This research includes synthesis of some new thiourea derivatives by two route, the first route include synthesis of 1-aroyl-3-aryl thiourea derivatives (1-17) by a reaction of substituted carboxylic acids with thionyl chloride then the resultant compounds treated the result with potassium thiocyanate to afford the corresponding 1-aroyl isothiocyanate which react directly with primary and secondary aryl amines. The second route, bromo benzene was allowed to react with potassium thiocyanate to afford the corresponding phenylisothiocyanate which was directly reacted with primary and secondary aryl amines to yield 1-phenyl-3-aryl thiourea derivatives (18- 28).The purity of the synthesized compounds were checked by measuring the melting point,Thin Layer Chromatography (TLC) and their structure, were identified by spectral methods [FTIR,1 H-NMR and 13C-NMR].furthermore, these compounds were investigated as corrosion inhibitors for carbon steel at 303 K in 1M H2SO4 solution by using weight loss method, the results showed that maximum inhibition efficiency of some 1-aroyl-3-aryl thiourea derivatives and 1-phenyl-3-aryl thiourea derivatives are ranging between (83-94) %.
Many diseases can produce cardiac overload, of these disease hypertension, valve disease congenital anomaly in addition to many other disease. One of the most common diseases causing left ventricle overload is hypertension. A long term hypertension can cause myocardium hypertrophy leading to changes in the cardiac contractility and reduced efficiency. The investigations were carried out using conventional echocardiography techniques in addition to the tissue Doppler imaging (TDI) from which many noninvasive measurements can be readily obtained. The study has involved the effect of hypertension on the myocardium stiffness index through the measurement of early diastolic filling (E) and the early velocity of lateral mitral annulus (E
... Show MoreMany diseases can produce cardiac overload, of these disease hypertension, valve disease congenital anomaly in addition to many other disease. One of the most common diseases causing left ventricle overload is hypertension. A long term hypertension can cause myocardium hypertrophy leading to changes in the cardiac contractility and reduced efficiency. The investigations were carried out using conventional echocardiography techniques in addition to the tissue Doppler imaging (TDI) from which many noninvasive measurements can be readily obtained. The study has involved the effect of hypertension on the myocardium stiffness index through the measurement of early diastolic filling (E) and the early velocity of lateral mitral annulus (Ea
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Background: Leishmaniasis is important public
health problem owing to its impact on morbidity
and mortality and difficulties in application of
effective control measures.
Objective: The aim of the study is to evaluate the
using of impregnate bed nets in the control of
leishmaniasis.
Methods: The study was conducted throughout
the years 2004 and 2005, in Diala Governorate
(about 60km north-east Baghdad). This is the first
study in Iraq for evaluation of the impregnated bed
net in control of leishmaniasis. Two villages were
selected to achieve this aim. The nets were
distributed for the first village to be used by their
population. The second village was served as
control.
Results: The
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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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This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n
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The 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
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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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The nonlinear refractive (NLR) index and third order susceptibility (X3) of carbon quantum dots (CQDs) have been studied using two laser wavelengths (473 and 532 nm). The z-scan technique was used to examine the nonlinearity. Results showed that all concentrations have negative NLR indices in the order of 10−10 cm2/W at two laser wavelengths. Moreover, the nonlinearity of CQDs was improved by increasing the concentration of CQDs. The highest value of third order susceptibility was found to be 3.32*10−8 (esu) for CQDs with a concentration of 70 mA at 473 nm wavelength.
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