This study tested the specificity of parent alcoholism effects on young adult alcohol and drug abuse/dependence, anxiety, and depression, and tested whether adolescent symptomatology and substance use mediated parent alcoholism effects. Participants were from a longitudinal study in which a target child was assessed in adolescence and young adulthood with structured interview measures (N = 454 families at Time 1). Results showed unique effects of parent alcoholism on young adult substance abuse/dependence diagnoses over and above the effects of other parental psychopathology. There was some evidence of parent alcoholism effects on young adult depression and of maternal alcoholism effects on young adult anxiety, although these were not found consistently across subsamples. Mediational models suggested that parent alcoholism effects could be partially (but not totally) explained by adolescent externalizing symptoms.

Many physics-based and surrogate models used in structural health monitoring are affected by different sources of uncertainty such as model approximations and simplified assumptions. Optimal structural health monitoring and prognostics are only possible with uncertainty quantification that leads to an informed course of action. In this article, a Bayesian neural network using variational inference is applied to learn a damage feature from a high-fidelity finite element model. Bayesian neural networks can learn from small and noisy data sets and are more robust to overfitting than artificial neural networks, which make it very suitable for applications such as structural health monitoring. Also, uncertainty estimates obtained from a trained Bayesian neural network model are used to build a cost-informed decision-making process. To demonstrate the applicability of Bayesian neural networks, an example of this approach applied to miter gates is presented. In this example, a degradation model based on real inspection data is used to simulate the damage evolution.