Search Results (9,580)

Early fault detection in microsatellite thermal control systems (TCS) faces fundamental challenges: high-dimensional redundant telemetry channels, overlapping multi-scale periodicities that obscure anomaly signatures, and severely limited daily data downlink (1–2 passes per day) that restricts the temporal window for diagnosis. Existing data-driven approaches either rely on supervised learning, requiring labeled fault data that are scarce in practice, or employ univariate analysis that fails to capture inter-sensor spatial correlations. To address these limitations, this paper introduces a hybrid framework integrating information-theoretic feature selection and spatiotemporal deep learning. The Generalized Maximum Information Coefficient (GMIC) quantifies nonlinear dependencies between temperature channels for key channel selection, reducing dimensionality by 82% while preserving diagnostic information. A dual-level Seasonal Trend Decomposition (STL) method disentangles orbital-periodic dynamics from diurnal cycles, effectively isolating distinct thermal characteristics at multiple timescales. Each decomposed component is modeled using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) networks to capture spatiotemporal dependencies for accurate temperature prediction. An adaptive threshold-based weighted error fusion mechanism enables early fault detection within a single day of telemetry data. Experimental validation on real satellite telemetry data demonstrates that the proposed framework achieves high-precision fault detection across multiple fault types using a minimal set of temperature channels, significantly outperforming existing benchmarks in both prediction accuracy and detection reliability. Full article

Grain moisture content is a key variable for safe storage, drying control, and quality management. Microwave sensing is attractive because water strongly modulates the complex relative permittivity (ε* = ε′ – jε″) of granular agricultural products, thereby shaping broadband scattering-parameter spectra. This study presents a meter-referenced feasibility evaluation of an interpretable S-parameter–permittivity–moisture chain using a vector network analyzer over 2–18 GHz. Wheat, maize, and mung bean were prepared at six moisture levels, and the moisture values were referenced to two commercial grain moisture meters (MC_ref) to represent rapid on-site benchmarking rather than absolute gravimetric moisture determination. Therefore, the reported errors should be interpreted as commercial-meter-referenced calibration indicators rather than absolute gravimetric moisture prediction accuracy. Two free-space configurations were compared on the same platform: a two-horn transmission setup under controlled packing and a metal-backed double-pass reflection setup intended to represent single-sided access under loose bulk packing. After SOLT calibration and empty-holder background normalization, ε′ and ε″ were retrieved via complex-domain nonlinear least-squares fitting of physics-based slab models to measured S21 spectra. The results show that moisture-dependent dielectric responses were grain- and configuration-dependent. In particular, ε″ generally provided a more robust moisture-sensitive feature in the free-space transmission configuration, whereas the optimal single-parameter predictor in the metal-backed configuration differed among grains. A mid-band frequency window of approximately 8–16 GHz provided more stable inversion by avoiding low-frequency coupling artefacts and high-frequency signal-to-noise degradation. The metal-backed configuration preserved moisture trends but yielded lower effective ε′ values, likely due to increased air fraction under loose packing. These results indicate that packing state, grain type, and frequency-window selection are critical factors for transferring microwave moisture calibration from laboratory measurements to practical grain-handling scenarios. Full article

The study examines the evolution, knowledge structure, and trends in snowmelt runoff prediction models. It identifies research hotspots, future directions, and offers a theoretical basis for accurate simulation and prediction. Utilizing CiteSpace software, 556 core Chinese and English publications from 2010 to 2025 were visually analyzed. Research on snowmelt runoff simulation shows: (1) Chinese publications are prominent in core journals like “Journal of Glaciology and Geocryology,” while English publications appear in high-impact journals like “Water Resources Research.” (2) Institutions like the University of Chinese Academy of Sciences, the Northwest Institute of Eco-Environment and Resources, and the University of California have formed a cross-regional research network. (3) International collaboration involves 42 countries, with a focus on China, the United States, and India. However, domestic institutional cooperation needs improvement. (4) Research trends in snowmelt runoff simulation have progressed from empirical statistics to remote sensing and model-driven physical mechanisms, and now to the integration of artificial intelligence with physical models. (5) The Chinese literature focuses on cold regions, while the English literature emphasizes intelligent modeling. This shift indicates a move towards “physical–intelligent” hybrid modeling. Future research should address challenges like model applicability in data-scarce areas, improving interpretability of complex models, quantifying uncertainties, and developing physically constrained deep learning models. Collaboration among institutions is crucial for enhancing water resource management and disaster warning systems in cold regions. Full article

The issue of water resources in a semi-arid country such as Morocco has been present for many years and is becoming increasingly critical. The droughts experienced over recent decades have demonstrated the country’s extreme vulnerability to any water deficit. In this context, the Berrechid plain represents a relevant case study illustrating both the practical and theoretical challenges of groundwater governance. The aquifer is heavily exploited to satisfy agricultural, industrial, and domestic needs. This study develops a hybrid “grey-box” modeling approach for predicting groundwater depth (GWD) fluctuations under climate change (CC). Unlike conventional black-box machine learning models, our framework combines a deterministic physical engine with a stochastic machine learning corrector. The physical component simulates aquifer mass balance using the Hargreaves method for evapotranspiration, linear drainage, climate memory via exponential decay, and an anthropogenic trend parameter (xi). The machine learning component—XGBoost with quantile regression—is trained exclusively on physical model residuals and predicts the 5th, 50th, and 95th percentiles, providing explicit 90% confidence intervals. Hydrological states (dry, normal, wet) are identified via K-means clustering for context-aware correction. The model is calibrated using historical data (1972–2019) and validated using blocked time-series cross-validation. Climate projections under the RCP 4.5 and RCP 8.5 scenarios were used to forecast GWD up to 2100. At piezometer 3933/20, the best performance was achieved, with an RMSE of 0.347 m and a KGE of 0.742 during the validation period. The proposed approach is suitable for seasonal GWD forecasting and offers practical value for water managers and decision-makers in the Berrechid region. Full article

Background: Acute pain after video-assisted thoracoscopic surgery (VATS) promotes respiratory splinting, impaired cough, and pulmonary complications, and predicts persistent opioid use. Surgeon-administered intercostal nerve block (ICNB) is a simple regional technique, but its independent effect on early pain and opioid requirement in a contemporary uniportal VATS (UVATS) pathway is incompletely defined. Methods: We performed a retrospective cohort study of 456 consecutive patients undergoing UVATS at a single Israeli center between 2017 and 30 May 2025. Patients receiving an intercostal block were compared with those who did not. Baseline covariates were balanced by 1:1 nearest-neighbor propensity-score matching (caliper 0.2 SD of the logit propensity score). The primary endpoints were pain on postoperative day (POD) 1 (visual analog scale, VAS) and postoperative opioid use; secondary endpoints included later pain, analgesic regimen, postoperative pneumonia, and mortality. Results: Matching yielded 159 patients per group (n = 318) with all clinically relevant covariates balanced (standardized mean difference [SMD] < 0.13). Median POD1 VAS was lower with the block (4 [IQR 3–4] vs. 5 [5–7]; p < 0.001), and 76.1% of block patients were opioid-free versus 10.7% who were not (p < 0.001). The effect was concentrated early and attenuated by POD3. In multivariable analysis the block was independently associated with lower POD1 VAS (adjusted β = −1.64, 95% CI −2.00 to −1.29; p < 0.001). Postoperative pneumonia was less frequent in the block group (5.7% vs. 20.1%; p < 0.001). Thirty-day and one-year mortality did not differ significantly. Conclusions: In UVATS, a surgeon-placed intercostal nerve block was associated with lower early postoperative pain that persisted after adjustment for operating surgeon and surgical era, concordant with pooled meta-analytic estimates; associated reductions in opioid use and pneumonia were confounded with surgeon and secular trend and are hypothesis-generating. These single-center, retrospective findings require prospective, protocol-randomized confirmation. Full article

Predicting landslide displacement is important for geological-hazard early warning. In reservoir areas, displacement evolution is affected by rainfall, reservoir water level, vegetation variation, and the intrinsic non-stationarity of the displacement sequence, which makes accurate prediction difficult for conventional single-sequence models. To address this problem, this study proposes a residual-increment-oriented landslide displacement prediction framework that fuses multi-source monitoring variables. The displacement sequence is first processed into trend and periodic-related fluctuation representations, and the residual increment is used as the prediction target. Rainfall, reservoir water level, and the normalized difference vegetation index (NDVI) are incorporated as external monitoring variables. A cross-branch attention mechanism models interactions among heterogeneous feature branches, and a sparse MoE-based fusion module is introduced to adaptively adjust branch contributions under different deformation conditions. The model predicts the displacement residual increment, from which the final displacement is reconstructed. A case study using the Baishui River (Baishuihe) landslide monitoring dataset was conducted, together with additional validation on the related Bazimen Z110 landslide monitoring dataset and comparisons against conventional recurrent, convolutional, statistical, and Transformer-based baselines. The results show that the proposed model achieves lower RMSE and MAE than the compared methods on the tested datasets. These findings suggest that residual-increment modeling, multi-source monitoring variables, and condition-dependent branch fusion can improve short-term displacement prediction for the tested reservoir-area landslide cases. Full article

Water loss and significant problems in the operation of water distribution networks caused by pipe failures are a global problem that needs immediate attention. This study is based on the experience-based assumption that the probability of water main breaks occurring is highest within a short time and a short distance from a previous (considered initial or base) break. The dataset used includes the historical pipe breaks recorded from 2007 to 2020 in the city of Larisa, Greece. A Geographic Information System (GIS) application is used for better data visualization, but also for effective operation and management of the developed water network database. Cluster analysis and Near and Spatial Join geoprocessing tools are the main tools used to detect and analyze trends in data related to space and time. In addition, the study attempts to identify relations between pipe attributes (material, age), environmental stressors (traffic load, soil type), and spatiotemporal clustering patterns. Finally, a machine learning-based water pipe failure Prediction Model is developed to serve as the computational engine of a Decision Support System (DSS) designed to optimize pipe replacement prioritization. Full article

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

Human digital twins (HDTs) are patient-specific computational models that combine medical imaging, physiological measurements and predictive algorithms. They are moving from an exciting concept to a realistic clinical opportunity. The key question is no longer whether HDTs can be built. The key question is which methods are mature enough to support clinical decisions and what is still missing for routine use. This systematic review maps the methodological landscape of HDTs and highlights practical bottlenecks that limit clinical translation. A PRISMA 2020 guided search of PubMed, Scopus, IEEE Xplore, and the Cochrane Library, covering publications from 2016 to 2026, identified 151 eligible studies. Bibliometric mapping and thematic synthesis were used to characterize research clusters, computational paradigms, and collaboration patterns. Three dominant application streams were identified: cardiovascular HDTs for hemodynamic simulation and procedural planning, musculoskeletal HDTs for biomechanics-driven orthopedic innovation, and neurological HDTs integrating neuroimaging with computational neuroscience. Across domains, the strongest technical trend is the rise in hybrid pipelines that combine physics-based simulation, including finite element and computational fluid dynamics models, with machine learning for segmentation, parameter identification, reduced-order modeling, and faster inference. However, reporting of verification, validation, uncertainty quantification, and explicit context of use remains uneven and prospective clinical evidence is still limited. Overall, the literature shows rapid progress toward clinically credible HDTs, while highlighting the need for scalable computation, standardized credibility pipelines, and workflow-integrated platforms to support safe and reproducible clinical adoption. Full article

Background: Stroke remains a leading cause of mortality, disability, and health-system burden in Korea’s rapidly aging population. We aimed to describe national stroke incidence trends from 2011 to 2023, characterize ecological associations between stroke incidence and health-screening indicators, and generate model-based projections through 2028 to support health-system planning. Methods: This retrospective ecological longitudinal analysis used three publicly available aggregate national data sources: (1) NHIS annual aggregate statistics on crude and age-standardized stroke incidence, stroke case counts, first-onset vs. recurrent stroke, and case-fatality rates (2011–2023); (2) regional standardized health-awareness survey rates for stroke symptoms, myocardial infarction symptoms, blood pressure, and blood glucose (2017–2025); and (3) national cancer-screening outcome tallies for breast and cervical cancer (2010–2024). All analyses used pre-aggregated annual summary data; individual-level NHIS records were not used. Annual trends were modeled with ordinary least-squares linear regression (n = 13 annual observations). Pearson correlations were computed only for overlapping observation windows. Model-based projections are presented with 95% prediction intervals and are explicitly distinguished from observed NHIS values. This study is purely descriptive and ecological; no causal inference is made. Results: Crude stroke incidence increased from 199.2 to 221.1 per 100,000 (2011–2023; slope +2.32/year, R² = 0.83), whereas age-standardized incidence declined from 158.3 to 113.2 per 100,000 (slope −3.41/year, R² = 0.96), a pattern consistent with demographic aging as a contributing factor to growing absolute burden, though formal age-decomposition analysis would be required to confirm this inference. Total cases increased from 99,837 to 113,098; the 30-day case-fatality rate declined from 8.5% to 7.5%. Ecological correlations showed that blood glucose awareness was strongly negatively correlated with age-standardized incidence (r = −0.944, p = 0.001, n = 7), though these are ecological associations and must not be interpreted as individual-level causal relationships. Model-based projections estimate crude incidence near 230.7 (95%PI 219.2–242.2) and age-standardized incidence near 103.2 (95%PI 95.7–110.8) per 100,000 by 2026. Conclusions: Concurrent increase in crude burden and decline in age-standardized incidence reflects demographic aging as the primary driver of Korea’s stroke burden. Projections support integrated cardiovascular prevention, public health education, and age-sensitive service planning. All projections are short-horizon statistical extrapolations intended for policy scenario planning only and must not be interpreted as observed future NHIS outcomes. Full article

Taking the Yellow River Basin (YRB) as an example, this study explores the non-stationary drought evolution features in large river basins under climate change. This study utilized precipitation and multiple climate factor data to establish the non-stationary standardized precipitation index (NSPI) through the GAMLSS model. Combined with the run theory, Copula function and a cascaded RF-LSTM machine learning model, the drought characteristics and retrospective predictive patterns were systematically assessed. The results show that: (1) The Arctic Oscillation, the Pacific Decadal Oscillation, the Southern Oscillation and the North Pacific Index are the primary climate drivers of non-stationary precipitation variation in the YRB, with the former three being selected most frequently and NPI additionally influencing April–June and September, and their effects are both different and lagging. Compared with the traditional SPI, the NSPI assigned higher drought grades and greater severity to typical drought years (e.g., the 1974 event was rated D3 with a severity of 17.935 by NSPI versus D2 with 11.733 by SPI), and thus better captured non-stationary drought evolution. (2) The duration of droughts exhibited a decreasing trend that was not statistically significant (p > 0.05), whereas drought intensity and severity decreased significantly (p < 0.05); the peak severity showed a significant upward trend (p = 0.0078). Spatially, the northwest of the Loess Plateau was a compound core area with high severity, high frequency and long duration of droughts, while the upper reaches were mainly characterized by low severity, short duration and sudden droughts. (3) The drought risk in the YRB shows a higher frequency in the lower reaches and a lower frequency in the upper reaches. The middle and lower reaches were high-risk areas, with shorter AND-type joint exceedance return periods for moderate drought (2.46–5.83 years) and severe drought (3.77–9.15 years). The upper reaches were low-risk areas, with longer return periods reaching up to 5.83 years for moderate drought and 9.15 years for severe drought. The study shows that the NSPI, considering the driving of multiple climate factors, can more effectively identify and assess non-stationary drought risks, providing a scientific basis for drought prevention and control in river basins. Full article

The prognostic value of post-radiotherapy (RT) lymphocyte recovery remains unclear in locally advanced esophageal squamous cell carcinoma (ESCC), and tools to predict recovery are lacking. This study evaluated lymphocyte recovery as a survival predictor and developed a prediction model. We analyzed 233 patients (2019–2024; training:validation = 7:3). Lymphocyte recovery was assessed at 1 and 3 months post-RT (ΔALC₁ > 0.41 and ΔALC₃ > 0.25 × 10⁹/L, calculated as ALC at each time point minus ALC at the end of RT). Patients were stratified into three groups by recovery status: no recovery (Group 0), recovery at both time points (Group 2), or at only one time point (Group 1). Multivariate logistic regression identified predictors of lymphocyte recovery, and a nomogram was developed and internally validated. Median overall survival (OS) was 26.4 months and median progression-free survival (PFS) was 13.9 months. OS differed significantly among groups: 16.0 months (Group 0), 26.0 months (Group 1), and 50.0 months (Group 2) (p < 0.001). Median PFS was 10.2, 12.0, and 36.6 months, respectively (p < 0.001). Independent predictors included ECOG 0 and thoracic spine V₅ < 57.3%; planning target volume < 210 cm³ showed a trend toward association (p = 0.051). The nomogram demonstrated AUCs of 0.77 and 0.75 in the training and validation cohorts. Superior lymphocyte recovery appears to be associated with improved survival. The model, if externally validated, may facilitate individualized risk stratification. Full article

To characterize the distinct expression profiles of microRNAs (miRNAs) in serum and tissue and to delineate the heterogeneity of their regulatory mechanisms in early-stage ovarian cancer (EOC), thereby identifying candidate biomarkers for non-invasive early diagnosis. Differentially expressed miRNAs were identified by integrating publicly available datasets of EOC tissues and serum samples from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Core miRNAs were subsequently screened through integrated differential expression analysis, weighted gene co-expression network analysis (WGCNA), and feature importance ranking derived from optimized machine learning models. Protein–protein interaction (PPI) networks and functional enrichment analyses (GO and KEGG) were performed on predicted target genes to systematically compare the functional discrepancies between serum- and tissue-derived miRNAs. No overlapping core miRNAs were observed between the two compartments. Serum miRNAs exhibited an overall up-regulated trend, whereas tissue miRNAs were predominantly down-regulated. Although the regulatory pathways demonstrated significant heterogeneity, they ultimately converged on the cell cycle and the PI3K-Akt signaling pathway, indicating high functional homology. Furthermore, serum miRNAs are not merely passive leakage products from tissues; current evidence suggests they may be selectively packaged into exosomes to participate in tumor regulation. Despite divergent expression profiles, serum and tissue miRNAs share homologous regulatory functions in EOC. These findings suggest that serum miRNAs accurately reflect the core molecular status of tumor tissues, providing a robust molecular foundation for liquid biopsy-based early detection strategies. Full article

Automotive Noise, Vibration, and Harshness (NVH) has emerged as a critical interdisciplinary field influencing vehicle performance, passenger comfort, brand perception, and regulatory compliance. This thematic literature review synthesizes key research trends, methodological approaches, and technological developments shaping contemporary NVH studies. Drawing on 255 scholarly and industry sources, the review identifies five dominant themes: (1) sources and characterization of noise and vibration in internal combustion, hybrid, and electric vehicles; (2) advanced modeling and simulation techniques—including finite element analysis, statistical energy analysis, and machine learning–based prediction models; (3) materials, components, and structural optimization strategies for NVH mitigation; (4) the rapidly evolving landscape of electric and autonomous vehicle NVH; and (5) emerging active noise and vibration control technologies and data-driven diagnostics. The analysis highlights a definite shift toward holistic, data-driven, and multi-physics approaches, driven by lightweighting imperatives, widespread electrification, and increasingly stringent occupant comfort expectations. Key gaps in current research—including the need for unified evaluation metrics, real-time in-vehicle NVH monitoring, closer integration of subjective psychoacoustic perception with objective physical measurement, and validated simulation workflows for novel EV architectures—are identified and discussed. This review provides a consolidated and expanded framework for understanding contemporary NVH research directions and articulates opportunities for transformative innovation in next-generation vehicle development. Full article

Two-dimensional heterostructures have attracted considerable attention in electrocatalytic hydrogen evolution due to their pronounced interfacial effects, tunable electronic properties, and large specific surface areas. In this work, two representative oxygen-terminated transition metal carbides (MXenes) and three typical transition metal dichalcogenides (TMDs) were selected to construct six heterostructures. Using first-principles density functional theory (DFT) calculations, their binding energies, structural stability, electronic structures, and HER catalytic performance were systematically investigated. The results showed that all heterostructures possessed good thermodynamic stability and favorable electronic properties. In particular, SnS₂/Ti₂CO₂, SnSe₂/Ti₂CO₂, SnTe₂/Ti₂CO₂, and SnTe₂/Zr₂CO₂ exhibited near-optimal hydrogen adsorption Gibbs free energy, indicating excellent HER activity. Moreover, the variation in Gibbs free energy of hydrogen adsorption from isolated monolayers to heterostructures could be effectively correlated with the work function difference. The predicted trends provided a useful descriptor for catalytic performance. Overall, this study provides theoretical insights into the rational design of efficient, advanced HER catalysts and contributes to the advancement of sustainable energy conversion technologies. As this work is based solely on first-principles calculations, the predicted catalytic activity of the heterostructure should be regarded as a theoretical prediction and awaits experimental confirmation. Full article