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Antimicrobial Effect of Moringa Oleifera L. and Red Pomegranate against Clinically Isolated Porphyromonas gingivalis: in vitro Study
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Moringa oleifera L. and red pomegranate extracts have been reported to inhibit gram-positive facultative anaerobe growth and inhibit the formation of biofilm on tooth surfaces. The current study aimed to assess the antibacterial effect of M. oleifera L. and red pomegranate extracts and their combinations against Porphyromonas gingivalis. The antimicrobial sensitivity, minimum inhibition concentrations (MIC), and minimum bactericidal concentrations after treatment with the aqueous extracts of M. oleifera L. and red pomegranate as well as their combination against clinically isolated P. gingivalis were determined using agar well diffusion and two-fold serial dilution. The anti-biofilm activity of the extracts and their combination was evaluated using the tube adhesion method. The phytochemical analysis was carried out using gas chromatography-mass spectrometry. It was found that P. gingivalis was sensitive to aqueous extract of M. oleifera L. seeds and red pomegranate albedo, however, not to M. oleifera L. leaves and red pomegranate seeds. The MIC value of M. oleifera L. seeds, red pomegranate albedo, and their combination were obtained at 12.5 mg/ml, 6.25 mg/ml, and 3.12 mg/ml against P. gingivalis, respectively. The extract combination had the highest anti-biofilm effect than M. oleifera L. seeds and red pomegranate albedo aqueous extracts at the minimum concentrations of 6.25 mg/ml, 25 mg/ml, and 12.5 mg/ml, respectively. The combination of red pomegranate albedo and M. oleifera L. seeds showed superior antibacterial and anti-biofilm effects against P. gingivalis, followed by red pomegranate albedo and M. oleifera L. seeds. This may highlight a promising alternative to the traditional chemicals that can be used as an adjunct in the treatment of periodontal diseases.

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Publication Date
Tue Sep 11 2018
Journal Name
Iraqi Journal Of Physics
Contrast enhancement of infrared images using Adaptive Histogram Equalization (AHE) with Contrast Limited Adaptive Histogram Equalization (CLAHE)
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The objective of this paper is to improve the general quality of infrared images by proposes an algorithm relying upon strategy for infrared images (IR) enhancement. This algorithm was based on two methods: adaptive histogram equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The contribution of this paper is on how well contrast enhancement improvement procedures proposed for infrared images, and to propose a strategy that may be most appropriate for consolidation into commercial infrared imaging applications.
The database for this paper consists of night vision infrared images were taken by Zenmuse camera (FLIR Systems, Inc) attached on MATRIC100 drone in Karbala city. The experimental tests showed sign

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Publication Date
Mon Jan 01 2024
Journal Name
Ieee Access
A Direct Solution Scheme for Wide-Angle Electromagnetic Scattering Problems Using Compressive Sensing-Based Method of Moments
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Publication Date
Sun Jun 26 2022
Journal Name
Electrical Engineering
Optimal insulation design of form-wound stator winding with stress grading system under fast rise-time excitation
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The effective insulation design of the stress grading (SG) system in form-wound stator coils is essential for preventing partial discharges and excessive heat generation under pulse-width modulation excitation. This paper proposes a method to find the optimal insulation design of the SG system aimed at reducing the dielectric and thermal stresses in the machine coil. The non-uniform transmission line model is used to predict the voltage propagation along the overhang, SG, and slot regions considering the variation in the physical properties of the insulation layers. The machine coil parameters for different insulation materials are calculated by using the finite element method. Two optimization algorithms, fmincon and particle swarm optimiz

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Publication Date
Sat Jun 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
Comparison Ridge regression method with some classical methods to estimate the parameters of Lomax distribution by simulation
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Abstract

In this research provide theoretical aspects of one of the most important statistical distributions which it is Lomax, which has many applications in several areas, set of estimation methods was used(MLE,LSE,GWPM) and compare with (RRE) estimation method ,in order to find out best estimation method set of simulation experiment (36) with many replications  in order  to get mean square error and used it to make compare , simulation experiment  contrast with (estimation method, sample size ,value of location and shape parameter) results show that estimation method effected by simulation experiment factors and ability of using other estimation methods such as(Shrinkage, jackknif

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Publication Date
Sat Apr 30 2022
Journal Name
Kufa Journal Of Engineering
Numerical Analysis for the Response of Skirt Circular Shallow Footing Resting on Sandy Soil under vertical loads
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Publication Date
Tue Jun 21 2022
Journal Name
Peerj Computer Science
Performance evaluation of frequency division duplex (FDD) massive multiple input multiple output (MIMO) under different correlation models
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Massive multiple-input multiple-output (massive-MIMO) is considered as the key technology to meet the huge demands of data rates in the future wireless communications networks. However, for massive-MIMO systems to realize their maximum potential gain, sufficiently accurate downlink (DL) channel state information (CSI) with low overhead to meet the short coherence time (CT) is required. Therefore, this article aims to overcome the technical challenge of DL CSI estimation in a frequency-division-duplex (FDD) massive-MIMO with short CT considering five different physical correlation models. To this end, the statistical structure of the massive-MIMO channel, which is captured by the physical correlation is exploited to find sufficiently

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Publication Date
Thu Jul 20 2023
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Bayesian Approach for estimating the unknown Scale parameter of Erlang Distribution Based on General Entropy Loss Function
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We are used Bayes estimators for unknown scale parameter  when shape Parameter  is known of Erlang distribution. Assuming different informative priors for unknown scale  parameter. We derived The posterior density with posterior mean and posterior variance using different informative priors for unknown scale parameter  which are the inverse exponential distribution, the inverse chi-square distribution, the inverse Gamma distribution, and the standard Levy distribution as prior. And we derived Bayes estimators based on the general entropy loss function (GELF) is used the Simulation method to obtain the results. we generated different cases for the parameters of the Erlang model, for different sample sizes. The estimates have been comp

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Publication Date
Mon Sep 01 2014
Journal Name
Al-khwarizmi Engineering Journal
Comparison of Fatigue Life Behavior between Two Different Composite Materials Subjected to Shot Peening at Different Times
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This paper investigated the fatigue life behavior of two composite materials subjected to different times of shot peening (2, 4 and 6 min).The first material prepared from unsaturated polyester with E-glass reinforcement by 33% volume fraction. While, the second one was prepared from unsaturated polyester with aluminum powder by2.5% volume fraction. The experimental results showed that the improvement in endurance limit was obtained (for the first material) at 2, 4 and 6 min shot peening times where the percentage of maximum improvement was 25% at shot peening time of 6 min. While, the endurance limit of the second material decreased at shot peening times of 2, 4 and 6 min where the percentage of maximum reduction was 29 % at shot peenin

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Publication Date
Fri Dec 01 2023
Journal Name
Al-khwarizmi Engineering Journal
Development of an ANN Model for RGB Color Classification using the Dataset Extracted from a Fabricated Colorimeter
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Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

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Publication Date
Sat Aug 04 2012
Journal Name
University Of Thi-qar Journal
Prediction of Ultimate Soil Bearing Capacity for Shallow Strip Foundation on Sandy Soils by Using (ANN) Techniqu
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Bearing capacity of soil is an important factor in designing shallow foundations. It is directly related to foundation dimensions and consequently its performance. The calculations for obtaining the bearing capacity of a soil needs many varying parameters, for example soil type, depth of foundation, unit weight of soil, etc. which makes these calculation very variable–parameter dependent. This paper presents the results of comparison between the theoretical equation stated by Terzaghi and the Artificial Neural Networks (ANN) technique to estimate the ultimate bearing capacity of the strip shallow footing on sandy soils. The results show a very good agreement between the theoretical solution and the ANN technique. Results revealed that us

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