Search Results (49)

Respiratory syncytial virus (RSV) is among leading global causes of lower respiratory tract infections, yet data from Russia and other states of the Former Soviet Union (FSU) remain fragmented and structurally inconsistent. This systematic review aims to map and synthesize existing evidence on RSV epidemiology and genotypic distribution across the FSU. Published studies from eLIBRARY and PubMed databases queried for RSV prevalence data, together with public health surveillance datasets, were used to summarize RSV prevalence research across eight FSU countries. Random-effects meta-analysis across age strata showed high prevalence in children before 6 (21%) and a progressive decline with age, which is in agreement with global data. Prevalence estimates showed a high degree of variability partially explained by study scope and clinical presentation. We observed COVID-19-related seasonal disruptions of RSV seasonality, followed by gradual post-pandemic stabilization. Genotypic data reflects global trends with two cosmopolitan clades, A.D and B.D, and their descendants, dominating in the region. The review is limited by uneven geographical and temporal coverage, and scarce data on adults. The review provides the first integrated summary of RSV epidemiology across the FSU and underscores the need for expanded regional surveillance and genomic reporting. Full article

Reliable estimation of oil palm carbon stock is essential for climate mitigation, concession management, and sustainability certification. While satellite-based approaches offer scalable solutions, redundancy among spectral indices and inter-sensor variability complicate model development. This study evaluates machine learning regressors for predicting oil palm carbon stock at tree (CO_tree, kg C tree⁻¹) and hectare (CO_ha, Mg C ha⁻¹) scales using spectral indices derived from Landsat-8, Landsat-9, and Sentinel-2. Fourteen vegetation indices were screened for multicollinearity, resulting in a lean feature set dominated by NDMI, EVI, MSI, NDWI, and sensor-specific indices such as NBR2 and ARVI. Ten regression algorithms were benchmarked through cross-validation. Ensemble models, particularly Random Forest, Gradient Boosting, and XGBoost, outperformed linear and kernel methods, achieving R² values of 0.86–0.88 and RMSE of 59–64 kg tree⁻¹ or 8–9 Mg ha⁻¹. Feature importance analysis consistently identified NDMI as the strongest predictor of standing carbon. Spatial predictions showed stable carbon patterns across sensors, with CO_tree ranging from 200–500 kg C tree⁻¹ and CO_ha from 20–70 Mg C ha⁻¹, consistent with published values for mature plantations. The study demonstrates that ensemble learning with sensor-specific index sets provides accurate, dual-scale carbon monitoring for oil palm. Limitations include geographic scope, dependence on allometric equations, and omission of belowground carbon. Future work should integrate age dynamics, multi-year composites, and deep learning approaches for operational carbon accounting. Full article

The histocompatibility gene complex plays a vital role in the body’s immune response to infections. In this work, we analyzed clinical data for 195 children hospitalized with signs of ARI in Siberia and performed genetic analysis for them. Genotyping was performed by high-throughput sequencing (NGS) using the HLA-Expert kit on the MiSeq Illumina platform. The frequencies of HLA allelic variants were calculated for each variant. For the variants detected in 20 patients or more, odds ratios (OR) were calculated for two pairs of conditions: severe/non-severe course of ARI and hypoxia/no hypoxia on admission. Six allelic variants were identified for which the odds ratio showed a significant (p < 0.05) association with one of the conditions. Allele HLA-A*11:01:01G is associated (OR = 5.654, 95% CI 1.631–19.600) with severe ARVI, which is consistent with the literature data, and HLA-A*03:01:01G allele is associated with ARVI without hypoxia in children (OR = 0.317, 95% CI 0.110–0.914). Alleles HLA-B*51:01:01G (OR = 4.457, 95% CI 1.355–14.663) and HLA-C*01:02:01G (OR = 4.743, 95% CI 1.538–14.629) are associated with severe ARI. HLA-DPB1*04:02:01G (OR = 0.462, 95% CI 0.244–0.876) is associated with ARI without hypoxia and HLA-DQA1*01:02:01G (OR = 1.811, 95% CI 1.003–3.268) is associated with ARI with hypoxia. Full article

A well-performing road network is essential for modern society. But any road is nothing without its users—cyclists, drivers, pedestrians. Road network cannot be managed without knowing who the roads serve. The gaps in this knowledge lead to decisions that hinder efficiency, equality, and sustainability. This is why monitoring traffic is imperative for road management. However, traditional short-term traffic counting methods fail to provide full coverage at a reasonable cost. This study assessed the economic feasibility of drone-enabled traffic monitoring systems across Estonian urban environments through comparative spatial and economic analysis. Hexagonal tessellation was applied to 255 urban locations, identifying 47,530 monitoring points across 4077 grid cells. Economic modeling compared traditional counting costs with drone-based systems utilizing ultralight drones and nomadic 5G infrastructure. Monte Carlo simulation evaluated robustness under varying operational intensities from 30 to 180 days annually. Analysis identified an 8-point density threshold for economic viability, substantially lower than previously reported requirements. Operational intensity emerged as the critical determinant: minimal operations (30 days) proved viable for 9.0% of locations, while semi-continuous deployment (180 days) expanded viability to 81.6%. The findings demonstrate that drone-based monitoring achieves 60–80% cost reductions compared to traditional methods while maintaining equivalent accuracy (95–100% detection rates for vehicles, cyclists, and pedestrians), presenting an economically superior alternative for 67% of Estonian urban areas, with viability extending to lower-density locations through increased operational utilization. Full article

Vegetated intertidal ecosystems, such as seagrass meadows, salt marshes, and macroalgal beds, are vital for biodiversity, coastal protection, and climate regulation; however, they remain highly vulnerable to anthropogenic and climate-induced stressors. This study aims to assess interannual changes in intertidal vegetation cover along the Portuguese mainland coast from 2015 to 2024 using Sentinel-2 satellite imagery calibrated with high-resolution multispectral unoccupied aerial vehicle (UAV) data, to determine the most accurate index for mapping intertidal vegetation. Among the 16 indices tested, the Atmospherically Resilient Vegetation Index (ARVI) showed the highest predictive performance. Based on a model relating intertidal vegetation cover to this index, an ARVI value greater than or equal to 0.214 was established to estimate the area covered with intertidal vegetation. Applying this threshold to time-series data revealed considerable spatial and temporal variability in vegetation cover, with estuarine systems such as the Ria de Aveiro and the Ria Formosa showing the greatest extents and marked fluctuations. At the national level, no consistent overall trend was identified for the study period. Despite limitations related to satellite image resolution and single-site validation, the results demonstrate the feasibility and utility of combining UAV data and satellite indices for long-term, large-scale monitoring of intertidal vegetation. Full article

The quantitative assessment of acoustic rain enhancement technology is highly significant for improving the ecological environment. A scientific and accurate evaluation of its operational effects provides an important basis for continued government and public support and investment in artificial weather modification activities. To effectively analyze the effects of acoustic rain enhancement on the vegetation of grassland ecosystems in arid and semi-arid areas and to clarify its mechanism, this study constructed eight vegetation indices based on Sentinel-2 satellite data. A comprehensive assessment of the changes in vegetation within the grassland ecosystem of the experimental zone was conducted by analyzing spatiotemporal distribution patterns, double-ratio analysis, and difference value comparisons. The results showed that (1) following the acoustic rain enhancement experiment, vegetation growth improved significantly. The mean values of all eight vegetation indices increased more substantially than before the experiment, with kNDVI showing the most notable gain. The proportion of the zone with kNDVI values greater than 0.417 increased from 52.62% to 71.59%, representing a relative increase of 36.05%. (2) The rain enhancement experiment significantly raised the values of eight vegetation indices: kNDVI increased by 0.042 (18.68%), ARVI by 0.043 (18.67%), and the remaining indices also increased to varying degrees (9.51–12.34%). (3) Vegetation improvement was more pronounced in areas closer to the acoustic rain enhancement site. Under consistent climate conditions, vegetation growth in the experimental zone showed significant enhancement. This study demonstrates that acoustic rain enhancement technology can mitigate drought and low rainfall, improve grassland ecosystem services, and provide a valuable foundation for ecological restoration and aerial water resource utilization in arid and semi-arid regions. Full article

This research proposes a green belt as a strategic response to urban expansion in Lomas del Paraíso, Villa María del Triunfo, Lima. Uncontrolled urban growth threatens the local ecosystem, exacerbates the lack of public spaces, and limits employment opportunities. The study employs an integrated methodology that includes urban, community, and especially environmental analysis. This involved the collection of climatic data, and the identification of local flora and fauna, supported by digital tools such as Google Earth, AutoCAD 2023, Revit, and 3D Sun-Path. The proposal incorporates urban, environmental, technological, and community-based design strategies grounded in permaculture principles, circular economy frameworks, and the Sustainable Development Goals (SDGs). These approaches emphasize the symbiotic relationship between the community and the Lomas ecosystem. The feasibility and potential impact of the proposed green belt were compared with similar case studies, such as Medellín’s metropolitan green belt (Jardín Circunvalar) and the Arví Ecotourism Park. These benchmarks highlight the relevance of community involvement and user awareness in ecological preservation. In conclusion, implementing a green belt in Lomas del Paraíso would not only curb unregulated urban sprawl but also enhance community–nature connectivity and promote sustainable development through integrated environmental, social, and urban strategies. Full article

This paper analyzes the exploitability, economic viability, and impact of fleet-based 5G network extensions implemented in maritime environments, focusing on the Baltic Sea and Mediterranean as a case study. Through cost–benefit analysis and business model validation, we demonstrate how multi-hop 5G connectivity can reduce communication costs while improving service quality for maritime operators. Our findings indicate that implementing vessel-based 5G relay stations can achieve 80–90% coverage in key maritime corridors with a break-even period of 2–3 years. The study reveals that combining vessel-to-vessel relaying with strategic floating base stations can reduce connectivity costs by up to 40% compared to traditional satellite solutions, while enabling new revenue streams through premium services. We provide a detailed economic framework for evaluating similar implementations across different maritime routes and suggest policy recommendations for facilitating cross-border 5G maritime networks and introduce key use cases value creation for network extension. Full article

In Vietnam, models for estimating Above-Ground Biomass (AGB) to predict carbon stock are primarily based on diameter at breast height (DBH), tree height (H), and wood density (WD). However, remote sensing has increasingly been recognized as a cost-effective and accurate alternative. Within this context, the present study aimed to develop correlation equations between Total Above-Ground Carbon (TAGC) and vegetation indices derived from Sentinel-2 imagery to enable direct estimation of carbon stock for assessing emissions and removals. In this study, the remote sensing indices most strongly associated with TAGC were identified using principal component analysis (PCA). TAGC values were calculated based on forest inventory data from 115 sample plots. Regression models were developed using Ordinary Least Squares and Maximum Likelihood methods and were validated through Monte Carlo cross-validation. The results revealed that Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Near Infrared Reflectance (NIR), as well as three variable combinations—(NDVI, ARVI), (SAVI, SIPI), and (NIR, EVI — Enhanced Vegetation Index)—had strong influences on TAGC. A total of 36 weighted linear and non-linear models were constructed using these selected variables. Among them, the quadratic models incorporating NIR and the (NIR, EVI) combination were identified as optimal, with AIC values of 756.924 and 752.493, R² values of 0.86 and 0.87, and Mean Percentage Standard Errors (MPSEs) of 22.04% and 21.63%, respectively. Consequently, these two models are recommended for predicting carbon stocks in Evergreen Broadleaf (EBL) forests within Vietnam’s South Central Coastal Ecoregion. Full article

Maize is a critical crop for global food security, yet excessive nitrogen (N) application, while sustaining yields, leads to reduced nitrogen use efficiency (NUE), and the application of controlled-release fertilizer (CRF) is one of the effective options to achieve sustainable maize production while improving NUE. This study evaluated the long-term effects of CRF with varying N input rates on maize growth using low-cost UAV-RGB imaging. UAV-RGB images were captured in different growth stages, and the non-canopy background was removed using the maximum between-class algorithm (OTSU). Eleven vegetation indices were constructed from the images to analyze maize growth under different N treatments. The results indicated that a single application of CRF with an equivalent N input rate to conventional treatment yielded significantly better outcomes. The optimal controlled-release N ratio was 40% of the total N input, increasing maize yield by 6.73% and NUE by 15%. Indices such as NRI, NBI, ARVI, RGBVI, ExR, ExG, and ExGR effectively reflected plant N status, with R² values exceeding 0.856 for yield estimation across growth stages. UAV-RGB imaging proved to be a viable method for rapid N status monitoring, aiding in the optimization of N management in maize production. Full article

Wheat (Triticum aestivum L.) is one of the world’s primary food crops, and timely and accurate yield prediction is essential for ensuring food security. There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but the optimal indices and time window for wheat yield prediction in arid regions remain unclear. This study was conducted to (1) assess the performance of widely recognized remote sensing indices to predict wheat yield at different growth stages, (2) evaluate the predictive accuracy of different yield predictive machine learning models, (3) determine the appropriate growth period for wheat yield prediction in arid regions, and (4) evaluate the impact of climate parameters on model accuracy. The vegetation indices, widely recognized due to their proven effectiveness, used in this study include the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Atmospheric Resistance Vegetation Index (ARVI). Moreover, four machine learning models, viz. Decision Trees (DTs), Random Forest (RF), Gradient Boosting (GB), and Bagging Trees (BTs), were evaluated to assess their predictive accuracy for wheat yield in the arid region. The whole wheat growth period was divided into three time windows: tillering to grain filling (December 15–March), stem elongation to grain filling (January 15–March), and heading to grain filling (February–March 15). The model was evaluated and developed in the Google Earth Engine (GEE), combining climate and remote sensing data. The results showed that the RF model with ARVI could accurately predict wheat yield at the grain filling and the maturity stages in arid regions with an R² > 0.75 and yield error of less than 10%. The grain filling stage was identified as the optimal prediction window for wheat yield in arid regions. While RF with ARVI delivered the best results, GB with EVI showed slightly lower precision but still outperformed other models. It is concluded that combining multisource data and machine learning models is a promising approach for wheat yield prediction in arid regions. Full article

Background/Objectives: This study aimed to evaluate the prevalence of viral agents identified by Multiplex PCR in acute respiratory viral infection (ARVI) patients at Edirne Sultan 1, Murat State Hospital, from April 2023 to April 2024, and to investigate the relationship between monthly average humidity and viral positivity rates. Methods: The study included 764 adult patients (aged 18 and older) diagnosed with influenza symptoms. Respiratory viral samples were collected and analyzed for COVID-19, influenza A and B, and RSV using Multiplex PCR, with results evaluated retrospectively. Continuous variables in the study were compared using a t-test, and categorical variables were compared with a chi-square test. A logistic regression analysis was performed for the analysis of COVID-19. In this analysis, PCR positivity was the dependent variable, while age, gender, and humidity level served as independent variables. Results: COVID-19 PCR positivity was detected in 142 patients (18.6%), with INF-A (influenza A) in 13 (3.7%), INF-B (influenza B) in 15 (4.2%), and RSV in 2 (0.6%). Higher humidity (over 60%) was associated with reduced viral PCR positivity rates for COVID-19 and influenza B, while low (up to 40%)/normal (40–60%) humidity correlated with positivity rate (p < 0.05 for both). Logistic regression analysis indicated that high humidity levels offer protection against COVID-19 (OR: 0.356; 95% CI: 0.245–0.518). Conclusions: Our study provides essential epidemiological data by summarizing monthly virus distribution in Edirne. Full article

Abundant and diverse urban bird communities promote ecosystem and human health in cities. However, the estimation of bird community structure requires large amounts of resources. On the other hand, calculating remotely sensed spectral indices is cheap and easy. Such indices are directly related to vegetation cover, built-up cover, and temperature, factors that also affect the presence and abundance of bird species in urban areas. Therefore, spectral indices can be used as proxies of the structure of urban bird communities. We estimated the abundance, taxonomic, functional, and phylogenetic diversity of the bird community at each of 18 50 m radius survey stations in the urban core area of Kavala, Greece. We also calculated eight spectral indices (means and standard deviations, SDs) around survey stations at 50 m, 200 m, and 500 m spatial scales. The land surface temperature SD (LST) was the most important proxy, positively related to bird abundance at the 50 m and 200 m spatial scales. At the same time, the mean green normalized difference vegetation index (GNDVI) was the most important proxy, negatively related to abundance at the 500 m spatial scale. Means and SDs of vegetation indices, such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI2), soil-adjusted vegetation index (SAVI), and atmospherically resistant vegetation index (ARVI), were the most important proxies, positively related to taxonomic and functional diversity at all the spatial scales. The mean and SDs of LST, normalized difference moisture index (NDMI), and normalized difference built-up index (NDBI) variously affected taxonomic and functional diversity. The mean and SDs of LST were the best proxies of phylogenetic diversity at the 50 m and 500 m spatial scales, while the SDs of NDBI and NDMI were the best proxies at the 200 m spatial scale. The results suggest that several spectral indices can be used as reliable proxies of various facets of urban bird diversity. Using such proxies is an easy and efficient way of informing successful urban planning and management. Full article

Estimating aboveground biomass (AGB) is vital for sustainable forest management and helps to understand the contributions of forests to carbon storage and emission goals. In this study, the effectiveness of plot-level AGB estimation using height and crown diameter derived from UAV-LiDAR, calibration of GEDI-L4A AGB and GEDI-L2A rh98 heights, and spectral variables derived from UAV-multispectral and RGB data were assessed. These calibrated AGB and height values and UAV-derived spectral variables were used to fit AGB estimations using a random forest (RF) regression model in Fuling District, China. Using Pearson correlation analysis, we identified 10 of the most important predictor variables in the AGB prediction model, including calibrated GEDI AGB and height, Visible Atmospherically Resistant Index green (VARIg), Red Blue Ratio Index (RBRI), Difference Vegetation Index (DVI), canopy cover (CC), Atmospherically Resistant Vegetation Index (ARVI), Red-Edge Normalized Difference Vegetation Index (NDVIre), Color Index of Vegetation (CIVI), elevation, and slope. The results showed that, in general, the second model based on calibrated AGB and height, Sentinel-2 indices, slope and elevation, and spectral variables from UAV-multispectral and RGB datasets with evaluation metric (for training: R² = 0.941 Mg/ha, RMSE = 13.514 Mg/ha, MAE = 8.136 Mg/ha) performed better than the first model with AGB prediction. The result was between 23.45 Mg/ha and 301.81 Mg/ha, and the standard error was between 0.14 Mg/ha and 10.18 Mg/ha. This hybrid approach significantly improves AGB prediction accuracy and addresses uncertainties in AGB prediction modeling. The findings provide a robust framework for enhancing forest carbon stock assessment and contribute to global-scale AGB monitoring, advancing methodologies for sustainable forest management and ecological research. Full article

Pest infestations significantly impact rice production and threaten food security. Remote sensing offers a vital tool for the non-destructive, rapid detection of rice pests. Existing studies often focus on laboratory conditions at the leaf level, limiting their applicability for precise pesticide application. Therefore, this study aimed to develop a method for detecting rice pests (rice leaf folders) in paddy fields based on unmanned aerial vehicle (UAV) hyperspectral data. Firstly, a UAV imaging system collected hyperspectral images of rice plants in both the jointing and heading stages. A total of 222 field plots for investigating rice leaf folders was established during these two periods. Secondly, 23 vegetation indices were calculated as candidates for identifying rice pests. Then, hyperspectral data and field investigation data from the jointing stage were used to construct a machine learning (extreme gradient boosting, XGBoost) algorithm for detecting rice pests. The results showed that the XGBoost model exhibited the best performance when eight vegetation indices were considered as the selected input features for model construction: the Red-edge Normalized Difference Vegetation Index (red-edge NDVI), Structure Insensitive Pigment Index (SIPI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), Soil-Adjusted Vegetation Index (SAVI), Red-edge Chlorophyll Index (CIred-edge), Pigment-Specific Simple Ratio₆₈₀ (PSSR₆₈₀), and Carotenoid Reflectance Index₇₀₀ (CPI₇₀₀). The training and testing accuracies reached 87.46% and 86%, respectively. Furthermore, the heading stage application confirmed the model’s feasibility. Thus, the XGBoost model with input features of eight vegetation indices provides an effective and reliable method for detecting rice leaf folders, supporting real-time, precise pesticide use in rice cultivation. Full article