Articles

<b>Background: </b>Parental employment is a significant social determinant of children's developmental outcomes, shaping their cognitive and behavioral trajectories. However, the effects of parental employment may not be equally protective across racial groups. The Minority Diminished Returns (MDRs) framework suggests that socioeconomic status (SES) factors, such as employment, yield fewer protective benefits for Black families compared to White families. <b>Objective: </b>This study investigates the diminished returns of parental employment on trauma and associated neurocognitive and behavioral outcomes in children, with a focus on racial variation in these effects. <b>Methods: </b>Using data from the Adolescent Brain Cognitive Development (ABCD) study, a large and diverse sample of children was analyzed. We applied MDRs theory and social determinants of health frameworks to examine the association between parental employment, trauma, and children's cognitive and behavioral outcomes. The analysis controlled for family SES, neighborhood factors, and racial group differences. <b>Results: </b>Preliminary findings suggest that while parental employment is generally protective against trauma, the strength of this association is diminished for Black children. Black families with employed parents experience higher levels of trauma and stress compared to their White counterparts, which may contribute to racial disparities in cognitive and behavioral outcomes. <b>Conclusion: </b>Parental employment may not equally buffer against trauma-related risks for Black children, reflecting the broader pattern of diminished returns for racially disadvantaged groups. These findings highlight the need for policies addressing the unequal benefits of SES across racial groups.

Machine Learning (ML) and Artificial Intelligence (AI) are having an increasingly transformative impact on all industries and are already used in many mission-critical use cases in production, bringing considerable value. Data engineering, which combines ETL pipelines with other workflows managing data and machine learning operations, is also significantly impacted. The Intelligent Data Engineering and Automation framework offers the groundwork for intelligent automation processes. However, ML/AI are not the only disruptive forces; new Big Data technologies inspired by Web2.0 companies are also reshaping the Internet. Companies having the largest Big Data footprints not only provide applications with a Big Data operational model but also source their competitive advantage from data in the form of AI services and, consequently, impact the cost/performance equilibrium of ETL pipelines. All these technologies and reasons help explain why the traditional ETL pipeline design should adapt to current and emerging technologies and may be enhanced through artificial intelligence.