An examination of the various types of human-modeled technology, Advances in Applied Human Modeling and Simulation not only covers the type of models available, but how they can be applied to solve specific problems. These models provide a representation of some human aspects that can be inserted into simulations or virtual environments and facilitate prediction of safety, satisfaction, usability, performance, and sustainability. Topics include: Anthropometry and human functional data Biomechanics, occupational safety, comfort and discomfort Biometric authentications Driving safety and human performance Enhancing human capabilities through aids or training Fuzzy systems and neural computing Human behavior and risk assessment modeling Integrating software with humans and systems International cooperation in education and engineering research Intelligent agents in decision training Intelligent data and text mining Machine learning and human factors Modeling physical aspects of work Monitoring systems and human decision Psychophysiological indicators of emotion Resilience engineering and human reliability Scenario-based performance in distributed enterprises Special populations Sustainability, earth sciences and engineering System-of-systems architecting and engineering Verification and validation Virtual interactive design and assessment The math and science provides a foundation for visualizations that can facilitate decision making by technical experts, management or those responsible for public policy. In considering a systems perspective and decisions that affect performance, these models provide opportunities for an expanded role of engineers and HF/E specialists to meet technical challenges worldwide. They can also be used to improve time-to-market, increase safety and ultimately the effectiveness of an organization. The book focuses on applications of these newly developed models and predictive capabilities useful to human factors and ergonomics engineers, cognitive engineers, human computer interaction engineers, human performance modeling engineers, and students in related fields.

APPLIED SIMULATION MODELING provides the student with both a conceptual introduction to the concepts of simulation modeling and practical experience with real examples using popular commercial simulation packages ARENA and @Risk. The coverage includes Risk Simulation, Dynamic Systems, and Discrete Event Simulation models. Throughout the text, the authors show readers how they can use simulation in the context of decision making. Practical examples from Operations Management, Manufacturing, Health Care, and Finance are included throughout to give students an appreciation for the wide scope of application and the robust nature of simulation modeling. Special student editions of ARENA and @Risk are packaged with the text.

This second edition is extensively revised throughout with expanded discussion of modeling fundamentals and coverage of advances in model calibration and uncertainty analysis that are revolutionizing the science of groundwater modeling. The text is intended for undergraduate and graduate level courses in applied groundwater modeling and as a comprehensive reference for environmental consultants and scientists/engineers in industry and governmental agencies. - Explains how to formulate a conceptual model of a groundwater system and translate it into a numerical model - Demonstrates how modeling concepts, including boundary conditions, are implemented in two groundwater flow codes-- MODFLOW (for finite differences) and FEFLOW (for finite elements) - Discusses particle tracking methods and codes for flowpath analysis and advective transport of contaminants - Summarizes parameter estimation and uncertainty analysis approaches using the code PEST to illustrate how concepts are implemented - Discusses modeling ethics and preparation of the modeling report - Includes Boxes that amplify and supplement topics covered in the text - Each chapter presents lists of common modeling errors and problem sets that illustrate concepts

Simulate reservoirs effectively to extract the maximum oil, gas and profit, with this book and free simlation software on companion web site.

The scope of this book is to identify and emphasize the successful link between computational materials modeling as a simulation and design tool and its synergistic application to experimental research and alloy development. The book provides a more balanced perspective of the role that computational modeling can play in every day research and development efforts. Each chapter describes one or more particular computational tool and how they are best used.

“Simulation-based Case Studies in Logistics” presents an intensive learning course on the application of simulation as a decision support tool to tackle complex logistic problems. The book describes and illustrates different approaches to developing simulation models at the right abstraction level to be used efficiently by engineers when dealing with strategic, tactical or operational decisions in logistic systems. 11 simulation-based case studies in logistics and supply chain management are discussed, based on the results of applied research, covering application areas such as production logistics, warehousing, transportation, material flow management, and hospital logistics. “Simulation-based Case Studies in Logistics” is an essential text for postgraduate engineering students and researchers working in the area of logistics modeling and simulation.

The tool for visualization is Microsoft Visual C++. This popular software has the standard C++ combined with the Microsoft Foundation Classes (MFC) libraries for Windows visualization. This book explains how to create a graph interactively, solve problems in graph theory with minimum number of C++ codes, and provide friendly interfaces that makes learning the topics an interesting one. Each topic in the book comes with working Visual C++ codes which can easily be adapted as solutions to various problems in science and engineering.

Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first half of the book focuses on general methods; the second half discusses model-specific algorithms. Exercises and illustrations are included.