Search Results (3,727)

High levels of photovoltaic (PV) generation in distribution networks create substantial uncertainty and voltage variability, which limits the effectiveness of conventional deterministic distribution network reconfiguration (DNR) strategies. In PV-dominated feeders, rare but severe operating conditions may considerably influence active power losses and voltage stability. To address this challenge, this paper proposes a risk-informed optimization framework for DNR that combines reinforcement learning with probabilistic performance assessment. A Deep Q-Network (DQN) agent is designed to support the selection of feasible radial switching configurations by interacting with the distribution network environment. Throughout the learning process, candidate network topologies are evaluated through radial load flow calculations, while a composite objective function incorporating active power losses and voltage deviation steers the agent toward improved configurations. The training stage is based on deterministic performance indices; however, the final reconfiguration solution is assessed under uncertainty to examine its operational robustness. For this purpose, extensive Monte Carlo simulations are performed to capture the stochastic behavior of PV generation and load demand. Tail-based risk metrics, including Value at Risk (VaR) and Conditional Value at Risk (CVaR), are computed for both loss and voltage deviation indices, providing insight into the performance of the selected configuration under unfavorable operating scenarios. The proposed framework is first validated on the IEEE 33-bus distribution system and then further investigated on the IEEE 69-bus network. The obtained results demonstrate that the proposed DQN-based reconfiguration approach can enhance voltage profiles and reduce power losses under high PV penetration. In addition, the probabilistic analysis identifies meaningful trade-offs between efficiency and voltage robustness, highlighting the importance of considering uncertainty-driven risk assessment in computational decision-making for modern active distribution networks. Full article

Decisions about vector-borne zoonotic disease control often have to be made before the evidence is complete. Human surveillance, entomological observations, animal reservoir or sentinel data, environmental indicators, and modelling outputs may provide warning before human disease patterns are clear. In some settings, early action can prevent harm, yet it can also impose social, economic, ecological, animal-welfare, liberty-related, and trust-related burdens. Technical risk assessment remains necessary, although it may need to be complemented by ethical analysis. In this conceptual manuscript, we propose a seven-step One Health ethical decision-making framework that links integrated evidence and transmission plausibility to precaution, proportionality, equity, One Health trade-offs, legitimacy, feedback, impact evaluation, and adaptation. Its contribution lies not in introducing new ethical principles, but in integrating them into an iterative governance sequence and proposing medical bioethics mediation for persistent value conflict. Illustrative domains include West Nile virus, dengue, Zika, Crimean–Congo haemorrhagic fever, and other mosquito-, tick-, and sand fly-borne zoonotic threats. Full article

High renewable penetration makes imbalance settlement inseparable from the physical decisions governing reserve procurement and flexibility activation. This paper develops a decision-focused learning-based optimization framework that trains renewable-deviation and flexible-resource deliverability representations through downstream dispatch, reliability, and settlement consequences. The mathematical contribution is a settlement-aware learning objective that couples learned uncertainty, resource-time credible-capacity certification, network-constrained multi-stage dispatch, and counterfactual marginal-contribution allocation while retaining an exact revenue-adequacy identity. The 33-node Zhangjiakou-type regional case uses 15 min intervals and comprises five resource classes: independent storage, data-center flexibility, industrial adjustable load, commercial demand response, and electric-vehicle aggregation. Relative to a fixed-ratio reserve rule, the proposed method lowers the regional balancing cost from 950 to 618 thousand USD (34.9%), achieves 97.8% renewable accommodation, limits the shortage probability to 0.7%, and attains a settlement-fairness index of 0.92. The framework solves a 500-asset instance in 118 s. External validation uses 4027 half-hour observations from the 2025 Elexon/BMRS market, including measured wind and solar output, day-ahead forecasts, load, imbalance prices, and procured-reserve prices. On the 1487-interval December test set, the proposed model reduces the replay cost from 2953.3 to 2598.2 thousand GBP (12.0%), decreases the shortage-interval frequency from 4.64% to 1.28%, and reaches 99.74% renewable accommodation. Comparisons with forecast-then-optimize, Wasserstein distributionally robust optimization, off-policy reinforcement learning, and graph-based behavioral cloning establish that the improvement comes from jointly learning which uncertainty matters for dispatch and which flexible capacity is deliverable. Full article

In recent years, interest in artificial intelligence has grown significantly, particularly in the development of advanced computational models for supply chain decision-making. Inventory management is one of the areas in which intelligent algorithms can support demand forecasting, replenishment, stock control, and operational optimization under uncertainty. This study presents an updated systematic literature review of intelligent algorithms applied to inventory management. The review followed PRISMA 2020 guidelines and combined database searches in Scopus, ScienceDirect, Web of Science, IEEE Xplore, SpringerLink, Taylor & Francis, and complementary manual searching. The original search covering January 2020 to December 2024 was updated in July 2026 to include studies published or available online up to June 2026. After applying strict eligibility criteria, 37 primary studies with quantitative evidence were included. The updated corpus confirms the predominance of deep learning, reinforcement learning, and hybrid intelligent models, while also showing the recent emergence of Transformer-based, graph neural network, multi-agent reinforcement learning, and prescriptive analytics approaches. The most frequent application areas were inventory control, inventory optimization, replenishment decision-making, and demand forecasting. Reported improvements were mainly associated with cost efficiency, service level, stockout reduction, and system performance; however, the magnitude of improvement varied across algorithms, data sources, sectors, and simulation or real-world settings. Overall, intelligent algorithms represent a relevant tool for improving inventory management, but their adoption requires careful validation, transparent reporting, and alignment with the operational context. Full article

Fuzzy multi-criteria decision-making is important for technology assessment when expert judgments contain uncertainty, hesitation, and inconsistent evidence. This study develops a T-Spherical Fuzzy-Valued Neutrosophic Set (T-SFVNS)-based MEREC-EDAS framework for evaluating low-carbon cooling and energy-management technologies for data centers. Expert linguistic assessments are represented by T-Spherical Fuzzy-Valued Neutrosophic Numbers and aggregated before a score function is used at the explicit scalarization boundary. Standard MEREC then derives objective criterion weights from criterion-removal effects, and standard EDAS ranks alternatives by their positive and negative distances from the average score profile. The application evaluates nine technologies against ten criteria using assessments from thirty domain specialists. The corrected MEREC calculation assigns the greatest weights to carbon reduction potential (0.127), electricity demand reduction (0.125), maintenance complexity (0.124), operational cost efficiency (0.123), and cooling efficiency (0.123). The final ranking is Direct-to-Chip Liquid Cooling, Liquid Immersion Cooling, AI-Enabled Energy Management, Water-Side Free Cooling, Free-Air Cooling, Rear-Door Heat Exchanger Cooling, Hot/Cold Aisle Containment, Renewable-Powered Cooling, and Thermal Storage-Assisted Cooling. Weight perturbation, q-parameter, leave-one-expert-out, alternative-deletion, dominated-alternative, and multi-method comparisons show that the leading tier is robust, although the exact order of the two liquid-cooling technologies is sensitive in some scenarios. The findings provide a transparent and reproducible decision-support basis while explicitly acknowledging the information compression and rank-reversal limitations of score-based MCDM. Full article

Convolutional Neural Networks (CNNs) are fundamental tools for image analysis and recognition in monitoring systems, particularly in photovoltaic (PV) installations, where visual inspection and thermal imaging play crucial roles in anomaly detection and energy optimization. This publication presents a Hybrid Fuzzy Convolutional Neural Network (HFCNN) that integrates a fuzzy dense layer utilizing Ordered Fuzzy Numbers (OFNs) into the CNN architecture. The architecture is additionally validated on the public ELPV benchmark of 2624 electroluminescence images of photovoltaic cells, where the HFCNN with Mean of Maxima defuzzification attains classification quality statistically indistinguishable from a CNN baseline while using a four times smaller dense layer and training two to three times faster. The methodology combines the feature extraction capabilities of traditional CNNs with the uncertainty handling properties of fuzzy logic. Experiments using the MNIST dataset demonstrate that HFCNN with Mean of Maxima (MOM) defuzzification achieves comparable accuracy to standard CNNs while using significantly fewer parameters (75% reduction in the dense layer). This efficiency gain is advantageous for deployment on edge computing devices. This work constitutes a methodological contribution—establishing, for the first time, the feasibility of integrating Ordered Fuzzy Numbers into CNN architectures without requiring expert membership function design. While the current study validates this approach on MNIST, actual photovoltaic applications require dedicated future research on real PV thermal imagery. Nevertheless, the proposed HFCNN framework could potentially support practical photovoltaic energy system applications in detecting panel degradation, performance anomalies, and autonomous decision-making in large-scale PV installations. Full article

Laboratory friction measurements are central to material screening for High-Friction Surface Treatments (HFST), yet metrological aspects including a proposed metrological traceability framework or uncertainty, and reproducibility are consistently underreported. This study applies a metrology-aligned framework to British Pendulum Tester (BPT) measurements performed in three states: dry, wet (water), and water–soap to assess operational adhesion and hysteresis components, document traceability to the International System of Units (SI), and report GUM-style uncertainty with covariance for the adhesion difference. Measurements were obtained for calcined bauxite (CB) and rhyolite (Rhy) in HFST and Coarse gradations across seven polishing protocols (baseline; LAA-1000/2000; MDA-105/180; PSV-10 h/20 h), using n = 3 replicates per Treatment × Material × State. Replicate-based Type A uncertainties were combined with instrument/system Type B components geometry, slider hardness, temperature, soap film consistency, and calibration to yield combined u_c and expanded uncertainty U_(k=2). The Wet–Soap cross treatment physical correlation ellipses demonstrate strong positive correlations (r ≈ 0.88–0.98; p < 0.001) between wet and soap states, supporting the interpretation that wet friction is governed primarily by hysteresis, with adhesion acting as a small offset under BPT kinematics. The slider hardness and temperature typically control the wet state uncertainty budget; including measured Wet–Soap covariance reduces adhesion U_(k=2) by up to ~6.5%, consistent with GUM’s law of uncertainty propagation. Together, these measurement science practices enhance road safety by making laboratory friction data traceable, comparable, and decision-ready. Full article

Decision-making in organizational and socio-technical systems often involves a responsible superior expert who must integrate committee judgments expressed under linguistic uncertainty, hesitation, and heterogeneous expertise. This paper proposes a systems-based superior–committee group decision-support method for criteria weighting under linguistic intuitionistic fuzzy uncertainty. First, the best–worst method is extended by using linguistic intuitionistic fuzzy numbers to represent pairwise preference information with linguistic membership, non-membership, and indeterminacy degrees. A utility transformation is then introduced to convert linguistic intuitionistic fuzzy comparisons into numerical preference values, enabling criteria weights to be derived through linear programming models. Second, a two-stage group decision-support framework is developed for superior–committee decision structures. In the first stage, committee expert influence is calculated by integrating prior expert weights obtained from the superior expert’s evaluation with judgment-derived expert weights based on certainty and agreement. In the second stage, the final criteria weights are obtained by combining the superior expert’s judgments with the weighted committee judgments. A constructed UAV criteria-weighting case and complementary numerical analyses are presented to illustrate the calculation process and examine the behavior of the proposed method. The results show that the framework provides a transparent mechanism for representing uncertain preferences, assigning expert influence, and deriving interpretable criteria weights in superior–committee group decision systems. Full article

Amid increasing upstream raw-material disruptions and battery supply uncertainty, NEV manufacturers may obtain emergency supply from vertically integrated competitors after a disruption, but must incur technology conversion costs. We develop a differentiated Cournot duopoly model with an outsourcing manufacturer and an integrated manufacturer to compare equilibrium, profit, and welfare outcomes with and without emergency procurement cooperation. The results show that cooperation has a clear feasibility boundary jointly determined by market potential, relative costs, and conversion costs, giving rise to competition-only, cooperation-only, and coopetition regimes. The cooperation option reshapes pre-disruption quantity decisions: the outsourcing manufacturer increases regular procurement, while the integrated manufacturer reduces its own-brand output by a larger amount. Within the cooperation region, higher conversion costs continuously reduce the outsourcing manufacturer’s profit, whereas the integrated manufacturer’s profit can be U-shaped. Greater product substitutability makes cooperation more fragile and widens the divergence between private profit incentives and supply-chain resilience. Consumer surplus and social welfare may also move in different directions, depending on the outsourcing manufacturer’s benchmark market share and conversion cost. A Nash-bargaining extension confirms the robustness of the activation condition and pre-disruption quantity-adjustment mechanism, while reducing emergency markups and improving consumer surplus. Full article

Prototyping has evolved from a simple representational artifact into a central mechanism for learning, communication, risk reduction, and decision-making in engineering design. Despite its widespread adoption across engineering disciplines, existing research remains fragmented across domains, methodologies, and application contexts, making it difficult to distinguish broadly applicable principles from context-specific practices. This paper presents a critical review of prototyping research in engineering design, synthesizing evidence from peer-reviewed journal articles and conference papers from foundational studies of the 1980s to recent developments in rapid prototyping, additive manufacturing, digital engineering, and Industry 4.0 systems. A thematic literature review was conducted to identify recurring principles, domain-dependent variations, emerging trends, and persistent limitations in current prototyping practices. The review examines key factors influencing prototyping effectiveness, including purpose, fidelity, timing, stakeholder involvement, modeling and analysis, risk management, economic considerations, and learning-oriented iteration. Particular attention is given to how uncertainty influences prototyping decisions and the ways in which different uncertainty conditions influence the selection, scope, and implementation of prototyping activities. The findings indicate that prototyping is best understood as a context-dependent decision-support activity whose effectiveness depends on the uncertainties, constraints, stakeholders, and design objectives associated with a specific engineering problem. Although several common principles emerge across engineering domains, substantial differences exist in how prototypes are used to support design decisions and system validation. The review identifies research gaps related to uncertainty-driven fidelity selection, integration of modeling, experimentation, and verification activities, and the limited availability of systematic guidance for selecting prototyping strategies across diverse engineering contexts. Future research should focus on generalized prototyping frameworks, quantitative decision-support methods for uncertainty management, enhanced stakeholder integration, and the continued convergence of physical and virtual prototyping environments in next-generation engineering systems. Full article

Public sector value generation increasingly relies on data provided by information systems, yet the impact of data quality (DQ) on decision-making remains underexamined. While prior research has identified associations between DQ and decision outcomes, strong causal evidence remains scarce. This study addresses the issue in two phases: first, through a scoping review of experimental research on the impact of value-critical DQ dimensions—accuracy, completeness, consistency, and timeliness—and second, through methodological development. Searches across Scopus, Web of Science, IEEE, and PsycInfo, supplemented by citation searching and Google Scholar to avoid inclusion bias, identified 20 studies after screening and full-text review. Rather than excluding studies based on methodological quality, the review critically examined existing experimental designs and their findings. The literature revealed substantial weaknesses, including unclear distinctions between objective and subjective variables, weak manipulations, ambiguous operationalizations, and small sample sizes. Consequently, existing understanding of DQ impact remains limited and often inconclusive. To address these limitations, the article proposes a formal model for DQ experiments and presents a proof of concept for completeness using open data. The findings suggest that completeness influences value appraisal and subsequent choice behavior, particularly under higher uncertainty and larger value differences between alternatives. Full article

Adaptive decision-making has emerged as an important direction in audio classification. Reinforcement learning (RL) and multi-armed bandit (MAB) methods offer principled frameworks for sequential and localized decision-making. However, despite growing interest in these approaches and the availability of review studies on audio classification and audio-based learning, their role within the audio classification pipeline remains fragmented and has not been systematically synthesized. This study presents a systematic review of RL- and MAB-based approaches in audio classification, to characterize how adaptive decision-making is formulated, where it is applied within the pipeline, and what impact it has on system performance, robustness, and efficiency. The review followed PRISMA guidelines. A literature search across IEEE Xplore, Scopus, Nature, and Google Scholar identified 1896 records. Thirty-one studies met the predefined inclusion criteria and data were extracted for analysis. Reporting quality was assessed using the TRIPOD framework, and methodological reliability was evaluated using a domain-specific risk-of-bias tool. The findings show that adaptive decision-making in audio classification is evolving toward a control-centric paradigm. RL-based optimization and control approaches dominated the literature, whereas bandit-based approaches remained comparatively underused. A central finding of the review is a structural mismatch between problem type and method choice. Many adaptive tasks are inherently local and repeated decision problems, yet they are predominantly addressed using full RL frameworks rather than lighter bandit formulations. This review introduces a taxonomy of adaptive decision-making mechanisms and proposes a unified framework that reframes audio classification as a layered decision-making process under uncertainty. The evidence suggests that the future of audio classification lies not only in improved prediction, but in adaptive decision-making architectures capable of managing uncertainty, variability, and real-world deployment constraints. Full article

Skin cancer represents a growing global health burden. Current diagnostic pathways combine clinical examination and dermoscopy with histopathological confirmation; however, overlap between benign and malignant lesions can create diagnostic uncertainty and lead to potentially avoidable biopsies. Electrical impedance spectroscopy (EIS) has emerged as a non-invasive technique with potential for portable and cost-efficient implementation that quantifies the dielectric contrast between malignant and healthy tissue, providing objective information that may support clinical decision-making. This review synthesizes the field across four levels. First, we describe the biophysical origins of the impedance contrast in skin cancer, spanning the cellular, tissue architecture, and molecular scales, together with the equivalent circuit and Cole–Cole frameworks used to interpret it. Second, we examine hardware advances, including electrode–skin interface strategies, flexible and wearable architectures, computational electrode design, and the translation from laboratory prototypes to commercial systems such as Nevisense. Third, we critically appraise clinical evidence from large multicenter trials, focusing on the sensitivity–specificity trade-off and the demonstrated reduction in the number needed to excise. Finally, we discuss emerging frontiers, including artificial intelligence-driven analysis and multimodal fusion with dermoscopy, reflectance confocal microscopy, optical coherence tomography, and near-infrared spectroscopy. We conclude that EIS is most valuable as a complementary component within an integrated, AI-supported multimodal diagnostic framework. Full article

Autonomous vehicles increasingly require capabilities that extend beyond perception towards contextual understanding, semantic reasoning, and explainable decision-making. Knowledge graphs (KGs) have emerged as a promising solution by integrating heterogeneous sensor data, traffic regulations, domain knowledge, and contextual information into unified semantic frameworks. This review systematically examines knowledge graph-based intelligent reasoning in autonomous driving through a PRISMA 2020-guided analysis of 47 peer-reviewed studies identified from the literature published from 1 January 2018 to 31 January 2026. The findings reveal that semantic scene understanding and ontology-based representations currently dominate the field, with 66.0% of studies integrating knowledge graphs with deep learning approaches. Neuro-symbolic methods and explainable AI components were identified in 38.3% and 34.0% of publications, respectively, indicating increasing research interest in hybrid and transparent AI architectures. The analysis further demonstrates that 80.9% of studies remain limited to benchmark datasets and simulation environments, whereas only 19.1% provide real-world validation, suggesting relatively low technological maturity and limited industrial readiness. Although KG-enabled approaches substantially improve contextual awareness, hidden hazard anticipation, and explainability compared with conventional perception-centric architectures, major challenges remain regarding scalability, ontology interoperability, semantic error propagation, real-time reasoning, and certification requirements. The review identifies the convergence of knowledge graphs, large language models, and neuro-symbolic AI as a promising direction for next-generation autonomous driving systems. Future research should therefore focus on uncertainty-aware reasoning, adaptive explainability, standardised evaluation methodologies, and certification-oriented real-world deployment strategies. Full article

The increasing use of autonomous mobile robots (AMRs) in internal logistics systems improves operational efficiency. However, it also introduces challenges related to safety, reliability, sensor-based monitoring and human–robot interaction. This study proposes a sensor-driven Digital Twin and fuzzy decision-support framework for operational risk monitoring in AMR-based transportation systems. The proposed approach integrates Digital Twin technology with fuzzy logic methods to support continuous sensing, operational data acquisition, and data-driven risk evaluation in autonomous logistics environments. In the proposed framework, the Digital Twin acts as a continuous monitoring and early-warning environment. It enables continuous observation of system states, robot condition, navigation performance, traffic intensity and operational disturbances. To support decision-making under uncertainty, the fuzzy Analytic Hierarchy Process (fuzzy AHP) is applied to determine the relative importance of selected safety and reliability indicators. These indicators include condition monitoring parameters, mean time between failures, sensor-related disturbances and task completion performance. Subsequently, a hierarchical Mamdani fuzzy inference system is used to evaluate the operational risk level of the transportation system based on aggregated KPI values derived from Digital Twin data. The applicability of the proposed approach is illustrated through a case study involving multiple AMRs operating in a dynamic intralogistics environment. The results indicate that the integration of sensor-based Digital Twin monitoring with fuzzy decision-support mechanisms improves operational risk visibility and supports more effective risk identification and management in Industry 4.0 intralogistics systems. Full article