN6OyNZ43S30PQGdzPwaOD+6NsNeDf6tIHXDuQ3A74S nXcXO8efgu7/KLf/lOOU635OOZ6Vg89ivwv6Wmg3InMH+yHQjMyzj+XrAzG/ Vb05vb2J+GwqtK/z/o8mnEN3N/BfL32w7whwITqs5mwN/41F92c4fyfnO4If kO+aq9p7HfhqzlZjbwTve1gPw38J/A/j28vBx7Nss2zvl/BdKecp9w1NuDZX wXMP+xX44xfsO8AdLEH+2fDoWuAaoVrxHHd0GrKacmY6+8qEc8c+/msP/17o 9BnyeyJvAPAWfDIB/2xF31lx1zjFl+KxB/yWIO8wcB3yFiPvbs7MLHMOvg3e PZOm1RnFpnoG9Q41ynfwa1npt6MYeRf6OuTNLnGO2qHcjY7bi1zD+nL2IDyG 5rqHaZJ2jlSuHAz94oxjXLGuHHgl+76s8dBPgGaU7Es4thVTiq0WwDV5junf pB2zit1p8GgJ3D7hXqE5/3UHdzX2LA5+o+vYH8LmIVmuMfnIvwb6YXmukaqV hei/nLtaoZ4g4Teqt6oe7VLgnemGt6B8w35Q3L5Rjvqswj2DegfdySjov8b+ Prl+kxnoh8VdO2rh9wX0rRLOzeWcuZSzn0Lfosg17A8p12TVZsXcAPYt8P+7 JX5zWxLOacptA7HvEuzrCM8e0HfC5g4VjmHFcmfob5Kvkb8gzzE1A1sn4I/f BcfcOdC3Y/Vk3wZ+OfAbrXjLcowoVt4AXgevpti0mv1mbKpU76OcDfwh8Jxc 9yynRo5Zxe7pyJvJvizpWjsXnRdiXw36z2P/HTQvy1b0G9LIObBHwm9Mb01v /kPuayj8x+Ofq4BfAd4Lz5uhHQnNt5FjRrHTB537cH4t9tUjby/4tuBej/mt qSdYBq/POTOskWPgaugngZ+PPgv03tFvP/jGes/Q1LHPR//z8c1fWcPQ/RD/ PcTZseB/YP9NZF1uZe1j36rCva5qynnsD0bmVYmPDkSWIVmt4ReSfsN6yx2R 2bPcNUa1ZgNwc3yxl/XvYvfMx7E/K3Ju05vOZv8zMrILnZNqK9yjq1ffyuqU ccwqdj8GviLjHkC9wHuslpF7WPWyJ1BnHb7Yjw6D0fVt8E3BL4o5d6jmLFbt Z6Wgz0G/TeASCdNuVH8R2efyvWaMUs0a8FuIflcSY+ej35HIvZh8KF+qZ1fv rhrbTr1CxroqJhQbRfy3ptQ9W+PIM4pmFdUQ1ZKbyh1br+kOU/aZfCef9QPf qyG/qcao1vw28mylHHEG+495X4+Uuqfdqf4cHq8W2qc/oe//It+1aqJq45mR a5lmKs1WXfHB5CLHSC74y4GnAOdyvp7zC+RTZO2ExznYWsxaj+2PA5dWuQdW L6weZka5e0L1hr/2bDHfoe5SPV1z8EfhuSzH9z1Z/XHDfCD71IvIB/KF3pje Wl9oUvmumTdAW1Jl2eqB1AtlsSYgazf/tQK3JuFeUj23em/1nOo9lYN+gdef k34Lsvf+uHt+9f6aKbaj77Mxn1cNVy3fm3QvpZqg2lBf7l5hF/LORd5S6A8q XwOfV+UZSbPSBarRsiftWUkz1VH1uknj5GP5ujZuWGemJt2Dqxe/XHcE/U1p 04qmCHx9wrPBI/jv3rjfjHypHLAIfYfB8y/QtwBuAZyddG6cox5PtQ387CL3 CB3A3QX/F4rcc6r3fBL8zOD/xoG7gzWdfX/0WVzpHla9rHqqFdCOAf8U+BTy Esj7T+TeVDlOuU4zp2ZPzdxroH8+cm5VTVRt/CnhXkQ9zyTsuS9ufXVHuqvu DfOp7lOzc1bSuUdvUm9TM6FmwxhwWcozuWbzZsTjh8jrnHStkU/km+q04z0H +uPYuzrtWXoK+r2R9jcDfTtQz7s55p5FvYt6pPeAd8XMuz1nbsYfI+LeDyK+ FhJrO2OeLdUz7tE8Gzm3q2fKjzzzavZVTVdtl0zJXol/i8EXRd6rpqm2dQa+ scQz6cWRa7xqvWaKieznQLNW/QP2NFe8Vnj2vBN5g+H9Y9J3r575KPtx+Kys 2DO1ZutNnNlQ6BlQs2CnpHP9Gngujfubjb7daMZeBH4lOp8o8jcIfYtYi4z7 5DvgMuDihu8F6tnVuzeKjFPNUu3SDK5ZXDO6ZnXNPJp9NMO9GneMKdaUox8H 3yxl2xSzit0nYp711bOpd9MMpVlKOVC5UDVOsaMYUuy/HXOuV83fgq4z0ell dJvDelHfHmL+9qCZUrPlioRtj3QnmhWS7n1UU1RbjqTs7ybAR9m3TPmt6T/d RZeMZ8dm8OvK/lxsGg9+AmsKtBen/G1J33g6y7bIvUlL6FuwbxK5FukNVrHP RO5VNMNqltU3An0r0Az5Ttw5Q7lDPn8w7m9w+hYnmZNT7tHUq6knqlC+jblW Kga6RZ5JNJs8QIztzHgG1iw8DpqHsPeDjHGaeTX76huXvnUpBlaxPzVlX8n+ ZfC/LGnfqOdaDvwO5+8qcYw9HXfOVO5UTVmoWhN3LlNNOJ5wjVGt0UwzXr1c he9KOUi5SD2yemV9M2smf8Ajr9gzkWaj7Wn7Xj3QlphrgmqDcvSymGu4arl6 zgvYf5L07KyeV72vZnbN7qrZ5di7Lu1ZSzGuWP8n9kwp8TfM7ex3pf0tQD2k esnp8Jxd4Jyo3Bgiz7bqWU+oF0551tE3A3072JH23cfg927MPZR6qWpkLMs4 pyi36JuGvm1MjLn3VI1XrT9R4VqtGqFa8VLcuVgxrlhXTVJt+nVGSfgbpL5F ymdr0r5j3XUFZ8rhNzvuvWYyzWYz4s6ts+D3tGIr7m9r+salb11bM9ZdPtnG /pm4v4XoznX36nnV++qb17Xgvqh0bVOP+zn7Axn3/prJDup7QIVzs3K0ehPN SJqVauQD8GPi9o1y4lLgRRnPZuqx1WuvzJhW33THxa2zdNedPMs+r6EfFn/V Ar1JvU31NFPxz/8BBykpDw== "]], PolygonBox[{{4485, 2051, 5628, 5509, 5510}, {5883, 2312, 5885, 5546, 5547}, {5547, 5546, 5678, 2087, 4534}, {5510, 5509, 5632, 2052, 4486}}]}]}, {RGBColor[0.6930474506790265, 0.7351965980697337, 0.912444709395743], EdgeForm[None], GraphicsGroupBox[{PolygonBox[CompressedData[" 1:eJxNmwe0lcW1x889t3Hbuefc7/QrYjSiWLBhyTOJvqgUATWACQooYBDRKKJg wYcQDZZoIpjoMxo1EUGNhSaCoIBRINFYwPYiVtSX9dQkRopK0fx//Dcrb637 rZkz38x8M3t2+e+9535j9PhBF6RTqdSldalUrcq5Ko/vkkrV6cfQmlSqopcn qH10Syo1Ss8G1c9QOVzP7no/ROWp0T65MZXaXp9KPaj6iXpX1Nj/U/0v7a4f rvrjifssUTk45uym9mEx5zOqH9Tp9XzVkUptbPO7RSm/HxF9HiinUhM059hc KjVL/TubU6lnS1qTyq56zte7+9XnXJWj1OdpffNJPT/QM7vsNQ9V+74Z74H1 39qUSk1R/SbV16jfaj17aPx9artccyZ6pmhd3USbb6i9qPGztc8l+VTq2HbT 6Gj9Xq/6G3rmt3ofXdW/m/o3ay9Xqc9Wtd1V5/Jh9f99i9eQ1Xd7aQ+/VXlZ 1W2Do525z4r5v695C1rLzVrvlS1ec53az9T8r6j9S83bmXZ5n8Y+J1p/pN8H qDy+3WNuV//5mni75likbx6s8hA9ozWmWnJ9d/U/Vf321tjjNc8rotsC/a5q 38/r3Wa1H6ryObVP1ZwPqP2Fsvf4vMor9e6nmq9e5Zta2/o2zzVFv69Ue1rl cv1+tdl0WKzf20TzW7Suotawn9bwpXhgrn5vUX1+yWfMWT+nco6ev6r9PpXX 13sPo4OurIG5X9CcX8B7rEVt0/Xka0yDqUGHwXo3SuMvEB9dpLLUIF7Wmf1N e/ih3t9RSaV6NHk9gzT35XX+Tf097eldPfvoG300z2kaP07z1Nb6XDm7/mof pvZz1X5Nxuu4V+2j6lxnPWl1rNEzTe/HJl7Hpeqf1V7b9Zyn+Q9OfM6r1K9W bbM0tp/mmd3i+ul6N0nrukSPSJL6bofpc6fmWSnZHKs+M1U+qG/8Xs86vVun eV5Web/G9kBWanzWZ+csX8jZbzTZ06rfUTKdRoZcLK63XCFT20SDrXp61/gd stO/xs+qeusB1si6WecElRc2my/pi7z1Up+VKp/RM7nO41ZH+8jow9x3a+19 9NxX73Wzj6TGfZ6J/vDZ0NjLUvVt1LdO0+9NOtsvm3yGzPOE+h+VNn2h82D1 X9FqutN2Y5PbUzXWH8ui/+M6g01qfkOyv0J0uUHzfar6wpLnn5c1naEt8nyl 6lMy1qeHtXttJ8TaOPMGfWM/PT2azQ+HavwhMI+e57Pe34eaf1Wr+8zSq646 oyVqTxVSqTFp04u9PxnnMkRtaznfZtOJvS6P9V/Rbl1xZ51pvCrOC/qtjHmY Y3HMMz3a4Xl0Ofodfd5bPDlU7eeIx/ZCj6jtcq13ftn66mKtcVHZvPeoyrka t5vGP4ytCb14kOoP1lhnoa9O0pzDNecAlX/U85ba91a5p+b9Rtb2hHF9ajwW nr5M6+qHTkOPaV/deKeyZ9TnNbn+hPqvzbrtSunYs7BRetdd7Xe1eE3vq21N xuOXqd9x8a2e6vNC1uczWWNXZXxme+nb41Se0+y13qc+76vvJzqviU2en99j Or22tZLL3+scP2ry9/vpGYasqM9rouF9mu+DvMd9FGPhwWzw4eGN1jPQC52C bjkRmmk9G7EzNZZv+A/eQ+ehZ9Dd8OODIfsDNM+aNp/ZzKhDx6+6eDxje2m+ peAAPTeqrV57u0HlkTqDI9qtI3qprU7PSWmX9VE/Rk+t6g/UmBbs4/7oS/ts /V7Q7nnPq/e8ddHO+lhDN60nn7GtuRuc0mh6P6Xf72i9b+v5LGX78lbU10c7 svae+m/Qc4HOdWKb52VO9npPxnt/UGuYlDHPPdxunY4+X5ZxfXPK5Yaodxd9 9tQcK9VnpPTJHqqviP7vhz3g+6xvap3lmDbO+IM292EedBg6CHl8tN11dNri jPfDXra3WbeiV/87Yx37v2q/U/W7MsYfZ0n+x2uOMTnPzTem67tJxv3oA19Q hzcO1Hr+pj5zArvw/uLEa2T8d9T3u3ra1H5ryn3/rucgtX2q8p96DlX9s6gX avyefh2qX5Q2bgOz/SPG0g6Oox3cyDjGz65z26Zo/0nGthCdeLvO6td66kMf Yh85F8p01OnH7xP1rS4Z60vo/BPJZxa8VbXsQjd0RU3oVPpvDdrSfmnY/nMS t0F3ZBlcsU5nfD/YNW+9+yuN/2ve631J9RF6/3L0WZp3fVmHz+fOkEkw76sN xsac420Zn+n3a60/kWUw0ZuBkeCBtwMznaa1vSPZ/zBnOwx9ixo7Im29WQxd 907oq55Zn/FWsJ7GHITN0772S/vM78Keat5nW62L2xpt17GtXwQeeyhl+WZ+ dPJrzW7f9f61wGxjpa+Wag9fF9z2erRva/HvL4Jn4Bf0AHhkUug05PKrZvM8 egbbgF04S/3ntdqeo4/PrLe+P7/T9e8ntmNPBPboHvYNG4kdgp4nqM+QWutG 9nix6HlRyPjJejcPnZYzVkOX9k3M348EfXrGeqDD2LLXeUDOOnli2IunNd/E jL8/MM5ijtpHtNlusraJsd9SYBLOE9t/tegzqNYY9Vtd7A9AN7ArbdRbRP+K 5i/q6cxYD2O/sE2rM7Znt3Uaw/UGm+fMi98TD7bqOVL1it5vSdsfQp7vxRfQ uqaKP75uMH+Dmder/dvqc1Zifh8R/Lyk1v1/kTavdsY+h2htg/Wcr7bLNOY6 9dmgb05KjBVez1v/wG/onMcC2y8Cjyfmya2J7dG6Xfiz2ZgY+wpueanZev/s ktdRn/ce4CPwM+e2y9dZE7i0V+irg4LXr80Yc8N/vTX+TH23e6dt5bCM8f9V otshav8qeGa3OPtHc+7fR+P65a3rT8zb3h0WNmy45PxutV+dt71jbayR9/A9 v8Ge4DvwYg02XM9GFY/kfHac4fPhu/5Z5SmB1/8gmeqtNQ4Fx2meI9X+G+23 pHU+2sXnO0PvOlR/Sc8EzXmh9N0Iyc78ivXe6WnjFvDe5HbTq6y5f1tnHj+q 0/WK5iw1en54+ULRciw8DPZq/rfPCn4GO4MD0We0odPeTgeWCiyHbkIWTk67 jo6aUjX++UvJuHqg1jk6ZXr8rM402dHkOtj43YJxNfiathui/cWsbc75edtQ aIR9x89Bv+AXXt5uHIXuwh6AsXaEzocu2FD8eHQeuHd94AVkc4docbD29pOc fffTA/8drfUMFo36qHy/4PnBa3/O2r79OG+++12d9ciezZYxMDYyAUaHP5dp TJ3eLS1ZtuZ3sXztXjW+30PloEafMedbVjlc7dPAHlVjzldKxjFgNbDHx1nb k6u0hj9mfY7wy2uqH41tUP/RWkttk7HT2drjeH37kk7z0cIu1uM8C1S/CZ8o 51jPQ2VjHzDQT+ObX8d3nyiZv59U2b/J78CNJ7f6fMGrR6jfoPTOaup0fXeM vjtB331R5QvhU8EjJwfPXJ+x7IAhi3E+8FWPOJ+tnGmr+Rx/GF6n/ssOx3CG RfuSkuXt8ZJxDZgIbJOIhk8QQ1F5k771i4zb19QZ+/eL78HP6Plbwj/ku8Qe RrcamwxstN44DJ5vsb+P3/9rfa9e72/Nmg/gh2/qW3sktt23JD4/zvHVkmXs iKDPrBrbaWzQnKijP4eGf44/+1SHMeeBRftrI8NfrhftH+liXZnRntr0nIOd i/jOvlXbQPTjwZprYavjWcSyiAMh28yDnekVPtqo+C57Lwa2A9eh78Gc6GFi hsQL2cOHmnNjk2n5edF02KJys2hxtsZvz1tXojO3gKEazIcXpi1PE8IHXKAx 87SXc7XX3SuOz23OOUYDVhmSWB8Q30MnsD7OCP8dfkJngqsP6fQeaxJjbDAh uKFHxAA4u7EFxy7HF+z/rAlf8nx9t037+XHFvgXYDIyGTj809v7TnGX3eu3l uoztDZgW+cNeokuvy1mOb8hbF8Fr+HroXewNevi5rH+jjwdFG9gDugwMufgo fEn48umS/cE/lByfIk51auL4KGvGLteHr4VfhW97dKvx1AD97p9xLPHkqmOJ qzTPbR3mb+Kp8N5BwYfgd3iY33O7+Fx6g7erpgcxQmR/SqvlH/sH3sB3aG10 DBT8tiX4Ahr/M7A/dHw2axuzVmv4U9Z680Vsfsbf41tzgyfxQ7frHLe1G3s9 02Ff7JS89ejc0KWLsvafzsnb1xsQMYKpGcdi8MvwFzgP7AL6ADvEt35Rsg6c pjlmZKwj0B2TcrbL2OffiVfmqH6Pytv13KP6HQXrOeRgrxrT8vbwX0bpbI5V +xDN8UXIBXoC/4Y+1+JfaPyjmmdpwW13RDsx2+PaHec9O3j1JZ35/jn7t68X jE3uabJewD+d1WS6jMvaPh+jb54rOlQbbdd3C0yJv/DDxLGxd6uOM2CbB4eO RZ9id4ips1bOFF8Nnw1d8LOM9fV5gRWYe6e+r7Weh4//I+zmnVpzRWu7RfNc ljL+Qt8Rb1/UYR4a22l/Pwm/Few7N+IP3QMTghXBhdSJCRDfWhftN7YaYxAH 2tpiHLmr79roQ9x8pp5xGruw6LPmzKdqnTO0zmkq/9hqPsR2P9VqbAZPL488 AroFmtDO+T4ZsTUw/4vRZ0h8t3usE39hn2b7Hny/g1hoyvHQ2sCHxAjBQOi0 CwM34qt+oPaWjHn28Yzt2uJaj6uLscviNzHhxVnjqb5Vf5M14B+Rw+C7NSGj 7IEzHa/zHNdm3EeM5e4m+7or9K3lGY9pabSPDB4CN6C3x4h3rii5/+SS9RXt 6Kx+ZccY+5Yt99gF7Cu6GR39suZfnTX9NuV9LoVY28hO16eJL1Zm3XZK1eth D9i1+1vs8+L7zi15/key5tOucS7gpmGBnRKd8y/j3MEf8OdpifENOGdjxfTm PKAnMZBjwm86OTAstm9QYj8Uf3RZ1rGccVr/FWqfAc+r/b9Un0lOotPfJGfD /spaf4k4juY7VOtZoLbnkKmq41OfoY9Ltk9fVWy38dHwxcC25Vb7k6yL9REr ezviTODX4Yn16m6J/ZNjos/DWethcmt9IqYJBuY8JwfGRTdgy5HfeyvGcO9X bKt7NjhWBqY7KXD754nbp+SMMckbMv57ac+P3wEG6Zs2n78bsSV07sKs7TL2 eWRi/XFGYrvEeHKC5PuGRZ5xob7xTc3fvej5+oUN3R5YfVtimYLG8Ap0nR+0 3bvosfsUjflnRqyD3MOFIV9Phtwui+eF4M+bS8Z/M0vO8R3T7pzlO+xZc/6n 5hyg5zjVv9Np3rw8+HNop+v/kzMP3By8N02/a9T/84L5g/UsIMdVcPsNJX// xdAhrHFC8OWfW1xn7Q2RQ6F9ZM5YiJwnMkYsGgzzVsG+B345vjW4DdyyoeA6 vjtx/enhO88LuhEfn6U5S1pPvmg/aEbI/jUF+wfT9f52PTn1aS963LygOfHw c3etIdZDnI1yXLT/qcV13iMzzP+Q6LBf0TbsNtHhwYLjrNvUVlM0xk0XjUn+ EbgEfflVk/UdOoLzf0rz/EBzHi5+OrXT8eqnQ4bhnb7hD75cdnx8rcpbE/ch 3wW2AGOgt4mdE0Mgfv6p5r+YuGfVfgPYgLgJOIY+0Jb4G2MPjrZrYyxtW2NO ZPTaiEtg62gHc31QtZ3+MHyTGTE/+OOm8FOQKfA96+8bv6kvD8yPrGEPD4/8 E5hioZ43sBHx+43ws7Df+NL4W9dHnZg8dfyv6Xlj5MlZr/e62At+DTHEt8EM sR54fkXe/uXyxOcPXuoXPiA+ArjoV4ll6uagM3YRm7hJfa/W+epvZwyPPAex nW1l6/rtZeuw6Q3mR7AdGO/RrHPJYFEwKXrlmgbLOjjz04hRrww/idjFpIjj EYf8ecY5CPDZc+Ln9fBHh9tuiPZVHc477K35vyz7TsDWss8DPkAXvVF17G99 1WN/HriIXPPxgd/I6XPngLw++PzGoHNL1fb7KPH7KznndLeUrWvoj/8D/2wP viJfsT0wMJgXLADuXdnhuED/vG32zNCHnA/n9EnaOUvs25KsdQU+AbJ5QcFY EcwI/dBL0BB9hF76ouAcJDhio9b7VuLY4ts5YzswHjGSUsQswWzkOnu1h/+Y du4Of54+2ELyl9jCUvR/PudY0n4F04M4CL7T0oiZtFaNG4dHLgNMBDbCt+T7 9Af//CCxTaxV2Vw1XiIeAD/ODJ5sirFdYr4REasnL4TPQL/HStYtfy9b7tdF zBRf6syITyNb2HL0HvgVHHtIybgVOuDHgWkLGWNR5J3f6AHOdlTc82A9zbEX 4jTgMGQJn50cFbHr1sCB9GHtTVHnXJtCD5DfYE/YNfoyBnuxPB95qsTvW6LP Mxl/C7zXFPRgnjsDh0Nn/Enivdy9+VHkm8g7PZazfkZPI5focGSzmnHseuf5 tnse9luIvc+O+HYp+nTN2CfBH8FXZA5wCm1dAy9NCjrA/8WYH9oSr+gM/lkd fg1x8s6Yk3b6sibyEe/lPN8BHfarkE3iwMRRyDugI4j3X9Hm3MTy8GceCp/9 Zw32209tdxwc2oxpM56k34DIX5+S2O98pNW+J7GcWREPH1g2Ll8nPklX3adG 5YSqY1fc9SBmhH2rJo51osPOVL1vxrkT1oZOwUdjDDGH6xscd+gRdwL2LTgm wBo5O2IsxEaJ+YPNr4iYMA/xYWIvlJdHHbx5SeQw0K/kMPD38TtPjb1DA+oX BE3ei3zr4KAPsWtkCDv9w4iTEi8dmNhfBHdiH4j9HBHf3S1wbFeVOyq2Qdii zyrGg+TfhoZ+Qbcsypm34fHxgdmxxWAi+uBPEbPET6bfi2XnYF4qGyviN4Jf OA/O5eWS5RK8BxY9PPQY852Udyx1YD618+4F/LtbznSDnviM3Nc6NrDi4pzl GNvI/sAi4BDKowKX7MgZ2+1dcfz8iOizT9XYf2zWsTNkfVPWOJm1EcOhPDbq +Pysje89UHLM6P6S184esLtHxRqg94aKY7Rv5R1j3xE4ilgO9CKe80nkDtgX MpENuchXHS85iXsZqq8hppo4t0mOc6cdrXq+x7KOrxBn4TujIo5LnIH5Pw6e ezNnHTxLsnmgxq5T/wMiLs78YNgzwl8YnZh36IPf8VrOerClw3l8csnEq/HN 8NGWag0LWh33JOZJnTt68HNj1b+pI9/kDZHxXkEz1tpQtX6orxqHEmcFB3Pf b0HMiW06LvQJskmd+Ma8snmiW8V2DD8d+0bsnZj7jLCB6ER0G5iTvcLD3arW zeSf8b+Iz3BmyC9xCs5lSMgfsoYOAD+hB07J2h5uQt+IV08QvboXHLemjlwV Nf9yzdOS+HyWxxl9XXZc+9vEJMv2tclF49szN3xK3I57jeSHuLP4W833y6zv x5DHW1f2PRv8iXVZ+w/4m/iGTWX7R/hJH5cdo/xE5V4djt2cVnGO6U+tzjPt VXV9z6pjicSaiSs8WzbGukjt3+xw7vn0imNPnCF2GB2KLu1MbGObAguRc1wV fEXej5w4ce/Nefu73GnamrOvsmfFdoQzQuaPLtrur845z8pZtuU9B9gYjLyp kNoJaDcWjN96Rl6eOAvxFvTf5ugzNed35O7xBbg3cmjccyC2t3cXx/fIYSFf 5HvwuY+L+7qpuDOGf42vfUiMJd7PnNhNyp5RJ15AvAnduC3uO+y8I9HsGBPy CnY+LO7zEHcnN4+d/HFi+QYvkxdC3pA14gHYvINDL6Afkrz7smZiDqPaTB/o XRN7ZO3IK/Ow5hWJ8faqyHWwd3DGVRn3I15JHALfk1jE5qpxL/g3HXMxDzFU 4rTEUf+SGFuCMbGf2FFkBtpBQ3I3xOiRxWvVZ2DGsfKjA4eAGcAS5BwZiw4s Fh3TnKP+c1udEyKv0D/Gsl/0M7+xI8Rtid+Sc+hdsGwio9xFwAcaUHb8o3+M 7VH03vfFB6/43jL3l7cXreuJf6OrRgU9B4TeZWxDYr+xMTEOAA8M545Q1by3 oeqYL7RsU3lFh2M+xH7AccT7kFfu3O28e4ddTXw/slT1PgeGDuG+855xN+/D gn258TljZHIAYLr9E8scejoT8gfO/F2jY3PERm5q9Fh8QPIt3IMhN8tdO3Aq 8ro2fPZXy4590eeziDUtDz8U32d5+MLEZTjf14nVlX0PdaPKXNHfvSvnGCL3 KrhTgT9F7gGfaudd79g7bY9HO7LBb/ibOy/Egrizwf7pj4/zRGLeJV/XEhgc vM39aXzoYYn9bPxt7Bk5JHy4q3OWuV7/705dr7hvQJ6GuCjxUXKa3DnDtm2O O4XbKt7DvIjhvBd90Ae14RPVRWySez/EweAL5IKc542xHr5LnvOOyE/c3eh7 BtwxyCWOiVe4Y1V0+x1a8zU520PiQ7VxR4JcGfctuEfRN+9cMXdeiV0dFT5e c95YDAwNfgbDrwlfgPtdS0Pe2dfqwPbI97RoJ+eNLjwvcd4D/INsbI97fbv8 5UsyxuWU/EbeyCniW8/JGgNhV1dknQeAF0vhH8F/6CP4nDPm7it3YKEZOWL8 p2eiD5jijegDViLviK4g37sqeJh4/MTwidDB5KTAxrV589eRBedyuR8AzgCX TYk+6KvaiKX8KGQcWUf2B4b8I4usAb6lXN/+b8yzPv43ARpMDDrsKPg73LUB 78+IvXMnkfXvzJk2e49gkclxF4RYyojwn8E84B0wEJgnU/V5c0eA+yml4B/k A7lCRvCHhoWPOTEx9gMDcscMvwufi32yX8xEffxPwdDEmJH7Ffgy2Dj8ZPwK 4hHEJcZoT/8g580dxKr7Ur8+7o6d0eZ8DXifGBRn8lbV7W9W7cPjyx9e8l1U sDA2hlwFuYU1WX/z4vguvulFwW/oiSVhQzlLzrSpaoz7cfhfTXljP+7QERd9 P+wvcU/in/sXfVeGfBZ3h7mjsSDuaRDnQHb6hW+AjzAI3ol4BXEL8izcjSFX RVya3ElH3En5OPA8OnJF6Ez69on+sxtt18jT9Y12bCV5TvQ17bVV27y6qnM+ 3B8CG2KT0RfYZfQNvA3WqIT/zZmSA7wn5tmZjw3ehkdmNZpPyCF8b5cta7a8 oz/xs7nDBL7Dts8O+14o+q7MvTljVrBrqmJ/cEv4hPgxn4Svwb0zci7Es/gm Y6elfK+M/6XBJ/44+kOrzyJHs6Xq+/JjQmdCF2gH9sYOfCtsATrqqvBBji26 /bsqeyXGEsQhiFGsDl2Hr3FY+JjgXfoTj/sX0CPuzw== "]], PolygonBox[CompressedData[" 1:eJwtmHl0VOUZxm8g+wIzc2cyk0kBRUFFrYpyWqW1WoWwGBcUFxaBYIISFAUV AVlEoEoXsYrHYosKCqgtBAgECIJLXdk3wVqkgpye416ToEAg/T0++eM793vv u37bu51eMXbAPW2CILg3Iwgy+Y6IBsFofvwe4D+JIDi/OAhOY/4G/9aAmxQL gmsLg6CcsagkCFqS0OcHwV2MU8yvhL5nbhDsA+6fCoJNEYQiOwL8Nvha/o0q QF5OEDyAvP7xIOiE/E3IXw38dGkQ9GhvG4YDL2aUIO9l6F/AnlHA94FbAs9h 4MPYcF2hdRxiHjBeQ37btsjGlkrox0L/WBZ46I/xLzsvCIZlB0FvdDdgzyLo X2bsgTaZxhbkVcPzVBgE85HXE3gB+C+hXcx6DqFmIHAHcCXQvwn+buhHwr8e HbXYWo+OOaz3Jf6lsH8R//4GrjP/nszxHqxG3iBknGD+I/zb0Hdr1LJvYrwE fh778cv2XnMVuDJk3Ah/X2zozbwPPLexth78u5r5a/AMgfd2RjX0/8O+8UVB 8Dg0XzOvQ18F9pZyHs9yll3ZjzOxLxN71wN/As1Q6G+G/mPmW6GfAn1/8KvB nwt911zfh8Xof4p/Q8EPZtRA+2fgIYVecwLafuzxcPa6BXgNuCPYdBDdS7Hv BLaWxX0WOpPXY5YhWZIhWQvRsZi1PQd+Anv3DvufmWGd92LfQNZ8C/C0dpwz e7UeuBnbJ8FTBe+twDO5KzPA3wu+I/rrM2zDAPT3YexhPow1J8G9lfRd1R0o gvco9j2PfRey303Mb+LfCOQXIb8F+/bDMxH6JaznmPYeuJK9mQXNAfQPhn4l +h5D/zj0nwf+FPSH4N8P/lLGgByf6WXMj7K+OzMsMw95d8I/DlkF4JvRty9u 2QNY0+fAz8GzENw89ucBbC+HPgb/ePRVl/oN6i1+xOiX8tBcd+5M+NtzRtFc v2m97TLwu8HvZPRifhx5F7D+FxhDwf8W/UOZ72KsA76N8zqOrCbWvFX7xZnc X+Q33cS8lH8vMv+Wf5diTxk8PbFvJDwrkN8N+z9t4z07h3kXeM6Avhv6uzJP MDZgW2/2P878Fvbv1QK/8Rb4jyL/59k+oyHI3s4edIb3fXi2Ma9A5gT2py37 sxb8Lva3E/ovgmYK8kYjb2iB34zezjDkJcDPYf/GY+8p4IuQXY6Mi2UrPMNZ ywhGX3CnAb8jX4h9nZhPh2YutA/Kx6B/Z8y6/gDNPvZqCeMG5k+BHw++C/AW dH/B+jcj7xvsG4T+++Bpiz3jo96rCkZNyv+EE89JcBXwVHMf/oi992Pvm/Bn wJ+A5jrsmcGIYOtkRhW8x1rPcyr670D/D9ibwflPA64EjnMfksA7GLOxvTP2 fYCuA9j3PrwtrfuxkDEs5jVpbSvhnwn/Ee0pukZh02fMMxnLwU/kTFeC/y7u 2DKHkY+sxpR9mXxwE/Pr+TcE3gdZzxjW8x7ryWI9HVnPVvivgaYG+CzgcubT Y16r9lR7ez3yOyP7Teyti9qnyLdkMX4o9h3UXdQb2YLs3sjYle87r7t/Xuiz KET/1dAWAyc5z7HYVMU8pQFcCTyI+edxx6IKxnfoakRmFbLz4b8K/knAF7O+ XvCclbAPki+6DZpbQvsQ+ZInkfewfEXE89ns76i437ze/lz+TQY/Jer7rvc4 E/xJ8IXgh8kngO/MSOf4DD5n7/8eMe8s6Kvi9pHyldOw9+bQMUOx41ZoeofW Kd1TwK+C9+mI7/bz7N/kpH2wfLFiiGLJaOgfh/6v0L8PbTXwHNkHvBl4MPrK 2/nN6+2Xg68AvwgZVREPzRVTunE+PULHwjQ8fUtts2yfgLx/RPym9LZS6J+K /klJ26Y3rrdeCc3CPMecvvCezR5cCLyUNZzFvAb8MeBG8Pvj9pHylbpz16K7 Ango8HzsXRjzndXdFc8q5lnwrCW3+BM2zuQs8oHrgecqxwBuwYb1wE8Azwbu FXpvtUfaq1PYfE6e78RJ5kVp34Uc7l875nmK0fBnAOcz34G9HcE3cp49sW0t cCO8meDrsWcNcAPwKWTWAX+Vcq5yGP3LgJtTjrUXQL8f+HjKsSoE/ifwDaHv jnIc5TriEW8D8pYz7wD+Zdb/PfovQ38O8CzFHuAewJmh79JB4IsVf7GnCXsO AV8CXAv8PfAB4O7AyyKWfQR9ZVof9jyKPRvRuUX+POVcTD5QvrAf8gdn+U7p blWGfosdgT8N7SPkK+QTrgPeC/+sAsfcPXrPnOFxZHeAph+0Z3PHuuT6juiu nIDnwmz7vz7K7eLOFUcoZsE7GZ4F8DYg4/7Qa9LalFNMDR0jFSsVA8+AfkvS vko5YRT8Va3xcg9jA/jL4W+Gvzf2fYm+CezBbnT3QMfNpd4T7c1R7T/4gaXG yUctizrnVu6tnHcy8nagbwG2fAB8BrxtWF9Wru/f49zF8/i3q9A+Qb6hc9q0 e7FnI/wPIf9Z5H8C/RjwtcibhLwaaLKhLcL+X0Bbi8wx0K9A/nLmKxjVMd8B 3QWdeX3Ed0R3RTF0O3CfmGXLB8gXZKUteyQ0B5lPZOQz78L6PpQviHmv9Qb1 FgNsWqX4wsgRL/Y9hH3LgNsALwX+gr16Fbgp5hpEtYhi1nH2KyNt2mtYf/eE cwLlBsr5+qZ8xjrr9/h3uvKN0HPF9I7As+OOdYp5M6L2GfIdzehcEXEMVSwN 2I8m1SPgW7Kdg3wAvixivGLuj8jeAP6jbOe8tcgr4N/GQuc4Ydp3UHdRMflo wjFVsbU7cM9ix2jhdWdPhs7JFAsUE5QLKSbLXuGbQ8cI4X+K+VHnHMo9QuRF kbcpZnuUMyt3Vg6pXFI1VzH4T4GXZ/oOnY99D3MmC9u5hlEtsw8bP2N+Nz7r o4R9gHyB3vzqiN+83v5UZEwJHfMV+7Xm4rR9mnzbOM78tNBvRG/lBPbUhc4x lWuqhvkO+nrg3dl+U3pbA+L27fq3Pu47vbd1Pevgr49bnuhXsv5Hot4b7ZHO dhwypxa4prhPvjh0bK9EX0rng7yvsK09a+6l+kH5T55jSgnrv1w5Ra5r3oOq NRmN7McTjCkJ5wTKDcSTTjrnV+6vPTs36himWPZf8JdDOxL5v0LfXPQ9UOqc U7mncqi2adeEqg2VAysX1p3W3db5/wDvIOWLbZxTfwzcNeHcQ/dXuVsydKxW TqPcRjWuat15jKeZzwt9Fm8znmE+MeLaR2eus98YOrdSjr6J+XTwK1n7KkYt e7E76dwsZD+3yr8Wu1ZUTtbMvD50ra+ewobQNb1qe9V4LzJfkbTv0xvXW5eP ka/5gjN5KXQNrVpaOeoi1hrGXRvUAK9E/njFqwz7bPnuzUnXusoh3424J6De wFXQX6NYEDq3lU+Wb9Yd1l0uUk3P2WSErblQoX3Rc+iMwfs8NjwD7ZNJ7516 FupdnCixL9IZ6Cy2cl4zCuzj5OvK0Xm17otqMuz5DfBrge/QFcwXRP02X0T+ X5D/VsK9EvVY1GtRza39VAw7B/pdyI9nOObtTrkmVG3YjT0sVq6W9Fmqh6Be wrGYccpBEuDGlHitqoFUCykGKxbL5m3MX+HftAK/kQ9TrqFUSymHuzbunoF6 B3qva9ifhoR9oXyefN+pmHM35ajKVatDxz7FHMWefRGfhfZEe6MegXoF6jmo 9/Bu1L5vFfx1Cedkys1k02bsKQmdayonV27+TMS5vnJ+9XrEI171ZNYxbyq2 vep5dGG+M+nYoxiqWKoejno5RxkHkFcDzbJcx9SMYvsA+QLV6KrVu4eufRP8 K1NuHfFdFY94f4a8tdCmwPcBvxT84cAx6xX0XSqfl+Ua7RHux5KkfYFolkTc M1DvQDnYENWH0IwrcoxRrDmY9F2Sj5avVo6nXE817k7lM9DfBf1Yxpdp94DU C5KMb9OuUVSryMedG7rmUe2jGqYpYh8kX6Sc747Qa9Ra9caKo16T1qYc6xLV Ekn3il7A/ksUayKeK8dRrtNQ4t6Uctrvma9t7dcoBik2zov4LumMdFarQ8Py 4fLlN6KzLss9qn+lXSOoVtAedwDXqcS1snp0HZkPVE8v2z2UUaFzQOWC6gGp FzQ6Zt/xU06DvrEJ9470Ju9JuGZW7ayezSjmDWn7NvU4GtN+o3qr6sHMZ74u 6dxdOXkda3k96dxeOfu6iH2GfId6LvOjztGUq/3kQ9H/u6hrCdUQeaz3m5Rz e/W4vlZuGzW9apCTCefwyuVV87+B/Eejrk1UQ7SFf3XSuYpyOuV2henW2gF8 QdoxSrFKPYtpCftw+XLlfIVx9wDUC5DPixQ7B1QuqJ6Velfqmal3ph5kWege hHoRV2DDCOA+rf09na9yjb1J93bks7ZF3GNUr/F2ZBxg/u+053oTehvqsajX ohqgv2Jzwm9TPd07Eu7RqFejHsjw0DFbsVs1xfaUexDqRegNHE4751TuqR7e jdD/GpsezXXMPoK8/wPJ7jsw "]], PolygonBox[{{5624, 2049, 4480, 5506, 5507}, {5544, 5543, 5878, 2309, 5880}, {5507, 5506, 4479, 2048, 5620}, {5671, 2084, 4531, 5543, 5544}}]}]}, {RGBColor[0.7526904929541466, 0.8310263711528192, 0.9138043870565347], EdgeForm[None], GraphicsGroupBox[{PolygonBox[CompressedData[" 1:eJxNmnfUVVWSxR/w5fjSfe977wECI4qC08Y1Ciomko45oIKtragoKCYUFAVj ty4VlKAYQJRGDGPOCQMYUFtAQFTMODpNK44EwdDO/rGL1fPHXefec0+oU6dO 1a6q0/XkUUec3T6VSnWqSqU6qKzOplKLG1Op7Uqp1EZVfFaXSj2kBndWp1Iz 9JzSTv9rUqmf9V5Un/2b/O8FtT1U9Z+o/YPtXX4c74uq3PetGO/zGPOFcip1 uebcoZhK/djB7fm+o9pjMtdvmVRqa/3r0OYxGIv6n1XfWfX/VN+mdCq1p+ae pjG7iO43RP8G/X+s4PpcPpXqW+O+w9T3hozb3qT6MQ3+R/1S/V+i51fRsEBj 76/6o/T+S/SfpH6n16ZS74nOv6r/W6J/qNotz6VSc5tTqXubTftBartYbeaq zad5jzNZfdvUdoFo+0j1gxtMZ6Pqfi96La8mqVRH1Rc0x9Rq0/K+nuNEQzeN c4rK6YxXcf06rXtRayp1YYPp79HoPrSvr/F6WEvXaMP7NdrfVZorL96co/ql 9TQW79V2tfZyksq1mmOj5n9C6zpN3w/oeUrN9lbfK9X3MvG4ocptpqq+j8bq re+rtI4zW1KpgzXeOn0/o/ZtKk/WuvYrem/Z8/56f1L0HqV58uLLDL3vq/3d Q/UL1eY8reuSomVsStpyxvtz4lvfovc/Ud8+FdO2SG16qn64+h6v90l6rtH7 UJUPi5cPVnnc9WozVHxYp3K+yo3i14AO5vdW+p6mdq9ozfM1Tzu12SQeZlQ/ EbnRul5r9lhrOCt6n1Jl2WVdS8WDAdrzY9W+t+h7rei+80TDk+JDorbvim// pvfDNGc/rXe4xjhdz0P6N05r6S2+jVX7iujpo/fXqywXNaLjG80xuGSau2iM rfUcqX8Hapxbsz6rT+v/OWXTtkDjnF/2+pZpTT9prKyeRtHWqWQ5nKM23fOW 161VLtec22kN92n+ffV/P+RTvDidvdS/0Xrvilzr/yzR09rqds/r/c+iYzu9 X6NykHh1WZ1l5WLJ1CV67tW845r8vU7jDBS/9lGbASrHSAb+rLpxouHWJvP9 OM15f9ZytkptNmoNO2meAWnzpnfwZ7TaTtQ8XfTvZNEzWzQs1PdI/R9R7/P4 gPjw76p7X2MszHtNB2nO1aLzHy3+N14yeonqL008NzQcizxojAbROU1rvrPW 5wM566r66/S9rL3PXPdGn6/5zZYT/v2i5+Za0/17lXnF3Lcl1g3TE8sEsvGU 1jozbTk9Kx1nq8rn65Q6yxpyhvwh02+In5MaTP9ElbMS69S7VD7aZp4M0Jgr 9P8DPS+p3z5l65tFOesk1od+Q97gJW3G6X2Y/nXX+yy1HaKx99Jc36t+uNbc pPe58F809ayy/min526Nc7bKs/TMqna/odF3jfpuEzqEtf5SZ/mhH/qBvvtr Xyp6f140j0jc/r9UzhcNR9Mn5X6/Rl/60R+dsKzF8yEDExO3H6Hztyx0G+Mv Q9fpGS/5ukf03a1ne7W7rNbz3qc287RvLzWb/l20Lx9pv1qzlgd4hf6FR/Bq YDu3Yxx02UUhw7MLPq8Lqnxm9xCvm9VmpXjfpDUOVN0TBe8d4yN7J4v+K0TX hFrLATw6UG2HVUz/j2r/XpP3Zm7oU9Z/sdY4ock0dNb3Bn33FS2rJD9LO5hu aP6H+jU0mIc7iI7eev9Ube/Ts1rjz1X7X1W3WOP0Cz3xpr7v0FibxI+NevYT 3+8pedyLRWP/Ous7dN1hofu+zVj2OQPI8esae0G9Zexgtemmth+kbcfvDDyw j2T1LWywyupWzzsp5fKtoOEQ9dXQqY8T2wnsRS5jO9o/aFiivov17Cd+fFzt uZkXPf+anulqU6+2f1H9gDhHrwWd2NJxNbaby9ALGve+svUe9m8BtkRjd9PY K9uHDayzvliq9v+jcq3oKojOb/VvdNH7tnSL7BWMbd5X+U7GY1xdMpaAn+wR mOuiBs+dEv1X1FivjqwxDZydx9rb9rAW9Av0Y3cvEk81dGpPlZcFry7U+KNE c62ekyRfu4j2I+sstytF60r1/w+1fbHZev9+9emsNd+m8ZKc5RO9Mlrtz80G HkqMHcB54DrOy1nRt1fe9T1V7qGxbwhZAusdXmPddJrm/U1jXlG23mIO5P+m Br8z1+6hz9iLkVnjyua8zwxnZ4LW+KHmXFFvnoxQm2fV5lH9f6TgvR2vNiur 3Y42g0THeTXW1RdW22agKxY0WD7gJ7x9LWQPfTuszvVnFPzelrUOPiXqWfe8 0BXXthrzbNT/r8q261+ovFd7sFK0TVDfmlbPhT1Gx9AXvv0invyuub/O2P6h Q9AlzLkgaBubtx79Ku15X4y+zM1YnH34gZ5lvdjq00NuC5LFtVrvKtGwSXN9 r/FXaPz1bbaV61SOV90xoT9/q7J+RDeCUY8JPAOuGRzvy1v8ji1ZX7DunCra 3sxab6O/z1JZwo7o311Fy8NMlX1EY5d66xp4Vt9svoGJGmuMT/KSv+mq+5PG 2agxz8W+6v2/1XcPzdtebTcVzINpmveEosdcEZioSeO0aZx3i8Z1b6jNN+p7 qtp8qz4XaW/qNO9k1R9aNu59JW06qH+GMxi6ttjO/gd4GvteV2udyfq+Tlvf onexCWCSq9U30fd5YAjRk8dGNxlff6J1ddI4p+BHxFngTIzXXnetsT45VTJT q/a7qe/3DX7Hx/gwZ34en7G+vyfsDpiVMcEJwwuWz/01/w+is1eD6Uffwzf0 b736NQeOfYCy2XjmqBZ/g2/OrPU7spspuDwfnKu+42t8RsZUfJZG5Ly3n+o5 s8r9H4gxscHY4kl5Y8Az64wDOUf4e+CZ1fq3CtnQOEnB9TdnrV/Hh479q/p2 qLVMn1FrPYMe+ly82KT/b2gP59UYx6J/wS3Qhjx/kjHefFl83b7otR0hWdpR /GhpNj8uLZsnS7SfuxetW8eqzfc11hvoj8vLtuPY87dbzFNwO77gj9X2B2kL LkTPVNI+X9iG5Wnz/uCwD9AJ/gOfzmm2Xnsl3sGa1M8NXxKfcm602fKNTsSf uyj8vvekW/qJRyeI7nTBvMHPmxnn7mLRPEJtxjb5fIFF9270GeFMYd+hb4PK n8LWv9zq71Xxf1O0YYxdWzzOjh2sr9HVK0XLJ3qeaucx6Htie9vo9c22SZzv n8IH2UH9erX4vOzU5HfwErhpRo33kDra4QO902I6oOfxxBj4CTBvxuf16Irn XB/zHldr3x/8eFCb9cK+4s2QWtsL7MQ+BePYvgWfrd3qrK84T7vX+Uxhi1gj evM79F+L9fUfO/gdG83/PaLNUPF9tv5tyPl/n2iPnqsLXcc+s9/s6bat5h17 gQ2DtjM1xol67iU2kLMumSWa5kB3m3Vlf5X9W70XrP+EmPfnmBdaoW11rW03 WKhT2uv9qb39Zmwk9gI8DwYFf3YPfY5eZz50LzoTOqBnG/F8TYN1Ez7IPRlj gTsT+4096tx/+4Kxz2lp6wP0C3rkt0bLCef2xcAP+JzTcqZnqsoFWttCjdWk Mf5Q8JjPap50q+dFHx7baj9zCxbjHQz0Wcbr21sft2V8Lqcl9uHBhfDhjBbH KYhR3KVylp5FxLc0zov19tlm1nm9O4MPY4+QbTAQ/LxR5fAW7yc6DZvLWPjI 4K4zYnywwfB4n5zYh5ySeB3fBQ/B/vAff+rZetPDeJ/XesxFoe+xzeh8cB/x CLDfslqPDw3Es4hrzREPH8sbO2fBpwXb03z4ImB9sNnAirFmx4zlBD8BHwE+ vFDvtaMraIMs3VFnnxZ/dkPi/wO1v2dmvO6RJeP9Yxs9HjoSXYntGRFtHmgz Vjmu0XilXbXnQP5vzju2s1ZjjqyyjeU84jfT5vmIf4GXKyoXtnh89PANYX9Y F/afcRmzXWBCsGFtwT7AjRnPjY3EPoJP54Xt+Cpx/ekZY5k10eZ2zbde5d4Z r4e5wSGfJ+bbn1T/ZWI/+VTse433C5lBb6G/0Ksb4h09RkwBeqAPnTAiYg1g 8zUhG4tjvxbpfUytx8cHp+63qEceV8feETOYFnGDCRXjE3AK+uObap/p/rXG Q5zrf9YY8zPm602O7fJNzG161LNe7AT6f3qb40K3qpyS85marPKTxGMO1dpP bLX8IEesnb6sd0VientlLNfINzLRM+N6/uPH48+DLSmXxzv2aF6r9fsx0Qb7 Dg6lDXWv6P9H/y/eQYlOPCJtPEKMkPFnRbyI2MiHLeYRfVfE+4u1fmcM5Bne HhW2ib7YKcZmrtuRp2rLK/oHnISeA/vfUrD+BMNOjXgOcckn845bPJKxr4jc 3Z4YjzQHJvk4fDR8tYuKju/dnYQtqbZsYANXRixoSLPlP6//w7WWwS3uUx/+ 34w4V5wvYqjoGHQNuoW92q3JOg+ZJUaC3IIziS/gC2zT6BjX+tBX46Oe/5fH O/VXRhyDeAvf6LQ38+FD563L59TbRryatz6rZOybwnf8U/zmy5oc3+latoyu yXrsK2Ku0RljhyfavA70JXOAeVNxZvcMv/jvks+xdY43XoD8NxkvEX8oaPws 8RHsZmJstYfKAbU+J+ifW+LMcF7Yx83x+SqfzSXh5y4JDEbssVvOOqlX2bi+ r9pfofoDmjw3845U+5ERC7252X4Re9Ep8TrgWb9oD66j7Ygm6/Scxr0eHKV5 Xsk7Xk3c+r2S+fa3kuUaGYanU0ve3ykq52XtLw7M2+Zg+9kj7BI2Fx98V7Xd OWIdg7L2PfFB54ddxj4TC2Rf0A+Toj998bHwN+HV2Ij3Ypu+TsyDvTTegRnH QYnFXhdnkzP6bGLs/HTi+YlZIA99G41X6dOj1d/gh1PTjgX20CaPSptv8O+D km3j4uy/8CXjvJB1rI9Y4zidgWF6fzlves8LnHN5yDAyhn5Fz2IzwYGrA+uS Z1kdcWMw44CQk+uK9hWeSyxnyBs2AgwEFjo4b1p2Dno+IX+kvf8QfzRiNdjz mwuWuUOy1sXoZPTxkFb3R/8gj7RhXvDf2ODz1ol1/Nqczyu5H/xW5OiAkCUw Jjh4WuT19g+ZJDZGPfYIbEJ8H5u7f/SlDbaYejAYMTpidsQ0iIH3CP3wYJPt DTzFvwT/4XcRQ50YMee5EWsmzkyMf0zYNXD32FrbL2JjxMjAcJzR6yMOfFpi GX4uZ11CDBd9cmPFeo6cEnyEn+Au9At6hrwT2PPTLX6K/i2qcbyTuHLXiC2T o0R/XhNYEf1JTHZV6Hl4ji/LHPB8cNqYhBwdcgEfjovYI3Gf14JPNcErxoM2 aN3yD5+aurqopw26m5g3420XvMXXxAfGxysXret+CN14eejeSsS10QvkALvU en+I28LTVsnXkyWvj1wZ54lzxRk7os2+TeeMdX63VssR8UjqkQew1da1xmTE XdCB6EL8dvz3R0qWO/oSzxxadJvrtV+XBA3EksHzyBKy9nnoP3DAprJjiGNz zilyljnTZ0V+hDwh8exsYB72b0T4tk9H3hqbSQ4WucPX3itj/oHjsNfEcdhL YgrEFoixnpT4nPXNm3bWD/3I45iQyReb7Fsii/jgXbesMXKOvUNv7xfnBf8O 3qKv4C/f4FL8iW2jHt6wXuwI4zI+fiv5asbBx4dP6Th3+A7p4NspeccdTo78 HT4V/hR5xYWBjYmLd4xc8eTAbMWyc4dHhn9MrIKYBfaLsmO8E78idoVv+5ei be21RfOna8gG49KHWMc5aceCZhXtE+MbEyd4TGezWf/yorNVZTrts7+i6FjM r5GzIhYJNsuXzeuE+Fza2Gvvin3YzmnbLPwyvvH3nqx4zCPz/mZd8HnbsnMn PcrOa7XGvFuVjXe7lO074k8iF9cmxuXtC85JkIO4oOyzWx1nmZwDcQ1iGtPa fH5uafP5HRU6ZwuG3oyr087rv100BsevJM77W8m68p8l6xtwG/hifdrn9oCK 9TqxG3DCiHhHzueGv72t6Jxf9D793Bb55FbbH+In2Ats4FVtluerVb6cdnv6 Md7IGB+MwTvzEAPb7F+2d+4D29O1yfGDTsF/dOnK8OW/iLj31yonZuzH1BFD qPiewb0qlybGq2Bb7kLAQ+LJVxTsw18qPjxeMP/ZBzD9+GjzQ8G68H9VzqjY x5hZsf+N/UMP05Zxac+Z2zZ0GnGyCyO30q3oOC359ksj5ztK9J6dWHeOUvlK ybyYV/L+vxlx+5PVpz3xcemw7QIjbp8Y42OLv414EWdlM47K2XfGz/4gfAX8 kaOKzk8fXfT9FeJb3AHpFbEu7Pv0sIvIT5+88WZa4x2W2CYenjiHh4/9WdHr 4/xh68lzckY4H/jZ+LP4sYvTPmucOXJZQ2NPsYHEdtGjlcRYtGNiW4Odwf6i i9FRf4wYKj4OevWCnPteUjGvsQngNGIS2EfOAjKI/IAnKYfGO/hrm4ix94u4 FvEtYlqb8wHtTB99vglbPCr8mQEV2++dNf+XJd8b+Erlwjbncu7LWDdh99D7 /SrWQ41ZY3KwKHs/J/F9hp6Sq0Pyzqtyb6VLzjEy7phgK4hFYC8eiXzJ+cSZ wm8Zor43pM2vB9Xv8azvOrxTsm1hHOxLZ32/Lpq2Khn/dI+1ty/aXo9MbDux oZOzzqeSV61X3WkZx02mVux744MvLMd5jTM7qGLbvFvONp+8Juu/oWIsMVHl 0Xn7eLvkfFcDXPFg2TjwjrjHhT+9LHzqrlljnbO1xhPyXsePWefDyFEdWnKe f0jkg/CNNscfqrzWttDpQyrW7wOz9l/JP+C34qPge2/xp3nvG7Hol8PfR2+D acAzUyr2yW8Sz2a3OdZ7bMYYGiz9MXk37cW3anNv0bJEnBR5In7AmMQivk87 r7OhzXh8p/B3sHuVsJXMTR/0PHofuSIfh+8LD/ERuCPFXY2/iz/9ipb597SP 4ytez+UVx6G5o7blLgPxGnDLmznfFegmHr6dc46pe8m5aGQVDPluznHz40rO ma6O/CjjNceY5J7Q8fgU6ADWhR44qOCYU0p9r0wc19yY2L9Cr78OLm6zPuwW uTlwHDiskPH54pyRX+BuDXjgx5LzMWtLvpPHXPByXdyt2lf875SxHCKPYHSw On7ndgWfNc4cMX5yNMRT8BHHhi/MHTvu2hXzlml0Ej4vd1mQH+TozbJtLraX fBix+19EzyCNv6/We6DKL3I+51/mnG8jpl/iHlDe8b4/qNyzaP94nui5v2z7 eFWciR5xN+Nc1VdJDs5TWd/mfdmQtn4jtov9IY95bdj9kzKWT+T0bznHIo8v 2SY/rOfLdr4LyP2bFwq2mdRjt8mB4WPcWvD5xtcC76ED4RN++Pq87+tsUHlN yXr8KpVPBP4BB2GTwUDgH/Aj+A8MObjoOzrHFl0PvgSjHh6xT3L0PXPWG9wx JIeBvSK3wLlYHDIwL/IoB8Q9He4okEfgrLGP2GNwInENZAkaWQP0v5U4hkAs gVjOsriTg/3izCG/2C/qe0Ysim/8NWxap7Br3+Y978vi5eycfVruAoCZwE69 cvZh8FkOVN02WecehopX67PW53PFh3VxLjgf/fPOKZNbBsO/F+vdWDDWAnON KzmmxJ2L5Yn9lg8Snw/a419fmHGc/DvidaGjBmccKydm/kvOOAJ9gt/3a+RW yPmMzPlckF9F7tZEO/AFsoHvuTKxDvso8biMjy7ENmAjbi96rxbFvuMf4ieu yjtPQ75mtvo9l7GvsCO6veJ47T0FY2R0Lvp2Vsgf9pd7n+RbTiw6T0J71vNc 3u2PyBvjkCcgR0A72k9InDdmzaz3hpx9HvzCQwvG0+NVzs76rJO7ICY5M8Yf lLdfTrznmUzEu1THHRvueKFTyAMsjFwDOgNMwlzgEt7Pz1l+loZcESMBi4PD 324zbXdnHRsgdo+80fb9kD30BPFgdOzxWcfIJpd81mjDeSOGiE/6Q96x01lx L4vYJbGyL1X/aPhB+EPYFzDw5v3rYN5tzq2krWfALNx3pG5tYh0BriemhJ4j rs77ovC/8Z2R2yVxD+q8WCe4lPuMxF6Iwfye9xqfFy8PzVq2iDkhM0tC5ktF Y5TOIZvsBbwit0O+g5j4wxnHTLqo79M522hibMSimyMuyr0n8AwyNzdyHOQ9 uZ+EXeVuF/h8TOSywc74m6Mj3wEGxu/mDijv92Udu74rcmSzCubb3QXnvYhp vaT3XYvWy7sVnf8Dw96esX89P+wvGAFsh99Hnpa8KfoCf5A6znnHvHn2TdZ0 TQra0NnobvQ5uIM1E3tfVbbPTQx4WOLYwakqC3nr2+1z9vXw84gXLC843vRU yX5GNs4IsohM4nP0T6xXBsS9BfATeYDVZc//Zdl3TFbHPZPVOWPVdVljZfwm 4lfkG8g7rNWczTnLw6tF61B06e8F5+fIyZDTY40PhW26K+6XzslYbpCfY/LW T9znABexLtZC/GXbxOfrYsnvfwYOAY98VDafjsw4zg7e5J7X9LgTOyPjuAbr Ax9yb4n47Aea66yS9S16F34PDTkjFpWKPOJDif0K4rvgLuwCMvForJ3cC/bn prhvdkzFeXnuBIHHhwUm5745vgB9Mjlj2neKzt1tiDsS2BXkpVfWugc+cR6J UxCv6J5zTgZaOQv/BzkN4Fc= "]], PolygonBox[CompressedData[" 1:eJwtmHucznUWx39zMTPPmOF55rnN84xEIbdKF68tl+iyLrVFhFGjG3JpopRW uWtV24ZySyWt3EbNKsUWW+liKCoUCqOEVqUtYcola9+fPvvH9/Wc85zzPed8 z+98z+Xb8PbhPYZlBkEwMiMIsvldmg6Cg/zRpHYQvAjcJB4ES7KC4ECtIDgH +LlUEFxZEARv8l8b8P+wTuYGwdy8ILipKAj2h4OgA7J21AmCcSVBsKg4CHYi rwp5Z7B3CzxtUDQA+kDolyWC4FfobdjfEfh65A1CXn9s6YOsSmxoxt7z0PcS cIeEeStCQTAEepL/dmHbNGQWRsFj8OQEwaj8IMhD93h4OiNrFfsXQp8E/ylo heyZCPxNJAhOo78t9vTFno/BWyM/hYwd8H8B/iP0S6GXQv8FfD329EHGoWQQ rOA8v8F/knUI267C/puw/1HsK4c+FbwK3b2wbxpwZ3g6wvsAayzwZciryfAZ H8HeTvyXmWWeLsAPsOcx5DVE3k50hfBhZ2idWN2gb8bG2lm2qQG0kynbfhf6 auAfWuSzt+O/V+HPY+Wz907k9cS+OZypU90geIMzLsOWaeAh8HnYNBV4H/Lm 8L37Im8hcEt4NmfYh4Ow90L0/wqtNvI/Bu4N/d/Yezk6uqKrAp5SbKtC363Q 7saetdhTyHdZjvyl0PtC/wL6HdDb8d+vhUFwC/achhaLmbeA9UqJfSBfjEBf f2gTwKuRdwPf51boZcg4gP5OrKb47lHoW7DlGvgbw78evDH2hpG3Av7t2FzG /lzOOJDzHUXnQuy5HR90h6d1kWk/ct4yaEdY32WapwZ4AfRM5I1HfjW6b2P9 kOsz6+zlyBwJ/x7+uwv4ev77Elm7Wd2BD+OjgPNUQF+EvV8grwF4W/CSuG2Q Lf10JvAj8K9GXgY8RxOOScVmK84QKnYMK5ZvBu8B3pg992DfYvBGwA/hg7f1 LcHL0LVYMcpdKEXHc+w9zH93QC/nPN/C+2zS32YHay5wXeg7OdsT2F+M/BR3 6Ax4Z7Mngi878V/3LMeUYmsDe2YhfzD4AvACbN7AWZ5jzcCe9vx3nO99Bd/o T9DCyJiFrGPsOYL+qfgzBG02/I/BfyJq2y/AbceAz4fnevTvEQ+27YKHLcEa 9FXKF/w3Hto41iTkD4T+ALIuAf8Sxu7wnIu/JuPTkeBLwO+u628yALgxOqqw JwcdH0R9Bp3lGDbMKbIP5IsnWSng95BfP8syJfuzqHOXfHZRyjGkWPqJ1a/Y OVC5sBIdO/BVS/i3AofQt1FnxeZHsO157F0E3DnuWLiKmGwbd85V7n2WNR/6 35DxFrZfyBF+gT4V/N2Q/fca9NbIzMC3Bcj/RLmjyLEzBnx7xHdId+kJbKhi 72Tom/Kck7sjbwEy5oFPB58Ivoo9TcGXgi8Ff5z9efhnG/gf2T8IfAjy4shb iayJusN1XCMmAA9UfoV3lHI++9cTT8c5bwf89QHwNdBrg18B/jT+6hnz3a3L eo39PdgzNNd3XHd9Dvbt53xPcaZu4E/EnHuVc5R7mkKfH/i+NQMehPxDuc5B ykUti3x3JuGPr6F1AZ8GPlLfA/wz9uxC127Wk0Wucap1byGjXdJ3SHfpIfZc Bv3zuO++cmYL9j8P/Za69mE/xSc61xILVax2wL2xd1COc4RyRQNkHKttHvFu 45t1zvJ/fVKOIcVSNTq2ousG9q9UfubMvYBPRh0LquG/AV+H3/rC+xbxE8O/ N2PzpeCf4rMp0N/mfLMzXcPWJJxDlUs7y1/AS7EnH9lPs75L+Jvp2+lODRCd M76a6Zy4EdtOKIfnu4YeB96LjoY5vpO6m0fivsuKp6PAB/ivlL1XygfY/zn2 npXhHKFc8WTStWQdazpwKfQh7D0s+9UvoGNQtnPundjyBjI257hGJcBXgW8B X8f5z+T8f0jYdy/rfqD/QeybUss+OQ59Afxrcpxjzov5G+lbDcD+IezdyZ79 ua55qn0xxR++rkT/AvAyvtGp2o7pPeD3ssZnOqaWI+vNhM+qHuEezn6UVZ7t GD6IviWc8duQc7RytXoK9RbKibnsLULeXPzzITZ1RF5l1L7QndXd7Q3/XfAP Y/UBHoGPNuDvtfizKf7ciH0P4osxrNfgDzhPo1zniDOQvSnuuzwUeQ3V62BT g1znqBpk/ZP9X0F7mK0rgf8FT4n6O860Gt6Loo5FxfRp8MuxuWeWe76vkD8K +uCQc8BedN0Wd2wlWbcCz2DPXGzpzxmnqx7x37fsr2D/M8DN1FPWds+6DXkZ nHEt8DjkZQJ3S/lb9WcN1n3H5jHQNnHec/i+Z8PTFv0l/FeD/CDlWqQeRL3I Ruj18l3DVMuU45XrVcM+RP8IZGQXOuY3QLs65lynHNMVeGHEsXAYm+Pon8p5 p8O/Cp2PJt0DqBdQjDQpck+m3qycdQPwtch4AVnFyLwOuF7cvYFkSNaauHNP T9mMrr9g/1mcrT3+6qreBfl/zrePaoE3Z8/mbPfIJ5E3E/7W6ufRd3ORa4xq jf6bBe0HePJyHYNLwu6p1VtLpmQfgWdRge+c7t73Cddu5YgXkTcd+oXQL2bd CL4Xf+3FtsHYvw94bMq1WW3QmJRjUrGpnvcSzj8+4V5XNX0c8H1px7py4L3A pUnH9nx0vo+tT4NvD7keHYO/V9K+VAwpluaDf1nLd/zv6n9Z20L2cXP4D8CT leszHcaeBmnnimc4T33NQ9j3Nva9CH8neK9OuNdVz67evWnasVUBfwrZ/0VG JfgI8Dh4PO3c+zh4FPjKpGNzIvKuSLqmq7ZrxtKs1RefXVRgH8qXfVPutdVj nQ3tK/a0yXeO3M73KYl5dlIOrQc8iTUvxz3MQejVac8+ZZxhd9o1X7X/M+gt iI3bY+6tf9R/wGORvzjkGn8+55ssH+b4TKfUz4BXBe4Z+wH3iPnu647prmlG 06w2AXwYZ/uNGGmV75nrZLFrrGptgE0XR11jVWvvBy8H/z7q2FBOPAg8GZ3V mY7xhzXr8D2WcJYu8BSg+8aUexvlmEb454W4e7Vl7JkPXAJ/ZaF7huacbXjc tk1UDAMfRUYi13d+HbaXx51L5AP5Qj2+ev2J2Hcf9rTCuGZ5zrHKtfvCvntl 7K8B/wf7mygfspYB70b+8RzPVHuVe5O+2yeQNxq4C/T62Y4xxVoD5DfM84yp WfOvSecO+Xw4uvYrZ2FfS/RtT7omqjZqJp4Zdc+u3r0afDZ4RdKz4UHdEeDN 2DChwDrvwL53NAPhq3L+exf4XXQcBP6B1T3tHKFc8XsPg32vgo8ucA35vZaE /VagGFGsVIStSzlEueRY3LVePap61UP8VwSej/1rwMeho06hY2QLe38hRhpl eebaiu+r+C8L+k/Y24rzdIi5VmgmWg79qWL3juoZrwafGbZvVfNV+9XTqrfV mbqhazk2z8y2zLvBZ6ScqxRjH7G/Tcy1TTNJW82bYceaemr11i34r1uOexD1 Io1injXVM6h3iMY8e6vnUu81QzkUXSHWKPTVjzpX6BumkbcmZd+rpi/Etzcl nNuVA5QLfk74rUEz+hT8PyHuXnM09o7XfBj1rKeZYVTEM45mHc1ou6PuWdW7 yuaWMc9Mmp1Uw7cn/Kaht41S1s+6+2G/3RQjswZZLyQ8a6iHH1/i/0RbB099 bN+acC5Tj7wN+Loi9+KaQZ8CvwCZQ+t4JtBsoBlNs5p6qGrV+yLXSn0TfZvX wbeGPJMt1vtLzLVaM6xmWc0cyvW6g6uxpSU+XZbtnHhu2jGr2FUP+g5nmR12 7laNUK1okfAsoBqgWhCN+O1DM4xmmXkR9xp6Y4jA2zzuXlhvRHorWhxxblFP koC+otizZBvkrQReB31bpt+82kKfGffsOxgbHgt75tPsV0g8jMXWj5Kepc/E 3o+Tnnk1u+nNZppmoahrh+5MAv6jYedu9WTqzfQmoLeBcZx5E/6bFfXdf0k8 Uc9Mmp3UI70Rcc1U7dTM8B7w6YRnf+WsD/D941HPBnqT6Ic978Scy/SGoreU y9POdXqD6ggcj7lXU4+RiDmnKrcqxyjXNE55ttWdPoX+9novzPVM8DW66qJ/ K7RF6u+gF8b81qgcr1w/LOFeVDlHuefGsH2pnK7cvj5s3ymHK5ePRObAkN+E dmv2D/vbqOdT76ecqdypGXAf9FnQlwbuYV9n7ycp+1IxqFjcHLYv1SOoVzgz 5dhXT/9Kse+M7o7e6Lpj63tJv01G+O/9pHOccp3eiPagrzTity69keqtNDPh 7603ywzg4RHf7Un44Bv496LzvznumdU7f5Ry766cvRvez6EfyfGZdfY7o66l 6gHUC3yTdu+lO7If+NOwY085V7m3XsS1WDOAZoFeJX4bVM7qXeI3OL3FaUac EnEOUy67Fn0X672g2G/HetNsr7fUpGm687r790ZdOzWTaTY7XuTeSDYnoD8b 8bfXDBVO+E1M91M18OWI3yD1FqkYGc3eeNS9mXzSMO2aoNqgnvwe8LNSfrvR m8wJ9p+T+P9bBcdrnPAbhuqR3rDvh74h7bcGvQl+CLwi7tlQM9PznHs1eNds 3+lVwGvC/raq6artu9LurWL6psD/A2tOTLQ= "]]}]}, {RGBColor[0.80726725, 0.861883, 0.894034], EdgeForm[None], GraphicsGroupBox[{PolygonBox[CompressedData[" 1:eJxFmgmUVdWVhq9V9V4N1PSm++q9py02ggYMaNutEnACUcCg4ICKQqLpOCSO xDaKcQAVDcigIGivBUGQKIqJWGDaAdHYiEqiMSAOMUYUZ20ZHKJZafv/6t9Z vVbddc4975x99tlnD//et/Y666ITLqxLkmRwfZLoL3m9IUmGNSfJu3r5YyVJ /lXv08pJ0qGxdj3f0vuV2SQZ1JYk/TNJsqYxSXZXv6j+px1JMkPP9t20XvN2 iMZk0XxFv63slSQZvddV3N9N7b1V01mh9paq6cxVW+hlOlXN787rXbQ2av7s QpJsEa3dS0lyQJPWaP5mvd+tNftp7gaNf6a1u/Rs15rfi88JeprVn9SaJGeL 5mzxtqPB/aPr3Z4T/YN0viWi+ZXONEr73qKx67Xvy2mSfKn+Qo2dKTqfac8v 9H6taF+g94YG87xGT5vGH2lwfy+d/cmK5zyhdpv2eVvPynbJq+a9CmofjDNe mJrfOvGR6v2VziQ5rCVJtkr+LVo3SeN/17k/Sz1nl9rZeu+rNVN1iR+p/7Ge 1eq/p9/+R/13gyZ95LBaexXV9tb45FavmS8+dzS5z9qPJOd+WvOh2lk177lD 898SPxPEzy7xsyqXJKdrzuHFJDlEZz1O575FdAoaG1NvOZ+Xuj+0K0nGZy1D 6F+u96LWLK76nmfGXSNDWuR2q+S1VP3TxO9arf2m1Tr1ZcZ8w/M87X2/fjtZ /AwtWAfniOZY7VvR2h0a/16r75f7Xy79eFj779Tv80U3o/4K0Ti9wfr+U9Fc rPMuV3+Sfj8XPRD9t/U+QvJ+XXJ4T3PL6s8Qvf31vkryeCpr3l7Q/KEZz2nP mod+ksMkzblZPOyp397R2W/XOdfnrLv0Z2b8+8yYsx09jf5Jze6vFc3tqe+O OxyidU+L/0cS69pDoXvTJbc+4u0GtQPEwwKN7a05u2VtyxvrrOfo+DL9dpHa U+Ju6htsN9wje86KfTNtvted6l+a8Tg2Dr/wfaLoHdnp3zolv0PE3zb9fmHR 9oW9oQ+HS+8vq/dvyzV+V+j0aD2j0GfNm6VzzNbzvPgs6RynaE5R7T16PhEP P9acvPojmkxrfIfntNRbd5aG/iyqWh6P6s7vTH3W08XXIPX3EY2rytYr7g0f 1Uv7f5Pxb/fkbSvvaO4knesaje+pc/0l4/1f1fwx4vHm4PPRBvMBD59I7ovF 38cV8804OnBOvec/rP408b5VtKZq7Abp7a/U/6Zqmc+Je+cudobcftxomujp BunSc1nLFHqzgoe3m+yXuMdp0rE+Gr+napveHjbOfv3CV5yv8z+t96MknztE /0adf4La70f/ZM0fpjkT1B+u9gI9P1f/eMnj44z7G/B1aoc22i8vFO3HxNfc er+/rOeoBv/+ln7bpPcr9QxvdpzBh2MDjG+O+fR7a3xx+MDFWju32euww9FZ 3xe8btC8RTrLS7EeO+pT9JxFsq/d1T9K/VrRsQJbY15f/faB5Hia5PRL6eSI rM9bqzn2TdAZt9R7LfSX6OxvavxOtXsUPX9+zvIfE3f6A42PFL+v5a3n3H2z aLymdXfp+Y3oD8nYj+AfLuq07H4umut1llPV3zdjG8AWsPO02XaLzS5qsi/A Hq8re3yRaOwrnsdqfHKn/fSksKOWjMfhCf0dHePd6h+n/v+qv0X7fk/9Efgf 0ZmofkHnurPDfXw6doJNnF+yfdwNn3qfrrXL41z3Rf+qRsv3pbj3l+Ne6F9d 9tpzxedFRftqfHZz+HT084F2r+UeP+1l3RtBXFc7PXSyu+D5D6p9UWuf0Jmf wY7y1o+VBcvxMMnn9jrjFXDLevXrxEMrMlU7UnOKWd/dfjXP+U/x9mze57ug ZLzSpTmT6nx21iKLazXeW+M6bvLDiuls0b5vFjx+Yc7y7Qi8tLXguYd2+v7w v/gj/OXBcaf4gMObfaYB6udE5xzxdmDF/U2i0b/Jfgoejmq0f8e3X58aA7xf sJ0Rn7CjUyr22y8V7Evo40/wB4OaLXP0FR25QuftXXHc+kPBdkwf33Wr5u/b 7L2Ie9AnllwRcZx4/nedbaZsqqT+AMlzndZcr7Fb8saBm1LzD+/cBfhpbNYx 942s5YWsurX2h3o/UTqSFn2PJbW5nH3fYaL/UupYvFntTXnH7hNKtivG2a9d e98tfbpP67M5x+mBWvtB6vOv0LpvF83D8pz9JnEQfLJOc9ZqzuOpfR19fGUv tY9Ff0HeZxgkGg/oLn4tmVynuQOLPtOyovUSOSwUzcl5+8sR4nNI0Xt9R+2h 2nsfyfAG8fZKapx0CD5M40dofGGXMd344I1YR1wk1gzTPY3Tnn+Un19Rslwf znk98/eoGT+iw+gytoWN3ZwxJu/B4+L1L+J9lcbfaHZcfUfnydY7zqOj6Cc4 H5tlHBkgF+Two6qx2XlV60IhcMiUjDH5qYnvAT+L/TZpfAD+pd5jWzssF/DM UD0tdR57S8+hWceCM9uN246WfA7X+2gwbawB/0zL+P2R2Iu10B6pdkrgBPw0 +2Lv4G9wOHb+aca+Hj9/ccG+crXktzJrPuChXnIcRE7SZfmtChkSB9hnQ8jq 08BFM6JPHIcOsuq5e809ocNxYEbJejE7Zz+5JWIfMuBcnAesMTLwxnkF38WV RZ+LGEN8+ani2sdZ+0xiDuPgK+R8cIPP+x+id6megzT/z1mfHRtDTsiLM2In d8cd3d9uGeHbwRH/3Mt3Cc4frKe5zv4KX/WZ6C/p8G+Mv571etaS79Bnrwuz ponMfyY96581zkbnkCn8k7dBB/zZiB/R701qR8a9c//HwUuH77Mn76k3ngFj g52IkZyVMy8hdnd5zZ1q92+zL8W/bi4Zk03scpwYELr6TJMxPzpBbFkX8eVM 7T1IbbPs+KZO682KgjHhilbfFb73gsDZxGZi9FOaMzDkDz/IfGvo53GBHzrr 7DuI2fT76fe+HV5HDERniIPko3MD33JX+DjujrNeFvTHNTvuQwuM807WuvDn RvsKxh+KXJU8FZ2AN2IN+fyrIQewAbgAv7xIZ3hb/QWp7Qn9Q/caaz4DMv66 Yjl8VbEOMge7e6po/36qYu6/12zLd9fsR/An6OqsnM/xQM24Crt6Qns1pvYx PVi1znaBfwALgh//tptter/wJz32F3Y3Vfc7XXOXicaxJd/nJaJzR9a6R14E znpMOrEnOXTN/b3VXpa1rJEzWKkj8BK6nOu0Po/N+a5Kov1ag+WGDOEFmcDP g4FV3hcPiySbx7PG8OB5WuJEd9Y4E/6GRO5IXjo7zoJfwb4+1zM841iDroD5 D2sMPF9v+8Cfg1XmRP6ILeCz8F0zu4w1Dgq8cWTBueraqvOXuZGrYh8HxVnI Z49u87z2mu3tA637Turcd5Pu+pc14/TfyC6uS83LUGJj5HfwjO6Sf4M9NrY4 9wOnlludB4GnfpS6BlPBvgrGqfmS9fakNusu9JYHTfrLOoxpp0cO1VJyvs9e yJA97oo5G0XnwNAb8o9hkYPgD8F2+CVyV3wsvnZd1Wd/vOo4SjwdXLScsBt0 A1/F3YCTyVeosyCLnpypzff5edwfvvGuvGtF6yquoyzI/n/uSIv8T496FdgM PSSX/4fdPxZzoHdn7EttgzMjO3ABMQCbwk7Jk7FBamfUl6gzofPoO/nF/C7H m6d1xs1Vy3mT2qf0jGJdyXYOTfZlv0LUB9ZXHaNe1Jxn1P+u1m5QOzXr/jHh z6nHjYrYsSv61Bh2hZ+/uub6wbZOn+XsqNXc2uVY899VY57xUQcY02a8DX6/ PGcMvKtg7It+9uhph30o/hrd7xe+tDv8GT6EmIbfx9etrliGD1asU9dEHrZN 9Ib38l7kofgdfCzxc1vEUHzCtqjpXZza7vvnzSPxhfh7TMRi8An6+EXo7rKw a3Tj8xhHt/EhAyLG4Vv6xlk4A31sc1xqDPhBzvICW6Dz1Jyaw5bv6DL/x2rO 1/j/dvvEiTX3f6F2a6txPro1OOIvdwQmvLzNOOfwgm3nQd3FV1WPTym4JoKs wE3kBD0+IeNYsW+r84Wloauci1rgiKgNPluyn6l02ScsjTnY6eA277e66n53 1TEQu8Km2HNZyG1C3jUf6oXIBnkhn1lRn+EOyKuI5eCVfpp/GrlHV8TVdvN0 Y/ichpprjtA8PbW91ofOZ/LGCWATcp2Hst6POnBDzNkR8zkj514auTQ5/r6B mWZGzYx6FfdMfEZPZnW6tnFqp2Pv2VnHvVlNzsGQJ7FrbpPj13/lrIPkTCcX Pf8ktdfUXC9YmjpeoaPgMupf5J2fSPZ/and8YN6YvOnNS13PA0vB37CcY2wb fjjnc/5Ocj43dX5+vPp75e0XXig5Xn0aMevKiv3RFRXjqceijgrWGBh448mc cWG94vLC1Gc8Lu+aLnICd92W+rzz07DpXrYF6gH3Rd3m1qgDUw9OS44VwyIX nx15+pcV1xD/WnEdERwEtv6w4tj8UcU+Ed9IrgFumhZ2i23DJ5j2y5zvbZ32 eaHiOjz1+EFxLuQ1qej5nA8seEHUcNaAPXoZf04sevwMtfOafMfgWOxmVtz1 jV2ef1jOY7NjDvlcNmJ9r8hHySWqeccBYukbovu15r2XN774OmpZc6LeN7tk HqnjcK6Xcv6u862K62dro84GTgG3vBu+Hx1Az4kr8ECsof4NbiHvJqZiu8iB GsNljY5lyHFk6Dn1HGqM8LGp6PzmLLUtUUNCVtST4I135NQQ4+TE+GV8Ktin sdEyeb/oOTt13vNT+7A91O9bNO/UCfgegH/cXjbWBHMW9ft1US+lbnq81q4i l6nYj+PPszXrI/ZLvtOStw6SE1RC5t0V5waM12n+mznHwjM0/rucY/k8avJB k1gxpMs+sbfGrkqdix+T2sdwF8QmfBV0iB1bUtccqD3AIzEeX0/tGPwDDsJv sRf4hJoi9RP4HpI3znpLc36ds47zPYS8oa3Jce2ynOMD2HlJ4Gzu8Z9S5+F7 qh1Rtj+jXoj/mxI5w6uR88Pzk6nzJupAMzodO+eX/B2I83JuathPtxgL7JHa 7vpo/usl1x9W1hwbiBFdai9pdv2CeNQUuWFZd/c5/IjO29pnXslnaU2doz8Q NY17i8awYN7m1DbTora9ZF0lvn1Y8jeRfM315uFRcx4YeRVyoMYzLuo86DP+ gXv5qOT4+rHa75es82eW7P+XRFx7PnI8coYbUn8DmJ4aA57WYkzJPYFJ0LGd ZY9vJcfrsqw6c5bb9ogxl+hOH6UmJTp7l1wT7Kt2o+Y81+Ka35rId7mfQ8rO DakZg6OJu/B2Sqe/dY3T2iO6vDavvfqXjZOpix+ft5+6XXv9otN5yGrutOxY AY4jlvZgkXqfA4zFWdDl/qEbjPWJ8WmpdRLdRDbjIm+FV3juyafqvYa1H+aM ecA+J6auM32isXFdtpPxuudHcq6DUQ+bk7NfpNZC3ON+0e9VNceOi7XvGV3G CP+Wd15OvYZaDXEd/YYX4ld35Gg937xjnLrdmjgbtKA5t2hMekfkmFOKrgkd nDOO5xsJWP6ZvP3dNzrHAVEb/Re13eJ1oPoHp87nyOvwC/hBvgfgA7nfnaED Y+PsyADa2AJYbk7EcXLhUaljxficMdoBgdPw4dT78OPkHwuCN3KyOVnfzc8q ztOuqhhrIv8p6t+mZ4nWLhftNRV/F39I7eDIDckRt4e//SLnb3qcCzuYGv4b 27ktchByETDC6MAJ5E8jAu+BLd+Kusq8yAvID5aGDuOP+M6HPqAXxMc1UcME q5Pjkd+Bs4jr2G5bxXWQVrUb8sZHTxSdH4C9yBFuKrsux/8dNJT9jYBvBXwz AWuNCLwIbiRn/W3UOqh5PFt1rvJc1bj+88D767OuOePHV9e8L/t3ha/ryJtv fOA/7oz2tZJrNoX4vwJqjPTRLdpi9JdF/Xnvor8Hro+9+Ab1emAYYjX+kHwB mx0S/adLtrdsYDAwK3Gvu2isPVK83Fd1HW1l1VgXf4Qe4q/h+U+i8YeSc8N9 RGe/onEB3wE2BH32AVOAFYjN2Ak5A5iH72zkHpsT+1RqhcuDf76l8a1pcdSj qEsRFyY3+xvTq6m/g76WOlZcEuPEAmgSO6iVEhOoNx6rOz2L789l2zp9Yhhz 3wgelse+yG1i2XvdrH3HqP8D9b+rdpbef6L+pLLbS2KvyWXXCakXgvlnRG0K DN9jO4FjNwQWIm6Q/4OXboxx8O3Ykm2Cb1z35v2/Gh+mtj3o3IU/LBsvN6Wu xVCHeB7dTl3TfCa17sMzcQtbfyTqPvyvA7zMr/ibKjyDFU8p+5vMeLVHVJ0r HkmdIPZiz/ExZ//U39LBgtz31VErvCZyqI2hJ9T/wBLwlyvZzkZJrz4qGst9 lvfZ+Q49IeTx+6iJEOsYgzfsr0+j8yNsjnoltovO8z8NfM+hJgvWHxk2zVz2 ezH0Ez3lO0yp0d9Wrq05L5pas41Sp8OW8IfU2qht/U187ql+f935rqLXdhb8 fYz4iw5/WXVd/q9V51fU24g/+Lxt4X/4VkuNgZwDv0g87anpZU2f+Dm75v/7 maO2VrIuvyL5fFP02Q8sOF6OC1yBzqLf6C1+i+/u+HnuhFoedk9NB2wMRh4T /zvA2l+VbFv3l+z7e4f/R37U4Hr2abdNoivYxPKwx+aS4/nQnO2aHB7bPrHi +s8Jan9S9f+RXFp1zZ2YS+zdFrWcHp/cZKwI3uKuiGvECOrB6MPJkRuTlwyQ DP4P1qwaLg== "]], PolygonBox[CompressedData[" 1:eJwtmHu8llMWx5/O5T2953TyXp7nec/7Ho1MEoUyZpBJ0YUuukccSmXMoKkU EyWXLmpouinS0AVRyf2cfGaICilymQ9pilAqVBpJU+Rjmu/Prz/251lrr8te e+2111r7OXHIiD7Di4IgGFQvCEr4nhcGQf9EEIwoC4KrUkFwfBwE/4ThhPpB 0Aj4vXwQxDAWJYPgfeAl1UEwuKHnngCOqoJgankQfAw+BP4PGd8VB8FW5D8C PpANguboH4d8R/TvBm8KPh36JZkgWAN+tDQIPmVuDbbsAT8ZuC32tIH/+9hr 1zD3XDoI3sGGu4CzrFeL/EFGWZll9iJ7S9rrzdOa6JuXsv4KdAS5IGgGfw32 TWBuBLqHw98Y3hmMG+G/krlHoRcYh6G9Cv4Ktn4D/xPoXw7PWngPgj8DPgn9 q6CvZrwG782FICitZN/Y1w7f9GC9+6BNY9wnXYyVwBWMJPAcxsz6tlm+ugz9 V7D2m5zBy6z/JvraVNjmtcA74d8L/3eMJ9F9gFHC/ochPwDZkxi3sNUv4W8K PBv+B4q9xv3AHzC3D96lDdgfvuwYsUax97hKvIwdpZbpju6l8L8O//OccQJ7 dkFPsrf/wP8V8Dh41gHXYEN7dC0B/6a+bdwB/Q188BfsPxt9b0HfGDs2OnEe p3Meq9G5och7+Do8tmfwJuBHwV8gxhri4+WyATjHXAt0PQjPTHi/Yr0j0Doy joc2hz09Dn2R9gv8a+a6QLsOff3Rszg2Tf5WrGzHhrbsrRSZbcBfxt6b4q0t svPh6Qq8kvEIeA/onaBn8N9o9L8B/UzwVozW0C5WvOL/duCdY8tIVjY8DO8U 7H0lYZ1NoD3KeBV4Pvr2oK+OGKhDQYS930ObjszcIp9RWew7INveZawIPQRf Kf9AmwX+FPD5jGngEXhb4Mvxdzv21xz9J5R5Loa2GbwqYR9sxj/VoX1ZqzsE fCo23Zawz0uxfXjsu9cD/ETwVNa2Kof8XufF2ABcAs9i6A+Dv5hwjvgZ/XOJ hwuIh4sVY8CtoV+WcMwodqYy1xL6T+Sn9tjfD/ofoV+N/Rdh/1WhY/0Lxnrg H/KO5aGsdwj4eebehraO9dLIryBm0qy7lbk64C2KT509d/Qw92kBI2S9SvYz H3i1zlfy4KPgfUg+Bm8Gz/noOyvr2JRNl0J7nLleCecE5YbzkJmecE7ZBu/d VZZtzn4nVdmntcfiQbF5IWtux/6V2H+Q/Y1hbi60WazfJeOcpNy0HfmH0PcG /K3R1wX66wXnSOVK0W9JOcakez3+XBf7juiu6Mx/QteH6EhDb8X+lyN/BTbt xlf1kR8p/pRz22BsWpF3jhO+A31bFM/KaayfQmcvdC/Ep70b+s4tAK7jjj/H WmfAXws8MeXcqZyn3LeMPe0/lh+Uy5RTRVfMKnbvwQcvcdabmPtrznvS3nLY Mw57lsH/balzjnLPJvAQ2e7s5y32swEduxLeU1Psm4MNC7CnEv7ZwG9D35lw DHaFvhSZJsCnYf+SwrGaoXhn3Jp2jCnW2qCjUeQapVp1LeMfaZ+J7JuA/rtV O5lbi2+acZ4twT+Cnk24JtyE/tfB6+nuscd3sf9W9D0AXoO9X7DXmZHvu2rK GuT/BX/DhGPkgoJrjGpNO+Sb6T7gk/EJ14yT0Teb9ecXOSd+hu4OkXPzy8iM hN6CuVGBa/Rpyk2xaZKR7EzWOB5bVyCzBXsSnOmAYp95GfBQ1Xf2dpFqJPra IHOwnn04scoxpFiSTAvoM3S/Sq1zFnAROloW+44XA09GpgX05uBTgNumDevO 6e49yFxn6B2YmwfcBXp5kWvi/eCtWO/UEvv8pbRrrmrv2/jzAuD1se/CJtbs hi8+xIZ+2PKpegb4L8eHnyWsoz/wOcztr+ecMAb5TNq6lNOU23SHdZdbouNJ dF3PONrAOV25/ULoG+AfyPmEyL7HmvvR/SZz50NLpQ0rRylXnc7clyXWcQO6 lhWsWzn3I3i7pb13xaxi98Cx/kw92B/wX5Nq92orkWkK/B30n8FDdO7XWWj/ xY6pGap3nOkV5Y45xd4h9jwQvB/27ko5BykXDWDuv8CvYv9LJZY5AL4MmVL5 Hx88qX6j2mt3QX4w8guJ1/FF7sn2Qt+Rsi7lNOW2ztiQLHIOm83ej2JfedI9 gHqBSvTNqnRNVW0di75bwT+HZyHr945cy3ewp17AjyE/ucg5aACyYejap55B vcMY5P9Wah0/4tu+oe+uaoJqw8exc9th9P0bOMPcj+hfBH8InK02fDbrd0Bm T+zeSzVatbqP7g/+KUd+D/a2wsau8N7JmgPhPxu8B3gt+BT4W2sP4M8yhqnf jpzrOqKjGriPckDSOevelGueat8m8Gr0h+homXQO3c3agxhjkR/CXDd4O0Wu XfdIf842y/ZzGI3B/xw5NzUALwKvlzOsHlm9uHjEezP4hcgOBZ8MPo/1Z0Tu odVLN2f9M1m7Z8prKYcr12tP2ptqwELgzRnvbRRzA3J+A+gtcETxgb6BzN2U 9Bn9clZZ845kXAVtGDynIL8E/A75Nue9qqdQb3EJ+DVJ53zl/utS5tV+1PuP g74YfDnrPYOuIcwtkC+w/1ex3wB6C0hHd3j/Dr0h8HGMUvBnI+cWzZXkPCea zlRnOwZ6Z+zrBX4u+E7k+yZ9p3S3alWviZ8NrP8/aDWM9oolRgvoWc50QNI5 Q7njA/h7lzpHbSyYR7yKCcXGqJTXUgwplnTndPcUc4o99dTqrTsgvxL5PpHf AlqzObQXmDur1DbVAb8fuZeTDZ2gr4r89tEe2uj+w3MRvO9jfxm2/sB5npL0 m2Zu7BqmWnY79BuQfaXg3CCZVQX3eOr1LgFfD76uYFg1a1vaNUy17Bl47s46 Zyp3lh9HH8XdG8/4hLs9n/XurHYPq152rfrjlPeovdYw1z7nHKZcJpm74C+P rFs1Kgn8aGxdj7HHR4DbYP/4Yr9JJ4E/HTlWtJ+hwHWh77JyVm3oOdGUs9Yj 2yB2bzGafLa2yjVTtVM9tXpr+Uy+0xtKb6lPkN9V5jeE3hITsPm3Je4JJgLv jpzPlIP2AK8O3bsqhhXL6kHUi6hHGpGyz2XPZMZo9TcF90KqmaqdM0O/nXSH dZf3Rd671hid9htNvdgo6s8T7KcYnuEJ37m3wH8T+67rDuguDEHflsAxfw3w qrx7/WHIrwHuGvvtMxD+LrHPSGf1NDrvC30HdBf05tHbpw8yBytcI1Ura/Pu La5GXx3wGEYEvJPzOxd7Po6sWz7cGjknKjeqhr8W+h+G/mWoZ1Uve0aV366q saq10yPnMvVE6o0GM0aXOacqt94WOvepBxgb2qfyrXwyUO+Ngt8OU+HZWeV/ GNOPve9nqX5EzvXqUdLA3+fc26lGqFbMzbjW6M2zOXYPol5Eb3K9zfsiM7TM b4rT9ZYruFdXj6ReqSv0QWV+ozTS+x7/jE74n0UNus+J3XtPx6dbq/wm114l I9kX8vatavgc+NtW+a2knk693Rf44C69NTnj7cAv5v0WOMTcJugNwF+scE+g 3uBr1XNoedVj6CNz7rXUo48CnpT3W6YJOiYDN0WmY5lziHJJT+a+RV8neKoy zvnK/aoJ07J+k+tt/hQ8RXnfMd01/VPQvwX1BOoNtjEWASf1BqywTbJtrd4/ Fe4p1FvozHR2mtsFfCht29VjN877Ta+3/Vh8eAf7aRy7l1LP9Sy8k2P3+soZ E4HvjM2rNXvGjgHFwino6JbxGegspjE+Aa6M/LZVDm4IHEV+Synnx8C/Q+ZP xX7TXIstw0PHvnroEaH/IUn/bEbPjP+56d+beupy8EYZvw11p/oi/2BsXvVk 6s3q5e3LzeibAb4v7bevzrQSWsvQZ61/SGeEtlm2y6aN2Dej2rlZPeY04G+Q WdDAMavY7ZBzLtaZXif/YU/3Yr+B+sObyPitpZ5FvYtyjnLPYfBa7CmP/W9M /areQvKhfKl/Ugv0ns47lhTz72D76rxzj/4Jfgp+e95vRcXwBOWi0P9GdMcH 6T5m/bZTDJ4U+R+P/qeoJ1BvcGnOuUo9cz/V+oz/XenNfBa69+b9r+ZKfL4b +Ee9ScE/V7+R9Zxo8sm+vGNasS0fXK//o3n/K7oX+kbgE2L3Ioov/XvVPwz9 y5DOIzqrrHtdvdmmYd/XsX2lf3BL9S8jci+lfK7aIZ3SrZryAfDYvHtT5dDb gB/J+a5sRN+N6Ps/0bsKPA== "]]}]}, {RGBColor[0.86083075, 0.879745, 0.8700376000000001], EdgeForm[None], GraphicsGroupBox[{PolygonBox[CompressedData[" 1:eJxFmXuY1WW1x38wMHvPZc/M3rMvv99vwqMy5uGUBGiJBlNy00CFo4+WYoSU 5RW8dKzUEkUY6HJQuZSKduw8ItnpImCjJ1MTzFCUVAikUyHeQuU5lmZe0qfv Z76rpz9m3ne/e73rXe+6fNda7z5o3oKT5g9NkmSL/jVp7OhMkpL+nmtNkl8X kmTi8CS5dkiSbKgmydbmJNmo8adar2l9lNbvGO55RXv/PUuSPs2nlZMkFY+G /oa2mdf/aM+Doj89TZLP66DZGpuHJcmk4H9/niRTS0nyc43rh/vcqxOPfUFT EP8fil+TxjaNl4vPs1qfoX27xH+a6BZp/kHND9W8VXTtonlF8szU59maD4h+ tWhW6W+i6M6WHOv03VXdSTKW+7aZz3WSo0c0yzV+R/euaf0ArX8l9fzIsu9d Dz1wv6VtvuOqhnTVonM01mtJcjj60viedPdfoj9IPBaJb1n8+zW+XLacyHtg apqFkucE7T+vmCQL6knyY+nw7Y4kuUQ6uWu4ec7SuKzVdz1L576gPb2S9SCd tVF8x4v/eo0/0ffj9PcF7V1a8j2nDPf9V+nchbL9LJ19nObvkyE/oPlYfX+M 5Nrd5Dn8/1f2vEd/S0rmfWTJfN8W/e3tSfKGxvMl73jZ9aHwjVHhKx/R3U+R bKdJthVN1iV6G1O2DZu1d7tkPVk8n8x9zou626Yh9hV8Zr5oLpSOL9LfhVq/ seDPzJ9vte/ia4eJ506tnad9V5VM88nwbeg+LPn2SZdnaP+pidfYh+2mS74d OmuSxvuKprllqH2kQ3+/TCzjSSXrf2TZ+57I7V/IjI+11OyHrTXzJqbuTvz9 H+Ne5ZptUanZf6CDBn/HFvj8g7rDRRr/1mUe3AH5iZExEZ9H6Oxp2vu8/GWR 6Ht13wmS69+kr8PEp1hx7B7d5nhbldrPPyGaXPeY0GoblEX3UdEfQ3xl9u1x Zd8DnqP0+ZCybfuo7HmIbN7bbt+9rOo4uVzj4vBtfJz74mvoZLJ0PqvTsuND xMs9if1zdMG+dL5k26vY+Z7u0lHzegmfSS3/cWVjCrrHxvj+qPBP1sGcLeL5 l3bHy/OSbb/uskaf92m8smA9osOyeN2kfXdK5mOk34O170Cdu1Ey36W/64dY xmUR161lrz2gez2d2oa9WjtZ8r0Izoj/ezrzbu0tDLO/rNbep+Q/72j9J62W h5F4/qb2D5SMgzdjY9FsaLUNziratjcFDTJdDx7q3O26w+M134d7vaxxT5t5 gg/NZfNep3sdUIy9Q4xn2BvcRMaBWIeG+QrtnTbMMu/Q/Iom04Lt2AO7/LfG TxT9Hesz9Pk0fZ4rjDpNOrxUa2dKP6/rLt8j1pps11tbjT+fqlk35+X2SfyR +2+UXBv0N79gHazX37ck2++LsTbUMqNfZMVnyQX47cfj/hMyy/7t0Pm7Habn PPi91mSeyIU8izTfXzQdNOfmts1h0vGwzPnwo12Os77ISXtEd6Y+f07fvSqa z+jz/oplxn6cM133eKLgu83pst3GNOx3+B8xjm++0/FPf2CO/Uotptkq+edJ p2dIvprkeSRwDMzj/siMvS4lvyC7xtmS/w35w2dy5xjyCPnkkNT5iLzUSG2T SehM+eJKzY+W/KemxsFPgjc6d77OPU3zouiKotmrve/qLpdIZ2vkVwen9tMT Msc1d+fz2NSxMya1jrE9eiYW+gvGz7ulh4FOY/O41HY9PORFv+i2JL4Duu9+ +fKMouf3an+/aJe0GvM/Hffl3r9InRfuT60jaAaxPbU/PKdxnujeFv3c3OeT O8HWSmq+3anxknXw5KUOr+/U2iNFy4dshYrpwFfOGcxDQ4y/4PALGk+SDqdr z2sN+xM2Hq3x9KJzKnrknK+HDNwDzOL+73SZ352Ss08yjOi0DV5odh1DfmWt vc3rrRX7Cf7SU3HuIJ//VuNrHbb3CVq/TjSrRbNd5yzU/qc0Xg7Oa7wss12R a7pkfih3XfVLjS/Jt97UHffVnOPJ9W9m5v168M/F/wvif17dtJN0rw8Osd8g 4xdFf1PJ6+AsvsF+MAHfmRyxM9DsegMdQTs5+JCDwHnwfq/0M0FrX5a8UzQf KR5/li//VWf8SutPan1P1bH5iuR6psUyf2moYxcdr9X80RbjJ9iNnbE39nwY Py3YB/ZI/olae6bhuhPMGZ7Zj97S3xywRfc6tMXnvaTvLgVjqlGntNm+YA66 4t7IsLzFvoQNiMHLJM+Jw/yZOfa5Mmx0U7fXLhTPD8seUlHyIY1jc+/5ve64 KTfubM5d23AX/PaGbt/7rcw8sT37L25YhosattfUVutxdtH5GLuMaXP+5fNL Zfszfn22xr+0OQ9NzJxzfqpzN+usoWJ+bY/yhOyyS+sXdRnXwGew9+KK66oH xGdbyTllZ9QT1BXHS56kbMzoVbys7bTtsGFHxb5WT12jgDfgEDZG9+DxxMgN vdp/RMV1Iz0HdmMdTJ8bOPA5jf9acd326cz6Rkfo5/EO85or3RXLlv1jWdir 0zYjDxI75MLHop6hjhudW8/jIn7viP5o0Mejr/ltl/36etF0tLlOhq6p5lrz HMn8gYpxGDzGp/Ht90uWxd2uZw+VrrIW04OrM9vcF9AfnJzZRiPE49cdxqWV kvPPDePofTpnXua7o4M/6G+mdPh/WjtqmLEGnME+6B5b7uqyXnfp/DFxR+56 QdFnUnf1d7um+4jOOaLNuZM7c/7UiOU5uWNwdLdjHSwl1iZnjpORqWtJegR6 Bfbho+wdKLj/pHcg/pZ0On4XaH5hu+so4pp1ahH0Wo8eFX4DsZdYfC3ikTtz 9z3SwZHRp/ygx3s4C6x5Ou5+bfhOf6fPX6s9Re19VjZ6Mfqvddq7Ofop8Hh8 xT7yFa1tyiznbRXL2B9yzutyPu/T+UPDj8GeeuRr8jay3BvygLPgbV9q3c8K /ZMTyA30x9Tg1EPk3mXi/6rGG6v2dew+SjJ8teg+kB7wli7H75aq+xL6YGr3 F8vGcGoB+LdF3UVtyBzfHhO9Brmxqc39/z/qdEZkw6+oz4nJA2v2x8Nz+9nM 6BH4Pg2aOZnXZ5Ytx7SggT88wY1zon+njyfWju10HMEDOvhQL1HjUuvOb7ev 4CfYh8/0Wb+oO49fi1zSW2+LfWJRl+uImRr7e6xDdPkj2fqH0tkPpKsVJdOC xROiFu3TuKPqdWKLWAE7oLmzZLwid9BfgtP0mIzwZ058UA+Qp6DD1zgPf1kS vkeOJE+Rs6eUjLPg1Gdz73lZ544uOZ+RO6k54XlJ9EdvxBye+DR+SP4jf3Bn 6vL5oatJ0RdfIZ4zKn43uE46+8+o+an9t5ZcZ4Ld5Hpit0/zxyvuty7nLaXT OZcYpXekh4TmuKLjDd+mn+Q74nRFzKFZJz7/L7o/6tyVmr+u79Zn5vH+oKHG /FGL8RBZOAf5lsVbEG8ty7uN60O0dke3dY5twfzHOpwntlRcizfRUzfM82p6 Q+2Zq7M2aPx80XPkWJpZhukVYx8YOLzhnoO6BsyHL/0XOMI7CbhJXOBD2Pa2 smtCalPqHOpj5tQP9Mu8idBzXl1yDc339K70iF3aO6pkfBvEuU7TwIf6BJ9b nLjPeDt6DTCZ3DA1sHlbh2uAO8Rzs3Dg+xqfa7gGe7Zh+/Lmg063FO0P+Cr9 6d3Rx1LPcgewYGTV/U1/xbw5a2XUpNSm1Bj0qPSqhXhroj5mHTtgD+w0q911 IXv42x178f3dkTsGe8SSsZjeixqF90vqsvVxd3pqbIYt0Mf6iMPB3lXrzYHT 32o2ttGH4bf0YpwFDbGGjLNDTuru02MdHaAL+r1zoy97TDwXVM1zvsZnij6b +ORdgZoBm9Kjs5dYI1/hq/j/8qJ1h976S36Dwxeq8tfvaqxoXJAZ685NvTYp 8iWxAPbTC/8s3hDAwZdrtjO1M73VvZFTv1oxBp2QW8/oHP2ST8BWsB5fgH9f 5GL2s5d6c1/Uz5zzs9DDiKr9hHc17rIy3hup3Yn1Z7pcVxL/fKbGpdadX/Ya NRb5BWw4LuYn5q7X6dta6saqn2s8oO4cSi6lZqHupW4hbtAbOYS6nPr8PdGM rbsXT+TbOwt+a6UGJR+dH7HJ2yvrh1b8lnVP8KEuOz/oD+nxfUf22Nfx+XMk 439Evrw49bvX5OiRd2j9G/Lp32i8tWqMv1n3fVKfl2l9e2bMgB5fRe/on7sf G7YgHy8sOp+gL7CFmKG+xec3xlsKdcTjgWnUC8QAeMF7BPY5U3r4W+46clvF 54N1+A5Y1RN4RV2QtzgWwABwHBwg7/BuTwwvye1vR3ebL/x7e1wPURfxPo39 meMDN+fGrFty17LUtA/WHSsj4p2KXMFZ2GxEyMM67883dFoWbIfNBqKepk7c 3eXvvxM09NL01A+ktj38NuTGpG2BgcRrGrmDM7g/dc6cqr/jrYU3IHAFHYLt 3J+cC55OCT28FT0lmE6t3Bv14+258/G63BgFH2oPsPbYkvlRe/LmxXvX0tzv cNfklisL2cbEG8spmW2Shl1u6LI/n91l3EHmTd1+K6RXOynzOVPjrAcz10IP ZX7TpD5qKfvNBp2Rx9HfDaF/+jr8gDrx/nhv2ZS6tng4+mXwEhrkoZaj5iOO qHP2RU9KPl8R+LY6ai2JOBgT9B3sQS5qj8EarWBc3xBverzZEef0pkfHbxDv qxvb/qVuG50VOE9O2Bj4f3vU6kdVnZvJ0TeGn+BP3LGaOoeNlU5+l1m2csN1 NPX0jsAL9Iy+6Xupoc7IncOw/32ajwh5kIteC4xtz/xOAfau1PpvUueA3anf nXkXmUydGTXN1zV+reE8dEHN9yC2F0f9Bs6AHZ9K/ZvCZzPvh093j3M2eN3J 7zK539l4b0O/48Mu5CJyEjyfrttnqW13ab5X69+u+Pu9QfOnzDJUG9b9+NA/ ddn+8NvNZeecUo9rRGrF46New9cH/bzk31HoD3h7xe/Zi1zEM7hKjl1c8vtD U4/nQ3vcm0HPb0rwuL3Tv8cMvhV0uk/+a+637KcrXlsbNNQF2yP2+M2Qs8AN 5OD7nfE7F7kWnL9G8l9Fbqo4x5Br6An4/RDfrUXPhj++UHfNRX1G3QU+sxea uyIvkt/RAzrAP+lpdoUMP47fgcbV/Xb8ROADPrg6fq98Lt4z38yNo/RvYOma sn3h1uhHVkZPwt6VISd5mLqNd6edddt1d901CDXFP2pL6kqweGzZbxNfTL2+ KO7FG8MpYbtHi+ZJHcj4SPB/J/f8a9LbrLLfJm5NHdPUidyLexBX2Il3+nfj bZ83M2Sg7h38jbJkH7ql2yP9x2FVYyy/vbwaciADdlsatiOXcI9ruvz2uTx0 BV4eHnUdPlsIbOHcR+Lu1ObfLLm3xi+2hW+Ai1sDG1fVzXOFxkKP6Zt7/EaD Dz6VW7e8DYE12+J3Q2qTNSX3SAdHn7I16mreW7YFDTg9JbD6goYxdKrue1Td 8UWcUYvBh3pse9QPT2Ve/27wn1Hzm+OvtG9Ywz6Gr2F/6lBiHh5rome7LXPN vTazPpeE/PTG9Mit+GfZ74PfSP8Ze8hMf4iffV+2mlr2G9By0WzoNvbyXsJ7 3tqI0+Ojl9zX7dr89YgLYpbYhubhun0bHydXo3Mw9Ircv1mcGvhHfTb4G2zd dn2+7lhYGDFIHKyIWDgx8282H0rdT6N7/Infcfmtb2dq/ELPD/EeWTefJRoX 111z8S4LjmyPemBX4Dm4vi/ezF+p+W2Iu5N/4IEM1G1/B2exfuE= "]], PolygonBox[CompressedData[" 1:eJwll3lwVeUZxj9yIXti7k3ucg4Nagl1KIsJ4AiUYMEBKZRFkSotjYrUKkvY rK2ggoQYwIUKwQUJWh0UmdpxCA50bK0QwKK0VYEm6tQJtCyyTPdq1S6/x+eP d+b9zrt/37udS2cvvK4xL4RwHugJRKkQbisM4XvA3EwIrUBfvq/nvAk8v3cI D5WFsIDzV+F9vyKEgT1CuLo4hD9lQ/g1PLcUhPA8St8A35sM4TD4UJQ3VMGf C2FtSQgdyLwHfiRGBnw6Oo+CfxKF8EKpv30MPrIyhPJeIWwHEml0w3MX/nwK z2zwsdBnIDsUmxno72PzOL69hM33wMvxdxN0SOEMvOfQuQXZu9B/FvwghIPI viEAX4TMfOQbgQHE15Z0LCcT3AP+f8i3KcQyFTjGeQg+HC31tzrwHui8Gf56 7iMPvAj+76Dr28At6J7IHS3F9kzOk8Cncn/fwrdZ2F/A+XbOeziv4Hwf563o 7I//fYj/R9zXYOBldD+Ejnx82w59MPT+0FdBuxYbE9A9ETgLfQwxXwN9f34I O/D3Ks4jOQ9GfznxPohMLe/xMvLrwC+K/Jaj0V8OfgiZPGSvIL7fppwTyo3v AzHnt9DxDromorMDX57D5xNFIXQRw3PEMh3+SfDuhudVeEejsxt7S6HXg89D pgDde7A5H7wCn5/C9lH463N+M73d/ehYja5CbA7iLerwdwDyD0BvwvYq4H/I dyKzr4dzrAt8PPpGw1uLvgoUjYK/Gt6HiWkhsd1f6dyK8Plz5R/wOLLj8elj 8I+Q/0ah71R32xedz+JrEzYCtOPEOAr8F+j/G7HX5Ew7jL5+Oces2PWGbejb hc3D2M5H/yJsP4bOR9G9kpxp43wZ8c3j3InM28gO5ls3vM/g43+RPQe9gfNN QDfnQ8gfgv8tYAW0jkrX2kHca8O3A/h4JM8xncSf++B5s9Aykj2eda7W4HMX /AnOZUWuWdXuM8i80ss1X0AtfRP6gR72cbLyF56p0E8R02H8GZrzW26Apxja EGz8tMA5XZfxG+mtbuDcjK/vEGMX9El8OwDtJDwX0P1nYDu+VmLzy/A+Db0K PD/l2lHNqnbXoGMNtBagJ/QzGcu24/9OaHl8ay5zDaoWlePKdX1LQJtBDr2C v234ez34UHxoJb/G820Y+Ahk7kW2hvhOo+8OYCP0LchXIP86+hYhu5j73Que wqe6Avsk39Qj1SvVU9RbDsaOdTw+tMI/n2+3F7qGVEunsratnvoB77E/dm2N hX8D/KNSroWVQBOyl6btm3y+BPyO2L1KPWIu+GRi+hrnds5TwD/Cp00J90T1 xqKk30p3UIf+d2P3YvVU9dYFafci5cQhbPdCx0vEmtC8iNwz1TvVg15A9vKc bT2MzkJ0t/AtWeYe/gB4qrff8hNkziK7DB2ZUteYam0p8jvgveaiEJ6E92Jg D/wbsNfAfcyBfwT5fRsODMOfWbF7yb/Q8d3YOahcfJDzMXjLuM8cvsfA2/Av QX8p/v8R/2qx92KVe+M/0NcKvb/6dcI53RA5J5Qbi9DXAX4emVLoGeglnPsQ Q32Bc0S5cif6y0scg2JRT1ZvruFbE/hcIAHeic5Bqo+kdfVHRy90zcx59qjG +iXts3xXD97Pe/wu5V6lmBTbZu7k58T/G+5oIHc1Lu1Z8ho6lsA7KmnbA/lW BO0n6J9a4ppWbc/k24x89+Abwdu4j92cpxHvNuJrRH4ZtbSCmLsr/SZ6m0LO p6FVY+OfJc5h5bJyWrldz7dNOfdc9d5hnMfBX5b27NHMGct5W8p3uxDYh70h wAfovgIoT3kGaRZ9Bfoa+VPl3n0P9OHQ/571rFMPPsFdXE4OQP5iJteCL4f/ iXzXrGr3ReAY+Bx41sC/M/Lb3wz/LvABxPcmd7kZH/cSz92RbSlHl4NPTNmX fkAL50eIcSfxTuNOtiDbF3hV8xqfqpHfjP2/QO9Ex73I1iS9+5yhRm5UrUS2 rZ1Lu9d5zqcT3lEugN8Az8liz7S+SfcA9YKR3N9G8H/D88M870yfgqfT3oX0 xtPgfz3jXNgKT6tmWdJvox6VTDvnlHvK2TpoE5LWrRnVXuWYFJt6SBL/bo3s u2pktuoh7VrWjngO/OKMY9/IuQ94R869cTH+78s5R5Wrmgk9ebvlsXcLxXgP +EhkWgo8s0aA/zj2bOqNzKPgF/Bvd4l3Cu0W63POLb2B3qISaCl2T/6swm+s t15PvSzB3uTYbyGeKbHvRHejnKgm3vzIvUE9riDyTNFsUY21kDsrKz2LNUM/ Q/7OyLWgnvID8Aspz+bLsPdXzr+EZ1yZdzDtYiOwUQi9nXM1ujdmvFtrB5ic 8k6g3UA9T73vtdi7nnT8Crw47VrWzqPdZxUx/Qxba3n/dyPPPM2+L2xUegfQ LiCepqx3UO2is4DilGeYZplm7Bh4x0auPe04V4PfFLu3agdpiL2jaFfRDNkK Pi/nXqadbz74nNi7uXaUW8E/r/Dbr0NflXpv5F1Jb1im/S3jWaYe0wy+OOu3 0j/EJGJtj72rD8fHXeDPR+5N6hHqFbpD3WUz/g/nbr9ETBMS3vmuhf77yLNA M0OzY2dsXdqBtQtXZf1Wjdibgb3ulHf7Gr79Af7rIu+i6nHTwY9EvmvlqHJV O5p2Ne0w2mUORO5l8mE/+FWRZ6F23q+DJ7PWrZmh2SGbsv2h/q+wvT52rmtH 1a46JuldVz1ZvXlb0v826hGDOKdzfkv9U/yHuz6Vce1phzoNPjrnXV07dRLZ K4F0iXviMuSLM+51+gcpAq/p7VmrHtYPvDHyrNXMWhj5jnXXJ9DZBf/jKeP6 5+rMOAbFoplyZZX/WfTvIpt3g6+L3Ds1Q9eCP5b1rqx/ukfwf3WFd0P1YPXi usi5o52/Fnw193MseKdojr3zavdVj93Bff0f1ZnvvA== "]]}]}, {RGBColor[0.91439425, 0.8976069999999999, 0.8460411999999999], EdgeForm[ None], GraphicsGroupBox[{PolygonBox[CompressedData[" 1:eJxFl2lsndURhs/Fjg332r6Lr+37XSrEFmUTkK2gWJQECA2KlCCqolKJRQTC D7YoJhaoJXGcpGwliQICiRIIwiwSP1pqUAX/WjDGSGyKY8eAQEAgDoFWdSTK kgj3fXgH5cd3z9zzzZkzZ+add8532pp1v7nthJTSrbmUGjQ+2JJSTzGlXRoP 51P6Ss+w3o3l/B95/gzpFFLao4X/qln+p8amFusXNT8qvWnJb8hmKljeq7kR 2X6jaDvofh02cwXrodNZtHxuY0qzmv2O+R8L9mFEr5fKh39IXimnF9Qsn1RO aZ3+N0pvn3y4pWb5Zo0H9X611jTrubw1pfsLx/8ztkn/QJP/4/Mf9P7tk1J6 TvNLSil168lJ/qzR/5GXVFLapP1O6UjpaF1rZHdzu+xo3YpG+9CYpfRX2ZqR +Ux3tfpcx+qW+6W/O++1E7JVls1F0tt9ss+CDeKDf2+f4LP/SrY+lc4FGv+H 7UbH4XXFrV+6G9CXnff1bqF02vS8LLmocU6cZXY8nGWb9J9S7nblLO/Us0XP Vu31neZPVw4+1R59muvBfrKduRGH4aJl9p+vPSa014LMsX80cvFK3jk7JDvr mz1uk515sjepR69SST9FPftyzjcYGtf87NCpSn5UcWmVzdtrnv9Cj0ylv+vn UOicolgu135dM6zDvMKc3qk4x29K6RJhpUl+lTqO78de52vttzrzL8MP/OHd n7Xn2gbngFwM5YyT5sAO8lDkiRxVZafS6jOBr5/XXdpqrP4cC8ZmzXc3eM9p 2RhqN0bek79L8z4j/hP77/XcmHx+4rBB446aMbOz5r3ANvsN5I1jMHyqznmR 4n6axis7nIPfa5xZs96ZGs9vsIz+IeVy4wznd6Jmm/s1vt9oH9bKhwWK4UFy 1+Gzj0ZMwMKcwAM4SrLRG/4jb4hYwSn7k2saeSTmxyMXm3OOB/rEBgzyv1O2 O0rmETAMbrFPDVAL4PbVzPIziuEV8u8bvat2pfRl0bU5kfN69qW+UtnnP6Pm vcAA+1FnYIK5hyu2g7175fO5rebCFXljDrwdLfldu+zMl81e6fdIzpddA4sk N2tulvacqedEzS/X/3M0f1bO9UAt8G5WYHtuzjhGHtTPC43WZb+2BuObOqfe n9U4lpmD3xLOl+WNIfL0jOZ/0Pi45s8Krryp5vWzo3Ymy8YVdXZG2bz5u5qx CCZbuxwnMALP3CUea9HZ/1g31sE8NfOnuuVtda9h7bsVc3tH0f2AWmQtNQTX gjfsDpTMoQNa11cxL77ZmdIa2TqqvFxXty4yfHxe1XkhP/gLv1MHxJt6o8au rjunV2n8sOScHZT9W/Q/SefmunFNT6O+qA/mqdEdJfv3iPSfb7c/+AVOhwLD xG9u5I4eSv+cF1il54G3FxTXC/POyVisBfNgBEyQc3rFxvhPjEZzx/tmZ2Ae 7CNjs6HDPbJbft2n/M7SvvdrPNKW0pSex/LH1+DnkN6tl87rGl9VXm/Xfq9R 1zVz3Aca75T+HUX3hPZW2yenXwRO4CL8+TLOxVknA7v0ArgD3iBHyOSUmkOf XjAZa6rRR6hh6rc7eIO1cNX+FvP6gManW5xjfBsIebVydljyKo0vNVmGnxbX vXaRxvl6xiSfo3Eqs51/V1xHzFNL3HfIGb0cLpkdPfEj6T+k+U8q3pN4ML+4 4rvODXr/2+jBV2TmuzlxljXwj3Suz4xdMLOp4j78YIPrdlf0XfrgZLv3504x GtgYDyw9FL5xJ4Dn4XvyQV7ABmeGh8Av3IPM3I68a5U6Jc+LG4xvOLgnOBMf p6O3cRegnrYF78J/m6Ln9ATnk8/hyOlerXsg73rhzrA5uJo7CPPsW9D5X5Le 12Wf+8WC8TNY8Dy8tD1vfbiJ94Ohgw+bwibvt4fNe+M+w72G932hQy+np/+6 bCwtCWwdkO6TWrsn7360M/SfyFveGv7zf3OcmzsPtpu1x9+0vknjfwvW2Rpr 94Q+2CPea6PH9UWsqMUXI9/ciUYjB9Q7NpsDA1siF+QWm5wBXE41uabg9e0R H8YdEYfxFuugS01PxV3wmrrvbdfWzTe9wS1X1pznM8uub2rpm7i/gq9yl/N/ rMkYuF7rj4Hzuu/p9At6JbVwZ/AD/ALPcF7kvsgZPY07DfHhfnskuIh+Si/t jrsU/sCB2ECHOvmkah++qjhGG6Nfck+ixxN/ehs97rKyeQa/4Y276+ase+q+ l4CvhWXzCP3yUNwZZ0Y/7Q9sg+HJ4Dc4HJ4aie8UsPB43ueq1pzDY8LVPZlz u1J+Lqg7Hwvr5pP18Q3Fnb8z+h0PvY87EiPz9EVqEpv4QG8mzxeWzRHYpxY4 OziBK+jP9JJl0UvIMecBPwcCQ2Nxv4HzycVnBcd8nnzeJ/nEiu9pw8HV7A8+ 8YE5uAyMLcu8dmnmmh+O+c+7fMf+oOSzcmYwxDdaanFu4Ed4clzjNZnn51Ts /7K4k8Ah+PND4OSxiDO+7A1/LtbazyVflBn7yPDaRIs5fDDu0WCDONGzGYnV xorr8y9V95+R6EF8ex6Ob1Xiuitiy52O71k4dEtgDryNVV1rH1eMI85OfuhT YKsanABHUL/UIjJrqcmpkKkZao+94KT/FJwf7nPUMfW+Ir5dsLs6s84qjev4 ltP62zLjuC2w8Vr0IHoR2F4aeAAHTwZH4cuR8A0s7444X555/hcV9wHy99P3 dWALOz9WXQNnt5uH+6NeiBHYpT9Qu9ikfsE9mKgGb++N/MIl1DW1ig0w1hu9 gP04S4p8wM9ww3T4A/9w9nXBK9PRb3rlf1nzGzLXATJ8Bw+BpbXxfcG5yDXf o9wtwQB7DkYPIk7Lg6+I2xPBw3BDf/Dz/wGSMUkQ "]], PolygonBox[CompressedData[" 1:eJwllXtolXUYx39zZ+fMc84u77vZ3vcY4S26SLpbBrYuM8sQtAtUmmk1EyrF MVMCUylaTY0sjIKiGy0jCkpLov7K1Ij+sOiyudlNnc1oK1zQhTasz5fvHw88 t9/zPL/nOr2j8+b1k0II3UAGuLsUwngxhK0VISyvDWE39HlVISzKh/Ad9PIp IXTlrD+vPoSnp4YwpQacx4frQuiBHqsOYaA8hLegextCGJocwvOFEF4Db8Ze P/b3oj8H/C7gZMY+O8CXAL+C788SQxxCnIQwmDFvKbJjxLAw75gU2+fEMITu Z8TzMrJhfMIKXcT0JrE8Ar0F3YD/e7H1fco7bM0uC+En7D9HTEeIbzPyZ8E/ wGYb+iegr0B3cgQPf9fDO4DsdmyMQGex0cr7WdC90G38N6A7A/pV6CHet/P+ Pugy8AvIWTm5uxMb//D2BH9aUXLMil05VC4b0V9cbp9N4A3ALvAngXMS51y5 fxD9t9HvwUYdsdVQr0eVS/7Qju5efFbifzpvXoKegf+zvF1MzLux/yHy7cjX Is+Ad8K7hPgvJ56t4O/AyyD/MnZtlcMJ7H8Kr4v4K+AdBL8D3t9F50y5uxHe Gd6OAVNj/1F/lc5K8BX4G807h8rlLHyuwd8T6A9C70NnHra2Ix+vdQ1Vy23o nKaW9yCfKLpn1Dsr8XeWt/8BF8aukWr1Ee/z2J5Ozhfk3BPqjTPo9/K+m/qP oj+CzvuqD7wdyJqhh5Hth1eAHo2MvwfkoStS5yZHPOXg8+tcW9XgcXzPqXOu VeNV0DtT21bOe8Bz+PyG9wNAI3RN6lpUIx+idpXIv4X+GrgY2VFyshP8Jux/ VfIMaZZUs3XIPgZeYYA3QH8CXpc4d5oZzU4WG+8i24T9Rv4ygv2JrHO4Gnvt 8P4ts8/ZqXtGvVMFnEa2MTGunDRFzplyJxvHSeoh5A8UHINiWa0cFV3TZdBX pp6l+dBt4G8AgwX73APelDoXrcjr0X8KaIHOYu/ayDlRbjTzp8D3xO69v9A5 xNvhBu+mffB+Bu/n/ZFJztkA+DTgxbxnQLNwLHGvSUe6P2JvLOud1EQ+boM3 jG6OeboV/JbUvjSTms0uenJRxjutt9Y7UrtSO2QXeH/q3VCD/b7UO0e7RztA u6AlcW/+go1m8AOJa6eeV+9rB2kXaaecn7jH1GuqYQ78IuINRfe8el89r95X zjug7088y9XQndAbgajonaDdsCFxL4i3CdkNkWOVzVXQ10XO/R/qOej1qW1p ZjW758aWacY169o52j0x8T9W8o7SrlKNq9Vrta6teuQgsoeZj81Vrolq8xBv ilXe4V/E7nn1vmZEs9KC/GjRMzyNWd6BvTXBN0a3RjOh2VDNZkaeUc2qZlqz vS32LZLPcXQXID9V8E7VbtUO1S79E53L6Ofj6D9T9I3Qregu+W/12kkl3yzd rj7kjSXXULUMyNeV3DPqHf1pC3TU4N0lmz+kngHNwkliuDr1jdCtUM9H+J6Z +JYoxmuQvVDvW6g/6+/1DY71deyNpd6x2rXayYehlwC/F3yDdIvmAgvLzVuK 7KrUvsWrjDwzmh3luJV455b8twHgUvC1Jf9NN38Z+f8ttm/tUO1S3VDdUs1Q H7H+D/UeJMI= "]]}]}}, {{}, TagBox[ TooltipBox[ {GrayLevel[0], Opacity[0.4], LineBox[{4155, 6404, 6405, 6403, 6407, 6406, 4400, 6258, 6517, 6516, 6114, 6408, 4583, 7065, 6259, 6260, 4401, 5381, 4402, 6411, 4588, 6410, 4587, 6537, 6538, 6535, 4682, 6536, 6414, 6415, 6115, 6116, 4314, 6802, 4315, 6118, 6117, 6708, 6707, 5408, 5409, 5094, 7080, 5095, 4665, 5406, 5905, 6621, 4832, 6620, 4831, 5333, 4830, 6705, 6706, 6702, 6704, 6703, 6709}], LineBox[{4209, 4259, 6051, 6050, 6252, 6251, 4397, 6250, 6511, 6510, 6235, 6234, 4660, 6500, 6614, 6613, 5324, 4431, 6494, 4649, 6493, 4648, 6498, 6499, 6232, 4388, 6361, 6362, 6358, 6360, 6359, 6865, 4387, 6231, 6230, 6780, 6779, 4814, 5321, 4254, 7056, 5060, 4816, 5901, 4815, 6908, 4993, 6248, 6249, 5379, 4922, 6790, 6791, 6787, 6789, 6788, 6792}], LineBox[CompressedData[" 1:eJwl0bkvRFEUB+CjshSCGbvC2pBgSGjsQkcQEmJfOkKhtnSioaIQSgoqjdZe +C/0SKiJ+CaKL7973n3n3DtvqpY2xtYzImKTnYKIl6KIbfldFrGbiKgsiaji NxnRqJ7zfJ5pZpnhhs7SiC466KGbrPyIJr3D1kNUW/fKfPNPyyMO1IcMeC+v MGJQjtqvsD8iy+WTfOaBc/WjHGeCZr0psvXduUOObFW3sKZeZdLcKdrc/8qZ 7fLInAnPksURX+Z8qD/lu3y1/yaX9a6wwBKLTOW5M8f6jqhzTi3h3BrZb0Yf xdZF5JrTor5I350zs3u925D+NvbqZaZslimSen7Mf5CP3HLPHdfO/7aXsC5I f1P1EFv6T8zdt7fn910m/v/HP18mOxo= "]]}, "0.8`"], Annotation[#, 0.8, "Tooltip"]& ], TagBox[ TooltipBox[ {GrayLevel[0], Opacity[0.4], LineBox[CompressedData[" 1:eJwNzjtOgmEQheGDtRoEvDe6Co2hMdophbgDOxuw5FJ6aZF4aYxsxMR1YAdW /qgNbsCneHO+OTNzvtm9aDdbpSQNHFSSSTVprSRXteTF+3YtucM16vo39Iu/ vJ0U9JQ35J3QkfoVz+qG+oE+4RFz/nE5+V5NflBghoqcGqo40t9ZTw5pWX1p p++OHvbc1N1IOhht+Qdv9s9l/+otbSaLeOfNZUxldOx90j9109zM3BktaM9s H13su7Vlto26Pweyh7jHh/0xb0ynciZYsFPCgP8PMpgwXw== "]], LineBox[CompressedData[" 1:eJwNz70uhHEQxeEjFpXELru+KgUSNBQSSp2EEJ1GoRIJS6NUUHABFCuRcAWu QXwkIhJ2t9FzC3apPMUvM++c8z8z79h2dWO/I8kaPktJayRp42so+cZTMekd TR7VhUqyiMZw0kQdJ4PJKa77kof+5E3Gbjk5wtZAcu9dJ/3FfM/sWL/DW0P0 s/LmcEcr+J6x64rWpZ/HmcyC2bla4iliWeal2y7w7NYnfJi/Y9KeqrxVb1fw 6oZx/k3eQ/Np+oE6pXbL7cEN7RbrfGUZFSzZV5fbRAMTtLqsln9u4wd/+EWN 9x/toTBC "]], LineBox[CompressedData[" 1:eJwl0stL1GEUxvFTguKAopam1ihi0SIMlwVqKAhhi0D3orPT7DbrVtlFciMl utFFQToWCDqztbREBcGtl22U2uVP0Pr8aPHlmfec5zznfflNU+Zh74MzETGF VE3ERHXEGr12MeJZRcQXv69eiHh+LuJvfcQLmuIpRVtlxGhtxFPk9Oaxqb6B 4qqIAbN3zPZg5nzEibxq3ox6Sn+QltKzdhXhld44uuSWySjHLfuW5RaQx6mM d7J+1kX8wjH+4Df6+Fv03pv/SBfN7ttxgF1M27VHb+il7btJ59W+qc3RfLIL R8455+/0ED/QwZuxu8HvRlxCGs1yruAyBvVbvbWfNjo/klUwl8dtd3rjba+x 7h1fse2+I0mOXX3u34sdtXaeCv4lc5W0w/keXz3fMK1L7spbwDImnRd4P6DT zhXZn/EJJeZz6sX0upwWDKsP4aW5Zr1Zvbd0hm7K30q+m3s89pYsxrxnQe4T /i71HqT5J/lXebPq3Wr3ee/yjmIl+bY1//9T/wCD/FTr "]]}, "0.6`"], Annotation[#, 0.6, "Tooltip"]& ], TagBox[ TooltipBox[ {GrayLevel[0], Opacity[0.4], LineBox[CompressedData[" 1:eJwV0LsuRHEUxeGt1LkLGhKewkhoMIoZUUg4ofAAdDMkDCN4AJdQjbh13sEL KNRo3BIxkRBjTOk7xS/rv/dae5+dM7C0MrPcFBEJRlointojnjHcFrHdFbHR E7GOIi+DD14VNX61I+LU+0Qur96nOXpMG3SoM2IQPzKrrfbIF+kl/wpH9h5i UbbSZxcWcMErmyvIftKEf6Z3jk1+GVt4c0+9O+KVltQZ7xv5P3OJ7Fx6k/3z 9IDe6u/SPewgq7/mphfzv2ZrGOuNGMWdm/tl6ma+9L8xbee7ulk/n96CEgrq HH3w7Wt3PNJ7zMpm9acwjklMpP8U//LTPMg= "]], LineBox[CompressedData[" 1:eJwV0DkvhFEYxfFnfAL7NhQa0ZGI4AsoaKxDMaMgKmt0I/QkKC3VUCpoZiSW ZoYC0xIRFEq1TjV+in/OPedZ7n3fjrnV8ZVERCxgrTritCniBDls1EX8tkZk 6Sy/R9N0l2boW21EsiGiDfuybdkOVu1plz3W60cGP3q/abEmotKoxi/qOccF Ns0V1R/khWTEJSb4PL0yU3IuuSOhrwpLZrZot13X6mf8De3hU/JpzODe3B2S akP8pPMUeu2q0Ft5io41R4yi0/3plogv+YNvf8SB/BDDsrLeEXrMH6Gs/oRX +xbM9f3PyzN48aYP+ol+78rzBeT8n2UMes867fKNA85ZtXfz8+6YwzP+AP+A P9w= "]], LineBox[CompressedData[" 1:eJwl0r9vzHEcx/G3QdpBotpr765naKUG9WuwKQ5tZ3pIRTogBg05hD9Bkcb1 rk4kYtRWWk3aHmVplEQHITrU2ZgkDRPOZvG4GJ55fd6v96/P55tv5/krufyG iJjDjfaIbCriMJq3RFQSERPNEcvpiDd4jS/8D/QZ/z2t0HxbRCddbY3oT0bc zUS0qDtiToKOiYso4Jfan3iuvoPOyU/q++18Qf4JpvBS/qP5q/gjV8O82kV3 WqBZelbfOSRaIr7LT+v5Rmfoj/oOfk1dE+2h+zHUFFHCorkv8FndA/V75fbg MbqwHXf4OfO7vSnjTmf07XTO0VHclytjkxkr3voW78RpmsJm/fvsWLP/qW87 i1v82zjkDY/sOEhHxDcxIz+NKm+Hvo36G/iNSKot8f/SbvPaaCuy7lNzl3vO ZZwSn8QJNLpXUc+oO43RBvFlfp942fkVpswa11eQn/fGhHOFLuCq/SXxJT3F +gz04hp/SW9Zz1f5dfFWmsFDbMOgOw2oPY4R+Vl7CrzT9W8ovwvj/DVzqviE 3fYsef9R2ouLvGO+Q0rdsPMB36EHHeJBftXM6+3//9t/hvJlQA== "]]}, "0.4`"], Annotation[#, 0.4, "Tooltip"]& ], TagBox[ TooltipBox[ {GrayLevel[0], Opacity[0.4], LineBox[CompressedData[" 1:eJwV0UsrhWEUxfHtG7jfiXIrhgykXFLimB0jhso5uZc5RUbmlPgAxFwOZWao w8BdEjIVKWLgdwb/1n72s9bez9tbPz6fnMuLiAWMFETcVkbc4QYPuEdnUcQy XUGqPCKNNt7rkohWekUvMVsRMYMpvT2eabpPP/U+8FYckc2P6KqKqDNzHRvY LovYwrp+SmY3t4M+5+bqv8i9F0ZUqmv5K2iLtzQjbV7SfQZHPP30mB7k5rv7 Ko1oRDN2zPumm/Ib7n7UA/xN9g7SE7mMXIov671pOo85TPL38PeiG4/e10eH vTWBBtkxmTP5DgyZWcMTstV00Lld/0ndxptwrlUnZUfVr+pf9R+uve/cviXf d0EXc/9Db4LvXPbKnlXnLvtP5Q7sOEQGa/r/q9xH3Q== "]], LineBox[CompressedData[" 1:eJwVz8sqBVAUxvHlDVyPW8iRa7wBphRKJuSSMiUGKOQWGXkGM5djRvECRkqm ZIDcRsw4J3d+Z/Dv22ut71t77+qxqb7JnIiYQVt+xFNRxFRexBJdwSpGiyOW SyPazbfKI3bVe1lyI14KI1J0X51CmueLd9COq4KIc9pkRyMaMKy+prNml7KP /Am5D1pEf2V/sGG2YG89bx3++Ed5vukXOs0y+psy73Qdb3hFh9k7zydGZDJ0 mK7bmUEaNXLzfIv+k3Q+Lok4wqz39eqdZO/MvgefeklvOrWnFu3mz7Jr9oyr 59QT9Iyv0z1D6g56aN+03gGtkq9EWr0rW+G8oz+g3qb99I+2yoZZVyKiG23q S/4e5wt66z03aCmLaEbSvY/0CXd4wD2e8Q99YE6t "]], LineBox[CompressedData[" 1:eJwl009oz3Ecx/H3ODHL5vfbb34UlhTmgDjgyPaLwlLEbTajkH+jre1gJ+Wi 7Lf0szWx3xoumv9zmn+jNr+b5GDS3FF+WxHl8cvh2evzfr3//r5rtc2n950q i4gCTiyKuJ+KOE4f0EQyYk9lRHJJRDUSKE9EpOh8+kN+rCbiS3XE4aqIO4sj mmjv0oir/B70qsuiRk8Ffcl7hTFzp/RPpyO+Yghd9uZpJ11of59Z18zup5Xi S/wkzYsH+EO0WtzDH9GXLd1Pz9g1bv56+856n8OE2+6qn6SD6prdmKefeOfV dYnb6KibUryMeKO4gENmrlRbrzeDBuTk76nbTw+g3Y6snR10k9+3GRtwTO6x 3oIZT+gu83eiVe1qt6/BsN9SR3Pq19KjcrtL3x6jpVvlO+W6St9T7hl/uT0r sKz0t3FjLf2udlquQzytp9V9LfRI6Y3P4im5FppWX+X9zswC3ppb1PuGToon kHd7zsynbm8XD5pxCzcxbEbKrRf4/eI+XMcNfhW/jX9bX7f+JnPGxXvtfGF+ I31OX/N++5a/8FHfQXXzvGfl6r3L9X7g17lzu7jI/+PGGbpDvI4/iVX2/FU3 V+83uVn6XvyTzqCIRn6D+zLYhgE9W+kWnLSnwty0mQvoI/d3q7/ivofel9XM oWUYwUW5ocT//5d/B+p4zQ== "]]}, "0.2`"], Annotation[#, 0.2, "Tooltip"]& ], TagBox[ TooltipBox[ {GrayLevel[0], Opacity[0.4], LineBox[CompressedData[" 1:eJwlj8tuQVEUhv9TWkdioC5xG5kYahHP0KQztxcQHBNMJAZoVZpOzT2D++Vd zDxATUUkbp8YfPnOv9dae+0TLTaydUNSDkwYOKQAbjqlGP6kmMZzl1Tg2ww/ 7MDySgYkItLSLVk+aYHfyR3Orx4pj+vkHN4y8wwvsGLJGub0b5ib4SV5AXbq Nkj5pQtz/Vfpj54ffCaX6TlQ65H7+BvvcYnzI/Uv8i/9XXwgJ6kZ3PcEs/tO mLLvgzzBU/IEdkHpH4YhKcNcjXdn8Zn/uMAbeUx/hbtHOE5uUz9Rq+IW2cIF 9t4AS1IwsA== "]], LineBox[{218, 1971, 203, 2480, 188, 442, 173, 3274, 430, 6878, 158, 419, 143, 2454, 128, 1955, 113, 1951, 98, 2444, 83, 353, 68}], LineBox[{4436, 4961, 7028, 4962, 4958, 4960, 7027, 4959, 5229, 4303, 6970, 4893, 4892, 4956, 7026, 4955, 4378, 6356, 6357, 6353, 6355, 6354, 6778, 4302, 6101, 6100, 6694, 6693, 4805, 5055, 5054, 5311, 4535, 5200, 4804, 5884, 5883, 5547, 4534, 5677, 4801, 5052, 5051, 5310, 4533, 5195, 4800, 6901, 6902, 6898, 6900, 6899, 5349, 4301, 6099, 6098, 6221, 6220, 4377, 4943, 7020, 4944, 4940, 4942, 7019, 4941, 5228, 4300, 6969, 4891, 4890, 6692, 430}], LineBox[{5083, 4963, 7029, 4964, 4870, 4871, 6965, 4283, 5225, 4282, 6964, 4990, 4869, 4952, 7024, 4951, 6880, 6669, 6670, 6076, 6077, 4281, 5338, 4280, 6075, 6074, 6668, 6667, 5432, 5130, 4488, 5259, 4487, 5129, 5991, 5631, 4486, 5510, 4485, 5629, 5990, 5125, 4484, 5257, 4483, 5123, 5431, 6665, 6666, 6072, 6073, 4279, 6728, 4278, 6071, 6070, 6382, 6381, 6555, 4949, 7023, 4950, 4867, 4868, 6963, 4277, 5224, 4276, 6962, 4989, 4866, 6664}], LineBox[{5086, 4965, 7030, 4966, 4886, 4887, 6967, 4296, 5226, 4933, 7016, 4934, 4932, 4936, 7017, 4935, 4375, 6216, 6217, 6094, 6095, 4297, 5347, 6894, 6895, 6893, 6897, 6896, 4793, 5186, 4530, 5307, 5045, 5046, 4794, 5670, 4531, 5543, 5878, 5879, 4797, 5191, 4532, 5308, 5048, 5049, 4798, 6690, 6691, 6096, 6097, 4298, 6777, 6349, 6350, 6348, 6352, 6351, 4376, 4953, 7025, 4954, 4888, 4889, 6968, 4299, 5227, 4938, 7018, 4939, 4937, 6878}]}, "0"], Annotation[#, 0, "Tooltip"]& ], TagBox[ TooltipBox[ {GrayLevel[0], Opacity[0.4], LineBox[CompressedData[" 1:eJwVzzssHWAYxvH3iCDpgmqPkhBx2VQtVnVZmLAdKpESm2ukibguQsRkqsss cU9ImEwmi3RlEEvLIIwkR+jvDP887+15v/er+DnSNZyIiHE0FESslUSs4zc2 sYHKjxFlyYhynH2KeKK3+RGPNPU5ohv36ofFEQfoV9+iKftWSyN25LvYyXiK xHRfvofnwojXLxE9Zq+984fW2/UNdeiV39Bfele8f80X86Vpkob73vmX9abt /Wq2Fh/kg2byaC7a9d7UVzIe9y6IX5FGm162mRwM8GTRfrpEX/CMar4pczP+ UyU+9b8TTLqvU+3cnoQ8kFardNOFm2vQqP/Au2jPkHxSPkwvM17v/JB30GPe CbUj2upfLagyt83bLJ63s8ncHP1O77w5Zn6WjtJ/tM9t/wGIJUP1 "]], LineBox[CompressedData[" 1:eJwV0M0r5XEUx/HDEosZj2OuMldJhlhYsGBjWLlzb6OkyMbKU5SHPSuL2Q8p /oBb9rO5JQo7zSAMNtL1kI1iM0VeFu8+55zv53N+p19ydKZ/uiAi5nFRGtFV GbH/MeJ3RcQe7dT/NT+tjjikZ/QZT7grN/sQcV4V8Q/JsoimzxErdBUbZuv4 lYgYsyv7KWKcXtt9Yp6Xf7SzRl3Ln6Bf5RsxYe+g9xy2eVJ0h+b4Vrz9d1cz WpC174Wuya96e1Vn+Bt89wfdlduSG+P74+5xuoB5TPF/4+9BN67c1/ued2sa 9bLDMgfyHUjZmeQplP1C+/Tt5tfqNt7v+jr1gOyQOq9+Ub/iwn1Hvjdp3zGd oJdmGb4pvk2eZf2s+RzOfGPRLWnvrd6X1D99q0hfgmKM6O/8r3vkcYsbPOAN QhFKag== "]], LineBox[CompressedData[" 1:eJwl07tvjQEYx/GnIkJCteco1TK0OjA1bp3PqbKUpSQMtFGNy+BepRIGi4SI ltRRkVZK0rq1sbDQoNKelkUMIpVw/gB6WdAYfE4M3/ze5/dc3/fkVDQfbzhW EBHvcTgR8WR5xCH6lBYui6gviigqiyjGUixKRiToQjolP7wi4ltJRFNxRH9p RCO9WR5xnd+BG+o6sRg1/JfmTer7vjIih1602tdDT9MRe0fN6TJnjN6iS3iX 5CZoltfLG6f3aIJ3RW5A/1X6kB5131t7qu3sV9Ng7wDtlt/v+Q79Ij4lf158 kj53Uwlvm3i9+AP2mFWpttauOmxBRv6Rup10F87Y1WlXG93gfTeiGgflBvVm zRiiafNTOKC2ws2VeOC7VdGM+jW0Ra5OzVY807NX/pxce/47yr3gl9uzCmVI unE1/ak2J3dWnNPT4r59tDH/u2BS/FWumZbmf0fPWTPH8c7cWb0jdEw8ij63 d5g55PY2ca8ZPbiLPjOSbm3l3xZn0IVufiH/RL5fX7v+JnPeiOvtHDZ/O31F X/N++5a/8FnfbnULPM/K1Xqer/cTf607U+Jp/pwbZ2havI4/gSp7/qqbp/dH /j3oR/EUncE0dvBT7kujJv8OejbTTThiT4G5pWYGHXL/RfXX3Dfo+bKaOfzB Y/EFufvJ//+TfzHDeGQ= "]]}, RowBox[{"-", "0.2`"}]], Annotation[#, -0.2, "Tooltip"]& ], TagBox[ TooltipBox[ {GrayLevel[0], Opacity[0.4], LineBox[CompressedData[" 1:eJwNz9sqhGEYxfHHHdiOZuRgjrkC0jjhAiaiSOYrp+SAhBRGyG5K2Z0YDuzu whXINXCAkk1hDMrv4N963/Wu9XzPl00m8uM1ETGE7tqIQnNEgu1URAk76KmP SDdFZDDdGDGFGdkcXhsi3vAp88w/dS7rDLof0QF6TKu0Xb8NHzLzdRGL8nP0 0vsVDtMRBxiTLbdEnGAEF96KerOy7zTxfsY7x7L3FRTxZJ8/uz/SJfcu52v5 b71h2VG9PfMLdJ/e8DfpFjaQ5y/Y6UH/V/cHvRn/j1s7Z2UqOl/8CvrNfHFP 8fucO3kd2JVvpWu8dawiZ/a9GXeY9I1/Jnk8Zw== "]], LineBox[CompressedData[" 1:eJwV0Dsvw3EYxfGni7C5XwcxGkwE78DEgEFbSUVqMKi4Fa2EF+KSiNHAoAwu iaAJqVrEYNPN6g34dPjm/M55fuf5XwYWV6dziYiYx2ZzxBnKbRHfrRGFjogi NmTZ9ogKMljAn/kvfWyJaOiKeOPX3L3AJfZlT+av8uu+iBvM8SV6q/Nc73pO o3tNWNc5oMOdEXfm5/w9HeGT8hTSKOu9oN9sgk86pzBqV0LnQZ7mZ7sjZjDk +ZmeiJq80hvxjkP5ESZlVXen6Al/jA/zKr7sW9Yb08/Kl/Bp/w+tYdx7XfEl nPo/OeRleTroG7ec98xW7C44F7GNXezUZ/gHz2Q6XQ== "]], LineBox[CompressedData[" 1:eJwl0r9L1HEcx/F3ZFOkd2f54wTTRTOCFoVUFINoqkgLh9zSIQfP/6AShNQ7 FSNaWhS9lvPUhtCCHO2nDdIULjm0VEpjS/T40vDk9Xm/3j8+78/3rvnu+EDu WESsYzwbcakuohOn0hHl0xHPMhFb9RGv8Qpf+W/pKn+HluhYTcRZunsm4kpt xFRDRKW6LnOq6Iw4j2n8VPsDZfUNtCS/rO+X87B8ESvYkH9v/gccyR1iVe2G ncq0h97RN4RMdcQ3+UU9+3SJHtAX/N/qKmkHbcdQKmIeZXPX8FndnPrzcm1Y RBOaMcm/Yf45b8raaVBfm3M/fYTHcgs4aca2t77BO3GappJvqP+iO/bcX/Rt n+MhfwLd3vDUHV30gfg+VuSXscdr0feXVvBPIKO2IP5DW81L0xR67XNklznn eQwk++EmKuyV1zNlpxl6XDzK7xVvOW+iaNasvunk9/bGKud1uoYx+xfE9/QU 9OTRgxz/pd4FPfvy38X1tA5P0Ihbdrqu9hom5EvuyfNuoyV5Q3In/6M5u/iE C+7Z9P4+ehnDvKu+Q0bdiHOH79CORvEg/4uZuez//+0/ar5jQA== "]]}, RowBox[{"-", "0.4`"}]], Annotation[#, -0.4, "Tooltip"]& ], TagBox[ TooltipBox[ {GrayLevel[0], Opacity[0.4], LineBox[CompressedData[" 1:eJwVjrkOgVEQhY8nsO+Vl0CjEXQasdVKBQkSCRFbZ0kUSMT+HgoPQmmp/CQa Kp/iy7lzZubcCRTKqZJJUhJiNulilypWqe6QVrxHLmkMA0jQH6IPfLNfMtAM 3gIvje6pd7ChzlIv0TWs4I2fsEhPp/SCBxjgJMcNLojTD7ilKGqlLrLT4Y42 hLmp6ZEasPNJWziwn/9n0/N5JS8c8T5kXMlosXdDv9Q55l7MZdEn2mO2D12I cmuV2RpE+HNK9hxmcGa/gn9C7+TcIIQfhAmzPyToMeg= "]], LineBox[{4829, 5221, 4554, 5332, 4553, 5902, 5903, 5496, 6021, 6022, 4256, 5061, 4820, 5488, 5495, 5217, 4547, 5898, 4812, 5491, 6017, 4203, 4251, 5895, 5685, 5686, 5554, 5899, 6957, 4252, 5900, 5943, 5001, 6917, 5002, 5396, 5395, 4428, 6299, 6298, 6860, 6859, 5587, 5586, 4654, 5746, 5967, 5966, 5328, 4393, 6241, 5748, 4658, 5747, 4657, 6920, 6921, 6306, 6307, 4434, 5399, 5400, 4931, 6870, 6872, 6871, 4394, 5087, 6375, 6376, 6374, 6378, 6377, 5018}], LineBox[CompressedData[" 1:eJwl0s1L1GEUxfE7hhWoYDn5MoplC40WLgqMQMpdMyEI7n0hsMCFs3KnCzNB oVBbZJsUoxqoSV2JLytFNxKUbUNzYeq/UAv1M7T4cn7Pveee+zzMNDzJdg4k IuINLlZGvLoWsUZv10Y8L49Y991YFTFaEXGainhBL/Ncwv0rEcPVEUN4rzeP bfUtnOl1mc2YTWMmGfFXXhlvj/qFqxHdtIiGXQmM602gzWyJjFI8sG9B7hIW 8U/GnKw/NRFHOMQJjtHBf0tv3nyOfjW7a8dPfMekXT/oHb06++7SWbVfau/o Ev8iDpznnPfob+yjlbfX7hrfKVShGjfk3EQDevSbvbWL1jtnC3nmFvDInaa9 bQqb3rGBHfd9Vsiy67H7Z/BNrYWnhD9vrpTec37Kl+TroxX0C28en/HS+SPv J7TZuSJ7DasoMv9BPUGb5DQWstT7MGruut5bvVk6Q7fkbxd+N/cY8JYsxrwn J3eQv1U9jRT/a/5V3qz6Q7V+3nbeESyrF1f+/0+dA6E6U14= "]]}, RowBox[{"-", "0.6`"}]], Annotation[#, -0.6, "Tooltip"]& ], TagBox[ TooltipBox[ {GrayLevel[0], Opacity[0.4], LineBox[{4158, 6417, 6418, 6416, 6420, 6419, 4403, 6261, 6520, 6519, 6121, 6421, 4590, 7066, 6262, 6263, 4404, 5382, 4405, 6424, 4595, 6423, 4594, 6544, 6545, 6542, 4691, 6543, 6427, 6428, 6122, 6123, 4318, 6807, 4319, 6125, 6124, 6718, 6717, 5410, 5411, 5100, 7081, 5101, 4675, 5407, 5906, 6623, 4835, 6622, 4834, 5334, 4833, 6715, 6716, 6712, 6714, 6713, 6719}], LineBox[{4213, 4261, 6053, 6052, 6257, 6256, 4398, 6255, 6513, 6512, 6243, 6242, 4662, 6504, 6619, 6618, 5329, 4433, 6497, 4656, 6496, 4655, 6502, 6503, 6240, 4392, 6368, 6369, 6365, 6367, 6366, 6869, 4391, 6239, 6238, 6784, 6783, 4821, 5326, 4257, 7057, 5062, 4823, 5904, 4822, 6909, 4994, 6253, 6254, 5380, 4923, 6796, 6797, 6793, 6795, 6794, 6798}], LineBox[CompressedData[" 1:eJwl0bsv5FEYxvFX0NgJw6yxO25RYRmxya5roUBcG9Zld0sNFYUE3XZTUS2J xBZ0IlFKMCj9G1oUlDYS4TNRfPO873nP85xzfr+mhZXp5aKIWMVGVcR1OmKd Pmci/qQiMp8iavHyMSKrn7E+iyn8wDRO8P1zRBe+oQfdKK6MaOEdVg+hTt1L k/L3aiM29VsYtK+i2h46ap42H6HV9JJeIY9/+gs6gUm08n5BCV/eHUppu74N S/pFzMmdR6f7HznzK92SM2MtVRPxKOde/0Dv6I35Lf3F+7vgx0/MF96ddGds 8/1Fo3Ma8GqtnvbL6EOFuhwJOR36A+xjV/ZA4Zuoi8yaadB2mkWC57+sPL3A Kc5xhmPnP5l9UJdhXD+GNf4duTmznPcdpt7/4xuueTmr "]]}, RowBox[{"-", "0.8`"}]], Annotation[#, -0.8, "Tooltip"]& ], {}, {}, {}}}], AspectRatio->1, Frame->True, PlotRange-> NCache[{{-Pi, Pi}, {-Pi, Pi}}, {{-3.141592653589793, 3.141592653589793}, {-3.141592653589793, 3.141592653589793}}], PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{3.397996156677252*^9, 3.397997545575139*^9, 3.3979979519458933`*^9, 3.461114711188981*^9, 3.4611163196903057`*^9, 3.461116390154529*^9}, ImageCache->GraphicsData["CompressedBitmap", "\<\ eJzsvWeUVVXW730egllARSUHc2gxZ1vFtu1uE+Y2YAAUs42xRTGhoqUYClGR VKQiFalIRSpCkYpQZCgQRAxl+/CBD4xxB3cMPqw7f3OvuWvtU4c6Ffq+433e tx2DAk/ts/de//9cM6+1buv2ylNPPN/tlad7dOvwl5e69Xrq6R4vd7jpHy/J Rw3/K5Vq0Eb+tOiQaiT/dqlU4ofbsWOH/mjHj1TqVP5q7uS/q+N/XRX/qzP/ OpJ//Tn+100Z/nX8/4ibHcMHqYby46Dbv3+//DmQalztAxrYNw4e0G8cOOhS jap9aPTr8BGN01/jaL3pYc4dKHc9I17kTyfXuXNXV1C2t2H8Rkf7p5fndXVG YEP/s3PfEv8gl9KXPlb/2djtKR7kunTupF/o1KWrG1S8J9UgHhCPlf8qSgfp BV169nSd5O++heUN42E1yHanaKAN3YFd+VXeK5Xq7XYdjN7rJntiNQOtCszh gt0el5eT4wpL90Qj3L/J9ZLv5pTsjV4gQAfk97uCrtxZ7lpU5AoKCuSPvHqq iyvZFyDUxMN5sDxPX2XQpgMqDB6Zw93BXQX6i77FFfq1A/7/88oPRM9VeBoe 6jbRcFUUBLu+0a0KCl2hvlCBy+nayfUq3BNAU4Ohhvg00682cuWDOrtUp1wZ t4vRqJTtfS5P0JAHpUx6D1ZscpsqDlRFYv+mSAxyy/ZFQ7hen3aU/NzqinqD aV9XYXLmDri8LvJZ13x3IPoggUeVm91UKUyHu12FvcJv6ntu2lRRCUbjzIML x6+3PeCKc3u7Xr17i4T2lp8NXa9eua48um/K7lKaI3dJ9TJRvNq+fmCXy+3V S7/YyH+9d2/5/9xi3ixlY5EZ4vqmIhR1Ql8VP19+VZInz6x8fm+5ocyN+KtZ kF3jBnUWFDsPsiEqFvpZpxy3t8bI2m/LcmWoXfK4W/DbQwBRBc6DblNRvsvL z5fh5MvPhi4vr9BVeDiNvQP79rqKij2uKLdnNIvleZ7JVIztwQpXmJend2nk 75WfL/9ftEme4vWmchBpg9zSvdH7htjuKy+WF6h8mXy5YdGmvTXBtpHbsW6K zHreTrFt4CEoUQi6uNL9tYO2kcJT1Fs0YO8ihlCp/GqCSwi1Ked95UWuZyfT hD1d8Z4DgUZr6l9qb3Fv+W1nV7rPM1DDSbu1SHVHUrQOuuK+tR1/JVVlrjPT oKgilT74agYTjtzGVF7QS+W7wqQq0OHguM8ddMF/+0rULuWU7qsVAjvWRSoq gcB+N6hLbSeXqPGD+/e6fQfCl9of3bxnob1rKoFWlSFmtG37y11+YXnlTQ+W q8zGI03gUtq3i8spC8j0NqdnfnkGXOy3abgcEanzXhFL5THOkYpL9S4+tG2r AsqBTbmuU6/iABS5CzLQOdftC0GpyVAz4iMk5cgN+xaVi/O015UM6hW/SVV8 DlZEFnpQUZkrK8l3Xb045u86EOAT2UY0YmQ8enbtnBydOQAHdhXqL3oXRUZv X2mu/n/BntAB8D5UhrsFUPmR4GPlFIqlK3W5PaPruuSWhW5UDYZc1Yc9zB3c Wxr4VGieXLfnoKuEyAa9qyin8iLmcl5ZoHYjxdRIB7OpOM/4CVCJBrvXHMau XfTvnOI96Q5j5puYr6h4VZS4XjKtG6iv2Mh16jqId7b/wkv98OJL/fDsv6q6 NbJl+yoqRB/vi2zN1Ynfige/b6/M6P0HA4veNPjuAe+1VjrLsfd/YL/bu1e1 QeD9NzrUdxPWeb9YiL379sfXhio0wzv7gVUJ0+rwwcnRBxr0yY/D+PFfwa8i l10viv6v6mfHZPjs2Hp89p/7/ed+tfmsAT//6z+C+5/7/Q+7X40E9/gMn91a j89uqcd3j///5AtX2sTMhERXxxc1rOft/h+9/HB+NnOF8/9bfjFt6TY3efFm t3jLf6ea6M+Gbsq8JW719j1u3a5fU031Z0O3YnWpW7thndu1Z3fqBP3Z0P28 e5MrXTbX/XfF9lQP/dnQLVk4g98mritZvtSVrC9P3G9x2RY3aeHGxHPHzC51 BbN+0jc71r/fwKFL3YiRv7lRo3+Xz/jZ0H2cs9QN+PR3/shn/GzoPnxns3vl 0YXunac3yDP42dC98USpe+jqIe6ZP89INdefDeX/B7u72n/p7u34Taq7/mzo Hvljnutx/Ti94gR/3es9lrleXYsS93v7qfWu9ytL9IlN/HO//marG/DVJn2z Jvw8stYU5M9e5UYUliSg+GbEWLdiy84EZLPmzHaLli5JQPvDjrVuSsFIBb+7 p6Dipy0uf9R3ekVzf13BlMmueNXGxP1mLl7phk9ZlHjuF8OnuHHTdiUoePvD sS5vxK8JCl55ZWwVCvq8tsI9e//UBGT/7F7i7rjk4wQFd1zykevSrn+Cgnsu +9w9eu3IBAUvPjzX9bxnXBoF69yT3UcnKOj/2SrX76PF9aFgbFFVCr4dOc4t 35ykYPbcIrd4aUmCgt3fl1Wh4JcfN7kJY4cmKJgwucAtWrs5cb/pC5e7vGlL qlAwfvruOlHw1j9L3TN/n1yFgjurUPCxu6P9ZwkK7r7sM/fYdaMTFDADnrx3 QlYKPvu/Q8Gg0eOrUFA0f64rXrIoQcGPu9a7ieOGJyjYvXOdmzRhRIKC0WPz 3bKNOxL3m7ZgqRtZuDTx3M+HTXITakDBSy+PqULBO71Xuaf/PikBGcqky8X9 EhTceekn7vZ2nyQogKbu149NUPDcA4VVZlVEwZgqs+Cjj5dUQ8EJhImNsvEw bs7qKiI5eMxEt3TD9krcGrh5xQsM1e27vndjRw9OoP/9ttVu3MTxCfSHjchz q8p/TKA/ee5iN2bWysTT+g+Z6CbO3JMV/RdfHFkF/ffeLBOBHZ80A4+vdLdf 9H4CfYT9trYfJdC//eIP3ROdJybQf/q+Se6Fh2Ym7ten5xr3VI+xCfQ/7V8q 6Jf8W9AfPnVxAo8hYye5Jeu3JXBjBjATEtI94lv3r1+2KQPdPA9bNi5X3R/O lu9373JDhg/VOzXz95s4e4E+O3zup99NcJNm/Zydh5dGuS9zfkvw8P5bG90T d+cncHuz52rX9dZvEjzcd0Wuu6XtBwkebr3oPffkn6YkeHjinrFqD9Lv9/Tj 4xM8fPLpCnUP6s3D3DVVbOOw8VPcknVbk26MGIOZRbOSil4UPwYg5GHD2iVu 2szpCR62bN/mRo4ZleBh3PS5buL8dYnn5gwa5yYX/VojbfTFx7+muUVbXI87 RyVwe+vJte7Bm79K8HD/lV+7v7Xpm+DhlgvecU/fWJjgofudI9yrjy2q4mY9 +8TEWvIQlx2tQG4psqzcTJhf5oZNXpjAKG9iYRX/ZlnpClc4c0aCmxnTxrry zSsT3CxbMht1luBm3cYNOu9CbkZPmVnFVf74m3w3Zc5vNTLWX3z8S4Kbj9/b 7rrdkacoNvNYPnDzAMF8egLzh68Z5v7c6g1lpZvn5okbCqrotEduHyyWZnmC m08/+ME99+SkSm4auJxPljfNyIh1F1jumj+dOnVyXXrmuvL9UYI4KzkI75BJ xQmQAG7eynUJclaVrYkVk5GzcN40t6Z0QYKcubML3PJVKxPklK5Z5UZNnpEg BwEglgmf2++rUbxVVnJefW28+6zfngQ5AAegITkor543TE5OCDHXnVu+lCDn sWtHqYFJxB23fK2GIyQnp+/37vmnpiQmDkbk009XVjNxIpqOcAf2FLucnDyr z7u9SltXtyn636xMEX0Nnjg/gdjYwjmuaOnqBFPrN22oYspXrZivkV7IFD7w +s0bq0R+qLSQqaHjJrsZK77XJx7rn7tw079cv4Gj9c2aVMPUa/+c6Pr3+zHB FMw988SEBFOPdhnqHu88IcEUJuXWC99LMEU0iOpLmHxReQ/8bUDifh+9V+56 PTM9jakl6nQdmqmmQXUjr3PKdR60qUbMoGS+mzAvwcz4mfM0YAuZMQMSMoPB nzV9vHLymGfm91/L3ZiRgzD88XULFhW7SXMWJZj5bvQEN3v17gQzxRt/UyWX jZnXXy9wn374Q4IZjNFTPfITSGJAul03JjE3YOWuDrnKyWOeGRwDFF/ITM8b Jskc/DZxP+L+l56flWCmX79F7rPPV1fDTCp7Q0fjmLw9rrcw1Tm3LCavMbdq HFI2RSj7dlxRgrJJRQs1wAspW/v9z27o8GFu548/xFSE0YtRRlCZHr0QfNr9 jDLyBHPLfkpQtmD9r+ozZKOs9xuT3Sfv70pA17Ob+goJNdXznvGaIklGLzlx 9PJYNdELwedjXYYlKHu/zwb36j/mJZ77wYcL3edfrKmkrHGMc0BZlj4R663Q 2tjeClexq9T17RIZsq555Z7QGiTEvs6flWBy2oISjVRC5Jl4m8u3JtSdsKqZ l4qftsRMbl6/1E2eNiXB5LQZhW7OsjWJ+w0YOtIt2FCRYHLeup/VC8/G5Btv TlVjEiJKhJJucJ69f5p74Kpv0zzwAe7mNu8nmLzlwndEXU5NMElyzTxJY5JY 9/WXFiWe+/4HC9yXueuyen4RTZsKKmvMnXrlW59FdpqmL9/uBo6emaBp5pJS 1ZOJwGbyJPU1QvinT813O7auUoIe9TStKClycxfMr5KxKV69KTGBy3b+4r4c PCLx3Llr92jQmo2mN/sUqtMXwoXjjAMd0tTr4Tnu3su/TNDU9Zqh7s+tIyfw 0cAJvC3NCeS7FgAbTW++usz1eW154rl935/ncgdsqIYm6w2xHrPOg3z3QVZu sO65I6YlMML/ww8MuZkxe5ZbsmxpAvOSRTOVi5CbmeK0r1q7OnHdmHFjkykJ vJLyH91Xw0Ynnlsk9uz9TyZl5eatt2a4j97dlsDo+acmV3GoX+u2RJVfiDme xu0Xf5DgBtt17+VfJK57/sHpcXbOuGH6vNN7deK577w72301cHMNfMA9xfmu uKKyu2FfCc2jnZ1v/MnKFNaefGOIGDnK4ROmJphasHhhlXTD9i2lbuqkUcrR I56pn37Y6MaNGaKexon+uuEjR7hV23YnmFq5dZfmV8Pnzl71g/ug/5SsTL39 ziwNa0PE/vFMoZqaRHrgiVVxuuFEzwAsWd7zEc/UXZd+6h7944gEUyTwnn9w RoKpV/4xV9Ma4XN5FyoA2ZlyFYXqnZfs2ef2793kcn3fcU29dXTMZ0MLEogt 3/S9pkhDpoiVpk6flmBKuNGkxO6d62KmiLMWzEmaJRJDg4cNSdyPuk26hMwq 3eU+/GxaVqaQ4w/e3pxA7MXnZsV1GkOWZMRTf5pWJSHx19bvKEcPW4a6w1fi K77LtTGjFAgsMWT36/XsDNfv3a2J56J7vx20PatZonk92fbUM6+mem/S7F/U 8Qqxoo6GaQ8xJaIaOyGKqGyWLFpQGNfSHvYc4cevXL0qwdHW7eVVkkbM2K9G zUg8d8byHe6jL6Zn5ejd9+ZUkWok/aVH5h0yojKOul8/zl3XoleCIxxFi32N I9wGSxrZ/dCt6c4nDul3g3dWw1FlT9Z+V1FR4fbu8x30WbmZOvdfGseATlOP 0VfDRlWpc1pyNORm2+aV6jPAStdA043PH+J2/LAzvm7N+rIqmhO7N2j83AQ3 hcvKXU7uzAQ37/QbV4UbbHTfPusTGL3+8iL3j4dmJbAk0WoazDDHL7iz/RfK StcMms6ue/i2QZogD+9HUvXzj35OPlfiuyFDd9c5sXqcH+dHX49xizb/nsCD uNPKMnGybWx+nE0w2Se5DeZ7dm2IeSB552vOMQ8a20rwFd5Pi51pifVpJVvd JwNmZ50juLTvvbkugcdb/1wZl2VimRb7btkEw/fvVw50f2vzrjLwkPltnSdq kvvpGwsr9ZjGtt8k7vfWk2Vxmaepfy65quF5P9el0mYUfDJovJu//pcEFCMK Ct2CVRsSkJE7JYETQhum6B70FEAL5mXTti2pk/x1RDxzV5Ql7gcllPjD505c sN59/s38rBSQbKG4GVIAJVYfjo30Ywu1shZS0P26/DhFZxRAi0VCdh0q7/G7 xyTuh4qy/KlRQL3DWhLqSAEFxiKfacmU4THIFpYs1rJ/ujYyv8soWL9msVoL rjgp8LtKt/5QNYW9aFMy51e0yg0YvDgrBWQl07sfyIulp7AJOh+8eWACWlLa aCTxveSdk9a85w2T5Z0P7XfhQbz8/OzEc8ksvfxKvr5Z5mR2VgoGjCx0M1fu TCrm4mVVFAdNL5b+PDFwqMjOfL9tdUwBBsKMtV2HAhs9doze6Th/vyH5BVWe O3L6MvdN3oqsFHzxZZl7rdeCZOn9o59EYUez4DgP2SO3DY4rmUbBg1cNcje1 flPBf8BTQFBJ5wVXGAVkawhyQgrefn2VGp5Evbnfj5qOrQcFX4+ZqTo4W5yI TR6WN9yV79wRS/faVcVu9owJCv79noKd5Ws0kUZyza6jQDd+xtwEBWGqJS7u TV7ohoxZm5UC4jKTRlMI6GiKaiFkdE10vWZIgoIoA/2uu6t9bkwBbQGWD7Xr 8IXxid9+al1MaRQnrkpQgK/U+40p9aGANHN6GxYuPxX/EDJKNenSrXp/3DBg jynAPhAshoqIbJcVFYxSAsCv85LuGG5S3vhNWSnAjSfgCymgjPLP7ksTFBAW pEs35vmm1n0U/L97CrAP1P7D615+tNh1v3NkYlYRupBuTrZ/bdPUQT0oQPIo Nx8qajAKKPmTAg6hpQ5DhQzw7/MU/Lpns8bg277fLg+PrssbNTJupAmjBkra IQVk1EZPLs9KwZAhlBULEhS88sKcKlEDJfqHbvkmAS0VNGbB3R0GxBTcc/kX 8Wyx68iU0PwSUoAppjIXUkD33bvvFdWHAvSveSYGBZl1pDQ9cCPxFFJAt50G 19+XxRRAy/w5kxPXbdy62Y3KH52glFkxpGBB4rnagVf4Q1YKhuf9Yi1gMQVv vFKiTmgIGYGbNTcatOR8/9L6bQX/XgsOOuRqIebJP00VwKLrMO2vdluctAVP rVeF92XOb/FzyffiJGehQBlIAA/o1nxlAORNmBb3yRnwdJSOGD0SEGOZxgJQ zgfye4KoDDLC61BHlhU+LoMras8N210yAd8kbrMYHbe7VLqiZbErasA/c/8U cTGTsq8WQDz/O9p/HgOPisI4p88RKsahBch5f2cVVzTsu1PgD49xrqns001h BXuDYsKs+XGlySCbPW+OBlahTOMHQcHqlQtiCjRpsXSOXmEUkOywRLrdj4hj 6pItiedSAp427/esFLz22oS4YG8UAM+jtw9JUIBFMM1uMk1ilmjgng5fp+6O kxYj1BATkNl1dH1ZIt3uR8RhlSujgC6XnE+W10f9hMVDg2J2yWqXP60oARmu aP74cQloKRRaTHyXp2Dd6kVu2uTRmoW16yQ4UytQtvOX1PH+fgOHj9ESU2h5 CAwJEHmzptVQgN8B5CEFzIrIFyqTZ0SQPXzbd3HeyKAlQLNmozvjaGCAzgxR VakWgfqxXKFR0PvlxRp7hxSgej7/Ym19KKBkQUQQUoAfRGoipABFBIzEui08 tEi7uaJ3pKWHJAKIr2P20GcX3o8e73Q/SJPywyZnpeCtt2fGiXCD4oWnp7rX fCI8kyIyCigdoe3vbP9lTAGK6P4rk9eRlsCAo4iMUtKr6c99551ZccmijhSE GW6DQkBSP2jNjp9iqcUHMkV0cuCKIvEi+TEFRTMnuuVLotjZKGD2mG0xCuaX rq8y+6gufvzFjKwUvNd3bpyhMyj++WKx+0fX2WmKqCRWRCbdD139nbuh5SsK fhdPAS3wXIcisuuIh60H2ygg4LMMXWz835wqrvGO+lBAkpQEHSA0C7oe0yEj 0WkJ7BaBItKW9+1rU7ebH7R8niucMkaTpC0CPwhTzp2MUhac4IOFFNAE03/g nKwU0OxhgVEsjb1XV4EsVEQGLQlR84Nusy6G9p+rIup+/dhUy9AP8glso5Rm F+t6bOafi10aOmxPXSg4PkjOmU6uzhyjiMiRChXyml7HrytxBePzWJeTujXI DBETb91RHl/HDLJ2BqOAfjprboyfK67Bl4MWZqUA80eNOWEW39seN34YBXjz uJ4hBXhENDLe2rafvLN3Rdvnarbo0T+OiCnAlJOrDu9HIwlRR/jc3E/+ZQk6 oaVOFHyZNzXO0BgUs0pWaXNcCBmpBSu1GbSUcFBEdIDc7CkgYWdOqV0nM0L7 4UX/p07w98MOoATD5+KksgQqGwW0O1knRuyV51SoOQ51N8k5HEuBPYbW0tG3 t/tE3rkyOYdHJEoq1crSEvdNjAMyux8JQdb9JJzSj35yr7w6Tt+sjhR8M3Z2 FeeQCoGZY6MAGMlzbt1u0t1InVLSEMVzp9L59lfPguglZWH1urUyooiFpSuX u0lTpyRuSZhspWnTgaxLGTRyVYKFsGBjLNBQYQLZLLDIVpo21NBNqH9wjXXM dWOUhS7t+qf+aha5w9fu7sv6Ex5UsvCnaWqRJbQWyfEW+ZnpccHTWAiTQ3Vk gczE+HlrE1AgpJZJOD4IDebMnxdMhEbaczhnVoFmqn/7eetNngXWq9EhtbFs ScwC6T2ypAvXbI7nAplY6/G2R5MfGj5uY9a58M23lc2axgKRMd1PIQusykGl YGwNXVp3rzv7WXdbu49TNykLjZQFetvwj56+cbpdyu3SjQw9BzReERzbo8lP UAmvBxFoAVsbZWjQVkvDU0gE/WhMB+xtq2A6kIsgTvvlx003Bkpp4IBPaIZP tQ66demX4mZGBPVM66u2R9Pbkz/1+6xEYAqpV4VEIKZWMTDxpSfU6pKt0qYD SulGTwRK6baL+uqUCIjAhDAj3nh8ZXzLd99YE2fK7dEEyx+Kt1APIkiVWjXX 0CCJM3rqrARqlL+sdBYSQbWSwsGPu9bfEPhI6KXN65fGRDAjKPKL3xXfkhYa mpsWbvqXOAHRo3EWLGWRiYimiZRFRZp1+E2bc996cm2M2kuPzI+rZ62DBneI uKXtB6kbPBGs3sRo0z4tRLSOZ8RUNRIht+TKsdMJGfhosS6YqgcRYYuZEUG2 DoQIcQ21so3rFUlC4dYBESSPQH3H1lXXKRGNdMkOYRyFnC3bt7XxXNCgRvY7 pJfgfMy02YmnU1CwRulm1XBBk4MFzgYImeWXH12QUCXk2ViAIBMh1Saw1OD+ 19bvpK6PW9iHaCQnmiu+LpoRX9NYGBNBMRm+v/gYJzJ6Ls0f1gRYRyLIF1jP i4klxQNbKBXOCFu8bNDSq06QjOj/0bPA4hyKCksXz4K0NmkzQm6bam7FTJl0 1I9DFmhNIoOajQVtTXprU4IF0gqs9wvFl4S2rV+O0G2k6PIZ1TRRSdfE2mmA dr5gKiS8bhuksZ+6ryBxV54M5zzXuAjDt1pycYIfKDzQ/B0CQsKBJa4hF4Rg mAkqBG39pKB2NnniCK0myEy40tOBGacdk7myddOKdkFVDUbDu9LaTkeNSEQs CWS2Pv2qKCsdBHEUGUM6aGi2hF5zDxyW4+E/Dk/Q8dSN07TTkokh1uKqWEd9 qkEEFR+x8G3TpoZY6viuROwWQhodpNatz6KOdKCfbKGL0cEaNWsRMjkmHLMi v9EhLpNOAXDfsHbJ5Z4OdlyYWThOO2LF520XRBPE1EvWb4vvSjcHqfRwYrLC M3fwoqx0sKzF6r0GCEtsrFHI5Bi/lgUZNOS183SwhBwjTbOyzI7LYzX1nZoR 1FT7oNpvE864oPeo/4e7E5JAtZ/FjbzccTUpLxgDmGqrbhoDkZiOxlrEWMlE 0AlBhNbeM8AKZpY0+QlxiWeArldIIbO088cf2ltfTMliN6UwySthO3s7hAyE kUR1DOA0WpnZGCC+s+qOwcXaDMvtGQNECITZt0UT4pLAe8Jzonu55w2TjQQr NRBP2F0J3mnQC58exhPH1a7UYHQw+HRA1I31XS8GHGASlgFtBHBjzfCZIsKL EkpSF3hORDFprEGeQ/zaDtYquaNcc7XMihOD9TK2xMlegWBz5MQtWTkBgXRU cGcf6zI8wQmzhFYuMQKpDp4TdWfFSv+p1Wt4TxcoJ41Jv7q/tH5LZwbtSOJM +a9Ena3WLa63buDefHXp8UHJjf7ALHOiOjqwnVZ9PC7QT2T8QjpY1TJh0kRo SXX0dLBmiZZVCs2035FyOlfpOEwL0FRAcXQJ//C1tu/6vkNgymmo4XZGC8vb 6WTmJY4P6nG2G0x1tNCKhXYg5jJayAKhsHo/vkKeEdFCThbbXIlxY7UdNMEw VcjFEm38wXNzW7sctefQhhF5vPMEGX5lcQhbJE5afH9Ew1Z32HvQtmarOevI EhaEjFTIEotQNBDf9H0MocV+VBxO8SxRhYAdnCyavUV5pc5UlhqrwSdNAoPE h/zuVFslPXuWdm1wZ7v/1PklcQxq72HOH296XAaKmmXweQ0aMkcWkRuEj981 OtZmEdSH6XJMTD6t+9Tt2C1AVNkZytJh6nX9rc17av3ZWIbynrB6iieKrC1z EU/YnoJ5wcyIkKRsOoX1izoSZSslF23+PZbhgtnFClwIJEknmggI0iPAD9PY gymDwWHaoOn27NrQ0c8oegzYQYDQBCVHxUls1mlB3M4dV2/fkzrJ2szGTtI+ 45Au/p8+8Wx0ERZYVeO4wBuj4yOki2Qi4JIqP1UBP1w7/UiikCyhy+PPrXqr K0a5taOfWYSPrO/k9/xNze+0tD5YIU7GUrnMzIJ5I+yf9CMP+aEuhDX3Q2fJ VlGaLTBFFBJGqp29G8RPS52ukB+hMSQeAjoQkwRxmCXSKmKqWilxh6sqLFu9 UGtRbJHC3ywIpEPt9GCrASbuso07YvasPYpg32SJTJxl46tjL+eTZXE23tBC N1lTn7FHZZX0Fw0fp3v21DuWgJKZhN67Udi7pe2H2qvTStk7XNNht4tuJPzR hGWUJtaU8unBGmrM4vMPUkqsbJ+ylQYmVSQl0N0jRzHOOrGI9Z7iCxcGFR72 nOVrEyzi49F/s2rtatGBQH90zCLTjbkFRfxN6phMPlSxj5AwmmqilB6lnjm5 ZAorMI5/CPvo0XlFk3SeirU707p15bEErvNWrpOxRK9CTMba68QrT10cO4bV sftl7npdnxGyS7IL1QnONmcIOa2r5Ez9eZTOLfSjzU0qjTgkeCCoVealqNYm SvRRUd6z/RdqCFGx7EGB88jqUAwppIuPc1ZQHrAtX+wt0BrYYWyzve2g776P Hck6co4dsvYdA5Buc+IdILZpxNTCA1y/aUPqHJ96QJey+JNpyWJDpiQqlwXy eCuEV3C6cH6h8otsoJqRB5x+ojLsJg4n16KPyWhT9YF/rtlYtkRlRKb52cHS YHKrcxfMj+XA1ivOLftJQiqfX8+fFTuj1clB6IzGPmHOb2rYSIQaA2wVhaci 3qU4aFFijzQSleYHr/pOI2eSGWz2QtjWueXLKhPobeSCNVisX6Bf469t3tFy KaHdja1eF4/pVdUUN1/wtnpQRBf3XzlQZYy0IWHhOcG6VNJZpBtNI9BKY039 mfzaOkqHbkHi/drjA4+Jqg6xgMHPNiJIh3hOqfOVpsM1zIA59Dgs4rvCvEhC qgHyItfAMFlbJAgNQcoKfxilfr6nm3IIWUNykfY80jDUEHkjo7v/4Eontzq6 cXJpeGcineChkmggDp9PCkwy3d/i1Ep4BPRNddpDB2YZTwoFj2qg1wPHFo+L uYwjTBqeWKXH9eNxo2Q8leVXcuw41EYfy5YpdpBRPCF2eCu3L6kjfYRnmOYQ JqCzfUQMThKzlI3IYj3nYWfLH7Ttqm274+sI+An7ZpXuiu9X5J/BE4/PAPtx HnZWDdqqYJNQGt/pvwglOQr5vtXsxnNBpgn7+tIj81MtovhNfN7mRp7cmebh OgB1on9hAiTb+8HkHJVCWxcAtPAAsKwdeQSoZzxQ5MW/HJCr+VS7jkIGXXkh 8OH6HAMqrHwaUNH6nKgty2SB6f2CX6pmQCFHuHcih/Iulc7CXTd+RG4jAioV 7VEFyAJ2fD/UG3OA0v4JdYNs0Lg5cUIoHqJYY+uGiGSmkbp88xcucB/0+5A8 21MetdXr1uoC2airq1K80Cwzlu+QiDm65dBJC93g0WuyiteArzbGWR4b5cd9 d2hfCaFlixi1EnfrdX1okU49lVgkMJAMUKqlieErJe6fLy5M3I9q6Jt9pukT 64gaORtbO2eoWbGFNFqLADUEjVhky/ZtPYNkMXp2VdkaedPK5XgjJ03Xmxlq 5O3HTtuZFbVhw3+KO59tOoGi7dNnqImuUlmjjaRnsFQf/fj03yfFqNGkjh77 rN+e+H7oMGusrSNqrMC1lkIbIl6f9SKHqJHxIoG7YcumHj7/SE53+qyZ2rIm cb4Bt2bHTypuM1fujO9qG75Mm8fLVgXueA+c7tHju+dNPAjL8RnQZgYIex6g uR66enDq8WBxHAbgxYfnSiRQ2YtpK8IMODrxEet6AMdmUATSBD82RBZEoNfI eLSyTo1VK1WuaIHq5lEjCwJixGsCVavY9JbEBTu7Jf0lrEDiodWhhjdCf3c4 SqJRS160DMwh9QoxqfI6UdoP1UaWm+az13ssaxWYC9/CJ/GJ15avjXdDh/1Y F+BOCgsOInXhKGcsWqF7o4GDoUEfMdJWvGRR6lFbSbJ2tYK5aOkS8RC9g7T1 BwV9zpof5TV9Q9KslXFDUnWoaQnBNyQZarRsM9HwTyMoGqnDwvpwWvHEYj4S 1NhoC+ghYY1c0tpi1hfmxGbG7gpogMdzm9ekOqBwNaDh8MTAO6RoKTGEH3wj 3VyLrCdlLonnunqcsApgJxhKMBHhxIxmRQE3s1sOGDXdjZ60LStOg4f8oMvo GIuJAiO0hag2cqQND4NcS9dg/T2fye/kXSpT95gPiazj+9EcZsszmtcudW+i RUOs1dttiAvXbFZVRNrJUCPMYaMQnDDxrx/w05ItYgESQMViGHBkW3DNKBna XS2nOHUus78qcCd44EgAWU6xeeAhsETi5UeLPSCNdEUwIPk64AMeO9bQYxwq L41iJdq1ZLaLQ+0Tg+/Ojkvk1UhXddix3J0REeadFLR0mkPbxmNHVy0ZCrR+ +c4d93nsxCZopydlQnE2DDs0IuhPWbw5viveGjUvnlsddnTB2DQyIUFwMAQy 22LsmJzsvEqKp8f14+4LCkzkF+jv7P34yrZmNl5eFHe/nRgzUqE+2/A8GK4V fCf7AbBHoW2JemJgDkjTkVVpq4A0VoNJJxq5NOwpLR93eQypeJPQZuZKjNAu 6CCACW5qGBI5Ds0vy4rhwIGb3YvPztRxmrDgf1kKpK3HkL4OjCmBtkRid1nl gTY+CbqwqTRVsmbCgKQ94NmeBWoe7NY8zhYa1hFIk45wtLRpkGjEjWvn5RCw KL8RXK1cvaqLLyawggSzCr6sqWLaG5A0cJN8wWTbrU3sJbxNnVgNkCNGRiJC GHlSMJGZib26FskzQKWxNrCgBSlWErISnt4e1HFoRtIdOu8Zp2Fqu6DND2Ob 0/d7wciX7WQCWJhaRzRp0mZ4DPnkIEwl90jU386LJe4dG5DhFLOhmEReqVs8 pHgrzHaFdFGxQtreQ8pCaOa3PCe+P8adWhBvkAnS5tbd8s6suOnKIMXxAwaB Q54RQUq7IhVk5JPon07Imz2kuC4sydRKpsi0OIX+e1GWAQGVZ4gb66vToo9t 85ZaQtrCluSLzbRew5MC00xQKhFaqoOHFCNDFQUBxaKIoUn9xUPK/jhsyAyk bOorAbH/XkPdLwx/ZsaK7+W1o/uz+vDrYcuyQooNtRqESRHLwSyn1cFDSoCL 7QY25jc5sJtiSKcrzJgjroGKDkG7F+pEVEgM6XeDd8VL7E+qG6S08NucNyki R4DynFWyKtVRoTlMpRR/mlUdukhm6RJs0A0eVTxGtgMBVdIJoNoxyGJyu6Kl q2NUceFJT0yc8WPqpGpQtUWsaDobNREFaS0C4I4eVSISBBVtydYd1GJv8KjS wEA6Cx8bd1GC5dQpQXs6dQdKfS2CuW8L9uqIKr1+zH3mpg2Zpmg0KSbpFIXm cEWVnAFqk25/5jqONqbpGl/6w8/EXKEfMPP8e9W23adYA6OX/4LZxRJp+Chy 4cY4csmE7olx5DI/7qoymaUyjpEWs546VVE6LNrA2/uXJPbJ34oDfo2vwKJc SRfjCaB8SfSLmJ8aLIqkMvjFx7/EGGP2WQIgvrk8uVYYt/QDIodizSKGMf4i E3hx2ZbUaR5jCixgNr5ggopniPFlHmOUAhKNsuAa6tvibZ0e1GOIkajcGsbk 4YeP25AVY3J4lodvEVgvFsmRZDhNUTpc5zebCmCdAPGuSz9RnNn95LK4dlqo SVq7hg0IOIED/XKaR5vFfajxT97fJe9amTNH5fNGdUSbLgPWHDJ4g4BeECR6 fun61BkebZNWLBkSjWSjN+Rzn/s+Qhs8UBhYNHLgoeSLA3tG0OLPTpo4//ZI lr+gN3D+T86A+kmWyeg9SRNlIepsr842EKJZ5XVB6wj1GXDAIt3xtZav8BNY EC9on++BZzWkCv294z3wIzRAoCfqjCAHd/89n+GcSfDqA7W3Z8aH/9QReBm3 Ak/6sJV5Tms3uzc/+FhVyVmK1pEq5tT5WXBEnI4ni0eLWFPmovhwhhJwpEZb qB1iVWpObDhMxw2BGUUPIcHfN8qMkMcTlRY/f/TMFXGbYHUssMm/xfgtAxZ6 PDKMbQfkGUB3pMZVNGIQM7AdAXubIP90PNEuSAX6DKXiSLWRJOls/0x0Eu4J c4IOHvo2zwo8ux6PDKeqHFPCHmVoHpLELWpHSSs/1FEzlsdnP4SU6HJpoeRc he5o9d5wQ+jdRHsDM/JuhwQh7zjNaKJo9eOxSg2fMT84UovroAmNhO3lHmgp NbLyfTzCc7xfTqaZmJEdhGSitAr8bNbNjp3GIsuqKXpjS5ERW4stjNBqpHqa pTKgKyydo8Aerdl32gMwCigh8szEgDRr4NRQJdIKkTg30UK+Y1V5YYr5HFKj StN3WhAkwcM9cJZYMkMxkCTQubaQ762NqtTwJ1tbPJqzNM7A1pFIW8tO4N3a o8WiblIOMxevTHXyKyrF0VGwUWz4jpCIb8k+gCQymEN8Tm6byIc5x1xE8TGf WFLWgFko0LFFCPORP5DMNVwPodyDJlFMlQT68h2hW7QUz+Izfsc1CAT313t8 //N5nn9S7vTmkcgs3vhb6wzeQIsMs/Vkzz9rla3LOeT/5ReKlCAh/TzfIMKE xZGFJ1oQdYNBP3FxwSgYs0ExrhhFZCqKTFg2f8SAUUGk4ogsoGrlbxksbT7H RVVIuZbv8X22fmHNCbLBvdl6X0tpXYbpa6GTeQcqScgk+YlOlrl/dakmuwjt WmdwPVrWTm5aB+0FVu9oHbh3eLlT55ekLlZCmuiERAkQ3sEXsoGM6EbtYvDQ z5r1EmMJv0xw0cMN+IeoOj5GnaM/oJyCJnssUZvjtix6v9TWmpZtUbGVCDPV NqiXWKtAdcQTlZEgJSXY2hOPG0ilCJQF0Yt94YRSCB4GWydRTkEBkL/G5UNH ELVACh0F9Pfw9fBvulCY4KwE4vvcB82P/y6PudTnRNAvVGxwksT1bxMUXaz2 Xw11lZnc1mmxOBO9jaLTSNdLMVtY2CwhytseSBby4tQsWLUhBpIZROzJzVpm ALJF3BdXFM8ge2f21UYEX+u2JPW2Hx6FJzxiEmLiC7YJJBMeBg/5QSZgrdKw baKUdd60JW398JA9cs6kCBau2dwn6DlnkWzR6t2pdn54ZAxGTtycdXg05rCC nR6Dtv6d2e+KrOBzDxSm+gSNY3xGmrSdUde/VBUMd2pVu1nXxr8SpWwLqtr5 UbKIAbGnPisjft1rQnbNIN3ERnHiuNhAWb9Okpr7tcow0JaWHnl3drzndls/ IahNECki2b0fX/m6pxK33bTMx+9tb5dGpUwtmVK1Gm7bYHGt9dPacCm4kJPE wi9Zt/U1TypHBeB8iMec6hCswbTN0qobK5ulkUOA1PZ+rHpEnZhbXFwZ4Kt+ rExSnAGO0/jso5/aW7/DZ6vi9at1HCsFSjpdpi/fLi/BABrr1h4MjMnIBn10 LLzk+cXb4pgY5q8Y7A6BX8pOHdy0dYYxt/Jj/uCD4rgMbmNmuwbyK5gxmasv BRluLkWeRSQ6BMkyjIj4TDLTazXmdv7FEEYce/KDHfyYSVszaT2ZquFf8GNm eTuWnQktuHT0Y2ZXdN8Jn2pTzZj5f3j+6N1tqY5+zKQASdNFGdGVLwSJVF3r JiJACZfAvaOps7emx+dw1HHgRDTk7EW4U6f4gbNTOYPT5RclGgamnvHZPCwr bq2BIq5NykZPooW9UoeNXZ9qm2H0rYNkPAUhcsjRUBrrHLVhEnkQOTzjIdBK j9gnaKdNRaRD3rWyZ/9tH023rR0E7QPHE9JINJ7qISDTTiIHfhkmKbSeHgK4 R3MDAal0grFTPQS66n1KFIxlgqCNh4ACn63ZOsVDQDBGTYKiK2aZhGPPWArW RiZeNBsuYM77tKNHEBDTIkzD81DFtYKgQ7BSGgcF8T/NIFi/TZkm3qRmSsdz dw8B24NhqlEHdMyTFTjNQ0AaiE4KsgLtqoGA9Xo6B94rl+8ylMMUAt2rWYZJ WE82QHRetyADDkIICW21H/fd4b8a7fCCkv920A5RI5lQqH7L6wiKRjoh2GEN 1SXB2uk6qsNU2TNS5gQjp2FZFEJXn5HF22d9P3oQi47jIlPjdI8JBVnNsM6k ZFAVk7Y2M77eooMQjZY63WNCfgrdgLIHF3YYeuOJ0q4+HcjkwMazLBLJQUf0 /3D36YHto3/3s89Xi2KrPSwdgzYP4hbxtFNn6qgOV68G/cBEQUpol8QHQF/c 54FBisCNFQ5cwy5p80vXn+mBwRfE1owq2JrqkAGYdh4Y9tJFZZB6PtMDQ0op sgbj1BHAQIqFuM/n8PCOyccxXVhKjftK/yoK9EyPDtOQvCsPqQs6pwQpZm2I KlqVOtujg8TQz0GJCVXBHKIIDzpdFJ0jFEEQQ50gWcQsmByuE+E7K0ga6Gaf M1ekOmZAqb1HicUE7LVK7HCWDvFwFR82ZUZxIh8EBDj6RGZ3eKhIzVDW5Ltc w9wiUQzaZ3uosDOsDBoxskLeofZQnRo4jjRzTphfljrXQ4VRoRrH7EFI8BTZ Qkm8idTffC6TCAFhw+Ni4TIeJZoZtfQH7tOAxQWnZECnQ6Bz7WCdc/TnETo9 bGMiRo4csQMbfQgys/4a9Brw0dP3TZLQWKx0Kiqgs7Eavtq5Qd2X+TtkCBYq E0hH+zMRyvO6KkbyJ0JLfnbuW6LHi0QIHgLH0/wIWTvPDowFxRtS5ymORypG KCZEDVFCPTMp0UakUqh0Xu9FD+8GNQ6GpNPBHaNPX6mo805WcZu+zL396Xcc m5k6NLiNtOWZirev3/xBATlSpY+WXJa4oMCYiEggK2MJNUmWX+/Tv3jvepD7 w3O0eKdOr0gkBkHsXSez+HK7bt0+JQASE3hojI+QH/tdgT+5paCoyBUUFMif Qa6LzL+SfYcW1dMDUf3wq5EsxE5doHgcpcEMcGIZUfQqigIfsxboPMyXKcpH KSNk5pFuVB+ssEUeSoHlZmxnJ6r1fB9SsCqNlgbihclFv9qcCROEHT3gI0f9 S0NcEBJH6nxF5ygVaMJAamkAzpZS9LnhUOBAALpM/csU86PUuyRXjqLE7bKv 0Jfzaq/5ulhFoosL4trF+miPmK82ieHNhH18UKzb5/IE+16Fe/xJbw3dwYpN ev5ltfJ9hh/znDU/qjOJY3qxwnOsFi+RWUOTLjDQ5G+MMqUh9Aao4qVVps6P VXmHHFaeIPPMEfPwkH0jkSiH0jPlpYs8LXbwErlmmQinZZgHpwS0kAyltiFR 6kUK3LE6D1he8+4bazUJH82FsbGjQ94MDYxmhg+0c5Q4P1bnBT2IKCBsnOX4 KGTBFY9ivhAossU/bvVFccrwN+0fiur9u07PyFkq+/mKDWNaK1zfVERr5VEu lScsnum9KGJILBb+sCB5mS9pY+8gBoIgCsJoLGZa5A4Z4b4dNV7DSJQ8M4wp QjYBwmlChhydMmIf8SyIVJhiEH2MXwbNM+i7xf5iWCCdKch3eSYuOyEsAoNX gt01IcLwIAQ8C0GT2X65pSsWbdL0NHbrjAwCcJq3MqTFcGsJUi73PR2kK1h7 DvvkJ7CvNMwgAWg53fX5rtEa+jELsUBIA1lUtCFuMBMXySBdhR8smvNYv9qc SUxoTGWYX7MJNJsDUYHhNtwSgcG4kel6svtoFTwibBwoFCxJfRr0RGAu87LD 0gl60VlgKqM8gwHW5aDHRiY7B8pdT5Gd3NK9WY5oOstLEU4nwSmSVLR69zVK RVN1h9DDpFlpvMEtwNPCz4Q9pjbqAYZxE0jQIGHihcmDRc5S0W66TH35Q95F fjZS6cNkog5EIhpQ8BSxQsXzMb9GYNAa3D7arKVEzQFCScwoJuMarzR4eVJG ODszV+48M4PMnO5lhpZF1GrfPutTf/RLLgmFIARlzvJeNDFEIT+4Hsx4XFtI /MfT01Rjs3gPjcC5oCJb4iMLzanj9XPkzH73RLeRKgSmgdAkuMd4QQgE1hpt glCgUYitRDCuidOMPkrpXypKKpNGsbN09xb3dpxwV7qv+nPTzg6CL5YKMtHE l87zUBKhsxSamEzM4lkZoDzD618yaLipQCOGK88a5z9ZrtnSb74tF4+2Oqfh wL59rvKIPj2lz3WS982JxnDIIZzrh8BiJeSV9xW7MdSrDxa9Igm48OdkeP8z Az8K+wG+4oIPDeYiaT1crKHD9pxT7RBK+3ZxOf5QQf47WJ6n79szv7zaIfxB 36uxssAZB54FzQoMCqjAQ8E0Fy4rPzfDUM72Q7E9SKgdiAYc5NMcFDMILsjY QQuric+tdjwHKwr0ZQcVlbmyknzXtVN01lv+rgPVjqeTHw+uBB0i1F1pq2Q8 A32Wg/HQVUYum9mKn/uHDINSxOV6+OGteXumociY3KtyZExV+h1YMSYGV1zz Q3tJjd2uouTpdb2ynl53gR8TGQUbE21juCtfBmNiLIyJZBRZh04ZxvQHPyaW 6FC8jcfUb08q1wseTOE8kGoc+PWW8zMOp/KgtwNu3969bt/+g6nqD3q7KBgG a8IZBkvmyaX198OANl4fMaTAR1L1ggzD6OSHQUKJFY0MAy2GBe4fpBf1/FsZ CnPo62+2pi6sQxR7iX9vdpzFtwFiTmWZVrI19ZFPFZEMoAUaLUDSB69x0qyf L8rw6hfoz8O0s85UFMqceocYgI/826ODiV5pCMAai5ITn7hWmb7L9OmHqWOG vBBhADraVoxWX//udBZR62BcLL9DbU2d+69LMrz7xR52TtSkmMTLkYQin9fX p2jY9ZqcCzMCrcymjiJtl9bu3a/07x6+HLkodJOo2zd9FgG3gE0zmM5U0VBR DA+5uTzDAC6Nwf9Zyya8IFaU0Em8tT5+DGxAgujQs8UYIULMyBW1G8PV+gJH aIQHqIwD4aDokDdtia5vkt+94sdC6IkMIWPIPwlV5IjGo6syjEXfRr5HVVA3 eBa/jXmARcfrZGXmK3HO4zc14Ux15gNCRxGGhWByv6tqN7Br9W2OVKNBTkL3 QBbJhyRsIIMjvUy19zkd3RFaJmG2k2dFEG0WMeJJs3/5Y4YBXq0/j1SyolXn 83WA+LMEqswY3CX6ZZ71A0X66M6HUCY97KHH6IQSoK6t3UBv0Lc6ShlEzMhz 45Dw8ogag8FqIqWiArr7hAHaAG2R6XImIr+bNu/36zOM+To/ZgwpY4YkZhLj wKiSzCKsQF939/E/Ngh3DTvEbLTrkV5CEzLMom06127sf9G3O1qJ46Vp90Qk WfiHV6P9l6Ki+R3jpx5wnwJwtIoyeIEN13At3+G7TFNUPMGVqMjUnzOg8Cf9 eYwyT6kDFU8rHx4SKxFIgOAMo2dwlnGVBIV7FZCjdTs6BANANPaSGcFMZ5dV MsroU9QSxvqm2qFyq75tEzVUzF8oNmSgOefbsSreTGLsMQhwHekEUVY3KUBN VKCYOdQgUWkAgqSgGtjfHzXBjEJl8HtuIY+8OQNWf9WfTdSesJSYczZ0sbfg RVDKoJkxZOniyK/PBk2li725STFrqkKE2rOYlZoLMatpFTaBYQdM1D45Z9JC CCm54Ztr0gpyl1+wgzVCXlAQKA6GiHwwQZAPmyz0ewAJ6KLeQRplgSZlmQVO t0y08/yyIywBThBA42EgXugblkMigsSUPAZyTBQhixwPj+Eadl/k9og8TuEd GXJxt/vmYpxClgaS/mXpAZAjV2DE5AMv4CfmB3py8WhfJjCKS+Osd7fp+ZXs x44SO8+vlUN+4QKKEG8WtyLq0erxYg10mAJo8uhZo/VZKDwUBsoOfgiMZArd WbtWlgd11B2VqCJDU1Bhots+11YtgyTIAknklroOExyk8SVAXddrCYnIOChD JilmmgW4H/LNNOH+uOoIBs8TmRdndNrSbeJwcAghcwB2+J2wK06zXJlqpd+D Oe7FPbk/z+GZPJt34F2YT8wvbCtZdP7m/7FYTFcEkffifXgPnoeg0vIs0nB/ hrl3n0/PskKKVa6YYgQCzY17QVT3wYcLVTAgByWEDuP8g8iSTdK0IIQiIFg1 9BrzFGHBiqHAEBrmLWsFEB4mKF468xhrJ0IiIMnUTXVQgaH4w+cIGdegCLme 73IP7sd9KRzyLJ7J8zW5IMKKEOMiIFjoW5QmikCXLYkvZLvmoWvQz+gdJoPo oAdqp097eq3AwSZYDZQiK4wgGUGACCVYBARiYpKFKOsC1JMhRSCVaCFSBFOX B8hTcRmRRf4QcyCzyAKX4X2ofMpXkU1uh7/FI5AnHltQvEFfA7nDoKFdOESc hfyPZxCIHr5fC8MFIADDrnVke5iR1DSpqmF96MhipqJNMXCEY+RtmcG4Omha bWgT0Gl3QKH7vxvwDzZ5fXe2XoJ88YevwA8yx+2QPx6BLPJY5JJXgTN8eOQV uaUX+4naEfe/M4z+f/3nFv8vuEVorf5HD+T/b7eo9JUO+cWaW3H5TO7hRkzY zGJpOtnlZ0P5uU4XT1d+1jj8zP9Zp1fW5vpG1fzuiAz3OqyGnzXO8FnTGn7W 5N/83Zq+86E/qz12teXBsH6m16eaFBDtL8/mZ2OXk7NMLbb9EWMkz5Z/0Yim hqnmv2uov2sY/o7cz7/1syYZPmtaw8/q891M79K4VuOoPZ715QHnIjH/a6Sg vJL4evhy98W3xSJY/Gyom5jKv4LPGoef6R/5f4Zeq+sbVfO7IzLc67AaftY4 w2dNa/hZk3/zd2v6zof+rPbY1ZYHw7rP+6PdR19M5488m58SRHxWWKtn1/a9 6o/P/13+avrdmsrhv5Pn+vIwYPDiOiiJ/43joR5JbTyf/3znP9/J/p3Q5bUr a+7j1uWNnlZn+jjN3xGlcwQaC2+J1slncsIPUTvhMakcQuUoel+nRpdcX/8o ek9pj0GquUbwOFve4dLUCNE91xHp8x2+i5HmfiTkeQbhN8/l+bwHKVx5ryd5 xaf8xgactj256Ffdg3bizD1uwvTdevo8+zDmT9nhRk8up5Na14+x3IgtEXAM h4xZ6waPWq1bVg8aUeq+yVuhdt7/3YB/pJrpr7jku1HRH76CLzls7Hq9HcsX eQSnyfHYiTN+1FchCTJlTsVTtQP/Yd/gHaZHAARwAI28EvkLUh7km8g7kX8i D0W+jCQnSc+otjhF81RUd8itkRqJ8iFzdI1inAfpt0jv+3HO0gbKV8eAr2Wa IiH7wiWkTLicLAxf1wW5dGC8PVMfw+N4NK/Fq/BKpPIs18sr8SqkZMI0GbID 58gXhXfaHx4Eiq5KcmeQVGRBGcRBHzbY9yb3u4Wu/8C57uMvq6YvSWRT0SUF SyKb5DYpWVKdpLw03TV+bpzmIlNmqS7SpGTSSHmFf5NZ43dcQyGPDBvf4buW YePePIPn8VzegXfhnUgF836UMN7/dLI6GJ8MmK2b1GKAEECEk8gUAWbcJNsf qp0o3ePz8MwbxAiM4RTcSbGmp1rhDQ7hhgyb7oMscxSnmnnOvGce0kl5lt8h jTw86TF4YxsRRJXrmddwq9u3iNjBuz0revYUFWHEL+I/ysXLu6bu8v1f/nhG FYA7fKEYMRg3bZdCA/3A9UnuLPfhZ1qzaEDRInWMIg3ysECCG5Yoc1DuoOxh iUrKIYs2/36m366J5jTWXvArCijkt8lvklqFdAjWnP7IQn0E+XyKMZCYkztT CUQgUQ9oHDTR1Ln/uqt2vN2m42+m8wAdi27UE6JlnlmNDgxppmEaMz9RD2RR 2W7J10quV4qaadkKEbDMe1SKKdGpiwbhVtwS+qGe21H0jkpXv93i2SDRYmz8 TX82QyhVy6JR0ZwqwDK5EHKqR2HliNQ0OWIqu9QLijf+1tl3LlJGAXJYIZvN HGMukppm3mjZhIKUzG/29ADeMZO3o2FTt9YO27/6sqniJEAg5+BIjYg5gPzz GfKLLGuROafiLsXyGK1i8FV+xVRCk4Kb4qdl0yLFFvyEvpsyYHej/jxGLRbm 46shS1SCTZ6oDZF3R9kge8ihyORditUxWsSjsmq1Y62sCuSIIfrEMEJ/yET5 S+3giarKR+k4mY5Mf8aEjKDSgQZR0yrqRz8/6svrVulj2Fh3JNW+ggYgsS6/ vyEDHNf7/gGmCkYZXY7YoCMRHROboqg6/KjvH6AQQsWLKgQQoPb5CkiiPjHI oituqN3wr9cXPEKHrkeQyPTAcvE3MxAv57N+e57xnRQMGTR02aIvNKLUcGiG DPnh2gzDvcb3heAgIMZUWqAOMcd0MB5mCI1rT/v1K0wPij2YGypxzCz0HVvY Mk6WFF5bu4Fe4weKO4XqhSQzCahtVPmXOb+95AdKgpWxQ79dhieAxRZRuSrD QK/wCpuqFS/Jy6KRUQ26G6bMc9G+L/neFzQvlUSk3npfPvt6noqEYHV17YZ3 pe/7QS4ZHt5J1CVWpLLIvhq9/UowuVJnOXLL7Oc64KAn9ooMA4taxg5X62Rt V7yx9iHJCGCut+9XwpZQkGc+cx2GHtZFMiOAaj6ky31bFjKHKKKsMKzqeOf8 9q5vy4JQ69pCMTGBxZW8NMNALvb9Y5hUNnqPe9/ETgoz7/reNxQOdtAklQkq Y7+sdq9/iX99FAsMaPlU/EFmmfgUH/rXhwwmH4Kms0kUiejaizO8/oW+5xBD YD2HCBbV8H7+3dES1Cpx/j7oL7YobyV69+LavftFvqWPWaDrGF6boC9Gt+Sn /sUZF5KGBIE9vpb4sBdkePHz/YvjxVpzgDZ5rv9Vbhc1eaL0ES5mCzMHS3dh 7d76guCtkW0EBm+BnTu+CODGw8AjR1rw3OStO2V46/Pit96s7g86Gk8Kcf/C vzVwY+q1yU3MGr1L59fura1rNWwoZjby1l/5vk/eGgOkby1ql2VX52V4Zes9 ZwtrhBvLKq88MHhb6wf+amjU43xeTeLw8+Km+h3KNU6AqPtvg9cDVH6Fj8UC 9HMzvJ519+MIwDUOgFi6Qb6lnNfjY6akKNA/1C7u/4N/IPNf90PJXZ8a4puX wRYnRffTyV1/ToZ3s6UmKDlegBdZsKFiiH839IG62KILRH2cUzuOz/H4IZlo ZPwv8VmH+9erhLUI1XBWhtezVSNMG6YI+0cPt+1WNlSoR8D7yRQ7u3bvFnbp I1uoWlGv1+q7Ha+9k/QRsmiNNjA6SKxrJNsyFHaxYFnJc08WNNAVKM30I1aZ 0KlEUwldeHyVW7DahI4mVrjQnELDCh3PdO3R+fTFx79cGxCKIo0W+m8/MwNi p3tC8dgJiJi+4lJdq6A1U9BwymnropkJgmnqoMEDDwzjRgwFsBgzGkVwRVgd Nmj0+Aa6VOgEXQvklw+pR8as0/VuYjt4KkEygbP2j4jzihOAZ0M7C7azqDIk S11r+xGLl4cISlwViWXNyTTRQRmjSJgEV/j9FllIRPMY0PZ5bYXCzmIfGGIx kG4Y9GSBssfndJZqg9pLi7TJjO/Q/0zjGVKANHA/dl1544nSRro3XAttu6R/ jZ3J6S6m1YjWJGsxon2J9iL6CHU3qWemaz8cr6E7XnRPb00s1cdFi9IqrvAL eVCHjFAccL/mJ0n/aZ5+vGyoJ7qSAOxyvwAR74sADEcbDxQljvNtK0lZ+gWn LBljjS6LE1kVzYJBFiOyeJBFhKz5YqU1i/fZ/4aV6oex3FCewaJE1oKxgJDl aSwLZkEhixg5UoTF/qzKZrEhq05Z9W6rtdlik78xl9h4xBHZQVTZaumKYO6r SZU4VBzPM2onLrZYiulEXgKLKNxdrBg30Tga2GGcrjDdHZV9WYQmKINCJAN6 kQaof/updaf59aosR2Te0nyGuuAWqAhbr6p7mIgEMO9pSpNrU5fEmyjvVC0u nl28Oi6k11ZEkxVBH4pelO9Gq4OxI/BKUK2tX6OiRaVgC4/gDndwwiJUdms8 zW8Ox8a9LBiFS/iCZ9b4sRjVlgSiBpjX1uQssnRxsG6bRxKTipk4vXZ0nB7Q gYnAzH7x8a8X+JgTNQwVTMhoh4Zo7a8dyMZs69NzzSV+STdsMHGAF5htgvn9 qnViBku6yWnho37Yb1G8hpWuIUPcFv3jLKOd2CXnAt/ajRSi1TRNKAiDFMtp +TeSzgxhCS77DlzsV8SzDpvluzBh+2My89CgKF0s3azSXRd4ZHHK0cc4tRNn 7jmtdsjagAgWnn9+KIYu1SncmkDMDJaMJdS6L5zoRhQWn7PH/7V+T1SsIeKM IsN0MRuYCSg1PmfDjfM8oBh7ABU7Fe1PkCbCtoEHoL38zqc6rTv54JB8B+aC lauIHH8jiojsyq27rvXby7LEGQ2DVmGxK9oL7O3wBTFwKdvAAcevd7+BGomd mhG9bPt2RENAOPH5xUdN/cFHlIgaSsB2zwAS4BQbkPqL39UBZYJNADoUCdKL KOJEiAmxnTO4/XPPDSYn59fSJ1GzzWFAB73OCoNzPWrMPuy4nsrpFSmi5ffo +Yvf8cK2bkCfs/wXu81X+KrM5XM9YJgDpA3ATqkHYFZ7YHxne8DwppiDTF9k iCmMV8XWxbf7zVhINOsODqI2WbjLtYDHcjbbjIWaCU6bBJh+Q4gkWrbHEJOT NmwEwvalwe3QNK9MUiYeK6QxTRy22MVvtsLaeiYo6g85xIrinuCunB0IFvqO pEzHOuBkLOv6BAlw2EflTI8TKo8xmzeJLkOI2GX+Ho+TXKmfgRM+DKrRr5uL 9/chiY+vOHDg5ngbmBAn258K7x9zwjldtruRLeOLdjcapeaAeYjZuDfY9omV 55hwpA5MRHnZvj2YBDuPr0MdIDJqyd9q1CpDO8MHXxherCoighfNXlgP+T2h mHO6SNvv+cPvZa6d4VEh7sCHYgeV9hlQsY3LqMbo/t5LtshjfaJBfGdUu27T M3WWzbGHPCD4PigsXUo/ZqYKYLyLVnTqNm4LLm5mQKKVsYe7A3uKXe8unRWT lCiznn3z3Z5oXXaqmj28osGQWIF2Fufa1mKoe6woqp4ppb7pU+se81uLUUpA ypAktlQU+FK2h5btV8exOO0y4GU7/ZEBxIyJ1KROt/zGimgvPfBibwJcxMc8 WDgd4McsAzDiD5E620jNttMTXyea0Ided+v2uF4CRufcslT1627bB9qc0JMd B06N18j+phMIQ2gQPOElytAjSAA9lJZImW1BR16YAHJUsJdZJoQIh6hUMcBT gwwQbjAIoHOYX0/4feYwcsw71DVzC61vW+0RSmkmQfR0uyzw7Hd5nWsDDzGm ZuQ+/T3eodDWrevOGRKksAXf01560EVMSAwc8EgIG+9QiO9hWw9kwsb2xhwv EYvufy26+hTv/bACAh+AkERMl23CiNohGtFJKN4uq6ZP8bCQwCBbEU/k+m33 st8NqhFyNi9s+2Kmnu1siSpi2oGOeFbPB6AR69uvALRjMOUwcCxGaFMNaCgT /HHG3iFId2H+8URRzmwO87zftxNvlNiL38k0DXeu1N0ZJm2rBrQs+5xUD1rl Hjltgzmoq+jeLEt1sI1PJajGaScSFif+xQAqFJYeCSTKnMK07X6Kj4EN5aat M0Bl+82yZg6VzIhtW1ddjSzqmvnFPjvMvRc9VPgAaCYgFrto27paRoxpp9O6 ypYwx9ZaeAwRcs56JGWwFSxZIZQybrno61f8VrB45AREmD+Rr/aB881kYxfI TGC0TDNtrEpuH0w2PGkUjviKr3oc2F0G246/Lm5ie48DkSCp31gaq1U/NcOh TSwZ0YbFHBvSzuOAW4ifjZvzRuWWx2gdPCS/e7VteUxhDiS5X6tqcLA8VeW+ xg3jAXNEkO3hzCZL5Cswb7NX77ZLbRdo8t/ZQcjtVBsQ+usQ5iT2fSatgH4l QSTC8IYHQbH5x1zVJDKD2sYxReW+z5lAsG3YKVEyCqyL7dhNoogNfognRGm8 mbYhObtAGQjEEKTV48l2CKemkasoGeR69ow2/uuSW1Y9Dq3ThAH1WLnR9xbb BDn1lgcBHYGJ5nMx63apwcjNWlYDAqOwHa5tV3ZCBcIDTJCohrdMiYqTx0Qh 3BSc2qQLw+xfDoGDCcO+XSUup2tNhMFAMHkON3Mn98Fg2XDi8uDwEnbH4jwE dkVlPzU2TGXPNI6p4pCfBrqN45H6GVvmsxfhP/xOWxycy/fYh5C9CS/1G0Ci lzH8EpTEz68Ur10ZkT05TbwI9A0rKt8YdQ5Ku0JhPUF3tyMvRXYEtxDbhVLG 0iOK4d/RpmgllbnIZWs0OGFLLE4HvNROqdzxk16HEhc22wTKy86FbVmHwKRV YMK0uPn+zviwAt1tncPEn55Kfjg6/aGZ32kwYoXdBkEdRtjzjFNL2KSw8kSJ b6I90e4cpYcBCVMN2L0w1Vy3RGMfQ/vD/mYQyR5nbKLHPmeca8Dt7AAMzjrg OCy2VYNoNtJjnzQRlk5+8uD+o1lZUS2Onh1PQVHJzjhocSiKGzBvDFnb/Z3D ac/3xDJfSAbbDnTETzgoTCtOmeDUEk4v4eglDhVLP2qCE0k47YTTfhvwL+oY i4r1lBJOr+A4Go52sjNbOXGI+7E/pSU+2fKQrQ+x6iJo53t9xpZpxBgcGtw6 mMc4opOLfq2XcFCM0DOXgiNM2AkPwdANG54si46UaaI71zFVwzOHOKUiOqFi vDKXPKFiGWeeNdfTbI5JHISBXHEABrLDKSWcZGO7unKkV5+ea871pCOpePKE 0fLvVvG8/i3exDUT6XbAGO0Eeo7F2j0xdsxNTAb1gOjkoabRTqurNqqjZSds cYgM/4Y2KIRODiXhgBoOkuHkLk4iOkFPIjpGj0vlaFl+jzhwdKodTsP9oBqR QheIDvlDcHC7np+zcmf8iroHx/Dllcq/Nkf1RGiQpCZFBV52lBQz6KH7viQl mjrba+Pej6/UuQ6FHPhnB35CJXsZPn3jdInPo6Ok0A9Qy4RmL8T0E4jCo6SI RjWf+Mz0+CQZunTsoEA7SyXMYxtrRAsU9hbHZ6dFSWbbC1Qmh53iBXFs7Kd7 CgrQetyTzE3mHUSU79whcX50jNr6zRv1eChOkuFYYEhhfmtmbd3Ws4NzQvEf kJlWgS14/5NJdSHEzI4eBiJ6mI0jjBCSs3jH4kCnzvQ5MwgBYLRjeE4XJ7Ce p0RUbsOLprRTHJlAkCbz9IzA3SBLgN8lfNhTyS0RuIU0ZJo8FEsp1lTS0FAd b2igXHBGsE0xBRl0G5hyuhM6keOXZYKcp/AfrmcGcngTh6ZxuhcHrmErF63d LIOv1HUc1CZ+fow9qWT6amJlXnvs6eCx45nDMwVJlUjQIhLiz20U7FFO6DUm AsbvtW5LLvWn2gE7thHdZZDjuSD3pwWYa/VcMBeqDXNajOnD4H1OyoC5nZRK 4ZOgh/3vDHPsAlEeQbId0ojTgAOCghmWN1yx5+g0UULiWESHNHLsMEqL40ex N4AtjsvpaYoHu2KPIr31+bcL6oK1qeHwBFIbPftk4G8A0Snx+Y3rdAdUjABY Y1fYFRWFc1UI+GOVgKN0/HkD8YmkdjakRFeViu7z1fHhOdWhrX31EjMy+BaW //SHNOKineqzgGw2jAlAukG6ZMUyNMvVHmjOnwNoHAUsCwqldOsPpxrQS6Km L2IXe4odyo4BryPQRA1WvDChJgLHdlMY6xicPvCCHs7+re5jjAV++sbC6/1h pPyaj/gV+gYdIm51Rw8vcZve8Z+l8VOI4KGYZqJM8Nop3yRbEWbOTrKB01UB vMz6U3yujIouShuNwBHDnH3Z2R+Uy9ldOE+cMor1ZC/5UwJtgdfMZLH76w4y o9dUKrLaAxuehRmfby0xPIGMaNf4ZGKQQzODGl4QBzrbycRo5+h41280gGGz aDuZGDNMoEIG0iAlO0sAFS2SODSkdLlZdtaOHyYzRqwncubPEG6sQQbOC3YO vcBpln/2kHJkIvoZnUB9AEfIzh4GYmLG8P5kcWnxrQOkZl6oYOum5AGkJAr0 uJhui+Pzs4EUbwRIUQwcPWznZ6OcUQQoC7SuXBufn00xmHuxzZrdv8oB74eA lOFaftKGTB6Fqi2gtLdEt7jlTH5CAfxAfL+/eUjx/XAtkGBKCZQQ7NxxKsFU 0OmDMCnFdye/FSuj2kOqfUQvzPHbV1eeJEKC5Z3eq+JT3vUcbJnfj9w2WIM4 ccL9Ke/aFqQn0AG3hNWGJqYSlRre2g6Vp8CSCc0TPJqsf7OT4wxN5imdOhgw O8WdLb8xXiAGcqB5m0eT824RUNQtgRgpz3YeTQI1gjcxlPH9dTOhUauzoVnZ NGkY0lhj55bZQDHc5K6Aoq036tgpHF10pIjrXR4+fGFCIfwAmfxtPXzqAkTl 81gYSe/Qkstzm1cDnx1IHy0B8rZC/FvcXjEp8kYRfBTK7eRa4k7s/p0KXyN1 tFCZ/E6CDUOOnCG3Rlvaran9sdgrRq7mTZ2GISUqaubREiw/2jfWqNFgirYJ Dn4jCwCOYtPv9RjyMbEDs1pMj2FIEEomnayFMUOrtkX61WFoK+KijeYqA2bM Dgddt/WJFyw94ufxu9fDR5igB4pyzuG23W09fKgE8xVODDQvCyDo1q3lNDa7 iS1lVQdhgcFHJkuNzeMrY/jYPB7xw9A89adp99sMlqgc8UNZihvaJjhCHAIo 2NtdTQdng08P/FtWnhgoxT/cUKBo4wHhcE5EbO2Gdan7PXZiZjT04vMFqzbY paSmQZ/GJBM96hx22GbzumHHig80fCh6JArIOoJD6+CIb1ScGOLUQ4HcEWYh d+JStg7nrrjvaNCTYt99ssZMPPSEDMAd74GbNCs6NJMOjFjuVpSp8ideau3l jnjJEgMyVx/y2HG4rR6OK266uPetPXZEVlAS0kGNlsWG9cCOOJzWqGgHwwHx sS0UKpi2Bsg/1HMEo+LUIx478MScYI3l2lZBApY5i+dvdNiS2mzY0cVhfkdz P0ry+7jTxas3xdjhiZOYIe8mZuJhjx0HDpOnwQ0SC2HYETWZabe7UkJiiWQ9 sKPsTFdkiB2zjcw0ULSMT2Ut07wKofwTNxQ85uHjoDhNoURnBBt81n0b3pU1 bHZUZHXwUVGmVzYcKGlMdBhQtDLrKfE3nrS4hKluil1DDWbUCotqtOuwznqo 64aK+H5kYFjVRshSS+DMYSBtZ7GhDZFeR+0LCoDDLyEG18M2bpzW3QMnv1SZ 41ei7FoGIQrqku6r5rGy2xMru+qAY2bR/8EQTwi0OoMn1Gjp5Y4+EBIX+NPr N23o4bHjoGLwFBMdY6dp4xnLE1yg7FiDG+vZ2mOHsqO5j/Gd4EdJvwP9V2JP U4YG8bI3pqknAuCQOBJ8YjFaxn5eaexCGnCkqFjWxUOPzwDccUFmFdOHN9E8 8POo3INDi6AqSWw3aeoUZukTHjgSqBgJYpaWHjh8GtolKMLYLbG4LGKuB3A0 vdAgHwJHEQ4PNzqkudJKUNng4OGnFLiG6rB06dxXMzx2HQkHPb22744YNdwS Fl9mQ40GPdJqCXHjHB7xaIkZDDViBmwBh1+LTX3So4Zr8kG/D8n8xKiR+bSz te2WNADQ91cP1AiCaTCPlppUihv2gRDC0MAHvvW6PpxQn3rGo0YqB3sbrTqI rmN5AimgkAUcOlJzIWphPthQs20OwiFSXaNoAggneygwrCSAZaLKu1RCRqEF QWvhr6OLHMRZ5BZOerQbO4XWEjJzplgij3YLh8iKDhO0kz0U+GzM0MeuHZV6 zkOGmcWkkic4OaHXxunG8HY/XQok1GQTNHIElrs9PhhiUtAaagyB4QSsZz1k HJXO3Jy3cl18HUoNryOkgGDCb4sQ6dPaQ2b2LYQMS0oVCBBOCg73o0QocX/q Qp8PePrGQtfzhsnu8c4TXPfrx7pu13F0EKA25t/y2Tj3ROeJFKE0qXihh5qj 1HGZyZEZ1NEap6UJaeeYMDxzIt5MUDcLI16RHNJ7Bg11YfIAoXQWL1nEYfUa 6V6oUB/ttn2/XbOHqMPV69ZqyYIT61etXa3+INJLOEd4d6FJ9KYNanOw13Zv chOEiLie9g66AYiPiutIDz12dhzj8YGTiM0JJZoCkFZvBeBz9GdLIFf4hRD3 0NXfufuuyHV3XvKxu/XCd93NF7ztbr3oPdfl4n7uzks/cXdd+qm757LP3d2X fSb//kQ+y3G3X/yhXPueXnvbRX31879f8ZXres0QpVeoT50b2DccLfL14tib 1NCizASi9d+kKzpZZJsO8LhqWCVPSdI2nEB4QLRAhsjDGpOFnOW5ylBLt3VH uRY/yGtQBSQRN3HccDdm5CA3Pn+ImzRhhJs+Nd/NLBznimZOdHNnF7hZ08e7 mdPGuikFI92EccNc/qjv3NjRgzWnzAQlv7xidalWGJGgc7002PPF643fiVwL icLw3fVAEd9zWEdpIIeBtxZKgy4T61qUmKz8Py6vhKmps5Sho1wPmYwPXPWN u+OSj9wtF7wj7H6gbN9/5dfu4WuG6QS+u8NXx7h7O34jl/e8YZKSDNn3XTFA hQSBQXgQhIeuHsI1Z/kpTTCHNy1TO5ZJAkH68cK3Jd9DbSAb+aRFWGMQAki1 BWCB+CQPNE2egC8kyEgjQiB/ybKlbvaMCaKOhG76Z4XYhfOmuVUr5rstG5e7 H3etTx3j/rtiuwz1t5+3uu+3rXbrVi9yy5bMdnNmFbjJE0eoAFC5wesUVXC2 vz3JXKIccmL2GgSZrHwKX5fVaKzwrwffmtnxLsBxlpqQKUXFkhDR+KZMTJ5b ZnvqDF+5QSnD7W0Xva8z+f4rv1GFfE+HgWJTIPlw5AO3QQlmpt8h2gFNIaGS v09DbRvAVuKU2fNI9tJhFjJLMcS6m6tj1jbED6GCVRYJAuaJQdKW+HzB4oXy LlEBkyCTXBHTdNyYIW7+nMlu8/ql7pcfN8mYYPMI969ftrmd5WvcipIiN3XS KDdh7FCNVQkUZNqe4VksnDkj9l6MRUJdC0WOSysTZWGxMrN5QhBpsQ8MCSDD iOIQ4RMonuixJK2Eq4bZPM1XhDCsj/xxhGph+HvwqkHw2d7ThmJnbv79yoGx EucSFPJpnjbsN8EG/Vn2KFIrGA78ZnslzdG/PTMrbVR1SBGkY0O4QFbZMJxZ NEtdGlA+zVfnsJdMPigrnjtVJ6DQ1F4ZO8zt3rnOrVo+TxUyypnuAFHep3mm KJbgiRPh2VPo1jKX24SIOhddZLETVvNMqnFG2KVbCARyTR7ZnKHmsTO0XjP5 aLtTPWcoTPQrhGBNsbr3dPj6NOXsMC7S6Xe/5wxj2+P68acGDjt9BAnJELuP WxTOflKItO5mo4s9Gchkh3TZwdDhLCP1QDeVgJw6xdNF8h8lBx8YzdJlc5lh p3u69uwST2nlArWTdBFwqXzlFE8XThMamVy5PYWGcvxeQlWjS4/qnrStkq6a q8cTAt/VomcDiIXcrJ7DtzQg8TcpXAN1h2CKMYcwgag+5pKoz3OUrsbkPNTE 4fdwiVCX6ui5IqlEDv3Fh+fGj7BSGknk2DK/kh/vRd6sGq5wXYkMQq7gibxj dFprpYsJsuKuyrtEXBEpYJ+YN2Lv3OYNy9zvv5afo1w1duWbV6qPUzA+T00i fktHTxR1XhrfQnFAERO6hq+iZ8F4L7aORIVe7HFBXIbvEs4rWjtQWhIweKIa q9+C/cJvwU/BfnXyLEEin90uOhLPFrvVwbNE7yJ2i8SBscRyerqCw/cgo2Pu aCaWmnqWWHXK0t4QGvKjKMBV23bHLBECkx+VIELeJSqnYbcWzJmiLJUsmqne x/lKUSO3Y+sqZY6ZtnzVSmOHFkXaFkIB4P/Rw+ErsBKYHWzrwQ6pE8oVISok CsyrNHbwKPEWwLe9RxlvAXVG6HdxwApzh2nF34R+7YN0jlnAWJuKVaSlNHw+ noT5idWxgrBaNd4gwZMwP9Ggo1QybUahgtvep6wJ+UB9ycIZ+IAXe0JwHtBt FAjElWwfBH3qaYrb1zxIY8A/es2ejgPR7/PC+hBC5yAuFlA0C5qlWKWIOBtw ZLu1a1OgbecDMWJtzA+uw13tcy9TThoxM9SF11nSeWI7TwfOB5Pk9R7L4rvS EkfMhiqLnc7+pfHymeroYMksUW9IhzXWRm2+lZOECHrj1s0pAxgXgAkiDl3q Ms8FzhwuOZ+LnbFLqa2SdQsZpgV/ss+3NPOPZsMtCv314EKXafXbk+CCVhQ9 gFLwOiFwCTAKTATjovt1+WpAcAvEFbjCc4Er0PWaoTo/xG1rFySgcCrCqcFT 0iWBLb5srUJ1XOBkW8LIuKDbjO75kAviWktAtfVTg6wIcTEugNj7K4OpgU9N E8bW7eVtg4ALNklrxbpq8coqkhAVaisqjV/N6Tg+9qor1KsOdQVrm8mth3TQ b2FTo42nA7cZN9nrq2uCqUFEi0cmZr9NENGi8V5+dEFMB6vSWf8a0kFdkgVq 2ehgmTN76ITyiUUn6Mg0NbDyRgc+FSkJbIdMiauD2bFy6Rz91frNG40OdJ35 eXZXqnaaOAnooK2TzVPrQUd0evHkBB2syqP5CsiOD2fHbdHsMDrIHKpKIv3U PvdaTwcs3Xv5F6qpRJkZHRRVoCMkGVOOSQ/pYNE/KeBsdIRlJ6PDjkYPgaML hoAEaNt4gBVzCSsljkldp1w0dBvLlqiWkjAzvg5Hmshlybqt8bzw/Z6J57J9 Ehup1YMIdiDFmwqhsOU4IWQ0etDEAKitPRFEKswJ5oaoqesDNYXmIlEkc6S1 J4JMH3cQ4xPfla4nNq1hxaWJAYlb836bVkMEwTUFmBAQdgpgLQiQHW8dDOU/ ajuSeLzy5pV9Npa2++mHjZ09F9u3lCo/EtzEXNAvZrVY45ZKGf5T+GgW/Zi3 W0cuyIRY3GZc0NBFjJecFOvU2+1+/diUoUtI8rcL+mA6UjcqEQ3Vw8WMSLgY X0dEShE2ofMkUmWpcPhcJieT9FAsNLHOidlRmiOEgo4dC+8NMlpLsL6A2sqz QOvd6BHfqqGW2ONPngXCevKs8hVPWDQjcKJoeLRbEubYWgN7NHbLVgvWkQU8 SDzLEA2q3CiSkAW8XDKX4NrKo/vwH4e7v53fx93Z/vPUTZ4FEqHXnf2s5tha BXMBpUT111iA6XT2/b6VWVlAIVmniUGB00/tJzEXtu1WG8FcaOWhZTEMCRZR TvLOEQUyJ3QikENrHVgHKxEfH/ixxOfhc7FV7KJeDwrwUthkCRCaWmO4bytL tw7abyoCbtCilBD6O9p/lvqrTQShBUUVXoehJ7YP70fK1NKTzTIEFNVRgAuJ qQ6hYLkFLYAhZLasLKo3RNDiNZE4Ll02V945oqDipy1KAbPAqGLCQB8N7jYL aFEye9Q0QyhRRwrCUMIooIJA2iSEjMgbfRJVjiJoibzxUbu065+62VPw6B9H aEaSVGPLIB9C9pnObLsfqUbCFfpMjAI7QCAbBXZ2ckgBe8vgGQHWcUGGGGVC ENEyUDDk+pcuniXvHFHwy4+bNAFCy41RQJqSJGJIKW1fLL8IKbCovx4UsCkK 6aGQAhxJc1kNMm0DviaioIWHFt+UWUBy+FZPwUNXD9ZkCMUdo4CyG03uCcX2 7Iw42RHPPpkBH/okfXUUoH+tgdOgwIU0Z9Ugo1JKpiKcBfOKJmliHvBvMVvw fZnOgjXry+LrCL9p5gnvx8o8y3DYc63Fsx4U4Ivgo4RQ6BKhRyNbcJyHjEXR SLKEATEFT/5pquafJGJL3eYpwDWiZiJ+akwB+SaUWEiBnqnuVz3ZcwnfbGVM dRSwn1u6NGIqrahtkNHWs6x0hYLawkpUi2fpLJAAQd45omDb5pWR07p9W0wB xXDzs2xW0XhGZBDOPjaiZ6vQ2G/LVgSx1DQOIGnQUBOjFsg7hMCzOkaXsAay D8BkZYH8dg88qSWqHY/8cUQMPP3fr3VbkrgfyUXayEPgbWfjbMBrpWPB+gTw rFilmy8ECiPMwi86Awx4QCalJFo/1cUDT/qcgDkkiE4EW91g96PBDTVXvPG3 GHj2EOEMmxj4mtc0jIKwpmFQoI6oIYaySrSM0QXUkwNvFG1/d4evUnd4CtQq iBEmR2tUocqe/vukBAUaffRMPtfCxWwUEBBZGtooIL9TRVbFqbes3smBcUXb E5zd4SkgUJ42ebQGBEYBmXOMMH3BNpfoVqVsGz4Xf4xDLbLIfnUUsB2rxakG BX7R495vSaqfr5HymAJKtFrCbfdJ6i5PAaXceyREDqmK0t3fJiilDkI9hCc2 ydCWWR0FYXzcNDDChGEhBSw1pSsipGBx8XSt8wH+nZ6CXdvXanwsIVtMQSb1 o/u+pqk9ErzWu1lHCtC6VioyCsjkmNGsVEQztZcmhrYBeujuQP1gCx69dmQs +6p+upck7hKaXgOeBC7H3WQDnilvCYImHgCW3NBcFAKFz048jPox4DXwHTfM /bpnc+puD/xyicpIZYcEoX6s+H1cNaaXfnAOc6kH8CypYwuiEHgSFSzoCSFD /aBuAPUkD22UP43Uzz2B+sEjErMcyz4kUlgI7xeZ3tLEc6MVk7/omzWphgK8 DSvIGQU0n7AAJYSM7iLLnJ4UOKDEAJs3LJN3rlQ/pE2Xr1oZU2AtfqifTKbX KKD6xslG9aAgqr6tSECBZqbHG7CamfrpuVq9n5ACsnEY3Nvb5aTui9XP12qY w+te77FcE3whBaxBsnZ6ey6WaIhvbs5EwbGeArwNvI4YigZk50xP085Fo14I PGbW3M57A6WDz0N124CnnYvlrtypWRBJsMtnCDwtr9b3XEfg8bTpuk1XA+zi TfuOAUUd53Ffn45l//qxqmqA3ICntkP/FabXZN93+SaIRPWQDEooOwkE2fEx G/BsXYkKCqFgywErRjcLvBW8H7Z3MGjJ9lDOBPz7PAU0U7EXR0gVsTO51nAu kRfl2eGco5pkHXN1pCCsbRoFeCYkqUNZJTdHYBVSQBb6b236Kvh/9xQg98h/ eB19G1YrNQrwsMi9hhSEWaDqKGCrC8sCGQV4JulSSwBFf1oILa01eDqA/3dP Ab1tWAWK/3adLpvwNVKjQPe4XbotQUGYBaojBRyBZPugGAX8P0m3kAISaWh3 QD0xDr4iiccC3O8poNEmmgVTYwpIa7NUJaSArBIzjQasOPXxwYJ4lUB1FIRl ZaOABBlGN6QAQ2pZIIOWulnZ6oUK/v2eApLUOKtccWLgqNIrv2bHTzEFJPps uZRRACW2lKCOFDD52QU4pADFZBlpg4ywjC1lqNgbtA9e9V0U6wr4D9gsuOIr rRaEVFlPfUgps4zZFj5XjxH069Gqo4AQzNajGRS6ob/o6VBqCa1YVBVCa7nQ 337eKu8cUYBioi+DdfhGFc4ra1BDSlFMttTBnss5GOx2XQ8K9PSlNCgoXLKO PqSAzTXMFpwYGGH8IHKhD3oKaO/EDwqpAn5oCO/H7jNWFIgVYFDNr44C/HDr sjMoKFmSqwwpwA0l9xlSQAqCxgrAf9BTgGJC8YTXsb7GbIFRwBZgVhGw51LK p8OvHhSEpfwmgSsK5CFkbIKVLt1ExrRUAP5DngL+3/ph7DqoM9fW7of7m+4G cLQSyigbBUgihwpUcUV9fdcgoykd6Q6hxRUl5Nq9c528c0QBislcVruO2WOu rd2PPYXNBtlzab7nnNJ6UOAbYhNQkKnHfQwhC6MBg5aullvafpigAJONcgqv 083uhMLwfsDPbAufS+erreysjgK6GKroZH9KSQgZ+p2u4hDa9WsWq+4HfKPg 592bVDlhjk8MogFWgIb3I9K2oozZIGYApbo4Wqk5BVYMpy+ZODT0TMKUXNPA fGILyIUatKSjtXm1w8BUV0+BfYYisuvIq5p5Nwq06uBTckZBmJKrjgI7ITCk IEzJGWRUJtPNLC0s9IgDfldPAfViFviE15ELSjfvHIxCj1/43DAlV2cKKuKs qEHByj4OGgohw68nqAql+4GrvnV/af2Wgv+wp4C2feIFrmhu5li+a+loux8L hywusOfayWkhBeEKQKMAfWwrWgwKyojo6hAyVprSrgKozT20VIJxQAH/YU8B HcRWSDYK+K7lg+x+NK2kxwWsCuLoonpQYHtkhVCwW7KVJg0yEsvo+ZAC4uEu 7fonKMBeWExs17E/I7WxcFaxTwfL68Ln0kHEkaLZZgHHZtmKQoOClgYrTRpk 2AGWtIUUEB0vWlCYoIBGbuwDHpFRQF3M2k7tfix2Yc1++FzSFOOzU1BZETDg WaJp+xAZAARLFpDFfosYUTqyQplmMRs+D5A/YupHP3s/ATyRhHUgGfD2jPC5 YTq6OuCJBCwdbQAQLKUHZHjzrFYIgUf9UBUA8kc88IVTxuiqsfA6yvLWe2T3 4xmWjrbnhunoJrWrCBgFuobgnxMTUCCXZDJDCmhINONqFNDve1PrPgr+o54C 0hQWuDUP/CDzq4wCLAzzK3yuVif8atrqKGC1QHpsihtKygywmnrI2JUPGkJo iQaQc6phjwZFGQvcmod+UJpfpWcLpc05NjzjHPZ6qJ8hQVEmDoxEO9NYFVJA ZhONH0JLr+Lt7XISFJCSgJrwOmrCpLOTRn2dxsThczn+BXHIRgHr29KlkaQ0 iztCCvBlqOuG0LK0jgZ4wH80SM6x9CRx3cYNbsjYSQkKKHhy8nX4XF08Mrm8 PhRwbrc1iRgUBEpWlDEKdMOza4YmoI1SEgMSFNAZka6wKGumx9hRSiKqRR8b NMXYqp7qKAiLMgaFX3+QoIAt+1g7F0JLMMbqR8B/zFPA8lZTWCf461irQGIv pIBSpTXK2XPDDTXqSAEbsqYbQyq36Zkctqt+9NqRCuoJlnS7YbI2pwD+Y4Et uN3bAqOARrkeaZTqPnUf/ZSgAOPPcT/ZKGDJfbox5DxCUsghBXRGWNnFKCAS IEEXUoB9SPeXcEXTKcU2cDhFYvaNK4r7RutIASlhuuKSmaHV8RqNMCAzF9Mo IECzmLibp+CWC9/RjonwOoI7a921+2lm6MMfEhSw2wJucTYKOJKMXVoB4VgP hZ6ZNDQZQLHgCX8mhJbi/I+71icoWDi/ME5R23WUJkl1hBSQprBtZYwCWsXy xm+qDwUsPrK9/owCKldW0TLIiAQItkJo6Rf6c+s3EhTQT03lLLwuWsT7beJ+ zDzbNs4oIDYnRs9GAS1RVqQ0CsjQsGlPSAHd02T7QwVD3pOl1YDfLUhLWDRg FNDYYsk5o4CALz0jRWRiRco6UsDOPumBEZtRWIuWQabx8J+mJaDF67cqgVGA 12StXHYdtWM2twnvB+0sJgmf++KLI3krfbNjq6FAT6wr+ylBAXVia9EyClj7 bNJtFFARoCAZUsBSQWvlMgrIFFnhxyjQOvGk4qQnJv8/NH9dNRQczV9HVLsj vpHx1cDNcSu7gUJDbbovQ2j1wM0D0nyjT91t7ZIqidAs3YeiyyI94cEz0zOl zAUSJtnIYJERq6FDMkihpfsyrIy2ZLWRQY7ICpdGBioKVRWSgbtqCQ8jg1Sg dasaGbRODs0v+/eQoesKXihKgKJnIz88JwGepRtCkAnL7mr/ZYIMWnjZhyS8 DsVmJUy7n535mMjVButpqyMjXE8b24fVmzScSqQqRNrZJCQkgx52lneEZPz+ a7luHBKSgWKztLWRoftW+6KRkREuJ6g3GWxfmp5Co6nFEs4GHu0oLJ8JQWYT l3s6DEyQQVKbxc3hdZliZ92M1W/SGj9XfCXW2mQjgyK+pdBiS7FyXbwWI0xa sF16SAaGee2qYqWhuyeDzKnVGuw6qnAWOxsZbL9MoJaI2actiXspqiGjgZ3G dPCA279/vztw0B9bVgOC1ulO6wl9/tyseM1BrGokMjbgzT6w4px9WaCmuycI jyudSDwx6zeKZ98rJXGfaTxLJZ6g2SsbQf2+GgU8CYLQ+RT9Q4Io8hAbhMCz bQ4Ln0KCaAKw7JNdR6OMtTsaQbQTW7OLEUTL8aARpdkISh2mR1xWlA5SKrr0 7Ok6yd99C8sb1IgmKsC24Njg0nNt0mBla/H/096bx905n/++a0cEHZB5nsRc Q6OlRJUQu1U0aq6Zkra7kw6/oq1SBDHHPEVERMgcQkIkIYTMIRERgqim7fGH c469f9vv9fLHva/3dX8/93OtO8t6nrWe/s7Z53Xq9Uqwnvu5131fn+81TwqA tPgd04oY1AXR7/ja6CqY6OQsww5EmnGl76U6HA+8LXxUhokeS5Vxiaw+6/qh B6uEFRMAFIO6IMSg8LYjTExyUGtI4ces3+ReflXQ0YxgLUepB9P22WebpjoM Vy3Y4pB9mv5//IZP24SU73RN1WPV+aJqCYWhJh0vpMiQEqqNSFFPAHpVUUXz LjXgo4iTXLp1vogwleYytqZ+ykjR/1yWUISlynESMqTERSJSDAdQ1lRI4V0q VKX74TEqXySkSB2qiKk2Ul/wf/Hx+mzuZayRvSrLsXK8svEj7bOzJvnCy9bh unlrsjFwhuLUSF7ScwpXCS4iiEf0/nUVXCcOHOu5parI4kmPbhVZJL2nnqsi uf77GR7jaYvpVoYrpvdEXjKsjDuJcFHoR2tDhAsnvyZcKcOq+9HNQkFthAsH XxvHWoNrha9LrAy/N/ukgOvj/LP9xmQftQUuGhU1gbgFrqlbGWOUdtP+FuHC wzyy92+r4Ro09nO4qxouhoKpMFDfSwu1GhUFV608FBnQKri8MFBKiiENuO5l l59GuDJIisDX46lYGFhkveYuK3oT64Fk5F89IxvJauaRDpLsisVj2Ng8Mlv6 ScKoZetpFTJbMdJUX00UKUlwcStkzGgY3vs3rTISUTE1wLQw0tbyNraQNspI mMjMCY00ReuUMaJgX2GZeoxEo4saYCIjlTGCkaow2mr/asFI6+f6VuhqRvos W3BVCaRG5V4sa6gH1w+PeDw7otevWoULg4LtPGU19Z8t92rBxXiUspqqBRfT pR5/6tlqNbXhfY9ZNyv3VucqqQquT7J7RzYg926+eWuyEQ0rWxVE4BhgE+HC 1ju81y+3guv4UtyZZBqbHLeyKkrylixQW6wKatLLcPmaxJJVwbxSSpzLOUhC PhEubMKyBMSPoqyibFX42oYAVyyNboNVcTFWxahsw2eCa0t2FRx32YL6i+qj EVhWF7TTqOlL5GXMl+q2BFdeQPrnKrhkGEa40qSolvt1oD1E9TGkx9pipFMd VQaJeFmZB3B0Va1VhHfMzFNyQCARowPQCBLTvuDTeD9ScJruUfhST76c3T3+ lTYY6Z9umu3Ev2xubvh9vHSs///UzW0z0hkiXcbHl96VlAt73Whdqo6KPpl9 96uXV+GT5xVu2Oo6oqfsHVP0FImrDn99L6UFzXq9lLKUlQsTJijMqor9zJvu QbuIFEYGBn1EirEhzNyL9yOnoO0yzXm9H8nrPWuk/3vMgs3Ne71Ek+igjzCR tFQpS3XC846tokdKeEZ2o4UkBrn5To2BLb43313WJq+X1E6EifFemlsXlQoB iggTXWgrlj5fBRMJT/IPESbyDBo+qvuxXVhT4otQXujEaUv06NNPso8++ij7 +NO2Ro+oCSW2WhUeML4iDBEBIvZarstAnp0w8OYqgCiOPD0B2ZKLe3mrXByH QFOFWwLfj7Yt1hqmtwggymLUW1DUEI17AHJXEZ6BXyoRVqyV4khqNSKQqDOC 6REg38iU5gNXxVrT4rl/Qqx1dTEtvzCtTHerXrtWKE9g0Jt5XP9rW00JER/S xC+BQbiw3C1BuWazWQgKZFSvXS+Ux+BIVFA5JUQbeQQDYciaiKoshAnOcql+ 61mIVrNyhLvLEDBFUj2bVeHuVKMnCLADvtP3ilJidMxWOWyEX7ncANtCMzOa ysqlcsUiK7funaJ7syXcPWmrKCkOENHTcmK0nMOGl8rlBtTsadxakZWb+nw2 7rE17YGgVvoHlUttUYQAv5Q5UVVpncMezkb0ubQKgtS9VgUBeT3dTxDgPGnw dUtuekvTuWlNKq4qapn6hBfKRAjYnkWqh8YRQeDdzGYZx+vIQMBF8X7E+DTY ujo3/Xq7c9MmFCIEqFPGJVSnQ1d6WrPKxTff8vBeF1cVyRCkwSKO1xGPwzqr Kj27aEoxtuSfUaFBEohCmUgyCmQIu8TTjc3LnMFYoeG+ZarXFgR5x0N1uQGh BpyTqgqNyfOyh59oV5FM7N8XBChMYluRZFg+5dNNWifPvd1VQKDCmQgBcJab CCnLV/9+M3VKNBJWFS4+OMELJKPgYDuCTrcgYILLaytfrIIAA5fKvgiBbz4o JVXjEB19bz7f7o321Slt3KpULDYRimSsKLmgVIvq04EH3lpVrUdFX9mGyncb L6i6HyX5lOY3U63nM7hL1Xq40uoyEARUZ1ClESFgej2d+7FgklFSGLDxuhgQ EwS1mgip1tO07SYhIEpBMD9CQFMhCcmqar2TH3PZX7aI1LsjCE75xm2FRaTr GEFVzsL5DrcLHt2qZvWStEC90ZpVCmQolKnyH0y40FQeSYtFpN4dQbB+7Sve PBKvYyEBp75Kvc98uhj2WRRq2rNoi3qzZcOpVDeS4oarN21l0cdISFTHR/X5 XVXxPAUBaihX8TxlYmoiFARERiiabKZym5O3VeX2Y9O2suhrxUQYnEDCn/0O goCiAEger6PETNO8BAFD21j2VSUAjSO1L6ndXQuIAY3XLExHk+Dl8hYKxiiL pIdZhKdtUCGOorPHwxurqwhPx2i5SDnvWpjRKuHjEB0RAKKow0aEp02QjHFs A0EDqIdZ7SKUwTBMgaGDuo6aPQJcVd7F4zO2EntoI42abbJrQSvPIymwRxhy EyEgRl4mrQ/5Tw7Z2cEhU3C3UMJHzdwqfIGIK1fI5h07s1vv2Hn82WImdRE8 nTHHC2CqugwmTSyUq840dRVyyM4ODpmniDe3NE3R/1zuALpv4hOF/dVgx049 CPLhy7kkVmg0n2WxplpcpJUxEQIKv+QNnFWIn7s8zTHqyGnFdR6gKDWiMM+C gGKEoK19a/ilZUlMOFaBOZGMKJBMTEHAYiDED9O8zg7eABOzIwQEKNSIovsx z4KK2Pi99K1p53iDEKiBNnVuVpECIVRu+4b8MkVbTnc+TCF2bwLLSQfdVAUV Qkzehe6Hx1GOTdG9+acrnm6VC6gOLXdvYqmX276JbNMGFUnL+BDFWs8MdhCw MEoqDlOga4FZtXGqCx4H36gGWro3tSSmSQgQQuWpWggIFWWJZJiTGqjW0sN8 Q3Z8/+uqepjp3ixfB/kV9iuGM/xuSTF8vOgaDbPg60FA7FLbaQVBrB4SyTAn tb0n9jBT6BUhoHsTHRGvwxvAp473I1KkxIQgSOu62wMBSTN1DxSR/18+V4Sy q9unSoLITE4JIg1T8B0j5iXEYQoM81dCsNAtF8/3xaVVZsAty32rWWsQ4JeW uwcIO6OSI8mICjE0PJL2r5vXFYJIEFB7oupVXYdgKjcRou7Loew4UvOfOFIk DlRrmWexoIiPdg8WUWqhKiDAalJJUJz+InVcjFarEZxjsOz1Y15qFYK4naJq tNojecl8vXkWJH049RBfI0WoqfNU+OaW6S+o47J6JzingbbFEIcV77swagcE DC8oF6xTM12OZ9aa6kKCR23kGqxDR5u8AV1HaXF5TKG3T5XmaPh45ZuXtQoB qrhcsE5kiE57ZHe9qS6ksAlD0McpCBBMVJfG6wjiASFbeXQ/uELLMPS9GKmj b57VHggY4VEWCJxO+cTFbCOzaC4ozTbSHKM42yifZ5H7D8UItlOnbTUlhmCf fGJZYoy6ImTeGgQ4YxIIksnRJ9apxclSWEKnG39YfoHGS1FbhXBiyJquo2iE YrnIVbV8YvyTG8Y+3R4IGGRTrn3Oh4w/Xn1qT5vms4wiafEBvtPviqohaxil 6ALTCcWQtbyYo1q9xyHjggBriHhtaxDQQFge9IceUBoylrOpRVakxQcgS2Du WAEBkSK6++N1tWY8kggqBwWxzG655/n2QMA4J9W1tPgFj/opLesCOnQiBD4+ ZOifnQtODd7xCansvUdwzRBicW7e1Ze/XmQn9L14xvSVtgYBrVOplrCAgBJ1 rKIIAUnLhyY87MamSGtE97zYaytfLObcEarAO2YYuebhEd6mQydCQAy2PFvS KwofeLE9EMTcsUhBvFQlSFFwkCVgeHjPggvGFrrglAQB+WQF56Iu+GFJF8B5 ZejzzoP3W4Vg5rN/V8VMAQElTSpBEskYqqOpLoJAg5bRBZr2SHczpU7xOoIb D+WTXaomp6qkSd9Ltcw9419tDwSxb60gBa1SKaovklHAfM5h40une0a+yXPA TcWkU6whcYugIuOg1ivdj/Q9XdPxe2PfWr1JpzF9r9OIwVgeDUiShmFpELUY IvvszCJ9r2GzTHrxHV4b1hfX+Yz46dUTwwhYIPAiBDFj3CQEsZu5mLoY5tyJ ZPgJMkpFWiA5otfF7h1r0PL3B97iwokARRzIjCBiT7wGLVOaSxy2HJtSrqze vF+CAuVYTV4j+UgVyTjJyTAtSMtmA4SOWUYFBAgmDNUIFX2cEmy6n8+5K434 j7myJiF44IF3izkrgoDJFmWLCEGU2mmLUcoEIo7pd6UT/4SgjmmBMmFUXAd8 ZaNUFhFb6/S9MUtQDwL6xMpjv/EJZBGJZIwalyASacmT4QMgiL4fvGNgIKIa x5LLKO1SwyIqZmyHLEGTEMTZRjuF06hIabFwwkgoQdQzqmMTRGy+OSEYpSem iKog4HflbQsCDN8y98GRipTWg4BaHkVKiwm8Rh4Wa0cI8AmUqOkZ1DFDN9l8 o4nv1FIgiNiipnHjjCnnd7mTIMDwFffpe8leK1Lapm0HWjNBcE7T7USAfBva ks8VPyIogTiJH207OAHxY2CwcqhXED8ocPY6F/tzLt16xvYdYbpdPcIjdZUs FAF8D1oqAxfhFWBD/Iigvl5o8jhMUnvmliwBGTLA6B3Ej7IExfaEJOL4Rm07 oH5J0+0a3HYgCOKMx2Lsdyhy7FxlB91dtccGO0jewHGFBrjNIWABi65jLhJJ zshLRIZU5FjwXJjxWA8CpLB802LpSYgM6axST6pUfVw4AQSbN71WbPrAJyZc UXVdKlgxshf3o1+Z9H383jjjscllK1jgqt0RKZhnoRofkYz/V3BOp5shCVRG mDlaObYIS9xRLGDR1icSnv923qKq+3nNUJpnoe+Nk07rQUADgmp3RAotd40Q 4NcSnEOy63RTY0owjqH72nfDoka4gBmPvYNDBoTxfhRy05UcvzdOOm0SAha6 a66ESMH/a7xUsZ/mwlc9U8DCid7BFKWU67v9rq4cE/LE5NROP+SuAgKq7zXx vUvgKopWUMJa9xLn/daDgOXymishgRDHS4lkhJhVM5Sf7o6uAURyU8THJBRA BleZGeVa/ESKmd7weMs4YUooMPIXF70dKFx19fxihoFQ0BpfxHfXGnq4d9DD 1MWDwrcDCkQmTj349mIPIA4BBdsRBXeLS7sgr7lmgcujiEJMlwkFMuaaYSAU aAmW+O5SQw/3Tigg+pE948fdCQrfTigwUiFVDxUoACBZs3hL0jXwm53/4qtn v7whGzN2TntQIDCs3eOiBvV0qvSJG9BYqwVd+4SgAz35phUqR6e1mKBw9L7/ 5q7ZT0Y8qUtxtCmdID4hYPMhFquqvpqcJYmz1thhrhkluAYRCOps1aHYJbgF 1OSuWfd6sZARdoDquAZbPnjj6AQEI5lvu3l02KC5jQfpFHXSLfn/8VNmV301 3HnrPQvaA0QMUexcQy+IaiReVKabUzff0UtIgtGD5hEcWWjnWx2z0w65s9Iv WKa/LHEEZRVw3c3XfVDsJ409PfWAIDpNG4JpxoIaUg0s74iLYlWpWwYCB8E0 9JEORL63F88Zrlj/1gZhAU8pgaa7EpLS2C99e2y/bxILTqBmThXr+Ux3QvuI BTlIWapaFwtToAyO6nMJzHBEwoL6Inxl1LSwICXKVJjIFCTz5SUUK61v+LuK 3Cv1lvbG3TiiBtNJVUukE4y1o6Bd34AFHQc+VuetlUckLHAUKKkg0Wy/JizI IRB5NRupanXv3OQr6Nupsdbkoyax8HGcv3vCSaHDyZhMtaNVr4u9lzxCWGk9 29dUDO/9a+ykw4KMIraNHr9w+BP9VeD4/UeKrLLgoA9Sy2EER5wIWQ+OOJRT BCF4p/Y0HWJmvCuToH28lLf4apY3lla+lQSUdiib/1zRMmtmUjCoJ94PuClx 4Ru1zNonQqYCxyaBmPDI3zWgvAAC30EBt7hbXJpCpMWQpQX0+AHXVw5NKBA9 8gEUBpC5bf2D4SSm0GJx1vHIZhYKcTh2PRQwmuTBCQXia4jwSDU6Ptg4hKbQ YvFiU7IZT3/7y/phQUCRT8B4emfDiv5VTPEoEcKCKWY892JhOQsLH5Gd6lwa xKJrzPCndQkiCP4Vu1YgmQhHWEm2U//EFJRXYyeR5T9p0O0Hx+2xJqCIJA1M WNB9o/is4AUHLa7TMYiuXD0sSPJreYSwyAc9TPQ9H6IaKUvFU4UF3jQ1FRDd cDk4YcF8CWri+dFbm94ekLCgtVpRVd2VskrFsfTtUl+znuNtmsLiT2GBi7Cg DB2NGg8x0h4TiJDegIQF5e/0oDEW4vj+1x0YBBT1v7CHCSjBQe0ePgVdnror wknN2g5HBxZada4DQtwcoiPJuCmNKhdDeLOH2T9mjFZysm7rWoJp8TAFpV6E 9Q4siaeVyxbYAWrJL1C9t2jlugIGil4YxILFoO9Hb2iPbJMw5NuklledS4YW ypAVwWARFf8OTDBgT0FrWMLE0lCHYVsPrlKOxI/QF8z6H1SwxsMFa+jW6G3t tdMjkHamDKk11nhqycZiIFdBlZeWFwatqMcMATQvrvPAhAr1L4wPJtIBCuQ+ hwb+WPjcLHfBTbQNEn8secn5AxNNtyYErhV3egTPQN/+THuAuSmsuBNVAEp1 GKIeCh1vm/XVOZW3dQVOVpoYEwYVK172TejQqUM8Ct7xXSPfeqQyOHTsKARe 3H/UcudJ9FeX9BxUaGt4Rz10aJBKg4sK0mhSgwmwgoSkQwmDz53/rD1Ljg5d 6+TdHIEVLzjP7OPodHJNAmJ45vAUPsjGd98RSiS40Unm5Fe6p6+gt1dRcT1K 3AffJEqoU8347FySYnjj3RMVWcaG3QQvDE4oIcWoFQYJaujZhrdXQmnkgBtd w1PKAQ+ZOKvsEjKmqHpyRbo/WkxD/PQc+djVR/1Ja0m2nWvYvyIN/KNi1m7B /iVMZbyUUOrkko1oIYMfGLfCYD1zDvdMQKF9liSfEbAAzVhwcIgfcsdnX1lV AKUJRs+/tsXOW7LGTe6q57BJoLShFNLoGDPlUCFcEZIaSbXw5ATv5OxE2hrz DBBhnxMH3jbEserkaue7/a7ONxp6SPEBtNUQBVOGT/GgsPnylR6qWfjF3CKI I7ji3oB6cMW9AYKLhIYCuwUhV690H57Y+pDEVFhmqHwqa8i0GnSVXR2rbR1G Ai38DDPt3Y0r0+/lSo3AjNnc9g75/alHU7ZVQFGRr1aIOkC1JDm6hfJX+CYS hGY4dWIJHmKB6HKk1m4JHvoRqTzDDGN41DH9PNPUP8FDpJcy/ZMTOzFdwIy5 3ULqAwfV7PECHnn2t17/YXFYKIJSUWw9eCiKVaua4CEIRW3Aq+s3FeSjLllF sbv639t55pV6AywzDDZsBZqDkH39E0uxcQzDmp8TASAcaRbcbqFpBRsQDaWv ojKdKsX4SITkU82svWJDWRFhRgZORZNR5lAjdekPlxTUzC24PCuyu1N9B2cp ypPZ+YZVjQw8us9lzkffG3ADw2/MogLAHfJk7cBb3aYAW++7oMTtoJucLaks 3CNELjVlvEeQjYpcdilsvS51MIRYKq3U0aYZS+krsRh2mpfpL3nJngAAdvAJ RuyBIFXLQGpyiPTdrV29GJZyLLs6ltv7XAIqryh+owMJSwRMEZl2nz1CXNMF 75vvFZhKjT676oPiETEHVf3ZpTExKUyJ6LJhNYpJbOUyTenrJYFOjH8vp/0A D6eBB5gSysH6oEWJMALYgh3teubNun7DkyUTRoUow474b5KT4A/Wx/a/Jvt2 3z+5BYNspeGVswL+rKZgN8KPjpq5V4KeR8Jxw3noWZRQTPM6gfg6MLEqGusx MlFi8ubxEJA2wAjH6xIStJhhbax+/TUjRZ5AwLTE4CdMRwwII4YOcMDl/zFY AJ1Dgdzl0FDlgr5ECLBEBxMUZscQwgBCMGCS2sHqgMdgVipnjI+QAXb5XoqC 2BMRqHpxNfUyygkt9hbO+DasIJGV2uR5IRWmGWVdSvYP9knPoFbBjWrhfZMM IORH+SpnBjxx3biGcwXexx9wdaUDp8iuBXc+41xxDSqa0ZzEB5m8hF21r/y9 8xZpaWHx/VQ0q9O3a1HU1zKCrt5BwIpNc83sd5N9NGNOsdGoR/AxOAgU6+2b pAGWKkACNFIAtFCyGEMECE2K2zuaeWTXEkPkgGD10qNFtpWALjYVUgJFvm+C mP0aeJlIHkGMliGa+PTSTcVzxpF1TUJMT9QvfzKrinRE3NXaH5Um6ppE0s9D NTJQGCTGFanW/qdPFZVlOjJ0vlDWNOGRv1dqCebONcqadIqpLCNcCxFECuJ7 SExCtT9XvYA5GHQWxuswcWXDiGSUK2m6QpMko0iO17ltzJaCZB4AumiKRyF6 BmuG5mQ7yZWfhmQ1JUw/P+OpgmT6XTKguh+LBtUnJZLF3JtIFgN5Ipm9vFft EnLrpfzMCwtdl3HKfppIRo0kU/qoodd1/J7CsyIZiWX1PzVIsu4huay2A70i jpXs857BHcUAJDnzk0AyTDgTMpXesrBN4SuuofuRtGNteSRZrVOG+lfSWK+o k9Jyejq628r8fJh+wzsbf5KoRjr+5ttucXUtqlGwSwtr1S1D9KZrc1QjSKAh aXpLUvJQg4PWK/Dm6d+5zXnzR4E3GWzwo1OeKKhG7w0HDWe/W8gtqvy8HtW8 /Hzc9KpXRCRR80Ygq1egGoeNhVrGn6NSoBIXA5uYULBRToRjIzhR+VmL19sT taQNb7l7fnsIR5+5et71ouRvlcjICdfRu1wwKvDF7ZhdmGhHMBi59osz51T6 BLmmvGzXYA6noVeVrnVoZ+/lVbOmaoq3JIzFkYEOVbRbtMBlm6mZCxPt8KMJ S+GiGe16B+mmUhzdFbNW46wapF0PRdEvm1akFQrpdtXb2X+76AnGKqTD1NFH LGCPk9M2/XxBsNNcJ5z/QkE7ziy2GgJOcFBvr470WrTrEor8tEyta0nAzV+6 xr6jRSfITrsgEU6VN3hNdtJ6B03KsX1yyVtm66f6vYdmFM3mTRKOWL9kkt6S Ei91t4lwdD7zGQaPGdXnKuptnipl3pzHP1y0rE/B22u8Sumay9fawyYT7NYV bsq3RjuyCmof1AmhgIsCRoR+n2DaEEswGlbOS2KORUheBL9iWaVvqDwidFR1 v9f/5kHAmc+CbVOE43WU5Bfh8m6cyYyVtq9vSSxztMwzqJydqIZ1iMKgWtvE YN/gFCpX3b3KM1jdKtVQfwo6dw8GoTKUfRK3YodjpGF1mKt4Vjp0eI3oCH60 ZO3bop0mrZsXV9yV4lXiPXxvk7TDwtWAFb0o/RWcJEjRNx06ulJdsZr3Zift jCLY+JxHr0wPV/oFswUtYdqiuCWSLs1frHSrQzsyR5wGJJ3eEq9K4ZG+iXZU hmJ+THp8MvHcMxLtmIaJlULx5/K3NvdLtMM5py0pIkJPpiYrNkm7PHf7qMeB 4ov+/MczPF7Ur5B0TM56Ik+kHz7pdIXTT5tuH93nkb/+Kgj6xbwi8aRbEkRW OXMt2nVNtMMyVcu73tJr/VJOtm/QEpJ28xc+f3piWMaSwcQLF79gj9PSMkE9 3PNr8GNaEq2qVG6QcD2DNaeUTvdgABMaMtGVqNHRy6MgGq7fqCOnn5x4lsgQ pgm5CNPA/QPPQnrM6h6hEJBO3dZohwmsLE33oCXQsHNeeDURpKOPZqXDCuY0 qXdyOnfM0YCNod/K11aLfOyxwOVCUYh81Pjhp/C93ZsjH0aqVnr0KHh2aQpv rEnk29Y1LFN5KN3GG8YLPiHkq7meotZfnzN/QCinB4UbR9NF0OIIM3ykNRrS 3YYjTApSb0s3A1Jr8ZoNiYbb+vnzufdmyjEBA4f4BKV1jIEpaEJzPLfgeW+h EjWZIubZ5oVL7NlacjRj71/UGjVbIsqioRcjpfFqelEcCm1fG5COIDMuyKmR Yz77sIeOV27z8El5GYBZ0iYDB4ZsG3bO6CvWFXel1EbD07rXIF+3RD6C4+Qr ebEewcjDUEHZDkjkQz/QJguJcC/Wb9xQOS6Rj+PJZ1RdUwCDjzEwkY/dOlRV cmeRjwIWDUnr3liYV4RkRE7uzP6teGX2XEIGbLhB6TDSyor/T4aEkA6hwe+k VBeZYqoiUDB4dBzigcE3Ib5gSsns3Bb7hULX1khKkbtG8/UIXA0Zps1daM+W k5QqFZwLkdROoT1bIunGDT6vDpJyDSQdpCbBBS/7iTQbpiApfZbY6kTOezTG 373Sq2BoKMoqkmICw5iXXfhKZXAiKUU9yEDOJplCIt0jEknhb36G+KSWCJIO VsTdbCVlA0VSVmHIjatHUtw45o3C5HplcnKcUiOHfUdOUsjGCYSkXoBuZvWI RFKkJSY1JIXcKPbBoT8AZf/sys32bPn9GSJCjo8naJKkvJoCfHplMmx0yvxh 1PLKLk6aTm7mEIxEZJIRgow/GfHkt1IuyYdDHHq/H1T6s6lVsAO6SyIsbX/S QPoWDCzVtfWoQdjuibBYjMrbibDagzRvyUp7QgjUyc8q/jEFIJg6VPW8tent b6WMK9LBTG4XrDrOdptdVEKdbCk0Uu+QlVMVW4Pk7R0qpzSYQy9OXoDoFXnu IYm8+MvUyfI5ljjWpJH4kEBeZAOyAAQwmkxu7BrcQHjgptHUf+ffQoGKFHw9 8qLgNQNC54rxSRCDONcQJ9B2rpxwZ8i9cT6ZCUHZE4MGDk55OGIRXIPG5xp8 awwqwv5DQp6asCPGgwjN7Aep/p7NEZqIjpa9iQSUgSizsZuTajtvssCTzgl9 jwfE6XAnwH6AU3s732ZMGRtSGWrTXsRk6Zb75JOxILmRvvg+ZuKi0EyxVXrW IbkkIbX1IgEzzn0Rx6o37DtykhOs1gwgyMnRpZKGUOMBieTY+2SsZRtQsowS RHTsHjobsXf5Jn0f8QyNuW+S5OpsvOU6HN6Wsw3/E8/Yw0m1vS8wYqwwNUyQ k1wEFZqQ/UdHzdzbqb59ngZLCymx/vGeCIPgSdnt9ii6ZF7JLjp/AkZt8a2k pdReV4vwPRLhcW1Uv907nPWb736QOnt7Ygi2vROV+m8cKYgqZ5TzvvbNN/Z2 2ueXMaUMcQ7dicxRHUtvkYnyPZJpTFQEcxl3w9y4AnE8OjsF05750BivIQT6 hMCIdi6IFgz4xwcgi7SX0+wL7mcx3pzgiESIqsz8oB/xODbwwFBgAxpYH6jP kw+6pdIBbqls4yYfggp1u7c6K85b5N2+GHd902MQYle2sB4kZAtZpA5N+oR6 WcQPkOzlRPyCixbqk5DqOLFIeaDB5+C8wwNm5OU1al/0y7H5+BUkEOyBS0zT JGl++gP4FSTTXiGQAE4TZ8yx10gF1Ate89GNPGxtkNowxV5w0X6LQiBaJzox pSlt+ars6/TcybtXaCOC1sgp0hooBRJ8BFRxWeAU8CFMg7wiKYgM+4Jj2NmD XsKQbe2M2OX3gFzJQpT1Gd+9w/f2wnHISvo/sDRbnifNGDdZd92VbxbP7dr7 d094k0CvOvgSofXOrZc3FESlYhx8n1m83L4D4u/kYMFyWJSoDXxw+AnWA2M+ B3vUC6ByPeFb40F7Z0Dv4ioeP4noJHwJu2IR8LucGYDnfpwbxrbzHaRp+Bln iN8zZt0n8S2SgWgm0/HMqusbrAK5Bk0fib7KPBmbqFNVpMXCzWtBXq4MdQi+ 7P4TVi3QwMWUlSJP8VKxZN2TPeZ2h5L/ts+ohvvatfa7wMznHCP+G5hV78G9 YGdq47g/6hG7ZGiIRdM4hYl407Xv9yvKTOd7CpuXqIV+z4Q+URdNNxL9Xn59 o9tuM+cvtq+B1Du6pmO1Dt4v0hN2RdthxxFnBjGmooIaLAxy5iVvC5z2hihH fkSUgV/hV7GtEcwAiwTwg2NfQbcQRT5DQ4YJRxxTsp+CQ4vXO8y8RO/2Qkwe mLkH+IGiHxk7IDbdVhmWTED8OcxAHGuUHH3q9u8OXhOU73QGDS4eFnxwkAEh Mz50d5I02CFkT3rXQYdaJje7jDfzV+/o5MFzo9XJeOGQxAukk7DcYDvTgB0Y +J4itYx+598mPg8NV1PzA/eYX9Q/kRVLXhnipsnaL4TKlDbKX7xjdv1VG12Y IsSMVn+UW2cHG2pffsnSyoBwgm+48RW/WS0a9dIJnr/ahybzBqIRhCBogGtn p/mP6a35mFQnen7+mg/7h8NEfo/79fn8t65sn+/43Lwgu2zkcH/vSmW/bNRV k7LN+a6uVkly191v+uujX/onkjBqguORb5Je8vsiJJjLmNFXvFGQ5IFwbPrU IQmGC/lXXKX+iSQED0ibwUlzXlz6+yBCyUIy8M1O24BEEsqDHpr8WmskKZb3 ZJt9K+Dwsasq9Zf39NfhuLylVmJAogReLa+M9W9O/+9CppKkt0u40e8NVBGl mVZyn2oRo3dwnzAQeLEBiRjE9mTwmR1+SSh490TFS8srAxMlMH90OPq2hRKf ZOOHN0IJbA7qdzHSByZKkAIiaet9++e/8JsUNUEAYXZAJHZYI7BEDqKCqHvO Rt865OBsoO7NbkrvuK2fDdwPVD6BIWJ2v0kHhOZvSMWfeSveF1nIfSGZHn5i nXFdm8hy71ZkaVnkOSA98bXXvVDssBmUiEF/NkkZjC2TpxenY0EfB+kfJh2P ufqdQcHFQ5jfeScPtjUd+iQ64NFS3UG6ZlA4FnS+wBBm2l6cAkI0qNDz7g2N Ly33ANKgRAd6udhawk2dDlttzmwDHWodD1GEihtMUlNIlcH+jtv6O9KjTZaQ OX14Ej9LZMHUp++EYI6Z+7tIn41eWEjTWmTpG/KmvA9vNygcD5qcCQp41t08 4J8l2hDnIT8F2zAHhXji4MA6BNymPf0XE0NNnpFatBkYxClnnizWLok21OiR hoJH6PogKfPjRBu3z4iUmwg24TOkiMmu9EzBwxO2VPrXoY2zjomC6YvW2te1 0AaxiiKlE2PhinX2dTlt0NQoWaIGyFeO2i6qa0/z9CZO32BS6Z9Im0Gh/UmZ 5SGJNuynoaaRGkZCJkaPyg8L42aNO/KcHeiDzWtqSjSiZgHVY5ZLZUANGvUT Wy3ZWLDVEH/XTu5eMxCB84NG5r/tsx8mMrHZnkJuyES6k8LfIcEFxyh59Jml Jq3aRKax+zVCJkIksJcdh8pu/q6dXCMTD4UMmOKY3iZ6zylyUytdCtP0SB6G 8oTdQrmfV9/c8XplYA0y9U9kYogJCSBEya6JTBwlYshIICQxraJ2hM5J0VNM NmJQnDJ+TN2rUXm3RCmq80mjcv86lLLn95Wii+/NRo06y+kyslViDU5vgv2B qsIm2SOdKTod4SnOFEHP3563sPKDdKbyuNLTrsqYpkXk3izAPdT0deUzxbSZ esTCCqYW5oFpC+xrc2IRDZbcQYtzppZteP/0FJjD0qONg8JgiMWxg03tPFV2 TxSjaQoVNn3uX03UtXq2Pt60OBtzVlvO1i7Br4BnTDtV9kxnC82Ot05eCBGF Ify7CxaflIKfdBSQ96TsBekOB9v58rD8nko3m9Hzx8ufdMd+UA2yDUhkQ0Wz wWPinFfsd3nn7ZwVaalBndGLRHEHfZ5o/BMT7VB3WEIcNK7BYn5q0SvedLZn qPAjYTp5FvmPJh2FIenhjVmcacx9reztL7mdiypUHISA0XCFifCi6o5NxMLZ 4jOOnduNdi3JZao3vlIyix4ct7kyuAaxBiZiUQrNYXhs7jJ7hpxYHCKIA0Mi nPg3hDGPonJcIhaREhgVj4yzSH4Oh5Umr71DXg2TyzwVk/9NEmvXUJd+2kk3 OrH28Zfc3ju/OVRYyfAZISlKNIgoGpFGJHpRYUp4QpfBlsg5khL7hloGcqJ2 diu71KDXoEQvDsBvrrjRdeE+/p7be+cG744wRx/CeHhh2ExmJ4xIsVvOIKXN sC8mVeF+rFwHq+9TEv0PzVhk6qCh6OtugVaYiLzgfv6CX/AqF/I9xMEpk4IQ xMM5YBCH0J4J+9yB38HDOtRykFynsAhDFMbUElDTFZX9Q8zg/PNv5MxVhtSg 3WBVG6zc7DpyyvNr7Hd52x1cniGgoBd0gzBkBTDHIRaDyIY5AXfwagLMcsr0 oTXCjTm5mOhcb2Zsum9Hn4519djxXms167l/NEjI3UOZGoLfhHdlqL/tl3JC mjvPCSKvA/cRY4aY5CHx4jAoyJ4b1+7nBP2iH8KWONlMn5DrgtCIyoHEr4Gw YHZAkHgICHOVKuKDWBG+SziU+L1IvAOcAF9ywsLEnDDGtCLRCG4h+Ygbojgg MFLPVGwe7fyS8z6+MUoEmoIL9gt0VvQFWQD/20kdmg4t4wQweXmC3Rqj9R7p xZhEiNbArDgw9RKI1hhlSDhqrXCHObwQjnQC7hEEJViIVMQR4DATMOQUmyDo k8LXeNR8zPwAICL3g61MzBG/ARi5ra/pMA0Er3C4kTkHJq8MO5sUAo78uIc+ 2K3GeffDZpciW1FEVJ4bgx+YKvZBBvLieaKncS049sy2RyYgbhmPAvldpJoL wrHHXgQtxE1R2rqzcwUfgwj5CbhDvUEAD1eBHigCPgcCjgNlQ/DAhCAdjjDn zBffMEuhIQT3TDTAVkbnXPn7FZVDU2kn5ELsEOOAnNS2e5eumU7YAh4UMzaA 9CzRw5xC1sBN2KCwCSY7us5EewegrvTy6DCg6w9SH5MCaQYTcij4NX6d23Bo +Bp9BdIRKYg4w2uifhLuo+rZRNuwELaDAalJm/DI33evAfauCWysDuxQDDYD fliqmAUdgANwDa2GpzAyQAdUYEv0LEtoYFMpB0VqkIegaU6Cb8QxJFC9cLH+ cFv0CmIRVYydA9icrQcnT3e1xCHgjPF1fMbPuZ57IQ0wsvFBhiURivom/84R nvLU+3s0dij2clp19FoqKlUA3GTbCKfsDs7ZnA3sHXQvVWyY1IAAtxMR3ZYT YzIJcCiWQ+JSbU1wlRgaARJ+h9/lHtiYdt8jVdJ0y3IXKLffsbayZw3kdkvI YWbjRSC/pr+w7uikmXh9vHyyArAfLgomJLyDWWjsa09onGbCHVYGXSQjpETz w7rwI0IW3oTtsY6OTOT1yYHmVZP6nfns3/dsjLx7J/ISJmFsIi9qtBkXgquU i1Nub97LXjVef4/0+ohuLEDKdezgjgteApYuynrvxp5tn/RsRPsx2GE0w+b+ 9GzUhMBUPPbDE7bsXePZ9gxMRUUYgske6P5EOgQrvJYq+L/y+Y/XUkW4X3oo L8cwlY46Ma/17vRQjOX+81XP5iG0u97Yp8ZD7Z0eigga3wynz3n1nbtTRITz 8ujTr/qzUoFPdfw+/ErbC/T292/d1gdrgZ6ekxDp7cnv5KBTbcbPsPOIe+5b 42mdKHY95iTtT5xuaGZPb/dKj7zmr15sB86U0tqRNHuxIayHxkcevdDPIaYM j3xLemTkP4+MboD8xJv2r/HI+9Z4ZCLYhPNvSY8M9MUjm16ltv6rjT3y1/yr O7lkYh8Nj0zsFVEy9oa/Xx+iIS6JTNFzKhAn9uRDazz4/v53J39wwu6wEyY8 VXD2wGPSs9NlQHULtRVwvQlVM9caevYD07M/NP4vbpzCXpCVZL2x2DXp2ZGI 5AkoguFUs5/b9NfXajz70PTsSEESQhCVB/TqvZc3XJPiDsTtON28GlURmAvT nvnw6409/sHp8e+7f1NBeoxfrAM7MX9Kjw8z+rDqy6b5H865vfFBNR7/6+nx OcqQm+QopjDP+vTSTX9Kj8+xoWICDhD5jRO+0djjD/MH2M6FLrOMIC0kxl5E SZn1ULkkxOOxHS/99aJKB+RdCkYTHOEuB9d4GX8cuz8ShkgZxIYJ0E40jhBk viQ56exngRFIYtN+oVYCilgPaey1DvNH2cFfi6pLkhHEhZ0xfGD1CjeMyN79 IrnecAd9+MCEOUxTBYlP+Nvk/jdrvN0w/3t791Y4PiQveXLegJggp8+E6C+T V41+hJngGGQquhKiwEUmW7/Z2DsOT+8I0xP3hjtQjvj8nEBsUxQTfu2oZKNg n2BjYCBiwvg7mjSAPnAUovfwGi96mP+9g2sv6kPJlfCyTOfgfAIn6nXeivft u3JrA9FB3oBzi8zjkKJkEMvIQq49orFXPtof7Yse6sW5wohFXvAaOK2Y4WgX mA94ibWd4a/+RYcX45efyfHyI2Gn/co/z3WOJIFHLO6oGiQY7n9/0V+L0Di1 bdiSAMmJhhwPzVjkrwagFBye4aT4oksiBl/QAsjvIZGQppwVbFEMAriB8rQR jZHkGH/UHf2kI4NgYpxsGJnTINKgDtBinHzUAoEmsyyPc+rsmDs1Rh2MUPie yjbsW9QEt+BWzDvj1nwFJUkmfr9dg1D/1f/+sstXGADGQDjBFOg4CMZ54MxA jOmL1jrrk2c4zgm2o58zuAWCIQPhEsQBZ42ZfshD7sHncBQW34x5W77TGO1G pi46WqhEOzJrGCpIgEv95Se6OkIgQkPqXFFNWO1Y9nBYsNQrBwU/n8/4GV1G EBZrn9/lHvnxy/FxC8O4Ecw40qhBpJZZIN8rYiSPFwQ+zv/u4gSGGcsEgtE4 Wfw3nyFuOGEQG3nK7yCPmadl0ukbyYlHoSDIoCWHEboixLg1B5ZCArCD/kgu x9AOvDCkKN9E9fcaw+A0f8FBLsVQJGDAOj3ai0h8QROkMKyKgciwXFgWLxtM mFLFYeVs41HB3oTZOeNgg0THWkMcoIRZnnnTte97dwgpHpSaYZVPKe/vsCEp +DGOHL8CX3AbYOS23D536PINUMRVCMTi/MMvCGAdGyD26hl7jWtTlThmF8KW 4AIC9+SEMSWc19/6lKN7sv/9BTcCYI4ICMIXIQoASI+bHpjSAUlsvjltcdgy xAgxd+Ax5O/jz630WwA9PImAAmeOAbiBO8eBI8U5wPI3QbdzKsWBHfmYcCeX 8kT8uisuuyW3xhaB1Uk7w+7IRlgekQ/bs1uBf3N0OErxZPJ7nEzuOfXpD05t 7AhdkJrC0ArktThG0BeliEdGYgrliJQnUaVYF+2MsB1HC1bHg+OIcbyMLTtw 1irb5BxqH9mPktmznUNbXG7cy89z2B+1awx84ydOKp9xDceCr+Hr+GpKEXkU fAikja8KMAXEtFUUG51u+B92PM4JfZ6+dX7q+sp5KVHLMlkwAQ9wgIYRA9L/ +HScFLCA7hwPMAEHjgu6C1yQGLD2tXc84v/m//lc1/gps/+o5KM0/FfunVzR yfNfNYixCPRrfAVfx9dyInkUYOfwetpUJ9OkB5YCEp8tKLOe+8d5jR2B/xlY CIMA8nza2C3+vURnE8CV/2iLG/7vpe/m//6jMS/5fyRPPnw3n9T5+np3CQ/S xF3+ew06dGjHfSJZmrnPJzWeZ5t23Cc+TzP3+b9rPE/HdtwnPk8z9/m/ajzP tu24T3yeZu7zf9Z4nk7tuA/Pg6TGLu/kf2+DgCisoX9v7Lb2Gb/J9ts/Xj3R TiR/b5P98ZpH+a/w2bbxs/zPNY9yZUPXd6zzs+1r3KtTGz/btsZnO7Xxsx3/ yb/b1mf+/M8ap93n49Bpq+t/8dux2b/94f7t0q/85Jc3tvvrG3209pPoPxfC tv5uW4/ifzb8baH/ldc9Uc36bZIP/5ES0Hi0SYFWXMO3XcQ0eIsWS+Jzf7Ht lsT/84///4tbUKLw//5T/OsWGzf+LmVUMAFwf6bP/StVydmUOZvtU/IP2/Hf PuqPz/EnuGbGvC2+CeJXNR7hNyHzyCQJUhEPjtvsKSC7HaEJRnOPe+gD/xF+ Jpdhi/y6sac/37+9my+A5gkZ1kXXO506TDxjuRGTRJgcx9RTNoBdf9uc7Lpb n3ST5ZqbZuQhdPOu8MII9ZjH1QHXi+S+OV985DFJ/DW7lh6EB6cWv2UeXAd+ bs+YXDj39HDd3Ouz/8fbu/qGaf5lfDEPwar6G++Y6w/GA9417uXs3gnLfG3x +MfX+kvwMrwUZD6nBpnPSVkC6Hd/HT+9TT66Od045sRZfvWrCf5vnG0+xznn Wpzu+G9+l5/VcNrdWeff/IzrorPOs3jvYOGsv+hhQuI4WKd3Bof94Qlbzm3s SJyaivE40KyOgZLsVYK6dz74UnbbvQud8iABiCAEWp5AMD+euDN+PqE4krRE AwnFe6hHYZ4lGz1GgINNHJqYAeVkRJeI7Sxa94/OKdRDeJuPiQRxKdEhfh3/ XOEeQj2EufHfCUZ5f3sK9xB05NH0iKNvmumH55Z7nvchr7wW7Uhs+rXDckqN c3KS/93deQ261gwJGi7lkCAYc16I89QKqlDM18HLIbf3+xLD5hwS5yNUSwyc 2N9tY/7WOYUDWc+lcCDwEiXmVzm61KMCfXGE7auY3klKjEfk2PA4HCcdTx4R 54qjpKER/K5He+1RECmntMVUGJliwIi1x2a94wKEEQNj71uYk9tOCvwMywML AVygIn5IHJGIHbACscF9sKPf2RMVIA/qCigp5Eg8kaSFYsBX3zjdv4qv5KsR A088+V7lezV05rEh0s57QjfYB1YCLuWtgBG2I9UQw6UcAegNZIq0H+wYdS7E CS0kOirCgdBrjOJDe+7Nz6F7nvDZUvleY1bWMf6KO7pimTh9g4vu2+9/wckB xZHDBL5gCkgHs0BOqG0MVpXlsI88iG+XEArlV5zNTSgj8RG494x/1anLwDMT E9+uwTNHpzQQBCaujFyCX/I00HQnAHIQOQZhOKwUa3Ptrdd/qDQQ512ReKYM Qn9YiXOMePRbpFp/2CrR7zuNibyjU3Ab/czSdeQC5IJsHFbkCWFM6DJvxftn piwaQWkC1hxKJxX1MuOm55FKozzK85Gp6+GJylE1aDQ8ZQ9R87wgOoeX4eBx AKEVNIN2TsOQPYQwHFyYnd+jpfUPf5xV5MZgZgjGOJIRjVFjeMqtIvw5SCDu r2WCnogp/IcQp+FBuVWC8VCHQC8Ug3LX3jLbVTPSlUaIw2uQQHlxzBdeBR5T ApXX4f85FtS6KGcMuZB58JXKdDgH/HfKjVeOaOyVD0t5c8wgELvjgRf9lREt gE8jsDGG8uZKucIjUATASZQgdUz4fbPGi+Z1Ddu7wOYhEQTgjFDm/xEWhu8v QzsusNv7F6UNQIqcQj58s7H3G5aqHjBQ2QnjNRr20L7i3uA0XVxUPfBypGSQ E6IB+0aJ8x9S482+kSo2YD3vDMOqsT/8N1qOU3tJar3g7eH0+PYkF8k6HdLY Kx2cqlJc2ZipodOJLWAKRFUpJJGACXbG7ER0mZo/qMaLfN3/zgt/gMRNvFQa Y2dKlTM6qoht1AMWonHvNxp7/APT4+MOMHCTvFmqL0fhqSYI8ULixM3gO/MZ 63ZIv1bj8Yemx0fiQndVzqTnU91SfDv+IDzMkvh6Y4//teT14NmI+qh0s8gq Y9Kzo00QjSR7OERwh4nCoTWeff/07LA250FmCf9t2kD1YlyuYwbXA4Fp4ANq PnsbGl+G6i1M0onKSDBKJVQNh4rkzXBO7h7/ikuy/Wu8wr4pQG3c6ZLJq7TN ksC8U5Uer6fiSKSVPXvlq80+/P7p4VHCekAenqmkt4eH14vx8GiifWs8/FfC w98UHt7EbFEVWbyY8TY/M41U2a/Zh983eM0IIO9XtaPPw9+dSlBhXV4MA2bC lHX71HhuleiiPS9NKsCIencNemMhYM3u0+wj75NqU82kLFLaxqkqmEUjwsCI SmPqvWs87Z7BrUex+arZO9aqaBfBAtMic+yFvvL5D2r33TA+77C0P/nD2t/D r1rsHZj5FZ/zGl9JD4ad48XH81dXxqV3gGMR+zjc9vO9arzDHukdMMowwnhg 49AHE8W5DOHIq/GKCPa96rzI9t5XO/UsXuSsbOpck99Tp9qfe7ORlZHZ4o8/ H469EhyobH9mOyUmLUeE0nIcB8xZhCiuIj4GAgmP1FxFBMpTS+xaDiA6gp9x DdfincI9eKXUBI4IMHum+s5n0aZ71CCRWhjwoVIJcaVmTf5Vb3sZByWMVONQ CUVNvs+FqnzJexSpzuEPtXNU61CXn9fkv+FVJFRN4X+YTzgiHSMEPNYgJpHx 6J51qN8xy//5OBtvCFw8e7MaUj/bsjZbu+XT+idJFeSEWLD5kPNFW0Z39+mh JN4dcglWRkFjc5RbMuiboImGlgz6JrwlY8Yc77Og54L+C9oAOvrgoO7efEP7 u+/knjXX+3now6B9ijHs3Is+DO5N6yS9GfAsj4ntg0xE3KjGA4E/LDABJwLz 1Rh59xoIqyMJbwSEzXirDEtNgNTwUKBDLRYFOaqtokKfFpm8G2dyh+xHF0xk p9eFk70J1adj2yXU7VDWRRMPtTy0Z9CkscTe2ku1fpP33nuDD7sXvKd+mg+W pWUr776Z5P9PmZ2XJHHPi+d7SRJlYl5uN9rL7dJj55tRiEEx22KPzz8yLd3w W7KrKn5mKrWGbogvcEbVtKbmLAxM2IuDQdUFh4IDAWD05QAkvTh0fNDpQXMW DZyl5qx8vUNn7/lkehvNH95wtWKdtwLRKMLYCM4HbT6cA1p/OAfEpziTxKUo bz8odWQQr0KIm05NvSvVwA9J0g8euyJFBI39DkyeP1xNMRacC4gQH4ABBOwB LQdhmV+nWj3AoHGOGZP5PoPOPgqAujA4PO/cecO5n5oyDgfgc3DsGPmZYlYJ hwMpwvnD7D4wgVvMgbl1RS6dak8S2UbgfrohG2Xgjl36UaVDXZ0o5lBnGwtH hqbuSPhIThI+lFpJgRq+ppkHnMBsxcYP9kvdkaBLLw+9PbA5YiAXFY84+1JB BtsSOly07h/qjsRD4wkenf5W6herDvjskpAjYINkxByw8z80udMggAyG5fKR Ao85C8E6sB1VfMhbpuLm7aZ5lyrowE4gStUfKNNn5xWuxnLUbiPjTT4PDa7d pf4Ei3NBUpvTXDbbA3+04DL70fBs6cf150DpuHKEiXkyKlgdwChBqiLphqvq Mn1xqdOaQYjqAIb+sJA6UxGn3thqXArMLMraPxGdalg6tSfNfDt1ZFazSzHc 4cH33BWhTWz/pAmRk8hI2gPVkQh/oOk463TjD0sjLKE0fML5R57CV+Ip+Inr 7bzvlygMd/7s5w/g2uQ9vZ9rfHz68cfZZ1n45+PF2X5G2TE5tet0zOcvS8D5 smvvdP2h5nIsD6wI0zHGPXZ07TN0GE1qzEJQr7rmSPAz6SxCmpxks1x0O1gI 852DvUsNGmtMA641HeLm+qWO++1cDiCLGCUCvTjN0BDajkhd6ASS8uvypnVO L4XW6C4+pyNBEw8wXImGmv+Vi8LPJezSq0ZmY1Z9UtD1sw3jnYqjJm1oZW5D /oZ48QhimiU1M0GnGOOAE8m/OaWmF4qZCSgBdAbNmd6Va241ypzafs1MwChB VCDkB9egqOaFkF7D2UFq7l30ZHzocgLeRrLD54y94QSa9D42Tb313kAz6/gR mt0nOtrBRRQYQfdK9MTiJ1hnPkouoD6Xnp9tmerEunfuqmzV4knZWfu56Z9N 2vRpK/NC8vcjW8I7036mgR0YaNAGwYyBxEGkm5Il0yelyTAapgAtMdwQ5mbj 7RmkLjAxhmlQDVJqnI9eFI7cMx1OCp0RABRM+wSXX+Tz3ekEPylNeUEscxCp ZEYcI55NEGhcCUlQPI2xt7+Wy5rPdZf4Z9PcMe4odUju0sXjV+l4VurMp8nf Ci8bd4kAx+4pwEFyAgsCgwVLFpFqovMHYaoOtgvKjh/D3OaK7BFsDXJodBMN rEE7jYsinvOnPEqeJuPk3VtYgQjOvLlrFQaDhuOglWgpkWHJj02G7h7Gt2Lr EZoY1AZb77NPs48/+ij7+JPPKq1NPspfg6AWx4IJHrslYiEW4V50OQTjpJk4 1AgipCEtv95F/ciTLgCMvsUIohmL3OWaHYbQ1CIWkSGCAEit3eIYIawl0zBY T+jwcxKl7Aq3wpCMvmclb/LTzCXiChhO5N0HNhs4cKp0QB/sGjosmRHAqUDH snP0wjSyCpWAZMMShjvpZ44jq3A+H5r8ek0qaPoaj63hZUPSkUHO57P9p7ne 5L9NYv0wTZ7iyCChOFEINwYL7RpmZGjC9oDG4oQ62MhcPHTeRvPNyOri2EEF muuZWv7jMLgLvsFohwq44LsEjejdys98WMxxilTQiEICVj7e7vK1iQodXeBo MIFZ1T9Ob49Y52ggYzBIWPSyS1B6yK7bxq5u9O2FEWF3cmgMfhic3h4s4Qck KyMCzLNJk+/yYYFMJIBXTMTqxQlzaIVfvzovjm1LaJTnHxw6woGdP6asfpZe HMsKAesKyrwH8iyDw1w2Dr/pwZy/Gn9xjHUthdQkRCQmXI5hby96cXpn7FC3 T01jm5TUEMSthkGW3jmO19aoS013REqiXRCDdgguDmCjSNDfuMPICo14JA+r heX9GntnHUXNaSGUOTC9M5EomBrD2zTEb9M7M8MAjCGTvagGYOoOhJr71njn 3sUB/5sHC+1Qp1md2zqb5/MbfeStu5S/STMKoQemHWfA+HtgOODcBi3a5Dtj GoBryyDUtIYtn/pZuSRMyYWtsXeNjfXCuIckS7lZnzovTDoAQRQnuxL9wh/w DtNRKzTZFcGeD32dzAEfGAQ6MoGduX0be9d+QZQhhJmvp3dlvgdAvrjmzcrv w5Yw3p9Io64jpI0VAbK1XrRXQBbTk3FjmtBLEAD5ZC5l5ffpLQkQ+MJX+2OE 0KWEJbSTr09zb4mrrbVpGn2MClekben6dzX6mAkeHG4Kf+yga/QxfE/Ogfv1 rvOulD1g3+QFLtVT0Fkq8MeAqESVfa5LqcIQonXetaVSpW/AEckCjnpDIr7E IDF5zTgZFvapMnGTfV0dHeR8ujFymre3Y61LCVlwEnCTNVF8bppoR/alFiV6 BtQ9NvHnDcVUawQ04R53NC589dA0wlljltiS1zH9zdxwhoKnSNIhYXlZMn2K EeR8M/m128auyYnW9lITkY/0hw6IprtTAYDGNg8sjVj/sofnoJFRza1hZrsQ 2cP+wfZhBCDLGZjqnubym4X+2BOPVzr7fH5GuusPu9G0G+C+B+/3f/t0ePNg UCdYERiRsB4WBHGNOOrdZ1uaAd8vHFHtQelVBxgqS3RE+0ruXPeB+9UoGDO9 Nas/B+YVH5GVz7962qcnMVyJzVOM02Jmf5jJb6973shxlZ39Z8zX49+sUPMN VqXZ/szwZzUNU5gYg8asMyY7mlE7NAGORETEmz4vACcXQba3kKaN7BvJCUPI FhOOsg8BDpAYqoztzTcndHHAGeZDfA83HHC1dEEz+tm5y4x+Vocx8j/O6GfK /Dvvv/tF3+uwo+9ZYdcDex9eWb7UL3963jM+5n/iY5N8MwC3JW1A1Bjv37Df N8FO4BjYTTsVTw0/YkDiedWDHYJpIaM2rGjZMxH5fVOoFkYEB6ZjMQnL8T0m 4WU4squBSVn8nGF/7MdijBq4MceUDYRf9NUdO/labjE3wwEZuebz0+z3ORcs xmFtB+O7GRrI/Zi1xs6dfZKkJPrgk7fMozb50ScwPZoBPd/kGaBRkiDgC8UO nXy2k2Lsxuja2cJULqIHqEZ2erC5AejBjr1GQG24DnKYv+Cos4KDFUecDFid U4O+4ZQRkrRbal0L3E0k04yMAlYilBosry0HMQSsRVhoDKJYEVb8AhQLvsLe KerIDgbID+l90aHByn/D0kDHLnCDK1+ck+/ZgfNBlllpnAQ4HNSYpI00+MNF y/ZKXOpbrn/+NP55sfyDSietp+/VGEK9g+tNuBDrUyt1tJeA6KO2HJFIIXjG yiKtLmL7HKtT3tr0trYcwXlwHYzKliNQ5HYkV5a9+d4eYYEaubbJ85YX3+rT rW5/pgqOyGWCw4cbpD1GWqZF4PHCcx/2KdLaJMVIVmgIBHABkhCp6gtrLlqq TVIMsmcIJMIRgculMAtMZHpyj2BNoFPhk9vGbOkdbGFCI/g8TSKgeRotPLKN KztsXnaPaMEXShEZCVk56aw9YSsRS23igi/WorCkhj0nWF6QHld5t0R7rVRh 6n5cL+XJ/afer0l77TIkLqctXpH2xOMwKrQ4DaJyrpn5h1RDA0H3UUdOLxan 2ZU+FBJth9RjqRB8cMkFi4vFaVCduBUJbrEe1Z9attCzOZITayLJz8v3kqlt +h43k82UWmOHasLncnlk5ETQcODffm9TscaOPUKQnPVfXMNp1xjjIUH6yJYQ yclk0oTAE/WoQ3J8GDxMXr7Y3HndX1ypGIkru6bIFLNNOd6Qm7VdrOn+0VEz tBSQFXSR0ugBky5DgnTBI7rkVwsKYH12pDkVWMoNElpaEgNPC7tEaEqztcxW 6xg52xhjnGt0PQQ1wsZ1jEgVfsQlsEdYxyidjQ2tbyH4j09ML0c98lI0SsCP V9aWUIgBn3PqtNKSmfRsAMXegn6ICyPv4WkLGuRl+TnUR4qYOVfZpaDtFvc4 MQFEW0phtCi0R3O0pS6BlDjvq1WXBAq16lI7RMl1I461zXbdhvVVO0T1M4JK jOvVDlEGK2KrkVIRVYm0Tpiyzp+gew2qarGx1nUSKtGhJVpI2BSiaJ0tVEUu yEplYrt2ryK/oTgnFt3IYdfuVTGAeezF/Smzp7qoCZJK5NGMQJkrJmvPIBpR hgz5HBRICim1etWMkWLTLRqQhXH4HYQuGFU+KDh72nCl+/twuhSkq0dSgnQE auJBZYgL0UmWcA1KigqVJ5LZKdRyYCiNROZjzvBvzl2QfmUbj+VizNxw9abi 1pL0FA83SU2Kgstvy1IF5qyi2LSKmQwyW6ql1NgbrFXMKLTnF+WCgdgvuSat YkbSlvcSEzjDnIHt61ETUx3Fxcv2CKa6lgMPLDa7PO/WAGSzw3lcoia6i6Uv ubU+wbevadUyUSQsBQoqRE13Bu9Y2yJ5GqRmB6pPREM0EsYBc7j7Bxpy6rQo GCY/Ia1Vx1TmM07knBeXDghr1bXEVrdGVlOCkzf5bU2+rol82sYdyYcb6fHw cxcWe79ZV5F7qOP4/IRwGLGU2VOIY0qGpX9Q+WlOVUE+ajIx+5olH0b8o3P8 QOa1Zi2aCAog9bSanrADS+RQ52vffEOr6fEm2QhIxNk4WsvUCVAABWkKHUGf APjgS63SkDiWtqKIhgQIiFlBin7FVr2VPg0759rnT06703H6MJegofG8yIcv R/yaSJjuSjkFse4myCfVSd0tPRq4CSIfoXaKc9jMKILgjnHOzDmvnO60M/X8 0ot+Llllpet8EvaMRVVwkKeh2641wlFMrH1d3dMrIhkJarYQLl/+CoPaCbNn yamGwclnDKQ2wupSsh2cXRJgotqleT1ki9honGqEQGhxjG+pEjPo0DcsfWVl LPEJO3Rn6NCZy8sKSqIixuB9w1JklLzRqzh0GO90I/G9XWvQrksIGdJIgQEv 2kn8U+UhgiD38IRMAVfOSLRDIcOtmD/mz+pS0kBEHCMcjGPUyscmaUflgNyi buktKa7FdESninY4psQJZj01G0f0rEQ7FAeqGV5euGKdaIfsRMgRxBUiFPfR sNoa7bDXCILGFyVkIobtkxgW6Yb3iP9v5+3sRD5ICrNihdslfQqG/dCpTwJV d6Udg5aXVsjXEqMW0SAYA6ki0agywGCGAH20H375WmfThYtfqJyX2BTHhs9g Z5EL/1E2ZrdgsFC03hq5SI3JYOkWchIeW/7RqoJcCDJ0KPafWdTnJnKxJ4CP CFoV+wJykwfbPN6VxC2HmnL9bo1Fp0U4AvtUCPKKXdOL4iijHrA5RDg2xEKk FWtWVS5IhMOI5phRaqzrUCMYz+ShRTikgeRbPcJRKCqXRK+I/4BlBxF6J8Lh 8XHGOFA/GfHk+YlwOn6YLEbb3kGvEuUlYqVzRpoaU72Qrm1nUxlX7mc9MKXq hCCviMFBil4hk8dqXqIXZpBcKNtk4wbnXg5psUBhGxeQ1MpFOIgbooLIhdUi X+dEPupfFKMQ+aitwiZrId827iAj4vArLky0w65zL9n8kd9f+KouBQ1+HXem uOXohd7e0g7aeYllUoJ6Sw4Oh45URKQdeoBQqLnCo9K5Yw8u5DTmrohwBNaQ kOhpwUHmRU5blzqE4ySQ143njmogBBd06JWoQVgam/ii4VMqowLhcIlP+a83 old7hUOHWv7zH1YVtyTRKD+tScLhVVBLGglH4JdcDnToGTTlHXffieVW+Uk6 ccQSPDc08+mWqvBt3PuVM9G1hiVXj3B4nhTz8n5dQ4CYKrRIOE4WhPvRUTPt cSDcNm4GEzTA6dB1VP3h1nHcutaw4RqkmmwpOl/k3eoVfW212RJEq0Q1spfY utNnzaj8NJ01qEYGxfi0IpIhH5Fx8X4kSZn3EUkWY+oiGQ0nWsirV/SK9/MW OhF6JlKQ+nAb7qhZ9iyJZMa42CCsU+wZDhoyktWUOmj5rq33W2Rr4ySjsoKY RzwVOPWSbiIZwQCCU++8/27lZ4E9OWiohV7BZVUStEuo/0WusZyg3ikj04up lo/nzV+RbJPkmkiBiYtcgz1/nkhGtA//gUCBrsOEhjcjBAQaaSNuB8kQahQe xVfEK6dqC49VJCPEh9Z88+23Kvs6yXZwT5WQNrvbMd6w6SCrr+l+4uHsicce rHTIJj/6gF37xORxbi+7BLSTicMGDOxxJzFk5E/33ca1zCOTJrpp0zO4bgpN Clrq4BkCw5t0rgHFzgkKCsZhxkg6cjOEqyBuj5B1R0Of+60J9ixA0Y2TnP3w iMezcw4bn/1g2N3ZqQePzU466Obs+1+/Pvve10Znxx9wVXbc0CuzY4dekR13 wJ/ts2uyE75+XXbigTdkJx90S3bawXdkZ33zgez8wx8lnL5vEMAY55hMf/rx aj0BoUwcG0I7elIMcwz0doCMnSm7UCDTX0V4B/L2CCIC18/c58peDsYAX/+N S0hI4smZk4ATYLM5s0yUzJ+dLX/1+ez1VS9m69e+kr395vLs/U1rsg/efT3b 8sEb2V83r/P/fu+d1dnG9cuyN15fkr228sXs1ZfmZfPnTc9mz3g0m/TIfX44 EGEcIg6EHbK90ll44603c9N3xbriLFDCgfcZX8dH0qTsYL2zQHZQXoHYksIp bHqKqYQEZhngmGlmpAC0AX4Wzjns4ez0Q+40fG807K/Ojv3qFY73yd+4NTtj 2L3+8/O+9Uh2wRGTYensxIF2Xgbdnp08yH5n0NjsoiOn+nniPJxr1571zQf9 jHBe4v1O+cZt2ZmHPpBdOPyJvZJU4MwQWcBBllTQQqj4OrgjSjM2eWAQoghT aNs5CD4Pv5g31yNobLjaQKvskXKKxF6ID86YOsGBXfT87GzV8oXZW28szT58 f2237P/Y8pZdtnnTa35mOD+cBc4VZ4CzZoJlD7k5q1a43EEW6VupAWBuScSf cmvlZ+rhnyv/aoJ5AY5ZQ1EWoPxxc8xSr+zuAOyAu5Od/c1xxv83Ob/D32ce en923uETHdeTB91lXHvK4LuNwTkrnAHkhp2XDggGu+3ph9xl8E/2G+6ecCVi hAX763PmF19PXIjaNqziLsGmIErfDlyJtZJ0iLiSSSPGGwXBzCdneWwXDHZN WR0U5Isvv5TNnPaIS/fFi+bA1P0dz+2yv/1lfbZuzUvZgmfNa5j0gAt6ijkI be6WwHzmuXyvFl/SPRp9pmwYx1/IpoWvF+Xg9cCkBVm55c4hG0SVM0FKUROT GMFuANqz5Bm2Cw6f5EyL8ObfMONJg+7o7/h1gu9M3t+TjfzatS7jTzOeN57d LWGGM0d0AKNG36KGsPg01Gsy7IRG6QYxk9NE/y3lLPG0Y9SQKI6EhNhUVJHt HJLi8QjvFxY86cw17+mpMJz9DMA6Zf/46wZnQIQ4Aj0l8oYEE5LaHpRwj2B1 010djw9RWSa3tYYVARK5yZ2DPYQZCP26F2HAV90eMi6xR82xgpkQlIDBHxSq CdQhCSvk7JmH3mdy83qXoWcMuw+jfUiwpmDl+C1EELH3oxigaITBM4UV1zhW 2NPKoYhAOML4eFSAF4ferCa0GrmTwYm/zBj1rOmc2ZMdj5dffAYhuWeC6+8f vul6dtb0iY7LkmWv8iuDE1yvvbHWVTfSuTgRy173euv4NOTHMF/JuNSDK23V rYKL0CHEhITdEiERXjhJZvlXdkn5AngHneasZTLS+MZ0KFht67rv1INvt5/9 2e0pWHJwAgqdSxjk52c8VQAFA2ERmSldAIU3ovrXJoGiyomy7CqgVq7zCm6I 1y2RkLJQys8IIA0K6W0UHMrtKbOCsGgMnX0dqI5u5GAouV5c8pIAwkgmHRFZ FneMarX4FGkjmz/nzjUA2ikYtd50GU4wmQRS2xGgUSc/7pYmJB6UAML4+L4b HFe50WJGihm8OUAoshygK93QwfsdlAAiyIfUI38mgGjQpK0mHhRNV2wHQLTw E1OOpCEsSr1ZBAhNRcYRIg9MMYO1b77hWgqzYtNbKw9IwLy57lU3SrAyjPEG JWCoFsGiiHfFMFZ4Qd/u+y2ShVkPGGrQqReIBKG7FXAiMCQk4RwsjIEJGMoD 0TTfMymGQjIhVxmaLArzHhwQQDMLU5jgcsM08daMnqBpPD7CncEqrINJS5Ba SBCPojAk0gILnI4OqNU10Qxdg0zDkxsoqWSsgTFg9l/lIIfBWOrlZ/0zY4ji OmxGfpfYrTCAHfHw4ve6lZc6CephgHQgKxkJ4MXlp06rIhSEg865fZ+7ZjCA W/QmucwgODCRH2MO5w5bwYTWgCCz2LT663OeK+5KdAO0yRF0rmG8dW4s6C0g POidpMXOiSCUqCNF4uHFDSfwDWn7h/zKwudmueFmLHFwYon33l7lsgoeMPwG JDjw9fAP412jrNK30x/KoM3W4KD+i4bFCAd6X+GjriWWaIFjG4cHZWH6v3Kw jOsRs9ySxuG2n+tSirKJNOHHdQviSWmNnYOJyIBCKqUaFE+K+JBgZSglwTQd Tq/2m/NcFVOQE+RgU/UnLFDVyKHn583ASx6WsMAeW/bKfNf9Zof1VzDkrQ1e uIJiFxakY5U3EhYxlFcPC99M8/uVVQShPgebORLO6wDMkYW0/RJrQHd0A3xg ivzQBAcODWYZPqtJsP4JDt8JndyqrsHuImseTwJsAXsU5kfjcNB4o7SG4FAX aoQDbbFw8QtO2n6JwKuXL3J5ZP+ufCvJKPgC9U7CTUDAUsoE635e25E8UQGh Z2kNCN+DZ+SIQBBjpUGxmimedwkTgcDizSl+KzLqsAQE6gOxdcxX/4je7peA oGsEk5qMse4KDzALgG19hYS0JyI+0g4gogEsgtAISkM/OZKCL5LAhy/6Jr7Y 8M5GV9nwhgFwRMKCQAEfPfPs3AIzxBm//vLrG4tbUiCDnU2ueOdge8OmBGvr YeHW7uj3qrCg84Nit4gFOIgp+ibq4uEjoM7+5rjKEQUQlBvOdIxGHTlNQFBy SGAh3pIyTvJd8avhUZpS2gEE4T7l44uT+fIK76OJJ3j6rBkecoGufQIQKAoU hrn1RyYBRTQP54S8oAHQN/AF/mi8K3UmqtjVt1PFy3A1nm+nGljsGGoekM6k U6LEpmg5nmDyqxTfRyxy4TQ6O2HgzZUjExb2E4+k4Rma0OpbxRR3eYxdt/SB TubkFFh0YEBrkyqCAUSyIUUD5IdszS6JVkS/cTHw2/sEoX/3nTeiIipHJ/Lj AD795OOOmF2rSwELxw/3UuSnNpe26vjVVFQx+7Q18mt+TTyP+O1k8aFSlxAg RVefd/jEQP6x2bf3uxSRZI8N+XP2+O5XL8esKq7DJ5Fm6BJsVnIj8XvhAZy9 dvABVjvDBiDCTsEHVn5DJCMnQTUURO0dUHA+mD+78p0kjTxcPW2KCy5BQGMF JI+okq+A5BEC+mAZYB8hiNk2QUDlCvmJSApKz0g+RpLBATQfQdQ+gQPQCngO 304QEJ8EFjNhi+uotqLrBZXfJditxMFvvnZz8b0MrkUvtAMC9AF6IULgFS2m oCPJ5EDACIKAoCMRj1dfmlf5boIAHYEij1CRJsBKQsfofrQooXew0QQBqS61 BNbjAk6eqv1ECiph6OeKEHh9blIIvRNp81DUdc4FxwgCuwZrFS4QBMSkVCSi +6H8qUjlG3dK33t/4sh2QEBDs1wHQeBD9lKhkUhGyQKhDYjaK5GWuNTcOVPQ BpVjEwTIIhJAc+c/W3ABHIQ9FO+HHqCOJn4vGUcmcLcGAXkv+vgjKejKxFCN JKOPi6AEfnTvQic/7PKewMaxCQICiOgGAhq6Dv1Olifej7Z6KQF9LzPfKVlq BwSpa6uKFBRBULefT7priYcjz9dv3FCcblIoiH4je+W4BAH5telTHvbUi64j SVvmKnwF5f70vfhtqgmrBwEbDdCJkRQatxJJhltNsByi9tLpPuIxN4tOGHiL PXOLKj79kLs9eWZw9Sp86NWOoDFXccvoKIgBsQooHisst7ajoFAnozowCKs0 4/hJXpYYUSDRgR8dGWHlsgUui0Dhe3IU3lntDpvJruI64oPSyEKBUhdCWhEF SmLYtNEaCpRSy2MTCri0VI3lHaotGhkymsFZoEATI3Jn5ICb7JlzRgAFUlhn DLu3YAT1rsb7UeqIixANMaoasBDaAQH+EbZgJAW2u8IYIpkaiyBqz6CRIfem t1ZWTkgQkEBEI0eoqM7FHY+Q0rSs4hB9L1lkhjS1BgFTKZjoGCHAP6BgPpKM gAYqITICASMOPcQfmSBwWIw5KPrQdbT54nRHxiKIpyKSAvrLn/QYYzsgiHUy IgWuMvUukWQ0ztASirEj0pJGWvLiM058QUDogki4ORLFddSnKt7dOWhk2nrR yPpeHYfWICCkSj1qJAUa+bKkkQWBN9UdmseQeiqG9K0JLvoxir6fICCRSxEH aqNnIYhyDrosCKJaGtlHUN3+WnsgYActLWFbaeQUdhPJaJ2XsSMuIKqKR4wg OjEIIqIYpFt7Bs2NIKKdtJZG3jFk9qk0aw2CS0NJ046l0FG1IFrt8QYEkUhL 7lyC6MQgiKh7OPPQ+wsuQBDJNehcRyMzCUz1nE1CQHpBOaDCODEHjYR4hADy AwNE7ZFIS9sYgujtN5dXTk4QLFvynOcWIlREUlXm2LmORk5BCn+yHetAQMYH uRwhcFWZvLNCEJ3bIogEwSkuiO504p+UICDY7YLI7FJdRxhVgqhzVMepjlsQ sJOKqvFWIGjJKIjwlAoTyY8EYMYHfbeQaOdEKPosMOrJKIigWKIvvfC0k/yU RHjKiCgbigB5eXwyhUR4NIJq3fS9NJ2SYf88wn85RCVY/BQJQLKf1v9q8fO4 W5qQskciKMY/WRvEzynBGiUFF6+jwMftoIuWFveDv/DJ4vfSY8z5Lwjf9lyC IIAMmvQgUiCZkdARghVrVnklRyQtSpiCArNAK6cmCCjumfr4eK+o7BHsIOAj qSMIGH9XFnskeVjz0RoEjINUGZ9IQemO3GKRzAc5DJ9SRVpK9o7tf40TXxAQ pCOSfcHhk1rElMFHwWq8H4GnMs8h/ZnB1g7xQzRAYkCSmMpLzNF4aukTpOk3 QvDK4rletwHxT0sQ4I+VxRQ2lGLggpQaYnU5CQJyfAoO1YOAmnN6MSMEOAOI G4i1s7IuFy31kxwhkKiBC06TEj7sIXcG4nX4ZKQOIgTUGqv/UxDEhGaTEBC6 RxFHCJhEUrZbkOpLV+SmaPdwukkRYIqeniCgnIa0f4SA8KiCQ4IAHw9uixDE 9HI9CAjQlyUxyUbZQYIAMmJ6RtKSM/t238ud+KcVXDA7hUYnF9eR50eBV2mU X8zzKvkIAdUydOS3AwJv9EzBIUFAhBQlWdYFDDLZ8M7GgrRrVy/2Uw/xfxB0 AVU0EapVr69x/RIhhcvgtvi9GAP3P7K8VQhgfsgRSZHnUyZ6PC2aotIF3ZUC GDHLueD7A2+zZ27RBQioCBVV9CrlKLjqty3uuL73gRAcahICyqrLJiHN27Q+ RgggIwVm8XSTD6AqKUJAFSAJ5QgBpiieRNQFcBmDi+P3EjBXGWc9CGIZp0jB NGEVWQgCyEg5UoQAoQO5If4PAhcoPicIqJqRbhEETK5T74i+l+AQ3dXtgIB8 IY5QJAWFkmRsIgTU+FEkHUnruUozRfEGzggOGcKJsISgwkcm7BrvB5cRDIrf 6+u07lvYKgQxqS9SEBxSPE0ko6qeREs83V6pN+B6J/4ZCQKEk4JIgsqnophK jveLpmihjm/4u1tn7YAgxudECoaOqcxFJCP7Re1dhIASS/nEZyYI+EyFSboO 6JjKFO+H00eqKH5vrH+tBwH1r6wmiKRg5Cl5xkgyUsg/PHFiFWlJ2lNmRNHy mQkC/8z85Hgdla6kPeP9KAzE9446KFW5tngrzcxSFhhsczVSVBEFiU2cKBKP dAHmKOTtFlIGmKcfvPt65awExoZ1r3pXSgSDKWXK5+t+TC9U1lTfi3Zi3WNr YJC0VaBAYMAfCquJeITtqFCNRCZxdky/Kx2GM4N3jGI28VVch2fNeDi0jO7H Z6yFIUwnMPJeqs3tA0MJc2KmhAciUUiss9AlEg9wSGRWqd7lC7Pn5k5zGM5O YCCsaMKNoFEnIxVdCLtlr7thGr+XyJWqXARGTKEJDGzzsrVCPkctPyIewor0 WN7sk+T+UbO8+pH8zdlBOFEpGUHDR6Z7K94vpc+qvpcmTcVM2w0GUwPwWSNR CF7TFVh23MpK+JmnniiUtcDAi1BFTLdg2mJrxfvF6Km+lwy/oqf1OMMXWafo qYhC9FSdqiKeDz1MylpgkMmRshYYpBaorIhgMLpM1Rq6H92wpJXj91LkrR7q OmBod9Znn2affPJJ9umnWcc2wROD2yITGoTpiVVOWJqDGMlOJv+xife7X31O godiYgW3BSPtMDQOc6ed0v2oAyTJF783NrDXgyc2sItMdBoSkI7kxCMgjBfh odpIfvXZwa+WLSV4qHopC0JS3OXvJcrLrO/W4OmUfbx0rMPAn/322y8bOWps tuGTrP5CI8FE04K6CUUu/GQSchEmMvv431WiasULHgQHoHNCPhSdw2QpwQT3 yd7aKYhI9Fj8XvKy7LZuDaa8geKNKnLhF6OgIehOQR8wnrRKpI2QSLu9cm6C iUoBupfidRQTl+0t2lrKeo1n0Ty0ejBtn326eUE2Zsx4XzfHPx85bGdlaz+t v+9ISCFjynKHLHPZPyRZh0aPSKF4QAuMzk1I0Z5Eci9eByOqKkdIRRe9+F6Q umFaq0iRoVGqQhQjkyMXXUipnD4iQKyQiAgYCam8XH9M1XUwp8q+hRTraNSA pO/1WYRplFUdpLSOKss+zcYPr2TD713bJnjw31TvJzJh4aqwoHA0zG/Ef4xk J15It2iEhxyrGs90HVa00iK6H9NIsUH4xi8reLZys6vG1uBBxjA8LJLJl4OM Wl4FD7pdhZciOw7Kd/pdUQUP9gLtgGSSdF0eALunCh5cd1z4+L3YKergbhM8 m7PLDIvhY1e1CR5GaLG3sezaqyFe5CRMS0oDgncNNZZUO5kvWTkvwUMTr+zo bsGOVlOf7kd1lZwpwcM0IPrYWoOHKSdlrU0amnlGER5ijoodFuqIhr7+1zow 5yV4CPGWrQrGbZfvR+1tOaRA6l2FmPXlXDIXso8/2pJt2bQ0u2pkrprOGt+y Eatlf6LwYYmC6oeFjza3RWnEvDgwinR/Z8MK73IBGeFDEyaYUUKl66gvxJGN +PjQyRRvED7ghRPcOj7TvWMaCn05RL/wQeJxx9SmIjzSHavthIE3V+EDZhQJ xuuwBKnrifj4NrlS1A0HmFBQgU/tzYeVbQp81k4dU2yI2u/iSdlHrW2IKgxv 88lpMo9Q4T6q5ElQ0XzEdOsIFQlbeUHnJ6hI4OL1RJbTaKIIFQlbzJUIFaPc yBi2BpUnbEe/WwVVTNgWhvIZT7qHA/G7KhFrVtx3v/onD9efn6Cisk3XCSoK EGUpCiqsOhUwRqhuSgnbOqxULKnMPs7udUW0qpUllQKIKk8GmFeFE2c+7Smn CBBqSCa1ACJ4Q04lAkR7hiyKKBMJY1bJTjPt1Suv7732jkd4Kn+yL9cBKHZZ CiDq0AmuVMUMTpte2GoCiHwKARygEUC4spT2RIAYqK06FQGEdVKOoVHspSFw 9WXd5gWTsgVbWhYrfrz4qowdlmkhYKtIEXEsR1koPdnKWl6+zKudI1LPPjPV 2SkiRSe6Gp50Hf6rkpm6H9+hTrYCqTsnFon3ekiReKT6MCJFV185NEZps6RZ 12DTHdn736qQQiPJhy2s9BBwFlJoQqz8iBRJd43UbkUrbZnt1vbizR9nn3y0 NhubNnG31fomP6kSCVGM8040IFKW7Ds1VxEBBNymt1ZWIUWtFjWm8ToEnuJ6 uh/2hBYO6XvpWmbib2tIxRIJIcV5L1vLMfQjpBBwSpCdH827r15RhdRPT59V uLNCCkGrpkshRYlEW5DK9dPmBdUbDEe1ZYOhoPJqljSdTCSjm5l0byQtzR5K IQiClM2vgoosjgqPdB25TWbExPsxE6CcxsaMYFNBa1ARM2bcdoTKUwgnTqwO DBmT0TgeoaJN9rv9cvF3QYIKKxxXN16HblOlpKAiQqf4dYRK1Sx1oGrZmvhJ tmXLluyjjz+t1N+aWAA0b7lPkY+EQrmXCYokYzJS1DukzyY+fI9Dc0ECiHZm RfgEELa5Eha6H8lnlXroe7HDVfVSD6Bf/WqCdwVEgLDD0CkRoLzsdHoV4Sk3 Orb/6CqAiGdT+xivY9+G+nIFEDkjxp9UpfdufLUtALUtjApBymCwTqAcV4sB IIGBo0SpXQQDQcdUhghGrQAQ7esqfdL3kvyb8tT7rYORlx9VgUFdMGnm6gDQ Gg8ARX8Uo+D4AWOqwED9yDESGDEApPsx4oEamDIY6vCvDcbnJtwiBKrGKiAw v6SsCxgaow41QUDOE/0SIWDQ1JTJD1VBgPVAJ068X6wC0/fSWq3etNYEVhkC Ii7YZ+XzW47sUGw6csBNVRBQ/Uh0J0JASwjR7HJMDx84QoAx/U+AYMLsl6pI QRl2GQJUdhkC3E+q4csQeHl2NANmz9zKBAeCslnhw9xmv9u6es/nmFdBQGSl LON9nPTx1eHN6H62QHDjVhCQmGCQbRkCXNwyBBrN1iQEcf5erUxnPQgounsq 1b80CgHTg9SQUAVBGj3SKAQ0JKi1NQbQyjYuHsv3B97WEATSMhedv7U2cghS LWSTEDAQi+BUJAVKmZKjSDISzeUgGVaTYpgXhGQAAipCQH8aIeVyUKdcgkSI ueyOsJu6DAE9YWVS4NkjeKpl9yp3FKscwuFTfBxdhIBrMHQjBMh94pgRghT4 qfpeJtHUTzbXgiDz/8pNqv/tL8+vLi5qrdS2S43PjmvHZ8e243e7tOX9/z/3 wNvU+OxLNT77cjs++9f9/nW/Rj5rk6T518H91/3+d7tf7YOb5TrP/uoUflT5 L/8LpYvKIw==\ \>"]] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"ParametricPlot", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"Sin", "[", "x", "]"}], ",", RowBox[{"Cos", "[", "x", "]"}]}], "}"}], ",", RowBox[{"{", RowBox[{"x", ",", RowBox[{"-", "\[Pi]"}], ",", "\[Pi]"}], "}"}], ",", RowBox[{"AspectRatio", "\[Rule]", "1"}]}], "]"}]], "Input"], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJw1m3c8lf8b/42KklGUSoUUKSQV7atQMrIqUn1kpCSbSKHSUFLZo2yyZWaF y95773WswznnTkYK9bu/v8fv5x+P5+O+neN+v6/3db2ej8c5wkZW2iYsTExM 9qxMTP/7zXaL74hobGQB0//9+YHTbLWKvj630UhCQmDlH4H1QxctPvpYoVBM uvkPkl2+epdPpDgiM/jO95OsuOkM9zOfp7ia15FRQvLd+3Ydb76/wJM5T+PC SBZfR+2oT3FH9qLgVjuSC/n38HJEvMOxk1JHz5O8lC8fKO/zEW2eKtevI/mi 3nHZE6Xe6FT6+1P5XwLpTfcb1b774pigRsRjkidCVYJvZvnj96Cb/ftJ1qj/ IWCSEoi7eY9oNK0SOJRs12caG4zyN2aWLEhWXZq5dTf8M5rtDulZQzLrfbF8 Ne9wXNHVOCewQuAC22bNHRURyBLnUflpmUDdNEUpj5JIlBWVfrmZ5Lq177Jv 5Udj5dlzsdO/yfUwKRsvyo5B0Z6fG1RJdgtz2rAz8wsmfwmPi14icM3W7LOV SXG4N9nz7Zlf5Pp0m7Vtjo/HB5x7m1wXCWQL+nNPLyYBLQjjy7kLBGYU8X9u C0nCx7oR87zz5OtjsO3D96l4NapuzuIHgdd7s+MqFNNQJVNvjQ1BYLWRV0R9 aRoyCpQ0zBkECpwfk20sSkdbUwV/VRqBXmbun0OyM9GxWoiaPEng46dDbi+P ZuEf9vXtNhMElvbdfXovIwurKp8vS40TyN366Mzur9/w5LgBs88ogeMt++xV v+RgQfjY7J9+AhM3mSexi+SimZt27fM+Ap9N0VmKI3LxvdafEaZeAiWfKAbt DMnDAmXr5slOAvmMZ1k9fL7jqmZS1vVmAt+7CuwRcEVU9Di5WFlC4FwU8i6z FONPtgC7OCTraYyVZexiMf41lel3LSQwwcanLa6xGAPi42025xE4wHP64MJA CSZcfIT1qQRaGXJ/818uw4jlj64fPhO4/gNPjzSUY/X9wn8DQQRWOryWqXQr R/1uBce9AQTeu/v0+ND6Cvx401ktwIvAaxoNdtX8lai7b1Ms5ysC8Qml8q5M NRZGT7eomhMo+EBTNuRhNdbfahPguE/gimNBbG1uNV6mlt4vMSGwmZp9ZjPU YAmn8xbe2+TzRSYeUFGtxakLl6tUtAh8+0nslJpRPcoavbPfeJTAqfd+xesv NCP/mizn4Z8MDKJ2Zv4wbkaJkjtJ9QwGrjX57tXm1oy1eldE0qcZmBAZMuVZ 3Iw/Z9fMGY0y8KmZ7omGUy14TnxEyqyFgWeI4ViXw62o6LKGnf0rA9vedEqa bm/HOMN3Qm8NGfhn6o9LoVw76jXsCXp2i4HSBWZlnDrtaJFv52Kty8DybbPc 4T7tqNyrGXDiMgNv2p7tf8HRgbv4avbaHGeg+l6VJI2VDhTaZdLjwMlAy2v/ sr73dGHkkYviE+l0pKdbatUvdmHcoMwyRzIdNcwtDnXxdmPDdeu0A7F0lNsv 79d3uRubotxqtT/R8XRj9tmwkm48c3xi7LIbHc8pDK5bG9+D1h2Xfv3QpKNO WK/0fus+1N8ryrMwRcMIHZkwO88+NChcv//kKA3F/+6zyY3vQ9fu0CuP+mj4 91a1hNRIHz7KvUIbaqBhuuuXMzVa/djQnaN1K4OG1X5WwzmHB3AmoPc2x2Ma nqT66FyiDaL0Vrcn6mtoqLlBimPdhiHUq9rlwLcyg3eMv/7NFxvCb3/2e7bO zSBPwMCzDcZDWLA0zCtHmcH6Y7/ZoGcIP4gGt0WWzKBUSrH5suEw+ng8EWdy mcH8/inqEeURFNUZUpulT6OEn0aKq+4IfqUoG+PYNLYkBHqWm4ygsQ2N8qpv GvnEMj+cdxtB024FqZXqafyVnCPN9H2ErLfP0h+ip/HwFl8Lz4Oj+IwyckZF dxoJzhd7KtgpKBLL4nQsj4pVUytf3LZQkKvn7NGyVCpu6NV2OrWHgjsWBSNU Yql4bYGXNfwUBa9OCPle9KViclCGqqQlBS3nosXmzKnIctl22biVgtRQuoCw IBXbQ4uM9waO4evQdxwjrlPIXGY+cyF6DP+r2PgkwX4Ke1vOGRqljuGFsE9T D8ym0Oz2Qw7PqjG06aFXDV6bwvVSm9iifo3hKDcMOktMoX7FGbZOnXH8YlgU E9I9iZHGTU3KmyYw5V5ZkbPEJF7yZ4jk7ZpA9k1yzOFCk3hzKHjrngMTKCn0 KjCfbxKPXlKsGZafQLNvDxoGViZwpXIn0yZ78jozvai9fgKlD3z+fKJjAul6 2mXuDyawm6fugK/PJMY4/mQXiBnHR6sDLw+FTWKloai3ZeA4/nfLm6siYRKF KTGD3z3GMXKLS3Rf8SQaDR3oVrAZxxQnTYdsBvm+ureT158dx/rOxAFB5Sms CTDit+4cQ7W6/A1JS1P4ItRKoZRlDH+JVS8dZaWiqMDIfb8FCuoHnfiZw0nF ATVbVsMpCj41DV6O30PFRe2YMGoDBXv1GVvl1Kj4zce6PzeIgm+oBjNMYVTM 3beYqS1JwYW/5upyp6ex2COb94P2KKqZ79o3cGEa2xxFur8pjuLOe49SnmhM o9Rz7jfdsqPIRXOujjKaRv7LrXlcAqPYGHJjIOjNNJYc1Gg5SxnBni1zx/a0 kfff3i6XbjuCo4MqSYFGMxidIvnfn/fDuFjZt5xrNoPdnzimnj4bxvtHpBPb bGfQMK/UntluGC0ULm9bcJtBRnt9xJLuML5a7WCdippBB82lsBzhYXzOe+u+ 58gMql7ftH/RdAh33bh3uVSPhsl6n8X1vw5g7dsjCRyGNOSRYzxz8BlAhTRx MXVTGgrtfJXm7jCA7J0f3IocaGj37G2m39kBdLZweK3mS0Pqi4B1Nxr70bpz jfzOOhoWaEa1BdL6UOJ3RK/rMTqK2Pn+4t7bi/f2iFnanaZj+H1jG8qaXswY vJp2W4GO1tLiL1PHezDZv/q4oBYdVcJ3BB2O60Fxk2ciJ83J61PxtoPiPdjO 9jhzIIKOZ043l4xKdmNS3c2ORlYGZuW2JbVLd+L9PIHP39Yz8PD53LB6nk6s yNrV4cvNwMzCFypFPzowOvt0uKwAAy+cVpv9kNaBJnYl/22RYeBkm+WTiUMd qPOsYIesPgOTHde9dpAk+3DVeN3vVAaanPWk3djbij09eRqPsxgY9Y1pqXpt K6pceCc7l8tAtheO72QmW/D269z9jSUMHHpYZrqU0II/heOe7W9joPflhy0a h1rQ11ufx3KegRm0KtajJ5qxXG+Hgv0SA5cruwT1dzRjR9xSle0KAzVzXz1h /9WEMp/2l19fQ869lLen3j5pwoPzmx9U8xLYz173VvJlI/4aPRN+7DCB3lbK 9Vx+9Rho5fC8mJxbp00nrKav1eNvvsFYheMExjlObET+ehxtLM6RAQKDnuVb X/lch85vp3sy1cicEFPw8UBULdoGtt49f5dAA6+faeFfq7GjPJVfk5yjju+O 799sXY3luZl/dcg5myWh5uV6uBqZdQ6bX7YlUGcvw/pCVhXWqTql9bsQOJwy I/o+rxKlAxXPvfQl8FEKwbS5rByF+4OE1pNz/bujZsy9V+Vol7Tt8Wty7gsY 9RrnKpVj+9EzAWahBFJMZG1V68vw1QuGUXcsgctvpu6d6ChFM8F7K0IJ5HVb 4zf3A0uRy8N1g1ESgeUXi07665VilpeJWwOZMwTTWL2GyNzxzu1Jq3IugRvl AoPPTxRj0u4LLDr5BFpEtY1rxBfj1n7fqBsFBD7Xmj5xw6wYxXd0ylwuJrDN wPx+ZT/ib6mpl11VBJomt+71ohWilhbL+2cdBFoWXC5o35CPVutvfuTtIjDb 797BZx/ysOLL7eWwbgL/PnJ+sW9zHu6yW+AKJ3PXVychE/3tuSjD7LhDcYTA LXy3JTzFstGp/YLBJzK3GSVdFd6Q9A1r7jBzUink6zMe09ykvuEOT+ZPVmTu Y6nd6GxyLAt1uIachGcIdB2zxFn5DCyTLWacJHOj2PYNX5Qr03Fbu3y1Gp3M zbo31EOV0/G5nHi8Hpk7j1j+MDqumYa0ae6vV8lcajWhxMsUl4rrGKH8l2YJ 3BPU1OGe8hU/ptXwbZsjkLmeI/55fjK6DS3FzZP8gS1Rar44CRXO/AytIXMu z/nT2wyqElFU73H6LTIX8/UKs+xrj8eQDQmNO8jc7PDhgZNrbxzGtIxcbiFZ u9H/fOtwLK5xrbARI3N3GMU94z49BrfuPKbeTrI/63F2u8Fo/HtIU+bRHwIH S9jqHJqi8B9O7eIlc/yxiuWLD4sj0V+njecLyaKHdlRbpkfgK5bJDVKkBwBX x9EqxXAc01nl+EpyuvIMb7xoKO7vi9njS3oDv3zdiNZ0EEpXLcgtkWzOVM60 sz4A17ZXX7lKeojNr4/5Qyl+SD2wxiGO5OtOqnGhH30wyckrbJbkr4/4buvY eGHcz+sNMqTndBfwzfPFemK9mirLA5L9Cz8LPbz/Br+U6Z75RLJECLN9o8RL vMFv41pMMksf/xnF4aeY7elZMfC/v89jjRy964j76ZGbf5KsfzN1ar7XAitf J5mskiz3OVj/eLMeakrEFv0j+fjiSDTTwXNw6Jj3rt8k81X+0v+8egccXCzd qCR/VS+/fnzJFoZN5OnNJLcJlqaG2jyBtjZe/VSSR3JnE1wWnoPeYVr7S5K1 FdVsxWReg7BFl5YWyYJnE5nqLD3A6Q+lnY9kjdnKvfeSPoBqyx79JvL5FwcP 6a7aecMOyzj6M5ItZT/Jyvv4wnKL+wtxkneXvQh6nuYP79z6BWvI9e270jSW 3xgIs2+LS2+TLGVu+IJOC4YmBy1z+v/2y0qofxtHCJy6lbrTluRntfM6p8XD IK9lpY1O7u9Br7GUJJEIkJrT9zEg2e3EhYQsmUjoMljVrSXrQ03MryX3fBQA sOw7SPLax7W9OZrRkPhwvqOF9Lh7W3puf7H8Ah1J8vn8JPuKJCn4usQCwZUX e5WsR0e1692PPeNge8VyUCbpcRduqYwdS0yAr8l21LU/CfxNEZW7Mp4CDFPP LWvI88Fpdlt73fxXOPmmhtRGAs/evnH7OnMaqDUPdlWT502/4pnkAnc6WBop OK5MEfg6z/jAvEQmcD0+tZpBetyDvoIO0axMoLEvfDQgz/Mlad4/V05lQWnj gYEI8rzbf9qwNVT5GzjrRQpnD5AeyVz5cMIkB17wDh07R3rcO93RE8OUHODZ xuFWSnqbvI3C/nbDXGBuiilLJfsPj181e+J/ebB35c2iRAuB1ntHn2y49h2a yzraHzWR3ma0+1hr63dY33GIUdBAoKxw2h5fzQJ4ZIJtkrXk8zX/EWRSKwRL q3CHjFICeflDlF7II5g+CDlRTPbD0PVTSpuKEQSE4VRlEYHCx93K6nYXg/Oc yUo+2U8DLN1FLoYUQ3/D0ZdKGQR6pp/OXx9YAswiVEF6OOnp/tMDRe/KQPPC FY6nZH+3NtSMO1dbBtPm/86sJz1wOU7ncyF7OUguvX6yjpwPR71PVMa/KgfZ 4hDTmHcEyvxVd5Z/WgH2ufqfLR8S+LLszRYHmyoYbCpUPq9A1ku3aaSdVh0I maoc9SPnW+iL8i0WfnWwxVdcbPAUWS/Ci+H6XXVQWrb3gC45Hz9cWZsg/l89 MLu4lQ3sJdCM29n+P9MG0OeZhLPkvF2NGJY86NIEjQTbMRYmsp972rCGYhOs DclTLFxmkJ6qOsjC3AzXXHJzt80xsOJSZuV/Cs3wYR/vVpMRBr4x36TSV9UM hc82Vl4tYGDjudg6WlMLJLC2rnjkMHB8h7d+948WaOaaN8rNIPMJn31d0aZW CHDsKV2KZ6BI/W1O5yutsGnv2yVufwZ2/GoSz+lshQKnItfgBwyU+ynmIdbf BpQAcz+RzQxcZDljkjraASt8C1L6GxkYclhW5zJLJ0zLLjl5rWOgxvyQ9Lhw J2jr9Iv1/6EjReXKMxbDTri2PYyHh0LHj/ue+P4c7IQ0289vZ0hPnJD6wIjo 6YJcRpakizIddXfmzf2u6QG3innBdHk6MtaOqZdP9QBngMOf/lN0jBoKeurO 1gvaN5utdkrRcXj9J1hS7IUfvKreJzfT0a3ujJob9oLaRWGjhW4abjNvlj6c 2QdtJekX7AxouCmw9Qb6DQC7qme1FJlHHaPpX73TBsDzQLwTRYuGPgmpg7fq B0BYK9HiuAINz7GaZw+wDoJ2okGj1z4amj36++6N7SC8TDiyZnVqBl+qc/5z Vh+ClcJnIs1kXta7Y26l92wYwvbrzmeRefqf9fx0zfthYOjlSfnemEG5xE0P ZD8Pw5GyhclTKjOo9EPYnDV7GCYFHkWfFZ9BYvIBv8XMMAxpbl/dMDGN15RN FZ2vjcCH537PGm5MY3OvdL6d6CgcXKVz7tGaRqdKfUmHo6Mgcahxv5XSNL6x /+5kJz8KaucFN88dmcaVJokP+vqj8C/AkClq4zQmptjJLfqPwt/kQ4VhBVTs pX7YuX8NBexGmTLF+KlI27iG/Uk/BXYwOsx8N1Kx29x2cwuVAuzv1m9cYqbi 3stqj4V/UUCby2trMm0KNXfJXEnfNAbhF6MjsGQKO2KSiz0vjsE6ufQjgaQH FksNXzyUOgZZLw/y7s6ZRJVb7ifXO49DEKGhYp08iQdPq/ytdx+H3jGDpILI SbRfc8rune84RNl3LSh4TmLV+GaTpcRxEA7TXNxoOIl2cQP8Pj3j4HKItuyx fhKvXlM8XnpsAqouePGr6EzgtHpPKPP0BGzYoV0+oTKBG28IrN26MAGXC/f/ dIEJHA5yKdvLNAl5PDr9n/dPoP6XgpOHt07Ccl/ViYA/48i8WbxqrfwkNBbt C3APHUf6gycrn4ImYedlWVaboTE8+bhD2fj8FNR9cFMwbB3D2RupoZ/UpuDy PYqzasUY7pUkpmp1pyCQl2OaM2kMF7W+Xt9qOQUSPBbbVB3GcLBgRf76pyng X/y1O5FjDMX2iz96NTsFoeIbl7bIUFDISNHZ6hMVfnIktqrtpWDK95dhGEOF NbM9e1y3UvCH2/ur7KlU8GDQjzT/GcWgC5utX5RRgXLkyFuZslG8nL91sxCN CkWHKt3nSE98WBBo++PUNNx6LWT8wnIE7V7GhPR0TAOzov+hqwYjeMmwfgPH 0DT4fRzfI6Q9glnyIb+PTk1D6KH59wnHRhBWltVt/0zDMRu+cbeVYSzbFe/t s3sGQhW3bL3ydhh1l1gu3DOZAV+a0mMOGEL+tzkXNOkzoHtD6/pr4SH0/Ra/ 6rkwA+c+Fr9eYh1Cuw7PqZLVGaj1aVworR5EIUM2dT4uGqj7iayZ1RpEOeLw tptSNIh9rL0vz2gAO84wMi0taDDOGspz1LUPHS8pt4SN04BZ7OS/b4Z9KP3a zcqQRoOLCmlKhy/0oa57EduuORroKfUz823sQ1PjbMGnTHSgqj4d8AjuxcWd KsWMHXTYd/SpsmJmD27JrtB/rE6HpaEvzPyjXcjfEn7+eBodjty2yWop78Kw B15b+bPpEJmfHPk6rgsvfn+TxfhOh1W9KaUh8y489Wr8wpsqOhSeW/RQ+9WJ rlfXBt4ZJK9HMkns4uhEsw6NouINDMhxXhCpkWrHZimjntX/GDDuKWFxhasd 1Tq34n/GDFj3vGWli96GEW7HlrJMGdAo5MLXnNyG/Vqf9JXtGIDT/4m7HGjD 608YLevfMGCfswdXG+mJUV8ub5hKZcDITP6OdWtaUZJZ+GFBFgPsVqxYjlJa MOb63p1v8hhg2zr69llkCy5VKGxfW8aAmzuCng7sakGDB4cS/TsYQP2mci51 SzMOfVBk5vlNvv4z2e791CasZWh66a4y4HXTKu90bBNypbZdDGAmQJkW80Jr TxPKcd7d93s9Ae4L5teytzXigQUr96s7CGDqbbw5xVqP3ePz/QYnCLjbv07O rr0KR0uWz1dbE/D07e7bWf5VuJ3+e1ehHQGp413xhE4VKqvYiiY6EGCUnjh0 tacSzdZO55k7E1DV5uLVN1CBo56cXb7uBFx0qk+bHC9D3K+mdT+EgICNapda Y8vwWG5b5HAYAQmvlpZz7pWh9fPtfJqRBNwWil+0p5ail9TDm/yxBND3X53N ppWgyZcfd5RSCTDpue7aMoZokPhyXKKYgKOJ9j+23UU0jn3OKVpKgE60lc/1 ySJU43ZQ5y8nYDhUcqyYWog+h69vH60iYJzrP5kTjO/4VWyhYlsT+f8l/gq4 YPUdP8v5xrc2E9Dvtyim+iMfW2afR7xsJcDP7tVl+Z95uMEiurG5g4DDtf8E xhZysGBz9LYN/QRskqu5XuiQgx/A5NPLAQIcmqeKvH5lY/FdDalfgwREXC1+ Jfr7G+6ukHhXPkLAo91y9ptWMtFBypabaZKABXYuW3TOxNm6+m2npggI/vU2 wHQ1A0eXYg5bUgmQdpFuTf6bjoVaYV4FMwT0eXx7x8Schv4DHctk7gRj2xHW beypaN77JOTHDwLCWx6/FOP6irlH/YOHZgkon5M+fHhHMn6UhfjYOQLe7hlq PyiUhLT1xnmu8wT842pbFhRNRCcfIVGNBQISLYpLFw7Ho+Cx6E09iwRs+abb 2yUXh5t3bPDy+UXWyyXrO1lnYpFFRf2+4hIBFn+s8D/lGBw6XXnQ+zcBNR/+ sR3UiMbtDxL5JP4QUHjKMGPuahQu59frIsmKV5YEHhlE4PWsc7TGZQJmeYq+ mNaHYamZ1hnVFQI6Que/9XSFYNmt3VNIsv1xlWEFyicMjH5Hk1glwMDWISSO EYStEk9VfEjuuytjtPZPAD7IH2b+QbJc2PGE/9b6Y9Fo8OaLfwk4NKtITePx RR+eAGc/ki+a8336J+CNe16UQh/J2ndr7/qpf8Cd6ly3dvwjgKOaprfHxANj 9cxaSA+CjgXu1YQnr9HEqCb8OcmKZt9AwscNXX8LViWQLN+Ke65quWDQyztK tSSf8BQtu+T6EIfPfBSikCz6VfOptc4DZDv6SXue5ER2GPA20EVVlecDpNfB 6TQXC45UAOvj8iWk10FL7KuuDSHGUOHRv0p6HcwHmFTzR9rA3igtXxrJIvu+ 0ffZOwEjM+Z5F8l1eOVvQPQz4K7trc4nWZ1rBwf/+5fwp/SPaQDJIxKJkj4O byBRi9PIjGS5X88d2Aw84dPqzixZkgN0eZbz93pB3R7Z28vkenB+fqFvud0H cmfvGueSzC7Sb7GLyw9uWmGxBckB126lV7EEgMhDLUcBku8VKaWb/wqEuTRp 9xJy/W0OUhY4SK8T+GL7g/Q8UD+vaCnXEQrl4y5Nr8j9PND2KaO0JhwGbj25 wEny5XxDKZGhCLAfUdjynqyH7l9Dzz+1RcFP94zSh2S9EGHBb5oaomHW4XPE IFlfi4N9rf+qYuAjZ0c3kNyY+kVeqyAWxKbbrs+Q9Rm24fUPm+w4yAubjjhG 8sLXiJ+eafGwtnte4RFZz2cIxTXpMYmQ0vrp8wRZ/xfP9bcmen6FBzv8Au6R 5+V942PR+s+pQN+nWnmbPE8WM5dUbK+lwXmf5CV18ryJDjyucr6TDhZyMXc3 0wlwDjBoveaWCVXM5taS5Pm1cBHML1qfBbOJ94sGJsj9Y+X4K+ydBbTaok2v xsn+5bPC3BX+DXaxLtbkjhLwvDCnabEgB7Cn7V0W2U/kus9aSinmwgbOHO5d fQRQPS8dv12XC5LvzoU49xAQ45WendSTByy1u1qEOgn4zZg+Qlv4DucUbXNW G8n+NFf2y35rMVhd0pxej+R+9eqdbNErBg865zFKAQEpl2pH94cWA6Nizisz n4DWTrPttSIlQPmS5noim4CrDy5ytEqXQuOPKoXeZAJymIZtJ5XLYSevNYd1 EAF8ypFHZj3L4YvO2tx8fwLMxyIPLjSVg6FG9bMVH3I+IPFw5loFrHH8cd/k PQFjcix3PhpWgqXC2vT652T9ZndIxjhWg8C8Rv2sKQF3Ovbzdnyrhp1MLqkT JgQoCPyk/purhgm+x8NtRmT/npmXUrCugafxVKnAW+R67rQxdzWthcEjkr4l GgQU3eAyzdeth1xe6sHYowTMOQYqvPCvh16uEBWhwwTEcb/cdaGtHqKiuMp9 JAlwSlz5mKneALOH02oNRAlwPM1UfeJCI2RvL4RIfgJK2M3FrLY1A1PriH/W EgOcL/1b5ZdrhjpO0YaseQZcK9jaV3CtGfTFfluk/GCAOFZ5/PEhvW7N+R/u Uwy4sFaIocjZAl1UW97RLgb8nrwvx83cCmUPtb4f/MaACouOyAvjbXB2T6D4 TjMGKOW/+jixth0ULU5fLrnDgIytN/Kei7ZDV2Fk2n+3GZCuS/kce68d5joo p52vMmDSfU1AArUdpv10AjXPktfvvpb2pHWAfUhugS4PA4RWd9Hc6F2wmTvT ZyaFDntVMjX0OLuh3Ntl+EkcHTzjbCv3S3bDh8nXMayRdHje21idYd4NIkt1 Ab996RA/wi5sO9MNau9Hrz91okM6BIsGTPaAxWkD5nl5OrA/S5A82tsHwqzX 2z820cDilfvad0t9sGLZX3WsmgZ3M9R39G7tB+RKKGorpoEnfaHb4Eo/jApo FP1Kp4G2Yypy1/eDdUIODPvSQOqu4ujM9wGQefFM+JgODdbo77U09h+CD7eE 36y2z4CL09vS9IwhsHrrwBZfPwNrX3Cr/moaglPRo00q5TPQ+4CZdmTtMKx/ r3LBKXMG1Ap1/1nCMPAYDLA+9p6Bh28u5XJkDkPa/YOUjWpk/h1NeVIWMAI8 k6Iyb79Pg3tQ5iQlegSU0hPej2RMQ8u0yyam9BG46LHV8UjiNBywyPU+WDcC I/WbVsqCyHzOfY+m/m8Els6Erg96OA07Ivarlt8dBWfPjzxcUtOwmDRDvSJD Aa6x0A3nST94KsLFPHGWAmGunzSbvKiw9ep3WztVCpQnvzHTdafC27ZAvWd3 SP59W+SaPRWGeCuHDQMoILJGj7pOnQpMntuOnv9NAUsHxcYLTFTQbpHZe+v7 GCQumrqfN5iC7tYjijrVY7BVlW+rzLUpeH8lzkWlYwxi5kULBFSmyHl7WEqY GAOVTIGbvUemQIT/nKD7nnGwVRo9vZZtCqzYNDfLvBmHCX24yZc8CU9tWnfP qE/AM5/ZUqnZCZAXZrIUvjkBUvLXXC6NT8B0g8IdrXsT0PjUP+dmzwRcj5Y7 HvZ0AuYdjudZlkyA0FmDXTOpExDPoqyp5D0BGbFiQ+rck3ClM7Ch4tAEhMqt +SZaMwkfdL4Y85iOg3RZk2dN2ySUmE7+PX5rHF4FOT29OzgJitmXtG5pjsOp lqnED3OT4PdMTMz3+DhYXNjYErhrCg6ItlNL2MchOGN2yzvrKYD9O4xF48fg IEtIMmUTuW7TNeJCFApE1DhNlAtQ4bu4g82XTgqoZ/NMh++jwsX56OZ9tRRQ TUtUVDhBhaYWygh/OgUWtBJZwYAK4bMdsSWuFAgarPzAnkKF7dXOD6W2U4A4 tXqj7fw0sGp+PPVCZRRmcitsv6tMw/Htso6ZZ0ZhzWNFldArpIdJfro1JD0K ARVzh9RNpqHOvVH6AP8oBA1f+3znzTTcz/p41ZMyAk+554WMGqbBlpft5Ksn I+BtKJrirT0DpjrNx27HD0O77K2SlBszIPvc9cuGz8Mg9Cf8apkR6WWi9nsy 3g9D76eoH722M+C4fkpzwXYY/sDemu8+M/D7ydoetTPk/X6Z1+RbZqBc3bbs ddQQJI/xrFxTpkGwxsGyGtoA7ParSz2tRYPB/s7azsYBsNkn6CCgRwO300qP +9IGwIyt7HCJKQ2mnnH8rLMfAAf2HrEUdxowqeZIq6yQ59rbTHJtOQ2OGlap l63vJ//Obm2cLB0ON8rHUHb3wsvh3p/vztDhD8WYd/FfDzjQM9NMFelwqOPI KeaRHvC7zRmwXpvsU46eXczRPVDLsrd1ypwOssxKsimiPZB9NNJCgOxT8W+D D2ZJdIOigVmwIisDev8WXC8+0gmX+1r7vrOT3pPt1CKwtRPmglzrD3IxwLdC Y8pmqQMMaq5mLmxjQN/3I5xchR2w4qZte1iKAWyOAX/XXeyAqyfFApR0GWCg 2trDrNsOl1qOdo9HM+DDuOwL9YetYGHpaW8dT/bp6rU9HLqtYFV+KmgumQEq /2Kzy4+3AuXuGRsK2ddZLo6Liq+0wFuJh6r3q8j3szyUWOjWAv+Gf2dupDJA QtQgW+BjM2zYE6WRRM6d1d7MXlRugKv7/Ra9DxAg0Mo2v5GtAbQOeaRZSRFQ kSxZqFVeDwas/Ho8sqTXXDBOKj1bD4/uX9WfUCTgg7Mot+KROhAcqVMfMyDA UoY1r2NHDTR45ceeukN6k3tTTWxXNbTHijN73CMg+ppiobVfNeDG0jwOSwJ4 Z8WCCc5q+HN6WTD6MZnbVlXOfWaqgmt6LH+EyDme/fiH5PexcgiDVc4pPzLX XLlpJxJZDrEsb7bGBpI5rUa9+dV/5eDqcpCFLZTM0U4Xd5/oLIMAVk2pW3EE 3AzeIn6rqhRqzf9r6U0gczW/jnIgmbmXev8YaZK5Iu603M4GKIUMcX4NoXQy F8m/EhPPLQF6Q7HcFTKX0ESV5mPiiyHJSuz0WzK36G1wqsm5UwxH7crFs4vI 3NVYTqsQKgbbU6JF86T3pXCwdMUfRkicyc9fU0fm8DrzjkebCkGeT+mVFZmb uAtH/vg05YKRwiaVpV4CAnkDh09r5sJWc/sdTmTuOmK2Lmu4OQdeKLn3Gw0R oHRVNXprazaU5vf0dY4RoHtYeTe0Z0Gp8fU5QTLXvagJo32/mgW19FheQ9Lj yvIV4o50ZMKFYXuLOtLbrqYPGfB1ZkC1iqfLOQaZ09NuG4Z1pUGnwNE2ZTJH shWIci3qpsElFqNDqmTO5DiupOablgqhK5p/jv0kvdrxvVRtegq0Pg80FSS9 rSD3rsGNjGSQ/GfZz0Tm1l62inuUjCRo+DrblkR6G8Xm7OJwZgJ8lJfRfUjm 3Lfh8yvXsuLhrbfUkCyZg98Nm3SXZ8WBFs/nlUjS26JFSjZ6f/sC1r5p3qpk jr71Tnuc8S0Gnj+qP0AnuT2d7q2UHQ2qq46m28hc7psYdnc6OxL8KQUbo0j+ T+4u17GcCFiTZ5slQuZ4nrRjvL8sw0Fml9PtUJKjlaWlna1CQe1rOic36QHf DB/O/bb6DILrmfARyabz2G9tHQw7apTtekkeyax/MGIdCHpvHA4cJb1Cb0pP SM3GH+KGrMZekXxKU+xxuo0vuN8Vjmwi+RzT4tImW2+QzXxksIn0mHXuPCf6 xz7Atxsme1RJPvlLvsZz3AMqfnRPOJPsxLwxSnbiNcyyVaXEkuzMuljaO+EG Xof3OVaTzOu9XXe+0QUqV6flR0lW8M3PPtj8EF4vb9r0P2+ryu5Q+h71APxz /Uf+52186rsdpVV0wbPELut/3hZHfzsma3Qe2yKT3v7P2+7fZwmS0bmDSZSz hjMk+/GYHaaK26IIz+FTHSSjzNf3Wfsfo2CqO38Oyaofu9MKeJ+jtpLKohfJ 7isHu3bwvkLQd+syIvnAmgv5dpvf4pFHp75LkPwtOOhG5ab3eGnbi0gGuR62 Eer9b9544a1iU494kktjTcwOvPFB7tnVhzdI/uTdqFvl7ocornNnDckR+VV7 b7sH4Kup19diyfWXLfPSmn0dhPunW861kvtntjuojuV1CL5m4gzXJ3lQ7pDO 4ydhOL99RoNC7v/OR+NKD49HoLsXe2IH6WnBD68E9khGoVjsmysKJJ+M+6z1 an805h+S1Ewg68neJNtWQiQG311xUtAn62/1Li492BaLvMbWJ1PI+uz+oD/O yhuHPC0HXs2T9asoLnMpgDMezyyEbDUn6/tU3sTTBJZE/JFVJvWP9DTnXRMi L2gpuOXA1hoeko/VPJOamviK746V5u4gz9N/u3QbL4ykYqvIlmB+8vzlMJIZ 89NpaPTz451v5Pm8XqN65d5QBn6tf5f2lvQ2xWibsUe3MhFO0K/okOe5njLb 9KonEynZ66O6yfN/cWbujHdbFiYnS915TvaHmIUhyQdV2bgmuslu2yBZH5cy la8q5uCZet2BOLKf9C6ONcmV5KCz+XNMJfvPy68u0fT8XEwJvVFr3EZ6R72H 0Kav+bh5g3JmYQvpSWzql2oPfMdd1Vt+czeTnnmzZto57js+iJgKCa0nYEfh 4f2NEQXYKn1I678KAgzvisIu3yLMLSuSeFxGQLrfymIYN2IUf7DpxxICVEou ndzuicjBysYaXUj2F5uRh0/ki/H69V3Nlt/Ifrd9c5/4qRIcDR88FRxN9uvf m+4VHCxDrx/ipeyR5PWNaz7/sSzD31v69azCCNgbz6Z9JKMM+dffKxYOJuCx oM6q7/FyJIYVngp8IIA/eeYYz4UK9GDBh8YOBJx/ovSZcqsKe+y5rRXtCPj4 Y3JXSXgVFphlvtlpTa6PWex84GgVqv19sy3jPumhv/5qHzatRpuplTpb0vMO bthSxmNXg6euNumoniP791HeXzbudbjnenls2GlyHjn2TPTV1OGK35O91OME eLkmr57ZWI8Hq4xTjEnvmwx0vTbtVY/K9Ry8hDBZb2FOkSPBDfg29d4+A2YC +lkjNE4mNmG/TNWXzDwGpPKwdrKPtiDnAJv/OXKeF0QeW3jG1IpHb7xYrEhj AHdxRez87laUGPbjy49jgNSB9dGNN1ux7711v5I/A24diJFS62jFgIM+5Lox oHl224Z7VW04zBp5umInA+TuUBsn4zow9bxHnhE/A1rbw+rLKjqwKuJF09Im BniFnrP6ROnAi+dW6rjYGLDvOeOVnGAnHr9QIjH6gw5q+8oOSgd0otU56xKx MjrkEGfm+V504XWlwaPHjegwZ3Up2Ol6D67du131zk06/HLY3srxqAdjjtyY cL9KBz99x9SAwB6M7RnM/HaRDkt+948EdPbg3vQNlKQDdNA49MHw1NVe5J5P vbFmlgbnkmXub1Xvw6wCd3t7RxrU+VtJNZ8aQHfthMP+VjRQuK3kbHJjAPcb Ezkp92hAW7TU+PlogNxfTvFKXRrsPGJs9PPbAP6j8A36ydEgJMBQ8d2hQeTq lwxPWpgBWuyS2jbhIXz47b5u04MZkJT2U0t3HUbb71uu8RvPwNlzlaJi74ex 1yUkQ5fMv27d0iZBn4exOO9PQMmlGRgR/PzXPHcYFY5lKZ/cR+bfTucjg7PD eEOj+0VJ/zQoHH4pNmA8gvsr43afuDgNNxZ5Px5WHMUo5qAl79PTsLZUBzZq j6L27TsvKTLTcKk1p2Pk9iienLpZYC04Da5FFl9cnoziWb/i30eWqMCdJ2bi mDGKyWJtPAbxVFgVdueyFqRg1PEsNSNWKkhEBH2+uUBB4VQWVc/fU3DGv1ww gXkMmbVOnE4jpuDObta6Wc4xZKpgME30TcEpsxYPe9Ex9NiQr7M+awrizeJu ge4YHn3vp33YeArs58qyJnPGsGhiXQN/4SS0pJq1Wj4cR7FOpfGXGZMwZEV5 J/B8HN20UoAWNwk5K6+5yzzHsVNNoiLBZxIaYY0ZS8w4Xgo9enP43iSsFG3d Jd86jjKllb/PbJoEj4uV68skJ1B36vyThtsTUGz8LCNneAKXL0o+8LsyAX6F j92+0CYwinX271WlCfjKn1nruTSBFlxLgmVSE9BppZGtxDOJLJmiiVdWxyGi KP+zJUxizeXfC5eCx2H+Uly6S8gkmhFNT0Prx6Cylp/JQ2sKZfaWNoXjGPhp BZtF3JrC0W3hsiEZY/B68dVY6r0pbBQidF8EjYGoPPufPJcp/OW3W4vjzhj8 8Fy48jhhCmM+TtxZ+4cC+sJ7Nyb8myLP863IAmEK7JE5diYhloqG5ZIyP/ko oCh75KVUGhVnm4XWibCT/jx63ywlj4oFaaIvHRijcOUdH0dQPRUdpNhlxvNH Iewx3hT6ScUNnV9ExrRHYaUmUWb51DQOvlfnevp0BBZ3qDbU1E6jSZKQgojt CDhtZH3r2jaNd+p5RMrujIDH/fDPEv3TKNLuN7ugTN5/bKrAnj6NVr8eiO7n G4FzobWZWdwz+Kqgqb8kbhj8mRuCZrVnMDDX8Ux54BDs9o2r0myfweFlaWkv 5yHgshzrXtM/g/sKn4ZfNRyCa1GXFDMoM3gIBzirDgzB6dyje37/nMGYiH9D 2oWDEMOeVSW7iYZaSiWbN44MgMlndTFrNRpq6lLbZET6galKdrWxkIabA05u FmDrh7mQrPHEchqa7X0QsTLdB1NrO388raOhupd8VVJGH8hXPjXd0kNDnONc zj/fB1f/k06jz9Fw8K32aJR+L3y94nhaez8df55/eKLatxu2BW54MvyOjnf5 12nZ2XdDQE1V8G0fOvoainjwX+sGrqN7zDqD6JiSce6l6tZumKu12Jb0hY6h 7bs/GgV1gUXATqcfRXQM69Mtuvq5EzTSLIqpBB2NwlN63EPaIST8wEcPVQZG iFnomjq3QyLLc48VTQY2xO3kU7jVDltMp5nv6jAw6a+Z5aRAOyQxj+kLGjJw yPZPNO1TG5xd5xR30IGB9nszmGcDW+GlNhxMCGPgQZ1uNeEPzTDaYH5aisrA nxe4H3taNQNry0FzeToDX3SdiJrTbIZqwpdDY5aBfBF6zNm8zeBx+oG/1m8G Nn/kCeaXaQINGpG3zE5gC2eJxpqLDaBjKmtLESWwJKScx5C9Ab53FYq7HiCw qYMtN7u2HvZ7zJ/fJEVgnH/UgppGPVTc/OkldIxAkV2DbaLX6yAs/oA3RZ7A VE933VDTGqja7W7w5xaBEr7L3h8P1MCRoUNFOwwI1DtSHvOEVg1sZx5YHTEm cCCM2+u8dTXoy/rVXL5P4AFDdrHXjlVwh3dWSfQhgS7BK7H/XlZAbOimBpN3 BJouVLsHXKwA/wdCnBwfCBSUfnZ0P3sFiAW/8o/3InBbe5nU6XflEP2rwrne n8ALDg+193uXwdmKZN+ccAL7DHfm7Q4pgXUN2f/tyCBw18avsrt1SiBuHyX3 Rdb/PpcU+2AbTwlErd41o2STrzcirrX6ohiWVHwbXn0n0OpbU/HWriL4d3zx mU45gdAnNFihUgT6jeef2FYSuKmXvfhBUSFElVtUvqwmcCQ8zznkSwEsa1DU POvJ9elYdg+wyweraK31Yu0EroPLj5gm86BAL8hpoYNA4cWMF0Y38oDn2FrZ /C4C33WIB3Kfz4WSOxLeon0EevKpGW7myoYNQVdKPUYIfGv/rff882+gnaX3 ioNCoLGXSqXZfBZ8WHrj5TZG4D5MCk/ozYQn+SEuVyYJXG8Zxjkcmw43brTF PqMROF+fVDu0PR0KD18Kz6ST+3vQWavXMw0Ci2P7+xnk80dFNz8b/wqvI/L2 bp4l8E9KZMj1uhSYSSiR2v2TwIvPN14VS0+GkL4bzkJzBGrFSA0kOieCdx37 CNsCgdn+xA0DowTY+O/NLxrJu/Sld3JdigeLCzaa1YsE1uvfLrnCGwv/hdhU Gy6R928x06ItxcDgyMZJwd/keptLurgORoN6zFal/33O0llAksk7IRKUs0Jq BZfJennjz7r5YwSMLmXSs0lW5tm1KEANg6h4Ky3FFQJDTO44bcgKAdnkmIUq kr13Hj/zw/UT9I0d6D2/SqA1rYXeoBwE9yUG/qaT/JrFUi6GLwBuXws23vaX wKQGlUq7IV+YPnCO/SHJJTXxF88kesOi9TdaFckmAX41LNMfQGJsiIf0OqT5 TQQ/f+oBa08m2GuSvFO/VWeJ7zVUC/3lf00yx8nx66aJz8EPGv+kk1y5cGv2 y9PHYCK7cVc7ybmSrrvsEm2APyzFlUbyGPuXxoeJhnCfPUlwmeT/93250/// +3L/B4b0cDg= "]]}}, AspectRatio->1, Axes->True, AxesOrigin->{0, 0}, PlotRange->{{-0.9999998782744886, 0.9999998592812047}, {-1., 0.9999998831351729}}, PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{3.397996156717444*^9, 3.3979975456121597`*^9, 3.397997951984847*^9, 3.461114711218544*^9, 3.461116319719145*^9, 3.461116390192553*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"ParametricPlot", "[", RowBox[{ RowBox[{"{", RowBox[{ FractionBox[ RowBox[{"Sin", "[", "x", "]"}], "2"], ",", FractionBox[ RowBox[{"2", " ", RowBox[{"Cos", "[", "x", "]"}]}], "3"]}], "}"}], ",", RowBox[{"{", RowBox[{"x", ",", RowBox[{"-", "\[Pi]"}], ",", "\[Pi]"}], "}"}], ",", RowBox[{"PlotRange", "\[Rule]", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"-", "1"}], ",", "1"}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"-", "1"}], ",", "1"}], "}"}]}], "}"}]}], ",", RowBox[{"AspectRatio", "\[Rule]", "1"}]}], "]"}]], "Input"], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJw123k4Vd8XMHCkSJEGkVKmCoUMUUnLkDI1UFQqIUmFRGn4hqiEVDIrY6aQ eci8zPN8jdd1D+69RaFMhaj3/N7nff1zn89ztn3O2Xuttff+44hZ3Ta+xsHG xnZ3BRvb/365Lm1S2pX4tsTk//6x8BtX49HAgFMYIlJfYky6mThm/ybgCgoI ic3ok3ZNf1v9Je06sslYzauTPrpefd2TgNv497/XzXtI29xw7vYudsZdj1Ju byItvWqsuzntPq7sKh7+fZaFpYLiG9fE/IdhjxvEe0jPF2mFagW4o0HKgGIG 6WMXDqgcrPTAPz1/1j8lPdF2o9Ww+ClOoHK5EekvkfrhF3OfY+e1APWtpE81 /9x6Le0Flq0V9yXOsJD45Dxgm+iDew9PJ0SRNpj/fskm+iVOlW14e570ihu7 iwzfvsGPh3y7S4xZOMe14bRwjT+yC3PJXyd9LvOonG/FW3y4RfT8WtJNK1/m XyoKxH7pJ1uOG5Hjca2KVZYfhA5qv7Nop1noGfWQZ1tOMDo53BS2J825Of9I bWoo2ovkXHQ7RY5P303Kho9hGJFTqfzvJAu5whavX4gPx6hoLfoj0tllgu8p Ee8x8Y1AnNUJsn8Md7r3KhpHNg/+ntFn4XlqflLN0RikNaudNyBdb+Uf01wZ g3oUed9IPRZu1WSqtJbFYqyO51VFXRb633zxPiI/DuOfl/8U0WHhI3fC85ly PCZmZo4YHGVh5YCN+/XseKT2pybd1Wbhus4H6tvTE3DXZHBqviYLWR077xok JKGUgHbskjoLU9bbpXJLfMQpUz8FdtJPRic4ymM+IvdwYgjbYRbK/nc0bFtE MnYO6A/9OMjCTVenVvgGpOLf/U4P/Pez8JXbVvGtbhkoe+nxQv8eFs58wI1/ ODLRcKX1h9syZDwxV3Awj2Vi8k8X9X9SLEy+E0BJas3Ewpoag7W7WDjIf3jP 3GAW1vYvdg3uYOFty3V5wX9ysDyJ55/5Bhaufs3fvw9ykfqe8i+Qn4W1Ll6K tZ65OHZIdrGSj4XXbdwPEKvzkEf94yDfGhaanGpxrhfMR6ZQvoP5Chbif4xa G8UClLKPUBmcZuKOW6dVIu4VoPeo34bsn0xcul+S2FhA+v3eqSeTTGwfy1ff AIX4xHy5YO03Jr6KTZHRNyjCd3L0Z7QhJvq8261maFWCrSFccZdbmDj6Kqh8 tU45rjvfsvwtlolhYz05P6+Wo0jPxDalaCauvFbsT/EsR68vfoIuEUxMjo0Y 9Ssvx18GKz6MhzDR/ea5gy1qFShTq7Ej4iUT1X8MJboqVGLfxzSzlXeZSPHu kbXdUo3CmtkCqzSZuDi66FqqWo3+SoL8qepM3Fdys4rXtBpndp34q3+IidVC U+uiA6px3+GXhf8pMfGi0xHa0zU1uLFgX1XMTiaelNRPPbVUg+W22l8WuJno YPIvt7i/Dje3yNVONjFwIsvBqPlXHd4Oal7iqmfgKTt7+d6N9aikFnNvWzUD VaW0ggZO1KOv3ScF1RIGHm7NPxJVUY9S7ZMTSp8YqKFNX7XyYwPGMJ6fMnjJ QNMo6j4pxyYcq3TIm9dhYIypYpSzXxN+k8oSfqzJQOm/O+8UfGzCv4dujCwe ZuDfS/V75YabMGHnq8hvSgzMcktQbzBqJu+/czhAjIH1QbeHPiu0YIT14WCu pRE8NBZgqjveio18n5J80kbwNI/cmlU8bahG3RjP+3EEra+m/y3a3YYSSY6X /T6MIH/I4BOeq23YmaDB+yh0BJv3L3BBfxuaV/O/2uwxgnJp5XZ/LNtxLdey o8mZESyijY4p6XVg9WtQ+jUzjHuDTqW5nevA7RMD410Tw9iRHOpXfa0DtX5N h6Z/HcZNu3Nea3p2oI6/Z4PxwDD+/vR5H1txBw6qb2gyrxxGBYFAe789ncg2 8Z3i9WYYf/A+Fa/hpqBgNXuixK5hrBtdSvAUoODqL9IPencMIw/V+KGaOAXX x98UeLFlGE3mNq6IVqPggaO3PvevHcZPYdkGsg4UjPotWGM4M4QcJ5z+XO2k 4Puymeu3cAi7IsuuSoZ2IeeNbK6Qs0PIXmX3XSeuCwOfchvXnhhCaoeGpVVG F/aVrX8/fWwIb165t8avrgsNvszIw8EhXC23nuvD7y4M4ZEJ+igyhOY16lw9 pt1I23vG678mAmOvtrXpre/Bw76cyX9YdNQNnpQoFOnBvM2zptI9dLxIhG8W l+nBLzKXdpyspaOy7tGGIa0eHJlirndNpONS7Ta29XfJ66Zv/zra0HGfzPv3 B7vJ/lrsOfOYg9jH3yQTGNCLz6PD8lupNHywPPhMPqoXL3t6V4w20PDypbd8 Ncm92COn4LZYQMNYAde4gfJe7JM+KLoylIZpD0+75E/24s/t7v6fjWnY3JMy uEOvD/Odn94Nrh9Aw6YintT5Pryw+9uF8+lU/L27fl55RT96P/QYqYmgonnY wenPvP342CaMc+9LKrrbhv/5KN6PulsGpEauU5FqPrlZ1bAfI6o0/vslSkXv MYvvbFH9GPMi8l3V236c+2t3UvUwFVOuzlRvcOhDQzuRnYM6VHTQlxeNvNiH 264/SPvvFBUD2G/miZLPxTf+uP6DFRW3l9h/2CjZh60RZoNh3lQ0PbRJMLm/ F/sFZvaLU6io3iU2G6LdiyN0/dRQqwGUC/i6K25DD/6qHfhTcHMAGyWKfFPY evCG0r4UitMAehimv/802Y322ieE5jwHUP2OTU9MYzc+X+5eMfphAKcFDKT1 PbvRY+OlG37DA0g7lC8c/7MLRcyun6i8QEOnLyVbgxoo2OijlLzGkoY8j/RS F/IpqJ0pvfukLQ3LN6TfM4unIHfPa88yFxp+MFnVwutOwcf2Ll6GgTT8ec6a 87AyBR17OLW2NdHQdWjzq6LITty7EEN12z+IlYZWi1fsO/C6+G4H58ODuGlq PCb0fAdm089mXtEexJh7W883anfgp+D6AzuMBtEL1/OLCXeg9LUnEofsBnHu naNhUG07dnE9yhmMGUQpqaE9odvbMbXpYnfrCjq+MxBmOYa04I3Cre/zVtMx hN3ArfhWC9bkinQHrqNjeFJGwbJGC8blH45W2UrHVX+KNG98b8ZrzhWXBRTp 6CM8tjAIzWj6pERYxZyOh/RzsnW/NuKFOlbTQgYdvT5eU3u4rx77+wtPPcql o+w0W0w+Vz3q67xUmSmg42pO/XWT9Dq84lUg1VpBR7fpCyoGr+pwWizpiRSF jhUlUxU4VouBb835HWbpGKaWl/EutgarLwhr352nY7x5o7vrgxrsTpqvc1qi 4/72Fctmp2pQ8Z1U9XlOAg9sz9q9arka98xuuFW/kcCATNrF7eer8feIevR+ BQLzowcUr/BWYehtF49yZQKlfk5GTzEqcWETPVH7AIG3FycvuxdV4khr+WdF IJA/tWPO73olPvb51p9jSGCtvVTenYoKdArttNG0IfAbz+97N++WY3d1huDp GwTGc/1Xk69fjtUFOX9N7QgUdx7k+SdajuymCnYnnAisf66uyfUescngYSbN lcDur0ylEsMy3Bd6VONZIIExh7+lVqYXoxgtTHR1CIEn9W/t871UjM6pQo+8 wgiU7GuRN+Apxi5l9ZCbkQTKDUZOFF4rwudPJ636Esn2li99DbcV4s0d15dE kwns15ZfM9JQgHy+bjxWqQSamX+Mu3O/AHP9r3m2ZBDoFpxx073zM770/K9T r4DAa3p375/zzsfU7TocpkUEfpEVHsT9+biZFvjBrIRAU0HGb3FGHkoL9yie KCdwJDjyfb96Hi7IjT7rrSPwnYhqFc9MDhoZcbx60k1glCu989WlLLy9+uKb jb3k8199V5eyOgtrEq78ieoj8G/C8qeq/EwUcZ7jix4gcLtRedgYfyYqst8X PjpM4DZHaTV+3zR82KVj8W6EwL6DtR68zE/YYM3OO8YgcFR9gbXiyCcU9mN/ d/sLgWlWNgm0qRQ05SMein0n0Hgg8KzWhY9YpVI+eWicQLoLL21tThIKdWnV G04QWNjQUtqxNgk9VKU/XvhB4NHRYDmD8gQc/7Yu/exPAqPDqWvZhBNw1WSk oO4UgRVFBCPDOR7fZDZsEpohkM07W5h9dxx6EvNJs6RzM2obPjz5gNrq05EN swRa7JHi16DG4q4Lj7Iu/SKw4MstdodXMRjBk9wq/JtA3Y2Hfi+3RGN8x/CJ DtL2tk7nhDyikNOt5s7uBQJd5fPCVL++x83b9p/sIl3LGZM36foO/8qfVnyw SGDnOR7rqE3h+A9HRTb+ITAi69Tq4+S+PNiUwp9AWjGohWdMMwSfc3zlkVsi sMuc0fKsLwiZpstr0kmLKkx/Fr4diFID8eKBywRae7wIUYnwx311c6rzpP3Z MlMfXXtNnmvqz5z9S6Bm9M0494CXOCbD6ZJE+p7hOuIxemPqQ/+oKdKjMY7S zuPPMWn6fIviP/L/pa6euLrlKTYbGnDcIs0nXSpw8tgTTKg6p/6O9KH2T+9s vzxCM8E7buWkTb+uc5PVuIf5fn41g6Tvfcjo6VO6jVITsRumSQd9vfvzxNer WOuVem2ZdFtJ8dij/UZ4em9i2T/S+rqyz1Nf7wf5/W9FFkg7XtaXblu6CC6u Dp5jpEdtM5vm9G/A0DWtiXbSqgJ775UZOAGFstE8g/Te2NCRpan7cEFhvOsZ afvMa2HKYa4gZt9rZEQa3e6wKUZ7wMNFRtcm0r4G8lVHnZ6BQYe4eRv5/rrS wQ1GOi9A2CFp4sn/xiOyRfe8kC/86XjxVJq0FOdKl3Pf/eClJ21HAzm+kjU3 vE6WvYEpn/LKK6S364aci9J9C20uRnYT5PzI6JcH8GcHgNqljG1OpE1oJ964 bg2Cwo4lygQ5v8ybA1+Zz4JBbsY8wIL0+d+rVI9NhkCvxfK5RjI+rik7Csae CwMAjp17SGd9KJCYKw+HlHuz3R3zBCrXuci8CIyA7lStIkHS1c8OmlUvRcIP vsLEs2Q86hXfE124Fg1bav6E5cyR9WbUa0JiWyykf3IeWzlN4OmJLY9UJ+Ng 0tZPgJPMD/OpoOH0D/FwyLvBYIHMp4/SEu6i5xLAsJ3eW0/mmxQXe8AUJoKD lfb9pVECsw2pr3TeJgPfI7Xl7K8EHmz7s81HJwXGuefeWJD5zPPd4UTNQgpU tsoMxpD5riPBV7/L6hM8vhArlj9Ivh8Hd6S3YgY83Ujs16CR8dUoe+IJNQP4 hdZ4VlIJ7InpFT6gmQnsbfFVGWT9seAdWLltLBMkl7x/7e0g40VoPCnrQDa0 V3V3PWgj0DD0hoSfXzas7pafLGkh52ezqKDVUDY8uIYU2UYCuYeMBNi8c8Dh drRLdiWBn61L5Tl6c8H2VsTBcrIe2q76KZ8vkwdbxUCttoyMp5g0zmtuefB4 5tpSEVlP60dXU7N35gOtRfnZ8WwCi8ZPz6+9+xnYJcZ2TEST+evE2J28rghO 65xZ407Wd41eAYLtahF8s/unvvo9ub74Oksa5xeB7LzXf6vI9eFdhSH38KVi UCmPsI1/SWDY8sY/acklcLfA/L3DPQIzOS3rpjUR6G2lepraBDqxdDafUKkE UVt95SByfSs713d1xqoSBAKld9PVyHqxOf5m0JtKqKySlDlHro+5qumOtaOV wO7qWTUoSeBUrvH86LsqMOf/CkfI9fbMLY2GbX+rofUH134ONrKeX/71yVam BlZGFB4t/UPH7JlC13TTGjBxLSgQmqHjpfzwnN0ZNfB658bN14bpmOdvx9t2 pRZKn6ytPVtCx9LUQesvWAfJKzqXfD/TUeFhhVTT9zpo55u1Ksim46Sjzrpk wXoIud9fOf+RjnL83etP366H9ZI+8+uC6XhHX+CV3vYGKHlY5hZ+i45RE4QV 8agRGCF2QRIb6BiZnqgAci2wtGlOznwtHR+FRopcP98C31TmH/qvomOZTvd3 L88WMDal7aYtDuIzvtts6T0tYLIlip+fQe7HFOOrw9xaIdPpvc/3rEH0PmIb 2NPcBgWTubKueoPI3LDrhJFCB3jWzO7I0hrE2RVhyn06HcAb4rJIUxvEzcH8 JmZmHWB8sf32NrlB1MiykTn9tAN+bjR4e2jDIJoqSnuzd3eA4TExq7k+GhZN +4q63O8ESkWWjrMFDdcUDb91L6AAt4FfvRy5Hz1VOdOwpokCfjIfHzKMaGhZ uwMCBykgZpRif0Cbht2fplsDObrAOMWi1X8nDacW/3JdNOyCZ8lKnMujA3he 2fzfMr0LlkqfSLST+2WXfWpwn60HoqTOzeaS++mzmu9fW23ogckLhXKBZgOo d6Dyta5kDyhVzX1V0x9Acw1Zf07dHvi69UHcEekBDPI9tOPwmx4gTm9Z5vlC xTtrX2YmbOuF1x5BT1rMqFguoTl/VKkP9ixP8IobUTGWW/rrtE4f7JVvlbp9 nIoW7+N/h5/vA0PNHRtmlKi4iGq2FNc++BdiyfZhLRVHDr+bna7rg7+f5Euj SvrxwcjGXUNm/eA8wpazW7AfWcVmeSf/o4LwZPfNwLX92KimXbX5FRW4X65e O8/ej8/XF5/ojaKCMZ//5k/jfVjeoVWtWUmF6GNxMVjRh7UD/qnF3AOwSjVL KfRmH7KNyayGoAHIfbZn4/bPvViw80qcZQINwn6c0nf81IuvE3jPrM+jAZVp kVoS24s6FxcmC6tp8OFu75y2Xy+aK26hzTFoIBZ1+tday14MV+7cISQ+CK7y 4398V/dizZ3MOY7IQajT8RfUN+3BV2Virude0YFH2Lj6i34P8j+QvCQSQYcT pVLTrtCDMmLGGQMpdCjkN6W9l+rBToXEOM16OvwZqDsYstiNpvbD5tkcBLSW 7Qx5EdmNbw+d+iPhQsC2Eyor7hBd+NjotVfoniFoeu2pbdlJnm+/3i1brzQE J64zHhvUdGFl/azOi0NDELpxzTfe1C48ObxdzVpvCPby2wsZuHThEcqyB8tm CAR//d6esqYLb9c4JqyIHYJI6bXzAooULA+R/Ri9fhim16R0GkpSsH73CnQX GgbOqX5xt80UnGNfzTDbMQy+kxNK7YudyN+fr88hOwwMJSUfxapOVHpsbSmg Owxl8rUvZow7cb5z3W9912G45CV69alDB35Is7f9whgG9qPB8mctOpCq4X2J 69swBL1hiYsad6B47wYjiZ/DECk/+yp5fwe6LycpGy4Nw/47m1ieS+3Yu9Ex 33LTCEQeFdh8xqcdmYvscu+0RyBw/PijNdCGRZltT/dHjcA5M6PzXmJtaNVa cyYnbgQ03pR7za9oww+Myz/3JI9AY0DrXGV9K5ZrmPnw5o7AySAJzimjVpTU F+MPaBiBxEfGOwutWpBBiRKPnhkB1opIfmW3Jlyk7lNBbQaw7z70L8+yCRvT 2ec0dBlwTDvzuIJOE77JkF5ZZsiAC8dp7JvWNqGOlqRAigkDxgzcB33DG3Gh 1Ktb7zoDdiq76x3NaUA+yf6xRB8GzBMJ7IIjdVg0hHvaGxmgdOVObkd1Hd6Y 5llX1MqA2KJPsV5JdSjXuVIiupMByxdGjxN2dbg7XbvfjMqAUo1fvoa/a/Hs gXTrsDHyeizbXpE1tXjsta/0Hi4mfH48J9EgV41DDSFqyUeYwPLba3+GrxoH X1u7H9FiwiqPjqXeiSoUONe62KLDhFZR103tn6pQfdgmaMCQCfjtsrSrTBWy lqJUssyYsPOxLx9FshIrqpQu6dxjwvD3IuFVnJW43fRWnOsDJjgv3eZQZlRg JatyQ+Z/THDqHPF5EluBCXKyNis8mXBROMx9UKQCg4831l59zYSxPH2NDIFy /Lb6aSpPItn/E5U+qTFEheghHtZHJni1LW/8lohovuPVs8JUJuiNxz81EkeU NA+vOp3FhBdzdib5QmV4J07f7EAJE9iorRdHV5TgofB59e/tTLChrVJ17vqM IT1JPdQ5Jrj7bL+SG/wZi3WNblB+MyGD1fvxh+ln9FIwrK5dYIJVVgpxtj8f j6Uf64hcZkIdxdV/YDAPQ7hXH+JdyYJjD5szv7Jy0N1xcOjnBhaErDXU7UzM QYl21YrgTSxIfj7/5/P1HIxJLbbav5kFV0Q//ro7lo0Wd+O+Wm9hwYTU2an8 8SwMq5qbeLaDBdf6z7t1MDNQY9uK8HoZFiin3P0pZJOB4w6m5lv2ssA07nbA +a/p6H1lgW4ty4KhSFlm+VgaWrqw+MflWcDiu6x4cDIVG27HyRUrk8+X8jtE 53YqXn/0uWZ4PwtoQb92G/xMwZe/UgVWqLIgyPn5Ca3pZFwVJUjsP8gChcZ/ W5lzSSj12Y7/kjoL1qs2nC91SUJzmUmb80dY4NI+Wub/OxFlfijfPQUsiDlb /nzXQgKW9Ylky2qy4MF21bvrl+IwvuFXbspRFsxx8znh4ziMUVQqfqzDgvDf PiG2yx9Q84+9i94xFuxz3df56W8suqlsWkM5zoIB37yXbOwxyDfGSErUZ8FV p+EVQtzRqHt4eYOBAQuiOx49280XhY8eXLX5Rrp6Zp+CgnAE5j50r9lyggU+ 4kTXHtH3qJs13p5M+h8f5c+OXe/wT75AtfJJFqTYl1fOKYQhPhC0VDvFAoG8 c9Re1VDMt8ni+UyaTdfROlc9BD95+byXPc0C+8XbeFkvCIOknjmtMWJBw+t/ XHtOBWJxxJ0CJ9KlapbZM2cD0Hi3EquL9NEz81sfWPijf0v3tI8xC6b4yxJs m1+jR8RLCo10d+RsXn+vH3qOlr2TOcOCuwf0h7QZvlgkeFnXmbSFk0tE0qQ3 +tWfpeWTHrBRtFq56IURHcGmc6RVow4kX175HLs013+WO8sC+amjY5n8T9HS onDZivQxu03v/m31wIpPHjIBpI1tGm2CTrphKd1cvYT0mvrxC+LXHqHDl2Mq w6S759YtJ//ngtrj+wTYTcjnv5kHewOc8O9pAepW0lqdKH7WyB6L0iaeKZA+ 6LerStftGn6j5Apokd6Vftrd0fQimrXY+J0gncINg28tjqMijW3sDOnDma72 azLkYe6Rxx4T0h2Jz3t5Is7A00ujZ4xIz4ZcqxeMtQQhOxUrXdISO/Mmdt69 AXot9qaHSDfhmb8hcY7AQ3u7bzfpk3zCawRf3YXEvUk/+UgP702RDXB5ALUv c0KmyPdR/e3hwmXxGGz+loq3kQ45x/+nSPIJ0Cpag5JI875/au6wxRPaDX6M PyLNLUGzF+F7Bof1Zfbo/a+9yaWsOg4vsIt7eWoD6etlx7Psfr+A3/dEzXrI 8b+zhzG3ZtwHSoR+6weTPql51EG1+xX0aScTK0jLUN5lVza8gSOBSU9zyPk+ UWQpJ0H4w7yGLJ856b7fhMc7SgD8yhDrjSPj5UdUuHdbSyD8WigRANK/6AOd /+qCIDfngFo3GX+tGQlaRiUhcHtKVW2ajM8oHq+fd/JDYRdOCTwkPZceM+2X GQYr/Od7F8n4Vv9xlDMr/h1sV72+4QcZ/8c0aJ0pflFwvMTK+h2ZL69aH+1q fh8NulsUn30j88v+u66+k0kMPLGsT1AmvWvwUd1j61iYkRT6ka/LgschFp0m nnEgz64b40zmr73rjqKy1fHw686xrkAyv2dXrPkr9jYedF90rUnXJutXwBJ7 b3QCbOSI92on64FH6ee2XyVJ0KX2KruFrCeqfUcc5I5+hCzuRzxlh1kw5qd7 4ErTR3Dm2mP7UY0F8f5Z+an9ycBO26hyi6xHC5PflMbnUkGm75dkMlnPFGaq ft/dnAnRz/dHrCLrYwj1wqGOC5mQNZPzIFSKBWm6jSNSkZkw+/SDhfhuFnT2 3NzSKJEF41X3zu6SZMHZW8fWdO7LBp8Yau8XERZ8Zhty+qpHnvs+62fr87Ng k16s0pRfLlQdTH/0mI8FdszYPXNtuRBCUE0+rmVBBv64990kD86fijk2yc0C piqH9RvLfJjdmy6qxE7Gb363bPz9AtgwTyjG/mSCdbfUxu68ArA1OdMYPskE 7a3TY/9mCuDoVSONV+NMGPo+K6ftWAj/hKcarEeZMLvtjp2bbREEiW+36iGY UGbGZ1t0rgR28h4TqW5hwsz9UO2nwSXwZbUt7WYTE5LWPRPRoZSAnV2u59oG JjxMWXqTc7IUDMREX2hUM+H+Ybb6gzpl8NCG4n6riAkV3Ha7bwuVQ4qKZmFL AhMe6/5bFlQtB1qOg5JEHBNMSjYPlJiUwxHhxCjnGCZIY53vYkA5SK3o0V/5 ngk6K0Unj/JWwJtjv9eOv2HCwtcbquvYK8HzpEpnHbne19h3x+qwqkDabt2d 0mNMOF70/M2XldXgOdPpvUebCdmbzQo9dlXDG7fNTYHAhKxzjPeJ16vhw2st ivEBJnx9wRmSPFYNU7xv1r2UJq/beO3zG6+B+Q3/SbF4mCC6LDLuOVEHa3nE Px9vYICkfs6pC7z14Jl/RmO5mgF+SU61UrL1kPvjglBaOQM8qK312Xb1IHh6 sPDPZwZ8HOYWc/peD4rm46yziQzIgvBdIV8bYKbzG9u8BwO4nyTLKlObYDOH /yue/Qywf/5i5cv5Jvg4G0CV2McAm+yTwtTNzeCe69iluoe838Rcn8WZZtAs Ve46JcYA4/sZuK65GeYcH8lo8DJAzuboyPfiFhDjSL0lwRwBTnNJh6vBbWA+ Wm7299UIuD70qczKboPZzAVOR+8RWPl0ncHvtjZ4LBjqS/McAeot9nGlle1w MUdBLvH+CBiWnvvnAO1gERzIsWQxAve8dQvW5LSD6NQ2rRxFcv87kvZfVUgH 8LyrdrnYOQwvwnK+MuI6gCp6xzykeRg6vrmuZ8vqgP5V6xaba4dBxr7g7Z6m DgjxzjeWLSb35+uuj5/81wEJbhuziuKGQThGyqDaphOWs69YrL47DL9Sv4+d UaTA6rCyrCfk+cBdgo/9yxEKbPpnM6S8Zhg2ny12cjaggKxz5hSTcxh8KKEX nlhToO8PVB6YHwJiY+2QZQgFBM+rHcugDwGbn5Cy5gIF9OJllftThsC4Q1Hy UnEXbDv1PmOPxhD0dSodNa3vAj7FAr7mA0Pw6kySq353FwjFVXdeVxgi11sF ObEfXeB1f0PAW/EhkBDU2PFCvBsMqSFS6ZxDcJvr9AZF727wdjsrudWfAPc7 ndu/n+wBk6rxFWMhdNASY3MQu9gDoxx1Rmte0OFbi7a10fUe2GBSNyt5nw7n 41QPRLn3gI6egeGRc3QQPWIh8j2jB4bOW5avFqRDduJu4uS6Xtji3FjCFTwI kaqcebsaesHWmfas3JcG+6ra/BoovfDppXLyr0c0eB720N2G3gtmuvkVO2/R QK1jNOX1TC8ImxJ0BwMa2Ous7QgV6YMD3jWqaWtoEJ49JfDSsQ+S9z/oH/Qd gD0cEZ8Y6/vB5JXViMwzKsQ0PPxSvbUfTo5Ma75wpsLJfP5v0Tv74fJP+c2D VlQwyEw5qn2wH25Uysve06TCnFHKCrDoB2ktEZ9zf/shjF77mjutH8QsEiNG Xfrhh9qyGYVsd0EA4y9c7YPvBTVOxfpU0G+ejjpq3Aecj47qR56hQsTblnZp zT4IqZmRP3mNCtY8Zd9HtvdB2JDJe2tvKqi8R0d+ai+4r5sVtWqhQli1+YlL p3rhreWutLfGA7Dfufx41/4e6FK5VJFmNgCR7H8V+CR7QHQx+myV1QBY0d/1 aW3oAeq7Dz+pTgMQtXboVsRkNyyCZENxwAAYJlwSW5/UDaJBOSZaHQPgqWzo ySnYDZ+Y/EsmejSw8a3bqzBDge1BTRmHjWgwpv6GdpCgwJ2dO1y2XqDBJ1f5 pcNNFLjJVaVQYUuDd2yXYxXjKODC3b877QUN2BQfidKMKTDy9qbsymoaiIfI E3uzOsHzsPPKJJVBcPHZKzRxowOeDVGnX6oPgtFE6OeTJh3gMpGTaXt0EBoP tDxO1eiAoCu8IauNB0FoMfuNqWAHNHJIdo7aDcLD1GcPnavbIV851n5r7CDU XLRxahFph6MWN8OPrqCDeHJik8m7Fjgx0DlQzE0H7qjfFW1OLTAT5ta8h48O DlwCL7UMWsCi4WzOnBAdHi4eLdmw1AxLnsZOCnJ0yI2q/2h+uRnOHtodcpyM 2+5Ce/G07U2g26Hcx4qjQyltJt02oh7sHfzuOn4k2+dxNa+7Ww+3q9XCZj7R weqsz0ymQT0wbNTvMPLokJat4DO8WAc+e+8Z3Kijg+TY2MjS+Tr4N7SQs3aM Dv6DMjOSm2qBR/zDqdRdBPx9K/7V3asKzkoF/XorQ8D14YwP3GZVYCTvm3lb joCn9c6Ul7JVYLFC8AK/CgGvn4bWeXRVwoMbZ82/HCUgd9nhiqx4JewYbjrJ tCBg1uXcf69LyqHFvyhRzZqAKDNfr+o35dCVKM3ue52ASoYnZc6qHHBtZeEa BwI+xwjb6K8uh8XDf3bEPSJAM4kv9WFqGZhc4FgUDSAgMk5DPX6gGKJgmXc0 iIAvW9rCn3kUQyKH9+bEUALuWZUJXN5dDG6uezi4Isn+Y7kpv5yKIGTFablL SQRI3q9TaV9dCI12lzuoyQQUbq+3vJtRAPPURavTnwign559tN6kALKlBU+J ZhHgdf4SoRLzGSZaylXPFBHAUsrVUVDJh9Tbuw/7lBDQzGv4OXogD5Sdq6Xz ywhYLL34hcsjD5zUdpXNVhJQz/WJWdOUCynfi4o4mwjYpThz4I9FDmhtOv78 dj8BPG4hS5a+mWClvV5/nkqAtdW67677M2Gz3V3hhzQCjh7IebenIgOeHn9B syIIMHe0fuZsnw6VRf0DPUwC7o7nuybXpELl1fMzO74QwGnlppawNRUaJxI3 Wn4l4MJJM+V3d1JAZ+iufdMYATWaMh6OIslQr+/nqjFJ3i/EfS/lbiL0bFWm 6P0g+09Qso1sTABdDit5g58EXNpJVbQSTYDIpdOL+6fJ+Vjk3kFrioNOj1Db HTMEuLaHB78ViwPZfw40tlkCDq+U09G6/wFa0qcoqXMEhP5yWwgWj4U3Worn 7v0iIBNPnDn4IAZ83soRKr8J4Ot5IZpF7muN+N8vxc4T8Ez+U/Hm6QhwDMx8 a7BAQHuh0Isjke/B40GzzARp4YnD9ld034HB8n1boT8EODCk6K+iQiGYUbL2 A+nHs4obI/RCgLPQKVdiiRxf0Yef42eDQFHk4ZVI0oIKoqIfowPBMD2Ld90y ARKaJz8k6gfAjtVs+ID0zw8ygTFz/iDcoOdMJe3xrvjNXs03cMHbRUb5LwFi L8X38If6QRJxm/mcdLwA8WGcPFe8sBGLbSNtwqYbW6n1AlRyHlis/0eOf0Ze e0DYM8gzuyZuQHrXosfA5UkPqPnZ9+Ux6V/s/H263m4wxVWXlkg6gVP3QqvS Q/BX2Hm/nvRnC5s3JwhnqF3+pjVCOn3Lyag3Avbg9Wf9+lnS1IevqY/trCC4 IHh4mbSwrdRsMO8p8Ktwzv1Hmn6Y1ytKVhUpsak+C6SHh9Maw0QuYirjiOX3 /7XffCB0WNUWJfgV1LpJd4ndNX/geAd3ZLwQ/EzaepNLyJ9qFzQ+rv/Ln3Tu adb2h1seI5h79lqR3nb+acfv/Ceo9ECteC/pBzb8X+Usn6Ku0NPYSXI8tPaU 7bRc44WXym19P5JOU1JkvMrzxnVTy/fMSLfYbbyXe+UlorSpNSdpmZM8Cl2r X+PzUS+TRHL8JT0tVH5d8Eepbx0aneT8HdvEP8jOGYhebLzR5qSP7Gx8bWQc hLNbvp9ikPO/M4Z2JSImGF/4c6d0L5LzNb1lh7h6GO5O9D6jTfqDT5vdpZfh WCQvezqZjKefCjNh/v3v8OWZh9rmZPwl0pMNWPciceNVx0NpZHyK3qAorKyO Qv4OmeezZPx6bm2m8q2OQfW5iM12ZHx3toenbD0Xiz9zq+T+TRFgXDYpyDET hwIymxv4SUt4se34GR6PL/dXFgiT+RS48ZByv0YCdkoIhAuS+RcV+GX0/etE tJp+Y51H5mejZdEWQjoZ05tfZvqMEmD2LHRfbHsywsGJM6ZkPvM/1xK4fD8F GfmrP/SR+X+Z+SOpojoVP32Ss/Yg68O+afeAg1fSkTOuzVmITuZzfvjnmpUZ qN58bjCJrCcPorxu6n/KwMd2HphB1p/zd//aRT3OxLRIs8arFAJOeV8qt2jI wg08ejmlHQRMZNCpXtuyUaReYGFdOwHVh70iPt7OxlsxoxGRzQSsd3OpoQvk YOc+eaPLNQR8n3Rw32iViwVVZXsfVZHjd/Vj/Ka8XPwgGG77poIAhZ7nHfzc ebhmBdeKuFIy/8c9phfS8vD8eZF2hzwCwsWEJF8t5ONINF0tPI6A1thKesKb QvT/KV3JHUtev7ZS8uRYIS4I0C7cjiLg+X4eoR9aRSi4+nq5WDgBJ9xFkoR/ FeGPIW33ra8JaDh0KFLgUgn6cuC9qy4EfFpY3fZsF2L/3XWOR50JuPGd2P3J HbHkZo73NkdyfF57v2/sQzT86y2UfYMAlYSzqi465XhndKnJ6RIBdav4PC1+ l6Pa2TZTAw0CrrSsXTQwq0Tx89WJUYfJ+Tz1JTzAqxKXgv6THDtA1p9/Tn8p 2ZW4p+5q2lUFAt4VJTOOralCveY1G3+IEXAG/5R/L65Cn4zrOy3YCQjgKCd2 batBmmJdQk4hHY5s5qxf01eHvINcwRrkep7QGzGVylaPymZPf9Vk0uGGSbbS Mel63DsUtKkoiQ4Bryz7bR/V48ArR9rxYDq0v3oeeG1bA4bsCXi/6ECH8lAX XuUrjTi0IvZwzTY6RD33KyOIZszQ9C20IvfFQnfcijO4WrAu5mnb/Ho63Nxt GvRAvgWPaSw18XHRgWYamD3r1oIHdCr2jvwchGu09DfBIq14W8OxYnfVIOh9 S9K5c6ENzx+nKx+wGoQP+vtsElntuFJyi4H1xUFYXOD3y15sx3glsy8vzg6C VdCpxsJ1HZjYT8/JOzYICQXv7xce7EDJLB5Gqswg+O7I/f74VQeum80w45yi QbdT808+5U7MLXlx9+59GtCu6t/d70rBF8bJCsG3abD327PlsLcUlLr643Pa dRronoomfiVQyPnlla49R4PS5vGQ2FYK/mNsogep0sDWOkQ2e0cX8tFko1Pn BmB6y5/BovIuvJd341zbrQE4rOFYrPm3G52KBUwErw7ARxNuH5/1PUh1jcg+ R+5/OVxCFJoke7C8cDGkQncAPGQk/6oZ9KD2/ly9QzsHYFbE6EdnaA+anep7 WkGjAmfoSt8O+V6Uqk3afvAYFR7o1pSNm/XhB/aw+beHqZDF0ZB8zb4Pja9Y P2MoUoGnKrqu170PD41eLHHcQYXl5cMvkuL78EhQ+YLSPHk+iCW8Zif68NNu Cr/Fx35Y6jXfqOHejx8O5BpareiHQ0+aJB9FUFEsg8PAb6EPlmoz3Yc/UZHd 6ODhzB998FyK01qrlIpsNZNsXwb64OQBwcKfg1T05SkyXZ3bB/tfK+/6t30A lV8FGSuQ5wqdHxnviegBLPuyqkWwtBfsvh2oCoyg4e6e46xn2b0gHfysSPIT DT2N0mA8qRf0Es0xvZiGPYZ7a5IDesnzWeZIGpWGupHKF4eu90LC3/pd54UG UbGydkF9fS9Y5Vy2uhAwiOdGNf9rudIDbM/ZJmef0PHPMdlbQWd6oGZpPo3m T8cPK6b+nj3eA+qNkwXFMXS055vfUSXXA/WAkVfK6ciRsyvlzHI3yPydlzzy j44NJxbmdMO7QcdSWcv4PwJv/mhzj2zugme0dWyXVIZQUbKyLRq7gHeY/3i5 +hCOCEWrRGR3wYX49IkdOkPYKvrj3NOwLqjuMTPqODOEv4O2G62x7oIY7gXV JcchjH/zxXrlIgXGyiuzHVOGyHy+FFsiRgE7sVap2i3DaFktqzi9iTxPtzZz 7BAdxql20VUS3BSYDTkW4bxrGEsydz1zmewEP66rXPxKw+gix63IKuqETUnu AQKGw8jTkyDBNO4EY4U6MWPXYaS/Osnn7t4B8SU0by/aMF5LFdWWcOqAxMvn Lp4dGUbrZn6JKusOWDW1qVRkdBgluoKm5vTI9q3vpOJmhvH271u7pDZ1QLct 7f4DnhF8XtJGq0hqh6umSsUGqiMYWnBfvTq0DcrW1si/fDWCQ3/27fN/3Eae TyZYaQEjuLPUPfqsZRu8rngu2xw6gvI4yFsn0wauOyXs/sSOYHzMP8K4tBXs M8WfS+aPoNHxig1rh1sg7LqycMzgCJ4+N0ZRlGgGE/m2j8rSDNwQcmjDVq5m +F3aol0ny8Cbkrdilr41gc6FgwYmigw86a9Vl5rdBB79/95dVWMgzvD+KdJs ggNHREX1TjCQ7mM88sG8ERpUFztVHBk4rXnvYH1gPRRZX0oKzmagjeAqI2fy nMSn8NzmWz4DAy0lfAVN6sG8ZXPEwWIGpmVrPDPYXA+GL6Ku1VUxMLJr+xur sDqIKiw9HdPFwKiBc2Vn39eCsqjek2NzDLSKTut/EVENpXjnyiVFJsbstj9n +7gatp1LzVrez8SWpG2btC9Vw1WdCNPwg0xM/XvT4etW8jp7WBBqMJFwWowb f1cFbhf+VNedZOJdyWz2qdBKSBvu2lZ7g4l7TPsMxV6Xg6aPrtTd90yc1ln3 yO92OXCoiOjujWLi096DH2ZOl8Nzfd7LRAwTN8VcYM/fWA6rKBFXVRKZ2P6G P1xQEcE3Ur8jKZOJHbwVpziPlQLz3cPY0GomVkRU81tyl0LnG7fav7VMbOvm KshvLIGaVXE9Fg1MTAr+MGd4qgT6vv7KE2hlooQInbLrfDFs2lpnodXLxAy/ F+cibQtBHjm/J44ycW/gn7dvZAqhOajRreQbEy8oVcf/N14AGVLTs83jTByM Wuev6VgAY61WXvSfTJSx5N7tdZ88V639JV89z0TX8KXEf8/yoHqjpVI4Fwtt 5+pfhBzLg6vTN3MurWbhjn1PlKW4yev8F/i2rmGhUFeV3OGXuSCYIHfGm4+F Oi73jKXe5kBXnwjvTgEWDlhuK9wekQXp3pJjNmIsFFmbrrLdNAuEt2j9xyHB Qq07ibeE+LNgc/LRiFBJsr9haaPlp5mQQFvrk76bhbfz2so396bDv53xbG