Clinical characteristics of 654 patients with COVID-19 in Dongxihu Shelter Hospital in Wuhan

Objective To analyze the clinical features of patients with coronavirus disease 2019COVID-19in Wuhan, and we provide reference for further prevention and control of the disease. Methods We collected the clinical data of patients with COVID-19 in Dongxihu Shelter Hospital of Wuhan from February 7 to March 6, 2020. The main symptoms, blood test results, lung CT results, and nucleic acid negative conversion were analyzed. Results A total of 654 patients were included, 17526.76%were mild, and 47973.24%were general. There were 344 males (52.60%), and 310 females (47.40%). The patients were with a mean age of49.36+/-10.30years, and 97 patients (14.83%) with a history of hypertension, 51 patients (7.80%) had a history of diabetes. The main clinical symptoms were fever with 547(83.64%) patients, 186 cases (28.44%) had chills, 15 cases (2.29%) had shiver, 342(52.29%) had fatigue symptoms, 413(63.15%) had cough, 137(20.95%) had chest tightness, and 109(16.67%) had diarrhea during the course of the disease. Blood routine tests of 395 patients, the white blood cell count (WBC) was (4.12+/-1.46)x109/L. The total white blood cell count was normal in 378 cases(95.70%), increased in 7(1.77%), and decreased in 10(2.53%). The lymphocyte percentage was (23.10+/-10.02)%, lymphocyte1.06+/-0.37x109/L. The percentage and count of lymphocyte were low. All the 654 cases were examined by CT, 175 cases (26.76%) showed normal lung CT, 422 cases (64.52%) showed patchy or segmental ground-glass opacity, and 57 cases (8.72%) showed multilobar consolidation, ground-glass shadow coexisted with consolidation or streak shadow. The interval between positive nucleic acid test before admission and negative test after admission was as short as 5 days and as long as 24 days, the average was (12.35+/-3.73) days. Conclusion Fever, coughing, and fatigue are the main symptoms in patients with COVID-19. The typical lung CT findings can be used as the basis for clinical diagnosis and disease evaluation. Patients with mild and common type had better prognosis.Copyright © 2021 Editorial Office of Chinese Journal of Schistosomiasis Control. All rights reserved.

Influence Factors of Small and Medium-Sized Enterprises and Micro-Enterprises in the Cross-Border E-Commerce Platforms

Small and medium-sized businesses (SMEs) are frequently exposed to a variety of difficulties during global epidemic crises like coronavirus (COVID-19), which may even threaten their lives. The purpose of this study explores the influencing factors of Taiwan's companies between small and medium-sized enterprises and micro-enterprises on the choice of the cross-border e-commerce platform. The findings are defined as taking into account small and medium-sized businesses and microenterprises when choosing cross-border e-commerce through a literature review and an examination of secondary data among the 10 participating businesses through interviews in various regions and business sectors in Taiwan. In this case we used study-based research, which included five small, medium-sized, and micro-enterprises, as well as five cross-border e-commerce projects and the company's management senior officers. According to the study's emphasis on the economic, social, technological, and legal aspects of various firms, these factors lead to a variety of decisions regarding the best cross-border e-commerce platform. The case study approach was utilized in this investigation to confirm the consideration of micro-and small-sized businesses that took part in cross-border e-commerce project counseling. This study summarizes five types of enterprises with different capabilities: product enhancement, marketing enhancement, cross-border potential, knowledge-based enhancement, and cross-border start-up. According to the results, it was found that different enterprise capabilities will affect the choice of cross-border e-commerce platforms. These five capabilities also have different types of consideration factors;among them, SMEs pay attention to marketing, pricing, market analysis, culture, customer service, payment, logistics, certification, taxation, etc. In addition to theoretical implications, this research also gives small and medium enterprises and micro-enterprises practice when choosing cross-border e-commerce platform, as well as suggestions for future research. © 2023 by the authors.

Masked Facial Region Recognition Using Human Pose Estimation and Broad Learning System

COVID-19 and its variants have been posing a large risk to people around the world since the outbreak of the disease. Many techniques like AI are explored to help combat epidemics. People are required or forced to wear a mask to fight against COVID-19 epidemics worldwide. It brings new challenges to the task of masked facial region recognition. When facial regions are occluded by masks, it will result in some failures of face detection algorithms. In this paper, we propose a method to recognize masked faces. It mainly includes three parts. Firstly, the human pose is estimated to produce a series of key points. It is implemented by OpenPose. Secondly, a key-points location strategy is designed to capture the masked facial regions. It can locate the positions of faces accurately. Thirdly, the broad learning system, which is also an incremental learning algorithm, is employed to recognize the classes of candidate regions. Experiments conducted on some datasets shed light on the effectiveness of the proposed method. © 2022 SPIE.

HCV core antigen and residual HCV RNA in the native liver of living donor liver transplant recipients with chronic hepatitis C

Background: With the highly effective direct-acting antiviral (DAA) therapy, the number of liver transplants for hepatitis C virus (HCV) has decreased worldwide. However, similar to the phenomenon occurring in COVID-19 infection, the residual virus reservoirs in target organ is warranted to be explored due to the potential replication and disease recurrence. Hence, we aim to investigate the significance of hepatic HCV RNA identification as well as the discrepancy between HCV RNA and HCV core antigen (HCV Ag) in native liver of chronic hepatitis C recipients undergoing living donor liver transplantation (LDLT). Methods: Between Feb 2016 to Aug 2019, we prospectively enrolled 80 serum anti-HCV positive recipients who underwent LDLT. HCV RNA extracted from the native liver tissues was subjected to one-step reverse transcribed qPCR, using the TopScript One Step qRT PCR Probe Kit with HCV qPCR probe assay and human GAPDH qPCR probe assay on ViiA 7 Real Time PCR System. Hepatic HCV Ag was identified from the native liver tissues by employing the qualitative enzyme immunoassay technique. All experimental steps were based on the protocol provided by Human HCV Ag ELISA Kit (Cat. No. MBS167758). Results: Among 80 recipients, 85% (68/80) positive HCV-RNA was significantly higher in the native liver tissues than in the serum before (29/80, 36.3%;p = 0.000) and after LDLT (3/80, 4.4%;p = 0.000). In contrast, hepatic HCV Ag was 100% negative identified in all 80 explanted native liver. Conclusions: Significant positive HCV-RNA identification in the native liver suggested that pre-LDLT serum HCV RNA should be underestimated in the real status of HCV activity. HCV Ag assay may have lack of sensitivity and negative predictive value in liver tissues. In contrast to serum HCV RNA and HCV Ag, a great discrepancy might be described between hepatic HCV RNA and HCV Ag in the liver tissue. (Figure Presented).

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

Coronavirus disease 2019 (COVID-19) continues to pose a great challenge to the world since its outbreak. To fight against the disease, a series of artificial intelligence (AI) techniques are developed and applied to real-world scenarios such as safety monitoring, disease diagnosis, infection risk assessment, and lesion segmentation of COVID-19 CT scans. The coronavirus epidemics have forced people wear masks to counteract the transmission of virus, which also brings difficulties to monitor large groups of people wearing masks. In this article, we primarily focus on the AI techniques of masked facial detection and related datasets. We survey the recent advances, beginning with the descriptions of masked facial detection datasets. A total of 13 available datasets are described and discussed in detail. Then, the methods are roughly categorized into two classes: conventional methods and neural network-based methods. The conventional methods are usually trained by boosting algorithms with hand-crafted features, which accounts for a small proportion. Neural network-based methods are further classified as three parts according to the number of processing stages. Representative algorithms are described in detail, coupled with some typical techniques that are described briefly. Finally, we summarize the recent benchmarking results, give the discussions on the limitations of datasets and methods, and expand future research directions. To our knowledge, this is the first survey about masked facial detection methods and datasets. Hopefully our survey could provide some help to fight against epidemics. © 2020 IEEE.

Masked Face Detection Using A Two-stage Classification Approach in the COVID-19 Era

Masked face detection is a challenging task in the surveillance applications due to complex backgrounds. In this paper, we propose a two-stage method for masked face detection: pre-detection and verification. Firstly, a masked face detector based on AdaBoost algorithm and histogram of orientation feature is exploited. It may provide sufficient candidate face regions. Secondly, a two-class classifier is trained by broad learning system, which is an incremental learning algorithm with high efficiency in training. It is used to distinguish realistic masked faces from background. Moreover, this paper proposes a masked face dataset that includes multiple masked faces captured from real-life scenes. It can be used for classifier training and evaluation. Experiments conducted on the dataset indicate the effectiveness of the proposed method with Recall 94.69% and Precision 97.72%. © 2021 IEEE.

Hospitality industry employees’ intention to stay in their job after the covid-19 pandemic

The COVID-19 pandemic had a devastating effect on the tourism and hospitality industries in Taiwan, causing some small companies to cease trading and large companies to place their employees on unpaid leave. Placing employees on unpaid leave may have negatively affected the intention of hospitality employees to remain in their jobs. This study examined whether employees’ job insecurity and organizational identification affected their intention to stay in their job during the COVID-19 pandemic. Previously developed scales were adopted to develop items measuring job insecurity, organizational identification, and intention to stay in a job. Responses to 515 returned questionnaires were examined. The results revealed that job insecurity significantly affects organizational identification. Both job insecurity and organizational identification significantly affected intention to stay. Few studies have used path analyses to investigate the relationships among intention to stay, job insecurity, and organizational identification. The indirect effect of organizational identification was analyzed, and evidence supporting a total effect and total indirect effect was obtained. This implies that hospitality companies seeking to retain staff during crises should promote organizational identification among staff. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

E-Learning in the Postpandemic Era: A Case Study in Taiwan

The COVID-19 pandemic is pressing educational institutions across the globe to transfer rapidly from face-to-face learning to e-learning. However, e-learning has some multifaceted problems waiting to be solved and optimized. This study uses a survey questionnaire to collect research data. By integrating two predictors and four moderators into expectation-confirmation model, this article develops a causal research model and studies the continuance intention of using e-learning after the pandemic and how we can optimize online learning systems to benefit the traditional education systems in the long term. This article fills the literature gap of studies on IS continuance intention of e-learning from a problem-driven perspective. Based on the research results, this article suggests policy-makers and educators not only develop continuous training for both instructors and students on e-learning to enhance their ability and acceptance of e-learning systems but also build a more comprehensive technical environment to accelerate the adoption as well as to increase the continuance usage intention of e-learning systems. In addition, high-quality educational designs of e-learning that contain interaction mechanisms and customizations according to students' perceptions and attitudes toward e-learning are needed. We also call on scholars from around the globe to continue investigating the critical factors of e-learning adaptation and online educational environment optimization for proposing better solutions to counter crises similar to the COVID-19 pandemic in the future.

Hybrid Transfer Learning and Broad Learning System for Wearing Mask Detection in the COVID-19 Era

In the era of Corona Virus Disease 2019 (COVID-19), wearing a mask can effectively protect people from infection risk and largely decrease the spread in public places, such as hospitals and airports. This brings a demand for the monitoring instruments that are required to detect people who are wearing masks. However, this is not the objective of existing face detection algorithms. In this article, we propose a two-stage approach to detect wearing masks using hybrid machine learning techniques. The first stage is designed to detect candidate wearing mask regions as many as possible, which is based on the transfer model of Faster_RCNN and InceptionV2 structure, while the second stage is designed to verify the real facial masks using a broad learning system. It is implemented by training a two-class model. Moreover, this article proposes a data set for wearing mask detection (WMD) that includes 7804 realistic images. The data set has 26403 wearing masks and covers multiple scenes, which is available at "https://github.com /BingshuCV/WMD." Experiments conducted on the data set demonstrate that the proposed approach achieves an overall accuracy of 97.32% for simple scene and an overall accuracy of 91.13% for the complex scene, outperforming the compared methods.

Mortality prediction for COVID-19 patients via Broad Learning System

The COVID-19 virus has been raging around the world for months and killing more than a million people. It is extremely infectious due to its easy transmission and long incubation period. Until now, the number of people diagnosed with COVID-19 infection has been increasing dramatically each day. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. For this purpose, the machine learning tool is an effective way to diagnose it. To support decision-making and logistical planning in healthcare systems, inspired by earlier works, we study the application of Broad Learning System (BLS) to predict the mortality of COVID-19 patients via their blood samples. We evaluated three models on the 375 patients' blood samples, and the BLS model achieves a sensitivity of 94.50% while having a specificity of 94.80%. Except for specificity and sensitivity rates, the area of under receiver operating characteristic (AUC), prediction accuracy, confusion matrix, and precision are also presented in this paper. The performance of BLS performs obviously better than the other models compared in this work. The encouraging results are inspired us to do a future analysis on a large set of COVID-19 blood samples to have a more reliable prediction. © 2020 IEEE.