Search Results (1,552)

Modern distribution networks are increasingly affected by the widespread adoption of photovoltaic (PV) generation and electric vehicles (EVs). The variability of PV output and the fluctuating demand of EVs may cause voltage violations that threaten the safe operation of active distribution networks (ADNs). This paper proposes a voltage control strategy for ADNs to address the voltage violation problem by utilizing the control flexibility of EV charging stations (EVCSs). In the proposed strategy, a state-driven margin algorithm is first employed to generate training data comprising response capability (RC) of EVs and state parameters, which are used to train a multi-layer perceptron (MLP) model for real-time estimation of EVCS response capability. To account for uncertainties in EV departure times, a relevance vector machine (RVM) model is applied to refine the estimated RC of EVCSs. Then, based on the estimated RC of EVCSs, a second-order cone programming (SOCP)-based voltage regulation problem is formulated to obtain the optimal dispatch signal of EVCSs. Finally, a broadcast control scheme is adopted to distribute the dispatch signal across individual charging piles and the energy storage system (ESS) within each EVCS to realize the voltage regulation. Simulation results on the IEEE 34-bus feeder validate the effectiveness and advantages of the proposed approach. Full article

Deep eutectic solvents (DESs) have emerged as sustainable and tunable alternatives to conventional solvents for the extraction of polysaccharides. This review presents a structure-informed framework linking DES composition to polysaccharide solubility, emphasizing the differential responsiveness of amorphous, interfacial, and crystalline domains. Amorphous polysaccharides are efficiently extracted under mild DES conditions, while crystalline polymers often require stronger hydrogen bond acceptors or thermal/mechanical activation. Beyond dissolution, DESs modulate key properties of the extracted polysaccharides, including molecular weight, monomer composition, and bioactivity. Comparative analysis highlights how acidic, basic, or metal-coordinating DESs selectively target distinct polymer classes. Emerging innovations, such as in situ DES formation, mechanochemical systems, and switchable solvents, enhance efficiency and reduce downstream processing demands. Furthermore, the integration of machine learning and COSMO-RS modeling enables predictive solvent design, reducing reliance on empirical screening. By combining mechanistic insight, compositional tailoring, and computational tools, this review provides a scientifically grounded perspective for advancing DES-mediated extraction processes and enabling structure-preserving, application-oriented recovery of polysaccharides in food, pharmaceutical, and biorefinery domains. Full article

Active learning promises richer engagement, yet transnational English-medium engineering classrooms can remain quiet even when students are motivated. This study aims to explain this silence by examining the factors that encourage students to participate, the barriers that discourage them, and how student characteristics and coping strategies influence their participation. We conducted a mixed-methods survey of 402 undergraduates (Years 2–4) in a China–United Kingdom (Sino-UK) joint engineering programme in China. We analysed the closed-ended responses using descriptive and inferential statistics (including effect sizes) and the open-ended responses using inductive thematic analysis. Quantitative results showed that interest in the subject (76.6%) and career relevance (72.8%) were the most potent motivators. In contrast, fear of making mistakes (56%) and low confidence in public speaking (51%) were the most common barriers to participation. Other constraints included language load, deference to instructors, and prior passive learning experiences. Gender and discipline differences were negligible (Cramér’s V ≤ 0.09; Cohen’s d < 0.20). A small year-of-study effect also emerged, with later-year students marginally more confident in English-medium interactions. Qualitative analysis revealed recurring themes of evaluation anxiety, demands for technical vocabulary, inconsistent participation expectations, and reliance on private coping strategies (e.g., pre-class preparation, peer support, and after-class queries). We propose a ‘motivated-but-silent’ learner profile and blocked-pathway model where cultural, linguistic, and psychological filters prevent motivation from becoming classroom voice, refining Self-Determination Theory/Expectancy–Value Theory (SDT/EVT) and Willingness to Communicate (WTC) theories for transnational engineering contexts. These findings inform practice by recommending psychological safety measures, discipline-specific language scaffolds, and culturally responsive pedagogy to unlock student voice in English-medium Instruction/Transnational Education (EMI/TNE) settings. Full article

This paper presents an optimal scheduling framework for a multi-energy hub (EH) that integrates electricity, natural gas, wind energy, energy storage systems, and demand response (DR) programs. The EH incorporates key system components including transformers, converters, boilers, combined heat and power (CHP) units, and both thermal and electrical energy storage. A novel aspect of this work is the joint coordination of multi-carrier energy flows with DR flexibility, enabling consumers to actively shift or reduce loads in response to pricing signals while leveraging storage and renewable resources. The optimisation problem is formulated as a mixed-integer linear programming (MILP) model and solved using the CPLEX solver in GAMS. To evaluate system performance, five case studies are investigated under varying natural gas price conditions and hub configurations, including scenarios with and without DR and CHP. Results demonstrate that DR participation significantly reduces total operating costs (up to 6%), enhances renewable utilisation, and decreases peak demand (by around 6%), leading to a flatter demand curve and improved system reliability. The findings highlight the potential of integrated EHs with DR as a cost-effective and flexible solution for future low-carbon energy systems. Furthermore, the study provides insights into practical deployment challenges, including storage efficiency, communication infrastructure, and real-time scheduling requirements, paving the way for hardware-in-the-loop and pilot-scale validations. Full article

This paper presents a deep reinforcement learning-based demand response (DR) optimization framework for active distribution networks under uncertainty and user heterogeneity. The proposed model utilizes a Double Deep Q-Network (Double DQN) to learn adaptive, multi-period DR strategies across residential, commercial, and electric vehicle (EV) participants in a 24 h rolling horizon. By incorporating a structured state representation—including forecasted load, photovoltaic (PV) output, dynamic pricing, historical DR actions, and voltage states—the agent autonomously learns control policies that minimize total operational costs while maintaining grid feasibility and voltage stability. The physical system is modeled via detailed constraints, including power flow balance, voltage magnitude bounds, PV curtailment caps, deferrable load recovery windows, and user-specific availability envelopes. A case study based on a modified IEEE 33-bus distribution network with embedded PV and DR nodes demonstrates the framework’s effectiveness. Simulation results show that the proposed method achieves significant cost savings (up to 35% over baseline), enhances PV absorption, reduces load variance by 42%, and maintains voltage profiles within safe operational thresholds. Training curves confirm smooth Q-value convergence and stable policy performance, while spatiotemporal visualizations reveal interpretable DR behavior aligned with both economic and physical system constraints. This work contributes a scalable, model-free approach for intelligent DR coordination in smart grids, integrating learning-based control with physical grid realism. The modular design allows for future extension to multi-agent systems, storage coordination, and market-integrated DR scheduling. The results position Double DQN as a promising architecture for operational decision-making in AI-enabled distribution networks. Full article

Within the framework of the European Higher Education Area, university students’ good practices are considered key indicators of educational quality. In light of the high levels of academic stress reported in this population, the present study aims to examine whether four specific practices—feedback-seeking, cooperative work, time management, and active learning—moderate the relationship between active coping and stress responses. A cross-sectional design was employed with a sample of 1014 university students (M = 20.56; SD = 3.50). Participants completed the Academic Stress Coping Scale (A-CEA), the Academic Stress Response Scale (R-CEA), and the Inventory of Good Practices in University Students (IBPEU). Moderation analyses were conducted using linear regressions with interaction terms, and conditional effects (simple slopes) were estimated at low and high levels of the moderator. Significant moderation effects emerged. Feedback-seeking, cooperative work, and time management strengthened the inverse association between active coping and academic stress, with stronger reductions when these practices were reported at high levels. In contrast, active learning showed a threshold pattern: active coping reduced stress only when this practice was actively implemented, suggesting that its effective implementation may be necessary for coping to be effective. Promoting good practices may enhance the benefits of active coping. Their integration into early psychoeducational programs could bolster students’ personal resources and reduce psychological distress in demanding academic settings. Full article

Gummy candies rely on sugar, gelatin, and synthetic colorants, but rising demand for natural alternatives makes replacement essential. Interactions between natural additives and main components (gelatin, sucrose), especially their effects on color and texture, remain unclear. This study examines the existence and amounts of sucrose, gelatin, and natural coloring agent, red beetroot extract powder (RBEP), in gummy candy compositions, along with main quality attributes and stability behavior. The effects of these variables on the main physicochemical, color, texture and bioactive properties were investigated. Model gummy samples’ hardness (10.68–19.18 N), resilience (0.57–0.89), cohesiveness (0.87–1.01), springiness (0.19–0.54 mm), gumminess (9.63–21.30 N), and chewiness (2.15–8.29 N × mm) properties were determined by texture profile analysis. The values of L*, a*, b*, chroma, and hue angle were determined in the ranges of 23.9–91.5, (−0.93)–43.6, 1.06–8.17, 6.20–44.0, and 5.97–97.4, respectively. The interactions between RBEP × gelatin and RBEP × sucrose were found to be effective in color parameters. Total phenolic content (TPC) values and inhibition values for antioxidant activity (AA, % inhibition) ranged from 25.6 to 257.4 mg GAE/kg and 0.00–49.8%, respectively. The optimum composition, determined by considering texture properties and stability behavior as the response, revealed the concentrations of sucrose, gelatin solution, and RBEP as 34.53 g/100 g, 18.33 g/100 g, and 0.44 g/100 g, respectively. The study is the first to investigate the effect of RBEP concentration on the quality parameters of gummy candies and interactions with other components of the formulation. The results will raise awareness about the use of colorants in the confectionery industry and contribute to developing novel products. Full article

Lawns have evolved from medieval European grasslands into globally accepted urban green surfaces, serving recreational, aesthetic and cultural purposes. Today lawn surfaces are essential components of public urban green space (PUGS), fulfilling ecosystem services such as urban heat mitigation, carbon sequestration and social well-being. However, their ecological and resource-intensive disservices, particularly in dry climates, have prompted growing concerns among environmental scientists, urban planners and landscape designers. In water-scarce regions like Perth, Western Australia, traditional lawns face increasing scrutiny due to their high irrigation demands and limited ecological diversity. This study contributed to the transdisciplinary LAWN as Cultural and Ecological Phenomenon project, focusing on the perspectives of professionals, landscape architects, park managers, turf producers and researchers responsible for the planning, design and management of urban lawn in PUGS. Using qualitative methods (semi-structured in-depth interviews), the research explores expert insights on the values, challenges and future trajectories of lawn use in a warming, drying climate. The interviews included 21 participants. Findings indicate that while professionals acknowledge lawns’ continued relevance for sports and active recreation, water scarcity is a major concern influencing design and species selection. Alternatives such as drought-tolerant plants, hard landscaping and multifunctional green spaces are increasingly considered for non-sporting areas. Despite growing concerns, the ideal lawn is still envisioned as an expansive, green, soft surface, mirroring entrenched public preferences. This study underscores the need to balance environmental sustainability with public preference and cultural expectations of green lawns. Balancing expert insights with public attitudes is vital for developing adaptive, water-conscious landscape design strategies suited to future urban planning and environmental conditions in Mediterranean climates. Full article

As the demand for clean water continues to rise, the development of reliable and environmentally sustainable purification methods has become increasingly important. In this study, we describe the production and characterization of electrospun polyimide (PID) nanofibers modified with MXene (Ti₃C₂Tx), tungsten trioxide (WO₃), and titanium dioxide (TiO₂) nanomaterials for improved photocatalytic degradation of rhodamine 6G (R6G), a model organic dye. Superior photocatalytic performance was achieved by suppressing electron–hole recombination, promoting efficient charge carrier separation, and the significant increase in light absorption through the addition of metal oxide nanowires and MXene to the PID matrix. Comprehensive characterization confirms a core–shell nanofiber architecture with TiO₂, WO₃, and MXene effectively integrated and electronically coupled, consistent with the observed photocatalytic response. The PID/TiO₂/WO₃/MXene composite exhibited the highest photocatalytic activity among the tested configurations, degrading R6G by 74% in 90 min of light exposure. This enhancement was ascribed to the synergistic interactions between MXene and the metal oxides, which reduced recombination losses and promoted effective charge transfer. The study confirms the suitability of PID-based hybrid nanofibers for wastewater treatment applications. It also points toward future directions focused on scalable production and deployment in the field of environmental remediation. Full article

Background and Objectives: Stroke and hemiplegia disrupts symmetrical activation of skeletal and abdominal muscles, impairing trunk control and functional movement. Although asymmetry is also present in healthy adults, its magnitude and patterns differ with neurological impairment. Understanding trunk muscle symmetry across functional tasks in healthy individuals and patients with stroke is essential for targeted rehabilitation strategies. Materials and Methods: A comparative cross-sectional study was conducted including healthy adults and patients with stroke. Muscle activation symmetry of the rectus abdominis, external oblique, internal oblique, and multifidus was analyzed across four trunk movements: flexion, extension, and lateral flexion to the dominant or non-dominant side. A two-way repeated measures ANOVA examined main and interaction effects of condition, muscle, and group. Results: Trunk muscle symmetry was significantly influenced by the movement conditions, and patterns of change differed between groups. While no consistent differences were observed across muscles, specific interactions revealed condition-dependent variations, particularly between abdominal and deep spinal muscles. Lateral flexion elicited the greatest asymmetry, with distinct response patterns in healthy individuals compared with patients with stroke. Conclusions: This study highlights the importance of addressing movement-specific demands in trunk rehabilitation. Rather than focusing on isolated muscles, interventions should consider the dynamic and condition-dependent nature of symmetry to optimize functional recovery in patients with stroke. Full article

This paper proposes a real-time energy management strategy for low-voltage microgrids that combines short-horizon forecasting with a rule-based supervisory controller to coordinate battery energy storage usage and reactive power support provided by flexible alternating current transmission technologies. The central contribution is the forecast-informed, joint orchestration of active charging and reactive power dispatch to regulate voltage and preserve stability under large photovoltaic variability and uncertain electric vehicle demand. The work also introduces a resilience response index that quantifies performance under external disturbances, forecasting delays, and increasing levels of electric-vehicle integration. Validation is carried out through time-domain numerical simulations in MATLAB/Simulink using realistic solar irradiance and electric vehicle charging profiles. The results show that the coordinated strategy reduces voltage deviation events, maintains stable operation across a wide range of scenarios, and enables electric vehicle charging to be supplied predominantly by renewable generation. Sensitivity analysis further indicates that support from flexible alternating current devices becomes particularly decisive at high charging demand and in the presence of forecasting latency, underscoring the practical value of the proposed approach for distribution-level microgrids. Full article

Emerging power electronic devices like soft open points (SOPs) and electric springs (ESs) play a vital role in enhancing active distribution network (ADN) efficiency. SOPs enable flexible active/reactive power control, while ESs improve demand-side management and voltage regulation. This paper proposes a two-stage stochastic programming model to optimize ADN’s operation by coordinating these fast-response devices with legacy mechanical equipment. The first stage determines hourly setpoints for conventional devices, while the second stage adjusts SOPs and ESs for intra-hour control. To handle ES nonlinearities, a hybrid data–knowledge approach combines knowledge-based linear constraints with a data-driven multi-layer perceptron, later linearized for computational efficiency. The resulting mixed-integer second-order cone program is solved using commercial solvers. Simulation results show the proposed strategy effectively reduces power loss by 42.5%, avoids voltage unsafety with 22 time slots, and enhances 4.3% PV harvesting. The coordinated use of SOP and ESs significantly improves system efficiency, while the proposed solution methodology ensures both accuracy and over 60% computation time reduction. Full article

Indoor sports facilities face distinctive indoor air quality (IAQ) challenges due to high occupant density, elevated metabolic emissions, and diverse pollutant sources associated with physical activity. This review presents a narrative synthesis of multidisciplinary evidence concerning IAQ in sports environments. It explores major pollutant categories, including carbon dioxide (CO₂), particulate matter (PM), volatile organic compounds (VOCs), and airborne microbial agents, highlighting their sources, behavior during exercise, and associated health risks. Research shows that physical activity can increase PM concentrations by up to 300%, and CO₂ levels frequently exceed 1000 ppm in inadequately ventilated spaces. The presence of semi-volatile organics and bioaerosols further complicates pollutant dynamics, especially in humid and densely occupied areas. Measurement technologies such as optical sensors, chromatographic methods, and molecular techniques are reviewed and compared for their applicability to dynamic indoor settings. Existing IAQ standards across China, the USA, the EU, the UK, and WHO are examined, revealing a lack of activity-specific thresholds and insufficient responsiveness to real-time conditions. Mitigation strategies (e.g., including demand-controlled ventilation, use of low-emission materials, liquid chalk substitutes, and integrated HEPA-UVGI purification systems) are evaluated, many demonstrating pollutant removal efficiencies over 80%. The integration of intelligent building management systems is emphasized for enabling real-time monitoring and adaptive control. This review concludes by identifying research priorities, including the development of activity-sensitive IAQ control frameworks and long-term health impact assessments for athletes and vulnerable users. Full article

The integration of renewable energy into distribution networks has led to voltage violations and increased network losses. Traditional control devices, with slow response, struggle to precisely control power flow in active distribution networks (ADNs). Optimizing from both supply and demand sides, an ADN power flow optimization method is proposed for accurate and dynamic power flow regulation to address these issues. On the demand side, the peak, valley, and flat periods are divided by the fuzzy transitive closure method. Balancing user satisfaction maximization and load fluctuation minimization, time-of-use (TOU) prices are solved by the non-dominated sorting genetic algorithm II (NSGA-II). On the supply side, operating cost and voltage deviation minimization are objectives, with a proposed optimization method coordinating precise continuous regulation devices and low-cost discrete ones. After second-order cone programming and linearization, the multi-objective model is solved via the normalized normal constraint (NNC) algorithm to get a solution set, from which the optimal solution is selected using Entropy Weight and Technique for Order Preference by Similarity to an Ideal Solution (EW-TOPSIS). The results indicate that, in comparison with the proposed method, ADN not implementing demand-side TOU pricing strategies exhibits an increase in operating costs by 13.83% and a rise in voltage deviation by 4.14%. Meanwhile, ADN utilizing only traditional discrete control devices demonstrates more significant increments, with operating costs increasing by 182.40% and voltage deviation rising by 113.02%. Full article

This study uses a U-shaped aqueduct structure in a specific irrigation area as the research object to examine the damage patterns of large-span elevated U-shaped aqueduct structures under seismic action. A single-span aqueduct model that integrates fluid–structure interaction is created with the finite element program ANSYS. The incremental dynamic analysis approach is utilized to perform nonlinear dynamic time–history assessments for three types of bearings—plate rubber bearings, pot rubber bearings and lead-core rubber bearings—under conditions of an empty condition, a half-full condition and a design water level. Seismic fragility curves for the bearings and piers subjected to transverse seismic stress are developed using capacity–demand ratio models and specified damage limit states. The findings demonstrate that the likelihood of aqueduct components being damaged increases substantially as seismic intensity increases, with bearings failing before piers. Under the conditions of empty, half-full and design water levels, the structural mass increases as a result of higher water levels. This alters the dynamic response characteristics and increases the likelihood of failure in a variety of damage states. The probability of plate rubber bearings experiencing minor damage exceedance increases from 11.75% to 61.6% as the water level rises from vacant to design conditions. Lead-core rubber bearings provide better seismic isolation than plate rubber bearings and pot rubber bearings. This greatly lowers the aqueduct structure’s displacement response and damage likelihood. Under design water level circumstances, the chance of mild damage to lead rubber bearings is 8.64%, at a peak ground acceleration of 0.4 g. The damage probabilities for the pot rubber bearings and the plate rubber bearings are 80.68% and 97.45%, respectively. The research findings establish a theoretical foundation for the seismic design and damage evaluation of aqueduct structures in places with high seismic activity, ensuring the stable operation of water transfer projects and sustainable water resource utilization, presenting considerable technical applicability. Full article