SCOPE OF PUBLICATION MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics is an open access, peer-reviewed journal that considers articles bearing manifestation of new ideas and original research across a range of mathematical science topics. It is an interdisciplinary journal containing research articles that treat scientific and industrial problems using techniques that are of mathematical interest. The journal publishes novel applications in all areas of industrial and applied mathematics including engineering, physics, chemistry, biology, ecology, economics, mathematical finance, mathematical biosciences, medical, information theory and more. Mathematical techniques of interests include asymptotic methods, control theory, bifurcation theory, dynamical systems theory, computational methods, numerical analysis, optimization, operational research, probabilistic and statistical methods. Articles on algebra and analysis related to scientific and industrial problems are also welcome. MATEMATIKA: MJIAM is published online with a frequency of three issues per year (April, August and December). Besides that, special issues of MATEMATIKA: MJIAM will be published non-periodically from time to time. Journal abbreviation: MATEMATIKA: MJIAM Indexed by: Web of Science (Emerging Sources Citation Index (ESCI)), MyCite, Google Scholar eISSN 0127-9602 | ISSN 0127-8274 Citations 2319 h-index 24 i10-index 62 Articles 576 2022 Google Scholar Citation Reports HISTORY OF MATEMATIKA: MALAYSIAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics started its publication in 1983. It was initiated and managed by UTM Department of Mathematical Sciences (formerly known as UTM Department of Mathematics) until 2013. It started as a platform for researchers to present high-level mathematical research within the local community but has since evolved to attract growing international readership. In 2014, the journal underwent a substantial relaunch and restructure, which includes online submission features under the administration of UTM Centre for Industrial and Applied Mathematics (UTM-CIAM).
Copyright of articles that appear in MATEMATIKA: MJIAM belongs exclusively to Penerbit UTM Press, Universiti Teknologi Malaysia. This copyright covers the rights to reproduce the article, including reprints, electronic reproductions or any other reproductions of similar nature.
Solution of Abel’s Integral Equation by Modified Laplace Transform
This paper provides a comprehensive and detailed analysis of the modified Laplace transform, exploring its applications in various mathematical and engineering domains. Initially, the modified Laplace transform is applied to various special functions including Mittag-Leffler function, k-Struve function, Bessel-Maitland function, and Generalized function. Following this, the study examines the modified Laplace transform of nth order derivative of a function. The paper also demonstrates how the modified Laplace transform can be utilized to solve Abel’s integral equation. Lastly, the research explores some practical applications of the modified Laplace transform in addressing real-world problems like chemical sciences and impulsive forces.
An Almost Unbiased Regression Estimator: Theoretical Comparison and Numerical Comparison in Portland Cement Data
Multicollinearity is the problem when there is linear dependency among the independent variables. The Ordinary least squares estimator (OLSE) that is commonly adopted is not suitable for the linear regression model when the independent variables are correlated. This is due to the high variance in OLSE and hence the accuracy of OLSE reduces in the presence of multicollinearity. Hence, the estimator named k-almost unbiased regression estimator (KAURE) was proposed as an alternative to OLSE in this paper. KAURE was developed by using the definition of an almost unbiased estimator to further reduce the bias of Liu-type estimator-special case (LTESC). The properties of KAURE including bias, variance-covariance and mean squared error (MSE) were derived. Theoretical comparison and real-life data comparison were carried out to evaluate the performance of the KAURE based on the MSE criterion. The application of the real-life data supported the theoretical comparison that showed the superiority of KAURE over OLSE and LTESC. The results revealed that KAURE could be considered as an alternative estimator for the linear regression model to combat the problem of multicollinearity.
Air Quality Analysis Using Non-Informative and Weakly Informative Priors for Autoregressive Coefficients in the BSTS Model
Understanding the complex dynamics of air quality is crucial for reducing environmental pollution and protecting public health. This study aims to investigate the impact of different prior specifications in Bayesian structural time series (BSTS) modelling on the accuracy of predicting air pollution levels (PM2.5 and PM10) across various monitoring stations. The study examines time series data from 5 July 2017 to 30 June 2019. Two priors are compared: non-informative prior regression coefficients (NIP) and weakly informative prior regression coefficients (WIP). The analysis focuses on forecast accuracy metrics, including observed and predicted standard deviations, relative goodness of fit, and R-squared. The results indicate that both approaches indicate that PM10 is more predictable than PM2.5 at all stations, but WIP has been seen to produce lower MAPEs and RMSEs, as well as tighter credible intervals that reduce forecast uncertainty in most stations. For instance, at station CA13A, for PM2.5, WIP lowers MAPE by 0.34% (from 10.68% to 10.34%) and reduces RMSE by 0.017 (0.391 to 0.374), while R² remains stable at 0.85, and relative gof improves slightly from 0.226 to 0.221. For PM10, Relative gof improves slightly from 0.249 to 0.246. Posterior distributions of coefficients (β) under both prior specifications identified ambient temperature and humidity as significant predictors across stations, confirming that the choice of prior does not drastically change which covariates are important. The WIP specification acts towards a more balanced model through enhancing the inclusion of less dominant predictors. Despite minor discrepancies in coefficient estimates, WIP models provided nuanced advantages in model fit and explanatory power. These findings underscore the importance of prior specifications in Bayesian modelling, offering valuable insights for air quality management and forecasting.
Green Manufacturing Evaluation Using Intuitionistic Trapezoidal Fuzzy DEMATEL Method
Green Manufacturing (GM) promotes environmentally responsible production practices that minimize waste, energy consumption, and ecological impact while sustaining product quality and profitability. However, existing decision-making methods such as fundamental Decision-Making Trial and Evaluation Laboratory (DEMATEL) and fuzzy DEMATEL often exhibit limitations in handling uncertainty, vagueness, and hesitation in expert evaluations, resulting in less accurate causal analysis. Therefore, a more robust approach is needed to effectively model such ambiguity in assessing interrelated GM factors. This study proposes an Intuitionistic Trapezoidal Fuzzy DEMATEL (ITF-DEMATEL) model to evaluate the key factors influencing GM adoption. The ITF-DEMATEL integrates intuitionistic and trapezoidal fuzzy theories to more accurately represent uncertainty and expert hesitation in complex decision contexts. Eight influential factors were analyzed: leadership dedication and support, company policies, influence from peer governments, social influence, customer characteristics, green supply chain management, government support, and integration of novel methods. The results reveal that leadership dedication and support is the most important criterion, while government support is the most influential factor. Comparative analysis with fundamental DEMATEL and fuzzy DEMATEL reveals that the proposed ITF-DEMATEL improves ranking stability, causal differentiation, and interpretive reliability, offering valuable insights for sustainable manufacturing decision-making.
Effects of Skin Thermoregulation Delay in a Dynamical Model of Heat Transfer in an Infant Incubator
Infant incubators are lifelines for millions of premature and sick infants worldwide. This device regulates an appropriate environment, where temperature control holds utmost importance. Heat transfer in infant incubators was previously studied using ODE models that did not account for temperature fluctuations due to fat and skin thickness. Hence, a system of delay differential equations describing heat transfer in an infant incubator considering skin thermoregulation delay, which is reflected using a time-delay parameter, is proposed and analyzed in this paper. Initial results include the existence of a unique positive equilibrium describing the ideal temperatures of the infant’s core, skin, and the incubator’s air space. To investigate the effects of the skin thermoregulation delay on the dynamical behavior near the equilibrium, a local stability analysis was performed using the time delay as the main parameter. Conditions for the absolute stability of the equilibrium and occurrence of stability switches were derived. In the latter case, a locally asymptotically stable equilibrium can become unstable due to the occurrence of a Hopf bifurcation at a threshold value of the time-delay parameter. Numerical simulations reveal that the limit-cycle solutions born from the Hopf bifurcation can lead to extreme temperature fluctuations, catastrophic for the infant. To maintain the ideal temperatures, the aim is to have conditions where the equilibrium is either absolutely stable or the time-delay parameter is kept lower than the threshold value. This paper helps to better understand the dynamics of heat transfer in an infant incubator, which benefits premature and sick infants.
Modelling Malaysian Mortality Improvement Using a Hybrid Logistic Spline
The unexpected rise in life expectancy and the declining mortality rates among older individuals have positioned Malaysia as an ageing nation. As this demographic shift becomes increasingly significant, the modeling of mortality rates is essential to address the specific needs of the elderly population effectively and promptly. However, the task of modeling these rates presents challenges, as mortality data frequently display irregular patterns attributed to various uncertainties. It cannot be formulated as a single mathematical equation as some age ranges have different shapes of mortality. The existing mortality models consisted of two separate mortality estimations between young and old age. This resulted in high correlation of parameters, rough mortality curve and incoherence estimation. To solve this problem, this research proposes a hybrid method that integrates a spline with three parametric logistic models. This research applies the proposed model to the Malaysian data from 2011 to 2021. The result is validated using Semi-Parametric Bootstrap. The bias and standard variation resulted from the bootstrap method declined as the number of replications increased, which indicates that the hybrid model able to estimate the underlying mortality risk. Thus, as this research provides a viable estimation of mortality improvement, this study will certainly bring a substantial benefit to the pension and health care provider and the insurance companies to calculate the mortality risk and build a sustainable reserve fund.
Efficacy of Three Semi-Analytical Techniques for Solving PDEs Arising in Turbulent Flow Motion
This research explores the solution of coupled 2-D and 3-D Burgers' equations, a partial differential equation arising in turbulent flow motion through three accurate and efficient techniques. The proposed schemes are the mixture of Sumudu transform with three classical techniques such as homotopy perturbation method (STHPM), Adomian decomposition method (STADM), and variational iteration method (STVIM). The research aims to compare these methods, evaluating their accuracy and efficacy in solving these systems of equations. The analysis seeks to uncover the strengths and limitations of each approach, contributing to the progress of numerical techniques for addressing coupled Burgers' equations. A comparison with the Finite Difference Method (FDM) is also performed to assess the efficiency of these hybrid techniques.
Flow of Micropolar Hybrid Nanofluid Via Permeable Stretching/Shrinking Sheet and Heat Transfer Optimization Through Response Surface Methodology
Numerical research has been done on the mathematical modelling of micropolar Cu-Al2O3/water nanofluid flow driven by a deformable sheet in a stagnation area with suction effect. Using appropriate similarity transformations, the governing partial differential equations are reduced to nonlinear ordinary differential equations, which are then solved numerically using bvp4c function in MATLAB. The hybrid nanofluids are made up of aluminium and copper nanoparticles dispersed in a base fluid called water. Due to variations in numerous relevant parameters, the given problem yields multiple solutions for both shrinking and stretching sheets. Interaction between these input parameters (hybrid nanoparticle, micropolar and suction) and their influences on heat transfer are assessed by a statistical Response Surface Methodology (RSM) model developed by BoxBehnken design approach. The Nusselt number is anticipated to have a maximum value of 18.2903. The RSM results indicate that the highest heat transfer coefficient is achieved when the suction parameter is at its maximum value and the hybrid nanoparticle and micropolar parameters are at their minimum values.
Logistic Regression Modelling of Complete Blood Count Parametersfor Predicting Diabetes Mellitus among Sabahan Women
According to the National Diabetes Registry, 1.6 million Malaysians have diabetes, with women having a higher prevalence than men (57.1% versus 42.9%). These figures highlight the necessity of customized diabetes care strategies that consider genderspecific characteristics. Therefore, this study investigates the relationship between diabetes in females and complete blood count (CBC) parameters. A dataset containing the CBC test results of 537 female patients obtained from the Clinical Laboratory at the Faculty of Medicine & Health Science at Universiti Malaysia Sabah was analyzed. The univariate analysis showed all variables were significant predictors of diabetes except for ethnicity. At the multivariate analysis, the following parameters were identified as significant: age (OR = 2.77, 95% CI: 1.402–5.486, p = 0.003), hemoglobin (OR = 22.56, 95% CI: 10.556–48.178, p = 0.000), MCV (OR = 26.20, 95% CI: 12.23–56.14, p = 0.000), MCH (OR = 2.29, 95% CI: 1.17–4.51, p = 0.016), MCHC (OR = 2.81, 95% CI: 1.40–5.65, p = 0.004), HCT (OR = 3.47, 95% CI: 1.72–6.70, p = 0.001), WBC (OR = 4.72, 95% CI: 2.39–9.32, p = 0.000), and platelets (OR = 7.95, 95% CI: 3.25–19.46, p = 0.000). The Hosmer-Lemeshow goodness-of-fit test indicates that the model aligns well, accurately reflects the data and satisfies the assumptions of logistic regression. When used and interpreted appropriately, these CBC parameters can be useful indicators to support decision-making for the monitoring and treatment of diabetes mellitus-related issues in females.
Cox-Integrated Spline Sieve Estimation for Doubly Censored Data
When considering doubly censored data, the failure time of interest refers to the duration between the initial and a subsequent event, where both events may be censored. Prior studies investigating the association between these events have yielded statistically inconclusive results due to their reliance exclusively on censored data (initial event). To address this limitation, a spline-based sieve maximum likelihood estimator (MLE) was developed by using integrated spline (I-splines) and M-splines (derivative of I-splines), integrated with Cox Proportional Hazards, to investigate the association between HIV infection and AIDS incubation period. Unlike prior studies, the proposed approach incorporates exactly observed event times and censored data to improve dependency estimates. The estimator treats the initial event as a covariate, allowing for a direct investigation of its relationship with the failure time. Spline functions simultaneously estimate parametric/non-parametric components, simplifying model implementation; multiple imputation ensures robustness to missing data. The asymptotic properties of the estimator are rigorously established. In simulation studies with sample sizes n = 50, 100, 200, 300, the proposed estimator achieved a reduction up to 93% in mean squared error and a 95% reduction in bias compared with existing methods, with coverage probabilities close to nominal levels. In the AIDS dataset, the proposed method revealed a statistically significant association between HIV infection timing and AIDS incubation (log HR = 0.580) that earlier analyses failed to detect (log HR = -0.11), and produced more precise treatment effect estimates. These findings highlight the enhanced sensitivity and statistical power of the proposed Cox-integrated spline sieve approach.
Comparative Analysis of Multivariate Copula Models for Extreme Rainfall Simulation in Pahang, Malaysia
Extreme rainfall events, often occurring at a regional scale, imply a degree of interdependence among several variables within the same locality. To replicate this dependency structure, a joint probability analysis utilizing multivariate approach is required. Conventional multivariate probabilistic distributions inherently imply that all variables adhere to the same distribution, which is realistically untrue. Alternatively, this study utilized the copula theory; a robust statistical method that enables the separate modelling of joint distributions and univariate marginals, thereby allowing for the simultaneous modelling and simulation of annual maximum daily rainfall data obtained from three rainfall stations in Pahang. From the modelling perspective, the efficacy of four distinct copula models was illustrated and the goodness-of-fit test indicates that the Gumbel copula is the superior model, followed by the Frank, Clayton, and Skew-t copulas, in that sequence. Moreover, the simulated data was able to be reverted back into meaningful value, allowing direct comparison to be done in terms of mean and variance of simulated and empirical values. The finding suggests all copula models successfully produced simulation outcomes that closely mimic the empirical distributions and characteristics of the observed extreme rainfall. These findings establish the basis for subsequent research on spatial and risk analysis of extreme events, which is essential for effective decision-making and catastrophe mitigation.
Sakiadis Flow with Thermal Radiation and Richardson Number via Inclined Stretching Plate
This study aims to elucidate a steady two-dimensional laminar mixed convection boundary layer over an inclined stretching plate immersed in an incompressible viscous fluid. The governing partial differential equations (PDEs) are first reduced to ordinary differential equations (ODEs) using a similarity transformation, before being solved numerically using MATLAB software, which is based on the boundary value problem fourth order (bvp4c) method. The effects of the radiation parameter, angle of the plate, Richardson number, and the Prandtl number on the heat transfer and fluid flow characteristics are obtained and discussed. The impacts of flow constraints on the velocity profile, temperature profile, heat transfer rate and skin friction coefficients are presented in tables and figures. The observation indicates that the rate of heat transfer at the surface rises with increases in the Richardson number, and inclination of plate angle but it diminishes as the radiation parameter increases. Meanwhile, the drag force experiences a decrease with an increase in radiation and the inclination of the plate angle. However, it demonstrates an increase with the rise values of Richardson number and Prandtl number. This leads to enhanced buoyancy-driven flow effects, ultimately resulting in a reduced skin friction along the surface Moreover, a further analysis using Computational Fluid Dynamics (CFD) simulation is applied to solve the governing equations of flow in interesting applications, enhancing the understanding of boundary layer flow.
Elasticity Effects on Hydromagnetic Convective Instability in a Salt Containing Maxwell Nanofluid Saturated Porous Layer
Maxwell fluids are important in both theoretical research and practical applications due to their ability to model real-world non-Newtonian behaviors that cannot be captured by simpler Newtonian fluid models. Maxwell fluids provide a realistic and manageable way to study non-Newtonian behavior that are widely applicable across engineering, biology and physics. In essence, Maxwell fluids enabling accurate modelling of materials with memory, where both elastic rebound and viscous flow occur simultaneously. The study examines the double diffusive instability resulting from temperature and nanoparticle concentration gradients. Specifically, it investigates the influence of a magnetic field on convective instabilities in a Maxwell nanofluid saturated porous layer containing salt. Linear stability analysis is utilized to determine the onset of both stationary and oscillatory convection. The Galerkin-type weighted residual method is applied to solve the system analytically. Key parameters examined include the relaxation parameter, solutal Darcy Rayleigh number and Chandrasekhar number. Findings suggest that the magnetic field plays a stabilizing role in both types of convective instability, while the relaxation parameter enhances oscillatory convection. Moreover, the solutal Darcy Rayleigh number contributes to the destabilization of the system in both stationary and oscillatory states. The findings on salt concentration and nanoparticle dynamics can be utilized to enhance targeted drug delivery using magnetic nanoparticles within blood flow.
Modeling of Double-Lane Roundabouts Using the Waiting System
Roundabouts are designed to improve intersection safety and efficiency, and in Malaysia, most are double-laned. However, existing methods for analysing roundabout performance remain limited. Some focus only on traffic parameters or signal controls without considering density and flux variations, while others treat double-lane roundabouts as separate, independent lanes or restrict analysis to entry and exit points. These approaches do not fully capture the dynamics of a complete double-lane four-arm roundabout. To address this gap, a new double-lane roundabout model is derived. The model represents the roundabout as two interconnected rings with four arms and incorporates a waiting system to better reflect real traffic conditions. It is formulated using hyperbolic partial differential equations within a macroscopic framework and solved numerically with the Godunov method. The Courant-Friedrichs-Lewy (CFL) condition is applied to ensure stability and accuracy of the simulations. Simulation results demonstrate logical changes in Total Travel Time and Total Waiting Time under different parameter settings, reflecting realistic traffic flow behaviour. This model provides a more comprehensive representation of double-lane roundabouts, offering valuable insights for researchers as well as government and private agencies in evaluating roundabout performance.
Bifurcation Analysis of a Modified SIR-Based COVID-19 Model with Nonlinear Incidence and Recovery Rates
In this work, a Susceptible-Infected-Recovered (SIR)-based epidemic model incorporating nonlinear incidence and recovery rates, with the consideration of limited medical resources (e.g., the availability of hospital beds) is examined. The model also emphasises the significance of factoring in distinct intervention strategies and considers some important epidemiological factors, in the light of COVID-19 endemicity. In particular, the study employs a Monod-type nonlinear incidence rate coupled with a nonlinear recovery equation, to uncover the intricate dynamics that emerge from the interplay of these epidemiological forces. The findings reveal the existence of disease-free and endemic equilibria, their stability conditions, and bifurcational changes in the dynamics of the system. Bifurcation analysis demonstrates the emergence of transcritical, saddle-node and Hopf bifurcations with the existence of distinct stable and unstable equilibria and limit cycles. Overall, this work highlights the importance of mathematical modeling and dynamical systems techniques in investigating the interplay among various epidemiological factors, thereby providing valuable insights to guide effective epidemic control strategies.
New COVRATIO Statistic for Outlier Detection in Simultaneous Linear Functional Relationship Model and Its Application in Malacca Environmental Dataset
Environmental studies, such as monsoon analysis, require examining relationships between multiple linear variables like wind speed, humidity, and temperature, with consideration of errors in each variable. Outliers in the data may affect the accuracy of analysis. This study aims to develop and validate a novel method for detecting outliers in simultaneous linear functional relationship model (LFRM) using the COVRATIO statistic. The objectives include deriving cut-off points for outlier detection using Monte Carlo simulations and demonstrating the method’s effectiveness on synthetic and real-world environmental datasets from Malacca. The findings confirm that the proposed method accurately identifies outliers, with detection performance improving as the variance of data contamination increases. Application to Malacca’s environmental data during the 2020 southwest monsoon season revealed significant outliers in the relationship between wind speed and humidity, while no outliers were found for wind speed and temperature. Removing detected outliers resulted in improved parameter estimates and reduced variance, enhancing the reliability of the LFRM. Data normality was verified through Q-Q plots and the Kolmogorov-Smirnov test statistic, demonstrating the robustness and applicability of the method in environmental studies.
Forecasting Flash Floods in Puchong Using Data-Driven Machine Learning Techniques
Flash floods are highly destructive in Malaysia. Mainly endangering lives, damaging infrastructure and disrupting the economy. Reliable and precise forecasting is essential to reduce their e ects and enhance preparedness. This study investigates the application of Machine Learning (ML) algorithms such as Logistic Regression (LR), Random Forest (RF) and Long Short-Term Memory (LSTM) to develop a robust flash flood prediction model. Historical water level and rainfall data recorded every 15 minutes during the flash flood at IOI Puchong Jaya on December 15-16 2023 were utilized for model training. Lag features were created to capture temporal dependencies and enhance predictive accuracy. Among the tested models, RF outperformed other models by achieving an accuracy of 0.50 and a perfect recall of 1.00 successfully detecting all flash flood events with an impressive computation time of just 0.01 seconds. Further real-world validation during the May 2024 flash floods demonstrated RF's reliability in predicting actual flood occurrences while minimizing false alarms. The findings underscore the potential of RF as a powerful tool for flash flood prediction. Its integration into flash flood warning systems can provide emergency responders with timely and accurate predictions. This research highlights the significance of ML-driven approaches in addressing the challenges of flash flood management and offers valuable insights for future disaster mitigation strategies.
Comparative Study and Prediction of Seasonal Adjustment Methods for Economic Time Series
Economic time series generally contain seasonal influences. In order to accurately analyze the development and changes of economic phenomena, internationally widely used methods such as X-12-ARIMA and TRAMO/SEATS are applied for seasonal adjustment to remove seasonal effects, thereby providing a theoretical foundation for further modeling, analysis, and forecasting of economic time series. This study takes the total retail sales of consumer goods in China from January 2009 to June 2023 as the research object. By comparing the residuals of two fitted models, it is found that the X-12-ARIMA method performs better. Therefore, the original series is modeled using the ARIMA approach, and data from July 2023 to June 2024 are forecasted, with prediction errors satisfying the national statistical error standards. After seasonal adjustment, seasonal factors are successfully separated from the total retail sales of consumer goods, restoring the underlying structure of the original series. This provides a basis for better understanding macroeconomic characteristics, capturing economic dynamics, and improving the accuracy of economic forecasting.
Enhancing Drought Forecasting Accuracy: A Wavelet-ARIMA Modelling Approach for Standardized Precipitation Index Data
Droughts are periods of inadequate precipitation that have severe and diverse impacts on human societies and ecosystems. However, a reliable forecasting technique can improve drought monitoring and mitigate impacts. This research aimed to assess the predictive accuracy of a hybrid forecasting model, Wavelet-ARIMA (W-ARIMA), for drought forecasting, using the Standardized Precipitation Index (SPI) with a traditional ARIMA model as a benchmark. Monthly rainfall data from the Badeggi district in the north-central part of Nigeria, covering the period from January 1968 to December 2018, was utilized for the analysis. Subsequently, SPI values for various time scales (3, 6, 9, and 12) were computed. The Wavelet Transform was then employed to decompose the data series into L components, encompassing details and approximations from A to D (AnD1D2 DL 1) of the SPIs, respectively. Furthermore, an ARIMA model was fitted to each of these details and approximations, and their sum constituted the forecasted W-ARIMA values. Evaluation based on the performance metric, the RMSE of W-ARIMA (0.4889, 0.4326, 0.3566, and 0.2177) while that of ARIMA (0.7771, 0.6667, 0.4648, and 0.3212), revealed that the hybrid model (W-ARIMA) consistently outperformed the ARIMA model among the metrics for all the SPI 3, 6, 9, and 12, respectively.
Modeling the Dual Nature of Ionizing Radiation: Analyzing DNA Double-Strand Break Repair Using Differential Equations Approach
The term "dual nature of ionising radiation" effectively captures the complex impacts of irradiation on human cells. This paper sheds light on the critical effects of irradiation on DNA structural integrity by examining double-strand breaks (DSBs). Our primary goal was to advance the existing model of bystander effects to facilitate the concurrent repair of multiple DNA DSBs, significantly enhancing the understanding of cellular response. We adopted the exponential matrix technique to solve a system of ordinary differential equations (ODEs) using MATLAB R2024a. Our refined model offers a comprehensive overview of cell populations and their DNA, explicitly addressing the number of DSBs and the incidence of mis-repaired breaks. Notably, this model considers the simultaneous repair of multiple DSBs, a crucial addition that allows for a more realistic representation of cellular dynamics. The simulation results reveal a remarkable 37% improvement in cell survival fractions compared to the existing model, underscoring a notable increase in DSB repair efficiency and overall cell viability. This indicates that our modified model outperforms the existing framework by facilitating greater DSB repair, ultimately leading to a higher survival fraction among cells exposed to radiation. These compelling findings demonstrate that our modified model provides a more accurate estimation of cellular response to irradiation than prior models. Furthermore, exploring alternative methods for solving the ODEs and integrating the cell cycle parameters into our framework could yield even more promising results. These insights are crucial for enhancing our understanding of DNA’s response to irradiation, paving the way for future innovations in cellular repair mechanisms.
Point Source Heavy Metal Migration in Soil with Adsorption and Desorption under Neumann Boundary Condition
Heavy metal pollution has always been a serious global environmental issue worldwide, and many scholars have demonstrated the feasibility of advection diffusion equation (ADE) to describe its transport in soil. In previous models, for boundary conditions involving instantaneous release of the source term, Dirichlet boundary condition was often used. This study examines Neumann boundary conditions, focusing on the evolution of concentration gradients, the influence of retardation factors, and the effect of the particle release ratio. Initially, sharp concentration gradients form near the point source, with the peak concentration shifting over time as the contaminant front progresses. Lower retardation factors increase migration speed and broaden the contaminant distribution. Additionally, a higher particle release ratio leads to higher local concentrations, underscoring the significant impact of soil porosity on contaminant transport. These findings provide insights for developing more accurate predictive tools for environmental remediation of heavy metal pollution.
Shortest Path Problem for a Graph of DNA String using Adenine and Guanine in Purine Bases as the Vertex
In the field of research on Deoxyribonucleic acid (DNA), graph theory can be applied to model the structure of a DNA molecule. In particular, the shortest path problem in graph theory can be used to identify the shortest path between vertices of a graph. Hence, it is possible to apply the shortest path problem for minimization of a DNA string so that the time taken for computation of genome assembly can be reduced. This paper presents a method to represent a DNA string graphically where the shortest path is calculated for the graph generated from the DNA string, and the shortest path is then used to minimize the DNA string. In this research, a DNA string is presented in graphical form by using base pairs of length two as the vertices where the initial bases used are Adenine (A) and Guanine (G) which are the main bases in purine. The number of base pairs between adjacent vertices in the DNA string is represented by the edges. The graph is then reduced by following a given set of rules where the shortest path is calculated for all start and end vertices of the reduced graph. Next, the simplification of the graph is done based on the shortest paths obtained by removing all the untraversed paths where the Euler path for the simplified graph is used to form a minimized DNA string. The result shows that a minimized DNA string can be obtained by simplifying the graph of the DNA string using the shortest path problem
Prioritizing Criteria for Reducing Single-Use Plastic Waste in Foodservice: A Fuzzy Analytic Hierarchy Process (FAHP)-Based Study with Key Player Insights
The increase in reliance on online meal delivery services and take-outs due to changes in consumer behavior has resulted in an escalation in the consumption and disposal of single-use plastics. With the mounting apprehension over the ecological consequences of the increased plastic waste, finding effective solutions is a crucial concern. This study aims to determine the priority of criterion for reducing plastic waste in the foodservice sector using the Fuzzy Analytic Hierarchy Process (FAHP) method. An onsite survey via questionnaire was conducted to gather responses from 16 key players who were involved directly and indirectly in plastic waste mitigation initiatives. The findings revealed that the key players prioritize the criteria related to harmonizing environmental impact, whereas public acceptance was assessed as being of lower priority. This article offers practical insights into single-use plastics in foodservice, using real data and perspectives from experts in the field to build on existing research.
Optimizing Food Manufacturing using Hybrid DES-DEA techniques: A Case Study at a Bean Curd Puff Factory
The competitiveness and expansion of small and medium-sized business (SMEs) engaged in food manufacturing is restricted by ineffective food production systems. This paper applied discrete event simulation to construct and simulate a representation of a food production line which enables better management and performance enhancement. The simulation results inform three improvement models (IMs) designed to reduce bottlenecks by adjusting operator allocation and soybean entry intervals. Data Envelopment Analysis (DEA) with the Banker, Charnes, and Cooper (BCC) output-oriented model is employed to determine the efficiency score of each improvement scenario. Additionally, Cross efficiency and Super efficiency BCC methods are used to rank these strategies, aiding in the selection of the best improvement model to maximise total production and average resource utilisation. This study found that IM1 which involves Operator 1 managing the grinding and coagulation process, Operator 2 handling the filtering and moulding process, and Operator 5 being assigned to the cutting process with a 15-minute interval between soybean entries selected as the most effective improvement model. IM1 significantly reduces the average cycle time by 30.25%, increases total production output from 144 kg to 198 kg and achieves a modest increase of 5.91% in average resource utilisation. This study demonstrates the value of discrete event simulation and DEA in operational decision-making by providing strategies that help in identifying and eliminating bottlenecks and optimise the food production process. The findings contribute to improve the competitiveness of SMEs by providing a reproducible framework for optimising production efficiency and resource usage in food manufacturing systems.
Modified Hungarian Method in Optimizing Preference Levels in Lecturer-To-Course Assignment
Effective allocating lecturers to courses is vital in Higher Education Institutions, ensuring that faculty expertise and resources are used to their best advantage. Previous studies have often utilized the Hungarian method for this task, but the Modified Hungarian Method (MHM) has yet to be extensively explored. Moreover, incorporating lecturer preferences, which range across four distinct levels from having never taught the course to having participated in training for the course, into the assignment process has yet to be previously implemented. This paper introduces a mathematical programming method to enhance the formulation of the MHM model. The preference-based MHM (P-MHM) model incorporates lecturers’ course preferences, aiming to maximize the preferences obtained from lecturer-to-course assignments. We gathered preference data from Mathematics lecturers at UiTM Shah Alam via an online survey, which served as the input for the P-MHM model. This model was solved using MATLAB’s intlinprog function, producing an optimal assignment where lecturers are assigned to a maximum of three courses. The optimal results of the P-MHM model determine the most suitable course assignments for each lecturer based on their preference levels. The model seeks to enhance educational quality and improve overall academic outcomes by aligning lecturer capabilities with the courses offered.
Time-Varying Autoregressive Models for Economic Forecasting
This research introduces a time-varying autoregressive (TVAR) model, developed to improve the precision of forecasting in economic time series data. The model advances the conventional TVAR framework by incorporating potential mean adjustments and utilizing the Kalman Filter within a Maximum Likelihood Estimation (MLE) framework, with further optimization through the NelderMead method. Applied to the real gross national product (GNP) data of the United States (U.S.), the model effectively captures dynamic patterns and structural changes that traditional models often overlook. The model’s performance is rigorously compared with the widely used Markov switching autoregressive (MSAR) model, demonstrating superior results in both training and testing forecasts. The TVAR model consistently achieves lower error metrics, underscoring its robustness and flexibility in capturing dynamic economic trends and providing reliable forecasts. This research emphasizes the TVAR models potential for broader applications in economic policy analysis, strategic planning, and decision-making processes, particularly in understanding and predicting economic growth. The models adaptability and precision make it a valuable tool for economists and policymakers aiming to navigate complex economic fluctuations with greater confidence as well as accuracy.
Triangular Z-Numbers Using Centroid Method: A Case Study of Flood Mitigation Measures
Z-numbers offer a robust framework for modeling human reasoning under uncertainty by integrating both a constraint and a reliability component. Despite their theoretical strengths, the practical application in Fuzzy Multi-Criteria Decision Making (FMCDM) has been limited due to the complexity of Z-numbers arithmetic and the lack of an effective defuzzification method that can preserve information during conversion to crisp values. To address these challenges, this study proposes a novel FMCDM methodology that employs triangular Z-numbers, a simplified representation to improve computational efficiency. The centroid method is adopted for defuzzification, selected for its ability to preserve the information during defuzzification process. This proposed methodology enhances both the computational feasibility and practical usability of Z-numbers, allowing decision-makers to express their judgments and confidence levels without relying on complex mathematical formulations. The effectiveness of the proposed methodology is demonstrated through a case study on flood mitigation measures, showing its ability to produce reliable and consistent rankings of alternatives. Comparative analysis with existing methods further validates the robustness and applicability of the proposed methodology, making it a viable tool for complex decision-making processes.
Derivative-Free Hybrid Conjugate Gradient Method for Constrained Nonlinear Equations with Projection in Line Searches
Robustness, efficiency, and accuracy are qualities that excellent algorithms should have. Due to the simplicity and minimal storage requirements, Conjugate gradient (CG) methods are useful for solving large-scale, unconstrained optimization problems. Despite that, it has a few drawbacks. Even if they have high numerical performance, certain approaches lack global convergence properties; therefore, the solutions might not be the most accurate. Various methods and modifications have been done. Some formulations would be difficult to comprehend and apply, and would lead to high CPU time. The proving process would also be impacted by the complex formulations. Over the past years, researchers have developed various globally convergent CG methods, but with a complicated algorithm, it rather hampered the implementation. Therefore, new CG methods with a derivate-free approach that have good convergence properties and outperform the existing CG coefficients in terms of number of iterations (NOI), number of function evaluations (NFE), and central processing time per unit (CPU time) are proposed. The proposed method will employ a non-derivative approach. This approach should make the algorithm’s processing time as minimal as possible. The comparison for derivative-free tools among the existed derivative-free CG. The proposed approach was chosen because it combines the strengths of the CG method with derivate-free optimization to optimize complicated objective functions without explicitly computing derivatives. This paper will show the derivate-free CG, which was proven to fulfil both convergence analysis and numerical performance.
Investigation of the Asymptotics of Quadratic Lotka-Volterra Mappings and Their Connections with Elements of Graph Theory
The relevance of studying discrete Lotka-Volterra mappings lies in their applicability to modeling epidemiological and environmental problems. In this regard, this work focuses on the asymptotic behavior of these mappings and their connection with graph theory. The paper demonstrates that, when these mappings are in a general position, they can be associated with complete oriented graphs. However, in degenerate cases, it may be associated with partially oriented graphs. Furthermore, the paper proves that, if the skew-symmetric matrix corresponding to a Lotka-Volterra mapping is not in general position, then the set of fixed points becomes infinite. In addition, sets of fixed points are constructed for systems that are discrete analogues of continuous compartmental models, SIR; SIRD; and the characteristics of these fixed points are studied by analyzing the spectrum of the Jacobian matrix and constructing a phase portrait of the trajectories of the interior points.
A Theoretical and Simulation Study in Formulating the Optimal Biasing Parameter for an Almost Unbiased Regression Estimator
To combat multicollinearity problem in linear regression model, a biased estimator named as the k-almost unbiased regression estimator (KAURE) was investigated in this study. KAURE is associated with the biasing parameter, and the mean squared error (MSE) of KAURE is sensitive to changes in the biasing parameter. Theoretical and numerical comparisons in the previous research showed that KAURE outperformed ordinary least squares estimator (OLSE) in terms of MSE when the biasing parameter was within a specific range. However, KAURE is not unique within the specified range of the biasing parameter, which complicates its practical use in linear regression modeling. Hence, there is a need to find an optimal biasing parameter for KAURE to enable practitioners across various fields to effectively use KAURE. In this paper, some new methods to estimate the optimal biasing parameter for KAURE that minimizes its MSE were proposed. Extensive Monte Carlo simulations were conducted to evaluate the performance of the proposed methods based on the average mean squared error (AMSE) criterion by varying the values of different factors (sample size, error standard deviation and degree of multicollinearity). The simulation results were confirmed by the empirical application. Thus, the proposed optimal biasing parameter provides a novel approach to formulating KAURE, enhancing its effectiveness as a practical alternative to OLSE for addressing multicollinearity issues in linear regression models.