The growing frequency and extent of wildfires constitute a significant environmental challenge, posing serious threats to ecosystems, biodiversity, and human livelihoods. This study presents a comprehensive wildfire susceptibility assessment for El Tarf Province, one of the most fire-prone yet understudied regions in Algeria. Long-term Landsat imagery (1995-2024) combined with four machine learning algorithms was used to produce high-resolution susceptibility maps and identify the key environmental and bioclimatic drivers of wildfire occurrence. Ten conditioning factors representing topographic, vegetative, edaphic, and climatic conditions were integrated, with elevation, Enhanced Vegetation Index (EVI), wind speed, and precipitation emerging as dominant predictors. Among the tested models, Random Forest achieved the highest predictive performance (ROC-AUC = 0.897), closely followed by XGBoost (0.896), while LightGBM provided an optimal balance between accuracy (0.875) and computational efficiency. Logistic Regression, though simpler, performed reasonably well (0.794). The Landsat-derived wildfire inventory comprised approximately 622,221 burned pixels and was subsequently split into a pre-2017 training set (72.8%) and a post-2017 testing set (27.2%) to evaluate model generalization over time. Spatial block cross-validation was applied to reduce spatial autocorrelation and enhance model generalization. This methodological framework, combining spatial and temporal validation, temporal hold-out, and spatial blocking, strengthens the robustness and reliability of wildfire susceptibility modeling. Interpretability analyses based on SHAP values, Gini importance, and permutation importance identified the contributions of underexplored variables, including vegetation type, soil type, and soil organic carbon (SOC). The resulting susceptibility maps provide valuable insights for spatial planning and ecosystem management, supporting evidence-based strategies to enhance environmental resilience and biodiversity conservation in Mediterranean landscapes.

The quality of wind power output data directly impacts the assessment of wind farm operational status and the accuracy of power forecasting models. However, due to factors such as sensor precision, communication interference, and the complex harbor environment, raw data collected from port-area wind turbines often contain noise, outliers, and missing values. Without effective cleaning, the resulting power curves can be distorted, reducing the generalization capability of predictive models. To overcome the limitations of traditional outlier detection methods in terms of adaptability and robustness, this study proposes a two-stage port-area wind power data cleaning approach based on dynamic interquartile range and an improved Sigmoid function fitting. In the first stage, an adaptive binning and density-weighting mechanism dynamically expands the interquartile range to identify and remove local outliers across different wind speed intervals. In the second stage, the cleaned wind speed–power data are subjected to secondary fitting and residual analysis using an improved Sigmoid model to detect hidden anomalies and boundary-type outliers. Using measured data from the #1 WT in the Chuanshan Port area as a case study, the experimental results demonstrate that the proposed method achieves high data retention while outperforming the conventional interquartile range, density-based spatial clustering of applications with noise and isolation forest algorithms in terms of the Pearson correlation coefficient (r = 0.93) and the coefficient of determination (R2 = 0.89), with mean squared error and root mean squared error reduced to 446.39 kW and 545.58 kW, respectively. The findings verify the efficiency, stability, and practical feasibility of the method for port-area wind power data cleaning, providing a reliable data foundation for wind power forecasting and operational optimization in port environments.

Abstract Cloud formation is due to a combination between water uptake and aerosol distribution and characteristics. Globally, the state of the terrestrial atmosphere is influenced by solar activity and galactic cosmic rays through the global electric circuit. This paper investigates the possible link between cloud cover and solar proxies, namely sunspot number (SSN), solar/plasma wind speed (PWS), and the associated interplanetary electric and magnetic fields (IEF, IMF), respectively, for two solar cycles. The seasonal variation on possible links is also investigated. Each solar driver may influence the atmospheric electricity, thus cloud formation and cloud cover. The study uses the first long-term cloud database, as provided by the International Satellite Cloud Climatology Project (ISCCP), since these data had sufficient time for validation and are ready to use as such. Solar proxies were taken from NASA’s OMNIWeb database, from measurements with instruments onboard several spacecraft with geocentric orbits. Cloud types were individually considered, and global distribution of cloud cover was analyzed. The study reveals that the cloud cover response to changes in various solar indicator depends on local conditions, and varies with season. E.g., high clouds cover exhibited anticorrelation with IEF in January on large areas, while low cloud cover was moderately positively correlated with PWS on extended regions in July.

Soil degradation due to wind erosion is a major concern in semi-arid agricultural regions, particularly in South Africa’s Overberg area. This study evaluates the effectiveness of an agroforestry windbreak composed of Eucalyptus cladocalyx F. Muell. in reducing wind speed and horizontal dust flux on a wheat farm during the fallow period. Aeolian transport was quantified by using meteorological data, dust collection with MWAC samplers, and remote sensing via aerosol optical depth. Results showed that the windbreak reduced wind speeds by up to 24%, with higher effectiveness under moderate wind conditions (<8 m·s−1) and in areas of denser vegetation. Dust transport was significantly lower on the leeward side, confirming the barrier’s mitigating influence. However, gaps within the windbreak channelled wind and elevated dust transport locally. The findings highlight agroforestry’s potential for soil protection and initiation of dust depositions in erosion-prone drylands, emphasizing the need for design optimization and broader implementation to enhance agricultural resilience under climate variability.

Asthma is a chronic respiratory condition shaped by environmental, physiological, and behavioral factors. Accurate prediction of asthma severity is vital for personalized care and reducing exacerbations. While Machine Learning (ML) has been widely explored in asthma prediction, many existing models lack generalizability, robustness, or comprehensive integration of multiple predictors, limiting their clinical applicability. This study presents a robust ML-based model for classifying asthma severity by incorporating diverse patient and environmental features. A supervised learning approach was employed using a publicly available dataset of 1,010 records with 14 features, including demographics, clinical symptoms, and environmental indicators (temperature, wind speed, and humidity). The dataset was pre-processed and stratified to balance severity classes. Three models—Decision Tree, Support Vector Machine (SVM), and Random Forest (RF) were evaluated using standard metrics: accuracy, precision, recall, F1-score, and ROC AUC. Among them, the RF model showed superior performance, achieving 96.70% accuracy, a 0.9668 F1-score, and a 0.9831 ROC AUC. Feature importance analysis highlighted environmental factors, particularly temperature and humidity, as key predictors of asthma severity. These results underscore RF's effectiveness in providing accurate, interpretable predictions and addressing limitations of earlier models. The proposed model offers a data-driven framework for real-time severity forecasting, supporting early interventions and personalized treatment. It holds promise for integration into clinical decision support systems, thereby enhancing asthma management and optimizing healthcare resource use. It is therefore recommended that the proposed model be operationalized within clinical triage protocols, embedded into electronic health record (EHR) infrastructures, or integrated into mobile health (mHealth) applications to facilitate data-driven, proactive asthma management across diverse care settings in Nigeria.

Study reveals unexpected relationship between droplet size and critical wind speed.

Shear-driven drop motion is a common phenomenon with critical implications for applications such as aircraft anti-icing, where efficient drop detachment is essential to reduce water retention, limit freezing, and suppress subsequent ice accumulation. While tangential air shear is typically associated with in-plane drop motion, it has not been considered effective for inducing detachment. Here, combining experiments, simulations, and theoretical analysis, we demonstrate that off-plane drop detachment can occur under specific conditions determined by surface wettability, drop radius, and wind speed. Notably, we identify a distinct scaling regime for the critical wind speed required for detachment as a function of drop radius, determined by the balance between lift and restoring force. Our findings advance the fundamental understanding of drop–airflow interactions and offer inspiring design strategies for efficient drop removal.

This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weather forecasting. The dataset includes processed imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI), spanning a solar cycle from May 2010 to December 2024. To ensure suitability for ML tasks, the data has been preprocessed, including correction of spacecraft roll angles, orbital adjustments, exposure normalization, and degradation compensation. We also provide auxiliary application benchmark datasets complementing the core SDO dataset. These provide benchmark applications for central heliophysics and space weather tasks such as active region segmentation, active region emergence forecasting, coronal field extrapolation, solar flare prediction, solar Extreme Ultraviolet (EUV) spectra prediction, and solar wind speed estimation. By establishing a unified, standardized data collection, this dataset aims to facilitate benchmarking, enhance reproducibility, and accelerate the development of AI-driven models for critical space weather prediction tasks, bridging gaps between solar physics, machine learning, and operational forecasting.

Source identification of sub-10nm particles through air dispersion modeling.

Wind speed, a key atmospheric parameter, results from the movement of air from high- to low-pressure regions driven primarily by temperature variations. This study modelled the wind speed (m/s) of Ikeja, Lagos State, using data from 2000 to 2020 through statistical techniques such as descriptive statistics, data visualization, and goodness-of-fit (GOF) tests, including chi-square, Kolmogorov-Smirnov, Anderson-Darling, and Cramer-von Mises analysis. Three positively skewed distributions, Gamma, Weibull, and Log-normal, were evaluated. Descriptive analysis indicated that the dataset was predominantly right-skewed (Skp > 0). The GOF results show that the Weibull distribution provides the best representation of the wind speed data (p=0.02), followed by the distributions of Log-normal and Gamma. The Weibull parameter (α > 1) further confirmed its suitability for the data. The findings suggest that Ikeja may experience higher wind speeds in the future, emphasizing the need for precautionary measures to mitigate potential damage to infrastructure and property. This study provides valuable insights for meteorologists and urban planners in anticipating and managing climate-related risks in the city.

Assessing algal blooms in Taihu Lake using hourly data: Seasonal characteristics and the relationship with environmental factors.

Incremental transfer learning based on temporal-frequency convolution interaction for multi-task prediction of wind speed and wind power.

Spatiotemporal evolution and anomaly assessment of wildfire-induced air pollution across Canada using satellite AOD analysis.

Impacts of land use/cover changes on local meteorology and air quality in the Yangtze River Delta region of China (2001-2021).

Sperm whale acoustic ecology around two sub-Antarctic islands.

In order to improve the accuracy and efficiency of resistance characteristic prediction of marine gas turbine intake system, a resistance characteristic prediction method based on component-level numerical simulation and neural network model is proposed in this paper. Aiming at the problems of high calculation cost and lack of flow field details in the traditional overall model, the intake system is divided into four parts: intake cabin, filter, muffler and intake shaft. Based on this, grid independence verification and numerical simulation are carried out respectively. The feasibility of the component-level calculation method is verified (total pressure loss relative error < 10 %, mass flow error < 0.1 %). Through the above component-level calculation method, this paper calculates the relationship between wind speed and total pressure loss under different wind directions. The BP neural network optimized by genetic algorithm is used to construct the total pressure loss surrogate model. This study provides a feasible solution for the performance prediction of marine gas turbine intake system, which has good regression and generalization performance. This study holds significance for the efficient and precise prediction of resistance characteristics in marine gas turbines.

Lipidomics profiling with instrumental variable analysis revealed distinct causal effect of exposure to ambient ozone and fine particulate matter on circulating lipids.

Seasonally adaptive data-driven ozone prediction in megacity environments.

Meteorological associations with out-of-hospital cardiac arrest: A national population-based time-series analysis.

Climatic and nutrient drivers affect long-term phytoplankton temporal trends in coastal lagoons.