Prevalence of Metallo-β-Lactamase producing Pseudomonas aeruginosa in wound infections in Duhok city, Iraq

Background: Pseudomonas aeruginosa is common pathogen causing nosocomial infection. Acquired drug resistance and Metallo-β-lactamases (MBL) production have recently emerged as one of the most worrisome resistance mechanism that hydrolyze all beta-lactam antibiotics including penicillins, cephalosporins and carbapenems, with the exception of aztreonam. The aim was to find out the prevalence of multi drug resistant (MDR) and Metallo-β-lactamase (MBL) positive isolates of P. aeruginosa in wounds samples which are a serious concern.Methods: Pseudomonas aeruginosa strains were obtained by standard isolation and identification techniques from 307 wound samples of hospital. Strains were then subjected to susceptibility testing for anti-pseudomonas drugs as per Clinical and Laboratory Standards Institute (CLSI) guidelines. Carbapenems resistant strains were selected for the detection of MBL enzyme production by disc potentiation test. Production of MBL was confirmed by enhancement of inhibition zone around imipenem and meropenem discs impregnated with EDTA, as compared to discs without EDTA.Results: Amongst the 71 isolates of P. aeruginosa, 62(87.3%) isolate were imipenem-sensitive, while 9(12.7%) isolates were found to be imipenem resistant and MBL producers. Very high resistance to antibiotics was recorded amongst MBL producers’ P. aeruginosa compared with non-MBL imipenem-sensitive strains.Conclusion: Study indicates that, surveillance for the detection of MBL is necessary. The rapid dissemination of MBL producers is worrisome and necessitates the implementation of proper and judicious selection of antibiotics especially carbapenem.

Classification of visualization exudates fundus images results using support vector machine

This paper classifies the characteristics of normal and exudates fundus images by determine its accuracy for diagnostic purposes. Image normalization was performed on 149 fundus images (81 normal and 68 exudates) from MESSIDOR databases to standardize the colours in the fundus images. The OD removed fundus image and fundus image with the exudates areas removed. The SVM1 classifier was applied to 30 test fundus images to determine the best optimal parameter. The kernel function settings; linear, polynomial, quadratic and RBF have an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using ts accuracy. It has proven to discriminated exudates and non exudates pixels in fundus image using linear kernel function of SVM1 to diagnose DR.Keywords: Diabetic retinopathy (DR); Optic disc (OD); Support Vector Machine (SVM); AC); Radial Basis Function (RBF)

Transformer Faults Classification Based on Convolution Neural Network

This paper studies the latest advances made in Deep Learning (DL) methods utilized for transformer inrush and fault currents classification. Inrush and fault currents at different operating conditions, initial flux and fault type are simulated. This paper presents a technique for the classification of power transformer faults which is based on a DL method called convolutional neural network (CNN) and compares it with traditional artificial neural network (ANN) and other techniques. The inrush and fault current signals of the transformer are simulated within MATLAB by using Fourier analyzers that provides the 2nd harmonic signal. The 2nd harmonic peak and variance statistic values of input signals of the three phases of transformer are used at different operating conditions. The resulted values are aggregated into a dataset to be used as an input for the CNN model, then training and testing the CNN model is performed. Consequently, it is obvious that the CNN algorithm achieves a better performance compared to other algorithms. This study helps with easy discrimination between normal signals and faulty signals and to determine the type of the fault to clear it easily

Production of Biodiesel from Jatropha curcas Seed Oil

This study was carried out to produce biodiesel from freshly harvested Jatropha curcas (J. curcas) seed oil. J. curcas seed oil has a high oil content (40%), the free fatty acids (FFA) range is (1.6-1.75%), peroxide value is 2.6 meq/kg, oil moisture content range is (0.2-0.3%) and saponification value range is (185-189) mg KOH/g oil. The main fatty acids are oleic 39.60 % and linoleic acids 34.64 %, unsaturated fatty acids in J. curcas oil are 75.54 wt%, while, saturated fatty acids are 24.46 wt%. The specifications of biodiesel produced are; Density is reduced from 0.9198 to 0.8810 g/cm3. The kinematic viscosity at 40 oC was reduced from 36.37 to 4.809 mm2/s, and the flash point is 187oC. Biodiesel produced complies with the requirements of the American Society for Testing and Materials (ASTM) standard D6751-09, and the Committee of Standardization in Europe (CEN) standard EN 14214 specifications

Esterification of High Free Fatty Acid Jatropha curcas Oil for Biodiesel Production

This study investigated the esterification of high free fatty acid (FFA) Jatropha curcas (J. curcas) seed oil (6.3 to14.6 %) to produce biodiesel using sulphuric acid with reaction parameters 1% H2SO4, 600 rpm at 60 oC and one hour reaction time. At methanol to oil ratio 3:1, FFAs were reduced to 4.73% with conversion 45%; at 6:1 methanol to oil ratio, FFAs were reduced to 2.31% with conversion 72%; at 7.5:1 methanol to oil ratio FFAs are decreased to less than 2% with conversion ≥85% and there is no considerable difference when increasing methanol to oil ratio to 9:1. Hence the optimum methanol to oil molar ratio is 7.5:1, moreover, the esterification process is not affected by the initial FFA

Detection of asphyxia in infants using deep learning Convolutional Neural Network (CNN) trained on Mel Frequency Cepstrum Coefficient (MFCC) features extracted from cry sounds

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Computational Fluid Dynamics for Gas-Liquid Flows

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Physicochemical characteristics of Bt (Seeni-1) Vs. local hamid cultivar cotton seed oils

n investigation on physicochemical characteristics of Bt (Seeni-1) vs local Hamid cultivar (cv) cottonseed oils (CSO) was conducted. Protein in Seeni-1 seed was relatively higher than Hamid cv seed. Oil content, ash and fibre of Hamid cv were relatively higher. Ash and oil content in black (chemical delinting) and white (mechanical delinting) seed were relatively higher in Hamid cv. There were no differences between the specific gravity (sp.gr.), refractive index (R.I.) and moisture content of both oils. Free fatty acids (FFA) and iodine value (IV) in Seeni-1 were relatively higher. Saturated fatty acids (SFAs) in Hamid cv oil proved to be more than Seeni-1 oil [automatically the USFA should be higher in Seeni-1]. Phosphorus content in Seeni-1 oil was lower than that of Hamid cv, whereas there was no significant difference in the peroxide value (PV)