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The agricultural sector of any country plays a pivotal role in its economy. Irrigation and the provision of appropriate nutrient levels in soil are essential for optimizing plant growth and enhancing crop productivity. To support the increasing need for food due to the growing population worldwide, synthetic fertilizers have been widely used in the agricultural sector.

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Continuous Near-Bed Movements of Microplastics in Open Channel Flows: Statistical Analysis.

Environ Sci Technol

January 2025

Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada.

The ubiquitous distribution of microplastics (MPs) in aquatic environments is linked to their transport in rivers and streams. However, the specific mechanism of bedload microplastic (MP) transport, notably their stochastic behaviors, remains an underexplored area. To investigate this, particle tracking velocimetry was employed to examine the continuous near-bed movements of four types of MPs under nine setups with different experimental conditions in a laboratory flume, with an emphasis on their streamwise transport.

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Autism spectrum disorder (ASD) is a globally recognized neurodevelopmental condition characterized by repetitive and restrictive behavior, persistent deficits in social interaction and communication, mental disturbances, etc., affecting approximately 1 in 100 children worldwide. A combination of genetic and environmental factors is involved in the etiopathogenesis of the disease, but specific biomarkers have not yet been identified.

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Objective: Asthma poses a significant health burden in South Asia, with increasing incidence and mortality despite a global decline in age-standardized prevalence rates. This study aims to analyze asthma trends from 1990 to 2021, focusing on prevalence, incidence, mortality, and disability-adjusted life years (DALYs) across South Asia. The study also assesses the impact of risk factors like high body mass index (BMI), smoking, and occupational exposures on asthma outcomes.

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IntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.

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