Cognitive Models for Sustainable Environment reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, along with a review of intelligent and cognitive tools that can be used. The book is centered on evolving novel intelligent/cognitive models and algorithms to develop sustainable solutions for the mitigation of environmental pollution. It unveils intelligent and cognitive models to address issues related to the effective monitoring of environmental pollution and sustainable environmental design. As such, the book focuses on the overall well-being of the global environment for better sustenance and livelihood. The book covers novel cognitive models for effective environmental pollution data management at par with the standards laid down by the World Health Organization. Every chapter is supported by real-life case studies, illustrative examples and video demonstrations that enlighten readers. - Explores the development and application of science, engineering and technology in achieving a sustainable lifestyle for humanity - Provides tools, connections and proactive solutions to take sustainability programs to the next level - Offers perspectives for design, development and commissioning of intelligent applications - Provides reviews on the latest intelligent technologies and algorithms related to state-of-the-art methodologies of monitoring and mitigation of environmental pollution

This book explores the modification of various synthesis processes to enhance the photocatalytic activity in varied applications in the fields of environmental remediation and energy production. It outlines the enhancement of photocatalytic activity via alloys synthesis, thin film coatings, electro-spun nanofibers and 3D printed photocatalysts. The book further states the diverse applications of materials for degrading organic pollutants and airborne pathogens, improving indoor air quality and as a potential antimicrobial agent. The application of photocatalysts in green organic synthesis, biomedical field and in hydrogen evolution are also presented in the book. It covers theoretical studies of photocatalytic material and conversion of CO2 to value added chemical feed stocks. The book is of relevance to researchers in academia and industry alike in the fields of material science, environmental science & technology, photocatalytic applications and in energy generation and conversion.

In the midst of our contemporary and swiftly evolving technological landscape, the pressing issue lies in the need for multifunctional materials that can transcend traditional boundaries and fuel innovation across diverse industries. This demand arises from the relentless pursuit of greater performance, efficiency, and adaptability in sectors ranging from electronics to aerospace, energy, and biomedical engineering. Academic scholars grapple with the challenge of comprehending and harnessing the untapped potential of nanomaterials with hybrid reinforcements, which represent a frontier in technological advancement. Technological Applications of Nano-Hybrid Composites comprehensively addresses this increasingly critical issue. Within its pages, this meticulously curated book embarks on a journey to explore the multifaceted aspects of nanocomposites, their hybrid reinforcements, and their significance in revolutionizing various technological domains. From the fundamental principles underpinning their design to the latest fabrication techniques and comprehensive characterization methods, this book offers a comprehensive roadmap to understanding and harnessing the unparalleled potential of these materials.

This book brings a high level of fluidity to analytics and addresses recent trends, innovative ideas, challenges and cognitive computing solutions in big data and the Internet of Things (IoT). It explores domain knowledge, data science reasoning and cognitive methods in the context of the IoT, extending current data science approaches by incorporating insights from experts as well as a notion of artificial intelligence, and performing inferences on the knowledge The book provides a comprehensive overview of the constituent paradigms underlying cognitive computing methods, which illustrate the increased focus on big data in IoT problems as they evolve. It includes novel, in-depth fundamental research contributions from a methodological/application in data science accomplishing sustainable solution for the future perspective. Mainly focusing on the design of the best cognitive embedded data science technologies to process and analyze the large amount of data collected through the IoT, and aid better decision making, the book discusses adapting decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures as well as big data and IoT problems can be handled in practice. This book is a valuable resource for scientists, professionals, researchers, and academicians dealing with the new challenges and advances in the specific areas of cognitive computing and data science approaches.

Indoor air quality (IAQ) and indoor air pollution (IAP) are a matter of concern in many countries because they can significantly influence the general health and well-being of those who spend most of their time inside, whether at home or work. Poor IAQ and repeated exposure to dangerous concentrations of pollutants can contribute significantly to the healthcare burden along with increased absenteeism and lost productivity worldwide. This book, Indoor Air Quality Assessment for Smart Environments, explores the problem of IAQ and highlights potential challenges, gaps, and opportunities in the field. As the title suggests, it focuses on assessing IAQ in smart environments using emerging technologies, such as the Internet of Things (IoT) and Wireless Sensor Networks (WSN), that can further contribute to the development of intelligent building management systems. The book contains 8 chapters, written by various experts in the field and addressing significant elements of IAQ management, including: definition, state-of-the-art and applications; sensing techniques; technological interventions and smart environments; smart monitoring devices; green and smart hospitals; health risks of nano building products; the optimization of household ventilation; and an assessment of smart environments. While providing a useful source of knowledge for researchers, policymakers, public health professionals and government agencies wishing to enhance the air quality in buildings, the book will also serve as a guide to building occupants who wish to take the necessary measures to enhance the built environment with improved ventilation arrangements.

Cognitive Computing for Human-Robot Interaction: Principles and Practices explores the efforts that should ultimately enable society to take advantage of the often-heralded potential of robots to provide economical and sustainable computing applications. This book discusses each of these applications, presents working implementations, and combines coherent and original deliberative architecture for human–robot interactions (HRI). Supported by experimental results, it shows how explicit knowledge management promises to be instrumental in building richer and more natural HRI, by pushing for pervasive, human-level semantics within the robot's deliberative system for sustainable computing applications. This book will be of special interest to academics, postgraduate students, and researchers working in the area of artificial intelligence and machine learning. Key features: - Introduces several new contributions to the representation and management of humans in autonomous robotic systems; - Explores the potential of cognitive computing, robots, and HRI to generate a deeper understanding and to provide a better contribution from robots to society; - Engages with the potential repercussions of cognitive computing and HRI in the real world. - Introduces several new contributions to the representation and management of humans in an autonomous robotic system - Explores cognitive computing, robots and HRI, presenting a more in-depth understanding to make robots better for society - Gives a challenging approach to those several repercussions of cognitive computing and HRI in the actual global scenario

This book provides knowledge into Cognitive Digital Twins for smart lifecycle management of built environment and infrastructure focusing on challenges and opportunities. It focuses on the challenges and opportunities of data-driven cognitive systems by integrating the heterogeneous data from multiple resources that can easily be used in a machine learning model and adjust the algorithms. It comprises Digital Twins incorporating cognitive features that will enable sensing complex and unpredicted behavior and reason about dynamic strategies for process optimization to support decision-making in lifecycle management of the built environment and infrastructure. The book introduces the Knowledge Graph (KG)-centric framework for Cognitive Digital Twins involving process modeling and simulation, ontology-based Knowledge Graph, analytics for process optimizations, and interfaces for data operability. It offers contributions of Cognitive Digital Twins for the integration of IoT, Big data, AI, smart sensors, machine learning and communication technologies, all connected to a novel paradigm of self-learning hybrid models with proactive cognitive capabilities. The book presents the topologies of models described for autonomous real time interpretation and decision-making support of complex system development based on Cognitive Digital Twins with applications in critical domains such as maintenance of complex engineering assets in built environment and infrastructure. It offers the essential material to enlighten pertinent research communities of the state-of-the-art research and the latest development in the area of Cognitive Digital Twins, as well as a valuable reference for planners, designers, developers, and ICT experts who are working towards the development and implementation of autonomous Cognitive IoT based on big data analytics and context–aware computing.

The theme of this book is sustainable e-waste management through effective amalgamation of information and communication technologies (ICT) and green recycling technologies to ensure development of intelligent, smart, and sustainable systems. It encompasses multidisciplinary interventions, including recent case studies from OEMs and IT industries as well as e-waste recyclers, and explores interdisciplinary research and industry–academia collaborations towards the development of smart and sustainable systems for e-waste management. Features: Covers the application of smart and intelligent systems for e-waste management. Explores recent advancements from the technological aspect – both recycling and ICT. Reviews supply chain criticalities for e-waste. Aims at cleaner production and intelligent systems for a green digital economy. Includes real-life case studies reflecting industry standards and the current paradigm. This book is aimed at graduate students and researchers in environmental engineering, waste management, urban mining, circular economy, waste processing, electronics and telecommunication engineering, electrical and electronics engineering, and chemical engineering.

The complex and multidisciplinary nature of environmental problems requires that they are dealt with in an integrated manner. Modeling and software have become key instruments used to promote sustainability and improve environmental decision processes, especially through systematic integration of various knowledge and data and their ability to foster learning and help make predictions. This book presents the current state-of-the-art in environmental modeling and software and identifies the future challenges in the field. - State-of-the-art in environmental modeling and software theory and practice for integrated assessment and management serves as a starting point for researchers - Identifies the areas of research and practice required for advancing the requisite knowledge base and tools, and their wider usage - Best practices of environmental modeling enables the reader to select appropriate software and gives the reader tools to integrate natural system dynamics with human dimensions