In this work was prepared three different types of modified screen printed carbon electrode (SPCEs) with drops casted method, the used carbone nanomaterials were the MWCNT, functionalized –MWCNT (f-MWCNT) and After several experiments were made to find an appropriate ratio to make good GOT/f-MWCNT nanocomposite, and found the suspension mixture (1:1) from GOT/f-MWCNT (f-MWCNT-GOT). The electrical and physical properties were performed with cyclic voltammeter technique, and studied the maximum current response, the effective surface area, effect of the pH value and the determination of active surface area for MWCNT-SPCE , f-MWCNT-SPCE and f-MWCNT-GOT/SPCE as (0.04 cm2), (0.119 cm2) and (0.115 cm2) respectively, the surface coverage concent

Aluminum Metal Matrix Composites (ALMMCs) was prepared by using stir casting technique for AA 7075 aluminum alloy as a matrix reinforced with SiC particles at various percentages (3, 6, 9 and 12 wt. % ) and 75µm in grain size. The prepared composite material can be used for many applications such as aerospace, automobiles and many industrial sectors. Abrasive wear test was carried out by two stages: the first stage was done by changing the emery papers at various grit sizes 180, 320, 500, and 1000µm with constant applied load 15N. While the second stage was carried out by changing the applied loads 5, 10, 15, 20 and 25N with constant emery paper at 320 µm grit size. Microstructure examination, hardness test and roughn

In this paper, the ability of using corn leaves as low-cost natural biowaste adsorbent material for the removal of Indigo Carmen (IC) dye was studied. Batch mode system was used to study several parameters such as, contact time (4 days), concentration of dye (10-50) ppm, adsorbent dosage (0.05-0.25) gram, pH (2-12) and temperature (30-60) ^oC. The corn leaf was characterized by Fourier-transform infrared spectroscopy device before and after the adsorption process of the IC dye and scanning electron microscope device was used to find the morphology of the adsorbent material. The experimental data was imputing with several isotherms where it fits with Freundlich (R² = 0.9

This paper presents the ability to use cheap adsorbent (corn leaf) for the removal of Malachite Green (MG) dye from its aqueous solution. A batch mode was used to study several factors, dye concentration (50-150) ppm, adsorbent dosage (0.5-2.5) g/L, contact time (1-4) day, pH (2-10), and temperature (30-60)   The results indicated that the removal efficiency increases with the increase of adsorbent dosage and contact time, while inversely proportional to the increase in pH and temperature. An SEM device characterized the adsorbent corn leaves. The adsorption's resulting data were in agreement with Freundlich isotherm according to the regression analysis, and the kinetics data followed pseudo-first-or

This research aims to identify the nutritional status of the individual's relationship to malnutrition using anthropometric measurements such as weight, height and impact on the values ??of the blood study sample included 200 male and 200 female of the inhabitants of the cities of Baghdad and Baquba reviewers Central Mahtbrat

The current research aims to identify the level of compulsive buying behavior and Histrionic Personality among a sample of primary school teachers for the academic year (2021-2022) and in the light of some variables

(sex, marital status). To measure the Histrionic Personality, the researcher applied two scales to a random stratified sample of (200) male and female teachers. The results showed statistically significant differences in the level of compulsive buying behavior according to the gender variable and in favor of female teachers. There are no statistically significant differences in terms of marital status. There are statistically significant differences in the Histrionic Personality based on gender variables in favor of f

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod