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This book explores computational fluid dynamics in the context of the human nose, allowing readers to gain a better understanding of its anatomy and physiology and integrates recent advances in clinical rhinology, otolaryngology and respiratory physiology research. It focuses on advanced research topics, such as virtual surgery, AI-assisted clinical applications and therapy, as well as the latest computational modeling techniques, controversies, challenges and future directions in simulation using CFD software. Presenting perspectives and insights from computational experts and clinical specialists (ENT) combined with technical details of the computational modeling techniques from engineers, this unique reference book will give direction to and inspire future research in this emerging field.

Systems Simulation and Modelling for Cloud Computing and Big Data Applications provides readers with the most current approaches to solving problems through the use of models and simulations, presenting SSM based approaches to performance testing and benchmarking that offer significant advantages. For example, multiple big data and cloud application developers and researchers can perform tests in a controllable and repeatable manner. Inspired by the need to analyze the performance of different big data processing and cloud frameworks, researchers have introduced several benchmarks, including BigDataBench, BigBench, HiBench, PigMix, CloudSuite and GridMix, which are all covered in this book. Despite the substantial progress, the research community still needs a holistic, comprehensive big data SSM to use in almost every scientific and engineering discipline involving multidisciplinary research. SSM develops frameworks that are applicable across disciplines to develop benchmarking tools that are useful in solutions development. - Examines the methodology and requirements of benchmarking big data and cloud computing tools, advances in big data frameworks and benchmarks for large-scale data analytics, and frameworks for benchmarking and predictive analytics in big data deployment - Discusses applications using big data benchmarks, such as BigDataBench, BigBench, HiBench, MapReduce, HPCC, ECL, HOBBIT, GridMix and PigMix, and applications using big data frameworks, such as Hadoop, Spark, Samza, Flink and SQL frameworks - Covers development of big data benchmarks to evaluate workloads in state-of-the-practice heterogeneous hardware platforms, advances in modeling and simulation tools for performance evaluation, security problems and scalable cloud computing environments

This book has a two-fold purpose: (1) An introduction to the computer-based modeling of influenza, a continuing major worldwide communicable disease. (2) The use of (1) as an illustration of a methodology for the computer-based modeling of communicable diseases. For the purposes of (1) and (2), a basic influenza model is formulated as a system of partial differential equations (PDEs) that define the spatiotemporal evolution of four populations: susceptibles, untreated and treated infecteds, and recovereds. The requirements of a well-posed PDE model are considered, including the initial and boundary conditions. The terms of the PDEs are explained. The computer implementation of the model is illustrated with a detailed line-by-line explanation of a system of routines in R (a quality, open-source scientific computing system that is readily available from the Internet). The R routines demonstrate the straightforward numerical solution ofa system of nonlinear PDEs by the method of lines (MOL), an established general algorithm for PDEs. The presentation of the PDE modeling methodology is introductory with a minumum of formal mathematics (no theorems and proofs), and with emphasis on example applications. The intent of the book is to assist in the initial understanding and use of PDE mathematical modeling of communicable diseases, and the explanation and interpretation of the computed model solutions, as illustrated with the influenza model.

This book introduces modeling and simulation of linear time invariant systems and demonstrates how these translate to systems engineering, mechatronics engineering, and biomedical engineering. It is organized into nine chapters that follow the lectures used for a one-semester course on this topic, making it appropriate for students as well as researchers. The author discusses state space modeling derived from two modeling techniques and the analysis of the system and usage of modeling in control systems design. It also contains a unique chapter on multidisciplinary energy systems with a special focus on bioengineering systems and expands upon how the bond graph augments research in biomedical and bio-mechatronics systems.

Arguably the first book of its kind, Computational Bioengineering explores the power of multidisciplinary computer modeling in bioengineering. Written by experts, the book examines the interplay of multiple governing principles underlying common biomedical devices and problems, bolstered by case studies. It shows you how to take advantage of the la

There is a tremendous interest among researchers for the development of virtual, augmented reality and games technologies due to their widespread applications in medicine and healthcare. To date the major applications of these technologies include medical simulation, telemedicine, medical and healthcare training, pain control, visualisation aid for surgery, rehabilitation in cases such as stroke, phobia and trauma therapies. Many recent studies have identified the benefits of using Virtual Reality, Augmented Reality or serious games in a variety of medical applications.This research volume on Virtual, Augmented Reality and Serious Games for Healthcare 1 offers an insightful introduction to the theories, development and applications of virtual, augmented reality and digital games technologies in medical and clinical settings and healthcare in general. It is divided into six sections: section one presents a selection of applications in medical education and healthcare management; Section two relates to the nursing training, health literacy and healthy behaviour; Section three presents the applications of Virtual Reality in neuropsychology; Section four includes a number of applications in motor rehabilitation; Section five aimed at therapeutic games for various diseases; and the final section presents the applications of Virtual Reality in healing and restoration.This book is directed to the healthcare professionals, scientists, researchers, professors and the students who wish to explore the applications of virtual, augmented reality and serious games in healthcare further.

Since 1992, when it began as the'Medicine Meets Virtual Reality'conference, NextMed/MMVR has been a forum for researchers utilizing IT advances to improve diagnosis and therapy, medical education, and procedural training. Scientists and engineers, physicians and other care providers, educators and students, military medicine specialists, futurists, and industry: all come together with the shared goal of making healthcare more precise and effective. This book presents the proceedings of the 20th NextMed/MMVR conference, held in San Diego, California, USA, in February 2013. It covers a wide range of topics: simulation, modeling, imaging, data visualization, haptics, robotics, sensors, interfaces, plasma medicine, and more. Key applications include simulator design, information-guided therapies, learning tools, mental and physical rehabilitation, and intelligence networking. During the past two decades, healthcare has been transformed by progress in computer-enabled technology, and NextMed/MMVR has played a prominent role in this transformation.

'This book introduces the development and application of game technologies for health-related serious games, providing cutting edge research from academia and industry, informing readers about the current and future advances in the area'--

Simulate realistic human motion in a virtual world with an optimization-based approach to motion prediction. With this approach, motion is governed by human performance measures, such as speed and energy, which act as objective functions to be optimized. Constraints on joint torques and angles are imposed quite easily. Predicting motion in this way allows one to use avatars to study how and why humans move the way they do, given specific scenarios. It also enables avatars to react to infinitely many scenarios with substantial autonomy. With this approach it is possible to predict dynamic motion without having to integrate equations of motion -- rather than solving equations of motion, this approach solves for a continuous time-dependent curve characterizing joint variables (also called joint profiles) for every degree of freedom. - Introduces rigorous mathematical methods for digital human modelling and simulation - Focuses on understanding and representing spatial relationships (3D) of biomechanics - Develops an innovative optimization-based approach to predicting human movement - Extensively illustrated with 3D images of simulated human motion (full color in the ebook version)

Features a solid foundation of mathematical and computational tools to formulate and solve real-world PDE problems across various fields With a step-by-step approach to solving partial differential equations (PDEs), Differential Equation Analysis in Biomedical Science and Engineering: Partial Differential Equation Applications with R successfully applies computational techniques for solving real-world PDE problems that are found in a variety of fields, including chemistry, physics, biology, and physiology. The book provides readers with the necessary knowledge to reproduce and extend the computed numerical solutions and is a valuable resource for dealing with a broad class of linear and nonlinear partial differential equations. The author's primary focus is on models expressed as systems of PDEs, which generally result from including spatial effects so that the PDE dependent variables are functions of both space and time, unlike ordinary differential equation (ODE) systems that pertain to time only. As such, the book emphasizes details of the numerical algorithms and how the solutions were computed. Featuring computer-based mathematical models for solving real-world problems in the biological and biomedical sciences and engineering, the book also includes: R routines to facilitate the immediate use of computation for solving differential equation problems without having to first learn the basic concepts of numerical analysis and programming for PDEs Models as systems of PDEs and associated initial and boundary conditions with explanations of the associated chemistry, physics, biology, and physiology Numerical solutions of the presented model equations with a discussion of the important features of the solutions Aspects of general PDE computation through various biomedical science and engineering applications Differential Equation Analysis in Biomedical Science and Engineering: Partial Differential Equation Applications with R is an excellent reference for researchers, scientists, clinicians, medical researchers, engineers, statisticians, epidemiologists, and pharmacokineticists who are interested in both clinical applications and interpretation of experimental data with mathematical models in order to efficiently solve the associated differential equations. The book is also useful as a textbook for graduate-level courses in mathematics, biomedical science and engineering, biology, biophysics, biochemistry, medicine, and engineering.

How can we optimize a bedridden patient's mattress? How can we make a passenger seat on a long distance flight or ride more comfortable? What qualities should a runner's shoes have? To objectively address such questions using engineering and scientific methods, adequate virtual human body models for use in computer simulation of loading scenarios are required. The authors have developed a novel method incorporating subject studies, magnetic resonance imaging, 3D-CAD-reconstruction, continuum mechanics, material theory and the finite element method. The focus is laid upon the mechanical in vivo-characterization of human soft tissue, which is indispensable for simulating its mechanical interaction with, for example, medical bedding or automotive and airplane seating systems. Using the examples of arbitrary body support systems, the presented approach provides visual insight into simulated internal mechanical body tissue stress and strain, with the goal of biomechanical optimization of body support systems. This book is intended for engineers, manufacturers and physicians and also provides students with guidance in solving problems related to support system optimization.

One of the major challenges in tissue engineering is the translation of biological knowledge on complex cell and tissue behavior into a predictive and robust engineering process. Mastering this complexity is an essential step towards clinical applications of tissue engineering. This volume discusses computational modeling tools that allow studying the biological complexity in a more quantitative way. More specifically, computational tools can help in: (i) quantifying and optimizing the tissue engineering product, e.g. by adapting scaffold design to optimize micro-environmental signals or by adapting selection criteria to improve homogeneity of the selected cell population; (ii) quantifying and optimizing the tissue engineering process, e.g. by adapting bioreactor design to improve quality and quantity of the final product; and (iii) assessing the influence of the in vivo environment on the behavior of the tissue engineering product, e.g. by investigating vascular ingrowth. The book presents examples of each of the above mentioned areas of computational modeling. The underlying tissue engineering applications will vary from blood vessels over trachea to cartilage and bone. For the chapters describing examples of the first two areas, the main focus is on (the optimization of) mechanical signals, mass transport and fluid flow encountered by the cells in scaffolds and bioreactors as well as on the optimization of the cell population itself. In the chapters describing modeling contributions in the third area, the focus will shift towards the biology, the complex interactions between biology and the micro-environmental signals and the ways in which modeling might be able to assist in investigating and mastering this complexity. The chapters cover issues related to (multiscale/multiphysics) model building, training and validation, but also discuss recent advances in scientific computing techniques that are needed to implement these models as well as new tools that can be used to experimentally validate the computational results.

Features a solid foundation of mathematical and computational tools to formulate and solve real-world ODE problems across various fields With a step-by-step approach to solving ordinary differential equations (ODEs), Differential Equation Analysis in Biomedical Science and Engineering: Ordinary Differential Equation Applications with R successfully applies computational techniques for solving real-world ODE problems that are found in a variety of fields, including chemistry, physics, biology, and physiology. The book provides readers with the necessary knowledge to reproduce and extend the computed numerical solutions and is a valuable resource for dealing with a broad class of linear and nonlinear ordinary differential equations. The author's primary focus is on models expressed as systems of ODEs, which generally result by neglecting spatial effects so that the ODE dependent variables are uniform in space. Therefore, time is the independent variable in most applications of ODE systems. As such, the book emphasizes details of the numerical algorithms and how the solutions were computed. Featuring computer-based mathematical models for solving real-world problems in the biological and biomedical sciences and engineering, the book also includes: R routines to facilitate the immediate use of computation for solving differential equation problems without having to first learn the basic concepts of numerical analysis and programming for ODEs Models as systems of ODEs with explanations of the associated chemistry, physics, biology, and physiology as well as the algebraic equations used to calculate intermediate variables Numerical solutions of the presented model equations with a discussion of the important features of the solutions Aspects of general ODE computation through various biomolecular science and engineering applications Differential Equation Analysis in Biomedical Science and Engineering: Ordinary Differential Equation Applications with R is an excellent reference for researchers, scientists, clinicians, medical researchers, engineers, statisticians, epidemiologists, and pharmacokineticists who are interested in both clinical applications and interpretation of experimental data with mathematical models in order to efficiently solve the associated differential equations. The book is also useful as a textbook for graduate-level courses in mathematics, biomedical science and engineering, biology, biophysics, biochemistry, medicine, and engineering.

This book reviews the frontier of research and clinical applications of Patient Specific Modeling, and provides a state-of-the-art update as well as perspectives on future directions in this exciting field. The book is useful for medical physicists, biomedical engineers and other engineers who are interested in the science and technology aspects of Patient Specific Modeling, as well as for radiologists and other medical specialists who wish to be updated about the state of implementation.

This book addresses patient-specific modeling. It integrates computational modeling, experimental procedures, imagine clinical segmentation and mesh generation with the finite element method (FEM) to solve problems in computational biomedicine and bioengineering. Specific areas of interest include cardiovascular problems, ocular and muscular systems and soft tissue modeling. Patient-specific modeling has been the subject of serious research over the last seven years and interest in the area is continually growing and this area is expected to further develop in the near future.

This book provides an introduction to two important aspects of modern bioch- istry, molecular biology, and biophysics: computer simulation and data analysis. My aim is to introduce the tools that will enable students to learn and use some f- damental methods to construct quantitative models of biological mechanisms, both deterministicandwithsomeelementsofrandomness;tolearnhowconceptsofpr- ability can help to understand important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data. The availability of very capable but inexpensive personal computers and software makes it possible to do such work at a much higher level, but in a much easier way, than ever before. TheExecutiveSummaryofthein?uential2003reportfromtheNationalAcademy of Sciences, “BIO 2010: Transforming Undergraduate Education for Future - search Biologists” [12], begins The interplay of the recombinant DNA, instrumentation, and digital revolutions has p- foundly transformed biological research. The con?uence of these three innovations has led to important discoveries, such as the mapping of the human genome. How biologists design, perform, and analyze experiments is changing swiftly. Biological concepts and models are becoming more quantitative, and biological research has become critically dependent on concepts and methods drawn from other scienti?c disciplines. The connections between the biological sciences and the physical sciences, mathematics, and computer science are rapidly becoming deeper and more extensive.

We are especially pleased to present our edited book in an area that is quickly emerging as one of the most active research areas that integrate both engineering and medicine. Preliminary research results show signi?cant potential in effecting major breakthroughsranging from a reduction in the number of corrective surgeries needed to the ‘scienti?c miracle'of generating tissue growth. Over $600 million has been in invested in tissue engineering last year alone—a large and signi?cant component of this is in the area of virtual and physical prototyping. Virtual & Physical prototyping can broadly be divided into three categories: Modeling, Manufacturing& Materials. This book focuses on the?rst part and some areas of the second.The second bookin this series will focuson the areas in the s- ondandthird categories.As youwill see fromthis book,the principlesutilizeddraw heavily from the more traditional engineering?elds including mechanical engine- ing, industrial engineering, civil engineering (structures), electrical engineering and bio engineering. The?rst chapter by Winder provides an insight into the practicalities of creating custom made implants for the skull details. The concept of cranioplasty, describing the correction of a bone defect or deformity in the cranium using a bio-compatible material, is described and explored from a computational point of view In Chapter 2, Ming Leu and his group of researchers review the use of virtual reality technology for the development of a virtual bone surgery system, which can be used for training in orthopedic surgery and planning of bone surgery procedures.

Simulation facilities are invaluable for training in medicine and clinical education, biomedical engineering and life sciences. They allow the practice of prevention, containment, treatment, and procedure in a risk-free setting. This book is a practical guide and reference to the latest technology, operations and opportunities presented by clinical simulation. It shows how to develop and make efficient use of resources, and provides hands-on information to those tasked with setting up and delivering simulation facilities for medical, clinical and related purposes, and the development and delivery of simulation-based education programs - A step-by-step manual to developing successful simulation programs - Shows how to design, construct, outfit and run simulation facilities for clinical education and research. - The Residency Review Committee of the US Accreditation Council on Graduate Medical Education has begun requiring residency programs to have simulation as an integral part of their training programs.

Molecular simulation is a widely used tool in biology, chemistry, physics and engineering. This book contains a collection of articles by leading researchers who are developing new methods for molecular modelling and simulation. Topics addressed here include: multiscale formulations for biomolecular modelling, such as quantum-classical methods and advanced solvation techniques; protein folding methods and schemes for sampling complex landscapes; membrane simulations; free energy calculation; and techniques for improving ergodicity. The book is meant to be useful for practitioners in the simulation community and for those new to molecular simulation who require a broad introduction to the state of the art.

Although neural modeling has a long history, most of the texts available on the subject are quite limited in scope, dealing primarily with the simulation of large-scale biological neural networks applicable to describing brain function. Introduction to Dynamic Modeling of Neuro-Sensory Systems presents the mathematical tools and methods that can de