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Objective: To examine the effect of stereotype threats on negative basic and self-conscious emotions among university students. Methods: Using gender-mathematics stereotype threat as the threatening scenario, this research recruited female university students who were not majoring in mathematics or psychology. Experiment 1 (n=162) manipulated stereotype threat, while Experiment 2 manipulated self-concept threat (Experiment 2a, n = 143) and group-concept threat (Experiment 2b, n 146), respectively, based on the multi-threat framework. Negative basic emotions (anger/irritable, fear) and self-conscious emotions (shame, guilt) were assessed. Results: Participants in the stereotype threat group demonstrated higher scores in negative basic and self-conscious emotions compared to those in the non-stereotype threat group (Ps <0.05). Individuals in the self-concept threat group exhibited higher scores in negative basic and self-conscious emotions than those in the no self-concept threat group (Ps <0.05). Individuals in the group-concept threat group displayed lower scores in negative basic and self-conscious emotions compared to those in the no group-concept threat group (Ps <0.05). Conclusion: This study suggests that the emotional effects of stereotype threat may vary depending on the type of threat. [ABSTRACT FROM AUTHOR]

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Influence maximization of social network is an optimization problem of finding a set of initial propagation nodes in the network such that the influence range invoked by these nodes is maximized. Most of the existing works have focused on single relation social networks, namely, there is only one relationship in social networks, but in reality, there are a variety of relationships between users of social networks, and these relationships affect the propagation of network information and the influence scope. This paper proposed the MRLT information propagation model, which based on the linear threshold model, combined with the various relationships existing between network nodes, and modeled the influence between nodes. And it proposed the MR-RRset algorithm based on reverse reachable set to solve the problem of low computational performance caused by greedy algorithm in the process of traditional influence maximization research. Finally, the experimental comparison on real data sets demonstrates that the proposed method has better influence propagation range and larger performance improvement. [ABSTRACT FROM AUTHOR]