Transportability refers to the ease with which people, goods, or services may be transferred. When transportability is high, distance becomes less of a limitation for activities. Transportation networks are frequently represented by a set of locations and a set of links that indicate the connections between those places which is usually called network topology. Hence, each transmission network has a unique topology that distinguishes its structure. The most essential components of such a framework are the network architecture and the connection level. This research aims to demonstrate the efficiency of the road network in the Al-Karrada area which is located in the Baghdad city. The analysis based on a quantitative evaluation using graph th

يتنامى يوما بعد يوم استخدام السيارات وتتعاضم اعدادها ، فهذا هو عصر السرعة، وخاصة في مجال النقل والمواصلات، والتي تتحقق باستخدام وسائل النقل المختلفة ومن بينها السيارات، وبالتالي اصبحت هذه الوسيلة ضرورية لتحقيق هذه السرعة ومن ضرورات الحياة في انجاز الاعمال.

وتتبارى مصانع السيارات فيما بينها لانتاج انواع السيارات بمواصفات عالية من المتانة والامان والراحة، وفي ذات الوقت اصبحت هندسة الطرق

In this study is the phenomenon of desertification risk assessment in the Abu Ghraib area west of Baghdad/Iraq, which has an area of about (384.168 km 2), that the annual mean temperature is more than (22 C). Rainfall was low, ranging from the (200 mm) per year for Iraq and (2.82) mm per year of the study area* temperature is high and evaporation is also high (mm 7.73) per year*, so the climate in general of the dry type and the system of soil moisture is the kind of Aridic (Torric). To this study was to identify three indicators to monitor for the period from 2001-2005 using GIS and these indicators are (soil, groundwater and the nature of land use), using ArcGIS 9.1. The results showed that the risk of desertification was part of the leve

In this work, a joint quadrature for numerical solution of the double integral is presented. This method is based on combining two rules of the same precision level to form a higher level of precision. Numerical results of the present method with a lower level of precision are presented and compared with those performed by the existing high-precision Gauss-Legendre five-point rule in two variables, which has the same functional evaluation. The efficiency of the proposed method is justified with numerical examples. From an application point of view, the determination of the center of gravity is a special consideration for the present scheme. Convergence analysis is demonstrated to validate the current method.

This work presents a comparison between the Convolutional Encoding CE, Parallel Turbo code and Low density Parity Check (LDPC) coding schemes with a MultiUser Single Output MUSO Multi-Carrier Code Division Multiple Access (MC-CDMA) system over multipath fading channels. The decoding technique used in the simulation was iterative decoding since it gives maximum efficiency at higher iterations. Modulation schemes used is Quadrature Amplitude Modulation QAM. An 8 pilot carrier were
used to compensate channel effect with Least Square Estimation method. The channel model used is Long Term Evolution (LTE) channel with Technical Specification TS 25.101v2.10 and 5 MHz bandwidth bandwidth including the channels of indoor to outdoor/ pedestrian

Objective: The study deafs with nursing performance in the surgical wards in general hospital at
Baghdad city.
Methodology : A descriptive evaluation design using, observational method was carried out. Non
probability (purposive) sample of (151) nurses was selected for the study and comprised all nurses who
worked in general surgical wards in the four health sectors( Rusaffa , Al-Karkh, Al-Yarmok, Medical
city health sector) at time of collecting the data. A check list questionnaire was constructed by the
researcher for the purpose of the study; it is composed of (2) major parts, part (I) is concerned with
socio-demographic data and the second part is composed of two minor parts thev concerned with
availability of

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod