About Emerging Science Journal (ESJ) Emerging Science Journal is a multidisciplinary, an open-access, internationally double-blind peer-reviewed journal which 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. ESJ covers the following main subjects: Engineering and Technical Sciences Natural Sciences Social and Management Sciences Formal Sciences Physical Sciences For more information about ESJ Focus and Scope, you can click here. Current areas of interest, along with related associate editors, are listed below: Engineering and Technical Sciences Prof. Francesco Tornabene Department of Innovation Engineering, University of Salento Lecce, Italy Associate Editor Natural Sciences Prof. Nour Shafik Emam El-Gendy Egyptian Petroleum Research Institute (EPRI) Cairo, Egypt Associate Editor Social and Management Sciences Prof. Ferda Halicioglu University of Lincoln Lincoln, United Kingdom Associate Editor Formal Sciences Prof. Sunil Kumar National Institute of Technology Jamshedpur, India Associate Editor Physical Sciences Prof. Shen Xiu-Min Beijing Normal University Beijing, China Associate Editor Editorial Team View More International Emerging Science Journal Open Special Issues Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education" Aim and Scope Current issues in education involve differing views on standardized tests, socioeconomic factors, educational equity, and adaptive learning. It is important to understand that these issues are distinct from current trends in education, such as changes in the higher education system, the rise of online education, and the adoption of standards-based learning. To address and resolve these challenges, innovative ideas must be conceived and implemented. This Special Issue prioritizes studies that integrate insights from diverse disciplines, contextualize research findings within a broader framework, advance methodological and theoretical understanding, and possess the potential to influence education policy and practice. It welcomes quantitative, qualitative, and mixed-method research papers, as well as comprehensive reviews of significant research studies and technical reports previously published. Guest Editor: Dr. Stamatis Papadakis Affiliations: Department of Preschool Education, School of Education, University of Crete, Crete, Greece. Interests: Distance Learning; Mobile Learning; Learning Analytics; Game-based Learning; Preschool Education; Primary Education; Secondary Education; Educational Robotics. 0000-0003-3184-1147 Co-Guest Editor: Prof. Paula Escudeiro Affiliations: Instituto Superior de Engenharia do Porto, Porto, Portugal. Interests: Educational Technology; Learning Technology; Information Technology in Education; Serious Games. 0000-0003-2528-572X Manuscripts should be submitted online at www.ijournalse.org by registering and logging in to this website. For more information Click Here. This Issue is now open for submissions. Deadline for manuscript submissions: 31 December 2025. Closed Special Issues Special Issue "Internet of Things, Internet of Vehicles, and Blockchain"-2020-2021 - Published Papers Special Issue "COVID-19: Emerging Research"-2021 - Published Papers Special Issue "COVID-19: Emerging Research"-2022 - Published Papers Special Issue "Current Issues, Trends, and New Ideas in Education"-2022 - Published Papers Most Cited & Viewed Articles The most cited articles from Emerging Science Journal published since 2019, extracted from Scopus. Citation Article Big Data, Knowledge Mapping for Sustainable Development: A Water Quality Index Case Study by: Lei Zhang Effect of Salinity on Evaporation and the Water Cycle by: Jude Iloabuchi Obianyo Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications by: Allan Ramone Araujo Scharnberg, Adrison Carvalho de Loreto, Annelise Kopp Alves Revisiting Agroforestry for Building Climate Resilient Communities: A Case of Package-Based Integrated Agroforestry Practices in Nepal by: Kishor Aryal, Prakash Singh Thapa, Dhananjaya Lamichhane Impact of Transportation on Carbon Dioxide Emissions from Locally vs. Non-locally Sourced Food by: Bradley Striebig, Eric Smitts, Samuel Morton Preliminary Studies on the Use of Sawdust and Peanut Shell Powder as Adsorbents for Phosphorus Removal from Water by: Marian Asantewah Nkansah, Moses Donkoh, Osei Akoto, James Hawkins Ephraim
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Performance Evaluation of Inclined-Step and Wall Roughness on Battery Thermal Management System
In this study, the effects of inclined steps and wall roughness on the step-like plenum of the Z-type battery thermal management system (BTMS) are examined, extending the literature on its design. Due to the performance of the design in achieving a reduction in maximum temperature (Tmax), additional modifications are required to provide more insight into further enhancing the thermal performance and overcoming the design’s drawback, such as higher pressure drop (ΔP). The performance of the system was evaluated in terms of the and maximum temperature difference (ΔTmax) of batteries in the systems and ΔP across the system. The temperature values were selected after comparing the maximum temperatures recorded on each battery. Investigations were carried out using a Computational Fluid Dynamics (CFD) method, which was validated by comparing with experimental data from the literature. Findings revealed that the step designs with inclined angles of 5°, 45° and 85° reduced the Tmax by 3.18 K, 3.9 K, and 4.34 K, respectively, when compared to the Z-type design. However, the Z-type design has the lowest ΔP value (16.50 Pa), while the original step-like design system produced the highest value (20.96 Pa). When considering the roughness, by increasing the roughness height from 5 μm to 10 μm, an increase in Tmax was observed, while wall roughness generally decreases the ΔP. From 0 to 10 μm, Tmax increased by 0.03 K (0.01%) and ΔP increased by 0.07 Pa (0.29 %), indicating negligible effects. The study, therefore, concludes that adequate selection of step design with different angles, air inlet velocity, temperature, and wall roughness will be highly beneficial for designing cost-effective and efficient BTMSs.
Comparative Assessment of Machine Learning Approaches for Early Lung Cancer Diagnosis
Lung cancer, a leading cause of cancer-related mortality worldwide, often escapes early detection due to the absence of distinct symptoms in its initial stages. This work investigates how Machine Learning (ML) might improve early diagnosis by analyzing Electronic Health Records (EHR) data. Multiple ML models were developed and evaluated on a synthetic dataset created to replicate real-world patient characteristics, allowing controlled experimentation while safeguarding privacy. Model performance was tuned using both conventional optimization methods and nature-inspired approaches, with the aim of balancing predictive accuracy and computational efficiency. In our synthetic dataset experiments, ensemble learners optimized with metaheuristic techniques reached accuracy levels approaching 99 percent while maintaining computational efficiency and generally outperformed simpler baselines. The contribution of this work lies in exploring the integration of GFO and WOA for feature selection and hyperparameter tuning of XGBoost, together with a soft-voting ensemble. This approach provides an experimental pathway for enhancing predictive performance under computational constraints. However, as the dataset is synthetic, the conclusion remains experimental; validation against clinical records will be essential before translation into practice.
Multi-Country GHG Emissions Forecasting by Sector Using a GCN-LSTM Model
This study developed a novel hybrid Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model to forecast greenhouse gas (GHG) emissions across multiple country sectors, aiming to enhance climate policy. We analyzed 52 years (1970–2022) of GHG emissions data (CO₂, CH₄, N₂O, F-Gases) from 163 countries and eight sectors (Agriculture, Buildings, Fuel Exploitation, Industrial Combustion, Power Industry, Processes, Transport, Waste), sourced from the EDGAR v8 database. The GCN adjacency matrix captures spatial relationships on a weighted sum of Haversine distance and cosine similarity, while the LSTM models temporal dynamics. Data preprocessing includes min-max scaling and outlier handling with Interquartile Range capping. The model was trained on 70% of the data, validated on 15%, and tested on 15%, using Mean Squared Error (MSE) loss and the Adam optimizer. The performance was evaluated with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The GCN–LSTM model outperformed baseline models (ARIMA, Simple LSTM, Stacked LSTM), achieving the lowest MAE (0.0207 in Waste) and highest R² (0.9756 in Waste). Model interpretability highlighted strong regional connections, such as Thailand–Cambodia in the Waste sector, suggesting that spatial and temporal dependencies offer superior forecasting accuracy, informing targeted climate action.
Mechanistic Multiphysics Optimization of Catalyst Layers for High Performance PEM Fuel Cells
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.
IoT-Driven Emotional Data Analytics for Medical Applications: Insights and Innovations
This study introduces the Internet of Things-based Emotional State Detection Model (IoT-ESDM), a comprehensive and intelligent emotional computing framework aimed at detecting and managing anxiety-related behavior in healthcare environments. The model leverages a multi-modal approach that combines facial expression analysis, physiological signal monitoring, and AI-driven classification to accurately identify emotional states in real time. Core components of the system include fuzzy color filtering, histogram analysis, and virtual face modeling, which work together to extract relevant emotional features from input data. These features are then analyzed to provide adaptive, personalized feedback to patients or caregivers, enhancing emotional well-being support. Experimental results demonstrate the superior performance of IoT-ESDM over existing emotion detection systems. The model achieved a feedback ratio of 97.54%, accessibility ratio of 95.3%, detection accuracy of 92.7%, and a classification accuracy of 98.13%. Additionally, it showed a quality assurance rate of 94.13%, contributed to a 29.1% reduction in anxiety levels, and yielded a health outcome ratio of 94.5%. These metrics validate the system's effectiveness in clinical and real-world applications. The success of IoT-ESDM highlights its potential as a powerful tool for emotion-aware AI interventions, paving the way for future advancements in mental health monitoring and personalized healthcare solutions.
Metaheuristic Hyperparameter Optimization and Explainable Deep Learning for Baggage Threat Detection
The American Statistical Association reports that Bangkok, the capital and largest city of Thailand, holds the top spot as the most visited city worldwide in 2023. X-ray imaging for security screening plays a crucial role in upholding transportation security by detecting a diverse range of threats or prohibited items carried by passengers. This study introduces an advanced deep learning model leveraging YOLOv8, renowned for its enhanced efficiency in automating baggage detection processes. To enhance the model's hyperparameters and adjust them finely during the training process using the baggage dataset, the system utilized a metaheuristic optimization algorithm known as Evolutionary Genetic Algorithm, which is based on evolutionary principles. Incorporating explainable artificial intelligence techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) allows for visual interpretation of predictions, aiding operators in utilizing the model effectively. We trained and tested the baggage dataset, which included 8,312 images and five classes: gun, knife, pliers, scissors, and wrench. The YOLOv8 model achieved the following metrics for the detection of prohibited objects in baggage inspection: an overall precision of 90.5%, recall of 83.3%, mAP50 of 91.3%, and mAP50-95 of 67%. The proposed method can fully automate the recognition of prohibited objects during baggage inspection. This approach is beneficial for designing an integrated, automatic, and non-destructive X-ray image-based classification system.
Regulatory Thresholds as Disciplinary Signals: Evidence from Bank Nonperforming Loan Supervision
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.
Unveiling the Power of Intellectual Capital in Driving Financial Performance: A Deep Dive into the IT Sector
This study aims to theoretically and empirically investigate the relationship between intellectual capital (IC) and the financial performance of firms in the U.S. information technology (IT) sector, with a particular focus on Return on Assets (ROA) as a key performance indicator. Data were collected from 345 publicly listed IT companies over the period 2011–2022, yielding 1,792 firm-year observations. The research employed descriptive statistics, correlation matrices, box plot analyses, and multiple regression models to examine the effects of IC and its components, human capital efficiency, structural capital efficiency, and capital employed efficiency on financial outcomes. The analysis revealed that, contrary to conventional expectations and prior literature, IC exhibited a negative and statistically significant association with financial performance, highlighting potential inefficiencies in the utilization of intangible assets within the IT industry. These findings underscore the complexity of translating investments in IC into measurable financial gains, suggesting that firms may be overinvesting or misallocating resources in areas that do not yield immediate profitability. The novelty of this research lies in uncovering an unexpected inverse IC-performance link in a knowledge-intensive sector, thereby offering executives and policymakers new insights into how IC strategies should be re-evaluated and aligned with long-term value creation.
Structure-Aware Chunking for Complex Tables in Retrieval-Augmented Generation Systems
Retrieval-Augmented Generation (RAG) is a hybrid method that combines information retrieval with large language models to generate context-aware, factually grounded responses. However, the RAG system relies heavily on well-structured input data to generate accurate and contextually relevant responses. Documents with complex table layouts pose significant challenges, as most chunking strategies are text-centric and often overlook table-rich documents containing multi-column and multi-row structures. Hence, this study proposes a customized structure-aware chunking framework specifically designed for university course documents containing multi-column, multi-row tables with nested headers. The framework employs Camelot for high-fidelity table extraction, followed by customized logic that constructs semantically coherent chunks by preserving academic term, subject name, credit hour, and category. This prevents semantic fragmentation during retrieval. The proposed method is evaluated using the RAGAS framework and compared against several baselines using standard parsing and chunking techniques. Results show that the proposed approach achieves the highest answer accuracy of 0.73 and substantially improves retrieval relevance and contextual precision. These findings demonstrate the framework’s effectiveness in handling structure-dependent academic queries. This study highlights that ensuring both parsing quality and chunking strategy is essential to retain semantic relationships in table-rich documents, offering a practical improvement for RAG systems in structurally complex scenarios.
Time-Varying Impacts of Robust Determinants on Greenhouse Gas Emissions: Panel Data Evidence
Understanding the key drivers of greenhouse gas (GHG) emissions is crucial for designing effective and adaptable climate policies, particularly given the complex interplay among structural, institutional, and energy-related factors. This study examines the time-varying impacts of key determinants of GHG emissions across 29 countries from 1993 to 2018, with an emphasis on the shadow economy, energy security risks, and geopolitical volatility. The analysis follows a four-step framework: countries are classified using principal component analysis (PCA) and K-means clustering, robust covariates are selected via Bayesian Model Averaging (BMA), and their impacts are estimated with time-varying coefficient panel models. Model robustness is evaluated through grouped cross-validation, confirming the superior performance of the time-varying random effects (tvRE) specification. The results reveal that the shadow economy and energy security risk exert more dynamic and substantial impacts in the Higher-income group, while their effects are comparatively muted in the Lower-income group. Geopolitical risk, however, shows limited explanatory power for emissions in both contexts. This study provides a novel empirical framework for capturing the dynamic influences of emissions drivers and contributes actionable insights toward achieving sustainable development goals.
The Leadership Attributes and Commercial Bank Financial Performance: The Mediating Role of Capital Adequacy Ratio
This paper aims to explore how factors such as age, gender, professional qualifications, and experience of the Chairman of the Board of Directors (COB) and Chief Executive Officer (CEO) influence the financial performance of Vietnamese commercial banks. Data are collected from 28 listed banks in Vietnam from 2013 to 2023. The study employs regression methods including OLS, FEM, REM, and FGLS. To ensure robustness and address potential model limitations, SGMM regression is also utilized. The results indicate that banks led by female COBs and CEOs tend to achieve better financial performance. Additionally, older COBs are associated with lower bank performance. The findings also highlight the significant impact of bank leaders' expertise and educational background on financial performance. To the author's knowledge, this is the first study to analyze the combined effect of bank leadership attributes on the profitability of Vietnamese commercial banks within an emerging economy. It is also among the first to examine the mediating role of the capital adequacy ratio (CAR). The results provide a solid basis for stakeholders to make informed decisions related to the recruitment, appointment, training, and development of bank leaders to enhance financial performance.
A New Scale for Evaluating Disclosure in Earnings Calls on Emerging Markets
Corporate disclosure via earnings calls is a vital mechanism for financial transparency and stakeholder communication, enabling investors to evaluate firms’ performance, governance practices, and strategic direction. Yet existing approaches to assessing disclosure quality are often outdated, fragmented, or narrowly focused on sustainability, thereby neglecting the governance dimension of transparency. This study develops and validates a comprehensive, reliable, and optimized scale to measure the quality of corporate disclosure in earnings calls, integrating both sustainability and governance perspectives. Using a systematic scale-development process grounded in a robust conceptual framework, we collected data from 74 investors and analysts across multiple stages, focusing on Brazilian listed companies. Exploratory factor analysis yielded a refined three-dimensional structure comprising Analyst Disclosure, ESG (environmental, social and governance), and Artificial Intelligence. Findings indicate that investors increasingly regard artificial intelligence as central to evaluating disclosure credibility and informing investment decisions. This research advances disclosure measurement by offering a novel, empirically validated instrument that captures evolving communication dynamics. The proposed scale provides theoretical and practical contributions by strengthening the links between governance, sustainability, and financial transparency, and it establishes a foundation for future cross-market validation in emerging economies.
Debt by Design: Exploring Market Forces Behind Leverage in Two Economies
Abstract This study investigates the determinants of capital structure by comparing firms listed on two prominent global stock indices: the S&P 500 (United States) and the NSE CNX 500 (India). Specifically, it examines how firm-specific factors—such as liquidity, asset tangibility, and sustainability practices—influence leverage decisions within differing economic and institutional contexts. Drawing on a comprehensive dataset of 3,575 firm-year observations from 406 S&P 500 companies and 4,180 observations from 419 NSE CNX 500 firms between 2011 and 2021, the analysis employs Two-Stage Least Squares (2SLS) regression, the Generalized Method of Moments (GMM), and a series of diagnostic tests addressing heteroskedasticity and model robustness. The empirical results indicate that liquidity, tangibility, and sustainability performance significantly affect firms’ capital structure decisions. Moreover, growth opportunities and profitability also play key roles. Cross-country differences highlight the influence of macroeconomic conditions and financial system structures on leverage behavior. This research enriches the capital structure literature by offering a comparative, cross-national perspective and provides actionable insights for corporate managers, investors, and policymakers seeking to optimize capital structure in diverse financial environments
Temporal ASTRA: Synthetic Evaluation and Hybrid CNN-BiLSTM Modeling for Calibration-Free Strabismus Detection
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.
Digital Financial Compliance Challenges: Applying Routine Activity Theory to Online Gambling Networks Analysis
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.
Job Characteristics, Affective Commitment, and Turnover Intentions: A Dual-Theory Examination of Generation Z
This study investigates the extent to which core job characteristics—autonomy, task identity, task significance, and skill variety—influence affective commitment and turnover intentions among Generation Z employees in Vietnam. Based on the Job Demands–Resources (JD-R) and Conservation of Resources (COR) theoretical frameworks, the study seeks to elucidate how early-career employees interpret job resources within a collectivist, hierarchical, and resource-constrained environment. A structured quantitative design was employed, drawing on survey data from 312 Gen Z respondents across diverse organizational settings. Measurement validity and reliability were established via Confirmatory Factor Analysis and Cronbach’s Alpha, while hypotheses were tested using Structural Equation Modeling (SEM). Empirical findings reveal that task identity exerts a negative influence on affective commitment, whereas autonomy, task identity, and skill variety are positively associated with turnover intentions. Task significance demonstrates no significant effect on either outcome. Conversely, affective commitment emerges as a strong inverse predictor of turnover intentions, underscoring its role as a stabilizing psychological resource. This research contributes to the existing literature by integrating JD-R and COR theories to challenge the presumed universality of job resource effects. It underscores the contextual sensitivity of job design, particularly for younger cohorts in emerging markets. The findings offer practical implications for designing culturally and generationally responsive retention strategies.
A Model for Fostering Labor Productivity and Wage Management: A Long-Term Outlook to 2034
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.
Hybrid Neural Networks vs. Econometric Models for Fresh Durian Export Value Forecasting: A Comparative Analysis
This study compares machine learning and econometric approaches for forecasting agricultural export values in volatile global markets, examining predictive accuracy and economic interpretability trade-offs. Monthly data from January 2014 to December 2023 were analyzed using five models: Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Hybrid ANN-LSTM, Ordinary Least Squares (OLS), and Autoregressive Distributed Lag (ARDL). Key predictors included durian, mangosteen, and longan export values/volumes, plus China's GDP. Performance evaluation used MAE, RMSE, MAPE, and R² metrics with systematic hyperparameter optimization through grid search and 5-fold cross-validation. ANN achieved the highest absolute accuracy (MAE: 1,684,667,401.55; RMSE: 2,602,671,952.28), while Hybrid ANN-LSTM delivered superior relative accuracy (MAPE: 1.58%). ARDL demonstrated exceptional explanatory power (R²=0.83) for structural economic relationships. China's GDP emerged as the strongest determinant across all models. Longan export value showed contrasting effects between approaches, positive in machine learning models versus negative in econometric models, reflecting different paradigmatic interpretations of market substitution dynamics. This research introduces the first comprehensive comparative framework integrating advanced hybrid neural networks with traditional econometric methods for multi-commodity agricultural forecasting, addressing cross-commodity substitution effects previously unexplored while offering complementary perspectives for both predictive accuracy and economic policy interpretation.
Antidiabetic Properties of Uncaria sclerophylla Roxb: In Vitro, Metabolite Profiling, and Molecular Docking
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.
Impact of Oil Price Shocks on GCC Stock Markets: Tail-Driven MTNARDL Evidence
This paper examines the asymmetric impact of recent oil price fluctuations on stock markets in the Gulf Cooperation Council (GCC) region from 2019 to 2024, a period marked by the COVID-19 pandemic and the prolonged Ukrainian crisis. Using the Nonlinear Autoregressive Distributed Lag (NARDL) and an enhanced Multiple Threshold Nonlinear Autoregressive Distributed Lag (MTNARDL) framework, the study examines whether extreme positive and negative shocks in oil prices, S&P 500, Bitcoin, and gold induce heterogeneous transmission effects on GCC equity indices. The empirical findings show that both extreme positive and negative oil price shocks exert a stronger and more persistent influence on GCC stock markets than fluctuations in global equities, cryptocurrencies, or precious metals. This confirms the dominant role of oil as a key driver of financial dynamics in oil-dependent economies, particularly during periods of heightened uncertainty. The main contribution of this study lies in the improvement of the MTNARDL specification, which allows for a clearer identification of tail-risk behavior and asymmetric volatility spillovers. The enhanced model captures multi-threshold nonlinearities more effectively than conventional approaches, offering a robust framework for policymakers and investors to better understand shock transmission mechanisms in hydrocarbon-based markets.
Brand Image and Customer Loyalty: Exploring the Power of Word of Mouth
This study aims to examine the moderating role of word of mouth (WOM) in the relationship between brand image and customer loyalty within the Vietnamese market context. While previous research has primarily explored WOM as a direct influencing factor, limited attention has been given to its moderating effect on this specific relationship. Employing a quantitative research design, the study collected data through a structured survey, distributing 950 questionnaires and obtaining 829 valid responses. The data were analyzed using PLS-SEM via SmartPLS 4.0 software. The findings indicate that WOM significantly moderates the relationship between brand image and customer loyalty, such that positive WOM reinforces consumer loyalty when brand perceptions are favorable, particularly when the message comes from influential sources such as celebrities or opinion leaders. Conversely, negative WOM can attenuate the effects of brand image and result in decreased loyalty. The study contributes to the existing literature by clarifying the contextual influence of WOM in a collectivist culture and offering practical implications for marketers in developing communication strategies that leverage WOM to enhance brand equity and customer retention. This research provides novel empirical evidence relevant to both theory development and managerial practice in emerging markets. JEL Classification: M30, M31, O35.
Artificial Intelligence and Business Process Management: A Responsible Framework for Sustainable Transformation
This study aims to develop a responsible and sustainable framework for implementing artificial intelligence (AI) in business process management (BPM), with a focus on aligning technological advancement with strategic economic transformation. It addresses the need for ethical, sector-sensitive AI adoption in emerging economies undergoing digital modernization and diversification. The research integrates enterprise information system considerations, privacy-preserving modular architectures, and national regulatory frameworks related to data localization and cybersecurity. A sectoral analysis is conducted to assess global AI adoption maturity and its implications for economic transformation, using Kazakhstan as a contextual reference point. The results reveal that consumer-facing sectors such as retail and financial services exhibit high near-term adoption potential, while healthcare requires gradual infrastructure and talent development. More significantly, mid-term opportunities in manufacturing, logistics, and transportation sectors present Kazakhstan with a comparative advantage. AI adoption in manufacturing is projected to grow by 83% within three to seven years, underscoring the importance of timely investments in automation, smart technologies, and workforce upskilling. This study contributes a context-aware framework for responsible AI-enabled BPM. It offers actionable insights for policymakers and business leaders in emerging economies, advocating for sectoral prioritization, strategic timing, and capacity-building to ensure sustainable digital transformation.
Probability Density Function Adjustment for Estimating Quantile Regression Coefficients
This study aims to improve the estimation of quantile regression coefficients by adjusting probability density functions using a selected τ-function that exhibits symmetric properties. The research focuses on five quantile levels Q(20)th, Q(25)th, Q(50)th, Q(75)th, and Q(80)th and compares the proposed method with conventional multiple regression through simulation experiments under varying sample sizes and distributional conditions. Performance is evaluated using the mean absolute error (MAE) as the primary metric. The findings indicate that for small sample sizes (n=8, n=15), both multiple and quantile regression methods perform well, especially at lower quantiles (Q(20)th to Q(50)th). However, as sample sizes increase (n=50, n=100), quantile regression at higher quantiles (Q(50)th, Q(75)th, Q(80)th) demonstrates superior estimation accuracy. In relation to kurtosis and skewness, the Q(50)th and Q(80)th quantiles are sensitive to distributional changes, effectively capturing transitions from high to normal kurtosis and central shifts in skewed distributions. The novelty of this research lies in the integration of the τ-function into the quantile regression framework, enhancing robustness and accuracy in coefficient estimation under non-normal conditions. This approach contributes to methodological advancements in regression analysis, particularly in applications involving non-standard data distributions.
Assessing the Impact of Ghost Car Attacks on Traffic Flow in Vehicular Ad Hoc Networks
Vehicular Ad Hoc Networks (VANETs) play a crucial role in enhancing road safety, traffic management, and driving efficiency through real-time communication between vehicles and infrastructure. However, VANETs are vulnerable to various security threats, one of which is the “ghost car” attack. In this attack, a malicious entity injects false information into the network, simulating the presence of a non-existent or “ghost” vehicle. This can lead to severe consequences such as traffic disruptions, accidents, and a compromised trust in the system’s reliability. This study aims to simulate and analyze the impacts of ghost car attacks on Vehicular Ad Hoc Networks (VANETs), focusing specifically on intersection waiting times and overall traffic flow. We used Simulation of Urban Mobility (SUMO) integrated with ns-3 for realistic VANET simulations, introducing varying numbers of ghost vehicles. Results indicate significant increases in waiting times and vehicle counts at intersections due to ghost cars, leading to traffic disruptions. This study evaluates ghost car attacks within realistic urban scenarios and proposes targeted detection and mitigation strategies, leveraging authentication, machine learning, and blockchain technologies.
Does Board Diversity Influence Green Revenue and Firm Value? Evidence From an Emerging Market
This study investigates the effect of board diversity on green revenue among Saudi-listed firms. It places particular emphasis on the moderating role of shareholder value. To achieve this objective, the study constructs a composite board diversity index using principal component analysis (PCA). It employs random-effects panel regression models on firm-level data covering the period 2020–2024. Robustness is ensured through alternative model specifications and Generalized Method of Moments (GMM) estimations. The findings reveal that board diversity is positively associated with green revenue. In contrast, higher shareholder value, as measured by market valuation, is negatively associated with green revenue. Importantly, board diversity significantly mitigates this negative relationship. This indicates that diverse boards encourage stronger engagement in sustainable activities, even in highly valued firms. The study contributes to the literature by integrating board diversity, green revenue, and shareholder value within a single empirical framework in an emerging market context. The results offer novel evidence that board diversity serves as an effective governance mechanism for aligning sustainability objectives with value-driven corporate strategies. From both theoretical and policy perspectives, the findings support agency and resource-dependence theories. They highlight the importance of inclusive board structures in embedding sustainability into corporate decision-making.
Dynamic Customer Experience, Satisfaction, and Word-of-Mouth in Telecom-IT Sector
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
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
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.
Data Governance Meets Generative Artificial Intelligence: Towards A Unified Organizational Framework
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.
Accent Classification Across Continents: A Deep Learning Approach
This study focuses on a deep learning based accent classification across continents and greatly enhances speech recognition systems by identifying the accents of Asia, Europe, North America, Africa, and Oceania. The Convolutional Neural Network (CNN) was trained on the Mozilla Common Voice dataset, which comprises the features extracted - Mel-Frequency Cepstral Coefficients, Delta, Delta-Delta, Chroma Frequency, and spectral features- and trained to classify accents. Multiple convolutional and dense layers for accent classification were combined with dropout and batch normalization layers to avoid overfitting during training. Out of the total validation data, 82% accuracy has been achieved. The Asian and European accents were classified with greater accuracy since their datasets were larger, whereas African and Oceanian accents were more misclassified due to limited representation and the greater diversity of languages. In contrast to the past research, which focused only on country-based accent classification, this work introduced a feature based deep learning approach of continent-based accent classification along the way. The recognition of this accent variation, in turn, helps integrate and improve various aspects of speech recognition systems and makes their application more inclusive for voice assistants and language learning tools with diverse linguistic patterns. The future work will concentrate on extending the dataset to the seven continents while enhancing classification accuracy via better feature engineering and model tuning.