Although insecure adult attachment is thought to be associated with depressive symptoms, results of research on the link between attachment dimensions (attachment anxiety and attachment avoidance) and depressive symptoms have been inconsistent. The aim of this study was to use meta-analysis to examine the strength of the correlations between the two attachment dimensions and depressive symptoms. A total of 64 papers and 78 independent samples were included in this meta-analysis. Results showed significant associations between both attachment dimensions and depressive symptoms. Additional analyses indicated that attachment anxiety was more strongly related to depressive symptoms, whereas attachment avoidance was weakly related to depressive symptoms. Cultural orientation and sex were found to moderate the relationship between attachment anxiety and depressive symptoms. Age was a significant moderator of the relationships between both attachment anxiety and avoidance and depressive symptoms. Results of the meta-analysis and the implications were discussed.

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Interpersonal trust is significant in the development of society. Attachment theory provides a framework for understanding the trust-building process. Although many studies have investigated the association between attachment and interpersonal trust, controversy still exists regarding their correlation, and the separate effects of attachment anxiety and avoidance on interpersonal trust. This meta-analysis summarized the combined relationship between both attachment dimensions and interpersonal trust in adults by including 53 articles reporting 149 effect sizes (N = 45,166). The results revealed that both attachment dimensions were negatively, concurrently, and longitudinally associated with interpersonal trust. Further, subgroup analyses indicated that attachment avoidance was more strongly related to interpersonal trust than attachment anxiety (F(1,291) = 459.568, p < .001). The effect sizes varied across cultures, trust figures, and sample sizes. The effect sizes between attachment anxiety (r=-.038) and attachment avoidance (r=-.15) on interpersonal trust in Chinese studies were both lower than in Western studies. Our results highlight the importance of attachment security in interpersonal relationships.

The influences of levels of processing at the encoding phase, misleading intensity at the misleading phase and warning at the retrieval phase on memory suggestibility were explored through the delayed retrieval errors paradigm, and the contributions of dynamic automatic processing and intentional processing to generation of memory suggestibility were deeply assessed through the Remember/Know/Guess paradigm. The results showed that levels of processing were an important influencing factor for memory suggestibility. The intentional processing at the encoding phase benefited the generation of memory suggestibility more than the automatic processing. The misleading intensity had a significant effect, where the misled-generate items were recalled at a higher level with a longer response time than the misled-read items. In addition, the warning increased both the memory suggestibility of misleading pictures and the memory accuracy of non-misleading pictures. This memory suggestibility was jointly caused by the automatic processing and intentional processing, but mainly intentional processing, and the warning increased the probability of intentional processing during the generation of memory suggestibility.

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Due to lack of training samples, overfitting is a severe problem in fault diagnosis for mechanical devices, especially for rotating machinery. In this paper, a graph neural network (GNN) method with one-shot learning is proposed for fault diagnosis of rotating machinery. Convolutional Neural Network (CNN) is applied to extract the feature vectors and generate codes for one-shot learning. With adjacency matrix in GNN, the proposed method can achieve fault classification for rotating machinery with small dataset. Rotate vector (RV) reducer of the industrial robot and bearing of the rotating shaft were chosen as experimental subjects. Experimental results show the high accuracy of classification in both experiments with the proposed method. To further verify the efficiency of this method, Siamese Net, Matching Net and SAE+RF were chosen as the comparisons. The results indicate the proposed method outperforms all the selected methods for fault classification in both rotating machineries.