Browse Articles

The authors examined the relationships between S&P 500 Technology and Financials equity sectors and their corresponding investment-grade fixed-income markets from 1995 to 2025.

This article explored the question of when do people decide to stop gambling and further tries to extrapolate why people stop gambling at that point. Their study showed that people tend to quit gambling after 4 consecutive losses, significantly more than 1-3 consecutive losses or a win previous to quitting. They also found that participants commonly quit at a point value approximately 5 points greater than or less than their starting balance. The authors concluded that these results may be important in understanding how to cut down on excessive gambling or in creating policies that make it easier for people to disengage from gambling.

The authors looked at the effects of different treatments using flies with neurodevelopmental disorders.

Lung cancer is highly fatal, largely due to late diagnoses, but early detection can greatly improve survival. This study developed three models to enhance early diagnosis: an MLP for clinical data, a CNN for imaging data, and a hybrid model combining both.

Building on previous work by earlier astronomers, the authors investigate the correlation between the density and size of celestial objects in the universe, including neutron stars, galaxies, and galaxy clusters.

The authors investigated whether the physician-patient relationship affected patient perceptions and treatment adherence.

The luxury market has grown through digital marketing, with many brands benefiting from exclusivity, which limits product availability. While exclusivity boosts sales, it may negatively affect consumers' mental health. This study of 129 respondents explored the main motivation for purchasing luxury goods and connection to mental health.

The authors investigate whether air quality impacts solar production.

The authors looked at potential downstream effects of raising the minimum wage. Specifically they focused on taxable wages, employment, and firm counts.

The authors use machine learning to develop an evidence-based detection tool for identifying human trafficking.