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This article describes the classification of medical text data using vector databases and text embedding. Various large language models were used to generate this medical data for the classification task.

The authors examine a relationship between tweet sentiment and stock market behavior during the early weeks of the COVID-19 pandemic.

This study utilizes machine learning models to predict missing and unclear signs from the Indus script, a writing system from an ancient civilization in the Indian subcontinent.

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.

Here the authors investigated the motivation of a short squeeze of GameStop stock where users of the internet forum Reddit drove a sudden increase in GameStop stock price during the start of 2021. They relied on both qualitative and quantitative analyses where they tracked activity on the r/WallStreetBets subreddit in relation to mentions of GameStop. With these methods they found that while initially the short squeeze was driven by financial motivations, later on emotional motivations became more important. They suggest that social phenomena can be dynamic and evolve necessitating mixed method approaches to study them.

The authors looked at the ability of machine learning algorithms to interpret language given their increasing use in moderating content on social media. Using an explainable model they were able to achieve 81% accuracy in detecting fake vs. real news based on language of posts alone.

The authors looked at fertilizer derived from coffee ground tea leaves, measuring the effectiveness by measuring the height, weight, and number of leaves on basil plants.

Seeking to investigate eco-friendly biological methods to control weeds and enhance food crop yields, here the authors considered the effects of three essential oils on seed germination and radicle length of both a weed and a common crop. They found that treatment with turmeric oil had phytotoxic potential, leading to a reduction in both seed germination and radicle length of the weed. In contrast, ginger oil possessed allelopathic properties towards both. The authors suggest that essential oils could be used as eco-friendly bio-herbicides.

Some spices have antimicrobial or antibacterial properties that people have already tested. Turmeric has a wide variety of uses and has even been implemented in alternative medicine as a treatment for cancer, inflammation, osteoarthritis, and other diseases. We tested the antimicrobial effects of turmeric under two different pHs to characterize this effect in vitro. Decreasing the pH of a solution of turmeric may increase antibacterial properties.

In this study, the authors investigate the disinfectant potential of many household spices and minerals. More specifically, they test whether these compounds can be used to disinfect mobile phones after daily use with the hope of identifying environmentally-friendly cleaning options.