4 resultados para 851
em Aston University Research Archive
Resumo:
In practical term any result obtained using an ordered weighted averaging (OWA) operator heavily depends upon the method to determine the weighting vector. Several approaches for obtaining the associated weights have been suggested in the literature, in which none of them took into account the preference of alternatives. This paper presents a method for determining the OWA weights when the preferences of alternatives across all the criteria are considered. An example is given to illustrate this method and an application in internet search engine shows the use of this new OWA operator.
Resumo:
The aim was to establish if the memory bias for sad faces, reported in clinically depressed patients (Gilboa-Schechtman, Erhard Weiss, & Jeczemien, 2002; Ridout, Astell, Reid, Glen, & O'Carroll, 2003) generalises to sub-clinical depression (dysphoria) and experimentally induced sadness. Study 1: dysphoric (n = 24) and non-dysphoric (n = 20) participants were presented with facial stimuli, asked to identify the emotion portrayed and then given a recognition memory test for these faces. At encoding, dysphoric participants (DP) exhibited impaired identification of sadness and neutral affect relative to the non-dysphoric group (ND). At memory testing, DP exhibited superior memory for sad faces relative to happy and neutral. They also exhibited enhanced memory for sad faces and impaired memory for happy relative to the ND. Study 2: non-depressed participants underwent a positive (n = 24) or negative (n = 24) mood induction (MI) and were assessed on the same tests as Study 1. At encoding, negative MI participants showed superior identification of sadness, relative to neutral affect and compared to the positive MI group. At memory testing, the negative MI group exhibited enhanced memory for the sad faces relative to happy or neutral and compared to the positive MI group. Conclusion: MCM bias for sad faces generalises from clinical depression to these sub-clinical affective states.
Resumo:
Book review
Resumo:
In the traditional TOPSIS, the ideal solutions are assumed to be located at the endpoints of the data interval. However, not all performance attributes possess ideal values at the endpoints. We termed performance attributes that have ideal values at extreme points as Type-1 attributes. Type-2 attributes however possess ideal values somewhere within the data interval instead of being at the extreme end points. This provides a preference ranking problem when all attributes are computed and assumed to be of the Type-1 nature. To overcome this issue, we propose a new Fuzzy DEA method for computing the ideal values and distance function of Type-2 attributes in a TOPSIS methodology. Our method allows Type-1 and Type-2 attributes to be included in an evaluation system without compromising the ranking quality. The efficacy of the proposed model is illustrated with a vendor evaluation case for a high-tech investment decision making exercise. A comparison analysis with the traditional TOPSIS is also presented. © 2012 Springer Science+Business Media B.V.