The aim of this study is to optimize stock portfolios by considering uncertain returns and the investment’ s utility function in the Tehran Stock Exchange. For this purpose, a two-stage recursive algorithm and a utility function have been used. Securities are selected according to the list of 50 most active companies that was published by the Securities and Exchange Organization per season between the years 1390 to 1394. Considering the returns in this five-year period, the returns vector and covariance matrix are determined and after modeling the optimal portfolio is presented. The results show that the optimal portfolio includes: Iran Transfo (0. 15), Eghtesad Novin Bank (0. 1), Saipa (0. 15), Ghadir Investment Company (0. 15), Foolad Mobarakeh Esfahan (0. 15), Mokhaberat Iran (0. 15) and Meli Sanaye Mess Iran (0. 15). The quality of the results is compared with real returns in the following year (1395). The results show the high accuracy of the algorithm.

In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex optimization problem that guarantees the global optimal solution. The convex optimization problem is efficiently solved by recently developed numerical interior–point methods. This passivity enforcement is also global which indicates that the passivity enforcement in one region does not lead to passivity violation in other regions.

This article presents an efficient Reliability-based optimization method for critical structural design, in which, the probability of failure (Pf) should remain in a small bound. Critical structures should be designed to be optimal and reliable. Despite significant progress in Reliability and optimization techniques, their interaction, which is considered in Reliability-based optimization (RBDO), is faced with several challenges, especially in Reliability analysis of critical constraints (constraints with Pf less than 10-5). To address this issue, in this paper the cross-entropy method is intended for Reliability analysis and is combined with Genetic Algorithm in a double-loop framework to implement RBDO. The cross-entropy method is based on Monte Carlo simulation, except that the variance of sampling is reduced, which leads to a faster and more accurate Reliability analysis method. The proposed RBDO method is demonstrated on two structural design. The obtained results show a high capability in Reliability-based optimization of critical structures. In addition, using the proposed method, in comparison with traditional safety factor method, provides a more substantial manner in Reliability improvement.

In this paper, a special class of redundancy optimization problem with fuzzy random variables is presented. In this model, fuzzy random lifetimes are considered as basic parameters and the Er-expected of system lifetime is used as a major type of system performance. Then a redundancy optimization problem is formulated as a binary integer programming model. Furthermore, illustrative numerical examples are also given to clarify the methods discussed in this paper.

Background: In spite of many advantages in Lenke classification of adolescent idiopathic scoliosis, the latest study of Richards et al showed that the King classification is better than had been reported recently and the Lenke classification system of adolescent idiopathic scoliosis is less reliable than previously reported.Methods: In this study, we performed a multi-surgeon comparison of these two classification systems. After teaching and discussing all available data of these classification systems with four spine surgeons, a pilot classification was performed. Then, they independently evaluated preoperative radiographs (standing posteroanterior, lateral, and two supine side-bending views) of 99 patients with adolescent idiopathic scoliosis. The results were determined by calculating the average percentage of intraobserver and interobserver agreement. Reliability was quantified using kappa statistics.Findings: The King classification demonstrated good intraobserver and interobserver Reliability, with an intraobserver agreement of 85.8% (kappa coefficient, 0.80). Interobserver percentage of agreement averaged 80.8% (kappa coefficient, 0.74). The complete Lenke classification, combining curve type, lumbar modifier, and sagittal thoracic modifier, demonstrated good Reliability for both intraobserver and interobserver measurements. The intraobserver percentage of agreement averaged 85.8% (kappa coefficient, 0.82). The interobserver percentage of agreement averaged 80.8% (kappa coefficient, 0.77).Conclusion: In this study, with each investigator performing the radiographic measurements, the King and Lenke classifications were almost similar (the Lenke classification had slightly better results). Such better results might be due to more training of this complex classification system. So, because of greater coverage of idiopathic scoliosis curve and usefulness of Lenke classification system, we prefer using this classification system in our center.

In Reliability optimization problems diverse situation occurs due to which it is not always possible to get relevant precision in system Reliability. The imprecision in data can often be represented by triangular fuzzy numbers. In this manuscript, we have considered different fuzzy environment for Reliability optimization problem of redundancy. We formulate a redundancy allocation problem for a hypothetical series-parallel system inwhich the parameters of the systemare fuzzy. Two different cases are then formulated as non-linear programming problem and the fuzzy nature is defuzzified into crisp problems using three different defuzzification methods viz. ranking function, graded mean integration value and a-cut. The result of the methods is compared at the end of the manuscript using a numerical example.

In this paper, the optimization of long term operation and determination of the Reliability level of a hydro-thermal power system are integrated into a unified model. Inflow to reservoirs is modeled as a random variable. Furthermore, the demand for energy is also assumed to be a random variable with normal distribution. In order to minimize the total cost, the Reliability level of the system is determined, rather than considering it as a priori input data. Since the resulting model is a large-scale stochastic nonlinear programming, it is necessary to develop a special method to solve it. This method, which provides an optimal solution within three stages, consists of a decomposition technique, Lagrangian relaxation and nonlinear and dynamic programming methods. To test the method, it has been implemented in Khuzestan power system and the results are compared with the existing operation procedures.

Maintenance costs are one of the major costs in plants and companies. The observation in many cases illustrates the lack of plans or mistakes in maintenance activities that incurred great costs. In this study, the number of equipment failures have been determined. Then the failure rate and Reliability of each equipment are calculated. The third step calculates total system Reliability so the initial plan is presented. After that, by using the obtained information, the sustainability aspects of the program will generate and the maintenance costs and sustainability functions will assess. At the end, this multi-objective optimization problem is solved by MOPSO algorithm and the results are compared with a simulation method. As a result, with this Reliability centered maintenance program, the Reliability of each equipment, as well as the whole system are improved; economic aspect of sustainability and customer satisfaction are increased; environmental pollutions and maintenance costs are decreased by offering more Reliability based program; a scheduling plan for each maintenance procedures is provided and also more stable internet connection is established by reducing the system failures.