ABSTRACT
We examine how institutional investors reacted to geographically dispersed local shocks during the early stages of the COVID-19 pandemic. A sample of real estate investment trusts (REITs) enables us to link two layers of geography: the locations of the assets in which the REITs were invested and the headquarters locations of institutional investors who owned REIT shares. We find that the institutional ownership of firms with an economic interest in the investors' home markets declined more if those markets were heavily affected by the pandemic. In addition, the ownership responses to the COVID-19 shock were larger in those markets in which REITs had larger portfolio allocations and in markets that were home to the investors. Importantly, we find that nonpassive and short-term investors may have overreacted to the local shocks because their REIT portfolios subsequently underperformed relative to passive and long-term investors. Our study highlights the importance of geography in the formation of investors' expectations during market crises. © 2023 American Real Estate and Urban Economics Association.
ABSTRACT
The COVID-19 pandemic has been going on for more than two years. Vaccination is believed to be one of the most efficient ways to achieve herd immunity and end pandemic. However, the contents about COVID-19 vaccines on social media have impacts on personal attitude towards vaccination. The present study aims to examine the current scenario and the echo chamber effect of COVID-19 vaccine videos on YouTube. A total of 1,646 videos with comments and replies were identified. An approach combining topic modeling, sentiment analysis, and social network analysis was employed to explore users' attitude towards COVID-19 vaccines and whether the echo chamber effect existed. The results indicate that, even if the misleading and anti-vaccination videos were removed by the platform, "anti-vaccination"contents still widely appear in the comments. Moreover, the community of "anti-vaccination"users was more homogeneous compared with that of "pro-vaccination"users. The findings of this study advanced theories of echo chamber effect and the network perspective to examine echo chambers. We propose that should be paid more attention ideology echo chamber, compared with exposure echo chamber. © 2022 IEEE.
ABSTRACT
Objective: To retrospectively analyze the clinical data of 52 patients with coronavirus disease-2019 COVID-19 and explore the clinical efficacy of modified Sanxiaoyin on mild/moderate COVID-19 patients. Method(s): The propensity score matching method was used to collect the clinical data of mild or moderate COVID-19 patients enrolled in the designated hospital of the Second Hospital of Jingzhou from December 2019 to May 2020. A total of 26 eligible patients who were treated with modified Sanxiaoyin were included in the observation group,and the 26 patients treated with conventional method were the regarded as the control. The disappearance of clinical symptoms,disappearance time of main symptoms,efficacy on traditional Chinese medicineTCMsymptoms,hospitalization duration,laboratory test indicators,and CT imaging changes in the two groups were compared. Result(s): The general data in the two groups were insignificantly different and thus they were comparable. After 7 days of treatment,the disappearance rate of fever,cough, fatigue,dry throat,anorexia,poor mental state,and poor sleep quality in the observation group was higher than that in the control groupP<0.05,and the difference in the disappearance rate of expectoration and chest distress was insignificant. For the cases with the disappearance of symptoms,the main symptomsfever, cough,fatigue,dry throat,anorexia,chest distressdisappeared earlier in the observation group than in the control groupP<0.01. After 7 days of treatment,the scores of the TCM symptom scale of both groups decreasedP<0.01,and the decrease of the observation group was larger that of the control groupP<0.01. All patients in the two groups were cured and discharged. The average hospitalization duration in the observation group12.79+/-2.68dwas shorter than that in the control group15.27+/-3.11dP<0.01. The effective rate in the observation group92.31%,24/26was higher than that in the control group76.92%,20/26. After 7 days of treatment,the lymphocyteLYMcount increasedP<0.05,and white blood cellWBCcount and neutrophilNEUTcount decreased insignificantly in the two groups. Moreover,levels of C-reactive protein CRP,erythrocyte sedimentation rateESR,and procalcitoninPCTreduced in the two groups after treatmentP<0.01and the reduction in the observation group was larger than that in the control group P<0.01. Through 7 days of treatment,the total effective rate on pulmonary shadow in the observation group 90.00%,18/20was higher than that in the control group77.27%,17/22P>0.05and the improvement of lung shadow in the observation group was better than that in the control groupP<0.01. Conclusion(s):Modified Sanxiaoyin can significantly alleviate fever,cough,fatigue,anorexia,chest distress,poor sleep quality,and other symptoms of patients with mild or moderate COVID-19,improve biochemical indicators,and promote the recovery of lung function. This paper provides clinical evidence for the application of modified Sanxiaoyin in the treatment of mild or moderate COVID-19.Copyright © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.
ABSTRACT
COVID-19 is the most widespread infectious disease in the world. There is an incubation period in the early stage of infection. At present, there are some difficulties in the diagnosis of COVID-19. Medical image analysis based on computed tomography (CT) images is an important tool for clinical diagnosis. However, the lesion size of COVID-19 is smaller, and the lesion shape of COVID-19 is more complex. The effect of the aided diagnosis model is not good. To solve this problem, an aided diagnostic model of COVID-ResNet was proposed based on CT images. Firstly, an improved attention ResNet model was designed based on CT images to focus on the focal lesion area. Secondly, the SE-Res block was constructed. The squeeze excitation mechanism with the residual connection was introduced into the ResNet. The SE-Res block can enhance the correlation degree among different channels and improve the overall accuracy of the model. Thirdly, MFCA (multi-layer feature converge attention) blocks were proposed, which extract multi-layer features. In this model, coordinated attention was used to focus on the direction information of the lesion area. Different layer features were concatenated so that the shallow layer and deep layer features were fused. The experimental results showed that the model could significantly improve the recognition accuracy of COVID-19. Compared with similar models, COVID-ResNet has better performance. On the COVID-19 CT dataset, the accuracy, recall rate, F1 score, and AUC value could reach 96.89%, 98.15%,96.96%, and 99.04%, respectively. Compared with the ResNet model, the accuracy, recall rate, F1 score, and AUC value were higher by 3.1%, 2.46%, 3.0%, and 1.16%, respectively. In ablation experiments, the experimental results showed that the SE-Res block and MFCA model proposed by us were effective. COVID-ResNet transfers the shallow features to the deep, gathers the features, and makes the information complementary. COVID-ResNet can improve the work efficiency of doctors and reduce the misdiagnosis rate. It has a positive significance for the computer-aided diagnosis of COVID-19. © 2023 by the authors.
ABSTRACT
The long-term viability of small businesses in the aftermath of multiple pandemics and consequent lockdowns has a crucial impact on the sustainable economic and social development of any region across the world. Thus, in order to investigate what has been the major impact of COVID-19 pandemic within local small businesses and to identify which main factors helped small businesses to survive none as well as multiple lockdowns, data were obtained from 382 small businesses in the main urban area of Wuhan, China, via two rounds of field investigations and surveys in July 2020 and July 2022. This paper presents the results of the field investigations and the surveys completed and describes the Bayesian methods applied to quantitatively explore the impact of different variables on the probability of each business to remain active and open even after experiencing none or multiple lockdowns. Results obtained show that the difference between survival rates associated with businesses hit by no pandemic outbreak with those hit by one or several waves is negligible. Furthermore, owners who had higher confidence in their abilities since the beginning or they implemented an accurate evaluation of their strategies to run their businesses since the start of the pandemic, demonstrated to have a higher probability to keep their business alive with none as well as additional waves of the pandemic. Reduction of employees, transition of operations and promotion activities online as well as rent subsidies and tax reduction were identified as crucial actions that enhanced the probability to maintain alive businesses that experienced at least one lockdown. Globally, there was no clear policy approach at the start of the pandemic, however this study clearly determines that in future governments should provide timely support to small businesses in regions experiencing more severe impacts of the pandemic, and this should consist of a mix of grants, loans, and temporary tax cuts since initial stages. Copyright © 2023 Li, Rubinato, Zhou, Li and Chen.
ABSTRACT
Currently, the epidemic of novel coronavirus pneumonia (NCP) is still ongoing. The pathogen of this disease was newly named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hernia and abdominal wall diseases, as common disorders among population, are likely need emergency surgery. Under the new situation of NCP outbreak, surgeons who practice surgery for hernia and abdominal wall diseases should properly conduct the classified diagnosis and treatment of the hernia and abdominal wall disease, and select the appropriate surgical procedure, following the guidelines and routine diagnosis and treatment methods and complying with the guidelines for diagnosis and treatment of NCP;pay sufficient attention to self-protection according to different risk levels in the meantime of proper diagnosis and treatment and nursing process optimization. The patients with hernia or abdominal wall disease should also actively cooperate with the medical staff to complete the examination and inpatient surgical treatment in accordance with the process. © 2020 Authors. All rights reserved.
ABSTRACT
COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Deep learning plays an important role in COVID-19 images diagnosis. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects;Firstly, 33 COVID-19 datasets and data enhancement methods are introduced;Secondly, COVID-19 classification methods based on supervised learning are summarized from four aspects of VGG, ResNet, DenseNet and Lightweight Networks. The COVID-19 segmentation methods based on supervised learning are summarized from four aspects of attention mechanism, multiscale mechanism, residual connectivity mechanism, and dense connectivity mechanism;Thirdly, the application of deep learning in semi-supervised COVID-19 images diagnosis in terms of consistency regularization methods and self-training methods. Fourthly, the application of deep learning in unsupervised COVID-19 diagnosis in terms of autoencoder methods and unsupervised generative adversarial methods. Moreover, the challenges and future work of COVID-19 images diagnostic methods in the field of deep learning are summarized. This paper reviews the latest research status of COVID-19 images diagnosis in deep learning, which is of positive significance to the detection of COVID-19. © 2023 by the authors.
ABSTRACT
Air transportation is eminent for its fast speed and low cargo damage rate among other ways. However, it is greatly limited by emergent factors like bad weather and current COVID-19 epidemic, where irregular flights may occur. Confronted with the negative impact caused by irregular flight, it is vital to rearrange the preceding schedule to reduce the cost. To solve this problem, first, we established a multi-objective model considering cost and crew satisfaction simultaneously. Secondly, due to the complexity of irregular flight recovery problem, we proposed a tabu-based multi-objective particle swarm optimization introducing the idea of tabu search. Thirdly, we devised an encoding scheme focusing on the characteristic of the problem. Finally, we verified the superiority of the tabu-based multi-objective particle swarm optimization through the comparison against MOPSO by the experiment based on real-world data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
We present a novel DenseNet framework with attention mechanisms (AM-DenseNet) to extract lung feature of 1 COVID-19 patient. In AM-DenseNet, a lightweight Efficient Channel Attention (ECA) structure is added at the end of each dense connection to introduce an attention mechanism to discovery local lesion domain. We compare our AM-DenseNet to VGG-16, ResNet-50 and DenseNet-121 on CT image dataset of COVID-19 patients. According to the experimental results, we conclude that the classification performance of AM-DensNet framework can be significantly enhanced under the effect of attention mechanism. The AM-DensNet shows better classification performance than the compared models. © 2022 IEEE.
ABSTRACT
The study focuses on how online education is used in the sphere of sports from 2001 to 2022. Especially in the era of COVID-19 popularity, the deepening cross-fertilization between the field of sports and other fields, coupled with the fact that scholars have not yet analyzed and organized the areas in which online education is combined with sports, and what opportunities there will be for the development of online education in the field of sports. In this article, it summarizes the past two decades of research with bibliometric and scientometric research methods, quantitatively exploring the development paths, research hotspots, and evolutionary trends in the field under the online medium, and systematically integrates the field through knowledge mapping to suggest and indicate the development process for the combination of the sports field and online education. The information of authors, keywords, and the number of national publications are used to determine that online education will continue to be studied in the field of sports with high explosive keywords such as children, online education, and COVID-19. This will provide suggestions and directions for the development of the sports field and online education.
ABSTRACT
Due to the coronavirus pandemic, portable electrical impedance tomography (EIT) systems [1]-[3] have been considered as the only variable wearable medical lung imaging solution for monitoring the treatment of pneumonia patients and their recovery. Generally, the EIT system is classified into passive EIT (P-EIT) [3]-[6] or active electrode EIT (AE-EIT) [2]. The AE-EIT system is preferred as it amplifies and digitalizes the small signals while minimizing the noises incurred by motion artifacts, complex long wire connection, large variation in electrode contact, and stray capacitance problems, which is important for high-performance imaging applications. © 2022 IEEE.
ABSTRACT
We study the impact of face-to-face (FTF) interactions on commercial real estate (CRE) performance. By linking tenants, properties, and CRE firms, we construct three novel FTF measures that capture tenant remote working, internal communication between coworkers, and external contact with customers. Using the COVID-19 pandemic as an exogenous shock to the FTF economy, we find that firms holding properties with tenants that are more resilient to social distancing perform better. These FTF effects weaken over the long term, however. As investors are capable of compiling valuable information at granular levels regarding how tenants operate, our findings support market efficiency and shed light on postpandemic CRE performance. © 2022 American Real Estate and Urban Economics Association.
ABSTRACT
Objectives To estimate the basic reproduction number of the novel coronavirus (2019-nCoV) and to provide support to epidemic preparedness and response. Methods Based on the susceptible-exposed-infected-removed (SEIR) compartment model and the assumption that the infection cases with symptoms occurred before January 26, 2020 were resulted from free propagation without intervention, we estimated the basic reproduction number of 2019-nCoV according to the reported confirmed cases and suspected cases, as well as theoretical estimated number of infected cases by other research teams, together with some epidemiological determinants learned from the severe acute respiratory syndrome. Results The basic reproduction number fall between 2.8 to 3.3 by using the real-time reports on the number of 2019-nCoV infected cases from People's Daily in China, and fall between 3.2 and 3.9 on the basis of the predicted number of infected cases from international colleagues. Conclusions The early transmission capability of 2019-nCoV is close to or slightly higher than SARS. It is a controllable disease with moderate-high transmissibility. Timely and effective control measures are capable to quickly reduce further transmission. Copyright © 2020 West China University of Medical Science. All rights reserved.
ABSTRACT
Most of the machine learning models are black box models. However, in practical applications, such as in many medical and health fields, it is very necessary to clearly understand the internal composition, combination or interaction of the model, study the system and predict the system behavior. Therefore, interpretable machine learning models have attracted more and more attention, especially when predicting based on models, the driving factors leading to prediction behavior are deeply studied. This paper proposes an interpretable machine learning model based on comparative learning and NARMAX. Because the input-output relationship of the model and the interaction relationship between input variables are clear, the model can not only be used for prediction, but also explain the relevant 'reasons' of prediction behavior. The novel coronavirus pneumonia epidemic data and influenza epidemic data were used to compare the model proposed in this paper. The experimental results show that the model is effective and reliable, and establish a dynamic model for the two diseases' spreads, and analyze the relationship between disease transmission factors. © 2022 IEEE.
ABSTRACT
The carbon market is a vital tool to achieve carbon neutrality. This paper uses daily closing price data of Shenzhen carbon trading market, energy, commodity and financial markets from 18 October 2018 to 19 August 2021, examining the transmission of risk/information from the perspective of market volatility spillover and tail risk transmission based on quantile spillover. The stock market crash and COVID-19 have increased the volatility of the system substantially. Next, the increase in trading frequency is accompanied by an increase in total volatility connectivity, and the carbon market transforms into a recipient of systemic shocks. Finally, the results of tail risk transmission reveal that the net effect of carbon reception increases significantly. These findings have implications for policymakers to improve the carbon market and provide important insights for investors to trade in turbulent periods.
ABSTRACT
SARS-CoV-2 can be transmitted through contact with fomite, respiratory droplets, and aerosolized viruses. Recent evidence suggests that aerosol transmission represents a significant route of infection. In relation to healthcare workers (HCWs), much attention has been focused on personal protective equipment, yet this is the lowest level of the Centers for Disease Control and Prevention hierarchy of controls. Although engineering controls are prominent in the hierarchy, little attention has been given to developing effective interventions. This study aims to evaluate the performance of a simple extraction device in a clinical setting. This was accomplished by using a high flow local extraction (HFLE) that was connected to the existing ventilation system of the hospital on one end and to an intake nozzle near the patient's airway on the other end. Propylene glycol was aerosolized through a physiological test apparatus to simulate the breath of a patient. The field of interest was illuminated using a laser sheet in two planes from the model, namely, the sagittal plane and the transverse plane, and the movement of the simulated aerosol was recorded using a video camera to assess the dispersion of the aerosol qualitatively. In the meantime, the concentration of the aerosol particles was measured using a particle meter to evaluate the effectiveness of the extraction quantitatively. It was found that the HFLE device could effectively reduce the dispersion of the exhaled aerosols to undetectable levels when it was positioned within 250 mm from the mouth. This result has significance in the safety of HCWs involved in the management of patients with infectious diseases and may also have potential applications in other clinical areas with high airflow in the ventilation systems. © 2022 Author(s).
ABSTRACT
Abnormal flights, which deviate from their scheduled plans, incurred huge costs for airlines and serious inconvenience for passengers. This phenomenon occurs frequently, especially under the influence of COVID-19 and requires high-quality solution within short time limits. To mitigate these negative effects, first, an integrated flight timetable and crew schedule recovery model with the aim of minimizing total cost is constructed in this paper. Second, an improved fireworks algorithm is proposed to effectively solve the model. Finally, an unscheduled temporary aircraft maintenance scenario is obtained to illustrate the superiority of the proposed algorithm in terms of computing time and solution quality. © 2022, Springer Nature Switzerland AG.
ABSTRACT
As with the rapid development of air transportation and potential uncertainties caused by abnormal weather and other emergencies, such as Covid-19, irregular flights may occur. Under this situation, how to reduce the negative impact on airlines, especially how to rearrange the crew for each aircraft, becomes an important problem. To solve this problem, firstly, we established the model by minimizing the cost of crew recovery with time-space constraints. Secondly, in view of the fact that crew recovery belongs to an NP-hard problem, we proposed an improved particle swarm optimization (PSO) with mutation and crossover mechanisms to avoid prematurity and local optima. Thirdly, we designed an encoding scheme based on the characteristics of the problem. Finally, to verify the effectiveness of the improved PSO, the variant and the original PSO are used for comparison. And the experimental results show that the performance of the improved PSO algorithm is significantly better than the comparison algorithms in the irregular flight recovery problem covered in this paper. © 2022, Springer Nature Switzerland AG.
ABSTRACT
The ubiquitous impacts resulting from the COVID-19 pandemic have profoundly changed the education sector and marked research interest in online or blended learning can be witnessed. As a pervasive learning activity of paramount significance, online language learning has aroused widespread attention. Nonetheless, few systematic reviews concerning the effectiveness of online language learning have been published. With the help of CiteSpace, this study systematically investigated 103 included articles from the SSCI of empirical studies from 44 journals for the purpose of filling the research gap in this field, providing a better understanding of the research trends, exploring effective ways to implement online language courses, and testifying to the ability of CiteSpace to track research hotspots. The findings show that effectiveness studies on online language learning principally focus on assisted tools (42.72%), instructional approaches (36.89%), and specific courses (20.39%). Lack of adequate cooperation among research institutions and the dominant position of online English learning (82.52%) can be witnessed. Despite the small sample size of 103 included articles, the validation of CiteSpace in terms of tracking the research trends or hotspots is confirmed. However, the proportion of each research focus is not compatible with the results of a comprehensive full-text analysis. This literature review also probes into various methods to measure effectiveness more scientifically and effective ways to implement online language courses. Theoretical as well as practical implications and future research directions are clarified.