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With the development of science and technology, electronic information technology has penetrated into many aspects of society now. Electronic informed consent is an effective way to adapt to development clinical trial. China is still at an early stage in this field. Affected by the outbreak of COVID-19 in 2020, the demand for electronic informed consent in clinical trial has become more urgent. Based on my own work experience, the author wants to analyze the problems in the traditional informed consent process and the current situations of electronic informed consent in China and explores the feasibility of electronic informed consent in clinical trials.Copyright © 2021 Drug Evaluation Research. All rights reserved.
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Amid the impact of COVID-19, the public's willingness to travel has changed, which has had a fundamental impact on the ridership of urban public transport. Usually, travel willingness is mainly analyzed by questionnaire survey, but it needs to reflect the accurate psychological perception of the public entirely. Based on Weibo text data, this paper used natural language processing technology to quantify the public's willingness to travel in the post-COVID-19 era. First, web crawler technology was used to collect microblog text data, which will discuss COVID-19 and travel at the same time. Then, based on the Naive Bayes classification algorithm, travel sentiment analysis was carried out on the data, and the relationship between public travel willingness and urban public transport ridership was analyzed by Spearman correlation analysis. Finally, the LDA topic model was used to conduct content topic research on microblog text data during and after COVID-19. The results showed that the mean values of compelling travel emotion were-0.8197 and-0.0640 during and after COVID-19, respectively. The willingness of the public to travel directly affects the ridership of urban public transport. Compared with the COVID-19 period, the public's fear of travel infection in the post-COVID-19 era has significantly improved, but it still exists. The public pays more attention to the level of COVID-19 prevention and control and the length of travel time on public transport.
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The COVID-19 pandemic has had an initial and outsized negative impact on bond exchange-traded funds (ETFs), causing concerns for financial stability. Using a large panel of US bond ETFs, we conduct a comprehensive examination of the impact of the pandemic on ETF valuation discounts. We find the change in COVID-19 deaths to be significantly related to the valuation discounts of government bond ETFs and corporate bond ETFs, with investment-grade corporate bond ETFs showing greater sensitivity. These valuation discounts reversed dramatically after the Federal Reserve announced its intentions to purchase corporate bonds and bond ETFs. Government economic policies to combat the pandemic are also negatively related to the valuation discounts of corporate bond ETFs. These findings are evidence of the efficacy of broad-based liquidity support on restoring financial stability in the bond ETF market at a time of enormously stressed market sentiment and massive pricing dislocations. Copyright 2022 With Intelligence LLC.
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Purpose of Study: Telemedicine has become a common option for healthcare delivery in the post-COVID-19 era. There are advantages, but the barriers to care for children with medical complexity (CMC) and marginalized populations have not been well-described. This study assessed parental perception of telemedicine in the care of their children. Methods Used: A REDcap survey was distributed to parents of hospitalized patients close to discharge to examine their attitudes regarding outpatient telemedicine with a focus on the post-discharge follow-up visit. Summary of Results: A total of 78 parents responded to our survey. A majority (58%) identified themselves as an ethnic minority. About 47% of parents completed college or postgraduate education;the rest had a high school diploma or some college education. Half (50%) of the parents reported a family income of <$100,000. Of the 78, 50% had used telemedicine previously, and a majority (76%) preferred in-person visits. Of those who belonged to a minority population, 80% preferred in-person visits after hospital discharge. Fifty-seven of the parents answered further questions about telemedicine and their child's medical complexity. Of these 57, 53% had a CMC, requiring specialized care and only 20% agreed or strongly agreed that it was difficult to take their child to in-person visits. Fifty-three out of the 78 parents provided a free text response about their thoughts on telemedicine visits. Common themes about advantages of in-person visits were a) preference for a physician's physical exam b) in-person visits were more personal and facilitated clearer communication and c) in-person visits provided more accurate? care compared to telemedicine (See Figure). Internet or computer access as a barrier was only mentioned by 3 parents. The main advantage of telemedicine mentioned was convenience. Conclusion(s): Our study shows that most parents prefer in-person visits, especially after hospital discharge. Our results may not apply to other populations as most of our patients were medically complex and belonged to a minority population. To increase parental support of telemedicine, techniques to improve family confidence in visual assessment and communication are required. Larger studies are needed to identify the needs of patients and families with a focus on the child's medical needs.
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Background: The increasing burden of mental health problems continues in the post-COVID-19 era, and nursing interns were particularly likely to experience negative emotions during the pandemic. Both psychological resilience and social support affect negative emotion, but the relationship among the three has not been explored in nursing interns in the postpandemic era. Objectives: To explore the current prevalence of negative emotions among nursing interns and the role of psychological resilience in mediating the relationship between social support and negative emotions in the postpandemic era. Methods: A cross-sectional survey of 788 nursing interns was conducted. The instruments included Psychological Resilience Scale, Social Support Scale, Beck Anxiety Scale and Beck Depression Scale. Structural equation modeling was applied to analyze the mediating role of psychological resilience. Results: The prevalence of anxiety disorder among nursing interns was 24.7%, while that of depression was 10.5%. Pearson correlation analysis showed that both social support and psychological resilience negatively correlated with negative emotions, while psychological resilience positively correlated with social support. Psychological resilience showed a partial mediating effect (53.9%) between social support and negative emotion, with an effect value of −0.1456. Conclusion: Psychological resilience and social support protect nursing students from negative emotions, and psychological resilience partially mediates the relationship between social support and negative emotion in the postpandemic era. © 2023 Wiley Periodicals LLC.
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With the abrupt and significant drop of PM2.5 concentrations during the lockdown in 2020, hourly direct radiation (Rdir) at surface substantially increased in East China, such as Zhengzhou, Wuhan and Baoshan, with the maximum enhancement of 86% at Wuhan. Most of these stations had decreased diffuse radiation (Rdif) except Zhengzhou. Zhengzhou had both enhanced Rdir and Rdif, as well as reduced but still high PM2.5 concentrations, indicating atmospheric particles were more scattering in this region. At Beijing and Harbin in North and Northeast China, intensification of aerosol pollution led to hourly Rdir (Rdif) falling (rising) up to −28% (59%) and −23% (40%), respectively. By contrast, surface solar radiation (SSR) in West China was also greatly influenced by the elevated dust/smoke layers, revealed by aerosol layer vertical distribution and the reduction of SSR and PM2.5 concentrations. This study highlighted the importance of aerosol optical properties and vertical structures in aerosol–radiation interactions. © 2023. The Authors.
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Passengers significantly affect airport terminal energy consumption and indoor environmental quality. Accurate passenger forecasting provides important insights for airport terminals to optimize their operation and management. However, the COVID-19 pandemic has greatly increased the uncertainty in airport passenger since 2020. There are insufficient studies to investigate which pandemic-related variables should be considered in forecasting airport passenger trends under the impact of COVID-19 outbreaks. In this study, the interrelationship between COVID-19 pandemic trends and passenger traffic at a major airport terminal in China was analyzed on a day-by-day basis. During COVID-19 outbreaks, three stages of passenger change were identified and characterized, i.e., the decline stage, the stabilization stage, and the recovery stage. A typical "sudden drop and slow recovery” pattern of passenger traffic was identified. A LightGBM model including pandemic variables was developed to forecast short-term daily passenger traffic at the airport terminal. The SHapley Additive exPlanations (SHAP) values was used to quantify the contribution of input pandemic variables. Results indicated the inclusion of pandemic variables reduced the model error by 27.7% compared to a baseline model. The cumulative numbers of COVID-19 cases in previous weeks were found to be stronger predictors of future passenger traffic than daily COVID-19 cases in the most recent week. In addition, the impact of pandemic control policies and passengers' travel behavior was discussed. Our empirical findings provide important implications for airport terminal operations in response to the on-going COVID-19 pandemic. © 2022
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Aiming at the problem of metro operation and passenger transport organization under the impact of the novel coronavirus (COVID-19), a collaborative determination method of train planning and passenger flow control is proposed to reduce the train load rate in each section and decrease the risk of spreading COVID-19. The Fisher optimal division method is used to determine reasonable passenger flow control periods, and based on this, different flow control rates are adopted for each control period to reduce the difficulty of implementing flow control at stations. According to the actual operation and passenger flow changes, a mathematical optimization model is established. Epidemic prevention risk values (EPRVs) are defined based on the standing density criteria for trains to measure travel safety. The optimization objectives of the model are to minimize the EPRV of trains in each interval, the passenger waiting time and the operating cost of the corporation. The decision variables are the number of running trains during the study period and the flow control rate at each station. The original model is transformed into a single-objective model by the linear weighting of the target, and the model is solved by designing a particle swarm optimization and genetic algorithm (PSO-GA). The validity of the method and the model is verified by actual metro line data. The results of the case study show that when a line is in the moderate-risk area of COVID-19, two more trains should be added to the full-length and short-turn routes after optimization. Combined with the flow control measures for large passenger flow stations, the maximum train load rate is reduced by 35.18%, and the load rate of each section of trains is less than 70%, which meets the requirements of COVID-19 prevention and control. The method can provide a theoretical basis for related research on ensuring the safety of metro operation during COVID-19.
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Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) currently has spread all over the world. However, the dynamic characteristics of SARS-CoV-2 infections have not previously been described in detail. Here, we report a cured patient in West China Hospital, and describe the dynamic detection of SARS-CoV-2-RNA in different specimens and viral specific IgM and IgG antibodies in blood. The findings suggest that the fecal SARS-CoV-2-RNA negativity may be considered as a new standard for de isolation. Serum IgM and IgG antibodies detection were helpful for early diagnosis of SARS-CoV-2 infection and judgment of patients in recovery stage, respectively. © 2022 Journal of Bone and Joint Surgery Inc.. All rights reserved.
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Six new species of Zaischnopsis Ashmead (Hymenoptera: Eupelmidae) from China are described, Zaischnopsis covid Jiang & Peng sp. nov., Zaischnopsis fuscolivida Tang & Peng sp. nov., Zaischnopsis lii Jiang & Peng sp. nov., Zaischnopsis pacis Jiang & Peng sp. nov., Zaischnopsis campaniformis Tang & Peng sp. nov., and Zaischnopsis zhongi Jiang & Peng sp. nov. All the new species are described and illustrated based on females, and partial mitochondrial cytochrome oxidase subunit I (COI) sequences are provided for the six new species as well as for the previously described Z. fumosa Peng & Xiang. Females of all the species of Zaischnopsis recorded from China are differentiated in a key. © 2022, Museum National d'Histoire Naturelle. All rights reserved.
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Due to the impact of the global COVID-19, numerous industries have suffered from the disruption propagating along the supply chain, i.e. the ripple effect. To reduce adverse impact of the ripple effect, supply chain (SC) risk management under it is becoming an increasingly hot topic in both practice and research. In our former research, a robust dynamic bayesian network (DBN) approach has been developed for disruption risk assessment, whereas the solution methods adopted before (commercial solvers and simulated annealling algorithm) are not efficient enough, especially for large-size instances. For this reason, a new reinforcement learning variable neighborhood search (QVNS) is developed for solving the robust DBN optimization model, where the Q-learning algorithm is implemented to select the most efficient neighborhood structure in different stages of the search process. We conduct computational experiments on randomly generated instances, which indicates that Q-learning algorithm can improve significantly the performance of the VNS on large-size instances of the robust DBN optimization problem. Copyright (C) 2022 The Authors.
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In order to explore the choice behavior of residents' travel mode in the post-COVID-19 era, a choice behavior experiment was conducted. A mixed Logit model and a latent class conditional Logit model of travel mode choice were constructed based on the data obtained from questionnaire surveys. Stata software was used to calibrate the model parameters, and the main factors influencing residents' travel mode choices were obtained. The results show that both models reflect the heterogeneity of individual travel mode choices. Compared with the mixed Logit model, the latent class conditional Logit model has an improvement of 13% in the goodness of fit and an increase of 3.03% in the prediction accuracy, which provides an effective tool for analyzing individual heterogeneity of travel behavior under public health emergencies. The latent class conditional Logit model divides residents into four and five groups according to the two scenarios of low and medium risk areas. From the perspective of travel mode attributes, the waiting time and the traveling time have become the most important influencing factors for residents to choose the travel modes. From the perspective of personal socio-economic attributes, women with higher incomes are more inclined to choose private cars to travel. The older are more sensitive to travel costs, and men are more willing to choose bus and subway travel. Copyright © 2022 by Science Press.
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With the development of science and technology, electronic information technology has penetrated into many aspects of society now. Electronic informed consent is an effective way to adapt to development clinical trial. China is still at an early stage in this field. Affected by the outbreak of COVID-19 in 2020, the demand for electronic informed consent in clinical trial has become more urgent. Based on my own work experience, the author wants to analyze the problems in the traditional informed consent process and the current situations of electronic informed consent in China and explores the feasibility of electronic informed consent in clinical trials. © 2021 Drug Evaluation Research. All rights reserved.
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Anthropogenic forcing has approximately halved the probability of 2020 June-July persistent heavy mei-yu rainfall event based on HadGEM3-GA6 simulations without considering the COVID-induced aerosol emission reduction.
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The aim of the project is to predict and analyse broad trends across the US economy using stock data from mainstream companies in six industries on Forbes 2000 and data from COVID-19. A time series analysis approach was used to predict the daily increases in each company's share price. The following five supervised learning techniques (logistic regression, random forest, decision tree, neural network and XGBoost) were used. As the accuracy of the results predicted by the different models for each company varies considerably, only the results predicted by the most accurate model for each company have been selected for analysed. The results show that the Electronic Pleased Technology Industry and the Social Entertainment Internet Industry remain break-even for COVID-19;the E-Commerce Industry shows a significant increase;The Financial Services Industry shows a significant drop in share price, while the Insurance Industry and Pharmaceutical Industry show a small drop in share price. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.
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From the world health organization hunger map, the global hunger population is 821 million in 2020. Moreover, COVID-19 caused the lockdown of the city boundary and the local food supply is not enough for the demand in some small country. Urban farming can help to increase the local food supply. However, it is not cost-effective and not efficient to supply food. Only limited kinds of crops with low efficiency and high cost can be provided. In this research, a newly invented Aero-Hydroponic Agriculture System (AHAS) aims to provide one more source for local food supply by adopting IoT technology to increase productivity. AHAS provides two different layers that allow growing vegetables on the top layer and carbohydrates on the bottom layer. With this IoT system, the temperature, humidity, pH value of nutrient and growth rate of crops can be monitored by the sensors and those environmental parameters can be controlled to offer suitable conditions to grow the crops. As a result, around 75-85% improvement in environment control is achieved by AHAS. With AHAS, more food can be produced in a limited space for the urban farming application. © 2021 IEEE.
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Background SARS-CoV-2 can spread both from symptomatic and asymptomatic individuals. Ocular manifestations due to SARS-CoV-2 have been described, being conjunctival inflammation the most common affectation. Evidence shows that conjunctivitis could be the first and/or only manifestation of COVID-19. This study aimed to develop and validate a COVID-19 screening method based on eyes photographs and artificial intelligence. Methods In this multicentre study, 1,200 participants were enrolled from Shanghai Public Health Clinical Center (SPHCC) Fudan University, AIMOMICS LAB and La Fe University and Polytechnic Hospital (LFUPH) of Valencia (Spain). Pictures of participants' ocular surface were taken in four different positions with mobile phone cameras, and a Deep Learning System (DLS) was developed through machine learning to identify characteristic conjunctival inflammation patterns. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committees of SPHCC and LFUPH. Results The area under the receiver-operating-characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated according to the results of our binary classification network. Bootstrapping with 1,000 replicates was used to estimate 95% confidence intervals of the performance metrics, with photography as the resampling unit. On the subject-level classification, the network achieved the AUC of 0.976 (95% CI 0.965-0.988) among Asian population and 0.892 (95% CI 0-763-1.000) among Caucasian population. Conclusions Preliminary results show that this DLS performed well in identifying probable asymptomatic COVID-19 cases through the analysis of participants' eyes pictures. This method could be an innocuous, accessible, low cost and quick COVID-19 screening method. Eventually, it could potentially contribute to pandemic control. Key messages In the context of the COVID-19 pandemic it would be useful to have a screening method to easily and quickly detect asymptomatic individuals, in addition to using temperature control. Preliminary results show that this Deep Learning System (DLS) based on eyes pictures taken with mobile phone cameras could be an innocuous, accessible, low cost and quick COVID-19 screening method.