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The authors looked at different treatments to clean up rainwater collected at home. They found that chlorine treatment and treatment with hydrogen peroxide catalyzed by yeast showed similar potential for cleaning up contaminated rainwater, but that further studies are needed to better assess impact on specific contaminant levels still present.

The authors looked at different combinations of risk-adjusted return measures to determine which combination would provide an optimal return for investors. They found that different combinations performed better dependent on investment timeframe.

Utilization of neural network to analyze Free Word Association to predict accurately age, gender, first language, and current country.

The discussion surrounding self-medication with non-prescription medicines has gained significance in healthcare and public health, particularly given the global increase in consumption of non-prescription drugs. This study aimed to examine the association between the frequency of general practitioner (GP) consultations and the proportion of economic resources spent on OTC medicine.

Aquaponics (the combination of aquatic plant farming with fish production) is an innovative farming practice, but the fish that are typically used, like tilapia, are expensive and space-consuming to cultivate. Medina and Alvarez explore other options test if mosquitofish are a viable option in the aquaponic cultivation of herbs and microgreens.

The authors use machine learning to develop an evidence-based detection tool for identifying human trafficking.

The authors looked at variables associated with identity fraud in the US. They found that national unemployment rate and online banking usage are among significant variables that explain identity fraud.

The authors compare the predicted versus actual geyser eruption times for the Old Faithful and Beehive Geysers at Yellowstone National Park.

The authors survey high school students regarding the frequency of microaggressions such as name mispronunciation.

This study hypothesized that a machine learning model could accurately predict the severity of California wildfires and determine the most influential meteorological factors. It utilized a custom dataset with information from the World Weather Online API and a Kaggle dataset of wildfires in California from 2013-2020. The developed algorithms classified fires into seven categories with promising accuracy (around 55 percent). They found that higher temperatures, lower humidity, lower dew point, higher wind gusts, and higher wind speeds are the most significant contributors to the spread of a wildfire. This tool could vastly improve the efficiency and preparedness of firefighters as they deal with wildfires.