Objective: To assess the Impact of Socio-economic status on age at menarche among secondary school students at
AL-Dora city in Baghdad, Iraq.
Methodology: This is a cross sectional study with multi-stage sampling was carried out during the period from the
3
th of December2013 to 12th of March 2014. The Sample comprised of 1760 girls, 1510 girls from urban area and
250 from rural area was included in the study. In first stage, selection of schools was done, and one class was
selected randomly from each level of Education, The data collection through a special questionnaire which Contain
the age of girl by year, class level, birth order, number of household, number of rooms, residency (urban/rural),
education level of parents, occupation of parents.
Results: The study showed that the mean age at menarche for adolescent secondary school girls in Al-Dora
was12.49±0.99 years, and the mean age at menarche of girls living in the urban area were 12.4±1.0 while 12.9±1.1
year for girls living in the rural area, which give a significant association, so the girls from urban area had earlier
menarche age than rural area, and earlier age at menarche of those girls who had fewer number of siblings than
those who had more siblings, Also the study discovered an earlier age at menarche in those girls whose Parents’
had a high educational level, occupation of mothers, While there was no association between occupation of father
and age at menarche.
Recommendation: According to the findings of the present study we recommended to further elaborated study is
required to estimate the age of menarche of Iraqi girls, because menarche age can vary by location, it may not be
possible to generalize these results to other communities in the Iraq government
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RM Abbas, AA Abdulhameed, AI Salahaldin, International Conference on Geotechnical Engineering, 2010
Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face ima
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Facial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) f
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Flow of water under concrete dams generates uplift pressure under the dam, which may cause the dam to function improperly, in addition to the exit gradient that may cause piping if exceeded a safe value. Cutoff walls usually used to minimize the effect of flow under dams. It is required to
1)minimize the flow quantity to conserve water in the reservoir, it is also required to
2)minimize the uplift pressure under the dam to maintain stability of the dam, and it is required to
3) minimize the exit gradient to prevent quick condition to occur at the toe of the dam where piping may occur and may cause erosion of the soil. Varying the angle of cutoff walls affects its influence on the factors aforementioned that are required to
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