ABSTRACT
Considerable research has explored the relationship between intangible assets and firm performance in manufacturing and tertiary industries, but such studies remain scarce for the agri-food industry. In fact, numerous stresses, including the COVID-19 pandemic, have disrupted many activities along agri-food production chains in developing countries, resulting in huge pressure on sustainable agri-food production. Based on a sample of 94 A-share listed agri-food firms in China from 2008 to 2017, this study examines the impact of intangible assets on agri-food enterprises' productivity using generalized method of moments estimation. The results reveal differences between private and state-owned enterprises, indicating that the promotional productivity effect of intangible assets held by private agri-food enterprises was insignificant in the current period, but will be manifested three periods later, in contrast to state-owned enterprises. Due to the soft budget constraints and incentive mechanism of state-owned agri-food enterprises, intangible assets will not promote productivity. In addition, by pursuing the financialization profit model, agri-food enterprises will sacrifice productivity improvement, which can further lead to selective abandonment in the use of intangible assets. Simultaneously, there is a crowding-out effect between intangible and fixed assets in promoting agri-food enterprises' total factor productivity. The findings highlight the importance of the quality and conversion rate of intangible assets, particularly for agri-food enterprises, which are closely related to food security and stability. © 2022 Economic Society of Australia, Queensland
ABSTRACT
This study uses the event study method to study and analyse the impact of the release of policy information related to the COVID-19 epidemic on the changes in the stock prices of listed companies of property service enterprises in China. The results show that the Hong Kong capital market has been greatly affected by the release of policy information related to the COVID-19 epidemic. Additionally, the study demonstrates that the policy effect of the introduction of policy information related to the COVID-19 epidemic exists in the short term, and that the effectiveness of policy information related to the COVID-19 epidemic will change over time.
ABSTRACT
Since 2019, COVID-19 has been a major problem for the world's population. COVID-19 is known for its fast transmission and strong infection. Therefore, how to reduce the burden of medical system is becoming a hot topic in current research. Previous researchers have used deep learning techniques to effectively classify COVID-19. Although the results are remarkable, the input method (X-ray images) is simple. Therefore, a new multi-modality fusion network is proposed in this paper. In this network, the spatial and structural feature information in the image were highlighted by means of thermal map. Experiments show the effectiveness of the proposed network. © 2021 IEEE.
ABSTRACT
Background: As of June 3rd, 2020, the number of confirmed cases of novel SARSCoV- 2, the causative agent of COVID-19, was approximately 6,538,456, with 386,503 deaths globally. Individuals with pre-existing conditions are particularly susceptible to and more likely to die from Covid-19. However, individuals with human immunodeficiency virus (HIV) are unique due to their use of antiretroviral therapy, including protease inhibitors, which have been used to treat COVID-19. We aimed to conduct a systematic review and meta-analysis exploring the prevalence and prevalence of HIV in patients hospitalized for COVID-19 and delineating the mortality rates. Methods: MEDLINE, SCOPUS, and Cochrane Library databases and medrxiv. org were searched from January 1st, 2020, to June 15th, 2020. Studies reporting on the prevalence of HIV among hospitalized COVID-19 patients among and outcome of mortality were extracted. Two reviewers independently extracted appropriate data of interest and assessed the risk of bias. All analyses were performed using random-effects models on log-transformed proportions and risk ratio estimates, and heterogeneity was quantified. Results: A total of 144,795 hospitalized COVID-19 patients were identified from 14 studies (United States 8, Spain 3, China 1, Italy1, and Germany 1). The pooled prevalence of HIV in COVID-19 patients was 1.22 % [95% confidence interval (CI): 0.61%-2.43%)] translating to a 2-fold increase compared to the respective local-level pooled HIV prevalence in the general population of 0.65% (95% CI: 0.48%-0.89%. When we stratified the analysis by country, pooled HIV prevalence among COVID- 19 patients in United States (1.43%, 95% CI: 0.98% -2.07%) was significantly higher compared to Spain (0.26%, 95% CI: 0.23%-0.29%) but not different from China (0.99 %, 95% CI: 0.25 %-3.85%). The pooled mortality rates in HIV-positive patients hospitalized for COVID-19 was 14.1 % 95% CI: 5.78%-30.50% and was substantially higher in the United States compared to other countries. Conclusion: The prevalence of HIV among COVID-19 patients may be higher compared to the general population, suggesting higher susceptibility to COVID-19. The mortality rates are high but vary significantly across countries.