The main purpose from this paper is to introduce a new kind of soft open sets in soft
topological spaces called soft omega open sets and we show that the collection of
every soft omega open sets in a soft topological space (X,~,E) forms a soft topology
  ~
on X which is soft finer than  ~
. Moreover we use soft omega open sets to define
and study new classes of soft functions called weakly soft omega open functions and
weakly soft omega closed functions which are weaker than weakly soft open functions
and weakly soft closed functions respectively. We obtain their basic properties, their
characterizations, and their relationships with other kinds of soft functions between
soft topological spaces.<

This work presents the simulation of a Low density Parity Check (LDPC) coding scheme with
multiuserMulti-Carrier Code Division Multiple Access (MC-CDMA) system over Additive White
Gaussian Noise (AWGN) channel and multipath fading channels. The decoding technique used in
the simulation was iterative decoding since it gives maximum efficiency with ten iterations.
Modulation schemes that used are Phase Shift Keying (BPSK, QPSK and 16 PSK), along with the
Orthogonal Frequency Division Multiplexing (OFDM). A 12 pilot carrier were used in the estimator
to compensate channel effect. The channel model used is Long Term Evolution (LTE) channel with
Technical Specification TS 25.101v2.10 and 5 MHz bandwidth including the chan

Since the inflammatory factor IL-6 known by its critical role in different metabolic reactions in the
body, therefore, this study was designed to detect it is effect in the thyroid gland function by
injecting three groups of rats, the first with IL-6 , the second with carbimazole (antithyroid drug) and
the third with normal saline (Control).The experiment included three groups of rat; each was injected
with recombinant human interleukin-6 (rhIL-6), Carbimazole, or normal saline (Control). The results
of experiments showed that both IL-6 and carbimazole caused a decrease in the levels of thyroid
hormones (T3 and T4) in animal sera, and a significant decrease in animal body weight, but had no
effect on the liver weight

   This paper concerns with deriving and estimating the reliability of the multicomponent system in stress-strength model R_(s,k), when the stress and strength are identical independent distribution (iid), follows two parameters Exponentiated Pareto Distribution(EPD) with the unknown shape and known scale parameters. Shrinkage estimation method including Maximum likelihood estimator (MLE), has been considered. Comparisons among the proposed estimators were made depending on simulation based on mean squared error (MSE) criteria.

In this paper, some estimators for the unknown shape parameters and reliability function of Basic Gompertz distribution were obtained, such as Maximum likelihood estimator and some Bayesian estimators under Squared log error loss function by using Gamma and Jefferys priors. Monte-Carlo simulation was conducted to compare the performance of all estimates of the shape parameter and Reliability function, based on mean squared errors (MSE) and integrated mean squared errors (IMSE's), respectively. Finally, the discussion is provided to illustrate the results that are summarized in tables.

In the present paper, we introduce two subclasses, S^*C(,,g,s,d) and  TS^*C(, ,g, s,d), of analytic functions . Coefficients bounds for these subclasses are calculated.

The main purpose of this article is to originate characteristic properties of the functions in the above subclasses.

         In the present paper, the authors introduce and investigates two new subclasses and,  of the class k-fold bi-univalent functions in the open unit disk. The initial coefficients for all of the functions that belong to them were determined, as well as the coefficients for functions that belong to a field determining these coefficients requires a complicated process. The bounds for the initial coefficients and are contained among the remaining results in our analysis are obtained. In addition, some specific special improver results for the related classes are provided.

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]).