Authors:J. W. E. W. De Silva, S. P. Abeysundara Pages: 1 - 10 Abstract: System dynamics simulation software, in general, depicts graphical interpretations. The values of the parameters, on the other hand, are required for prediction. The goal of this research is to develop a novel multivariate model that can predict flow parameters while simulating flow under various scenarios. The project involves looking for variations in the streamline and constructing a new multivariate model for each elliptic cylinder system's velocity magnitude. Furthermore, the flow zones were split into three groups based on streamline behavior. As a result, utilizing simulation outputs, new models for flow zones are developed using linear and semiparametric regression. The best fitted model for each flow region was determined using mean square error (MSE), root of mean square error (RMSE), and mean absolute percentage error (MAPE). Based on the fitted smoothing curve of the velocity magnitude, a summary statistic and variability may be assessed. The presented models can be used to predict magnitude in any point of fluid flow using these models. PubDate: 2021-12-19 DOI: 10.9734/arjom/2021/v17i1230344
Authors:Desmond C. Bartholomew, Oyinebifun E. Biu, Christopher O. Arimie Pages: 11 - 27 Abstract: According to World Health Organization (WHO), normal weight of baby at term delivery is (2.5 – 4.2) kilograms. Every child’s birth weight below 2.5 kilograms, regardless of gestational age, is regarded as Low birth weight (LBW). WHO estimates that globally, over 20 million LBW babies are born annually and nearly 95.6% of them in developing countries. Half of all perinatal and 1/3rd of all infant deaths occur in babies with LBW. It is therefore, essential to study some of the factors that causes LBW. Hierarchical Multiple Regression analysis was used to study the effects of mother’s weight, age and height above and beyond mother’s education level in predicting the weight of the child at birth. The results showed that mother’s education level explains about 6.1% of the unexplained variations in the weight of the child at birth in block 1 and mother’s age, weight and height explained about 3.9% above and beyond mother’s education level. This implies that all the variables studied affects the baby’s weight at birth but, the mother’s educational level affects the baby’s weight much more. It was concluded that mother’s education level plays a vital role in predicting the weight of the child at birth because it has a causal effect on the use of prenatal care and improves marriage prospects. PubDate: 2021-12-20 DOI: 10.9734/arjom/2021/v17i1230345
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