Organizational Identification and Alternative Job Opportunities of Hotel Internship Students During COVID-19

This study highlights the major challenges faced by hotel interns in their career development and the human resource management of hotels in the current macroeconomic environment, particularly during the COVID-19 pandemic. The paper developed a conceptual model for organizational identification, turnover intention, and perceived alternative job opportunities in the context of hotel internships. A total of 350 samples were collected from hotel internships in Macau. The presented results indicate that organizational identification has a significant negative impact on turnover intention. In addition, alternative job opportunities do not moderate the relationship between organizational identification and turnover intention. The results also showed that females had a higher level of evaluative identification for hotel internships compared to males. In addition, interns from high-income families had a higher level of evaluative identification compared to those from low- and middle-income families. The theoretical contribution extends the concept of organizational socialization to include internship stages in the field of hospitality management. Finally, this paper proposes measures for managing hotel internships during the COVID-19 pandemic.

An Improved COVID-19 Lung X-Ray Image Classification Algorithm Based on ConvNeXt Network

Aiming at the new coronavirus that appeared in 2019, which has caused a large number of infected patients worldwide due to its high contagiousness, in order to detect the source of infection in time and cut off the chain of transmission, we developed a new Chest X-ray (CXR) image classification algorithm with high accuracy, simple operation and fast processing for COVID-19. The algorithm is based on ConvNeXt pure convolutional neural network, we adjusted the network structure and loss function, added some new Data Augmentation methods and introduced attention mechanism. Compared with other classical convolutional neural network classification algorithms such as AlexNet, ResNet-34, ResNet-50, ResNet-101, ConvNeXt-tiny, ConvNeXt-small and ConvNeXt-base, the improved algorithm has better performance on COVID dataset.

Analysis of adverse reactions of lopinavir/ritonavir in the treatment of coronavirus disease 2019.

Objective: To explore the occurrence of adverse reactions of lopinavir/ritonavir (LPV/r) in the treatment of coronavirus disease 2019 (COVID-19). Method(s): The medical records of patients with COVID-19 who received LPV/r treatment in the Fourth People's Hospital of Nanning from January 24th to February 6th, 2020 were collected and the occurrence of adverse events during the treatment was retrospectively analyzed. According to the 5 principles of adverse drug reaction correlation evaluation proposed in the Handbook of Adverse Drug Reaction Reporting and Monitoring in China, adverse events that were certainly related, probably related, and possibly related to LPV/r were defined as LPV/r-related adverse reactions. The incidence of adverse reactions was calculated and the main clinical manifestations and severity of adverse reactions [grade 1 (mild), grade 2 (moderate), grade 3 (severe), grade 4 (life-threatening), and grade 5 (death);grade 3-5 was defined as severe adverse reaction] were analyzed. Result(s): A total of 28 patients were enrolled in the analysis, including 13 males and 15 females, aged from 18 to 70 years with an average age of 44 years. The courses of treatment with LPV/r of patients ranged from 2 to 12 days, with a median course of 6 days. Of the 28 patients, 18 developed LPV/r related adverse reactions, with an incidence of 64.3%. The LPV/r-related adverse reactions in 18 patients included gastrointestinal reactions in 14 patients (grade 1 in 13 patients and grade 2 in 1 patient), bradycardia in 2 patients (grade 2 in both patients), and acute hemolysis in 1 patient (grade 3), and liver injury in 1 patient (grade 3), and no grade 4 or 5 adverse reactions occurred. The incidence of severe adverse reactions was 7.1%. Thirteen patients with grade 1 adverse reactions did not affect the treatment, and the symptoms were relieved after 2-7 days of continuous medication. LPV/r was discontinued in 5 patients with grade 2 or 3 adverse reactions, 4 of whom received symptomatic treatment, and the symptoms disappeared 2-10 days later. Conclusion(s): The incidence of adverse reactions in COVID-19 patients treated with LPV/r in our hospital was 64.3%. LPV/r mainly leads to mild gastrointestinal reactions and can also lead to bradycardia, acute hemolysis, and liver injury. Blood routine, liver function, and electrocardiogram need to be monitored during the treatment.Copyright © 2020 by the Chinese Medical Association.

Time and Frequency Spillovers between the Green Economy and Traditional Energy Markets

The green economy is aimed at decreasing the dependence of the global economy on traditional fossil energy, thereby resolving conflicts between economic development and environmental issues and achieving sustainable economic development. Thus, the relation between the green economy and traditional energy markets is of great importance for both policymakers and portfolio managers. In this study, we investigate the dynamic spillover effects between the green economy and traditional energy markets by applying time and frequency spillover measures based on the TVP-VAR model. The results reveal a strong spillover relationship between the green economy and traditional energy system, and the spillover direction is mainly from green economy markets to traditional energy markets. Our analysis further reveals the heterogeneity of these spillover effects, both within green economy markets and between these markets and traditional energy markets. The performance of the U.S. green economy market is similar to that of Europe, whereas the Asian green economy market is more complex. The frequency domain results demonstrate that the spillover effects are mainly dominated by short-term (1–5 days) components, whereas medium- and long-term components have less of an effect. In addition, we find a sharp increase in the level of spillover effects during the COVID-19 pandemic. © 2023 by the authors.

Lipid carriers for mRNA delivery

Messenger RNA (mRNA) is the template for protein biosynthesis and is emerging as an essential active molecule to combat various diseases, including viral infection and cancer. Especially, mRNA-based vaccines, as a new type of vaccine, have played a leading role in fighting against the current global pandemic of COVID-19. However, the inherent drawbacks, including large size, negative charge, and instability, hinder its use as a therapeutic agent. Lipid carriers are distinguishable and promising vehicles for mRNA delivery, owning the capacity to encapsulate and deliver negatively charged drugs to the targeted tissues and release cargoes at the desired time. Here, we first summarized the structure and properties of different lipid carriers, such as liposomes, liposome-like nanoparticles, solid lipid nanoparticles, lipid-polymer hybrid nanoparticles, nanoemulsions, exosomes and lipoprotein particles, and their applications in delivering mRNA. Then, the development of lipid-based formulations as vaccine delivery systems was discussed and highlighted. Recent advancements in the mRNA vaccine of COVID-19 were emphasized. Finally, we described our future vision and perspectives in this field.Copyright © 2023 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences

Path Planning Algorithms for Mobile Robots in Hospital Environment during Covid-19

Since 2019, the COVID-19 virus has spread worldwide, posing a significant health and safety concern. The application of mobile robots in the medical field has gradually demonstrated their unique advantages. Therefore, we focus on the application of mobile robots inwards. By collating and summarizing some of the most popular existing path planning algorithms, this paper illustrates that different algorithms can produce varying outcomes depending on different environments and hardware used. MATLAB is used in this study to simulate four algorithms: To determine the most efficient path, A∗, RRT, RRT∗, and PRM in a specific hospital map are compared, as well as parameters including path length, average execution time, and resource consumption. Modelling a single-layer hospital map makes it possible for mobile robots in the medical field to execute tasks more efficiently between entry and ward in the COVID-19 hospital environment. Based on a comparison and comprehensive consideration of the data derived from the simulations, it is found that the A∗algorithm is superior in terms of optimality, completeness, time complexity, and spatial complexity. Therefore, the A∗algorithm is more valuable in finding the best path for a mobile robot in a hospital environment. © 2022 ACM.

Stacking based prediction of COVID-19 Pandemic by integrating infectious disease dynamics model and traditional machine learning

Accurate prediction of 2019 novel coronavirus diseases (COVID-19) has been playing an important role in making more effective prevention and control policies during pandemic crises. The aim of this paper was to develop an innovative stacking based prediction of COVID-19 pandemic cumulative confirmed cases (StackCPPred) by integrating infectious disease dynamics model and traditional machine learning. Based on population migration characteristics, five feature indicators were first extracted from the population flow data in the early stage of this epidemic, which were collected from the National Health Commission of the People's Republic of China. Then, stacking based ensemble learning (SEL) model was established for COVID-19 prediction using traditional machine learning, including the multiple linear regression (MLR) and the tree regression model (XGBoost and LightGBM). By introducing the variable "death state", an improved Susceptible-Infected-Recovered (ISIR) model was established. Finally, a hybrid model, StackCPPred was proposed by incorporating the ISIR model outputs and the five feature indicators into the SEL model. Real data on population movements and daily cumulative number of newly confirmed cases across the country from January 23 to February 6 were used to validate our model. The results positively proved that the proposed StackCPPred model outperformed the existing models for COVID-19 prediction, as quantified by the root mean square error (RMSE), the root mean square logarithmic error (RMSLE) and the coefficient of determination (R2) (g1/41841 persons, g1/40.1 and >0.9, respectively). Furthermore, this study confirms the validity and usefulness of the StackCPPred model for COVID-19 prediction. © 2022 ACM.

Critique of “ Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team from Peking University

Ankit Srivastava et al. [1] proposed a parallel framework for Constraint-Based Bayesian Network (BN) Learning via Markov Blanket Discovery (referred to as ramBLe) and implemented it over three existing BN learning algorithms, namely, GS, IAMB and Inter-IAMB. As part of the Student Cluster Competition at SC21, we reproduce the computational efficiency of ramBLe on our assigned Oracle cluster. The cluster has 4x36 cores in total with 100 Gbps RoCE v2 support and is equipped with Centos-compatible Oracle Linux. Our experiments, covering the same three algorithms of ramBLe, evaluate its strong and weak scalability of the algorithms using real COVID-19 data sets. We verify part of the conclusions in the paper and propose our explanation of the differences. IEEE

IMPACT OF PERSISTENT COVID-19 SYMPTOMS ON PHYSICAL ACTIVITY AND CARDIORESPIRATORY PERFORMANCE IN VULNERABLE CHILDREN AND ADOLESCENTS

SESSION TITLE: Late Breaking Chest Infections Posters SESSION TYPE: Original Investigation Posters PRESENTED ON: 10/18/2022 01:30 pm - 02:30 pm PURPOSE: The science continues to develop in terms of the epidemiology of persistent, or long COVID, especially in the pediatric population. The impact of persistent COVID-19 on cardiorespiratory fitness in the form of physical activity and athletic performance among children/adolescents is not well described, especially among vulnerable populations. METHODS: A retrospective electronic health record review identified children/adolescents with previously diagnosed COVID (N=312, 52.9% male, mean age at diagnosis 6.6 [SD 5.9] years, 20.5% non-Hispanic White [NHW], 19.2% non-Hispanic Black [NHB], and 54.5% Hispanic, 85.26% hospitalized due to COVID-19 illness) from one pediatric healthcare system that serves predominantly Medicaid-dependent families. Patients or caregivers completed a follow-up telephone survey from March 2021- February 2022 to estimate the prevalence of persistent COVID symptoms, defined as the presence of symptoms lasting ≥ 30 days. Multiple logistic regression models explored the association between physical activity and the presence of long COVID. RESULTS: 71 (22.8%) patients reported long COVID and the most prevalent symptoms included tiredness (21 [6.7%]), shortness of breath (18 [5.8%]), cough (16 [5.1%]), headache (14 [4.5%]), difficulty with thinking/concentration (14 [4.5%]), disrupted sleep (14 [4.5%]), other symptoms (12 [3.8%]), anxiety (11 [3.5%]), body aches (11 [3.5%]), joint pain (10 [3.2%]) chest pain (9 [2.9%]), intermittent fever (6 [1.9%]), and loss taste/smell (5 [1.6%]). Almost a third (32%, N = 24) of patients who participated in any athletics or physical activity in or outside of school reported a negative impact on physical or athletic performance, and 66.7% reported it was directly related to COVID-19 illness. Specific complaints when returning to physical activity post-COVID illness included tiredness (7 [36.8%]) and shortness of breath (2 [10.5%]). The odds of a decline in physical activity performance was over twice that (OR 2.17, 95% CI 0.54-8.71, p = 0.28) among children with long COVID versus those reporting no long COVID after adjusting for demographics. There was no difference by age (mean 9.8 vs. 9.7 years, p = 0.93), sex (50% girls vs. 50% boys, p =0.71), or race/ethnicity (25% NHW vs. 25% NHB vs. 37.5% Hispanic, p = 0.25) in terms of decline in physical activity performance. Two children were recommended to delay re-entry into physical activity. CONCLUSIONS: A substantial proportion of ethnically diverse children from low resource backgrounds who had severe COVID illness are reporting long-term impacts on physical activity and cardiorespiratory fitness. Findings can inform pediatricians about this vulnerable population in post-COVID-19 recovery efforts. CLINICAL IMPLICATIONS: Pediatric pulmonologists and other sub-specialists should screen and monitor patients who have had previous severe COVID-19 illness for persistent cardiorespiratory impacts. DISCLOSURES: No relevant relationships by Kubra Melike Bozkanat No relevant relationships by Jackson Francis No relevant relationships by Weiheng He No relevant relationships by Alejandra Lozano No relevant relationships by Matthew Mathew No relevant relationships by Sarah Messiah No relevant relationships by Angela Rabl No relevant relationships by Sumbul shaikH No relevant relationships by Nimisha Srikanth No relevant relationships by Apurva Veeraswamy No relevant relationships by Sitara Weerakoon No relevant relationships by Luyu Xie

Preparation of polydopamine-induced nanosilver-modified polypropylene meltblown nonwoven fabric and study on its antibacterial properties

Polypropylene (PP) nonwoven fabric has good physical and mechanical properties, and the production process is simple and cost is low, so it is widely used in the fields of adsorption and filtration, medical and health care. After the outbreak of COVID-19 pandemic, the market demand for PP nonwovens for medical and health care, such as masks and protective clothing has surged. Nanosilver, with high efficiency and broad-spectrum antibacterial activity, has potential application prospects in the new generation of medical nonwoven materials. However, due to the undiversified macromolecular structure of polypropylene and the lack of polar functional groups on the surface, it is not easy for inorganic antimicrobial agents to hind to PP nonwoven substrates, thus limiting its application and development to a certain extent. Therefore, it is of great practical significance to research and develop PP nonwoven fabric with antibacterial and antiviral properties to achieve long-lasting antibacterial effects. Surface modification methods such as radiation grafting, plasma treatment and chemical etching are commonly used for nonwoven fabric to improve the binding properties of fabrics with antimicrobial agents, but these methods involve harsh treatment conditions and complicated preparation processes. Therefore, this study intends to develop a simple nonwoven modification pathway to enhance the surface binding of inorganic antimicrobial agents to fabrics and achieve sustainability of the antimicrobial effect of PP nonwoven fabric. Dopamine is a low-molecular-weight catecholamine that mimics an adhesion protein and can polymerize spontaneously on various organic-inorganic surfaces to form uniform polydopamine (PDA) films under mild conditions. In addition, the phenolic hydroxyl groups in PDA have redox activity and thus can be used as metal reducing agents. In this paper, inspired by mussel adhesion proteins, PDA coatings were deposited on the surface of PP nonwovens by impregnation using nonwovens with different structures as substrates, and the optimal process conditions for dopamine impregnation were optimally selected using orthogonal tests. Further, nanosilver was generated in situ on the modified PP nonwoven surface by chemical plating method, and the antibacterial PP nonwoven fabric was characterized using FTIR , XRD, SEM, and pore testing. The results showed that the best film formation and coating effect of PDA was achieved when the dopamine concentration was 4 g/L, the pH of the buffer solution was 8.5, and the impregnation time was 24 h. When the amount of PVP was small, irregular silver nanoparticles were easily generated on the fabric surface, and the appropriate amount of PVP could effectively prevent the agglomeration of Ag nanoparticles;when mPVp∗mAKN03 = 1:1, spherical Ag with narrow particle size distribution range and good dispersion was obtained, the antibacterial performance was tested using agar plate diffusion method, and the results showed that 25 g/nr sample fabric after 30 times of water washing. The antibacterial activity of the sample was 74. 22% against influenza A ( HI N1) virus. In this study, an effective antibacterial and potentially antiviral PP nonwoven fabric was prepared based on the surface modification of mussel mimicry, which expands its application in the medical and health care field. © 2022 China Silk Association. All rights reserved.

Effects of Online Self-Regulated Learning on Learning Ineffectiveness in the Context of COVID-19

Within the COVID-19 pandemic and the new normal period, online learning has become one of the main options for learning. Previous studies on self-regulated learning have shown that it was a better predictor of online learning effectiveness. However, this discussion has not been extended to the situation of the COVID-19 pandemic. To address this gap, this study aims to explore the relationship between the three stages of self-regulated learning (SRL) and learning ineffectiveness (LI). Data of 370 high school students were collected during the period of COVID-19. Structural equation modeling was used to perform confirmatory factor analysis on the data. Findings show that the preparatory stage was positively related to the stages of performance and appraisal, and the performance stage was positively related to the appraisal stage;on the other hand, the stages of performance and appraisal were negatively related to learning ineffectiveness. In addition, the preparatory stage had no direct relation to learning ineffectiveness, but the preparatory stage was correlated with learning ineffectiveness, mediated by the stages of performance and appraisal. These results suggest that better performance in the three stages of self-regulated learning decrease learners' perceived online learning ineffectiveness. This understanding can have implications for global education.

How does cognitive detachment from work influence employee creativity? A curvilinear relationship based on the cognitive perspective

During the corona virus disease of 2019 (COVID-19) pandemic, employees have begun to lack a sense of cognitive detachment from their work. Furthermore, employees with high creativity are better able to help their organizations to survive the economic decline caused by this pandemic. However, scholars currently know relatively little about how and why cognitive detachment influences employee creativity. Leveraging boundary theory and a dual pathway to creativity model (DPCM), the present study hypothesized that cognitive detachment from work will influence employee creativity in an inverse U-shaped pattern, with cognitive flexibility as a mediator and the boundary condition of intrinsic motivation for creativity being included. Our results, which were gained from a sample of 304 research and development (R&D) employees, indicate that employees' cognitive detachment from work and their degree of creativity possess a curvilinear relationship and that cognitive flexibility is a likely mediator between them. Notably, this inverse U-shaped relationship is significant only if the employees have high intrinsic motivation for creativity. This study uncovers the complicated influence of cognitive detachment from work on individual creativity, while also investigating the underlying cognitive processes (i.e., cognitive flexibility) involved and the importance of intrinsic motivation. © 2022 John Wiley & Sons Ltd.

Structure-Based Discovery and Structural Basis of a Novel Broad-Spectrum Natural Product against the Main Protease of Coronavirus

Over the past 20 years, the severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome CoV (MERS-CoV), and SARS-CoV-2 emerged, causing severe human respiratory diseases throughout the globe. Developing broad-spectrum drugs would be invaluable in responding to new, emerging coronaviruses and to address unmet urgent clinical needs. Main protease (Mpro;also known as 3CL(pro)) has a major role in the coronavirus life cycle and is one of the most important targets for anti-coronavirus agents. We show that a natural product, noncovalent inhibitor, shikonin, is a pan-main protease inhibitor of SARS-CoV-2, SARS-CoV, MERS-CoV, human coronavirus (HCoV)-HKU1, HCoV-NL63, and HCoV-229E with micromolar half maximal inhibitory concentration (IC50) values. Structures of the main protease of different coronavirus genus, SARS-CoV from the betacoronavirus genus and HCoV-NL63 from the alphacoronavirus genus, were determined by X-ray crystallography and revealed that the inhibitor interacts with key active site residues in a unique mode. The structure of the main protease inhibitor complex presents an opportunity to discover a novel series of broad-spectrum inhibitors. These data provide substantial evidence that shikonin and its derivatives may be effective against most coronaviruses as well as emerging coronaviruses of the future. Given the importance of the main protease for coronavirus therapeutic indication, insights from these studies should accelerate the development and design of safer and more effective antiviral agents. IMPORTANCE The current pandemic has created an urgent need for broad-spectrum inhibitors of SARS-CoV-2. The main protease is relatively conservative compared to the spike protein and, thus, is one of the most promising targets in developing anticoronavirus agents. We solved the crystal structures of the main protease of SARSCoV and HCoV-NL63 that bound to shikonin. The structures provide important insights, have broad implications for understanding the structural basis underlying enzyme activity, and can facilitate rational design of broad-spectrum anti-coronavirus ligands as new therapeutic agents.

Increasing Use of Interferon-Gamma Release Assay to Test for Pediatric Tuberculosis in a Low-Burden Setting

Background. The American Academy of Pediatrics recommends tuberculin skin tests (TSTs) or interferon gamma release assays (IGRAs) to test for tuberculosis (TB) infection in children ≥2 years old, and prioritizes IGRA testing in Bacille Calmette-Guerin vaccine recipients due to cross-reactivity. TSTs require a return visit, which frequently results in loss to follow up. Growing evidence supports accuracy of IGRA testing in pediatric patients, including young children, leading to calls for preferential use of IGRA over TST. We sought to evaluate trends in IGRA use in children over time. Methods. We identified all TB infection tests conducted in children 5-17 years old at 2 academic medical systems in Boston from October 2015-January 2021. TSTs were identified using medication administration records, and IGRAs were identified using laboratory records. We computed the proportion of tests per month that were IGRA and TST. We used Pearson correlation to determine the association between month of testing and proportion of tests that were IGRAs. Results. 21,471 TB infection tests were obtained from 16,778 patients during our timeframe. Median age of testing was 13.4 years (IQR 9.2 - 16.2 years). During the study period, there was a significant increase in the monthly proportion of TB infection tests that were IGRAs (Pearson correlation coefficient 0.92, P < 0.001). The total number of tests performed per month also increased, with seasonal increases in testing in late summer and early fall and a substantial decline in testing early in the COVID-19 pandemic. Tuberculosis infection tests and proportion IGRA. Total number of tuberculosis infection tests per month and proportion of tests that were interferon gamma release assays, from October 2015 - January 2021. Conclusion. Use of IGRAs among patients age 5-17 years of age increased significantly overall and compared to TST in two large Boston healthcare systems over a 5-year period. These results suggest a shift towards blood-based TB infection testing in a low-burden setting, which may improve completion of the pediatric TB infection care cascade. Future research is needed to determine reasons for changing testing modalities, and similar patterns in other settings.

Development and challenges of mRNA-based therapeutics

mRNA-based drug has already become a focus as a potential new type of drug in recent years. With the rapidly evolving technologies, mRNA has been widely applied in various areas of clinical research, such as immune disease, oncological disease, infectious disease, and congenital metabolic disorder. Since the outbreak of COVID-19, there has been a great leap in mRNA vaccine development, forecasting that much more mRNA medicines will enter the market in the near future. In order to meet industrial requirements, this article provides an overview of the product profiles of mRNA-based drugs and their industrial landscape, and further discusses the key considerations in the manufacturing of such medicines. © 2021, Chinese Journal of New Drugs Co. Ltd. All right reserved.

Using data mining to track the information spreading on social media about the COVID-19 outbreak

Purpose: COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence people behaviour in social media. This paper aims to question of information spreading on COVID-19 outbreak are addressed with a massive data analysis on Twitter from a multidimensional perspective. Design/methodology/approach: The evolutionary trend of user interaction and the network structure is analysed by social network analysis. A differential assessment on the topics evolving is provided by the method of text clustering. Visualization is further used to show different characteristics of user interaction networks and public opinion in different periods. Findings: Information spreading in social media emerges from different characteristics during various periods. User interaction demonstrates multidimensional cross relations. The results interpret how people express their thoughts and detect topics people are most discussing in social media. Research limitations/implications: This study is mainly limited by the size of the data sets and the unicity of the social media. It is challenging to expand the data sets and choose multiple social media to cross-validate the findings of this study. Originality/value: This paper aims to find the evolutionary trend of information spreading on the COVID-19 outbreak in social media, including user interaction and topical issues. The findings are of great importance to help government and related regulatory units to manage the dissemination of information on emergencies, in terms of early detection and prevention. © 2021, Emerald Publishing Limited.

Design and engineering application of medical oxygen supply system in novel coronavirus pneumonia treatment hospital

Respiratory supporting, as an important medical treatment for new coronavirus pneumonia patients, must be effectively guaranteed by medical oxygen supply. However, the medical oxygen system designed and configured by the existing hospitals according to the current specifications cannot meet the oxygen needs for patients with new coronavirus pneumonia. This paper aimed to study the design of medical oxygen system in new coronavirus pneumonia emergency hospital. By investigating the oxygen treatment plan for the novel coronavirus pneumonia patients in the health emergency hospital, the oxygen treatment characteristics of different patients were studied. The oxygen characteristics of different respiratory support terminals were explored to study the oxygen demands of new coronavirus pneumonia emergency hospitals. Through calculating flow rates of medical gas system air source referring to 'technical code for medical gases engineering', the proportion coefficient of severe patients converted into respiratory distress patients was introduced, and the model of calculating flow rates of medical oxygen system air source in emergency hospital was proposed. The cases were verified in a typical health emergency hospital that the developed calculation flow model of medical oxygen source met the demands of hospital oxygen. The outcomes provide a reference for the design and construction of medical oxygen in such health emergency hospitals.