cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
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 30 Documents
 1   2   3 
Search results for , issue "Vol. 9 No. 6 (2025): December" : 30 Documents clear
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.
Policy Recommendations for Enhancing the Green Banking and Sustainable Development Dien Vy, Phan; Tam, Phan Thanh
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-2025-09-06-011

Abstract

This study examines the key factors influencing green banking and sustainable development in Vietnam to provide evidence-based policy recommendations to promote the integration of sustainability within financial institutions. A mixed-method approach combining qualitative and quantitative analyses was adopted. The research process began with focus group discussions with 30 banking experts, followed by in-depth interviews with senior managers to refine measurement scales. Subsequently, a structured survey was conducted among 900 commercial bank managers in the Southeast region, and the collected data (n = 845 valid responses) were analyzed using exploratory factor analysis, confirmatory factor analysis, and structural equation modeling (SEM). The findings reveal seven knowledge-driven factors that significantly affect green banking and sustainable development: the legal framework and supporting policies, awareness and trends in sustainable consumption, financial technology, leadership commitment and corporate culture, pressure from investors and international organizations, climate change and environmental risk management, and public-private partnerships. Among them, the legal framework and supporting policies emerged as the most influential drivers. Green banking practices are also shown to directly contribute to sustainable development by financing environmentally friendly projects and integrating ESG principles. The study’s novelty lies in its knowledge-based economy perspective, demonstrating how policy knowledge, financial technology, and organizational learning interact to enhance sustainability. Practical implications highlight the need for regulatory reform, technology adoption, and cross-sectoral collaboration to accelerate Vietnam’s transition to a green economy.
The Use of Eye-Tracking Technology in Mathematics Education: A Mapping Study with Bibliometric Analysis Farman; Siswono, Tatag Y. E.; Lukito, Agung; Dewi, Ratna Sari; Hali , Fitriyani; Herlina
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-2025-09-06-029

Abstract

This study aims to identify trends, domains, and research topics of eye-tracking methodology in mathematics education published in Scopus-indexed journals. A systematic mapping study with bibliometric was employed to investigate the field. The analysis identified 119 eye-tracking studies in mathematics education published between 2013 and 2023, reflecting fluctuating publication trends. In this period, 333 authors, 78 sources, 156 organizations, and 38 countries contributed to the field. Schindler authored the most documents, while Germany and the United States recorded the highest output. The most cited work was by Cortina et al., and the International Journal of Science and Mathematics Education was the most frequently cited journal. Collaborations analysis identified Brockmole and Hannula as the most collaborative authors, and the University of Helsinki as the most active institution. Topic and domain analysis showed that the studies primarily focused on numbers and arithmetic, problem-solving, reasoning, individual differences, mathematical anxiety, creativity, mathematical representation, multimedia in learning, embodied cognition, mathematics learning, learning difficulties, geometry, and preschool mathematics. The findings suggest that several mathematical domains remain underexplored, offering opportunities for further eye-tracking research in mathematics education.
Evaluating Digital Transformation Risks in Logistics and Supply Chain Management with PLS-SEM-ANN-fsQCA Nguyen, H. T. M.; Dang, H. B.; Nguyen, A. V. T.; Nguyen, H. Ngoc; Nguyen, P. V.
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-2025-09-06-023

Abstract

This study investigates the risks associated with digital transformation (DT) implementation in Vietnam’s logistics and supply chain management (SCM) sector, utilizing a hybrid PLS-SEM-ANN-fsQCA methodology to analyze data from 243 valid questionnaires. Anchored in the Technology-Organization-Environment framework augmented with human factors (TOE+H), the research aims to examine how technological, organizational, environmental, and human factors influence DT adoption and associated risks, including financial, operational, cybersecurity, and reputational risks, while exploring the moderating roles of firm size and digital literacy. Findings reveal that TOE+H factors significantly drive DT implementation, but misalignment, ineffective management, market volatility, and limited digital literacy amplify risks, particularly cybersecurity vulnerabilities. Moderation analyses indicate that high digital literacy, larger firm size, and regulatory compliance mitigate these risks. Artificial neural network (ANN) analysis highlights non-linear relationships, emphasizing technological and human factors as key drivers, while fuzzy-set qualitative comparative analysis (fsQCA) identifies configurations, such as strong technological-human factor alignment, linked to successful DT outcomes. Importance-Performance Map Analysis (IPMA) prioritizes technological and human factors for resource allocation to enhance sustainability. This study advances the TOE+H framework by integrating a hybrid methodology, offering novel insights into DT risk dynamics and practical strategies for sustainable logistics in Vietnam’s SCM sector.
Linking Psychological Safety Climate to Dual Innovation Through AI-Enabled Dynamic Capabilities Tao, Ke; Tan, Chai Ching
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-2025-09-06-022

Abstract

Objective: This study develops and empirically validates an integrated model that explains how the psychological safety climate influences dual innovation through AI-enabled dynamic capabilities in Chinese design organizations. Methods: A cross-sectional survey was conducted among 281 designers from industry design firms and departments. Data analysis employed partial least squares-structural equation modeling, including mediation bootstrapping analysis, importance-performance map analysis, necessary condition analysis, and quadratic effect analysis. Findings: All hypotheses received strong empirical support. The psychological safety climate has a significant influence on AI-enabled dynamic capabilities, with a path coefficient of 0.452 at p <0.001, and on dual innovation, with a coefficient of 0.383 at p < 0.001. AI-enabled dynamic capabilities have a positive impact on dual innovation, with a coefficient of 0.384 at p < 0.001, and significant mediation effects, indicating an indirect effect of 0.174 at p < 0.001. The model explains 42.7% of the variance in dual innovation. Importance-performance analysis reveals a psychological safety climate as highly important but moderately performing, indicating strategic opportunities for improvement for organizations. Necessary condition analysis confirms both constructs as essential requirements for innovation outcomes. The findings demonstrate that psychological safety climate, as a higher-order cultural resource, enables lower-order AI-enabled dynamic capabilities, supporting socio-technical systems structure for dual innovation. Organizations should prioritize investments in psychological safety while maintaining their AI capabilities. Novelty: This research introduces AI-enabled dynamic capabilities as a second-order formative construct and establishes the meta-capability role of psychological safety climate in AI-enabled dynamic capabilities and dual innovation, thereby extending the resource-based view and dynamic capabilities theories through micro-foundational perspectives.
Exploring Contextual and Behavioral Determinants of Environmental Auditing Adoption Thu Hoai, Nguyen
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-2025-09-06-06

Abstract

This study's primary objective is to identify the determinants of environmental auditing (EA) adoption intention in environmentally sensitive industries. Its novelty lies in developing and testing an integrated model that extends the Theory of Planned Behavior (TPB) by incorporating four contextual factors: Coercive Pressure, Stakeholder Pressure, Internal Resources, and Corporate Culture. For its methodology, the research analyzed survey data from 336 senior managers using Structural Equation Modeling (SEM). The findings confirm that the three core TPB constructs - Attitude, Subjective Norms, and Perceived Behavioral Control - are all direct, positive, and significant predictors of EA intention, with Attitude emerging as the strongest. Crucially, the study finds that the four contextual factors only influence intention indirectly, as their effects are fully mediated by the TPB constructs; Corporate Culture and Internal Resources exhibited the greatest total indirect effects. This research provides a significant improvement over existing models by empirically demonstrating this dual-pathway mechanism, suggesting that efforts to promote EA in Vietnam must focus not only on external pressures but also on cultivating positive managerial attitudes and enhancing internal organizational capabilities.
Credit Allocation to Private Sector and Growth: An ARDL Analysis for a Transitional Economy Nguyen, Tran Phuc
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-2025-09-06-019

Abstract

This study examines the role of credit allocation to the private sector in driving economic growth in Vietnam’s transitional economy. The primary objective is to evaluate whether bank credit allocation fosters sustainable output expansion or, conversely, produces diminishing returns when it surpasses optimal levels. Employing the Autoregressive Distributed Lag (ARDL) bounds testing framework, the analysis uses annual data for 1990–2024 and compares three specifications: credit to the private sector, aggregate credit to the economy, and credit to the state sector. Findings indicate a robust long-run cointegration between credit and output, but with a clear nonlinear pattern: private credit enhances growth up to a threshold of roughly 91% of GDP, beyond which its marginal effect declines. While capital formation and moderate inflation consistently support long-term growth, foreign direct investment exerts mainly short-term benefits, and state-directed credit shows no significant contribution. The novelty of this paper lies in extending previous studies through a longer time horizon, updated post-GDP-revision data, and explicit disaggregation between private and state credit. By highlighting threshold effects and sectoral inefficiencies, this research improves understanding of the credit–growth nexus in transitional economies and underscores the need to prioritize credit quality, efficiency, and SME access in credit policy. JEL Classification: E51, G21, O47, P27.

Page 2 of 3 | Total Record : 30

 1   2   3 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 10 No. 1 (2026): February Vol. 9 No. 6 (2025): December Vol. 9 No. 5 (2025): October Vol. 9 No. 4 (2025): August Vol. 9 No. 3 (2025): June Vol 9, No 1 (2025): February Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education" Vol 8, No 6 (2024): December Vol 8, No 5 (2024): October Vol. 8 No. 5 (2024): October Vol 8, No 4 (2024): August Vol 8, No 3 (2024): June Vol 8, No 2 (2024): April Vol 8, No 1 (2024): February Vol 8 (2024): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 7 (2023): Special Issue "COVID-19: Emerging Research" Vol 7, No 6 (2023): December Vol 7, No 5 (2023): October Vol 7, No 4 (2023): August Vol 7, No 3 (2023): June Vol 7, No 2 (2023): April Vol 7, No 1 (2023): February Vol 7 (2023): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 6 (2022): Special Issue "COVID-19: Emerging Research" Vol 6, No 6 (2022): December Vol 6, No 5 (2022): October Vol 6, No 4 (2022): August Vol 6, No 3 (2022): June Vol 6, No 2 (2022): April Vol 6, No 1 (2022): February Vol 6 (2022): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 5 (2021): Special Issue "COVID-19: Emerging Research" Vol 5, No 6 (2021): December Vol 5, No 5 (2021): October Vol 5, No 4 (2021): August Vol 5, No 3 (2021): June Vol 5, No 2 (2021): April Vol 5, No 1 (2021): February Vol 4 (2020): Special Issue "IoT, IoV, and Blockchain" (2020-2021) Vol 4, No 6 (2020): December Vol 4, No 5 (2020): October Vol 4, No 4 (2020): August Vol 4, No 3 (2020): June Vol 4, No 2 (2020): April Vol 4, No 1 (2020): February Vol 3, No 6 (2019): December Vol 3, No 5 (2019): October Vol 3, No 4 (2019): August Vol 3, No 3 (2019): June Vol 3, No 2 (2019): April Vol 3, No 1 (2019): February Vol 2, No 6 (2018): December Vol 2, No 5 (2018): October Vol 2, No 4 (2018): August Vol 2, No 3 (2018): June Vol 2, No 2 (2018): April Vol 2, No 1 (2018): February Vol 1, No 4 (2017): December Vol 1, No 3 (2017): October Vol 1, No 2 (2017): August Vol 1, No 1 (2017): June More Issue