RESUMEN
The outbreak of COVID-19 has caused concerns globally. To reduce the rapid transmission of the virus, strict city lockdown measures were conducted in different regions. China is the country that takes the earliest home-based quarantine for people. Although normal industrial and social activities were suspended, the spread of virus was efficiently controlled. Simultaneously, another merit of the city lockdown measure was noticed, which is the improvement of the air quality. Contamination levels of multiple atmospheric pollutants were decreased. However, in this work, 24 and 14 air fine particulate matter (PM2.5) samples were continuously collected before and during COVID-19 city lockdown in Linfen (a typical heavy industrial city in China), and intriguingly, the unreduced concentration was found for environmentally persistent free radicals (EPFRs) in PM2.5 after normal life suspension. The primary non-stopped coal combustion source and secondary Cu-related atmospheric reaction may have impacts on this phenomenon. The cigarette-based assessment model also indicated possible exposure risks of PM2.5-bound EPFRs during lockdown of Linfen. This study revealed not all the contaminants in the atmosphere had an apparent concentration decrease during city lockdown, suggesting the pollutants with complicated sources and formation mechanisms, like EPFRs in PM2.5, still should not be ignored. © 2022
RESUMEN
With the rapid growth of the coronavirus disease of 2019 (COVID-19) cases, massive amounts of relevant data are being trained on machine learning models for countering communicable infectious diseases. Federated Learning (FL) is a paradigm of distributed machine learning to deal with the individual COVID-19 data, and enable the protection of data privacy. However, FL has low efficiency in Edge-Based wireless communication systems with system heterogeneity. In this paper, we propose an "Asynchronous-Adaptive FL" (AAFL) scheme. Specifically, we allow that medical devices with different performances have a heterogeneous number of local SGD iterations in each communication round, called asynchronous iteration strategy which is balanced under adaptive control. We theoretically analyze the convergence of the AAFL scheme under a given time budget and obtain a mathematical relationship between the heterogeneous number of local SGD iterations and the optimal model parameters. Based on the mathematical relationship, we design an algorithm for parameter server and work nodes to adaptively control the heterogeneous number of local SGD iterations. Subsequently, we build a prototype heterogeneous system and conduct experiments on various scenarios for analyzing the general properties of our algorithm, and then apply our algorithm to public COVID-19 databases. The experimental results and application performance demonstrate the effectiveness and efficiency of our AAFL scheme.
RESUMEN
Based on the stimulus–response framework, this study examines the external environmental stimuli influencing online rumour sharing about COVID-19 and considers the contingent effect of fear. A large-scale online survey was used to test the proposed research model and hypotheses. The final data set comprised 2807 valid responses. The results indicate that perceptions of community safety and infection risk negatively affect online rumour sharing, while social influence positively affects online rumour sharing. Fear weakens the negative effects of community safety on online rumour sharing but strengthens the positive effect of social influence on online rumour sharing. This study provides a comprehensive analysis by applying the stimulus–response framework to explore the underlying drivers of online rumour sharing with regard to COVID-19 and the moderating effects of fear in the Chinese context.
RESUMEN
Globalization has brought larger spillovers of global risks across borders since the 2000s. Specifically, global policy risk has sharply increased due to policy uncertainty in major countries in the recent decade as are shown in the Brexit, the US-China trade friction and the COVID-19 pandemic. This paper empirically investigates effects of both global policy risk and global financial risk on macroeconomy and financial markets in eight major countries from January 1997 to June 2020. We employed a structural vector autoregressive framework to obtain interesting empirical results. First, global risks have recessionary effects on the macroeconomy, reducing production, deteriorating employment, lowering long-term interest rates, depressing prices and reducing global trade. Second, global risks also have recessionary effects on financial markets, plunging stock prices, appreciating the safe-haven currencies and depreciating the other currencies. Third, the macroeconomies and the financial markets respond to the global financial risk more significantly than the global policy risk. Fourth, the recessionary effects of global risks vary depending on countries.
RESUMEN
Background and purpose: Coronavirus disease 2019 (COVID-19) was spreading all over the world. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) primarily invades and infects the lungs of humans leading to COVID-19. Mild to severe clinical symptoms such as fever, cough, and shortness of breath were existed in those patients. One of the most common changes in these patients was abnormal blood routine. However, uncertainty remains regarding the dynamic characteristics of platelet in COVID-19 patients due to limited data. Therefore, we aimed to analyze the association between dynamic characteristics of blood platelet and disease severity, and to identify new monitoring indicators to treat the COVID-19 patients.Methods:In this cohort study, 398 COVID19 patients treated in the Shenzhen Third People's hospital from December 16, 2019 to March 26, IP: 182.75.148.10 On: Thu, 20 Jan 2022 08:58:32 Copyright: American Scientific Publishers 2020 were collected and participated. All data of participants including the clinical characteristics, Delivered by Ingenta imaging and laboratory information were collected. All patients included in our study were classified as four groups (mild, common, severe, and critical types) regarding clinical symptoms and relevant severe failures based on the Diagnosis Criteria. Platelet count was examined at the baseline and every 3-5 days during hospitalization. Results: The platelet count varied with clinical classifications. The platelet count in mild type was normal without significant fluctuation. While the blood platelet count of most common and severe patients had obvious fluctuations, showing as a dynamic change that first rose and then fell to the level at admission, which was consistent with the trend of lung inflammation. Bone marrow smears further showed that bone marrow hyperplasia was normal in mild, common and severe type patients, and megakaryocytes and their platelet-producing functions were not abnormal. Conclusions: Our results suggested that the dynamic changes of platelet count might be a predictor of lung inflammation alteration for COVID-19 patients. The changes in platelet count might be a responsive pattern secondary to lung inflammation. The function of bone marrow may be slightly affected by SARS-CoV-2 infection.
RESUMEN
Due to the epidemic of COVID-19, more social activities have been moved to the internet, such as online education and online learning. The education management to avoid burst events is a basic requirement of online education, especially when a huge number of persons are visiting at the same time. In order to monitor the abnormal and burst access in online education systems, this paper proposes an anomaly detection method by using data flow to mining high frequency events among massive network traffic data during online education. First, the data flow in traffic network is described as a special structure which is used to establish an efficient high frequent event detection algorithm. Second, the network traffic flow is reduced to make it possible to monitor large-scale concurrent network visiting. The effectiveness of the abnormal network behavior detection method is verified through the experiment on a real network environment for online education. © 2021 IGI Global. All rights reserved.
RESUMEN
BACKGROUND: Patients on dialysis were susceptible to coronavirus disease 2019 (COVID-19) and were prone to severe clinical characteristics after infection; acute kidney injury was related to mortality in COVID-19 cases. Limited is known about the characteristics of COVID-19 patients with end-stage renal disease not requiring renal replacement therapy (RRT). AIM: Evaluate clinical characteristics, course and outcomes of COVID-19 patients with chronic kidney disease (CKD) who did not require RRT and those on dialysis. DESIGN: A two-center retrospective study. METHODS: A total of 836 adult patients with COVID-19 (24 CKD not on dialysis; 15 dialysis-dependent CKD) were included. The study includes no patients with renal transplantation. Risk factors were explored. RESULTS: CKD not requiring RRT is an independent risk factor for in-hospital death [adjusted odds ratio (aOR) 7.35 (95% CI 2.41-22.44)] and poor prognosis [aOR 3.01 (95% CI 1.23-7.33)]. Compared with COVID-19 cases without CKD, those with CKD not requiring RRT showed similar percentage of initial moderate cases (75.00% vs. 73.65%) but higher incidence of in-hospital neutrophilia (50.00% vs. 27.30%) or death (50.00% vs. 9.03%). The odds ratio of dialysis associated to mortality in CKD patients was 2.00 (95% CI 0.52-7.63), suggesting COVID-19 patients with dialysis-dependent CKD were at greater risk of in-hospital death. For COVID-19 patients with CKD not requiring RRT, statins reduced the risk of neutrophilia [OR 0.10 (95% CI 0.01-0.69)] while diuretics increased the risk of neutrophilia [OR 15.4 (95% CI 1.47-160.97)], although both showed no association to mortality. CONCLUSION: COVID-19 patients with CKD presented high incidence of neutrophilia, poor prognosis and in-hospital death, with dialysis patients being more vulnerable.