After offering an overview of some of the main themes of Popper’s political thought, the paper argues that his account faces two PROBLEMS relating to institutions. The first is that while Popper stresses the ‘rational unity of mankind’, and the potential for any of us to furnish criticisms of public policy, it is not clear what institutional means currently exist for this to enable this to take place. Second, Popper has stressed the conjectural character of even our best theories. However, at any point, some theories will have fared better in the face of criticism than others, and they may give us important information about constraints on our actions. At the same time, as ordinary citizens we may not be in a good position to understand the theories in question, let alone appraise the state of the specialised discussion of them. There is, it is suggested, a case for thinking of ways to institutionally entrench such fallible theories, especially in the current setting in which social media play an important role

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.

Over the last decade, evolutionary and meta-heuristic algorithms have been extensively used as search and OPTIMIZATION tools in various problem domains, including science, commerce, and engineering. Ease of use and broad applicability may be considered as the primary reasons for their success. The honey-bee mating process has been considered as a typical swarm-based approach to OPTIMIZATION, in which the search algorithm is inspired by the process of real honey-bee mating. In this paper, the honey-bee mating OPTIMIZATION (HBMO) algorithm is applied to three well-known mathematical PROBLEMS. To demonstrate the efficiency of the algorithm, these three PROBLEMS are chosen from well-known constraint and unconstraint mathematical PROBLEMS with continuous decision and state variables having all the complexities of OPTIMIZATION PROBLEMS. The proposed HBMO algorithm results in a feasible, near-optimal solution in an acceptable number of mating flights. To emphasize the capability of the developed algorithm in handling these PROBLEMS, results are compared with those of a well-developed genetic algorithm (GA). Although the HBMO algorithm is at the early stages of development the results obtained by the present algorithm are if not better but surly equally convenient to those employing genetic algorithms. A real-world water resources problem, in the field of water resources management is selected as a case study. The developed model is applied to a reservoir with 60 operational periods with the objective function of minimizing the root mean square error of release and demand. The results show that in this problem the algorithm arrives at the global optimum which is gained by non-linear programming. The results are promising and the algorithm arrives at the global optimum which is gained by nonlinear programming.

We establish a relationship between general constrained pseudoconvex OPTIMIZATION PROBLEMS and globally projected dynamical systems. A corresponding novel neural network model, which is globally convergent and stable in the sense of Lyapunov, is proposed. Both theoretical and numerical approaches are considered. Numerical simulations for three constrained nonlinear OPTIMIZATION PROBLEMS are given to show that the numerical behaviors are in good agreement with the theoretical results.

This paper addresses several algorithms based on the self-organizing neural network approach for distribution PROBLEMS. The algorithm for Traveling Salesman Problem is elaborated and the extension of the proposed algorithm to more complex PROBLEMS namely, Multiple Traveling Salesmen and Vehicle Routing is discussed. In order to investigate the performance of the algorithms an empirical study has been provided. The simulations, which are conducted on standard data, evaluate the overall performance of this approach by comparing the results with the best known or optimal solutions of the problem.