The effect of substitution of Ni on Cu in (Bi0.8Pb0.2)2(Sr0.9Ba0.1)2 Ca2Cu3-x Nix O10+? for (x=0,0.1….1,2,3) superconductor system and sintering time has been investigated .The samples were prepared by solid-state reaction methods. The results show that the optimum sintering temperature is equal to 850 ºC, and the sintering time is equal to 140 h. The highest transition temperature (Tc) obtained for (Bi0.8Pb0.2)2(Sr0.9Ba0.1)2 Ca2Cu3-x NixO10+? composition was 113 with x=0.8 Phase analyses of the samples by X-ray diffraction (XRD) analysis showed an orthorhombic structure with a high Tc phases (2223) as a dominant phase and low Tc phase (2212) in addition to some impurity phases.

Absorption properties (Attenuation coefficient, the percentage of the reflection, and the percentage of absorption) in x-band have been investigated in this paper for novolac – alumina- graphite mixture. Using novolac as the host material, the samples are prepared with alumina concentrations (5%,10%,15%,20%) and graphite concentrations (5%,10%) with thickness equal to 2.2mm .Network analyzer produced by HP-8510 was used in this work to measure the attenuation coefficient. The samples (3, 5) have good attenuation of wave with bandwidth of frequencies. The maximum of attenuation is -25dB at frequency 10.28GHZ in sample (3) which has concentrations (80%novolac,10%alumina,and 5% graphite) and -24 dB at frequency 10.56GHZ in sample (5) whic

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

During of Experimental result of this work , we found that the change of electrical conductivity proprieties of tin dioxide with the change of gas concentration at temperatures 260oC and 360oC after treatment by photons rays have similar character after treatment isothermally. We found that intensive short duration impulse annealing during the fractions of a second leads to crystallization of the films and to the high values of its gas sensitivity.

Tin dioxide doped silver oxide thin films with different x content (0, 0.03, 0.05, 0.07) have been prepared by pulse laser deposition technique (PLD) at room temperatures (RT). The effect of doping concentration on the structural and electrical properties of the films were studied. Atomic Force Measurement (AFM) measurements found that the average value of grain size for all films at RT decrease with increasing of AgO content. While an average roughness values increase with increasing x content. The electrical properties of these films were studied with different x content. The D.C conductivity for all films increases with increasing x content. Also, it found that activation energies decrease with increasing of AgO content for all films.

         According to Chandra Survey Observatory Near-Asteroid Belt Comets, the solar wind's contact with the comet produces a variety of spectral characteristics. The study of X-ray spectra produced by charge exchange is presented here. The spectrum of a comet can reveal a lot about its composition. This study has concentrated on the elemental abundance in six different comets, including 17P/Holmes, C/1999T1, C/2013A1, 9p/Temple1, and 103p/Hartley2 (NEAT). Numerous aspects of the comet's dynamics allow it to behave in a unique manner as it gets closer to the Near-Asteroid Belt. These characteristics are being examined, and some studies are still ongoing. The computations allow us to observe, for instance, how the composition of

Ferrite with general formula Ni1-x Cox Fe2O4(where x=0.0.1,0.3,0.5,0.7, and 0.9), were prepared by standard ceramic technique. The main cubic spinel structure phase for all samples was confirmed by x-ray diffraction patterns. The lattice parameter results were (8.256-8.299 ^°A). Generally, x -ray density increased with the addition of Cobalt and showed value between (5.452-5.538gm/cm³). Atomic Force Microscopy (AFM) showed that the average grain size and surface roughness was decreasing with the increasing cobalt concentration. Scanning Electron Microscopy images show that grains had an irregular distribution and irregular shape. The A.C conductivity was found to increase with the frequency and the addition of Cobal