Abstract Applications of cooperative multicast communication techniques for emerging broadband multimedia services, such as Internet Protocol TV (IPTV), over WiMAX air interface have an important and attractive feature of reducing the network resource consumption, by broadcasting the data once to multiple users (e.g., TV channel group members) simultaneously independently of their number, using a common shared downlink channel. But users distributed around the cell face different channel conditions due to fading, user location, mobility, and other related characteristics. Several schemes were reported in the literature that attempt to introduce cooperative multicasting reliably and efficiently, some of which utilize subscribers with good-channel as relay agents (RA) to provide coverage for subscribers with bad-channel but fail to consider the huge amount of energy consumed in the process. In this thesis, we propose energy-efficient cooperative multicasting schemes by properly selecting RAs based on their location, channel condition and coverage. By exploiting the channel state information (CSI) and the location based service (LBS) techniques, protocol based on nearest neighbor, transmission distance and subscriber-subscriber interlink instantaneous CSI were proposed and implemented in order to achieve much improvement in the system performance. The number of RAs was varied in accordance to channel conditions and treated on a frame-per-frame basis. This approach considerably reduces the amount of energy consumed as proven by analysis and simulation models, providing a lower cost coverage solution with no dereliction in achieving high throughput for all group members.
سارة مفتاح الربيعي (2010)

A review of previous array Pascals leads on to a description of the Glasgow Pascal compiler. The compiler is an ISO-Pascal superset with semantic extensions to translate data parallel statements to run on multiple SIMD cores.
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—Predicting the groundwater level has recently become very important research topic especially with the rise of population density and consequently increasing the water demand. This paper uses the Random Forest and linear regression neural network models to predict the groundwater level of Wadi-Alshaty district in the South West part of Libya. The results are compared with that obtained using the hydrologic long-term forecasting graphical approach. One of the most important findings of this study is the effectiveness of the neural network models to investigate the fluctuation of the groundwater levels over time (20 years)
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