Search Results (4,861)

Background/Objectives: Insulin clearance is a key determinant of circulating insulin availability, but its diurnal variation and relationship with circadian metabolic programs in metabolic dysfunction associated steatotic liver disease (MASLD) remain unclear. This study aimed to explore diurnal insulin clearance in humans and examine associated metabolic gene signatures in MASLD. Methods: A single-subject pilot assessment was performed to explore daytime-nighttime differences in insulin clearance rate (ICR) surrogate index, followed by evaluation using public hyperinsulinemic-euglycemic clamp datasets from healthy individuals and patients with MASLD. Public circadian transcriptomic datasets, spatial transcriptomic data, and a time course high-fat diet (HFD)-induced mouse dataset were integrated. A predefined panel of insulin clearance-related and circadian genes, including carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1), insulin receptor (INSR), insulin-degrading enzyme (IDE), clock circadian regulator (CLOCK), basic helix-loop-helix ARNT like 1 (BMAL1), nuclear receptor subfamily 1 group D member 1/2 (NR1D1/2), period circadian regulator 1/2 (PER1/2), and cryptochrome 1/2 (CRY1/2), was analyzed. Results: The pilot assessment showed higher nighttime than daytime ICR, and independent clamp datasets showed a similar pattern in healthy individuals. In MASLD, nighttime ICR remained relatively higher, whereas overall insulin clearance was reduced compared with controls. Human blood-based circadian transcriptomic datasets identified rhythmic expression patterns of selected genes involved in insulin clearance and circadian regulation, including CEACAM1, CLOCK, NR1D1, CRY1, PER1, and PER2. MASLD liver datasets showed reduced expression of insulin clearance-related and circadian genes, while spatial transcriptomics suggested altered lobular distribution of these signatures. In HFD mouse model, rhythmic expression of selected genes was attenuated. Conclusions: These integrative findings suggest that insulin clearance may exhibit diurnal variation and may be linked to circadian metabolic gene signatures across systemic and hepatic datasets in MASLD. Larger controlled human studies are needed to validate the temporal regulation of insulin clearance and its metabolic relevance. Full article

High-resolution remote sensing understanding requires models to preserve small spatial evidence, account for acquisition-dependent appearance, and separate genuine geographic change from nuisance variation. We introduce GeoGATE, a geo-sensor-guided framework that combines typed acquisition conditioning, budget-constrained adaptive token acquisition, metadata-compatible evidence retrieval, and reliability-aware temporal reasoning. LoRA adaptation and NF4 quantization support efficient training and deployment. On the VRSBench test split, GeoGATE reaches 53.4 BLEU-1, 36.8 BLEU-2, 18.2 BLEU-4, 56.4 Acc@0.5, 82.3 VQA, 25.1 METEOR, and 42.6 ROUGE-L, outperforming the controlled GeoGATE (Base) configuration across captioning, question answering, and grounding. Component ablations associate adaptive slicing most strongly with localization, retrieval with language and VQA, and language model adaptation with all reported tasks. NF4 reduces measured video memory from 24.5 GiB to 7.2 GiB with only minor metric changes. These experiments support the single-image language and grounding components. Dedicated cross-sensor and bi-temporal benchmarks are not reported; the corresponding modules are therefore presented as architectural extensions rather than validated performance claims. Full article

Regional 10 m wind-speed products extend coverage beyond sparse station networks, but their errors vary with wind regime and location. This study evaluates a station-trained LightGBM correction of a 5 km WRF/HNR wind-speed product over Hainan Island. Raw and corrected fields were collocated with stations using a four-neighbour inverse-distance operator. A spatially separated 2022 holdout retained 67 stations after five coordinate-overlap exclusions. Across 582,610 common hourly pairs, corrected RMSE decreased from 3.198 to 1.211 m s⁻¹ and Pearson R increased from 0.368 to 0.651. The paired RMSE reduction was 1.986 m s⁻¹ [95% CI 1.839, 2.122]. Improvement was largest below 3.4 m s⁻¹ (2.203 m s⁻¹) and remained positive for 3.4–7.9 m s⁻¹ (0.566 m s⁻¹). In contrast, corrected RMSE increased by 0.990 m s⁻¹ for 8.0–10.7 m s⁻¹ and by 1.656 m s⁻¹ for ≥10.8 m s⁻¹. A time-only adjustment reduced pooled RMSE to 1.870 m s⁻¹ but also degraded the two upper strata. The 2016–2022 maps show a lower corrected product climatology, with a mean corrected-minus-raw increment of −2.273 m s⁻¹ across the Hainan buffer. Temporal partitions and AWS-selected station groups show variation across season, time of day, and setting. The correction reduces station-sampled errors for light-to-moderate wind speeds; it changes speed magnitude while retaining the raw-product wind direction. Full article

Atmospheric oxidation capacity is a key parameter for measuring the atmosphere’s self-cleaning ability. Its strength directly affects the degradation rate of pollutants such as methane, carbon monoxide, and nitrogen oxides, and has a profound impact on regional air quality, ecosystem health, and even global climate chemistry processes. Against the backdrop of increasing global anthropogenic emissions and rapid climate system changes, quantitatively assessing the spatiotemporal evolution of atmospheric oxidation capacity is particularly important. This study systematically simulated and assessed the atmospheric oxidation capacity of Guangdong Province in October 2017 based on the Weather Research and Forecasting with Chemistry model (WRF-Chem). To more scientifically quantify atmospheric oxidation capacity, this study examined a new observational index, AOC_TOX (the rate atmospheric oxidants being reduced), and compared it to the traditionally used index AOC_ODT (the rate atmospheric reductants being oxi32dized). The results show that the AOC_TOX and AOC_ODT indices exhibit a high degree of consistency in both temporal variation trends and spatial distribution patterns. Temporally, the diurnal variation characteristics of the AOC_TOX and AOC_ODT indices are highly consistent, with 24 h averages of 1.8 × 10⁷ cm⁻³ s⁻¹ and 2.0 × 10⁷ cm⁻³ s⁻¹, respectively, showing minimal difference. Spatially, the high-value and low-value regions of both indices significantly overlap. This result demonstrates the internal consistency of the new AOC_TOX index in characterizing atmospheric oxidation capacity. Furthermore, by analyzing the contributions of different oxidants, this study clarifies that hydroxyl radicals (OH) are the main driving force of atmospheric oxidation capacity during the day, while nitrate radicals (NO₃) play a dominant role at night. Finally, simulation analysis further reveals that human activity emissions are a key factor regulating the spatial pattern of regional atmospheric oxidation capacity. Full article

Severe soil salinization and low fertility significantly constrain sustainable agricultural development in the Yellow River Delta, one of the three major estuarine deltas in China. Despite their ecological importance, the regional-scale spatial interactions between soil salinity and nutrients, particularly regarding their vertical variability, remain poorly understood. This study analyzed 228 soil samples from 76 sites distributed across a distinct salinity gradient, which was determined by constructing a spatial salinity distribution map after sampling. Samples were collected at three depths (0–15, 15–30, and 30–45 cm) to investigate the spatial distribution of soil organic carbon (SOC), total nitrogen (TN), and the C/N ratio, along with their underlying driving factors. SOC and TN exhibited similar spatial patterns, with higher values distributed along both banks of the Yellow River. Horizontally, SOC and TN in the 0–15 cm layer decreased gradually from west to east, whereas the 15–30 cm and 30–45 cm layers showed an opposite trend, increasing eastward. Vertically, SOC and TN contents declined significantly with soil depth (p < 0.05), although the magnitude of this decline varied regionally: the 0–15 cm layer in the western area contained markedly higher nutrient levels than deeper layers, while vertical variation was less pronounced in the eastern and estuarine regions. Both variables were positively associated with total phosphorus (TP), available potassium (AK), soil moisture content (MC), clay content, and pH, but negatively correlated with electrical conductivity (EC), particularly in the 0–15 cm layer. Our results highlight that soil texture, moisture, and salinity affect the spatial heterogeneity and vertical decline of SOC and TN in the Yellow River Delta. Future research should focus on the long-term temporal distribution of the coupling of multiple elements under changing hydrological and salinity regimes. Full article

Real-time event detection from social media has become increasingly important for emergency response, public safety, and situational awareness. However, accurately identifying emerging events from Thai social media remains challenging because Thai is a low-resource language without explicit word boundaries, while social media text is often characterized by informal writing, spelling variations, and noisy user-generated content. This paper proposes an entity-centric real-time event detection framework for Thai social media that employs a Multi-Granularity Thai Character Cluster (TCC)-Aware Named Entity Recognition (NER) model as its core information extraction component. The proposed NER architecture integrates contextual word embeddings, character-level representations, Thai Character Cluster features, and Part-of-Speech embeddings through an attention-based feature fusion mechanism to improve entity recognition under noisy conditions. Recognized entities are subsequently used as semantic anchors for event construction, clustering, temporal trend analysis, event ranking, and alert generation within a unified streaming framework. Event discovery combines Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Exponential Moving Average (EMA)-based temporal analysis to identify emerging events in real time. Experiments conducted on a large-scale Thai social media corpus demonstrate that the proposed model achieves an F1-score of 94.14% for named entity recognition and 92.2% for downstream event detection, outperforming representative baseline methods. Additional ablation studies, qualitative error analysis, and statistical significance tests confirm the effectiveness of the proposed multi-granularity representation. These results demonstrate that the proposed framework provides an effective solution for real-time event monitoring in low-resource language environments. Full article

The water resources carrying capacity (WRCC) lays a foundational basis for long-term coordinated water resource governance. Based on the Driving–Pressure–State–Impact–Response (DPSIR) framework, this study constructed a WRCC evaluation system containing 21 indicators and adopted a combined weighting method integrating entropy weight and coefficient of variation. Weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and geographical detector tools were jointly applied to quantify spatial-temporal WRCC disparities within the Jialu River Basin, alongside extraction of core driving forces during 2010–2022. Marked spatial disparities existed across administrative units, with basin-average WRCC ranging from 0.18–0.35. Zhengzhou maintained relatively high carrying levels, Kaifeng stayed chronically low, Xuchang improved after 2019, while Zhoukou experienced an overall decline, forming a relatively stable spatial pattern: Zhengzhou > Zhoukou > Xuchang > Kaifeng. Socioeconomic factors stood among the major drivers of spatial divergence. R&D expenditure and urbanization rate exhibited the highest explanatory capacity, with respective q statistics of 0.58 and 0.57. In contrast, natural factors including precipitation and groundwater reserves showed limited impacts, with q values of only 0.11 and 0.09. Factor interaction analysis showed that bivariate enhancement was the primary interaction type (70.53%), followed by nonlinear enhancement (21.05%) and nonlinear weakening (8.42%). The mean q value of the interactive effects reached 0.58, which was 45.0% higher than that of individual factors, suggesting prominent multi-factor synergistic effects. These results deliver empirical evidence for differentiated watershed regulation and cross-jurisdictional water–ecological coordination, and offer actionable governance insights for densely urbanized plain tributary basins with intense human–water conflicts. Full article

Purple corn is an anthocyanidin-rich plant, with anthocyanidins being particularly abundant in the waste it produces. This study explored the variation in anthocyanidins in each part of the plant during the growth period of a novel type of purple corn (Zea mays L., cv Jizi-01). The results showed that vacuum freeze-drying had advantages over cabinet drying in protecting anthocyanidins, except in the cornsilk part. The anthocyanidin contents of the different plant parts varied with time. Purple corn cob anthocyanidins (PCCAs) were isolated and identified owing to their abundance (14.95 g/kg, dry basis (DB)) among all plant parts. PCCA comprises three anthocyanidins, namely cyanidin, pelargonidin and peonidin, forming 11 types of anthocyanins when combined with glycosidic bonds. Finally, the in vitro scavenging effects of PCCA on DPPH, ABTS·⁺ and ·OH radicals were evaluated, and the scavenging rates reached up to 89.92%, 96.95% and 90.76%. This study shows that purple corn (Zm Jizi-01) is a novel resource rich in anthocyanidins, with high economic potential. Full article

Drought is a complex and periodic issue that has a significant impact on agriculture and water resources, particularly in semi-arid areas. This study evaluated meteorological and agricultural drought conditions in the Thanjavur district by combining rainfall data with remote-sensing methods. Meteorological drought was analyzed using 33 years of rainfall data and the Standardized Precipitation Index (SPI) (1992–2024) using 20 rainfall stations for the data available between 1992 and 2024. The spatial variation in rainfall was analyzed using Kriging interpolation in GIS. Agricultural droughts were analyzed using the Normalized Difference Vegetation Index (NDVI) and Vegetation Con0dition Index (VCI) using multi-temporal Landsat satellite images (Landsat 5 and Landsat 8). Land Use and Land Cover (LULC) classification was included to determine drought vulnerability across different land types. The NDVI and VCI indices showed an intensification of agricultural drought in 2010. The results demonstrated temporal and spatial differences in drought conditions for the years 1992, 1997, 2004, 2009, 2014, 2019, and 2024 and indicated that the region experienced periodic severe drought conditions of 3%, 3%, 3%, 8%, 19%, 9%, and 11% in the study area, respectively. During the drought period, the vegetation indices showed a strong sensitivity of agricultural areas to changes in rainfall, and low NDVI and VCI values indicated increased vegetation stress. Meteorological and agricultural droughts were integrated using the Analytical Hierarchy Process (AHP) method by combining various indicators to analyze the drought condition across the Thanjavur district. The multiple criteria decision-making (MCDM) method uses pairwise comparisons of various factors, such as giving high importance to SPI and rainfall, followed by vegetation indices and LULC. The consistency ratio validated the reliability of the weighting term. This method shows that combining meteorological and remote sensing indicators advances a robust framework for monitoring and assessing droughts. Conceptual droughts illustrate how meteorological droughts are associated with the development of agricultural droughts. The results of this study can be adopted for effective drought management, irrigation planning, and sustainable agricultural practices in this region. Full article

Conventional exposure assessments for toxic release events often rely on spatial hazard representations that provide limited information on how exposure evolves while evacuation is underway. This study proposes a dynamic exposure assessment framework that integrates time-dependent concentration profiles with scenario-specific evacuation completion times, including vulnerability-related evacuation delay. Exposure is quantified as accumulated exposure from release onset until evacuation completion, while the duration above a selected Acute Exposure Guideline Level (AEGL) reference threshold is used as a complementary temporal exposure metric. The framework is demonstrated using an ammonia-release scenario simulated with ALOHA and three illustrative evacuation-delay scenarios evaluated under identical hazard conditions. The results show that progressively delayed evacuation increases both accumulated exposure and the duration above the selected AEGL-2 reference threshold. However, the exposure contribution of additional evacuation time depends strongly on its temporal position within the evolving concentration profile. Additional evacuation time coinciding with the peak concentration plateau produced the largest marginal increase in accumulated exposure, whereas additional time during the later decay phase contributed comparatively less. A structured sensitivity analysis showed that the principal qualitative findings remained stable under $\pm 15\%$ perturbations of the reconstructed concentration profile, although the AEGL-2 exceedance duration was more sensitive to concentration variations. These findings demonstrate that exposure outcomes depend not only on evacuation duration but also on the temporal alignment between evacuation timing and hazard evolution. The proposed framework provides a structured approach for linking time-dependent dispersion outputs with evacuation assumptions and can be adapted to other toxic release scenarios when appropriate concentration–time data and substance-specific reference criteria are available. The approach may provide additional information for temporally informed emergency planning, particularly for populations requiring additional evacuation time or assistance. Full article

In this study, a method capable of simultaneously extrapolating the position, shape, and intensity of weather radar echoes is proposed. As the method is an improved version of the previously proposed nonlinear grid transformation (NGT) method, it is referred to as the enhanced NGT (ENGT) method. By extending the nonlinear transformation matrix to include radar reflectivity as the third dimension in addition to the grid coordinates X and Y, a 3 × 9 transformation matrix is used to describe the continuous spatial variation in the radar reflectivity field. The transformation matrix is solved using historical near-term data, enabling the extrapolation of subsequent time steps. In a set of ideal extrapolation experiments combining translation, temporal increments, and path variations, the ENGT method demonstrated better qualitative and conceptual performance than the NGT and traditional optical flow (OF) methods. In a real squall line case, the ENGT method could predict the overall movement direction of the cloud system synthesized by moving and emerging cells. In a real enhanced convective cloud cluster case, the ENGT method achieved higher scores because it generated stronger reflectivity. Although there are still mathematically unsolved and statistically insignificant problems, the ENGT method shows potential in predicting strong reflectivity, and the computational efficiency for a single weather radar is considerable. Full article

Global warming poses a serious environmental challenge worldwide. Agriculture, as a significant source of greenhouse gas (GHG) emissions, exerts considerable influence on the atmospheric environment. Chengdu, renowned for its thriving agricultural sector, serves as a key grain production center in China. Reducing agricultural greenhouse gas (AGHG) emissions is essential for mitigating the impact of climate change on Chengdu. Firstly, this paper employed the IPCC (Intergovernmental Panel on Climate Change) coefficient method and the Super-SBM-Undesired model to calculate the AGHG emissions and emission efficiency in Chengdu, respectively. Then, center of gravity shift analysis, kernel density estimation and spatial autocorrelation theory were used to analyze the spatial–temporal evolution characteristics of AGHG emissions. Finally, this paper conducted an in-depth analysis based on the STIRPAT model to identify key factors affecting AGHG emissions. The results show that: (1) From 2007 to 2021, Chengdu experienced an overall decline in both AGHG emissions and emission intensity, with reductions of 22.32% and 66.20%, respectively. And the AGHG emission efficiency was largely low. (2) AGHG emissions display regional variations and spatial clustering phenomena, characterized by a pattern of “high in the east, low in the west, high outside and low inside”. (3) AGHG emissions are highly increased by the sown area (S) and pesticide and fertilizer utilization (F) and may be reduced by the agricultural industrial structure (V) and the urbanization rate (U). These findings provide valuable scientific insights into the spatial–temporal dynamics of regional agricultural emissions. Furthermore, this study offers practical references for local governments to optimize agricultural resource allocation, formulate tailored low-carbon agricultural policies, and promote sustainable rural development. Full article

High-altitude wetlands in the Caucasus Anatolia transition zone remain among the least-documented avian habitats in the Western Palearctic. Standardised avian biodiversity assessment in such systems provides essential evidence for sustainable wetland management, supporting the conservation planning and long-term ecological monitoring needed to safeguard these ecosystems under increasing anthropogenic pressure. We report on the avifauna of Çalı Lake (2237 m a.s.l.; 391 ha; Kars Province, Türkiye), a nationally designated wetland located on the Central Asian Flyway, based on five systematic survey periods conducted from March 2024 to Spring 2026 using line transects and point counts, combined with a 25 × 25 m grid-based GIS analysis encompassing 498 cells. Approximately 31 ha of the core open-water and marsh perimeter within the 391 ha designated boundary is covered; upland steppe and pasture zones beyond the active survey perimeter were excluded. A total of 154 species belonging to 18 orders and 41 families were recorded, representing approximately 30.5% of Turkey’s national checklist. IUCN status assessment identified two Endangered species, Neophron percnopterus and Oxyura leucocephala, two Vulnerable, five Near Threatened, and 145 Least Concern species. Grid-level species richness averaged 1.47 ± 1.20 per cell per period; cumulative richness per grid reached 7.62 ± 2.73 across all five survey periods. Spearman rank correlation between per-grid richness (S) and abundance (N) was consistently strong across all five periods (ρ = 0.52–0.60; all p < 0.001). A Friedman test indicated significant overall variation across periods (χ²(4) = 127.73, p < 0.001, Kendall’s W = 0.064, a negligible effect size by conventional benchmarks, indicating that the statistically significant variation reflects trivially small per-cell richness differences at this block size). Bonferroni-corrected post hoc Wilcoxon tests revealed that all significant contrasts involved the 2024 Spring–Summer period or the 2026 partial Spring window, while the four fully comparable 2024 Autumn–2025 periods showed no significant differences. A Lorenz concentration curve yielded a Gini coefficient of 0.351, with the top 10% of grid cells concentrating 24.0% of all individual detections in the central and south-western lake zones. Collectively, these findings document Çalı Lake as a species-rich high-altitude wetland with significant conservation value, and establish a reproducible spatial and temporal baseline for long-term ornithological monitoring. These results demonstrate the value of standardised biodiversity assessment as a practical tool for sustainable wetland governance and align with international sustainability frameworks, including the UN Sustainable Development Goals on life on land and clean water and sanitation. Full article

Skeleton-based action recognition has attracted considerable research interest because skeleton data are inherently robust to illumination changes, viewpoint variation, background clutter, and camera motion. Nevertheless, extracting informative representations from skeleton sequences remains a challenging problem, as it requires capturing both the spatial co-occurrence patterns among body joints and the fine-grained kinematic cues that distinguish different actions. In this paper, we propose a single-stream architecture that constructs an action-specific skeleton graph directly from motion and processes it with a kinematic-aware Transformer. Rather than relying on a fixed skeleton topology, a motion-guided dynamic-graph construction module infers a per-frame adjacency matrix from short-term motion cues through a differentiable edge predictor and Gumbel-Softmax sparsification, allowing the model to discover action-driven connections between distant joints that lack direct bone connectivity (e.g., coordinated hand motion during clapping). Each joint is described by kinematic node features that combine its 3D position, instantaneous velocity, and limb-angle encodings within a single descriptor, so that both motion dynamics and higher-order limb configurations are available to the spatial encoder from the outset. A graph-attention network (GAT) encodes the spatial configuration of every frame over the learned graph, and the resulting sequence of frame descriptors is processed by a Transformer encoder that models long-range temporal dependencies; a learnable classification token aggregates the sequence, and a multi-layer perceptron (MLP) produces the final action classification. The entire model is trained end-to-end from action labels alone. We conduct a comprehensive ablation study and evaluate the proposed method on the large-scale NTU RGB+D 60 and NTU RGB+D 120 benchmarks, where the results demonstrate that our approach achieves competitive performance compared to state-of-the-art architectures. Full article

Multi-site water quality sequences are jointly affected by seasonal periodicity, meteorological disturbances, and inter-site differences in the Jianghuai Watershed region. Conventional quality prediction models struggle to simultaneously achieve multi-scale feature extraction, spatial heterogeneity characterization, and prediction uncertainty expression. This study used daily-scale monitoring data on dissolved oxygen (DO), pH, and ammonia nitrogen (NH3N) from 32 monitoring stations within the region in 2025 and proposed the FT-TransONet (Fourier-enhanced Temporal Transformer Operator Network) multi-site probabilistic water quality prediction model. Within a Transformer framework, the model employed a FourierTime learnable frequency-domain filtering module, a GeoBias (Geographic Bias) attention bias mechanism, and a multi-residual ensemble strategy composed of a multilayer perceptron (MLP), a gated recurrent unit (GRU), and a temporal convolutional network (TCN) combined with a mass conservation constraint, thereby achieving both point and interval prediction of key water quality indicators. The results showed that FT-TransONet achieved the lowest Macro_RMSE among all compared methods on the multi-site water quality prediction task. At a prediction horizon of three days, its Macro_RMSE reached 0.2255, which was 21.89% lower than that of the long short-term memory network and 5.57% lower than that of the strongest baseline MC-Dropout. For the three individual indicators, the model attained coefficients of determination of 0.9135, 0.9338, and 0.8660 for dissolved oxygen, pH, and ammonia nitrogen, with corresponding root-mean-square errors of 0.5145, 0.1098, and 0.0523, confirming its potential to characterize the temporal variation in the main water quality indicators. Under multi-step prediction, the error grew gently, with the Macro_RMSE rising only from 0.2255 to 0.2384 as the horizon extended from three to seven days, and the ablation experiments, together with the probabilistic prediction results, further supported the effectiveness of the proposed structural design. Validated on 32 water quality monitoring stations in the Jianghuai Watershed, the method improved multi-site prediction accuracy while accounting for stability and uncertainty quantification, providing a preliminary reference for regional water quality early warning and management. Full article