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Go to Editorial ManagerThis study has been performed to compare the compartmental modeling of two types of extravascular routes, sustained-release (SR) oral dosage forms and intramuscular (IM) injection. Twenty healthy volunteers received a single dose of 100 mg Diclofenac Sodium (DS) sustained-release tablet, then 75 mg DS Intramuscular injection after two weeks washout period. The concentrations of DS in plasma were measured using reverse-phase high-performance liquid chromatography (HPLC). The data analyzed using compartmental modeling, with single time-variant input and output. Primary kinetic parameters for both formulations, ( , , ) and other kinetic parameters were evaluated. The result shows that the IM injection needs a shorter time to reach the maximum concentration with convergent bioavailability to SR oral dosage forms, in another hand the data of IM injection fitted to single-compartment model with a correlation coefficient of 0.93 and the data of SR tablet fitted to two-compartment models with a correlation coefficient of 0.97.
Porosity is one of the most important reservoir characteristics because it indicates to fluid collection. Several techniques used to get good porosity prediction, so, in this study we employed seismic attributes and well log data in a genetic algorithm to get the best porosity prediction. The study attempt to enhance the performance of genetic algorithm for attribute selection and therefore porosity prediction by applying genetic algorithm on different types of fitness functions like average mean square error fitness, average correlation coefficients fitness and performance index fitness. Also, used two methods to represent attributes in genetic algorithm. Different witnesses applied to choose the appropriate fitness function that gives high porosity prediction.
This study implements the soft computing techniques such as Artificial Neural Network (ANN) and an adaptive Neuro-Fuzzy (ANFIS) approach. Thus to model the rutting prediction with the aid of experimental uniaxial creep test results for asphalt mixtures. Marshall samples, having Maximum Nominal Size of 12.5 mm, have been selected from previous studies. These samples have been prepared and tested under different conditions. They were also subjected to different loading stress (0.034, 0.069, 0.103) MPa, and tested at various temperature (10, 20, 40, and 55) °C. The modeling analysis revealed that both approaches are powerful tools for modeling creep behavior of pavement mixture in terms of Root Mean Square Error and Correlation Coefficient. The best results are obtained with the ANFIS model.