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Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 30 Documents
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Search results for , issue "vol. 10 no. 2 (2026): april" : 30 Documents clear
How Perceived Accuracy Drives Adoption of AI Personalized Recommendations: A Moderated Mediation Model Xiaolan Zhu; Siwarit Pongsakornrungsilp; Pimlapas Pongsakornrungsilp; Archana Kumari
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-023

Abstract

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.
Mg, Si, Al, and P Particle-Doped Epoxy: A Synergistic Approach for Enhanced Fire Performance Qandeel Fatima Gillani; Almagul Mentbayeva; Muhammad Faisal Javed; Sandugash Kalybekkyzy
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-021

Abstract

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.
Strategic Positioning for Knowledge-Based Industry Growth: Bridging Innovation and Competitiveness Gaps Haitham Al Qahtani; Jayendira P. Sankar
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-013

Abstract

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.
Trusted AI-Based Method for Predicting Controller Load and PSO-Based Structure for Reducing Latencies Vladimir Zh. Kuklin; Islam Alexandrov; Maxim Mikhailov; Naur Z. Ivanov; Elena Yu. Linskaya
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-017

Abstract

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.
Green Technology Innovation and Financial Performance: Roles of Executive Green Perception and Carbon Performance Yan Li; Pankaewta Lakkanawanit; Muttanachai Suttipun; Wilawan Dungtripop; Rizqa Anita
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-022

Abstract

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.
T-CER-Net: Attention-Based Temporal Cross-Eye Regression for Noise-Resilient Detection of Intermittent Strabismus Wattanapong Kurdthongmee; Karanrat Thammarak; Md Eshrat E. Alahi; Yun Hui; Piyadhida Kurdthongmee
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-014

Abstract

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.
Detecting Genuine Versus Fake Emotions: A Dual-Task Deep Learning Approach Using Facial Expression Analysis Sarah Tasnim Diya; Most. Jannatul Ferdos; Md. Mizanur Rahman; Yadab Sutradhar; Zahura Zaman; Suman Ahmmed; Ohidujjaman
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-018

Abstract

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 Renya Rosari; Anis Chariri; Dwi Cahyo Utomo
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-019

Abstract

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.
From Awareness to Action: Mindfulness Brief Interventions Shaping Positive Affect and Decision Certainty Yani Duan; Nor Akmar Bt. Nordin; Siti Aisyah Panatik; Huayi Liu
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-027

Abstract

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.
Time Redistribution Based on Temporal Risk Matrices for Operational Optimization in Public Security Institutions Pacheco, Luis Palacios; La Fuente, Henry Tapia; Gordón, Antonio Castillo; Quijada Acuña, Eduardo; Villegas-Ch, William
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-030

Abstract

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.

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