This book focuses on advanced techniques to reduce the impact of the transport sector and, more specifically, of Spark-Ignition (SI) Internal Combustion Engines (ICEs) on atmospheric air pollution and climate change. Hybrid vehicles represent the most suitable option for addressing these issues in the medium term, since hybridization allows us to overcome the major disadvantages of ICEs, electric units, and energy storage devices and merge their respective benefits. In this scenario, ICEs will remain the core component of automotive propulsion systems in the years to come. Of course, further efforts to improve the efficiency and reduce the pollutant and CO2 emissions of ICEs are necessary.

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Ultra-precision machining technology has been widely used in the manufacture of many mission-critical components for various industrial areas, such as advanced optics, photonics aerospace, the automotive industry, telecommunications, biomedical areas, energy, the environment, etc. Nowadays, ultra-precision machining technology is capable of producing workpieces with submicron shape accuracy, nanometer surface roughness, and high geometric complexity. Due to increasing geometrical complexity, high-precision requirements and the evolution of advanced materials of the workpiece being machined lead to numerous research challenges in different fields, including ultra-precision machining technologies, novel machining processes, cutting mechanics, surface generation mechanisms, novel machine design, advanced sensing, and machine metrology. In addition, the machining process can be accurately controlled through the modeling and simulation of ultra-precision machining processes, error compensation, materials sciences, measurement and on-machine metrology, as well as advanced applications for functional uses. This reprint aims to collate the latest research results on ultra-precision machining technology and applications in order to promote the development of related industrial technology with high efficiency, high precision, and intelligence.

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Imaging and analysis are widely involved in various research fields, including biomedical applications, medical imaging and diagnosis, computer vision, autonomous driving, and robot controls. Imaging and analysis are now facing big changes regarding intelligence, due to the breakthroughs of artificial intelligence techniques, including deep learning. Many difficulties in image generation, reconstruction, de-noising skills, artifact removal, segmentation, detection, and control tasks are being overcome with the help of advanced artificial intelligence approaches. This Special Issue focuses on the latest developments of learning-based intelligent imaging techniques and subsequent analyses, which include photographic imaging, medical imaging, detection, segmentation, medical diagnosis, computer vision, and vision-based robot control. These latest technological developments will be shared through this Special Issue for the various researchers who are involved with imaging itself, or are using image data and analysis for their own specific purposes.

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Solving nonlinear equations in Banach spaces (real or complex nonlinear equations, nonlinear systems, and nonlinear matrix equations, among others), is a non-trivial task that involves many areas of science and technology. Usually the solution is not directly affordable and require an approach using iterative algorithms. This Special Issue focuses mainly on the design, analysis of convergence, and stability of new schemes for solving nonlinear problems and their application to practical problems. Included papers study the following topics: Methods for finding simple or multiple roots either with or without derivatives, iterative methods for approximating different generalized inverses, real or complex dynamics associated to the rational functions resulting from the application of an iterative method on a polynomial. Additionally, the analysis of the convergence has been carried out by means of different sufficient conditions assuring the local, semilocal, or global convergence. This Special issue has allowed us to present the latest research results in the area of iterative processes for solving nonlinear equations as well as systems and matrix equations. In addition to the theoretical papers, several manuscripts on signal processing, nonlinear integral equations, or partial differential equations, reveal the connection between iterative methods and other branches of science and engineering.

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Multiple-input, multiple-output (MIMO), which transmits multiple data streams via multiple antenna elements, is one of the most attractive technologies in the wireless communication field. Its extension, called ‘massive MIMO’ or ‘large-scale MIMO’, in which base station has over one hundred of the antenna elements, is now seen as a promising candidate to realize 5G and beyond, as well as 6G mobile communications. It has been the first decade since its fundamental concept emerged. This Special Issue consists of 19 papers and each of them focuses on a popular topic related to massive MIMO systems, e.g. analog/digital hybrid signal processing, antenna fabrication, and machine learning incorporation. These achievements could boost its realization and deepen the academic and industrial knowledge of this field.

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This book includes papers in cross-disciplinary applications of mathematical modelling: from medicine to linguistics, social problems, and more. Based on cutting-edge research, each chapter is focused on a different problem of modelling human behaviour or engineering problems at different levels. The reader would find this book to be a useful reference in identifying problems of interest in social, medicine and engineering sciences, and in developing mathematical models that could be used to successfully predict behaviours and obtain practical information for specialised practitioners. This book is a must-read for anyone interested in the new developments of applied mathematics in connection with epidemics, medical modelling, social issues, random differential equations and numerical methods.

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This book deals with metal processing and its numerical modelling and simulation. In total, 21 papers from different distinguished authors have been compiled in this area. Various processes are addressed, including solidification, TIG welding, additive manufacturing, hot and cold rolling, deep drawing, pipe deformation, and galvanizing. Material models are developed at different length scales from atomistic simulation to finite element analysis in order to describe the evolution and behavior of materials during thermal and thermomechanical treatment. Materials under consideration are carbon, Q&T, DP, and stainless steels; ductile iron; and aluminum, nickel-based, and titanium alloys. The developed models and simulations shall help to predict structure evolution, damage, and service behavior of advanced materials.

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This reprint is mainly concerned with quantum algorithms, their mathematics from behind and the complexity issues arising from them. It comprises seven papers: Two of them are focused on improving the performance by means of quantum algorithms over the best instances of classical ones: “A Fast Quantum Image Component Labeling Algorithm” [1] by Yan Li, Dapeng Hao, Yang Xu and Kinkeung Lai. “Quantum Algorithms for Some Strings Problems Based on Quantum String Comparator” [2] by Kamil Khadiev, Artem Ilikaev and Jevgenijs Vihrovs. Another four papers deal with enhanced quantum algorithms: “Progress towards Analytically Optimal Angles in Quantum Approximate Optimisation”: [3] by Daniil Rabinovich, Richik Sengupta, Ernesto Campos, Vishwanathan Akshay and Jacob Biamonte. “Quantum Weighted Fractional Fourier Transform” [4] by Tieyu Zhao, Tianyu Yang and Yingying Chi. “On the Amplitude Amplification of Quantum States Corresponding to the Solutions of the Partition Problem” [5] by Mauro Mezzini, Jose J. Paulet, Fernando Cuartero, Hernan I. Cruz and Fernando L. Pelayo. “On the Complexity of Finding the Maximum Entropy Compatible Quantum State” [6] by Serena Di Giorgio and Paulo Mateus. Last, but not least, one paper researched a classical operational research problem by means of quantum annealing: “GPS: A New TSP Formulation for Its Generalizations Type QUBO” [7] by Saul Gonzalez-Bermejo, Guillermo Alonso-Linaje and Parfait Atchade-Adelomou. As Guest Editors, we would like to thank the authors and reviewers who made this possible by promoting, spreading research on and supporting quantum computing.

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This reprint of a Special Issue in the journal Algorithms is dedicated to scheduling algorithms and their various applications. In addition to the Editorial, it contains 12 papers including a survey of assembly and production line design and scheduling under uncertainty. Other topics addressed in this reprint are the energy management of power systems, cost scheduling for financial data supply chains, staff scheduling in the construction industry, various assignment problems, single-lane roundabouts, robotic arm-based batching systems with conveyor belts, as well as single-machine and flow-shop scheduling problems.

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In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem-solving. These algorithms have the capacity to generalize and discover knowledge for themselves and to learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves the mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topics range from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspired filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensor signal processing.

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This Special Issue aims to provide a forum for the latest advances in hydraulic modeling based on the use of shallow water and related models as well as their novel application in practical engineering. Original contributions, including those in but not limited to the following areas, will be considered for publication: new conceptual models and applications, flood inundation and routing, sediment transport and morphodynamic modelling, pollutant transport in water, irrigation and drainage modeling, numerical simulation in hydraulics, novel numerical methods for the shallow water equations and extended models, case studies, and high-performance computing.

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The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things.

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Applied mathematics and symmetry work together as a powerful tool for problem reduction and solving. We are communicating applications in probability theory and statistics (A Test Detecting the Outliers for Continuous Distributions Based on the Cumulative Distribution Function of the Data Being Tested, The Asymmetric Alpha-Power Skew-t Distribution), fractals - geometry and alike (Khovanov Homology of Three-Strand Braid Links, Volume Preserving Maps Between p-Balls, Generation of Julia and Mandelbrot Sets via Fixed Points), supersymmetry - physics, nanostructures -chemistry, taxonomy - biology and alike (A Continuous Coordinate System for the Plane by Triangular Symmetry, One-Dimensional Optimal System for 2D Rotating Ideal Gas, Minimal Energy Configurations of Finite Molecular Arrays, Noether-Like Operators and First Integrals for Generalized Systems of Lane-Emden Equations), algorithms, programs and software analysis (Algorithm for Neutrosophic Soft Sets in Stochastic Multi-Criteria Group Decision Making Based on Prospect Theory, On a Reduced Cost Higher Order Traub-Steffensen-Like Method for Nonlinear Systems, On a Class of Optimal Fourth Order Multiple Root Solvers without Using Derivatives) to specific subjects (Facility Location Problem Approach for Distributed Drones, Parametric Jensen-Shannon Statistical Complexity and Its Applications on Full-Scale Compartment Fire Data). Diverse topics are thus combined to map out the mathematical core of practical problems.

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