text:
東山

Proper noun

 * 1) the eastern peak of
 * 2)  Dongshan Town various towns in Mainland China
 * 3)  Dongshan Township various townships in Mainland China
 * 1)  Dongshan Town various towns in Mainland China
 * 2)  Dongshan Township various townships in Mainland China
 * 1)  Dongshan Township various townships in Mainland China

title: 東山


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James' Journey to Jerusalem

James' Journey to Jerusalem (מסעות ג'יימס בארץ הקודש) is a 2003 Israeli film directed by Ra'anan Alexandrowicz and produced by Renen Schorr.

Plot
The film's plot focuses on an African teenager named James (Siyabonga Melongisi Shibe), who goes on a pilgrimage to the Holy Land on behalf of his village. Upon arriving in Israel, James is suspected to be an illegal foreign worker and, as a result, is arrested. Shimi (Salim Daw), a contractor of foreign workers, releases him on bail to work with him. After James explains to him that he did not travel to Israel to work, Shimi clarifies to him that since he paid for his release, James now owes him. Therefore, James is forced to interrupt his journey and begin working for Shimi.

Shimi tries to gain a profit at James' expense and makes him work for other people as well. Shimi's wife sees him as a kind of an amusement.

Salah, Shimi's father, soon discovers that James is exceptionally lucky rolling dice and he decides to exploit this in order to win in backgammon games against his friends. James hopes to pay his debt to Shimi so that he can finally reach Jerusalem, but as time passes he learns how to conduct with the locals. Salah keeps telling good-hearted and guileless James "Don't be a frayer (sucker)", and eventually James ceases to be one: he starts managing his foreign worker friends, and soon he becomes a cheap labor contractor himself, just like Shimi. James buys himself nice clothes, a mobile phone and a TV. As a result, he forgets about the pilgrimage.

Eventually James remembers the original reason for which he arrived in Israel, but it is already too late – he is arrested by the immigration police and transferred to an Israeli prison. The prison is located in the Russian Compound in Jerusalem, and so as he is handcuffed, James finally gets to see the city for which his village prays to.

Film history
Alexandrowicz said that the plot was inspired by a true history of a Nigerian man he knew, who came to Israel on a tourist visa and became an illegal worker. He changed the nationality of James to Zulu intending for James to be from the place most remote from Western culture values. Accordingly, he picked the actor, Siyabonga Shibe, from South Africa.

The situation described in the film is quite uncommon, but legally possible: after an arrest for an illegal stay in Israel, a person is either deported or someone may post a bail and give a work permit.

source: wikimedia

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: James' Journey to Jerusalem


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Talk:Wikimania/2007

Virtual Wikimania
The Wikiversity School of Media Studies is planning to get Wikiversity involved in producing a "Virtual Wikimania" from Taipei via a "Wikiversity kiosk" located at the main event. Some of the participating content development groups are:
 * Topic:Wikimania - Wikimania, Wikimania 2007 etc.
 * Topic:Internet Audio and Video - Setting up the Sandbox Server (or equivalent) with Icecast and and other Internet Audio/Video services.
 * Topic:VoIP - Setting up the Sandbox Server (or equivalent) with Asterisk Voice over Internet Protocol hub linked to something like an OpenMoko/Internet Kiosk
 * Topic:Server administration - Setting up the Sandbox Server (or equivalent) in general
 * Topic:Internet Audio - Wiki Campus Radio, Hypatia, etc. to work on Topic:Audio Engineering learning projects for the "Sound of Wikiversity" Internet radio project
 * ...several others... just follow the breadcrumbs...

If you know of people, companies or groups that could help out, please let us know, below. Thanks in advance for your input! CQ 17:40, 22 April 2007 (UTC)

title: Talk:Wikimania/2007


text:
Numerical Methods/Equation Solving

An equation of the type $$f(x)=0$$ is either algebraic or transcendental.

E.g, these equations are algebraic.
 * $$2x=5; \quad x^2+x=1; \quad x^7=x(1+2x). $$

and these are transcendental
 * $$ \sin x = 0; \quad \quad e^\sqrt{x} = \pi; \quad \tan x = x.$$

While roots can be found directly for algebraic equations of fourth order or lower, and for a few special transcendental equations, in practice we need to solve equations of higher order and also arbitrary transcendental equations.

As analytic solutions are often either too cumbersome or simply do not exist, we need to find an approximate method of solution. This is where numerical analysis comes into the picture.

Some Useful Observations

 * The total number of roots an algebraic equation can have is the same as its degree.
 * An algebraic equation can have at most as many positive roots as the number of changes of sign in $$f(x)$$.
 * An algebraic equation can have at most as many negative roots as the number of changes of sign in $$f(-x)$$.
 * In an algebraic equation with real coefficients, complex roots occur in conjugate pairs
 * If $$f(x)=a_0x^n+a_1x^{n-1}+a_2x^{n-2}+...+a_{n-1}x+a_n$$ with roots $$\alpha_1, \alpha_2, ..., \alpha_n$$ then the following hold good:
 * $$\sum_i\alpha_i=\frac{-a_1}{a_0}$$
 * $$\sum_{i<j}{\alpha_i\alpha_j}=\frac{a_2}{a_0}$$
 * $$\prod_i\alpha_i=(-1)^n \frac{a_n}{a_0}$$
 * If $$f(x)$$ is continuous in the interval $$[a,b]$$ and $$f(a)f(b)<0$$ then a root must exist in the interval $$(a,b)$$

Initial Approximation
The last point about the interval is one of the most useful properties numerical methods use to find the roots. All of them have in common the requirement that we need to make an initial guess for the root. Practically, this is easy to do graphically. Simply plot the equation and make a rough estimate of the solution. Analytically, we can usually choose any point in an interval where a change of sign takes place. However, this is subject to certain conditions that vary from method to method.

Convergence
A numerical method to solve equations will be a long process. We would like to know, if the method will lead to a solution (close to the exact solution) or will lead us away from the solution. If the method, leads to the solution, then we say that the method is convergent. Otherwise, the method is said to be divergent.i.e, in case of linear and non linear interpolation convergence means tends to 0.

Rate of Convergence
Various methods converge to the root at different rates. That is, some methods are slow to converge and it takes a long time to arrive at the root, while other methods can lead us to the root faster. This is in general a compromise between ease of calculation and time.

For a computer program however, it is generally better to look at methods which converge quickly. The rate of convergence could be linear or of some higher order. The higher the order, the faster the method converges.

If $$e_i$$ is the magnitude of the error in the $$i$$th iteration, ignoring sign, then the order is $$n$$ if $$\frac{e_{i+1}}{e_i^n}$$ is approximately constant.

It is also important to note that the chosen method will converge only if $$e_{i+1}<e_i$$.

Bisection Method
This is one of the simplest methods and is strongly based on the property of intervals. To find a root using this method, the first thing to do is to find an interval $$[a,b]$$ such that $$f(a) \cdot f(b) < 0 $$. Bisect this interval to get a point $$(c,f(c))$$. Choose one of $$a$$ or $$b$$ so that the sign of $$f(c)$$ is opposite to the ordinate at that point. Use this as the new interval and proceed until you get the root within desired accuracy.

Example
Solve $$e^x-2x^2+3x+1$$ correct up to 2 decimal places.

 * $$f(x)=e^x-2x^2+3x+1$$

$$\Rightarrow f(2) \cdot f(3) < 0 $$ $$\Rightarrow \epsilon = \frac{1}{2}10^{-2}$$ $$\Rightarrow i = \frac{2.3010}{0.3010} = 8$$ $$\Rightarrow x_1=2.5$$
 * $$f(x_1)=5.625 > 0$$

$$\Rightarrow x_2 = 2.25$$ $$\Rightarrow ... x_8 = 2.09$$

Error Analysis
The maximum error after the $$i$$th iteration using this process will be given as
 * $$\epsilon_i=\frac{|b-a|}{2^i}$$

$$\Rightarrow i\ge\frac{[\log(b-a)-\log\epsilon_i]}{\log 2}$$

As the interval at each iteration is halved, we have $$\frac{\epsilon_{i+1}}{\epsilon_i} = \frac{1}{2}$$. Thus this method converges linearly.

If we are interested in the number of iterations the Bisection Method needs to converge to a root within a certain tolerance than we can use the formula for the maximum error.

Example
How many iterations do you need to get the root if you start with a = 1 and b = 2 and the tolerance is 10−4?

The error $$\epsilon_i$$ needs to be smaller than 10−4. Use the formula for the maximum error:

 * $$\epsilon_i = 2^{-i} \cdot (2-1) < 10^{-4} $$

 * $$\epsilon_i = 2^{-i} < 10^{-4} $$

Solve for i using log rules

 * $$\displaystyle\log_{10}{2^{-i}} < \log_{10}{10^{-4}} $$

 * $$\displaystyle -i \cdot \log_{10}{2} < -4 $$

 * $$\displaystyle i > \frac{4}{\log_{10}{2}} = 13.29 = 14 $$

Hence 14 iterations will ensure an approximation to the root accurate to $$\displaystyle 10^{-4}$$. Note: the error analysis only gives a bound approximation to the error; the actual error may be much smaller.

False Position Method
The false position method (sometimes called the regula falsi method) is essentially same as the bisection method -- except that instead of bisecting the interval, we find where the chord joining the two points meets the X axis. The roots are calculated using the equation of the chord, i.e. putting $$y = 0$$ in

 * $$y-f(x_0)=\frac{f(x_1)-f(x_0)}{x_1-x_0}(x-x_0)$$.

The rate of convergence is still linear but faster than that of the bisection method. Both these methods will fail if f has a double root.

Example
Consider f(x)=x2-1. We already know the roots of this equation, so we can easily check how fast the regula falsi method converges.

For our initial guess, we'll use the interval [0,2].

Since f is concave upwards and increasing, a quick sketch of the geometry shows that the chord will always intersect the x-axis to the left of the solution. This can be confirmed by a little algebra.

We'll call our nth iteration of the interval [an, 2]

The chord intersects the x-axis when

 * $$-(a_n^2-1)=\frac{(2^2-1)-(a_n^2-1)}{2-a_n} (x-a_n)$$

Rearranging and simplifying gives

 * $$x=\frac{1+2a_n}{2+a_n}$$

Since this is always less than the root, it is also an+1

The difference between an and the root is en=an-1, but

 * $$e_{n+1}=\frac{1+2a_n}{2+a_n}-1 = \frac{a_n-1}{a_n+2} =

\frac{1}{a_n+2}e_n $$

This is always smaller than en when an is positive. When an approaches 1, each extra iteration reduces the error by two-thirds, rather than one-half as the bisection method would.

The order of convergence of this method is 2/3 and is linear.

In this case, the lower end of the interval tends to the root, and the minimum error tends to zero, but the upper limit and maximum error remain fixed. This is not uncommon.

Fixed Point Iteration (or Staircase method or x = g(x) method or Iterative method)
If we can write f(x)=0 in the form x=g(x), then the point x would be a fixed point of the function g (that is, the input of g is also the output). Then an obvious sequence to consider is

 * $$x_{n+1}=g(x_n) \,$$

If we look at this on a graph we can see how this could converge to the intersection.

Any function can be written in this form if we define x=g(x), though in some cases other rearrangements may prove more useful(must satisfy the condition |g(x)|<1). This method is useful for finding a positive root to the infinite series also.

Fixed Point Theorem (Statement)

If g is continuous on[a,b] and a ≤ g(x) ≤ b for all x in [a,b], then g has at least a fixed point in [a,b].

Further, suppose g'(x) is continuous on (a,b) and that a positive constant c exists with |g'(x)| ≤ c <1, for all x in (a,b). Then there is a unique fixed point α of g in [a,b]. Also, the iterates xn+1 = g(xn) n≥0 will converge to α for any choice of x0 in [a,b].

Error analysis
We define the error at the nth step to be

 * $$e_n=x_n-x \mbox{ where } x=g(x) \,$$

Then we have

 * $$\begin{matrix}

e_{n+1} & = & x_{n+1}-x & \\ & = & g(x_n)-x & \\ & = & g(x+e_n) -x & \\ & = & -x + g(x)+ e_n g^\prime(x) + \cdots & \\ & = & e_n g^\prime(x) + \cdots & \mbox{since } g(x)=x \end{matrix}$$

So, when |g′(x)|<l, this sequence converges to a root, and the error will be approximately proportional to n. Because the relationship between en+1 and en is linear, we say that this method converges linearly, if it converges at all.

When g(x)=f(x)+x this means that if

 * $$x_{n+1}=x_n+f(x_n) \,$$.

converges to a root, x, of f then

 * $$ -2 < f^\prime(x) < 0 \, $$

Note that this convergence will only happen for a certain range of x. If the first estimate is outside that range then no solution will be found.

Also note that although this is a necessary condition for convergence, it does not guarantee convergence. In the error analysis we neglected higher powers of en, but we can only do this if en is small. If our initial error is large, the higher powers may prevent convergence, even when the condition is satisfied.

If |g′(x)|<1 is true at the root, the iteration sequence will converge in some interval around the root, which may be smaller than the interval where |g′(x)|<1. If |g′(x)| isn't smaller than one at the root, the iteration will not converge to that root.

Example
Lets consider $$f(x)=x^3+x-2$$, which we can see has a single root at x=1. There are several ways f(x)=0 can be written in the desired form, x=g(x).

The simplest is

1):$$x_{n+1}=x_n+f(x_n)=x_n^3 +2x_n -2$$

In this case, $$g'(x)=3x^2+2$$, and the convergence condition is

 * $$1>|g'(x)|=3x^2+2, \qquad -1>3x^2$$

Since this is never true, this doesn't converge to the root.

2)An alternate rearrangement is

 * $$x_{n+1}=2-x_n^3$$

This converges when

 * $$1 > |g'(x)|= |-3x^2|, \qquad x^2 < \frac{1}{3},

\qquad |x| < \frac{1}{\sqrt{3}}$$

Since this range does not include the root, this method won't converge either.

3)Another obvious rearrangement is

 * $$x_{n+1}=\sqrt[3]{2-x_n}$$

In this case the convergence condition becomes

 * $$\frac{1}{3}\left| (2-x_n)^{-\frac{2}{3}} \right| <1, \qquad

\left(2-x_n \right)^{-2} < 3^3, \qquad
 * x_n-2|>\sqrt{27} $$

Again, this region excludes the root.

4)Another possibility is obtained by dividing by x2+1

 * $$x_{n+1}=\frac{2}{x_n^2+1}$$

In this case the convergence condition becomes

 * $$\frac{4|x|}{(1+x^2)^2}<1, \qquad 4|x| < (1+x^2)^2 $$

Consideration of this inequality shows it is satisfied if x>1, so if we start with such an x, this will converge to the root.

Clearly, finding a method of this type which converges is not always straightforwards.

Newton-Raphson
In numerical analysis, Newton's method (also known as the Newton–Raphson method or the Newton–Fourier method) is an efficient algorithm for finding approximations to the zeros (or roots) of a real-valued function. As such, it is an example of a root-finding algorithm.

Any zero-finding method (Bisection Method, False Position Method, Newton-Raphson, etc.) can also be used to find a minimum or maximum of such a function, by finding a zero in the function's first derivative, see Newton's method as an optimization algorithm.

Description of the method
The idea of the Newton-Raphson method is as follows: one starts with an initial guess which is reasonably close to the true root, then the function is approximated by its tangent line (which can be computed using the tools of calculus), and one computes the x-intercept of this tangent line (which is easily done with elementary algebra). This x-intercept will typically be a better approximation to the function's root than the original guess, and the method can be iterated. Suppose f : [a, b] → R is a differentiable function defined on the interval [a, b] with values in the real numbers R. The formula for converging on the root can be easily derived. Suppose we have some current approximation xn. Then we can derive the formula for a better approximation, xn+1 by referring to the diagram on the right. We know from the definition of the derivative at a given point that it is the slope of a tangent at that point.

We can get better convergence if we know about the function's derivatives. Consider the tangent to the function:

Near any point, the tangent at that point is approximately the same as f('x'') itself, so we can use the tangent to approximate the function.

The tangent through the point (xn, f(xn)) is

 * $$\frac{y-f(x_n)}{x-x_n}=f'(x_n)$$

The next approximation, xn+1, is where the tangent line intersects the axis, so where y=0. Rearranging, we find

 * $$x_{n+1}=x_n-\frac{f(x_n)}{f'(x_n)}$$

Error analysis
Again, we define the root to be x, and the error at the nth step to be

 * $$e_n=x_n-x \,$$

Then the error at the next step is

 * $$e_{n+1} = e_n - \frac{f(x)+e_n f'(x)+ \frac{1}{2}e_n^2 f''(x)+ \cdots}

{f'(x)+e_n f''(x)+ \cdots }$$ (1)

where we've written f as a Taylor series round its root, x. Rearranging this, and using f(x)=0, we get

 * $$\begin{matrix}

e_{n+1} & = & e_n & - & e_n \left( f'(x)+\frac{1}{2}e_n f''(x) \right) /f'(x) + \cdots \\ & = & -\frac{f''(x)}{f'(x)}e_n^2 & + & \cdots \end{matrix}$$

where we've neglected cubic and higher powers of the error, since they will be much smaller than the squared term, when the error itself is small.

Notice that the error is squared at each step. This means that the number of correct decimal places doubles with each step, much faster than linear convergence.

This sequence will converge if

 * $$ \left| \frac{f''(x)}{f'(x)}e_n^2 \right| < |e_n|, \qquad

 * e_n|< \frac{f'(x)}{f''(x)} $$

If f′ isn't zero at the root, then there will always be a range round the root where this method converges.

If f′ is zero at the root, then on looking again at (1) we see that we get

 * $$e_{n+1} = e_n /2 \,$$

and the convergence becomes merely linear.

Overall, this method works well, provided f does not have a minimum near its root, but it can only be used if the derivative is known.

Example
Let's consider f(x)=x2-a. Here, we know the roots exactly, so we can see better just how well the method converges.

We have

 * $$x_{n+1} = x_n-\frac{f(x_n)}{f'(x_n)} = x_n-\frac{x_n^2-a}{2x_n}

= \frac{x_n^2+a}{2x_n} = \frac{1}{2}\left( x_n + \frac{a}{x_n} \right) $$

This method is easily implemented, even with just pen and paper, and has been used to rapidly estimate square roots since long before Newton.

The nth error is en=xn-√a, so we have

 * $$\begin{matrix}

e_{n+1} & = & \frac{(e_n+\sqrt{a})^2+a}{2(\sqrt{a}+e_n)} - \sqrt{a} \\ & = & \frac{2a+2e_n\sqrt{a}+e_n^2}{2(\sqrt{a}+e_n)} - \sqrt{a} \\ & = & \frac{2(\sqrt{a}+e_n)\sqrt{a}+e_n^2}{2(\sqrt{a}+e_n)} - \sqrt{a} \\ & = & \frac{e_n^2}{2(\sqrt{a}+e_n)} \end{matrix}$$

If a=0, this simplifies to en/2, as expected.

If a>0, en+1 will be positive, provided en is greater than -√a, i.e provided xn is positive. Thus, starting from any positive number, all the errors, except perhaps the first will be positive.

The method converges when,

 * $$ |e_{n+1}| = \left| \frac{e_n^2}{2(\sqrt{a}+e_n)} \right| < |e_n| $$

so, assuming en is positive, it converges when

 * $$ e_n < 2 \left( e_n + \sqrt{a} \right) $$

which is always true.

This method converges to the square root, starting from any positive number, and it does so quadratically.

Higher order methods
There are methods that converge even faster than Newton-Raphson. e.g

 * $$x_{n+1} = x_n-\frac{f(x_n)}{f'(x_n)}

+ \frac{1}{2} \frac{f''(x_n)f^2(x_n)}{{f'}^3(x_n)} $$

which converge cubicly, tripling the number of correct digits at each iteration, which is 50% faster than Newton-Raphson. However, if iterating each step takes 50% longer, due to the more complex formula, there is no net gain in speed. For this reason, methods such as this are seldom used. Ostrowski's efficiency index describes the trade-off well: if each step takes $n$ units of work (e.g. evaluating a function) for an algorithm of convergence order $p$, the "effective work" required for a given amount of convergence is $n^{1/p}$.

A quite honorable mention here is the ITP method, an improvement of the false position method. It provides a guaranteed (tunable, typically) $$\sqrt{2}$$ order of convergence without requiring a derivative.

Main Page - Mathematics bookshelf - Numerical Methods

title: Numerical Methods/Equation Solving


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User:Wonderfool~enwikibooks

Hello Wikiauthouts. Wikibooks is currently my third fling with wikimedia. I have pages on wikipedia and wiktionary, but I'm winging away from editing -pedia cos there's nothing I can do to it thats worthwhile anymore (Cleanup isnt really my style - I much prefer adding and adding more stuff). And I decided rashly to stop Witionary too, so I'm gonna give books a shot. My speciality is French, so I'm gonna see what I can do about the French book here. Plus, I always fancied myself of a bit of an authout. Watch this space

title: User:Wonderfool~enwikibooks


text:
Portal:Latin America/Featured article/Day 30

The following are known battles of the Mexican–American War. This list includes all major engagements and most reported skirmishes. Included also are two major engagements, the Battle of Palo Alto, and the Battle of Resaca de la Palma, which took place prior to the official start of hostilities between the two countries.

The Mexican–American War lasted from 1846 until 1848. It grew out of unresolved border disputes between the Republic of Texas and Mexico after the annexation of the former by the United States during the Texas Revolution of 1836. Before 1836, Anglo settlers in Texas (which was then a part of Mexico) had increasingly desired to align with the United States and its ongoing westward expansion. Texas was admitted into the United States in 1845 and as a consequence, tension with Mexico increased over the disputed border.

The U.S. government claimed that the southern border of Texas was the Rio Grande; Mexico maintained it to be the Nueces River. The U.S. Congress declared war on May 13, 1846, while Mexico did so on May 23. Most of the formal fighting effectively ended in October 1847, soon after General Winfield Scott and his forces had stormed Mexico City. The Treaty of Guadalupe Hidalgo was signed on February 2, 1848 in which Mexico, under pressure, surrendered a vast tract of land to the United States for the sum of USD $15 million.


text:
James C. Magee

James Carre Magee (January 23, 1883 – October 15, 1975) was an American medical officer and later Surgeon General of the United States Army from 1939 – 1943. He went with the American Expeditionary Force during World War 1 and worked as an assistant to the chief surgeon.

Personal life
James Magee was born on January 23, 1883, in County Donegal, Ireland to Edward Carre Magee and Elizabeth Armstrong Magee. He was the fourth son of a Pennsylvania businessman who was working in Ireland. The Magees returned to Pennsylvania where James Magee spent his youth. He attended Jefferson Medical College in Philadelphia, and received his M.D. in 1905. After graduating he joined the Medical Department as a contract surgeon in 1907 and was commissioned in the Medical Reserve Corps in 1908. He graduated from the Army Medical school in 1909 and joined the medical corps as a first lieutenant. He was part of the American Expeditionary force and went to France from 1917-1919.

World War 1
Magee, who was promoted to Major on May 15, 1917, and traveled to Base Hospital No. 12 in France. In August 1918, he was assigned as assistant to the chief surgeon Colonel Alexander Stark. While working with Stark, he participated in the St. Mihiel and Meuse-Argonne offensives from September - November 1918. He was awarded the Purple Heart for meritorious services in these operations.

During World war I, Magee achieved the rank of captain and chief surgeon.

Interwar period
He returned to the United States in 1919 and was transferred to Fort Sam Houston in December. While at Fort Sam Huston he worked as an instructor for the National Guard Medical Department. In June 1922, he went to Washington, D. C., for the Advanced Course in Preventive Medicine at the Army Medical School. Upon completion, he returned to Fort Sam Houston where he served as the division surgeon for the 2nd Division and commanding officer of the 2nd Medical Regiment.

Death
He died at age 92 at Walter Reed General Hospital and was buried at Arlington National Cemetery.

Surgeon general
On June 1, 1939, he was appointed by President Roosevelt to replace Major General Charles R. Reynolds as Surgeon General of the United States Army. The outbreak of World War II in Europe in September 1939 and the declaration of a limited national emergency by President Franklin D. Roosevelt that month confronted the new Surgeon General with enormous difficulties as the Army slowly began preparing for war. Magee had to confront the huge problems in preparing the Army Medical Department for its wartime expansion.

General Magee's tenure was marked by both successes and criticism. Magee was not a favorite of Army Chief of Staff, General George Marshall and Lieutenant General Brehon B. Somervell, commander of the Services of Supply. This eventually resulted in a major investigation by the War Department's Wadhams Committee from August through November 1942. The committee's final report was issued in November 1942 and outlined recommendations to improve the efficiency of the Army Medical Services. Many of the recommendations were never implemented until early in 1943. Magee's adversaries may have encouraged the Wadhams Committee investigation of the Medical Department in an attempt to remove him from his position but failed.

George Marshall was able to convince president Franklin D. Roosevelt to not reappoint him to the position after his four-year term as surgeon general was over. He retired from the position on May 31, 1943.

title: James C. Magee


text:
Billy Joe Green

Billy Joe Green is an Anishinaabe rock and blues musician from Canada. He is most noted as a three-time Juno Award nominee for Indigenous Music Album of the Year, receiving nominations at the Juno Awards of 2002 for My Ojibway Experience: Strength & Hope, at the Juno Awards of 2006 for Muskrat Blues and Rock & Roll and at the Juno Awards of 2009 for First Law of the Land.

Green, a member of the Lac Seul First Nation from Kejick Bay, was the son of David Green, a country musician. A survivor of the Indian residential school system, he turned to music as an outlet for healing. He launched his own musical career in the late 1960s with the band The Feathermen, although he did not record an album until Roughin' It in 1996. He has since released eight further CDs.

Green has also won two Indigenous Music Awards, and a Western Canadian Music Award.

Discography

 * Roughin' It (1996)
 * My Ojibway Experience: Strength & Hope (2000)
 * Muskrat Blues and Rock & Roll (2004)
 * The Best of Billy Joe Green (2008)
 * First Law of the Land (2008)
 * String Twister: Billy Joe Green Hits & Misses (2010)
 * Swingin' Tomahawk (2013)
 * Fender Bender (2017)
 * The Feathermen Family: Keeping The Circle Strong, Vol. One (2019)

title: Billy Joe Green


text:
User:At-Talib

At-Talib means "the student" in arabic. At-Talib is not an arab (he's American with an 'A'!), but he is a student (by temporary profession, not by hobby). Which is good, or else he'd just be a weirdo who knows a miniscule bit of arabic and a decent amount about most everything else. He's a noob to wikipedia.

In the real world, he is a sophomore working on his BA/BA in Mathematics and Religious Studies.

title: User:At-Talib