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Background: Vietnam's economy and intellectual standards have witnessed significant development, improving conditions for residents to acquire novel mHealth applications. Additionally, the outbreak of the COVID-19 pandemic has influenced Vietnamese awareness of healthcare; however, previous studies have only been clinician-centered rather than customer-centered. Methods: This study addresses this literature gap by interviewing 50 Vietnamese participants grouped by age, namely Generation X, Generation Y, and Generation Z, and health conditions, namely whether participants or family members have chronic illness. The study utilized semi-structured and in-depth interviews to collect the data and used thematic analysis to analyze the data under the unified theory of acceptance and use of technology framework. Results: Most participants were willing to adopt this technology and demanded a convenient and user-friendly one-stop-shop solution, endorsements from credible and authoritative sources, and professional customer services. However, each group also had distinctive demands and behaviors. Conclusion:This study contributes theoretically by providing context-rich demand for Vietnamese customers across three generations and healthcare conditions during the COVID-19 pandemic and comparing their behavior with pre-COVID literature. While this research provides helpful information for potential app developers, this study also suggests that mHealth developers and policymakers should pay more attention to the differences in the demand of age groups and health conditions.

Weighted frequent itemset mining is more practical than traditional frequent itemset mining, because it can consider different semantic significance (weight) of items. Many models and algorithms for mining weighted frequent itemsets have been proposed. These models assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of the items may vary with time. Therefore, reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. Recently, Chowdhury F. A. et al. have introduced a novel concept of adaptive weight for each item and propose an algorithm AWFPM (Adaptive Weighted Frequent Pattern Mining). AWFPM can handle the situation where the weight (price or significance) of an item may vary with time. In this paper, we present an improved algorithm named AWFIMiner. Experimental computations show that our AWFIMiner is more efficient and scalable for mining weighted frequent itemsets using adaptive weights. Moreover, because it only requires one single database scan, the AWFIMiner is applicable for mining these itemsets on data streams.

Ensuring food safety for foodservice businesses is extremely necessary during the current complicated situation of the Covid-19 pandemic. This study has evaluated the knowledge and practices on food safety of establishment owners, food processors, and customers to prevent the Covid-19 pandemic at foodservice businesses in Son La city in 2020. The results show that over 75 % of establishment owners knew regulations to ensure food safety in the prevention of the Covid-19 pandemic, and 100 % of establishment owners were trained in disseminating and guiding pandemic prevention documents. Food processors who have good knowledge and practices of regulations of the Ministry of Health on ensuring food safety to prevent Covid-19, such as wearing masks when working, keeping contact distance with food, washing hands, disinfecting correctly, and do not gather in large numbers in production facilities have reached a high rate of over 90 %. All customers know about the 5K regulations. It is necessary to strengthen the propaganda to ensure food safety to prevent the Covid-19 pandemic so that the subjects can better understand the regulations of the Government, the Ministry of Health, Departments on ensuring food safety and prevention of the Covid-19 pandemic, and good practices of the above regulations.