Browse Articles

Domestic waste classification using convolutional neural network

In this study, the authors address the current climate concern of high CO₂ levels by testing solid forms of hydroxide for CO₂ reduction and designing a drone to fly it in ambient air!

The authors investigated the impact of light pollution on astrophotography.

This manuscript investigates the urban heat island (UHI) effect by utilizing two satellite datasets: Landsat (high spatial resolution, lower temporal resolution) and MODIS (lower spatial resolution, high temporal resolution). The authors hypothesized that Landsat would provide better spatial detail, while MODIS would better capture temporal variations. Their analysis in the Washington D.C.–Baltimore region supports these hypotheses, demonstrating that Landsat offers finer spatial details, whereas MODIS provides more consistent seasonal patterns and better detects heatwave frequencies.

This study investigates PFAS contamination in Santa Clarita groundwater, focusing on potential sources. The study employs statistical analysis to assess data quality and trends which allowed them to identified domestic waste, fire extinguisher materials, and food packaging as the most likely sources of contamination.

The authors looked at how nanosilver particles may negatively impact the pond water environment. They used D. magna as an indicator species to look at the impact of different concentrations of nanosilver particles.

The authors studied the ability of deep learning models to predict droughts in the midwestern United States.

The authors looked at the impact of different harvest and feeding treatments on Heterandria formosa over three generations as a model for changes in marine ecosystems.

Pediatric asthma remains a significant health issue for Dallas students. This study examined the relationship between microclimatic parameters, vegetation, and pediatric asthma vulnerability (PAV) in urban schools.

Here the authors investigated the use of an affordable gas sensor to detect volatile organic compound (VOC) emissions as an early indicator of tree disease, finding statistically significant differences in VOCs between diseased and non-diseased ash, beech, and maple trees. They suggest this sensor has potential for widespread early disease detection, but call for further research with larger sample sizes and diverse locations.