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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.
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.
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.
The removal of Congo red dye from industrial wastewater before its discharge to the environment is a critical problem, as it is non-biodegradable, toxic, and carcinogenic. Activated carbon was synthesized from a suggested sustainable source (sugarcane waste) and used as an adsorbent for the dye. Isotherm adsorption models and adsorption kinetics were investigated, and a simulated adsorption column model was proposed and developed based on the modified general rate model. The batch results showed that the highest removal rate achieved was 87.1% of the initial dye concentration (30 mg/L). The Freundlich isotherm model best described the equilibrium data, as evidenced by the high correlation coefficient (0.9535). Meanwhile, the maximum adsorption capacity was found to be 149.25 mg/g using the Langmuir isotherm. Furthermore, the kinetic results indicate that the pseudo-second-order model accurately describes the concentration-time relationship. A simulated maximum dynamic adsorption capacity of 0.096 mg/g was achieved at an optimal volumetric flow rate of 0.1 mL/min, with a dye concentration of 30 mg/L and a bed length of 39.69 cm. The continuous column efficiency increased with column length, reaching a maximum of 75% at 66.15 cm. Also, as the dye concentration increased, the column efficiency and dynamic adsorption capacity decreased. The study demonstrated an innovative approach to removing Congo red dye from its aqueous solution in a neutral medium using sustainably produced activated carbon derived from a sustainable source. A continuous removal process was simulated by developing a mathematical model based on adsorption curves and adsorption kinetics, which successfully described the adsorption process. This type of modeling, rather than relying on pre-existing equations, enables more efficient, scalable design.