Search Results (27,431)

Against the backdrop of an urgent energy crisis, solar energy has attracted sufficient attention as one of the most inexhaustible and friendly types of environmental energy. Faced with long service and harsh environment, the poor performance ratios of photovoltaic arrays and safety hazards are frequently boosted worldwide. In particular, the hot spot effect plays a vital role in weakening the power generation performance and reduces the lifetime of photovoltaic (PV) modules. Here, our research reports a spatial–temporal hot spot management system integrated with fiber Bragg grating (FBG) temperature sensor arrays and cooling hydrogels. Through finite element simulations and indoor experiments in laboratory conditions, a superior cooling effect of hydrogels and photoelectric conversion efficiency improvement have been demonstrated. On this basis, field tests were carried out in which the FBG arrays detected the surface temperature of the PV module first, and then a classifier based on an optimized artificial neural network (ANN) recognized hot spots with an accuracy of 99.1%. The implementation of cooling hydrogels as a feedback mechanism achieved a 7.7 °C reduction in temperature, resulting in a 5.6% enhancement in power generation efficiency. The proposed strategy offers valuable insights for conducting predictive maintenance of PV power plants in the case of hot spots. Full article

Background: Small airway dysfunction (SAD) plays a critical role in the management of severe asthma, particularly in patients at risk of requiring biological therapies (BTs). Short-term studies have shown that switching to single-inhaler triple therapy (SITT) with extrafine beclomethasone–formoterol–glycopyrronium improves outcomes and helps achieve quiet asthma, a state marked by symptom control, no exacerbations or oral steroids, reduced inflammation, and better small airway function. This study investigated whether, over one year, patients could maintain this state as Steady Quiet Asthma (SQA) and whether baseline measures could predict this sustained response. Methods: Twenty-six patients with severe asthma and SAD were transitioned from open triple-inhaler therapy to a closed, single-inhaler triple therapy containing extrafine beclomethasone–formoterol–glycopyrronium. Assessments at baseline (T0) and at one-year follow-up (T12) included clinical evaluations, spirometry, and impulse oscillometry, with a focus on Fres as a predictor for the need for BT. When prescribed, biologic therapies included mepolizumab, benralizumab, and dupilumab. Results: Of the 26 patients, 9 (34.6%) achieved SQA and did not require biologic therapy at the one-year follow-up, while 17 patients (65.4%) initiated biologic treatment. At T0, patients who required biologics had significantly higher median Fres (21 (19.47; 24.58) vs. 17.61 (15.82; 20.63); p = 0.049) compared to those who remained biologic-free. They also exhibited higher residual volume to total lung capacity ratio (%RV/TLC) values and lower forced expiratory volume in one second/forced vital capacity ratios (FEV₁/FVC). At T12, patients spared from BT showed significant reductions in Fres (p = 0.014) and improvements in small airway function (difference in airway resistance between 5 Hz and 20 Hz (R5–20), forced expiratory flow between 25% and 75% of FVC (%FEF25–75), and better asthma control (ACT). In contrast, patients on BT demonstrated less favorable changes in these parameters. Conclusions: Baseline Fres, FEV1/FVC ratio, and %FEV25–75 are valuable predictors of achieving Steady Quiet Asthma (SQA) and sparing biologic therapy. These findings support the use of SITT in severe asthma and highlight the importance of early functional assessments to guide personalized management. Full article

Soil moisture is a key factor in the exchange of energy and matter between the soil and atmosphere, playing a vital role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in achieving large-scale, real-time observations, while deep learning offers new avenues to model the complex nonlinear relationships between spectral features and soil moisture content. This study focuses on the Wei-Ku Oasis in Xinjiang, using multi-source remote sensing data (Landsat series and Sentinel-1) and in situ multi-layer soil moisture measurements. The BOSS feature selection algorithm was applied to construct 46 feature parameters, including vegetation indices, soil indices, and microwave indices, and to identify optimal variable sets for each depth. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and their hybrid model (CNN-LSTM) were used to build soil moisture inversion models at various depths. Their performances were systematically compared on both training and testing sets, and the optimal model was used for spatiotemporal mapping. The results show that the CNN-LSTM-based multi-depth soil moisture inversion model achieved superior performance, with the 0–10 cm model showing the highest accuracy and a testing R² of 0.64, outperforming individual models. The testing R² values for the soil moisture inversion models at depths of 10–20 cm, 20–40 cm, and 40–60 cm were 0.59, 0.54, and 0.59, respectively. According to the mapping results, soil moisture in the 0–60 cm profile of the Wei-Ku Oasis exhibited a vertical gradient, increasing with depth. Spatially, soil moisture was higher in the central oasis and lower toward the periphery, forming a “center-high, edge-low” pattern. This study provides a high-accuracy method for multi-layer soil moisture remote sensing in arid regions, offering valuable data support for oasis water resource management and precision irrigation planning. Full article

Detecting steel defects is a vital process in industrial production, but traditional methods suffer from poor feature extraction and low detection accuracy. To address these issues, this research introduces an improved model, EB-YOLOv8, based on YOLOv8. First, the multi-scale attention mechanism EMA is integrated into the backbone and neck sections to reduce noise during gradient descent and enhance model stability by encoding global information and weighting model parameters. Second, the weighted fusion splicing module, Concat_BiFPN, is used in the neck network to facilitate multi-scale feature detection and fusion. This improves detection precision. The results show that the EB-YOLOv8 model increases detection accuracy on the NEU-DET dataset by 3.1%, reaching 80.2%, compared to YOLOv8. Additionally, the average precision on the Severstal steel defect dataset improves from 65.4% to 66.1%. Overall, the proposed model demonstrates superior recognition performance. Full article

Mechanical processes such as mixing, extrusion, and drying are key operations in food engineering, with a significant impact on product quality and process efficiency. The increasing complexity of food materials—due to non-Newtonian properties, multiphase structures, and thermal–mechanical interactions—requires advanced modeling approaches for process analysis and optimization. Computational Fluid Dynamics (CFD) has become a vital tool in this context. This review presents recent progress in the use of CFD for simulating key mechanical operations in food processing. Applications include the analysis of fluid flow, heat and mass transfer, and mechanical stresses, supporting improvements in mixing uniformity, energy efficiency during drying, and optimization of extrusion components (e.g., shaping dies). The potential for integrating CFD with complementary models for system-wide optimization is also discussed, including challenges related to scale-up and product consistency. Current limitations are outlined, and future research directions are proposed. Full article

DIALOGUE (DIagnostic AI Learning through Objective Guided User Experience) is a generative artificial intelligence (GenAI)-based training program designed to enhance diagnostic communication skills in medical students. In this single-arm pre–post study, we evaluated whether DIALOGUE could improve students’ ability to disclose a type 2 diabetes mellitus (T2DM) diagnosis with clarity, structure, and empathy. Thirty clinical-phase students completed two pre-test virtual encounters with an AI-simulated patient (ChatGPT, GPT-4o), scored by blinded raters using an eight-domain rubric. Participants then engaged in ten asynchronous GenAI scenarios with automated natural-language feedback. Seven days later, they completed two post-test consultations with human standardized patients, again evaluated with the same rubric. Mean total performance increased by 36.7 points (95% CI: 31.4–42.1; p < 0.001), and the proportion of high-performing students rose from 0% to 70%. Gains were significant across all domains, most notably in opening the encounter, closure, and diabetes specific explanation. Multiple regression showed that lower baseline empathy (β = −0.41, p = 0.005) and higher digital self-efficacy (β = 0.35, p = 0.016) independently predicted greater improvement; gender had only a marginal effect. Cluster analysis revealed three learner profiles, with the highest-gain group characterized by low empathy and high digital self-efficacy. Inter-rater reliability was excellent (ICC ≈ 0.90). These findings provide empirical evidence that GenAI-mediated training can meaningfully enhance diagnostic communication and may serve as a scalable, individualized adjunct to conventional medical education. Full article

Cobalt, a rare element in the Earth’s crust, is widely used in industries due to its hardness and antioxidant properties. It also plays a vital role in physiological functions, being a key component of vitamin B₁₂. However, excessive cobalt intake can cause health issues. Detecting cobalt ions, especially Co²⁺, in food is crucial due to potential contamination from various sources. Fluorescent probes offer high sensitivity, selectivity, a rapid response, and ease of use, making them ideal for the accurate and efficient recognition of Co²⁺ in complex samples. In this context, a highly selective fluorescent probe, 2,2′-((3-(1H-benzo[d]imidazol-2-yl)-1,2-phenylene) bis(oxy)) bis(N-(quinolin-8-yl) acetamide) (DQBM-B), was synthesized using chloroacetyl chloride, 8-aminoquinoline, 2,3-dihydroxybenzaldehyde, and benzidine as raw materials for the recognition of Co²⁺. Probe DQBM-B can exhibit fluorescence alone in DMF. However, as the concentration of Co²⁺ increased, Photoinduced Electron Transfer (PET) occurred, which quenched the original fluorescence of the probe. Probe DQBM-B shows better selectivity for Co²⁺ than other ions with high sensitivity (detection limit: 3.56 μmol L⁻¹), and the reaction reaches equilibrium within 30 min. Full article

Wireless sensor networks have become a vital technology that is extensively applied across multiple industries, including agriculture, industrial operations, and smart cities, as well as residential smart homes and environmental monitoring systems. Security threats emerge in these systems through hidden routing-level attacks such as Wormhole and Sinkhole attacks. The aim of this research was to develop a methodology for detecting security incidents in WSNs by conducting real-time analysis of Wormhole and Sinkhole attacks. Furthermore, the paper proposes a novel detection methodology combined with architectural enhancements to improve network robustness, measured by hop counts, delays, false data ratios, and route integrity. A real-time WSN infrastructure was developed using ZigBee and Global System for Mobile Communications/General Packet Radio Service (GSM/GPRS) technologies. To realistically simulate Wormhole and Sinkhole attack scenarios and conduct evaluations, we developed a modular cyber–physical architecture that supports real-time monitoring, repeatability, and integration of ZigBee- and GSM/GPRS-based attacker nodes. During the experimentation, Wormhole attacks caused the hop count to decrease from 4 to 3, while the average delay increased by 40%, and false sensor readings were introduced in over 30% of cases. Additionally, Sinkhole attacks led to a 27% increase in traffic concentration at the malicious node, disrupting load balancing and route integrity. The proposed multi-stage methodology includes data collection, preprocessing, anomaly detection using the 3-sigma rule, and risk-based decision making. Simulation results demonstrated that the methodology successfully detected route shortening, packet loss, and data manipulation in real time. Thus, the integration of anomaly-based detection with ZigBee and GSM/GPRS enables a timely response to security threats in critical WSN deployments. Full article

Biodiversity conservation and sustainable development in high-density forest urban areas have attracted growing attention and are increasingly recognized as critical for achieving the Sustainable Development Goals (SDGs). University campus forests, functioning as ecological islands, possess unique acoustic characteristics and play a vital role in supporting urban biodiversity. In this case study, acoustic monitoring was conducted over the course of a full year to objectively reveal the ecological patterns across temporal scales of the campus sound environment, by combining acoustic indices’ visualization combined with statistical analysis. The findings indicate (1) the existence of ecological sound patterns across different temporal scales, closely associated with phenological cycles; (2) the identification of the specific timing affected by the different species‘ activities, such as the breeding season of birds, the chirping time of cicadas and other insects, as well as the fluctuations in the intensity of human activities, and (3) the development of a methodological framework integrating a visualization technique with statistical analysis to enhance the understanding of long-term ecological dynamics. The results offer a foundation for promoting the sustainable conservation of campus biodiversity in high-density urban settings. Full article

Forest tenure functions as a critical institutional mechanism globally for curbing deforestation and degradation and advancing sustainable forest administration, ultimately underpinning the provision of vital ecosystem services. However, research on robust forest tenure system models both globally and within China remains underdeveloped, hindering their potential as an effective administration tool. The study addresses this gap by conceptualizing China’s forest tenure system through the lens of systems thinking and proposing a Forest Cadastral System based on the Land Administration Domain Model (LADM). We conduct a comprehensive review of the evolution of China’s forest tenure system and an in-depth analysis of the current “person–right–land” configuration. Subsequently, we construct an integrated forest cadastral model structured around three core LADM-compliant packages: party, administrative, and spatial unit. The model is then tested in Ningbo’s forested highlands: trials confirm its efficacy in reconciling tenure security with ecological governance. The findings offer valuable insights for policymakers and practitioners engaged in forest tenure reform and administration, while advancing the academic discourse on leveraging land administration systems for ecosystem service outcomes through robust institutional mechanisms. Full article

Transit systems play a vital role in urban mobility, yet predicting individual travel behavior within these systems remains a complex challenge. Traditional machine learning approaches struggle with transit trip data because each trip may consist of a variable number of transit legs, leading to missing data and inconsistencies when using fixed-length tabular representations. To address this issue, we propose a transformer-based dynamic-sequence approach that models transit trips as variable-length sequences, allowing for flexible representation while leveraging the power of attention mechanisms. Our methodology constructs trip sequences by encoding each transit leg as a token, incorporating travel time, mode of transport, and a 2D positional encoding based on grid-based spatial coordinates. By dynamically skipping missing legs instead of imputing artificial values, our approach maintains data integrity and prevents bias. The transformer model then processes these sequences using self-attention, effectively capturing relationships across different trip segments and spatial patterns. To evaluate the effectiveness of our approach, we train the model on a dataset of urban transit trips and predict first-mile and last-mile travel times. We assess performance using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Experimental results demonstrate that our dynamic-sequence method yields up to a 30.96% improvement in accuracy compared to non-dynamic methods while preserving the underlying structure of transit trips. This study contributes to intelligent transportation systems by presenting a robust, adaptable framework for modeling real-world transit data. Our findings highlight the advantages of self-attention-based architectures for handling irregular trip structures, offering a novel perspective on a data-driven understanding of individual travel behavior. Full article

Background: Medical clearance for competitive sports is vital to safeguarding athletes’ health, particularly in high-intensity disciplines like football. In Italy, fitness assessments follow stringent protocols set by the Commissione di Vigilanza per il controllo dell’Idoneità Sportiva (COCIS), with a strong focus on cardiovascular screening. The primary goal is to prevent sudden cardiac death (SCD), a rare but catastrophic event in athletes. Methods: This paper provides an in-depth narrative review of the 2023 COCIS guidelines, examining the cardiological screening process, required diagnostic tests, management of identified cardiovascular conditions, and the protocols’ role in reducing SCD risk. Results: Comparisons with international standards underscore the effectiveness of the Italian approach. Conclusions: The COCIS 2023 guidelines provide clear, evidence-based protocols for cardiovascular risk assessment, significantly enhancing athlete safety and reducing the incidence of SCD in high-intensity sports. Full article

Gut microbiota plays a vital role in host resilience but may be disrupted under environmental antibiotic pressure. The goitered gazelle (Gazella subgutturosa), a keystone ungulate in the Qaidam Basin, is crucial for ecosystem stability. We aimed to investigate how this species responds to antibiotic pressure through gut microbial adaptation. Using 16S rRNA sequencing and weighted gene co-expression network analysis (WGCNA) on fecal and soil samples from six regions, we identified 18 microbial modules, of which three were strongly associated with antibiotics (|r| ≥ 0.75, p < 0.05). Gut microbial α-diversity was lowest in the antibiotic-rich, vegetation-poor TGL region and highest in XRH, where diverse vegetation appeared to buffer antibiotic impact. Antibiotic pressure can reshape gut microbial communities, exerting both adaptive benefits and adverse effects. High-quality habitats may alleviate the negative impacts of antibiotic pressure. Full article

Dermal fibroblasts, the primary stromal cells of the dermis, exhibit remarkable plasticity in response to various stimuli, playing crucial roles in tissue homeostasis, wound healing, and ECM production. This study examines the molecular mechanisms underlying fibroblast plasticity, including key signaling pathways, epigenetic regulation, and microRNA-mediated control. The impact of aging on ECM synthesis and remodeling is discussed, and the diminished production of vital components such as collagen, elastin, and glycosaminoglycans are highlighted, alongside enhanced ECM degradation through upregulated matrix metalloproteinase activity and accumulation of advanced glycation end products. The process of cellular senescence in dermal fibroblasts is explored, with its role in skin aging and its effects on tissue homeostasis and repair capacity being highlighted. The senescence-associated secretory phenotype (SASP) is examined for its contribution to chronic inflammation and ECM disruption. This review also presents therapeutic perspectives, focusing on senolytics and geroprotectors as promising strategies to combat the negative effects of fibroblast senescence. Current challenges in translating preclinical findings to human therapies are addressed, along with future directions for research in this field. This comprehensive review explores the complex interplay between dermal fibroblast plasticity, cellular senescence, and extracellular matrix (ECM) remodeling in the context of skin aging. In conclusion, understanding the complex interplay between dermal fibroblast plasticity, cellular senescence, and extracellular matrix (ECM) remodeling is essential for developing effective anti-aging interventions, which highlights the need for further research into senolytic and geroprotective therapies to enhance skin health and longevity. This approach has shown promising results in preclinical studies, demonstrating improved skin elasticity and reduced signs of aging. Full article

Species and phylogenetic diversity play vital roles in sustaining the structure, function, and resilience of plant communities, particularly in tropical rainforests. However, the mechanisms according to which environmental filtering and competitive exclusion influence diversity across forest layers remain insufficiently understood. In this study, we investigated the species and phylogenetic diversity patterns in two representative tropical rainforest sites—Bawangling and Jianfengling—within Hainan Tropical Rainforest National Park, China, focusing on communities associated with the endangered species Hopea hainanensis. We employed a one-way ANOVA and Pearson’s correlation analyses to examine the distribution characteristics and interrelationships among diversity indices and used Mantel tests to assess the correlations with environmental variables. Our results revealed that the plant community in Jianfengling exhibited a significantly higher species richness at the family, genus, and species levels (a total of 288 plant species have been recorded, belonging to 82 families and 183 genera) compared to that in Bawangling (a total of 212 plant species, belonging to 75 families and 162 genera). H. hainanensis held the highest importance value in the middle tree layer across both sites (IV(BWL) = 12.44; IV(JFL) = 5.73), while dominant species varied notably among other forest layers, indicating strong habitat specificity. Diversity indices, including the Simpson index, the Shannon–Wiener index, and Pielou’s evenness, were significantly higher in the large shrub layer of Jianfengling, whereas Bawangling showed a relatively higher Shannon–Wiener index in the middle shrub layer. Phylogenetic diversity (PD) and the phylogenetic structure indices (NRI and NTI) displayed distinct vertical stratification patterns between sites. Furthermore, the PD in Bawangling’s large shrub layer was positively correlated with total phosphorus in the soil, while community evenness was influenced by soil organic carbon and total nitrogen. In Jianfengling, species richness was significantly associated with soil bulk density, altitude, and pH. These findings enhance our understanding of the ecological and evolutionary processes shaping biodiversity in tropical rainforests and highlight the importance of incorporating both species and phylogenetic metrics into the conservation strategies for endangered species such as Hopea hainanensis. Full article