Enterprises' Green Growth Model and Value Chain Reconstruction: Theory and Method

The goal of this book is to improve the ability of enterprises to implement the green growth model and value chain reconstruction. China's environmental development strategies, such as carbon peak emission and carbon neutrality, have created new challenges and requirements for enterprises to "go green.” In addition, anti-globalization and the complex dynamic uncertainty caused by COVID-19 have changed the operational environment that enterprises face. The application of new technologies, including the new generation of information technologies and the whole process management technology, provides solutions for the implementation of enterprises' green growth model and value chain reconstruction. Based on China's enterprise management cases, this book reveals the connotative features of enterprises' green growth model and their evolutionary regularities, the overall framework and decision optimization of value chain reconstruction under the green growth model, and the approach to implementing the green growth model and value chain reconstruction. The theoretical framework of the green growth model and value chain reconstruction established in this book has enriched and developed the research results in this field. Cases of enterprises implementing the green growth model can provide references for the green transformation of enterprises and help enterprises appreciate the synergy between sustainability and growth. This book can also serve as a research reference for scholars engaged in the field of sustainable operations, as well as decision-makers and managers of relevant government departments. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

Synthesis and crystal characteristics of nirmatrelvir

Several synthetic routes of nirmatrelvir (the ingredient of a new drug to treat COVID-19 made by Pfizer) have been reported. We focused on a second route to improve the synthetic method of nirmatrelvir with a methodology that included different steps. The first step was an analysis of reaction byproducts using acetonitrile as a solvent of the condensation reaction to improve the inversion rate. Then, we used isobutyl acetate as a crystalline solvent to obtain the key intermediate as a solvate, which was a stable crystal product with high purity. Complementarily, we also used trifluoroacetic anhydride as the primary-amide dehydrating agent, and 2-methyl tetrahydrofuran as the solvent to prepare nirmatrelvir, which led to an overall yield of 48% via four steps and a purity of 99.5% according to high-performance liquid chromatography. We also investigated the crystal form of nirmatrelvir: the single-crystal features and transformation from a crystal form to nirmatrelvir were dependent upon temperature. Our data have great value for study of the synthetic method and crystal stability of nirmatrelvir. © 2023 The Royal Society of Chemistry.

YOLO Based Thermal Screening Using Artificial Intelligence (Al) for Instinctive Human Facial Detection

Covid19 remains the world's greatest public health emergency. It has become indispensable to measure the temperature of people entering or leaving croweded places to ease the identification of potentially infected and to isolate them from spreading and preventing the spread of the ongoing global pandemic of coronavirus disease. This research work is focusing on thermal screening for an automated scanner using Artificial Intelligence (AI) for instinctive temperature measurement on human faces. The framework used for facial detection is known as YOLOv5 which is a family of compound-scaled object detection models trained on the COCO, a large-scale object detection, segmentation, and captioning dataset. YOLOv5 is able to detect several different objects simultaneously by using its available pre-trained models and robustness of detecting faces even at the vicinity of face masks. The research presents the application, training procedure and capability of the Yolov5. This system is not only used for the human face detection, but also for the detection of some commonly-used objects as an extension to its overall application and performance. Yolov5 is readily available to be implemented in Python, the core programming language working under an Ubuntu-based Operating System providing users the best experience. One of the important outcomes of this research work is the development of a Graphical User Interface (GUI) to work alongside the main programme flow. © 2022 IEEE.

Air quality improvement assessment and exposure risk of Shandong Province in China during 2014 to 2020.

As one of the most polluted provinces in China, air pollution events occur frequently in Shandong. Based on the hourly (or daily) concentrations of six air pollutants (PM2.5, PM10, O3, NO2, SO2 and CO), the situations of air quality improvement in three kinds of cities (key cities, coastal cities and general cities) are assessed comprehensively during 2014-2020. Contrary to the daily maximum 8-h average ozone (MDA8 O3), the annual average concentrations of other pollutants show the downward trends during 2014-2020. Therein, the improvement rates of annual average concentrations of air pollutants in key cities are highest. By 2020, the day proportions of O3 as the primary pollutant are up to 38% in three kinds of cities. Besides, due to the impact of COVID-19, the monthly average concentrations of PM2.5, PM10, NO2, SO2 and CO in February 2020 decrease by 32.1-49.5% year-on-year. There are still about 50% of population exposed to high-risk regions (R i > 2), which are mainly concentrated in main urban areas and industrial areas. Thus, the adjustment of industrial structure and energy composition in the context of carbon peak and carbon neutrality should be implemented in the future. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04651-5.

Geographical detector-based assessment of multi-level explanatory powers of determinants on China’s medical-service resumption during the COVID-19 epidemic

Knowing the multi-level influences of determinants on medical-service resumptions is of great benefits to the policymaking for medical-service recovery at different levels of study units during the post-COVID-19 pandemic era. This article evaluated the hospital- and city-level resumptions of medical services in mainland China based on the data of location-based service (LBS) requests of mobile devices during the two time periods (December 2019 and from February 21 to March 18, 2020). We selected medical-service capacity, human movement, epidemic severity, and socioeconomic factors as the potential determinants on medical-service resumptions and then explicitly assessed their multi-level explanatory powers and the interactive effects of paired determinants using the geographical detector method. The results indicate that various determinants had different individual explanatory powers and interactive relationships/effects at different levels of medical-service resumptions. The current study provides a novel multi-level insight for assessing work resumption and individual/interactive influences of determinants, and considerable implications for regionalized recovery strategies of medical services.

Study on the Active Compounds in Yinlian Jiedu Decoction Treating COVID-19 Based on Network Pharmacology and Molecular Docking

Objective: To explore the potential components and mechanism of Yinlian jiedu decoction in the treatment of COVID-19. Method(s): The blood components in the formula of Yinlian jiedu decoction or compounds conforming to drug-like parameters were selected as the research objects.The components that meet the requirements in Lonicerae Japonicae Flos, Forsythiae Fructus, Bupleuri Radix, Scutellariae Radix, aboveground part of Agastache rugosa, Saposhnikoviae Radix, Menthae Haplocalycis Herba, Bombyx Batryticatus, Belamcandae Rhizoma, Platycodonis Radix, Aurantii Fructus, Fritillariae Thunbergii Bulbus, Phragmitis Rhizoma, fried Stemonae Radix, Eriobotryae Folium, Citri Reticulatae Pericarpium, Astragali Radix, Codonopsis Radix, fried Atrictylodis Macrocephalac Rhizoma, Coicis Semen, Salviae Miltiorrhizae Radix et Rhizoma, Chuanxiong Rhizama, Chebulae Fructus, Glycyrrhizae Radix et Rhizoma were searched and predicted through multiple network pharmacological data platforms.The Perl command was used to batch retrieve the upstream gene name of the prediction target in the UniProt database.The target genes were brought into the ClueGO software for GO function enrichment analysis, to explore the core metabolic pathways and signal pathways and clarify the mechanism of the treatment of COVID-19 with Yinlian jiedu decoction. Result(s): The compounds-targets network consisted of 309 compounds and 1 016 corresponding targets.The key targets involved MMP1, FASN, MPO, MMP3, NQO1, MMP12, ALOX5, PTGS2, GCLM, MMP2, EGFR, GSTP1, MET, ACEII, etc.There were 238 GO items in GO functional enrichment analysis(P<0.05), including 202 biological processes(BP), 9 cellular components(CC)and 27 molecular functions(MF).The results of molecular docking showed that puerarin had the best affinity with COVID-19. Conclusion(s): Puerarin in Yinlian jiedu decoction has a direct effect on ACEII.At the same time, multiple components of Yinlian jiedu decoction play a regulatory role in multiple pathways related to respiratory diseases by acting on multiple related targets. Copyright © 2021, Central Station of Chinese Medicinal Materials Information, National Medical Products Administration. All right reserved.

Bioinformatics analysis of changes in genes associated with tumor-associated macrophages before and after neoadjuvant chemotherapy for breast cancer

Background and Aims: Breast cancer is the most prevalent malignancy in women worldwide, and chemotherapy is one of the most important treatment modalities for breast cancer. Recent studies have shown that chemotherapy may exert anti-tumor effects by enhancing anti-tumor immunity in the tumor microenvironment. Therefore, this study was conducted to identify the changes in tumor-associated macrophages (TAMs) and relevant genes before and after neoadjuvant chemotherapy (NAC) in breast cancer patients by bioinformatics analysis and to evaluate the effect of NAC on immune functions in breast cancer patients. Methods: Information searching was performed by entering "Breast Cancer", "TAMs", "Chemotherapy" and selecting the human breast cancer tissue in the GEO database, and the GSE134600 dataset was selected for analysis. Differentially expressed genes (DEGs) in tissue samples from breast cancer patients before and after NAC were screened by R package (limma function). GO function enrichment and KEGG pathway analysis were performed for all DRGs. The protein interaction network of DEGs was visualized by Cytoscape software, and hub genes were screened and 10 hub genes were analyzed for mutations by cBioPortal. Immune cell distribution and correlation in GSE134600 data were evaluated using the R package“CIBERSORT”. Results: A total of 751 DEGs (409 up-regulated and 342 down-regulated genes) were identified before and after NAC for breast cancer. The biology of DEGs was analyzed by GO enrichment for biological process（BP）, cellular component （CC）, and molecular function（MF）. In BP function, they were mainly enriched in type I interferon(IFN-I) signaling pathway/viral response and defense and viral life cycle;in CC function, they were mainly enriched in extrinsic components of cell membrane and cytoplasmic side of cell membrane;in MF function, they were mainly enriched in cytokine receptor binding, double-stranded RNA binding and lipopeptide binding. In the analysis of KEGG pathway enrichment, DEGs were mainly enriched in influenza A (H1N1), measles, hepatitis C, coronavirus disease COVID-19, NF-κB signaling pathway, EBV virus infection, NOD-like receptor signaling pathway, and amoeba disease signaling pathway. The top 10 hub genes with the highest degree of interaction with TAMs before and after NAC for breast cancer were screened by CytoHubba plug-in: IFIT1, ISG15, MX1, MX2, IRF7, RSAD2, IFIT3, IFI35, IFI6, and IFITM1. Multi-omics analysis revealed that IFIT1, MX1 and MX2 were mainly deletion mutations, IFIT1 mainly had deep gene deletion, while MX1 and MX2 were mainly associated with gene amplifications. The content of M0 macrophages, CD8+T cells and M2 macrophages in breast cancer tissues decreased after NAC, and M0 macrophages were positively correlated with memory B cells (r=0.64) and negatively correlated with unactivated CD4+ memory T cells (r=-0.66). Conclusion: The identified DEGs associated with TAMs in breast cancer patients before and after NAC are closely related to interferon signaling pathway, suggesting that interferon signaling pathway may play an important role by altering TAMs in NAC. Meanwhile, M0 macrophages are significantly altered before and after NAC, indicating that chemotherapy may regulate the immune response to tumor by changing the distribution of M0 macrophages and immune function. © 2022 Central South University. All right reserved.

Covid-19 Diagnosis via Voice Using Online Sequential Extreme Learning Machine

With the worldwide spreading of Coronavirus disease 2019 (Covid-19) pandemic, besides the traditional diagnosing approach, Artificial Intelligence provides additional support for the pre-diagnosis of Covid-19 by using data such as patients' images, and sounds, etc. Being able to recognize Covid-19 positive patients quickly and correctly is the key to preventing the expansion of the disease. However, the existing Covid-19 diagnosis models still face challenges due to the complex network structure and additional medical examination. It takes much time to return a diagnosis result. In this paper, a diagnostic model is proposed as an early work for Covid-19 diagnosis using sound samples. The features of sound signals are expressed by Mel Frequency Cepstral Coefficients, which are input into the Online Sequential Extreme Learning Machine for normal/abnormal detection. Data from an open-source database were used to train the proposed model, the experiments show that using vowel pronunciations the model can achieve an accuracy of 96.4% on average, with about 10 times faster for testing than the Support Vector Machine. © 2022 IEEE.

Green spaces, especially nearby forest, may reduce the SARS-CoV-2 infection rate: A nationwide study in the United States

The coronavirus pandemic is an ongoing global crisis that has profoundly harmed public health. Although studies found exposure to green spaces can provide multiple health benefits, the relationship between exposure to green spaces and the SARS-CoV-2 infection rate is unclear. This is a critical knowledge gap for research and practice. In this study, we examined the relationship between total green space, seven types of green space, and a year of SARS-CoV-2 infection data across 3,108 counties in the contiguous United States, after controlling for spatial autocorrelation and multiple types of covariates. First, we examined the association between total green space and SARS-CoV-2 infection rate. Next, we examined the association between different types of green space and SARS-CoV-2 infection rate. Then, we examined forest-infection rate association across five time periods and five urbanicity levels. Lastly, we examined the association between infection rate and population-weighted exposure to forest at varying buffer distances (100 m to 4 km). We found that total green space was negative associated with the SARS-CoV-2 infection rate. Furthermore, two forest variables (forest outside park and forest inside park) had the strongest negative association with the infection rate, while open space variables had mixed associations with the infection rate. Forest outside park was more effective than forest inside park. The optimal buffer dis-tances associated with lowest infection rate are within 1,200 m for forest outside park and within 600 m for forest inside park. Altogether, the findings suggest that green spaces, especially nearby forest, may significantly mitigate risk of SARS-CoV-2 infection.

The application of interpretable machine learning model based on comparative learning and NARMAX in epidemic research

Most of the machine learning models are black box models. However, in practical applications, such as in many medical and health fields, it is very necessary to clearly understand the internal composition, combination or interaction of the model, study the system and predict the system behavior. Therefore, interpretable machine learning models have attracted more and more attention, especially when predicting based on models, the driving factors leading to prediction behavior are deeply studied. This paper proposes an interpretable machine learning model based on comparative learning and NARMAX. Because the input-output relationship of the model and the interaction relationship between input variables are clear, the model can not only be used for prediction, but also explain the relevant 'reasons' of prediction behavior. The novel coronavirus pneumonia epidemic data and influenza epidemic data were used to compare the model proposed in this paper. The experimental results show that the model is effective and reliable, and establish a dynamic model for the two diseases' spreads, and analyze the relationship between disease transmission factors. © 2022 IEEE.

The link between genetic variation and variability in vaccine responses: a narrative review

Vaccines are one of the biggest successes in modern history and are particularly important in light of the multiple ongoing epidemics. Recently, vaccines have protected peoples' health and lives around the world during the coronavirus disease 2019 pandemic. Different types of vaccines have their own characteristics and advantages and are used in the context of different epidemics. Responses to vaccination are also different, and can include adverse reactions and absent responses. These individual differences are thought to be influenced by host genes. In this review, we first discuss vaccine types and characteristics. Second, we discuss different responses to vaccination, primarily focusing on the association between genetic variation and inter-individual differences.

Two-stage game model of code sharing agreement choosing and tickets' pricing

The code sharing is the popular and effective cooperation of the airlines. First, this paper applies the spokes model to describe the different preferences of passengers for three airlines, and proposes the twostage game model and the simple method for the code sharing agreement choosing stage and the tickets' pricing stage under the free-sale model. Second, we use the numerical example to show the feasibility of the proposed models and method. By comparison and analysis, the Nash equilibrium situations of the two-stage game can be obtained. This study demonstrates: The code sharing agreement saves the total operating cost, but it raises the average airfare of the aviation market, so that the airlines' profits increase and the passengers' surpluses decrease;the government should promote the realization of better Nash equilibrium situation in order to maximize the total social welfare;to further improve the total social welfare during the period of Corona Virus Disease-19, the government could give the airlines some cost subsidies or adjust the passengers' psychological costs. Therefore, this paper provides significant theoretical and methodical supports to optimize the choices of the code sharing agreements and to improve the total social welfare in the aviation market. © 2022, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

Team huddle, education and infection screening as effective tools in continuing cancer care delivery during the peak of COVID19 pandemic in 2020 in the Bronx, NY: Experience from a public teaching hospital

Background: COVID-19 pandemic has affected healthcare delivery, particularly in the hard-hit areas. During the peak of COVID-19 pandemic crisis in the New York city, our outpatient oncology infusion center, located within a public teaching hospital in the South Bronx remained active. We implemented twice daily team huddle, staff and patient education, and infection screening tools and modified treatment plans based on social, personal and disease related factors. We evaluate the effectiveness of the above strategies in timely delivery of critical oncological care. Methods: Patients treated from the March 1, 2020 to the May 8, 2020 were included. De-identified data from medical charts were analyzed using IBM SPSS Version 27.0. Bivariate logistic regression analysis was applied to identify factors associated with COVID-19. Results: In total, 170 patients were treated in 576 visits. Median age was 60.7 years, 44% Hispanic and 41% Black, median Charlson Comorbidity index (CCI) was 6.6. Fifty percent received cytotoxic chemotherapy, 44% targeted therapies and the remaining received immune-checkpoint inhibitors. Of the 170 patients, six developed severe COVID-19 requiring hospitalization. Their median age was 63 years with average of 10.5 days from infusion center visit to COVID-19 and median CCI score was 9, higher than the rest of the cohort. Two patients died, 3 made complete recovery, 1 enrolled in hospice. Two patients contracted mild COVID-19 managed in the outpatient setting. Diabetes mellitus was associated with severe COVID-19 [OR: 25.9 (95%CI: 1.3-519, p=0.03)]. Age, gender, type of cancer and oncological treatment, smoking, CCI, growth factor support, nursing home residence, statin use were not associated with risk of developing severe COVID-19 Conclusions: Cancer treatment in the outpatient setting using an approach focused on careful patient selection, infection prevention strategies and strong team communication is feasible and allows for continuity of critical oncological care. Receipt of cancer directed therapy was not associated with higher risk for infection compared to risks associated with communitybased transmission. In communities with high community-based transmission, careful selection of patients for oncological based treatment is paramount.

Does the stringency of government interventions for COVID19 reduce the negative impact on market growth? Evidence from Pacific and South Asia

The objective of this study is to empirically assess the effect of government decisions on market growth in response to social distancing initiatives, government reactions, economic support provision, and containment and health responses, to name a few. A panel dataset of daily stock market returns is analysed in this study, changes in COVID-19 cases, and government responses to 17 countries in the Pacific and South Asia from 1st January 2020 to 31st December 2020. Findings indicate that social distancing policies have a significant negative effect on stock returns but a substantial positive impact on market growth when new cases' growth rate declines after accounting for country characteristics and systematic risk due to foreign factors. A direct negative effect is seen almost immediately, and a subsequently indirect positive effect is noted. As expected, policies regarding social distancing have an immediate negative impact, attributed mainly to the expected negative effect on economic activity. Subsequently, we see an indirect positive effect on market return because social distancing measures reduced the growth of confirmed COVID-19 cases. Both awareness, containment, and health index (ACHI) and Income Support and Debt Relief Index (ISDRI) positively affect market growth, as they are perceived to support individuals' socio-economic well-being and mainly result in positive market returns. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Toward A Multilingual and Multimodal Data Repository for COVID-19 Disinformation

The COVID-19 epidemic is considered as the global health crisis of the whole society and the greatest challenge mankind faced since World War Two. Unfortunately, the fake news about COVID-19 is spreading as fast as the virus itself. The incorrect health measurements, anxiety, and hate speeches will have bad consequences on people's physical health, as well as their mental health in the whole world. To help better combat the COVID-19 fake news, we propose a new fake news detection dataset MM-COVID1 (Multilingual and Multidimensional COVID-19 Fake News Data Repository). This dataset provides the multilingual fake news and the relevant social context. We collect 3981 pieces of fake news content and 7192 trustworthy information from English, Spanish, Portuguese, Hindi, French and Italian, 6 different languages. We present a detailed and exploratory analysis of MM-COVID from different perspectives. © 2020 IEEE.

Logistic regression combined with ROC curve model to predict risk of critically ill-patients with COVID-19

Objective: To build a model to predict critically ill-patients with coronavirus disease 2019 (COVID-19), and provide a new idea for the rapid identification of clinical progression in the early stage of critically ill-patients. Methods: A retrospective analysis of the general data of 152 general patients and 323 critically ill-patients diagnosed with COVID-19 from Jan 17th, 2020 to Feb 25th, 2020 in Wuhan Third Hospital was carried out;At the same time, the differences in fever, blood routine, liver and kidney function, coagulation function, C-reactive protein (CRP), and nucleic acid reagent testing results from the day of admission were statistically analyzed. Factors with statistical significance were included in a multivariate logistic regression analysis to obtain independent relevant factors that affect the critical ill-patients with COVID-19. Then a prediction model was built based on these factors and its accuracy was evaluated by the receiver operating characteristic (ROC) curve. Results: The sensitivities of age, fever, neutrophil ratio, lymphocyte ratio, serum creatinine (Scr) and combined diagnosis were 0.664, 0.671, 0.607, 0.669, 0.302 and 0.710, respectively;The specificities were 0.669, 0.585, 0.795, 0.685, 0.895 and 0.802, respectively;The area under the curve (AUC) were 0.725, 0.628, 0.721, 0.681, 0.590 and 0.795, respectively;The AUC of combined diagnosis was higher than that of single diagnosis (P < 0.05). Conclusion: The logistic regression and combined with ROC curve model based on multi-factors, including age, fever status, neutrophil ratio, lymphocyte ratio, and Scr, can play a good role in predicting the occurrence of critically ill-patients with COVID-19, which is worthy of further promotion and application.

Oxygen therapy strategies and techniques to treat hypoxia in COVID-19 patients.

OBJECTIVE: Hypoxia is one of the primary causes that leads to multiple organ injuries and death in COVID-19 patients. Aggressive oxygen therapy for the treatment of hypoxia is important in saving these patients. We have summarized the mechanisms, efficacy, and side effects of various oxygen therapy techniques and their status or the potential to treat hypoxia in COVID-19 patients. The benefit to risk ratio of each oxygen therapy technique and strategy to use them in COVID-19 patients are discussed. High flow nasal cannula oxygen (HFNO) should be considered a better choice as an early stage oxygen therapy. Supraglottic jet oxygenation and ventilation (SJOV) is a promising alternative for HFNO with potential benefits.

Could dantrolene be explored as a repurposed drug to treat COVID-19 patients by restoring intracellular calcium homeostasis?

Dantrolene, an FDA approved drug to treat malignant hyperthermia and muscle spasm, has been demonstrated to inhibit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mediated toxicity of host cells. Ryanodine receptor overactivation and associated disruption of intracellular Ca2+ homeostasis play important roles in SARS-CoV-2 infection and replication of host cells. Dantrolene, as an inhibitor of RyRs, is expected to ameliorate these detrimental effects of SARS-CoV-2 in host cells. Additionally, dantrolene has also been shown to inhibit multiple cell or organ damage induced by hypoxia/ischemia, mitochondria damage, oxidative stresses, inflammation, impairment of autophagy and apoptosis, etc., which are often the causes of severity and mortality of COVID-19 patients. We have repurposed that dantrolene has a high potential at treating COVID-19 patients and reducing its morbidity and mortality.

[Suggestions for thoracic surgery clinical practice in non-epidemic area of 2019 coronavirus disease].

In this paper, the mechanism of destroying human alveolar epithelial cells and pulmonary tissue by 2019 novel coronavirus (2019-nCoV) was discussed firstly. There may be multiple mechanisms including killing directly the target cells and hyperinflammatory responses. Secondly, the clinical features, CT imaging, short-term and long-term pulmonary function damage of the 2019 coronavirus disease (COVID-19) was analyzed. Finally, some suggestions for thoracic surgery clinical practice in non-epidemic area during and after the epidemic of COVID-19 were provided, to help all the thoracic surgery patients receive active and effective treatment.