BN Rashid

In this paper, a numerical model for fluid-structure interaction (FSI) analysis is developed for investigating the aeroelastic response of a single wind turbine blade. The Blade Element Momentum (BEM) theory was adopted to calculate the aerodynamic forces considering the effects of wind shear and tower shadow. The wind turbine blade was modeled as a rotating cantilever beam discretized using Finite Element Method (FEM) to analyze the deformation and vibration of the blade. The aeroelastic response of the blade was obtained by coupling these aerodynamic and structural models using a coupled BEM-FEM program written in MATLAB. The governing FSI equations of motion are iteratively calculated at each time step, through exchanging data between

According to grammarians In ( نإ) and Itha (اذإ) are conditionals and sometimes they may be used interchangeably. However, when they are mentioned in the Holy Qur’an, they have their own specific use. This paper attempts to investigate their meanings in the source language as well as investigate their translations and find out any differences or similarities. The translations that are adopted in this research are as follows: Pickthall, Al-Hilali & Khan, and Shakir.

ATAW Eqbal Abdul Ameer'. Shifaa Jameel Ibrahim?, HISTORY Of MEDICINE, 2023

Background: Implant stability is considered one of the most important factors affecting healing and successful osseointegration of dental implants. The aims of the study were to measure the implant stability quotient (ISQ) values during the healing period and to determine the factors that affect implant stability. Materials and methods: Thirty patients enrolled in the study (17 female, 13 male). They received 44 Implantium® Dental Implants located as the following: 22 implants in maxillary jaw, 22 implants in mandibular jaw from them 17 implants in anterior segment and 27 in posterior segment. The bone density determined using interactive CT scan and classified according to the Misch bone density classification (29 implants in (D3), 15 i

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

Background: The most crucial mechanism of genetic variation in N. meningitidis is the slipped strand mispairing, this mechanism generates Phase variation using simple sequence repeat (SSR) and is commonly used by the N. meningitidis to escape the immune system despite its function in eradicating the pathogenic and commensal bacteria. Some of simple sequence repeats (SSRs) that located within the genome works as phase variation while other SSRs have no role in generating phase variation mechanisms. Therefore, Aim: the main goal of the current in silico study was to detect the probability of SSR to enroll with phase variation for the entire N. meningitidis genome. Methods: Different criteria were used to judge SSR as

The present study included the microscopic and molecular identification of Entamoeba histolytica by using specific primers to detect four virulence factors possessed by Entamoeba histolytica. Virulence factors included Active Cysteine proteinase, Galactose/N-acetyl-D-galactose-lectin, Amoeba pore C and Phospholipase. Titanium dioxide nanoparticles (TiO₂NPs) were synthesized from Pseudomonas aeruginosa which producing Pyocyanin pigment as a reducing agent to form it. After that we studied the ability ofTiO₂NPs to inhibit virulence factors production and curing the genes responsible for encoding them by using four different dose 2 ,3, 4, 6 mg/Kg and administered by intraperitoneal injection