The present study introduces description of a new species of genus Arboridia Zakhvaticin 1946, based on a large collection of Cicadellids. External morphological characters particularly male genitalia were discussed and illustrated. The genus Arboridia Zalchvatkiia (Typhlocybinae: Erythroneurini) contains small slender, fragil and attractively coloured and patterned leafhoppers. It was erected by Zakhvatkin in 1946 (Zalchvatkin, 1946). The overall length of adults ranges from 2.5 to 3.4 mm. Members of this genus can be recognized by inner apical cell of forewing which is long with oblique base; Cu confluent with this base at a point near the middle of the length of inner apical cell; two prominent circular deep brown spots on vertex (Zalchvatkin, 1946; Young, 1952 and Lequesne & paynr, 1981). The taxonomic status of this genus in Iraq is still poorely studied, the first taxonomic work was made by Gliatui (1964), who described and illustrated Arbooridia hussaini as a new species.
A mounted specimen of a mustelid animal deposited in the Kurdistan Museum of Natural History, Salahaddin University, Erbil proved to be Mustela erminea (Linnaeus, 1758) and represents a new record for the mammalian fauna of Iraq. Its measurements and some biological noted are provided. Also, two passerine birds; the Red-headed bunting, Emberiza bruniceps Brandt, 1841(Family, Emberizidae) and the Variable wheatear, Oenanthe picata (Blyth, 1847) (Family, Muscicapidae) were recorded for the first time in Iraq. Furthermore, the tree frog Hyla savignyi Audouin, 1829 was found in two locations north east of Iraq with spotted dorsum and having interesting behavior in having the capabil
... Show MoreEnzyme activity were studied in the sera of children with leukemia than healthy children, where 31 cases were studied, including 21 cases of patients with acute lymphatic leukemia
Purpose/objective:
1 - To explain the financial impact of the activities and areas of human resources management and the adoption of the methodology for estimating costs on the basis of conduct and statement of how to assess costs and benefits of human resource activities.
2 - Measuring human capital, and its impact on the financial statements.
Design/methodology/approach:
Concentrated dimensions of the research paper's lack of financial statements prepared by the organizations for information mandated human resource its components of the three (attraction - development
... Show MoreThe presented research investigated the pollen morphology of endemic Iraqi Hypericum species. The study revealed phenotypic features of pollen grains in the polar and equatorial views and their quantitative and qualitative characteristics. The results showed that the pollen grains of the genus Hypericum were radially symmetrical and isopolar, and their apertures were simple and tricolporate, except the species H. davisii, distinguished as tetracolprate. Dividing the studied species based on pollen grain sizes comprised two groups. Small pollen grains with an average length of the equatorial view ranged between 10–16 μm in H. lysimachioides and H. vermiculare. Medium-sized pollen grains with an average extent between 17–26 μm e
... Show MoreAdhrt all fungal biological control ability Tdhadah less than 2 repel Alaftran Almamradan showed leaky mushroom Biological control is thermally laboratories and different concentrations of 5, 10 and 20% inhibition in the growth of fungus colonies amounted to 3.8 cm and 3.1 and 2.4 respectively in comparison with control 9 cm
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 MoreThe Cu(II) was found using a quick and uncomplicated procedure that involved reacting it with a freshly synthesized ligand to create an orange complex that had an absorbance peak of 481.5 nm in an acidic solution. The best conditions for the formation of the complex were studied from the concentration of the ligand, medium, the eff ect of the addition sequence, the eff ect of temperature, and the time of complex formation. The results obtained are scatter plot extending from 0.1–9 ppm and a linear range from 0.1–7 ppm. Relative standard deviation (RSD%) for n = 8 is less than 0.5, recovery % (R%) within acceptable values, correlation coeffi cient (r) equal 0.9986, coeffi cient of determination (r2) equal to 0.9973, and percentage capita
... Show MoreRecently, numerous the generalizations of Hurwitz-Lerch zeta functions are investigated and introduced. In this paper, by using the extended generalized Hurwitz-Lerch zeta function, a new Salagean’s differential operator is studied. Based on this new operator, a new geometric class and yielded coefficient bounds, growth and distortion result, radii of convexity, star-likeness, close-to-convexity, as well as extreme points are discussed.
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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