Post-COVID-19 Pulmonary Fibrosis: Facts-Challenges and Futures: A Narrative Review.

Patients with coronavirus disease 2019 (COVID-19) usually suffer from post-acute sequelae of coronavirus disease 2019 (PASC). Pulmonary fibrosis (PF) has the most significant long-term impact on patients' respiratory health, called post-COVID-19 pulmonary fibrosis (PC19-PF). PC19- PF can be caused by acute respiratory distress syndrome (ARDS) or pneumonia due to COVID-19. The risk factors of PC19-PF, such as older age, chronic comorbidities, the use of mechanical ventilation during the acute phase, and female sex, should be considered. Individuals with COVID-19 pneumonia symptoms lasting at least 12 weeks following diagnosis, including cough, dyspnea, exertional dyspnea, and poor saturation, accounted for nearly all disease occurrences. PC19-PF is characterized by persistent fibrotic tomographic sequelae associated with functional impairment throughout follow-up. Thus, clinical examination, radiology, pulmonary function tests, and pathological findings should be done to diagnose PC19-PF patients. PFT indicated persistent limitations in diffusion capacity and restrictive physiology, despite the absence of previous testing and inconsistency in the timeliness of assessments following acute illness. It has been hypothesized that PC19-PF patients may benefit from idiopathic pulmonary fibrosis treatment to prevent continued infection-related disorders, enhance the healing phase, and manage fibroproliferative processes. Immunomodulatory agents might reduce inflammation and the length of mechanical ventilation during the acute phase of COVID-19 infection, and the risk of the PC19-PF stage. Pulmonary rehabilitation, incorporating exercise training, physical education, and behavioral modifications, can improve the physical and psychological conditions of patients with PC19-PF.

A Network Analysis of Return Connectedness in Financial Stability: Insights into Disease and Economic Policy Uncertainties

This paper studies how return connectedness exhibits potential linkages among 17 economies over a 20-year period starting in 2001. We obtained three main results by employing the dynamic connectedness approach, which is based on vector auto-regression (VAR), to calculate generalized forecast error decompositions. First, although the financial crisis (2007-2008) experienced a high level of connectedness, the spillover index spiked during the early stages of the COVID-19 outbreak. Second, the "return shock sender" is a community of countries that includes the United States, Australia, and European countries, while Vietnam is immune to financial linkages. Third, we discovered the predictive power of U.S. economic policy uncertainty and disease fear with market volatility for the Vietnamese return connectedness. As a result, our research identifies a range of relevant policies to mitigate spillover risks in the context of financial stability.

Volatility spillovers from the Chinese stock market to the U.S. stock market: The role of the COVID-19 pandemic.

The COVID-19 pandemic, which originated in Wuhan, China, precipitated the stock market crash of March 2020. According to published global data, the U.S. has been most affected by the tragedy throughout this outbreak. Understanding the degree of integration between the financial systems of the world's two largest economies, particularly during the COVID-19 pandemic, necessitates thorough research of the risk transmission from China's stock market to the U.S. stock market. This study examines the volatility transmission from the Chinese to the U.S. stock market from January 2001 to October 2020. We employ a variant form of the EGARCH (1,1) model with long-term control over the excessive volatility breakpoints identified by the ICSS algorithm. Since 2004, empirical evidence indicates that the volatility shocks of the Chinese stock market have frequently and negatively affected the volatility of the U.S. stock market. Most importantly, we explore that the COVID-19 pandemic vigorously and positively promoted the volatility infection from the Chinese equity market to the U.S. equity market in March 2020. This precious evidence endorses the asymmetric volatility transmission from the Chinese to the U.S. stock market when COVID-19 broke out. These experimental results provide profound insight into the risk contagion between the U.S. and China stock markets. They are also essential for securities investors to minimize portfolio risk. Furthermore, this paper suggests that globalization has carefully driven the integration of China's stock market with the international equity markets.

The Determinants of the Usage of Accounting Information Systems toward Operational Efficiency in Industrial Revolution 4.0: Evidence from an Emerging Economy

The purpose of this study is to determine the factors affecting the application of accounting information systems (AIS) in small and medium enterprises (SMEs) in Vietnam. Drawing upon the Technology–Organization–Environment (TOE) theoretical framework, Diffusion of Innovations theory (DOI), and Resource-based theory (RBV), we proposed a research model to investigate the antecedents and influence of AIS usage in Vietnamese SMEs. This study used an online survey of individuals who work in Vietnamese SMEs for data collection. The result was assembled by applying the PLS-SEM model to test the proposed hypotheses based on 132 valid responses. First, the factors that have a significant impact on AIS usage are as follows: relative advantage;owner/manager commitment;and impact of COVID-19. Second, the research results also confirm that there is a positive relationship between AIS usage and AIS effectiveness;AIS performance has a positive impact on business performance. Research implications are to help business owners and leaders decide whether to use AIS to strengthen the company's position and reduce the burden on departments, particularly the accounting department.

An Embedded Machine Learning System for Real-time Face Mask Detection and Human Temperature Measurement

In this paper, an efficient embedded machine learning system is proposed to automatically detect face masks and measure human temperature in a real-time application. In particular, our system uses a Raspberry-Pi camera to collect realtime video and detect face masks by implementing a classification model on Raspberry Pi 3 in public places. The face mask detector is built based on MobileNetV2, with ImageNet pre-trained weights, to detect three cases of correctly wearing, incorrectly wearing and not wearing a mask. We also design a human temperature measurement framework by deploying a temperature sensor on the Raspberry Pi 3. The numerical results prove the practicality and effectiveness of our embedded systems compared to some state-of-the-art researches. The results of accuracy rate in detecting three cases of wearing a face mask are 98.61% based on the training results and 97.63% for validation results. Meanwhile, our proposed system needs a short time of 6 seconds for each person to be tested through the whole process of face mask detection and human forehead temperature measurement. © 2021 IEEE.