Jeffrey Huang – författare

Understanding Forensic Digital Imaging offers the principles of forensic digital imaging and photography in a manner that is straightforward and easy to digest for the professional and student. It provides information on how to photograph any setting that may have forensic value, details how to follow practices that are acceptable in court, and recommends what variety of hardware and software are most valuable to a practitioner.

In addition to chapters on basic topics such as light and lenses, resolution, and file formats, the book contains forensic-science-specific information on SWGIT and the use of photography in investigations and in court. Of particular note is Chapter 17, Establishing Quality Requirements, which offers information on how to create a good digital image, and is more comprehensive than any other source currently available.

The field of Computer-Supported Collaborative Learning (CSCL) has traditionally focused on virtual learning environments and has somewhat neglected the physical environments in which students interact with each other and with their teachers. However, over the last years, scholars have devoted growing attention to the interplay between digital and physical environments. Interactive Artifacts and Furniture Supporting Collaborative Work and Learning reveals how these advances in computer science and human-computer interaction impact CSCL environments.

The underlying theme of the contributions is the social affordances of physical objects, i.e. the understanding of how technology-augmented physical objects support interactions among groups in a way more intuitive or natural than traditional computers. This collection of leading researchers investigates how artifacts may trigger rich interactions among groups, which is a central quest for researchers in CSCL.

Interactive Artifacts and Furniture Supporting Collaborative Work and Learning illustrates a turn in the field of CSCL and emphasizes an important message for a generation of CSCL designers and researchers.

Digital Twins for Smart Metabolic Circular Cities: Innovations in Planning and Climate Resilience explores the advanced convergence of smart city technologies, digital twin applications, smart urban metabolism, and circular economy principles. This comprehensive resource offers insights into sustainable urban practices and innovative approaches to address the multifaceted challenges posed by rapid urbanization, environmental degradation, and climate change. In a rapidly changing world, this book provides a detailed understanding of how digital twin technologies and smart urban metabolism frameworks can be applied to foster sustainable urban development. This holistic perspective bridges urban planning, environmental sustainability, and the transformative potential of digital twins.The book equips readers with practical insights and solutions to navigate contemporary urban development complexities. It is an invaluable resource for urban planners, environmental scientists, policymakers, and technology experts interested in sustainable practices. Through real-world applications, foundational theory, and forward-thinking strategies, it empowers readers with the knowledge to address pressing environmental and infrastructural challenges associated with the future of smart cities.

The rapid rise of generative artificial intelligence (GenAI) is reshaping urban computing and intelligence, marking the next frontier of sustainable smart cities. This book explores the transformative potential of deep generative models (DGMs), hybrid architectures, and foundation models (FMs) in redefining the urban landscape. These technologies enhance environmental sustainability, climate resilience, infrastructure performance, operational efficiency, spatial organization, and livability through innovative approaches to urban planning, design, and management—often via digital twins and other emerging technologies.

DGMs enable synthetic data generation, data augmentation, data imputation, scenario simulation, and predictive modeling, offering deeper insights into complex urban and environmental systems. Hybrid architectures integrate generative and conventional models to improve robustness, scalability, adaptability, and flexibility across diverse urban operational and planning tasks. FMs extend these capabilities by enabling multi-modal knowledge generalization, cross-domain integration, large-scale simulation, advanced reasoning, and adaptive decision-making under uncertainty. Strengthened by integrative frameworks and cross-disciplinary insights, these technologies offer powerful tools for developing sophisticated urban and environmental solutions. They advance how urban ecosystems are environmentally and strategically planned, efficiently and dynamically managed, aesthetically and functionally designed, and spatially and contextually organized.

Rich in conceptual frameworks, theoretical insights, operational models, applied innovations, practical case studies, and policy guidance, this book equips researchers, planners, designers, technologists, and policymakers to harness generative intelligence in shaping adaptive, context-aware, and continuously refined urban environments that are ecologically responsible, climate-resilient, and future-ready. Essential for anyone exploring the frontier of GenAI-driven sustainable urban development, it offers actionable insights to guide the next generation of sustainable smart cities.

The rapid rise of generative artificial intelligence (GenAI) is reshaping urban computing and intelligence, marking the next frontier of sustainable smart cities. This book explores the transformative potential of deep generative models (DGMs), hybrid architectures, and foundation models (FMs) in redefining the urban landscape. These technologies enhance environmental sustainability, climate resilience, infrastructure performance, operational efficiency, spatial organization, and livability through innovative approaches to urban planning, design, and management—often via digital twins and other emerging technologies.

DGMs enable synthetic data generation, data augmentation, data imputation, scenario simulation, and predictive modeling, offering deeper insights into complex urban and environmental systems. Hybrid architectures integrate generative and conventional models to improve robustness, scalability, adaptability, and flexibility across diverse urban operational and planning tasks. FMs extend these capabilities by enabling multi-modal knowledge generalization, cross-domain integration, large-scale simulation, advanced reasoning, and adaptive decision-making under uncertainty. Strengthened by integrative frameworks and cross-disciplinary insights, these technologies offer powerful tools for developing sophisticated urban and environmental solutions. They advance how urban ecosystems are environmentally and strategically planned, efficiently and dynamically managed, aesthetically and functionally designed, and spatially and contextually organized.

Rich in conceptual frameworks, theoretical insights, operational models, applied innovations, practical case studies, and policy guidance, this book equips researchers, planners, designers, technologists, and policymakers to harness generative intelligence in shaping adaptive, context-aware, and continuously refined urban environments that are ecologically responsible, climate-resilient, and future-ready. Essential for anyone exploring the frontier of GenAI-driven sustainable urban development, it offers actionable insights to guide the next generation of sustainable smart cities.

This book fills the need for a single comprehensive work on oncological anesthesia written by anesthesiologists. Divided into 14 distinct sections, this title includes the most up-to-date surgical oncologic procedures/treatments (new adjunctive treatments, new surgical techniques), perioperative care and anesthetic considerations and protocols. The highly experienced authors explain how the new treatments affect anesthesia implementations.

An invaluable resource for anesthesiologists, certified registered nurse anesthetists (CRNA), certified Anesthesiologist assistants (CAA), anesthesia trainees (anesthesiology residents/fellows, students registered nurse anesthetists, student Anesthesiologist assistants), and oncological surgeons.