The plants of genus Heliotropium L. (Boraginaceae) are well-known for containing the toxic metabolites called pyrrolizidine alkaloids (PAs) in addition to the other secondary metabolites. Its spread in the Mediterranean area northwards to central and southern Europe, Asia, South Russia, Caucasia, Afghanistan, Iran, Pakistan, and India, Saudi Arabia, Turkey, and over lower Iraq, Western desert. The present study includes the preparation of various extracts from aerial parts of the Iraqi plant. Fractionation, screening the active constituent, and identification by chromatographic techniques were carried out.Heliotropium  europaeum

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

A numerical model for Polypropylene 575 polymer melts flow along the solid conveying screw of a single screw extruder under constant heat flux using ANSYS-FLUENT 17.2 software has been conducted. The model uses the thermophysical properties such as Viscosity, thermal conductivity, Specific heat and density of polypropylene 575 that measured as a function of temperature, and residence time data for process simulation. The numerical simulation using CFD models for single screw extruder and the polymer extrusion was analysed for parameters such as (thermal conductivity, specific heat, density and viscosity) reveals a high degree of similarity to experimental data measured. The most important outcome of this study is that geometrical, parame

Abstract\

In this research we built a mathematical model of the transportation problem  for data of General Company for Grain Under the environment of variable demand ,and situations of incapableness to determining the supply required quantities as a result of economic and commercial reasons, also restrict flow of grain amounts was specified to a known level by the decision makers to ensure that the stock of reserves for emergency situations that face the company from decrease, or non-arrival of the amount of grain to silos , also it took the capabilities of the tanker into consideration and the grain have been restricted to avoid shortages and lack of processing capability, Function has been adopted

In order to obtain a mixed model with high significance and accurate alertness, it is necessary to search for the method that performs the task of selecting the most important variables to be included in the model, especially when the data under study suffers from the problem of multicollinearity as well as the problem of high dimensions. The research aims to compare some methods of choosing the explanatory variables and the estimation of the parameters of the regression model, which are Bayesian Ridge Regression (unbiased) and the adaptive Lasso regression model, using simulation. MSE was used to compare the methods.

this paper presents a novel method for solving nonlinear optimal conrol problems of regular type via its equivalent two points boundary value problems using the non-classical

Today’s academics have a major hurdle in solving combinatorial problems in the actual world. It is nevertheless possible to use optimization techniques to find, design, and solve a genuine optimal solution to a particular problem, despite the limitations of the applied approach. A surge in interest in population-based optimization methodologies has spawned a plethora of new and improved approaches to a wide range of engineering problems. Optimizing test suites is a combinatorial testing challenge that has been demonstrated to be an extremely difficult combinatorial optimization limitation of the research. The authors have proposed an almost infallible method for selecting combinatorial test cases. It uses a hybrid whale–gray wol