Emerging Science Journal (ESJ)
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A Cross-Cultural Study of University Students' e-Learning Adoption
Get PDFThis study aims to investigate the differences in e-learning adoption among university students in Indonesia, Albania, Russia, and Kazakhstan and examine the role of cultural dimensions in explaining these differences. This research draws upon Hofstede's Cultural Dimensions Theory to explore the impact of culture on e-learning adoption in diverse global contexts. A cross-sectional survey was conducted with a sample of university students from the four countries, and the data were analyzed using structural equation modeling (SEM) and multi-group analysis (MGA) techniques. The findings reveal significant differences in e-learning adoption among the four countries, with learner engagement, learning satisfaction, and technology accessibility exhibiting varying levels of influence on e-learning adoption. The multi-group analysis indicates that cultural dimensions partially explain these differences, highlighting the importance of considering cultural factors when examining e-learning adoption in diverse settings. This study fills a gap in cross-cultural e-learning research, offering key insights into factors shaping students' adoption of online platforms worldwide. The findings emphasize the importance of cultural considerations for educators, policymakers, and e-learning developers in global higher education. This study contributes to the theoretical understanding of the complex interplay between culture and e-learning adoption by demonstrating the explanatory power of cultural dimensions. Doi: 10.28991/ESJ-2024-08-03-015 Full Text: PD
Demystifying Knowledge Work Practices and Performance in the Public Sector
Get PDFThe performance of the public sector, especially its officers, is vital to a nation's growth in light of the challenges clouding public service. Despite numerous efforts and initiatives, the level of efficiency of Malaysian public sector officers remains feeble, and public dissatisfaction has led to criticism of the administration. Therefore, addressing issues surrounding the performance of public sector officers is imperative to improve public perception. Guided by Drucker's knowledge work productivity theory, this research aims to discover the relationship between knowledge work practices toward affective commitment (AC) and knowledge worker performance (KWP). This research adopted a cross-sectional design involving a survey of 395 administrative and diplomatic officers who were recruited via stratified random sampling. A variance-based structural equation modeling using Smart PLS 4.0 was conducted to analyze the data. Results show that job crafting (JC) and continuous learning (CL) improve KWP, job-related innovation (JRI) does not impact KWP, and AC exerts a mediating impact on the relationship between knowledge work practices and KWP. This study provides impetus to knowledge productivity and human behavior by integrating JC into Drucker's theory. Doi: 10.28991/ESJ-2024-08-05-015 Full Text: PD
Design of Modified UWB Microstrip Antenna for UHF Partial Discharge Sensor
Get PDFThe 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 í— 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: PD
Visualization and Analysis Method of Defect Manifestation in Electromechanical Equipment
Get PDFThis study focuses on the problem of diagnosing electromechanical equipment and aims to prevent its failures by timely detecting hidden signs of defects in diagnostic signals. This paper considers the possibility of improving systems whose equipment monitoring relies on measuring and analyzing the diagnostic signal of vibration or motor current. Fourier series decomposition for processing complex signals is not always effective because the contribution of harmonics reflecting the specific effect of the defect is less than that of non-specific harmonics and is comparable to the influence of noise. It has been proposed to apply the singular spectral analysis method for visualizing and analyzing the regularities of defect manifestations. It is reasonable to supplement the classical algorithm of this method by comparing the analyzed eigenvalue spectrum corresponding to the operating condition. Detection of hidden defects for the first time involves analyzing initial data projections in the directions of the singular basis that reflect deviations under the defect influence. Numerical and field experiments confirm the possibility of analyzing comparatively weak generations essential for equipment condition identification. The experiments demonstrate the opportunity for timely defect detection due to preprocessing when the probability of defect detection using the frequency method is close to zero. Thus, the approach to timely detection of equipment defects and making adequate decisions to manage its condition is justified. Doi: 10.28991/ESJ-2024-08-04-04 Full Text: PD
Digital Collaboration Models for Empowering SMEs: Enhancing Public Organization Performance
Get PDFThis study aims to examine the effectiveness of SiBakul Jogja, a digital platform initiated by the government in Yogyakarta Province, in supporting small businesses and fostering collaboration among various stakeholders. Through interviews and research analysis, we investigate the mechanisms through which SiBakul Jogja facilitates small business growth and innovation. The findings reveal that SiBakul Jogja serves as a comprehensive resource hub for small businesses, offering assistance in record-keeping, advisory services, and fostering partnerships for innovation. Collaboration among government entities, businesses, academics, and the media plays a crucial role in enhancing the platform's impact. Positive outcomes include job creation and improved access to financial resources. However, challenges such as digital skills shortages and internet connectivity issues persist. The novelty of this study lies in its examination of SiBakul Jogja's collaborative approach in alignment with principles of new public service, contributing to improved public service delivery and economic growth. Addressing these challenges collectively presents an opportunity to leverage SiBakul Jogja's potential to significantly boost the local economy. Through effective teamwork and organizational strategies, digital support for small businesses can be optimized, fostering economic growth and resilience. Doi: 10.28991/ESJ-2024-08-04-015 Full Text: PD
A Hybrid Ant Colony and Grey Wolf Optimization Algorithm for Exploitation-Exploration Balance
Get PDFThe Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) are well-known nature-inspired algorithms. ACO is a metaheuristic search algorithm that takes inspiration from the behavior of real ants. In contrast, GWO is a grey wolf population-based heuristic algorithm. The important procedure in optimization is exploration and exploitation. ACO has excellent global and local search capabilities, and the exploration process is performed better than the exploitation process. In the case of regular, GWO is a greatly competitive algorithm compared to other common meta-heuristic algorithms, as it has super performance in the exploitation phase. This study proposed hybrid ACO and GWO algorithms. This hybridization is to acquire the balance between exploitation and exploration in optimization Swarm Intelligence algorithm”comprehensive examination using CEC 2014 benchmark functions. Detail investigations indicate that ACO-GWO could find solutions to unimodal, multi-modal, and hybrid problems in evaluation functions. The results show that the ACO-GWO algorithm outperforms its predecessors in several benchmark function cases. In addition, the proposed ACO-GWO algorithm could achieve an exploitation-exploration balance. Even though ACO-GWO has one disadvantage: since ACO-GWO directly combines two algorithms (ACO and GWO) with two different agents, it has superior demands on computational complexity. Doi: 10.28991/ESJ-2024-08-04-023 Full Text: PD
Blockchain and AI-Driven Framework for Measuring the Digital Economy in GCC
Get PDFThe rapid growth of the digital economy presents opportunities and challenges, particularly in the Gulf Cooperation Council (GCC) region, where economic diversification is essential. Accurate measurement of digital economic activity is crucial for developing effective policies and strategic decision-making. This study introduces a comprehensive Digital Economy Measurement (DEM) framework tailored for the GCC. The framework integrates blockchain technology for secure and transparent data management, FinGPT for advanced financial data analysis, and Conversational Agent (CA) for enhanced user interaction. The research methodology involves a step-by-step design, starting with identifying and categorizing relevant data sources, collecting data through APIs and web scraping, and utilizing smart contracts and oracles for validation and recording. The data is managed securely using decentralized storage solutions and regional nodes. We propose using FinGPT and CA to analyze data in-depth and extract valuable insights. User interaction is prioritized through CA, interactive dashboards, and natural language processing, which prioritize user interaction with interfaces tailored to GCC-specific languages and cultures. The study's contribution to the literature lies in its novel, integrated approach to measuring the digital economy in the GCC, addressing challenges related to data accuracy, privacy, and regulatory compliance. By leveraging blockchain, FinGPT, and CA, the DEM-GCC framework offers a robust and adaptable solution for understanding and fostering the region's digital economy. Doi: 10.28991/ESJ-2024-08-04-019 Full Text: PD
Analyzing Socio-Academic Factors and Predictive Modeling of Student Performance Using Machine Learning Techniques
Get PDFUnderstanding the factors that influence student performance is crucial for improving educational outcomes. Thus, this study aims to examine the impact of socio-economic and psychological factors on student performance, less is known about how students' personal attitudes and behaviors across different departments and activities correlate with their academic success. This study employs exploratory data analysis (EDA) to identify trends and relationships within the dataset. Machine learning techniques, such as K-means clustering and Long Short-Term Memory (LSTM) networks, are utilized to model and predict student performance based on their reported behaviors and preferences. The dataset is reduced using Principal Component Analysis (PCA) to enhance the clustering process. The findings suggest significant variations in academic performance based on departmental affiliation, gender, and engagement in certification courses. The LSTM model achieved an accuracy of 91% on the test set, demonstrating substantial predictive capability. However, the classification report reveals that while the model was highly effective in identifying the majority class (label 1), achieving a precision of 91% and a recall of 100%, it failed to correctly predict any instances of the minority class (label 0). The insights from this study could help educators tailor interventions to address the specific needs of students based on their behaviors and departmental affiliations, leading to more personalized education strategies and potentially improving academic outcomes. Doi: 10.28991/ESJ-2024-08-04-05 Full Text: PD
Transformational Leadership and Project Success: The Role of Leader-Member Exchange and Professional Commitment
Get PDFThe purpose of this study is to explore the effects of transformational leadership (TL) on project success (PS), focusing on the indirect impacts of leader-member exchange (LMX) and the moderating influence of professional commitment (PC). This study aims to address the inconsistent findings in existing literature regarding the transformational leadership-project success relationship and to uncover the mechanisms that affect project outcomes. The unit of analysis is finished projects. The 509 project managers on projects completed within the past five years responded to the poll to collect data. The data were then assessed using Smart-PLS software. The results confirmed the study's hypotheses, demonstrating that LMX mediates the relationship between transformational leadership and project success. Additionally, professional commitment was found to moderate both the relationship between transformational leadership and LMX and the connection between LMX and project success. Furthermore, transformational leadership was shown to have a direct positive effect on project success. These findings contribute to the theoretical foundations of leadership and project management by emphasizing the critical role of relationship quality within teams, with LMX serving as an intervention mechanism and professional commitment acting as a moderating role influencing project success. The insights from this study offer practical value for developing project management strategies tailored to specific organizational contexts, enhancing efficiency in project-based organizations. Future research should consider longitudinal studies to explore how the relationships between these antecedents and project outcomes evolve over time. Doi: 10.28991/ESJ-2024-08-06-022 Full Text: PD
Extreme Rainfall Trends and Hydrometeorological Disasters in Tropical Regions: Implications for Climate Resilience
Get PDFHydrometeorological disasters due to extreme weather events represent a significant threat to the security of life in Jambi Province. In order to develop effective strategies for mitigating this threat, it is essential to gain a comprehensive understanding of the underlying dynamics that give rise to such disasters. Despite the high frequency of these events, more research is needed on the complex relationship between trends in extreme indices and the frequency of hydrometeorological disasters in this region. This study addresses this gap by utilizing rainfall data from 2008 to 2020 from the Integrated Multi-satellite Retrievals for GPM (IMERG) and hydrometeorological disaster data from the National Disaster Management Agency (BNPB). A range of extreme rainfall indices, including PRCPTOT, R85P, R95P, R99P, CWD, CDD, R1mm, R10mm, R20mm, R50mm, RX1Day, RX5Day, and SDII, were subjected to careful analysis concerning hydrometeorological disasters, including floods, landslides, tornadoes, droughts, and forest fires. Notable results indicate a significant increasing trend (p < 0.05) for the CWD index, while decreasing trends are observed for R85P, R95P, R99P, R50mm, RX1Day, RX5Day, and SDII. PRCPTOT and R20mm show decreasing trends, and CDD shows an increasing trend, although it is not statistically significant (p > 0.05). Subsequently, there was a significant increase in landslides and tornadoes, while forest fires and floods showed an insignificant increase (p > 0.05). Drought exhibited a significant decreasing trend in Jambi. Correlation analysis revealed the complex relationship between extreme weather indices and hydrometeorological disasters. The positive correlations observed between most extreme rainfall indices and floods and landslides, except for CDD, indicate that extreme rainfall is the primary cause of these disasters in Jambi. The correlation is particularly pronounced in areas with mountainous topography, where landslides are more prevalent. The positive correlations observed between CDD and droughts and forest fires suggest that periods of reduced rainfall and increased drought contribute to these disasters. This correlation is more robust in districts with extensive peatlands. The results provide valuable insights into the vulnerability of Jambi Province to hydrometeorological disasters and highlight the importance of understanding regional variations in extreme weather events. These findings improve our understanding of the interactions between climate indices and disasters and provide the basis for informed risk reduction and adaptation strategies in changing climatic conditions. Doi: 10.28991/ESJ-2024-08-05-012 Full Text: PD