Impact of COVID-19 Pandemic on Cultural Products Interests

The COVID-19 pandemic has had a significant impact on human behaviors and how it influenced peoples' interests in cultural products is an unsolved problem. While prior studies mostly adopt subjective surveys to find an answer, these methods are always suffering from high cost, limited size, and subjective bias. Inspired by the rich user-oriented data over the Internet, this work explores the possibility to leverage users' search logs to reflect humans' underlying cultural product interests. To further examine how the COVID-19 mobility policy might influence cultural interest changes, we propose a new regression discontinuity design that has the additional potential to predict the recovery phase of peoples' cultural product interests. By analyzing the 1592 search interest time series in 6 countries, we found different patterns of change in interest in movies, music, and art during the COVID-19 pandemic, but a clear overall incremental increase. Across the six countries we studied, we found that changes in interest in cultural products were found to be strongly correlated with mobility and that as mobility declined, interest in movies, music, and art increased by an average of 35, 27 and 20, respectively, with these changes lasting at least eight weeks. © 2023 ACM.

Performance of in-duct bipolar ionization devices on pollutant removal and potential byproduct formation in indoor environments

The COVID-19 pandemic has highlighted the importance of indoor air quality (IAQ) since SARS-CoV-2 may be transmitted through virus-laden aerosols in poorly ventilated spaces. Multiple air cleaning technologies have been developed to mitigate airborne transmission risk and improve IAQ. In-duct bipolar ionization technology is an air cleaning technology that can generate ions for inactivating airborne pathogens and increasing particle deposition and removal while without significant byproducts generated. Many commercial in-duct ionization systems have been developed but their practical performance on pollutant removal and potential formation of byproducts have not been investigated comprehensively. The results in this study showed that the in-duct bipolar ionization technology can significantly improve the particle removal efficiency of the regular filter, while no significant ozone and ion were released to the indoor air. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Long-COVID Inducement Mechanism Based on the Path Module Correlation Coefficient

As the number of COVID-19 cases increases, the long-COVID symptoms become the focus of clinical attention. Based on the statistical analysis of long-COVID symptoms in European and Chinese populations, this study proposes the path module correlation coefficient, which can estimate the correlation between two modules in a network, to evaluate the correlation between SARS-CoV-2 infection and long-COVID symptoms, providing a theoretical support for analyzing the frequency of long-COVID symptoms in European and Chinese populations. The path module correlation coefficients between specific COVID-19-related genes in the European and Chinese populations and genes that may induce long-COVID symptoms were calculated. The results showed that the path module correlation coefficients were completely consistent with the frequency of long-COVID symptoms in the Chinese population, but slightly different in the European population. Furthermore, the cathepsin C (CTSC) gene was found to be a potential COVID-19-related gene by a path module correlation coefficient correction rate. Our study can help to explore other long-COVID symptoms that have not yet been discovered and provide a new perspective to research this syndrome. Meanwhile, the path module correlation coefficient correction rate can help to find more species-specific genes related to COVID-19 in the future.

Financial Well-Being and Financial Capability among Low-Income Entrepreneurs

Financial well-being is a key component of quality of life and overall well-being and is likely to affect other aspects of quality of life, such as health and health care. The COVID-19 pandemic presents an immense crisis of financial well-being among low-income entrepreneurs and has left many small-scale entrepreneurs financially fragile. We argue that promoting the financial capability of low-income entrepreneurs is effective in protecting their financial well-being from a crisis. To examine the association between financial capability and the financial well-being of low-income entrepreneurs, we use the 2016 National Financial Well-Being Survey, which provides the latest and comprehensive measurement of financial capability, including financial knowledge, financial skills, and access to financial products and services. Our analyses show that, compared to their higher-income counterparts, low-income entrepreneurs have statistically lower levels of financial well-being, financial knowledge, financial skills, and access to mainstream financial products;they also have a statistically higher risk of using high-fee alternative financial products. In addition, low-income entrepreneurs have larger barriers to accessing mainstream financial products than low-income non-entrepreneurs. The results indicate that financial capability plays a significant role in promoting the financial well-being of low-income entrepreneurs. © 2023 by the authors.

Advances in mRNA nanomedicines for malignant brain tumor therapy

Nowadays, malignant brain tumors are still mostly lethal diseases with poor prognosis and a clinical median survival rate of fewer than 2 years after therapeutic intervention. It is difficult to achieve complete remission of brain tumors due to blood-brain barrier (BBB) and a lack of efficient drug delivery systems to targeted transportation of brain tumor medicines. Nanoparticle delivery systems have shown merits including stability and high carrier capacity for the transportation of different drugs to treat brain tumors. The application of mRNA nanomedicines brings in great promise not only in COVID-19, but also for malignant brain tumor immunotherapy. The appropriate delivery system facilitates mRNA delivery efficiency and enhances the immune response successfully, for optimal treatment outcomes on malignant brain tumors. Herein, we do an updated review on the development of mRNA nanomedicines for malignant brain cancer treatment. We focus on how to design mRNA-loaded nanoparticle-based delivery systems with optimized pharmacokinetics and pharmacodynamics for efficient therapy of brain cancers. In addition, we point out the challenges and solutions for further development of mRNA nanomedicines for brain cancer therapy. We hope this review would stimulate interest among researchers with different backgrounds and expedite the translation from bench to bedside for the mRNA nanomedicines. © 2022 The Authors

Bioinformatics analysis of SARS-CoV-2 infection-associated immune injury and therapeutic prediction for COVID-19

Background: Severe acute respiratory syndrome coronavirus 2 is a highly contagious viral infection, without any available targeted therapies. The high mortality rate of COVID-19 is speculated to be related to immune damage. Methods: In this study, clinical bioinformatics analysis was conducted on transcriptome data of coronavirus infection. Results: Bioinformatics analysis revealed that the complex immune injury induced by coronavirus infection provoked dysfunction of numerous immune-related molecules and signaling pathways, including immune cells and toll-like receptor cascades. Production of numerous cytokines through the Th17 signaling pathway led to elevation in plasma levels of cytokines (including IL6, NF-kB, and TNF-a) followed by concurrent inflammatory storm, which mediates the autoimmune response. Several novel medications seemed to display therapeutic effects on immune damage associated with coronavirus infection. Conclusions: This study provided insights for further large-scale studies on the target therapy on reconciliation of immunological damage associated with COVID-19. Copyright © 2021 Shandong University, published by Wolters Kluwer, Inc.

MDN: Meta-transfer Learning Method for Fake News Detection

The rapid development of social media has brought convenience to people’s lives, but at the same time, it has also led to the widespread and rapid dissemination of false information among the population, which has had a bad impact on society. Therefore, effective detection of fake news is of great significance. Traditional fake news detection methods require a large amount of labeled data for model training. For emerging events (such as COVID-19), it is often hard to collect high-quality labeled data required for training models in a short period of time. To solve the above problems, this paper proposes a fake news detection method MDN (Meta Detection Network) based on meta-transfer learning. This method can extract the text and image features of tweets to improve accuracy. On this basis, a meta-training method is proposed based on the model-agnostic meta-learning algorithm, so that the model can use the knowledge of different kinds of events, and can realize rapid detection on new events. Finally, it was trained on a multi-modal real data set. The experimental results show that the detection accuracy has reached 76.7%, the accuracy rate has reached 77.8%, and the recall rate has reached 85.3%, which is at a better level among the baseline methods. © 2022, Springer Nature Singapore Pte Ltd.

Factors associated with depression in residents in the post-epidemic era.

OBJECTIVE: To explore the factors associated with depression in residents in the post-epidemic era of COVID-19. METHODS: A multi-stage stratified random sampling method was used to conduct a questionnaire survey among community residents through self-designed questionnaires and self-rating depression scale (SDS). Multivariate logistic regression analysis was performed on the influencing factors of depressive symptoms. RESULTS: A total of 1993 residues completed the survey of depression status. The incidence of depressive symptoms was 27.04%. The multivariate logistic regression analysis showed that female (odds ratio (OR): 6.239, 95% confidence interval (CI): 2.743-10.698), body mass index (BMI) > 24 (OR: 2.684, 95% CI: 1.059-3.759) and drinking (OR: 1.730, 95% CI: 1.480-3.153) were the risk factors for developing depressive symptoms. Married (OR: 0.417, 95% CI: 0.240-0.652), monthly income (3001-5000 yuan, OR: 0.624, 95% CI: 0.280-0.756; >5000 yuan, OR: 0.348, 95% CI: 0.117-0.625), ordinary residents (OR: 0.722, 95% CI: 0.248-0.924) and urban residents (OR: 0.655, 95% CI: 0.394-0.829) were the protective factors of depressive symptoms. CONCLUSIONS: Under the post-epidemic era of COVID-19, depressive symptoms are still common among community residents in China. Gender, BMI, drinking, marriage, monthly income and nature of personnel and residential area are associated with the incidence of depressive symptoms.

A critical analysis of collaborative and disruptive digital-driven built environment education

The COVID-19 pandemic has driven the teaching and learning provisions more towards virtual platforms, exposing lack of resilience and technology preparedness. This study aims to provide a critical appraisal of existing pedagogical studies on built environment (e.g., Building Information Modelling or BIM) challenging the opportunism and agency theories in response towards remote education provision provoked by the pandemic. The study consists of critical review of two literature samples, namely how the education sector as a whole has been responding to the pandemic, and the digitalisation-based pedagogy in built environment especially how the pedagogy addresses the pandemic. The review of the second literature sample evaluates longitudinally how BIM-based built environment education had evolved. A conceptual framework incorporating multiple factors from the review of the two literature samples is finally proposed. These factors include educational theories (e.g., Bloom's Taxonomy), curriculum development addressing assessment, student experience, collaborative learning, delivery approaches, and teaching methods. This review-based study not only provides an overview of the digital built environment pedagogical work in higher education, but also contests the opportunism response to remote or blended learning and how the post-pandemic era could embrace the remote delivery-platforms to engender a variety of pedagogical principles, for example, cross-disciplinary team-based information sharing, experiential learning, and projectbased learning. The findings of this study represent a barometer and roadmap for measuring the resilience of higher education and built environment programmes towards pandemic and technological disruptions. © 2021 Proceedings of the 37th Annual ARCOM Conference, ARCOM 2021. All Rights Reserved.

Effective Cross-Region Courier-Displacement for Instant Delivery via Reinforcement Learning

With the rapid development of mobile phones and the Internet of Things, instant delivery services (e.g., UberEats and MeiTuan) have become a popular choice for people to order foods, fruits, and other groceries online, especially after the impact of COVID-19. In instant delivery services, it is important to dispatch massive orders to limited couriers, especially in rush hours. To meet this need, an efficient courier displacement mechanism not only can balance the demand (picking up orders) and supply (couriers’ capacity) but also improve the efficiency of order delivery by reducing idle displacing time. Existing studies on fleet management of rider-sharing or bike rebalancing cannot apply to courier displacement problems in instant delivery due to unique practical factors of instant delivery including region difference and strict delivery time constraints. In this work, we propose an efficient cross-region courier displacement method Courier Displacement Reinforcement Learning (short for CDRL), based on multi-agent actor-critic, considering the dynamic demand and supply at the region level and strict time constraints. Specifically, the multi-agent actor-critic reinforcement learning-based courier displacement framework utilizes a policy network to generate displacement decisions considering multiple practical factors and designs a value network to evaluate decisions of the policy network. One month of real-world order records data-set of Shanghai collecting from Eleme (i.e., one of the biggest instant delivery services in China) are utilized in the evaluation and the results show that our method offering up to 36% increase in courier displacement performance and reduce idle ride time by 17%. © 2021, Springer Nature Switzerland AG.

Detection of Novel Coronavirus Pneumonia Based on CT Image with Convolutional Neural Network Processing

To improve the ability to distinguish novel coronavirus pneumonia from common pneumonia and assist medical staff in chest CT examination of pneumonia patients, a detection method using convolution neural network and CT image based on artificial intelligence image analysis was proposed. First, a convolution neural network model was built, and the influence of model depth on detection results was evaluated to select the best network structure. Second, a tabu genetic algorithm was proposed to obtain the optimal hyper-parameter combination of the network model and to enhance the performance of the model. Finally, the best network model was employed to distinguish novel coronavirus pneumonia from common pneumonia. Experimental results show that the accuracy, MCC, and F1Score of the proposed detection algorithm are 93.89%, 93.32% and 91.40%, respectively, which has higher detection accuracy than other algorithms. © 2021, Acta Metrologica Sinica Press. All right reserved.

Mechanism of corrections to false information in online social network

The mechanism of corrections on false information in real social network environments was analyzed. The effect of corrections was evaluated and its influencing factors were explored. Eight factors that affect the effectiveness of correction were summarized based on existing research and our hypotheses, such as the proportion of the original false information, whether it contains text warnings of false information, whether to explain the explanation, user influence, etc.. The effectiveness of correction posts was evaluated by sentiment analysis and the social context of themselves. Statistical methods were used to test the relationship between the pre-determined influencing factors and the effectiveness of correction. The experiment was conducted based on the false information data about COVID-19 epidemic collected from Sina Weibo. Results show that a higher proportion of false information in a correction reduces the effectiveness, and explaining the reason improves the effectiveness. Six conclusions that improve the effectiveness of corrections on social networks were proposed such as mentioning original misinformation less, explaining why original misinformation is wrong. Guidance was provided for related media to correct false information on social network. Copyright ©2021 Journal of Zhejiang University (Engineering Science). All rights reserved.

Performance of urea-mediated dissociation in reducing false-positive of 2019-nCoV IgM test

Objective: To investigate the interference factors causing false-positive result of novel coronavirus IgM antibody detected by gold immunochromatography assay (GICA) and enzyme-linked immunosorbent assay (ELISA). Methods: A total of 71 serum from different pathogen infections and related chronic diseases patients were collected from the Affiliated Hospital of North Sichuan Medical College from January 22, 2020 to February 15, 2020. GICA and ELISA were used to detect 2019-nCoV IgM in 71 serum, including 5 influenza A virus (Flu A) IgM positive serum, 5 influenza B virus (Flu B) IgM positive serum, 5 Mycoplasma pneumonia (MP) IgM positive serum, 5 Legionella pneumophila (LP) IgM positive serum, 29 rheumatoid factor (RF) IgM positive serum, 5 hypertension patients serum, 5 diabetes mellitus patients serum, 6 human immunodeficiency virus (HIV) infection patients serum and 6 COVID-19 patients serum. The interference factors causing false positive results of the two methods were analyzed, and urea dissociation test was employed to dissociate the 2019-nCoV IgM positive serum using the best dissociation concentration. Statistical analyses were performed by SPSS, version 19.0. Results: 2019-nCoV IgM was positive in 18 cases of middle-high level RF-IgM positive serum and 6 cases of 2019-nCoV-infected serum detected by two methods, and the other 47 serum were negative. When the dissociation concentration of urea was 6 mol/L, 2019-nCoV IgM was negative in 17 cases of middle-high level RF-IgM positive serum and positive in 6 cases of 2019-nCoV-infected serum detected by GICA. When the urea dissociation concentration was 4 mol/L, dissociation time was 10 min and the avidity index<0.46 was set as negative, 2019-nCoV IgM was negative in 15 cases of middle-high level RF-IgM positive serum and positive in 6 cases of 2019-nCoV-infected serum detected by ELISA. Conclusion: The middle-high level of RF-IgM could cause false positive results of 2019-nCoV IgM detected by GICA and ELISA, and the urea dissociation test would be helpful for reducing the probability of false-positive results of 2019-nCoV IgM test.