Search Results (7,502)

Effective asset management is crucial for improving the reliability, resilience, and cost efficiency of distribution networks throughout the asset life cycle. Distribution transformers are among the most critical components, as their failures can cause extensive service interruptions and substantial economic impacts. Therefore, robust and transparent maintenance prioritization strategies are essential, particularly for utilities managing several transformers. Traditional time-based maintenance, while simple to implement, often results in inefficient resource allocation. Condition-based maintenance provides a more effective alternative; however, its performance depends strongly on the reliability of indicator selection and weighting. This study proposes a systematic weighting framework for distribution transformer maintenance prioritization using a multi-criteria decision-making (MCDM) approach. Each transformer is evaluated across two dimensions, including health condition and operational impact, based on indicators identified from the literature and expert judgment. To address uncertainty and judgmental inconsistency, particularly when the consistency ratio (CR) exceeds the conventional threshold of 0.10, the Fuzzy Analytic Hierarchy Process (FAHP) is employed. Seven condition parameters characterize transformer health, while impact is quantified using five indicators reflecting failure consequences. The proposed framework offers a transparent, repeatable, and defensible decision-support tool, enabling utilities to prioritize maintenance actions, optimize resource allocation, and mitigate operational risks in distribution networks. Full article

Landfills serve as a primary disposal method for municipal solid waste in China, with over 20,000 operational sites nationwide; however, long-term operations risk leachate leakage and groundwater contamination. Amid intensifying climate change and human activities, understanding contaminant evolution mechanisms in landfills has become critically urgent. Focusing on a representative plain-based landfill in North China, this study integrated field investigations and groundwater monitoring to establish a monthly coupled groundwater flow–solute transport model (using MODFLOW and MT3DMS codes) based on site-specific hydrogeological boundaries and multi-year monitoring data, analyzing spatiotemporal plume evolution under the coupled impacts of precipitation variability (climate change) and intensive groundwater extraction (human activities), spanning the historical period (2021–2024) and future projections (2025–2040). Historical simulations demonstrated robust model performance with satisfactory calibration against observed water levels and chloride concentrations, revealing that the current contamination plume exhibits a distinct distribution beneath the site. Future projections indicate nonlinear concentration increases: in the plume core zone, concentrations rise with precipitation, whereas at the advancing front, concentrations escalate with extraction intensity. Spatially, high-risk zones (>200 mg/L) emerge earlier under wetter conditions—under the baseline scenario (S0), such zones form by 2033 and exceed site boundaries by 2037. Plume expansion scales positively with extraction intensity, reaching its maximum advancement and coverage under the high-extraction scenario. These findings demonstrate dual drivers—precipitation accelerates contaminant accumulation through enhanced leaching, while groundwater extraction promotes plume expansion via heightened hydraulic gradients. This work elucidates coupled climate–human activity impacts on landfill contamination mechanisms, proposing a transferable numerical modeling framework that provides a quantitative scientific basis for post-closure supervision, risk assessment, and regional groundwater protection strategies, thereby aligning with China’s Standard for Pollution Control on the Landfill Site of Municipal Solid Waste and the Zero-Waste City initiative. Full article

Individuals born after intrauterine growth restriction (IUGR) are at increased risk of long-term cardiovascular complications, including elevated blood pressure, endothelial dysfunction, and arterial stiffness. Endothelial progenitor cells (EPCs), particularly endothelial colony-forming cells (ECFCs), play a critical role in maintaining vascular homeostasis. Previously, Simoncini et al. observed that in a rat model of IUGR, six-month-old males exhibited elevated systolic blood pressure (SBP) and microvascular rarefaction compared with control (CTRL) rats. These vascular alterations were accompanied by reduced numbers and impaired function of bone marrow-derived ECFCs, which were associated with oxidative stress and stress-induced premature senescence (SIPS). In contrast, IUGR females of the same age and from the same litter did not exhibit higher SBP or microvascular rarefaction, raising the question of whether ECFC dysfunction in IUGR female rats can be present without vascular alterations. So, we investigated ECFCs isolated from six-month-old female IUGR offspring (maternal 9% casein diet) and CTRL females (23% casein diet). To complete the vascular assessment, we performed in vivo and in vitro investigations. No alteration in pulse wave velocity (measured by echo-Doppler) was observed; however, IUGR females showed decreased aortic collagen and increased elastin content compared with CTRL. Regarding ECFCs, those from IUGR females maintained their endothelial identity (CD31⁺/CD146⁺ ratio among viable CD45⁻ cells) but exhibited slight alterations in progenitor marker expression (CD34) compared with those of CTRL females. Functionally, IUGR-ECFCs displayed a delayed proliferation phase between 6 and 24 h, while their ability to form capillary-like structures remained unchanged, however their capacity to form capillary-like structures was preserved. Regarding the nitric oxide (NO) pathway, a biologically relevant trend toward reduced NO levels and decreased endothelial nitric oxide synthase expression was observed, whereas oxidative stress and SIPS markers remained unchanged. Overall, these findings indicate that ECFCs from six-month-old female IUGR rats exhibit only minor functional alterations, which may contribute to vascular protection against increase SBP, microvascular rarefaction, and arterial stiffness. Full article

In high-speed railways, the reliability of jointless track circuits largely hinges on the operational integrity of compensation capacitors. These capacitors are periodically installed along the track to mitigate rail inductive impedance and stabilize signal transmission. The induced voltage response, referred to as the compensation-capacitor signal, serves as a critical diagnostic indicator of circuit health. Yet it is often distorted by electromagnetic interference and structural resonance, posing significant challenges for robust feature extraction. To address this challenge, we propose a Disturbance-Robust Feature Distillation (DRFD) framework that performs multi-perspective modeling and validation of robust features. The framework formulates a unified multi-objective optimization model that jointly considers statistical significance, environmental stability, and structural separability. These objectives are harmonized through an adaptive Bayesian weighting mechanism, enabling automatic identification of disturbance-resistant and discriminative features under complex operating conditions. Experimental evaluations on real-world datasets collected at a 100 kHz sampling rate from roadbed, tunnel, and bridge environments demonstrate that the DRFD framework achieves 96.2% accuracy and 95.4% F1-score, outperforming the best-performing baseline by 4.2–7.8% in accuracy and 6.5% in F1-score. Moreover, the framework achieves the lowest cross-condition relative variance (RV < 0.015), confirming its high robustness against electromagnetic and structural disturbances. The extracted core features—Root Mean Square (RMS), Peak Factor (PF), and Center Frequency (CF)—faithfully capture the intrinsic electromagnetic behaviors of compensation capacitors, thus linking statistical robustness with physical interpretability for enhanced reliability assessment of railway signal systems. Full article

Ice-and-snow tourism cities face pronounced seasonal fluctuations that place strong pressure on urban accommodation systems. Understanding the spatial distribution, accessibility, and service capacity of hotels is therefore critical for sustainable tourism management in cold-region cities. Taking Harbin, China, as a representative winter tourism destination, this study develops a GIS-based spatial analytical framework to examine the spatial organization and service performance of star-rated hotels. Using data from 553 three-star and above hotels, combined with questionnaire survey data (N = 224), we apply the Nearest Neighbor Index (NNI), Kernel Density Estimation (KDE), and raster-based cost-distance accessibility analysis to identify spatial clustering patterns, accessibility differentiation, and mismatches between hotel supply and peak seasonal demand. We find that available hotel rooms can only meet about 60% of peak-season demand, indicating a severe capacity deficit. The results reveal a clear core–periphery spatial structure of star-rated hotels, significant accessibility disparities among hotel categories, and a pronounced mismatch between accommodation capacity and tourism demand during peak winter seasons. Peripheral areas exhibit limited accessibility and insufficient service capacity, while central districts experience high concentration and pressure. These findings highlight the importance of integrating spatial equity and seasonal demand considerations into accommodation planning and infrastructure optimization, providing policy-relevant insights for sustainable tourism development in cold-region cities. Full article

Agriculture faces increasing challenges in ensuring food security under a changing climate, where abiotic stresses such as salinity and drought represent major constraints to crop productivity. These stresses induce complex physiological and biochemical alterations in plants, including osmotic imbalance, oxidative damage, and disruption of metabolic pathways, ultimately impairing growth and yield. In this context, the application of biostimulants has emerged as a sustainable strategy to enhance plant resilience. While synthetic products are widely available, growing attention is being directed toward natural bio-based products, particularly those derived from renewable biomasses and organic wastes, in line with circular economy principles. This review critically examines the current literature on bio-based products with biostimulant properties, with particular emphasis on vermicompost-derived extracts, humic-like substances, and macro- and microalgae extracts, focusing on their role in mitigating salt and drought stress in plants. The reviewed studies consistently demonstrate that these bio-products enhance plant tolerance to abiotic stress by modulating key physiological and biochemical processes, including hormonal regulation, activation of antioxidant defence systems, accumulation of osmoprotectants, and regulation of secondary metabolism. Moreover, evidence indicates that these bio-based inputs can improve nutrient use efficiency, photosynthetic performance, and overall plant growth under stress conditions. Overall, this review highlights the potential of non-microbial bio-based biostimulants as effective and sustainable tools for climate-resilient agriculture, while also underlining the need for further research to standardize formulations, clarify mechanisms of action, and validate their performance under field conditions. Full article

Climate change increasingly threatens global food security through disrupted precipitation patterns and extreme weather events, requiring resilient systems for assessing agricultural vulnerability. This study developed and compared machine learning approaches for predicting cropland vulnerability using Earth Observation data, operationalized through NDVI anomalies as a defensible biophysical metric. We employed both a dual-stream deep learning architecture and a Random Forest model to predict 2023 NDVI anomalies across Bangladesh croplands using a 22-year time series (2001–2023) of MODIS vegetation indices, ERA5 climate variables, and static environmental covariates. A spatially aware block cross-validation strategy ensured rigorous, independent performance evaluation. Results demonstrated that the Random Forest model (R² = 0.70, RMSE = 197.03) substantially outperformed the deep learning architecture (R² = 0.02, RMSE = 357.57), explaining 70% of cropland stress variance and enabling early detection of vulnerable areas three months before harvest. Feature importance analysis identified recent climate variables, March precipitation, February NDVI, and vapor pressure deficit as primary vulnerability drivers. Spatial analysis revealed distinct vulnerability patterns, with Natore and Magura districts exhibiting elevated stress consistent with 2023 drought conditions, threatening the productivity of the region’s critical irrigation-dependent rice cultivation. These findings demonstrate that simpler, interpretable models can sometimes outperform complex architectures while providing useful information for early warning systems and precision targeting of climate adaptation interventions. Full article

Targeted coverage of crushed grass segments under the fruit tree canopy synergistically achieves the agronomic goals of soil moisture conservation, weed suppression, and soil fertility improvement. To address issues like incomplete grass cutting and high risk of damaging fruit trees in complex orchard environments with traditional mowing devices, a lateral grass discharge blade for orchard mowers was designed. Based on airflow field theory, the dynamic basis of the airflow field, critical conditions for carrying crushed grass segments, and their movement laws on the blade and in the air were analyzed to identify key factors affecting discharge. CFD simulations were conducted using the Flow Simulation module of SolidWorks 2021 to explore the effects of the blade airfoil’s long side, short side lengths, and horizontal included angle on the outlet velocity and outlet volumetric flow rate of crushed grass segments, determining the reasonable parameter range. With these three as test factors and the two indicators above, orthogonal tests and parameter optimization were performed via Design-Expert 13.0 software, yielding optimal parameters: long side 125 mm, short side 35 mm, horizontal included angle 60°, corresponding to 9.105 m/s outlet velocity and 0.045 m³/s volume flow rate. A prototype mowing device with these parameters was fabricated for orchard field tests. Results show an average stubble stability coefficient of 94.2%, average over-stubble loss rate of 0.39%, and crushed grass segment distribution variation coefficient of 23.8%, meeting orchard mower operation requirements and providing technical support for orchard weed mowing, coverage, and utilization. Full article

Water-saving agricultural irrigation systems depend heavily on the hydraulic stability of pressure-compensating (PC) emitters, whose performance is fundamentally shaped by internal flow-path geometry. This study analyzes six commercial PC emitters (E1–E6) operated under pressures of 0.8–2.0 bar to quantify how key geometric descriptors influence hydraulic parameters critical for efficient water use, including actual discharge (q_act), discharge coefficient (k), pressure exponent (x), emission uniformity (EU), and flow variability. All emitters had discharge deviations within ±7% of nominal values. Longer and more tortuous labyrinths enhanced compensation stability, while emitters with wider cross-sections and shorter paths produced higher throughput but weaker regulation efficiency. Linear mixed-effects modeling showed that effective flow area increased k, whereas normalized path length and tortuosity reduced both k and x. Predictive equations derived from geometric indicators closely matched measured values, with deviations below ±0.05 L/h for k and ±0.05 for x. These results establish a geometry-based hydraulic framework that supports emitter selection and design in water-saving agricultural irrigation, aligning with broader Agricultural Water–Land–Plant System Engineering objectives and contributing to more efficient and sustainable water-resource utilization. Full article

Deep reinforcement learning policies are hard to deploy in safety-critical settings, because they fail to explain why a sequence of actions is taken. We introduce an intrinsically interpretable framework that learns compact summaries of recurring behavior and uses them for case-based decision making. Our method (i) discovers global regimes by grouping trajectories into a small set of recurrent patterns and (ii) learns a prototype-conditioned local policy that maps the current short-horizon pattern to an action (“this matches prototype X → take action Y”). Each action is accompanied by a similarity score to relevant prototypes, which provide the explanations. We evaluate our approach on two domains: (1) CarRacing (pixel-based continuous control) and (2) a real voltage-control problem in low-voltage distribution networks. Our results indicate that the method provides clear pre hoc explanations while keeping task performance close to the reference policy. Full article

Accurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, and land cover classification. Four advanced deep learning segmentation models—Proportional–Integral–Derivative Network (PIDNet), Mask2Former, Swin Transformer, and Kernel Network (K-Net)—were systematically evaluated via cross-validation. Their outputs were combined using a weighted average ensemble strategy. The proposed ensemble model achieved an Intersection over Union (IoU) of 0.9422 and an F1-score of 0.9703 in blind testing, indicating high accuracy. While the ensemble gains over the best single model (IoU: 0.9371) were moderate, the enhanced operational reliability through balanced Precision–Recall performance provides significant practical value for flood and water resource monitoring with high-resolution SAR imagery, particularly under data-constrained commercial satellite platforms. Full article

Background: H3K27-altered diffuse midline glioma (DMG) is a highly aggressive central nervous system malignancy with limited therapeutic options and poor prognosis. Precision medicine strategies that integrate molecular profiling with individualized treatment selection represent a critical avenue for improving outcomes. Case presentation: We describe a 31-year-old woman with H3K27-altered DMG who, after standard chemoradiotherapy, was treated with a personalized, mechanism-guided combination regimen based on her tumor’s molecular profile. Next-generation sequencing identified pathogenic alterations in ATRX, H3F3A, and NF1, with a high NF1 mutation allelic fraction indicating RAS/MAPK pathway activation. Immunohistochemistry demonstrated elevated phosphorylated mTOR consistent with PI3K/AKT/mTOR pathway upregulation. The individualized regimen comprised trametinib and everolimus for dual pathway inhibition, the tissue-agnostic agent dordaviprone (ONC201), metabolic modulation with 2-deoxy-D-glucose, and electric field-based therapy. At seven months, MRI showed approximately a 60% volumetric reduction in the enhancing tumor component, accompanied by marked T2-weighted signal regression. Clinically, the patient remained neurologically intact with a Karnofsky Performance Score of 100%. Conclusions: This case illustrates the potential clinical value of a genomics-guided, multimodal treatment strategy in H3K27-altered DMG. The systematic integration of comprehensive molecular profiling with mechanistically rational treatment selection may contribute to meaningful radiological and clinical benefit in this otherwise uniformly fatal disease. These observations support further investigation of individualized, pathway-targeted approaches in prospective studies and N-of-1 trial frameworks. Full article

Dental caries remains a major global public health problem characterized by pronounced social inequalities. This study aimed to identify, critically appraise, and synthesize the most recent evidence on the relationship between socioeconomic indicators and dental caries among adults aged 19–60 years, providing an updated systematic review that builds upon our previous reviews from 2012 and 2018. Reported following the PRISMA 2020 guidelines, we conducted a systematic search of eight electronic databases for observational studies published between March 2017 and April 2024 (PROSPERO: CRD42017074434). Two independent reviewers performed study selection, data extraction, and risk of bias assessment using the Newcastle–Ottawa Scale. Due to substantial methodological heterogeneity across the 22 included studies, a narrative synthesis was undertaken. The findings demonstrated a strong inverse association between socioeconomic position and caries experience. Lower income, lower educational attainment, and unemployment or employment in manual/unskilled occupations were associated with a higher overall caries experience. Advanced analytical approaches in recent studies, including life-course, reinforced that education and income are key contributors of these oral health inequalities, with persistent social disadvantage conferring the greatest risk. In conclusion, dental caries in adults aged 19–60 years is a social condition reflecting the cumulative effects of socioeconomic inequality across the life course. Addressing adult dental caries requires integrated approaches that combine clinical prevention with social and public policies aimed at reducing structural inequalities. Full article

Background: Accurate risk stratification in acute pulmonary embolism (PE) is critical for guiding management. This study assessed the prognostic value of computed tomography (CT) indicators of right-heart strain and thrombus burden for predicting in-hospital adverse events. Methods: In this retrospective cohort of 300 patients with CT-confirmed acute PE, the right-to-left ventricular (RV/LV) diameter ratio, Pulmonary Artery Obstruction Index (PAOI), and inferior vena cava (IVC) contrast reflux were measured. The primary endpoint was in-hospital adverse events, including hemodynamic collapse, vasopressor or ventilatory support, rescue reperfusion therapy, or death. Logistic regression and receiver operating characteristic (ROC) analyses were performed. Results: Adverse events occurred in 106 patients (35.3%). Compared with stable patients, those with events had higher RV/LV ratios (1.45 vs. 1.03), higher PAOI (38.8 vs. 24.3), and more frequent IVC reflux (74% vs. 7%) (all p < 0.001). Independent predictors were RV/LV ratio (aOR 3.22 per 0.1), PAOI (aOR 5.53 per 10 points), and IVC reflux (aOR 428.5; all p < 0.001). The model showed excellent discrimination (AUC = 0.96). Conclusions: CT-derived indices of right-heart strain and thrombus burden are strong, independent predictors of in-hospital adverse events in acute PE and should be integrated into routine CT-based risk assessment. Full article

Life-cycle assessment is crucial for evaluating materials’ environmental impact and guiding the development of low-carbon and sustainable buildings. However, conventional LCA methods often overlook critical impacts during the operation and maintenance stage. To address this gap, this study proposes an improved framework using four composite indicators to enable systematic evaluation of six wall materials across China’s five climate zones. Using a university teaching building in the Hot Summer and Cold Winter Zone as a case study, this study quantitatively analyzed the economic viability and carbon reduction potential of each material. Results indicate that lower thermal conductivity does not necessarily imply superior economic and carbon reduction performance. Factors including the material carbon emission factor, cost, and thermal properties, must be comprehensively considered. Buffering materials also exhibit climate dependency—WPM and BWPM (moisture-buffering plastering mortars) perform better in hot–humid zones than temperate zones. All five buffer materials reduce operational energy consumption; WPM and BWPM stand out with 15.7% and 16.7% life-cycle cost savings and 17.3% and 18.0% carbon emission reductions, respectively. This study addresses the limitations of traditional LCC/LCA and provides theoretical and practical support for scientific material selection and low-carbon building design. Full article