leishmaniasis is a disease of global concern, Leishmania types and distribution rates vary from country to another. For visceral leishmaniasis serum samples were examined by the kala-azar dipstick rapid test. Meanwhile smears were taken from lesions, air dried, fixed and Leishman stained then microscopically examined for amstigote stage in cutaneous leishmaniasis. From April 2014 to April 2015, 150 samples for cutaneous leishmaniasis (CL) and 108 serum sample for visceral leishmaniasis (VL) from in and out patients in Pediatric, Azadi Teaching and Kirkuk General Hospitals were examined. The incidence of visceral leishmaniasis was very low (2.7%) comparing to that in cutaneous leishmaniasis (64.6%). No significant differences had appeared between the cutaneous leishmaniasis incidence in male or female. The most age group infected with cutaneous leishmaniasis in both genders was 5 ≤ years old, with rate of 26.1% in male and a rate of 15.8% in female. Significantly high frequency of the patients were presented with only one or two lesions with rate of 39.17, 25.7 % for each type of lesions respectively. The most location that infected with CL was hand (27.7%) and high rate of them being moist type (71.13%). In Kirkuk city CL is more prevalent than VL. High rate of CL is moist type locating in the limbs and face. The infection is more frequent in children than in adult. For CL a smear taken from the lesion is much worthy than a lesion blood sample or re aspirated saline injected to the lesion.
The aim of the research is to design a test with cognitive achievement through the use of the inverted class strategy for some basic soccer skills for gyms for first-average students, within the curriculum of the Ministry of Education, where the researcher used the experimental approach. For the same division (B) and the number of each group (10) students, i.e. the total total of the research sample (20) students, and the pre-test for the research sample was applied through the design of the test prepared by the research after which the educational program with the strategy was applied to the research sample and continued (8) weeks after Completion of the tutorial has been post-tested for the research sample, In it, he reached significant r
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Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d
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Experimental investigation for small horizontal portable wind turbine (SHPWT) of NACA-44, BP-44, and NACA-63, BP-63 profiles under laboratory conditions at different wind velocity range of (3.7-5.8 m/s) achieved in present work. Experimental data tabulated for 2, 3, 4, and 6- bladed rotor of both profiles within range of blade pitch angles . A mathematical model formulated and computer Code for MATLAB software developed. The least-squares regression is used to fit experimental data. As the majority of previous works have been presented for large scale wind turbines, the aims were to present the performance of (SHPWT) and also to make a comparisons between both profiles to conclude which is the best performance. The overall efficiency and el
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The main object of this article is to study and introduce a subclass of meromorphic univalent functions with fixed second positive defined by q-differed operator. Coefficient bounds, distortion and Growth theorems, and various are the obtained results.
During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
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