Search Results (246)

In this paper we aimed to compare seasonality, clinical characteristics, and outcomes of Influenza A and COVID-19 in the context of influenza reemergence and ongoing Omicron circulation. We performed a retrospective comparative analysis at the Teaching Hospital of Infectious Diseases in Cluj-Napoca, Romania. We included adult patients hospitalized with Influenza A or COVID-19 between 1 November 2022 and 31 March 2024. Data were collected on demographics, clinical presentation, complications, and in-hospital mortality. We included 899 COVID-19 and 423 Influenza A patients. The median age was 74 years for COVID-19 and 65 for Influenza A (p < 0.001). The age-adjusted Charlson comorbidity index was higher in COVID-19 patients (5 vs. 3, p < 0.001). Despite this age gap, acute respiratory failure was more common in Influenza A (62.8% vs. 55.7%, p = 0.014), but ventilation rates did not differ significantly. Multivariate models showed Influenza A was associated with increased risk of intensive-care unit (ICU) admission or ventilation, whereas older COVID-19 patients had higher in-hospital mortality (5.67% vs. 3.3%, p = 0.064). Omicron COVID-19 disproportionately affected older patients with comorbidities, contributing to higher in-hospital mortality. However, Influenza A remained a significant driver of respiratory failure and ICU admission, underscoring the importance of preventive measures in high-risk groups. Full article

Understanding how prey and predator species partition activity patterns across time and space is essential for elucidating behavioral adaptation and ecological coexistence. In this study, we examined the diel and seasonal activity rhythms of two sympatric rodent species—Rhombomys opimus (Great gerbil) and Meriones meridianus (Midday gerbil)—and their primary predators, Otocolobus manul (Pallas’s cat) and Vulpes vulpes (Red fox), in a desert-steppe ecosystem on the northern slopes of the Qilian Mountains, China. Using over 8000 camera trap days and kernel density estimation, we quantified their activity intensity and spatiotemporal overlap. The two rodent species showed clear temporal niche differentiation but differed in their synchrony with predators. R. opimus exhibited a unimodal diurnal rhythm with spring activity peaks, while M. meridianus showed stable nocturnal activity with a distinct autumn peak. Notably, O. manul adjusted its activity pattern to partially align with that of R. opimus, whereas V. vulpes maintained a crepuscular–nocturnal rhythm overlapping more closely with that of M. meridianus. Despite distinct temporal rhythms, both rodent species shared high spatial overlap with their predators (overlap index OI = 0.64–0.83). These findings suggest that temporal partitioning may reduce predation risk for R. opimus, while M. meridianus co-occurs more extensively with its predators. Our results highlight the ecological role of native carnivores in rodent population dynamics and support their potential use in biodiversity-friendly rodent management strategies under arid grassland conditions. Full article

Agriculture accounts for 29% of the Gross Domestic Product of the Nusa Tenggara Islands (NTIs). However, recurring agricultural droughts pose a major threat to the sustainability of agriculture in this region. The interplay between precipitation, solar radiation, and surface temperature as meteorological factors plays a key role in affecting vegetation (Soil-Adjusted Vegetation Index) and agricultural drought (Temperature Vegetation Dryness Index) in the NTIs. Based on the analyses of interplay with temporal lag, this study investigates the effect of each factor on agricultural drought and attempts to provide early warnings regarding drought in the NTIs. We collected surface information data from Moderate-Resolution Imaging Spectroradiometer (MODIS). Meanwhile, rainfall was estimated from Himawari-8 based on the INSAT Multi-Spectral Rainfall Algorithm (IMSRA). The results showed reliable performance for 8-day and monthly scales against gauges. The drought analysis results reveal that the NTIs suffer from mild-to-moderate droughts, where cropland is the most vulnerable, causing shifts in the rice cropping season. The driving factors could also explain >60% of the vegetation and surface-dryness conditions. Furthermore, our monthly and 8-day TVDI estimation models could capture spatial drought patterns consistent with MODIS, with coefficient of determination (R²) values of more than 0.64. The low error rates and the ability to capture the spatial distribution of droughts, especially in open-land vegetation, highlight the potential of these models to provide an estimation of agricultural drought. Full article

Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar Vegetation Index (NPRVI) using dual-polarization Sentinel-1 C-band SAR data from agricultural fields at the Beltsville Agricultural Research Center (BARC). Field-measured precipitation and Global Precipitation Measurement (GPM) precipitation datasets were temporally aligned with Sentinel-1 acquisition times to assess the sensitivity of radar signals to precipitation events. NPRVI exhibited a strong sensitivity to precipitation, particularly within the 1 to 7 h prior to the satellite overpass, even for small amounts of precipitation. A quality assessment (QA) framework was developed to flag and correct precipitation-affected radar observations through interpolation. The adjusted NPRVI values, based on the QA framework using precipitation within a 6 h window, showed strong agreement between field- and GPM-derived data, with an RMSE of 0.09 and a relative RMSE of 19.8%, demonstrating that GPM data can serve as a viable alternative for quality adjustment despite its coarse spatial resolution. The adjusted NPRVI for both soybean and corn fields significantly improved the temporal consistency of the time series and closely followed NDVI trends, while also capturing crop-specific seasonal variations, especially during periods of NDVI saturation or limited variability. These findings underscore the value of the proposed radar-based QA framework in enhancing the interpretability of vegetation dynamics. NPRVI, when adjusted for precipitation effects, can serve as a reliable and complementary tool to optical vegetation indices in agricultural and environmental monitoring. Full article

With the increasing penetration of renewable energy, power grids face significant challenges in balancing fluctuating renewable generation with flexible demand-side resources. Industrial loads, characterized by substantial consumption and high adjustability, provide critical flexibility to address these challenges; however, existing methods for quantifying their response potential lack sufficient accuracy and comprehensive uncertainty characterization. This study proposes an integrated quantitative assessment framework combining Seasonal-Trend decomposition using Loess (STL), load-step feature extraction, and Gaussian Process Regression (GPR). Historical industrial load data are first decomposed using STL to isolate trend and periodic patterns, while mathematically defined load-step indicators quantify intrinsic adjustability. Concurrently, a multi-dimensional willingness index reflecting past response behaviors and participation records comprehensively characterizes user response capabilities and inclinations. A GPR-based nonlinear mapping between extracted load features and response potential enables precise quantification and robust uncertainty estimation. Case studies verify the effectiveness of the proposed approach, achieving an assessment accuracy of 91.4% and improved confidence interval characterization compared to traditional methods. These findings demonstrate the framework’s significant capability in supporting precise flexibility utilization, thereby enhancing operational stability in power grids with high renewable energy penetration. Full article

Increasing species richness through rotation is considered a promising measure to enhance agroecosystem functions and services. However, the legacy effects of introducing legumes into a wheat–maize rotation in the North China Plain on soil microecology, especially the soil metabolome, in the succeeding wheat season have not been elucidated. This study established three cropping systems: (1) a continuous winter wheat–summer maize rotation (M), (2) a winter wheat–summer peanut (summer maize) rotation (PM), and (3) a winter wheat–summer soybean (summer maize) rotation (SM). The soil physicochemical properties, microbial communities, and metabolomes were analyzed at the stage of the succeeding wheat crop. Introducing peanuts or soybeans into a wheat–maize rotation significantly reduced the soil bacterial abundance and increased the soil fungal Shannon index. This rotation adjustment had a substantial impact on the structure and taxa composition of the soil microbial community. Crop diversification increased the number of total edges, the average degree, and the average number of neighbors in the soil microbial co-occurrence network. Different crop rotations significantly affected the soil metabolic profiles in the positive and negative ion modes. Crop diversification significantly reduced the abundance of coumarin and coumaric acid in the soils. In conclusion, introducing peanuts or soybeans into a wheat–maize rotation could increase the soil fungal community diversity, change the soil microbial community structure and taxa composition, increase the complexity of the soil microbial ecological network, and reduce the abundance of soil allelochemicals. Our study demonstrated the continuity of the impact of crop rotation on soil ecology, and revealed the ecological advantages of crop diversification from the perspective of soil microbiology and metabolomics. Full article

Enhancing nitrogen use efficiency (NUE) through optimized application methods can benefit agronomic productivity and environmental sustainability. This study examined three nitrogen application strategies, flat rate, soil-based sensing, and remote sensing-based prescription maps, for corn in southeast South Dakota, USA. Soil-based sensing utilized an electrical conductivity (EC) sensor while the normalized difference vegetation index (NDVI) was extracted from remote sensing data using Sentinel-2 images to create different zones. In the flat-rate method, nitrogen is applied uniformly at all plots, regardless of field variations. On the other hand, the sensor-based methods recommended variable rates of nitrogen applications to address field variations. The results of the present study showed that remote sensing-based methods significantly identify field variations as different zones (p < 0.05). The remote sensing-based method improved NUE compared to the flat-rate method, with increases of 2.21, 29.24, 29.6, and 82.09% in zones 1, 2, 3, and 4, respectively. However, adjusting the spatial and temporal nitrogen requirement rates using a soil-based sensor was difficult. The findings suggest remote sensing-based method can offer nitrogen optimization by incorporating in-season environmental variability, enhancing agronomic efficiency and sustainability. Full article

The scarcity of information on agricultural development in the semi-arid region of Alagoas limits the spatial understanding of this activity. Government data are generally numerical and lack spatial detail. Remote sensing emerges as an efficient alternative, providing accessible visualization of agricultural areas. This study evaluates the performance of MapBiomas in monitoring agricultural areas in the semi-arid region of Alagoas, comparing it to a Random Forest model adjusted for the region using higher-resolution images. The first methodology is based on land use and land cover (LULC) data from MapBiomas, an initiative that provides information on land use and land cover in Brazil. The second method employs the Random Forest model, calibrated for the region’s dry season, addressing cloud cover issues and allowing for the identification of irrigated agriculture. LULC data were subjected to a precision analysis using 200 points generated within the study areas, extracting LULC information for each coordinate. These points were overlaid on high-resolution images to assess model accuracy. Additionally, field visits were conducted to validate the identification of agriculture. The irrigated area data from the Random Forest model were correlated with irrigation records from SEMARH. MapBiomas presented a Kappa index of 0.74, with precision exceeding 90% for classes such as forest, natural pasture, non-vegetated area, and water bodies. However, the agriculture class obtained an F1 score of 0.56. The Random Forest model achieved a Kappa index of 0.82, with an F1 score of 0.79 for agriculture. The correlation between the total annual irrigated area data from Random Forest and SEMARH records was high (R = 0.85). The Random Forest model yielded better results in classifying agriculture in the semi-arid region of Alagoas compared to MapBiomas. However, classification limitations were observed in lowland areas due to spectral confusion caused by soil moisture accumulation. Full article

This paper introduces a novel weighted fusion methodology for grid-level short-term load forecasting that addresses the critical limitations of direct aggregation methods currently used by regional dispatch centers. Traditional approaches accumulate provincial forecasts without considering regional heterogeneity in load characteristics, data quality, and forecasting capabilities. Our methodology implements a comprehensive evaluation index system that quantifies forecast trustworthiness through three key dimensions: forecast reliability, provincial impact, and forecasting complexity. The core innovation lies in our principal component analysis (PCA)-based weighted aggregation mechanism that dynamically adjusts provincial weights according to their evaluated reliability, further enhancing through time-varying weights that adapt to changing load patterns throughout the day. Experimental validation across three representative seasonal periods (moderate temperature, high temperature, and winter conditions) substantiates that our weighted fusion approach consistently outperforms direct aggregation, achieving a 24.67% improvement in overall MAPE (from 3.09% to 2.33%). Performance gains are particularly significant during critical peak periods, with up to 62.6% error reduction under high-temperature conditions. The methodology verifies remarkable adaptability across different temporal scales, seasonal variations, and regional characteristics, consistently maintaining superior performance from ultra-short-term (1 h) to medium-term (168 h) forecasting horizons. Analysis of provincial weight dynamics reveals intelligent redistribution of weights across seasons, with summer months characterized by Jiangsu dominance (0.30–0.35) shifting to increased Anhui contribution (0.30–0.35) during winter. Our approach provides grid dispatch centers with a computationally efficient solution for enhancing the integration of heterogeneous forecasts from diverse regions, leveraging the complementary strengths of individual provincial systems while supporting safer and more economical power system operations without requiring modifications to existing forecasting infrastructure. Full article

Background: In Australia, 2017 was a severe A/H3N2 season and, therefore, we estimated the effectiveness of standard-dose quadrivalent influenza vaccine in preventing hospitalization for cardiovascular disease (CVD) among New South Wales (NSW) residents aged ≥50 years. Methods: We conducted a nested, matched case–control study within the 45 and Up study, linking data from the Australian Immunization Register, NSW Admitted Patient Data Collection and Pharmaceutical Benefits Schedule. Cases were individuals hospitalized for CVD and controls were those who were hospitalized for gastrointestinal diseases. The two groups were balanced using 1:1 propensity score matching based on age group (50–64, 65–74, 75–84, and ≥85 years) and sex. After adjusting for confounders (smoking, body mass index and income), we calculated the adjusted odds ratio (aOR) for vaccination during the season using multivariable logistic regression. E-values were estimated to assess residual confounding. Vaccine effectiveness (VE) was calculated as (1 − aOR) × 100. Results: There were 10,445 (4452 cases and 5993 controls) study participants. After matching, 8904 (85.2%) were retained with a mean age of 76.4 ± 10.4 years and 58.3% men. Following adjustment for confounders, the aOR of averting a CVD hospitalization was 0.15 (95% CI: 0.13 to 0.17; p < 0.001). The estimated VE against CVD hospitalization was 85% (95% CI: 83 to 87). We found an E-value of 12.82, indicating strong evidence of minimal residual confounding. Conclusions: In the severe 2017 influenza A/H3N2 season in Australia, we observed a high VE in preventing cardiovascular hospitalization despite a low VE against influenza infection prevention. Improving vaccine uptake may reduce cardiovascular burden. Full article

Yield maps and in-season forecasts help optimize agricultural practices. The traditional approaches to predicting yield during the growing season often rely on ground-based observations, which are time-consuming and labor-intensive. Remote sensing offers a promising alternative by providing frequent and spatially extensive information on crop development. In this study, we evaluated the feasibility of high-resolution satellite imagery for the early yield prediction of an under-investigated crop, Japanese squash (Cucurbita maxima), in a small farm in Hollister, California, over the growing seasons of 2022 and 2023 using vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI). We identified the optimal time for yield prediction and compared the performances across satellite platforms (Sentinel-2: 10 m; PlanetScope: 3 m; SkySat: 0.5 m). Pearson’s correlation coefficient (r) was employed to determine the dependencies between the yield and vegetation indices measured at various stages throughout the squash growing season. The results showed that SkySat-derived vegetation indices outperformed those of Sentinel-2 and PlanetScope in explaining the squash yields (R² = 0.75–0.76; RMSE = 0.8–1.9 tons/ha). Remote sensing showed very strong correlations with yield as early as 29 days after planting in 2022 and 37 and 76 days in 2023 for the NDVI and the SAVI, respectively. These early dates corresponded with the vegetative stages when the crop canopy became denser before fruit development. These findings highlight the utility of high-resolution imagery for in-season yield estimation and within-field variability detection. Detecting yield variability early enables timely management interventions to optimize crop productivity and resource efficiency, a critical advantage for small-scale farms, where marginal yield changes impact economic outcomes. Full article

This study investigates the spatial–temporal dynamics of vegetation indices in olive orchards across two traditionally rainfed regions of the Iberian Peninsula, namely the “Trás-os-Montes” (TM) agrarian region in Portugal and the Badajoz (BA) province in Spain, in response to drought conditions. Using satellite-derived vegetation indices, derived from the Harmonized Landsat Sentinel-2 project (HLSL30), such as the Normalized Difference Moisture Index (NDMI) and Soil-Adjusted Vegetation Index (SAVI), this study evaluates the impact of drought periods on olive tree growing conditions. The Mediterranean Palmer Drought Severity Index (MedPDSI), specifically developed for olive trees, was selected to quantify drought severity, and impacts on vegetation dynamics were assessed throughout the study period (2015–2023). The analysis reveals significant differences between the regions, with BA experiencing more intense drought conditions, particularly during the warm season, compared to TM. Seasonal variability in vegetation dynamics is clearly linked to MedPDSI, with lagged responses stronger in the previous two-months. Both the SAVI and the NDMI show vegetation vigour declines during dry seasons, particularly in the years of 2017 and 2022. The findings reported in this study highlight the vulnerability of rainfed olive orchards in BA to long-term drought-induced stress, while TM appears to have slightly higher resilience. The study underscores the value of combining satellite-derived vegetation indices with drought indicators for the effective monitoring of olive groves and to improve water use management practices in response to climate change. These insights are crucial for developing adaptation measures that ensure the sustainability, resiliency, and productivity of rainfed olive orchards in the Iberian Peninsula, particularly under climate change scenarios. Full article

Nuclear receptor subfamily 1 group D member 1 (NR1D1 or REV-ERBα) is a crucial element of the circadian clock’s transcriptional and translational feedback loop. Understanding its expression in humans is critical for elucidating its role in circadian rhythms and metabolic processes, and in finding potential links to various pathologies. In a longitudinal survey, we examined REV-ERBα expression at 08:00 using a real-time polymerase chain reaction (qRT-PCR) in blood mononuclear cells from Arctic native and non-native residents during equinoxes and solstices. REV-ERBα expression exhibited a pronounced seasonality, peaking at the summer solstice, and reaching a nadir at the winter solstice in both natives and non-natives, with a relatively higher summer peak in natives. After adjusting for age, sex, and body mass index, the amount and timing of light exposure, the amount of physical activity, and indigeneity emerged as significant predictors of REV-ERBα expression. Full article

The application of reflective vegetation indices is crucial for advancing precision agriculture, particularly in monitoring crop growth and development. Among these indices, the Normalized Difference Vegetation Index (NDVI) is the most widely used due to its reliability in capturing vegetation dynamics. This study focuses on the applicability and temporal dynamics of the NDVI in monitoring winter wheat (Triticum aestivum) under the specific climatic conditions of Southern Dobrudja, Bulgaria. Using a Survey3W Camera RGN mounted on DJI unmanned aerial vehicles (Phantom 4 Pro and Mavic 2 Pro) at an altitude of 100 m, NDVI data were collected over a five-year period (2019–2024). Results reveal distinct NDVI trends, with maximum values reaching 0.56 during favorable conditions, and sharp declines during late spring frosts or drought periods. These NDVI variations correlate strongly with environmental factors, including precipitation and temperature fluctuations. For instance, during the 2019–2020 season, the NDVI decreased by 30% due to severe drought and high winter temperatures. In this study, vegetation indices, including the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), were utilized to compare the results with the NDVI. The high-resolution UAV methodology demonstrated in this study proves highly effective for breeding and agronomic applications, offering precise data for optimizing wheat cultivation under variable agro-climatic conditions. These findings highlight the NDVI’s potential to enhance crop monitoring, yield prediction, and stress response management in winter wheat. Full article

Orchards are complex agricultural systems with various characteristics that influence crop evapotranspiration (ET_c), such as variety, tree height, planting density, irrigation methods, and inter-row management. The preservation of biodiversity and improvement of soil fertility have become important goals in modern orchard management. Consequently, the traditional approach to weed control between rows, which relies on herbicides and soil mobilization, has gradually been replaced by the use of permanent living mulch (LM). This study explored the potential of a remote sensing (RS)-assisted method to monitor water use and water productivity in apple orchards with permanent mulch. The experimental data were obtained in the Lis Valley Irrigation District, on the Central Coast of Portugal, where the “Maçã de Alcobaça” (Alcobaça apple) is produced. The methodology was applied over three growing seasons (2019–2021), combining ground observations with RS tools, including drone flights and satellite images. The estimation of ET_a followed a modified version of the Food and Agriculture Organization of the United Nations (FAO) single crop coefficient approach, in which the crop coefficient (K_c) was derived from the normalized difference vegetation index (NDVI) calculated from satellite images and incorporated into a daily soil water balance. The average seasonal ET_a (FAO-56) was 824 ± 14 mm, and the water productivity (WP) was 3.99 ± 0.7 kg m⁻³. Good correlations were found between the K_c’s proposed by FAO and the NDVI evolution in the experimental plot, with an R² of 0.75 for the entire growing season. The results from the derived RS-assisted method were compared to the ET_a values obtained from the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) surface energy balance model, showing a root mean square (RMSE) of ±0.3 mm day⁻¹ and a low bias of 0.6 mm day⁻¹. This study provided insights into mulch management, including cutting intensity, and its role in maintaining the health of the main crop. RS data can be used in this management to adjust cutting schedules, determine K_c, and monitor canopy management practices such as pruning, health monitoring, and irrigation warnings. Full article