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The authors looked at unionization petitions from Starbucks workers between August 2021 and July 2024 to determine what factors influence votes for or against unionization.

In cognitive psychology, typed responses are used to assess thinking skills and creativity, but research on factors influencing typing speed is limited. This study examined how language familiarity affects typing speed, hypothesizing that familiarity with a language would correlate with faster typing. Participants typed faster in English than Latin, with those unfamiliar with Latin showing a larger discrepancy between the two languages, though Latin education level did not significantly impact typing speed, highlighting the role of language familiarity in typing performance.

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

Pay is a widely debated factor in workplace motivation, influencing both incentives and job satisfaction. This work analyzed employee reviews across various industries to examine the relationship between pay importance and job satisfaction. Findings suggest that job satisfaction decreases as the importance of pay increases, particularly in construction, food, and finance industries, as well as among entry-level and experienced workers, though the results were not statistically significant.

Here recognizing the growing amount of plastic waste in the oceans, the authors sought to develop and test laser imaging for the identification of waste in water. They found that while possible, limitations such as increasing depth and water turbidity result in increasing blurriness in laser images. While their image processing methods were somewhat insufficient they identified recent methods to use deep learning-based techniques as a potential avenue to viability for this method.

The authors look at malnutrition in children and how socio-economic factors impact this.

Pediatric cancers pose unique challenges due to their rarity and distinct biological factors, emphasizing the need for accurate survival prediction to guide treatment. This study integrated generative AI and machine learning, including synthetic data, to analyze 9,184 pediatric cancer patients, identifying age at diagnosis, cancer types, and anatomical sites as significant survival predictors. The findings highlight the potential of AI-driven approaches to improve survival prediction and inform personalized treatment strategies, with broader implications for innovative healthcare applications.

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

The authors analyzed racial and ethnic representation in studies on PFAS and neurological health outcomes.

The authors examine the relationships between race, racist beliefs, and perceptions of beauty across cultures and generations.