Search Results (4,276)

Streak cameras, essential for ultrahigh temporal resolution diagnostics in laser-driven inertial confinement fusion, underpin the streak tube imaging LiDAR (STIL) system—a flash LiDAR technology offering high spatiotemporal resolution, precise ranging, enhanced sensitivity, and wide field of view. This study establishes a theoretical model of the STIL system, with numerical simulations predicting limits of temporal and spatial resolutions of ~6 ps and 22.8 lp/mm, respectively. Dynamic simulations of laser backscatter signals from targets at varying depths demonstrate an optimal distance reconstruction accuracy of 98%. An experimental STIL platform was developed, with the key parameters calibrated as follows: scanning speed (16.78 ps/pixel), temporal resolution (14.47 ps), and central cathode spatial resolution (20 lp/mm). The system achieved target imaging through streak camera detection of azimuth-resolved intensity profiles, generating raw streak images. Feature extraction and neural network-based three-dimensional (3D) reconstruction algorithms enabled target reconstruction from the time-of-flight data of short laser pulses, achieving a minimum distance reconstruction error of 3.57%. Experimental results validate the capability of the system to detect fast, low-intensity optical signals while acquiring target range information, ultimately achieving high-frame-rate, high-resolution 3D imaging. These advancements position STIL technology as a promising solution for applications that require micron-scale depth discrimination under dynamic conditions. Full article

Urbanization has emerged as one of the most significant global challenges and opportunities of the 21st century, driven by a complex interplay of dynamic processes. In Ethiopia, cities have undergone rapid expansion in recent decades, largely due to state-led economic reforms and infrastructure development. This study aims to investigate the spatiotemporal dynamics, driving forces, and future projections of urban expansion along the Ethio–Djibouti trade corridor, with a focus on Dire Dawa City in eastern Ethiopia. Landsat imagery from 1993, 2003, 2013, and 2023 was utilized to detect land use and land cover (LULC) changes and analyze urban growth patterns. Additionally, maps illustrating the city’s demographic, economic, and topographic characteristics were developed to identify the key driving factors behind land conversion and urban expansion. The spatial matrix and landscape expansion index were employed to examine the spatial patterns of urban growth. Furthermore, the study applied the Multi-Layer Perceptron–Markov Chain (MLP–MC) model to simulate future LULC changes and urban expansion. The results indicate that the built-up area in Dire Dawa has increased significantly over the past three decades, growing from 6.21 km² in 1993 to 21.54 km² in 2023. This urban growth is predominantly characterized by edge expansion, reflecting a pattern of unidirectional, unsustainable development that has consumed large areas of agricultural land. The analysis shows that socioeconomic development and population growth have had a greater influence on LULC conversion and urban expansion than physical factors. Based on these identified drivers, the study projected land conversion and simulated urban expansion for the years 2043 and 2064. The findings underscore the urgent need for context-sensitive urban growth strategies that harmonize local realities with national development policies and the Sustainable Development Goals. Full article

This paper proposes Feature fusion and Dilated causal convolution model for Bearing Remaining useful life Prediction (FDBRP), an integrated framework for accurate Remaining Useful Life (RUL) prediction of rolling bearings that combines three key innovations: (1) a data augmentation module employing sliding-window processing and two-dimensional feature concatenation with label normalization to enhance signal representation and improve model generalizability, (2) a feature fusion module incorporating an enhanced graph convolutional network for spatial modeling, an improved multi-scale temporal convolution for dynamic pattern extraction, and an efficient multi-scale attention mechanism to optimize spatiotemporal feature consistency, and (3) an optimized dilated convolution module utilizing interval sampling to expand the receptive field, and combines the residual connection structure to realize the regularization of the neural network and enhance the ability of the model to capture long-range dependencies. Experimental validation showcases the effectiveness of proposed approach, achieving a high average score of 0.756564 and demonstrating a lower average error of 10.903656 in RUL prediction for test bearings compared to state-of-the-art benchmarks. This highlights the superior RUL prediction capability of the proposed methodology. Full article

Driven by both natural and anthropogenic factors, farmland abandonment and recultivation constitute complex and widespread global phenomena that impact the ecological environment and society. In the Inner Mongolia Yellow River Basin (IMYRB), a critical tension lies between agricultural production and ecological conservation, characterized by dynamic bidirectional transitions that hold significant implications for the harmony of human–nature relations and the advancement of ecological civilization. With the development of remote sensing, it has become possible to rapidly and accurately extract farmland changes and monitor its vegetation restoration status. However, mapping abandoned farmland presents significant challenges due to its scattered and heterogeneous distribution across diverse landscapes. Furthermore, subjectivity in questionnaire-based data collection compromises the precision of farmland abandonment monitoring. This study aims to extract crop phenological metrics, map farmland abandonment, and recultivation dynamics in the IMYRB and assess post-transition vegetation changes. We used Landsat time-series data to detect the land-use changes and vegetation responses in the IMYRB. The Farmland Abandonment and Recultivation Extraction Index (FAREI) was developed using crop phenology spectral features. Key crop-specific phenological indicators, including sprout, peak, and wilting stages, were extracted from annual MODIS NDVI data for 2020. Based on these key nodes, the Landsat data from 1999 to 2022 was employed to map farmland abandonment and recultivation. Vegetation recovery trajectories were further analyzed by the Mann–Kendall test and the Theil–Sen estimator. The results showed rewarding accuracy for farmland conversion mapping, with overall precision exceeding 79%. Driven by ecological restoration programs, rural labor migration, and soil salinization, two distinct phases of farmland abandonment were identified, 87.9 kha during 2002–2004 and 105.14 kha during 2016–2019, representing an approximate 19.6% increase. Additionally, the post-2016 surge in farmland recultivation was primarily linked to national food security policies and localized soil amelioration initiatives. Vegetation restoration trends indicate significant greening over the past two decades, with particularly significant increases observed between 2011 and 2022. In the future, more attention should be paid to the trade-off between ecological protection and food security. Overall, this study developed a novel method for monitoring farmland dynamics, offering critical insights to inform adaptive ecosystem management and advance ecological conservation and sustainable development in ecologically fragile semi-arid regions. Full article

Understanding the spatiotemporal dynamics of water quality parameters and microbial communities in drinking water distribution systems (DWDS) and their interrelationships is critical for ensuring the safety of tap water supply. This study investigated the diurnal, monthly, and annual variation patterns of water quality and the stage-specific succession behaviors of microbial communities in a DWDS located in southeastern China. Results indicated that hydraulic shear stress during peak usage periods drove biofilm detachment and particle resuspension. This process led to significant diurnal fluctuations in total cell counts (TCC) and metal ions, with coefficients of variation ranging from 0.44 to 1.89. Monthly analyses revealed the synergistic risks of disinfection by-products (e.g., 24.5 μg/L of trichloromethane) under conditions of low chlorine residual (<0.2 mg/L) and high organic loading. Annual trends suggested seasonal coupling: winter pH reductions correlated with organic acid accumulation, while summer microbial blooms associated with chlorine decay and temperature increase. Nonlinear interactions indicated weakened metal–organic complexation but enhanced turbidity–sulfate adsorption, suggesting altered contaminant mobility in pipe scales. Microbial analysis demonstrated persistent dominance of oligotrophic Phreatobacter and prevalence of Pseudomonas in biofilms, highlighting hydrodynamic conditions, nutrient availability, and disinfection pressure as key drivers of community succession. These findings reveal DWDS complexity and inform targeted operational and microbial risk control strategies. Full article

Fluid mechanics often involves complex systems characterized by a large number of physical parameters, which are usually described by experimental and numerical sparse data (temporal or spatial). The difficulty of obtaining complete spatio-temporal datasets is a common issue with conventional approaches, such as computational fluid dynamics (CFDs) and various experimental methods, particularly when evaluating and modeling turbulent flows. This review paper focuses on the integration of machine learning (ML), specifically physics-informed neural networks (PINNs), as a means to address this challenge. By directly incorporating governing physical equations into neural network training, PINNs present a novel method that allows for the reconstruction of flow from sparse and noisy data. This review examines various applications in fluid mechanics where sparse data is a common problem and evaluates the effectiveness of PINNs in enhancing flow prediction accuracy. An overview of diverse PINNs methods, their applications, and outcomes is discussed, demonstrating their flexibility and effectiveness in addressing challenges related to sparse data and illustrating that the future of fluid mechanics lies in the synergy between data-driven approaches and established physical theories. Full article

To overcome the limitations of traditional convolutional and recurrent neural networks in capturing spatio-temporal dynamics in flow fields on unstructured grids, this study proposes a novel Spatio-Temporal Graph Neural Network (ST-GNN) model that integrates a Graph Neural Network (GNN) with a Long Short-Term Memory (LSTM) network. The GNN component captures spatial dependencies among irregular grid nodes via message passing, while the LSTM component models temporal evolution through gated memory mechanisms. This hybrid framework enables the joint learning of spatial and temporal features in complex flow systems. Two variants of ST-GNN, namely, GCN-LSTM and GAT-LSTM, were developed and evaluated using the SUBOFF AFF-8 benchmark dataset. The results show that GAT-LSTM achieved higher accuracy than GCN-LSTM, with average relative errors of 2.51% for velocity and 1.43% for pressure at the 1000th time step. Both models achieved substantial speedups over traditional CFD solvers, with GCN-LSTM and GAT-LSTM accelerating predictions by approximately 350 and 150 times, respectively. These findings position ST-GNN as an efficient and accurate alternative for spatio-temporal flow modeling on unstructured grids, advancing data-driven fluid dynamics. Full article

The vegetation cover in the Red River Basin (RRB) has undergone considerable changes over the past 20 years. Identifying vegetation cover and its transboundary differences is crucial for assessing the ecological health of the region. This study utilized normalized difference vegetation index (NDVI) data (2000–2023) to analyze the spatiotemporal dynamics of fractional vegetation cover (FVC) and its transboundary differences within the RRB. The results revealed the following: (1) From 2000 to 2023, overall FVC in the basin increased, with a mean value of 0.64, indicating favorable vegetation conditions. (2) In terms of spatial distribution, the RRB in China (RRBC) generally exhibited higher FVC in the west and lower FVC in the east, whereas the RRB in Vietnam and Laos (RRBVL) exhibited higher FVC in the east and lower FVC in the west. Regarding spatiotemporal changes, in RRBC, the changes were primarily characterized by both non-significant improvement (56.01%) and extremely significant improvement (21.45%). Conversely, RRBVL exhibited both areas of extremely significant improvement (25.4%) and areas of extremely significant degradation (18%). (3) Anthropogenic activities exerted a stronger influence than precipitation on both spatiotemporal changes and transboundary differences in FVC. In conclusion, an overall increase in FVC is observed throughout the RRB, with notable transboundary variations. Full article

Traffic violations, the primary cause of road accidents, threaten public safety by disrupting traffic flow and causing substantial casualties and economic losses. Accurate spatiotemporal prediction of violations offers critical insights for proactive traffic management. While Graph Attention Network (GAT) methods excel in spatiotemporal forecasting, their practical deployment is hindered by prohibitive computational costs when handling dynamic large-scale data. To address this issue, we propose a Lightweight Traffic Violation Prediction with Graph Attention Distillation (LTVPGA) model, transferring spatial topology comprehension from a complex GAT to an efficient multilayer perceptron (MLP) via knowledge distillation. Our core contribution lies in topology-invariant knowledge transfer, where spatial relation priors distilled from the teacher’s attention heads enable the MLP student to bypass explicit graph computation. This approach achieves significant efficiency gains for large-scale data—notably accelerated inference time and reduced memory overhead—while preserving modeling capability. We conducted a performance comparison between LTVPGA, Conv-LSTM, and GATR (teacher model). LTVPGA achieved revolutionary efficiency: consuming merely 15% memory and 0.6% training time of GATR while preserving nearly the same accuracy. This capacity enables practical deployment without sacrificing fidelity, providing a scalable solution for intelligent transportation governance. Full article

This paper proposes a Dynamic Flow Spatio-Temporal Generative Adversarial Network (DFST-GAN) model for high-quality precipitation nowcasting. Current spatio-temporal prediction models struggle with two key limitations: the inability to adaptively capture complex motion patterns and the tendency to generate blurry predictions over time. To address these challenges, DFST-GAN integrates a dynamic flow feature extraction mechanism with a novel specialized meteorological discriminator, enabling adaptive modeling of complex precipitation system trajectories and generating sharper, physically consistent predictions. We evaluate our approach on the HKO-7 dataset using metrics including CSI, HSS, POD, FAR and ETS. Experimental results demonstrate that DFST-GAN consistently outperforms existing methods across all evaluation metrics, with particularly notable improvements for moderate to heavy rainfall events (dBZ ≥ 50), showing a 18.8% relative improvement in CSI compared to PredRNN-V2. The ablation studies confirm that each component makes a meaningful contribution to overall performance, validating the potential of our approach for operational precipitation nowcasting applications. Full article

Land degradation is one of the significant ecological and environmental issues threatening regional sustainable development. Datong Coalfield is located in an arid and semi-arid ecologically fragile area and is also an important energy base, the mining of coal resources and natural factors have caused serious land degradation problems. Therefore, dynamic monitoring and influencing factor analysis of land degradation in the Datong Coalfield is particularly important for land degradation prevention and land reclamation in mining areas. This study focuses on the Datong Coalfield, using remote sensing technology to dynamically extract soil erosion, net primary productivity of vegetation, land desertification, soil moisture content. Based on the Analytic Hierarchy Process (AHP), a comprehensive assessment model for land degradation was constructed to analyze the spatiotemporal evolution of land degradation in the Datong Coalfield from 2000 to 2021, and the influencing factors of land degradation were explored using a geographic detector. The results indicate that (1) from 2000 to 2021, the land degradation level in Datong Coalfield changed to mild degradation and non degradation, with the mild degradation area increasing by 30.48% and the non degradation area increasing by 13.9%, and spatially expanding contiguously from localized areas outwards. (2) Over the past 21 years, the land degradation situation in Datong Coalfield predominantly showed an improving trend, accounting for 69.11%, indicating an overall positive trajectory. However, 0.54% of the area experienced significantly intensified land degradation, scattered in the eastern and southwestern parts of the Datong Coalfield, which are areas requiring focused governance efforts. (3) Vegetation and land use are the main factors affecting land degradation in Datong Coalfield. At the same time, the influence of land use has gradually increased over the years, and the influence of vegetation and land use interaction is the highest in the two-factor interaction. Full article

Dominant species form species-specific fine-scale vegetation matrices in grasslands that regulate community dynamics, diversity and ecosystem functioning. The structure of these dynamic microscale landscapes was analyzed and compared between primary and secondary plant communities. We explored fine-scale monitoring data along permanent transects over seven consecutive years. Spatial and temporal patterns of dominant grass species (Festuca valesiaca, Alopecurus pratensis and Poa angustifolia) were analyzed using information theory models. These matrix-forming species showed high spatiotemporal variability in all grasslands. However, consistent differences were found between primary and secondary grasslands in the spatial and temporal organization of the vegetation matrix. Alopecurus pratensis and Poa angustifolia had coarse-scale patchiness with stronger aggregation in secondary grasslands. The spatial patterns of Festuca valesiaca were nearly random in both types of grasslands. Strong associations were observed among the spatial patterns of each species across years, with a stronger dependence in secondary grasslands. In contrast, the rate of fine-scale dynamics was higher in primary grasslands. The complexity of microhabitats within the matrix was higher in primary grasslands, often involving two to three dominant species, while, in secondary grasslands, patches formed by a single dominant species were more frequent. In the spatial variability of small-scale subordinate species richness, significant, temporally consistent differences were found. Higher variability in secondary grasslands suggests stronger and more spatially variable microhabitat filtering. We recommend that grassland management and restoration practices be guided by preliminary information on the spatial organization of primary grasslands. Enhancing the complexity of the matrix formed by dominant species can further improve the condition of secondary grasslands. Full article

Carbon stock plays a crucial role in regulating atmospheric carbon dioxide concentrations and represents a vital ecological function for mitigating climate change and supporting long-term environmental sustainability. Jiangsu Province, a typical region experiencing rapid urbanization and land-use transformation in eastern China, serves as a representative case for regional-scale carbon assessment. This study employs the InVEST model, integrated with multi-source remote sensing data, a random forest algorithm, and a control variable approach, to simulate the spatiotemporal dynamics of carbon stock in Jiangsu Province under a set of climate, productivity, and population scenarios. Three scenario groups were designed to isolate the individual effects of climate change, gross primary productivity, and population density from 2020 to 2060, enabling a clearer understanding of the dominant drivers. The results indicate that the coupled model estimates Jiangsu’s 2020 carbon stock at 1.52 × 10⁹ t C, slightly below the 1.82 × 10⁹ t C estimated by the standalone InVEST model, with the coupled results closer to previous estimates. Compared with InVEST alone, the integrated model significantly improves numerical accuracy and spatial resolution, allowing for finer-scale pattern recognition. By 2060, carbon stock is projected to decline by approximately 24.4% across all scenarios. Among the features, climate change exerts the most significant influence, with an elasticity coefficient range of −37.76–1.01, followed by productivity, while population density has minimal impact. These findings underscore the dominant role of climate drivers and highlight that model integration improves both predictive accuracy and spatial detail, offering a more robust basis for scenario-based assessment. The proposed approach provides valuable insights for supporting sustainable carbon management, real-time monitoring, and provincial-scale decarbonization planning. Full article

The long-term sustainability of conservation efforts in critical reforestation regions requires timely, spatiotemporal assessments of ecological quality. In alignment with China’s environmental initiatives, this study integrates Google Earth Engine (GEE) and MODIS data to construct an enhanced Remote Sensing Ecological Index (RSEI) for two decades of ecological monitoring. Hotspot analysis (Getis-Ord Gi*) revealed concentrated high-quality zones, particularly in Xinjiang’s Altay Prefecture, with ‘Good’ and ‘Excellent’ areas increasing from 21.64% in 2000 to 31.30% in 2020. To uncover driving forces, partial correlation and geographic detector analyses identified a transition in the Three-North Shelter Forest Program (TNSFP) from climate–topography constraints to land use–climate synergy, with land use emerging as the dominant factor. Socioeconomic influences, shaped by policy interventions, also played an important but fluctuating role. This progression—from natural constraints to active human regulation—underscores the need for climate-adaptive land use, balanced ecological–economic development, and region-specific governance. These findings validate the effectiveness of current conservation strategies and provide guidance for sustaining ecological progress and optimizing future development in the TNSFP. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article