US To Launch New Moon Mission In 2010 The United States could launch a mission in 2010 that would land two stationary robots on the moon to collect rock samples before returning to earth, a US scientist said here Thursday. Carle Pieters of Brown University's Department of Geological Sciences, who is involved in the US space programme, said the aim of the Moonrise Mission was to land at the moon's largest and oldest crater - the South Pole Aitken Basin. "The purpose is to study how long ago the basin was formed and return materials derived from the deep interior to earth for analysis," Pieters said. "It will also help us to understand the unique process of how basins are formed." Pieters is also the chairwoman of the International Lunar Exploration Working Group, an organisation formed to promote cooperation between nations. She said scientists in the United States were still identifying which landing spots in the basin would be good for the twin robots to gather samples. All rights reserved. 2004 Agence France-Presse.
Ant colony optimization algorithms Ant behavior was the inspiration for the metaheuristic optimization technique This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. Overview[edit] Summary[edit] In the natural world, ants (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails. Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength. Common extensions[edit] Here are some of most popular variations of ACO Algorithms. to state where to
Alien From Earth Alien From Earth PBS Airdate: November 11, 2008 NARRATOR: It is the dream of every archaeologist who slogs through backbreaking days of excavation, the find that changes everything. ABC NEWS REPORTER (Archival Footage):A team of Australian and Indonesian archeologists has discovered the remains of what's believed to be a new species of human. HENRY GEE (Nature Magazine): This is a major discovery. CHRIS STRINGER (Natural History Museum, United Kingdom): It implies we are missing a huge amount of the story of human evolution. NARRATOR: Paradoxically, the discovery is huge because its pieces are not: a skeleton of an adult, the size of a three-year old child; a skull one-third the size of a modern human's. To many, the evidence is irrefutable. BILL JUNGERS (Stony Brook University): This is not a little person. NARRATOR: But some scientists just aren't buying it. RALPH HOLLOWAY (Columbia University): It just invites tremendous skepticism. NARRATOR: An astonishing discovery, a bitter controversy.
Knapsack problem Example of a one-dimensional (constraint) knapsack problem: which boxes should be chosen to maximize the amount of money while still keeping the overall weight under or equal to 15 kg? A multiple constrained problem could consider both the weight and volume of the boxes. (Answer: if any number of each box is available, then three yellow boxes and three grey boxes; if only the shown boxes are available, then all but the green box.) The knapsack problem or rucksack problem is a problem in combinatorial optimization: Given a set of items, each with a mass and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. The problem often arises in resource allocation where there are financial constraints and is studied in fields such as combinatorics, computer science, complexity theory, cryptography and applied mathematics. Applications[edit] Definition[edit] Let there be to .
Life Science Technologies: Sanger Who? Sequencing the Next Generation In November 2008 Elaine Mardis of Washington University in St. Louis and colleagues published the complete genome sequence of an individual with acute myeloid leukemia. Coming just a few years after the decade-long, multibillion dollar Human Genome Project, the paper was remarkable on several levels. By Jeffrey M. Inclusion of companies in this article does not indicate endorsement by either AAAS or Science, nor is it meant to imply that their products or services are superior to those of other companies. Elaine Mardis's acute myeloid leukemia work comprised about nine months of collecting 32-base snippets at the rate of about a billion bases per instrument every five days, with five instruments running in parallel, she says. The instruments in question, Illumina Genome Analyzers, are one of a cadre of so-called next-generation DNA sequencers. Yet today, says Mardis, those heady gigabase-a-week days seem "sort of like ±ho hum, that took a really long time.'" Out with the Old± Jeffrey M.
Ant robotics Ant robotics is a special case of swarm robotics. Swarm robots are simple (and hopefully, therefore cheap) robots with limited sensing and computational capabilities. This makes it feasible to deploy teams of swarm robots and take advantage of the resulting fault tolerance and parallelism. Swarm robots cannot use conventional planning methods due to their limited sensing and computational capabilities. Thus, their behavior is often driven by local interactions. Invention[edit] In 1991, American electrical engineer James McLurkin was the first to conceptualize the idea of "robot ants" while working at the MIT Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Background[edit] See also[edit] References[edit] ^ Jump up to: a b J. External links[edit] Ant robot by Sven KoenigAnt algorithm by Israel Wagner
Genetic algorithm The 2006 NASA ST5 spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields. Methodology[edit] In a genetic algorithm, a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. A typical genetic algorithm requires: a genetic representation of the solution domain,a fitness function to evaluate the solution domain. Initialization of genetic algorithm[edit] Selection[edit] Genetic operators[edit] Termination[edit]
Collective intelligence: Ants and brain's neurons CONTACT: Stanford University News Service (415) 723-2558 Collective intelligence: Ants and brain's neurons STANFORD - An individual ant is not very bright, but ants in a colony, operating as a collective, do remarkable things. A single neuron in the human brain can respond only to what the neurons connected to it are doing, but all of them together can be Immanuel Kant. That resemblance is why Deborah M. "I'm interested in the kind of system where simple units together do behave in complicated ways," she said. No one gives orders in an ant colony, yet each ant decides what to do next. For instance, an ant may have several job descriptions. This kind of undirected behavior is not unique to ants, Gordon said. Gordon studies harvester ants in Arizona and, both in the field and in her lab, the so-called Argentine ants that are ubiquitous to coastal California. Argentine ants came to Louisiana in a sugar shipment in 1908. The motions of the ants confirm the existence of a collective. -jns/ants-
Cellular automaton The concept was originally discovered in the 1940s by Stanislaw Ulam and John von Neumann while they were contemporaries at Los Alamos National Laboratory. While studied by some throughout the 1950s and 1960s, it was not until the 1970s and Conway's Game of Life, a two-dimensional cellular automaton, that interest in the subject expanded beyond academia. In the 1980s, Stephen Wolfram engaged in a systematic study of one-dimensional cellular automata, or what he calls elementary cellular automata; his research assistant Matthew Cook showed that one of these rules is Turing-complete. Wolfram published A New Kind of Science in 2002, claiming that cellular automata have applications in many fields of science. These include computer processors and cryptography. The primary classifications of cellular automata as outlined by Wolfram are numbered one to four. Overview[edit] A torus, a toroidal shape Cellular automata are often simulated on a finite grid rather than an infinite one. History[edit]
Bees algorithm In computer science and operations research, the Bees Algorithm is a population-based search algorithm which was developed in 2005.[1] It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both combinatorial optimization and continuous optimization. The only condition for the application of the Bees Algorithm is that some measure of topological distance between the solutions is defined. The effectiveness and specific abilities of the Bees Algorithm have been proven in a number of studies. [2][3] The Bees Algorithm is inspired by the foraging behaviour of honey bees. Honey bees foraging strategy in nature[edit] A colony of honey bees can extend itself over long distances (over 14 km) [4] and in multiple directions simultaneously to harvest nectar or pollen from multiple food sources (flower patches). The Bees Algorithm[edit] Applications[edit] See also[edit]
Why the Theory of Evolution Exists Introduction to the Mathematics of Evolution Chapter 1 Why the Theory of Evolution Exists "In the preface to the proceedings of the [Wistar] symposium, Dr. Kaplan commented about the importance of mathematics in such matters as theorizing about origins [of life]. 's Enigma, Luther D. Introduction Many times students hear that the theory of evolution is a "proven fact of science." The reality is that the theory of evolution is NOT a proven fact of science. For example, the theory of evolution requires that life be created from simple chemicals. Such a conversion has never been demonstrated and such a conversion has never been proven to be possible. Even the simplest life on earth, which does not require a host, is far too complex to form by a series of accidents. The theory of evolution also requires massive amounts of new genetic information form by totally random mutations of DNA. Before getting into the heart of the issue, it is necessary to distinguish between a "scientist" and "science."
The Probability of Evolution Introduction to the Mathematics of Evolution Chapter 15 The Probability of Evolution "A statistician is a person who stands in a bucket of ice water, sticks their head in an over and says: 'on average, I feel fine!'" K. Gene Complexes In prior chapters we talked about genes and DNA, among other things. A gene would be useless without the rest of the gene complex. No one really knows what the average number of nucleotide pairs (generally just referred to as "nucleotides") are in the average "gene complex." "While only a small fraction of the [DNA] directly encodes for proteins, every protein-encoding sequence is embedded within other functional sequences that regulate the expression of such proteins. Genetic Entropy & The Mystery of the Genome, page 38 The numbers he quotes are for humans. Before getting into human evolution, let us apply the concept of "gene complex" to the "first living cell." The Probability of the "First Living Cell" What if we randomly modified pure gibberish? And so on.
Swarm intelligence Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.[1] The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems. Example algorithms[edit] Particle swarm optimization[edit] Ant colony optimization[edit] Artificial bee colony algorithm[edit] Artificial bee colony algorithm (ABC) is a meta-heuristic algorithm introduced by Karaboga in 2005,[5] and simulates the foraging behaviour of honey bees. Bacterial colony optimization[edit] Differential evolution[edit] Differential evolution is similar to genetic algorithm and pattern search. The bees algorithm[edit] Artificial immune systems[edit] Bat algorithm[edit]