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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.

Irrespective of the final application of a molecule, synthetic accessibility is the rate-determining step in discovering and developing novel entities. However, synthetic complexity is challenging to quantify as a single metric, since it is a composite of several measurable metrics, some of which include cost, safety, and availability. Moreover, defining a single synthetic accessibility metric for both natural products and non-natural products poses yet another challenge given the structural distinctions between these two classes of compounds. Here, we propose a model for synthetic accessibility of all chemical compounds, inspired by the Central Limit Theorem, and devise a novel synthetic accessibility metric assessing the overall feasibility of making chemical compounds that has been fitted to a Gaussian distribution.

Sequence accessibility is an important factor affecting gene expression. Sequence accessibility or openness impacts the likelihood that a gene is transcribed and translated into a protein and performs functions and manifests traits. There are many potential factors that affect the accessibility of a gene. In this study, our hypothesis was that the content of nucleotides in a genetic sequence predicts its accessibility. Using a machine learning linear regression model, we studied the relationship between nucleotide content and accessibility.

In this study, the authors address potential reasons why employees may voluntarily resign. This is in response to the currently observed economic trend The Great Resignation. Through analysis of federal and local government data along with survey results from Fairfax County, they concluded that adding additional benefits will help companies retain talented empolyees.

Here, recognizing that years of training in saber fencing could expectedly result in optimized movements that result in elite skill levels, the authors used motion tracking and statistical analysis to assess the difference in velocity and blade tip velocity of novice and elite fencers during a vertical blade thrust. They found statistically significant differences in blade tip velocity and elbow joint angle kinematics.

The authors explore sex-specific differences in the formation of fear memories across several rat brain regions.

Alzheimer’s disease (AD) is a common disease affecting 6 million people in the U.S., but no cure exists. To create therapy for AD, it is critical to detect amyloid-β protein in the brain at the early stage of AD because the accumulation of amyloid-β over 20 years is believed to cause memory impairment. However, it is difficult to examine amyloid-β in patients’ brains. In this study, we hypothesized that we could accurately predict the presence of amyloid-β using EEG data and machine learning.

The authors found that SARIMAX-GARCH is more accurate than SARIMAX for load forecasting with respect to energy consumption.

Pillai et al. look at whether exposure to Schistosoma mansoni, a parasitic blood fluke, has any relation to peanut allergies. They found that cockroaches exposed to an antigen found in S. mansoni eggs exhibited an allergic reaction to peanuts.

In this study, the authors assess the factors that allow some speedcubers to solve Rubik's Cubes faster than others.