Search Results (1,220)

The Guidelines for Sustainable Aquaculture (GSAs) emphasize that formulating proper regional development plans is essential for achieving sustainable growth in aquaculture. Conventional research has limitations in allocating resources from a regional scale, subject to various constraints relating to the social, economic, environmental, and other domains. To fill this research gap, this study proposes an inexact regional aquaculture system planning model (IRAM) to optimally allocate resources including energy, water, area, media, and labor among various aquaculture species across multiple aquafarms under uncertainty. The model is applied to Fujian Province, China, over a 15-year planning horizon (2026–2040, three 5-year periods). As model-based scenario insights derived under specific assumptions, the main findings are as follows: (i) the system cost would increase by [18.75, 20.00]%, while the product output would greatly rise by [41.57, 47.54]% from period 1 to period 3; (ii) renewable energy (wind and solar) would supply [60.32, 81.05]% of the total electricity by period 3; (iii) shallow-sea culture ([48.6, 50.2]%) and pond culture ([17.6, 18.8]%) would receive the highest media allocation via internet platforms, supporting community communication; and (iv) pollutant and carbon emissions would decline due to the adoption of low-carbon feed and renewable energy penetration. The findings are scenario-dependent, and their practical applicability relies on the sensitivity analysis and model code/data availability discussed in the main text. One limitation lies in its inadequate ability to handle stochastic and fuzzy information commonly encountered in real-world planning problems. Full article

Cost control in engineering, procurement, and construction (EPC) public works is shaped by interacting drivers, nonlinear feedback, and qualitative judgments. Existing studies usually apply the three relevant method families separately: DEMATEL-ISM maps causal structure but does not propagate hesitation-aware expert judgments into cost trajectories; fuzzy systems represent uncertainty but commonly lack a verified causal hierarchy; and system dynamics (SDs) capture dynamic accumulation but often rely on crisp inputs. The resulting absence of a traceable causal screening to uncertainty to dynamic cost link is the specific gap addressed in this study. We therefore develop a transparent three-stage pipeline combining the Decision-Making Trial and Evaluation Laboratory–Interpretive Structural Modeling (DEMATEL-ISM) method, triangular intuitionistic fuzzy reasoning (TIFR), and SDs. DEMATEL-ISM identifies the causal hierarchy; TIFR represents membership, non-membership, and hesitation in design complexity and human–technology synergy judgments; and SDs evaluate stage-specific cost trajectories. Recalculation from the supplied 17 × 17 direct influence matrix produced a six-level hierarchy in which senior management decision-making capability and the level of integration occupy the two deepest driving levels. For the Urumqi Civic Center case, the baseline terminal cost absolute percentage error was 0.492%. A coordinated intervention scenario shifted the simulated terminal cost by CNY 12.1243 million (6.1%) relative to the baseline; this is a model-based scenario difference, not an observed project saving. Integration had the largest simulated effects on design and transportation costs, whereas senior management decision-making capability had the largest effects on procurement and construction costs. Security cost curves showed a complementary pattern between managerial capability and workers’ professional competence, but no statistical interaction effect is claimed. The framework is intended for within-case scenario comparison and intervention prioritization; multi-project and time-series validation remains necessary. Full article

This paper presents an artificial intelligence (AI)-based energy management and control framework for renewable-powered smart microgrids operating under dynamic conditions. The proposed system integrates photovoltaic (PV) generation, battery energy storage, and grid interaction within a MATLAB/Simulink-R2024B environment to improve operational reliability, energy efficiency, and renewable energy utilization. Seven control scenarios were investigated, including baseline operation, Rule-Based Energy Management System (EMS), Proportional–Integral–Derivative (PID), Model Predictive Control (MPC), Fuzzy Logic Control (FLC), Artificial Neural Network (ANN)-assisted forecasting, and Reinforcement Learning (RL)-based optimization. Comparative results demonstrate progressive performance improvements with increasing controller intelligence. The RL-based EMS achieved the highest operational cost reduction (95%), voltage regulation performance (95%), battery state-of-charge management (95%), renewable energy utilization (92%), grid dependency reduction (92%), overall system efficiency (95%), and an overall performance score of 95.4%. The ANN forecasting model attained a forecasting accuracy of 96.2%, corresponding to a Mean Absolute Percentage Error (MAPE) of 3.8%, while achieving a Mean Absolute Error (MAE) of 1.84 kW, Root Mean Square Error (RMSE) of 2.37 kW, and coefficient of determination ($R^2$) of 0.982. For voltage regulation, the RL controller reduced the RMSE, settling time, and overshoot to 1.50 V, 1.8 s, and 0.5%, respectively, compared with 20.0 V, 15.0 s, and 10.0% for the baseline case. Ultimately, the results demonstrate that AI-driven control strategies substantially enhance microgrid stability, battery utilization, renewable energy penetration, and operational efficiency, providing a practical and scalable solution for next-generation intelligent microgrids. 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

The Fuzzy Cognitive Map (FCM) framework provides a useful tool for representing the complex interdependencies involved in wastewater treatment selection, particularly when social, ecological, climatic, and economic criteria are considered simultaneously. In this study, the FCM approach was applied to compare two wastewater treatment approaches in Ahvaz, Iran: constructed wetlands (CWs) as a nature-based solution and energy-based wastewater treatment plants. The developed model included 30 components and 127 causal links, and was used to examine four scenarios representing CWs, energy-based treatment, a hybrid approach, and direct wastewater discharge. The results showed that both CWs and energy-based solutions had similar effects on public health, while the hybrid scenario produced the greatest improvement. CWs had a positive effect on ecosystem restoration and showed better performance in heavy metal removal, whereas energy-based solutions had a greater negative influence on environmental conditions and climate-related components. In addition, the economic results indicated that CWs were more favorable in terms of capital cost, energy consumption, and operational cost. Sensitivity analysis using ±10% variations in causal weights showed that the main scenario-response patterns remained generally unchanged. Overall, the findings suggest that CWs and energy-based systems each have specific advantages and limitations, while the hybrid approach offers the most balanced performance across the evaluated criteria. This study demonstrates the usefulness of the FCM approach for supporting wastewater management decisions and for identifying trade-offs among treatment alternatives in sustainable water resource planning. Full article

Digital transformation in agricultural production requires automated approaches for monitoring, risk assessment, and decision support under uncertainty. This study proposes an automated risk assessment and control framework for feed production in digital agroengineering systems. The framework is designed for the quantitative evaluation of producer and consumer risks arising during the control of key feed-quality parameters, using the case of feed production for cattle in the OHMK agricultural holding. The proposed approach integrates probabilistic modeling, simulation-based risk estimation, fuzzy logic, expert evaluation, and a multi-agent representation of agroengineering processes. A three-dimensional risk model is developed to represent producer risk, consumer risk, and actuarial risk as interconnected components of a digital control environment. In addition, a fuzzy model is introduced to assess the robustness and digital maturity of management functions, including organization, planning, motivation, and control. Computer experiments based on statistical data for crude protein content in silage demonstrate that control risks depend nonlinearly on measurement uncertainty, parameter variability, and normative thresholds. In the analyzed single-indicator case study, the arithmetic mean of crude protein content in silage was 7.5% of dry matter, the standard deviation was 0.5, and the Weibull approximation parameters were α = 1.0, β = 2.5, and γ = 6.0. Under the most sensitive normative threshold scenario, producer risk increased to approximately 25%, while consumer risk showed a lower but nonlinear increase with measurement uncertainty. The results show that producer risk may reach significant levels when measurement uncertainty becomes comparable with the variability of the controlled parameter. The proposed framework can serve as a computational basis for future automated monitoring, risk-aware control, and decision-support systems in Industry 4.0-oriented agricultural production. Full article

Power Flow (PF) analysis is a fundamental tool in distribution systems since it determines the steady-state operating point for specified input conditions. Modern distribution networks face high distributed energy resource (DER) penetration and variable operating conditions, requiring repeated PF evaluations in time-series and scenario-based studies. Their unbalanced operation and high $R/X$ ratios can challenge conventional PF solvers, thereby requiring accurate, robust, and scalable methods. Prior studies have examined nonlinear distribution PF solvers and uncertainty-based formulations, but the review literature remains limited to specific categories and lacks a unified discussion of linearized models, numerical robustness, and acceleration techniques. Therefore, this paper presents a state-of-the-art review of PF methods for modern distribution networks, covering 205 studies published between 2000 and 2026. It summarizes conventional nonlinear PF formulations, reviews linearized models with their assumptions and applicability, and surveys numerical robustness strategies for improved convergence. The methods are compared according to their applicability to radial, weakly meshed, and unbalanced networks, while practical selection criteria are provided based on accuracy, convergence reliability, and computational requirements. Probabilistic, interval, and fuzzy approaches are also reviewed under renewable and load uncertainty. Finally, acceleration strategies for repeated PF evaluation are discussed, emphasizing sparse numerical implementations, topology-based schemes, and physics-informed surrogate models. Full article

Accurately assessing the fatigue levels of air traffic controllers is crucial for reducing human errors in ATC and ensuring the safe and orderly operation of the civil aviation transportation system. In remote tower scenarios, air traffic controllers’ work environments and task interaction modes differ significantly from those in traditional towers, and traditional fatigue detection approaches relying on physiological monitoring can cause intrusive disruptions to ATC operations. To overcome these limitations, this study proposes a scenario-based, non-invasive assessment framework for accurate and low-interference fatigue recognition. Taking three key scenario elements (traffic load, main operation screen brightness, and core work area illuminance) as the basis for measuring fatigue, the framework bridges the mapping from scenario elements to fatigue status, thereby enabling the transition of assessment inputs from physiological metrics to scenario features. In this mapping, fatigue labels are determined using a fusion strategy. Specifically, objective fatigue labels are derived from optimal wave features extracted from electroencephalogram data using one-way analysis of variance (OW-ANOVA), which are then fused with subjective labels based on the Karolinska Sleepiness Scale (KSS) self-reports through fuzzy C-means (FCM) clustering. Ultimately, a hybrid intelligent classification model integrating the Gannet optimization algorithm (GOA) and random forest (RF) is constructed to perform the primary assessment task. The experimental results indicate that the proposed framework achieves a recognition accuracy of 95.00%, outperforming six other commonly used classification or combination models. Ablation experiments and robustness tests validate the effectiveness of the fused labeling strategy and GOA modules, as well as the method’s excellent stability in resisting data noise. Furthermore, feature interpretability analysis reveals the quantitative influence of the three core fatigue drivers used. The research findings confirm the feasibility of non-invasive fatigue assessment for remote tower controllers leveraging scenario-based elements, which can offer intelligent decision support for controller shift scheduling, visual environment optimization, and targeted safety interventions. Full article

Transportation networks are subject to multiple sources of uncertainty, ranging from stochastic fluctuations in demand and travel times to epistemic indeterminacy in infrastructure condition, disruption risk, and user behavior. A diverse body of mathematical frameworks has emerged in response, including stochastic programming and probabilistic reliability analysis, fuzzy and possibilistic approaches, Liu’s uncertainty theory and uncertain programming, and robust or distributionally robust optimization. This article delivers a comprehensive, mathematically oriented synthesis of these paradigms for transportation networks, with emphasis on network-level structures—paths, flows, spanning trees, and network design problems—and on reliability notions including connectivity, travel-time, capacity, and max-type reliability. A central theme is that modelling choices about uncertainty representation and reliability indices are inseparable from questions of stability and sensitivity: how robust are optimal or near-optimal configurations when parameters vary within plausible ranges? Building on deterministic post-optimal analysis, this paper reviews tolerance-based stability concepts for uncertain most reliable paths, maximum reliable transmission paths, and uncertain minimum spanning trees under Liu-type uncertainty and demonstrates how inverse-distribution mappings yield exact deterministic equivalents and belief-based robustness margins. A dedicated comparative framework is developed, summarizing the data requirements, core advantages, typical limitations, and suitable engineering scenarios of each uncertainty paradigm to guide model selection in practice. The discussion extends to practical applications in post-disaster planning, infrastructure investment prioritization, and supply chain network design and identifies open research directions including network-wide travel-time reliability under belief-based uncertainty, unified stability frameworks across paradigms, and the integration of machine learning for uncertainty distribution elicitation. The emphasis throughout is on conceptual structure, modelling assumptions, and interpretability of reliability and stability indices, thereby positioning uncertain transportation networks as a rich interface between applied mathematics, operations research, and infrastructure planning. Full article

This study presents a small-scale application-oriented vehicle vision-sharing prototype for blocked-view observation in vehicle-following scenarios. The system was implemented using two DuckieBot vehicles and integrates socket-based image transmission, JPEG image compression, You Only Look Once (YOLO)-based object detection, road-plane-aligned image fusion, and fuzzy proportional–integral–derivative (fuzzy-PID) motor control. These modules are adopted as existing techniques and integrated for prototype-level experimental evaluation rather than proposed as new perception, compression, fusion, or control algorithms. Experiments were conducted under controlled small-scale indoor conditions. JPEG compression was quantitatively evaluated using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), encoded file size, and processing time. Q75 provided a mean PSNR of 39.8777 dB, a mean SSIM of 0.970522, and an average encoded size of 27.78 KB, representing a practical trade-off between reconstructed image quality and encoded data size. The YOLOv8n obstacle detector achieved a precision of 0.9724, a recall of 0.9571, an mAP@0.5 of 0.9851, and an mAP@0.5:0.95 of 0.8585 on an independent test set. Image-fusion evaluation showed that α = 0.60 produced the highest global mean PSNR, whereas α = 0.90 produced the highest global mean SSIM, indicating that the preferred blending coefficient depends on the selected image-quality criterion. A system-level ablation further distinguished shared-view visualization from a warning-only configuration, with the expected obstacle information presented in all 35 positive trials and no false alarms observed in 10 negative trials. The vehicle-following experiment verified the functional operation of the complete perception-to-control pipeline. The results should be interpreted within the controlled miniature-vehicle setting and should not be directly generalized to full-scale vehicles or real-road advanced driver assistance systems. Full article

Autonomous unmanned aerial vehicle (UAV) swarms execute mission-critical tasks through distributed sensing, communication, routing, and control. Existing trust-management approaches can assign a numerical trust score to a UAV, yet they rarely explain why trust changed, which evidence chain caused the change, or how an individual UAV contributed to mission degradation. This paper introduces ProvTrust-UAV, a mission-provenance trust-management framework for explainable UAV swarm security and causal accountability. The framework formalizes a typed Mission Provenance Graph (MPG), computes bounded adaptive trust from behavior, communication, provenance integrity, and mission contribution, and estimates Mission Impact Attribution (MIA) through counterfactual interventions and approximate Shapley-style contribution. To make explainability measurable rather than decorative, the paper defines fidelity, stability, faithfulness, compactness, and operator interpretability metrics for trust-chain explanations. The proposed framework incorporates a structural causal model, a Monte Carlo convergence analysis for Shapley approximation, a probabilistic false-trust reduction analysis, and a sensitivity analysis of trust-weight parameters. Controlled synthetic mission-event simulations over 120 scenarios and 2400 event windows indicate that ProvTrust-UAV improves macro-F1, root-cause attribution accuracy, and false-trust reduction compared with Bayesian, fuzzy, blockchain, deep-learning, and graph-trust baselines. The paper explicitly treats these results as first-stage computational validation and provides an anonymized additional package with simulation summaries, a public-dataset feature-mapping template, and a reproducible scaffold for external validation. Full article

Picture Fuzzy Sets (PiFSs) have appeared as an effective tool to tackle ambiguity in decision-making and offer greater flexibility than traditional extensions of Fuzzy Sets (FSs). Under the framework of evidence theory (ET), the concepts of belief and plausibility significantly boost the representative capacity of PiFSs, which enables the management of uncertain and ambiguous data. We constructed both distance and similarity measures specifically for Belief and Plausible Picture Fuzzy Sets (BP-PiFSs). The constructed measures detect the differences and connections between BP-PiFSs and addressed the key shortcomings in current methodologies. They are mathematically validated and applied to real-world scenarios, such as fault detection in complex systems and antenna design optimization, where managing uncertainty is critical. A modified decision-making method, Belief and Plausible SMART (BP-SMART), extends the classical SMART approach to more effectively handle multi-criteria decision-making (MCDM) in uncertain environments. Numerical evaluations across pattern recognition, clustering, fault detection, and MCDM demonstrates the effectiveness and robustness of the suggested framework, contributing significantly to both the theoretical and practical development of fuzzy set theory. Full article

This paper presents the design, control, and validation of a solar photovoltaic (PV)-powered DC microgrid (MG) integrated with a battery energy storage system (BESS), which was studied at laboratory scale as a step towards remote electrification in resource-constrained regions. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based maximum power point tracking (MPPT) algorithm is implemented to maximise solar energy extraction under varying irradiance. An Adaptive Power Management (APM) framework is proposed to maintain DC bus stability when the BESS is unavailable to support the bus—a condition that may arise from battery degradation, sensor or communication failures, converter malfunctions, protection trips, or physical damage. In this work, BESS unavailability is represented at the system level as the withdrawal of BESS support; the individual fault mechanisms that may cause it are not separately modelled. The APM operates across three hierarchical layers—monitoring, decision, and control—and reuses only the voltage and current measurements already present in the MG, requiring no additional sensing. The system is evaluated under three operating scenarios: (i) intermittent renewable generation; (ii) varying load demand; (iii) stochastic fluctuations in both irradiance and load. During BESS unavailability, the APM activates prioritised adaptive load shedding or PV generation curtailment as appropriate, preserving critical loads and preventing DC bus overvoltage. In the scenarios studied, the APM reduces worst-case voltage sag from 35.9% to 2.4% and worst-case swell from 53.51% to 0.14%, while maintaining BESS State of Charge (SOC) within 20%–80% during normal operation. Compared with the conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, the ANFIS-based MPPT achieves a mean point-wise tracking and conversion efficiency of 99.46%, a 1.78% improvement and a 0.86% improvement, respectively, which were corroborated by independent energy-based assessments (1.76% and 0.92%), with voltage deviations of 2.34% and oscillations of only 0.57 V peak-to-peak. Lyapunov-based analysis establishes asymptotic stability of the DC bus voltage in the BESS-regulated operating modes under stated assumptions. The proposed control strategies are validated through MATLAB/Simulink (R2025b) simulations and laboratory-scale experimental results, with the latter demonstrating coordinated PV–BESS–converter operation and bus voltage regulation. Full article

Transport decarbonization through electric mobility depends not only on how many charging stations are deployed but where, and whether expansion balances accessibility, grid readiness, land-use protection and regional equity. Türkiye, targeting net-zero by 2053 with electric car sales exceeding 10% of the market in 2024, shows a highly uneven charging network: provincial provision ranges from 9.0 to 155.6 points per 100,000 inhabitants, with the least-served half of the population holding only 22.3% of installed capacity (Gini = 0.311). This study develops a GIS-based multi-criteria framework treating charging expansion as a sustainability-constrained planning problem. Six criteria, namely population, GDP, transformer and transmission-line proximity, road-network proximity, and city-centre proximity, were harmonized to a 100-m grid via fuzzy membership functions, with an exclusion mask protecting sensitive land uses. Three weighting scenarios were compared: equal weights (EVCSI-A), Random Forest-derived weights (EVCSI-B), and expert AHP weights (EVCSI-C). Road accessibility (41.12%) and economic capacity (29.84%) dominated existing placement, explaining ~71% of feature importance, stable across algorithms and bootstrap replicates. National results reveal an efficiency–equity trade-off: EVCSI-B concentrates suitability in metropolitan corridors, EVCSI-A preserves broader coverage, and EVCSI-C reinforces metropolitan bias. Central and Eastern Anatolia remain underserved. We recommend sustainability-constrained screening followed by grid-capacity verification, positioning EVCSI as a transferable equity-monitoring tool supporting SDG 7, 9, 11 and 13. Full article

Construction delays remain a persistent challenge in Australian construction projects, contributing to cost escalation, disrupted work sequences, contractual claims, and reduced confidence in project delivery. Although delay causes have been widely investigated, existing studies often provide broad factor lists and prioritise risks using single-dimension or inconsistent scoring approaches. This limits guidance on which delay risks should receive priority attention when project teams face constrained time, cost, and management resources. This study addresses this limitation by quantifying and prioritising 22 validated delay risk factors in Australian construction projects. Probability of occurrence and schedule impact were evaluated as separate judgement dimensions before being integrated into an overall measure of risk criticality. Data were collected from 48 experienced Australian construction professionals. A dual-dimension Fuzzy Best–Worst Method was applied to derive separate ratio-scale weights for probability of occurrence and schedule impact, with dimension-specific consistency screening used to improve judgement reliability. The resulting weights were integrated using a probability–impact formulation and mapped onto a 5 × 5 Probability–Impact Matrix through quantile-based discretisation. A 10,000-iteration Monte Carlo robustness analysis was subsequently conducted to assess the stability of the resulting rankings under alternative expert-selection and weighting scenarios. The results indicate that the delay risks perceived by the participating professionals as having the highest combined probability and schedule impact are predominantly governance-, approval-, and coordination-related, particularly owner late decisions, change-approval delays, owner requirement changes, cost-estimation deficiencies, design-approval delays, and inadequate planning. The Monte Carlo analysis further indicated that the principal risk rankings remained relatively stable under variations in expert aggregation. Overall, the integrated FBWM–PIM framework provides a structured and practically interpretable approach for eliciting and prioritising expert perceptions of construction delay risk and translating them into an actionable classification tool for allocating limited risk management resources. Full article