The dream of building intelligent robotic systems to interact and communicate with people and help them in their lives is a very old and ongoing study. In this research, the massive parallel autonomous checkers agent "MPACA" can autonomously play checkers with a human upto Grandmaster level without requiring a special checkers board for detecting human movements. The main aim and contribution of this research is proposing enhanced algorithms for a game tree search using two different approaches. The first was a task-based approach on CPU with a parallel database, while the second was a threads-based approach on the GPU with no divergence and dynamic parallelism. The two approaches were compared with previous studies using various approaches, including threads on CPU for up to 6x speedup for an 8-core processor and threads on GPU using iterative dependence and fixed grid and block size of up to 40x speedup at 14 depth. Furthermore, the approaches were tested with different depths on the CPU and the GPU. The result shows speed up for parallel CPU tasks up to 7x for an 8core processor and parallel GPU of up to 80x at 14 depth.
Clustering is carried out to explore and solve power dissipation problem in wireless sensor network (WSN). Hierarchical network architecture, based on clustering, can reduce energy consumption, balance traffic load, improve scalability, and prolong network lifetime. However, clustering faces two main challenges: hotspot problem and searching for effective techniques to perform clustering. This paper introduces a fuzzy unequal clustering technique for heterogeneous dense WSNs to determine both final cluster heads and their radii. Proposed fuzzy system blends three effective parameters together which are: the distance to the base station, the density of the cluster, and the deviation of the node's residual energy from the average network energy. Our objectives are achieving gain for network lifetime, energy distribution, and energy consumption. To evaluate the proposed algorithm, WSN clustering based routing algorithms are analyzed, simulated, and compared with obtained results. These protocols are LEACH, SEP, HEED, EEUC, and MOFCA.
The aim of this study is the integration of mud logging and wire-line logging data to detect overpressure zones in Kareem Formation (Middle Miocene), Ashrafi Field, Gulf of Suez, Egypt. The study is performed for the three wells Ash_H_1X_ST2, Ash_I_1X_ST, and Ash_K_1X. The prediction of the abnormal pressure is a quite important factor in the design of the well, where it contributes to avoid many problems during the drilling process and maintain the formation fluids. The abnormal pressure zones occur due to major changes in lithology, petrophysical properties, and fluid type, where these factors lead to differences in pore pressure from hydrostatic pressure, and their prediction is achieved by utilizing rock cuttings, D-exponent, and methane gas; in addition, the porosity and water saturation are estimated from mud logging as real-time data and compared to wire-line logging (resistivity, porosity, and permeability) to determine these zones. The concept of detecting abnormal pressure zones in this study is based on defining the marked changes in the D-exponent trends that arise from the variations of the fluids and the lithology of the Kareem Formation. Therefore, these trends are integrated with the petrophysical parameters such as resistivity, porosity, and permeability from wire-line logging to detect the overpressure zones. So, the overpressure zones are detected in the intercalated sand and shale intervals of the studied wells within the Kareem Formation and are mostly marked by a decrease in the reservoir quality such as permeability, as well as an increase in the resistivity and D-exponent. The thickness of the overpressure zone in Ash_H_1X_ST2 well is influenced by the marl content that reaches up to 80%. The integration results are summarized to determine the average depths of the overpressure zones for the Kareem Formation in the three studied wells. The zone average depths in the Ash_H_1X_ST2 well range from 6022.50 to 6093.30 ft, whereas the zone top is detected in the Ash_I_1X_ST well at the top of the Kareem Formation (6580.00 ft), and the zone bottom at average depth of approximately 6704.20 ft, in addition, the zone average depths in the Ash_K_1X well range from 7718.33 (top) to 7833.33 ft (bottom).
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