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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Hybrid Parametric and Non-Parametric Identification of PEMFC Dynamics in SISO and MIMO Workflow
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.
Fractional White Smell Agent Optimization for CNN-Based Transfer Learning in Melanoma Classification
Melanoma is the deadliest form of skin cancer, and early diagnosis and treatment can significantly reduce mortality rates. However, existing strategies for classifying melanoma from dermoscopic skin images still face significant challenges. Therefore, this study aims to develop an accurate method for melanoma classification using dermoscopic skin images. A novel melanoma classification framework, termed Fractional White Smell Agent Optimization-enabled Convolutional Neural Network-based Transfer Learning (FWSAO_CNN-based TL), is proposed. First, the input skin image is preprocessed using an Adaptive Kalman Filter. Subsequently, skin lesion segmentation is performed using LinkNet, where the network is trained using White Smell Agent Optimization (WSAO). Following segmentation, image augmentation is applied, and feature extraction is conducted. Finally, melanoma classification is performed using a CNN-based transfer learning model trained with the proposed Fractional White Smell Agent Optimization (FWSAO), which integrates the Fractional concept, Smell Agent Optimization (SAO), and White Shark Optimizer (WSO). The CNN utilizes hyperparameters derived from a pretrained GoogLeNet model. The performance of the proposed FWSAO_CNN-based TL framework was evaluated using accuracy, True Positive Rate (TPR), and True Negative Rate (TNR). The proposed method achieved values of 91.565%, 90.090%, and 91.269%, respectively. Furthermore, the proposed model demonstrated performance improvements of 18.4%, 8.1%, 17.5%, 12.55%, 8.2%, and 6.23% compared with conventional approaches.
LeukocyteNet: An Explainable Transfer-Transformer Fusion Learning Model for Leukocyte Classification
White Blood Cells (WBCs), or leukocytes, are essential components of the immune system that protect the body against infections and malignant disorders. Even minor fluctuations in leukocyte count can indicate serious pathological conditions, including life-threatening malignancies such as leukemia, lymphoma, and myelodysplastic syndromes. Conventional diagnosis through manual microscopic examination is time-consuming, subjective, and heavily dependent on the pathologist’s expertise. To overcome these challenges, this study introduces LeukocyteNet, a transfer–transformer fusion model designed for the automated classification of ten malignant leukocyte categories. The model integrates convolutional feature extraction from VGG19 with the Swin Transformer’s global attention mechanism, enabling robust representations of both local morphology and global spatial dependencies. The LeukocyteNet model was trained on three publicly available datasets “ALL-IDB, the American Society of Hematology Image Bank, and Tehran Taleqani Hospital” and achieved an overall accuracy of 97.34%, a macro-averaged F1-score of 0.95, and a recall of 0.93, outperforming all evaluated baseline models. Furthermore, the inclusion of explainable AI techniques Grad-CAM, LIME, and Saliency Map enhances explainability by visualizing class-specific decision regions, thereby increasing clinical transparency and reliability. These findings demonstrate that LeukocyteNet not only achieves state-of-the-art predictive performance but also provides interpretable insights critical for trustworthy medical diagnostics.
Steady and Transient CFD Analysis of a Vertical Axis Ocean Current Turbine
Vertical-axis turbines (VATs) are a promising hydrokinetic technology for harvesting renewable energy from ocean currents. Still, their performance depends strongly on design parameters and flow interaction at different azimuthal angles. This study evaluates the performance of a VAT using computational fluid dynamics (CFD) simulations carried out under both steady-state and transient flow conditions. ANSYS CFX is used in the steady-state analysis to estimate torque and power output at various flow velocities and rotational speeds (RPM). At the same time, ANSYS Fluent is applied in the transient analysis to examine time-dependent torque behavior and azimuthal effects under unsteady flow. The steady-state results show that torque and power increase with higher flow velocity and rotational speed, reaching maximum values of 37,079 Nm and 291.86 kW at 4 m/s and 40 RPM. The transient results indicate periodic torque oscillations that become more stable at higher flow velocities, with peak turbine efficiency at 3 m/s, followed by a decrease at 4 m/s due to possible hydrodynamic losses. These findings provide clearer insight into VAT performance under realistic operating conditions and may support future efforts to improve hydrokinetic turbine design.
Nonparametric Mixed Moving Average-Extended Exponentially Weighted Moving Average Signed-Rank Control Chart
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.
Bank Stability Under ESG Uncertainty: Evidence from Threshold Regression, Causal Forest and SHAP Explanations
This paper investigates the nonlinear effects of ESG-related macroeconomic uncertainty on bank stability in Vietnam, an emerging market undergoing rapid financial transformation. Using an integrated empirical framework that combines panel threshold regression, Causal Forest estimation, and SHAP explanations, the analysis explores how ESG-related uncertainty interacts with income diversification and FinTech development to influence bank resilience. The results indicate an inverted U-shaped relationship between ESG uncertainty and bank stability, suggesting that moderate uncertainty enhances governance discipline, whereas excessive uncertainty erodes resilience. Income diversification (IDI) and FinTech growth (G_FINTECH) also display threshold-dependent and nonlinear impacts, where moderate diversification strengthens stability, and FinTech becomes stabilizing only beyond a maturity threshold. Robustness tests using alternative measures of bank stability (non-performing loans - NPL) and ownership heterogeneity confirm that private banks are more sensitive to ESG shocks than state-owned counterparts. The study contributes by introducing a novel hybrid framework integrating threshold models with causal machine learning to capture nonlinear and heterogeneous effects, providing new evidence from Vietnam’s nascent ESG and FinTech landscape, and offering policy implications for regulators and banks to manage ESG uncertainty, optimize diversification, and promote sustainable FinTech-driven stability.
Detecting Fake Images Generated by Artificial Intelligence Using Deep Learning Approach
The rapid progress in artificial intelligence has enabled the creation of highly realistic images, leading to concerns about the credibility and genuineness of visual content. This study aims to address the growing challenge of verifying the authenticity of digital images in the era of advanced generative artificial intelligence by developing an effective method to detect AI-generated (deepfake) images. To achieve this objective, we employed a Machine Learning (ML) framework based on Convolutional Neural Networks (CNNs), evaluating five established architectures, VGG19, ResNet50, Xception, DenseNet-121, and InceptionV3, through a systematic pipeline involving dataset compilation, image preprocessing, feature extraction, model training, and rigorous validation. Our experimental analysis demonstrates that DenseNet-121 and InceptionV3 achieve state-of-the-art performance, both attaining 98% accuracy in distinguishing AI-synthesized images from real ones, despite a non-negligible error rate observed in other models. These findings highlight the viability of CNN-based approaches for reliable deepfake detection. The novelty of this work lies in its comparative assessment of multiple CNN architectures on a curated dataset of AI-generated imagery, offering practical insights into model selection for forensic and security applications. The proposed method contributes a robust, scalable solution with significant implications for digital content moderation, cybersecurity, and multimedia forensics, where timely and accurate identification of synthetic media is increasingly critical.
Corn Cob-Derived Activated Carbon for Chloramphenicol Removal: An Optimization and Mass Transfer Model Study
This study developed a sustainable activated carbon (AC) from corn cob agricultural waste for efficient chloramphenicol (CP) removal from aqueous solutions and to improve the predictive understanding of the adsorption process. Microwave-assisted physicochemical activation using potassium hydroxide (KOH) was optimized through response surface methodology (RSM), with activation time, microwave radiation power, and impregnation ratio (IR) identified as the key preparation variables. Under the optimal conditions (3.86 min, 616 W, and 2.5 g/g), the resulting AC achieved a yield of 16.6% and a CP adsorption capacity of 20.2 mg/g. The optimized AC exhibited a high BET surface area (832.68 m²/g), a mesopore-dominated pore structure (mesoporous surface area of 623.45 m²/g), a pore volume of 0.09067 cm³/g, and an average pore diameter of 1.93 nm, leading to a maximum experimental adsorption capacity of 20.68 mg/g at 30 °C. In addition, a mass transfer (MT) model was successfully applied to predict an equilibrium adsorption capacity of 21.48 mg/g with a low average error of 3.29% and R² ≥ 0.90. By integrating process optimization with mass transfer modeling, this study improves the understanding of CP adsorption and provides a practical framework for designing efficient, waste-derived adsorbents for antibiotic-contaminated water treatment.
Scenario-Robust Hydropower Suitability Mapping in Geothermal Regions Using Multi-Paradigm Spatial Modeling
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.
A Hierarchical Hybrid Closest Access Point–Medoids Algorithm for Improved Clustering-Based Fingerprint Localization
Traditional clustering algorithms in fingerprint-based localization often struggle with outliers, overlapping clusters, and irregular RSS variations in fingerprint databases, which reduces clustering accuracy. To address these issues, this study proposes a hierarchical hybrid approach, the closest access point–medoids (CAP-medoids) algorithm, which combines the closest access point (CAP) method with k-medoids clustering. The CAP algorithm generates initial clusters based on the strongest received signal strength (RSS) from nearby wireless access points (APs), while k-medoids refines clusters by selecting actual fingerprint vectors as cluster centers, improving robustness against noise and irregular RSS variations. The algorithm was evaluated on four publicly available fingerprint databases of varying size and density. Performance was assessed using Euclidean, Manhattan, and cosine similarity distances as similarity metrics, with silhouette scores and Davies Bouldin (DB) indices as clustering performance metrics. Results show that the CAP-medoids algorithm consistently produces more compact and well-separated clusters than standard k-medoids in small databases, with silhouette scores increasing up to 75% and DB indices decreasing up to 63%. For larger, high-density databases, performance declines, indicating sensitivity to database size. Comparisons with other hybrid algorithms, including CAP+k-means++ and k-density-based spatial clustering of applications with noise (k-DBSCAN) algorithms, confirm its overall robustness and adaptability.
Purchase Behavior in AI-Enabled Livestream Commerce: Evidence from an Emerging Economy
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.
Determinants of Customers' Purchasing Intention of Fresh Agricultural Products: SEM Analysis
With the ongoing advancement and widespread use of internet technologies, e-commerce has become a dominant retail channel, and the emergence of live-streaming platforms now offers companies a dynamic new way to market and sell products. Nevertheless, this business model also heightens the issue of information asymmetry, as consumers often face challenges in accurately assessing product quality and determining the credibility of sellers, making the enhancement of consumer trust a key concern for e-commerce enterprises. This study examines how information acquisition ability and online word of mouth regarding fresh agricultural products on Chinese live-streaming platforms influence consumers’ purchase intentions, with a particular focus on the mediating role of perceived value. Analysis of 372 valid survey responses reveals that both information acquisition ability and online word of mouth significantly increase purchase intentions, and that perceived value acts as a critical mechanism linking these factors. The findings also indicate the importance of real-time interaction in boosting sales and fostering deeper consumer engagement. Based on these insights, the study recommends strengthening consumers’ ability to access and evaluate product information, cultivating credible and positive online word of mouth, and leveraging interactive live-streaming features to enhance product displays and review systems. These contributions advance theoretical understanding of consumer behavior in digital marketing contexts and provide practical guidance for enterprises seeking to improve their live-streaming e-commerce strategies.
A Sustainable E-Learning Ecosystem: Linking Readiness, Teaching Efficiency, Culture, and Employability
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.
Application of New Type UHF Sensor for Partial Discharge Measurement in Air Insulated Switchgear
Accurate detection of partial discharge (PD) is crucial for monitoring high voltage (HV) and medium voltage (MV) of electrical power assets. Among the various methods used for partial discharge (PD) monitoring, ultra-high-frequency (UHF) monitoring is particularly advantageous for on-site/online applications due to its anti-interference and noise-canceling capabilities. Therefore, this study aimed to focus on the application of an ultrawideband antenna, operating between 0.9 to 4 GHz frequency range, for detecting PD defects in air-insulated switchgear. Specifically, the proposed ultrawideband antenna was designed, optimized, and fabricated on FR4 substrate with a thickness of 1.6 mm and dielectric constant of 4.4. To evaluate the sensitivity performance of the antenna, laboratory experiments were conducted using a PD protrusion model located inside a test chamber. Subsequently, the results were evaluated through waveform analysis, PD power spectrum analysis, scalogram analysis in both the time as well as frequency domains, and finally, the phase-resolved PD (PRPD) pattern analysis. The experimental results show that the proposed UHF antenna could detect PD signals with high sensitivity and high signal-to-noise ratio (SNR) when placed at 120 cm from the source. This implied that the proposed antenna was a suitable sensor for detecting PD in HV and MV air-insulated switchgear.
Attention-Driven Hybrid Deep Learning for Automated Alzheimer’s Disease Severity Assessment via MRI Neuroimaging
Early, accurate diagnosis of Alzheimer’s Disease (AD) is vital for effective intervention. Properly classifying its progression from cognitively normal to moderate dementia is essential for tailoring treatment and management plans. The proposed research is a hybrid deep learning framework that integrates the EfficientNet-B3 and the ResNet50 with sophisticated attention units in order to classify the MRI scans of Alzheimer's disease in multi-classes. The model proposed combines both a Convolutional Block Attention Module (CBAM) based on the refinement of channels and space features and Multi-Head Self-Attention based on cross-branch feature interaction. The dual-branch architecture yields complementary features, with EfficientNet-B3 being able to pick fine-grained patterns and ResNet50 being able to pick strong hierarchical representations. The characteristics in both branches are mapped to 512 dimensions, operated by multi-head attention classification. The model and extensive preprocessing were implanted on a series of 33,984 augmented Alzheimer's MRI images in four categories (MildDemented, ModerateDemented, NonDemented and VeryMildDemented. This hybrid model had outstanding performance of 98.21% test accuracy, 98.23% precision, 98.21% recall, and 98.21% F1-score, which was far much better than the accuracies of other baseline architectures such as VGG16 (74.11%), ResNet50 (93.68%), EfficientNetB0 (63.52%), DenseNet121 (64.12%), and CustomCNN (68.87%). These findings support the usefulness of hybrid systems consisting of attention mechanisms to diagnose Alzheimer's disease automatically by using neuroimaging information.
Gold Price Forecasting Using Machine Learning Models with Hyperparameter Optimization for Inflation Hedging
Gold serves as a hedge against inflation, particularly in emerging markets such as Malaysia, where macroeconomic volatility is pronounced. This study evaluates the predictive performance of six machine learning models; comprising ensemble models (Random Forest, XGBoost, Gradient Boosting Machine, and LightGBM) and deep learning models (Long Short-Term Memory and Gated Recurrent Units) in forecasting Malaysia's gold prices using monthly macroeconomic data from 2009 to 2024. Key indicators include inflation rates, interest rates, exchange rates, oil prices, and stock indices. Hyperparameter tuning is performed using the Optuna framework by comparing three optimization strategies: Tree-structured Parzen Estimator (TPE), Grid Search, and Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Experimental results show that Gradient Boosting, optimized via CMA-ES, achieves the best performance (RMSE = 101.26, R² = 0.9972) using the complete feature set. While deep learning models demonstrate improvements following optimization, ensemble models consistently outperform them due to better alignment with the static, cross-sectional nature of the dataset. Feature importance analysis identifies GP_Low, GP_High, and both domestic and international inflation and interest rates as the most significant predictors. This study contributes by benchmarking ensemble and deep learning models, evaluating multiple hyperparameter optimization strategies, and identifying key macroeconomic indicators relevant to gold price forecasting. The findings provide valuable insights for investors, financial analysts, and policymakers in economies sensitive to inflation.
Net Working Capital and Firm Performance: The Moderating Role of Firm Size in Emerging Markets
This study examines the relationship between net working capital and business performance and analyzes the moderating role of firm size in this relationship in emerging markets. Drawing on trade-off theory, the resource-based perspective, and agency theory, the study suggests that the impact of net working capital on performance varies with firm size. Using panel data on 370 non-financial publicly traded companies in Vietnam from 2013 to 2024, the study employs an interaction regression model with firm size as the moderating variable. The analysis proceeds in multiple steps, including OLS, fixed-effects, random-effects, and FGLS models, and, in particular, a GMM-robust model to address endogeneity, heteroskedasticity, and autocorrelation. Empirical results show that net working capital significantly affects business performance, as measured by ROA and ROE. Firm size not only directly affects operational efficiency but also moderates the relationship between net working capital and operational efficiency. Specifically, the impact of net working capital differs significantly across firm sizes, reflecting differences in resource access and management efficiency. This research contributes to the existing literature by clarifying the moderating mechanism of firm size in working capital management and by providing new empirical evidence from emerging markets, offering important implications for managers and policymakers.
PTP1B-Mediated Dephosphorylation of SRC Controls Fibrogenic Cellular Activation
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.
Dual-Agent Q-Learning for Cross-Layer IEEE 802.11bd Optimization in Dense VANETs
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.
Modelling Pre-Service English Teachers' Readiness for AI Integration: A TPACK–TAM Mixed-Methods Study
Artificial Intelligence (AI), particularly large language models such as ChatGPT, has advanced rapidly recently, revolutionizing English Language Teaching (ELT); nonetheless, its pedagogically meaningful integration remains uneven and contingent on teacher preparation. Emerging research indicates that AI adoption is shaped more by teachers’ professional knowledge and acceptance views than by technological hurdles. However, empirical information on their interaction, particularly in underexplored contexts, remains scarce. Using an integrated Technological Pedagogical Content Knowledge (TPACK) and Technology Acceptance Model (TAM) framework, this study investigates pre-service English teachers' preparedness for AI integration, conceptualizing readiness as competence-informed acceptance, a novel construct that differs from traditional readiness frameworks by emphasizing the cognitive professional interplay between knowledge and beliefs rather than mere willingness or attitude. An explanatory sequential mixed-methods single-case design was utilized, with survey data (n = 78) analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and qualitative responses examined through reflexive thematic analysis. The results demonstrated that pedagogical knowledge was the strongest predictor of reported usefulness (β = 0.607, p < 0.001) and perceived ease of use (β = 0.546, p < 0.001). Prior AI experience directly predicted intention (β = 0.208, p < 0.001) and moderated the usefulness–intention link (β = 0.061, p = .044), although perceived ease of use had a greater impact on planned future use (β = 0.299, p < 0.001) than perceived usefulness (β = 0.192, p = 0.003). The qualitative results identified the importance of pedagogical rationale and context limitations. The research contributes to the theory, as it combines TPACK and TAM and offers context-related evidence in the MENA region, which supports the preparation of AI in ELT with pedagogy as a priority. Qualitative findings highlighted the role of pedagogical reasoning and contextual constraints. The study advances theory by integrating TPACK and TAM, demonstrating that professional knowledge operates indirectly through acceptance beliefs, and provides context-sensitive evidence from the Middle East and North Africa (MENA) region, supporting pedagogy-first AI preparation in ELT.
Monte Carlo-Based Assessment of Machine Flexibility in Group-Configured Part-Feeding Systems
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.
Enhancing Strategic Decision Performance Through AI: The Task-Technology Fit and Managerial Behaviour Link
This study examines how artificial intelligence improves strategic decision-making by focusing on the alignment between managerial task requirements and AI capabilities through the lens of Task Technology Fit. The objective is to explain whether the performance benefits associated with AI emerge from mere adoption or from a more precise fit between task demands, AI characteristics, and actual patterns of use. Methodologically, the study relies on a quantitative design based on survey data collected from 360 managers working in medium and large enterprises in Morocco. The proposed research model was tested using Partial Least Squares Structural Equation Modeling to assess the direct and indirect relationships among task characteristics, AI characteristics, Task AI Technology Fit, effective AI use, and strategic decision-making performance. The findings show that both task characteristics and AI characteristics positively and significantly influence Task AI Technology Fit. In turn, this fit strongly enhances effective AI use and strategic decision-making benefits, while effective AI use partially mediates the relationship between fit and performance. These results indicate that organizational value does not arise from symbolic or superficial AI adoption but from purposeful integration aligned with strategic requirements. The study’s novelty lies in extending Task Technology Fit theory to AI enabled strategic contexts and in demonstrating that AI should be understood not as a substitute for managerial judgment but as a mechanism for cognitive augmentation and performance enhancement.
Stroke Detection Using EEG and Deep Learning: A Comparative Study of Feature Engineering Techniques
Strokes remain one of the leading causes of disability and mortality worldwide, underscoring the need for effective early detection and intervention methods. Recently, researchers have shown a growing interest in harnessing bio-signals, natural indicators produced by the human body, as potential markers for stroke detection. Multiple types of bio-signals, such as electroencephalography (EEG), are currently being explored in stroke diagnostic studies. This approach is promising because it offers a non-invasive, cost-effective, accurate, and portable means of detecting strokes. The objectives of this research are to investigate the effectiveness of deep learning (DL) techniques, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), Recurrent Neural Networks (RNN), CNN-LSTM hybrid models, and CNN-Gated Recurrent Unit (CNN-GRU) models, for detecting early-stage strokes based on EEG data. In addition, the impact of diverse feature extraction techniques, including utilizing all features, selecting features with a Decision Tree (DT) based on different thresholds, Principal Component Analysis (PCA), and Independent Component Analysis (ICA), is analyzed to evaluate their influence on model performance. A comparative discussion is conducted across multiple experimental setups to identify the most effective DL and feature engineering combinations for stroke detection. Across 35 different experiments, the CNN-LSTM model with seven selected features using the DT method yields the best results, achieving 86% accuracy, 99% precision, 81% recall, and an F1-score of 89%.
Response Surface Methodology for Enhanced Recovery of Eucalyptus Pellita Essential Oil through Hydrodistillation
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.
Impact of Liquidity and Basel III Regulation on Bank Profitability
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.
The Impact of Socio-Technical Determinants and Mediating Mechanisms on AI Adoption in Internal Auditing
This study examines how socio-technical factors shape the adoption of artificial intelligence (AI) in internal auditing. A theory-driven model links organisational readiness, management support, auditors’ perceptions, and attitudes to AI adoption through direct and mediated pathways. Survey data from 340 listed firms were analysed using covariance-based structural equation modelling (CB-SEM) in AMOS, employing bias-corrected bootstrapping with 5,000 resamples to assess construct validity, model fit, and mediation effects. The results indicate that management support is the strongest driver, enhancing auditors’ perceptions and attitudes. Attitude emerges as the most potent predictor of adoption, whereas perception affects adoption only indirectly through attitude, confirming indirect-only mediation. Organisational readiness is not statistically significant, implying that infrastructure alone does not ensure adoption without leadership commitment and behavioural alignment. By integrating the Resource-Based View and the Technology Acceptance Model with institutional insights, the study advances understanding of how organisational resources, behavioural mechanisms, and institutional pressures jointly influence sustainable AI adoption in internal auditing. The findings emphasise the importance of executive sponsorship, role-specific AI literacy, and participatory system design while informing policy on competency and governance frameworks for effective AI integration.
Supply Chain Integration and Sustainable Organizational Performance: Moderating Role of Blockchain Adoption
This research aims to investigate the impact of supply chain integration (SCI) on sustainable organizational performance (SOP). SCI includes supply chain agility (SCA), supply chain agility visibility (SCV), and supply chain agility flexibility (SCF), which examined the effect on SOP and green supply chain management (GSCM). This query also examines the moderating role of blockchain adoption (BA) on the relationship between SCI and GSCM, and the mediating role of GSCM in the relationship between SCI and SOP. A purposive sampling approach is used to collect data from 364 blockchain adopters who are also directly connected to SCI within the Bangladeshi manufacturing sector. The dataset is analyzed by the Structural Equation Model (SEM). The results revealed a substantial impact of SCA, SCV, and SCF on SOP. Additionally, GSCM plays a significant role in SCA, SCF, and SOP, whereas there is no significant effect of GSCM on SCV and SOP. In the moderation analysis, BA significantly and positively moderates the relationships among SCA, SCF, and GSCM. However, no significant effect of BA was found between SCV and GSCM. This research adds value to the existing literature on the Bangladeshi manufacturing sector by integrating SCA, SCV, and SCF, which were examined separately in previous studies.
Determinants of Climate Change Disclosure in Carbon-Intensive Firms: Evidence from the GCC Region
This study examines Climate Change Disclosure (CCD) among carbon-intensive firms in the Gulf Cooperation Council (GCC) region and identifies the key firm- and country-level factors shaping such disclosure. Using cross-sectional data from 212 listed firms operating in carbon-intensive sectors in 2022, the study employs a disclosure index tailored to the GCC context and conducts a content analysis of annual and sustainability reports to measure disclosure practices. The results reveal low disclosure levels and limited external assurance of the disclosed information. A multiple regression model is developed to assess the effect of financial attributes, governance characteristics, and national environmental performance on CCD. The results confirm that firm size, leverage, Global Reporting Initiative (GRI) adoption, the presence of sustainability or environmental committees, and countries’ Environmental Performance Index (EPI) scores significantly and positively influence CCD, whereas firm age, profitability, and board independence do not have a significant impact on disclosure. To the best of the authors’ knowledge, this study provides the first empirical evidence of CCD determinants in the GCC, a carbon-reliant region with limited scholarly attention, and introduces a disclosure index adapted to regional reporting practices. The insights provided by the study offer practical value for regulators and stakeholders aiming to advance corporate transparency and climate accountability.
Epistemological Beliefs, Digital Literacy, and Cultural Attitudes in Shaping Students Entrepreneurial Knowledge
The development of higher education in the digital era presents new challenges for students, particularly in developing entrepreneurial competencies that are relevant to global needs while also rooted in local cultural values. The integration of cognitive aspects, digital skills, and cultural attitudes is becoming increasingly important in producing a young generation capable of innovating and competing in the face of globalization. Although various previous studies have highlighted the role of epistemological beliefs, digital literacy, and cultural attitudes separately, studies that integrate all three within the framework of strengthening entrepreneurial knowledge are still limited, especially in Indonesia. This study investigates the impact of epistemological beliefs and digital literacy on shaping students' cultural attitudes and entrepreneurial knowledge within the context of higher education in Indonesia. Using a quantitative approach and an explanatory survey method, data were collected from 398 students and analyzed using Structural Equation Modelling with Partial Least Squares (SEM-PLS). The results indicate that epistemological beliefs have a significant and positive impact on digital literacy, cultural attitudes, and entrepreneurial knowledge, thereby confirming their pivotal role in the development of entrepreneurial competencies. Digital literacy also has a significant, albeit relatively weaker, influence on cultural attitudes and entrepreneurial knowledge. Furthermore, cultural attitudes proved to be a significant mediator, strengthening the relationship between cognitive and digital factors and entrepreneurial achievement. This research model demonstrated high predictive power, accounting for 69.0% of the variance in entrepreneurial knowledge. These findings enrich the literature by integrating cognitive, technological, and cultural dimensions into a comprehensive framework for entrepreneurship education.
Exploring AI-Enabled Cloud Transformation Towards Digitalization Value in Accounting Firms
This study examines how AI-enabled, cloud-based accounting platforms generate digitalization value within Thai accounting firms by identifying the internal mechanisms through which cloud capability and cloud user capability translate into economic outcomes. Using survey data from 360 cloud-using firms, we employed covariance-based structural equation modeling with maximum likelihood estimation and bias-corrected bootstrapping (5,000 resamples) to test direct and mediated pathways. The results show that the qualitative of accounting information produced by cloud systems strongly enhances digitalization value added (β = 0.49, p < 0.001) and promotes proactive work behavior (β = 0.59, p < 0.001). Proactive work behavior further contributes to digitalization value added (β = 0.30, p < 0.01). Cloud user capability improves information quality (β = 0.19, p < 0.05) and proactive behavior (β = 0.35, p < 0.01), while cloud attributes improve information quality (β = 0.10, p < 0.05). Mediation analysis confirms that value arises indirectly via the sequential chain from cloud capability to information quality and then to proactive behavior, indicating that technical readiness alone is insufficient without informational uplift and behavioral execution. Theoretically, we extend information-systems and capability-based perspectives by positioning qualitative accounting information and proactive work behavior as the conversion channels that render cloud technology economically meaningful. Practically, firms and regulators should shift emphasis from infrastructure deployment to managing information quality, building user capability, and institutionalizing proactive work routines within AI-enabled cloud settings so that cloud adoption yields measurable value.
An Empirical Analysis of the Relationship Between the Omega Ratio and Yield Skewness in European Government Bonds
This study examines the empirical relationship between the Omega ratio and yield skewness in European sovereign bond markets, addressing whether distributional asymmetry is systematically reflected in Omega-based performance evaluation. The analysis is guided by two research questions: whether a statistically significant association exists between the Omega ratio and yield skewness across different time horizons, and whether this relationship exhibits cross-country heterogeneity consistent with a core–periphery structure. Using daily data for 10-year government bonds from 27 European countries over the period 2015–2025, we construct constant-maturity total returns and apply a robust Omega ratio formulation with inflation-adjusted thresholds. Yield skewness is measured using time-adjusted daily yield changes. The empirical strategy combines rolling-window correlation analysis, hierarchical clustering based on Kendall’s τ, and Independent Component Analysis to capture both short-term dynamics and latent structural patterns. The results provide strong and consistent evidence of a significant relationship between the Omega ratio and yield skewness across short-, medium-, and long-term horizons, confirming that the Omega ratio captures meaningful aspects of return asymmetry in fixed-income markets. Importantly, the findings reveal pronounced regional heterogeneity: core and Northern European markets exhibit stable positive associations, while several peripheral and emerging markets display weaker or negative relationships. These results imply that Omega-based performance measures reflect not only statistical asymmetry but also underlying differences in market liquidity, risk premia, and institutional structure. Overall, the study highlights the relevance of distribution-sensitive performance measures for sovereign bond evaluation and contributes novel evidence from the European fixed-income context.
Investigating Demographic Variations in the Use of Online Public Services: A UTAUT-Based Multigroup Analysis
This study aims to investigate demographic disparities in the adoption of online public services (OPS) in Vietnam, a nation experiencing rapid digital transformation while grappling with unequal access. Employing the Unified Theory of Acceptance and Use of Technology (UTAUT) as the analytical framework, data from 1,186 citizens were analyzed using PLS-SEM and multigroup analysis to determine whether the fundamental UTAUT relationships differ across these demographic segments. The findings indicate that women are more affected by family and community encouragement; ethnic minority users place greater reliance on performance expectancy “valuing time savings, reduced travel, and service accessibility” and rural users depend more on effort expectancy and facilitating conditions due to infrastructure constraints. These differences affirm that social and contextual factors significantly influence citizens’ behavioral intentions toward OPS use. The study’s novelty lies in incorporating demographic segmentation into the UTAUT framework within a developing-country context, thereby extending its theoretical applicability and providing actionable insights for an inclusive digital government. The results offer policymakers evidence-based guidance for designing targeted interventions to enhance accessibility and equity in Vietnam’s digital transformation process.
HyDNN: A Hybrid Deep Learning Approach for Phishing URL Detection
Phishing is an online attack in which attackers trick victims into disclosing their sensitive information, such as credentials, financial portal pins, and OTPs, with the intention of identity or financial theft, jeopardizing reputations, and posing a risk to netizens. As the stakes are high, attackers invest considerable effort and time in committing organized crimes to steal valuable user information. The research carried out aims to detect phishing websites using machine learning and deep learning models. In this research, the classification models are applied to three different phishing website datasets, namely the Mendeley phishing dataset and the UCI dataset, which belong to binary classification, and one dataset that falls under multi-class classification. These datasets are publicly available for research. A custom data set is also prepared from recently available websites to reduce the potential bias in the already available data set. The reason for choosing a publicly available dataset is to validate and compare the results obtained from the custom dataset. To optimize the process, various feature selection techniques and dimensional reduction methods are applied, and a comparison of all approaches is summarized. Performance metrics are used for binary and multi-class classification, and then the outcomes obtained are summarized. The Random Forest model performs well with most feature selection techniques by achieving the best accuracy of 98.24% using the embedded feature selection approach for the Mendeley data set, 94.78% for the UCI data set, and 90.57% for the custom data set. Hence, using Random Forest as the base model, deep learning approaches, namely, CNN and LSTMs, are used to check the efficiency. This study shows that the proposed Hybrid Deep Neural Network approach, HyDNN, performs better, providing the best result with an accuracy of 98.87% for the Mendeley dataset, 97.63% for the UCI dataset, and 93.77% for the custom dataset.
Construction, Validity, and Reliability of the Pedagogical Competence Assessment Instrument for Proficient Teachers
This study aims to develop and evaluate the Pedagogical Competence Assessment for Proficient Teachers (PCAI-PT) as an instrument for assessing teachers’ pedagogical competence in Indonesia. The research employed a development method based on the ADDIE framework (Analyze, Design, Develop, Implement, and Evaluate). Thirteen pedagogical competence items representing core skills of novice expert teachers were formulated from document analysis. These items were validated by six expert raters and tested on 231 teachers across various educational levels. Validation results indicated that all PCAI-PT items were valid after revision, with Aiken’s V ranging from 0.78 to 1.00. Intraclass correlation analysis showed increased inter-rater consistency from the initial validation to revalidation, demonstrating strong inter-rater reliability. Construct testing using CFA revealed that all items met the criteria, with factor loadings >0.70, Cronbach’s alpha and composite reliability >0.70, and AVE >0.50. Discriminant validity was confirmed, and model fit indices indicated good to excellent fit (RMSEA = 0.088; SRMR = 0.034; CFI = 0.955). Concurrent validity testing using the Kruskal-Wallis test showed no significant differences across educational levels (p>0.05), indicating consistent application of the instrument. Overall, PCAI-PT is valid, reliable, and representative, providing a comprehensive and sustainable tool for assessing teachers’ pedagogical competence across diverse educational contexts.
A Synergistic Model of Technological Capacity, Institutions, and Culture in the Transition Towards Society 5.0: Cross-Country Evidence
This study elucidates global variation in achieving Society 5.0 objectives by identifying the foundational requirements, thresholds, and synergistic configurations of technological and AI capacity, institutional quality, and cultural values across nations. The research develops a novel conceptual model that integrates core determinants while accounting for country-specific conditions in the transition towards a human-centered society. Utilizing a harmonized dataset encompassing 102 countries, this study employs hierarchical multiple regression and three-way interaction modeling to evaluate the direct, conditional, and higher-order effects of technological readiness and its interaction with institutional and cultural values on Human-Centered Outcomes (HCO). The empirical results demonstrate that technological advancement and AI readiness alone are insufficient to generate meaningful societal progress. Instead, institutional quality emerges as the most robust predictor, significantly amplifying the relationship between digital capacity and human-centered outcomes. Among cultural dimensions, power distance exhibits the most pronounced constraining effect, while individualism and long-term orientation display consistent but statistically weaker patterns. These findings elucidate the paradox of divergent national outcomes arising from AI integration, leading to a new country taxonomy based on empirical proximity to the Society 5.0 benchmark. Ultimately, the article challenges technocentric narratives and advances a co-evolving concept of socially embedded change, providing actionable insights into how technological, institutional, and cultural pillars must be aligned to foster inclusive and well-being-oriented digital transformation.