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 lays the foundations for quality modeling and analysis in the context of supply chains through a synthesis of the economics, operations management, as well as operations research/management science literature on quality. The reality of today's supply chain networks, given their global reach from sourcing locations to points of demand, is further challenged by such issues as the growth in outsourcing as well as the information asymmetry associated with what producers know about the quality of their products and what consumers know. Although much of the related literature has focused on the micro aspects of supply chain networks, considering two or three decision-makers, it is essential to capture the scale of supply chain networks in a holistic manner that occurs in practice in order to be able to evaluate and analyze the competition and the impacts on supply chain quality in a quantifiable manner. This volume provides an overview of the fundamental methodologies utilized in this book, including optimization theory, game theory, variational inequality theory, and projected dynamical systems theory. It then focuses on major issues in today's supply chains with respect to quality, beginning with information asymmetry, followed by product differentiation and branding, the outsourcing of production, from components to final products, to quality in freight service provision. The book is filled with numerous real-life examples in order to emphasize the generality and pragmatism of the models and tools. The novelty of the framework lies in a network economics perspective through which the authors identify the underlying network structure of the various supply chains, coupled with the behavior of the decision-makers, ranging from suppliers and manufacturers to freight service providers. What is meant by quality is rigorously defined and quantified. The authors explore the underlying dynamics associated with the competitive processes along with the equilibrium solutions. As appropriate, the supply chain decision-makers compete in terms of quantity and quality, or in price and quality. The relevance of the various models that are developed to specific industrial sectors, including pharmaceuticals and high technology products, is clearly made. Qualitative analyses are provided, along with effective, and, easy to implement, computational procedures. Finally, the impacts of policy interventions, in the form of minimum quality standards, and their ramifications, in terms of product prices, quality levels, as well as profits are explored. The book is filled with many network figures, graphs, and tables with data.

Global networks, which are the primary pillars of the modern manufacturing industry and supply chains, can only cope with the new challenges, requirements and demands when supported by new computing and Internet-based technologies. Cloud Manufacturing: Distributed Computing Technologies for Global and Sustainable Manufacturing introduces a new paradigm for scalable service-oriented sustainable and globally distributed manufacturing systems. The eleven chapters in this book provide an updated overview of the latest technological development and applications in relevant research areas. Following an introduction to the essential features of Cloud Computing, chapters cover a range of methods and applications such as the factors that actually affect adoption of the Cloud Computing technology in manufacturing companies and new geometrical simplification method to stream 3-Dimensional design and manufacturing data via the Internet. This is further supported case studies and real life data for Waste Electrical and Electronic Equipment (WEEE) remanufacturing. This compilation of up to date research and literature can be used as a textbook or reference for mechanical, manufacturing, and computer engineering graduate students and researchers for efficient utilization, deployment and development of distributed and Cloud manufacturing systems, services and applications.

This book presents new approaches to logistics solutions in global environments, with a special focus on collaborative logistics and intermodality. Contributions in this book are linked to two major initiatives in global logistics - H2020 MSCA-RISE-EU project EC-Asia Research Network on Integration of Global and Local Agri-Food Supply Chains Towards Sustainable Food Security (GOLF), and the International Conference on Logistics & Supply Chain (CiLOG). Topics covered in this book are: global logistics environments in manufacturing industries, key logistic decision-making parameters, global logistics management and its impact on container logistics processes, logistic market clusters and many more.

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 is an open access book. Important tasks must be completed on time and with guaranteed quality; that is the consensus reached by system designers and users. However, for too long, important tasks have often been given unnecessary urgency, and people intuitively believe that important tasks should be executed first so that their performance can be guaranteed. Actually, in most cases, their performance can be guaranteed even if they are executed later, and the "early" resources can be utilized for other, more urgent tasks. Therefore, confusing importance with urgency hinders the proper use of system resources. In 2007, mixed criticality was proposed to indicate that a system may contain tasks of various importance levels. Since then, system designers and users have distinguished between importance and urgency. In the industrial field, due to the harsh environment they operate in, industrial wireless networks' quality of service (QoS) has always been a bottleneck restricting their applications. Therefore, this book introduces criticality to label important data, which is then allocated more transmission resources, ensuring that important data's QoS requirements can be met to the extent possible. To help readers understand how to apply mixed-criticality data to industrial wireless networks, the content is divided into three parts. First, we introduce how to integrate the model of mixed-criticality data into industrial wireless networks. Second, we explain how to analyze the schedulability of mixed-criticality data under existing scheduling algorithms. Third, we present a range of novel scheduling algorithms for mixed-criticality data. If you want to improve the QoS of industrial wireless networks, this book is for you.

This book covers key issues related to Geospatial Semantic Web, including geospatial web services for spatial data interoperability; geospatial ontology for semantic interoperability; ontology creation, sharing, and integration; querying knowledge and information from heterogeneous data source; interfaces for Geospatial Semantic Web, VGI (Volunteered Geographic Information) and Geospatial Semantic Web; challenges of Geospatial Semantic Web; and development of Geospatial Semantic Web applications. This book also describes state-of-the-art technologies that attempt to solve these problems such as WFS, WMS, RDF, OWL and GeoSPARQL and demonstrates how to use the Geospatial Semantic Web technologies to solve practical real-world problems such as spatial data interoperability.

Support vector machines (SVMs) are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar with what they are and how they work. Presenting a clear bridge between theory and application, Support Vector Machines and Their Application in Chemistry and Biotechnology provides a thorough description of the mechanism of SVMs from the point of view of chemists and biologists, enabling them to solve difficult problems with the help of these powerful tools. Topics discussed include: Background and key elements of support vector machines and applications in chemistry and biotechnology Elements and algorithms of support vector classification (SVC) and support vector regression (SVR) machines, along with discussion of simulated datasets The kernel function for solving nonlinear problems by using a simple linear transformation method Ensemble learning of support vector machines Applications of support vector machines to near-infrared data Support vector machines and quantitative structure-activity/property relationship (QSAR/QSPR) Quality control of traditional Chinese medicine by means of the chromatography fingerprint technique The use of support vector machines in exploring the biological data produced in OMICS study Beneficial for chemical data analysis and the modeling of complex physic-chemical and biological systems, support vector machines show promise in a myriad of areas. This book enables non-mathematicians to understand the potential of SVMs and utilize them in a host of applications.

Overweight students often suffer negative consequences with regard to low physical ability, skills, and fitness; obesity-related health implications; teasing and exclusion from physical education by their peers; and psychosocial and emotional suffering as a result of weight stigma. Widespread obesity and its negative consequences have presented an unprecedented challenge for teachers, who must include overweight students in physical education activities while striving to provide individualized instruction for diverse learners and foster positive learning environments. Educators stand to benefit greatly from specific knowledge and skills for reducing bias and including overweight students. Teaching Overweight Students in Physical Education offers a compact and easy-to-read take on this problem. It begins by summarizing information on the obesity trend, weight stigma, and coping mechanisms. Next, it introduces the Social Ecological Constraint Model, which casts the teacher as an agent of change who is aware of and manipulates a variety of factors from multiple levels for effective inclusion of overweight students in physical education. Finally, it provides detailed strategies guided by the conceptual model for instructors to implement into their physical education classes. In all, this book provides a map for successfully including overweight students and offers practical strategies to help physical education teachers create inclusive and safe climates, and design differentiated instruction to maximize overweight or obese students' engagement and learning. Comprehensive, evidence-based, and timely, this book is tailored for physical education educators and practitioners, but will also benefit parents of overweight children by providing them with strategies for educating their children on how to cope with stigma and weight-related teasing.

Overweight students often suffer negative consequences with regard to low physical ability, skills, and fitness; obesity-related health implications; teasing and exclusion from physical education by their peers; and psychosocial and emotional suffering as a result of weight stigma. Widespread obesity and its negative consequences have presented an unprecedented challenge for teachers, who must include overweight students in physical education activities while striving to provide individualized instruction for diverse learners and foster positive learning environments. Educators stand to benefit greatly from specific knowledge and skills for reducing bias and including overweight students. Teaching Overweight Students in Physical Education offers a compact and easy-to-read take on this problem. It begins by summarizing information on the obesity trend, weight stigma, and coping mechanisms. Next, it introduces the Social Ecological Constraint Model, which casts the teacher as an agent of change who is aware of and manipulates a variety of factors from multiple levels for effective inclusion of overweight students in physical education. Finally, it provides detailed strategies guided by the conceptual model for instructors to implement into their physical education classes. In all, this book provides a map for successfully including overweight students and offers practical strategies to help physical education teachers create inclusive and safe climates, and design differentiated instruction to maximize overweight or obese students' engagement and learning. Comprehensive, evidence-based, and timely, this book is tailored for physical education educators and practitioners, but will also benefit parents of overweight children by providing them with strategies for educating their children on how to cope with stigma and weight-related teasing.

The six volume set LNCS 11361-11366 constitutes the proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.

The six volume set LNCS 11361-11366 constitutes the proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.

The six volume set LNCS 11361-11366 constitutes the proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.

The six volume set LNCS 11361-11366 constitutes the proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.

The six volume set LNCS 11361-11366 constitutes the proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.

Collaborative Product Design and Manufacturing Methodologies and Applications introduces a wide spectrum of collaborative engineering issues in design and manufacturing. It offers state-of-the-art chapters written by international experts from academia and industry, and reflects the most up-to-date R & D work and applications, especially those from the last three to five years. The book will serve as an essential reference for academics, upper-level undergraduate and graduate students and practicing professionals.

The six volume set LNCS 11361-11366 constitutes the proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.

Global networks, which are the primary pillars of the modern manufacturing industry and supply chains, can only cope with the new challenges, requirements and demands when supported by new computing and Internet-based technologies. Cloud Manufacturing: Distributed Computing Technologies for Global and Sustainable Manufacturing introduces a new paradigm for scalable service-oriented sustainable and globally distributed manufacturing systems. The eleven chapters in this book provide an updated overview of the latest technological development and applications in relevant research areas. Following an introduction to the essential features of Cloud Computing, chapters cover a range of methods and applications such as the factors that actually affect adoption of the Cloud Computing technology in manufacturing companies and new geometrical simplification method to stream 3-Dimensional design and manufacturing data via the Internet. This is further supported case studies and real life data for Waste Electrical and Electronic Equipment (WEEE) remanufacturing. This compilation of up to date research and literature can be used as a textbook or reference for mechanical, manufacturing, and computer engineering graduate students and researchers for efficient utilization, deployment and development of distributed and Cloud manufacturing systems, services and applications.

Collaborative Product Design and Manufacturing Methodologies and Applications introduces a wide spectrum of collaborative engineering issues in design and manufacturing. It offers state-of-the-art chapters written by international experts from academia and industry, and reflects the most up-to-date R & D work and applications, especially those from the last three to five years. The book will serve as an essential reference for academics, upper-level undergraduate and graduate students and practicing professionals.

Support vector machines (SVMs) are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar with what they are and how they work. Presenting a clear bridge between theory and application, Support Vector Machines and Their Application in Chemistry and Biotechnology provides a thorough description of the mechanism of SVMs from the point of view of chemists and biologists, enabling them to solve difficult problems with the help of these powerful tools. Topics discussed include: Background and key elements of support vector machines and applications in chemistry and biotechnology Elements and algorithms of support vector classification (SVC) and support vector regression (SVR) machines, along with discussion of simulated datasets The kernel function for solving nonlinear problems by using a simple linear transformation method Ensemble learning of support vector machines Applications of support vector machines to near-infrared data Support vector machines and quantitative structure-activity/property relationship (QSAR/QSPR) Quality control of traditional Chinese medicine by means of the chromatography fingerprint technique The use of support vector machines in exploring the biological data produced in OMICS study Beneficial for chemical data analysis and the modeling of complex physic-chemical and biological systems, support vector machines show promise in a myriad of areas. This book enables non-mathematicians to understand the potential of SVMs and utilize them in a host of applications.

Debut collection of poems that weaves stories of family history, war, and migration. Dong Li's The Orange Tree is a collection of narrative poems that braids forgotten legends, personal sorrows, and political upheavals into a cinematic account of Chinese history as experienced by one family. Amid chaos and catastrophe, the child narrator examines a yellowed family photo to find resemblances and learns a new language, inventing compound words to conjure and connect family stories. These invented words and the calligraphy of untranslated Chinese characters appear in lists separating the book's narrative sections. Li's lyrical and experimental collection transcends the individual, placing generations of family members and anonymous others together in a single moment that surpasses chronological time. Weaving through stories of people with little means, between wars and celebrations, over bridges and walls, and between trees and gardens, Li's poems offer intimate perspectives on times that resonate with our own. The result is an unflinching meditation on family history, collective trauma, and imaginative recovery. The Orange Tree is the recipient of the inaugural Phoenix Emerging Poet Book Prize for 2023.

This book presents cutting-edge multidisciplinary work on the characterization of ancient materials; the technologies of selection, production and usage by which materials are transformed into objects and artifacts; the science underlying their deterioration, preservation and conservation; and sociocultural interpretation derived from an empirical methodology of observation, measurement and experimentation. Of particular interest are contributions which explore the interface and overlap among traditional materials science, the history of technology and the archaeological and conservation sciences, or that investigate new methods and applications of materials science in art and archaeology. Topics include: analytical chemistry and spectroscopy; ancient and historical metallurgy; natural and artificial glass; characterization, sources and production of ceramics; organic materials technologies; architectural conservation and materials characterization; conservation of archaeological and historical materials; and other studies of ceramics and metals.

This is an open access book. Important tasks must be completed on time and with guaranteed quality; that is the consensus reached by system designers and users. However, for too long, important tasks have often been given unnecessary urgency, and people intuitively believe that important tasks should be executed first so that their performance can be guaranteed. Actually, in most cases, their performance can be guaranteed even if they are executed later, and the "early" resources can be utilized for other, more urgent tasks. Therefore, confusing importance with urgency hinders the proper use of system resources. In 2007, mixed criticality was proposed to indicate that a system may contain tasks of various importance levels. Since then, system designers and users have distinguished between importance and urgency. In the industrial field, due to the harsh environment they operate in, industrial wireless networks' quality of service (QoS) has always been a bottleneck restricting their applications. Therefore, this book introduces criticality to label important data, which is then allocated more transmission resources, ensuring that important data's QoS requirements can be met to the extent possible. To help readers understand how to apply mixed-criticality data to industrial wireless networks, the content is divided into three parts. First, we introduce how to integrate the model of mixed-criticality data into industrial wireless networks. Second, we explain how to analyze the schedulability of mixed-criticality data under existing scheduling algorithms. Third, we present a range of novel scheduling algorithms for mixed-criticality data. If you want to improve the QoS of industrial wireless networks, this book is for you.