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Adrenomedullin (ADM) is a strong vasodilator peptide that was first identified in human pheochromocytoma and known to expressed in numerous cell types and believed to have pleiotropic impacts on pregnancy-related vascular adaptations and fetal growth. Mid-regional pro-adrenomedullin (MR-proADM) peptide which secreted in an equimolar concentration to ADM used to quantify ADM in plasma since it has longer half-life with more availability than ADM and for that reason the present study determine the serum MR-proADM level in the plasma of pregnant women with and without GDM and determine any potential correlation between MR-proADM and gestational diabetes mellitus (GDM) in an observational case–control study conducted on

This study includes the application of non-parametric methods in estimating the conditional survival function of the Beran method using both the Nadaraya-Waston and the Priestley-chao weights and using data for Interval censored and Right censored of breast cancer and two types of treatment, Chemotherapy and radiation therapy Considering age is continuous variable, through using (MATLAB)  use of the (MSE) To compare weights The results showed a superior weight (Nadaraya-Waston) in estimating the survival function and condition of Both for chemotherapy and radiation therapy.

Teachers' self-efficacy plays a very important role in the process of teaching English language, and teachers have primary role in determining what is needed or what would work best with their students. The sample consists of eighty-seven English language teachers in secondary schools in Baghdad/Kaarkh2. Self-efficacy questionnaire of fifteen items was constructed. Face, content validity and reliability were verified. Then, pupils' marks for the first semester were taken as their achievement.The main finding of the study indicates that:
English language teachers' self-efficacy has effect on their pupils' achievement.
In the light of the findings of the present study, several conclusions, recommendations and suggestions for further

The current study is considered a field study to measure the level of satisfaction of the academic programs of Media Masters students. That was accomplished through surveying the attitudes of the students who are enrolled in the faculties of media at the following universities: (Petra University, The Middle East University (MEU), and Al-Yarmouk University). Those students were enrolled in the master’s program within the formal educational system during the second semester of the academic year (2015 – 2016). This survey aims to identify the services, facilities and academic programs provided by the concerned faculties. It, also, aims to identify the public relations, administrative, educational and services aspects of those faculties.

This paper proposes a self organizing fuzzy controller as an enhancement level of the fuzzy controller. The adjustment mechanism provides explicit adaptation to tune and update the position of the output membership functions of the fuzzy controller. Simulation results show that this controller is capable of controlling a non-linear time varying system so that the performance of the system improves so as to reach the desired state in a less number of samples.

Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steerin

Transformers are a specific category of neural network design. Transformers often depend on extensive pre-training on a large scale and exhibit a notable degree of computational complexity. The disadvantage of using this method is a significant increase in computational complexity, which necessitates a significant commitment of time and computing resources in order to successfully work with these models. Transformer networks possess the desirable benefit of extracting distant characteristics effectively via their self-attention mechanism. In this paper, the Global Self-Attention Transformer module is applied to tackle these issues. The model is based on a segmentation problem called Brain-GS that works as a mechanism and encompasses