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.

In this paper, Particle Swarm OPTIMIZATION (PSO) is employed to optimize Markowitz portfolio based on different risk measurement criteria, namely, mean-variance, semi-variance and mean absolute deviation considering constraints of real markets such as "fixed stock numbers" and "cardinality constraints". To investigate the efficiency of the proposed algorithm, the data obtained from 186 companies of Tehran Stock Exchange in duration of Tir of 1385 until Tir of 1390 are used. The obtained results demonstrate the efficiency of PSO as a promising method in calculating appropriate constraint of Markowitz based on different definitions of risk measurement.

The PROBLEM of personal identity among others may stem from the following question—what does be the person that you are, from one day to the next, necessarily consist of? The diachronic PROBLEM of personal identity raises question on the necessary and sufficient conditions for the identity of the person over time. The synchronic PROBLEM is grounded in the question of what features or traits characterize a given person at one time. To answer these questions, John Locke discarded the soul and the body as necessary and sufficient substances for personal identity over time. He accepted consciousness as the only criterion for personal identity; the only thing capable of remaining the same and preserving personal identity through change. Though Locke’s argument is somewhat clear and coherent but what remains vague and incoherent is embedded in the question—what exactly is consciousness? How and why should it be the basis or criterion for the determination of personal identity? Using the method of critical analysis, I argue that Locke’s choice of consciousness as the determinant of personal identity, though quite novel, is incoherent and vague. Secondly, Locke had already presumed and anticipated clearly though fallaciously the very thing he wishes to substantiate. I therefore conclude that Locke’s argument is just another way of trying to escape but inadvertently prolonging the difficulty of apparently articulating a distinction between the psychological approach and physiological approach to the PROBLEM of personal identity. However, in my submission, I propose the concept of the “other” as alternative approach— a sort of an extrinsic-intrinsic approach to the PROBLEM of personal identity.

Portfolio selection is of great importance among financiers, who seek to invest in a financial market by selecting a portfolio to minimize the risk of investment and maximize their profit. Since there is a covariant among portfolios, there are situations in which all portfolios go high or down simultaneously, known as systemic risks. In this study, we proposed three improved meta-heuristic algorithms namely, genetic, dragonfly, and imperialist competitive algorithms to study the portfolio selection PROBLEM in the presence of systemic risks. Results reveal that our Imperialist Competitive Algorithm are superior to Genetic algorithm method. After that, we implement our method on the Iran Stock Exchange market and show that considering systemic risks leads to more robust portfolio selection.

In this paper we consider unconstraint OPTIMIZATION PROBLEM as representation theorem and a control PROBLEM. Then by using Riesz admissible paires of control and state we transfer the PROBLEM to a PROBLEM in measure space. Then the PROBLEM is reduced to an infinitedimensional linear programming. The later PROBLEM is approximatedto a finite-dimentional linear programming and then by the solution of this linear programming we obtain the approximate optimal control function as a piecewise constant function. Then we optimal control function. The final end point of this trajectory is an approximate solution of the original unconstraint OPTIMIZATION PROBLEM. Finally we consider the OPTIMIZATION PROBLEMs with constraints. We compare the approximate solution obtained by this method by approximate solutions obtained by some other well-known methods. At the end we solve some examples by this method to show the efficiency of this method.

We address the throughput maximization PROBLEM for downlink transmission in DF-relay-assisted cognitive radio networks (CRNs) based on simultaneous wireless information and power transfer (SWIPT) capability. In this envisioned network, multiple-input multiple-output (MIMO) relay and secondary user (SU) equipment are designed to handle both radio frequency (RF) signal energy harvesting and SWIPT functional tasks. Additionally, the cognitive base station (CBS) communicates with the SU only via the MIMO relay. Based on the considered network model, several combined constraints of the main PROBLEM complicate the solution. Therefore, in this paper, we apply heuristic guidelines within the convex OPTIMIZATION framework to handle this complexity. First, consider the PROBLEM of maximizing throughput on both sides of the relay separately. Second, each side progresses to solve the complex PROBLEM optimally by adopting strategies for solving sub-PROBLEMs. Finally, these optimal solutions are synthesized by proposing a heuristic iterative power allocation algorithm that satisfies the combinatorial constraints with short convergence times. The performance of the optimal proposed algorithm (OPA) is evaluated against benchmark algorithms via numerical results on optimality, convergence time, constraints’ compliance, and imperfect channel state information (CSI) on the CBS-PU link.