The rapid growth of modern industry has resulted in a growing demand for construction materials with excellent operational properties. However, the improved features of these materials can significantly hinder their manufacture and, therefore, they can be defined as hard-to-cut. The main difficulties during the manufacturing/processing of hard-to-cut materials are attributed especially to their high hardness and abrasion resistance, high strength at room or elevated temperatures, increased thermal conductivity, as well as resistance to oxidation and corrosion. Nowadays, the group of hard-to-cut materials is extensive and still expanding, which is attributed to the development of a novel manufacturing techniques (e.g., additive technologies). Currently, the group of hard-to-cut materials mainly includes hardened and stainless steels, titanium, cobalt and nickel alloys, composites, ceramics, as well as the hard clads fabricated by additive techniques. This Special Issue, “Advances in Hard-to-Cut Materials: Manufacturing, Properties, Process Mechanics and Evaluation of Surface Integrity”, provides the collection of research papers regarding the various problems correlated with hard-to-cut materials. The analysis of these studies reveals the primary directions regarding the developments in manufacturing methods, characterization, and optimization of hard-to-cut materials.

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The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment.

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Computational Intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Traditionally, the three main pillars of CI have been neural networks, fuzzy systems, and evolutionary computation. However, in time, many nature-inspired computing paradigms have evolved. Thus, CI is an evolving field, and, at present, in addition to the three main constituents, it encompasses computing paradigms such as ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a major role in developing successful intelligent systems, including games and cognitive developmental systems. Over the last few years, there has been an explosion of research on deep learning, specifically deep convolutional neural networks, and deep learning has become the core method for artificial intelligence. In fact, some of the most successful AI systems today are based on CI. Therefore, this reprint focuses on the theoretical study of computational intelligence and its applications.

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The present reprint contains the 11 papers that were accepted and published in the Special Issue “Applied and Computational Mathematics for Digital Environments” of the MDPI Mathematics journal. The topics of interest include, among others, scientific research, applied tasks, and problems in the following areas: The construction of mathematical and information models of intelligent computer systems for monitoring and controlling the parameters of digital environments; The development of intelligent optimization algorithms that search for optimal parameter values of mathematical and information models in digital environments; Software and mathematical technologies in the implementation of intelligent monitoring and computer control of the parameters of digital environments; The development and application of mathematical and information models, machine learning methods, and artificial intelligence for the analysis and processing of big data in digital environments. I hope that this reprint will be useful to those who are interested in the real-world applications of applied and computational mathematics for digital environments in terms of solving actual, practical problems in all spheres of human life and activity.

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This Special Issue, "Artificial Intelligence Advances for Medical Computer-Aided Diagnosis," explores the transformative role of AI and ML in modern diagnostics. Featuring contributions from leading researchers, this collection highlights innovative algorithms, models, and applications that enhance diagnostic accuracy, speed, and predictive power across various medical fields, including radiology, pathology, genomics, and personalized medicine. This issue includes 12 articles, spanning theoretical advancements, algorithm development, and practical implementations, showcasing the broad impact and future potential of AI-driven diagnostics. Each contribution offers unique insights into overcoming current challenges, improving clinical decision-making, and ultimately enhancing patient outcomes. This compilation serves as a valuable resource for researchers, clinicians, and technologists, fostering further innovation and collaboration in advancing medical diagnostics. Join us in exploring the latest breakthroughs and future directions in AI-driven healthcare, as we strive to improve diagnostic accuracy and efficiency for better patient care.

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Complex systems have long been an integral part of modern life and can be encountered everywhere. Undertaking a comprehensive study of such systems is a challenging problem, one which is impossible to solve without the use of contemporary mathematical modeling techniques. Mathematical models form the basis for the optimal design and control of complex systems. The present reprint contains all the articles accepted and published in the Special Issue of Mathematics entitled "Control, Optimization, and Mathematical Modeling of Complex Systems". This Special Issue is focused on recent theoretical and computational studies of complex systems modeling, control, and optimization. The topics addressed in this Special Issue cover a wide range of areas, including numerical simulation in physical, social, and life sciences; the modeling and analysis of complex systems based on mathematical methods and AI/ML approaches; control problems in robotics; design optimization of complex systems, modeling in economics and social sciences; stochastic models in physics and engineering; mathematical models in material science; and high-performance computing for mathematical modeling. It is our hope that the scientific results presented in this reprint will serve as valuable sources of documentation and inspiration to those seeking to delve into complex systems modeling, control, and optimization and examine their wide-ranging applications.

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Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc.

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Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological world, mimic the physiological structure and bodily functions of the organism,

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Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms.

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This book develops efficient methods for the application of Evolutionary Algorithms on stochastic problems. To achieve this, procedures for statistical selection are systematically analyzed with respect to different measures and significantly improved. It is shown how to adapt one of the best procedures for the needs of Evolutionary Algorithms and Evolutionary operators for efficient implementation in stochastic environments are identified.

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This reprint compiles the articles, communications, and review articles from top researchers describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles. This reprint encompasses the key topics related to drone design, communications as well as autonomous Flight and Navigation. Special attention is paid to the drones’ applications to environmental and earth sciences, including in agriculture, forestry, water, and marine environments; other innovative applications are also explored including in relation to the field of application, such as the inclusion of new deep learning techniques.

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This reprint compiles the articles, communications, and review articles from top researchers describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles. This reprint encompasses the key topics related to drone design, communications as well as autonomous Flight and Navigation. Special attention is paid to the drones’ applications to environmental and earth sciences, including in agriculture, forestry, water, and marine environments; other innovative applications are also explored including in relation to the field of application, such as the inclusion of new deep learning techniques.

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Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections:•Reviews of Computational Methods•Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels•The Interface of Biotic and Abiotic Processes•Processing of Large Data Sets for Enhanced Analysis•Parameter Optimization and Measurement

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Recently, microfluidic, nanofluidic and lab-on-a-chip devices have gained particular attention in biomedical applications. Due to their advantages, such as miniaturization, versatility, ease of use, cost-effectiveness, and the potential to replace animal models for drug development and testing, these devices hold tremendous potential to revolutionize the research of more effective treatments for several diseases that threaten human life. With integrated biosensors, these devices allow the development and design of micro- and nanoparticles to be studied in detail, modelling human physiology, investigating the molecular and cellular mechanisms underlying disease formation and progression, and gaining insights into the performance and long-term effects of responsive drug delivery nanocarriers. This Special Issue gathered research papers, and review articles focusing on novel microfluidic, nanofluidic and lab-on-a-chip devices for biomedical applications, addressing all steps related to fabrication, biosensor integration and development, characterization, numerical simulations and validation of the devices, optimization and, the translation of these devices from research labs to industry settings.

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The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems.

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Machine learning and computational intelligence have been applied to various areas and witnessed many successes. The research in this publication explorse many intelligent algorithms which are characterized by computational adaptability, robustness, and high performance. These algorithms facilitate intelligent behavior in complex and dynamic environments and the development of technology that enables machines to think, behave, or act in a more humanesque fashion. This reprint aims to present and discuss the most recent innovations, trends, concerns, challenges, solutions, and application fields in the areas of machine learning and computational intelligence.

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