Entropy Generation For Non-Newtonian Fluid With Constant Viscosity
Publication Date: 03/04/2024
DOI: 10.52589/AJMSS-3FOWTMHB
Author(s):
Obi Boniface Inalu.
Volume/Issue:
Volume 7
,
Issue 2
(2024)
Abstract:
In this research, entropy generation for non-Newtonian fluid flow with constant viscosity is considered. The governing nonlinear equations of motion are solved analytically with regular perturbation techniques. Third grade fluid is employed to account for the non-Newtonian influence. The influence of some physical parameters involved in the analysis is studied. Results show that the parameter has the tendency of increasing the velocity of the fluid flow as well as the temperature of the cylindrical pipe. It is observed that as the Brinkman parameter increases, the cylindrical wall temperature is enhanced. Entropy generation number for both heat transfer and fluid friction for various values of and is examined. Results indicate that increase in these parameters increases the temperature of the cylinder, thereby increasing the entropy generation number.
Keywords:
Entropy, Non-Newtonian, Fluid, Viscosity.
No. of Downloads:
0
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Modelling the Effect of Police Motivating Factors on Effective Crime Management in Nigeria
Publication Date: 22/03/2024
DOI: 10.52589/AJMSS-7C49NQTE
Author(s):
Okunowo B. Olukorede, Taiwo A. Ishola, Olatayo T. Olabisi.
Volume/Issue:
Volume 7
,
Issue 1
(2024)
Abstract:
This study investigates the relationship between police motivating factors' and the crime rate in Nigeria. The fixed effect panel data model with geopolitical zones and states as dummy variables results indicated the significance of motivating factors aside from logistics and insurance with multiple R-squared values of 76.65%. The random effect panel data model showed every motivating factor was significant with multiple R-squared values of 53.81%. Therefore, based on multiple R squared values, the fixed effect panel data model explained the variations in crime rate better—random effect study. Conclusively, police motivating factors have a significant impact on the crime rate in Nigeria.
Keywords:
Crime management, Estimation techniques, Panel data, Crime modelling, Police Motivating factors.
No. of Downloads:
0
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Autocovariances and Autocorrelation Properties of Diagonal Vector Autoregressive and Multivariate Autoregressive Distributed Lag Models
Publication Date: 13/03/2024
DOI: 10.52589/AJMSS-LUF1ET0Z
Author(s):
Emediong Udoh, Anthony Usoro.
Volume/Issue:
Volume 7
,
Issue 1
(2024)
Abstract:
The primary aim of this study was to conduct a comparative analysis of the performance of parsimonious models, specifically the Diagonal Vector Autoregressive (VAR) and Multivariate Autoregressive Distributed Lag (MARDL) Models, using their respective Autocovariance and Autocorrelation properties. This comparison was driven by the imposition of restrictions on parameters within the coefficient matrices, specifically limiting them to diagonal elements. To assess the efficacy of these novel multivariate lag models, we utilised data derived from key macroeconomic variables, including Nigeria's Gross Domestic Product (GDP), Crude Oil Petroleum (C/PET), Agriculture (AGRIC), and Telecommunication (TELECOM). The data was subjected to first-order differencing of the logarithm of the series to ensure stationarity. Subsequently, the models were estimated, and autocovariances and autocorrelations of the processes were derived for the analysis. The empirical findings revealed notable patterns, particularly the direct converse autocorrelation observed in both VAR and MARDL models. The negative autocorrelation identified in the macroeconomic variables suggests that periods of economic expansion were succeeded by contractions and vice versa. This implies a complementary relationship between the two models in effectively capturing the dynamics of multivariate lag variables. In conclusion, our study underscores the significance of considering the Diagonal Vector Autoregressive and Multivariate Autoregressive Distributed Lag Models with restricted parameters in the diagonal elements when modelling multivariate lag variables. These findings contribute to a nuanced understanding of the interplay between economic variables and provide valuable insights for researchers and practitioners in the field.
Keywords:
Variances, Autocovariance, Autocorrelation, Upper and lower diagonal VAR Models, Upper and lower diagonal MARDL models.
No. of Downloads:
0
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Modeling the use of Trace-Test-Isolate-Treat Strategy for Controlling the Spread of Covid-19
Publication Date: 01/03/2024
DOI: 10.52589/AJMSS-ASOHFR9Q
Author(s):
Michael C. Anyanwu, Emmanuel C. Duru.
Volume/Issue:
Volume 7
,
Issue 1
(2024)
Abstract:
During the COVID-19 pandemic that ravaged the entire world between 2019 and 2021, the Trace-Test-Isolate-Treat Strategy was devised as an emergency way of managing the spread of the disease. As the name implies, the Trace-Test-Isolate-Treat Strategy involves identifying those who had contact with an infected person through contact tracing, and subsequent isolation and treatment if confirmed to be infected with the disease. This paper aims to model the transmission dynamics of COVID-19, with the Trace-Test-Isolate-Treat Strategy as a control strategy. To do this, we propose a simple nonlinear system of ordinary differential equations that models COVID-19 dynamics and incorporates the Trace-Test-Isolate-Treat strategy as a way of controlling the spread of the disease. The analysis of the model shows that the disease-free equilibrium is locally asymptotically stable if the reproduction number, R_eff is less than one. Furthermore, the model is shown to possess a unique and stable endemic equilibrium if, R_eff>1. This confirms the global asymptotic stability of the disease-free equilibrium and the absence of backward bifurcation in the model. Numerical plots show the effectiveness of isolation and treatment of infected persons in reducing the spread of the disease.
Keywords:
Coronavirus; Trace-Test-Isolate-Treat Strategy; disease-free equilibrium; endemic equilibrium; local stability
No. of Downloads:
0
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