- Table View
- List View
Computational Data and Social Networks: 7th International Conference, CSoNet 2018, Shanghai, China, December 18–20, 2018, Proceedings (Lecture Notes in Computer Science #11280)
by My T. Thai Xuemin Chen Arunabha Sen Wei Wayne LiThis book constitutes the refereed proceedings of the 7th International Conference on Computational Data and Social Networks, CSoNet 2018, held in Shanghai, China, in December 2018.The 44 revised full papers presented in this book toghether with 2 extended abstracts, were carefully reviewed and selected from 106 submissions. The topics cover the fundamental background, theoretical technology development, and real-world applications associated with complex and data network analysis, minimizing in uence of rumors on social networks, blockchain Markov modelling, fraud detection, data mining, internet of things (IoT), internet of vehicles (IoV), and others.
Computational Data and Social Networks: 8th International Conference, CSoNet 2019, Ho Chi Minh City, Vietnam, November 18–20, 2019, Proceedings (Lecture Notes in Computer Science #11917)
by Hanghang Tong Andrea TagarelliThis book constitutes the refereed proceedings of the 8th International Conference on Computational Data and Social Networks, CSoNet 2019, held in Ho Chi Minh City, Vietnam, in November 2019. The 22 full and 8 short papers presented in this book were carefully reviewed and selected from 120 submissions. The papers appear under the following topical headings: Combinatorial Optimization and Learning; Influence Modeling, Propagation, and Maximization; NLP and Affective Computing; Computational Methods for Social Good; and User Profiling and Behavior Modeling.
Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings (Lecture Notes in Computer Science #12575)
by Kim-Kwang Raymond Choo Sriram Chellappan NhatHai PhanThis book constitutes the refereed proceedings of the 9th International Conference on Computational Data and Social Networks, CSoNet 2020, held in Dallas, TX, USA, in December 2020. The 20 full papers were carefully reviewed and selected from 83 submissions. Additionally the book includes 22 special track papers and 3 extended abstracts. The selected papers are devoted to topics such as Combinatorial Optimization and Learning; Computational Methods for Social Good Applications; NLP and Affective Computing; Privacy and Security; Blockchain; Fact-Checking, Fake News and Malware Detection in Online Social Networks; and Information Spread in Social and Data Networks.
Computational Design for Landscape Architects
by Brendan HarmonThis book is a guide to computational design for landscape architects replete with extensive tutorials. It introduces algorithmic approaches for modeling and designing landscapes. The aim of this book is to use algorithms to understand and design landscape as a generative system, i.e. to harness the processes that shape landscape to generate new forms. An algorithmic approach to design is gently introduced through visual programming with Grasshopper, before more advanced methods are taught in Python, a high-level programming language. Topics covered include parametric design, randomness and noise, waves and attractors, lidar, drone photogrammetry, point cloud modeling, terrain modeling, earthworks, digital fabrication, and more. The chapters include sections on theory, methods, and either visual programming or scripting. Online resources for the book include code and datasets so that readers can easily follow along and try out the methods presented. This book is a much-needed guide, both theoretical and practical, on computational design for students, educators, and practitioners of landscape architecture.
Computational Design of Battery Materials (Topics in Applied Physics #150)
by Dorian A. H. HanaorThis book presents an essential survey of the state of the art in the application of diverse computational methods to the interpretation, prediction, and design of high-performance battery materials. Rechargeable batteries have become one of the most important technologies supporting the global transition from fossil fuels to renewable energy sources. Aided by the growth of high-performance computing and machine learning technologies, computational methods are being applied to design the battery materials of the future and pave the way to a more sustainable energy economy. In this contributed collection, leading battery material researchers from across the globe share their methods, insights, and expert knowledge in the application of computational methods for battery material design and interpretation. With chapters featuring an array of computational techniques applied to model the relevant properties of cathodes, anodes, and electrolytes, this book provides the ideal starting point for any researcher looking to integrate computational tools in the development of next-generation battery materials and processes.
Computational Design: Technology, Cognition and Environments
by Michael J. Ostwald Ning Gu Rongrong YuNew computational design tools have evolved rapidly and been increasingly applied in the field of design in recent years, complimenting and even replacing the traditional design media and approaches. Design as both the process and product are changing due to the emergence and adoption of these new technologies. Understanding and assessing the impact of these new computational design environments on design and designers is important for advancing design in the contemporary context. Do these new computational environments support or hinder design creativity? How do those tools facilitate designers’ thinking? Such knowledge is also important for the future development of design technologies. Research shows that design is never a mysterious non-understandable process, for example, one general view is that design process shares a common analysis-synthesis-evaluation model, during which designers interact between design problem and solution spaces. Understanding designers’ thinking in different environments is the key to design research, education and practice. This book focuses on emerging computational design environments, whose impact on design and designers have not been comprehensively and systematically studied. It comprises three parts. The history and recent developments of computational design technologies are introduced in Part I. The main categories of technologies cover from computer-aided drafting and modelling tools, to visual programming and scripting tools for algorithmic design, to advanced interfaces and platforms for interactions between designers, between designers and computers, and between the virtual environment and the physical reality. To critically explore design thinking, especially in these new computational design environments, formal approaches to studying design thinking and design cognition are introduced and compared in Part II, drawing on literature and studies from the 70s to the current era. Part III concludes the book by exploring the impact of different computational design technologies on design and designers, using a series of case studies conducted by the author team building on their close collaboration over the past five years. The book offers new insights into designers’ thinking in the rapidly evolving computational design environments, which have not been critically and systematically studied and reported in the current literature. The book is meant for design researchers, educators and students, professional practitioners and consultants, as well as people who are interested in computational design in general.
Computational Diffusion MRI: 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings (Lecture Notes in Computer Science #13006)
by Daan Christiaens Suheyla Cetin-Karayumak Matteo Figini Pamela Guevara Noemi Gyori Vishwesh Nath Tomasz PieciakThis book constitutes the proceedings of the International Workshop on Computational Diffusion MRI, CDMRI 2021, which was held on October 1, 2021, in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 full papers included were carefully reviewed and selected for inclusion in the book. The proceedings also contain a paper about the design and scope of the MICCAI Diffusion-Simulated Connectivity Challenge (DiSCo) which was held at CDMRI 2021. The papers were organized in topical sections as follows: acquisition; microstructure modelling; tractography and connectivity; applications and visualization; DiSCo challenge – invited contribution.
Computational Diffusion MRI: 13th International Workshop, CDMRI 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings (Lecture Notes in Computer Science #13722)
by Elizabeth Powell Daan Christiaens Suheyla Cetin-Karayumak Matteo Figini Pamela Guevara Tomasz Pieciak Francois RheaultThis book constitutes the proceedings of the International Workshop on Computational Diffusion MRI, CDMRI 2022, which was held 22 September 2022, in conjunction with MICCAI 2022. The 12 full papers included were carefully reviewed and selected for inclusion in the book. The papers were organized in topical sections as follows: Data processing, Signal representations, Tractography and WM pathways.
Computational Diffusion MRI: 14th International Workshop, CDMRI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings (Lecture Notes in Computer Science #14328)
by Elizabeth Powell Stefan Winzeck Francois Rheault Muge Karaman Remika MitoThis book constitutes the proceedings of the 14th International Workshop, CDMRI 2023, held in conjunction with MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Vancouver, BC, Canada, on October 8, 2023. The 17regular papers presented in this book were carefully reviewed and selected from 19 submissions. These contributions cover various aspects, including preprocessing, signal modeling, tractography, bundle segmentation, and clinical applications. Many of these studies employ novel machine learning implementations, highlighting the evolving landscape of techniques beyond the more traditional physics-based algorithms.
Computational Diffusion MRI: 15th International Workshop, CDMRI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings (Lecture Notes in Computer Science #15171)
by Muge Karaman Remika Mito Maxime Chamberland Tom Hendriks Nancy Newlin S. Shailja Elinor ThompsonThis book constitutes the proceedings of the 15th International Workshop, CDMRI 2024, held in conjunction with MICCAI 2024, the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Marrakesh, Morocco, October 6, 2024. The 19 full papers presented in this book were carefully reviewed and selected from 22 submissions.
Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018 (Mathematics and Visualization)
by Francesco Grussu Lipeng Ning Chantal M. Tax Elisenda Bonet-Carne Farshid SepehrbandThis volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI’18), which was held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention in Granada, Spain on September 20, 2018. It presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find papers on a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as harmonisation and frontline applications in research and clinical practice. The respective papers constitute invited works from high-profile researchers with a specific focus on three topics that are now gaining momentum within the diffusion MRI community: i) machine learning for diffusion MRI; ii) diffusion MRI outside the brain (e.g. in the placenta); and iii) diffusion MRI for multimodal imaging. The book shares new perspectives on the latest research challenges for those currently working in the field, but also offers a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. It includes rigorous mathematical derivations, a wealth of full-colour visualisations, and clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics alike.
Computational Diffusion MRI: International MICCAI Workshop, Lima, Peru, October 2020 (Mathematics and Visualization)
by Fan Zhang Jana Hutter Marco Palombo Marco Pizzolato Noemi Gyori Vishwesh NathThis book gathers papers presented at the Workshop on Computational Diffusion MRI, CDMRI 2020, held under the auspices of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), which took place virtually on October 8th, 2020, having originally been planned to take place in Lima, Peru.This book presents the latest developments in the highly active and rapidly growing field of diffusion MRI. While offering new perspectives on the most recent research challenges in the field, the selected articles also provide a valuable starting point for anyone interested in learning computational techniques for diffusion MRI. The book includes rigorous mathematical derivations, a large number of rich, full-colour visualizations, and clinically relevant results. As such, it is of interest to researchers and practitioners in the fields of computer science, MRI physics, and applied mathematics. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as diffusion-relaxometry and frontline applications in research and clinical practice.
Computational Diffusion MRI: MICCAI Workshop, Boston, MA, USA, September 2014 (Mathematics and Visualization)
by Lauren O'Donnell Gemma Nedjati-Gilani Yogesh Rathi Marco Reisert Torben SchneiderThis book contains papers presented at the 2014 MICCAI Workshop on Computational Diffusion MRI, CDMRI’14. Detailing new computational methods applied to diffusion magnetic resonance imaging data, it offers readers a snapshot of the current state of the art and covers a wide range of topics from fundamental theoretical work on mathematical modeling to the development and evaluation of robust algorithms and applications in neuroscientific studies and clinical practice.Inside, readers will find information on brain network analysis, mathematical modeling for clinical applications, tissue microstructure imaging, super-resolution methods, signal reconstruction, visualization, and more. Contributions include both careful mathematical derivations and a large number of rich full-color visualizations.Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into the clinic. This volume will offer a valuable starting point for anyone interested in learning computational diffusion MRI. It also offers new perspectives and insights on current research challenges for those currently in the field. The book will be of interest to researchers and practitioners in computer science, MR physics, and applied mathematics.
Computational Diffusion MRI: MICCAI Workshop, Shenzhen, China, October 2019 (Mathematics and Visualization)
by Fan Zhang Elisenda Bonet-Carne Farshid Sepehrband Jana Hutter Marco Palombo Marco PizzolatoThis volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI 2019), held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), which took place in Shenzhen, China on October 17, 2019. This book presents the latest advances in the rapidly expanding field of diffusion MRI. It shares new perspectives on the latest research challenges for those currently working in the field, but also offers a valuable starting point for anyone interested in learning about computational techniques in diffusion MRI. The book includes rigorous mathematical derivations, a wealth of rich, full-colour visualisations and extensive clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics. Readers will find contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in vivo recovery of microstructural and connectivity features, as well as diffusion-relaxometry and frontline applications in research and clinical practice. This edition includes invited works from high-profile researchers with a specific focus on three new and important topics that are gaining momentum within the diffusion MRI community, including diffusion MRI signal acquisition and processing strategies, machine learning for diffusion MRI, and diffusion MRI outside the brain and clinical applications.
Computational Discrete Mathematics
by Sriram Pemmaraju Steven SkienaThis book was first published in 2003. Combinatorica, an extension to the popular computer algebra system Mathematica®, is the most comprehensive software available for teaching and research applications of discrete mathematics, particularly combinatorics and graph theory. This book is the definitive reference/user's guide to Combinatorica, with examples of all 450 Combinatorica functions in action, along with the associated mathematical and algorithmic theory. The authors cover classical and advanced topics on the most important combinatorial objects: permutations, subsets, partitions, and Young tableaux, as well as all important areas of graph theory: graph construction operations, invariants, embeddings, and algorithmic graph theory. In addition to being a research tool, Combinatorica makes discrete mathematics accessible in new and exciting ways to a wide variety of people, by encouraging computational experimentation and visualization. The book contains no formal proofs, but enough discussion to understand and appreciate all the algorithms and theorems it contains.
Computational Drug Discovery: Methods and Applications
by Vasanthanathan Poongavanam Vijayan RamaswamyComputational Drug Discovery A comprehensive resource that explains a wide array of computational technologies and methods driving innovation in drug discovery Computational Drug Discovery: Methods and Applications (2 volume set) covers a wide range of cutting-edge computational technologies and computational chemistry methods that are transforming drug discovery. The book delves into recent advances, particularly focusing on artificial intelligence (AI) and its application for protein structure prediction, AI-enabled virtual screening, and generative modeling for compound design. Additionally, it covers key technological advancements in computing such as quantum and cloud computing that are driving innovations in drug discovery. Furthermore, dedicated chapters that addresses the recent trends in the field of computer aided drug design, including ultra-large-scale virtual screening for hit identification, computational strategies for designing new therapeutic modalities like PROTACs and covalent inhibitors that target residues beyond cysteine are also presented. To offer the most up-to-date information on computational methods utilized in computational drug discovery, it covers chapters highlighting the use of molecular dynamics and other related methods, application of QM and QM/MM methods in computational drug design, and techniques for navigating and visualizing the chemical space, as well as leveraging big data to drive drug discovery efforts. The book is thoughtfully organized into eight thematic sections, each focusing on a specific computational method or technology applied to drug discovery. Authored by renowned experts from academia, pharmaceutical industry, and major drug discovery software providers, it offers an overview of the latest advances in computational drug discovery. Key topics covered in the book include: Application of molecular dynamics simulations and related approaches in drug discovery The application of QM, hybrid approaches such as QM/MM, and fragment molecular orbital framework for understanding protein-ligand interactions Adoption of artificial intelligence in pre-clinical drug discovery, encompassing protein structure prediction, generative modeling for de novo design, and virtual screening. Techniques for navigating and visualizing the chemical space, along with harnessing big data to drive drug discovery efforts. Methods for performing ultra-large-scale virtual screening for hit identification. Computational strategies for designing new therapeutic models, including PROTACs and molecular glues. In silico ADMET approaches for predicting a variety of pharmacokinetic and physicochemical endpoints. The role of computing technologies like quantum computing and cloud computing in accelerating drug discovery This book will provide readers an overview of the latest advancements in computational drug discovery and serve as a valuable resource for professionals engaged in drug discovery.
Computational EEG Analysis: Methods and Applications (Biological and Medical Physics, Biomedical Engineering)
by Chang-Hwan ImThis book introduces and reviews all of the currently available methods being used for computational electroencephalogram (EEG) analysis, from the fundamentals through to the state-of-the-art. The aim of the book is to help biomedical engineers and medical doctors who use EEG to better understand the methods and applications of computational EEG analysis from a single, well-organized resource. Following a brief introduction to the principles of EEG and acquisition techniques, the book is divided into two main sections. The first of these covers analysis methods, beginning with preprocessing, and then describing EEG spectral analysis, event-related potential analysis, source imaging and multimodal neuroimaging, and functional connectivity analysis. The following section covers application of EEG analysis to specific fields, including the diagnosis of psychiatric diseases and neurological disorders, brain-computer interfacing, and social neuroscience. Aimed at practicing medical specialists, engineers, researchers and advanced students, the book features contributions from world-renowned biomedical engineers working across a broad spectrum of computational EEG analysis techniques and EEG applications.
Computational Economics
by Hans M. Amman P. Ruben Mercado David A. KendrickThe ability to conceptualize an economic problem verbally, to formulate it as a mathematical model, and then represent the mathematics in software so that the model can be solved on a computer is a crucial skill for economists. Computational Economics contains well-known models--and some brand-new ones--designed to help students move from verbal to mathematical to computational representations in economic modeling. The authors' focus, however, is not just on solving the models, but also on developing the ability to modify them to reflect one's interest and point of view. The result is a book that enables students to be creative in developing models that are relevant to the economic problems of their times. Unlike other computational economics textbooks, this book is organized around economic topics, among them macroeconomics, microeconomics, and finance. The authors employ various software systems--including MATLAB, Mathematica, GAMS, the nonlinear programming solver in Excel, and the database systems in Access--to enable students to use the most advantageous system. The book progresses from relatively simple models to more complex ones, and includes appendices on the ins and outs of running each program. The book is intended for use by advanced undergraduates and professional economists and even, as a first exposure to computational economics, by graduate students. Organized by economic topics Progresses from simple to more complex models Includes instructions on numerous software systems Encourages customization and creativity
Computational Engineering
by Jürgen GeiserDas Buch bietet ein ausgewogenes Verhältnis zwischen Theorie und praktischen Anwendungen des berechnenden Ingenieurswesens. Es illustriert sowohl die mathematischen Modelle im Computational Engineering, wie auch die zugehörigen Simulationsmethoden für die verschiedenen Ingenieursanwendungen und benennt geeignete Softwarepakete. Die umfangreichen Beispiele aus der berechnenden Ingenieurswissenschaft, welche Wärme- und Massentransport, Plasmasimulation und hydrodynamische Transportprobleme einschließen, geben dem Leser einen Überblick zu den aktuellen Themen und deren praktische Umsetzung in spätere Simulationsprogramme. Übungsaufgaben und prüfungsrelevante Fragen schließen die einzelnen Kapitel ab.
Computational Engineering 2: Theorie und Anwendungen im Bereich der Elektrodynamik
by Jürgen GeiserDas Buch zeigt Theorie und praktische Anwendungen im Bereich des Computational Engineering (berechnendes Ingenieurwesen) für elektrodynamische Anwendungen. Es illustriert sowohl die mathematischen Modelle wie auch die zugehörigen Simulationsmethoden für die verschiedenen Ingenieursanwendungen. Außerdem präsentiert es Strategien zur Verbesserung der numerischen Methoden wie z. B. Zeit-Raum-Verfahren, hyperbolische Löser, Multiskalenlöser oder strukturerhaltende Verfahren sowie Kopplungsverfahren für elektrodynamische und hydrodynamische Modelle auf verschiedenen Zeit- und Raumskalen. Dabei werden Ansätze zur Zerlegung in einfachere und effizient lösbare Teilprobleme vorgestellt. Gerade im Bereich der Multikomponenten- und Multiskalenmodelle bei komplizierten Ingenieursproblemen sind solche neuartigen Multiskalenverfahren wichtig. Weiter werden auch stochastische Modelle im Bereich der Partikelmodelle und deren Einbindung in deterministische Modelle besprochen. Diese neueren Problemstellungen brauchen iterative Löser zur Kopplung der verschiedenen Zeit- und Raumskalen. Die umfangreichen Beispiele aus dem Bereich der Elektrodynamik (inkl. elektromagnetische Felder, Antennenmodelle, Teilchenmodelle im Bereich der Plasmasimulation) geben dem Leser einen Überblick zu den aktuellen Themen und deren praktischer Umsetzung in spätere Simulationsprogramme.
Computational Epidemiology: From Disease Transmission Modeling to Vaccination Decision Making (Health Information Science)
by Jiming Liu Shang XiaThis book provides a comprehensive introduction to computational epidemiology, highlighting its major methodological paradigms throughout the development of the field while emphasizing the needs for a new paradigm shift in order to most effectively address the increasingly complex real-world challenges in disease control and prevention. Specifically, the book presents the basic concepts, related computational models, and tools that are useful for characterizing disease transmission dynamics with respect to a heterogeneous host population. In addition, it shows how to develop and apply computational methods to tackle the challenges involved in population-level intervention, such as prioritized vaccine allocation. A unique feature of this book is that its examination on the issues of vaccination decision-making is not confined only to the question of how to develop strategic policies on prioritized interventions, as it further approaches the issues from the perspective of individuals, offering a well integrated cost-benefit and social-influence account for voluntary vaccination decisions. One of the most important contributions of this book lies in it offers a blueprint on a novel methodological paradigm in epidemiology, namely, systems epidemiology, with detailed systems modeling principles, as well as practical steps and real-world examples, which can readily be applied in addressing future systems epidemiological challenges.The book is intended to serve as a reference book for researchers and practitioners in the fields of computer science and epidemiology. Together with the provided references on the key concepts, methods, and examples being introduced, the book can also readily be adopted as an introductory text for undergraduate and graduate courses in computational epidemiology as well as systems epidemiology, and as training materials for practitioners and field workers.
Computational Evolution of Neural and Morphological Development: Towards Evolutionary Developmental Artificial Intelligence (Natural Computing Series)
by Yaochu JinThis book provides a basic yet unified overview of theory and methodologies for evolutionary developmental systems. Based on the author’s extensive research into the synergies between various approaches to artificial intelligence including evolutionary computation, artificial neural networks, and systems biology, it also examines the inherent links between biological intelligence and artificial intelligence. The book begins with an introduction to computational algorithms used to understand and simulate biological evolution and development, including evolutionary algorithms, gene regulatory network models, multi-cellular models for neural and morphological development, and computational models of neural plasticity. Chap. 2 discusses important properties of biological gene regulatory systems, including network motifs, network connectivity, robustness and evolvability. Going a step further, Chap. 3 presents methods for synthesizing regulatory motifs from scratch and creating more complex regulatory dynamics by combining basic regulatory motifs using evolutionary algorithms. Multi-cellular growth models, which can be used to simulate either neural or morphological development, are presented in Chapters 4 and 5. Chap. 6 examines the synergies and coupling between neural and morphological evolution and development. In turn, Chap. 7 provides preliminary yet promising examples of how evolutionary developmental systems can help in self-organized pattern generation, referred to as morphogenetic self-organization, highlighting the great potentials of evolutionary developmental systems. Finally, Chap. 8 rounds out the book, stressing the importance and promise of the evolutionary developmental approach to artificial intelligence. Featuring a wealth of diagrams, graphs and charts to aid in comprehension, this book offers a valuable asset for graduate students, researchers and practitioners who are interested in pursuing a different approach to artificial intelligence.
Computational Exome and Genome Analysis (Chapman & Hall/CRC Computational Biology Series)
by Peter N. Robinson Rosario Michael Piro Marten JagerExome and genome sequencing are revolutionizing medical research and diagnostics, but the computational analysis of the data has become an extremely heterogeneous and often challenging area of bioinformatics. <P><P> Computational Exome and Genome Analysis provides a practical introduction to all of the major areas in the field, enabling readers to develop a comprehensive understanding of the sequencing process and the entire computational analysis pipeline.
Computational Fluid Dynamics 2010: Proceedings of the Sixth International Conference on Computational Fluid Dynamics, ICCFD6, St Petersburg, Russia, on July 12-16, 2010
by Alexander KuzminThe International Conference on Computational Fluid Dynamics is held every two years and brings together physicists, mathematicians and engineers to review and share recent advances in mathematical and computational techniques for modeling fluid flow. The proceedings of the 2010 conference (ICCFD6) held in St Petersburg, Russia, contain a selection of refereed contributions and are meant to serve as a source of reference for all those interested in the state of the art in computational fluid dynamics.
Computational Fluid Dynamics and the Theory of Fluidization: Applications of the Kinetic Theory of Granular Flow
by Dimitri Gidaspow Huilin Lu Shuyan WangThis book is for engineers and students to solve issues concerning the fluidized bed systems. It presents an analysis that focuses directly on the problem of predicting the fluid dynamic behavior which empirical data is limited or unavailable. The second objective is to provide a treatment of computational fluidization dynamics that is readily accessible to the non-specialist. The approach adopted in this book, starting with the formulation of predictive expressions for the basic conservation equations for mass and momentum using kinetic theory of granular flow. The analyses presented in this book represent a body of simulations and experiments research that has appeared in numerous publications over the last 20 years. This material helps to form the basis for university course modules in engineering and applied science at undergraduate and graduate level, as well as focused, post-experienced courses for the process, and allied industries.