Resources to mark the 100th day of school with math activities. Challenge students to generate 100 different ways to represent the number 100. Students will easily generate 99 + 1 and 50 + 50, but encourage them to think out of the box. Challenge them to include examples from all of the NCTM Standards strands: number sense, numerical operations, geometry, measurement, algebra, patterns, data analysis, probability, discrete math, Create a class list to record the best entries. Some teachers write 100 in big bubble numeral style and then record the entries inside the numerals.

This video is part of CARLINK PROJECT simulation videos. It presents the performance of communications in Vehicular Ad-hoc Networks (VANETs) using the IEEE 802.11b standard in the transmission of files.- Jamal Toutouh -

This video is part of CARLINK PROJECT simulation videos. It presents the performance of communications in Vehicular Ad-hoc Networks (VANETs) using the IEEE 802.11b standard in the transmission of files.- Jamal Toutouh -

This module includes the basics and theories of ICT, including types of computer, networks, how, why and who people access information using ICT. This module is the first under the ECDL (AKA ICDL) qualification, written for Windows XP and Office 2003

Simulation of tumor cord growth where conversion of the tumor to glycolytic (anaerobic) metabolism takes place under hypoxia. This video shows evolution of the region where the aerobic cells suffer from hypoxia (ATP deficit) as well as the limit where the glycolytic cells start suffering too. This video reflects work in progress and may be different from the final results.

This is a simulation of tumor cord growth, where cells suffer from hypoxia (energy deficit shown with color). The tumor grows along the blood vessel (coincides with x-axis). Red line shows the position of the tumor–host interface. This particular simulation was programmed in FreeFEM++ out of curiosity. The source code for simulation may be found at http://code.google.com/p/cord. This video reflects work in progress and may be different from the final results.

Projects to facilitate collaboration between biologists and computer scientists. Lecture from the Women in Bioinformatics series. Fran Lewitter, Ph.D. Director of the Bioinformatics and Research Computing Department, Whitehead Institute, MIT

This course will present advanced topics in Artificial Intelligence (AI), including inquiries into logic, artificial neural network and machine learning, and the Turing machine. Upon successful completion of this course, students will be able to: define the term 'intelligent agent,' list major problems in AI, and identify the major approaches to AI; translate problems into graphs and encode the procedures that search the solutions with the graph data structures; explain the differences between various types of logic and basic statistical tools used in AI; list the different types of learning algorithms and explain why they are different; list the most common methods of statistical learning and classification and explain the basic differences between them; describe the components of Turing machine; name the most important propositions in the philosophy of AI; list the major issues pertaining to the creation of machine consciousness; design a reasonable software agent with java code. (Computer Science 408)

This course will expand upon SQL as well as other advanced topics, including query optimization, concurrency, data warehouses, object-oriented extensions, and XML. Additional topics covered in this course will help you become more proficient in writing queries and will expand your knowledge base so that you have a better understanding of the field. Upon successful completion of this course, the student will be able to: write complex queries, including full outer joins, self-joins, sub queries, and set theoretic queries; write stored procedures and triggers; apply the principles of query optimization to a database schema; explain the various types of locking mechanisms utilized within database management systems; explain the different types of database failures as well as the methods used to recover from these failures; design queries against a distributed database management system; perform queries against database designed with object-relational extensions; develop and query XML files. (Computer Science 410)

This course focuses on the fundamentals of computer algorithms, emphasizing methods useful in practice. Upon successful completion of this course, the student will be able to: explain and identify the importance of algorithms in modern computing systems and their place as a technology in the computing industry; indentify algorithms as a pseudo-code to solve some common problems; describe asymptotic notations for bounding algorithm running times from above and below; explain methods for solving recurrences useful in describing running times of recursive algorithms; explain the use of Master Theorem in describing running times of recursive algorithms; describe the divide-and-conquer recursive technique for solving a class of problems; describe sorting algorithms and their runtime complexity analysis; describe the dynamic programming technique for solving a class of problems; describe greedy algorithms and their applications; describe concepts in graph theory, graph-based algorithms, and their analysis; describe tree-based algorithms and their analysis; explain the classification of difficult computer science problems as belonging to P, NP, and NP-hard classes. (Computer Science 303)

This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.

This course examines computers anthropologically, as meaningful tools revealing the social and cultural orders that produce them. We read classic texts in computer science along with works analyzing links between machines and culture. We explore early computation theory and capitalist manufacturing; cybernetics and WWII operations research; artificial intelligence and gendered subjectivity; the creation and commodification of the personal computer; the hacking aesthetic; non-Western histories of computing; the growth of the Internet as a military, academic, and commercial project; the politics of identity in cyberspace; and the emergence of "evolutionary" computation.

The items in this collection were developed by the author for the support of open courseware. Some modules are particularly useful for students taking courses that use computers and others could be used for all courses.

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

This course provides a challenging introduction to some of the central ideas of theoretical computer science. Beginning in antiquity, the course will progress through finite automata, circuits and decision trees, Turing machines and computability, efficient algorithms and reducibility, the P versus NP problem, NP-completeness, the power of randomness, cryptography and one-way functions, computational learning theory, and quantum computing. It examines the classes of problems that can and cannot be solved by various kinds of machines. It tries to explain the key differences between computational models that affect their power.

This video is courtesy of EU-IndiaGrid and shows the setup of a simple simulation using BEMuSE, software for faster folding of proteins using grid computing. (http://euindia.ictp.it/bemuse/video-tutorials/) This video shows:
* The structure of the restarts * The configuration of a simulation * The submission of the jobs

This course is designed for teachers who are considering the use of video game design in the classroom. It contains software recommendations, tips on creating units and links to research.

Dr. Laura Elnitski, Head of the Genomic Functional Analysis Section, Genome Technology Branch NHGRI/NIHDr. Elnitski uses experimental and Bioinformatic methods to discover non-coding functional elements in the human genome. On 7 March 2008, Dr. Elnitski came to MSU-Bozeman to participate in the Women In Bioinformatics Seminar Series.

Simulation created using the CFD code named CEBAM (Computational Explosion And Blast Assessment Model). CEBAM is capable of simulating blast loads from high explosive, vapor clouds, and bursting vessels. Blast loading on a Humvee from an IED detonated near the vehicle. Research performed by Risknology, Inc.