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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 903 Documents
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QLAF: Q-Learning Adaptive Forwarding for Disaster Emergency Communication in Named Data Networking Ratna Mayasari; Galih N. Nurkahfi; Nana R. Syambas; Eueung Mulyana
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-03

Abstract

Reliable communication is essential for effective disaster response; however, conventional IP-based networks often fail when the network infrastructure is damaged. Disaster communication networks need adaptive forwarding strategies that maintain reliability under rapid topology changes, various link qualities, and resource constraints. This research proposes a Q-Learning-based Adaptive Forwarding (QLAF) strategy designed to enhance reliability in heterogeneous disaster emergency communication networks. QLAF implements reinforcement learning into the NDN forwarding plane, enabling each router to autonomously learn optimal forwarding faces based on multiple performance metrics: Round-Trip Time (RTT), throughput, and link stability. The proposed strategy was implemented in the Named Data Networking Forwarding Daemon (NFD) and evaluated using the MiniNDN emulator over a BRITE-generated 25-node disaster topology that integrates terrestrial, cellular, and satellite links. We compared QLAF and Adaptive Smoothed RTT-based Forwarding (ASF), Access strategy, and Self-Learning. Experimental results show that QLAF achieves a Packet Delivery Ratio (PDR) of 99.91%. These results show that QLAF gives a robust solution for reliability-sensitive disaster communication, guaranteeing high data delivery performance under unstable network conditions. However, its latency overhead limits its applicability to real-time scenarios.
CFOs as Innovators in Debt Maturity Choices: New Evidence from an Asian Emerging Market Uyen To Diep
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-016

Abstract

This study aims to examine the role of the Chief Financial Officer (CFO) as an agent of innovation in shaping corporate debt maturity decisions within the framework of Upper Echelons Theory. Data were collected from 312 non-financial firms listed in Vietnam from 2011 to 2023, spanning 10 sectors, with focused analysis on three sectors - Industrials, Materials, and Consumer Staples - to highlight their specific characteristics. The personal traits of CFOs, including gender, age, education, and expertise, were analyzed in relation to debt maturity structures, measured by the long-term debt ratio (LTDR) and weighted average debt maturity (WAM). To explore these relationships, various regression methods - OLS, FEM, REM, FGLS, and GMM - were applied. The results reveal that CFO attributes significantly affect debt maturity choices, emphasising their innovative role in corporate financial management. Moreover, this influence varies among sectors: Industrials, Materials, and Consumer Staples exhibit notable differences from other sectors. The study advances the literature by emphasising the pivotal role of CFOs in financial innovation and by extending Upper Echelons Theory. Empirically, it provides novel evidence from an emerging market context, a setting that remains underexplored. Practically, the findings offer insights for firms and investors in selecting and developing CFOs as strategic resources to enhance adaptability and financial stability.
Design and Evaluation of a Blockchain-Based Traceability Model for Organic Rice Supply Chain Rohmat Taufiq; Harco Leslie Hendric Spits Warnars; Haryono Soeparno; Tanty Oktavia
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-025

Abstract

This study aims to design and evaluate a blockchain-based traceability model and prototype for the traceability of organic rice supply chains in Banten Province, Indonesia. The main problem identified is the absence of a digital system that can transparently trace the origin of organic rice, which has reduced consumer trust in product authenticity. This research adopts the Design Science Research Methodology (DSRM) to develop a traceability model and prototype capable of recording all supply chain activities from farmers to end consumers. The prototype was validated using the ISO/IEC 25010 standard with supply chain actors and further validated with end consumers. Validation results from supply chain actors indicate strong performance across maintainability (4.91), functional suitability (4.29), security (4.11), performance efficiency (3.98), compatibility (3.89), usability (3.85), reliability (3.80), flexibility (3.93), and safety (3.77). Meanwhile, validation with end consumers yielded an average score of 4.22 on a 1–5 Likert scale. These findings indicate that the system meets key quality attributes—particularly functionality, reliability, security, and maintainability—at a very good level. In conclusion, implementing blockchain technology for organic rice supply chain traceability can enhance transparency, improve data security, and strengthen consumer trust in organic rice products.
Enhancing Small Language Models for Code Generation via Strategic Decomposition and Filtering Yuriy Perezhohin; Fabian Collao; Mauro Castelli
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-011

Abstract

This study addresses the challenge of enhancing Small Language Models (SLMs) for complex code generation tasks requiring structured planning, which current models struggle with due to their monolithic, single-pass generation approach. A three-stage pipeline architecture is proposed that decouples strategic planning from implementation: (1) an SLM generates diverse natural language strategies at high temperature, (2) a filtering mechanism selects high-quality strategies while removing noise, and (3) refined strategies guide a specialized coding model for final implementation. The approach was evaluated on the ClassEval benchmark for class-level code generation. The pipeline enabled a 1.5B parameter model to achieve 13% class success rate, representing a 30% relative improvement over direct generation (10%) and competitive performance with models 5-8 times larger. Critically, effective strategy filtering proved more important than strategy diversity, with simple pattern-based filters successfully mitigating SLM artifacts like few-shot contamination. This work demonstrates that structured, inference-time computation offers an efficient alternative to parameter scaling, with strategic noise reduction being the key driver of performance gains in resource-constrained models.
How Perceived Accuracy Drives Adoption of AI Personalized Recommendations: A Moderated Mediation Model Xiaolan Zhu; Siwarit Pongsakornrungsilp; Pimlapas Pongsakornrungsilp; Archana Kumari
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-023

Abstract

Artificial intelligence (AI)-powered personalized recommendation systems are reshaping how consumers search, evaluate, and purchase products, yet the psychological mechanisms through which perceived accuracy drives adoption remain underexplored. This study examines how perceived accuracy of AI recommendations influences consumer adoption willingness through perceived benefit and how this process is conditioned by product involvement. Drawing on the Technology Acceptance Model (TAM) and Product Involvement Theory, we develop an accuracy-centred moderated mediation model in which perceived accuracy (PA) leads to perceived benefit (PB), which in turn leads to consumer adoption willingness (AW) or (PA → PB → AW). The study uses survey data from 518 Chinese consumers with experience of using AI-personalized recommendations. The data are analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with multigroup analysis to examine age-based heterogeneity on consumer adoption willingness. The results show that perceived accuracy has a significant direct and indirect effect on adoption willingness, with perceived benefit acting as a partial mediator. Product involvement positively moderates the relationship between perceived accuracy and perceived benefit, and the proposed mechanisms are stable across age groups. The study opens the “black box” linking perceived accuracy to adoption, identifies key boundary conditions, and extends TAM by positioning perceived accuracy as an antecedent of perceived usefulness in AI recommendation contexts.
Mg, Si, Al, and P Particle-Doped Epoxy: A Synergistic Approach for Enhanced Fire Performance Qandeel Fatima Gillani; Almagul Mentbayeva; Muhammad Faisal Javed; Sandugash Kalybekkyzy
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-021

Abstract

This study presents the development of a low-toxicity, high-performance intumescent fire-retardant coating (IFRC) through a hybrid epoxy binder doped with Mg, Si, Al, and P particles. The objective was to improve thermal stability and char cohesion and reduce the toxic aromatic emissions typically released from bisphenol-A epoxy systems during combustion. Modified epoxy resins were prepared by dispersing Mg(OH)₂ and incorporating hydroxyl-terminated PDMS, followed by formulation with APP, melamine, expandable graphite, PER, and nano-alumina. Comprehensive analyses using FTIR, ¹³C NMR, DSC, TGA, SEM–EDS, TEM, XRD, and GC–MS, along with ISO-834 furnace and ASTM E-119 flame tests, were employed to evaluate chemical structure, thermal behavior, char morphology, and fire performance. The optimized formulation produced a dense Mg–Al–silicate–phosphate char network, achieved a 6.1× expansion ratio, limited backside steel temperature to 227°C, and retained 36% char at 800°C, which significantly outperformed the unmodified epoxy system. GC–MS confirmed a substantial (≈53%) reduction in toxic volatile emissions. A machine-learning model further validated char compactness with >94% classification accuracy. Collectively, the results demonstrate that synergistic inorganic–siloxane modification offers a scalable, halogen-free pathway to next-generation epoxy-based IFRCs with enhanced fire resistance and markedly lower toxicity.
Strategic Positioning for Knowledge-Based Industry Growth: Bridging Innovation and Competitiveness Gaps Haitham Al Qahtani; Jayendira P. Sankar
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-013

Abstract

This study examines the strategic positioning of knowledge-based industries (KBIs) of Bahrain to address the innovation and competitiveness gaps within the Gulf Cooperation Council (GCC) region. This study adopted Porter’s Diamond model to identify critical factors that enhance the attractiveness of Bahrain as a KBI hub, which include key strengths of Bahrain’s location-based advantage, regulatory efficiency, and human capital readiness. This study used a mixed-method approach that integrates both qualitative and quantitative analyses by utilizing secondary data sources from GCC comparative benchmarks, policy documents, and international databases. The findings highlight the significant strengths of Bahrain in business-friendly policy, financial ecosystem, and information and communication technology (ICT) infrastructure. This study also identifies strategic pathways to further build upon these strengths, including continued domestic market diversification, increased research and development (R&D) investment, and expanded venture capital availability, reinforcing Bahrain's promising growth trajectory. Recommendations for actionable policy are proposed to expand fintech and artificial intelligence (AI) ecosystems and foster university-industry linkages. Thus, this study advocates for enhanced cross-GCC cluster collaboration to support the Bahrain Vision 2030 by applying and extending the competitive framework to KBI and contributing research to theoretical discussions on knowledge-driven economies. Overall, this study offers practical strategies for industry stakeholders and policymakers to strengthen the innovation ecosystem of Bahrain.
Trusted AI-Based Method for Predicting Controller Load and PSO-Based Structure for Reducing Latencies Vladimir Zh. Kuklin; Islam Alexandrov; Maxim Mikhailov; Naur Z. Ivanov; Elena Yu. Linskaya
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-017

Abstract

The objective of this study is to develop a trusted AI-based framework for predicting software-defined networking (SDN) controller load and optimizing fog/edge microservice orchestration to reduce end-to-end latency in dense 5G scenarios. The proposed approach integrates user-aware spatial clustering with evolutionary resource selection to maintain stable quality of service (QoS) under high mobility and traffic variability. In the analysis stage, k-means clustering partitions users into spatial sectors and identifies sector centroids. Particle swarm optimization (PSO) is then applied to fog-node selection, resource sizing, and adaptive microservice placement and migration. To enhance system resilience, a recurrent neural network (RNN) is employed to forecast SDN controller load using correlation-informed features extracted from service-channel dynamics. Numerical experiments on heterogeneous fog-node topologies indicate that the framework reduces microservice execution time by 69% relative to baseline placement strategies under identical load conditions, while controller-load prediction attains an RMSE of 0.00387. These findings confirm the effectiveness of both the latency-reduction mechanism and the controller-load estimation workflow. The novelty of this work lies in the unified optimization of microservice placement, migration, and SDN controller-load anticipation within a single reproducible architecture, extending existing fog and edge orchestration approaches that typically address these components as independent subproblems.
Green Technology Innovation and Financial Performance: Roles of Executive Green Perception and Carbon Performance Yan Li; Pankaewta Lakkanawanit; Muttanachai Suttipun; Wilawan Dungtripop; Rizqa Anita
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-022

Abstract

Existing research on the linkage mechanisms among green technology innovation (GTI), executive green perception (EGP), carbon performance (CP), and financial performance (FP), particularly systematic investigations within the context of China’s high‑carbon industries, remains insufficient. To address this gap, this study explored the pathway through which GTI influences FP, as well as the mediating effect of EGP and the moderating effect of CP. Grounded in stakeholder theory and innovation diffusion theory, an integrated analytical framework was developed and tested using panel data from listed companies in China’s coal, energy, and manufacturing sectors spanning 2015 to 2023 (N = 11,302). The analysis employed a two-way fixed effects model. The results revealed that GTI significantly and positively impacts FP (β = 0.199, p < 0.01), with EGP serving a partial mediating role in this relationship. Furthermore, CP positively moderates the connection between GTI and FP (β = 0.096, p < 0.01). The key innovation of this research is its unique simultaneous examination of both mediating and moderating mechanisms within a single model. The approach provides a deeper theoretical explanation and practical managerial insights for utilizing green innovation to enhance financial outcomes in high-carbon transitional settings.
T-CER-Net: Attention-Based Temporal Cross-Eye Regression for Noise-Resilient Detection of Intermittent Strabismus Wattanapong Kurdthongmee; Karanrat Thammarak; Md Eshrat E. Alahi; Yun Hui; Piyadhida Kurdthongmee
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-014

Abstract

Automated strabismus screening using video is difficult in unconstrained settings, where brief events such as blinking, head movement, or tracking errors can easily be mistaken for true ocular misalignment. The objective of this study is to improve diagnostic specificity while maintaining sensitivity in automated pre-screening scenarios. To address this problem, a temporal analysis framework, termed the Temporal Cross-Eye Regression Network (T-CER-Net), is proposed. The method introduces the Cross-Eye Regression Error (CERE), a scale- and position-invariant temporal signal that characterizes deviations in binocular coordination by measuring prediction error between the two eyes. Rather than relying on frame-level deviation estimates, the approach analyzes extended CERE sequences using a Transformer Encoder to assess temporal consistency. In addition, the training procedure explicitly accounts for real-world variability through oversampling of normal sequences containing common artifacts and the use of class weighting. The proposed method was evaluated against static threshold-based classifiers and a CNN–LSTM temporal baseline. On a held-out test set, T-CER-Net achieved an area under the ROC curve of 0.9140, with a sensitivity of 0.8421 and a specificity of 0.8500, showing improved robustness to noise-induced false positives. The findings suggest that treating binocular misalignment as a temporal pattern, together with attention-based sequence analysis, offers a practical and robust basis for automated strabismus pre-screening in real-world settings.

Page 90 of 91 | Total Record : 903

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