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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 874 Documents
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Antidiabetic Properties of Uncaria sclerophylla Roxb: In Vitro, Metabolite Profiling, and Molecular Docking Triadisti, Nita; Hanafi, Muhammad; Mohd Hashim, Najihah; Berna Elya
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-019

Abstract

Uncaria sclerophylla Roxb is a traditional medicinal plant used to treat diabetes mellitus in Kalimantan, Indonesia, and the antidiabetic properties of its stem bark have not been previously investigated. This research will focus on investigating the potential of U. sclerophylla stem bark as an antidiabetic with the mechanism of inhibiting dipeptidyl peptidase-4, α-glucosidase, and antioxidants from extracts to chromatographic fractions, including the exploration of the major compounds contained in the most active chromatographic fraction. Extraction using a four-grade maceration technique, bioassays were carried out using spectrophotometric methods, fractionation using gradient column chromatography, and compound profiling using UHPLC-Q-ToF-MS/MS. The profiled compounds were predicted for their bioactivity in silico. The stem bark of U. sclerophylla demonstrated antidiabetic potential, and the methanol extract showed superior antidiabetic potential compared with the other extracts. From the extract, the most active chromatographic fraction, FUS2, was successfully obtained, which had the best activity with DPP-4 inhibition IC50 of 83.07 ± 6.3393 µg/mL, α-glucosidase inhibition IC50 of 58.06 ± 1.6226 µg/mL, and antioxidant IC50 of 8.47 ± 0.0443 (DPPH method) and 8.47 ± 0.0234 µg/mL (FRAP method). Compound profiling of FUS2 and in silico bioassays revealed potential antidiabetic compounds, including rhynchophyllic acid, arecatannin A2, silydianin, and procyanidin A2.
Digital Financial Compliance Challenges: Applying Routine Activity Theory to Online Gambling Networks Analysis Mawardi, Rizal; Nuraeni, Vira; Jasman, Jasman; Trihatmoko, Huda; Sorongan, Fangky A.; Aripin, Septian; Malik, Syaefulloh Maulana
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-015

Abstract

This study examines Indonesia's Financial Intelligence Unit (INTRAC) "follow the money" investigative techniques through Routine Activity Theory, analyzing criminal convergence in online gambling money laundering operations. Using qualitative methodology with interviews, observation, and document analysis (December 13-19, 2024), the research applied Cohen and Felson's framework to understand criminal patterns in digital financial ecosystems. Data analysis using Audit Command Language (ACL) revealed criminal convergence patterns where motivated offenders (84.63% male, 50% private sector employees, 53% aged 20-30) exploited digital infrastructure vulnerabilities. Sophisticated schemes included multiple nominee accounts, 5-8 layered transactions, and cryptocurrency laundering in low-surveillance environments. Transaction analysis showed expanding criminal opportunities, increasing to IDR 691.88 trillion (2017-2024). The study demonstrates how digital transformation creates suitable targets faster than regulatory adaptation. Research contributes theoretical insights explaining financial irregularity patterns through routine activity theory while offering practical risk reduction models for global financial intelligence units, advancing regulatory compliance theory and digital financial risk prevention.
Regulatory Thresholds as Disciplinary Signals: Evidence from Bank Nonperforming Loan Supervision Phung, Thi Lan Nhi; Le, Chi Dat; Pham, Van Kien
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-07

Abstract

Persistent high levels of nonperforming loans (NPLs) remain a key threat to banking stability, yet limited evidence exists on how regulatory thresholds influence bank discipline and risk behavior. This study investigates whether supervisory NPL ceilings serve as effective disciplinary mechanisms that balance profitability and credit risk in commercial banking systems. Using a balanced panel dataset from multiple emerging-market banks between 2013 and 2023, we employ Hansen’s (1999) panel threshold regression to identify critical points at which bank behavior changes significantly. The findings indicate that when NPL ratios exceed an optimal threshold, banks exhibit heightened self-discipline by tightening credit growth and accepting lower short-term returns, demonstrating a strong regulatory disciplining effect rather than moral hazard. Conversely, when NPLs remain below the threshold, the traditional risk–return trade-off weakens, suggesting stability and prudence. The results highlight the importance of threshold-based supervision as a prudential instrument that enhances banking stability through behavioral signaling. The study contributes to signaling theory by conceptualizing regulatory thresholds as negative signals that trigger pre-emptive risk management and to policy design by offering empirical insights into optimizing supervisory frameworks.
Data Governance Meets Generative Artificial Intelligence: Towards A Unified Organizational Framework Bernardo, Bruno M. V.; Mamede, Henrique S.; Barroso, João M. P.; Naranjo-Zolotov, Mijail; Duarte dos Santos, Vítor M. P.
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-029

Abstract

As technology continues to evolve, organizations face growing and complex challenges and opportunities that affect their ability to govern, manage and harness data as a key source of competitive advantage. Equally, data are considered a powerful and unique source of success for organizations, which in turn, can impact their decision-making capabilities and play a critical role in their success. Hence, this article aims to provide a detailed identification, analysis and discussion over the current data governance context and its existing frameworks, highlighting their commonalities, differences and gaps, including ones related to data governance relationship with Generative Artificial Intelligence (GenAI). This article conducts an extensive methodological and in-depth analysis over a set of sixteen data governance frameworks based on different key data governance attributes, denoting that although there are numerous frameworks, they hold weaknesses, limitations and challenges which prevent them from being capable of incorporating and governing the use and management of AI, particularly the demands originating from GenAI. Our findings provide and propose a new and enhanced data governance framework which integrates the best features and ideas from the existing ones and initiatives derived from the advancements and particularities of AI and GenAI models, systems, and overall usage.
Dynamic Customer Experience, Satisfaction, and Word-of-Mouth in Telecom-IT Sector Nguyen, Hung Q.; Nguyen, Hau V.; Nguyen, P. V.
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-026

Abstract

This study examines how Dynamic Customer Experience (DCX) affects Customer Satisfaction (CS) and Word-of-Mouth (WOM) intentions among VNPT customers in Vietnam, identifying AI-Driven Service Personalization (AISP), Integrated Service Quality (ISQ), Cultural Resonance (CR), and Sustainable IT-Telecom Practices (SITP) as key antecedents, with Customer Empowerment (CEMP), Perceived Value Co-Creation (PVCC), Emotional Engagement (EE), and CS as mediators, and AI Trust (AIT), Service Innovation Maturity (SIM), and Regional Cultural Dynamics (RCD) as moderators. A multi-theoretical framework (Customer Experience Framework, Social Exchange Theory, Expectancy-Disconfirmation Theory, TAM, SERVQUAL) guided the research. Survey data from 677 VNPT customers were analysed using hybrid PLS-SEM (SmartPLS 4.0) for explanatory power and Artificial Neural Network (ANN) in SPSS 25.0 for predictive accuracy. PLS-SEM confirmed significant positive effects of AISP, ISQ, CR, and SITP on DCX (β = 0.24–0.33, p < 0.01), and DCX on CS (β = 0.43) and WOM (β = 0.30). CS was the strongest mediator (indirect effect = 0.20, VAF = 67%). Moderation analyses showed stronger effects in rural areas due to cultural dynamics. ANN validated results with high predictive power (R² testing = 0.83–0.87), identifying AISP and CS as top predictors. This is the first study to integrate sustainability and cultural resonance into DCX for Vietnam's collectivist telecom market using a hybrid PLS-SEM-ANN approach, outperforming single-method studies and providing VNPT actionable strategies for AI personalization and green 5G deployment. JEL Code: M14, M30, M31, M37.
Role of Cost Efficiency, Capital Leverage, and Cost of Capital in Determining Shareholders' Value Hágen, István Zsombor; Ahmed, Amanj Mohamed
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-027

Abstract

The objective of this research is to explore the impact of cost efficiency, capital leverage, and cost of capital on shareholder value regarding the GCC, a dynamic and inventive economy. Data from 41 banks that were listed on the stock exchange from 2015 to 2023 were collected. The Refinitiv Eikon interface provided bank-level data to achieve the aim of this study. GLS with cross-sectional weight as a panel econometric method was applied. The findings display that cost efficiency has a significant impact on shareholder value in the GCC banking sector. Reduced operational expenses, increased asset utilization, and effective tax planning have a positive effect on shareholders' return. Although financing with debt has a minimal effect on GCC banks' productivity, it is nevertheless important for evaluating the market performance, with DMC possessing a negative effect and DTA holding a positive influence. Moreover, revenue generation and the value of shareholders are all continuously improved by an optimum cost of capital (WACC). The outcomes of this study enrich the current literature by proposing a combined framework for assessing cost efficiency, capital leverage, and cost of capital. It also fills the gap in earlier regional research by providing new perspectives for creating shareholder value in the GCC banking industry.
Synergizing Innovation: Examining the TOE Readiness Influence, Ambidextrous Capabilities, Organization Resilience on Craft SMEs Soeprapto, Etwin Fibrianie; Partiwi, Sri Gunani; Widyaningrum, Retno
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-028

Abstract

This study examines how technology-organization-environment (TOE) readiness (TR), ambidextrous organization (AO), and organizational resilience (OR) influence innovation performance (IP) and competitive advantage (CA) in small and medium-sized enterprises (SMEs), focusing on craft-based firms operating in dynamic and uncertain environments. A quantitative research design was chosen to collect survey data from 200 Indonesian craft-based SME owners and managers. Structural equation modelling (SEM) was employed to analyze the data and examine both direct and indirect relationships among the proposed constructs. The findings demonstrate that TR, AO, and OR each have a significant positive effect on IP. In turn, IP has a strong positive effect on CA, highlighting its key role in converting organizational capabilities into market success as well as a complementary mediating mechanism linking TR, AO, and OR to competitive outcomes. The novel contribution of this study is the empirical integration of TR with internal dynamic capabilities, namely AO and OR, in a single structural model that explains how SMEs transform innovation-related capabilities into CA. This integrative perspective contributes to the literature on SME innovation and offers practical implications for managers and policymakers seeking to strengthen innovation-driven competitiveness and long-term sustainability amid turbulent environmental conditions in emerging market contexts.
A Model for Fostering Labor Productivity and Wage Management: A Long-Term Outlook to 2034 Kostyrin, Evgeniy V.; Moussa Pascal, Loua; Kostyrin, Dmitriy
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-017

Abstract

This research aims to develop a complex system of managerial decision-making support, which includes an economic-mathematical model of maximizing the salary share in the company’s revenue, consistent with the financial interests of business owners and the state, analyzing the sensitivity of the maximum wage share to key parameters of the model, and developing software based on MS Excel and PTC Mathcad Prime 3.1. The research methodology included system analysis, non-linear programming, sensitivity analysis, and dynamic system modeling linking time-related changes in wages, labor productivity, enterprises’ reinvestment, and financial stability. Modeling results showed that a balanced policy allows employees to increase wages by 59.83% and net profit by 2.97. The return on sales increased by 2.28 times, and government revenues from taxes and social contributions increased by 58.78%. The maximum sustainable share of wages in revenue reaches 54.73%, which is 9.83% higher than the base indicator. The novelty of the research lies in the development and practical implementation of an economic and mathematical model that maximizes the share of wages in the company’s revenue while balancing the financial results of employees, owners, and the state within the framework of the Russian fiscal system. JEL Classification: C44, I11, J31, M21.
Temporal ASTRA: Synthetic Evaluation and Hybrid CNN-BiLSTM Modeling for Calibration-Free Strabismus Detection Udomwech , Lunla; Kurdthongmee, Wattanapong; Kurdthongmee, Piyadhida
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-014

Abstract

Strabismus screening in pediatric and remote-care settings remains difficult because many existing methods depend on patient cooperation, individual calibration procedures, and static image capture, which are insufficient for detecting intermittent or transient ocular misalignment. The objective of this study is to introduce a calibration-free pre-screening approach that relies on temporal binocular behavior rather than absolute gaze measurements. We present Temporal ASTRA (Automatic Strabismus Tracking and Risk Assessment), a video-based framework that analyzes interocular disparity and its temporal evolution, including velocity and acceleration, from short binocular video segments. To address the limited availability of annotated clinical time-series data, a synthetic data generation process was developed to reproduce physiologically plausible normal and abnormal vergence patterns, such as gradual drift, intermittent phoria, and nystagmus-like oscillations. A hybrid convolutional neural network and bidirectional long short-term memory (CNN–BiLSTM) model with attention pooling was trained on the synthetic dataset and subsequently fine-tuned using real video recordings. The proposed system achieved 93.3% accuracy on held-out synthetic data and 90.9% accuracy with an AUC of 93.7% on real-world videos following synthetic pretraining. Evaluation on a clinical validation set of 24 videos yielded 100% sensitivity and 66.7% specificity at a high-sensitivity screening threshold. This study demonstrates that modeling temporal vergence dynamics provides a practical and robust basis for calibration-free, video-based strabismus pre-screening suitable for telemedicine and community-scale deployment.
Mechanistic Multiphysics Optimization of Catalyst Layers for High Performance PEM Fuel Cells Rawashdea, Sa′ed
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-04

Abstract

The work set out to optimize the catalyst layer (CL) structure of proton exchange membrane fuel cells (PEMFCs) to maximize electrochemical performance, transport efficiency, and durability simultaneously. The coupled effects of platinum loading distribution, ionomer pathways, and porous microstructure on charge transport, oxygen diffusion, water management, and electrochemical kinetics are investigated through the development of a mechanistic multiphysics modeling framework. The model combines mass and charge conservation, Butler–Volmer reaction kinetics, and effective transport formulations to enable systematic comparison between a standard reference CL and three successively optimized architectures. The findings indicate that the optimized CL designs provide clear improvements in power density, voltage stability, oxygen transport, and electrochemically active surface utilization, while exhibiting lower ohmic losses and reduced transport resistance. Quantitative comparison with experimental data shows that the predictive accuracy improves significantly, with the root mean square error decreasing to 2.7 mAcm⁻² and the coefficient of determination increasing to 0.997 for the most developed design. Moreover, degradation-sensitive aspects, such as platinum loss and interfacial instability, are noticeably alleviated through controlled microstructural design. The main contribution of this work lies in integrating multi-parameter optimization of the catalyst layer architecture within a single mechanistic framework, offering a scalable and robust route toward high-performance.

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