Search Results (129)

Deep and ultra-deep hydrocarbon resources are of strategic importance for energy security. The pronounced ductility of deep reservoirs and faults makes hydraulic fracture propagation and induced seismicity mechanisms fundamentally different from those in conventional brittle reservoirs. This review systematically synthesizes recent theoretical, experimental, and numerical advances in hydraulic-fracturing-induced seismicity, covering triggering mechanisms, fault reactivation risk evaluation, and perspectives. A core distinction is identified: brittle faults exhibit instantaneous stick-slip rupture with high seismic frequency and significant magnitude, whereas ductile faults undergo stable aseismic creep and progressive slip, with long-term deformation prone to delayed large earthquakes—their nucleation shows unique mechanical responses including high stress drop, low rupture velocity, and low seismic radiation efficiency. Subsequently, four major challenges are distilled for risk evaluation systems: insufficient dynamic characterization of mechanical parameters in ductile reservoirs, lack of fracturing-control strategies adapted to ductile behavior, poor understanding of multi-scale slip transition mechanisms, and inadequacy of multi-field coupling models in capturing long-term delayed evolution. Traditional brittle-based risk frameworks cannot characterize the time-dependent slip and progressive reactivation of ductile faults, limiting their applicability to deep reservoirs. Future works are proposed, including refined characterization of mechanical parameters under high temperature and pressure, intelligent full-cycle hydrofracturing control, quantitative criteria for slip activation, and optimization of long-term multi-field coupling models. This study elucidates recent progress in hydrofracturing-induced seismicity mechanisms and quantitative risk assessment in ductile reservoirs, filling a gap in the conventional brittle-dominant research. It also provides theoretical support for seismic risk evaluation and early warning in deep fracturing operations, with significant implications for improving induced seismicity management and ensuring safe, efficient, and sustainable deep-resource development. Full article

As extreme heat events increase in frequency, intensity, and duration due to climate change, forecasting these events has become vital for early warning systems, public health preparedness, and climate adaptation strategies, especially in parts of the world that are already subject to extreme heat, such as tropical regions. In recent years, machine learning (ML) has increasingly been applied to environmental and meteorological data to improve the prediction of heatwaves and extreme heat events. This scoping review examines global peer-reviewed literature on the application of ML techniques for extreme heat prediction using environmental variables. This includes heatwave prediction, environmental and meteorological predictors used in these models, and the geographical distribution of existing research. A total of 23 peer-reviewed studies meeting the inclusion criteria were included in the review, following the PRISMA-ScR guidelines. The findings indicate that artificial neural networks and random forest models were most frequently reported as high performing within individual studies. However, direct comparisons across studies are limited by heterogeneity in prediction targets, validation strategies, lead times, heatwave definitions, and performance metrics. Temperature-related variables, especially maximum temperature, were consistently identified as the most influential predictors across studies. Furthermore, the evidence base was heavily concentrated in Europe, Asia, and North America, with comparatively limited representation from low- and middle-income countries respective to population, despite these regions often experiencing disproportionate impacts of climate change and extreme heat exposure. By synthesising current evidence on ML-based heatwave prediction, associated environmental predictors, and geographical research trends, this review provides insights to support the development of more robust, context-aware, and globally representative heatwave forecasting frameworks. Full article

The steel–concrete interface in steel–concrete composite structures is susceptible to interfacial void defects during both casting and service, posing a significant threat to structural load-bearing capacity. For early-stage micro-voids exceeding 2 mm in height, signal variations are weak and exhibit response characteristics similar to dense states, leading to feature ambiguity when using conventional criteria based on time-domain amplitude and attenuation or frequency-domain peak values and resulting in a high risk of missed detections. To address this limitation for early warning purposes, this study proposes a high-sensitivity identification method integrating an impact elastic wave response feature system with a feature-attention gated multi-layer perceptron (Feature-attention MLP). Based on full-scale model experiments from an engineering project, the temporal and spectral evolution patterns of impact elastic wave responses under varying dense conditions were analyzed. A comprehensive feature system, including time-domain statistical descriptors, spectral peaks, and sub-band energy distributions, was constructed, with Random Forest used for feature importance ranking and Top-K selection. An MLP classifier was then developed for automatic discrimination of dense states. A feature-level attention gating mechanism was introduced to enable adaptive weighting across feature dimensions, enhancing sensitive features while suppressing noise and structural variability. The final lightweight classifier contains 4052 trainable parameters, enabling rapid execution with an average CPU inference time of approximately 1.24 ms per sample. The average CPU inference time was approximately 1.24 ms per sample. Under the original train–validation split, the recall-prioritized operating point achieved a Void recall of 0.978 and a weighted F1-score of 0.780, accompanied by a non-negligible false-positive screening burden. Stratified five-fold internal validation yielded a balanced accuracy of 0.682 ± 0.021 and a Void recall of 0.845 ± 0.035 under the inner-validation-optimized threshold. These results demonstrate the preliminary potential of the proposed lightweight framework for engineering-oriented micro-void screening under the investigated full-scale conditions. Full article

Reliable precipitation forecasts are critical for hydrological modelling and flood early warning in West African river basins, where rainfall is dominated by highly variable monsoon-driven convection. This study evaluates and improves the precipitation forecasting skill of six numerical weather prediction (NWP) models over the Ouémé River basin in Benin, with particular emphasis on lead-time dependence, basin-scale effects, and the added value of statistical bias correction. Daily precipitation forecasts, over the period 1985–2015 across lead times of one to seven days, are assessed across six sub-basins using complementary continuous and event-based verification metrics. The results indicate that precipitation forecast skill varies with model choice, forecast horizon, and spatial scale. Among the raw forecasts, the ECMWF and UK Met Office models consistently outperform the other systems with KGE values reaching 0.5. ECMWF exhibits the highest overall skill at short to medium lead times, while the UK Met Office model shows relatively low volumetric bias across most sub-basins (Pbias less than 25%). For some models, forecast performance improves with increasing basin size, reflecting the smoothing effect of spatial aggregation, although this relationship remains model-specific. Distribution-based methods outperform regression-based approaches, with empirical quantile mapping providing the most robust and consistent improvements across lead times and sub-basins. Following bias correction, Empirical quantile mapping achieved median Likelihood Ratio values of approximately 6 during validation, with upper-range values reaching 15–18 across sub-basins for both ECMWF and UK Met Office forecasts. This represents a substantial improvement over raw predictions whose distributions remained consistently bounded below 10 throughout the calibration and validation phases (more than 50% improvement). Overall, the combination of ECMWF or UK Met Office precipitation forecasts with empirical quantile mapping offers a reliable framework for improving precipitation inputs to hydrological models and flood early warning systems in the Ouémé basin. The findings highlight the importance of multi-criteria evaluation and appropriate bias correction when applying NWP precipitation forecasts in monsoon-influenced hydrological environments and flood forecasting. Full article

This study investigates the structural sensitivity of a large-span steel space frame at Yanjiao Station to environmental disturbances during the critical “flexible suspension” stage of asymmetric hydraulic lifting. First, by analyzing the offset between the center of mass and the center of stiffness—induced by the asymmetric lifting configuration—the study systematically examines the spatial eccentric amplification effect under a coupled thermal-wind field. To this end, a non-uniform solar radiation model based on the Axis-Aligned Bounding Box (AABB) algorithm is integrated with a refined finite element model, enabling a full-factor parametric analysis under 20 coupled load conditions. The results reveal a significant time lag in the structural temperature field, with 12:00 identified as the critical time for maximum thermal deformation. The wind-induced response follows a “bimodal evolution” pattern, and the maximum translational-torsional coupling effect occurs at wind direction angles of 60° and 120°. Further analysis of the multi-field coupling mechanism indicates that the wind field dominates the deformation mode, while the temperature field amplifies the resulting response. Consequently, the peak displacement reaches 192.50 mm, which represents a 360.81% increase compared to the dead load baseline. The cantilever end is identified as the primary vulnerable region. Based on these findings, a “wind direction–time” two-dimensional monitoring strategy is proposed. This strategy provides scientific quantitative criteria and theoretical support for the construction safety of large-span structures, as well as for the development of a comprehensive early warning and health monitoring system. Full article

Introduction: Integrated One Health-based surveillance of pathogens in wastewater suggests its potential for monitoring community health and preventing the emergence and spread of infectious diseases. Despite the growing popularity of Wastewater Surveillance (WWS) and its clinical utility, its uniformity remains poorly understood, especially concerning its clinical evidence. This review systematically synthesizes evidence on the role of wastewater surveillance in early pathogen detection and outbreak preparedness, with particular emphasis on its implications for One Health surveillance. Methods: We systematically searched PubMed, EMBASE, ProQuest and EBSCO CINAHL databases. Retrieved articles were screened by two reviewers, and conflicts were resolved by a third reviewer. Initially, 539 studies were retrieved as potentially eligible published articles, of which 16 articles fulfilled the inclusion criteria. Results: Most pathogens identified in the included studies were associated with respiratory and gastrointestinal infections. Studies found a positive link between the presence of pathogens in wastewater and clinical cases, depicting potential exposure and transmission within the communities. A season-specific upsurge was observed among the identified pathogens in circulation. In addition, the duration and frequency of sample collection in socio-vulnerable areas provide early warning of disease outbreaks. Few studies have explicitly operationalized a One Health framework, highlighting the need for integrated human, animal, and environmental surveillance systems in future wastewater surveillance programmes. Conclusion: The review emphasized wastewater surveillance as a promising complementary approach for the early detection and tracking of pathogens. Future research is needed to standardize surveillance approaches and strengthen One Health integration across human, animal and environmental health systems. Full article

A large heel can develop within seconds on a small fishing vessel, so a short-horizon forecast of inclination is useful for safety only if it reports both the predicted angle and its uncertainty and is turned into an explicit warning decision. We present an uncertainty-aware early-warning benchmark and decision layer for the total inclination angle, framing the task as warning of large-inclination exceedance—that the heel will cross a fixed operational threshold (${15}^{\circ }/{20}^{\circ }/{25}^{\circ }$)—rather than predicting a vessel-specific capsize or dynamic-stability limit. Models are trained and evaluated on 90 time series generated from 6-DOF simulations spanning five tonnages, three sea states, and six independent wave-phase realizations under a leakage-safe, multi-seed protocol. The strongest in-distribution forecaster (an LSTM) reaches $R^2=0.677$ (RMSE ${3.51}^{\circ }$) pooled over the 1–5 s horizon, and converting its predictive distribution into a probabilistic exceedance alarm lowers the event-level false-alert burden at matched recall; the receiver-operating advantage is threshold-specific—clear at ${15}^{\circ }$, marginal at ${20}^{\circ }$, and reversed at the rarest ${25}^{\circ }$—so the alarm is not uniformly better than a point alarm. Leave-one-realization-out folds confirm that forecasting is robust to wave phase, whereas the smallest (10 t) vessel remains the dominant generalization failure mode. Because nominal interval coverage drops in the large-inclination tail, a conformal recalibration layer is required at deployment. The study is a simulation benchmark: operational value remains conditional on validation against measured seakeeping data and on mapping the thresholds to vessel-specific stability criteria. Full article

Background: Early and accurate risk stratification of patients suspected of serious infection is essential for improving outcomes, but existing diagnostic and predictive tools have limited accuracy. The objective was to compare the performance of an FDA-authorized AI diagnostic test, the Sepsis ImmunoScore, against widely available biomarkers and clinical tools for diagnosis of sepsis and prediction of in-hospital mortality and intensive care unit (ICU) admission. Methods: This multicenter observational study included 6027 adult patients suspected of infection across 7 U.S. hospital sites. The Sepsis ImmunoScore’s predictive performance was compared to the sequential organ failure assessment (SOFA) score, procalcitonin (PCT), C-reactive protein (CRP), Systemic Inflammatory Response Syndrome (SIRS) score, National Early Warning Score (NEWS), and quick SOFA (qSOFA). Primary outcomes included sepsis as defined by Sepsis-3 criteria, in-hospital mortality, and ICU admission. Predictive accuracy was assessed using area under the receiver operating characteristic curve (AUC), and 95% confidence intervals were generated and hypothesis testing conducted using the bootstrap method. Results: The Sepsis ImmunoScore demonstrated statistically significant superior performance across all outcomes. For sepsis prediction, the Sepsis ImmunoScore achieved an AUC of 0.82, compared to SOFA (0.72), procalcitonin (PCT) (0.70), C-reactive protein (CRP) (0.61), SIRS (0.59), NEWS (0.69), and qSOFA (0.67). For in-hospital mortality prediction, the Sepsis ImmunoScore achieved an AUC of 0.80, outperforming SOFA (0.72), PCT (0.67), CRP (0.58), SIRS (0.60), NEWS (0.72), and qSOFA (0.69). For ICU admission, the Sepsis ImmunoScore reached an AUC of 0.74, superior to SOFA (0.63), PCT (0.64), CRP (0.54), SIRS (0.60), NEWS (0.70), and qSOFA (0.65). All differences between the Sepsis ImmunoScore and comparators were statistically significant. Conclusions: The Sepsis ImmunoScore significantly improved predictive accuracy for sepsis, in-hospital mortality, and ICU admission compared to six conventional clinical scores and biomarkers. This AI-based tool may enhance risk stratification and clinical decision-making, potentially leading to more timely sepsis interventions and improved outcomes. Full article

Knowledge of river flood risk in semiarid rural areas is often based on outdated, low-resolution geoinformation. Consequently, identification of exposed settlements, assets and risk-reduction measures remains challenging. This dataset provides up-to-date, fine-grained information for a rural area spanning 931 km² that is exposed to flooding from the Niger River and the Karma Wadi. The dataset includes information on (i) areas exposed to the two flood types that characterise the river’s hydrological regime and flash floods from the wadi, (ii) flood-prone crops, buildings and (iii) measures for risk treatment. Discharge data, a 4 m horizontal-resolution digital elevation model, and two-dimensional hydraulic modelling with BASEMENT were used to identify flood-prone areas. Visual interpretation of high-resolution satellite imagery in Google Earth, together with field inspections, enabled the identification of exposed assets. The Information System on Rural Markets of Niger and house compensation values recognised during resettlement-related works enabled asset valuation. Risk was expressed in monetary terms as the product of flood probability and expected damage. Risk-reduction measures were identified with stakeholders through a SWOT analysis and prioritised using eight criteria. The dataset can support emergency plans, flood early warning systems, rescue and recovery operations and flood risk management. Full article

Identifying corrosion-prone conditions early is a major maintenance challenge in closed, hard-to-access structural zones. This paper reports an in-service validation of the first monitoring layer of a multi-sensor data fusion approach for early warning of such conditions in selected closed zones of a medical rescue aircraft. The work covers sensor selection, installation in restricted-access compartments, and analysis of data from helicopter operations. Environmental, conductance, and electrochemical channels are combined to identify persistent conditions favorable to long-term corrosion development and to assign warning levels linked to maintenance actions. The thresholds proposed here are empirical screening criteria from the 82-day campaign, not universal damage thresholds or proof of existing corrosion. PZT and eddy-current sensing are planned as follow-up diagnostic layers in the overall architecture. These technologies have been validated separately under laboratory or controlled conditions but were not installed on the flying helicopter during this initial period. Although persistent severe early-warning episodes were detected, they did not coincide with an approved maintenance-access window suitable for additional PZT/EC hardware installation. The present results therefore characterize the corrosion-prone environment and the likelihood of corrosion initiation, not the type, exact location, pit depth, mass loss, or crack initiation of actual damage. Field inspection evidence of corrosion in hidden zones supports the practical relevance of early warning, while full end-to-end validation of localization and damage-growth monitoring remains future work. Full article

Background/Objectives: Cross-border meteorological disaster medical rescue policies in the Guangdong–Hong Kong–Macao Greater Bay Area face challenges in coordination, completeness, and effectiveness. Existing policy systems lack systematic quantitative evaluation. This study aims to assess the current policy landscape and provide evidence-based recommendations for optimizing cross-border medical rescue policy supply and enhancing regional emergency coordination. Methods: We reviewed policy documents on cross-border meteorological disaster medical rescue issued from 2005 to 2025 and used a combination of text mining and the PMC index model to quantitatively analyze and evaluate selected policy texts. The PMC scoring criteria (0–10 scale) define scores ≥ 7 as “excellent” and 5–6.99 as “good”. Results: Policy word frequency analysis showed that “emergency,” “disaster,” “meteorology,” and “management,” were core high-frequency words; semantic network clustering revealed five major thematic modules: monitoring and early warning, emergency rescue, medical treatment, material support, cross-border coordination. The PMC indices of the 26 policies ranged from 5.65 to 9.42, with an average score of 6.95, which corresponds to the “good” level. Policy 14 scored 9.42, reaching the “perfect” level; eight policies received an “excellent” rating, indicating generally high policy quality. From a dimensional perspective, X9 (policy evaluation), X1 (Nature of policy), and X8 (policy guarantee) scored relatively high, while X4 (policy type) and X2 (policy timeliness) scored relatively low. Conclusions: The overall performance of the cross-border meteorological disaster medical rescue policy system is good, with relatively sound policy transparency and institutional guarantees. However, the policy system has the following shortcomings: insufficient cross-border coordination mechanisms, shallow integration of medical rescue professional content into comprehensive policies, and an emphasis on short-term emergency response with inadequate medium- and long-term strategic planning. It is recommended to strengthen medium- and long-term top-level strategic planning, enhance the functional allocation of health departments in meteorological disaster emergency plans, and establish a cross-regional joint policy evaluation and dynamic revision mechanism. Full article

In cold-region open-pit mine slopes, damage accumulation and mechanical deterioration induced by in situ stress and seasonal freeze–thaw alternation can easily trigger sudden instability. To investigate the effects of temperature difference under coupled constant loading and freeze–thaw action on the mechanical response and failure precursors of rock, based on the self-developed TCDR-I temperature–stress coupled testing system, uniaxial compression tests and real-time acoustic emission monitoring were conducted on water-saturated sandstone under a constant load of 1.4 MPa and multiple freeze–thaw temperature gradients. The mechanical behavior of freeze–thawed water-saturated sandstone and the acoustic emission characteristics during failure were analyzed. Combined with critical slowing down theory, the failure precursor characteristics of water-saturated sandstone under freeze–thaw action were investigated, and the internal mechanism of damage accumulation and defect evolution under the coupled effects of constant load and freeze–thaw temperature difference was revealed. The results show that, with increasing freeze–thaw temperature difference, the number of cracks and crack ratio in the loaded water-saturated sandstone gradually increased, whereas the compressive strength, elastic modulus, and total strain energy gradually decreased. After freeze–thaw treatment at −40 to 20 °C, the compressive strength, elastic modulus, and total strain energy decreased by 19.24%, 13.72%, and 44.77%, respectively, compared with those of the unfrozen–thawed specimens. During specimen failure, the dominant crack type gradually shifted from shear cracking to tensile cracking. The acoustic emission b-value and precursor points identified from multiparameter variance can both be used as criteria for predicting specimen failure. The warning lead time increased with increasing freeze–thaw temperature difference. After freeze–thaw treatment at −40 to 20 °C, the predicted failure times based on these two indicators preceded the actual failure time by 11.05 s and 16.19 s, respectively. The findings provide a theoretical basis for the early warning of sudden disasters in rock masses in cold-region engineering. Full article

Due to the strong heterogeneity of the reservoir, early gas breakthrough and low CO₂ displacement efficiency are common issues in the CO₂ flooding process of domestic gravel reservoirs. This study focuses on a gravel reservoir in Xinjiang, proposing a quantitative evaluation method that combines early gas breakthrough identification and the inversion of gas channel characteristic parameters. The aim is to provide theoretical support and technical guidance for gas breakthrough risk warning, injection-production system optimization, and control measures during the CO₂ flooding process. The research method includes the following several key steps: first, clarifying the criteria for determining the time of gas breakthrough and proposing a classification method for early gas breakthrough types based on CO₂ concentration levels; second, adopting a “matrix-dominant gas channel” dual-medium model, considering the geometric and physical characteristics of inter-well gas channels, and deriving a theoretical calculation formula with gas breakthrough time and CO₂ concentration in the produced gas as the target; third, using actual gas breakthrough time and CO₂ concentration as constraints, constructing a method to invert the characteristic parameters of gas channels, quantitatively representing key parameters such as gas channel thickness ratio, permeability variation, and equivalent permeability; finally, through the combined analysis of CO₂ concentration and gas channel characteristic parameters, establishing a method for identifying gas channel types suitable for domestic gravel reservoirs. The practical application results show that the test area has formed localized dominant gas channels, but the overall stage is still in the early phase of weak gas breakthrough. Most gas breakthrough phenomena are weak, with only a few well groups experiencing severe gas breakthrough issues. The gas channel thickness ratio is generally less than 0.05, and the permeability variation mainly ranges from 2 to 20. The gas channels are primarily of the fracture type, with some areas also containing ordinary fractures and main control fractures. The method proposed in this study, which combines early gas breakthrough identification with the inversion of gas channel characteristic parameters, not only provides a new approach to revealing the characteristics of gas breakthrough during CO₂ flooding but also offers solid theoretical and technical support for optimizing CO₂ flooding technology and controlling gas breakthrough risks. Full article

Background/Objectives: Failure to rescue deteriorating ward patients before irreversible organ injury remains a leading cause of preventable in-hospital mortality, yet current rapid response team (RRT) research relies predominantly on cross-sectional comparisons at the moment of activation, overlooking the short-horizon physiological changes that precede it. Methods: This retrospective cohort study at a tertiary academic hospital in South Korea included 549 adults (191 ICU-transferred, 358 ward-remaining) with a first RRT activation between September 2023 and August 2025. Generalised estimating equations (GEE) with a time × group interaction modelled differential changes in 12 laboratory variables and the DeepCARS AI-derived risk score between 24 h before activation (T−24 h) and the moment of activation (T0). At T−24 h, physiological profiles were largely similar between groups, indicating that conventional static assessment failed to identify patients destined for ICU transfer. Results: Over the ensuing 24 h, patients subsequently transferred to the ICU showed a steeper decline in SpO₂/FiO₂ (S/F) ratio (383.4 → 167.1 vs. 369.1 → 260.3; B = −0.547, p < 0.001) and steeper increases in lactate (2.91 → 4.02 vs. 2.05 → 2.98 mmol/L; B = 0.154, p = 0.045), creatinine (B = 0.076, p = 0.038), potassium (B = 0.019, p = 0.001), and DeepCARS score (B = 0.073, p = 0.028) compared with patients remaining on the ward. All five variables retained significance under Benjamini–Hochberg false discovery rate correction (q < 0.10). Seven inflammatory and haematological markers showed no differential change. Procalcitonin was excluded from the primary analysis because of very high missingness at the pre-activation time point (approximately 75%). Conclusions: These findings demonstrate that short-horizon deterioration in oxygenation, perfusion, and renal function—rather than any single earlier measurement—distinguishes patients requiring ICU transfer, supporting the development of change-based early warning criteria to enable earlier clinical escalation. Full article

Geological hazards induced by large-scale and high-intensity mining activities worldwide are primary drivers of regional ecological degradation and pose significant threats to human safety and property. To construct efficient monitoring systems and enhance early warning capabilities, it is essential to clarify the formation mechanisms of various hazards and the suitability of corresponding technologies. Focusing on five typical geological hazards prevalent in mining areas (surface subsidence, ground fissures, landslides, collapses, and sinkholes), this paper characterizes their specific features and monitoring requirements. It systematically analyzes the physical principles, accuracy levels, and technical advantages and limitations of ground-based, aerial, and spaceborne monitoring, as well as multi-source remote sensing data fusion and emerging technologies (e.g., distributed optical fiber, light detection and range, microseismical monitoring, and deep learning). Utilizing case studies from an open-pit coal mine in Turkey and a loess gully mining area in China, the paper evaluates the effectiveness of methods like multi-temporal InSAR and UAV photogrammetry in identifying the evolution of these hazards. The findings indicate that the technological framework for mining area monitoring is transitioning from single-method approaches to integrated systems. However, given the complex mining environment, several bottleneck challenges remain, including single data dimensions, the limited environmental adaptability of aerospace remote sensing, insufficient stability of deep monitoring equipment, and weak anti-interference capabilities under extreme operating conditions. Consequently, this paper proposes that future innovations in geological hazard monitoring in mining areas will focus on multi-platform hierarchical collaboration, the development of multi-parameter fusion early warning criteria, and the construction of digital and visual platforms. Constructing a comprehensive monitoring system characterized by multi-scale collaboration and dynamic prediction capabilities is vital for improving safety standards in mining areas and achieving coordinated development between resource exploitation and environmental protection. The findings provide a theoretical foundation for the precise prevention and control of mining hazards, as well as for land ecological restoration. Full article