Search Results (2,389)

We investigate the quantum dynamics of coherence in the hyperfine structure of hydrogen atoms subjected to dephasing noise, modeled using the Lindblad master equation. The effective Hamiltonian describes the spin–spin interaction between the electron and proton, with dephasing introduced via Lindblad operators. Analytical solutions for the time-dependent density matrix are derived for various initial states, including separable, partially entangled, and maximally entangled configurations. Quantum coherence is quantified through the $l_1$-norm measures, while purity is evaluated to assess mixedness. Results demonstrate that coherence exhibits oscillatory decay modulated by the dephasing rate, with antiparallel spin states showing greater resilience against noise compared to parallel configurations. These findings highlight the interplay between coherent hyperfine dynamics and environmental dephasing, offering insights into preserving quantum resources in atomic systems for applications in quantum information science. Full article

Aroma and flavor are central to consumer perception, product acceptance, and market positioning of animal-derived foods such as meat, milk, and eggs. These sensory traits arise from volatile organic compounds (VOCs) formed via lipid oxidation (e.g., hexanal, nonanal), Maillard/Strecker chemistry (e.g., pyrazines, furans), thiamine degradation (e.g., 2-methyl-3-furanthiol, thiazoles), and microbial metabolism, and are modulated by species, diet, husbandry, and post-harvest processing. Despite extensive research on food volatiles, there is still no unified framework spanning meat, milk, and eggs that connects production factors with VOC pathways and links them to sensory traits and consumer behavior. This review explores how production systems, feeding strategies, and processing shape VOC profiles, creating distinct aroma “fingerprints” in meat, milk, and eggs, and assesses their value as markers of quality, authenticity, and traceability. We have also summarized the advances in analytical techniques for aroma fingerprinting, with emphasis on GC–MS, GC–IMS, and electronic-nose approaches, and discuss links between key VOCs and sensory patterns (e.g., grassy, nutty, buttery, rancid) that influence consumer perception and willingness-to-pay. These patterns reflect differences in production and processing and can support regulatory claims, provenance verification, and label integrity. In practice, such markers can help producers tailor feeding and processing for flavor outcomes, assist regulators in verifying claims such as “organic” or “free-range,” and enable consumers to make informed choices. Integrating VOC profiling with production data and chemometric/machine learning pipelines can enable robust traceability tools and sensory-driven product differentiation, supporting transparent, value-added livestock products. Thus, this review integrates production variables, biochemical pathways, and analytical platforms to outline a research agenda toward standardized, transferable VOC-based tools for authentication and label integrity. Full article

Decision Support Systems (DSSs) are increasingly shaping high-performance sport by translating complex time series data into actionable insights for coaches and practitioners. This paper outlines a structured, five-stage DSS development pipeline, grounded in the Schelling and Robertson framework, and demonstrates its application in professional basketball. Using changepoint analysis, we present a novel approach to dynamically quantify Most Demanding Scenarios (MDSs) using high-resolution optical tracking data in this context. Unlike fixed-window methods, this approach adapts scenario duration to real performance, improving the ecological validity and practical interpretation of MDS metrics for athlete profiling, benchmarking, and training prescription. The system is realized as an interactive web dashboard, providing intuitive visualizations and individualized feedback by integrating validated workload metrics with contextual game information. Practitioners can rapidly distinguish normative from outlier performance periods, guiding recovery and conditioning strategies, and more accurately replicating game demands in training. While illustrated in basketball, the pipeline and principles are broadly transferable, offering a replicable blueprint for integrating context-aware analytics and enhancing data-driven decision-making in elite sport. Full article

The present study investigated factors influencing cybersecurity in railway critical infrastructure by identifying relevant factors and criteria and then prioritizing them in order of importance. To address the lack of multi-criteria analysis in previous studies on this topic, the present study applied the analytical hierarchy process to identify factors and criteria influencing cybersecurity and then selected the top 70% of influencing criteria to serve as a reference for railway cybersecurity project management. A total of 25 valid expert questionnaires were collected for weight vector analysis, revealing that the influencing criteria in the top 70% were inability to monitor train occupancy in track sections (locations); inability of controllers to issue commands to safety control systems; inability to provide drivers with information on upcoming signals, block status, and train occupancy; failure to automatically apply brakes when the train exceeds the speed limit; increased risk of catastrophic accidents due to power system security vulnerabilities; and inability of the dispatching system to automatically track train numbers. Full article

This article proposes a scientifically grounded approach to risk assessment for infrastructural and functional systems that underpin the development of digitally transformed regional territories under conditions of high threat dynamics and sociotechnical instability. The core methodology is based on modeling of multifactorial threats through the application of fuzzy set theory and logic–linguistic analysis, enabling consideration of parameter uncertainty, fragmented expert input, and the lack of a unified risk landscape within complex infrastructure environments. A special emphasis is placed on components of technogenic, informational, and mobile infrastructure that ensure regional viability across planning, response, and recovery phases. The results confirm the relevance of the approach for assessing infrastructure resilience risks in regional spatial–functional systems, which demonstrates the potential integration into sustainable development strategies at the level of regional governance, cross-sectoral planning, and cultural reevaluation of the role of analytics as an ethically grounded practice for cultivating trust, transparency, and professional maturity. Full article

The Internet of Medical Things (IoMT) is the latest advancement in the Internet of Things (IoT). Researchers are increasingly drawn to its vast potential applications in secure healthcare systems. The growing use of internet-connected medical device sensors has significantly transformed healthcare, necessitating the development of robust methodologies to assess their integrity. As access to computer networks continues to expand, these sensors have become vulnerable to a wide range of security threats, thereby compromising their integrity. To prevent such lapses, it is essential to understand the complexities of the operational environment and to systematically identify technical vulnerabilities. This paper proposes a unified hesitant fuzzy-based healthcare system for assessing IoMT sensor integrity. The approach integrates the hesitant fuzzy Analytic Network Process (ANP) and the hesitant fuzzy Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). In this study, a hesitant fuzzy ANP is employed to construct a comprehensive network that illustrates the interrelationships among various integrity criteria. This network incorporates expert input and accounts for inherent uncertainties. The research also offers sensitivity analysis and comparative evaluations to show that the suggested method can analyse many medical device sensors. The unified hesitant fuzzy-based healthcare system presented here offers a systematic and valuable tool for informed decision-making in healthcare. It strengthens both the integrity and security of healthcare systems amid the rapidly evolving landscape of medical technology. Healthcare stakeholders and beyond can significantly benefit from adopting this integrated fuzzy-based approach as they navigate the challenges of modern healthcare. Full article

Quantifying uncertainty in complex systems is a central problem in reliability analysis and engineering applications. In this work, we develop an information-theoretic framework for analyzing linear consecutive k-out-of-n:G systems using the cumulative residual Tsallis entropy (CRTE). A general analytical expression for CRTE is derived, and its behavior is investigated under various stochastic ordering relations, providing insight into the reliability of systems governed by continuous lifetime distributions. To address challenges in large-scale settings or with nonstandard lifetimes, we establish analytical bounds that serve as practical tools for uncertainty quantification and reliability assessment. Beyond theoretical contributions, we propose a nonparametric CRTE-based test for dispersive ordering, establish its asymptotic distribution, and confirm its statistical properties through extensive Monte Carlo simulations. The methodology is further illustrated with real lifetime data, highlighting the interpretability and effectiveness of CRTE as a probabilistic entropy measure for reliability modeling. The results demonstrate that CRTE provides a versatile and computationally feasible approach for bounding analysis, characterization, and inference in systems where uncertainty plays a critical role, aligning with current advances in entropy-based uncertainty quantification. Full article

Background: Inflammatory Bowel Disease (IBD), comprising Crohn’s disease and ulcerative colitis, requires accurate assessment over time. Imaging techniques play a crucial role in diagnosis, monitoring disease activity, and guiding therapeutic response. This review summarizes the current evidence on radiologic imaging techniques in IBD, focusing on intestinal ultrasound (IUS), computed tomography enterography (CTE), magnetic resonance enterography (MRE), and other emerging technologies. Methods: A literature review was conducted using PubMed, EMBASE, Scopus, and the Cochrane Library, encompassing publications up to 31 October 2024. Results: IUS offers a non-invasive tool for assessing bowel wall thickness, vascularity, and complications. CTE and MRE provide detailed visualization of luminal and extraluminal disease, with MRE preferred for routine monitoring due to the absence of ionizing radiation. Standardized indices and scoring systems aid in objective disease activity assessment. Emerging technologies like Positron Emission Tomography (PET)/MRI and radiomics show promise in combining metabolic and morphological information for complex cases. Conclusions: Imaging has a central role in IBD management, with IUS, CTE, and MRE demonstrating high diagnostic accuracy. Radiomics and Artificial Intelligence (AI) are paving the way for precision imaging. Integrating advanced imaging techniques, scoring systems, and AI-driven analytics represents a transformative step toward more effective and individualized care for patients with IBD. Full article

Public electric vehicle (EV) charging infrastructure is crucial for accelerating EV adoption and reducing transportation emissions; however, disparities in infrastructure access have raised significant equity concerns. This review synthesizes existing knowledge and identifies gaps regarding equity in EV public charging research. Following structured review protocols, 91 peer-reviewed studies from Scopus and Google Scholar were analyzed, focusing explicitly on equity considerations. The findings indicate that current research on EV public charging equity mainly adopts geographic information systems (GIS), network optimization, behavioral modeling, and hybrid analytical frameworks, yet lacks consistent normative frameworks for assessing equity outcomes. Equity assessments highlight four key dimensions: spatial accessibility, cost burdens, reliability and usability, and user awareness and trust. Socio-economic disparities, particularly income, housing tenure, and ethnicity, frequently exacerbate inequitable access, disproportionately disadvantaging low-income, renter, and minority populations. Additionally, infrastructure-specific choices, including charger reliability, strategic location, and pricing strategies, significantly influence adoption patterns and equity outcomes. However, the existing literature primarily reflects the contexts of North America, Europe, and China, revealing substantial geographical and methodological limitations. This review suggests the need for more robust normative evaluations of equity, comprehensive demographic data integration, and advanced methodological frameworks, thereby guiding targeted, inclusive, and context-sensitive infrastructure planning and policy interventions. Full article

Organisations are increasingly adopting the Chain of Custody (CoC) standards in the mining industry to enhance the traceability of minerals. It ensures that the minerals they have received are from credible sources and accompanied by verifiable information. However, unlikeother industries such as timber, where the effectiveness and benefits of CoC standards are mainly explored, this study subtly shifts the focus towards identifying strategic opportunities and innovation areas within the CoC standards that could extend beyond traceability. Four CoC standards were selected, and their provisions examined. It was found that implementing these requirements could not only enhance transparency but also support broader sustainability goals across the entire value chain. The study also identifies several challenges that could act as barriers to the CoC system, and these are seen as opportunities for innovative approaches to enhance the effectiveness of the standards. These are labelled as transformative innovation areas, and while they do include blockchains and analytical proof of origin technologies, this study also seeks to advocate for solutions that are more pragmatic and scalable. By identifying opportunities and areas of innovation, the findings will help improve the practical implementation of the standards and suggest areas for future evaluations of effectiveness that could consider aspects beyond traceability, such as sustainability and transparency. Full article

This paper presents an analytical framework for the channel capacity evaluation of simultaneous wireless information and power transfer (SWIPT)-enabled opportunistic amplify-and-forward (OAF) relaying systems over Rayleigh fading channels. For the SWIPT, we employ a power splitter (PS) at the relay, which splits the received signal into the information transmission and the energy-harvesting parts. By modeling the partial channel state information (P-CSI)-based SWIPT OAF system as an equivalent non-SWIPT OAF configuration, a semi-lower bound and a new upper bound on the ergodic channel capacity are derived. A refined approximation is then obtained by averaging these bounds, yielding a simple yet accurate analytical estimate of the true capacity. Simulation results confirm that the proposed approximations closely track the actual performance across a wide range of signal-to-noise ratios (SNRs) and relay configurations. They further demonstrate that SR-based relay selection provides higher capacity than RD-based selection, primarily due to its direct influence on energy harvesting efficiency at the relay. In addition, diversity advantages manifest mainly as SNR improvements, rather than as gains in diversity order. The proposed framework thus serves as a practical and insightful tool for the capacity analysis and design of SWIPT-enabled cooperative networks, with direct relevance to energy-constrained Internet of Things (IoT) and wireless sensor applications. Full article

This paper proposes a solution for the design of high-performance propellers optimized for various flight conditions. Considering both propulsion and electric motor efficiencies, a new design optimization methodology is proposed. The optimization of the electric propulsive system is directly achieved by simultaneously analyzing the aerodynamic performance of the propeller and the motor. This study is focused on small, low-speed Remotely Piloted Aircraft Systems, addressing the design of fixed pitch propellers that operate efficiently over the entire speed range. The aerodynamic methodology uses combined blade element and momentum theory, which is adequate for a preliminary design phase with low computational time. For the aerodynamic coefficients of the airfoils used in these applications, at low Reynolds numbers, a new database was developed that incorporates airfoil experimental data and analytical methods to cover a wide range of angles of attack, beyond stall. For the modelling of the motor behavior, an idealization of the circuit was carried out, which considers its basic electric parameters. The results show significant improvements with respect to the information available for a current commercial propeller. Full article

Accurate gender identification from written text is critical for author profiling, recommendation systems, and demographic analytics in digital ecosystems. This study introduces a scalable framework for gender classification in Turkish, combining contextualized BERTurk and subword-aware FastText embeddings with three meta-heuristic feature selection algorithms: Genetic Algorithm (GA), Jaya and Artificial Rabbit Optimization (ARO). Evaluated on the IAG-TNKU corpus of 43,292 balanced Turkish news articles, the best-performing model—BERTurk+GA+LSTM—achieves 89.7% accuracy, while ARO reduces feature dimensionality by 90% with minimal performance loss. Beyond in-domain results, exploratory zero-shot and few-shot adaptation experiments on Turkish e-commerce product reviews demonstrate the framework’s transferability: while zero-shot performance dropped to 59.8%, few-shot adaptation with only 200–400 labeled samples raised accuracy to 69.6–72.3%. These findings highlight both the limitations of training exclusively on news articles and the practical feasibility of adapting the framework to consumer-generated content with minimal supervision. In addition to technical outcomes, we critically examine ethical considerations in gender inference, including fairness, representation, and the binary nature of current datasets. This work contributes a reproducible and linguistically informed baseline for gender profiling in morphologically rich, low-resource languages, with demonstrated potential for adaptation across domains such as social media and e-commerce personalization. Full article

Digital Entrepreneurial Capability (DEC) is the integrated and learnable capacity that equips individuals, or founding teams, to sense, evaluate, and exploit entrepreneurial opportunities within digitally intermediated, platform-centric markets. The construct synthesises four interlocking elements. First, it requires technical dexterity: mastery of data engineering, AI-driven analytics, low-code development, cloud orchestration, and cybersecurity safeguards. Second, it draws on accumulated human capital—formal education, sector experience, and tacit managerial know-how that ground vision in operational reality. Third, DEC hinges on an opportunity-seeking mindset characterised by cognitive alertness, creative problem framing, a high need for achievement, and autonomous motivation. Finally, it depends on calculated risk tolerance, encompassing the ability to price and mitigate economic, technical, algorithmic, and competitive uncertainties endemic to platform economies. When these pillars operate synergistically, entrepreneurs translate digital affordances into scalable, resilient business models; when one pillar is weak, capability bottlenecks arise and ventures falter. Because each pillar can be intentionally developed through education, deliberate practice, and ecosystem support, DEC serves as a practical roadmap for stakeholders. It now informs scholarship across entrepreneurship, information systems, innovation management, and public-policy disciplines, and guides interventions ranging from curriculum design and accelerator programming to due-diligence heuristics and national digital literacy initiatives. Full article

Microplastics (MPs) are increasingly recognized as widespread environmental contaminants, with confirmed presence in human tissues and biological fluids through ingestion, inhalation, and direct systemic exposure. Their potential impacts on human health have become an important subject of scientific investigation. The detection and quantification of MPs, particularly nanoplastics, in complex biological matrices remain challenging because of their low concentrations, diverse physicochemical properties, and interference from organic and inorganic matter. This review presents a critical assessment of current methods for the separation and detection of MPs from human-relevant samples. It examines pre-treatment, separation, and analytical approaches including physical filtration, density-based separation, chemical and enzymatic digestion, vibrational spectroscopy, thermal analysis, and electron microscopy, highlighting their principles, advantages, and limitations. Key challenges such as low sample throughput, absence of standardized procedures, and the difficulty of nanoplastic detection are identified as major barriers to accurate exposure assessment and risk evaluation. Recent advances, including functionalized adsorbents, improved anti-fouling membranes, integrated microfluidic systems, and artificial intelligence-assisted spectral analysis, are discussed for their potential to provide sensitive, scalable, and standardized analytical workflows. By integrating current challenges with recent innovations, this review aims to guide multidisciplinary research toward the development of reliable and reproducible detection strategies that can support MPs exposure assessment and inform evidence-based health policies. Full article