In this research, the X-ray diffraction pattern was used, which was obtained experimentally after preparation of barium oxide powder. A program was used to analyze the X-ray diffraction lines of barium oxide nanoparticles, and then the particle size was calculated by using the Williamson-Hall method, where it was found that the value of the particle size is 25.356 nm. Also, the dislocation density was calculated, which is equal to1.555 x1015 (lines/nm2), and the value of the unit cell number was also calculated, as it is equal to 23831.

The basic analytical formula for particle-hole state densities is derived based on the non-Equidistant Spacing Model (non-ESM) for the single-particle level density (s.p.l.d.) dependence on particle excitation energy u. Two methods are illustrated in this work, the first depends on Taylor series expansion of the s.p.l.d. about u, while the second uses direct analytical derivation of the state density formula. This treatment is applied for a system composing from one kind of fermions and for uncorrected physical system. The important corrections due to Pauli blocking was added to the present formula. Analytical comparisons with the standard formulae for ESM are made and it is shown that the solution reduces to earlier formulae providing m

Abstract

Much attention has been paid for the use of robot arm in various applications. Therefore, the optimal path finding has a significant role to upgrade and guide the arm movement. The essential function of path planning is to create a path that satisfies the aims of motion including, averting obstacles collision, reducing time interval, decreasing the path traveling cost and satisfying the kinematics constraints. In this paper, the free Cartesian space map of 2-DOF arm is constructed to attain the joints variable at each point without collision. The D*algorithm and Euclidean distance are applied to obtain the exact and estimated distances to the goal respectively. The modified Particle Swarm Optimization al

This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO) for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In ord

     Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem.  In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a