Search Results (1,747)

Background: Recurrent haemarthrosis leads to progressive and degenerative joint damage in patients with haemophilia from an early age. Haemophilic arthropathy is characterised by chronic pain, restricted range of motion, proprioceptive deficits, and structural alterations. The aim of this study was to evaluate the effectiveness of a manual therapy protocol in patients with haemophilic ankle arthropathy. Methods: A randomised, crossover, double-blind clinical trial was conducted. Thirteen patients with haemophilia were allocated to two sequences: A–B (intervention phase followed by placebo control) and B–A (placebo control followed by intervention). The intervention comprised joint mobilisation techniques, high-velocity low-amplitude manipulations, and myofascial release. In the placebo control condition, a simulated protocol was applied, consisting of intermittent contact and light pressure. Both conditions involved three physiotherapy sessions, delivered once weekly over three consecutive weeks. Outcome measures included functional capacity (2-Minute Walk Test), pain intensity (visual analogue scale), range of motion (goniometer), pressure pain threshold (algometer), joint status (Haemophilia Joint Health Score), kinesiophobia (Tampa Scale of Kinesiophobia), and postural stability (pressure platform). Following a four-week washout period, participants crossed over to the alternate condition. Results: No participants experienced ankle haemarthrosis or other adverse events during the intervention, confirming the safety of the protocol. Significant time*sequence interactions (p < 0.05) with high post hoc power (≥0.80) were observed for functional capacity, range of motion, and joint status. A significant sequence effect was also found for most clinical outcomes, with no evidence of a carry-over effect. Conclusions: This manual therapy protocol might be safe for patients with haemophilia. The physiotherapy intervention demonstrated improvements in functionality, range of motion, and joint status in individuals with haemophilic ankle arthropathy. Full article

Traditional pumped storage capacity configuration uses static, year-targeted approaches, leading under-capacity in the early planning stages—wasting renewable energy—and over-capacity in later stages, thus wasting resources. In order to solve the above problems, this article innovatively proposes a dynamic, time-sequenced construction timeline and annual capacity configuration strategy, synchronized with new energy and load development, enhancing sustainability through optimized investment allocation and efficient resource utilization. It presents a two-layer model that considers multiple scenario operational dispatch. The upper layer aims to minimize the curtailment of wind and solar energy, providing a planning scheme to the lower layer, which focuses on multi-scenario economic dispatch, taking into account the peak-valley difference indicators. The models co-iterate: lower-layer operational outcomes feed back to refine the upper-layer’s capacity plan. This process continues until the predicted curtailment calculated by the upper layer aligns closely with that observed in the lower-layer operational simulations, or until capacity changes stabilize, ultimately determining the optimal time-phased capacity configuration. Simulations on a provincial power grid during three typical scenarios in winter, transitional seasons, and summer, as well as extreme weather scenarios, confirm that timely, dynamic configuration strategy significantly enhances renewable absorption, proving the model’s effectiveness. Full article

Although logistics underpins the spatial architecture of supply chains, the causal contribution of logistics industry clustering to green total factor productivity (GTFP) remains under-identified relative to aggregate or manufacturing clustering. This study investigates both the local and spatial spillover effects of logistics industry clustering on green total factor productivity, utilizing panel data from 30 Chinese provinces spanning 2010 to 2023. The empirical results demonstrate that logistics industry clustering significantly enhances green total factor productivity within the local province and generates robust positive spillover effects in adjacent regions. Regional heterogeneity analysis reveals that in the eastern provinces, clustering of the logistics industry bolsters green total factor productivity both locally and regionally. In contrast, in the central region, such clustering only benefits neighboring provinces, while in the western region, its impact is not statistically significant for either local or neighboring green total factor productivity. Temporal heterogeneity analysis further indicates that the positive influence of logistics industry clustering on green total factor productivity has become more pronounced since 2018.Additionally, spatial mediation effect analysis uncovers that improvements in local green total factor productivity stem from logistics industry clustering’s capacity to enhance resource allocation efficiency and foster industrial upgrading. Notably, the spatial spillover effect dissipates entirely beyond a distance of 350 km. These findings establish logistics industry clustering as a high-leverage, cross-boundary tool for aligning regional logistics planning with green objectives, delineating the effective radius of collaboration to internalize externalities and providing practical guidance for developing economies. Full article

As electric vehicle (EV) adoption accelerates, residential buildings—particularly multi-dwelling structures—face increasing challenges to electrical infrastructure, notably due to conservative sizing practices of electrical feeders based on maximum simultaneous demand. Current sizing methods assume all EVs charge simultaneously at maximum capacity, resulting in unnecessarily oversized and costly electrical installations. This study proposes an optimized methodology to estimate accurate coincidence factors, leveraging simulations of EV user charging behaviors in multi-dwelling residential environments. Charging scenarios considering different fleet sizes (1 to 70 EVs) were simulated under two distinct premises of charging: minimization of current allocation to achieve the desired battery state-of-charge and maximization of instantaneous power delivery. Results demonstrate significant deviations from conventional assumptions, with estimated coincidence factors decreasing non-linearly as fleet size increases. Specifically, applying the derived coincidence factors can reduce feeder section requirements by up to 86%, substantially lowering material costs. A fuzzy logic inference model is further developed to refine these estimates based on fleet characteristics and optimization preferences, providing a practical tool for infrastructure planners. The results were compared against other studies and real-life data. Finally, the proposed methodology thus contributes to more efficient, cost-effective design strategies for EV charging infrastructures in residential buildings. Full article

Air Traffic Flow Management (ATFM) delay refers to the difference between a flight’s Target Take-Off Time (TTOT) and its Calculated Take-Off Time (CTOT), reflecting congestion levels in the air traffic network. ATFM delays are assigned to balance demand and capacity at key points in the network. The traditional First-Come, First-Served (FCFS) approach allocates delays strictly in the order flights are ready to depart, which is simple but inflexible. This study proposes a dynamic priority-based aircraft sequencing method at critical waypoints under multi-resource scenarios, aiming to reduce ATFM delays. An improved Constrained Position Shifting (CPS) constraint is introduced into the optimization model to enhance the influence of flight priority during decision-making. Additionally, three different priority strategies are designed to compare their respective impacts on ATFM delay. Finally, a dynamic priority-based ATFM delay optimization model is developed to address the identified challenges. Experimental results demonstrate that, compared with the FCFS scheme, the three priority strategies achieve maximum ATFM delay reductions of 30.5%, 44.1%, and 19.9%, respectively. The proposed model effectively allocates shorter delays to critical flights, optimizing resource utilization and improving the operational efficiency of the air route network. The research provides a reference framework for air traffic managers in allocating spatiotemporal resources across multiple congestion hotspots. By aligning priorities with network-wide efficiency goals, it overcomes traditional model limitations, avoids local optima, and supports globally optimal ATFM policy and practice. Full article

Background: Diabetic foot ulcers (DFUs) in patients with peripheral artery disease (PAD) are difficult to treat and associated with poor healing outcomes. Photobiomodulation therapy (PBMT) and exercise have shown individual benefits, but evidence on their combined effects is limited. Objective: To evaluate whether PBMT combined with resistance exercise improves wound healing and walking ability in patients with DFU and PAD. Methods: In this pilot randomized trial, 11 patients with DFU and PAD were allocated to either PBMT plus supervised exercise or exercise alone for 4 weeks. Outcome measures included wound size, skin temperature, and 6-min walking distance. Results: PBMT combined with exercise improved wound healing and walking capacity compared with baseline; however, no significant between-group differences were observed. A positive correlation was observed between post-PBMT plantar skin temperature and percentage of wound reduction. Conclusions: PBMT combined with resistance exercise may enhance wound healing and functional mobility in patients with DFU and PAD. Full article

Topic modeling in digital news faces the dual challenge of thematic overlap and evolving semantic boundaries, especially in morphologically rich languages like Turkish. To address these obstacles, we propose a topic modeling framework enhanced with knowledge graphs that explicitly incorporates uncertainty in topic assignment. We focus on the diversity of Fuzzy Latent Semantic Analysis (FLSA) and compare the performance with Latent Dirichlet Allocation (LDA), BERTopic, and embedding-based Top2Vec on a corpus drawn from two Turkish news agencies. We evaluate each model using standard metrics for topic coherence, diversity, and interpretability. We propose Shannon entropy of node-degree distributions to measure the network complexity of knowledge graphs as topic similarity. Our results indicate that FLSA achieves perfect topic diversity, 1.000 and improved interpretability, 0.33 over LDA, 0.09 while also enhancing coherence, 0.33 vs. 0.27. Top2Vec demonstrates the strongest coherence, 0.81 and interpretability, 0.78 with high diversity, 0.97, reflecting its capacity to form semantically cohesive clusters. Entropy analysis further shows that FLSA produces the most information-rich topic networks. These findings suggest that fuzzy modeling and embedding-based approaches offer complementary strengths, uncertainty-aware flexibility, and semantic precision, thereby improving topic discovery in complex, unstructured news environments. Full article

Sea–rail intermodal transport offers high efficiency and environmental benefits, yet its development in China remains limited. Existing studies have mainly assessed the macro-level benefits of sea–rail intermodal transport policies, but rigorous evidence on whether incentive policies work and how their effects differ across policy types remains scarce, which limits evidence-based policy design and efficient allocation between subsidies and capacity expansion. To address this gap, a dual-policy identification framework was established that combines a multi-period difference-in-differences model with event study analysis and used station–month data from China to assess the independent effects, underlying mechanisms, and spatiotemporal heterogeneity of railway freight price subsidies and freight train expansion on container throughput. The results indicate that both policies significantly increased container throughput. Railway freight price subsidies exhibited stronger and more persistent effects with a certain lag, whereas freight train expansion produced rapid but short-lived responses. The impacts of both policies were more pronounced in short-distance transport, but weakened or even turned negative over longer distances. Moreover, the number of participating entities served as a key mediating pathway, while information sharing positively moderates policy impacts. This study makes theoretical contributions to the identification of heterogeneity, mechanism analysis, and spatiotemporal characterization of SRIT incentive policy effects, while offering refined and actionable guidance for SRIT policy optimization. Full article

To enhance the overall operational efficiency of heavy haul railway port stations, which serve as critical hubs in rail–water intermodal transportation systems, this study develops a novel scheduling optimization method that integrates operation plans and resource allocation. By analyzing the operational processes of heavy haul trains and shunting operation modes within a hybrid unloading system, we establish an integrated scheduling optimization model. To solve the model efficiently, a dual-agent advantage actor–critic with Pareto reward shaping (DAA2C-PRS) algorithm framework is proposed, which captures the matching relationship between operations and resources through joint actions taken by the train agent and the shunting agent to depict the scheduling decision process. Convolutional neural networks (CNNs) are employed to extract features from a multi-channel matrix containing real-time scheduling data. Considering the objective function and resource allocation with capacity, we design knowledge-based composite dispatching rules. Regarding the communication among agents, a shared experience replay buffer and Pareto reward shaping mechanism are implemented to enhance the level of strategic collaboration and learning efficiency. Based on this algorithm framework, we conduct experimental verification at H port station, and the results demonstrate that the proposed algorithm exhibits a superior solution quality and convergence performance compared with other methods for all tested instances. Full article

This study addresses the critical need for accurate tourism demand (TD) forecasting in Bulgaria using economic indicators, developing robust predictive models to navigate post-pandemic market volatility. The COVID-19 pandemic exposed tourism’s vulnerability to systemic shocks, highlighting deficiencies in traditional forecasting approaches. Bulgaria’s tourism industry, characterized by strong seasonal variations and economic sensitivity, requires enhanced methodologies for strategic planning in uncertain environments. The research employs comprehensive comparative analysis of machine learning (ML) and deep machine learning (DML) methodologies. Monthly overnight stay data from Bulgaria’s National Statistical Institute (2005–2024) were integrated with COVID-19 case data, Consumer Price Index (CPI) and Bulgarian Gross Domestic Product (GDP) variables for the same period. Multiple approaches were implemented including Prophet with external regressors, Ridge regression, LightGBM, and gradient boosting models using inverse MAE weighting optimization, alongside deep learning architectures such as Bidirectional LSTM with attention mechanisms and XGBoost configurations, as each model statistical significance was estimated. Contrary to prevailing assumptions about deep learning superiority, traditional machine learning ensemble approaches demonstrated superior performance. The ensemble model combining Prophet, LightGBM, and Ridge regression achieved optimal results with MAE of 156,847 and MAPE of 14.23%, outperforming individual models by 10.2%. Deep learning alternatives, particularly Bi-LSTM architectures, exhibited significant deficiencies with negative R² scores, indicating fundamental limitations in capturing seasonal tourism patterns, probable data dependence and overfitting. The findings, provide tourism stakeholders and policymakers with empirically validated forecasting tools for enhanced decision-making. The ensemble approach combined with statistical significance testing offers improved accuracy for investment planning, marketing budget allocation, and operational capacity management during economic volatility. Economic indicator integration enables proactive responses to market disruptions, supporting resilient tourism planning strategies and crisis management protocols. Full article

To address urban flooding challenges exacerbated by climate change and urbanization, this study develops an integrated assessment framework combining the analytic hierarchy process (AHP), entropy weight method, and cloud model to quantify urban flood resilience. Resilience is deconstructed into resistance, adaptability, and recovery and evaluated through 24 indicators spanning water resources, socio-economic systems, and ecological systems. Subjective (AHP) and objective (entropy) weights are optimized via minimum information entropy, with the cloud model enabling qualitative–quantitative resilience mapping. Analyzing 2014–2024 data from 27 Chinese sponge city pilots, the results show resilience improved from “poor to average” to “good to average”, with a 2.89% annual growth rate. Megacities like Beijing and Shanghai excel in resistance and recovery due to infrastructure and economic strengths, while cities like Sanya enhance resilience via ecological restoration. Key drivers include water allocation (27.38%), economic system (18.41%), and social system (17.94%), with critical indicators being population density, secondary industry GDP ratio, and sewage treatment rate. Recommendations emphasize upgrading rainwater storage, intelligent monitoring networks, and resilience-oriented planning. The model offers a scientific foundation for urban disaster risk management, supporting sustainable development. This approach enables systematic improvements in adaptive capacity and recovery potential, providing actionable insights for global flood-resilient urban planning. Full article

It is of particular interest to use eco-friendly working fluids in ocean thermal energy conversion (OTEC) systems. In response, this study develops a thermo-economic model to evaluate the feasibility of fourth-generation refrigerants, including R1234yf, R1234ze(Z), and R1336mzz(Z), as potential alternatives to ammonia. The analysis examines the effects of system scale and cold seawater pumping depth on capital investment distribution and key economic indicators, such as the levelized cost of energy (LCOE) and net present value (NPV). The findings highlight the viability of R1234ze(Z) as a substitute for ammonia, demonstrating a slightly lower LCOE and requiring 8.6% less installed capacity to achieve financial breakeven. Additionally, the economic impact of pumping depth varies with system scale: in small-scale OTEC systems, LCOE initially decreases with depth before rising beyond an optimal point, while in large-scale systems, it continuously declines and eventually stabilizes. Moreover, capital investment allocation shifts with system size, making pipeline optimization crucial for small-scale systems, whereas minimizing heat exchanger costs is key to enhancing the economic feasibility of large-scale OTEC plants. The results offer guidance for cost-effective OTEC deployment and refrigerant selection, supporting a sustainable energy supply for tropical islands. Full article

Background/Objectives: Nanobubble water (NBW) is being studied increasingly for its potential benefits in sports nutrition. This study aimed to evaluate whether supplementation with ozone-enriched NBW (O₃-NBW) could improve integrated exercise capacity—encompassing endurance performance, muscle strength, and postexercise recovery as well as body composition and metabolic adaptations in mice. Methods: Male ICR mice (n = 24) were allocated into Control, Air-NBW, or O₃-NBW (0.2–1 mg/L ozone) groups for 4 weeks. Results: O₃-NBW treatment considerably enhanced forelimb grip strength and treadmill running endurance compared to the Control group (both p < 0.05). Analyses of body composition revealed a higher proportion of lean mass and muscle glycogen storage in NBW groups, notably with O₃-NBW. Serum markers gathered post-exercise demonstrated a reduction in ammonia and blood urea nitrogen (BUN), suggesting improved nitrogen metabolism. Levels of resting serum creatine kinase (CK) and uric acid were also lower in O₃-NBW mice, indicating potential benefits for muscle recovery. In addition, O₃-NBW treatment significantly enhanced oxygen consumption (VO₂) and reduced the respiratory quotient (RQ), signifying amplified fat oxidation, while also lowering total energy expenditure (all p < 0.05). Spontaneous wheel-running activity remained consistent across all the groups. Conclusions: Taken as a whole, these findings emphasize that O₃-NBW supplementation offers ergogenic and metabolic advantages by improving integrated exercise capacity and efficiency of gas exchange, without adverse effects. Full article

To resolve the issue of state of charge (SOC) inconsistency among energy storage units under traditional equal-power allocation strategies, this paper proposes a multi-unit SOC balancing control strategy based on battery life degradation characteristics. Prior to system operation, the proposed strategy optimizes power distribution according to each unit’s state of health (SOH) and predefined depth of discharge (DOD), ensuring SOC balance at the end of each charge–discharge cycle. Simulation and experimental results demonstrate that, compared with traditional equal-power distribution control, the proposed strategy significantly improves capacity utilization and extends the overall system lifetime. For instance, in Simulation Scenario 1, the available capacity per cycle is increased by 8.14%, and the overall system lifetime is prolonged by 11.04%. Furthermore, the strategy eliminates the need for dynamic power redistribution, thus reducing communication overheads and effectively meeting engineering requirements for SOC balancing. This research provides valuable insights for the safe and economical operation of energy storage power stations. Full article

The European energy transition, marked by the increasing share of renewable sources in the production mix, brings to the fore the issue of maintaining power quality under conditions of high variability. This study proposes an adaptive monitoring model based on a zonal classification of electrical networks according to the volatility of net renewable production (wind and photovoltaic). The approach relies on a proprietary Renewable Variability Index (RVI), developed using publicly available European datasets, to assess the mismatch between electricity consumption and renewable generation in six representative countries: Germany, Denmark, Spain, Poland, Romania, and Sweden. Based on this index, the model defines three zonal risk levels and recommends differentiated power quality monitoring strategies: continuous high-resolution observation in critical areas, adaptive monitoring in medium-risk zones, and conditional event-based activation in stable regions. The results demonstrate a significant reduction in data acquisition requirements, without compromising the capacity to detect disruptive events. By incorporating adaptability, risk sensitivity, and selective allocation of monitoring resources, the proposed framework enhances operational efficiency in smart grid environments. It aligns with current trends in smart grid digitalization, enabling scalable, context-aware control and protection mechanisms that support Europe’s sustainability and energy security objectives while contributing to the broader goals of sustainable energy transition and long-term grid resilience. Full article