INFLUENCE OF THE COVID-19 PANDEMIC ON REDUCING THE INCOME OF WORKERS

In Vietnam, the impact of COVID-19 on the economy is also huge, the economy was severely affected with the gross domestic product (GDP) growth at the lowest level in a decade, and most important industries saw a decline in growth, employment, and growing income is seriously affected (Dat, 2020). The COVID-19 pandemic has been affecting many aspects of the economy and society;many enterprises, business households, and cooperatives had to suspend operations, reduce production scale or rotate production, directly affecting the income of employees. This article uses the employment survey data of the General Statistics Office in 2021 and uses labor law (National Assembly, 2019) to analyze the influence of the COVID-19 pandemic on reducing the income of workers in Vietnam. The model estimation results show that the influence of the COVID-19 pandemic on income reduction does not differ between male and female workers, and has a strong influence on the group without professional and technical qualifications. Based on the findings, several suggestions are proposed to improve workers' income in the context of COVID-19. © 2023 The Authors.

The Intentions To Use E-Wallet Services During The Covid-19 Pandemic: Lessons From Vietnam

This study analyses the dimensions of self-service technology that affect intentions of using e-wallets on mobile platforms. Our study is unique because the Covid-19 pandemic alters consumer intentions to use the E-wallet. We collect data from 307 Vietnamese users through online questionnaires and focus group interviews. We analyze the data sample by applying the Theory of Technology Acceptance and a part of Behavior Intention. Our findings indicate that Perceived Usefulness, Perceived Ease of Use, Trust, Risk, and Personal Innovativeness in Information Technology increase the intention to use E-wallet services in Vietnam. Our findings extend the literature related to essential characteristics of mobile wallets that support the marketing of mobile wallet products. Our study also recommends increasing the attractiveness of using electronic payment via mobile wallets. © 2023, SRAC - Romanian Society for Quality. All rights reserved.

Identification and Localization COVID-19 Abnormalities on Chest Radiographs

Solutions to screen and diagnose positive patients for the SARS-CoV-2 promptly and efficiently are critical in the context of the COVID-19 pandemic's complex evolution. Recent researches have demonstrated the efficiency of deep learning and particularly convolutional neural networks (CNNs) in classifying and detecting lung disease-related lesions from radiographs. This paper presents a solution using ensemble learning techniques on advanced CNNs to classify as well as localize COVID-19-related abnormalities in radiographs. Two classifiers including EfficientNetV2 and NFNet are combined with three detectors, DETR, Yolov7 and EfficientDet. Along with gathering and training the model on a large number of datasets, image augmentation and cross validation are also addressed. Since then, this study has shown promising results and has received excellent marks in the Society for Imaging Informatics in Medicine's competition. The analysis in model selection for the trade-off between speed and accuracy is also given. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Method for Flexible Definition and Automatic Implementation of Laboratory Environment in Online Training Platforms

Cloud infrastructure enables individuals, organizations, and enterprises to offer scalable and elastic resources to support business operations remotely. The demand for digital transformation encourages communities and technical professionals to adopt cloud computing and automation platforms for facilitating their resource capacity, including operating systems, networks, and applications. Of cloud-based applications for social good, virtual education platforms play an important role to re-duce the cost and effort for trainees and trainers during practical courses, especially in the context of pandemics such as Covid-19. Nonetheless, the task of setting up practical environments with virtual machines, network elements, and software programs is the burden of the system that hosts many training courses with numerous trainees or resources. Hence, this research provides the mechanism for defining and automatically implementing the hands-on laboratory environments for information technology (IT) training. Specifically, we design and implement a concurrent scheme and local repository for deploying multiple environments with high performance in large virtual classrooms. The total time to finish environmental settings for learners is kept stable to meet the satisfaction of users in case of the remarkable growth in the number of environments and trainees. © 2022 IEEE.

The Effects of Online Peer Feedback on Students' Writing Skills during Corona Virus Pandemic

The purpose of this study is to compare the impacts of two different modes of e-peer feedback on the students' writing skills to see which mode should be better used to train the students' writing skills. The current study included 66 first-year students from the University of Foreign Language Studies in Danang City, Vietnam. The quasi-experimental research design was employed in the study. The two groups were taught how to compose a paragraph writing with peer comments in two rounds (1st & 2nd drafts). One group used Moodle (LMS), while the other used Facebook for e-peer feedback. Data collection was from pre-and post-tests for quantitative analysis. The research indicated that both types of e-peer feedback had a significant influence on students' writing abilities. However, the Moodle peer comments had little effect on students' writing length, but the e-peer remark on Facebook had a considerable impact. Post-test comparisons show that students who conducted peer comments on Facebook were considerably more diverse than their peers who conducted them in Moodle. © 2023 Eskisehir Osmangazi University. All rights reserved.

A case study for evaluating learners' behaviors from online cybersecurity training platform on digital forensics subject

Virtual cybersecurity training platforms play an important role in developing the knowledge and practice skills of students in educational institutions and universities. It helps learners can access virtual laboratories through web interfaces without any geolocation restriction, especially in the Covid-19 pandemic. Furthermore, instructors can monitor and understand learners' behaviors in practice sessions by analyzing actions and logs from the virtual platform. But, to realize this feature, such a platform must gather data during cybersecurity training for data mining tasks. In this paper, we introduce a virtual laboratory platform to facilitate cybersecurity training courses, namely vLab. In addition, we apply clustering analysis to the actions of learners to better understand the capabilities of trainees in resolving given challenges in digital forensics subject. With the built-in behavior analyzer in vLab, instructors can find out the common mistakes, and the reasons for learners' failure results, or identify whether they actually conduct experiments to get answers for digital forensics challenges or not. © 2022 IEEE.

The Proposed Context Matching Algorithm and Its Application for User Preferences of Tourism in COVID-19 Pandemic

Currently, many applications of information search tourism are limited in the COVID-19 pandemic using a search engine. However, most application service online has not supported directly, matching end users with their preferences to find suitable tourist places. This paper has presented a proposed model using the Context Matching algorithm mostly based on the Smartphones;matching with user’s preferences and behaviors allows users to find tourism packages and regions. The experimental results show that the proposed model achieves significant improvements in matching user preferences for the domain under dynamic uncertainty. We posit that our novel approach holds the prospect of improvements in user preferences for tourism and weather in the COVID-19 Pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Umbilical cord-derived mesenchymal stem cell transplantation for COVID -19 patients: long-term benefits for lung regeneration

Coronavirus disease 2019 (COVID-19) has affected hundreds of millions of people worldwide. Currently, mortality due to COVID-19 is significantly reduced by vaccination, antiviral drugs, and some improved treatments. Mesenchymal stem cell (MSC) transplantation—particularly umbilical cord-derived MSC (UC-MSC)—has been used as an adjuvant therapy for COVID-19 with some clinical evidence (reviewed in the publication). Moreover, a recent piece published in eBiomedicine (part of The Lancet, https://doi.org/10.1016/j.ebiom.2021.103789) in the previous month showed the long-term effects of UC-MSC transplantation in COVID-19 in a 1-year follow-up randomized, double-blind, placebo-controlled trial, demonstrating significantly recovered lung lesions and symptoms compared to the control group (i.e., without UC-MSC transplantation). In this commentary, we would like to discuss the value of UC-MSC transplantation for COVID-19 patients based on the results from this study and suggest applying this therapy for COVID-19 patients.

Casirivimab and Imdevimab for Treatment of Hospitalized Patients with COVID-19 Receiving Low Flow or No Supplemental Oxygen

Background. Casirivimab and imdevimab (CAS/IMDEV) is authorized for emergency use in the US for outpatients with COVID-19. We present results from patient cohorts receiving low flow or no supplemental oxygen at baseline from a phase 1/2/3, randomized, double-blinded, placebo (PBO)-controlled trial of CAS/IMDEV in hospitalized patients (pts) with COVID-19. Methods. Hospitalized COVID-19 pts were randomized 1:1:1 to 2.4 g or 8.0 g of IV CAS/IMDEV (co-administered) or PBO. Primary endpoints were time-weighted average (TWA) change in viral load from baseline (Day 1) to Day 7;proportion of pts who died or went on mechanical ventilation (MV) through Day 29. Safety was evaluated through Day 57. The study was terminated early due to low enrollment (no safety concerns). Results. Analysis was performed in pooled cohorts (low flow or no supplemental oxygen) as well as combined treatment doses (2.4 g and 8.0 g). The prespecified primary virologic analysis was in seronegative (seroneg) pts (combined dose group n=360;PBO n=160), where treatment with CAS/IMDEV led to a significant reduction in viral load from Day 1-7 (TWA change: LS mean (SE): -0.28 (0.12);95% CI: -0.51, -0.05;P=0.0172;Fig. 1). The primary clinical analysis had a strong positive trend, though it did not reach statistical significance (P=0.2048), and 4/6 clinical endpoints prespecified for hypothesis testing were nominally significant (Table 1). In seroneg pts, there was a 47.0% relative risk reduction (RRR) in the proportion of pts who died or went on MV from Day 1-29 (10.3% treated vs 19.4% PBO;nominal P=0.0061;Fig. 2). There was a 55.6% (6.7% treated vs 15.0% PBO;nominal P=0.0032) and 35.9% (7.3% treated vs 11.5% PBO;nominal P=0.0178) RRR in the prespecified secondary endpoint of mortality by Day 29 in seroneg pts and the overall population, respectively (Fig. 2). No harm was seen in seropositive patients, and no safety events of concern were identified. Conclusion. Co-administration of CAS/IMDEV led to a significant reduction in viral load in hospitalized, seroneg pts requiring low flow or no supplemental oxygen. In seroneg pts and the overall population, treatment also demonstrated clinically meaningful, nominally significant reductions in 28-day mortality and proportion of pts dying or requiring MV.

Umbilical Cord Tissue-derived Mesenchymal Stem Cells Should be Considered as Adjuvant Therapy for COVID-19 Treatment: An Opinion from Pooled Clinical Evidence

In 2020, we suggested that umbilical cord-derived mesenchymal stem cell (UC-MSC) transplantation can significantly improve coronavirus disease 2019 (COVID-19) symptoms based on evidential relations (10.4252/wjsc.v12.i8.721). One year later, this review aims to summarize and update the clinical evidence regarding UC-MSC usage in COVID-19 treatment. The publications on applications of UC-MSCs were searched in the PubMed, Web of Science, and Google scholar databases with the keywords ``umbilical cord-derived mesenchymal stem cells'' and ``COVID-19''. All publications about clinical studies, from case reports to randomized controlled trials (RCTs), were used as clinical evidence in this review. The results showed 16 publications (4 randomized clinical trials, 3 pilot studies/phase 1 clinical trials, 3 case series reports, and 6 case reports) with a total of 395 COVID-19 patients that were provided with UC-MSC transplantation. All publications demonstrated that UC-MSC transplantation is safe, well tolerated, improved COVID-19 symptoms, and significantly decreased mortality. These findings support our suggestion for the usage of off-the-shelf UC-MSCs for COVID-19 as an adjuvant therapy.

Utilization of information technology: An effective means of public investment management at autonomous universities in Vietnam considering the covid-19 pandemic

Covid-19 has been causing a large-scale pandemic, along with the rapid development of information technology. Information technology means such as digital applications will be one of the effective tools to help manage public investment and investment in general in Vietnam. Public investment management at autonomous universities in Vietnam is in the final stage of completion in terms of procedures, legal documents, and implementation. This study was designed to shed light on the factors that affect the management of public investments at Vietnamese autonomous universities. The study's data was gathered from a survey of 126 public investment managers at autonomous universities in Vietnam. Factor analysis and multivariate regression methods were used to analyze the influence of factors on public investment management at autonomous universities in Vietnam. According to research findings, the capacity of public investment management agencies at autonomous universities;Distribution of funds for the implementation of public investment projects;administrative procedures, legal provisions, and the actual context affect public investment management at autonomous universities in Vietnam. With Standardized Coefficients = 0.482, distribution of funds for implementation of public investment projects at schools at autonomous universities were the most influential factors. The research results are the foundation for proposing solutions to improve the efficiency of public investment management at these universities in Vietnam such as promulgating legal documents, building public investment management processes suitable to the characteristics of universities, developing, and proactively implementing works in investment management and operating results of public investment in autonomous University. © 2021 by the authors;licensee Growing Science, Canada.

A Novel Approach to Detect and Monitor COVID-19 Infection Using Transfer Learning Concept in AI

COVID-19 is an ongoing pandemic that is continuing to spread after recording one hundred million cases, causing millions of casualties, overwhelming health care systems of many countries, and threatening the whole world. Monitoring and assessing the severity of COVID-19 through artificial intelligence would be a practical support for medical practitioners reviving patients and offloading the burden from medical system. Previous works exploited deep learning, for this purpose, which produces inexplainable diagnosis results and lacks medical evidence. Integrating clinical symptom into diagnosis with deep learning will support generating results more compelled and validated. In this study, we focus on verifying the effectiveness of applying the human lung lesion, specifically Ground Glass Opacity and Consolidation, caused by typical pneumonia for COVID-19 detection or severity assessment on chest X-ray image with deep learning technology. We have conducted multiple experiments with state-of-art machine learning architectures (MobileNetV2, ResNet, Faster R-CNN) on many datasets to establish the conclusion. The experiment result demonstrates that lung lesion is useful when incorporating with deep learning solutions for monitoring COVID-19 progression and will provide solid pathway to develop an improved model and support better research in the future. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 and the Global Shift Progress to Clean Energy

The ongoing COVID-19 pandemic has presented us with an unprecedented global emergency that severely threatens the lives of millions of people around the world. In response to this existential crisis, state governments have taken drastic containment measures to limit the spread of the coronavirus. The consequential lockdowns have disrupted production and economic activity and had profound impacts on major industries. The heightened financial risk has significantly slowed down new investments while stoking fears of a looming economic recession. Indeed, the pandemic-induced strong reduction in global demand has resulted in sharp falls in energy prices leading to a temporary market distortion that may impede progress in the deployment of new renewable energy projects. Besides, some countries could be at risk of falling short of their 2020 national binding targets given the lack of effective policies required to facilitate new renewable energy investments. Considering the present impacts of the COVID-19 pandemic on the global energy market, there are opportunities to highlight the important role of renewables while making the case for clean energy investments, especially in the electricity sector. This review aims to provide important insights by analyzing the significant impacts of the COVID-19 pandemic on the global energy market. On the other hand, sustainable energy strategies are also discussed in detail. Finally, lessons learned amidst the costly battle against this invisible enemy could help policymakers and government leaders in identifying the appropriate renewable energy paths for their countries in a post-pandemic future.

Deep Learning Based COVID-19 Diagnosis by Joint Classification and Segmentation

COVID-19 is currently one the most life-threatening problems around the world. Fast and accurate detection of the COVID-19 infection play an important role to identify, take better decisions as well as ensure treatment for the patients. In this paper, we propose a fast and efficient method to identify COVID-19 patients based on deep learning approach. The proposed approach includes segmentation and classification stages. The segmentation stage is performed by employing U-Net neural network to accurately segment the lung position from chest CT images. The classification stage is achieved by DenseNet169 model. We applied the proposed model to dataset contains 349 CT scans that are positive for COVID-19 and 397 negative CT scans that are normal or contain other types of diseases. Experiment show that our model outperforms other methods in term of accuracy, sensitivity, F1, and AUC evaluation metrics. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A remarkable review of the effect of lockdowns during COVID-19 pandemic on global PM emissions

COVID-19 was labeled as a global pandemic when it caused a larger number of death cases in the world. Facing the dangerous and serious global pandemic, many countries have issued the nationwide lockdown, resulting in the shutdown in all places of social gatherings, the traffic, and industrial and manufacturing activities, causing negative impacts on the socio-economical activities. However, a noticeable reduction in NO2, CO2, and PM emissions was globally recorded although this reduction was different for each nation. In this paper, an overall picture of global PM pollution was exhibited during the city-to-nation lockdowns in the COVID-19 period. Overall, the obtained results in this current work might provide confidence to the stakeholders associated with the development of policy for air quality control aiming to achieve a significant improvement in air quality when better control strategies for air pollution are strictly executed. © 2020 Taylor & Francis Group, LLC.

A study on the COVID-19 awareness affecting the consumer perceived benefits of online shopping in Vietnam

The Covid-19 outbreak has turned out an opportunity for a recent noticeable increase in online shopping in Vietnam. Hence, this paper aims to discover how online shoppers react to their purchasing behaviour during Covid-19 time in terms of perceived benefits perspective. Based on the collected data from 427 Vietnamese online respondents during the social distancing period due to Covid-19, the research analyses the role of Covid-19 as a moderator variable in the relationship between benefits perception of the consumer and their online shopping activity. The result shows Covid-19 plays a moderating role in consumer’s awareness of utilities, which encourages shoppers towards Online shopping. However, the affection of society could be a reason that consumers become more hesitant to shop online. In the meanwhile, awareness of the Covid-19 pandemic and marketing policies do not impact on online shopping significantly during the Covid-19 pandemic which is a conflict with previous researches. These findings can contribute to understanding consumer behaviour comprehensively, help companies deal with similar situation as well as recommendations for the government to support businesses effectively in the future. © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Rapid and sensitive diagnostic procedure for multiple detection of pandemic Coronaviridae family members SARS-CoV-2, SARS-CoV, MERS-CoV and HCoV: a translational research and cooperation between the Phan Chau Trinh University in Vietnam and University of Bari "Aldo Moro" in Italy.

OBJECTIVE: A new pandemic coronavirus causing coronavirus disease-2019 (COVID-19), initially called 2019-nCoV and successively named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The COVID-19 refers to the disease while the SARS-CoV-2 refers to the virus and is characterized by a rapid contagious capacity able to spread worldwide in a very short time. The rise in the number of infected patients and deaths is of great concern especially because symptoms are vague and similar to other forms of flu infection and corona syndrome infections characterized by fever, fatigue, dry cough, and dyspnea. According to the latest guidelines published by the World Health Organization (WHO), the diagnosis of COVID-19 must be confirmed by quantitative reverse transcription polymerase chain reaction (rRT-PCR) or gene sequencing of specimen obtained from throat, sputum and blood samples. However, the limitations due to logistics, as well as low sensitivity and specificity diagnostic tools currently available have been reported as the main cause of high incidence of either false-negative or positive results. PATIENTS AND METHODS: The purpose of the present translational research protocol is to discuss and present the original findings from our research team on new diagnostic technique to detect four Coronaviridae family members (SARS-CoV-2, SARS-CoV, HCoV and MERS-CoV), highlighting the methodology, the procedure and the possible advantages. Moreover, the authors review the current epidemiology, precautions and safety measures for health personnel to manage patients with known or suspected COVID-19 infection. RESULTS: Implementation of an effective and rapid plan of diagnosing, screening and checking is a key factor to reduce and prevent further transmission. This procedure based on rRT-PCR could be of great help to decisively validate the results obtained from more conventional diagnostic procedures such as chest computed tomography (CT) imaging and chest ultrasound. CONCLUSIONS: This translational diagnostic tool will assist emergency and primary care clinicians, as well as out-of-hospital providers, in effectively managing people with suspected or confirmed SARS-CoV-2.