Search Results (4,590)

Satellite remote sensing has become an increasingly important approach for detecting and quantifying methane emissions across spatial and temporal scales. While most reviews in the literature have addressed aspects of methane monitoring, they often focus primarily on satellite platforms or provide discussions on retrieval methodologies. This review offers an integrated assessment of recent developments in satellite-based methane detection, combining technical evaluations of satellite instruments with detailed analysis of retrieval techniques and sector-specific applications. The paper distinguishes between area flux mappers and point-source imagers and reviews both established and recent satellite missions, including GHGSat, MethaneSAT, and PRISMA. Retrieval methods are critically compared, covering full-physics models, CO₂ proxy approaches, optimal estimation, and emerging data-driven techniques such as machine learning. The review further examines methane emission characteristics in key sectors, i.e., oil and gas, coal mining, agriculture, and waste management, and discusses how satellite data are applied in emission estimation and mitigation contexts. The paper concludes by identifying technical and operational challenges and outlining research directions to enhance the accuracy, accessibility, and policy relevance of satellite-based methane monitoring. Full article

Current methods for the high-precision real-time monitoring and parameter inversion of industrial methane point source leakage are insufficient. This research introduces a novel laser-based methane leakage monitoring approach for deployment on an unmanned aerial vehicle platform. An enhanced two-dimensional integral Gaussian diffusion model paired with a point sampling technique is employed to simultaneously determine the leakage rate and source location, integrating a genetic algorithm and an interior point penalty function algorithm for optimization. Simulations incorporating observational error sources are performed to quantitatively assess the accuracy of leakage parameter inversion under diverse errors, demonstrating the scheme’s viability. The accuracy of leakage parameter inversion achieved by the algorithm across various point sampling methods, gas plume characteristics, and wind speeds was examined, validating the assessment under multivariable influences in real observations. The proposed methodology was compared with two other leakage inversion optimization techniques, demonstrating its efficiency in addressing wind speed and directional effects. This study offers a practical method with significant implications for monitoring and quantifying industrial methane point source leakages. Full article

Ginkgo biloba L. plays an important role in biodiversity conservation. Accurate identification of Ginkgo in forest environments remains challenging due to its visual similarity to other broad-leaved species during the green-leaf period and to species with yellow foliage during autumn. In this study, we propose a novel two-stage segment-then-classify (STC) strategy to improve the accuracy of Ginkgo identification from unmanned aerial vehicle (UAV) imagery. First, the Segment Anything Model (SAM) was fine-tuned for canopy segmentation across the green-leaf stage and the yellow-leaf stage. A post-processing pipeline was developed to optimize mask quality, ensuring independent and complete tree crown segmentation. Subsequently, a ResNet-101-based classification model was trained to distinguish Ginkgo from other tree species. The experimental results showed that the STC strategy achieved significant improvements compared to the YOLOv8 model. In the yellow-leaf stage, it reached an F1-score of 92.96%, improving by 24.50 percentage points over YOLOv8. In the more challenging green-leaf stage, the F1-score improved by 31.27 percentage points, surpassing YOLOv8’s best performance in the yellow-leaf stage. These findings demonstrate that the STC framework provides a reliable solution for high-precision identification of Ginkgo in forest ecosystems, offering valuable support for biodiversity monitoring and forest management. Full article

Ultraviolet B (UVB) light exerts biological effects beyond the skin; however, its influence on systemic energy metabolism remains unclear. We investigated the effects of chronic, low-dose narrowband UVB irradiation on substrate utilization, circulating metabolites, and thermogenesis of brown adipose tissue (BAT) in mice. Male and female C57BL/6J mice were daily exposed to sub-erythemal UVB (308 nm, 50 or 100 mJ/cm², 3 h) for up to 7 weeks using a custom light-emitting diode-based device. Metabolic outcomes were assessed by indirect calorimetry, locomotor activity monitoring, and infrared thermography. Plasma metabolites were profiled by capillary electrophoresis–time-of-flight mass spectrometry. Gene expression in BAT and skin was measured by reverse transcription quantitative polymerase chain reaction. UVB exposure lowered the respiratory exchange ratio at specific time points, indicating greater lipid utilization, and transiently increased oxygen consumption. Metabolomic profiling revealed reduced succinate levels and enrichment of nicotinate/nicotinamide and propanoate metabolism pathways. Infrared thermography showed elevated surface temperature after irradiation and that prolonged UVB exposure modestly upregulated thermogenic genes in BAT, along with increased cutaneous expression of Cidea. These findings suggested that sub-erythemal UVB exposure modestly modulates systemic metabolism, circulating metabolites, and BAT activity, highlighting UVB as a potential environmental regulator of energy balance. Full article

Nowadays, information obtained through Global Navigation Satellite Systems (GNSSs) is widely employed in modern geodesy. The Precise Point Positioning (PPP) approach, which leverages signals from multiple GNSS constellations (e.g., GPS, GLONASS, Galileo, and BeiDou), enables high-precision positioning—crucial for seismic monitoring and early tsunami warning systems (EEWs). Recent advances, such as increased satellite availability and additional frequency bands, have significantly improved PPP performance, particularly in terms of positioning accuracy and convergence time. This study focuses on the Rete Integrata Nazionale GNSS (RING) network, managed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV), which comprises dual-frequency GNSS receivers distributed across the Italian peninsula and parts of the Mediterranean Basin. We evaluate the performance of the RING data (GPS and GNSS) acquired in a period of three weeks between 19 January 2024 and 9 February 2024 and analyzed in real time by using different PPP strategies: standard PPP and PPP with Regional Augmentation (PPP-RA). The preliminary results show that the PPP-RA approach enhances positioning accuracy and reduces convergence time, especially when comparing GPS-only datasets with those incorporating full multi-GNSS configurations. For the daily solution, in the optimal setup (i.e., full GNSS with RA), real-time solutions exhibit average accuracies of 2.05, 1.73, and 4.35 cm for the North, East, and vertical components, respectively. Sub-daily accuracies’ analysis, using 300 s sliding windows, showed even better uncertainties, exhibiting median values of 0.41, 0.32, and 0.9 cm for the North, East and vertical components, respectively. Based on the outcomes for network-wide sub-daily accuracies, 84% of the stations demonstrate average errors within 2 cm for North and East components and 3 cm for the vertical one. The analysis on the convergence time after data gaps occurred during the investigation period shows that 87% of the RING stations experienced convergence times lower than five minutes in the GNSS PPP-RA solution. These findings underscore the potential of RT-GNSS RING data for enhancing seismic monitoring and early warning systems, particularly in tectonically active regions. Full article

Methane emissions from coal mines, especially surface operations, are spatially diffuse, presenting significant challenges for accurate quantification. Satellites such as TROPOMI, GHGSat, PRISMA, GaoFen-5, and GOSAT have been extensively used for detecting methane emissions at various scales, from individual point sources to regional and global assessments. Despite various advancements, methane quantification via satellite observations remains subject to several challenges. Various quantification methods for the same observation can produce variable results. Also, meteorological conditions, terrain complexity, and surface heterogeneity introduce uncertainties in emission estimates. The selection of wind speed and direction, along with retrieval-algorithm limitations, can lead to significant discrepancies in reported emissions. Additionally, satellite-based observations capture emissions only at specific overpass times, which may introduce temporal uncertainties compared to inventories derived from continuous emission estimations. This study provides a comprehensive review of satellite-based coal mine methane (CMM) monitoring, evaluating current methodologies, their limitations, and recent technological advancements. We discussed the potential of emerging machine-learning techniques, improved atmospheric modelling, and integrated observational approaches to enhance methane emission quantification. By refining satellite-based monitoring techniques and addressing existing challenges, this research will support the development of more accurate emission inventories and effective mitigation strategies for the coal mining sector. Full article

As intelligent transportation systems continue to advance, on-board surveillance video has become essential for train safety and intelligent scheduling. However, high-resolution video transmission faces bandwidth limitations, and existing deep learning-based super-resolution models find it difficult to meet real-time requirements due to high computational complexity. To address this, this paper proposes an “edge–end” collaborative multi-terminal task inference framework, which improves inference speed by integrating resources of in-vehicle end devices and edge servers. The framework establishes a real-time-priority mathematical model, uses game theory to solve the problem of minimizing multi-terminal task inference latency, and proposes a multi-terminal task model partitioning strategy and an adaptive adjustment mechanism. It can dynamically partition the model according to device performance and network status, prioritizing real-time performance and minimizing the maximum inference delay. Experimental results show that the dynamic model partitioning mechanism can adaptively determine the optimal partition point, effectively reducing the inference delay of each end device in high-speed mobile and bandwidth-constrained scenarios and providing high-quality video data support for safety monitoring and intelligent analysis. Full article

Neutralizing antibodies (nAbs) are key indicators of protection against SARS-CoV-2, and their measurement remains essential for monitoring vaccine responses and population immunity. While the plaque reduction neutralization test (PRNT) is the gold standard, it relies on replicative viruses and is not suited for high-throughput applications. Here, both an in-house and a commercial pseudovirus-based neutralization (PBN) assay were standardized and compared with PRNT to assess performance and concordance. The in-house PBN employed a VSV-ΔG pseudovirus encoding NanoLuc and displaying the SARS-CoV-2 Spike from the Wuhan or Omicron BA.1 variants in HEK293T-hACE2 cells, whereas the commercial assay (Integral Molecular, Philadelphia, PA, USA) used a lentiviral backbone with Renilla or GFP reporters and Wuhan or Omicron XBB.1.5/XBB.1.9 Spikes in Vero E6-ACE2-TMPRSS2 cells. Both assays showed strong correlations with PRNT, the commercial assay; moreover, they offered superior reproducibility and scalability, while the in-house version provided a cost-effective alternative suitable for BSL-2 settings. A total of 600 serum samples from vaccinated individuals were analyzed by commercial PBN at collection time points, from pre-vaccination to twelve months post–second dose, enabling large-scale screening, revealing marked differences in neutralization between Wuhan and Omicron XBB.1.5/1.9, and allowing unbiased classification of low, medium, and high responders using k-means clustering. The geometric mean titers (log₁₀ GMT) highlighted a ~1.5 log₁₀ (eightfold) reduction in neutralizing activity against Omicron, reflecting antibody waning and antigenic drift. Altogether, this study integrates assay standardization, PRNT comparison, and large-scale immune profiling, establishing a robust framework for harmonized pseudovirus-based neutralization testing. Full article

Bathing water quality (BWQ) monitoring and prediction are essential to safeguard public health by informing bathers about the risk of exposure to faecal indicator bacteria (FIBs). Traditional monitoring approaches, such as manual sampling and laboratory analysis, while effective, are often constrained by delayed reporting, limited spatial and temporal coverage, and high operational costs. The integration of artificial intelligence (AI), particularly machine learning (ML), with automated data sources such as environmental sensors and satellite imagery has offered novel predictive and real-time monitoring opportunities in BWQ assessment. This systematic literature review synthesises current research on the application of AI in BWQ assessment, focusing on predictive modelling techniques and remote sensing approaches. Following the PRISMA methodology, 63 relevant studies are reviewed. The review identifies dominant modelling techniques such as Artificial Neural Networks (ANN), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Hybrid and Ensemble Boosting algorithms. The integration of AI with remote sensing platforms such as Google Earth Engine (GEE) has improved the spatial and temporal solution of BWQ monitoring systems. The performance of modelling approaches varied depending on data availability, model flexibility, and integration with alternative data sources like remote sensing. Notable research gaps include short-term faecal pollution prediction and incomplete datasets on key environmental variables, data scarcity, and model interpretability of complex AI models. Emerging trends point towards the potential of near-real-time modelling, Internet of Things (IoT) integration, standardised data protocols, global data sharing, the development of explainable AI models, and integrating remote sensing and cloud-based systems. Future research should prioritise these areas while promoting the integration of AI-driven BWQ systems into public health monitoring and environmental management through multidisciplinary collaboration. Full article

Detecting anomalous pedestrian behaviors is critical for enhancing safety in dense urban environments, particularly in complex back streets where movement patterns are irregular and context-dependent. While extensive research has been conducted on trajectory-based anomaly detection for vehicles, ships, and aircraft, few studies have focused on pedestrians, whose behaviors are strongly influenced by surrounding spatial and environmental conditions. This study proposes a pedestrian anomaly detection framework based on a Variational Autoencoder (VAE), designed to identify and interpret abnormal trajectories captured by large-scale Closed-Circuit Television (CCTV) systems in urban back streets. The framework extracts 14 movement features across point, trajectory, and grid levels, and employs the VAE to learn normal movement patterns and detect deviations from them. A total of 1.88 million trajectories were analyzed, and approximately 1.05% were identified as anomalous. These were further categorized into three behavioral types—wandering, slow-linear, and stationary—through clustering analysis. Contextual interpretation revealed that anomaly types differ substantially by time of day, spatial configuration, and weather conditions. The final optimized model achieved an accuracy of 97.80% and an F1-score of 94.63%, demonstrating its strong capability to detect abnormal pedestrian movement while minimizing false alarms. By integrating deep learning with contextual urban analytics, this study contributes to data-driven frameworks for real-time pedestrian safety monitoring and spatial risk assessment in complex urban environments. Full article

The proliferation of algorithmically generated malicious URLs necessitates a shift from syntactic detection to semantic analysis. This paper introduces PhishGraph, a disk-aware Approximate Nearest Neighbor (ANN) search system designed to perform billion-scale semantic similarity searches on URL embeddings for threat intelligence applications. Traditional in-memory ANN indexes are prohibitively expensive at this scale, while existing disk-based solutions fail to address the unique challenges of the cybersecurity domain: the high velocity of streaming data, the complexity of hybrid queries involving rich metadata, and the highly skewed, adversarial nature of query workloads. PhishGraph addresses these challenges through a synergistic architecture built upon the foundational principles of DiskANN. Its core is a Vamana proximity graph optimized for SSD residency, but it extends this with three key innovations: a Hybrid Fusion Distance metric that natively integrates structured attributes into the graph’s topology for efficient constrained search; a dual-mode update mechanism that combines high-throughput batch consolidation with low-latency in-place updates for streaming data; and an adaptive maintenance policy that monitors query patterns and dynamically reconfigures graph hotspots to mitigate performance degradation from skewed workloads. Our comprehensive experimental evaluation on a billion-point dataset demonstrates that PhishGraph’s adaptive, hybrid design significantly outperforms strong baselines, offering a robust, scalable, and efficient solution for modern threat intelligence. Full article

Recent innovations in task-agnostic imagery featurization have lowered the computational costs of using machine learning to predict ground conditions from satellite imagery. These methods hold particular promise for the development of imagery-based monitoring systems in low-income regions, where data and computational resources can be limited. However, these relatively simple prediction pipelines have not been evaluated in developing-country contexts over time, limiting our understanding of their performance in practice. Here, we compute task-agnostic random convolutional features from satellite imagery and use linear ridge regression models to predict maize yields over space and time in Zambia, a country prone to severe droughts and crop failure. Leveraging Landsat and Sentinel 2 satellite constellations, in combination with district-level yield data, our model explains 83% of the out-of-sample maize yield variation from 2016 to 2021, slightly outperforming a model trained on Normalized Difference Vegetation Index (NDVI) features, a common remote sensing approach used by practitioners to monitor crop health. Our approach maintains an $R^2$ score of 0.74 when predicting temporal variation alone, while the performance of the NDVI-based approach drops to an $R^2$ of 0.39. Our findings imply that this task-agnostic featurization can be used to predict spatial and temporal variation in agricultural outcomes, even in contexts with limited ground truth data. More broadly, these results point to imagery-based monitoring as a promising tool for assisting agricultural planning and food security, even in contexts where computationally expensive methodologies remain out of reach. Full article

Air quality monitoring and forecasting has been a challenging problem for years. In addition to traditional ground-based observational stations, in recent years there have been more geostationary and polar orbiting satellite observations on air quality. However, evaluation of performance of these observations is lacking, especially for the region of southern China, which is rarely affected by severe sand/dust weather. In the spring of 2025, two events of sand/dust weather, one case of sand/dust spreading to southern China in April and another case of sand/dust confining to northern China in May, provide a good opportunity for detailed case study and examination of the performance of the tools. The surface particulate matter (PM) concentration retrieved from a geostationary satellite, Geostationary Korea Multi-Purpose Satellite—2B (GEO-KOMPSAT-2B, or GK2B), is studied by checking consistency with the analysis of two numerical models: the Copernicus Atmosphere Monitoring Service model of the European Centre of Medium Range Weather Forecast (ECMWF-CAMS) and Chinese Unified Atmospheric Chemistry Environment model of the China Meteorological Administration (CMA-CUACE). The former shows comparable PM concentration with satellite observations, while overestimation is found with the latter. It is also found that there may be latitude dependence of the quality of the satellite-based data. To further validate the satellite observation data, it is directly compared with the ground-based station measurements in Hong Kong for the event in mid-April 2025, the performance of satellite data points near Hong Kong is generally satisfactory. For polar orbiting satellite, there is information about the aerosol classification in addition to aerosol optical depth, and the classification result is found to be reasonable by comparison with ground-based observation, though some refinements appear to be necessary. The geostationary satellite images provide high spatial coverage and frequently updated air quality data, which are confirmed to be useful in monitoring the southward spread of sand/dust weather to southern China which is a very rare event. The monitoring can be both qualitative and quantitative. The performance of various monitoring and forecasting tools is examined in details based on the cases. It also forms a reference for the use in operation, and opens up a new era for air quality study for southern China. Full article

To address the issues of the insufficient accuracy and weak generalization capabilities of single models in landslide displacement prediction, this paper proposes a machine learning model fusion prediction method for landslide displacement based on stacking. Taking the landslide displacement data (F) and rainfall (RAINFALL) of the Baishui River landslide in the Three Gorges Reservoir area as the research object, input sequences were constructed through data preprocessing and feature engineering. Prediction models including SVR, XGBoost, Bayesian optimization, and random forest were established. Based on the stacking framework, an integrated landslide displacement prediction model was developed by dynamically weighting the outputs of the base models using prediction accuracy and stability as fusion indicators. The Baishui River landslide, a typical colluvial landslide, was selected as a case study, with typical displacement data from monitoring points ZG118 and XD-01 from December 2006 to December 2012. The results show that the evaluation metrics (R², ERMSE, and EMAE) for ZG118 and XD-01 demonstrate satisfactory prediction performance. Compared with traditional single models such as a TCN and XGBoost, the proposed integrated model exhibits improved prediction accuracy, providing scientific support for the real-time monitoring and early warning of landslide hazards. Full article

This paper presents the implementation of the Grid-Forming (GFM) control technique in renewable energy source inverters to synchronize with the grid and provide frequency support. Specifically, the GFM Droop Control technique, based on the Power–Frequency relationship, is employed. The proposed model was developed and validated in the Matlab-Simulink environment. By using electromagnetic transient (EMT) simulations, we were able to precisely monitor and analyze voltage and current waveforms, thereby confirming the approach’s effectiveness in enhancing grid stability and power quality. The implementation of the GFM control technique in islanded mode demonstrated high system frequency stability. In response to sudden load changes up to 5 MW (equivalent to over 30% of the total load), a maximum frequency deviation of 0.04 Hz and a maximum Rate of Change of Frequency (RoCoF) of 4 Hz/s were observed. The system ensured the frequency’s return to its nominal value of 60 Hz, thanks to the virtual inertia and frequency regulation provided by the GFM. The total harmonic distortion (THD) of current and voltage in steady-state operation consistently remained below 1%, thus complying with IEEE 1547 standards. In tests with the GFM interconnected to the grid, the droop+LPF control provided dynamic support to the external system, effectively mitigating both frequency deviations and RoCoF. The GFM contributes to the grid’s frequency stability by providing virtual inertia. The power quality at the point of common coupling (PCC) was excellent, as the voltage distortion was maintained below 0.5%, confirming that the injection of harmonic currents does not violate established limits. Full article