Malaysian Journal of Science and Advanced Technology
The Malaysian Journal of Science and Advanced Technology (MJSAT), e-ISSN: 2785-8901 is a multidisciplinary journal that publishes high-quality research papers in various fields of science and technology. The journal is dedicated to promoting the advancement of science and technology in Malaysia, the region, and globally by providing a platform for researchers to share their latest findings and developments. The journal welcomes submissions from all areas of science and technology, including but not limited to engineering, physics, chemistry, biology, and mathematics. The journal is committed to promoting high-quality research and providing a valuable resource for scientists, researchers, and practitioners in the field. The journal is also committed to fostering collaboration and networking among researchers from different fields and promoting the exchange of ideas and knowledge. The journal is peer-reviewed and publishes original research papers, review articles, and short communications. The journal also features special issues and sections on specific topics of interest. The editorial board is composed of experts from various fields, ensuring that the journal maintains a high standard of quality. The journal also encourages submissions from early career researchers and graduate students, providing them with the opportunity to showcase their work and gain visibility in the scientific community. Editor-in-Chief: Mohammed Reyasudin Basir Khan, Ph.D., CEng, EUR ING, PEng, PTech
Terrain, Learning and Unit Cost in Mechanised In-Field Evacuation of Oil Palm Fresh Fruit Bunches: A Seven-Month Operational Record of a Mini Platform Collection System in Kedah, Malaysia
Mechanisation of in-field fresh fruit bunch (FFB) evacuation is central to reducing the dependence of the Malaysian oil palm sector on manual field labour, yet published performance figures come mostly from short trials rather than routine commercial operation. This study analysed 175 machine-days of operating records from a single mini platform collection (MPC) unit working ten blocks of a commercial oil palm estate in Sungai Petani, Kedah, from 1 January to 31 July 2026. Daily throughput, output per harvester served and unit operating cost were tested against terrain class and elapsed operating time. The unit evacuated 3,552.4 t of FFB, averaging 20.30 t per machine-day (SD 5.62). Terrain class had no significant effect on machine throughput (F = 0.71, p = 0.491), but output per harvester served differed sharply (F = 32.46, p < 0.001), falling from 6.21 t on undulating blocks to 2.95 t on hilly blocks, where gangs averaged 7.4 workers against 3.3. Throughput rose from 14.86 t per day in January to 25.69 t in June, the second half of the record exceeding the first by 27.9 per cent. Operating cost averaged RM 84.91 per machine-day, 86 per cent of it the operator wage, giving a unit cost of RM 4.18 per tonne. The terrain penalty of platform-dependent evacuation falls on harvesters, not on the machine.
Copyright (c) 2026 Muhamad Zahid Muhamad, Nurul Azreena Azman, Mohd Muhyiddin Mustafa, Fairuz Khalid, Ismail Rakibe https://creativecommons.org/licenses/by-nc/4.0
Crystallographic Phase Analysis of Anisotropic Cubic Halite Nanocrystal by X-ray Diffraction and Selected Area Electron Diffraction Pattern
The structural geometry of crystalline granular powder halite was studied using powder X-ray diffraction (XRD), which provided a detailed analysis of its crystal symmetry. Rietveld refinement confirmed that the powder consisted of 100 % crystalline halite phase with lattice parameters of a=b=c= 5.64244 Å, α=β=γ= 90.0 ° in a cubic crystal system. The concluded Na–Cl bond length is 3.98 Å. The strongest diffraction was observed at 2θ = 31.837° predominant (2 0 0) plane. Unique models were used to estimate crystallite size, with the size-strain plot model indicating an average size of 84.05 nm, which is anisotropic. The crystallographic analysis showed dislocation density 1.42×10⁻⁴ nm⁻², crystallinity index 1.96, atomic packing factor 63.60 %, unit cell density 2.16 g/cm³, and specific surface area 33.05 m²/g that provides insight into the symmetrical characteristics of halite. The microscopic view also revealed the cubic morphology of halite with uniform crystal growth.
Copyright (c) 2026 Md. Monirul Islam, Md. Ashraful Alam, Md. Khalid Hossain Shishir, Raton Kumar Bishwas, Md. Shoyeb Akand, Fariha Zannat, Md. Hasnain Mustak, Shirin Akter Jahan https://creativecommons.org/licenses/by-nc/4.0
Blockchain Technology for Cross-Border Remittances: A Smart Contract Approach
This research explores a blockchain-based solution to address inefficiencies in traditional cross-border remittance systems, including high fees, slow processing times, and limited transparency. By deploying Ethereum-based smart contracts and a custom wallet system, the project streamlines user registration, transaction processing, and fee management. Users benefit from a secure and intuitive interface to send, receive, and track transactions with transparent fee calculations. The system significantly outperforms traditional remittance methods in cost, speed, and security. Transactions are processed within seconds at minimal fees, while the decentralized architecture ensures data integrity and transparency. Results demonstrate the system's potential to increase financial inclusion, particularly in underserved regions, by providing a secure and accessible alternative to traditional methods. Future work includes integrating multi-currency support and Central Bank Digital Currencies (CBDCs) to enhance scalability and usability, further establishing blockchain as a transformative technology for global remittance solutions.
Copyright (c) 2026 Md. Shafiur Rahman, M. Rakibul Hasan Rafsan, Nusrat Jahan Zerin, Pranta Das Gupta, Nyme Ahmed https://creativecommons.org/licenses/by-nc/4.0
Geoengineering Site Exploration for Building, Bridge, Landfill, Pavement, Embankment Construction, and Excavation (Road Cut/Open Pit Mines): Case Study from Southwestern Nigeria
Engineering site exploration of RUGIPO was conducted using integrated methods involving in situ CPT testing, geophysical, geotechnical laboratory studies, geochemical analysis, groundwater measurement and quality analysis, and geological mapping. This was done to assess the geomaterial across the study area for civil engineering construction. The result revealed schistose quartzite and quartzite as the main rocks in the area, capable of providing (compressive) strength of 150 to over 350MPa, and 20 – 60 MPa, respectively. The abundance of SiO2 in the soil mineral content (52.4 %) implies a good engineering tendency that can translate to high CBR, shear strength, and PI, and low compressibility/swelling potential. The soils across the area are dominated by sand - silt mixture, with average CBR (> 10%), cohesion (23.2 kN/m2), friction angle (29.1°), PI (< 20 %), allowable bearing capacity (172.6 kN/m2), modulus of elasticity (18715 kN/m2), soil behaviour index (2.40), and elastic settlement (34.19 mm for 250 kN load). Thus, the soil is expected to have good applications in basement or foundation, reinforced cement concrete structures (including slabs, lintels, and columns), bridge, pavement, metallic grounding systems/utilities, and embankment. However, the soils fail in some major properties for landfill/liners application in terms of PI, permeability, and porosity. The groundwater level is higher than 8 m across the study area; hence, it cannot threaten the basement/foundation structure. The quality of the water is within the recommended threshold for all the parameters tested for, including T°, SG, pH, TDS, EC, salinity, sulfate, and EC, for concrete cement interaction
Copyright (c) 2026 Abayomi Solomon Daramola, Olumuyiwa Olusola Falowo, William Kunle Olabisi, Oluwatobi Emmanuel Ajiboye https://creativecommons.org/licenses/by-nc/4.0
Exploring the New Sine Unit Gompertz Distribution: Theory, Inference, and Practical Relevance to Financial and Medical Analytics
This study proposes the New Sine Unit Gompertz distribution (NSUGD), a flexible bounded lifetime model derived from the Unit Gompertz distribution. The probability density function accommodates decreasing, near-symmetric, right-skewed, and left-skewed shapes, while the hazard rate function exhibits increasing, J-shaped, or bathtub forms, enabling effective modelling of unit-interval data across diverse applications. Some statistical properties are derived, including the survival and hazard rate functions, quantile function, moments, median, mean, skewness, and kurtosis. Parameters are estimated via maximum likelihood, maximum product of spacings, and Cramér-von Mises methods; a Monte Carlo simulation study, evaluating average estimates and mean squared error, identifies maximum likelihood as the most efficient approach. Empirical validation on financial and medical datasets demonstrates the NSUGD’s superior fit relative to competitors such as the Unit Gompertz, Kumaraswamy, and beta distributions, as evidenced by lower AIC and BIC values and non-significant Kolmogorov-Smirnov statistics with higher p-values. Owing to its adaptability, the NSUGD holds promise for diverse fields, including engineering, insurance, environmental science, and other disciplines where unit bounded data are prevalent.
Copyright (c) 2026 Ehinomen Emmanuel Ehizojie https://creativecommons.org/licenses/by-nc/4.0
A Unified Ontology-Based Knowledge Graph Framework for Multi-Domain Vietnamese Legal Document Retrieval
Legal information retrieval systems face the challenge of handling diverse legal domains with varying terminology, document structures, and semantic relationships. This paper presents a unified ontology-based knowledge graph framework that enables consistent knowledge representation and retrieval across multiple Vietnamese legal domains. The objective of this study is to develop a modular architecture that maximizes reuse while accommodating domain-specific requirements. Our methodology extends the Legal-Onto model with a three-layer architecture: Core Layer defining fundamental legal document structures based on Vietnamese legal formatting regulations, Domain Extension Layer providing specialized concept hierarchies for different legal areas, and Application Layer implementing query interfaces with natural language processing pipelines. The framework is validated through implementation and evaluation across three distinct legal domains: Land Law, Electronic Transaction Law, and Labor Law, comprising 53 legal documents with 1,398 concepts and 5,257 relationships. The results demonstrate that the unified framework achieves average accuracy of 84.2% and F1-score of 0.843 across all domains. Cross-domain evaluation reveals that domain-specific ontology extensions improve retrieval precision by 12.7% compared to generic approaches, while the shared core structure enables knowledge transfer and reduces development effort by 40% per new domain. In conclusion, the modular ontology architecture effectively balances reusability with domain specificity, providing a practical foundation for scalable legal information systems.
Copyright (c) 2026 Vuong Thi Pham, Minh Phan, Cao Bao Quynh, Truong Cong Phuc, Nguyen Thanh Duy, Tien Minh Vy, Nguyen Ngoc Son https://creativecommons.org/licenses/by-nc/4.0
Design and Simulation of a Predictive Maintenance Model for a Power Transformer: A Case Study of a Transmission Substation in Nigeria
The most crucial component of a high-voltage transmission system are power transformers, whose unplanned failure can cause major operational interruptions, monetary losses, and widespread power outages. A predictive maintenance framework and simulation for a 300-MVA power transformer at a 330/132 kV transmission substation in Lagos State, Nigeria, is presented in this study. Gradient Boosting algorithm (GBA) was utilized to analyze the transformer's historical data in order to predict potential transformer faults. Important data were collected and pre-processed, including Dissolved Gas Analysis (DGA), load profile, insulation resistance, top-oil and ambient temperature. The GB model was then trained and tested using the dataset. The obtained results indicates that the most significant indicators of transformer malfunctions were the presence of acetylene and hydrogen compounds. The model achieved 93% accuracy, with recall, Area Under the Curve (AUC), and average precision of 88%, 91% and 88.4% respectively. This suggests that the model has an effective fault prediction capability. Because it helps with problem identification, the study has demonstrated that GB can be utilized to increase the reliability of power transformer operations. Utilities seeking to minimize transformer downtime will find the model useful.
Copyright (c) 2026 Gerard Nonso Obiora, Olusegun Julius Adebowale https://creativecommons.org/licenses/by-nc/4.0
Heavy Metal Contamination in Water and Sediments of the Gongola River Complex, Nigeria: A Seasonal and Spatial Assessment
Heavy metal contamination poses a significant threat to aquatic ecosystems globally. This study investigates the presence and seasonal variations of heavy metals (Cd, Cr, Pb, Ni, Fe, Cu, As, Mn) in water and sediment samples from two anthropogenically impacted corridors (Almakashi and Gwani) of the Gongola River, Nigeria, using Balanga Dam as a reference site. Water and sediment samples were collected over a 12-month period covering dry and wet seasons and analyzed using Atomic Absorption Spectrophotometry. Results indicate that while most metal concentrations in water and sediments were within permissible limits set by NESREA and WHO, spatial and seasonal variations were significant. Almakashi corridor exhibited the highest mean concentrations for most metals in water, notably Cd (5.07 µg/L) and Ni (9.10 µg/L), while the highest Fe concentration (50.57 µg/L) was found at Balanga Dam. Sediment analysis revealed higher accumulations of Ni, As, and Mn in the river corridors compared to the dam, with concentrations peaking during the dry season. Statistical analysis (p < 0.05) confirmed significant differences in contamination levels between the reference dam and the river corridors. The study concludes that despite current levels being largely sub-threshold, the elevated metal concentrations in Almakashi and Gwani signal growing anthropogenic pressure, necessitating continuous monitoring and preemptive integrated water resource management strategies.
Copyright (c) 2026 Nuru A. Garkuwa, K. A. Abubakar, Haruna Saidu, Baraatu Abubakar, Zainab Rabiu Gamawa https://creativecommons.org/licenses/by-nc/4.0
Design and Evaluation of a Smart Poultry Farm Device Integrating Internet of Things and Machine Learning for Monitoring Temperature and Humidity
Rising global temperatures and humidity are significantly impacting birds' welfare, productivity, and mortality rates, leading to economic losses and inefficiencies in poultry farm management. This research aims to design and implement an Internet of Things (IoT) device integrated with Machine Learning (ML) models to forecast temperature and humidity values in a poultry farm. This study designed a prototype of an IoT device consisting of DHT11 sensors connected to an ESP32 microcontroller to collect real-time temperature and humidity data from a poultry farm. The collected data are used to train ML models, namely: Auto-Regressive Integrated Moving Average (ARIMA), Random Forest (RF) and Support Vector Machine (SVM) to forecast future temperature and humidity values. The measured and predicted data are displayed on an LCD screen connected to the ESP32 microcontroller, enhancing proactive decision-making. Simulation testing was carried out on Python software to compare and analyse the measured and predicted temperature and humidity values. The performance of the ML models was evaluated in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R2). The results show that RF predicted data gave a close fit to the actual data, SVM provided a tendency toward lower predicted value and ARIMA model showed the most accurate predictions, providing lowest RMSE (1.56) and MAE (0.34) at highest R2 (88%) than RF and SVM. This approach demonstrates that ARIMA model offers effective solution than other ML models in poultry farming through real-time monitoring and predictive analysis.
Copyright (c) 2026 Folashade Olamide Ariba, David Abolarin, John Onyemenam, Ester Toyin Olawole https://creativecommons.org/licenses/by-nc/4.0
Phytochemical Screening and Antimicrobial Activity of Catharanthus roseus: Comparative Efficacy of Leaf, Flower and Root Extracts Against Escherichia coli and Candida albicans
The rise in antibiotic-resistant pathogens has led to a search for new and effective natural antimicrobial agents. Catharanthus roseus (C. roseus), locally known as Kemunting Cina is one of the widely recognised herbs that have a great number of medicinal uses mostly attributed to the various disease. The leaves, flowers and roots of the C. roseus collected from Sungai Siput, Perak C. roseus were identified and then subjected to a cold maceration extraction method using ethanol. The resultant crude extracts were analysed for preliminary phytochemicals to identify bioactive compounds. The cup plate method assessed the antimicrobial activity with Escherichia coli (E. coli) and Candida albicans (C. albicans) as representative bacterial and fungal pathogens, respectively. The results indicated high antimicrobial activity in the leaves, while the flowers and root extracts showed the lowest MIC values. The extraction yields were 12.3% for flowers, 16.25% for leaves, and 5.6% for roots. The antimicrobial properties of the 3 plant parts were then studied through cup plate method assay using concentrations of 200mg/ml, 100mg/ml, 50mg/ml, 25mg/ml, and 12.5mg/ml. The leaves showed the strongest antimicrobial activity, with inhibition zones of 12–14 mm against E. coli and 10 mm against C. albicans, demonstrating dose-dependent effects. These results were comparable to standard antibiotics such as Amoxycilin and Griseofulvin. The data supporting the potential use of C. roseus in developing plant-based antimicrobial agents.
Copyright (c) 2026 Siti Khadijah Padzil, Auf Ali, Nirosha Palaraman, Sangeeta Thamorthiran https://creativecommons.org/licenses/by-nc/4.0
Assessing the Interaction of Blanching and Drying Parameters in Yam Drying through Response Surface Methodology
Blanching facilitates thermal energy utilization in yam drying by softening the tissue structure, so that heat penetrate more efficiently to minimize drying time. Higher drying temperatures further improve thermal energy utilization by accelerating moisture removal, though excessively high temperatures may reduce energy efficiency and product quality. The aforementioned conditions, including blanching time, has not been collectively run in any Response Surface Methodology (RSM) tool prior to this study, to test their effect on yam drying. In this study, Central Composite Design (CCD) in Design Expert 7.0 software was employed to assess the moisture content as response variable. Yam slices of 7–8 mm thickness were blanched at varying temperatures (70–90℃) and durations (15–35 min) and subsequently dried in a hot air tray dryer at temperatures between 50–70℃ for 4–10 h. The quadratic model showed an excellent fit with an R2 value of 0.9750 and 95% confidence intervals. Among the tested interactions, the combination of blanching temperature and blanching duration exhibited a strong synergistic effect on moisture removal efficiency. The optimal conditions – blanching at 90℃ for 23.9 min and drying at 70℃ for 10 h, resulted in a minimum moisture content of 3.99% with a desirability of 0.812. It further signifies a high thermal efficiency and minimal energy loss. Hence, the findings demonstrate the process’s potential for yam flour production, due to its effective dehydration, nutrient preservation, and energy-efficient drying performance. Attention must be given to differences in yam types, which currently includes, white yam, yellow yam, bitter yam, water yam and three-leaved yam, when opting for drying RSM, kinetics and thermodynamic studies.
Copyright (c) 2026 Yusufu Luka, Abdulhalim Musa Abubakar, Michael Mshirshima Tsaha, Semiu Adebayo Kareem https://creativecommons.org/licenses/by-nc/4.0
Isolation, Screening, and Characterization of PHA-Producing Bacteria from Dumpsite Fadama Soil
The growing environmental burden caused by persistent petroleum-based plastics has intensified the search for biodegradable alternatives such as polyhydroxyalkanoates, which are microbially synthesized biopolymers with promising industrial applications. This study focused on isolating and screening efficient polyhydroxyalkanoate-producing bacteria from dumpsite fadama soil and evaluating their potential to accumulate bioplastics. Bacteria were isolated from soil samples and screened for PHA accumulation using Sudan Black B and Nile Blue staining and UV fluorescence microscopy. The competent isolates were subjected to morphological, biochemical, and molecular 16S rRNA) identification. A total of 112 isolates were recovered, of which 59 showed positive Sudan Black B staining. Subsequent Nile Blue screening identified eight potential producers, and three isolates demonstrated strong fluorescence, indicating high intracellular polymer accumulation. Molecular identification using partial 16S ribosomal RNA sequencing revealed the isolates as Shigella flexneri SMI1, Priestia megaterium SMI2, and Bacillus subtilis SMI3. The sequences were deposited in GenBank under accession numbers PX567900, PX567901, and PX567902, respectively. These species, particularly Bacillus subtilis and Priestia megaterium, exhibited strong traits associated with polyhydroxyalkanoate biosynthesis and accumulation. The findings highlight dumpsite fadama soil as a rich source of metabolically versatile microorganisms with the capacity for efficient intracellular biopolymer biosynthesis. This work provides a foundation for further optimization of culture conditions and bioprocess scale-up to support sustainable bioplastic production.
Copyright (c) 2026 Sa'adatu Mukhtar Ismail, Aminu Bukar, Sani Yahaya, A.M Magashi, Baita Nafisa, Yusuf Saadatu Abba, Salisu Nainna Zainab https://creativecommons.org/licenses/by-nc/4.0
Comparative Study of CO₂ Dissolution in Aqueous Media for Applications in Bioelectrochemical Systems
In alignment with the Sustainable Development Goals (SDG 13: Climate Action and SDG 7: Affordable and Clean Energy), the urgent need to mitigate the high atmospheric level of carbon dioxide (CO₂) has lead research toward sustainable carbon capture and utilization technologies. Bioelectrochemical systems (BESs), especially microbial electrosynthesis (MES), have emerged as promising technology for converting CO₂ into value-added products with the assistance of the electroactive microbial metabolisms. However, the efficiency of MES remains constrained by the inherently low solubility and limited mass transfer of CO₂ in aqueous systems, which minimizes the availability of CO2 for microbial uptake and conversion. This study investigates the CO₂ dissolution behavior in various aqueous media—deionized (DI) water, reverse osmosis (RO) water, tap water, phosphate-buffered saline (PBS), PBS with anode additives, and microbial growth media including acetogen and sulfate-reducing bacteria formulations. Carbon dioxide (CO₂) dissolution was observed through changes of pH and conductivity to examine dissolution efficiency and media stability. Based on results achieved, this study highlights DI and RO water enhances CO₂ uptake but lack of buffering leads to pH stability, while PBS-based medium not only offers superior pH stability but also effectively support CO₂ dissolution, making them more suitable for long-term operation in microbial electrosynthesis systems. These findings provide crucial insights for selecting optimal aqueous environments to enhance CO₂ dissolution and support the design of more robust, efficient MES systems for future carbon-neutral technologies.
Copyright (c) 2026 Yashawini Phriya Rauichandran, Hsien Han Yoong Adam, Wai Yin Wong, Mimi Hani Abu Bakar, Ebrahim Mahmoudi, Peer Mohamed, Swee Su Lim https://creativecommons.org/licenses/by-nc/4.0
Effects of Different Types of Moina Preparation On The Growth and Survival of Giant Freshwater Prawn Post-Larvae, Macrobarchium rosenbergii (De Man, 1879)
The giant freshwater prawn Macrobrachium rosenbergii is an economically important aquaculture species, with post-larval nutrition playing a critical role in early growth and survival. This study evaluated the effects of different Moina feed preparations on the growth performance and survival of M. rosenbergii post-larvae (PL10). A 14-day feeding trial was conducted using a randomized complete block design with four dietary treatments: commercial pellet (control), frozen Moina, freeze-dried Moina, and live Moina, each in triplicate. A total of 552 post-larvae (initial weight ≈ 0.01 g) were stocked and fed daily at 20% body weight. Growth performance was assessed through mean weight gain (MWG) and specific growth rate (SGR), while survival rate was recorded at the end of the experiment. Post-larvae fed with live Moina exhibited significantly higher MWG (0.024 ± 0.004 g) and SGR (8.741 ± 1.15% day⁻¹) compared to those fed commercial pellets and freeze-dried Moina (P < 0.05), and showed comparable performance to frozen Moina. Survival was also highest in the live Moina group (35.3 ± 1.0%). The superior performance of live Moina is likely attributed to its higher palatability, nutritional availability, and movement-induced feeding response. These findings indicate that live Moina is a promising and effective live feed for improving growth and survival of M. rosenbergii post-larvae
Copyright (c) 2026 Encik Ahmad Baihaqi Othman, Encik Hanan Mohd Yusof, Md. Ali Amatul-Samahah, Encik Muhamad Zudaidy Jaapar https://creativecommons.org/licenses/by-nc/4.0
Heart Rate Variability and Batting Decision-Making Under Varying Bowler Familiarity: A Real-Time Physiological and Biomechanical Analysis
Cricket batting demands rapid decision-making under intense psychological pressure, where shot selection accuracy directly influences match outcomes. Heart Rate Variability (HRV) is a validated physiological marker for stress and cognitive load, yet its specific relationship with batting decision-making under varying bowler familiarity remains underexplored. This study proposes an integrated framework using a custom-built IoT wearable device powered by a 3.7V LiPo battery, ESP32 microcontroller, and MAX30102 sensor to monitor HRV in real-time during batting scenarios against familiar versus unfamiliar bowlers. The device acquires raw photoplethysmographic (PPG) signals at 100 Hz through its integrated 16-bit analogue-to-digital converter, which provides the temporal resolution necessary for accurate RR interval detection and subsequent HRV computation. From these high-resolution raw data, processed heart rate values are derived and transmitted at a rate of three data points per second for real-time display and session logging. Preliminary validation against the Xiaomi Smart Band 8 Pro, a consumer-grade wrist-worn device with established optical heart rate sensing capability, across four physiological states (morning post-sleep, noon post-meal, evening post-exercise, and night pre-sleep) demonstrated a 98% accuracy rate based on primary data from batsmen. Combined with synchronized 4K 60 FPS video analysis, this study aims to uncover neurophysiological correlates of shot selection behaviour in cricket. Findings are expected to inform personalized training and stress management strategies, offering significant implications for modern cricket coaching.
Copyright (c) 2026 Anik Das, Saumik Saha Niloy, Robayed Mahmud Rohan, S M Abdullah Shafi https://creativecommons.org/licenses/by-nc/4.0
Influence of Injection Timing on Combustion Characteristics, Performance, and Emission Behaviour of Dual-Fuel Engines
At low loads, dual-fuel engines typically exhibit lower thermal efficiency and higher emissions. In this study, Ansys Forte 2023 R1 was used to model the combustion, performance, and emission characteristics of a methane-diesel dual-fuel engine at injection timings (IT) of 3°, 5°, 7°, and 9° bTDC under a 0.44 MPa load. The CFD simulation employed a modified re-entrant bowl piston with a 50% methane energy share and a 110° spray angle, where methane was premixed through intake manifold injection. Results show that maximum cylinder pressure, temperature, and apparent heat release rate increase with advancing IT. The highest thermal efficiency (34.11 %) and lowest indicated specific fuel consumption were observed at 7° bTDC. O and OH radical formation rose when shifting IT from 3° to 9° bTDC due to improved mixing, which also enhanced premixed combustion. Therefore, CO, UHC, VOC, and soot emissions decreased as IT advanced. However, earlier injection produced higher peak temperatures, leading to increased NOx formation compared to later IT. However, 7° bTDC was identified as the optimal injection timing, providing the best balance of combustion efficiency, engine performance, and acceptable emission levels.
Copyright (c) 2026 Abdullah Al Rifat, Md. Arafat Rahman, Md. Mizanur Rahman https://creativecommons.org/licenses/by-nc/4.0
Comprehensive Feasibility Assessment of Integrated Valorization of EFB and POME in a Circular Bioeconomy Framework: Compost and Biogas
The palm oil industry produces vast amounts of palm oil mill effluent (POME) and empty fruit bunches (EFB), yet current disposal and treatment practices underexploit their resource potential. This study addresses a key research gap: existing works typically evaluate EFB and POME independently and lack a unified techno-economic assessment that quantifies combined capital–operating costs, operational interdependencies, and market-driven feasibility. Additionally, limited attention has been given to how enriched compost pricing and operational stability influence the long-term viability of integrated biorefinery configurations. Techno-economic analysis confirmed strong feasibility, achieving a 21.52% return on investment (ROI), a payback time (PT) of 4.65 years, and a positive net present value (NPV). Unlike electricity export, which had limited impact, project profitability was primarily driven by enriched compost price, operating stability, and capital efficiency. Sensitivity analysis revealed that feedstock cost fluctuations exerted only marginal influence. These findings underscore that market-driven compost valorization and operational optimization are decisive for financial resilience. By transforming waste into revenue-generating products, the approach offers a scalable pathway to enhance sustainability, reduce environmental burden, and strengthen Malaysia’s position in the global bioeconomy.
Copyright (c) 2026 Mohd Noriznan Mokhtar, Mohd Rafein Zakaria, Rabitah Zakaria https://creativecommons.org/licenses/by-nc/4.0
Digital Technologies for Road Maintenance Management: A Narrative Review
Effective road maintenance management is vital in the area of transportation safety and mobility as well as infrastructure sustainability. Nonetheless, the traditional methods of road maintenance involve a large degree of manual work and are rather inefficient and costly in nature. Fortunately, recent developments in the realm of digitalization have provided an opportunity to improve road condition evaluation and prediction, maintenance optimization, and other associated operations. In order to provide an overview of digital technologies used in road maintenance management, six different digital tools, namely, Artificial Intelligence, GIS, IoT, Digital Twin, mobile applications, computer vision, and decision support systems, will be discussed. A narrative review method was employed based on the use of the Scopus database. Initially, 413 articles were found; however, applying pre-determined inclusion/exclusion criteria led to narrowing down the list to 35 papers for further discussion. It is evident from the results that digital technologies can help improve the efficiency of the maintenance process via automatic defect detection and identification, monitoring, predictive analytics, maintenance prioritizing, and lifecycle management. Nevertheless, several drawbacks persist, including data issues, implementation expenses, interoperability, integration, and unavailability of standardized frameworks. Moreover, previous studies focus on exploring each technology individually, which emphasizes the necessity of considering several types of technologies together. In this regard, this literature review offers a detailed analysis of the current applications, advantages, challenges, and future areas of research related to intelligent road maintenance management.
Copyright (c) 2026 Ummu Najwa Nur Yusrizal, Noram Irwan Ramli, Putri Zulaiha Razi, Siti Nurizzati Ismail https://creativecommons.org/licenses/by-nc/4.0
Correlation and ANOVA-Based Validation of IoT-Derived Motion Metrics in Post-Stroke Hand Rehabilitation
Reliance on intermittent and subjective clinical assessment in stroke rehabilitation has remained a persistent weakness with limited information on daily recovery progress and it has impeded access to patients in the remote or underserved regions. To overcome this, the paper proposes RIoTv2, which is a system that can monitor motor activity in an objective and continuous manner. The vision-based system employed in the solution is that of MediaPipe Pose to extract 4D skeletal arm motion values using regular webcam when doing important key hand rehabilitation exercises, including Hand Strengthening and Hand Opposition. The algorithmic method of kinematic measurements (velocity, smoothness, range of motion, repetition accuracy) was conducted in a quasi-experimental study of 200 post-stroke patients. These RIoTv2 measurements were correlated with and compared to Fugl-Meyer Assessment (FMA) and Barthel Index (BI) scores under different degrees of impairment with the help of correlation analysis and ANOVA. This system was seen to be accurate in most cases (largely over 75 percent), which corresponds to pragmatic clinical limits (the error rate is often not far below 25 to 30 percent) with normalized kinematic errors of about 7 to 20 percent and FMA thresholds of remote monitoring literature. The RIoTv2 system is an efficient device that can be used as a remote-monitoring platform offering healthcare providers with objective and real-time information to use in personalized therapy and intervention. The critical social repercussion comprises the development of health equity by providing cost-effective and administrable technology to break geographic and economic boundaries, improving patient agency amid home-based care, and improving a more transparent and data-based paradigm on the care of post-stroke patients.
Copyright (c) 2026 Md Sariful Islam, Dr Ahmad Anwar Zainuddin, Ainul Hani Binti Mohd Manoj, Siti Maisarah Binti Abdul Aziz, Siti Aishah Binti Abdul Aziz, Nurul Farah Hanim Muhamad Nasir https://creativecommons.org/licenses/by-nc/4.0
Performance Evaluation of Esters Extracted from Waste Cooking Palm Oil on the Rheology, Filtration and Mud Cake Properties of Water Based Drilling Mud
The synthesis of high-purity biodegradable esters from Waste Cooking Palm Oil (WCPO) represents a transformative shift toward sustainable drilling operations. By utilizing acid-catalyzed transesterification, this research successfully converts a low-value waste stream into a functional chemical additive capable of enhancing Water-Based Mud (WBM) performance. A critical aspect of this innovation is the achieved chemical purity of 98.8%, which ensures the bioester remains stable under high-shear environments. While commercial ester benchmarks at 4% concentration resulted in 15 mL fluid loss, the synthesized WCPO ester achieved a 61.5% improvement in fluid loss compared to the base mud, reducing loss to 10 mL and forming an ultra-thin 2/32-inch mudcake. Furthermore, rheological analysis confirmed that the ester stabilizes the Yield Point (YP) and Plastic Viscosity (PV) within operational limits, maintaining a predictable density range (7.7 to 8.5 ppg) compared to the erratic fluctuations observed in commercial alternatives. Although current results validate performance under Low-Pressure Low-Temperature (LPLT) conditions, this work establishes a foundation for future High-Pressure High-Temperature (HPHT) thermal stability validation. Therefore, this study demonstrates a viable waste-to-value pathway that supports the Malaysian circular economy and local SME scalability by providing a low-energy (65°C), cost-effective alternative to synthetic lubricants.
Copyright (c) 2026 Muhammad Emre Bin Mohd Nazran, Norida Binti Ridzuan, Mohd Shahrul Azuar Bin Mat Roseh, Awang Harun Alrashid Bin Hamzah https://creativecommons.org/licenses/by-nc/4.0
VIBRI-SAFE: Quantification of Motorcycle Rider Whole-Body Vibration Exposure Using a Low-Cost IoT Monitoring System with ISO 2631-1 Normalization
Motorcycle riders are continuously exposed to mechanical vibrations from engines and uneven roads, which can lead to long-term musculoskeletal disorders (MSDs) affecting the spine, shoulders, and limbs. Current safety systems focus on accident prevention, while chronic vibration exposure remains largely unaddressed. This study presents VIBRI-SAFE, a low-cost vibration monitoring system using an Arduino Uno MPU6050 with multi-axis sensors to capture real-time vibration intensity and rider posture stability. Weighted Root Mean Squared (RMS), Vibration Dosage Value (VDV), and Static compression dose (Sed) for the spine were calculated and normalized to an 8-hour ISO reference period, which allowed short rides to be compared with long-term occupational thresholds. Results indicate higher vibrations in motorcycles with older suspensions and prolonged exposures, with RMS variability reflecting mechanical instability. VIBRI-SAFE shifts motorcycle safety from reactive crash protection to proactive physiological monitoring through combining IoT and ergonomic principles. Its affordability and diagnostic potential support integrating human health analytics into sustainable mobility.
Abeykoon Mudiyanselage Akila Thenuka Abeykoon
Copyright (c) 2026 Abeykoon Mudiyanselage Akila Thenuka Abeykoon, Khairul Rizuan Suliman https://creativecommons.org/licenses/by-nc/4.0
Immersive and Interactive Visualization of Otological Anatomy: Advancing an AI-Integrated Medical Education Platform
Aural anatomy remains one of the most spatially complex domains in medical education due to the intricate three-dimensional relationships within the middle and inner ear. While immersive technologies have demonstrated measurable improvements in anatomical learning, existing systems such as OtoVIS primarily focuses on photorealistic visualization without fully integrating artificial intelligence (AI), curriculum alignment, multi-user collaboration, or commercialization frameworks. This study introduces OtoVisionXR, an AI-integrated extended reality (XR) platform designed to enhance anatomical comprehension, learner engagement, and clinical relevance. A controlled pre-test/post-test validation study was conducted among 30 first-year medical students. Statistical analysis demonstrated significant improvement in knowledge scores following XR exposure (Pre-test: M = 56.4, SD = 8.7; Post-test: M = 78.9, SD = 7.5; t (29) = 14.62, p < 0.001; Cohen’s d = 1.92). Learner perception ratings exceeded 6.2/7 across engagement and visualization domains. Percentage improvement reached 39.9%. The platform further demonstrates scalability for rural medical centres and continuing professional development programs. These findings position OtoVisionXR as a validated, societally relevant, and commercially viable immersive learning solution.
Copyright (c) 2026 Manjit Sidhu, Kirandeep Sidhu, Avin Mahajan https://creativecommons.org/licenses/by-nc/4.0
Reliability And Cost Integration Using Trade-Space Exploration for Satellite Anomalies
A satellite is an important component in supplying crucial services such as communications, navigation, and earth observation, which profoundly rely on satellite reliability. Satellite reliability is described as the possibility that a satellite system is operational in stated environments, confirming the satellite operates efficiently and continuously without service disruptions. The effect of satellite anomalies as a major reason for satellite failures, cost, and incompetence of satellite systems has been emphasized in the previous research. But these works generally lack a thorough reliability analysis using constructed mathematical models. Thus, this work fills the gap by working on 87 satellite reliability data points and 42 cost data points from Seradata database using trade-space exploration (TSE). The work was done by considering parametric models (Weibull, Exponential, and Poisson) and non-parametric models (Kaplan-Meier and Monte Carlo Simulation) for reliability. Meanwhile, for cost, we employed parametric models (Weibull, Exponential, and Poisson). The results from both reliability and cost show that the Exponential model proves the best model in terms of performance, with the lowest Root Mean Square Error (RMSE) values for all communication subsystems and the lowest RMSE and RRMSE for one variable (considered cost only) and two-variable (considered cost and design life). Integrating both reliability and cost models shows a good deployment in an engineering decision-support system (DSS).
Copyright (c) 2026 Nadirah Abdul Rahim, Muhamad Imran Haris Ahmad Mahmood, Muhammad Aryss Zafran Shahrul Ariffin, Abdul Mutholib https://creativecommons.org/licenses/by-nc/4.0
Development of Wearable Optical Fiber Sensors Integrated with Machine Learning Monitoring for Rehabilitation Gait Analysis
A wearable optical fiber sensor system integrated with machine learning is developed to support rehabilitation gait analysis. Human gait monitoring plays a crucial role in evaluating the recovery progress of individuals with lower limb injuries or neurological disorders. Conventional gait analysis methods are often expensive, non-portable, and reliant on expert interpretation, which limits their practicality for continuous use. To overcome these challenges, a flexible insole embedded with six fiber optic pressure sensors is designed to capture plantar pressure data during walking. The collected data is wirelessly transmitted to a MATLAB interface, where it is processed and analysed using a decision tree classifier to identify gait phases and detect abnormalities. The output is visualized through graphical representations and classification trends, enabling clinicians to monitor and interpret patient progress effectively. This system enables the assessment of gait patterns in both healthy individuals and those undergoing rehabilitation. Expected outcomes include a cost-effective, portable, and intelligent solution capable of distinguishing between normal and abnormal gait patterns, identifying stages of rehabilitation, and offering interpretable feedback for improved clinical decision-making. The approach aims to advance non-invasive, automated, and personalized rehabilitation monitoring.
Copyright (c) 2026 Nur Nadiah Mazlan, Wan Suhaimizan Wan Zaki, Ariffuddin Joret, Ashok Vajravelu, Muhammad Mahadi Abdul Jamil https://creativecommons.org/licenses/by-nc/4.0
A New Single-Switch Bridgeless PFC Dual-Output-Polarities Converter with Series-Line-Diode-Clamped Configuration for Elimination Circulating Current and Capacitive Coupling Loop
This paper presents a new single-switch bridgeless PFC Dual-Output-Polarities (SSBPFC DOP) converter with a Series-Line-Diode-Clamped (SLDC) configuration, designed to eliminate circulating currents and capacitive coupling loops. The proposed topology addresses critical drawbacks in existing designs, including circulating currents, high maximum current stress on input capacitors and line diodes, and capacitive coupling loops. To overcome these issues, the line diodes are repositioned in series with the input inductors. The converter operates in discontinuous conduction mode (DCM) to achieve a near-unity power factor. The paper thoroughly explains the operational principles and design considerations of the new structure. It also discusses the transition from a two-switch to a single-switch design for both shared and non-shared operation modes. Detailed analyses of circulating current, maximum current stress, and capacitive coupling loop mechanisms are provided. Simulation results demonstrate the complete elimination of circulating currents and capacitive coupling loops, with maximum current stress on input capacitors and line diodes reduced from 13.91 A to 7.6 A.
Copyright (c) 2026 Mohamad Kamil Romai Noor, Asmarashid Ponniran, Afarulrazi Abu Bakar, Mohd Amirul Naim Kasiran, Mohd Hafizie Yatim, Mohd Nasri Abd Samat https://creativecommons.org/licenses/by-nc/4.0
Effect of Charge Existence on Electric Field Distribution in String Insulator
The charge existence at any region of insulator sting will distort the original electric field distribution and may result in the enhancement of local electric fields to the highest level over a long period of time. This situation would then lead to the occurrence of partial discharge and further cause premature failure of insulating material due to degradation process which can lead to electrical breakdowns and electrostatic discharges. Therefore, this paper investigates the effect of charge existence on the electric field distribution along glass insulator string. The measurement of charge distribution was experimentally collected using charge sensors to be applied in the simulation modelling. Finite element software was also adopted in this paper to assess the effect of charge existence on electric field distribution. It is found that the charge distribution is successfully collected with negative charge distribution pattern is obtained. It is proven that the value of charge collected in the experimental works indeed affects the distribution of electric field when the insulator is injected with that value. Also, it was found that the negative charge has more impact toward the electric field distribution compared with positive charge. The findings suggest that the existence of charge on the surface of insulator is certainly affect the local distribution which after a long period of time may affect the high voltage insulation performance and finally may threaten the reliability of power system network.
Copyright (c) 2026 Nurul Adha Shamira Salleh, Nordiana Azlin Othman, Muhammad Syazani Abid Sani, Md Aris Nor Asyidi Md Nadzir https://creativecommons.org/licenses/by-nc/4.0
Enhancing Solar Panel Efficiency via MPPT with CUK Converter
This project focuses on improving the efficiency of photovoltaic (PV) systems through the development of a CUK converter integrated with the Maximum Power Point Tracking (MPPT) algorithm. This is because the increasing global emphasis on renewable energy has caused the reliability and efficiency of solar energy systems to become increasingly important. Although conventional boost converters used in PV systems have shown good performance, they still fail to achieve optimal power extraction under varying irradiance conditions. This failure has also been the main cause of interrupted input current and even increases the voltage stress on the switch. Therefore, the use of a CUK converter has been introduced in this project to address the limitations that have been encountered by providing a smoother output and reducing the ripple effect. In the implementation of this project, the main objective is to increase the input current generation and energy efficiency by 2-5% across different irradiance conditions while minimizing the voltage stress on the converter components. Therefore, a systematic flow chart has been developed to ensure the smooth implementation of this project by identifying critical areas that require more attention. The implementation of this project also outlines a comprehensive methodology that includes a detailed literature review to identify gaps between knowledge followed by design and simulation for CUK converters using advanced software tools such as MATLAB Simulink and Proteus 8 Professional.
Copyright (c) 2026 Jabbar Al-Fattah Yahaya, Nur Atiqah Adilah Binti Kamarozaman https://creativecommons.org/licenses/by-nc/4.0
Effect of Pretreatment on the Profiling of Cassava Peels by Morphological and Chemical Characterization
Cassava is mainly grown for its root, whereas the peels are mostly considered a byproduct. Cassava peels, a byproduct of cassava processing, have gained significant attention in recent years as a potential feedstock and other bioproduct. This study investigated the impact of pretreatment on the lignocellulose content, functional groups, morphological characteristics (SEM), chemical composition (XRF), and crystalline phases (XRD) of dried and autoclaved cassava peels. The Chemical analysis showed cellulose content increased from 42.80% to 45.51%, hemicellulose decreased from 25.33% to 19.66%, and lignin content decreased from 15.33% to 10.33% after autoclaving. The autoclaved cassava peels µXRF results also revealed the presence of high iron (Fe) and potassium, as well as traces of manganese at 0.52%, and an amorphous and semi-crystalline structure. Although the SEM images showed no morphological difference between the dried and autoclaved cassava peels. The Brunauer Emmette Teller (BET) analysis indicated a higher surface area of 2.713 m²/g for autoclaved cassava compared to 2.097 m²/g for dried cassava peels. Thermal pretreatment via the autoclave method improved the structural properties and increased the degradable cellulose content of cassava peels. Thus, the cassava peels can be biotechnologically converted into value-added products to maximize effective utilization.
Copyright (c) 2025 Fatimah Ibrahim Jumare, Madihah Md. Salleh, Nurashikin Ihsan, Huszalina Hussin https://creativecommons.org/licenses/by-nc/4.0
Applications of the Chaos Approach on Transportation Systems – A Review
Chaos theory offers a robust analytical lens for interpreting the nonlinear and dynamic nature of transportation systems, particularly in relation to congestion management and incident propagation. This review consolidates global applications of chaos theory in traffic studies by examining its integration with classical mathematical models, machine learning techniques, and sensitivity analyses of complex traffic datasets. The methodology synthesizes findings from studies conducted in the United States, Slovenia, Germany, Iran, and China. For example, several studies reported prediction accuracy improvements of up to 15–25% when Lyapunov exponent-based features were combined with machine learning models. Chaos-based simulations also demonstrated a 30% reduction in noise sensitivity compared to conventional approaches, with observed Lyapunov exponents typically ranging from 0.1 to 0.5, indicating pronounced chaotic behaviour in short-term traffic dynamics. Despite these promising outcomes, practical challenges persist, particularly in embedding chaos-based models into real-time Intelligent Transportation Systems (ITS), due to noise interference and infrastructure constraints. The novelty of this paper lies in bridging theoretical foundations with empirical case studies to propose a conceptual framework for integrating chaos theory into real-time traffic forecasting systems, thereby offering actionable insights for adaptive, data-driven urban mobility management.
Copyright (c) 2025 Yessy Yusnita, Nur Hamiza Adenan, Angelalia Roza https://creativecommons.org/licenses/by-nc/4.0
Emerging Strategies in the Management of Heart Failure with Preserved Ejection Fraction: Integrating SGLT2 Inhibitors, Lifestyle Interventions, and Multidisciplinary Care
Heart failure with preserved ejection fraction (HFpEF) is a complex clinical syndrome characterized by heart failure symptoms despite a left ventricular ejection fraction ≥50%, representing about 50% of all heart failure cases. Its increasing prevalence, driven by an aging population and comorbidities like hypertension, diabetes, and obesity, poses significant challenges due to frequent hospitalizations, impaired quality of life, and high mortality rates. The pathophysiology of HFpEF is multifactorial, involving systemic inflammation, endothelial dysfunction, myocardial fibrosis, and increased ventricular stiffness, indicating a systemic syndrome requiring a multidisciplinary treatment approach. While current guidelines emphasize symptom relief, comorbidity management, and lifestyle changes, the emergence of sodium-glucose cotransporter 2 (SGLT2) inhibitors has revolutionized HFpEF management. Large randomized controlled trials like EMPEROR-Preserved and DELIVER have demonstrated their ability to significantly reduce heart failure hospitalizations and improve quality of life. Additionally, structured exercise training, dietary modifications, and behavioral support have shown benefits in enhancing functional capacity and overall well-being. Multidisciplinary care models involving collaboration among various healthcare professionals have the potential to optimize patient-centered care and improve outcomes. However, challenges persist, including the heterogeneity of HFpEF phenotypes, underrepresentation of high-risk populations in trials, and inconsistent implementation of lifestyle and multidisciplinary interventions. Future directions should focus on personalized treatment strategies guided by deep phenotyping and biomarkers, equitable access to care, and the integration of pharmacological therapies with scalable lifestyle interventions and technology-enabled monitoring systems. Addressing these issues will be crucial in alleviating the global burden of HFpEF and improving patient outcomes.
Copyright (c) 2025 Ayesha Shoukat, Mehvish Ambreen https://creativecommons.org/licenses/by-nc/4.0