The thermal properties (thermal transfer and thermal expansion coefficient) of the enhanced epoxy resin (MWCNT / x-TiO₂) were studied by weight ratios with the values (0%, 3%, 5%, 7% and 10%) and a constant ratio of 3% of MWCNT. The ultrasonic technology was used to prepare the neat and composites which were then poured into Teflon molds according to standard conditions. Thermo-analyzer sensor technology was used to measure thermal transfer (thermal conductivity, thermal flow, thermal diffusion, thermal energy and heat resistance). The thermal conductivity, flow, and thermal conductivity values were increased sequentially by increasing the weight ratio of the filler while the results of stored energy values an

         Polymer blended electrolytes of various concentrations of undoped PAN/PMMA (80/20, 75/25, 70/30, 65/35 and 60/40 wt%) and doped with lithium salts (LiCl, Li₂SO₄H₂O, LiNO₃, Li₂CO₃) at 20% wt have been prepared by the solution casting method using dimethylformamide as a solvent. The electrical conductivity has been carried out using an LCR meter. The results showed that the highest ionic conductivity was 2.80x10^-7 (Ω.cm)^-1 and 1.05x10^-1 (Ω.cm)^-1 at 100 kHz frequency at room temperature for undoped (60% PAN + 40% PMMA) and (80% PAN + 20% PMMA) doped with 20%wt Li₂CO₃ composite blends, respect

Cancer constitutes a serious disease and a major health problem in worldwide, a lot of people were infected with this dangerous disease, Therefore, there must be attention to this disease through diagnosis and prevention there. In this study, we determined the relationship between the Cancer and the concentration of trace elements by comparing the concentration trace elements for infected and non-infected people. The trace elements concentrations to nails are one of the diagnostic criteria that easily to detect and dated this disease without any harm to the patient. Eight nails samples were collected from cancer-infected and eight samples from non-infected of the relatives of first-degree. All samples were measured by the concentrations

With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121

The molar ratio(x) of Li-Ni ferrites in the formula Li0.5-0.5xNixFe2.5-
0.5xO4 was varied in range 0.1-1.0 by hydrothermal process. The
XRD, SEM, and TEM tests were conducted to examine the samples
crystalline phase and to characterize the particles shapes and sizes.
The high purity spinel structure was obtained at med and high x
values. SEM and TEM images showed the existence of different
ferrite particles shapes like nanospheres and nanorods. The
maximum particle size is around (20nm). These size encourage
occurrence of super paramagnetic state. The reflection loss and
insertion loss as microwave losses of Li-Ni ferrite-epoxy composite
of 1mm thickness and mixing ratio 39.4 wt was investigated. The
mini

We studied the effect of Ca- doping on the properties of Bi-based superconductors by
adding differ ent amounts of CaO
to the Bi
2
Sr2La2-xCaxCu3O10+δ
compound. consequently, we
obtained three samples A,B and C with x=0.0, 0.4 and 0.8 respectively. The usual solid-state
reaction method has been applied under optimum conditions. The x-ray diffraction analy sis
showed that the samples A and B have tetragonal structures conversely the sample C has an
orthorhombic structure. In addition XRD analysis show that decreasing the c-axis lattice
constant and thus decreasing the ratio c/a for samples A,B and C resp ectively. The X-ray
florescence proved that the compositions of samples A,B and C with the ra