Suchergebnisse

A study of the relationships between the image of a country and media use is one of the most appropriate methods to gain knowledge on various stakeholders' different perceptions of the country's sustainability. Through an online survey of China's post-90s generation, this paper first studies the respondents' domestic image of China (including social, political, economic, and cultural images), second, their media use behaviors, and third, the relationships between their perceptions of China's image and their behaviors. Based on the CFA model, with 16 items obtained from the survey data, the results of the empirical analysis indicated that China's domestic image, as well as its political, economic, and cultural images, were generally neutral for the respondents, while they tended to disagree with the social image. Furthermore, neither traditional media use time nor new media use time of the respondents had any statistically significant influence on their perceptions of China's image, where the latter was significantly more than the former. However, the type of media contact had a significant influence on their perceptions of political image and on their perception of some items concerning economic and cultural images.

In biomedical studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as outcomes to identify the progression of a disease. In cancer studies, interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme, termed interval sampling, in which the first failure event is identified within a calendar time interval, the time of the initiating event can be retrospectively confirmed and the occurrence of the second failure event is observed subject to right censoring. In a cancer data application, the initiating, first and second events could correspond to birth, cancer onset and death. The fact that the data are collected conditional on the first failure event occurring within a time interval induces bias. Interval sampling is widely used for collection of disease registry data by governments and medical institutions, though the interval sampling bias is frequently overlooked by researchers. This paper develops statistical methods for analysing such data. Semiparametric methods are proposed under semi-stationarity and stationarity. Numerical studies demonstrate that the proposed estimation approaches perform well with moderate sample sizes. We apply the proposed methods to ovarian cancer registry data.

Objective: To clarify the correlation between temperature and the COVID-19 pandemic in Hubei. Methods: We collected daily newly confirmed COVID-19 cases and daily temperature for six cities in Hubei Province, assessed their correlations, and established regression models. Results: For temperatures ranging from −3.9 to 16.5°C, daily newly confirmed cases were positively correlated with the maximum temperature ~0–4 days prior or the minimum temperature ~11–14 days prior to the diagnosis in almost all selected cities. An increase in the maximum temperature 4 days prior by 1°C was associated with an increase in the daily newly confirmed cases (~129) in Wuhan. The influence of temperature on the daily newly confirmed cases in Wuhan was much more significant than in other cities. Conclusion: Government departments in areas where temperatures range between −3.9 and 16.5°C and rise gradually must take more active measures to address the COVID-19 pandemic.