Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

Conclusion :

The conflict between the fundamentalist and news theory in the methodology that each of the two schools pursued with the Twelver Shi'a Imamiyyah in deriving the legal rulings from the sources of legislation, especially in the second source, which is the noble hadith and narration after the Holy Qur’an, which is the first source has gone through multiple and complex stages that led This struggle to excess, stress, exaggeration, negligence, neglect and indulgence by each of the two schools in dealing with the noble hadith and narration and in the manufacture of rules and laws and fundamentalist and juristic theories of the process of jurisprudential development of legal rulings where every school believed and proceed

Media, especially press plays an important role in fighting corruption and tackling this phenomenon, which has become widespread in our society, through its effective role in raising awareness of the seriousness of spreading of corruption of all its forms in society.
All international conventions and agreements stress the necessity of the role of media and its importance in the light of corruption. All countries also commit themselves to the necessity of guaranteeing the freedom of media and the circulation of information and preparing it as a prerequisite for activating the People’s Control Mechanism and supporting measures and means to prevent and combat financial and administrative corruption more actively and effectively.
Co

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

The letters are exposed in some texts of the classical Arabic language (poetry or prose), or in some of the Qur’anic texts, which are the main sources that were adopted on the day when provisions, rules and linguistic controls were established. I say that some of these letters are exposed in some contexts to deletion, mention, or change in the structure and shape of the letter. As for the omission, it is one of the aspects that distinguished Arabic, as well as other languages, for many purposes that differ among their user.

Abstract\

In this research, estimated the reliability of water system network in Baghdad was done. to assess its performance during a specific period. a fault tree through static and dynamic gates was belt and these gates represent logical relationships between the main events in the network and analyzed using dynamic Bayesian networks . As it has been applied Dynamic Bayesian networks estimate reliability by translating dynamic fault tree to Dynamic Bayesian networks and reliability of the system appreciated. As was the potential for the expense of each phase of the network for each gate . Because there are two parts to the Dynamic Bayesian networks and two part of gate (AND), which includes the three basic units of the

Wellbore instability is one of the most common issues encountered during drilling operations. This problem becomes enormous when drilling deep wells that are passing through many different formations. The purpose of this study is to evaluate wellbore failure criteria by constructing a one-dimensional mechanical earth model (1D-MEM) that will help to predict a safe mud-weight window for deep wells. An integrated log measurement has been used to compute MEM components for nine formations along the studied well. Repeated formation pressure and laboratory core testing are used to validate the calculated results. The prediction of mud weight along the nine studied formations shows that for Ahmadi, Nahr Umr, Shuaiba, and Zubair formations