Search Results (7,658)

The Normalized Difference Vegetation Index (NDVI) derived from polar-orbiting satellites is widely used for vegetation monitoring; however, its temporal continuity is often limited by cloud contamination and fixed revisit cycles. To address this limitation, this study investigates the feasibility of using geostationary satellite observations to enhance the spatial completeness of Sentinel-2 NDVI at its standard revisit intervals through cloud gap-filling applications. Geostationary Ocean Color Imager II (GOCI-II) data (250 m) was used as input, while Sentinel-2 Multispectral Instrument (MSI) NDVI (10 m) served as the reference dataset. To enable cross-sensor integration, a data-driven transformation framework was developed to convert GOCI-II NDVI into MSI-like NDVI while preserving dominant spatial variation patterns rather than pursuing strict pixel-level super-resolution. The transformed NDVI was assessed through spatial comparisons and statistical metrics, including correlation coefficient, mean absolute error, root mean square error (RMSE), normalized RMSE, and structural similarity index measure. Results show that geostationary-derived NDVI captures broad spatial organization and field-scale variability observed in MSI NDVI. Building on this cross-scale consistency, cloud gap-filling experiments demonstrate that temporally adjacent transformed NDVI scenes maintain consistent variation patterns, supporting their complementary use for compensating cloud-induced gaps. Although reduced contrast and magnitude-dependent biases remain, primarily due to the large spatial resolution difference and sub-pixel heterogeneity, an intermediate-resolution (80 m) sensitivity analysis indicates improved stability when the resolution gap is reduced. Overall, these findings highlight the practical potential of integrating geostationary and polar-orbiting observations to improve NDVI spatial continuity in cloud-prone regions. Full article

This study develops a bottom-up carbon emission accounting framework at the urban community scale and applies it to 642 communities in Guangzhou, China, using the Local Climate Zone (LCZ) classification. Carbon emissions from buildings, transportation, water use, waste, and urban road lighting, together with green space carbon sinks, are quantified to establish a high-resolution spatiotemporal emission dataset. The results show that total community-scale carbon emissions range from 0 to 5852.88 tCO₂, with building-related emissions dominating the carbon footprint and accounting for approximately 75% of the total emissions, followed by water use (15%) and waste (8%), while transportation and road lighting together contribute less than 3%. Building and transportation emissions exhibit pronounced temporal variability, with citywide building emissions peaking at 21:00 (994.6 tCO₂ h⁻¹). Strong spatial heterogeneity is observed across LCZ types and administrative districts. LCZ1 records the highest total emissions (60,401.71 tCO₂), whereas LCZ6 exhibits substantially lower emissions due to greater green space coverage. Spatial autocorrelation analysis reveals significant clustering of high-emission communities (Global Moran’s I = 0.2486, p < 0.0001), indicating an outward diffusion of carbon emissions from central urban areas. These findings demonstrate the role of building energy use in carbon emissions and validate LCZ-based bottom-up accounting for mitigation. Full article

The growing complexity of maritime navigation creates safety challenges that drive the shift toward autonomous systems. Maritime vessel behaviour modelling is critical for safe and efficient autonomous operations. Representation learning offers a systematic approach to learn feature embeddings encoding vessel behaviour for improved situational awareness and decision-making. We introduce a three-stage representation learning pipeline evaluating six architectures on real-world AIS trajectories. Grouped Masked Autoencoder (GMAE)-Risk Extrapolation (REx) combines group-wise masked autoencoding at the semantic feature level with risk extrapolation regularisation, forcing encoders to learn cross-group dependencies between temporal, kinematic, spatial, and interaction features. DAE and EAE provide robust and uncertainty-aware baselines. Evaluation uses a dual-pipeline framework on two years of Kiel Fjord AIS data (176,787 trajectories, 527,225 segments). Pipeline 1 applies three-stage representation learning using vessel-type classification as encoder selection probe. GMAE-REx achieves 86.03% validation accuracy, outperforming DAE (85.63%), EAE (85.56%), and baselines Transformer (84.93%), TCN (76.27%), LiST (85.12%). Pipeline 2 applies unsupervised clustering to discover intrinsic behavioural structure. Learnt representations consistently outperform expert features on DBCV, conductance, and modularity metrics, organising trajectories by operational context rather than vessel type. This behaviour-oriented organisation enables cross-vessel knowledge transfer for autonomous navigation, VTS monitoring, and safety analysis. Full article

Herpes simplex virus type 1 (HSV-1) is a neurotropic alphaherpesvirus that interacts dynamically with host cells within structured tissue environments. Conventional two-dimensional (2D) cultures do not fully recapitulate these spatial and microenvironmental features. In this study, we established a three-dimensional (3D) culture system using the self-assembling peptide RADA16-I to generate an extracellular matrix–mimetic hydrogel scaffold. This platform supported the formation of stable Vero cell spheroids that remained viable for more than 30 days. Following HSV-1 infection, viral spread initiated at the spheroid periphery and progressively extended toward the core. Sustained viral replication was detected for up to 22 days, indicating long-term maintenance of infection within the 3D structure. Ultrastructural examination identified viral particles and vesicular compartments consistent with autophagy-related organelles. Comparative analysis of autophagy-associated markers revealed distinct temporal patterns between 2D monolayer cultures and 3D spheroids. In the 3D system, LC3B-II levels progressively increased, accompanied by a reduction in p62, suggesting altered regulation of autophagic flux relative to conventional 2D conditions. These findings demonstrate that the RADA16-I-based 3D culture model supports prolonged HSV-1 infection and reproduces key spatial features of viral dissemination. The differential autophagic responses observed between 2D and 3D systems highlight the influence of cellular architecture on host–virus interactions and support the application of 3D culture platforms for mechanistic studies of HSV-1 pathogenesis. Full article

Forest and landscape restoration (FLR) represents a critical nexus of climate change mitigation, biodiversity conservation, and sustainable development. Despite substantial federal investments and commitments, empirical subnational research quantifying the relationships between governance structures, funding mechanisms, and restoration outcomes remains scarce, and integrated implementation frameworks bridging institutional, technical, and socio-economic dimensions are largely absent from the literature. This study presents a mixed-methods analysis of FLR implementation gaps across Maryland, Virginia, and West Virginia. Three Mid-Atlantic Appalachian states selected for their contrasting ecological conditions, governance structures, and restoration trajectories that collectively represent the heterogeneity of subnational restoration challenges. We examined 147 restoration projects (2019–2024), conducted 25 stakeholder interviews, and analyzed federal funding allocations ($428 million) through spatial and temporal frameworks. Our findings reveal five critical implementation barriers: (1) policy incoherence across federal–state–local jurisdictions creating 34% project delays; (2) chronic underfunding with 63% of projects receiving less than 60% of planned budgets; (3) technical capacity deficits affecting 71% of rural communities; (4) inadequate stakeholder engagement mechanisms reducing project sustainability by 45%; and (5) insufficient monitoring frameworks limiting adaptive management. We introduce an Integrated Restoration Implementation Framework (IRIF) that uniquely integrates policy coordination, sustainable financing, technical capacity building, and community engagement within a unified adaptive management cycle, operationalized through empirically derived thresholds, to guide evidence-based interventions. Quantitative analyses demonstrate that multi-stakeholder governance models increase restoration success rates by 2.3-fold (p < 0.001), while integrated funding mechanisms improve long-term sustainability by 67%. Theoretically, this study advances socio-ecological systems scholarship by providing empirical evidence that multi-scalar governance configurations and integrated stakeholder engagement mechanisms are principal determinants of restoration success, advancing the evidence base for adaptive governance approaches in complex federal systems. Our findings provide actionable intelligence for policymakers and practitioners, while underscoring that sustainable FLR in complex federal systems depends on coherent multi-level governance architectures coordinating institutional mandates, financial resources, technical capacity, and community agency across jurisdictional scales. Full article

Ecological resilience (ER) describes an ecosystem’s capacity to resist, adapt to, and recover from external shocks. Enhancing ER has become a crucial issue of high-quality development in urban agglomerations. Based on the perspective of human–land relationship, this study takes the Chengdu–Chongqing urban agglomeration (CCUA) as its research subject and constructs a three-dimensional evaluation framework of “Resistance-Adaptation-Recovery” (Res-Ada-Rec), evaluates the spatial and temporal evolution characteristics of ER from 2003 to 2022, and uses a partial least squares structural equation model (PLS-SEM) to reveal the interaction mechanism of human and natural factors on ER. Results indicate that: (1) Temporally, ER in the CCUA showed a significant upward trend, with resistance, adaptation, and recovery demonstrating fluctuating evolutionary processes. (2) Spatially, ER presented a pattern of “small agglomeration and large dispersion”, with clear spatial heterogeneity observed across the three dimensions. (3) PLS-SEM analysis revealed that green innovation, institutional policies, and the natural environment had significant positive direct effects on ER, with path coefficients of 0.54, 0.53, and 0.12, respectively. Urbanization exerted a significant indirect negative effect on ER through its impact on the natural environment. These findings deepen our understanding of how green innovation, institutional policies, and urbanization influence ER, providing scientific references for urban agglomeration to achieve modernization characterized by harmonious coexistence between humans and nature. Full article

Carbon emissions (CEs) are a primary driver of global climate change, particularly pronounced in China’s Yangtze River Delta (YRD) region, where rapid economic development and urbanization have led to a substantial increase in CEs. At fine spatial scales (e.g., county level) or in regions with limited statistical data, traditional methods for CE accounting are constrained by data gaps and inconsistencies, which hinders the accurate characterization of regional disparities. Therefore, this study proposes a CE spatial downscaling method based on nighttime light (NTL) data. By integrating remote sensing data with the IPCC emission inventory model, energy consumption-related carbon emissions (ECCEs) across the YRD region from 2000 to 2020 were quantified. Through global spatial autocorrelation analysis and standard deviation ellipse (SDE) analysis, the spatial distribution characteristics and temporal variation trends of ECCEs were revealed. Results indicate that total CEs increased significantly over the study period. CE hotspots were concentrated in the Hangzhou Bay area and the Shanghai–Nanjing corridor, while coldspots were identified in southwestern Anhui and Zhejiang. From 2010, the CE centroid shifted toward the southwest or northwest, and the regional CE distribution evolved from a point pattern to a band-shaped pattern. These findings offer a novel approach for CE monitoring and can provide scientific support for low-carbon development policies and precise emission reduction strategies in data-scarce regions of developing countries. Full article

To develop and assess MyoNet, a deep learning (DL)-based network for measuring myocardial regional function from cine cardiac magnetic resonance (CMR) images, and compare its efficacy with ResMyoNet as an efficient alternative to SinMod-derived reference. MyoNet was tested alongside ResMyoNet on datasets from Dahl salt-sensitive rat models undergoing radiation therapy (RT). Both networks were designed to extract displacement maps from cine images, were specifically optimized for detailed myocardial deformation, employed advanced convolution operations with alternating kernel sizes for spatial and temporal analysis, and robust loss functions. MyoNet demonstrated superior performance in myocardial strain measurement, achieving high consistency with the SinMod-derived reference strains. It outperformed ResMyoNet, achieving higher performance metrics, including SSIM of 0.961 and 0.960, ICC of 0.973 and 0.975, and Pearson CC of 0.973 and 0.953 for circumferential (Ecc) and radial (Err) strains, respectively. Its accuracy and efficiency in generating strain measurements were validated through comprehensive statistical analyses. MyoNet offers a significant advancement in myocardial strain analysis from cine CMR images, potentially revolutionizing cardiac imaging in pre-clinical studies. Its ability to provide detailed and reliable measurements positions it as a valuable tool for clinical applications, particularly in monitoring the cardiac health of cancer patients. Full article

Accurate spatial–temporal crime prediction is a critical component of proactive public safety governance, yet it remains challenging due to complex dependency structures and severe data sparsity in real-world crime datasets. Most existing methods either focus on local spatial–temporal correlations or attempt to model global dependencies at fine-grained region levels, which limits their robustness under highly sparse and imbalanced crime distributions. In this paper, we propose GL-MoPA, a global–local modulated prototype attention framework for city-scale crime prediction. GL-MoPA integrates three key components. First, a local dependency modeling module is designed to capture fine-grained spatial and short-term temporal patterns. Second, a prototype-aware global attention mechanism aggregates region-level representations into semantically meaningful prototypes to efficiently model long-range dependencies. Third, a two-stage occurrence-aware prediction strategy decouples crime occurrence estimation from intensity regression to explicitly address data sparsity. We evaluate GL-MoPA on a real-world crime dataset from New York City covering four major crime categories. The experimental results show that GL-MoPA achieves state-of-the-art performance, consistently outperforming both classical statistical models and recent deep learning baselines. In particular, a robustness analysis shows substantial error reductions in sparse regions, while ablation studies reveal the complementary roles of individual model components. These results indicate that GL-MoPA provides an effective and robust solution for spatial–temporal crime forecasting under sparse-data scenarios. Full article

The identification of potential landslides and targeted risk analysis is crucial for the warning and prevention of geological landslide disasters. This article presents a time series deformation prediction framework based on a Long Short-Term Memory (LSTM) network deep learning model for analyzing Interferometric Synthetic Aperture Radar (InSAR) data. By employing an advanced stacked LSTM network model, we effectively capture temporal dependencies and move beyond traditional methods that depend on explicit deformation. This approach enables short- to medium-term deformation prediction through structured time dynamic modeling, identifies potential landslide targets in the high-altitude regions upstream of Lake Sarez, and classifies associated risk levels. The results indicate that: (1) In short-term forecasting, the stacked LSTM model effectively captures trend turning points, producing stable and reliable predictions with a Mean Absolute Error (MAE) of 0.164 mm and a Root Mean Square Error (RMSE) of 0.194 mm; (2) From 2019 to 2022, regional surface deformation characteristics exhibited significant spatial heterogeneity, with the potential landslide on the right bank identified as the most critical settlement center, demonstrating a line of sight (LOS) deformation rate consistently exceeding 49 mm per year, while the Usoi Dam displayed relatively good stability during this period; (3) By integrating InSAR deformation rate maps with Sentinel-2 optical images, we identified a total of 72 potential landslide targets in the region, four of which exhibited deformation rates exceeding −30 mm per year, indicating significant activity and classifying them as high-risk areas requiring attention. This provides a targeted reference list for the prevention and control of geological landslides around Lake Sarez and establishes a reliable technical pathway for the early identification of landslides under complex geological conditions in high-altitude mountainous areas. Full article

Bamboo shoots are valued as traditional vegetables, but their palatability is often compromised by bitter and astringent compounds. The spatial and temporal distribution of these compounds across species, tissues, and developmental stages remains poorly characterized. This study systematically investigated key taste-active compounds (tannins, oxalic acid, flavonoids, cyanide compounds, and free amino acids) in three sympodial bamboo species (Bambusa chungii, Dendrocalamus farinosus, and Bambusa oldhamii). We integrated quantitative chemical analysis of shoots at different emergence stages and tissue parts with descriptive sensory evaluation. The results revealed pronounced, species-specific accumulation patterns. For instance, tannin content increased with shoot emergence in all species, whereas oxalic acid and cyanide showed divergent temporal trends among them. Tissue-specific gradients were also evident for most compounds. Correlation analysis with sensory data indicated distinct associations for each species. Bitterness in D. farinosus was most strongly correlated with oxalic acid, while in B. oldhamii, it was closely linked to tannins and cyanide. In B. chungii, specific amino acids (aspartic acid, histidine) and tannins showed significant correlations with bitterness perception. The perception of astringency involved multiple contributing factors. These findings elucidate the distinct biochemical bases of flavor variation in sympodial bamboos. They provide a scientific rationale for optimizing harvest timing and tissue selection, offering targeted strategies for post-harvest processing to improve edible quality and market value. Full article

Pitting corrosion is a prevalent and highly detrimental form of localized corrosion, which can severely compromise the local load-bearing capacity of metallic materials and, in extreme cases, trigger structural failure. In response to the pronounced susceptibility of Q235 galvanized steel plates to localized pitting under the extreme service conditions of the South China Sea—characterized by high temperature, high salinity, high humidity, and coupled chemical corrosive effects—this study conducts a systematic investigation combining experimental characterization and numerical simulation. First, a novel accelerated pitting corrosion apparatus was designed and developed, and chloride ion cyclic corrosion (CICC) tests were performed on Q235 galvanized steel plates. The morphology and temporal evolution of pitting damage were comprehensively characterized. Subsequently, based on a coupled Cellular Automata (CA) and Finite Element Analysis (FEA) framework, a corrosion evolution model termed CAFE (Cellular Automata-Finite Element) was established. This model elucidates the initiation, growth, and corrosion product evolution of pitting pits under varying temperature and salinity conditions and further quantifies the spatial distributions of stress and temperature fields in the vicinity of pitting sites. Finally, experimental results were employed to validate the rationality and effectiveness of the proposed electro-thermo-mechanical-chemical (ETMC) multi-field coupling model. The results demonstrate that temperature and salinity are the dominant environmental parameters governing the evolution of localized pitting corrosion rates. A strong agreement between numerical predictions and experimental observations is achieved in both qualitative trends and quantitative metrics. Notably, the model reveals that under elevated current-driving conditions, localized plastic deformation plays a critical role in promoting pit propagation and accelerating the pitting corrosion process. Full article

This study addresses the critical need for accurate, continuous monitoring of surface water quality parameters (SWQPs) using remote sensing, overcoming limitations in existing models that often rely on pre-trained networks ill-suited for complex aquatic environments. We present a customized convolutional neural network (CNN) architecture, implemented in the MATLAB environment, designed to simultaneously predict optically active (Total Organic Carbon, TOC) and non-optically active (Dissolved Oxygen, DO) parameters from eighteen Sentinel-2 Level-2A satellite images, acquired between 2023 and 2024. Our approach integrates spatial and spectral data through a customized CNN with three convolutional layers and two dense layers, optimized via adaptive learning strategies, data augmentation, and rigorous regularization to enhance predictive performance and prevent overfitting. The models were trained and validated on fused datasets of satellite imagery and in situ measurements, organized into comprehensive four-dimensional arrays capturing spectral, spatial, and sample dimensions. The results demonstrated high accuracy, with coefficient of determination (R²) values exceeding 0.97 and low root mean square error (RMSE) across training, validation, and testing subsets. Spatial prediction maps generated at high resolution revealed realistic ecological and hydrological patterns consistent with known regional water quality dynamics in New Brunswick. Our contribution, accessible to users with MATLAB, lies in the development of a transparent, adaptable, and reproducible CNN framework tailored for multiparameter water quality estimation, which extends beyond traditional empirical, site-specific regression models by enabling non-invasive, cost-effective, and continuous monitoring from satellite platforms over a large, heterogeneous province-scale domain. Additionally, model interpretability was enhanced through SHapley Additive exPlanations (SHAP) analysis, which identified key spectral bands influencing predictions and provided ecological insights, offering guidance for future sensor design and data reduction strategies. This study addresses a significant research gap by providing a dual-parameter focused, end-to-end deep learning solution optimized for province-scale remote sensing data, facilitating more informed environmental management. This study can support water managers and agencies by providing province-wide DO and TOC maps derived from freely available Sentinel-2 imagery, reducing reliance on sparse field sampling alone and helping to identify areas of low oxygen or high organic carbon. Future work will extend this framework temporally and spatially and explore hybrid CNN architectures incorporating temporal dependencies for improved generalization and accuracy. Full article

Optical microscopy is a fundamental tool in the physical, chemical, and life sciences, enabling direct investigation of structure, dynamics, and function across multiple spatial and temporal scales. Advances in optical design, detectors, and computational techniques have greatly enhanced performance, but have also increased the complexity of modern microscopes, which are now software-driven and embedded in data-intensive workflows. Artificial intelligence has become an important component of this landscape, particularly through task-specific machine learning approaches for image analysis, optimization, and limited instrument control. While effective, these solutions are often fragmented and lack the ability to integrate experimental intent, contextual knowledge, and multi-step reasoning. Recent progress in large language models (LLMs) offers a new paradigm for intelligent microscopy. As foundation models trained on large-scale text and code, LLMs exhibit emergent capabilities in reasoning, abstraction, and tool coordination, allowing them to act as natural interfaces between users and complex experimental systems. This perspective highlights how LLMs can function as cognitive and orchestration layers that connect experiment design, instrument control, data analysis, and knowledge integration. Emerging applications include conversational microscope control, workflow supervision, and scientific assistance for data exploration and hypothesis generation, alongside important technical, ethical, and governance challenges. Full article

Current research lacks systematic understanding of cross-scale correlations between micro-texture geometry and macro-lubrication behavior. This study presents a multi-scale collaborative optimization methodology for gear Micro-Textured Meshing Interface (MTMI). An objective function targeting macroscopic interfacial performance is formulated, and a topology optimization strategy is employed to achieve optimal MET configuration. The homogenization analysis captures the modulating effects of MET on local flow and stress fields, while topology optimization transcends conventional parametric geometric constraints, enabling the generation of non-regular MET topological patterns tailored to complex operating conditions, thereby ensuring optimal macroscopic ASLBC. The proposed scheme is validated through numerical simulations of two representative problems capturing distinct lubrication regimes: (1) IEL, characterizing transient load-bearing dynamics governed by temporally evolving MET configurations; and (2) ASLBC, elucidating steady-state load-bearing capacity modulation via spatially heterogeneous MET distributions. A Taylor expansion-based surrogate model is developed to efficiently explore the MET configuration design space, significantly enhancing computational efficiency and solution accuracy for multi-scale optimization. While the gradient-based algorithm cannot guarantee global optimality, extensive numerical simulations and cross-validation studies demonstrate consistent convergence toward high-performance MET configurations, with sensitivity analyses of design parameters further confirming the engineering applicability of the optimized solutions. Full article