Journal of Computational Innovation and Analytics (JCIA)
Journal of Computational Innovation and Analytics (JCIA) is an initiative of the School of Quantitative Science (SQS), Universiti Utara Malaysia to further promote high-impact scholarly articles among the professionals globally. The meaning of each word from the JCIA title is as follows: · Computational: Involving the calculation of answers, amounts, results, etc. · Innovation: The development of new products, designs, or ideas. · Analytics: A process in which a computer examines information using mathematical methods in order to find useful patterns. JCIA is an international free open access journal of double-blind techniques that aims to publish original papers every 6 months (January and July) in the form of concepts, research results, as well as case studies, related to ideas, consulting activities, designs, and new products, as well as quantitative methods for identifying useful patterns in the applications of mathematics, computational mathematics and statistics. JCIA in particular will build its identity by focusing on innovative aspects and computational - based analytical methods. In particular, JCIA is designed to focus on the publication of papers in fields which are still new and even rapidly growing and are currently focusing on innovation in the production of mathematical methods to identify useful patterns in pure mathematics, computational mathematics, pure statistics, applied mathematics and statistics, probability and uncertainty, as well as computer science applications.
A Novel Extension of the Rayleigh Distribution for Biomedical Data: Properties, Estimation and Performance
This study introduces a new compound distribution by combining the Rayleigh distribution with the sine type II Topp-Leone (STIITL) generator, resulting in the sine type II Topp-Leone Rayleigh (STIITLR) distribution. The proposed model incorporates a trigonometric function and an additional shape parameter, offering greater flexibility in modelling diverse data behaviours. Several statistical properties of the distribution are derived, including moments, the moment-generating function, hazard and survival functions, and entropy. To estimate the model parameters, four methods were employed: maximum likelihood, Anderson-Darling, Cramér-von Mises, and maximum product of spacings. A simulation study was conducted to evaluate the consistency and accuracy of these estimators. As sample sizes increased, the estimators showed improved performance, with reductions in bias and root-mean-square error. The STIITLR model was then applied to a biomedical dataset involving survival times of patients with acute myeloid leukaemia. Compared to competing distributions, the proposed model provided a superior fit. These findings demonstrate that the STIITLR distribution is a promising tool for modelling complex survival patterns in biomedical research, where flexibility and adaptability are essential.
Copyright (c) 2026 Journal of Computational Innovation and Analytics (JCIA) https://creativecommons.org/licenses/by/4.0
Ant Colony Optimization for Efficient Emergency Ambulance Routing in Urban Environments
Efficient ambulance routing plays an important role in emergency medical services. However, solving the ambulance routing problem remains challenging. This study investigates the performance of ant colony optimization to solve the ambulance routing problem, aiming to improve the quality of route planning under constraints such as traffic, patient urgency, and ambulance capacity. To simulate realistic emergency scenarios, 27 benchmark instances from the classical vehicle routing problem were adapted to the ambulance routing context by mapping depots to ambulance stations, customer nodes to emergency sites, and incorporating patient urgency and ambulance capacity. The performance of ant colony optimization was compared with the genetic algorithm and particle swarm optimization. Each algorithm was independently applied to all instances, and route quality was evaluated based on best route cost, average cost, and standard deviations. The experimental results show that ant colony optimization consistently outperformed both genetic algorithm and particle swarm optimization across most instances. Specifically, ant colony optimization achieved shorter total route distances, which are measured as the cumulative route distance of ambulances required to serve the emergency sites. These improvements were accompanied by greater consistency in solution quality across multiple runs. These findings suggest that ant colony optimization is a robust and effective tool for ambulance routing optimization. This study contributes to the growing body of work on intelligent emergency logistics by demonstrating the practical advantages of ant colony optimization in critical decision-making. The findings are valuable for optimization researchers in enhancing ambulance routing efficiency.
Computational Analysis of Engineering Licensure Outcomes Under Curricular Change using Propensity Score Matching
The Philippine higher education sector underwent a significant curriculum transition with the implementation of the K–12 program, which reduced the engineering curriculum from five to four years. This study examines the impact of this change on the licensure examination performance of engineering graduates. A causal-comparative design was used, with propensity score matching (PSM) applied to control for confounding variables such as age, sex, academic performance, exam timing, and degree program. Both nearest-neighbor and full matching methods were tested, with full matching selected for its superior covariate balance and sample retention. Using the matched dataset, logistic regression modeling revealed that graduates under the new 4-year curriculum had significantly lower odds (OR = 0.94) of passing the board exam than those under the 5-year track. The marginal mean comparison showed a 6.06 percentage point decrease in passing probability for the new curriculum group (p < .001), with a Cohen’s d of –0.53, indicating a moderate negative effect. These findings suggest that transitioning to the restructured 4-year engineering curriculum has not yet translated into improved licensure performance. A closer review of curriculum content, implementation fidelity, and academic support programs is needed to address this gap and strengthen outcomes under the reformed education system.
Copyright (c) 2025 Journal of Computational Innovation and Analytics (JCIA) https://creativecommons.org/licenses/by/4.0
Analyzing the Impact of External Factors on Stock Market Performance in the Fast Food Industry During Boycott Movements Using Multiple Linear Regression
This study examines the impact of boycott movements on the stock market performance of McDonald’s and Pizza Hut from October 2023 to January 2025. Using Multiple Linear Regression (MLR), the research analyzes key factors affecting stock prices, including war-related news, boycott-related news, trading volume, and boycott duration. The findings indicate that historical stock prices significantly influence both companies’ stock performance, with McDonald’s showing an R-squared value of 83.0% and Pizza Hut 67.7%. War-related and boycott-related news were measured based on weekly article frequency, trading volume was obtained from official stock data, and boycott duration was calculated as the number of days since the boycott announcement, measured weekly. Trading volume was found not to be statistically significant in affecting McDonald’s stock price at the 99% confidence level. At the same time, other factors, such as boycott-related news and war-related news, do not have a significant impact on either company. The results suggest that investors prioritize historical stock trends over external socio-political events. This study provides insights into the financial consequences of consumer activism and can assist investors, policymakers, and business strategists in understanding market reactions to boycott movements.
A Robust Cumulative Sum for Monitoring High-Risk Stock in Malaysia
This study analyzes stock price fluctuations and develops a robust monitoring approach for high-risk stocks. It identifies key factors influencing stock price volatility, constructs a median CUSUM control chart as an alternative to the mean CUSUM chart, which requires a normality assumption unsuitable for stock price data, and compares the median CUSUM chart’s performance during and after COVID-19. A Fishbone diagram is used to determine factors contributing to stock price fluctuations, while a risk matrix helps identify high-risk stocks. The median CUSUM control chart is then applied to monitor stock performance, focusing on FINTEC Global Berhad. Findings indicate that during and after COVID-19, FINTEC’s stock performance declined due to financial management challenges that affected investor sentiment. The study highlights the effectiveness of the median CUSUM chart in detecting small shifts in stock fluctuations compared to the mean CUSUM chart. This approach provides a more reliable framework for monitoring stock risk and volatility. By integrating quality tools such as the Fishbone diagram and risk matrix, this study enhances risk assessment strategies in financial market analysis, offering valuable insights for investors and analysts navigating uncertain market conditions.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Assessing the Impact of Fintech on Financial Inclusion in Algeria: An Analysis of Digital Financial Transformation Indicators (2016-2022)
This research examines the correlation between financial inclusion and financial technology (fintech), particularly emphasizing how fintech can expedite the digital financial transformation in Algeria. This research examines how individuals and institutions are able to access financial products and services, contributing to broader economic development. The study covers the period from 2016 to 2022, employing a descriptive-analytical methodology and incorporating the deductive method for analyzing economic data derived from official statistics and World Bank reports. According to the World Bank data, the adoption of fintech is slowing down due to the deficiency in financial knowledge of the Algerian banking system. According to World Bank data, the results indicate that Algeria is making slow progress in implementing fintech solutions to enhance financial inclusion. This stagnation is attributed to low financial literacy levels and the underdeveloped banking system. The originality of this paper lies in its focus on the intersection between fintech adoption and financial inclusion in the Algerian context, a relatively underexplored area. The study affirms the need to accelerate the digitization of financial and banking services in Algeria by promoting fintech products, improving software infrastructure, and enhancing network readiness indicators. The research concludes with recommendations for policymakers to improve digital financial literacy and foster innovation in banking technologies to achieve broader financial inclusion.
Adequacy of Inflation-Indexed Life Annuity: A Case of Defined Contribution Pension Plan in Malaysia
Defined Contribution (DC) pension plans in some Asian countries are currently operated by the lump-sum withdrawal scheme, which allows retirees to withdraw all accrued savings in a single withdrawal. This type of withdrawal does not protect pensioners from the longevity risk. This study examines the adequacy of an alternative for the lump-sum withdrawal system, namely the withdrawal system based on the Inflation-Indexed Life Annuity (IILA). This study proposes two types of IILA: the immediate IILA and the deferred IILA. The adequacy of a life annuity is measured using the ratio of projected wealth to the projected consumption at the retirement age. For the deferred IILA, this study uses the Euler-Lagrange method to assess the optimal amount that will be set aside to finance the annuity scheme. The IILA and deferred IILA will protect retirees from purchasing under-valued annuities by adjusting the IILA to inflation risk. Furthermore, deferred IILA may improve the life quality of retirees by providing adequate retirement income during the deferment years. Analysis of data from the Household Income and Expenditure Survey (HEIS, 2014) indicates that the immediate IILA offers an average lifetime income of over 50%, while the deferred IILA provides more than 40%.
Integrating Analytical Hierarchical Process and Quadrant Matrix Analysis in a Decision Analytics Framework for Flood Management Planning
Flood management planning (FMP) requires decision analytics approaches that can prioritize multiple criteria while accounting for uncertainty in expert judgment and data availability. This study presents a decision analytics framework that integrates the Analytic Hierarchy Process (AHP) and Quadrant Matrix Analysis (QMA) to identify key flood and trade-off criteria, and to develop a structural plan to support FMP decision making. Using the PESTEL framework, a refined set of 40 criteria was assessed through structured elicitation involving 10 experts from diverse disciplines. AHP was applied to find the relative importance of each criterion, while QMA mapped the criteria by both importance and certainty. The AHP results indicate that the Political, Legal, and Economic domains were the main driving criteria for FMP. At the criterion level, P1: Fair distribution of resources was ranked first (0.1917), followed by L1: Land ownership for flood protection (0.0739) and L4: International constitutional standards and guidelines (0.0543). QMA, in contrast, identified 25 “Must‑Have” criteria, primarily within the Environmental, Social, and Technological domains. A comparison of AHP and QMA outputs reveals that criterion certainty significantly affects practical applicability, even for criteria that are highly ranked in importance. By jointly considering importance and certainty, EC7: Economic loss, EC5: Financial budget, and L3: Flood Disaster Institutional emerged as "Very Highly Ranked" and "Must-Have". The framework also incorporates a structured criterion‑reflection stage, informed by the Weather Model, to refine expert evaluations. Overall, the proposed decision analytics framework enhances clarity and consistency in the selection of criteria and offers practical guidance for FMP in Malaysia and other planning contexts.
Game-Based Pedagogy Meets Simulation: Using Board Games to Model Seaport Operations in Maritime Education
Game-based learning has gained traction as an innovative educational initiative, particularly in fields requiring strategic decision-making and operational management. This study explores the effectiveness of a board game, Port Management Game, as a pedagogical tool for enhancing students’ understanding of seaport management concepts. The primary objective of this study is to examine how board games can facilitate experiential learning, thus improving knowledge retention and supporting the development of critical decision-making skills among higher education students studying seaport management. A quasi-experimental research design involving a control and an experimental group was employed. The experimental group participated in board game sessions integrated into their coursework, while the control group underwent traditional lecture-based instruction. Data were collected through pre-tests and post-tests to assess students' learning outcomes. A split-plot ANOVA was conducted to determine the statistical significance of the differences in performance between the two groups. The findings revealed that students in the experimental group demonstrated a statistically significant improvement in their understanding of seaport management concepts compared to those in the control group. The results align with existing literature supporting the use of game-based learning as an effective pedagogical strategy in higher education. The significance of this study lies in its contribution to maritime education by validating the use of board games as an interactive learning medium and as a tool in enhancing students’ understanding of lessons. The study also highlights the potential of board games to bridge theoretical knowledge with practical application, preparing students for real-world challenges in the maritime industry. Furthermore, the research underscores the suitability of board games as a stepping stone toward apprenticeship training, as they provide a structured yet flexible environment for learning by doing.
PLS-SEM Approach to Analyzing the Marketing Mix and Its Impact on Repurchase Intentions in the Parboiled Rice Market
The market landscape is marked by intense competition, necessitating continuous product enhancements, particularly within the food sector. To meet evolving consumer demands and adapt to changing consumption patterns, the marketing mix serves as a strategic tool to foster profitable relationships rooted in consumer satisfaction and loyalty. This study aims to investigate the impact of the marketing mix on consumer satisfaction with parboiled rice and to examine its influence on repurchase intentions. Data were collected from 300 respondents and analyzed using PLS-SEM. Findings indicate that marketing mix elements; product, price, place, and promotion, positively and significantly influence consumer satisfaction. However, the relationship between these variables and repurchase intentions varied. The final model supports the connection between satisfaction and the intention to repurchase. Product attributes, such as the health benefits of parboiled rice, play a crucial role, alongside the significance of the sales location. Remarkably, there was no evidence to support the notion that price and repurchase intention were related, and promotion had a negative correlation with repurchase intention, which calls for more research. These insights are invaluable for parboiled rice producers and marketing managers, highlighting the need to enhance these product aspects to boost consumer engagement and loyalty.
A Novel Probability Distribution with Robust Parametric Properties
The continuous evolution of statistical modelling has spurred the development of novel probability distributions to address complex data challenges. This paper introduces the Topp-Leone Epsilon (TopLE) distribution, derived by combining the Topp-Leone and epsilon distributions. The TopLE distribution exhibits attractive parametric properties, including flexible shape and scale parameters, a closed-form quantile function, and efficient parameter estimation using computational tools. Simulation studies demonstrate the distribution's efficiency, consistency, and stability, making it suitable for a wide range of applications in lifetime data analysis. Two comparative applications with four standard distributions have shown the TopLE distribution to be a competitive choice in modelling real-life datasets. This work contributes to the statistical literature by providing a versatile and competitive model for modern data complexities.
Greedy Heuristic Algorithms for Multi-Objective Tourist Trip Design Problem on Penang Island
Tourism is a vital sector in the global economy, significantly contributing to the development of many countries by creating jobs, generating income, and promoting cultural exchange. However, planning a tour itinerary presents a significant challenge due to the necessity of identifying points of interest (POI) and organizing them into a proper itinerary. This study focused on solving the multi-objective TTDP for Penang Island by developing two greedy heuristic techniques: Nearest Neighbour (NN) and Nearest Greedy Insertion (NGI). The study aimed to achieve three objectives: identifying tourists’ preferences and constraints relevant to the TTDP, proposing solutions using NN and NGI techniques, and evaluating the effectiveness of these methods. Adopted a hybrid weighted objective, both algorithms balanced maximizing the number of POIs visited and popularity index while satisfying several constraints such as time windows, popularity index, restaurant selection, POI categories, and budget limitations. The findings showed that the NN technique performed better for a one-day tour, delivering a higher popularity index (212.66) compared to NGI (171.43). However, the NGI technique outperformed in a two-day tour by balancing POI coverage and popularity index of 294.91, while NN had 268.68. For a three-day tour, NN achieved a higher popularity index (359.86) than NGI (337.03), while NGI visited more POIs. Future studies should explore metaheuristic methods, broader geographic applications, incorporate time-dependent variables, and tailored solutions for halal-conscious travellers.
An Empirical Study on the Construction of A Non-Convex Risk Parity Portfolio Using A Genetic Algorithm
Risk-based portfolio optimization has become increasingly crucial due to the limitations and underperformance of traditional Mean-Variance (MV) portfolios. In the context of the Risk Parity (RP) portfolio, capital is allocated in such a way that each asset contributes equally to the overall risk of the portfolio. However, optimizing a non-convex RP portfolio presents significant challenges. While conventional numerical methods can be applied, they often struggle with inefficiency and fail to deliver optimal results. This study addresses these challenges by proposing the Genetic Algorithm (GA) and comparing its effectiveness against Successive Convex for RIsk Parity (SCRIP) to solve the non-convex RP portfolio. Using stock price data from companies listed on the Jakarta Islamic Index (JII), we demonstrate that the GA, although yielding a solution that slightly deviates from the ideal equal-risk-contribution, offers a more efficient and practical approach than SCRIP. Notably, the RP portfolio outperforms both the Equal Weight (EW) and Global Minimum Variance (GMV) portfolios in terms of lower Value at Risk (VaR) and Turnover (TO) ratios. This research contributes to the field by showcasing the GA as a viable tool for overcoming the limitations of traditional optimization methods, particularly in addressing the complexities of non-convex RP portfolios while considering practical investment constraints.
A Flexible Inverse Lomax Weibull-Weibull Model for Survival and Reliability Data
In this paper, an extension of the Weibull distribution using Inverse Lomax Weibull-G (ILWG) called Inverse Lomax Weibull-Weibull Distribution (ILWWD) was introduced. The statistical properties of the ILWWD were also derived. This includes the moment, moment generating function, quantile function, and distributions of order statistics. The parameters of the ILWWD were estimated using the maximum likelihood estimates (MLE) method. Simulation studies were presented for three distinct cases. Survival regression was also presented. Goodness-of-fit statistics were employed to assess the effectiveness of the ILWWD and Log-ILWWD. This indicated that the proposed distribution is superior in fitting leukemia data. Lastly, the flexibility of the ILWWD was demonstrated using a real-life dataset. These suggest that the proposed distribution is superior in fitting the datasets presented in this study.
Analyzing Economic, Community, and Financial Performances of Philippine Social Enterprises Using R Statistics Environment and Statistical Package for the Social Sciences (SPSS) Software
This research examines sustainability in a social enterprise, delving into the quantitative variables, namely, social impact, social mission, and financial responsibility towards financial performance. This study evaluated whether businesses practicing social entrepreneurship were sufficiently sustainable in light of media accounts that report their economic and community performance. By examining key indicators across economic, community, and financial performance, this study provides evidence-based insights into their contributions to sustainable development and identifies areas for improvement. The participants are Metro Manila-based social enterprises that have been operating for over five years. The inclusion criteria for the social enterprises were the following: (1) sought a mission to solve social problems, (2) established a sustainable model, and (3) recognized by domestic opinion leaders to be innovative and potentially leading to more considerable systematic change. The quantitative data were analyzed using R Statistics Environment and Statistical Package for the Social Sciences (SPSS) version 23. The data was summarized in frequency tables, and the output was presented using descriptive statistics. The ratio and gathered data were tested using the Multiple Regression Analysis (MRA) to show the effect between dependent and independent variables. The results show that Economic Performance and Community Performance significantly influence most sustainability metrics of the study subjects. Among the sustainability variables, only social mission had a significant impact on Return on Assets and Return on Equity. Social enterprises may utilize the results from this study to formulate better ideas in their respective fields and create opportunities to make their businesses more sustainable.
A Modification of Conjugate Gradient Parameter and Its Global Convergence for Solving Unconstrained Optimization Problems
The methods of nonlinear conjugate gradient coefficients are significant and helpful in solving large-scale unconstrained optimization problems, due to their simplicity and lower storage requirement. Research activities on its applications to handle higher-dimensional systems of nonlinear equations are of paramount importance. Many authors studied and developed different kinds of conjugate gradient coefficients. Recently, some conjugate gradients were proposed with large dimensions, but they have small sample sizes. So, they cannot solve problems that have higher dimensions. Therefore, this research proposed a modified algorithm with a huge dimension and a large sample size. The strategy of strong Wolfe line search was applied in the convergence analysis, which makes it possible to converge globally with a descent property. Finally, the numerical results show that the proposed algorithm performs more efficiently and is superior to the existing Conjugate Gradient (CG) coefficients.
Magnetohydrodynamics Inclination Effects on Couette Fluctuating Micro-Gas Flow
An analytical investigation is conducted in the laminar flow regime to examine the impacts of Magnetohydrodynamics (MHD) inclination on Couette fluctuating basic micro-gas-flow, in which the bottom plate remains fixed while the top plate swings in its plane with sinusoidal velocity. Using a transformation technique, the partial differential equations that govern the flow situation have been reduced to a system of analytically solvable ordinary differential equations. The impact of MHD inclination on the dimensionless velocity and skin friction's numerical values are displayed in a detailed analytical and graphical form. The analysis's conclusion reveals that velocity rises with increasing phase angle values but drops with increasing inclination angle, magnetism, and frequency of the fluctuating driving force.
ENSURING SUSTAINABLE WAQF-ZAKAT MICROTAKAFUL PROTECTION THROUGH A SYSTEM DYNAMICS APPROACH
The Islamic financial ecosystems, such as zakat and waqf, have roles in tackling poverty and can be integrated into providing financial protection, such as death benefits to the poor through microtakaful. However, unpredictable payouts and irregular contributions can hinder the implementation of microtakaful, affecting cash flows. This paper employs a system dynamics approach to simulate the cash flows of microtakaful funds, emphasizing the influence of three key factors: the rate of new participants, the mortality rate, and the contribution rate. Note that this approach is chosen for its ability to model complex systems and predict the behavior of microtakaful funds over time. The data collected were from various institutions, including the Life Insurance Association of Malaysia (LIAM), the Central Bank of Malaysia (BNM), and the Department of Statistics Malaysia (DOSM) from 2018 to 2020. The model indicates a minimum contribution rate of RM130 per year, with a 2% mortality rate and 0.8% annual increase in the new participants. The model serves as a foundation for designing a simple and understandable non-profit microtakaful product.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
DESIGN AND IMPLEMENTATION OF A WEB-BASED INFORMATION SYSTEM FOR UNIVERSITY STAFF UNION
The Academic Staff Union of Universities (ASUU) in Nigerian public universities has long championed the welfare and interests of academic staff. However, operational inefficiencies stemming from manual processes hinder its effectiveness in addressing contemporary challenges. This inefficiency hampers ASUU’s effectiveness and limits its ability to interact efficiently with its members and stakeholders. Therefore, this study proposes that the ASUU Web-Based Information System (AWBIS) should modernize data management, communication, and decision-making within the union. Drawing on related work in online information systems across various sectors, the methodology involves requirements gathering, leading to the development and implementation of AWBIS. Leveraging development technologies, AWBIS offers modules for member management, communication, event organization, and biometric attendance. This project uses the waterfall model approach to developing software as the research methodology. The data collection techniques include interviews and questionnaires. The AWBIS enables various operations to be performed online, consisting of registering members by the admin, logging members, and allowing members to manage their profiles. The system also provides a login page for both members and admins, each with their own username and password. A secure and accessible database for storing member profiles, academic information, and other relevant data was created. The successful implementation of AWBIS will significantly streamline ASUU’s operations, resulting in improved communication, reduced administrative burden, and enhanced transparency. Members will have access to up-to-date information, and the system will facilitate better coordination of ASUU’s activities. Future enhancements, including financial management, election processes, loan applications, and mobile app development, are suggested to further optimize AWBIS’s functionality and accessibility. AWBIS is a model for leveraging technology to advance organizational efficiency and effectiveness within the education sector.
STATISTICAL INFERENCE ON SINE-EXPONENTIAL DISTRIBUTION PARAMETER
The Sine-Exponential (Sine-E) distribution is a probability distribution that combines the periodic behavior of the sine function with the decay characteristic of the exponential function. This study addresses the problem of identifying the most accurate and reliable estimation method for the parameter of the Sine-E distribution. The objective is to evaluate various parameter estimation techniques, including Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE), Weighted Least Squares Estimation (WLSE), Maximum Product of Spacing Estimation (MPSE), Cramer-von-Mises Estimation (CVME), and Anderson-Darling Estimation (ADE), using Mean Square Error (MSE) as the criterion for determining the technique with the minimum error. The study’s findings reveal that as sample size increases, the parameter estimates for all techniques converge to the true parameter value, with decreases in bias, MSE, and mean relative estimates. Among the techniques evaluated, the MPSE method consistently provides estimates closest to the true parameter value and exhibits the least bias and lowest MSE across small, moderate, and large sample sizes, making it the best estimator for the Sine-E distribution.
DEVELOPMENT AND IMPLEMENTATION OF A NEW PATIENT APPOINTMENT SCHEDULING SYSTEM AT ADUSTECH WUDIL: AN EVALUATION OF USER SATISFACTION AND SYSTEM IMPACT
Globally, the healthcare sector is the pivot and integral part of human lives. Any error committed in the clinic services might lead to defects or the termination of life. In the rapidly evolving field of healthcare, efficient and accessible appointment scheduling plays a crucial role in ensuring timely and quality care for patients. This article is centered around creating a Patient Appointment Scheduling System, with a focus on handling registration and management, as well as managing bookings or making appointments and scheduling sessions at Aliko Dangote University of Science and Technology, Wudil (ADUSTECH Wudil). Identification of the drawbacks of the existing traditional system leads to the design of a computerized system, which is more user-friendly and GUI-oriented. HTML, PHP, CSS, JavaScript, and MySQL were used for the development. Note that performance was improved to 92% from using the traditional system. Similarly, the productivity of participants increased to 90%. Furthermore, the waiting time for patients to be attended was enhanced to 90% in the proposed system.
DECENTRALIZED ELECTRONIC VOTING WITH ETHEREUM BLOCKCHAIN IN DEMOCRATIC AND POLITICAL ELECTIONS
The Ethereum blockchain-based electronic voting (e-voting) systems can emerge as a viable strategy in this era of contemporary democracies to revolutionize political elections and augment the efficacy of the electoral process. There are myriad advantages that the Ethereum blockchain has to offer, from fairness to increased voting rates. Unlike traditional voting protocols, the Ethereum blockchain can assure substantial cost savings and eliminate the necessity for electoral intermediaries. The use of the Ethereum blockchain in political contexts also ensures that elections are held with integrity while preserving the voters’ privacy. Due to its popularity, provision of smart contracts logic, and various promising advantages, this systematic review aims to examine the potential deployment of decentralized e-voting systems integrated with Ethereum blockchain technology for democratic political elections. A systematic literature review (SLR) and the PICO approach, which stands for population, intervention, control, and outcomes, were adopted in this study to systematically analyze the existing literature. Key technological approaches identified in the voting system include the hybrid blockchain and privacy-preserving score voting. Among the noteworthy findings are the following: while adoption and complexity remain challenges across numerous e-voting frameworks, scalability, end-to-end security, enhanced efficiency, and effectiveness are key benefits. An exploration into the prospective future innovations, such as the integration of artificial intelligence and big data analytics into the Ethereum blockchain, was also included to further improve the reliability of the e-voting systems. It is believed that the Ethereum blockchain has a promising transformative impact on electoral politics and democratic processes, presenting a ray of hope for future elections.
Mathematical Modeling of Viscous Incompressible Fluid Flow in Tubular Networks: Analysis of Laminar and Turbulent Regimes
The article delves into the mathematical modeling of the flow of viscous, incompressible fluids through a network of tubes enclosed within an outer pipe. It comprehensively examines both laminar and turbulent flow regimes, providing a thorough analysis of the underlying physical phenomena. Specifically, it investigates the fluid dynamics through a configuration consisting of n tubes, each with a length L and radius r situated within the outer tube. The article derives formulae for calculating key parameters such as the maximum velocity of fluid flow, the volumetric flow rate through the tube's cross-section, the coefficient of frictional resistance along the tube's length, and the maximum value of tangential stress.
EXPLORING THE EFFICACY OF DIGITAL MEME MARKETING CAMPAIGNS IN GENERATING LEADS WITHIN THE GENERATION Z DEMOGRAPHIC
Quite recently, marketers have used memes because they entice customers’ attention. It serves as a means to have a dialogue between customers and companies. Some companies would even hire people to browse the internet for relevant memes. The emergence of Generation Z, who are digital natives, has brought significant changes to how businesses interact with their target audience. One potential strategy is to use digital memes as a marketing tool to engage them. The study, using 271 respondents, investigates the use of the aforementioned tool to determine its appropriateness in lead generation based on the Attention, Interest, Desire, Action (AIDA) model. Using the software Analysis of Moment Structures (AMOS), a descriptive exploratory design using Structural Equation Modeling (SEM) was used to investigate the relationship between the different variables in the study. The findings show that attention impacts interest, interest influences desire, and desire significantly affects action at a significance level of p < 0.01. The study revealed that Digital memes are an effective tool that can be utilized to generate leads. Businesses can use digital memes to engage with the Generation Z segment. However, prudence in creating memes must align the message with corporate values and be relevant to the target audience.
Forecasting Labour Force Trends Among Older Persons in Malaysia using Time Series Analysis
Malaysia has transitioned into an ageing society faster than the previous demographic model suggested. The latest report from the Department of Statistics Malaysia (DOSM) confirms that as of 2025, individuals aged 65 and older already comprise 8% of the total population. To address the objectives of Sustainable Development Goal (SDG) 8.5 regarding productive employment, this study forecasts labour force participation trends among Malaysians aged 60 to 64 through the year 2030. This specific age group represents a critical segment for extending working lives and maintaining national productivity. The analysis utilises annual time series data from 1982 to 2021 for model estimation and evaluation, while actual observations from 2022 to 2024 serve as an ex-post benchmark to verify the forecast accuracy. This study applies four forecasting techniques, including double exponential smoothing (DES), Holt’s exponential smoothing (HES), autoregressive integrated moving average (ARIMA), and time series regression (TSR). Following evaluation via mean absolute percentage error (MAPE), root mean square error (RMSE), and geometric root mean square error (GRMSE), the HES emerged as the most reliable, achieving a precision rate of 99.27% (0.73% error) against 2024 actuals. The final forecast trends indicate steady expansion, with the labour force participation expected to reach 533,020 older workers by 2030, which is a 10.47% cumulative increase from 2024. These findings confirm that prolonged workforce participation is no longer a temporary shift but a structural reality. Consequently, Malaysia requires immediate policy interventions focused on flexible retirement frameworks, targeted reskilling, and the creation of an age-inclusive workplace environment to sustain economic stability.
THE MODIFIED MEAN ABSOLUTE DEVIATION INTEGER LINEAR PROGRAMMING MODEL FOR GLOBAL PORTFOLIO ASSET ALLOCATION
This study develops a practical yet robust Mean Absolute Deviation (MAD) Mixed-Integer Linear Programming (MILP) model for a global investment portfolio tailored to the constraints faced by Malaysian retail investors, such as transactional fees and foreign currency exchange spreads, to provide more accurate estimations of portfolio returns. The MAD ILP model is modified to address these factors, aiming to enhance utility and efficiency. The study utilizes a Maybank 12-month fixed deposit cash account and ten selected Exchange-Traded Funds (ETFs) from iShares, Vanguard, and State Street Global Advisors for global portfolio construction. Forecasting techniques were utilized to generate expected returns for the ETFs and foreign currency exchange spreads. Based on the MAD model proposed by Konno and Wijayanayake (2001), the study introduces three modifications: incorporating transaction cost elements specific to Malaysian investors, including foreign currency exchange spreads in return calculations, and transforming the model from mixed-integer to pure integer model to accommodate minimum transaction lot constraint. Verification and validation exercises demonstrate the model’s ability to replicate real-world portfolio construction and reliable simulations. The model offers a practical tool for self-ascribed investors aiming to optimize global investment portfolios while considering unique constraints and transaction costs.
SOLVING BIPOLAR FULLY FUZZY SYLVESTER MATRIX EQUATIONS WITH NEGATIVE FUZZY NUMBERS
Sylvester matrix equations play a crucial role in control theory for controller design. Bipolar Fully Fuzzy Sylvester Matrix Equations (FFSME), incorporating both positive and negative components, are employed in controller design to address uncertainties that may affect a system’s performance and stability. However, there is not much existing research on combining bipolar fuzzy numbers and FFSME, and most of them mainly deal with positive coefficients. Thus, this paper presents a method that enables solving the negative coefficient of bipolar FFSME in the form of Left-Right (LR) triangular fuzzy numbers using an Associated Bipolar Linear System (ABLS). To obtain the ABLS, bipolar FFSME is transformed into a bipolar Fully Fuzzy Linear System (FFLS) using the Kronecker product and Vecoperator. Subsequently, the solution is derived through the inverse method, and the equation of the ABLS is rearranged as a bipolar fuzzy matrix. Additionally, this paper provides two numerical examples to illustrate the applicability of the constructed method.
THE IMPLEMENTATION OF THE ALEXANDER-GOVERN TEST IN FACTORIAL DESIGN ANALYSIS
This study proposed to evaluate the performance of the Alexander-Govern test (AG test), Analysis of Variance (ANOVA), and t-test by analyzing the Type I error rate. The AG test is regarded as a reliable control Type I error rate. This technique is insensitive in the presence of heteroscedasticity under a normal distribution. Simulation research was carried out using Statistical Analysis Software (SAS) to assess the effectiveness of the tests that are based on the rate of Type I error. By creating the conditions that could highlight the strengths and weaknesses of each test, three variables are being manipulated: sample size, variance heterogeneity, and type of pairings. The performance of the AG tests is convincing when it is able to control the Type I error rates better compared to ANOVA under all conditions of heterogeneous variances. Meanwhile, the ANOVA performs best only when the variances are homogenous. A real data experiment was applied to validate the result. In the battery life design experiment, the p-value using the AG test and ANOVA are computed and compared. The AG test provides valid results when it can test the main effect and the interaction effect, as well as the ANOVA. With good performance in the simulation study, the AG test can be considered a good alternative to the ANOVA when the assumptions of the homogeneity of variances are violated in the case of factorial design.
FORECASTING RAINFALL VOLUME IN SELANGOR WITH A COMBINED ARIMA MODEL
Flash flood is the most hazardous type of flooding, mainly caused by extensive rainfall. It also can cause significant harm to a community’s economy, ecology, and society without warning at an irrational pace. Therefore, this study was conducted to detect the time series element within the rainfall data, select the optimal model, and make predictions about the volume of rainfall in Selangor. A variety of univariate time series models were utilized, including the naïve model, decomposition model, Autoregressive Integrated Moving Average (ARIMA) model, exponential models, and combined models. Historical monthly rainfall data collected from Petaling station and Subang station from 2018 to 2022 were used to estimate the parameters of the models, and the model was evaluated for the smallest error of measurements. Previous research mostly focused on complex methodologies for forecasting rainfall. However, this research aimed to identify a simple tool for fast prediction of rainfall. The results showed that the combination of the ARIMA (2,0,3) model from Petaling Station and the ARIMA (4,0,4) model from Subang station were able to capture the trends and seasons in the time series with the lowest error of measurement on short-term predictions of rainfall volume. Furthermore, the study delves into the concept of combined time series models, which are blended using weighted performance measures to enhance prediction accuracy further. The research acknowledges certain limitations of univariate time series models, notably their inability to account for intricate interactions among environmental variables and potential long-term trends, such as those stemming from climate change. Overall, the study explores the potential of combining models to refine predictions for forecasting rainfall volume in Klang Valley.
A NEW FRAMEWORK ON SUCCESS FACTORS OF SOCIAL MEDIA APPLICATIONS USAGE
Based on many past studies regarding Social Media Applications (SMAs) rarely focus on aspects of continuous use and Perceived Privacy (PP). The increasing user awareness issue regarding PP in using SMA is escalating due to various cybersecurity threats, such as malware, software, password and Man-in-the-Middle (MitM) attacks. Accordingly, this study examines the effect of users' PP on System Quality (SQ), Information Quality (IQ), Service Quality (SVQ), User Satisfaction (US) and Usage (USG) based on the DeLone and McLean Information System (IS) Success Model. A total of 345 samples were used for this study using the convenience sampling approach and SmartPLS 3 as the analysis tool. Based on the study's results, it was discovered that SVQ and IQ affect the use of SMA. In addition, it was discovered that PP affects the relationship between SQ, SVQ and the use of SMA. The analysis results related to PP reveal that it is a dominant construct in the study model. Note that the importance of PP should be a solid foundation for developing an SMA application. The PP component in this study can be used as a guide and checklist for software developers to improve security features and SVQ, which is a catalyst for US and ensure that an SMA application is used continuously.
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