COVID-19, American Influence, and Economic Relations: An Empirical Analysis of China's Image in the European Union

As one of the major powers at the center of the global landscape, it is natural that China attaches great importance to its national image worldwide. COVID-19, which was first reported in Wuhan, has put China at the center of attention and controversies. The unprecedented pandemic and the strains of the relations with the United States, accentuated under the Trump Administration, have started to show consequences on China's relations with the rest of the world, especially with Western countries. In the context of an increasingly severe US–China strategic competition and the pandemic, China tries to strengthen its economic relations with Europe to cope with the constraints of the United States. In this chapter, we focus on an examination of how COVID-19, bilateral economic ties, and Americans' views shape China's image in EU countries. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

FedUSC: Collaborative Unsupervised Representation Learning from Decentralized Data for Internet of Things

Federated Learning (FL) lately has shown much promise in improving the shared model and preserving data privacy. However, these existing methods are only of limited utility in the Internet of Things (IoT) scenarios, as they either heavily depend on high-quality labeled data or only perform well under idealized conditions, which typically cannot be found in practical applications. In this paper, we propose a novel federated unsupervised learning method for image classification without the use of any ground truth annotations. In IoT scenarios, a big challenge is that decentralized data among multiple clients is normally non-IID, leading to performance degradation. To address this issue, we further propose a dynamic update mechanism that can decide how to update the local model based on weights divergence. Extensive experiments show that our method outperforms all baseline methods by large margins, including +6.67% on CIFAR-10, +5.15% on STL-10, and +8.44% on SVHN in terms of classification accuracy. In particular, we obtain promising results on Mini-ImageNet and COVID-19 datasets and outperform several federated unsupervised learning methods under non-IID settings. IEEE

The Benefits of Physical Activity and Positive Mental Health for Reducing the Burden of COVID-19: Validation from a Cross-sectional and Longitudinal Investigation in China and Germany.

Many people experience high burden by the outbreak of the coronavirus disease (COVID-19) and its consequences for health and everyday life. The present cross-national study investigated potential factors that can reduce the burden by COVID-19 in China and Germany. Cross-sectional and longitudinal (China: N = 474, baseline, BL: 2015, follow-up, FU: 2020; Germany: N = 359, BL: 2019, FU: 2020) data on physical activity (e.g., jogging) (BL/FU), positive mental health (PMH) (BL/FU), and burden by COVID-19 (FU) were collected via online surveys. In both countries, physical activity was positively associated with PMH, and both variables were negatively related to burden by COVID-19. Furthermore, PMH mediated the link between physical activity and burden. The mediation model was significant when physical activity and PMH were assessed at the BL, while burden was measured at the FU; and it was also significant when all variables were assessed at the FU. The present findings reveal that physical activity in combination with PMH can reduce the experience of burden by COVID-19. Conscious fostering of physical activity and PMH is supported as an effective strategy to reduce the negative impact of the pandemic outbreak on mental and physical health. Additional benefits such as increased adherence to governmental measures around COVID-19 are discussed.

Perception shifts in seafood consumption in the United States

Perceptions about specific seafood attributes play an essential role in American consumers' choices of a seafood entre ' e served in casual and fine dining restaurants. However, the trends and determinants of consumer per-ceptions are underexplored. This research analyzes how consumers perceive specific attributes of a seafood entre ' e and examines the effect of socioeconomic and demographic characteristics on their perceptions. Factor analysis and multivariate Tobit models were employed to analyze data collected online via Qualtrics' consumer panels in the U.S. The results indicate that the proportion of participants who deem farm-raised and sustainability-certified fish safer to eat, better tasting, higher quality, more fairly traded, and more environ-mentally friendly is the highest. However, the difference in perceptions of domestic and imported fish is ambiguous. The perceptions of nutritional value for human consumption are near neutral among these seafood attributes. The main determinants of these perceptions are the frequency they eat fish, whether having children in a household, gender, ethnicity, and the age of consumers. These perceptions and determinants are relatively consistent before and during Covid-19. This study contributes to the literature on seafood perceptions at casual and fine dining restaurants and provides the most recent trend on American seafood perceptions and their de-terminants. These results are beneficial to seafood producers, distributors, and policymakers for providing more appropriate regulations related to future seafood supply in the U.S.

The economic impact of COVID-19 on biotech companies

This article looks at the impact on biological and medical companies in the context of covid-19. Through linear analysis, data model plots, and variance comparisons, the study finds that the economy of the traditional medical sector continues to decline and eventually plateaus, however, the pharmaceutical as well as biological research sectors, which are associated with the epidemic, continue to rise. The advantages and disadvantages of combining the internet and healthcare are presented. Although internet healthcare is a more convenient way for people to see a doctor. Some people can even see a doctor for treatment without leaving home, but diagnosis lacks accuracy and large testing machines. And a more rational approach to healthcare must still be researched in the future. © 2023 SPIE.

[Advances in epidemiology, etiology, and treatment of community-acquired pneumonia].

Community-acquired pneumonia (CAP) is the third leading cause of death worldwide and one of the most commonly infectious diseases. Its epidemiological characteristics vary with host and immune status, and corresponding pathogen spectrums migrate over time and space distribution. Meanwhile, with the outbreak of COVID-19, some unconventional treatment strategies are on the rise. This article reviewed the epidemiological characteristics, pathogen spectrum and treatment direction of CAP in China over the years, and aimed to provide guidance for the diagnosis and treatment of CAP in clinical practice.

The economic impact of COVID-19 on biotech companies

This article looks at the impact on biological and medical companies in the context of covid-19. Through linear analysis, data model plots, and variance comparisons, the study finds that the economy of the traditional medical sector continues to decline and eventually plateaus, however, the pharmaceutical as well as biological research sectors, which are associated with the epidemic, continue to rise. The advantages and disadvantages of combining the internet and healthcare are presented. Although internet healthcare is a more convenient way for people to see a doctor. Some people can even see a doctor for treatment without leaving home, but diagnosis lacks accuracy and large testing machines. And a more rational approach to healthcare must still be researched in the future. © 2023 SPIE.

Mechanism of Cold-Dampness Epidemic Prescription in Treatment of Novel Coronavirus Pneumonia Based on Network Pharmacology

Background:The emergence of novel coronavirus pneumonia has seriously affected people's normal life and health. Cold-dampness epidemic prescription has a good effect in the prevention and treatment of novel coronavirus pneumonia. Methods:TCMSP, PubChem, Swiss Target Prediction, PharmMapper database and related literatures were used to retrieve and predict the main chemical components and corresponding targets of Traditional Chinese Medicine (TCM)TCM. GeneCard, OMIM, NCBI and TTD databases were used to collect disease targets. Uniprot disease database was used to standardize target names. Cytoscape3.8.2 software was used to establish the 'active components-action target' network. Protein interaction (PPI) network was established by using protein interaction database (STRING), and core genes were screened by CytoNCA plug-in of Cytoscape3.8.2 software.GO enrichment analysis and KEGG pathway enrichment analysis were carried out through DAVID network database, and Hiplot network platform was used for visualization. Molecular docking technology was used to verify the docking between core components and targets. Results:After preliminary screening, 102 effective components, 255 potential targets and 2230 COVID-19 disease targets were obtained, and it was speculated that the mechanism might be related to 177 pathways such as TNF signaling pathway, IL-17 signaling pathway and AGE-RAGE signaling pathway in diabetic complications. The absolute values of docking binding energy between active components such as quercetin, luteolin and wogonin and targets such as PTGS2, AR, TP53 and CASP3 were greater than 5.0 Kcal/mol, and the docking results were good. Conclusion:Cold-dampness epidemic prescription has the characteristics of multiple components, multiple targets and multiple pathways in the prevention and treatment of COVID-19, and may play a therapeutic role through anti-inflammatory, antiviral and immune regulation. © 2022 IEEE.

A Novel Lightweight Swin-Unet Network for Semantic Segmentation of COVID-19 Lesion in CT Images

The Corona Virus Disease 2019 (COVID-19) is highly infectious, has been spread worldwide, caused a global pandemic, and seriously endangered human health and life. The most effective methods for halting and stopping the transmission of the Corona Virus include early detection, quarantine, and successful treatment. Because it exhibits significant imaging characteristics for COVID-19 lesions in chest computed tomography (CT), it can be used to diagnose COVID-19. Aiming at the inaccuracies of uneven gray distribution, irregular regions, multi-scale, and multi-region segmentation in COVID-19 CT images. This paper proposed a novel Swin-Unet network to improve the accuracy of multi-scale lesion segmentation in COVID-19 CT images. First, in the double-layer Swin Transformer blocks of the Swin-Unet, a residual multi-layer perceptron (ResMLP) module was introduced and replaced the multi-layer perceptron (MLP) module to reduce the loss of features during the transmission process, thereby improving the segmentation precision of multi-scale lesion areas. Second, the uncertain region inpainting module (URIM) was added after Linear Projection, which can refine the uncertain regions in the segmentation features map, thereby improving the segmentation accuracy of different lesion regions. Third, a new loss function DF was designed. It can effectively improve the small target segmentation effect and thus improve the multi-scale segmentation result. Finally, the proposed method was compared to other methods on the public dataset. The Dice, Precision, Recall, and IOU of the proposed method are 0.812, 0.780, 0.848, and 0.683, respectively, which are better than the other models. Moreover, our model has fewer parameters and faster reasoning speed. The proposed method achieves excellent segmentation results for multi-scale and multi-region lesions, and it will be more beneficial in aiding COVID-19 diagnosis and treatment.

Traditional Chinese Medicine Pattern Classification and Herbal Medicine for COVID-19: A Comparative Study of Data from Different Sources

Background: Traditional Chinese medicine (TCM) plays a crucial role in the prevention and control of coronavirus disease 2019 (COVID-19). Objective: The study aimed to reveal the distribution characteristics of COVID-19 TCM syndrome types and syndrome elements and the law of TCM treatment and medication. Methods: The TCM diagnosis and treatment protocol for COVID-19 and clinical research data were obtained through network retrieval, and Revman 5.3 and SPSS 23.0 were employed to analyze the composition of TCM syndromes and the situation of TCMs in meta and frequency. Results: The top three TCM syndromes of COVID-19 included damp-heat accumulation in the lung pattern, damp abundance due to spleen deficiency, and epidemic toxin invading the lung pattern, while the syndrome elements were dampness, heat, and toxin. Gypsum fibrosum, Pogostemonis herba, and Armeniacae semen were identified as the commonly used drugs. Different syndrome elements were identified at lung disease location: Forsythiae fructus, Glycyrrhizae radix, and Armeniacae semen can be used for "wind;" Glycyrrhizae radix, Armeniacae semen, and Scutellariae radix can be used for "Heat;" Armeniacae semen, Sheng Gypsum fibrosum, and Ephedrae herba can be used for "Toxin;" Ephedrae herba, Armeniacae semen, and Atractylodis rhizome can be used for "Damp;" Magnoliae officinalis Cortex, Ephedrae herba, and Zingiberis Rhizoma recens can be used for "cold;" and Armeniacae semen, Gypsum fibrosum, Ephedrae herba, and Lepidii/Descurainiae semen can be used for "epidemic. " Conclusion: The establishment of a treatment scheme based on the classification of disease syndrome elements should be considered for sudden infectious diseases, such as COVID-19. Pogostemonis herba, Armeniacae semen, Gypsum fibrosum, and Glycyrrhizae radix should be considered as effective drugs from TCM for the treatment of COVID-19.

Research on Prediction Model of COVID-19 Based on Deep Learning

The accurate prediction of COVID-19 is of great significance for the prevention and control of the epidemic. Based on the current situation and demand of COVID-19 prediction research, this paper mainly analyzes the convolutional neural network (CNN) model by using the deep learning algorithm, It uses 1dcnn model and Gabor filter to build the G-ldcnn model, and introduces back propagation to update The model has the high-efficiency learning ability of CNN model and the feature extraction ability of Gabor filter at the model. The model has the high-efficiency learning ability of CNN model and the feature extraction ability of Gabor filter at the same time, improves the prediction efficiency of the model while ensuring the accuracy, and can better adapt to By comparing the prediction model proposed in this paper with the current By comparing the prediction model proposed in this paper with the current mature model, it shows that the improved and optimized model has a high accuracy. © 2022 IEEE.

Procurement management mode of electrostatic protection materials in supply chain environment-Taking state-owned enterprises as an example

for every enterprise, material procurement is a very important link. It is not only related to cost control and production and management quality, but also determines the final economic benefits of the enterprise. Novel coronavirus pneumonia is a key issue for the enterprises. The overall management of material procurement is carried out throughout the whole business process. Especially in the current market economy environment, the new crown pneumonia epidemic continues to affect the enterprises and face more severe competition environment. If we want to gain more market share in the industry, we must innovate and adjust the original material procurement management mode and benchmark the international and domestic advanced standards. Nowadays, the shadow of Internet technology has penetrated into all walks of life, and new Internet technology has also been applied to the management of enterprise supply chain, which makes the relationship between enterprises closer and plays an important role in the process of enterprise development. Therefore, based on the supply chain environment, this paper gives some targeted measures and suggestions to strengthen the procurement management of electrostatic protection materials in state-owned enterprises for reference. © 2022 IEEE.

Polyurethane With Instant And Persistent Antimicrobial Efficacy Against Bacteria And SARSCoV- 2

Purpose/Objectives: Gram-negative bacteria including E. coli and P. aeruginosa can survive for months on dry hard surfaces, and SARS viruses can persist for days. These contaminated surfaces along with patients' damaged skin barriers, due to wounds or central line insertion sites, increase the risk healthcare-acquired infections (HAI) and subsequent serious complications. Furthermore, with increased frequency and duration of hospitalizations due to the current pandemic, the number of HAIs is on the rise. Currently there are no antimicrobial surfaces that provide both instant and long-lasting antimicrobial protection against a broad spectrum of infectious microbes. Liquid- or radiation-based disinfection techniques are kill microbes quickly, but their effect does not last long before needing reapplication. Antimicrobial surfaces based on heavy metals remain antimicrobial for long durations, but complete disinfection can take hours. In this work, we developed a new class of plant-inspired antimicrobial surfaces and wound dressings that incorporate plant secondary metabolites capable of rapid disinfection (> 4-log reduction) of common bacteria and viruses and maintain their efficacy over time (> 6 months). Methodology: We developed a method for stabilizing naturally antimicrobial essential oils components from plants such as, alpha terpineol (AT) and cinnamaldehyde (CMA), within a polyurethane polymer. Using a modified standard method for evaluating the performance of different nonporous solids (ISO 22196) and median tissue culture infection dose assay, these antimicrobial polyurethane coatings were tested and found to be effective in killing E. coli, P. aeruginosa, methicillin-resistant S. aureus (MRSA), and SARS-CoV-2. The durability of the coatings was tested by linear abrasion, UV and airflow exposure. Application methods such as spray coating and dip coating allow the coating to be applied to a variety of surfaces. Results: Polyurethane surfaces containing 35% AT content (PU-35%AT) showed a ∼5.8-log reduction in E. coli colony forming units per cm2 (CFU/cm2) in under 2 minutes, a shorter time than common commercial disinfectants. Additionally, when subjected to 8 consecutive rounds of inoculation the PU- 35%AT surface reduced the E. coli by >99.99% for all 8 rounds. We achieved a ∼5.8-log reduction of MRSA within 5 minutes on PU-60%AT. The PU-35%AT surfaces showed a 4.0-log reduction in SARS-CoV- 2 in 60 minutes. A PU-70%AT showed a 1.6-log reduction after 10 minutes and maintained virucidal capabilities after 2 weeks. PU+35%AT surfaces maintained a ∼5.3-log reduction in CFU/cm2 in MRSA and E. coli after 1000 abrasion cycles, 12 hours of UV exposure, 25 hours of exposure to -17°C, or 5 months of air flow. Lastly, to demonstrate the coating's real world functionality the PU+35%AT coating was successfully applied to a computer keyboard, cell phone screen protector and medical gauze. Conclusion/Significance: This work demonstrates a novel approach for fabricating a broad-spectrum antibacterial and antiviral polymer surface based on plant essential oil components. This antimicrobial polyurethane coating has not only rapid bactericidal and virucidal capabilities but maintains this efficacy over time. Additionally, the coating can be applied to a variety of surfaces including medical gauze to create wound dressings that significantly reduce bacterial burden and decrease chances of HAIs.

CS2 : A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention

The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance. To address such a problem of data and label scarcity, generative models have been developed to augment the training datasets. Previously proposed generative models usually require manually adjusted annotations (e.g., segmentation masks) or need pre-labeling. However, studies have found that these pre-labeling based methods can induce hallucinating artifacts, which might mislead the downstream clinical tasks, while manual adjustment could be onerous and subjective. To avoid manual adjustment and pre-labeling, we propose a novel controllable and simultaneous synthesizer (dubbed CS$$

[Analysis of common viral infection in surveillance cases of febrile respiratory syndrome in 9 provinces of China from 2009 to 2021].

Objective: To understand the common viral infection among the surveillance cases of fever respiratory syndrome (FRS) in nine provinces in China. Methods: The research data were obtained from nine provinces (Anhui, Beijing, Guangdong, Hebei, Hunan, Jilin, Shandong, Shaanxi and Xinjiang) in the "Infectious Disease Surveillance Technology Platform Information Management System" of the Chinese Center for Disease Control and Prevention from January 2009 to June 2021. Finally, 8 243 FRS cases with nucleic acid detection results of eight viruses [human influenza virus (HIFV), human respiratory syncytial virus (HRSV), human adenovirus (HAdV), human parainfluenza virus (HPIV), human rhinovirus (HRV), human metapneumovirus (HMPV), human coronavirus (HCoV) and human Boca virus (HBoV)] were included in the study. The χ2 test/Fisher exact probability method was used to analyze the difference of virus detection rate in different age groups, regions and seasons. Results The M (Q1, Q3) age of 8 243 FRS cases was 4 (1, 18) years old, and 56.56% (4 662 cases) were children under 5 years old. Males accounted for 58.1% (4 792 cases) of all cases. All cases were from outpatient/emergency department (2 043 cases) and inpatient department (6 200 cases). The virus detection rates of FRS cases from high to low were HRSV, HIFV, HPIV, HRV, HAdV, HMPV, HCoV and HBoV. Two or more viruses were detected simultaneously in 524 cases, accounting for 15.66% of virus-positive cases. The difference of the virus detection rate in different age groups was statistically significant (all P values<0.05), and the virus detection rate in children<5 years old was higher (49.96%). The positive rate of any virus in south China was higher than that in north China (P<0.001). The virus-positive FRS cases were detected throughout the year. The detection rate of HRSV was higher in autumn and winter. The detection rate of HIFV was higher in winter. The detection rate of HMPV was higher in winter and spring. The detection rates of HPIV, HRV, HCoV and HBoV were higher in summer and autumn, while there was no significant difference in the detection rate of HAdV in different seasons. Compared with 2009-2019, the detection rate of any virus in 2020-2021 decreased from 41.37% to 37.86%. The detection rate of HIFV decreased sharply from 10.62% to 1.37%. The detection rate of HPIV decreased from 8.24% to 5.88%. The detection rate of HRV and HBoV increased from 5.43% and 1.79% to 9.67% and 3.19%, respectively. Conclusion: HRSV and HIFV infections are more common among FRS cases in nine provinces in China from 2009 to 2021, and the epidemiological characteristics of eight common respiratory viruses vary in different age groups, regions and seasons.

Effects of market liquidity on price dynamics in a heterogeneous belief model

To understand the effect of liquidity on asset pricing, this study constructs a boundedly rational asset pricing model, introducing market liquidity and heterogeneous beliefs. Based on our model, we conduct empirical tests using the S&P 500 index from 1991 to 2021 and the CSI 500 index from 2007 to 2021. We find that market liquidity significantly influences investors’ expectations and belief switching. When market liquidity is scarce, fundamentalists in both markets expect the price to converge more quickly to its fundamental value, whereas chartists perceive that the price deviates from its fundamental value less rapidly. Lack of liquidity mitigates the investors’ original switching strategy, resulting in positive feedback as a net effect. Moreover, the S&P 500 index is efficient, whereas the CSI 500 index is slightly undervalued in the long run. Both markets exhibit large fluctuations and inefficiency during short periods such as the 2008 financial crisis and COVID-19 pandemic. As such, safeguards should be implemented against sudden shocks and the resulting price deviation and market inefficiency. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Suicidal ideation, suicide attempts and positive mental health in Chinese medical students

Introduction: Students worldwide are at marked risk of suicidal ideation/behavior, depression, and low positive mental health (PMH). Medical students are suspected to be a particularly burdened group. On this background, the aim of the present study was to determine prevalence rates of suicidal ideation/behavior, depression and PMH, among Chinese medical students compared to students from other disciplines. Methods: A total of 2,695 Chinese students (41.2% women;age: M = 18.86, SD = .60, range: 18–21), including 461 medical students, filled out self-report measures on suicidal ideation/behavior, depression, and PMH. Results: Twelve-month suicidal ideation was reported by 10.2% of the medical students and by 11.5% of the other students. Medical students had significantly lower levels of depression symptoms and significantly higher levels of PMH than students from other disciplines. Complete mental health – according to the dual-factor model of mental health – was reported by more than 84% of the total sample. Limitations: Data were assessed by a self-report survey that is prone to social desirability. Conclusions: Students in the present study displayed high rates of complete mental health – although the survey was conducted in the midst of the Corona pandemic. No evidence was found that medical students are particularly stressed. © 2022

Nano-Glass Composite Scintillators: Opportunities and Challenges

Scintillators as the core materials of radiation detection play an important role in industrial nondestructive testing, medical imaging, high energy physics and safety inspection, etc.. Theexisting scintillator research faces both opportunities and challenges, especially in the context of COVID-19 pandemic period. It is of great practical significance to develop cost-effective scintillators and optimize their overall performance. The nano-glass composites (i.e., glass ceramics) have some advantages like high emission efficiency of scintillator crystals, simple preparation and low cost as an effective star scintillator. Based on the different luminescence centers, such scintillators can be broadly divided into rare-earth element ions doped or rare-earth-free luminescent nanocrystals embedded materials. This review represented recent development on the preparation of these materials, the relationship between the types of nanocrystals and their luminescence properties, and the potential applications of these materials in high-energy X-ray and gamma-ray detection. In addition, the existing problems in the research were discussed and the future development direction of nano-glass composite scintillators was also prospected. © 2022, Editorial Department of Journal of the Chinese Ceramic Society. All right reserved.