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The research aims to reveal the relationship between the use of social networking sites and the image that females make about their physical formation, the nature of the effects, their value judgments about the image of their bodies, their attitudes toward plastic surgery, the most important types of these processes for them, their motivations to conduct them, and the cultural pressures they are exposed to. The study, moreover, investigates in the effects of those plastic surgery on their behavior as active and interacting users with what is published on social media, according to the theory of social comparison. This paper is an attempt to understand the pattern of social networking operation and its implications for the human self. The research community formed of "females", and the sample included (204) respondents from the followers of "fashionista/ fashionist". The survey approach was used over the intentional sample method to obtain research data through the electronic questionnaire. The questionnaire contained closed questions and phrases according to Likert scale. The research reached several results, including: The sample of the research mostly ranges between 18-28 years of age, having a university education, and are not married and economically sufficient. Always interested in following "fashion" from "fashionista", and sometimes learning cooking techniques, and rarely follow "fashionist". They have a desire to perform plastic surgery, and they were respectively "filler and Botox, remove wrinkles", rhinoplasty, plastic surgery and whitening teeth, sculpting the waist and buttocks, getting the lip of the duck, and finally liposuction and stomach cutting. As for their motives of plastic surgery, mostly, are done in order to satisfy themselves, and in the second place to satisfy people, and in the third position to satisfy their husbands, and in the fourth position to obtain a husband. Finally, they do that to get a job.
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In this study, simply supported reinforced concrete (RC) beams were analyzed using the Extended Finite Element Method (XFEM). This is a powerful method that is used for the treatment of discontinuities resulting from the fracture process and crack propagation in concrete. The mesoscale is used in modeling concrete as a two-phasic material of coarse aggregate and cement mortar. Air voids in the cement paste will also be modeled. The coarse aggregate used in the casting of these beams is a rounded aggregate consisting of different maximum sizes. The maximum size is 25 mm in the first model, and in the second model, the maximum size is 20 mm. The compressive strength used in these beams is equal to 26 MPa.
The subje
... Show MoreThis paper presents an experimental and numerical study which was carried out to examine the influence of the size and the layout of the web openings on the load carrying capacity and the serviceability of reinforced concrete deep beams. Five full-scale simply supported reinforced concrete deep beams with two large web openings created in shear regions were tested up to failure. The shear span to overall depth ratio was (1.1). Square openings were located symmetrically relative to the midspan section either at the midpoint or at the interior boundaries of the shear span. Two different side dimensions for the square openings were considered, mainly, (200) mm and (230) mm. The strength results proved that the shear capacity of the dee
... Show MoreThis paper presents an experimental and numerical study which was carried out to examine the influence of the size and the layout of the web openings on the load carrying capacity and the serviceability of reinforced concrete deep beams. Five full-scale simply supported reinforced concrete deep beams with two large web openings created in shear regions were tested up to failure. The shear span to overall depth ratio was (1.1). Square openings were located symmetrically relative to the midspan section either at the midpoint or at the interior boundaries of the shear span. Two different side dimensions for the square openings were considered, mainly, (200) mm and (230) mm. The strength results proved that the shear capacity of the dee
... Show MoreThe research aims to identify the level of selective visual attention among students of the faculties of education at the University of Mosul. To achieve the goal of the research, the researchers chose a stratified random sample of students from the faculties of education at the University of Mosul for the academic year (2020-2021). The sample size was (652) students from the scientific and humanitarian specializations, the second and fourth stages. The researchers developed a test of multiple-choice to measure the selective visual attention, which consisted of (42) items. The results revealed that the students of the faculties of education for human sciences have an appropriate level of selective visual attention. There are statisticall
... Show MoreNAA Mustafa, University of Sulaimani, Ms. c Thesis, 2010 - Cited by 4
A QR code is a type of barcode that can hold more information than the familiar kind scanned at checkouts around the world. The “QR” stands for “Quick Response”, a reference to the speed at which the large amounts of information they contain can be decoded by scanners. They are being widely used for advertising campaigns, linking to company websites, contest sign-up pages and online menus. In this paper, we propose an efficient module to extract QR code from background and solve problem of rotation in case of inaccurate image taken from mobile camera.
Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.
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