ABSTRACT
Today, there is greater awareness on the association between oral diseases and respiration diseases after the outbreak of COVID-19. However, confusion regarding the oral health management and medical risk prevention for patients with chronic airway diseases has been remained among dental clinicians. Therefore, the dental experts of the Fifth General Dentistry Special Committee, Chinese Stomatological Association, combined with the experts of respiratory and critical care medicine, undertook the formation of consensus on the oral health management of patients with chronic airway diseases in order to help dental clinicians to evaluate medical risks and make better treatment decision in clinical practice. In the present consensus report, the relationship of oral diseases and chronic airway diseases, the oral health management and the treatment recommendations of patients with chronic airway diseases are provided.
Subject(s)
COVID-19 , Oral Medicine , Consensus , Humans , Oral HealthABSTRACT
Purpose Taking the COVID-19 as the background, this study aims to investigate the direct influencing factors regarding knowledge sharing behavior (KSB) on new media platforms and discuss how the characteristics of the users could enhance the KSB through moderation effect, and provide empirical evidences. Design/methodology/approach Based on the social exchange theory and after the text analysis of the data collected from the Tiktok platform in 2020, this paper uses the quantitative method to evaluate the factors influence KSB on short video social platform during the COVID-19 outbreak. Findings KSB on new media platform could be enhanced by richer knowledge content of the video posted and the attribute of the platform users directly. Platform users could affect the trustworthiness of the knowledge shared, thus influence the knowledge sharing. On the early stage of the COVID-19, the richer content of the knowledge released by users could effectively enhance the KSB. On the early stage of the emergency events, the official users could play a significant role on KS. During the mitigation stage of COVID-19, the KSB of the knowledge shared by unofficial users with richer content could be enhanced and the moderation effect is relatively stronger. Originality/value The research extends the social exchange theory to a disaster management context. The authors provide an effective reference for future governments to effectively cope with the epidemic and spread public knowledge in an emergency response context. By analyzing the influence of knowledge content and influencer characteristics, it could help the social media platform to improve content management and optimize resource allocation.
ABSTRACT
The Covid-19 outbreak, the disease elicited by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), poses many significant challenges to scientific communities around the world, including computer scientific communities. At the same time, the rise of computer science fueled by advanced in connectivity of social media and smartphones throughout the world, the fields of augmented reality (AR) and artificial intelligence (AI) have recently grown very rapidly. Augmented reality is an emerging field of a physical scene where the things that reside in the physical world are mixed by virtual world, while artificial intelligence is a popular field for the machine simulation of human intelligence that is programmed to see, think and understand like humans. This paper presents the current development of augmented reality and artificial intelligence during the Covid-19 outbreak. First, we highlight a summary of recent tools using augmented reality to tackle the Covid-19 crisis. For instance, augmented reality-based thermal imaging glasses for detecting virus symptoms and methods of augmented reality on educational tasks that help people overcome the isolation for online learning effectively are reviewed. Second, we discuss an overview of recent tools using artificial intelligence to smartly fight against the Covid-19 pandemic. Our discussion include the artificial intelligence methods to approximate and prepare people for prevention the virus, a method for forecasting of the Covid-19 outbreak using non-linear regressive network (NAR) to predict the size, lengths and ending time of the virus, and susceptible-exposed-infectious-removed (SEIR) model for estimating the outbreak trend of the deadly virus. Finally, we suggest benefits and promising future integrations between augmented reality and artificial intelligence to tackle the research problems after the Covid-19 crisis. © 2020, Springer Nature Switzerland AG.