Search Results (772)

Academic anxiety frequently emerges when students perceive academic demands as uncertain, uncontrollable, or threatening. Intolerance of uncertainty is widely recognized as a key cognitive antecedent of such anxiety, influencing how learners appraise stressors and mobilize coping resources. This study investigates the relationships among intolerance of uncertainty, academic self-efficacy as a coping mechanism, and academic resilience among vocational high school students in a collectivist educational context. Data were collected from 387 vocational high school students across Istanbul, Turkey via online forms. Contrary to expectations, the results revealed that intolerance of uncertainty positively affects academic self-efficacy. Furthermore, academic self-efficacy was positively associated with academic resilience. Academic self-efficacy partially mediated the relationship between these two variables. These findings provide new insights into how uncertainty is managed in collectivist educational contexts and suggest directions for future educational practices and research. Full article

The ramping requirement in new power systems primarily stems from net load variations and forecast errors of renewable energy and load. Designing an equitable cost allocation mechanism for ramping services based on these factors facilitates incentives for generation and load to actively reduce ramping demands, thereby alleviating system ramping pressure. Accordingly, this paper proposes a fair ramping cost allocation mechanism based on the ramping responsibility coefficients of market participants. Under this mechanism, a market-oriented operation model for wind–hydro-storage joint operation is established to verify its effectiveness in market applications. First, a ramping cost allocation mechanism is constructed based on ramping responsibility coefficients. According to the responsibility coefficients of market participants for deterministic and uncertain ramping requirements, ramping costs are allocated to the corresponding contributors in proportion to the ramping demands caused by net load variations, load forecast deviations, and renewable energy forecast deviations. Specifically, for costs arising from renewable energy forecast errors, an allocation mechanism is designed based on the difference between the declared error range and the actual error. Second, within this allocation framework, hydropower and storage (including cascade hydropower and hybrid pumped storage) are utilized as flexible resources to mitigate wind power uncertainty and reduce its ramping costs. A two-stage day-ahead and real-time bi-level game model for wind–hydro-storage cooperative decision-making is developed. The upper level optimizes bilateral trading and market bidding strategies for wind–hydro-storage, while the lower level simulates the market clearing process. Through Stackelberg game modeling, joint optimal operation of wind–hydro-storage is achieved, ensuring mutual benefits. Finally, simulation results validate that the proposed ramping cost allocation mechanism can guide renewable energy to improve output controllability through economic signals. Furthermore, the bilateral trading and coordinated market participation of wind–hydro-storage realize win–win outcomes, reduce the ramping cost allocation for wind power by 23.10%, effectively narrow peak-valley price differences, and enhance market operational efficiency. Full article

With the rapid growth of passenger demand, the imbalance between transport capacity and passenger flow has become increasingly severe. Existing studies seldom consider the impacts induced by passenger demand uncertainty under a flexible train composition mode. To address this issue, this study investigates the integrated optimization of train timetabling and rolling stock circulation planning under a flexible train composition mode. The objective is to minimize the number of stranded passengers and operational costs. A scenario-based robust optimization framework is introduced, and a mean risk objective is formulated by combining the expected objective value with the expected absolute deviation of each scenario’s objective value from the expectation. By using linearization techniques, the model is transformed into a mixed integer programming (MIP) problem, which balances the operating cost and robustness while satisfying safety and service level requirements. The model is validated through a case study of Shanghai Metro Line 16. Numerical experimental results indicate that, in a single scenario, compared with the fixed train composition scheme, the proposed scheme reduces the objective function value by 28.3%. Simultaneously, it can enhance the robustness of the train timetable and rolling stock circulation plan under the condition of uncertain passenger demands. The related findings provide decision support for the design of urban rail transit operating plans. Full article

Polymeric membranes are promising materials for post-combustion CO₂ capture (PCC), yet their life cycle environmental performance remains uncertain. This review synthesizes 21 life cycle assessment (LCA) studies of polymeric membrane-based PCC systems to examine methodological choices, quantify environmental trade-offs, and identify research gaps. Google Scholar, Web of Science, ScienceDirect, and MDPI were searched up to January 2026. Methodological quality and risk of bias were assessed against a 10-criteria framework derived from ISO 14044. Results indicate widely varying system boundaries and functional units, with only four studies performing formal uncertainty analysis. Within individual study contexts, polymeric membrane gas separation systems can reduce global warming potential (GWP) by up to 89% compared to no-capture plants, though other impacts, like ozone depletion potential, increase by up to 780%. Compared to amine-based absorption, membranes showed superior performance, with reductions up to 26% in GWP and 98% in other categories. In some cases, large relative reductions are driven by scenario-specific baselines and should be interpreted with caution. Outcomes were most sensitive to background energy mixes and raw material demand. The absence of commercial-scale data highlights the need for harmonized frameworks and standardized functional units. Future research should prioritize membrane material selection, renewable energy integration, and coordinated policy–industry collaboration. Full article

Against the backdrop of intensified climate change and increasingly prominent imbalances in resource supply and demand, achieving multi-objective collaborative optimization of the Water-Energy-Food-Carbon (WEFC) nexus under uncertain conditions has become a pivotal task for regional sustainable development. Taking the Heihe River Basin, a typical arid inland river basin in northwest China with a complex WEFC nexus, as the research area, this study develops a multi-objective collaborative optimization model for the WEFC nexus, targeting three core goals: maximizing crop irrigation water productivity, minimizing carbon emissions, and enhancing low-carbon agricultural competitiveness. The model embeds constraints of regional water security, food security, land policy, and total water resource availability, introduces the uncertainty parameter τ to quantify fluctuations in available surface water, and adopts the ideal point method to convert the multi-objective problem into a single-objective optimization task by minimizing the Euclidean distance between feasible solutions and the ideal solution, with a case application in the oasis area of the basin’s middle reaches. Results show the model exhibits excellent stability across varying uncertainty levels: crop irrigation water productivity stabilizes around 1.5 kg/m³, low-carbon agricultural competitiveness at approximately 0.1003 kg/yuan, and spatial differences in resource allocation are evident. Linze gains the most water resources (16.47 × 10⁸ m³) due to geographical advantages, while Gaotai obtains the least (6.51 × 10⁸ m³). In terms of planting structure, vegetables dominate the sown area owing to low carbon emissions and high water use efficiency, while wheat planting is relatively limited by climate adaptability and market demand. Carbon sink analysis confirms vegetables as the primary carbon sequestration contributor in Ganzhou and Linze, offering a practical pathway for agricultural carbon reduction. These findings provide tailored theoretical and practical support for balancing food security, efficient resource utilization, low-carbon development, and ecological protection in arid and semi-arid regions, facilitating regional carbon neutrality and sustainable agricultural development. Full article

Nitrate pollution in freshwater has become an increasing concern for both environmental sustainability and human health, especially in water reuse systems and intensive aquaculture. Photocatalytic reduction in nitrate to nitrogen gas (N₂) represents a promising low-chemical treatment strategy that can operate under sunlight or LED irradiation, and in general, enable nitrate removal without generating concentrated waste streams. Over the past decade, the development of advanced photocatalytic materials, including heterojunction semiconductors, plasmonic catalysts, and single-atom co-catalysts, has significantly enhanced visible-light absorption and overall photocatalytic performance. Despite these advances in photocatalyst design and synthesis, several critical challenges still limit the large-scale implementation of photocatalytic nitrate reduction to N₂. First, selectivity toward N₂ remains limited, as competing reaction pathways often lead to the formation of undesirable byproducts, such as nitrite (NO₂⁻), ammonium (NH₄⁺), and nitrous oxide (N₂O). Second, nitrogen reaction pathways are often uncertain, because many studies lack isotopic labeling or nitrogen mass balances, making it difficult to verify that the detected N₂ originates from nitrate reduction. Third, practical implementation is restricted by several technical challenges, including catalyst fouling or leaching, limitations in reactor design, excessive addition of hole scavengers, and the relatively high energy demand associated with indoor LED-driven systems. This review critically surveys advances from 2015 to 2025 in photocatalytic materials and reaction mechanisms for nitrate conversion to N₂. It highlights best practices for reliable product quantification and reaction pathway validation, and evaluates the feasibility of integrating these systems into recirculating aquaculture systems (RAS), where effective nitrate management is essential. In addition, the potential role of modern inline nitrate sensors (optical and electrochemical) and automated process control is discussed, outlining pathways toward hybrid photocatalytic–biological nitrate removal systems for sustainable aquaculture applications. Full article

Football refereeing involves rapid decision-making in dynamic, uncertain, and socially demanding environments. This study examined an integrative cognitive-behavioral model of football refereeing performance, focusing on perceptual processing speed (PPS), attentional control (AC), and social intelligence (SI). Sixty-one male football referees (M_age = 30.04, SD = 4.06) enrolled in a national talent development program across multiple competitive seasons participated in the study. At the beginning of each season, referees completed standardized, ability-based assessments of PPS (processing speed task), AC (selective and inhibitory task), and SI (performance-based social intelligence measure). Refereeing performance was operationalized using season-standardized end-of-season officiating ratings assigned by the national refereeing authority. Mediation analyses did not support AC or SI as mechanisms transmitting the effect of PPS on performance. However, moderation analyses revealed a significant PPS × AC interaction, indicating that attentional control amplified the positive association between perceptual processing speed and refereeing performance. PPS emerged as a robust predictor of performance, particularly among referees with high attentional control. Social intelligence showed a positive bivariate association with performance but did not function as a mediator or moderator in multivariate models. These findings support an interactive and ecological view of applied intelligence in football refereeing, emphasizing functional coordination highlighting the functional coordination of cognitive resources rather than isolated cognitive abilities as key to performance under real-world competitive demands. Full article

This study examines retailers’ information sharing aimed at enhancing product greenness within green supply chains, with consumer participation as a pivotal factor and the overarching goal of advancing the sustainable development of the whole supply chain ecosystem. Each supply chain comprises a green product supplier and a retailer with uncertain demand information. A tripartite evolutionary game model involving manufacturers, retailers, and consumers is constructed to analyze the factors influencing information sharing behavior, which serves as a critical pathway to achieve environmental and economic sustainability in green supply chain operations. The findings highlight two key insights: First, strong consumer willingness to purchase green products may inhibit retailers’ inclination towards information sharing, a counterintuitive outcome that needs to be addressed to align individual stakeholder behaviors with long-term sustainable development goals. Second, lower information sharing costs can motivate retailers to share information with manufacturers; otherwise, manufacturers must adopt technological measures to assist retailers in reducing information sharing-related costs, thereby achieving win–win outcomes across the supply chain and fostering a sustainable and collaborative green supply chain system that balances ecological benefits, economic gains, and social value co-creation. Full article

In order to tackle resilient supplier selection (RSS) of high uncertainty in resilient supply chain management, an effective correlation coefficients-based multicriteria group decision-making (MCGDM) methodology has been constructed. The major contribution of the present study is twofold. Firstly, in view of that extant criteria systems are all in lack of theoretical rationality, this paper establishes a capabilities-based analytical framework for intensive evaluation of supplier resilience by taking processual viewpoints of dynamic capabilities theory and risk management theory. Secondly, to empower the proposed correlation coefficients-based MCGDM methodology, probabilistic dual hesitant fuzzy uncertain unbalanced linguistic set (PDHF_UUBLS) is employed to capture hybrid uncertainties in decision processes of RSS. Then, theoretically compliant correlation coefficients (CCs) for PDHF_UUBLS are developed, including statistics-based CC, information energy-based CC and their weighted versions. Especially, information energy-based CCs overcome limitations of statistics-based CCs in special cases, thus exhibiting general applicability. In addition, a compatibility-based programming model has also been developed to objectively derive an unknown weighting vector for DMUs. Furthermore, illustrative case studies and comparative experiments have been carried out to verify effectiveness and stability of the proposed methodology. Taken together, this paper satisfies the new normal demand of resilience building in supply chain management and presents an effective MCGDM methodology for handling the key problems of RSS. Full article

During pandemic emergencies, demand for relief supplies in affected areas surges abruptly and evolves randomly and dynamically, resulting in highly asymmetric supply and demand. Ensuring timely and reliable supply requires robust decision-making under risk. This study addresses a stochastic multi-objective location-routing problem (LRP) that simultaneously considers demand uncertainty and travel time variability. A multi-scenario stochastic programming model is developed with three objectives: minimizing total system cost, minimizing total waiting time, and minimizing the composite conditional value at risk (CVaR–Rcomp) to capture tail risks under extreme scenarios. A novel regret-based risk mechanism is introduced to unify temporal and cost dimensions, enabling joint evaluation of uncertainties within a single framework. To solve this challenging high-dimensional problem, a reinforcement learning-enhanced NSGA-III (RL-NSGAIII) is proposed. Specifically, Q-learning generates high-quality initial solutions, which accelerate convergence and improve population diversity for NSGA-III. Case studies demonstrate that the proposed method outperforms traditional evolutionary algorithms in convergence efficiency and Pareto solution quality, while effectively revealing potential risk blind spots. The results provide quantitative decision support and robust optimization insights for emergency logistics networks operating under uncertain conditions. Full article

Emergency facility location and layout are critical to the efficiency of emergency rescue and resource allocation. However, practical emergency scenarios are plagued by two key challenges: the risk of facility failure due to various uncertain factors and the presence of complex polygonal barriers (including convex and concave polygons) that hinder transportation. Existing studies often overlook concave polygonal barriers or fail to prioritize time satisfaction, a core demand in emergency response. To address these gaps, this paper proposes a reliable emergency facility location optimization model with the objective of maximizing time satisfaction, considering constraints such as capacity, cost, and demand. The model integrates three key methods: a convex hull algorithm to convert concave barriers into convex ones for simplified calculation, a path optimization algorithm to find the shortest bypass routes around barriers, and an Artificial Ecosystem Optimization (AEO) algorithm to solve the nonlinear programming model. Through numerical experiments (single-facility, multi-facility, and medium-scale scenarios) and a practical case study in the Meknès region of Morocco for ambulance deployment, the feasibility and effectiveness of the model and algorithms are verified. The results show that the model achieves high time satisfaction (all above 0.8, with most exceeding 0.9) and efficiently optimizes facility locations and resource allocation. Sensitivity analysis indicates that increased failure risk parameters ($\alpha$ and $\theta$) lead to a gradual decrease in average time satisfaction. This research provides a systematic mathematical model and practical method for emergency facility location decision-making, effectively addressing the challenges of complex barriers and facility failure. Full article

With the increasing penetration of distributed energy resources in power systems, the coupling between transmission and distribution networks has become increasingly complex. How to ensure short-term voltage stability (STVS) and maintain the supply–demand flexibility balance under complex transmission–distribution interactions and uncertain renewable generation has become a key challenge that must be addressed for coordinated transmission–distribution operation. To this end, this paper proposes a transmission–distribution coordinated optimal scheduling strategy that accounts for STVS and the supply–demand flexibility balance. First, the causes of short-term voltage instability were analyzed, and a time-domain simulation model of the power system was developed that incorporates the active voltage support capability of distribution networks. Second, an improved flexibility demand model was established based on the probability-box (p-box) method. Then, economic models for the transmission network and the distribution network were formulated, and a coordinated transmission–distribution operation model was constructed by considering both the short-term voltage instability risk and the supply–demand flexibility imbalance risk. Finally, a test system was built by connecting two modified IEEE 13-node feeders to buses 14 and 13 of the IEEE 14-bus system, and simulation studies were conducted. The results demonstrate that the proposed coordinated scheduling strategy can effectively reduce the risk of short-term voltage instability and ensure flexibility balance across the transmission and distribution networks. Full article

For logistics facilities at the county level, workforce scheduling is a basic operational concern. Although these facilities are developing rapidly, they still mostly rely on human and semi-automated work. Significant differences in employee productivity and skill levels, along with regular changes in demand, exacerbate this challenge. This study proposes a sustainability-oriented dual-objective optimization model to coordinate operational cost control with employee well-being enhancement. To address this issue, we designed an improved Genetic Algorithm that combines heuristic initialization with specialized repair operators, forming a systematic optimization framework. The effectiveness of the proposed system design and algorithm has been validated through real-world case studies. Experimental results demonstrate that this model not only achieves a balance between cost and employee satisfaction under uncertain demand conditions but also provides county-level logistics centers with sustainable scheduling solutions adaptable to business changes. Management recommendations based on the experimental results are proposed, such as implementing differentiated scheduling strategies, easing restrictions on maximum working hour variations, establishing a progressive optimization mechanism, and optimizing staffing and employee structure in accordance with corporate characteristics. This study provides scientific decision support for county-level logistics systems to achieve sustainable operations and human resource management transformation. Full article

Design guidance for headed-bar development remains uncertain for large-diameter bars at cutoff points, where bar termination increases anchorage demand and confinement is often limited. This study quantified the anchorage behavior of 43 and 57 mm headed bars and established a regression-based strength model grounded in a splitting-controlled bond–bearing mechanism. Nineteen reinforced concrete beam specimens were tested under four-point loading configured to place the bending-moment inflection point at the head location. The primary variables were the development length (l_dt = 12–28d_b), concrete compressive strength (f_c′ = 42 and 70 MPa), clear side cover, clear spacing, and transverse reinforcement index (K_tr/d_b = 0–2.0). All the specimens failed by splitting prior to bar yielding, characterized by longitudinal cracking along the development region and cover spalling near the head. The anchorage strength increased with concrete compressive strength and development length and was most strongly enhanced by transverse reinforcement (up to ~60%). At failure, the bond contributed 70–86% of the developed stress, while the head-bearing contribution increased with confinement. Existing ACI 318-19 and KDS-2021 provisions were generally unconservative, particularly for unconfined specimens. The proposed bond–bearing model showed a close agreement with the test database (mean test/prediction = 0.99; COV = 4.72%) within stated parameter limits. Full article

Climate change and growing water scarcity necessitate that irrigation districts allocate limited water resources more efficiently, with explicit consideration of multi-source uncertainties. To maximize the effective utilization coefficient of irrigation water, an uncertainty-informed optimization and dynamic regulation framework for agricultural water allocation (UODRA) was developed. The framework quantifies and characterizes uncertainties arising from meteorological forcings, soil heterogeneity, irrigation practices, and water losses during conveyance and field application. The fractional programming model derived therefrom is solved via Dinkelbach’s algorithm, and Monte Carlo simulation is adopted in a reduced scenario space to propagate the dominant uncertainty drivers and assess the distribution characteristics of outcomes and associated risks. A case study was conducted in the Fendong Irrigation District to evaluate three water supply scenarios. The results indicate that with sufficient water supply and diminishing marginal returns, the effective utilization coefficient of irrigation water increases accordingly. Uncertainty mainly exerts an impact on the degree of dispersion and downside risks rather than at the average level. Sensitivity analysis shows that efficiency-related perturbations are the primary drivers of output variability, and their impacts are greater than those of supply-side perturbations and demand-side variation in simulated irrigation demand. Further technical comparison reveals that the adoption of high-efficiency irrigation can significantly improve the performance at the regional level: under drip irrigation conditions, the efficiency reaches 0.614, while that of sprinkler irrigation is 0.499, with a simultaneous improvement in operational stability. Overall, UODRA provides a quantitative decision support method for robust irrigation water resource allocation and adaptive management under uncertain conditions. Full article