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design pattern is a widely accepted solution to a recurring design problem in OOP a design pattern describes how to structure classes to meet a given requirement provides a general blueprint to follow when implementing part of a program does not describe how to structure the entire application does not describe specific algorithms focuses on relationships between classes

design patterns: make you more productive help you write cleaner code Observer and Singleton are just two of the many available if you like design patterns, try these resources: GoF book -- Design Patterns: Elements of Reusable Object-oriented Software design pattern examples in Java, see Design Patterns in Java Reference and Example Site

learn what a design pattern is

OpenGL (Open Graphics Library) is a standard specification defining a cross-language, cross-platform API for writing applications that produce 2D and 3D computer graphics. The interface consists of over 250 different function calls which can be used to draw complex three-dimensional scenes from simple primitives. OpenGL was developed by Silicon Graphics Inc. (SGI) in 1992[1] and is widely used in CAD, virtual reality, scientific visualization, information visualization, and flight simulation. It is also used in video games, where it competes with Direct3D on Microsoft Windows platforms (see Direct3D vs. OpenGL). OpenGL is managed by the non-profit technology consortium, the Khronos Group.

Mark Segal and Kurt Akeley authored the original OpenGL specification

In telecommunications and software engineering, scalability is a desirable property of a system, a network, or a process, which indicates its ability to either handle growing amounts of work in a graceful manner, or to be readily enlarged.[1] For example, it can refer to the capability of a system to increase total throughput under an increased load when resources (typically hardware) are added. An analogous meaning is implied when the word is used in a commercial context, where scalability of a company implies that the underlying business model offers the potential for economic growth within the company.

A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (EA) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).

A typical genetic algorithm requires: a genetic representation of the solution domain, a fitness function to evaluate the solution domain.

A Voyage to Functional Programming in F#

Finally I settled on F#. Both LISP and F# are belong to functional programming (FP) paradigm.Functional programming is a complete different paradigm from imperative (and OO) programming.I found it terse as well yet I can understand it (though some of the concepts are new to me).The advantage is it can use .NET Framework. Thus it gives me a break from OOP and yet taps the power from the familiar .NET Framework.Therefore I decided to dive into it to seem whether functional programming can deliver its claims in terms of productivity and expression power.I would like to share my findings in this blog to those interested in learning functional programming.

COUNT(*) returns the number of items in a group. This includes NULL values and duplicates. COUNT(ALL expression) evaluates expression for each row in a group and returns the number of nonnull values. COUNT(DISTINCT expression) evaluates expression for each row in a group and returns the number of unique, nonnull values.

The Clockwork Universe Theory is a theory, established by Isaac Newton, as to the origins of the universe. A "clockwork universe" can be thought of as being a clock wound up by God and ticking along, as a perfect machine, with its gears governed by the laws of physics. What sets this theory apart from others is the idea that God's only contribution to the universe was to set everything in motion, and from there the laws of science took hold and have governed every sequence of events since that time. This idea was very popular during the Enlightenment, when scientists realized that Newton's laws of motion, including the law of universal gravitation, could explain the behavior of the solar system. A notable exclusion from this theory though is free will, since all things have already been set in motion and are just parts of a predictable machine. Newton feared that this notion of "everything is predetermined" would lead to atheism. This theory was undermined by the second law of thermodynamics ( the total entropy of any isolated thermodynamic system tends to increase over time, approaching a maximum value) and quantum physics with its unpredictable random behavior.

The Clockwork Universe Theory is a theory, established by Isaac Newton, as to the origins of the universe. A "clockwork universe" can be thought of as being a clock wound up by God and ticking along, as a perfect machine, with its gears governed by the laws of physics. What sets this theory apart from others is the idea that God's only contribution to the universe was to set everything in motion, and from there the laws of science took hold and have governed every sequence of events since that time. This idea was very popular during the Enlightenment, when scientists realized that Newton's laws of motion, including the law of universal gravitation, could explain the behavior of the solar system. A notable exclusion from this theory though is free will, since all things have already been set in motion and are just parts of a predictable machine. Newton feared that this notion of "everything is predetermined" would lead to atheism. This theory was undermined by the second law of thermodynamics ( the total entropy of any isolated thermodynamic system tends to increase over time, approaching a maximum value) and quantum physics with its unpredictable random behavior.

Stochastic optimization (SO) methods are optimization algorithms which incorporate probabilistic (random) elements, either in the problem data (the objective function, the constraints, etc.), or in the algorithm itself (through random parameter values, random choices, etc.), or in both [1]. The concept contrasts with the deterministic optimization methods, where the values of the objective function are assumed to be exact, and the computation is completely determined by the values sampled so far.

Deep Blue was a chess-playing computer developed by IBM. On May 11, 1997, the machine won a six-game match by two wins to one with three draws against world champion Garry Kasparov.[1] Kasparov accused IBM of cheating and demanded a rematch, but IBM declined and dismantled Deep Blue.[2] Kasparov had beaten a previous version of Deep Blue in 1996

Deep Blue was then heavily upgraded (unofficially nicknamed "Deeper Blue")[11] and played Kasparov again in May 1997, winning the six-game rematch 3½–2½, ending on May 11, finally ending in game six, and becoming the first computer system to defeat a reigning world champion in a match under standard chess tournament time controls.

The system derived its playing strength mainly out of brute force computing power.

A database is a structured collection of records or data that is stored in a computer system. The structure is achieved by organizing the data according to a database model. The model in most common use today is the relational model. Other models such as the hierarchical model and the network model use a more explicit representation of relationships

increase their speed

common kind of index is a sorted list of the contents of some particular table column, with pointers to the row associated with the value

In computer science, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is relatively simple to implement, making it a popular first choice. Although more advanced algorithms may give better results, in some situations hill climbing works just as well. Hill climbing can be used to solve problems that have many solutions, some of which are better than others. It starts with a random (potentially poor) solution, and iteratively makes small changes to the solution, each time improving it a little. When the algorithm cannot see any improvement anymore, it terminates. Ideally, at that point the current solution is close to optimal, but it is not guaranteed that hill climbing will ever come close to the optimal solution. For example, hill climbing can be applied to the traveling salesman problem. It is easy to find a solution that visits all the cities but will be very poor compared to the optimal solution. The algorithm starts with such a solution and makes small improvements to it, such as switching the order in which two cities are visited. Eventually, a much better route is obtained. Hill climbing is used widely in artificial intelligence, for reaching a goal state from a starting node. Choice of next node and starting node can be varied to give a list of related algorithms.

IPv4 Internet Protocol version 4

IPv6 Internet Protocol version 6

TCP Transmission Control Protocol UDP User Datagram Protocol

A production system (or production rule system) is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behavior. These rules, termed productions, are a basic representation found useful in AI planning, expert systems and action selection. A production system provides the mechanism necessary to execute productions in order to achieve some goal for the system. Productions consist of two parts: a sensory precondition (or "IF" statement) and an action (or "THEN"). If a production's precondition matches the current state of the world, then the production is said to be triggered. If a production's action is executed, it is said to have fired. A production system also contains a database, sometimes called working memory, which maintains data about current state or knowledge, and a rule interpreter. The rule interpreter must provide a mechanism for prioritizing productions when more than one is triggered.

A production system (or production rule system) is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behavior. These rules, termed productions, are a basic representation found useful in AI planning, expert systems and action selection. A production system provides the mechanism necessary to execute productions in order to achieve some goal for the system. Productions consist of two parts: a sensory precondition (or "IF" statement) and an action (or "THEN"). If a production's precondition matches the current state of the world, then the production is said to be triggered. If a production's action is executed, it is said to have fired. A production system also contains a database, sometimes called working memory, which maintains data about current state or knowledge, and a rule interpreter. The rule interpreter must provide a mechanism for prioritizing productions when more than one is triggered.

A pattern must explain why a particular situation causes problems, and why the proposed solution is considered a good one. Christopher Alexander describes common design problems as arising from "conflicting forces" -- such as the conflict between wanting a room to be sunny and wanting it not to overheat on summer afternoons. A pattern would not tell the designer how many windows to put in the room; instead, it would propose a set of values to guide the designer toward a decision that is best for their particular application. Alexander, for example, suggests that enough windows should be included to direct light all around the room. He considers this a good solution because he believes it increases the enjoyment of the room by its occupants. Other authors might come to different conclusions, if they place higher value on heating costs, or material costs. These values, used by the pattern's author to determine which solution is "best", must also be documented within the pattern. A pattern must also explain when it is applicable. Since two houses may be very different from one another, a design pattern for houses must be broad enough to apply to both of them, but not so vague that it doesn't help the designer make decisions. The range of situations in which a pattern can be used is called its context. Some examples might be "all houses", "all two-story houses", or "all places where people spend time." The context must be documented within the pattern. For instance, in Christopher Alexander's work, bus stops and waiting rooms in a surgery are both part of the context for the pattern "A PLACE TO WAIT."