Translating Against COVID-19 in the Chinese Context: A Multi-agent, Multimedia and Multilingual Endeavor

In the COVID-19 crisis, multilingual and multimodal translation services, provided by both professional translators and volunteer communities, have greatly facilitated the communication between different parties. This chapter gives an overview of how diverse agents have engaged in this large-scale translation endeavor and draws attention to the importance of translation planning in crisis contexts. It not only discusses the operations of volunteer translators, highlighting their contribution in providing language assistance in crisis situations, but also analyzes the significant role played by government-sponsored institutions in mobilizing language professionals and organizing translation projects, such as Language Service Group Against COVID-19 and China International Publishing Group. Moreover, this chapter focuses on the relevance of translation to waixuan (often translated as international publicity) in this crisis situation and investigates how translation is strategically adopted by the government as a medium of communicating the Chinese experience of fighting COVID-19. After analyzing all these disparate efforts, it argues for the significance of translation in overcoming language barriers in crisis contexts and concludes with suggestions on how to enhance translation planning for health emergencies. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

Trash to treasure: Sulfonation-assisted transformation of waste masks into high-performance carbon anode for sodium-ion batteries

The global pandemic of COVID-19 poses significant challenge to the recycling of disposable polypropylene (PP)-based waste masks. Herein, a simple but effective sulfonation route has been proposed to transform PP-based waste masks into value-added hard carbon (CM) anode materials for advanced sodium-ion batteries. The sulfonation treatment improves the thermal stability of the PP molecule, preventing their complete decomposition and the release of massive gas molecules during the carbonization process. Meanwhile, the oxygen functional groups introduced during sulfonation effectively facilitates the cross-linking between the PP chains, hindering the rearrangement of carbon microcrystalline structures and enhancing its structural disorder. As a result, the prepared hard carbon anode (CM-180) with a high disorder degree and minimal surface defects realizes a high sodium storage capacity of 327.4 mAh g−1 with excellent cycle and rate capability. In addition, when coupled with O3–NaNi1/3Fe1/3Mn1/3O2 cathode, the fabricated sodium-ion full cell delivers a high energy density of 238 Wh kg−1 and achieves an outstanding rate capability with a retained capacity of 75 mAh g−1 even at an ultrahigh current rate of 50 C. This work offers a novel insight into transforming the waste masks to value-added hard carbons with promising prospects for sodium-ion batteries. © 2023

Rashes caused by arbidol in a patient with novel coronavirus pneumonia.

A 50-year-old male patient with agitated depression and hyperlipemia received oral amoxicillin and clavulanate potassium 0.5 g once daily and 2 lopinavir and ritonavir tablets twice daily for novel coronavirus infection, based on previous drugs including quetiapine, clonazepam, and atorvastatin calcium. After 3 days, lopinavir and ritonavir was changed to oral arbidol 200 mg, thrice daily due to suspicious drug interaction. After taking arbidol for 3 days, the patient developed red papules on the whole body. Considering that it might be related to amoxicillin and clavulanate potassium, the drug was stopped and loratadine was given. But the rashes were aggravated. Considering that the drug eruption was caused by arbidol, arbidol was discontinued and the rashes subsided in a large area the next day. Then vitamin C injection, calcium gluconate injection, and ribavirin were added. After 5 days, the rashes subsided completely. After 17 days, the patient recovered from pneumonia.Copyright © 2020 by the Chinese Medical Association.

COVID, Democratic Fallout Versus New National Humiliation: China's Perception and Strategy in the Middle East

China sees an enormous opportunity in the power vacuum left by the US withdrawal from the Middle East, to formulate a strategic alliance with Russia against the US. China sees the Middle East as its "playground” and/or new frontier, in which to challenge US world dominance and seek revenge for 100 years of shame and humiliation that has culminated in the emergence of a democratic Taiwan. Furthermore, the US response to COVID combined with Trumpism has emboldened both China's and Russia's authoritarian and anti-democratic strategies. The twin national psychological traumas of shame and rage, built up over decades, even centuries, drives both Russia and China. At the heart of the China-Russia alliance is deep national sentiment on the parts of both of these very different powers, that revenge against the United States must be waged through geopolitical partnership, and through the strategic centerpiece of the Middle East. For Russia, revenge is sought for the shameful dissolution of the Soviet Union, perceived as the greatest tragedy of the 20th Century. For China, revenge is sought for the century-long, western-imposed humiliation in the late nineteenth century and early twentieth century, and modern-day humiliation of a divided nation as a result of democratization in Taiwan. The US response to COVID combined with the anti-intellectual, anti-democratic ideals of Trumpism, have revealed to Russia and China that US democracy is more fragile than previously imagined, and unable to successfully deal with emerging global-scale issues that will inevitably be shaping geopolitics and economics (pandemics, climate change, etc.) into the future. The American characteristic hubris, combined with its short-term, ahistoric, perspective, and narcissistic, "island” world view of its own exceptionalism, have the strong potential to culminate as truly fatal flaws leading-from China and Russia's point of view, inevitably-to the US losing its place of dominion in the global world order. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

An Effective COVID-19 CT Image Denoising Method Based on a Deep Convolutional Neural Network

Background: Faced with the global threat posed by SARS-CoV-2 (COVID-19), low-dose computed tomography (LDCT), as the primary diagnostic tool, is often accompanied by high levels of noise. This can easily interfere with the radiologist's assessment. Convolutional neural networks (CNN), as a method of deep learning, have been shown to have excellent effects in image denoising. Objective: The objective of the study was to use modified convolutional neural network algorithm to train the denoising model. The purpose was to make the model extract the highlighted features of the lesion region better and ensure its effectiveness in removing noise from COVID-19 lung CT images, preserving more important detail information of the images and reducing the adverse effects of denoising. Methods: We propose a CNN-based deformable convolutional denoising neural network (DCDNet). By combining deformable convolution methods with residual learning on the basis of CNN structure, more image detail features are retained in CT image denoising. Results: According to the noise reduction evaluation index of PSNR, SSIM and RMSE, DCDNet shows excellent denoising performance for COVID-19 CT images. From the visual effect of denoising, DCDNet can effectively remove image noise and preserve more detailed features of lung lesions. Conclusion: The experimental results indicate that the DCDNet-trained model is more suitable for image denoising of COVID-19 than traditional image denoising algorithms under the same training set. © 2023 Bentham Science Publishers.

What drives the volatility of non-fungible tokens (NFTs): macroeconomic fundamentals or investor attention?

Non-fungible tokens (NFTs) have experienced wild market fluctuation during the past years, which leads to the high volatility of NFT's daily price. This paper examines two potential volatility drivers of NFTs: macroeconomic fundamentals and investor attention. We employ the global and local economic policy uncertainty (EPU) indices as the economic fundamentals' proxies. The investor attention is represented by the Google search volumes (GSV) or NFTs attention index. Based on the empirical results of a modified generalized autoregressive conditional heteroskedasticity –mixed-data sampling (G-M) model, we find that either economic fundamentals or investor attention can increase the volatility of NFTs significantly. The monthly global EPU index adjusted by the current GDP and weekly GSV contain complementary information. Macroeconomic fundamentals and investor attention can jointly model the volatility of NFTs better than considering only one explanatory variable, as suggested by the G-M model with two explanatory variables. The results remain robust to alternative Twitter-based EPU indices and the ongoing COVID-19 pandemic period. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Integration of a hybrid scan approach and in-house high-resolution MS2 spectral database for charactering the multicomponents of Xuebijing Injection

Xuebijing (XBJ) Injection is a reputable patent Chinese medicine widely used to cure sepsis, among the Chinese ″Three Medicines and Three Prescriptions″ solution to fight against COVID-19. We were aimed to achieve the comprehensive multicomponent characterization from the single drugs to traditional Chinese medicine (TCM) formula, by integrating powerful data acquisition and the in-house MS2 spectral database searching. By ultra-high performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS), a hybrid scan approach (HDMSE-HDDDA) was developed, while the HDMSE data for five component drugs and 56 reference compounds were acquired and processed to establish an in-house MS2 spectral database of XBJ. Good resolution of the XBJ components was accomplished on a Zorbax Eclipse Plus C18 column within 24 min, while a fit-for-purpose HDMSE-HDDDA approach was elaborated in two ionization modes for enhanced MS2 data acquisition. XBJ MS2 spectral library was thus established on the UNIFITM platform involving rich structure-related information for the chemicals from five component drugs. We could identify or tentatively characterize 294 components from XBJ, involving 81 flavonoids, 51 terpenoids, 42 phthalides, 40 organic acids, 13 phenylpropanoids, seven phenanthrenequinones, six alkaloids, and 54 others. In contrast to the application of conventional MS1 library, this newly established strategy could demonstrate superiority in the accuracy of identification results and the characterization of isomers, due to the more restricted filtering/matching criteria. Conclusively, the integration of the HDMSE-HDDDA hybrid scan approach and the in-house MS2 spectral database can favor the efficient and more reliable multicomponent characterization from single drugs to the TCM formula. © 2022 The Author(s)

Sustainable Market Environment and Consumer Confidence in Food Safety in China after Covid-19: Urban Consumer Perspectives

Previously, several food safety incidents have lowered Chinese consumer confidence regarding the purchase of meat and domestically-produced infant formulas ( DIF). Recently, the COVID-19 pandemic was caused by a food safety issue, and it is, therefore, important to investigate sustainable consumer confidence and its influence on consumption behavior by first estimating consumer confidence in meat and DIF products. The purpose of this study is thus to estimate the sustainable market environment and Chinese consumer confidence behavior post- COVID-19 regarding the purchase of meat and DIF products by applying a choice experiment. An online survey was conducted with 704 consumers residing in Nanjing. The survey obtained information on the features of meat and DIF products that affected consumer purchase consideration and determined the perceived estimated value of the product (price 15.24%, quality certification 31.57%, organic 23.69%, traceability 18.51%). These findings provide insight into consumer perceived premium pay behavior concerning the value estimation of product features according to the market environment (price 191.6 CNY, quality certification 285.3 CNY, organic product 189.1 CNY, and traceability 143.7 CNY). Chinese consumers' low confidence in the safety of meat and DIF products appears less sustainable. Quality certification is the product feature that is most valued, followed by organic origin and production-to-consumption traceability. Apart from these, price is seen as an indication of high quality by consumers with low confidence, which results in a positive image of DIF products. In addition, consumer preferences for production-toconsumption traceability are highly affected by consumer confidence.

Technology-Enhanced Social-Emotional Learning Community

COVID-19 has increased the level of stress and loneliness experienced by teens, and there is an urgent need for them to learn social-emotional skills and feel supported by their peers. A technology-enhanced social-emotional learning community was designed to help students acquire effective coping skills and connect meaningfully with others. A study was conducted with 38 high school students, and their coping skills, feeling of social support, and level of stress were measured before and after engaging in the community. Results suggested that social-emotional skill development can be enhanced using an online learning community, where students share their struggles anonymously and learn coping skills by browsing effective coping strategies to support their peers. The results offered opportunities to leverage technology-enhanced learning communities to broaden access to quality social emotional learning experiences and help foster psychological well-being of more high school students. © ISLS.

What Does It Mean to Take Online Classes as an International Student During COVID-19?

Using the Theory of Social Support and the Community of Inquiry as theoretical frameworks, this qualitative study explored international students' experiences, challenges, and perceptions of online learning environments during the COVID-19 pandemic in the United States. By conducting three virtual focus group interviews with 18 international students, we identified four key themes that encapsulate participants' challenges and experiences of online learning: social isolation in online learning spaces, difficulties with engaging in online class discussions and activities, limited opportunities for improving English proficiency in the online setting and limited academic support from faculty and advisors. Implications for research and practice to support international students are discussed. © 2022, The Online Learning Consortium. All rights reserved.

[Research on the interaction between COVID-19 and influenza].

Since the global pandemic of COVID-19, different countries have implemented various prevention and control measures, which has affected the epidemic characteristics of respiratory infectious diseases such as influenza. From 2020 to 2021, the level of influenza activity was relatively low, but it is necessary to be alert that with the adjustment of national prevention and control measures, influenza may have a relatively strong epidemic rebound. In order to deal with influenza epidemic, experts were organized to publish a series of influenza studies in this issue, suggesting that influenza prevention and control cannot be underestimated during the COVID-19 pandemic. It is suggested to carry out research on the interaction between COVID-19 and influenza to explore the epidemic characteristics of the disease, develop new technologies and tools to improve the efficiency of monitoring and early warning, identify obstacles to vaccination, promote the scientific implementation of intervention measures, and achieve joint prevention and control of multiple diseases.

Open-Source Internships With Industry Mentors

Internships help students connect what they have learned in the classroom to the real world, and students with access to internships are more likely to graduate and secure employment. However, many students are unable to find an internship by the time they graduate. This experience report describes a program where volunteer software engineers mentor students as they work on open-source projects in the summer, offered as an alternative to a traditional internship experience. We catalog the considerations involved in providing an experience similar to a traditional internship, describe our program's design, and provide two years' worth of participant evaluations and career outcomes as a measure of efficacy. The program served mostly undergraduates from non-R1 schools who are underrepresented in technology, and achieved similar educational outcomes to a traditional internship program. Most promisingly, mentors were willing to serve as a professional reference for 80% of students and the number of graduating seniors who secured full-time employment in technology was 7 points higher than average (despite occurring during the COVID-19 pandemic). © 2022 ACM.

AIR QUALITY ASSESSMENT OF IMMIGRANT RURAL COMMUNITIES BASED ON THE THEORY OF HEALTHY AND SUSTAINABLE DEVELOPMENT - A CASE STUDY OF IMMIGRANT COMMUNITIES IN HUBEI, CHINA

In the context of the global outbreak of COVID-19, health issues have attracted worldwide attention. Building a healthy ecological environment is particularly important for human beings, and among the ecological environmental factors, air quality is particularly prominent. The study takes the air quality of newly-built immigrant relocation communities in Western China as the research object, and adopts a number of technical methods, such as professional laboratory test report, instrument test, calculation test and so on. Obtain the data of regional ambient air quality and building indoor air quality, and comprehensively judge the regional environment and building ventilation efficiency of the experimental point. So as to comprehensively grade the air quality of the experimental point. A number of technologies and methods are studied and integrated to form a comprehensive three-dimensional air quality detection technology integration. From the perspective of air quality inspection, provide technical support for the healthy and sustainable development of relocated new rural communities. It is of great practical significance to supervise and urge the construction of a healthy and sustainable new township village.

Structure and function of SARS-CoV-2 spike protein and its receptor

Corona virus disease 2019 is an acute infectious disease caused by SARS-CoV-2 infection and has entered the state of global pandemic. Spike protein ( S protein) , a key protein that mediates SARS-CoV-2 to infect host cells, has the characteristics of specific receptor binding and membrane fusion, playing an important role in host tropism and virulence. The spontaneous closed and open conformation of S protein trimer is crucial for receptor binding and initiation of conformational changes in membrane fusion, and its unique furin recognition site may be a crucial factor leading to high infectivity. Therefore, to study the structure and function of SARS-CoV-2 S protein and its receptor has important implications for invasion mechanisms of SARS- CoV-2 and the development of relevant targeted drugs.

Document Classification with Termolator for COVID-19 Literature

The COVID-19 pandemic has led to a multiplicity of research publications related to various aspects of coronavirus. Research topics range from COVID-19 transmission mechanisms to the public health response of various countries, and the publications need to be categorized for easier and more efficient access to resources. This paper explores various machine learning-based document classification techniques to categorize COVID-19 related literature. We integrate a novel terminology dictionary with machine learning models to study the dictionary's impact on the effectiveness of various classification techniques. We report a slight boost to F1 scores as a result of our modifications. © 2021 IEEE.

Motivating Ethnic Minority Students in Hong Kong to Learn Chinese Culture with EduVenture VR

This paper presents our initiative for leveraging SV-IVR (spherical video-based immersive virtual reality) to give Hong Kong ethnic minority students exposure to local Chinese culture via EduVenture VR-an interactive learner-immersed virtual interactive learning application. The work was conducted in the COVID-19 pandemic context;outdoor fieldwork-based learning was not recommended in the circumstance. The research participants were 63 ethnic minority students (from Grade 7 to Grade 9) from a Hong Kong secondary school. The ARCS model of instructional motivation was employed to evaluate the motivational effectiveness of the Chinese culture learning activity supported by EduVenture VR. In the study, we obtained positive results in terms of the 4 motivational dimensions: "Attention," "Relevance," "Confidence' and "Satisfaction."

RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic

Coronavirus 2019 has made a significant impact on the world. One effective strategy to prevent infection for people is to wear masks in public places. Certain public service providers require clients to use their services only if they properly wear masks. There are, however, only a few research studies on automatic face mask detection. In this paper, we proposed RetinaFaceMask, the first high-performance single stage face mask detector. First, to solve the issue that existing studies did not distinguish between correct and incorrect mask wearing states, we established a new dataset containing these annotations. Second, we proposed a context attention module to focus on learning discriminated features associated with face mask wearing states. Third, we transferred the knowledge from the face detection task, inspired by how humans improve their ability via learning from similar tasks. Ablation studies showed the advantages of the proposed model. Experimental findings on both the public and new datasets demonstrated the state-of-the-art performance of our model. © 2021 IEEE.

LDA Topic Mining of Light Food Customer Reviews on the Meituan Platform

Light food refers to healthy and nutritious food that has the characteristics of low calorie, low fat, and high fiber. Light food has been favored by the public, especially by the young generation in recent years. Moreover, affected by the COVID-19 epidemic, consumers’ awareness of a healthy diet has been improved to a certain extent. As both take-out and in-place orders for light food are growing rapidly, there are massive customer reviews left on the Meituan platform. However, massive, multi-dimensional unstructured data has not yet been fully explored. This research aims to explore the customers’ focal points and sentiment polarity of the overall comments and to investigate whether there exist differences of these two aspects before and after the COVID-19. A total of 6968 light food customer reviews on the Meituan platform were crawled and finally used for data analysis. This research first conducted the fine-grained sentiment analysis and classification of the light food customer reviews via the SnowNLP technique. In addition, LDA topic modeling was used to analyze positive and negative topics of customer reviews. The experimental results were visualized and the research showed that the SnowNLP technique and LDA topic modeling achieve high performance in extracting the customers’ sentiments and focal points, which provides theoretical and data support for light food businesses to improve customer service. This research contributes to the existing research on LDA modeling and light food customer review analysis. Several practical and feasible suggestions are further provided for managers in the light food industry. © 2021, Springer Nature Singapore Pte Ltd.

SARS-CoV-2 detection by a clinical diagnostic RT-LAMP assay [version 1;peer review: 2 approved with reservations]

The ongoing pandemic of SARS-CoV-2 calls for rapid and cost-effective methods to accurately identify infected individuals. The vast majority of patient samples is assessed for viral RNA presence by RT-qPCR. Our biomedical research institute, in collaboration between partner hospitals and an accredited clinical diagnostic laboratory, established a diagnostic testing pipeline that has reported on more than 252,000 RT-qPCR results since its commencement at the beginning of April 2020. However, due to ongoing demand and competition for critical resources, alternative testing strategies were sought. In this work, we present a clinically-validated procedure for high-throughput SARSCoV-2 detection by RT-LAMP in 25 minutes that is robust, reliable, repeatable, sensitive, specific, and inexpensive © 2021. Buck MD et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

RBOTUE: Rumor Blocking Considering Outbreak Threshold and User Experience

When a rumor breaks out in online social network (OSN), it can lead to significant negative impact on human society, especially in the context of public emergencies, such as pandemic. Toward restraining rumor outbreak in OSN, one of the effective containment measures is to block influential users to minimize the spread of rumors. However, most of previous efforts ignore the imbalance between the cost and effect of rumor suppression. To fill this gap, from the perspective of public opinion crisis, a dynamic rumor spread model called PISIR model is established, which takes into account the overall popularity and individual tendency of rumors. Based on this model, two rumor blocking algorithms considering outbreak threshold and user experience, called 1-Hop and 2-Hop RBOTUE algorithms, are proposed, respectively. In the algorithms, a hyperbolic discount effect-based user experience mode is introduced as the constraint to ensure the user experience in OSN, then the blocking strategy is implemented on the selected subset of nodes to keep the rumor spread scale always below the outbreak warning line. The experimental results in two synthetic networks and four real OSNs indicate that both 1-Hop and 2-Hop RBOTUE algorithms have lower rumor infection rate and require less number of blocked nodes, which means that proposed algorithms can achieve better blocking performance with less restraining cost of rumors in mainstream social networks, and the two algorithms also have different adaptability for different OSNs. IEEE