• 한국의류산업학회지
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• 제24권5호
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• pp.593-604
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• 2022

The purpose of this study is to analyze the flow of change in perception of metaverse before and after COVID-19 through big data analysis. This research method used Textom to collect all data, including metaverse for two years before COVID-19 (2018.1.1~2019.11.30) and after COVID-19 outbreak (2020.1.11~2021.12.31), and the collection channels were selected by Naver and Google. The collected data were text mining, and word frequency, TF-IDF, word cloud, network analysis, and emotional analysis were conducted. As a result of the analysis, first, hotels, weddings, and glades were commonly extracted as social issues related to metaverse before and after COVID-19, and keywords such as robots and launches were derived, so the frequency of keywords related to hotels and weddings was high. Second, the association of the pre-COVID-19 metaverse keywords was platform-oriented, content-oriented, economic-oriented, and online promotion-oriented, and post-COVID-19 clusters were event-oriented, ontact sales-oriented, stock-oriented, and new businesses. Third, positive keywords such as likes, interest, and joy before COVID-19 were high, and positive keywords such as likes, joy, and interest after COVID-19. In conclusion, through this study, it was found that metaverse has firmly established itself as a new platform business model that can be used in various fields such as tourism, travel, festivals, and education using smart technology and metaverse.

• The Journal of Asian Finance, Economics and Business
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• 제7권7호
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• pp.131-137
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• 2020

The research investigates the impact of the lockdown period caused by the COVID-19 to the stock market of India. The study examines the extent of the influence of the lockdown on the Indian stock market and whether the market reaction would be the same in pre- and post-lockdown period caused by COVID-19. Market Model Event study methodology is used. A sample of 31 companies listed on Bombay Stock Exchange (BSE) are selected at random for the purpose of the study. The sample period taken for the study is 35 days (24 February-17 April, 2020). An event window of 35 days was taken with 20 days prior to the event and 15 days during the event. The event (t1) being the official announcement of the lockdown. The results indicate that the market reacted positively with significantly positive Average Abnormal Returns during the present lockdown period, and investors anticipated the lockdown and reacted positively, whereas in the pre-lockdown period investors panicked and it was reflected in negative AAR. The study finds evidence of a positive AR around the present lockdown period and confirms that lockdown had a positive impact on the stock market performance of stocks till the situation improves in the Indian context.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.170-176
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• 2024

Since the World Health Organization (WHO) has declared COVID-19 as pandemic, many researchers have started working on developing vaccine and developing AI systems to detect COVID-19 patient using Chest X-ray images. The purpose of this work is to improve the performance of pre-trained Deep convolution neural nets (DCNNs) on Chest X-ray images dataset specially COVID-19 which is developed by collecting from different sources such as GitHub, Kaggle. To improve the performance of Deep CNNs, data augmentation is used in this study. The COVID-19 dataset collected from GitHub was containing 257 images while the other two classes normal and pneumonia were having more than 500 images each class. There were two issues whike training DCNN model on this dataset, one is unbalanced and second is the data is very less. In order to handle these both issues, we performed data augmentation such as rotation, flipping to increase and balance the dataset. After data augmentation each class contains 510 images. Results show that augmentation on Chest X-ray images helps in improving accuracy. The accuracy before and after augmentation produced by our proposed architecture is 96.8% and 98.4% respectively.

• 유통과학연구
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• 제19권10호
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• pp.43-54
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• 2021

Purpose: The restrictions posed by the COVID-19 pandemic have affected the normal functioning of the economy. A country like India is facing a lot of concerns in all its sectors especially, in its fiscal system. This paper makes an attempt to examine the impact of COVID-19 first wave on Goods and Service Tax revenue collection and distribution in India and also studies the impact of COVID-19 first wave on the state wise GST revenue of the country. Research Design, Data and Methodology: Our study is based on published GST revenue data. Tools such as Paired Sample t-test, Wilcoxon signed rank test are employed to analyze the data. Results: Our results provide evidence that there is a sharp decline in the GST revenue in the months after the lockdown announcement. The large states show no significance impact of COVID-19 pandemic on GST collection. Whereas, small states like Manipur and Goa show significant difference in GST revenue collection & distribution between the pre and post lockdown period. Conclusion: The outcome of this study will help the policymakers to analyze the extent of the GST revenue loss to the government treasury and will allow them to take appropriate measures in the future.

• 경제분석
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• 제27권1호
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• pp.37-66
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• 2021

본 논문은 COVID-19가 아세안 빈곤에 미칠 잠재적 영향을 실증적으로 추정한다. 빈곤 감소는 국제개발협력의 가장 보편적인 목적이다. 지속가능발전목표(SDGs)에서도 2030년까지 빈곤종식을 목표로 전 세계적 노력을 촉구한 바 있으나 COVID-19는 이러한 노력에 부정적인 영향을 줄 것으로 예상된다. 이러한 배경에서 빈곤에 대한 COVID-19의 영향을 추정하는 것은 개발협력정책의 방향성 수립 및 효과성 제고에 있어 시사하는 바가 크다. 본 논문은 다양한 추정방법을 통해 COVID-19의 빈곤에 대한 잠재적 영향을 정량적으로 추정한다. 첫 번째는 Summer et al. (2020), Nonvide (2020)가 제안한 가계소득 감소 시나리오를 구성한 후 국제빈곤선 조정을 통한 빈곤추정이다. 두 번째는 회귀분석을 통한 추정으로 국가 간 이질성, 불균형데이터, 내생성을 통제한 상관임의효과 모형을 통해 추정한다. 분석결과 COVID-19는 아세안 각국의 빈곤에 악영향을 줄 것으로 나타났다. 특히 빈곤감소를 위해서는 아세안 각국이 경제성장과 더불어 소득불평등도를 감소시키는 정책적 노력을 함께 추진해야 하며, 이는 COVID-19 이전의 빈곤수준으로 빠르게 회복시킬 뿐만 아니라 더 빠른 빈곤감소에 기여할 것으로 분석되었다.

• Journal of Korean Neurosurgical Society
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• 제65권2호
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• pp.224-235
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• 2022

Objective : Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, neurointerventionists have been increasingly concerned regarding the prevention of infection and time delay in performing emergency thrombectomy procedures in patients with acute stroke. This study aimed to analyze the effects of changes in mechanical thrombectomy protocol before and after the COVID-19 pandemic on procedure time and patient outcomes and to identify factors that significantly impact procedure time. Methods : The last-normal-to-door, first-abnormal-to-door, door-to-imaging, door-to-puncture, and puncture-to-recanalization times of 88 patients (45 treated with conventional pre-COVID-19 protocol and 43 with COVID-19 protection protocol) were retrospectively analyzed. The recanalization time, success rate of mechanical thrombectomy, and modified Rankin score of patients at discharge were assessed. A multivariate analysis was conducted to identify variables that significantly influenced the time delay in the door-to-puncture time and total procedure time. Results : The door-to-imaging time significantly increased under the COVID-19 protection protocol (p=0.0257) compared to that with the conventional pre-COVID-19 protocol. This increase was even more pronounced in patients who were suspected to be COVID-19-positive than in those who were negative. The door-to-puncture time showed no statistical difference between the conventional and COVID-19 protocol groups (p=0.5042). However, in the multivariate analysis, the last-normal-to-door time and door-to-imaging time were shown to affect the door-to-puncture time (p=0.0068 and 0.0097). The total procedure time was affected by the occlusion site, last-normal-to-door time, door-to-imaging time, and type of anesthesia (p=0.0001, 0.0231, 0.0103, and 0.0207, respectively). Conclusion : The COVID-19 protection protocol significantly impacted the door-to-imaging time. Shortening the door-to-imaging time and performing the procedure under local anesthesia, if possible, may be required to reduce the door-to-puncture and door-to-recanalization times. The effect of various aspects of the protection protocol on emergency thrombectomy should be further studied.

• Journal of Information Technology Applications and Management
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• 제29권3호
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• pp.1-23
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• 2022

This study is about the change in the importance of shopping value for beauty products of social commerce due to the social crisis or risk caused by COVID-19. It was analyzed focusing on whether the importance of shopping value changed before and after COVID-19. We checked the importance of shopping value after COVID-19 through the AHP results of previous papers before COVID-19, and analyzed the importance by adding variables of risk reduction behavior and delivery convenience according to the situation of COVID-19. The AHP method was used to check the change in the importance of shopping value before and after COVID-19, and the study was conducted using 48 data. The results were as follows. As for the importance of shopping value of social commerce beauty products, it was ranked in the order of time convenience, convenience of delivery, third-ranked trust business operators, fourth-ranked economic aspects, fifth-ranked decision support, sixth-ranked risk reduction behavior, and seventh-ranked business reputation. Compared to previous studies, decision-making support, which was in the second place, fell to the fifth place. This result was confirmed to be a drop in ranking due to the improvement of delivery convenience due to the influence of COVID-19. In addition, in the case of beauty products, it was confirmed that risk reduction behavior related to COVID-19 infection is not a key factor in shopping value. These results confirmed changes in the importance of shopping value compared to pre-COVID-19 studies, and in the case of product groups other than beauty products, further studies are expected as there is a possibility of other results.

• Journal of information and communication convergence engineering
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• 제20권3호
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• pp.219-225
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• 2022

In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.

• Pediatric Infection and Vaccine
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• 제31권1호
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• pp.83-93
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• 2024

목적: 코로나19 판데믹이 시작된 이후, 다양한 주요 변이 바이러스가 출현했다. 코로나 19 판데믹 기간 동안 대표적인 주요 변이 바이러스 유행 시기를 네 가지로 나누고, 네 가지의 주요 변이 바이러스 시기로부터 임상적 그리고 혈액학적 검사의 특징을 파악하고자 하였다. 방법: 코로나19 확진으로 입원한 19세 이하 환자의 의무기록을 후향적으로 분석하였다. 변이전시기(2020년 2월 1일-2020년 9월 30일), 알파와 베타 변이 시기(2020년 10월 1일-2021년 5월 31일), 델타 변이 시기(2021년 6월 1일-2021년 10월 31일), 오미크론 변이 시기(2021년 11월 1일-2022년 5월 31일)를 비교하였다. 결과:대상환자 827명중에서 163명(19.7%)가무증상이었고, 발열과기침의빈도는각각 320명(38.7%), 399명(48.2%)이었다. 38.5℃ 이상의 발열이 있었던 경우는 12세 미만인 경우에 오미크론 변이 시기에 높게 관찰되었다. 혈액학적 검사에서 백혈구 감소증, 임파구 감소증 그리고 호중구 감소증은 각각 33%, 30.2%, 24.9%로 관찰되었다. 결론: 코로나 19의 주요 변이 바이러스 우세 시기에 다른 특징들이 있었다. 델타 변이 시기에 4일 이상의 발열이 지속되는 경우가 더 많았고, 오미크론 변이 시기에는 38.5℃ 이상의 발열을 가지는 경우가 많았다.

• The Journal of Asian Finance, Economics and Business
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• 제8권2호
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• pp.943-952
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• 2021

The purpose of this study is to evaluate the effect of COVID-19 on corporate governance attributes and firm performance association. This research used a sample of 188 non-financial firms from the Malaysian market for the years 2019-2020. We found that the COVID-19 has affected all firm characteristics including firm performance, governance structure, dividend, liquidity, and leverage level, yet, the difference between prior and post COVID-19 pandemic is not significant. Also, the investigation revealed that board size exerts a significant positive impact on firm performance. After splitting the sample based on year, however, we found that board size does not matter in the uncertain time of the current crisis, while board diversity appeared to be significantly enhancing firm performance in the crisis time compared to the prior year where it has an inverse association with firm performance in both indicators. Board meetings and audit committee meetings seemed to have a significant negative influence on firm performance pre and post-COVID-19. This study contributes to the limited literature by providing the first empirical evidence on the impact of Coronavirus on the firm performance and corporate governance association.