A little hand, big idea :-) by James You will need some fine settings on your printer to print the 60% scale hand. The model pictured is 100% and prints easily with .25mm layers. But for 60% I used 0.18 layer and 0.3mm extrusion width with 12-18mm/s feed rates. For the fingertips you will need to slow it down to 5mm/s feed rate. After the print finishes you need to cut off the support pads some are circular others are rectangular. You will need some fishing line 0.5-0.8mm dia and 1.5-2mm dia self-tapping screws. not easy to get but available, go to a hobby store. The wrist and thumb base parts I recommend screwing together for initial assembly, then when it all works well glue and screw it together. The tips are a separate print because I printed them hollow, 0.3mm wall thickness with soft pliable PLA, any printable plastic will do though. The hand should probably be put in a rubber glove to enhance its grip. I'm working on a mod to include duel tendons that will enhance the movement. Updating files be patient
Ask Nature - the Biomimicry Design Portal: biomimetics, architecture, biology, innovation inspired by nature, industrial design - Ask Nature - the Biomimicry Design Portal: biomimetics, architecture, biology, innovation inspired by nature, industrial desi iTunes U iTunes U You must use an iOS device to access this course. Learn more about iTunes U courses on your iPad, iPhone, or iPod touch. This Course is Available on iTunes U Get iTunes U Learn more about iTunes U voidhaze's Home 3.8 Institute The Biomimicry 3.8 Institute is a not-for-profit organization that promotes the study and imitation of nature’s remarkably efficient designs, bringing together scientists, engineers, architects and innovators of all ages who can use those models to create sustainable technologies. The Institute was founded in 2006 by science writer and consultant Janine Benyus in response to overwhelming interest in the subject following the publication of her book, Biomimicry: Innovation Inspired by Nature. See Janine’s TED Talk video for her groundbreaking introduction to biomimicry. Today, the Biomimicry 3.8 Institute focuses on three areas: Developing our online database of nature’s solutions, AskNature.org.Hosting our annual, international Biomimicry Student Design Challenge.Growing our Global Network of regional biomimicry practitioners. See examples of biomimicry in action! Meet executive director Beth Rattner, our staff, and the Institute board.
Neptune's Pride. Explore, Expand, Exploit, Exterminate! bigthink's Channel There are only two events in the universe that defy the laws of physics: black holes and the big bang, and while scientists try to explain them, crucial evidence may be eaten up in the meantime. Christophe Galfard's book is "The Universe in Your Hand A Journey Through Space, Time, and Beyond" ( Read more at BigThink.com: Follow Big Think here:YouTube: Transcript - The interesting thing about trying to unravel the laws of nature is that yes, we have found some laws. For a long time gravity has told us that nothing can escape the gravitational grip of a black hole. And everything we had known about black holes until the mid-1970s was only related to gravity. Neuro-linguistic programming Not to be confused with Natural language processing (also NLP) Neuro-linguistic programming (NLP) is an approach to communication, personal development, and psychotherapy created by Richard Bandler and John Grinder in California, United States in the 1970s. Its creators claim a connection between the neurological processes ("neuro"), language ("linguistic") and behavioral patterns learned through experience ("programming") and that these can be changed to achieve specific goals in life.[1][2] Bandler and Grinder claim that the skills of exceptional people can be "modeled" using NLP methodology, then those skills can be acquired by anyone.[3][4][5][6][7] Bandler and Grinder also claim that NLP can treat problems such as phobias, depression, habit disorder, psychosomatic illnesses, myopia,[8] allergy, common cold,[9] and learning disorders, often in a single session.[10][11][12][13] NLP has been adopted by some hypnotherapists and in seminars marketed to business and government.[14][15]
High-voltage engineers create nearly 200-foot-long electrical arcs using less energy than before (Update) Photos taken by the researchers show plasma arcs up to 60 meters long casting an eerie blue glow over buildings and trees at the High Voltage Laboratory at the University of Canterbury in New Zealand. A team of engineers at Canterbury University in New Zealand has developed a method to create nearly 200-foot-long electrical arcs -- visible currents of electricity traveling through air that has been broken down into electrically charged particles. Others have created longer arcs, but the traditional technique requires large amounts of energy in order to break down the air. The new technique requires much less energy. Daniel Sinars, who researches fusion at Sandia National Laboratory in Albuquerque, N.M., has also worked with exploding wires, but at a much smaller scale. "It's hard to make a plasma that size," said Sinars. The team occasionally created plasma arcs during other exploding wire experiments and pursued the new research in order to better understand how the arcs formed.
Decision support system A Decision Support System (DSS) is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management, operations, and planning levels of an organization (usually mid and higher management) and help to make decisions, which may be rapidly changing and not easily specified in advance (Unstructured and Semi-Structured decision problems). Decision support systems can be either fully computerized, human or a combination of both. While academics have perceived DSS as a tool to support decision making process, DSS users see DSS as a tool to facilitate organizational processes.[1] Some authors have extended the definition of DSS to include any system that might support decision making.[2] Sprague (1980) defines DSS by its characteristics: DSSs include knowledge-based systems. Typical information that a decision support application might gather and present includes: History[edit] Taxonomies[edit] Components[edit] Classification[edit]
Expert system An expert system is divided into two sub-systems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. History[edit] Edward Feigenbaum in a 1977 paper said that the key insight of early expert systems was that "intelligent systems derive their power from the knowledge they possess rather than from the specific formalisms and inference schemes they use" (as paraphrased by Hayes-Roth, et al.) Expert systems were introduced by the Stanford Heuristic Programming Project led by Feigenbaum, who is sometimes referred to as the "father of expert systems". In addition to Feigenbaum key early contributors were Bruce Buchanan, Edward Shortliffe, Randall Davis, William vanMelle, and Carli Scott. In the 1980s, expert systems proliferated. In 1981 the first IBM PC was introduced, with the MS-DOS operating system. Software architecture[edit] R1: Man(x) => Mortal(x) Truth Maintenance.
How Khan Academy is using Machine Learning to Assess Student Mastery | David Hu See discussion on Hacker News and Reddit. The Khan Academy is well known for its extensive library of over 2600 video lessons. It should also be known for its rapidly-growing set of now 225 exercises — outnumbering stitches on a baseball — with close to 2 million problems done each day. To determine when a student has finished a certain exercise, we award proficiency to a user who has answered at least 10 problems in a row correctly — known as a streak. It turns out that the streak model has serious flaws. First, if we define proficiency as your chance of getting the next problem correct being above a certain threshold, then the streak becomes a poor binary classifier. False positives is not our only problem, but also false negatives. In Search of a Better Model These findings, presented by one of our full-time volunteers Jace, led us to investigate whether we could construct a better proficiency model. to this: and when full: % likely to get the next problem correct, for some threshold . .