Based on a finite element analysis using Matlab coding, eigenvalue problem has been formulated and solved for the buckling analysis of non-prismatic columns. Different numbers of elements per column length have been used to assess the rate of convergence for the model. Then the proposed model has been used to determine the critical buckling load factor () for the idealized supported columns based on the comparison of their buckling loads with the corresponding hinge supported columns . Finally in this study the critical buckling factor () under end force (P) increases by about 3.71% with the tapered ratio increment of 10% for different end supported columns and the relationship between normalized critical load and slenderness ratio was g

Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites

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He lived Abadan, age in the request to talk and take the elders, and Awali, and attribution, which affected him by taking, and update about (300) Sheikh or more.
Abadan also excelled in the novel and its origins, which made students talk to him, and ask for the novel, with his hardness and hardship. As we will see in the folds of the search

Background: Hormonal changes during pregnancy have been suggested to predispose women to gingivitis. Furthermore, advance periodontal infection in pregnant women may pose a threat to the placenta and uterus and may increase the like hood of preterm delivery. The aim of this study was to investigate the effect of salivary interleukin -6 (IL-6) level and periodontal health among preterm postpartum women. Materials and Methods: Salivary samples were taken from 33 preterm postpartum women (study group) and 33 full term postpartum women (control group). The supernatant salivary samples were assayed using atomic absorption spectrophotometer. Plaque, calculus and gingival indices were used for recording the oral hygiene and gingivitis also probing

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k

The rate of births delivered by cesarean section (CS) has gone up substantially all over the world. Post-cesarean surgical site infection (SSI) is a common cause of maternal morbidity and mortality that results in prolonged period of hospitalization with increased cost and direct health implications, especially in low socioeconomic population, resource- restricted settings, and war- related conditions with internal forced movement. This study was aimed to find incidence of post cesarean section surgical site infection withthe accompanying risk factors.Pregnant ladies admitted to department of obstetrics and gynecology at Medical City Hospital in Baghdad who had undergone CSs were followed up prospectively from first of January 2017 till end

The aim of the current study was to evaluate the knowledge and perception of the fifth stage pharmacy students (college of pharmacy/ University of Baghdad /Iraq) regarding generic medicines. This study is a cross-sectional study carried in a college of pharmacy /University of Baghdad during the period from (November 2018- March 2019). The number of students included in the current study was 168 undergraduate stager pharmacists. A questionnaire was used to collect data of the study. Nearly 86% of the students said that they had heard of generic and brand medicines, and pharmacy was the main source of knowledge regarding generic medicines (66.7%).  About (33.3%) of the respondents agreed that generic medicines are bioequivalent to br

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

Objectives: The study aim was to explore the knowledge, perceptions, and attitudes of Iraqi physicians regarding generic and locally manufactured medicines. Methods: A total of 124 physicians were involved in this cross -sectional study. The convenience sample was collected from five public hospitals in Baghdad. A self-administered questionnaire was distributed and collected in-person. Fisher's Exact Test was used to measure the association between physician years of experience, gender and categorical (perception and knowledge) variables. Results: Most respondent answers regarding the knowledge of generic medicines were incorrect. Only up to one-third of the participants knew that generic medicines are therapeutically eq