How do the sectoral indices react to COVID-19? Evidence from an emerging economy

The study empirically examines the impact of the COVID-19 on different sectoral indices of the National Stock Exchange (India) using the event study method and a generalised autoregressive conditional heteroskedasticity (GARCH) model. We provide evidence of positive impacts on the auto, oil and gas, healthcare, and pharma sectors. While the bank, financial services, and private bank sectors are the most adversely impacted sectors, the PSU bank, media, and reality sectors are the least impacted, and the rest are moderately impacted sectors. The overall impact of COVID-19 was negative until the implementation of nationwide lockdowns and the announcement of stimulus packages. The GARCH results exhibit more substantial evidence for the negative impact of the pandemic on the FMCG, IT, metal, oil and gas, and PSU bank sectors. We also find a more favourable impact on FMCG, pharma, and healthcare sectors in India.

Do dividend announcements override the pandemic impacts? Evidence from the BSE 500 constituent firms

With a sample of 332 dividend announcements from January 2019 to December 2020, using the event study methodology with the market model, we provide evidence that the dividend announcements failed to influence the stock prices under the pandemic stress. Although the pre-pandemic period announcements significantly impacted the stock returns, the pandemic period dividend announcements failed to generate significant abnormal returns even for an increase in dividend over the previous year. The pre-pandemic period results are consistent with previous literature with significant returns for constant, increase, and decrease in dividends. During the pre-pandemic period, we also find the possibility of information leakage in the Indian stock market as the pre-announcement period is marked with positive significant abnormal returns while the post-announcement period seems to be profit booking. The industry-wise analysis reveals the presence of positive returns in the Information Technology, Media and Telecommunication sector. However, the rest of the results are in line with the previous analysis. The findings suggest that before making such announcements, the companies should wait for the market to recover;else, the positively impacting dividend announcement will fail to influence the stock prices when the market is already under pandemic stress. We conduct the first-ever study to examine the impacts of dividend announcements during a pandemic stress period with also comparing the impacts during the pre-pandemic period. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of College of Management, National Cheng Kung University.

A Database Application of Monitoring COVID-19 in India

A quick user-friendly application of any pandemic situation can reduce the huge value of mortality with producing the graph of cases. Simple database application can make sense to the people about the pandemic and transmissions. This research aimed to develop a simple application which shows the real-time cases of COVID-19 and analyzes different states condition of India and a proper graphical prediction of cases. This application notifies people to get alert about the transmission and precautions to get rid of this pandemic. This application also helps clinical doctors, ministry and decision makers to improve the gap of any unfilled section. We have used the platform of APEX Oracle to develop this application and analyzed the dataset. The accuracy of the data is 78% rather than any other existing techniques. Combining the application and advance techniques, this study can create a vital framework for the prediction of any pandemics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

How the global airline industry behaved to restrictions on air travel to India? An event study analysis

We examine the impact of the recent restrictions/bans imposed by several nations on air travel to India in the light of the increasing number of infections amid the second wave of covid-19. We employ the standard event study method on a sample of 34 airline stocks across seven nations to find that the recent restrictions/bans on air travel significantly impact the global airline industry, although the country-specific impacts are not similar. We find that the post-event reaction in all nations has been different from those evidenced during the global pandemic declaration. We are the first to examine these impacts during the current wave of the pandemic. It contributes to the literature on the effects of the pandemic on the global airline industry. Further, it also provides practical explanations to the investors on how the airline stocks react to the persistence of the pandemic. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Venture capital financing during crises: A bibliometric review

We present the publication trends in the literature on venture capital financing during crises and highlight the top publishing source with the most contributing authors in their affiliated countries using bibliometric and content analysis of 115 documents retrieved from the Scopus database. This study provides insight into the theme with the help of co-occurrence, co-citation, and bibliographic coupling analysis. The authors' keyword co-occurrence analysis shows the spatial links among the articles based on venture capital during the financial crisis and the COVID-19 pandemic. The top productive and influential source is the journal Venture Capital, followed by Small Business Economics and the Journal of Business Venturing. The Journal of Business Venturing is the top journal in terms of citations per document. The United States is the most contributing affiliated country having strong links with several nations. The publications on crisis-led venture capital increased significantly after the financial crisis of 2008.

POSSIBLE DESTRUCTION OF SARS-COv-2 BY SODIUM DEOXYCHOLATE

The objective of this study was to focus on the antiviral activity of a bile salt, namely sodium deoxycholate. There is a possibility of killing severe acute respiratory syndrome corona virus-2 due to the destruction of its protein and lipid overcoat by sodium deoxycholate alone or with drugs those showing response against severe acute respiratory syndrome corona virus-2. Destruction of inner viral constituents and hence disintegration of the virion is possible at very small concentration. This study can be an important platform for further investigations. Copyright © 2022 Indian Drug Manufacturers' Association. All rights reserved.

An Interdisciplinary Approach in the Post-COVID-19 Pandemic Era

COVID-19 is not only a medical science issue, but it is also a critical issue for other experts such as social scientists, economists, technologists, psychiatrists, statisticians, sociologists, policymakers, politicians, and administrators, among others. Therefore, it is important to make collective efforts to deal with this pandemic. Interdisciplinary research is one of the best ways to achieve this. Interdisciplinary research is capable of bridging traditional divides between disciplines and also combines research excellence with relevant impact. Interdisciplinary research should be treated as policy research. The quality of the interdisciplinary research structure not only provides new ideas and areas of research, but also flexibility and expanded possibilities for traditional disciplines. This manuscript will likely inspire researchers and policymakers to further their interdisciplinary research on the coronavirus pandemic. In the present book, authors from diverse backgrounds have expressed their views on this specific problem. They have contributed their ideas on how the pandemic has affected every aspect of human life, including education, economics, social life, finance, information technology, etc. © 2022 by Nova Science Publishers, Inc. All rights reserved.

Assessing the impacts, trends and responses to COVID-19 in developing countries using machine learning techniques

On March 11, 2020, the World Health Organization (WHO) declared the novel coronavirus, otherwise known as COVID-19, a pandemic. Originating from Wuhan in Hubei province, China in December 2019, the pandemic has spread to more than 200 countries across the globe. The number of people infected globally by the disease has risen to more than 2.2 million, and over 154,219 people have died as a result of this disease as of April 18, 2020. Although China, the epicentre of the pandemic, has in recent weeks recorded only a few new cases of the disease and very few deaths, The number of cases and deaths has continued to upsurge in mostly developed nations such as the United States of America (USA), Spain, Italy, France, and Germany. There is, however, speculation that developing countries, many of which have continued to record lower cases of the disease, will likely see a rise in the near future. In this chapter, we apply machine learning models for the assessment of the trend and spread of the virus in Ethiopia, Nigeria, Pakistan, and India. The analysis showed a similar trend in the rise of the epidemic in these countries and an increase in the number of cases forecast for the near future in all four countries. We evaluated the social, cultural, and economic impacts of this pandemic on these countries and how the disease is impacting the mental well-being of the masses. Finally, we analysed how government response policies have affected the spread of the pandemic and the contributions of social media to the dissemination of vital information to facilitate the containment of the global spread of the virus. © 2022 Nova Science Publishers, Inc..

The impact of digital change on student learning and mental anguish in the COVID era

There is a lot of change in the learning of the students after the pandemic COVID-19. To study the resulting impact on their learning is the main aim of this article. To review this, a dataset of the various students is created and subsequently processed and visualized. The data is undergone to the various classification techniques using machine learning. It is observed after the analysis that the support vector machine (SVM) method is best in terms of the classification accuracy while random forest (RF) method is best in terms of the classification sensitivity. © 2022 Nova Science Publishers, Inc..

Evaluation of the SARS-COV-2 Omicron variant's features, prospective consequences, and upcoming prospects

According to reports, the 2019 Corona-virus infection COVID-19 caused significant damage to world-wide demographic wellbeing. In South Africa, a new level severe respiratory syndrome coronavirus 2 (SARSCov- 2) variant, B.1.1.529, was found awhile back, resulting in a substantial increase in COVID-19 patients. Then On November 24, 2021, the World Health Organization outlined B.1.1.529, also known as omicron, as a variant under inspection. The Omicron variability must have been proclaimed as a variation of worry. This variant includes a huge number of modifications, particularly 15 inside the spike's receptorbinding region (RBD). The Omicron variant seems to be likewise similar to the preceding VOC Alpha, Beta, and Gamma variants, generating issues regarding virus infectiousness, pathogenicity, and immunity resistance. In this paper, the identification and features of the Omicron variant were discussed, contrasting the spiking alterations inside the five VOCs and discussing prospective avoidance and mitigation measures. © 2022 Nova Science Publishers, Inc..

The impact of digital transformation on story writing skills in the COVID Era

"This pre-experimental analysis sought to determine the impact of Webcentered teaching in Google Classroom upon the story-writing skills of 5th grade students at YPS Singkole Elementary School in East Luwu Township. One such study appears to be online learning with Google Classroom Focused, as well as 28 students in 5th grade at YPS Singkole Elementary School in East Luwu District. A data analysis of the data obtained by administering scriptwriting exams with a pre and posttest on the instructional concept of ""My Living Place,"" sub theme 2: the variety of living organisms in my environment, was conducted. Descriptive analytics and inferential analysis are two data analysis tools. Depending on the outcomes of the inference statistical methods, the Wilcoxon test acquires a value of just 0.0001 or less than 0.05. It is reasonable to conclude that Web-based learning with Google Classroom Focus appears to have had a significant impact on the story writing abilities of the 5th grade classroom at YPS Singkole Elementary School in East Luwu District. © 2022 Nova Science Publishers, Inc.."

Development and validation of an electronic version of the Sydney Swallow Questionnaire

Background and Aim: Accurate assessment of patient-reported oropharyngeal dysphagia (OPD) is essential to guide appropriate management and evaluate its response. The Sydney Swallow Questionnaire (SSQ) is a paper-based 17-item inventory developed and validated to objectively assess OPD. An easy-to-use electronic questionnaire version with automated answer upload has significant potential to streamline remote patient assessment, especially in COVID-19-affected populations. The aim of this study was to develop an electronic version of the SSQ (eSSQ) and validate it against the original paper version. Method(s): The eSSQ was translated from the paper version on the online REDCAP platform and developed to be accessible on computer and mobile devices. Recruited patients with OPD and asymptomatic controls completed both electronic and paper versions in randomized order. Patients with stable symptoms during the study period then repeated the eSSQ after >=14 days for test-retest reliability. Agreement of total scores between both versions and eSSQ test-retest reliability were calculated using two-way mixed-effects intraclass correlation coefficient (ICC). Result(s): A total of 44 dysphagic patients and 32 controls were recruited. The most common underlying etiology for dysphagia was head and neck cancer. Mean eSSQ total score was 800 in dysphagic patients and 67 in controls. eSSQ had excellent agreement with the paper SSQ in total scores among all participants (ICC, 0.99;95% CI, 0.98-0.99) and in dysphagic patients (ICC, 0.97;95% CI, 0.94-0.98), as well as excellent test-retest reliability (ICC, 0.96;95% CI, 0.90-0.98). Conclusion(s): The newly developed eSSQ shows excellent agreement with the paper SSQ and test-retest reliability. Future applications of its use may allow for more efficient and accessible patient assessment.

Design and Development of IoT Wearable Device for Early Detection of COVID-19 and Monitoring Through Efficient Data Management Framework in Pre-pandemic Life

COVID-19 virus named CORONA is a vigorous disease spread all over the world very quickly and creates a pandemic situation to the human beings normal life. As per the doctors and researchers from the laboratory point of view, it will spread to a huge volume when humans are not followed certain principles. Moreover, this disease is easily transferred to neighbors and others in a short period which leads to death. To rectify the remedy for this virus, various spread countries and research peoples are creating the vaccines and some precautionary methods for living hood situation. Recent techniques are used to detect and monitoring the COVID-19- affected person's lifestyle and insisting they take precaution steps for early pre-pandemic life. IoT is a framework that is used to generate data from the human body from the sensors opted for human conditions. Wearable devices have been created with these sensors and communicated with human bodies directly or indirectly. The generated data will send through the server using any connectivity techniques such as Bluetooth or Wi-Fi. Analytics will be done at the server side for taking actions like the human body is affected by the COVID-19 virus or not. Finally, the generated data from a human can continuously store in real time in a cloud server which will be managed as a framework efficiently. This research work proposes a framework for data management in the early detection and monitoring of COVID-19 persons through IoT wearable devices in a pre-pandemic life. The experiments have been done at different zones, and the results are shown symptoms of COVID-19 disease. Parallel work reveals the data management in a cloud server since data have generated continuously in real time and tracking details also stored genuinely. Data management is the typical process in this research because all the data were generated in real time and analytics will be done whenever required. For that large amount of space and effective retrieval technique is required for data extraction. This research work data set is derived from various Internet sources like government web sites and mobile applications, and then, results have displayed the COVID-19 disease details accurately in real time.

A Study on COVID–19, Its Origin, Phenomenon, Variants, and IoT-Based Framework to Detect the Presence of Coronavirus

An ongoing pandemic SS-RNA viral infection initiated from the Chinese province has threatened people throughout the globe. Coronavirus or COVID-19 or 2019-nCoV as a contagious infection is spreading day-by-day threatening the livelihood of people. The main objective of this paper is to find out solutions for the detection of this contagious viral infection at the earliest. Computer-based artificial intelligence can be used to monitor and detect the symptoms of coronavirus. For detection of coronavirus infection, computers or smartphones can be embedded with biosensors that will perceive the information and will convert the information into digital data. In this paper, a study on the coronavirus is done and an IoT-based framework is proposed to detect the coronavirus using IoT-based sensors. The proposed approach will be able to detect the pandemic in its early stages, and better options for prevention and cure will be discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A blockchain approach on security of health records for children suffering from dyslexia during pandemic COVID-19

The Electronic Health Record (EHR) systems provide health information about patients. Data security, integrity, and management of EHR are crucial problems. Records can be modified and altered by different stockholders as the different users may be using them in more than one form. We provide a new approach, methodology, and system for calculating dyslexia symptoms in this research with a machine learning algorithm and secure dyslexia data storage using blockchain technology. The major role of our paper is to test a primary-age group student against dyslexia, a student detected in such early years of his life for such a disability then he or she can be easily cured for the disabilities and can spend the rest of his life normally. For this, we will be using various machine learning algorithms. Dyslexic patterns and a large amount of data can be shared for future clinical research, statistical analysis, and quality assurance because the framework is language-independent and built on Blockchain and a decentralized big data repository. This paper presents the design, execution, and test results, demonstrating the dyslexia health management system's high potential for worldwide deployment using blockchain technology. © 2022 Elsevier Inc. All rights reserved.

A Database Application of Monitoring COVID-19 in India

A quick user-friendly application of any pandemic situation can reduce the huge value of mortality with producing the graph of cases. Simple database application can make sense to the people about the pandemic and transmissions. This research aimed to develop a simple application which shows the real-time cases of COVID-19 and analyzes different states condition of India and a proper graphical prediction of cases. This application notifies people to get alert about the transmission and precautions to get rid of this pandemic. This application also helps clinical doctors, ministry and decision makers to improve the gap of any unfilled section. We have used the platform of APEX Oracle to develop this application and analyzed the dataset. The accuracy of the data is 78% rather than any other existing techniques. Combining the application and advance techniques, this study can create a vital framework for the prediction of any pandemics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Effective Overview of Different ML Models Used for Prediction of COVID-19 Patients

The goal of this research is to see how well is a fast primary screening method for COVID-19 that relies only on cough sounds collected from 2200 clinically verified samples utilizing the laboratory molecular testing performs (1100 Covid-19 positive and 1100 Covid-19 negative). The clinical labels were applied to the results, and severity of the samples may be judged based on quantitative RT-PCR (qRT-PCR), cycle threshold, and patient lymphocyte counts. The fast spread of the COVID-19 virus poses a significant danger of serious pulmonary disease, and it also causes the most heinous harm to humanity. As a result, a quick and clear disease classification model to distinguish between normal and COVID-19 infected individuals is critical. In this article, we describe the various machine learning and other models that have been used to predict COVID-19 patients. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Peritoneal Rupture of Hematocolpos in Distal Vaginal Atresia

An adolescent girl with vaginal atresia, massive hematocolpos and bilateral hydroureteronephrosis presented with an acute abdomen secondary to spontaneous rupture of the hematocolpos into the cul-de-sac. Diagnosis, treatment, postoperative course and complications of this unique case are briefly summarized. Spontaneous rupture of hematocolpos into the abdominal cavity is an extremely rare manifestation of vaginal atresia. Tertiary care management, which involves a multidisciplinary team of experienced gynecologists, plastic surgeons, urosurgeons, and critical care physicians, is recommended for optimal management of these patients. Patient education is also crucial;regular follow-ups visits and strict adherence to the postoperative vaginal dilatation schedule can reduce risk of stenosis after vaginoplasty.

How the global airline industry behaved to restrictions on air travel to India? An event study analysis

We examine the impact of the recent restrictions/bans imposed by several nations on air travel to India in the light of the increasing number of infections amid the second wave of covid-19. We employ the standard event study method on a sample of 34 airline stocks across seven nations to find that the recent restrictions/bans on air travel significantly impact the global airline industry, although the country-specific impacts are not similar. We find that the post-event reaction in all nations has been different from those evidenced during the global pandemic declaration. We are the first to examine these impacts during the current wave of the pandemic. It contributes to the literature on the effects of the pandemic on the global airline industry. Further, it also provides practical explanations to the investors on how the airline stocks react to the persistence of the pandemic.

An Approach to Identify Covid Infected People Due to Contact by using Machine Learning

Since the year 2020, the world has faced an unprecedented situation resulting from the spread of a deadly and highly contagious Coronavirus disease. Ever since the inception of the Coronavirus disease 2019 (Covid- 19), scientists all over the world have started preparing methods to fight the disease to save as many human lives as possible. One preliminary solution was hidden in the spreading mechanism of the virus, which was physical contact. So, the solution was to follow social distancing as much as possible. Another solution, which was the second stage solution, was to find the affected patients and isolate them to prevent the disease from spreading. Thus, contact tracing mechanisms were developed to find potential patients in as little time as possible. The Density-based spatial clustering of applications with noise (DBSCAN) algorithm proves to be an efficient solution to implement the proposed solution by making use of the physical locations of patients and geotagging them. This paper focuses on the development of a standalone web application that performs the task of contact tracing by making use of machine learning clustering algorithms. This reduces the time taken by competent authorities and helps them to find probable covid -positive patients in a much lesser period. © 2022 Bharati Vidyapeeth, New Delhi.