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During the formation of medical images, they are easily disturbed by factors such as acquisition devices and tissue backgrounds, causing problems such as blurred image backgrounds and difficulty in differentiation. In this paper, we combine the HarDNet module and the multi-coding attention mechanism module to optimize the two stages of encoding and decoding to improve the model segmentation performance. In the encoding stage, the HarDNet module extracts medical image feature information to improve the segmentation network operation speed. In the decoding stage, the multi-coding attention module is used to extract both the position feature information and channel feature information of the image to improve the model segmentation effect. Finally, to improve the segmentation accuracy of small targets, the use of Cross Entropy and Dice combination function is proposed as the loss function of this algorithm. The algorithm has experimented on three different types of medical datasets, Kvasir-SEG, ISIC2018, and COVID-19CT. The values of JS were 0.7189, 0.7702, 0.9895, ACC were 0.8964, 0.9491, 0.9965, SENS were 0.7634, 0.8204, 0.9976, PRE were 0.9214, 0.9504, 0.9931. The experimental results showed that the model proposed in this paper achieved excellent segmentation results in all the above evaluation indexes, which can effectively assist doctors to diagnose related diseases quickly and improve the speed of diagnosis and patients’quality of life. Author
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Understanding a concept that people cannot observe directly in real life is always challenging in education. It could be even more difficult for public health education topics such as viruses or bacteria. However, public health education is critical for understanding the knowledge of the virus in the age of COVID-19. Thus, this paper proposes a distributed mixed reality environment to enhance public health education in the internet of things (IoT) context. We introduce the design methodology based on the mixed reality interaction characteristics, the implementation, and the initial evaluation. © 2023 Author(s).
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Objective: Although the neurological and olfactory symptoms of coronavirus disease 2019 have been identified, the neurotropic properties of the causative virus, severe acute respiratory syndrome-associated coronavirus 2 (SARS-CoV-2), remain unknown. We sought to identify the susceptible cell types and potential routes of SARS-CoV-2 entry into the central nervous system, olfactory system, and respiratory system. Method(s): We collected single-cell RNA data from normal brain and nasal epithelium specimens, along with bronchial, tracheal, and lung specimens in public datasets. The susceptible cell types that express SARS-CoV-2 entry genes were identified using single-cell RNA sequencing and the expression of the key genes at protein levels was verified by immunohistochemistry. We compared the coexpression patterns of the entry receptor angiotensin-converting enzyme 2 (ACE2) and the spike protein priming enzyme transmembrane serine protease (TMPRSS)/cathepsin L among the specimens. Result(s): The SARS-CoV-2 entry receptor ACE2 and the spike protein priming enzyme TMPRSS/cathepsin L were coexpressed by pericytes in brain tissue;this coexpression was confirmed by immunohistochemistry. In the nasal epithelium, ciliated cells and sustentacular cells exhibited strong coexpression of ACE2 and TMPRSS. Neurons and glia in the brain and nasal epithelium did not exhibit coexpression of ACE2 and TMPRSS. However, coexpression was present in ciliated cells, vascular smooth muscle cells, and fibroblasts in tracheal tissue;ciliated cells and goblet cells in bronchial tissue;and alveolar epithelium type 1 cells, AT2 cells, and ciliated cells in lung tissue. Conclusion(s): Neurological symptoms in patients with coronavirus disease 2019 could be associated with SARS-CoV-2 invasion across the blood-brain barrier via pericytes. Additionally, SARS-CoV-2-induced olfactory disorders could be the result of localized cell damage in the nasal epithelium.Copyright © Wolters Kluwer Health, Inc. All rights reserved.
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The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decreased by 27.9% and 17.82%, respectively. This approach can serve as a basis for dispatching emergency materials during public health emergencies.
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The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decreased by 27.9% and 17.82%, respectively. This approach can serve as a basis for dispatching emergency materials during public health emergencies. © 2023 by the authors.
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In light of the fast-spreading number of COVID-19 cases worldwide, contact tracing proved to be an effective measure to slow down the infection rate and mitigate the casualties caused by this virus. However, because of several concerns in terms of privacy, as well as security, several countries and their population around the globe are reluctant to adopt contact tracing solutions to contain the spread of the virus. Because of its distributed, public, and auditable nature, blockchain can be a groundbreaking solution contact tracing, given that it would provide a privacy-oriented contact tracing solution. Therefore, in this chapter, we discuss and compare the two alternatives proposed by the BeepTrace framework, active and passive, and also present some initial results based on an early implementation of it. As it can be seen, by utilizing blockchain together with contact tracing, user privacy, security, and decentralization can be guaranteed, giving back the trust needed for these applications to work. © 2022 by John Wiley & Sons Ltd. All rights reserved.
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PurposeThis study aims to identify and analyse the gains and strains associated with flexible working practices (FWPs) introduced and adopted prior to and during the pandemic and consider how these experiences are likely to shape the future of workplace flexibility post-pandemic. Design/methodology/approachA case study research strategy was adopted to explore the FWPs implemented by a state-owned organisation in the Chinese publishing industry. A mixed data collection method was used. Quantitative data was collected from 50 valid questionnaires, which was followed by 7 qualitative interviews to gain rich insights into the availability and effectiveness of various FWPs and associated benefits and drawbacks. FindingsWhile the results confirm positive effects FWPs have on employee engagement and retention and on business continuity and employee well-being during the pandemic, the empirical analysis highlights the performance-driven patterns in use and impacts of some FWPs, which caused concerns and dilemmas. Besides the increasing intense market competition, the changing face of state-owned enterprises and managerial attitudes have been found to have significant effects on the use of FWPs. Originality/valueThis paper has contributed to a better understanding of flexible working in an under-researched setting, reflected in changes before and during the pandemic, offering an insight into the commercialised nature of flexible working in the Chinese context. It has implications for organisations and HR practitioners as they envision future workplace flexibility.
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In 2022, the COVID-19 epidemic significantly affected the effective supply of films in China, whereby the market fluctuated, and box-office sales decreased. However, China's resilient film industry continued to persevere with quality products being released. New government policies were launched to help the industry's enterprises alleviate their difficulties. Due to these policies, film enterprises endured to reduce costs, increase efficiency, and survive the difficult times. While the top-grossing films supported the market, a number of small and medium budget films were successful, with the reputation of young filmmakers growing rapidly. Creating healthy industry ecology, rebuilding the confidence of practitioners, giving full play to the main role of the market, increasing the effective supply of diversified products, and promoting the vertical and horizontal integration of film within other industries are of great significance to the recovery of both the Chinese film industry and its market in the post-epidemic period. © 2023 Walter de Gruyter GmbH, Berlin/Boston.
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In China, there has been a significant increase in carbon emissions in the new era. Therefore, evaluating the influence of industrial structure upgrades and energy structure optimization on reducing carbon emissions is the objective of this research. Based on the provincial panel data of 30 provinces and cities across China from 1997 to 2019, this paper builds up a fixed-effect panel quantile STIRPAT model to investigate the differences in the impact of industrial structure on carbon emission intensity at different quantile levels from the provincial perspective, and as a way of causality test, the mediation effect model is adopted to empirically test the transmission path of "industrial structure upgrading-energy structure optimization-carbon emission reduction". The research results show that: (1) Both industrial structure upgrades and energy structure optimization have significant inhibitory effects on carbon emissions, and there are regional heterogeneities. (2) The upgrading of industrial structure has a significant positive effect on optimizing energy structure. (3) The upgrading of industrial structure can not only directly restrain carbon emissions but also indirectly have a significant inhibitory effect on carbon emissions by promoting the optimization of energy structure. Based on the above conclusions, corresponding policy recommendations are proposed to provide suggestions for China to achieve the goal of carbon neutrality.
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COVID-19 pandemic has paused many ongoing research projects and unified researchers' attention to focus on COVID-19 related issues. Our project traces 712,294 scientists' publications related to COVID-19 for two years, from January 2020 to December 2021, in order to detect the dynamic evolution patterns of COVID-19 collaboration network over time. By studying the collaboration network of COVID-19 scientists, we observe how a new scientific community has been built in preparation for a sudden shock. The number of newcomers grows incrementally, and the connectivity of the collaboration network shifts from loose to tight promptly. Even though every scientist has an equal opportunity to start a study, collaboration disparity still exists. Following the scale-free distribution, only a few top authors are highly connected with other authors. These top authors are more likely to attract newcomers and work with each other. As the collaboration network evolves, the increase rate in the probability of attracting newcomers for authors with higher degree increases, whereas the increase rates in the probability of forming new links among authors with higher degree decreases. This highlights the interesting trend that COVID pandemic alters the research collaboration trends that star scientists are starting to collaborate more with newcomers, but less with existing collaborators, which, in certain way, reduces the collaboration disparity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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In a low-carbon context, the connectedness among carbon, stock, and renewable energy markets has been strengthening. This study examines the effect of Brexit, the launch of the European Green Deal and the COVID-19 pandemic on the connectedness among carbon, stock, and renewable energy markets by employing Time Varying Parameter -Vector Auto Regression (TVP-VAR). First, equal interval impulse response analysis shows that in the short term, the renewable energy market suffers from a positive shock from the carbon market and this shock gradually decreases from the initial 1.6×10−3. In the long run, the connectivity between the carbon market and the stock market, and between the carbon market and the renewable energy market is almost 0. Second, we can conclude that the positive connectivity between stock market to carbon market and renewable energy market to carbon market is enhanced by COVID-19 in the short term, with values of 7.5×10−3 and 3.6×10−3 respectively. Finally, renewable energy market received a greater negative impact from the carbon market during COVID-19 than during the release of the European Green Deal, while Brexit allowed positive carbon price spillover to renewable energy price. © 2023 Elsevier Ltd
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In order to improve risk prevention and control capabilities for international sports events under the background of COVID-19, case data of 23 international sports since the pandemic outbreak were collected, and an evolutionary network model with COVID-19 as risk source was established. Then, risk analysis on the model was carried out based on in-and-out degree, number of sub-net nodes, the shortest path and average path of complex network theory, key risk event nodes were identified, and preventive measures were put forward. Finally, critical chains were obtained by analyzing causal mechanism and types of risk chains, and countermeasures and suggestions for chain disconnection and disaster mitigation were put forward. The results show that severe epidemic situation and rising risk of virus transmission in host cities are the key nodes in evolutionary network, and cycle chain of political relations and public opinion is the most destructive one. Therefore, it is necessary to promote the development of a public opinion monitoring system and strengthen positive publicity of sports events. © 2020 China Safety Science Journal. All rights reserved.
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In order to improve risk prevention and control capabilities for international sports events under the background of COVID-19, case data of 23 international sports since the pandemic outbreak were collected, and an evolutionary network model with COVID-19 as risk source was established. Then, risk analysis on the model was carried out based on in-and-out degree, number of sub-net nodes, the shortest path and average path of complex network theory, key risk event nodes were identified, and preventive measures were put forward. Finally, critical chains were obtained by analyzing causal mechanism and types of risk chains, and countermeasures and suggestions for chain disconnection and disaster mitigation were put forward. The results show that severe epidemic situation and rising risk of virus transmission in host cities are the key nodes in evolutionary network, and cycle chain of political relations and public opinion is the most destructive one. Therefore, it is necessary to promote the development of a public opinion monitoring system and strengthen positive publicity of sports events. © 2020 China Safety Science Journal. All rights reserved.
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Disruptions in maritime networks may cause significant financial burden and damage to business. Recently, some international ports have been experiencing unprecedented congestions due to the COVID19 pandemic and other disruptions. It is paramount for the maritime industry to further enhance the capability to assess and predict impacts of disruptions. With more data available from industrial digitization and more advanced technologies developed for big data analytics and simulation, it is possible to build up such capability. In this study, we developed a discrete event simulation model backed with big data analytics for realistic and valid inputs to assess impacts of the Suez Canal blockage to the Port of Singapore. The simulation results reveal an interesting finding that, the blockage occurred in the Suez Canal can hardly cause significant congestion in the Port of Singapore. The work can be extended to evaluate impacts of other types of disruptions, even occurring concurrently. © 2022 IEEE.
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COVID-19 pandemic has paused many ongoing research projects and unified researchers' attention to focus on COVID-19 related issues. Our project traces 712,294 scientists' publications related to COVID-19 for two years, from January 2020 to December 2021, in order to detect the dynamic evolution patterns of COVID-19 collaboration network over time. By studying the collaboration network of COVID-19 scientists, we observe how a new scientific community has been built in preparation for a sudden shock. The number of newcomers grows incrementally, and the connectivity of the collaboration network shifts from loose to tight promptly. Even though every scientist has an equal opportunity to start a study, collaboration disparity still exists. Following the scale-free distribution, only a few top authors are highly connected with other authors. These top authors are more likely to attract newcomers and work with each other. As the collaboration network evolves, the increase rate in the probability of attracting newcomers for authors with higher degree increases, whereas the increase rates in the probability of forming new links among authors with higher degree decreases. This highlights the interesting trend that COVID pandemic alters the research collaboration trends that star scientists are starting to collaborate more with newcomers, but less with existing collaborators, which, in certain way, reduces the collaboration disparity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.