In this study, gold nanoparticle samples were prepared by the chemical reduction method (seed-growth) with 4 ratios (10, 12, 15 and 18) ml of seed, and the growth was stationary at 40 ml. The optical and structural properties of these samples were studied. The 18 ml seed sample showed the highest absorbance. The X- ray diffraction (XRD) patterns of these samples showed clear peaks at (38.25^o, 44.5^o, 64.4^o, and 77.95^o). The UV-visible showed that the absorbance of all the samples was in the same range as the standard AuNPs. The field emission-scanning electron microscope (FE-SEM) showed the shape of AuNPs as nanorods and the particle size between 30-50 nm. Rhodamine-610 (RhB) was prepared at 10<

Anew mixed compound complexes derived from 2-phenyl-2-(o-tolylamino) Acetonitrile as primary ligand (L1) and histidine (L2) as secondary ligand have been prepared and characterized by conventional techniques, elemental microanalysis (C.H.N), Fourier transform infrared, ultra violet-visible spectra, , flame atomic absorption, molar conductivity, magnetic susceptibility measurement and 1H-NMR spectra. From IR data which appear chelating behavior of the amino acid ligand (L2) toward transition metal ions is via carboxylate oxygen, amino nitrogen and imidazol nitrogen as tridentate ligand while second ligand (L1) chelating through N-nitrile and N-aniline, according to all above technics the octahedral shapes were expected for these complexes as

An ingrowing toenail is a common problem affecting mainly adolescents and young adults, with a male predominance of 3:1. The disorder generally occurs in big toes. It is painful and often chronic and it affects work and social activities. Most patients initially complain of pain and later discharge, infection and difficulty in walking occur. The Objectives: The purpose of the study was to evaluate the efficacy and safety of (10600nm) CO2 laser in the treatment of ingrowing toe nail. Patients, Materials & Methods: This study was done in laser medicine research clinics from July 2013 to the end of December 2013; 10 patients including 7(70%) males and 3 (30%) females with age ranging from 18 years to 70 years with mean age of 44 years o

This research presents a method for calculating stress ratio to predict fracture pressure gradient. It also, describes a correlation and list ideas about this correlation. Using the data collected from four wells, which are the deepest in southern Iraqi oil fields (3000 to 6000) m and belonged to four oil fields. These wells are passing through the following formations: Y, Su, G, N, Sa, Al, M, Ad, and B. A correlation method was applied to calculate fracture pressure gradient immediately in terms of both overburden and pore pressure gradient with an accurate results. Based on the results of our previous research , the data were used to calculate and plot the effective stresses. Many equations relating horizontal effective stress and vertica

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security