COVID-19 and its impact on tourism sectors: implications for green economic recovery

The COVID-19 pandemic posed huge hurdles to green economic recovery and the tourism sector. This paper examines challenges in the Zhejiang, Fujian, and Shandong provinces of China' coastal tourism and green economic growth in the wake of the COVID-19 pandemic. To assess this impact, the study used econometrics models based on the Chinese provincial data from March 2020 to April 2021. According to the results, coastal tourism's related income fell drastically regardless of where the people live. Fisherman's earnings dropped by 26%, while captains and owner's earnings dropped by 49% on average. This also resulted in a shortage of food supply that endangered food security. During the pandemic, the number and duration of tourism trips dropped in all study locations. In addition, results indicated that in the wake of COVID-19, lower economic growth and recessions resulted in a significant decline in green investments. The paper proposes that to achieve green recovery and the recovery of the tourism sector, the local and central governments need to increase green investments and the literacy of the people in charge of coastal tourism.

Research on Auxiliary Diagnosis Technology of Chronic Disease Based on Case-Based Reasoning

In recent years, with the continuous acceleration of social progress, the number of patients with chronic diseases worldwide has increased year by year, and the pressure on medical institutions and volunteer workers has also increased. Especially in the current situation of rampant CoviD-19, if the number of admissions and treatments for chronic patients can be reduced, it will help reduce the pressure on hospitals and medical staff during the special period at this stage, and will also reduce the spread and infection rate of CoviD-19. The research team established big health data through questionnaire surveys, and established a case database based on these data, and proposed an assisted diagnosis technology for chronic diseases based on case-based reasoning. With the continuous expansion of the application field of Case-Based Reasoning (CBR) technology, the problem of programmers' difficulty in obtaining and expressing professional knowledge has become increasingly prominent. Therefore, this article first gives the standardized expression of health data, and combines the method based on case-based reasoning with the scientific measurement of keyword weight (Term Frequency-Inverse Document Frequency, TF-IDF) to give the weight in the case retrieval process It also designs the calculation of the dispersion degree between classes for the distribution of characteristic words, which provides an effective method for the establishment of an auxiliary diagnosis model for chronic diseases. © 2021 IEEE.