Cost and safety are two most important factors involved in the design of civil engineering systems and structures. Thus, the optimal design of structure dimensions is of big importance in order to reduce the cost of construction while meeting the safety requirement and the design constraints. Up to now several algorithms have been developed and used for optimization of different civil engineering problems. One of these algorithms is the PARTICLE Swarm Optimization (PSO) which is a swarm intelligence based algorithm inspired by the social behavior of animals such as fish schooling and bird flocking to solve continuous problems. In this paper, an optimization model is developed for the optimal design of flood control systems which contain both detention dam and bottom outlet. The proposed model uses the powerful PSO algorithm as the search engine and The "Transport Module" of "SWMM" as the hydraulic analyzer of the system. The applicability of the model to solve real world problem is verified by the optimal design of of South Pars flood control system which contains both detention dam and bottom outlet. The results show that the proposed optimization model can considerably reduce the total costs of flood control systems.

Background: Online Monte Carlo (MC) treatment planning is very crucial to increase the precision of intraoperative radiotherapy (IORT). However, the performance of MC methods depends on the geometries and energies used for the problem under study. Objective: This study aimed to compare the performance of MC N-PARTICLE Transport Code version 4c (MCNP4c) and Electron Gamma Shower, National Research Council/easy PARTICLE PROPAGATION (EGSnrc/Epp) MC codes using similar geometry of an INTRABEAM® system. Material and Methods: This simulation study was done by increasing the number of PARTICLEs and compared the performance of MCNP4c and EGSnrc/Epp simulations using an INTRABEAM® system with 1. 5 and 5 cm diameter spherical applicators. A comparison of these two codes was done using simulation time, statistical uncertainty, and relative depth-dose values obtained after doing the simulation by each MC code. Results: The statistical uncertainties for the MCNP4c and EGSnrc/Epp MC codes were below 2% and 0. 5%, respectively. 1e9 PARTICLEs were simulated in 117. 89 hours using MCNP4c but a much greater number of PARTICLEs (5e10 PARTICLEs) were simulated in a shorter time of 90. 26 hours using EGSnrc/Epp MC code. No significant deviations were found in the calculated relative depth-dose values for both in the presence and absence of an air gap between MCNP4c and EGSnrc/Epp MC codes. Nevertheless, the EGSnrc/ Epp MC code was found to be speedier and more efficient to achieve accurate statistical precision than MCNP4c. Conclusion: Therefore, in all comparisons criteria used, EGSnrc/Epp MC code is much better than MCNP4c MC code for simulating an INTRABEAM® system.

In this paper, we present a Mont Carlo simulation by using the inhomogeneous diffusion equation suggested by Lagutin et al. in a fractal interstellar medium for a supernova, as being the main source of Galactic cosmic rays in an energy range up to 1016ev. When the numerical results of this simulation are compared with the predicted energy density of cosmic rays from the supernova model of Erlikyn and Wolfendale (EW), they show a strong consistency with the observed experimental parameters of cosmic rays, i.e. Galactic radial gradient, percentage of total supernova energy transfer to cosmic PARTICLEs etc., which is also confirmation of a supernova being of cosmic ray origin.

A gyroscope is a device for measuring or maintaining orientation, based on the principles of conservation of angular momentum. The device is a spinning wheel or disk whose axle is free to take any orientation. This orientation changes much less in response to a given external torque than it would without the large angular momentum associated with the gyroscope's high rate of spin. Since external torque is minimized by mounting the device in gimbals, its orientation remains nearly fixed, regardless of any motion of the platform on which it is mounted. A new method of optimization, PARTICLE swarm optimization (PSO), is able to accomplish the same goal as GA optimization in a new and faster way. The purpose of this paper is to investigate the foundations and performance of the two algorithms when applied to  solve of gyroscope equation.