Test results of nine reinforced concrete one way slab with and without lacing reinforcement are reported. The tests were designed to study the effect of the lacing reinforcement on the flexural response of one way slabs. The test parameters were considered is the lacing steel ratios of (0, 0.0025, 0.0045, and 0.0065), flexural steel ratios of (0.0025, 0.0045, and 0.0065) and span to the effective depth ratios of (11, 13, and 16). Two specimens had no lacing reinforcement and the remaining seven specimens had the lacing reinforcement. Four point bending test were carried out, one of the specimens was tested under the static load applied gradually up to failure and the other specimens were tested under repeated load (5 cyc

Background: Tumor associated tissue eosinophilia (TATE) has been described in a variety of neoplasms. In regard to squamous cell carcinoma, some studies worldwide done to assess stromal eosinophilia in oral and cervical squamous cell carcinoma. The objectives of this study is to evaluate the association of stromal eosinophilic infiltration of cutaneous squamous cell carcinoma and to detect the significance of this association. Aim of the study The aim of our study is to establish the relationship between the degree of stromal eosinophilia and the level of invasion and the histological grade in cutaneous squamous cell carcinoma. Methods: In this retrospective study done at the histopathology department of al Wasity teaching hospital for orth

 Accuracy in multiple objects segmentation using geometric deformable models sometimes is not achieved for reasons relating to a number of parameters. In this research, we will study the effect of changing the parameters values on the work of the geometric deformable model and define their efficient values, as well as finding out the relations that link these parameters with each other, by depending on different case studies including multiple objects different in spacing, colors, and illumination. For specific ranges of parameters values the segmentation results are found good, where the success of the work of geometric deformable models has been limited within certain limits to the values of these parameters.

Background: Tumor associated tissue eosinophilia (TATE) has been described in a variety of neoplasms. In regard to squamous cell carcinoma, some studies worldwide done to assess stromal eosinophilia in oral and cervical squamous cell carcinoma. The objectives of this study is to evaluate the association of stromal eosinophilic infiltration of cutaneous squamous cell carcinoma and to detect the significance of this association.

Aim of the study

The aim of our study is to establish the relationship between the degree of stromal eosinophilia and the level of invasion and the histological grade in cutaneous squamous cell carcinoma.

Methods: In this retrospective study

Purpose: To identify the risk factors of urinary incontinency for menopausal women.
Methodology: A descriptive analytic study was conducted to identify the risk factor for urinary incontinency
and selected non-probability sample (purposive sample) from (200) menopausal women (45-65) who have
urinary incontinence as visitors and caregiver women who attend at Hila surgical teaching hospital during the
period 1/11/2010-30/3/2011. Questionnaire format used for data collection was designed and constructed
after reviewing related literatures and previous studies and consists of the following variables: Demographic
and reproductive characteristics of menopausal women who suffers from urinary incontinence
Results: The study

The importance of efficient vehicle detection (VD) is increased with the expansion of road networks and the number of vehicles in the Intelligent Transportation Systems (ITS). This paper proposes a system for detecting vehicles at different weather conditions such as sunny, rainy, cloudy and foggy days. The first step to the proposed system implementation is to determine whether the video’s weather condition is normal or abnormal. The Random Forest (RF) weather condition classification was performed in the video while the features were extracted for the first two frames by using the Gray Level Co-occurrence Matrix (GLCM). In this system, the background subtraction was applied by the mixture of Gaussian 2 (MOG 2) then applying a number

This paper presents a numerical analysis using ANSYS finite element program to simulate the reinforced concrete slabs with spherical voids. Six full-scale one way bubbled slabs of (3000mm) length with rectangular cross-sectional area of (460mm) width and (150mm) depth are tested as simply supported under two-concentrated load. The results of the finite element model are presented and compared with the experimental data of the tested slabs. Material nonlinearities due to cracking and crushing of concrete and yielding of reinforcement are considered. The general behavior of the finite element models represented by the load-deflection curves at midspan, crack pattern, ultimate load, load-concrete strain curves and failure m

The Diwan of Imam Al-Shafi’i acquires great importance, as Al-Shafi’i is an authority in the language, and when I saw that no one had preceded me in exploring its depths, I took my tool and turned my face towards it intending to study the triple verb in it. I stop at these verbs and the student pauses for their morphological forms, looking at the significance of the triple verb more with one letter, two letters, and three letters, and I found that they are many, and such research cannot contain them all, so the choice came to choose the triple verb more with one letter, and the significance of the increase in it, as the increase in The building necessitates an increase in the meaning, and from here the study was limited to the triple

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D