KS Ismaeil, BR Jawad, Journal of Physical Education, 2023

      In this paper, the researcher suggested using the Genetic algorithm method to estimate the parameters of the Wiener degradation process,  where it is based on the Wiener process in order to estimate the reliability of high-efficiency products, due to the difficulty of estimating the reliability of them using traditional techniques that depend only on the failure times of products. Monte Carlo simulation has been applied for the purpose of proving the efficiency of the proposed method in estimating parameters; it was compared with the method of the maximum likelihood estimation. The results were that the Genetic algorithm method is the best based on the AMSE comparison criterion, then the reliab

The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an in

The platforms of social networking sites, with their distinctive communication and technological features, create a social movement that led to the establishment of a new pattern of communication in a modern context. This allows the users on the internet to carry out many social interactions based on the interests and commonalities among them. Algerian women have a share of this digital presence by representing their views and discussing their issues on several sites like Facebook, for example.
In this research, we have analyzed the pages of Algerian women on Facebook site to find out the most important issues addressed by Algerian women so that we can organize their concerns in the digital channels and discover their different orie

BACKGROUND: The rapidly growing knowledge regarding factors controlling tumour growth, with the new modalities of therapy acting on the biological activity of the tumours draw the attention of most cancer researches nowadays and represent a major focus for clinical oncology practice. For the detection of HER2/neu protein overexpression and gene amplification, immunohistochemistry (IHC) and in-situ hybridisation (ISH) is the recommended techniques, respectively, with high concordance between the two techniques. The current United Kingdom recommendations for HER2/neu testing are either for a two-tier system using IHC with reflex ISH testing in equivocal positive cases, or a one-tier ISH strategy. AIM: To compare the results of HER2/neu gene s

This paper deals with the modeling of a preventive maintenance strategy applied to a single-unit system subject to random failures.

According to this policy, the system is subjected to imperfect periodic preventive maintenance restoring it to ‘as good as new’ with probability

p and leaving it at state ‘as bad as old’ with probability q. Imperfect repairs are performed following failures occurring between consecutive

preventive maintenance actions, i.e the times between failures follow a decreasing quasi-renewal process with parameter a. Considering the

average durations of the preventive and corrective maintenance actions a

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par