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Etim Uduak James

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Etim Uduak James is a registered researcher in their academic field.

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3 research works linked to this profile

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Research · 2026 · Ktrend - International Journal of Computational Mathematics and Scientific Computing

Ordinary Differential Equation Models of Chemical Kinetics, HIV-Prevention Pathways, Epidemic Spread, and Growth–Decay Processes

Ordinary differential equations provide a common language for representing rates of change in chemical, biological, epidemiological, and financial systems. This study develops and computationally examines four model families drawn from physical and life-science applications: the dimensionless Lengyel–Epstein model for the chlorine dioxide–iodine–malonic acid reaction; a six-compartment demographic, exposure, infection, and AIDS-progression model motivated by delayed first sexual intercourse; the classical susceptible–infectious–removed epidemic model; and exponential growth and radioactive-decay models. Equilibria and local stability conditions are derived analytically, while numerical solutions are obtained with adaptive Runge–Kutta integration. For the chemical model with illustrative parameters $a=12$ and $b=0.30$, the positive equilibrium is unstable and the numerical trajectory approaches sustained oscillation. The delayed-intercourse model is locally asymptotically stable when the feedback between the sexually active and under-age compartments is weaker than total demographic removal, specifically when $(d_1+m_1)(d_2+m_2+b_2)>b_1m_1$. For the epidemic illustration, the effective transmission rate is $0.8$ per day, the recovery rate is $0.125$ per day, and $R_0=6.4$; the infectious population peaks at approximately $554$ persons near day $13$ in a population of $1,000$. Continuous $5%$ financial growth increases $20,000$ monetary units to $23,236.68$ after three years, whereas an $800,\mathrm{mg}$ bismuth-210 sample with a five-day half-life declines to $12.5,\mathrm{mg}$ after $30$ days. The results demonstrate how a shared differential-equation framework supports model formulation, stability analysis, simulation, and transparent comparison across distinct applications. All numerical outcomes are illustrative and are not fitted to clinical or laboratory observations.

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conformal mapping; Joukowski transformation; circular domains; airfoil profile; complex analysis; potential flow; aerodynamics. · 2026 · African Journal of Mathematics, Statistics and Computer Science

Joukowski Mapping of Circular Domains to Aerodynamic Profiles

Conformal mapping is one of the central geometric techniques of complex analysis because it permits a complicated planar domain to be represented by a simpler one while preserving local angles. This paper develops the analytic conditions for conformality from the principal linear part of a differentiable complex mapping, relates the Cauchy--Riemann equations to local rotation and dilation, and presents explicit mappings between standard domains. Particular attention is given to the Joukowski transformation and its constructive action on circular boundaries. Actual mappings are worked out for the upper half-plane and unit disk, for a strip and half-plane, and for circles mapped by the Joukowski transformation into a line segment, an ellipse, and an airfoil-like profile. The derivations are accompanied by graphical realizations and a discussion of potential-flow aerodynamics. The results make explicit the connection between the analytic formula, its critical points, transformed geometry, and the distinction between local conformality and global one-to-one behavior.

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Lafarge Africa; state-space model; Markov switching; time series; Value-at-Risk; Expected Shortfall; transformed data · 2026 · Ktrend – Nigerian Journal of Mathematical and Computational Sciences

Robust State-Space and Markov Regime-Switching Analysis of a Transformed Lafarge Africa Plc Monthly Price Scenario

This study develops a new, reproducible framework for analysing a transformed monthly price scenario associated with Lafarge Africa Plc. Unlike polynomial-trend and simple present-value approaches, the proposed methodology combines log-return transformation, a two-state Markov regime-switching model, a local-linear-trend state-space model, benchmark forecasting, residual diagnostics, and downside-risk estimation. The input series contains 36 monthly observations from January 2023 to December 2025 and was deliberately transformed so that it does not reproduce the source values directly; consequently, the findings are interpreted as a methodological case study rather than as a verified record of actual traded prices. Monthly log returns were computed using $r_t=\log\left(\frac{P_t}{P_{t-1}}\right)$, where $P_t$ denotes the transformed monthly price at time $t$. The transformed price has a mean of NGN 65.344, a median of NGN 63.125, and a standard deviation of NGN 10.847. Monthly log returns are stationary under the Augmented Dickey–Fuller (ADF) test, while the Ljung–Box and ARCH-LM diagnostics do not detect statistically significant residual serial correlation or conditional heteroskedasticity at conventional significance levels. Returns nevertheless exhibit pronounced positive skewness and excess kurtosis, motivating regime-switching and tail-risk analyses. A two-regime Markov specification is employed to estimate time-varying latent market states, whereas a local-linear-trend state-space model generates probabilistic forecasts through Kalman filtering. In a six-month holdout evaluation, the ARIMA(1,1,1) benchmark achieved the lowest forecasting error with an RMSE of 2.276 and a MAPE of 3.961%, compared with 6.576 and 11.029%, respectively, for the structural state-space model. Historical 95% Value-at-Risk (VaR) and Expected Shortfall (ES) were estimated at 9.083% and 10.808%, respectively. The findings demonstrate that forecasting performance should be evaluated separately from structural interpretability: while ARIMA provides superior point forecasts for the transformed dataset, the regime-switching and state-space models offer richer insights into latent market dynamics, uncertainty quantification, and downside financial risk. The proposed framework provides a transparent and reproducible methodology for advanced financial time-series modelling and investment risk assessment.