Authors address the gender disparity in STEM fields, examining changes in gender diversity across male-dominated undergraduate programs over 19 years at 24 top universities. Analyzing data from NCES IPEDS, it identifies STEM as persistently male-dominated but notes increasing gender diversity in many disciplines, particularly in recent years. Results indicate that higher-ranked universities in disciplines like computer science and mechanical engineering show a weak correlation with improved gender diversity, suggesting effective initiatives can mitigate the gender gap in STEM, despite ongoing challenges.
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High school students show some reluctance to COVID-19 guidelines
COVID-19 has officially been downgraded from the status of a global health emergency, but have COVID-19 safety practices become a new way of life for students? The authors collected survey data on COVID-19-related knowledge and behaviors of high-school students in Punjab, Pakistan and Santa Clara County, California, USA, so see where high-schoolers stand on pandemic safety today.
Read More...The effect of youth marijuana use on high-risk drug use: Examining gateway and substitution hypothesis
The authors looked at whether youth use of marijuana related to later high-risk drug use. Using survey data from 2010-2019 they found that youth marijuana use did correlate to an increased risk of high-risk drug use.
Read More...Identifying factors, such as low sleep quality, that predict suicidal thoughts using machine learning
Sadly, around 800,000 people die by suicide worldwide each year. Dong and Pearce analyze health survey data to identify associations between suicidal ideation and relevant variables, such as sleep quality, hopelessness, and anxious behavior.
Read More...Identification of potential therapeutic targets for multiple myeloma by gene expression analysis
A central challenge of cancer therapy is identifying treatments that will effectively target cancer cells while minimizing effects on healthy cells. To identify potential targets for treating a multiple myeloma, a frequently incurable cancer, Kochenderfer and Kochenderfer analyze RNA sequencing data from the Cancer Cell Line Encyclopedia to find genes with high expression in multiple myeloma cells and low expression in normal tissues
Read More...Contribution of environmental factors to genetic variation in the Pacific white-sided dolphin
Here the authors sought to understand the effects of different variables that may be tied to pollution and climate change on genetic variation of Pacific white-sided dolphins, a species that is currently threatened by water pollution. Based on environmental data collected alongside a genetic distance matrix, they found that ocean currents had the most significant impact on the genetic diversity of Pacific white-sided dolphins along the Japanese coast.
Read More...Pruning replay buffer for efficient training of deep reinforcement learning
Reinforcement learning (RL) is a form of machine learning that can be harnessed to develop artificial intelligence by exposing the intelligence to multiple generations of data. The study demonstrates how reply buffer reward mechanics can inform the creation of new pruning methods to improve RL efficiency.
Read More...A comparison of use of the mobile electronic health record by medical providers based on clinical setting
The electronic health record (EHR), along with its mobile application, has demonstrated the ability to improve the efficiency and accuracy of health care delivery. This study included data from 874 health care providers over a 12-month period regarding their usage of mobile phone (EPICĀ® Haiku) and tablet (EPICĀ® Canto) mEHR. Ambulatory and inpatient care providers had the greatest usage levels over the 12-month period. Awareness of workflow allows for optimization of mEHR design and implementation, which should increase mEHR adoption and usage, leading to better health outcomes for patients.
Read More...A novel encoding technique to improve non-weather-based models for solar photovoltaic forecasting
Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.
Read More...Employee resignation study in Fairfax County
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.
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