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Safety-seeking has fueled the growth of charter schools. Descriptive evidence suggests different possible factors underlying safety in charter schools. This study investigates characteristics media...

With the rapid expansion of higher education, educational meritocracy has received both applause and skepticism among scholars, citizens, and policy-makers. Focusing on China’s urban-rura...

Neurons exhibit complex geometry in their branched networks of neurites which is essential to the function of individual neuron but also brings challenges to transport a wide variety of essential materials throughout their neurite networks for their survival and function. While numerical methods like isogeometric analysis (IGA) have been used for modeling the material transport process via solving partial differential equations (PDEs), they require long computation time and huge computation resources to ensure accurate geometry representation and solution, thus limit their biomedical application. Here we present a graph neural network (GNN)-based deep learning model to learn the IGA-based material transport simulation and provide fast material concentration prediction within neurite networks of any topology. Given input boundary conditions and geometry configurations, the well-trained model can predict the dynamical concentration change during the transport process with an average error less than 10% and $$120 \sim 330$$ 120 ∼ 330 times faster compared to IGA simulations. The effectiveness of the proposed model is demonstrated within several complex neurite networks.

Sociological research examines the gender gap reversal in higher education and the gender division in paid and unpaid labor for adult women, especially “the second shift literature,” as two distinct topics. In this study, we extend the insights of the second shift literature to research on youth labor and adolescents’ enrollment in higher education. Using data from the Youth Development Study from 1988 to 1992, we find that the negative association of unpaid labor with adolescents’ college enrollment odds was at least as large as, if not greater than, that of paid labor. Although labor engagement had adverse impacts both for female and male adolescents during this time, the negative associations of youth labor with college enrollment were more pronounced for male students. We discuss the implications of these findings and explain their relevance to more contemporary cohorts of high school students in the conclusion.