Abstract

The net profit reported in the annual financial statements of the companies listed in the financial markets, is considered one of the Sources of information relied upon by users of accounting information in making their investment decisions. At the same time be relied upon in calculating the bonus (Incentives) granted to management, therefore the management of companies to manipulate those numbers in order to increase those bonuses associated to earnings, This practices are called earnings management practices. the manipulation in the figures of earnings by management will mislead the users  of financial statements who depend on reported earnings in their deci

This study was conducted for evaluating the cytotoxic effect of heat stable enterotoxin a (STa) produced by enterotoxigenic Escherichia coli on the proliferation of primary cancer cell cultures, obtained from tumor samples that were collected from (13) cancer patients and as follows: (five colon cancer patients, two bladder cancer patients, two breast cancer patients, two stomach cancer patients and two lung cancer patients), and on normal cell line (rat embryonic fibroblast / REF) (in vitro) with the use of different concentrations starting from (1) mg/ml and ending with (0.0002) mg/ml by making two fold serial dilutions by using the 96- well microtiter plate, and in comparison with negative (PBS) and positive (MMC, at concentration

Abstract Software-Defined Networking (commonly referred to as SDN) is a newer paradigm that develops the concept of a software-driven network by separating data and control planes. It can handle the traditional network problems. However, this excellent architecture is subjected to various security threats. One of these issues is the distributed denial of service (DDoS) attack, which is difficult to contain in this kind of software-based network. Several security solutions have been proposed recently to secure SDN against DDoS attacks. This paper aims to analyze and discuss machine learning-based systems for SDN security networks from DDoS attack. The results have indicated that the algorithms for machine learning can be used to detect DDoS