Welcome to our book review site www.go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Modelling, Analysis and Optimization of Biosystems
Mathematical models in biology and medicine cannot be based on natural laws as it is the case with physics and chemistry. This is due to the fact that biological and medical processes are concerned with living organisms. Mathematical models, however, can be used as a language by which certain aspects of biological or medical processes can be expressed. In general, several mathematical models can be designed in order to describe a biological or medical process and there is no unique criterion which model gives the best description. This book presents several of these models and shows applications of them to different biological and medical problems. The book shows that operations research expertise is necessary in respect to modeling, analysis and optimization of biosystems.
Dynamics of Disasters
Based on the “Fourth International Conference on Dynamics of Disasters” (Kalamata, Greece, July 2019), this volume includes contributions from experts who share their latest discoveries on natural and unnatural disasters. Authors provide overviews of the tactical points involved in disaster relief, outlines of hurdles from mitigation and preparedness to response and recovery, and uses for mathematical models to describe natural and man-made disasters. Topics covered include economics, optimization, machine learning, government, management, business, humanities, engineering, medicine, mathematics, computer science, behavioral studies, emergency services, and environmental studies will engage readers from a wide variety of fields and backgrounds.
Reliability Modeling in Industry 4.0
Reliability Modeling with Industry 4.0 explores the emerging theoretical and practical developments in reliability engineering in highly digitized industries, including power, computer systems, railway systems, and robotics. Drawing on leading research from around the globe, as well as the latest in industry practice, this book provides cutting edge advice on how to integrate a fully digitized industry 4.0 system for enhanced reliability and reduced maintenance cost. Technologies such as big data, artificial intelligence, and the industrial internet of things are addressed in the context of reliability engineering, providing practical advice on applications. - Provides innovative reliability modeling tools related to the application of Industry 4.0 technologies - Includes case studies from industries such as rail, energy, and computer systems - Describes techniques for the successful digital transformation of industries for sophisticated reliability systems
Advances in Human Factors in Simulation and Modeling
This book focuses on computational modeling and simulation research that advances the current state-of-the-art regarding human factors in simulation and applied digital human modeling. It reports on cutting-edge simulators such as virtual and augmented reality, on multisensory environments, and on modeling and simulation methods used in various applications, such as surgery, military operations, occupational safety, sports training, education, transportation and robotics. Based on the AHFE 2017 International Conference on Human Factors in Simulation and Modeling, held on July 17–21, 2017, in Los Angeles, California, USA, the book is intended as a timely reference guide for researchers and practitioners developing new modeling and simulation tools for analyzing or improving human performance. It also offers a unique resource for modelers seeking insights into human factors research and more feasible and reliable computational tools to foster advances in this exciting research field.
Analysis, Controllability and Optimization of Time-Discrete Systems and Dynamical Games
J. P. La Salle has developed in [20] a stability theory for systems of difference equations (see also [8]) which we introduce in the first chapter within the framework of metric spaces. The stability theory for such systems can also be found in [13] in a slightly modified form. We start with autonomous systems in the first section of chapter 1. After theoretical preparations we examine the localization of limit sets with the aid of Lyapunov Functions. Applying these Lyapunov Functions we can develop a stability theory for autonomous systems. If we linearize a non-linear system at a fixed point we are able to develop a stability theory for fixed points which makes use of the Frechet derivativ...
Operations Research Proceedings 2021
This book gathers a selection of peer-reviewed papers presented at the International Conference on Operations Research (OR 2021), which was hosted online by the University of Bern from August 31 to September 3, 2021, and was jointly organized by the Operations Research Societies of Switzerland (SVOR/ASRO), Germany (GOR e.V.), and Austria (ÖGOR). The respective papers discuss classical mathematical optimization, statistics and simulation techniques. These are complemented by computer science methods, and by tools for processing data, designing and implementing information systems. The book also examines recent advances in information technology, which allow massive volumes of data to be processed and enable real-time predictive and prescriptive business analytics to drive decisions and actions. Lastly, it presents a selection of problems that are modeled and treated while taking into account uncertainty, risk management, behavioral issues, etc.
Big Data of Complex Networks
Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks. Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for re...
Mathematical Reviews
None
