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INDONESIA
Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 30 Documents
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Search results for , issue "Vol. 9 No. 5 (2025): October" : 30 Documents clear
Integrated AI, IoT, and Blockchain for Enhancing Security and Traceability in Perishable Logistics Villegas-Ch, William; Gutierrez, Rommel; Govea, Jaime; Garcí­a-Ortiz, Joselin
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-011

Abstract

The perishability of food products in the supply chain poses a significant challenge in ensuring quality and safety. Inefficient monitoring of temperature, humidity, and storage time results in substantial economic losses and increased health risks. Traditional traceability systems rely on manual audits or essential IoT platforms that lack predictive capabilities, leading to delayed anomaly detection and inefficient intervention. Blockchain-based solutions improve transparency but primarily focus on record verification rather than active anomaly detection and automated decision-making. This study proposes an integrated system combining Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain to optimize food traceability through real-time monitoring, predictive analytics, and secure decentralized record management. The system deploys smart sensors across storage and transportation units to continuously collect environmental data, which is processed by a deep learning model trained to detect deviations with 92.4 % accuracy. Detected anomalies trigger automated responses via smart contracts in a blockchain network, ensuring immediate corrective actions while maintaining immutable audit records. Results demonstrate a 64.3 % reduction in response time, improving reaction efficiency to critical storage failures. Additionally, false positive alerts decreased by 73.1 %, optimizing operational efficiency and minimizing unnecessary interventions. The blockchain implementation reduced storage overhead by 76.9%, ensuring scalability and long-term feasibility. This research establishes a foundation for intelligent, automated food supply chain management, demonstrating that integrating AI, IoT, and blockchain enhances safety, reduces waste, and optimizes logistics. Future work will focus on improvements in large-scale deployment and computational efficiency to refine this innovative approach.
Real-Time FPGA-Based ADAS Solution for Driver Drowsiness Detection and Autonomous Stopping Almomany, Abedalmuhdi; Marouf, Zaid; Jarrah, Amin; Sutcu, Muhammad
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-023

Abstract

This study addresses driver drowsiness, a leading cause of traffic accidents, by developing a real-time Advanced Driver Assistance System that integrates biometric detection and autonomous vehicle control. The objective of this study is to enhance road safety through the early detection of drowsiness and automated intervention. The proposed system detects signs of drowsiness by monitoring facial and ocular features using a real-time video stream. Once a predefined threshold is exceeded, an audible alert is triggered. If the driver remains unresponsive, the system gradually reduces the vehicle’s speed and initiates an automated stop procedure. Methodologically, the system employs OpenCV for image processing and a convolutional neural network for lane detection and vehicle control. It is implemented on a high-performance hardware platform using field-programmable gate arrays programmed via Vivado High-Level Synthesis to ensure low-latency operation. The results confirm the system’s real-time capability, accuracy in drowsiness detection, and effective vehicle control under drowsy driving conditions. The system’s novelty lies in its combination of biometric monitoring, deep learning, and hardware acceleration to provide faster and more reliable intervention than existing Advanced Driver Assistance System technologies. This integration sets a new benchmark for proactive road safety measures.
Dynamic Capabilities and Technological Innovation for Firm Resilience: A Configurational Analysis Rana, Jewel; Ahmad, Md Zubair; Jihad, Md Nazmul Islam; Rashed, Md.; Rahman, Siddiqur; Islam, Mohammad Fakhrul
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-01

Abstract

Firm resilience is essential to manage response and rapid recovery from disruptive events for a firm. Moreover, there is limited literature that investigates the combined effects of dynamic capability and technological innovation that are interrelated with firm resilience. This study used the dimensions of firm resilience, which were investigated with both necessary condition analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) methods using survey questionnaires from 308 respondents operating in Bangladeshi corporate industries that are currently facing uncertainties due to unforeseen crises. NCA results showed that visibility, market position, and digitalization achieved firm resilience as these antecedents reached the full percentile to achieve an optimal level of outcome. On the contrary, the influence of reserve capacity and big data analytics was not empirically significant for achieving firm resilience. Moreover, fsQCA results appreciated NCA results and showed four solutions that are sufficient for achieving a high level of firm resilience. The study reveals the configurational effects of dynamic capabilities and technological innovation to achieve firm resilience. The results show the necessary effects of configurational relationships that lead to outcomes. The configurational method is applied to identify the combined effects of antecedents that help managers predict high levels of firm resilience in a turbulent environment.
Microstructural and Elemental Characterization of TPU/Jute CNFs Nanocomposites via FESEM and EDX Analysis Nordi, Siti Syazwani; Mhd Noor, Ervina Efzan; Abdul Kadir, Aeslina Binti; Baig, Mirza Farrukh
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-016

Abstract

This study aims to investigate the microstructural and elemental characteristics of thermoplastic polyurethane (TPU) nanocomposites reinforced with jute cellulose nanofibers (CNFs), with the objective of understanding the dispersion behavior and interfacial interactions within the polymer matrix. CNFs were extracted from jute fibers through a chemo-mechanical process involving alkaline treatment, acid hydrolysis, bleaching, and high-energy milling, followed by melt blending with TPU to fabricate nanocomposites at varying filler loadings (1–5 wt%). Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray (EDX) spectroscopy were employed to analyze the surface morphology and elemental distribution of the nanocomposites. The FESEM results revealed that uniform CNF dispersion was achieved up to 4 wt%, beyond which noticeable agglomeration occurred. EDX analysis confirmed the successful incorporation of CNFs and identified performance-enhancing elements such as Si, Ca, Na, and Al in the reinforcement phase. These findings suggest that CNF content strongly influences microstructure and bonding quality, which are key factors for mechanical performance. The novelty of this work lies in its exclusive focus on microstructural and elemental characterization—providing essential insight into filler distribution and matrix compatibility—offering a foundation for optimizing sustainable, high-performance TPU/CNF nanocomposites for advanced industrial applications.
Extreme Value Model to Forecast PM2.5 Concentration Through a Non-Stationary Process Guayjarernpanishk, Pannarat; Remsungnen, Tawun; Chutiman, Nipaporn; Chiangpradit, Monchaya; Kong-ied, Butsakorn
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-025

Abstract

The objectives of this research were to develop a model to forecast and estimate the return levels for daily maximum PM2.5 concentrations in Thailand, applying Extreme Value Theory (EVT) with the Generalized Extreme Value (GEV) distribution under eight models for stationary and non-stationary process. This research utilized reanalysis data from the NASA EARTHDATA satellite, represented as grid points with a spatial resolution of 50 × 62.5 km, enabling the analysis of daily maximum PM2.5 concentrations across 176 grid points from January 1, 2009 to October 31, 2024. The analysis revealed that Model 2 (μ(t)=β0+β1t where σ and ξ are constants) is the most suitable model for five grid points, namely Sa Kaeo Province, Uthai Thani Province, Nakhon Ratchasima Province, Bueng Kan Province and Mae Hong Son Province, whereas Model 1 (μ, σ and ξ are constants) is suitable for the remaining 171 grid points. Estimating the return levels for return periods of 5, 10, 25, and 50 years showed that Northern Thailand had the most extreme daily PM2.5 concentrations, for all return periods especially Mae Hong Son Province. The results of this analysis can serve as valuable information to support decision-making for response planning in high-risk areas, aiding in efficient resource allocation and preventive measures.
Europe’s Energy Shift: From Fossil Fuels to Renewable Energy Jenei, Szonja; Módosné Szalai, Szilvia; Singh, Dhruv Pratap; Afadzinu, Kobla Sewornu; Poyda-Nosyk, Nina; Kálmán, Botond Géza; Dávid, Lóránt Dénes
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-06

Abstract

Objectives: This study explores the transformation of energy consumption in Europe between 2002 and 2022, focusing on the declining role of fossil fuels and the increasing significance of renewable and nuclear energy sources. The study also considers how countries with varying levels of economic development adopt different energy strategies and how these strategies correlate with shifts in energy usage. A circular economy approach that includes energy recovery from waste and resource reuse is a complementary aspect of sustainable energy transitions. Methods/Analysis: The per capita energy consumption data were analyzed through decile classification and cluster analysis to group countries with similar energy profiles. To explore the relationship between GDP and energy use—both total and renewable—linear and exponential regression models were applied. Outlier countries with atypical consumption trends were excluded to improve model reliability. Statistical analyses were conducted using SPSS, and Excel was used to support the visualization process. Findings: Six distinct clusters of energy consumption patterns emerged. In lower- and middle-GDP countries, renewable energy use showed a stronger exponential correlation with GDP growth than total energy use. While fossil fuel dependence has declined across most countries, the pathways taken have been diverse. High-GDP nations such as Iceland and Norway have demonstrated unique, resource-driven strategies. Novelty/Improvement: This study introduces a novel methodological blend of decile-based classification and clustering to enable clearer cross-country comparisons of energy use. The results also highlight the importance of excluding statistical outliers to improve regression precision. By integrating insights relevant to circular economy principles, the findings contribute to designing more effective and regionally adapted energy transition strategies.
Mediating Role of Digital Service Adoption in Enhancing Human Resource Management Capacity of SMEs Nga, Lu Phi; Tam, Phan Thanh
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-024

Abstract

In the context of rapid digital transformation, small and medium enterprises (SMEs) increasingly recognize the necessity of adopting digital services to enhance their human resource management (HRM) capabilities. This study aims to investigate the mediating role of digital service adoption in improving HRM capacity among Vietnamese SMEs. The Resource-Based View draws upon the Technology-Organization-Environment framework; the research examines how leadership support, organizational readiness, policy mechanisms, perceived benefits, and organizational culture influence digital service adoption and HRM development. A mixed-method was employed, combining qualitative interviews with 30 digital transformation experts across five major cities in Vietnam and a quantitative survey of 1,000 SME managers from July 2024 to February 2025. Structural equation modeling was utilized to test 11 proposed hypotheses. The results demonstrate that digital service adoption significantly enhances HRM capacity and mediates the relationships between organizational factors and HRM outcomes. Leadership support is the strongest predictor of digital adoption and HRM capacity, followed by perceived benefits and policy mechanisms. These findings emphasize the importance of strategic leadership, organizational preparedness, supportive policies, and a digital-friendly culture in fostering successful digital transformation in HRM. The novelty contributes a novel integrated model bridging digital transformation and HRM theories and offers practical insights for SME managers seeking to strengthen workforce capabilities in emerging economies.
Innovation Adoption and Resistance of Functional Postbiotics: Consumer Intentions for Sleep and Mental Wellbeing Piyapinyo, Chawin; Assarut, Nuttapol; Borompichaichartkul, Chaleeda
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-018

Abstract

This study investigates the key factors influencing consumer purchase intention toward functional postbiotic beverages designed to enhance sleep quality and mental health. It focuses on perceived innovation characteristics (PIC), innovation resistance (IR), attitude, and the moderating role of product knowledge. Data were collected through a structured questionnaire from 400 health-conscious Thai consumers aged 18–65 with prior experience in functional foods. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the relationships among PIC, IR, attitude, and purchase intention, including the moderating effect of product knowledge. The findings reveal that relative advantage, compatibility, and attitude positively influence purchase intention, while the claim skepticism barrier has a negative impact. Complexity and trialability were found to be non-significant. Additionally, compatibility significantly influences attitudes across high- and low-product-knowledge consumers. However, product knowledge did not moderate the direct relationship between PIC and purchase intention. Attitude emerged as a key mediator. This study contributes to innovation adoption theory by highlighting the roles of compatibility and attitude while introducing trust and claim skepticism as critical resistance factors. It offers actionable implications for marketers aiming to enhance consumer trust and align products with daily routines.
Institutional Co-Evolution and Hybrid Regulation in the Digital Economy: A Case Study of BRICS Nations Shkalenko, Anna V.; Kozlova, Svetlana A.; Nazarenko, Anton V.
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-014

Abstract

This study investigates the institutional co-evolution associated with digitalization processes in BRICS countries, emphasizing the development of hybrid regulatory frameworks that integrate state intervention, platform-based self-regulation, and entrepreneurial institutional agency. The primary objective is to analyze how these frameworks operate within heterogeneous governance environments and address the sustainability challenges arising in emerging digital economies. Grounded in the theory of institutional co-evolution, the research applies a mixed-methods design, combining bibliometric mapping, comparative policy analysis, and multiple linear regression on cross-national panel data from Brazil, Russia, India, China, and South Africa (2018 - 2022). The findings demonstrate that increasing levels of digitalization and innovation are significantly correlated with reductions in environmental risks, while GDP growth remains positively associated with CO2 emissions; underscoring a structural tension between economic expansion and ecological resilience. To address this contradiction, the study proposes and empirically validates an Optimized Hybrid Model of institutional regulation, which improves sustainability indicators by 18.5%. The novelty of this research lies in the operationalization of institutional co-evolution within digital governance, offering a transferable policy model for flexible, adaptive regulation in complex, data-intensive economies. These results contribute to the advancement of institutional theory and provide actionable insights for the governance of transitional digital systems.
Driving Mangrove Recovery: Community Engagement and Socio-Economic Shifts in Aquaculture Areas Rattanarat, Jantira; Jaroensutasinee, Krisanadej; Jaroensutasinee, Mullica; Sparrow, Elena B.
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

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

Abstract

Land-use change and recovery patterns of mangroves in the Tha Sak subdistrict, Nakhon Si Thammarat, Thailand, were examined utilizing multi-temporal Landsat images and socio-economic data from 1988 to 2023. Land use was classified through visual interpretation, and potential changes were predicted using a Markov chain model. The results showed a significant expansion of mangrove forests (1.11 km² to 9.10 km²), indicating a clear recovery. At the same time, the aquaculture area decreased drastically (from 25.69 km² to 8.79 km²), indicating a significant change in land use. The recovery of mangroves is primarily attributed to the cessation of aquaculture and the active involvement of the Tha Sak subdistrict's Small-Scale Fishermen Group, highlighting the success of community-based restoration. This study provides evidence of the critical role local communities play in bringing about positive environmental change and enabling Sustainable Development Goals (SDGs) 15: Life on Land from ecosystem restoration, SDG 14: Life Below Water for conservation of coastal areas, and SDG 11: Sustainable Cities and Communities for increasing community resilience. Involving local communities in mangrove restoration and preservation is key to long-term sustainability.

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