Author(s): S. A. Jimoh, Ebenezer Ajomole, E. C. Ajila.

Volume/Issue: Volume 8 , Issue 2 (2025)

Abstract:

This paper investigates the transient response of Timoshenko beams under moving loads in turbulent environments. The study incorporates the effects of shear deformation, rotary inertia, and dynamic aerodynamic forces caused by turbulence. The governing equations are derived from Timoshenko beam theory and coupled with an aerodynamic force model that accounts for mean and fluctuating wind velocities. A spectral representation of turbulent forces is employed to simulate realistic wind-induced forces. Numerical simulations are conducted using both the spectral element method (SEM) and the finite element method (FEM), enabling a comparison of their accuracy and computational efficiency. Results are presented for various load velocities and turbulence intensities, highlighting the advantages and limitations of each method. This study provides valuable insights into the dynamic behavior of beams in challenging environmental conditions, offering practical applications in civil, mechanical, and aerospace engineering.

Keywords:

Timoshenko beam, Turbulent environments, Shear deformation, Aerodynamic force, Finite Element Method, Spectral Element Method.

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Author(s): Moffat Victoria Ekpedeme, Iseh Matthew Joshua.

Volume/Issue: Volume 8 , Issue 2 (2025)

Abstract:

This study adopts the concept of stepwise regression analysis with the aim of generating a model to examine the influence of basket of commodities on consumer price index (CPI) in both rural and urban areas in Nigeria. Data used is gotten from the CBN data bank. The result of analysis shows that (housing, water, electricity, gas and other fuel), (food and non alcoholic beverages), (alcoholic beverage, tobacco and kola) (clothing and foot wear), (health), (furnishing and household equipment maintenance), (restaurant and hotels) and (education) are the variables that mostly affect the urban area. Also, for the Rural area, it is observed that (housing, water, electricity, gas and other fuel), (food and non alcoholic beverages), (alcoholic beverage, tobacco and kola), (clothing and foot wear), (health), (furnishing and household equipment maintenance), (miscellaneous goods and services), and (transport) are the variables that contributes significantly. Therefore, the independent variables that have contributed significantly and common to both urban and rural areas are , , , , , and . However, it was noticed that, among the indicators in the basket of commodities, (restaurant and hotels) and (education) have effect only on the urban area while (miscellaneous goods and services) and (transport) were significant in the rural areas only. On this note, the choice of stepwise regression model has really paid off by distinguishing the cause and effect of basket of commodities on CPI for rural and urban areas.

Keywords:

Basket of commodities, Consumer price index, Stepwise regression, Rural area, Urban area.

No. of Downloads: 0

Author(s): Ayodele Olakiitan Owolanke, Obarhua Friday Oghenerukevwe, Olushola C. Akeremale.

Volume/Issue: Volume 8 , Issue 1 (2025)

Abstract:

This paper is concerned with the formulation of a scheme via construction of Canonical with Shifted-Chebyshev Polynomials (SCP), for the direct solution of fractional order integro-differential equations (FIDEs). Perturbation collocation method (PCB) is the approximate method developed, to handle a special singular class of fractional multi-order Volterra type for approximation. The process involves the incorporation of perturbation variables otherwise known as parameters, to the given mathematical models under consideration. Systems of equations are evolved, and the embedded unknown constants are sought for.

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Author(s): Peter Chimwanda, Edwin Rupi.

Volume/Issue: Volume 8 , Issue 1 (2025)

Abstract:

The Ordinal scale of measurement is not understood by many researchers, especially in the social and business fraternities. The thinking that coding values of ordinal scale variables converts data from being qualitative into being quantitative is held by these researchers. A sample of randomly selected articles on factors affecting students’ academic performance is studied to establish how ordinal level variables are analyzed. Results show that the greater part of researchers do not know that, although it is correct that where there is quantity there is number, the converse is incorrect. Parametric techniques dominate in the analysis of ordinal data. Scenarios are forwarded for purposes of sending home the message of differentiating when number is quantity and when it is not. Techniques that are designed for the analysis of ordinal data are then shared.

Keywords:

Ordinal, non-parametric, rank-correlation coefficient, regression analysis, variable.

No. of Downloads: 0