ABSTRACT
The low-carbon development of air transport industry is of great significance for China to achieve the commitment of carbon peak and carbon neutrality goals. In order to improve the basic data of aviation CO2 emissions, this study continuously collected full flight information in China from January 2017 to December 2020, and established a flight information database and an aircraft-engine parameter database. On the basis of IPCC's Tier 3B accounting method, this study established a long-term aviation CO2 emissions inventory of China from 2017 to 2020 by calculating and accumulating CO2 emissions of each flight. And aviation CO2 emissions of various provinces and cities in China were calculated combined with spatial allocation method. The results showed that aviation CO2 emissions in China was 104.1, 120.1, 136.9, and 88.3 Mt in 2017, 2018, 2019, and 2020, respectively, with annual growth rates of 15.4%, 14.0%, and −35.3% in 2018, 2019, and 2020, respectively. Affected by the COVID-19 pandemic, aviation CO2 emissions in all 31 provinces and 93% of cities decreased in 2020 compared with 2019. China is in the stage of rapid development of air transport industry, and aviation fossil energy consumption and CO2 emissions have continued to grow in recent years. © 2022 Elsevier Ltd
ABSTRACT
This study aims to explore the relationship between social capital and the innovation performance of digital firms. In addition, we examine the mediation effect of cross-border knowledge search on this relationship and investigate the serial mediation effect of cross-border knowledge search and absorptive capacity between social capital and innovation performance. Using data collected from 217 Chinese digital companies, we tested the proposed hypotheses by constructing structural equation models through SPSS 22.0 and AMOS 24.0. Based on the results of theoretical deductions and empirical tests, some conclusions can be drawn. First, for digital firms, social capital remains significantly and positively associated with innovation performance during the COVID-19 pandemic. Meanwhile, digital firms with higher social capital are likely to generate higher innovation performance even if they experience a more severe impact of the COVID-19 pandemic. Second, cross-border knowledge search effectively mediates the relationship between social structural capital, social relational capital, and innovation performance, whereas this mediation effect is not significant between social cognitive capital and innovation performance. Finally, the serial mediation effect of cross-border knowledge search and absorptive capacity on the relationship between social capital and innovation performance is confirmed. Some managerial implications are summarized based on our findings. On the one hand, digital firms should still actively build social capital to enhance innovation performance during the pandemic. On the other hand, social capital can help digital firms implement cross-border knowledge search and develop absorptive capacity. Thus, digital firms can effectively utilize heterogeneous knowledge to enhance their innovation performance. © 2022 The Author(s)
ABSTRACT
PURPOSE: In this paper, based on the age, sex, disease type, and consultation time of dental emergency during COVID-19 epidemic, a comprehensive analysis of dental emergency management and prevention and control of COVID-19 was conducted. METHODS: A total of 739 emergency dental cases were collected from January 29 2020 to February 28 2020. They were divided into 3 groups,including adolescents (<=18 years), young adults (18 ~ 60 years), and elderly ( >=60 years old). The data was analyzed using SPSS 21.0 software package. RESULTS: The ratio of male to female was 1.24:1. There were 655 emergency cases during the day and 84 cases during the night. The types of diseases included pericoronitis (15.83%), apical periodontitis (14.21%), pulpitis (13.40%), periodontitis (12.31%), oral mucosal disease (12.18%), Oral and maxillofacial trauma (10.55%), oral and maxillofacial space infection (8.39%), dental disease in children (5.41%), oral and maxillofacial tumors (2.84%), temporomandibular joint dislocations and disorders (1.76%), and others (3.11%). CONCLUSIONS: Under the epidemic situation of the new coronavirus, as one of the high-risk departments, it is of great significance to enhance the clinical emergency skills and ability of emergency treatment, improve patients' oral health awareness, address the diagnosis and treatment of essential diseases, for the improvement the quality of dental medical care and the prevention and control of COVID-19 epidemic.
ABSTRACT
The new coronavirus epidemic (COVID-19) has received widespread attention, causing the health crisis across the world. Massive information about the COVID-19 has emerged on social networks. However, not all information disseminated on social networks is true and reliable. In response to the COVID-19 pandemic, only real information is valuable to the authorities and the public. Therefore, it is an essential task to detect rumors of the COVID-19 on social networks. In this paper, we attempt to solve this problem by using an approach of machine learning on the platform of Weibo. First, we extract text characteristics, user-related features, interaction-based features, and emotion-based features from the spread messages of the COVID-19. Second, by combining these four types of features, we design an intelligent rumor detection model with the technique of ensemble learning. Finally, we conduct extensive experiments on the collected data from Weibo. Experimental results indicate that our model can significantly improve the accuracy of rumor detection, with an accuracy rate of 91% and an AUC value of 0.96. © 2020 IEEE.