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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.
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Introduction: Overweight and obesity in youth with serious emotional disturbance (SED) is exceedingly common. In 2015 the AHA called attention to mental illnesses in youth as important risk conditions for early CVD and the need for transformational change in management of overweight and obesity in this group. Our objective was to test a 12-month, innovative healthy weight intervention in youth with SED.Hypothesis: The active intervention is more effective than control in decreasing BMI Z-score compared at 12 m. Method(s): We conducted a two-arm randomized trial in 2 outpatient pediatric mental health settings in 112 youth, ages 8-18 yrs. The active intervention group was offered 12m of in-person and virtual individual weight management sessions led by health coaches who provided guidance on improving diet and increasing physical activity, and engaged parents. Result(s): At baseline, mean (SD) age was 13.0 (2.7) yrs with 46% ages 8-12 and 54% 13-18;55% were male, 46% Black, 39% had household income less than $50K/yr and 31% lived in a single-parent household. Primary diagnoses were ADHD (41%), major depression (23%), and anxiety (23%). Mean BMI Z-score (SD) was 2.0(0.4), BMI 30.4 (6.4) kg/m2.Mean(SD) psychotropic medications were 2.1(1.4).At 12m, 111 (99%) had a follow-up weight;42 were collected after the onset of the COVID pandemic). The intervention group compared to the control group had 0.15 decrease in BMI Z-Score (95% CI 0.26 to 0.04), p<0.007) between baseline and 12 m (Figure) and a 1.43 kg/m2 decrease in BMI (95% CI 2.43, 0.42, p<0.006). Estimated net effect on BMI Z-score for intervention vs. control was enhanced during the pandemic but not statistically different from net effects pre-pandemic (p=0.06). Conclusion(s): A weight control intervention designed for children with SED decreased BMI Z-score substantially over 12 months, including during the COVID-19 pandemic. These results provide empirical evidence in support of weight control programs in a population at high risk for early development of CVD risk factors.
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The Railway industry is facing a productivity issue as is often publicised with regular delays in rolling stock projects [1]. Plus, there is a growing need for innovation in remote services and management that have become the new normal during the COVID-19 pandemic. It drives a need for better Systems Engineering (SE) methods which include increased automation and dependence between systems and system performance, increasing number of disparate specialist engineering teams. [2] The aim of this paper is to develop an adaptable model which expresses the operational behavior of a train system in different railway environments, this model will be quickly and accurately configured to a specific environment to define the needs for a specific passenger service mission. Preventing late changes (cost and time-saving) by generating the right system requirements at the very early design phase through agile Model-Based Systems Engineering (MBSE) approach is the key benefit. Another goal includes increased productivity by minimizing unnecessary manual transcription of concepts when coordinating the work of large teams. This Generic* functional model of a Rolling Stock system can be configured to define specific products for an operator or Original Equipment Manufacturer (OEM).
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With the Internet of Things and medical technology development, patients use wearable telemedicine devices to transmit health data to hospitals. The need for data sharing for public health has become more urgent under the COVID-19 pandemic. Previously, security protection technology was difficult to solve the increasing security risks and challenges of telemedicine. To address the above hindrances, Federated learning (FL) solves the difficulty for companies and institutions to share user data securely. The global server iterative aggregates the model parameters from the local server instead of uploading the user's data directly to the cloud server. We propose a new model of federated distillation learning called FedTD, which allows the different models between local hospital servers and global servers. Unlike traditional federated learning, we combine the knowledge distillation method to solve the non-Independent Identically Distribution (non-IID) problem of patient medical data. It provides a security solution for sharing patients' medical information among hospitals. We tested our approach on the COVID-19 Radiography and COVID-Chestxray datasets to improve the model performance and reduce communication costs. Extensive experiments show that our FedTD significantly outperforms the state-of-the-art. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19. © ACL 2020.All right reserved.
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Introduction & Objectives: Operative volume is often used as an indicator of competency. During the COVID-19 pandemic many New Zealand hospitals limited elective operating. A retrospective review was performed, to quantify the impact of restrictions on trainee operative volume. Method(s): New Zealand based urology trainee operative numbers were obtained from the Morbidity Audit & Logbook Tool (MALT) from 2018- 2021. Several index operations were selected as markers of operative exposure. Both the mean and median number of cases per trainee each quarter per year were calculated as well as average total annual number of each operation per trainee. Result(s): During the second quarter of 2020 when NZ was in its strictest lockdown, operative numbers across almost all procedures assessed dropped from the preceding quarter, with the exception of radical prostatectomy. This contrasts with other years where second quarter operative numbers tended to increase compared to the prior quarter. However, with the extension in the NZ training year in 2020, overall operative numbers were not significantly different to the adjacent years. Conclusion(s): There does not appear to have been a significant impact on the operative volume for NZ based trainees. This may reflect the higher priority given to cancer operations, the extension of the 2020 training year, and the low numbers of noncancer operations being performed prior to the pandemic.
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Internal control is an important internal governance mechanism of enterprises and plays an important role in preventing and controlling corporate risks. This paper utilizes COVID-19 shocks and uses data from listed companies in China for 2019–2021 in order to study the impact of internal control on enterprise resilience and its functioning mechanism. The findings show that internal control significantly improves enterprise resilience during a crisis. By using firm characteristic quantile regressions, it is found that under a crisis, larger firms with sufficient cash flow from operating activities are more protected by internal control and more resilient. Mechanistic analysis suggests that internal control further increases enterprise resilience by improving resource allocation efficiency, reducing operating risk, and increasing innovation output. Further analysis shows that government support can enhance the resilience of firms during crises through tax and fiscal policies;a better business environment enhances firms' ability to withstand risks in crisis situations and helps them gain a competitive advantage in crisis situations. Based on this, this paper provides empirical evidence for revising and improving the internal control system of enterprises to reduce the negative impact of public health emergencies in the context of epidemics. © 2022 by the authors.
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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.
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We examined the relationship between COVID-19 stressors and individuals' career insecurity and the moderating effect of family support and openness to experience on that relationship. Participants were 207 young Chinese employees (female = 52.2%;mean age = 25.5 years, SD = 4.673 years). They completed the COVID-related stressors, Family Support, Career Insecurity, and Openness Questionnaires. Regression analysis results showed that COVID-related stressors were associated with higher career insecurity. Openness to experience buffered such a link between the COVID-related stressors and career insecurity so that when openness was high, career insecurity from COVID-related stressors was lower. Family support did not moderate the relationship between COVID-related stressors and career insecurity. These findings suggest the importance of personality traits in the relationship between COVID-19 pandemic stressors and work participation for resilient careers.
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The outbreak of the coronavirus pandemic in 2019, declared as a major public health emergency, profoundly affected the mental health of older adults. Several studies have provided reasonable recommendations to alleviate these effects. A vital role was played by healthcare robots in providing the psychosocial care to the older adults. This review analyzes relevant studies and addresses the research progress on the effects, recommendations, and robot-mediated therapy to alleviate mental health problems developed among older adults during the pandemic. Social robots can provide strong support for mental health. © 2022 IEEE.
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Coronavirus disease 2019 (COVID-19), characterized by high infectivity and invisibility, has spread quickly throughout the world and brought great challenges to hospital security work. Regarding the medical service reality of large general hospitals, the Security Department actively responded to the epidemic prevention and control work. They classified the risk areas in the hospital and formulated corresponding security strategies. Current paper summarize the improvement of the existing security management and control system, the management level of major public health emergencies and the emergency management. Then the control measures for scientific deployment of personnel and proper planning of the diagnosis and treatment process of risk patients during the epidemic are discussed. This study aimed to provide helpful reference for ensuring the stability of medical reception order in large general hospitals under COVID-19 epidemic. Copyright © 2022, Editorial Board of Journal of Xi'an Jiaotong University (Medical Sciences). All right reserved.
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Li–air batteries have received significant attention for their ultrahigh theoretical energy density. However, the byproducts induced by attacking air hinder the conversion of Li–O2 batteries to Li–air batteries. Humidity is one of the main obstacles, not only causing side reactions with the discharge products but also leading to rapid corrosion of the lithium anode. Here, we fabricated a novel composite hydrophobic catalyst by loading RuO2 and graphene on N-doped porous carbon. The catalyst was endowed with hydrophobicity and showed superior catalytic performance and low affinity to water in the air. A Li–air battery equipped with this novel composite catalyst exhibited eminent cycling performance in pure oxygen (over 470 h), humid oxygen [∼40% relative humidity (RH), over 310 h], and ambient air (∼42% RH, over 330 h) at a current density of 500 mA g−1, and the discharge specific capacity increased from 13122.1 to 19358.6 mAh g−1. © 2022
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The Railway industry is facing a productivity issue as is often publicised with regular delays in rolling stock projects [1]. Plus, there is a growing need for innovation in remote services and management that have become the new normal during the COVID-19 pandemic. It drives a need for better Systems Engineering (SE) methods which include increased automation and dependence between systems and system performance, increasing number of disparate specialist engineering teams. [2] The aim of this paper is to develop an adaptable model which expresses the operational behavior of a train system in different railway environments, this model will be quickly and accurately configured to a specific environment to define the needs for a specific passenger service mission. Preventing late changes (cost and time-saving) by generating the right system requirements at the very early design phase through agile Model-Based Systems Engineering (MBSE) approach is the key benefit. Another goal includes increased productivity by minimizing unnecessary manual transcription of concepts when coordinating the work of large teams. This Generic* functional model of a Rolling Stock system can be configured to define specific products for an operator or Original Equipment Manufacturer (OEM). Copyright © 2022 by ASME
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The influence of asymptomatic patients on disease transmission has attracted more and more attention, but the mechanism of some factors affecting disease transmission needs to be studied urgently. Considering the self-healing rate of asymptomatic patients, the cure rate of symptomatic patients, the transformation rate from asymptomatic to symptomatic and the infection delay, a type of infectious disease dynamics model SIsIaS with asymptomatic infection and infection delay is established in this paper. It is found that both the infection delay and the difference size between the cure rate and the self-healing rate not only affect the minimum value of the total number of patients in the persistent state of the disease, but also lead to disease extinction to be controlled by the proportion of symptomatic patients in patients. Moreover, the infection delay can lead to local Hopf bifurcation of periodic solutions. By using the normal form and center manifold theory the direction of Hopf bifurcations and the stability of bifurcated periodic solutions are discussed. At last, sensitivity analysis shows that the infection delay can change the correlation of the proportion of symptomatic patients in patients and the transformation rate to the total number of patients. (C) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
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COVID-19 is spread mainly through respiratory droplets. With the development of COVID-19 worldwide, international airports are facing unprecedented imported risks, becoming the forefront of overseas epidemic prevention. The transmission mechanism of the disease is easy to implement due to the general human susceptibility. Despite the ongoing development of COVID-19 vaccines, the public health community still needs to establish nonpharmaceutical interventions to mitigate the spread of COVID-19 in the population, especially among individuals in close contact with confirmed cases. Since the outbreak of COVID-19, relevant authorities in China have taken active prevention and control measures, strictly tracked down and isolated those involved, and effectively contained the spread of the epidemic. Medical workers have played an important role in epidemic prevention and control. Medical workers are putting their lives and health at risk because of a lack of knowledge about COVID-19. This review summarizes the work of preventing cross-infection in the transport of high-risk groups by ambulance in primary hospitals in Jiangsu province during the COVID-19 outbreak. Through standardized management, the cross infection caused by ambulance has been effectively prevented. Therefore, during the COVID-19 outbreak, establishing a safe disinfection management system, strengthening the disinfection management of ambulance transport, and training personnel in personal protection, work requirements and emergency response skills can effectively prevent the spread of the COVID-19. © 2022 Journal of Innovation Management. All rights reserved.
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The COVID-19 pandemic has led to a burgeoning demand for active travel (walking or cycling), which is a healthy, pollution-free, and affordable daily transportation mode. Park green space (PGS), as an open natural landscape, have become a popular destination for active travel trips in metropolitan areas. Pedestrians and cyclists are often at high crash risk when exposed to complicated traffic environments in urban areas. Therefore, this study aims to propose a safety assessment framework for evaluating active travel traffic safety (ATTS) near PGS from the perspective of urban planning and exploring the effect of the point-of-interest (POI) aggregation phenomenon on ATTS. First, links between ATTS and the environment variables were investigated and integrated into the framework using the catastrophe model. Second, the relationship between the POI density and ATTS was investigated using three spatial regression models. Results in the Wuhan Metropolitan Area as a case study have shown that (1) the population density, road density, nighttime brightness, and vegetation situation near PGS have pronounced effects on ATTS;(2) pedestrians near PGS enjoy safer road facilities than cyclists. Active travel traffic near PGS requires more attention than non-park neighborhoods;(3) among four park categories, using active travel to access theme parks is the safest;and (4) SEM has the best fit for POI cluster research. Increases in leisure facility density and residence density may lead to deterioration and improvement in ATTS safety levels near PGSs, respectively. The safety framework can be applied in other regions because the selected environment indicators are common and accessible. The findings offer appropriate traffic planning strategies to improve the safety of active travel users when accessing PGS. Copyright © 2022 Luo, Liu, Xing, Wang and Rao.
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In this paper, a reaction-diffusion SIRE epidemic model in contaminated environments is proposed, in which the effect of protection for susceptible individuals is included by the nonlinear incidence functions b(S)E$b(S)E$ and g(S)I$g(S)I$. When the space is heterogeneous, the basic reproduction number R0$\mathcal {R}_{0}$ is derived, by which we find that if R0 <= 1$\mathcal {R}_{0}\le 1$, the disease-free steady state is globally asymptotically stable, while R0>1$\mathcal {R}_{0}>1$, the disease is uniform persistent. Furthermore, when R0>1$\mathcal {R}_{0}>1$ and additional conditions hold, the global asymptotic stability of special endemic steady state is obtained in homogeneous space. Finally, the theoretical results are validated by numerical simulations, some open questions are illustrated.
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Objective To systematically review the impact of ACEI/ARB (angiotensin converting enzyme inhibitor/angiotensin receptor antagonist) treatment on the clinical outcomes of Chinese patients with COVID-19 infections. Methods PubMed, EMbase, Web of Science, The Cochrane Library, CNKI, WanFang Data, and VIP databases were electronically searched to collect cohort studies on the impact of the treatment with ACEI/ARB on the clinical outcomes of Chinese patients with COVID-19 infections from January 2020 to January 2022. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of the included studies. Then, meta-analysis was performed using RevMan 5.3 software. Results A total of 17 cohort studies involving 4 912 subjects were included. The results of meta-analysis showed that patients who were prescribed ACEI/ARB had shorter hospital stays (SMD=-0.28, 95%CI -0.46 to -0.11, P=0.002) and a lower mortality rate (OR=0.47, 95%CI 0.36 to 0.62, P<0.000 01) than patients who did not take ACEI/ARB. Conclusion Current evidence shows that the use of ACEI/ARB drugs can improve the clinical prognosis of Chinese patients with COVID-19 infections. Due to the limited quality and quantity of the included studies, more high-quality studies are needed to verify the above conclusion.
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BACKGROUND: In the past decade, the U.S. immigration detention system regularly detained more than 30,000 people per day; in 2019 prior to the pandemic, the daily detention population exceeded 52,000 people. Inhumane detention conditions have been documented by internal government watchdogs, and news media and human rights groups who have observed over-crowding, poor hygiene and sanitation and poor and delayed medical care, as well as verbal, physical and sexual abuse. METHODS: This study surveyed health professionals across the United States who had provided care for immigrants who were recently released from immigration detention to assess clinician perceptions about the adverse health impact of immigration detention on migrant populations based on real-life clinical encounters. There were 150 survey responses, of which 85 clinicians observed medical conditions attributed to detention. RESULTS: These 85 clinicians reported seeing a combined estimate of 1300 patients with a medical issue related to their time in detention, including patients with delayed access to medical care or medicine in detention, patients with new or acute health conditions such as infection and injury attributed to detention, and patients with worsened chronic or special needs conditions. Clinicians also provided details regarding sentinel cases, categorized into the following themes: Pregnant women, Children, Mentally Ill, COVID-19, and Other serious health issue. CONCLUSIONS: This is the first survey, to our knowledge, of health care professionals treating individuals upon release from detention. Due to the lack of transparency by federal entities and limited access to detainees, this survey serves as a source of credible information about conditions experienced within immigration detention facilities and is a means of corroborating immigrant testimonials and media reports. These findings can help inform policy discussions regarding systematic changes to the delivery of healthcare in detention, quality assurance and transparent reporting.
Subject(s)
COVID-19 , Emigrants and Immigrants , Transients and Migrants , COVID-19/epidemiology , Child , Emigration and Immigration , Female , Health Status , Humans , Pregnancy , United States/epidemiologyABSTRACT
Computer vision techniques always had played a salient role in numerous medical fields, especially in image diagnosis. Amidst a global pandemic situation, one of the archetypal methods assisting healthcare professionals in diagnosing various types of lung cancers, heart diseases, and COVID-19 infection is the Computed Tomography (CT) medical imaging technique. Segmentation of Lung and Infection with high accuracy in COVID-19 CT scans can play a vital role in the prognosis and diagnosis of a mass population of infected patients. Most of the existing works are predominately based on large private data sets that are practically impossible to obtain during a pandemic situation. Moreover, it is difficult to compare the segmentation methods as the data set are obtained in various geographical areas and developed and implemented in different environments. To help the current global pandemic situation, we are proposing a highly data-efficient method that gets trained on 20 expert annotated COVID-19 cases. To increase the efficiency rate further, the proposed model has been implemented on NVIDIA-Jetson Nano (System-on-Chip) to completely exploit the GPU performance for a medical application machine learning module. To compare the results, we tested the performance with conventional U-Net architecture and calculated the performance metrics. The proposed state-of-art method proves better than the conventional architecture delivering a Dice Similarity Coefficient of 99%. © 2021 IEEE.