Search Results (523)

In the last decades, the introduction of intermittent renewable energy sources has transformed the operation of power systems. In this framework, ancillary service markets (ASMs) play an important role, due to their contribution in supporting system operators to balance demand and supply and managing real-time contingencies. Usually, ASMs require that energy is committed before actual participation, hence scheduling systems of plants and microgrids are required to compute the dispatching program and bidding strategy before needs of the market are revealed. Since possible ASM requirements are given as input to scheduling systems, the chance of accessing accurate estimates may be helpful to define reliable dispatching programs and effective bidding strategies. Within this context, this paper proposes a methodology to estimate the revenue range of energy exchange proposals in the ASM. To this end, the possible revenues are discretized into ranges and a classification pattern recognition algorithm is implemented. Modeling is performed using extreme gradient boosting. Input data to be fed to the algorithm are selected because of relationships with the production unit making the proposal, with the location and temporal indication, with the grid power dispatch and with the market regulations. Different tests are set up using historical data referred to the Italian ASM. Results show that the model can appropriately estimate rejection and the revenue range of awarded bids and offers, respectively, in more than 82% and 70% of cases. Full article

Busan’s major port is among the largest trading ports worldwide; however, it is also one of the ten most polluted ports globally. This study aims to assess the effectiveness of satellite-derived aerosol data for monitoring particulate matter levels in Busan. Aerosol optical depth (AOD) from the Visible Infrared Imaging Radiometer (VIIRS) Deep Blue product tends to be sparse near coastlines due to higher retrieval uncertainties. To increase the number of samples along the coastal area, we established optimized quality control criteria, resulting in more than three times the number of samples. The VIIRS AOD showed a positive correlation with surface particulate matter (PM_2.5) measurements (r = 0.42). The ratios of VIIRS AOD to surface PM_2.5 and PM₁₀ were higher in coastal areas, probably due to greater hygroscopic growth of particles. This connection can assist in estimating surface PM concentrations using satellite data. Both VIIRS AOD and surface PM concentrations exhibit a negative correlation with terrain elevation, primarily due to the locations of emission sources and altitude-dependent weather factors such as temperature and humidity. We expect that combining higher-resolution ancillary databases, including digital elevation maps and meteorology, with satellite-based AOD will enhance the detail of air quality evaluations in port cities. Full article

This study presents an operational and robust method for detecting and dating cereal harvest events using temporal stacks of Copernicus Sentinel-2 imagery and crop and fields border information from ancillary records. The proposed approach is exempt from training data, thereby enabling its application across diverse geographical contexts. The method was used to generate 10 m resolution maps of harvest dates for all wheat and barley fields in 2021, 2022, and 2023 in Castilla y León, a major cereal-producing region of Spain. This work also investigates the use of a reference dataset derived from real time kinematic records (RTK) in agricultural machinery as an alternative source of large-scale in situ data reference as for Earth observation-based agricultural products. The initial comparison of annual harvest date maps with the RTK-based reference datasets revealed that the temporal lag in the detection of harvest events between Earth observation-derived maps and reference harvest dates was less than 10 days for 65.7% of fields, while the temporal lag was between 10 and 30 days for 26.1% of the fields. The 3-year average root mean square error of the lag between harvest dates in the reference dataset and maps was 16.1 days. An in-depth visual analysis of the Sentinel-2 temporal series was carried out to understand and evaluate the potential and limitations of the RTK-based reference dataset. The visual inspection of a representative sample of 668 fields with large temporal lags revealed that the date of harvest of 41.11% of these fields had been correctly identified in the Sentinel-2 based maps and 16.43% of them had been incorrectly identified. The visual inspection could not find evidence of harvest in 10.52% of the analyzed fields. Monte Carlo simulations were parameterized using the findings of the visual inspection to build a series of synthetic reference datasets. Accuracy metrics calculated from synthetic datasets revealed that the quality of the harvest maps was higher than what the initial comparison against the RTK-based reference dataset suggested. The date of harvest was registered within 10 days in both the maps and the synthetic reference datasets for 90.5% of the fields, the root mean squared error of the comparison was 9.5 days, and harvest dates were registered in the Sentinel-2 based maps 2 days (median) after the dates registered in the reference dataset. These results highlight the feasibility of mapping harvest dates in cereal fields with time series of high-resolution satellite imagery and expose the potential use of alternative sources of calibration and validation datasets for Earth observation products. More generally, these results contribute to defining plausible targets for monitoring of agricultural practices with Earth observation data. Full article

Accurate real-time information about soil moisture (SM) at a large scale is essential for improving hydrological modeling, managing water resources, and monitoring extreme weather events. This study presents a framework using convolutional long short-term memory (ConvLSTM) network to produce short- (1, 3, and 7 days ahead) and mid-term (14 and 30 days ahead) forecasts of SM at surface (0–10 cm) and subsurface (10–40 and 40–100 cm) soil layers across the contiguous U.S. The model was trained with five-year period (2018–2022) datasets including Soil Moisture Active Passive (SMAP) level 3 ancillary covariables, North American Land Data Assimilation System phase 2 (NLDAS-2) SM product, shortwave infrared reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS), and terrain features (e.g., elevation, slope, curvature), as well as soil texture and bulk density maps from the Soil Landscape of the United States (SOLUS100) database. To develop and evaluate the model, the dataset was divided into three subsets: training (January 2018–January 2021), validation (2021), and testing (2022). The outputs were validated with observed in situ data from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN) soil moisture networks. The results indicated that the accuracy of SM forecasts decreased with increasing lead time, particularly in the surface (0–10 cm) and subsurface (10–40 cm) layers, where strong fluctuations driven by rainfall variability and evapotranspiration fluxes introduced greater uncertainty. Across all soil layers and lead times, the model achieved a median unbiased root mean square error (ubRMSE) of 0.04 cm³ cm⁻³ with a Pearson correlation coefficient of 0.61. Further, the performance of the model was evaluated with respect to both land cover and soil texture databases. Forecast accuracy was highest in coarse-textured soils, followed by medium- and fine-textured soils, likely because the greater penetration depth of microwave observations improves SM retrieval in sandy soils. Among land cover types, performance was strongest in grasslands and savannas and weakest in dense forests and shrublands, where dense vegetation attenuates the microwave signal and reduces SM estimation accuracy. These results demonstrate that the ConvLSTM framework provides skillful short- and mid-term forecasts of surface and subsurface soil moisture, offering valuable support for large-scale drought and flood monitoring. Full article

Background/Objective: Thyroid nodules are rare in the pediatric population but carry a higher malignancy risk compared to adults. Evaluation and management of cytologically indeterminate nodules vary considerably between institutions and countries. The aim was to systematically review current evidence on the management of indeterminate thyroid nodules in the pediatric population. Methods: A systematic review of the literature was conducted, focusing on cytological classification systems, surgical strategies, and the use of ancillary tools such as molecular testing. Results: Most studies (42.9%) recommend lobectomy for indeterminate thyroid nodules in children; however, considerable heterogeneity in management strategies was observed among institutions. This variability precluded the possibility of conducting a meta-analysis of surgical outcomes. Additionally, a lack of pediatric-specific risk of malignancy (ROM) data for the British Thyroid Association (BTA) and SIAPEC cytological classification systems was noted. Conclusions: We propose the development of a pediatric-specific, multiparametric risk stratification model that incorporates clinical features, biochemical markers, ultrasound characteristics, cytological classification, and molecular profiling. This comprehensive score could help standardize the management of indeterminate thyroid nodules in children and guide clinical decision-making, ranging from observation to total thyroidectomy. Prospective validation in multicenter pediatric cohorts is essential to confirm its clinical utility. Full article

Space-based, photon-counting lidar instruments are effective tools for observing cloud and aerosol layers in the atmosphere. Cloud phases and several different kinds of aerosols are presently identified and typed using sophisticated, fine-tuned classification algorithms that operate using processed lidar data. We present a deep neural network semantic segmentation model that was trained using raw, uncalibrated photon count data and data products from the Cloud/Aerosol Transport System’s (CATS) 1064 nm lidar. Our approach successfully types layers in complex scenes using only raw photon counts, bin altitudes, and ground surface type at 14 to 171 times the spatial resolution of the CATS operational data product. We observe comparable cloud detection and phase determination to the CATS operational algorithm while also exhibiting a 15-point improvement in finding tenuous aerosol layers. Because the model is lightweight, does not rely upon ancillary information, and is optimized to leverage GPU computing, it has the potential to be deployed on-instrument to perform cloud and aerosol typing in real time. Full article

This study employs a mixed method. In-depth interviews and observational studies are among the data collection approaches used in qualitative research. The quantitative method measures the weight of respondents’ answers to the distributed questionnaire. The questionnaire, containing 82 items, was distributed to 202 tourists to collect their perceptions based on the 4A tourist components. The results indicate that tourists’ perceptions of attractions, accessibility, and ancillary services are generally positive. In contrast, perceptions of amenity services are less favorable. Using the scores from IFAS, EFAS, and the I-E matrix, the total weighted scores for IFAS and EFAS are 2.68 and 2.83, respectively. The appropriate strategy for BTV is one of aggressive growth in a position of strengths and opportunities. The study highlights key techniques, including the application of information technology in service and promotion, the strengthening of community and government roles, the development of infrastructure and facilities, the utilization of external resources, sustainable innovation, and the encouragement of local governments to issue regulations for halal tourism villages. By identifying drivers and barriers from an economic, environmental, social, and cultural perspective, the SWOT analysis results help design strategies that can make positive contributions to the development of sustainable, community-based halal tourism and digital information in the future. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

Background: The number of women veterans has been rising steadily since the Gulf War and many assume the functions of their male counterparts. Women face unique obstacles in their service, and it is imperative that differences in physiology not be overlooked so as to provide better and appropriate care to our women in uniform. Despite this influx and incorporation of female talent, dedicated reports contrasting female and male veterans are rare, outside of specific psychological studies. We therefore attempt to contrast gut constituents, absorption, innate immune system, and nutritional differences to provide a comprehensive account of similarities and differences between female and male veterans, from our single-center perspective, as this has not been carried out previously. Herein, we obtained a detailed roster of commonly used biomedical tests and some novel entities to detect differences between female and male veterans. The objective of this study was to detect differences in the innate immune system and other ancillary test results to seek differences that may impact the health of female and male veterans differently. Methods: To contrast biochemical and sociomedical parameters in female and male veterans, we studied the data collected on 450 female veterans and contrasted them to a group of approximately 1642 males, sequentially from 1995 to 2022, all selected because of above-average risk for CRC. As part of this colorectal cancer (CRC) screening cross-sectional and longitudinal study, we also collected stool, urine, saliva, and serum specimens. We used ELISA testing to detect stool p87 shedding by the Adnab-9 monoclonal and urinary organ-specific antigen using the BAC18.1 monoclonal. We used the FERAD ratio (blood ferritin/fecal p87), a measure of the innate immune system to gauge the activity of the innate immune system (InImS) by dividing the denominator p87 (10% N-linked glycoprotein detected by ELISA) into the ferritin level (the enumerator, a common lab test to assess anemia). FERAD ratios have not been performed elsewhere despite past Adnab-9 commercial availability so we have had to auto-cite our published data where appropriate. Results: Many differences between female and males were detected. The most impressive differences were those of the InImS where males clearly had the higher numbers (54,957 ± 120,095) in contrast to a much lower level in females (28,621 ± 66,869), which was highly significantly different (p < 0.004). Mortality was higher in males than females (49.4% vs. 24.1%; OR 3.08 [2.40–3.94]; p < 0.0001). Stool p87, which is secreted by Paneth cells and may have a protective function, was lower in males (0.044 ± 0.083) but higher in females (0.063 ± 0.116; p < 0.031). Immunohistochemistry of the Paneth cell-fixed p87 antigen was also higher in females (in the descending colon and rectum). In contrast, male ferritin levels were significantly higher (206.3 ± 255.9 vs. 141.1 ± 211.00 ng/mL; p < 0.0006). Females were less likely to be diabetic (29.4 vs. 37.3%; OR 0.7 [0.55–0.90]; p < 0.006). Females were also more likely to use NSAIDs (14.7 vs. 10.7%, OR 1.08 [1.08–2.00]; p < 0.015). Females also had borderline less GI bleeding by fecal immune tests (FITs), with 13.2% as opposed to 18.2% in males (OR 0.68 [0.46–1.01]; p = 0.057), but were less inclined to have available flexible sigmoidoscopy (OR 0.68 [0.53–0.89]; p < 0.004). Females also had more GI symptomatology, a higher rate of smoking, and were significantly younger than their male counterparts. Conclusions: This study shows significant differences with multiple parameters in female and male veterans. Full article

Landslides are among the most destructive natural hazards in the Western Ghats region of Kerala, driven by complex interactions between geological, hydrological, and anthropogenic factors. This study aims to generate a high-resolution Landslide Susceptibility Level Map (LSLM) using a machine learning (ML)-based Random Forest (RF) model integrated with Geographic Information Systems (GIS). A total of 231 historical landslide locations obtained from the Bhukosh portal were used as reference data. Eight predictive factors—Stream Order, Drainage Density, Slope, Aspect, Geology, Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Moisture Stress Index (MSI)—were derived from remote sensing and ancillary datasets, preprocessed, and reclassified for model input. The RF model was trained and validated using a 50:50 split of landslide and non-landslide points, with variable importance values derived to weight each predictive factor of the raster layer in ArcGIS. The resulting Landslide Susceptibility Index (LSI) was reclassified into five susceptibility zones: Very Low, Low, Moderate, High, and Very High. Results indicate that approximately 17.82% of the study area falls under high to very high susceptibility, predominantly in the steep, weathered, and high rainfall zones of the Western Ghats. Validation using Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) analysis yielded an accuracy of 0.890, demonstrating excellent model performance. The output LSM provides valuable spatial insights for planners, disaster managers, and policymakers, enabling targeted mitigation strategies and sustainable land-use planning in landslide-prone regions. Full article

Hurricanes can cause massive power outages and pose significant disruptions to society. Accurately monitoring hurricane power outages will improve predictive models and guide disaster emergency management. However, many challenges exist in obtaining high-quality data on hurricane power outages. We systematically evaluated machine learning (ML) approaches to reconstruct historical hurricane power outages based on high-resolution (1 km) satellite night light observations from the Defense Meteorological Satellite Program (DMSP) and other ancillary information. We found that the two-step hybrid model significantly improved model prediction performance by capturing a substantial portion of the uncertainty in the zero-inflated data. In general, the classification and regression tree-based machine learning models (XGBoost and random forest) demonstrated better performance than the logistic and CNN models in both binary classification and regression models. For example, the xgb+xgb model has 14% less RMSE than the log+cnn model, and the R-squared value is 25 times larger. The Interpretable ML (SHAP value) identified geographic locations, population, and stable and hurricane night light values as important variables in the XGBoost power outage model. These variables also exhibit meaningful physical relationships with power outages. Our study lays the groundwork for monitoring power outages caused by natural disasters using satellite data and machine learning (ML) approaches. Future work should aim to improve the accuracy of power outage estimations and incorporate more hurricanes from the recently available Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data. Full article

Surface soil moisture (SM) is a critical factor in hydrological modeling, agricultural management, and numerical weather forecasting. This paper presents a highly effective soil moisture retrieval algorithm developed for the FY-3E (FengYun-3E) GNOS-R (GNSS Occultation Sounder II-Reflectometry) instrument. The algorithm incorporates a first-order vegetation model that considers vegetation density and volume scattering. Utilizing multi-angle GNOS-R observations, the algorithm derives surface reflectivity, which is combined with ancillary data on opacity, vegetation water content, and soil moisture from SMAP (Soil Moisture Active Passive) to optimize the retrieval process. The algorithm has been specifically tailored for different surface conditions, including bare soil, areas with low vegetation, and densely vegetated regions. The algorithm directly incorporates the angle-dependence of observations, leading to enhanced retrieval accuracy. Additionally, a new approach parameterizes surface roughness as a function of angle, allowing for refined corrections in reflectivity measurements. For vegetated areas, the algorithm effectively isolates the soil surface signal by eliminating volume scattering and vegetation effects, enabling the accurate estimation of soil moisture. By leveraging multi-angle data, the algorithm achieves significantly improved retrieval accuracy, with root mean square errors of 0.0235, 0.0264, and 0.0191 (g/cm³) for bare, low-vegetation, and dense-vegetation areas, respectively. This innovative methodology offers robust global soil moisture estimation capabilities using the GNOS-R instrument, surpassing the accuracy of previous techniques. Full article

Integrating offshore renewable energy (ORE) into power systems is vital for sustainable energy transitions. This paper examines the challenges and opportunities in integrating ORE, focusing on offshore wind and floating solar, into grid systems. A simulation was conducted using a 5 MW offshore wind turbine and a 2 MW floating PV (FPV) system, complemented by a 10 MWh battery energy storage system (BESS). The simulation utilized the typical load profile of Belgium and actual 2023 electricity price data, along with realistic wind and solar generation patterns for a location at the sea border of Belgium and the Netherlands. The use of real operational and market data ensures the practical relevance of the results. This study highlights the importance of BESS, targeting a significant revenue by participating in system imbalance and providing ancillary services (aFRR and mFRR). Key findings emphasize the need for grid infrastructure transformation to support ORE’s growing investments and deployment. This research underscores the essential role of technological innovation and strategic planning in optimizing the potential of ORE sources. Full article

Background/Objectives: Primary prevention, germline, familial, or other pre- or post-diagnostic and standard treatment-elevated progression or recurrence risk and mitigating adverse events from systemic treatment are all clinical opportunities to reduce the risk of lethal prostate cancer. This review attempted to provide a practical and realistic consensus via an international committee of experts who, in general, harbor career-long experience in this discipline. Methods: A PubMed review primarily utilizing the latest meta-analyses, systematic reviews, and methodologically robust epidemiologic recent data adjusting for multiple confounding variables was conducted. The goal of this committee was to highlight tangible options for clinicians and patients. Results: Behavioral patterns and metrics known to reduce cardiovascular morbidity, mortality, and all-cause mortality (premature death) appear to prevent numerous lethal common cancers, including prostate cancer. This practical approach allows for the greatest probability of patient success since cardiovascular disease (CVD) is the primary cause of death in men with and without prostate cancer, and a notable source of morbidity and mortality in men with advanced disease due to systemic conventional treatment as well as the inflammatory contribution of cancer itself. Heart-healthy dietary patterns, exercise, healthy weight/waist circumference, eliminating tobacco, minimizing alcohol exposure, and other behaviors to reduce the risk of CVD should be prioritized. CVD-preventive medications, including aspirin, GLP-1 agonists, metformin, statins, etc., should receive attention to improve compliance for those that already qualify for these agents and to increase the probability of enhancing the quality and quantity of life. Dietary supplements do not have favorable data currently to espouse their utilization to prevent lethal prostate cancer but may have an ancillary role in mitigating some adverse effects of treatment. Conclusions: Remarkably, heart-healthy lifestyle changes, metrics, and promising repurposed medications known to reduce cardiovascular events, promote longevity, and improve mental health could simultaneously prevent lethal prostate cancer. This serendipitous association provides clinicians and their patients a higher probability of success, regardless of their prostate cancer pathway or circumstance. Full article

A semi-empirical method for estimating actual evapotranspiration (ETa) based on ancillary and NDVI data, named NDVI-Cws, is currently being refined for improved applicability to wetlands. The investigation, in particular, addresses the case of semi-natural ecosystems where the impact of meteorological water stress (WS) is limited by groundwater resources. To adapt to this situation, the application of the NDVI-Cws method is preceded by a calibration phase based on spatially enhanced Land Surface Analysis Satellite Application Facility (LSA SAF) evapotranspiration products. This calibration is currently performed in the main wetlands of Tuscany (Central Italy) identified in the Ramsar Convention. The calibrated NDVI-Cws version is then applied to all regional Ramsar areas, yielding outputs that are first examined all over Tuscany. Next, the model estimates are quantitatively assessed versus ETa observations taken in a forest and a grassland Ramsar site. The results of these independent tests show the improvement achieved by the calibration phase with respect to the original model version. This supports the potential of the refined NDVI-Cws method to yield reasonably accurate daily ETa estimates for wetlands at a spatial resolution that is mainly dependent on the NDVI data used. Full article