Search Results (5,083)

To address the operational challenges posed by the high penetration of photovoltaic systems in distribution networks—such as system congestion, voltage violations, and increased distribution losses—this study proposes a spatiotemporal sparsified dynamic reconfiguration scheduling method considering practical implementation in real distribution system operations. The proposed framework comprises two complementary sparsification mechanisms. Spatial sparsification is achieved by clustering hourly net-load distributions in a high-dimensional net-load space to aggregate characteristic net-load patterns, thereby restricting power flow evaluations and configuration screening to a small set of representative patterns and substantially reducing the computational burden. Temporal sparsification is realized by solving an integer linear programming problem to optimize the reconfiguration schedule under a daily reconfiguration frequency constraint, which optimizes the reconfiguration timing while mitigating excessive switching operations. Numerical experiments under deterministic forecast assumptions demonstrated that the proposed method can effectively eliminate congestion and voltage violations while achieving loss reduction by 4.56% and 27.4% respectively in two scenarios from the conventional method with the computational scalability significantly improved. Full article

Efficient multi-hop prediction over large-scale spatio-temporal knowledge graphs of aircraft taxiing trajectories remains challenging, as existing methods focus either on static multi-hop relations or on accuracy improvement for spatio-temporal single-hop predictions, leading to computational inefficiency. This paper proposes a vector-index-supported multi-hop prediction method. First, a knowledge graph embedding technique that integrates spatio-temporal features maps the trajectory graph into a low-dimensional complex vector space. Then, a hierarchical query acceleration structure based on IndexIVFFlat is constructed. A clustering strategy guided by the distribution of trajectory data partitions the vector space into subspaces, and approximate nearest neighbor search within those subspaces rapidly prunes the candidate set to accelerate multi-hop retrieval. Experiments on real aircraft taxiing trajectory datasets and general benchmarks show that the proposed method substantially improves prediction efficiency while maintaining competitive accuracy. The results demonstrate that the vector index mechanism effectively balances accuracy and efficiency, and the efficiency has been improved by at least 56.65%. This work provides a key technical foundation for real-time analysis and intelligent prediction of large-scale aircraft taxiing trajectories. Full article

This study focuses on the quantitative simulation of the spatiotemporal distribution characteristics of permafrost area, providing scientific value for Mongolian Plateau permafrost dynamics. Understanding the permafrost area of the Mongolian Plateau and accurately predicting future changes in permafrost area are crucial for sustainable environmental development. In this study, ERA5-Land surface temperature (LST) combined with the temperature at the top of permafrost (TTOP) model are used to calculate the annual permafrost area from 1980 to 2024. In addition, this study used the long short-term memory (LSTM) model to predict permafrost area on the Mongolian Plateau from 2025 to 2100. In this study, it is concluded that (1) the study area is not uniformly covered with permafrost, and its distribution is mainly limited to the northern part of the Mongolian Plateau, with a permafrost area of 53.20 × 10⁴ km²; (2) the permafrost area is estimated with an accuracy and precision of 0.94 when compared to the baseline value derived from borehole permafrost data; (3) under the CMIP6 three different shared socioeconomic pathway (SSP) 1-2.6, 2-4.5, and 5-8.5 future scenarios, the distribution of permafrost area shows a downward trend. This study provides a theoretical reference for distribution permafrost area in geographical space, which can help achieve the sustainable development of ice and snow resources. Full article

The Amazon coastal zone exhibits remarkable habitat diversity and species richness, with nutrient-rich estuaries playing a crucial role in local food webs and supporting fish and other aquatic organisms. To examine the distribution of fish larvae and juveniles in the Taperaçu Estuary and their relationship with environmental variables, monthly sampling was conducted at two fixed stations in 2008. Samples were collected during flood and ebb spring tides using 500 μm mesh nets. In situ measurements of salinity, temperature, and dissolved oxygen were recorded, while pH and turbidity were determined in the laboratory. Abiotic variables did not differ significantly between tides, but salinity and dissolved oxygen were higher during the dry season. A total of 5175 individuals were identified, representing 17 families and 37 species. The ichthyoplankton community was dominated by Rhinosardinia amazonica, Anchovia clupeoides, Stellifer stellifer, and Microgobius meeki. Stations 1 and 2 showed differing abundance ranges, with higher values at station 1 during the rainy season. Preflexion stages were abundant at both stations, indicating the estuary’s importance as a nursery and development area for several fish species. Multivariate analyses revealed spatial and seasonal structuring of larval assemblages along the estuarine gradient, driven primarily by salinity, temperature, and turbidity. Our results emphasize the role of upper estuary sectors of eastern Amazonia as areas of spawning, larval development, and subsequent juvenile settlement, contributing to the dispersal of fish species throughout the estuary and adjacent coastal environments. The present findings also reinforce the ecological value of the studied Extractive Reserve and other protected areas along the Amazon littoral as essential habitats for larval refuge and development. The need for continued monitoring and preservation of these protected zones is evident. Full article

Acute cardiovascular events driven by atherosclerosis primarily originate from thrombosis triggered by vulnerable plaque rupture or endothelial erosion. Endothelial barrier destabilization—characterized by glycocalyx impairment, intercellular junction disassembly, and abnormal cytoskeletal tension—is a core upstream pathological stage that promotes atherogenic lipoprotein leakage, inflammatory cell infiltration, and matrix degradation. Hemodynamics, primarily through wall shear stress (WSS), shape the spatial distribution and plaque phenotypes of atherosclerosis; notably, low or oscillatory shear stress is associated with, and in experimental systems can promote, pro-inflammatory, pro-oxidant and pro-permeability endothelial phenotypes that contribute to plaque initiation and vulnerability. Conversely, regular exercise training, as an intervention that modulates hemodynamics, is widely suggested to promote anti-inflammatory, antioxidant, and antithrombotic endothelial phenotypes by significantly increasing antegrade shear stress and reducing detrimental retrograde/oscillatory shear stress. With a central focus on the axis of “exercise-shear stress-glycocalyx-cytoskeleton/junction-permeability-plaque stability,” this review integrates evidence from in vitro flow chambers, animal models and human studies to critically discuss: (1) the spatiotemporal heterogeneity of WSS and its relationship with plaque vulnerability; (2) the composition, barrier function, and plasticity of the glycocalyx as the primary interface for shear stress; (3) the mechanosensory complexes at the glycocalyx and junctions that transduce shear stimuli to protective pathways such as Phosphoinositide 3-kinase (PI3K)-Akt-endothelial nitric oxide synthase (eNOS) and Krüppel-like factor 2 (KLF2), thereby stabilizing adherens/tight junctions; (4) how improved barrier homeostasis promotes the maintenance of the fibrous cap collagen scaffold by reducing lipoprotein leakage and dampening the inflammation–matrix metalloproteinase (MMP) axis. Finally, this review highlights the boundary conditions of the biological effects of shear stress: low/oscillatory shear stress is primarily associated with plaque initiation and susceptible sites, whereas focal, extremely high WSS in established stenotic lesions may contribute to late-stage high-risk remodeling. Therefore, the protective hemodynamic adaptations induced by exercise should not be simply equated with the pathologically high WSS found at stenotic sites. Full article

Remote-sensing land surface deformation (LSD) is a powerful and effective approach for investigating regional alpine permafrost variations. However, alpine permafrost is often distributed in areas characterized by earthquakes, and the LSD of alpine permafrost is potentially contaminated or diminished by earthquake-related LSD. Therefore, this study aimed to derive the effective LSD in the alpine permafrost of the Source Area Yellow River (SAYR) by removing LSD originating from the M_w 7.4 Maduo earthquake in 2021-05-22 and analyzing the spatiotemporal variations in LSD during 2017–2024. Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) was used to obtain the initial LSD time series from Sentinel-1 images acquired during 2017–2024. The LSD of the M_w 7.4 Maduo earthquake, its aftershocks and the post-seismic relaxation in SAYR was simulated separately by considering its temporal process and removed from the LSD time series in SAYR. The final LSD was validated against in situ Global Navigation Satellite System (GNSS) measurements, and the spatiotemporal variations in LSD in SAYAR were subsequently analyzed. The study found the following: (1) the removal of the earthquake-related LSD was successful both spatially and temporally and the final LSD has mean absolute error (MAE) of 3.22 mm and root mean squared error (RMSE) of 3.92 mm; (2) during 2017–2024, the vertical LSD in SAYR was mostly −8–8 mm/y; (3) soil moisture determined the spatial distribution of the LSD direction in SAYR as a result of local drainage conditions, air temperature, precipitation and snow melt. This study demonstrated the necessity of removing the earthquake-related LSD when investigating the alpine permafrost LSD in tectonically active areas. The strategy adopted in this study serves as a technical reference for future investigations of this kind. The findings in this study provide insight for a thorough understanding of permafrost evolution on the Tibetan Plateau in the context of climate change. Full article

Hyporheic zone exchange processes are strongly influenced by channel morphology, producing heat transfer patterns with distinct vertical stratification. To evaluate the effects of different channel geomorphic units on hyporheic temperature dynamics, monitoring sites were established along a segment of the Xiajiasi River (Hubei Province, China) encompassing four representative channel types: a meandering reach, a pool–riffle reach, a weir reach, and a straight reach. Hyporheic temperatures were recorded at multiple depths (0, 0.1, 0.2, and 0.3 m) during both summer and winter. The results indicate that channel morphology strongly controls the spatiotemporal distribution of hyporheic temperatures. Across all channel types, sediment temperatures exhibited depth-dependent amplitude attenuation and phase lag, with mean temperatures decreasing with depth in summer and increasing with depth in winter. The meandering reach exhibited the highest summer temperatures (28.3–30.6 °C), whereas the pool–riffle reach displayed the steepest thermal gradients (deep sediment temperatures as low as 25.6 °C). In contrast, the straight reach exhibited the weakest thermal buffering capacity. The presence of the weir markedly modified downstream thermal conditions, reducing sediment temperatures by approximately 1.6–3.2 °C during summer, whereas overall winter observations demonstrated a pronounced thermal inversion with deep sediment temperatures increasing by 1.2–2.9 °C. These findings demonstrate that distinct geomorphic units create diverse thermal niches; river managers can incorporate diverse geomorphic features into river restoration designs to create localized thermal refugia, thereby protecting temperature-sensitive aquatic species. Full article

Natural climate variations and human activities have significantly altered the river–lake hydrological regimes in the middle and lower reaches of the Yangtze River, leading to substantial changes in the inundation patterns of the Dongting Lake wetland, which in turn profoundly affect the spatial distribution and landscape patterns of wetland vegetation. Determining the response mechanisms and appropriate thresholds of wetland landscape patterns to hydrological rhythm changes is of great importance for maintaining the health of wetland ecosystems and optimizing the ecological operation of water conservancy projects. Based on long-term measured water level data (1992–2023) and multi-temporal Landsat remote sensing images (1997–2022), combined with a digital elevation model (DEM), this study systematically analyzed the spatiotemporal evolution characteristics of the inundation processes in Dongting Lake before and after the operation of the Three Gorges Project (TGP) and their driving mechanisms on the plant landscape patterns of the floodplain wetland. The results show that after the TGP operation, the inundation pattern of Dongting Lake exhibited a drying trend, with a significant decline in annual mean water level (the largest drop of approximately 0.7 m in East Dongting Lake) and a marked reduction in the lake-wide average inundation duration (T) and inundation frequency (F). From 1997 to 2022, the total area of wetland vegetation in Dongting Lake showed a significant expansion trend, and the succession of the landscape pattern experienced a nonlinear process of stability, fragmentation, and recovery. The stepwise regression model revealed that the three elements of the inundation process explained more than 80% of the landscape pattern variation, among which inundation frequency (F) and inundation duration (T) were the core driving factors. Specifically, inundation frequency primarily regulated landscape diversity (SHDI) and contagion (CONTAG) through an environmental filtering effect, while maximum inundation depth (H) mainly maintained the physical connectivity (COHESION) of the landscape. Furthermore, the study quantified the stable hydrological range of the Dongting Lake wetland ecosystem: when the inundation frequency is maintained at 0.40–0.50 and the annual inundation duration is controlled at 4–5 months, the wetland landscape is in an optimal structural state. Once the warning thresholds are breached (e.g., F < 0.35 or T < 90 days), it may trigger the rapid expansion of cultivated poplar forests under combined hydrological and anthropogenic influences, leading to severe habitat fragmentation. These findings deepen the understanding of the response mechanisms of vegetation landscape patterns in large lake wetlands under altered hydrological rhythms. Full article

Extreme weather events exacerbated by global warming pose severe threats to urban safety, underscoring the urgent need for highly accurate precipitation nowcasting. Short-term local heavy precipitation remains a particular challenge for traditional forecasting due to its suddenness and high disaster potential. To address this, we propose a multi-modal fusion framework that integrates ground-based GNSS-derived Precipitable Water Vapor (GNSS-PWV) and ground-based Radar Composite Reflectivity (CR). While GNSS-PWV keenly captures pre-convective atmospheric water vapor accumulation, radar CR details the morphological distribution of hydrometeors. Specifically, we developed the Spatio-Temporal Enhanced Attention Swin U-Net (STEA-Swin) model to synergize these heterogeneous datasets over the Beijing–Tianjin–Hebei region. High-precision PWV was retrieved from 250 Continuously Operating Reference Stations (CORS) using the dual-frequency ionosphere-free Precise Point Positioning (PPP) method, achieving a strong correlation (>0.97) with ERA5 reanalysis data. Validated against measured data from the 2025 flood season, the STEA-Swin model achieved a Probability of Detection (POD) of 0.68 for torrential rain events at a +1 h forecast lead time. Notably, compared to single-source models, the Critical Success Index (CSI) and POD for torrential rain improved by 18.5% and 21.5%, respectively. These findings demonstrate that coupling deep learning with ground-based GNSS-derived atmospheric thermodynamic information can significantly enhance early warning capabilities, providing a promising technical approach for regional disaster prevention and climate resilience. Full article

Understanding the spatiotemporal variability of nanoplastics (NPs) in porous media is vital for environmental risk assessment, yet quantitative in-media analysis of NP distributions during transport remains limited. To address this, we innovatively applied low-field nuclear magnetic resonance (LF-NMR) as a non-invasive approach to dynamically monitor magnetic polystyrene nanoplastic (MPSNP) transport in saturated quartz sand. By establishing the relationship between LF-NMR transverse relaxation rate [1/T_2,I − 1/T_2,0] and MPSNP concentrations, we reconstructed spatiotemporal concentration profiles via T₂ inversion. This methodology enabled systematic evaluation of the effects of ionic strength (IS), flow velocity, initial concentration, and flow direction. Three mathematical models were further applied to analyze MPSNP transport behavior. Results revealed IS as the dominant factor; increasing IS (0.001 to 1 mM) dropped mass recovery from 85.7% to 0%, the migration front no longer advanced at IS > 5 mM. Lower flow rates, higher initial concentrations, and horizontal flow also enhanced retention. The two types of two-site kinetic models provide a better fit for the features of the breakthrough curves. This novel use of LF-NMR demonstrates its robust capability to resolve spatial transport heterogeneity, underscoring that flow velocity, flow direction, and ionic strength are critical regulatory parameters that should be carefully accounted for when evaluating nanoplastic transport in porous media. Full article

Traditional villages are important carriers of rural cultural heritage, yet their spatio-temporal distribution and underlying mechanisms remain insufficiently understood, particularly regarding the interaction between environmental and socio-cultural drivers over long historical periods. Focusing on 131 nationally recognized traditional villages in southern Hebei, China, this study integrates GIS-based spatial analysis with historical interpretation to examine their spatial patterns, temporal evolution, and driving factors from the pre-Sui period to the Qing Dynasty and post-Qing period. The results show that traditional villages exhibit a highly clustered and uneven distribution, primarily concentrated in mountain-front zones in the western and southwestern parts of the region. Spatial analysis reveals a multi-core clustering structure, and spatial autocorrelation confirms that this pattern is statistically significant. Temporally, village formation follows a non-linear process of concentration, expansion, and stabilization, with the Ming Dynasty representing a key peak period. The findings further indicate that dominant driving mechanisms shifted over time: early settlement was mainly constrained by environmental conditions, whereas later development increasingly depended on socio-cultural processes such as migration, defense, clan organization, and regional exchange. In the contemporary context, economic development and accessibility introduce complex and non-linear effects. These results suggest that traditional villages should be understood as dynamic cultural landscapes shaped by long-term human–environment interactions. This study provides an integrated framework for understanding rural settlement dynamics and offers insights relevant to rural heritage conservation and sustainable development in transitional regions. Full article

With the large-scale integration of electric vehicles (EVs), charging demand exhibits significant spatiotemporal variability, further intensified by climatic factors, which makes it difficult for existing uncertainty models to capture underlying dependency structures. To address this issue, this paper proposes a Copula–Wasserstein-based distributionally robust optimization (C-WDRO) framework for the coordinated operation of distribution networks and charging stations. A climate-sensitive physical mapping model of electric vehicle energy consumption is first developed to establish a coupled climate–energy–load mechanism. Copula functions are then used to characterize dependencies among temperature, precipitation, and charging demand, and are incorporated into a bi-level optimization formulation. The model is solved using Karush–Kuhn–Tucker (KKT) conditions and a column-and-constraint generation (C&CG) algorithm. Case studies on the IEEE 33-bus system show that the proposed method reduces total operating cost by 4.26% compared with robust optimization (RO), while maintaining economic efficiency, and reduces the load shedding rate by 0.14 percentage points compared with Wasserstein distributionally robust optimization (WDRO), while keeping voltage security. These results demonstrate that explicitly modeling dependency structures can enhance operational efficiency and support more sustainable and reliable power–transportation system operation under uncertainty. Full article

Aiming at the problem of weak fault features and difficult localization of adjacent nodes in distribution networks, we constructed a two-stage cascaded architecture to decouple the diagnosis task into fault classification and section location. The feature layer fuses MS-CNN, SimAM, and Transformer to form a spatio-temporal dual attention mechanism that synchronously captures spatial saliency and global temporal logic. A prototype network is introduced at the fault location decision layer, and metric learning is used to solve the problem of feature aliasing of adjacent nodes. The experimental results show that the accuracy of fault classification and localization are 98.61% and 94.22%, respectively, and it exhibits graceful degradation under extremely low-SNR conditions, which verifies the effectiveness of the proposed strategy in the refined fault diagnosis of distribution networks. Full article

Holotrichia parallela is a globally distributed soil-dwelling pest that poses a major threat to peanut cultivation in China. Neuropeptides, as critical signaling molecules, regulate multiple physiological and behavioral processes in insects and represent highly promising targets for pest management. To date, the functional characteristics of neuropeptides in H. parallela remain unreported. In this study, we isolated and cloned one NPF and one NPFR gene, respectively. Bioinformatics analysis revealed that alternative splicing of the NPF gene produces two transcript variants, NPFa (255 bp) and NPFb (369 bp). The NPFR gene spans a length of 1188 bp, encoding 395 amino acids that contain seven α-helical transmembrane domains, indicating that it belongs to the family A G protein-coupled receptor (GPCR) family. Spatiotemporal expression profiles demonstrated that NPF was most abundant in the adult brain, whereas NPFR was highly enriched in the brain and antennae. NPF expression peaked in second-to-third-instar larvae, while NPFR was highly expressed in eggs. Starvation stress significantly upregulated the expression of both genes. RNA interference (RNAi)-mediated silencing of NPF and NPFR significantly reduced food intake, female fecundity, and glycogen content in adults. These findings enhance our understanding of insect neuropeptides signaling networks and support the development of behavior-based pest control strategies. Full article

Student behavior recognition in classroom environments is important for teaching quality assessment and intelligent education, yet it remains challenging due to dense student distributions, frequent occlusion, substantial scale variation, and the subtle nature of common classroom activities. To address these issues, this paper proposes RepYOLOv5-SF3D, a cascaded visual perception framework for fine-grained student behavior recognition in complex classroom scenes. The framework integrates a lightweight RepYOLOv5m detector with a dual-stream SlowFast-3D recognition branch, enabling automated inference from raw video input to behavior labels. To improve robustness in dense and occluded scenes, the front-end detector serves as a spatial-prior module, while a decoupled training strategy reduces the impact of localization instability on back-end spatiotemporal learning. In addition, two task-oriented modules are introduced in the recognition branch: the Spatiotemporal Depthwise-Separable 3D module (SDS3D) and the Normalization-Based Temporal Attention Mechanism (NTAM). Experimental results on a real classroom dataset show that RepYOLOv5-SF3D achieves a mean average precision (mAP) of 88.83%, outperforming the baseline SlowFast model by 3.36% and surpassing the existing LSTC method by 2.05%, while maintaining a front-end inference latency of 12.5 ms per frame and a total model size of 151.46 MB. These results demonstrate a favorable balance between fine-grained recognition accuracy and edge-deployment efficiency in practical classroom visual sensing. Full article