Stunning Infographics and Data Visualization Feb 02 2010 Information graphics, or infographics, are visual representations of information, data or knowledge. The graphics are used where complex information needs to be explained quickly and clearly, such as on signs and maps and in journalism, technical writing and education. Creating an effective infographic requires both artistic sense and a clear vision of what to tell the audience. You may be interested in the following related posts: Beautiful Infographics And Data Visualization 37 Minute Bus Ride Population of dead Feel Good Hospital Aleman: Tree Pink Floyd Timeline 1960-2000 Genealogy of Pop and Rock Music Digital Podge Ancient Hebrew Cosmology Twitter Territory Digital NostalgiaA set of infographic posters documenting the progress of technology and some of the social events that have been influenced by it. ‘Simpsons’ comedy tree Overnewsed but uninformed Knot Tied Jules & Jim: Entropy of Love Feltron: 2009 Annual Report Ouch? Nonsense Chart No.6 by 1chord & a fib. High-Speed Trains Storage10
Usenet Physics FAQ Version Date: February 2018 This list of answers to frequently asked questions in physics was created by Scott Chase in 1992. Its purpose was to provide good answers to questions that had been discussed often in the sci.physics and related Internet news groups. The articles in this FAQ are based on those discussions and on information from good reference sources. They were later maintained and enlarged by Michael Weiss and Philip Gibbs. Most of the entries that you'll find here were written in the days when the Internet was brand new. So because of their age, the FAQ entries that you'll find here have a great deal of academic credibility—but they are not always perfect and complete. This document is copyright. General Physics Particle and Nuclear Physics Quantum Physics Relativity and Cosmology Speed of Light Special Relativity General Relativity and Cosmology Black Holes Reference Topics There are many other places where you may find answers to your question. Australia: England: Netherlands:
Physics I: Classical Mechanics - Download free content from MIT www-klinowski.ch.cam.ac.uk/pmshome.html | Take a look at our Periodic Minimal Surfaces Image Gallery | Important events have recently taken place in structural crystallography. First, the rigid concept of a “perfect crystal” has been relaxed to embrace more general structures, such as quasi-crystals.[1] Second, an advance has been made from the classical geometry of coordination polyhedra to three-dimensional differential geometry. The decisive step in this direction has been the use of curved surfaces in describing a great variety of structures.[2] The essence of the key concept of a minimal surface is as follows. It is imperative that the TPMS be properly examined in the practical context. The application of minimal surfaces to the physical world has so far been descriptive, rather than quantitative. The coordinates of points lying on minimal surfaces are described in terms of integrals involving the (“Weierstrass”) function of the form where and are parameters which fully describe the surface.
100 Open Courses to Learn Any New Language Learning a new language can be a great way to challenge your mind, meet people from different cultures and even add a valuable asset to your resume and hireability. While traditional courses can be great, there are a number of free courses on the web that can help teach you the basics of language learning and get you on the path to fluency without having to spend a fortune. Here are 100 resources we’ve found that will help you become multilingual in your choice of languages. French French is one of the most widely spoken languages in the world and an official language in countries on nearly every continent. These courses will help you learn the famously romantic language for personal advancement or pleasure. French I: Take this course to learn things like French grammar, pronunciation and get an introduction to French culture. Spanish Italian Whether you’re headed to Italy for business or pleasure or just want to impress the in-laws, these courses will help you brush up on your Italian.
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
Maze Design We've been working on producing mazes by computer, with input from a human designer. We're interested in two complementary questions with respect to maze design: Complexity: What makes a maze difficult to solve? The more we consider this question, the more elusive it becomes. It's certainly possible to begin defining mathematical measures of a maze's complexity, but complexity must depend on aspects of human perception as well. Gallery We have addressed these two aspects of maze design in separate sub-projects. We then studied maze design as a problem in non-photorealistic rendering. Clicking on the examples below will link you to a very high-res PNG. Papers Other resources Karan Singh has put together an excellent page about maze and labyrinth design, motivated by his NPAR 2006 paper on labyrinths. Mazes can be used to represent images in a couple of different ways. Thank you to Ronald Koster and Claudio Pozas for the use of the source images for the snails and minotaur mazes.
Complexity, Artificial Life and Self-Organising Systems Glossary In this glossary each entry is an hypertext link that takes you to an introduction describing that concept in a wider context. Alternatively, to read all the introductions in sequence start with "Setting The Scene". This is a brief glossary, for a more detailed one see: ISAAC's. Some of the terms included here are specific to the wider CALResCo viewpoint and may not be common in the work of other more specialised groups. Adaptability The ability of an organism to learn in response to changes in its environment over the course of its lifetime. Adaptation The ability of a species to change in response to changes in its environment over many generations. Agents Individuals within an interacting population, each may have only limited freedom to react to their neighbours yet the behaviour of the whole (emergent) may be much more complex. Aggregate ALife Attractor A point to which a system tends to move, a goal, either deliberate or constrained by system parameters (laws). Arms Race Autocatalysis
Computational complexity theory Computational complexity theory is a branch of the theory of computation in theoretical computer science and mathematics that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. A computational problem is understood to be a task that is in principle amenable to being solved by a computer, which is equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying the amount of resources needed to solve them, such as time and storage. Closely related fields in theoretical computer science are analysis of algorithms and computability theory. Computational problems[edit] Problem instances[edit] Turing machine[edit]
Figures for &Impossible fractals& Figures for "Impossible fractals" Cameron Browne Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Figure 7. Figure 8. Figure 9. Figure 10. Figure 11. Figure 12. Figure 13. 45° Pythagorean tree, balanced 30° Pythagorean tree and extended tri-bar. Figure 14. Figure 15. Figure 16.