This collection focuses on energy efficient technologies including innovative ore beneficiation, smelting technologies, recycling and waste heat recovery. The volume also covers various technological aspects of sustainable energy ecosystems, processes that improve energy efficiency, reduce thermal emissions, and reduce carbon dioxide and other greenhouse emissions. Papers addressing renewable energy resources for metals and materials production, waste heat recovery and other industrial energy efficient technologies, new concepts or devices for energy generation and conversion, energy efficiency improvement in process engineering, sustainability and life cycle assessment of energy systems, as well as the thermodynamics and modeling for sustainable metallurgical processes are included. This volume also includes topics on CO2 sequestration and reduction in greenhouse gas emissions from process engineering, sustainable technologies in extractive metallurgy, as well as the materials processing and manufacturing industries with reduced energy consumption and CO2 emission. Contributions from all areas of non-nuclear and non-traditional energy sources, such as solar, wind, and biomass are also included in this volume.Papers from the following symposia are presented in the book:Energy Technologies and CO2 ManagementAdvanced Materials for Energy Conversion and Storage Deriving Value from Challenging Waste Streams: Recycling and Sustainability Joint SessionSolar Cell SiliconStored Renewable Energy in Coal

This volume compiles topics from the REWAS 2013 symposium at the TMS Annual Meeting, focusing on different aspects of sustainability. It discusses how to realize sustainability in such areas as transportation, the built environment, electrical and electronic equipment and infrastructure, energy production, and water systems. Enabling sustainability topics include the use of metals and materials processing, recycling and recovery, as well as process design and modeling. The book focuses on understanding sustainability through life cycle management and analysis, systems modeling and design, and education and consumer awareness.

This collection focuses on energy efficient technologies including innovative ore beneficiation, smelting technologies, recycling and waste heat recovery. The volume also covers various technological aspects of sustainable energy ecosystems, processes that improve energy efficiency, reduce thermal emissions, and reduce carbon dioxide and other greenhouse emissions. Papers addressing renewable energy resources for metals and materials production, waste heat recovery and other industrial energy efficient technologies, new concepts or devices for energy generation and conversion, energy efficiency improvement in process engineering, sustainability and life cycle assessment of energy systems, as well as the thermodynamics and modeling for sustainable metallurgical processes are included. This volume also includes topics on CO2 sequestration and reduction in greenhouse gas emissions from process engineering, sustainable technologies in extractive metallurgy, as well as the materials processing and manufacturing industries with reduced energy consumption and CO2 emission. Contributions from all areas of non-nuclear and non-traditional energy sources, such as solar, wind, and biomass are also included in this volume.Papers from the following symposia are presented in the book:Energy Technologies and CO2 ManagementAdvanced Materials for Energy Conversion and Storage Deriving Value from Challenging Waste Streams: Recycling and Sustainability Joint SessionSolar Cell SiliconStored Renewable Energy in Coal

This book covers various technological aspects of sustainable energy ecosystems and processes that improve energy efficiency, and reduce and sequestrate carbon dioxide (CO2) and other greenhouse emissions. Papers emphasize the need for sustainable technologies in extractive metallurgy, materials processing and manufacturing industries with reduced energy consumption and CO2 emission. Industrial energy efficient technologies include innovative ore beneficiation, smelting technologies, recycling, and waste heat recovery. The book also contains contributions from all areas of non-nuclear and non-traditional energy sources, including renewable energy sources such as solar, wind, and biomass. Papers from the following symposia are presented in the book: Energy Technologies and Carbon Dioxide Management Recycling and Sustainability Update Magnetic Materials for Energy Applications V Sustainable Energy and Layered Double Hydroxides

EPD Congress is an annual collection that addresses extraction and processing metallurgy. The papers in this book are drawn from symposia held at the 2016 Annual Meeting of The Minerals, Metals & Materials Society. The 2016 edition includes papers from the following symposia: *Materials Processing Fundamentals *Advanced Characterization Techniques for Quantifying and Modeling Deformation

This collection provides researchers and industry professionals with complete guidance on the synthesis, analysis, design, monitoring, and control of metals, materials, and metallurgical processes and phenomena. Along with the fundamentals, it covers modeling of diverse phenomena in processes involving iron, steel, non-ferrous metals, and composites. It also goes on to examine second phase particles in metals, novel sensors for hostile-environment materials processes, online sampling and analysis techniques, and models for real-time process control and quality monitoring systems.

This SpringerBrief provides a concise guide to applying wireless energy transfer techniques in traditional battery-powered sensor networks. It examines the benefits and challenges of wireless power including efficiency and reliability. The authors build a wireless rechargeable sensor networks from scratch and aim to provide perpetual network operation. Chapters cover a wide range of topics from the collection of energy information and recharge scheduling to joint design with typical sensing applications such as data gathering. Problems are approached using a natural combination of probability theory, optimization, algorithm and protocol designs. All proposed mechanisms are evaluated by extensive simulations. Wireless Rechargeable Sensor Networks targets professionals and researchers working in networks, wireless communications, energy technology and information technology. Advanced-level students studying electrical engineering and computer science will also find this material useful as a study guide.

Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way. A Deterministic View of Learning in Dynamic Environments The authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems. A New Model of Information Processing This book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).

This book comprehensively reviews searchable encryption, which represents a series of research developments that directly enable search functionality over encrypted data. The book majorly covers: 1) the design and implementation of encrypted search algorithms, data structures, and systems that facilitate various forms of search over always-encrypted databases; 2) different threat models, assumptions, and the related security guarantees, when using searchable encryption in the real-world settings; and 3) latest efforts in building full-fledged encrypted database systems that draw insights from searchable encryption constructions. The book fits in the timely context, where the necessity of safeguarding important and sensitive data has been globally recognized. Traditional security measures, such as storing data behind network firewalls and layers of access control mechanisms to keep attackers out, are no longer sufficient to cope with the expanding landscape of surging cyber threats. There is an urgent call to keep sensitive data always encrypted to protect the data at rest, in transit, and in use. Doing so guarantees data confidentiality for owners, even if the data is out of their hands, e.g., hosted at in-the-cloud databases. The daunting challenge is how to perform computation over encrypted data. As we unfold in this book, searchable encryption, as a specific line of research in this broadly defined area, has received tremendous advancements over the past decades. This book is majorly oriented toward senior undergraduates, graduate students, and researchers, who want to work in the field and need extensive coverage of encrypted database research. It also targets security practitioners who want to make well-informed deployment choices of the latest advancements in searchable encryption for their targeted applications. Hopefully, this book will be beneficial in both regards.

This book constitutes the refereed post-conference proceedings of the 15th EAI International Conference on Quality, Reliability, Security and Robustness in Heterogeneous Networks, QShine 2020, held in November 2020. Due to COVID-19 pandemic the conference was held virtually. The 19 revised full papers were carefully reviewed and selected from 49 submissions. The papers are organized thematically in tracks on Network Reliability and Security an Emerging Applications

This collection addresses the need for sustainable technologies with reduced energy consumption and pollutants and the development and application of alternative sustainable energy to maintain a green environment and energy supply. Contributions focus on energy-efficient technologies including innovative ore beneficiation, smelting technologies, and recycling and waste heat recovery, as well as emerging novel energy technologies. Papers also cover various technological aspects of sustainable energy ecosystems, processes that improve energy efficiency, reduce thermal emissions, and reduce carbon dioxide and other greenhouse emissions. Papers from the following symposia are presented in the book: Energy Technologies and Carbon Dioxide Management Solar Cell Silicon Advanced Materials for Energy Conversion and Storage

This book constitutes the proceedings of the 12th International Conference on Network and System Security, NSS 2018, held in Hong Kong, China, in August 2018. The 26 revised full papers and 9 short papers presented in this book were carefully reviewed and selected from 88 initial submissions. The papers cover a wide range of topics in the field, including blockchain, mobile security, applied cryptography, authentication, biometrics, IoT, privacy, and education.

It has been suggested that Ti-containing inclusions could cause clogging of the submerged entry nozzle, decrease continuous casting productivity, and is a source of surface defects on the steel products. It is the aim of this project to investigate whether the existence of Ti-containing inclusions can be the results of transient conditions, which can be caused by (i) local and temporary variation of the Ti/Al, (ii) fluctuations in melt temperature and (iii) changes in O availability. The approach undertaken in this study is to simulate transient conditions for the Fe-Al-Ti-O melts and sample the melts as a function of time during a time period that is of relevance to industrial ladle processing. The samples are characterized for inclusion shape, size and chemistry through scanning electron microscopy (SEM), inclusion structure and detailed chemistry through transmission electron microscopy (TEM) and chemical analysis. The influences of the following variables on the inclusion characteristics have been investigated: 1) Ti source, 2) Ti vs. Al ratio, 3) temperature and 4) time between Al and Ti additions. The starting O content was in all cases in the range between 330-460 ppm.

Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way.

A Deterministic View of Learning in Dynamic Environments

The authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems.

A New Model of Information Processing

This book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).