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The authors looked at variables associated with identity fraud in the US. They found that national unemployment rate and online banking usage are among significant variables that explain identity fraud.

The authors use the Census Household Pulse Survey issued by the US Census Bureau to examine the prevalence of stress and depression among people across socioeconomic statuses.

The authors looked at the presence of coliform bacteria present in ground beef after cooking it various CDC standards. They found that no coliform bacteria was present when CDC guidelines for cooking ground beef were properly followed.

This study examined the relationship between citizenship status, racial background, and the use of marijuana and cigarettes among males in California using data from the 2017–2018 California Health Interview Survey. Findings indicated that non-citizens and naturalized citizens were less likely to use marijuana compared to US-born citizens, while Asian and Latino males were less likely to consume marijuana than White males. Additionally, various racial groups were more likely to smoke cigarettes compared to White males, suggesting that targeted health interventions based on citizenship status and race could be beneficial.

The authors test different machine learning algorithms to remove background noise from audio to help people with hearing loss differentiate between important sounds and distracting noise.

This study explores how different economic sectors, geographic regions, and greenhouse gas types might affect future global mean surface temperature (GMST) anomalies differently from historical patterns. Using the Finite Amplitude Impulse Response (FaIR) model and four Shared Socioeconomic Pathways (SSPs) — SSP126, SSP245, SSP370, and SSP585 — the research reveals that future contributions to GMST anomalies.

The authors looked at the effects of commonly used anesthetics in rodents on brain activity (specifically quasi-periodic patterns). Understanding effects on brain activity is important for researchers to understand when choosing rodent models for disease.

Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.

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.

The authors explore how diversity in data sets contributes to bias in artificial intelligence.