This book provides comprehensive coverage of various solutions that address issues related to real-time performance, security, and robustness in emerging automotive platforms. The authors discuss recent advances towards the goal of enabling reliable, secure, and robust, time-critical automotive cyber-physical systems, using advanced optimization and machine learning techniques. The focus is on presenting state-of-the-art solutions to various challenges including real-time data scheduling, secure communication within and outside the vehicle, tolerance to faults, optimizing the use of resource-constrained automotive ECUs, intrusion detection, and developing robust perception and control techniques for increasingly autonomous vehicles.

"Introduction to Data Mining" presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

This book provides an effective review and critical analysis of the recently demonstrated room-temperature sodium-sulfur batteries. Divided into three sections, it highlights the status of the technologies and strategies developed for the sodium metal anode, insight into the development of sulfur cathode, and electrolyte engineering. It reviews past, present, and future perspectives for each cell component including characterization tools unveiling the fundamental understanding of the room-temperature sodium-sulfur batteries. Features: • Highlights scientific challenges in developing room-temperature sodium-sulfur batteries • Covers pertinent anode, cathode, and electrolyte engineering • Provides scientific and technical interpretation for each of the cell components • Discusses how Na-S batteries relates to the more extensively researched Li-S batteries • Explores importance of the SEI and CEI in developing stable sodium-sulfur batteries This book is aimed at graduate students and researchers in energy science, materials science, and electrochemistry.

Modern society depends critically on computers that control and manage the systems on which we depend in many aspects of our daily lives. While this provides conveniences of a level unimaginable just a few years ago, it also leaves us vulnerable to attacks on the computers managing these systems. In recent times the explosion in cyber attacks, including viruses, worms, and intrusions, has turned this vulnerability into a clear and visible threat. Due to the escalating number and increased sophistication of cyber attacks, it has become important to develop a broad range of techniques, which can ensure that the information infrastructure continues to operate smoothly, even in the presence of dire and continuous threats.

This book brings together the latest techniques for managing cyber threats, developed by some of the world s leading experts in the area. The book includes broad surveys on a number of topics, as well as specific techniques. It provides an excellent reference point for researchers and practitioners in the government, academic, and industrial communities who want to understand the issues and challenges in this area of growing worldwide importance."

Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML

The text covers four important areas: digital manufacturing, modern manufacturing processes, modeling and simulation in smart industry, and nanotechnology. It further presents mathematical models to represent physical phenomena and applies modern computing methods and simulations in analyzing the same. The text covers key concepts such as abrasive flow machining (AFM), abrasive water jet (AWJ) machining, and hybrid machining for micro/nanomanufacturing. It will serve as an ideal reference text for senior undergraduate, graduate students, and researchers in fields including mechanical engineering, aerospace engineering, manufacturing engineering, and production engineering. Features Discusses sustainable development aspects of additive manufacturing in industry 4.0 Studies electrochemical machining processes for micro-machining Presents experimental Investigation of friction factor and heat transfer rate in the laminar regime Examines the mechanical and microstructural characterization of titanium chips using large strain machining Covers hybrid approaches like electrochemical machining and magnetic abrasive flow machining The book emphasizes linking the computer interface with the digital manufacturing process and their demonstration using commercially available software like Solid-Edge, ProE, and CATIA. It further discusses important aspects of digital manufacturing, advanced composites, artificial intelligence, and modern manufacturing processes.

Microbial Inoculants: Recent Progress and Applications fulfills the need to explore and learn about an efficient delivery mechanism, selection of microbial strain as inoculants, and related technological advances for the efficient and productive use of microbial inoculants. Moreover, factors like methods of formulation, interaction between host plant and microbe, impact of inoculation on the metabolomics of plants, the effect of microbial inoculants on soil dynamics, proteomics approach of plant-microbe interaction, as well as the registration and regulation process of bio inoculants for commercial production are described by leading academicians and researchers from different parts of the world in this comprehensive book. In the recent past, beneficial microorganisms have been sustainably used in agriculture as a safe, economic and effective alternative to chemical fertilizers or pesticides. These beneficial microbes, including bacteria, actinomycetes, and yeast were efficiently applied in soil, seeds, fruits, or plants as inoculants, to achieve the optimum agricultural yield. An efficient delivery method or enhanced shelf life of microbial inoculants in the soil or seed is still a matter of concern.

Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm.

The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics?including classification, clustering, statistical learning, association analysis, and link mining?in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses.

By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.

Drawn from the US National Science Foundation's Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field.

Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms.

The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.

Introducing the fundamental concepts and algorithms of data mining Introduction to Data Mining, 2nd Edition, gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals. Presented in a clear and accessible way, the book outlines fundamental concepts and algorithms for each topic, thus providing the reader with the necessary background for the application of data mining to real problems. The text helps readers understand the nuances of the subject, and includes important sections on classification, association analysis, and cluster analysis. This edition improves on the first iteration of the book, published over a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth.

Recent research results in the area of parallel algorithms for problem solving, search, natural language parsing, and computer vision, are brought together in this book. The research reported demonstrates that substantial parallelism can be exploited in various machine intelligence and vision problems. The chapter authors are prominent researchers actively involved in the study of parallel algorithms for machine intelligence and vision. Extensive experimental studies are presented that will help the reader in assessing the usefulness of an approach to a specific problem. Intended for students and researchers actively involved in parallel algorithms design and in machine intelligence and vision, this book will serve as a valuable reference work as well as an introduction to several research directions in these areas.

Search is an important component of problem solving in artificial intelligence (AI) and, more generally, in computer science, engineering and operations research. Combinatorial optimization, decision analysis, game playing, learning, planning, pattern recognition, robotics and theorem proving are some of the areas in which search algbrithms playa key role. Less than a decade ago the conventional wisdom in artificial intelligence was that the best search algorithms had already been invented and the likelihood of finding new results in this area was very small. Since then many new insights and results have been obtained. For example, new algorithms for state space, AND/OR graph, and game tree search were discovered. Articles on new theoretical developments and experimental results on backtracking, heuristic search and constraint propaga tion were published. The relationships among various search and combinatorial algorithms in AI, Operations Research, and other fields were clarified. This volume brings together some of this recent work in a manner designed to be accessible to students and professionals interested in these new insights and developments."

Modern society depends critically on computers that control and manage the systems on which we depend in many aspects of our daily lives. While this provides conveniences of a level unimaginable just a few years ago, it also leaves us vulnerable to attacks on the computers managing these systems. In recent times the explosion in cyber attacks, including viruses, worms, and intrusions, has turned this vulnerability into a clear and visible threat. Due to the escalating number and increased sophistication of cyber attacks, it has become important to develop a broad range of techniques, which can ensure that the information infrastructure continues to operate smoothly, even in the presence of dire and continuous threats.

This book brings together the latest techniques for managing cyber threats, developed by some of the world's leading experts in the area. The book includes broad surveys on a number of topics, as well as specific techniques. It provides an excellent reference point for researchers and practitioners in the government, academic, and industrial communities who want to understand the issues and challenges in this area of growing worldwide importance.

The five-volume set LNCS 3980-3984 constitutes the refereed proceedings of the International Conference on Computational Science and Its Applications, ICCSA 2006. The volumes present a total of 664 papers organized according to the five major conference themes: computational methods, algorithms and applications high performance technical computing and networks advanced and emerging applications geometric modelling, graphics and visualization information systems and information technologies. This is Part V.

The natural mission of Computational Science is to tackle all sorts of human problems and to work out intelligent automata aimed at alleviating the b- den of working out suitable tools for solving complex problems. For this reason ComputationalScience,thoughoriginatingfromtheneedtosolvethemostch- lenging problems in science and engineering (computational science is the key player in the ?ght to gain fundamental advances in astronomy, biology, che- stry, environmental science, physics and several other scienti?c and engineering disciplines) is increasingly turning its attention to all ?elds of human activity. In all activities, in fact, intensive computation, information handling, kn- ledge synthesis, the use of ad-hoc devices, etc. increasingly need to be exploited and coordinated regardless of the location of both the users and the (various and heterogeneous) computing platforms. As a result the key to understanding the explosive growth of this discipline lies in two adjectives that more and more appropriately refer to Computational Science and its applications: interoperable and ubiquitous. Numerous examples of ubiquitous and interoperable tools and applicationsaregiveninthepresentfourLNCSvolumescontainingthecontri- tions delivered at the 2004 International Conference on Computational Science and its Applications (ICCSA 2004) held in Assisi, Italy, May 14-17, 2004.

The natural mission of Computational Science is to tackle all sorts of human problems and to work out intelligent automata aimed at alleviating the b- den of working out suitable tools for solving complex problems. For this reason ComputationalScience, thoughoriginatingfromtheneedtosolvethemostch- lenging problems in science and engineering (computational science is the key player in the ?ght to gain fundamental advances in astronomy, biology, che- stry, environmental science, physics and several other scienti?c and engineering disciplines) is increasingly turning its attention to all ?elds of human activity. In all activities, in fact, intensive computation, information handling, kn- ledge synthesis, the use of ad-hoc devices, etc. increasingly need to be exploited and coordinated regardless of the location of both the users and the (various and heterogeneous) computing platforms. As a result the key to understanding the explosive growth of this discipline lies in two adjectives that more and more appropriately refer to Computational Science and its applications: interoperable and ubiquitous. Numerous examples of ubiquitous and interoperable tools and applicationsaregiveninthepresentfourLNCSvolumescontainingthecontri- tions delivered at the 2004 International Conference on Computational Science and its Applications (ICCSA 2004) held in Assisi, Italy, May 14-17, 2004

The natural mission of Computational Science is to tackle all sorts of human problems and to work out intelligent automata aimed at alleviating the b- den of working out suitable tools for solving complex problems. For this reason ComputationalScience, thoughoriginatingfromtheneedtosolvethemostch- lenging problems in science and engineering (computational science is the key player in the ?ght to gain fundamental advances in astronomy, biology, che- stry, environmental science, physics and several other scienti?c and engineering disciplines) is increasingly turning its attention to all ?elds of human activity. In all activities, in fact, intensive computation, information handling, kn- ledge synthesis, the use of ad-hoc devices, etc. increasingly need to be exploited and coordinated regardless of the location of both the users and the (various and heterogeneous) computing platforms. As a result the key to understanding the explosive growth of this discipline lies in two adjectives that more and more appropriately refer to Computational Science and its applications: interoperable and ubiquitous. Numerous examples of ubiquitous and interoperable tools and applicationsaregiveninthepresentfourLNCSvolumescontainingthecontri- tions delivered at the 2004 International Conference on Computational Science and its Applications (ICCSA 2004) held in Assisi, Italy, May 14-17, 2004

The three-volume set, LNCS 2667, LNCS 2668, and LNCS 2669, constitutes the refereed proceedings of the International Conference on Computational Science and Its Applications, ICCSA 2003, held in Montreal, Canada, in May 2003. The three volumes present more than 300 papers and span the whole range of computational science from foundational issues in computer science and mathematics to advanced applications in virtually all sciences making use of computational techniques. The proceedings give a unique account of recent results in computational science.

The three-volume set, LNCS 2667, LNCS 2668, and LNCS 2669, constitutes the refereed proceedings of the International Conference on Computational Science and Its Applications, ICCSA 2003, held in Montreal, Canada, in May 2003. The three volumes present more than 300 papers and span the whole range of computational science from foundational issues in computer science and mathematics to advanced applications in virtually all sciences making use of computational techniques. The proceedings give a unique account of recent results in computational science.

The three-volume set, LNCS 2667, LNCS 2668, and LNCS 2669, constitutes the refereed proceedings of the International Conference on Computational Science and Its Applications, ICCSA 2003, held in Montreal, Canada, in May 2003. The three volumes present more than 300 papers and span the whole range of computational science from foundational issues in computer science and mathematics to advanced applications in virtually all sciences making use of computational techniques. The proceedings give a unique account of recent results in computational science.

Drawn from the US National Science Foundation's Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field. Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms. The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.

Microbial Endophytes: Prospects for Sustainable Agriculture discusses the practical and theoretical aspects regarding the use of endophytic microorganisms in agriculture, providing insights on the biotechnological applications associated with long-term crop production. Chapters deal with the various aspects of endophytic microorganisms, including isolation, enumeration, characterization procedures, diversity analysis, and their role as biofertilizer, biocontrol agent and microbial inoculants. Framed to discuss the present and future potential of microbial endophytes in biotic and abiotic stress management, bioremediation, bioactive compounds production, and in nanotechnology, this book provides a single-volume resource that will be valuable to academics and researchers interested in microbiology, agricultural sciences and biotechnology.

Advances in Cyanobacterial Biology presents the novel, practical, and theoretical aspects of cyanobacteria, providing a better understanding of basic and advanced biotechnological application in the field of sustainable agriculture. Chapters have been designed to deal with the different aspects of cyanobacteria including their role in the evolution of life, cyanobacterial diversity and classification, isolation, and characterization of cyanobacteria through biochemical and molecular approaches, phylogeny and biogeography of cyanobacteria, symbiosis, Cyanobacterial photosynthesis, morphological and physiological adaptation to abiotic stresses, stress-tolerant cyanobacterium, biological nitrogen fixation. Other topics include circadian rhythms, genetics and molecular biology of abiotic stress responses, application of cyanobacteria and cyanobacterial mats in wastewater treatments, use as a source of novel stress-responsive genes for development of stress tolerance and as a source of biofuels, industrial application, as biofertilizer, cyanobacterial blooms, use in Nano-technology and nanomedicines as well as potential applications. This book will be important for academics and researchers working in cyanobacteria, cyanobacterial environmental biology, cyanobacterial agriculture and cyanobacterial molecular biologists.