Engineering topics

Zhong, Wen

Publications and source records attributed to Zhong, Wen.

2 records

DOE OSTI2024

The effect of anxiety on sleep disorders in medical students: a moderated mediation model

The relationship between anxiety and sleep disorders is a key research topic in the academic community. However, evidence on the mechanism through which anxiety influences sleep disorders remains limited. The purpose of this study was to investigate the roles of flourishing and neuroticism in the mechanism through which anxiety influences sleep disorders in medical students. We constructed a moderated mediation model and tested the mediating role of flourishing and the moderating role of neuroticism in medical college students. The results showed that: (1) anxiety was significantly and positively related to sleep disorders and significantly and negatively related to flourishing; flourishing was significantly and negatively related to sleep disorders; neuroticism was significantly and positively related to sleep disorders; (2) flourishing had a mediation effect on the relationship between anxiety and sleep disorders; (3) neuroticism moderated the process through which flourishing mediated the effect of anxiety on sleep disorders. Our research expands the literature on the mechanism underlying the effects of anxiety on sleep disorders and provides insights into the potential prevention and intervention of sleep and emotional problems in medical students.

Yu, Chuang↗

DOE OSTI2018

Visual Analytics of Heterogeneous Data Using Hypergraph Learning

For real-world learning tasks (e.g., classification), graph-based models are commonly used to fuse the information distributed in diverse data sources, which can be heterogeneous, redundant, and incomplete. Thesemodels represent the relations in different datasets as pairwise links. However, these links cannot deal with high-order relations which connect multiple objects (e.g., in public health datasets, more than two patient groups admitted by the same hospital in 2014). In this article, we propose a visual analytics approach forthe classification on heterogeneous datasets using the hypergraph model. The hypergraph is an extension to traditional graphs in which a hyperedge connects multiple vertices instead of just two. We model various high-order relations in heterogeneous datasets as hyperedges and fuse different datasets with a unified hypergraph structure. We use the hypergraph learning algorithm for predicting missing labels in the datasets.To allow users to inject their domain knowledge into the model-learning process, we augment the traditional learning algorithm in a number of ways. In addition, we also propose a set of visualizations which enable the user to construct the hypergraph structure and the parameters of the learning model interactively during the analysis. Here, we demonstrate the capability of our approach via two real-world cases.

97 MATHEMATICS AND COMPUTING↗