This study aimed to conduct a comparative analysis of seven calliphorid species of third- instar larvae that were collected from central Iraq. The purpose was to morphologically characterize these species and develop an identification key to differentiate them from closely related species. Scanning Electron Microscope (SEM) graphical images and high-resolution traditional optical microscopes were used to analyze the morphological characteristics of the species Calliphora vomitoria (Linnaeus, 1758), C. vicina Robineau-Desvoidy, 1830, Chrysomya albiceps (Widemann, 1819), Ch. rufifacies (Macquart, 1844), Ch. megacephala (Fabricius, 1794), Lucilia cuprina (Wiedemann, 1830), and L. sericata (Meigen, 1826). An identification key was const

Leuciscidae species are the abundant and widely distributed fish species in Iraq's inland waters. They are complex species, and morphology makes them difficult to identify. Molecular analysis achieved and confirmed the morphological characters. Twenty specimens of Acanthobrama marmid were collected from two localities at Tigris River, in the middle of Iraq; 15 specimens from the Al-Zubaydia sub-district and five specimens from Al-Tharthar Lake. We used the mitochondrial DNA cytochrome b (cytb) gene to sequence the DNA of A. marmid. The following analysis are compared the sequences with those of other fish genera and species found in the Gene Bank. The barcoding result (DNA sequencing) in fishes found in the same family (Leuciscidae) showed

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

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Background The application of nanotechnology to biomedical surfaces is explained by the ability of cells to interact with nanometric features. The aim of this study was to consider the role of nanoscale topographic modification of CPTi dental implant using chemical etching method for the purpose of improving osseointegration. Materials and methods: Commercial pure titanium rod was machined into 20 dental implants. Each implant was machined in diameter about 3mm, length of 8mm (5mm was threaded part and 3mm was flat part). Implants were prepared and divided into 2 groups according to the types of surface modification method used: 1st group (10 implant) remained without nano surface modification (control), 2nd group include (10 implant) etche

Background: In recent years, the immediate loading of dental implants has become more accepted as a standard protocol for the treatment of the edentulous area. Success in implant dentistry depends on several parameters that may improve phenomenon of osseointegration and new bone formation in close contact with the implant. The aim of study was to evaluate the effect of strontium chloride coating of screw shape commercially pure titanium dental implant osseointegration at bone - implant interface by histomorphometric analysis and compare with hydroxyapatite coating at 2 time periods (2 weeks and 6 weeks). Materials and methods: Electrophoretic Deposition Technique (EPD) was used to obtain a uniform coating layer on commercially pure titanium