Study on International Students' Anxiety and Related Factors Under COVID-19

To investigate the correlation between anxiety and related factors among international students in Wenzhou during the COVID-19 epidemic,international students from Wenzhou were selected as subjects for our research.We administered a self-developed questionnaire on anxiety among our subjects in question during the specific time of the COVID-19 epidemic,in which a self-assessment scale was included.Overall,an anxiety questionnaire for international students studying in Wenzhou during the outbreak of COVID–19,a self-rating anxiety scale,and statistical methods were utilized to conduct our research.During the COVID-19 epidemic,international students in Wenzhou experienced varying degrees of anxiety,which were related to concerns about contracting the virus,exam-related stress,and differences in living standards.Therefore,intervention is crucial.

A Chengjiang-type fossil assemblage from the Hongjingshao Formation(Cambrian Stage 3) at Chenggong, Kunming, Yunnan

A new Chengjiang-type fossil assemblage is reported herein from the lower part of the Hongjingshao Formation at Xiazhuang village of Chenggong,Kunming,Yunnan.The fossil assemblage,named as Xiazhuang fossil assemblage,yields predominantly soft-bodied fossils,including arthropods,brachiopods,priapulids,lobopods and some problematic taxa,with arthropods being the most dominant group.Preservation and composition of the fossil assemblage are very similar to the typical Chengjiang biota,which is preserved in the middle Yu’anshan Formation in the large area of eastern Yunnan.The associated trilobites demonstrate that the soft-bodied fossil assemblage belongs to the late Qiongzhusian in age(Stage 3,Cambrian),suggesting that the Hongjingshao Formation is probably a diachronous lithostratigraphic unit ranging from the upper Qiongzhusian to the lower Canglangpuan stages in eastern Yunnan.The fossil assemblage from the Xiazhuang area fills up the missing link between the typical older Chengjiang biota and the younger Malong and Guanshan biotas,making eastern Yunnan a unique area in the world to reveal the early evolutionary history of animals and palaeocommunity dynamics during the‘‘Cambrian explosion’’.

A novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder for small negative samples

This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder(DCAE)for small negative samples.The proposed DCAE scheme combines the advantages of supervised learning and unsupervised learning.In order to reduce the high cost of training Deep Neural Networks,this paper pre-trained the Convolutional Neural Networks(CNN)through open labelled datasets.Through transferring learning,the encoder part of the traditional Convolutional Auto-Encoder was replaced by the first three layers of the CNN,and a small number of defect samples were used to fine-tune the parameters.A threshold discrimination scheme was designed to evaluate the model detection,realising the self-explosion detection of insulator by judging the residual result and abnormal score.The experimental results show that compared with the existing insulator self-explosion detection schemes,the proposed scheme can reduce the model training time by up to 40%,and the recognition accuracy can reach 97%.Moreover,this model does not need a large number of insulator labelled data and is especially suitable for small negative sample application.