Books in Physical sciences and engineering

Functional Analysis: Theory and Applications

1st Edition
January 1, 2026
Ravi P. Agarwal + 2 more
English

An applied understanding of functional analysis is essential for students pursuing research or careers in pure mathematics, applied mathematics, mathematical physics, and engineering, among other disciplines.Function... Analysis: Theory and Applications offers a comprehensive exploration of functional analysis. Authored by esteemed mathematicians with extensive expertise in the field, this book thoroughly introduces fundamental concepts in functional analysis, including Banach spaces, Hilbert spaces, operator theory, nonlinear analysis, linear operators, and normed spaces, and implements these in real-world problems across various scientific and engineering disciplines.The book's rigorous mathematical treatment is combined with worked examples, exercises and solutions, visual aids, application case studies, and future directions across all chapters to reinforce learning, while appendices offer supplementary materials, proofs of theorems, and tables of important results, among other resources.

Autonomous Vehicle Safety Solutions

1st Edition
January 1, 2026
Aparna Kumari
English

Autonomous Vehicle Safety Solutions: Foundations, Technologies, and Prospects for the Future addresses the dynamically evolving state of the art that traverses and brings together automotive engineering, electronics engineering, and computer science. Its technical overview of smart vehicles' capabilities, propelled by headway in artificial intelligence and sensor technologies, enables in-depth understanding of the safety-assurance complexities that guide their design and operation.The comprehensive volume distinguishes itself for its treatment of several exciting, emerging opportunities and trends, including machine learning algorithms, V2X connectivity, and cybersecurity. Up-to-date applications are featured through practical examples and expert insights, bringing additional value to a discourse which underscores the critical need for ongoing safety advancements and adaptability in the fast-moving context of autonomous driving systems, with their related implications on sustainability and society as a whole.The outcome is a cornerstone resource for academia and industry alike that encourages further transformative, interdisciplinary investigations to bring safety solutions to maturity and subsequent rigorous testing for validation standards to be defined, thus realizing, in a future not distant from now, a scenario where self-driving vehicles can co-exist seamlessly with traditional modes of transportation and also integrate reliably, efficiently, and without risks within the larger infrastructure.

Challenges and Applications of Generative Large Language Models

1st Edition
January 1, 2026
Anitha S. Pillai + 2 more
English

Large Language Models (LLMs) are a form of generative AI, based on Deep Learning, that rely on very large textual datasets, and are composed of hundreds of millions (or even billions) of parameters. LLMs can be trained and then refined to perform several NLP tasks like generation of text, summarization, translation, prediction, and more. Challenges and Applications of Generative Large Language Models assists readers in understanding LLMs, their applications in various sectors, challenges that need to be encountered while developing them, open issues, and ethical concerns. LLMs are just one approach in the huge set of methodologies provided by AI. The book, describing strengths and weaknesses of such models, enables researchers and software developers to decide whether an LLM is the right choice for the problem they are trying to solve. AI is the new buzzword, in particular Generative AI for human language (LLMs). As such, an overwhelming amount of hype is obfuscating and giving a distorted view about AI in general, and LLMs in particular. Thus, trying to provide an objective description of LLMs is useful to any person (researcher, professional, student) who is starting to work with human language. The risk, otherwise, is to forget the whole set of methodologies developed by AI in the last decades, sticking with only one model which, although very powerful, has known weaknesses and risks. Given the high level of hype around such models, Challenges and Applications of Generative Large Language Models (LLMs) enables readers to clarify and understand their scope and limitations.

Engineering Materials for 3D Printing

1st Edition
January 1, 2026
Rupinder Singh + 3 more
English

3D printing processes have promising advantages over conventional manufacturing processes from a functional ability viewpoint. In the past 30 years, a variety of additive manufacturing-based 3D printing processes have been reported for the fabrication of functional prototypes, using materials such as thermoplastic, resins, metal and their alloys. While there is a significant amount of information on various 3D printing processes, and materials that may be used in 3D printing-based manufacturing practices for engineering applications, there is far less information on 3D printable materials for structural and non-structural engineering applications. Engineering Materials for 3D Printing fills this gap. The book includes three sections and provides an insight on the development and characterization of polymer-based 3D printed materials by different processes such as extrusion, sol-gel, and pulverization along with meta-structure properties for various structural applications

Decision Systems

1st Edition
January 1, 2026
Pallavi Vijay Chavan + 3 more
English

Decision Systems: Integrating Machine Learning, Fuzzy Logic, and Artificial Neural Networks provides readers with a comprehensive understanding of the principal techniques used to build effective decision-making systems. This book covers the fundamental principles and concepts of machine learning, fuzzy logic, and artificial neural networks, and explains how these techniques can be used to build intelligent decision-making systems that can learn from data, reason, and make accurate predictions. The book also presents a wide range of applications of machine learning, fuzzy logic, and artificial neural networks in various domains, such as engineering, medicine, finance, and robotics. The book also provides practical guidance on how to design and implement effective decision-making systems using these techniques and discusses the potential challenges and limitations of machine learning, fuzzy logic, and artificial neural networks, and how to overcome them. The book provides a stepwise approach to provide readers with the knowledge and tools they need to build intelligent decision-making systems, including a robust introduction to the mathematical concepts and principles necessary to understand the concepts and applications of Decision Systems and Machine Learning algorithms. Next, the book provides readers with an in-depth explanation and demonstration of two of the major machine learning techniques – Fuzzy Logic/Fuzzy Set Theory and Artificial Neural Networks – followed by an in-depth look at more advanced topics that play essential roles in making machine learning algorithms more useful in practice, including creating full-fledged Recurrent Networks and their mathematical foundations, Associative Memories, and Deep Learning networks such as Convolutional Neural Networks, Generative Adversarial Networks, Radial Basis Function Networks, Multilayer Perceptrons, and Self-Organizing Maps. The lynchpin of the book provides readers with an understanding of how the various types of techniques can be integrated to create dynamic Decision Systems. The book wraps up with coverage of challenges and opportunities in Decision Systems along with real-world applications of Decision Systems with case studies in healthcare, finance, education, social media, and agriculture.

Floating Photovoltaics

1st Edition
January 1, 2026
Anik Goswami + 3 more
English

Floating Photovoltaics: Design, Performance, Degradation, Financial Sustainability, and Environmental Impacts offers broad coverage of floating photovoltaic (FPV) systems, enabling the reader to understand design, deployment, and performance, as we all economic feasibility and environmental factors. The book introduces a range of pre-feasibility analysis methods such as location selection, bathymetric studies, design of anchors and mooring, effect of waves, and tilt angle. Design criteria of FPV systems are then discussed in detail, from module selection and inverter selection to conditions and operating temperature. The types of degradation and the effect of degradation on long-term performance of FPV systems are analyzed, with a parameter level study helping to determine the exact long-term degradation of FPV systems, as well as the effect of maintenance on performance of FPV systems. The final chapters in the book focus on financial sustainability, environmental impacts, and policy recommendations, supporting further development, deployment, and decision-making with regard to FPV technology and projects. Researchers, advanced students, faculty, engineers, R&D, scientists, and policy makers with an interest in floating photovoltaic systems will find this book to be an interesting resource.

Embedded Systems in Automotive Applications

1st Edition
January 1, 2026
Aboubakr El Hammoumi + 1 more
English

With its well-thought-out overview of the topic, Embedded Systems in Automotive Applications is a resource that serves a variety of audiences both in academia and industry. Its comprehensive coverage includes both software and hardware aspects, model-based design methods for control systems, practical observations with real-world illustrations, and a focus on keeping abreast of the most recent industry developments, particularly in the realms of electric and autonomous vehicles, encompassing facets of security and connectivity (including AI, blockchain, cybersecurity, and Internet of Vehicles). The volume also bridges interdisciplinary gaps through discussions on challenges such as system complexity, timely software updates, interoperability issues, power constraints, cost management, regulatory compliance, and adapting to rapid technological advancements, contributing to a more well-rounded understanding of the integration of dynamic, ever-evolving, modern techniques while ensuring synergy with standards of the highest quality for next-generation automotive applications.

Reliability Assessment of Flow Networks and Industrial Systems

1st Edition
January 1, 2026
Amit Kumar + 2 more
English

Reliability Assessment of Flow Networks and Industrial Systems explores real-life flow networks and industrial systems, employing various methods (including both exact and approximation techniques) to assess the reliability of these systems. Through numerous examples, the book explains algorithms, ensuring readers gain a deep understanding of the discussed approaches. The complexity results for these algorithms are computed and compared with alternative methods. Furthermore, the book presents experimental results tied to specific methods, providing a comprehensive and practical understanding throughout.

Graph Theory

1st Edition
January 1, 2026
Sovan Samanta
English
eBook
9 7 8 0 4 4 3 3 3 9 4 2 4

Graph theory is a rapidly evolving and expanding mathematical discipline, with new discoveries, challenges, and techniques emerging every year. Graph Theory: Fundamentals and Applications provides a fully up-to-date and accessible introduction to graph theory, covering both the classical and the modern topics, as well as algorithms and evolving challenges addressed by discipline. Based on the latest syllabi and research trends worldwide, this book includes practical, solved problems that are user friendly to undergraduate, postgraduate, and PhD students, and acts as a key aid in learning the fundamentals and the frontiers of graph theory, as well as developing independent problem-solving and critical thinking skills. This book includes clear instruction in graph representation, basic graph operations, graph connectivity, trees and forests, matching theory, planar graphs and graph drawing, algebraic graph theory, graph traversals, network flows, topological graph theory, and cryptography, among other topics. Each chapter features key term definitions, proofs and algorithms, summary points, and unique exercises to reinforce learning, as well as open problems and research challenges that present unsolved or conjectural problems in graph theory for discussion. Supporting student and instructor sites offer additional exercises, solutions, examples, and case studies in graph theory applications.

Applied Machine Learning in Chemical Process Engineering

1st Edition
January 1, 2026
Zafar Said + 1 more
English

As machine learning capabilities and functionality increases, more industry experts and researchers are integrating applied machine learning into their research. Applied Machine Learning in Chemical Process Engineering: A Practical Approach serves as a comprehensive guide to equip the reader with the fundamental theory, practical guidance, methodologies, experimental design and troubleshooting knowledge needed to integrate machine learning into their processes. This book offers a comprehensive overview of all aspects of machine learning, from inception to integration that will allow readers from any scientific discipline to begin to examine the capabilities of machine learning. This book will then build upon this overview to offer worked examples and case studies, alongside practical methods-based guidance to walk the reader through integrating machine learning end-to-end. Finally, this book will offer critical discussion of concepts that are interwoven into the ever-evolving principles of machine learning such as ethics, safety and culpability that are crucial when working with machine learning. Applied Machine Learning in Chemical Process Engineering: A Practical Approach will be an invaluable resource for researchers, professionals in industry and academia, and students at graduate level and above who work in chemical engineering and are looking to automate, optimize or intensify their chemical processes. This book will also help professionals in other disciplines and industries looking into integrate machine learning into their work, such as though looking to scale up their processes to an industrial scale or conduct novel research.

Related subjects

Astronomy astrophysics space science

Built environment

Chemical engineering

Chemistry

Computer science

Earth and planetary sciences

Energy and power

Engineering and technology

Environmental sciences

Materials science

Mathematics

Physics

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health