Programmers often tend to be sedentary people. Sitting in a chair and pressing keys, testing code, and planning out one logical step-wise strategy after another to get the computer to process data the way you want it to is just what life as a programmer is all about. But, is being too sedentary hindering a programmers max potential? In other words, will getting up, moving around, and getting the blood pumping make us better programmers? To answer this question more efficiently, we will need to consider the impact of exercise on various aspects of programming.

Alertness And Focus

It is no surprise that working up a sweat makes the mind wake up and become more alert. As the blood starts pumping, the body physically reacts in ways that helps the mind to better focus. And improving our focus might make us better programmers in the sense that we are more able to wrap our mind around a problem and deal with it more efficiently than if we feel sluggish and not so alert. However, improving one's focus with exercise can be augmented by taking such vitamins as B6, Coleen, and eating more saturated fats rather than so many sugars. Exercise alone may be a good start, but it is important to realize that the impact of exercise on overall focus can be enhanced when combined with other dietary practices. However, it never hurts to begin a day of programming with fifteen minutes of rigorous workout to give the mind a little extra push.

Increase In Intellect

Does exercise cause a programmer to become a smarter programmer? This is perhaps a trickier question. In some sense, it might seem as if exercise makes us more intelligent. But, this may be more because our focus is sharper than because of any increase in actual knowledge. For example, if you don't know how to program in Python, it is highly doubtful that exercising harder will all of a sudden transfer such insights directly to your brain. However, exercise might have another indirect impact on a programmer’s intellect that will help them to become a better programmer. The more a person exercises, the more stamina and energy they will tend to have, as compared to programmers who never exercise all that much. That additional energy and stamina might help a programmer to be able to push themselves to learn things more efficiently, simply because they aren't getting tired as much as they study new languages or coding techniques. If you have more energy and stamina throughout the day, you will likely be more productive as a programmer as well. Greater productivity can often make one program better simply because they actually push themselves to finish projects. Other programmers who do not exercise on a regular basis may simply lack the energy, stamina, and motivation to follow through and bring their programming projects to completion.

Memory

The ability to remember things and recall them quickly is key to being an efficient programmer. Getting up and getting real exercise may be central to making sure that one does not lose control of these cognitive abilities. According to the New York Times, article, Getting a Brain Boost Through Exercise, recent research studies on mice and humans have shown that, in both cases, exercise does in fact appear to promote better memory function as well as other cognitive factors like spacial sense. (1) Consequently, if a person intends to be a programmer for a long time and wants their mind to be able to remember things and recall them more easily, then exercise may need to become an essential part of such a programmer's daily routine.

As much as one might want to resist the need for exercise and be sedentary programmers, the simple fact is that exercise very well could improve our ability to program in numerous ways. More importantly, exercise is critical to improving and maintaining good health overall. Even if a person does not have much time to get up and move around during the day, there are exercises that one can do while sitting, which would be better to do than no exercise at all.

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Computers. They’re a part of our everyday lives. Most of us couldn’t imagine living a day without them. We use them for school, work, and fun and use them to stay connected to those we love and care about. Since the invention of the web cam, millions of us use webcams to communicate with loved-ones and business contacts far away.

Web camera use has leveled the playing field for business entrepreneurs and given teenagers a fun way to chat with friends. However, solid citizens aren’t the only ones who make use of this popular modern technology. Recently, there have been reports of criminals using a type of webcam spy hack to insert themselves unseen into the living rooms and bedrooms of millions of unsuspecting users.

The Webcam Spy Hack

The most popular way criminals gain access to your webcam is through innocent-looking emails. You may receive an e-card from someone in your contacts list. When you click on it, you’re directed to another website to view the e-card. While you’re listening to music and watching animated puppies scroll across the screen, a Trojan horse is silently installed into your computer’s hard drive.

Python and Ruby, each with roots going back into the 1990s, are two of the most popular interpreted programming languages today. Ruby is most widely known as the language in which the ubiquitous Ruby on Rails web application framework is written, but it also has legions of fans that use it for things that have nothing to do with the web. Python is a big hit in the numerical and scientific computing communities at the present time, rapidly displacing such longtime stalwarts as R when it comes to these applications. It too, however, is also put to a myriad of other uses, and the two languages probably vie for the title when it comes to how flexible their users find them.

A Matter of Personality...

That isn't to say that there aren't some major, immediately noticeable, differences between the two programming tongues. Ruby is famous for its flexibility and eagerness to please; it is seen by many as a cleaned-up continuation of Perl's "Do What I Mean" philosophy, whereby the interpreter does its best to figure out the meaning of evening non-canonical syntactic constructs. In fact, the language's creator, Yukihiro Matsumoto, chose his brainchild's name in homage to that earlier language's gemstone-inspired moniker.

Python, on the other hand, takes a very different tact. In a famous Python Enhancement Proposal called "The Zen of Python," longtime Pythonista Tim Peters declared it to be preferable that there should only be a single obvious way to do anything. Python enthusiasts and programmers, then, generally prize unanimity of style over syntactic flexibility compared to those who choose Ruby, and this shows in the code they create. Even Python's whitespace-sensitive parsing has a feel of lending clarity through syntactical enforcement that is very much at odds with the much fuzzier style of typical Ruby code.

For example, Python's much-admired list comprehension feature serves as the most obvious way to build up certain kinds of lists according to initial conditions:

a = [x**3 for x in range(10,20)]
b = [y for y in a if y % 2 == 0]

first builds up a list of the cubes of all of the numbers between 10 and 19 (yes, 19), assigning the result to 'a'. A second list of those elements in 'a' which are even is then stored in 'b'. One natural way to do this in Ruby is probably:

a = (10..19).map {|x| x ** 3}
b = a.select {|y| y.even?}

but there are a number of obvious alternatives, such as:

a = (10..19).collect do |x|
x ** 3
end

b = a.find_all do |y|
y % 2 == 0
end

It tends to be a little easier to come up with equally viable, but syntactically distinct, solutions in Ruby compared to Python, even for relatively simple tasks like the above. That is not to say that Ruby is a messy language, either; it is merely that it is somewhat freer and more forgiving than Python is, and many consider Python's relative purity in this regard a real advantage when it comes to writing clear, easily understandable code.

And Somewhat One of Performance

I will begin our blog on Java Tutorial with an incredibly important aspect of java development:  memory management.  The importance of this topic should not be minimized as an application's performance and footprint size are at stake.

From the outset, the Java Virtual Machine (JVM) manages memory via a mechanism known as Garbage Collection (GC).  The Garbage collector

• Manages the heap memory.   All obects are stored on the heap; therefore, all objects are managed.  The keyword, new, allocates the requisite memory to instantiate an object and places the newly allocated memory on the heap.  This object is marked as live until it is no longer being reference.
• Deallocates or reclaims those objects that are no longer being referened. 
• Traditionally, employs a Mark and Sweep algorithm.  In the mark phase, the collector identifies which objects are still alive.  The sweep phase identifies objects that are no longer alive.
• Deallocates the memory of objects that are not marked as live.
• Is automatically run by the JVM and not explicitely called by the Java developer.  Unlike languages such as C++, the Java developer has no explict control over memory management.
• Does not manage the stack.  Local primitive types and local object references are not managed by the GC.

So if the Java developer has no control over memory management, why even worry about the GC?  It turns out that memory management is an integral part of an application's performance, all things being equal.  The more memory that is required for the application to run, the greater the likelihood that computational efficiency suffers. To that end, the developer has to take into account the amount of memory being allocated when writing code.  This translates into the amount of heap memory being consumed.

Memory is split into two types:  stack and heap.  Stack memory is memory set aside for a thread of execution e.g. a function.  When a function is called, a block of memory is reserved for those variables local to the function, provided that they are either a type of Java primitive or an object reference.  Upon runtime completion of the function call, the reserved memory block is now available for the next thread of execution.  Heap memory, on the otherhand, is dynamically allocated.  That is, there is no set pattern for allocating or deallocating this memory.  Therefore, keeping track or managing this type of memory is a complicated process. In Java, such memory is allocated when instantiating an object:

String s = new String();  // new operator being employed
String m = "A String";    /* object instantiated by the JVM and then being set to a value.  The JVM
calls the new operator */