Some authors studied modules with annihilator of every nonzero submodule is prime, primary or maximal. In this paper, we introduce and study annsemimaximal and coannsemimaximal modules, where an R-module M is called annsemimaximal (resp. coannsemimaximal) if ann_RN (resp. ) is semimaximal ideal of R for each nonzero submodule N of M.

 In this paper, we give a comprehensive study of min (max)-CS modules such as a closed submodule of min-CS module is min-CS. Amongst other results we show that a direct summand of min (max)-CS module is min (max)-CS module. One of interested theorems in this paper is, if R is a nonsingular ring then R is a max-CS ring if and only if R is a min-CS ring.

       A submodule N of a module M  is said to be s-essential if it has nonzero intersection with any nonzero small submodule in M. In this article, we introduce and study a class of modules in which all its nonzero endomorphisms have non-s-essential kernels, named, strongly -nonsigular. We investigate some properties of strongly -nonsigular modules. Direct summand, direct sums and some connections of such modules are discussed.        

An -module  is extending if every submodule of   is essential in a direct summand of . Following Clark, an -module  is purely extending if every submodule of   is essential in a pure submodule of . It is clear purely extending is generalization of extending modules. Following Birkenmeier and Tercan, an -module     is Goldie extending if, for each submodule      of , there is a direct summand D of such that . In this paper, we introduce and study class of modules which are proper generalization of both the purely extending modules and -extending modules. We call an -module  is purely Goldie extending if, for each , there is a pure submodule P of such that  . Many c

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               The aim of the research is to evaluate the practice of banking departments for strategic foresight represented by its dimensions (environmental scanning, future vision, scenarios, reconfiguration) and its relationship to the organizational brilliance represented by dimensions (brilliance of leadership, brilliance of service and innovation, brilliance of knowledge, brilliance of employees), as the research was applied in A number of private Iraqi commercial banks represented by (Baghdad, Iraqi Investment, Iraqi Middle East Investment, Commercial Gulf, Ashur International Investment, Al Mansour Investment, Via Iraq Invest

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            The approach maintenance and replacement one of techniques of operations research whom cares of the failure experienced by a lot of production lines which consist of a set of machines and equipment, which in turn exposed to the failure or work stoppages over the lifetime, which requires reducing the working time of these machines or equipment below what can or conuct  maintenance process once in a while or a replacement for one part of the machine or replace one of the machines in production lines. In this research is the study of the failure s that occur in some parts of one of the machines for the General Company for Vege

Recently, wireless charging based RF harvesting has interfered our lives [1] significantly through the different applications including biomedical, military, IoT, RF energy harvesting, IT-care, and RFID technologies. Wirelessly powered low energy devices become significantly essential for a wide spectrum of sensing applications [1]. Such devices require for low energy resources from sunlight, mechanical vibration, thermal gradients, convection flows or other forms of harvestable energy [2]. One of the emerging power extraction resources based on passive devices is harvesting radio frequency (RF) signals powers [3]–[5]. Such applications need devices that can be organized in very large numbers, so, making separate node battery impractical.

          The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed