Search Results (5,625)

With the large-scale integration of offshore wind farms (OWFs), harmonic issues caused by the interaction between high-frequency switching of converters and complex network impedances pose severe challenges to power quality. Traditional harmonic monitoring heavily relies on post-event fixed-threshold alarm mechanisms, which struggle to achieve early warning during the low-distortion sub-health operation stage and lack the capability for multi-dimensional tracing of harmonic degradation sources. To address these limitations, this paper proposes a deep warning network for grid harmonics combining multi-step regression and multi-dimensional tracing within a unified multi-task learning (MTL) architecture. First, a deep shared feature encoder, integrating a bi-directional long short-term memory (Bi-LSTM) network with a multi-head self-attention (MHSA) mechanism, is utilized to extract high-order temporal coupling features between meteorological evolution and multi-node electrical states. Subsequently, the main task branch executes a k-step-ahead multivariate time-series regression to accurately predict the evolution trend of total harmonic distortion (THD) at both the point of common coupling (PCC) and the turbine terminal. Simultaneously, the auxiliary task branch performs multi-label micro-state classification based on relative degradation thresholds, achieving fine-grained multi-dimensional tracing covering spatial nodes, electrical attributes, and their joint micro-states. Experimental results on real-world OWF operational data demonstrate that through the joint optimization of regression and tracing tasks, the proposed MultiDimKStepMTL model significantly improves time-series prediction accuracy, achieving a $10.3\%$ relative improvement over single-task baselines, while substantially reducing computational overhead. This research successfully advances grid harmonic monitoring from passive response to proactive micro-state early warning, providing a solid, highly interpretable data-driven foundation for active filter control of offshore wind clusters. Full article

Urban park use is a key indicator of sustainable urban development, reflecting the accessibility and social value of urban green infrastructure. However, existing studies often struggle to distinguish stable spatial differences from short-term temporal dynamics. Using monthly data for 125 urban parks in Las Vegas from 2022 to 2024, this study examines how park visitation is shaped by spatial, temporal, and contextual factors. It addresses three objectives: identifying cross-park determinants of visitation, examining within-park monthly dynamics, and assessing spatial variation in key relationships. Park visitation is measured using observed visit counts, with dwell time and travel distance used as alternative behavioral outcomes for robustness tests. To address these research questions, this study asks: (1) what structural and contextual factors explain cross-park differences in park visitation; (2) how park visitation responds to changing contextual conditions within parks over time at the monthly scale; and (3) whether the relationships between park visitation and its key determinants vary across space. To answer these questions, the analysis combines annual cross-sectional ordinary least squares (OLS) regression, monthly panel models, Random Forest analysis, robustness tests, and geographically weighted regression. This study employs a triangulated analytical framework combining cross-sectional ordinary least squares (OLS) regression monthly fixed-effects (FE) panel models, and Random Forest (RF) analysis. These factors function as stable support for sustainable park use. Crime exposure shows no stable global linear effect, but its association with visitation appears conditional on temporal and spatial context. Overall, the findings suggest that park visitation is shaped by the interaction of physical design, safety conditions, and urban context. By explicitly separating cross-sectional spatial and economic inequalities from within-park temporal dynamics, this study offers policy-relevant evidence for urban planners and park managers seeking to promote more inclusive, efficient, and sustainable urban park systems through integrated design, economic activation, and safety-oriented interventions. Full article

In the context of high renewable energy penetration, the demand for power grid flexibility regulation is growing, yet existing load-side flexibility resource research ignores spatio-temporal coupling effects, leading to fragmented mining, insufficient quantification adaptability and low aggregation efficiency. This paper proposes an adaptive mining and quantification method for load-side flexibility resources considering spatio-temporal coupling. First, an adaptive mining framework integrating multi-energy time patterns, regional spatial clustering and spatio-temporal coupling matching is constructed to locate resources accurately. In this framework, the ST-MLCM (spatio-temporal multi-layer chain model) and ST-GMM (spatio-temporal Gaussian mixture model) are mathematically extended with spatial coupling terms, enabling genuine joint modeling of temporal dynamics and spatial dependencies. Second, a quantification model combining AHP-entropy weight method and fuzzy multi-attribute decision-making with spatio-temporal dynamic correction is established to balance weights and heterogeneity. Third, a ST-GNN-based distributed aggregation method is designed to realize high-efficiency aggregation of large-scale resources. Experimental results verify the effectiveness of the method in resource mining, quantitative evaluation and aggregation application, providing technical support for spatio-temporal coordinated dispatching of new power systems. Full article

Semantic change detection aims to localize changed regions and identify the corresponding land-cover transitions from bi-temporal remote sensing images, which is crucial for applications such as urban expansion analysis, disaster assessment, and environmental monitoring. Although vision foundation models such as the Segment Anything Model 2 provide strong visual priors and powerful feature representations, directly transferring them to semantic change detection remains challenging. In particular, the high-frequency details required for precise boundary delineation are often weakened during feature extraction, while the joint optimization of binary change localization and semantic recognition can introduce task interference. To address these challenges, we present FreqSCD, a SAM2-based framework built on a frozen backbone with three task-specific components: a High–Low-Frequency Adapter for frequency-aware feature adaptation, Task-Decoupled Decoding and Semantic Consistency for reducing task interference, and Local Spatial–Semantic Alignment for improving multi-scale feature aggregation. Experiments on the SECOND and Landsat-SCD benchmarks show that FreqSCD achieves strong semantic change detection performance, obtaining an $F_1$ score of 56.72% and a SeK of 24.17% on SECOND, as well as an $F_1$ score of 85.46% and a SeK of 53.76% on Landsat-SCD. Full article

This study presents the first systematic evaluation of the capability of the Surface Water and Ocean Topography (SWOT) satellite Level-2 High Rate Pixel Cloud (L2_HR_PIXC) product for retrieving topography in reservoir drawdown zones under varying terrain conditions in arid and semi-arid regions. Three representative reservoirs in southern Xinjiang, China—characterized by plain, canyon, and pocket-shaped canyon morphologies—were selected to establish a terrain-dependent validation framework. A novel multi-feature clustering strategy integrating elevation and radar backscatter coefficients was explored to reduce the misclassification of wet mudflats as water pixels in the PIXC product, aiming to improve DEM accuracy in reservoir drawdown zones. Based on this framework, multi-cycle SWOT-derived digital elevation models (DEMs) were generated and quantitatively evaluated against high-resolution unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) DEMs. Results demonstrate a strong terrain dependency in SWOT-derived elevation accuracy. In low-relief environments, sub-meter accuracy is achieved, with the root mean square error (RMSE) below 0.25 m, confirming the suitability of SWOT for high-precision monitoring. However, errors increase significantly in steep and complex terrains, reaching up to ±6 m, primarily due to interferometric decorrelation, geometric distortion, and slope-induced biases. Despite these limitations, multi-temporal observations exhibit generally similar spatial error patterns across terrains, indicating reasonable repeatability under the tested conditions. This study reveals the performance boundaries of SWOT-derived DEMs in dynamic land–water transition zones and provides a robust methodological framework for improving DEM extraction in similar environments. The findings contribute to advancing the application of SWOT data in hydrological monitoring and geomorphological analysis at regional scales. Full article

Change detection in open-pit mining areas from remote sensing imagery is of great importance for mining supervision, ecological monitoring, and restoration planning. Nevertheless, mining-related changes usually exhibit multi-scale patterns, irregular boundaries, and fragmented spatial distributions, which make accurate detection difficult. Existing CNN- and Transformer-based methods often cannot effectively balance global context perception and local detail preservation, resulting in incomplete boundary extraction and insufficient sensitivity to subtle changes. To overcome these limitations, we propose GLMECD-Net, a Global–Local Multi-scale Cross-fusion Enhanced Change Detection Network for remote sensing image change detection in open-pit mining areas. Specifically, a Siamese encoder is used to extract hierarchical bi-temporal features, while a Global–Local Feature Mixing Embedding (GLME) module is introduced to jointly capture long-range contextual information and local spatial details. Furthermore, multi-scale feature aggregation and cross-temporal feature fusion are employed to improve change representation and boundary recovery. Experimental results on mining area datasets show that the proposed method achieves 71.66% Precision, 83.78% OA, 77.53% F1-score, and 53.82% IoU. The results demonstrate that GLMECD-Net provides effective and robust performance for detecting complex and subtle changes in open-pit mining areas. Full article

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. Full article

As urban waterfront public spaces have increasingly become important settings for residents’ daily leisure activities, there remains a lack of empirical evidence based on objective image data regarding how street-level visual environments influence different types of leisure activities. The existing studies have largely relied on macro-scale built environment indicators and paid limited attention to micro-scale visual perception from the pedestrian perspective. To address this gap, this study focuses on the core waterfront section of the Pearl River in Guangzhou. Behavioral observations were conducted across nine spatial units during different time periods on weekdays and weekends, yielding 54 samples of passive, active, and social activity intensity. Meanwhile, 109 street-view sampling points were established, generating 436 pedestrian-view images. Using Mask2Former with an ADE20K pre-trained model, visual environmental indicators—including the Green View Index (GVI), Sky View Index (SVI), built environment proportion, road proportion, and visual diversity (Entropy)—were extracted. Spearman correlation and multiple linear regression were applied to examine their effects on activity intensity. The results show that leisure activities are generally more active in the evening and on weekends, with social activities exhibiting the strongest temporal variation. Active activities remain relatively stable, passive activities show temporal dependence, and social activities display localized high-intensity clustering. Regression results reveal differentiated environmental responses: visual diversity has a stable positive effect on passive activities, active activities show weak associations with visual variables, and social activities are the most sensitive, with GVI, SVI, and built proportion showing significant negative effects, while visual diversity shows a significant positive effect. The social activity model also demonstrates the highest explanatory power (Adj. R² = 0.488). Overall, this study develops a street-view semantic segmentation-based method for quantifying waterfront visual environments, demonstrates the critical role of visual environmental composition in shaping activity patterns, and provides empirical support for the fine-grained and activity-oriented optimization of waterfront public spaces. Full article

This study proposes and tests an analytical framework for interpreting digitally monitored municipal biowaste collection services through comparable diagnostics of operational performance, additional effort, and emissions intensity. The framework was applied to 572 collection services recorded between July and December 2025 in the Municipality of Barreiro, Portugal, covering seven circuits operating under different urban morphologies and collection configurations. Service-level operational records were transformed into physically interpretable performance indicators and an additional operational effort index was derived from robust normalization of serviced container density and service time per kilometer. The results showed marked heterogeneity across service regimes, with the highest effort observed in residential circuits characterized by greater spatial and temporal demand, while the non-domestic and communal circuits remained at or below municipal reference conditions. At the municipal scale, operational effort was moderately associated with mass collected per kilometer (ρ = 0.490, n = 572), weakly and non-significantly associated with mass per hour (ρ = 0.075, p = 0.074), and negatively associated with mass per container (ρ = −0.325). For services operating above municipal reference conditions (Eesf > 0, n = 286), emissions intensity was negatively associated with both effort components and with the aggregate effort index, with the strongest association observed for Eesf (ρ = −0.554). The results indicate that higher operational effort tends to coincide with greater spatial mass recovery, but not with higher container-level yield or proportionate improvements in emissions performance. More broadly, the study shows that the analytical value of digital monitoring depends not only on data availability, but also on the ability to convert routine service records into interpretable diagnostics for municipal decision-making. Full article

Wind power plays an increasingly important role in the global energy transition. However, its power output exhibits significant uncertainty due to rapid variations in meteorological conditions. Existing forecasting methods still face challenges in large-scale wind farm cluster scenarios. In such cases, spatial heterogeneity and temporal asynchrony among wind farms cannot be fully characterized, which limits the overall prediction accuracy. To address these issues, this study proposes a novel hierarchical and adaptive collaborative forecasting framework for wind farm clusters by integrating meteorology-driven dynamic clustering with deep learning-based prediction. First, a multidimensional feature system is constructed by jointly considering static wind farm attributes and dynamic meteorological variation trends. Based on a sliding time window, real-time meteorological similarity among wind turbines is evaluated, allowing meteorological data to actively drive the formation and continuous evolution of adaptive subcluster structures. Subsequently, a deep learning model is developed to perform short-term power forecasting at the dynamic subcluster level. This approach enables the framework to flexibly capture spatio-temporal heterogeneity while maintaining robust prediction capability under varying cluster structures. Experimental results based on real-world wind farm cluster data demonstrate that the proposed method achieves superior accuracy and robustness compared with conventional whole-farm forecasting and static clustering approaches. The proposed framework enhances forecasting reliability, thereby supporting renewable energy integration and sustainable low-carbon power systems. Full article

Proton exchange membrane water electrolysis is a promising technology for large-scale green hydrogen production due to its high efficiency, compact design, and rapid dynamic response. However, its commercialization is strictly limited by high material costs, durability issues, and complex multiphysics coupling within the membrane electrode assembly. This work provides a comprehensive and critical review of key physicochemical processes and advanced predictive modeling approaches for PEMWEs. To capture recent paradigm shifts, we introduce an innovative multi-dimensional classification framework—incorporating spatial resolution, temporal dynamics, and methodological paradigms—to critically evaluate lumped-parameter, continuum, microscale, and multiscale models, explicitly defining their applicability bounds and inherent limitations. The fundamental mechanisms governing electrode kinetics, membrane water transport, and gas–liquid two-phase flow are analyzed, establishing state-of-the-art quantitative benchmarks for microstructural parameters and advanced 3D flow field topologies under high-current-density and high-pressure regimes. Furthermore, we systematically examine model validation rigor, typical prediction errors, and the critical failure of static models in capturing dynamic property shifts during extreme bubble breakthrough. Recent breakthroughs integrating in situ diagnostics, pore-scale simulations, density functional theory, and Physics-Informed Neural Networks are extensively discussed. Future efforts must prioritize mechanical–electrochemical–thermal coupling, transient degradation prognostics, and machine learning-driven predictive digital twin technologies to overcome current empirical limitations and accelerate the gigawatt-scale deployment of PEMWE systems. Full article

In the context of the rapid growth of the digital economy and the continued implementation of China’s “dual carbon” strategy, clarifying the interactive relationships among the digital economy, low-carbon logistics, and the ecological environment is crucial for promoting sustainable regional development and green transformation. Based on the theoretical mechanisms underlying the coordinated development of these three systems, this study constructs a comprehensive evaluation index system for the Digital Economy–Low-Carbon Logistics–Ecological Environment (DLE) system. The entropy weighting method, a modified coupling coordination model, kernel density estimation, spatial autocorrelation analysis, and the barrier model are integrated to investigate the spatiotemporal evolution and driving mechanisms of coupling coordination among the three systems. The results indicate that (1) the development levels of the digital economy, low-carbon logistics, and the ecological environment have generally increased, although their evolutionary trajectories differ across stages. The digital economy shows the most rapid improvement, low-carbon logistics maintains steady progress, and the ecological environment exhibits gradual optimization. (2) From a temporal perspective, the overall coupling coordination of the national DLE system has shown a fluctuating upward trend, with the coordination type gradually evolving from a near-coordination stage to an initial coordination stage, though it remains at a low-to-medium coordination level overall. (3) From a spatial perspective, the coupling coordination degree presents a stable gradient pattern, with higher levels in eastern China, intermediate levels in central China, and lower levels in western China. Medium- and high-coordination areas are gradually extending from coastal regions to inland areas, while regional disparities remain evident. (4) The spatial autocorrelation results reveal significant positive spatial clustering at the provincial level. Both high-value and low-value clusters show a certain degree of stability, indicating clear spatial spillover effects. (5) An analysis of constraining factors reveals that insufficient scale of digital economic development and innovation application capabilities, constraints on ecological and environmental resource carrying capacity and governance, as well as low operational efficiency and delayed transformation of low-carbon logistics, are the primary types of obstacles hindering the coordinated improvement of the three systems. These findings provide empirical evidence and policy implications for leveraging the digital economy to facilitate low-carbon logistics transformation and enhance coordinated regional sustainability. Full article

Background: Macrophages were long regarded as passive executors of erythrophagocytosis responsible for systemic iron recycling. However, increasing evidence has reframed them as immunometabolic hubs that sense diverse environmental cues to modulate systemic iron homeostasis. Main body: This review examines the molecular architecture underlying macrophage iron metabolism and outlines how iron metabolic processes are dynamically regulated across spatial and temporal scales through the integration of mechanotransductive, mitochondrial, and epigenetic signaling pathways. Across disease contexts, macrophage iron handling displays marked heterogeneity, exemplified by contact-dependent iron transfer in tumors and ferroptosis-driven instability in cardiovascular disease. In cardiovascular pathologies, iron overload is associated with enhanced ferroptosis-related cascades that contribute to atherosclerotic plaque instability. Furthermore, at mucosal interfaces, host–pathogen competition over nutritional immunity highlights epigenetic strategies by which pathogens perturb host iron machinery. Conclusions: Linking these mechanistic insights to clinical translation, emerging therapeutic strategies are discussed that move beyond non-specific systemic iron chelation toward more targeted interventions. These include engineering macrophages for targeted drug delivery, exploiting nanomedicine-based redox modulation to influence macrophage phenotypes, and non-invasive regulation via the gut microbiota–epigenetic axis. Collectively, elucidating macrophage iron metabolic networks provides a conceptual framework for the development of precision approaches to inflammatory, metabolic, and malignant diseases. Full article

In recent years, wildfires have occurred with increasing frequency. Pixel-level annotation of high-resolution remote sensing wildfire imagery is costly and labor-intensive. Therefore, there is an urgent need for a weakly supervised wildfire detection method that balances detection accuracy and annotation efficiency. To address the key limitations of existing weakly supervised approaches based on class activation maps (CAMs), including imprecise delineation of fire boundaries, insufficient utilization of cross-modal information, and limited capability in modeling temporal characteristics, this paper proposes a dual-branch multi-scale feature fusion framework for weakly supervised wildfire detection. The proposed framework consists of a multispectral branch and a shortwave infrared (SWIR) temporal branch, which are designed to capture the spatial structural information of fire regions and the temporal variation of thermal anomalies, respectively. Attention-guided feature fusion modules are introduced at each network stage to enable complementary integration of cross-modal information. In addition, a multi-scale CAM-weighted fusion strategy is designed to jointly enhance region localization accuracy and semantic discrimination capability. Experimental evaluations are conducted on a high-resolution wildfire dataset covering 29 regions and consisting of 2206 images. The results demonstrate that the proposed method achieves an IoU of 58.7% and an F1-score of 73.5%, outperforming the state-of-the-art methods by 4.6% and 3.2%, respectively. Ablation and comparative experiments further verify that the dual-branch architecture and feature fusion strategy significantly improve fire localization accuracy and effectively reduce the missed detection rate. Full article

Pacific sardine (Sardinops sagax) are an economically important forage fish in the Northeast Pacific Ocean that undergo large changes in abundance over decadal scales and exhibit high individual variation in somatic growth. Past studies have suggested that somatic growth in Pacific sardine may be density-dependent and vary regionally in response to environmental conditions. We analyzed somatic growth in Pacific sardine off the U.S. Pacific Coast during the recent period of low abundance (2012–2021) and compared the results to those of previous studies to evaluate evidence of spatial or temporal variation in growth. Sampled fish (n = 3228) ranged in length from 30 to 291 mm SL and in age from 0 to 9 years and displayed high individual variation in length-at-age and age-at-length. Length-at-age data were best explained by the von Bertalanffy growth model, and sample distribution simulations showed the dataset to be robust and unbiased. Estimated growth parameters (L_∞ = 243, K = 0.795, t₀ = −0.638) were consistent with an opportunistic life history strategy characterized by rapid growth, early maturation, and a short lifespan. While the estimated growth rate (K) was higher than in a previous study conducted during a period of high abundance and indicated that growth may be density-dependent, the parameter estimates from the previous study were influenced by sample distribution bias. Similarly, differences in study region, season, collection method, aging methods, and other factors precluded any definitive conclusions on the source of reported differences in growth patterns among studies. Full article