Enhanced Knowledge in Sciences and Technology
The Periodical of Enhanced Knowledge in Sciences and Technology (EKST) is an open access, peer-reviewed, refereed and international articles published by the Faculty of Applied Sciences and Technology (FAST), Universiti Tun Hussein Onn Malaysia (UTHM). EKST will provide an intellectual platform to a multitude of topics ranging from the sciences to technology. It will publish twice a year (April-June and October-December). The scope of topics will cover Mathematics, Statistics, Physic, Biology, Biodiversity, Chemistry and Food Technology. EKST aims to publish empirical research such as survey or questionnaire, experiment, case study, observation, analytical and simulation models.
Differential Scanning Calorimetry and Fourier Transform Infrared Analyses of Filled Polymer Pellets
This study investigates the effects of filler incorporation on the chemical structure and thermal properties of polymer masterbatch pellets using Fourier Transform Infrared (FTIR) spectroscopy and Differential Scanning Calorimetry (DSC). FTIR analysis revealed that both with and without filler samples are based on a polyolefin matrix, as confirmed by the characteristic C–H stretching vibrations at 2916–2921 cm⁻¹ and 2849–2855 cm⁻¹. However, the masterbatch with filler exhibited additional absorption bands within the regions of 1600–1500 cm⁻¹, 1260–1150 cm⁻¹, and 1110–1010 cm⁻¹, indicating the presence of oxygen- and nitrogen-containing functional groups associated with surface-treated fillers, compatibilizers, or dispersing agents. DSC analysis showed that both samples followed the expected thermal transition sequence of Tg < Tc < Tm. The masterbatch with filler exhibited a lower crystallization enthalpy (0.62 J/g) compared to the masterbatch without filler (0.96 J/g), suggesting reduced crystallization ability due to restricted polymer chain mobility caused by the filler particles. In addition, the melting temperature (Tm) of the masterbatch with filler was slightly lower (123.32°C) than that of the masterbatch without filler (123.65°C), indicating disruption of crystalline perfection within the polymer matrix. Overall, the findings demonstrate that filler incorporation significantly modifies the chemical structure and thermal behaviour of the masterbatch pellets while maintaining the fundamental characteristics of the polyolefin-based polymer system.
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Thermal and Functional Group Analyses of Potato Starch and Corn Starch Biopolymer Film
Potato starch and corn starch are widely used to produce biopolymers because they contain amylose and amylopectin, the two main components responsible for polymer formation. In this study, potato and corn starch were used as raw materials to prepare biopolymers with glycerol added as a plasticizer to improve elasticity. Four biopolymer samples were prepared with different starch weights. A biopolymer sample was prepared from potato and corn starch at different starch contents were evaluated for their physical properties. The potato starch biopolymer with 2.0g showed the highest elasticity and transparency compared to the other samples. This difference is attributed to variations in the amylose and amylopectin content of each starch type. The thermal analysis results showed that the biopolymer sample with higher starch content has greater weight loss and lower thermal stability. Among the samples, potato starch biopolymers showed lower thermal stability compared to corn starch biopolymers, which may be attributed to their higher amylopectin content. The spectra of all samples showed similar functional groups, including C–O (1000–1300 cm⁻¹), C–H (700–900 cm⁻¹), C=O (1500–1800 cm⁻¹), C–H stretching (2800–3000 cm⁻¹), and O–H (3300–3500 cm⁻¹). The presence of the O–H group confirms amylose and amylopectin structures. The addition of glycerol introduced C=O groups associated with ester formation and slightly changed the intensity of certain peaks. These findings provide insight into how starch type and formulation composition influence the properties and stability of starch-based biopolymers, supporting their potential development as an environmentally friendly alternative material.
UV-Induced Degradation Behaviour of HDPE, PET and NBR Films: An FTIR Analysis
This study investigates the ultraviolet (UV) degradation behaviour of high-density polyethylene (HDPE), polyethylene terephthalate (PET), and nitrile butadiene rubber (NBR) under controlled UV irradiation at 254 nm. Chemical changes induced by photo-oxidation were analysed using Fourier Transform Infrared (FTIR) spectroscopy. The formation of oxygen-containing functional groups, particularly carbonyl and hydroxyl groups, was used as an indicator of degradation. Results showed that HDPE exhibited early- stage oxidation and increased brittleness, while NBR underwent moderate degradation involving both chain scission and crosslinking. In contrast, PET demonstrated minimal chemical and physical changes, indicating superior UV resistance. These findings highlight the strong dependence of UV stability on polymer molecular structure.
Surface and Cross-Sectional Morphology of Wood Plastic Composite Using Scanning Electron Microscopy
The natural-fibre-reinforced polymer materials based on wood plastic composites (WPCs) are broadly utilized because they offer better performance in terms of stiffness, dimensional stability and environmental advantages. Microstructural features, including fibre dispersion, interfacial adhesion, and the creation of void, have a very significant impact on the performance of WPCs. This study evaluates the surface and cross-sectional morphology of a commercial polyvinyl chloride (PVC)-based WPC reinforced with 40% wood fibre. Characterization was performed using Scanning Electron Microscopy (SEM) combined with Energy Dispersive X-ray Spectroscopy (EDS) to qualitatively analyze surface elemental composition. SEM measurements showed an anisotropic and non-uniform surface morphology that was exposed to the wood fibres and micro-voids. Cross-sectional observations showed heterogeneous fibre distribution, interfacial gaps and internal voids in the cross-section, which indicated the incomplete polymer encapsulation and low fibre-matrix compatibility. Analysis on the surface through EDS revealed the presence of carbon and oxygen peaks that are predominantly in the polymer matrix and lignocellulosic fibres respectively and several inorganic elements that are linked to natural fibre components and polymer additives. All in all, the findings indicate that morphological characteristics on the surface and inside are directly connected with the quality of WPC materials and the interfacial bonding, dispersing of the fibres, and the optimum processing condition play a significant role in creating high-quality wood plastic composites.
Comparative Study on the Surface Morphology and Structural Degradation of Bio-based Polylactic Acid and Conventional High-Density Polyethylene
This work studied the surface morphology and molecular structure of bio-based and conventional plastics, specifically polylactic acid (PLA) and commercial-grade high-density polyethylene (HDPE). The degradation process was conducted over a period of 45 days under controlled hydrolytic conditions and natural sunlight exposure. The FTIR result revealed the formation of a new hydroxyl (-OH) peak at 3396 in PLA after degradation, indicating ester bond cleavage and the formation of degradation products. HDPE showed a very minimal spectral change, revealing its resistance to both hydrolytic and photo-oxidative degradation remained stable. The surface morphology analysis proved that HDPE had a comparatively constant and intact structure, while PLA showed increased roughness and surface defects. Overall, the results show that PLA is more particularly susceptible to environmental degradation compared to HDPE, which provides insights on how conventional and bio-based polymers degrade differently.
Hydrolytic and Photo-Oxidative Degradation
Thermal Degradation and Fourier Transform Infrared Spectroscopy Characterization of Polypropylene under Multiple Mechanical Recycling Cycles
This study investigates the effect of repeated mechanical recycling on the thermal and chemical properties of polypropylene (PP). Virgin PP (v-PP) was subjected to three recycling cycles (r1-PP, r2-PP, r3-PP) through repeated melting. Thermal degradation behavior was analyzed using thermogravimetric analysis (TGA), while chemical structural changes were examined using Fourier transform infrared spectroscopy (FTIR). TGA result showed a single step degradation for all samples. The onset degradation temperature increased from 418.73°C (v-PP) to 440.64°C (r2-PP), indicating improved thermal stability after initial recycling cycles, but decreased to 414.62°C in r3-PP due to stabilizer depletion and chain scission. Weight loss remained high (~97–98 wt%) with minimal residue (~1–2 wt%). FTIR analysis revealed increased intensity of C–H, C–C, and C=O functional groups in recycled samples, indicating oxidative degradation and structural modification. These findings suggest that limited recycling cycles do not significantly degrade PP performance, while excessive recycling reduces thermal stability.
Analysing Electricity Consumption in Malaysia via Linear Programming, Time Series Analysis and Clustering Analysis
Malaysia faces increasing electricity consumption that later creates challenges in long-term sustainability. The lack of accurate forecasting and clear consumption pattern classification limits effective energy planning. This study aims to minimize electricity transmission losses using linear programming, forecast future electricity consumption using time series analysis and categorize consumption levels using clustering analysis. Monthly electricity consumption data from January 2018 to May 2025 were obtained from the Department of Statistics Malaysia (DOSM) and analysed using Python. In the first phase, linear programming was applied to optimize electricity distribution among industrial, domestic and export sectors while satisfying supply and demand constraints. The results showed that optimized allocation reduced annual electricity losses by 60 to 80 MKWh that proves the model's effectiveness. In the second phase, time series analysis such as Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed to forecast future monthly electricity consumption. Mean Absolute Percentage Error (MAPE) showed the high predictive accuracy below 2% which indicates a stable increasing consumption trend. In the third phase, clustering analysis such as K-means clustering and Principal Component Analysis (PCA) were used to categorize monthly consumption levels. Results showed upward shift toward higher consumption after 2023 because of industrial growth and rising domestic demand. Overall, linear programming, time series analysis and clustering analysis successfully provide a comprehensive data-driven framework for sustainable electricity management. The findings offer valuable insights for policymakers and the Energy Commission in formulating strategies to reduce losses, enhance forecasting accuracy and improve energy distribution efficiency across Malaysia’s electricity sector.
Assessing Flood Factors through Comparative Analysis of Machine Learning Techniques
Floods are among the most frequent natural disasters, leading to loss of life, spreading of diseases, and destruction of property. Therefore, accurate and timely flood prediction is essential for effective disaster risk management. This study explores the application of Machine Learning (ML) techniques to improve flood forecasting systems in Malaysia using geological and hydrological features. The purposes of the study are to identify the factors influencing flood occurrence and their significance using Random Forest (RF), analyse hidden trends or patterns related to flood occurrence using Logistic Regression (LR) and K Nearest Neighbors, and evaluates the performance of the machine learning models proposed using evaluation metrics during the modelling process. A dataset consisting of 4217 samples was compiled for 42 study areas at 42 weather stations across Malaysia, incorporating 16 explanatory variables, including seven-day continuous rainfall characteristics, seasonal and geographical factors, and other relevant environmental attributes. Three classification models, RF, LR and KNN were trained to predict flood occurrence. The results indicate that recent rainfall (0.1444), cumulative (0.1088) and maximum daily rainfall (0.1058) are the most influential features. Among the models, LR performs better at classifying flood events with higher recall value (0.7218), while RF shows best performance in all other metrics. Overall, the best model identified is RF based on Receiver Operating Characteristic (ROC) curve (AUC=0.877).
Analysis on Students’ Perspective Toward Academic, Career Aspirations and Destinations After SPM in Muar, Johor
This study examines upper secondary school students' views on their higher education and career goals, as well as the variables that affect their planned career paths after completing the Sijil Pelajaran Malaysia (SPM). Many students find it challenging to make accurate decisions due to the influence of circumstances, social, and personal factors, even though many educational and professional opportunities are available after SPM. It is crucial to understand these difficulties to help students make better-informed, more suitable decisions after SPM. A structured questionnaire was the primary data collection tool in a quantitative study design. A survey method was applied to gather information from secondary school students. Students' after-SPM plans and demographic characteristics were summarised using descriptive statistics. To investigate variations in ambitions and influencing factors across demographic groups, the Mann-Whitney U test and the Kruskal-Wallis H test were used. To find correlations between demographic factors and students' post-SPM choices, the chi-square test of independence was used. To determine the fundamental factors influencing students' post-SPM decision-making, an exploratory factor analysis (EFA) was conducted. The results show that the majority of students (70.7%) intend to pursue further education after SPM, with work coming in second and entrepreneurship the least prevalent choice. Students' academic or career goals and after SPM decisions were not substantially influenced by age, gender, household wealth, or religion (p > 0.05). The academic stream had a substantial impact on the factors influencing students' decisions (p < 0.05) but was not associated with career goals (p > 0.05). Self-efficacy and goal clarity, financial limitations, social influence, knowledge access, and media and environmental impacts were identified as the five main determinants. In general, contextual and personal factors influence after SPM decisions more than demographic traits.
A Statistical Comparison of Cancer Mortality Rates Between Developed and Developing Countries
This study addresses disparities in cancer deaths worldwide through the analysis of long-term patterns in 20 countries in developed and developing countries by secondary data from 1990 to 2016 incorporating five major types of cancers including lung, breast, colorectal, liver, and stomach cancers. The Principal Component Analysis (PCA) is used to eliminate data dimensions and find major patterns of mortality, which proves two main components that explain a significant percentage of all the variances, the first component is significantly related to the lifestyle-associated cancer such as lung, breast, and colorectal and the second element is associated with the infection-related and region-specific mortality such as liver and stomach. According to these aspects, K-means cluster analysis (K = 2) can group the countries into high- and low-mortality groups regardless of the specified economic groups, which indicates that the statistical groupings and development status are highly consistent. The findings reveal that developed nations are largely concentrated into the category of mortality due to lung and colorectal cancer whereas developing nations are largely gathered under the category of mortality due to liver and stomach cancer showing a different epidemiology of the same arising out of the lifestyle, access to healthcare, and prevention measures. The integrative multivariate method offers strong empirical data of self-perpetuating structural disparities in the world healthcare institutions and cancer control initiatives. The current research will be useful in practice because it provides a data-based model to facilitate the implementation of specific interventions in the field of public health, a more efficient distribution of resources, and cancer prevention and early diagnosis policies that are specific to the region, which will result in the reduction of international health disparities in cancer mortality and the enhancement of international health outcomes.
Multinomial Logistic Regression for Diabetes Prediction using LASSO and Elastic Net Regularization
Diabetes is a chronic metabolic disorder characterized by persistently elevated blood glucose levels and is associated with severe long-term health complications if not detected early. Accurate classification of individuals into Non-Diabetic, Prediabetic, and Diabetic categories are therefore essential for effective prevention and clinical intervention. This study is a multiclass diabetes prediction model by using Multinomial Logistic Regression integrated with LASSO and Elastic Net regularization techniques. A dataset of 1,000 patient records containing demographic and biochemical variables was analysed following data cleaning, normalization, and an 80:20 train–test split. Model training and hyperparameter tuning were conducted using 10-fold cross-validation. Performance was evaluated by using accuracy, Cohen’s Kappa, F1-score, Brier score, macro-AUC, and information criteria. Elastic Net model achieves the highest accuracy of 0.9141, the highest Cohen’s Kappa of 0.6340, the highest macro sensitivity of 0.6607 and the highest macro balanced accuracy of 0.7303 among the models. In addition, it consists of fewest variables and provided the most balanced and stable result of AIC, BIC, Pseudo-R2, F1-Macro, F1-Weighted, Brier score and Macro specificity which is 387.8591. 401.9204, 0.5556, 0.9559, 0.5737, 0.8909 and 0.8665 respectively. The results indicate that Elastic Net regularization provides the most stable and balanced classification performance while effectively handling correlated variables. Key variables that influenced diabetes status include body mass index, age, HbA1c, triglycerides, and cholesterol. The proposed approach offers an interpretable and reliable framework for multiclass diabetes classification and early risk stratification.
Forecasting Electricity Consumption in Malaysia using Holt-Winters’ Exponential Smoothing, SARIMA and FB Prophet
Electricity consumption forecasting plays a vital role in supporting power system reliability, efficient energy planning and sustaining economic and country development. With rising demand, robust and accurate forecasting models are important to support development and decision making. This study aims to apply and compare different time series forecasting models to predict future electricity consumption in Malaysia across total, local domestics and local commercial sectors. Holt-Winters’ method, Box-Jenkins Seasonal ARIMA (SARIMA) model and FB Prophet model were used and forecast evaluation is conducted using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The future electricity consumption was forecasted using the most accurate forecasting model. The dataset consist of 78 observations for each of the sectors and was obtained from January 2018 to June 2024 based on Department of Statistics Malaysia. (DOSM) website. During statistical analysis it revealed that each model’s outcomes are very similar to each other in terms of the fitted values are plotted closely with the actual values indicating all models able to effective capture the underlying trend and seasonality in the data. During the forecast evaluation comparison, it shows that for total electricity consumption, FB Prophet model outperforms Holt-Winters’ methods and SARIMA model with lowest MAPE, MAE and RMSE values. This outcome is also similar for the local domestic and local commercial data forecasting analysis. Hence, this model is selected as the best forecasting model with high accuracy. The use of larger datasets is recommended to further improve models’ understanding of historical patterns to deliver good and more accurate predictions.
Forecasting Natural Gas Prices in Malaysia Using Double Exponential Smoothing, Arima and XGBoost Models
Natural gas is an integral component of Malaysia’s energy structure, accounting for a significant share of overall primary energy consumption, which makes the market prone to price volatility due to the influence of changes in policies, demand, and global factors. As reliable natural gas price forecasts play a pivotal role in making informed energy policies and other related decisions, this research work examines and compares the ability and performance of three models: Double Exponential Smoothing (DES), Autoregressive Integrated Moving Average (ARIMA), and eXtreme Gradient Boosting (XGBoost) in making accurate forecasts of the prices of natural gas in Malaysia using historical price time series data on the basis of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) on the training and testing datasets. Findings indicate the poor performance of the DES model in terms of accuracy in conditions of price volatility, while XGBoost yields an impressive performance in terms of accuracy on the training dataset but performs poorly on the testing dataset; hence, the ARIMA(2,1,0) model is shown to provide the lowest forecast errors on the testing dataset, making it the model of superior performance on the test datasets in terms of precision and accuracy. The forecasts of the selected model indicate a stabilisation of prices towards the end of the forecast time period, ranging between the values of RM 37-38/MMBtu.
Differentiation of Imported and Local Rice using Terahertz Time-Domain Spectroscopy (THz-TDS)
Accurate authentication of rice origin is critical for ensuring food quality and market integrity. This study investigates the feasibility of differentiating between imported (Thai Jasmine, Indian Basmathi) and local (Selangor, Kelantan) rice varieties using Terahertz Time-Domain Spectroscopy (THz-TDS). Transmission-mode measurements were conducted on powdered rice samples over the frequency range of 0.1 THz to 3.0 THz. The extracted frequency-domain data revealed distinct absorbance profiles for each variety, attributed to variations in compositional and structural properties. To evaluate the discrimination capability of the system, Principal Component Analysis (PCA) was employed on the spectral data. The PCA results demonstrated clear clustering and separation between imported and local rice samples, confirming the method's effectiveness. These findings establish THz-TDS, combined with multivariate analysis, as a promising rapid and non-destructive technique for rice origin verification and food safety applications.
Detection of Milk Spoilage Using Au D-Shaped Fiber Sensor based on Localized Surface Plasmon Resonance
The effectiveness of gold nanoparticle coated D-shaped fiber sensor by utilizing localized surface plasmon resonance (LSPR) effect for detection of spoiled milk is studied. Milk is a highly perishable food product that is susceptible to spoilage due to microbial activity and improper storage conditions. Conventional methods for milk spoilage detection are often time-consuming, subjective, and require laboratory facilities. Therefore, this study investigates the feasibility of using an optical fiber–based sensor for the detection of milk spoilage through changes in optical response. An Au-coated D-shaped optical fiber sensor was developed based on the localized surface plasmon resonance (LSPR) effect to enhance sensitivity to refractive index variations in milk samples. Different types of milk, namely fresh milk, low-fat milk, full cream milk, and condensed milk, were prepared and allowed to undergo natural spoilage at room temperature over exposure periods of 0, 24, 48, and 72 hours. A thin gold (Au) layer was deposited using a sputter coater, and UV–Vis spectroscopy was employed to confirm the plasmonic characteristics of the coating. The sensor’s surface morphology was examined using field emission scanning electron microscopy (FESEM). Optical measurements were carried out using an Optical Spectrum Analyzer (OSA) and an Optical Power Meter (OPM) to monitor spectral changes and variations in transmitted optical power. The results demonstrated clear time-dependent changes in both spectral response and optical power for all milk samples, with more pronounced variations observed in full cream and condensed milk. These changes indicate sensitivity to compositional and refractive index variations associated with spoilage.
Distribution of Non Volant Small Mammals in Different Landscape in Universiti Tun Hussein Onn Malaysia, Johor
The distribution of non volant small mammals was investigated across different landscape areas within Universiti Tun Hussein Onn Malaysia (UTHM), Johor. This study aimed to (1) catalogue the variety of non volant small mammal species present in the study area and (2) assess how different habitat types influence their distribution and abundance. Field sampling was conducted between August and November 2025 using ground level live trapping and direct observation across three habitat types: agricultural monoculture area (oil palm), disturbed area (lake edge), and coastal area. A total of eight non volant small mammal species were recorded through direct live trapping, comprising 41 individuals, while additional species were documented through direct observation. The captured assemblages were dominated by generalist rodent species, particularly Rattus rattus, Rattus exulans, and Rattus tiomanicus. Forest associated species such as Maxomys rajah and Maxomys whiteheadi were recorded in low number, indicating limited suitability of highly modified habitats for disturbance sensitive taxa. Observational records revealed the presence of arboreal species including Tupaia glis, Callosciurus notatus, and Ratufa bicolor, which were not effectively sampled by ground level live traps. Species richness and distribution varied among habitat types. The disturbed lake edge habitat supported the highest species richness and diversity. In contrast, the agricultural monoculture area exhibited lower diversity with strong dominance by generalist to overall species presence. The Shannon-Wiener diversity index indicated moderate species diversity with uneven species distribution across habitats. Overall, the findings demonstrate that habitat type and structural complexity play important roles in shaping non volant small mammal communities. This study provides essential baseline data for future ecological research and highlights the importance of integrating multiple sampling approaches when assessing small mammal assemblages in human modified landscapes.
Non volant small mammal, habitat types, species distribution, diversity index, Johor, Malaysia
Department of Technology and Natural Resources, Faculty of Applied Sciences and Technology, UTHM Kampus Cawangan Pagoh,
Sales Prediction for Fashion Retailer Based on State Space Model
This study builds a novel model to predict sales for fashion retailers, an industry in which demand forecasting is notoriously challenging. Fashion sales are very volatile and are affected by new trends, highly seasonal patterns, and frequent promotional activities. Traditional forecasting methods often fail as they assume that sales patterns are stable over time. However, these patterns are always changing. In order to overcome this obstacle, the authors of this paper recommend the use of a state space model. This kind of model is very effective since it considers the actual sales level as a latent state which changes. It can also explain in a very organized manner how factors such as past sales, price changes, and holidays affect the demand to come. The main objective of this project is to develop and perform an experiment with this model using the real historical sales data of fashion retailers. We will examine the data and then apply the state space model to make sales predictions. To determine the model's success, we will gauge the forecasts' accuracy by computing the mean square error, which is a standard measure of prediction error. We will also compare its results to those obtained by a traditional method such as ARIMA to determine which is more accurate.
Validation of SAWI Transformation for Nonlinear Differential Equations Using Fourth‑Order Runge‑Kutta
This paper investigates the method of Shifted and Weighted Integral (SAWI) transformation for solving a nonlinear system of ordinary differential equations (ODEs) with quadratic nonlinearity. The approach semi-analytical solution is validated by comparison with the classical fourth-order Runge-Kutta (RK4) method. Numerical results show that the absolute errors demonstrating strong agreement between both approaches. The SAWI technique transforms the connected nonlinear system into a set of differential equations in the sigma-domain. The agreement between the two approaches for the quadratically nonlinear system confirms the reliability and efficiency of the SAWI transformation as a semi-analytical technique, providing analytically valid and computationally efficient analysis of such systems.
Nonlinear Ordinary Differential Equations
Solving Two Species Lotka-Volterra Biological Model Using Sawi Transformation
The paper explores the use of Sawi Transformation to solve the two species Lotka-Volterra predator-prey model, which is ruled by an ordinary differential equation (ODE) system that is nonlinear. The main aim is to show how this fundamental transformation is working to simplify nonlinear systems through the reduction of the complexity of the system by transforming it into algebraic equations to enable semi-analytical solutions with less computation effort. Maple 2015 is used to obtain iterative solutions to both the classical and modified Lotka-Volterra models. The findings are then compared the Variational Iteration Method (VIM). The comparison proves that the Sawi Transformation method has highly accurate results which are equally efficient. These results confirm that Sawi Transformation is a robust valid instrument in solving nonlinear dynamical systems with a lot of success in ecological and biological modelling.
Modelling Vitamin D Concentration Dynamics in the Human Body Using Partial Differential Equation
Vitamin D is important for bone health, the immune system, regulating metabolic processes, and maintaining the calcium and phosphate balance. Many people, however, encounter vitamin D deficiencies, which can be attributed to insufficient sun exposure, a lack of vitamin D-rich food, and certain health issues. Most existing models have used ordinary differential equations (ODEs) to model vitamin D dynamics. The problem with the existing models, however, is that the models assume a uniform distribution, which does not account for the differing spatial levels of vitamin D in various body compartments. This research focuses on vitamin D dynamics in sunlight exposure, supplementation, and body mass index (BMI), and aims to apply it to an existing partial differential equation (PDE) model. The model is built using the Crank-Nicolson finite difference method to solve the equations and is validated with a synthetic dataset of 1000 people. The model shows that sunlight exposure and vitamin D supplementation positively contribute to serum 25-hydroxyvitamin D (25(OH)D) levels, and negatively impact it is BMI. Unlike traditional ODE models, the PDE model does a better job of mimicking the distribution, conversion, and destruction of vitamin D in principal body compartments. The study shows that, for simulating the dynamics of vitamin D, the PDE approach has more biological realism, which may be applied to future studies of vitamin D metabolism and public health.
Prediction of Rice Production in Malaysia Using State Space Model
Rice is not only a staple food in Malaysia but also the primary agricultural product, influencing the food security, employment rate, and economic stability. Hence, an accurate prediction of rice production can help in farm planning and policy construction. However, rice production is constantly evolving due to various factors, including climate change, uncertain pricing, and limited technological advancements. This paper proposes a state-space model to predict rice production, capturing the underlying dynamics of rice production data. A historical rice production dataset from 1980 to 2019 was analysed, and descriptive statistics were calculated to understand the trend in rice production. The state space model is formulated after standardising the data. The model parameters are estimated through an optimisation process to reduce the prediction error. The accuracy of the proposed model is evaluated using a mean square error (MSE) metric. The results showed that the proposed model can accurately fit the historical rice production data, with the smallest MSE value compared to the simple moving average and the exponential smoothing method. Therefore, the state space model demonstrates its effectiveness in predicting rice production, supporting data-driven decision-making in agricultural planning and policy formation.
University Student Enrolment Prediction in Malaysia with State Space Model
Accurate university student enrolment is crucial for informing decisions about academic infrastructure, personnel, and funding as it significantly impacts institutional capacity planning, resource allocation and national education policy. However, the accuracy of enrolment forecasting is influenced by demographic shifts, economic conditions and policy factors. This paper proposes the use of a state space model to predict undergraduate student enrolment in Malaysian public and private universities. The enrolment dataset from 2002 to 2024, obtained from the Ministry of Higher Education, are normalized to ensure numerical stability and comparability for modelling. Descriptive statistics, such as mean and standard deviation, are processed for analysis of standardization of the data. The performance of the model is evaluated using the mean squared error (MSE) to assess its effectiveness. Hence, the dynamic changes and long-term enrolment trends can be detected by the state space model, which produces low prediction errors. Moreover, the state space model expresses the lowest MSE values compared to the simple moving average and exponential smoothing models for predicting enrolment in both public and private universities. These findings confirm that the study objectives have been fulfilled and the effectiveness of the state space model in predicting the student enrolment in Malaysian universities.
Optimizing the Shortest Route Problem in Pagoh Using Ant Colony Optimization Algorithm
In this research, the model of the Pagoh Education Hub was made using the graph model, and the Ant Colony Optimization technique was employed for finding the shortest path problem between critical locations on the campus. With the help of the ACO technique, it was possible to find the shortest possible path from the Universiti Tun Hussein Onn Malaysia (UTHM) campus to the Politeknik Tun Syed Nasir (PTSN), going through the Education Hub Library, which measured a total distance of 1.1 km. These findings clearly state that the ACO technique can be effectively used for finding the shortest possible route, which will be reliable as well as optimized according to the shortest path on the network graph. In this regard, it has been found that the ACO technique performs much more effectively compared to the human inspection process, especially while dealing with larger networks. The results obtained clearly justify that the model developed has been accurate, as the optimized route clearly depicts the correctness of the network developed. Further, it has been found that this research clearly has implications on finding more complex routes while dealing with larger education systems or larger geographical areas. In summary, the application of the ACO algorithm offers a promising solution for shortest route problems in campus and similar real-world. environments, supporting smarter and more adaptive navigation systems.
Predicting Residential Housing Rent Prices Using Machine Learning and Statistical Modelling Approaches
Accurate estimation of residential rental prices is critical for property valuation, investment decision-making, and housing market analysis in urban areas. The rapid urban development in Selangor and Kuala Lumpur has heightened the complexity of determining rental prices, thereby necessitating data-driven analytical approaches. This study identifies key factors influencing residential rental prices and evaluates the predictive performance of statistical and machine learning models using housing data from Selangor and Kuala Lumpur. Stepwise regression was initially applied to identify significant explanatory variables affecting monthly rental prices. Multiple Linear Regression served as the baseline statistical model, while Random Forest and Extreme Gradient Boosting (XGBoost) were implemented to enhance predictive accuracy through nonlinear learning. The dataset was partitioned into training (80%) and testing (20%) subsets, and model performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results show that property size, furnishing status, location, and completion year are among the most influential factors affecting rental prices. In terms of predictive performance, machine learning models outperformed the traditional regression approach, with XGBoost achieving the lowest test-set prediction error, followed by the Random Forest. These findings demonstrate the effectiveness of machine learning techniques for predicting residential rental prices and offer valuable insights for property analysts, policymakers, and real estate practitioners in Malaysia.
Shooting Method to Solve Nonlinear Boundary Value Problem in Heat Transfer
This study investigates the application of the shooting method to solve nonlinear boundary value problems (BVP) in heat transfer focusing on a one-dimensional fin with temperature dependent conductivity and conductive-convective-radiative boundary conditions. The objective of this study are to solve the nonlinear BVP using the shooting method, validate the results by comparing with existing literature, and analyses the method’s accuracy and computational efficiency. The governing equation is converted into a dimensionless form and transformed into a system of first-order ordinary differential equations. The shooting method, implemented in MATLAB with a fourth-order Runge-Kutta (RK4) method, then iteratively adjusts the unknown initial condition at the fin tip until the boundary condition at the base is satisfied. Numerical solutions are obtained for various values of the convection parameter, , radiation parameter, , and thermal conductivity parameter, . The results show that higher radiation led to more heat loss and lower fin temperatures, while increased thermal conductivity helped retain heat. The temperature distributions closely match those reported in previous studies confirming the accuracy of the shooting method. Furthermore, the method demonstrated computational efficiency, converging within 1-6 iterations and requiring less than 0.15 seconds of Central Processing Unit (CPU) time for all cases. In conclusion, the shooting method is an accurate, and efficient numerical tool for solving nonlinear BVPs in heat transfer involving conductive, convective, and radiative effects.
Solving the Linear Arms Race Model Using SAWI Transformation
This research explores the application of SAWI transformation to solve the arms race model, offering an analytical alternative to traditional numerical methods. such as the Runge-Kutta algorithm. By converting complex systems of linear first-order ordinary differential equations into simpler algebraic forms, the SAWI method enables the derivation of exact, closed-form analytical solutions that describe how nations and armament levels evolve. Numerical simulations in MATLAB further validate the method’s computational precision, highlighting how factors such as reaction coefficients and economic fatigue determine whether a system leads to a runaway arms race or a stable balance of power. While the study demonstrates that the SAWI transformation effectively reduces numerical approximation errors for linear systems, it also notes the need for future improvements to handle more complex nonlinear dynamics found in real-world international relations.
System of Ordinary Differential Equations (ODEs)
Numerical Study of Eigenvalue Problem Using Iterative Methods
This study focuses on solving generalized eigenvalue problems by iterative method which are power method and Rayleigh quotient iteration. The primary purpose is to compare the performance of these two methods with respect to convergence speed, accuracy and computational efficiency when applied to a workforce transition model among three sectors which are information technology (IT), education, and media. The transition matrix used is a symmetric, stochastic with dominant eigenvalue corresponds to the steady-state distribution of workers. The power method algorithm is used to approximate the largest eigenvalue and corresponding eigenvector through repeated matrix-vector multiplication and normalization, while the Rayleigh quotient iteration used inverse iteration with dynamic shifting for better convergence. Rayleigh quotient iteration achieves faster convergence with 2 to 3 iterations than the power method that up to 250 iterations. Both methods yield the same result with a dominant eigenvalue of 1.0027 and consistent steady-state eigenvectors. The results are verified with eigen function which comes in MATLAB.
Analytical Study of the Correlation between Consumer Price Index and Inflation in Malaysia
The measurement of inflation is central to the study and the formulation of any economic policy, and the Consumer Price Index (CPI) has become the primary tool to measure the change in the price levels in Malaysia. This study analyzed the correlation between CPI and inflation in three economic periods such as before the COVID-19 pandemic, during the COVID-19 pandemic and after the COVID-19 pandemic. Time-series analysis is used to analyze monthly CPI and inflation data that are available at the Department of Statistics Malaysia (DOSM). The Augmented Dickey-Fuller (ADF) test is used to test stationarity, correlation and regressions analysis is carried out to determine the direction, strength and explanatory power of the CPI-inflation relationship. The findings indicate that there is a significant positive relationship between CPI and inflation during the COVID-19 pandemic and before the COVID-19 pandemic, but there is no significant linear relationship after the COVID-19 pandemic. These results imply that whilst CPI is useful in monitoring inflation in both stable and crisis times, it is not effective in monitoring inflation during periods of economic recovery, which suggests that CPI-based measure of inflation must be carefully interpreted.
Integer Programming for Solving Hospital Hypertension Patient Menu Planning
The Delete-Reshuffle Algorithm (DRA) and an Integer Programming (IP) model are combined in this study to develop an ideal seven-day food plan for hypertension patients in Malaysian hospitals. The RNI 2017 standards' requirements for six key nutrients such as sodium, protein, potassium, vitamin B2, calcium, and vitamin C are met while minimizing expenses using a database of 426 Malaysian items in ten categories. Using MATLAB R2025b, the model produced a weekly plan that cost RM 151.20 (RM 21.60 per day) and included 126 different meals spread over six different occasions each day, ensuring full dietary diversity. All nutrients met RNI standards, and daily salt intake was kept considerably below the 2000 mg limit, ranging from 556 to 1717 mg. While preserving cost-effectiveness and nutritional compliance, the DRA effectively removed meal repetitions. With the help of this framework, hospital meal plans that are clinically acceptable, economical, and culturally relevant can be created through mathematical optimization.
Optimization of Fixed Deposit Rules Using the Bisection Method
The fixed deposit investment is generally considered as low-risk financial tools but it is difficult to establish the best parameters like interest rate or investment time because of the nonlinearity of compound interest. This paper suggests the use of the Bisection Method, a method of numerical root-finding, in order to optimise the fixed deposit investment parameters. A compound interest formula mathematical model was written and converted to a nonlinear equation to be solved numerically. Bisection Method was used with MATLAB and tested on five cases of simulation to either determine the interest rate required or the investment period to be used to obtain a given maturity value. The findings show that it exhibits a solid convergence of within a set tolerance limit, and high numerical accuracy and stability across all instances. These results show that the Bisection Method is a simple, stabilised and efficient technique of optimising fixed deposit, which demonstrates the existence of practicality of numerical techniques in financial planning and decision-making.