Search Results (950)

The Metaverse—an evolving convergence of virtual and physical realities—has emerged as a transformative platform, particularly within the fashion and retail industries. Its immersive nature aligns closely with the principles of flow theory, which describes the optimal psychological state of deep engagement and enjoyment. This study investigates the dynamics of fashion retail shopping in the Metaverse through a novel multi-criteria decision-making (MCDM) framework. Specifically, it integrates the CRITIC and ARAS methods within a spherical fuzzy environment to address decision-making under uncertainty. Flow theory is employed as the theoretical lens, with its dimensions serving as evaluation criteria. By incorporating spherical fuzzy sets, the model accommodates expert uncertainty more effectively. The findings provide empirical insights into the relative importance of flow constructs in shaping immersive consumer experiences in Metaverse-based retail environments. This study offers both theoretical contributions to the literature on digital consumer behavior and practical implications for fashion brands navigating immersive virtual ecosystems. Sensitivity analyses and comparative validation further demonstrate the robustness of the proposed framework. Full article

This paper analyzes how the generalized uncertainty principle (GUP) affects the thermodynamic properties in a regular black hole spacetime in the context of $f(\mathcal{Q},{\mathcal{B}}_{\mathcal{Q}})$ symmetric teleparallel gravity, with an arbitrary action f as a function of non-metric scalar $\mathcal{Q}$ and the boundary ${\mathcal{B}}_{\mathcal{Q}}$. We analyze a GUP-influenced semi-classical technique in regular black hole spacetime that incorporates the quantum tunneling mechanism. The GUP-influenced temperature results show that the GUP term reduced the vector particles’ radiation in the context of $f(\mathcal{Q},{\mathcal{B}}_{\mathcal{Q}})$ gravity. Moreover, we explore the GUP-influenced entropy as well as the GUP-influenced emission energy, it can help to explain the complex interactions between quantum gravity and astrophysics and highlights the important role of GUP-influenced thermodynamic properties (Hawking temperature, entropy and emission energy) in regular black hole spacetime in the context of $f(\mathcal{Q},{\mathcal{B}}_{\mathcal{Q}})$ gravity. We graphically analyze the effects of different parameters on black hole geometry. Full article

With the development of uncertain finance, uncertain stock models have become increasingly popular for modeling stock prices. This paper explores the symmetric properties inherent in the uncertain mean-reverting stock model, particularly in the structure of its differential equations and the resulting pricing formulas. The primary findings comprise the derivation of explicit pricing formulas, via uncertain differential equations, for European, American, Asian, and geometric average Asian options under the uncertain mean-reverting stock model. The symmetry in the inverse uncertainty distributions and the duality between call and put options are highlighted, demonstrating the model’s alignment with symmetric financial principles. Furthermore, several numerical examples are provided to illustrate the applicability and the symmetry-related characteristics of the derived formulas. Full article

Demand-Driven Material Requirements Planning (DDMRP) has been proposed as a solution for managing uncertainty and variability in supply chains by combining decoupling, buffer management and demand-driven planning principles. A key element of DDMRP is its inventory replenishment model, which relies on dynamically adjusted inventory buffers rather than fixed stock levels. However, parameterization of these buffers often involves subjective choices, raising concerns about consistency and performance. This paper assesses the DDMRP replenishment model through discrete-event simulation of a multi-echelon, capacity-constrained production system. Two alternative formulations of the safety stock term in the red zone are compared: the original factor-based approach and a revised formula that incorporates measurable variability coefficients. While both safety stock formulations yield similar numerical results, the revised formula enhances transparency and reduces subjectivity. Assessing the impact of introducing a buffer for components in addition to a finished goods buffer further shows that the components buffer can reduce finished goods inventory requirements while maintaining service levels. These findings contribute to a better understanding of the DDMRP replenishment model, offering practical insights for parameter selection and supply chain design. Full article

Reliable schedule-risk estimation in hybrid software development lifecycles is strategically important for organizations adopting AI in software engineering. This study addresses that need by transforming routine process telemetry (CI/CD, SAST, traceability) into explainable, quantitative predictions of completion time and rework. This paper introduces an integrated probabilistic model of the hybrid software development lifecycle that combines Generalized Evaluation and Review Technique (GERT) network semantics with I-AND synchronization, explicit artificial-intelligence (AI) interventions, and a fuzzy treatment of epistemic uncertainty. The model embeds two controllable AI nodes–an AI Requirements Assistant and AI-augmented static code analysis, directly into the process topology and applies an analytical reduction to a W-function to obtain iteration-time distributions and release-success probabilities without resorting solely to simulation. Epistemic uncertainty on critical arcs is represented by fuzzy intervals and propagated via Zadeh’s extension principle, while aleatory variability is captured through stochastic branching. Parameter calibration relies on process telemetry (requirements traceability, static-analysis signals, continuous integration/continuous delivery, CI/CD, and history). A validation case (“system design → UX prototyping → implementation → quality assurance → deployment”) demonstrates practical use: large samples of process trajectories are generated under identical initial conditions and fixed random seeds, and kernel density estimation with Silverman’s bandwidth is applied to normalized histograms of continuous outcomes. Results indicate earlier defect detection, fewer late rework loops, thinner right tails of global duration, and an approximately threefold reduction in the expected number of rework cycles when AI is enabled. The framework yields interpretable, scenario-ready metrics for tuning quality-gate policies and automation levels in Agile/DevOps settings. Full article

Uncertainty from heterogeneous degradation paths, limited experimental samples, and exogenous perturbations often complicates accelerated lifetime modeling and prediction. To confront these intertwined challenges, a Wiener process-based robust framework is developed. The proposed approach incorporates random-effect structures to capture unit-to-unit variability, adopts interval-based inference to reflect sampling limitations, and employs a hybrid estimator, combining Huber-type loss with a Metropolis–Hastings algorithm, to suppress the influence of external disturbances. In addition, a quantitative stress–parameter linkage is established under the accelerated factor principle, supporting consistent transfer from accelerated testing to normal conditions. Validation on contact stress relaxation data of connectors confirms that this methodology achieves more stable parameter inference and improves the reliability of lifetime predictions. Full article

Global agriculture faces the twin challenges of meeting rising food demand while minimizing environmental impacts, necessitating transformative approaches to nutrient management. Recycled nitrogen fertilizers (RNFs), derived from diverse organic and waste sources such as urine, manure, compost, digestate, biosolids, and struvite, offer a groundbreaking pathway to close nutrient loops, reduce reliance on synthetic inputs, and foster regenerative agroecosystems. This comprehensive review synthesizes peer-reviewed studies published over the last two decades, selected based on relevance, study quality, and applicability to agronomic and environmental outcomes. Unlike earlier reviews that focus on individual RNF types, this work provides a novel cross-sectoral synthesis linking agronomic performance, environmental trade-offs, and socio-economic feasibility within the regenerative agriculture framework. Using a structured analytical framework, we critically assess RNF technologies and applications across agronomic efficacy, ecological implications, economic viability, and socio-regulatory landscapes. Despite promising benefits, including enhanced soil health, greenhouse gas mitigation, and alignment with circular economy principles, widespread RNF adoption remains constrained by logistical complexities, variable nutrient quality, regulatory uncertainties, and social acceptance challenges. By integrating multidisciplinary evidence and identifying system-level synergies and bottlenecks, this review advances a unified understanding of how RNFs can be strategically scaled in regenerative agricultural systems. Key knowledge gaps and integrated research and policy strategies are identified to unlock the full potential of RNFs. Embracing recycled nitrogen within tailored, context-sensitive frameworks has the potential to revolutionize sustainable agriculture, delivering resilient food systems, restoring ecosystem services, and advancing global climate goals. Full article

The development of smart sustainable cities is closely linked to the advancement of city manufacturing, which aims to meet local demand while maintaining economic, social, and environmental balance. This concept is realised in large cities through City Multifloor Manufacturing Clusters (CMFMCs) equipped with City Logistics Nodes (CLNs) that manage intra- and extra-cluster logistics. These flows depend on supplies arriving via Intermodal Logistics Nodes (ILNs) located on city outskirts, where disruptions caused by intermodal supply chain uncertainty can significantly affect production continuity and urban sustainability. This study aims to develop a stochastic inventory management model for city manufacturing clusters operating under intermodal supply chain uncertainty. The model is designed to ensure stable and resilient material supply to city manufacturers by optimising buffer stock (BS) levels, reducing delivery delays, and improving transport and storage efficiency. Based on the Multi-Layer Bayesian Network Method (MLBNM), the model integrates probabilistic reasoning and resilience principles to support decision-making under uncertainty. A simulation-based case study of a representative CMFMC system was used for model verification and validation. The results show that the MLBNM-based approach enhances Sustainable Supply Chain Resilience (SSCR), improves inventory flexibility, and reduces environmental impacts. The study contributes to theory and practice by providing a quantitative framework for ensuring resilient and sustainable inventory management in city manufacturing systems. Full article

Antimicrobial resistance (AMR) constitutes a major global health challenge, driven significantly by inappropriate antibiotic use in human medicine. Despite the existence of evidence-based guidelines, variability in antibiotic prescribing persists, influenced by psychosocial factors, diagnostic uncertainty, patient expectations, and local prescribing cultures. Outpatient care, the setting in which most antibiotics are prescribed, is particularly affected by such challenges. Traditional top-down interventions, such as national guidelines, often fail to achieve sustained behavioral change among prescribers. In this comprehensive review, we provide an overview of the psychological and behavioral factors influencing antimicrobial stewardship (AMS) implementation, as well as describe a bottom-up project working to meet these challenges: the “Antibiotic Therapy in Bielefeld” (AnTiB) initiative. AnTiB employs a cross-sectoral strategy aimed at developing rational prescribing culture by means of locally developed consensus guidelines, interdisciplinary collaboration, and regularly held trainings. By addressing both the organizational and psychological aspects of prescribing practices, AnTiB has facilitated a harmonization of antibiotic use across specialties and care interfaces at the local level. The initiative’s success has led to its expansion within Germany, including through the creation of the AMS-Network Westphalia Lippe and the development of AnTiB-based national pediatric recommendations. These projects are all grounded in social structures designed to strengthen the long-term establishment of AMS measures. Our efforts underscore the importance of considering local social norms, professional network, and real-world practice conditions in AMS interventions. Integrating behavioral and social science approaches into outpatient antimicrobial stewardship—exemplified by the practitioner-led AnTiB model—improves acceptability and alignment with stewardship principles; wider adoption will require local adaptation, routine outpatient resistance surveillance, structured evaluation, and sustainable support. Full article

This work presents the design, the development and the experimental validation of a portable, low-cost sensing system for the detection of waterborne pollutants. The proposed system is based on Electrochemical Impedance Spectroscopy and PPF+Ni nanomembrane sensors. Designed in response to the increasing demand for in situ water quality monitoring, the system integrates a simplified, scalable EIS acquisition architecture compatible with microcontroller-based platforms. The sensing configuration utilises the voltage divider principle, ensuring simplicity in signal conditioning by allowing compatibility with different electrode types through passive impedance matching. In addition, new merit figures have been proposed and implemented to analyse the measures. The proposed platform was experimentally characterised for its measurement stability, accuracy and environmental robustness. Sensitivity tests using benzoquinone as a target analyte demonstrated the capability of detecting concentrations as low as 0.1 mM with a monotonic response over increasing concentrations. A comparative study with a commercial electrochemical system (PalmSens4) under identical conditions highlighted the higher resolution and practical advantages of the proposed method despite operating with a lower impedance range. Additionally, the system exhibited reliable discrimination across tested concentrations and greater adaptability for integration into field-deployable environmental monitoring platforms. Future developments will focus on optimising selectivity through new sensor materials and analytical modelling of uncertainty propagation in the analysis based on defined figures of merit. Full article

An extension of the Dirac equation for an initially massless particle carrying an electric charge, assumed to be embedded via minimal coupling into an external fluctuating electromagnetic four-potential of the vacuum, is suggested. We conjecture that appropriate averages of the four-vector can lead to observable quantities, such as a particle mass in its rest frame. The conditions on the potential mean values to become gauge-invariant are obtained. The mass is found to be proportional to the magnitude of the charge times the associated mean Lorentz scalar of the four-potential, and the relation holds for both spacelike and timelike types of four-vectors. For the latter, the extended Dirac equation violates Lorentz covariance, but the violation can be argued to occur within a time scale allowed by the uncertainty principle. For larger times, the particle has acquired a mass and Lorentz covariance is restored. This mathematical scenario is applied to acquire estimates of the neutrino millicharge and magnetic moment, in good agreement with the present upper bounds obtained experimentally. The issue of unstable particle decay is considered by focusing, for illustration, on the main decay channels of the selected particles. From the lifetime of the $\tau$ lepton, a lower bound of the effective neutrino mass is predicted, which can be tested in future experiments. Full article

Uncertainty quantification (UQ) for open-ended language generation remains a critical yet underexplored challenge, particularly in settings where token-level log-probabilities are available during decoding. We present Token-Entropy Conformal Prediction (TECP), which treats a log-probability-based token-entropy statistic as a nonconformity score and integrates it with split conformal prediction to construct prediction sets with finite-sample coverage guarantees. We work in a white-box regime in which per-token log-probabilities are accessible during decoding. TECP estimates episodic uncertainty from the token-entropy structure of sampled generations and calibrates thresholds via conformal quantiles to ensure provable error control. Empirical evaluations across six large language models and two QA benchmarks (CoQA and TriviaQA) show that TECP consistently achieves reliable coverage and compact prediction sets, outperforming prior self-UQ methods. These results provide a principled and efficient solution for trustworthy generation in white-box, log-probability-accessible LLM settings. Full article

Mass-balanced compartmental systems defy classical deterministic entropy measures since both metric and topological entropy vanish in dissipative dynamics. By interpreting open compartmental systems as absorbing continuous-time Markov chains that describe the random journey of a single representative particle, we allow established information-theoretic principles to be applied to this particular type of deterministic dynamical system. In particular, path entropy quantifies the uncertainty of complete trajectories, while entropy rates measure the average uncertainty of instantaneous transitions. Using Shannon’s information entropy, we derive closed-form expressions for these quantities in equilibrium and extend the maximum entropy principle (MaxEnt) to the problem of model selection in compartmental dynamics. This information-theoretic framework not only provides a systematic way to address equifinality but also reveals hidden structural properties of complex systems such as the global carbon cycle. Full article

By extending the four-dimensional semi-Riemann geometry to higher-dimensional Finsler/Hamilton geometry, the canonical quantization of the fundamental metric tensor of general relativity, i.e., an approach that tackles a geometric quantity, is derived. With this quantization, the smooth continuous Finsler structure is transformed into a quantized Hamilton structure through the kinematics of a free-falling quantum particle with a positive mass, along with the introduction of the relativistic generalized uncertainty principle (RGUP) that generalizes quantum mechanics by integrating gravity. This transformation ensures the preservation of the positive one-homogeneity of both Finsler and Hamilton structures, while the RGUP dictates modifications in the noncommutative relations due to integrating consequences of relativistic gravitational fields in quantum mechanics. The anisotropic conformal transformation of the resulting metric tensor and its inverse in higher-dimensional spaces has been determined, particularly highlighting their translations to the four-dimensional fundamental metric tensor and its inverse. It is essential to recognize the complexity involved in computing the fundamental inverse metric tensor during a conformal transformation, as it is influenced by variables like spatial coordinates and directional orientation, making it a challenging task, especially in tensorial terms. We conclude that the derivations in this study are not limited to the structure in tangent and cotangent bundles, which might include both spacetime and momentum space, but are also applicable to higher-dimensional contexts. The theoretical framework of quantization of general relativity based on quantizing its metric tensor is primarily grounded in the four-dimensional metric tensor and its inverse in pseudo-Riemannian geometry. Full article

The challenge of optimally controlling energy storage systems under uncertainty conditions, whether due to uncertain storage device dynamics or load signal variability, is well established. Recent research works tackle this problem using two primary approaches: optimal control methods, such as stochastic dynamic programming, and data-driven techniques. This work’s objective is to quantify the inherent trade-offs between these methodologies and identify their respective strengths and weaknesses across different scenarios. We evaluate the degradation of performance, measured by increased operational costs, when a reinforcement learning policy is adopted instead of an optimal control policy, such as dynamic programming, Pontryagin’s minimum principle, or the Shortest-Path method. Our study examines three increasingly intricate use cases: ideal storage units, storage units with losses, and lossy storage units integrated with transmission line losses. For each scenario, we compare the performance of a representative optimal control technique against a reinforcement learning approach, seeking to establish broader comparative insights. Full article