This book focuses on cases and studies of interest to mechanical engineers and industrial technicians. The considered applications in this volume are widely used in several industrial fields particularly in the automotive and aviation industries. Readers will understand the theory and techniques which are used in each application covered in each chapter. Volume 3 includes the following topics: Numerical simulations of three-dimensional laminar mixed convection heat transfer of water-based-Al2O3 nanofluid in an open cubic cavity with a heated block. Nonlinear formulations of Element-Free Galerkin Method (EFGM) for large deformation analysis of Ogden’s hyperelastic materials, emphasizing incompressibility and mesh distortion avoidance. Development of a 3D numerical model with LS-DYNA using a coupled SPH-FEM method to simulate hydraulic behavior of a Ski-Jump Spillway with dentates, showcasing precision through validation. Exploration of enhancing the inlet system of an LPG-H2 fueled engine using a static inclined blade turbine, analyzed through Computational Fluid Dynamics (CFD) simulations. Effective utilization of Artificial Neural Networks (ANN) in heat transfer applications, addressing issues like fouling in heat exchangers, showcasing their accuracy compared to experimental data. Investigation of the impact of nitrogen concentration on the structure and properties of ZrN coatings deposited by magnetron sputtering, evaluating variations in structural and mechanical properties. Forced convection in a horizontal cylindrical pipe with pseudoplastic fluid, considering uniform constant heat flux and uniform temperature as boundary conditions. Modeling and experimental study of a water solar collector coupled to an optimized solar still, aiming to enhance freshwater production in a solar distillation system under specific climatic conditions. Exploration of the effect of film thickness on the structure and properties of Ti-N films deposited by magnetron sputtering, utilizing theoretical and experimental analysis to confirm the rock salt TiN structure. The presented case studies and development approaches aim to provide readers with basic and applied information broadly related to mechanical engineering and technology.

This book focuses on cases and studies of interest to mechanical engineers and industrial technicians. The considered applications in this volume are widely used in several industrial fields particularly in the automotive and aviation industries. Readers will understand the theory and techniques which are used in each application covered in each chapter. Volume 2 includes the following topics: Numerical investigation of turbulent slot jets with various nanoparticle shapes Experimental study on a sweeping gas membrane distillation unit Development of design processes for multi-spindle drilling using a neural network and expert systems Experimental investigation of a new hybrid solar collector (PV/t) system Theoretical study of the effects of combustion duration on engine performance Effects of preheating temperature and fuel-air equivalence ratio on pollution control in hydrocarbon combustion Numerical study of natural convection between two concentric ellipses with different shapes and imposed temperatures Theoretical study of the geometrical parameters effect on the behavior of a solar chimney power plant Numerical investigations of the effect of packed bed porosity on the flow behavior Comparison between a conventional and a four-stage Savonius wind rotor The presented case studies and development approaches aim to provide readers with basic and applied information broadly related to mechanical engineering and technology.

The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications. Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.

Blockchain, whether public or private, is capable enough to maintain the integrity of transactions by decentralizing the records for users. Many IoT companies are using blockchain technology to make the world a better-connected place. Businesses and researchers are exploring ways to make this technology increasingly efficient for IoT services. This volume presents the recent advances in these two technologies. Chapters explain the fundamentals of Blockchain and IoT, before explaining how these technologies, when merged together, provide a transparent, reliable, and secure model for data processing by intelligent devices in various domains. Readers will be able to understand how these technologies are making an impact on healthcare, supply chain management and electronic voting, to give a few examples. The 10 peer-reviewed book chapters have been contributed by scholars, researchers, academicians, and engineering professionals, and provide a comprehensive yet easily digestible update on Blockchain on IoT technology.

Augmented intelligence is an alternate approach of artificial intelligence (AI), which emphasizes AI’s assistive role. Augmented intelligence enhances human skills of reasoning in a robotic system or software by simulating expectancy, educational mining, problem solving, recollection, sequencing, and decision-making capabilities. It is based on a combination of techniques such as machine learning, deep learning and cognitive computing. This book explains artificial intelligence models that support assistive processes in different situations. The contributors aim to provide information to a diverse audience with groundbreaking developments in mathematical computing. The book presents 8 chapters on these topics: - Educational data mining in augmented reality virtual learning environment - Brain and computer interfaces - Tree-based tools for chemometric analysis of infrared spectra - Applications of deep learning in medical engineering - Bankruptcy prediction model using an enhanced boosting classifier - Reputation systems for mobile agent security - The crow search algorithm - COVID-19 diagnosis and treatment The contents attempt to integrate various facets of augmented Intelligence, by describing recent research developments and advanced topics of interest to academicians and researchers working on machine learning problems and AI.

A smart city uses technology to provide services and solve problems to improve urban policy efficiency, reduce waste, improve quality of life, and maximize social inclusion. By 2050, 66% of the world's population is expected to be urban, which is a key driver of a global trend toward the creation of smart cities. This trend creates many opportunities for urban planning committees to learn how to design, modernize, and operate smart cities intelligently and effectively. Facets of a Smart City: Computational and Experimental Techniques for Sustainable Urban Development is a collection of topics that are relevant to the design of a smart city. This book aims to complement technical journal articles that require advanced knowledge of the subject of smart cities and applications for readers. It aims to bridge knowledge gaps in sustainable urban design by providing background information via case studies to facilitate students, recent graduates and new practitioners in urban design and planning. Key Features: - This book features 9 chapters that cover 6 major domains, which include (i) information modelling, (ii) internet of things, (iii) intelligent transportation systems, (iv) water supply, (v) waste management and (vi) sustainable environment - Computational techniques are included in the book. These include artificial neural networks, stochastic models, particle swarm optimization, machine learning, and adaptive neuro-fuzzy Inference systems. - Goals of case studies presented in this book use computational techniques to offer readers examples of supervised, unsupervised and reinforcement learning strategies in the context of smart city applications - References are provided for further reading

Soft robotics is a subfield of robotics that encompasses the design and fabrication of robots with soft and compliant materials. Soft robots represent components like human prosthetics or biomimicking systems. Soft robotics relies on technically astute designs based on the correct choice of materials to enable a level of dexterity not possible with rigid components alone. The basic prime movers (actuators) and perception (sensors) require control systems capable of accommodating imprecise feedback data and often unpredictable reaction times. Mobility in such robots is more akin to entomological or marine systems than conventional guided vehicles. This reference is a guide to materials and systems used in soft robotics. If features 6 chapters contributed by robotics experts that review fundamental and applied topics that are important for understanding the requirements of soft robotics design projects and the physics of the polymers involved. Chapters are organized for easy reading and include references. The topics include: - Aspects of materials processing and engineering for the development of soft robotic devices - A review on biological gripping principles and their application to robotics - Information about self-sensing electroadhesive polymer grippers with magnetically controllable surface geometry - Theoretical and experimental investigations of magnetic hybrid materials - Modeling and dynamic analysis of a novel rotary soft robotic arm by transfer matrix method - Design and control of a portable continuum robot for pipe inspection assisted by a rigid manipulator This book is a suitable reference for scholars and engineers who are seeking knowledge about materials and design principles in soft robotics with its practical applications.

This reference presents a collection of studies which highlight new developments in improving manufacturing processes and performance. The book includes 11 chapters which cover unique topics of interest to production engineers. These topics include the production of advanced composite materials and alloys to the use of sensors to evaluate production processes, product testing and evaluation (in production and storage environments), and the development and testing of an unmanned aerial vehicle. Chapters have been contributed by several scholars, who give a unique perspective on specific aspects of manufacturing and industrial production. The book also covers recent technologies such as additive manufacturing. Each chapter presents the study in an experimental manner highlighting the theory, methods, and results, where applicable. References for further reading are also provided. This book is intended to provide researchers and engineers with new data and cases for industrial manufacturing processes which may be applied in different industrial sectors.

Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments is a 2-part book set which presents discoveries, innovative ideas, concepts, practical solutions, and novel applications of Human-Computer Interaction (HCI) and related disciplines such as artificial intelligence, machine learning, data mining, computer vision, and natural language processing. The book provides readers with information about HCI trends which are shaping the future of smart, interconnected urban and industrial environments. This is the second of the two volumes of the edited books. The chapters of this volume cover topics like ERP usability in educational settings, the role of AI in enhancing HCI functionality, usability of local mobile healthcare apps, analyzing the usage of social media apps and a review of HCI systems for disaster management and systems for tracking traffic safety violations. Contributions are authored by experts and scientists in the field of HCI and its interrelated disciplines from 9 different countries – Albania, China, India, Indonesia, Nigeria, Pakistan, Spain, the United Kingdom, and the United States. Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments is an informative reference for scientists, researchers, and developers in both academia and industry who wish to learn, design, implement, and apply these emerging technologies in HCI in different sectors, with the goal of realizing futuristic technology-driven living and functional smart cities and environments.

Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, which include the types of machine learning algorithms and the statistical knowledge required for devising relevant computer algorithms. The book also covers advanced topics such as deep learning and feature engineering. <p> Key features: <p> - 8 organized chapters on core concepts of machine learning for learners <p> - Accessible text for beginners unfamiliar with complex mathematical concepts <p> - Introductory topics are included, including supervised learning, unsupervised learning, reinforcement learning and predictive statistics <p> - Advanced topics such as deep learning and feature engineering provide additional information <p> - Introduces readers to python programming with examples of code for understanding and practice <p> - Includes a summary of the text and a dedicated section for references <p> Machine Learning and Its Application: A Quick Guide for Beginners is an essential book for students and learners who want to understand the basics of machine learning and equip themselves with the knowledge to write algorithms for intelligent data processing applications.