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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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Food Supply Chain: Possible Impact and Consequence Analysis of Reducing Working Hours of Food Retailers Pilvere, Aija; Pilvere, Irina; Proskina, Liga; Cerina, Sallija; Nipers, Aleksejs
Emerging Science Journal Vol 9, No 1 (2025): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-01-05

Abstract

Grocery shops constantly follow trends and developments in consumer demand; therefore, solutions are sought to enhance food retailing, and one solution is to limit the working hours of supermarkets to balance the interests of stakeholders and those employed in the food supply chain. Accordingly, the present research aims to identify the possible socio-economic impact of reducing the working hours of food supermarkets in Latvia. The research analyzed primary information sources: publicly available information from databases and annual reports by companies from the industry. Three potential scenarios were designed to identify the socio-economic impact of reducing the working hours of supermarkets. The research found that if the working hours of the four leading food supermarkets (Maxima, Rimi, Lidl, Sky) in Latvia were reduced, their turnover, market shares, and taxes paid to the national government, as well as the hours worked by their employees, would decrease, thereby leading to some redundancies causing some negative socio-economic consequences. The novelty of the research is that retail is an essential link in the food supply chain from farm to fork, making food available to consumers. The calculations show that we should be careful when reducing the working hours of supermarkets because this has socio-economic consequences. It is also necessary to evaluate the attitude of consumers. Doi: 10.28991/ESJ-2025-09-01-05 Full Text: PDF
Invisible Scout: A Layer 2 Anomaly System for Detecting Rogue Access Point (RAP) Arisandi, Diki; Ahmad, Nazrul M.; Kannan, Subarmaniam
Emerging Science Journal Vol 9, No 1 (2025): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-01-016

Abstract

Rogue Access Points (RAPs) pose a significant security threat by mimicking legitimate Wi-Fi networks and potentially compromising sensitive data. To address this issue, this research has proposed an innovative mechanism called Invisible Scout, which uses a multi-module system to identify RAPs. This study aimed to develop and validate a mechanism capable of accurately detecting RAPs in controlled setups, real-world environments, and under de-authentication attack scenarios. The proposed system consists of four key modules: sniffer, detection, probing, and comparison. To evaluate its effectiveness, tests were conducted in controlled and open environments and under de-authentication scenarios, using decision tree models and various metrics to assess performance. The decision tree model showed promising results in the controlled setup, achieving an Area Under the Curve (AUC) score of 0.921 and classification accuracy (CA) of 0.875, indicating that the model effectively distinguished between legitimate access points and RAPs. When tested in an open environment, the model's performance improved, achieving an AUC score of 0.952 and a CA of 0.994. Furthermore, under a de-authentication attack, the model achieved an AUC score of 0.955 and a CA of 0.996. To gain a deeper understanding of RAP behaviors, linear regression analysis was conducted, revealing patterns and visualizing the existence of RAPs, which could assist in further analysis. In conclusion, the results demonstrated that the proposed mechanism was highly effective in identifying RAPs. Future research should focus on refining the detection mechanism, incorporating real-time response capabilities, and expanding testing to diverse network scenarios. Doi: 10.28991/ESJ-2025-09-01-016 Full Text: PDF
Estimating Ruin Probability in an Insurance Risk Model Using the Wang-PH Transform Through Claim Simulation Ieosanurak, Weenakorn; Moumeesri, Adisak
Emerging Science Journal Vol 9, No 1 (2025): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-01-011

Abstract

The accurate estimation of ruin probability is a fundamental challenge in non-life insurance, impacting financial stability, risk management strategies, and operational decisions. This study aims to propose an approach for estimating ruin probability using claim simulation enhanced by the Wang-PH transform to fit various loss distributions, including Gamma, Weibull, Lognormal, Log-logistic, Inverse Weibull, and Inverse Gaussian, to actual claim data. Methods involve the transformation of loss distributions via the Wang-PH transform and rigorous evaluation to select the optimal distribution model that best reflects actual claim characteristics. This model serves as the foundation for estimating finite-time ruin probability through claim simulation, employing the acceptance-rejection technique to generate random samples. Additionally, a regression-based methodology estimates the minimum capital reserve required to safeguard against financial risk. Findings indicate the proposed method's computational efficiency, making it a valuable tool for insurers and risk analysts in assessing and mitigating financial risks in the non-life insurance sector. The novelty of this study lies in the integration of the Wang-PH transform with empirical data fitting and simulation techniques, applied to estimating ruin probability and determining capital reserves. Doi: 10.28991/ESJ-2025-09-01-011 Full Text: PDF
Investigating the Effectiveness of Coal-Fired Power Plant Operations: Management, Technical and Air Pollution Aspects Ramly, Zamzam T. A.; Ishak, Mohd Y.; Abdullah, Ahmad M.; Ismail, Marzuki; Abdullah, Samsuri; Abu Mansor, Amalina
Emerging Science Journal Vol 9, No 1 (2025): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-01-06

Abstract

Coal-fired energy has been a major part of Malaysia's power supply, causing environmental pollution and slowing sustainable growth. To address these issues, we evaluated a coal-fired power plant's efficiency using a questionnaire completed by industry experts. This study seeks to find factors affecting coal-fired power generation efficiency and create a statistical model. The questionnaire covered five areas: best management practices, technology efficiency, cost efficiency, fuel efficiency, air pollution control, and the best available technique. Principal Component Analysis (PCA) was used to simplify large data sets. The results showed that 15 principal components were valid, with a KMO value of 0.836 (greater than 0.50) and a Bartlett Test value below 0.05. The results show a strong correlation between the best available technique and various indicators: best management practice (r=0.614, p<0.01), technology efficiency (r=0.719, p<0.01), cost efficiency (r=0.529, p<0.05), fuel efficiency (r=0.662, p<0.01), and air pollution control efficiency (r=-0.752, p<0.01). The model indicates that verifying the standard operating procedure (SOP) is crucial for improving power generation efficiency and reducing human error (R²=0.914). This study pinpoints issues reducing power plant efficiency, particularly regarding emissions, and shows that the regression model is strong (R² = 0.916–0.647). It will assist policymakers and researchers in creating sustainable environmental management plans. Doi: 10.28991/ESJ-2025-09-01-06 Full Text: PDF
The Influence of Quality of Work Life and Perceived Organizational Support on Turnover Intention in Private Higher Education Institutions Zeng, Dongxiu; Swatdikun, Trairong; Aujirapongpan, Somnuk; Huang, Shi-Zheng
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-SIED1-020

Abstract

The study addresses faculty turnover in private higher education institutions, a challenge that disrupts institutional progress and educational continuity. It examines the influence of Quality of Work Life (QWL) on turnover intention (TI) and the mediating role of Perceived Organizational Support (POS). Utilizing a stratified random sample of 396 educators across 24 private colleges, data were collected through structured questionnaires and analysed using Structural Equation Modeling (SEM). The findings reveal a significant negative relationship between QWL and TI, indicating that improved work-life balance reduces educators' intent to resign. Additionally, QWL positively influences POS, which further diminishes TI, with POS mediating the QWL-TI relationship. By integrating POS as a mediator, the study provides actionable insights for educational administrators, emphasizing the importance of enhancing QWL and POS to mitigate faculty turnover. The findings offer a foundation for developing targeted policies aimed at improving workplace conditions and organizational support to ensure institutional stability. Doi: 10.28991/ESJ-2024-SIED1-020 Full Text: PDF
Enhancing Student Motivation and Competencies: Integrating E-Learning, Technological Literacy, and Cultural Alignment Shannaq, Boumedyen; Saleem, Imran; AlRawahi, Said; Almhlawi, Saad; AlMaqbali, Said
Emerging Science Journal Vol 9, No 1 (2025): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-01-025

Abstract

This study explores the integration of open innovation in education with a focus on technology and cultural aspects, aiming to foster improvements in educational management and policy within Oman learning environments. The research investigates the relationships between technological literacy (TL), cultural compatibility (CC), human face-to-face communication methods (HFtFCM), and student motivation and engagement (SMaE). A survey was conducted among 1,436 Oman students, and the data were analyzed using partial least squares and structural equation modeling to assess the moderating effects of TL and CC on these relationships. Results indicate that incorporating TL and CC as moderators significantly strengthens the mediation framework, enhancing the connection between HFtFCM and student perception of communication effectiveness (SPoCE). Specifically, the p-value decreased from 0.11 to 0.005, highlighting increased statistical significance, and the path from HFtFCM to SPoCE to SMaE improved from -0.051 to 0.071, demonstrating a stronger mediation effect. Conversely, the indirect effect from Technology-Based Communication Methods (TBCM) to SPoCE to SMaE decreased from 0.047 to 0.008. Additionally, notable paths such as TL → SPoCE and CC → SPoCE emerged, illustrating the enhanced explanatory power of these moderators. Conclusion: These findings underscore the potential of TL and CC to elevate student engagement and communication effectiveness, offering valuable insights for educational policy development and leadership programs. Doi: 10.28991/ESJ-2025-09-01-025 Full Text: PDF
Organizational Internal Factors and Sustainable Performance: A Serial Mediation Model Ejaz, Faisal; Abid, Sidra; Nasir, Aemin; Esponda-Pérez, Jorge Alberto; Ejaz, Sarmad; Hossain, Md Billal
Emerging Science Journal Vol 9, No 1 (2025): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-01-020

Abstract

Objective: The present study aims to explore the relationships between big data analytics capability, circular economy practices, and SMEs’ sustainable performance in Pakistan. It investigates notable factors determining SMEs’ sustainable performance, including employees’ perceived usefulness, data-driven culture, and leadership competency mediating the mentioned relationships. Method: The study employs quantitative research based on a positivist philosophy orientation. Data were collected through a structured questionnaire distributed among the employees of 350 SMEs operating in Pakistan’s different regions. Findings: The study's results demonstrated the direct effects of big data analytics capability on sustainable performance, employee perceived usefulness, and data-driven culture. Additionally, circular economy practices influence sustainable performance; employee perceived usefulness and leadership competency. Finally, the results highlighted that each relationship is subject to partial mediation, which indicates the role of employee-perceived usefulness and data-driven culture in the relationship between big data analytics and sustainable performance and employee-perceived usefulness associated with the relationship between circular economy practices and sustainable performance. Novelty:The present study highlights that all three of the previous topics are consistent and significantly contribute to the existing literature by providing a model with the main factors that determine SMEs’ sustainable performance, which can be sufficient for countries’ developing economies. Doi: 10.28991/ESJ-2025-09-01-020 Full Text: PDF
Reducing the Incidence of Bullying in Secondary Schools Gabrhelová, Gabriela; Pasternáková, Lenka; Barnová, Silvia
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-SIED1-026

Abstract

The present study discusses the issues of bullying prevention in secondary schools with the objective of finding out about the efficiency of the experiential learning method in this context. Within a more extensive research study under realisation, the method of pedagogical experiment was applied to a sample of 100 vocational school students, and its partial results are presented. A set of experiential activities was prepared for teachers and used by them in the experimental group (50 students) within a 10-month bullying prevention program. In the control group (50 students), traditional methods of bullying prevention were used. To examine the effect of the intervention, the Olweus Bullying Questionnaire was administered to the participating students before and after the intervention (pre-test and post-test). The obtained research results suggest that the implementation of experiential learning activities contributed to a more positive school climate and favourable conditions for the realisation of bullying prevention in the participating school. Although given the limits of the research study, the present findings cannot be generalised to the entire population of vocational school students, the study brings unique data that fill the gap in current knowledge and create a basis for further research activities. Doi: 10.28991/ESJ-2024-SIED1-026 Full Text: PDF
Innovative Chemical Engineering Education: Social Media-Enhanced Project-Based Learning Approaches Guaya, Diana E.; Jaramillo-Fierro, Ximena V.; Meneses, Miguel A.; Valarezo, Eduardo
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-SIED1-021

Abstract

This study investigates the integration of social media platforms, specifically YouTube and TikTok, as educational tools in Project-Based Learning (PBL) within chemical engineering courses, with a particularly focus on Unit Operations. The research involved seventy-eight students from the Universidad Técnica Particular de Loja across two consecutive semesters (April-August 2022 and October 2022-February 2023). Students were tasked with creating educational videos to communicate complex engineering concepts. YouTube was utilized for longer, detailed explanations, while TikTok was employed for short, engaging content. The results demonstrate the effectiveness of this method in enhancing student engagement and comprehension of both theoretical and practical concepts. Instructors observed substantial improvements in student creativity and digital literacy. Quantitative data, such as average course scores, and qualitative feedback from instructors highlight both the strengths and challenges of leveraging social media as a learning tool. A project evaluation rubric was developed to assess performance across several dimensions, including content mastery, practical application, creativity, and engagement. The study concludes that the combination of PBL with social media platforms creates a dynamic, interactive learning environment that cultivates essential skills for future engineers. However, it also identifies areas for refinement, particularly in terms of effective communication through digital media formats. Doi: 10.28991/ESJ-2024-SIED1-021 Full Text: PDF
HSTCN-NuSVC: A Homogeneous Stacked Deep Ensemble Learner for Classifying Human Actions Using Smartphones Raja Sekaran, Sarmela; Pang, Ying Han; Shih Yin, Ooi; Zheng You, Lim
Emerging Science Journal Vol 9, No 1 (2025): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-01-026

Abstract

Smartphone-based human activity recognition (HAR) is an important research area due to its wide-ranging applications in health, security, gaming, etc. Existing HAR models face challenges such as tedious manual feature extraction/selection techniques, limited model generalisation, high computational cost, and inability to retain longer-term dependencies. This work aims to overcome the issues by proposing a lightweight, homogenous stacked deep ensemble model, termed Homogenous Stacking Temporal Convolutional Network with Nu-Support Vector Classifier (HSTCN-NuSVC), for activity classification. In this model, multiple enhanced TCN networks with diverse architectures are organised parallelly to capture hierarchical spatial-temporal patterns from raw inertial signals. Each base model (i.e., TCN) incorporates dilations and residual connections to preserve longer effective histories, allowing the model to retain longer-term dependencies. Additionally, dilations can diminish the number of trainable parameters, reducing the model complexity and computational cost. The base models’ predictions are concatenated and fed into a meta-learner (i.e., Nu-SVC) for final classification. The proposed HSTCN-NuSVC is evaluated using a publicly available database, i.e., UCI HAR, and a subject-independent protocol is implemented. The empirical results demonstrate that HSTCN-NuSVC achieves 97.25% accuracy with only 0.51 million parameters. The results exhibit the model’s effectiveness in enhancing generalisation across individuals with better accuracy and computational efficiency. Doi: 10.28991/ESJ-2025-09-01-026 Full Text: PDF

Page 72 of 81 | Total Record : 803

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