The aim of this work is to determine the uranium concentration in soil samples
taken from the north, south, east, west and center of the city of Baghdad and
measure its impact on the rate of cell division for non-smokers peoples and living in
those areas and that between the ages 25-30 year.
The uranium concentration in the samples determined by using CR-39 track
detector . As calculated for the ten samples of each site when irradiated by thermal
neutrons from the (Am - Be) source with flux (5x 103 n S-1cm-2), the concentration
values were calculated by a comparison with standard geological samples. The
results indicate that the extent of the concentration of uranium in the soil north and
east of Baghdad was 12.9 ± 0.7 in Al- Taji north of Baghdad and 12.4 ± 0.23ppm
in the Diyala- Bridge area east of Baghdad and the results were recorded lower
concentration of uranium in the western, central and southern Baghdad, which stood
at 0.60 ± 0.21 in the Abu Ghraib area west of Baghdad, and 4.6 ± 0.7ppm in the
Bab-Al-Sharqee of central Baghdad and 0.87 ± 0.7ppm in Al-Mhmodya area south
of Baghdad . The mitotic index assay MI in the north and east of Baghdad was 2.3 ±
0.059 in the north and 2.43 ± 0.059 in eastern Baghdad, while the lowest rate in
West and Central and South compared with the threshold level of 0.6 . Which
indicates contamination north and east of Baghdad as a result of uranium wars on
Iraq passed in 2003 which negatively affects the behavior of lymphocytes and on
the rate of division.
ABSTRACT
In this research been to use some of the semi-parametric methods the based on the different function penalty as well as the methods proposed by the researcher because these methods work to estimate and variable selection of significant at once for single index model including (SCAD-NPLS method , the first proposal SCAD-MAVE method , the second proposal ALASSO-MAVE method ) .As it has been using a method simulation time to compare between the semi-parametric estimation method studied , and various simulation experiments to identify the best method based on the comparison criteria (mean squares error(MSE) and average mean squares error (AMSE)).
And the use
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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
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