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Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 40 Documents
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Search results for , issue "Vol 7 (2023): Special Issue " : 40 Documents clear
Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19 Saravanan Muthaiyah; Thein Oak Kyaw Zaw; Kalaiarasi Sonai Muthu Anbananthen; Byeonghwa Park; Myung Joon Kim
Emerging Science Journal Vol 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-02

Abstract

The primary objective of this research is to identify commonly used data-driven decision-making techniques for contact tracing with regards to Covid-19. The virus spread quickly at an alarming level that caused the global health community to rely on multiple methods for tracking the transmission and spread of the disease through systematic contact tracing. Predictive analytics and data-driven decision-making were critical in determining its prevalence and incidence. Articles were accessed from primarily four sources, i.e., Web of Science, Scopus, Emerald, and the Institute of Electrical and Electronics Engineers (IEEE). Retrieved articles were then analyzed in a stepwise manner by applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISM) that guided the authors on eligibility for inclusion. PRISM results were then evaluated and summarized for a total of 845 articles, but only 38 of them were selected as eligible. Logistic regression and SIR models ranked first (11.36%) for supervised learning. 90% of the articles indicated supervised learning methods that were useful for prediction. The most common specialty in healthcare specialties was infectious illness (36%). This was followed closely by epidemiology (35%). Tools such as Python and SPSS (Statistical Package for Social Sciences) were also popular, resulting in 25% and 16.67%, respectively. Doi: 10.28991/ESJ-2023-SPER-02 Full Text: PDF
Response of Financial Markets to COVID-19 Pandemic: A Review of Literature on Stock Markets Bayu Arie Fianto; Masagus M. Ridhwan; Syed Alamdar Ali Shah; Muhammad Faris; Rafiatul Adlin Hj Mohd Ruslan
Emerging Science Journal Vol 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-03

Abstract

The objective of this research is to consolidate the literature published on the COVID-19 crisis impact on global stock markets to gain managerial implications from the crisis. It performs a thematic bibliometric review of the literature published in Scopus-ranked journals since the beginning of the pandemic using FCWI, Piecharts, and VOSViewer. It identifies the most under-researched regions and eight emerging sub-themes. The research finds that the benchmark theme is market behavior during the COVID-19 crisis, whereas an emerging benchmark theme is the markets after the COVID-19 crisis. The holistic view of the literature supporting eight sub-themes suggests that the government's role is of utmost importance to handle the impact of the COVID-19 crisis, which should be industry-specific. It identifies that all eight sub-themes of the research are the future research directions in all and specifically in the South American, African, South East Asian, and Oceania regions till the crisis continues. Doi: 10.28991/ESJ-2023-SPER-03 Full Text: PDF
Does National Governance Affect the Capital Structure of Listed Firms during the COVID-19 Pandemic? Kim Quoc Trung Nguyen
Emerging Science Journal Vol 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-04

Abstract

This study estimates the macro-economic factors affecting the listed small and medium enterprises' capital structures in Vietnam from 2010 to 2020. The author conducts the quantitative method (generalized method of moments—GMM) with valid instrument variables to solve the endogeneity in regression models, which refers to the determinants of capital structures. Based on the trade-off theory and the pecking order theory, the author provides evidence of macro-economic factors and firm-specific factors in explanations for the capital choices of the Vietnamese firms, including national governance, inflation, COVID-19, firm age, and asset structure. In particular, this study highlights how national governance and COVID-19 influence the capital structure of small and medium enterprises in Vietnam. Doi: 10.28991/ESJ-2023-SPER-04 Full Text: PDF
Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX Ahmad Aljaafreh; Ahmad Abadleh; Saqer S. Alja'Afreh; Khaled Alawasa; Eqab Almajali; Hossam Faris
Emerging Science Journal Vol 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-05

Abstract

This paper proposes a fully automated vision-based system for real-time COVID-19 personal protective equipment detection and monitoring. Through this paper, we aim to enhance the capability of on-edge real-time face mask detection as well as improve social distancing monitoring from real-live digital videos. Using deep neural networks, researchers have developed a state-of-the-art object detector called "You Only Look Once Version Five" (YOLO5). On real images of people wearing COVID19 masks collected from Google Dataset Search, YOLOv5s, the smallest variant of the object detection model, is trained and implemented. It was found that the Yolov5s model is capable of extracting rich features from images and detecting the face mask with a high precision of better than 0.88 mAP_0.5. This model is combined with the Density-Based Spatial Clustering of Applications with Noise method in order to detect patterns in the data to monitor social distances between people. The system is programmed in Python and implemented on the NVIDIA Jetson Xavier board. It achieved a speed of more than 12 frames per second. Doi: 10.28991/ESJ-2023-SPER-05 Full Text: PDF
Analyzing the Effect of Basic Data Augmentation for COVID-19 Detection through a Fractional Factorial Experimental Design Mateo Hidalgo Davila; Maria Baldeon-Calisto; Juan Jose Murillo; Bernardo Puente-Mejia; Danny Navarrete; Daniel Riofrío; Noel Peréz; Diego S. Benítez; Ricardo Flores Moyano
Emerging Science Journal Vol 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-01

Abstract

The COVID-19 pandemic has created a worldwide healthcare crisis. Convolutional Neural Networks (CNNs) have recently been used with encouraging results to help detect COVID-19 from chest X-ray images. However, to generalize well to unseen data, CNNs require large labeled datasets. Due to the lack of publicly available COVID-19 datasets, most CNNs apply various data augmentation techniques during training. However, there has not been a thorough statistical analysis of how data augmentation operations affect classification performance for COVID-19 detection. In this study, a fractional factorial experimental design is used to examine the impact of basic augmentation methods on COVID-19 detection. The latter enables identifying which particular data augmentation techniques and interactions have a statistically significant impact on the classification performance, whether positively or negatively. Using the CoroNet architecture and two publicly available COVID-19 datasets, the most common basic augmentation methods in the literature are evaluated. The results of the experiments demonstrate that the methods of zoom, range, and height shift positively impact the model's accuracy in dataset 1. The performance of dataset 2 is unaffected by any of the data augmentation operations. Additionally, a new state-of-the-art performance is achieved on both datasets by training CoroNet with the ideal data augmentation values found using the experimental design. Specifically, in dataset 1, 97% accuracy, 93% precision, and 97.7% recall were attained, while in dataset 2, 97% accuracy, 97% precision, and 97.6% recall were achieved. These results indicate that analyzing the effects of data augmentations on a particular task and dataset is essential for the best performance. Doi: 10.28991/ESJ-2023-SPER-01 Full Text: PDF
Pre and Present COVID-19 Situation: A Framework of Educational Transformation in South Asia Region Sunjida Khan; Nurul Mohammad Zayed; Saad Darwish; Vitalii Nitsenko; K. M. Anwarul Islam; Md. Arif Hassan; Oksana Dubrova
Emerging Science Journal Vol 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-06

Abstract

This study is designed to support the development of strategies to recover from the disrupted impact of COVID-19 on HE institutes of the South Asian Region (SAR), as the nations in this region are severely cursed by poverty and unemployment. During the unusual phase of the COVID-19 pandemic, the face-to-face learning method is no longer appropriate, and the crisis leads to force on distance learning instead of physical learning. Like all other educational institutions, HE institutions are also in big trouble. Changes in educational structure change the pattern of academic work, which may have an inverse impact on acquiring knowledge and improving skills. Not only students but also a greater number of teachers at the HE institutions required to continue their service through online during this closure period. However, well digital infrastructure and digital contents appear to be the prime requirements for this educational transmission, which are extensively accessible in SAR countries. By following a mixed-methods strategy, the study specifically examines the impact of the pandemic on higher education in the South Asian Region, with an emphasis on the impact on learners, educators, and institutions, and to identify the measures that have been taken by these countries to survive and continue the education system with all the obstacles of the crisis. It concludes with some vital suggestions that may be applied to mitigate the crisis moment and assist in moving forward with more technological advancements for a new future. Doi: 10.28991/ESJ-2023-SPER-06 Full Text: PDF
Space Law and Space Mining, Exploring New Horizons Amid COVID-19 Pandemic Marcin Jakub Drobnik; Ivan Bimbilovski; Shubham Pathak
Emerging Science Journal Vol 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-09

Abstract

This study analyses the current scenario with COVID-19 affecting the international and Thai space law, and its impacts and corresponding repercussions upon the Thai economy, ASEAN region and then at international level. The methodology adopted for this study is a mixed method with qualitative research tools collected from key informant interviews and focus group discussions. The data analysis involves the Strength, Weakness, Opportunity, and Threat (SWOT) analysis, which has been integrated with Hierarchical Thematic areas to provide the supporting model for wholesome recommendations through analyzing the findings from the research. The key respondents involved several government officials associated with Thai space agencies and departments, along with judges, lawyers, researchers, academicians, non-government organizations (NGO) officials, and law students. The findings provided the need for adoption of Treaty leading to the creation of a space organization which would be accountable towards setting up a legal framework for commencement of space mining operations. The international space tribunal is to be created under this international space organization to resolve any disputes arising out of space mining. The overall implications of this research would lead to the sharing of the benefits of space mining with both developed and developing countries to enhance sustainable development for all mankind. Doi: 10.28991/ESJ-2023-SPER-09 Full Text: PDF
Behavior of Russian Premium Fashion Consumers and Designers after the COVID-19 Pandemic and International Sanctions Irina I. Skorobogatykh; Irina P. Shirochenskaya; Galina S. Timokhina; Taira V. Murtuzalieva; Sergey V. Mkhitaryan
Emerging Science Journal Vol 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-010

Abstract

The purpose of this paper was to investigate the emerging changes in Russian premium fashion brand consumers' behavior on the eve of the COVID-19 pandemic and international economic sanctions, the impact on foreign fashion brands' decisions to leave the market, and the willingness of some Russian fashion designers to scale their businesses and occupy vacated market niches. This problem had arisen for the first time; the situation is unexpected and unique. Therefore, the researchers combined multiple methods of data collection: (1) Observation; (2) Netnography to identify emerging changes in Russian consumers’ behavior, which increases the objectivity of the data obtained since personal contact was excluded; and (3) Expert in-depth interviews to assess the situation by Russian fashion designers. QDA and qualitative content analysis were used. Fashion designers in Russia percept the situation as an opportunity for business development, similar to the situation that occurred in Iran, but entrepreneurs understand the market risks and expect more serious measures of state support for business. The results may inform state policymakers and stakeholders about the stated changes in consumer behavior and the capabilities of Russian entrepreneurs to scale the business, which will help identify possible growth vectors for domestic fashion designers in the premium sector. Doi: 10.28991/ESJ-2023-SPER-010 Full Text: PDF
The Effects of COVID-19 on Informal Traders in Undesignated Spaces Emmanuel Ndhlovu; David Mhlanga
Emerging Science Journal Vol 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-07

Abstract

The literature on COVID-19 impacts overlooks the pandemic’ impact on informal traders who operate in undesignated public spaces. While studies on the impact of COVID-19 on informal traders exist, there remains little focus on how the socio-economic livelihood activities of informal traders in undesignated public spaces, such as parks, who rely on both domestic and international tourists as customers, have been impacted. This paper fills this gap by focusing on two case studies of urban public spaces in the city of Tshwane, South Africa. These spaces are Jubilee Square and Magnolia Dell Park. The study is predicated on the spatial triad framework which enables it to interrogate how the restriction on access and utilisation of public spaces during the COVID-19 lockdown impacted on the socio-economic activities of informal traders. It found that informal traders in these two parks were the most vulnerable category of traders during the COVID-19 lockdown and faced huge socio-economic and livelihood challenges. They lost their income sources and had their social networks disrupted. The article proposes social policy interventions in the governance of public spaces as part of an effort to save both lives and livelihoods in the face of a pandemic. Doi: 10.28991/ESJ-2023-SPER-07 Full Text: PDF
Diagnosis of Covid-19 Via Patient Breath Data Using Artificial Intelligence Özge Doğuç; Gökhan Silahtaroğlu; Zehra Nur Canbolat; Kailash Hambarde; Ahmet Alperen Yiğitbaşı; Hasan Gökay; Mesut Yılmaz
Emerging Science Journal Vol 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-08

Abstract

Using machine learning algorithms for the rapid diagnosis and detection of the COVID-19 pandemic and isolating the patients from crowded environments are very important to controlling the epidemic. This study aims to develop a point-of-care testing (POCT) system that can detect COVID-19 by detecting volatile organic compounds (VOCs) in a patient's exhaled breath using the Gradient Boosted Trees Learner Algorithm. 294 breath samples were collected from 142 patients at Istanbul Medipol Mega Hospital between December 2020 and March 2021. 84 cases out of 142 resulted in negatives, and 58 cases resulted in positives. All these breath samples have been converted into numeric values through five air sensors. 10% of the data have been used for the validation of the model, while 75% of the test data have been used for training an AI model to predict the coronavirus presence. 25% have been used for testing. The SMOTE oversampling method was used to increase the training set size and reduce the imbalance of negative and positive classes in training and test data. Different machine learning algorithms have also been tried to develop the e-nose model. The test results have suggested that the Gradient Boosting algorithm created the best model. The Gradient Boosting model provides 95% recall when predicting COVID-19 positive patients and 96% accuracy when predicting COVID-19 negative patients. Doi: 10.28991/ESJ-2023-SPER-08 Full Text: PDF

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