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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 874 Documents
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Big Data Analytics and Auditing: A Review and Synthesis of Literature Yaseen A. A. Hezam; Lilian Anthonysamy; Susela Devi K. Suppiah
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-2023-07-02-023

Abstract

The use of data analytics in auditing is increasingly growing. The application of common data analytics to audit engagements appears to be lagging behind other areas of practice, even though data analytics is thought to represent the future of audit, and there are still few publications that have examined this influence. This article reviews data analytics in audits and its potential for future audit engagements to describe the evolution of this research trend and picture its future growth directions. Future audit research potential and difficulties are also discussed. Data analytics application in auditing has enormous potential for refining audit quality, decreasing errors, increasing process transparency, and enhancing stakeholders’ confidence. We conducted a systematic literature review using the PRISMA approach. A total of 100 articles published in English from January 2011 to November 2021 were identified through a systematic search of reputed databases, including Web of Science and Scopus and many others. Our analysis reveals that data analytics is a promising domain for the auditing practice as it improves audit efficiency and promotes audit work digital transformation. While reviewing the most pertinent literature in the context of data analytics in auditing, this study offers insights on potential new directions and waning views on big data analytics in auditing. Doi: 10.28991/ESJ-2023-07-02-023 Full Text: PDF
A Causal Model of Relationship between Organizational Climate Influencing Happiness at Work and Organization Engagement Ekkasit Sanamthong; Suebpong Prabyai
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-2023-07-02-018

Abstract

This research aimed to study the level of organizational climate, happiness at work, and organization engagement. It included research into the relationship between organizational climate and employee happiness and engagement, as well as the direct and indirect effects of organizational climate on employee happiness and engagement. A sample of 400 employees was used. The findings revealed the following: 1) Organizational Climate (OC) directly influencing Happiness at Work (HW) had an influence coefficient of 0.92 with a statistical significance level of 0.05. 2) Organizational Climate (OC) directly influencing Organization Engagement (OE) had the influence coefficient of 0.32 with the statistical significance at the level of 0.05 and indirectly influencing Organization Engagement (OE) through Happiness at Work (HW) had the influence coefficient of 0.59 with the statistical significance at the level of 0.05. 3) Happiness at Work (HW) directly influencing Organization Engagement (OE) had an influence coefficient of 0.64 with a statistical significance level of 0.05. 4) Organizational Climate (OC) could forecast 85 percent of Happiness at Work (HW). 5) Organizational Climate (OC) and Happiness at Work (HW) could jointly forecast 89 percent of Organization Engagement (OE). Doi: 10.28991/ESJ-2023-07-02-018 Full Text: PDF
Artificial Intelligence-based Control Techniques for HVDC Systems Ali Hadi Abdulwahid
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-2023-07-02-024

Abstract

The electrical energy industry depends, among other things, on the ability of networks to deal with uncertainties from several directions. Smart-grid systems in high-voltage direct current (HVDC) networks, being an application of artificial intelligence (AI), are a reliable way to achieve this goal as they solve complex problems in power system engineering using AI algorithms. Due to their distinctive characteristics, they are usually effective approaches for optimization problems. They have been successfully applied to HVDC systems. This paper presents a number of issues in HVDC transmission systems. It reviews AI applications such as HVDC transmission system controllers and power flow control within DC grids in multi-terminal HVDC systems. Advancements in HVDC systems enable better performance under varying conditions to obtain the optimal dynamic response in practical settings. However, they also pose difficulties in mathematical modeling as they are non-linear and complex. ANN-based controllers have replaced traditional PI controllers in the rectifier of the HVDC link. Moreover, the combination of ANN and fuzzy logic has proven to be a powerful strategy for controlling excessively non-linear loads. Future research can focus on developing AI algorithms for an advanced control scheme for UPFC devices. Also, there is a need for a comprehensive analysis of power fluctuations or steady-state errors that can be eliminated by the quick response of this control scheme. This survey was informed by the need to develop adaptive AI controllers to enhance the performance of HVDC systems based on their promising results in the control of power systems. Doi: 10.28991/ESJ-2023-07-02-024 Full Text: PDF
Improving Metacognitive Ability and Learning Outcomes with Problem-Based Revised Bloom's Taxonomy Oriented Learning Activities I Gusti Agung Lanang Parwata; I Nyoman Laba Jayanta; I Wayan Widiana
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-2023-07-02-019

Abstract

This study aims to analyze the impact of Bloom's revised taxonomy-oriented learning activities with problem-based learning models on students' metacognitive skills and learning outcomes. A quasi-experimental design is used as the research method, and the quasi-experimental design is implemented as a pure post-test-control design. All fourth-year students participated in the study, with a total of 132 students participating. The sample was randomly selected and corresponded to 84 students. A 10-question test was used to collect the data. MANOVA with SPSS support was used as the analytical method. The significance of the test results was < 0.00. According to the results, 0.05. This means that learning that uses a combination of problem-based learning models and learning activities aligned with the revised Bloom taxonomy can influence students' metacognitive skills and learning outcomes. Students are at the central of their learning, so they are actively involved in the learning process. This learning activity develops students' metacognitive skills and provides an opportunity to reflect on what they know about themselves and to be honest and confident in their knowledge. Additionally, learning activities are organized by learning objectives to help students improve their learning outcomes. Doi: 10.28991/ESJ-2023-07-02-019 Full Text: PDF
New Approach to Image Segmentation: U-Net Convolutional Network for Multiresolution CT Image Lung Segmentation Sugiyarto Surono; Muhammad Rivaldi; Deshinta Arrova Dewi; Nursyiya Irsalinda
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-2023-07-02-014

Abstract

Image processing is the main topic of discussion in the field of computer vision technology. With the increase in the number of images used over time, the types of images with different resolution qualities are becoming more diverse. Low image resolution leads to uncertainty in the task of image processing. Therefore, a method with high performance is needed for image processing. In image processing, there is a Convolutional Neural Networks (CNN) architecture for semantic segmentation of pixels called U-Net. U-Net is formed by an encoder network and decoder network that will later produce segmented images. In this paper, researchers applied the U-Net architecture to the lung CT image dataset, which has different resolutions in each image, to segment the image that produces a segmented lung image. In this study, we conducted experiments for many training and testing data ratios while also comparing the model performances between the single resolution dataset and the multiresolution dataset. The results showed that the segmentation accuracy using a single resolution dataset is as follows: 5 to 5 ratio is 66.00%, 8 to 2 ratio is 88.96%, and 9 to 1 ratio is 94.47%. For the multiresolution dataset, the application is: 5 to 5 ratio is 82.42%, 8 to 2 ratio is 90.12%, and 9 to 1 ratio is 93.66%. And for the result, the training time using single resolution dataset are: 5 to 5 ratio is 59.94 seconds, 8 to 2 ratio is 87.16 seconds, and 9 to 1 ratio is 195.34 seconds, as for multiresolution data application are: 5 to 5 ratio is 49.60 seconds, 8 to 2 ratio is 102.08 seconds, and 9 to 1 ratio is 199.79 seconds. Based on those results, we obtained the best accuracy for single resolution at a 9:1 ratio and the best training time for multiresolution at a 5:5 ratio. Doi: 10.28991/ESJ-2023-07-02-014 Full Text: PDF
Reputational Risk: A Bibliometric Review of Relevant Literature Haitham Nobanee; Fayrouz Aksam Elsaied; Maryam Alhajjar; Ghada Abushairah; Safaa Al Harbi
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-2023-07-02-025

Abstract

This paper focuses on analyzing the level of research development regarding reputational risk on a general basis to identify what topics remain to be investigated. As a result, it offers a broader scope of research, including research debates, resolutions, and gaps that are relevant to the topic. A bibliometric analysis has been employed in this study to identify the topic’s trends and pinpoint potential gaps in the literature. The data were collected from the Scopus database for the period of 1994–2022, where the search resulted in a total of 659 documents relating in any way to reputational risk that fit the selection criteria. Research shows that conducted investigations are in favor of reputation risk and e-commerce, reputation insurance, corporate social responsibility, operational risk, risk management, and sustainability reporting. However, some of the articles' results on related topics were contradictory, and others found no evidence relating to reputation risk; some other topics were not fully examined or presented in the literature. Therefore, the current topic-related literature does not suffice, and further research is required to cover more topics on reputation risk and further highlight alignment between similar studies. This study has brought to light the relevant papers related to reputational risk and demonstrated potential gaps in the literature by investigating articles’ contradictory results on the researched topics, in turn conveying which topics need further examination. Thus, the literature will continue to evolve as members of the global academic community strive to fill the gaps and identify potential rescue strategies for jeopardized business entities. Doi: 10.28991/ESJ-2023-07-02-025 Full Text: PDF
Climatic Factor Differences and Mangosteen Fruit Quality between On- and Off-Season Productions Krisanadej Jaroensutasinee; Mullica Jaroensutasinee; Piyatida Boonsanong
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-2023-07-02-020

Abstract

The objective of this study was to investigate the differences in climatic factors and fruit quality between on- and off-season production periods. Climate, soil, and mangosteen measurements were all studied during on- and off-season production. We chose 40 mangosteen trees and observed flowering and fruit set rates over two production periods. The results showed that the number of flowers per branch, the number of fruits per branch, the circumference of fruits, and the fruit weight were higher during the on-season mangosteen production period than during the off-season mangosteen production period. However, the number of edible pulp segments, peel thickness, percentage of translucent flesh, and fruit gumminess were lower in the on-season mangosteen production period than in the off-season mangosteen production period. The percentage of fruit scars did not differ between the on- and off-season mangosteen production periods. When compared to the on-season mangosteen production period, there was lower relative humidity, soil moisture at 120 cm depth, and leaf wetness at 15 cm above ground during the off-season mangosteen production period; however, there was higher air temperature, soil moisture, and soil temperature at all four depth levels. Doi: 10.28991/ESJ-2023-07-02-020 Full Text: PDF
The Role of Cultural and Institutional Distances in International Trade Nguyen Tien Long; Nguyen Thi Gam; Vu Hong Van; Bui Hoang Ngoc
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-2023-07-02-015

Abstract

Despite the effectiveness of the observed barriers such as taxes and quotas to adjust bilateral trade, they are still not well supported by governments in general and the World Trade Organization in particular. Therefore, in recent years, unobserved barriers have been critical tools to modify the trade flows between nations worldwide. China’s exports account for a massive proportion of global trade. However, the role of cultural and institutional distance in China’s trade flow has not been much explored. This study analyzes the impact of cultural and institutional differences on China's exports between 2006-2017 by adopting a system-GMM estimator. The main findings are, first, that cultural and institutional differences between China and its trading partners reduce China's exports. Second, cultural and institutional distances have the strongest influence on China's exports to high-income countries, followed by low-income countries, and finally middle-income countries. Third, manufactured products are the most sensitive to cultural and institutional distances. Based on these findings, several policies for China, as well as for emerging economies in general, are suggested for reducing cultural and institutional distances and boosting their exports. Doi: 10.28991/ESJ-2023-07-02-015 Full Text: PDF
Managing Coal Enterprise Competitiveness in the Context of Global Challenges Ahmad Kultur Hia; Nurdelima Waruwu; Aan Komariah; Dedy Achmad Kurniady; Herlan Suherlan; Mikhail E. Kosov; Inna Rykova; Konstantin Ordov; Izabella Elyakova; Elena Romanenko
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-2023-07-02-021

Abstract

Increased geopolitical tensions and economic sanctions imposed by the U.S., the European Union, and other countries against Russian sectors of the economy have caused a slowdown of economic growth in Russia and significantly restricted access to international capital markets, creating many problems for coal enterprises due to the rapid growth of competition. Russian and Indonesian coal companies need to adopt coping strategies and implement effective management practices to successfully counter the various global challenges facing the coal sector. The article aims to develop coal enterprise competitiveness management in the context of global challenges in 2022, as exemplified by Russia and Indonesia, considering the main role of these two countries in global coal exports. The management process was empirically assessed, and a comprehensive qualitative focus group session was conducted to achieve this goal. Fifty-five top managers of Russian and Indonesian coal companies participated in the focus group session to collect data for identifying all the factors and indicators to be accounted for in a holistic assessment of the companies’ competitiveness. Suggestions were worked out for the development of coal companies in Russia and Indonesia, regarding current changes, to increase their competitiveness. Doi: 10.28991/ESJ-2023-07-02-021 Full Text: PDF
A Digital Model of Full-Cycle Training Based on the Zettelkasten and Interval Repetition System Gevorg T. Malashenko; Mikhail E. Kosov; Svetlana V. Frumina; Olga A. Grishina; Roman A. Alandarov; Vadim V. Ponkratov; Tatyana A. Bloshenko; Lola D. Sanginova; Svetlana S. Dzusova; Munther F. Hasan
Emerging Science Journal Vol 7 (2023): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SIED2-01

Abstract

The study aims to propose and validate a new digital model based on the Zettelkasten and interval repetition system for comprehensive and full-cycle training of the students. The core idea is to enhance the learning experience and effectiveness of the given training course by enhancing the student's attention during the learning and long-term information retention. In pursuit of the aforementioned research aim, the study incorporates a quantitative research methodology by combining experiments and a survey. In particular, the effectiveness of the proposed model was assessed via an achievement assessment involving two groups of students and assessing their scores on the same test. This was followed by a metacognitive awareness survey of the two groups to investigate their perceived understanding and performance (with and without the use of a model). The proposed model was found to be effective in enhancing the learning experience and effectiveness of the students on the training course. The Zettelkasten facilitates the management of the student's attention, while the interval repetition system contributes to increased retention. The students that used this model in their learning and preparation scored better than their peers. Also, they reported a significantly higher understanding and awareness of their learning than their peers. The model can be incorporated into the learning process or the provision of training courses to the students. This study is the first to suggest the integration of Zettelkasten and the interval repetition system into one learning model for the students. The article proposes a practical model that can be incorporated by teachers to improve the learning effectiveness of their students. This article has some limitations as well that must be acknowledged. Doi: 10.28991/ESJ-2023-SIED2-01 Full Text: PDF

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