ABSTRACT
Recent studies in the epidermis have shown that Far-UVC (200-230nm) is a promising candidate against Novel Coronavirus (SARS-Cov-2) with little DNA damage. Due to the consideration that conventional Far-UVC KrCl excilamps may emit 200-230 nm radiation (typically 222-nm peak wavelength) but with some harmful UV radiation beyond 230 to 280 nm, a novel design of Far-UVC KrCl excilamps with the filter and reflector is introduced to reduce the harmful UV radiation from 10.9% to 2.5% at the cost of 30%similar to 40% reduction in the total irradiance. In our study, the radiant characteristics and service life of the novel Far-UVC KrCl excilamps of 40 similar to 75 Watt (electrical power) with 222-nm peak wavelength were investigated. The service life was assessed under aging at the ambient temperatures (T-a) of 25 degrees C and 85 degrees C for 500 hours, respectively. The results showed that both the ambient temperature and the root mean square of current (I-rms) into the excilamps have a substantial effect on the lifetime of the KrCl excilamps. Furthermore, although no significant change of the off-nominal emission ratio existed during the lifetime test, it was observed that the high ambient temperature has a negative effect on the filtering of the harmful radiation.
ABSTRACT
As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians. © 2022 IEEE.
ABSTRACT
As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians. © 2022 IEEE.