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The Experimental and Theoretical Effect of Fire on the Structural Behavior of Laced Reinforced Concrete Deep Beams
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A Laced Reinforced Concrete (LRC) structural element comprises continuously inclined shear reinforcement in the form of lacing that connects the longitudinal reinforcements on both faces of the structural element. This study conducted a theoretical investigation of LRC deep beams to predict their behavior after exposure to fire and high temperatures. Four simply supported reinforced concrete beams of 1500 mm, 200 mm, and 240 mm length, width, and depth, respectively, were considered. The specimens were identical in terms of compressive strength (  40 MPa) and steel reinforcement details. The same laced steel reinforcement ratio of 0.0035 was used. Three specimens were burned at variable durations and steady-state temperatures (one hour at 500 °C and 600 °C, and two hours at 500 °C). The flexural behavior of the simply supported deep beams, subjected to the two concentric loads in the middle third of the beam, was investigated with ABAQUS software. The results showed that the laced reinforcement with an inclination of 45˚ improved the structural behavior of the deep beams, and the lacing resisted failure and extended the life of the model. The optimal structural response was observed for the specimens. The laced reinforcement improved the failure mode and converted it from shear to flexure-shear failure. The parametric study showed that the lacing bars remarkably improved the strength of the deep beams and they were not affected more by the steady-state temperature and duration. Furthermore, a greater increase in load-carrying capacity was associated with an increase in the flexural diameter of approximately 12 and 16 mm by approximately 24.77% and 87.61%, respectively, compared to the reference LRC deep beams.

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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network
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Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

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Publication Date
Sun Jan 30 2022
Journal Name
Iraqi Journal Of Science
A Survey on Arabic Text Classification Using Deep and Machine Learning Algorithms
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    Text categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy th

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Publication Date
Wed May 10 2023
Journal Name
Diagnostics
A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning
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Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

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Publication Date
Sun May 08 2011
Journal Name
Journal Of Planner And Development
The Effect of the Natural and Cultural Environments Factors on the Architectural and Planning Characteristics of Sulaymaniyah Mosques
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Most of the mosques in the Islamic world fall under specific and known forms and patterns to a large extent, and such patterns have grown and evolved from the few basic and uniform models, but they changed slowly due to the impact with a mixture of changing functional requirements and cultural landscapes because of the variables of time and place to form patterns known and famous in this day across parts of the Islamic world and its borders. There was no exception to these patterns, but small numbers of mosques that were probably the result of personal experiences or improvisational resolutions, or in response to specific or temporary stimuli. However, the emergence of a specific pattern which does not belong to any of these patt

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Publication Date
Mon Jan 01 2018
Journal Name
Aip Conference Proceedings
The study of behavior titanium pure commercially coated with hydroxyapatite and zirconia
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Publication Date
Mon Mar 07 2022
Journal Name
Journal Of Educational And Psychological Researches
The Impact of Cognitive Behavior Program Based on Meichenbaum Theory in Reducing the Negative Emotional Sensitivity among the Intermediate Stage Students
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The aim of this research is to construct a cognitive behavior program based on the theory of Meichenbaum in reducing the emotional sensitivity among Intermediate school students. To achieve the aims of the research, two hypotheses were formulated and the experimental design with equal groups was chosen. The population of research and its sample are determined. The test of negative emotional sensitivity, which is constructed by the researcher, was adopted. The test contains (20) items proved validity and reliability as a reliable test by presenting it to a group of arbitrators and experts in education and psychology. An educational program is constructed based on the theory of Meichenbaum. The test was applied to a sample of (60) second i

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Publication Date
Sat Dec 01 2018
Journal Name
Proceedings Of International Structural Engineering And Construction
ON THE REDUCTION OF PRESTRESSING FORCE NEAR SUPPORTS IN PARTIALLY PRESTRESSED CONCRETE FLEXURAL MEMBERS
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Straight tendons in pretensioned members can cause high-tensile stresses in the concrete extreme fibers at end sections because of the absence of the bending stresses due to self-weight and superimposed loads and the dominance of the moment due to prestressing force alone. Accordingly, the concrete tensile stresses at the ends of a member prestressed with straight tendons may limit the service load capacity of the member. It is therefore important to establish limiting zone in the concrete section within which the prestressing force can be applied without causing tension in the extreme concrete fibers. Two practical methods are available to reduce the stresses at the end sections due to the prestressing force. The first method based

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Publication Date
Sun Jun 01 2014
Journal Name
Baghdad Science Journal
The effect of Nitrofurantoin on liver on Albino mice
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the current study Included, evaluation the impact of Nitrofurantoin drug on liver in albino mice, 128 male albino mice have been used . Animals treared with (150,200 Mg/Kg) for 8 weeks . NFI caused histological changes in liver represented by , swelling of hepatocytes, disappearance of radial arrangement , vaculation of liver cells , increasing of kupffer cells and appearance of giant cells. NFT caused Congestion of blood vessels and infiltration of inflammatory cells in liver in all used concentrations.

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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction
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Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

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Publication Date
Sun Apr 08 2018
Journal Name
Al-khwarizmi Engineering Journal
Evaluation the Mechanical Properties of Kaolin Particulate Reinforced Epoxy Composites
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Epoxy resin has many chemical features and mechanical properties, but it has a small elongation at break, low impact strength and crack propagation resistance, i.e. it exhibits a brittle behavior. In the current study, the influence of adding kaolin with variable particle size on the mechanical properties (flexural modulus E, toughness Gc, fracture toughness Kc, hardness HB, and Wear rate WR) of epoxy resin was evaluated. Composites of epoxy with varying concentrations (0, 10, 20, 30, 40 weights %) of kaolin were prepared by hand-out method. The composites showed improved (E, Gc, Kc, HB, and WR) properties with the addition of filler. Also, similar results were observed with the decrease in particle size. In addition, in this study, mult

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