The ability of parallel computing to process large data sets and handle time-consuming operations has resulted in unprecedented advances in biological and scientific computing, modeling, and simulations. Exploring these recent developments, the Handbook of Parallel Computing: Models, Algorithms, and Applications provides comprehensive coverage on all aspects of this field.The first section of the book describes parallel models. It covers evolving computational systems, the decomposable bulk synchronous model, parallel random access machine-on-chip architecture, the parallel disks model, mobile agents, fault-tolerant computing, hierarchical performance modeling, the partitioned optical passive star network, and the reconfigurable mesh model. The subsequent section on parallel algorithms examines networks of workstations, grid and packet scheduling, the derandomization technique, isosurface extraction and rendering, suffix trees, and mobile computing algorithmics. The final part of the text highlights an array of problems and offers ways to combat these challenges.This volume provides an up-to-date assessment of the models and algorithms involved in applying parallel computing to a variety of fields, from computational biology to wireless networking Logic (both classical and non-classical) is being increasingly related with other fields in almost every scientific discipline and human activity. This work covers its role in artificial intelligence, robotics, informatics, technology, and correlated themes. Cover; Title page; Contents; Retriever Prototype of a Case Based Reasoning: A Study Case; Dynamic Compaction Process of Metal Powder Media within Dies; Automated Theorem Proving for Many-sorted Free Description Theory Based on Logic Translation; Annotated Logic and Negation as Failure; Multi-agent System for Distribution System Operation; ArTbitrariness: Putting Computer Creativity to Work in Aesthetic Domains; An Overview of Fuzzy Numbers and Fuzzy Arithmetic; The Brain and Arithmetic Calculation; Evolving Arithmetical Knowledge in a Distributed Intelligent Processing System