Advanced Topology Optimization
This Reprint presents a curated collection of peer reviewed articles from the Special Issue Advanced Topology Optimization: Methods and Applications published in the journal Computation. It provides an up-to-date overview of recent progress in topology optimization, a computational design methodology that determines optimal material distributions for high performance engineering structures and systems.The contributions in this Reprint reflect the growing maturity of the field and its transition from theoretical development toward practical engineering implementation. The included studies address key topics such as manufacturing aware design, CAD ready structural representation, computational efficiency, and the treatment of multi physics and dynamic problems. Together, they demonstrate how modern topology optimization frameworks are being adapted to meet realistic engineering constraints and industrial design requirements. Covering both methodological advances and application-oriented research, this Reprint highlights current trends that bridge optimization theory with manufacturable and deployable solutions. It serves as a valuable reference for researchers, graduate students, and practicing engineers working in structural optimization, computational mechanics, and advanced engineering design.
Zero Trust Architecture Guidance
Enterprise security didn't fail because attackers became unstoppable - it failed because organizations kept trusting systems, identities, and networks that no longer deserved it. As your manuscript states, "The perimeter held the entire time. The castle wall never fell. The attacker walked through the front gate." That single insight captures why Zero Trust is no longer optional: the old model of "inside equals safe" has collapsed.Zero Trust Architecture Guidance is the definitive playbook for leaders who must rebuild security from the inside out. Written for CISOs, IT directors, architects, and modernization leaders, this book explains Zero Trust not as a product, but as an organizational transformation that reshapes identity, access, data protection, network design, governance, and culture.Readers will learn why perimeter-based security failed under cloud adoption, remote work, SaaS sprawl, and increasingly sophisticated adversaries. Through vivid scenarios, the book shows how credential harvesting, lateral movement, supply-chain infiltration, and cloud misconfigurations bypass traditional defenses. It reframes Zero Trust as a practical, enforceable architecture built on continuous verification, least privilege, microsegmentation, and the assumption that any part of the environment may already be compromised.This guide equips leaders to: Replace implicit trust with identity-driven, context-aware accessRedesign networks using microsegmentation and east-west traffic controlsImplement data classification, encryption, and access governance that actually workBuild policy engines, control planes, and enforcement points that scaleGovern cloud and hybrid environments with shared-responsibility clarityPrepare for modern threats with telemetry, behavioral analytics, and anomaly detectionStructure security teams around Zero Trust roles and responsibilitiesLead the cultural and organizational shift required for long-term successBuild a continuous, never-finished Zero Trust program that evolves with the threat landscapeThe book includes maturity models, checklists, sequencing guidance, rollout strategies, and real-world architectural patterns for federal, healthcare, and enterprise environments. It shows leaders how to move from whiteboard concepts to production-ready controls - and how to sustain the program long after the initial excitement fades.Zero Trust is not a tool you install; it is the architecture you choose to trust. As the book emphasizes, "Zero trust is an architectural commitment to verifying every interaction, limiting every privilege, and assuming that any segment of your environment may already be compromised."If you are responsible for protecting an enterprise in a world where the perimeter no longer exists, this book gives you the clarity, structure, and roadmap to build a security program that can withstand modern threats - and prove it.
Managing Post-Quantum Cryptography
Every encrypted transaction your organization relies on today - every financial transfer, patient record, classified message, and identity verification - rests on mathematical assumptions that a sufficiently powerful quantum computer will break. As your manuscript warns, "Every encrypted message your organization sent today rests on a mathematical problem that a sufficiently powerful quantum computer will solve." The threat is no longer theoretical. The algorithms are published. The standards are finalized. Adversaries are already harvesting encrypted traffic to decrypt later.Managing Post-Quantum Cryptography is the essential guide for leaders who must secure their enterprise before the machines catch up. Written for CISOs, IT executives, architects, and modernization leaders, this book explains the quantum threat in clear, accessible terms - without requiring a background in physics or number theory - and provides a complete roadmap for transitioning to quantum-safe cryptography at enterprise scale.Readers will learn why classical encryption has an expiration date, how quantum algorithms break RSA and ECC, and why symmetric encryption survives but must be re-evaluated. The book demystifies the new post-quantum algorithm families, decodes the NIST selections, and explains what they mean for certificates, key exchange, authentication, and long-horizon data protection.This guide equips leaders to: Identify which systems, protocols, and data categories are most at riskConduct a full cryptographic census across keys, certificates, algorithms, and dependenciesImplement hybrid cryptography to bridge classical and post-quantum systemsBuild crypto-agility into enterprise architecture to avoid future forced migrationsPrioritize migration using risk-tiered sequencing and dependency mappingGovern the transition across budget cycles, leadership changes, and vendor ecosystemsProtect long-horizon data from "harvest now, decrypt later" adversariesBuild a workforce and governance model capable of sustaining quantum resilienceThe book includes detailed playbooks, maturity models, migration templates, and decision frameworks that help leaders move from awareness to execution. It shows how to design hybrid deployments, evaluate vendor readiness, integrate new standards, and build monitoring systems that keep the enterprise secure long after the initial migration.Quantum computing will not wait for your organization to be ready. As the book emphasizes, "The window for acting ahead of this threat is open now, but it is not indefinite." This guide gives you the clarity, structure, and operational strategy to secure your enterprise before the machines catch up - and before your encrypted data becomes readable to anyone who has already captured it.If you are responsible for protecting sensitive data in a world racing toward quantum capability, this is the roadmap you cannot afford to postpone.
Managing Post-Quantum Cryptography
Every encrypted transaction your organization relies on today - every financial transfer, patient record, classified message, and identity verification - rests on mathematical assumptions that a sufficiently powerful quantum computer will break. As your manuscript warns, "Every encrypted message your organization sent today rests on a mathematical problem that a sufficiently powerful quantum computer will solve." The threat is no longer theoretical. The algorithms are published. The standards are finalized. Adversaries are already harvesting encrypted traffic to decrypt later.Managing Post-Quantum Cryptography is the essential guide for leaders who must secure their enterprise before the machines catch up. Written for CISOs, IT executives, architects, and modernization leaders, this book explains the quantum threat in clear, accessible terms - without requiring a background in physics or number theory - and provides a complete roadmap for transitioning to quantum-safe cryptography at enterprise scale.Readers will learn why classical encryption has an expiration date, how quantum algorithms break RSA and ECC, and why symmetric encryption survives but must be re-evaluated. The book demystifies the new post-quantum algorithm families, decodes the NIST selections, and explains what they mean for certificates, key exchange, authentication, and long-horizon data protection.This guide equips leaders to: Identify which systems, protocols, and data categories are most at riskConduct a full cryptographic census across keys, certificates, algorithms, and dependenciesImplement hybrid cryptography to bridge classical and post-quantum systemsBuild crypto-agility into enterprise architecture to avoid future forced migrationsPrioritize migration using risk-tiered sequencing and dependency mappingGovern the transition across budget cycles, leadership changes, and vendor ecosystemsProtect long-horizon data from "harvest now, decrypt later" adversariesBuild a workforce and governance model capable of sustaining quantum resilienceThe book includes detailed playbooks, maturity models, migration templates, and decision frameworks that help leaders move from awareness to execution. It shows how to design hybrid deployments, evaluate vendor readiness, integrate new standards, and build monitoring systems that keep the enterprise secure long after the initial migration.Quantum computing will not wait for your organization to be ready. As the book emphasizes, "The window for acting ahead of this threat is open now, but it is not indefinite." This guide gives you the clarity, structure, and operational strategy to secure your enterprise before the machines catch up - and before your encrypted data becomes readable to anyone who has already captured it.If you are responsible for protecting sensitive data in a world racing toward quantum capability, this is the roadmap you cannot afford to postpone.
The Symbiotic Relationship Between FHIR and AI
Most organizations didn't decide to become "AI organizations." It crept up on them. This book is the practical, strategic playbook for leaders who must turn that accidental reality into a deliberate advantage. The Symbiotic Relationship Between FHIR and AI explains why artificial intelligence in healthcare succeeds only when paired with the right data architecture, governance, and human oversight-and it shows exactly how to build those foundations so AI delivers measurable clinical, operational, and financial value.This is not a technical manual for data scientists, nor a marketing brochure for vendors. It is a manager's guide: clear, evidence-based, and relentlessly practical. The book opens by diagnosing the real problem: healthcare produces enormous volumes of data, but that data is fragmented, inconsistently coded, and often inaccessible when decisions must be made. It then makes a simple but powerful claim: FHIR (Fast Healthcare Interoperability Resources) is the missing link that converts scattered clinical records into structured, machine-readable intelligence, and Human-in-the-Loop (HITL) governance is the mechanism that keeps AI aligned with clinical judgment. As the book states, HITL ensures that humans "actively review, validate, override, and improve" AI decisions.Readers will find a concise primer on FHIR's core concepts-Resources, terminology bindings (LOINC, SNOMED CT, RxNorm), and RESTful APIs-paired with actionable guidance on how to prioritize and implement FHIR endpoints across an enterprise. The book explains why legacy standards and document-centric approaches fail modern AI pipelines, and it provides a step-by-step framework for reducing the 3:1 data-preparation burden that typically consumes AI projects. Practical checklists help leaders inventory data assets, map clinical concepts across systems, and set realistic timelines for interoperability work.Security, trust, and governance receive equal attention. The book demystifies OAuth and OpenID Connect in plain language, shows how SMART on FHIR enables an app ecosystem, and lays out governance patterns that scale oversight without creating bottlenecks. Real-world case studies-sepsis prediction, NLP for social determinants, patient matching, and administrative automation-illustrate common failure modes and the concrete fixes that prevent them. Each case ties back to the central thesis: better data and better governance produce safer, more equitable AI.This guide also addresses the human and organizational dimensions of transformation. It explains how to design HITL workflows that protect safety while preserving speed, how to communicate AI's limits to clinicians and patients, and how to build the cultural muscle for interoperability as a strategic capability rather than a compliance checkbox. Leaders will find templates for measuring ROI, prioritizing investments, and aligning technical work with clinical outcomes.If you are responsible for technology strategy, clinical operations, or enterprise data, this book gives you the vocabulary, the frameworks, and the operational playbook to lead intelligent interoperability. It moves beyond hype to the hard work that makes AI reliable in production: data quality, real-time access, standards adoption, and governance. Read it to understand not just what AI can do, but what your organization must become to make AI safe, trustworthy, and transformative.
Artificial Intelligence Remote Sensing for Earth Observation
Advancements in Optical Measurement Devices and Technologies
Fractional Differential Equations, Inclusions and Inequalities with Applications II
The following Reprint contains a total of 19 articles accepted and published in the Special Issue "Fractional Differential Equations, Inclusions and Inequalities with Applications II" of the MDPI journal Mathematics and covers various aspects of recent developments in the theory and applications of fractional differential equations, inclusions, inequalities, and systems of fractional differential equations and inclusions. Topics include, but are not limited to, the following themes: Oscillation for fractional-order differential equations, (p, q)-difference equations, q-difference inclusions, Integral inequalities, Fractional integro-differential equations, Sequential fractional differential equations, Sequential fractional differential inclusions, Numerical fractional differential equations, Λ-fractional differential equations, Positive solutions for fractional boundary value problems, Stochastic fractional differential equations, Fractional-order systems, Caputo fractional differential equations, Delay difference equations, and Black-Scholes fractional equations. In total, 80 manuscripts were submitted, and 19 papers by 68 authors from 21 countries were successfully published. It is hoped that the Reprint together with the ideas and publications therein will be of interest to readers and will inspire new studies on fractional differential equations, inclusions and inequalities.
Deep Learning in Video and Image Processing
The Reprint titled "Deep Learning in Video and Image Processing Challenges Solutions and Future Directions" presents a comprehensive collection of high-quality research addressing the growing demand for intelligent visual systems operating in real time. This Reprint focuses on the practical deployment of deep learning and machine learning techniques for video and image processing on edge devices with limited computational resources. It highlights recent advances that enable efficient model execution under strict constraints related to power consumption memory capacity and processing speed. The Reprint covers a wide range of topics, including lightweight neural network architectures, algorithm optimization, hardware-software co design, and energy-efficient learning models. Emphasis is placed on real-world applications where low latency and on-device intelligence are critical, such as medical imaging, surveillance systems, autonomous driving, smart city monitoring, industrial automation, and wearable health devices. Unlike conventional cloud-based solutions, this Reprint promotes localized data processing to reduce latency enhance privacy and improve system reliability. By bridging theoretical research and practical implementations, the collected works provide valuable insights into scalable and deployable edge intelligence solutions. This Reprint serves as a key reference for researchers, engineers, and practitioners seeking to design robust real-time visual processing systems.
Sensor-Based Human Activity Recognition
Recognising or monitoring human activities has significant potential for building innovative systems across a wide range of domains, including healthcare, smart home, sports, industry, security, and human-computer interaction. This reprint features nine of the latest studies on sensor-based Human Activity Recognition (HAR), which aims at recognising activities by exploiting and integrating data obtained from various sensors, like wearable, object, and ambient sensors, as well as vision sensors. In particular, these studies address various key aspects of sensor-based HAR, spanning methodologies, sensor technologies, and dataset collection, as well as the development and evaluation of actual systems. The editor hopes that this reprint will encourage many researchers to explore this attractive and important research field.
How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture
The advent of smart agriculture marks a paradigm shift from experience-driven to data-driven decision-making, fundamentally reshaping centuries-old farming practices. At the heart of this transformation lies the synergistic integration of optical sensors and deep learning (DL) technologies. Modern optical sensors range from cost-effective RGB cameras and near-infrared-modified consumer devices to sophisticated multispectral, hyperspectral, and fluorescence imaging systems. When combined with these powerful DL approaches, the datasets are transformed into actionable intelligence, enabling precise yield forecasting at the individual plant or plot level, the automated phenotyping of thousands of genotypes per day, and the real-time variable-rate application of water, fertilizers, and pesticides.This Reprint, entitled "How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture," assembles thirteen high-quality contributions that exemplify the current state of the art and illuminate viable paths forward. The collected works span staple crops (rice, maize, wheat), high-value horticultural species, and complex real-world field scenarios. By bridging the gap between algorithmic sophistication and agricultural practicality, this Special Issue not only documents tangible progress but also establishes a benchmark and inspiration for the next wave of optical-DL innovations in global food production.
Computational Intelligence and Machine Learning
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.
Entropy Method for Decision Making with Uncertainty
This Reprint brings together recent advances in uncertainty-aware decision making, highlighting how entropy-based methods, fuzzy and rough set formalisms, cloud-model reasoning, and distributed learning architectures are reshaping analytical practice across scientific and technical domains. The collected contributions illustrate a clear shift toward integrated uncertainty pipelines that combine robustness, interpretability, and privacy awareness in decision support systems. They address challenges such as heterogeneous information fusion, instability of classical entropy weights, conflict management in distributed environments, and the need for transparent, explainable mechanisms that operate reliably under noisy, incomplete, or adversarial data. By presenting applications spanning medicine, cybersecurity, environmental assessment, multi-criteria evaluation, and coalition-based classification, this Reprint demonstrates how treating uncertainty as structured information enables more resilient, adaptive, and trustworthy decision processes.
The Digital Leader's Playbook
This practical guide, written by a seasoned technology leader with over 25 years of experience, is packed with real-world advice and battle-tested solutions. Whether you're a new or aspiring IT leader or a seasoned one, this book will equip you with the knowledge and tools you need to navigate the challenges of managing and transforming digitally your company or department.The Digital Leader's Playbook is not your typical IT leadership book. It's a concise and easy-to-use reference guide filled with 60 key maxims - each one a shot of practical wisdom for becoming a successful tech leader. The author offers transferable insights and lessons from his successes working in technology leadership from sectors as varied as banking, retail, transport, chemicals and construction, and solving real-business challenges. As a result, readers will learn how to manage their team better, manage their time more effectively, introduce new technology and manage innovation, manage suppliers and business partners better, and engage with the technology that the company really values.
Fuzzy Sets, Fuzzy Numbers, Fuzzy Modeling, and Their Applications in Management and Engineering, 2nd Edition
In recent years, contemporary business operations have been impacted by a number of conditions that can be described as uncertain, imprecise, and vague. These business conditions are, in turn, impacted by a variety of factors, such as a global pandemic, geopolitical instability, climate change, and other issues that cannot be easily brought under control. At the same time, other unpredicted events may occur, so existing quantitative and qualitative models within the scope of management and engineering cannot be accurate and reliable. Considering the above, the exact values of variables that are used in conventional management and engineering models are hard to predict, and their previous values are not reliable for future use. The development of fuzzy sets theory has enabled the description of uncertainties, which makes it a suitable tool for overcoming the aforementioned issues. This Special Issue focuses on the use of current advances in fuzzy sets, fuzzy numbers, fuzzy modeling, and their applications in management and engineering. This Special Issue provides a platform for researchers from academia and industry to present their novel and unpublished work in the domain of management and engineering. This will help to foster future research in the emerging fields of management and engineering.
Numerical Optimization and Algorithms
The Special Issue "Numerical Optimization and Algorithms (3rd Edition)", hosted by the MDPI journal Algorithms, provides a crucial forum for the latest advancements in computational mathematics. As science and engineering problems grow increasingly complex, this edition focuses on the urgent need for robust solvers capable of navigating high-dimensional and nonlinear landscapes. The scope of the Special Issue encompasses a broad range of scientific fields, including physics, data science, economics, and biology. It specifically addresses modern challenges like the "curse of dimensionality," uncertainty in modeling, and the integration of data science with traditional numerical methods. Key areas of interest include the following: Graph Theory and Algebra: Developing fundamental structures for modern computing; Linear and Nonlinear Programming: Refining optimization under complex constraints; Stochastic Modeling: Handling the inherent unpredictability in real-world data; Differential Equations: Advancing numerical solvers for mechanical and environmental systems. Led by Guest Editors Prof. Dr. Dunhui Xiao and Prof. Dr. Shuai (Steven) Li, this collection highlights the shift from purely theoretical algorithms to scalable, practical solutions. By fostering multidisciplinary collaboration, the Special Issue aims to promote innovative techniques-from metaheuristics to AI-driven optimization-that empower researchers to solve high-stakes problems in global logistics, finance, and industrial automation more efficiently.
Advances in Offshore Wind and Wave Energies
Renewable energies are currently emerging as the most promising technologies for mitigating severe environmental impacts, contributing to the achievement of global sustainability goals. Offshore renewables, such as offshore wind and wave energy, are some of the most promising candidates for accomplishing environmental objectives, and this Reprint provides an updated overview of the progress in this area, as well as highlighting promising directions for future research. The Reprint includes a broad spectrum of themes, ranging from fundamental modeling and control strategies to hybrid wind-wave energy system design and long-term resource assessment.
Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring
The integration of optical sensors and machine learning (ML) technologies has transformed agricultural monitoring, delivering precise, real-time insights into crop health, growth patterns, and environmental interactions. These sensors-spanning multispectral, hyperspectral, and RGB cameras-capture intricate spectral signatures that detect subtle physiological shifts in crops. When paired with ML algorithms, the resulting data streams yield actionable intelligence, informing key decisions in precision agriculture, yield forecasting, and disease control.This Reprint of the second edition of the Special Issue entitled "Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring" builds upon the foundational research established by prior studies in the field. Featuring 17 original research articles, this collection tackles key methodological challenges, including data imbalance, improved model transferability, and the fusion of multisensor data for resilient monitoring. Together, these works highlight the advancing capabilities of optical sensors in acquiring high-resolution, multidimensional datasets, which ML models exploit for advanced pattern recognition and predictive modeling. Beyond refining current approaches, the studies within this Reprint pave the way for emerging innovations, such as edge computing and AI-powered automation in agricultural ecosystems.
Advances in Statistical Simulation and Computing
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.
Mathematical and Statistical Methods and Their Applications, 2nd Edition
This Reprint contains eight articles that have been accepted and published in the Special Issue "Mathematical and Statistical Methods and Their Applications, 2nd Edition". The collected papers cover a wide range of topics concerning the development, analysis, and application of modern mathematical and statistical methodologies. The key topics addressed in this volume include queueing theory and load balancing models, stochastic and reliability modeling, hybrid probability distributions, stress-strength analysis, change-point estimation in mixture regression models, statistical process monitoring schemes (such as EWMA, Shiryaev-Roberts, and CUSUM procedures) under censoring, kinetic equations and optimal transport, fuzzy competence space-based learning path recommendation, and trust-enhancing randomized response techniques, among others.We hope that this Reprint will be interesting and useful for researchers and practitioners working in the areas of applied mathematics, statistics, quality control, reliability engineering, and intelligent systems, as well as for readers with a solid mathematical background who wish to familiarize themselves with recent advances and practical applications of mathematical and statistical methodologies.
Data Mining and Machine Learning in the Era of Big Knowledge and Large Models
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.
Polynomials
This book presents a comprehensive collection of peer-reviewed articles accepted and published in the Special Issue "Polynomials: Theory and Applications, 2nd Edition" of the MDPI journal Mathematics. The volume reflects the central role of polynomial theory in modern mathematical research and its growing impact on science, engineering, and applied disciplines.The contributions span a wide range of contemporary topics, including modern umbral calculus, Appell and Sheffer polynomial sequences, q-calculus and quantum calculus-based polynomial structures, orthogonal and multiple orthogonal polynomials, matrix and determinant approaches to polynomial systems, fractional calculus, symbolic computation, and analytic number theory. In addition, several papers explore applications of special polynomial sequences and special functions to physics, engineering, statistics, and related scientific fields.By bringing together both theoretical developments and application-oriented studies, this volume aims to serve as a valuable reference for researchers, graduate students, and practitioners seeking a deeper understanding of recent advances in polynomial theory and its interdisciplinary applications.
Algorithms and Applications of Machine Learning Techniques for Healthcare
Improving human health and providing access to high-quality healthcare for everyone is a global concern. Modern technologies help to promote and maintain health, while avoiding unnecessary disabilities and premature health issues. Machine learning is a subfield of artificial intelligence, which is mainly defined as the capability of a machine to imitate "intelligent" human behavior. This capacity is being widely applied in many areas of our lives such as virtual personal assistants, self-driving cars, security cameras, product recommendations, or disaster alerts. With the union of machine learning and healthcare, researchers around the world have opened new horizons providing impressive advances in healthcare. Thus, a great number of works focus on areas such as patient diagnosis, automating health-related tasks, new treatments and drugs, improvements in diagnosis, cost reduction, better tracking, or telemedicine. However, despite the huge amount of work carried out, we are still very far from being able to consider machine learning to be integrated into healthcare. The aim of this Reprint is to enhance the state-of-the-art in this area significantly, improving the application of machine learning techniques for healthcare.
3D Computer Vision and Smart Building and City, 2nd Edition
This Topic is a continuation of the previous successful Topic, "3D Computer Vision and Smart Building and City". Three-dimensional computer vision is an interdisciplinary subject involving computer vision, computer graphics, artificial intelligence and other fields. Its main contents include 3D perception, 3D understanding and 3D modeling. In recent years, 3D computer vision technology has developed rapidly and has been widely used in unmanned aerial vehicles, robots, autonomous driving, AR, VR and other fields. Smart buildings and cities use various information technologies or innovative concepts to connect as well as various systems and services so as to improve the efficiency of resource utilization, optimize management and services and improve quality of life. Smart buildings and cities can involve some frontier techniques, such as 3D CV for building information models, digital twins, city information models, simultaneous localization and mapping robots. The application of 3D computer vision in smart buildings and cities is a valuable research direction, but it still faces many major challenges. This topic focuses on the theory and technology of 3D computer vision in smart buildings and cities.
Emerging Distributed/Parallel Computing Systems
This Reprint presents eight papers from the Special Issue "Emerging Distributed and Parallel Computing Systems" that span the core system challenges where computation, networking, and trust meet. Two contributions develop lightweight authentication and key agreement schemes for distributed wireless sensor networks and for the 5G Internet of Vehicles, using practical hardware security and anonymous negotiation to strengthen resilience in resource constrained settings. Privacy-preserving distributed analytics is represented by a framework for origin-destination matrix computation that balances utility and overhead via hybrid differential privacy. Regarding data-driven computing, the Reprint includes advances in machine learning methods, including a deep reinforcement learning recommender with multi-level attention and a tensor-based multi-view projection clustering approach. Three surveys consolidate current knowledge on trends in parallel and distributed systems, analysing TLS 1.3-encrypted traffic, and software-defined wide-area networks, outlining design trade-offs and open research directions. Together, these papers provide an up-to-date snapshot of methods and insights for building scalable, secure, and intelligent distributed computing platforms.
Causality and Complex Systems
Complex systems are unified wholes formed by many interacting units. One key source of their complexity lies in the intricate entanglement of causal structures. A notable feature of these systems is the phenomenon of emergent causality, where stronger causal relationships may arise at the macro-scale rather than the micro-scale, as seen in fields like statistical mechanics. This Special Issue focuses on "Causality and Complex Systems", examining the interplay of causal relationships and the emergence of causal structures within complex systems. The 17 articles in this issue encompass a broad spectrum of topics, ranging from theoretical frameworks to practical applications, with the shared goal of advancing our understanding of causality in dynamic, interconnected systems. Key contributions of this issue include novel theoretical frameworks for quantifying causal emergence, innovative causal machine learning algorithms, and information-theoretic measures for analyzing causality. Methodological advancements are also presented for causal discovery in complex and nonlinear systems. Furthermore, this issue features interdisciplinary applications across neuroscience, biology, sociology, and environmental science, demonstrating the versatility of causal methods in addressing real-world phenomena. Ultimately, this Special Issue enhances both the theoretical understanding of causality in complex systems and provides practical tools and methodologies to tackle challenges in data-driven research.
Multimedia-Based Digital Learning
This Reprint brings together a curated selection of peer-reviewed articles published within the Topical Collection on multimedia-based digital learning in the journal Digital. The collection addresses recent advances in the design, implementation, and evaluation of digital learning environments that leverage multimedia technologies to enhance learning effectiveness, accessibility, and engagement. The Reprint covers a broad range of topics, including e-learning and blended learning frameworks, multimedia educational content, learning analytics, educational software and games, digital platforms for distance education, and emerging technologies supporting data-driven and personalized learning experiences. Particular attention is given to interdisciplinary approaches that combine computer science, education, and human-computer interaction, reflecting the evolving role of digital technologies in modern education systems. By consolidating high-quality contributions from international researchers, this Reprint provides a comprehensive overview of the current research trends and methodological approaches in multimedia-based digital learning, serving as a valuable reference for researchers, educators, instructional designers, and practitioners interested in the development and assessment of innovative digital learning solutions.
Time Series Analysis in Earthquake Complex Networks
Earthquake time series are complex due to the variety of forces affecting earthquake generation, the complexity of the Earth's crust and mantle structure, where the earthquake source develops, and the complexity of the earthquake development process in space (generation of elementary cracks, their multiplication and coalescence, leading to main fault formation and final rupture) and time (kinetics of the different stages of the fracturing process). The early stages of the process developing in the deep layers of the Earth's crust cannot be studied in detail experimentally; at present, the only mechanism through which to model these initial stages of earthquake source development is experimental and theoretical modelling. New physical/mathematical methods of data analysis, such as nonlinear dynamics, artificial intelligence/machine learning/deep learning, non-extensive statistical analysis, natural time analysis, and complex network approaches, enable us to obtain mathematical regularities underlying physical/geophysical processes using only the time series of experimental observations.The aim of this Special Issue is to examine the progress in the analysis of complex systems' time series during the fracture process using new approaches to understanding the complexity of earthquake time series.
SAP S/4HANA aATP
In today's fast-paced and customer-driven market, supply chain precision is no longer optional-it is essential. Master SAP S/4HANA aATP delivers a practical, implementation-focused guide to leveraging Advanced Available-to-Promise (aATP) capabilities to transform supply chain performance and customer fulfillment.Designed for SAP consultants, supply chain professionals, and ERP practitioners, this book provides clear, step-by-step guidance on configuring and optimizing aATP within SAP S/4HANA. Readers will learn how to improve inventory visibility, execute real-time order fulfillment, manage backorders efficiently, and enhance customer satisfaction through intelligent availability confirmation.Inside, you will discover how to: Configure and implement SAP S/4HANA aATP effectivelyOptimize inventory allocation and product availabilityExecute real-time availability checks and substitutionsManage backorder processing with advanced rules-based logicImprove supply-demand matching across complex networksAlign SAP capabilities with modern supply chain strategiesWhether you are implementing aATP for the first time or enhancing an existing SAP S/4HANA landscape, this guide bridges the gap between system functionality and real-world supply chain execution.Transform your order fulfillment processes, increase operational transparency, and unlock the full potential of SAP S/4HANA aATP to drive supply chain excellence.
Text Mining
Encompassing fields such as information retrieval, extraction, classification, summarization and understanding, text mining has become indispensable across diverse application domains. Methodological advances span rule-based systems, statistical approaches, support vector machines, clustering, neural networks and most recently, deep learning, with distance and similarity estimation persisting as central challenges.This Special Issue comprises two survey papers and sixteen research articles, all addressing practical aspects of text mining. The surveys review methods for financial market prediction through social media analysis and sentiment detection, and provide a synthesis of techniques, evaluation strategies, and applications of recommender systems across domains including e-commerce, social media, and online learning.The research articles extend to text classification, sentiment analysis, summarization, and natural language understanding, as well as corpus construction and e-governance applications. Contributions include a large-scale Chinese toponym corpus, CHTopo, deep learning models for tourist behaviour prediction, semantic optimization of artifact presentation, BERT-based knowledge extraction for agricultural ontology construction, and transformer-based approaches for detecting moral features in television narratives.
Hacked and Hunted
In the digital world, one click can change everything.Social media promises fame, friendship, and freedom-but beneath the surface lies a darker reality. Hacked and Hunted: A Smart Girl's Guide to Online Safety is a gripping cyber-thriller that exposes how quickly online success can turn into a nightmare-and why digital awareness is no longer optional.Meet Aarushi, Kaneera, and Sophia-three young women connected by friendship and unknowingly bound by danger. Aarushi is a brilliant tech mind who understands the language of machines. Kaneera is a rising social media influencer, basking in likes, followers, and viral fame. Sophia, a determined law student, believes justice can protect the vulnerable. Their lives collide when a celebration of online success spirals into a chilling game of cyber manipulation.What begins as a harmless posting and online engagement soon unravels into a terrifying chain of events-phishing attacks, spyware infections, account takeovers, cyberstalking, doxxing, and personal data being sold on the dark web. A faceless hacker watches their every move, exploiting overshared information and digital blind spots. Trust fractures. Fear escalates. And the line between predator and prey begins to blur.As the threat intensifies, Aarushi is forced to push her technical skills to the limit, venturing into the hacker's world where one mistake could cost everything. Kaneera's online fame becomes a weapon used against her, while Sophia uncovers a painful truth: the greatest danger doesn't always come from strangers-it can emerge from within the systems we trust most.This is more than a fast-paced thriller-it's a wake-up call.Seamlessly blending storytelling with real-world cybersecurity lessons, this book educates readers on: Phishing scams and how attackers manipulate trustWeak passwords, account takeovers, and recovery strategiesSpyware, malware, and device compromiseCyberstalking, doxxing, and digital harassmentDark web data trading and identity exposureEthical hacking, digital footprints, and online traceabilityLong-term practices for protecting privacy and digital identityEach chapter reveals not only what happens to the characters-but how it happens, empowering readers to recognize threats before they become victims themselves.Written by Anand Shinde, an internationally recognized cybersecurity expert and mentor, and Vandana Keswani, a senior enterprise cybersecurity leader and passionate advocate for empowering girls online, this book draws directly from decades of real-world cyber defense experience. Their combined expertise transforms complex threats into relatable, actionable insights-without losing the intensity of a page-turning thriller.Hacked and Hunted is especially powerful for: Teen girls and young women navigating social mediaParents seeking awareness-driven stories for their childrenEducators and mentors promoting digital safetyAnyone who believes "it won't happen to me"This story will make you rethink every password, every post, and every moment you share online.Because in today's digital world, privacy is fragile, trust is exploitable, and awareness is survival.Once you step into this story, there's no turning back.
Smart Parent's Guide to Cybersecurity
Parenting doesn't come with a manual-especially in the digital age.Today's children grow up surrounded by screens, social media, online games, and limitless information. While the internet opens doors to learning, creativity, and connection, it also exposes young minds to risks many parents feel unprepared to handle: cyberbullying, online predators, privacy breaches, scams, screen addiction, and harmful content.Smart Parent's Guide to Cybersecurity is a practical, empowering roadmap designed to help parents confidently protect, guide, and educate their children in an increasingly connected world. Rather than promoting fear or excessive restriction, this book focuses on awareness, communication, trust, and practical action-the foundations of safe and responsible digital parenting.Written by two seasoned cybersecurity professionals with decades of global experience, this guide translates complex cybersecurity concepts into clear, relatable, and actionable advice for parents of all backgrounds. Whether your child is taking their first steps online or already navigating social media and gaming platforms, this book meets you where you are.Inside, you'll learn how to: Understand how children of different age groups use the internetIdentify real-world online threats such as cyberbullying, phishing, scams, malware, and online groomingCreate age-appropriate and secure digital environments across devicesSet healthy boundaries around screen time without damaging trustImplement and manage parental controls effectivelyTeach children digital etiquette, privacy awareness, and online responsibilityRecognize warning signs of online trouble and take the right steps earlySupport your child emotionally when online issues ariseBecome an active, positive presence in your child's digital lifeWhat sets this book apart is its balanced approach. Cybersecurity is not just about tools, filters, or monitoring-it's about equipping children with the judgment and confidence to make smart decisions even when parents aren't watching. Through real-life scenarios, expert insights, and practical strategies, parents learn how to move from control to collaboration, and from fear to preparedness.The book is structured to be easy to follow, with clearly defined chapters covering everything from devices and apps to social media, gaming, and connected technologies. It also includes curated resources, tools, and support networks parents can rely on beyond the book.Together, Anand and Jessen wrote this book with one clear mission: to help parents feel confident, capable, and supported in protecting their children online.If you're a parent who wants to raise a digitally aware, resilient, and responsible child-without panic, guilt, or confusion-Smart Parent's Guide to Cybersecurity is your trusted companion.Because protecting children online starts with informed parents-and meaningful conversations at home.
Innovative Technologies and Services for Unmanned Aerial Vehicles
This Special Issue Reprint compiles cutting-edge research on Unmanned Aerial Vehicles (UAVs), highlighting transformative advancements that broaden their capabilities across diverse sectors. Bringing together contributions from researchers, practitioners, and industry experts, this collection fosters interdisciplinary dialogue while delivering rigorous peer-reviewed insights into UAV technology and services. It covers a comprehensive range of critical topics, such as the following: autonomous navigation and control systems (including advanced algorithms for obstacle detection, path planning, and formation flying); innovative sensing/imaging technologies (hyperspectral imaging, LiDAR, thermal imaging for data acquisition); efficient communication/networking solutions (reliable, secure connectivity, network coordination, swarm intelligence for networked UAVs); payload design and service delivery (medical supply transport, infrastructure inspections); energy efficiency research (energy harvesting, optimization algorithms, sustainable power sources); and regulatory/ethical considerations for safe, responsible UAV deployment. Featuring original research articles, reviews, and case studies, this Reprint captures the latest breakthroughs pushing UAV application boundaries. It serves as an invaluable resource for professionals and researchers seeking to keep up to date with industry progress, inspiring further innovation and collaboration to advance UAV technology's real-world impact.
Applied Mathematics to Mechanisms and Machines II
This reprint compiles the nine articles from the Special Issue "Applied Mathematics to Mechanisms and Machines II" of the MDPI Mathematics journal, specifically from its "Engineering Mathematics" section. The articles, while diverse in their specific topics, uniformly focus on mechanisms as their object of study and employ mathematics as their foundational methodology. Key areas explored within the reprint include the following: Synthesis, design, and optimization of mechanisms; Robotics; Automotive applications; Maintenance 4.0; Machine vibrations; Control systems; Biomechanics; Medical devices.This reprint is intended for professionals and researchers in the field of Mechanism and Machine Science, with the aim of contributing to advancements in both mechanical engineering and applied mathematics.
Symmetry/Asymmetry of Differential Equations in Biomathematics
Symmetry principles play a fundamental role in understanding the complex dynamics of ecological and biological systems. This Special Issue brings together cutting-edge research that applies mathematical modeling, stability analysis, and bifurcation theory to explore symmetry-related phenomena across diverse biological contexts. The collected works demonstrate how symmetry concepts can reveal universal patterns in population dynamics, disease transmission, and resource management.
Machine Learning for Pattern Recognition (2nd Edition)
In the recent digital age, machine learning technology has made significant progress, revolutionizing applications in fields such as image recognition, speech processing, and natural language processing. These technologies have not only changed our daily lives but have also had a profound impact on medicine, finance, transportation, and other fields. However, pattern recognition, as an important branch of machine learning, still has many challenges and problems. This 2nd edition Reprint brings together contributions from leading experts in their fields. Each paper provides valuable insights into the latest trends, methods, and challenges in state-of-the-art applications of machine learning for pattern recognition. In addition, the studies in each paper not only showcase the latest advancements in machine learning algorithms but also discuss their successful applications and the challenges encountered in real-world scenarios.As Guest Editors, we are honored to present this 2nd edition Reprint, and we hope that readers, whether researchers, engineers, or students, will find inspiration and guidance in these papers as they explore the growing field of machine learning for pattern recognition. We express our gratitude to the authors for their outstanding contributions, to the reviewers for their critical evaluation, and to the Assistant Editor, Mr. Musea Wu, for his enthusiastic help. We are also sincerely grateful to our readers, whose curiosity and enthusiasm continue to drive innovation in this exciting field.
Application of Vision Technology and Artificial Intelligence in Smart Farming
Artificial intelligence (AI) and computer vision (CV) are increasingly applied in smart agriculture, offering significant potential to enhance farming efficiency, reduce manual labor, and lower production costs. In livestock farming, CV and AI enable behavior monitoring to improve animal health and welfare. In crop production, they support phenotype extraction, plant organ detection, and pest management. This Special Issue (Volume 2) presents original research and reviews on topics such as agricultural sensor optimization, livestock behavior recognition, AI-based automation, agricultural robotics, and big data analytics in farming.
Big Data Mining and Analytics with Applications
This Reprint contains all of the articles accepted and published in the Special Issue titled "Big Data Mining and Analytics with Applications" in the journal Mathematics. It compiles 10 papers related to span predictive modeling for spatiotemporal data (e.g., traffic, air quality, crowd flow), data quality and repair, automated data understanding (e.g., table type prediction), advanced clustering and user segmentation, scalable statistical learning (e.g., Bayesian variable selection), and text mining applications (e.g., document classification, fake review detection). Together, they highlight a strong focus on efficient, robust, and interpretable methods for big data mining and analysis across domains. It is our hope that these contributions will serve as an inspiring source for researchers in the domains of big data analytics and machine learning.
Recent Advances in Finite Element Methods with Applications
This Reprint contains all of the articles that were accepted and published in the Special Issue of Mathematics titled "Recent Advances in Finite Element Methods with Applications". This Special Issue aimed to collect recent advances in the construction, theoretical analysis, implementation, and application of finite element methods. The finite element method is an important tool used in applied sciences. In close association with computational mechanics, it has been increasingly applied across various fields, such as engineering, material sciences, environmental sciences, medicine, biology, as well as physics and chemistry, and so forth. It is hoped that this Reprint will inspire research in the theoretical study and application areas of finite element methods.
Modeling Aboveground Forest Biomass
Forest biomass modelling is essential for monitoring and storage. However, biomass in stands and forests varies according to the species, stand structure, and site. Biomass models can be developed using data obtained from destructive sampling, forest inventory, remote sensing, and ancillary. There is a wide range of data science methods and techniques that are currently applied in order to fit the models and evaluate their uncertainties. Biomass models are utilized in order to produce management alternatives. This reprint offers an overview of the various datasets and modelling methods employed to develop biomass functions, as well as their applicability at both the tree and area levels.The topics covered include the following: Biomass models at the tree level; Biomass models at the stand level; Datasets used in biomass modelling; Data science methods and techniques used in biomass modelling; Model performances and uncertainties; Development of management alternatives with biomass models.
Deep Learning for Perception and Recognition
The rapid advancement of deep learning technology has brought about transformative breakthroughs in perception and recognition systems across a wide range of applications. In addition to driving innovation in industrial sectors, it has opened up significant opportunities in fields such as intelligent transportation, smart cities, healthcare, and robotics.Deep learning significantly enhances the accuracy and robustness of perception and recognition systems through hierarchical feature extraction in multilayer neural networks, achieving remarkable results in areas such as soft sensing, image classification, natural language processing, and object detection. By training on large volumes of labeled data, deep learning algorithms are able to automatically learn complex feature representations and efficiently recognize objects during the perception process.As application scenarios grow more complex and data become increasingly diverse, deep learning models continue to face significant challenges in solving real-world perception and recognition problems. These challenges include ensuring model generalization when dealing with noisy, imbalanced, or limited data; enhancing performance through self-supervised, few-shot, or transfer learning in cases of insufficient labeled data; integrating information across different scales, dimensions, and modalities.
Numerical Simulation and Methods in Computational Fluid Dynamics
The present Reprint contains 8 articles accepted and published in the Special Issue "Numerical Simulation and Methods in Computational Fluid Dynamics" in the MDPI journal Mathematics, which cover a wide range of topics connected to the theory, numerical methods, and simulation techniques of complex fluid dynamic systems. These topics include, among others, magnetic field-induced Joule-heating thermal convection, a Tiwari and Das approach to buoyant convective thermal transport, as well as a computational and machine learning-based approach, and the use of an influence matrix method for accurate implementation of rotating magneto-hydrodynamics. Moreover, Newton's method using implicit Coriolis integration, transition modelling from shear-driven turbulence to convective turbulence, and convective heat transfer via uniformly accelerated and decelerated turbulent pipe flows are also explored.It is hoped that the Reprint will be interesting and useful for those working in the area of computational fluid dynamics (CFD), complex systems, and numerics, as well as for those with a mathematical background who wish to become familiar with recent advances in CFD, which now plays a crucial role in almost all sectors of human life and activity.
Mathematical Models and Simulations, 2nd Edition
This Reprint continues the work initiated in our previous edition, Mathematical Models and Simulations, emphasizing the central role of mathematical modeling in understanding complex systems across physics, biology, finance, and engineering. Mathematical models provide the structural framework through which the behavior of physical and abstract phenomena can be interpreted, predicted, and optimized. Alongside these theoretical foundations, numerical simulations serve as a crucial complement, enabling researchers to explore system dynamics, test hypotheses, and forecast quantities of interest with high fidelity.The Reprint brings together contributions spanning a broad spectrum of mathematical physics, highlighting both deterministic and stochastic modeling approaches. Deterministic models typically rely on ordinary or partial differential equations to describe well-defined system evolutions, while stochastic models account for randomness through stochastic processes or stochastic differential equations. Emphasis is placed on rigorous mathematical analyses of these models to ensure clarity, reliability, and applicability.
Eye-Tracking Technologies
This reprint gathers recent advances in eye-tracking technologies across theory, methods, and applications. It features contributions on core topics-gaze estimation, oculomotor measurement, event-camera-based pupil tracking, and area-of-interest analytics-alongside application-driven studies in transportation, human-computer interaction, vision science, and education. The articles demonstrate how improved sensors, computer vision, and neural networks are reshaping attention analysis, enabling reliable metrics in naturalistic environments and clinical settings. Collectively, the reprint highlights methodological rigor (from data quality to validation), practical deployment (from simulators to on-road and classroom contexts), and emerging trends such as deep learning pipelines and remote, video-based assessment. It is intended for researchers and practitioners seeking a clear view of the state of the art and the challenges ahead in building robust, interpretable, and ethically grounded eye-tracking solutions.
The Future of Healthcare
Biomedical technology, which is fundamental today, incorporates various disciplines such as signal, image, and data processing, greatly benefiting healthcare systems. Artificial intelligence plays a crucial role in allowing the creation of embedded systems that monitor and adjust patient treatment in real-time, ensuring more personalized and effective care. Additionally, this integration facilitates the development of automatic diagnostic techniques, such as advanced medical image analysis and biometric data interpretation, which have transformed the diagnosis and treatment of diseases, thus improving patients' quality of life. This advance is essential to respond to the demands of a growing population with expectations of high-quality medical care.This Special Issue aims to showcase innovators who are using artificial intelligence as a main topic for solving problems in biomedical technology through the development of technologies with integrated systems for health and quality of life.
Discrete Math in Coding Theory
This Special Issue comprises eleven articles on the algebraic, combinatorial, and geometric aspects of Coding Theory. The topics range from applied (error detection probability, sonar sequences, optical codes, codes for constrained systems, and storage codes) and algebraic (AG codes for the Hermitian curve, LCD codes, and codes over the Eisenstein integers) aspects to combinatorial (permutation arrays, two-weight codes, and Boolean functions) ones.
Continuous/Discrete-Time Fractional Systems
Over the past thirty years, fractional calculus has become an integral part of all scientific fields. Although not all formulations are suitable for use in applications, there are several tools that constitute true generalizations of classical operators and are suitable for describing real phenomena. In fact, many systems can be classified as displacement-invariant or scale-invariant and have fractional characteristics, either in time or in frequency/scale. This means that some of the known fractional operators, specifically those described by ARMA-type equations, are very useful in many areas, such as diffusion, viscoelasticity, fluid mechanics, bioengineering, dynamics of mechanical, electronic, and biological systems, signal processing, control, economics, and others.The aim of this Reprint is to continue advancing research on topics such as modeling, design, and estimation related to fractional-order systems.
