Nano gamma alumina was prepared by double hydrolysis process using aluminum nitrate nano hydrate and sodium aluminate as an aluminum source, hydroxyle poly acid and CTAB (cetyltrimethylammonium bromide) as templates. Different crystallization temperatures (120, 140, 160, and 180) 0C and calcinations temperatures (500, 550, 600, and 650) 0C were applied. All the batches were prepared at PH equals to 9. XRD diffraction technique and infrared Fourier transform spectroscopy were used to investigate the phase formation and the optical properties of the nano gamma alumina. N2 adsorption-desorption (BET) was used to measure the surface area and pore volume of the prepared nano alumina, the particle size and the morphology of the surface of

Based on the results of standard penetration tests (SPTs) conducted in Al-Basrah governorate, this research aims to present thematic maps and equations for estimating the bearing capacity of driven piles having several lengths. The work includes drilling 135 boreholes to a depth of 10 m below the existing ground level and three standard penetration tests (SPT) at depths of 1.5, 6, and 9.5 m were conducted in each borehole. MATLAB software and corrected SPT values were used to determine the bearing capacity of driven piles in Al-Basrah. Several-order interpolation polynomials are suggested to estimate the bearing capacity of driven piles, but the first-order polynomial is considered the most straightforward. Furthermore, the root means squar

In this study, cloud point extraction combined with molecular spectrometry as an eco-friendly method is used for extraction, enrichment and determination of bendiocarb (BC) insecticide in different complex matrices. The method involved an alkaline hydrolysis of BC followed Emerson reaction in which the resultant phenol is reacted with 4-aminoantipyrene(4-AAP) in the presence of an alkaline oxidant of potassium ferric cyanide to form red colored product which then extracted into micelles of Triton X-114 as a mediated extractant at room temperature. The extracted product in cloud point layer is separated from the aqueous layer by centrifugation for 20 min and dissolved in a minimum amount of a mixture ethanol: water (1:1) followed

Exchange of information through the channels of communication can be unsafe. Communication media are not safe to send sensitive information so it is necessary to provide the protection of information from disclosure to unauthorized persons. This research presented the method to information security is done through information hiding into the cover image using a least significant bit (LSB) technique, where a text file is encrypted using a secret sharing scheme. Then, generating positions to hiding information in a random manner of cover image, which is difficult to predict hiding in the image-by-image analysis or statistical analyzes. Where it provides two levels of information security through encryption of a text file using the secret sha

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings