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The epidemic of coronavirus disease 2019 (COVID-19) has seriously affected people's normal work, life, and medical treatment. Since Mar. 2022, there has been a pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant in Shanghai. In order to meet the needs of hospitalization for patients, and at the same time for better control of epidemic and nosocomial infections, a large hospital in Shanghai innovatively set up a centralized transition ward in the hospital, and established scientific rules of medical work, regulations for prevention of nosocomial infections and efficient norms for patient admission. During the operation of the ward, a total of 211 patients were treated and one of the patients was confirmed of COVID-19 recurrence. All work was carried out methodically, and neither hospitalized patients nor medical staff had nosocomial infection of COVID-19. The preparation, operation and management of the central transition ward in our hospital are summarized here to provide guidance and reference for general hospitals to carry out similar work under the epidemic.Copyright © 2022, Second Military Medical University Press. All rights reserved.
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Background: The coronavirus disease pandemic has caused significant disruption in the field of education, resulting in the need for more online classes and a blended offline and online teaching model. Therefore, understanding what makes this model effective is important. Accordingly, this study explored the structural relationships among academic pressure, independent learning ability, and academic self-efficacy in a blended teaching environment during the pandemic and independent learning ability's mediating effect on the relationship between academic pressure and academic self-efficacy. Methods: Adopting a random sampling method, this study surveyed 761 Chinese college, Shaanxi Province, China in 2022 and university students. Factor analysis, correlation analysis, structural equation modeling, and path analysis were used to analyze the data. Results: The results show that the academic pressure faced by Chinese English majors had a significant negative impact on academic self-efficacy (P<0.001). However, academic pressure had no statistical effect on stu-dents' independent learning ability (P=0.317). Moreover, independent learning ability had a significant positive effect on academic self-efficacy (P<0.001) and a mediating effect on the relationship between academic pressure and academic self-efficacy (P=0.032). Conclusion: Independent learning ability can directly and indirectly affect academic self-efficacy. Thus, in an online and offline blended teaching model, teachers should guide students regarding self-exploration, com-munication, and cooperation based on existing knowledge and experience. They should also enable students to improve their learning process and independent learning ability. Various language learning situations should be established for learning English so that by experiencing success and failure, students can ultimately improve their academic self-efficacy. © 2023 Zhao et al.
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During an emergency, timely and effective distribution of emergency supplies is critical in rescue. In the context of Covid-19, given the difficulties in distributing supplies to communities due to super infectious viruses, unmanned vehicle distribution is studied by taking into account the priority and satisfaction of communities to improve distribution safety and effectiveness of supplies. Furthermore, the influence of distribution time on the overall efficiency is also taken into account, thus ultimately establishing an unmanned distribution model with the shortest distribution time while meeting community satisfaction. The improved whale algorithm is used to solve the dual-objective model and compared with the basic whale optimization algorithm. The results show that the improved whale algorithm demonstrates better convergence, searchability, and stability. The constructed model can scientifically distribute daily necessities to communities while considering their priority and satisfaction. © 2022 IEEE.
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The 21st century has begun with digital economy platforms increasingly defining the lives of humans. It is thus, inevitable that digital economy platforms have proliferated extensively in Malaysia to transform the way businesses, organizations, and economic agents interact with each other and with their customers. Especially, internet platforms and mobile devices have enabled consumers to share resources from others more cheaply, including in collectively sourced platforms. This chapter examines the impact of the COVID-19 pandemic on the digital economy (gig), and in doing so, the manner with which it is influencing the way firms operate and consumer attitudes towards their products and services. It finishes by articulating its potential role and future prospects in raising the appropriation of economic synergies for Malaysian consumers by strengthening the digital ecosystem of through a focus on its shared economy properties. © 2023 selection and editorial matter, Rajah Rasiah, Wah Yun Low, and Nurliana Kamaruddin;individual chapters, the contributors.
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The wine industry in China has developed rapidly and occupied an important position in the domestic and foreign wine markets. China is the fifth largest consumer of wine in the world. However, under the impact of COVID-19, the competition to secure a limited market share in the domestic wine market has become increasingly fierce and the previous marketing strategies of the Changyu Wine Company can no longer adapt to the changing consumer environment. In addition, the sales of imported and domestic wine continued the ‘double decline' trend in 2018 and some wine production and operation enterprises encountered difficulties. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.
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Neutrophils play an important role in infectious diseases by clearing pathogens in the early stages of the disease and damaging the surrounding tissues along with the disease progress. Low-density neutrophils (LDNs) are a crucial and distinct subpopulation of neutrophils. They are a mixture of activated and degranulated normal mature neutrophils and a considerable number of immature neutrophils prematurely released from the bone marrow. Additionally, they may be involved in the occurrence and development of diseases through the changes in phagocytosis, the generation of reactive oxygen species (ROS), the enhancement of the ability to produce neutrophils extracellular traps and immunosuppression. We summarizes the role of LDNs in the pathogenesis and their correlation with the severity of infectious diseases such as COVID-19, severe fever with thrombocytopenia syndrome (SFTS), AIDS, and tuberculosis.
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While higher education institutions promptly responded to the transition to online or blended practices as a result of COVID-19, there is limited current understanding of how first-year PhD students committed themselves to various online networking experiences during their initial stage of professional development. By drawing on Kolb's experiential learning cycle, this chapter elicits two first-year international PhD students' professional trajectories of forming our professional identities in academia during the COVID-19 pandemic. Despite engaging with different professional socialisation activities, we both underwent three transformative stages which we classify as acquiring knowledge, establishing networks, and gaining validation. Our findings indicate that our dynamic and consecutive professional identity formation transitioned through three stages: a doctoral student, an institutional member, and an early career researcher. This chapter reveals how this linear three-stage process respectively unfolds for different international doctoral students. In this regard, relevant implications are proposed for current and prospective international doctoral students and their institutions to refer to in better facilitating international doctoral students' professional identity development during and beyond COVID-19 pandemic. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.
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The coronavirus disease 2019 (COVID-19) pandemic has resulted in a considerable increase in hospitalizations, leading to an increasing demand for accurate and efficient techniques for diagnosis. The CT-based diagnosis can provide pathologic information to assist treatment but be restricted due to inefficient and relatively complicated implementation. With the advent of deep learning and advanced hardware, an AI-assisted method diagnosis and segmentation for COVID-19 are proposed. In this paper, many traditional machine learning methods for imaging classification and segmentation are discussed, such as k-Nearest Neighbours (KNN), support vector machines (SVM), edge-based or region-based segmentation. In addition, we proposed a ResNet-based model and an improved U-Net for medical tasks of classification and segmentation, respectively. Our proposed model achieved desirable accuracy in medical applications. © 2023 SPIE.
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Traffic flowprediction has always been the focus of research in the field of Intelligent Transportation Systems, which is conducive to the more reasonable allocation of basic transportation resources and formulation of transportation policies. The spread of COVID-19 has seriously affected the normal order in the transportation sector. With the increase in the number of infected people and the government's anti-epidemic policy, human outgoing activities have gradually decreased, resulting in increasingly obvious discreteness and irregularities in traffic flow data. This article proposes a deep-space time traffic flow prediction model based on discrete wavelet transform (DSTM-DWT) to overcome the highly discrete and irregular nature of the new crown epidemic. First, DSTM-DWT decomposes traffic flow into discrete attributes, such as flow trend, discrete amplitude, and discrete baseline. Second, we design the spatial relationship of the transportation network as a graph and integrate the new crown pneumonia epidemic data into the characteristics of each transportation node. Then, we use the graph convolutional network to calculate the spatial correlation of each node, and the temporal convolutional network to calculate the temporal correlation of the data. In order to solve the problem of high discreteness of traffic flow data during the epidemic, this article proposes a graph memory network (GMN), which is used to convert discrete magnitudes separated by discrete wavelet transform into highdimensional discrete features. Finally, use DWT to segment the predicted traffic data, and then perform the inverse discrete wavelet transform between the newly segmented traffic trend and discrete baseline and the discrete model predicted by GMN to obtain the final traffic flow prediction result. In simulation experiments, this work was compared with the existing advanced baselines to verify the superiority of DSTM-DWT.
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Based on a broad definition of the digital economy, this study constructs a digital technology capital measure-ment framework and measures China's digital technology capital services from 2002 to 2018 to illustrate the growth of digital technology. In addition, the relationship between digital technology and economic growth is examined using the growth accounting method and econometric panel models from the perspective of the substitution and penetration effects of information and communication technologies. The measurement results show that China's digital technology capital services grew faster than non-digital ones from 2002 to 2018. In terms of the substitution effect, the contribution of traditional capital to economic growth is diminishing, whereas digital technology capital's contribution is expanding. Regarding the penetration effect, the contribution of digital technology capital to labor productivity is gradually overtaking that of traditional capital, with the impact varying by sector. These results may provide developing countries with a strategy to seek new devel-opment drivers and sustain economic growth in the post-pandemic era.
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BACKGROUND: Gender disparity in authorship broadly persists in medical literature, little is known about female authorship within pulmonary medicine. METHODS: A bibliometric analysis of publications from 2012 to 2021 in 12 journals with the highest impact in pulmonary medicine was conducted. Only original research and review articles were included. Names of the first and last authors were extracted and their genders were identified using the Gender-API web. Female authorship was described by overall distribution and distribution by country/region/continent and journal. We compared the article citations by gender combinations, evaluated the trend in female authorship, and forecasted when parity for first and last authorship would be reached. We also conducted a systematic review of female authorship in clinical medicine. RESULTS: 14,875 articles were included, and the overall percentage of female first authors was higher than last authors (37.0% vs 22.2%, p<0.001). Asia had the lowest percentage of female first (27.6%) and last (15.2%) authors. The percentages of female first and last authors increased slightly over time, except for a rapid increase in the COVID-19 pandemic periods. Parity was predicted in 2046 for the first authors and 2059 for the last authors. Articles with male authors were cited more than articles with female authors. However, male-male collaborations significantly decreased, whereas female-female collaborations significantly increased. CONCLUSIONS: Despite the slow improvement in female authorship over the past decade, there is still a substantial gender disparity in female first and last authorship in high-impact medical journals in pulmonary medicine.