Search Results (315)

This study constructs a time-dependent model to predict the nighttime suspended sediment concentration near Wenzhou based on the convolutional neural network U-Net, which integrates the high-resolution Delft3D (version 4.03.01) hydrodynamic model and GOCI satellite observation data. The model’s prediction accuracy is significantly improved by replacing the original tide level with the tide level variation and increasing the temporal resolution of the flow field to 15 min via sensitivity analysis of the model’s input parameters. The validation results show that the model can maintain high consistency with GOCI observations in short-term prediction, with a structural similarity index (SSIM) of 0.82. For multi-hour continuous nighttime predictions, while quantitative uncertainty increases with the forecast horizon, the model successfully captures the spatial evolution patterns and maintains stable structural characteristics. The model effectively provides missing remote sensing nighttime observations as well as a new method for full-cycle prediction of nearshore SSC. Full article

Validation of ICON model configurations optimized over a limited domain is essential before accepting new semi-empirical parameters that influence the behavior of subgrid-scale schemes. Because such parameters can modify the dynamics of a numerical weather prediction (NWP) model in highly nonlinear ways, we analyze one season of forecasts (December 2022, January and February 2023) generated with the NWP ICON-LAM through the lens of machine learning–based diagnostics as a complement to traditional evaluation metrics. The goal is to extract physically interpretable information on the model behavior induced by the optimized parameters. This work represents the first part of a wider study exploring machine learning tools for model validation, focusing on two specific approaches: Empirical Orthogonal Functions (EOFs), which are widely used in meteorology and climate science, and autoencoders, which are increasingly adopted for their nonlinear feature extraction capability. In this first part, EOF analysis is used as the primary tool to decompose weather fields from observed reanalysis and forecast datasets. Hourly 2-m temperature forecasts for winter 2022–2023 from multiple regional ICON configurations are compared against downscaled ERA5 data and in situ observations from ground station. EOF analyses revealed that the optimized configurations demonstrate a high skill in predicting surface temperature. From the signal error decomposition, the fourth EOF mode is effective particularly during night-time hours, and contributes to enhancing the performance of ICON. Analyses based on autoencoders will be presented in a companion paper (Part II). Full article

Background/Objectives: Insulin resistance and ambulatory blood pressure monitoring (ABPM) abnormalities represent distinct but interrelated pathways contributing to cardiovascular risk. The triglyceride–glucose (TyG) index reflects metabolic burden, whereas arterial load—captured through arterial stiffness, blood pressure variability, and morning surge—reflects hemodynamic instability. Whether the coexistence of these domains identifies a particularly high-risk ambulatory phenotype remains unclear. To evaluate the independent and combined effects of metabolic burden (TyG) and arterial load on circadian blood pressure pattern and short-term systolic blood pressure variability. Methods: This retrospective cross-sectional study included 294 adults who underwent 24 h ABPM. Arterial load was defined using three ABPM-derived indices (high AASI, high SBP-ARV, high morning surge). High metabolic burden was defined as TyG in the upper quartile. The “double-high” phenotype was classified as high TyG plus high arterial load. Primary and secondary outcomes were non-dipping pattern and high SBP variability. Multivariable logistic regression and Firth penalized models were used to assess independent associations. Predictive performance was evaluated using ROC analysis. Results: The double-high phenotype (n = 15) demonstrated significantly higher nighttime SBP, reduced nocturnal dipping, and markedly elevated BP variability. It was the strongest independent predictor of non-dipping (adjusted OR = 42.0; Firth OR = 11.73; both p < 0.001) and high SBP variability (adjusted OR = 41.7; Firth OR = 26.29; both p < 0.001). Arterial load substantially improved model discrimination (AUC = 0.819 for non-dipping; 0.979 for SBP variability), whereas adding TyG to arterial load produced minimal incremental benefit. Conclusions: The coexistence of elevated TyG and increased arterial load defines a distinct hemodynamic endotype characterized by severe circadian blood pressure disruption and exaggerated short-term variability. While arterial load emerged as the principal determinant of adverse ambulatory blood pressure phenotypes, TyG alone demonstrated limited discriminative capacity. These findings suggest that TyG primarily acts as a metabolic modifier, amplifying adverse ambulatory blood pressure phenotypes predominantly in the presence of underlying arterial instability rather than serving as an independent discriminator. Integrating metabolic and hemodynamic domains may therefore improve risk stratification and help identify a small but clinically meaningful subgroup of patients with extreme ambulatory blood pressure dysregulation. Full article

This study focused on developing and validating machine learning models to predict pavement marking retroreflectivity using Light Detection and Ranging (LiDAR) intensity data. The retroreflectivity data was collected using a Mobile Retroreflectometer Unit (MRU) due to its increasing acceptance among states as a compliant measurement device. A comprehensive dataset was assembled spanning more than 1000 miles of roadways, capturing diverse marking materials, colors, installation methods, pavement types, and vehicle speeds. The final dataset used for model development focused on dry condition measurements and roadway segments most relevant to state transportation agencies. A detailed synchronization process was implemented to ensure the accurate pairing of retroreflectivity and LiDAR intensity values. Using these data, several machine learning techniques were evaluated, and an ensemble of gradient boosting-based models emerged as the top performer, predicting pavement retroreflectivity with an R² of 0.94 on previously unseen data. The repeatability of the predicted retroreflectivity was tested and showed similar consistency as the MRU. The model’s accuracy was confirmed against independent field segments demonstrating the potential for LiDAR to serve as a practical, low-cost alternative for MRU measurements in routine roadway inspection and maintenance. The approach presented in this study enhances roadway safety by enabling more frequent, network-level assessments of pavement marking performance at lower cost, allowing agencies to detect and correct visibility problems sooner and helping to prevent nighttime and adverse weather crashes. Full article

This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Floods are among the most frequent and devastating natural hazards, particularly in developing countries such as Myanmar, where monsoon-driven rainfall and inadequate flood-control infrastructure exacerbate disaster impacts. This study presents a satellite-driven and interpretable framework for high-resolution flood susceptibility and risk assessment by integrating multi-source remote sensing and geospatial data with ensemble machine-learning models—Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)—implemented on the Google Earth Engine (GEE) platform. Eleven satellite- and GIS-derived predictors were used, including the Digital Elevation Model (DEM), slope, curvature, precipitation frequency, the Normalized Difference Vegetation Index (NDVI), land-use type, and distance to rivers, to develop flood susceptibility models. The Jenks natural breaks method was applied to classify flood susceptibility into five categories across Myanmar. Both models achieved excellent predictive performance, with area under the receiver operating characteristic curve (AUC) values of 0.943 for XGBoost and 0.936 for LightGBM, effectively distinguishing flood-prone from non-prone areas. XGBoost estimated that 26.1% of Myanmar’s territory falls within medium- to high-susceptibility zones, while LightGBM yielded a similar estimate of 25.3%. High-susceptibility regions were concentrated in the Ayeyarwady Delta, Rakhine coastal plains, and the Yangon region. SHapley Additive exPlanations (SHAP) analysis identified precipitation frequency, NDVI, and DEM as dominant factors, highlighting the ability of satellite-observed environmental indicators to capture flood-relevant surface processes. To incorporate exposure, population density and nighttime-light intensity were integrated with the susceptibility results to construct a natural–social flood risk framework. This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Full article

Scutellaria baicalensis is an important medicinal plant, and the diversity of its rhizosphere microbiota may influence its growth, development, and yield. Numerous studies have reported that warming associated with global climate change significantly altered plant-associated soil microbial diversity. To reveal the effects of night-time warming on the rhizosphere microbial community of S. baicalensis, soil microbial diversity in the rhizosphere (RS) and bulk soil (BS) of S. baicalensis were analyzed by employing bacterial 16S rRNA and fungal ITS sequencing technology. Warming significantly altered both bacterial and fungal communities in the rhizosphere and bulk soils of S. baicalensis, with pronounced changes in OTU composition, relative abundances at both phylum and species levels. The analysis of alpha and beta diversity showed that warming significantly altered the fungal community structure in the rhizosphere soil (R² = 0.423, p < 0.05) and significantly reduced the species richness in the bulk soil of S. baicalensis (Shannon and Simpson index, p < 0.05). LEfSe and functional prediction analyses revealed that warming altered the taxonomic composition of both bacterial (35 taxa, LDA > 3) and fungal (24 taxa, LDA > 4) communities in rhizosphere and bulk soils of S. baicalensis, with multiple bacterial and fungal taxa serving as treatment-specific biomarkers. Functional predictions indicated that fungal functional groups, including saprotrophic and mycorrhizal guilds, were more strongly affected by warming than bacteria. Overall, warming has a significantly stronger impact on fungal communities in the rhizosphere and bulk soils of S. baicalensis than on bacteria, and has a significantly greater effect on the diversity of microbial communities in bulk soils than that in rhizosphere soils. This study provides important data for understanding the impact of global climate change on the rhizosphere microbial communities of cultivated plants. Full article

Photovoltaic (PV) power generation is essential for achieving carbon neutrality and advancing renewable energy development. In Northwest China, the rapid expansion of PV installations requires accurate and timely spatial data to support effective monitoring and planning. Addressing the limitations of existing datasets in spatiotemporal resolution and driver analysis, this study develops a scalable solar facility inventory framework on the Google Earth Engine (GEE) platform. The framework integrates Sentinel-1 SAR, Sentinel-2 multispectral imagery, and interpretable machine learning. Feature redundancy is first assessed using correlation-based metrics, after which a Random Forest classifier is applied to generate a 10 m resolution distribution map of utility-scale photovoltaic power plants as of December 2023. To elucidate model behavior, SHAP (SHapley Additive exPlanations) is used to identify key predictors, and MaxEnt is incorporated to provide a preliminary quantitative assessment of spatial drivers of PV deployment. The RFECV-optimized model, retaining 44 key features, achieves an overall accuracy of 98.4% and a Kappa coefficient of 0.96. The study region contains approximately 2560 km² of PV installations, with pronounced clusters in northern Ningxia, central Shaanxi, and parts of Xinjiang and Gansu. SHAP analysis highlights the Enhanced Photovoltaic Index (EPVI), the Normalized Difference Built-up Index (NDBI), Sentinel-2 Band 8A, and related texture metrics as primary contributors to model predictions. High EPVI, NDBI, and Sentinel-2 Band 8A values contribute positively to PV classification, whereas vegetation-related indices (e.g., NDVI) exhibit predominantly negative contributions; these results indicate that PV mapping relies on the integrated discrimination of multiple spectral and texture features rather than on a single dominant variable. MaxEnt results indicate that grid accessibility and land-use constraints (e.g., nighttime light intensity reflecting human activity) are dominant drivers of PV clustering, often exerting more influence than solar irradiance alone. This framework provides robust technical support for PV monitoring and offers high-resolution spatial distribution data and driver insights to inform sustainable energy management and regional renewable-energy planning. Full article

Introduction: Nocturnal enuresis is defined as involuntary urination during sleep in children, particularly those aged 5 years or older. Primary monosymptomatic nocturnal enuresis (PMNE) involves nighttime wetting without daytime symptoms, and although factors like reduced bladder capacity, nocturnal polyuria, and impaired arousal contribute, predictors of long-term relapse remain uncertain. Methods: This retrospective cohort study included 227 children aged ≥5 years with strictly defined PMNE who achieved complete remission following a standardized 3-month treatment protocol (alarm therapy, desmopressin, or desmopressin plus oxybutynin). All children underwent ICCS-based assessment, including physical examination, urinalysis, ultrasonography, UFM, a 48 h frequency/volume (F/V) diary, and post-void residual measurement. One year after treatment discontinuation, patients were reassessed using a 14-day wet-night diary. Predictors of relapse were analyzed using comparative statistics. Result: At 1-year follow-up, 48.5% of children experienced relapse. Age, sex, treatment modality, family history, and baseline wet-night frequency were not associated with relapse (p > 0.05). Diary-based FBC was significantly higher than UFM-based capacity (p < 0.001). Reduced diary-based mean FBC/EBC ratios were significantly more common among relapsing children (p < 0.001), whereas UFM-derived ratios showed no significant difference (p = 0.052). ROC analysis demonstrated moderate discriminatory performance for diary-based FBC/EBC (AUC 0.671). A ratio > 79% predicted sustained remission with 83.6% specificity and a positive predictive value of 73.5%. Conclusions: Diary-derived bladder capacity is the strongest predictor of long-term relapse in PMNE and outperforms UFM-based assessment. A mean FBC/EBC ratio > 79% provides a clinically useful threshold for identifying children at low risk of recurrence. Those with reduced diary-based capacity may benefit from closer follow-up or extended maintenance therapy. Full article

Earth Embedding (EMB) is a method that adapts embedding techniques from Large Language Models (LLMs) to compress the information contained in multiple remote sensing satellite images into feature vectors. This article introduces a new approach to measuring economic activity from EMBs. Using the Google Satellite Embedding Dataset (GSED), we extract a 64-dimensional representation of the Earth’s surface that integrates optical and radar imagery. A neural network maps these embeddings to nighttime light (NTL) intensity, yielding a 32-dimensional “income-aware” feature space aligned with economic variation. We then predict GDP levels and growth rates across countries and compare the results with those of traditional NTL-based models. The Earth-Embedding (EMB) based estimator achieves substantially lower mean squared error in estimating GDP levels. Combining the two sources yields the best overall accuracy. Further analysis shows that EMB performs particularly well in low-statistical-capacity and high-income economies. These results suggest that satellite embeddings can provide a scalable, globally consistent framework for monitoring economic development and validating official statistics. Full article

Reliable estimates of regional energy consumption are essential to planning sustainable development and achieving decarbonization; however, this information is still not available for several regions worldwide. In this work, we propose a methodological framework that uses satellite-derived Nighttime Light (NTL) imagery and machine learning to predict regional electricity consumption one year ahead. The methodology follows three stages: First, a Random Forest regression model is used to identify the relationship between NTL data and regional energy consumption. Thereafter, NTL values for the year ahead are forecasted using NTL values from previous years. Lastly, the obtained result is applied to estimate regional energy consumption from predicted NTL values for the year ahead. The country of Mexico is considered a case study to apply and validate this methodology, reproducing spatial consumption patterns with high correlation to official data ($R^2>0.85)$, thus confirming the success of this proposal. The proposed methodology demonstrates how energy demand can be estimated, even in areas of scarce information, providing a transparent and replicable approach for energy monitoring in data-limited regions. Full article

Rice (Oryza sativa L.) production in the Philippines remains highly vulnerable to recurrent outbreaks of the Rice Black Bug (RBB; Scotinophara coarctata F.) and White Stemborer (WSB; Scirpophaga innotata W.), two of the most destructive pests in Southeast Asian rice ecosystems. Classical economic threshold levels (ETLs) are difficult to estimate in smallholder settings due to the lack of cost–loss data, often leading to either delayed or excessive pesticide application. To address this, the present study developed an adaptive outbreak-forecasting framework that integrates the Number–Size (N–S) fractal model with machine learning (ML) classifiers to define and predict pest regime transitions. Seven years (2018–2024) of light-trap surveillance data from the Philippine Rice Research Institute–Midsayap Experimental Station were combined with daily climate variables from the NASA POWER database, including air temperature, humidity, precipitation, wind, soil moisture, and lunar phase. The N–S fractal model identified natural breakpoints in the log–log cumulative frequency of pest counts, yielding early-warning and severe-outbreak thresholds of 134 and 250 individuals for WSB and 575 and 11,383 individuals for RBB, respectively. Eight ML algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Balanced Bagging, LightGBM, XGBoost, and CatBoost were trained on variance-inflation-filtered climatic and temporal predictors. Among these, CatBoost achieved the highest predictive performance for WSB at the 94.3rd percentile (accuracy = 0.932, F1 = 0.545, ROC–AUC = 0.957), while Logistic Regression performed best for RBB at the 75.1st percentile (F1 = 0.520, ROC–AUC = 0.716). SHAP (SHapley Additive exPlanations) analysis revealed that outbreak probability increases under warm nighttime temperatures, high surface soil moisture, moderate humidity, and calm wind conditions, with lunar phase exerting additional modulation of nocturnal pest activity. The integrated fractal–ML approach thus provides a statistically defensible and ecologically interpretable basis for adaptive pest surveillance. It offers an early-warning system that supports data-driven integrated pest management (IPM), reduces unnecessary pesticide use, and strengthens climate resilience in Philippine rice ecosystems. Full article

Understanding extreme precipitation (EP) evolution is crucial for global climate adaptation and hazardous disasters prevention. However, spatial non-stationarity of urbanization relationships with EP variations has been rarely discussed in a complex topographic context. Taking the city Liuzhou in China as the example, this study separately quantified the evolution of EP intensity, magnitude, duration, and frequency on different temporal scales with Innovative Trend Analysis (ITA). Based on a finer spatial (5 km grid) scale and multiple temporal (daily, daytime, nighttime, and 14 h) scale analyses, it innovatively identified spatially varying urbanization effects on EP with more details in different elevations. Our results indicate that: (1) from 2009 to 2023, EP events became more intense, persistent, and frequent, particularly for higher-grade EPs and in the steeper north of Liuzhou; (2) despite the globally negative correlations, spatial correlations between comprehensive urbanization (CUB) and each EP index on individual temporal scales were still explicitly categorized into four types using LISA maps—high-high, high-low, low-low, and low-high; (3) Geographically Weighted Regression (GWR) was demonstrated to precisely explain the response of most EP characteristics to multiple manifestation of urbanization with respect to population (POP), economy (GDP), and urban area (URP) expansion (adjusted R²: 0.5–0.8). The predictive accuracy of GWR on urbanization and EPs was spatially non-stationary and variable with temporal scales. The local influential strength and direction varied significantly with elevations. The most significant and positive influences of three urbanization predictors on EPs occurred at different elevation grades, respectively. Compared with POP and GDP, urban area percent (URP) was indicated to positively relate to EP changes in more areas of Liuzhou. The spatial and quantitative relationships between urbanization and EPs can help to guide effective urban planning and location-specific management of flood risks. Full article

This study investigates the ionospheric responses to two successive geomagnetic storms that occurred on 7–8 and 10–11 October 2024 over the Indian equatorial and low-latitude sector. Using GNSS-derived vertical total electron content (VTEC) measurements and the Global Ionosphere Map (GIM)-derived VTEC variation, supported by O/N₂ ratio variations, equatorial electrojet (EEJ) estimates, and modeled equatorial electric fields from the Prompt Penetration Equatorial Electric Field Model (PPEEFM), the distinct mechanisms driving storm-time ionospheric variability were identified. The 7–8 October storm produced a strong positive phase in the morning sector, with VTEC enhancements exceeding 100 TECU, followed by sharp afternoon depletions. This short-lived response was dominated by prompt penetration electric fields (PPEFs), subsequently suppressed by disturbance dynamo electric fields (DDEFs) and storm-induced compositional changes. In contrast, the 10–11 October storm generated a more complex and prolonged response, including sustained nighttime enhancements, suppression of early morning peaks, and strong afternoon depletions persisting into the recovery phase. This behavior was mainly controlled by DDEFs and significant reductions in O/N₂, consistent with long-lasting negative storm effects. EEJ variability further confirmed the interplay of PPEF and DDEF drivers during both events. The results highlight that even storms of comparable intensity can produce fundamentally different ionospheric outcomes depending on the dominance of electrodynamic versus thermospheric processes. These findings provide new insights into storm-time ionospheric variability over the Indian sector and are crucial for improving space weather prediction and GNSS-based applications in low-latitude regions. Full article

Urban areas and their surroundings feature unique, horizontally inhomogeneous spatial distributions of land use and land cover, leading to urban heat islands (UHIs) for both air and land surface temperature that complicate the estimation of urban sensible heat flux. The urban dispersion option in AERMOD, the American Meteorological Society (AMS)/Environmental Protection Agency (EPA) Regulatory Model, incorporates this effect at night through a “convective like boundary layer” that modifies the single column meteorology based on a population number representative of the urban area. The model produces positive nighttime sensible heat flux values that often significantly overestimate observed values from the literature. This study re-examines the formulation of the AERMOD urban option assumptions, methodology, and original evaluation against a field study of a power plant in Indianapolis. We investigate replacing the population-based parameterizations of urban–surrounding temperature differences ($\Delta \mathit{T}$) with observations of remotely sensed land surface temperature (LST) data from the Advanced Baseline Imager on the GOES-16/R/East geostationary satellite. We generated a monthly averaged, hourly, wind direction-dependent, clear sky land surface urban heat island $\Delta \mathit{T}$ database for 480 continental United States (CONUS) urban areas, as defined by the 2010 US Census. These $\Delta \mathit{T}$ values are used to advise city-specific horizontal advection corrections to sensible heat flux estimates that are neglected from simple energy balance models. The four cities of Cleveland, Amarillo, Atlanta, and Baltimore are highlighted, showing that the AERMOD predicted nighttime $\Delta \mathit{T}$ values are 794%, 416%, 1048%, and 758% higher, respectively, than the GOES-16 observations. These overestimated $\Delta \mathit{T}$ values in AERMOD lead to nighttime sensible heat flux values > 100 W/m² that rival daytime values. However, using the GOES-16 observations as horizontal advection corrections to sensible heat flux results in trends that match the expected neutral to slightly positive nighttime values from observations recorded in the literature. The annual nighttime average in 2021 was −0.8 W/m², 8.6 W/m², 3.0 W/m², and 3.1 W/m² in Cleveland, Amarillo, Atlanta, and Baltimore, respectively, using this approach. Finally, reviewing the initial evaluation with the Indianapolis database against independent studies from the literature suggest that the AERMOD urban option inadvertently implements an urban heat island modeling approach to account for what was a low-level jet during the field study. Full article

The temperature–humidity index (THI) remains one of the most widely used tools for assessing heat stress in dairy farming; however, its application is often limited by methodological inconsistencies and insufficient integration with welfare indicators. This study proposes a unified analytical framework for evaluating thermal load at the herd level by combining daily THI values with productivity, feed intake, and clinical indicators such as mastitis and lameness. The analysis was based on two years of herd-level data from a commercial dairy farm with naturally ventilated barns. General linear models (GLM) were applied to assess both direct and delayed effects of heat stress and to compare model reproducibility across years. The results confirmed that maximum daily THI had the strongest association with milk composition and dry matter intake, while cumulative heat load and elevated night-time THI contributed to increased mastitis and lameness incidence. The inclusion of welfare indicators substantially improved the explanatory power of THI-based models, providing a more biologically relevant assessment of heat stress. The proposed framework enhances the accuracy of herd-level monitoring and supports the development of predictive models for welfare-oriented management in dairy systems. Full article