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We employed publicly available user-generated content (UGC) data from the website Tripadvisor and developed an autoregressive integrated moving average (ARIMA) model using the R language to analyze the seasonality of the use of urban green space (UGS) in Okinawa under normal conditions and during the COVID-19 pandemic. The seasonality of the use of ocean-area UGS is primarily influenced by climatic factors, with the peak season occurring from April to October and the off-peak season from November to March. Conversely, the seasonality of the use of non-ocean-area UGS remains fairly stable throughout the year, with a relatively high number of visitors in January and May. The outbreak of the COVID-19 pandemic greatly impacted visitor enthusiasm for travel, resulting in significantly fewer actual postings compared with predictions. During the outbreak, use of ocean-area UGS was severely restricted, resulting in even fewer postings and a negative correlation with the number of new cases. In contrast, for non-ocean-area UGS, a positive correlation was observed between the change in postings and the number of new cases. We offer several suggestions to develop UGS management in Okinawa, considering the opportunity for a period of recovery for the tourism industry.
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The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the reso-lution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at x3 SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.
ABSTRACT
Compound Qinlan Oral Liquid (,CQOL) is derived from Yinqiao San (), which is composed of Jinyinhua (Lonicerae Japonicae Flos), Huangqin (Scutellariae Radix), Lianqiao (Forsythiae Fructus) and Banlangen (Isatidis Radix). It is a common clinical herbal medicine for clearing heat and detoxification, and has antiviral effects. By reviewing the active ingredients of CQOL and the research progress on its anti-influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) efficacy, with view to providing a basis for the clinical use of CQOL in treatment of respiratory diseases caused by SARS-CoV-2.Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.
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To discuss the decision-making scheme of crowding risk management during the COVID-19 pandemic, this paper constructs an evolutionary game model based on the changes of pedestrian and government strategies, and simulates the strategy selection under different states. The results show that under the condition of pedestrian rationality, when the difference between the benefits and costs of the government's active response strategy is less than the benefits of inaction, the government will choose the strategy of inaction. If the benefit of rational action is less than the additional benefit of irrational action, pedestrians will choose irrational action. By establishing the replication dynamic equations of governments and pedestrians, the stability strategy of the system is analyzed. It is found that the values of R1, R2, R3, R4, R5, C1, C2, C3, C4, C5, C6, C7 will affect the strategy choices of the players, and how to measure the benefits and costs under different circumstances becomes the key to the problem. These findings provide a theoretical basis for the risk control decision of human crowding during the COVID-19 epidemic. © 2023 SPIE.
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Coronavirus Disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV2) has spread around the world in a global pandemic [1-4]. Early and daily detection of suspected COVID-19 patients is the most important approach not only for tracing close contacts to prevent further spread [5], but also providing crucial information for healthcare providers and officials to make resource allocation and policy decisions [6]. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Teamwork is usually a component of the learning assessment framework of online courses, and the timely sharing of information and feedback through synchronous communication is beneficial for team-based assignments. However, during the COVID-19 pandemic, university students did not always actively engage in virtual teamwork in online environments because they were learning from home and were subject to the effect of disruptions at home. This study explored the perceptions of university students who engaged in synchronous discussions involving teamwork during the COVID-19 pandemic. Semi-structured individual interviews were conducted with 25 Taiwanese university students. Through qualitative methods that incorporated individual interviews and a content analysis, six content concepts were established. Afterward, relational content analysis was conducted. This study revealed that the students benefited from visible synchronous discussions in terms of inquiry dialogue, project creation, and learning satisfaction. Notably, the interviewees mostly did not switch on their webcams but recognized that webcam use can benefit discussions. The participants' decision to switch on/off their webcams was primarily influenced by their perception of the importance of a given project than by their privacy concerns. Moreover, when home-based leaning was implemented during the COVID-19 pandemic, group leaders encountered an obstruction when they were guiding their group members to engage in teamwork involving physical tasks. Future studies should explore how members should be guided to engage in teamwork involving physical tasks during synchronous discussions.
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The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which extracts knowledge index structures and knowledge representations for exercises. Unfortunately, to the best of our knowledge, existing tagging approaches based on exercise content either ignore multiple components of exercises, or ignore that exercises may contain multiple concepts. To this end, in this paper, we present a study of concept tagging. First, we propose an improved pre-trained BERT for concept tagging with both questions and solutions (QSCT). Specifically, we design a question-solution prediction task and apply the BERT encoder to combine questions and solutions, ultimately obtaining the final exercise representation through feature augmentation. Then, to further explore the relationship between questions and solutions, we extend the QSCT to a pseudo-siamese BERT for concept tagging with both questions and solutions (PQSCT). We optimize the feature fusion strategy, which integrates five different vector features from local and global into the final exercise representation. Finally, we conduct extensive experiments on real-world datasets, which clearly demonstrate the effectiveness of our proposed models for concept tagging. IEEE
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The COVID-19 pandemic may have been a difficult time to join a new organization. Drawing on the feelings-as-information theory, this study explores how COVID-19 lockdown anxiety influenced newcomers' job satisfaction during their first few months of work. We tested 357 new employees working in 84 cities across China. We conducted a longitudinal study, and participants were invited to complete the same survey at two time points. Cross-lagged panel analysis was conducted to test our hypotheses. We confirmed that COVID-19 lockdown anxiety at Time 1 predicted less job satisfaction at Time 2, whereas the data did not support the idea of reverse causality. These findings suggest public health crises like the pandemic can impact newcomers' job satisfaction, especially during China's Zero-Covid Policy.
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The epidemiological data were collected from travellers who returned from Guinea on the 23rd of September, 2020 and were diagnosed with malaria at a C OVID -19 quarantine site in Qingdao, Shandong Province. The epidemiological characteristics, diagnosis and treatment of the cases and the epidemiology investigation and the rapid test screening results for other travellers on from the same flight and the interventions in reaction to the imported malaria cases were analyzed. The results showed that 4 out of 231 Guinean returned travellers had developed malaria symptoms, including chills and fever, during the isolation period. Rapid diagnostic test (RDT) indicated Plasmodium falciparum infection. Considering the patients ' travel history, clinical manifestations, and laboratory RDT test results, a confirmed diagnosis of imported P. falciparum malaria was made. The four malaria cases, who are male workers aged 29 to 55, were transferred to Jiaozhou People ' s Hospital for treatment. All four patients were administrated of artemether tablets upon diagnosis. One of the cases experienced severe malaria complications and were administrated with 12 doses (60 mg/dose) of artesunate intravenously for five days. The other three patients were treated with dihydroartemisinin and piperaquine phosphate tablets for one course of 8 tablets in 2 days (40 mg dihydroartemisinin and 320 mg piperaquine phosphate), respectively. Among the 231 returned travellers, 111 (48.1 %) had a history of malaria overseas. There were 23 positive cases detected by RDT, including the four symptomatic cases. The other 19 cases were asymptomatic. One of the asymptomatic cases became symptomatic three months later and was diagnosed as an imported P. malariae infection. Laboratory blood smear microscopic tests at the Jiaozhou City and Qingdao Municipal Center For Disease Control and Prevention showed negative results for the four malaria cases and the 19 RDT positive case. The samples from the four malaria cases were rechecked by the provincial reference laboratory of Shandong Institute of parasitic Disease. The results were negative for malaria infection by microscopic examination but positive for P. falciparum infection by nucleic acid test. It is suggested that during the routine control of COVID-19, the awareness of COVID-19 and malaria should be established among the returned travellers from high malaria-endemic areas. The health education "gate" should be moved forward to improve the treatment compliance for malaria cases and reduce the relapse or recrudescence caused by sub-optimal treatment.Copyright © 2022, Chin J Parasitol Parasit Dis. All rights reserved.
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Objective To analyze the epidemiological characteristics of COVID-19 cluster epidemic in Huizhou from January to February in 2020, and we provide experience and reference for the prevention and control of cluster epidemic. Methods Descriptive epidemiology was used to analyze the clusters of COVID-19 in Huizhou city. Results From January to February in 2020, a total of 19 COVID-19 cluster outbreaks were reported in Huizhou. The most common cluster outbreaks were in Huidong county (8 cases), Boluo county (3 cases) and Huiyang district (3 cases). There were 59 cases involved in 19 outbreaks, among which 46 were confirmed cases, and 13 were asymptomatic infected. The sex ratio of male to female was 0.84: 1, the age was 1-85 years old. The 19 cases of outbreaks were all caused by imported cases, among which 13 cases were imported from Wuhan (68.4%), 3 cases were imported from Hubei province except Wuhan (15.8%), and 3 cases were imported from other provinces and cities (15.8%). There were 13 cases (68.4%) in the first generation, and 6 cases (31.6%) in the second generation. Events exposed place were variety, including 3 (15.8%) simple family exposure, 13 (68.4%) joint exposure, exposure family, 1 (5.26%) of the joint exposure, family exposure, family dinners, 1 (5.26%) of the joint exposure, family exposure, exposure (hotel) exposed in public places, 1 (5.26%) of the collective unit (workplace) exposure. Conclusion All the COVID - 19 cluster outbreaks in Huizhou city were caused by imported cases, most of which occurred in the family and were caused by families living together and eating together. As the number of people returning to work, production and school increases, various prevention and control measures should be implemented in key areas, key populations and key places to prevent the outbreak from rebounding.Copyright © China Tropical Medicine 2021.
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Objective To analyze the epidemiological characteristics of community transmission of the coronavirus disease 2019 (COVID-19) caused by four imported cases in Hebei Province, and to provide a scientific basis for the prevention and control of the disease. Methods Descriptive epidemiological methods were used to analyze the epidemiological characteristics of four community-transmitted COVID-19 outbreaks reported in the China Disease Control and Prevention Information System from January 1, 2020 to December 31, 2021 in Hebei Province. Results From January 1, 2020 to December 31, 2021, four community-transmitted COVID-19 outbreaks caused by imported COVID-19 occurred in Hebei Province, respectively related of Hubei (Wuhan) Province, Beijing Xinfadi market, Overseas cases and Ejina banner of Inner Mongolia Autonomous Region. Total of 1 656 cases (1 420 confirmed cases and 236 asymptomatic cases) were reported, including 375 cases in phase A (From January 22 to April 16, 2020), and phase B (from June 14 to June 24, 2020) 27 cases were reported, with 1 116 cases reported in the third phase (Phase C, January 2 to February 14, 2021), and 138 cases reported in the fourth phase (Phase D, October 23 to November 14, 2021). The 1 656 cases were distributed in 104 counties of 11 districts (100.00%), accounting for 60.46% of the total number of counties in the province. There were 743 male cases and 913 female cases, with a male to female ratio of 0.81:1. The minimum age was 13 days, the maximum age was 94 years old, and the average age (median) was 40.3 years old. The incidence was 64.01% between 30 and 70 years old. Farmers and students accounted for 54.41% and 14.73% of the total cases respectively. Of the 1 420 confirmed cases, 312 were mild cases, accounting for 21.97%;Common type 1 095 cases (77.11%);There was 1 severe case and 12 critical cases, accounting for 0.07% and 0.85%, respectively. 7 patients died from 61.0 to 85.7 years old. The mean (median) time from onset to diagnosis was 1.9 days (0-31 days), and the mean (median) time of hospital stay was 15 days (1.5-56 days). Conclusions Four times in Hebei province COVID-19 outbreak in scale, duration, population, epidemic and type of input source, there are some certain difference, but there are some common characteristics, such as the outbreak occurs mainly during the legal holidays or after starting and spreading epidemic area is mainly in rural areas, aggregation epidemic is the main mode of transmission, etc. To this end, special efforts should be made to strengthen the management of people moving around during holidays, and strengthen the implementation of epidemic prevention and control measures in places with high concentration of people. To prevent the spread of the epidemic, we will step up surveillance in rural areas, farmers' markets, medical workers and other key areas and groups, and ensure early detection and timely response.Copyright © 2022 China Tropical Medicine. All rights reserved.
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The outbreak of COVID-19 has impacted traditional teaching methods in schools, and blended teaching in the post-pandemic has gradually become a hot topic of research in higher education. Computational thinking, as one of the core literacies to be acquired in the 21st century, can help students realize the importance of computers as well as enable them to solve specific problems more effectively when facing real-life situations. The article takes the C language programming course as an example, analyzes the problems faced in teaching in the post-pandemic, introduces the concept of computational thinking and integrates it into all aspects of blended teaching design, pays attention to students' individual differences, and proposes a blended teaching model based on computational thinking and puts it into practice. The results show that this teaching model can improve students' learning performance, exercise students' computational thinking skills, and promote blended teaching reform and students' personalized development. © 2022 IEEE.
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Compound Qinlan Oral Liquid (,CQOL) is derived from Yinqiao San (), which is composed of Jinyinhua (Lonicerae Japonicae Flos), Huangqin (Scutellariae Radix), Lianqiao (Forsythiae Fructus) and Banlangen (Isatidis Radix). It is a common clinical herbal medicine for clearing heat and detoxification, and has antiviral effects. By reviewing the active ingredients of CQOL and the research progress on its anti-influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) efficacy, with view to providing a basis for the clinical use of CQOL in treatment of respiratory diseases caused by SARS-CoV-2.Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.
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This introductory chapter first discusses some key terms in the book (e.g., remote, distance, and online) and then provides a brief history of remote teaching and learning of Chinese language, including its origin and development over the past 120 years, as well as its relationship to other instructional modes. The chapter then introduces the other 14 chapters, contributed by 21 authors from 10 countries on 5 continents. Next, it discusses four key findings across the chapters. The overview concludes with a summary and remarks on new opportunities and trends that have emerged during the COVID-19 pandemic, suggesting that we build on what we have learned from the crisis to advance Chinese language education globally. © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.
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Artificial Intelligence (AI) is a transformative force in communication and messaging strategy, with potential to disrupt traditional approaches. Large language models (LLMs), a form of AI, are capable of generating high-quality, humanlike text. We investigate the persuasive quality of AI-generated messages to understand how AI could impact public health messaging. Specifically, through a series of studies designed to characterize and evaluate generative AI in developing public health messages, we analyze COVID-19 pro-vaccination messages generated by GPT-3, a state-of-the-art instantiation of a large language model. Study 1 is a systematic evaluation of GPT-3's ability to generate pro-vaccination messages. Study 2 then observed peoples' perceptions of curated GPT-3-generated messages compared to human-authored messages released by the CDC (Centers for Disease Control and Prevention), finding that GPT-3 messages were perceived as more effective, stronger arguments, and evoked more positive attitudes than CDC messages. Finally, Study 3 assessed the role of source labels on perceived quality, finding that while participants preferred AI-generated messages, they expressed dispreference for messages that were labeled as AI-generated. The results suggest that, with human supervision, AI can be used to create effective public health messages, but that individuals prefer their public health messages to come from human institutions rather than AI sources. We propose best practices for assessing generative outputs of large language models in future social science research and ways health professionals can use AI systems to augment public health messaging. © 2023 ACM.
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HostSeq was launched in April 2020 as a national initiative to integrate whole genome sequencing data from 10,000 Canadians infected with SARS-CoV-2 with clinical information related to their disease experience. The mandate of HostSeq is to support the Canadian and international research communities in their efforts to understand the risk factors for disease and associated health outcomes and support the development of interventions such as vaccines and therapeutics. HostSeq is a collaboration among 13 independent epidemiological studies of SARS-CoV-2 across five provinces in Canada. Aggregated data collected by HostSeq are made available to the public through two data portals: a phenotype portal showing summaries of major variables and their distributions, and a variant search portal enabling queries in a genomic region. Individual-level data is available to the global research community for health research through a Data Access Agreement and Data Access Compliance Office approval. Here we provide an overview of the collective project design along with summary level information for HostSeq. We highlight several statistical considerations for researchers using the HostSeq platform regarding data aggregation, sampling mechanism, covariate adjustment, and X chromosome analysis. In addition to serving as a rich data source, the diversity of study designs, sample sizes, and research objectives among the participating studies provides unique opportunities for the research community.
Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/genetics , COVID-19/epidemiology , Canada/epidemiology , Genomics , Whole Genome SequencingABSTRACT
Metapopulation models have been a popular tool for the study of epidemic spread over a network of highly populated nodes (cities, provinces, countries) and have been extensively used in the context of the ongoing COVID-19 pandemic. In the present work, we revisit such a model, bearing a particular case example in mind, namely that of the region of Andalusia in Spain during the period of the summer-fall of 2020 (i.e., between the first and second pandemic waves). Our aim is to consider the possibility of incorporation of mobility across the province nodes focusing on mobile-phone time-dependent data, but also discussing the comparison for our case example with a gravity model, as well as with the dynamics in the absence of mobility. Our main finding is that mobility is key toward a quantitative understanding of the emergence of the second wave of the pandemic and that the most accurate way to capture it involves dynamic (rather than static) inclusion of time-dependent mobility matrices based on cell-phone data. Alternatives bearing no mobility are unable to capture the trends revealed by the data in the context of the metapopulation model considered herein.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Pandemics , Models, Biological , Mathematical Concepts , TimeABSTRACT
The increasing emergence of infectious diseases like COVID-19 has created an urgent need for filtration/purification materials coupled with multifunctional features such as mechanical integrity, excellent airflow/filtration, and antibacterial/antimicrobial properties. Polymer membranes and metal-organic frameworks (MOFs) have demonstrated high effectiveness in air filtration and purification. MOF nanoparticles have been introduced into electrospun polymer nanofibrous membranes through embedding or postsolution growth. However, the derived hybrids are still facing the issue of (1) limited MOF exposure, which leads to low efficacy;and (2) uncontrollable growth, which leads to pore blocking and low breathability. In this work, we customized an electrospray-on-electrospinning in situ process to dynamically integrate MOF nanoparticles into a robust and elastic continuous nanofibrous membrane for advanced properties including high mechanical strength and flexibility, excellent breathability, particle filtration, and good antimicrobial performance. Biodegradable polylactic acid was reinforced by the poly(hydroxybutyrate)-di-poly(DLA-CL)x copolymer (PHBR) and used as an electrospinning matrix, while MOF nanoparticles were simultaneously electrically sprayed onto the nanofibers with easily controllable MOF loading. The MOF nanoparticles were homogeneously deposited onto nanofibers without clogging the pores in the membrane. The collision of PLA and MOF under the wet status during electrospinning and the hydrogen bonding through C=O and N-H bonds strengthen the affinity between PLA nanofibers and MOF nanoparticles. Because of these factors, the MOF-incorporated PLA/PHBR nanofibrous membrane achieved over 95% particle filtration efficiency with enhanced mechanical properties while maintaining high breathability. Meanwhile, it exhibits excellent photocatalytic antibacterial performance, which is necessary to kill microbes. The electrospray-on-electrospinning in situ process provides an efficient and straightforward way to hybridize one-dimensional (1D) or two-dimensional (2D) nanomaterials into a continuous nanofibrous membrane with strong interaction and controllable loading. Upon integrating proper functionalities from the materials, the obtained hybrids are able to achieve multifunctionalities for various applications.