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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 30 Documents
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Search results for , issue "Vol. 10 No. 2 (2026): April" : 30 Documents clear
Motivation Profiles, Personal Values, and Personality Traits: The Interplay in Research Management and Administration José M. R. C. A. Santos; Melinda Fischer; Simon Kerridge
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-2026-010-02-029

Abstract

This study addresses a significant gap in the literature by examining the motivation profiles of Research Managers and Administrators (RMAs) and their correlation with personal values and personality traits. Drawing on Self-Determination Theory (SDT), the research sought to propose and validate a conceptual framework specifically for RMAs, introducing the distinct profile of outcome-driven motivation. Empirical data were collected using a quantitative, cross-sectional survey (N=1,095 valid responses) distributed via snowball sampling. The methodological rigor was demonstrated through Exploratory Factor Analysis (EFA), with highly suitable data (KMO=0.915, p<0.001) and high reliability (Cronbach’s alpha ranging from 0.757 to 0.880). The EFA validated the construct of three distinct motivation profiles. RMAs were found to exhibit a predominantly autonomous (intrinsic) drive, confirmed by the highest mean score among the profiles, with statistically significant differences between all three types of motivation. This intrinsic motivation aligns with personal values that emphasize benevolence and universalism while downplaying power and tradition, and personality traits showing high conscientiousness, openness, and agreeableness. This work extends the use of SDT in Science and Technology Studies by validating a specific measurement scale for RMA motivation profiles. The results offer practical guidance, supporting the need for flexible, tailored motivational strategies and policies that enhance intrinsic factors such as autonomy and competency to boost RMA performance.
Castor-Based Ester Oil Production Using SnCl₂/HZSM-5 Catalyst for Sustainable Transformer Istiqomah; Widayat Widayat; John Philia; Sriyono; Kevin Gausultan Hadith Mangunkusumo; Aji Suryo Alam; Hadiyanto
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-2026-010-02-06

Abstract

Mineral oil remains the most common insulating fluid in power transformers; nevertheless, its non-biodegradable nature and carbon-based emissions have prompted the quest for environmentally safer alternatives. Castor oil, a sustainable and non-edible derivative, provides a promising source for high-performance ester-based insulating lubricants. This study investigates the synthesis and process optimization of castor-based polyol esters via esterification with trimethylolpropane using a heterogeneous SnCl₂/HZSM-5 catalyst. The alcohol-to-oil molar ratio, temperature, and catalyst loading were among the critical reaction parameters that were modeled and optimized using response surface methods with a central composite design. Under optimized conditions of 130°C and 2.207 wt% catalyst, an ester yield of 81.77% was obtained. The resulting ester oil demonstrated advantageous characteristics, such as acceptable color and dielectric performance, while viscosity and acidity were improved by a two-step process to comply with the IEC 62975:2021 standards for distribution transformer insulating oils. The statistical study validated the model's reliability, with ANOVA indicating a substantial quadratic regression (R² = 0.952). The key novelty of this work lies in demonstrating the potential of SnCl₂/HZSM-5 to catalyze the synthesis of castor-derived polyol esters with tailored physicochemical properties, supporting their future scalability and use as sustainable insulating oils.
BIoT-DApp: A Prototype for Real Time Traceability in Agricultural Supply Chains Sajid Safeer; Victoria Lemieux; Chang Lu; Cataldo Pulvento
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-2026-010-02-02

Abstract

Agricultural supply chains frequently experience inefficiencies, including a lack of transparency, post-harvest losses, and inequitable compensation for stakeholders. This study aims to develop and evaluate a Blockchain-IoT decentralized application (BIoT-DApp) that enhances traceability, efficiency, and resilience in agri-food supply chains. Utilizing a lab-based prototyping methodology, the system integrates Ethereum smart contracts with IoT sensors to automate workflows from cultivation to retail, employing a hybrid architecture that stores raw sensor data off-chain while anchoring cryptographic hashes on-chain. The methods involve designing role-specific smart contracts, managing batch life cycles across six stages, and conducting real-time environmental monitoring through IoT data processed by Raspberry Pi, with deployment and testing performed on the Sepolia testnet. The findings demonstrate automated quality control, reduced storage costs through optimized on-chain practices, and seamless product ownership transfers validated by four role-based MetaMask accounts representing a farmer, wholesaler, retailer, and end-user. Core functions were executed successfully, with gas costs ranging from 30,000 (data logging) to 112,300 (batch initialization), confirming both cost efficiency and scalability. The novelty of this work lies in bridging blockchain theory and practice by providing a modular, adaptable prototype capable of supporting perishable agricultural supply chains globally. This offers policymakers and agri-tech developers actionable insights for decentralized solutions in resource-constrained environments.
Strategic Dividend Policy Adaptation and Stock Market Reactions in State-Owned Enterprises Across Crises Georgina Maria Tinungki; Powell Gian Hartono; Nurhafifah Amalina; Dewie Tri Wijayati Wardoyo; Reniati Karnasi; Gatri Lunarindiah; Marieta Ariani; Lidia Wahyuni
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-2026-010-02-012

Abstract

This study investigates the strategic adaptation of dividend policy in Indonesian state-owned enterprises across the pre-crisis, crisis, and recovery phases. Adaptation is operationally defined as firm-level, measurable changes in cash dividend indicators during the crisis and post-crisis phases relative to the pre-crisis average. Empirically, dividend behavior is estimated using a dynamic panel framework with system GMM, and an event-study approach evaluates abnormal returns and cumulative abnormal returns around dividend announcement dates in each phase. The results indicate that SOEs increased dividends during the crisis relative to pre- and post-crisis periods, and that the market exhibited stronger positive reactions in the crisis and recovery phases than in the pre-crisis phase. These patterns suggest adaptive choices consistent with managing uncertainty and reinforcing policy credibility within Indonesia’s state-ownership setting. The findings highlight the strategic role of dividend signals in shaping investor perceptions during economic shocks, while theoretically challenging the core cash-conservation premise of the pecking order and reinforcing the relevance of signaling theory for state-controlled firms with complex fiscal and political mandates.
Thermoelectric Generator Efficiency Enhancement Through Copper Electrical Contact Optimization N. Jagadesh Babu; Rajesh Kumar Burra
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-2026-010-02-028

Abstract

Thermoelectric generators (TEGs) can transform heat into electricity and have considerable potential for diverse applications. Nonetheless, their widespread use is limited by their low efficiency, mainly owing to their high internal electrical resistance. This study aims to improve TEG performance by reducing the electrical contact resistance through the application of copper. This study addresses the critical challenge of improving the performance of thermoelectric generators (TEG) by reducing the electrical contact resistance, which directly affects the output power and conversion efficiency, particularly at low temperatures. The main objective of this study was to investigate how the contact resistance influences the electrical conductance and overall energy conversion efficiency of TEGs and to optimize the contact geometries to enhance the performance. Copper contacts with different shapes (flat and circular) were designed and fabricated to evaluate their impact on electrical resistance. Experimental investigations using a commercial TEG module were conducted to measure the contact resistances and analyze their effects on electrical parameters, including the output voltage, power, and conversion efficiency. A comprehensive theoretical model was developed to assess the contact area, energy loss, and thermal factors. Equations were applied to quantify the contact resistance and its influence on the power output and efficiency. Notably, small circular copper contacts exhibited a significant reduction in contact resistance compared to flat contacts, leading to an 18.6% improvement in efficiency at low temperatures. This study demonstrates that optimizing the geometry and size of copper contacts can substantially reduce energy losses at the interfaces, thereby enhancing the current flow and boosting the TEG conversion efficiency. These findings provide a novel approach for addressing the prevalent issue of high internal resistance in thermoelectric devices, paving the way for more effective energy harvesting and waste-heat recovery. This study underscores the critical role of contact engineering in TEG technology and offers promising strategies for improving device efficiency and output power for future applications.
Macroeconomic Uncertainty and Banking Stability in ASEAN Emerging Markets: A Causal Machine Learning Approach Truong Nguyen Tuong Vy; Dao Le Kieu Oanh; Pham Anh Thuy
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-2026-010-02-04

Abstract

This study aims to examine the causal impact of macroeconomic uncertainty on banking stability across six ASEAN emerging markets from 2010 to 2023, with particular attention to structural regime shifts triggered by the COVID-19 pandemic. To achieve this objective, a novel country-specific uncertainty index is constructed using Principal Component Analysis (PCA) based on three indicators World Uncertainty Index (WUI), World Pandemic Uncertainty Index (WPUI), and World Sentiment Index (WSI). Employing advanced causal inference methods, including Double Machine Learning (DML) and Causal Forests, the study estimates both Average Treatment Effects (ATEs) and Conditional Average Treatment Effects (CATEs). The results reveal that a one-unit rise in macroeconomic uncertainty reduces the Z-Score by 10.7% on average, signaling increased financial instability. The adverse effect is most pronounced for small banks (21.9% decline), reflecting limited capital buffers and structural vulnerability, and becomes more severe after the COVID-19 outbreak. CATEs results highlight significant cross-country heterogeneity, with Singapore and Thailand showing resilience, while Indonesia and the Philippines exhibit greater fragility. This study contributes to the literature by integrating SHAP-based model interpretability into causal machine learning for banking stability analysis, offering novel, policy-relevant insights for uncertainty management in emerging ASEAN economies.
Unified Numerical FFR with Adaptive Bandwidth for 5G and Beyond Multilayer Multisector Networks Muhammad Yaser; Iskandar; M. Sigit Arifianto; Khoirul Anwar
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-2026-010-02-09

Abstract

The present research introduces a unified numerical formulation for Fractional Frequency Reuse (FFR) and a user composition-based adaptive bandwidth allocation strategy for multi-layer/multi-sector cell architectures. The proposed FFR metric explicitly accumulates sub-band usage across the inner–outer and inter-sector layers, thereby normalizing diverse reuse patterns into a single, consistent number. This formulation remains consistent with the classical definition (reducing to 1/N reuse under certain conditions), approaches full reuse when multi-layer/sector coordination is applied, and provides a simple yet powerful link between reuse configurations and capacity predictions in 5G and beyond networks. Comprehensive simulations based on a realistic urban macrocell environment show that increasing the architectural complexity from a single-layer to a 2-layer 6-sector network results in a remarkable 184% increase in average cell capacity. Furthermore, in the dynamic bandwidth allocation, the inner user-dominated scenario achieves the highest cell capacity, which is 41% higher than that in static bandwidth allocation. At the same time, dynamic allocation also improves fairness in the outer user-dominated scenario, increasing the Jain fairness index by up to 0.444. These results confirm that the combination of the new FFR formulation and adaptive resource allocation significantly improves spectrum efficiency, cell capacity, and fairness, and provides practical guidance for optimizing the implementation of 5G and beyond cellular network deployments.
Pattern Recognition Tasks with Personalized Federated Learning Md. Arifur Rahman; Isha Das; Mushfiqur Rahman Abir; B. M. Taslimul Haque; Abdullah Al Noman; Abir Ahmed; Md. Jakir Hossen
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-2026-010-02-020

Abstract

Personalized Federated Learning (PFL) constitutes a novel paradigm that tailors Machine Learning (ML) models to individual clients, thereby furnishing personalized model updates whilst upholding stringent data privacy principles. Diverging from conventional standard Federated Learning (FL) approaches, PFL adapts models to distinct client data distributions, engendering heightened levels of accuracy, customization, and data security, all while minimizing communication overhead. This methodology proves particularly salient in contexts marked by pattern recognition tasks reliant upon heterogeneous data sources and underpinned by paramount privacy apprehensions. In the present research endeavor, this article undertake a comprehensive comparative analysis of seven distinct PFL algorithms deployed across three diverse datasets, namely MNIST, SignMNIST, and Digit5. The overarching objective entails ascertaining the preeminent PFL algorithm, within the framework of pattern recognition tasks, through a rigorous evaluation anchored in metrics encompassing Accuracy, Precision, Recall, and F1 Score. Concurrently, an in-depth scrutiny of these PFL algorithms is conducted, elucidating their operative workflows, advantages, and limitations. Through empirical investigation, the findings evince that APPLE, FedGC, and FedProto emerge as stalwart contenders, consistently furnishing superior performance across the spectrum of assessed datasets, while acknowledging the contextual specificity of alternative algorithms and the potential for iterative refinement to realize optimal outcomes.
Perceived Risk and Trust as Moderators Between Online Shopping Intention and Purchase Decisions Tu Ngoc Tran; Le Dinh Nghi; Dinh Thi Kieu Chinh
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-2026-010-02-026

Abstract

This study aims to identify the factors influencing online shopping intention and purchase decisions among consumers in Ho Chi Minh City, while examining the moderating roles of perceived risk and trust in e-commerce. Data were collected through a structured survey of online shoppers and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The empirical results indicate that product quality, price, promotion programs, and website quality have significant positive effects on online shopping intention, which in turn exerts a strong influence on purchase decisions. Moreover, perceived risk negatively moderates, whereas trust in e-commerce positively moderates the relationship between online shopping intention and purchase decisions. These findings underscore the pivotal roles of perceived value, trust formation, and risk reduction in converting online shopping intention into actual purchasing behavior. The novelty of this study lies in simultaneously examining perceived risk and trust as moderating mechanisms within a PLS-SEM framework in an emerging market context. The results provide practical insights for e-commerce firms by highlighting the importance of improving website quality, enhancing security systems, adopting flexible pricing strategies, and designing targeted promotions to increase online shopping conversion rates.
Business Process Remaining Time Prediction Based on Bidirectional QRNN with Attention Mechanism Na Guo; Ting Lu; Cong Liu; Xingrong Xu; Qingtian Zeng
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-2026-010-02-01

Abstract

Business process prediction is essential for monitoring workflows and ensuring service quality. A key task in this area, remaining time prediction, focuses on estimating process duration and has been extensively studied. While Long Short-Term Memory (LSTM) networks are widely adopted, their limited parallelization and sequential modeling capabilities constrain performance. To address these limitations, we propose a remaining time prediction approach based on a bidirectional Quasi-Recurrent Neural Network (QRNN) with an attention mechanism. Specifically, the bidirectional QRNN is employed to construct the prediction model, while the attention mechanism enhances its ability to extract feature information. Next, a transfer training iteration strategy based on different trace prefix lengths is designed to address the imbalance in trace lengths. Then, a Word2Vec-based event representation learning approach is introduced to generate similarity vector of adjacent events, further improving prediction accuracy. Finally, using five publicly real-life event logs, the proposed approach is evaluated against state-of-the-art approaches. Experimental results demonstrate that it improves average prediction accuracy by nearly 15% while reducing average model training time by approximately 26%.

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