In this work, the modified Lyapunov-Schmidt reduction is used to find a nonlinear Ritz approximation of Fredholm functional defined by the nonhomogeneous Camassa-Holm equation and Benjamin-Bona-Mahony. We introduced the modified Lyapunov-Schmidt reduction for nonhomogeneous problems when the dimension of the null space is equal to two.  The nonlinear Ritz approximation for the nonhomogeneous Camassa-Holm equation has been found as a function of codimension twenty-four.

This paper introduces the Multistep Modified Reduced Differential Transform Method (MMRDTM). It is applied to approximate the solution for Nonlinear Schrodinger Equations (NLSEs) of power law nonlinearity. The proposed method has some advantages. An analytical approximation can be generated in a fast converging series by applying the proposed approach. On top of that, the number of computed terms is also significantly reduced. Compared to the RDTM, the nonlinear term in this method is replaced by related Adomian polynomials prior to the implementation of a multistep approach. As a consequence, only a smaller number of NLSE computed terms are required in the attained approximation. Moreover, the approximation also converges rapidly over a

The operation and management of water resources projects have direct and significant effects on the optimum use of water. Artificial intelligence techniques are a new tool used to help in making optimized decisions, based on knowledge bases in the planning, implementation, operation and management of projects as well as controlling flowing water quantities to prevent flooding and storage of excess water and use it during drought.

 In this research, an Expert System was designed for operating and managing the system of AthTharthar Lake (ESSTAR). It was applied for all expected conditions of flow, including the cases of   drought, normal flow, and during floods. Moreover, the cases of hypothetical op

In spite of the disappearing of a clear uniform textbook for teaching ESP at different departments and different colleges in both scientific and humanistic studies, the practitioners at those departments and colleges have to teach translation as one of the important requirements to pass the English language exam. The lack of defined translation activities is a noticeable problem therefore; the problem of teaching translation is diagnosed in that the students lack the ability to comprehend the text in English language and other translation knowledge and skills.

The study aims to suggest a translation strategy and then find out the effect of the translation strategy on ESP learners’ achievement in translation. A sample of 50 stud

   The research aims to indicate the relationship between lean production tools included seven {constant improvement , and Just in time (JIT), and the production smoothing , and quality at the source, and standardized work, Visual management, and activities 5S }  and  Mass Customization strategy for the model (Pine & Gilomer, 1997) {collaborative, adaptive, cosmetic, transparent}, as well as providing a conceptual framework and applied for variables search to clarify how they will choose a Mass Customization strategy through the lean production tools, , and recognize the reality of the practices of Iraqi industries in such a field. Moreover, aims to highlight the positive aspects that accrue to companies a

The influence of the grounded electrode area on the ignition voltage in capcitively coupled radio frequency discharge at 13.56 MHz in argon gas is studied experimentally. The results indicate a systematic decrease of the breakdown voltage with increasing grounded electrode area for the same pd value. Results show that the secondary ionization coefficient γ increases with the increase of grounded electrode area. Furthermore, results also the discharge current at the breakdown voltage increases almost linearly with the increase of electrode area suggesting an almost constant current density.

Deep Learning Techniques For Skull Stripping of Brain MR Images

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog