Search Results (3,279)

In this paper, composite structures were fabricated by incorporating silver nanowires (AgNWs) with various metal oxides via the sol–gel method. This approach enhanced the electrical performance of AgNW-based transparent electrodes while simultaneously improving their stability under damp heat conditions and modifying the local medium environment surrounding the AgNW meshes. The randomly distributed AgNW meshes fabricated via drop-coating were treated with plasma to remove surface organic residues and reduce the inter-nanowire contact resistance. Subsequently, a zinc oxide (ZnO) coating was applied to further decrease the sheet resistance (R_sheet) value. The pristine AgNW mesh exhibits an R_sheet of 17.4 ohm/sq and an optical transmittance of 93.06% at a wavelength of 550 nm. After treatment, the composite structure achieves a reduced R_sheet of 8.7 ohm/sq while maintaining a high optical transmittance of 92.20%. The use of AgNW meshes as window electrodes enhances electron injection efficiency and facilitates the coupling mechanism between localized surface plasmon resonances and excitons. Compared with conventional ITO transparent electrodes, the incorporation of the AgNW mesh leads to a 17-fold enhancement in ZnO emission intensity under identical injection current conditions. Moreover, the unique scattering characteristics of the AgNW and metal oxide composite structure effectively reduce photon reflection at the device interface, thereby broadening the angular distribution of emitted light in electroluminescent devices. Full article

Sepsis-associated acute kidney injury (S-AKI) is a frequent and life-threatening condition, characterized by rapid functional decline, which is followed by intense inflammation and tissue injury. Experimental lipopolysaccharide (LPS)-induced sepsis reproduces functional and morphological features of human S-AKI and enables investigation of melatonin which has numerous beneficial properties, such as antioxidant properties. In this study, the effects of melatonin (50 mg/kg) on kidney dysfunction, oxidative damage, inflammation, apoptosis, and histopathological alterations in a rat model of S-AKI induced by LPS application (10 mg/kg) were studied. Acute LPS exposure caused statistically significant (p ≤ 0.05) marked renal dysfunction, increased lipid and protein oxidation, suppression of antioxidant enzymes, enhanced NO/iNOS signaling, elevated pro-inflammatory cytokines (TNF-α, IL-1β, IL-6), activation of apoptotic pathways, and pronounced tubular and glomerular injury. Co-administration of melatonin statistically significantly (p ≤ 0.05) attenuated oxidative stress, reduced production of inflammatory cytokines, suppressed apoptosis, and ameliorated structural kidney damage, leading to partial restoration of renal function. These findings suggest that melatonin exerts renoprotective effects in S-AKI through combined antioxidant, anti-inflammatory, and anti-apoptotic actions, likely involving modulation of different signaling pathways. Full article

During liquid drainage from intermediate vessels in various industrial processes such as continuous steel casting, aircraft fuel supply, and chemical separation, free-surface vortices commonly occur. The formation and evolution of these vortices not only entrain surface slag and gas, but also lead to deterioration of downstream product quality and abnormal equipment operation. The vortex evolution process exhibits notable three-dimensional unsteadiness, multi-scale turbulence, and dynamic gas–liquid interfacial changes, accompanied by strong coupling effects between temperature gradients and flow field structures. Traditional macroscopic numerical models show clear limitations in accurately capturing these complex physical mechanisms. To address these challenges, this study developed a mesoscopic numerical model for gas-liquid two-phase vortex flow based on the lattice Boltzmann method. The model systematically reveals the dynamic behavior during vortex evolution and the multi-field coupling mechanism with the temperature field while providing an in-depth analysis of how initial perturbation velocity regulates vortex intensity and stability. The results indicate that vortex evolution begins near the bottom drain outlet, with the tangential velocity distribution conforming to the theoretical Rankine vortex model. The vortex core velocity during the critical penetration stage is significantly higher than that during the initial depression stage. An increase in the initial perturbation velocity not only enhances vortex intensity and induces low-frequency oscillations of the vortex core but also markedly promotes the global convective heat transfer process. With regard to the temperature field, an increase in fluid temperature reduces the viscosity coefficient, thereby weakening viscous dissipation effects, which accelerates vortex development and prolongs drainage time. Meanwhile, the vortex structure—through the induction of Taylor vortices and a spiral pumping effect—drives shear mixing and radial thermal diffusion between fluid regions at different temperatures, leading to dynamic reconstruction and homogenization of the temperature field. The outcomes of this study not only provide a solid theoretical foundation for understanding the generation, evolution, and heat transfer mechanisms of vortices under industrial thermal conditions, but also offer clear engineering guidance for practical production-enabling optimized operational parameters to suppress vortices and enhance drainage efficiency. Full article

Glucocorticoids are widely applied intra-articularly to alleviate inflammation and pain in osteoarthritis (OA). However, repeated administration and high local concentrations can lead to crystal deposition on the cartilage surface, contributing to chondrocyte damage and extracellular matrix (ECM) degradation, potentially accelerating OA progression. Calcium-dependent mechanosensors play a critical role in mediating catabolic responses in chondrocytes, but it remains unclear whether glucocorticoids affect chondrocyte mechanosensitivity or biomechanical properties. This in vitro study examined the dose-dependent effects of triamcinolone acetonide (TA) on chondrocyte biomechanics and mechanosensitivity. Primary human chondrocytes (N = 23) were cultured for one week with TA (2 µM–2 mM) or control medium. Cytoskeletal organization was visualized by F-actin staining (N = 6), and cellular elasticity (N = 5) was quantified via atomic force microscopy (AFM). Mechanotransduction was analyzed by Ca²⁺ imaging (Fluo-4 AM) upon AFM-based indentation (500 nN). Expression of matrix-related and mechanosensitive genes (N = 9) was assessed by qPCR. TA exposure induced a concentration-dependent reorganization of the F-actin cytoskeleton, pronounced at 0.2 mM, accompanied by a significant increase in the elastic modulus (p < 0.001). TA further augmented Ca²⁺ fluorescence intensity under basal conditions and during mechanical stimulation. Blocking cationic mechanosensitive channels with GsMtx4 (N = 3) markedly reduced the TA-evoked Ca²⁺ influx (p < 0.0001). Significant reduction in MMP1 was observed on the transcriptional level (N = 9) after TA-treatment (p < 0.05). In summary, TA enhances chondrocyte stiffness through cytoskeletal condensation and amplifies Ca²⁺-dependent mechanotransduction but reduces MMP1 expression, indicating a dual biomechanical response of chondrocytes to OA under exposure of potent corticosteroid. Full article

Urban underpasses are critical flood-prone hotspots during extreme rainfall, posing significant threats to urban resilience and infrastructure safety. However, a scale gap persists between catchment-scale hydrological models, which often oversimplify local geometry, and high-fidelity hydrodynamic models, which typically lack realistic boundary conditions. To bridge this gap, this study develops a multi-scale framework that integrates the Storm Water Management Model (SWMM) with 3D Computational Fluid Dynamics (CFD). The framework employs a unidirectional integration (one-way forcing), utilizing SWMM-simulated runoff hydrographs as dynamic inlet boundaries for a detailed CFD model of a 2.8 km underpass in Shanghai. Simulations across six design rainfall events (2- to 50-year return periods) revealed two distinct flooding mechanisms: a systemic response at the hydraulic low point, governed by cumulative inflow; and a localized response at entrance concavities, where water depth is rapidly capped by micro-topography. Informed by these mechanisms, an intensity-graded drainage strategy was developed. Simulation results show significant differences between different drainage strategies. Through this framework and optimized drainage system design, significant water accumulation within the underpass can be prevented, enhancing its flood resistance and reducing the severity of disasters. This integrated framework provides a robust tool for enhancing the flood resilience of urban underpasses and offers a basis for the design of proactive disaster mitigation systems. Full article

Background: Cardiovascular-Kidney-Metabolic (CKM) syndrome involves interconnected cardiovascular, renal, and metabolic conditions. The dose–response relationship between physical activity and bidirectional CKM stage transitions remains unclear. Methods: Using data from the China Health and Retirement Longitudinal Study (CHARLS), cross-sectional analysis pooled 14,310 observations from 10,868 participants. Logistic regression with clustered robust standard errors accounted for intra-individual correlation. Longitudinal analysis (n = 3442) employed continuous-time multi-state Markov models with a 5-state structure (Stages 0–4). To evaluate physical activity effects, stages were regrouped into low-risk (Stages 0–2) and high-risk states (Stages 3–4) using a 2 × 2 transition intensity matrix. Physical activity was measured in MET-min/week and categorized into quartiles (Q1–Q4). Results: Compared with Q1, Q2, Q3, and Q4 were associated with 43.1%, 52.5%, and 53.1% lower risk of high-risk CKM stages, respectively. RCS analysis demonstrated nonlinear dose–response relationships between physical activity and CKM stage progression. Subgroup analyses showed more pronounced protective effects in older adults and single individuals. During 4-year follow-up, 31.6% experienced progression and 6.8% showed improvement. Stage 4 acted as a complete absorbing state without any reversal. Transition intensity analysis revealed that transitions between adjacent stages were notably higher than cross-stage transitions. The Q4 physical activity level significantly reduced transitions from low-risk to high-risk states (HR = 0.598, 95% CI: 0.459–0.777) and promoted transitions from high-risk to low-risk states (HR = 2.995, 95% CI: 1.257–7.134). Conclusions: Moderate-to-high physical activity effectively reduces CKM progression risk and promotes improvement, providing evidence for CKM prevention and management. Full article

This study examines the rapid growth of energy demand in Paraguay, primarily driven by intensive air conditioning use and reduced hydroelectric output due to adverse Paraná River conditions. Employing a Vector Autoregressive (VAR) model, we quantify how temperature shocks significantly elevate peak electricity demand within the National Interconnected System. Our findings reveal that air conditioning accounts for 34–36% of the peak demand, pushing the hydroelectric system towards its operational limits. To address this challenge, we propose a technological transition strategy focused on energy efficiency improvements and labeling programs aimed at reducing peak demand, delaying system saturation, and achieving substantial power savings. These measures offer a practical approach to climate adaptation while supporting Paraguay’s international commitments and Sustainable Development Goals (SGDs) 7 (affordable and clean energy) and 13 (climate action). This work represents the first pioneering effort in Paraguay to quantify the influence of the SIN’s AC at the national level. This research provides policymakers with an evidence-based framework for energy planning, marking a pioneering effort in Paraguay to quantify cooling loads and set actionable efficiency targets. Full article

A key challenge in precision agriculture is acquiring reliable spatial soil information under varying sampling densities, from sparse surveys to intensive monitoring. The individual predictive soil mapping (iPSM) method performs well in data-scarce conditions but neglects spatial proximity, limiting its predictive accuracy where spatial autocorrelation exists. To overcome this, we developed an enhanced framework, iPSM-Spatial Distance (iPSM-SD), which systematically integrates spatial proximity through multiplicative (MUL) and additive (ADD) strategies. The framework was validated using two contrasting cases: sparse soil organic carbon density data from Yunnan Province (n = 118) and dense soil organic matter data from Bayi Farm (n = 2511). Results show that the additive model (iPSM-ADD) significantly outperformed the original iPSM and benchmark models, including random forest, regression kriging, geographically weighted regression, and multiple linear regression, under sufficient sampling, achieving an R² of 0.86 and reducing RMSE by 46.6% at Bayi Farm. It also maintained robust accuracy under sparse sampling conditions. The iPSM-SD framework thus provides a unified and adaptive tool for digital soil mapping across a wide range of data availability, supporting scalable soil management decisions from regional assessment to field-scale variable-rate applications in precision agriculture. Full article

Background/Objectives: Burning Mouth Syndrome (BMS) is a common oral condition in older women and is characterized by a multifactorial etiology. To date, no standardized treatment strategy has been established. The aim of this study was to evaluate the effectiveness of topical application of capsaicin (0.025 mg/cm²) in the form of a mucoadhesive bilayer polymer reducing burning sensations in BMS. The study assessed levels of depression, sleep disturbances, and quality of life. Material and Methods: The proof-of-concept study included 29 patients with symptoms of BMS. The peripheral origin of BMS was confirmed by lingual nerve block. Pain intensity was assessed using the Numeric Rating Scale (NRS-11) and the Short-Form McGill Pain Questionnaire (SF-MPQ). Depression, sleep disturbances, and quality of life were evaluated using the Beck Depression Inventory (BDI), Athens Insomnia Scale (AIS), and WHO Quality of Life Questionnaire (WHOQoL). Results: A reduction in pain was observed in over 86% patients. Decrease in burning at treatment sites was recorded immediately after treatment and also at the 3-month follow-up. Gender, taste disturbances, depression, and age were found to have a significant effect on final NRS-11 scores. Conclusions: Significant reduction in pain intensity was achieved in nearly all treated patients, with adverse effects being rare. Full article

Hospitals are among the most energy-intensive buildings, yet their heating systems often operate below optimal efficiency due to outdated controls and limited sensing. Existing facilities often provide only a few accessible measurement points, many of which are locked within proprietary master controllers and not integrated into the Building Automation System (BAS). To address these limitations, this study proposes a data-driven feature selection approach that supports the development of gray-box emulators for complex, real-world central heating plants. A year of operational and weather data from a large hospital was used to train multiple machine learning models to predict the heating demand and return water temperature of a hospital heating plant system. The model’s performance was evaluated under reduced-sensor conditions by intentionally removing unpredictable values such as the VFD speed and flow rate. XGBoost achieved the highest accuracy with full sensor data and maintained a strong performance when critical sensors were omitted. An explainability analysis using Shapley Additive Explanations (SHAP) is applied to interpret the models, revealing that outdoor temperature and time of day (as an occupancy proxy) are the dominant predictors of boiler load. The results demonstrate that, even under sparse instrumentation, an AI-driven digital twin of the heating plant can reliably capture system dynamics. Full article

A key challenge in automated marigold harvesting lies in the accurate identification of picking points under complex environmental conditions, such as dense shading and intense illumination. To tackle this problem, this research proposes a lightweight instance segmentation model combined with a harvest position estimation method. Based on the YOLOv11n-seg segmentation framework, we develop a lightweight PDS-YOLO model through two key improvements: (1) structural pruning of the base model to reduce its parameter count, (2) incorporation of a Channel-wise Distillation (CWD)-based feature distillation method to compensate for the accuracy loss caused by pruning. The resulting lightweight segmentation model achieves a size of only 1.3 MB (22.8% of the base model) and a computational cost of 5 GFLOPs (49.02% of the base model). At the same time, it maintains high segmentation performance, with a precision of 93.6% and a mean average precision (mAP) of 96.7% for marigold segmentation. Furthermore, the proposed model demonstrates enhanced robustness under challenging scenarios including strong lighting, cloudy weather, and occlusion, improving the recall rate by 1.1% over the base model. Based on the segmentation results, a method for estimating marigold harvest positions using 3D point clouds is proposed. Fitting and deflection angle experiments confirm that the fitting errors are constrained within 3–12 mm, which lies within an acceptable range for automated harvesting. These results validate the capability of the proposed approach to accurately locate marigold harvest positions under top-down viewing conditions. The lightweight segmentation network and harvest position estimation method presented in this work offer effective technical support for selective harvesting of marigolds. Full article

Rainfall-induced failures in red clay slopes are common, yet the coupled influence of soil structure degradation and rainfall temporal patterns on slope hydromechanical behavior remains poorly understood. This study advances the understanding by investigating a cut slope failure in Yunnan through integrated field monitoring, laboratory testing, and numerical modeling. Key advancements include: (1) elucidating the coupled effect of structure degradation on both shear strength reduction and hydraulic conductivity alteration; (2) systematically quantifying the impact of rainfall temporal patterns beyond total rainfall; and (3) providing a mechanistic explanation for the critical role of early-peak rainfall. Mechanical and hydrological parameters were obtained from intact and remolded samples, with soil-water retention estimated via pedotransfer functions. A hydro-mechanical finite element model of the slope was constructed and calibrated using recorded rainfall, displacement data and failure surface. Six simulation scenarios were designed by combining three strength conditions (intact at natural water content, intact at saturation, remolded at natural water content) with two hydraulic conductivity values (intact vs. remolded). Additionally, four synthetic rainfall patterns, including uniform, peak-increasing, peak-decaying and bell-shaped rainfall, were simulated to evaluate their influence on pore water pressure development and slope stability. Results show remolding reduced hydraulic conductivity 4.7-fold, slowing wetting front advance and increasing shallow pore water pressure. Intact soil facilitated deeper drainage, elevating pressure near the soil-rock interface. Strength reduction induced by structure degradation (water saturating and remolding) enlarged the slope deformation zone by 1.5 times under same hydraulic conductivity. Simulations using saturated intact strength best matched field observations. The results from this specific slope indicate that strength parameters primarily control stability, while permeability affects deformation depth. Simulations considering different rainfall patterns indicate that slope stability depends more critically on the temporal distribution of rainfall intensity than on the total amount. Overall, peak-decaying rainfall led to the most rapid rise in pore water pressure, earliest instability and lowest failure rainfall threshold, whereas peak-increasing rainfall showed the opposite trends. Our findings outline a practical framework for assessing red clay slope stability during rainfall. This framework recommends using saturated intact strength parameters in stability analysis. It highlights the important influence of rainfall temporal patterns, especially those with an early peak, on failure timing and rainfall threshold. Full article

The abrupt COVID-19 lockdown in early 2020 offered a unique natural experiment to examine vegetation productivity responses to sudden declines in human activity. Although vegetation often responds to environmental changes with time lags, how such lags operate under short-term, intensive disturbances remains unclear. This study combined multi-source environmental data with an interpretable machine learning framework (XGBoost-SHAP) to analyze spatiotemporal variations in net primary productivity (NPP) across the Beijing-Tianjin-Hebei region during the strict lockdown (March–May) and recovery (June–August) periods, using 2017–2019 as a baseline. Results indicate that: (1) NPP showed a significant increase during lockdown, with 88.4% of pixels showing positive changes, especially in central urban areas. During recovery, vegetation responses weakened (65.31% positive) and became more spatially heterogeneous. (2) Integrating lagged environmental variables improved model performance (R² increased by an average of 0.071). SHAP analysis identified climatic factors (temperature, precipitation, radiation) as dominant drivers of NPP, while aerosol optical depth (AOD) and nighttime light (NTL) had minimal influence and weak lagged effects. Importantly, under lockdown, vegetation exhibited stronger immediate responses to concurrent temperature, precipitation, and radiation (SHAP contribution increased by approximately 7.05% compared to the baseline), whereas lagged effects seen in baseline conditions were substantially reduced. Compared to the lockdown period, anthropogenic disturbances during the recovery phase showed a direct weakening of their impact (decreasing by 6.01%). However, the air quality improvements resulting from the spring lockdown exhibited a significant cross-seasonal lag effect. (3) Spatially, NPP response times showed an “urban-immediate, mountainous-delayed” pattern, reflecting both the ecological memory of mountain systems and the rapid adjustment capacity of urban vegetation. These findings demonstrate that short-term removal of anthropogenic disturbances shifted vegetation responses toward greater immediacy and sensitivity to environmental conditions. This offers new insights into a “green window period” for ecological management and supports evidence-based, adaptive regional climate and ecosystem policies. Full article

Reducing energy intensity is critical for combating climate change, yet current progress remains insufficient to meet international targets. Green-oriented policy narratives hold significant potential for mitigating energy intensity, but existing research lacks regional-level quantitative analysis. This study examines how green-oriented policy narratives influence urban energy intensity. We analyze textual data from Chinese provincial Party newspapers using large language models and LDA topic modeling to measure narrative-related variables, then combine these measures with panel data from 288 Chinese cities spanning 2010–2022. The findings reveal that green-oriented policy narrative exposure significantly reduces urban energy intensity through promoting green credit development and stimulating green innovation, with the negative effect strengthening as the prominence of the public and narrativity of narratives increase. Heterogeneity analysis further shows that narrative effectiveness is amplified in cities with higher internet penetration and marketization levels. This study broadens research on energy intensity determinants beyond traditional policy instruments, extends green-oriented narrative effects from the micro to macro level, and offers insights for leveraging narratives and contextual conditions to promote energy conservation. Full article

Precipitation in high mountain areas is of critical importance as these regions are major sources of freshwater, supporting river basins, ecosystems, and downstream communities. Changes in precipitation regimes in these regions can have cascading impacts on water availability, agriculture, hydropower, and biodiversity. The present study aims to give new information about precipitation variability in high mountain regions of Bulgaria (Musala, Botev Peak, and Cherni Vrah) and to assess the role of large-scale atmospheric circulation patterns for the occurrence of extreme precipitation months. The study period is 1937–2024, and the classification of extreme precipitation months is based on the 10th and 90th percentiles of precipitation distribution. The temporal distribution of extreme precipitation months was analyzed by comparison of two periods (1937–1980 and 1981–2024). The impact of atmospheric circulation was evaluated by correlation between the number of extreme precipitation months and indices for the North Atlantic Oscillation (NAO) and Western Mediterranean Oscillation (WeMO). Results show a statistically significant decrease in winter and spring precipitation at Musala and Cherni Vrah, and a persistent drying tendency at Cherni Vrah across all seasons. The frequency of extremely wet months in winter and autumn has sharply declined since 1981, whereas extremely dry months have become more common, particularly during the cold season. Precipitation erosivity also exhibits station-specific responses, with Musala and Cherni Vrah showing reduced monthly concentration, while Botev Peak retains pronounced warm-season erosive rainfall. Circulation analysis indicates that positive NAOI phases favor dry extremes, while positive WeMOI phases enhance wet extremes. These findings reveal a shift toward drier and more seasonally uneven conditions in Bulgaria’s alpine zone, increasing hydrological risks related to drought, water scarcity, and soil erosion. The identified shifts in precipitation seasonality and intensity offer essential guidance for forecasting hydrological risks and mitigating soil erosion in vulnerable mountain ecosystems. The study underscores the need for adaptive water-resource strategies and enhanced monitoring in high-mountain areas. Full article