rhabditid Mesorhabditis franseni Fuchs, 1933 (Family, Mesorhabditidae) and pratylenchid nematode Pratylenchus goodeyi Sher and Allen, 1953 (Family, Pratylenchidae). They were illustrated by molecular aspects. All specimens of both genera were cultured and reproduced for DNA extraction. M. franseni (IRQ.ZAh2 PP528819.1 isolate) was characterized. P. goodeyi (IRQ.ZAh5 PP535537 isolate) was also characterized. Selected specimens of these two species were molecularly characterized using the partial ITS-rRNA gene sequences. The ITS-rRNA sequence of IRQ.ZAh2 PP528819.1 isolate had a range of (98.62%-100%) sequence homology with ITS-rRNA sequence of M. franseni available in NCBI database. While, the ITS-rRNA sequence of IRQ.ZAh5 PP535537 isolate had (100%)sequence homology with ITS-rRNA sequence of P. goodeyi available in NCBI database. M.franseni (IRQ.ZAh2 PP528819.1 isolate) and P. goodeyi (IRQ.ZAh5 PP535537 isolate) are Iraq's first documented instance of these species.
A revised checklist of the robber fly genera (Diptera, Asilidae) was given during this study in Iraq. The investigation showed (21) genera belonging to seven subfamilies, two genera new recorded to entomofauna of Iraq (Promachus Loew, 1848 and Genus: Dysmacus Loew, 1860). Eight genera showed in this investigation and eleven genera were recorded previously to Iraq.
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
... Show MoreImage 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
... Show MoreTwo compounds,[2-amino-4-(4-nitro phenyl) 1,3-thiazole],(4) and [2-amino-4-(4-bromo phenyl) 1,3-thiazole],(5), were synthesized by refluxing thiourea (1) with each of para-ntiro and para-bomophanacyl bromides(2) and (3) respectively, in absolute methanol. Then, by reaction of [5] with 3,5-dinitrobenzoyl chloride in dimethylformamide (DMF) yielded (6) .On the other hand, reaction of (4) with chloroacetyl chloride in dry benzene afforded (7), which is upon treatment with thiourea in absolute methanol, af
... Show MoreThe reaction of starting materials (L-asCl2):bis[O,O-2,3;O,O-5,6-(chloro(carboxylic) methylidene)]- -L-ascorbic acid] with glycine gives new product bis[O,O-2,3,O,O-5,6-(N,O-di carboxylic methylidene N-glycine)-L-ascorbic acid] (L-as-gly) which is isolated and characterized by, Mass spectrum UV-visible and Fourier transform infrared spectrophotometer (FT-IR) . The reaction of the (L-as-gly) with M+2; Co(II) Ni(II) Cu(II) and Zn(II) has been characterized by FT- IR , Uv-Visible , electrical conductivity, magnetic susceptibility methods and atomic absorption and molar ratio . The analysis showed that the ligand coordinate with metal ions through mono dentate carboxylic resulting in six-coordinated with Co(II) Ni(II) Cu(II) ions while with
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