International Journal of Advances in Applied Sciences
International Journal of Advances in Applied Sciences (IJAAS), p-ISSN 2252-8814, e-ISSN 2722-2594, is a peer-reviewed and open access journal dedicated to publish significant research findings in the field of Applied Sciences, Engineering and Information Technology. The journal is designed to serve researchers, developers, professionals, graduate students and others interested in state-of-the art research activities in applied science, engineering and information technology areas, which cover topics including: applied physics; applied chemistry; applied biology; environmental and earth sciences; electrical & electronic engineering; instrumentation & control; telecommunications & computer science; industrial engineering; materials & manufacturing; mechanical, mechatronics & civil engineering; food, chemical & agricultural engineering; and acoustic & music engineering. IJAAS is published by Intelektual Pustaka Media Utama (IPMU). This journal is ACCREDITED (recognised) by the Ministry of Education, Culture, Research, and Technology, Republic of Indonesia (Decree No: 79/E/KTP/2023). Scopus has indexed articles published in this journal since 2019 issues. If you have access to scopus.com, you can <a href="https://id.elsevier.com/as/authorization.oauth2?platSite=SC%2Fscopus&ui_locales=en-US&scope=openid+profile+email+els_auth_info+els_analytics_info+urn%3Acom%3Aelsevier%3Aidp%3Apolicy%3Aproduct%3Aindv_identity&els_policy=idp_policy_indv_identity_plus&response_type=code&redirect_uri=https%3A%2F%2Fwww.scopus.com%2Fauthredirect.uri%3FtxGid%3D1296069b61a14ad54b64ac4c1cbee01f&state=forceLogin%7CtxId%3D4B6FDB664E7E5E38C50C3A445963F1E2.i-0e06c2e91cabf8067%3A14&authType=SINGLE_SIGN_IN&prompt=login&client_id=SCOPUS&isValidNewDocSearchRedirection=false">click here. Scimagojr also has been indexing it as of 2019. See our Scimagojr profile here. IJAAS Features: Plagiarism: CrossCheck plagiarism powered by iThenticate. DOI: DOI Number is indexed by Crossref. Open Access: Open access provides immediate and unrestricted access to the latest research. Full Archive Available: Every issue of Journal of IJAAS is available online. ISSN Record Information: The ISSN of IJAAS journal is registered at https://portal.issn.org/resource/ISSN/2722-2594 <!--[if mso]> <xml><w:WordDocument xmlns:w="urn:schemas-microsoft-com:office:word"><w:DontUseAdvancedTypographyReadingMail/></w:WordDocument> <o:OfficeDocumentSettings><o:PixelsPerInch>96</o:PixelsPerInch><o:AllowPNG/></o:OfficeDocumentSettings></xml> <![endif]--><!--[if !mso]><!--><!--<![endif]--><!--[if mso ]> <![endif]--> <!--[if mso]> <v:roundrect xmlns:v="urn:schemas-microsoft-com:vml" xmlns:w="urn:schemas-microsoft-com:office:word" href="https://youtu.be/B9lB-4sZNI4?si=o5H0_HS2NaXlxe-Z" style="height:46px;width:210px;v-text-anchor:middle;" arcsize="9%" fillcolor="#3202b6"> <v:stroke dashstyle="Solid" weight="0px" color="#3202b6"/> <w:anchorlock/> <v:textbox inset="0px,0px,0px,0px"> <![endif]-->HOW TO REGISTER<!--[if mso]> </v:textbox></v:roundrect><![endif]--> <!--[if mso]> <v:roundrect xmlns:v="urn:schemas-microsoft-com:vml" xmlns:w="urn:schemas-microsoft-com:office:word" href="https://youtu.be/hQD9-HeNy1k?si=_Q7Dsawb-OEvh2Gl" style="height:46px;width:187px;v-text-anchor:middle;" arcsize="9%" fillcolor="#ff47ce"> <v:stroke dashstyle="Solid" weight="0px" color="#ff47ce"/> <w:anchorlock/> <v:textbox inset="0px,0px,0px,0px"> <![endif]-->HOW TO SUBMIT<!--[if mso]> </v:textbox></v:roundrect><![endif]-->
Institute of Advanced Engineering and Science
COPYRIGHT AND LICENSING AGREEMENT This is to certify that the submitted manuscript is an original work, has not been published previously, and is not being considered for publication elsewhere. Upon acceptance, the article will be published by Intelektual Pustaka Media Utama (IPMU) under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). The authors retain the copyright to the work. We declare that: This manuscript has not been published previously in the same or substantially similar form. It is not currently under review or consideration for publication by any other journal. All necessary permissions have been obtained for the use of any third-party material included in this manuscript. We agree that upon publication, the article will be distributed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). License Grant We, the authors, retain all copyrights to the submitted article, and grant to IPMU the non-exclusive right to reproduce, publish, translate, and distribute the article in any form or medium, including print and electronic formats. The article may be used, shared, or adapted by others, provided that: Proper attribution is given to the original author(s) and source, A link to the license is included, Any changes are indicated, and Any derivative works are licensed under the same terms (ShareAlike). Full license details: https://creativecommons.org/licenses/by-sa/4.0 Author Responsibility The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who has obtained the consent of the co-author(s), if any. After submission of this agreement signed by the corresponding author, changes in authorship or order of authors will not be accepted. Retained Rights Authors retain all proprietary rights in any process, method, or material described in the work. Authors may distribute or reuse the work for personal or institutional use, or include it in other works, with appropriate credit and under the same license. Authors may deposit the published version of the article in institutional or public repositories.
Optimizing smart grids with blockchain-driven automation and demand response
To increase resilience, efficiency, and engagement in the network, it shall develop and test its smart grid system integrating blockchain-based authentication and automated demand response management. Simulations are made on the dynamic behavior of the grid in energy generation, consumption, and management through demand responses through MATLAB/Simulink assessment of performance and stability. Ethereum is used in implementing and managing smart contracts that automate and secure events of demand response and consumer interactions for transparency in transactions. It uses Python with Pandas to process, analyze, and visualize simulation data that gives insight into the effectiveness of demand response strategies; PostgreSQL supports the structured storage and querying of data with comprehensive data management. Proper integration of such tools can result in the proper robust simulation of the smart grid system that is highly reliable, efficient usage of energy, and can empower consumers through secure, efficient demand response mechanisms. These immediate issues about managing the grid can thus solve the way toward the future development of such smart grid technologies and their possible integration with the blockchain.
Copyright (c) 2025 B. Jyothi, Bhavana Pabbuleti, Ravi Ponnala, Kambhampati Venkata Govardhan Rao, S. Sai Srilakshmi, Putta Dhanush Narasimha, Mareboyina Karthik Yadav, Malligunta Kiran Kumar, Ch. Rami Reddy http://creativecommons.org/licenses/by-sa/4.0
Harvesting insights: exploring machine learning for crop selection and predictive farming
Modern agriculture has undergone a significant evolution, adopting advanced techniques to streamline crop management processes. One such advancement is the integration of machine learning (ML) technology, which shows great promise in optimizing crop selection and enhancing economic returns. Key determinants of crop productivity, including water availability, soil quality, weather conditions, and timely resource allocation, play pivotal roles in the farming ecosystem. Harnessing these factors, ML algorithms facilitate the identification of optimal crop choices and provide continuous monitoring of cultivation processes to anticipate evolving crop needs. This paper investigates various ML techniques employed for crop selection and evaluates their effectiveness in agricultural settings. Through a comparative analysis, we highlight the advantages of these techniques and provide insights into their potential impact on current farming management practices. By leveraging ML for predictive farming, stakeholders can make informed decisions to maximize yields, minimize resource wastage, and promote sustainable agricultural practices. This study contributes to the ongoing discourse on the integration of technology in agriculture and underscores the transformative potential of ML in shaping the future of crop management. We investigate recent papers from the years 2020 to 2024.
Copyright (c) 2025 Tanvi Deshmukh, Anand Singh Rajawat, Amol Potgantwar http://creativecommons.org/licenses/by-sa/4.0
A convolutional neural network with attention mechanism-based malaria detection from blood smear images
With 249 million cases and 608,000 fatalities recorded in 2022, malaria is one of the major worldwide health concerns, particularly in areas with low resources. In this paper, we propose a custom convolutional neural network (CNN) with an integrated attention mechanism to inspect malaria from blood smear images. To improve model robustness, we combined three publicly available datasets from the NIH and Kaggle. The proposed model achieved 98.20% accuracy, 97.85% precision, 98.55% recall, and 98.20% F1-score, outperforming conventional di agnostic methods. In addition, we conduct comparative analyses using two transfer learning models, ResNet50 and DenseNet. ResNet50 attained 95.06% precision, 95.44% recall, with 95.05% F1-score, while DenseNet achieved a pre cision of 87.96%, recall of 88.33%, and F1-score of 87.90%. For interpretability, Grad-CAMandsaliency map visualizations highlighted key image regions, with saliency maps offering finer pixel-level localization. These results highlight the potential of our attention-based CNN as a feasible, interpretable diagnostic tool for malaria, particularly in low-resource settings.
Copyright (c) 2025 Kingkar Prosad Ghosh, Ferdaus Anam Jibon, Shahina Haque, Md. Suhag Ali, Md. Monirul Islam, Jia Uddin http://creativecommons.org/licenses/by-sa/4.0
Multi-dimensional brand experiences in co-branded products across generations
As consumer expectations evolve, brands are tasked with creating multifaceted experiences that resonate with different generations. This study examines the influence of sensory, affective, behavioral, and cognitive brand experiences on consumer perceptions of co-branded products, with a focus on two key cohorts: Generation Y (Gen Y) and Generation Z (Gen Z). A mixed-methods approach, integrating quantitative surveys and qualitative focus groups, was employed to gain deeper insights into generational differences in brand engagement. The findings reveal that Gen Y consumers prioritize emotional and behavioral experiences, seeking meaningful interactions and emotional connections that align with their values and life stages. In contrast, Gen Z consumers are more interested in sensory novelty and cognitive engagement, favoring brands that emphasize originality, digital interactions, and distinctive experiences. Both generations showed strong reactions to behavioral factors, particularly direct product interactions. These insights highlight the importance of tailoring brand experience strategies to the unique preferences of each generation. By embedding sensory, emotional, and cognitive elements into brand experiences, companies can create deeper emotional connections with consumers, enhance brand value, and build long-term loyalty. The results offer actionable strategies for brand managers seeking differentiation and sustainable success in today’s competitive market environment.
Copyright (c) 2025 Yana Erlyana, Lim Jing Yi http://creativecommons.org/licenses/by-sa/4.0
Implementation of XGBoost for diabetes mellitus risk prediction based on health history
Diabetes mellitus (DM) is a chronic disease with a growing global burden and specific challenges for early management, particularly in regions with limited access to healthcare. This study develops a web-based system to classify diabetes risk from medical history using extreme gradient boosting (XGBoost), an ensemble model of decision trees. The dataset comprised 520 respondents (320 DM, 200 non-DM) and underwent labeling, standardization, and an 80:20 train–test split, followed by hyperparameter selection via grid search and 5-fold cross-validation (CV). On the test set, the model achieved an accuracy of 0.9888, precision of 1.0000, recall of 0.9718, and an F1-score of 0.9857; discriminative performance was also strong with an area under the receiver operating characteristic curve (AUC ROC) of 0.839. These findings confirm that XGBoost effectively handles complex or imbalanced medical data while providing probabilistic outputs that are clinically meaningful. Deployed as a web application, the system can support early screening, triage, and clinical decision-making, thereby expediting referrals and personalizing interventions in primary care and hospital settings, especially in resource-constrained environments. This work lays the groundwork for further development, including the integration of explainable artificial intelligence (XAI) techniques to enhance clinical transparency.
Copyright (c) 2025 Andi Riansyah, Ghufron Ghufron, Lailatul Fitriyah, Suyanto Suyanto http://creativecommons.org/licenses/by-sa/4.0
Optimized transfer learning for detection susceptibility vessel sign in stroke using gorilla troops optimizer
The blockage of blood vessels causes ischemic stroke due to clots. The susceptibility vessel sign (SVS), observed through susceptibility-weighted imaging (SWI) via magnetic resonance imaging (MRI), is a key indicator that reveals clots within brain vessels. Early detection of these clots is crucial for timely and effective treatment. Image-based detection methods, particularly non-invasive techniques like MRI, offer a superior approach compared to other modalities. This study proposes an optimized method using transfer learning to classify SVS. The deep convolutional neural network (DCNN) residual network 50 version 2 (ResNet50V2) was applied for classification, with hyperparameters fine-tuned using the gorilla troops optimizer (GTO). The optimized proposed model achieved an accuracy of 94%, sensitivity of 100%, specificity of 88%, and an F1-score of 93%. This significantly outperforms the standard ResNet50V2 model using the default parameter, which achieved an accuracy of 91%, sensitivity of 82%, specificity of 100%, and an F1-score of 77%. These results demonstrate that the proposed method significantly enhances the detection of SVS, offering a promising tool for early ischemic stroke diagnosis.
Copyright (c) 2025 Nur Lyana Shahfiqa Albashah, Ibrahima Faye, Nur Syahirah Roslan, Rohani Bakar, Norliana Muslim http://creativecommons.org/licenses/by-sa/4.0
Fit to-organization amplifies unethical pro-organizational behavior
The study of unethical pro-organizational behavior (UPB) has seen a sharp rise in recent years, with research results explaining that a potential cause of UPB is organizational identification (OI). However, there are inconsistencies in the findings regarding the effect of OI on UPB in the workplace. This study seeks to test the direct effect of OI on UPB, and to explore the mediating role of fit to-organization (Fit-O). To evaluate data collected from employees of micro and small enterprises, this study utilized structural equation modeling-partial least squares (SEM-PLS) analysis, along with the variance accounted for (VAF) method to test the mediation between OI and UPB. The findings confirm that the fit or compatibility of an individual with the organization can strengthen the effect of OI on UPB. The intervening role of Fit-O in the OI-UPB relationship is a crucial theoretical contribution. This research also implies that organizations must balance increasing OI with strong ethical standards to mitigate UPB.
Copyright (c) 2025 Institute of Advanced Engineering and Science (IAES) http://creativecommons.org/licenses/by-sa/4.0
Antimicrobial activity of hard candy with basil (Ocimum sanctum L.) essential oil addition
The basil plant belongs to the Lamiaceae family and contains various active compounds, including phenols, saponins, alkaloids, flavonoids, tannins, and essential oils. These compounds have antimicrobial activity against Streptococcus mutans and Candida albicans, two types of bacteria that can cause bad breath. The addition of basil essential oil to hard candy has the potential to reduce bad breath. This study aimed to determine the concentration effect of basil essential oil on hard candy in inhibiting the growth of Streptococcus mutans and Candida albicans and its acceptance by the panelists. This research was conducted with five treatments with variations in the concentration of basil essential oil, which were 0, 0.25, 0.5, 0.75, and 1%. The results showed that the higher basil essential oil concentration in hard candy inhibited the growth of Streptococcus mutans and Candida albicans. The best treatment was at 0.75% basil essential oil, with sensory panelist acceptance for color 69%, aroma 57%, taste 43%, and overall 58%. Several compounds in basil essential oil, including linalool, eugenol, caryophyllene, and trans-α-bergamotene, are thought to contribute to the ability of this candy to inhibit microbial growth.
Copyright (c) 2025 Maria Belqis, Giyarto Giyarto, Moch Yusuf Irvanto, Fitri Setyoningrum, Siti Susanti http://creativecommons.org/licenses/by-sa/4.0
Development of a hydraulic jack system bending tool for improved manufacturing efficiency
This article presents the design, fabrication, and testing of a hydraulic sheet metal bending tool. The main objective was to create a tool capable of bending sheets of various thicknesses, ranging from 2 to 4 mm, with high precision and minimal operator effort. The design incorporates a hydraulic ram for easy operation, allowing multiple plates to be bent in a short period of time. Key calculations, including bending force, spring load, and hydraulic force, are performed to ensure the efficiency and safety of the tool. Experimental results show that the tool is able to achieve the desired bending angles, with minimal spring return, and can handle up to three 10 cm wide sheets in approximately 10 minutes. The performance of the tool has been proven by tests, and the results confirm that it can meet the requirements of industrial sheet metal bending. Based on these results, the tool demonstrates its effectiveness in small and medium-scale operations, providing a cost-effective solution for sheet metal production.
Copyright (c) 2025 Muhammad Arsyad Suyuti, Rusdi Nur, Ahmad Nurul Muttaqin, Arminas Arminas, Zainal Sudirman http://creativecommons.org/licenses/by-sa/4.0
New approach of the neighborhood structure of fuzzy points
This paper provides a comparative analysis of the fuzzy Q-neighborhood and the fuzzy neighborhood system of a fuzzy point. Specifically, we investigate the relationship between the elements of these systems when both are defined at the same fuzzy point. We address questions such as: how are these elements interconnected, and which system contains the other? Furthermore, we give the dual of the fuzzy Q-neighborhood system, which is named the fuzzy DQ-neighborhood system, and prove that these two systems are not equivalent. Finally, we examine the properties of these systems to determine whether they satisfy the conditions of fuzzy topology, Supra topology, or filter theory.
Copyright (c) 2025 Amer Himza Almyaly, Jwngsar Moshahary http://creativecommons.org/licenses/by-sa/4.0
Redesigning retail spaces based on customer habits and halal standards using market basket analysis
The halal retailing standard was first introduced by the Department of Malaysian Standards in 2010, known as MS 2400-3:2010. This standard was developed to protect Muslim consumers with the assurance of halal products. However, this management system needs to be more prescriptive on how the retail layout should be organized. In addition, this management also overrides the consideration of customer purchase behaviors or preferences. This research aims to design the layout of retail outlets by considering customer buying behavior and halal retailing standards. This study used the association rule-market basket analysis (AR-MBA) to determine the pattern of customer shopping behavior. One Islamic retail in Indonesia was used as a case study, where one-month sales transaction data was analyzed using AR-MBA. In addition, the activity relationship chart (ARC) was used to qualitatively analyze the placement of a product department by considering halal retailing standards. The results of AR-MBA obtained 21 associations among product departments, which were then used as a basis for proposed layouts while still considering the product characteristics and halal retailing standards. This research output provided a proposed product layout for retail outlets by considering quantitative factors (AR-MBA output) and qualitative factors (MS standard).
Copyright (c) 2025 Roaida Yanti, Qurtubi, Danang Setiawan, Prita Nurkhalisa Maradjabessy, Nasruddin Faisol http://creativecommons.org/licenses/by-sa/4.0
Development of a leakage detection and alert system for liquefied petroleum gas via a mobile application
Nowadays, ensuring the comfort and safety of house users is a top priority, and this may be accomplished by implementing smart technology to lead a convenient and safe life. Leakage of liquefied petroleum gas (LPG), which is mostly utilized in the home kitchen for cooking, is one of the frequent risks. Using a gas sensing device, a gas control system, and wireless communication units, the goal of this study is to create an LPG gas leakage warning and management system to prevent the gas from exploding by detecting the leak. When LPG gas is brought near the sensor, it detects the leakage and the buzzer is activated by activating the audio-visual alarm and closing the gas cylinder valve. The system also generates alert messages and sends them to the fire station when the LPG gas leakage has reached a critical level. Testing results of the proposed LPG leakage system show a satisfactory performance of the developed device with a quick response to LPG gas leakage. In addition, powerful audio and visual alarms are activated. An immediate message was sent to homeowners and the fire station department regarding the leakage incident to prevent the risk of gas leakage.
Copyright (c) 2025 Wael A. Salah, Anees Abu Sneineh http://creativecommons.org/licenses/by-sa/4.0
AI-driven emotion recognition systems for sustainable mental health care: an engineering perspective
Emotion recognition systems are transforming human-computer interaction (HCI) applications by enabling AI-driven, adaptive, and responsive mental health interventions. This study explores AI-based emotion recognition technologies using facial expressions, voice analysis, text-based sentiment processing, and physiological signals to develop scalable, real-time mental health support systems. Utilizing datasets such as FER2013, JAFFE, and CK+, our research examines deep learning models, including EfficientNet XGBoost, which achieved over 90% accuracy across key evaluation metrics. Unlike traditional mental health interventions, AI-driven systems provide cost-effective, accessible, and sustainable solutions through telemedicine, wearable biosensors, and virtual counselors. The study also highlights critical challenges such as algorithmic bias, ethical AI compliance, and the energy consumption of deep learning models. By integrating machine learning, cloud-based deployment, and edge computing, this research contributes to the development of sustainable, ethical, and user-centric AI solutions for mental health care. Future directions include AI model optimization for energy-efficient deployments and the creation of diverse, inclusive datasets to improve performance across global populations.
Copyright (c) 2025 Akram Ahmad, Vaishali Singh, Kamal Upreti http://creativecommons.org/licenses/by-sa/4.0
The latest trends in internet of things usage in smart homes: a systematic literature review
The Internet of Things (IoT) has recently developed very quickly. Equipped with increasingly mature information technology, especially for use in smart homes. This technology is integrated with IoT systems, which can now solve this problem. This paper helps discover the latest research trends and offers a broad perspective on what factors are used in intelligent housing by utilizing a systematic literature review (SLR). This article takes 2256 documents from Springer, IEEE, ACM, MDPI, ScienceDirect, Hindawi, IAES, and Google Scholar. 59 articles passed the specified exclusion and inclusion criteria. Furthermore, the new findings are that several research factors exist in smart homes, such as Artificial Intelligence (AI), Assistant Technology, Blockchain, IoT, energy-saving IoT, Network IoT, Robot IoT, and Security IoT. It is hoped that future research will provide insights to examine smart homes based on their factor.
Copyright (c) 2025 Mochammad Haldi Widianto, Puji Prabowo http://creativecommons.org/licenses/by-sa/4.0
Li-Fi technology for automated transport
India is now one of the countries that is growing quickly worldwide. Today, practically for everything, a vehicle is necessary. Vehicle production is growing rapidly. One of the downsides of this enormous increase is the ineffective management of traffic. The well-planned expansion of transport organizations has resulted in a variety of challenges with travel. It is detrimental to both mankind and the economy when emergency vehicles like ambulances and fire engines are late in arriving. Smart transport is the most effective strategy to lower vehicle accidents and communicate with other cars to open a way for emergency vehicles. Here, the preliminary ideas and findings of a small-scale model of an automated transport system are presented using an innovative discovery known as Li-Fi, also known as light-fidelity. Full duplex communication is accomplished with Li-Fi, in which light is modified at speeds that are too rapid for the eye to follow. Li Fi may be used to create intelligent transportation systems since it offers various advantages over other communication protocols.
Copyright (c) 2025 Popuri Rajani Kumari, Chalasani Suneetha, Maddali Anil Kumar, Tangirala Mrudula, Anbumani Venkatachalam, Bodapati Venkata Rajanna, Giriprasad Ambati http://creativecommons.org/licenses/by-sa/4.0
Analysis of feature reduction for identifying stress levels electroencephalogram signal based
Stress identification based on electroencephalogram (EEG) signals has become a rapidly growing research topic, with the main approaches utilizing features from the frequency domain and time-frequency domain. This research aims to combine principal component analysis (PCA) and independent component analysis (ICA) for feature extraction to improve the accuracy of stress identification. Additionally, PCA+ICA features are reduced from 64 to 32 columns to optimize computational efficiency without losing important information from the EEG signal. The stress identification models used in this research include Ensemble, naive Bayes, and support vector machine (SVM). The data used are from the SAM-40 task Stroop color trials 1, 2, and 3. Experimental results indicate that the combination of PCA+ICA features improves accuracy only in the ensemble method. Reducing PCA+ICA features from 64 to 32 columns led to an improvement in accuracy only for Stroop trial 2 data with the naive Bayes method.
Copyright (c) 2025 Setyorini, Ilham Ari Elbaith Zaeni, Hakkun Elmusyah http://creativecommons.org/licenses/by-sa/4.0
Prediction index drought use neural network based rainfall
Prolonged dry seasons compared to rainy seasons often lead to drought, making drought index observations essential. In Indonesia, drought monitoring commonly uses the standardized precipitation index (SPI), yet there is no common standard for drought index measurement. Therefore, this research applies the Z-score index (ZSI) and China-Z index (CZI), which, like SPI, are rainfall-based drought indices but have rarely been explored in previous research. To predict ZSI and CZI, this research compares the weighted moving average (WMA) and multilayer perceptron (MLP) methods. Two input scenarios are tested: the previous two periods (t-2, t-1) and the previous three periods (t-3, t-2, t-1). The results show that MLP outperforms WMA, with the best performance achieved by the MLP model at a mean absolute percentage error (MAPE) of 4.177% using the three variable input scenario and MLP architecture 3-6-10-1.
Copyright (c) 2025 Nur Nafiiyah, Ali Mokhtar http://creativecommons.org/licenses/by-sa/4.0
Design and implementation of an internet of things-based automatic waste sorting system
This paper presents the design and development of an internet of things (IoT)-based automatic waste sorting system that classifies waste into four categories: organic, non-organic, metal, and others. The system integrates an Arduino Mega for control, multiple proximity sensors (inductive, capacitive, and infrared), and ultrasonic sensors for level detection, and a NodeMCU ESP8266 for real-time monitoring via the Blynk platform. A total of 100 tests (25 per bin) were conducted. Classification success rates were 92% (metal), 80% (inorganic), 84% (organic), and 100% (others), resulting in an overall accuracy of 89%. The main contribution is a combined automatic sorting and IoT monitoring framework suitable for campus-scale deployment.
Copyright (c) 2025 Akhmad Taufik, Paisal Paisal, Muhammad Ruswandi Djalal, Zahran Atha Dillah, Haryono Ismail http://creativecommons.org/licenses/by-sa/4.0
Mandailing smoked fish cuisine: cultural, nutritional, and local wisdom insights
This study explores the uniqueness of Mandailing traditional cuisine, focusing on the cultural and nutritional significance of its iconic smoked fish dishes, such as smoked fish rendang, smoked fish curry, smoked fish with chili sauce, and smoked fish with vegetables. These dishes showcase the traditional fish smoking practices developed as a preservation method, allowing the Mandailing community to adapt to the abundance of rivers and natural resources in their highland environment. Smoking fish not only extended its shelf life but also became a cornerstone of Mandailing culinary identity, reflecting the community’s ingenuity and resourcefulness. Mandailing cuisine is deeply influenced by neighboring culinary traditions from West Sumatra and North Tapanuli, resulting in a rich fusion of bold flavors, often characterized using coconut milk and fresh spices. The preparation of smoked fish combines traditional high-heat cooking techniques with unique flavor profiles that distinguish Mandailing dishes from other Indonesian cuisines. This research highlights the importance of Mandailing smoked fish practices in sustaining local food systems and preserving cultural heritage. By emphasizing both cultural and nutritional aspects, it underlines the relevance of these traditional practices in promoting food diversity, environmental sustainability, and the recognition of Indonesia’s rich culinary landscape.
Copyright (c) 2025 Esi Emilia, Nila Reswari Haryana, Risti Rosmiati, Erli Mutiara, Laili Fitria, Rachmat Mulyana, Wisnu Prayogo http://creativecommons.org/licenses/by-sa/4.0
Optimizing VR-UX: analysis and adaptive recommendations for enhancing immersion and reducing motion sickness
This study presents an adaptive recommendation framework to enhance comfort and immersion in virtual reality (VR) by actively reducing motion sickness. Unlike prior research that views VR user experience (UX) as static, this approach integrates statistical analysis with dynamic system design. Using a Kaggle dataset of 1,000 entries, we applied descriptive statistics, Spearman correlation, Kruskal-Wallis tests, and regression to explore relationships among session duration, motion sickness, immersion, headset type, and user demographics. Findings show that session duration alone does not significantly predict motion sickness or immersion (R²=0.00, p>0.05), but certain user profiles, such as individuals over 30 using PlayStation VR, are more prone to discomfort. These insights inform a four-module framework: user profiling, real-time duration monitoring, rule-based adaptation logic (such as slowing navigation speed or adding a virtual nose for visual stability), and personalized in-VR recommendations. The system is compatible with Unity and Unreal Engine and integrates with commercial headset software development kits (SDKs). Future validation will use A/B testing, standardized questionnaires, simulator sickness questionnaire /immersion presence questionnaire (SSQ/IPQ), and physiological metrics. This work shifts VR design toward personalized, responsive systems that prioritize user well-being and immersive engagement.
Copyright (c) 2025 Fendi Aji Purnomo, Fatchul Arifin, Herman Dwi Surjono http://creativecommons.org/licenses/by-sa/4.0
Investigating relationships between reading comprehension and oral reading fluency through AI-driven tool reading progress
This study investigates the relationship between reading comprehension and oral reading fluency components—accuracy and rate—among 113 Vietnamese EFL university students using the AI-powered tool Microsoft Reading Progress. Over 14 weeks, students engaged in weekly oral reading and comprehension tasks using integrated Microsoft Teams features. Fluency metrics (accuracy and rate) and comprehension scores were automatically collected and analyzed using Pearson correlation. The results revealed weak but statistically significant positive correlations between reading comprehension and accuracy (r = .257, p < .01), and between comprehension and rate (r = .289, p < .01), suggesting that improvements in fluency modestly support comprehension. A strong correlation between accuracy and rate (r = .765, p < .01) was also observed. The study highlights the effectiveness of Reading Progress in capturing fluency data and promoting self-paced improvement. However, limitations such as the short duration, localized sample, and constraints of accent recognition in AI-based speech analysis affect the generalizability and validity of results. The findings support the pedagogical integration of AI tools in EFL instruction while calling for future research with larger samples, extended timelines, and diversified digital tools to further validate and expand on these results.
Copyright (c) 2025 Pham Duc Thuan, Pham Thi Tam http://creativecommons.org/licenses/by-sa/4.0
Bioecological characteristics of modern soil cover in subtropic regions of Azerbaijan
The purpose of this study is to introduce innovation in the field of agriculture in Azerbaijan by determining the abundance of various ecotrophic groups of microorganisms (involved in the formation and mineralization of humic substances) in natural and cultivated gray-brown soils. Studying the microbiological indicators of humic substance transformation in virgin soils and determining the direction of these processes under the influence of anthropogenic factors in agrocenoses soils is considered relevant for the development of the agricultural sector in the Lankaran region. It was found that perennial woody vegetation increased the abundance of pedotrophic microorganisms by 17-21% and humate decomposers by 12-14% compared to completely natural soil. The correlation coefficient between the abundance of humate decomposers and the pedotrophic index was r=-0.685±0.09. Plowing natural gray-brown soils reduces the total humus content and the abundance of micromycetes, which form the peripheral portion of humic substances.
Copyright (c) 2025 Farida Verdiyeva Bahram, Turkan Hasanova Allahverdi, Mahsati Ismayilova Eyvaz, Elnur Huseynov Yusif, Telli Jabiyeva Elshad, Gunel Asgarova Farhad http://creativecommons.org/licenses/by-sa/4.0
Quantum-inspired magnetic resonance imaging sequence optimization for detecting neurological diseases
According to a research study by the National Institutes of Health, India, a magnetic resonance imaging (MRI) holds 89% diagnostic accuracy for acute stroke, while a computed tomography (CT) holds only 54%. Means there is still 11% area of improvement for accuracy measures required and there is 84% specific in identifying nerve enlargement. The possible solution is to use quantum computing; this is new era of technology in advanced design and implementation for computing techniques as compared with that of classical computers. With the goal of improving patient care, this is the area-of research using quantum technology to solve the neurological disorders. MRI and Microsoft’s quantum-inspired algorithms to enhance approach to detecting neurological disorders. To improve accuracy of MRI results in less time, an approach called magnetic resonance fingerprinting (MRF) was explored. This paper mainly focused on optimizing the sequence using Microsoft azure simulator. By generating an optimized pulse sequence and map to the accurate predefined patterns, able to create a solution that improves the diagnostic capability of MRI. Conventional computers will take long time to predict, but accuracy may alter. The proposed quantum-inspired optimization improved MRI diagnostic accuracy up to 92%, with faster sequence optimization compared to classical methods. This simulation-based proof of concept demonstrates potential for enhanced neurological disease detection while acknowledging current limitations such as simulator dependency and limited datasets.
Kotichintala Venkata Narasimha Savan Kumar
Copyright (c) 2025 Kotichintala Venkata Narasimha Savan Kumar, Nitin Kumar http://creativecommons.org/licenses/by-sa/4.0
Cybersecurity skills in new graduates: a Philippine perspective
This study investigates the key skills and competencies needed by new cybersecurity graduates in the Philippines for entry-level positions. Using a descriptive cross-sectional research design, it combines analysis of job listings from Philippine online platforms with surveys of students, teachers, and professionals. The aim is to identify required skills and areas needing improvement, highlighting the balance between technical skills and other competencies like ethical conduct, suggesting a shift away from traditional cybersecurity skills towards a more diverse skillset. Furthermore, the results revealed common agreement on the importance of communication, critical thinking, problem-solving, and adaptability skills, albeit with slight variations in their prioritization. It recommends that aspiring cybersecurity professionals develop an inclusive skill set encompassing technical knowledge, soft skills, and personal competencies, with a focus on adaptability, continuous learning, and ethics. Skills such as business acumen are considered less vital for entry-level roles, proposing a preparation strategy that aligns with the changing demands of the cybersecurity industry.
Copyright (c) 2025 John Paul P. Miranda, Marlon I. Tayag, Joel D. Canlas http://creativecommons.org/licenses/by-sa/4.0
Financing model for demand response information services with bundled incentives
This study attempts to build a new model for information service financing schemes by considering utility functions to measure heterogeneous consumer satisfaction. This model was developed by involving a combination of reverse charging, demand response, and heterogeneous incentive models, and considering the quality of user service measured by a quasi-linear utility function against the information service financing scheme. The incentive financing scheme is applied to a local data server, including traffic during peak hours and off-peak hours. This internet incentive financing model is solved using the LINGO 13.0 application. Furthermore, the development model for incentive financing for information services based on demand response and bundling in the information service financing scheme is subjected to sensitivity analysis with the aim of identifying parameters that affect model performance. Based on the analysis that has been done, the results of this study indicate that the new model in the incentive financing scheme for information services with a quasi-linear utility function involving a combination of reverse charging, demand response, and heterogeneous incentive models produces an optimal solution in a fixed cost financing scheme for data traffic usage during peak hours and off-peak hours.
Copyright (c) 2025 Evi Yuliza, Fitri Maya Puspita, Fridha Aprisa Rahayu http://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence-based multi-key security for protected and transparent medical cloud storage
Ensuring the security and privacy for the patient medical records and medical reports data is a crucial challenge as cloud-based healthcare technologies become more prevalent. For cloud-hosted medical data, internet of things (IoT) and artificial intelligence (AI) technologies shows best solutions for the challenges in the medical domain. This study suggests a Secure and Transparent Multi-Key Authentication Framework that makes use of AI. Using Z-score normalization, the framework first preprocesses the data before clustering to create a multi-level multi-key security structure. The physics-informed triangulation aggregation neural network (PITANN) model in the study reduces computation costs by minimizing overhead, ensuring secure handling of location-based and medical data for enhanced data classification and encryption effectiveness. A multi-key derivation of an elliptic curve, the ElGamal cryptography scheme is presented, which allows for safe multi-key encryption with little increase in the length of the ciphertext. This method guarantees safe, confidential access to cloud-hosted encrypted health information. An envisioned amalgamation improves flexibility by enhancing performance metrics such as speed of computation while safeguarding patient information through enhanced security measures and ensuring precise medical record integrity within virtual healthcare systems.
Copyright (c) 2025 Ravi Kiran Bagadi, Neelima Santoshi Koraganji, Bandreddi Venkata Seshukumari, Kavya Ramya Sree Karuturi, Sireesha Abotula, Bodapati Venkata Rajanna, Mahalakshmi Annavarapu, Nitalaksheswara Rao Kolukula, Jayasree Pinajala, James Stephen Meka http://creativecommons.org/licenses/by-sa/4.0
A review of direct-to-device satellite technology in bridging connectivity gaps in Malaysia
The connectivity gap in rural and remote regions of Malaysia remains a significant challenge due to the high costs and technical challenges of deploying fixed-line infrastructure like fiber optics and copper cables. Similarly, expanding cellular networks such as 4G and 5G to these areas is often economically unfeasible for mobile network operators (MNOs) due to low returns on investment. Direct-to-device (D2D) satellite technology has recently emerged as a promising solution, gaining interest from both industry and academia. D2D enables direct communication with standard mobile phones and internet of things (IoT) devices, extending coverage to underserved regions without requiring extensive ground infrastructure. This paper provides a comprehensive overview of D2D technology, its technical aspects, and its suitability as a complementary solution to terrestrial networks. This paper also discusses how D2D technology can bridge the connectivity gap in Malaysia, offering a possible solution to enhance digital inclusion in rural and remote communities.
Copyright (c) 2025 Siti Maisurah Mohd Hassan, Mardeni Roslee, Azah Syafiah Mohd Marzuki http://creativecommons.org/licenses/by-sa/4.0
Stacking architecture-endpoint detection: a hybrid multi layered architecture for endpoint threat detection
Modern endpoint threat detection systems face persistent challenges in balancing detection accuracy, resilience against zero-day attacks, and the interpretability of artificial intelligence (AI) models. Although deep learning (DL) approaches often achieve high accuracy on benchmark datasets, they remain vulnerable to adversarial perturbations and operate as opaque “black boxes,” thereby reducing trust and limiting practical adoption in critical infrastructures. This research introduces stacking architecture-endpoint detection (STACK-ED), a hybrid multi-layered architecture for endpoint threat detection. STACK-ED integrates three complementary paradigms: supervised learning for known attack patterns, self-supervised Fgraph-based learning for structural relationships, and unsupervised anomaly detection for emerging or unknown threats. The outputs are consolidated by a meta learner, followed by a post-hoc correction (PHC) mechanism to minimize false negatives. The framework was evaluated on a combined benchmark dataset (CSE-CIC-IDS2018 and UNSW-NB15, hereafter referred to as HIDS-Set). Experimental results demonstrate state-of-the-art performance, achieving an F2-score of 98.89% after hybrid integration and active learning, with the primary optimization objective being the reduction of undetected attacks. Furthermore, the Shapley additive explanations (SHAP) method enhances interpretability by revealing feature contributions, while the PHC successfully recovered 62.64% of missed zero-day candidates. The findings position STACK-ED not only as a highly accurate detection model but also as an adaptive, resilient, and transparent framework, offering practical implications for enterprise-grade endpoint defense and future zero-trust cybersecurity systems.
Copyright (c) 2025 Abd Rahman Wahid, Desi Anggreani, Muhyiddin A. M. Hayat, Aedah Abd Rahman, Muhammad Faisal http://creativecommons.org/licenses/by-sa/4.0
Cloud-based secure data storage in healthcare using elliptic curve cryptography
The growth of cloud computing in the healthcare field has led to significant developments, but ensuring the confidentiality and protection of medical records such as electronic health records (EHRs) remains a major concern for healthcare service applications. In cloud computing, the basic authentication provided by most service providers is insufficient to ensure secure access to critical or sensitive resources. Moreover, most of the existing healthcare management systems are ineffective in handling a number of patient data, which leads to single points of failure. To address these issues, elliptic curve cryptography (ECC) with Curve25519 is utilized to enhance security in cloud storage, particularly within healthcare management systems. The ECC with Curve25519 is optimized for efficient and fast scalar multiplication, which reduces computational overhead and enhances performance. The curve parameters are selected to prevent vulnerabilities and ensure security against known attacks. Moreover, it is efficient in maintaining the integrity of patient records, which reduces storage and bandwidth requirements. The ECC with Curve25519 achieves lower Key-Gen, prove, verify, proving key size, and verification key size of 13.7 s, 48 s, 0.608 s, 13.27 Mb, and 123.70 Kb, respectively, in comparison with proxy re-encryption algorithm with zero-knowledge proof (ZKP).
Copyright (c) 2025 Gayathri Govindappa Nalina, Channakrishna Raju http://creativecommons.org/licenses/by-sa/4.0
E-bikes unplugged: exploring the evolution and environmental benefits of electric cycling
Electric bicycles (e-bikes) have rapidly emerged as a sustainable alternative to conventional modes of transportation. This study reviews the evolution, technological advancements, and environmental benefits of e-bikes through comparative data analysis, survey results, and case studies. The findings demonstrate that the developments in lithium-ion batteries, lightweight materials, and smart motor systems have significantly improved e-bike performance, efficiency, and affordability. From an environmental perspective, e-bikes can cut greenhouse gas emissions by more than 90% compared to cars, while simultaneously improving urban air quality and reducing overall pollution levels. Survey responses indicate that e-bike users often substitute short car trips, promoting sustainable commuting behaviors and supporting public health. Despite these benefits, challenges persist regarding insufficient infrastructure, inconsistent policy support, and limited battery recycling programs. In summary, e-bikes constitute a transformative element in sustainable urban mobility and climate change mitigation. Beyond policy reforms, future work should prioritize renewable-powered charging systems and circular battery utilization models to ensure e-bikes contribute to a more resilient and environmentally friendly transportation ecosystem.
Copyright (c) 2025 Vasupalli Manoj, Malleti Sreedhar, Rebba Sasidhar, Praveen Kumar Yadav Kundala, Dasyam Chandra Mouli, Ramana Pilla http://creativecommons.org/licenses/by-sa/4.0
AI-integrated pharmacy systems: bridging technology, ethics, and patient care
The operation of pharmacy systems undergoes transformation through artificial intelligence (AI), which advances from manual procedures to intelligent adaptive tools. These technologies enhance daily operations through prescription verification, drug interaction alerts, and inventory management while decreasing human mistakes. Through AI, patients gain access to customized medication recommendations, automatic appointment alerts, and virtual support services. The advancement of technology creates multiple new difficulties for healthcare systems. The increasing integration of AI in healthcare creates growing concerns about data privacy alongside algorithmic bias and the requirement for decision-making explanations. This paper evaluates AI systems against conventional pharmacy methods through an assessment of their precision and speed and their impact on patient safety and ethical preparedness. The adoption of AI systems requires strong ethical protections together with defined regulatory frameworks to maintain human clinical decision-making authority in patient care.
Copyright (c) 2025 Adi El-Dalahmeh, Nevien Nedal, Khulood Abu Maria, Sara Abu Tarboosh http://creativecommons.org/licenses/by-sa/4.0
Ambulance tracking system using GPS module and IoT based telegram messenger to find fastest route
Traffic congestion in urban areas affects ambulance trips to hospitals. This research aims to find the fastest route for ambulances to travel. The fastest route has criteria such as road shape, road width, shortest distance traveled, and fewer road users. This detection system applies internet of things (IoT) technology to each ambulance equipped with global positioning system (GPS), NodeMCU, and Wi-Fi modem that can send GPS coordinates to the cloud server, which will then mark the shortest distance from its current location to the hospital through the place where the emergency call is raised. The components used in this research are Neo6M GPS, NodeMCU ESP8266, cloud computing, and smartphone. This system can provide realtime information on all ambulance positions via android applications and Telegram messenger. The results obtained can determine the fastest path, distance, and travel time. In addition, the operation of this system takes 2-3 minutes to find the GPS signal at the beginning, then there is a 1-2 second delay from the GPS Tracking movement. Testing the route accuracy of this system and google maps by driving by motorcycle shows the results of this GPS system are more accurate in terms of distance and travel time.
Copyright (c) 2025 Rizalul Akram, Novianda, Teuku Hadi Wibowo Atmaja, M. Khairul Anam, Banta Cut http://creativecommons.org/licenses/by-sa/4.0
Advancements in electric vehicle safety and charging infrastructure
In electric vehicles (EVs), safety measures must be taken to prevent dangerous accidents. Safety regulations must be in place for two important things: electric or EV batteries and EV equipment. Operating an electric vehicle charging stations (EVCS) is a challenging task. This holistic approach is used to evaluate when renewable energy is produced. It's best to focus on the popularity of EVs as more and more people choose this mode of transportation. It is important to know that power plants can be risky. Therefore, safety issues related to EV charging must be addressed quickly and appropriately. Potential safety issues with EVs include overcurrent, ground faults, and overheating. If the charging system does not work, the electric car's battery may heat up and catch fire, and overcharging may cause other problems. To avoid security risks, you must comply with security regulations, use payment devices that meet security requirements, and follow the manufacturer's instructions.
Copyright (c) 2025 Debani Prasad Mishra, Nisha Kedia, Sanchita Sahay, Raj Alpha Swain, Surender Reddy Salkuti http://creativecommons.org/licenses/by-sa/4.0
Generative adversarial network for intelligent haze removal from high quality images
Suspended atmospheric particulates like haze, mist, and fog greatly degrade captured images, creating considerable challenges for computer vision applications operating in safety-sensitive areas such as autonomous driving, surveillance, and remote sensing. In this paper, we treat the important challenge of single-image haze removal by proposing a novel and robust conditional generative adversarial network (cGAN)-based framework. The proposal utilizes a U-Net-based generator with self-attention and skip connections to preserve spatial fidelity, and a PatchGAN discriminator to enforce local realism. At the heart of our contribution is a carefully weighted multi-component loss function that applies reconstruction, perceptual, edge, structural similarity (SSIM), and adversarial losses to optimize pixel-level accuracy and perceptual quality. We trained and evaluated our proposal on the large-scale real-world LMHaze dataset. Experimental results demonstrate state-of-the-art performance with a peak signal-to-noise ratio (PSNR) of 33.42 dB and SSIM of 0.9590. Our qualitative and comparative analyses further support our claims by assessing our proposed model's capacity to recover clear and artifact-free images from hazy images - outperforming the existing methods on this challenging real-world benchmark.
Copyright (c) 2025 Ali Abdulazeez Mohammed Baqer Qazzaz, Hayfaa T. Hussein, Shroouq J. Al-janabi, Yousif Mudhafar http://creativecommons.org/licenses/by-sa/4.0
Convolutional neural network model for fingerprint-based gender classification using original and degraded images
Fingerprint-based gender classification is a crucial component of soft biometrics, providing valuable additional information to narrow the search space in forensic investigations and large-scale identification systems. Although deep learning models, particularly convolutional neural networks (CNNs), have demonstrated significant potential, performance validation is typically performed on high-quality fingerprint images. This creates a gap between laboratory results and real-world applications, where fingerprint evidence is often found in a degraded state, such as smudged, distorted, or partially damaged. This study attempts to bridge this gap by proposing a more realistic training approach. We design a lightweight and computationally efficient CNN and train it on a comprehensive combined dataset. The main contribution of this study lies in the data training strategy, which explicitly combines real and synthetically modified fingerprint images from the Sokoto coventry fingerprint (SOCOFing) dataset into a single, unified training set. Experimental results show that the proposed model achieves very high classification accuracy (97.39%) on a test set that also includes a combination of original and degraded images. This finding not only confirms the effectiveness of diverse data-based training to produce more robust models but also establishes a new benchmark for fingerprint based gender classification research under conditions more representative of practical scenarios.
Copyright (c) 2025 Risqy Siwi Pradini, Wahyu Teja Kusuma, Agung Setia Budi http://creativecommons.org/licenses/by-sa/4.0
Influence of potassium bromide phosphor on optical properties of white light-emitting diodes
Conventional phosphor-converted light-emitting diodes (LEDs) using silicone binders often suffer from yellowing, moisture degradation, and limited spectral tunability, restricting their performance in high-power street lighting. To overcome these limitations, this study aims to develop an advanced LED illumination system integrating a KBr-doped sol-gel/silica phosphor with total internal reflection (TIR) lenses and a reflective housing, encapsulated by an atomic layer deposition (ALD)-coated minilens panel. The sol-gel matrix, synthesized from MTEOS, TEOS, and silica granules, was engineered to achieve uniform KBr particle dispersion, reduced thermal quenching, and improved chromatic stability. The ALD laminate provides an additional moisture and heat barrier, sealing micro-defects and minimizing stress-induced cracking. Optical performance was quantitatively assessed using Monte Carlo beam-tracking simulations under various street configurations, including focal, zigzag, and single-plane pole layouts. Results demonstrated enhanced luminous efficacy, precise glare control, and high uniformity in street illumination. Overall, this integrated sol-gel/ALD LED design effectively addresses the durability and color instability problems of traditional silicone systems, offering a scalable and energy efficient solution for next-generation street lighting.
Copyright (c) 2025 Pham Hong Cong, Nguyen Thi Phuong Loan, Nguyen Doan Quoc Anh, Hsiao-Yi Lee http://creativecommons.org/licenses/by-sa/4.0
Thermally stable sol-gel yttrium aluminum garnet cerium phosphors for white light-emitting diodes
This study aims to develop structurally controlled TiO2-based materials that serve a dual purpose as high-performance photocatalysts and optical scattering agents for white light-emitting diodes (LEDs). Hollow spherical TiO2, TiO2/Ag, and TiO2/Au particles were synthesized via a one-step spray thermolysis process using aqueous titanium citrate and titanium oxalate precursors. The method enables precise control of morphology and crystalline phase composition, producing hollow microspheres with tunable anatase–rutile ratios (10–100%) and crystallite sizes ranging from 12 to 120 nm. Photocatalytic performance, evaluated through the ultraviolet (UV) driven oxidation of methylene blue, showed that as-prepared TiO2 exhibited comparable activity to Degussa P25, while metal doping accelerated the anatase-to-rutile transition with minimal plasmonic enhancement under UV light. For LED applications, incorporating hollow TiO2 particles into YAG:Ce phosphor films improved luminous intensity, reaching a peak of ∼71 lm at 1 wt.% TiO2, and enhanced color uniformity, achieving a D-CCT as low as ∼60 K at 5 wt.%. These results confirm that spray thermolysis provides a scalable route to tailor morphology and phase composition, enabling multifunctional TiO2 materials optimized for both environmental photocatalysis and high-quality LED lighting.
Copyright (c) 2025 Phan Xuan Le, Nguyen Thi Phuong Loan, Nguyen Doan Quoc Anh, Hsiao-Yi Lee http://creativecommons.org/licenses/by-sa/4.0
Exploring the need for a kickstarter toolkit for special education educators to teach authoring and illustrating
This study examines the need for developing a Kickstarter toolkit designed to support special education teachers in teaching entrepreneurship through the creation of children’s storybooks. Entrepreneurship education is increasingly recognized as a valuable addition to special education, promoting skills such as creativity, problem-solving, and independence. A survey conducted among 170 special education teachers from the Kuala Kangsar district, selected through judgmental sampling, revealed strong support for introducing entrepreneurship education into special education curricula (M=3.69, SD=0.71). Teachers also emphasized the importance of employing pedagogical approaches to effectively teach entrepreneurship concepts (M=3.89, SD=0.69). Moreover, the findings indicate an urgent need for a dedicated toolkit to facilitate such activities, with educators expressing high levels of agreement regarding its necessity (M=3.99, SD=0.73). These results underscore the positive perception of special education teachers toward entrepreneurship education and the crucial role a specialized toolkit could play in equipping educators with the resources needed to foster entrepreneurial competencies in students with special needs.
Copyright (c) 2025 Shyielathy Arumugam, Mazlina Che Mustafa, Norazilawati Abdullah, lylia Dayana Shamsudin, Mohd Ridhuan Mohd Jamil, Masayu Dzainudin http://creativecommons.org/licenses/by-sa/4.0
Computer simulation and software engineering in optical analysis of phosphor-converted white light-emitting diodes utilizing barium sulfate
Achieving uniform nanoparticle dispersion in electrospun polymer nanofibers remains a critical challenge, as conventional electrospinning often leads to particle agglomeration and nozzle clogging, reducing fiber uniformity and functional efficiency. This study explicitly addresses this problem by developing poly (vinyl alcohol) (PVA)/BaSO4 composite nanofibers through both conventional and ultrasonic-assisted electrospinning. Scanning electron microscopy (SEM) revealed that ultrasonication effectively disrupted nanoparticle agglomerates, yielding smoother and more uniform fiber morphologies. X-ray diffraction (XRD) analysis further confirmed that ultrasonic processing reduced the crystalline intensity of PVA and BaSO4, indicating enhanced polymer–filler interaction and finer BaSO4 distribution. Quantitatively, the agglomeration slope decreased from 0.039 (conventional) to 0.006, and the mean crystallite size was reduced from approximately 470 to 300 nm. These results are consistent with recent advances showing that ultrasonic electrospinning improves nanoparticle dispersion and stability in polymer matrices, thereby enhancing optical and mechanical properties. Ultimately, this work demonstrates that ultrasonic-assisted electrospinning provides a robust and scalable strategy to fabricate lightweight, flexible, and multifunctional PVA-based radiation shielding materials with superior nanoparticle dispersion and structural homogeneity.
Copyright (c) 2025 Le Thi Trang, Nguyen Thi Phuong Loan, Pham Hong Cong, Nguyen Doan Quoc Anh, Hsiao-Yi Lee http://creativecommons.org/licenses/by-sa/4.0
Methods used to enhance the physicochemical properties of natural ester insulating oils for transformers: a review
Natural ester insulating oils, derived from vegetable-based feedstocks, are increasingly regarded as sustainable alternatives to conventional mineral oils due to their high fire point, biodegradability, and lower environmental impact. However, their widespread adoption in high-voltage equipment is constrained by their inherent limitations, such as lower oxidation stability, higher viscosity, and poor low-temperature performance. In this review, the three principal enhancement strategies developed to address these shortcomings are examined. The use of antioxidants is analysed for its role in improving oxidative resistance and flow characteristics. Transesterification is evaluated as a chemical modification method to alter the molecular structure, thereby enhancing viscosity and thermal stability. Refining and adsorbent treatments are discussed with respect to oil purification and regeneration, emphasising their adsorption efficiency and influence on dielectric performance. A comparative evaluation of these methods highlights their relative effectiveness, scalability, and practical challenges in implementation. This review underscores that no single approach is sufficient, and a combination of different methods is desirable to achieve optimal performance. These insights provide researchers with clear directions for further investigation while offering practitioners a knowledge base to guide the selection and application of enhanced natural ester insulating oils for reliable, long-term transformer operation.
Copyright (c) 2025 Muhammad Syahrani Johal, Sharin Ab Ghani, Mohd Shahril Ahmad Khiar, Imran Sutan Chairul, Norazhar Abu Bakar http://creativecommons.org/licenses/by-sa/4.0
Design and analysis of a portal frame test rig for vertical load testing of goalpost pipeline support
Pipe support is a crucial infrastructure in the oil and gas industry, requiring robust designs to withstand various loads and maintain operational stability. While numerical analysis is commonly used to assess the interaction between pipelines and supports, experimental testing remains essential for validation. However, field testing is often costly and difficult due to safety constraints. To overcome this, a reliable test rig with minimal deflection is needed to ensure accurate experimental results. This study uses finite element analysis (FEA) to evaluate both a goalpost pipeline support and a newly developed portal frame test rig. The test rig was analyzed under two conditions: the failure load of the goalpost support and an amplified load with a factor of 2.5 to simulate unexpected scenarios. Results show the test rig can safely withstand loads up to 40 kN, meeting the EN 1990 safety factor requirement of 1.5. Furthermore, critical components remained within the deflection limit specified by the British Constructional Steelwork Association (BCSA), which is under L/1,000 of the beam length. These results confirm the structural integrity and suitability of the portal frame test rig for accurate testing of the goalpost pipeline support structure.
Copyright (c) 2025 Tsaqif Al Farrel Ghazali, Mohd Shukri Yob, Mohd Juzaila Abd Latif, Ojo Kurdi, Fudhail Abdul Munir http://creativecommons.org/licenses/by-sa/4.0
Optimizing inventory management in the textile industry: a comprehensive evaluation of UHF-RFID technology integration
The integration of ultra-high frequency radio frequency identification (UHF RFID) technology presents a transformative solution to inventory management challenges in the textile industry. This study examines the implementation of a web-based inventory management system incorporating UHF-RFID technology at AK-Tekstil Solo, focusing on its impact on inventory accuracy, operational efficiency, and product traceability. The developed system facilitates real-time tracking of yarn products, streamlines inventory audits, and minimizes manual errors, resulting in substantial improvements in inventory control and warehouse management processes. By enabling automated data capture and tracking, UHF-RFID technology supports the transition to smart warehousing by providing real-time insights into inventory movements. The findings demonstrate that UHF-RFID technology offers significant advantages, including enhanced inventory visibility, cost savings, and improved customer satisfaction through better product availability. Despite potential implementation challenges, the study shows that the long-term benefits of UHF-RFID integration outweigh the initial costs, proving it to be an effective solution for optimizing inventory management in the textile industry. Future research may explore the integration of complementary technologies such as the internet of things (IoT) and artificial intelligence (AI) to further enhance UHF-RFID enabled inventory management systems.
Copyright (c) 2025 Ahmad Darmawi, Rita Istikowati, Galuh Yuli Astrini http://creativecommons.org/licenses/by-sa/4.0
Unveiling anomalies in industrial control systems: a kernel SHAP-based approach with temporal convolution autoencoder
Industrial control systems (ICS) are often the target of cyber-attacks, leading to undesirable consequences. ICSs operate without human supervision, making them vulnerable to adversaries. In recent years, numerous deep learning-based solutions have demonstrated their efficiency in detecting anomalies in ICSs. However, there is a lack of ability to pinpoint the sensors and actuators that contributed to the anomaly. In this research work, we use kernel Shapley additive explanations (SHAP) to explain anomalies detected by a temporal convolution autoencoder (TCAE). The proposed TCAE model handles the long-term dependency effectively and is computationally effective on a large dataset. A comprehensive explanation is provided, focusing on the feature that contributed to the anomaly for each identified attack. The SHAP values are extracted for each identified attack and visually depict the feature that contributed to the anomaly for each attack, helping the expert to handle the attack and build user trust.
Copyright (c) 2025 Sangeeta Oswal, Subhash Shinde, Vijayalaksmi Murli http://creativecommons.org/licenses/by-sa/4.0
Forecasting internet traffic patterns for the campus Metro-E network using a hybrid machine learning model
Complex traffic patterns lead to crucial campus Metro-E network management and resource allocation. This paper presents an internet traffic forecasting by pre-processing data to offer better bandwidth quality of service (QoS). Eight (8) campuses' traffic data were analysed for modelling predictions using statistical analysis. A Metro-E campus network presents four (4) locations: A, E, F, and H have is a strong correlation between inbound and outbound traffic, with correlation values between 0.4547 and 0.5204. As the inbound traffic increases, outbound traffic tends to rise as well. Conversely, locations B, C, and G have weak correlations, indicating more independent traffic patterns. Data outliers were found for locations C and F, where unusual traffic spikes require further network exploration and show key trends in traffic data. Descriptive statistics reveal notable differences, with H has the highest average traffic at about 75 Mbps, while C has the lowest at around 30 Mbps. Location F shows the greatest traffic fluctuation with a standard deviation of 0.4076, whereas Location G has very little fluctuation with a standard deviation of 0.0240. Overall, this pre process data is use to combine machine learning (ML) to improve prediction abilities for better bandwidth management and real-time handling in digital campus environments.
Copyright (c) 2025 Norakmar Arbain, Murizah Kassim, Darmawaty Mohd Ali, Shuria Saaidin http://creativecommons.org/licenses/by-sa/4.0
Production of hydrogen gas from water via electrolysis for community power generation
Rural and remote communities often rely on diesel generators, which are costly, inefficient, and emit greenhouse gas and particulate pollutants. This study combines real-time hydrogen production via electrolytic water separation with a conventional 5,871-cc diesel backup generator to enhance combustion performance and reduce environmental impacts. A self-built electrolyzer was powered by a direct current (DC) battery and precisely controlled by an electronic control unit (ECU) to provide hydrogen output based on engine load conditions. The results of testing co-fueling improved fuel efficiency by 20-25%, with a peak 24.9% reduction in fuel consumption at 50% load. Emission measurements revealed significant reductions in black smoke, PM₂.₅, PM₁₀, and CO₂, with the maximum CO₂ reduction of 23.4 kg CO₂-e/hr. The system operates without the need for a hydrogen storage tank, thus improving safety and reliability. These findings demonstrate that this low-cost and low-emission approach represents a practical alternative for backup power in remote areas. Future work will focus on long-term stability and monitoring hydrogen flow rates for varying load conditions.
Copyright (c) 2025 Wichien Hermharn, Sittichot Kradang-nga, Pongsakorn Kachapongkun, Sirichai Jirawongnuson http://creativecommons.org/licenses/by-sa/4.0