solation of candida spp. From cancer patients who suffered oral candidiasis due to immunodeficiency

The cancer is one of the biggest health problems that facing the world . And  the bladder cancer has a special place among the most spread cancers in Arab countries specially in Iraq and Egypt⁽²⁾ . It is one of the diseases which can be treated and cured if it is diagnosed early . This research is aimed at studying the assistant factors that diagnose bladder cancer such as (patient's age , gender , and other major complains of hematuria , burning or pain during urination and micturition disorders) and then determine which factors are the most effective in the possibility of diagnosing this disease by using the statistical model (logistic regression model) and depending on a random sample of (128) patients . After

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

Abstract

The study aims to examine the relationships between cognitive absorption and E-Learning readiness in the preparatory stage. The study sample consisted of (190) students who were chosen randomly. The Researcher has developed the cognitive absorption and E-Learning readiness scales. A correlational descriptive approach was adopted. The research revealed that there is a positive statistical relationship between cognitive absorption and eLearning readiness.

This study presents experimental and numerical investigations on seven one-way, reinforced concrete (RC) slabs with a new technique of slab weight reduction using polystyrene-embedded arched blocks (PEABs). All slabs had the same dimensions, steel reinforcement, and concrete compressive strength. One of these slabs was a solid slab, which was taken as a control slab, while the other six slabs were cast with PEABs. The main variables were the ratio of the length of the PEABs to the length of the slab (lp/L) and the ratio of the height of the PEABs to the total slab depth (hP/H). The minimum decrease in the ultimate load capacity was about 6% with a minimum reduction in the slab weight of 15%. In contrast, the maximum decrease in the

Astronomy image is regarded main source of information to discover outer space, therefore to know the basic contain for galaxy (Milky way), it was classified using Variable Precision Rough Sets technique to determine the different region within galaxy according different color in the image. From classified image we can determined the percentage for each class and then what is the percentage mean. In this technique a good classified image result and faster time required to done the classification process.

Test results of eight reinforced concrete one way slab with lacing reinforcement are reported. The tests were designed to study the effect of the lacing reinforcement on the flexural behavior of one way slabs. The test parameters were the lacing steel ratio, flexural steel ratio and span to the effective depth ratio. One specimen had no lacing reinforcement and the remaining seven had various percentages of lacing and flexural steel ratios. All specimens were cast with normal density concrete of approximately 30 MPa compressive strength. The specimens were tested under two equal line loads applied statically at a thirds part (four point bending test) up to failure. Three percentage of lacing and flexural steel ratios wer