Search Results (2,061)

With global warming and rapid urbanization, short-duration summer rainstorms are becoming more intense and localized, posing growing challenges to urban flood resilience. However, their spatiotemporal characteristics, vertical structures, and environmental drivers remain poorly understood. Here, we combine multi-source remote sensing datasets and China’s new-generation satellite-borne dual-frequency precipitation radar observations to investigate summer rainstorms in Nanjing, China, during 2017–2024. Results reveal pronounced spatiotemporal heterogeneity, with higher rainfall intensities concentrated over urban and adjacent areas. During the study period, rainstorm intensity and duration increased by 7.44% and 38.63%, respectively, while the affected area decreased by 8.18%, indicating a transition toward more localized yet more intense rainfall events. Environmental analyses suggest that large-scale thermodynamic conditions and regional topographic forcing provide a favorable background for convection development, while local urban thermal effects may further modulate rainfall enhancement. Three-dimensional radar detection of an illustrative rainstorm event indicates an inverted-cone vertical structure, suggesting a mixed convective-stratiform precipitation structure involving both warm-rain and ice-phase processes. An Explainable Bayesian-Optimized XGBoost (EBOX) model further identifies near-surface air temperature and specific humidity as the primary environmental factors associated with rainstorm occurrence and development. Overall, this study highlights the value of integrating satellite remote sensing with explainable artificial intelligence to improve understanding of urban extreme rainfall and provide new insights into how climate change, topography, and urbanization jointly shape precipitation extremes in rapidly urbanizing monsoon regions. Full article

Land Use and Land Cover (LULC) change is widely acknowledged as a pivotal driver of environmental change, exerting an escalating influence on surface hydrological processes. The accelerating pace of LULC alterations in response to burgeoning human populations underscores the pressing need for a comprehensive evaluation of their ramifications on surface runoff dynamics. This study investigates the impacts of LULC changes on flood behavior in a Mediterranean watershed in Crete, Greece (Geropotamos watershed). LULC data spanning the years 1990, 2006, and 2018 were procured from the European CORINE Land Cover database at a refined spatial resolution. The HEC-HMS hydrological model is employed to simulate peak discharge and associated hydrograph characteristics under varying recurrence intervals. Subsequently, selected river segments within the studied catchments undergo hydrodynamic flood modelling using the HEC-RAS hydraulic model. Flood depth maps are generated to illustrate the evolution of inundated areas relative to LULC change. The overarching objective of this research is to furnish a comprehensive understanding of how spatiotemporal variations in land use and land cover in-fluence flood characteristics, thereby facilitating informed decision making for sustainable planning. Full article

Pumped-storage hydropower requires pump-turbines to operate safely and efficiently under off-design conditions, where stall-induced unsteady flows can redistribute hydraulic losses and reduce operational stability. Unlike previous analyses focused mainly on spatial correlations, this study develops a spatiotemporal framework to clarify how hydraulic loss (HL) and vortex evolution (VE) co-vary under different stall states at the valley point of the pump-mode hump region in a low-specific-speed, ultra-high-head pump-turbine. Detached eddy simulations (DESs) were performed for an original-runner scheme (ORI) and an optimized-runner scheme (OPT), with identical stationary components, boundary conditions, and numerical settings. The comparative cases cover four representative flow states: non-stall, fixed stall, rotating stall, and mixed stall. The local hydraulic-loss rate (LHLR) was decomposed into dissipation (DIS) and transport (TRANS) terms, and Liutex-based vorticity decomposition was used to distinguish shear- and rigid-rotation-related vortex quantities. Pearson correlation analysis was then applied in both space and time. The results show that DIS is consistently associated with shear enstrophy Ω_S, whereas the spatiotemporal correlation associated with TRANS and VE parameters exhibits stronger regional and stall-state dependence. These findings provide a quantitative basis for identifying loss-sensitive vortex features and support flow-control and runner-optimization strategies for improving pump-turbine efficiency and stability. Full article

Fire occurrence in Southwest China is jointly shaped by meteorological conditions, topography, vegetation status, and human activities. To improve the interpretability and validation rigor of regional fire susceptibility assessment, this study developed a grid-day-based susceptibility assessment framework for Yunnan, Sichuan, Guizhou, and Chongqing using MODIS active-fire detections and multi-source environmental data from 2015 to 2024 at a 5 km grid resolution. A sensitivity analysis was conducted to determine the training sample configuration, and a 1:2 positive-to-negative sampling ratio was adopted. Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) were compared, and SHapley Additive exPlanations (SHAP), together with partial dependence plots (PDP), were used to interpret key drivers and their interactions. Data from 2015 to 2018 were used for model training, while data from 2019 to 2024 were used to evaluate the model’s cross-year transferability within the same study domain, rather than full spatiotemporal independence. The results show that the 1:2 sampling ratio achieved a favorable balance between fire detection and false-alarm control. In five-fold stratified cross-validation, RF outperformed LR and SVM (AUC = 0.9167; F1-score = 76.70%). In the cross-year transferability test, areas classified as high and very high susceptibility captured 62.04–68.95% of the observed fire points while accounting for less than 32% of the total area. Soil moisture and maximum temperature contributed most strongly to the model output, and their interaction revealed a pronounced dry-hot statistical response pattern associated with elevated susceptibility. Fire susceptibility also exhibited stable positive spatial autocorrelation, with hotspot areas concentrated in the dry-hot valleys near the Sichuan-Yunnan border and in central-southern Yunnan. Because the model was built with under-sampled negatives and same-day environmental matching, the output should be interpreted as a relative fire susceptibility index for spatial assessment and statistical attribution rather than as a calibrated occurrence probability or a forward-looking daily forecast. Full article

The performance of haul trucks in open-pit mining is strongly affected by haul-road geometry, surface condition, rolling resistance, and operational traffic regimes. However, existing studies often consider road-surface mapping, vehicle dynamic response, and onboard telemetry as separate information streams, which limits the reproducible assessment of how road-related factors are associated with VIMS-derived fuel-use proxy burden, mechanical dynamic response, and transport-cycle instability. This study proposes a field-based, segment-level multisensor framework that integrates unmanned aerial vehicle/light detection and ranging (UAV/LiDAR) road-surface reconstruction, global positioning system/inertial measurement unit (GPS/IMU) trajectory and vibration data, and Caterpillar Vial Information Management System (VIMS) telemetry into a unified spatiotemporal analytical dataset. The methodological contribution consists in the synchronization of heterogeneous data sources at the road-segment level, the calculation of interpretable road-condition and vehicle-response indicators, and the statistical assessment of road-related effects while explicitly accounting for confounding factors such as longitudinal grade, payload state, speed regime, truck class, and operational variability. Unlike studies that use LiDAR mapping, vibration monitoring, or onboard telemetry as separate diagnostic channels, the proposed approach introduces a segment-level analytical framework in which road morphology, truck response, and operational penalties are aligned within the same spatial unit, interpreted under confounder-aware conditions, and verified through repeat-pass reproducibility and robustness checks. The framework was tested on haul roads around the Ekibastuz open-pit coal mine. The field analysis identifies road segments where degraded surface morphology, increased waviness, unfavorable longitudinal profile, and higher rolling resistance coincide with increased mechanical dynamic response, VIMS-derived fuel-use proxy burden, braking instability, and travel-time variability. The results are interpreted as controlled field-supported associations rather than as isolated causal effects. The proposed maintenance ranking should therefore be regarded as a decision-support output, while the operational effectiveness of specific repair interventions requires future before–after validation. Full article

As a critical alpine transition zone linking the Qinghai–Tibet Plateau and the Loess Plateau, Gannan Prefecture acts as an important water conservation area in the upper Yellow River basin of China. Based on GLDAS-2.1 surface soil moisture (SSM) datasets spanning 2000–2022 and interpretable machine learning tools (SHAP and ALE), this paper analyzes the spatiotemporal evolution, future trend sustainability, and nonlinear statistical associations between environmental predictors and SSM. The main results were as follows: (1) SSM exhibited a significant upward trend with an annual growth rate of 0.18 kg·m⁻²·a⁻¹ (p < 0.001), and an abrupt turning point occurred in 2017. The spatial pattern of high SSM in the southeast and low SSM in the northwest remained relatively stable, with the centroid migration distance being less than 1.81 km; most regions presented statistically significant moistening trends (p < 0.05). (2) Natural environmental predictors jointly carried 95.79% of the total statistical explanatory weight for modeled SSM variability. Precipitation possessed the highest explanatory proportion (37.93%), followed by temperature (27.30%), potential evapotranspiration (ET_p, 12.26%), elevation (10.44%), and fractional vegetation cover (FVC, 7.77%). One-dimensional ALE curves identified sample-limited statistical breakpoints: SSM gradually plateaued when precipitation reached 650–700 mm, while modeled SSM decreased substantially once ET_p exceeded 800 mm·a⁻¹. Two-dimensional ALE further characterized combined statistical correlations among precipitation, temperature, and ET_p. Model outputs also indicated that FVC above 0.45 corresponded to enhanced soil water retention within the observed sample range, which only reflects statistical patterns captured in this dataset rather than universal regulatory standards. This study offers quantitative statistical understanding of SSM variations across alpine transition zones. Full article

For advancing carbon peaking and neutrality objectives and regional socio-ecological sustainability, it is critical to examine how land use change and ecosystem carbon storage may evolve under different development scenarios, and to reveal the spatiotemporal patterns and key drivers of carbon sink capacity across the Lower Yangtze River Basin. Such analysis bears both substantial scientific insight and practical relevance. By coupling the PLUS, InVEST, and Geographical Detector models, the present study conducted a comprehensive assessment of land use and carbon storage dynamics in the Lower Yangtze River region from 2000 to 2025. We further explored how different factors drive the spatiotemporal variation in carbon storage, and predicted the potential land use patterns and associated carbon storage values in the research area by 2030 under three hypothetical scenarios. Collectively, our analysis yielded four core conclusions. (1) Between 2000 and 2025, the land use transformation in the research area was dominated by the continuous shrinkage of arable land and the expansion of construction land. (2) The total carbon storage in the study area declined steadily throughout the study period, showing distinct phased characteristics with a steep drop in the early stage and a slower decline thereafter. (3) Implementing the S2 scenario could effectively curb regional carbon storage loss, whereas the S3 Scenario would result in the most severe carbon stock depletion. (4) The spatial configuration of carbon storage is primarily structured by natural environmental factors. In light of these research outcomes, several recommendations are proposed to guide regional sustainable development. Specifically, efforts should be made to improve the intensive use of urban construction land, thereby minimizing carbon storage loss caused by urbanization. Additionally, develop scientific and targeted ecological conservation policies based on the spatial distribution patterns of high carbon storage zones. Finally, implementing regionally tailored management measures will help achieve coordinated and sustainable development across the study area. Full article

Urban ecological resilience (UER) has become a central concern in rapidly urbanizing regions where development pressures increasingly interact with ecological constraints. Focusing on the Guanzhong Plain Urban Agglomeration (GPUA), a semi-arid urban agglomeration in western China, this study examines the non-static and locally heterogeneous drivers of UER across 11 prefecture-level cities from 2000 to 2023. UER is measured through resistance, adaptability, and recovery. An extended STIRPAT model, Elastic Net with stability selection, two-way fixed-effects period interactions, and Geographically and Temporally Weighted Regression (GTWR) are integrated to identify robust drivers, test post-2011 shifts, and estimate city-year local associations. Residual Moran’s I diagnostics and Spatial Lag GTWR (SLM-GTWR) are used as supplementary checks. The results show that UER remains relatively stable at the aggregate regional level but becomes increasingly divergent across cities. Ten robust drivers are retained, with fiscal investment intensity, human capital, medical and health level, and total energy consumption emerging as key variables. Period heterogeneity results indicate that fiscal investment becomes more favorably associated with UER after 2011, while the marginal association of energy consumption weakens. GTWR reveals clear local heterogeneity: human capital shows the most stable positive association, medical and health level remains generally negative, fiscal investment is positive but context-dependent, and energy consumption is predominantly negative but locally differentiated. Supplementary spatial diagnostics suggest that the GTWR specification captures the main spatiotemporal structure of UER, while spatial-lag checks broadly support the robustness of the local coefficient patterns, although estimates of spatial interaction remain sensitive to how inter-city linkages are defined. These findings indicate that UER drivers are dynamic rather than fixed, with resilience formation shaped mainly by governance-regime shifts and localized heterogeneity. The study contributes a sequential screening–heterogeneity framework for identifying non-static resilience drivers and suggests that resilience governance should combine stage-sensitive policy adjustment, place-based intervention, and regional coordination where ecological functions and environmental risks cross administrative boundaries. Full article

Background: Stroke represents a major contributor to long-term disability and is commonly associated with impaired gait, balance, and mobility, which reduce independence and increase fall risk. Gait biofeedback training provides real-time performance-related feedback and may facilitate motor relearning. This study aimed to synthesize the available evidence of gait biofeedback training on spatiotemporal gait parameters in stroke survivors. Methods: PubMed, Embase, Web of Science, and the Cochrane Library were searched up to 7 April 2026. RCTs involving stroke survivors with gait impairment that compared gait biofeedback training with non-biofeedback rehabilitation and reported spatiotemporal gait outcomes were included. Risk of bias and certainty of evidence were assessed using RoB-1 and GRADE, respectively. Meta-analyses were conducted using mean difference (MD) with 95% confidence intervals (CIs). Heterogeneity was assessed using I² and τ², and 95% prediction intervals (PI) were calculated where possible. Results: 10 RCTs involving 304 participants were included. Compared with control interventions, gait biofeedback training may improve gait velocity (MD = 9.78 cm/s, 95% CI 6.06 to 13.50, p < 0.001, 95% PI 2.14 to 17.41) and step length (MD = 5.88 cm, 95% CI 1.14 to 10.61, p = 0.01, 95% PI −10.18 to 21.94). However, the certainty of evidence was stronger for gait velocity than for step length. A significant effect on cadence was observed in the primary analysis, but this finding was unstable in the sensitivity analysis. No significant pooled effects were found for stride length or stance time. The wide PI for step length, stride length, and stance time indicates that the expected effects may vary across future clinical settings. Conclusions: Gait biofeedback training may improve gait velocity after stroke. Evidence for step length improvement is more tentative, while evidence for cadence, stride length, and stance time remains insufficient or unstable. Additional well-designed high-quality RCTs are needed to confirm these findings and determine optimal feedback modes and training protocols. The review was registered in PROSPERO (CRD420261354683). Full article

This study examines the evolving patterns and zoning characteristics of gas migration and storage zones during coal seam mining, taking the 215 fully mechanized longwall face at Huangling No. 2 Coal Mine as the engineering background. By integrating theoretical analysis, physical similarity simulation experiments, and field measurements, the research systematically explores the zonal linkage evolution mechanism of mining-induced depressurization gas migration and storage zones, together with the associated depressurization gas extraction technology. A flow regime determination equation, driven by the fracture expansion coefficient and permeability, is established on the basis of the fluid Reynolds number criterion. According to differences in gas flow states and medium morphology, the mining-induced fracture field is divided into five distinct zones: a high-permeability zone dominated by turbulent transport, a medium-to-high permeability zone with transitional flow as the secondary dominant region, a low-permeability zone featuring linear laminar flow with micro-permeability, an extremely low-permeability zone characterized by linear laminar flow in a locked state, and a zone of abrupt permeability change associated with gas enrichment. The dynamic evolution of depressurization gas migration and storage zones and their regional linkage mechanisms are clarified. On the basis of these findings, a dynamic targeted layout strategy for high-level boreholes is proposed that is consistent with the spatiotemporal evolution of the overburden permeability field. Field engineering practice shows that the optimized high-level borehole layout maintains the overall gas extraction rate at the drilling site stably above 70%, with a peak value of 93.7%, thereby ensuring safe and efficient mining of the working face. Full article

As accessibility development in public buildings has gradually shifted from facility compliance toward experience- and performance-oriented evaluation, the quantitative assessment of indoor mobility experiences among blind users still lacks a systematic sensor-supported analytical framework. To address this gap, this study proposes an indoor accessibility assessment approach that integrates multi-sensor data acquisition with explainable machine learning, using a tactile museum as the experimental setting. Sixty-four participants with first-level blindness were recruited to complete a real-world directed walking task. A multimodal database was constructed by integrating objective data collected from an ultra-wideband (UWB) indoor positioning system, an intelligent gait analysis system, and video-based behavioral recording, including spatiotemporal trajectories, gait characteristics, and behavioral events, together with post-task accessibility satisfaction ratings. Based on this dataset, a random forest model was developed using the Overall Accessibility Satisfaction Score (OAS) as the response variable. SHAP, partial dependence analysis, and GAM smoothing were further applied to interpret the associations between key variables and predicted satisfaction. The results showed that walking distance, number of turns, self-reported collision perception, and selected gait indicators made relatively high contributions to the model interpretation, and these variables exhibited certain nonlinear associations with predicted satisfaction. These findings suggest that combining multi-source sensor-based behavioral measurement with explainable machine learning has potential for sensor-supported post-occupancy evaluation of indoor accessibility environments and can provide exploratory references for the quantitative assessment and optimization of accessibility in public buildings. Full article

Resource-based cities face complex land-use pressures. Examining the evolution of production–living–ecological spaces (PLESs) and the spatial conflicts associated with this evolution provides an important basis for reducing land-use tensions and promoting more coordinated and sustainable spatial development. Drawing on land-use records spanning 2000–2020, this study integrates a transfer-matrix approach, a PLES conflict assessment model, and spatial autocorrelation analysis to examine the spatiotemporal evolution of PLESs and their conflict patterns in Shenmu City, China. The results show that (1) industrial production land expanded more rapidly than any other land category, mainly through the conversion of agricultural production land. Agricultural production land continued to decrease as it was converted into both industrial production land and ecological land. Grassland served as an important transitional space between production and ecological spaces, with its evolution shifting from rapid expansion in the early period to relative stability in the later period. (2) In terms of spatial conflicts, moderate conflict remained the dominant category and generally increased over time. By contrast, strong and relatively strong conflicts decreased, while weak and relatively weak conflicts gradually increased. Spatially, conflict patterns shifted from highly concentrated areas in the southeastern resource-extraction zone to a more dispersed and balanced regional distribution. (3) Global <!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> Full article

Wildfires have intensified in several regions worldwide, and the Brazilian Pantanal has become increasingly vulnerable due to the combined effects of human activities and climate change. This study analyzed the spatiotemporal patterns of burned areas and burn severity in the Brazilian Pantanal over 39 years (1985–2023), integrating burned-area dynamics, land use and land cover information, and hydroclimatic variables. Burned areas were quantified using MapBiomas Fire Project data, including annual burned areas, affected land use and land cover classes, seasonal fire distribution, fire-scar size, and fire recurrence. Burn severity was assessed using the Differenced Normalized Burn Ratio (ΔNBR), and hydroclimatic trends were evaluated using the Mann–Kendall test. The largest burned areas occurred in 1999 (27,260.65 km²) and 2020 (25,602.65 km²), with grassland representing the most affected land use and land cover class throughout the historical series. Fires were concentrated during the late dry season, and recurrent burning was more evident in the southwestern Pantanal and in smaller northern areas. The 2020 fire season showed the greatest extent of high-, moderate–high-, and moderate–low-severity classes. Wildfire occurrence, recurrence, extent, and severity were associated with hydroclimatic variability, especially reduced precipitation and relative humidity and increased air and land surface temperatures. These findings provide a long-term basis for understanding changes in fire regimes in the Brazilian Pantanal and can support fire management, ecological restoration, biodiversity conservation, and climate adaptation strategies. Full article

Existing seizure detection methods cannot fully exploit the spatiotemporal features of multimodal signals. They also fail to capture deep associations among cross-modal features. This limits their ability to learn unified representations of spatiotemporal dependencies. This work proposes CMEpiNet (Complex-valued Multimodal Epilepsy detection Network model) to address this issue. CMEpiNet first uses complex-valued convolutions for feature extraction. It explicitly models phase synchronization, phase shifts, and cross-frequency coupling. Thus, EEG, ECG, and EMG features are represented in the complex-valued domain. During feature fusion, CMEpiNet uses a two-level semantic alignment-based fusion method. It applies cross-modal consistency constraints in a shared alignment space. It also performs distribution-level alignment in an epilepsy-related semantic latent space. These operations ensure the consistency of multimodal features in the global semantic structure. Finally, CMEpiNet uses a spatial attention-guided 3D convolutional classifier. The classifier jointly models the temporal, feature, and modality dimensions. Experimental results on the SeizeIT2 dataset show that CMEpiNet improves seizure detection sensitivity, reduces the false alarm rate, and maintains stable performance under perturbations. Full article

Virtual fencing is increasingly used in grazing systems as a flexible alternative to physical fencing, yet detailed assessments of cattle behavior within such systems remain limited. This study investigates the use of collar-mounted tri-axial accelerometers combined with supervised machine learning to characterize cattle behavior in a virtual fencing system. Seven free-ranging Angus cattle were monitored using accelerometers mounted on a virtual fencing system, GNSS positioning, and virtual fence warning logs. A random forest classifier was developed and trained to identify key behaviors (grazing/feeding, ruminating, lying, standing and locomotion) using features derived from tri-axial accelerometer data. The model achieved high classification performance for grazing/feeding, ruminating, and lying (mean accuracy = 0.87, range = 0.83–0.90), enabling estimation of individual behavioral time budgets. Daily activity patterns were generally stable over time and across individuals. Spatial analyses revealed significant differences in behavior between areas near the virtual fence boundary and interior pasture locations, with increased grazing and reduced ruminating near the boundary, potentially reflecting spatial variation in habitat type or forage availability. In the virtual fencing system, cattle are equipped with collars that emit an auditory warning when they approach a virtual boundary, followed by a low-energy electrical impulse when the warning is ignored over a directional distance of 5–10 m. Event-based analyses showed no consistent short-term changes in either movement intensity and direction nor locomotion following auditory warning events, indicating that cattle habituated to the system did not exhibit uniform behavioral disturbance in response to warnings. These results suggest that accelerometer-based behavior classification can provide fine-scale, non-invasive insights into spatio-temporal cattle behavior in virtual fencing systems. The finding indicates that, in a habituated herd, virtual fencing was not associated with pronounced disruption to the measured behavioral patterns, while highlighting the potential of embedded sensor data for animal-based behavioral monitoring. Full article