Abstract
Regulators of electricity distribution networks have typically applied Data Envelopment Analysis (DEA) to cross-section data for benchmarking purposes. However, the use of panel data to analyse the impact of regulatory policies on productivity change over time is less frequent. The main purpose of this paper is to construct a Malmquist productivity index to examine the recent productivity change experienced by Norwegian distribution companies between 2004 and 2007. The Malmquist index is decomposed in order to explore the sources of productivity change, and to identify the innovator companies that pushed the frontier forward each year. The input and output variables considered are those used by the Norwegian regulator. In order to reflect appropriately the exogenous conditions where the companies operate, the efficiency model used in this paper incorporates geography variables as outputs of the DEA model. Unlike the model used by the regulator, we included virtual weight restrictions in the DEA formulation to correct the biases in the DEA results that may be associated to a judicious choice of weights by some of the companies. Journal of the Operational Research Society (2012) 63, 982-990. doi: 10.1057/jors.2011.82 Published online 26 October 2011

Abstract
This paper presents a sequential Monte Carlo simulation algorithm that can simultaneously assess composite system adequacy and detect wind power curtailment events. A simple procedure at the end of the state evaluation stage is proposed to categorize wind power curtailment events according to their cause. Furthermore, the dual variables of the DC optimal power flow procedure are used to identify which transmission circuits are restricting the use of the total wind power available. In the first set of experiments, the composite system adequacy is assessed, incorporating different generation technologies. This is conducted to clarify the usual comparisons made between wind and thermal technologies which, in fact, depend on the performance measure selected. A second set of experiments considering several wind penetration scenarios is also performed to determine the operational rules or system components responsible for the largest amount of wind energy curtailed. The experiments are carried out on configurations of the IEEE-RTS 79 power system.

Abstract
The measurement of shear wave velocity (Vs) is an established approach in contributing to earthquake site response. Currently, in terms of sensors, horizontal geophones have been added as an option to the more common vertical geophones and thus being able to measure Love waves. In terms of processing the most recent development has been the possibility of joint inversion of data sets of different types (Rayleigh+Love, Rayleigh+HVSR, Rayleigh+Refraction). Since some of most important earthquake site response measurements have to be performed in urban environments this can pose a problem to the in situ measurements due to the lower signal to noise ratio. Thus we aim to show a case study of results of dispersive wave tests, made in the urban environment of the city Porto in Northern Portugal, with the objective of contributing towards a microzoning GIS map that we are currently engaged in improving. Porto is set in a crystalline environment with a rock mass that is variably weathered thus our test results will be compared with the geotechnical map of Porto.

Abstract
Objectives: The purposes of the present study were to assess the factorial structure and reliability of the Greene Climacteric Scale (GCS), and provide normative data for a sample of postmenopausal Portuguese women. Methods: A sample of 401 Caucasian women, with ages between 47 and 91 years, divided into four age groups (47-57: 31.4%, 58-68: 40.4%, 69-79: 21.4% and >= 80: 6.7%), voluntarily participated in the study. The Greene Climacteric Scale aims to measure psychological symptoms divided into anxiety and depression, somatic and vasomotor symptoms with a total of 21 items. Data were analyzed by reliability, correlation and confirmatory factor analyses. Age group differences in the raw and the standardized scores of symptoms clusters were investigated by means of ANOVA procedures. Results: The CFA performed supported the 4-factor structure specified by Greene (*CFI = 0.937; SRMR = 0.046; *RMSEA (90%IC) = 0.050 (0.042-0.058). The computed internal consistency estimates ranged from 0.73 to 0.90. Vasomotor symptoms (hot flushes and sweating at night) were experienced most frequently by the younger age group (47-57 years) while nonspecific symptoms (e.g. difficulty in concentrating, feeling tired or lacking in energy, breathing difficulties) were reported more frequently by the older age groups. Conclusions: Our results suggest that the Portuguese version of the GCS is a reliable and a valid instrument for the measurement of climacteric-related factors in postmenopausal women.

Abstract
A good relationship between companies and customers is a crucial factor of competitiveness. Market segmentation is a key issue for companies to develop and maintain loyal relationships with customers as well as to promote the increase of company sales. This paper proposes a method for market segmentation in retailing based on customers' lifestyle, supported by information extracted from a large transactional database. A set of typical shopping baskets are mined from the database, using a variable clustering algorithm, and these are used to infer customers lifestyle. Customers are assigned to a lifestyle segment based on their purchases history. This study is done in collaboration with an European retailing company.

Abstract
Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.