In recent years, substantial efforts are being made in the development of reliability theory including fuzzy reliability theories and their applications to various real-life problems. Fuzzy set theory is widely used in decision making and multi criteria such as management and engineering, as well as other important domains in order to evaluate the uncertainty of real-life systems. Fuzzy reliability has proven to have effective tools and techniques based on real set theory for proposed models within various engineering fields, and current research focuses on these applications. Advancements in Fuzzy Reliability Theory introduces the concept of reliability fuzzy set theory including various methods, techniques, and algorithms. The chapters present the latest findings and research in fuzzy reliability theory applications in engineering areas. While examining the implementation of fuzzy reliability theory among various industries such as mining, construction, automobile, engineering, and more, this book is ideal for engineers, practitioners, researchers, academicians, and students interested in fuzzy reliability theory applications in engineering areas.

Reliability is a fundamental criterium in engineering systems. This book shows innovative concepts and applications of mathematics in solving reliability problems. The contents address in particular the interaction between engineers and mathematicians, as well as the cross-fertilization in the advancement of science and technology. It bridges the gap between theory and practice to aid in practical problem-solving in various contexts.

Reliability is one of the fundamental criteria in engineering systems. Design and maintenance serve to support it throughout the systems life. As such, maintenance acts in parallel to production and can have a great impact on the availability and capacity of production and the quality of the products. The authors describe current and innovative methods useful to industry and society.

This book presents the latest research in the fields of reliability theory and its applications, providing a comprehensive overview of reliability engineering and discussing various tools, techniques, strategies and methods within these areas. Reliability analysis is one of the most multidimensional topics in the field of systems reliability engineering, and while its rapid development creates opportunities for industrialists and academics, it is also means that it is hard to keep up to date with the research taking place. By gathering findings from institutions around the globe, the book offers insights into the international developments in the field. As well as discussing the current areas of research, it also identifies knowledge gaps in reliability theory and its applications and highlights fruitful avenues for future research. Covering topics from life cycle sustainability to performance analysis of cloud computing, this book is ideal for upper undergraduate and postgraduate researchers studying reliability engineering.

Supply chains are significant in improving business efficiency. Sustainable supply chains help industries enhance their ecological, monetary, and social performance. Innovative research frameworks as well as the modelling of sustainability issues are significant to different stakeholder's perspectives. This book guides researchers and practitioners through developing effective sustainable supply chains to meet UN Sustainable Development Goals (SDGs).

This book presents the most important tools, techniques, strategy and diagnostic methods used in industrial engineering. The current widely accepted methods of diagnosis and their properties are discussed. Also, the possible fruitful areas for further research in the field are identified.

The application of mathematical concepts has proven to be beneficial within a number of different industries. In particular, these concepts have created significant developments in the engineering field. Mathematical Concepts and Applications in Mechanical Engineering and Mechatronics is an authoritative reference source for the latest scholarly research on the use of applied mathematics to enhance the current trends and productivity in mechanical engineering. Highlighting theoretical foundations, real-world cases, and future directions, this book is ideally designed for researchers, practitioners, professionals, and students of mechatronics and mechanical engineering.

The evolution of soft computing applications has offered a multitude of methodologies and techniques that are useful in facilitating new ways to address practical and real scenarios in a variety of fields. In particular, these concepts have created significant developments in the engineering field. Soft Computing Techniques and Applications in Mechanical Engineering is a pivotal reference source for the latest research findings on a comprehensive range of soft computing techniques applied in various fields of mechanical engineering. Featuring extensive coverage on relevant areas such as thermodynamics, fuzzy computing, and computational intelligence, this publication is an ideal resource for students, engineers, research scientists, and academicians involved in soft computing techniques and applications in mechanical engineering areas.

This book presents original studies describing the latest research and developments in the area of reliability and systems engineering. It helps the reader identifying gaps in the current knowledge and presents fruitful areas for further research in the field. Among others, this book covers reliability measures, reliability assessment of multi-state systems, optimization of multi-state systems, continuous multi-state systems, new computational techniques applied to multi-state systems and probabilistic and non-probabilistic safety assessment.

Computational intelligence (CI) lies at the interface between engineering and computer science; control engineering, where problems are solved using computer-assisted methods. Thus, it can be regarded as an indispensable basis for all artificial intelligence (AI) activities. This book collects surveys of most recent theoretical approaches focusing on fuzzy systems, neurocomputing, and nature inspired algorithms. It also presents surveys of up-to-date research and application with special focus on fuzzy systems as well as on applications in life sciences and neuronal computing.

Reliability theory is a multidisciplinary science aimed at developing complex systems that are resistant to failures. Reliability engineering has emerged as a main field not only for scientists and researchers, but also for engineers and industrial managers. This book covers the recent developments in reliability engineering. It presents new theoretical issues that were not previously published, as well as the solutions of practical problems and case studies illustrating the applications methodology. This book is written by a number of leading scientists, analysts, mathematicians, statisticians, and engineers who have been working on the front end of reliability science and engineering. Reliability Engineering: Theory and Applications covers the recent developments in reliability engineering. It presents new theoretical issues that were not previously presented in the literature, as well as the solutions of important practical problems and case studies illustrating the applications methodology. Features Covers applications to reliability engineering practice Discusses current advances and developments Introduces current achievements in the field Considers and analyses case studies along with real world examples Presents numerous examples to illustrate the theoretical results

This book describes the latest research developments in modeling and simulation in industrial engineering. Topics such as decision and performance analysis and industrial control systems are described. Case studies in industry and services as well as engineering economy and cost estimation are also covered.

The sequential analysis of data and information gathered from past to present is called time series analysis. Time series data are of high dimension, large size and updated continuously. A time series depends on various factors like trend, seasonality, cycle and irregular data set, and is basically a series of data points well-organized in time. Time series forecasting is a significant area of machine learning. There are various prediction problems that are time-dependent and these problems can be handled through time series analysis. Computational intelligence (CI) is a developing computing approach for the forthcoming several years. CI gives the litheness to model the problem according to given requirements. It helps to find swift solutions to the problems arising in numerous disciplines. These methods mimic human behavior. The main objective of CI is to develop intelligent machines to provide solutions to real world problems, which are not modelled or too difficult to model mathematically. This book aims to cover the recent advances in time series and applications of CI for time series analysis.

The concepts of telemedicine and e-healthcare have eased as well as improved the reachability of experienced doctors and medical staff to remote patients. A patient who is living in a remote village area can directly connect to specialist doctors across the globe though his/her mobile phone using telemedicine systems and e-healthcare services. In pandemic situations like COVID-19, these online platforms helped society to get medical treatment from their residence without any physical movement. Technology is transforming human lives by playing an important role in the planning, designing, and development of intelligent systems for better service. This book presents a cross-disciplinary perspective on the concept of machine learning, blockchain and IoT by congregating cutting-edge research and insights. It also identifies and discusses various advanced technologies such as internet of things (IoT), big data analytics, machine learning, artificial intelligence, cyber security, cloud computing, sensors and so on that are vital to foster the development of smart healthcare and telemedicine systems by providing effective solutions to the medical challenges faced by humankind.

Covers recent developments in materials like graphene reinforced magnesium metal matrix, magnesium, and its alloys. Discusses design and Analysis of Stainless Steel 316L for Femur Bone fracture healing. Covers advanced materials, their properties, and processing techniques. Provides advanced integrated design and nonlinear simulation problems occurring in the biomechanical engineering field.

Provides real-life reliability studies on industrial operations along with solutions Discusses modelling and optimization of reliability and safety aspects in industry Covers reliability and maintenance issues in process industries Presents cost optimization and life-cycle costing analysis Offers MCDM application for risk and Safety analysis

Fuzzy logic techniques have had extraordinary growth in various engineering systems. The developments in engineering sciences have caused apprehension in modern years due to high-tech industrial processes with ever-increasing levels of complexity. Advanced Fuzzy Logic Approaches in Engineering Science provides innovative insights into a comprehensive range of soft fuzzy logic techniques applied in various fields of engineering problems like fuzzy sets theory, adaptive neuro fuzzy inference system, and hybrid fuzzy logic genetic algorithms belief networks in industrial and engineering settings. The content within this publication represents the work of particle swarms, fuzzy computing, and rough sets. It is a vital reference source for engineers, research scientists, academicians, and graduate-level students seeking coverage on topics centered on the applications of fuzzy logic in high-tech industrial processes.

This book shows how machine learning (ML) methods can be used to enhance cyber security operations, including detection, modeling, monitoring as well as defense against threats to sensitive data and security systems. Filling an important gap between ML and cyber security communities, it discusses topics covering a wide range of modern and practical ML techniques, frameworks and tools.

This book includes research studies, novel theory, as well as new methodology and applications in mathematics and management sciences. The book will provide a comprehensive range of mathematics applied to engineering areas for different tasks. It will offer an international perspective and a bridge between classical theory and new methodology in many areas, along with real-life applications. Features Offers solutions to multi-objective transportation problem under cost reliability using utility function Presents optimization techniques to support eco-efficiency assessment in manufacturing processes Covers distance-based function approach for optimal design of engineering processes with multiple quality characteristics Provides discrete time sliding mode control for non-linear networked control systems Discusses second law of thermodynamics as instruments for optimizing fluid dynamic systems and aerodynamic systems

In recent years, mathematics has experienced amazing growth in the engineering sciences. Mathematics forms the common foundation of all engineering disciplines. This book provides a comprehensive range of mathematics applied in various fields of engineering for different tasks such as civil engineering, structural engineering, computer science, and electrical engineering, among others. It offers chapters that develop the applications of mathematics in engineering sciences, conveys the innovative research ideas, offers real-world utility of mathematics, and has a significance in the life of academics, practitioners, researchers, and industry leaders. Features Focuses on the latest research in the field of engineering applications Includes recent findings from various institutions Identifies the gaps in the knowledge in the field and provides the latest approaches Presents international studies and findings in modeling and simulation Offers various mathematical tools, techniques, strategies, and methods across different engineering fields

This book focuses on artifi cial intelligence in the field of digital signal processing and wireless communication. The implementation of machine learning and deep learning in audio, image, and video processing is presented, while adaptive signal processing and biomedical signal processing are also explored through DL algorithms, as well as 5G and green communication. Finally, metaheuristic algorithms of related mathematical problems are explored.

This era of science and engineering has attracted researchers tasked with evaluating performance and optimization of problems in the field of operations research. The book covers mathematical analysis, methods and applications involving processes such as system performance, optimization, inventory theory, reliability theory, and queueing theory. Operations Research: Methods, Techniques, and Advancements explores recent and innovative methods and advancements associated with the mathematical theory of operations research. It offers a detailed overview of mathematical modelling for general industrial systems and emphasizes the latest ideas for the benefit of society and the research community. Intended for a broad range of readers, this book is useful to academicians, industrialists, researchers, students, academia and specialists from various disciplines and those working in the industry.

Provides a comprehensive review on new swarm intelligence Offers practical implementation of PSO with MATLAB code Presents statistical analysis techniques so that researchers can analyze their own experiment design Discusses swarm intelligence algorithms in social sector and oil and gas industries Covers recent findings and the implementation techniques to Machine Learning

Transitioning to DevOps requires a change in culture and mindset. At its simplest, DevOps is about removing the barriers between two traditionally siloed teams, development, and operations. In some organizations, there may not even be separate development and operations teams; engineers may do both. With DevOps, the two teams work together to optimize both the productivity of developers and the reliability of operations. They strive to communicate frequently, increase efficiencies, and improve the quality of services they provide to customers. They take full ownership for their services, often beyond where their stated roles or titles have traditionally been scoped by thinking about the end customer’s needs and how they can contribute to meeting those needs. Quality assurance and security teams may also become tightly integrated within these teams. Organizations using a DevOps model, regardless of their organizational structure, have teams that view the entire development and infrastructure lifecycle as part of their responsibilities. In this book, we introduce the DevOps culture, and the tools and techniques under this technical cultural umbrella. We explain microservice, containers, Docker Container, Kubernetes, etc., and the significance of these in adopting the DevOps culture for successful software development.

Real-world issues can be translated into the language and concepts of mathematics with the use of mathematical models. This book provides these real-world examples, explores research challenges in numerical treatment, and demonstrates how to create new numerical methods for resolving problems. Theories and practical applications in the sciences and engineering are also discussed. Students of engineering and applied mathematics, as well as researchers and engineers who use computers to solve problems numerically or oversee those who do, will find this book focusing on advance numerical techniques to solve linear and nonlinear differential equations useful. Models guided by differential equations with intuitive solutions can be used throughout engineering and the sciences. Almost any changing system may be described by a set of differential equations. They may be found just about anywhere you look in fields including physics, engineering, economics, sociology, biology, business, healthcare, etc. The nature of these equations has been investigated by several mathematicians over the course of hundreds of years and, consequently, numerous effective methods for solving them have been created. It is often impractical to find a purely analytical solution to a system described by a differential equation because either the system itself is too complex or the system being described is too vast. Numerical approaches and computer simulations are especially helpful in such systems.