The implicit Colebrook equation has been the standard for estimating pipe friction factor in a fully developed turbulent regime. Several alternative explicit models to the Colebrook equation have been proposed. To date, most of the accurate explicit models have been those with three logarithmic functions, but they require more computational time than the Colebrook equation. In this study, a new explicit non-linear regression model which has only two logarithmic functions is developed. The new model, when compared with the existing extremely accurate models, gives rise to the least average and maximum relative errors of 0.0025% and 0.0664%, respectively. Moreover, it requires far less computational time than the Colebrook equation. It is therefore concluded that the new explicit model provides a good trade-off between accuracy and relative computational efficiency for pipe friction factor estimation in the fully developed turbulent flow regime.

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Friction factor estimation is essential in fluid flow in pipes calculations. The Colebrook equation, which is a referential standard for its estimation, is implicit in friction factor, f. This implies that f can only be obtained via iterative solution. Sequel to this, explicit approximations of the Colebrook equation developed using analytical approaches have been proposed. A shift in paradigm is the application of artificial intelligence in the area of fluid flow. The use of artificial neural network, an artificial intelligence technique for prediction of friction factor was investigated in this study. The network having a 2-30-30-1 topology was trained using the Levenberg-Marquardt back propagation algorithm. The inputs to the network consisted of 60,000 dataset of Reynolds number and relative roughness which were transformed to logarithmic scales. The performance evaluation of the model gives rise to a mean square error value of 2.456 × 10 −15 and a relative error of not more than 0.004%. The error indices are less than those of previously developed neural network models and a vast majority of the non neural networks are based on explicit analytical approximations of the Colebrook equation.

E-commerce has contributed immensely to the economies of developed countries and a factor in its success can be attributed to the adoption of e-commerce by their citizens. As such, it is perceived that ecommerce can also be an economic driver for developing countries. However, security has been identified as a major barrier that prevents citizens from adopting e-commerce in developing countries. Therefore, this paper examines Security Authentication Techniques (SAT), particularly Digital Signature (DS) and Digital Fingerprint Systems (DFS), including the limitations of these two security techniques, and then proposes Contactless Palm Vein Authentication (CPVA) as a potentially much better solution to increase adoption of e-commerce in developing countries. The architecture of this new CPVA technique is discussed in relation to Security, Privacy, Trust and Reliability. Participants are treated to a Design Fiction Documentary (DFD) and Design Fiction Simulation Experiment (DFSE) in our experimental design method to measure the potential Technology Acceptance (adoption) of the proposed CPVA technique over DS and DFS authentication techniques. The result of our pilot study indicates that citizens may be willing to adopt the proposed CPVA technique, which may increase their trust and likely adoption of more e-commerce applications. A larger main study is planned in the field in Nigeria starting January 2020.