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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 16 Documents
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Search results for , issue "Vol 8, No 2 (2024): April" : 16 Documents clear
Impact of Projects with Future Potential on the Global Competitiveness Index of Countries Akbota Akzambekkyzy; Laszlo Vasa; Jeffrey Yi-Lin Forrest; Shynara Sarkambayeva; Satyanand Singh
Emerging Science Journal Vol 8, No 2 (2024): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-02-012

Abstract

The concept of project success has evolved from the perspective of conforming to the project triangle to that of benefiting the environment, and then from the perspective of the following generation. Scientists increasingly assert that successful projects require a set of criteria that include such item(s) as future potential. The meaning of project success varies depending on where it is executed. The purpose of this study is to identify whether projects with future potential have a certain effect on indicators of the Global Competitiveness Index (GCI) of the Republic of Kazakhstan (RK) and what other success criteria are inherent in such projects. By using the method of descriptive analysis of data collected from 107 experts and analyzing 19 influential projects, the study revealed that projects oriented towards the future have a significant impact on the indicators of the GCI in the RK. This finding confirms the necessity of considering the long-term sustainability and social significance of projects when assessing their successes. Additionally, a specific combination of success criteria that contributes most to this impact was identified. This research provides a brand-new understanding of project success criteria in the context of their impact on the GCI and emphasizes the importance of considering future potential in project planning and evaluation. Doi: 10.28991/ESJ-2024-08-02-012 Full Text: PDF
Using Semicircular Sampling to Increase Sea Water/Ice Discrimination Altitude Alexey Nekrasov; Alena Khachaturian; Colin Fidge
Emerging Science Journal Vol 8, No 2 (2024): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-02-07

Abstract

The rapid development of aircraft and unmanned aerial vehicles (UAV) increases their use, including in polar areas, which are characterized by their remoteness and rather harsh conditions. The dominant trends in airborne radar development are expanding their functionality and increasing the altitude of their applicability. Our study focuses on the functionality enhancement of airborne high-altitude conical scanning radars currently used for circular clouds and precipitation observations as well as for sea wind measurements. Recently, we showed how a semicircular observation scheme, instead of a circular one, can double the maximum applicable altitude of sea wind measurements made with such radars. Here we apply this approach to show how an airborne high-altitude conical scanning radar’s functionality can also be expanded for sea water/ice discrimination within a semicircular observation scheme, again doubling the maximum discrimination altitude compared to circular observations. The discrimination is performed in scatterometer mode using the minimum statistical distance of the measured normalized radar cross sections (NRCSs) to the geophysical model functions (GMFs) of the sea water and ice underlying surfaces. However, as no sea ice GMF is available for the considered horizontal transmit and receive polarization at the Ku band, we instead used a substitute sea ice GMF having the same azimuth isotropic property setting for its NRCSs as the averaged value of the measured azimuth NRCSs within the semicircular observations scheme. Our analysis found that incidence angles of 30°, 45°, and 60° are well suited to our sea water/ice discrimination method, and that incidence angles higher than 30° are preferable as they provide a higher difference in the statistical distance of the measured NRCSs to the sea ice and water GMFs, whereas an incidence angle of 30° provides the highest applicable altitude for sea water/ice discrimination and wind retrieval. We also demonstrated the ability of the sea water/ice discrimination procedure’s implementation for any airborne wind scatterometer or multimode radar operated in scatterometer mode over freezing seas to avoid entirely erroneous sea wind measurement results when a sea ice surface is observed. The obtained results can also be used for enhancing aircraft and UAV radars and for developing new remote sensing systems. Doi: 10.28991/ESJ-2024-08-02-07 Full Text: PDF
The Effect of Felt Accountability on User Satisfaction with Accounting Information Dang Anh Tuan
Emerging Science Journal Vol 8, No 2 (2024): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-02-023

Abstract

Felt accountability affects an account-givers’ behavior, decisions, and organizational performance. Accounting information (AI) is provided for decision-making and accountability in the public sector. This study investigated the effects of felt accountability on expertise, legitimacy, and AI disclosure level for accountability on users’ satisfaction. Survey data included 401 responses across public institutions in Vietnam, and SEM linear structure analysis was used to examine the results. The research findings indicate that felt accountability directly affects users’ satisfaction and their expertise and legitimacy, and the level of AI disclosure. The expertise and legitimacy of the account-holder and the level of AI disclosure partially mediate the relationship between felt accountability and users’ satisfaction. This implies that AI's needs, purposes, and importance are determined based on hypothetical users that are not useful in reality. In practice, AI must meet accountability requirements to bring satisfaction to users. The satisfaction level of actual users of AI is influenced by the account-givers’ perceived accountability regarding the needs, expertise, and legitimacy of the account-holder. Therefore, it is essential to identify the type of information needed, the timing of AI disclosure, and the actual AI users to reduce the gap between the supply and demand of AI. The research results provide evidence supporting agency and social contingency theories in accountability relationships. Doi: 10.28991/ESJ-2024-08-02-023 Full Text: PDF
Improved Fingerprint-Based Localization Based on Sequential Hybridization of Clustering Algorithms Abdulmalik Shehu Yaro; Filip Maly; Pavel Prazak; Karel Malý
Emerging Science Journal Vol 8, No 2 (2024): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-02-02

Abstract

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PDF
Prioritizing Critical Success Factors for Reverse Logistics as a Source of Competitive Advantage Mpho Sharon Makaleng
Emerging Science Journal Vol 8, No 2 (2024): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-02-018

Abstract

Reverse logistics has received a lot of attention due to the negative impact it has on the environment and the growing demand for green products. This especially occurred in the fast-moving consumer goods retail sector due to recalls and waste management. This sector significantly contributes to the gross domestic product growth of all countries. This has therefore led to the growing significance of reverse logistics since the fast-moving consumer goods retail sector cannot avoid reverse logistics. The primary objective of this study was for fast-moving consumer goods retailers to prioritize critical success factors for reverse logistics as a source of competitive advantage in the fast-moving consumer goods retailers’ sector. This is because it is essential for the fast-moving consumer goods retail sector to implement critical success factors in reverse logistics that can lead to firm competitiveness. The study employed a positivist research philosophy, where data were collected from 418 fast-moving consumer goods retailers and consumers via SurveyMonkey using two close-ended questionnaires. The Statistical Package for the Social Sciences and the Analysis of Moment Structures software version 27 were employed to analyze the data. The results offer insight into the critical success factors in reverse logistics that should be carried out to achieve firm competitiveness. Through the implementation of critical success factors, this sector will achieve several goals, such as meeting environmental protocols, decreasing operational costs, cultivating the cumulative value of the brand, and improving customer satisfaction. Doi: 10.28991/ESJ-2024-08-02-018 Full Text: PDF
Assessment of the Development of the Circular Economy in the EU Countries: Comparative Analysis by Multiple Criteria Methods Dainora Gedvilaitė; Giedrė Lapinskienė; Marek Szarucki
Emerging Science Journal Vol 8, No 2 (2024): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-02-013

Abstract

In recent decades, attention to environmental resource management has increased worldwide. Circular economy (CE) is a concept that is increasingly being considered as a solution to this range of challenges. Therefore, it is important to monitor the development of CE. This research is an attempt to contribute to the CE surveillance literature by providing a framework for comparing the positions of states and their classifications. The main goal of the article is to assess the level of circular economy development in EU countries according to the chosen methodology. The indicators used in this study are sourced from the European Commission Monitoring Framework database, which includes data from 27 European Union (EU) countries over the time frame from 2016 to 2020. The analysis was carried out using Multi-Criteria Decision Methods (MCDM), such as Simple Additive Weighing (SAW), and the objective method of estimating weights in accordance with proportional differences (APROD), which helped to assess the state of CE. The results showed that EU countries can be divided into three groups based on the level of performance of the CE, and their level of development in relation to the circular economy is different. The level of circular economy development in most EU countries is low. Germany, the Netherlands, France, and Italy demonstrated the best positions. The study findings were derived from the combination of two MCDMs, thus increasing the refinement of the overall methodology. Doi: 10.28991/ESJ-2024-08-02-013 Full Text: PDF
Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis Viviana Moya; Angélica Quito; Andrea Pilco; Juan P. Vásconez; Christian Vargas
Emerging Science Journal Vol 8, No 2 (2024): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-02-08

Abstract

In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant’s natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise and time-consuming. With the rise of computer vision and pattern recognition technologies, new opportunities in image recognition have emerged, offering solutions to these challenges. This research proposes an affordable solution for object detection and classification, specifically through version 5 of the You Only Look Once (YOLOv5) model, to determine the location and maturity state of rocoto chili peppers cultivated in Ecuador. To enhance the model’s efficacy, we introduce a novel dataset comprising images of chili peppers in their authentic states, spanning both immature and mature stages, all while preserving their natural settings and potential environmental impediments. This methodology ensures that the dataset closely replicates real-world conditions encountered by a detection system. Upon testing the model with this dataset, it achieved an accuracy of 99.99% for the classification task and an 84% accuracy rate for the detection of the crops. These promising outcomes highlight the model’s potential, indicating a game-changing technique for chili small-scale farmers, especially in Ecuador, with prospects for broader applications in agriculture. Doi: 10.28991/ESJ-2024-08-02-08 Full Text: PDF
Agriculture 5.0 and Explainable AI for Smart Agriculture: A Scoping Review Siti Fatimah Abdul Razak; Sumendra Yogarayan; Md Shohel Sayeed; Muhammad Izzat Faiz Mohd Derafi
Emerging Science Journal Vol 8, No 2 (2024): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-02-024

Abstract

The visionary paradigm of Agriculture 5.0 integrates Industry 4.0 principles into agricultural practices. Our scoping review explores the landscape of Agriculture 5.0, emphasizing the pivotal role of Explainable AI (XAI) in shaping this domain. Guided by the Preferred Reporting Items for Systematic Review and Meta-Analysis Scoping Review, we rigorously analyzed 84 articles published from 2018 to September 2023. Our findings highlight XAI’s potential within Agriculture 5.0, recognizing its influence on intelligent farming. We propose a conceptual framework for integrating XAI, emphasizing its impact on model transparency and user trust. Despite transformative applications, existing literature often lacks XAI discussions. Our objective is to bridge this gap and provide a reference for academics, practitioners, policymakers, and educators in the field of smart agriculture that is both environmentally friendly and technologically advanced. Doi: 10.28991/ESJ-2024-08-02-024 Full Text: PDF
An Empirical Analysis of Influencing Factors of Government Decision-Making on Public Crisis Tuo Wang; Shiqing Chen; Chuleerat Kongruang
Emerging Science Journal Vol 8, No 2 (2024): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-02-03

Abstract

As the times and the world are changing in an unprecedented way, public security issues have become increasingly linked, transnational, and diverse, causing huge impacts on the global economy and society, as well as posing great challenges to the government in dealing with public crisis decision-making. This study aims to determine the factors affecting the government's crisis decision-making and analyze the interrelationship among the factors affecting the government's crisis decision-making and the performance of its crisis decision-making. The questionnaire was developed and used to collect data from 400 samples of various groups, including government department personnel, scientific research institute practitioners, university lecturers, the public, and university students, both online and face-to-face. The structural equation model (SEM) is used to evaluate the structural relationships of the relevant variables, including the crisis decision-making body, crisis decision-making procedure, crisis decision-making performance, the decision environment, and value identification. It is found that the diversified decision-making subject and decision-making environment have a positive and significant impact on the public crisis decision-making process, value identification, and decision-making performance, respectively. This study has contributed to the following issues. Firstly, it developed new measurement tools and indicators for better evaluating the quality and effect of public crisis decision-making and exploring the influence of different factors on the crisis decision-making of the government. Secondly, it employed cross-industry and cross-cultural comparative research to find commonalities and differences and provide targeted recommendations. Doi: 10.28991/ESJ-2024-08-02-03 Full Text: PDF
Multilingual Question Answering for Malaysia History with Transformer-based Language Model Qi Zhi Lim; Chin Poo Lee; Kian Ming Lim; Jing Xiang Ng; Eric Khang Heng Ooi; Nicole Kai Ning Loh
Emerging Science Journal Vol 8, No 2 (2024): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-02-019

Abstract

In natural language processing (NLP), a Question Answering System (QAS) refers to a system or model that is designed to understand and respond to user queries in natural language. As we navigate through the recent advancements in QAS, it can be observed that there is a paradigm shift of the methods used from traditional machine learning and deep learning approaches towards transformer-based language models. While significant progress has been made, the utilization of these models for historical QAS and the development of QAS for Malay language remain largely unexplored. This research aims to bridge the gaps, focusing on developing a Multilingual QAS for history of Malaysia by utilizing a transformer-based language model. The system development process encompasses various stages, including data collection, knowledge representation, data loading and pre-processing, document indexing and storing, and the establishment of a querying pipeline with the retriever and reader. A dataset with a collection of 100 articles, including web blogs related to the history of Malaysia, has been constructed, serving as the knowledge base for the proposed QAS. A significant aspect of this research is the use of the translated dataset in English instead of the raw dataset in Malay. This decision was made to leverage the effectiveness of well-established retriever and reader models that were trained on English data. Moreover, an evaluation dataset comprising 100 question-answer pairs has been created to evaluate the performance of the models. A comparative analysis of six different transformer-based language models, namely DeBERTaV3, BERT, ALBERT, ELECTRA, MiniLM, and RoBERTa, has been conducted, where the effectiveness of the models was examined through a series of experiments to determine the best reader model for the proposed QAS. The experimental results reveal that the proposed QAS achieved the best performance when employing RoBERTa as the reader model. Finally, the proposed QAS was deployed on Discord and equipped with multilingual support through the incorporation of language detection and translation modules, enabling it to handle queries in both Malay and English. Doi: 10.28991/ESJ-2024-08-02-019 Full Text: PDF

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