Search Results (542)

Xizang is characterized by high altitude, low air pressure, strong atmospheric transparency, and complex terrain, while sparse ground stations coexist with continuously available remotely sensed data, and systematic studies on SSR bias correction and meteorological influences under plateau conditions remain limited. This study focuses on a short-term spring case at one urban observation site in Lhasa, using observations collected from 4 to 30 April 2025 to investigate the bias correction of remotely sensed surface solar radiation (SSR) and the influence of meteorological factors. Ground observations and Himawari-8 remotely sensed data were first spatially and temporally matched and preprocessed. Spearman correlation analysis was then used to select key input variables. Support vector regression, random forest, XGBoost, and multiple linear regression models were subsequently developed, followed by a Stacking ensemble model for bias correction. Finally, local sensitivity analysis was conducted to examine the local response of the correction model to selected meteorological variables at a representative baseline point. The results showed that the correlation coefficient between remotely sensed SSR and ground-observed SSR was 0.88 ($p<0.001$). The Stacking ensemble model achieved the best performance, with a test set $R^2$ of 0.8796, an MAE of 118.54 W/m², and an RMSE of 152.41 W/m². Local sensitivity analysis showed that a +10 hPa perturbation in air pressure increased the model output by 173.45 W/m², while a +10 °C perturbation in air temperature increased the output by 23.76 W/m². This study provides a reference for improving the accuracy of remotely sensed SSR and for solar resource assessment in plateau regions. Full article

Urban flood susceptibility mapping is essential for disaster risk management in rapidly urbanizing regions. Although high-resolution Earth observation (EO) data provide detailed information for fine-scale flood analysis, existing studies are often limited by inadequate representation of drainage capacity, inappropriate spatial scales, and model uncertainty under sparse flood sample conditions. To address these issues, this study develops a multi-scale urban flood susceptibility mapping framework based on the Maximum Entropy (MaxEnt) model, integrating multi-source high-resolution geospatial data. A three-tier spatial unit system, including catchment, street, and grid scales, was constructed. Two models were developed at each scale using per capita drainage density (PCDD) and pipe density (PipeDen) as drainage capacity indicators. The results reveal significant scale-dependent differences in spatial autocorrelation, model performance, and variable responses. Compared with the PipeDen-based model, the standard deviation of AUC decreased by 37.5% and 25.0% at the catchment and street scales, respectively, and the model produced a more physically consistent relationship between drainage capacity and urban flood susceptibility. Considering the combined results of model performance, spatial autocorrelation, and response-curve analysis, the street scale PCDD-based model achieved the best overall performance among the six multi-scale models. Impervious area ratio, distance to roads, and annual maximum daily precipitation were identified as dominant factors influencing urban flood susceptibility. Based on the optimal street scale PCDD-based model, a 2 m resolution susceptibility map was generated, showing that high-susceptibility areas are mainly concentrated in highly urbanized central districts and along major transportation corridors. This study highlights the importance of spatial scale and drainage capacity representation in high-resolution urban flood susceptibility mapping. Full article

In many developing regions, the lack of on-site weather data impedes the estimation of rainfall-driven processes, such as soil erosion. Satellite-based precipitation products (SPPs) can support hydrological modelling in gauge-sparse regions by providing continuous rainfall estimates. Accurate rainfall estimation is crucial to soil erosion modelling, particularly in data-scarce regions such as the TRB. In this study, seven satellite-based precipitation products—CHIRPS, IMERG, TRMM, ERA5, GLDAS, and PERSIANN-CDR, along with the IMD gridded dataset—were evaluated for their ability to represent rainfall patterns and support R-factor estimation in the RUSLE framework. This is the first comprehensive evaluation of multiple SPPs for RUSLE-based soil erosion modelling in the Tungabhadra river basin (TRB), providing insights for ungauged watersheds in India. CHIRPS and IMERG displayed relatively smooth and continuous patterns, while PERSIANN-CDR and TRMM exhibited fragmented rainfall zones. ERA5 and GLDAS demonstrated consistent but moderate values across the basin. IMD data served as the reference product for comparison. The findings reveal that the choice of precipitation dataset directly affects the accuracy of erosion estimation. Therefore, multi-dataset evaluation is recommended for reliable assessment of soil loss and watershed planning in ungauged or partially gauged catchments. Full article

Dissolved oxygen (DO) is a critical indicator for assessing marine ecological health and hypoxia risk. Most existing DO prediction studies rely on time-series forecasting models, which require continuous temporal observations and are often unreliable in practical marine monitoring scenarios due to sparse sampling, missing records, and heterogeneous measurement conditions. To address this limitation, this paper investigates the problem of non-temporal DO prediction, aiming to learn a direct nonlinear mapping between environmental drivers and DO concentration. To explicitly model nonlinear pairwise interaction effects between environmental variables, we propose a Factor-Interaction Neural Network (FINN), which decomposes DO estimation into main effects and structured pairwise interaction effects. This interaction-driven design enhances both representation capacity and interpretability compared with conventional multilayer perceptrons. Furthermore, we develop a physics-informed extension, termed PI-FINN, by incorporating oceanographic-consistent regularization priors that reflect key DO formation mechanisms, including temperature-related solubility behavior, depth-wise smoothness associated with stratification, and chlorophyll-driven biological oxygen production tendencies. To evaluate the physical plausibility of model predictions beyond standard accuracy metrics, we introduce a physics-consistency assessment protocol based on Physics Consistency Violation Rate (PCVR) and its robust variant, and further analyze their convergence stability under different driver-weight configurations. Extensive experiments on a real-world marine dataset demonstrate that FINN achieves competitive predictive accuracy compared with strong machine learning baselines (e.g., SVR, Random Forest, and XGBoost), while the proposed physics-informed design mainly improves the physical consistency, robustness, and interpretability of DO estimation under heterogeneous environmental regimes, although it does not necessarily guarantee superior RMSE or MAE performance compared with purely data-driven models. Specifically, FINN achieves an RMSE of 0.3130, an $R^2$ of 0.9831, and a PCVR of 0.4826 on a dataset composed of key environmental variables, including depth, temperature, salinity, and chlorophyll-a, collected under sparse and irregular sampling conditions. Ablation studies confirm the effectiveness of both factor-interaction modeling and physics-guided regularization components. Overall, the proposed framework further provides a reliable tool for sustainable environmental monitoring by enabling physically consistent dissolved oxygen prediction under sparse observational conditions. Such capability is critical for supporting sustainable water resource management, hypoxia risk assessment, and long-term ecological protection. Full article

The Northern Tibetan Plateau is among the most climate-sensitive alpine regions globally. To address the limited applicability of the traditional Remote Sensing Ecological Index (RSEI) in sparsely vegetated areas, this study developed a Soil-Adjusted Remote Sensing Ecological Index (SRSEI) tailored to cold and arid environments. The ecological quality of the Northern Tibetan Plateau from 2000 to 2025 was systematically evaluated and analyzed. The results indicate that: (1) The improved SRSEI achieved a first principal component (PC1) contribution of 72.76%, a significant enhancement over traditional models that effectively mitigates noise from soil backgrounds and anthropogenic features. (2) Between 2000 and 2025, ecological quality was predominantly moderate, following a characterized east-to-west declining spatial gradient. Overall mean SRSEI values fluctuated between 0.420 and 0.476, exhibiting a marginal downward trend. (3) Ecological degradation affected 50.17% of the region, with 26.14% facing risks of sustained decline. Conversely, 40.11% of the area displayed potential recovery trends, suggesting potential spatial divergence in future ecological trajectories. (4) Regional ecological dynamics are governed by a topographic-thermal compound driving mechanism. Elevation (DEM), temperature (TEMP), and surface shortwave radiation (SRAD) emerged as the dominant explanatory variables. Furthermore, dual-factor interactions exhibited significant enhancement effects, while the influence of anthropogenic factors was comparatively weak at the regional scale. These findings provide a scientific basis for the long-term monitoring of fragile alpine ecosystems and the strategic development of the Qiangtang National Park. Full article

Background/Objectives: Exocrine pancreatic insufficiency (EPI) is increasingly recognized in patients with diabetes; however, its clinical correlates remain poorly defined. This study aimed to determine the prevalence and clinical characteristics of EPI in patients with type 1 (T1DM) and type 2 diabetes mellitus (T2DM) and to evaluate its associations with diabetes-related complications and insulin therapy. Methods: A total of 200 patients with diabetes were screened, and 182 who met the inclusion criteria were included in the final analysis. EPI was diagnosed using fecal elastase-1 (FE-1). Clinical, biochemical, and complication-related data were collected. Factors associated with EPI were evaluated using univariate and multivariate logistic regression analyses. Among patients with T2DM, an inverse probability of treatment weighting–average treatment effect on the treated (IPTW-ATT) model was constructed to evaluate the association between insulin therapy and EPI. Propensity scores were estimated using baseline demographic and clinical covariates, and covariate balance after weighting was assessed using standardized mean differences (SMDs). Results: Exocrine pancreatic insufficiency was detected in 18.1% of patients, with prevalences of 21.2% in T1DM and 17.4% in T2DM. Cardiovascular disease was the only variable independently associated with EPI in multivariate analysis (OR = 3.25; 95% CI: 1.12–6.75; p = 0.028. Among patients with T2DM, insulin therapy was significantly associated with EPI in both unadjusted and IPTW-ATT analyses (weighted OR = 10.76; 95% CI: 1.85–62.76; p = 0.008) with a wide confidence interval reflecting sparse data. Cardiovascular disease also remained significantly associated with EPI in the weighted model (OR = 3.52; 95% CI: 1.22–10.15; p = 0.020). Conclusions: Exocrine pancreatic insufficiency is a clinically relevant condition in diabetes and shows a significant cross-sectional association with cardiovascular disease. In T2DM, insulin therapy was associated with a higher prevalence of EPI, although confounding by indication cannot be excluded. These findings suggest that evaluation of exocrine pancreatic function may be considered in high-risk diabetic subgroups, pending confirmation in prospective longitudinal studies. Full article

Background: Long-read, single-CpG-resolution sequencing is redefining the information-to-depth ratio in epigenomics. While conventional methylome analysis often requires high coverage, we propose a scalable pipeline designed to extract high-density regulatory logic from shallow sequencing data. Methods: By utilizing the progenitor-like HepaRG cell line as a model for liver plasticity, we validated this framework across two divergent developmental trajectories: hepatic maturation and sphere-induced retrodifferentiation. Our technical approach combines CpG-centric enrichment and regional methylation aggregation to reconstruct regulatory landscapes from sparse data. Using long-read Nanopore sequencing, we mapped the dynamics of 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). Results: Our pipeline revealed that these trajectories are not inverse processes but engage distinct epigenetic strategies. Hepatic maturation is characterized by the accumulation of 5hmC that partially targets repressive heterochromatin (H3K9me3, H4K20me3) and pioneer factors such as FOXA2. In contrast, retrodifferentiation increases 5mC, potentially silencing adult regulators such as HNF1A via Polycomb-associated networks. In addition, aggregation-based analysis can distinguish widespread focal perturbations from a restricted subset of transcription factors that translate epigenetic changes into regional accessibility. Conclusions: This study provides a scalable computational framework for investigating cellular fate transitions, proving that high-value epigenetic insights are attainable even at reduced sequencing depths. Full article

Against the background of industrial transformation and urban regeneration in old industrial bases, understanding the spatial pattern and driving mechanisms of industrial heritage is essential for its conservation and sustainable use. This study investigates 277 industrial heritage sites in Liaoning Province (including nationally designated sites, potential heritage within cultural relic protection units at all levels, and sites recognized by the China Association for Science and Technology) using kernel density estimation, standard deviation ellipse, and the GeoDetector model. The results reveal a significantly clustered distribution characterized by “dense in central–southern Liaoning, sparse in the periphery,” forming three major agglomerations: the Shenyang core, the Anshan–Benxi–Liaoyang heavy industry triangle, and the Dalian coastal industrial belt. Temporally, the distribution shows distinct phases closely linked to industrial development history and major socio-political events. Land use, GDP, and climatic factors dominate the spatial differentiation, with GDP and annual average temperature exhibiting the strongest combined explanatory power (41.67%). Based on these dominant factors and the identified core agglomeration areas, differentiated protection and utilization strategies should be formulated for core versus peripheral areas, different industrial types, and various historical periods. This provides direct empirical evidence for industrial heritage management and cultural revitalization in old industrial regions. Full article

Background: Rheumatoid arthritis (RA) and ankylosing spondylitis (AS) are chronic inflammatory rheumatic diseases associated with impaired quality of life, persistent disease burden, and increased healthcare utilization. Treatment adherence and psychosocial factors may influence outcomes, but their longitudinal associations in real-world hospitalized populations remain insufficiently characterized. Methods: We conducted a single-center, retrospective, mid-term longitudinal observational study including 50 adults with RA or AS who experienced repeated hospitalizations over a four-year period. The final dataset comprised 196 hospitalization episodes analyzed as repeated observations nested within individual patients. Disease activity was assessed using DAS28 in RA and ASDAS and/or BASDAI in AS, according to data availability and routine clinical practice. Treatment adherence, quality of life, anxiety, social isolation, patient–provider communication, dietary support, inflammatory markers, and hospitalization-related outcomes were extracted from medical records and structured inpatient assessments. Linear mixed-effects models were used for continuous outcomes, and ordinal mixed-effects models were used for ordered categorical outcomes, with adjustment for age, sex, and time where appropriate. Results: In RA, higher treatment adherence was associated with lower disease activity over time. In AS, comparable associations were not detected, possibly reflecting disease-specific factors, limited variability in adherence, and reduced statistical power in the smaller AS subgroup. Better patient–provider communication was associated with higher adherence and lower anxiety, whereas greater social isolation was associated with poorer quality of life. More favorable dietary support was associated with better adherence, although the magnitude of this association should be interpreted cautiously because of sparse categories and wide confidence intervals. Lower inflammatory burden, particularly lower CRP over time, was associated with lower hospitalization-related costs. Conclusions: In this selected cohort of repeatedly hospitalized patients with RA or AS, treatment adherence, psychosocial factors, and supportive care indicators were associated with clinically relevant longitudinal outcomes. The findings support a multidisciplinary, patient-centered approach to inflammatory rheumatic disease care. However, because of the retrospective design, modest sample size, selected inpatient population, non-standardized assessment of several variables, and possible instability of some ordinal model estimates, the results should be interpreted as exploratory and confirmed in larger prospective cohorts. Full article

Background: Concomitant HIV/HBV infection results in worse health outcomes, with HBV reactivations being observed in immunocompromised individuals. However, data on HBV infection in people with advanced HIV disease (AHD) remains sparse in Botswana. We aimed to determine the prevalence and molecular characteristics of HBV in people living with HIV (PLHIV) with CD4+ T-cell counts ≤100 cells/µL in Botswana. Methods: Plasma samples (n = 1097) of PLHIV with CD4+ T-cell count ≤100 cells/uL collected between 2014 and 2016 were screened for hepatitis B surface antigen (HBsAg) and HBV core antibodies (anti-HBc). A 415bp region of the HBV surface gene was amplified and sequenced using Sanger sequencing. Genotypic and mutational analysis was performed using Geno2pheno. Adjusted prevalence ratios (aPRs) were estimated from a modified Poisson regression model to explore factors associated with HBV infection. p-values < 0.05 indicated statistical significance. Results: The median age was 37 years (IQR: 32–43), and 565/1097 (51.5%) were male. HBsAg prevalence was 10.6% (95%CI: 8.8–12.5%) and anti-HBc prevalence was 50.0% (95%CI:46.9–52.9%). Factors associated with HBV infection were male sex [aPR: 1.6 (p < 0.01)] and those that were ART-experienced [aPR: 1.43 (p = 0.04). Eighteen samples were successfully genotyped. The prevalence of genotype A was (12/18, 66.7%) and D (6/18, 33.3%). Sixty-three mutations were identified as associated with drug resistance and immune and diagnostic escape. Highly prevalent immune escape mutations in the surface region were S207N (12/63, 19%) and A194V (9/63, 14.3%). V163I (12/63, 19%) and M129L (12/63, 19%) were highly prevalent in the reverse transcriptase region. Two classical lamivudine-associated drug resistance mutations were observed, each occurring in one participant (L180M and V173L). Conclusions: The prevalence of HBV in people with AHD is high, highlighting the importance of HBV screening and HIV/HBV co-management in this population. Full article

The Performance Efficiency Factor (PEF) is a key composite metric in broiler production that integrates livability, average body weight, feed conversion ratio (FCR) and age. While tree-based machine learning models have shown promising results for live-weight prediction, they often struggle with temporal dependencies and sparse mortality data. This study evaluates five tree-based algorithms, Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Category Boosting (CatBoost) on daily sensor data from commercial broiler farms. Data were stratified into six age categories, pre-processed with outlier retention and feature engineering, and assessed using R², RMSE, MAE, paired t-tests for statistical significance and SHapley Additive exPlanations (SHAP) for explainability. Cross-farm validation was performed to assess generalizability. The models achieved high accuracy for live-weight prediction (R² up to 0.97, RMSE as low as 9.23 g in mid-cycle categories), with Random Forest and LightGBM performing best. Mortality prediction remained challenging (R² −1.63 to 0.53) due to its sparse and stochastic nature. Nevertheless, Day 21 to Day 28 PEF forecasts showed relative errors of only 4.42–5.40%, as live-weight predictions dominated the PEF calculation. SHAP analysis consistently identified bird age, feed intake per bird and temperature as the most influential predictors. Tree-based models offer a robust, interpretable and computationally efficient solution for live-weight and PEF forecasting in commercial broiler production. The findings support proactive farm management and highlight the need for hybrid approaches to improve mortality prediction. Full article

Origin–destination (OD) matrix forecasting is essential for urban railway operations because it enables simultaneous understanding of the direction and magnitude of passenger flows. However, OD matrices in large-scale subway networks are difficult to predict owing to their high dimensionality and sparsity, and existing approaches often rely on station-level predictions or complex structural designs. This study addresses the offline full-matrix OD forecasting problem, where complete historical OD sequences are available at prediction time, and proposes Metro-GATF, a spatiotemporal forecasting framework that jointly models railway topology and dynamic OD interactions. The model employs a GATv2-based spatial encoder to learn static inter-station relationships and encodes time-varying interactions using sparse OD graphs. A non-autoregressive transformer decoder generates future multi-step node representations in parallel, whereas origin–destination factorization and sparsity-aware gating are used to reconstruct the full OD matrix. Experiments on minute-level AFC-based OD data from a 637-station metropolitan subway network demonstrated that Metro-GATF achieved the lowest sMAPE among the compared full-matrix models. These results indicate that the proposed framework effectively captures complex spatiotemporal OD patterns and offers a practical end-to-end framework for forecasting urban railway demand. Full article

In predicting tubing corrosion rates under multi-factor coupling, traditional methods often struggle to effectively analyze the nonlinear interactions among variables such as temperature, pressure, CO₂ partial pressure, and H₂S partial pressure, and they also lack interpretability in the prediction process. To address this, this study first establishes a corrosion dataset covering three typical steels (2205DSS, CT80, N80) through high-temperature and high-pressure weight-loss experiments. A machine learning framework is then proposed, integrating feature coupling analysis with a SHAP-Sobol-based interpretability framework. By incorporating the Context-Aware Sparse Attention (CASA) mechanism into the XGBoost ensemble, a CASA-XGBoost prediction model is constructed to systematically analyze interactions among multiple features and convert them into effective predictive information. Bayesian optimization enables adaptive hyperparameter tuning, while five-fold cross-validation tailored to different materials enhances model generalization and stability. Furthermore, the SHAP-Sobol weighting method systematically evaluates feature contributions and interaction effects across global sensitivity analysis and local sample interpretation, enabling feature coupling reconstruction. Experimental results demonstrate that the proposed framework outperforms benchmark models (Random Forest and Gaussian Process Regression) on three steel corrosion datasets, achieving test set R² values up to 0.98 with a low MAE and RMSE. The SHAP-Sobol-based interpretability framework also reveals material-specific sensitivities: 2205DSS is highly influenced by CO₂-H₂S interaction, CT80 by temperature–pressure coupling, and N80 shows reduced performance at high corrosion rates due to localized mechanisms. This study provides a reference for corrosion prevention and control by delivering high-accuracy and interpretable corrosion rate prediction for tubing under multi-factor coupling conditions, offering practical value for industrial modeling and decision-making. Full article

Material selection in architectural design necessitates a multifaceted evaluation of economic, technical, esthetic, and cultural variables. Beyond fundamental requirements such as cost, structural integrity, and transparency, architects must synthesize subjective attributes, including warmth and formality, with objective constraints like multifunctionality and cultural heritage. Despite the strategic impact of material choice on project performance, empirical research systematically categorizing these governing criteria remains sparse. Furthermore, existing methodologies often overlook the psychophysical principles of human perception essential for construction material evaluation. Thus, this study identifies the fundamental factors influencing material selection and establishes a hierarchical framework to prioritize their relative significance within the design process. The research employs a weighted Analytic Hierarchy Process integrated with the Weber–Fechner law (W-AHP) to structure and quantify selection criteria. By incorporating perceptual scaling principles into the AHP framework, the methodology accounts for variations in judgment sensitivity across different evaluation scales. A hierarchical decision model was developed to categorize criteria and sub-criteria, followed by pairwise comparisons to derive priority weights. Results reveal a distinct priority hierarchy among the identified criteria and confirm that judgment sensitivity varies significantly across evaluation scales. The W-AHP method produced differentiated weightings that accurately reflect the psychological intensity of professional decision-making, offering a structured mechanism to balance functional performance with complex design intentions. This study contributes to the field of construction management by introducing the W-AHP method as a novel decision-support tool. The integration of Weber–Fechner perceptual principles enhances weight differentiation and addresses the inherent subjectivity of architectural evaluation, providing a transparent methodology to justify material procurement within a rigorous engineering management context. Full article

Accurate short-term prediction of urban waterlogging depth is essential for real-time flood risk management in rapidly urbanizing areas under climate variability. Departures from quasi-stationary operating conditions, caused by changes in drainage efficiency, inflow patterns, or measurement quality, weaken historical rainfall–water depth relationships, making purely data-driven models prone to error accumulation. In this study, a GeoAI-based, physics-enhanced machine learning framework is proposed, which translates the water balance principle into Physical Violation Scores (PVSs) and incorporates them as additional input features. PVSs remain zero under expected rainfall–water depth behavior and become positive only under departure scenarios, providing sparse and lightweight diagnostic signals without modifying model structures or loss functions. The framework is implemented on five algorithms (Support Vector Machine, Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and XGBoost) to construct physics-enhanced models (PEMs). These are evaluated against original feature models (OFMs) across 1 h and 2 h forecasting horizons. Results show that most PEMs improve prediction performance compared with their corresponding OFMs, with more pronounced gains at the 2 h horizon. Bootstrap analysis and RMSE-based error amplification factor further indicate comparable or lower R² variability and reduced recursive error amplification for most PEMs. Interpretability analyses show that rainfall forcing and water-depth persistence remain dominant predictors, whereas PVSs act as auxiliary diagnostic signals. Overall, the proposed framework provides a lightweight, reliable, interpretable, and scalable GeoAI approach for incorporating water balance knowledge into short-term urban waterlogging prediction, supporting climate resilience and smart urban water management. Full article