It has become the subject of the environment and its problems during the last three decades of the important topics and dangerous that gained the attention of researchers in this regard as especially with the worsening repercussions severe, and turned out to be hot issues impose themselves urgently everywhere in the world, not concerned with the environment and specialists out, but also on all people wherever they are, regardless of their standard of living, and the circumstances of their lives, and their level of education and culture because the relationship of man with environment relationship since antiquity as relied upon to provide what they need him to stay, and through the stages of the development of civilization has made some a

تشخيص عوامل النجاح الحرجة لتفعيل استخدام الحاسوب الشخصي

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

It is not often  easy to identify a certain group of words as a lexical bundle, since the same set of words can be, in different situations, recognized as idiom,  a collocation, a lexical phrase or a lexical bundle. That is, there are many cases where the overlap among the four types is plausible. Thus, it is important to extract the most identifiable and distinguishable characteristics with which a certain group of words, under certain conditions, can be recognized as a lexical bundle, and this is the task of this paper.

In this paper, a subspace identification method for bilinear systems is used . Wherein a " three-block " and " four-block " subspace algorithms are used. In this algorithms the input signal to the system does not have to be white . Simulation of these algorithms shows that the " four-block " gives fast convergence and the dimensions of the matrices involved are significantly smaller so that the computational complexity is lower as a comparison with " three-block " algorithm .

The use of deep learning.