Background:

Multiple sclerosis is a chronic disease believed to be the result of autoimmune disorders of the central nervous system, characterised by inflammation, demyelination, and axonal transection, affecting primarily young adults. Disease modifying therapies have become widely used, and the rapid development of these drugs highlighted the need to update our knowledge on their short- and long-term safety profile.

Objective:

The study aim is to evaluate the impact of disease-modifying treatments on thyroid functions and thyroid autoantibodies with subsequent effects on the outcome of the disease.

Materials and Methods:

A retro prospective study

      In this paper, we introduce the notion of Jordan generalized Derivation on prime and then some related concepts are discussed. We also verify that every Jordan generalized Derivation is generalized Derivation when  is a 2-torsionfree prime .

This paper is concerned with introducing and studying the new approximation operators based on a finite family of d. g. 'swhich are the core concept in this paper. In addition, we study generalization of some Pawlak's concepts and we offer generalize the definition of accuracy measure of approximations by using a finite family of d. g. 's.

Breast cancer was one of the most common reasons for death among the women in the world. Limited awareness of the seriousness of this disease, shortage number of specialists in hospitals and waiting the diagnostic for a long period time that might increase the probability of expansion the injury cases. Consequently, various machine learning techniques have been formulated to decrease the time taken of decision making for diagnoses the breast cancer and that might minimize the mortality rate. The proposed system consists of two phases. Firstly, data pre-processing (data cleaning, selection) of the data mining are used in the breast cancer dataset taken from the University of California, Irvine machine learning repository in this stage we

     Machine learning-based techniques are used widely for the classification of images into various categories. The advancement of Convolutional Neural Network (CNN) affects the field of computer vision on a large scale. It has been applied to classify and localize objects in images. Among the fields of applications of CNN, it has been applied to understand huge unstructured astronomical data being collected every second. Galaxies have diverse and complex shapes and their morphology carries fundamental information about the whole universe. Studying these galaxies has been a tremendous task for the researchers around the world. Researchers have already applied some basic CNN models to predict the morphological classes

The exception of contract non-Performance represents an important mechanism in the implementation of obligations. It enables a contractor to refrain from implementing its obligation until the other party has fulfilled its obligations. It is a means of defense, pressure and guarantee of its rights. It is stipulated in articles 161 of the Egyptian Civil Code and Article 123 of the Civil Code While it was not provided for in the French Civil Code except after the issuance of Order No. 2016-131 on the reform of the system of contracts and public order and the establishment of obligations in articles 1217, 1219 and 1220 thereof. These texts, although they did not show the basis on which the exception of contract non-Performance, leaving this

In this paper, we define the concept of soft -connected sets and soft -connected spaces by using the notion of soft -open sets in soft topological spaces. Several properties of these concepts are investigated.

The main goal of this paper is to study and discuss a new class of meromorphici "functions[ which are multivalent defined by [fractional  calculus operators. Coefficients iestimates , radiisi of satarlikeness , convexityi and closed-to-iconvexity are studied. Also distortion iand closure theorems for the classi" ,  are considered.

In this paper, the error distribution function is estimated for the single index model by the empirical distribution function and the kernel distribution function. Refined minimum average variance estimation (RMAVE) method is used for estimating single index model. We use simulation experiments to compare the two estimation methods for error distribution function with different sample sizes, the results show that the kernel distribution function is better than the empirical distribution function.

     This work is devoted to define new generalized gamma and beta functions involving the recently suggested seven-parameter Mittag-Leffler function, followed by a review of all related special cases. In addition, necessary investigations are affirmed for the new generalized beta function, including, Mellin transform, differential formulas, integral representations, and essential summation relations. Furthermore, crucial statistical application has been realized for the new generalized beta function.