Study on the Foreign Publicity Translation of Marine Culture—Take Zhoushan for Example

The development of foreign publicity translation of marine culture is of great significance to the internationalization of Zhoushan Archipelago New Area. Based on Lasswell 5W mode (Harold Lasswell, 1948), this paper will combine some successful cases of foreign publicity translation of marine culture at home and abroad and analyze the present situation of foreign publicity translation of marine culture in Zhoushan Archipelago New Area by means of questionnaire surveys and the literature study on existing newspapers, periodicals and websites in Zhoushan. Accordingly, some suggestions are put forward to improve the deficiencies of the translation of marine culture in Zhoushan and innovate the ways in translating the marine culture, thus boosting the development of marine tourism economy and promoting the establishment of International Ecological Leisure Tourism Island (2016) in Zhoushan

Influence of Lisu People’s Religious Beliefs on their Traditional Medicine

The Lisu, inhabitants of Nujiang River Canyon in China’s northwestern Yunnan Province, believe in three set of religious beliefs: their own primitive religion, Christianity and Catholicism, introduced by Western missionaries in the 18th century (Yang et al. (Eds.), 1993). Religious convictions permeate all aspects of life conducted by Lisu and do have a profound impact upon various aspects of their traditional culture. The present article explores how religious tenets have helped shape and have affected traditional Lisu medicine, investigating the relationship between religion, culture, and traditional medicine, tracking the path from Lisu ancient history down to modern times

Faults Detection for Power Systems

Non

Transformer Convolutional Neural Networks for Automated Artifact Detection in Scalp EEG

It is well known that electroencephalograms (EEGs) often contain artifacts due to muscle activity, eye blinks, and various other causes. Detecting such artifacts is an essential first step toward a correct interpretation of EEGs. Although much effort has been devoted to semi-automated and automated artifact detection in EEG, the problem of artifact detection remains challenging. In this paper, we propose a convolutional neural network (CNN) enhanced by transformers using belief matching (BM) loss for automated detection of five types of artifacts: chewing, electrode pop, eye movement, muscle, and shiver. Specifically, we apply these five detectors at individual EEG channels to distinguish artifacts from background EEG. Next, for each of these five types of artifacts, we combine the output of these channel-wise detectors to detect artifacts in multi-channel EEG segments. These segment-level classifiers can detect specific artifacts with a balanced accuracy (BAC) of 0.947, 0.735, 0.826, 0.857, and 0.655 for chewing, electrode pop, eye movement, muscle, and shiver artifacts, respectively. Finally, we combine the outputs of the five segment-level detectors to perform a combined binary classification (any artifact vs. background). The resulting detector achieves a sensitivity (SEN) of 60.4%, 51.8%, and 35.5%, at a specificity (SPE) of 95%, 97%, and 99%, respectively. This artifact detection module can reject artifact segments while only removing a small fraction of the background EEG, leading to a cleaner EEG for further analysis.Comment: This is an extension to a paper presented at the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Scottish Event Campus, Glasgow, UK, July 11-15, 202