An integrated interval type-2 fuzzy rough technique for emergency decision making

This paper aims to develop a hybrid emergency decision-making (EDM) method by combining best–worst method (BWM), multi-attributive border approximation area comparison (MABAC) and prospect theory (PT) in trapezoidal interval type-2 fuzzy rough (TrIT2FR) environment. In this hybrid method, the decision information is represented by trapezoidal interval type-2 fuzzy rough numbers (TrIT2FRNs). Firstly, this paper defines the TrIT2FRN and analyzes its desirable properties. Then, the TrIT2FR-BWM is developed to determine criteria weights. To develop the TrIT2FR-BWM, this paper completes the following three core issues: (i) propose an effective theorem to normalize the TrIT2FR weights;(ii) build a crisp programming model to transform the minmax objective of weight-determining model for the TrIT2FR-BWM;(iii) design a consistency ratio for the TrIT2FR-BWM to check the reliability of the determined criteria weights. Afterwards, this paper extends the classical MABAC into TrIT2FR environment to calculate the border approximation area (BAA). Subsequently, the PT is used to rank the alternatives, in which the calculated BAA is selected as the reference point. Lastly, the validity of the proposed method is certificated with a real site selection case of makeshift hospitals on COVID-19. Sensitivity analysis and comparative analyses are conducted to illustrate the robustness and superiorities of the proposed method. Some valuable results are summarized as follows: (i) the best alternative determined by the proposed method conforms with the actual selection result, (ii) the proposed models in the TrIT2FR-BWM have strong robustness, (iii) PT is helpful to improve the decision quality of EDM. © 2023 Elsevier B.V.

EdgeVR360: Edge-Assisted Multiuser-Oriented Intelligent 360-degree Video Delivery Scheme over Wireless Networks

The COVID-19 situation has determined many people all over the world to experience remote work, study and play although most of them were not prepared for such a change in their lifestyle. With the coming of the high demand of virtual interaction, 360-degree Virtual Reality (VR) technologies and applications have established stronger relationships with your peers and friends if it applies. However, higher quality of VR streaming brings users deeper immersive experience which requires greater network bandwidth and latency, and more powerful computation capability for individuals. To address these issues, the proposed intelligent video delivery scheme in this paper takes advantage of the edge-assisted computational power to improve the multi-user oriented watching experience of high quality 360-degree video over wireless networks, which reduces network resource utilization, and also optimizes edge cache hit ratio and user's Field of View (FoV) quality.

Dynamic lead–lag relationship between Chinese carbon emission trading and stock markets under exogenous shocks

Using the non-parametric thermal optimal path method, we investigate the dynamic lead–lag relationship between carbon emission trading and stock markets in China, and further consider the impact of different types of exogenous shocks on the lead–lag relationship. The empirical results show that the stock market leads the carbon market on most trading days, and the relationship reverses when the mean values of carbon market return are significantly smaller than zero. In addition, the lead–lag relationships when the carbon market leads the high energy-consuming stock market sectors are more obvious. We also find that there exist significant heterogeneous effects of different types of exogenous shocks on the lead–lag relationship between the two markets, including government policy, the Sino-US trade war and the Covid-19 outbreak. These findings have the potential to help regulators understand the interrelationship between components of the financial market, and be of great value for investors to optimize portfolio allocation by incorporating carbon assets into the portfolio. © 2023 Elsevier Inc.

Hand-Hygiene Education and Facilitation System for Children

Hand hygiene has become even more im-portant in light of the COVID-19 pandemic, where hands are one of the high-risk transmission routes. Existing hand-hygiene education is focused on one-time training and does not ensure that correct handwashing procedures are undertaken. Our study, therefore, proposes a hand-hygiene education and facilitation system. Compared to previous systems, through an external RGB camera with our proposed image preprocessing and use the 3-D convo-lution and convolutional long short-term memory (Con-vLSTM) models to detect correctness of handwashing postures, which also facilitates children's ability to wash their hands properly through an on-screen tutorial. It also encourages children to develop good handwashing habits through a positive competition and reward system, and helps teachers to understand children's learning pro-gresses. The experimental results showed that the model was able to identify handwashing postures in real-time with 95.12% accuracy in a realistic and variable environ-ment. © 2022 IEEE.

[Course of disease and related epidemiological parameters of COVID-19: a prospective study based on contact tracing cohort].

Objective: To analyze the course of disease and epidemiological parameters of COVID-19 and provide evidence for making prevention and control strategies. Methods: To display the distribution of course of disease of the infectors who had close contacts with COVID-19 cases from January 1 to March 15, 2020 in Guangdong Provincial, the models of Lognormal, Weibull and gamma distribution were applied. A descriptive analysis was conducted on the basic characteristics and epidemiological parameters of course of disease. Results: In total, 515 of 11 580 close contacts were infected, with an attack rate about 4.4%, including 449 confirmed cases and 66 asymptomatic cases. Lognormal distribution was fitting best for latent period, incubation period, pre-symptomatic infection period of confirmed cases and infection period of asymptomatic cases; Gamma distribution was fitting best for infectious period and clinical symptom period of confirmed cases; Weibull distribution was fitting best for latent period of asymptomatic cases. The latent period, incubation period, pre-symptomatic infection period, infectious period and clinical symptoms period of confirmed cases were 4.50 (95%CI:3.86-5.13) days, 5.12 (95%CI:4.63-5.62) days, 0.87 (95%CI:0.67-1.07) days, 11.89 (95%CI:9.81-13.98) days and 22.00 (95%CI:21.24-22.77) days, respectively. The latent period and infectious period of asymptomatic cases were 8.88 (95%CI:6.89-10.86) days and 6.18 (95%CI:1.89-10.47) days, respectively. Conclusion: The estimated course of COVID-19 and related epidemiological parameters are similar to the existing data.

A MULTI-STAGE PROGRESSIVE LEARNING STRATEGY FOR COVID-19 DIAGNOSIS USING CHEST COMPUTED TOMOGRAPHY WITH IMBALANCED DATA

In this paper, a multi-stage progressive learning strategy is investigated to train classifiers for COVID-19 Diagnosis using imbalanced Chest Computed Tomography Data acquired from patients infected with COVID-19 Pneumonia, Community Acquired Pneumonia (CAP) and from normal healthy subjects. In the first learning stage, pre-processed volumetric CT data together with the segmented lung masks are fed into a 3D ResNet module, and an initial classification result can be obtained. However, due to categorical data imbalance, we observe large differences in sensitivity between COVID-19 and CAP cases. In the second stage, five learning models are independently trained over data with only COVID-19 and CAP cases, and are then ensembled to further discriminate the two classes. The final classification results are obtained by combining the predictions from both stages. Based on the validation dataset, we have evaluated our method and compared it with up-to-date methods in terms of overall accuracy and sensitivity for each class. The validation results validate the accuracy of the proposed multi-stage learning strategy. The overall accuracy of the validation dataset is 88.8%, and the sensitivities are 0.873, 0.789 and 1 for COVID-19, CAP and normal cases, respectively.

[Analysis on the imported Coronavirus Disease 2019 related cluster epidemic in rural areas of Chengdu].

An epidemiological investigation was carried out on a local cluster of outbreak caused by imported cases of Coronavirus Disease 2019 (COVID-19) in rural areas of Chengdu in December 2020, to find out the source of infection and the chain of transmission. According to Prevention and Control Protocol for COVID-19 (Version 7), field epidemiological investigation was adopted, combined with big data technology, video image investigation, gene sequencing and other methods to carry out investigation into COVID-19 cases and infections source tracing, analyze the epidemiological association, and map the chain of transmission. From December 7 to 17, 2020, 13 local COVID-19 confirmed cases and 1 asymptomatic case were diagnosed in Chengdu, of which 12 cases (85.71%) had a history of residence and activity in the village courtyard of Taiping (TP), Pidu (P) District, Chengdu. From November 8, 2020 to November 28, 2020, a group of inbound people form Nepal were transferred to the designated entry personnel quarantine hotel of P District which was adjacent to the TP village. During quarantine, there were 5 cases who tested positive for COVID-19. Through gene sequencing alignment, genes of local cases and Nepalese imported cases from the same period are homologous, all belong to the lineage of L2.2.3 (B.1.36 according to Pangolin lineage typing method). According to the results of field epidemiological investigation and gene sequencing analysis, the index case was most likely infected by contact with household waste of quarantine site. Under the situation of normalization prevention and control of COVID-19, sentinel monitoring of fever clinics in primary medical institutions is the key to early detection of the epidemic. The multi-department joint epidemiological investigation and the application of gene technology are the core links of the investigation and traceability of modern infectious diseases. The allocation of public health resources in rural areas needs to be strengthened. We need to improve the capacity for early surveillance and early warning of the epidemic in rural areas.

COVID-19 chest radiography employing via Deep Convolutional Stacked Autoencoders

This paper applies AI (artificial intelligence) technology to analyze CT (chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training. The model improves the existing techniques used for CT images inspection through an application of Stacked Autoencoders (SAEs) structures using the segmentation function for the area object detection model on Fast-RCNN. As a result, the proposed approach can quickly analyze X-ray images in detecting abnormalities in patients with lab-confirmed coronavirus even before clinical symptoms appear. In addition to detecting early abnormalities, area object detection model reveals a finding not seen in the latest cases of COVID-19. Most noteworthy, the study has shown that all COVID-19 patients exhibit an associated bilateral pleural effusion. The features are augmented to the model for the improvement of detection quality improvement and the shorten of the examination period.

[Effectiveness of the "14 plus 7 day quarantine" and "nucleic acid plus total antibody testing" strategy for screening imported patients with COVID-19 in Xiamen].

Objective: To analysis effectiveness of the "14 plus 7 day quarantine" and "nucleic acid plus total antibody testing" strategy (combined screening strategy) for screenin the imported patients with COVID-19 in Xiamen. Methods: The study populations were overseas travelers arriving in Xiamen from March 17 to December 31, 2020, and overseas travelers who had quarantine outside Xiamen for less than 21 days from July 18 to December 31, 2020. Data were collected and analyzed on the timing of detection, pathways, and test results of the imported patients with COVID-19 after implementing combined screening strategy. Results: A total of 304 imported patients with COVID-19 were found from 174 628 overseas travelers and 943 overseas travelers from other cities. A total of 163 cases (53.6%) were diagnosed by multitime, multisite intensive nucleic acid testing after positive finding in total antibody testing. Among them, 27 (8.9%) were first positive for nucleic acid in 14 plus 7 day quarantine and 136 were first positive for nucleic acid in 14-day quarantine. Only 8 of these individuals were tested positive for nucleic acid after positive total antibody testing. The other 128 individuals were tested positive for nucleic acid after being negative for average 2.3 times (maximum of 6 times). Aditional 155 cases might be detected by using the combined "14 plus 7 day quarantine" and " nucleic acid plus total antibody testing" strategy compared with "14-day quarantine and nucleic acid testing" strategy, accounting for 51.0% of the total inbound infections. So the combined screening strategy doubled the detection rate for imported patients with COVID-19. No second-generation case caused by overseas travelers had been reported in Xiamen as of February 26, 2021. Conclusions: Xiamen's combined screening strategy can effectively screen the imported patients with COVID-19 who were first positive for nucleic acid after 14 day quarantine. Compared with "14 day quarantine and nucleic acid testing", the combined screening strategy improved detection rate and further reduced the risk of the secondary transmission caused by the imported patients with COVID-19.

Author Correction: The dual role of anti-viral therapy in the treatment of Coronavirus disease 2019.

Correction to: European Review for Medical and Pharmacological Sciences 2020; 24 (22): 11939-11944-DOI: 10.26355/eurrev_202011_23854-PMID: 33275267, published online 30 November, 2020. The authors state that "Figures 3 and 4 were used twice due to a careless mistake during the preparation of Figures". There are amendments to this paper.  The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/23854.

The dual role of anti-viral therapy in the treatment of Coronavirus disease 2019.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has become a worldwide public health emergency; unfortunately, there is currently no treatment for improving outcomes or reducing viral-clearance times in infected patients. The aim of the present study was to evaluate the efficacy of interferon (IFN) with or without lopinavir and ritonavir as antiviral therapeutic option for treating COVID-19 infection. PATIENTS AND METHODS: The present study enrolled 148 patients that received either standard care, treatment with IFN alfa-2b, or IFN alfa-2b combined with lopinavir plus ritonavir. Viral testing was performed using Reverse-Transcription Polymerase Chain Reaction (RT-PCR). RESULTS: There was no significant difference in the viral-clearance time at 28 days after treatment between patients receiving standard care and those receiving anti-viral treatments. However, the average viral-clearance time of patients receiving standard care (14 days) was shorter than that for patients receiving IFN alfa-2b or IFN alfa-2b combined with lopinavir plus ritonavir (15.5 or 17.5 days) (p<0.05). Patients treated with IFN alfa-2b within five days or IFN alfa-2b combined with lopinavir plus ritonavir after three days of symptoms exhibited shorter viral-clearance times than the other groups (p<0.05). Moreover, viral-clearance times were significantly longer in patients receiving standard care or anti-viral treatment 5 days after symptoms appeared than those of patients who received these treatments within five days of symptom onset (p<0.05). CONCLUSIONS: Early symptomatic treatment is most critical for maximizing amelioration of COVID-19 infection. Anti-viral treatment might have complicated effect on viral-clearance.

[Structure of SARS-CoV-2 and treatment of COVID-19]

Corona virus disease 2019 (COVID-19) is a new type of coronavirus pneumonia, which is caused by infection of a novel coronavirus, SARS-CoV-2. The virus infects lung cells by binding angiotensin-converting enzyme 2 (ACE2) of cell surface, which leads to leukocyte infiltration, increased permeability of blood vessels and alveolar walls, and decreased surfactant in the lung, causing respiratory symptoms. The aggravation of local inflammation causes cytokine storm, resulting in systemic inflammatory response syndrome. In December 2019, a number of new pneumonia cases were reported by Wuhan Municipal Health Commission, after then a novel coronavirus was isolated and identified as SARS-CoV-2. To the date of Sep. 13th, 2020, COVID-19 is affecting 216 countries or regions, causing 28 637 952 cases, 917 417 deaths, and the mortality rate is 3.20%. This review will summarize the structure of SARS-CoV-2 and the pharmaceutical treatment of COVID-19, and their potential relationships.

[Preliminary study of the relationship between novel coronavirus pneumonia and liver function damage: a multicenter study].

Objective: To analyze the clinical characteristics of cases of novel coronavirus pneumonia and a preliminary study to explore the relationship between different clinical classification and liver damage. Methods: Consecutively confirmed novel coronavirus infection cases admitted to seven designated hospitals during January 23, 2020 to February 8, 2020 were included. Clinical classification (mild, moderate, severe, and critical) was carried out according to the diagnosis and treatment program of novel coronavirus pneumonia (Trial Fifth Edition) issued by the National Health Commission. The research data were analyzed using SPSS19.0 statistical software. Quantitative data were expressed as median (interquartile range), and qualitative data were expressed as frequency and rate. Results: 32 confirmed cases that met the inclusion criteria were included. 28 cases were of mild or moderate type (87.50%), and four cases (12.50%) of severe or critical type. Four cases (12.5%) were combined with one underlying disease (bronchial asthma, coronary heart disease, malignant tumor, chronic kidney disease), and one case (3.13%) was simultaneously combined with high blood pressure and malignant tumor. The results of laboratory examination showed that the alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB), and total bilirubin (TBil) for entire cohort were 26.98 (16.88 ~ 46.09) U/L and 24.75 (18.71 ~ 31.79) U/L, 39.00 (36.20 ~ 44.20) g/L and 16.40 (11.34 ~ 21.15) µmol/L, respectively. ALT, AST, ALB and TBil of the mild or moderate subgroups were 22.75 (16.31 ~ 37.25) U/L, 23.63 (18.71 ~ 26.50) U/L, 39.70 (36.50 ~ 46.10) g/L, and 15.95 (11.34 ~ 20.83) µmol/L, respectively. ALT, AST, ALB and TBil of the severe or critical subgroups were 60.25 (40.88 ~ 68.90) U/L, 37.00 (20.88 ~ 64.45) U/L, 35.75 (28.68 ~ 42.00) g/L, and 20.50 (11.28 ~ 25.00) µmol/L, respectively. Conclusion: The results of this multicenter retrospective study suggests that novel coronavirus pneumonia combined with liver damage is more likely to be caused by adverse drug reactions and systemic inflammation in severe patients receiving medical treatment. Therefore, liver function monitoring and evaluation should be strengthened during the treatment of such patients.