Journals of Universiti Tun Hussein Onn Malaysia (UTHM)
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Optimisation of NaOH Concentration for Sustainable Soap Production Using Waste Cooking Oil
Full text linkThis study presents a sustainable approach for synthesizing soap bars from WCO. The process involves collecting and filtering WCO, followed by an optimized saponification reaction using alkaline, resulting in soap bars with desirable properties. Comparative analysis reveals that WCO appears darker, with higher free fatty acid content (4.00%) and slightly increased viscosity (79.13 cP) compared to new cooking oil. The study highlights the significant impact of NaOH concentration on soap consistency, where 10% NaOH concentration (sample BS3) has achieved the most stable foam formation. BS3 has no appearance of a gel phase upon cutting and shows 1.78% moisture content. This research emphasizes the utilization of waste cooking oil as a valuable resource for soap production, contributing to waste reduction and supporting sustainable practices within the circular economy framework
Oil Absorption Analysis of Vegetable Oil onto Kapok Fiber Using pH and Temperature
Full text linkOil spills pose serious environmental and economic threats, driving the search for sustainable sorbent materials. This study examines the effect of pH and temperature on the oil absorption performance of treated kapok fiber, a natural and hydrophobic material with a hollow tubular structure. Kapok fibers were treated and tested under varying pH (3, 7, and 11) and temperature (40 °C and 70 °C) conditions using coconut and canola oils. Characterization through SEM, FTIR, and contact angle analysis revealed that alkaline treatment effectively removed surface waxes, improving oil affinity while maintaining structural integrity. The highest oil uptake occurred under alkaline conditions (pH 11) and elevated temperature, indicating enhanced sorption efficiency. These results demonstrate the potential of chemically treated kapok fiber as an eco-friendly sorbent for oil spill remediation applications
An Intelligent Botnet Detection System for IoT Using Neural Networks and an Enhanced Moth Search Optimize
Full text linkBotnets, distributed networks of compromised devices under remote control, continue to pose a serious cybersecurity threat, as they are challenging to detect with traditional methods because of their evasion capabilities. The evolving nature of botnets demands stronger detection systems that can effectively detect malicious traffic patterns. To confront this problem, we introduce a new botnet detection framework called BDS (Botnet Detection System), aimed at improving the accuracy of detection and reducing the number of false positive results. We integrate the Enhanced Moth Search Algorithm (EMSA) with Multi-Layer Perceptron (MLP) for the enhancement of training of the neural network model, titled EMSA-MLP. For this, we test our model with a representative Bot-IoT dataset with diverse botnet attack scenarios in terms of separating honest and malicious traffic with the help of efficient optimization by EMSA. To assess the proposed EMSA-MLP, the Bot-IoT benchmark dataset was used, containing a variety of botnet attack methods. The detection performance of the model was demonstrated to be excellent. The model achieved a high detection performance in identifying botnet attacks, with an accuracy rate of 97.09%. They also preserved a good accuracy of 91.59%, and their false alarm rate was kept low at 0.0291. Overall, compared to some common classifiers: random forest, decision tree, and base MLP-this model did pretty well. It also outperformed relatively newer and more complex models. This architecture has achieved good accuracy, while it has not made the growth of IoT settings very large, and it has been formulated to be used by devices. This model provides high accuracy without burdening the IoT infrastructure, so it is applicable to devices with restricted capabilities. It is suited for practical applications, as it can adapt to different types of data
The Use of Transformer-Based Models for Automatic Short-answer Scoring in Education: A Systematic Literature Review
Full text linkThis review focuses on recent advancements in the Automatic Short-Answer Scoring (ASAS) system in education. The primary objective of this review is to identify current trends in utilizing transformer-based models for the ASAS system. This review also aims to discuss future directions for ASAS technology. ASAS’s conventional machine learning methods were inconsistent because they rely on statistical similarity and are prone to bias. Meanwhile, transformer-based models were typically used for feature extraction, embedding, and score calculation via classification or regression. They generally served as a similarity calculator comparing students’ answers to the reference answer. We applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to explore ASAS’s state of the art and uncover its future trends. Our findings reveal that transformer-based models significantly outperform traditional machine learning approaches by capturing complex context. On the other hand, LLMs excel at providing feedback and score justification. Recent studies have shown a shift toward using transformer-based models for ASAS’s complex tasks, including data augmentation and feedback generation. However, further research is needed to use LLMs and GPTs to generate explainable, fairer scores and to address data scarcity through reasoning and augmentation
AI-Driven Secure Emergency Message Dissemination in 5G-Enabled VANETs Using LSTM-Based Intrusion Detection and CP-ABE Encryption
Full text linkThe number of vehicles has expanded rapidly due to advances in automobile technology and global population growth, resulting in an increase in the frequency of traffic accidents. Wireless Vehicle-to-Vehicle (V2V) connections are used by event-driven safety applications to alert drivers to potentially dangerous situations. For emergency vehicles to respond to urgent emergency services, there must be uninterrupted traffic on the roads. Even a small delay in an emergency journey time can be costly and potentially result in lost lives. To overcome this limitation in this research, we designed an AI-driven emergency message dissemination framework for “Vehicular Ad hoc Networks (VANETs)” with 5G, for the efficient, secure, and privacy-preserving communication of messages at critical times. Using time-series data analysis, an AI-based Intrusion Detection System (IDS) that uses Long Short-Term Memory (LSTM) networks classifies emergency messages through recognizing abnormalities and false warnings. To ensure secure emergency message dissemination in the VANET environment, this research proposes a Multi-Authority Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Priority-based scheduling leverages are the Edge-DENM Prioritization Algorithm, which categorizes emergency messages based on urgency levels to prevent delays in high-risk scenarios. The proposed approach ensures secure, scalable, and privacy-preserving emergency communication, improving overall VANET performance by dynamically adapting to traffic conditions and vehicle mobility
Effect of Two Different Medias on Selected Liberica Coffee Clones Stem Cuttings Performance
Full text linkCoffee production in Malaysia has been declining, and the demand for high-quality coffee is increasing. To address this issue, Malaysian Agricultural Research and Development Institute (MARDI) has released new coffee clones, MKL 8, 9, and 10 with promising yield quality. Under current guidelines, planting materials must be produced through grafting, but huge drawback are the time needed to produce matured planting materials. A new potential technique was developed and could save a lot of time and this technique is known as stem cuttings was studied using standard media sand and soil (1:1). Therefore, study aimed to evaluate the effects of two different media on the vegetative performance of stem cuttings on selected clone MKL 8, 9 and 10 was carried out. The experiment was conducted using a randomized complete block design with a factorial arrangement with five replications. The results showed that the type of media significantly affected root fresh weight and root dry weight, with media 1 (soil, peat moss, and sand in a ratio of 4:2:1) performing better than media 2 (soil, sand, and CIRP in a ratio of 4:4:1). The clones also exhibited significant differences in SPAD meter readings, height, and leaf number, with MKL 8 performing better than MKL 9 and MKL 10. Correlation analysis revealed that SPAD readings were positively associated with leaf number and survival, while plant height was positively associated with leaf number, survival, fresh root weight, and dry root weight. This study highlights the importance of selecting suitable clones and media for coffee stem cuttings to ensure better performance and improve coffee production in Malaysia
Design Optimization and Performance Enhancement of Small-Scale Vortex-Induced Turbines for Sustainable Energy Generation
Full text linkThis study presents the development and evaluation of a vortex bladeless wind turbine prototype designed to harness wind energy in low wind speed conditions, making it suitable for regions such as Malaysia. The prototype consists of a 2-meter-tall, 0.2-meter-diameter cylindrical structure mounted on a flexible shaft, which utilizes vortex-induced vibrations to generate mechanical energy. Testing was conducted in a wind tunnel with airflow ranging from 1 m/s to 10 m/s, as well as in outdoor field locations around UTHM. Results demonstrated the turbine’s effectiveness at low to moderate wind speeds. Vibrations were converted into electrical energy using an electromagnetic induction system. Analysis of oscillation frequency, voltage output, and efficiency indicated promising performance under conditions where traditional turbines are less effective. Design enhancements further improved the turbine’s performance
Leaching Kinetics of Crucial Metals from Bismutite Ore: Parameter Determination
Full text linkThis study investigated the leaching kinetics of the crucial metals-aluminum (Al), iron (Fe), and zirconium (Zr) from bismutite ore using sulfuric acid (H2SO4) as the lixiviant. The influences of acid concentration (0.1–3 mol/L), leaching time (5–120 min), and temperature (28–80 °C) on metal extraction efficiency were systematically evaluated. The results demonstrated that increased acid concentrations and higher temperatures significantly enhanced metal recovery. The optimal leaching conditions were 2.5 mol/L H2SO4, a solid-to-liquid ratio of 100 g/L, and a temperature of 80 °C for 120 minutes, yielding leaching efficiencies of 99.82% for Al, 85.4% for Fe, and 99.2% for Zr. Kinetic modelling via the shrinking core model indicated that the process is chemically controlled, with activation energies of 31.06, 38.02, and 42.79 kJ/mol for Al, Fe, and Zr, respectively. The enriched leachate produced under these conditions is suitable for the subsequent selective recovery of target metals. This work supports sustainable metallurgical practices by promoting efficient, multi-element extraction from complex ores
Investigation of Circular Construction Waste Management (CCWM) Practices in Malaysia
Full text linkThis research investigates the adoption of Circular Construction Waste Management (CCWM) practices in Malaysia, focusing on the environmental and economic challenges posed by construction waste. The study aims to identify key challenges, determine effective waste management practices, and propose sustainable solutions to enhance resource efficiency. A quantitative survey was conducted with data analyzed using SPSS for reliability and descriptive insights involving 130 construction personnel such as those engineers, project managers, main contractors, construction manager, site consisting of workers, and supervisors. The findings reveal that the high cost of recycled materials mean value 4.85 are the most critical barriers to adopting circular economy principles. Among the recommended practices, the "3R" approach Reduce, Reuse, Recycle emerged as the highly effective practice with the highest mean value of 4.89. By adopting these practices, stakeholders can reduce environmental impacts, improve resource efficiency, and achieve a more sustainable construction industry
Bond Strength of Self-Sensing Concrete (SSC) Incorporating Biomass Activated Carbon
Full text linkSelf-sensing concrete (SSC) has emerged as a multifunctional smart material that combines structural performance with self-monitoring capabilities, offering significant potential for structural health monitoring. In this study, biomass-activated carbon (BAC) was incorporated into SSC as a supplementary cementitious material to investigate its impact on bond strength. While BAC is known to enhance electrical resistivity and compressive strength, its effect on interfacial bonding behaviour has not been comprehensively evaluated. Therefore, this study aims to assess the bond strength of SSC incorporating BAC (SSC-BAC). The mix design of SSC-BAC is based on C25/30, which consists of ordinary Portland cement (OPC), silica fume, BAC, sand, and water. Cube specimens were prepared for the compression test. Meanwhile, rectangular and prism specimens were prepared for the slant shear and three-point bending tests. Experimental tests revealed that the compressive strength decreases as BAC content increases. A similar trend was also observed in the bond strength. The threshold for the compressive strength is 31.54 MPa produced by 1% BAC. Meanwhile, 3% BAC has the lowest acceptable bond strength. The findings of this study suggest that SSC-BAC exhibits a strong bonding behaviour, which is suitable for application in structural health monitoring