Search Results (10,304)

The Turpan–Hami Basin, a critical energy hub in northwestern China, is plagued by frequent ground collapses induced by extensive mining over karst geology, threatening ecology and safety. Current hazard assessment methods, mainly single linear or traditional machine learning models, fail to capture the complex nonlinear interactions inherent to this coupled geo-mining environment. This study addresses this gap by establishing a multi-dimensional “Geology-Mining-Hydrology-Environment” index system comprising 14 critical factors—including lithology, goaf distribution, mining intensity, and their interaction terms. A coupled gradient boosting decision tree and logistic regression (GBDT-LR) model, optimized for the multi-factor coupling characteristics of ground collapse in arid mining basins, was applied for the hazard assessment. The results reveal a distinct spatial pattern of “core agglomeration with multi-level gradient differentiation.” Extremely high-hazard areas, covering 9.21% of the area, are concentrated in the core mining areas northwest of Turpan and southwest of Hami, while high-hazard areas (4.63%) form surrounding belts. The GBDT-LR model (AUC = 0.871) demonstrated significantly superior performance over a single logistic regression model (AUC = 0.813), proving its enhanced capability to identify high-hazard areas by modeling complex factor interactions. This work provides an essential scientific foundation for implementing zonal hazard management and prioritizing disaster prevention projects in key areas of the basin. Full article

Background/Objective: Women with a history of gestational diabetes mellitus (GDM) are at increased risk of type 2 diabetes mellitus (T2DM), dysglycemia, and dyslipidemia. However, the role of postpartum weight change in long-term metabolic outcomes remains unclear. Here, we determined the long-term incidence of dysglycemia and dyslipidemia after GDM and evaluated whether postpartum changes in body mass index (BMI) independently predicted these outcomes. Methods: This single-center prospective cohort study included 205 Japanese women diagnosed with GDM. All participants underwent a 75 g oral glucose tolerance test at 6–12 weeks postpartum. The incidence of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), T2DM, and dyslipidemia was evaluated over a median follow-up of 3.6 years. Cumulative incidence was estimated using the Kaplan–Meier method, and Cox proportional hazards models identified independent risk factors, particularly postpartum BMI change. Results: During follow-up, 42.4%, 6.3%, and 35.6% of women developed IFG or IGT (prediabetes), T2DM, and dyslipidemia, respectively. The estimated cumulative incidence rates at 6 years postpartum were 57.1% and 50% for IFG/IGT and dyslipidemia, respectively, whereas the 5-year incidence of T2DM was 10.3%. Postpartum BMI increase was independently associated with new-onset dysglycemia. No independent predictor of T2DM progression was identified. Dyslipidemia was independently associated with higher pre-pregnancy BMI and multiparity, whereas postpartum BMI change was not independently associated after multivariable adjustment. Conclusions: Postpartum BMI change was independently associated with dysglycemia in women with a history of GDM. These findings suggest that postpartum weight change may help identify women at higher risk of subsequent metabolic abnormalities, particularly dysglycemia, in this high-risk population, although causal relationships cannot be inferred from this observational study. Full article

The Landslide susceptibility assessment in complex mountainous terrain is frequently limited by static modelling frameworks that inadequately capture nonlinear deformation characteristics and temporally evolving hazard processes. To bridge this gap, a continuous-time dynamic assessment framework is proposed for Shazhou Town, Sichuan Province, integrating slowly moving scatterogram interferometric radar (S(BAS-InSAR))-derived deformation time series with Liquid Neural Networks (LNN). By incorporating a liquid time-constant architecture, the model accommodates irregular temporal sampling and captures non-stationary environmental responses through adaptive multimodal feature fusion. Analysis of long-term SBAS-InSAR observations (January 2021–May 2025) reveals distinctive deformation patterns, identifying eight active zones with maximum annual displacement rates of 107 mm yr⁻¹ and cumulative subsidence of 535.7 mm, which serve as critical dynamic inputs for the susceptibility model. Comparative experiments demonstrate that the LNN framework outperforms benchmark models (including LSTM, GRU, Random Forest, and SVM), achieving a coefficient of determination (R²) of 0.95 and an RMSE of 0.50. Furthermore, multi-temporal validation against 189 historical landslide records (2008–2025) confirms the model’s robustness, yielding a 91.5% capture rate within high-susceptibility zones. Interpretability analyses via SHAP and Layer-wise relevance propagation identify rainfall and vegetation cover as dominant dynamic controls, while characterising a distinct slope threshold effect at approximately 20°. These findings demonstrate that explicit continuous-time neural modelling enables physically consistent representation of irregular satellite acquisition intervals and delayed hydro-mechanical responses, thereby advancing landslide susceptibility assessment from static spatial classification toward dynamic state evolution inference under asynchronous Earth observation data streams. Full article

Tidal-scale wave modulation is a critical yet complex process in macro-tidal estuaries. This study investigates semidiurnal wave modulations in the Changjiang River Estuary (CRE) using unique, long-term in situ observations and high-resolution ADCIRC–SWAN coupled simulations. Pronounced semidiurnal signals are identified in significant wave height (H_s), mean wave period, and wave direction. Observational results demonstrate that the modulation intensity is highest in Hangzhou Bay and the CRE mouth, decreasing gradually offshore. A key finding is that semidiurnal H_s maxima systematically coincide with peak flood currents and precede high water by approximately three hours. Long-term records confirm that this modulation persists year-round and intensifies during energetic events such as typhoons. The expression of the tidal signal depends on wave composition: wind-sea-dominated conditions exhibit stronger period modulation, whereas swell-dominated conditions favor coherent H_s modulation as kinematic tidal effects remain more apparent in the absence of strong local wind forcing. Numerical sensitivity experiments demonstrate that tidal currents are the primary driver of the observed wave modulation, while water-level effects are largely confined to shallow shoals. The results highlight that accurately reproducing the observed frequency–directional structure requires the inclusion of current-induced Doppler shifts and refraction. Beyond the classical following-current effects, the analysis suggests that the spatial deceleration of currents along the wave path acts as a kinematic trap that focuses wave action and sustains H_s intensification. This mechanism provides a physically plausible explanation for the observed phase relationship and points to the non-local nature of estuarine wave dynamics, where the wave state appears as an integrated response to cumulative current gradients along the propagation path. These findings emphasize the necessity of incorporating wave–current coupling in future coastal modeling and hazard forecasting. Full article

Background: Delays in diagnosis are unfortunately quite common in most health systems. It is apparent that timely diagnosis is more likely to have a favourable outcome. However, there may be many reasons why timely diagnosis is not always achieved. The aim of our study was to report on the impact of delays on IBD-related adverse outcomes (AOs). Methods: New patients referred for suspected IBD to a single tertiary care centre between January 2013 to December 2017 were identified using EMR. For purposes of the study, a cut-off time was set by investigators for each delay-type based on best average hospital waiting times. The reasons for delays in patient journey until start of treatment and data on pre-defined AOs (steroid & other rescue therapies, hospitalisations, surgery) were recorded for each patient until end of June 2021. The data were analysed using multiple Pearson correlations and Cox proportional Hazard model to determine whether there is a difference in survival without AOs between patients with and without a delay. Results: Total of 105 patients were identified using stringent criteria (M = 58; median age = 32 y) with a long median follow-up of 55 months. 65, 27 and 13 patients had final diagnosis of Ulcerative colitis, Crohn’s disease and Unclassified colitis respectively, and analysed collectively. In our cohort, the longest delay-types noted were—patients seeking medical attention (median = 4 months; range 1 to 84 months), arranging gastroenterology clinic review after referral from primary care (median = 5 weeks; range 1 to 30 weeks), and waiting for index endoscopy (median = 3 weeks; 1 to 36 weeks). Patient stratification based on delay-type using specific cut-off times for each showed a statistically significant difference in survival without AOs for all (when comparing delay v/s no delay). Conclusions: In our cohort we report that delays, and subsequent untreated chronic inflammation, leads to poor outcomes in patients with newly diagnosed IBD regardless of whether delays are patient-related or health-system-related. Also, cumulative delays in the hospital appear to increase the use of biologics in consecutive years. Understanding these factors help rectify and offer long-term solutions. Full article

Floods are among the most frequent and destructive natural hazards in Kazakhstan, particularly in the Abai Region, Kazakhstan, where topographic, hydrological, and climatic factors strongly influence flood occurrence. This study presents a comprehensive spatial assessment of flood susceptibility in the Abai Region using a multi-criteria Geographic Information System (GIS) approach. The analysis integrates twelve flood-conditioning factors representing hydrological, topographic, environmental, and anthropogenic variables. The relative importance of these factors was determined using the Analytical Hierarchy Process (AHP). The results indicate that distance to rivers (20%) and precipitation (16%) are the most influential drivers of flood susceptibility, followed by Height Above Nearest Drainage (HAND) (11%) and drainage density (9%). The resulting flood susceptibility map classifies the study area into five susceptibility levels. Approximately 56.6% of the study area falls within the moderate susceptibility class, while 25.0% is categorized as high susceptibility, mainly concentrated in low-lying floodplains and foothill regions. Low-susceptibility areas account for 18.1% of the region, whereas the very high and very low susceptibility classes together represent less than 1% of the territory. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis, yielding an Area Under the Curve (ROC–AUC) value of 0.893, indicating strong agreement between predicted susceptibility patterns and observed flood occurrences. Additional validation metrics derived from the confusion matrix show an overall accuracy of 83.3%, precision of 0.75, recall of 1.0, and a Kappa coefficient of 0.67, confirming reliable predictive performance. Sensitivity analysis with ±10% variation in factor weights further demonstrated the spatial stability of the susceptibility results. The resulting susceptibility map provides an important spatial basis for infrastructure planning, flood mitigation, and disaster preparedness in the Abai Region and offers a transferable framework for flood-susceptibility assessment in other semi-arid regions of Central Asia. Full article

Sustainable groundwater management is essential for water security and human health protection. Fluoride contamination is a serious concern for the sustainable drinking water supply in many parts of Pakistan, including Balochistan, where arid climate conditions and geological formations support the enrichment of fluoride. The toxic nature of fluoride contamination has resulted in negative health impacts on the local population. Conventional geostatistical techniques are usually ineffective to delineate the nonlinear relationships that affect the distribution of fluoride. This study aims to develop a machine learning-driven spatial modelling framework for classifying the spatial distribution of fluoride contamination in groundwater across the study area. The model will help to understand the spatial variability of fluoride contamination and its controlling factors, essential for effective mitigation and early warning systems. Physiochemical elements were used as predictive features in this study, utilizing a unified feature importance framework combining hydrogeochemical analysis, spatial distribution assessment, and ensemble SHAP-based interpretation to identify consistent predictors. Model performance was evaluated using a nested cross-validation framework, followed by validation on an independent geology-informed spatial holdout test set to ensure realistic generalization. Among machine learning models, the Logistic Regression (LR), Support Vector Classifier (SVC), XGBoost (XGB), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbours (KNN) were evaluated. Support Vector Classifier (SVC) demonstrated a high predictive performance. On the independent spatial holdout dataset, SVC achieved an overall accuracy of 0.75 and an area under the receiver operating characteristic curve (AUC) of 0.821. In addition to classification, a human health risk assessment was conducted using chronic daily intake (CDI) and hazard quotient (HQ) calculations for children and adults, identifying several high-risk water supply schemes. The prediction maps successfully delineated high-risk fluoride points across specific areas, offering a tool for sustainable groundwater management. This study helps to achieve a Sustainable Development Goal (Clean Water and Sanitation, SDG#6) and promotes long-term sustainable planning in water-stressed areas by integrating spatial machine learning mapping and health risk assessment. Full article

Background/Objectives: Cirrhosis is associated with increased mortality. In this study, we aimed to investigate the prognostic relevance of 6-min-walk-distance- and cardiopulmonary exercise testing (CPET)-derived peak oxygen uptake (VO₂) as estimates of exercise capacity in outpatients with cirrhosis. Methods: Patients underwent a comprehensive clinical characterization including cardiopulmonary exercise testing, six-minute-walking-test-derived distance, and echocardiography. We stratified the cohort using established prognostic thresholds for the six-minute-walking-test-derived distance (440 m) and peak VO₂ (65% predicted) and Child–Pugh class (A vs. B/C). Competing risk analyses were performed using cumulative incidence functions and subdistribution hazard models to assess the impact of baseline variables on mortality, accounting for liver transplantation (LT) as a competing event and for age and sex. The prognostic value of exercise performance was analyzed first, followed by the stepwise inclusion of additional variables; multicollinearity precluded a full multivariable model. Results: We enrolled 197 patients in Child–Pugh Class A, B, and C (N = 92, N = 80, N = 25 patients; male N = 146, age: 56 ± 9 years). During the observation time of 85 (25–105) months, 48 patients underwent a liver transplant, and 88 died. Both the six-minute-walking-test-derived distance ≤ 440 m (p = 0.002, sHR: 0.996 95% CI: 0.993–0.998) and peak VO₂ ≤ 65% predicted (p = 0.023, sHR: 0.987 95% CI: 0.976–0.998) were strong independent predictors of mortality. While the six-minute-walking-test-derived distance consistently remained significant across most models, the peak VO₂ retained significance only when adjusted for creatinine. Combining exercise capacity and the Child–Pugh classification identified patients at a particularly high mortality risk. Conclusions: In patients with liver cirrhosis outside the liver transplant setting, the impaired six-minute-walking-test-derived distance and peak VO₂ serve as predictors of mortality and may help to identify patients at a particularly high mortality risk. These results suggest that functional capacity provides complementary information to established liver disease severity scores and could be considered in a multidimensional risk assessment approach in patients with liver cirrhosis. Full article

Toxoplasma gondii establishes latent infection in a substantial proportion of the global population, yet the long-term health consequences of this infection remain incompletely characterized. We conducted a retrospective observational matched cohort study using longitudinal electronic health record data from a nationwide integrated healthcare provider, including members aged 18–45 years who underwent routine Toxoplasma serologic screening, typically performed in obstetric evaluation, excluding patients with clinical toxoplasmosis, immunosuppression, or HIV. Seropositive individuals were matched 1:1 without replacement to seronegative controls to align demographic, temporal, and socioeconomic variables. Time-to-event associations with predefined medical conditions were evaluated using Cox proportional hazards models with false discovery rate correction. The final cohort included 19,443 seropositive individuals and 19,443 matched controls (96.7% female), with a tight baseline balance of demographic and temporal characteristics. During follow-up, seropositivity was associated with increased risks of tobacco dependence (aHR 1.65), alcohol dependence (2.32), suicide attempt (1.82), motor vehicle accidents (1.22), and work accidents (1.27), as well as multiple infectious conditions, including hepatitis B (1.55), hepatitis C (2.15), and syphilis (2.43), with an overall trend toward increased all-cause mortality (1.32, 95% CI [1.00–1.74]). These findings suggest that asymptomatic Toxoplasma infection in young adults is associated with increased long-term behavioral and medical comorbidity. Full article

Background: Patients with estrogen receptor (ER)-positive ductal carcinoma in situ (DCIS) derive a greater benefit from endocrine therapy than patients with ER-negative disease. The relevance of ER status and progesterone receptor (PR) status in DCIS to radiation therapy has not been well explored. Methods: Patients undergoing breast-conserving surgery with or without radiation were grouped by ER and PR status and matched using rank-based Mahalanobis optimal matching with respect to lesion size and grade and patient age and race. Cumulative incidences were estimated and competing risk regressions with subdistribution hazard ratios (sHRs) were calculated. Results: Among patients who underwent breast-conserving surgery only, 369 patients with ER-PR- disease were matched to 738 patients with ER+PR+ disease (1:2 matching). In multivariate models, patients with ER-PR- disease were at increased risk of any invasive events (sHR = 2.47, p = 0.007) and early ipsilateral invasive events (sHR = 2.64, p = 0.02 in the 0-to-4-year period) relative to patients with ER+PR+ disease. Among patients who underwent breast-conserving surgery with adjuvant radiation, 1498 patients with ER+PR+ disease were matched to 1498 patients with ER-PR- disease. No significant differences were noted with respect to cumulative incidence of any invasive event (5.6% vs. 5.6%) or ipsilateral invasive events (1.9% vs. 2.9%). In multivariate models, no significant differences were noted. Patients with ER-PR+ lesions had similar cumulative incidences of ipsilateral invasive events to patients with ER-PR- disease in the absence of radiation (5.9% vs. 5.9%) and similar cumulative incidences of contralateral invasive events to patients with ER+PR+ disease when radiation was administered (3.2% vs. 4.2%). Conclusion: The statuses of ER and PR carry independent prognostic and therapeutic implications beyond those of traditional clinicopathologic risk factors. Given that ER and PR statuses are routinely collected for patients with DCIS, incorporation of these variables into clinicopathologic risk classification systems is warranted. Full article

With the advancement of global industrialization, the market for the transportation of hazardous materials is also expanding, which poses an increasingly serious threat to public safety, environmental protection, and economic stability. This study explores solutions to improve the safety and sustainability of transportation by integrating a variety of transportation modes, such as highways, railways, and waterways. We have built a comprehensive assessment system that takes into account safety considerations, operating costs, and environmental impact. The methodological contributions include an improved NSGA-II algorithm featuring population invasion and homologous competition mechanisms, combined with entropy-weighted TOPSIS for objective route selection. We use the improved NSGA-II algorithm combined with the entropy weighted TOPSIS method to model the solution, screen the optimal scheme, and determine the actual feasible route. We used the real transportation route from Berlin to Paris as a case to verify the validity of the model and proved the improved effect of the algorithm by comparing it with the baseline NSGA-II and MOQPSO. The experimental results demonstrated that the improved algorithm achieved a 133% higher hypervolume than the baseline NSGA-II and 58.8% higher than MOQPSO, while the optimal solution reduced operating costs by approximately 7.3% and carbon emissions by 12.7%. The experimental results proved that the framework effectively reduced the accident rate, operating costs, and carbon emissions. The research results provide important references for logistics planners, fully demonstrating that under the increasingly complex world pattern, it is a feasible plan to improve the efficiency of hazardous materials transportation through multimodal transportation. Full article

The safety and stability of underground coal mining are largely determined by the structural features of coal seams and surrounding rocks. Geological heterogeneities such as faults, fracture zones, and lithological variations strongly influence the distribution of rock pressure and the occurrence of geodynamic hazards. This highlights the need for reliable geophysical methods capable of identifying such zones under mining conditions. Electrical prospecting represents a promising diagnostic approach, as it is highly sensitive to changes in the physical properties of rocks. Unlike conventional geological mapping, it enables the detection of hidden structures and weakened zones often invisible to direct observation. Advances in instrumentation and data processing have further expanded the applicability of electrical methods in complex environments. This study introduces a methodology of electrical prospecting observations for the diagnosis of coal seams. The analysis focuses on conductivity anomalies that reflect tectonic disturbances, fracture systems, and lithological heterogeneities. Field investigations demonstrated the sensitivity of the method to local environmental variations. Comparison with geological records confirmed the validity of the approach: the identified anomalous zones correlated well with documented tectonic features. The methodology showed a stable performance and revealed potential for integration into mine monitoring systems. It allows the identification of areas associated with elevated rock pressure and possible geodynamic activity, thereby contributing to safer underground operations. In the longer term, electrical prospecting may be applied to other coal deposits, including those with a high gas content and complex structure. The development of automated interpretation tools and machine learning algorithms could further increase processing efficiency and improve predictive reliability. Overall, the results confirm that electrical prospecting in mining environments can become an effective instrument for enhancing safety and building more accurate geological–geophysical models of coal seams. Full article

Seismic collapse can cause catastrophic losses, and acceptable annual collapse probability with its CMR target is a core metric in performance-based design. Existing ATC-63-based CMR research mainly addresses non-damped systems and often uses a single lumped dispersion, obscuring damper-reliability contributions and hindering alignment with CECS 392 limits. This study proposes a unified, code-consistent decision framework for acceptable annual collapse probability and CMR that jointly accounts for seismic hazard and damper-related uncertainty. The total collapse dispersion is decomposed as ${\sigma }_{\mathrm{total},\mathrm{damp}}^2={\sigma }_{\mathrm{base}}^2 +{ \sigma }_{\mathrm{damper}}^2$, where ${\sigma }_{\mathrm{b}\mathrm{a}\mathrm{s}\mathrm{e}}$ represents background dispersion independent of dampers and ${\sigma }_{\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{e}\mathrm{r}}$ captures incremental uncertainty induced by degradation and partial failure. A code-designed viscous-damped RC frame is evaluated under three scenarios (nominal damping, 20% damping-coefficient reduction, and 7% random damper failures). Using the same 14 records and $\mathrm{S}\mathrm{a}\left({\mathrm{T}}_1,5\%\right)$ as the intensity measure, multi-stripe IDA and Probit-based lognormal fragility fitting yield median collapse intensities $\mathrm{S}\mathrm{c}\approx 2.18-2.24 \mathrm{g}$, with only ~2–3% reduction under mild degradation/failure. A random-effects variance decomposition identifies σ_damper ≈ 0, indicating a limited marginal contribution of damper-related uncertainty within the degradation range considered in this study. Closed-form relationships between annual collapse rate, $\mathrm{S}\mathrm{c}$, and ${\sigma }_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l},\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}}$ are then derived under a power-law hazard model and inverted to generate acceptable-risk intervals and CMR target curves/matrices. Results show that higher design intensity and larger ${\sigma }_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l},\mathrm{d}\mathrm{a}\mathrm{m}\mathrm{p}}$ demand substantially higher CMR, highlighting potential risk underestimation when relying solely on nominal CMR. The framework enables explicit identification of damper-related uncertainty from limited collapse data and provides a practical workflow for collapse-prevention design and post-assessment under explicitly defined scenario conditions, with a clear pathway for extension to broader scenario spaces. Full article

Industrial volatile organic compound (VOC) emissions from large-scale petroleum storage represent a persistent environmental challenge, particularly in agricultural perimeters where atmospheric “breathing” cycles drive localized soil loading. This study investigates the thermodynamic and spatial relationship between gasoline storage emissions and chemical contamination in the Constanta South terminal area using a multi-layered analytical approach. By integrating gas chromatography (GC-MS) headspace analysis with an artificial intelligence (AI) framework utilizing high-order polynomial regression, we quantified the source–path–receptor dynamics across a thermal gradient (12 °C to 70 °C). The results reveal a non-linear surge in VOC emissions at temperatures exceeding 37 °C, characterized by a shift toward medium-weight hydrocarbons (C4–C6) that act as carriers for heavier aromatics. The AI risk model identified a significant spatial gradient, identifying a 500 m “critical zone” where the Hazard Quotient (HQ) is elevated, necessitating technological upgrades like Vapor Recovery Units (VRUs) to mitigate ecological risks. Full article

Background/Objectives: Our study aims to identify potential new MRI features of brain metastases (BMs) that could be further used in overall survival (OS) assessment. Methods: A total of 109 patients with BMs were included. Kaplan–Meier analysis, the log-rank test, and Cox Regression were implemented in the survival analysis. The first ten significant features were incorporated into four distinct machine learning (ML) algorithms to predict six-month survival. Results: Survival analysis revealed that multiple brain lesions and synchronous presentation were associated with a poor prognostic value (HR > 1; p = 0.01, p = 0.02). Other features demonstrated a protective effect on OS including the absence of extracranial lesions (HR < 1, p = 0.04) and the presence of solid enhancement (HR < 1, p < 0.05). In this observational cohort, treatment was associated with longer OS—including surgery, gamma knife radiosurgery, whole brain radiation therapy, and chemotherapy—compared to best supportive care (HR < 1, p < 0.005); these treatment-related hazard ratios are not interpreted causally. The shallow Neural Networks model was the top-performing ML model, achieving an AUC of 0.93 (CI = 0.89–0.97). According to the Shapley Additive Explanations analysis, the solid enhancement type had a positive impact on OS, whereas a higher number of lesions, larger volumes and a cystic morphology were associated with negative outcomes. Conclusions: Our results confirm that including morphological MRI features of BMs in the prediction of OS significantly contributes to the enhancement of ML algorithms’ prediction and discriminatory capacity. Full article