(Mass spectrometric) non-target screening is a preferably comprehensive and untargeted (predominantly organic molecules detecting) approach combining (robust) analytical measurements with adapted data evaluation concepts, systematic compound identification workflows, and statistical data interpretation. It is well suitable for the identification of new, unexpected and/or unknown organic compounds as well as monitoring ‘molecular fingerprints’ and profiling ‘process-relevant’ molecules via statistical methods. In recent years, 14 articles in various disciplines were published and presented in this Special Issue, whereby it contains 4 peer-reviewed review articles and 10 peer-reviewed research articles dealing with non-target screening strategies and solutions.

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The journal Axioms has achieved a remarkable increase in the number of papers published, as well as a significant growth in citations over the past decade. In order to mark this significant milestone, I am glad to lead this Special Issue entitled “10th Anniversary of Axioms: Mathematical Analysis".

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The year 2021 marks the 10th anniversary of Journal of Clinical Medicine, and as one of the major sections of JCM, we are launching a 10th anniversary Special Issue in the section “Gastroenterology & Hepatopancreatobiliary Medicine”. We accepted papers for the Special Issue, titled "Recent diagnostic and therapeutic advance in Gastroenterology & Hepatopancreatobiliary Medicine". This medical field is a complex and wide-ranging field that deals with diseases in multiple organs such as the gastrointestinal tract and hepatobiliary pancreas. With recent advances in diagnostic imaging and interventional treatment, those in endoscopic diagnosis and treatment, those in functional test and those in genetic diagnosis and drug therapy, including the molecular-targeted therapy, many new medical findings have been accumulated. In this Special Issue, we looked for reports that make full use of advances in diagnostics and therapeutics in the field of Gastroenterology and Hepatopancreatobiliary Medicine.

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This Special Issue contains selected papers from works presented at the 11th EASN (European Aeronautics Science Network) International Conference on Innovation in Aviation & Space, which was held from the 1st until the 3rd of September 2021. About 420 remote participants contributed to a high-level scientific gathering providing some of the latest research results on the topic, as well as some of the latest relevant technological advancements. Nineteen interesting papers, which cover a wide range of topics including characterization, analysis and design, as well as numerical simulation, are contained in this Special Issue.

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This Special Issue contains selected papers from works presented at the 12th EASN (European Aeronautics Science Network) International Conference on Innovation in Aviation & Space for Opening New Horizons, which was held from the 18th until the 21st of October 2022, in Barcelona, with physical participation. About 470 participants contributed to a high-level scientific gathering providing some of the latest research results on the topic, as well as some of the latest relevant technological advancements. Twenty interesting papers, which cover a wide range of topics including characterization, analysis and design, as well as numerical simulation, are contained in this Special Issue.

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The atrial substrate undergoes electrical and structural remodeling during atrial fibrillation. Detailed multiscale models were used to study the effect of structural remodeling induced at the cellular and tissue levels. Simulated electrograms were used to train a machine-learning algorithm to characterize the substrate. Also, wave propagation direction was tracked from unannotated electrograms. In conclusion, in silico experiments provide insight into electrograms' information of the substrate.

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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

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This Special Issue sheds light on the remarkable resilience of plants as they confront a wide array of abiotic stresses, such as drought, extreme temperatures, high salinity, waterlogging, and nutrient imbalances, among other challenges. By delving into the molecular intricacies, this collection offers a comprehensive journey from nucleic acids to proteins, from cellular responses to tissue-level adaptations, and from individual plants to the broader ecosystem. Through this exploration, we can unveil fascinating, yet unexplored, natural laws and interplays. The research presented in this Special Issue not only enhances our understanding of how plants respond to environmental changes but also offers valuable insights into mitigating the impacts of climate change on agriculture and ecosystems. The collection encompasses a diverse range of research articles and comprehensive reviews, providing a deep dive into the molecular essence of plant responses to various environmental cues. Finally, the special issue serves as an invaluable resource for researchers, scientists, and practitioners seeking to expand their knowledge of plant resilience and adaptability in the face of current environmental challenges.

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A new approach for defining causality and such related notions as degree of responsibility, degrees of blame, and causal explanation. Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C “actually caused” event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. The philosophy literature has been struggling with the problem of defining causality since Hume. In this book, Joseph Halpern explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression. Halpern applies and expands an approach to causality that he and Judea Pearl developed, based on structural equations. He carefully formulates a definition of causality, and building on this, defines degree of responsibility, degree of blame, and causal explanation. He concludes by discussing how these ideas can be applied to such practical problems as accountability and program verification. Technical details are generally confined to the final section of each chapter and can be skipped by non-mathematical readers.

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Additive manufacturing (AM), as an advanced manufacturing technology, has overturned the traditional concept of subtractive manufacturing. It has revolutionized advanced integrated structural design, high-performance material preparation, and the manufacturing of complex components. AM technology is changing the way products are developed, produced, and commercialized, leading to disruptive changes in the economy and society. AM technology has received extensive attention and research from the research community since its inception, and it is leading manufacturing technology and continues to be used with great effectiveness in the aerospace industry, automotive industry, medical plant applications, and many other fields. However, the development of additive manufacturing still faces demanding technical challenges; for example, due to insufficient process planning and inadequate process control, many defects are often observed in the products of AM processes, reducing production efficiency and deteriorating product quality. To promote AM technology toward high efficiency, high precision, high performance, and low cost in a green and intelligent direction, many advanced design and manufacturing technologies are in urgent need of further breakthroughs, such as numerical and analytical models for structural design, experimental methods, performance prediction, and process optimization.

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The introduction of metal AM processes in such industrial sectors as the aerospace, automotive, defense, jewelry, medical and tool-making fields, has led to a significant reduction in waste material and in the lead times of the components, innovative designs with higher strength, lower weight, and fewer potential failure points from joining features. This Special Issue on “Additive Manufacturing (AM) of Metallic Alloys” contains a mixture of review articles and original contributions on some problems that limit the wider uptake and exploitation of metals in AM.

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This Special Issue book covers a wide scope in the research field of 3D-printing, including: the use of 3D printing in system design; AM with binding jetting; powder manufacturing technologies in 3D printing; fatigue performance of additively manufactured metals, such as the Ti-6Al-4V alloy; 3D-printing methods with metallic powder and a laser-based 3D printer; 3D-printed custom-made implants; laser-directed energy deposition (LDED) process of TiC-TMC coatings; Wire Arc Additive Manufacturing; cranial implant fabrication without supports in electron beam melting (EBM) additive manufacturing; the influence of material properties and characteristics in laser powder bed fusion; Design For Additive Manufacturing (DFAM); porosity evaluation of additively manufactured parts; fabrication of coatings by laser additive manufacturing; laser powder bed fusion additive manufacturing; plasma metal deposition (PMD); as-metal-arc (GMA) additive manufacturing process; and spreading process maps for powder-bed additive manufacturing derived from physics model-based machine learning.

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The presence of pollutants (i.e., emerging contaminants, radionuclides, potential toxic metals, dyes, etc.) in water has a negative impact on the environment and presents a potential health risk for inhabitants. Among the existing technologies (i.e., advanced oxidation process, membrane filtration, biodegradation, etc.) that are used to remove them from water, adsorption has garnered significant interest due to its low cost and fast removal. The development of advanced materials and their application for water treatments have recently caught the attention of researchers.This reprint aimed to establish updated information on adsorption technology for water and wastewater treatments. The submissions selected for this SI focus on (1) applying reality technology for water and wastewater treatments and (2) exploring detailed adsorption mechanisms using current data (i.e., comparing changes in the properties of adsorbents before and after adsorption using appropriate techniques).

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The book provides a comprehensive overview of nanostructures and methods used to design biosensors, as well as applications for these biosensor nanotechnologies in the biological, chemical, and environmental monitoring fields. Biological sensing has proven to be an essential tool for understanding living systems, but it also has practical applications in medicine, drug discovery, food safety, environmental monitoring, defense, personal security, etc. In healthcare, advancements in telecommunications, expert systems, and distributed diagnostics are challenging current delivery models, while robust industrial sensors enable new approaches to research and development. Experts from around the world have written five articles on topics including:Diagnosing and treating intraocular cancers such as retinoblastoma; Nanomedicine in cancer management; Engineered nanomaterials in osteosarcoma diagnosis and treatment; Practical design of nanoscale devices; Detect alkaline phosphatase quantitatively in clinical diagnosis; Progress in the area of non-enzymatic sensing of dual/multi biomolecules; Developments in non-enzymatic glucose and H2O2 (NEGH) sensing; Multi-functionalized nanocarrier therapies for targeting retinoblastoma; Galactose functionalized nanocarriers; Sensing performance, electro-catalytic mechanism, and morphology and design of electrode materials; Biosensors along with their applications and the benefits of machine learning; Innovative approaches to improve the NEGH sensitivity, selectivity, and stability in real-time applications; Challenges and solutions in the field of biosensors.

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Computers and machines were developed to reduce time consumption and manual human efforts to complete projects efficiently. With fast-growing technologies in the field, we have finally reached a stage where almost everyone in the world has access to these high technologies. However, this is just a starting phase because future development is taking a more advanced route in the shape of artificial intelligence (AI). Although AI is under the computer science umbrella, nowadays there is no field unaffected by this high technology. The overall aim of using intelligence learning methods is to train machines to think intelligently and make decisions in different situations the same as humans. Previously, machines were doing what they were programmed to do, but now with AI, devices can think and behave like a human being. This book aims to present the application of advanced analytics and AI in different industries as practical tools to develop prediction, optimization, and make decision models.

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The field of Artificial Intelligence (AI) has undergone enormous expansion since its inception in the mid-20th century, as demonstrated by its application across an array of engineering and scientific challenges. Particularly in the last decade, AI has witnessed a significant breakthrough with the advent of deep learning, which has facilitated the employment of various AI models across a multitude of domains. This reprint features ten papers accepted for publication in the Special Issue titled "Advanced Artificial Intelligence Models and Their Applications," published in the MDPI Mathematics journal. These papers explore numerous facets of advanced artificial intelligence models and their applications, covering areas such as cybersecurity, image classification, logistics optimization, automatic music generation, human capital investment, writer recognition, remote sensing image indexing, target tracking, and more. These diverse subjects highlight the extensive scope and capability of AI models in tackling intricate challenges across distinct fields, underlining the vast potential inherent in this cutting-edge technology.

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Global climate changes, particularly extreme events, affect terrestrial carbon, water, and energy exchanges between the atmosphere, biosphere, and lithosphere, thus controlling freshwater availability, floods, and droughts. Therefore, it is urgent and necessary to develop advanced climate simulation and observation approaches and models related to extreme climate events. Advanced climate simulation and observation can improve the accurate prediction of climate change and long-term trends, which can mitigate climate events' impacts on human society. Under these conditions, this reprint aims to introduce advanced climate simulation and observation approaches to various practical studies related to climate variations, including the global climate models (GCMs) and regional climate models (RCMs), mitigation studies of high-impact climate events, predictions of climate variations, and some new artificial intelligence. Twenty-two papers have been collected in this reprint, with eight original research articles reporting on climate change and six papers reporting on climate change's impact on society and the economy. Meanwhile, three papers reported climate change's impact on agriculture, and climate change's impact on human health was studied in five articles.

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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 –

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[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.]

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This Special Issue includes 14 contributions, with 2 review contributions and 12 research contributions. The review contributions provide a survey with an overview of the state of the art in detecting and projecting cyber-attack scenarios, and the review of a specific application area, the safety of autonomous haulage systems in the mining environment related to both cybersecurity and communication. 10 research contributions are addressing the area of advanced services for intrusion detection systems: (a) the use of different machine learning models depending on the specific scenarios and datasets; (b) the use of deep learning techniques for the detection of zero-day attacks; (c) a proposal of an integrated scalable framework aimed at efficiently detecting anomalous events on large amounts of unlabeled data logs; (d) a spatiotemporal characterization of cyber-attacks for detecting such attacks; (e) a two-stage intrusion detection system for industrial control networks; (f) a chatbot for detecting online sex offenders, based on an artificial conversational entity (ACE); and (g) an open-source platform for manipulating both streaming and archived network flow data in real time. This Special Issue also contains two protection-related research contributions, including: (a) a countermeasure for on–off web defacement attacks and (b) the evaluation of multi-path routing as a protection feature against network attacks and failures.

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The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.

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Offers a refreshing, contemporary and European perspective on IT auditing Explores and presents best practices from around the world Covers a blend of academic and non-academic perspectives in the areas of strategic information systems management This book is open access, which means that you have free and unlimited access

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This Special Issue reviews advanced digital technologies in modelling and control of technological processes. The methods of numerical modelling, artificial intelligence, machine learning, automatic control, and optimization theory are considered in the published manuscripts of this Special Issue.

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The rapid proliferation of the Internet has been driving communication networks closer and closer to their limits, while available bandwidth is disappearing due to an ever-increasing network load. Over the past decade, optical fiber communication technology has increased per fiber data rate from 10 Tb/s to exceeding 10 Pb/s. The major explosion came after the maturity of coherent detection and advanced digital signal processing (DSP). DSP has played a critical role in accommodating channel impairments mitigation, enabling advanced modulation formats for spectral efficiency transmission and realizing flexible bandwidth. This book aims to explore novel, advanced DSP techniques to enable multi-Tb/s/channel optical transmission to address pressing bandwidth and power-efficiency demands. It provides state-of-the-art advances and future perspectives of DSP as well.

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In collaboration with the MDPI publishing house, we are pleased to introduce the reader to our new project, a Special Issue entitled “Advanced Eco-friendly Wood-Based Composites”. This Special Issue provides an opportunity to investigate advanced eco-friendly wood-based composites from a broader perspective. The coronavirus pandemic and shutdown measures employed to contain it, as well as the ongoing war in Ukraine, have influenced and decelerated the world economy and adversely impacted research activities on most levels in all countries. Surprisingly, researchers in the field of wood-based composites have continued to make progress, which is also described in this Special Issue.

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Advanced intelligent control is a rapidly developing, complex and challenging field with significant practical importance and potential applications. The authors aim to stimulate advancements in science and technology by addressing this field and presenting new trends in the design, control and applications of real-time intelligent sensor system control using advanced intelligent control methods and techniques. The purpose of the Special Issue is to promote in-depth research and communication regarding these topics. The authors focus on innovative multi-sensor fusion techniques integrated into robots, which are combined with computer vision, virtual and augmented reality (VR&AR) and intelligent communication including remote control, adaptive sensor networks and human–robot (H2R) interaction systems. Special attention is given to advancements in sensors, actuators, computation technology and communication networks that provide the necessary tools for implementing intelligent control hardware. These advancements are targeted toward various scientific research fields, including machine learning (such as deep learning), bio-inspired algorithms, recurrent neural networks, neuro-fuzzy control and artificial intelligence in general. The Special Issue includes original research papers that report on the recent advancements in intelligent control using intelligent sensors. It serves as a further extension of the previously successful Special Issue, “Advanced Intelligent Control through Versatile Intelligent Portable Platforms”.

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This is an open access book. This comprehensive and timely methodological book introduces several novel topics under the overarching sections of advanced learning analytics (LA), artificial intelligence (AI), precision education, and complex systems. These topics are presented using accessible language, beginning with introductory chapters that cover the fundamentals of each section, followed by step-by-step tutorials featuring code and datasets for various methods within each area. Although the title refers to “advanced LA,” the book is written for the broader educational research community and is of interest to quantitative researchers from diverse backgrounds. The first section focuses on Explainable AI and machine learning (ML), with an introduction to the methods, their applications, and tutorials. The second section outlines the foundational concepts of LLMs, their potential applications, and related methodologies, with a tutorial on using LLMs in various analytical tasks. The third section focuses on complex systems, which have become integral to many disciplines and have enabled breakthroughs in modeling intractable problems. Here, three chapters cover Transition Network Analysis (TNA), which fills a critical gap in modeling the temporal unfolding of learning processes over time from a complex systems perspective. The final section addresses precision education, with a particular emphasis on person-centered and person-specific (idiographic) methodologies.

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