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Mikailalsys Journal of Mathematics and Statistics
Published by Lembaga Yasin Alsys
ISSN : 30308399     EISSN : 3030816X     DOI : https://doi.org/10.58578/mjms
Core Subject : Education, Engineering,
Engineering Mathematics Mechanical Engineering
The journal contains scientific articles covering topics such as mathematical theory, statistical methods, the application of mathematics in various disciplines, and statistical data analysis. The primary objective of this journal is to promote a better understanding of mathematical and statistical concepts and to encourage advancements in the methods and applications of mathematics and statistics in various contexts. The journal serves as a platform for researchers, academics, and practitioners to share knowledge and the latest research findings in the fields of mathematics and statistics. MJMS publishes three editions a year in February, June, and October.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 93 Documents
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A Study of Some Geometric Structures on Inner Metric Spaces Morawo, Monsuru A; F. F, Otoide; E. O, Shobanke; O. O, Adegbemi
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 1 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i1.7869

Abstract

Inner metric spaces, characterized by the approximate midpoint property, play an important role in metric geometry and in the analysis of length structures on metric spaces. This paper investigates structural properties of inner metric spaces, focusing on the relationship between the inner metric condition, approximate midpoints, and geodesicity. We first revisit the definition of inner metric spaces and establish that every inner metric space admits an approximate midpoint. We then show that, when an inner metric space is proper, it is geodesic. The arguments rely on the notions of length of curves and rectifiable curves to relate distance and curve length within this class of spaces. These results clarify how the inner metric property is linked to geodesic behavior and contribute to a deeper understanding of metric spaces that can be treated as length spaces.
Econometrics Analysis of Economic Factor Affecting Student Academic Performance Using Correlation and Regression Analysis Garba, B. A; Anyah, A. D; Nyong, E. G; Delle, J. O
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 1 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i1.8049

Abstract

This study investigates the relationship between students’ daily feeding times and physical fitness, specifically body weight, using econometric tools such as correlation and regression analysis in a university context. The research aimed to explore the economic and behavioral factors underlying students’ daily eating habits and their potential impact on physical fitness. A sample of 21 students from the Faculty of Science was selected, and data on gender, daily feeding times, and weight were collected. Descriptive statistics, Pearson correlation, and simple linear regression were employed to analyze the data. The results revealed a weak positive correlation between daily feeding time and weight (r = 0.417), indicating no statistically significant relationship, and the regression model showed that only 13% of the variance in students’ weight could be explained by their daily feeding habits. These findings suggest that, within this sample, daily feeding time alone is not a major determinant of physical fitness as measured by body weight. The study concludes that there is no significant link between students’ daily feeding times and their physical fitness and recommends that future research consider broader nutritional patterns, meal frequency, and psychosocial factors such as physical and mental stress to better understand and support healthy weight maintenance among university students.
Logistic Regression Analysis on Cardiovascular Diseases in Jos Metropolis Delle, J. O.; Garba, B. A.; Anyah, A. D.; Shepan, A.
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 1 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i1.8067

Abstract

Cardiovascular diseases (CVDs) remain a leading cause of morbidity and mortality worldwide, with a rising burden in low- and middle-income countries such as Nigeria, yet localized evidence on CVD risk determinants in Jos Metropolis is limited. This study aimed to develop and validate a multivariate logistic regression model to identify and quantify significant predictors of CVD among adults in Jos Metropolis using routinely collected data. A descriptive cross-sectional analysis was conducted among 489 adults (≥18 years) using retrospective electronic health records (2015–2023) and patient survey data from Jos University Teaching Hospital and the Plateau State Ministry of Health. Candidate predictors included hypertension, diabetes, obesity, smoking, physical inactivity, age, gender, and occupation. Logistic regression with backward elimination was employed for model development, and model performance was evaluated using split-sample validation and goodness-of-fit assessments. The findings revealed hypertension as the strongest predictor, with hypertensive individuals having 4.3-fold higher odds of CVD (95% CI: 2.74–6.88, p < 0.001). Smoking, diabetes, and obesity increased CVD odds by 2.7-, 2.8-, and 1.8-fold, respectively, while age showed a modest but significant effect, with each additional year associated with a 2.3% increase in CVD risk (p = 0.002). Gender approached statistical significance, suggesting potential male vulnerability (OR = 1.47, p = 0.053). Overall, the model demonstrated moderate explanatory power (Nagelkerke R² = 0.21) and acceptable discrimination (AUC = 0.73). The study concludes that hypertension and other modifiable lifestyle-related factors are critical drivers of CVD risk in Jos Metropolis and supports the prioritization of community-based hypertension screening, smoking cessation initiatives, and lifestyle-focused health education as key public health strategies.
Mathematical Modelling of Kidnapping Activities Tasiu, A. R.; O, Kabir; Muhammad, Aminu; Ayu, M. S.; Ishaq, M. M.
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 2 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i2.8764

Abstract

Kidnapping has become one of the most severe security challenges in Nigeria, particularly in the northern regions, where it has evolved into a profitable criminal enterprise. This study develops a mathematical model to analyze the dynamics and control of kidnapping activities. The population is classified into five compartments: susceptible individuals, exposed individuals, informants, kidnappers, and repentant kidnappers. The model describes the transition of individuals from vulnerability to involvement as informants or kidnappers, as well as the possibility of repentance through rehabilitation. A basic reproduction number, (R_0), is derived to determine whether kidnapping activities will persist or decline. The analysis indicates that kidnapping can be eliminated when (R_0 < 1), whereas (R_0 > 1) implies its continued persistence. Numerical simulations further show that increasing the rehabilitation rate of kidnappers promotes repentance, while strengthening intelligence gathering through informants and reducing recruitment into kidnapping significantly suppress the expansion of this criminal activity. The study concludes that the proposed model provides useful quantitative insight into the mechanisms driving kidnapping and offers practical implications for policy interventions aimed at reducing kidnapping in Nigeria.
Inference and Simulation Study for the Exponentiated Novel α-Power Gumbel Model Bitrus, Bako B.; Adubisi, Obinna D.; John, David I.; Reuben, Israel P.
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 2 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i2.8840

Abstract

This study introduces and investigates a new flexible lifetime model, termed the Exponentiated Novel α-Power Gumbel (ENAPG) distribution, by applying the exponentiation technique to the recently proposed novel α-power Gumbel model. The proposed distribution extends the classical Gumbel family through the inclusion of an additional shape parameter, thereby enhancing its flexibility for modeling right-skewed and heavy-tailed data. To establish its theoretical usefulness, the study derives key statistical properties of the ENAPG distribution, including the survival and hazard rate functions, quantile function, moments, moment-generating function, Rényi and Tsallis entropies, and order statistics. Parameter estimation is carried out using the maximum likelihood estimation approach, with the resulting nonlinear likelihood equations solved numerically through iterative optimization routines. A comprehensive Monte Carlo simulation is further conducted to assess the finite-sample performance of the estimators across different sample sizes using bias, mean square error, root mean square error, and mean relative error criteria. The results indicate that the maximum likelihood estimators exhibit consistency and improved efficiency as sample size increases. Overall, the ENAPG distribution provides a robust and flexible alternative to existing Gumbel-type models and offers potential applications in reliability analysis, survival studies, and extreme-value modeling.
Spatial Epidemiology of Lassa Fever in Nigeria: Mapping and Predictive Analytics for Improved Disease Control Hassan, Aliu Abbas; Olaitan, Aliu Tawakalitu
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 2 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i2.8884

Abstract

Lassa fever remains a major public health concern in Nigeria because of its recurrent outbreaks, high morbidity, and fluctuating case fatality rates. This study investigates the geographical distribution and temporal dynamics of Lassa fever in Nigeria from 2020 to 2025 using spatial epidemiology and predictive analytics. Surveillance data obtained from the Nigeria Centre for Disease Control were analyzed through geospatial mapping to visualize confirmed cases and deaths at the state and Local Government Area levels, while Bayesian hierarchical spatial models, specifically the Integrated Nested Laplace Approximation and Besag-York-Mollié models, were applied to generate predictions and identify persistent and emerging hotspots. The findings show that a small cluster of states, particularly Ondo, Edo, and Bauchi, consistently accounted for more than 70% of annual confirmed cases. Case fatality rates ranged from 16% to 21% during the study period, with notable increases in 2023 and 2025. The hotspot maps further reveal marked spatial heterogeneity in disease risk, shaped by ecological suitability for rodent reservoirs, population density, and disparities in health systems. In addition, the predictive outputs show strong agreement with historical data, confirming the usefulness of the models for early warning. The study concludes that integrating spatial mapping with predictive modeling provides a robust framework for strengthening Lassa fever surveillance and response in Nigeria. These findings contribute a scalable and adaptable methodological approach that can support outbreak forecasting, resource optimization, timely intervention in high-risk areas, and broader data-driven epidemic intelligence for infectious disease control.
Small Area Estimation of Child Multidimensional Poverty in Nigeria: A Linear SAE Approximation Using MICS 2021 and WorldPop 2020 Data Adeyemo, Samuel O.
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 2 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i2.8922

Abstract

This study estimates child multidimensional poverty across Nigeria’s 774 Local Government Areas (LGAs) by integrating the 2021 Multiple Indicator Cluster Survey (MICS) with WorldPop 2020 high-resolution population density data. Using the Alkire–Foster framework, the analysis produced a national weighted Multidimensional Poverty Index (MPI) of 0.292 and a raw MPI of 0.23632, based on a poverty incidence value of H = 0.56, which implies approximately 55.7 million children in multidimensional poverty, and an average deprivation intensity of A = 0.422. To generate LGA-level estimates, this study applied the Fay–Herriot small area estimation (SAE) model by regressing MPI on the logarithm of population density, conflict indicators, and infrastructure measures. The model explained more than 80% of the variance, improving the goodness of fit from R² = 0.695. Non-linear specifications were tested but were not retained based on the Akaike Information Criterion (AIC). A logit transformation was applied to bound predictions within a plausible range of 0–0.569, thereby eliminating negative estimates. Uncertainty estimation incorporated MICS sampling variance through bootstrapping, with coefficients of variation below 15% for 90% of LGAs. The findings reveal substantial regional disparities in child multidimensional poverty, with the North West recording a zonal MPI of 0.447 compared with 0.090 in the South East. Although constrained by data limitations, the study demonstrates the utility of SAE for producing granular poverty estimates and contributes to policy-oriented poverty measurement by strengthening evidence for geographically targeted child poverty reduction in Nigeria.
Analysis of Steady Radiative MHD Nanofluid Flow in a Porous Medium: Effects of Magnetic Field, Prandtl Number, and Internal Heat Source/Sink Garba, Mohammed; Tahiru, Garba Adamu; Hussaini, Abubakar Assidiq
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 2 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i2.9099

Abstract

This study presents a numerical investigation of steady magnetohydrodynamic (MHD) nanofluid flow under the combined effects of thermal radiation, Prandtl number, porous medium permeability, magnetic field strength, and internal heat generation or absorption. The objective is to examine how these governing parameters influence velocity profiles, temperature distributions, and surface heat transfer characteristics. The nonlinear partial differential equations describing coupled momentum and energy transport were reduced to a system of dimensionless ordinary differential equations through suitable similarity transformations and solved numerically. The results show that thermal radiation and internal heat generation substantially increase the temperature field, while momentum transport is suppressed due to intensified thermal–magnetic interactions and resistive forces. An increase in the Prandtl number reduces thermal diffusion and produces thinner thermal boundary layers. Higher porous medium permeability introduces porous resistance that decelerates the flow but enhances surface heat transfer through boundary layer thinning. The applied magnetic field also regulates both momentum and thermal transport through Lorentz forces. Mathematically, these trends are consistent with the structure of the dimensionless governing equations and boundary conditions, indicating strong nonlinear coupling among diffusion, convection, radiation, porous drag, and electromagnetic effects. The study concludes that surface heat transfer performance, represented by the Nusselt number, is primarily governed by wall temperature gradients. These findings contribute to the numerical understanding of MHD nanofluid transport in porous media and provide a useful theoretical basis for applications involving thermal regulation and heat transfer enhancement.
Bayesian Hierarchical and Decomposition Analysis of Pregnancy-Related Mortality in Nigeria Using NDHS 2018-2023/24 S.O, Adeyemo; E.U, Ohaegbulam; P, Duruojinkeya
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 2 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i2.9166

Abstract

Nigeria continues to face one of the highest burdens of pregnancy-related mortality globally, yet recent measurement evidence remains constrained by gaps in direct reporting. This policy and measurement note examines the availability of direct sibling-history estimates in the Nigeria Demographic and Health Surveys (NDHS), with particular attention to the absence of an updated pregnancy-related mortality ratio (PRMR) in the 2023–24 reporting cycle. The NDHS 2018 reported a national PRMR of 512 per 100,000 live births (95% CI: 447–578). However, the NDHS 2023–24 Key Indicators and Summary Reports do not publish an updated sibling-history-based PRMR, thereby limiting direct assessment of mortality trends. Proxy indicators suggest modest progress, including a decline in the total fertility rate from 5.3 to 4.8 and an increase in skilled birth attendance from 43% to 46%, although rural–urban and zonal disparities persist. Exploratory Bayesian hierarchical modeling using NDHS 2023–24 microdata produced an illustrative national PRMR estimate of approximately 462 per 100,000 live births (CrI: 392–532), suggesting a possible modest decline, while rural estimates remain high at approximately 612. The findings indicate that the absence of a published direct PRMR weakens evidence-based monitoring of SDG 3.1 and limits independent assessment of progress in reducing pregnancy-related mortality. This note contributes to policy measurement by highlighting the need for routine inclusion of sibling-derived PRMR estimates in future DHS reports, alongside microdata access to support independent verification and more equitable maternal health planning.
Semi-Analytical Study of Pulsatile Nanofluid Flow in Porous Stenosed Arteries Under Magnetic and Thermal Effects Musa, Ali; Yakubu, D.G
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 2 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i2.9187

Abstract

This study presents an extended fractional Maxwell fluid model for pulsatile blood flow through a stenosed artery by incorporating the combined effects of a magnetic field, porous medium, chemical reaction, heat source, and suspended nanoparticles. Blood is modeled as a compressible, viscoelastic, and electrically conducting fluid, and the governing fractional-order coupled nonlinear partial differential equations for momentum, energy, and nanoparticle concentration are formulated in cylindrical coordinates. To capture fluid memory effects, the Caputo fractional derivative is employed, and the resulting system is solved semi-analytically using the Laplace transform method. The inverse Laplace transforms, involving modified Bessel functions, are computed numerically through the Concentrated Matrix-Exponential method implemented in Python to improve stability and accuracy. Validation against existing literature demonstrates excellent agreement. The parametric results show that increasing the Hartmann number, stenosis length, particle mass, and chemical reaction parameter reduces both velocity and nanoparticle concentration, whereas higher heat source, Peclet number, and nanoparticle concentration parameters enhance flow and particle dispersion. The findings further indicate that fractional-order effects strongly influence velocity behavior, with lower fractional orders producing stronger memory effects and smoother gradients. The study concludes that the proposed model improves the prediction of hemodynamic behavior under pathological arterial conditions and offers useful implications for magnetic-assisted therapies and nanoparticle-based drug delivery.

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