Minia University

About: Minia University is a education organization based out in Minya, Egypt. It is known for research contribution in the topics: Population & Medicine. The organization has 4967 authors who have published 8986 publications receiving 108384 citations.

Continental back-arc basin origin of some ophiolites from the Eastern Desert of Egypt

Abstract: Geochemical and petrographical data of three ophiolitic pillow metavolcanic occurrences from the central Eastern Desert of Egypt are presented The investigated rocks show a subalkaline, tholeiitic affinity Chemical data indicate that the metavolcanics have transitional within-plate basalt to island-arc basalt features, which are characteristics of basalts formed in ensialic back-arc basins The association of the investigated ophiolites with volcanoclastic metasedimentary rocks of marine to continental facies is a further confirmation of their ensialic evolution This suggestion, along with the geochronologic, isotopic and crustal growth rate evidences, revives interest in models that involve contribution from a pre-Pan-African continental crust at least in the southern part of the Egyptian Shield Mixing between a depleted mantle-derived magma and an enriched crustal melt, a process similar to AFC (assimilation and fractional crystallization), is suggested for the evolution of the investigated rocks This study provides evidence for formation of some ophiolites in the Eastern Desert of Egypt in continental (ensialic) back arc basins

Extracorporeal shock wave lithotripsy as first line treatment for urinary tract stones in children: outcome of 500 cases

Abstract: Purpose The continued evolution of stone treatment modalities, such as endourologic procedures, open surgery and shock wave lithotripsy, makes the assessment of continuous outcomes are essential. Pediatric urolithiasis are an important health problem allover the world, especially in Middle East region. We evaluate the safety, efficacy and factors affecting success rate and clearance of stones in children treated with shock wave lithotripsy.

Optimal Sink Node Placement in Large Scale Wireless Sensor Networks Based on Harris’ Hawk Optimization Algorithm

Abstract: Large-scale wireless sensor network (LSWSN) is composed of a huge number of sensor nodes that are distributed in some region of interest (ROI), to sense and measure the environmental conditions like pressure, temperature, pollution levels, humidity, wind, and so on. The objective is to collect data for real-time monitoring so that appropriate actions can be taken promptly. One of the sensor nodes used in an LSWSN is called the sink node, which is responsible for processing and analyzing the collected information. It works as a station between the network sensor nodes and the administrator. Also, it is responsible for controlling the whole network. Determining the sink node location in an LSWSN is a challenging task, as it is crucial to the network lifetime, for keeping the network activity to the most possible extent. In this paper, the Harris' hawks optimization (HHO) algorithm is employed to solve this problem and subsequently the Prim's shortest path algorithm is used to reconstruct the network by making minimum transmission paths from the sink node to the rest of the sensor nodes. The performance of HHO is compared with other well-known algorithms such as particle swarm optimization (PSO), flower pollination algorithm (FPA), grey wolf optimizer (GWO), sine cosine algorithm (SCA), multi-verse optimizer (MVO), and whale optimization algorithm (WOA). The simulation results of different network sizes, with single and multiple sink nodes, show the superiority of the employed approach in terms of energy consumption and localization error, and ultimately prolonging the lifetime of the network in an efficacious way.

MOGOA algorithm for constrained and unconstrained multi-objective optimization problems

Abstract: Grasshopper Optimization Algorithm (GOA) was modified in this paper, to optimize multi-objective problems, and the modified version is called Multi-Objective Grasshopper Optimization Algorithm (MOGOA). An external archive is integrated with the GOA for saving the Pareto optimal solutions. The archive is then employed for defining the social behavior of the GOA in the multi-objective search space. To evaluate and verify the effectiveness of the MOGOA, a set of standard unconstrained and constrained test functions are used. Moreover, the proposed algorithm was compared with three well-known optimization algorithms: Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Ant Lion Optimizer (MOALO), and Non-dominated Sorting Genetic Algorithm version 2 (NSGA-II); and the obtained results show that the MOGOA algorithm is able to provide competitive results and outperform other algorithms.