Search Results (3,856)

Net solar radiation is an essential parameter that characterizes surface energy exchange and plays a critical role in climate change, solar power generation, and agricultural irrigation. Although the global GLASS surface all-wave daily net radiation (NR) product exhibits high overall accuracy, a comprehensive quality assessment for continental China remains lacking, resulting in unclear regional applicability. Therefore, this study focuses on mainland China. Based on solar net radiation observations from 50 meteorological stations (2000–2016) and 37 ecological stations (2000–2020), four evaluation metrics were used: the correlation coefficient (R), mean bias error (MBE), root mean square error (RMSE), and coefficient of determination (R²). The results indicate that, during the study period, GLASS NR showed relatively small deviations from the observed values across most regions of China, with significant discrepancies observed only in southern Yunnan, Guangdong, Guangxi, and Hainan. Seasonally, GLASS NR performed better in autumn and winter than in spring and summer. Interannually, there was only a slight decline in data quality for a few individual years; however, overall, an upward trend was observed. Regarding land cover types, GLASS NR accuracy was lower for shrublands, forests, and grasslands, whereas it performed better for other land cover types. Overall, the GLASS NR product demonstrates high accuracy and good temporal continuity across mainland China. However, significant regional variations exist, and localized applications require optimization and refinement. This study provides valuable insights for improving net radiation products across multiple spatiotemporal scales. Full article

The K-homology (KH) domain is a crucial RNA-binding motif central to post-transcriptional regulation. However, its corresponding gene family remains poorly characterized in tomato (Solanum lycopersicum), a key model species for studying fleshy fruit development. Here, we performed a genome-wide identification and comprehensive characterization of 47 SlKH genes in S. lycopersicum. Phylogenetic and synteny analyses indicated that the gene family expanded mainly through segmental duplications. While the core RNA-binding GXXG loop has evolved under strict purifying selection, specific orthologs, such as the SlKH3/AtKH6 pair (Ka/Ks = 1.78), exhibited putative signatures of positive selection. Haplotype variations in SlKH47, SlKH43, and SlKH35 are associated with significant differences in fruit weight, revealing their potential roles in crop domestication. Furthermore, expression profiling revealed distinct spatiotemporal patterns, highlighting several members that are significantly upregulated during fruit ripening. Structural modeling with AlphaFold 3 provided predictive insights into how the conserved GXXG motif mediates RNA recognition. This study provides a comprehensive genomic resource and foundational insights into the evolutionary and functional significance of KH proteins in S. lycopersicum development and breeding. Full article

Owing to their high-altitude vantage point and maneuverability, unmanned aerial vehicles (UAVs) have emerged as an effective technical solution for real-time parking detection in expressway scenarios. Using UAV cruise-perspective images, this paper proposes an unauthorized parking detection method by analyzing the time-series variations in the relative distances between the moving vehicle and static structure as a reference. Firstly, vehicle and static structure targets are recognized and tracked by the DeepSort, and a Vehicle–Structure (V-S) distance matrix is further constructed to describe their frame-wise relative positions in the pixel coordinate system. Then, to eliminate the radial scale errors caused by perspective distortion, a scale factor (SF) index is introduced to correct the original V-S matrix and provide a more accurate spatiotemporal representation. Finally, the stationarity of the distance series in the V-S matrix is tested using the Augmented Dickey–Fuller (ADF) test, and a parking detection method is proposed by introducing the parking support ratio (PSR) to establish a multi-structure joint decision scheme. Experimental results show that the corrected V-S matrix can faithfully describe the spatial positional relationship between road vehicles and static structures. With the optimal PSR threshold ${\psi }_0$ and time window T, the proposed method achieves better overall parking-detection performance in terms of accuracy, precision, recall, and F1-score in comparison with a traditional speed threshold approach. Full article

With the acceleration of urbanization, public transit systems face prominent challenges, including insufficient passenger flow prediction accuracy and low scheduling efficiency. This study analyzes passenger flow variation patterns from both spatial and temporal dimensions, constructs spatiotemporal matrices, and employs matrix dimensionality reduction methods to extract key features. We propose a passenger flow prediction model based on GCN-AM-LSTM and a dynamic real-time intelligent scheduling strategy. For passenger flow prediction, the model first utilizes Graph Convolutional Networks (GCNs) to extract spatial features of the transit network, then employs Attention Mechanism-enhanced Long Short-Term Memory networks (AM-LSTM) to perform weighted extraction of temporal features, and finally integrates external factors such as weather conditions to generate prediction outputs. For scheduling optimization, a dynamic real-time scheduling mode is adopted: the foundational framework optimizes dynamic departure timetables using a multi-objective particle swarm optimization algorithm, which is then combined with real-time passenger flow data to adjust departure intervals at the route level and implement stop-skipping strategies at the station level. Validation was conducted using Xiamen BRT Line 1 as a case study. Experimental results demonstrate that the proposed GCN-AM-LSTM prediction model reduces Mean Absolute Error (MAE) by 14% and 22% compared to CNN and LSTM models, respectively, achieving significantly improved prediction accuracy. Regarding scheduling optimization, the number of departures decreased by 15.24%, passenger waiting time costs were reduced by 3.7%, and transit operating costs decreased by 3.19%, effectively balancing service quality and operational efficiency. Full article

The characteristics and causes of the long-term trends of diurnal variation of sea surface temperature (DSST) in the South China Sea (SCS) are investigated in this study based on the global hourly sea surface temperature data generated by the mixed layer model (MLSST) from the National Marine Environmental Forecasting Center (NMEFC) of China. Validation of the MLSST dataset demonstrates excellent agreement with in-situ buoy observations in the SCS with a correlation coefficient of 0.951, confirming its reliability in the SCS. Based on this dataset, the long-term trend of DSST in the SCS exhibits significant seasonal variations with the strongest magnitude in spring and the weakest in winter. Specifically, a significant decreasing trend of −0.0014 °C yr⁻¹ during 1982–2009 transitioned to a pronounced increasing trend of 0.0057 °C yr⁻¹ from 2010–2019. Both climatic factors and local atmospheric variables jointly modulate the DSST in the SCS. On the long-term timescale, the Pacific Decadal Oscillation (PDO) served as the dominant factor driving DSST changes in most areas of the SCS. After 2010, the PDO shifted to a persistent positive phase, providing a crucial climatic background for the basin-wide DSST increase. While the El Niño–Southern Oscillation (ENSO) showed enhanced correlation with DSST post-2010, the Indian Ocean Dipole (IOD) had negligible influence overall. In addition, the SCS summer monsoon played an important regulatory role in shaping the long-term trend of summer DSST by altering air–sea heat exchange processes. Among local atmospheric variables, sea surface wind speed was significantly negatively correlated with DSST, and net heat flux was significantly positively correlated with DSST, with their effects showing regional differentiation. The regulatory role of wind speed dominated in the western SCS, whereas the net heat flux exerted a more prominent impact in parts of the eastern SCS. This work clarifies the spatiotemporal patterns and multi-driver framework governing DSST variability in the SCS, providing a basis for understanding regional ocean–atmosphere interactions. Full article

High-fidelity 4D reconstruction of dynamic scenes is pivotal for immersive simulation yet remains challenging due to the photometric inconsistencies inherent in multi-view sensor arrays. Standard 3D Gaussian Splatting (3DGS) strictly adheres to the brightness constancy assumption, failing to distinguish between intrinsic scene radiance and transient brightness shifts caused by independent auto-exposure (AE), auto-white-balance (AWB), and non-linear ISP processing. This misalignment often forces the optimization process to compensate for spectral discrepancies through incorrect geometric deformation, resulting in severe temporal flickering and spatial floating artifacts. To address these limitations, we present Lumina-4DGS, a robust framework that harmonizes spatiotemporal geometry modeling with a hierarchical exposure compensation strategy. Our approach explicitly decouples photometric variations into two levels: a Global Exposure Affine Module that neutralizes sensor-specific AE/AWB fluctuations and a Multi-Scale Bilateral Grid that residually corrects spatially varying non-linearities, such as vignetting, using luminance-based guidance. Crucially, to prevent these powerful appearance modules from masking geometric flaws, we introduce a novel SSIM-Gated Optimization mechanism. This strategy dynamically gates the gradient flow to the exposure modules based on structural similarity. By ensuring that photometric enhancement is only activated when the underlying geometry is structurally reliable, we effectively prioritize geometric accuracy over photometric overfitting. Extensive experiments validate the quantitative superiority of Lumina-4DGS. On the Waymo Open Dataset, our method achieves a state-of-the-art Full Image PSNR of 31.12 dB while minimizing geometric errors to a Depth RMSE of 1.89 m and Chamfer Distance of 0.215 m. Furthermore, on our highly challenging self-collected surround-view dataset featuring severe unconstrained illumination shifts, Lumina-4DGS yields a significant 2.13 dB PSNR improvement over recent driving-scene baselines. These results confirm that our framework achieves photorealistic, exposure-invariant novel view synthesis while maintaining superior geometric consistency across heterogeneous camera inputs. Full article

Precise spatiotemporal monitoring of soil moisture is fundamental to the efficient regulation and sustainable utilization of agricultural water resources in arid and semi-arid irrigation districts. This study focuses on the Yichang Irrigation District within the Hetao Irrigation Area to elucidate the spatiotemporal dynamics of surface soil moisture during the crop growing season. Multi-year Landsat 8/9 remote sensing imagery (2022–2024) was integrated with the Temperature Vegetation Dryness Index (TVDI) framework to construct two feature spaces, namely Normalized Difference Vegetation Index–Land Surface Temperature (NDVI–LST) and Enhanced Vegetation Index–Land Surface Temperature (EVI–LST). A dual-index complementary inversion strategy was applied for soil moisture estimation, and the outputs were validated against Soil Moisture Active Passive (SMAP) soil moisture products and MOD16 evapotranspiration products. Results indicated that the dry edges of the feature spaces derived from both vegetation indices exhibited double-inflection-point characteristics, with optimal fitting intervals located between the inflection points. The inflection point positions shifted dynamically with variations in crop coverage. During bare-soil and low-vegetation-coverage periods (May, June, and September), the minimum thresholds for low NDVI and EVI values were 0.07 and 0.06, respectively, whereas during high-vegetation-coverage periods in July and August, the minimum thresholds for both indices increased to 0.15. NDVI demonstrated superior performance during May, June, and September, whereas EVI exhibited greater advantages during active crop growth periods in July–August. The optimized model achieved robust inversion accuracy, with a validation R² of 0.81 for the measured soil moisture in the 0–20 cm layer on 12 May 2024. The inversion results exhibited strong correlations with the SMAP soil moisture products (R² = 0.663 during low crop coverage; R² = 0.625 during high crop coverage) and MOD16 evapotranspiration data (R = 0.751). The spatiotemporal patterns of soil moisture were distinctly discerned. Following spring irrigation in May, abundant moisture in certain areas resulted in bimodal distribution patterns in the inversion results. June exhibited the lowest soil moisture content across the study area, with arid zones making up 36.67% of the total area. From July to August, concentrated precipitation coupled with summer irrigation reduced the proportion of extremely arid zones to below 0.98%. Full article

Autism classification is challenging due to the subtle, heterogeneous, and overlapping neural activation profiles that occur in individuals with autism. Novel deep learning approaches, such as Convolutional Neural Networks (CNNs) and their variants, as well as Transformers, have shown moderate performance in discriminating between autism and normal cohorts; yet, they often struggle to jointly capture the spatial–structural and temporal–functional variations present in autistic brains. To overcome these shortcomings, we propose a novel hierarchical deep learning framework that extracts the inherent spatial dependencies from the dual-modal MRI scans. For sMRI, we develop a 3D Hierarchical Convolutional Neural Network to capture both fine and coarse anatomical structures via multi-view projections along the axial, sagittal, and coronal planes. For the fMRI case, we introduced a bidirectional LSTM-based temporal encoder to examine regional brain dynamics and functional connectivity. The sequential embeddings and correlations are combined into a unified spatiotemporal representation of functional imaging, which is then classified using a multilayer perceptron to ensure continuity in diagnostic predictions across the examined modalities. Finally, a cross-modality fusion scheme was employed to integrate feature representations of both modalities. Extensive evaluations on the ABIDE I dataset (NYU repository) demonstrate that our proposed framework outperforms existing baselines, including Vision/Swin Transformers and various newly developed CNN variants. For the sMRI branch, we achieved 90.19 ± 0.12% accuracy (precision: 90.85 ± 0.16%, recall: 89.27 ± 0.19%, F1-score: 90.05 ± 0.14%, and focal loss: 0.3982). For the fMRI branch, we achieved an accuracy of 88.93 ± 0.15% (precision: 89.78 ± 0.18%, recall: 88.29 ± 0.20%, F1-score: 89.03 ± 0.17%, and focal loss of 0.4437). These outcomes affirm the superior generalization and robustness of the proposed framework for integrating structural and functional brain representations to achieve accurate autism classification. Full article

With the advancement of intelligent manufacturing strategies, Cyber–Physical Production Systems (CPPSs) generate massive amounts of multidimensional, dynamic, and non-stationary data, posing significant challenges to real-time Process Monitoring. Existing anomaly detection methods often suffer from insufficient feature robustness when dealing with complex spatio-temporal dynamics, high computational complexity, and difficulties in effectively capturing incipient faults within deep topological structures. To address these issues, this paper proposes a Spatio-Temporal Variational Graph Statistical Attention Autoencoder (ST-VGSAE). First, the framework performs end-to-end multi-scale temporal decomposition via an Adaptive Lifting Wavelet Module, which enhances feature robustness while effectively suppressing noise. Furthermore, a spatio-temporal Token statistical self-attention mechanism with linear complexity is incorporated. By modulating local features via global statistics, it significantly reduces computational costs while enhancing anomaly discriminability. Experiments on the Tennessee Eastman (TE) process dataset demonstrate that the proposed model significantly outperforms state-of-the-art methods in key metrics such as the Fault Detection Rate and the False Alarm Rate, exhibiting superior noise robustness and real-time performance. Full article

Agricultural water footprint is an important indicator for assessing water-use efficiency and resource carrying capacity in agricultural systems, especially in arid regions. From the perspective of ecological function zones, this study examines the spatiotemporal dynamics of the agricultural water footprint in Xinjiang, China, and evaluates its adaptability to socioeconomic factors. The blue and green water footprints of crop production and the water footprint of animal products during 2000–2020 were quantified to estimate the total agricultural water footprint. The Gini coefficient and imbalance index were used to quantitatively evaluate the spatial adaptability between the agricultural water footprint and socioeconomic factors, including sown area, population, and agricultural Gross Domestic Product (GDP) across different ecological function zones. The results indicate that the agricultural water footprint increased from 2.54 × 10¹⁰ m³ to 5.85 × 10¹⁰ m³, with a clear spatial gradient characterized by higher values in southwestern Xinjiang and lower values in the northeastern region. Crop production accounted for more than 85% of the total footprint, with cotton as the dominant contributor, while beef consumption drove the growth in the animal product water footprint. The adaptability between the agricultural water footprint and sown area improved overall, whereas coordination with population distribution remained weak, and notable regional differences were observed in water footprint intensity relative to agricultural GDP. These findings indicate that the spatiotemporal differentiation of the agricultural water footprint is closely linked to resource endowments and development characteristics across ecological function zones, providing support for region-specific agricultural water management in arid areas. Full article

The artificial lateral line sensing principle provides a promising approach for underwater target perception and the navigation of underwater vehicles in complex flow environments. However, the highly nonlinear hydrodynamic mechanisms in complex flow fields make it difficult to establish accurate analytical models, which limits the development of high-precision perception and localization methods for underwater moving targets. In this study, a high-fidelity simulation model is established to characterize the pressure field variations induced by a moving source on an artificial lateral line pressure array. The influences of source velocity and sensing distance on the sensitivity and discretization characteristics of the pressure array are systematically investigated. Simulation results indicate that the sensor density of the pressure array is strongly correlated with the spatial resolution of the acquired pressure data, and a resolution of 50 sensors per meter is selected as the best-performing configuration by balancing sensing accuracy and sensor quantity. Under this configuration, the pressure distribution induced by the moving source exhibits clear and distinguishable spatiotemporal features, making it suitable for deep learning-based modeling. Furthermore, a large-scale temporal pressure dataset is constructed based on high-fidelity simulations under multiple motion directions and velocity conditions, and a spatiotemporal neural network is employed to predict the position of the underwater moving source. Experimental results demonstrate that, for straight-line underwater motion scenarios, the average localization error is within 7 cm, and a classification accuracy of 71% is achieved in practical engineering experiments. These results indicate that the proposed artificial lateral line pressure array design and deep learning-based prediction framework provide a feasible and effective solution for underwater target perception and localization in complex flow environments. Full article

Rapid urbanization has intensified the Surface Urban Heat Island (SUHI) effect, which poses particular challenges for coastal cities where marine environments, climatic regulation, and distinctive urban morphology interact in complex ways. Current research on coastal SUHI remains limited, especially in terms of systematic analyses using the Local Climate Zone (LCZ) framework. Key gaps include insufficient cross-climate comparisons and limited understanding of spatial differentiation patterns linked to LCZ-based SUHI dynamics. This study employs LCZ classification to analyze coastal cities across diverse climatic backgrounds, integrating Pearson’s correlation analysis and coastal distance gradient zoning to investigate the spatio-temporal distribution and influencing factors of Surface Urban Heat Island Intensity (SUHII). The findings reveal that: (1) SUHII exhibits a distinct spatial pattern, with elevated intensities in built-up areas and reduced values in natural zones, alongside seasonally differentiated variations across climate zones. (2) The normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) emerge as dominant drivers, exerting heating and cooling effects, respectively. Elevation alleviates SUHII, whereas anthropogenic factors dominate during summer. (3) Coastal SUHII is governed by dual regulatory mechanisms: land–sea interactions modulate spatial patterns, with NDVI cooling and NDBI heating effects amplifying with distance from the coastline, while nearshore marine regulation suppresses heat accumulation. Additionally, cities across different climatic zones exhibit distinct thermal responses, with vegetation cooling efficiency and building-induced heating intensity showing clear latitudinal gradients. These findings advance understanding of multi-scale drivers of coastal SUHI and provide a scientific basis for climate-adaptive urban planning strategies that optimize coastal morphology. Full article

Loop Closure Detection (LCD) is a key component of Simultaneous Localization and Mapping (SLAM) systems, responsible for correcting odometric drift and maintaining global consistency in localization and mapping. However, single-modality LCD methods suffer from inherent limitations: LiDAR-based approaches are affected by point cloud sparsity, limiting feature representation in unstructured environments, while vision-based methods are sensitive to illumination and weather variations, reducing robustness. To address these issues, this paper presents a LiDAR–vision multimodal fusion LCD algorithm. Spatiotemporal alignment between LiDAR point clouds and images is achieved through extrinsic calibration and timestamp interpolation to ensure cross-modal consistency. Harris corner detection and BRIEF descriptors are employed to extract visual features, and a LiDAR-projected sparse depth map is used to complete depth information, mapping 2D features into 3D space. A hybrid feature representation is then constructed by fusing LiDAR geometric triangle descriptors with visual BRIEF descriptors, enabling efficient loop candidate retrieval via hash indexing. Finally, an improved RANSAC algorithm performs geometric verification to enhance the robustness of relative pose estimation. Experiments on the KITTI and NCLT datasets show that the proposed method achieves average F1 scores of 85.28% and 77.63%, respectively, outperforming both unimodal and existing multimodal approaches. When integrated into a SLAM framework, it reduces the Absolute Error (ATE) RMSE by 11.2–16.4% compared with LiDAR-only methods, demonstrating improved loop detection accuracy and overall system robustness in complex environments. Full article

Accurate estimation of forest aboveground carbon (AGC) is crucial for understanding the carbon cycle and formulating climate policies, yet it remains challenging in complex mountainous regions. This study used machine learning framework to estimate the spatiotemporal dynamics of AGC in the Three Parallel Rivers region of China from 2003 to 2024. By integrating China’s National Forest Continuous Inventory (NFCI) data with multispectral satellite imagery, we employed a two-stage feature selection strategy to identify key predictor variables. Among three ensemble algorithms tested, the Random Forest model achieved the optimal performance (R² = 0.74). The results indicated a net increase of 67.05 Tg in total AGC over the two decades, with a spatial pattern characterized by higher densities in the west and north. Geographical Detector analysis revealed that the driving forces were synergistic, with the interaction between temperature and population density exhibiting the most prominent explanatory capacity. This study provides a high-resolution (30 m) benchmark for AGC in a global biodiversity hotspot and underscores the critical role of ecological protection policies in enhancing carbon sequestration, offering valuable insights for managing similar mountain ecosystems worldwide. Full article

On 8 February 2025, an Mw 7.6 strike-slip earthquake ruptured the Swan Islands Transform Fault in the northern Caribbean near its junction with the Mid-Cayman Spreading Center, providing an important offshore case for investigating rupture dynamics along oceanic transform faults. In this study, we jointly apply teleseismic high-frequency back-projection and low-frequency finite-fault full-waveform inversion to image the multi-scale spatiotemporal evolution of the rupture process. Back-projection results reveal a two-stage rupture characterized by an initial sub-shear propagation lasting approximately 20 s, followed by rapid acceleration to supershear velocities of ~5–6 km/s and westward propagation over ~80–100 km. Finite-fault inversion shows that coseismic slip is primarily concentrated within ~20 km west of the epicenter, with a peak slip of ~5.6 m and an overall rupture duration of ~40 s. Comparison between high-frequency radiation and low-frequency slip indicates that the most seismic moment was released during the early slow rupture stage, whereas the later fast-propagating segment produced enhanced high-frequency energy but relatively small slip. These observations reveal a pronounced along-strike complementary relationship between slip amplitude and rupture speed, suggesting a transition in rupture dynamics controlled by variations in fault strength, fracture energy, and/or geometric complexity. By combining high-frequency back-projection with low-frequency finite-fault inversion, we obtain a more complete view of the rupture process of offshore earthquakes, which helps clarify rupture propagation characteristics, including supershear behavior, along oceanic transform faults. Full article