The Corrosion protection effectiveness of Alimina(Al2O3,50nm)and Zinc oxide (ZnO,30nm) nanoparticales were studied on carbon steel and 316 stainless steel alloys in saline water (3.5%NaCl)at four temperatures: (20,30,40,50 OC)using three electrodes potentiostat. An average corrosion protection efficiencies of 65 %and 80% was achieved using Al2O3 NP's on carbon steel and stainless steel samples respectively, and it seems that no effect of rising temperature on the performances of the coated layers. While ZnO NP'S showed protection efficiency around 65% for the two alloys and little effected by temperature rising on the performanes of the coated layers. The morphology of the coated spesiemses was examined by Atomic force microscope.

This paper proposes a self organizing fuzzy controller as an enhancement level of the fuzzy controller. The adjustment mechanism provides explicit adaptation to tune and update the position of the output membership functions of the fuzzy controller. Simulation results show that this controller is capable of controlling a non-linear time varying system so that the performance of the system improves so as to reach the desired state in a less number of samples.

Abstract 

The Phenomenon of Extremism of Values ​​(Maximum or Rare Value) an important phenomenon is the use of two techniques of sampling techniques to deal with this Extremism: the technique of the peak sample and the maximum annual sampling technique (AM) (Extreme values, Gumbel) for sample (AM) and (general Pareto, exponential) distribution of the POT sample. The cross-entropy algorithm was applied in two of its methods to the first estimate using the statistical order and the second using the statistical order and likelihood ratio. The third method is proposed by the researcher. The MSE comparison coefficient of the estimated parameters and the probability density function for each of the distributions were

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

In this paper, a new equivalent lumped parameter model is proposed for describing the vibration of beams under the moving load effect. Also, an analytical formula for calculating such vibration for low-speed loads is presented. Furthermore, a MATLAB/Simulink model is introduced to give a simple and accurate solution that can be used to design beams subjected to any moving loads, i.e., loads of any magnitude and speed. In general, the proposed Simulink model can be used much easier than the alternative FEM software, which is usually used in designing such beams. The obtained results from the analytical formula and the proposed Simulink model were compared with those obtained from Ansys R19.0, and very good agreement has been shown. I

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

An analytical approach based on field data was used to determine the strength capacity of large diameter bored type piles. Also the deformations and settlements were evaluated for both vertical and lateral loadings. The analytical predictions are compared to field data obtained from a proto-type test pile used at Tharthar –Tigris canal Bridge. They were found to be with acceptable agreement of 12% deviation.

               Following ASTM standards D1143M-07e1,2010, a test schedule of five loading cycles were proposed for vertical loads and series of cyclic loads to simulate horizontal loading .The load test results and analytical data of 1.95

ABSTRACT Background: Cortical bone thickness is important for the stability of mini implants. Placing mini implants in sites of favorable cortical bone thickness would guarantee better initial stability and long-term success. The aim of this study was to investigate gender, side and jaw differences of the buccal cortical bone thickness as a guide for orthodontic mini screw placement. Materials and Methods: The sample was selected from the patients attending the Specialized Health Center in Al-Sadr City / 3D department. Thirty patients (15 males and 15 females) were selected and cone beam computerized tomographic images were done. Then the buccal cortical bone thickness was measured at thirteen inter radicular sites in the maxilla and mandib