The soft sets were known since 1999, and because of their wide applications and their great flexibility to solve the problems, we used these concepts to define new types of soft limit points, that we called soft turning points.Finally, we used these points to define new types of soft separation axioms and we study their properties.

In this work we define and study new concept of fibrewise topological spaces, namely fibrewise soft topological spaces, Also, we introduce the concepts of fibrewise closed soft topological spaces, fibrewise open soft topological spaces, fibrewise soft near compact spaces and fibrewise locally soft near compact spaces.

We introduce and discuss recent type of fibrewise topological spaces, namely fibrewise soft bitopological spaces. Also, we introduce the concepts of fibrewise closed soft bitopological spaces, fibrewise open soft bitopological spaces, fibrewise locally sliceable soft bitopological spaces and fibrewise locally sectionable soft bitopological spaces. Furthermore, we state and prove several propositions concerning these concepts.

Waters of some wells at Almuqdadea region Diyala province, east of Iraq have been
compared with wells at Alfalluja region, Alanbar province west of Iraq. Five wells were
selected randomly at each of the two regions to measure several factors represented by
temperature (C), P
H
, Electric Conductivity (EC), Sodium (Na),Calcium (Ca), Magnesium
(Mg),Total Hardness (TH), Carbonate (Co3),Chloride (Cl), Nitrite (NO2), Nitrate (NO3)
Phosphate (PO4) Sulfate (So4), in addition to the heavy metals such as Chromium (Cr),
Cadmium (Cd) Lead (Pb) & Iron (Fe). The mean concentration of the above factors in water
of wells at the above regions had been measured during the period from April to September
(2010).

Safe drinking water is essential for the present and future generations' health. This study aims to assess drinking water quality in Baghdad's Al-Rusafa neighborhood. Water samples were taken from 32 neighborhoods on this side. The quality of the examined potable water samples differed depending on the water source. This investigation's pH, chlorine, EC, TDS, TSS, Cd, and Pb levels were below acceptable ranges. TDS levels in Al-Mada'in are more significant than acceptable (>600ppm) water levels. Bacteria have polluted six communities (Shigella, Salmonella, Escherichia coli, and Klebsiella). Bacterial quality of drinking water and gram-negative bacteria resistant to chlorine in Baghdad's municipal water supply. Regarding pH, the w

      In this work, we introduced and studied  a new kind of soft mapping  on soft topological spaces with an ideal, which we called  soft strongly generalized mapping with respect an ideal I, we studied the concepts like SSIg-continuous, Contra-SSIg-continuous, SSIg-open, SSIg-closed and SSIg-irresolute mapping  and  the relations between these kinds of mappings and the composition of two mappings of the same type of two different types, with proofs or counter examples

Abstract 

For sparse system identification,recent suggested algorithms are  -norm Least Mean Square (  -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named  -ZA-LMS, 

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a