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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Business Process Remaining Time Prediction Based on Bidirectional QRNN with Attention Mechanism
Business process prediction is essential for monitoring workflows and ensuring service quality. A key task in this area, remaining time prediction, focuses on estimating process duration and has been extensively studied. While Long Short-Term Memory (LSTM) networks are widely adopted, their limited parallelization and sequential modeling capabilities constrain performance. To address these limitations, we propose a remaining time prediction approach based on a bidirectional Quasi-Recurrent Neural Network (QRNN) with an attention mechanism. Specifically, the bidirectional QRNN is employed to construct the prediction model, while the attention mechanism enhances its ability to extract feature information. Next, a transfer training iteration strategy based on different trace prefix lengths is designed to address the imbalance in trace lengths. Then, a Word2Vec-based event representation learning approach is introduced to generate similarity vector of adjacent events, further improving prediction accuracy. Finally, using five publicly real-life event logs, the proposed approach is evaluated against state-of-the-art approaches. Experimental results demonstrate that it improves average prediction accuracy by nearly 15% while reducing average model training time by approximately 26%.
BIoT-DApp: A Prototype for Real Time Traceability in Agricultural Supply Chains
Agricultural supply chains frequently experience inefficiencies, including a lack of transparency, post-harvest losses, and inequitable compensation for stakeholders. This study aims to develop and evaluate a Blockchain-IoT decentralized application (BIoT-DApp) that enhances traceability, efficiency, and resilience in agri-food supply chains. Utilizing a lab-based prototyping methodology, the system integrates Ethereum smart contracts with IoT sensors to automate workflows from cultivation to retail, employing a hybrid architecture that stores raw sensor data off-chain while anchoring cryptographic hashes on-chain. The methods involve designing role-specific smart contracts, managing batch life cycles across six stages, and conducting real-time environmental monitoring through IoT data processed by Raspberry Pi, with deployment and testing performed on the Sepolia testnet. The findings demonstrate automated quality control, reduced storage costs through optimized on-chain practices, and seamless product ownership transfers validated by four role-based MetaMask accounts representing a farmer, wholesaler, retailer, and end-user. Core functions were executed successfully, with gas costs ranging from 30,000 (data logging) to 112,300 (batch initialization), confirming both cost efficiency and scalability. The novelty of this work lies in bridging blockchain theory and practice by providing a modular, adaptable prototype capable of supporting perishable agricultural supply chains globally. This offers policymakers and agri-tech developers actionable insights for decentralized solutions in resource-constrained environments.
QLAF: Q-Learning Adaptive Forwarding for Disaster Emergency Communication in Named Data Networking
Reliable communication is essential for effective disaster response; however, conventional IP-based networks often fail when the network infrastructure is damaged. Disaster communication networks need adaptive forwarding strategies that maintain reliability under rapid topology changes, various link qualities, and resource constraints. This research proposes a Q-Learning-based Adaptive Forwarding (QLAF) strategy designed to enhance reliability in heterogeneous disaster emergency communication networks. QLAF implements reinforcement learning into the NDN forwarding plane, enabling each router to autonomously learn optimal forwarding faces based on multiple performance metrics: Round-Trip Time (RTT), throughput, and link stability. The proposed strategy was implemented in the Named Data Networking Forwarding Daemon (NFD) and evaluated using the MiniNDN emulator over a BRITE-generated 25-node disaster topology that integrates terrestrial, cellular, and satellite links. We compared QLAF and Adaptive Smoothed RTT-based Forwarding (ASF), Access strategy, and Self-Learning. Experimental results show that QLAF achieves a Packet Delivery Ratio (PDR) of 99.91%. These results show that QLAF gives a robust solution for reliability-sensitive disaster communication, guaranteeing high data delivery performance under unstable network conditions. However, its latency overhead limits its applicability to real-time scenarios.
Macroeconomic Uncertainty and Banking Stability in ASEAN Emerging Markets: A Causal Machine Learning Approach
This study aims to examine the causal impact of macroeconomic uncertainty on banking stability across six ASEAN emerging markets from 2010 to 2023, with particular attention to structural regime shifts triggered by the COVID-19 pandemic. To achieve this objective, a novel country-specific uncertainty index is constructed using Principal Component Analysis (PCA) based on three indicators World Uncertainty Index (WUI), World Pandemic Uncertainty Index (WPUI), and World Sentiment Index (WSI). Employing advanced causal inference methods, including Double Machine Learning (DML) and Causal Forests, the study estimates both Average Treatment Effects (ATEs) and Conditional Average Treatment Effects (CATEs). The results reveal that a one-unit rise in macroeconomic uncertainty reduces the Z-Score by 10.7% on average, signaling increased financial instability. The adverse effect is most pronounced for small banks (21.9% decline), reflecting limited capital buffers and structural vulnerability, and becomes more severe after the COVID-19 outbreak. CATEs results highlight significant cross-country heterogeneity, with Singapore and Thailand showing resilience, while Indonesia and the Philippines exhibit greater fragility. This study contributes to the literature by integrating SHAP-based model interpretability into causal machine learning for banking stability analysis, offering novel, policy-relevant insights for uncertainty management in emerging ASEAN economies.
How AI Literacy Drives Digital Entrepreneurial Intention: Evidence from an Emerging Economy
The objective of this study is to examine how artificial intelligence literacy influences digital entrepreneurial intention through cognitive and motivational mechanisms. Drawing on the Theory of Planned Behavior and entrepreneurial cognition, the model incorporates perceived feasibility and perceived opportunity as mediating factors, while self-efficacy and risk-taking propensity are included as moderating variables. Data were collected from 312 adults in Thailand using a structured questionnaire, and the proposed relationships were analyzed using partial least squares structural equation modeling. The results indicate that artificial intelligence literacy has a direct and positive effect on digital entrepreneurial intention and also enhances individuals’ perceptions of feasibility and opportunity. Further analysis confirms that perceived feasibility and perceived opportunity partially mediate the relationship between artificial intelligence literacy and entrepreneurial intention. The moderating analysis shows that self-efficacy strengthens the effect of artificial intelligence literacy on perceived feasibility, while risk-taking propensity has no significant influence on the relationship between artificial intelligence literacy and perceived opportunity. These findings contribute to the understanding of digital entrepreneurship by identifying artificial intelligence literacy as a critical personal capability that stimulates entrepreneurial engagement through both cognitive and motivational pathways, particularly within the context of emerging economies.
Castor-Based Ester Oil Production Using SnCl₂/HZSM-5 Catalyst for Sustainable Transformer
Mineral oil remains the most common insulating fluid in power transformers; nevertheless, its non-biodegradable nature and carbon-based emissions have prompted the quest for environmentally safer alternatives. Castor oil, a sustainable and non-edible derivative, provides a promising source for high-performance ester-based insulating lubricants. This study investigates the synthesis and process optimization of castor-based polyol esters via esterification with trimethylolpropane using a heterogeneous SnCl₂/HZSM-5 catalyst. The alcohol-to-oil molar ratio, temperature, and catalyst loading were among the critical reaction parameters that were modeled and optimized using response surface methods with a central composite design. Under optimized conditions of 130°C and 2.207 wt% catalyst, an ester yield of 81.77% was obtained. The resulting ester oil demonstrated advantageous characteristics, such as acceptable color and dielectric performance, while viscosity and acidity were improved by a two-step process to comply with the IEC 62975:2021 standards for distribution transformer insulating oils. The statistical study validated the model's reliability, with ANOVA indicating a substantial quadratic regression (R² = 0.952). The key novelty of this work lies in demonstrating the potential of SnCl₂/HZSM-5 to catalyze the synthesis of castor-derived polyol esters with tailored physicochemical properties, supporting their future scalability and use as sustainable insulating oils.
Evaluating Differential Privacy Mechanisms in Machine Learning with Emphasis on Utility and Robustness
Federated learning enables collaborative model training across distributed clients without sharing raw data, yet it remains susceptible to inference threats such as membership inference attacks. This study aims to enhance the privacy of federated learning by integrating differential privacy and systematically evaluating its effects on model utility and adversarial robustness. A synthetic multimodal dataset was developed by combining demographic attributes from the UCI Adult dataset, mobility indicators from Google COVID-19 Mobility Reports, and semantic descriptors from LAION-400M, creating a high-dimensional and bias-reduced benchmark for privacy-preserving experimentation. Differentially private stochastic gradient descent (DP-SGD) was applied under multiple privacy budgets and ablation settings to isolate the individual contributions of gradient clipping and noise injection. Experimental results reveal that model accuracy increases with larger privacy budgets, while membership inference attack accuracy remains close to random guessing, confirming strong defense capability. Gradient clipping proved essential for training stability, whereas excessive noise caused measurable degradation in learning utility. The proposed framework establishes reproducible benchmarks for tuning differential privacy parameters in federated environments and demonstrates that robust privacy guarantees can be achieved without substantial loss of performance, providing practical guidance for deploying trustworthy, privacy-preserving machine learning systems across domains such as healthcare, finance, and mobility.
Supply Chain Digitalization and Performance: The Serial Mediation of Visibility and Resilience
This study aims to examine how supply chain digitalization enhances supply chain performance through the serial mediation of supply chain visibility and supply chain resilience. Grounded in the Resource-Based View and Dynamic Capabilities Theory, the research model was empirically tested using survey data collected from 379 managers of manufacturing firms operating in Vietnamese industrial parks. The data was analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that supply chain digitalization positively influences both visibility and performance. However, while visibility improves performance when examined independently, its direct effect becomes insignificant once resilience is incorporated, suggesting that visibility alone is insufficient to achieve superior performance without strong resilience capabilities. Moreover, the serial mediation path digitalization → visibility → resilience → performance was statistically significant, confirming the sequential mechanism through which digitalization improves performance outcomes. The study contributes to the literature by clarifying the complementary roles of visibility and resilience and highlighting resilience as the key capability linking digitalization to performance. From a managerial perspective, the results emphasize that investing in digital technologies should be accompanied by initiatives to strengthen resilience capabilities to maximize the benefits of digital transformation in supply chains.
Unified Numerical FFR with Adaptive Bandwidth for 5G and Beyond Multilayer Multisector Networks
The present research introduces a unified numerical formulation for Fractional Frequency Reuse (FFR) and a user composition-based adaptive bandwidth allocation strategy for multi-layer/multi-sector cell architectures. The proposed FFR metric explicitly accumulates sub-band usage across the inner–outer and inter-sector layers, thereby normalizing diverse reuse patterns into a single, consistent number. This formulation remains consistent with the classical definition (reducing to 1/N reuse under certain conditions), approaches full reuse when multi-layer/sector coordination is applied, and provides a simple yet powerful link between reuse configurations and capacity predictions in 5G and beyond networks. Comprehensive simulations based on a realistic urban macrocell environment show that increasing the architectural complexity from a single-layer to a 2-layer 6-sector network results in a remarkable 184% increase in average cell capacity. Furthermore, in the dynamic bandwidth allocation, the inner user-dominated scenario achieves the highest cell capacity, which is 41% higher than that in static bandwidth allocation. At the same time, dynamic allocation also improves fairness in the outer user-dominated scenario, increasing the Jain fairness index by up to 0.444. These results confirm that the combination of the new FFR formulation and adaptive resource allocation significantly improves spectrum efficiency, cell capacity, and fairness, and provides practical guidance for optimizing the implementation of 5G and beyond cellular network deployments.
Metaverse Marketing: Avatars as the Future of Brand Engagement
The Metaverse, a virtual space where users interact via avatars, presents a burgeoning platform for marketing endeavors. Unlike traditional social media marketing, which broadcasts to a passive audience, Metaverse marketing facilitates two-way engagement in immersive settings. This is especially relevant for brands aiming to connect with digitally savvy generations, such as Gen X, Millennials, and Gen Z, whose expectations for interactivity and personalization continue to grow. While previous studies have assessed the broader pros and cons of Metaverse marketing, this study examines the avatar as a catalyst for consumer interaction and brand loyalty. The central question was to investigate how avatars influence brand engagement, with a focus on emotional connection and self-expression within the virtual environment. This study explores consumer perceptions, attitudes, and behaviors towards avatars among 398 Malaysian adults. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze the data. Findings reveal that avatars are effective in enhancing brand recall, trust, and emotional connection. The results also indicate that metaverse brand awareness, customer awareness, trust, and credibility, as well as emotional connection, serve as mediators in the relationship between avatar usage and Metaverse brand engagement. Implications for marketers and future study directions are discussed, highlighting the significance of avatar-based marketing in the evolving digital commerce landscape.
Enhancing Small Language Models for Code Generation via Strategic Decomposition and Filtering
This study addresses the challenge of enhancing Small Language Models (SLMs) for complex code generation tasks requiring structured planning, which current models struggle with due to their monolithic, single-pass generation approach. A three-stage pipeline architecture is proposed that decouples strategic planning from implementation: (1) an SLM generates diverse natural language strategies at high temperature, (2) a filtering mechanism selects high-quality strategies while removing noise, and (3) refined strategies guide a specialized coding model for final implementation. The approach was evaluated on the ClassEval benchmark for class-level code generation. The pipeline enabled a 1.5B parameter model to achieve 13% class success rate, representing a 30% relative improvement over direct generation (10%) and competitive performance with models 5-8 times larger. Critically, effective strategy filtering proved more important than strategy diversity, with simple pattern-based filters successfully mitigating SLM artifacts like few-shot contamination. This work demonstrates that structured, inference-time computation offers an efficient alternative to parameter scaling, with strategic noise reduction being the key driver of performance gains in resource-constrained models.
Strategic Dividend Policy Adaptation and Stock Market Reactions in State-Owned Enterprises Across Crises
This study investigates the strategic adaptation of dividend policy in Indonesian state-owned enterprises across the pre-crisis, crisis, and recovery phases. Adaptation is operationally defined as firm-level, measurable changes in cash dividend indicators during the crisis and post-crisis phases relative to the pre-crisis average. Empirically, dividend behavior is estimated using a dynamic panel framework with system GMM, and an event-study approach evaluates abnormal returns and cumulative abnormal returns around dividend announcement dates in each phase. The results indicate that SOEs increased dividends during the crisis relative to pre- and post-crisis periods, and that the market exhibited stronger positive reactions in the crisis and recovery phases than in the pre-crisis phase. These patterns suggest adaptive choices consistent with managing uncertainty and reinforcing policy credibility within Indonesia’s state-ownership setting. The findings highlight the strategic role of dividend signals in shaping investor perceptions during economic shocks, while theoretically challenging the core cash-conservation premise of the pecking order and reinforcing the relevance of signaling theory for state-controlled firms with complex fiscal and political mandates.
Strategic Positioning for Knowledge-Based Industry Growth: Bridging Innovation and Competitiveness Gaps
This study examines the strategic positioning of knowledge-based industries (KBIs) of Bahrain to address the innovation and competitiveness gaps within the Gulf Cooperation Council (GCC) region. This study adopted Porter’s Diamond model to identify critical factors that enhance the attractiveness of Bahrain as a KBI hub, which include key strengths of Bahrain’s location-based advantage, regulatory efficiency, and human capital readiness. This study used a mixed-method approach that integrates both qualitative and quantitative analyses by utilizing secondary data sources from GCC comparative benchmarks, policy documents, and international databases. The findings highlight the significant strengths of Bahrain in business-friendly policy, financial ecosystem, and information and communication technology (ICT) infrastructure. This study also identifies strategic pathways to further build upon these strengths, including continued domestic market diversification, increased research and development (R&D) investment, and expanded venture capital availability, reinforcing Bahrain's promising growth trajectory. Recommendations for actionable policy are proposed to expand fintech and artificial intelligence (AI) ecosystems and foster university-industry linkages. Thus, this study advocates for enhanced cross-GCC cluster collaboration to support the Bahrain Vision 2030 by applying and extending the competitive framework to KBI and contributing research to theoretical discussions on knowledge-driven economies. Overall, this study offers practical strategies for industry stakeholders and policymakers to strengthen the innovation ecosystem of Bahrain.
T-CER-Net: Attention-Based Temporal Cross-Eye Regression for Noise-Resilient Detection of Intermittent Strabismus
Automated strabismus screening using video is difficult in unconstrained settings, where brief events such as blinking, head movement, or tracking errors can easily be mistaken for true ocular misalignment. The objective of this study is to improve diagnostic specificity while maintaining sensitivity in automated pre-screening scenarios. To address this problem, a temporal analysis framework, termed the Temporal Cross-Eye Regression Network (T-CER-Net), is proposed. The method introduces the Cross-Eye Regression Error (CERE), a scale- and position-invariant temporal signal that characterizes deviations in binocular coordination by measuring prediction error between the two eyes. Rather than relying on frame-level deviation estimates, the approach analyzes extended CERE sequences using a Transformer Encoder to assess temporal consistency. In addition, the training procedure explicitly accounts for real-world variability through oversampling of normal sequences containing common artifacts and the use of class weighting. The proposed method was evaluated against static threshold-based classifiers and a CNN–LSTM temporal baseline. On a held-out test set, T-CER-Net achieved an area under the ROC curve of 0.9140, with a sensitivity of 0.8421 and a specificity of 0.8500, showing improved robustness to noise-induced false positives. The findings suggest that treating binocular misalignment as a temporal pattern, together with attention-based sequence analysis, offers a practical and robust basis for automated strabismus pre-screening in real-world settings.
Multimodal Emotion Recognition Using Hybrid Large Language Models and Metaheuristic Algorithms
Emotion recognition is a vital component of human–computer interaction and intelligent systems, yet robust multimodal emotion recognition remains challenging due to high-dimensional input space, noisy features, and the complexity of integrating heterogeneous modalities. This study proposes a novel hybrid multimodal framework that enhances both accuracy and computational efficiency by combining the semantic representation capability of Large Language Models (LLMs) with the optimization strengths of metaheuristic algorithms. In the proposed approach, an LLM is utilized to extract high-level contextual features from text and audio streams, while the Binary Artificial Hummingbird Algorithm (BAHA) performs feature selection to remove redundant attributes. Subsequently, the Goose Algorithm (GA) optimizes classifier hyperparameters, and the Komodo Mlipir Algorithm (KMA) conducts late fusion of the final multimodal outputs. Experiments conducted on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset, evaluated on six emotion categories, demonstrate that this hybrid approach successfully captures subtle affective cues and surpasses state-of-the-art baselines, achieving an accuracy of 87.5%. Integrating LLMs with multiple specialized metaheuristics therefore yields a substantially more robust emotion recognition pipeline and represents a promising direction toward the development of more emotionally intelligent systems.
CFOs as Innovators in Debt Maturity Choices: New Evidence from an Asian Emerging Market
This study aims to examine the role of the Chief Financial Officer (CFO) as an agent of innovation in shaping corporate debt maturity decisions within the framework of Upper Echelons Theory. Data were collected from 312 non-financial firms listed in Vietnam from 2011 to 2023, spanning 10 sectors, with focused analysis on three sectors - Industrials, Materials, and Consumer Staples - to highlight their specific characteristics. The personal traits of CFOs, including gender, age, education, and expertise, were analyzed in relation to debt maturity structures, measured by the long-term debt ratio (LTDR) and weighted average debt maturity (WAM). To explore these relationships, various regression methods - OLS, FEM, REM, FGLS, and GMM - were applied. The results reveal that CFO attributes significantly affect debt maturity choices, emphasising their innovative role in corporate financial management. Moreover, this influence varies among sectors: Industrials, Materials, and Consumer Staples exhibit notable differences from other sectors. The study advances the literature by emphasising the pivotal role of CFOs in financial innovation and by extending Upper Echelons Theory. Empirically, it provides novel evidence from an emerging market context, a setting that remains underexplored. Practically, the findings offer insights for firms and investors in selecting and developing CFOs as strategic resources to enhance adaptability and financial stability.
Trusted AI-Based Method for Predicting Controller Load and PSO-Based Structure for Reducing Latencies
The objective of this study is to develop a trusted AI-based framework for predicting software-defined networking (SDN) controller load and optimizing fog/edge microservice orchestration to reduce end-to-end latency in dense 5G scenarios. The proposed approach integrates user-aware spatial clustering with evolutionary resource selection to maintain stable quality of service (QoS) under high mobility and traffic variability. In the analysis stage, k-means clustering partitions users into spatial sectors and identifies sector centroids. Particle swarm optimization (PSO) is then applied to fog-node selection, resource sizing, and adaptive microservice placement and migration. To enhance system resilience, a recurrent neural network (RNN) is employed to forecast SDN controller load using correlation-informed features extracted from service-channel dynamics. Numerical experiments on heterogeneous fog-node topologies indicate that the framework reduces microservice execution time by 69% relative to baseline placement strategies under identical load conditions, while controller-load prediction attains an RMSE of 0.00387. These findings confirm the effectiveness of both the latency-reduction mechanism and the controller-load estimation workflow. The novelty of this work lies in the unified optimization of microservice placement, migration, and SDN controller-load anticipation within a single reproducible architecture, extending existing fog and edge orchestration approaches that typically address these components as independent subproblems.
Detecting Genuine Versus Fake Emotions: A Dual-Task Deep Learning Approach Using Facial Expression Analysis
Facial expression recognition (FER) is a relevant field of study with applications in human-computer interaction, healthcare, and security. Although recent approaches demonstrate excellent outcomes on the recognition of basic emotions, the authenticity of expressions (genuine versus fake) remains unexplored. In this work, we propose a dual-task deep learning framework based on EfficientNet-B0, enhanced with a lightweight squeeze-and-excitation (SE) attention mechanism, to collaboratively work on multiclass emotion recognition (seven categories: angry, disgust, fear, happy, neutral, sad and surprise) and authenticity classification (genuine vs fake). The architecture leverages a shared backbone for representing feature, followed by task-dedicated branches trained using categorical cross-entropy and focal loss, respectively. To overcome the lack of publicly available benchmarks incorporating authenticity labels, we designed a curated dataset annotated with both emotional categories and authenticity information. Experimental evaluation demonstrates that the proposed dual-task model with the SE attention mechanism achieves 98.5% accuracy for emotion recognition and 92.2% accuracy for authenticity prediction, emphasizing both the effectiveness of the framework and the inherent challenges of authenticity detection. Moreover, we present a deployable real-time system demonstrating the feasibility of integrating authenticity-aware FER into practical applications such as e-learning analytics, security surveillance, and affective computing.
Academic Dishonesty Among University Students: Gender, Semester Differences, and Influencing Factors
This study examines differences in academic dishonesty among university students based on gender and semester level and identifies factors influencing such behavior using an explanatory sequential mixed-methods design. Quantitative data were collected from 405 undergraduate students across five semester levels (II, IV, VI, VIII, and X) using the Academic Dishonesty Scale (ADS) and analyzed with non-parametric statistical tests. The results show significant differences in examination-related cheating across semesters (p = 0.012) and significant gender differences across several indicators (p < 0.05), with male students and those in early semesters displaying higher levels of dishonest behavior. To further explain these findings, qualitative data were obtained through in-depth interviews with seven informants and analyzed thematically. The qualitative results indicate that academic dishonesty is influenced by pressure to achieve high grades, insufficient study preparation, permissive peer environments, and limited understanding of academic ethics. The novelty of this study lies in combining a validated measurement instrument with qualitative follow-up to provide contextual explanations of academic dishonesty in Indonesian higher education. The findings highlight the need for stricter supervision, strengthened academic ethics education, improved time management skills, and clearer institutional policies to foster an academic culture that promotes integrity.
Pattern Recognition Tasks with Personalized Federated Learning
Personalized Federated Learning (PFL) constitutes a novel paradigm that tailors Machine Learning (ML) models to individual clients, thereby furnishing personalized model updates whilst upholding stringent data privacy principles. Diverging from conventional standard Federated Learning (FL) approaches, PFL adapts models to distinct client data distributions, engendering heightened levels of accuracy, customization, and data security, all while minimizing communication overhead. This methodology proves particularly salient in contexts marked by pattern recognition tasks reliant upon heterogeneous data sources and underpinned by paramount privacy apprehensions. In the present research endeavor, this article undertake a comprehensive comparative analysis of seven distinct PFL algorithms deployed across three diverse datasets, namely MNIST, SignMNIST, and Digit5. The overarching objective entails ascertaining the preeminent PFL algorithm, within the framework of pattern recognition tasks, through a rigorous evaluation anchored in metrics encompassing Accuracy, Precision, Recall, and F1 Score. Concurrently, an in-depth scrutiny of these PFL algorithms is conducted, elucidating their operative workflows, advantages, and limitations. Through empirical investigation, the findings evince that APPLE, FedGC, and FedProto emerge as stalwart contenders, consistently furnishing superior performance across the spectrum of assessed datasets, while acknowledging the contextual specificity of alternative algorithms and the potential for iterative refinement to realize optimal outcomes.
Mg, Si, Al, and P Particle-Doped Epoxy: A Synergistic Approach for Enhanced Fire Performance
This study presents the development of a low-toxicity, high-performance intumescent fire-retardant coating (IFRC) through a hybrid epoxy binder doped with Mg, Si, Al, and P particles. The objective was to improve thermal stability and char cohesion and reduce the toxic aromatic emissions typically released from bisphenol-A epoxy systems during combustion. Modified epoxy resins were prepared by dispersing Mg(OH)₂ and incorporating hydroxyl-terminated PDMS, followed by formulation with APP, melamine, expandable graphite, PER, and nano-alumina. Comprehensive analyses using FTIR, ¹³C NMR, DSC, TGA, SEM–EDS, TEM, XRD, and GC–MS, along with ISO-834 furnace and ASTM E-119 flame tests, were employed to evaluate chemical structure, thermal behavior, char morphology, and fire performance. The optimized formulation produced a dense Mg–Al–silicate–phosphate char network, achieved a 6.1× expansion ratio, limited backside steel temperature to 227°C, and retained 36% char at 800°C, which significantly outperformed the unmodified epoxy system. GC–MS confirmed a substantial (≈53%) reduction in toxic volatile emissions. A machine-learning model further validated char compactness with >94% classification accuracy. Collectively, the results demonstrate that synergistic inorganic–siloxane modification offers a scalable, halogen-free pathway to next-generation epoxy-based IFRCs with enhanced fire resistance and markedly lower toxicity.
Green Technology Innovation and Financial Performance: Roles of Executive Green Perception and Carbon Performance
Existing research on the linkage mechanisms among green technology innovation (GTI), executive green perception (EGP), carbon performance (CP), and financial performance (FP), particularly systematic investigations within the context of China’s high‑carbon industries, remains insufficient. To address this gap, this study explored the pathway through which GTI influences FP, as well as the mediating effect of EGP and the moderating effect of CP. Grounded in stakeholder theory and innovation diffusion theory, an integrated analytical framework was developed and tested using panel data from listed companies in China’s coal, energy, and manufacturing sectors spanning 2015 to 2023 (N = 11,302). The analysis employed a two-way fixed effects model. The results revealed that GTI significantly and positively impacts FP (β = 0.199, p < 0.01), with EGP serving a partial mediating role in this relationship. Furthermore, CP positively moderates the connection between GTI and FP (β = 0.096, p < 0.01). The key innovation of this research is its unique simultaneous examination of both mediating and moderating mechanisms within a single model. The approach provides a deeper theoretical explanation and practical managerial insights for utilizing green innovation to enhance financial outcomes in high-carbon transitional settings.
How Perceived Accuracy Drives Adoption of AI Personalized Recommendations: A Moderated Mediation Model
Artificial intelligence (AI)-powered personalized recommendation systems are reshaping how consumers search, evaluate, and purchase products, yet the psychological mechanisms through which perceived accuracy drives adoption remain underexplored. This study examines how perceived accuracy of AI recommendations influences consumer adoption willingness through perceived benefit and how this process is conditioned by product involvement. Drawing on the Technology Acceptance Model (TAM) and Product Involvement Theory, we develop an accuracy-centred moderated mediation model in which perceived accuracy (PA) leads to perceived benefit (PB), which in turn leads to consumer adoption willingness (AW) or (PA → PB → AW). The study uses survey data from 518 Chinese consumers with experience of using AI-personalized recommendations. The data are analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with multigroup analysis to examine age-based heterogeneity on consumer adoption willingness. The results show that perceived accuracy has a significant direct and indirect effect on adoption willingness, with perceived benefit acting as a partial mediator. Product involvement positively moderates the relationship between perceived accuracy and perceived benefit, and the proposed mechanisms are stable across age groups. The study opens the “black box” linking perceived accuracy to adoption, identifies key boundary conditions, and extends TAM by positioning perceived accuracy as an antecedent of perceived usefulness in AI recommendation contexts.
ZigBee Based Low Latency IoT and AI Integrated Framework for Real Time Telehealth Monitoring
The Internet of Things (IoT) and Artificial Intelligence (AI) have opened up new frontiers in remote health monitoring with the integration of technologies and transformative solutions in order to detect real-time health monitoring and disparities. This article shows an innovative and integrated wireless health surveillance system, which is aimed at auxiliary environments, especially for elderly and chronically ill patients. The system links IoT sensors to monitor heart rate, body temperature, and oxygen level with cloud-based AI-driven systems for continuous real-time health monitoring of data shared by IoT sensors. Taking advantage of the ZigBee protocol for low-power, reliable communication ensures spontaneous data transmission from a system wearer to a centralized processing unit. At the most basic level, the system uses advanced machine learning algorithms such as random forest, support vector machine (SVM), and logistic regression to identify health discrepancies with a high degree of accuracy. The random forest model in particular gets an impressive 95% accuracy and recalls 100%, ensuring reliable detection of minimum false negatives and important health issues. The modular structure of the system allows for the addition of more sensors, including blood pressure and glucose monitors, to ensure scalability and adaptability to suit the varying needs of different patients. In a real-world care facility, strict testing was carried out on the capability of monitoring the capacity system with just a 120 ms delay and a power consumption of 3.8 mW/h, which made it very suitable for long-term, energy-skilled deployment. By addressing some of the major issues such as high delays, false alarms, and lack of integration in current systems, this research provides a scalable, reliable, and user-friendly solution for telehealth. The proposed system not only adds more accuracy and freedom to the patient in the clinical setting but also lessens the burden of the healthcare providers, paving the way to a new generation of intelligent health solutions.
Design and Evaluation of a Blockchain-Based Traceability Model for Organic Rice Supply Chain
This study aims to design and evaluate a blockchain-based traceability model and prototype for the traceability of organic rice supply chains in Banten Province, Indonesia. The main problem identified is the absence of a digital system that can transparently trace the origin of organic rice, which has reduced consumer trust in product authenticity. This research adopts the Design Science Research Methodology (DSRM) to develop a traceability model and prototype capable of recording all supply chain activities from farmers to end consumers. The prototype was validated using the ISO/IEC 25010 standard with supply chain actors and further validated with end consumers. Validation results from supply chain actors indicate strong performance across maintainability (4.91), functional suitability (4.29), security (4.11), performance efficiency (3.98), compatibility (3.89), usability (3.85), reliability (3.80), flexibility (3.93), and safety (3.77). Meanwhile, validation with end consumers yielded an average score of 4.22 on a 1–5 Likert scale. These findings indicate that the system meets key quality attributes—particularly functionality, reliability, security, and maintainability—at a very good level. In conclusion, implementing blockchain technology for organic rice supply chain traceability can enhance transparency, improve data security, and strengthen consumer trust in organic rice products.
Perceived Risk and Trust as Moderators Between Online Shopping Intention and Purchase Decisions
This study aims to identify the factors influencing online shopping intention and purchase decisions among consumers in Ho Chi Minh City, while examining the moderating roles of perceived risk and trust in e-commerce. Data were collected through a structured survey of online shoppers and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The empirical results indicate that product quality, price, promotion programs, and website quality have significant positive effects on online shopping intention, which in turn exerts a strong influence on purchase decisions. Moreover, perceived risk negatively moderates, whereas trust in e-commerce positively moderates the relationship between online shopping intention and purchase decisions. These findings underscore the pivotal roles of perceived value, trust formation, and risk reduction in converting online shopping intention into actual purchasing behavior. The novelty of this study lies in simultaneously examining perceived risk and trust as moderating mechanisms within a PLS-SEM framework in an emerging market context. The results provide practical insights for e-commerce firms by highlighting the importance of improving website quality, enhancing security systems, adopting flexible pricing strategies, and designing targeted promotions to increase online shopping conversion rates.
From Awareness to Action: Mindfulness Brief Interventions Shaping Positive Affect and Decision Certainty
Purpose: This study aims to explore the effect of a five-minute mindfulness audio intervention on improving state positive emotion and decision-making effectiveness under uncertainty, and to examine whether trait maximization moderates these effects among Chinese university students. Method: A randomized between-subjects experiment (N = 320) was conducted, in which participants were assigned to either a brief mindfulness exercise or a time-matched neutral audio control. State positive emotion was measured immediately after the manipulation using the PANAS positive affect scale. Participants then completed five worst-case scenario tasks (least-worst decision scenarios). Decision time, perceived decision difficulty, and the percentage of approach choices were recorded. Structural equation modeling was used to test mediation effects, and interaction modeling was applied to examine moderation. Findings: Participants in the mindfulness condition reported higher levels of positive emotion and demonstrated more effective decision-making patterns, characterized by faster decisions, lower perceived difficulty, and a higher proportion of approach-oriented choices. Positive emotion partially mediated the relationship between mindfulness and decision effectiveness. However, the benefits of mindfulness on approach choices were reduced among individuals with higher maximization tendencies. Originality/Implications: This study contributes to the literature on least-worst decision making by incorporating an affective mechanism and an individual difference moderator within a Chinese sample. The findings suggest that brief, scalable mindfulness interventions can support approach-oriented decision behavior under uncertainty, while also indicating that such interventions may need to be tailored for individuals with high maximization tendencies.
Thermoelectric Generator Efficiency Enhancement Through Copper Electrical Contact Optimization
Thermoelectric generators (TEGs) can transform heat into electricity and have considerable potential for diverse applications. Nonetheless, their widespread use is limited by their low efficiency, mainly owing to their high internal electrical resistance. This study aims to improve TEG performance by reducing the electrical contact resistance through the application of copper. This study addresses the critical challenge of improving the performance of thermoelectric generators (TEG) by reducing the electrical contact resistance, which directly affects the output power and conversion efficiency, particularly at low temperatures. The main objective of this study was to investigate how the contact resistance influences the electrical conductance and overall energy conversion efficiency of TEGs and to optimize the contact geometries to enhance the performance. Copper contacts with different shapes (flat and circular) were designed and fabricated to evaluate their impact on electrical resistance. Experimental investigations using a commercial TEG module were conducted to measure the contact resistances and analyze their effects on electrical parameters, including the output voltage, power, and conversion efficiency. A comprehensive theoretical model was developed to assess the contact area, energy loss, and thermal factors. Equations were applied to quantify the contact resistance and its influence on the power output and efficiency. Notably, small circular copper contacts exhibited a significant reduction in contact resistance compared to flat contacts, leading to an 18.6% improvement in efficiency at low temperatures. This study demonstrates that optimizing the geometry and size of copper contacts can substantially reduce energy losses at the interfaces, thereby enhancing the current flow and boosting the TEG conversion efficiency. These findings provide a novel approach for addressing the prevalent issue of high internal resistance in thermoelectric devices, paving the way for more effective energy harvesting and waste-heat recovery. This study underscores the critical role of contact engineering in TEG technology and offers promising strategies for improving device efficiency and output power for future applications.
Motivation Profiles, Personal Values, and Personality Traits: The Interplay in Research Management and Administration
This study addresses a significant gap in the literature by examining the motivation profiles of Research Managers and Administrators (RMAs) and their correlation with personal values and personality traits. Drawing on Self-Determination Theory (SDT), the research sought to propose and validate a conceptual framework specifically for RMAs, introducing the distinct profile of outcome-driven motivation. Empirical data were collected using a quantitative, cross-sectional survey (N=1,095 valid responses) distributed via snowball sampling. The methodological rigor was demonstrated through Exploratory Factor Analysis (EFA), with highly suitable data (KMO=0.915, p<0.001) and high reliability (Cronbach’s alpha ranging from 0.757 to 0.880). The EFA validated the construct of three distinct motivation profiles. RMAs were found to exhibit a predominantly autonomous (intrinsic) drive, confirmed by the highest mean score among the profiles, with statistically significant differences between all three types of motivation. This intrinsic motivation aligns with personal values that emphasize benevolence and universalism while downplaying power and tradition, and personality traits showing high conscientiousness, openness, and agreeableness. This work extends the use of SDT in Science and Technology Studies by validating a specific measurement scale for RMA motivation profiles. The results offer practical guidance, supporting the need for flexible, tailored motivational strategies and policies that enhance intrinsic factors such as autonomy and competency to boost RMA performance.
Time Redistribution Based on Temporal Risk Matrices for Operational Optimization in Public Security Institutions
The current “9-3” operational scheduling model used by the Ecuadorian National Police imposes rigid 8-hour rotational shifts over nine consecutive days, followed by three days off, without accounting for the spatiotemporal distribution of criminal activity. This leads to structural inefficiencies, including officer overload exceeding public-sector standards by 57%, unbalanced shift coverage, and an increase in fatigue-related incidents. This study aims to optimize staff allocation by proposing a data-driven redistribution model based on a normalized hour-day matrix. The method integrates multi-source institutional data, including ECU-911 dispatch logs, crime reports, and homicide records, and applies weighted normalization to construct proportional risk matrices per time slot. These matrices guide the redistribution of personnel while adhering to institutional criteria, including target monthly workload, equitable shift rotation, and guaranteed minimum coverage. The model was implemented in four pilot sectors characterized by varying urban, residential, and peripheral conditions. Results demonstrated improved adequacy in night-shift coverage of up to 30%, a 41% reduction in temporal imbalance, and decreased workload variability, with coefficients of variation below 6%. The proposed approach offers a replicable, low-cost planning solution that combines empirical risk modeling, operational transparency, and institutional scalability, representing a significant methodological improvement over the traditional static scheduling model.