Objective: Automatic detection of auditory stimuli, represented by the mismatch negativity, facilitates rapid processing of salient stimuli in the environment. The amplitude of MMN declines with ageing. However, whether automatic detection of auditory stimuli is affected by visually perceived negative emotions with normal ageing remains unclear. We aimed to evaluate how fearful facial expressions affect the MMN amplitude under ageing.Methods: We used a modified oddball paradigm to analyze the amplitude of N100 and MMN in 22 young adults and 21 middle-aged (...) adults.Results: We found that the amplitude of N1 elicited by standard tones was smaller under fearful facial expressions than neutral facial expressions and was more negative for young adults than middle-aged adults. The MMN amplitude under fearful facial expressions was greater than neutral facial expressions, but the amplitude in middle-aged adults was smaller than in young adults.Conclusion: Visually perceived negative emotion promotes the extraction of auditory features. Additionally, it enhances the effect of auditory change detection in middle-aged adults but fails to compensate for this decline with normal ageing.Significance: The study may help to understand how visually perceived emotion affects the early stage of auditory information processing from an event process perspective. (shrink)

Presently, the State Grid Corporation of China has accumulated a large amount of maintenance records for power primary equipment. Unfortunately, most of these records are unstructured data which lead to difficultly analyze and utilize them. The emergence of natural language processing technology and deep learning methods provide a solution for unstructured text data. This paper proposes a progressive multitype feature fusion model to recognize Chinese named entity of unstructured maintenance records for power primary equipment. Firstly, the textual characteristics and word (...) separation difficulties of maintenance records are analyzed, then 7 main entity categories of power technical terms from unstructured maintenance records are chosen, and 3452 maintenance records are labeled by these categories, which is so called EPE-MR training dataset. Secondly, the standard test reports, standard maintenance, and fault analysis reports for three types of power primary equipment are employed as corpus to train character embedding in order to obtain certain words representation ability of maintenance records. After that, progressive multilevel radicals feature extraction module is designed to get detailed and fine semantic information in a hierarchical manner. Further, radicals feature representation and character embedding are concatenated and sent to BiLSTM module to extract contextual information in order to improve Chinese entity recognition ability. Moreover, CRF is introduced to handle the dependencies among prediction labels and to output the optimal prediction sequence, which can easily obtain structured data of maintenance records. Finally, comparative experiments on public MSRA dataset, China People’s Daily corpus, and EPE-MR dataset are implemented, respectively, which show the effectiveness of the proposed method. (shrink)