In the present research, the electrical properties which included the ac-conductivity (σac), loss tangent of dielectric (tan δ) and real dielectric constant (ε’) are studied for nano polycarbonate in different pressures and frequencies as a function of temperature these properties were studied at selective temperature gradients which are (RT-50-100-150-250)°C. The results of the study showed that the values of dielectric constant and dissipation factor increase with increasing pressure and temperature and decreases by increasing frequency. And the results of electrical conductivity showed that it increases with increasing temperature, pressure and frequency.

An experimental study was conducted on pressure drop of water flow through vertical cylindrical packed beds in turbulent region and the influence of the operating parameters on its behavior. The bed packing was made of spherical and non-spherical particles (spheres, Rasching rings and intalox saddle) with aspect ratio range 3.46  D/dp  8.486 obtaining bed porosities 0.396 0.84 and Reynolds number 1217  21758. The system is consisted of 5 cm inside diameter Perspex column, 50 cm long; distilled water was pumped through the bed with flow rate 875, 1000, 1125, 1250,1375 and 1500 l/h and inlet water temperature 20, 30, 40 and 50 ˚C. The packed bed system was monitored by using LabVIEW program, were the result

In this work the interaction of plasma jet with water and hydrogen peroxide liquids used for assisted teeth bleaching by plasma jet had been study. A homemade plasma jet system was used. The plasma jet supply by 15 W electrical power generated by high voltage power supply of 9.6 kV peak to peak and frequency of 33 kHz .this power supply generate high electric field on electrodes that would be enough to ionize the argon gas. Some important agents were study such as the effect of the Ar gas flow rates on the length of the plasma jet, the influence of plasma jet on some properties of water and two hydrogen peroxide concentrations 25 % and 30 % like pH, conductivity and liquid temperature for different exposure time. The liquids temperature

In this paper, some series of new complexes of Mn(II), Co(II), Ni (II) Cu(II) and Hg(II) are prepared from the Schiff bases (L1,L2). (L1) derived from 4-aminoantipyrine and O-phenylene dia mine then (L2) derived from (L1) and 2-benzoyl benzoic acid. Structural features are obtained from their elemental microanalyses, molar conductance, IR, UV–Vis, 1H, 13CNMR spectra and magnetic susceptibility. The magnetic susceptibility and UV–Vis, IR spectral data of the ligand (L1) complexes get square–planar and tetrahedral geometries and the complexes oflig and (L2) get an octahedral geometry. Antimicrobial examinations show good results in the sharing complexes.

Obesity is a risk factor for a number of chronic conditions. Obesity is clinically defined using the body mass index (BMI) as weight in kg divided by (height)² in m² correlated with obesity. Currently, genetic markers of obesity are being studied. This study focused on the association between the angiotensin II receptor AGTR1 gene (A1166C) and fat mass and obesity-associated protein also known as alpha-ketoglutarate-dependent dioxygenase (FTO) (rs9939609) in obese children and adolescents patients in Rostov region, Russia.  Five-hundreds of Russian nationality child and adolescent were recruited for the obesity-control studies. The relationship between the A1166C polymorphism of the AGTR1 gene in

The splicing design of the existing road and the new road in the expansion project is an important part of the design work. Based on the analysis of the characteristics and the load effect of pavement structure on splicing, this paper points out that tensile crack or shear failure may occur at the splicing under the repeated action of the traffic load on the new/old pavement. According to the current structure design code of asphalt pavement in China, it is proposed that the horizontal tensile stress at the bottom of the splicing layer and the vertical shear stress at other layers of the splicing line should be controlled by adjusting the position and size of the excavated steps in addition to the conventional design ind

Global concerns are rising due to complications associated with the use of chemical agents and antibiotic resistance. Consequently, research focus has shifted towards the quest for effective agents of biological origin. The aim of the present study was to assess the antioxidant and antimicrobial potentials of aqueous and organic extracts derived from various parts of Alcea kurdica. Different parts of A. kurdica were obtained and prepared into leaf, flower and root powders. The powders were extracted with aqueous and organic solvents. The antimicrobial activity of these extracts was assessed against bacterial pathogens using the agar well-diffusion assay. Additionally, the antioxidant effects of the extracts were evaluated using the

Thermomechanical analysis (TMA) and differential scanning calorimetry (DSC) are used to investigate the effect of molding and annealing of polyester on the behavior of thermal expansion and crystallization since these factors play role in the reprocessing or recycling of the polymer. The dynamic mode of the TMA provides enhanced characterization information about the polyester since it separates the transitions into reversible and irreversible signals, and also reveals the progress of the amorphous regions as the polyester loses strength with the increasing temperature approaching melting. Slow cooling after annealing brings crystallization that may be attributed to molecular chain straightening due to orientation.

     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