Total of 46 isolates of Klebsiella pneumoniae were collected from patients attending (Al-Yarmook Hospital and Education Labs / medical city), and isolates were re-identified, depending on morphology and biochemical tests . Disk diffusion method was employed to determine antibiotic susceptibility of forty six isolates by using eleven antibiotics .The results revealed the sensitivity of six isolates (9.3%) to Imipenem and Meropenem . On the other hand the isolates were showed 23.9% resistant against Ciprofloxacin, while some isolates shown higher resistant against several antimicrobial agents such as 65.2%, 69.0% for Amikacin and Cefepime consequently , 71.1%, 71.7 % for Amoxicillin -Clauvulanic acid and Gentamicin and 82.6% against Piperacillin , Nitrofurantoin and Ceftazidime. The isolates also appeared high level of resistance against Cefotaxime at a percentage 91.3% . Depending on the obtained results 6 isolates were selected assigned (K21, K32, K33, K37, K38, K43) for detection of blaTEM, blaSHV, blaKPC and AmpC because of its resistance of almost chosen antibiotics. The selected isolates were PCR-positive for blaTEM which showed bands in 209 pb., on the other hand the result revealed that the isolates K33,K32 posses encoding to blaSHV of 590pb in size,. In regard to blaKPC gene only K37 (16%)gave 811 pb . All selected isolate gave negative results to AmpC. The selected isolates were detected of beta-lactamase production by using acidimetric tests (tube method) , all isolates gave positive result.
In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho
... Show More In this paper, we introduce a new type of functions in bitopological spaces, namely, (1,2)*-proper functions. Also, we study the basic properties and characterizations of these functions . One of the most important of equivalent definitions to the (1,2)*-proper functions is given by using (1,2)*-cluster points of filters . Moreover we define and study (1,2)*-perfect functions and (1,2)*-compact functions in bitopological spaces and we study the relation between (1,2)*-proper functions and each of (1,2)*-closed functions , (1,2)*-perfect functions and (1,2)*-compact functions and we give an example when the converse may not be true .
The diseases presence in various species of fruits are the crucial parameter of economic composition and degradation of the cultivation industry around the world. The proposed pear fruit disease identification neural network (PFDINN) frame-work to identify three types of pear diseases was presented in this work. The major phases of the presented frame-work were as the following: (1) the infected area in the pear fruit was detected by using the algorithm of K-means clustering. (2) hybrid statistical features were computed over the segmented pear image and combined to form one descriptor. (3) Feed forward neural network (FFNN), which depends on three learning algorithms of back propagation (BP) training, namely Sca
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The charge density distributions (CDD) and the elastic electron scattering form
factors F(q) of the ground state for some odd mass nuclei in the 2s 1d shell, such
as K Mg Al Si 19 25 27 29 , , , and P 31
have been calculated based on the use of
occupation numbers of the states and the single particle wave functions of the
harmonic oscillator potential with size parameters chosen to reproduce the observed
root mean square charge radii for all considered nuclei. It is found that introducing
additional parameters, namely; 1 , and , 2 which reflect the difference of the
occupation numbers of the states from the prediction of the simple shell model leads
to very good agreement between the calculated an
The charge density distributions (CDD) and the elastic electron scattering form
factors F(q) of the ground state for some odd mass nuclei in the 2s 1d shell, such
as K Mg Al Si 19 25 27 29 , , , and P 31
have been calculated based on the use of
occupation numbers of the states and the single particle wave functions of the
harmonic oscillator potential with size parameters chosen to reproduce the observed
root mean square charge radii for all considered nuclei. It is found that introducing
additional parameters, namely; 1 , and , 2 which reflect the difference of the
occupation numbers of the states from the prediction of the simple shell model leads
to very good agreement between the calculated an
In this paper, estimation of system reliability of the multi-components in stress-strength model R(s,k) is considered, when the stress and strength are independent random variables and follows the Exponentiated Weibull Distribution (EWD) with known first shape parameter θ and, the second shape parameter α is unknown using different estimation methods. Comparisons among the proposed estimators through Monte Carlo simulation technique were made depend on mean squared error (MSE) criteria
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