ABSTRACT
The ongoing COVID-19 has caused a great amount of serious troubles for people around the world. Even though vaccination has been proven to be safe and highly effective against the COVID-19, it is far away to prevent thoroughly the spread of the disease and truly halt the pandemic. Therefore, we need to apply additional methods aside from vaccine injection, such as keeping the distance between people and always using the face masks during the ordinary conversations, in efforts to further reduce the COVID-19 contagion rate. To implement such methods, this research aims to investigate an efficient approach to detect and warn people that they should wear mask whenever they go to public places. Our proposed system studies the benefits of Local Binary Pattern (LBP) and deep learning model to provide accurate face mask detection and classification system. After comprehensive testing, we found that our system provided the detection rate up to 90% with the Kaggle, Face-Mask-Net, and our own datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Background and Objective: With the onset of the Coronavirus Disease 2019 (COVID-19) pandemic, various aspects of health care have been affected;however, there has been an unknown effect on hospital admissions for gastrointestinal (GI) diseases and the potential consequences on specific illnesses. Our study aims to characterize the rates of GI disease hospitalizations during the pandemic as compared to prior and any differences between specific gastrointestinal diseases throughout this period. This will be important in highlighting any gaps of care as related to gastroenterology during COVID-19.Methods: We conducted a retrospective, cross-sectional study between the months of January to May from the years 2016-2020 in a regional integrated health care system. January –May 2020 was delineated as the COVID-19 period. ICD-10 codes were used to identify principal diagnoses related to the most common GI hospitalizations in the United States (upper GI hemorrhage, pancreatitis, liver disease, diverticular disease, cholelithiasis). Rates of hospitalization were then calculated per 100,000 members for each calendar month and each respective year. Rates for the 5 most identified GI diseases were then calculated using a similar method from 2019 as compared to 2020. The rate of percent change for each month for these diseases were then analyzed during the pandemic year of 2020 versus the preceding year of 2019.Results: A total of 4589 (rate of 19.57 per 100,000) hospitalizations for GI related diseases occurred between January – May 2020 as opposed to 5328 (rate of 23.10 per 100,000) hospitalizations from January – May 2019 (p=0.03). The median age in 2020 was 59.1 (p= 0.27 compared to 2019) with a 51% female to male ratio. 38% of patients were White, 42% Hispanic, 10% Black, 8% Asian (p=0.58 compared to 2019 for all ethnicities). There was a decrease in the rate of hospitalization in each month from January – April 2020 compared to 2019 with a subsequent rise in May. There was a 2.86 increase in rate of hospitalization (p<0.01) from April to May 2020. There were only significant differences (p<0.05) in hospitalization rates between the months of March – May from 2020 versus 2019. Of the 5 most common GI diseases, upper gastrointestinal hemorrhage showed the highest average rate change of -20% from 2020 to 2019. Cholelithiasis had a change of -15%, pancreatitis with a change of -14%, diverticular disease with a change of -11%, and liver disease with a change of -9%.Conclusion: GI related hospitalizations decreased during the COVID-19 pandemic as com-pared to the previous year. Upper gastrointestinal hemorrhage showed the most average rate change of the GI diseases. Further studies highlighting the implications of these findings, such as mortality and severity of illness during the pandemic, need to be completed to assess the impact COVID-19 on GI disease.(Figure presented)(Table Presented)
ABSTRACT
Nowadays, even if the Covid-19 pandemic has not left a significant impact, the market of the tourism industry has relentlessly become competitive. At the same time, the interests of tourists have also continuously changed rapidly in industry 4.0. Facing such unpredictable challenges, firms must constantly have different innovations to bring new and other values to the tourism industry. With the ability to create appropriate new values, Artificial Intelligence (AI) can force firms to redesign products and services, and at the same time to restructure the entire innovation method. However, AI may also appear as a preferred option when the advantages of conventional innovation methods are challenging to realize. This paper aims to explore how AI will help the innovation of tourism and hospitality enterprises create new values that match new and safe experiences for customers during the Covid-19 pandemic and beyond. © 2021 CEUR-WS. All rights reserved.
ABSTRACT
The pandemic has caused several health issues and deaths and numerous severe devastations to the global economy. Due to the extreme impacts of the epidemics, it is crucial to investigate the pandemics and their pessimistically hidden influences to devise proper strategies. However, although this topic gains enormous recognition from scholars and researchers, there is still an inadequacy of a comprehensive literature review on the issues, and in-depth research on individual aspects of the world economy. Accordingly, the objective of this paper is to contribute a literature review that synthesises and evaluates prior research, to examine the current state of the pandemic impacts on the global economy, as well as become the requisite foundation for the practitioners to perceive how to respond to a pandemic in the future. A literature review is conducted, with 254 most relevant articles are analysed and classified based on the proposed framework. Thus, the findings of this study contribute diverse theoretical and practical insights concerning pandemics and the global economy. The results of this paper indicate the current status of the literature review and discover future research directions. Additionally, this paper proposes an integrated framework of the most influenced industries, followed by some emerging solutions to the most suffering sectors. Future research directions arc further suggested based on the identified research gaps and the analysis results.