Accurate rock typing is essential for reservoir characterization because heterogeneous carbonate reservoirs exhibit complex pore systems that weaken conventional porosity- permeability relationships. This study presents a comprehensive comparative evaluation of conventional and machine learning-based rock typing techniques for the Jeribe-Euphrates carbonate reservoir in the Fauqi Oil Field, southern Iraq. Unlike previous studies that focused on a single classification approach, this work systematically compares Hydraulic Flow Units, Discrete Rock Types, the Winland (R35) method, Lorenz curve analysis, Accumulated Correlation analysis, and machine learning clustering techniques using a unified dataset. The analysis was conducted on 1

This work involves separating and studying the aminoacylase-1 (ACY1) of amniotic fluid from healthy pregnant, mainly one peak with higher activity has been isolated by DEAE-Cellulose ion exchange from the proteinous supernatant produced by deposition of proteins using ammonium sulfate  (65%) after dialysis. The purification folds reaching to 19 folds also gave one protein peak when injected into the gel filtration column, a high ACY1 purity was obtained, with 38 folds of purification. It was found that the molecular weight of the isolated ACY1 was up to 46698 Dalton when using gel chromatography technique.The effect of ACY1 isolate was studied on rats with oxidative stress caused by lead acetate(LA) at 40 mg / kg body weight and compare

This paper performance for preparation and identification of six new complexes of a number of transition metals Cr (lII), Mn (I1), Fe (l), Co (II), Ni (I1), Cu (Il) with: N - (3,4,5-Trimethoxy phenyl-N - benzoyl Thiourea (TMPBT) as a bidentet ligand. The prepared complexes have been characterized, identified on the basis of elemental analysis (C.H.N), atomic absorption, molar conductivity, molar-ratio ,pH effect study, I. Rand UV spectra studies. The complexes have the structural formula ML₂X₃ for Cr (III), Fe (III), and ML₂X₂ for Mn (II), Ni (II), and MLX₂ for Co (Il) , Cu (Il).

Most reinforced concrete (RC) structures are constructed with square/rectangular columns. The cross-section size of these types of columns is much larger than the thickness of their partitions. Therefore, parts of these columns are protruded out of the partitions. The emergence of columns edges out of the walls has some disadvantages. This limitation is difficult to be overcome with square or rectangular columns. To solve this problem, new types of RC columns called specially shaped reinforced concrete (SSRC) columns have been used as hidden columns. Besides, the use of SSRC columns provides many structural and architectural advantages as compared with rectangular columns. Therefore, this study was conducted to explain the structura

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

In this work Laser wireless video communication system using intensity modualtion direct
detection IM/DD over a 1 km range between transmitter and receiver is experimentally investigated and
demonstrated. Beam expander and beam collimeter were implemented to collimete laser beam at the
transmitter and focus this beam at the receiver respectively. The results show that IM/DD communication
sysatem using laser diode is quite attractive for transmitting video signal. In this work signal to noise
ratio (S/N) higher than 20 dB is achieved in this work.

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

The researchers aimed to develop a novel azo ligand as a continuation of their prior investigations. They synthesized the ligand, identified as N-(3-acetyl-2- hydroxy-5-methyl-phenyl)N-(4-carboxy-cyclohexylmethyl)-diazonium salt, and proceeded to synthesize a series of chelate complexes with Ru+3, Rh+3, Pd+2, Pt+4, and Au+3 ions. Characterization of these compounds includes advanced techniques including elemental analysis, UV-Vis spectroscopy, FT-IR spectroscopy, LC-Mass spectrometry, NMR spectroscopy plus thermal analysis, conductivity measurements, magnetic quantification using TGA and DSC are used to further clarify the and synthesized complexes have been developed.Analysis revealed that the complexes formed with Ru+3, Rh+3, Pt+4, and Au