This study presents a comparative analysis of the Inverse Weibull and Inverse Lognormal distributions
using both simulated and real-world data with Maximum Likelihood Estimation (MLE). Model selection
criteria included Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Anderson
Darling (AD), and Kolmogorov-Smirnov (KS) tests. Six simulated sample sizes (50, 100, 150, 200, 250,
and 500) were used, with 1000 replications each. Results showed the Inverse Lognormal distribution
consistently had lower AIC and BIC values at small to moderate sample sizes. Furthermore, real-world
stock price data (sample size 100) from the Nigerian Stock Exchange was analyzed. Descriptive statistics
and goodness-of-fit tests favored the Inverse Lognormal model. These findings support the utility of AIC,
BIC, AD, and KS in lifetime data modeling and model selection.
keywords: Inverse Weibull distribution; Inverse Lognormal distribution; Maximum Likelihood
Estimation; AIC; BIC; Anderson-Darling; Kolmogorov-Smirnov; Simulation study; Lifetime analysis;
Model selection; Stock price data.
Data mining algorithms may hamper due to missing attribute values. Classification accuracy of the data
mining may hamper due to improper handling of missing values. For data processing Rough set is an
important tool. It can handle uncertainty and impreciseness without any additional or prior information
about data. Crisp equivalence classes are the main concept of Rough set. In this paper it has been shown
that Rough set is an important tool for data pre processing in applications like imputation of missing
values, attributes reduction, etc. keywords: Rough set, Attribute Reduction, Missing value Imputation.
The heat and mass transfer problem of a viscous dissipative fluid with symmetric wall concentration
conditions is solved analytically using the homotopy perturbation method. Physical components of
engineering importance in terms of Sherwood number, Nusselt number, and skin friction are also calculated,
along with the governing equations controlling the flow configuration. The study's main findings suggest
that reverse flow can be produced and controlled when mass flow with a wall concentration effect is present.
Furthermore, a graphic comparison of the present work's findings with previous research for the limiting
scenario establishes the validity and accuracy of the current study. A high degree of consistency was
verified.
keywords: Couette Flow, Mass flow, Wall concentration, Viscous dissipation, Convective boundary
conditions, Homotopy perturbation method.
Understanding fluid flow in microchannels, especially when hydrodynamic factors are present, has advanced
significantly in the past few decades. The role of heat radiation and constant suction/injection in these unique
interactions has, however, received little research. Thus, in the presence of a constant suction/injection action,
the influence of optically thick thermal radiation on buoyancy-induced magnetized flow with viscous dissipation
via a superhydrophobic microchannel is investigated in this work. While the other parallel plate has a no-slip
surface (NSS), one of the plates is purposefully altered to create a superhydrophobic surface (SHS). The nonlinear
coupled ordinary differential equations that are modeled are semi-analytically solved. A visual representation
shows how the main parameters govern the flow behavior in terms of momentum and energy distributions.
Because the current study is supported by a comparison with previous research, it is valid for the limiting case.
The study's main findings are: A larger fluid injection parameter results in a stronger temperature and velocity,
respectively, and increasing values of the thermal radiation R function as an additional assisting force. Medical
sciences and technical applications like the cooling and drying of paper and textiles, and the molding of metals
and polymers, to mention a few, depend heavily on the research of fluid suction or injection flow.
keywords: Suction/injection, Viscous dissipation, Thermal radiation, Magnetohydrodynamics (MHD),
Superhydrophobic microchannel.
This study presents a comprehensive statistical analysis of diphtheria outbreaks across several African
countries using a cleaned dataset from WHO data. Summary statistics reveal Nigeria as the most
affected nation in terms of suspected cases and deaths, suggesting a possible correlation with population
size, reporting efficiency, or outbreak severity. A notably high case fatality rate (CFR) in Mauritania
may be attributed to limited healthcare access and delayed diagnoses. Correlation analysis indicates a
strong positive association between deaths and both suspected and confirmed cases, with perfect
correlations noted between deaths and both epidemiologically linked and clinical compatible cases.
These findings suggest that increased diagnostic efforts may mitigate the CFR by identifying and
managing cases earlier. A multiple linear regression model further underscores the significant
contributions of lab-confirmed, epidemiologically linked, and clinical compatible cases to death
outcomes, all with statistically significant p-values. The model’s prediction accuracy is supported by a
near-perfect regression fit. The analysis identifies Mauritania and Niger as high-risk countries for
elevated CFRs, warranting urgent public health interventions. This study underscores the value of
integrated data analytics in enhancing disease surveillance and informing evidence-based responses to
diphtheria outbreaks.
keywords: : Diphtheria, Machine learning, Infectious disease surveillance, Intervention strategies,
Multiple Linear Regression.
This paper presents a novel framework for solving the third-order nonlinear neutrosophic Korteweg–De
Vries (KdV) equation by combining the Hybrid Homotopy Perturbation Method (HPM) with the Kharrat
Toma transform under a one-dimensional isometric transformation. The neutrosophic partial differential
equation is first converted into a system of classical differential equations. The Kharrat–Toma transform is
then applied to obtain an integral representation that facilitates the implementation of the homotopy
perturbation technique. This hybrid approach yields analytical, rapidly convergent series solutions that
accurately capture the dynamic behavior of neutrosophic channel waves. A detailed numerical example
demonstrates that the obtained approximate solutions agree closely with the exact solutions over a wide
range of parameters, confirming the accuracy and robustness of the proposed methodology for nonlinear
neutrosophic partial differential equations.
keywords: Neutrosophic Korteweg–De Vries equation, Hybrid Homotopy Perturbation Method (HPM),
Kharrat-Toma Transform, One-dimensional isometric transform, Analytical solution, Exact solution.
1Department of fundamental sciences
Higher Institute for Applied Sciences and Technology, Aleppo, Syrian Arab Republic. 2Department of mathematics,
Faculty of Science, Aleppo University, Aleppo, Syrian Arab Republic.
In this paper, a general class of inhomogeneous linear neutrosophic ordinary differential equations with
constant coefficients and arbitrary order is investigated. By employing the one-dimensional isometric
transform (Khatib–Abu Balla transform), the original neutrosophic problem is rigorously reduced to an
equivalent system of classical linear ordinary differential equations.
The solution procedure begins with the analysis of the corresponding homogeneous equation, followed by
the construction of particular solutions for the inhomogeneous neutrosophic equations, where the forcing
terms may take exponential, trigonometric, or polynomial forms. The differential operator method is then
applied within the classical framework, and the inverse isometric transform is used to recover the exact
neutrosophic solutions.
The general solution of the inhomogeneous neutrosophic equation is obtained as the superposition of the
homogeneous and particular solutions. Several illustrative examples are provided to demonstrate the
effectiveness, consistency, and applicability of the proposed method in solving higher-order
inhomogeneous neutrosophic differential equations.
keywords: Ordinary differential equation, Isometric transform, Differential operator method.
This study investigated the extent to which students’ proficiency in mathematical construction skills
predicts their achievement in biology among secondary school students in Bayelsa State, as well as the
influence of gender on this predictive relationship. A correlational research design was adopted to explore
the nature of the relationship between the variables. The population comprised all 4567 senior secondary
school biology students in Bayelsa State, from which a sample of 200 students was selected using a stratified
random sampling technique to ensure adequate gender representation. Data were collected using two
standardized instruments: the Mathematical Construction Skills Test (MCST) and the Biology Achievement
Test (BAT). The instruments were validated by experts in Measurement and Evaluation, and their reliability
coefficients, obtained through Cronbach’s Alpha, were 0.82 and 0.86 respectively, indicating strong internal
consistency. Data were analyzed using simple linear regression at a 0.05 level of significance. Findings
revealed a significant positive predictive relationship between students’ proficiency in mathematical
construction skills and their achievement in biology (β = 0.590, p < 0.05), while gender had no significant
influence on this relationship (p > 0.05). The study concluded that mathematical construction skills are
crucial in enhancing students’ academic performance in biology, regardless of gender. It was recommended
that teachers integrate mathematical construction activities into biology instruction and that gender
inclusive pedagogical strategies be adopted to ensure equitable learning outcomes for all students.
keywords: Mathematical Construction Skills, Biology Academic Achievement, Secondary School Students.
We consider the problem of finding a function 𝑤(𝑥,𝑡) that satisfy the equation 𝑤𝑡𝑡(𝑥,𝑡) − 𝑤𝑥𝑥(𝑥,𝑡) + 𝜒(𝑥,𝑡,𝑤) = Φ(𝑥,𝑡) under Dirichlet boundary conditions or Neumann boundary
conditions. We will see that an approximate solution can be found using the techniques of generalized
keywords: :generalized moment problem; integral equations; hiperbolic equation, Cauchy
conditions.
Farming is a prerequisite of the existence of human beings, but the agricultural sector is still subject to
chronic issues, including pests, illnesses, soil erosion, water scarcity, and the consequences of the climate
change. To cope with these challenges, the smart solutions to them are necessary, which will optimize the
efficient use of land, water, and energy and will, at the same time, improve the resiliency and productivity
of crops and soil. Remote sensing is important in the measurement of crops and soil parameters, such as
temperature, moisture content, nutrient, stress, and disease, but the traditional types of remote sensing e.g.
satellite imagery and aerial imagery have constraints of low resolution, high noise, high data and energy
requirements, high costs and the complexity of remote sensing. In order to reduce these constraints,
quantum computing is proposed to enhance quantum compressive ghost imaging using quantum metrology.
It combines quantum enhanced sensing, that minimizes the need of measurements, and energy costs, with
quantum enhanced estimation, that enhances the level of precision and accuracy using quantum light
sources and quantum interferometry and entangled photons. Compared to conventional approaches,
quantum computing enhanced quantum compressive ghost imaging has better resolution, lower noise, and
lower cost as well as new capabilities and applications that cannot be established by classical means. The
paper also addresses time and space complexity, applicability and challenges of quantum computing
enhanced quantum compressive ghost imaging in agricultural practices with the help of algorithm design,
testing protocols, experimental findings, and graphical illustrations. The results show that quantum
computing quantum compressive ghost imaging is much better at estimating crop and soil parameters,
which leads to sustainability and productivity in agricultural systems.
keywords: Quantum Metrology, Algorithm, Remote Sensing, Ghost Imaging.
Equity-focused mathematics education research has increasingly sought to address disparities in learners’
access, participation, and achievement, promoting inclusive and socially just learning environments. This
study provides a systematic review of the evolution and impact of equity-oriented research in mathematics
education, examining themes, trends, and outcomes reported in empirical and theoretically grounded studies
from 2000 to 2025. Using a structured review approach, key themes identified include achievement gaps
and access, culturally responsive and inclusive pedagogy, classroom participation and discourse, critical
and social justice mathematics, and policy, curriculum, and teacher preparation. Findings indicate that
equity-focused interventions have positively influenced learners’ access to mathematics opportunities,
classroom engagement, academic performance, and learner agency. However, these impacts are often
mediated by contextual factors such as teacher preparedness, institutional support, and policy
implementation. The study highlights the importance of multi-dimensional strategies integrating inclusive
pedagogies, professional development, and systemic support to advance equity in mathematics education.
Recommendations focus on curriculum reform and sustained teacher training to maximise equitable
outcomes.
keywords: Equity-focused Mathematics Education, Inclusion, Social Justice, Learner participation,
Academic Achievement, Educational policy.
