A longitudinal examination of factors predicting maternal permissiveness toward underage student drinking across the first three years of college.

Parental permissiveness of drinking is a reliable predictor of college drinking, but there is little known about factors that predict such permissiveness. This study seeks to examine factors that predict two potentially different facets of permissiveness: perceived general approval of alcohol use and perceived drinking limits. Additionally, we explored how these facets mediate the relationship between the predictors of permissiveness and subsequent college drinking and related consequences.

Serious harm reduction protective behavioral strategies reduce consequences associated with alcohol-induced blackouts in college students.

Objective: Alcohol-induced blackouts (AIBs) have been associated with increased alcohol-related consequences. Serious harm reduction (SHR) protective behavioral strategies may reduce consequences when students are drinking heavily. We examined whether SHR weakened the relationship between AIBs and a) total consequences and b) serious consequences (e.

QCD challenges from pp to AA collisions: 4th edition.

This paper is a write-up of the ideas that were presented, developed and discussed at the fourth International Workshop on QCD Challenges from pp to AA, which took place in February 2023 in Padua, Italy. The goal of the workshop was to focus on some of the open questions in the field of high-energy heavy-ion physics and to stimulate the formulation of concrete suggestions for making progresses on both the experimental and theoretical sides. The paper gives a brief introduction to each topic and then summarizes the primary results.

Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices.

Introduction: Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices in , and is crucial. In this work, we summarize and strictly adhere to such practices to ensure reproducible and reliable ML.