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AIP Conference Proceedings ; 2685, 2023.
Article in English | Scopus | ID: covidwho-20240575


Understanding a concept that people cannot observe directly in real life is always challenging in education. It could be even more difficult for public health education topics such as viruses or bacteria. However, public health education is critical for understanding the knowledge of the virus in the age of COVID-19. Thus, this paper proposes a distributed mixed reality environment to enhance public health education in the internet of things (IoT) context. We introduce the design methodology based on the mixed reality interaction characteristics, the implementation, and the initial evaluation. © 2023 Author(s).

5th International Conference on Data Science and Information Technology, DSIT 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2161385


China's port investments in countries along the Belt and Road are growing, while the global investment environment has deteriorated due to the Sino-US trade friction and the COVID-19 epidemic. However, the recent quantitative research on overseas port investment risk has not considered the time weight, and the related research is less. Therefore, this paper proposes a dynamic evaluation method specially for port investment risk of countries along the B&R based on entropy weight-TOPSIS and BP neural network. First, we figure out the static comprehensive risk value by entropy weight-TOPSIS method, and get the dynamic comprehensive risk value by time weighting method. Second, select three-dimensional data of 32 indicators in 18 host countries from 2010 to 2019 for empirical analysis, and obtain the risk level of each country. Lastly, compared with multiple regression, ridge regression, partial least square, we find BP neural network is the most effective means to estimate the simulation weight of evaluation indicators. The experimental result shows that, the proposed dynamic risk assessment approach for overseas port investment is able to assess risk well and can be extended to other fields. © 2022 IEEE.

5th International Conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022 ; : 272-275, 2022.
Article in English | Scopus | ID: covidwho-2161365


With the normalization of COVID-19 epidemic prevention and control, big data analysis technology has become an important support for scientific and accurate prevention and control. As a transportation means for water traffic, ship management plays an important role in the whole COVID-19 epidemic prevention system. It is the key to improve the efficiency of water traffic epidemic prevention to identify and track the key ships. To improve the efficiency and accuracy of key ships identification and tracking in epidemic prevention and control, a real-time ship tracking and control platform for epidemic prevention is proposed. By fusing the multi-source information, the platform changes the epidemic prevention way of water traffic from the manual query method to the data analysis method. The platform has been applied in the local maritime regulatory department, and it improves the identifying, tracking and control efficiency of key ships. © 2022 IEEE.