In the current research, an eco-biosynthesis method for synthesizing silver nanoparticles (AgNPs) is reported using thymus vulgaris leaves (T. vulgaris) extracts. The optical and structural properties of the nanoparticles is determined using UV-visible, x-ray diffraction (XRD) and field emission scanning electron microscope (FESEM). In addition, the synthesis factors such as the temperature, the molar ratio of silver nitride and thymus vulgaris leaves extract have been investigated. The XRD pattern presented higher intensity for the five characteristic peaks of silver. FESEM images for same samples indicated that the particle size was distributed between 24-56 nm. In addition, it’s observed the formation of some aggregated Ag particles

The present work reports a direct experimental comparison of the catalytic hydrodesulfurization of
thiophene over Co-Mo/Al2O3 in fixed- and fluidized-bed reactors under the same conditions. An
experimental pilot plant scale was constructed in the laboratories of chemical engineering department,
Baghdad University; fixed-bed unit (2.54 cm diameter, and 60cm length) and fluidized-bed unit (diameter of 2.54 cm and 40 cm long with a separation zone of 30 cm long and 12.7 cm diameter). The affecting
variables studied in the two systems were reaction temperature of (308 – 460) oC, Liquid hourly space
velocity of (2 – 5) hr-1, and catalyst particle size of (0.075-0.5) mm. It was found in both operations that the
conversion

Sorting and grading agricultural crops using manual sorting is a cumbersome and arduous process, in addition to the high costs and increased labor, as well as the low quality of sorting and grading compared to automatic sorting. the importance of deep learning, which includes the artificial neural network in prediction, also shows the importance of automated sorting in terms of efficiency, quality, and accuracy of sorting and grading. artificial neural network in predicting values and choosing what is good and suitable for agricultural crops, especially local lemons.

Fingerprint recognition is one among oldest procedures of identification. An important step in automatic fingerprint matching is to mechanically and dependably extract features. The quality of the input fingerprint image has a major impact on the performance of a feature extraction algorithm. The target of this paper is to present a fingerprint recognition technique that utilizes local features for fingerprint representation and matching. The adopted local features have determined: (i) the energy of Haar wavelet subbands, (ii) the normalized of Haar wavelet subbands. Experiments have been made on three completely different sets of features which are used when partitioning the fingerprint into overlapped blocks. Experiments are conducted on

Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

 إن المقصود باختبارات حسن المطابقة هو التحقق من فرضية العدم القائمة على تطابق مشاهدات أية عينة تحت الدراسة لتوزيع احتمالي معين وترد مثل هكذا حالات في التطبيق العملي بكثرة وفي كافة المجالات وعلى الأخص بحوث علم الوراثة والبحوث الطبية والبحوث الحياتية ,عندما اقترح كلا من   Shapiro والعالم Wilk  عام 1965 اختبار حسن المطابقة الحدسي مع معالم القياس
(

In this research a computational simulation  has been carried out on the design and properties of the electrostatic mirror and a mathematical expression has been suggested  to represent the axial potential of an electrostatic mirror. The electron beam path using the Bimurzaev  technique had been investigated as mirror trajectory with the aid of Runge – Kutta  method. The spherical and chromatic aberration coefficients of mirror has computed and normalized in terms of the focal length. The choice of the mirror depends on the operational requirements. The Electrode shape of mirror two electrodes has been determined by using package SIMION computer program. Computations have shown that the suggested potentials giv