Population density represents an important indicator for determining the growth of the size of urban areas. Population density has a direct impact on the quality of life, and its excessive rise may lead to the deterioration of basic service facilities. The problem of the research is that the increase in the total population densities of the residential neighborhood may reflect negatively on the nature of the services provided to the residents in these shops, and thus the failure to achieve sustainability in the residential locality. The research assumes that the increase in population densities negatively affects the achievement of sustainability in the residential neighborhood.

Objective: The study aimed to assess the postoperative nurses' intervention for the patients with laparoscopic
cholecystectomy and to determine the relationship between Nurses' interventions and their demographic
characteristics.
Methodology: Quantitative design (a descriptive study) was started from 20th November 2012 up to 1st
September 2013. Non-probability (purposive sample) of (50) nurses, who were working in surgical wards, were
selected from Baghdad teaching hospitals (Baghdad Teaching Hospital, Digestives System and Liver Teaching
Hospital, AL-Kindy Teaching Hospital, and AL-Kadhimiyia Teaching Hospita). The data were collected through
the use of a constructed questionnaire, which consisted of two parts; the

داء المشوكات الكيسي (CE) هو مرض وبائي يسبب مرضًا خطيرًا وخسائر اقتصادية في معظم بلدان العالم. MiRNAs هي عامل جيني ضروري لتنظيم الاستجابة المناعية من خلال قدرته على التدخل في التعبير الخلوي ؛ واحد هذه الحوامض النووية الدقيقة -146 أ. هدفت الدراسة الحالية تقييم إذا كان بإمكاننا استخدام microRNA 146a كمؤشر حيوي للكشف عن    CEو تحديد العلاقة بين التعبير الجيني microRNA 146a و IL-17 في مرضى CE.حيث اشتملت الدراسة على 50 مريضًا من CE تم إد

This study examined the problematic of the ambiguous relationship between the media and terrorism and the problems that result from press coverage of terroristic incidents. The paper sought to show the classification and confrontation of such incidents had been established from the point of view of a sample of media professionals, researchers and writers who are frequenters of Al-Mutanabi Street in Baghdad. The media outlets that carry this coverage would not give up their media mission as well as the terrorists would not be given an opportunity to take advantage of this coverage in achieving their goals and objectives. Furthermore, the terrorist organizations would have no chance to exploit these means to deliver their terroristic messa

The aim of this work is to design an algorithm which combines between steganography andcryptography that can hide a text in an image in a way that prevents, as much as possible, anysuspicion of the hidden textThe proposed system depends upon preparing the image data for the next step (DCT Quantization)through steganographic process and using two levels of security: the RSA algorithm and the digitalsignature, then storing the image in a JPEG format. In this case, the secret message will be looked asplaintext with digital signature while the cover is a coloured image. Then, the results of the algorithmare submitted to many criteria in order to be evaluated that prove the sufficiency of the algorithm andits activity. Thus, the proposed algorit

Nowadays, information systems constitute a crucial part of organizations; by losing security, these organizations will lose plenty of competitive advantages as well. The core point of information security (InfoSecu) is risk management. There are a great deal of research works and standards in security risk management (ISRM) including NIST 800-30 and ISO/IEC 27005. However, only few works of research focus on InfoSecu risk reduction, while the standards explain general principles and guidelines. They do not provide any implementation details regarding ISRM; as such reducing the InfoSecu risks in uncertain environments is painstaking. Thus, this paper applied a genetic algorithm (GA) for InfoSecu risk reduction in uncertainty. Finally, the ef

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet