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Objectives: Sleep deprivation (SD) has detrimental effects on cognition. Emotional processing, a critical component of social cognition, is also affected by SD. However, current research on how SD affects emotion recognition and the specific emotion recognition that declines with SD is inconsistent. The present study meta-analyzed results of studies examining emotion category recognition changes in SD compared with controls. Methods: PubMed, Web of Science, Embase, PsychINFO, MEDLINE, and China National Knowledge Infrastructure databases were searched. Studies on the impact of acute SD or insomnia on emotional recognition and participants aged 18 years or older were included in this review. The JBI Critical Appraisal Checklist and GRADE approach were used to assess the quality of the studies and evidence. Results: Twelve studies with 414 SD and 399 control participants were included in this meta-analysis. The SD group performed poorer on facial sadness (MD = –4.35; 95% CI, –7.99 to –0.71) and happiness (MD = –1.75; 95% CI, –3.25 to –0.26) recognition than the control group (normal sleep condition). The reaction time of the SD group was significantly longer than that of the control group for all emotional categories. The intensity rating of facial emotions showed no difference between the two groups. Conclusions: Sleep deprivation slows individuals’ reactions in facial emotion recognition tasks and weakens their ability to recognize sadness and happiness. Future studies should identify the effects of SD, SD duration, and recovery time on different types of emotion recognition.

The photosynthesis process of plant requires fast removal of surface contaminant of leaves. The removal of surface contaminants is highly related to the drainage capacity of the leaf. This paper conducted a comprehensive study on the drainage capacity of plant leaves. The effects of both leaf shape and leaf hydrophilicity/hydrophobicity are investigated. This paper uses Computational Fluid Dynamics (CFD) technology to simulate the drainage process of plant leaves. The numerical simulation results indicate that hydrophilic leaves requires the sharp parts on the edges to remove the water. On the other hand, the drainage capacity of hydrophobic leaves is independent of the shape of the leaves.

The safe operation of smart distribution network is highly dependent on the powerful technical guarantee provided by the function of information link, which makes the network vulnerable to the threat of malicious data injection and other network attacks during the operation. In order to ensure that this kind of malicious data injection attack can be detected sensitively in the operation of power grid, this paper proposes a kind of power system state estimation malicious data attack defense model based on historical data. Firstly, the Long Short-Term Memory(LSTM) network is trained with the historical state quantity to realize the state prediction model. The prediction results are used as a reference, and the deviation between the prediction and the real-time estimate is calculated to break the concealment of malicious data. Simulation results of IEEE33-bus power system verify the accuracy of prediction and the effectiveness of the proposed method for online detection of hidden malicious data.

This paper introduces the working principle of a lifting mechanism and analyzes the end factors of the fault using the fault tree analysis method [1]. This paper also conducts strength analysis and physical and chemical analysis of the fractured supporting head, and determines that the cracks generated on the component surface due to the defect is the main cause of the supporting head fracture. Based on this, this paper proposes relevant solutions to improve the safety of the lifting mechanism.