Overweight or obesity is related to a decrease in cognitive control, especially conflict control. However, research on conflict control in overweight/obese individuals are still controversial. This study was conducted to explore general and food-related conflict control in overweight Chinese females (OWs) with a color–word Stroop task and a food-related conflict task. Event-related potentials (ERPs) were recorded during the food-related conflict task. Behavioral results showed that, OWs had a longer reaction time (RT) than normal-weight Chinese females (NWs), in both tasks. ERP results in the food-related conflict task showed that there was a reduction of N2 and N450 response strength in OWs, and the P3 and late positive component (LPC) response strength was enhanced. Results indicated that OWs might be less efficient in monitoring and resolving conflict, and OWs tended to have a higher motivational or emotional involvement in processing food-related stimuli, which was likely to contribute to their difficulty in losing weight.

A large number of studies have examined the association between depressive symptoms and sleep quality, however, the psychological mechanism underlying the association remains nebulous. Using moderated mediation analysis, the present study aimed to examine to what extent the association was mediated by rumination and whether the mediation effect was moderated by self-compassion. Self-reported measures on depressive symptoms, rumination, self-compassion, and sleep quality were collected from 564 college students. The results showed that (a) rumination mediated the association between depressive symptoms and sleep quality, and (b) self-compassion moderated the mediation effect. Specifically, the mediating effect of rumination was stronger for students with low self-compassion than those with high self-compassion. These findings suggest that selfcompassion may be a useful intervention target for health care practitioners to evaluate and improve sleep quality for individuals suffering from depressive symptoms.

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Traditional electricity price forecasting tends to adopt time-domain forecasting methods based on time series, which fail to make full use of the regional information of the electricity market, and ignore the extra-territorial factors affecting electricity price within the region under cross-regional transmission conditions. In order to improve the accuracy of electricity price forecasting, this paper proposes a novel spatio-temporal prediction model, which is combined with the graph convolutional network (GCN) and the temporal convolutional network (TCN). First, the model automatically extracts the relationships between price areas through the graph construction module. Then, the mix-jump GCN is used to capture the spatial dependence, and the dilated splicing TCN is used to capture the temporal dependence and forecast electricity price for all price areas. The results show that the model outperforms other models in both one-step forecasting and multi-step forecasting, indicating that the model has superior performance in electricity price forecasting.