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Item Socio-demographic factors associated with dietary behaviour among young Ghanaians aged 15-34 years(Cambridge University Press, 2016) Amoateng, A. Y.; Doegah, P. T.; Udomboso, C.This study used data from the 2008 Ghana Demographic and Health Survey to investigate the association between selected sociodemographic factors and dietary behaviour as measured by fruit and vegetable consumption among a sample of 6139 young people aged 15–34 years in Ghana. Overall, fruit and vegetable consumption was low in young people, but females were likely to consume more fruit and vegetables than their male counterparts. Respondents from the Mande ethnic group, those who resided in rural areas and those living in the Brong/Ahafo, Ashanti and the Eastern regions consumed more fruit and vegetables than those from other regions. Females who were Catholic/Anglican, Methodist/Presbyterian and Pentecostal/Charismatic were more likely than those of other religions to consume fruit and vegetables, while Muslim males generally consumed more fruit and vegetables. The findings point to the need for interventions to educate young people in Ghana about the health benefits of eating fruit and vegetables.Item Prediction of oilfield scale formation using artificial neural network (ANN)(SCIENCEDOMAIN International, 2016-07) Falode, O. A.; Udomboso, C.; Ebere, F.Scale formation and deposition is a recurring problem in many oil producing fields leading to operational problems, problems in reservoirs, pumps, valves and topside facilities. Scale is described economically as a menace to an oil-field because its build-up clogs the flow lines and causes loss of millions of dollars yearly. The ability to predict the onset and amount of scale formation has been a major challenge in the oil industry. Previous models for predicting scale formation have focused mainly on thermodynamics and limited solubility data, and can predict only the potential or tendency to form scale. However, no studies have considered the influence of kinetic and transport factors. In this paper, a comprehensive and robust model incorporating other factors that have been ignored in past studies is developed using the technique of artificial neural network (ANN). Field data on two types of scale namely Barium and Calcium sulphate were obtained, processed, trained and tested with Artificial Neural Network. The model obtained was validated with actual data. Results show that at constant pressure, the neural network structure with optimum performance for BaSO(4) was ANN {1,2,1} with the lowest Mean Square Value (MSE) of 0.0025 and the highest correlation determination R(2) of 0.9966 while at constant temperature, it was ANN{1,1,1} with MSE of 0.0017 and R(2) of 0.9956. The neural network structure with optimum performance for CaSO4 precipitation kinetics with temperature and pressure was ANN{2,5,1} with MSE of 8.7745e-005 and R(2) of 0.8206 while at constant flow rate it was ANN{1,4,1} with MSE of 2.3007e-006 and R(2) of 0.9953. This gave a very close agreement with actual data in terms of prediction and performance. The results of this study therefore will greatly help to reduce the amount of risk incurred (such as NORM, etc.) due to the deposition and formation of scale in an oilfieldthe cost of stimulating an oil flow line and also improve the productivity of an oil well, hence, increase revenue to the oil industry.Item Predictive modeling of gas production, utilization and flaring in Nigeria using TSRM and TSNN: a comparative approach(Scientifc Research Publishing, 2016-02) Falode, O.; Udomboso, C.Since the discovery of oil and gas in Nigeria in 1956, much gas has been flared because the operators pay little or no concern to its utilization, and as such, trillions of dollars have been lost. In this paper, a model is proposed using Time Series Regression Model (TSRM) and Time Series Neural Network (TSNN) to model the production, utilization and flaring of natural gas in Nigeria with the ultimate aim of observing the trend of each activity. The results show that TSNN has better predictive and forecasting capabilities compared to TSRN. It is also observed that the higher the hidden neurons, the lower the error generated by the TSNN.Item Does religion affect alcohol and tobacco use among students at North-West University, South Africa?(2017) Amoateng, A. Y.; Setlalentoa, B. M. P.; Udomboso, C.The present study used multidimensional measures of religion to asses religion's influence in engedering positive behavioursas measured by alcohol and tobaco use among a sample of undergraduate students at the North-West University in South Africa. Multinomial logistic regression model was used to examine the effect of religion on youth alcohol and tobaco use. Zero-order correlations showed that measures of religion not only correlated positively with each other, but they correlated negatively with both current use of alcohol and tobacco. Religious affiliation was insignificant, but self-related religiosity was positvely associated with drinking among females who reported that they always drink alcohol, frequency of church attendance increased the odds of drinking among females who reported that they never drink compared to those who reported that they drink occasionally. Social class, as measured by father's education was negatively associated with both alcohol and tobacco use. On the whole, religious commitment continues to act as the protective factor against these two anti-social behaviours of teh youth.