* Interdisciplinary approach and multiple theoretical perspectives on intercultural conflict management * Addresses intercultural conflict management and harmony building in the virtual space across cultures * Looks at how intercultural conflicts are managed and harmonized in different cultural contexts

* Interdisciplinary approach and multiple theoretical perspectives on intercultural conflict management * Addresses intercultural conflict management and harmony building in the virtual space across cultures * Looks at how intercultural conflicts are managed and harmonized in different cultural contexts

This book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) - Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework of Granular Computing. The innovative perspective established with the aid of information granules provides a high level of human centricity and transparency central to the development of AI constructs. The chapters reflect the breadth of the area and cover recent developments in the methodology, advanced algorithms and applications of XAI to visual analytics, knowledge representation, learning and interpretation. The book appeals to a broad audience including researchers and practitioners interested in gaining exposure to the rapidly growing body of knowledge in AI and intelligent systems.

This book describes some of the key epidemiological principles, scientific approaches and quality assurance frameworks required to design and conduct biobank studies in various settings. Using examples from contemporary biobanks, the book addresses the design features and practical procedures needed in order to launch and manage biobank studies, including consent and regulatory approval, the organisation of field work, management of data and biological samples, follow-up and verification of disease outcomes, development of IT systems for data collection, quality assurance and study management. Over the last two decades, several large biobank studies have been initiated in different populations, intended to greatly enhance the development of precision medicine. Contemporary biobank studies are extremely large and complex, and involve several decades of follow-up. Such studies pose major challenges in terms of ensuring rapid recruitment, obtaining high-quality data, minimising loss to follow-up, reliably classifying disease outcomes, and optimising the use of the biological samples collected. In this regard, the key to success lies not in planning the perfect study, but in planning the most appropriate, reliable, sustainable and future-proof study given the practical constraints of available resources, time and capacity. The authors of this handbook are epidemiologists, clinicians, software engineers, and laboratory and data scientists with extensive experience in conducting large biobank studies. The eight chapters can be read separately or together, and provide readers with essential information on how to design, implement and manage these studies. The state-of-the-art, innovative and scalable approaches and methodologies presented here are intended to stimulate the development of further population-based and hospital-based biobank studies in diverse populations.

This book introduces readers to the use of formic acid for efficient organic synthesis. It describes the N-methylation of aromatic and aliphatic amines with formic acid using a boron-based catalyst [B(C6F5)3] in combination with silanes and without the need for an expensive transition metal catalyst. It also shows that formic acid interacts with alkynes and allyl alcohols through a carbonylation process that generates carbon monoxide in situ from nickel and palladium catalysis, respectively, doing away with the need to use high-pressure CO gas and offering a user-friendly and practical method for preparing functionalized , - and , -unsaturated carboxylic acids. The findings presented not only enrich the field of 'C1 chemistry,' but also support the advancement of green and sustainable chemistry.

This book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) - Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework of Granular Computing. The innovative perspective established with the aid of information granules provides a high level of human centricity and transparency central to the development of AI constructs. The chapters reflect the breadth of the area and cover recent developments in the methodology, advanced algorithms and applications of XAI to visual analytics, knowledge representation, learning and interpretation. The book appeals to a broad audience including researchers and practitioners interested in gaining exposure to the rapidly growing body of knowledge in AI and intelligent systems.

This book investigates the mineralogy and shock effects of Yanzhuang chondrite, using modern micro-mineralogical experimental techniques, including SEM, TEM, EPMA, Raman microprobe spectroscopy, instrumental neutron activation analysis, X-ray micro-diffraction analysis, micro-PIXE analysis and laser ablation ICP-MS. The micro-structural and micro-morphological characteristics as well as chemical composition of minerals were studied in details. Based on the studies in the shock effects of rocks and minerals, and the detailed study in the shock-produced melt, the book concludes that Yanzhuang chondrite is the most heavily shocked ordinary H group chondrite ever found and that it contains the most abundant shock induced melt among all known shock-melt-bearing chondritic meteorites.

This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.

This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.

This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm's algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.

This book introduces readers to the use of formic acid for efficient organic synthesis. It describes the N-methylation of aromatic and aliphatic amines with formic acid using a boron-based catalyst [B(C6F5)3] in combination with silanes and without the need for an expensive transition metal catalyst. It also shows that formic acid interacts with alkynes and allyl alcohols through a carbonylation process that generates carbon monoxide in situ from nickel and palladium catalysis, respectively, doing away with the need to use high-pressure CO gas and offering a user-friendly and practical method for preparing functionalized , - and , -unsaturated carboxylic acids. The findings presented not only enrich the field of 'C1 chemistry,' but also support the advancement of green and sustainable chemistry.

This book investigates the mineralogy and shock effects of Yanzhuang chondrite, using modern micro-mineralogical experimental techniques, including SEM, TEM, EPMA, Raman microprobe spectroscopy, instrumental neutron activation analysis, X-ray micro-diffraction analysis, micro-PIXE analysis and laser ablation ICP-MS. The micro-structural and micro-morphological characteristics as well as chemical composition of minerals were studied in details. Based on the studies in the shock effects of rocks and minerals, and the detailed study in the shock-produced melt, the book concludes that Yanzhuang chondrite is the most heavily shocked ordinary H group chondrite ever found and that it contains the most abundant shock induced melt among all known shock-melt-bearing chondritic meteorites.

This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.

The book presents a comprehensive and up-to-date review of fuzzy pattern recognition. It carefully discusses a range of methodological and algorithmic issues, as well as implementations and case studies, and identifies the best design practices, assesses business models and practices of pattern recognition in real-world applications in industry, health care, administration, and business. Since the inception of fuzzy sets, fuzzy pattern recognition with its methodology, algorithms, and applications, has offered new insights into the principles and practice of pattern classification. Computational intelligence (CI) establishes a comprehensive framework aimed at fostering the paradigm of pattern recognition. The collection of contributions included in this book offers a representative overview of the advances in the area, with timely, in-depth and comprehensive material on the conceptually appealing and practically sound methodology and practices of CI-based pattern recognition.

This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business.Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today's knowledge-driven economy.Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence with its underlying methodologies and tools helps address data analytics needs.The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.

This book describes algorithmic methods and parallelization techniques to design a parallel sparse direct solver which is specifically targeted at integrated circuit simulation problems. The authors describe a complete flow and detailed parallel algorithms of the sparse direct solver. They also show how to improve the performance by simple but effective numerical techniques. The sparse direct solver techniques described can be applied to any SPICE-like integrated circuit simulator and have been proven to be high-performance in actual circuit simulation. Readers will benefit from the state-of-the-art parallel integrated circuit simulation techniques described in this book, especially the latest parallel sparse matrix solution techniques.

This book explains how investor behavior, from mental accounting to the combustible interplay of hope and fear, affects financial economics. The transformation of portfolio theory begins with the identification of anomalies. Gaps in perception and behavioral departures from rationality spur momentum, irrational exuberance, and speculative bubbles. Behavioral accounting undermines the rational premises of mathematical finance. Assets and portfolios are imbued with "affect." Positive and negative emotions warp investment decisions. Whether hedging against intertemporal changes in their ability to bear risk or climbing a psychological hierarchy of needs, investors arrange their portfolios and financial affairs according to emotions and perceptions. Risk aversion and life-cycle theories of consumption provide possible solutions to the equity premium puzzle, an iconic financial mystery. Prospect theory has questioned the cogency of the efficient capital markets hypothesis. Behavioral portfolio theory arises from a psychological account of security, potential, and aspiration.

The book presents a comprehensive and up-to-date review of fuzzy pattern recognition. It carefully discusses a range of methodological and algorithmic issues, as well as implementations and case studies, and identifies the best design practices, assesses business models and practices of pattern recognition in real-world applications in industry, health care, administration, and business. Since the inception of fuzzy sets, fuzzy pattern recognition with its methodology, algorithms, and applications, has offered new insights into the principles and practice of pattern classification. Computational intelligence (CI) establishes a comprehensive framework aimed at fostering the paradigm of pattern recognition. The collection of contributions included in this book offers a representative overview of the advances in the area, with timely, in-depth and comprehensive material on the conceptually appealing and practically sound methodology and practices of CI-based pattern recognition.

This edited volume provides the reader with a fully updated, in-depth treatise on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of models of sentiment analysis and ontology -oriented engineering. The volume involves studies devoted to key issues of sentiment analysis, sentiment models, and ontology engineering. The book is structured into three main parts. The first part offers a comprehensive and prudently structured exposure to the fundamentals of sentiment analysis and natural language processing. The second part consists of studies devoted to the concepts, methodologies, and algorithmic developments elaborating on fuzzy linguistic aggregation to emotion analysis, carrying out interpretability of computational sentiment models, emotion classification, sentiment-oriented information retrieval, a methodology of adaptive dynamics in knowledge acquisition. The third part includes a plethora of applications showing how sentiment analysis and ontologies becomes successfully applied to investment strategies, customer experience management, disaster relief, monitoring in social media, customer review rating prediction, and ontology learning. This book is aimed at a broad audience of researchers and practitioners. Readers involved in intelligent systems, data analysis, Internet engineering, Computational Intelligence, and knowledge-based systems will benefit from the exposure to the subject matter. The book may also serve as a highly useful reference material for graduate students and senior undergraduate students.

This thesis introduces novel and significant results regarding the analysis and synthesis of positive systems, especially under l1 and L1 performance. It describes stability analysis, controller synthesis, and bounding positivity-preserving observer and filtering design for a variety of both discrete and continuous positive systems. It subsequently derives computationally efficient solutions based on linear programming in terms of matrix inequalities, as well as a number of analytical solutions obtained for special cases. The thesis applies a range of novel approaches and fundamental techniques to the further study of positive systems, thus contributing significantly to the theory of positive systems, a "hot topic" in the field of control.

This volume of Advances in Intelligent Systems and Computing highlights papers presented at the 11th International Conference on Genetic and Evolutionary Computing (ICGEC 2017). Held from 6 to 8 November 2017 in Kaohsiung, Taiwan, the conference was co-sponsored by Springer, Fujian University of Technology in China, National University of Kaohsiung, Harbin Institute of Technology, National Kaohsiung University of Applied Sciences, and VSB -Technical University of Ostrava. The conference was intended as an international forum for researchers and professionals engaged in all areas of genetic computing, intelligent computing, evolutionary and grid computing.

Approaches in Integrative Bioinformatics provides a basic introduction to biological information systems, as well as guidance for the computational analysis of systems biology. This book also covers a range of issues and methods that reveal the multitude of omics data integration types and the relevance that integrative bioinformatics has today. Topics include biological data integration and manipulation, modeling and simulation of metabolic networks, transcriptomics and phenomics, and virtual cell approaches, as well as a number of applications of network biology. It helps to illustrate the value of integrative bioinformatics approaches to the life sciences. This book is intended for researchers and graduate students in the field of Bioinformatics. Professor Ming Chen is the Director of the Bioinformatics Laboratory at the College of Life Sciences, Zhejiang University, Hangzhou, China. Professor Ralf Hofestädt is the Chair of the Department of Bioinformatics and Medical Informatics, Bielefeld University, Germany.

This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business.Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today's knowledge-driven economy.Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence with its underlying methodologies and tools helps address data analytics needs.The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.

This book describes algorithmic methods and parallelization techniques to design a parallel sparse direct solver which is specifically targeted at integrated circuit simulation problems. The authors describe a complete flow and detailed parallel algorithms of the sparse direct solver. They also show how to improve the performance by simple but effective numerical techniques. The sparse direct solver techniques described can be applied to any SPICE-like integrated circuit simulator and have been proven to be high-performance in actual circuit simulation. Readers will benefit from the state-of-the-art parallel integrated circuit simulation techniques described in this book, especially the latest parallel sparse matrix solution techniques.

This monograph is the first academic work to apply a neo-Marxist approach to 20th-century Canadian social realist novels, pursuing a refreshingly (neo-)Marxist approach to such issues as Bakhtinian notions of the novelistic form and dialogism as applied to Canadian socio-political novels influenced by various socialisms, socialist-feminist concerns, economic and sexual politics, and the genre of social realism. In so doing, it demonstrates that Marxist socialism is as relevant today as it was in the 1930s, just as social realist novels continue to thrive as a critique of capitalism. Readers will find valuable insights into the social significance, formal innovations, moral sensitivity, aesthetic enrichment, and ideological complexity of Canadian social realist novels.