This short textbook covers roughly 13 weeks of lectures on advanced statistical mechanics at the graduate level. It starts with an elementary introduction to the theory of ensembles from classical mechanics, and then goes on to quantum statistical mechanics with density matrix. These topics are covered concisely and briefly. The advanced topics cover the mean-field theory for phase transitions, the Ising models and their exact solutions, and critical phenomena and their scaling theory. The mean-field theories are discussed thoroughly with several different perspectives - focusing on a single degree, or using Feynman-Jensen-Bogoliubov inequality, cavity method, or Landau theory. The renormalization group theory is mentioned only briefly. As examples of computational and numerical approach, there is a chapter on Monte Carlo method including the cluster algorithms. The second half of the book studies nonequilibrium statistical mechanics, which includes the Brownian motion, the Langevin and Fokker-Planck equations, Boltzmann equation, linear response theory, and the Jarzynski equality. The book ends with a brief discussion of irreversibility. The topics are supplemented by problem sets (with partial answers) and supplementary readings up to the current research, such as heat transport with a Fokker-Planck approach.

This edited book provides a platform to bring together researchers, academia and industry collaborators to exchange their knowledge and work to develop better understanding about the scope of blockchain technology in business management applications of different sectors such as retail sector, supply chain and logistics, healthcare sector, manufacturing sector, judiciary, finance and government sector in terms of data quality and timeliness. The book presents original unpublished research papers on blockchain technology and business management on novel architectures, prototypes and case studies.

This book reports on the latest research and applications in the fields of sustainable manufacturing and remanufacturing, as well as process planning and optimization technologies. It introduces innovative algorithms, methodologies, industrial case studies and applications. It focuses on two topics: sustainable manufacturing for machining technologies and remanufacturing of waste electronic equipment, and various methods are covered for each one, including macro process planning, dynamic scheduling, selective disassembly planning and cloud-based disassembly planning. The experimental analysis provided for every method explains the benefits, as well as how they are sustainable for various real-world applications. Further, a theoretical analysis and algorithm design is presented for each, accompanied by the contributors' relevant research, including: * step-by-step guides; * application scenarios; * relevant literature surveys; * implementation details and case studies; and * critical reviews on the relevant technologies. This book is a valuable resource for researchers in sustainable manufacturing, remanufacturing and product lifecycle management communities, as well as practicing engineers and decision-makers in industry and all those interested in sustainable product development. It is also useful reading material for postgraduates and academics wanting to conduct relevant research, and a reference resource for manufacturing engineers developing innovative tools and methodologies.

This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress. This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0.

Connected vehicles and intelligent vehicles have been identified as key technologies for increasing road safety and transport efficiency. This book presents and discusss the recent advances in theory and practice in connected vehicle systems. It covers emerging research that aims at dealing with the challenges in designing the essential functional components of connected vehicles. Major topics include intra- and inter-vehicle communications, mobility model of fleet and ramp merging, trace and position data analysis, security and privacy.

This edited book provides a platform to bring together researchers, academia and industry collaborators to exchange their knowledge and work to develop better understanding about the scope of blockchain technology in business management applications of different sectors such as retail sector, supply chain and logistics, healthcare sector, manufacturing sector, judiciary, finance and government sector in terms of data quality and timeliness. The book presents original unpublished research papers on blockchain technology and business management on novel architectures, prototypes and case studies.

This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress. This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0.

This book discusses an emerging field of decision science that focuses on business processes and systems used to extract knowledge from large volumes of data to provide significant insights for crucial decisions in critical situations. It presents studies employing computing techniques like machine learning, which explore decision-making for cross-platforms that contain heterogeneous data associated with complex assets, leadership, and team coordination. It also reveals the advantages of using decision sciences with management-oriented problems. The book includes a selection of the best papers presented at the 2nd International Conference on Decision Science and Management (ICDSM 2019), held at Hunan International Economics University, China, on 20-21 September 2019.

This work covers sequence-based protein homology detection, a fundamental and challenging bioinformatics problem with a variety of real-world applications. The text first surveys a few popular homology detection methods, such as Position-Specific Scoring Matrix (PSSM) and Hidden Markov Model (HMM) based methods, and then describes a novel Markov Random Fields (MRF) based method developed by the authors. MRF-based methods are much more sensitive than HMM- and PSSM-based methods for remote homolog detection and fold recognition, as MRFs can model long-range residue-residue interaction. The text also describes the installation, usage and result interpretation of programs implementing the MRF-based method.

The authors present a study of the H-infinity control problem and related topics for descriptor systems, described by a set of nonlinear differential-algebraic equations. They derive necessary and sufficient conditions for the existence of a controller solving the standard nonlinear H-infinity control problem considering both state and output feedback. One such condition for the output feedback control problem to be solvable is obtained in terms of Hamilton-Jacobi inequalities and a weak coupling condition; a parameterization of output feedback controllers solving the problem is also provided. All of these results are then specialized to the linear case. The derivation of state-space formulae for all controllers solving the standard H-infinity control problem for descriptor systems is proposed. Among other important topics covered are balanced realization, reduced-order controller design and mixed H2/H-infinity control. "H-infinity Control for Nonlinear Descriptor Systems" provides a comprehensive introduction and easy access to advanced topics.

Connected vehicles and intelligent vehicles have been identified as key technologies for increasing road safety and transport efficiency. This book presents and discusss the recent advances in theory and practice in connected vehicle systems. It covers emerging research that aims at dealing with the challenges in designing the essential functional components of connected vehicles. Major topics include intra- and inter-vehicle communications, mobility model of fleet and ramp merging, trace and position data analysis, security and privacy.

This book discusses an emerging area in computer science, IT, and management, i.e., decision sciences and management. It includes studies that employ various computing techniques like machine learning to generate insights from huge amounts of available data; and which explore decision making for cross-platforms that contain heterogeneous data associated with complex assets; leadership; and team coordination. It also reveals the advantages of using decision sciences with management-oriented problems. The book includes a selection of the best papers presented at the Third International Conference on Decision Science and Management 2021 (ICDSM 2021), held at Hang Seng University of Hong Kong in China.

This book reports on the latest research and applications in the fields of sustainable manufacturing and remanufacturing, as well as process planning and optimization technologies. It introduces innovative algorithms, methodologies, industrial case studies and applications. It focuses on two topics: sustainable manufacturing for machining technologies and remanufacturing of waste electronic equipment, and various methods are covered for each one, including macro process planning, dynamic scheduling, selective disassembly planning and cloud-based disassembly planning. The experimental analysis provided for every method explains the benefits, as well as how they are sustainable for various real-world applications. Further, a theoretical analysis and algorithm design is presented for each, accompanied by the contributors' relevant research, including: * step-by-step guides; * application scenarios; * relevant literature surveys; * implementation details and case studies; and * critical reviews on the relevant technologies. This book is a valuable resource for researchers in sustainable manufacturing, remanufacturing and product lifecycle management communities, as well as practicing engineers and decision-makers in industry and all those interested in sustainable product development. It is also useful reading material for postgraduates and academics wanting to conduct relevant research, and a reference resource for manufacturing engineers developing innovative tools and methodologies.

Taiwan's modern legal system--quite different from those of both traditional China and the People's Republic--has evolved since the advent of Japanese rule in 1895. Japan has gradually adopted Western law during the 19th-century and when it occupied Taiwan--a frontier society composed of Han Chinese settlers--its codes were instituted for the purpose of rapidly assimilating the Taiwanese people into Japanese society. Tay-sheng Wang's comprehensive study lays a solid foundation for future analyses of Taiwanese law. It documents how Western traditions influenced the formation of Taiwan's modern legal structure through the conduit of Japanese colonial rule and demonstrates the extent to which legal concepts diverged from the Chinese legal tradition and moved toward Western law.