A low speed open circuit wind tunnel has been designed, manufactured and constructed at the
Mechanical Engineering Department at Baghdad University - College of Engineering. The work is one of
the pioneer projects adapted by the R & D Office at the Iraqi MOHESR. The present paper describes the
first part of the work; that is the design calculations, simulation and construction. It will be followed by a
second part that describes testing and calibration of the tunnel. The proposed wind tunnel has a test
section with cross sectional area of (0.7 x 0.7 m2) and length of (1.5 m). The maximum speed is about (70
m/s) with empty test section. The contraction ratio is (8.16). Three screens are used to minimize flow
distu

It has aimed by this research at presenting new interpretations to diversification phenomenon and its possible effects on firm value. These interpretations are culminated by the disclosure of the distinct role of resource-based view in the study of relationship between diversification strategy and firm value. The study has conducted on a sample consisted of the largest American companies listed in Fortune 500 along with seven years period
(2005-2011). This first local attempt, which has based on panel dat--a analysis for interpreting an expected variance of firm value due to diversification strategy, may helps with a clear vision to solve the contradiction and confusion surrounded the true linkages between t

The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

Information processing has an important application which is speech recognition. In this paper, a two hybrid techniques have been presented. The first one is a 3-level hybrid of Stationary Wavelet Transform (S) and Discrete Wavelet Transform (W) and the second one is a 3-level hybrid of Discrete Wavelet Transform (W) and Multi-wavelet Transforms (M). To choose the best 3-level hybrid in each technique, a comparison according to five factors has been implemented and the best results are WWS, WWW, and MWM. Speech recognition is performed on WWS, WWW, and MWM using Euclidean distance (Ecl) and Dynamic Time Warping (DTW). The match performance is (98%) using DTW in MWM, while in the WWS and WWW are (74%) and (78%) respectively, but when using (

Background: Chronic periodontitis is a bacterial infection that result in bone destruction associated with the increasing level of salivary tumor necrosis alpha and interleukin6 that affect Mother-infant bonding status. The aim of the present study was to assess the relationship between the Mother-infant bonding status in mothers with chronic periodontitis in relation to Salivary Tumor necrosis factor alpha and Salivary Interleukin6. Materials and Methods: The selected sample consisted of mothers with chronic periodontitis compared with mothers with healthy periodontium in postpartum period, their age ranged between 30-40 years. Both groups were subjected to postpartum Bonding Questionnaire (PBQ). Periodontal health status was assessed f

Aromaticity, antiaromaticity and chemical bonding in the ground (S0), first singlet excited (S1) and lowest triplet (T1) electronic states of disulfur dinitride, S2N2, were investigated by analysing the isotropic magnetic shielding, σiso(r), in the space surrounding the molecule for each electronic state. The σiso(r) values were calculated by state-optimized CASSCF/cc-pVTZ wave functions with 22 electrons in 16 orbitals constructed from gauge-including atomic orbitals (GIAOs). The S1 and T1 electronic states were confirmed as 11Au and 13B3u, respectively, through linear response CC3/aug-cc-pVTZ calculations of the vertical excitation energies for eight singlet (S1–S8) and eight triplet (T1–T8) electronic states. The aromaticities of S

    The Atoms in Molecules (AIM) analysis for triosmium cluster, which contains trihydridede, carbon, carbonyl and 2-methylbenzothiazolide ligands, [Os₃(µ-H)₃(µ³-ɳ²-CC₇H₃(2-CH₃)NS)(CO)₈] is reported. Bonding features in this cluster has been analyzed based on QTAIM ("Quantum Theory of Atoms in Molecules") in this work. The topological indices derived from electron density of relevant interactions in triosmium compound have been studied. The major interesting point of the AIM analyses is that the core of part (Os₃H₃) reveals the absence of any critical points and bond paths connecting any pairs of O

Background: Environmental tobacco smoking is produced by active smokers burning the tip of a cigarette and breathed by nonsmokers and measured by cotinine level. It has the potential to raise the risk of periodontal disease. One of the most frequent chronic diseases in adults is periodontal disease. The lower maternal-fetal attachment has been found to predict smoking status in previous studies, but no research has examined whether maternal-fetal attachment predicts environmental tobacco smoking. This study assessed the effects of maternal environmental tobacco smoke exposure on periodontal health and mother-infant bonding concerning salivary cotinine levels. Materials and methods: This is a comparative cross-sectional study comparing en

Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed