Search Results (5,589)

Quantitatively characterizing the dynamic evolution of fracture swarms under offset well low-frequency distributed acoustic sensing (LF-DAS) monitoring remains a significant challenge. This study proposes a physics-data dual-driven closed-loop inversion framework to address this problem. The framework consists of three core modules: (1) a fluid–solid coupled semi-analytical forward model applicable to variable-rate injection and shut-in conditions; (2) an automatic key feature identification method based on multi-scale scanning and physical polarity constraints; and (3) a dynamic inversion model for fracture swarms based on adaptive particle swarm optimization (APSO). Validation against the classical PKN model confirms that the proposed forward model accurately reproduces the fundamental fracture propagation behavior, with good agreement in fracture half-length and net pressure evolution. In synthetic inversion cases, the method successfully recovers the number of fractures, the dynamic flow rate allocation history, fracture length evolution, and the spatiotemporal strain rate response. A field application further demonstrates that three dominant fractures were generated during stimulation, reaching the vicinity of the monitoring well at 18, 27, and 46 min with corresponding spacings of approximately 21 m and 16 m. The proposed framework provides a new route for advancing LF-DAS monitoring from qualitative interpretation to quantitative dynamic inversion. Full article

Spatial and temporal ontologies play a foundational role in modeling dynamic real-world phenomena across domains such as geographic information systems, artificial intelligence, and the Semantic Web. Although decades of research have advanced spatial reasoning, temporal logic, and ontology engineering, fully integrated spatio-temporal frameworks remain fragmented across disciplinary traditions. This paper presents a comprehensive review of spatial, temporal, and spatio-temporal ontologies, examining their conceptual foundations, formal logical models and Semantic Web standards. The literature is analyzed to classify major modeling paradigms and to evaluate their theoretical assumptions, representational capabilities, and computational trade-offs. The review proposes a taxonomy distinguishing foundational ontologies, spatial-centric models, temporal-centric frameworks, integrated spatio-temporal systems. Comparative discussion highlights tensions between logical expressiveness and scalability, as well as challenges related to interoperability and dynamic reasoning. The analysis identifies persistent gaps, including limited native temporal support in description logics, complexity in modeling evolving spatial relations, absence of unified spatio-temporal standards, and lack of standardized evaluation benchmarks. The paper concludes by outlining research directions focused on hybrid ontology–knowledge graph architectures, multi-scale modeling, event-driven semantics, and neuro-symbolic integration. By synthesizing theoretical and applied perspectives, this review provides a structured foundation for advancing interoperable and scalable spatio-temporal knowledge systems capable of supporting next-generation intelligent applications. Full article

With the increasing depth of mining operations, accurate identification and assessment of rock mass instability risks are crucial for ensuring mine safety. This study proposes an integrated framework combining the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), fuzzy comprehensive evaluation (FCE) and kernel density estimation (KDE) for the identification and dynamic assessment of high-risk zones in deep mining. Using microseismic monitoring data from a lead–zinc mine in Northwest China (January–June 2023), the HDBSCAN algorithm adaptively identified 86 high-density clusters from 11,638 events. The weights of five evaluation indicators (moment magnitude, apparent stress, stress drop, peak ground acceleration, and ringing count) were determined objectively using the Euclidean distance method. FCE was then applied to classify cluster risk levels, revealing that 70.9% of the clusters were rated as high-risk (Level IV). KDE further illustrated the spatiotemporal migration of high-risk zones, showing a systematic shift from northeast to southwest along stopes and roadways, driven by mining unloading and geological structures. The integrated HDBSCAN-FCE-KDE framework demonstrates strong applicability and reliability in identifying and predicting rock mass instability risks, providing a scientific basis for proactive risk management in deep mining environments. Full article

The sustainable conservation of linear industrial heritage corridors remains challenged by a limited understanding of their formation mechanisms and driving forces. Addressing this gap, this study develops a transferable analytical framework to explain the spatio-temporal evolution of such systems. Using Jiangsu Province (China) as a case study and a dataset of 344 industrial heritage sites, we apply an integrated spatial-analytical approach to examine distribution patterns and underlying drivers. The results reveal an evolving dual-axis spatial structure shaped by transportation networks and regional development dynamics, with railway density emerging as a key influencing factor. Furthermore, the interaction of infrastructural, demographic, and institutional variables highlights a synergistic mechanism underpinning corridor formation. Building on these findings, the study proposes a “corridor-as-process” framework, conceptualizing industrial heritage corridors as dynamic socio-spatial products of long-term interactions between institutions, networks, and economic activities. This perspective advances beyond static, descriptive approaches by offering a process-oriented and explanatory understanding of heritage systems. This study contributes to sustainability by providing a spatially explicit basis for adaptive reuse, vulnerability assessment, and differentiated conservation strategies, supporting the integration of heritage preservation within broader regional sustainability transitions. The proposed framework offers a transferable methodological reference for analyzing industrial heritage corridors in comparable global contexts. Full article

Tourist congestion and load imbalance during peak periods pose critical challenges to the safe operation and experience assurance of large scenic areas. To address the limitations of traditional management approaches in capturing the dynamic and stochastic nature of tourist flows, this study develops a dynamic visitor guidance modeling and analysis framework based on a Timed Petri Net. The proposed model provides a formal representation of tourist movements, scenic spot load evolution, and guidance decision mechanisms within a scenic area. Under unified parameter settings and controlled random conditions, multiple visitor guidance strategies with different information coverage scopes are designed, and minute-level simulation experiments are conducted using the Huangshan Scenic Area as a case study. The simulation results show that, compared with unguided tourist flows, the proposed strategies significantly reduce average load levels, alleviate spatial load imbalance, and enhance TS. Using mean–standard deviation analysis, distributional analysis, and dynamic evolution analysis, differences among guidance strategies in terms of load control, visitor experience, and operational stability are systematically evaluated. Furthermore, a quantitative relationship model between tourist satisfaction and scenic area load is constructed, revealing a consistent inverted-U pattern. Robustness tests under multiple random seeds indicate that the main conclusions are not sensitive to specific stochastic realizations. Overall, the simulation results suggest that dynamic visitor guidance may improve load control, visitor experience, and system stability by optimizing the spatiotemporal distribution of tourist flows, thereby providing simulation-based quantitative insights for peak-period management in large scenic areas. Full article

The growing number of on-orbit spacecraft and the increasing volume of telemetry data have made intelligent anomaly detection in multi-channel telemetry essential for mission operations. Current spacecraft anomaly detection methods primarily rely on statistical models or time-series deep learning approaches, which often fail to explicitly model spatiotemporal dependencies across multiple telemetry channels. This shortcoming limits their ability to capture the dynamically evolving and intricately coupled relationships between variables. To overcome this limitation, a Progressive Spatiotemporal Graph (PSTG) model is proposed for anomaly detection in multi-channel spacecraft telemetry. PSTG employs a multi-scale patch embedding module to extract hierarchical semantic features from multi-channel time series, effectively reducing the dimensionality of the spatiotemporal graph. It constructs a sparse adjacency matrix using a multi-head attention mechanism that integrates intra-channel temporal dynamics, inter-channel spatial correlations, and cross-channel spatiotemporal interactions. An improved multi-head graph attention network then captures pairwise dependencies among nodes within the adjacency matrix. As a result, PSTG encodes rich spatiotemporal representations derived from intricate variable interactions, enabling accurate, real-time prediction of multi-channel telemetry. Furthermore, a dynamic thresholding mechanism is incorporated into PSTG to perform online anomaly detection based on prediction residuals. Extensive experiments on real-world spacecraft telemetry data collected over 84 months show that PSTG outperforms eleven state-of-the-art benchmark methods in almost all cases across multiple evaluation metrics. Finally, visualizations of the learned adjacency and attention matrices are presented to interpret the spatiotemporal modeling process, providing operators with actionable insights into the detected anomalies and facilitating root cause analysis. Full article

Monitoring thermal effluent is critical for assessing aquatic ecosystem health, yet traditional satellite remote sensing and in situ point measurements often fail to capture fine-scale thermal dynamics in narrow streams and complex coastal areas due to spatiotemporal resolution limitations. This study establishes a high-precision surface water temperature mapping protocol using a low-cost Unmanned Aerial Vehicle (UAV) equipped with an uncooled thermal infrared sensor (FLIR Vue Pro R) to overcome these observational gaps. We investigated two distinct hydrological environments—an inland stream and a coastal sea—to provide initial evidence for the applicability of an in situ-based linear regression calibration model across contrasting aquatic settings. The initial uncalibrated radiometric temperatures exhibited significant bias errors reaching up to 9.2 °C in the stream and 9.4 °C in the coastal area, primarily driven by atmospheric attenuation and environmental factors. However, the proposed calibration method dramatically reduced these discrepancies, achieving Root Mean Square Errors (RMSE) of 0.43 °C and 0.42 °C, respectively, with high determination coefficients (R² > 0.87). The derived high-resolution thermal maps successfully visualized the detailed diffusion patterns of thermal plumes, revealing a steep temperature gradient of approximately 13 °C in the stream discharge zone and a distinct 5 °C elevation in the coastal effluent area relative to the ambient water. These findings demonstrate that UAV-based thermal remote sensing, when coupled with a rigorous radiometric calibration strategy, can serve as a cost-effective and reliable tool for environmental monitoring, bridging the critical scale gap between local point measurements and regional satellite observations. Full article

The transition from acute kidney injury (AKI) to chronic kidney disease (CKD) represents a dynamic and multistage pathological process driven by maladaptive intercellular communication. Rather than resulting from isolated cellular injury, AKI-CKD progression unfolds through a spatially and temporally coordinated dysregulation of cellular networks. In the acute phase, damaged tubular epithelial cells act as instigators, releasing damage-associated molecular patterns (DAMPs) and activating a storm of inflammatory crosstalk among immune cells, endothelium, and fibroblasts. During the subacute repair phase, imbalance in macrophage polarization (M1 persistence/M2 dysfunction) and the emergence of senescent tubular cells with a senescence-associated secretory phenotype (SASP) together create a pro-fibrotic microenvironment. In the chronic phase, activated myofibroblasts—derived from multiple sources—establish self-sustaining feedback loops via autocrine signaling, mechanical memory from the stiffened extracellular matrix (ECM), and ongoing dialogue with immune and resident cells, ultimately leading to irreversible fibrosis. Current therapeutic strategies focused on single molecular targets often fail to disrupt this resilient network homeostasis. Therefore, we propose a paradigm shift toward spatiotemporally precise network-remodeling therapies, which require integrated use of liquid biopsy-based staging, smart nanocarriers for cell-specific delivery, and AI-powered multi-omics modeling. This review systematically delineates the evolving cell-to-cell communication networks across AKI-CKD continuum and highlights innovative strategies to intercept disease progression by targeting the pathophysiology of cellular crosstalk. Full article

Land cover (LC) change is reshaping terrestrial ecosystems and profoundly impacting sustainable development in Africa, yet the long-term, continental-scale spatiotemporal dynamics of these shifts remain obscured. To address the above issue, this study systematically explores the spatiotemporal dynamics of LC across Africa from 1985 to 2022 by leveraging the fine-resolution remote-sensing-derived GLC_FCS30D LC dataset within a stratified Intensity Analysis framework. To decompose landscape changes into interval, category, and transition levels across five climatic sub-regions of Africa, we systematically evaluate the temporal consistency of land systems. This hierarchical approach disentangles systematic transition pathways from random fluctuations, thereby revealing the distinct regional regimes governing continental transformation of LC. Our results ultimately show a strong LC change acceleration in Africa after 2010, mainly in Southern, Eastern, and Western Africa, which together made up 80 to 90% of the continent’s LC dynamics. During the whole study period, shrubland and grassland had the highest gross turnover due to their high bidirectional volatility. Intensity-wise, forest remained inactive even though it was a persistent net loser to crop in East Africa (2010–2020), to shrub in Southern Africa (1990–2022), and to wetland in West Africa during the post-2000 intervals. Wetland had a major change in dynamics from historical growth during 1985–1990 to systematic decline in 2015–2022. Cropland increased by systematically targeting shrubland and grassland, mainly in East Africa. Additionally, the Sahel contributed 40% of continental grassland to bare area transitions, despite some recovery of grassland in the region. These findings show that aggregate net-change metrics obscure the volatility in African LC; therefore, distinct regional regimes such as agricultural expansion and forest degradation necessitate spatially differentiated management strategies. Full article

Astrocytes undergo pronounced reactivity during traumatic brain injury (TBI); however, the temporal dynamics of this response and the signaling mechanisms regulating astrocyte proliferation remain incompletely defined. In this study, we characterized the spatiotemporal profile of astrocyte reactivity and proliferation in the hippocampus during TBI and investigated the involvement of mammalian target of rapamycin complex 1 (mTORC1) signaling in these processes. Using a mouse model of TBI, we found that injury triggered a rapid astrocytic response in the hippocampus, characterized by increased glial fibrillary acidic protein (GFAP) expression and morphological hypertrophy as early as 4 h post-injury. Astrocyte proliferation emerged subsequently, peaked during the acute phase (48 and 72 h), and declined to baseline levels at 7 days post-trauma, indicating a transient proliferative response during TBI. Concurrently, mTORC1 signaling was robustly activated in reactive astrocytes in the hippocampus and was specifically associated with proliferative reactive astrocytes during injury. Pharmacological inhibition of mTORC1 signaling with rapamycin significantly reduced reactive astrocyte proliferation during TBI without altering astrocytic hypertrophy. Together, these findings demonstrate that TBI induces a rapid but transient astrocyte activation and proliferation response in the hippocampus and that mTORC1 activation is required for the proliferation, but not the hypertrophic activation, of reactive astrocytes during traumatic brain injury. Full article

In response to the problem that sensors cannot be directly installed at key local positions on the surface of ship hull structures during the transient strong shock process of underwater explosions due to spatial constraints or large plastic deformations, this paper investigates the chaotic-like nonlinear transient behavior of structural dynamic response systems under strong shock and proposes a key position structural response reconstruction method based on dynamic inversion. Since the structural response under a transient strong shock exhibits significant non-stationarity and nonlinearity, signals from neighboring measurement points cannot directly characterize the dynamic behavior at key positions. Therefore, the shock response signals are discretized in both time and space dimensions. The phase space reconstruction method is employed to characterize the motion trajectory of acceleration responses in a two-dimensional phase space, establish mapping functions for system motion evolution, and use their control parameters to characterize the system’s nonlinear dynamic behavior. Furthermore, based on the spatiotemporal dynamic equations, a spatiotemporal coupled mapping model for spatial state points is established to achieve the theoretical inversion of acceleration responses at key positions. This method provides theoretical support for analyzing the dynamic characteristics of structures at key positions under strong shock environments, characterizing the shock environment, and assessing and designing equipment for shock safety. However, the current validation is based on high-fidelity numerical simulations rather than physical prototype tests; therefore, the predictive capability of this method in actual physical environments requires further validation through subsequent physical model tests. Full article

Background: Efficient parking navigation in large and dynamic parking areas requires systems that can adapt to real-time conditions and provide precise vehicle localization. Methods: This paper presents a smart car parking navigation module that integrates camera-based vehicle perception, homography-based ground-plane localization, mobile GNSS positioning, and dynamic route planning into a unified framework. Instance segmentation (YOLOv8n-seg) is used to detect vehicles and extract ground-contact regions, which are associated with parking slots defined in a GeoJSON-based site model. Mobile GNSS data are fused with visual observations via spatio-temporal proximity scoring to enable robust user–vehicle matching without optical identification. An A* routing algorithm dynamically computes and updates navigation paths, adapting to lane obstructions and slot availability in real time. Results: Experimental evaluation on a real six-camera parking facility shows that the proposed segmentation-based localization reduces mean error from 0.732 m to 0.283 m (61.3% improvement), with the 95th-percentile error dropping from 1.892 m to 0.908 m, and outperforming the bounding-box baseline in 85.3% of detections. Conclusions: These results demonstrate that sub-meter vehicle localization and reliable user–vehicle association are achievable using standard surveillance cameras without specialized infrastructure, offering a scalable and cost-effective solution for intelligent parking navigation. Full article

Climate-induced drought and intensifying land-use pressures threaten ecosystem services and agricultural productivity, particularly in regions with distinctive soil and ecological characteristics. Alabama’s Black Belt, defined by its clay-rich soils and shaped by a legacy of plantation agriculture, uneven land tenure, and persistent socioeconomic disadvantage, is increasingly vulnerable to these interacting stressors. This study analyzes long-term (2000–2023) spatiotemporal patterns of Land Use Land Cover (LULC) change and vegetation response to drought to inform sustainable resource management. Multi-temporal Landsat imagery and National Land Cover Database (NLCD) products were used to quantify LULC dynamics. At the same time, vegetation condition and moisture stress were assessed using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI). Drought conditions were evaluated using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), which incorporates temperature-driven evaporative demand. Results indicate substantial landscape change, including declines in deciduous forest (−17.78%) and pasture/hay (−13.17%), alongside increases in medium-intensity developed land (+20.25%) and evergreen forest (+10.62%). Declining NDVI and NDMI values indicate increasing vegetation stress, particularly during prolonged droughts. Vegetation response exhibited a weak relationship with SPI (R = 0.37) but a stronger association with SPEI (R = 0.59), underscoring the importance of accounting for atmospheric water demand. These findings highlight the growing vulnerability of Black Belt ecosystems to coupled climate and land-use pressures and provide insights to strengthen climate-resilient agricultural management. Full article

Accurate precipitation nowcasting plays an important role in disaster prevention and hydrometeorological applications, yet it remains highly challenging due to the complex spatiotemporal variability and multi-scale structural characteristics of precipitation systems. Existing deep learning methods are largely data-driven and often struggle to effectively exploit multi-source observations or learn physically meaningful representations. To address these limitations, this study proposes a Multi-Scale Frequency–Temporal Network (MS-FTNet) for precipitation nowcasting. The framework leverages Fourier transform-based frequency-domain modeling to achieve an interpretable multi-scale decomposition of precipitation dynamics. Specifically, low-frequency components capture large-scale stratiform patterns and their temporal evolution, while high-frequency components represent localized convective structures and abrupt variations. Building on this, a Global Feature Collaboration (GFC) module integrates global frequency-domain representations with multi-scale convolutional features, and an Adaptive Temporal Fusion (ATF) module enhances temporal dependency modeling. Experiments on the SEVIR dataset demonstrate that MS-FTNet consistently outperforms representative baseline models in terms of MSE, CSI, and LPIPS, particularly for heavy precipitation events and longer forecast lead times. Full article