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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 1,093 Documents
 106   107   108   109   110 
Impact of Liquidity and Basel III Regulation on Bank Profitability Duong Thuy Nguyen; Trang Thi Thu Nguyen; Oanh Thi Kim Nguyen
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-025

Abstract

This study examines the relationship between liquidity on bank profitability in the context of Vietnamese commercial banks, focusing on the moderating effect of Basel III regulations. Using data from 26 commercial banks listed on Vietnam’s stock exchanges between 2012 and 2023, the research employs multiple econometric models, including OLS, FEM, REM, and Generalized Method of Moments (GMM), to explore the effects of liquidity measures such as liquid assets to total assets (LATA) and the liquidity transformation gap (TLGAP) on key profitability indicators; Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM). The findings suggest a negative relationship between liquidity and profitability of commercial banks in Vietnam, with higher liquidity levels constraining profit generation. Additionally, the study reveals that adopting Basel III regulatory standards, particularly its liquidity and capital requirements, mitigates the negative effects of liquidity on the profitability of commercial banks in Vietnam. The results highlight the trade-off commercial banks face between maintaining sufficient liquidity for financial stability and optimising profitability. This research contributes to the understanding of liquidity management in emerging markets, emphasising the role of Basel III in balancing regulatory compliance with financial performance.
Dual-Agent Q-Learning for Cross-Layer IEEE 802.11bd Optimization in Dense VANETs Galih Nugraha Nurkahfi; Suyoto; Agus Subekti; Budi Prawara; Ratna Mayasari; Andy Triwinarko; Nasrullah Armi; Eueung Mulyana; Nana Rachmana Syambas
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-019

Abstract

Dense vehicular ad hoc networks face critical challenges in reliably delivering safety messages due to channel congestion, packet collisions, and interference. This study develops a dual-agent Q-learning framework for cross-layer IEEE 802.11bd optimization to improve latency and power efficiency while maintaining acceptable packet delivery ratios in dense traffic. We propose a decomposed architecture separating PHY-layer power control and MAC-layer beacon rate adaptation, with deterministic SINR-based MCS selection ensuring IEEE 802.11bd compliance. The framework is evaluated using a Python-based VANET simulator implementing the IEEE 802.11bd PHY/MAC stack with realistic SUMO mobility, multi-class background traffic, and omnidirectional/sectoral antennas across 20-90 vehicles/km densities. Results show dual-agent Q-learning reduces average latency by 44.6% (31.1ms to 17.2ms) and transmission power by 55% (15-20dBm to 9dBm) compared to static baselines, with acceptable 5-11% PDR reduction (94.2% to 88.6%). The approach converges within 8,500 episodes, significantly faster than single-agent Q-learning (12,500) and dual-agent DQN (14,000-35,000). This work introduces the first dual-agent tabular Q-learning for joint power-rate-MCS optimization in IEEE 802.11bd VANETs, demonstrating that agent decomposition reduces state-action complexity while enabling interpretable, fast-converging control suitable for sub-100ms vehicular applications.
Response Surface Methodology for Enhanced Recovery of Eucalyptus Pellita Essential Oil through Hydrodistillation Lia Cundari; Susila Arita; Poedji L. Hariani; Subhash Maheswari; Siswanto
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-024

Abstract

Essential oils (EOs) are valuable natural products with diverse industrial applications. The present study aimed to optimize the hydrodistillation (HD) process to maximize essential oil (EO) recovery of Eucalyptus pellita leaves. Response Surface Methodology (RSM) with a Box-Behnken Design (BBD) was employed to model and analyze the effects of three key factors: extraction time (A), agitation speed (B), and solvent-to-leaf mass ratio (C). Seventeen experimental runs were conducted by using a Clevenger apparatus. The results showed that the ANOVA quadratic models were statistically significant for both mass and yield EO, with excellent coefficients of determination       (R² > 0.99), adjusted R², predicted R², adequate precision, and non-significant lack-of-fit. All three linear parameters (A, B, C) and their interactions (AB, AC, BC) had a significant impact on extraction efficiency. Nonetheless, the quadratic terms (A2, B2, C2) had a non-significant effect on the responses. The RSM predicted optimum conditions at 4.96 h, 874 rpm, and a solid-to-solvent mass ratio of 1:5.4 (g/g). This condition predicted 0.62-0.66 g of mass and 0.42-0.44% of yield EO. Experimental validation under these conditions produced an average EO mass of 0.635 g and an average yield of 0.4235%, both values closely matching the predicted outcomes. These results demonstrate the accuracy application of the RSM-BBD in optimizing HD for Eucalyptus pellita EO. The optimized parameters and validated model provide a practical and scalable framework for industrial EO production, supporting the transition from laboratory research to commercial implementation.
A Sustainable E-Learning Ecosystem: Linking Readiness, Teaching Efficiency, Culture, and Employability Boumedyen Shannaq; Said Almaqbali
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-013

Abstract

This paper hypothesizes and justifies a Sustainable E-Learning Ecosystem Framework that incorporates the readiness, teaching efficiency, and cultural enablers to improve the reputation of the institution, as well as graduate competency and employability. The model that was built based on the data collected by higher education institutions, and which was run on the Partial Least Squares Structural Equation Modeling (PLS-SEM), displayed a high explanatory power with high path coefficients. The direct effects were strong: readiness had a positive impact on the teaching efficiency (b = 0.131, t = 3.649, p < 0.001), teaching efficiency had a significant impact on the e-learning adoption (b = 0.522, t = 13.729, p < 0.001), and the e-learning adoption had the impact on the student performance (b = 0.512, t = 13.734, p < 0.001). The highest influence on sustainability indicators had student performance and competence; competence was a strong predictor of the university's reputation (b = 0.581, t = 16.134, p < 0.001), and reputation led to job employment (b = 0.814, t = 46.630, p < 0.001). Other critical effects were also present, like TE - AeL - SP - SC - UR - JE (b = 0.100, t = 6.548, p < 0.001), which proved the cascading impact of drivers of operation on long-term outcomes. There were mixed outcomes in terms of cultural factors: the mediating effect of information culture on competence and reputation (b = 0.289, t = 7.556, p < 0.001) was significant, and organizational and national cultural moderation was not significant. Policy support and training were both found to have a good direct influence on teaching efficiency (Ps b = 0.380, t = 7.914; TP b = 0.387, t = 8.869; p < 0.001) but did not act as moderators. According to these results, readiness and teaching efficiency are identified as key drivers, and competence and reputation are key to sustainable education outcomes and employability.
PTP1B-Mediated Dephosphorylation of SRC Controls Fibrogenic Cellular Activation Bootsakorn Boonkaew; Nuchanart Suntornnont; Chaiyaboot Ariyachet
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-018

Abstract

Liver fibrosis, the precursor to cirrhosis and hepatocellular carcinoma (HCC), represents a global health crisis with millions affected and few effective treatments available. This progressive pathology is characterized by excessive extracellular matrix (ECM) deposition, primarily driven by the activation of hepatic stellate cells (HSCs) into profibrogenic myofibroblasts. Elucidating the molecular control of HSC activation is paramount for therapeutic development. Protein-tyrosine phosphatase 1B (PTP1B), an established target in metabolic diseases, has emerging pro-fibrotic roles, as evidenced by protection against liver fibrosis in PTP1B-deficient mice. However, the regulatory function of PTP1B in human-relevant models remains poorly understood. Here, we define the role and mechanistic significance of PTP1B in primary human HSCs and investigate its clinical relevance in human fibrotic liver. Using loss- and gain-of-function approaches in primary human HSCs, we demonstrate that PTP1B significantly promotes HSC activation, evidenced by enhanced proliferation, migration, and increased expression of key ECM genes and collagen production. Mechanistically, PTP1B acts as a positive regulator of the pro-fibrotic SRC kinase, directly dephosphorylating the inhibitory tyrosine 527 (Y527) residue to induce its activation. Pharmacological inhibition of SRC effectively reversed the PTP1B-driven pro-fibrotic phenotypes. Consistent with our findings, re-analysis of human fibrotic liver tissues revealed that PTP1B and SRC expressions are significantly upregulated and positively correlated with ECM genes. Collectively, these findings establish a novel PTP1B-SRC signaling axis that critically drives human HSC activation and hepatic fibrogenesis, positioning PTP1B as a high-potential therapeutic target for liver fibrosis.
Nonparametric Mixed Moving Average-Extended Exponentially Weighted Moving Average Signed-Rank Control Chart Khanittha Talordphop; Saowanit Sukparungsee
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-05

Abstract

Control charts are strong statistical monitoring instruments extensively utilized in both manufacturing and non-manufacturing operations. In numerous ongoing processes, the concept of normality is challenging to achieve, resulting in erroneous evaluations within parametric monitoring systems. When the actual variability of a performance parameter is unknown, nonparametric control charts provide a reliable and adaptable approach to evaluating the process. The benefits of utilizing combination control charts encompass increased sensitivity, thorough monitoring, and the capacity to adapt by altering mixtures to satisfy workflow and company needs. To overcome this limitation, this work introduces a hybrid moving average-extended exponentially weighted moving average control chart utilizing the Wilcoxon signed-rank statistic, namely, the MA-EEWMA-WSR, to identify shifts in the process mean. A Monte Carlo simulation was used to estimate the average run length, and the average extra-quadratic loss (AEQL) was calculated to comprehensively assess the performance of the control chart for some selected symmetric distributions: normal, Laplace, and logistic. The study shows that the proposed technique demonstrated efficacy in detecting all alterations across various distributions, outperforming other charts, such as MA-EEWMA, EWMA-WSR, and EEWMA-WSR under the zero-state scenario. The efficiency of the proposed chart in identifying process adjustments is demonstrated in a case study of the dry bleach products dataset.
Hybrid Parametric and Non-Parametric Identification of PEMFC Dynamics in SISO and MIMO Workflow Eduardo Benavides-Farías; Abel Rubio-Roldán; Wilton Agila; Edwin Valarezo-Añazco
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-01

Abstract

Reliable control-oriented models of PEM fuel cells remain challenging because PEMFC dynamics are nonlinear, coupled, and hard to excite under practical constraints. This paper presents a hybrid identification workflow in a controlled MATLAB/Simulink simulation environment. After discretization, bounded multisine excitation is applied, and correlation-based response analysis (CRA) is used to obtain non-parametric dynamics; low-order parametric structures (ARX, ARMAX, Box–Jenkins, OE, and FIR) and a grey-box state-space model are then estimated and validated using Fit%, information criteria (AIC/BIC), and residual diagnostics. In SISO, ARMAX provides the best accuracy–parsimony compromise (Fit = 96.84% with the lowest AIC/BIC and residuals mostly within confidence bounds), while Box–Jenkins achieves the highest fit (i.e., 98.75%) at higher complexity. In MIMO, most channels achieve an accuracy over 92% fit, with the most coupled pathway remaining the limiting case (best fit = 86.38% with BJ), and ARMAX/BJ emerging as the dominant structures across channels. The grey-box model attains 97.35% fit for voltage and 86.47% for power. This paper establishes a unified, control-oriented hybrid workflow that links CRA non-parametric estimation with low-order parametric and grey-box models, providing compact, physically interpretable PEMFC dynamics and practical model-selection guidance for control and energy-management applications.
Scenario-Robust Hydropower Suitability Mapping in Geothermal Regions Using Multi-Paradigm Spatial Modeling Ahmad Saikhu; Ira Mutiara Anjasmara; Widya Utama; Rista Fitri Indriani
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-09

Abstract

Hydropower planning in geothermal and environmentally sensitive regions involves substantial uncertainty due to complex terrain, ecological constraints, and competing land-use priorities. This study aims to evaluate whether the suitability patterns of hydropower that are robust to planning assumptions can be identified through cross-method spatial consistency rather than single-model optimization. We propose a scenario-based spatial decision-support framework that integrates knowledge-driven multi-criteria decision analysis (MCDA), supervised machine learning (XGBoost), unsupervised RLKM, and patch-based convolutional neural networks (PCNN) using harmonized satellite-derived spatial datasets. Three alternative planning scenarios, balanced, conservation-oriented, and energy-priority, are implemented through consistent feature-weighting schemes applied across all analytical paradigms. The evaluation focuses on internal robustness indicators, including cross-method agreement, scenario sensitivity, and spatial coherence, rather than external field validation. The results show that supervised learning models exhibit high performance stability across scenarios, whereas PCNN substantially improves spatial coherence by reducing the fragmentation of suitable zones. The MCDA provides a transparent and spatially contiguous baseline, whereas the RLKM reveals scenario-sensitive intrinsic suitability regimes. Areas consistently identified as suitable across methods and scenarios represent high-confidence zones for screening-level planning, whereas scenario-dependent areas indicate elevated uncertainty. This framework advances hydropower suitability assessment toward transparent, risk-aware, and adaptive spatial decision support in complex geothermal environments by shifting emphasis from single-model accuracy to scenario robustness and cross-method synthesis.
Purchase Behavior in AI-Enabled Livestream Commerce: Evidence from an Emerging Economy Thanh D. Nguyen; Hoan D. D. Ly
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-011

Abstract

This study aims to examine consumer purchase behavior in AI-enabled livestream commerce as an emerging form of AI-driven digital commerce. Specifically, the research investigates how technological and social–psychological factors influence perceived ease of use, perceived usefulness, intention to use, and purchase behavior. Data were collected through an online survey using convenience and snowball sampling methods, resulting in 248 valid responses. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the proposed relationships. The findings indicate that compatibility and self-satisfaction positively influence both perceived ease of use and perceived usefulness, while perceived risk negatively affects these perceptions. Social influence significantly enhances perceived usefulness but does not have a significant effect on perceived ease of use. In addition, perceived ease of use and perceived usefulness significantly strengthen intention to use, which subsequently drives purchase behavior. This study contributes to the literature by extending the TAM–UTAUT framework within the context of AI-enabled livestream commerce and by offering new insights into how AI streamers reshape consumer–platform interactions. These findings provide both theoretical contributions and practical implications for AI-driven commerce in emerging markets.
Monte Carlo-Based Assessment of Machine Flexibility in Group-Configured Part-Feeding Systems Islam Alexandrov; Ilya Melikov; Nikita Karpov; Naur Ivanov; Dmitry Krasovsky; Alexander Shurpo
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-021

Abstract

Modern high-mix, low-volume manufacturing faces significant downtime during setup changes in part-feeding systems, yet no quantitative model currently exists that links group-based reconfiguration strategies to a measurable flexibility index under stochastic batch-size conditions. This study therefore aims to develop and experimentally validate a probabilistic mathematical model for assessing machine flexibility when a group-based reconfiguration approach is applied to part-feeding systems. The methodology combines Monte Carlo simulation to model random batch-size distributions with physical validation using a rotary orienting device across eight distinct sleeve types. Simulation results indicate that the proposed strategy reduces setup labor by 51-61% in systems handling 100 different part types. When fewer than one-third of parts require reconfiguration, the machine flexibility index reaches 0.088 ± 0.014, meeting established thresholds for high system flexibility. Experimental tests confirm that a uniform group-level adjustment maintains operational efficiency deviations within 3-5% across varying part geometries. The primary novelty of this work lies in introducing a confidence-bounded flexibility coefficient that explicitly incorporates auxiliary loading subsystems, which are consistently overlooked in existing deterministic approaches. This provides a practical, data-driven tool for production planning that enhances responsiveness without sacrificing throughput or increasing system complexity.

Page 109 of 110 | Total Record : 1093

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