Background: Non-small cell lung cancer (NSCLC) is caused of 85% of all lung cancers. Among the most important factors for lung tumor growth and proliferation are the tyrosine kinase receptors that coded by the epidermal growth factor recep-tor (EGFR) gene. Activation of EGFR ultimately leads to developing of lung cancer. The present study was undertaken with an objective to detect EGFR mutations in bronchial wash from Iraqi patients with NSCLC before treatment. Methods: DNA was extracted from bronchial wash samples collected from 50 patients with NSCLC by using a Qiamp DNA Mini Kit (Qiagen, Hilden, Germany). Then, EGFR mutations were determined by using real-time RCR combined with two technologies, Amplification Refractory Mutation System (

In this work, the dyes Rhodamine B and Coumarin 102 containing titanium dioxide nanoparticles were used as scattering centers to fabricate a random gain medium. The laser dye was dissolved in hexanol and methanol solvent respectively. The titanium dioxide nanoparticles were synthesized by DC reaction magnetron spraying technique. The random-gain medium was made by adding 2.5 mg of titanium dioxide nanoparticles to Rhodamine and coumarin 102 dyes by coating the glass cell with two-sided titanium dioxide with high spectral efficiency and low production cost. A narrow line optical emission was detected at 565 nm for Rhodamine B and 534 nm for coumarin 102, where it was found that rhodamine B dye has FWHM 8 nm and coumarin dye 102 has FWHM 9 nm

S Khalifa E, N Adil A, AS Mazin M…, 2008

Carbon dioxide geo-sequestration (CGS) into sediments in the form of (gas) hydrates is one proposed method for reducing anthropogenic carbon dioxide emissions to the atmosphere and, thus reducing global warming and climate change. However, there is a serious lack of understanding of how such CO2 hydrate forms and exists in sediments. We thus imaged CO2 hydrate distribution in sandstone, and investigated the hydrate morphology and cluster characteristics via x-ray micro-computed tomography in 3D in-situ. A substantial amount of gas hydrate (∼17% saturation) was observed, and the stochastically distributed hydrate clusters followed power-law relations with respect to their size distributions and surface area-volume relationships. The layer-

Automated detection of Dubas palm infestation by image processing techniques has practical significance as it can improve agricultural efficiency, increase crop yield and quality, protect the environment, and provide data-driven insights. It also reduces the human effort required for pest control and enhances sustainability. In this study, we aimed to automate the detection of Dubas bug infestation in palm trees using deep learning with transfer learning residual neural networks. Based on four models: InceptionResNetV2, ResNet18, ResNet50, and ResNet101, the data used in this study were obtained by drone photography, many images were taken, and then the infected area was extracted. Using two types of data, 185 infected images and 185 health

Abstract: non-alcoholic fatty liver disease (NAFLD) is one of the widespread chronic liver diseases; it is ranging from simple fat buildup in the liver (steatosis) to non-alcoholic steatohepatitis (NASH) presence of inflammation and hepatocyte injury.                                                                                                                                        &nb

     This article aims to determine the time-dependent heat coefficient together with the temperature solution for a type of semi-linear time-fractional inverse source problem by applying a method based on the finite difference scheme and Tikhonov regularization. An unconditionally stable implicit finite difference scheme is used as a direct (forward) solver. While by the MATLAB routine lsqnonlin from the optimization toolbox, the inverse problem is reformulated as nonlinear least square minimization and solved efficiently. Since the problem is generally incorrect or ill-posed that means any error inclusion in the input data will produce a large error in the output data. Therefore, the Tikhonov regularization technique is applie