This study investigates the use of mobile phones and tablets for learning purposes among university students in Vietnam. For this purpose, the research is based on relevant technology acceptance literature and the Technology Acceptance Model (TAM) is proposed to analyze the adoption of mobile devices and smart phones by Vietnam students for accessing course materials, searching the web for information related to their discipline, sharing knowledge, conducting assignments etc. Employing structural equation modeling (SEM) technology, the model was assessed based on the data collected from 301 participants using a survey questionnaire. These results validate the power of TAM constructs and its appropriateness for predicting acceptance of mobile learning. Usefulness had the highest path coefficients and was a strong predictor of behavioral intention and attitude to use and thus actual use. The proposed TAM model also can improve the understanding of students' motivation by suggesting what external factors are the most important in enhancing students acceptance of mobile learning.

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As COVID-19 enters the pandemic stage, the resulting infections, deaths and economic shocks are emerging. To minimize anxiety and uncertainty about socioeconomic damage caused by the COVID-19 pandemic, it is necessary to reasonably predict the economic impact of future disease trends by scientific means. Based on previous cases of epidemic (such as influenza) and economic trends, this study has established an epidemic disease spread model and economic situation prediction model. Based on this model, the author also predict the economic impact of future COVID-19 spread. The results of this study are as follows. First, the deep learning-based economic impact prediction model, which was built based on historical infectious disease data, was verified with verification data to ensure 77% accuracy in predicting inflation rates. Second, based on the economic impact prediction model of the deep learning-based infectious disease, the author presented the COVID-19 trend and future economic impact prediction results for the next 1 year. Currently, most of the published studies on COVID-19 are on the prediction of disease spread by statistical mathematical calculations. This study is expected to be used as an empirical reference to efficient and preemptive decision making by predicting the spread of diseases and economic conditions related to COVID-19 using deep learning technology and historical infectious disease data.

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