Shear wave velocity is an important feature in the seismic exploration that could be utilized in reservoir development strategy and characterization. Its vital applications in petrophysics, seismic, and geomechanics to predict rock elastic and inelastic properties are essential elements of good stability and fracturing orientation, identification of matrix mineral and gas-bearing formations. However, the shear wave velocity that is usually obtained from core analysis which is an expensive and time-consuming process and dipole sonic imager tool is not commonly available in all wells. In this study, a statistical method is presented to predict shear wave velocity from wireline log data. The model concentrated to predict shear wave velocity fr

n this research, some thermophysical properties of ethylene glycol with water (H2O) and two solvent mixtures dimethylformamide/ water (DMF + H2O) were studied. The densities (ρ) and viscosities (η) of ethylene glycol in water and a mixed solvent dimethylformamide (DMF + H2O) were determined at 298.15 K, t and a range of concentrations from 0.1 to1.0 molar. The ρ and η values were subsequently used to calculate the thermodynamics of mixing including the apparent molar volume (ϕv), partial molar volume (ϕvo) at infinite dilution. The solute-solute interaction is presented by Sv results from the equation ∅_v=ϕ_v^o+S_v √m. The values of viscosity (B) coefficients and Falkenhagen coefficient(A) of the Jone-Dole equation and Gibbs free

Background: Different diagnostic definition and criteria have been recommended by different expert groups for the diagnosis of metabolic syndrome, however, it’s prevalence in the same population could differ depending on the definition used yielding different results. In Iraq, there is a lack of research comparing these different diagnostic definitions. Objective: To find out the most suitable metabolic syndrome definition to be used for Iraqi people.  Methods: 320 participants were recruited for this study, 53.4% men and 46.6% women, aged between 25-85 years, visiting Baghdad Teaching Hospital, the prevalence of metabolic syndrome according to different definitions were compared and the agreement was assessed by the Kappa st

Background: Different diagnostic definition and criteria have been recommended by different expert groups for the diagnosis of metabolic syndrome, however, it’s prevalence in the same population could differ depending on the definition used yielding different results. In Iraq, there is a lack of research comparing these different diagnostic definitions.

Objective: To find out the most suitable metabolic syndrome definition to be used for Iraqi people.

 Methods: 320 participants were recruited for this study, 53.4% men and 46.6% women, aged between 25-85 years, visiting Baghdad Teaching Hospital, the prevalence of metabolic syndrome according to different definition

The current research aims to identify the psychological well-being of the educational counselors, as well as to identify the significance of differences in psychological well-being according to the variables (gender, duration of service). The number of scale's items reached (42) items distributed evenly on six dimensions (autonomy, environmental mastery, personal growth, positive relationships with others, purpose in life, self-acceptance), for each field (7) items. The scale was applied to a sample of (400) school counselors (male-female) who were randomly selected from the six directorates in the Baghdad governorate for the academic year 2020/2021. The results showed that the educational counselors in the Baghdad governorate for the si

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes