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In this study, the authors investigate whether microplastics affect terrestrial plant growth and soil quality.

Here, the authors aimed to apply home soil testing to identify the cause of the growth differences between two lemon trees. They hypothesized that differences in physical and chemical soil characteristics were influencing differences in soil productivity and plant growth. Overall, the study demonstrated the effectiveness of home soil testing to characterize soils and help homeowners solve common gardening problems.

In this study, preparations of ginger were tested for an effect on the growth of four common bacterial species.

Plants in the wild compete with each other for nutrients and sunlight. Kudzu is a weed that is thought to secrete compounds that inhibit the growth of other plants. Here the authors find that certain parts of kudzu plants can block the germination of clover and dandelion seeds. These experiments may lead to a weed killer that is safe and naturally derived.

This study investigated whether thiamine (vitamin B1) could mitigate the harmful effects of lead contamination on plant growth. Using garden cress (Lepidium sativum), we found that lead exposure reduced plant growth and biomass, while thiamine treatment partially alleviated these effects, improving shoot length and overall plant length. These findings highlight the potential of thiamine as a simple strategy to improve plant resilience to heavy metal stress, with implications for agricultural productivity and food security.

The authors demonstrate computationally that two types of nanoplastics can bind to estrogen receptor beta and potentially disrupt endocrine systems.

Here the authors investigated air quality forecasting in India, comparing traditional time series models like SARIMA with deep learning models like LSTM. The research found that SARIMA models, which capture seasonal variations, outperform LSTM models in predicting Air Quality Index (AQI) levels across multiple Indian cities, supporting the hypothesis that simpler models can be more effective for this specific task.

This study explores how to predict and minimize distortion in 3D printed parts, particularly when using affordable PLA filament. The researchers developed a model using a gradient boosting regressor trained on 3D printing data, aiming to predict the necessary CAD dimensions to counteract print distortion.

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.

Digital compasses are essential in technology that we use in our everyday lives: phones, vehicles, and more. Li and Liu address the accuracy of these devices by presenting a new algorithm for accurately calibrating low-cost magnetometers.