In this paper, the first integrals of Darboux type of the generalized Sprott ET9 chaotic system will be studied. This study showed that the system has no polynomial, rational, analytic and Darboux first integrals for any value of . All the Darboux polynomials for this system were derived together with its exponential factors. Using the weight homogenous polynomials helped us prove the process.

Background: overweight and obesity are the fifth leadingrisk for global deaths. At least 2.8 million adults die eachyear as a result of being overweight or obese. Numerousstudies show that weight loss, even if only 5-10%,significantly improves dyslipidemia, hypertension, diabetesmellitus, risk for osteoarthritis and its symptoms and risk forselected cancers.Objectives: is to evaluate the effect of diet and exerciseprogram on anthropometric and biochemical status of adultobese patients.Methods: descriptive study. 124 adult obese patientsattending Al Kindy obesity research and therapy unit duringDecember 2012 were included. Measurement of Wt, heightHt and WC performed and BMI was calculated. Laboratorytest analysis, on the fasting state, w

The effect of some environmental factors in the loss rate for high weights virgins are full to the screwworm fly of the ancient world and included temperatures 15,20,25,30,35,40 study showed that the rate of loss in weight virgins advanced to full participants at a temperature of 15 C while notgets evolution

      In this study, different methods were used for estimating location parameter  and scale parameter for extreme value distribution, such as maximum likelihood estimation (MLE) , method of moment  estimation (ME),and approximation  estimators based on percentiles which is called white method in estimation, as the extreme value distribution is one of exponential distributions. Least squares estimation (OLS) was used, weighted least squares estimation (WLS), ridge regression estimation (Rig), and adjusted ridge regression estimation (ARig) were used. Two parameters for expected value to the percentile  as estimation for distribution f

The aim of this paper is to approximate multidimensional functions f∈C(R^s) by developing a new type of Feedforward neural networks (FFNS) which we called it Greedy ridge function neural networks (GRGFNNS). Also, we introduce a modification to the greedy algorithm which is used to train the greedy ridge function neural networks. An error bound are introduced in Sobolov space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result in [1]).