Search Results (2,004)

Electrocatalytic water splitting for hydrogen production is a crucial technology in achieving carbon neutrality. The development of efficient and stable hydrogen evolution reaction (HER) electrocatalysts is a core challenge in this field. This review systematically summarizes the latest research advancements in nanoporous transition metal-based catalysts, covering metal alloys and compounds. Through strategies such as compositional optimization, crystal structure modulation, interface engineering, and nanoporous structure design, these non-precious metal catalysts exhibit outstanding performance comparable to commercial platinum-carbon catalysts across a wide pH range. This paper thoroughly discusses the catalytic mechanisms of different material systems, including electronic structure regulation, active site exposure, and mass transport optimization. Finally, the challenges faced in current research are summarized, and future directions are projected, including scalable fabrication processes and performance validation in real electrolysis cell environments. This review provides significant insights into designing next-generation efficient and stable non-precious metal electrocatalysts. Full article

Traditional image watermarking technology focuses on the robustness and imperceptibility of the copyright information embedded in the cover image. However, in addition to copyright theft, the cover images stored and transmitted in the open network environment is facing the threat of being identified and retrieved by deep neural network (DNN) with malicious purpose, which is a new privacy threat. Therefore, it is essential to protect the copyright and the privacy of cover image simultaneously. In this paper, a novel cover-concealed image watermarking (CCIW) is proposed, which combines conditional generative adversarial networks with channel attention mechanisms to generate adversarial examples of the cover image containing invisible copyright information. This method can effectively prevent privacy leakage and copyright infringement simultaneously, since the cover image cannot be collected and processed by DNNs without permission, and the embedded copyright information is hardly to be removed. The experimental results show that the proposed method achieved a success rate of adversarial attack over 98% on the Caltech256 dataset, and the generated adversarial examples have good image quality. The accuracy of copyright information extraction is close to 100%, and it also exhibits good robustness in different noise environments. Full article

This study investigated differences in running performance between starters and substitutes during their first 15 min of match play in professional football. The investigation was designed as a retrospective observational study. A time–motion analysis was conducted on one professional football team from the Swiss Challenge League during the 2023–2024 season. The first 15 min of players’ match participation were analyzed and divided into three 5 min periods. Running performance variables included total distance covered (TDC), high-speed running (HSR; 19.8–25.2 km·h⁻¹), and sprint distance (>25.2 km·h⁻¹) using GPS technology. Statistical analyses were performed using paired t-tests and repeated-measures ANOVA with Bonferroni post hoc corrections. Starters covered significantly greater TDC than substitutes over the 15 min period (p = 0.002), driven by higher values in the 5–10 min and 10–15 min epochs (p = 0.01 and p < 0.001, respectively). No between-group differences were observed for HSR and sprint distance. Within-group analyses revealed a significant decline in TDC during the 10–15 min epoch compared with earlier intervals for both starters and substitutes (p < 0.001 and p = 0.02, respectively). Substitutes also exhibited a reduction in distance covered at HSR after the initial 0–5 min period (p = 0.02). Starters face higher TDC demands than substitutes in the opening 15 min, although HSR and sprint distance remain stable. The results indicate that starters covered greater TDC than substitutes during the first 15 min of play; however, no significant differences were found in HSR and sprint distance between the two conditions. Full article

This study presents an integrated field- and model-based assessment of how rapid urbanization is transforming water infiltration and storm runoff dynamics in Dhaka—a megacity facing escalating flood risks. Unlike conventional studies that rely solely on secondary or modeled datasets, this research combines extensive in situ field measurements of soil infiltration with scenario-based hydrological modeling to capture the localized impacts of land use change. Using the SCS Curve Number and Water Balance methods, the study quantifies how variations in land cover under different urban growth trajectories alter surface runoff behavior. Results show that Dhaka’s annual infiltration rates—measured at 2034 mm, 1546 mm, and 1074 mm during wet (2017), normal (2018), and dry (2020) years—could decline by nearly 50% if current urban expansion trends persist. Concurrently, surface runoff volumes are projected to nearly double, amplifying flood hazard potential across the city. By grounding scenario modeling in empirical local data, this work offers a context-specific understanding of the evolving hydrological regime of a rapidly urbanizing South Asian metropolis, providing a framework for flood resilience planning in other data-limited cities. Full article

In the complex operational environment of offshore wind turbines, the drivetrain system faces multiple uncertainties including wind speed fluctuations, wave disturbances, and dynamic coupling effects, which significantly increase the difficulty of fault identification. To address this challenge, this paper proposes a deep learning model integrating Temporal Convolutional Networks (TCN) and Gated Recurrent Units (GRU) to enhance fault detection capability. The TCN module extracts multi-scale temporal features from vibration signals, while the GRU module captures long-term dependencies in drivetrain degradation patterns. The study utilizes a publicly available Zenodo dataset containing simulated acceleration signals from a 5-MW reference drivetrain under three offshore conditions, covering healthy and faulty states of the main shaft, high-speed shaft, and planet bearings. Experimental validation under different operational conditions demonstrates that the proposed TCN-GRU model outperforms baseline models in terms of accuracy, precision, and recall. Full article

Background: Knee osteoarthritis (OA) is a prevalent musculoskeletal condition associated with pain, functional limitation, and reduced quality of life. Telerehabilitation has emerged as a scalable intervention, yet many platforms lack neuromuscular feedback or objective-monitoring capabilities. The KneE-PAD system uniquely integrates electromyographic and inertial sensing to provide personalized feedback and remote performance tracking. Objective: To evaluate the clinical effectiveness of a sensor-augmented telerehabilitation system (KneE-PAD) compared to conventional face-to-face physiotherapy in older adults with mild-to-moderate knee OA. Methods: In this single-blind randomized controlled trial, 42 older adults (mean age 68.4 ± 5.7 years) were randomly assigned to either KneE-PAD telerehabilitation or conventional physiotherapy for eight weeks. KneE-PAD sessions incorporated real-time electromyographic and motion feedback, while physiotherapists remotely supervised training. Assessments were performed at baseline, post-intervention, and 12-week follow-up. Primary outcomes included quadriceps strength, neuromuscular activation, and WOMAC scores. Secondary outcomes covered functional mobility, psychological distress, self-efficacy, and fear of movement. Results: The telerehabilitation group demonstrated notable improvements in neuromuscular activation, quadriceps strength, and functional capacity, all exceeding clinically meaningful thresholds. Functional mobility and pain outcomes showed substantial gains compared with the control group, while psychological indicators (self-efficacy and depressive symptoms) exhibited modest but positive trends. Between-group comparisons consistently favored KneE-PAD, with effects maintained at the 12-week follow-up, confirming both clinical and functional robustness. Conclusions: Sensor-augmented telerehabilitation using the KneE-PAD platform appears to be a feasible and potentially effective alternative to conventional physiotherapy for knee OA. By combining real-time feedback, motor learning reinforcement, and remote monitoring, the system may enhance neuromuscular and functional recovery. These findings should be confirmed in larger and longer-term trials. Trial Registration: ClinicalTrials.gov: NCT06416332. Full article

Raptors wintering in temperate regions face strong energetic constraints due to low temperatures and limited prey availability. Understanding how intrinsic traits and environmental conditions shape hunting performance helps to clarify the mechanisms underlying winter foraging efficiency. We studied wintering Hen Harriers (Circus cyaneus) in Austria over five consecutive winters (2020/21–2024/25) to quantify touchdown attempt rate and outcome in relation to sex, age, territorial status, and weather. Using generalized linear mixed models, we analyzed 1829 recorded touchdowns with individual identity as a random effect. Territorial females showed slightly higher attempt rates than males, whereas non-territorial females exhibited a tendency toward lower touchdown success. Adult males achieved the highest per-attempt efficiency, but age alone had no significant effect. Touchdown outcomes improved under cloud cover and declined with wind speed, while temperature showed no influence. Attempt rate was unaffected by any environmental variable. These results demonstrate that both intrinsic and extrinsic factors jointly determine winter foraging performance: males, with their smaller body size and agility, can persist even in vole-poor habitats by compensating through efficient flight and prey capture; territorial females benefit from stable access to vole-rich patches; and non-territorial females remain constrained by competition and limited access to prey. The findings highlight the need to maintain open farmland and vole-rich habitat to support diverse overwintering strategies. Full article

Object detection in high-resolution satellite imagery is a critical technology for various applications, yet it faces persistent challenges due to extreme variations in object scale, orientation, and density. The development of numerous public datasets has been pivotal for advancing the field. To continue this progress and expand the diversity of sensor data available for research, we introduce the KOMPSAT Fine-Grained Object Detection (KFGOD) dataset, a new large-scale benchmark for fine-grained object detection. KFGOD is uniquely constructed using 70 cm and 55 cm resolution optical imagery from the KOMPSAT-3 and 3A satellites, sources not covered by existing major datasets. It provides approximately 880,000 object instances across 33 fine-grained classes, encompassing a wide range of ships, aircraft, vehicles, and infrastructure. The dataset ensures high quality and sensor consistency, covering diverse geographical regions worldwide to promote model generalization. For precise localization, all objects are annotated with both oriented (OBB) and horizontal (HBB) bounding boxes. Comprehensive experiments with state-of-the-art detection models provide benchmark results and highlight the challenging nature of the dataset, particularly in distinguishing between visually similar fine-grained classes. The KFGOD dataset is publicly available and aims to foster further research in fine-grained object detection and analysis of high-resolution satellite imagery. Full article

Agriculture is one of the many sectors facing significant risks from climate change. To manage potential crop losses, whether climate-related or not, farmers widely rely on crop insurance to increase their resilience. However, farmers in Sri Lanka demonstrate a limited acceptance of crop insurance schemes. This study aimed to investigate the perceptions and Willingness-to-Pay (WTP) for crop insurance schemes among the paddy farmers in Kurunegala district. A total of 248 paddy farmers from the Kurunegala district were recruited as the study sample using the stratified random sampling approach. A pre-tested structured questionnaire and choice cards were used for primary data collection. The Conditional Logit Model (CLM) was used for data analysis. Around 77.8% of respondents were males engaged only in paddy farming, while the majority (62.5%) received an income of LKR 50,000 to 75,000. Complications experienced during the claim form-filling process (mean = 4.6), gaps in covering all crops on the crop land (mean = 4.6), and poor service quality (mean = 4.5) were perceived as the major limitations in existing crop insurance schemes. Outcomes of the CLM indicated that farmers with a positive attitude toward crop insurance significantly prefer plans with drought coverage (β = 0.823; p < 0.05), on-field assessments (β = 0.251; p < 0.05), and higher no-hazard returns (β = 0.318; p < 0.05) while showing a notable sensitivity to premium costs (β = −0.590; p < 0.05). The model also revealed an apparent willingness to switch from the status quo when presented with better-designed alternatives. The findings emphasized the need to implement responsive crop insurance schemes to enhance climate resilience and ensure the sustainability of paddy production in Sri Lanka. Full article

Pleurotus ostreatus is valued for its nutritional, medicinal, economic, and ecological benefits and is widely used in the food, pharmaceutical, and environmental protection industries. Pleurotus ostreatus, as a highly perishable edible fungus, faces significant challenges in supply chain quality control and food safety due to its short shelf life. As consumer demand for food freshness and full traceability increases, there is an urgent need to establish a reliable traceability system that enables real-time monitoring, spoilage prevention, and quality assurance. This study focuses on the Pleurotus ostreatus supply chain and designs and implements a multi-role flexible traceability system that integrates blockchain and the Internet of Things. The system collects key production and storage environment parameters in real time through sensor networks and enhances data accuracy and robustness using an improved adaptive weighted fusion algorithm, enabling precise monitoring of the growth environment and quality risks. The system adopts a “link-chain” mapping mechanism for multi-chain storage and dynamic reorganization of business processes. It incorporates attribute-based encryption strategies and smart contracts to support tiered data access and secure sharing among multiple parties. Key information is stored on the blockchain to prevent tampering, while auxiliary data is stored in off-chain databases and the Interplanetary File System to ensure efficient and verifiable data queries. Deployed at Shandong Qihe Ecological Agriculture Co., Ltd., No. 517, Xilou Village, Kunlun Town, Zichuan District, 255000, Zibo City, Shandong Province, China, the system covers 12 cultivation units and 60 sensor nodes, recording over 50,000 traceable data points. Experimental results demonstrate that the system outperforms baseline methods in query latency, data consistency, and environmental monitoring accuracy. The improved fusion algorithm reduced the total variance of environmental data by 20%. In practical application, the system reduced the spoilage rate of Pleurotus ostreatus by approximately 12.3% and increased the quality inspection pass rate by approximately 15.4%, significantly enhancing the supply chain’s quality control and food safety capabilities. The results show that the framework is feasible and scalable in terms of information credibility and operational efficiency and significantly improves food quality and safety monitoring throughout the production, storage, and distribution of Pleurotus ostreatus. This study provides a viable technological path for spoilage prevention, quality tracking, and digital food safety supervision, offering valuable insights for both food science research and practical applications. Full article

This study investigates the nonlinear, interactive, and temporally dynamic effects of urban form on street vitality within community-oriented metro station areas (MSAs) in Beijing. It offers potential reference value for other cities facing comparable challenges in MSA implementation and increasing motorization. This research addresses gaps in prior studies concerning the integration of multi-source data, nonlinearity, and diurnal variation. Utilizing an extended node-place-design framework, urban form is conceptualized through network, interface, and functional dimensions. The empirical analysis employs multi-source datasets, including 128,199 mobile device trips recorded in April 2024, OpenStreetMap for network data, Baidu points of interest for functional data, and Grasshopper for interface metrics, covering 183 street samples within a 1000 m radius of metro stations. Traditional regression models—such as ordinary least squares and spatial autocorrelation and cross-correlation—are used as baselines, while a novel gradient-boosting decision tree with latitude and longitude features is applied to enhance predictive performance. The results indicate that key contributors include road network density (16.89%), road intersections (10.56%), and point-of-interest density (9.74%), with Shapley Additive Explanations dependence plots demonstrating nonlinear thresholds. The analyses reveal synergistic or antagonistic interactions among features. Temporal fluctuations in feature importance further support the presence of diurnal dynamics. The study provides insights for time-sensitive urban planning aimed at enhancing MSA vitality, sustainability, and resident quality of life, while acknowledging that the conclusions are context-specific to Beijing and require additional validation in other urban environments. Full article

Unlike most other measures in flood risk management, flood forecasting stands out because it is not designed to address a pre-defined return period. In principle it is applicable to a whole range of possible events and can be operated continuously in real time. This makes flood forecasting an effective non-structural measure for saving lives and property, even in the face of increased hydro-meteorological variability and extremes. In Germany, a series of regional and transregional flood forecasting centres and services have been established that cover the entire national territory. For large basins, the existing forecasting centres are well equipped to provide accurate real-time forecasts. Nevertheless, there are remaining challenges that need to be met when the focus is on small to medium-sized catchments. This study focuses on discussing the capabilities of six state-of-the-art flood forecasting centres and derives the most important requirements for significant improvements in flood forecasting capabilities for small to medium-sized catchment areas. We emphasise that future research must focus on flood-driven predictions, including the prediction of flood inundation and consequences for buildings and infrastructure, as well as geotechnical failure mechanisms in Germany. Full article

The sustainable development of resource-based cities faces challenges due to the imbalance between ecosystem service supply and demand. This study examines Xingtai, a typical resource-based city located in northern China, using ecosystem service bundle theory to analyze the supply–demand relationships of six ecosystem services—water yield, soil retention, habitat quality, urban cooling, PM_2.5 removal, and carbon sequestration—from 2000 to 2020. Based on the ratio of supply–demand, we identify ecosystem service bundles and explore their driving factors using redundancy analysis (RDA) and the geographically and temporally weighted regression (GTWR) model. Results show a clear “mountain–plain” supply gradient, with high supply in the western Taihang Mountains and low supply in urbanized eastern plains. Demand follows a “center-high, periphery-low” pattern, with urban centers showing higher demand for urban cooling and PM_2.5 removal. A severe supply–demand imbalance exists: soil retention, PM_2.5 removal, habitat quality, and carbon sequestration are undersupplied in urbanized areas, while water yield and urban cooling are oversupplied in the western mountains. Natural factors (precipitation and temperature) shape western mountain supply, while human activities (GDP and nighttime light) drive demand polarization in the east. GTWR results reveal that urban GDP growth and land expansion intensify demand, while stable supply in mountain areas relies on precipitation and forest cover. This study provides scientific support for the sustainable development of resource-based cities. Full article

A scientifically robust evaluation system for ecological products value realisation is urgently needed in China. Approaches that rely solely on objective indicators face significant challenges due to data limitations and regional heterogeneity. This study innovatively constructed an experts’ perceived effectiveness evaluation scale for ecological products value realisation, establishing a dual mechanism of “objective data + expert experience calibration” and covering the entire chain of “ecological background–economic conversion–social well-being–benefit feedback”. This framework was applied to the Beijing–Tianjin–Hebei (BTH) region, with results indicating that the perceived effectiveness index for value realisation of material-supply-oriented ecological products (MSEPs), regulatory service-oriented ecological products (RSEPs), and cultural service-oriented ecological products (CSEPs) was 0.7054, 0.6482, and 0.6052, respectively. Significant regional differences exist. Beijing holds a central and leading role, while effectiveness in the northern mountainous areas of Hebei Province is stronger than in the central and southern regions. Regions with weaker performance should prioritise leadership strategies over comprehensive development, as disparities arising from regional differentiation call for more sophisticated coordination mechanisms. The study offers new insights for policy decision-making and optimisation, enhancing both the applicability and precision of evaluation methods. Nonetheless, the designed scales remain exploratory and warrant verification through a broader empirical basis. Full article

The present study examines the influence of material and structural parameters on the fit and air permeability of textile face masks, surgical masks, and certified respirators. Nine samples were tested using the AccuFIT 9000 for quantitative fit factor (FF) measurements and the FX-3340 MinAir for air permeability in both airflow directions. Results show that increased thickness moderately improves FF, supporting better facial sealing. However, mass per unit area and bulk density show weak or no correlation with FF. Air permeability correlates weakly and negatively with FF, especially during exhalation, but remains essential for wearer comfort. Notably, some textile masks outperformed certified respirators in terms of fit, highlighting the importance of design, elasticity, and edge sealing. The findings suggest that effective mask performance depends on more than filtration materials or certification levels. A balanced design combining breathability, structural optimisation, and ergonomic fit is essential for both comfort and protection. These insights can guide the development of more effective reusable and disposable face coverings, particularly in aerosol-rich environments. Full article