Search Results (2,578)

As a critical link between regional economic development and ecological security, understanding the dynamics of water retention is essential for sustainable water resource management in the Huaihe River Economic Belt. This study explores the spatio-temporal evolution and spatial explanatory factors of water retention across five temporal snapshots (2003, 2008, 2013, 2018, and 2023). Based on the InVEST model, we assessed water retention capacity at both grid and spatial development levels, thereby obtaining the retention characteristics of different land-use types and their responses to land-use transitions. Furthermore, a parameter-optimized geographical detector was employed to quantify the relative contributions of climatic-environmental and social-economic factors to the spatial variance of the modeled water retention index. Results indicate that the total water retention capacity exhibited significant interannual fluctuations, with the net capacity in 2023 being lower than the initial level in 2003. Retention values displayed obvious spatial heterogeneity, with high levels concentrated in the southwest and north and low levels distributed in the central area, closely mirroring precipitation distribution. While forest land exhibited the strongest unit water retention capacity, cropland contributed the most to the total volume (50.49%) due to its predominant areal proportion (73.92%). Notably, the conversion of forest to cropland was spatially associated with the most substantial loss in the modeled retention capacity. Soil saturated hydraulic conductivity and land-use type were identified as the dominant factors explaining the spatial variance of water retention. These findings underscore the methodological utility of coupling the InVEST model with a parameter-optimized geographical detector. For practical ecosystem management, the results suggest that spatial planning policies should strictly limit the conversion of ecological lands to agricultural use and prioritize targeted soil hydrological improvements in the central plains to secure long-term water resources. Full article

As a crucial national ecological barrier, China’s Southern Collective Forest Region (SCFR) plays an essential role in maintaining regional ecological security and promoting sustainable development. Understanding the mechanisms driving the evolution of its ecosystem service value (ESV) is of great significance. Based on county-level data from 2000 to 2023, this study integrated the equivalent factor method, spatial autocorrelation analysis, the XGBoost-SHAP model, geographically and temporally weighted regression (GTWR), and partial least squares structural equation modeling (PLS-SEM) to examine the spatio-temporal evolution patterns and driving mechanisms of ESV in the SCFR. The results showed that ESV in the SCFR exhibited an overall downward trend, with a cumulative loss of 1973.77 × 10⁸ CNY. This was primarily due to marked reductions in hydrological and climate regulation services. The spatial distribution of ESV exhibited a significant heterogeneity—higher in the southwestern and southeastern mountainous regions, and lower in the northern plains and coastal zones, with the center of gravity shifting first to the northeast and then to the southwest. Local spatial autocorrelation revealed relatively stable “High–High” and “Low–Low” clustering characteristics, where high-value clusters were consistently distributed in core forest zones, while low-value clusters overlapped highly with urban agglomerations. Socio-economic factors exerted a significantly stronger influence on ESV than natural factors. Population density (POP), land use intensity (LUI), and gross domestic product (GDP) were identified as the dominant drivers, exhibiting distinct non-linear threshold effects and significant spatio-temporal heterogeneity. PLS-SEM analysis further quantified LUI as the dominant direct inhibitory pathway on ESV, highlighting urbanization’s indirect negative effect mediated through intensified LUI. Meanwhile, terrain effects were confirmed to positively influence ESV indirectly by constraining LUI and modulating local climate. The analytical framework of “threshold identification–spatio-temporal heterogeneity–causal pathway analysis” proposed in this study elucidated the complex driving mechanisms of ESV evolution, providing valuable guidance for ecological restoration evaluation and differentiated environmental governance. Full article

This study systematically examines the low-carbon transition challenges faced by the Yellow River Basin, a core strategic energy base in China with a coal-dominated energy system, under the dual carbon goals. Existing studies based on traditional econometric models or single-province analyses are mostly limited to static analysis, failing to simultaneously capture the nonlinear spatiotemporal evolution, cross-regional spillover effects, and long-term changing trends of carbon emissions in the basin. To fill this gap, this study builds an Energy–Economy–Carbon (EEC) analytical framework, and develops an integrated TFT-ASTGCN deep learning framework. Specifically, we employ the Temporal Fusion Transformer (TFT) for high-precision multivariate time-series simulation and peak forecasting, while the Attention-based Spatial–Temporal Graph Convolutional Network (ASTGCN) is used to identify complex spatial dependencies of inter-provincial emissions. The empirical results confirm that: (1) Basin carbon emissions show significant coal-driven carbon lock-in, with initial decoupling between economic growth and emissions. (2) Most provinces will maintain rising emissions under the current development mode, posing severe challenges to carbon peaking. (3) Asymmetric spatial spillover effects are prominent, underscoring cross-regional collaborative governance as a critical pathway for achieving an early and stable carbon peak in the basin. Full article

Urban districts along major rivers are undergoing rapid transformation, yet long-term evidence on how redevelopment reshapes land cover and vegetation structure remains limited in post-socialist cities. This study examines the spatio-temporal evolution of land use and land cover (LULC) and vegetation dynamics along the Sava River corridor in Belgrade from 1990 to 2024. CORINE Land Cover (CLC) datasets were combined with Landsat-derived NDVI and MSAVI time series, while high-resolution Esri Wayback imagery was used for visual interpretation and qualitative corroboration of the detected land-cover and vegetation patterns. Beyond conventional NDVI/LULC assessments, the study integrates multi-decadal spectral trends with functional vegetation structure classification to evaluate canopy continuity and ecological configuration under contrasting redevelopment models. Results reveal a pronounced divergence between the two riverbanks. The left bank (New Belgrade) maintains stable land-cover composition and consistently higher NDVI and MSAVI values, indicating preserved green infrastructure and sustained canopy continuity. In contrast, the right bank (Belgrade Waterfront) experienced substantial land-cover conversion after 2006, with a statistically significant decline in vegetation greenness (NDVI −0.020 dec⁻¹, p < 0.001) and a marked increase in impervious surfaces. MSAVI-based functional classes indicate a shift from mixed low vegetation to predominantly sealed land, while tree canopy remained persistently low throughout redevelopment. The findings demonstrate measurable ecological simplification and canopy loss, even where nominal green areas remain present. By providing a rare multi-decadal, spatially explicit comparison of two contrasting planning paradigms within the same river corridor, the study contributes new empirical evidence on how governance and redevelopment models shape riparian ecological trajectories and sustainable urbanism in post-socialist cities. Strengthening blue-green infrastructure and restoring native riparian vegetation are essential for enhancing climate resilience and ensuring long-term riverfront sustainability. Full article

Accurately predicting the temperature field of turbine blades is of great significance for evaluating the thermal reliability and service life of high-temperature components in aero-engines. However, due to the high computational cost of numerical simulations and the limitations imposed by complex geometric structures and harsh operating environments, experimental measurements can usually only obtain sparse sensor data, making the acquisition of complete temperature distributions still challenging. Therefore, reconstructing the complete temperature field under sparse measurement conditions has become a key research issue in turbine thermal analysis. To address this problem, this paper proposes an attention-enhanced CNN–LSTM framework for reconstructing transient turbine blade temperature fields from sparse data. The model combines the spatial feature extraction capability of Convolutional Neural Networks (CNNs) with the time-series modeling capability of Long Short-Term Memory networks (LSTM). An SE channel attention module is introduced in the CNN feature extraction stage to achieve adaptive recalibration of channel features, and a temporal attention mechanism is incorporated after the LSTM layer to highlight key transient thermal features. A multi-condition temperature field dataset was constructed by conducting Computational Fluid Dynamics (CFD) simulations on low-pressure turbine guide vanes, and the model was experimentally validated through thermal shock tests. The results show that the proposed model can accurately reconstruct the spatial distribution and transient evolution of the turbine blade temperature field under sparse measurement conditions. Under different operating conditions, the predicted temperature fields are highly consistent with the CFD results, with the maximum Reconstruction error remaining below 19 °C. Error distribution analysis indicates that the model has stable Reconstruction performance and good generalization ability. Full article

Quercus pyrenaica (Willd.), a sub-Mediterranean oak, is expected to experience substantial distribution shifts under climate change, with some populations in Portugal at risk. Beyond climate-driven pressures, long-standing anthropogenic pressures have likely contributed to the species’ current vulnerability. This work aims to characterize the current status of closed-canopy Q. pyrenaica forests by providing a spatio-temporal assessment of forest fragmentation and its recent evolution. Using multispectral bands from Sentinel-2 time-series data, vegetation indices, embedding vectors generated by Google’s AlphaEarth foundational model, and topographic variables, we applied a machine learning Random Forest classifier to map Q. pyrenaica forests in 2019 and 2024 and to analyze their spatial configuration patterns. The findings indicate robust predictive performance (spatial cross-validation OA of 95.1%, Kappa of 83.7%, and F1 of 86.9%) and reveal the prominent role of AlphaEarth embedding features in the RF classifier, suggesting that these features are well-suited for classifying forest habitats of conservation importance. Quercus pyrenaica occurs predominantly at mid-elevations (~820 m a.s.l.), on gentle slopes (~9°), topographically neutral terrain, and northwestern-facing aspects, consistently across both years. Between 2019 and 2024, the Q. pyrenaica forest area showed an increasing signal. However, the results point to a landscape in an initial phase of forest recovery, constrained by land-use legacies, with cover increasing predominantly through the sprawl of small, geometrically complex, and poorly connected patches. Together, these results provide a baseline to track recent changes in Q. pyrenaica distribution and fragmentation, highlighting a contrast between apparent area expansion and declining overall structural integrity. In the future, patch connectivity and full recovery of secondary succession should be a priority for policymakers and forest owners. Full article

Urban–suburban PM_2.5 differences are widely used to characterize spatial disparities in air pollution, yet their long-term trends may depend on urban definitions. For China during 2013–2020, this study used nationwide ground PM_2.5 monitoring data and 1 km × 1 km gridded population density data to analyze the sensitivity of urban–suburban PM_2.5 trends to spatial structure-based and population-density-based classification (300, 1500, 2200, 2500 people km⁻²) at national, Eastern and Western China scales. Results showed significant national PM_2.5 decline, with urban reduction rates of −3.1 to −3.3 µg m⁻³ yr⁻¹ in summer and −6.0 to −6.3 µg m⁻³ yr⁻¹ in winter, and faster air quality improvement in winter. Urban–suburban PM_2.5 differences were highly sensitive to classification methods: the spatial structure-based framework showed minimal differences (0.09 µg m⁻³ in summer, 5 µg m⁻³ in winter), while the 300 people km⁻² threshold yielded much larger ones (11 µg m⁻³ in summer, 29 µg m⁻³ in winter) with faster urban declines. Higher population density thresholds narrowed such differences and converged trends with the spatial structure-based results. Pronounced spatial heterogeneity existed: Eastern China had larger PM_2.5 declines with consistent response patterns to national trends, while Western China showed weaker declines, with urban–suburban differences highly sensitive to classification methods and opposite temporal evolution trends. This study confirms that urban definition is a critical methodological factor for interpreting China’s long-term urban–suburban PM_2.5 trends, as different methods cause notable inferential deviations. Future air pollution spatial heterogeneity studies should carefully select and specify urban classification methods to ensure comparable, scientifically rigorous findings. Full article

The proliferation of marine data presents both an opportunity for ocean governance and a challenge, contributing to fragmentation across disciplines, institutions, and sectors. Marine Spatial Data Infrastructure (MSDI) stands out as a major framework for integrating marine information. However, an integrated synthesis that combines quantitative mapping of publication patterns with qualitative analysis of thematic evolution remains absent. This study employs a two-step approach combining systematic review and bibliometric analysis of Scopus-indexed literature (2000–2024). Based on a focused corpus of 20 publications rigorously screened for explicit MSDI relevance, we examine publication trends, collaboration patterns, thematic structures, and evolutionary trajectories. Results indicate accelerating scholarly interest in MSDI, with European institutions contributing 75% of the analysed publications. Policy frameworks such as the INSPIRE Directive (Infrastructure for Spatial Information in the European Community) and the Marine Strategy Framework Directive (MSFD) emerge as key drivers of research activity. Temporal analysis of this corpus suggests a tentative five-phase evolution in MSDI research: (1) foundational technical standardisation, (2) governance model implementation, (3) semantic interoperability enhancement, (4) policy integration, and (5) advanced applications incorporating FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) principles and Artificial Intelligence (AI). These phases, derived from systematic coding of thematic focus across publications, represent observed patterns within the analysed literature rather than definitive stages. This paper concludes that MSDI is moving toward a more socio-technical approach that requires the consideration of a technical-focused tool in present-day ocean governance. Future work should combine semantic AI, decentralised architectures, polycentric governance models, and impact assessment frameworks to align MSDI development with the objectives of equity, inclusion, and sustainability. Full article

Colmation phenomena play a critical role in long-term gas flow through porous media, significantly influencing methane migration, mine ventilation efficiency, and emission control in both active and abandoned coal mines. In colmation modeling, three fundamental kinetic types are commonly distinguished, with the third kinetic providing a generalized nonlinear formulation capable of describing state-dependent and spatially variable permeability degradation. However, the strong nonlinearity of the coupled transport–colmation equations prevents the derivation of closed-form solutions, which necessitates the application of linearization techniques. In this study, gas flow with colmation governed by third-kinetics is analyzed with particular emphasis on methane migration in underground mining environments. Linearization of nonlinear kinetic terms is applied at the level of the coupled mass balance and colmation equations, resulting in an approximate form of Darcy’s law and an explicit analytical solution describing the evolution of the porous medium state. The primary objective of the study is to quantify the error introduced by the adopted linearization and to analyze its spatial and temporal propagation with respect to the nonlinear reference solution. A rigorous error estimation based on Taylor series truncation is developed, yielding an explicit criterion that defines the validity range of the linearized solution. The results demonstrate that the approximation remains reliable within the regime of weak colmation, while the associated error is locally generated and propagates through transport mechanisms without exhibiting uncontrolled growth. Full article

Cu/Ni/Ag composite materials are widely used in the manufacturing of electrical connector contacts due to their excellent electrical conductivity and good wear resistance. During hot plugging and unplugging operations, electrical connectors inevitably generate arc discharge, leading to melting, splashing, and erosion of the contact material, which severely threaten system reliability and service life. To investigate the arc behavior of Cu/Ni/Ag composite electrical connectors during plugging and unplugging, this paper establishes a multiphysics coupling model incorporating electric field, fluid heat transfer, and laminar flow based on the COMSOL simulation software (version 6.2). The model employs a multiphysics coupling approach, incorporating electric field, fluid heat transfer, and laminar flow, to systematically simulate the formation and evolution mechanisms of the arc during plugging and unplugging. The study focuses on analyzing the effects of plugging and unplugging speed, operating voltage, and arc gap distance on the arc, exploring the temporal and spatial evolution characteristics and distribution patterns of arc temperature. The simulation results reveal that the arc temperature follows a radially decreasing gradient, with the core region exceeding 10,000 K. When the operating voltage increases to 1000 V, the arc peak temperature rises to 1.3 × 10⁴ K. As the arc gap distance increases, the arc coverage area expands, and the peak arc temperature increases by approximately 2% to 8%. As the plugging/unplugging speed is increased to 500 mm/s, the peak temperature of the arc increases from 1.19 × 10⁴ K to 1.3 × 10⁴ K. The distribution characteristics of the magnetic field are clearly correlated with the arc temperature field and the electric field intensity distribution and the current density also exhibits typical constriction characteristics. Prolonged arc duration is correlated with an upward trend in peak temperature. Further analysis indicates that the temperature distribution characteristics of the arc are constrained by the competition mechanism of energy deposition and diffusion, while the evolution characteristics of the arc are regulated by the coupling effect of electromagnetic field and mechanical work. The research results provide a theoretical basis and simulation methods for the design of arc-resistant structures in Cu/Ni/Ag composite electrical connectors. Full article

Granary systems formed a core institutional foundation of state governance, famine relief, and social stabilization in premodern China. Using the complete corpus of the Twenty-Six Dynastic Histories, this study employs digital humanities methods—including text preprocessing, word-frequency analysis, collocation analysis, time-series comparison, and geographic co-occurrence analysis—to examine the long-term evolution and institutional structure of three major granary types: ever-normal granaries (ChangpingCang), charitable granaries (Yicang), and community granaries (Shecang). The results reveal significant temporal and spatial variation closely associated with dynastic stability, fiscal capacity, and disaster conditions. Ever-normal granaries evolved from early formation in the Western Han to institutional consolidation in the Tang, peak expansion in the Song, and functional diversification thereafter, operating as a centralized mechanism integrating price regulation, fiscal management, and famine relief. Charitable and community granaries, by contrast, display increasingly differentiated roles, reflecting a shift toward localized and socially embedded relief in later periods. Spatial analysis further demonstrates a hierarchical deployment pattern centered on political and agrarian cores and extended through transport corridors and frontier zones. Overall, the study highlights a multilayered relief system combining state authority and social participation, offering a data-driven reinterpretation of Chinese charity and governance. Full article

This study investigates the thermo-mechanical response of geocell-reinforced concrete pavements through scaled model tests and three-dimensional finite element analyses. Static, thermal, traffic, and coupled temperature–loading tests were conducted to clarify the deformation evolution, strain distribution, and damage-related response of the reinforced structure. The results show that, under static loading, pavement settlement evolves through three stages, namely initial compaction, plastic development, and stable strengthening, indicating progressive mobilization of geocell confinement. Under thermal loading, slab strain exhibits pronounced spatial and temporal non-uniformity, and the slab center is identified as the thermally sensitive zone. Under coupled temperature–loading conditions, both strain and settlement show a non-monotonic response near 1.1–1.3 kN, suggesting a potential damage-initiation range. Post-test crack observations further provide direct qualitative evidence that local cracking damage occurred in the slab under representative loading conditions. Under traffic loading, permanent deformation accumulates with load repetitions and is highly sensitive to load amplitude, indicating a load-sensitive transition in cumulative deformation behavior rather than a definitive fatigue threshold. Numerical results further show that geocell reinforcement reduces central settlement by 17.4% relative to plain concrete pavement and by 7.6% relative to doweled pavement, while producing a smoother deflection basin and a more uniform stress distribution. Parametric analyses indicate that the optimum geocell height is approximately one-third of the slab thickness; beyond this range, the marginal reinforcement benefit decreases. Overall, the results demonstrate that geocell reinforcement can effectively improve load transfer, deformation compatibility, and thermo-mechanical stability of concrete pavements under the investigated conditions. 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

Background: Meningiomas, the most common primary intracranial tumors, are predominantly benign, but high-grade variants show marked aggressiveness, histo-molecular heterogeneity, and treatment resistance. Although the 2021 WHO CNS classification integrates molecular and histopathologic criteria, substantial inter- and intratumoral variability still limits prognostic accuracy and treatment effectiveness. The goal was to provide insight regarding the histo-molecular intratumoral heterogeneity (ITH) of meningioma and examine its clinical implications. Methods: A narrative review was performed in accordance with PRISMA guidelines. PubMed and Google Scholar were screened for studies on “meningioma” and “intratumoral heterogeneity” published up to 28 July 2025. Eligible studies included original human research reporting histological or molecular heterogeneity with clinical relevance. Results: Eighteen studies comprising 2952 meningioma patients (mean age 59.4 ± 14.8 years, range 16–85) were included. Integrated cytogenetic, molecular, and spatial analyses, including FISH, karyotyping, scRNA-seq, CNV profiling, and spatial transcriptomics, revealed multilayered histo-molecular heterogeneity. Histologically, regional variations in morphology and proliferative index increased with tumor grade. Genomic diversity, marked by recurrent losses of 1p, 14q, and 22q and transcriptionally distinct subclones, defined a complex tumor architecture. Spatial and temporal analyses demonstrated subclonal expansion, stepwise clonal evolution, and therapy resistance, particularly in recurrent tumors. Functionally, SULT1E1⁺ subclones and COL6A3-mediated macrophage–tumor interactions emerged as potential key drivers of malignancy, recurrence, and radioresistance. Conclusions: Histo-molecular diversity underlies meningioma progression, recurrence, and therapeutic resistance. Standardization of ITH assessment, integration of AI-based spatial analytics, and the development of subclone-specific therapies are essential next steps toward advancing precision neuro-oncology. Full article

Street-view imagery supports fine-grained urban monitoring, but most street-scene change detection methods are pixel-centric or object-centric and cannot explicitly capture the evolution of inter-entity spatial relations needed for interpretable tasks (e.g., compliance inspection and post-disaster assessment). To address this, we propose SSPRCD, a scene graph-based framework that extracts entity-relation triplets with pixel locations, builds spatial knowledge graphs, and achieves stable node alignment via intra-/inter-temporal consistency. Graph differencing then identifies added, removed, and unchanged entities/relations, while nGED and graph2vec jointly quantify structural discrepancies between temporal scenes. Experiments on the TSUNAMI dataset, with comparisons across two object detectors and seven scene graph generation backbones, show that SSPRCD achieves a macro-F1 of 0.65 for the object-level task, F1 of 0.72 for binary change detection, and F1 of 0.89 for relation-level detection, consistently outperforming baseline methods. Overall, SSPRCD delivers relation-aware and topology-informed change explanations that improve the interpretability of street-block level change analysis for geospatial in-formation updating and urban applications. Full article