Search Results (305)

Underground blasting vibrations are a critical factor influencing the stability of mine slopes. However, existing studies have yet to establish a quantitative relationship or clarify the underlying mechanisms linking blasting-induced vibrations and slope deformation. Taking the Shilu Iron Mine as a case study, this research develops a dynamic mechanical response model of slope stability that accounts for blasting loads. By integrating slope radar remote sensing data and applying the Pearson correlation coefficient, this study quantitatively evaluates—for the first time—the correlation between underground blasting activity and slope surface deformation. The results reveal that blasting vibrations are characterized by typical short-duration, high-amplitude pulse patterns, with horizontal shear stress identified as the primary trigger for slope shear failure. Both elevation and lithological conditions significantly influence the intensity of vibration responses: high-elevation areas and structurally loose rock masses exhibit greater dynamic sensitivity. A pronounced lag effect in slope deformation was observed following blasting, with cumulative displacements increasing by 10.13% and 34.06% at one and six hours post-blasting, respectively, showing a progressive intensification over time. Mechanistically, the impact of blasting on slope stability operates through three interrelated processes: abrupt perturbations in the stress environment, stress redistribution due to rock mass deformation, and the long-term accumulation of fatigue-induced damage. This integrated approach provides new insights into slope behavior under blasting disturbances and offers valuable guidance for slope stability assessment and hazard mitigation. Full article

Background/Objectives: Total thymectomy is currently the gold standard operation for treating thymoma. However, recent studies have suggested the potential health consequences of thymus removal in adults, including possible increased autoimmune disease and all-cause mortality. In this context, we assess oncologic outcomes following total vs. partial thymectomy for early-stage thymoma. Methods: We identified patients diagnosed with WHO types A–B3 thymoma between 2010–2021 from a national hospital-based dataset. We excluded patients with stage II or higher disease, open resections and perioperative chemo-/radiation therapy. We stratified patients into total and partial thymectomy cohorts. We used propensity score matching to minimize confounding, Kaplan–Meier analysis to estimate survival, and Cox proportional hazards to identify associations. Results: Of 1598 patients with early-stage thymoma, 495 (31.0%) underwent partial and 1103 (69.0%) total thymectomy. Patients undergoing partial thymectomy were similar in sex (female 53.7% vs. 53.4%; p = 0.914), race (white 74.5% vs. 74.0%; p = 0.921), comorbidities (0 in 77.0% vs. 75.5%; p = 0.742), and tumor size (48.7 mm vs. 50.4 mm; p = 0.455) compared to total thymectomy. There were no differences in 30-day (0.8% vs. 0.6%, p = 0.747) or 90-day mortality (0.8% vs. 0.8%, p > 0.999), which persisted after matching. Moreover, 10-year survival was similar in both unmatched (p = 0.471) and matched cohorts (p = 0.828). Partial thymectomy was not independently associated with survival (aHR = 1.00, p = 0.976). Conclusions: In patients with early-stage thymoma, partial and total thymectomy were associated with similar short- and long-term outcomes. In light of recent attention to the role of the thymus gland, the results add important insights to shared decision-making discussions. Full article

A systems-oriented review of machine learning (ML) over the entire flood management spectrum, encompassing fluvial flood control, pluvial flood management, and resiliency-risk characterization was undertaken. Deep learners like long short-term memory (LSTM) networks perform well in predicting reservoir inflows and outflows. Convolution neural networks (CNNs) and other object identification algorithms are being explored in assessing levee and flood wall failures. The use of ML methods in pump station operations is limited due to lack of public-domain datasets. Reinforcement learning (RL) has shown promise in controlling low-impact development (LID) systems for pluvial flood management. Resiliency is defined in terms of the vulnerability of a community to floods. Multi-criteria decision making (MCDM) and unsupervised ML methods are used to capture vulnerability. Supervised learning is used to model flooding hazards. Conventional approaches perform better than deep learners and ensemble methods for modeling flood hazards due to paucity of data and large inter-model predictive variability. Advances in satellite-based, drone-facilitated data collection and Internet of Things (IoT)-based low-cost sensors offer new research avenues to explore. Transfer learning at ungauged basins holds promise but is largely unexplored. Explainable artificial intelligence (XAI) is seeing increased use and helps the transition of ML models from black-box forecasters to knowledge-enhancing predictors. Full article

This study investigates the impact of nonlinear constitutive models on one-dimensional seismic site response analysis (SRA) for soft, reclaimed soil deposits in Saemangeum, South Korea. Two widely used models, MKZ and GQ/H, were applied to three representative soil profiles using the DEEPSOIL program. Ground motions were scaled to bedrock peak ground accelerations (PGAs) corresponding to annual return periods (ARPs) of 1000, 2400, and 4800 years. Seismic response metrics include the ratio of GQ/H to MKZ shear strain, effective PGA (EPGA), and short- and long-term amplification factors (F_a and F_v). The results highlight the critical role of the site-to-motion period ratio (T_g/T_m) in controlling seismic behavior. Compared to the MKZ, the GQ/H model, which features strength correction and improved stiffness retention, predicts lower shear strains and higher surface spectral accelerations, particularly under strong shaking and shallow conditions. Model differences are most pronounced at low T_g/T_m values, where MKZ tends to underestimate amplification and overestimate strain due to its limited ability to reflect site-specific shear strength. Relative to code-based amplification factors, the GQ/H model yields lower short-term estimates, reflecting the disparity between stiff inland reference sites and the soft reclaimed conditions at Saemangeum. These findings emphasize the need for strength-calibrated constitutive models to improve the accuracy of site-specific seismic hazard assessments. Full article

Long-term sea level variations in the northern South China Sea (SCS) are known to significantly impact coastal ecosystems and socio-economic activities. To improve sea level prediction accuracy, four models—harmonic analysis and three artificial neural networks (ANNs), namely genetic algorithm-optimized back propagation (GA-BP), radial basis function (RBF), and long short-term memory (LSTM)—are developed and compared using 52 years of observational data (1960–2004). Key evaluation metrics are presented to demonstrate the models’ effectiveness: for harmonic analysis, the root mean square error (RMSE) is reported as 14.73, the mean absolute error (MAE) is 12.61, the mean bias error (MBE) is 0.0, and the coefficient of determination (R²) is 0.84; for GA-BP, the RMSE is measured as 29.1371, the MAE is 24.9411, the MBE is 5.6809, and the R² is 0.4003; for the RBF neural network, the RMSE is calculated as 27.1433, the MAE is 22.7533, the MBE is 2.1322, and the R² is 0.4690; for LSTM, the RMSE is determined as 23.7929, the MAE is 19.7899, the MBE is 1.3700, and the R² is 0.5872. The key findings include the following: (1) A significant sea level rise trend at 1.4 mm/year is observed in the northern SCS. (2) Harmonic analysis is shown to outperform all ANN models in both accuracy and robustness, with sea level variations effectively characterized by four principal and six secondary tidal constituents. (3) Despite their complexity, ANN models (including LSTM) are found to fail in surpassing the predictive capability of the traditional harmonic method. These results highlight the continued effectiveness of harmonic analysis for long-term sea level forecasting, offering critical insights for coastal hazard mitigation and sustainable development planning. Full article

Background/Objectives: The optimal surgical management of perforated gastric cancer (PGC) in emergency settings remains controversial. Urgent upfront one-stage gastrectomy (1SG) and two-stage gastrectomy (2SG) with damage-control surgery followed by elective gastrectomy have been proposed. The aim of the present systematic review is to compare short- and long-term outcomes between 1SG and 2SG in the treatment of PGC. Methods: A systematic review and individual patient data (IPD) meta-analysis of studies reporting data of patients undergoing 1SG vs. 2SG for PGC was conducted. The time-dependent effects of surgical interventions were assessed using a likelihood ratio test. Hazard function plots were generated via marginal prediction. Results: Ten retrospective series (579 patients) were included. Overall, 482 patients (83%) underwent 1SG, while 97 patients (17%) were treated with 2SG. A trend toward better short-term oncological outcomes and safety profiles for 2SG compared to 1SG was observed. Long-term outcomes were comparable between 1SG and 2SG, and the IPD meta-analysis showed no statistically significant difference between the two approaches in terms of OS or hazard for mortality at all time points. A trend towards a higher hazard for mortality was observed for 1SG in the first 20 months postoperatively. Conclusions: Our analysis suggests that 1SG and 2SG yield comparable short-term outcomes, although 2SG may be associated with a lower medium-term mortality risk. Further research is needed to identify key factors to improve clinical judgments and decision-making in PGC. Full article

Climate change has intensified natural hazards, including droughts, which have caused substantial damage in South Korea. Drought, characterized by prolonged moisture deficiency, is typically assessed using drought indices that reflect meteorological conditions. This study examined the influence of various meteorological and climate indices on drought variability in the Yeongsan and Seomjin River basins. The Standardized Precipitation Index (SPI) was used to represent drought conditions, and its monthly variations were predicted using the Long Short-Term Memory (LSTM) algorithm. To assess model performance, four statistical indicators—Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Nash–Sutcliffe Efficiency (NSE), and the Coefficient of Determination (R²)—were employed. The LSTM model that utilized both precipitation and drought indices as input data showed the best performance, achieving an MSE of 0.2, RMSE of 0.477, NSE of 0.77, and R² of 0.78. Overall predictive performance ranged from 0.298 to 0.347 (MSE), 0.546 to 0.589 (RMSE), 0.578 to 0.628 (NSE), and 0.580 to 0.675 (R²). This study highlights the effectiveness of LSTM in predicting drought conditions and the value of incorporating meteorological and climatic indices. The results can support improved drought hazard assessment and management strategies in South Korea. Full article

Landslides are among the most destructive geological hazards, necessitating precise landslide susceptibility mapping (LSM) for effective risk management. This study focuses on the northeastern region of Leshan City and investigates the influence of various non-landslide sampling strategies and machine learning (ML) models on LSM performance. Ten landslide conditioning factors, selected by SHAP analysis, and six models were utilized: Convolutional neural networks (CNNs), Long Short-Term Memory (LSTM), CNN-LSTM, CNN-LSTM with an attention mechanism (AM), Random Forest (RF), and eXtreme Gradient Boosting combined with Logistic Regression (XGBoost-LR). Three non-landslide sampling strategies were designed, with the certainty factor-based approach demonstrating superior performance by effectively capturing geological and physical characteristics, applying spatial constraints to exclude high-risk zones, and achieving improved mean squared error (MSE) and area under the curve (AUC) values. The results reveal that traditional ML models struggle with complex nonlinear relationships and imbalanced datasets, often leading to high false positive rates. In contrast, deep learning (DL) models—particularly CNN-LSTM-AM—achieved the best performance, with an AUC of 0.9044 and enhanced balance in accuracy, precision, recall, and Kappa. These improvements are attributed to the model’s ability to extract static spatial features (via CNNs), capture dynamic temporal patterns (via LSTM), and emphasize key features through the attention mechanism. This integrated architecture enhances the capacity to process heterogeneous data and extract landslide-relevant features. Overall, optimizing non-landslide sampling strategies, incorporating comprehensive geophysical information, enforcing spatial constraints, and enhancing feature extraction capabilities are essential for improving the accuracy and reliability of LSM. Full article

Acid drainage resulting from mining operations has led to significant iron contamination in surface waters, posing serious ecological and public health hazards. Elevated iron levels in freshwater ecosystems can severely affect aquatic organisms and human health. However, there remains a considerable gap in the establishment of benchmark values and ecological risk assessments (ERAs) for iron in surface waters in China. This study collected and screened 47 acute and chronic toxicity data points of 22 species for ferric iron (Fe³⁺) from various studies and databases. Three widely utilized methodologies were applied to derive long-term and short-term water quality criteria (LWQC and SWQC, respectively) for Fe³⁺; the logistic fitting curve based on the species sensitivity distribution (SSD) method was identified as the most optimal method, yielding an acute HC₅ of 689 μg/L and an SWQC of 345 μg/L. The LWQC of Fe³⁺ was estimated to be 28 μg/L by dividing HC₅ by the acute-to-chronic ratio (ACR), owing to the inadequacy of chronic toxicity data for model fitting. Utilizing these benchmarks, an ecological risk assessment (ERA) was conducted to compare the benchmarks with 68 iron exposure data points collected from surface waters across 30 provinces from eight river basins of China. The findings of 30% of the acute risk quotients and 83% of the chronic risk quotients raise substantial ecological concerns, primarily regarding the Yellow River Basin, Huaihe River Basin, and Songhua and Liaohe River Basin. This research provides critical insights into Fe³⁺ toxicity data collection and benchmark derivations, offering a benchmark data foundation for the remediation of surface water iron contamination and water quality management in China. Full article

Extreme rainfall events directly increase flood risks and further trigger environmental geological hazards (i.e., landslides and debris flows). Meanwhile, rainfall-induced risks are determined by climate and geographical factors and spatial socioeconomic factors (e.g., population density and gross domestic product). However, the middle stream of Yellow River Basin, where geological hazards frequently occur, lacks systematic analyses of rainfall-induced risks. In this study, we propose a comprehensive quantification framework and apply it to the Loess Plateau of northern China based on 40 years of climate data, streamflow measurements, and multiple spatial and geographical attribute datasets. A deep learning algorithm of long short-term memory (LSTM) was used to predict runoff, and the analytic hierarchy index was utilized to evaluate the comprehensive spatial risk considering natural and socioeconomic factors. Despite a decrease in annual precipitation in our study area of 1.46 mm per year, the intensity of heavy rainfall has increased since the 1980s, characterized by increases in rainstorm intensity (+4.68%), rainfall intensity (+7.07%), and rainfall amount (+5.34%). A comprehensive risk assessment indicated that high-risk areas accounted for 20.30% of the total area, with rainfall, geographical factors, and socioeconomic variables accounting for 53.90%, 29.72%, and 16.38% of risk areas, respectively. Rainfall was the dominant factor that determined the risk, and geographical and socioeconomic properties characterized the vulnerability and resilience of disasters. Our study provided an evaluation framework for multi-hazard risk assessment and insights for the development of disaster prevention and reduction policies. Full article

Cruise construction involves a lengthy logistical cycle, complex processes, and large volumes of diverse materials, inevitably generating reverse flows. To mitigate risks such as stock congestion, production disruption, and occupational hazards, this study proposes a novel reverse logistics network optimization model that integrates cost, efficiency, and Health, Safety, Environment (HSE) risk factors. Realistic factors including vehicle load, transport cost, loading time, and risk weight were considered to improve model applicability. Fuzzy time windows quantify worker risk exposure and operational efficiency, adding decision-making complexity. A three-phase Levy mutation discrete crow search algorithm (DCSA) was developed, introducing the Levy flight strategy to replace random search and enhance the discretization and solution diversity. The comparative analysis shows that DCSA performs as well as NSGA-II, while outperforming DGWO, demonstrating both stability and efficiency. Comparative analysis with a cost-only scenario revealed that although short-term economic gains may be achieved under cost minimization, such approaches often overlook risks with potential long-term impacts. This highlights the necessity of integrating safety concerns into reverse logistics planning, and confirms the model’s robustness and practical value, thus supporting decision makers in aligning reverse logistics planning in shipyards with sustainability and operational efficiency goals. Full article

Zhejiang Province in China is one of the most typhoon-prone regions globally, making typhoon and storm surge hazard analysis critically important for disaster mitigation. This study integrates the Tropical Cyclone Risk Model (TCRM) with a machine learning-based storm surge forecasting model to analyze typhoon hazards and storm surge risks at four representative coastal sites in Zhejiang Province: Haimen, Ruian, Wenzhou, and Zhapu. Firstly, the input database of the TCRM has been updated and subsequently used to generate a 1000-year synthetic typhoon event catalog for the Northwest Pacific region. Secondly, four machine learning models—Long Short-Term Memory (LSTM), Back Propagation (BP), Support Vector Regression (SVR), and Random Forest (RF)—were developed to forecast storm surge component at the four sites, with sensitivity analysis conducted on the input parameters. Among the four models, RF consistently outperformed the others across all four sites. Thirdly, by integrating the storm surge forecasting model with the Yan Meng (YM) typhoon wind field model, extreme wind speed sequences and extreme surge component sequences were derived for the four coastal sites. Finally, four extreme value distribution models—empirical distribution, Weibull, Gumbel, and Generalized Pareto Distribution (GPD)—were applied to fit the extreme wind and surge sequences. Goodness-of-fit tests indicated that the GPD best captured extreme wind speeds at all four sites and extreme surge levels at Haimen, Ruian, and Wenzhou. Using the optimal distributions, return periods (10-, 50-, 100-, and 200-year) for extreme wind speeds and surge components were calculated, providing actionable references for disaster risk management authorities. Full article

The viability and host-seeking behavior of Strongyloides larvae are significantly influenced by soil conditions, emphasizing the critical role of environmental control in disease management. This is particularly relevant given the growing concerns about drug resistance resulting from mass chemotherapy or the use of chemical nematicides. Strongyloides stercoralis was effectively inactivated by exposure to 50 °C for both 12 and 24 h (long-term exposure). Strongyloides ratti was inactivated by 50 °C for 20 min (short-term exposure), 9% saline for 50 min, and a combination of 4% saline and 40 °C for 50 min. The combined treatment successfully inactivated S. ratti in four soil mediums using 5% saline at a central temperature of 40 °C. Thermotaxis responses to noxious heat revealed attraction at 40 °C, increased localized searching at 45 °C, and complete inactivation at 50 °C. Larvae migrating within agar at 45 °C were more readily inactivated. Long-range heat attraction at 5 cm resulted in the inactivation of up to 50% of incoming larvae; however, heat-high concentration saline traps at 3 cm distance proved ineffective. Thermal–saline agar trapping demonstrated promise for larval removal in sand, loam, and laterite soils. This method offers a promising approach to larval removal while minimizing hazards to non-target organisms. Full article

The northern Portuguese coast has been increasingly subjected to wave-induced coastal flooding, highlighting a critical need for comprehensive overwash assessment in the region. This study systematically evaluates the total water levels (TWLs) and swash regimes over a 120 km stretch of the northern coast of Portugal. Traditional approaches to overwash assessment often rely on detailed models and location-specific data, which can be resource-intensive. The presented methodology addresses these limitations by offering a pragmatic balance between accuracy and practicality, suitable for extended coastal areas with reduced human and computational resources. A coastal digital terrain model was used to extract essential geomorphological features, including the dune toe, dune crest, and/or crown of defense structures, as well as the sub-aerial beach profile. These features help establish a critical threshold for flooding, alongside assessments of beach slope and other relevant parameters. Additionally, a wave climate derived from a SWAN regional model was integrated, providing a comprehensive time-series hindcast of sea-states from 1979 to 2023. The wave contribution to TWL was considered by using the wave runup, which was calculated using different empirical formulas based on SWAN’s outputs. Astronomical tides and meteorological surge—the latter reconstructed using a long short-term memory (LSTM) neural network—were also integrated to form the TWL. This integration of geomorphological and oceanographic data allows for a straightforward evaluation of swash regimes and consequently overwash potential. The accuracy of various empirical predictors for wave runup, a primary hydrodynamic factor in overwash processes, was assessed. Several reports from hazardous events along this stretch were used as validation for this method. This study further delineates levels of flooding hazard—ranging from swash and collision to overwash at multiple representative profiles along the coast. This regional-scale assessment contributes to a deeper understanding of coastal flooding dynamics and supports the development of targeted, effective coastal management strategies for the northern Portuguese coast. Full article

Research on climate-related disasters and healthcare infrastructure has largely focused on short-term, localized impacts. This study examined the long-term association between climate-related disasters and healthcare facilities across 3108 contiguous United States counties from 2000 to 2014. Utilizing databases like the National Establishment Time Series and the Spatial Hazards and Events Losses Database, we classified county-level infrastructure changes (“never had”, “lost”, “gained”, and “always had”) and disaster severity (minor, moderate, severe), respectively. Autoregressive linear models were used to estimate the total number of moderate and severe disasters (2000–2013) associated with the change in the number of healthcare establishments in 2014, after adjusting for healthcare establishments, total population, and poverty in 2000. Results demonstrate that an increase in one moderate disaster was significantly associated with increased hospital infrastructure (Count, 0.14; 95% CI, 0.03–0.25), while severe disasters were significantly associated with a decrease (Count, −0.31; 95% CI, −0.47–−0.14). Similar but stronger associations were observed for ambulatory care (Moderate: Count, 2.52; 95% CI 0.91–4.12 and Severe: Count, −5.99; 95% CI, −8.53–−3.64, respectively). No significant associations were found among pharmacies. These findings highlight the varying impacts of climate-related disasters on healthcare accessibility. Future initiatives should prioritize strengthening existing infrastructure and enhance disaster recovery strategies. Full article