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This study examined the mediating effect of psychological capital in the relationship between job stress and burnout of psychiatric nurses. The participants were 108 psychiatric nurses working in three psychiatric hospitals located in South Korea. Data were collected from 10 August to 15 September 2018 using self-report questionnaires. Data were analyzed using descriptive statistics, t-test, one-way ANOVA, Pearson’s correlation coefficient, and multiple linear regression by IBM SPSS 24.0 program. In addition, a bootstrapping test using the SPSS PROCESS macro was conducted to test the statistical significance of the mediating effect. There was significant correlation between job stress, psychological capital, and burnout. Psychological capital showed partial mediating effects in the relationship between job stress and burnout. Job stress explained 29.7% of the variance in burnout, and the model including job stress and psychological capital explained 49.6% of the variance in burnout. The bootstrapping showed that psychological capital was a significant sub-parameter and decreased job stress and burnout (LLCI = −0.1442, ULCI = −0.3548). These findings suggest that psychiatric nurses’ burnout can be reduced by implementing various health care programs designed to increase psychological capital.

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Random walks on graphs are often used to analyse and predict epidemic spreads and to investigate possible control actions to mitigate them. In this study, we first show that models based on random walks with a single stochastic agent (such as Google’s popular PageRank) may provide a poor description of certain features of epidemic spread: most notably, spreading times. Then, we discuss another Markov chain based method that does reflect the correct mean infection times for the disease to spread between individuals in a network, and we determine a procedure that allows one to compute them efficiently via a sampling strategy. Finally, we present a novel centrality measure based on infection times, and we compare its node ranking properties with other centrality measures based on random walks. Our results are provided for a simple SI model for epidemic spreading.

In this paper, we determine a formula for Kemeny's constant for a graph with multiple bridges, in terms of quantities that are inherent to the subgraphs obtained upon removal of all bridges and that can be computed independently. With the formula, we consider several optimization problems for Kemeny's constant for graphs with bridges, and we remark on the computational benefit of this formula for the computation of Kemeny's constant. Finally, we discuss some potential applications.

In spectral bisection, a Fielder vector is used for partitioning a graph into two connected subgraphs according to its sign pattern. In this article, we investigate graphs having Fiedler vectors with unbalanced sign patterns such that a partition can result in two connected subgraphs that are distinctly different in size. We present a characterization of graphs having a Fiedler vector with exactly one negative component, and discuss some classes of such graphs. We also establish an analogous result for regular graphs with a Fiedler vector with exactly two negative components. In particular, we examine the circumstances under which any Fiedler vector has unbalanced sign pattern according to the number of vertices with minimum degree.