The techniques of fractional calculus are applied successfully in many branches of science and engineering, one of the techniques is the Elzaki Adomian decomposition method (EADM), which researchers did not study with the fractional derivative of Caputo Fabrizio. This work aims to study the Elzaki Adomian decomposition method (EADM) to solve fractional differential equations with the Caputo-Fabrizio derivative. We presented the algorithm of this method with the CF operator and discussed its convergence by using the method of the Cauchy series then, the method has applied to solve Burger, heat-like, and, couped Burger equations with the Caputo -Fabrizio operator. To conclude the method was convergent and effective for solving this type of

The proposal of nonlinear models is one of the most important methods in time series analysis, which has a wide potential for predicting various phenomena, including physical, engineering and economic, by studying the characteristics of random disturbances in order to arrive at accurate predictions.

In this, the autoregressive model with exogenous variable was built using a threshold as the first method, using two proposed approaches that were used to determine the best cutting point of [the predictability forward (forecasting) and the predictability in the time series (prediction), through the threshold point indicator]. B-J seasonal models are used as a second method based on the principle of the two proposed approaches in dete

‎ This research aims to estimate stock returns, according to the ‎Rough Set Theory ‎approach, ‎test ‎its effectiveness and accuracy in predicting stock returns and their potential in the ‎field of ‎financial ‎markets, and rationalize investor decisions. The research sample is totaling (10) ‎companies traded at Iraq Stock Exchange. The results showed a remarkable ‎ ‎Rough Set Theory application in data reduction, contributing to the rationalization of ‎investment ‎decisions. The most prominent conclusions are the capability of rough set theory ‎in ‎dealing with financial data and applying it for forecasting stock ‎returns.‎The ‎research provides those interested in investing stocks in financial

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

In this study, light elements Li ,10B for (a,n) and (n,a) reactions
as well as o-particle energy from threshold energy to 10 MeV are
used according to the available data of reaction cross sections. The
more recent cross sections data of (a,n) and (n,a) reactions are
reproduced in fine steps 42 Kev for 10B(n,o) Li in the specified
energy range, as well as cross section (o,n) Values were derived from
the published data of (n,a) as a function of a-energy in the same fine
energy steps by using the principle inverse reactions. This calculation
involves only the ground state of Li OB in the reactions 'Li(a,n) B
B (n,a) Li
Introduction
When two charged nuclei overcome their Coulomb repulsion, a
rearrangement

Abstract in English  The aim of this study is to identify the effectiveness of rehabilitation exercises in conjunction with aquatic therapy for those suffering from sacroiliitis in football players. The researcher used the experimental approach, using the experimental design (pre-measurement, post-measurement). Similar to the objectives and procedures of the research, the researcher assumed the following: the existence of significant differences. Statistics between pre- and post-measurements in variables in favor of post-measurement for the following variables (degree of pain and level of balance). The experimental approach was used with a pre- and post-measurement design on one group of football players with sacroiliitis, nu

The research work was conducted to investigate the effect of oral administration of water extract of black pepper at doses of (1, 5) mg/kg body weight for two weeks daily by determining the genotoxic effect (mitotic index), evaluation of immunological effect (IgG, IgM, IgA, C3, C4) and measuring fertility hormones (follicles stimulation hormone/FSH, lutenising hormone/LH) levels with histopathological examinations of female albino swiss mice ovaries in comparison with control (normal saline). A clear effect in increasing mitotic activity was reveled for both doses in comparison with control. Results also showed a significant increase in the value of the all immunological parameters at both doses in comparison with control. Also obvious rais

Levofloxacin belongs to the fluoroquinolone family; it is a potent broad-spectrum bactericidal agent. The pharmacophore required for significant antibacterial activity is the C-3 carboxylic acid group and the 4-pyridine ring with the C-4 carbonyl group, into which binding to the DNA bases occur. In this work, we tried to show that by masking the carboxyl group through amide formation using certain amines to form levofloxacin carboxamides, an interesting activity is kept. Levofloxacin carboxamides on the C-3 group were prepared, followed by the formation of their copper complexes. The target compounds were characterized by FT-IR, elemental analysis. The antimicrobial activity of the target compounds was evaluated and showed satisfactory resu

Abstract

  Bivariate time series modeling and forecasting have become a promising field of applied studies in recent times. For this purpose, the Linear Autoregressive Moving Average with exogenous variable ARMAX model is the most widely used technique over the past few years in modeling and forecasting this type of data. The most important assumptions of this model are linearity and homogenous for random error variance of the appropriate model. In practice, these two assumptions are often violated, so the Generalized Autoregressive Conditional Heteroscedasticity (ARCH) and (GARCH) with exogenous varia