revised Separation of variables & Eigen value problems

This commit is contained in:
Sasserisop 2023-12-03 17:47:59 -07:00
parent 522f5a0422
commit 5f795e0c33
24 changed files with 6207 additions and 785 deletions

View File

@ -23,7 +23,7 @@ $u'(t-a)=\delta(t-a)$
What is $\mathcal{L}\{\delta(t-a)\}$? What is $\mathcal{L}\{\delta(t-a)\}$?
$\mathcal{L}\{\delta(t-a)\}=\int _{0} ^\infty \delta(t-a)e^{-st} \, dt$ for $a>0$ $\mathcal{L}\{\delta(t-a)\}=\int _{0} ^\infty \delta(t-a)e^{-st} \, dt$ for $a>0$
Using the definition earlier: Using the definition of Dirak $\int _{-\infty} ^{\infty} \delta(t-a)f(t)\, dt=f(a)$:
$$\mathcal{L}\{\delta(t-a)\}=\int _{-\infty} ^{\infty} e^{-st} \delta(t-a) \, dt=e^{-as}$$ $$\mathcal{L}\{\delta(t-a)\}=\int _{-\infty} ^{\infty} e^{-st} \delta(t-a) \, dt=e^{-as}$$
## Examples of DE's with Dirak ## Examples of DE's with Dirak
#ex #second_order_nonhomogenous #dirak_delta #IVP #ex #second_order_nonhomogenous #dirak_delta #IVP

View File

@ -49,10 +49,10 @@ If $f$ and $f'$ are piecewise continuous on $[-L,L]$, then the fourier series co
$\frac{1}{2}(f(x^-)+f(x^+))$ for all $x \in (-L,L)$ $\frac{1}{2}(f(x^-)+f(x^+))$ for all $x \in (-L,L)$
Basically meaning, the fourier series converges. Basically meaning, the fourier series converges.
At $x=\pm L$ the fourier series converges to $\frac{1}{2}(f(-L^+)+f(L^-))$ At $x=\pm L$ the fourier series converges to $\frac{1}{2}(f(-L^+)+f(L^-))$
![[Partial differential equations (lec 26-27) 2023-11-22 13.15.26.excalidraw]] ![draw](drawings/Drawing-2023-11-22-13.15.26.excalidraw.png)
### Theorem: ### Theorem:
If f(x) is continuous on $(-\infty,\infty)$ and $2L$ periodic and if $f'$ is continuous, then the taylor series converges to $f(x)$ everywhere If f(x) is continuous on $(-\infty,\infty)$ and $2L$ periodic and if $f'$ is continuous, then the taylor series converges to $f(x)$ everywhere
![[Partial differential equations (lec 26-27) 2023-11-22 13.14.05.excalidraw]] ![draw](drawings/Drawing-2023-11-22-13.14.05.excalidraw.png)
#ex lets compute the fourier transform of: #ex lets compute the fourier transform of:
$f(x)=\begin{cases}1, & -\pi\leq x\leq 0 \\x, & 0<x\leq \pi\end{cases}$ $f(x)=\begin{cases}1, & -\pi\leq x\leq 0 \\x, & 0<x\leq \pi\end{cases}$
$L$ here is $\pi$ clearly. $L$ here is $\pi$ clearly.
@ -124,7 +124,7 @@ then $\frac{a_{0}}{2}+\sum_{n=1}^\infty a_{n}\cos\left( \frac{n\pi x}{L} \right)
#ex Fourier sine series for $f(x)=x^2$ from $0\leq x\leq \pi$ #ex Fourier sine series for $f(x)=x^2$ from $0\leq x\leq \pi$
well that means we want the odd extension: well that means we want the odd extension:
![[Lec 30 2023-11-24 13.15.17.excalidraw]] ![draw](drawings/Drawing-2023-11-24-13.15.17.excalidraw.png)
the $\cos()$ ($a_{n}$) terms are zero. the $\cos()$ ($a_{n}$) terms are zero.
the b terms are: the b terms are:
$b_{n}=\frac{2}{\pi}\int _{0}^\pi x^2\sin(nx) \, dx$ $b_{n}=\frac{2}{\pi}\int _{0}^\pi x^2\sin(nx) \, dx$
@ -134,7 +134,7 @@ $b_{n}=-\frac{2}{n\pi}\left[ \pi^2(-1)^n-\frac{2}{n^2}\cos(nx)|_{0}^\pi \right]=
for $n=1,2,3,\dots$ note no $n=0$ so no divison by zero problems here. for $n=1,2,3,\dots$ note no $n=0$ so no divison by zero problems here.
#ex fourier cosine series of $f(x)=\sin(x)$ for $0\leq x\leq \pi$ #ex fourier cosine series of $f(x)=\sin(x)$ for $0\leq x\leq \pi$
![[Lec 30 2023-11-24 13.23.08.excalidraw]] ![draw](drawings/Drawing-2023-11-24-13.23.08.excalidraw.png)
$a_{n}=\frac{2}{\pi}\int _{0}^\pi \sin(x)\cos(nx)\, dx$ for $n=0,1,2,\dots$ $a_{n}=\frac{2}{\pi}\int _{0}^\pi \sin(x)\cos(nx)\, dx$ for $n=0,1,2,\dots$
use trig identity: (by the way the identites will be provided in the exam.) use trig identity: (by the way the identites will be provided in the exam.)

View File

@ -35,7 +35,7 @@ $k=1,l=-3$
so $x=u+1 \quad y=v-3$ so $x=u+1 \quad y=v-3$
$(-3u+v)du+(u+v)dv=0$ //Beautiful! It's homogenous now $(-3u+v)du+(u+v)dv=0$ //Beautiful! It's homogenous now
$\frac{ dv }{ du }=\frac{{3u-v}}{u+v}$ $\frac{ dv }{ du }=\frac{{3u-v}}{u+v}$
divide top and bottom by u so we turn the homogenous equation into the form #de_h_type1 and solve it using the tools we developed from lecture 2. divide top and bottom by $u$ so we turn the homogenous equation into the form #de_h_type1 and solve it using the tools we developed from lecture 2.
$\frac{ dv }{ du }=\frac{{3-\frac{v}{u}}}{1+\frac{v}{u}}$ $\frac{ dv }{ du }=\frac{{3-\frac{v}{u}}}{1+\frac{v}{u}}$
$\frac{v}{u}=w \quad v=uw \quad \frac{ dv }{ du }=w+u\frac{ dw }{ du }$ $\frac{v}{u}=w \quad v=uw \quad \frac{ dv }{ du }=w+u\frac{ dw }{ du }$

View File

@ -8,7 +8,7 @@ $u(t,0)=u(t,L)=0, \quad t>0$
$u(0,x)=f(x), \quad 0\leq x\leq L$ $u(0,x)=f(x), \quad 0\leq x\leq L$
lets choose $L=\pi$ lets choose $L=\pi$
$f(x)=\begin{cases}-x & 0\leq x\leq \frac{\pi}{2} \\1-x & \frac{\pi}{2}<x\leq \pi\end{cases}$ $f(x)=\begin{cases}-x & 0\leq x\leq \frac{\pi}{2} \\1-x & \frac{\pi}{2}<x\leq \pi\end{cases}$
![[Lec 30 2023-11-24 13.42.29.excalidraw]] ![draw](drawings/Drawing-2023-11-24-13.42.29.excalidraw.png)
So we have a non-uniformly heated rod with both ends insulated. What happens to the temperature inside the rod over time? <i>"\[...\]. Very interesting problem."</i> -Prof (I agree.) So we have a non-uniformly heated rod with both ends insulated. What happens to the temperature inside the rod over time? <i>"\[...\]. Very interesting problem."</i> -Prof (I agree.)
If we made this a series, where would it converge? Well it's continuous from 0 to pi and its windowed form when repeated will be convergent everywhere, this is good news for us. If we made this a series, where would it converge? Well it's continuous from 0 to pi and its windowed form when repeated will be convergent everywhere, this is good news for us.
Separation of variables: $u(t,x)=T(t)X(x)$ Separation of variables: $u(t,x)=T(t)X(x)$
@ -229,7 +229,7 @@ ok i spent 5 minutes talking nonsense but
you complaining that your exams are hard theyre not hard. I'm talking about 40 years ago, no computers no cellphones, and it wasnt so bad because we had to use our brain more. you complaining that your exams are hard theyre not hard. I'm talking about 40 years ago, no computers no cellphones, and it wasnt so bad because we had to use our brain more.
now we consider a guitar string: now we consider a guitar string:
![[Partial differential equations (lec 30-32) 2023-12-01 13.49.58.excalidraw]] ![draw](drawings/Drawing-2023-12-01-13.49.58.excalidraw.png)
assuming the thickness of the string is much smaller than the length of the string, which is true. assuming the thickness of the string is much smaller than the length of the string, which is true.
$\frac{ \partial u^2 }{ \partial t^2 }=\alpha^2 \frac{ \partial^2 u }{ \partial x^2 } \quad 0\leq x\leq L, t>0$ $\frac{ \partial u^2 }{ \partial t^2 }=\alpha^2 \frac{ \partial^2 u }{ \partial x^2 } \quad 0\leq x\leq L, t>0$
^ Reminds me of the wave equation from phys 130. ^ Reminds me of the wave equation from phys 130.

View File

@ -189,7 +189,6 @@ if $p(x)$ and $q(x)$ are <u>analytic</u> functions in a vicinity of $x_{0}$ then
we expect that the solution $y$ can be represented by a power series. This is true according to the following theorem: we expect that the solution $y$ can be represented by a power series. This is true according to the following theorem:
Theorem: If $x_{0}$ is an ordinary point then the differential equation above has two linearly independent solution of the form $\sum_{n=0} ^\infty a_{n}(x-x_{0})^n, \qquad\sum_{n=0}^\infty b_{n}(x-x_{0})^n$. Theorem: If $x_{0}$ is an ordinary point then the differential equation above has two linearly independent solution of the form $\sum_{n=0} ^\infty a_{n}(x-x_{0})^n, \qquad\sum_{n=0}^\infty b_{n}(x-x_{0})^n$.
The radius of convergence for them is at least as large as the distance between $x_{0}$ and the closest singular point (which can be real or complex). The radius of convergence for them is at least as large as the distance between $x_{0}$ and the closest singular point (which can be real or complex).
![draw](drawings/Drawing-2023-10-30-13.12.57.excalidraw.png)
## Examples for calculating $\rho$ ## Examples for calculating $\rho$
#ex #ex
@ -306,7 +305,7 @@ $\left( 30a_{6}+a_{3}-\cancelto{ 0 }{ \frac{a_{1}}{6} } \right)t^4=0 \implies a_
</br> </br>
substitute back $x-\pi=t$ substitute back $x-\pi=t$
$$y(x)=1-\frac{1}{6}(x-\pi)^3+\frac{1}{120}(x-\pi)^5+\frac{1}{180}(x-\pi)^6+\dots$$ $$y(x)=1-\frac{1}{6}(x-\pi)^3+\frac{1}{120}(x-\pi)^5+\frac{1}{180}(x-\pi)^6+\dots$$
there's no general formula here for the constants (or maybe he said no formula for y(x)?), but we can write the solution in the following form^. there's no general formula here for the constants (or maybe he said no formula for $y(x)$?), but we can write the solution in the following form^.
#ex #powseries #ex #powseries
Here's a non-homogenous example: (RHS$\ne 0$) Here's a non-homogenous example: (RHS$\ne 0$)

View File

@ -5,140 +5,185 @@ We start with some thermodynamics
Heat equation not only describes thermodynamics but it can also model the diffusion of gasses. It is a partial differential equation. Heat equation not only describes thermodynamics but it can also model the diffusion of gasses. It is a partial differential equation.
Strikingly, it can also model option prices in the stock market. However, using it as a strategy to make money is not so simple, because if it worked then everyone would try to use it to make money, which would cause the overall strategy to be less effective as the option prices start to get priced to accommodate for the prediction (🤯). Strikingly, it can also model option prices in the stock market. However, using it as a strategy to make money is not so simple, because if it worked then everyone would try to use it to make money, which would cause the overall strategy to be less effective as the option prices start to get priced to accommodate for the prediction (🤯).
![draw](drawings/Drawing-2023-11-08-13.07.19.excalidraw.png) ![draw](drawings/Drawing-2023-11-08-13.07.19.excalidraw.png)
We assume that the tube is perfectly insulating along its surface, this helps reduce the problem into a one dimensional problem. Heat can only travel inside and along the x axis. We assume that the tube is perfectly insulating along its cylindrical surface, this helps reduce the problem into a one dimensional problem. Heat can only travel inside and along the x axis.
We can express the heat along the tube as $u(t,x)$
>I like to visualize $u(t,x)$ as a wavetable that smooths out as the time variable progresses. Something like this: [(image source)](https://markmoshermusic.com/2015/07/29/intro-to-loading-custom-waldorf-blofeld-wavetables/)
![wavetable.jpg](drawings/wavetable.jpg)
## Derivations (I'd skip this)
Fourier figured out that: Fourier figured out that:
$\text{Heat flux} = -k(x)a\frac{\partial u}{\partial x}(t,x) \Delta t$ $\text{Heat flux} = -k(x)a\frac{\partial u}{\partial x}(t,x) \Delta t$
heat flux is in the positive $x$ direction heat flux is in the positive $x$ direction
where du/dx is the opposite sign of the flux (because hot flows to cold.) $k(x)$ is the thermal conductivity of the tube at point $x$
where $u(t,x)$ is a function that describes the temperature in the tube. $\frac{ \partial u }{ \partial x }$ is always opposite in sign of flux (because hot flows to cold.)
$-k(x+\Delta x)a\frac{\partial u}{\partial x}(t,x+\Delta x) \Delta t$ $a$ is the area of the cross-section.
$=-k(x+\Delta x)a\frac{\partial u}{\partial x}(t,x+\Delta x) \Delta t$
$\Delta u=u(t+\Delta t,x)-u(t,x)$ $\Delta u=u(t+\Delta t,x)-u(t,x)$
Amount of heat to change temperature over $\Delta t$ with $\Delta u$ is $\underbrace{ C(x) }_{ \text{specific heat capacity } }\underbrace{ \rho(x) }_{ \text{density} }\Delta u\underbrace{ a\Delta x }_{ \text{volume} }$ Amount of heat to change temperature over $\Delta t$ with $\Delta u$ is $\underbrace{ C(x) }_{ \text{specific heat capacity } }\underbrace{ \rho(x) }_{ \text{density} }\Delta u\underbrace{ a\Delta x }_{ \text{volume} }$
$c(x)\rho(x)a\Delta x(u(t+\Delta t,x)-u(t,x))=a\Delta t(k(x+\Delta x) \frac{\partial u}{\partial x}(t_{1}x+\Delta x)-k(x) \frac{\partial u}{\partial x}(t,x))+Q(t,x)\Delta xa\Delta t$ $C(x)\rho(x)a\Delta x(u(t+\Delta t,x)-u(t,x))=a\Delta t(k(x+\Delta x) \frac{\partial u}{\partial x}(t,x+\Delta x)-k(x) \frac{\partial u}{\partial x}(t,x))+Q(t,x)\Delta xa\Delta t$
>I'm gonna be honest, I'm lost in this derivation already. What is $Q(t,x)$?
divide by $a\Delta x\Delta t$ divide by $a\Delta x\Delta t$
$c(x)\rho(x)\frac{(u(t+\Delta t,x)-u(t,x))}{\Delta t}=a\Delta t\left( k(x+\Delta x) \frac{\partial u}{\partial x}(t,x+\Delta x)-k(x) \frac{\partial u}{\partial x}(t,x) \right)+Q(t,x)$ $C(x)\rho(x)\frac{(u(t+\Delta t,x)-u(t,x))}{\Delta t}=\frac{ k(x+\Delta x) \frac{\partial u}{\partial x}(t,x+\Delta x)-k(x) \frac{\partial u}{\partial x}(t,x)}{\nabla x}+Q(t,x)$
$\lim_{ \Delta x,\Delta t \to 0 }: c(x)\rho(x)\frac{ \partial u }{ \partial t }(t,x)=\frac{ \partial }{ \partial x }\left( k(x)\frac{ \partial u }{ \partial x }(t,x) \right)+Q(t,x)$ $\lim_{ \Delta x,\Delta t \to 0 }: C(x)\rho(x)\frac{ \partial u }{ \partial t }(t,x)=\frac{ \partial }{ \partial x }\left( k(x)\frac{ \partial u }{ \partial x }(t,x) \right)+Q(t,x)$
Any differential equation, to his knowledge, is to balance some volume and to take the limit to produce a pointwise / instantaneous balance (in the form of a differential equation) Any differential equation, to his knowledge, is to balance some volume and to take the limit to produce a pointwise / instantaneous balance (in the form of a differential equation)
the Maxwell equations that describes magnetic and electric fields are a system of partial differential equations. the Maxwell equations that describes magnetic and electric fields are a system of partial differential equations.
thermodynamics can be very important for electrical engineers, for instance the heat produced in a transformer, or a battery. It has applications. thermodynamics can be very important for electrical engineers, for instance the heat produced in a transformer, or a battery. It has applications.
## Separation of variables & Eigen value problems
#ex #SoV #evp
(This is more of a case study than an example.)
Rewrite the equation we derived by grouping the constant terms into one constant $D$
$\frac{ \partial u }{ \partial t }=D \frac{ \partial^2 u }{ \partial x^2 }, \quad 0\leq x\leq L, \quad t>0$ $\frac{ \partial u }{ \partial t }=D \frac{ \partial^2 u }{ \partial x^2 }, \quad 0\leq x\leq L, \quad t>0$
boundary conditions: boundary conditions:
$u(t,0)=u(t,L)=0 , \quad t>0$ (simple case) $u(t,0)=u(t,L)=0 , \quad t>0$ (simple case)
$u(0,x)=f(x) , \quad 0\leq x\leq L$ $u(0,x)=f(x) , \quad 0\leq x\leq L$
These three equations form an IBVP (initial boundary value problem) These three equations form an IBVP (initial boundary value problem)
we will work on this equation next lecture: Separation of variables starts with the assumption that the solution can be written as:
$u(t,x)=T(t)X(x)$ $u(t,x)=T(t)X(x)$
$T'x=DTx''$ plug into equation:
divide by DTx: $\frac{ \partial}{ \partial t }(T(t)X(x))=D \frac{ \partial^2 }{ \partial x^2 }(T(t)X(x))$
$\frac{T'}{DT}=\frac{x''}{x}$ $T'X=DTX''$
on the left is a function of time only, on the right is a function of x only. divide by $DTX$:
$\frac{T'}{DT}=\frac{x''}{x}=-\lambda$ where $\lambda$ is a constant $\frac{T'}{DT}=\frac{X''}{X}$
$x''+\lambda x=0$ recall $T$ and $X$ are single variable functions ($u(t,x)=T(t)X(x)$)
$u(t,0)=\cancel{ T(t) } \quad x(0)=0$ that means the left is a function of $t$ only, on the right is a function of $x$ only.
$x(0)=x(L)=0$ $\frac{T'}{DT}=\frac{X''}{X}=-\lambda$ where $\lambda$ is a constant
this is called an eigen value problem. we first consider the problem:
$\frac{X''}{X}=-\lambda$
$X''+\lambda X=0$ (called an eigen value problem on a second derivative operator)
$u(t,0)=0=T(t)X(0)$
$\implies X(0)=0$
$T(t)$ cannot be the 0 term because if it was then you get $u(t,x)=T(t)X(x)=0$ for all $t$ and $x$ which can't be.
$u(t,L)=0=T(t)X(L)$
$\implies X(L)=0$
</br>
$\begin{cases}X''+\lambda X=0 \\X(0)=0 \\X(L)=0\end{cases}$ <-This is called an eigen value problem.
#end of lec 26 #start of lec 27 #end of lec 26 #start of lec 27
Recall from last lec:
$\frac{ \partial u }{ \partial t }=D \frac{ \partial^2 u }{ \partial x^2 }, \quad 0\leq x\leq L, \quad t>0$ It's an eigen value problem because unlike before when we did second order linear equations with constant coefficients, lambda is not fixed, it can be any number.
boundary conditions: recall in linear algebra $Ax+\lambda x=0$ where $A$ is a matrix
$u(t,0)=u(t,L)=0 , \quad t>0$ (simple case) eigen is a Germanic word meaning intrinsic, important
$u(0,x)=f(x) , \quad 0\leq x\leq L$ We find the characteristic equations by splitting into three cases, just like we've done since lecture 7
This is an IBVP $X''+\lambda X=0$
$\frac{T'}{DT}=\frac{x''}{x}=-\lambda$ case 1) $\lambda<0$
$x''+\lambda x=0$ (called an eigen value problem on a second derivative operator) $r^2+\lambda=0 \implies r_{1,2}=\pm \sqrt{ -\lambda }$ (real solutions only since $\sqrt{ -(-) }=\sqrt{ + }$)
$x(0)=x(L)=0$ $X(x)=c_{1}e^{\sqrt{ -\lambda }x}+c_{2}e^{-\sqrt{ -\lambda }x}$
its an eigen value problem because lambda is not fixed, it can be any number.
recall in linear algebra $Ax+\lambda x=0$ where A is a matrix
eigen is a germanic word meaning intrinsic, important
(i)$\lambda<0$
$r^2+\lambda=0 \implies r_{1,2}=\pm \sqrt{ -\lambda }$
$X(x)=c_{1}e^{\sqrt{ -\lambda }x}+c_{2}^{-\sqrt{ -\lambda }x}$
but we have a boundary condition: but we have a boundary condition:
$X(0)=0=c_{1}+c_{2}$
$c_{1}+c_{2}=0$ $c_{1}+c_{2}=0$
$X(L)=c_{1}e^{\sqrt{ -\lambda }L}+c_{2}^{-\sqrt{ -\lambda }L}=0$ $X(L)=c_{1}e^{\sqrt{ -\lambda }L}+c_{2}e^{-\sqrt{ -\lambda }L}=0$
this has a solution, as the determinant $\ne 0$ this has a unique solution, as the determinant is non-zero: $\det\left(\begin{matrix}1 & 1 \\e^{\sqrt{ -\lambda }L} & e^{-\sqrt{ -\lambda }L}\end{matrix}\right)=e^{-\sqrt{ -\lambda }L}-e^{\sqrt{ -\lambda }L}\ne 0$ (as long as $L\ne 0$, which is true since we are assuming the tube has non-zero length.)
the solution is $c_{1}=0,\ c_{2}=0$ the only solution is $c_{1}=0,\ c_{2}=0$
which gives $X(x)=0$ for all $x\in[0,L]$ very boring solution! which gives $X(x)=0$ for all $x\in[0,L]$ very boring solution!
other option: </br>
(ii) $\lambda=0$ case 2) $\lambda=0$
$x''=0$ $X''=0$
integrate both sides twice: $r^2=0$
$X(x)=c_{1}x+c_{2}$ $r=0$ (repeated root)
boundary conditions: $X(x)=c_{1}e^{0}+c_{2}0e^0=c_{1}$
$X(0)=0\implies c_{2}=0$ $X(0)=0\implies c_{1}=0$
$X(L)=0=c_{1}L \implies c_{1}=0$ $X(L)=0 \implies c_{1}=0$
again a boring solution! $X(x)=0$ this is called a trivial solution. there are no restrictions on $c_{2}$
We are looking for non-trival solutions. but our solution is $X(x)=0$ again for all $x$. This is called a trivial solution.
try the last other possible case: >alternatively, we could have found this in a different approach:
(iii) $\lambda>0$ >$X''=0$
$r_{1,2}=\pm \sqrt{ \lambda }i$ >integrate both sides twice:
>$X(x)=c_{1}x+c_{2}$
>boundary conditions:
>$X(0)=0\implies c_{2}=0$
>$X(L)=0=c_{1}L \implies c_{1}=0$
>$\implies X(x)=0$
>This method worked because the LHS is just one term of $X^n$ so we treated it as a separable equation.
We are looking for non-trival solutions. Try the last other possible case:
case 3) $\lambda>0$
$r^2+\lambda=0$
$r_{1,2}=\pm \sqrt{ -\lambda }=\pm i\sqrt{ \lambda }$
$X(x)=c_{1}\cos(\sqrt{ \lambda }x)+c_{2}\sin(\sqrt{ \lambda }x)$ $X(x)=c_{1}\cos(\sqrt{ \lambda }x)+c_{2}\sin(\sqrt{ \lambda }x)$
boundary conditions: boundary conditions:
$X(0)=0\implies c_{1}=0$ $X(0)=0\implies c_{1}=0$
$X(L)=0 \implies c_{2}\sin(\sqrt{ \lambda }L)=0$ $X(L)=0 \implies c_{2}\sin(\sqrt{ \lambda }L)=0$
if you take $c_{2}=0$ you lose, another boring solution. if you take $c_{2}=0$ you lose, another boring solution ($X(x)=0$).
but if $\sin(\sqrt{ \lambda }L)=0 \implies \sqrt{ \lambda }L=n\pi$ but if $\sin(\sqrt{ \lambda }L)=0 \implies \sqrt{ \lambda }L=n\pi$
where $n=1,2,3,\dots$ notice that $n\ne 0$ becuase that implies lambda=0 but lambda is >0 so that cant be. where $n=1,2,3,\dots$ notice that $n\ne 0$ because that implies $\lambda=0$ but $\lambda>0$ in case 3 so that cant be.
$\lambda_{n}=\frac{n\pi}{L}$ $\lambda_{n}=(\frac{n\pi}{L})^2$ <- These are called <u>eigen values</u>.
$X_{n}(x)=c_{2}\sin(\sqrt{ \lambda }x)=\sin\left( \frac{n\pi}{L}x \right)$ this is an eigen function. It's the only non-trivial solution. The corresponding <u>eigen functions</u> are:
$X_{n}(x)=c_{2}\sin(\sqrt{ \lambda_{n} }x)=c_{n}\sin\left( \frac{n\pi}{L}x \right)$
lets go back to the problem: ^They are the only non-trivial solutions.
$\frac{T'}{DT}=\frac{x''}{x}=-\lambda$ </br>
lets go back to the problem and focus on $T$:
$\frac{T'}{DT}=\frac{X''}{X}=-\lambda$
$\frac{T'}{T}=-\left( \frac{n\pi}{L} \right)^2D$ $\frac{T'}{T}=-\left( \frac{n\pi}{L} \right)^2D$
this is a separable equation. this is a separable equation.
$T_{n}(t)=c_{n}e^{-(n\pi/2)^2Dt}$ We can treat the function T as a variable:
you might have thought you could forget the material before the midterm to make more space in your brain, your brains are a lot more emptier than mine, mine is filled with garbage [...] so don't forget anything you learned before the midterm! $\frac{dT}{dt} \frac{1}{T}=-\left( \frac{n\pi}{L} \right)^2D$
you'll see that as you continue in education, you'll see a lot of completely new things and you'll have to find shortcuts and tricks to make the content fit what you already know. $\int{dT} \frac{1}{T}=\int-\left( \frac{n\pi}{L} \right)^2Ddt$
okay enough with life stuff, back to mathematics: $\ln(T)=-\left( \frac{n\pi}{L} \right)Dt+c_{n}$
$\implies u_{n}(t,x)=c_{n}e^{-(n\pi/2)^2Dt}\sin\left( \frac{n\pi x}{L} \right)$ $n=1,2,3,\dots$ $T_{n}(t)=c_{n}e^{-(\frac{n\pi}{L})^2Dt}$
applying superposition (sum of any solutions is also a solution) >Yes this looks illegal, but it works, you could also integrate more rigorously if you did a u-sub: $u=T(t) \quad \frac{du}{dt}=T'(t)$)
$u(t,x)=\sum_{n=1}^\infty c_{n}e^{-(n\pi/L)^2Dt}\sin\left( \frac{n\pi x}{L} \right)$ this is the most general form of the solution. </br>
$u(0,x)=f(x)=\sum_{n=1}^\infty c_{n}\sin\left( \frac{n\pi x}{L} \right),\ 0\leq x\leq L$ we have never seen anything like this before, an infinite number of sin terms added together, usually its polynonmials we sum. ><i>"You might have thought you could forget the material before the midterm to make more space in your brain, your brains are a lot more emptier than mine, (class laughs) mine is filled with garbage [...] so don't forget anything you learned before the midterm!"</i>
this is when fourier (?) steps in. he proved that you can represent various functions as a sum of sines. ><i>"You'll see that as you continue in education, you'll see a lot of completely new things and you'll have to find shortcuts and tricks to make the content fit what you already know."</i>
fun stories about pioncre and cauchy euler: cauchy euler was an engineer, and pioncre had his theorem released around 2008 and it was really long, like 400 pages, he posted it online and asked if anyone wanted to prove it, after a while 4-5 or so mathematicians checked his proof and said, yep okay the proof looks correct. His theorem has a lot to do with the material world.
f doesn't even have to be analytic in order for it to be expressed as a sum of sines.
take $f(x)=\sin(x)$ and $L=\pi$ can we pick the coefficients $c_{n}$ to make the expressian above true? Okay enough with life stuff, back to mathematics:
of course $c_{1}=1$ and all the other coefficients equals 0 $\implies u_{n}(t,x)=c_{n}e^{-(n\pi/L)^2Dt}\sin\left( \frac{n\pi x}{L} \right)$ $n=1,2,3,\dots$
applying superposition (sum of any solutions is also a solution):
done with class! more history time: $$u(t,x)=\sum_{n=1}^\infty c_{n}e^{-(n\pi/L)^2Dt}\sin\left( \frac{n\pi x}{L} \right)$$ ^This is the most general form of the solution.
why is this important? in 1979 a team of engineers and mathematicians from a company philips they discovered, or practically implemented: that an audio signal has billions of datapoints over time if you represent it as a fourier seriers, and truncate some of the coeffecients we can represent many signals really well. which condenses down the data to just a handful of coefficients $u(0,x)=f(x)=\sum_{n=1}^\infty c_{n}\sin\left( \frac{n\pi x}{L} \right),\ 0\leq x\leq L$ we have never seen anything like this before, an infinite number of sin terms added together, usually its polynomials that are summed.
this is how EQ's are made too to filter out noise, just set the c_n of the frequencies you dont want to 0 This is when Fourier (?) steps in. He proved that you can represent various functions as a sum of sines.
so philips used math, math that is simillar to what we are discussing in the lecture, to make a digital record, the first digital cd. Not only that but fourier series are used for image and video compression as well, although they often use a sum of wavelets instead of a sum of trigonometric functions. fun stories about Pioncre and Cauchy Euler: Cauchy Euler was an engineer, and Pioncre had his theorem released around 2008 and it was really long, like 400 pages, he posted it online and asked if anyone wanted to prove it, after a while 4-5 or so mathematicians checked his proof and said, yep okay the proof looks correct. His theorem has a lot to do with the material world.
#end of lec 27 </br>
<i>reading week</i> $f$ doesn't even have to be analytic in order for it to be expressed as a sum of sines.
Take $f(x)=\sin(x)$ and $L=\pi$ can we pick the coefficients $c_{n}$ to make the expression above true?
of course! $c_{1}=1$ and all the other coefficients equals 0.
</br>
Done with class! More history time:
Why is this important? in 1979 a team of engineers and mathematicians from a company Philips they discovered, or practically implemented that an audio signal has billions of datapoints over time if you represent it as a Fourier series, and truncate some of the coefficients we can represent many signals really well. Which condenses down the data to just a handful of coefficients
This is how filters are made too to filter out noise, just set the $c_{n}$ of the frequencies you don't want to $0$.
So Philips used math, math that is similar to what we are discussing in the lecture, to make a digital record, the first digital cd. Not only that but Fourier series are used for image and video compression as well, although they often use a sum of wavelets instead of a sum of trigonometric functions.
#end of lec 27 (Nov 10)
<i>*reading week*</i>
#start of lec 28 (Nov 20) #start of lec 28 (Nov 20)
## Example Eigen value problem
#ex #evp
$y''-2y'+\lambda y=0 \qquad y(0)=0, \quad y'(\pi)+y(\pi)=0$ $y''-2y'+\lambda y=0 \qquad y(0)=0, \quad y'(\pi)+y(\pi)=0$
eigen value problem, find $\lambda$ such that the initial conditions are true. Find $\lambda$ such that the initial conditions are true.
find the characteristic polynomial. find the characteristic polynomial:
$r^2-2r+\lambda=0$ $r^2-2r+\lambda=0$
$r_{1,2}=\frac{2\pm \sqrt{ 4-4\lambda }}{2}=1\pm \sqrt{ 1-\lambda }$ $r_{1,2}=\frac{2\pm \sqrt{ 4-4\lambda }}{2}=1\pm \sqrt{ 1-\lambda }$
</br>
case 1: $1-\lambda>0$ case 1: $1-\lambda>0$
$y(x)=c_{1}e^{(1+\sqrt{ 1-\lambda })x}+c_{2}e^{ (1-\sqrt{ 1-\lambda })x }$ $y(x)=c_{1}e^{(1+\sqrt{ 1-\lambda })x}+c_{2}e^{ (1-\sqrt{ 1-\lambda })x }$
$y(0)=0=c_{1}+c_{2}$ $y(0)=0=c_{1}+c_{2}$
$c_{1}(1+\sqrt{ 1-\lambda })e^{(1+\sqrt{ 1-\lambda })\pi}+c_{2}(1-\sqrt{ 1-\lambda })e^{ (1-\sqrt{ 1-\lambda })\pi }+c_{1}e^{(1+\sqrt{ 1-\lambda })\pi}+c_{2}e^{ (1-\sqrt{ 1-\lambda })\pi }=0$ $y'(\pi)+y(\pi)=0$
$\implies c_{1}(1+\sqrt{ 1-\lambda })e^{(1+\sqrt{ 1-\lambda })\pi}+c_{2}(1-\sqrt{ 1-\lambda })e^{ (1-\sqrt{ 1-\lambda })\pi }+c_{1}e^{(1+\sqrt{ 1-\lambda })\pi}+c_{2}e^{ (1-\sqrt{ 1-\lambda })\pi }=0$
substitute $c_{1}=-c_{2}$
$-c_{2}(1+\sqrt{ 1-\lambda })e^{(1+\sqrt{ 1-\lambda })\pi}+c_{2}(1-\sqrt{ 1-\lambda })e^{ (1-\sqrt{ 1-\lambda })\pi }-c_{2}e^{(1+\sqrt{ 1-\lambda })\pi}+c_{2}e^{ (1-\sqrt{ 1-\lambda })\pi }=0$ $-c_{2}(1+\sqrt{ 1-\lambda })e^{(1+\sqrt{ 1-\lambda })\pi}+c_{2}(1-\sqrt{ 1-\lambda })e^{ (1-\sqrt{ 1-\lambda })\pi }-c_{2}e^{(1+\sqrt{ 1-\lambda })\pi}+c_{2}e^{ (1-\sqrt{ 1-\lambda })\pi }=0$
$c_{2}=0=c_{1}$ boring solution :( (also called a trivial solution) $\implies c_{2}=0$
$\implies y(x)=0$ boring solution :( (also called a trivial solution)
</br>
case 2: $1-\lambda=0$ case 2: $1-\lambda=0$
$y(x)=c_{1}e^x+c_{2}xe^x$ $r=r_{1,2}=1\pm \sqrt{ 0 }$ (repeated root)
$y(x)=c_{1}e^{rx}+c_{2}xe^{rx}$
$y(0)=0=c_{1}$ $y(0)=0=c_{1}$
$y'(\pi)+y(\pi)=c_{2}(\pi e^\pi+e^\pi+\pi e^\pi)=0 \implies c_{2}=0$ $y'(\pi)+y(\pi)=c_{2}(\pi e^\pi+e^\pi+\pi e^\pi)=0 \implies c_{2}=0$
(same trivial solution.) (same trivial solution.)
</br>
case 3: $1-\lambda<0$ case 3: $1-\lambda<0$
$r_{1,2}=1\pm i\sqrt{\lambda-1}$ $r_{1,2}=1\pm i\sqrt{\lambda-1}$
</br>
$y(x)=e^x(c_{1}\cos(\sqrt{ \lambda-1 }x)+c_{2}\sin(\sqrt{ \lambda-1 }x))$ $y(x)=e^x(c_{1}\cos(\sqrt{ \lambda-1 }x)+c_{2}\sin(\sqrt{ \lambda-1 }x))$
$y(0)=0=c_{1}$ $y(0)=0=c_{1}$
$y(x)=c_{2}e^x\sin \sqrt{ \lambda-1 }x$ $y(x)=c_{2}e^x\sin (\sqrt{ \lambda-1 }x)$
$y'(\pi)+y(\pi)=0=c_{2}(e^\pi \sin \sqrt{ \lambda-1 }\pi+e^\pi \cos( \sqrt{ x-1 }\pi)\sqrt{ \lambda-1 })+c_{2}e^\pi \sin \sqrt{ \lambda-1 }\pi$ $y'(\pi)+y(\pi)=0=c_{2}\underbrace{ (e^\pi \sin \sqrt{ \lambda-1 }\pi+e^\pi \cos( \sqrt{ x-1 }\pi)\sqrt{ \lambda-1 }+e^\pi \sin \sqrt{ \lambda-1 }\pi) }_{ =0 }$
$$2\sin(\sqrt{ \lambda-1 }\pi)-\sqrt{ \lambda-1 }\cos(\sqrt{ \lambda-1 }\pi)=0$$ $$2\sin(\sqrt{ \lambda-1 }\pi)+\sqrt{ \lambda-1 }\cos(\sqrt{ \lambda-1 }\pi)=\frac{0}{e^\pi}=0$$
This is a transcendental equation, non algebraic, cannot be solved explicitly. This is a transcendental equation, non algebraic, cannot be solved explicitly.
Only option we have is to approximate the eigen values. there are an infinite number of them. Btw the eigen function is $y(x)=ce^x\sin(\sqrt{\lambda-1}x)$
We have to use software in order to obtain a finite number of approximations. I like software, software is good, as long as your being mindful of how you're using it. Only option we have is to approximate the eigen values. There are an infinite number of them.
"engineers over design things." first they use software and add a 50%, sometimes 300% margin on it. But if your using that big of a margin I can just tell you how far you need to build your house away from the river. -Prof We have to use software in order to obtain a finite number of approximations. I like software, software is good, as long as your being mindful of how you're using it. (I'm not putting quotes as I can't recall for certain what he said.)
"Engineers over design things." first they use software and add a 50%, sometimes 300% margin on it. But if your using that big of a margin I can just tell you how far you need to build your house away from the river. -Prof
</br>
Here's a plot, every crossing of red line with the $x$ axis is a solution for $\lambda$. Blue is a solution curve corresponding with $\lambda\approx8.305027$. Green shows the DE is being satisfied. And the equation on the bottom left shows the second initial condition is being met. The first condition is also met as you can see $Y(0)=0$ on the plot.
![evp.png](drawings/evp.png)
We are done. We are done.

View File

@ -70,7 +70,7 @@ $y'=z$
$\begin{cases}y'-z=0 \\az'+bz+cy=f\end{cases}$ $\begin{cases}y'-z=0 \\az'+bz+cy=f\end{cases}$
last example of the chapter: last example of the chapter:
#ex #ex #SoLE #IVP
$x'+y=0, \qquad x(0)=0$ $x'+y=0, \qquad x(0)=0$
$x+y'=1-u(t-2) \qquad y(0)=0$ $x+y'=1-u(t-2) \qquad y(0)=0$
This is a review problem in chapter 7 of the textbook. This is a review problem in chapter 7 of the textbook.

View File

@ -23,7 +23,7 @@ I have written these notes for myself, I thought it would be cool to share them.
[Dirak δ-function (lec 21)](dirak-δ-function-lec-21.html) [Dirak δ-function (lec 21)](dirak-δ-function-lec-21.html)
[Systems of linear equations (lec 21-22)](systems-of-linear-equations-lec-21-22.html) [Systems of linear equations (lec 21-22)](systems-of-linear-equations-lec-21-22.html)
[Power series (lec 22-25)](power-series-lec-22-25.html) [Power series (lec 22-25)](power-series-lec-22-25.html)
[Separation of variables & Eigen value problems (lec 26-28)](separation-of-variables-eigen-value-problems-lec-26-28.html) (raw notes, not reviewed or revised yet.) [Separation of variables & Eigen value problems (lec 26-28)](separation-of-variables-eigen-value-problems-lec-26-28.html)
[Fourier series (lec 28-29)](fourier-series-lec-28-29.html) (raw notes, not reviewed or revised yet.) [Fourier series (lec 28-29)](fourier-series-lec-28-29.html) (raw notes, not reviewed or revised yet.)
[Partial differential equations (lec 30-33)](partial-differential-equations-lec-30-33.html) (raw notes, not reviewed or revised yet.) [Partial differential equations (lec 30-33)](partial-differential-equations-lec-30-33.html) (raw notes, not reviewed or revised yet.)
</br> </br>

File diff suppressed because it is too large Load Diff

Binary file not shown.

Before

Width:  |  Height:  |  Size: 29 KiB

After

Width:  |  Height:  |  Size: 30 KiB

View File

@ -0,0 +1,762 @@
---
excalidraw-plugin: parsed
tags: [excalidraw]
---
==⚠ Switch to EXCALIDRAW VIEW in the MORE OPTIONS menu of this document. ⚠==
# Text Elements
%%
# Drawing
```compressed-json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```
%%

Binary file not shown.

After

Width:  |  Height:  |  Size: 8.2 KiB

View File

@ -0,0 +1,796 @@
---
excalidraw-plugin: parsed
tags: [excalidraw]
---
==⚠ Switch to EXCALIDRAW VIEW in the MORE OPTIONS menu of this document. ⚠==
# Text Elements
%%
# Drawing
```compressed-json
N4KAkARALgngDgUwgLgAQQQDwMYEMA2AlgCYBOuA7hADTgQBuCpAzoQPYB2KqATL
ZMzYBXUtiRoIACyhQ4zZAHoFAc0JRJQgEYA6bGwC2CgF7N6hbEcK4OCtptbErHAL
RY8RMpWdx8Q1TdIEfARcZgRmBShcZQUARm0ATm1YhJo6IIR9BA4oZm4AbXAwUDBS
iBJuCAB5fX0OAHV6ADMADgBVABUjOAArAFkAYQAGKoAhAA1lAE000shYREqmwIRP
Kn4yzG5nAGYdoe0eABYANgB2E4BWWJ3z2Jazy43IGG2W+L2eS54z2JOhk5HI6XHb
PCAUEjqbgnc7JHj/BItE47HjvL5gyQIQjKaTcY7aQGPHgpM7wy4ndFFSDWZTBbhD
MHMKCkNgAawQAzY+DYpEqAGJYghBYLZmVNLhsKzlCyhBxiJzubyJMzrMw4LhAjlR
ZAmoR8PgAMqwOkSSQSjSBbUQJks9n1SGSbixRnMtkII0wE3oQQeK0ynEccJ5NDOq
kQNjq7BqV4hoYMsPS4RwACSxGDqHyAF0wctcFlU9wOEJ9WCZXLA8x00WS2Gwqsnb
FYvCEoihkcwYwWOwuGgjqCw53WJwAHKcMTcI4tFoJHZ9oYtMGEZgAEQyUHraCaBD
CYM0wjlAFFglkclXi/gwUI4MRcOviE6znGTi0jjcEkNLmcwUQOKzC+fvzYSUN1QL
d8B3MM4DYQhTwKKkwEKOZSnjJCwCGeDs3gxCkJQnDtEuI4LkRBIzlOIYZ0eDDngQ
+C0OosB4nIgjUVOc4EmfSikMwpDsLmHhtDOHYWkuJEji+M40SeWjGISZiXxhM52I
eS4qKw6SDiOAFBJaVEmyk1D0K46jeNKe5tCGUidISWIhliclKVQ5wZIIkTET7Ti5
m4uYTLAL4BLE1tZMfHY9Po5x+O0k4/gSPsdkuCiVKMtTUNuZIjnYkSeCGEKHKQ3Z
tBaWcLPin5BPcxLPOM2jCIJa5hIIjj9Lyo5Eh4WS2x2GKQT01SeNo+LkgIj8flnH
ZHjCy5tEI0k9inO5lN67zaLOM5DlRKd7jihKwpahI2suDqupyirSi80ofPfVqUlu
bLrnspq5mcFqhNiFb0qGHhRo806qtQmzJr+H4STJCkHtKfLUWRU4+0uBqFqSvq/q
yqbiIpTTPvKsL4kBEKp0Iq5QcW87pKBfDEUeScdMk+jnLE+S2MaomaL+ik4REgj0
uJZT6J2Q4kRC+LXqU76wDO5mkPuA5LMI19iXIsawbAXb9sO4FjqZnyUiSayHgpLL
gtC2iTnM44Z0hm5dK+DXaJ4NqUc/H4QXfUH6NWmE0RuMbpwBK2EaW1DTcOG5SOuV
77nG2ikhCptFLaxm/eJgPH3w0jZMbX5ufUxIObGr52YS62A+sxJbjs0kQdyuYmzJ
x5ZOJSdysLpCURar5XsfWzZOOmnJr2CkmzsnTsp6hPxbmWdjb+AEgW6yvTONvs/l
+WIxM/SSm/H2FvnuA784VmnVu+R8WwRBWN9KISkjnUkDpIgm54YlpzMstqbLswnR
5818Pk6+L2PliOf0n561hsJAun9qp9mSMVPsR1DZ/SSK9D6U55zxTPhA1C0MpqNR
psA22oC0EizFl/JE2gxoXHqqcTOQDn6TlfrZe658wAHVWtpIis9FZ/CmiFO4K9xL
rwwUhJ2+EtI3CRBnQBEteYnBnB+V8LYuZEN+kImRBIQqnBIhXThGktIWQuLcCRJ1
RbKLmCJVhPtERU3gUhJ++iCKtioUo5KSF/gtWfOSZ8cCH6rSbNlHWwIDa+0qs4uY
FJjbxTIkCPa4dFY+Kyp1e4ATh5BJ+iE0oyJea/HSlcIe3dlpwj8Ykz8ySjHEKNsC
IOvxkSoO2rRJ+xJMolKYVcFq3wXxiSEpbRWSQwFxgeN7D+wTEYuMBCXOqAJAQ+20
dnIEucCHgKGf7EZcSET8y6TTA4slZk3z3k44ZoTYRNlWcidZWctnuTzmA9BnkqRn
QgHAQIlYRDhDgnhV6qI7ou3ggceuL597fMOO4uGkjkJkNuNZTSXy8JjRsrU6Fgk2
w4IBWNSxpy8Kvn2FckFaEpqvkhXPDSodDHUQ0sicRMSSXYMaXk9F5wVpfTBqSlaZ
V/m0s0ffRlU1BInOpgCyciLqFzA0oVTqfzsXCr2G5VlQqpoiqleK2VPLrGgsnHtN
eyqcXpQHgAzl2SdWUvSi0AVCrDXGs5eaylB0UgcMtQCQqhDzXpwpQColzq8KusFa
C640MxXmqyiw3l7rbZkThTK74xxT4KvDSG6VXrg2Rr9R+Cygaw3vwJbcxkhB9DFl
vAgAACo85gzzuBgQgnMCA+BQhQE5LUNQd483QS1GgEyCk4THJRCm0ymyc47KxSdT
MRQAC+GwShlAqBIegVQAD6zAEg9EkAALUkKMM0TQABSC7UwLqGAAGVGFaBY4h0DL
AQKscg6wwxbDQOFKW9r5z7GTcqiAMZUDhSivhT4vwJKyQOguMMEJiBQjQHEtsttb
gdqfZibEuI0AhV5i+A6IVtJorKDSL0uEBCunZAqHk/JhRCiQLuCUUoyzyi5Lh5U5
AOBqg1NkKAVpdT6g9F6KQ5pi0ultAge0gHHQhg426ZjR7rRcgqKWYQAYgxOjBBGC
U0YnRxjBImK8qZ0xZhzOQfM940DVgvGGUjFYzw1nLXWLTqBf6Pnsn+8tg5uzcBRB
2JgQ4OCjg4OONAP6XwryBIuFca4QKloQLufcxAjyZDo4Z3T5arw3jvA+J8nnNJ2Q
wxWmCf5tMATDNyYCpmAtgigjBXIryllCvooZRZiccI03Mls1yvqmEYdMgSRegliT
MoZUw5wBwCIrTapzN1SFnrvEUrZKKFsBHlbHuDbGi88YXHujTeIB1iQKX5Y40pJj
wb8RSORIWSKWaJEUppAEs45uDNSfszbAkhIiUBPwjVvkWrkRIntVb8cJs+XCskd8
1lFJ7ebvxfmucjUjUboIx603cYvlOw/CpIbny2UeBJEe73aJOSa5D/G83+rAOO59
Y4ij1tpLAJ97bP3haxIB4RGyMi9gOo67zSGAsyd/bmA8NR9lbJh3hijxy8GirAlf
AwqFEtbJkMhqRHh+LCcXeJ3zzSAu35Y6RvxTxL5EQgk+TwDrg327vQxrG0ynUCQy
K+G2KcXTtdkKG6nD6DKe6TzjHXFedWwfgx129d8+vsUMVJB++EePrJC61672XBV+
cEUV8LqubOkRzXigpJSLR6d82RHZZnnrShZS2+RQEGVclNmT4VeXEeg+K3roNGr0
4Xc87ynLtsJe01l78jCMO8IfV9iTyHp6Vvdee7tzbMaAlGz/CnOS7n53ivgzrwr0
v9EvhuMD0xX5HeOsRWu6JO7D8yRNY5nSllezJ/E4h/cKHHK58rTShcIEoHycdeP7
Ns/NtLptTjuP4xRPPvaRu6vJHD99j8VnFRB/UfRSXfxl2cEmmmk+iEjH29xyn8iC
kdk6imQ63CVJBNweFhQNzADgwKmBGHmQ3Gwnwq0ehAzEk+iVT/xuDwMQ1ek6SILA
MP2cDILA0oMVlwIQwIPoOR1OgzUgkLWeVUwBXbn305RSApAGQJTIRqQNwOAVjviV
xlV/gDQ1R0S911RXimQNUly0L5T/gTQNRkXQMUJVSMK0QNUeF2V1R0jEn6xVQeH2
DWwNRik0Kj01RcKl2cLxV0PRQ8J8JlXSm8LcI0j8OCJRiCKkMCNcMiNCJiIiN1Vi
ISIML5X/lDS9TtS2lkJEWtRpRlX1hKRJT4OMyzRzXXALSDGLU3G3EC0yyrRrX0Dr
VWAbQKyK3BhYIoIgzng4PwKQ24JSQHVKGHSKFHUgHHXQB3Q4CqB2AXQADEoBLgOh
MBkwhgEAABxZMKdPNDgGYvoPoA9eAITE9M9SgK0K9V9UubBIwlsI1JwsMF9JyQfL
KW3WcCJdAk4MEADIDXgVaISKKGRWKB1DELEHEejNANfJEaKAEtIiANDI9ZLG0N0H
DJUdAAUAjEUIjSUJTOUJEyoFUajdUTUejHMPUQ0Y0ITM0bAC0QjWsLDLjB0KTGkz
jQTSoH0UTPTcTSQAzBk8tGTKMWAeTZLJTFMNMAoO5XMTTf8IzMofTSTdLKUgQU9U
zGRWRb4bzAcRzWzWMBzLsEcMcI9E/LVPYP4HzVcYIWLKo8CGo8tPcWUELY8cLSUy
LMoaLXNUzJBO1PFa1d4zLVLR0wCbLEtaovLRtQrZtInBrNCerfCKAmKaJDPSMkPc
EwkGNb3LrGMvrN/MpVCY2D6bJPYb9fwquarOSViX7TMjbBibtIEPffvAOL7ecB4Y
EJ1csonZGd4E7R/OsyKSE2nBZYgybB7arZ7Y4G4lnTPK7CE/43s65fsnyH4K3P4m
Kac73Lbd8KcEOZsg/EgzPH4ycpczIlc+s9cpsrnIhIosoB5Cox5VonFAEZicnSlI
STHMIqErIqJAnA1beBuN8r831A1U4IAwsnFTaQE75c8gQEoytMogQy0C0stMoStJ
keoxo4gZoptDMeCec34nsg8sGVchsjc08/tIdEdMMMYiAZQNkHgUYHdKYGASQTQV
YScYcIYPoVkGdPNfYxYCQI4tYU47YFEY2f+d8uw59bYP4VpfmGaCmc3KzMoT43jX
gJ+Scd+QgyDYEmDVAMSASMDa4CyWwz1GEjgWkOE/jbDcjZEiAVE/DK0cUTE0jHEy
jVUAkujBjEk5k00NjS0MyuknjbksoBE9kDy70ETe8MTPwTk2U1AUMHkyMOTWMQUm
UYU1TMUjTBAAsOUp0yAGUysP02sRUp0NWLKFIScbUpzPEfsazDU3U1zfU6pbKMaB
TMi3zM0/zIMsMG0w8e02CTKy8a8V0uLD0vhb4SqhC303qzLICdkHLdq8tfLHqjCm
XCMsrWc2iNMnrWMj8taphWmWrTGNa6MjajMrcgc3aqvfav6dHMuWSc6mcxg7chiK
6h/Ew0oSAnrWaWA6XQ/fC9Pb3HpeKPpAss7e6gcpMmWZIv6aRFU+ROMk6ucic5Mi
GiWPBS5OnEPBnKSmRG1G2NxQibedlRhdGsPYvQXRvHmXmb+CyEQ9rImxndhXI8eJ
+IqT8KKQxJhMG0iJGuYV8Z+ayGcRsEa0HGvPiBG8G/VaqLbTm74J7BNdm0Wzm8Wz
BCKXSmyUiAnJhafBvF6pWSW04aWk+HVJhZWdqWBNWe7bS16PWrKA2vskGnyWxZEe
xUc+Mi2qW621Iu6rMiWXuW4fHc4dVGHbGZsW2f4NWmJHaxBR8daGQ73HmiyPm6OQ
Wz2isz6cyVWbGlKbWQqK/QEavVagOZS94ckNSv/VhPaemMsuGm2Qu1Svo9gsuliB
PN7Xg8re5GCl5MMt5RsTosQ44W3C6vI4SMm5FJiQJTlAxOwnFTqRIg1HbSujQxDZ
2SIz6KxSI2ZXCz8zuDOgI2GEA3VMleaE1McrrecIGqQpbPaB8l1D6S+4+kRBQsIg
6Z8De6+8iaHMQruVQ8C60SC3Ncop5WC0CWahCuogwBomQJokMm81EWVWu1esGQfW
OCu2/G5QY0i8tcipoOAVkZwKAIYA0ZcA0NoAAQX0GcB6AACUEghBYg2g1190wRD0
lgVg+KwQziRoyEYZ3YvEHoxLr0vMCpn7usSJwNUQPj6SwSa7i666gToNQSzNJ5sl
1z8iGajKTL6QfLHKUT8N0SOriMsSyNFRcSqMaNCS3KmMyTKgKSqSrRArfKviYqAr
aTgrhNfRwqJNcq+MwxeT4roqmry0hSVNRT1M8x0rTMdNwrywoqIn8qQJtsUh4Q0Q
yrNTeBksbMaq3NoryQGqVp/Gx0WqEBzSgHLSgtbTQsTxQzUAYmot+qin3Tnw8Uxo
xJvxxqqmMty0stprAySnIIQyhClrSt6swp1rSRNrw6u8RnetYbFYVq7bUd79T9ta
zr5UvqHqSdvtdt4z8LjyPUq7ecU8xosaGbSgo5Gwfhb6WzwCdd7U0jTIWoJIh7cZ
cLLcXobnsDkYyRSJHwx7LdL7yR7yxzfIpHzg66Os3rRnjr2CAD35Yp/o1swWuUIX
pmeZWk7z9gLgiLfn9oWlX84DJo5FbYRG2COtJmxn4y4oCQt69oPlG8mFwX3kh7ta
KWCW2puUe6dr+J4pHhZxNm8XzJmJWXRGGCvbo8CR8ErCUWRErh0XWbxnhbM94g1z
2zny/9UXpWLJZXLnvreY4wxJSQUz2C1WQQNXMXEydXQN9WuaL4jWZXTXUHiC26ry
O7FqZVJ6fk2xI8pDbYl4r7oUBb1DHy25P6pCURrhg3x69XRDHyrgA3kUZxYZbj0U
4xkCXzhoo2+U57fWAj17QLaVvyTUVonYgLhUzULCDXN6civ7W7WBs0oL8127un4L
IBELq0wGUK0L5GTJU7dWfgI1Fbx4bWTW2b7WhjSgRjyhTMIA81MBkRnAAAJYccYP
oNgKYKofAbAHoKoA8XAYgKdHYLiw4lh89fi69AWKaTKMN7el4bYdiY3XWREW4H/O
SyABSuzA5tPXl3hqDEEvEAGOub1XOge6kYy9DTRiyvDNE6k60/Rhy8Dpy/E2jLUY
kixz0ckryqDpxzjbjBxnylx1ksK9kiKrkrx2K2TfkhKxTJKoJ5tVK0JjKtp+UiAH
KiLRkAqkMQGFpCkNJ6qnsaKxxyAdJ5zPUuzal6cE/E0vzGanp604Lcph0iampmLO
JjuBpleFpZ9lLX8PKjpqatq6Ti8vpm8nyZaqM7rJFragyQ68zuV/OiWYs4EW6w87
bHZzc1Z0Go88kGcF+zBVqE2zqM20AkV8cr/DfAOxWB2uKU1eFxMjzmnbzqRNKDXa
NWWmL5zzz5czhFG+ZZO1sic7/TfMvc1vsSZG4Q+tznyDG1PQWeeufP974AD/N8r2
iSrpnT92rr7ertTxrjCb+y8gB51kyA4ciNZTtYC/t0FCzvI+lIDqeoNq9qe0srN0
FX2xwu+uKQqFZylFeBW25lau5Gt0o+tp1xtq0kBpCttiB1CqBzu8ed96rpb3yOrt
udvGcgYsAUd4oMiydnoZME4DoDoI4egBdPoWIAAaU0FiDXWIZaHqH0GXGTAoYPeY
cVOPbYYEuoPhEEnakrcrj4dfUnGwX8WUkpjEf/QkdQHpdJEZajykA0vkfnK89j00
nz1x9hI0cZMRLg+0cg9spg+Cy0egBMZcqQ7DEY1JNQ6sfQ9sdpOw8Uv4+tGccsYk
Hw79A5OI+iukzivI78cSqTGo4zFo4lIU+lOC3V+qYCrY+ivhGyjuHs3VJ1N48bGS
Yyf1MUh0gMXnAk9aqk6bYgE6rtLCwWvN8gBdLqZU4S2fvl5/DSwY6yorV0599O8g
HmsqZMmM8GYmcOvdiSWOceqYgc5Wbvyup+CBGiMVnBegLmmHfldD0Zw/Zq8jgKgB
tPskKa8cmNoOlNvm4Ylbk0gknBTL4RemgZc10y9oTASighRQK70p5pe1uJESHnHf
Avtxbb7yln+p6315lhiygpFRWFYrO7xeg937vedWkZ4afnHiR4Lmfb57xP9jaThL
l+JfCv6aS4l6/bv6ddYz3dbHsfNUJkJc2ShYSGHV/5kJNucbP8simnCAcsiK8Gem
BWra/1oKx3OCknwrSgNa0l3DtjeSP7W49ctZPiOf1miX8WexFNBsMS+6VBWQMxA0
FOmcCjBhwO6EgDwFwAaBRgygVkMQA4BsBlwSPHikexOJo9T2XCWGDCnG549Jo+sD
JJoh9YyJxGflMEvWV+pfs6eeIXclFHvSQDy0bPNAPCVpIC9rKBGXnvZX55c9Bezl
RDkSVF7uUle6Aaxuxg552hye8vOxnh1Cqq8iOUVeXj42142RdeymEUjRxCZG9Y+k
THgdE3aYW84mLcYEKAm44O95MzvITrVWhBXBFILYUkF70KZ6dfe/vOTkH2iEh9am
yneLHimXgado+2nBCgnxO7BkCs3/B6iZxDwRkFmKrNfmGnTLIsbYsXLzmjRr7LNo
B2ZE2HmS9hL1OhjWHGAaXfobIpoWkUFqlw2YN99szWEbP8FpYh52hswm2PxGBAwg
2wybCYTtXs57Uz+C5GyBtzzq387O+fM4SuU0GXDRUQtFug6z65FpryN3UFGXy24v
UDgw9PCPjjdaApKEd9VEB9HTaAidIIdP4YCkRyjc9umaWtn/QbboDvwWA8BvWmu4
utGsdwxznhUeHaC86b3D7uO3IpHh6gFAYhqyAACKkgGAMQGHAUA/AHATQGcAobLg
F2Ag49EIIvTloziuwPyEhmt5gCQUePGEPhHq7FdEsBiPgGTyUFmZRcxwCXDGyIGQ
Bv2mlYNPhBhBfBbg/ceWBpz0GoADBnGIwToww6QA7KJGcwUY3g6mNXKyHcXixkcH
eVnB9jOXrh3sGuM2S5af0JFU8Ya9vGWvF9AEMo569ghBvUIWExqHZVTeUQxjiZm4
AIZvYZEFIdCDlFVUHeLmTJpLAjzhxvSGDApkU1ywdVZO3VSpsHwgCh8yhQ1K1MSB
aZadjezbOoWiN6aNCjOa1DPjX06xWcpmk3cHKcPxFy00ucXYAY9G2bpdnmtNfmPX
we7E5wS+XMLkwgi5O1S2qOHMqbFP57MEuK8EEMqIsgFFUcK4qLs3RuFVwhKikRqq
5yxj8Q2wiIDITCOBpBdfIBwZVjsMcgrwBIU4a4E8wGG2c+I2/aWnv1kr3YnIT8fS
lZE9aTDfISQT8FHWRBlcwoqdHfhYhAmBcKys4KVsawxbEpUcWFCEkSJeFnjSgmkD
9AgPiJhQUQZCd+DHAubbjuaOZecDCGVL9ip8DdZBqeOfHkgpodkTSEfDVFu5GJSI
GEAohs7ESwArMSxMNy8zd8ICmyY/mIjK7NIkgtkfYFFCTre4ICk0cRP7V/zQTHg5
kFFAfVwmOR30s4d6OxAZhasHq7wAkNTmkbwMwosIcFA4TNRMJLo0SGcP8DigAi8o
vuKHBq1fAbD7I7koSlQhbAUTUcF+W2KPhAq21nxNkfiCRHuAUhY4c45wLZIsS9sD
WO1ciNGSiQAVW+YUNnC3w2H6UuYneQYanU+gyVHJqOREOZA6Q/AUEVySqf+NMjzk
qaVwfiTNyPxxhDgnwOyJ8Hf6DDUonSGcDn1AnU4CoH4B4BQgTbPg2p4kgRvFGJ5o
TFYTkagntDCThsdq3wQFA02KS59NpV8W6NlFfDz0dqoyUSFanDY3jbE23HSFkmuE
JTSIOlHyeVMnpH4KW9qS2tkzhhLSEphUWVI+H+DAoNpK8eIEiFUmXivEEdeIHZDu
ilcTJeUNTl+PYgdJJUfyQGSnVFy2Qgps+eZvtPhCHSfmiZauNcEdqiiIZ76dVLDG
uDwiC8iZBfuc3fDlxOyqM58INHnCvRMChtM1kNz7CTSH4TkQSGKyNQMylxiZHmi5
BxbpTEkhkvGk7m/I4zWy6UASFFDFq3Mj8l0D8JojahHCfY7NWGANIeDzg9U2spyD
OAKgURI+ciRaezVGRxh8EhUOMHpKQl4zIZwUp8SnQlHXRXoO02SVnlqhlQRuN/Z8
T8C6ypxdmG0/1IkGK6Ahd6ek9mgZM77xCPEnhPCXlOZSkQ2wEkTXCnPqRzgYarE+
cTZAKiCQ/g04ALtBPOZgphIjud8XlGJDxAxIS/F6SnUUj1yDob9DmeOMbCJBnwqU
uibXK7lzRPoa4xyC3MHlIhzmq/dmkakSDvBPENcpCQPKUjDz55iZHSM/Gem9TwoF
Mv4HFGpkLzW48OD8BIMtnEgdWkyckJ1DgGqyZcqIQ+ARBRBNgipeEuyIcHsQd4Oh
C84BOlAkgQiBJZc42HFEOGogUQI09qUCySAhxPoaqd2Z/ONhV4LYLcdWomWLgwFI
2ICg+ef3jah1A8aadmrJDWjKj1Eq8vCcjBazU0iJEc29kiA8SAwsCmk71gVB+AIT
q+MC1loPP+hBRDxU822K1DUl1QFpU4EhU/F/Rn1Y5QivaCIp3gAySFKk/5gosTbN
zU6hEeOqcDawr50a/UuyCSG5YTCkJ+IDxLqJ1FYFH5h+B9ASCyEHjjpWosxSCAsU
GiNa/U5Ut8wEXqLWkOo5xfqJnCAzP+TrJobeQlYApp4lCvCPODdkIjzIauK1v8Ln
kPd/h3sqQrzN+DjdgK1c+bv8MRBAjwBxIQCmETxwGUFUpuB+l61vhNyJuZiD+YCN
qXGKAU8ICegUs37j1ABfcKfmEU6U/DkUQIYwt0u8mj9Z6fiYtvMJ6lvkoJBqJLCm
2XoyM+UmPMLgakWVIL0UglIiv+V6XopIp2ygZZEV/IzdCUy+H8jbTfJN1wBailVJ
4uOYaRrlgAzRf/z5SWE+0FhMlgWzeW6pDsPQ9FF8tYm3KPlryyFganeAzwblsqLG
YcvBXPC3yrky5e4R2UBEHEd9E8RcqRXgDzULfRpe6kxVjLF6xbb+gdzrb/0PhR6E
sR0wxHttsRJkGxR4vsUapwopi3xXqLlgBLbkJFKgRg0nZ5ooAgoegMQCGBCAhAU6
NoMOH0AnAhAoPKHhQA4D7tGGBxZHscT5GbBtgC/fpOxCp7DK7iKq5WhSHMk3QfJ5
IRQV8UcIfoXoHZEwrTzkYNh4ggkHOP61GiGiQOplN0WaJ54YlrRtpAXniXtEi9y0
YvFxi6ItEK8sOrgz0RL2V6eD3G/o9ML4ODECkwxQQlKlGPo6VjmOMY60JbyOTnB3
EmkNMWgEKgpDsxR6ISCtEbCaEchxY4BpaLLGB8KxxQqsaULdLh9XwGuV8A2Jj6Vj
OmeQjASnxCXp9tqrQqrHiML6tDexAK6SE9UWY+yic2wvucF2wpTkpxNfDmuW2qg9
4JZJcsSc+I77p1c+FfD6kpJDztEhW92NAsHT5nxSKy/1d8DircJgA3Yz4AePmVb4
7Ve+yaAfpnL+g6IQW8DHaofCBj/BAQkSiWItg+jAwOU7NV8TcA6E0xOWd8cSOgq3
Up0nuDXYYd7TBRowVlB/VsrBNybNLEJ0kC8TFCfDmrp1MuClhsOIg7dsCKQZIAbG
hkGqyNh+OOrHA6IoY+IUG0jehKJyw4EQ6uRjVvj2EeIXixy+iSRJ6Q4Lep+IJ7NC
OuJuSQ83EltaxExRjjM8xsA2WxH5qTImND1f4IcHSgxzz8z/Jnm/3Vgh5RkqeSGP
4uwL7A4QhEXURdJQY19VEqkgEHZEf7NwbVbEMRHFJy4y5fcbUCiKRARXjwKaZaj6
H2KQ1E5op60d4G8z5aUaSoc/HTQOUyl79spVrHAqtGODepcmKhbjTLhfA2zgoH6o
CjgSfij4+49wCStPxr7ThDgDzGOuwSzpjQeWKmy9UTmtkHDkumSpWIxEUj+z3NNN
GviRHCISEsVA2ZWivVnGcSKyAQr8QELCWYJdoaC4SLqOOFbC2wdk9KH3l6k1QbIL
a3PPER2rlzJk+MuAd7muCyo0Eb4C7dBIFraiuoRqPevRBBAEgKE6UW4BpPu1CK+E
7c3qSbPESgy2o60jli1BQSCQEUcK5hAvF4nHyiFhNKqUkH9yfhEN8ZESPy1nliRd
J2GmXKVxLgTJP1LiH5IETRCc4uFy0nmtDNVqlLFYZkgqbZFBHWTTqpwdGecGS0Px
JJG0KzSyr82H53NB0hDI8uzKPZ7UJM7upbD2msJS+RO0JADG/T1R+FOUK6ZNFkin
LvcEo6EZbQsiPjg8gw5OFOGI0BRAWLmm4MPDjB7wrFD1V6NIN+B90PNcwZOPfBKo
2EKpO1ReU8SEbazfgAkSmRJEW4OythU4QeTDHBmuwWoeuXOogrRAR14MFEffvdgu
DJA9gasUrRSAjr1JHgUMapaUAkhi53InsM+pBpVyo7JwbBV2NrHOCGoVoFzK3aDQ
siGT/ER0+7DvMHhBTLIAqOvXOWoKjZl5su0oEVunAdxeiq9dmq9ESBeTvt9EUhEN
BSB/A8V6e5mQAW5YuLtZbOR2lcGOwqt2a8IX3aSDA2Ndp9tiEKGbhEjQqu9A+CKD
pAO1ArI4myYkJZJ34soL9AcVKH8V51/VOWVwRGTfVfwv6kIxwRbPsIBbxkYohwK/
jYTuV67uFNUCLeOtQikL9gTTXcflvZrqzr5hFFGXMFIVD1NFMUPZcbO34eY4WgLU
bYESuAxQV6kuxMp+CmjYsQDf1SRTetR1Q7A9y6vTb8AiRUHRuYAFsMnpWix5VRui
9g49ltzXR4uVcfqdARy3xLXFiZLmfzQuDNhu+82oBSRBBCOa2D3Cn3ecA9aEyv1H
wZ+WbghVCQU5Ee8iB+BwlfS34FcnGH2HhBc5/9xA2xGzL3k0aPoB2TaLbCMVGzt5
t40tYfvUiPZ2d1kL4BUtPkFRrq9w7RG4iSz6xL2yu7eQApIjA5IRIuSaBksIhEtO
iJC18ZCjyW06Nk6mxpKJwLmYK9hhUKvtYYBAzS0EDwFRkzOXWkLbYDmtrupB+J+L
r9RS6A+JJ4UYo9gMS+7JcNxTOx39lipRfy2/1M7vcwxk+IpH+bG7xFeig4LSuF0g
ahu1LBqL8QzhOGL4HhupSBoRlzTSuE8qhLsZwLZRuE9qESvGTLXJBjjK3TvW4t5j
3jCJNGgebatmT2rhDMCk1SCDNUwbpIHxyLovCG0/HxJfx2aNBpz0MRTt09TzO1tM
N6LWkUJrjdoifhzHZIcsrQxCa23/G3xc6yshifVVYmQDgS1uu8MEI3l3W14kejbt
WU70t6YK+IS9r0Lq796VkgtuYSeU+GXycqNDfYRMMmpQjLCpIpkq6y2ovdWRQzS6
lpNBok0DJuNEW0frrbcVPLFYWGkvnSnZl5qUmrCJcj4jLUJPWJUnJNMggMlu3SUT
CH71dYM5uK+KM3vPrZ651XWbE+itHGWmHY3J91OzP1MBGBTOKT8AjsfrvB7Z6K0M
2HpdQRm4VXWaM3fREgkHwziZqNK7NZPupUzipwM56cskamvUOZ5JWTHOVRp8D3ps
NCWZdNkx7NFZ+PJyfNT5m76qSzlK4lv14Rmz3ymVIBtBWADOzwG0FD2ZyWUsgNA5
qKKcYbNNhVu4AhJZS0tNTnZz4p6c1kTnMzn5zo2HupSlXPsacUG52Jdue7MS6dzY
2Pc1PoBSNnKUI5SeUKgJUoCju/XeobUXO7YCsRLRL4RcZRMAmc9sxkkwsb/1srKB
Y7agRIE0D1AAAjmwGAu4A+V9QVYqsRmLENiGHACgE0D6Cg9lwC6bkRAF4qo9L0eI
YSBAPzKDxr+uPF9NxN3ESz1uVfDMfJVcGDidBZQDUfIzPUYELTsaNRqBxdUWDjBu
jaDmYM9UWDvVwvGwX6rsHhqHBUvHyrL38qYYmSXolXlGvV6xqyOIYvJpAECYRi1M
ovNKimvrVpqmxGakCCiFNzHYCxZQQTg2ELXCc0AT9HmQCGSxLhTSuQxPqUy6q1qW
OYYasU2vKGIzZk7a9NV2scttiFqafTsQOpr7LUx1kLUzt0NLlDC6Fc2miwGcerTD
nqKWzWPFd6kxW+d1utK9gVmYJTsrrCvLqF2TlbD8rG0ndV3zM2hWuxrw+6o6364h
KUlNpw4HJpjN0HXsFy8bWMsIgnrIiVSXM5qnlyr8tuRqa0/iuQHIjUBt51seSofO
YjIGz5nEXn0ry0W3cvnTvv5w4S/n3u6DMdJO3qCaATgmgJoG0GYAUBmAjwCgKMAo
YDBCAQgIwAADUEgHQdC5heEHYXJG4O/YW3MNNaqQw2sdiNt0Rk4TKLL7cnqSxbN0
X1B7maibRKGthgjRJoznraO542V3VBjL1UL2sHmMnRaHSkk4OMwy9Q1bojwW40I4
eMY1mvRS/GoTBUc1LhvaMbpZ0vhDYmpma2tPWyjKWGAPHJMeZbSGWXe2YnY4BWu7
VOWA+FTVy4pwGohhm1iMvfj5d0t+W7zc1Qzi+f7UGRIrR1dswOOHUdzwylE4mvXj
1MpXgrpk0YexGfUbaqrR4oONSnDkVkIyzgU5rDac0wKHbTtpJZxKCX1XqTg0CZQq
hH6+SJuKRzVdCnjaRnQ7iAt5NpvTTjXDuxKyosU194ttkKOAqld8moiO2YbHt8fC
SJ2ujFJ2CFnYGuiOAGgzgbAPNHml2D1AjgcAE4AuimA9AZiAwF67yJPZKVe4XnTg
+QLBD3EF+1SQtk2QOHvwjVilcG1rctU/t81GsyLmirFEI2wOyNqyuaNMEersSfFz
G2Y0dEBqxLboiSyR0w4CYZLkasm9Gskvhg41FHGm+GKTUaW6O4TbS3GIDGVjExll
4KMJCHh5rUAX4e3k5iLUPhd6oAu3oWPsuVr9O1aspuWIlvOlG1g1VTqAjVIdNWmn
alsYnZ7Uq3FratnCCS3Cta3Ls3ZRdX+PEk9izOkW+Ms4A3FjDpF2Dkh/Ab8l3sn1
4w2rTAuIdRWvpGU6e6uOi7dix7pc9h3YhRVnkKTX/H29tkWNlLUalpkviFpxTKGw
VjYW6IHYTIOtCVKItAag/RGzXKVC1kyCw81usS+HjtARxQO2scrdrlQTQKyEuAIB
RgJwYgJIDODjBiAlwYgHOzXQ0i4AXAotC3ZR5vX+ROF1XZTRp39i8eEBCPS4Xbi3
A+9AIEe3iBUFtHy09F6EGbYyTSKwQ899i4vc4tBqrR6N9e1YM3u2CUOzonewTZDU
Ki3BivES96II6+i1ePgym3ySUuBDkqwTW+2ENTWP2oHCpECNjpIh+JjLAnbm+5l5
s5j0CEW4rsLf8sycIHLl9Ne5dgeeZ/pyWaoQrZQdkqDO7Y1WybaweDrUcPDqLeAX
2dkOMrYmo/KVaQlHlGyMc1fHE/6vE5yrG1yq8w5+rxO8oFD824w+NsGRqrosL2yS
oavJ6MZAZ/4U/oEn/D+rPyVI2C+kL0nYlVbZR9efjuAN1nzbClane0fwR1mmbMcu
Q+ScW2mHud0x/ncqDbF6gxDJcKyEaAwAoAC6YC5yKmDOBJAPALTnKu4o8ifHSqyA
AKLMglRv9LSsUfcX2lIGJ5HyIc6NVBsKijcxXBRxasSewZ8WzEf4KqZp7pPSnSNi
jCjZMFo3YOi9/i1ja3tejA10vMpzh2JtH3SbtT7wQGIUuNPqbATWmzfb9WaX77jH
Jm8/ct7/N4dRqT+/FBGdHp2yhioB/kxAci3SxMz8W3M5gfS3PLgsJEPLeZs6cAy0
1jZ4Fb1shXXbwzHBxgqtuWdusMiIfTlMz55vSZXiostMKDPRGi+0wqR1subh3dVB
yeOvvd0Bbu4bc6hLFjFFfKx01ru6u2x/hAylw1za64/m2+G3MPjxs99gvW8/Z35Q
N183VReZFr4P9yhD58U5B9pRQond62mI3VrN34iNV4jZem6Ie0akE0dF5XhMueYG
DnTBD5gTAY0h3m5CNHCqu8P4L8lW17sh628IGxWP8+IRGr1uYIFIEkQu6BUQ78jW
pRHcNxyPUkHi/oH0xV7sbvo01RF+9xOa9YDRfVd5d9lRz5yLIpnwbUd+S05+FEJF
XD95X8qcJ1FJDyDmdH2fCVoPI/YEwJ4/ePHPqlyr51NzsVD2VoPlkJRslhLUyR78
hq0+auD+cUV0faqTD1SHwCbvxB11Sp5GRgKEaiV19vwC77giieSwObYUFT5ciB0l
/XYefk97oZYo/nEkCX+zPQi9BJY8QT6E5n8KCpJfwu2T3G76rdHdjm4b4J/L2z+X
POQLDFPteRJXjSsIdYH1qn0zaBNs1NhQvfaIfqWo1WOeYvyo7LiR5a4zayHKXuL6
+4/zORqPtD8cc55HlDM8JlnkzeQOHHLD0pDHsRNZEbNG111EhzbMp5B2j0wPz495
yk4OPjjcaRIcDYjpgUReDF0xmReexhAibncv7wrdPcY/QrWFHwAT+IOYunOMPt6k
WXe8/APKOvc2qstoum7MezIEEtj10oK38653A3u9U5AXhIZ1hpeDlo1IUQ46ppUN
TFFTj3dbCLvBNK79XDvGwD7dY75aXt5rIUfv1JdaCWt7VW8fYP7Me9IMbO82SbbG
0NE05Lxd4f9JBScgj1bKs9uKr6nw/F1/xdXeWu0MOKODMa9/Ne3+HydX/KWHYv4y
61fNyRELehWs31h+zru8ulFv8YJb3PsfgrdDjsPfQjLlVnLfzHBf3Yjfo+7LeLwB
fI67se863GcIOfHEuj9s8vMf8hHwSkR55627feQ2hOsZSL+RT1cdTj5R8HKaUI8N
tCYik1PoXnMaI9lny0s1crS/WFWDqKnQi+RbBK/LUQT4EahshXHmg0yp8+sq7D+h
+/Ukfy1OH8dR3TZTvnv34n4T8Eb3UyMgP+n/RWZ+o02fx1Mn7T9HuC/OnrrJb69S
l/Az5fkv4X9TTV+y/tfiv/X6r/F/8Iqi+M/6jTNhoHdgZ2PzH+j8uoe//fvvyH7N
+D+R/w/ibUqbH+d+h/qaWEYUp69KParKjyaySqVtndW2j5+a+hR0eK+u/O71X99E
Jf/nOVlQK4AgCODAWYAy4SQMmFB5NAqgRwaULgB2BTo10UALYKy8PYcu27UCifQK
2x8922wEnrtQRIF8AZmGnK+whgN3jVo08crrwBPc1tFWYWqqrgfbmUmTsvbauNoh
q6WCCHAU5CWRTrjY2M4lkTZquQVOa4+iJvFa4U2QYlTaX29rtfatOTrnfbpqbrvW
ov2qAD76sseMJ/ZC2P9t2B/27HM9ihme0JM5r+4Ds5YRuulvM7RuQ1N1KdQ8bsg5
Ju6jgFap8abura7OH4qVZVe8dNEZYwV1HL6625GmHjzeEMvoHi+8vjAru4ikjp5n
OYvjdQWB4ksbQ5slsvz7mBhgQT5m2vvvMxaBIeDD7d2ezmz5q+CBk3w3qUPld5HO
pzgPJLYxUDjwzMD3lywaGrzl0J6ON7tbpvqvpnepJkL7h1p467nlu4w49/KO7Te/
OtAG6+RsCnAkyn0BribCgwqUFoeE7s7SRBdQWVpRBYGitiNB92lDSBaXvg/BcIi8
LwhPs+PtbpVkLxGsZVwcCpLCwuQwalocORjnPiKsS/Emgues2tFrAeUUEO6v6kov
J4J6CPgOQReFhv4GbBurHsCzgHtJEFwa3LJoZbMi3unDLeopvEHqG6pnOIL8pcDb
rmm9wZr5vCwji+bguHOmITJWXrAAFNKsSlwHS+MjsHaKOPyPYHAunDFF4hszPlOa
DBkRGpJlBayiaiFSE/pqj/SrVgHoXKMIT+Kp+nfjLq4qkCjZ6WoAmnn6Eh3wqd6M
oV5hNY3mq/sm6oumjui7b+8EAR6XBSQb5A3BbwUJ7GOpIgBboA+AKICg82QHOxVA
ExEcAHgxDDuwJAw4PQDEg/Ap/4KqrDO9ZmYV2gpD5yYrk+iCuTNLpBeSicgGgxOY
JEJrMSC+rjxwByEkBIKe7GqxbOqpARyAcWGAXow8Wa9rq4b2DooU442kvHjauiDo
XvaBiDoSTYUBsYlQFn2fgk04JqLTiEJtODNgm6UBUTE/ZsBmau+AqUk4BcCf2TYH
65OgocAmauIogUyF+8NapIHxhJQkpweWsga8QKB9aoraFhvah2LfOx7s+IRBMzIE
GnOLznc4thjXq8zNei/uJIRcVIatYjuP7planUvMLuLXY1MljB7ecyGF5B6tCGnD
1+R+KUHLwSIbXImeW3lAYQyWXHOHLqpoTCDmhscgsHewK/K54Ry+4ZvpT+44pJ4p
6ZwbXIXhh4UhI3hpwSlz2stVpSafCi1kNxWGBSrR4KoCSKeYpEXge6hgyrVrbbn0
hwWGg/hUaEdhXhMjstgQudIXHaoiygTNYb+c1ldwYuABg+GjKbhOFDPhFsq9zsqx
/mY4SAVQAgCww+gC0D0ApAMQxroygKsRGg9QMdbOA4wOMCCWZQEwyCC3/iILqh5h
jjAn64dmUD3EeemgiB4rWDSwDO4IOTx2wcEh9CcKolJaEbhVSKC5AcdoezwOhrqq
jYuhq9oYzYBerngFlA/qoa4lOqAe6Jn27guQE1OlAeTbhhF9jrxRh+vOpZMB7Tg/
a2kZvMmEgQ7Or8DWQQboM5JCwznwEu8hVNpoKI7YM1QhuUzmKDFh8nKWENq5YQs5
BSm7t/aIOjYnFG1hqESm6qBAzE2EVk1zL2ELiRVrjqH47tmlKAs+wWuQ2ee0hhpI
GK3pwjjhRVNt5POvRn15n68PpwiR0yCEajxeiZGR4mBNMBMFu8oMmGbth3nvJH5+
ABspHz6ftrXJjR+GjYGbeKkTNE9cWvt7Y/BMLozJOmEEak4uo9ZuipqeEEStYyO/
QTaZIRRKihEoumAiyFPmbIQAZzRCkRniLR00SDhERf5p9wn+EgDMR5ow4OSBsRuo
CcBroBDFUAdAzgF0DMA9QPdbeOiqm3a7AFWmfpNMwEcJEqqrOiSA303dPTTGhWlP
1r1eYRtUoT2moseF7QXUTISOq6jPoIL22AVk4r2uTu6H5OnofgHehnlL6FBqdjAG
EVO0llU6yWJ9vJYNOvjKGJX2iaowFGRzriwGdO6auwEC0m7jGzhRmYuVRgk8vIJw
CBVvGiCyw3wAWGZR4gWLaxRlYtIHRUMtiCzZCPpGlGKBXTHWHoOQVo2HqB3Dm2Fx
BXeCc62x1sTQ4Q2j0F2ElWTEAV7OxU2GYHQhxQbpoVBqePFobSjFrqrVBg3stK3i
hqA+Iu+U2BHGowAPr7GnUzQT96lBokNJ47G7umKwSOh3p1HnuuXk/I8h+BLEFry/
Ho2BsyPsUibLqx4Vc7l+ZciXHbYbgeCYRyBEXeEeykRhPBm6Wpucb/ueNA/pO+WM
OBLSw0rgJokKJsCPLTh8wj+oW46NPEC2wDCEJBNaE0IiyV8n1BrRhaqtLQrMe2zN
XF2svxpNCUaz8qKY/Oz4kJCwhLNEuGuxNfEbgPswUOsGbmfYcfEtaN8YeYPwuVn8
5Um60QHZz+G8WUommRyPH7uoYKgaZHR61G0pGm0Hl0JTKLqHBFdY0CR+iYhMCfAn
Rk60paiP0bplGhoJ5qE8FgibrGdGqOU1hrFXR6EVo63R48I/E28z8bSEjsedhOyV
AgkKQB5oPAMBb4AJwPgA0ilwPdZwAU6KyDMATQOMAcAc7EMBQxqoX45/W4Bh9B3y
gJuWi92rOsFqPA0Mg6bBQmMdJpZCL2B0GyMk9lpTI67UJDCMKGMDLGoYTqhpFmRW
kVq46RNMfpEehvqkZHCWxTszHGuboGzFhqLGFzGWutkfvaQAEYXa5lAqlo67CxzA
YzZixuluwH5y04PziZhvDIrEWW0VP0rSez4OrGXRBQpA6RuCUTIGqc5wMJDiumnB
2o1hazlWr3I5sWoE7OrPk7HieuVvbY2xegfnyc+Z4eUklJvDvuowEMnsw4thWMBd
7tB8mkh63ONXjyGCedUTc6k4XIbLgheucv8CzSdRtQ6sOWXuxK4hpzhl7NuX7gzg
dwEsqNZfOSEBVFwhZVj4gesssHFCMaO1A1F7iTUaBLoG2iYIaC0RwBHR0axMRe7t
8WiUthnJeiWPqL8LnEuFPQT8FrAiQhEe2FVxn7ppIE8HybIbxSb8Z+GDcdGs95pK
/8TvT5aupp7GxmCHjuYjRTZiubIpA/umbtRlqLDArJYftC6ialCQi70hSLqSp5Jy
dhdw3RnbJhQ/J2npPRvJX2DdRfJ/IdQnkUhAAugUM9AMoA4gkgKyDjAC6KQAJAMA
DSIwA7CRQyjAmgEIlYWIidFRwKWRu/b9Rv1q+iJMtUHqLgYpvgkCYxn4g8w/ivmm
KJwBmtEbas8hieTEZOlMc6HcWukRjZ0xViTqA2JhAfjZmRjiWa6cxx9q4mn27ief
a0BDkQLHRhkYrGFaWrroElxREse8gHa5ECDZc2AUagAackSXzbRUEshFIcK8SXkm
JJszlIFRuesZ5bw4Yois7pRuSWA75Jmzhg7q+yEBrbZ8jphtIjMfCCcEOBeVtW7B
atbtrbnIs4T1E18Uvo55bxvyfdp7egXnfGf4vxCNigezUQlL3M34jcBapG0rqlQS
r6l+KPMo6frZF4hthOmfB74d8Ffh0CEx7+2AIZ8p0q59NCkoJKPk0po64qLgkr+C
dpdEkpm/phEkJdzFOmapgcRnbjp93lQlEuNCRIAHgQgAkBtAJwKDy3WLQGwC4Aqx
FUCg8OwMmBzsbQBBbN2yodxHQxvEYKK9w7yKHSFGcqVzIS4pwFPCj47Okon3RE0e
qJQ2ZmIOzQRvDCgFSW6rpZRUxmAbxa0xuAfTHWJBAT6FEBu9iQFmRwYdZGhhbiYG
FlAniXQHeJDrkLE6gIsQEnuR8YnHzsBako2SJMn9vIFBRqQpkzxIHcG5qhpdlpJx
iBRYeG7ax9arrH1MnmNHKhpWaSbGhuytvmkWxlWLlGFJyQdZxBBRmZdQ62CcZg4i
0I4v0I5BTBC0nNc07lz7dis6trTfuu2qOEfYcyaoL0QnmUr4du61tb7OZTbg25d4
hPuj6i+i8DW4ceXeOsmQRLsTbEdYDQYu54OvxFrLeZ9UiEGYemIcTgNJVRuZmPQk
PjtEweswZO4dYJWQv5H4X3k74pZSPmSFEyBUBtw0eH3t2LHq5evMw5xLUnnFMEkW
aVnNyNwbvyIpQWVT6xyzca+HPOXSYCz4RWwahJGexSZMmsKE2fzI18AWV37zinGr
T51a5WWonzMO4U2kwKv3pHFemhJuu7NZ1HghE1JM6tpItZV2TNnHZT3ptH3aA8XQ
jBmIsqllcOR2d1ndRqmvllLxB6hToJSEwUahTBG3tNlFZpkCDkI4Syr0J2ZGXBnH
2e72XTr+xv4g5lpB16SOm3pjfNHDZ2kQWD7zKwQb0jre0EutkgK5cpCjg+LzAQJe
ZMxsD46KWWY5AJZjWV+oTxVOV3h+BLOSLhs5iwu1l3sBwVznYGWdmVGQ5xOP1nVZ
zOTt4f44uXlmS5A6cCkDc4So8BFxTSlumco/uEcwdK9KY+TAEmZoSg/WvhD0GYJg
AvTJYp5qBgkoJ4Pign2Z0ph6bSmU4S6j+mQfhkHn0LueahjBgZlKboJqIWGhnxzf
uSBgmtuT7leoHuXCnopu0aVDQuoyUtodmNkBSHrmeOU2bbGwIhbknm1SDckdm6ea
porG4EU2awioMBUrq5sOUUlL+iLhdHEpaLmSl4CMuUBRy5PBEf7vRpEegDKAO6KD
z4AqxLEBToRgMQySqQEGwBNAO6AQyrEzAGuhipvjsqqSMATvI7++hlC+h7QeBKGz
M8YdoRBKJpoQwZqCVqmCTFe4CcBxkxxohTEkZJqWKB885GRYkWpHEVak0ZTMXRn+
hDGURlkBjqRa42RLqWxkeJ9kfzH0BgsTGEuRcYR04CZSYQmKZqakmbpZQn9s0ySZ
Sse+AvEvaQmm5pSaSWE6xqaRpkXSLSClFjUxsTklKBl0fWFbOlsSXniSbQsXy1pc
WY7FLZyvjWnEhqyQOJJWU6i/GF4QprHLy0LPsw584sqY5AFZK8V3isFR0cTjDeoi
uT5cFKePPjIJ8zATkLZU2QMl3OTkBcEQadsec5NZfcH7TPZGtmZmaSfQbd5/ADXm
7HLWCVtd7cISCJoUPpy6hDlqF0ARb5D2YcdupNef2edlUecgm1kwKZ6ryb4eQ6dO
mBxy4rtlpZ30rYr7AEeKJKi5/nrvgM5ahbHHbSyhVsIHJk4QhmOQpUSV5bCrhVt6
8OHWeyxbCt2ZpDDSx0g848MV0gdiFe6WaJDUakQa9mQS+hrcI6F1mdJAHZf2fek1
BR2VUXayNeVQWmQ9RdgSc5UuXjpEansZWQ854hctJpFDVILldoMyPt6iED3neJhF
xeRLCxxjCm7m5SwxSD40a0xVHGyFI2sLljx0kBcEOmuKUwh15vQZsXKy7gX7HiJu
ZNI4MQ+xQcqM5dbnTSa5vQRd6PGX2ctLyFROc3xhBuwR9j2x0+g1ntFTBO5l3qjF
mjmXF1BTNi0FisGTkVFlmeUU5WyWXT5+Z3gVZmAlyEEfEK5ALpLBm5evrg6EoTJi
bkGW0cVHLRFYaOAEm5lUaW5Zm0ptmalplqAdF1mFJbtE0l7qIXmUlLcSeZtgsKYO
ZdmUhP2ZgqWgvHknmFCQnkNGHJTaE7mGwR2Z7prZsnmTmwfpnmlFfZh3qtWCxQqi
1m65vkFNm2Oa2b6+TZpCl9m/hYqValW5s9kJ5ReTHlFpsdudFqOp6ZXlb+5KQAYm
FYMGjjwlh/sRGN5xLhIALoa6JgBGAqxPdb0APQPqCEAAwJoAMCowDAADAw4GuipA
EGey5QZaoW+grGYCKRDao0JPcSNg9zDDDEQKKLJRqpDUu7zEeG+Ronr6ZPtOD2+A
Smk4Gp++UamH5bqmYk6uZ+ZRmWpGFtam0ZtqffnmRrqZZGP5IYUxx1O1rrzH+CnN
j4k8ZGFnxlxRrAYAUgQ2dIAa5wPAWgX+Rv9lEm78fCICD/A6scyBCAGAvAWqZjHO
pky2o0CvDVhjHBlHYFBSTlFWxrtlny8+U0j4FrZhQSOGaSyRVPGDC6QYVAyIdaZt
hDZNlgIXLqwLFbmo4iDOXSlqGpUbQwJK8EaSIpE0HITL8v+tdky4V2pOAvFA2Y9A
DQFvhJAtGKxY4VJSKCIiAySx0lzqqeIKjXLNIKCt6yh60OqE5rQAuPybglLiD8T5
uHvppLMsiruIk6mzSDRXkwE5moWve5sn1bQVBPipJm6urCIU+cw4bTn6SLxqvBm4
QXuMFXJucejl7BrCHNinhgLN3GpwKBnvTLiDOOaaaZzwX15aoLJoh4wKt7DFCeIk
duhp2FQUoeEna+LBJGgJAQXUmpBLOq0jkwBEIJXNyrXoFClpV0lthgBHMOJ67Aij
MlJyKvRa9K266cGJ68OrtFbQy0q2XUXGw9KIcwvcfyfBi8yi2odnLSYBsRDiEwRW
VZTaL0PMmRBDUo7AsG7FWVbAVPHk0UMQ2ZSUq8OJ8ZVofgZuRnrsKB6WOkVaVHnV
WeeK0V8Ha+H8apFZExVbPQvkdJdmzcG9ysb7ooWUAbkBEU8JCHzCuEZERps0Lt9b
AJ57DulQJJuZPEmma6Uik8lrZqOaTmu1YqX7VTZh55NWt8QiljJnpg7mAiQISXn7
cZeeaUV510VaU3kKQBVpVV1KTVWtVdpmdgN5ZIpOwwAxDAgALoowMuDWAeaEYCjA
+gBMQnAyYGugcAHpUqFhgXEVGXCJE+fKljy9kHrBq5gAfwwN6uqvkSlGxpPKJfEs
MIxBL8ZaiKXYZm+VpRJVzKFsiQJugmWWI2aAcalVlpqeYmWUBkVRmX5jMaJZ2JxA
eU5OJQmC4nP5PMTQG2unGSpbcZ3+X4muRfqf/ldOelm6QjkqeI+CRpQzlpRq1WYl
ElF003HEkRRimWgCrl65TFFFCW5UgU7lbvPonNsSDpgWmxBCTgUFpeBRr6LZFwBe
V0FJVhQWnF+/jMnQSNRfPxXuVKaLm7FU7mFkzuWwucW4p/mTeUiVORc9iHuNgRXw
NUL5SQUwGQ5LHDtW3uAO5N6ZMpfEhe/xK7pfSwdRrTZagdd7ifZKwTLikwX2oRA2
VwQbjki5pzvtJJ1tUYfFZwlOXogPRpzvToWSRZpwjTF7EPHEIl96mE6IxVcBu6FK
BFt8U2S4OuxAaluCOPwOelhVeqtwV/Akb3YT0d1IvRkQeXIhpwaImXvMK2bJWawy
MIagD16MCAoQedcIgF1ZERbeK2Qw3J1lP8jPOj6vqSUqbnB5exu9o5ak4AwV7Sk0
OIJ/AzCr1p7A2cCgh/xm1lsJiy0SjiWEmf5dUkV1/OkVrnSozIbJ3qUrjdCIoEpf
lXGwJyNdiopA2NjGDa32mPqKsQ8onn+ZiyX0htVKdb0ZYUZuMNFCR3NDTU16WtOV
WBwGLJlmx0zDfMZ6ptclcDVYiXn8H+ZOVXpQny28uOE3wpwFg2glhDfzSB5o0Tqw
CwJNQaXNcj8W1pNaGtPOQggv2drJv6IjPewQNl8RUgjm1kI+HNce8VSwxSo/BrRc
yAIDdTh5KUC8YiQoObI7TBFXMHq04KIG4ZwE0LAHmJ5RtF/J0EDDdDq2a7OhRW/1
IeDDBLyGcA4YLRuGsqSfJd4UbSjI0OrV5vGpzvZrJ6ZAoMW+QentnRYea2cHrkgL
4PhXd8IngFDBZBFQprVw7sLeGZaVodsGg6CmnbBPY6hsaUKszyZBXLBouUND8sQI
F25l4PSXcHRV4kg1Bis3zGvVb4hMUsFxFzaQdDYIjYCqXzBnTTM1byczaTVic+5u
vXTNilT01XafSNZBbNUzSs27Ngjp1VrRK6eTWbmkLogkU1U9GCFyEn1NMrZKzJpH
mQqQIMNVr0nzbqhIBL5L837KzJgOaFxepZiUq56KLdJAtkLYC2AtK1eC2wtARJPy
vltyriVcoKLXBXbVARBkkPNUKktXphfVdfRgts/t74iVzhCS3X07TSfR65LflhkV
+o3jH4KmJpl35dYW9ZgnYt7wfOYz+XqLAmctU9MvnH0R6QyEnpD1UQmsh1paYj7N
ZNUc14UOzVBXN0JIuADcQMJHABwARoLmjcAo6NACYgWQJUBEAP7BsAMAhAAgCXWJ
+W6GUxTQOa0WtooBADYAIgISTJg64PoBGgpok6Gs1kADa2kAdrQ62jAJrXpEc1li
RxHWttrXRj2tmQDMSNl1+bamBtHrcG0OtTrQ4kMZUbZ62ZAcbQ/nOJTqW61BtOQC
G36AFDN2XUBZQO61Jt+gFUDv5eTIm0xtobZwBQAMxHmB6gIYvq2FtFbfoAzEVbQa
CEARgEeigFRQOW1ZtDrR0BYAUAMQy6tvHBWgIATQAG2Ntvbcm1RApAIO0etbABQC
YgO7DUI9tUANm0HgcoMQzzti7SECTsGoCyBKq1oNgAsg+oOMDcAzKIPIpGFBCU0E
Q+rcwDHtXIPgAzAllsDJicucslJxaxlhABGAbAAYDqtA4AQBrl9IBrJ1QlkMawDG
gkE6Wrt2bbm3y1lQKRhWt0oCQBttHbSu1Idvrf+3loowFyCTsfIAMAJA+Hfh0zEM
xFaAUMCAMoA5oJGQeDLg1HdR3EdEAFB2TtUACm0IAJbVADdgUDo6xmAwgMwCrEpA
Mh3ttR6ObxDloTGR1ygTADBDKAmHWUDZAuAJoDBApmEbVgg2AEQBwA3AEp1hgHAK
EzqdpAGuXSYQgFAA/gR6EbVDo4AKOwNlwQOmDAAg6CACDoQAA===
```
%%

Binary file not shown.

After

Width:  |  Height:  |  Size: 25 KiB

View File

@ -0,0 +1,788 @@
---
excalidraw-plugin: parsed
tags: [excalidraw]
---
==⚠ Switch to EXCALIDRAW VIEW in the MORE OPTIONS menu of this document. ⚠==
# Text Elements
%%
# Drawing
```compressed-json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=
```
%%

Binary file not shown.

After

Width:  |  Height:  |  Size: 23 KiB

View File

@ -0,0 +1,778 @@
---
excalidraw-plugin: parsed
tags: [excalidraw]
---
==⚠ Switch to EXCALIDRAW VIEW in the MORE OPTIONS menu of this document. ⚠==
# Text Elements
%%
# Drawing
```compressed-json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=
```
%%

Binary file not shown.

After

Width:  |  Height:  |  Size: 21 KiB

View File

@ -0,0 +1,926 @@
---
excalidraw-plugin: parsed
tags: [excalidraw]
---
==⚠ Switch to EXCALIDRAW VIEW in the MORE OPTIONS menu of this document. ⚠==
# Text Elements
%%
# Drawing
```compressed-json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```
%%

Binary file not shown.

After

Width:  |  Height:  |  Size: 28 KiB

View File

@ -0,0 +1,554 @@
---
excalidraw-plugin: parsed
tags: [excalidraw]
---
==⚠ Switch to EXCALIDRAW VIEW in the MORE OPTIONS menu of this document. ⚠==
# Text Elements
%%
# Drawing
```compressed-json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```
%%

Binary file not shown.

After

Width:  |  Height:  |  Size: 9.9 KiB

BIN
content/drawings/evp.png Normal file

Binary file not shown.

After

Width:  |  Height:  |  Size: 229 KiB

Binary file not shown.

After

Width:  |  Height:  |  Size: 60 KiB