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The Association of Age and Gender with the Histopathological Features among Thyroid Cancer Patients in Erbil City, Iraq: Clinical Analysis of 153 Cases
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     Background: Thyroid cancer (TC) is an increasingly prevalent malignancy throughout the world. It has long been recognized that the incidence of TC is higher in women which increases with age. However, the association of gender disparity and age with TC aggressiveness and outcomes are still controversial. The aim of this study was focused on the association of age and gender with histopathological characteristics in TC. Methods: 153 patients who met the criteria, were selected.  The included cases were divided into four age groups (≤24 years, 25-44 years, 45-64 years, and ≥65 years). Demographic, age and pathological parameters were compared among them. The association of gender and age with histopathological features were then evaluated. Results: Females were significantly more frequent in almost all age groups with the highest female frequency found the age group of 25-44 years old. Females are more susceptible for TC even when they are young. The four groups showed highly significant differences regarding extrathyroidal extension (ETE) which is more aggressive in older individuals’ tumor. However, there were no significant differences regarding tumor size, multifocality, LV invasion and LN metastasis. Moreover, increasing age was significantly associated with increases risk of ETE. In addition, old patients and males were significantly more likely to have larger tumor size. Nonetheless, both gender and age non-significantly associated with multifocality and LV invasion. Conclusion: Our results confirmed that increasing age could really exert a negative prognostic effect, at least in terms of ETE risk and larger tumor size. In addition, TC risk in females was more frequent in all age groups and significantly more likely than men to present at younger, nonetheless, males represented larger tumor size.

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Publication Date
Sat Jun 03 2023
Journal Name
Iraqi Journal Of Science
Effect Effect Effect Effect Effect Effect Effect of Thickness on Some Physical PropertiesThickness on Some Physical PropertiesThickness on Some Physical PropertiesThickness on Some Physical PropertiesThickness on Some Physical Properties Thickness on Some
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The effect of thickness variation on some physical properties of hematite α-Fe2O3 thin films was investigated. An Fe2O3 bulk in the form of pellet was prepared by cold pressing of Fe2O3 powder with subsequent sintering at 800 . Thin films with various thicknesses were obtained on glass substrates by pulsed laser deposition technique. The films properties were characterized by XRD, and FT-IR. The deposited iron oxide thin films showed a single hematite phase with polycrystalline rhombohedral crystal structure .The thickness of films were estimated by using spectrometer to be (185-232) nm. Using Debye Scherrerś formula, the average grain size for the samples was found to be (18-32) nm. Atomic force microscopy indicated that the films had

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Selecting Optimum Dimensions for a Three-Phase Horizontal Smart Separator for Khor Mor Gas-Condensate Processing Plant
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      The Khor Mor gas-condensate processing plant in Iraq is currently facing operational challenges due to foaming issues in the sweetening tower caused by high-soluble hydrocarbon liquids entering the tower. The root cause of the problem could be liquid carry-over as the separation vessels within the plant fail to remove liquid droplets from the gas phase. This study employs Aspen HYSYS v.11 software to investigate the performance of the industrial three-phase horizontal separator, Bravo #2, located upstream of the Khor Mor sweetening tower, under both current and future operational conditions. The simulation results, regarding the size distribution of liquid droplets in the gas product and the efficiency gas/liquid separation, r

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Publication Date
Thu Sep 30 2021
Journal Name
Iraqi Journal Of Science
PFDINN: Comparison between Three Back-propagation Algorithms for Pear Fruit Disease Identification
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     The diseases presence in various species of fruits are the crucial parameter of economic composition and degradation of the cultivation industry around the world. The proposed pear fruit disease identification neural network (PFDINN) frame-work to identify three types of pear diseases was presented in this work. The major phases of the presented frame-work were as the following: (1) the infected area in the pear fruit was detected by using the algorithm of K-means clustering. (2) hybrid statistical features were computed over the segmented pear image and combined to form one descriptor. (3) Feed forward neural network (FFNN), which depends on three learning algorithms of back propagation (BP) training, namely Sca

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