Search

Summary: This reprint, as a continuation of a previous Special Issue entitled "Efficiency and Optimization of Buildings Energy Consumption", gives an up-to-date overview of new technologies based on Machine Learning (ML) and Internet of Things (IoT) procedures to improve the mathematical approach of algorithms that allow control systems to be improved with the aim of reducing housing sector energy consumption.

Summary: This book represents one of the most up-to-date collections of articles on clinical practice and research in the field of Autism Spectrum Disorders (ASD). The scholars who contributed to this book are experts in their field, carrying out cutting edge research in prestigious institutes worldwide (e.g., Harvard Medical School, University of California, MIND Institute, King's College, Karolinska Institute, and many others). The book addressed many topics, including (1) The COVID-19 pandemic; (2) Epidemiology and prevalence; (3) Screening and early behavioral markers; (4) Diagnostic and phenotypic profile; (5) Treatment and intervention; (6) Etiopathogenesis (biomarkers, biology, and genetic, epigenetic, and risk factors); (7) Comorbidity; (8) Adulthood; and (9) Broader Autism Phenotype (BAP). This book testifies to the complexity of performing research in the field of ASD. The published contributions underline areas of progress and ongoing challenges in which more certain data is expected in the coming years. It would be desirable that experts, clinicians, researchers, and trainees could have the opportunity to read this updated text describing the challenging heterogeneity of Autism Spectrum Disorder.

Summary: In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas.

Summary: In recent years, improving the sustainability of the steel industry and reducing its CO2 emissions has become a global focus. To achieve this goal, further process optimization in terms of energy and resource efficiency and the development of new processes and process routes are necessary. Modeling and simulation have established themselves as invaluable sources of information for otherwise unknown process parameters and as an alternative to plant trials that involves lower costs, risks, and time. Models also open up new possibilities for model-based control of metallurgical processes. This Special Issue focuses on recent advances in the modeling and simulation of unit processes in iron and steelmaking. It includes reviews on the fundamentals of modeling and simulation of metallurgical processes, as well as contributions from the areas of iron reduction/ironmaking, steelmaking via the primary and secondary route, and continuous casting.

Summary: Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the "real world" frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis - be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies.

Summary: Unlike other construction materials, road materials have developed minimally over the past 100 years. However, since the 1970s, the focus has been on more sustainable road construction materials such as recycled asphalt pavements. Recycling asphalt involves removing old asphalt and mixing it with new (fresh) aggregates, binders, and/or rejuvenators. Similarly, there are various efforts to use alternative modifiers and technical solutions such as crumb rubber, plastics, or various types of fibres. For the past two decades, researchers have been developing novel materials and technologies, such as self-healing materials, in order to improve road design, construction, and maintenance efficiency and reduce the financial and environmental burden of road construction. This Special Issue on "Advances in Asphalt Pavement Technologies and Practices" curates advanced/novel work on asphalt pavement design, construction, and maintenance. The Special Issue comprises 19 papers describing unique works that address the current challenges that the asphalt industry and road owners face.

Summary: The inverse dynamics problem was developed in order to provide researchers with the state of the art in inverse problems for dynamic and vibrational systems. Contrasted with a forward problem, which solves for the system output in a straightforward manner, an inverse problem searches for the system input through a procedure contaminated with errors and uncertainties. An inverse problem, with a focus on structural dynamics, determines the changes made to the system and estimates the inputs, including forces and moments, to the system, utilizing measurements of structural vibration responses only. With its complex mathematical structure and need for more reliable input estimations, the inverse problem is still a fundamental subject of research among mathematicians and engineering scientists. This book contains 11 articles that touch upon various aspects of inverse dynamic problems.

Summary: Human activity recognition (HAR) and human behavior recognition (HBR) play increasingly important roles in the digital age. High-quality sensory observations applicable to recognizing users' activities and behaviors, including electrical, magnetic, mechanical (kinetic), optical, acoustic, thermal, and chemical biosignals, are inseparable from sensors' sophisticated design and appropriate application. Traditional sensors suitable for HAR and HBR, including external sensors for smart homes, optical sensors such as cameras for capturing video signals, and bioelectrical, biomagnetic, and biomechanical sensors for wearable applications, have been studied and verified adequately. They continue to be researched in depth for more effective and efficient usage, and brand-new areas facilitated by sensor-based HAR/HBR are emerging, such as interactive edutainment, single-motion duration analysis, time series information retrieval, handcrafted and high-level feature design, and fall detection. Meanwhile, innovative sensor research for HAR or HBR is also very active in the academic community, including new sensors appropriate for HAR/HBR, new designs and applications of the above-mentioned traditional sensors, and the usage of non-traditional HAR-/HBR-related sensor types, among others.

Summary: Regular physical activity (PA) is both a preventive measure and a cure for non-communicable diseases. Moreover, PA improves mental health, quality of life, and well-being. Conversely, physical inactivity and sedentary lifestyles have negative impacts on individuals, families, and society, as evidenced in particular by the spread of the obesity epidemic. PA has proven to be a low-cost alternative for the treatment and prevention of disease. Therefore, interventions to prevent avoidable diseases by increasing the proportion of physically active people are fundamental. The Special Issue "Physical Activity, Wellness and Health: Challenges, Benefits and Strategies" was collected research articles on anthropometric determinants of health and performance, PA and healthy habits, exercise and diet, exercise and body composition, interventions to promote PA for people of all ages, strategies for the implementation of an active life, and the beneficial effects of exercise on metabolic syndrome. A total of 20 articles were published, falling mainly into the following three areas: anthropometry, health, and sport; health benefits of exercise; population studies and strategies for an active life. All of the studies support strategies to promote PA and reduce sedentary behavior among adolescents, adults and the elderly. There is no doubt that regular exercise is beneficial to health, but the general population should be encouraged to engage in more of it.

Summary: This reprint was established after the 9th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.

Summary: Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.

Summary: This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on "Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules", presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots' ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science.

Summary: Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries' "preferred methods" were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.

Summary: With increasing awareness about environmental sustainability issues, the geotechnical engineering community is in search of sustainable materials, innovative applications and ground improvement technologies which are resilient to climate change, involve low carbon emissions, are ecologically friendly and affordable, yet effective. As such, this reprint brings together 18 novel contributions providing the state-of-the-art in "Sustainable Geotechnics" with special focus on the reuse and recycling of waste materials in geotechnical applications as well as sustainable design, construction, and monitoring of the performance of geostructures. This reprint may be of particular interest to geotechnical professional engineers and researchers who are facing the challenging task of increasing the sustainability and resiliency of geotechnical assets and infrastructures.

Summary: This reprint collects 10 original research contributions published in the Special Issue entitled "Artificial Intelligence for Multisource Geospatial Information" of the ISPRS International Journal of Geo-Information. The focus is on different methods of Geospatial Artificial Intelligence (GeoAI) based on deep learning using different network architectures, clustering, soft computing, and semantic approaches. They are proposed to deal with a variety of Geospatial Big Data (GBD), such as georeferenced texts and photos in social networks, remote sensing images, cartographic maps, multidimensional geo databases, metadata in spatial data infrastructures, and for different tasks, such as for multisource georeferenced text integration and geodata flexible querying, for social sensing by applying sentiment analysis, clustering and geo analysis, for segmentation of roads, clouds and snow, and for detection of small targets and people on the streets.

Summary: This Special Issue, entitled "Non-Destructive Testing in Civil Engineering", aims to present to interested researchers and engineers the latest achievements in the field of new research methods, as well as the original results of scientific research carried out with their use-not only in laboratory conditions but also in selected case studies. The articles published in this Special Issue are theoretical-experimental and experimental, and also show the practical nature of the research. They are grouped by topic, and the main content of each article is briefly discussed for your convenience. These articles extend the knowledge in the field of non-destructive testing in civil engineering with regard to new and improved non-destructive testing (NDT) methods, their complementary application, and also the analysis of their results-including the use of sophisticated mathematical algorithms and artificial intelligence, as well as the diagnostics of materials, components, structures, entire buildings, and interesting case studies.

Summary: The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field.

Summary: In this Special Issue on human health engineering, we invited submissions exploring recent contributions to the field of human health engineering, which is the technology used for monitoring the physical or mental health status of individuals in a variety of applications. Contributions focused on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main aspects of the monitoring and control engineering scheme applied to human health applications, including papers focusing on measuring/sensing physiological variables, contributions describing research on the modelling of biological signals, papers highlighting health monitoring applications, and finally examples of control applications for human health. In comparison to biomedical engineering, the field of human health engineering also covers applications on healthy humans (e.g., sports, sleep, and stress) and thus not only contributes to develop technology for curing patients or supporting chronically ill people, but also more generally for disease prevention and optimizing human well-being.

Summary: Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE'2020 conference. This conference and journal's Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling.

Summary: The present reprint contains 33 articles accepted and published in the Special Issue entitled "Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2022" in the MDPI journal, Mathematics, which covers a wide range of topics connected to the theory and applications of feature representation learning for image processing, artificial intelligence, data mining and robotics. These topics include, among others, elements from image blurring, image aesthetic quality assessment, pedestrian detection, visual tracking, vehicle re-identification, face recognition, 3D reconstruction, the stability of switched systems, domain adaption, deep reinforcement, sentiment analysis, graph convolutional networks, knowledge graphs, geometric metric learning, etc. It is hoped that this reprint will be interesting and useful for those working in the area of image processing, computer vision, machine learning, natural language processing and robotics, as well as for those with backgrounds in machine learning who are willing to become familiar with recent advancements in artificial intelligence, which, today, is present in almost all aspects of human life and activities.

Summary: In recent years, we have been witnessing the exponential proliferation of the Internet of Things (IoT), networks of physical devices, vehicles, appliances and other items embedded with electronics, software, sensors, actuators and connectivity that enables these objects to connect and exchange data. Enabling the introduction of highly efficient IoT, wireless sensing and network technologies will reduce the need for traditional processes that are currently be carried out manually, thus freeing up the precious resources of dwindling working staff, to do more meaningful and human-centered work. This reprint aims to bring together innovative developments in areas related to IoT, wireless sensing and networking. The aspects covered include software-defined network (SDN)-based IoT networks, artificial intelligence (AI) for IoT, industrial IoT, smart sensors, optimization of energy efficiency for IoT, and wireless sensor networks, IoT applications for agriculture, smart cities, healthcare, localization and environment monitoring.

Summary: Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue "Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicine

Summary: In recent years, bioeconomy strategies have been implemented and adapted internationally. In the bioeconomy, materials are to a certain extent circular by nature. However, biomaterials may also be used in a rather linear way. Lately, a transition towards a circular economy, a more restorative and regenerative economic model, is being promoted worldwide. A circular economy offers an alternative model aiming at "doing more and better with less". It is based on the idea that circulating matter and energy will diminish the need for new input. Its concept lies in maintaining the value of products, materials, and resources for as long as possible and at the same time minimizing or even eliminating the amount of waste produced. Focused on "closing the loops", a circular economy is a practical solution for promoting entrepreneurial sustainability, economic growth, environmental resilience, and a better quality of life for all. The most efficient way to close resource loops is to find value in the waste. Different modes of resource circulation may be applied, e.g., raw materials, by-products, human resources, logistics, services, waste, energy, or water. To that end, this Special Issue seeks to contribute to the circular bioeconomy agenda through enhanced scientific and multidisciplinary knowledge to boost the performance efficiency of circular business models and support decision-making within the specific field. The Special Issue includes innovative technical developments, reviews, and case studies, all of which are relevant to green, closed-loop, circular bioeconomy.

Summary: This book collects recent research in the field of care for neurodevelopmental disorders, emphasizing transdisciplinary work in clinical, educational and family contexts. It presents an opportunity to learn about the impact of participation on children and adolescents with neurodevelopmental disorders. Mainly, new therapeutic approaches are presented in children and adolescents with autism spectrum disorder, attention-deficit/hyperactivity disorder, or motor coordination disorders.

Summary: Carpal tunnel syndrome (CTS) is a very common median nerve compression neuropathy at wrist level. It causes unplesant symptoms to patient as well as a financial burden for society. Conservative treatment helps with mild and transient symptoms, but often this syndrome requires surgical treatment. Surgical decompression of the median nerve is one of the most common surgical operations. If the symptom persists for a long time and treatment is delayed, this can result in a lack of sensation in the median area in addition to thenar atrophy and weakness of the thumb opposition. There are many impressive things behind CTS that we do not know yet; anatomical causes, links to other diseases and medication, occupational exposures and predisposing lifestyle risk factors. Diagnostics and differential diagnostics of CTS have their own challenges. Conservative treatment practices of CTS are diverse and unambiguous best practice is not clear. Clinical symptoms, electromyography (EMG) finding, and possible differential diagnostic challenges should be considered when deciding on surgery. Surgical treatment varies somewhat from hospital to hospital and from country to country. When the CTS diagnosis and treatment is done too late for nerves to recover or there is perioperative complication, some late reconstructive surgeries might be needed. These nerve surgery techniques can be done by experienced hand surgeons. The quality of care, complications, and cost effectiveness of different methods require further research. This Special Issue will present the latest research on this interesting and clinically significant syndrome.

Summary: This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs.

Summary: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe chronic health condition that is often misunderstood or ignored by health establishments. The lack of definitive diagnostic markers to separate ME/CFS patients from the healthy population as well as from other chronic disorders is problematic for both health professionals and researchers. A consortium of Australian researchers gathered to systematically understand ME/CFS, ranging from a deep analysis of clinical and pathology data to metabolomic profiles and the investigation of mitochondrial function. From this broad collaboration, a number of compelling insights have arisen that may form the basis of specific serum, blood, and/or urinary biomarkers of ME/CFS. This Special Edition reports on a conference centred on these biomedical discoveries, with other contributions, with a translation focus for predictive markers for ME/CFS diagnosis. By supporting health professionals with developments in diagnostics for this condition, the patients and their families will hopefully benefit from an improved recognition of the biomedical underpinnings of the condition and will be better able to access the care that is urgently required. This Special Edition contains a mix of speaker submissions and other accepted manuscripts that contributed to our objective of advancing biomedical insights to enable the accurate diagnosis of ME/CFS.

Summary: Dual disorders, meaning the coexistence in the same individual of a substance use disorder and another mental disorder, have become an area of great interest in the fields of psychiatry and addiction and in health overall due to their high prevalence, clinical relevance and multicultural presence. This book includes a compilation of papers published between the end of 2021 and the beginning of 2023 focused on recent advances in dual disorders. The contributions present a broad look at various relevant aspects. Among them are the characterization of patients treated in different services, with special interest in the risk of overdoses, suicidal attempts and HIV, as well as the so-forgotten gender perspective and the impact of the COVID-19 pandemic on the attention of addiction centers. One of the first studies to consider the comorbidity of gambling disorders with substance use is also presented. The possible usefulness of explanatory models of machine learning for diagnosis and for performing chronobiological measurements that allow a more individualized treatment stand out among the novel contributions to the field of dual disorders. The aim of each of the contributions of this reprint is to be transferred for the benefit of clinical practice.

Summary: Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods.

Summary: In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems.

Summary: The deployment of distributed renewable energy resources (DRERs) has accelerated globally due to environmental concerns and an increasing demand for electricity. DRERs are considered to be solutions to some of the current challenges related to power grids, such as reliability, resilience, efficiency, and flexibility. However, there are still several technical and non-technical challenges regarding the deployment of distributed renewable energy resources. Technical concerns associated with the integration and control of DRERs include, but are not limited, to optimal sizing and placement, optimal operation in grid-connected and islanded modes, as well as the impact of these resources on power quality, power system security, stability, and protection systems. On the other hand, non-technical challenges can be classified into three categories-regulatory issues, social issues, and economic issues. This Special Issue will address all aspects related to the integration and control of distributed renewable energy resources. It aims to understand the existing challenges and explore new solutions and practices for use in overcoming technical challenges.

Summary: Since the launch of Seasat, TIROS-N, and Nimbus-7 satellites equipped with ocean observation sensors in 1978, a new era of ocean remote sensing has opened. Today, remotely sensed data have been widely used in oceanographic studies. This reprint collects various advanced ocean remote sensing technologies and their applications, including the use of artificial intelligence techniques to explore ocean information and bibliometric analysis to assess researchers and trends in this scientific field. The observations of various sensors enrich the application of ocean environment monitoring and ocean dynamical analysis. If you are interested in understanding the application of ocean remote sensing, this monograph should be very helpful.

Summary: Personalised medicine is the next step in healthcare, especially when applied to genetically diverse diseases such as cancers. Naturally, a host of methods need to evolve alongside this, in order to allow the practice and implementation of individual treatment regimens. One of the major tasks for the development of personalised treatment of cancer is the identification and validation of a comprehensive, robust, and reliable panel of biomarkers that guide the clinicians to provide the best treatment to patients. This is indeed important with regards to radiotherapy; not only do biomarkers allow for the assessment of treatability, tumour response, and the radiosensitivity of healthy tissue of the treated patient. Furthermore, biomarkers should allow for the evaluation of the risks of developing adverse late effects as a result of radiotherapy such as second cancers and non-cancer effects, for example cardiovascular injury and cataract formation. Knowledge of all of these factors would allow for the development of a tailored radiation therapy regime. This Special Issue of the Journal of Personalised Medicine covers the topic of Radiation Response Biomarkers in the context of individualised cancer treatments, and offers an insight into some of the further evolution of radiation response biomarkers, their usefulness in guiding clinicians, and their application in radiation therapy.

Summary: In the computer sciences, virtual reality (VR) is usually described as a set of fancy technologies. However, in medicine and neuroscience, VR is instead defined as an advanced form of human-computer interface that allows the user to interact with and become present in a computer-generated environment. The sense of presence offered by VR makes it a powerful tool for personal change because it offers a world where the individual can stay and live a specific experience. For this reason, the use of VR in mental health shows promise: different types of research support its clinical efficacy for conditions including anxiety disorders, stress-related disorders, obesity and eating disorders, pain management, addiction, and schizophrenia. However, more research is needed to transform VR according to a clinical standard for mental health. This Special Issue aims to present the most recent advances in the mental health applications of VR, as well as their implications for future patient care.

Summary: This book features the manuscripts accepted for the Special Issue "Applications in Electronics Pervading Industry, Environment and Society-Sensing Systems and Pervasive Intelligence" of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the "Applications in Electronics Pervading Industry, Environment and Society" (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs.

Summary: We all agree that people with MS need to be cared in a profoundly personalized way. The care of the patient with MS is still based on the presence of relapses, so their successful diagnosis and treatment is fundamental and will condition the therapeutic strategies to follow with the patient. The treatment strategies are a highly controversial topic of debate that is increasingly supported by robust objective biological markers of response and that also increasingly take into account the dynamics and predictors of cognitive impairment along the disease course, which includes the adoption of new trends in the field of machine learning techniques. However, we all know that patient care goes beyond being treated with drugs and we cannot overlook reminding patients of the importance of their lifestyle behaviors that vary according to the MS phenotype, in order to improve their quality of life. Teleconsultation is a new care strategy proved to be feasible and well-received by patients with MS that will undoubtedly become reinforced because it will allow a closer follow-up of the patient without the need for displacement.

Summary: The present reprint contains all of the articles accepted and published in the Special Issue " Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education" from the MDPI journal Mathematics. This Special Issue aims to develop more efficient and effective approaches to healthcare and education, leveraging the increasing availability of big data and advancements in artificial intelligence. By sharing new methods, applications, and case studies, this reprint is dedicated to the development of innovative solutions that improve healthcare and education for all. The topics addressed in this Special Issue cover a wide range of areas, including data mining, machine learning, learning analytics, prediction methods, pattern recognition, decision analysis, probabilistic reasoning, fuzzy systems, student or patient modelling, adaptive systems, collaborative systems, recommendation systems, experimental design, and empirical study cases. We hope that this reprint will enable the scientific community in both medicine and education to leverage the techniques from statistics and artificial intelligence to drive significant advances in their respective fields. These approaches hold promise for improving patient outcomes and enhancing the quality of education for students around the world.

Summary: This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems.

Summary: 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.

Summary: Digital health, virtual assistance, and telemedicine are terms often used interchangeably to refer to remote medical assistance, monitoring and care. Several studies and insights have developed these issues, analyzing the advantages and disadvantages and successes and failures and offering reflections on the implications and issues of these technologies in the health domain. The results of these investigations are affecting the redesign of hospital and outpatient management based on digital innovation using eHealth and mHealth. During the COVID-19 pandemic, this approach made it possible to offer assistance and continue care at home, protecting patients, preserving health workers, limiting the spread of the virus, and reducing the need for hospitalization. This reprint contains contributions dealing with the development of DH during the COVID-19 pandemic. The contributions are from various experts in different fields regarding the application of digital health, which, in some cases is also integrated with artificial intelligence, including digital contact tracing, mHealth, virtual reality, mental health, physiology, and rehabilitation.

Summary: The "Age-Friendly Cities & Communities: States of the Art and Future Perspectives" publication presents contemporary, innovative, and insightful narratives, debates, and frameworks based on an international collection of papers from scholars spanning the fields of gerontology, social sciences, architecture, computer science, and gerontechnology. This extensive collection of papers aims to move the narrative and debates forward in this interdisciplinary field of age-friendly cities and communities.

Summary: Composites have increasingly been used in various structural components in the aerospace, marine, automotive, and wind energy sectors. The material characterization of composites is a vital part of the product development and production process. Physical, mechanical, and chemical characterization helps developers to further their understanding of products and materials, thus ensuring quality control. Achieving an in-depth understanding and consequent improvement of the general performance of these materials, however, still requires complex material modeling and simulation tools, which are often multiscale and encompass multiphysics. This Special Issue aims to solicit papers concerning promising, recent developments in composite modeling, simulation, and characterization, in both design and manufacturing areas, including experimental as well as industrial-scale case studies. All submitted manuscripts will undergo a rigorous review process and will only be considered for publication if they meet journal standards. Selected top articles may have their processing charges waived at the recommendation of reviewers and the Guest Editor.

Summary: Minimally invasive surgery has become a common term in visceral as well as gynecologic surgery. It has almost evolved into its own surgical speciality over the past 20 years. Today, being firmly established in every subspeciality of visceral surgery, it is now no longer a distinct skillset, but a fixed part of the armamentarium of surgical options available. In every indication, the advantages of a minimally invasive approach include reduced intraoperative blood loss, less postoperative pain, and shorter rehabilitation times, as well as a marked reduction of overall and surgical postoperative morbidity. In the advent of modern oncologic treatment algorithms, these effects not only lower the immediate impact that an operation has on the patient, but also become important key steps in reducing the side-effects of surgery. Thus, they enable surgery to become a module in modern multi-disciplinary cancer treatment, which blends into multimodular treatment options at different times and prolongs and widens the possibilities available to cancer patients. In this quickly changing environment, the requirement to learn and refine not only open surgical but also different minimally invasive techniques on high levels deeply impact modern surgical training pathways. The use of modern elearning tools and new and praxis-based surgical training possibilities have been readily integrated into modern surgical education,which persists throughout the whole surgical career of modern gynecologic and visceral surgery specialists.

Summary: This book is a reprint of the Special Issue entitled "The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?". Artificial intelligence is extending into the world of both digital radiology and digital pathology, and involves many scholars in the areas of biomedicine, technology, and bioethics. There is a particular need for scholars to focus on both the innovations in this field and the problems hampering integration into a robust and effective process in stable health care models in the health domain. Many professionals involved in these fields of digital health were encouraged to contribute with their experiences. This book contains contributions from various experts across different fields. Aspects of the integration in the health domain have been faced. Particular space was dedicated to overviewing the challenges, opportunities, and problems in both radiology and pathology. Clinal deepens are available in cardiology, the hystopathology of breast cancer, and colonoscopy. Dedicated studies were based on surveys which investigated students and insiders, opinions, attitudes, and self-perception on the integration of artificial intelligence in this field.

Summary: This book reprints articles from the Special Issue "Advances in Computer-Aided Technology" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of thirteen published articles. This Special Issue belongs to the "Mechatronic and Intelligent Machines" section. Industry 4.0 is characterized by the integration of advanced technologies, such as artificial intelligence, the Internet of Things, and cloud computing, into traditional manufacturing and production processes. CAx (Computer-Aided Systems) systems are a set of computer software tools used in engineering and product design, covering various stages of the product development cycle. Advanced CAx tools combine many different aspects of product lifecycle management (PLM), including design, finite element analysis (FEA), manufacturing, production planning and product. In connection with the transition to Industry 4.0 concepts, the concept of the digital twin comes to the fore, and existing CAx systems must adapt to this trend. The Special Issue deals with a number of research areas, such as: - New trends in CAx systems; Digital manufacturing; Internet of Things in manufacturing; Simulation of production systems and processes; Systems for advanced finite element analysis; Material engineering; Digitization and 3D scanning.

Summary: Despite being one of the most popular sports worldwide, basketball has received limited research attention compared to other team sports. Establishing a strong evidence base with high-quality and impactful research is essential in enhancing decision-making processes to optimize player performance for basketball professionals. Consequently, the book entitled Improving Performance and Practice in Basketball provides a collection of novel research studies to increase the available evidence on various topics with strong translation to practice in basketball. The book includes work by 40 researchers from 16 institutions or professional organizations from 9 countries. In keeping with notable topics in basketball research, the book contains 2 reviews focused on monitoring strategies to detect player fatigue and considerations for travel in National Basketball Association players. In addition, 8 applied studies are also included in the book, focused on workload monitoring, game-related statistics, and the measurement of physical and skill attributes in basketball players. This book also has a strong focus on increasing the evidence available for female basketball players, who have traditionally been under-represented in the literature. The outcomes generated from this book should provide new insights to inform practice in many areas for professionals working in various roles with basketball teams.

Summary: Energy and its economic implications have been in the spotlight of policymakers, academics, traders, speculators and the industry for decades now. It has been an active research topic for more than 150 years. From the 19th century, the problem of creating, processing, storing and transporting energy was well defined. The issues of efficiently producing, pricing, distributing, and forecasting the demand, supply and prices of energy-related products and services are central to most modern economies irrespective of their level of development. These issues are apparent in times of relative tranquility in the respective markets but become central for all stakeholders in times of turbulence. This volume focuses on emerging methodologies of analysis, description, modelling, and forecasting in the topical area of Energy Economics. Includes emerging and innovative methodological approaches from the areas of machine learning, artificial intelligence, econometrics, and statistics aimed to model, describe or forecast the energy markets at all levels. Additionally, the volume also presents a bibliographical review, summarizes and compares results of different studies in the energy-sustainable economic growth and development nexus. The practical importance of the results to all energy market stakeholders in terms of regulating, pricing, and distributing energy is evident. Theoretical robustness, methodological innovation, and possible direct applicability of the conclusions were the basic requirements for research work to be included in this publication.

Summary: As the scaling of electronic semiconductor devices displays signs of saturation, the main focus of research in microelectronics is shifting towards finding new computing paradigms. Electron spin offers additional functionality to digital charge-based devices. Several fundamental problems, including spin injection to a semiconductor, spin propagation and relaxation, and spin manipulation by the gate voltage, have been successfully resolved to open a path towards spin-based reprogrammable electron switches. Devices employing electron spin are nonvolatile; they are able to preserve the stored information without external power. Emerging nonvolatile devices are electrically addressable, possess a simple structure, and offer endurance and speed superior to flash memory. Having nonvolatile memory very close to CMOS offers a prospect of data processing in the nonvolatile segment, where the same devices are used to store and process the information. This opens perspectives for conceptually new low-power computing paradigms within Artificial Intelligence of Things (AIoT). This Special Issue focuses on all topics related to spintronic devices such as spin-based switches, magnetoresistive memories, energy harvesting devices, and sensors that can be employed in in-memory computing concepts and in Artificial Intelligence.

Summary: Recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice. When processing images, videos, or other types of multimedia, one needs efficient solutions to perform fast and reliable processing. Computational intelligence is used for medical screening where the detection of disease symptoms is carried out, in prevention monitoring to detect suspicious behavior, in agriculture systems to help with growing plants and animal breeding, in transportation systems for the control of incoming and outgoing transportation, for unmanned vehicles to detect obstacles and avoid collisions, in optics and materials for the detection of surface damage, etc. In many cases, we use developed techniques which help us to recognize some special features. In the context of this innovative research on computational intelligence, the Special Issue "Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments" present an excellent opportunity for the dissemination of recent results and achievements for further innovations and development. It is my pleasure to present this collection of excellent contributions to the research community. - Prof. Marcin Woźniak, Silesian University of Technology, Poland -

Summary: This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field.