Abstract Kidney stones are one of the most common and most painful medical problems known (1). Nurses assess and monitor patients through diagnosis and treatment and teach patients how to avoid recurrence of stones (2). A descriptive study was conducted on 150 patients diagnosed with recurrent kidney stones, who were attending the out patients consultation urology disease clinics at surgical specialties, Al-Kadhimia, Al-Yarmook, and Al-Karama Teaching Hospital and Extracorporeal shock wave lithotripsy (ESWL) departments for the period from the 1st of Feb. 2002 through to the end of May 2004. The aim of the study is to assess the post-operative follow-up for patients with recurrent kidney stones. None- probability (purposive sample) of 150 patients with recurrent kidney stones The reliability and validity of the instrument were determined through a pilot study. The data were collected through the use of constructed questionnaire and analysis through the application of descriptive statistical analysis procedures which included. (Frequency, percentage, mean score, stander deviation, relative sufficiency) and application of inferential statistical analysis which included the (Chi-square)
Back ground: Glass ionomer materials lack resistance to wear and pressure and are susceptible to moisture during the initial stages of setting and dehydration. So this study was done to assess diametral tensile strength and microhardness of glass ionomer reinforced by different amounts of hydroxyapatite. Materials and methods: In this study a hydroxyapatite material was added to glass monomer cement at different ratios: 10%, 15%, 20%, 25% and 30% (by weight). The diametral tensile strength test described by the British standard specification for zinc polycarboxylate cement was used in this study and the microhardness test was performed using Vickers microhardness testing machine and the microhardness values were calculated and statistical c
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Abstract. Al-Abbawy DAH, Al-Thahaibawi BMH, Al-Mayaly IKA, Younis KH. 2021. Assessment of some heavy metals in various aquatic plants of Al-Hawizeh Marsh, southern of Iraq. Biodiversitas 22: 338-345. In order to describe the degree of contamination of aquatic environments in Iraq, heavy metals analysis (Fe, Ni, Cr, Cd, Pb, and Zn) was conducted for six aquatic macrophytes from different locations of Al-Hawizeh Marsh in southern Iraq. The six species were Azolla filiculoides (floating plant), Ceratophyllum demersum, Potamogeton pectinatus, Najas marina (submerged plants), Phragmites australis, and Typha domingensis (emergent plants). The results indicate that cadmium, chromium, and iron concentrations in aquatic plants were above the
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Vitiligo is an acquired idiopathic skin disorder characterized by depigmented macules due to loss of cutaneous melanocytes. A potential role of the immune dysfunction has been suggested in vitiligo, so to test this hypothesis, certain cytokines (IL-17A and TNF-?) and immunoglobulins (IgM, IgG, IgA and total IgE) were investigated in all participants. The study included: 60 patients with age range between (6-55) year; 30(11 males and 19 females) were untreated and 30(12 males and 18 females) were treated with Narrow Band Ultraviolet-B (NB-UVB) and 30 (14 males and 16 females) apparently healthy control. Serum was separated and cytokines (IL-17A and TNF-?) and total immunoglobulin E (IgE) were detected by using Enzyme Linked Immunosorbent Ass
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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The analysis of survival and reliability considered of topics and methods of vital statistics at the present time because of their importance in the various demographical, medical, industrial and engineering fields. This research focused generate random data for samples from the probability distribution Generalized Gamma: GG, known as: "Inverse Transformation" Method: ITM, which includes the distribution cycle integration function incomplete Gamma integration making it more difficult classical estimation so will be the need to illustration to the method of numerical approximation and then appreciation of the function of survival function. It was estimated survival function by simulation the way "Monte Carlo". The Entropy method used for the
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Metal cutting processes still represent the largest class of manufacturing operations. Turning is the most commonly employed material removal process. This research focuses on analysis of the thermal field of the oblique machining process. Finite element method (FEM) software DEFORM 3D V10.2 was used together with experimental work carried out using infrared image equipment, which include both hardware and software simulations. The thermal experiments are conducted with AA6063-T6, using different tool obliquity, cutting speeds and feed rates. The results show that the temperature relatively decreased when tool obliquity increases at different cutting speeds and feed rates, also it
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