Machine learning-based estimation of crude oil-nitrogen interfacial tension.

Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil - nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs.

Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods.

Precise estimation of rock petrophysical parameters are seriously important for the reliable computation of hydrocarbon in place in the underground formations. Therefore, accurately estimation rock saturation exponent is necessary in this regard. In this communication, we aim to develop intelligent data-driven models of decision tree, random forest, ensemble learning, adaptive boosting, support vector machine and multilayer perceptron artificial neural network to predict rock saturation exponent parameter in terms of rock absolute permeability, porosity, resistivity index, true resistivity, and water saturation based on acquired 1041 field data.

Antiviral activity of on HSV-1, 2 .

Background And Objectives: Medicinal plants are the primary treatment for many infectious and non-infectious diseases. In this study, we evaluated the antiviral activity of against herpes simplex viruses 1 and 2, and compared it with the antiviral drug acyclovir.

Materials And Methods: In our experimental study, was dissolved in DMSO, then diluted in DMEM medium.

Fair charging management of PHEVs in radial distribution networks with DG resources-a case study.

Considering the widespread use of PHEVs in advanced societies and the issues ahead, researchers' thinking has focused more on this issue. The important issue is that the use of EVs is increasing due to the advantages, but the necessary infrastructure for their charging stations in the distribution networks does not exist. The high penetration level of EVs can create a potential risk for the existing distribution network; the fair charging of EVs has a special value.