Three growth models (Richards, Gompertz, and Weibull) were estimated using a computer program employing a modified version of the Levenberg-Marquardt approach for solving non-linear regression models. With both small and high sample sizes, three data sets were collected. The most suitable model for growth studies was added by decomposing the growth models into their component parts: additive and multiplicative error factors. The analysis based on the mathematical properties was conducted using Gretl statistics software. Prior to using an iterative strategy, a third-order polynomial (cubic function) solves the issue of the initial parameters. The final estimate of the parameters, standard errors, p-values, and model adequacy standards used to choose the best growth model are included in the result. Out of the five growth models considered suitable for the agricultural data, this study was able to pinpoint the Weibull Growth Model with Multiplicative Error Term. The Hill Growth Model with Additive Error Term is the best growth model for the engineering data out of the five growth models. Among the Five Growth Models for Population Growth, the Weibull Growth Model with Additive Error Term is a viable growth model. Several growth models are suggested or recommended by this study for use in forecasting this growth behavior.
Cardiovascular flow maintains the integrity of animated life, especially of humans. This flow goes fuzzy in the event of turbulence. In pathological states, vascular stenosis is usually the culprit in turbulence. Whether turbulence ensues from a physiological state or else, there must be a concomitant sound that is at variance with the normative flow acoustics. This sound emanates from a source point which is more or less within the inception of stenosis. This paper aims to identify the vascular sound field that emanates from a source point, maybe, due to stenosis. As a prelude, it furnished the windowed equations of motion for a fluid (blood) occupying a vascular region to derive the fluid density form of the acoustic analogy. However, the parameter of higher interest in vascular acoustics is turbulent pressure. So, it was well considered, and its relationship with acceleration at the monitor points on the aortic surface was supplied. An aspect of this work is the analysis of heart murmur. The heart was presumed here to admit Kelvin-Voigt’s viscoelastic model and, therefore, the equations governing the model hold well in the present case. Details here suggest that the hemodynamic pressure fluctuation on the aortic lumen boundary and the concomitant turbulent flow precipitates aortic stenosis murmur.
Insurance serves as a protection against the unexpected and it is one of the most effective risk management tools that protect individuals from being bankrupt due to various contingencies. The binary logistic regression model approach was used to model the described dataset; the model so obtained was statistically significant. All the levels of education were statistically significant in predicting the odds of having insurance cover except for primary education level. Also, employment status and age were statistically significant in predicting the likelihood for insurance cover in Nigeria. The results showed that individuals who move from no formal education to obtain Higher education level are 21.66 times more likely to obtain insurance cover and individuals who move from no formal education to obtain Secondary education level are 2.63 times more likely to obtain insurance cover. The odd ratio is not significant for moving from no formal education to Primary education and therefore should not be interpreted. Further, individuals who move from being unemployed to being employed are more likely to obtain insurance cover. Education has the highest impact in predicting the likelihood for one to have insurance cover in Nigeria. This paper recommends overhauling of the educational system in order to revamp this sector.
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