Search Results (4,979)

To effectively manage and conserve ecosystems, it is crucial to understand how vegetation changes over time and space and what drives these changes. The Normalized Difference Vegetation Index (NDVI) is a key measure of plant growth that is highly sensitive to climate variations. Despite its importance, there has been limited research on vegetation changes in the upper sections of the Irtysh River. In our study, we combined various datasets, including NDVI, temperature, precipitation, soil moisture, elevation, and land cover. We conducted several analyses, such as Theil–Sen median trend analysis, Mann–Kendall trend and mutation tests, partial correlation analysis, the geographical detector model, and wavelet analysis, to reveal the region’s pronounced warming and moistening trend in recent years, the response relationship between NDVI and the climate, and the primary drivers influencing NDVI variations. We also delved into the spatiotemporal evolution of NDVI and identified key factors driving these changes by analyzing atmospheric circulation patterns. Our main findings are as follows: (1) Between 1901 and 2022, the area’s temperature rose by 0.018 °C/a, with a noticeable increase in the rate of warming around 1990; precipitation increased by 0.292 mm/a. From 1950 to 2022, soil moisture exhibited a steady increase of 0.0002 m³ m⁻³/a. Spatial trend distributions indicated that increasing trends in temperature and precipitation were evident across the entire region, while trends in soil moisture showed significant spatial variation. (2) During 1982 to 2022, the vegetation greening trend was 0.002/10a, indicating a gradual improvement in vegetation growth in the study area. The spatial distribution of monthly average NDVI values revealed that the main growing season of vegetation spanned April to November, with peak NDVI values occurring in June–August. Combined with serial partial correlation and spatial partial correlation analysis, temperatures during April to May effectively promoted the germination and growth of vegetation, while soil moisture accumulation from June to August (or January to August) effectively met the water demand of vegetation during its growth process, with a significant promoting effect. Geographical detector results demonstrate that temperature exhibits the strongest explanatory power for NDVI variation, whereas land cover has the weakest. The synergistic promotional effect of multiple climatic factors is highly pronounced. (3) Wavelet analysis revealed that the periodic characteristics of NDVI and climate variables over a 2–15-year timescale may have been associated with the impacts of atmospheric circulation. Taking NDVI and climatic factors from June to August as an example, before 2000, temperature was the dominant influencing factor, followed by precipitation and soil moisture; after 2000, precipitation and soil moisture became the primary drivers. The North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were the primary atmospheric circulation patterns influencing vegetation variability in the region. Their effects were reflected in the inverse relationship observed between NAO/AO indices and NDVI, with typical phases of high and low NDVI closely corresponding to shifts in NAO and AO activity. This study helps us to understand how plants have been changing in the upper parts of the Irtysh River. These insights are critical for guiding efforts to develop the area in a way that is sustainable and beneficial for the environment. Full article

To enhance the assessment accuracy of tunnel face instability risks of active collapse during shield tunneling, this study establishes a novel unified analytical framework that couples the effects of unsaturated transient seepage induced by excavation drainage with soil stratification and heterogeneity. Grounded in unsaturated effective stress theory, the framework explicitly incorporates matric suction into the Mohr–Coulomb failure criterion via suction stress and apparent cohesion. By employing a horizontal two-layer nonhomogeneous soil model and solving the one-dimensional vertical Richards’ equation, an analytical solution for the face drainage boundary is derived to quantify the spatiotemporal evolution of suction stress and apparent cohesion. Subsequently, the critical support pressure is evaluated using the upper bound theorem of limit analysis, incorporating a horizontal layer-discretized rotational failure mechanism and the power balance equation. The validity of the proposed framework is confirmed through comparative analyses. Parametric studies reveal that in the upper hard and lower soft strata, the critical support pressure decreases and converges over time, indicating that unsaturated transient seepage exerts a significant influence in the short term that stabilizes over the long term. Additionally, sand–silt stratum exhibits lower overall stability and higher sensitivity to groundwater levels and temporal factors compared to silt–clay stratum. Conversely, silt–clay stratum displays a non-monotonic evolution with increasing cover-to-diameter ratios (C/D), reaching a minimum critical support pressure at approximately C/D = 1.1. Regarding heterogeneity, the internal friction angle of the lower layer exerts dominant control over the critical support pressure compared to seepage velocity, while the influence of other strength parameters remains secondary. These findings provide a theoretical basis for the time-dependent design of tunnel face support pressure under excavation drainage conditions. Full article

China has made significant progress in paleontological heritage conservation. However, research and conservation efforts have predominantly focused on exquisitely preserved, movable specimens of high scientific value, leading to the relative neglect of in situ paleontological geosites which are critical for understanding fossil distribution patterns. To address this gap, this study employs a GIS approach to conduct a multifaceted spatial analysis of paleontological geosites in the BTH region as a representative case study. Our results reveal a pronounced spatiotemporal imbalance in the distribution of these geosites. Furthermore, their spatial configuration exhibits significant correlations with key physiographic factors—including elevation, stratigraphic distribution, and slope—as well as socioeconomic indicators such as population density, GDP density, and fiscal self-reliance ratio. This uneven distribution creates substantial conservation challenges, resulting in fragmented governance, a mismatch between local conservation capacities and needs, and potential biases in protection priorities toward specific regions or geological periods. In the BTH region, the distribution patterns of paleontological geosites are jointly shaped by physiographic, socioeconomic, and anthropogenic process factors. Elucidating the relationships between these drivers and the spatial distribution of geosites constitutes a critical foundation for advancing their scientific conservation and sustainable management. Drawing on broader interdisciplinary insights, currently peripheral paleontological heritage can be further transformed into strategic and sustainable resources. Full article

This study introduces a mathematical framework for analyzing unsteady gas filtration in porous media with pressure-dependent porosity variations. The physical process is formulated as a nonlinear parabolic boundary value problem that captures the coupled interaction between pressure evolution and porosity changes during gas production. To solve the equation, a numerical strategy is developed by integrating the Alternating Direction Implicit (ADI) scheme with quasi-linearization iterations, employing finite difference discretization on a two-dimensional spatial grid. Extensive computational experiments are performed to investigate the influence of key reservoir parameters—including porosity coefficient, permeability, gas viscosity, and well production rate—on the spatiotemporal behavior of pressure and porosity during long-term extraction. The results indicate significant porosity variations near the wellbore driven by local pressure depletion, reflecting strong sensitivity of the system to formation properties. The validated numerical model provides valuable quantitative insights for optimizing reservoir management and improving production forecasting in gas field development. Overall, the proposed methodology serves as a practical tool for oil and gas engineers to assess long-term reservoir performance under diverse operational conditions and to design efficient extraction strategies that incorporate pressure-dependent formation property changes. Full article

Adaptive locomotion requires the integration of cognitive and motor processes and is challenged in neurological disorders. Dual-task (DT) training may improve cognitive–motor coordination, but its feasibility across heterogeneous clinical populations is uncertain. This pilot study aimed to understand if the effects of a secondary motor or cognitive task added to a walking task depend on the functional walking abilities of the subjects. We enrolled 30 participants with neurological disorders not related to traumatic events, 5 for each one of the following groups: healthy young subjects (HeY), healthy control subjects (HeC), subjects with stroke (ictus, IC), Parkinson’s disease (PD), multiple sclerosis (MS), and Long-COVID sequelae (LC). Spatiotemporal gait parameters were recorded using a wearable inertial magnetic unit, and subjective workload was assessed with the visual analog scale (VAS) and NASA-Task Load Index. Regression models revealed strong baseline–DT coupling for stride duration (slopes 1.11–1.37; R² 0.85–0.97), stride length (slopes 0.93–0.94; R² 0.86–0.93), walking speed (slopes 0.87–0.98; R² 0.78–0.93), and gait ratio (stance/swing, slopes 0.38–0.60; R² 0.21–0.52). Mixed-effects analyses identified significant group effects for walking speed (F(5) = 7.218, p < 0.001), stride length (F(5) = 4.834, p = 0.001), gait cycle duration (F(5) = 5.630–5.664, p < 0.001), Walking Quality (F(5) = 4.340–4.373, p = 0.001), and propulsion index (F(5) = 5.668–6.843, p < 0.001). The incongruent DT condition was the most sensitive in differentiating clinical groups. NASA-TLX indicated higher perceived workload in IC and MS compared with non-clinical groups. The protocol was completed by all participants without adverse events, supporting the feasibility of the procedure in this pilot sample. Its predictable scaling across baseline gait metrics supports its use as a personalized rehabilitation tool for diverse neurological populations. (ClinicalTrials.gov NCT07254377). Full article

Accurate joint estimation of heterogeneous hydraulic conductivity fields and time-varying contaminant source parameters in groundwater systems constitutes a challenging high-dimensional inverse problem, particularly under sparse observational conditions and high computational demands. To alleviate this limitation, this study proposes an autoregressive depthwise convolutional neural network (AR-DWCNN) as a lightweight surrogate model for coupled groundwater flow and contaminant transport simulations. The proposed model employs depthwise separable convolutions and dense connectivity within an encoder–decoder framework to capture nonlinear flow and spatiotemporal transport dynamics while reducing model complexity and computational demand relative to conventional convolutional architectures. The AR-DWCNN is further integrated with an enhanced Iterative Local Updating Ensemble Smoother incorporating Levenberg–Marquardt regularization, enabling efficient joint inversion of high-dimensional hydraulic conductivity fields and multi-period contaminant source strengths. Numerical experiments conducted on a synthetic two-dimensional heterogeneous aquifer demonstrate that the surrogate-assisted inversion framework achieves posterior estimates that closely match those obtained using the numerical forward model, while significantly improving computational efficiency. These results indicate that the AR-DWCNN-based inversion method provides an effective and scalable solution for high-dimensional groundwater contaminant transport inverse problems, offering practical potential for uncertainty quantification and remediation design in complex subsurface systems. Full article

Under the background that urban–rural integrated development continuously deepens and the common prosperity goal continuously advances, systematically identifying the actual results of urban–rural integrated development and its influence mechanism on common prosperity holds important significance for understanding regional development differences and optimizing policy implementation paths. Based on land use data, NTL data, and POI facility data from 2013 to 2025, this study comprehensively employs spatial analysis and deep learning methods to conduct an empirical analysis on the spatiotemporal evolution characteristics and coupling relationship of urban–rural integrated development and common prosperity levels from dimensions including urban–rural spatial form evolution, economic activity intensity, and public service facility diversity. The research results indicate that urban–rural integration significantly promotes urban spatial expansion and the improvement in overall economic activity levels during the study period, but the difference in development magnitude among different regions remains obvious. The common prosperity level generally presents a rising trend, but it highly concentrates in the Pearl River Delta and city–county center areas in space, and the promotion effect of urban–rural integration on common prosperity exhibits obvious characteristics of regional heterogeneity, stages, time lags, and diminishing marginal effects. This study considers that urban–rural integration does not inevitably and synchronously transform into an elevation in common prosperity levels. Combining regional development basis and structural conditions to optimize urban–rural integration development paths by region and by stage and to improve the realization quality of common prosperity possesses important practical reference value. Full article

Tea bud recognition and localization constitute a fundamental step toward enabling fine-grained tea plantation management and intelligent harvesting, offering substantial value in improving the picking quality of premium tea materials, reducing labor dependency, and accelerating the development of smart tea agriculture. However, most existing methods for detecting tea buds are built upon Artificial Neural Networks (ANNs) and rely extensively on floating-point computation, making them difficult to deploy efficiently on energy-constrained edge platforms. To address this challenge, this paper proposes an energy-efficient tea bud detection model, GAE-SpikeYOLO, which improves upon the Spiking Neural Networks (SNNs) detection framework SpikeYOLO. Firstly, Gated Attention Coding (GAC) is introduced into the input encoding stage to generate spike streams with richer spatiotemporal dynamics, strengthening shallow feature saliency while suppressing redundant background spikes. Secondly, the model incorporates the Temporal-Channel-Spatial Attention (TCSA) module into the neck network to enhance deep semantic attention on tea bud regions and effectively suppress high-level feature responses unrelated to the target. Lastly, the proposed model adopts the EIoU loss function to further improve bounding box regression accuracy. The detection capability of the model is systematically validated on a tea bud object detection dataset collected in natural tea garden environments. Experimental results show that the proposed GAE-SpikeYOLO achieves a Precision (P) of 83.0%, a Recall (R) of 72.1%, a mAP@0.5 of 81.0%, and a mAP@[0.5:0.95] of 60.4%, with an inference energy consumption of only 49.4 mJ. Compared with the original SpikeYOLO, the proposed model improves P, R, mAP@0.5, and mAP@[0.5:0.95] by 1.4%, 1.6%, 2.0%, and 3.3%, respectively, while achieving a relative reduction of 24.3% in inference energy consumption. The results indicate that GAE-SpikeYOLO provides an efficient and readily deployable solution for tea bud detection and other agricultural vision tasks in energy-limited scenarios. Full article

Neonatal seizures are a critical early indicator of neurological injury, yet effective automated detection is challenged by significant inter-subject variability in electroencephalogram (EEG) signals. To address this generalization gap, this study introduces the Domain-Adversarial Spatiotemporal Network (DA-STNet) for robust cross-subject seizure detection. Utilizing Short-Time Fourier Transform (STFT) spectrograms, the architecture employs a hierarchical backbone comprising a Channel-Independent CNN (CI-CNN) for local texture extraction, a Spatial Bidirectional Long Short-Term Memory (Bi-LSTM) for modeling topological dependencies, and Attention Pooling to dynamically prioritize pathological channels while suppressing noise. Crucially, a Gradient Reversal Layer (GRL) is integrated to enforce domain-adversarial training, decoupling pathological features from subject-specific identity to ensure domain invariance. Under rigorous 5-fold cross-validation, the model achieves State-of-the-Art performance with an average Area Under the Curve (AUC) of 0.9998 and an F1-score of 0.9952. Data scaling experiments further reveal that optimal generalization is attainable using only 80% of source data, highlighting the model’s superior data efficiency. These findings demonstrate the proposed method’s capability to reduce reliance on extensive clinical annotations while maintaining high diagnostic precision in complex clinical scenarios. Full article

Agricultural watersheds are undergoing rapid rural–urban transitions, yet the relative contributions of diffuse agricultural runoff versus rural domestic and point sources to organophosphate esters (OPEs) pollution remain poorly understood. This study investigated the occurrence, spatiotemporal distribution, and potential risks of 17 legacy and emerging OPEs in the Dalongdong River, China. Combined non-target and target analyses revealed mean OPE concentrations of 111.94 ng/L in water and 8.76 ng/g in sediments. Spatially, total OPE concentrations increased progressively from upstream to downstream, with pronounced hotspots downstream of townships and near wastewater treatment facilities, indicating that rural domestic effluents and urban runoff, alongside agricultural activities, are critical contributors to OPE pollution in this watershed. Seasonally, concentrations of six legacy OPEs were significantly higher during the wet season. Furthermore, high-throughput phenotypic screening using Caenorhabditis elegans, combined with toxicological priority index analysis, showed that emerging OPEs generally pose higher integrated health and ecological risks, although certain legacy compounds, such as triphenyl phosphate, still display substantial toxic potential. These findings clarify the potential biological hazards of these compounds and provide baseline data on the fate of OPEs in riverine systems influenced by mixed agricultural and rural–urban anthropogenic activities. Full article

Amid global freshwater scarcity and soil salinization, brackish irrigation is a potential alternative, yet its effects under low-leaching soilless systems remain unclear. We tested brackish irrigation (30 mmol L⁻¹ NaCl; EC ≈ 4.8 dS m⁻¹, including fertilizer) on lettuce (Lactuca sativa L.) grown in peat–perlite substrates with non-saline (CK), mildly saline (M), and moderately–severely saline (S) initial salinity. Substrate moisture and bulk electrical conductivity (EC_b) were monitored at upper, middle, and deep layers with multi-depth sensors; lettuce physiological and growth traits were measured. Under negligible drainage, salt moved downward promptly after irrigation in CK, accumulated at the surface in M, and remained high with spatiotemporal variability in S. Brackish irrigation had minimal effects on biomass and water use efficiency in CK and M, but significantly reduced both in S. These findings support tailoring brackish irrigation to initial salinity severity and motivate future work to measure drainage and calibrate EC indices to establish operational thresholds. Full article

Beautiful Leisure Tourism Villages (BLTVs) represent an effective pathway for advancing high-quality rural industrial development and promoting comprehensive rural revitalization. They are of great significance to enriching new rural business formats and new functions. The analysis is interpreted within an integrated location–distance attenuation framework. Based on the methods of spatial clustering analysis, geographical linkage rate and geographical weighted regression, the spatio-temporal evolution of 1982 BLTVs in China up to 2023 was examined to uncover the underlying driving mechanisms. Findings indicated that (1) a staged expansion in the number of villages across China, with the most pronounced growth occurring between 2014 and 2018, averaged 124 new villages per year; their stage characteristics showed an obvious “unipolar core-bipolar multi-core-bipolar network” development model; (2) the barycenters of villages were all located in Nanyang City of Henan Province; they migrated from east to west, and formed a push and pull migration trend from east to west and then east; (3) the spatial distribution of villages was highly aggregated and demonstrated marked regional heterogeneity, following a south–north and east–west gradient, with the highest concentration in Jiangzhe and the lowest in Ningxia Hui Autonomous Region; and (4) natural ecology, hydrological and climatic conditions, socioeconomic context, transportation accessibility, and resource endowment collectively shaped the spatial layout of villages, exhibiting pronounced spatial variation in the intensity of these driving factors. On the whole, topography, social economy, traffic condition and precipitation condition had greater influences on the spatial distribution of villages in the western than in the eastern part of China. In contrast, the effects of resource endowment and temperature on the spatial distribution of BLTVs were stronger in eastern China than in western China. These findings enhance the theoretical understanding of tourism-oriented rural development by integrating spatio-temporal evolution with a location–distance attenuation perspective and provide differentiated guidance for the sustainable development of BLTVs across regions. Full article

Coastal waters worldwide are increasingly threatened by excessive nutrient inputs, a key driver of eutrophication. Dissolved inorganic nitrogen (DIN) serves as a vital indicator for assessing the eutrophic status of nearshore marine environments, underscoring the necessity for precise monitoring to ensure effective protection and restoration of marine ecosystems. To address the current limitations in DIN retrieval methods, this study builds on MODIS satellite imagery data and introduces a novel one-dimensional convolutional neural network (1D-CNN) model synergistically co-optimized by the Bald Eagle Search (BES) and Bayesian Optimization (BO) algorithms. The proposed BES-BO-CNN framework was applied to the retrieval of DIN concentrations in the coastal waters of Shandong Province from 2015 to 2024. Based on the retrieval results, we further investigated the spatiotemporal evolution patterns and dominant environmental drivers. The findings demonstrated that (1) the BES-BO-CNN model substantially outperforms conventional approaches, with the coefficient of determination (R²) reaching 0.81; (2) the ten-year reconstruction reveals distinct land–sea gradient patterns and seasonal variations in DIN concentrations, with the Yellow River Estuary persistently exhibiting elevated levels due to terrestrial inputs; (3) correlation analysis indicated that DIN is significantly negatively correlated with sea surface temperature but positively correlated with sea level pressure. In summary, the proposed BES-BO-CNN framework, via the synergistic optimization of multiple algorithms, enables high-precision DIN monitoring, thus providing scientific support for integrated land–sea management and targeted control of nitrogen pollution in coastal waters. Full article

The Chongming Dongtan wetland, a representative coastal wetland in East Asia, faces a significant ecological threat from the invasive species Spartina alterniflora. The mixed ecotone formed between this invasive species and the native Phragmites australis serves as a highly sensitive and critical indicator of alterations in wetland ecosystem structure and function. Using spring and autumn Sentinel-2 imagery from 2016 to 2023, this study developed an integrated method that combines a three-dimensional feature space with multi-threshold Otsu segmentation to accurately extract the mixed S. alterniflora–P. australis ecotone. The spatiotemporal dynamics of the mixed ecotone were analyzed at multiple temporal scales using a centroid migration model and a newly defined Seasonal Area Ratio (SAR) index. The results suggest that: (1) Near-infrared reflectance and NDVI were identified as the optimal spectral indices for spring and autumn, respectively. This approach led to a classification achieving an overall accuracy of 87.3 ± 1.4% and a Kappa coefficient of 0.84 ± 0.02. Notably, the mixed ecotone was mapped with producers’ and users’ accuracies of 85.2% and 83.6%. (2) The vegetation followed a distinct land-to-sea ecological sequence of “pure P. australis–mixed ecotone–pure S. alterniflora”, predominantly distributed as an east–west trending belt. This pattern was fragmented by tidal creeks and micro-topography in the northwest, contrasting with geometrically regular linear features in the central area, indicative of human engineering. (3) The ecotone showed continuous seaward expansion from 2016 to 2023. Spring exhibited a consistent annual area growth of 13.93% and a stable seaward centroid migration, whereas autumn exhibited significant intra-annual fluctuations in both area and centroid, likely influenced by extreme climate events. (4) Analysis using the Seasonal Area Ratio (SAR) index, defined as the ratio of autumn to spring ecotone area, revealed a clear transition in the seasonal competition pattern in 2017, initiating a seven-year spring-dominant phase after a single year of autumn dominance. This spring-dominated era exhibited a distinctive sawtooth fluctuation pattern, indicative of competitive dynamics arising from the phenological advancement of P. australis combined with the niche penetration of S. alterniflora. This study elucidates the multiscale competition mechanisms between S. alterniflora and P. australis, thereby providing a scientific basis for effective invasive species control and ecological restoration in coastal wetlands. Full article

Accurate estimation of the barometric coefficient ($\beta$) is important for correcting pressure effects in soil moisture data from cosmic-ray neutron sensing (CRNS) due to the barometric effect. To evaluate estimation strategies for $\beta$, we compared analytical and empirical approaches using 71 CRNS and 46 neutron monitor (NM) stations across the United States, Europe, and globally. Our results show spatio-temporal variation in the barometric effect, with $\beta$ ranging from 0.66 to 0.82 %hPa for NM and from 0.63 to 0.80 %hPa for CRNS. These coefficients exhibit higher variability than previously published semi-analytical models. In addition, we found that the analytically determined $\beta$ values were systematically lower compared with empirical estimates, with stronger agreement between the two empirical methods ($r\approx 0.67$) than between empirical and analytical approaches. Furthermore, NM stations produced higher $\beta$ values than CRNS, indicating that differences in detector energy sensitivity affected the values of $\beta$. Principal Component Analysis (PCA) further showed that the analytical and empirical $\beta$ estimates clustered together, reflecting shared sensitivity to elevation. In contrast, soil moisture and atmospheric humidity projected nearly orthogonally to the $\beta$ vectors, indicating negligible influence, while cut-off rigidity contributed to a separate, inverse gradient. Analytical $\beta$ estimates were fully orthogonal to AH, while empirical methods showed only slight deviations beyond orthogonality. The barometric coefficient ($\beta$), therefore, varies with location, altitude, atmospheric conditions, and sensor type, highlighting the necessity of station-specific values for precise correction. Overall, our study emphasizes the need for atmospheric correction in CRNS measurements and introduces a method for deriving site- and sensor-specific $\beta$ values for accurate soil moisture estimation. Full article