In this study, a mathematical model for the kinetics of solute transport in liquid membrane systems (LMSs) has been formulated. This model merged the mechanisms of consecutive and reversible processes with a “semi-derived” diffusion expression, resulting in equations that describe solute concentrations in the three sections (donor, acceptor and membrane). These equations have been refined into linear forms, which are satisfying in the special conditions for simplification obtaining the important kinetic constants of the process experimentally.

Variable selection in Poisson regression with high dimensional data has been widely used in recent years. we proposed in this paper using a penalty function that depends on a function named a penalty. An Atan estimator was compared with Lasso and adaptive lasso. A simulation and application show that an Atan estimator has the advantage in the estimation of coefficient and variables selection.

Static reservoir modeling is the interacting and analysis of the geological data to visualize the reservoir framework by three-dimensional model and distribute the static reservoir properties. The Petrel E&P software used to incorporate the data. The interpreted log data and core report used in distribution of petrophysical properties of porosity, water saturation and permeability for Zubair reservoir in Luhais oil field.

   The reservoir discretized to 274968 cells in increments of 300, 200 and 1 meter in the direction of X, Y, and Z respectively. The geostatistical approach used in the distribution of the properties of porosity and water saturation overall the reservoir units. The permeability has been calculated

The purpose behind building the linear regression model is to describe the real linear relation between any explanatory variable in the model and the dependent one, on the basis of the fact that the dependent variable is a linear function of the explanatory variables and one can use it for prediction and control. This purpose does not cometrue without getting significant, stable and reasonable estimatros for the parameters of the model, specifically regression-coefficients. The researcher found that "RUF" the criterian that he had suggested accurate and sufficient to accomplish that purpose when multicollinearity exists provided that the adequate model that satisfies the standard assumpitions of the error-term can be assigned. It

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

Generalized multivariate transmuted Bessel distribution belongs to the family of probability distributions with a symmetric heavy tail. It is considered a mixed continuous probability distribution. It is the result of mixing the multivariate Gaussian mixture distribution with the generalized inverse normal distribution. On this basis, the paper will study a multiple compact regression model when the random error follows a generalized multivariate transmuted Bessel distribution. Assuming that the shape parameters are known, the parameters of the multiple compact regression model will be estimated using the maximum likelihood method and Bayesian approach depending on non-informative prior information. In addition, the Bayes factor was used

The non-isothermal crystallization kinetics and crystalline properties of nanocomposites poly butyleneterephthalate, [PBT] /multiwalled-carbon nanotubes (MWCNTs) were tested by differential scanning calorimetry (DSC). PBT/(MWCNTs) nanocomposite was prepared by ultrasonicated of MWCNTs (0.5, 1, 2, 4 wt %) in dichloromethane (DCM) and after that the powdered PBT polymer was added to the MWCNTs solution. The non-isothermal crystallization results show that increasing the MWCNTs contents, decreased the melting temperature (Tm) of PBT/(MWCNTs) nanocomposite as compared with pure PBT, while resulting in improving the degree of crystallinity. These results indicated that a little amount of MWCNTs can be evident strong nucleating agent in PBT na