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INDONESIA
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 874 Documents
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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
Financial Solace: Malaysian Credit Counselling and Debt Management Agency Responses to COVID-19 Challenges Ibtisam @ Ilyana Ilias; Nadzratun Naim Hammad Azizi; Noraiza Abdul Rahman; Mazlina Mahali
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-011

Abstract

This study evaluates the measures undertaken by the Credit Counselling and Debt Management Agency (AKPK) to assist those financially distressed due to their inability to meet their financial commitments amidst the COVID-19 pandemic. Adopting secondary analysis of qualitative data, relevant secondary data, including journal articles, annual reports, and newspaper articles, were analyzed. The study finds that measures adopted by AKPK in response to the COVID-19 pandemic include reinforcing the workforce, enhancing IT infrastructures, deploying digital platforms, using various media channels, introducing online apps, online portals, online webinars, online learning modules, and online payment facility for all debt management participants. AKPK is also entrusted with handling small and medium enterprises (SMEs) under the Small Debt Resolution Scheme. A dedicated SME Helpdesk is established to facilitate the process. AKPK’s continual support to provide financial aid is reflected in its collaborative effort with the banking industry under the Financial Management and Resilience Program and the Financial Resilience Support Program. However, the government should seriously consider strengthening personal data protection laws because of AKPK’s significant reliance on digital platforms. Similarly, appropriate government bodies must take quick action to address the digital divide issue and promote inclusion to reduce disparity in terms of access to online services offered by AKPK. Also, since certain individuals or SMEs with credit facilities with entities not regulated by Bank Negara Malaysia are deprived of this incentive, relevant regulators should undertake actions to provide a similar facility. This study is significant in that it provides lessons to be learned by other credit counseling and debt management agencies in adopting effective measures to enable them to adapt to the new normal. Doi: 10.28991/ESJ-2023-SPER-011 Full Text: PDF
Probabilistic Analysis Depending on the Distance from A COVID-19 Outbreak Yupaporn Areepong; Rapin Sunthornwat
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-012

Abstract

COVID-19 has been affecting human beings since the end of 2019. Studying the characteristics of a COVID-19 outbreak is significant because it will add to the knowledge that is necessary for protecting the general public and controlling future viral outbreaks. The aims of the present research are to analyze COVID-19 outbreaks in Thailand depending on the distance from the outbreak center by using a differential equation, to construct a probability density function from the solution of the differential equation, and to prove the theorem for the probability density function depending on the distance from the outbreak. The least-squares-error method is adopted to estimate the parameters of the function describing the COVID-19 outbreak. Moreover, a cumulative distribution function, a quantile function, a sojourn function, a hazard function, the median, the expected value, variance, skewness, and kurtosis are derived, and their practicability is shown. Applying the exponentially weighted moving average control chart to monitor a COVID-19 outbreak based on distance is proposed and compared with monitoring the COVID-19 outbreak based on time. The results show that using the former more quickly detected the out-of-control first passage time of the COVID-19 outbreak than the latter. Doi: 10.28991/ESJ-2023-SPER-012 Full Text: PDF
Factors Affecting Business Angels Investment in Vietnam Nguyen Thi Kim Anh; Vu Thanh Huong; Nguyen Thi Minh Phuong; Dang Thanh Dat
Emerging Science Journal Vol 7, No 2 (2023): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-02-07

Abstract

The paper aims at investigating and comparing the factors determining investment decisions by business angels (BAs) from the viewpoints of BAs and startups in Vietnam based on a framework synthesized from a literature review and primary data from in-depth interviews conducted with 8 startups and 15 angel investors. The results show that the startups’ founder, working team, financial issues, product and market, and strategy related to exit and the roles of BAs are startup-related factors determining BAs’ investment in Vietnam. For BA-related factors, the BAs’ experience, investment objectives and preferences, and culture are key determinants. The novelty of the paper is to find out the gaps between the perspectives of BAs and startups, and the difference between Vietnamese and foreign BAs’ viewpoints. The finding is that BAs, more strictly than startups, assess their business plan, financial state, product, market, and targeted consumers. Startups neglect the exit strategy and role of BAs in invested startups. In addition, foreign and domestic BAs have different opinions on startups’ market scale, and expectation of profits and BAs’ roles in startups. The paper ends by providing some implications for Vietnamese startups to attract more angel investment, focusing on improving the quality of human resources, developing a profitable, honest, and realistic business plan, and setting up a long-run vision towards the global market. Doi: 10.28991/ESJ-2023-07-02-07 Full Text: PDF
Reaction of Carbon Dioxide Gas Absorption with Suspension of Calcium Hydroxide in Slurry Reactor Zahrul Mufrodi; L. M. Shitophyta; Hary Sulistyo; . Rochmadi; Muhammad Aziz
Emerging Science Journal Vol 7, No 2 (2023): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-02-02

Abstract

Chemical phenomena involving three phases (solid, liquid, and gas) are often found in the industry. Carbonate (CaCO3) is widely used in industries as a powder-making material in the cosmetic industry, a pigment in the paint industry, and filler in the paper and rubber industry. This research aim to study the ordering process carbonate deposits (CaCO3) from the absorption process of CO2 gas with Ca(OH)2 suspension. The absorption reaction of CO2 gas with Ca(OH)2 suspension was carried out in a stirred slurry tank reactor. Initially, the reactor containing water was heated to a certain temperature, then Ca(OH)2 was added to the reactor. Furthermore, CO2 gas with a certain flow rate and temperature (according to the reactor temperature) is flown with the help of a gas distributor. Samples were taken every 1 min until the concentration of Ca(OH)2 could not be detected (completely reacted). The variables in this study were: stirrer rotation speed (5.66711.067 rps), CO2 gas flow rate (34.0127–60.5503 c/s), and temperature (30–50°C). The mass transfer coefficient and the reaction rate coefficient were determined by minimizing Sum of Squares of Errors (SSE). This experimental process follows a dynamic regime. A dimensionless number relationship for the gas-liquid mass transfer for the value range is Re1 = 18928.76-38217.20, Sh = 0.07928 Reg0.4383 Rel0.4399 Sc0.6415 with an error of 5.19%. The dimensionless number relationship for solid-liquid mass transfer is Sh = 0.0001179 Reg0.4674 Rel0.5403 Sc1.444 with an error of 7.31%. The relationship between the reaction rate constant and the temperature in the 30-50 °C range can be approximated by the Arrhenius equation, namely kr = 1771000 e-2321.4/T cm3/mgmol/s with an error of 3.63%. Doi: 10.28991/ESJ-2023-07-02-02 Full Text: PDF
Smart Farm-Care using a Deep Learning Model on Mobile Phones Mercelin Francis; Kalaiarasi Sonai Muthu Anbananthen; Deisy Chelliah; Subarmaniam Kannan; Sridevi Subbiah; Jayakumar Krishnan
Emerging Science Journal Vol 7, No 2 (2023): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-02-013

Abstract

Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PDF
How Does Environmental Data from ESG Concept Affect Stock Returns: Case of the European Union and US Capital Markets Giedrė Lapinskienė; Dainora Gedvilaitė; Aušra Liučvaitienė; Kęstutis Peleckis
Emerging Science Journal Vol 7, No 2 (2023): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-02-08

Abstract

This article examines the environmental, social, and governance (ESG) performance of firms, with a focus on the environmental pillar of the ESG concept. It is believed that the price of equities as well as sector-specific characteristics may be affected by ESG data. It also contributes to the argument that environmental performance and governance quality are related. The purpose of this paper is to statistically validate the separated environmental data from the ESG concept and investigate its impact on the equity price in the EU and the United States. Using simple linear regressions and a fixed effect panel data model, the association between environmental score and governance score, as well as equity price and environmental score, was estimated. This study examines the 500 largest US corporations comprising the S&P 500 index (S&P) and the 600 largest EU companies comprising the STOXX Europe 600 index (STOXX) (SXXP). This article analyzes ESG statistics for the period 2015–2020. The results indicate that a higher government score has a favorable effect on environmental pledges and that changes in stock price depend in part on environmental data. The novel contribution of this paper is that the results suggest a sector-specific contribution to the model, and it would be fascinating to analyze sector disparities and their ESG-related policies in greater detail. Doi: 10.28991/ESJ-2023-07-02-08 Full Text: PDF
Batch and Streaming Data Ingestion towards Creating Holistic Health Records Argyro Mavrogiorgou; Athanasios Kiourtis; George Manias; Chrysostomos Symvoulidis; Dimosthenis Kyriazis
Emerging Science Journal Vol 7, No 2 (2023): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-02-03

Abstract

The healthcare sector has been moving toward Electronic Health Record (EHR) systems that produce enormous amounts of healthcare data due to the increased emphasis on getting the appropriate information to the right person, wherever they are, at any time. This highlights the need for a holistic approach to ingest, exploit, and manage these huge amounts of data for achieving better health management and promotion in general. This manuscript proposes such an approach, providing a mechanism allowing all health ecosystem entities to obtain actionable knowledge from heterogeneous data in a multimodal way. The mechanism includes diverse techniques for automatically ingesting healthcare-related information from heterogeneous sources that produce batch/streaming data, managing, fusing, and aggregating this data into new data structures (i.e., Holistic Health Records (HHRs)). The latter enable the aggregation of data coming from different sources, such as Internet of Medical Things (IoMT) devices, online/offline platforms, while to effectively construct the HHRs, the mechanism develops various data management techniques covering the overall data path, from data acquisition and cleaning to data integration, modelling, and interpretation. The mechanism has been evaluated upon different healthcare scenarios, ranging from hospital-retrieved data to patient platforms, combined with data obtained from IoMT devices, having produced useful insights towards its successful and wide adaptation in this domain. In order to implement a paradigm shift from heterogeneous and independent data sources, limited data exploitation, and health records, the mechanism has combined multidisciplinary technologies toward HHRs. Doi: 10.28991/ESJ-2023-07-02-03 Full Text: PDF
Fusion Landsat-8 Thermal TIRS and OLI Datasets for Superior Monitoring and Change Detection using Remote Sensing Hayder Dibs; Alaa Hussein Ali; Nadhir Al-Ansari; Salwan Ali Abed
Emerging Science Journal Vol 7, No 2 (2023): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-02-09

Abstract

Currently, updating the change detection (CD) of land use/land cover (LU/LC) geospatial information with high accuracy outcomes is important and very confusing with the different classification methods, datasets, satellite images, and ancillary dataset types available. However, using just the low spatial resolution visible bands of the remotely sensed images will not provide good information with high accuracy. Remotely sensed thermal data contains very valuable information to monitor and investigate the CD of the LU/LC. So, it needs to involve the thermal datasets for better outcomes. Fusion plays a big role to map the CD. Therefore, this study aims to find out a refining method for estimating the accurate CD method of the LU/LC patterns by investigating the integration of the effectiveness of the thermal satellite data with visible datasets by (a) adopting a noise removal model, (b) satellite images resampling, (c) image fusion, combining and integrating between the visible and thermal images using the Grim Schmidt spectral (GS) method, (d) applying image classification using Mahalanobis distances (MH), Maximum likelihood (ML) and artificial neural network (ANN) classifiers on datasets captured from the Landsat-8 TIRS and OLI satellite system, these images were captured from operational land imager (OLI) and the thermal infrared (TIRS) sensors of 2015 and 2020 to generate about of twelve LC maps. (e) The comparison was made among all the twelve classifiers' results. The results reveal that adopting the ANN technique on the integrated images of the combined TIRS and OLI datasets has the highest accuracy compared to the rest of the applied image classification approaches. The obtained overall accuracy was 96.31% and 98.40%, and the kappa coefficients were (0.94) and (0.97) for the years 2015 and 2020, respectively. However, the ML classifier obtains better results compared to the MH approach. The image fusion and integration of the thermal images improve the accuracy results by 5%–6% from the proposed method better than using low spatial-resolution visible datasets alone. Doi: 10.28991/ESJ-2023-07-02-09 Full Text: PDF

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