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The world is facing dual challenges of generating an economic recovery from the COVID-19 crisis, and transitioning to a low-carbon economy to tackle climate change. Strongly interrelated global challenges will require an integrated and coordinated response by all countries to manage the risk and lay the foundation for building back better. As the world's biggest emitter and the second-largest economy, China is a very important player in international collaboration and coordination in climate action. Against this backdrop, this paper looks into the increasingly crucial role that China is playing in global climate action, especially focusing on three aspects: China's domestic and foreign policymaking for the energy transition;its role in promoting multilateralism and international collaboration on building a sustainable world;and how it could accelerate climate action and diplomacy through research, development and innovation. In the critical decade of the 2020s, China has a great opportunity to further transform and upgrade its energy and industrial structures, promote research, development and the application of green and low-carbon technologies and intensify international climate cooperation on climate change. China should aim to be at the forefront of raising climate ambition and accelerating climate action for a sustainable and more equitable world. © 2023 IOP Publishing Ltd.
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The hazards of epoxy resin (EP) are not only reflected in the large amount of smoke and heat released during combustion, but also in the long survival time of bacterial on their surfaces at a time when COVID-19 are prevalent. Therefore, it is crucial to improve the antibacterial properties and fire-resistance of EP. Herein, this paper reports a multifunctional nanoparticle (Cu2O@KF) to overcome this issue. It is found that Cu2O@KF can confer great fire-resistance (LOI = 34.7% and pHRR reduced by 56.3%), antibacterial properties (over 99.99% antibacterial efficiency), and mechanical properties (hardness and Young's modulus increased by 80.0% and 24.0%, respectively) at a low loading level (7 wt%). These ideal characteristics are derived from the multi-synergistic properties among Cu2O and KF. © 2022
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Automatic longitudinal assessment of the disease progression of coronavirus disease 2019 (COVID-19) is invaluable to ensure timely treatment for severe or critical patients. An artificial intelligence system that combines chest computed tomography (CT) and laboratory examinations may provide a more accurate diagnosis. To explore an artificial intelligence solution to longitudinally assess the condition of COVID-19 using CT imaging and laboratory findings, from January 27, 2020, to April 3, 2020, multiple follow-up examinations of COVID-19 inpatients were retrospectively collected. CT imaging features were automatically extracted using a deep learning method and combined with laboratory tests. The progression sequences were generated with two follow-ups, each of which contained 60 imaging and 24 laboratory features. Pearson's correlation was conducted to rank the importance of each univariate feature, and multivariate logistic regression was adopted for feature selection. The selected features were used to train a 2-layer long short-term memory network (LSTM) with pulse oxygen saturation (SpO(2)) as an indicator of disease progression in three classes: alleviated, stable, and aggravated. The performance of models trained on various feature subsets was compared with five-fold cross validation.559 patients with 1734 examinations were collected, and 1450 progression sequences were generated. Of the 559 patients, 262 (46.9%) were male. The mean age of the patients was 60 +/- 14 years. The mean hospitalization duration was 31 +/- 12 days. Based on the ranking of importance, 26 features from the imaging and laboratory tests were selected, achieving the best accuracy of 0.85 for progression assessment. The comparisons demonstrated that CT features outperformed laboratory features. The best sensitivities for alleviated and aggravated obtained with CT features alone were 0.83 and 0.85, respectively, while laboratory features improved the assessment precision by about 3%. Longitudinal assessment using deep learning with combined features from CT imaging and laboratory tests better predicts the progression of COVID-19 than either of them.
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The COVID-19 pandemic has hampered consumers' psychological well-being and the travel industry. Secondary evidence suggests a revenge travel phenomenon. This study theorizes and empirically demonstrates that nostalgia helps consumers cope with pandemic distress and cultivates the desire for leisure travel. Drawing on the Stimulus-Organism-Response (SOR) framework, consumer perceived severity of COVID-19 (stimulus) triggers nostalgia (organism), which increases the desire for leisure travel (response). The effect of nostalgia on desire to travel is robust when manipulated via a marketing communication (ad-hoc experiment). This study further explains travel pursuit heterogeneity with the interaction of a consumer's approach-avoidance motivation system and traveler personality.
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Digital polymerase chain reaction (PCR), as a nucleic acid detection technology with wide application prospect, has become one of the most accurate nucleic acid detection technology at present. Multiplex detection is an important direction for the development of digital PCR technique. With the development of microfluidic technology, multiplex digital PCR technique has become more and more mature. This paper reviewed the research progresses of multiplex digital PCR in recent years, especially summarized the implementation of multiplex digital PCR technique in the past five years, and introduced the application of multiplex digital PCR technique in hot areas such as liquid biopsy, transgenic detection, and SARS-Cov-2 detection. Finally, the issues and challenges faced by multiplex digital PCR technique were discussed and the future direction of the technology was foreseen.
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medical service robot, human-robot interaction, COVID-19, sensor fusion The cabin hospitals used for COVID-19 victims have an infectious environment and thus require highly autonomous systems to provide various services. This article presents a multirobot architecture designed for cabin hospitals with special focus on the sensors used by service robots. The robots' functions include autonomous disinfection, delivery, cleaning, temperature measurement, physical interaction, and conversation assistance. The robots collaborate with each other by using multiple sensors in large and complicated scenes and evolve together. The robots are controlled by cloud controllers considering a human-centered multi-robot collaboration mechanism. This article presents the details of these robots, including the hardware and software architecture of the robots with sensors, the communication modules, and the cloudbased central controllers. It also presents several applications of the robots and discusses open problems. © 2021 M Y U Scientific Publishing Division. All rights reserved.
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Under the wake of COVID-19, agricultural production and people's consumption are seriously affected, in addition, the number of methods to quantitatively analyze of the degree of influence is limited. The big data of electrical power can accurately reflect the business situation of enterprises in the industry. This paper select hundreds of leading enterprises in agricultural industrialization as the analysis objects, and use Keyword Index Technology to construct the corresponding system of enterprise name-electric household number, and use Multilevel Coordination Algorithm to fit the electricity curve of leading enterprises in agricultural industrialization, to study the impact degree difference and resilience of the epidemic situation on the subdivision industry, and use Covariance Analysis Algorithm to analyze the correlation of subdivision industry under the epidemic situation, and to give the prospect of development opportunities in the period after the COVID-19 epidemic situation. © 2020 IEEE.
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Background: The "Work Plan for Psychological Counseling of Patients Under Normalization of Prevention and Control of New Coronary Pneumonia" in September 2020 pointed out that it is important to strengthen the follow-up of the psychological status of COVID-19 patients after recovery. At present, cross-sectional studies have found that patients have frequent mental health problems after recovery, but there is still a lack of dynamic follow-up studies. Objective: A dynamic follow-up survey was conducted to investigate the mental health status and its influencing factors of COVID-19 patientstwo weeks and three months after recovery and discharge. Methods: COVID-19 patients diagnosed in two designated hospitals in Anhui Province from February to April 2020 were selected as the research objects, the thegeneral demographic data were collected, and the Streaming Depression Self-Rating Scale (CES-D), Insomnia Severity Index Scale (ISI), Somatization Symptom Self-rating Scale (SSS) and Post-Traumatic Stress Disorder Checklist (PCL) were used for mental health status evaluation. Results: One hundred and twenty five questionnaires were distributed. The results showed that the incidences of depression, insomnia, PTSD and somatization symptoms in COVID-19 patients at 2 weeks after discharge were 9.92% (12/121), 26.45%(32/121), 1.65%(2/121) and 4.96%(6/121), respectively. The incidences of depression, insomnia, PTSD and somatization symptoms at 3 months were 36.47% (31/85), 83.53%(71/85), 8.24%(7/85) and 18.82%(16/85), respectively. There was no significant difference in gender, age, health status, family support, need for psychological assistance and acceptance of psychological assistance between 2 weeks and 3 months after discharge (P>0.05). There were significant differences in the economic status, physical and mental impairment and care about the views of the people around them at 2 weeks and 3 months after discharge(P<0.05). The total scores of CES-D, ISI, SSS and PCL at 3 months after discharge were higher than those at 2 weeks after discharge(P<0.05). In the Multivariate Logistic regression analysis with depression as the dependent variable, age and the need for psychological assistance were the risk factors of depression at 2 weeks after discharge (P<0.05). Physical and mental impairment was the risk factor of depression at 2 weeks and 3 months after discharge(P<0.05). The risk factor of depression at 3 months after discharge was caring about the views of the people around them. Patients who cared about the views of others were 3.997 times more likely to suffer from depressive symptoms than those who didn't 〔OR (95%CI)= 3.997 (1.708, 9.351), P=0.001〕. In the Multivariate Logistic regression with insomnia as the dependent variable, age, physical and mental impairment and the need for psychological assistance were the risk factors of insomnia at 2 weeks after discharge (P<0.05). The risk factor of insomnia at 3 months after discharge was caring about the views of the people around them or not. Patients who cared about the views of others were 10.255 times more likely to suffer from insomnia than those who didn't 〔OR (95%CI)= 10.255 (2.796, 37.611), P<0.001〕. Conclusion: After recovery and discharge, COVID-19 patients have obvious mental health problems such as depression, insomnia, post-traumatic stress disorder and somatization symptoms, and the longer the follow-up period, the higher the incidence of symptoms. Psychiatric professionals need to carry out psychological crisis intervention as soon as possible. Copyright © 2021 by the Chinese General Practice.
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This article summarizes the nursing experience in a critically ill patient with novel coronavirus pneumonia after extracorporeal membrane oxygenation (ECMO) treatment for lung function improvement. After oral intubation-assisted ventilation, anti-infection, and other symptomatic support treatments, the patient was still unable to breathe without the ventilator. For the sustained carbon dioxide retention and severe gas exchange impairment, he was treated with tracheotomy and ECMO. During the treatment, a series of nursing measures to improve lung function were adopted, such as sputum suction care, atomized inhalation therapy, bronchial irrigation, and lateral ventilation combined with postural drainage. After 7 days of ECMO treatment and nursing, the patient's lung function improved and then he was weaned from the machine.
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SARS-CoV-2 can spread by close contact through large droplet spray and indirect contact via contaminated objects. There is mounting evidence that it can also be transmitted by inhalation of infected saliva aerosol particles. These particles are generated when breathing, talking, laughing, coughing or sneezing. It can be assumed that aerosol particle concentrations should be kept low in order to minimize the potential risk of airborne virus transmission. This paper presents measurements of aerosol particle concentrations in a gym, where saliva aerosol production is pronounced. 35 test persons performed physical exercise and aerosol particle concentrations, CO2 concentrations, air temperature and relative humidity were obtained in the room of 886 m³. A separate test was used to discriminate between human endogenous and exogenous aerosol particles. Aerosol particle removal by mechanical ventilation and mobile air cleaning units was measured. The gym test showed that ventilation with air-change rate ACH = 2.2 h-1, i.e. 4.5 times the minimum of the Dutch Building Code, was insufficient to stop the significant aerosol concentration rise over 30 min. Air cleaning alone with ACH = 1.39 h-1 had a similar effect as ventilation alone. Simplified mathematical models were engaged to provide further insight into ventilation, air cleaning and deposition. It was shown that combining the above-mentioned ventilation and air cleaning can reduce aerosol particle concentrations with 80 to 90% , depending on aerosol size. This combination of existing ventilation supplemented with air cleaning is energy efficient and can also be applied for other indoor environments.
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Background. The course of disease in mild and moderate COVID-19 has many implications for mobile patients, such as the risk of spread of the infection, precautions taken, and investigations targeted at preventing transmission. Methods. Three hundred thirty-one adults were hospitalized from January 21 to February 22, 2020, and classified as severe (10%) or critical (4.8%) cases;1.5% died. Two hundred eighty-two (85.2%) mild or moderate cases were admitted to regular wards. Epidemiological, demographic, clinical, chest computed tomography (CT) scan, laboratory, treatment, and outcome data from patient records were analyzed retrospectively. Results. Patients were symptomatic for 9.82 +/- 5.75 (1-37) days. Pulmonary involvement was demonstrated on a chest CT scan in 97.9% of cases. It took 16.81 +/- 8.54 (3-49) days from the appearance of the first symptom until 274 patients tested virus-negative in naso- and oropharyngeal (NP) swabs, blood, urine, and stool, and 234 (83%) patients were asymptomatic for 9.09 +/- 7.82 (1-44) days. Subsequently, 131 patients were discharged. One hundred sixty-nine remained in the hospital;these patients tested virus-free and were clinically asymptomatic because of widespread persisting or increasing pulmonary infiltrates. Hospitalization took 16.24 +/- 7.57 (2-47) days;the time interval from the first symptom to discharge was 21.37 +/- 7.85 (3-52) days. Conclusions. With an asymptomatic phase, disease courses are unexpectedly long until the stage of virus negativity. NP swabs are not reliable in the later stages of COVID-19. Pneumonia outlasts virus-positive tests if sputum is not acquired. Imminent pulmonary fibrosis in high-risk groups demands follow-up examinations. Investigation of promising antiviral agents should heed the specific needs of mild and moderate COVID-19 patients.
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With the outbreak and spread of 2019 novel coronavirus (2019-nCoV, or SARS-CoV-2) infection in Wuhan, China, the number of children infected has also increased significantly, and even severe and critically ill children have appeared. In order to curb the spread of the epidemic, it is important to understand the clinical manifestations of 2019-nCoV infection in children and strengthen the key points for family prevention and control. According to the diagnostic standards and protective measures issued by the National Health Commission, this article summarizes the epidemiological characteristics and clinical manifestations of some child cases, and proposes to focus on the clinical manifestations, treatment procedures, and family protection and daily health care. © 2020, Editorial Board of Medical Journal of Wuhan University. All right reserved.
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Objective: To analyze the effects of angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACEI/ARB) on coronavirus disease 2019 (COVID-19) patients with hypertension, and to provide an evidence for selecting antihypertensive drugs in those patients. Methods: Clinical data were retrospectively analyzed in 58 COVID-19 patients with hypertension admitted to Shanghai Public Health Clinical Center from January 20 to February 22, 2020, including epidemiological history, clinical manifestations, laboratory findings, chest CT and outcome. Patients were divided into ACEI/ARB group and non-ACEI/ARB group. Results: Twenty-six patients were in ACEI/ARB group and the other 32 patients in non-ACEI/ARB group, with median age 64.0 (49.5, 72.0) years and 64.0 (57.0, 68.8) years respectively. The median time to onset was 5(3, 8) days in ACEI/ARB group and 4 (3, 7) days in non-ACEI/ARB group, the proportion of patients with severe or critical illness was 19.2% and 15.6% respectively. The main clinical symptoms in two groups were fever (80.8% vs. 84.4%) and cough (23.1% vs. 31.3%). The following parameters were comparable including lymphocyte counts, C-reactive protein, lactate dehydrogenase, D-dimer, bilateral involvement in chest CT (76.9% vs. 71.9%), worsening of COVID-19 (15.4% vs. 9.4%), favorable outcome (92.3% vs. 96.9%) between ACEI/ARB group and non-ACEI/ARB group respectively (all P>0.05). However, compared with non-ACEI/ARB group, serum creatinine [80.49 (68.72, 95.30) µmol/L vs. 71.29 (50.98, 76.98) µmol/L, P=0.007] was higher significantly in ACEI/ARB group. Conclusions: ACEI/ARB drugs have no significant effects on baseline clinical parameters (serum creatine and myoglobin excluded) , outcome, and prognosis of COVID-19 patients with hypertension. Antihypertensive drugs are not suggested to adjust in those patients, but the potential impairment of renal function as elevation of serum creatinine should be paid attention in patients administrating ACEI/ARB drugs.