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Sediment Assessment of Al-Hindyia and Al-Abbasyia River / Iraq by aquatic oligocheata community as bioindicators
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             Bioindicators have an important role in assessing the quality of water bodies. Aquatic oligocheates, was used as a bioindicator to assess the sediment quality of Al-Hindyia and AL-Abbasyia river (branches of Euphrates River in Iraq). Two sites in each river have been   chosen for this purpose, site S1 was located at Al-Hindyia River and S2 at Al-Abbasyia River. Some kinds of biological indices were used in this study, comprising the percentage of oligochaetes in benthic invertebrates, ranged from 20.3-60.16%. While the percentage of Tubificidae within benthic invertebrates was close 43.3-43.9%.Index of pollution D ranged from 0.13-0.21. The maximum percentage of aquatic oligochaetes to insects larvae of family Chironomidae larvae was recorded at S2 90% while at S1 60%. I0 was scored high value at S1 36.06 whilst only 30.56 at S2.E0 was A8 at S1 and A9 at S2.while IOBS was 15.26 at S2 and 7.07 at S1.The percentage of subfamily Tubificidae (TUSP) showed the highest value 21.21% at S1 while 11.79%at S2 .

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Publication Date
Thu Mar 01 2018
Journal Name
Materials Today Communications
Improved mechanical properties of sol-gel derived ITO thin films via Ag doping
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Publication Date
Sat Apr 15 2023
Journal Name
Journal Of Robotics
A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification
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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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Publication Date
Fri Nov 24 2023
Journal Name
International Journal Of Statistics In Medical Research
A Novel Algorithm for Predicting Antimicrobial Resistance in Unequal Groups of Bacterial Isolates
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Choosing antimicrobials is a common dilemma when the expected rate of bacterial resistance is high. The observed resistance values in unequal groups of isolates tested for different antimicrobials can be misleading. This can affect the decision to recommend one antibiotic over the other. We analyzed recalled data with the statistical consideration of unequal sample groups. Data was collected concerning children suspected to have typhoid fever at Al Alwyia Pediatric Teaching Hospital in Baghdad, Iraq. The study period extended from September 2021 to September 2022. A novel algorithm was developed to compare the drug sensitivity among unequal numbers of Salmonella typhi (S. Typhi) isolates tested with different antibacterials.

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Publication Date
Fri Oct 11 2019
Journal Name
Open Access Macedonian Journal Of Medical Sciences
Immunohistochemical Expression of Epidermal Growth Factor Receptor in Astrocytic Tumors in Iraqi Patients
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BACKGROUND: Diffuse astrocytomas constitute the largest group of primary malignant human intracranial tumours. They are classified by the World Health Organization (WHO) into three histological malignancy grades: diffuse astrocytomas (grade II), anaplastic astrocytomas (grade III) and glioblastoma (grade IV) based on histopathological features such as cellular atypia, mitotic activity, necrosis and microvascular proliferation. Epidermal growth factor receptor (EGFR) is a 170-kDa transmembrane tyrosine kinase receptor expressed in a variety of normal and malignant cells regulating critical cellular processes. When activated, epidermal growth factor receptor (EGFR) triggers several signalling cascades leading to increased proliferatio

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Publication Date
Thu Oct 01 2020
Journal Name
Alexandria Engineering Journal
The operational matrix of Legendre polynomials for solving nonlinear thin film flow problems
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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Enhanced Photocatalytic Activity of Cu2O/ZnO/GO Nanocomposites on the Methylene Blue Degradation
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This study synthesized nanocomposite photocatalyst materials from a mixture of Cu2O nanoparticles, ZnO nanoparticles, and graphene oxide (GO) through coprecipitation and hydrothermal methods. This study aims to determine the optimum composition of Cu2O/ZnO/GO nanocomposites in degrading methylene blue. The nanocomposite was synthesized in two steps:  1  the synthesis of Cu2O and ZnO nanoparticles through the coprecipitation method and the preparation of GO through the modified Hummer method.  2  The preparation of Cu2O and ZnO nanoparticles mixtures with GO through the hydrothermal method to form Cu2O/ZnO/GO nanocomposites. The adsorption-photocatalysis process of methylene blue

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Publication Date
Mon Sep 04 2023
Journal Name
2023 International Conference On Advanced Mechatronic Systems (icamechs)
Performance Analysis of Finite-Time Generalized Proportional Integral Observer for Uncertain Brunovsky Systems
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This paper proposes a novel finite-time generalized proportional integral observer (FTGPIO) based a sliding mode control (SMC) scheme for the tracking control problem of high order uncertain systems subject to fast time-varying disturbances. For this purpose, the construction of the controller consists of two consecutive steps. First, the novel FTGPIO is designed to observe unmeasurable plant dynamics states and disturbance with its higher time derivatives in finite time rather than infinite time as in the standard GPIO. In the FTGPO estimator, the finite time convergence rate of estimations is well achieved, whereas the convergence rate of estimations by classical GPIO is asymptotic and slow. Secondly, on the basis of the finite and fast e

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Publication Date
Thu Aug 31 2017
Journal Name
Journal Of Engineering
Enhanced Performance of Consensus Wireless Sensor Controlled System via Particle Swarm Optimization Algorithm
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     This paper describes the application of consensus optimization for Wireless Sensor Network (WSN) system. Consensus algorithm is usually conducted within a certain number of iterations for a given graph topology. Nevertheless, the best Number of Iterations (NOI) to reach consensus is varied in accordance with any change in number of nodes or other parameters of . graph topology. As a result, a time consuming trial and error procedure will necessary be applied
to obtain best NOI. The implementation of an intellig ent optimization can effectively help to get the optimal NOI. The performance of the consensus algorithm has considerably been improved by the inclusion of Particle Swarm Optimization (PSO). As a case s

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Publication Date
Thu Feb 09 2017
Journal Name
International Journal Of Pharmacy Practice
Factors influencing the degree of physician–pharmacist collaboration within Iraqi public healthcare settings†
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Abstract<sec> <title>Objectives

Medication safety and effectiveness can be improved through interprofessional collaboration. The goals of this study were to measure the degree of physician–pharmacist collaboration within Iraqi governmental healthcare settings and to investigate factors influencing this collaboration.

Methods

This cross-sectional study was conducted in Al-Najaf Province using the Collaborative Working Relationship Model and Physician–Pharmacist Collaborative Instrument (PPCI). Four phar

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Publication Date
Mon Apr 15 2019
Journal Name
Proceedings Of The International Conference On Information And Communication Technology
Optimal control of compliant planar robot for safe impact using steepest descent technique
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