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Domestic waste classification using convolutional neural network

In this study, the authors investigate whether Moringa Oleifera seeds can serve as material to aid in purifying water. M. oleifera seeds have coagulating properties and the authors hypothesized that including it in a water filtration system would reduce particles, specifically bacteria, in water. Their results show that this system removed the largest percent of bacteria. When used in combination with cilantro, it was actually more efficient than the other techniques! These findings have important implications for creating better and more economical water purification systems.

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!

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.

The authors looked at the ability of Spirulina platensis to reduce contaminants in wastewater in order to develop a more accessible treatment option. They found that S platensis did reduce the concentration of pollutants present within simulated agricultural wastewater.

The authors investigate whether air quality impacts solar production.

Soil stores three times more carbon than the atmosphere, making small changes in its storage and release crucial for carbon cycling and climate models. This study examined the impact of the 2020 California Silverado Fire on pyrogenic carbon (PyC) deposits using nitrogen and carbon isotopes as proxies. While the results showed significant variability in δ¹⁵N, δ¹³C, total carbon, and total nitrogen across sites, they did not support the hypothesis that wildfire increases δ¹⁵N while keeping δ¹³C constant, emphasizing the need for location-based controls when using δ¹⁵N to track PyC.

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.

The authors compare rates of renewable energy adoption between states that historically vote for democrats versus republicans in presidential elections.