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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 803 Documents
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Design of Modified UWB Microstrip Antenna for UHF Partial Discharge Sensor Khayam, Umar; Hamdani, Yuda M.; Rachmawati, .
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-03

Abstract

The development of printable ultrahigh-frequency (UHF) antennas as partial discharge (PD) sensors for high-voltage equipment has been extensively studied. However, achieving ultrawideband (UWB) UHF PD sensors frequently requires larger sizes, unsuitable for certain applications requiring compact sensors for dielectric windows in HV equipment. This research objective is to obtain PD sensors with a wider bandwidth (0.3–3 GHz) and a compact size fitting a less-than-100mm-length gas-insulated switchgear (GIS) dielectric window. A circular patch microstrip antenna (CPMA) was chosen for its small size and potential for UWB performance. This paper discusses the design modification of the CPMA to obtain a wider bandwidth for PD detection in GIS. Simulations and lab-scale experimental verifications were conducted to evaluate the optimized sensor. The modified sensor, with a size of 60 × 73 mm², achieved a bandwidth of 3.08–3.14 GHz, a reflection coefficient of -44 dB, and several resonant frequencies of 0.3–2.3 GHz. This is a seven-time wider bandwidth compared to earlier bowtie antennas while keeping a dimension of less than 100 mm². These properties allow for efficient PD detection in GIS and other insulating media. Experimental results indicate the sensor's capacity to reliably detect and analyze PD signals while responding appropriately to variations in voltage. Doi: 10.28991/ESJ-2024-08-05-03 Full Text: PDF
Modelling School Zone Border as Rich Modelling Problem for Secondary School Students Suweken, Gede
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-019

Abstract

This article explores the potential of using the school zoning problem in Indonesia as a vehicle for teaching mathematical modeling to secondary school students. This problem is highly suitable for students as a modeling challenge because it is (i) contextual, (ii) rich, (iii) challenging, and (iv) within students' Zone of Proximal Development (ZPD). School zoning involves a concept called Voronoi, essentially a partitioning problem. For simpler or special-case problems, these partitions can be created using concepts already taught in secondary schools, such as perpendicular bisectors and radical axes. However, for more complex problems with multiple sites, an algorithm is required, which involves advanced mathematical concepts beyond the typical secondary curriculum. Yet, with the rise of visual programming languages like Scratch, Snap!, StarLogo, and TurboWarp, it becomes possible to tackle these partitioning challenges using coding and only basic mathematical principles. This approach not only enhances students' understanding of foundational mathematical concepts but also fosters the integration of computational thinking and coding within mathematics. In summary, the school zoning problem serves as an ideal topic for mathematical modeling for secondary school students, promoting the integration of mathematical concepts, computational thinking, and coding skills. Doi: 10.28991/ESJ-2024-08-05-019 Full Text: PDF
Green Technology Innovation and Corporate Reputation: Key Drivers of ESG and Firm Performance Chen, Xiyi; Lakkanawanit, Pankaewta; Suttipun, Muttanachai; Swatdikun, Trairong; Huang, Shi-Zheng
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-021

Abstract

The purpose of this study is to investigate the impact of Environmental, Social, and Governance (ESG) performance and firm performance within China's manufacturing sector, with a novel focus on the mediating effect of green technology innovation and the moderating influence of corporate reputation. Using a 2011-2022 dataset from A-share listed manufacturing companies on the Shanghai and Shenzhen Stock Exchanges, the study employs multiple regression analysis with a two-way fixed-effects model to examine these relationships. Findings indicate that robust ESG practices significantly enhance company performance, mediated by green technological innovation. However, a negative moderating effect of corporate reputation suggests that higher corporate reputation weakens the ESG-financial performance relationship. Further analyses reveal that privately-owned firms, those in China's eastern region, and environmentally sustainable industries benefit most from strong ESG initiatives. This study addresses the challenge of disentangling key variables by analyzing their interconnected effects. The findings fill a gap in the existing literature by contributing to a deeper understanding of the relationship between ESG and corporate success, particularly through the mediating role of innovation and the moderating influence of reputation. Additionally, the study provides practical recommendations for managers and policymakers to enhance ESG strategies, promote growth, and support sustainable development. Doi: 10.28991/ESJ-2024-08-06-021 Full Text: PDF
New Assessment Model of Financing Treatment of Patients with Complete Tooth Loss Grachev, Dmitry I.; Martynenko, Aleksandr V.; Perekhodov, Sergey N.; Kostyrin, Evgeniy V.; Mustafaev, Magomet Sh.; Akhmedov, Kamalutdin G.; Deshev, Aslan V.; Rozanov, Daniil G.; Korotkova, Nadezhda L.; Kerasov, Stefan N.; Arutyunov, Sergey A.
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-014

Abstract

According to the World Health Organization, the global prevalence of complete tooth loss is estimated to be 7% among individuals aged 20 years and older, while for those aged 60 and over, this rate significantly increases to 23%. This study is relevant due to the psychological trauma, social challenges, and functional limitations caused by tooth loss, as well as the uneven availability of dental care worldwide. The goal of this research is to develop and implement a new model to assess the socioeconomic feasibility of investing in digital technologies for diagnosing and treating patients with complete tooth loss using removable polymer prostheses produced through additive 3D printing. The study employs scenario analysis, the clustered rankings coordination method, statistical methods, expert opinion assessment using Kendall’s coefficient of rank concordance, system analysis and design, questionnaires, sociometry, and functional modeling. The practical significance of this research lies in providing a quantitative assessment of economic opportunities for effectively using RPDs in three groups: RPDs without additional fixation means; those with special adhesive agents for improved fixation; and implant-supported prosthetics with conditionally removable dentures similar to RPDs. The scientific novelty of this study is the development of a new evaluation model that justifies the choice of prosthetic treatment technology for patients with complete tooth loss, enabling the most rational use of resources. Doi: 10.28991/ESJ-2024-08-05-014 Full Text: PDF
Federated Risk-Based Access Control Model for P2P Lending Platforms: A Multi-Agent Systems (MAS) Approach Muthaiyah, Saravanan; Nguyen, Lan Thi Phuong; Choong, Yap Voon; Zaw, Thein Oak Kyaw
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-05

Abstract

This study addresses the inherent risk management challenges in decentralized finance, particularly for peer-to-peer (P2P) lending platforms. We propose a novel framework that leverages a Multi-Agent System (MAS) to establish a collaborative network encompassing loan originators, investors, regulators, and service providers. This distributed approach facilitates federated risk management, where risk assessment and mitigation responsibilities are shared across these entities. The MAS employs a comprehensive nine-factor assessment (detailed in Table 5) to evaluate industry risk profiles, considering industry environment, competition, and internal capabilities. This data is further visualized using a color matrix (Tables 5 & 6) and utilized alongside state diagrams (Figure 2) to depict the workflow and manage tasks within the P2P lending process. Additionally, the MAS informs a novel Federated Risk-Based Access Control (FRkBAC) system that tailors access permissions (lending origination, disbursement, etc.) based on dynamic risk assessments of industry trends and individual borrower profiles. This data-driven approach fosters trust within the P2P ecosystem and represents a significant advancement in decentralized finance risk management compared to traditional methods. Doi: 10.28991/ESJ-2024-08-06-05 Full Text: PDF
SHAP-Instance Weighted and Anchor Explainable AI: Enhancing XGBoost for Financial Fraud Detection Thanathamathee, Putthiporn; Sawangarreerak, Siriporn; Chantamunee, Siripinyo; Mohd Nizam, Dinna Nina
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-016

Abstract

This research aims to enhance financial fraud detection by integrating SHAP-Instance Weighting and Anchor Explainable AI with XGBoost, addressing challenges of class imbalance and model interpretability. The study extends SHAP values beyond feature importance to instance weighting, assigning higher weights to more influential instances. This focuses model learning on critical samples. It combines this with Anchor Explainable AI to generate interpretable if-then rules explaining model decisions. The approach is applied to a dataset of financial statements from the listed companies on the Stock Exchange of Thailand. The method significantly improves fraud detection performance, achieving perfect recall for fraudulent instances and substantial gains in accuracy while maintaining high precision. It effectively differentiates between non-fraudulent, fraudulent, and grey area cases. The generated rules provide transparent insights into model decisions, offering nuanced guidance for risk management and compliance. This research introduces instance weighting based on SHAP values as a novel concept in financial fraud detection. By simultaneously addressing class imbalance and interpretability, the integrated approach outperforms traditional methods and sets a new standard in the field. It provides a robust, explainable solution that reduces false positives and increases trust in fraud detection models. Doi: 10.28991/ESJ-2024-08-06-016 Full Text: PDF
The Internal Work Environment and Job Alienation: The Case of Faculty Members Bougherza, Rédha; Azieb, Samia; Abdo Noufal, Zezit M.; Mallek, Mohamed; Abderrahmane, Yasser; Brachene, Imed Eddine; Menighed, Ahmed
Emerging Science Journal Vol 8 (2024): 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-2024-SIED1-07

Abstract

The objective of this study is to examine the nature of the relationship between the internal work environment including its all-encompassing dimensions (organizational structure, participation in decision-making, incentives) and job alienation in Higher education institutions through focusing on faculty members at Jijel University in Algeria. That being the case, the study aims to fill a research gap by investigating the underexplored relationship between the Internal Work Environment and Job Alienation, hence while there is ample literature on work alienation, studies specifically focusing on work alienation within higher education institutions through the prism of internal work environment are notably scarce. The study utilized a descriptive approach, employing a survey sampling method to collect data from the target population with a specifically designed questionnaire for this purpose. The questionnaire consisting of 60 items was administered to a randomly selected sample of 167 faculty members at Jijel University. The collected data were analyzed through the Statistical Package for Social Sciences (SPSS), version 22. The study's findings illustrate that faculty members perceive their work environment as inadequate for carrying out their activities, coupled with a notably high level of job alienation. Additionally, the research underscores a significant correlation between the internal work environment, encompassing its various dimensions, and the prevalence of job alienation among faculty members at Jijel University. Doi: 10.28991/ESJ-2024-SIED1-07 Full Text: PDF
Short and Effective: A Reasoned Proposal for Organizational Climate Measurement Ramos, Valentina; Ramos-Galarza, Carlos; Pazmiño, Pablo; Tejera, Eduardo
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-09

Abstract

Generally, organizational climate research does not focus on the work environment because the mindset and emotions of employees are often mistaken for organizational culture. Additionally, surveys to evaluate the organizational climate tend to be long, and therefore, organizational climate studies are conducted only once a year—that too if an organization is concerned about its employees. This research proposes a methodology to evaluate organizational climate; the methodology has the following characteristics: it is a short evaluation named “pulse”; it is oriented toward specific elements of culture that influence the organizational climate and its variability; and it considers organizational contexts. The study was conducted in three organizations encompassing three sectors (N=3,331 employees). The survey included three questions regarding employees’ feelings and climate perception at the individual, group, and organizational levels. Additionally, it had 56 questions related to the elements of organizational culture, grouped into six components after an exploratory analysis: Structure, Recognition, Leadership, Accountability, Work Team, and Ethics. The results showed significant differences between organizations based on the organizational climate perception, its strength, and the behavior of the variables associated with the organizational culture that impacts the climate. Additionally, cultural elements were reduced because of their relationship with the organizational climate. This research suggests that organizational climate studies should be conducted for specific organizational contexts. Additionally, it proposes a methodology to reduce the duration of organizational climate studies by focusing on specific cultural dimensions associated with the climate, which can be applied longitudinally throughout the year to monitor climate changes. Doi: 10.28991/ESJ-2024-08-05-09 Full Text: PDF
Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method Hedayati-Dezfooli, M.; Moayyedian, Mehdi; Dinc, Ali; Abdrabboh, Mostafa; Saber, Ahmed; Amer, A. M.
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-025

Abstract

This research explores multi-objective optimization in injection molding with a focus on identifying the optimal configuration for the moldability index in aviation propeller manufacturing. The study employs the Taguchi method and fuzzy analytic hierarchy process (FAHP) combined with the Technique for the Order Performance by Similarity to the Ideal Solution (TOPSIS) to systematically evaluate diverse objectives. The investigation specifically addresses two prevalent defects—shrinkage rate and sink mark—that impact the final quality of injection-molded components. Polypropylene is chosen as the injection material, and critical process parameters encompass melt temperature, mold temperature, filling time, cooling time, and pressure holding time. The Taguchi L25 orthogonal array is selected, considering the number of levels and parameters, and Finite Element Analysis (FEA) is applied to enhance precision in results. To validate both simulation outcomes and the proposed optimization methodology, Artificial Neural Network (ANN) analysis is conducted for the chosen component. The Fuzzy-TOPSIS method, in conjunction with ANN, is employed to ascertain the optimal levels of the selected parameters. The margin of error between the chosen optimization methods is found to be less than one percent, underscoring their suitability for injection molding optimization. The efficacy of the selected optimization method has been corroborated in prior research. Ultimately, employing the fuzzy-TOPSIS optimization method yields a minimum shrinkage value of 16.34% and a sink mark value of 0.0516 mm. Similarly, utilizing the ANN optimization method results in minimum values of 16.42% for shrinkage and 0.0519 mm for the sink mark. Doi: 10.28991/ESJ-2024-08-05-025 Full Text: PDF
BSHPC: Improve Big Data Privacy Based on Blockchain and High-Performance Computing (HPC) Alsumayt, Albandari
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-011

Abstract

The vast expansion and sharp rise in data across many facets of society have made it increasingly difficult to manage big data effectively. Using traditional methods to ensure the security and privacy of users’ data is no longer sufficient. In keeping with this worry, massive data storage is still crucial. High-Performance Computing (HPC) is examined to determine the need for handling blockchain issues and protecting large data in a decentralized manner that strives for resilience. This study proposes the Big Data Storage High-Performance Computing (BSHPC) approach, which addresses big data considerations in storage management to maintain accuracy and enables the usage of blockchain. The best storage management is the primary benefit of BSHPC, as only critical data is kept on the blockchain, and other data may be kept in an off-chain database using the interplanetary file system (IPFS). Furthermore, the network's node authentication in this strategy depends on trustworthy nodes. On HPC computers, data authenticity and provenance tracking would be guaranteed, and managing large data across blockchains would be more secure. The proposed method is simulated using the Python-MPI version, and the results confirm the effectiveness of the proposed method based on performance and transactions. Moreover, the proposed method is evaluated with another study in the literature on MEC-based sharing, and it proves its effectiveness. Doi: 10.28991/ESJ-2024-08-06-011 Full Text: PDF

Page 69 of 81 | Total Record : 803

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