Malaysian Journal of Science and Advanced Technology

Comparative Study of Fresh Water and Sea Water for Cooling System Solar Panel Energy

Rindi Wulandari — 2025-04-27

Solar panels are one of the renewable energy sources that have the most significant potential in the world. However, one of the problems of solar panels is the effect of temperature on their performance so that it requires other alternatives to overcome this problem, one of which is the installation of a cooling system. In the active cooling system that is made, namely using water spray, the purpose of this study is to compare the effectiveness of the cooling fluid between fresh water (aquades) and sea water. The method used is experimental and data testing. From the results of the study, it was found that the comparison of current and voltage data between fresh water (aquades) and sea water has a characteristic curve shape that is almost the same with a difference in value that is not too significant. The average current value for fresh water and sea water coolers is 0.66 A and 0.63 A. While the average voltage value for fresh water and sea water coolers is 14.55 V and 14.44 V. The total power generated during 6 hours of operation by solar panels with fresh water cooling is 125.81 Watts, while the total power of solar panels with sea water cooling is 119.45. The efficiency obtained is 5.32%. The average value of the temperature of the freshwater and seawater coolers is 39.2ºC and 40ºC. Based on the results of the study, fresh water is highly recommended as a cooling fluid for solar panels with the water spray method. In addition, review the effects of seawater, which will also cause corrosion on solar panel materials. However, seawater can be an option when the installation of solar panels is carried out at sea because of the seawater sources available at the installation location.

Integrating Carbon Pricing Mechanism in Use-Stage Life Cycle Costs for Automotive Sector and Green Heavy-Duty Vehicles

Kamal Muhammad Raini — 2025-05-12

The automotive industry is undergoing a significant transformation towards environmental sustainability, driven by the urgent need to mitigate climate change. Green heavy-duty vehicles have emerged as a promising solution to reduce the environmental impact of transportation, particularly in the freight and logistics sectors. Life cycle costing is a well-established method for assessing the total cost of ownership of assets, but it traditionally overlooks environmental externalities. This paper investigates the integration of carbon pricing into LCC analysis, focusing on its implications for GHDVs. The study examines the impact of carbon pricing on three key cost components during the use phase of GHDVs: initial costs, operation and maintenance costs, and salvage value. By incorporating carbon costs across all stages of the vehicle lifecycle—manufacturing, operation, maintenance, and disposal—the research develops a comprehensive framework for evaluating the financial viability of GHDVs in a carbon-constrained environment. The findings of this study provide valuable insights for policymakers and industry stakeholders in Malaysia, supporting the nation's transition towards a low-carbon transportation system and its ambitious net-zero targets. Integrating carbon pricing into LCC analysis can incentivize the adoption of low-emission technologies and contribute to a more sustainable and environmentally responsible automotive industry.

AI-Powered Early Detection and Prognostic Modeling of Restrictive Cardiomyopathy Using Multimodal Non-Invasive Data

Md Juniadul Islam — 2025-05-17

Restrictive Cardiomyopathy (RCM) is a rare but severe heart disease that is often diagnosed in advanced stages, leading to significant clinical consequences. Detecting RCM at an early stage is essential to slowing disease progression and improving patient outcomes. This study introduces a novel approach that leverages multimodal non-invasive data, including electronic health records (EHRs), medical imaging, and genetic information, to enhance early detection and prognosis. The model underwent rigorous training and validation using the ACDC, MIMIC-IV, ClinVar datasets, employing deep learning techniques for feature extraction and classification. The system demonstrated high accuracy (93%), precision (0.90), and recall (0.91%), surpassing conventional diagnostic methods. By analyzing longitudinal patient data, the proposed method identifies subtle biomarkers and predictive patterns indicative of RCM onset. Additionally, it provides personalized prognostic insights, such as assessing the likelihood of heart failure or arrhythmias, all while seamlessly integrating into existing clinical workflows without requiring additional hardware. This research contributes to the advancement of cardiology by incorporating AI-driven methodologies that improve diagnostic accuracy and enhance patient-centered care.

Smart Maintenance System (SMAT) : Predictive Maintenance of Electrical Motor Applications

Anas Mohd Noor — 2025-05-28

Predictive maintenance is crucial for the efficient operation of electrical motors, ensuring timely maintenance and preventing unexpected failures. This study proposes a Smart Maintenance System (SMAT) specifically designed for electrical motors, aimed at optimizing maintenance activities through predictive techniques. By utilizing mechanical vibration, electrical current, and motor body temperature sensors, the system monitors motor conditions in industrial applications such as pumps, generators, and other critical motor-driven systems to reduce unexpected downtime, lower maintenance costs, and extend motor lifespan. The sensor data is transmitted using Raspberry Pi and the TCP/IP communication protocol, with the data stored chronologically on a programmable interface controller. MATLAB is employed for data preprocessing, modeling, and prediction to facilitate maintenance decisions. A comparison of the K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) algorithms reveals accuracies ranging from 92.5% to 95.8% in classifying normal and failure conditions. The future enhancement of the system will focus on real-time data collection and improved prediction of motor conditions

Development of a Sustainable Water Supply through Combined Rooftop Rainwater Harvesting and Groundwater System

Noorul Iqhlima Najwa Ismail — 2025-06-02

Increased population often leads to water supply deficit as the demand for water rises. In water resource, rainwater harvesting (RWH) holds great potential for providing a sustainable water supply in Malaysia, while groundwater serves as an alternative water source especially in limited surface water places. This study aims to evaluate the feasibility of sustainable water supply system that combines RWH and groundwater. The first objective is to design an integrated RWH and groundwater extraction to meet the required water demand. The second objective is to evaluate the groundwater quality index on the groundwater based on Malaysia's National Water Quality Standards. This study was conducted at UiTM Kampus Jengka, Pahang. The first step of the methodology involves evaluating the efficiency of the rainwater harvesting system by comparing monthly rainfall from April to September 2024 against the volume of harvested rainwater. In general, volume of shortfall in rainwater supply in UiTM Jengka is about 5m³ per month. In this study, volume of monthly harvested rainwater ranges from 2.74m³ to 4.25m³. The second step was to identify volume of water needed for landscaping purposes around campus of UiTM Jengka which is about 6.3m³ per month. Finally, the water quality index was assessed through laboratory testing of the groundwater sample, using four different types of treatment. This process could evaluate the suitability of the groundwater for consumption and guide on future treatment processes for more efficient use. It is found that the harvested rainwater was not sufficient to meet the area's water demand on most days for landscaping purpose, necessitating reliance on groundwater. Moreover, the water quality index for groundwater was only complied with the required standards for non-potable uses, such as general cleaning, livestock consumption, and landscaping. It is recommended that the groundwater undergo additional treatment, particularly for the removal of heavy metals, to ensure consumer safety.

Analysis of Bacterial Communities and Physico-chemical Properties of Grain Corn Silage Using 16S Amplicon Metagenomics in Malaysia

Minhalina Badrul Hisham — 2025-06-03

Tropical regions produce silage that is susceptible to spoiling due to excessive humidity and warmth. Consequently, identifying native bacteria as a possible inoculant is important to enhancing the quality of silage. The aim of this work was to use amplicon metagenomics to identify the bacterial community and functional changes related to ensiling and to forecast possible bacterial inoculant related to grain corn silage quality in the Malaysian climate. The fermentation characteristics and functional bacterial populations grain corn were produced and studied. After fermentation, the grain corn silage had a lactic acid bacteria (LAB) predominance. The dominant taxa in fresh grain corn, Leuconostoc & Pseudomonas were displaced by LAB, namely Weissella and Lactobacillus and showed high silage quality with an increase in lactic acid (LA) and acetic acid (AA), conversely decrease in water-soluble carbohydrates (WC). Tax4fun's functional prediction revealed metabolic pathways of coenzyme and transport and metabolism were depleted while synthesis of secondary metabolites which associated to fermentation activities (p<0.05) was enriched in after ensiling, likely to support bacterial growth during silage fermentation and produce metabolic byproducts like lactic acid. This study highlighted the presence and potential roles of homolactic and heterolactic bacterial populations before and after ensiling, which can be utilized to produce more effective bacterial additives for improving the fermentation quality of grain corn silage in tropical climates like Malaysia.

Chemically Reactive Unsteady Flow of Casson Fluid over a Stretching Surface

Md. Maruf Hasan — 2025-06-14

This study aims to investigate the unsteady flow behaviour of Casson fluid over a stretching surface in the presence of a magnetic field. The effects of magnetic field, Casson parameter and chemical reaction are also accounted for in this model. A Casson non-Newtonian constitutive model is utilized to describe the fluid transport. By employing appropriate similarity transformations, the model equations are reformulated as ordinary differential equations. Then the reduced differential equations are solved numerically using the Nachtsheim-Swigert shooting technique along with a sixth-order Runge-Kutta method. The graphical representation illustrates the influence of key physical parameters on flow characteristics. It is indicated that fluid velocity declines with a rise in the unsteadiness parameter and temperature significantly decreases due to this unsteadiness. Moreover, increasing chemical reaction parameter diminishes both velocity and concentration field. Again to validate the accuracy of the developed code, the results are compared with those from earlier research, demonstrating good agreement.

In-silico Assessment of Radiation Shielding Effectiveness of Some Materials used in Interior Building Decorations

Paul Ayanlola — 2025-06-18

The increasing trend in the use of decorative building materials for aesthetically pleasing purposes while neglecting the shielding effectiveness of these materials against penetrative radiation is a cause of concern radiologically. It is in view of this, that the present study investigates the radiation shielding properties of concrete, gypsum, glass and ceramic tile in order to intimate the end users of the radiological safety of these materials as regard interior building decorations. The linear- and mass-attenuation coefficient (LAC, MAC), half- and tenth-value layers (HVL, TVL), mean free path (MFP), effective atomic number (Z_eff), and electron density (N_eff) of the selected building materials were calculated for photon energies in the range between 0.05 to 1.408 MeV. The results obtained revealed that gypsum has the highest MAC and LAC values at low energies and slightly higher than other materials at high energies. The HVL, TVL, MFP and Z_eff of the studied materials showed that ceramic tiles have the greater value than that of other materials with gypsum having the least. Thus, while concerted effort are being made towards achieving atheistically pleasing interior building decorations, the end users are hereby safe radiologically as these materials are effective in shielding against penetrative radiation.

Machine Learning Techniques for the Early Detection of Alzheimer's Disease: A Systematic Review

Mohamad Al Saeed — 2025-06-29

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that poses a significant global health challenge. Early and accurate detection is crucial for timely intervention and for the development of new therapies. Machine learning (ML) has emerged as a powerful tool for analyzing the complex, high-dimensional data associated with AD. This systematic review was conducted by searching major academic databases for peer-reviewed literature published between 2020 and 2025. We identified studies that applied ML models to neuroimaging data for the early detection of AD. Information on ML models, datasets, and performance metrics was extracted and synthesized to provide a comprehensive overview of the field. A range of ML models are employed, from traditional supervised learning algorithms like Support Vector Machines (SVM) to more advanced ensemble (e.g., Random Forest) and deep learning methods (e.g., CNNs). Studies consistently show that ensemble and deep learning models achieve high performance (>90% accuracy in many cases), particularly in multiclass classification. However, the field faces persistent challenges, including severe class imbalance in common datasets, issues of data quality and anomalies, and the "black box" nature of complex models, which limits their interpretability and clinical trust. ML models show immense promise for the early and accurate detection of AD. However, for these tools to be successfully translated into clinical practice, future research must focus on developing robust, generalizable, and interpretable models that can effectively address the challenges of data imbalance and pathological heterogeneity.