Search Results (3,662)

In the context of global climate change and intensifying resource and environmental constraints, improving agricultural eco-efficiency (AEE) has become critical to achieving the green transformation of agriculture. This study develops a comprehensive evaluation index system for AEE that incorporates factor inputs, expected outputs, and undesirable outputs. Using county-level panel data from 2010 to 2022 for the Yangtze River Economic Belt (YEB), it applied the super-efficiency slacks-based measure (SBM) model to quantify AEE. Furthermore, spatial autocorrelation analysis and the spatial Durbin model (SDM) are employed to reveal its spatiotemporal characteristics and influencing factors of AEE. The results indicate that the overall AEE of the YEB exhibited a fluctuating upward trend over the study period, yet significant regional heterogeneity persisted. AEE showed pronounced positive spatial correlations, with regional disparities primarily stemming from hyper-variance intensity, suggesting that high- and low-efficiency counties are spatially interwoven. The SDM results indicate that local temperature, economic development, urbanization, fiscal support for agriculture, and agricultural production structure positively influence local AEE, while rural residents’ income and educational attainment exert negative effects. These factors also demonstrate significant spatial spillover effects, with economic development and ecological conditions in adjacent regions generating positive externalities, while neighboring urbanization and temperature producing negative impacts. This study deepens the understanding of the driving mechanisms underlying AEE from a spatial interdependence perspective, providing a scientific basis for formulating cross-regional collaborative policies aimed at promoting green agricultural development in major river basins. Full article

Surface water-groundwater interaction zones are critical ecohydrological interfaces in arid regions, yet quantitative spatiotemporal patterns and soil-vegetation responses under coupled water-salt-heat gradients remain poorly documented. Based on a one-year monitoring period (August 2024–August 2025) at four sites along a river-to-desert transect (LW3: 25 m, LW2: 200 m, LW1: 300 m, LW4: 400 m from the Niya River) in the hyper-arid Tarim Basin, this study reveals the following quantitative patterns. Groundwater depth increased with distance from the river and followed an annual decrease-increase trend, with an anomalous shallow peak in March 2025 (−20 cm) linked to precipitation recharge. Soil temperature stability increased with depth: the 20 cm layer recorded the widest annual fluctuation (e.g., −1.5 °C to 24 °C at LW1), whereas the 80 cm layer varied only between approximately −0.2 °C and 28 °C. Proximity to the river dampened thermal extremes. Shallow soil moisture was highly dynamic (with a coefficient of variation [CV] reaching 40–50% at LW1 and LW4), while deeper layers remained stable; LW3 near the river stayed saturated year-round (CV = 0). Soil electrical conductivity (EC) decreased with distance from the river: LW3 exhibited the highest surface values (5000–16,000 μS cm⁻¹), whereas LW1 recorded the lowest (1000–2700 μS cm⁻¹). Vegetation performance was governed by coupled water-salt conditions rather than moisture alone: P. australis at LW1 achieved the tallest growth (>200 cm) and highest photosynthetic rates (20.25–37.38 μmol m⁻² s⁻¹), outperforming LW3 (104 cm, winter photosynthesis dropping to 2.01) and LW4 (~100 cm). Correlation analysis further showed strong vertical temperature coupling (r > 0.96 across all depths) and depth-stratified water-salt relationships (e.g., EC-volumetric water content r = 0.95 at 20 cm in LW4), reflecting spatial differentiation driven by freeze-thaw cycles, evaporative enrichment, and homogeneous silt-textured soils (54–96% fine fraction). These quantitative findings provide a detailed observational baseline for riparian ecohydrology in hyper-arid inland rivers and underscore that sustainable vegetation management requires balancing water availability against salinity stress. Full article

Early fault detection in microsatellite thermal control systems (TCS) faces fundamental challenges: high-dimensional redundant telemetry channels, overlapping multi-scale periodicities that obscure anomaly signatures, and severely limited daily data downlink (1–2 passes per day) that restricts the temporal window for diagnosis. Existing data-driven approaches either rely on supervised learning, requiring labeled fault data that are scarce in practice, or employ univariate analysis that fails to capture inter-sensor spatial correlations. To address these limitations, this paper introduces a hybrid framework integrating information-theoretic feature selection and spatiotemporal deep learning. The Generalized Maximum Information Coefficient (GMIC) quantifies nonlinear dependencies between temperature channels for key channel selection, reducing dimensionality by 82% while preserving diagnostic information. A dual-level Seasonal Trend Decomposition (STL) method disentangles orbital-periodic dynamics from diurnal cycles, effectively isolating distinct thermal characteristics at multiple timescales. Each decomposed component is modeled using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) networks to capture spatiotemporal dependencies for accurate temperature prediction. An adaptive threshold-based weighted error fusion mechanism enables early fault detection within a single day of telemetry data. Experimental validation on real satellite telemetry data demonstrates that the proposed framework achieves high-precision fault detection across multiple fault types using a minimal set of temperature channels, significantly outperforming existing benchmarks in both prediction accuracy and detection reliability. Full article

Traffic accidents significantly impact societies and economies. The risk of collision is highest in urban areas, leading to devastating loss of life and escalating socioeconomic costs. In this context, numerous studies have focused on accurately predicting accident risk, severity, and duration using various methodologies. This paper presents an overview of traditional statistical models for accident prediction and a comprehensive systematic review of the literature on statistical modeling, machine learning (ML), and deep learning (DL) techniques employed in this field. Different methodologies and techniques are compared by categorizing studies that adopt similar approaches and analyzing them comparatively. Furthermore, a distinction is made between temporal and spatiotemporal models to describe how these approaches influence the accuracy of future predictions regarding accident occurrence and the duration of impact. This review distinguishes itself from similar works by not only comparing models and approaches, but also by analyzing how external features, such as meteorological data, road geometric design, and land usage, affect the probability of accidents and the models’ accuracy in forecasting road safety. The study explores the performance levels and limitations associated with a set of forecasting approaches, offering an analytical discussion of their differences and similarities, and potential future developments in this research space, including the use of hybrid models and reinforcement learning (RL). The results of this review indicate that DL models tend to be better suited to complex forecasting problems due to their superior ability to represent features and extract non-linear spatiotemporal correlations. This article concludes by describing various directions for further research, ranging from optimizing model architectures to integrating real-time big data into proactive prediction systems. Full article

Investigating the spatial correlation characteristics and configurational pathways of ecological space service value (ESSV) is of importance for alleviating urban ecological pressure. This study, focusing on the northern slope of the Tianshan Mountains in China, employs the modified value equivalent method, gravity model, and configurational analysis model to elucidate the spatiotemporal evolution mechanisms of ESSV. The results demonstrate that: (1) The extent of ecological space decreased sharply (328.25 km²), primarily converting to other ecological space. Among these, the grassland ecological space experienced the largest reduction (215.34 km²), whereas the decline in forest ecological space was relatively modest (58.85 km²). (2) ESSV showed a fluctuating but overall increasing trend, with ΔESSV dominated by negative changes. Spatially, the pattern was characterized by higher values in the west, lower values in the east, and a contiguous high-value area in the central region. (3) The network of ESSV exhibited multiple connections and multiple cores, with the strength of network linkages continuously strengthening and showing a trend of expansion from the central region toward the south and north. (4) High ESSV depends on the configurational effects of multidimensional resilience factors. Several configurational modes were identified, including single-core resilience-driven and multi-dimensional resilience synergy-driven modes. Full article

Quantifying vegetation ecological quality and ecological water requirement is essential for understanding ecosystem sustainability in arid regions. However, large-scale assessments of vegetation ecological quality and ecological water requirement thresholds remain limited in Central Asia. In this study, we developed a Vegetation Ecological Quality Index (VEQI) for Central Asia based on fractional vegetation cover (FVC) and net primary productivity (NPP) and estimated vegetation ecological water requirement quota (VEWRq) and total vegetation ecological water requirement (VEWR) using the Penman–Monteith method, the soil moisture limitation coefficient (SMLC), and GIS-based spatial analysis. We further examined the spatiotemporal variations in VEQI and VEWR during 2001–2020 and identified VEWRq thresholds corresponding to different VEQI levels. The results showed that (1) the multi-year mean VEQI in Central Asia was 28.46 and exhibited a slight increasing trend during 2001–2020; (2) the annual mean minimum, maximum, and optimal VEWRq were 147.53, 179.71, and 162.52 mm, respectively, corresponding to mean annual VEWR values of 146.98 × 10⁹ m³, 179.04 × 10⁹ m³ and 161.91 × 10⁹ m³, respectively; and (3) VEQI was positively correlated with VEWRq in 89.48% of the vegetation area. The VEWRq threshold increased with vegetation ecological quality. The five VEQI levels in Central Asia, namely very poor, poor, moderate, good, and very good, corresponded to VEWRq thresholds of 28.62–35.96, 88.33–107.81, 190.69–233.32, 362.86–432.81, and 678.59–838.31 mm, respectively. This study provides a remote sensing-based framework for evaluating vegetation ecological quality and quantifying ecological water requirement thresholds in arid regions and offers scientific support for regional ecological management and water resource allocation. Full article

Incomplete spatio-temporal (ST) data from sensor networks in precision agriculture often limits environmental modeling and decision-making accuracy. To address this, we propose the Spatio-Temporal Conditional Diffusion Framework (ST-CDF), a generative approach for high-fidelity data reconstruction. The framework’s core is a deep denoising network that integrates a Graph Attention Network (GAT) to explicitly model non-Euclidean spatial correlations, a Differential Attention Transformer to capture abrupt temporal dynamics, and an Inverse Discrete Wavelet Transform (IDWT) module to preserve multi-scale signal details. The generative process is constrained by a physics-informed training objective, which injects known physical laws (i.e., the Penman–Monteith equation for reference evapotranspiration, $E{T}_0$) as an inductive bias, ensuring the imputed data maintains physical consistency. For privacy-preserving deployment on resource-constrained IoT devices, we extend the framework with a Federated Cluster-Guided Distillation (Fed-CGD) strategy. We conducted extensive experiments against established methods on two real-world agricultural datasets. ST-CDF demonstrated improved imputation accuracy across evaluated metrics. Its efficacy was most pronounced in the physically-demanding $E{T}_0$ calculation task, where data imputed by ST-CDF at an 80% missing rate achieved a Root Mean Square Error (RMSE) of 0.3485 and a Coefficient of Determination ($R^2$) of 0.7558, outperforming the baseline models. Furthermore, we explore ST-CDF as an explainable (XAI) framework for active agricultural decision support, demonstrating its utility in performing counterfactual simulations of “what-if” interventions, such as irrigation. The findings highlight ST-CDF as an effective, physically-grounded, and interpretable tool for data-driven scientific computation and precision agriculture. Full article

Clarifying the mutual feedback relationship and coordinated evolution characteristics between low-carbon transportation (LCT) and high-quality economic development (HQED) is of great significance for the green transformation of resource-based and ecologically fragile urban agglomerations. Taking 18 cities in the Yellow River Jiziwan Metropolitan Area as the research objects, this paper constructs an evaluation indicator system for LCT and HQED based on panel data from 2013 to 2022, and comprehensively applies the ISM-MICMAC model, a modified coupling coordination degree model, a gravity model, an obstacle degree model, and a combined GM-ARIMA forecasting model to analyze the interaction relationships, spatiotemporal evolution, spatial correlations, and scenario differences between the two systems. The results indicate that: (1) A hierarchical mutual feedback relationship exists between LCT and HQED, in which the relevant factors exhibit a hierarchical association within the system structure, extending from basic input, transportation supply, and economic operation to green and low-carbon outcomes. (2) During the study period, the comprehensive development levels of the two systems generally improved, with the mean coupling coordination degree rising from 0.4374 in 2013 to 0.4702 in 2022, remaining overall at a borderline coordination stage, while inter-city divergence was relatively pronounced. (3) The spatial connection network gradually exhibited multi-node linkage characteristics, yet strong connections remained concentrated in a few core cities. (4) Scenario predictions reveal that the synergistic development scenario is most conducive to enhancing the coupling coordination level, and the differences among scenarios gradually widen after 2026. Simultaneously advancing LCT and HQED is an important pathway to enhance the regional synergy level of the Yellow River Jiziwan Metropolitan Area. Full article

Skeleton-based human action recognition (HAR) requires models that preserve the local kinematic structure of the human body while capturing long-range spatiotemporal dependencies under noisy or incomplete joint observations. Traditional Graph Convolutional Networks (GCNs) provide topology-aligned inductive bias but are often limited by local information aggregation from neighboring joints. In contrast, attention-based mechanisms capture global interactions, yet they may attend to spurious correlations when skeletal constraints are weakly enforced. This paper proposes Differential Hyperedge Attention-enhanced GCN (DHA-eGCN), a hybrid architecture that couples structure-aware Differential Hyperedge Attention with multi-scale temporal convolution for spatiotemporal skeleton sequence processing. DHA injects skeletal structure into attention via hop-distance relative positional encoding and hyperedge context tokens generated via joint-to-part pooling. It further employs differential attention to suppress shared noisy correlations and enhance interaction selectivity. To strengthen spatial grounding, an explicit GCN branch is added under partial- or full-depth configurations, where the first four or all ten layers are applied with graph convolutions. The model further employs an ensemble strategy that combines predictions from multiple complementary model instances. Our experiments on NTU RGB+D 60 under the X-Sub and X-View protocols, NTU RGB+D 120 under the X-Sub and X-Set protocols, and Northwestern-UCLA demonstrate that DHA-eGCN consistently outperforms or remains competitive with strong graph-based, transformer-based, and hybrid state-of-the-art methods based on the same four-stream architecture. The best configuration achieves 93.7% and 97.0% on NTU RGB+D 60 X-Sub and X-View, respectively; 90.9% and 91.9% on NTU RGB+D 120 X-Sub and X-Set, respectively; and 97.6% on Northwestern-UCLA. Full article

Short-term traffic forecasting is a critical component of intelligent transportation systems. While deep learning architectures for this task have proliferated rapidly, the sensor-level data characteristics—zero-value prevalence, distributional heterogeneity, and cross-sensor correlation structure—that drive architecture-specific failure modes remain insufficiently understood, and their implications for evidence-based model selection in real deployments have not been systematically addressed. This study addresses that question through a sensor-network diagnostic framework applied to the METR-LA dataset (Metropolitan Los Angeles; 207 inductive loop detectors, 5-min resolution). The framework integrates systematic characterization of sensor data properties, a controlled benchmark of four representative architectures—Transformer, Spatio-Temporal Graph Convolutional Network (STGCN), Diffusion Convolutional Recurrent Neural Network (DCRNN), and Gated Temporal Convolutional Network (Gated TCN)—under a unified 12→3 prediction setting, and a novel per-sensor regression analysis that quantitatively links zero-value ratios to model-specific prediction errors across all 207 sensors. Building on these findings, this study further proposes Graph-Enhanced Transformer (GETFormer), a lightweight hybrid architecture that augments the Transformer with a single-hop Graph Convolutional Network (GCN) layer and a gated residual fusion module. The diagnostic findings and condition-dependent model-selection guidelines provide an empirically grounded foundation for principled hybrid architecture development in urban traffic sensing. Full article

The past few years has witnessed the rapid development of China’s logistics industry. However, the industry still faces problems such as uneven regional development, low-cost efficiency, insufficient technology application, and pressure for green transformation. To support more effective policy and strategic planning, this study used composite location entropy, spatial autocorrelation analysis, and kernel density estimation to analyze the spatiotemporal evolution of logistics industry agglomeration based on China’s city-level panel data from 2010 to 2023. Geographic detectors and geographically weighted regression were used to explore its driving mechanisms from multiple perspectives. The results indicated that (1) China’s logistics industry agglomeration exhibited a decreasing gradient from east to west and the regional disparities gradually narrowed down over time. (2) China’s logistics industry showed significantly positive spatial autocorrelation, characterized mainly by high-high and low-low clusters. Northeastern China experienced the most active and tortuous local spatial evolution of logistics agglomeration, while Eastern China exhibited high tortuosity but stable spatial structure. Western China showed a smooth evolution, and Central China followed a relatively independent evolutionary path. Spatially, China’s logistics industry presented a pattern of high concentration in the southeast and sparse distribution in the northwest, with high-value zones expanding toward the central and western regions. (3) Transportation accessibility was the primary factor influencing logistics industry agglomeration, and the interaction among factors was stronger than the effect of individual factors. Specifically, the degree of openness exhibited a driving pattern centered on coastal areas and decreasing towards inland regions; the level of commercial development showed a positive correlation in the west and a negative correlation in the east; the spatial pattern of transportation capacity shifted from a pronounced east–west polarization to a more fragmented multi-cluster distribution; and transportation accessibility demonstrated spatial heterogeneity, with positive correlation in the southeast coastal areas and negative correlation in the west. Full article

Infrared small target detection (ISTD) has been a critical technology in various civilian and industrial applications over the past several decades, such as civilian patrol missions aboard UAVs or shipboard systems, and industrial inspection tasks like factory defect scanning. Nevertheless, moving infrared small target detection still faces considerable challenges: existing models suffer from insufficient spatio-temporal semantic correlation and are not lightweight-friendly, while algorithms that perform reliably across diverse scenarios are in great demand for real-world applications. To address these issues, we propose FeedbackSTS-Det, a sparse-frames-based spatio-temporal semantic feedback network. A closed-loop spatio-temporal semantic feedback strategy with paired forward and backward refinement modules that work cooperatively across the encoder and decoder is adopted to enhance information exchange between consecutive frames, effectively improving detection accuracy and reducing false alarms. Moreover, we introduce an embedded sparse semantic module (SSM), which operates by strategically grouping frames by interval, propagating semantics within each group, and reassembling the sequence to efficiently capture long-range temporal dependencies with low computational overhead. Extensive experiments on many widely adopted multi-frame infrared small target datasets demonstrate the consistent effectiveness of our proposed network across diverse scenes. Full article

Focusing on contrasting climate and land use pathways, this analysis explores the changing trajectories of water conservation function over time. An integrated framework combining the PLUS and InVEST models with Spearman’s correlation analysis and geographically weighted regression (GWR) was applied to examine the spatiotemporal heterogeneity and underlying drivers of water conservation function in the Chengdu–Chongqing Economic Zone during the period 2000–2020. Thus, it further predicted the evolution trend under two scenarios, namely SSP1-1.9 (Sustainable Development Pathway) and SSP2-4.5 (Medium Development Pathway), for the period 2030–2050. The findings reveal that: (1) Between 2000 and 2020, the spatial distribution of water conservation function shifted markedly, with low-value areas contracting and high-value zones expanding, alongside a progressive transition toward a predominantly medium-to-high functional structure. (2) In mountainous and hilly transition zones, precipitation (PRE) and forest cover proportion (FCP) exhibited notably positive effects, whereas evapotranspiration (PET) exerted a negative effect. In contrast, in plain and urbanized areas, built-up land proportion (BUP), population density (POP), and gross domestic product density (GDP) demonstrated pronounced negative effects. (3) Future simulations indicate that under the sustainable development pathway (SSP1-1.9), the combined area of high and extreme functional zones will recover by 2050, whereas under the moderate development pathway (SSP2-4.5), such extreme functional zones will be nearly eliminated. These results underscore the substantial impact of development pathways on regional water security and sustainability. Full article

Multiple sclerosis (MS) is a neuro-inflammatory disease characterized by demyelination in the central nervous system (CNS). Although a substantial endogenous capacity for remyelination has been demonstrated, this process is frequently incomplete and exhibits marked intra- and inter-individual heterogeneity. Several factors influence the extent of spontaneous myelin regeneration, including age, sex, disease course, and lesion localization. Oligodendrocytes (OL), derived from oligodendrocyte progenitor cells (OPCs), are the principal myelinating cells of the CNS. The regenerative cascade involves several key stages, including OPC activation, recruitment, differentiation into oligodendrocytes (OL), and myelin deposition. This process is orchestrated in a spatiotemporal manner by a complex interplay of intracellular signaling pathways, genetic determinants, and dynamic microenvironmental cues, which together balance inhibitory and pro-remyelinating influences. Several lines of evidence indicate that chronically demyelinated axons are vulnerable to degeneration, whereas successful remyelination may confer neuroprotection. These observations underscore remyelination as a promising neuroprotective therapeutic target for preventing or slowing disability progression in MS, a condition in which gradual neuroaxonal degeneration is believed to underlie irreversible disability progression. In this review, we aim to bridge the gap between fundamental biological mechanisms of remyelination and their clinical relevance. We examine recent advances in in vivo techniques for assessing remyelination and discuss how these measures correlate with clinical and disability outcomes. In addition, we review recent clinical trials of remyelination-promoting therapies and analyze the challenges that have limited their advancement beyond phase II. Overall, we seek to provide a comprehensive overview of the remyelination process from bench to bedside, highlighting both the obstacles and the therapeutic potential of remyelination strategies in MS. Full article

The integration of the digital and real economies is a pivotal engine driving the development of new, quality productive forces. Tourism ecological governance is the concrete manifestation of the green dimension of new-quality productive forces in the cultural and tourism sector, as well as being a path for converting ecological value to drive regional sustainable development. The relationship and mechanisms between digital–real integration and tourism ecological governance are critical issues requiring urgent breakthroughs. However, existing research primarily explores the economic factors influencing tourism ecology and has yet to systematically reveal the intrinsic mechanisms through which digital–real integration affects tourism ecological efficiency from the perspective of typical ecological functional zones. Based on data from 78 counties (municipalities, districts) in China’s Taihang Mountains from 2011 to 2023, this study examines the impact of digital–real integration on tourism ecological efficiency and its operational pathways. The findings are as follows: Firstly, from a temporal evolution perspective, tourism ecological efficiency in the Taihang Mountains underwent a phase of dynamic adjustment and gradual improvement between 2011 and 2023, while the level of digital–real integration experienced a phase of general enhancement and phased advancement. From a spatial evolution perspective, neighboring sub-regions within the Taihang Mountains exhibit positive spatial correlations in terms of both digital–real integration and tourism ecological efficiency. From the perspective of spatiotemporal transfer characteristics, changes in tourism ecological efficiency and the level of integration of the digital and real economies in the Taihang Mountains are influenced by neighboring regions. The development processes of tourism ecology and digital–real integration exhibit a relatively stable and gradually improving pattern, driving the agglomeration of regions toward higher levels. Secondly, digital–real integration has a positive impact on improving tourism ecological efficiency by releasing ecological pressure, promoting industrial synergy agglomeration, and driving green innovation development. Heterogeneity analysis reveals that the positive effect of this integration on tourism ecological efficiency is more pronounced in national e-commerce demonstration cities. Digital–real integration has had a positive impact on improving tourism ecological efficiency in the Southern and Western Taihang Mountain regions, while its impact on the Eastern Taihang Mountain region was not statistically significant. This study incorporates digital–real integration with tourism ecological efficiency, as well as environmental, structural, and capacity factors, into a unified analytical framework, providing theoretical references and practical insights for exploring pathways of digital transformation and innovative tourism ecological governance in ecologically sensitive functional zones. Full article