In the last decade there has been a phenomenal growth in interest in crime pattern analysis. Geographic information systems are now widely used in urban police agencies throughout industrial nations. With this, scholarly interest in understanding crime patterns has grown considerably. ""Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems"" discusses leading research on the use of computer simulation of crime patterns to reveal hidden processes of urban crimes, taking an interdisciplinary approach by combining criminology, computer simulation, and geographic information systems into one comprehensive resource.

Real world problems are complex and full of uncertainty. Fuzzy computing technologies embedded into data analysis methodologies and decision-making processes help to address the complexity of these problems. Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization presents the most innovative systematic and practical facets of fuzzy computing technologies to students, scholars, and academicians, as well as practitioners, engineers, and professionals. This premier reference source focuses on up-to-date theoretical views of fuzzy computing while highlighting empirical approaches useful to real world utilization.

The healthcare industry is starting to adopt digital twins to improve personalized medicine, healthcare organization performance, and new medicine and devices. These digital twins can create useful models based on information from wearable devices, omics, and patient records to connect the dots across processes that span patients, doctors, and healthcare organizations as well as drug and device manufacturers. Digital twins are digital representations of human physiology built on computer models. The use of digital twins in healthcare is revolutionizing clinical processes and hospital management by enhancing medical care with digital tracking and advancing modelling of the human body. These tools are of great help to researchers in studying diseases, new drugs, and medical devices. Digital Twins and Healthcare: Trends, Techniques, and Challenges facilitates the advancement and knowledge dissemination in methodologies and applications of digital twins in the healthcare and medicine fields. This book raises interest and awareness of the uses of digital twins in healthcare in the research community. Covering topics such as deep neural network, edge computing, and transfer learning method, this premier reference source is an essential resource for hospital administrators, pharmacists, medical professionals, IT consultants, students and educators of higher education, librarians, and researchers.

The discovery and development of new computational methods have expanded the capabilities and uses of simulations. With agent-based models, the applications of computer simulations are significantly enhanced. Multi-Agent-Based Simulations Applied to Biological and Environmental Systems is a pivotal reference source for the latest research on the implementation of autonomous agents in computer simulation paradigms. Featuring extensive coverage on relevant applications, such as biodiversity conservation, pollution reduction, and environmental risk assessment, this publication is an ideal source for researchers, academics, engineers, practitioners, and professionals seeking material on various issues surrounding the use of agent-based simulations.

This book addresses the experimental calibration of best-estimate numerical simulation models. The results of measurements and computations are never exact. Therefore, knowing only the nominal values of experimentally measured or computed quantities is insufficient for applications, particularly since the respective experimental and computed nominal values seldom coincide. In the author's view, the objective of predictive modeling is to extract "best estimate" values for model parameters and predicted results, together with "best estimate" uncertainties for these parameters and results. To achieve this goal, predictive modeling combines imprecisely known experimental and computational data, which calls for reasoning on the basis of incomplete, error-rich, and occasionally discrepant information. The customary methods used for data assimilation combine experimental and computational information by minimizing an a priori, user-chosen, "cost functional" (usually a quadratic functional that represents the weighted errors between measured and computed responses). In contrast to these user-influenced methods, the BERRU (Best Estimate Results with Reduced Uncertainties) Predictive Modeling methodology developed by the author relies on the thermodynamics-based maximum entropy principle to eliminate the need for relying on minimizing user-chosen functionals, thus generalizing the "data adjustment" and/or the "4D-VAR" data assimilation procedures used in the geophysical sciences. The BERRU predictive modeling methodology also provides a "model validation metric" which quantifies the consistency (agreement/disagreement) between measurements and computations. This "model validation metric" (or "consistency indicator") is constructed from parameter covariance matrices, response covariance matrices (measured and computed), and response sensitivities to model parameters. Traditional methods for computing response sensitivities are hampered by the "curse of dimensionality," which makes them impractical for applications to large-scale systems that involve many imprecisely known parameters. Reducing the computational effort required for precisely calculating the response sensitivities is paramount, and the comprehensive adjoint sensitivity analysis methodology developed by the author shows great promise in this regard, as shown in this book. After discarding inconsistent data (if any) using the consistency indicator, the BERRU predictive modeling methodology provides best-estimate values for predicted parameters and responses along with best-estimate reduced uncertainties (i.e., smaller predicted standard deviations) for the predicted quantities. Applying the BERRU methodology yields optimal, experimentally validated, "best estimate" predictive modeling tools for designing new technologies and facilities, while also improving on existing ones.

This unique, new book covers the whole field of electronic warfare modeling and simulation at a systems level, including chapters that describe basic electronic warfare (EW) concepts. Written by a well-known expert in the field with more than 24 years of experience, the book explores EW applications and techniques and the radio frequency spectrum. A detailed resource for entry-level engineering personnel in EW, military personnel with no radio or communications engineering background, technicians and software professionals, the work explains the basic concepts required for modeling and simulation that today's professionals need to understand. Practitioners find clear explanations of important mathematical concepts, such as decibel notation and spherical trigonometry, necessary for modeling and simulation. Moreover, the book describes specific types of EW equipment, how they work and how each is mathematically modeled.

This book offers a comprehensive overview of the challenges in hydrological modeling. Hydrology, on both a local and global scale, has undergone dramatic changes, largely due to variations in climate, population growth and the associated land-use and land-cover changes. Written by experts in the field, the book provides decision-makers with a better understanding of the science, impacts, and consequences of these climate and land-use changes on hydrology. Further, offering insights into how the changing behavior of hydrological processes, related uncertainties and their evolution affect the modeling process, it is of interest for all researchers and practitioners using hydrological modeling.

This book examines the origins and dynamical characteristics of atmospheric inertia-gravity waves in the Antarctic mesosphere. Gravity waves are relatively small-scale atmospheric waves with a restoring force of buoyancy that can transport momentum upward from the troposphere to the middle atmosphere. In previous studies, the dynamical characteristics of mesospheric gravity waves have not been fully examined using numerical simulations, since performing a numerical simulation with a high resolution and a high model-top requires considerable computational power. However, recent advances in computational capabilities have allowed us to perform numerical simulations using atmospheric general circulation models, which cover the troposphere to the mesosphere with a sufficiently fine horizontal resolution to resolve small-scale gravity waves. The book first describes the simulation of mesospheric gravity waves using a high-resolution non-hydrostatic atmospheric model with a high model top. The accuracy of the numerical results was confirmed by the first Mesosphere-Stratosphere-Troposphere/Incoherent Scattering (MST/IS) radar observation in the Antarctic. It also depicts the origins and propagation processes of mesospheric gravity waves on the basis of the results of the high-resolution numerical model. The behaviors of mesospheric gravity waves can be clearly explained using both fundamental and cutting-edge theories of fluid dynamics

The papers contained in this volume were originally presented at the 2015 International Conference on Complex Systems in Business, Administration, Science and Engineering. Included are the latest works of practitioners from a variety of disciplines who have developed new approaches for resolving complex issues that cannot be formulated using conventional, mathematical or software models.Complex Systems occur in an infinite variety of problems, not only in the realm of physical sciences and engineering, but also in such diverse fields as economics, the environment, humanities, and social and political sciences.The papers in the book cover such topics as: Complex ecological systems; Complexity science and urban developments; Complex energy systems; Complex issues in biological and medical sciences; Extreme events: natural and human made disasters; Climate change; Complexity of the internet-based global market; Complex business processes; Supply chain complexity; Transportation complexity; Logistics complexity; Closed and open systems; Attractions and chaotic systems; Complex adaptive software; Complexity of big data; Management of complexity; Global economy as a complex system; Complexity in social systems; Complex political systems; Administrations as complex systems; Complexity in engineering; Complexity and environment; Complexity and evolution; Complexity in linguistics, literature and arts.

The book discusses the theoretical fundamentals of CAD graphics to enhance readers' understanding of surface modeling and free-form design by demonstrating how to use mathematical equations to define curves and surfaces in CAD modelers. Additionally, it explains and describes the main approaches to creating CAD models out of 3D scans of physical objects. All CAD approaches are demonstrated with guided examples and supported with comprehensive engineering explanations. Furthermore, each approach includes exercises for independent consolidation of advanced CAD skills. This book is intended for engineers and designers who are already familiar with the basics of modern CAD tools, e.g. feature based and solid based modeling in 3D space, and would like to improve and expand their knowledge and experience. It is also an easy-to use guide and excellent teaching and research aid for academics and practitioners alike.

Probabilistic modeling represents a subject spanning many branches of mathematics, economics, and computer science to connect pure mathematics with applied sciences. Operational research also relies on this connection to enable the improvement of business functions and decision making. Analyzing Risk through Probabilistic Modeling in Operations Research is an authoritative reference publication discussing the various challenges in management and decision science. Featuring exhaustive coverage on a range of topics within operational research including, but not limited to, decision analysis, data mining, process modeling, probabilistic interpolation and extrapolation, and optimization methods, this book is an essential reference source for decision makers, academicians, researchers, advanced-level students, technology developers, and government officials interested in the implementation of probabilistic modeling in various business applications.

Computational Modelling of Nanoparticles highlights recent advances in the power and versatility of computational modelling, experimental techniques, and how new progress has opened the door to a more detailed and comprehensive understanding of the world of nanomaterials. Nanoparticles, having dimensions of 100 nanometers or less, are increasingly being used in applications in medicine, materials and manufacturing, and energy. Spanning the smallest sub-nanometer nanoclusters to nanocrystals with diameters of 10s of nanometers, this book provides a state-of-the-art overview on how computational modelling can provide, often otherwise unobtainable, insights into nanoparticulate structure and properties. This comprehensive, single resource is ideal for researchers who want to start/improve their nanoparticle modelling efforts, learn what can be (and what cannot) achieved with computational modelling, and understand more clearly the value and details of computational modelling efforts in their area of research.

From Digital Traces to Algorithmic Projections describes individual digital fingerprints in interaction with the different algorithms they encounter throughout life. Centered on the human user, this formalism makes it possible to distinguish the voluntary projections of an individual and their systemic projections (suffered, metadata), both open (public) and closed. As the global algorithmic projection of an individual is now the focus of attention (Big Data, neuromarketing, targeted advertising, sentiment analysis, cybermonitoring, etc.) and is used to define new concepts, this resource discusses the ubiquity of place and the algorithmic consent of a user.

This book describes for readers various technical outcomes from the EU-project IoSense. The authors discuss sensor integration, including LEDs, dust sensors, LIDAR for automotive driving and 8 more, demonstrating their use in simulations for the design and fabrication of sensor systems. Readers will benefit from the coverage of topics such as sensor technologies for both discrete and integrated innovative sensor devices, suitable for high volume production, electrical, mechanical, security and software resources for integration of sensor system components into IoT systems and IoT-enabling systems, and IoT sensor system reliability. Describes from component to system level simulation, how to use the available simulation techniques for reaching a proper design with good performance; Explains how to use simulation techniques such as Finite Elements, Multi-body, Dynamic, stochastics and many more in the virtual design of sensor systems; Demonstrates the integration of several sensor solutions (thermal, dust, occupancy, distance, awareness and more) into large-scale system solutions in several industrial domains (Lighting, automotive, transport and more); Includes state-of-the-art simulation techniques, both multi-scale and multi-physics, for use in the electronic industry.

Interval Finite Element Method with MATLAB provides a thorough introduction to an effective way of investigating problems involving uncertainty using computational modeling. The well-known and versatile Finite Element Method (FEM) is combined with the concept of interval uncertainties to develop the Interval Finite Element Method (IFEM). An interval or stochastic environment in parameters and variables is used in place of crisp ones to make the governing equations interval, thereby allowing modeling of the problem. The concept of interval uncertainties is systematically explained. Several examples are explored with IFEM using MATLAB on topics like spring mass, bar, truss and frame.

Recent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the control of single-input, single-output systems. This volume provides a definitive survey of the latest model-predictive control methods available to engineers and scientists today. The initial set of chapters present various methods for managing uncertainty in systems, including stochastic model-predictive control. With the advent of affordable and fast computation, control engineers now need to think about using "computationally intensive controls," so the second part of this book addresses the solution of optimization problems in "real" time for model-predictive control. The theory and applications of control theory often influence each other, so the last section of Handbook of Model Predictive Control rounds out the book with representative applications to automobiles, healthcare, robotics, and finance. The chapters in this volume will be useful to working engineers, scientists, and mathematicians, as well as students and faculty interested in the progression of control theory. Future developments in MPC will no doubt build from concepts demonstrated in this book and anyone with an interest in MPC will find fruitful information and suggestions for additional reading.

This book presents the state-of-the-art in supercomputer simulation. It includes the latest findings from leading researchers using systems from the High Performance Computing Center Stuttgart (HLRS) in 2017. The reports cover all fields of computational science and engineering ranging from CFD to computational physics and from chemistry to computer science with a special emphasis on industrially relevant applications. Presenting findings of one of Europe's leading systems, this volume covers a wide variety of applications that deliver a high level of sustained performance.The book covers the main methods in high-performance computing. Its outstanding results in achieving the best performance for production codes are of particular interest for both scientists and engineers. The book comes with a wealth of color illustrations and tables of results.

This book is a survey of the research work done by the author over the last 15 years, in collaboration with various eminent mathematicians and climate scientists on the subject of tropical convection and convectively coupled waves. In the areas of climate modelling and climate change science, tropical dynamics and tropical rainfall are among the biggest uncertainties of future projections. This not only puts at risk billions of human beings who populate the tropical continents but it is also of central importance for climate predictions on the global scale. This book aims to introduce the non-expert readers in mathematics and theoretical physics to this fascinating topic in order to attract interest into this difficult and exciting research area. The general thyme revolves around the use of new deterministic and stochastic multi-cloud models for tropical convection and convectively coupled waves. It draws modelling ideas from various areas of mathematics and physics and used in conjunction with state-of-the-art satellite and in-situ observations and detailed numerical simulations. After a review of preliminary material on tropical dynamics and moist thermodynamics, including recent discoveries based on satellite observations as well as Markov chains, the book immerses the reader into the area of models for convection and tropical waves. It begins with basic concepts of linear stability analysis and ends with the use of these models to improve the state-of-the-art global climate models. The book also contains a fair amount of exercises that makes it suitable as a textbook complement on the subject.

This book presents the state of the art in High Performance Computing on modern supercomputer architectures. It addresses trends in hardware and software development in general, as well as the future of High Performance Computing systems and heterogeneous architectures. The contributions cover a broad range of topics, from improved system management to Computational Fluid Dynamics, High Performance Data Analytics, and novel mathematical approaches for large-scale systems. In addition, they explore innovative fields like coupled multi-physics and multi-scale simulations. All contributions are based on selected papers presented at the 24th Workshop on Sustained Simulation Performance, held at the University of Stuttgart's High Performance Computing Center in Stuttgart, Germany in December 2016 and the subsequent Workshop on Sustained Simulation Performance, held at the Cyberscience Center, Tohoku University, Japan in March 2017.

This book is intended for researchers, practitioners and students who are interested in the current trends and want to make their GI applications and research dynamic. Time is the key element of contemporary GIS: mobile and wearable electronics, sensor networks, UAVs and other mobile snoopers, the IoT and many other resources produce a massive amount of data every minute, which is naturally located in space as well as in time. Time series data is transformed into almost (from the human perspective) continuous data streams, which require changes to the concept of spatial data recording, storage and manipulation. This book collects the latest innovative research presented at the GIS Ostrava 2017 conference held in 2017 in Ostrava, Czech Republic, under the auspices of EuroSDR and EuroGEO. The accepted papers cover various aspects of dynamics in GIscience, including spatiotemporal data analysis and modelling; spatial mobility data and trajectories; real-time geodata and real-time applications; dynamics in land use, land cover and urban development; visualisation of dynamics; open spatiotemporal data; crowdsourcing for spatiotemporal data and big spatiotemporal data.

This book contains a selection of papers presented during a special workshop on Complexity Science organized as part of the 9th International Conference on GIScience 2016. Expert researchers in the areas of Agent-Based Modeling, Complexity Theory, Network Theory, Big Data, and emerging methods of Analysis and Visualization for new types of data explore novel complexity science approaches to dynamic geographic phenomena and their applications, addressing challenges and enriching research methodologies in geography in a Big Data Era.