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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 803 Documents
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New Development Model of the Agro-Industrial Complex Under Green Economy Conditions Saparova, Dana; Saginova, Saniya; Belgibayeva, Kuralay; Bekbussinova, Gulnafiz; Kasenova, Asiya; Tleulin, Chingiz
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-019

Abstract

This study empirically examines opportunities and provides a conceptual rationale for developing a green economy model tailored to Kazakhstan’s agro-industrial complex (AIC). The research focuses on identifying effective strategies for sustainable agricultural development by combining environmental and economic approaches. The methodological basis includes the analysis of theoretical concepts in economic and mathematical modeling, identification of key factors influencing the sector, and the construction of a block structure to represent these factors. Forecasting models were developed to evaluate the efficiency of various strategic directions for transitioning to sustainable agriculture. The findings indicate that the most influential drivers of sustainable development include the expansion of green financial instruments, increased public and international investment, and the use of digital technologies for collecting and processing data. The forecast of key indicators of the green economy in Kazakhstan up to 2030 confirms the reliability and applicability of the proposed evaluation method. The main contribution of this research is the creation of an integrated model that connects theoretical frameworks with practical tools for managing ecological and economic performance in agriculture. The results can be applied in strategic planning, decision-making for the AIC, and academic programs focused on the green economy and sustainable development in Kazakhstan.
Factors Influencing Female Managerial Performance: A Pilot Study for Model Validation Pálmai, László; Kőmüves, Zsolt; Végvári, Bence; Szécsi, Gabriella
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-026

Abstract

This pilot study examines factors influencing the performance of female managers by testing a structural model that integrates psychosocial and organizational dimensions. The model includes organizational culture, managerial power, discrimination, prejudice, insecurity, and family roles. Data were collected through a survey of 179 female managers in Hungary. As the sample is geographically limited, the findings should be interpreted with caution. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to analyze the relationships among variables. Results indicate that a supportive organizational culture enhances perceptions of managerial power, which positively influences leadership performance. Conversely, experiences of discrimination reinforce prejudices, while family-related obligations heighten perceived bias toward women in leadership. Insecurity was also found to negatively impact managerial performance. The model showed strong internal reliability and acceptable discriminant validity, supporting its use in further research. This study offers novel insights by jointly examining individual, organizational, and societal barriers within a unified framework. Beyond its theoretical contribution, the findings provide practical guidance for organizations and policymakers aiming to foster inclusive leadership environments and promote gender equity in the workplace.
Stakeholder Engagement Based Moral Hazard Analysis Model in FPSO-Tanker Oil Transfer Palippui, Habibi; Rosyid, Daniel M.; Silvianita; Sade, Juswan
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-022

Abstract

This study aims to address moral hazard risks in FPSO-tanker oil transfer operations by introducing a semi-quantitative model rooted in stakeholder engagement theory. The model, named SEMHAM (Stakeholder Engagement-based Moral Hazard Analysis Model), incorporates four engagement indicators to calculate the total involvement percentage (PTI): Occurrence Frequency Value (OFV), Responsibility Weight Value (RWV), Critical Role Value (CRV), and Process Impact Value (PIV) to calculate the Total Involvement Percentage (PTI). This metric quantifies the behavioral influence of each stakeholder in the offloading process. Using operational data from 17 offshore zones based on Pertamina's 2023 report, eight primary stakeholder roles were evaluated using a weighted activity matrix. The findings indicate that FPSO Crew and Ship Crew possess the highest PTI scores, signifying greater control and potential risk, whereas administrative actors such as agents and port authorities were identified as lower-risk participants. The SEMHAM model facilitates risk classification and recommends appropriate digital oversight, including IoT-based monitoring, smart contracts, and role-based dashboards. This approach enables the integration of behavioral risk metrics into digital governance systems, thus supporting real-time operational monitoring. The model also demonstrates potential scalability to more complex offshore energy domains, such as LNG terminals and deepwater operations, enabling broader stakeholder governance beyond the current FPSO-tanker context.
Adaptive Segmentation of Information Sequences for Machine Learning Modular Regression Models Lebedev, Ilya; Sukhoparov, Mikhail; Semenov, Viktor; Khasanov, Dmitry
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-08

Abstract

The research objective is to construct an adaptive model for modular machine learning structures that improves the processing quality of information sequences. The novelty of the proposed methodology is that it can identify segments of an information sequence obtained using various methods and assign models with the best quality indicator values to subsequences. Classical methods allow tuning of the model to the entire data sample. The improvement consists of the proposed solutions that consider the inverse problem of forming segments of data sequences, such that their properties correspond to the processing model. The proposed methodology was tested on various models and datasets. Segmentation and assignment of regression models with the best characteristics to individual segments allow the reduction of the mean square error (MSE) and mean absolute error (MAE) to 8%. The findings show an opportunity to increase of 5-8% for weak LR, SVM, and GR models, while strong DT, CNN, ANN, ANFIS, and XGBoost models improve by 1-4% in scenarios with limited data. Segmentation enables more efficient training and reduces sensitivity to noise and outliers. The proposed solution allows the selection of segmentation strategies and model combinations considering local data properties. Its application enables the implementation of existing machine learning architectures to improve the quality of training and analysis of information sequences and increase adaptability, scalability, and interpretability.
Study on Preparation of Nano Humic Acid and Adsorption Effect of Heavy Metals in Soil Qian Sun; Kamaruddin, Mohamad Anuar Bin; Kai Huang; Yun Cao; Mohd Suffian Yusoff; Yong Cheng
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-04

Abstract

Nano humic acid (NHA) offers a promising strategy for remediating agricultural soils contaminated by livestock and poultry manure. This study investigates the adsorption behavior of NHA for heavy metals (Cu, Zn, As, Mg) and nitrogenous compounds (nitrate and ammonium nitrogen) in real-world polluted soil collected from a poultry farm in Changzhou, China. NHA was synthesized via high-shear, acid-precipitation, and surfactant-assisted methods, and its structure was characterized using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and particle size analysis. FTIR revealed the emergence of new functional groups (e.g., amino, ester, sulfonic), enhancing the active sites available for pollutant binding. At 30 days, NHA treatments achieved substantial reductions in Cu (76.1%), Zn (57.5%), and As (12.9%), with NANO3 and NANO4 showing the highest adsorption capacity. At 90 days, Cu and Mg continued to exhibit strong dose-responsive removal (up to 49.9% and 26.8%, respectively), while Zn and As showed nonlinear responses, likely due to saturation effects. NHA also outperformed traditional humic acid in nitrate and ammonium nitrogen adsorption, with the 25 g/kg application (NANO2) achieving up to 55% nitrate and 20% ammonium reduction. Correlation analysis confirmed that material type, rather than dosage alone, was the dominant factor influencing pollutant immobilization. These findings demonstrate that NHA is an effective, dual-function soil amendment capable of long-term remediation of both heavy metal and nitrogen pollution, offering a cost-effective and scalable solution for improving soil quality in degraded agricultural regions such as the Yellow River basin.
Predicting EFL Students’ Use of Artificial Intelligence Tool in Advancing Their Writing Skills Alrishan, Amal Mohammad Husein
Emerging Science Journal Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-SIED1-018

Abstract

This study examines the factors influencing the adoption and use of artificial intelligence (AI) tools to enhance writing skills among English as a Foreign Language (EFL) learners in Oman, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT). The objectives were to assess the impact of performance expectancy, effort expectancy, social influence, and facilitating conditions on students’ behavioral intention and actual AI usage, and to test the moderating role of prior AI experience. A cross-sectional quantitative design was employed, with data collected from 255 undergraduate female EFL students through a validated questionnaire. Structural equation modeling (SEM) and confirmatory factor analysis were used to validate the measurement model and test hypothesized relationships. Findings indicate that behavioral intention and facilitating conditions significantly predicted actual AI tool use, while performance expectancy, effort expectancy, and social influence strongly shaped behavioral intention. Mediation tests confirmed that behavioral intention served as a key pathway linking UTAUT constructs to actual adoption, and moderation analysis showed that prior AI experience strengthened the intention–usage relationship. This research contributes to a context-specific, evidence-based framework for AI adoption in EFL writing, offering novel insights for educators, institutions, and technology designers to integrate AI ethically and effectively in language learning.
Combining a Moving Average with a Triple EWMA Chart to Improve Detection Performance Saesuntia, Piyatida; Areepong, Yupaporn; Sukparungsee, Saowanit
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-05

Abstract

This article aims to introduce the novel mixed triple exponentially weighted moving average-moving average (MTEM) chart to accurately detect position changes for both symmetric and non-symmetric distributions. The MTEM chart constructs a moving average (MA) structure to filter out fluctuations in the raw data and then applies triple exponential weighting to improve the ability to identify minor shifts. The average run length (ARL) and median run length (MRL), which are run length profiles derived from the Monte Carlo simulation (MC) strategy, were used to compare the performance of the suggested chart with that of MA, EWMA, TEWMA, and mixed moving average-triple exponentially weighted moving average (MMTE) charts. In addition, the expected average run length (EARL) and expected median run length (EMRL) were also used to rate the overall results. Results of the study indicate that the MTEM chart surpasses competitor charts in detecting minor to moderate changes. The MMTE chart responds slightly slower than the proposed chart. Due to its smoothed and re-averaged structure, it may lose significant information. The MA chart worked better for greater shifts. Furthermore, the MTEM chart competency was applied to two real-world datasets, confirming its practicality.
Predicting Dropout in MENA STEM Higher Education Using Explainable AI: A Machine Learning Approach Al Hashmi, Reem A. M.; D. Zervopoulos, Panagiotis; M. Elmehdi, Hussein; Ozturk, Ilhan
Emerging Science Journal Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-SIED1-016

Abstract

This study aims to develop an explainable machine learning–based early warning system to predict dropout risk among Science, Technology, Engineering, and Mathematics (STEM) students in the MENA region. Using longitudinal data from 6,798 undergraduate STEM students enrolled at a major UAE university, we evaluated six supervised classifiers: XGBoost, Gradient Boosting Machine (GBM), Random Forest, CART, Logistic Regression, and K-Nearest Neighbors. Models were trained on institutional student information system (SIS) data spanning ten cohorts (2010–2019), with class imbalance addressed through ROSE sampling. The top-performing models (XGBoost, GBM, and Random Forest) achieved AUC-ROC scores exceeding 0.91 and F1-scores above 0.84, significantly outperforming baseline models. Key predictors of dropout included the number of withdrawn semesters, second-term credit load, academic probation history, and performance in mathematics and physics. To improve interpretability, we applied SHapley Additive exPlanations (SHAP) analysis, enabling both global and individual-level feature attribution. The system offers scalable, real-time predictive capabilities using only routinely available SIS data, with no need for external surveys or learning management system inputs. The novelty of this research lies in its integration of explainable AI into a regional context, enabling early, transparent, and actionable interventions to reduce dropout. These findings contribute to data-driven retention strategies in higher education systems where predictive tools remain underutilized.
From Teaching to Employability: The Cultural and Performance Pathways to Success Almaqbali, Said; Meng-Chew , Leow; Shannaq , Boumedyen; Marhoubi, Asmaa H.; Ong, Lee-Yeng
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-027

Abstract

The current research examines the possible mediating and moderating effects of Teaching Efficacy (TE) and National Culture (NC) on the nexus of Readiness of Students (RS), Interactive Online Collaboration (IOC), Faculty Training (FT), and Policy Support (PS) and the ensuing results of Student Performance (SP), Job Employment (JE), Student Competency (SC), and University Reputation (UR). We have evaluated both the direct and indirect association between the stipulated constructs by utilizing Partial Least Squares Structural Equation Modeling (PLS -SEM) on a sample of 291 respondents who were sampled using structured questionnaires. The empirical evidence suggests that TE is a medium of connecting between RS, PS, and SP and therefore enhances its impact on JE, SC, and UR. Notably, the influence of SP on JE is statistically significant in case of concurrent TE activity (O for indirect path = 0.215, p<0.001). Similarly, mediation helped students score better on SC (O = 0.327, t = 6.261, p < 0.001) and UR (O = -0.065, t = 1.911, p = 0.028). A substantial direct correlation was found between RS and TE (r = 0.282, t = 4.175, p < 0.001). The outcome of the moderate analysis indicated that Organizational Culture exerted a strong influence, leading to a positive impact on the correlation between TE and SP (O = 0.087, t = 1.994, p = 0.023). In addition, Information Culture (IC) acted as a protective factor, moderating the relationship between RS and TE (O = -0.093, t = 1.945, p = 0.026). Taking TE as the main factor and cultural elements as moderators significantly improved the model's performance, demonstrating that student results and university reputation can be enhanced when there is strong teaching competence and a positive organizational environment within these institutions.
From Silos to Synergy: Collaborative Laboratories and the Transformation of Knowledge Production Santos, José M. R. C. A.; Brandão, Ana Sofia
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-020

Abstract

The increasing societal importance of cutting-edge science and technology calls for a closer examination of public policies' influence on the evolving dynamics of knowledge production and transfer. This focus is especially pertinent in peripheral economies such as Portugal, where persistent structural challenges include the limited integration of highly qualified human resources within the economy. The purpose of this research is to investigate how the knowledge coproduction and transfer dynamics of ‘Collaborative Laboratories’ (CoLABs), a new form of intermediary organization in Portugal, differ from those of more traditional science-industry interface set-ups, in the Portuguese context. This research employed a deductive, quantitative, multiple-case, cross-sectional design, utilizing scientific publications as collaboration indicators and applying Social Network Analysis to map and analyze the knowledge coproduction and transfer networks of CoLABs in Portugal, comparing them to Technology Centers. The results reveal that CoLABs prioritize the creation of flexible collaboration networks and the broad coproduction and dissemination of knowledge. CoLABs are found to function as value-occupying hub organizations and serve as crucial bridging entities and are characterized by high connectivity, diverse collaboration, and cohesive research and innovation communities. The need for public agencies and CoLAB governance structures to devise strategies to enhance communication and collaboration within the CoLAB network is highlighted. This is the first study to investigate CoLABs as a new form of intermediary organization in Portugal, specifically examining how their knowledge coproduction and transfer dynamics differ from more traditional science-industry interface set-ups in the Portuguese context.

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