In this paper, a new analytical method is introduced to find the general solution of linear partial differential equations. In this method, each Laplace transform (LT) and Sumudu transform (ST) is used independently along with canonical coordinates. The strength of this method is that it is easy to implement and does not require initial conditions.

This paper proposes a neuro-fuzzy system to model β-glucosidase activity based on the reaction’s pH level and temperature. The developed fuzzy inference system includes two input variables (pH level and temperature) and one output (enzyme activity). The multi-input fuzzy inference system was developed in two stages: first, developing a single input-single output fuzzy inference system for each input variable (pH, temperature) separately, using the robust adaptive network-based fuzzy inference system (ANFIS) approach. The neural network learning techniques were used to tune the membership functions based on previously published experimental data for β-glucosidase. Second, each input’s optimized membership functions from the ANF

In this work the concept of semi-generalized regular topological space was introduced and studied via semi generalized open sets. Many properties and results was investigated and studied, also it was shown that the quotient space of semi-generalized regular topological space is not, in general semi-generalizedspace.

Dans la langue française, une forme d'auxiliarité, composée de deux éléments cohérents l'auxiliant et l'auxilié, fournit, en effet, à la phrase une diversité significative et structurale. L'auxiliarité, renvoie à l'unification de deux éléments grammaticaux afin de localiser l'énoncé sur l'axe du temps, d'aspect ou de mode. É. Benveniste définit l'auxiliarité en : « Il s'agit d'une forme linguistique unitaire qui se réalise, à travers des paradigmes entiers, en  deux éléments, dont chacun assume une partie des fonctions grammaticales, et qui sont à la fois liés et autonomes, distincts et complémentaires »[1]. Ces deux éléments d'auxiliarité possèden

Let R be a commutative ring with identity 1 ¹ 0, and let M be a unitary left module over R. A submodule N of an R-module M is called essential, if whenever N ⋂ L = (0), then L = (0) for every submodule L of M. In this case, we write N ≤e M. An R-module M is called extending, if every submodule of M is an essential in a direct summand of M. A submodule N of an R-module M is called semi-essential (denoted by N ≤sem M), if N ∩ P ≠ (0) for each nonzero prime submodule P of M. The main purpose of this work is to determine and study two new concepts (up to our knowledge) which are St-closed submodules and semi-extending modules. St-closed submodules is contained properly in the class of closed submodules, where a submodule N of

In this paper, we proposed to zoom Volterra equations system Altfazlah linear complementarity of the first type in this approximation were first forming functions notch Baschtdam matrix and then we discussed the approach and stability, to notch functions

A new approach for baud time (or baud rate) estimation of a random binary signal is presented. This approach utilizes the spectrum of the signal after nonlinear processing in a way that the estimation error can be reduced by simply increasing the number of the processed samples instead of increasing the sampling rate. The spectrum of the new signal is shown to give an accurate estimate about the baud time when there is no apriory information or any restricting preassumptions. The performance of the estimator for random binary square waves perturbed by white Gaussian noise and ISI is evaluated and compared with that of the conventional estimator of the zero crossing detector.

In this research, some robust non-parametric methods were used to estimate the semi-parametric regression model, and then  these methods were compared using the MSE comparison criterion, different sample sizes, levels of variance, pollution rates, and three different models were used. These methods are S-LLS S-Estimation -local smoothing, (M-LLS)M- Estimation -local smoothing, (S-NW) S-Estimation-NadaryaWatson Smoothing, and (M-NW) M-Estimation-Nadarya-Watson Smoothing.

The results in the first model proved that the (S-LLS) method was the best in the case of large sample sizes, and small sample sizes showed that the