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This study analyzed the changes in ecotourism flow in Hunan Province before and after the COVID-19 epidemic by obtaining the ecotourism route data for 2019 and 2020 from online tourism platforms such as wasp nest, poor travel, Ctrip, tuniu and qunar, and determining geographical distribution characteristics, node characteristics and network characteristics with the help of software such as UCINET, Netdraw and ArcGIS. The results re-vealed major changes in spatial distribution, the roles of nodes, and the structure and composition of the flow net-work. (1) COVID-19 has changed the spatial distribution pattern of ecotourism flows in Hunan Province. Under the influence of the epidemic, the spatial geographical concentration trend in Hunan Province decreased from 30.42 in 2019 to 28.94 in 2020, the flow in the North weakened, and the hot spots shifted to the south. In order to promote the development of global tourism in Hunan Province, we should focus on how to correctly handle the differences in tourism resources and the imbalance in tourism development between the Xiangxi region and other tourism regions. (2) The COVID-19 epidemic triggered changes of the roles of some nodes in the ecotourism flow network in Hunan Province. The comprehensive efficiency value of the node decreased from 266 to 222, indicating that crisis events such as the epidemic have caused damage to the ecotourism flow in Hunan Province, and the surrounding low-density ecotourism sites with good natural scenery have become more favored by tourists after the COVID-19 epidemic. (3) The COVID-19 situation has affected the structure and composition of the ecotourism flow network in Hunan Province. The overall network density of ecotourism flow in Hunan Province increased from 0.18 to 0.14, the degree of separation between ecotourism destinations increased, and tourists' demand for health and safety has driven the development of ecotourism flow in Hunan Province towards multinode and multiregional characteristics. © 2023, Editorial office of Journal of Resources and Ecology. All rights reserved.
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Objective To improve the diagnosis and treatment level for tuberculous meningitis (TBM) under the Coronavirus disease 2019 (COVID-19) epidemic. Methods The diagnosis and treatment course of a female patient under the COVID-19 epidemic was analyzed for high fever, vomiting for 23 days, headache, talk nonsense for 10 days, inability to stand, and double vision lasting 5 days. The patient was successively misdiagnosed to suffer from viral pneumonia and acute disseminated encephalomyelitis (ADEM) in other hospitals. The patient had a history of transit at Hankou railway station (wearing a mask without departing the station throughout the process) under the COVID-19 epidemic. The patient had a history of leukopenia and long-term medical therapy. The patient was diagnosed as TBM by applying the diagnostic scheme for the 2019 China Central Nervous System Tuberculosis Diagnosis and Treatment Guidelines after physical examination, cerebrospinal fluid test, magnetic resonance imaging (MRI) plain scan and enhanced examination. The analysis on reasons for extramural hospital misdiagnosis showed it was related to the lack of careful physical examination and lack of scientific analysis of laboratory test results. Results The intracranial pressure reduction, anti-tuberculosis treatment, adrenal cortex hormone treatment and symptomatic treatment were immediately administered according to the 2019 China Central Nervous System Tuberculosis Diagnosis and Treatment Guidelines. Intensive anti-tuberculosis treatment (4 months) was implemented firstly and followed by the anti-tuberculosis treatment (12 months) during the consolidation phase, clinically enabling the patient to be cured. Conclusion Careful inquiry of medical history, careful physical examination, timely cerebrospinal fluid examination and MRI examination and scientific analysis on clinical data are critical to confirmation of TBM. Standard anti-tuberculosis treatment, rational use of adrenal cortex hormones and lowering intracranial pressure are critical factors for curing. © 2023, Editorial Department of Medical Pest Control. All rights reserved.
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Objective To improve the diagnosis and treatment level for tuberculous meningitis (TBM) under the Coronavirus disease 2019 (COVID-19) epidemic. Methods The diagnosis and treatment course of a female patient under the COVID-19 epidemic was analyzed for high fever, vomiting for 23 days, headache, talk nonsense for 10 days, inability to stand, and double vision lasting 5 days. The patient was successively misdiagnosed to suffer from viral pneumonia and acute disseminated encephalomyelitis (ADEM) in other hospitals. The patient had a history of transit at Hankou railway station (wearing a mask without departing the station throughout the process) under the COVID-19 epidemic. The patient had a history of leukopenia and long-term medical therapy. The patient was diagnosed as TBM by applying the diagnostic scheme for the 2019 China Central Nervous System Tuberculosis Diagnosis and Treatment Guidelines after physical examination, cerebrospinal fluid test, magnetic resonance imaging (MRI) plain scan and enhanced examination. The analysis on reasons for extramural hospital misdiagnosis showed it was related to the lack of careful physical examination and lack of scientific analysis of laboratory test results. Results The intracranial pressure reduction, anti-tuberculosis treatment, adrenal cortex hormone treatment and symptomatic treatment were immediately administered according to the 2019 China Central Nervous System Tuberculosis Diagnosis and Treatment Guidelines. Intensive anti-tuberculosis treatment (4 months) was implemented firstly and followed by the anti-tuberculosis treatment (12 months) during the consolidation phase, clinically enabling the patient to be cured. Conclusion Careful inquiry of medical history, careful physical examination, timely cerebrospinal fluid examination and MRI examination and scientific analysis on clinical data are critical to confirmation of TBM. Standard anti-tuberculosis treatment, rational use of adrenal cortex hormones and lowering intracranial pressure are critical factors for curing. © 2023, Editorial Department of Medical Pest Control. All rights reserved.
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Environmental-friendly and efficient strategies for triclosan (TCS) removal have received more attention. Influenced by COVID-19, a large amount of TCS contaminants were accumulated in medical and domestic wastewater discharges. In this study, a unique g-C3N4/Bi2MoO6 heterostructure was fabricated and optimized by a novel and simple method for superb photocatalytic dechlorination of TCS into 2-phenoxyphenol (2-PP) under visible light irradiation. The as-prepared samples were characterized and analyzed by XRD, BET, SEM, XPS, etc. The rationally designed g-C3N4/Bi2MoO6 (4:6) catalyst exhibited notably photocatalytic activity in that more than 95.5% of TCS was transformed at 180 min, which was 3.6 times higher than that of pure g-C3N4 powder. This catalyst promotes efficient photocatalytic electron-hole separation for efficient dechlorination by photocatalytic reduction. The samples exhibited high recyclable ability and the dechlorination pathway was clear. The results of Density Functional Theory calculations displayed the TCS dechlorination selectivity has different mechanisms and hydrogen substitution may be more favorable than hydrogen ion in the TCS dechlorination hydrogen transfer process. This work will provide an experimental and theoretical basis for designing high-performance photocatalysts to construct the systems of efficient and safe visible photocatalytic reduction of aromatic chlorinated pollutants, such as TCS in dechlorinated waters. © 2022 Elsevier Ltd
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According to the World Health Organization, Artificial Intelligence (AI) technology may assist in COVID-19 management. However, existing image segmentation using AI suffers from a lack of accuracy and explainability, which prevents its adoption in actual clinical practice. In this paper, we investigated an attention-based image segmentation method for COVID-19 CT imaging with enhanced interpretation capabilities. Specifically, we developed U-Net architecture-based for segmentation with attention coefficients to produce a salient feature map. We use the DICE score and accuracy to perform a comprehensive model evaluation. We compared to other well-known methods such as Light U-Net, COPLE-Net, and Res U-Net and demonstrated that attention U-Net is superior for COVID-19 segmentation tasks in terms of performance and explainability. We also developed the tool as a web-application with a graphic user interface with the goal to translate this AI-driven clinical decision-support system for real-world clinical use. © 2022 IEEE.
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Purpose: This study clarifies the operational performance of fashion companies during the coronavirus pandemic. Meanwhile, improvement strategies have been provided in the post-pandemic era. Design/methodology/approach: The static and dynamic perspectives were combined to comprehensively analyze the operational performance of fashion companies before, during and after the COVID-19 outbreak. A comparative analysis among five representative countries was conducted to achieve global conclusions. Additionally, data envelopment analysis (DEA) theory and various DEA models were employed for the analysis. Findings: The fashion industry has not achieved overall effectiveness. American companies have the best operational performance, followed by European and Chinese companies. In contrast, the impact of the pandemic on American companies was severe, whereas Chinese and European companies showed operational resilience. In addition, the pandemic had a devastating influence on the global fashion industry. This resulted in a decline in total factor productivity, and the main reason was technological regress. Furthermore, labor redundancy is a critical issue for the fashion industry in the post-pandemic era, even if it shows a decrease because of the pandemic. Originality/value: The existing theory on the fashion industry during the pandemic was improved by expanding the time and geographical dimensions and integrating the advantages of various DEA models. Scientific improvement strategies were presented in the post-pandemic era with application value. © 2022, Emerald Publishing Limited.
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As more and more people begin to wear masks due to current COVID-19 pandemic, existing face recognition systems may encounter severe performance degradation when recognizing masked faces. To figure out the impact of masks on face recognition model, we build a simple but effective tool to generate masked faces from unmasked faces automatically, and construct a new database called Masked LFW (MLFW) based on Cross-Age LFW (CALFW) database. The mask on the masked face generated by our method has good visual consistency with the original face. Moreover, we collect various mask templates, covering most of the common styles appeared in the daily life, to achieve diverse generation effects. Considering realistic scenarios, we design three kinds of combinations of face pairs. The recognition accuracy of SOTA models declines 5%–16% on MLFW database compared with the accuracy on the original images. MLFW database can be viewed and downloaded at http://whdeng.cn/mlfw. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The traceability of COVID-19 is of great importance for Epidemiological implications. Two robust and non-parametric traceability methods were employed in the study to evaluate COVID-19's traceability for the prevention of epidemics. We first corrected the initial dates for eight countries using Solow and smith's method, and then the OLE method was applied to the corrected dataset for traceability. Our model suggests that the first global case of COVID19 originated on 25 September 2019 (95%CI 23 September) and that the first case emerged in Europe and spread rapidly to neighbouring countries, expanding globally in early January 2021. Our study suggests that the spread of the epidemic may be more rapid and earlier than we thought. The two methods we use can be used in a robust traceability approach for small sample estimates, providing additional explanations for epidemic traceability studies.
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Background: The global spread of the COVID-19 has brought great damage to the economic development, infrastructure and social development of various countries, and has also had a huge negative impact on people's daily life, work and study, bringing a lot of inconvenience. In the fear and tension of the epidemic, the psychological state of college students is worrying and prone to anxiety. Anxiety will affect the secretion of body hormones and the normal operation of organ functions, damage the physical and mental health of patients, and even seriously damage the cerebral cortex of patients, resulting in cognitive impairment, cognitive disorders, emotional and mental disorders and other diseases with different symptom dimensions. Based on learning psychology, the study explored the psychological changes of students in their study and life, and puts forward the idea of educating students on the consciousness of the Chinese nation community, so as to strengthen students' beliefs, improve students' psychological cognition, and avoid students' anxiety. Subjects and methods: Experiment design in a university. First, recruit volunteer students on the platform to cooperate with the experiment to complete the research. A total of 132 students were recruited. After screening, 80 students were left as experimental subjects. The two groups of the Institute are mainly composed of 80 students. They are respectively expressed as intervention and control groups, for subjects who were subject to research intervention, in addition to the normal courses, they also received the awareness education of the Chinese national community based on school psychology;For the control group, only normal courses were accepted. Under different education modes, after three months of college education, the degree of anxiety and learning outcomes of the subjects of the two teams were compared. Result(s): Students were investigated anonymously by using the self-made College Students' Anxiety survey scale (CSAQ). Students filled in all the options on the college students' anxiety survey scale, collected them, and counted and analyzed the scores of the college students' anxiety survey scale. Under different education modes, after three months of college education, the average score of the people who were intervened by the study design was higher than that of those who were intervened by the common method the research group is much lower than that of the control group. Conclusion(s): Most contemporary college students' growth environment and learning environment are relatively smooth, and they rarely experience setbacks, so their psychological tolerance is generally poor. In the fear and tension of the epidemic situation, college students are easy to have anxiety. Under the guidance of learning psychology, the study puts forward the education of the consciousness of the Chinese nation community for students, so as to strengthen students' beliefs and relieve students' anxiety. Copyright © Medicinska naklada - Zagreb, Croatia.
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This paper reviewed the research on moral injury among foreign medical staff in the background of the COVID-19 pandemic. It was found that foreign medical staff bore multiple moral pressures and impacts in the face of the epidemic, including the collision between medical practice and utilitarian policy, the inability to meet personal needs, the rapid transformation of medical mode, and a variety of comprehensive factors. Therefore, the moral injury of foreign medical staff is particularly prominent. In order to avoid and reduce the occurrence of moral injury, it is necessary to strengthen the cultivation of moral resilience, provide psychological and social support, and carry out personalized treatment for medical staff. © 2022, Editorial department of Chinese Medical Ethics. All rights reserved.
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The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe from the beginning of 2020 and people worldwide have been receiving news about the same from government offices, press conferences and various other media outlets. The COVID-19 Information Watcher Project started in 2020 to collect and organize reliable information sources worldwide. However, it is difficult to automatically identify reliable information sources in foreign countries for several reasons. First, what kind of information sources are reliable heavily depend on each county situation. In some countries people trust their government's official information but in other countries they do not. Secondly, such reliable information sources often provide information in their local languages. Reliable information sources are not necessarily top-ranked by search engines. Crowdsourcing is a promising way to deal with such a case. However, crowd-sourcing platforms do not cover crowds in all countries. In this study, we report some results of our attempt to collect local information regarding COVID-19 from several countries through multi-hop crowdsourcing, in which we allow crowd workers on a crowdsourcing platform to use other platforms in other countries. We show two case studies, Russia and Afghanistan. Our results show that the multi-hop crowdsourcing is a promising way to collect COVID-19 information from different countries. © 2021 IEEE.
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beta-Nucleosides are fundamental building blocks of biological systems and are widely used as therapeutic agents for the treatment of cancer and viral infections, among others. In the last two years, nucleoside analogues have been investigated with renewed urgency in the search for agents that are effective against SARS-CoV-2, the cause of the ongoing global pandemic of COVID-19. This has resulted in an explosion of activities in the field of beta-nucleoside synthesis. This review summarizes the historical perspective and the recent advances in the stereoselective synthesis of beta-nucleosides and their analogues. The synthetic strategies to obtain beta-nucleosides can be divided into three categories: (1) N-glycosylation;(2) intramolecular sugar ring formation;and (3) enzymatic transglycosylation.
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The influence of internet financial news on financial markets has been verified from multiple perspectives. This paper examines whether the emotional tendency of COVID-19 news affect financial markets. Based on constructing the sentiment dictionary of COVID-19 news, we measure the sentiment indicators of COVID-19 news, and then study the dynamic relationship between the emotional tendency of COVID-19 news and the rate of return in financial markets. Compared with the news before the outbreak of COVID-19, our results show that the emotional tendency of COVID-19 news does affect the rate of return in the stock and bond markets. © 2021 The Authors. Published by Elsevier B.V.
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Effective health communication is a crucial method to control the COVID-19 outbreak. The exchange of real- time information, advice, and opinions between experts and people becomes essential, especially during pandemics. Using social media channels is a method to ensure appropriate health communication and an ongoing, consistent media presence. This chapter aims to analyze the impact of social media on health communication and examine public engagement during the COVID- 19 outbreak between the United States and China. First, the study compares the number of posts and analyzes different social media platforms' functions in health communication from March 1st to July 31st 2020, using social media announcements of the Centers for Disease Control and Prevention (CDC) as the national public health institute for both countries. The social media platforms used for public awareness and notifications are quite different in the United States and China. Sina Blog is the most popular social media microblogging platforms in China, while Americans prefer to use Twitter. And lastly, it examined the functions/ categories of text messages on each social media about health communication. © Peter Lang GmbH Internationaler Verlag der Wissenschaften Berlin 2021. All rights reserved.
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Current classification and retrieval methods are affected by the amount of data in the classification of multimedia learning resources, and there are problems such as low classification accuracy, low retrieval rate, and long retrieval time. To solve this problem, a new multimedia learning method is proposed. Combine decision tree and hash algorithm to design resource classification and retrieval method. The decision tree algorithm is used for the collection and classification of multimedia learning resources, the hash algorithm is introduced to solve and preprocess the resources, and the Lyapunov theorem is used to obtain features. By using two different deep convolutional networks as non-linear hash functions, joint training enables the corresponding hash codes of the network to interpret the similar relations contained in the semantic information. Use annotated propagation algorithm to realize multimedia classification and retrieval of learning resources. The experimental results show that the improved method can effectively improve the retrieval accuracy and efficiency of multimedia learning resources, and has certain practicability.
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Utilizing the content analysis approach, this study analyzed 153 textual information sources to explore Chinese restaurant enterprises' innovative activities during and after COVID-19. An innovative crisis management model was synthesized from a micro-level perspective by adopting the life cycle model to dynamically measure the innovative strategies applied by Chinese restaurant enterprises in their emergency responses and recovery efforts. Two types of innovative activities that led to internal and external performances were identified based on the push and pull theory of business motivation. Findings further indicated five dimensions of innovative strategies at two different stages: pandemic prevention and control, government and community, corporate social responsibility, marketing response, and management response at the stage of emergency responses;and pandemic prevention and control, cooperation with third parties, customer service innovation, product innovation, and innovative marketing strategy at the stage of recovery efforts. Theoretical and empirical implications are also carefully discussed. © 2021 The Authors
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Traditional Chinese medicine has been used to treat and prevent infectious diseases for thousands of years, and has accumulated a large number of effective prescriptions. Deep learning methods provide powerful applications in calculating interactions between drugs and targets. In this study, we try to use the method of deep learning to reposition molecules of Chinese medicines (CMs) and the targets of syndrome coronavirus 2 (SARS-CoV-2). A deep convolution neural network with residual module (DCNN-Res) is constructed and trained on KIBA dataset. The accuracy of predicting the binding affinity of drug-target pairs is 85.33%. By ranking binding affinity scores of 433 molecules in 35 CMs to 6 targets of SARS-Cov-2, DCNN-Res recommends 30 possible repositioning molecules. The consistency between our result and the latest research is 0.827. The molecules in Gancao and Huangqin have a strong binding affinity to targets of SARS-CoV-2, which is also consistent with the latest research.
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Public health emergency is a huge challenge for all countries or regions. It is impossible for any country or region to make adequate preparations in advance. How to effectively prevent and control the spread of the epidemic is an unprecedented challenge for China and even the whole international community. This paper holds that the current prevention and control measures and efforts of the Chinese government are effective and important for preventing and controlling the disease rapidly, but the ethical concerns cannot be ignored. In fact, good ethical practices can help to achieve long-term prevention and control goals. Based on the current situation of the epidemic, we propose six ethical points to further stimulate dialogue on how to implement disease prevention and control measures to better protect public safety, protect personal privacy, prevent stigma and discrimination, and guide society to follow the right ethical value orientation.
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Purpose: Use artificial intelligence technology to identify the characteristics of covid-19 in CT images, quickly screen COVID-19 patients, achieve rapid diversion and treatment of suspected patients, reduce the risk of infection and control the spread of the disease.Materials and methods: This article combines deep learning target detection and image classification methods to study the CT images of COVID-19 patients. By extracting and analyzing the features of lesions in different periods, a new detection model of covid-19 based on time-spatial sequence convolution is obtained. The algorithm is based on a recurrent neural network structure and a 2D convolutional layer structure.Results: The spatiotemporal sequence convolution kernel based on time and space attributes can effectively extract the latent image semantic features of multiple image data of COVID-19 patients. By comparing with Faster RCNN, YOLO3 and SSD algorithm models, the detection method proposed in this paper can obtain more accurate comprehensive detection results.Conclusion: The time-spatial sequence convolution model can quickly complete the automatic detection of COVID-19 and improve the efficiency of preliminary diagnosis. By correlating images from different stages of the same patient, more accurate auxiliary preliminary screening results can be obtained. © 2020 IEEE.
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Objective: To study the novel coronavirus pneumonia (COVID-19) patients′ serum myocardial injury markers, coagulation markers and inflammatory markers, and to explore their value in prognosis. Methods: A retrospective study was performed on 67 patients with covid-19 admitted to Tongji Hospital of Huazhong University of Science and Technology from February 1, 2020 to March 31, 2020. According to the severity of the disease, they were divided into general group (n=20), severe group (n=20) and critical group (n=27). According to the discharge condition of the patients, the patients were divided into cure group (n=12) and death group (n=15), and the end point of observation was patients′ improvement and discharge or death during hospitalization. The clinical data of the patients were collected, the venous blood was collected and the serum levels of hs-cTnI, MYO, CK-MB, NT-proBNP, D-dimer, WBC, PT, APPT, FIB, hs-CRP and ferritin were detected. The differences of the above indexes were compared among the three groups. Results: There was no significant difference in Red Blood Cell, Platelet (PLT), FIB and hemoglobin among the three groups (P>0.05). However, the levels of WBC, hs-CRP, ferritin, PT, APPT and D-dimer in the normal group, the severe group and the critical recombination group were significantly different (P<0.05), and the critical recombination group was higher than that of the severe group and the ordinary group (P<0.05). NT-proBNP, CK-MB, MYO, hs-cTnI of critical patients were significantly higher than those of normal group and severe group, and pro BNP, CK-MB, MYO, hs-cTnI of death patients in critical severe group were higher than those of cured patients (all P<0.05). Multivariate logistic regression analysis showed that the increase of hs-cTnI (OR=1.053, 95%CI 1.010-1.098, P=0.016) and D-dimer (OR=1.253, 95%CI 1.026-1.531, P=0.005) were the risk factors of death in patients with COVID-19. Conclusion: COVID-19 critical and severe patients were accompanied with coagulation dysfunction and the increase of inflammatory factors. There were different degrees of myocardial injury, and the increase of hs-cTnI and D-dimer are the risk factors of death in patients with severe COVID-19.