The effect of mixed corrosion inhibitors in cooling system was evaluated by using carbon steel specimens and weight loss analysis. The carbon steel specimens immersed in mixture of sodium phosphate (Na₂ HPO₄) used as corrosion inhibitor and sodium glocunate (C₆ H₁₁ NaO₇) as a scale dispersant at different concentrations (20,40, 60, 80 ppm) and at different temperature (25,50,75 and 100)ºC for (1-5) days. The corrosion inhibitors efficiency was calculated by using uninhibited and inhibited water to give 98.1%. The result of these investigations indicate that the corrosion rate decreases with the increase the corrosion inhibitors concentration at 80 ppm and at 100ºC for 5 days, (i.e,

Many extracted material were used as mouthwash to settle different kinds of oral fungi and bacteria. In this study an extractive of Cyperus rotundus plant E.C.R. which is very active as a mouthwash, was used in concentration range (10-25) ppm in artificial saliva at pH=6.4 and temperature range (288-318) K, to study it’s inhibitory effect on the corrosion of dental amalgam. Potentiostatic and cyclic polarization were used to follow this study. The presence of E.C.R. lead to shift the corrosion potential to the active direction and decreased the corrosion rate to about 98.87%, the inhibition action of E.C.R. resulting from the adsorption of C.R.E. layer between amalgam and saliva as FTIR spectrums shows, where the variety of FTIR adsorptio

   The inhibitive action of a blend of sodium nitrite/sodium hexametaphosphate (SN+SHMP) on corrosion of carbon steel in simulated cooling water systems (CWS) has been investigated by weight loss and electrochemical polarization technique. The effect of temperature, velocity, and salts concentrations on corrosion of carbon steel were studied in the absence and presence of mixed inhibiting blend. Also the effect of inhibitors blend concentrations (SN+SHMP), temperatures, and rotational velocity, i.e., Reynolds number (Re) on corrosion rate of carbon steel were investigated using Second-order Rotatable Design (Box-Wilson Design) in performing weight loss and corrosion potential approach. Electrochemical polarization measurements

The steel jetty selected for strengthening is in Baghdad city, over Tigris River, consists of 55 short spans, each of approximately 4 meters and one naviga-tional opening of 12 m. The bridge is 224 meters length and 8 meters in width. The strengthening system was designed to remove overstresses that occurred when the bridge was subjected to abnormal loads of 380 tons. A strengthening system which installed in spring 2008 was used where the main concept is to depend on added side supporting elements which impose reversal forces on the bridge to counteract most of the loads expected from the abnormal heavy loads. The bridge was load tested before and after the strengthening system was activated. The load test results indicate that the strengt

This work was included external morphological study of horse fly Tabanus indrae Hauser 1939 new record in Iraq, which belongs to family: Tabanidae order: Diptera. The study was involved the most important taxonomic external characters of the: head, thorax, abdomen and their appendages which are: antenna, maxillary palp, wings, legs, spotting in coloring pattern and female genitalia of abdomen

This research presents a new study in kinetics under reactive distillation by using consecutive two – step reaction : the saponification reaction of diethyl adipate with sodium hydroxide solution . The distillation process takes the role of withdrawing the intermediate product (sodium monoethyladipate SMA) which otherwise converts to the final product of low purity.The effect of three parameters were studied through a design of experiments applying 2³ factorial design. These parameters were : the mole ratio of DA to NaOH solution (0.1 and 1) , NaOH solution concentration (3 N and 8 N) , and batch time (1.5 hr. and 3.5 hr.) . The conversion of DA to sodium monoethyladipate(SMA)(intermediate product) was the effect of these pa

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

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

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