Search Results (5,742)

For the combination of finite elements and control circuits, the calculation is complex and time-consuming, making direct optimization impractical. In this paper, a new distributed node and magnetic circuit model is proposed to simulate the spatial and temporal variation of the distributed air-gap magnetic density with the current and rotor angle and solve the electromagnetic force wave variation. Compared to other distributed flux-linkage models, the proposed model not only considers the radial magnetic path but also connects adjacent magnetic paths tangentially. The inclusion of this tangential path enhances the mutual interaction between magnetic circuits, leading to a more accurate model. Based on the control circuit model, the electromagnetic force wave changes caused by the harmonic currents under various circuits and operating conditions are calculated, the topology is analyzed and optimized to mitigate critical harmonics, the electromagnetic force wave is reduced, and finally, the model accuracy is verified experimentally. While most distributed flux-linkage models are applied to the optimization of motor performance metrics such as the magnetomotive force (MMF), power, and torque, this paper applies the model to the optimization of the magnetic field strength, the harmonic content, and the corresponding noise, vibration, and harshness (NVH), demonstrating a broader range of applications. This method can be coupled with the control circuit to analyze the changes in electromagnetic force waves and quickly optimize them, improving the accuracy and efficiency of research and development. Full article

In the field of water process engineering, accurately predicting the total nitrogen (TN) concentration distribution in the Semi-Enclosed Bay area is of great importance for water quality assessment, pollution control, and scientific management. Due to the coupling of multiple influencing factors, the pollution process is complex, and traditional monitoring methods struggle to achieve large-scale, long-term real-time observation. Although numerical simulations can reproduce TN transport processes, they are computationally expensive and have low prediction efficiency. To address this, this study develops a deep learning hybrid model that integrates a Temporal Convolutional Network (TCN) and a Long Short-Term Memory (LSTM) network, referred to as the TCN-LSTM-Hybrid Model, to predict the spatiotemporal distribution of TN concentration fields in Shenzhen Bay. Comparative experiments show that this model outperforms traditional models such as TCN, LSTM, GRU, and MLP in terms of prediction accuracy and spatial generalization, offering higher computational efficiency and breaking through the limitations of “point-based prediction” by achieving “field-based prediction,” thereby providing a new path for pollutant simulation in complex ocean environments, supporting more informed decision making in ocean and coastal management. Full article

Post-fire soil and vegetation changes can intensify erosion and sediment yield by altering the factors controlling the runoff–infiltration balance. Erosion barriers (EBs) are widely used in hydrological and forest restoration to mitigate erosion, reduce sediment transport, and promote vegetation recovery. However, precise spatial assessments of their effectiveness remain scarce, requiring validation through operational methodologies. This study evaluates the impact of EB on post-fire vegetation recovery at two temporal and spatial scales: (1) Remotely Piloted Aircraft System (RPAS) imagery, acquired at high spatial resolution but limited to a single acquisition date coinciding with the field flight. These data were captured using a MicaSense RedEdge-MX multispectral camera and an RGB optical sensor (SODA), from which NDVI and vegetation height were derived through aerial photogrammetry and digital surface models (DSMs). (2) Sentinel-2 satellite imagery, offering coarser spatial resolution but enabling multi-temporal analysis, through NDVI time series spanning four consecutive years. The study was conducted in the area of the Luna Fire (northern Spain), which burned in July 2015. A paired sampling design compared upstream and downstream areas of burned wood stacks and control sites using NDVI values and vegetation height. Results showed slightly higher NDVI values (0.45) upstream of the EB (p < 0.05), while vegetation height was, on average, ~8 cm lower than in control sites (p > 0.05). Sentinel-2 analysis revealed significant differences in NDVI distributions between treatments (p < 0.05), although mean values were similar (~0.32), both showing positive trends over four years. This study offers indirect insight into the functioning and effectiveness of EB in post-fire recovery. The findings highlight the need for continued monitoring of treated areas to better understand environmental responses over time and to inform more effective land management strategies. Full article

One of the leading causes of climate change and global warming is the rise in carbon dioxide (${\mathrm{CO}}_2$) levels. For a precise assessment of ${\mathrm{CO}}_2$’s impact on the climate and the creation of successful mitigation methods, it is essential to comprehend its distribution by analyzing ${\mathrm{CO}}_2$ sources and sinks, which is a challenging task using sparsely available ground monitoring stations and airborne platforms. Therefore, the data retrieved by the Orbiting Carbon Observatory-2 (OCO-2) satellite can be useful due to its extensive spatial and temporal coverage. Sparse and missed retrievals in the satellite make it challenging to perform a thorough analysis. This work trains machine learning models using the Orbiting Carbon Observatory-2 (OCO-2) ${\mathrm{XCO}}_2$ retrievals and auxiliary features to obtain a monthly, high-spatial-resolution, gap-filled ${\mathrm{CO}}_2$ concentration distribution. It uses a multi-source aggregated (MSD) dataset and the generalized stacked ensemble model to predict country-level high-resolution (1 ${\mathrm{km}}^2$) ${\mathrm{XCO}}_2$. When evaluated with TCCON, this country-level model can achieve an RMSE of 1.42 ppm, a MAE of 0.84 ppm, and ${\mathrm{R}}^2$ of 0.90. Full article

SHORT INTERNODE (SHI)-Related Sequence (SRS) transcription factors play crucial roles in plant growth, development, and stress responses and have been extensively studied in various plant species. However, the molecular functions and regulatory mechanisms of SRS genes in the economically important tropical fruit crop pitaya (Hylocereus undatus) remain poorly understood. This study identified 9 HuSRS genes in pitaya via bioinformatics analysis, with subcellular localization predicting nuclear distributions for all. Gene structure analysis showed 1–4 exons, and conserved motifs (RING-type zinc finger and IXGH domains) were shared across subclasses. Phylogenetic analysis classified the HuSRS genes into three subfamilies. Subfamily I (HuSRS1–HuSRS4) is closely related to poplar and tomato homologs and subfamily III (HuSRS6–HuSRS8) contains a recently duplicated paralogous pair (HuSRS7/HuSRS8) and shows affinity to rice SRS genes. Protein structure prediction revealed dominance of random coils, α-helices, and extended strands, with spatial similarity correlating to subfamily classification. Interaction networks showed HuSRS1, HuSRS2, HuSRS7 and HuSRS8 interact with functional proteins in transcription and hormone signaling. Promoter analysis identified abundant light/hormone/stress-responsive elements, with HuSRS5 harboring the most motifs. Transcriptome and qPCR analyses revealed spatiotemporal expression patterns: HuSRS4, HuSRS5, and HuSRS7 exhibited significantly higher expression levels in callus (WG), which may be associated with dedifferentiation capacity. In seedlings, HuSRS9 exhibited extremely high transcriptional accumulation in stem segments, while HuSRS1, HuSRS5, HuSRS7 and HuSRS8 were highly active in cotyledons. This study systematically analyzed the characteristics of the SRS gene family in pitaya, revealing its evolutionary conservation and spatio-temporal expression differences. The research results have laid a foundation for in-depth exploration of the function of the SRS gene in the tissue culture and molecular breeding of pitaya. Full article

We present a visual–inertial odometry (VIO) system that integrates a deep feature extraction and filtering strategy with optical flow to improve tracking robustness. While many traditional VIO methods rely on hand-crafted features, they often struggle to remain robust under challenging visual conditions, such as low texture, motion blur, or lighting variation. These methods tend to exhibit large performance variance across different environments, primarily due to the limited repeatability and adaptability of hand-crafted keypoints. In contrast, learning-based features offer richer representations and can generalize across diverse domains thanks to data-driven training. However, they often suffer from uneven spatial distribution and temporal instability, which can degrade tracking performance. To address these issues, we propose a hybrid front-end that combines a lightweight deep feature extractor with an image pyramid and grid-based keypoint sampling to enhance spatial diversity. Additionally, a forward–backward optical-flow-consistency check is applied to filter unstable keypoints. The system improves feature tracking stability by enforcing spatial and temporal consistency while maintaining real-time efficiency. Finally, the effectiveness of the proposed VIO system is validated on the EuRoC MAV benchmark, showing a 19.35% reduction in trajectory RMSE and improved consistency across multiple sequences compared with previous methods. Full article

Understory vegetation, particularly dwarf bamboo, plays a crucial role in regulating forest nutrient cycles by intercepting litter and altering decomposition processes, yet its overall impacts remain understudied and insufficiently quantified. This study employs a combination of field surveys and decomposition bag experiments to investigate how understory dwarf bamboo (Fargesia decurvata) alters the spatial–temporal patterns of leaf litter production and decomposition. We found that the dwarf bamboo intercepted more than 25% of canopy litterfall, altering its spatial distribution and reducing decomposition efficiency in the bamboo crown (BC). Leaf trait-decomposition relationships differed strongly across habitats, being positive for saturated fresh weight (SFW), leaf thickness (LFT), and leaf area (LA) and dry weight (DW) in bamboo habitats but weaker in the bamboo-free habitat (NB). Potassium release was significantly higher in the BC treatment, whereas carbon release showed the opposite trend. In contrast, nitrogen and phosphorus exhibited net enrichment across all treatments, with phosphorus enrichment being slower in BC than in bamboo-covered ground surface (BG) and NB. Our results demonstrate that the understory dwarf bamboo reshapes the spatial distribution of litter and nutrient release dynamics during decomposition, resulting in element-specific nutrient release patterns. These findings provide mechanistic insights into how understory dwarf bamboo mediates nutrient cycling dynamics in forest communities. Full article

Understanding the temporal dynamics and spatial distribution of key soil properties is essential for sustainable land management and informed decision-making. This study assessed the spatial variability and decadal changes (2013–2023) of topsoil properties in Córdoba, central Argentina, using digital soil mapping (DSM) and machine learning (ML) algorithms. Three ML methods—Quantile Regression Forest (QRF), Cubist, and Support Vector Machine (SVM)—were compared to predict soil organic matter (SOM), extractable phosphorus (P), and pH at 0–20 cm depth, based on environmental covariates related to site climate, vegetation, and topography. QRF consistently outperformed the other models in prediction accuracy and uncertainty, confirming its suitability for DSM in heterogeneous landscapes. Prediction uncertainty was higher in marginal mountainous areas than in intensively managed plains. Over ten years, SOM, P, and pH exhibited changes across land-use classes (cropland, pasture, and forest). Extractable P declined by 15–35%, with the sharpest reduction in croplands (−35.4%). SOM decreased in croplands (−6.7%) and pastures (−3.1%) but remained stable in forests. pH trends varied, with slight decreases in croplands and forests and a small increase in pastures. By integrating high-resolution mapping and temporal assessment, this study advances DSM applications and supports regional soil monitoring and sustainable land-use planning. Full article

Traditional villages (TVLGS) have significantly declined as a result of China’s fast urbanization, especially in Fujian Province, where efficient conservation efforts are hampered by a lack of thorough study. The geographical and temporal distribution features of Fujian’s traditional villages (FTVLGS) are investigated using ArcGIS 10.8 and GeoDa software. Additionally, it identifies 18 driving factors to investigate the primary influences and interaction mechanisms through a combination of Python 3.7 and GeoDa 1.16. The results show that: (1) FTVLGS are distributed both spatially and temporally in a pattern that is oriented from northeast to southwest to east. Over time, the distribution center of gravity moves from north to southeast, increasing directional tendencies and broadening the distribution area. (2) The impact of each driving factor on the spatial distribution of TVLGS varies, with the strongest influence being the interaction between average annual precipitation and the straight-line distance from provincial highways. The straight-line distance between TVLGS and provincial highways is found to be the most significant factor affecting their distribution. This study clarifies the intricate dynamics associated with the distribution of TVLGS and the factors that influence them, providing evidence-based recommendations for the future preservation and advancement of these TVLGS. It also aims to enhance the connectivity of developmental elements at a regional scale and to foster the advancement of global tourism within TVLGS. Full article

In urban intelligent transportation systems, the real-time acquisition of network-wide traffic states is constrained by limited sensor density and high deployment costs. To address this challenge, this paper proposes a learnable Detection Point Selection Module (DPSM), which adaptively determines the most informative observation points through an end-to-end attention mechanism to support full-map traffic state estimation. Distinct from conventional fixed deployment strategies, DPSM provides an adaptive detector configuration that, under the same number of loop sensors, achieves significantly higher estimation accuracy by intelligently optimizing their placement. Specifically, the module takes normalized spatial and temporal information as input and generates an attention-based distribution to identify critical traffic flow readings, which are subsequently fed into various backbone prediction models, including fully connected networks, convolutional neural networks, and long short-term memory networks. Experiments on the real-world NGSIM-US101 dataset demonstrate that three variants—DPSM-NN, DPSM-CNN, and DPSM-LSTM—consistently outperform their corresponding baselines, with notable robustness under sparse observation scenarios. These results highlight the advantage of adaptive detector placement in maximizing the utility of limited sensors, effectively mitigating information loss from sparse deployments and offering a cost-efficient, scalable solution for urban traffic monitoring and control. Full article

Chlorophyll-a is a key indicator characterizing the health of marine ecosystems. This study aimed to assess eutrophication risk by investigating the spatio-temporal evolution of chlorophyll-a in the South China Sea (SCS). Based on MODIS-Aqua remote sensing data from 2003 to 2024, five spatial interpolation methods were compared, and Ordinary Kriging was selected as the optimal method (r = 0.96) for reconstructing the chlorophyll-a distribution. The findings indicate that chlorophyll-a is higher in winter and autumn than in summer and spring, with significant enrichment observed near coastal areas. Concentrations decrease with increasing distance from the shore. The Mekong River estuary consistently exhibits high values, while the concentration in the SCS Basin remains persistently low. Furthermore, the spatial extent where chlorophyll concentrations exceed the bloom threshold was evaluated to highlight potential eutrophication risk. These results provide a scientific basis for understanding the response mechanism of the SCS ecosystem to climate change and have important implications for regional marine environmental management and ecological conservation. Full article

Urban park green space (UPGS) is a key component of urban green infrastructure, yet it faces multiple contradictions, such as insufficient quantity and uneven distribution. Taking Zhengzhou City as a case study, this research explored the impacts of temporal thresholds and the modifiable areal unit problem (MAUP) on UPGS accessibility and equity. An improved multi-modal Gaussian two-step floating catchment area (G2SFCA) method was employed to measure UPGS accessibility, while the Gini coefficient and Lorenz curve were used to analyze its equity. The results show that (1) UPGS presents a dual-core agglomeration feature, with accessibility blind spots surrounding the edge of the study area and relatively low equity in the western and southern regions; (2) changes in temporal thresholds and spatial scales have a significant impact on UPGS accessibility (p < 0.001), whereas their impact on equity is minor; and (3) UPGS distribution suffers from spatial imbalance, with a huge disparity in resource allocation. This study overcomes the limitations of traditional evaluation methods that rely on a single mode or ignore scale effects and provides a more scientific analytical framework for accurately identifying the spatial heterogeneity of UPGS accessibility and the imbalance between supply and demand. Full article

Remotely sensed data have been instrumental in improving our understanding of the nature of fires within tropical landscapes. However, most studies have depicted fires in a negative light, highlighting how land-use and land-cover changes make forests more vulnerable to fire damage. In contrast to such fires, indigenous peoples utilize fires as a key part of their livelihood practices, and such relationships have not been extensively examined using remotely sensed data. In this paper, we utilize MODIS Active Fire data to examine the spatial and temporal distribution of fires relative to indigenous lands across Guyana. We employed the DBSCAN clustering algorithm and Voronoi polygons to examine the patterns of fire distribution across the Guyanese landscape. We found that while indigenous territories accounted for approximately 15% of Guyana’s terrestrial landscape, 25% of fires occurred within Amerindian lands, and 71% within 16 km of village boundaries. A strong linear distance decay (R² = 0.97) was observed between the occurrence of fires and Amerindian village boundaries. Four previously undefined fire regions emerged for Guyana–Coastal, Forest, Forest Edge North, and Forest Edge South–with the Forest Edge regions hosting the second highest number of fires but the highest indigenous peoples’ presence. The spatial distribution of fires relative to each region suggested that Forest Edge indigenous villages had a strong reliance on fires as a part of their toolkit for maintaining the rich ecological processes characteristically observed around their lands. Full article

Bitcoin transaction anomaly detection is essential for maintaining financial market stability. A significant challenge is capturing the dynamically evolving transaction patterns within transaction networks. Dynamic graph models are effective for characterizing the temporal evolution of transaction systems. However, current methods struggle to mine long-range temporal dependencies and address the class imbalance caused by the scarcity of abnormal samples. To address these issues, we propose a novel approach, the Bidirectional EvolveGCN with Class-Balanced Learning Network (Balanced-BiEGCN), for Bitcoin transaction anomaly detection. This model integrates two key components: (1) a bidirectional temporal feature fusion mechanism (Bi-EvolveGCN) that enhances the capture of long-range temporal dependencies and (2) a Sample Class Transformation (CSCT) classifier that generates difficult-to-distinguish abnormal samples to balance the positive and negative class distribution. The generation of these samples is guided by two loss functions: the adjacency distance adaptive loss function and the symmetric space adjustment loss function, which optimize the spatial distribution and confusion of abnormal samples. Experimental results on the Elliptic dataset demonstrate that Balanced-BiEGCN outperforms existing baseline methods in anomaly detection. Full article

As an important ecosystem service, water conservation is influenced by land use related to human activities. In this study, we first evaluated spatial and temporal changes in water conservation in Baicheng City, western Jilin Province, from 2000 to 2020. Then, we identified three different scenarios: the natural development scenario (NDS), cropland protection scenario (CPS), and ecological protection scenario (EPS). We coupled the Patch-generating Land Use Simulation (PLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models to predict the distribution of land use types and water conservation in Baicheng City under these scenarios for 2030. The results showed the following: (1) The average water conservation in Baicheng City from 2000 to 2020 was 7.08 mm. (2) Areas with higher water conservation were distributed in the northwest and northeast, while lower water conservation areas were distributed in the central and southwest of Baicheng City. (3) The simulation results of the future pattern of land use show an increasing water conservation trend in all three scenarios. Compared with the other two scenarios, the ecological protection scenario is the most suitable option for the current development planning of Baicheng City. Under the ecological protection scenario (EPS), ecological land is strictly protected, the area of agricultural land increases to some extent, and the overall structure of changes in land use becomes more rational. This study provides a reference for land resource allocation and ecosystem conservation. Full article