Is Peer Victimization Associated with Higher Online Trolling among Adolescents?The Mediation of Hostile Attribution Bias and the Moderation of Trait Mindfulness

Background:In recent years,online trolling has garnered significant attention due to its detrimental effects on mental health and social well-being.The current study examined the influence of peer victimization on adolescent online trolling behavior,proposing that hostile attribution bias mediated this relationship and that trait mindfulness moderated both the direct and indirect effects.Methods:A total of 833 Chinese adolescents completed the measurements of peer victimization,hostile attribution bias,trait mindfulness,and online trolling.Moderated mediation analysis was performed to examine the relationships between these variables.Results:After controlling for gender and residential address,the study found a significant positive correlation between peer victimization and online trolling,with hostile attribution bias serving as a mediator.In addition,trait mindfulness moderated the direct relationship between peer victimization and online trolling.Specifically,the effect of peer victimization on online trolling was attenuated when adolescents had high levels of trait mindfulness.The results of the study emphasized the joint role of peer and personal factors in adolescents’online trolling behavior and provide certain strategies for intervening in adolescents’online trolling behavior.Conclusion:The results of the study suggest that strategies focusing on peer support and mindfulness training can have a positive impact on reducing online trolling behavior,promoting adolescents’mental health,and their long-term development.

Quantifying the attribution of model bias in simulating summer hot days in China with IAP AGCM 4.1

Using lAP AGCM simulation results for the period 1961-2005, summer hot days in China were calculated and then compared with observations. Generally, the spatial pattern of hot days is reasonably reproduced, with more hot days found in northern China, the Yangtze and Huaihe River basin, the Chuan-Yu region, and southern Xinjiang. However, the model tends to overestimate the number of hot days in the above-mentioned regions, particularly in the Yangtze and Huaihe River basin where the simulated summer-mean hot days is 13 days more than observed when averaged over the whole region, and the maximum overestimation of hot days can reach 23 days in the region. Analysis of the probability distribution of daily maximum temperature （Trnax） suggests that the warm bias in the model-simulated Tmax contributes largely to the overestimation of hot days in the model. Furthermore, the discrepancy in the simulated variance of the Tmax distribution also plays a non- negligible role in the overestimation of hot days. Indeed, the latter can even account for 22% of the total bias of simulated hot days in August in the Yangtze and Huaihe River basin. The quantification of model bias from the mean value and variability can provide more information for further model improvement.