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1.
Sustainability ; 14(4):2474, 2022.
Article in English | MDPI | ID: covidwho-1707883

ABSTRACT

Our research aims to establish an evaluation framework and evaluate the sustainability of scientific research in universities. Based on the concept of Education for Sustainable Development and the function of scientific research activities, an evaluation framework was constructed including three dimensions: the sustainable trend of scientific research activity, research performance related to the topic of sustainable development, and sustainability of scientific research contributions. Descriptive analysis, Data Envelopment Analysis, and a Statistical Index Method were used to calculate the sustainability of scientific research of world-class universities in China. Results show that China’s world-class universities published more articles related to sustainable development than the best-performing universities in the UK and USA. They make sustainable contributions to society through cultivating Ph.D. graduates, publishing research papers, and transforming science and technology. However, the sustainable trend of the scientific research of universities is still to be improved. The result of resource efficiency is relatively low, and attention should be paid to the waste of human and financial resources. In addition, universities should improve their ability to withstand external risks to minimize the influence of external public emergencies such as COVID-19.

2.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-309025

ABSTRACT

This paper investigates the impact of economic policy uncertainty (EPU) on the crash risk of US stock market during the COVID-19 pandemic. To this end, we use the GARCH-S (GARCH with skewness) model to estimate daily skewness as a proxy for the stock market crash risk. The empirical results show the significantly negative correlation between EPU and stock market crash risk, indicating the aggravation of EPU increase the crash risk. Moreover, the negative correlation gets stronger after the global COVID-19 outbreak, which shows the crash risk of the US stock market will be more affected by EPU during the pandemic.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-313410

ABSTRACT

The transmission dynamics of COVID-19 is investigated in this study. A SINDy-LM modeling method that can effectively balance model complexity and prediction accuracy is proposed based on data-driven technique. First, the Sparse Identification of Nonlinear Dynamical systems (SINDy) method is used to discover and describe the nonlinear functional relationship between the dynamic terms in the model in accordance with the observation data of the COVID-19 epidemic. Moreover, the Levenberg–Marquardt (LM) algorithm is utilized to optimize the obtained model for improving the accuracy of the SINDy algorithm. Second, the obtained model, which is consistent with the logistic model in mathematical form with small errors and high robustness, is leveraged to review the epidemic situation in China. Otherwise, the evolution of the epidemic in Australia and Egypt is predicted, which demonstrates that this method has universality for constructing the global COVID-19 model. The proposed model is also compared with the extreme learning machine (ELM), which shows that the prediction accuracy of the SINDy-LM method outperforms that of the ELM method and the generated model has higher sparsity.

4.
Nonlinear Dyn ; 105(3): 2775-2794, 2021.
Article in English | MEDLINE | ID: covidwho-1372807

ABSTRACT

The transmission dynamics of COVID-19 is investigated in this study. A SINDy-LM modeling method that can effectively balance model complexity and prediction accuracy is proposed based on data-driven technique. First, the Sparse Identification of Nonlinear Dynamical systems (SINDy) method is used to discover and describe the nonlinear functional relationship between the dynamic terms in the model in accordance with the observation data of the COVID-19 epidemic. Moreover, the Levenberg-Marquardt (LM) algorithm is utilized to optimize the obtained model for improving the accuracy of the SINDy algorithm. Second, the obtained model, which is consistent with the logistic model in mathematical form with small errors and high robustness, is leveraged to review the epidemic situation in China. Otherwise, the evolution of the epidemic in Australia and Egypt is predicted, which demonstrates that this method has universality for constructing the global COVID-19 model. The proposed model is also compared with the extreme learning machine (ELM), which shows that the prediction accuracy of the SINDy-LM method outperforms that of the ELM method and the generated model has higher sparsity.

5.
Appl Math ; 36(2): 287-303, 2021.
Article in English | MEDLINE | ID: covidwho-1274931

ABSTRACT

OBJECTIVES: Firstly, according to the characteristics of COVID-19 epidemic and the control measures of the government of Shaanxi Province, a general population epidemic model is established. Then, the control reproduction number of general population epidemic model is obtained. Based on the epidemic model of general population, the epidemic model of general population and college population is further established, and the control reproduction number is also obtained. METHODS: For the established epidemic model, firstly, the expression of the control reproduction number is obtained by using the next generation matrix. Secondly, the real-time reported data of COVID-19 in Shaanxi Province is used to fit the epidemic model, and the parameters in the model are estimated by least square method and MCMC. Thirdly, the Latin hypercube sampling method and partial rank correlation coefficient (PRCC) are adopted to analyze the sensitivity of the model. CONCLUSIONS: The control reproduction number remained at 3 from January 23 to January 31, then gradually decreased from 3 to slightly greater than 0.2 by using the real-time reports on the number of COVID-19 infected cases from Health Committee of Shaanxi Province in China. In order to further control the spread of the epidemic, the following measures can be taken: (i) reducing infection by wearing masks, paying attention to personal hygiene and limiting travel; (ii) improving isolation of suspected patients and treatment of symptomatic individuals. In particular, the epidemic model of the college population and the general population is established, and the control reproduction number is given, which will provide theoretical basis for the prevention and control of the epidemic in the colleges.

6.
Environ Pollut ; 285: 117485, 2021 Sep 15.
Article in English | MEDLINE | ID: covidwho-1252841

ABSTRACT

The consumption of disposable face masks increases greatly because of the outbreak of the COVID-19 pandemic. Inappropriate disposal of wasted face masks has already caused the pollution of the environment. As made from plastic nonwoven fabrics, disposable face masks could be a potential source of microplastics for the environment. In this study, we evaluated the ability of new and used disposable face masks of different types to release microplastics into the water. The microplastic release capacity of the used masks increased significantly from 183.00 ± 78.42 particles/piece for the new masks to 1246.62 ± 403.50 particles/piece. Most microplastics released from the face masks were medium size transparent polypropylene fibers originated from the nonwoven fabrics. The abrasion and aging during the using of face masks enhanced the releasing of microplastics since the increasing of medium size and blue microplastics. The face masks could also accumulate airborne microplastics during use. Our results indicated that used disposable masks without effective disposal could be a critical source of microplastics in the environment. The efficient allocation of mask resources and the proper disposal of wasted masks are not only beneficial to pandemic control but also to environmental safety.


Subject(s)
COVID-19 , Microplastics , Humans , Masks , Pandemics , Plastics , SARS-CoV-2
7.
Financ Innov ; 7(1): 31, 2021.
Article in English | MEDLINE | ID: covidwho-1206161

ABSTRACT

This paper investigates the impact of economic policy uncertainty (EPU) on the crash risk of US stock market during the COVID-19 pandemic. To this end, we use the GARCH-S (GARCH with skewness) model to estimate daily skewness as a proxy for the stock market crash risk. The empirical results show the significantly negative correlation between EPU and stock market crash risk, indicating the aggravation of EPU increase the crash risk. Moreover, the negative correlation gets stronger after the global COVID-19 outbreak, which shows the crash risk of the US stock market will be more affected by EPU during the epidemic.

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