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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 837 Documents
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The Influence of Work Motivation on Job Performance: Engagement and Burnout as Mediators Zeng, Jing; Pathak, Shubham; Zhaowen, Shuai
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-018

Abstract

Based on self-determination theory, a conceptual model is proposed in which work motivation operates as the antecedent variable, with work engagement and occupational exhaustion acting as dual mediators. To test this framework, data were collected through a structured questionnaire from 469 academic staff members across 24 private higher education colleges in Jiangxi Province and analyzed using structural equation modeling (SEM). The results demonstrate that greater levels of educators' work motivation are significantly correlated with improved job performance and that this effect is channeled through increased work engagement and reduced burnout. By elucidating these mediatory pathways, the findings deepen theoretical comprehension of how motivation drives performance and yield practical guidance for devising effective motivation and performance-management strategies within private higher education institutions.
Student Perspective on Employability Skills in Business Education Madi, Hisham Kamil; Abdelfattah, Fadi; Al-Washahi, Maryam; Abdel Qader, Ahmad
Emerging Science Journal Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-SIED1-023

Abstract

This study aims to examine how academic motivation and perceived relevance of curriculum influence career readiness and perceived employability skills among business students in Omani higher education institutions. It further investigates the mediating roles of skill development perception and learning engagement, and the moderating role of internship opportunities in these relationships. A quantitative survey-based approach was employed, collecting data from 386 business students using stratified random sampling to ensure representation across academic programs. Data were analyzed using SmartPLS-based Structural Equation Modeling (SEM), incorporating Confirmatory Factor Analysis, mediation, and moderation analyses. The findings reveal that both academic motivation and perceived curriculum relevance significantly enhance career readiness and employability skills, with skill development perception and learning engagement serving as significant mediators. Internship opportunities strengthen these effects, demonstrating their role as a key moderator. The novelty of this research lies in integrating motivational, curricular, and experiential factors within a single empirical framework for the Omani higher education context. The study contributes practical recommendations for curriculum design, teaching practices, and industry-academia collaboration, while providing policymakers with evidence-based insights to bridge the skills gap and better prepare graduates for the evolving labor market.
A Novel Statistical Process Control Approach for PM2.5 Monitoring Using Time Series Modeling Supharakonsakun, Yadpirun; Areepong, Yupaporn
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-05

Abstract

This research seeks to create a novel control chart capable of managing autocorrelated time series data by proposing a modified Exponentially Weighted Moving Average (EWMA) approach tailored to processes following the ARMA(p,q) model, which also makes use of exponential white noise. The key methodological contribution involves an explicit formula to compute the Average Run Length (ARL), while the Numerical Integral Equation (NIE) approach is utilized for verification purposes. The proposed formula not only demonstrated 100% agreement with NIE results but also significantly reduced computational time, requiring less than 0.001 seconds per run, compared to the 3–4 seconds typically needed by NIE. To assess the performance, simulation experiments and real-world case studies on PM2.5 air pollution data from Nakhon Phanom, Nan, and Nonthaburi provinces in Thailand were conducted. Our modified control chart was better at identifying minimal changes than a standard EWMA chart, as shown by lower ARL1, SDRL1, AEQL, and optimal PCI values. The one-sided chart structure, designed to monitor upward shifts in pollutant levels, further supports its application in environmental surveillance. Overall, the study introduces a fast, accurate, and practical tool for quality control in autocorrelated environments, offering both analytical and computational advantages over existing methods.
Human-Centered Organizational Culture in the Global Workplace: Strategic Approaches, Trends, and Practical Models Barabanova, Yelena; Tyulyupergeneva, Raushan; Nazyrova, Larissa; Ladzina, Natalya; Bekbayeva, Malika
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-027

Abstract

Amid accelerating global transformations, the need to reconsider human resource management strategies through the lens of human-centricity is becoming increasingly urgent. This study aims to examine the systemic implementation of human-centered organizational culture within international labor contexts, with a focus on enhancing employee well-being, adaptability, and organizational resilience. A mixed-methods approach was employed, combining comparative policy analysis, content analysis of regulatory documents, and empirical case studies. The empirical sample included 320 employees from multinational companies across four sectors (education, IT, healthcare, and manufacturing). The findings revealed statistically significant improvements following the implementation of the proposed model: autonomy increased from 5.48 to 5.86 (p = 0.012), competence from 5.33 to 5.61 (p = 0.038), and relatedness from 5.07 to 5.58 (p = 0.004). Positive emotion expression scores rose from 3.98 to 4.42 (p = 0.009), while the Human-Centeredness Index increased from 4.18 to 4.71 (p = 0.002). These results underscore the limitations of hierarchical management models and highlight the value of flexible, emotionally supportive systems. The scientific contribution of the study lies in the typologization of human-centric management models and the empirical validation of a scalable integration framework that combines emotional intelligence development, inclusive feedback cycles, and leadership support. This model provides a strategic foundation for building sustainable, inclusive, and ethically grounded organizational environments.
Feature Transformation on Big Data for Species Classification in Machine Learning Yow, Li Wen; Ong, Lee Yeng; Tan, Joon Liang
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

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

Abstract

Classification of bacterial species, particularly for closely related taxa, remains a major challenge in many areas, e.g., public health, food industries, and many others. The issues are mainly caused by overlapping genetic features of organisms and data complexities. In this study, a bacterial taxonomic identification framework that integrates genome-derived motif sequences with machine learning was introduced. Two hundred and forty genome sequences from Salmonella enterica, representing six subspecies and ten serovars, were used for modelling. Sequence motifs were predicted from single-copy orthologous core genes of the downloaded genomes. Single nucleotide polymorphisms (SNPs) within these motifs were extracted and numerically encoded as machine learning features. The 20 top-most informative predictors from feature selections were used for model training in Random Forest and Support Vector Machine. Comparing the output from multiple analyses, the Random Forest model achieved the highest accuracy of 97.92%, demonstrating reliable differentiation of Salmonella at both subspecies and serovar levels. This research presents two key innovations: i) the use of sequence motifs as molecular signatures for bacterial classification; ii) a novel feature engineering method that transforms genome-derived data into machine learning-readable features. The proposed framework offers a practical and scalable solution for fine-level bacterial classification and has high potential to be applied for other microbial taxa.
Enhance Multimodal Retrieval-Augmented Generation Using Multimodal Knowledge Graph How, Shue-Kei; Ong, Lee-Yeng; Leow, Meng-Chew
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-025

Abstract

Large Language Models (LLMs) have shown impressive capabilities in natural language understanding and generation tasks. However, their reliance on text-only input limits their ability to handle tasks that require multimodal reasoning. To overcome this, Multimodal Large Language Models (MLLMs) have been introduced, enabling inputs such as images, text, video and audio. While MLLMs address some limitations, they often suffer from hallucinations because of over-reliance on internal knowledge and face high computational costs. Traditional vector-based multimodal RAG systems attempt to mitigate these issues by retrieving supporting information, but often suffer from cross-modal misalignment, where independently retrieved text and image content cannot align meaningfully. Motivated by the structured retrieval capabilities of text-based knowledge graph RAG, this paper proposes VisGraphRAG to address the challenge by modelling structured relationships between images and text within a unified MMKG. This structure enables more accurate retrieval and better alignment across modalities, resulting in more relevant and complete responses. The experimental results show that VisGraphRAG significantly outperforms the vector database-based baseline RAG, achieving a higher answer accuracy of 0.7629 compared to 0.6743. Besides accuracy, VisGraphRAG also shows superior performance in key RAGAS metrics such as multimodal relevance (0.8802 vs 0.7912), showing its stronger ability to retrieve relevance information across modalities. These results underscore the effectiveness of the proposed Multimodal Knowledge Graph (MMKG) methods in enhancing cross-modal alignment and supporting more accurate, context-aware generation in complex multimodal tasks.
Design and Evaluation of C-Band Microstrip Antenna Array for Portable Ground Surveillance Radar Matheus Edward, Ian Josef; Hariyadi, Tommi; Shalannanda, Wervyan; Bharata, Endon; Danudirdjo, Donny; Hidayat, Yosi A.; Hariyanto, Dharma Favitri; Mustafa, Alvin; Kusmadi; Nugroho, Sapto Adi; Ridwan, Nerissa Arviana
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-015

Abstract

This study aims to design, simulate, fabricate, and evaluate a high-gain C-band microstrip antenna array with a corrugation plate for Portable Ground Surveillance Radar (PGSR) applications, addressing the need for compact, high-performance antennas in border security operations. The proposed design targets a minimum gain of 20 dBi, a horizontal beamwidth of ≤ 2.8°, a vertical beamwidth of ≤7.5°, horizontal polarization, and compact physical dimensions for field portability. The methodology involved electromagnetic simulations to optimize the slit-patch array geometry, fabrication using Rogers RO-4350B substrate for its stable dielectric properties, and performance validation in an anechoic chamber using a vector network analyzer. The fabricated prototype achieved strong agreement with simulations in key metrics: realized gain exceeded 20 dBi, return loss reached -27.35 dB, and SWR was approximately 1.2, confirming effective impedance matching. The corrugation plate enhanced impedance matching, improved transmission efficiency (S21), and reduced reverse isolation (S12), while S22 remained stable. Despite these strengths, the measurement beamwidths, especially vertical beamwidth (~30°), exceeded both simulation and target values, highlighting fabrication precision and alignment as areas for improvement. The novelty of this work lies in integrating a corrugation plate to improve impedance matching and the correlation between simulation and measurement, offering a practical, tuneable enhancement to microstrip antenna arrays for PGSR and similar radar systems.
DML-IDS: Distributed Multi-Layer Intrusion Detection System for Securing Healthcare Infrastructure Yoosuf, Mohamed Sirajudeen; Vijaya, P.; Mani, Joseph
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-016

Abstract

In recent years, the number of cyberattacks targeting healthcare resources has rapidly increased. Conventional IDSs rely heavily on predefined rules and attack signatures. However, modern zero-day attacks with unpredictable behavior and multi-vector attack patterns can still breach healthcare networks. When a new type of cyberattack targets a specific server, an existing IDS may fail to detect it because it depends on static, predefined rules. To address these issues, we propose DML-IDS: Distributed Multi-Layer Intrusion Detection System, designed to operate across multiple nodes in a network to collaboratively detect suspicious activities. The proposed approach employs a multi-layer ensemble strategy to improve detection accuracy while reducing computational overhead on a single machine. All incoming network packets are first analyzed by the Distributed Threat Analysis Module (DTAM), which runs a Random Forest-based model as the base classifier to distinguish between benign and malicious traffic. Based on the nature and severity of the threat, malicious packets are flagged as highAlert (HA) in the Threat Prioritization Layer (TPL) and then forwarded to the respective Confirmatory Ensemble Model (CEM) for further, attack-specific analysis. These CEM models are designed to scale efficiently and detect zero-day as well as multi-vector attacks. The proposed model was trained on the CICIDS-2017 dataset. DTAM achieved an accuracy of 98.5%, while the CEM models for DDoS, Patator, and Web Attack achieved 99.01%, 98.87%, and 98.91% accuracy, respectively. Furthermore, the computational overhead of the DML-IDS architecture was evaluated and compared with an existing ensemble learning-based IDS.
Extrusion Technology for Complex Processing of Brewery Waste Into Feed Products for Livestock and Poultry Yazykbayev, Yerkin; Iztayev, Auyelbek; Kulazhanov, Talgat; Yakiyayeva, Madina; Baigazieva, Gulgaisha
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-021

Abstract

An energy-efficient extrusion technology for the complex processing of wet brewing waste into feed products for animals and poultry is proposed and evaluated. The study aims to replace traditional energy-intensive drying methods – typically involving natural gas, steam, or boiler exhaust gases – with a more sustainable extrusion process. The approach allows direct utilization of wet brewing by-products, such as brewers’ grains and brewers’ yeast, without preliminary drying, thereby reducing energy consumption by up to 50%. The technological development was based on systems analysis and synthesis of extrusion processes, combining wet brewing waste with dry feed components. The research identified optimal parameters for extrusion: a feed mixture to compound feed component ratio of 1:1.85–2; initial moisture content of 28–30%; extrusion temperature of 140–150 °C; and barrel pressure of 4–8 MPa. The final product was a partially dehydrated mass with a moisture content of 60–65%, suitable for use as a feed additive or complete compound feed. The results demonstrate improved product quality and extended shelf life due to thermal and mechanical treatment during extrusion. The novelty of the approach lies in bypassing the conventional drying step, offering a cost-effective and environmentally friendly way to increase the value of brewing industry waste.
Neuroleadership in Twenty-First-Century Education: A Systematic Review Revilla-Briceño, Yuli T.; Cieza-Mostacero, Segundo E.; Hidalgo-Lama, Jenry A.
Emerging Science Journal Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-SIED1-022

Abstract

In today’s volatile and interconnected world, characterized by economic uncertainty, competitiveness, and constant demands for transformation, organizations are challenged to adapt effectively to new requirements. This study presents a systematic review of 31 peer-reviewed articles published between 2014 and 2025 that examine neuroleadership in both managerial and educational contexts. The review offers a comprehensive framework that links leadership challenges with organizational strategies designed to transform work and academic practices. Findings highlight the benefits of neuroleadership in enhancing emotional well-being, engagement, decision-making, organizational resilience, and the development of both cognitive and emotional skills, as well as sustainability. The evidence further underscores the need to cultivate leaders who are capable of managing teams with empathy and strategic insight, thereby fostering more adaptive and human-centered workplace cultures. Overall, neuroleadership emerges as an innovative and essential paradigm for twenty-first-century leadership, rooted in cognitive processes and focused on the holistic development of human talent. Its successful implementation requires strategic vision, specialized training, and organizational commitment to address emerging challenges.

Page 82 of 84 | Total Record : 837

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