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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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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
Evaluation of Machine Learning Algorithms for Emotions Recognition using Electrocardiogram Chy Mohammed Tawsif Khan; Nor Azlina Ab Aziz; J. Emerson Raja; Sophan Wahyudi Bin Nawawi; Pushpa Rani
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-011

Abstract

In recent studies, researchers have focused on using various modalities to recognize emotions for different applications. A major challenge is identifying emotions correctly with only electrocardiograms (ECG) as the modality. The main objective is to reduce costs by using single-modality ECG signals to predict human emotional states. This paper presents an emotion recognition approach utilizing the heart rate variability features obtained from ECG with feature selection techniques (exhaustive feature selection (EFS) and Pearson’s correlation) to train the classification models. Seven machine learning (ML) models: multi-layer perceptrons (MLP), Support Vector Machine (SVM), Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), Logistic Regression, Adaboost and Extra Tree classifier are used to classify emotional state. Two public datasets, DREAMER and SWELL are used for evaluation. The results show that no particular ML works best for all data. For DREAMER with EFS, the best models to predict valence, arousal, and dominance are Extra Tree (74.6%), MLP and DT (74.6%), and GBDT and DT (69.8%), respectively. Extra tree with Pearson’s correlation are the best method for the ECG SWELL dataset and provide 100% accuracy. The usage of Extra tree classifier and feature selection technique contributes to the improvement of the model accuracy. Moreover, the Friedman test proved that ET is as good as other classification models for predicting human emotional state and ranks highest. Doi: 10.28991/ESJ-2023-07-01-011 Full Text: PDF
The Necessity of Close Contact Tracing in Combating COVID-19 Infection – A Systemic Study Thein Oak Kyaw Zaw; Saravanan Muthaiyah; Kalaiarasi Sonai Muthu Anbananthen; Min Thu Soe; Byeonghwa Park; Myung Joon Kim
Emerging Science Journal Vol 6 (2022): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2022-SPER-019

Abstract

Many contact tracing solutions developed by countries around the globe in containing the Covid-19 pandemic are in the area of location-based tracing, which does not enable them to identify close contacts accurately. As location-based tracing implementations continuous on, the results have not been as effective as intended. Thus, in providing some closure, this study will dissect the need for close contact tracing solutions for the pandemic by providing a comprehensive contact tracing characteristic framework (CCTCF) for Covid-19, which will help authorities toward better pandemic management. In this study, CCTCF for Covid-19 was constructed by applying several methods. Using Problem, Intervention, Comparison, Outcome (PICO) as the framework, methods conducted were: (1) Case study to analyze the contact tracing systems in 30 countries; (2) Systematic literature review (n=2056) regarding solutions’ elements, (3) Thematic analysis for characteristics framework development. A total of 25 items were obtained for CCTCF, along with valuable insights that necessitate close contact tracing for the pandemic. Results from CCTCF have also shown that the best contact tracing solution for Covid-19 is bi-directional human-to-human close contact tracing, which uses a retrospective approach and is able to identify the source as well as groups of infection using a personal area network (PAN). Doi: 10.28991/esj-2022-SPER-019 Full Text: PDF
Examining the Key Factors that Drives Live Stream Shopping Behavior Sudaporn Sawmong
Emerging Science Journal Vol 6, No 6 (2022): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-06-011

Abstract

The purpose of this research was to examine the key factors that drive live-stream shopping behavior in Thailand. The specific objective was to determine the live stream shopping factors that influence purchase intention. The study was driven by the increasing role of e-commerce and live streaming shopping trends through social media marketing. For marketers, live-stream shopping is considered a valuable marketing strategy for commercial businesses to enhance sales, save expenses, and create unique marketing impacts. The study adopted the Uses and Gratification Theory and the Source Credibility Theory. Through these theories, the variables of the study were considered to be entertainment, informativeness, attractiveness, expertise, trustworthiness, culture, and purchase intention. A quantitative research methodology was adopted, with primary data collected from 370 respondents. The model was evaluated using reliability, validity, and CFA. SEM was used to analyse the hypotheses using AMOS and SPSS software. The results of the study indicated that four factors (entertainment, informativeness, expertise, and trustworthiness) have a significant and positive effect on purchase intention. Trustworthiness and entertainment had the highest effect. Attractiveness was found not to influence purchase intention, while culture did not moderate the effect of any variable on purchase intention. The research recommended that live streaming should be trustworthy in terms of sincerity, non-exaggeration, correct information, correct thoughts, and opinions. They should also be entertaining to establish positive interconnectivity between the user and the product or service, and also informative to contribute towards awareness of a product/service and offer insights that influence perceptions and behavioral intentions. Doi: 10.28991/ESJ-2022-06-06-011 Full Text: PDF
Ownership Concentration and Accounting Conservatism: The Moderating Role of Board Independence Thi Minh Hang Nguyen; Anh Tho To; Thi Huyen Phan; Nhat Phuong Diem Ngo; Thi Thu Hong Ho
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-07

Abstract

The purpose of this study is to examine the moderating effect of board independence on the relationship between ownership concentration and accounting conservatism. Using fixed-effect regressions for a sample of 165 Vietnamese listed companies from 2007 to 2017, the results revealed that the proportion of outstanding shares owned by the largest shareholder is negatively associated with accounting conservatism and board independence plays a moderating role in this relationship. Our results are robust after applying alternative measures of the largest ownership and correcting for potential endogeneity using fixed-effects regression with instrumental variables. Overall, our evidence shows that firms with concentrated ownership should keep a high non-executive ratio to maintain accounting conservatism. In other words, increasing the number of non-executive directors on boards in firms with a substantial proportion of shares held by the largest shareholder is likely to strengthen the information environment, giving financial reporting more credibility.JEL Classification: G30; G32. Doi: 10.28991/ESJ-2023-07-01-07 Full Text: PDF
Development of a Technique for the Spectral Description of Curves of Complex Shape for Problems of Object Classification Aslan Tatarkanov; Islam Alexandrov; Alexander Muranov; Abas Lampezhev
Emerging Science Journal Vol 6, No 6 (2022): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-06-015

Abstract

Vascular pathology symptoms can be determined by retinal image segmentation and classification. However, the retinal images from non-invasive diagnostics have a complex structure containing tree-like vascular beds, multiple segment boundaries, false segments, and various distortions. It should be noted that complex structure images’ segmentation does not always provide a single solution. Thus, the goal is to increase the efficiency of vascular diagnostics. This study aims to develop a technique for describing the geometric properties of complexly structured image segments used for classifying vascular pathologies based on retinal images. The advantages and disadvantages of the existing methods and algorithms of segmentation were considered. The most effective use areas of the mentioned methods and algorithms are revealed. Through detecting retinal thrombosis, the algorithm's efficiency for constructing a mathematical model of an arbitrary shape segment based on the morphological processing of binary and halftone images was justified. A modified variant of this algorithm based on the spectral analysis procedure of arbitrary shape boundary curves was used for the spectral description of complex shape curves for classifying vascular pathologies based on retinal images. Two approaches have been developed. The first one allows obtaining a closing segment of the curve from a symmetric mapping of the initial parametric curves. The second involves intelligent data processing and obtaining contours of minimum thickness, forming convex sets. The results of experiments confirm the possibility of practical use of the developed technique to solve problems of vascular pathology classification based on retinal images, showing the correct forecast probability was 0.93 with all associated risk factors. Doi: 10.28991/ESJ-2022-06-06-015 Full Text: PDF
Microstrip Patch Antenna with C Slot for 5G Communication at 30 GHz Siddharth Kishore; A. R. Abdul Rajak
Emerging Science Journal Vol 6, No 6 (2022): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-06-06

Abstract

A novel design of a 30 GHz microstrip line-fed antenna for 5G communication has been presented in this paper. 5G is the latest industry standard in mobile communication, which is designed to deliver higher data speeds, lower latency, greater network capacity, and higher reliability. It uses major parts of the mmWave spectrum (28 GHz to 40 GHz), allowing for a wide range of applications like mobiles, vehicles, medical devices, and other IoT networks. This mmWave network requires efficient antennas for its effective communication. Patch antennas use the function of oscillating their physical structure to the wavelength of the transmitting wave. Thus, higher efficiency can be achieved in the mmWave spectrum due to its proximity to the actual dimensions of the patch antenna, which also allows us to design antennas at small sizes and high reliability. The design in this report has a patch antenna with a centre frequency of 30 GHz. The antenna was optimized for this frequency based on the best reflection coefficient and gain while keeping the restraints of staying within the FR-2 band of 28 GHz to 33 GHz. The proposed antenna has been implemented using Rogers RT5880 substrate for high gain and performance across a wide range of frequencies. The feed is also accompanied by a quarter-wave feed cut for performance increase and impedance matching. The design has a gain of 8.45, with a reflection coefficient of -8 dB at a resonant frequency of 30 GHz. It shows great directivity of 5o and VSWR of 2.3 over a bandwidth of 3.5 GHz. It also employs a 0.4 mm C slot, which induces a dipole effect, thereby increasing the directivity and gain of the antenna. Hence, it is recommended for use in applications related to 5G mobile communication. Doi: 10.28991/ESJ-2022-06-06-06 Full Text: PDF
How Instagram Influencers Contribute to Consumer Travel Decision: Insights from SEM and fsQCA Wen-Kuo Chen; Pantas H. Silaban; Widya Elisabeth Hutagalung; Andri Dayarana K. Silalahi
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-02

Abstract

In recent years, social media influencers (SMIs) have become independent entities capable of influencing their audiences. Businesses look towards SMIs on Instagram as a marketing communication tool due to their popularity and effectiveness. In tourism, few studies investigate the role of SMI in influencing travel behaviors. The study examines how SMI can influence Instagram followers' travel behaviors. This study determined SMI based on attractiveness, similarity, and expertise. SMI can generate parasocial interactions with followers and build trust by promoting destinations based on these three dimensions. This study tested the research hypothesis on 364 respondents using a dual-approach analysis of structural equation modeling and fuzzy set qualitative comparative analysis. The results of the SEM analysis confirm that consumers were more likely to trust SMI based on their expertise and similarity. Also, this study demonstrated that highly attractive SMI and similarity with followers can lead to parasocial interactions. When consumers trust and feel parasocial interaction with SMI, they are more likely to consider traveling. FsQCA results confirm the presence of two configurations with high travel intention. The causal conditions configuration presented in this study demonstrated the interdimensional relationship between SMIs (attractiveness, similarity, and expertise), trust, and parasocial interaction in terms of travel intention. This study also achieved theoretical contributions and managerial implications on how scholars and tourism managers leverage SMIs to create high travel intentions. Doi: 10.28991/ESJ-2023-07-01-02 Full Text: PDF
A Framework to Create a Deep Learning Detector from a Small Dataset: A Case of Parawood Pith Estimation Wattanapong Kurdthongmee; Korakot Suwannarat; Jeremy Kiplagat
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-017

Abstract

A deep learning-based object detector has been successfully applied to all application areas. It has high immunity to variations in illumination and deviations among objects. One weakness of the detector is that it requires a huge, undefinable dataset for training the detector to avoid overtraining and make it deployable. This research proposes a framework to create a deep learning-based object detector with a limited-sized dataset. The framework is based on training the detector with the regions surrounding an object that typically contain various features over a more extensive area than the object itself. Our proposed algorithm further post-processes the detection results to identify the object. The framework is applied to the problem of wood pith approximation. The YOLO v3 framework was employed to create the detector with all default hyperparameters based on the transfer learning approach. A wood pith dataset with only 150 images is used to create the detector with a ratio between training to testing of 90:10. Several experiments were performed to compare the detection results from different approaches to preparing the regions surrounding a pith, i.e., all regions, only close neighbors, and only diagonal neighbors around a pith. The best experiment result shows that the framework outperforms the typical approach to create the detector with approximately twice the detection precision at a relative average error. Doi: 10.28991/ESJ-2023-07-01-017 Full Text: PDF
Public Perceptions on Application Areas and Adoption Challenges of AI in Urban Services Tan Yigitcanlar; Rita Yi Man Li; Tommi Inkinen; Alexander Paz
Emerging Science Journal Vol 6, No 6 (2022): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-06-01

Abstract

Artificial intelligence (AI) deployment is exceedingly relevant to local governments, for example, in planning and delivering urban services. AI adoption in urban services, however, is an understudied area, particularly because there is limited knowledge and hence a research gap on the public's perceptions-users/receivers of these services. This study aims to examine people’s behaviors and preferences regarding the most suited urban services for application of AI technology and the challenges for governments to adopt AI for urban service delivery. The methodological approach includes data collection through an online survey from Australia and Hong Kong and statistical analysis of the data through binary logistic regression modeling. The study finds that: (a) Attitudes toward AI applications and ease of use have significant effects on forming an opinion on AI; (b) initial thoughts regarding the meaning of AI have a significant impact on AI application areas and adoption challenges; (c) perception differences between the two countries in AI application areas are significant; and (d) perception differences between the two countries in government AI adoption challenges are minimal. The study consolidates our understanding of how the public perceives the application areas and adoption challenges of AI, particularly in urban services, which informs local authorities that deploy or plan to adopt AI in their urban services. Doi: 10.28991/ESJ-2022-06-06-01 Full Text: PDF

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