Search Results (5,462)

Supply–demand balance in wind–solar dominant energy transition is challenged by the volatility of wind–solar power. Complementarity of wind–solar power has been introduced to suppress this volatility. Although multiple indices have been developed to quantify complementarity, a quantitative index with explicit physical meaning remains lacking. Additionally, complementarity’s temporal stability, which is imperative for wind–solar site selection, is unclear. In this study, these knowledge gaps are closed through developing a Daily Complementarity Index of wind–solar generation (DCI) and a nuanced national assessment of complementarity in China. The results of the comparison of our index with existing indices and site validation confirm the reasonability of the DCI and its improvements in interpretability. The average DCI of China ranges from 0.06 to 0.88, with a pronounced low-DCI zone across the Sichuan Basin and Chongqing municipality, and a high–DCI zone along the Three-North Shelterbelt. Temporally, the complementarity of wind–solar power in China follows a slight increase trend (3.96 × 10⁻⁵ year⁻¹), with evident seasonal characteristics, in which the highest and lowest are 0.37 and 0.17, respectively. This study introduces an effective tool for quantifying complementarity, and these findings can offer valuable reference for China’s renewable energy transition. Full article

Ecosystem health assessment is essential for informing ecological protection and sustainable management, yet current evaluation frameworks often overlook the foundational role of natural background conditions and struggle with methodological uncertainties in indicator weighting, particularly in ecologically fragile regions. To address these dual challenges, this study proposes a novel Base–Pressure–State–Response (BPSR) framework that systematically integrates key natural background factors as a fundamental “Base” layer. Focusing on the Qinling Mountains—a critical ecological barrier in China—we implemented this framework at the county scale using multi-source data (2000–2023) and introduced a Monte Carlo simulation with triangular probability distributions to quantify and synthesize weight uncertainties from multiple methods, thereby enhancing assessment robustness. Furthermore, the Geodetector method was employed to quantitatively identify the driving forces behind the spatiotemporal heterogeneity of ecosystem health. Supported by 3S technology, our analysis demonstrates a sustained improvement in ecosystem health: the composite index rose from 0.723 to 0.916, healthy areas expanded from 60.17% to 68.48%, and nearly half of the region achieved a higher health grade. Spatially, a persistent “low–south, high–north” pattern was observed, shaped by human disturbance gradients, while temporally, the region evolved from localized improvement (2000–2010) to broad-scale recovery (2010–2023), despite lingering degradation in human-dominated zones. Driving force analysis revealed a shift from early dominance by natural and land use factors to a later complex interplay where urbanization pressure and climatic conditions jointly shaped the health pattern. The BPSR framework, combined with probabilistic weight optimization and driving force quantification, offers a methodologically robust and spatially explicit tool that advances ecosystem health evaluation and supports targeted ecological governance, policy formulation, and sustainable management in fragile mountain ecosystems, with transferable insights for similar regions globally. Full article

Satellite-aided multi-unmanned aerial vehicle (UAV) collaborative localization systems combine the extensive coverage of satellites with the flexibility of UAVs, offering new opportunities for locating highly dynamic emitters across large areas. However, the openness of space-air communication links and the increasing complexity of cybersecurity threats make these systems vulnerable to false data injection attacks. Most existing detection approaches focus only on temporal dependencies in time-frequency features and lack diagnostic mechanisms for identifying malicious UAVs, which limits their ability to effectively detect and mitigate such attacks. To address this issue, this paper proposes an intelligent collaborative localization framework that safeguards localization integrity by identifying and correcting false ranging information from malicious UAVs. The framework captures spatio-temporal correlations in multidimensional ranging sequences through a graph attention network (GAT) coupled with a time-attention-based variational autoencoder (VAE) to detect anomalies through anomalous distribution patterns. Malicious UAVs are further diagnosed through an anomaly scoring mechanism based on statistical analysis and reconstruction errors, while detected anomalies are corrected via a K-nearest neighbor-based (KNN) algorithm to enhance localization performance. Simulation results show that the proposed model improves localization accuracy by 25.9%, demonstrating the effectiveness of spatial–temporal feature extraction in securing collaborative localization. Full article

In response to the urgent demands of global climate governance, China has systematically integrated the green transition into its “dual-carbon” goals. The practical exploration of cultivated land emission reduction is not only crucial for promoting green transition but also embodies the synergistic effects of emission reduction and carbon sequestration in high-carbon-emission pressure areas. Existing studies have paid relatively less attention to high-carbon-emission pressure areas, necessitating more systematic research. In this study, we selected Henan Province as the study area and quantitatively analyzed the spatial-temporal differentiation of cultivated land net carbon sink from 2000 to 2023 along with their driving factors using an integrated methodological framework including Intergovernmental Panel on Climate Change (IPCC)-based carbon accounting, spatial autocorrelation analysis, and trajectory modeling. Analysis of the results indicates that the total net carbon sink of cultivated land in Henan Province showed a fluctuating yet overall upward trend with an average annual growth rate of 2.51%. The spatial distribution exhibits a pattern of “higher in the south and lower in the north” and “higher in the east and lower in the west.” This spatial pattern was significantly shaped by the cultivation area and fertilizer application intensity of three major crops—wheat, maize, and vegetables. Specifically, the net carbon sink contributions from these crops increased from 82.12% in 2000 to 85.93% in 2023, while the share of carbon emissions attributable to fertilizer use in the net carbon sink increased from 4.61% in 2000 to 5.22% in 2023, representing the activity with the largest contribution ratio among carbon emission activities. These findings provide valuable scientific evidence for further optimizing the green transition in high-carbon-emission areas and promoting the synergistic effects of emission reduction and carbon sequestration. Full article

Inspired by the Tesla valve, the island-type fishway is a novel design whose biological performance remains unelucidated. This study integrated hydraulic experiments, CFD modeling, and 3D computer vision to investigate the passage performance and swimming behavior of juvenile silver carp (Hypophthalmichthys molitrix). The results confirmed high biological feasibility, with upstream success rates exceeding 70%. The island and arc-baffle configuration create a heterogeneous flow field with an S-shaped main flow and low-velocity zones; each island unit contributes 8.9% to total energy dissipation. Critically, fish utilize a multi-dimensional navigation strategy to avoid high-velocity cores: temporally adopting an intermittent “rest-burst” pattern for energetic recovery; horizontally following an “Ω”-shaped bypass trajectory; and vertically preferring the bottom boundary layer. Passage failure was primarily linked to suboptimal path selection near the high-velocity main flow. These findings demonstrate that fishway effectiveness depends less on bulk hydraulic parameters and more on the spatial connectivity of hydraulic refugia aligning with fish behavioral traits. This study provides a scientific basis for optimizing eco-friendly hydraulic structures. Full article

Pan evaporation (PE) is widely used as an indicator of atmospheric evaporative demand and is relevant to irrigation demand and climate-related hydrological changes. Using daily records from 759 meteorological stations across China during 2002–2018, this study investigated the temporal trends, spatial patterns, and climatic controls of PE across seven major climate zones. Multiple decomposition techniques revealed a dominant annual cycle and a pronounced peak in 2018, while a decreasing interannual trend was observed nationwide. Spatial analyses showed a clear north–south contrast, with the strongest declines occurring in northern China. A random forest (RF) model was employed to quantify the contributions of climatic variables, achieving high predictive performance. RF results indicated that the dominant drivers of PE varied substantially across climate zones: sunshine duration (as a proxy for solar radiation) and air temperature mainly controlled PE in humid regions, while wind speed and relative humidity (RH) exerted stronger influences in arid and semi-arid regions. The widespread decline in northern China is consistent with concurrent changes in wind speed and sunshine duration, together with humidity conditions, which modulate evaporative demand at monthly scales. These findings highlight substantial spatial heterogeneity in PE responses to climate forcing and provide insights for drought assessment and water resource management in a warming climate. Full article

Based on GRACE RL06 data, this study reconstructs a monthly Terrestrial Water Storage Anomaly (TWSA) series in Shandong Province (2003–2024) using Singular Spectrum Analysis (SSA) and derives Groundwater Storage Anomaly (GWSA) via the water balance equation. The spatiotemporal evolution characteristics of GWSA were systematically examined, and the relative contributions of climatic factors and human activities to groundwater storage changes were quantitatively assessed, with the aim of contributing to the development, utilization, and protection of groundwater in Shandong Province. The results indicate that temporally, GWSA in Shandong Province exhibited a statistically significant decreasing trend at a rate of −8.45 mm/a (p < 0.01). The maximum GWSA value of 17.15 mm was recorded in 2006, while the Mann–Kendall abrupt change-point analysis identified 2013 as a significant transition point. Following this abrupt change, GWSA demonstrated a persistent decline, reaching the minimum annual average of −225.78 mm in 2020. Although moderate recovery was observed after 2020, GWSA values remained substantially lower than those in the pre-abrupt change period. Seasonal analysis revealed a distinct “higher in autumn and lower in spring” pattern, with the most pronounced fluctuations occurring in summer and the most stable conditions in winter. Spatially, approximately 99.1% of the study area showed significant decreasing trends, displaying a clear east–west gradient with more severe depletion in inland regions compared to relatively stable coastal areas. Crucially, human activities emerged as the dominant driving factor, with an average contribution rate of 86.11% during 2003–2024. The areal proportion where human activities served as the decisive factor (contribution rate > 80%) increased dramatically to 99.58%. Furthermore, the impact of human activities demonstrated bidirectional characteristics, transitioning from negative influences during the depletion phase to positive contributions promoting groundwater recovery in recent years. At present, the GWSA in Shandong Province is expected to continue declining in the future, with an overall downward trend. Countermeasures must be implemented promptly. Full article

Enhancing agricultural green total factor productivity (AGTFP) under ecological and environmental constraints is essential for advancing green agricultural development in the Beijing–Tianjin–Hebei (BTH) region. Using panel data from 13 prefecture-level cities from 2001 to 2022, this study applies a super-efficiency EBM model incorporating undesirable outputs together with the Malmquist–Luenberger index to measure AGTFP. Global and local Moran’s I indices as well as the spatial Durbin model are then employed to examine the temporal evolution, spatial disparities, and spatial interaction effects of AGTFP during 2001–2022. The findings indicate that: (1) From 2001 to 2022, the AGTFP in the BTH region grew at an average annual rate of 7.7%. This trend reflects a growth pattern primarily driven by green technological progress in agriculture, while substantial disparities in AGTFP persist across different subregions. (2) the global Moran’s I values show frequent shifts between positive and negative spatial autocorrelation, suggesting that a stable and effective regional coordination mechanism for green agricultural development has yet to be formed; (3) the determinants of AGTFP exhibit pronounced spatiotemporal heterogeneity, and the fundamental drivers of the region’s green agricultural transition increasingly rely on endogenous growth generated by technological innovation and rural human capital; (4) policy recommendations include strengthening benefit-sharing and policy coordination mechanisms, promoting cross-regional cooperation in agricultural science and technology, and implementing differentiated industrial layouts to support green agricultural development in the BTH region. These results provide valuable insights for promoting coordinated and sustainable green agricultural development across regions. Full article

Non-repetitive scanning LiDARs provide high coverage yet exhibit irregular sampling patterns, which destabilize local features and correspondences. To address this, we propose a novel spatiotemporal unified energy framework that integrates fractional calculus into rigid pose estimation. Spatially, we introduce a Riesz fractional regularization term to impose non-local smoothness constraints on the residual field, mitigating structural inconsistencies. Temporally, we design a Grünwald–Letnikov fractional dynamics solver that leverages long-memory effects of historical gradients to reduce the risk of being trapped in local minima. Comparative experiments on the Stanford 3D, MVTec ITODD, and HomebrewedDB (HB) datasets demonstrate that our method significantly outperforms state-of-the-art geometric and learning-based approaches. Specifically, it maintains a success rate exceeding 90% even under severe sampling perturbations where traditional methods fail. Ablation studies further validate that the introduction of non-local spatial constraints and historical gradient memory significantly reshapes the energy landscape, ensuring robust convergence. This work provides a rigorous theoretical foundation for applying fractional operators to point cloud processing. Full article

This study presents an analysis of data from patients with ascending thoracic aortic aneurysms (ATAAs). Two databases of 87 patients were available: one containing clinical variables and the other consisting of measurements of the maximum diameter taken along the ascending aorta. For the clinical database, both a supervised and an unsupervised learning method were applied to explore patterns within the data. On the other hand, for the ascending aorta dataset, experimental variograms were calculated, from which key parameters of interest were extracted. These parameters were then analyzed over time to assess temporal patterns. This analysis aimed to assess the emergence of similar patterns or behaviour in patients with aneurysms of comparable sizes. Based on the analyses conducted, the clinical variables with the greatest importance in surgical decision-making were identified, while the spatial statistical analysis revealed patterns that may be related to elasticity, stiffness, or deformations of the aorta. Full article

Extensive coal mining activities can exert substantial negative impacts on surface ecosystems. Vegetation indices are widely recognized as effective indicators of land ecological conditions and provide valuable insights into long-term ecological changes in mining areas. In this study, the Zhaogu mining area of the Jiaozuo Coalfield was selected as the study site. Using the Google Earth Engine (GEE) platform, the Kernel Normalized Difference Vegetation Index (kNDVI) was constructed to generate a vegetation dataset covering the period from 2010 to 2024. The temporal dynamics and future trends of vegetation coverage were analyzed using Theil–Sen median trend analysis, the Mann–Kendall test, the Hurst index, and residual analysis. Furthermore, the relative contributions of climatic factors and human activities to vegetation changes were quantitatively assessed. The results indicate that: (1) vegetation coverage in the Zhaogu mining area exhibits an overall improving trend, affecting approximately 77.1% of the study area, while slight degradation is mainly concentrated in the southeastern region, accounting for about 15.2%; (2) vegetation dynamics are predominantly characterized by low and relatively low fluctuations, covering approximately 78.5% of the region, whereas areas with high fluctuations are limited and mainly distributed in zones with intensive mining activities; although the current vegetation trend is generally increasing, future projections suggest a potential decline in approximately 55.8% of the area; and (3) vegetation changes in the Zhaogu mining area are jointly influenced by climatic factors and human activities, with climatic factors promoting vegetation growth in approximately 70.6% of the study area, while human activities exert inhibitory effects in about 24.2%, particularly in regions affected by mining operations and urban expansion. Full article

Population activity drives urban development, and high-spatiotemporal-resolution population distribution provides critical insights for refined urban management and social services. However, mixed population activity patterns and spatial heterogeneity make simultaneous high-temporal- and -spatial-resolution estimation difficult. Therefore, we propose the High-Spatiotemporal-Resolution Population Distribution Estimation Based on the Strong and Weak Perception of Population Activity Patterns (SWPP-HSTPE) method to estimate hourly population distribution at the building scale. During the weak-perception period, we construct a Modified Dual-Environment Feature Fusion model using building features within small-scale grids to estimate stable nighttime populations. During the strong-perception period, we incorporate activity characteristics of weakly perceived activity populations (minors and older people). Then, the Self-Organizing Map algorithm and spatial environment function purity are used to decompose mixed patterns of strongly perceived activity populations (young and middle-aged) and to extract fundamental patterns, combined with building types, for population calculation. Results demonstrated that the SWPP-HSTPE method achieved high-spatiotemporal-resolution population distribution estimation. During the weak-perception period, the estimated population correlated strongly with actual household counts (r = 0.72) and outperformed WorldPop and GHS-POP by 0.157 and 0.133, respectively. During the strong-perception period, the SWPP-HSTPE model achieves a correlation with hourly population estimates that is approximately 4% higher than that of the baseline model, while reducing estimation errors by nearly 2%. By jointly accounting for temporal dynamics and population activity patterns, this study provides valuable data support and methodological insights for fine-grained urban management. Full article

The Three Gorges Reservoir Area (TGRA) faces mounting pressures from urbanization and hydrological regulation, threatening the sustainability of its ecosystem services (ESs). The InVEST model, coupled with optimal parameter geographical detector (OPGD) and geographically and temporally weighted regression (GTWR), was employed to assess spatiotemporal changes, trade-offs/synergies, and driving mechanisms of four ESs, water yield (WY), habitat quality (HQ), carbon storage (CS), and soil conservation (SC), from 2000 to 2020. Results revealed that WY and SC increased significantly by 24.54% and 5.75%, respectively, while HQ declined by 3.02% and CS remained relatively stable, with high-value ES zones mainly concentrated in the eastern and northern forest-dominated areas. Regarding interactions, strong synergies existed among HQ, CS, and SC, whereas WY exhibited persistent trade-offs with other services, particularly in the central agricultural-urban transitional zone. Furthermore, landscape diversity increased linearly, driven by forest expansion and urban growth. Mechanistically, land use type (LUT) dominated the spatial distribution of WY, HQ, and CS, while slope primarily controlled SC patterns, with all driver interactions demonstrating enhanced effects. By coupling OPGD with GTWR, this study uniquely elucidates the spatiotemporal instability of ES trade-offs/synergies and the spatial heterogeneity of their driving mechanisms, providing a novel scientific basis for implementing spatially differentiated management strategies in large-scale reservoir-impacted regions. Full article

Against the backdrop of intensifying global climate change and expanding human encroachment into mountainous regions, landslides have increased markedly in both frequency and destructiveness, emerging as a key risk to socio-ecological security and development in mountain areas. Rigorous assessment and forward-looking prediction of landslide disaster vulnerability (LDV) are essential for targeted disaster risk reduction and regional sustainability. However, existing studies largely center on landslide susceptibility or risk, often overlooking the dynamic evolution of adaptive capacity within affected systems and its nonlinear responses across temporal and spatial scales, thereby obscuring the complex mechanisms underpinning LDV. To address this gap, we examine Yunnan Province, a landslide-prone region of China where intensified extreme rainfall and the expansion of human activities in recent years have exacerbated landslide risk. Drawing on the vulnerability scoping diagram (VSD), we construct an exposure–sensitivity–adaptive capacity assessment framework to characterize the spatiotemporal distribution of LDV during 2000–2020. We further develop a multi-model, multi-scale integrated prediction framework, benchmarking the predictive performance of four machine learning algorithms—backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and XGBoost—across sample sizes ranging from 2500 to 360,000 to identify the optimal model–scale combination. From 2000 to 2020, LDV in Yunnan declined overall, exhibiting a spatial pattern of “higher in the northwest and lower in the southeast.” High-LDV areas decreased markedly, and sustained enhancement of adaptive capacity was the primary driver of the decline. At approximately the 90,000-cell grid scale, XGBoost performed best, robustly reproducing the observed spatiotemporal evolution and projecting continued declines in LDV during 2030–2050, albeit with decelerating improvement; low-LDV zones show phased fluctuations of “expansion followed by contraction”, whereas high-LDV zones continue to contract northwestward. The proposed multi-model, multi-scale fusion framework enhances the accuracy and robustness of LDV prediction, provides a scientific basis for precise disaster risk reduction strategies and resource optimization in Yunnan, and offers a quantitative reference for resilience building and policy design in analogous regions worldwide. Full article

Accurate golf swing correction requires modeling not only static pose deviations but also temporal rhythm and biomechanical stability throughout the swing sequence. Most existing pose-based approaches rely on frame-wise similarity and therefore fail to capture timing, velocity transitions, and coordinated joint dynamics. This study proposes a reinforcement learning-based framework that generates frame-level corrective motions by formulating swing correction as a sequential decision-making problem optimized via Proximal Policy Optimization (PPO). A multi-term reward function is designed to integrate geometric pose accuracy, incremental correction improvement, hip-centered stability, and temporal rhythm consistency measured using a Velocity-DTW metric. Experiments conducted with swing sequences from one professional and five amateur golfers demonstrate that the proposed method produces smoother and more temporally coherent corrections than static pose–based baselines. In particular, rhythm-aware rewards substantially improve the motion of highly dynamic joints, such as the wrists and shoulders, while preserving lower-body stability. Visual analyses further confirm that the corrected trajectories follow expert patterns in both spatial alignment and timing. These results indicate that explicitly incorporating temporal rhythm within a reinforcement learning framework is essential for realistic and effective swing correction. The proposed method provides a principled foundation for automated, expert-level coaching systems in golf and other dynamic sports requiring temporally coordinated whole-body motion. Full article