The current research aims to identify the level of extension of the sense of self and the cognitive style (complexity - simplification) among university students, as well as the significance of the difference according to gender (males - females), as well as the correlation between the two variables, and in order to verify this, the researcher has constructed The measure of the extension of the sense of self based on the theory of Alport 1961, as the number of its final paragraphs reached (28) paragraphs, and the cognitive style scale (complexity - simplification) prepared by (Abdullah 2017) was adopted, as the scale consisted of (32) paragraphs in its final form. To verify their psychometric properties, and the two tools were applied to

Automatic Programming Assessment (APA) has been gaining lots of attention among researchers mainly to support automated grading and marking of students’ programming assignments or exercises systematically. APA is commonly identified as a method that can enhance accuracy, efficiency and consistency as well as providing instant feedback on students’ programming solutions. In achieving APA, test data generation process is very important so as to perform a dynamic testing on students’ assignment. In software testing field, many researches that focus on test data generation have demonstrated the successful of adoption of Meta-Heuristic Search Techniques (MHST) so as to enhance the procedure of deriving adequate test data for efficient t

Survivin, a member of inhibitor of apoptosis family is increasingly used as a target for cancer therapy design because it has a key role in cell growth and inhibition of cell apoptosis. Also it can be used as a biomarker for targeting cancer because it is found in almost all cancer but not normal cells. Our strategy was to design (computationally) a molecule to be used as survivin inhibitor. This molecule was named lead10 and was used furthermore to find (virtually) existing drugs with a good survivin inhibition activity.

This paper aims to evaluate the reliability analysis for steel beam which represented by the probability of Failure and reliability index. Monte Carlo Simulation Method (MCSM) and First Order Reliability Method (FORM) will be used to achieve this issue. These methods need two samples for each behavior that want to study; the first sample for resistance (carrying capacity R), and second for load effect (Q) which are parameters for a limit state function. Monte Carlo method has been adopted to generate these samples dependent on the randomness and uncertainties in variables. The variables that consider are beam cross-section dimensions, material property, beam length, yield stress, and applied loads. Matlab software has be

     In this article, a new efficient approach is presented to solve a type of partial differential equations, such (2+1)-dimensional differential equations non-linear, and nonhomogeneous. The procedure of the new approach is suggested to solve important types of differential equations and get accurate analytic solutions i.e., exact solutions. The effectiveness of the suggested approach based on its properties compared with other approaches has been used to solve this type of differential equations such as the Adomain decomposition method, homotopy perturbation method, homotopy analysis method, and variation iteration method. The advantage of the present method has been illustrated by some examples.

Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. One of the main advantages of using these techniques is that they require no knowledge or gradient information about the response surface. The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of algorithms. One such class of these algorithms is compact Genetic Algorithm (cGA), it dramatically reduces the number of bits reqyuired to store the poulation and has a faster convergence speed. In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). Simulation results

Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id