Maximizing the coefficient of generalizability under the constraint of limited resources

A procedure for maximizing the coefficient of generalizability under the constraint of limited resources is presented. The procedure uses optimization techniques that offer an investigator or test constructor the possibility of employing practical constraints. The procedure is illustrated for the two-facet random-model crossed design.     

Multicriteria Optimization Technique for Optimal Design of Orthotropic Bridges

The design improvement of large‐scale structures such as cable stayed and suspension bridges with large spans is one of the major engineering optimization problems faced by design engineers. In many real‐life engineering design problems, it is necessary to carry out large‐scale experimental physical models for only one prototype to construct the feasible solution set that is too expensive and not practical. For these reasons, an experimental search for optimal solutions is often not carried out at all. This paper presents a technique for multicriteria analysis, which involve the finite element analysis of the prototype in the optimization process. The improvement of the Suez Canal Bridge in Egypt is introduced as a real‐life large‐scale case study. The parameter space investigation method, the visual basic for application programming language, and Femap as finite element analysis software provide an implementation tools to construct the feasible and Pareto solution sets for the studied bridge. An efficient combination between the parameter space investigation method and the finite element programme was successfully investigated to obtain the Pareto solution set. This study shows possibility to apply the multicriteria optimization method for more applications on different large‐scale structural systems in the future. Copyright © 2013 John Wiley & Sons, Ltd.     

Multiobjective Optimization for the Asset Allocation of European Nonlife Insurance Companies

An optimal asset allocation is crucial for nonlife insurance companies. The most previous studies focused on this topic use a mono‐objective technique optimization. This technique usually allows the maximization of shareholders' expected utility. As nonlife insurance company is a complex system, it has many stakeholders other than shareholders. So, the satisfaction of the shareholders' expected utility cannot lead usually to the satisfaction of other stakeholders' objectives. Therefore, the focus on utility maximization can be a destruction source of other objectives such as productivity, competitiveness and solvency. Our developed model integrates simulation approach with a multiobjective particle swarm optimization algorithm. This model insures an optimal asset allocation that maximizes, simultaneously, shareholders expected utility and technical efficiency of European nonlife insurance companies. The empirical application conducts a comparison between the attained results with multiobjective optimization technique and mono‐objective technique to search the optimal asset allocation for nonlife insurance companies. Our results show that the investment portfolio will be more diversified between most available investment assets. In addition, any decision maker should take account of different stakeholders' objectives. Accordingly, multiobjective optimization allows us to find the best asset allocation that maximizes simultaneously expected utility and technical efficiency of nonlife insurance companies. Copyright © 2013 John Wiley & Sons, Ltd.     

Perceived Occurrence of the Second of Two Closely Spaced Stimuli in a Long Movement

A 75-deg. movement of the arm in the vertical plane was used to examine the conscious perception of the apparent time of occurrence of a reversal signal S₂. A marked perceptual delay was found whether S₂ occurred during or after the reaction time to the first stimulus. This finding was consistent with Henry’s memory drum theory of neuromotor response, as well as with the efference theory of conscious perception proposed by Festinger and associates.     

The effects on classifier performance of 2D discrete wavelet transform analysis and whale optimization algorithm for recognition of power quality disturbances

Power Quality (PQ) is becoming more and more important day by day in the electric network. Signal processing, pattern recognition and machine learning are increasingly being studied for the automatic recognition of any disturbances that may occur during the generation, transmission, and distribution of electricity. There are three main steps to identify the PQ disturbances. These include the use of signal processing methods to calculate the features representing the disturbances, the selection of those that are more useful than these feature sets to prevent the creation of a complex classification model, the creating a classification model that recognizes multiple classes using the selected feature subsets. In this study, one-dimensional (1D) PQ disturbances signals are transformed into two-dimensional (2D) signals, 2D discrete wavelet transforms (2D-DWT) are used to extract the features. The features are extracted by using the wavelet families such as Daubechies, Biorthogonal, Symlets, Coiflets and Fejer-Korovkin in 2D-DWT to analyze PQ disturbances. Whale Optimization Algorithm (WOA) and k-nearest neighbor (KNN) classifier determine the feature subsets. Then, WOA and k nearest neighbor (KNN) classifier are used to determine the feature group. By using KNN and Support Vector Machines (SVM) classification methods, Classifier models that distinguish PQ disturbances are formed. The main aim of the study is to determine the features derived from 2D wavelet coefficients for different wavelet families and to determine which of them has a better classification performance to distinguish PQ disturbances signals. At the same time, different classification methods are simulated and a model which can classify PQ disturbances signals with high performance is created. Also, the generated models are analysed for their performance in terms of different noise levels (40 dB, 30 dB, 20 dB). The result of this simulation study shows that the model developed to classify PQ disturbances is superior to conventional models and other 2D signal processing methods in the literature. In addition, it was concluded that the proposed method can cope better with noisy signals by low computational complexity and higher classification rate.     

中国养老机构治理地方立法现状——基于85部地方立法文本分析

对我国现行有效的85部涉及养老机构治理的地方立法进行文本统计分析,发现普遍存在相关法规可执行性不强、缺乏监管部门之间协同治理的法律规范、鲜见规范养老机构服务合同的条款、忽视机构评价标准和信用体系的重要性、医养结合相关法律规范亟需完善等问题。因此,应当加强地方立法可执行性、完善各部门协同治理策略、细化养老机构服务合同条款、重视机构评价标准和信用体系的重要性、健全医养结合养老地方立法规范,从制度层面切实有效地支持机构养老产业良性健康发展。

     

过度医疗、适度医疗与诊疗最优化

过度医疗与过度服务有所不同。适度医疗应当是为患有某种疾病的病人提供有效、安全、便捷、耗费少的医疗服务。最优化的医疗是一种包括以优质的医疗服务实现了最佳疗效、最安全、痛苦最少、最便捷和费用低的医疗。一般地说,最优化的医疗首先应当是适度的医疗。最优化的医疗比适度医疗有更高的要求。不宜用优质医疗取代适度医疗,也不宜将适度医疗改称为最优化医疗。我们可以将适度医疗作为保健服务的基本要求,而将最优化医疗作为努力目标,一种理想化的目标。在满足人民的基本医疗、实行基本医疗保险过程中,我们更应当提倡适度医疗。     

Analytical Inverse Optimization in Two-Hand Prehensile Tasks

The authors explored application of analytical inverse optimization (ANIO) method to the normal finger forces in unimanual and bimanual prehensile tasks with discrete and continuously changing constraints. The subjects held an instrumented handle vertically with one or two hands. The external torque and grip force changed across trials or within a trial continuously. Principal component analysis showed similar percentages of variance accounted for by the first two principal components across tasks and conditions. Compared to unimanual tasks, bimanual tasks showed significantly more frequent inability to find a cost function leading to a stable solution. In cases of stable solutions, similar second-order polynomials were computed as cost functions across tasks and condition. The bimanual tasks, however, showed significantly worse goodness-of-fit index values. The authors show that ANIO can be used in tasks with slowly changing constraints making it an attractive tool to study optimality of performance in special populations. They also show that ANIO can fail in multifinger tasks, likely due to irreproducible behavior across trials, more likely to happen in bimanual tasks compared to unimanual tasks.     

Relating reference points and weights in MOLP

A frequent problem for decision makers (DMs) analysing decisions involving multiple objectives is the identification and selection of the most preferred option from the set of non‐dominated solutions. Two techniques, weighted sum optimization and reference point optimization, have been developed to address this problem for multiobjective linear programming problems (MOLP). In this paper, we examine the relationship between these two techniques. We demonstrate that the values of the dual variables associate with auxiliary constraints of the reference point technique are equal to the weight values used to compute the same non‐dominated solution via the weighted sum technique. This insight will enable the development of new interactive solution procedures for MOLPs which allow the DM to readily switch from one method to the other during the search for the most preferred non‐dominated solution. The advantages of the approach are discussed in the paper. Copyright © 1999 John Wiley & Sons, Ltd.