Optimization Environment Definition for Beam Steering Reflectarray Antenna Design

Reflectarray antennas are low-profile high-gain systems widely applied in the aerospace industry. The increase in their application is leading to the problem of getting more advanced performance while keeping the system as simple as possible. In these cases, their design cannot be conducted via analytical methods, thus evolutionary optimization algorithms are often implemented. Indeed, the design is characterized by the presence of many local minima, by high number of design variables, and by the high computational burden required to evaluate the antenna performance. The purpose of this paper is to develop, implement, and test a complete Optimization Environment that can be applied to achieve high scanning capabilities with a reflectarray. The design of the optimization environment has been selected to be flexible enough to be applied also with other different algorithms.

Risk Stratification in Cost-Effectiveness Analyses of Cancer Screening: Intervention Eligibility, Strategy Choice, and Optimality

Introduction There is increasing interest in risk-stratified approaches to cancer screening in cost-effectiveness analysis (CEA). Current CEA practice regarding risk stratification is heterogeneous and guidance on the best approach is lacking. This article suggests how stratification in CEA can be improved. Methods I use a simple example of a hypothetical screening intervention with 3 potential recipient risk strata. The screening intervention has 6 alternative intensities, each with different costs and effects, all of which vary between strata. I consider a series of alternative stratification approaches, demonstrating the consequences for estimated costs, effects, and the choice of optimal strategy. I supplement this analysis with applied examples from the literature. Results Adopting the same screening policy for all strata yields the least efficient strategies, where efficiency is understood as the volume of net health benefit generated across a range of cost-effectiveness threshold values. Basic stratification that withholds screening from lower-risk strata while adopting a common strategy for those screened increases efficiency. Greatest efficiency is achieved when different strata receive separate strategies. While complete optimization can be achieved within a single analysis by considering all possible policy combinations, the resulting number of strategy combinations may be inconveniently large. Optimization with separate strata-specific analyses is simpler and more transparent. Despite this, there can be good reasons to simulate all strata together in a single analysis. Conclusions If the benefits of risk stratification are to be fully realized, policy makers need to consider the extent to which stratification is feasible, and modelers need to simulate those choices adequately. It is hoped this analysis will clarify those policy and modeling choices and therefore lead to improved population health outcomes.

Two-Layer Network Caching for Different Service Requirements

Network caching is a technique used to speed-up user access to frequently requested contents in complex data networks. This paper presents a two-layer overlay network caching system for content distribution. It is used to define some caching scenarios with increasing complexity, which refers to real situations, including mobile 5G connectivity. For each scenario our aim is to maximize the hit ratio, which leads to the formulation of NP-complete optimization problems. The heuristic solutions proposed are based on the theory of the maximization of monotone submodular functions under matroid constraints. After the determination of the approximation ratio of the greedy heuristic algorithms proposed, a numerical performance analysis is shown. This analysis includes a comparison with the Least-Frequently Used (LFU) eviction strategy adapted to the analyzed systems. Results show very good performance, under the hypotheses of either known or unknown popularity of contents.

Signature PSO: A novel inertia weight adjustment using fuzzy signature for LQR tuning

Particle swarm optimization (PSO) is an optimization algorithm that is simple and reliable to complete optimization. The balance between exploration and exploitation of PSO searching characteristics is maintained by inertia weight. Since this parameter has been introduced, there have been several different strategies to determine the inertia weight during a train of the run. This paper describes the method of adjusting the inertia weights using fuzzy signatures called signature PSO. Some parameters were used as a fuzzy signature variable to represent the particle situation in a run. The implementation to solve the tuning problem of linear quadratic regulator (LQR) control parameters is also presented in this paper. Another weight adjustment strategy is also used as a comparison in performance evaluation using an integral time absolute error (ITAE). Experimental results show that signature PSO was able to give a good approximation to the optimum control parameters of LQR in this case.

Exploring Further Advantages in an Alternative Formulation for the Set Covering Problem

The set covering problem (SCP) is an NP-complete optimization problem, fitting with many problems in engineering. The traditional SCP formulation does not directly address both solution unsatisfiability and set redundancy aspects. As a result, the solving methods have to control these aspects to avoid getting unfeasible and nonoptimized in cost solutions. In the last years, an alternative SCP formulation was proposed, directly covering both aspects. This alternative formulation received limited attention because managing both aspects is considered straightforward at this time. This paper questions whether there is some advantage in the alternative formulation, beyond addressing the two issues. Thus, two studies based on a metaheuristic approach are proposed to identify if there is any concept in the alternative formulation, which could be considered for enhancing a solving method considering the traditional SCP formulation. As a result, the authors conclude that there are concepts from the alternative formulation, which could be applied for guiding the search process and for designing heuristic feasibilit\y operators. Thus, such concepts could be recommended for designing state-of-the-art algorithms addressing the traditional SCP formulation.

ON THE NP-COMPLETENESS OF THE PROBLEM OF PLACEMENT OF THE GRAPH

This paper provides a proof of NP-completeness of the problem of the placement of the graph. Thus, it is concluded that the algorithmization of the graph placement problem requires an approach based on heuristic or stochastic methods. To prove the NP-completeness of the graph placement problem, we use the well-known NP-complete Hamiltonian cycle search problem. It is shown that the graph placement problem is a generalization over the Hamiltonian cycle problem. Also the problem of the placement of the graph as the optimization and presents some auxiliary results, which are set nalivayut the connection between the two problems: NP-complete optimization problem and embed the graph.

Unification of optimal targeting methods in Transcranial Electrical Stimulation

One of the major questions in high-density transcranial electrical stimulation (TES) is: given a region of interest (ROI), and given electric current limits for safety, how much current should be delivered by each electrode for optimal targeting? Several solutions, apparently unrelated, have been independently proposed depending on how “optimality” is defined and on how this optimization problem is stated mathematically. Among them, there are closed-formula solutions such as ones provided by the least squares (LS) or weighted LS (WLS) methods, that attempt to fit a desired stimulation pattern at ROI and non-ROI, or reciprocity-based solutions, that maximize the directional dose at ROI under safety constraints. A more complete optimization problem can be stated as follows: maximize directional dose at ROI, limit dose at non-ROI, and constrain total injected current and current per electrode (safety constraints). To consider all these constraints (or some of them) altogether, numerical convex or linear optimization solvers are required. We theoretically demonstrate in this work that LS, WLS and reciprocity-based closed-form solutions are particular solutions to the complete optimization problem stated above, and we validate these findings with simulations on an atlas head model. Moreover, the LS and reciprocity solutions are the two opposite cases emerging under variation of one parameter of the optimization problem, the dose limit at non-ROI. LS solutions belong to one extreme case, when the non-ROI dose limit is strictly imposed, and reciprocity-based solutions belong to the opposite side, i.e., when this limit is loose. As we couple together most optimization approaches published so far, these findings will allow a better understanding of the nature of the TES optimization problem and help in the development of advanced and more effective targeting strategies.

Влияние толщины базы на эффективность фотопреобразования текстурированных солнечных элементов на основе кремния

A theory is developed, which describes the experimental external quantum efficiency, EQE (λ) as a function of photon wavelength for structured Si-based solar cells. Short-circuit current density as a function of base thickness, d , is calculated for the high-efficiency solar cells with the photoconversion efficiency η ≥ 25% The procedure allows one to carry out a complete optimization of such solar cells, in particular, to find the optimal base thickness, d _opt.

INSTITUTIONAL CONDITIONALITY OF DEVELOPMENT OF SOVREMEN-NYH OF ECONOMIC SYSTEMS

Approaches of the institutional theory to differences between hierarchical and market mechanisms of economic interactions are researched. affirms that modern evolution leads to deleting of borders between these two forms and to their mutual interlacing. The research of the fundamental principles of organizational forms and coordination of economic activity as in each of two main forms of cooperation of economic actors - the organization (hierarchy) and the market, and between them is one of basic elements of modern economic science. With respect thereto there is a question of basic differences between them, their borders and forms of interactions. the existing universal typology not absolutely precisely reflects reality. The entities aren’t capable to perform complete optimization of transactional expenses because of complexity and the uncertainty of the environment, and also owing to limited rationality making the decision. Therefore the choice of this or that organizational “ideal model" is utopian as borders of the entities don’t give in to a limitation in connection with variety of the purposes, interdependence of actions of other economic actors as components of the external environment. The state stimulation of these or those links of economic system shall be aimed at the development not of the separate entities, but market structures in general into which they enter only as components.

Constructal Design of Double-T Shaped Cavity with Stochastic Methods Luus-Jaakola and Simulated Annealing

In this paper it is proposed a comparison between two stochastic methods, Simulated Annealing and Luus-Jaakola algorithms, applied in association with Constructal Design to the geometric optimization of a heat transfer problem. The problem consists in a solid body with an internal uniform heat generation, which is cooled by an intruded cavity that is maintained at a minimal temperature. The other surfaces are kept as adiabatic. The objective is to minimize the maximum excess of temperature (θmax) in the solid domain through geometric optimization of the isothermal double-T shaped cavity. The problem geometry has five degrees of freedom, but in this study four degrees of freedom are evaluated, keeping fixed the ratio H/L (ratio between the height and length of the solid domain) as well as the cavity constraints. The search for the optimal geometry is performed by Simulated Annealing and the Luus-Jaakola algorithm with different configurations or set of main parameters. Each algorithm is executed twenty times and the results for θmax, and corresponding geometry ratios, are recorded. Results of two heuristics are compared in order to select the best method for future studies about the complete optimization of the cavity, as well as, the evaluation of constraints over the thermal performance of the problem. The method employed to compare and rank the different versions of the two algorithms is a statistical tool called multi-comparison of Kruskal-Wallis. With this statistical method it is possible to classify the algorithms in three main groups. Results showed that the Simulated Annealing with hybrid parameters of Cooling Schedule (BoltzExp and ConstExp2) and traditional ones (Exponential) led to the highest probability to find the global optimal shape, while the results obtained with the Luus-Jaakola algorithm reached to several local points of minimum far from the best shape for all versions of the algorithm studied here. However, the Luus-Jaakola algorithm led to the lowest magnitude of maximum excess of temperature, showing that the implementation of hybrid methods of optimization can be an interesting strategy for evaluation of this kind of problem.