The impact of China's financial policy on economic resilience during the pandemic period

The COVID-19 has impacted the social economy of various provinces in China to varying degrees. How to quickly restore the social economy has become the most concerned issue of the Party, the country and all sectors of society. This paper combines the entropy weight method and TOPSIS method-technique for order performance by similarity to ideal solution, taking the financial policy transmission mechanism as the theoretical basis, and selects the data of 29 provinces in China to obtain the contribution of finance in the socio-economic resilience under the pandemic situation. The empirical analysis results show that the weights of financial policy, pandemic situation and financial basis are different. It can be clearly seen from the weight data that the financial basis is crucial to the socio-economic resilience. Although the COVID-19 pandemic will cause huge losses to the whole society and will also seriously hinder the socio-economic recovery, the effective implementation of financial policies and the good trend of the pandemic situation have a significant promoting effect on the socio-economic recovery. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

The role of heritage sites and other influential factors in domestic tourism inflow to Australian states: a gravity model approach

Domestic tourism plays a crucial role in the Australian economy, generating revenue, creating employment opportunities, fostering cultural identity, and facilitating tourism growth and development. The remote regions of Australia are particularly reliant on domestic inbound tourism to stimulate their local economies. This study investigates the influence of heritage sites and various factors on domestic tourism inflows to eight states in the Australia between 1998-2021. The gravity method and random effect model are employed for the empirical analysis. The results indicate that the macro determinants, including population of origin state, gross state product per capita, infrastructural development, shared border between states, and the number of heritage sites, have significant and positive impact on domestic tourism inflow. Conversely, the consumer price index, distance, and pandemic outbreak have a negative influence on domestic tourism inflow. These findings hold important practical implications. Given Australia's geographical remoteness, promoting domestic tourism becomes imperative to boost the tourism industry and local economies. Therefore, it is recommended that authorities prioritize domestic tourism flows and invest in infrastructure, preserve heritage sites, stabilize prices, implement effective marketing strategies, and respond swiftly to public emergencies such as the Covid-19 pandemic.

Towards a Model for Inclusive Healthcare Access post COVID-19

The devastation caused by the COVID-19 pandemic has exposed years of cyclic inequalities faced by disadvantaged and minority communities. Unequal access to healthcare and a lack of financial resources further exacerbates their suffering, especially during a pandemic. In such critical conditions, information technology-based healthcare services can be an efficient way of increasing access to healthcare for these communities. In this paper, we put forward a decision model for guiding the distribution of IT-based healthcare services for racial minorities. We augment the Health Belief Model by adding financial and technology beliefs. We posit that financial inclusion of minority populations increases their ability to access technology and, by extension, IT-based healthcare services. Financial inclusion and the use of secure private technologies like federated learning can indeed enable greater access to healthcare services for minorities. Therefore, we incorporate financial, health, and technology tools to develop a model for equitable delivery of healthcare services and test its applicability in different use-case scenarios. © 2022 IEEE Computer Society. All rights reserved.

Study on Online Education Students' Attention based on Multi-source Data in COVID-19

With the frequent occurrence of COVID-19 virus, online learning is currently the response of most educational institutions. However, in online learning, teachers do not have enough grasp of students' learning status, so how to assess student concentration in online learning is of great significance. In order to reduce the interference of classroom teaching, this paper adopt a non-contact observation method to analyze and evaluate the students' facial features. Considering that student concentration is a fuzzy variable, a reasonable weight of each factor is constructed in combination with the analytic hierarchy method, and a framework for identifying the concentration of online learning that integrates multi-source data is proposed. Finally, the effect of concentration assessment was verified by experiments. © 2022 IEEE.

Public Opinion Analysis for the Covid-19 Pandemic Based on Sina Weibo Data

The outbreak of Covid-19 has been continuously affecting human lives and communities around the world in many ways. In order to effectively prevent and control the Covid-19 pandemic, public opinion is analyzed based on Sina Weibo data in this paper. Firstly the Weibo data was crawled from Sina website to be the experimental dataset. After preprocessing operations of data cleaning, word segmentation and stop words removal, Term Frequency Inverse Document Frequency (TF-IDF) method was used to perform feature extraction and vectorization. Then public opinion for the Covid-19 pandemic was analyzed, which included word cloud analysis based on text visualization, topic mining based on Latent Dirichlet Allocation (LDA) and sentiment analysis based on Naïve Bayes. The experimental results show that public opinion analysis based on Sina Weibo data can provide effective data support for prevention and control of the Covid-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis of medication regularities of epidemic disease prescription in Treatise on Febrile Diseases and mechanism of core drugs in treatment of COVID-19

Objective To analyze the medication rules of related epidemic disease prescription in Treatise on Febrile Diseases based on data mining, and the mechanism of "Chaihu (Bupleuri Radix)-Huangqin (Scutellariae Radix)” as the core drugs in the treatment of coronavirus disease 2019 (COVID-19) by network pharmacology, in order to explore the contemporary value of classical prescriptions in the treatment of epidemic diseases. Methods The prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were screened, and the medication rules such as drug frequency, flavor and meridian tropism as well as correlation, apriori algorithm were analyzed by using software such as R language. The mechanism of the core drugs in the medication pattern in the treatment of COVID-19 was explored by the network pharmacology. A "disease-drug-ingredient-target” network was constructed on the selected components and targets with Cytoscape. The key targets were introduced into String database for network analysis of protein-protein interaction (PPI), and gene ontology (GO) functional analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were conducted in R language. Results A total of 61 prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were included, including 52 traditional Chinese medicines (TCMs). In the top 20 high-frequency drugs, warm drugs, spicy drugs and qitonifying drugs were mainly used, mostly in the spleen and lung meridian. Chaihu (Bupleuri Radix) and Huangqin (Scutellariae Radix) herb pair had the strongest correlation. A total of five clusters were excavated: supplemented formula of Xiaochaihu Decoction (小柴胡汤), Sini Decoction (四逆汤), supplemented formule of Maxing Shigan Decoction (麻杏石甘汤), Fuling Baizhu Decoction (茯苓白术汤) and Dachengqi Decoction (大承气汤). A total of 45 active ingredients, 189 action targets of Bupleuri Radix-Scutellariae Radix herb pair, and 543 targets of COVID-19 were obtained from TCMSP and Genecards, and 64 intersection targets were generated. The results of the network analysis showed that the main components of core drugs pair against COVID-19 may be quercetin, wogonin, kaempferol baicalein, acacetin etc., and the core targets may be VEGFA, TNF, IL-6, TP53, AKT1, CASP3, CXCL8, PTGS2, etc. A total of 1871 related entries and 164 pathways were obtained by GO and KEGG enrichment analysis, respectively. Conclusion In Treatise on Febrile Diseases, the treatment of epidemic diseases mainly chose pungent, warm, spleen-invigorating and qi-tonifying herbs, such as Xiaochaihu Decoction, Sini Decoction and Dachengqi Decoction, etc. It was found that Bupleuri Radix-Scutellariae Radix core herb pair prevent and treat COVID-19 through multi-target targets such as PTGS2, IL-6 and TNF. The ancient prescriptions for treating epidemic disease in Treatise on Febrile Diseases may have significant reference value for the prevention and treatment of new epidemic diseases today. © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Analysis of medication regularities of epidemic disease prescription in Treatise on Febrile Diseases and mechanism of core drugs in treatment of COVID-19

Objective To analyze the medication rules of related epidemic disease prescription in Treatise on Febrile Diseases based on data mining, and the mechanism of "Chaihu (Bupleuri Radix)-Huangqin (Scutellariae Radix)" as the core drugs in the treatment of coronavirus disease 2019 (COVID-19) by network pharmacology, in order to explore the contemporary value of classical prescriptions in the treatment of epidemic diseases. Methods The prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were screened, and the medication rules such as drug frequency, flavor and meridian tropism as well as correlation, apriori algorithm were analyzed by using software such as R language. The mechanism of the core drugs in the medication pattern in the treatment of COVID-19 was explored by the network pharmacology. A "disease-drug-ingredient-target" network was constructed on the selected components and targets with Cytoscape. The key targets were introduced into String database for network analysis of protein-protein interaction (PPI), and gene ontology (GO) functional analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were conducted in R language. Results A total of 61 prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were included, including 52 traditional Chinese medicines (TCMs). In the top 20 high-frequency drugs, warm drugs, spicy drugs and qitonifying drugs were mainly used, mostly in the spleen and lung meridian. Chaihu (Bupleuri Radix) and Huangqin (Scutellariae Radix) herb pair had the strongest correlation. A total of five clusters were excavated: supplemented formula of Xiaochaihu Decoction (), Sini Decoction (), supplemented formule of Maxing Shigan Decoction (), Fuling Baizhu Decoction () and Dachengqi Decoction (). A total of 45 active ingredients, 189 action targets of Bupleuri Radix-Scutellariae Radix herb pair, and 543 targets of COVID-19 were obtained from TCMSP and Genecards, and 64 intersection targets were generated. The results of the network analysis showed that the main components of core drugs pair against COVID-19 may be quercetin, wogonin, kaempferol baicalein, acacetin etc., and the core targets may be VEGFA, TNF, IL-6, TP53, AKT1, CASP3, CXCL8, PTGS2, etc. A total of 1871 related entries and 164 pathways were obtained by GO and KEGG enrichment analysis, respectively. Conclusion In Treatise on Febrile Diseases, the treatment of epidemic diseases mainly chose pungent, warm, spleen-invigorating and qi-tonifying herbs, such as Xiaochaihu Decoction, Sini Decoction and Dachengqi Decoction, etc. It was found that Bupleuri Radix-Scutellariae Radix core herb pair prevent and treat COVID-19 through multi-target targets such as PTGS2, IL-6 and TNF. The ancient prescriptions for treating epidemic disease in Treatise on Febrile Diseases may have significant reference value for the prevention and treatment of new epidemic diseases today. Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Forecasting the dynamic of the COVID-19 pandemic by an adaptive Cauchy Quantum-Behaved Particle Swarm Optimization Algorithm

Accurate identification of parameters is critical to the epidemiological utility of the results obtained from the COVID-19 transmission model. In order to optimize the model parameters, we propose an adaptive Cauchy quantum particle swarm optimization (QPSO) algorithm. We introduce a piecewise Cauchy mutation operator and the mutation probability is adjusted adaptively according to the fitness to enhance the global search ability of QPSO. The experimental results show that the improved QPSO algorithm has higher accuracy than original QPSO and PSO algorithms. © 2022 SPIE.

The Boosting Effect of Online Retailing on Household Consumption during Covid-19

The outbreak of Covid-19 has posed severe negative impact on household consumption. This paper investigates the boosting effect of online retailing on household consumption during the epidemic period. Based on the data of Anhui Province in China, this paper show that during the epidemic period, every 1% increase in the growth rate of online retail sales could increase the proportion of total retail sales of consumer goods above the quota in GDP by 4.27%. Therefore, we provide reliable empirical evidence of promoting consumer consumption through the development of online retail under the normalization of the epidemic situation. © 2021 ACM.

Constructing Mobile Crowdsourced COVID-19 Vulnerability Map with Geo-Indistinguishability

Preventing COVID-19 disease from spreading in communities will require proactive and effective healthcare resources allocations, such as vaccinations. A fine-grained COVID-19 vulnerability map will be essential to detect the high-risk communities and guild the effective vaccine policy. A mobile-crowdsourcing-based self-reporting approach is a promising solution. However, an accurate mobile-crowdsourcing-based map construction requests participants to report their actual locations, raising serious privacy concerns. To address this issue, we propose a novel approach to effectively construct a reliable community-level COVID-19 vulnerability map based on mobile crowdsourced COVID-19 self-reports without compromising participants’location privacy. We design a geo-perturbation scheme where participants can locally obfuscate their locations with the geo-indistinguishability guarantee to protect their location privacy against any adversaries’prior knowledge. To minimize the data utility loss caused by location perturbation, we first design an unbiased vulnerability estimator and formulate the location perturbation probability generation into a convex optimization. Its objective is to minimize the estimation error of the direct vulnerability estimator under the constraints of geo-indistinguishability. Given the perturbed locations, we integrate the perturbation probabilities with the spatial smoothing method to obtain reliable community-level vulnerability estimations that are robust to a small-sampling-size problem incurred by location perturbation. Considering the fast-spreading nature of coronavirus, we integrate the vulnerability estimates into the modified susceptible-infected-removed (SIR) model with vaccination for building a future trend map. It helps to provide a guideline for vaccine allocation when supply is limited. Extensive simulations based on real-world data demonstrate the proposed scheme superiority over the peer designs satisfying geo-indistinguishability in terms of estimation accuracy and reliability. IEEE

Quality Evaluation of Image Dataset based on Label File

The quality of the dataset affects the accuracy of the artificial intelligence model, but it is a lot of work to manually detect errors related to the quality evaluation of the dataset, and it may not be possible to perform quality evaluation through simple viewing. Therefore, we propose an image dataset quality measurement model, including nine evaluation metrics, and analyze the evaluation metrics from three aspects: definition, calculation formula and description. Based on the label file, the quality of the dataset file and the content of the dataset is evaluated, and the evaluation standard is given to judge whether the quality of the dataset is qualified. The measurement model and evaluation criteria proposed in this article were verified against the Cifar-10 dataset and the COVID-CT dataset, and the problems of label accuracy and label category imbalance were found, which proved the effectiveness of the method in this paper. © 2021 IEEE.

COVID-19 Vulnerability Map Construction via Location Privacy Preserving Mobile Crowdsourcing

The pandemic of the coronavirus (COVID-19) has caused an unprecedented global public health crisis, and most countries in the world are running out of the healthcare resources. A fine-grained COVID-19 vulnerability map will be essential to track the number of people with covid-like symptoms, so that the the potential outbreak communities can be identified and the valuable healthcare resources can proactively and dynamically be allocated. Mobile crowdsourcing based symptom reporting is a promising and convenient option to construct such a map, while it may compromise the location privacy of crowdsourcing participants. In this work, we propose a novel approach to establish the COVID-19 vulnerability map based on the crowdsourced reporting without disclosing the participants' location privacy to a semi-honest crowdsourcing aggregator. Briefly, based on the differentially private geo-indistinguishability, the mobile participants are able to locally perturb their geographic data. With the masked geographic information, we employ the best linear unbiased prediction estimator with spatial smoothing to obtain the reliable vulnerability estimates in the areas of interest and construct the map. Given the fast spreading nature of coronavirus, we integrate the vulnerability estimates with a susceptible-exposed-infected-removed (SEIR) model to build up a future trend map. Extensive simulations based on real-world data verify the effectiveness of the proposed method.

Teacher Agency in Adapting to Online Teaching during COVID-19: A Case Study on Teachers of Chinese as an Additional Language in Macau

The COVID-19 university closure forced a rapid transition and adaptation to online teaching. This paper reports on a case study that examined teacher agency in response to online teaching from February to September 2020. In the study we collected multiple data from three teachers of Chinese as an additional language, including semi-structured interviews, institutional documents, and field notes, to investigate their exercise of agency in adapting to online teaching. The analysis revealed that the participants displayed strong agency to build digital competence and develop student-centered pedagogy at different stages. At the same time, the shift to online from classroom-based teaching allowed them an opportunity to transform existing practices and seek innovative pedagogy, such as a hybrid model blending asynchronous and synchronous online teaching. This study also suggests the influence of flexible and collaborative institutional culture and teacher professional digital competence in shaping the participants' agency in addressing the diverse challenges of online teaching. These findings offer insights into the value of an agency-oriented approach to professional learning and development in educational change. Educational stakeholders should pay more attention to the dynamic interaction between educational institutional systems and teacher agentic practice.

Discounting lives: Analyzing the causes of Hispanic Adversity raging in the Healthcare sector during Covid-19 using Cumulative Inequality Theory

In the wake of the devastation caused by Covid-19 in the community, one racial minority has been the most heavily hit by this pandemic - Hispanics. There have been numerous studies that bring out the difference in the level and quality of healthcare received by racial and ethnic minorities. However, most of these studies have focused on using socioeconomic status to account for minorities being disproportionately affected by health-related issues and ailments. We investigate the cumulative and ever-increasing gap in healthcare facilities and the resultant inequality which leads to Hispanics being severely affected by the novel coronavirus more than any other race or ethnicity. The study highlights the importance of considering a cumulative inequality effect that can help explain the reason for minorities being more prone to Covid-19. Even with an increase in their socioeconomic status, Hispanics have a way higher infection rate than other races. It is here that we use the cumulative inequality theory to explain the counterintuitive observation above. A cumulative inequality in healthcare facilities over the years helps to account for the disproportionate infection rate among the Hispanic population. We conduct empirical case comparisons (ECCs) to test our hypotheses and find that socioeconomic status is not sufficient to explain the higher infection rates among the minority population. We propose using cumulative inequality theory to fight both the current infection rate among minorities and fortify them from being negatively affected by future pandemics as well. © 2021 IEEE Computer Society. All rights reserved.

Association of acute kidney injury with the severity and mortality of SARS-CoV-2 infection: A meta-analysis

BACKGROUND: we aimed to explore the relationship of acute kidney injury (AKI) with the severity and mortality of coronavirus disease 2019 (COVID-19). METHODS: A systematic literature search was conducted in PubMed, EMBASE, Scopus, Web of Science, MedRxiv Database. We compared the laboratory indicators of renal impairment and incidences of AKI in the severe versus non-severe cases, and survival versus non-survival cases, respectively. RESULTS: In 41 studies with 10,335 COVID-19 patients, the serum creatinine (sCr) in severe cases was much higher than that in non-severe cases (SMD = 0.34, 95% CI: 0.29-0.39), with a similar trend for blood urea nitrogen (BUN) (SMD = 0.66, 95%CI: 0.51-0.81), hematuria (OR = 1.59, 95% CI: 1.15-2.19), and proteinuria (OR = 2.92, 95% CI: 1.58-5.38). The estimated glomerular filtration rate decreased significantly in severe cases compared with non-severe cases (SMD = -0.45, 95% CI: -0.67- -0.23). Moreover, the pooled OR of continuous renal replacement therapy (CRRT) and AKI prevalence for severe vs. non-severe cases was 12.99 (95%CI: 4.03-41.89) and 13.16 (95%CI: 10.16-17.05), respectively. Additionally, 11 studies with 3759 COVID-19 patients were included for analysis of disease mortality. The results showed the levels of sCr and BUN in non-survival cases remarkably elevated compared with survival patients, respectively (SMD = 0.97, SMD = 1.49). The pooled OR of CRRT and AKI prevalence for non-survival vs. survival cases was 31.51 (95%CI: 6.55-151.59) and 77.48 (95%CI: 24.52-244.85), respectively. CONCLUSIONS: AKI is closely related with severity and mortality of COVID-19, which gives awareness for doctors to pay more attention for risk screening, early identification and timely treatment of AKI.