Indoor air quality becomes more critical as people stay indoors longer, particularly children and the elderly who are vulnerable to air pollution. Natural ventilation has been recognized as the most economical and effective means of improving indoor air quality, but its benefit is questionable when the external air quality is unacceptable. Such risk-risk tradeoffs would require evidence-based guidelines for households and policymakers, but there is a lack of research that examines spatiotemporal long-term air quality trends, leaving us unclear on when to ventilate. This study aims to suggest the appropriate time for ventilation by analyzing the hourly and quarterly concentrations of particulate matter (PM)10 and PM2.5 in seven metropolitan cities and Jeju island in South Korea from January 2015 to September 2019. Both areas’ PM levels decreased until 2018 and rebounded in 2019 but are consistently higher in spring and winter. Overall, the average concentrations of PM10 and PM2.5 peaked in the morning, declined in the afternoon, and rebounded in the evening, but the second peak was more pronounced for PM2.5. This study may suggest ventilation in the afternoon (2–6pm) instead of the morning or late evening, but substantial differences across the regions by season encourage intervention strategies tailored to regional characteristics.
Malware is any malicious program that can attack the security of other computer systems for various purposes. The threat of malware has significantly increased in recent years. To protect our computer systems, we need to analyze an executable file to decide whether it is malicious or not. In this paper, we propose two malware classification methods: malware classification using Simhash and PCA (MCSP), and malware classification using Simhash and linear transform (MCSLT). PCA uses the symmetrical covariance matrix. The former method combines Simhash encoding and PCA, and the latter combines Simhash encoding and linear transform layer. To verify the performance of our methods, we compared them with basic malware classification using Simhash and CNN (MCSC) using tanh and relu activation. We used a highly imbalanced dataset with 10,736 samples. As a result, our MCSP method showed the best performance with a maximum accuracy of 98.74% and an average accuracy of 98.59%. It showed an average F1 score of 99.2%. In addition, the MCSLT method showed better performance than MCSC in accuracy and F1 score.
Based on a literature review regarding shift work, it is recognized that it has an adverse effect on workers' health. Especially, the night shift rather than the day shift imposes severe disorders on workers, which are indicated to dyssomnia, maladaptation to social life, and health problems such as gastroenteric trouble, cardiovascular diseases and depression. As the shift work can be explainable by using workers' labor ability necessarily to maintain company business consistently, it does not consider biorhythm, active mass and health condition of workers Actually duration of shit work would deprive workers of fundamental life rights by causing physical and mental effects. As a result of reviewing previous case studies related to effect of work pattern (day shift and night shift) on workers' health, an incidence of physical diseases like dyssomnia, gastroenteric trouble, cardiovascular diseases and premature delivery was higher in shift workers than normal workers. Additionally the incidence of mental disorders such as busy brain, social isolation, depression and work stress was also higher in shift workers than normal workers. These adverse physical and mental problems were intensified to night shift workers compared to day shift workers. Considering current various reports and study results, it is recommended that the shift work, especially the night work pattern, should not apply to contemporary work situation for sustaining workers' health condition constantly.
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