The promises and realities of digital innovation have come to suffuse everything from city regions to astronomy, government to finance, art to medicine, politics to warfare, and from genetics to reality itself. Digital systems augmenting physical space, buildings, and communities occupy a special place in the evolutionary discourse about advanced technology. The two Intelligent Environments books edited by Peter Droege span a quarter of a century across this genre. The second volume, Intelligent Environments: Advanced Systems for a Healthy Planet, asks: how does civilization approach thinking systems, intelligent spatial models, design methods, and support structures designed for sustainability, in ways that could counteract challenges to terrestrial habitability? This book examines a range of baseline and benchmark practices but also unusual and even sublime endeavors across regions, currencies, infrastructure, architecture, transactive electricity, geodesign, net-positive planning, remote work, integrated transport, and artificial intelligence in understanding the most immediate spatial setting: the human body. The result of this quest is both highly informative and useful, but also critical. It opens windows on what must fast become a central and overarching existential focus in the face of anthropogenic planetary heating and other threats-and raises concomitant questions about direction, scope, and speed of that change.

This ground-breaking and timely book explores how big data, artificial intelligence and algorithms are creating new types of agency, and the impact that this is having on our lives and the rule of law. Addressing the issues in a thoughtful, cross-disciplinary manner, the authors examine the ways in which data-driven agency is transforming democratic practices and the meaning of individual choice. Leading scholars in law, philosophy, computer science and politics analyse the latest innovations in data science and machine learning, assessing the actual and potential implications of these technologies. They investigate how this affects our understanding of such concepts as agency, epistemology, justice, transparency and democracy, and advocate a precautionary approach that takes the effects of data-driven agency seriously without taking it for granted. Scholars and students of law, ethics and philosophy, in particular legal, political and democratic theory, will find this book a compelling and invaluable read, as will computer scientists interested in the implications of their own work. It will also prove insightful for academics and activists working on privacy, fairness and anti-discrimination. Contributors include: J.E. Cohen, G. de Vries, S. Delacroix, P. Dumouchel, C. Ess, M. Garnett, E.H. Gerding, R. Gomer, C. Graber, M. Hildebrandt, C. Maple, K. O'Hara, P. Ohm, m.c. schraefel, D. Stevens, N. van Dijk, M. Veale

The Designer's Guide to the Cortex-M Microcontrollers, Third Edition provides an easy-to-understand introduction to the concepts required to develop programs in C with a Cortex-M based microcontroller. Sections cover architectural descriptions that are supported with practical examples, enabling readers to easily develop basic C programs to run on the Cortex-M0/M0+/M3 and M4 and M7 and examine advanced features of the Cortex architecture, such as memory protection, operating modes and dual stack operation. Final sections examine techniques for software testing and code reuse specific to Cortex-M microcontrollers. Users will learn the key differences between the Cortex-M0/M0+/M3 and M4 and M7; how to write C programs to run on Cortex-M based processors; how to make the best use of the CoreSight debug system; the Cortex-M operating modes and memory protection; advanced software techniques that can be used on Cortex-M microcontrollers, and much more.

Cardiovascular and Coronary Artery Imaging, Volume Two presents the basics of echocardiography, nuclear imaging and magnetic resonance imaging (MRI) and provides insights into their appropriate use. The book covers state-of-the-art approaches for automated non-invasive systems for early cardiovascular and coronary artery disease diagnosis. It includes several prominent imaging modalities such as MRI, CT and PET technologies. Other sections focus on major trends and challenges in this area and present the latest techniques for cardiovascular and coronary image analysis.

Customized Production Through 3D Printing in Cloud Manufacturing explains how to combine the latest cloud manufacturing and additive manufacturing technology to find innovative solutions to important problems in research and industry. The manufacturing industry strives constantly to improve levels of product personalization for its customers, who have become increasingly demanding in this respect in recent decades. Among the tools currently growing in use in the industry, there is great potential to address this demand. Cloud manufacturing maps manufacturing resources and capabilities to the cloud, adding the capacity to gather decentralized manufacturing resources and use manufacturing services on-demand, and 3D printing provides strong support for truly individualized manufactured components. This is the first book to cover the whole lifecycle of 3D printing services in a cloud environment, including topics like: cloud servitization of 3D printers, 3D printing model design, supply-demand matching and scheduling, on-demand using and pricing, printing monitoring in cloud, and printing service evaluation. With a systematic introduction to this promising manufacturing paradigm, as well as coverage of models and service management to practical applications, this book will meet the needs of a broad range of researchers as well as practitioners.

Coulomb Interactions in Particle Beams, Volume 223 in the Advances in Imaging and Electron Physics series, merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. The series features articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science, digital image processing, electromagnetic wave propagation, electron microscopy, and computing methods used in all these domains, with this release exploring Coulomb Interactions in Particle Beams.

Intelligent Edge Computing for Cyber Physical Applications introduces state-of-the-art research methodologies, tools and techniques, challenges, and solutions with further research opportunities in the area of edge-based cyber-physical systems. The book presents a comprehensive review of recent literature and analysis of different techniques for building edge-based CPS. In addition, it describes how edge-based CPS can be built to seamlessly interact with physical machines for optimal performance, covering various aspects of edge computing architectures for dynamic resource provisioning, mobile edge computing, energy saving scenarios, and different security issues. Sections feature practical use cases of edge-computing which will help readers understand the workings of edge-based systems in detail, taking into account the need to present intellectual challenges while appealing to a broad readership, including academic researchers, practicing engineers and managers, and graduate students.

Lean thinking involves more than just eliminating waste; through its five guiding principles-value, value chain, continuous flow, pull production, and perfection-its successful applications are commonly found in the manufacturing sector. Although its application and benefits to companies is no longer contested, it is rare to find works that consolidate applications of lean thinking in sectors that are unconventional, such as healthcare and government. Cases on Lean Thinking Applications in Unconventional Systems allows readers to broaden their view on lean thinking applications and visualize insights for research. It presents case studies and applications of lean thinking within several different industries. Covering topics such as emergency care units, standardized work, and national humanization policy, this case book is an essential resource for engineers, hospital administrators, healthcare professionals, IT managers, government officials, students and faculty of higher education, researchers, and academicians.

In today's competitive market, a manager must be able to look at data, understand it, analyze it, and then interpret it to design a smart business strategy. Big data is also a valuable source of information on how customers interact with firms through various mediums such as social media platforms, online reviews, and many more. The applications and uses of business analytics are numerous and must be further studied to ensure they are utilized appropriately. Data-Driven Approaches for Effective Managerial Decision Making investigates management concepts and applications using data analytics and outlines future research directions. The book also addresses contemporary advancements and innovations in the field of management. Covering key topics such as big data, business intelligence, and artificial intelligence, this reference work is ideal for managers, business owners, industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.

Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach - putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning. Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.

Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.

Artificial Intelligence and Machine Learning in Smart City Planning shows the reader practical applications of AIML techniques and describes recent advancements in this area in various sectors. Owing to the multidisciplinary nature, this book primarily focuses on the concepts of AIML and its methodologies such as evolutionary techniques, neural networks, machine learning, deep learning, block chain technology, big data analytics, and image processing in the context of smart cities. The text also discusses possible solutions to different challenges posed by smart cities by presenting cutting edge AIML techniques using different methodologies, as well as future directions for those same techniques.

Integrated Human-Machine Intelligence: Beyond Artificial Intelligence focuses on deep situational awareness in human-computer integration, covering the interaction and integration mechanisms of human intelligence, machine intelligence and environmental systems. The book also details the cognitive, philosophical, social, scientific and technological, and military theories and methods of human-computer division, cooperation and collaborative decision-making to provide basic theoretical support for a development strategy in the field of national intelligence. Sections focus on describing a new form of intelligence produced by the interaction of human, machine and environmental systems which will become the next generation of AI. From the perspective of deep situational awareness in human-computer integration, the book studies the interaction and integration mechanisms of human intelligence, machine intelligence and environmental systems. In addition, it details the cognitive, philosophical, social, scientific and technological, and military theories and methods of human-computer division, cooperation and collaborative decision-making, so as to provide basic theoretical support for a development strategy in the field of national intelligence.

Mathematical Methods in Data Science introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for data analysis and prediction. The mathematics is accompanied with examples and problems arising in data science to demonstrate advanced mathematics, in particular, data-driven differential equations used. Chapters also cover network analysis, ordinary and partial differential equations based on recent published and unpublished results. Finally, the book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for data analysis and prediction. There are a number of books on mathematical methods in data science. Currently, all these related books primarily focus on linear algebra, optimization and statistical methods. However, network analysis, ordinary and partial differential equation models play an increasingly important role in data science. With the availability of unprecedented amount of clinical, epidemiological and social COVID-19 data, data-driven differential equation models have become more useful for infection prediction and analysis.

Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations. The book introduces how to design MDM platforms that adapt to the evolving mobility environment-and new types of transportation and users-based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This third volume looks at various cases studies to illustrate and explore the methods introduced in the first two volumes, covering topics such as Intelligent Transportation Management, Smart Emergency Management-detailing cases such as the Fukushima earthquake, Hurricane Katrina, and COVID-19-and Urban Sustainability Development, covering bicycle and railway travel behavior, mobility inequality, and road and light pollution inequality.

Algebraic Theory for True Concurrency presents readers with the algebraic laws for true concurrency. Parallelism and concurrency are two of the core concepts within computer science. This book covers the different realms of concurrency, which enables programs, algorithms or problems to be broken out into order-independent or partially ordered components to improve computation and execution speed. There are two primary approaches for executing concurrency: interleaving concurrency and true concurrency. The main representative of interleaving concurrency is bisimulation/rooted branching bisimulation equivalences which is also readily explored. This work eventually founded the comprehensive axiomatization modulo bisimulation equivalence -- ACP (Algebra of Communicating Processes).The other approach to concurrency is true concurrency. Research on true concurrency is active and includes many emerging applications. First, there are several truly concurrent bisimulation equivalences, including: pomset bisimulation equivalence, step bisimulation equivalence, history-preserving (hp-) bisimulation equivalence, and hereditary history-preserving (hhp-) bisimulation equivalence, the most well-known truly concurrent bisimulation equivalence.

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Case Studies in Geospatial Applications to Groundwater Resources provides thorough the most up-to-date techniques in GIS and geostatistics as they relate to groundwater, through detailed case studies that prove real-world applications of remote sensing applications to this subject. Groundwater is the primary source of fresh water in many parts of the world, while come regions are becoming overly dependent on it, consuming groundwater faster than it is naturally replenished and causing water tables to decline unremittingly. India is the largest user of groundwater in the world followed by China and the USA, with developing countries using groundwater at an unsustainable rate. Systematic planning of groundwater usage using modern techniques is essential for the proper utilization, management and modeling of this precious but shrinking natural resource. With the advent of powerful and highspeed personal computers, efficient techniques for water management have evolved, of which remote sensing, GIS (Geographic Information Systems), GPS (Global Positioning Systems) and Geostatistical techniques are of great significance. This book advances the scientific understanding, development, and application of geospatial technologies related to water resource management. Case Studies in Geospatial Applications to Groundwater Resources is a valuable reference for researchers and postgraduate students in Earth and Environmental Sciences, especially GIS, agriculture, hydrology, natural resources, and soil science, who need to be able to apply the latest technologies in groundwater research in a practical manner.

Elgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences and law, expertly written by the world's leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas. Woodrow Barfield and Ugo Pagallo present a succinct introduction to the legal issues related to the design and use of artificial intelligence (AI). Exploring human rights, constitutional law, data protection, criminal law, tort law, and intellectual property law, they consider the laws of a number of jurisdictions including the US, the European Union, Japan, and China, making reference to case law and statutes. Key features include: a critical insight into human rights and constitutional law issues which may be affected by the use of AI discussion of the concept of legal personhood and how the law might respond as AI evolves in intelligence an introduction to current laws and statutes which apply to AI and an identification of the areas where future challenges to the law may arise. This Advanced Introduction is ideal for law and social science students with an interest in how the law applies to AI. It also provides a useful entry point for legal practitioners seeking an understanding of this emerging field.

Applying mechanisms and principles of human intelligence and converging the brain and artificial intelligence (AI) is currently a research trend. The applications of AI in brain simulation are countless. Brain-inspired intelligent systems will improve next-generation information processing by applying theories, techniques, and applications inspired by the information processing principles from the brain. Exploring Future Opportunities of Brain-Inspired Artificial Intelligence focuses on the convergence of AI with brain-inspired intelligence. It presents research on brain-inspired cognitive machines with vision, audition, language processing, and thinking capabilities. Covering topics such as data analysis tools, knowledge representation, and super-resolution, this premier reference source is an essential resource for engineers, developers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.

This insightful book discusses how states deploy frontier and digital technologies to manage and control migratory movements. Assessing the development of blockchain technologies for digital identities and cash transfer; artificial intelligence for smart borders, resettlement of refugees and assessing asylum applications; social media and mobile phone applications to track and surveil migrants, it critically examines the consequences of new technological developments and evaluates their impact on the rights of migrants and refugees. Chapters evaluate the technology-based public-private projects that govern migration globally and illustrate the political implications of these virtual borders. International contributors compare and contrast different forms of political expression, in both personal technologies, such as social media for refugees and smugglers, and automated decision-making algorithms used by states to enable migration governance. This timely book challenges hegemonic approach to migration governance and provides cases demonstrating the dangers of employing frontier technologies denying basic rights, liberties and agencies of migrants and refugees. Stepping into a contentious political climate for migrants and refugees, this provocative book is ideal reading for scholars and researchers of political science and public policy, particularly those focusing on migration and refugee studies. It will also benefit policymakers and practitioners dealing with migration, such as humanitarian NGOs, UN agencies and local authorities.

Since the advent of the internet, online communities have emerged as a way for users to share their common interests and connect with others with ease. As the possibilities of the online world grew and the COVID-19 pandemic raged across the world, many organizations recognized the utility in not only providing further services online, but also in transitioning operations typically fulfilled in-person to an online space. As society approaches a reality in which most community practices have moved to online spaces, it is essential that community leaders remain knowledgeable on the best practices in cultivating engagement. Community Engagement in the Online Space evaluates key issues and practices pertaining to community engagement in remote settings. It analyzes various community engagement efforts within remote education, online groups, and remote work. This book further reviews the best practices for community engagement and considerations for the optimization of these practices for effective virtual delivery to support emergency environmental challenges, such as pandemic conditions. Covering topics such as community belonging, global health virtual practicum, and social media engagement, this premier reference source is an excellent resource for program directors, faculty and administrators of both K-12 and higher education, students of higher education, business leaders and executives, IT professionals, online community moderators, librarians, researchers, and academicians.