A cross-sectional analysis of the relationships between anxiety sensitivity and youth irritability: the mediated roles of insomnia and selective attention for threat.

BACKGROUND: Irritability is common in multiple psychiatric disorders and is hallmark of disruptive mood dysregulation disorder. Child irritability is associated with higher risk of suicide and adulthood mental health problems. However, the psychological mechanisms of irritability are understudied. This study examined the relationship between anxiety sensitivity and irritability among youth, and further explored three possible mediated factors: selective attention for threat, delayed reward discounting, and insomnia. METHODS: Participants were 1417 students (51.7% male; mean age 13.83 years, SD = 1.48) recruited from one high school in Hunan province, China. Self-report questionnaires were used to measure irritability (The Affective Reactivity Index and The Brief Irritability Test), anxiety sensitivity (The Childhood Anxiety Sensitivity Index), selective attention for threat (The Davos Assessment of Cognitive Biases Scale-attention for threat bias subscale), insomnia (The Youth Self-Rating Insomnia Scale), and delayed reward discounting (The 27-item Monetary Choice Questionnaire). Structural equation modal (SEM) was performed to examine mediated relations. RESULTS: Anxiety sensitivity was modestly related to irritability and insomnia (r from 0.25 to 0.54) and slightly correlated with selective attention for threat (r from 0.12 to 0.28). However, there is no significant relationship of delayed rewards discounting with anxiety sensitivity and irritability. The results of SEM showed that selective attention for threat (indirect effect estimate = 0.04) and insomnia (indirect effect estimate = 0.20) partially mediate the relationship between anxiety sensitivity and irritability, which explained 34% variation. CONCLUSIONS: Anxiety sensitivity is an important susceptibility factor for irritability. Selective attention for threat and insomnia are two mediated mechanisms to understand the relationship between anxiety sensitivity and irritability.

Fault Diagnosis of Power Transformer Based on Time-Shift Multiscale Bubble Entropy and Stochastic Configuration Network.

In order to accurately diagnose the fault type of power transformer, this paper proposes a transformer fault diagnosis method based on the combination of time-shift multiscale bubble entropy (TSMBE) and stochastic configuration network (SCN). Firstly, bubble entropy is introduced to overcome the shortcomings of traditional entropy models that rely too heavily on hyperparameters. Secondly, on the basis of bubble entropy, a tool for measuring signal complexity, TSMBE, is proposed. Then, the TSMBE of the transformer vibration signal is extracted as a fault feature. Finally, the fault feature is inputted into the stochastic configuration network model to achieve an accurate identification of different transformer state signals. The proposed method was applied to real power transformer fault cases, and the research results showed that TSMBE-SCN achieved 99.01%, 99.1%, 99.11%, 99.11%, 99.14% and 99.02% of the diagnostic rates under different folding numbers, respectively, compared with conventional diagnostic models MBE-SCN, TSMSE-SCN, MSE-SCN, TSMDE-SCN and MDE-SCN. This comparison shows that TSMBE-SCN has a strong competitive advantage, which verifies that the proposed method has a good diagnostic effect. This study provides a new method for power transformer fault diagnosis, which has good reference value.