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.

In this study, the authors investigate the effect of disorganization and messiness on high school students' ability to perform well on a standardized test.

Water scarcity affects upwards of a billion people worldwide today. This project leverages the potential of capturing humidity to build a high-efficiency water condensation device that can generate water and be used for personal and commercial purposes. This compact environment-friendly device would have low power requirements, which would potentially allow it to utilize renewable energy sources and collect water at the most needed location.

In this study, the authors looked at a proto-oncogene, KRAS, and searched for molecules that are predicted to be able to bind to the inactive form of KRAS. They found that a modified version of Irbesartan, a derivative of benzimidazole, showed the best binding to inactive KRAS.

The authors looked at using genetic algorithms to look at the Indian labor market and what features might best explain any variation seen. They found that features such as economic growth and household consumption, among others, best explained variation.

The purpose of this study was to test the anti-cancer properties and pro-apoptotic effects of the polyherbal formulation MAT20 as a complementary treatment. Moringa oleifera (Moringa), Phyllanthus emblica (Amla) and Ocimum sanctum (Tulsi), these 3 herbs were used to formulate MAT20, which contain phytochemicals that are known to display anti-cancer properties. In this study, we hypothesized that MCF-7 breast cancer cells treated with MAT20 would show increased cytotoxicity compared to its individual plant extracts.

The purpose of the study was to determine whether graph-based machine learning techniques, which have increased prevalence in the last few years, can accurately classify data into one of many clusters, while requiring less labeled training data and parameter tuning as opposed to traditional machine learning algorithms. The results determined that the accuracy of graph-based and traditional classification algorithms depends directly upon the number of features of each dataset, the number of classes in each dataset, and the amount of labeled training data used.

In this study, the authors investigate whether a new compound has anti-cancer properties. Using the crude extract from the Amaranthus spinosus plant, HeLa cancer cells were assessed for cell death. Findings reveal that the extract (AS20) has cytotoxic effects on HeLa cells. Their findings introduce a new compound to potentially pursue in the hunt for novel cancer treatments.

Here, recognizing the effects of the COVID-19 pandemic on young peoples' mental health and wellbeing the authors used an online survey which included the short General Health Questionnaire (GHQ-12) to probe 102 young adults. Overall they found that young adults perceived the pandemic to be detrimental to many areas of their wellbeing, with females and those aged 18-19 and 22-23 reporting to be the most significantly impacted.

Naturally occurring neuroactive alkaloids are often studied for their potential to treat Neurological diseases. This team of students study Rivastigmine, a potent cholinesterase inhibitor that is a synthetic analog of physostigmine, which comes from the Calabar bean plant Physostigma venenosum. By comparing the effects of optimized synthetic analogs to the naturally occurring alkaloid, they determine the most favorable analog for inhibition of acetylcholinesterase (AChE), the enzyme that breaks down the neurotransmitter acetylcholine (ACh) to terminate neuronal transmission and signaling between synapses.