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This book is a printed edition of the Special Issue “Energy Harvesters and Self-Powered Sensors for Smart Electronics” that was published in Micromachines, which showcases the rapid development of various energy harvesting technologies and novel devices. In the current 5G and Internet of Things (IoT) era, energy demand for numerous and widely distributed IoT nodes has greatly driven the innovation of various energy harvesting technologies, providing key functionalities as energy harvesters (i.e., sustainable power supplies) and/or self-powered sensors for diverse IoT systems. Accordingly, this book includes one editorial and nine research articles to explore different aspects of energy harvesting technologies such as electromagnetic energy harvesters, piezoelectric energy harvesters, and hybrid energy harvesters. The mechanism design, structural optimization, performance improvement, and a wide range of energy harvesting and self-powered monitoring applications have been involved. Th

This Reprint brings together selected contributions from the Special Issue “Computational Intelligence and Machine Learning: Models and Applications”, showcasing recent advances at the intersection of intelligent algorithms and real-world problem solving. The collected papers reflect the growing maturity of computational intelligence and machine learning as core technologies driving innovation across science, engineering, and society. This Reprint highlights methodological advances in distributed, semi-supervised, and weakly supervised learning, addressing challenges such as data uncertainty, decentralization, and limited labeling. It also presents application-driven studies showing how modern models adapt to domains including agriculture, cybersecurity, sports analytics, and document intelligence. Further emphasis is placed on human-centered AI, examining trust, interpretability, user behavior, and technology acceptance. By combining theoretical insights with domain-specific applications, this Reprint emphasizes a shift toward scalable, context-aware, and interpretable machine learning solutions. The contributions collectively illustrate current trends in computational intelligence, including multimodal learning, edge and distributed computing, and responsible AI design. This Reprint is intended for researchers, practitioners, and graduate students working in machine learning, data science, and applied artificial intelligence, as well as for readers interested in how advanced models translate into impactful applications across domains.

This Reprint presents a collection of cutting-edge research and review articles focusing on the integration of machine learning and deep learning theories for intelligent fault diagnosis in industrial and engineering systems. With the rapid advancement of computational intelligence, data-driven fault diagnosis has become a cornerstone of modern Prognostics and Health Management (PHM), enabling early detection, prediction, and mitigation of system failures. The contributions in this Reprint highlight innovative applications of artificial neural networks, convolutional and recurrent neural architectures, transfer learning, and hybrid intelligent systems for diagnosing faults in rotating machinery, robotic systems, power plants, and manufacturing processes. In addition, the included studies explore explainable AI models, data augmentation, and sensor fusion methods that enhance model interpretability and robustness under real-world operating conditions. By bringing together theoretical insights and practical implementations, this Reprint aims to serve as a valuable reference for researchers, engineers, and practitioners engaged in machine learning–based condition monitoring and intelligent fault diagnosis. It reflects recent trends shaping the future of autonomous and resilient industrial systems within the framework of Industry 4.0.

This reprint provides research on how technologies such as artificial intelligence-based machine learning and deep learning can be applied to remote sensing. Through this, we can see the process of solving the existing problems of image and image signal processing for remote sensing. These techniques are computationally intensive and require the help of high-performance computing devices. With the development of devices such as GPUs, remote sensing technology, and aerial sensing technology, it is possible to monitor the Earth with high-resolution images and to obtain vast amounts of Earth observation data. The papers published in this reprint describe recent advances in big data processing and artificial intelligence-based technologies for remote sensing technology. Related Books Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II Environmental & Earth Sciences ... Artificial Intelligence-Based Learning Approaches for Remote Sensing Environmental & Earth Sci

This Reprint collects recent advances in statistical simulation and computing, highlighting their central role in modern statistical methodology and data driven research. Simulation-based approaches provide flexible and powerful tools for analyzing complex systems characterized by high dimensionality, stochastic behavior, and computational constraints that limit the use of purely analytical techniques. The Reprint brings together methodological, computational, and applied contributions that reflect current developments in Monte Carlo methods, Bayesian computation, simulation-based inference, and stochastic modeling. Particular attention is given to algorithmic efficiency, scalability, and reproducibility, as well as to the integration of statistical simulation with emerging computational paradigms such as parallel and high-performance computing. By presenting both theoretical developments and practical applications, this Reprint illustrates how statistical simulation supports rigorous inference, uncertainty quantification, and decision making across a broad range of scientific domains, including health sciences, environmental modeling, engineering, and complex data analysis. The collected works provide a comprehensive overview of contemporary trends while offering perspectives that inform future research directions in statistical simulation and computing.

The aim of this reprint is to present the latest advances in plant genetic engineering and biotechnology published in Plants. In this Special Issue, several important economic and cereal crops, Brassica napus, Brassica juncea, Coffea arabica, Nasturtium officinale, Nicotiana tabacum, Oryza sativa, Passiflora edulis, Solanum betaceum, Solanum tuberosum, and Solanum lycopersicum, are included. Several important gene families, including the cytochrome P450 superfamily, the R2R3-MYB subfamily, the MYB superfamily in Brassica, and the NAC in Passiflora edulis, are extensively analyzed, and the function and potential application of several genes, such as BnCYP704B1a and BnCYP704B1b for genic male sterility, PeNAC-19 for low temperature, and the SUPERMAN-like gene for fruit size control, is determined. A set of new methods is presented for gene transformation and plant propagation. Efficient markers for CMS breeding in oilseed rape and novel reference genes in rice are also reported. In add

The main aim of this reprint was to collect research focusing on data processing using machine learning and deep learning. We invited investigators to contribute both original and review articles, covering the research and development in the areas of data processing using machine learning (ML) and deep learning (DL). These areas include solutions that are designed for smart devices. In this reprint, leading experts in the field share their insights, research findings, and visions for the future. Together, we embark on a journey to unlock the potential of effective data processing that involves transforming data from a given format into a more usable and desirable form, rendering them more meaningful and informative. Machine learning (ML), deep learning (DL), and artificial intelligence (AI) have proven to be effective methods for this purpose. Through the utilization of machine learning algorithms, mathematical modeling, or various statistical techniques, the entire process can be au

The Special Issue on Advances in Modeling and Management of Urban Water Networks (UWNs) explores four important topics of research in the context of UWNs: asset management, modeling of demand and hydraulics, energy recovery, and pipe burst identification and leakage reduction. In the first topic, the multi-objective optimization of interventions on the network is presented to find trade-off solutions between costs and efficiency. In the second topic, methodologies are presented to simulate and predict demand and to simulate network behavior in emergency scenarios. In the third topic, a methodology is presented for the multi-objective optimization of pump-as-turbine (PAT) installation sites in transmission mains. In the fourth topic, methodologies for pipe burst identification and leakage reduction are presented. As for the urban drainage systems (UDSs), the two explored topics are asset management, with a system upgrade to reduce flooding, and modeling of flow and water quality, with

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 combin

Plants face constant challenges from biotic and abiotic stresses, which trigger complex defense mechanisms. This Reprint focuses on research related to biotic and abiotic stresses. Under biotic stress, plants employ pattern-triggered immunity and effector-triggered immunity to recognize and counteract invaders. Key responses include the production of reactive oxygen species, phytohormone signaling, and the synthesis of antimicrobial compounds. Under abiotic stress, the following are involved: ROS mitigation by antioxidants; protein and membrane stabilization by heat shock proteins (HSPs) and cold-responsive genes (COR), and osmoprotectants; and the upregulation of aquaporins. Hormonal pathways, particularly abscisic acid (ABA), play central roles in coordinating stress responses and regulating stomatal closure and gene expression. Understanding these mechanisms is critical for developing stress-resistant crops through genetic engineering or breeding, ensuring food security under changing environmental pressures.

The survival of plants under stressful environments requires the study of the mechanisms that help in reversing the adverse impacts of reactive oxygen species (ROS) excessively generated under these conditions. Understanding the mechanisms helps in adopting strategies to induce such mechanisms for the better acclimation of plants. These ROS, if not scavenged, cause damage of cellular components, including lipids, proteins, nucleic acids, and metabolites, and, in extreme cases, even the death of cells in plants. Confronted with abiotic stress, there is an initial elevation in ROS that activates redox signaling to initiate defense in plants through the activation of antioxidant activity. The improvement in the capacity of antioxidant machinery is one of the essential strategies with which to develop tolerance and relieve the pressure of abiotic-stress-induced oxidative changes for the survival of plants. This reprint provides knowledge on the following aspects: Impact of abiotic stress

This Special Issue is a collection of research articles in which computational methods were utilized for the development of new drugs, or the repurposing of an old drug for the treatment of new diseases. Moreover, these approaches helped to predict the metabolic fate of a drug candidate and to highlight the potential toxicity of the drug candidate, reducing the number of compounds to be tested. Virtual screening and machine learning tools were revealed to be of great interest in the field of pharmaceutical sciences, pharmacology, chemical biology, and bioinformatics.

The present Reprint contains all 13 articles accepted and published in the Special Issue “Data Mining and Machine Learning in the Era of Big Knowledge and Large Models, 2026” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory, models, algorithms and applications related to advancing data mining and machine learning in the current age. The topics of these articles focus on Transformer-based named entity recognition, knowledge recommendation, time series analysis, model integration, data quality improvement, hallucinations, and innovative application development. We hope that the Reprint will be both interesting and useful for those working in the area of data mining and machine learning, as well as for those with the proper mathematical background and a willingness to become familiar with recent advances in this field.

This Reprint compiles selected peer-reviewed contributions from the Special Issue “Advanced Nanomaterials-Based (Bio-)Sensors for Electrochemical Detection and Analysis”, presenting recent advances in nanomaterials applied to chemical and biological sensing platforms. While electrochemical sensing represents a central theme, this Reprint also encompasses complementary sensing strategies, including colorimetric, plasmonic, and gas-sensing approaches, reflecting the multidisciplinary nature of modern sensor research. The collected articles explore the synthesis, functionalization, and integration of advanced nanomaterials such as nanoparticles, two-dimensional materials, polymers, metal oxides, and hybrid heterostructures into sensing interfaces. Emphasis is placed on structure–property–performance relationships, signal transduction mechanisms, and analytical applicability across environmental monitoring, food analysis, biomedical diagnostics, and industrial safety. Overall, this Reprint provides a comprehensive overview of how advanced nanomaterials enable diverse sensing modalities, highlighting converging trends that bridge electrochemical and non-electrochemical detection technologies.

We are experiencing the fourth industrial revolution, a time of significant change and innovation. Independent studies have their limitations, and multidisciplinary convergence that transcends the boundaries of academic disciplines is indispensable. This Special Issue on “Biotechnology and Sports Engineering” aims to share the latest research trends and explore future directions for development. In particular, biotechnology and sports engineering are intimately connected; together, they may significantly improve the quality of human life. Knowledge building is not just a matter of linearly adding to previous information, but making more synergistic and complementary choices. Knowledge must be exchanged continuously between disciplines to advance of human health and wellbeing. I would like to thank all who have contributed to this Special Issue, which would not have been possible without the talent and dedication of many authors, reviewers, and the editorial team of Applied Sciences

Climate change and adverse soil conditions impose substantial abiotic stress on crops, presenting a major challenge to agricultural productivity and global food security. Advances in molecular genetics and genomics have enabled extensive genetic analysis and crop improvement initiatives aimed at enhancing abiotic stress tolerance, leading to significant achievements. This reprint comprises 13 articles focusing on abiotic stress in crops, including 10 research studies that investigate the response mechanisms of wheat, rice, maize, cotton, common bean, and Kentucky Bluegrass to various stresses (such as drought, heat, frost, salinity, and cadmium contamination), along with three review articles that synthesize research and technological advances in improving abiotic stress tolerance. These contributions provide key insights and a molecular foundation for molecular breeding and improvement of crops with enhanced abiotic stress tolerance.

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome. Related Books Advanced Sensing and Image Processing Techniques for Healthcare Applications Engineering Imaging: Sensors and Technologies Engineering Optical Sensors Tec

Advances in Earth observation and high-performance computing are revolutionizing how we monitor land cover and support sustainable development. This volume explores cutting-edge methods in land cover classification, highlighting deep learning applications such as semantic segmentation, object detection, and temporal analysis. Key contributions include the ABNet model for enhanced feature representation, accuracy assessments of 30-meter land cover products, CNN-based wildfire mapping, and the segmentation of China’s coastal wetlands. These studies showcase AI's growing role in environmental monitoring and promote innovative and interdisciplinary solutions for managing landscape changes.

At present, data-intensive applications are one of the most prominent lines of work in data science. Most of these projects occur in the context of sensor data analytics, in which different devices, equipment, and software applications provide dynamic datasets to be processed and analyzed with machine learning algorithms. Application domains span multiple areas, including smart cities and intelligent transport, economy and finance, energy management, biomedical applications, geographical systems, agriculture, and livestock or cybersecurity. This volume is centered on emerging challenges, methods, algorithms, and tools that address sensor data analytics, with a special emphasis on approaches that leverage information theory to accomplish projected goals. It includes practical applications following interdisciplinary approaches using real-world data. Articles are focused on new methods applied to a wide range of applications, from electricity markets to healthcare, among others. Pract

Over the past few decades, global warming and climate change have impacted the hydrologic cycle. Many models have been developed to simulate hydrologic processes. Obtaining accurate climatic data on local/meso, and global scales is essential for the realistic simulation of hydrologic processes. However, the limited availability of climatic data often poses a challenge to hydrologic modeling efforts. Hydrologic science is currently undergoing a revolution in which the field is being transformed by the multitude of newly available data streams. Historically, hydrologic models that have been developed to answer basic questions about the rainfall–runoff relationship, surface water, and groundwater storage/fluxes, land–atmosphere interactions, have been optimized for previously data-limited conditions. With the advent of remote sensing technologies and increased computational resources, the environment for water cycle researchers has fundamentally changed to one where there is now a flood

The book is the reprint of the Special Issue 'Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems' published in the journal Applied Science (MDPI). It contains 17 articles, including 1 Editorial, 14 Research Papers, and 2 Reviews. Related Books Smart Sustainable Manufacturing Systems Environmental & Earth Sciences ... Smart Manufacturing Engineering Advanced Digital Design and Intelligent Manufacturing Engineering Recent Advances in Smart Design and Manufacturing Technology Engineering Innovations in Manufacturing Processes and Systems for Sustainable Practices Chemistry & Materials Science ... The recommendations have been generated using an AI system. About: About MDPI Books Advisory Board Legal Information: MDPI Privacy Policy Terms and Conditions Terms of Use Accessibility Sales: Order and Delivery Indexing and Distribution Further Information: Book Series Reprints Book/Chapter Processing Charges Pay an Invoice Open Acces

Life in biological systems is maintained by the cooperative actions of various biomolecules. With the development of chemical and biological technologies related to nucleic acids, the details of the mechanisms of such cooperative actions between nucleic acids and other biomolecules have been elucidated and further applied in various applications.In the papers published in this Special Issue, advanced research works involved in nucleic acid conjugates are reported in wide application fields, such as artificial gene regulation, biomolecular sensing, and therapeutics from leading scientists in nucleic acids chemistry and engineering. Related Books Nucleic Acid Architectures for Therapeutics, Diagnostics, Devices and Materials Biology & Life Sciences Biophysics of Nucleic Acids Celebrating the 75th Birthday of Professor Kenneth J. Breslauer Biology & Life Sciences Impacts of Molecular Structure on Nucleic Acid-Protein Interactions Biology & Life Sciences Oligonucleotide, Therapy, and Ap

“Computational Mathematics, Algorithms, and Data Processing” of MDPI consists of articles on new mathematical tools and numerical methods for computational problems. Topics covered include: numerical stability, interpolation, approximation, complexity, numerical linear algebra, differential equations (ordinary, partial), optimization, integral equations, systems of nonlinear equations, compression or distillation, and active learning. Related Books Applied and Computational Mathematics for Digital Environments Computer Science & Mathematics Applied Mathematics and Computational Physics Computer Science & Mathematics Computational Mechanics and Applied Mathematics Computer Science & Mathematics Advances in Computational Mathematics and Applied Mathematics Computer Science & Mathematics Computational and Mathematical Methods in Information Science and Engineering Computer Science & Mathematics The recommendations have been generated using an AI system. About: About MDPI Books Advisor

The aim of this reprint is to provide an overview of the current challenges within the cyber security community today, as recognized by our contributors. This reprint provides 16 papers from the Topical Collection on “Machine Learning and Data Analytics for Cyber Security” that cover topics of large language models for cybersecurity claim classification, adversarial machine learning attacks against intrusion detection systems, the detection of PLC process control anomalies, identifying session-replay bots compared to human users, and mitigating against side-channel attacks.