Abstract
In this paper, two Persistent Scatterer Interferometry (PSI) methodologies are compared in order to further understand their potential in the detection of surface deformation. A comparison of these two algorithms is a comparison of the two classes of PSI techniques available: coherence estimation based on a temporal model of deformation (represented by DePSI) and coherence estimation based on spatial correlation (represented by StaMPS). Despite the similarity between the results obtained from the application of the two independent PSI methodologies, significant differences in PS density and distribution were detected, motivating a comparative study between both techniques. We analyze which approach might be more appropriate for studying specific areas/environments, which is helpful in evaluating the benefits that could be derived from an integration of the two methodologies. Several experiments are performed to assess the sensitivity of both PSI approaches to different parameter settings and circumstances. The most significant differences in the processing chain of both procedures are then investigated. We apply both methodologies to the Granada Basin area (southern Spain) and realize that coherence does not improve significantly as function of the methodology applied. If oversampling is implemented in the StaMPS processing chain, the PS density increases so that the density in the urbanized areas is similar to the results provided by DePSI but in all the remaining covers the density is significantly higher. The general results provided by both approaches are very similar in the relative deformations estimated.

Abstract
Several meta-learning approaches have been developed for the problem of algorithm selection. In this context, it is of central importance to collect a sufficient number of datasets to be used as meta-examples in order to provide reliable results. Recently, some proposals to generate datasets have addressed this issue with successful results. These proposals include datasetoids, which is a simple manipulation method to obtain new datasets from existing ones. However, the increase in the number of datasets raises another issue: in order to generate meta-examples for training, it is necessary to estimate the performance of the algorithms on the datasets. This typically requires running all candidate algorithms on all datasets, which is computationally very expensive. One approach to address this problem is the use of active learning, termed active meta-learning. In this paper we investigate the combined use of active meta-learning and datasetoids. Our results show that it is possible to significantly reduce the computational cost of generating meta-examples not only without loss of meta-learning accuracy but with potential gains.

Abstract
A probabilistic forecast, in contrast to a point forecast, provides to the end-user more and valuable information for decision-making problems such as wind power bidding into the electricity market or setting adequate operating reserve levels in the power system. One important requirement is to have flexible representations of wind power forecast (WPF) uncertainty, in order to facilitate their inclusion in several problems. This paper reports results of using the quantile-copula conditional Kernel density estimator in the WPF problem, and how to select the adequate kernels for modeling the different variables of the problem. The method was compared with splines quantile regression for a real wind farm located in the U.S. Midwest. © 2011 IEEE.

Abstract
WirelessHART currently appears as a leading solution for interconnection of wireless devices in industrial process control applications. However, the lack of knowledge about the influence of transient faults in WirelessHART networks can lead to the choice of less reliable topologies. In this work,we propose a simulation model to evaluate WirelessHART networks in the presence of transient faults. We assume that these faults result from noisy environments that disturb communications between devices. The model was developed using the Stochastic Petri Net (SPN) formalism, supported by the Mobius tool. For further developments, we target the development of an application that automates the assessment of a WirelessHART network in transient fault scenarios.

Abstract
In the present paper, management strategies are analysed in order to evaluate the degree of entrepreneurship in firms' management by the use of innovation, risk and proactivity strategies. Since we are dealing with management strategies, it is possible to relate them to the concept of Intrapreneurship. This study was done in a region of northern (Portugal Vale do Sousa) and focus on Industrial and Construction sectors. The region is composed of six concelhos1 in some of which it is possible to identify some industrial districts. In order to get a valid sample, a group of 251 firms were analysed. Each strategy was analysed individually and the results pointed to a lack of culture of entrepreneurship in firms' management. Only Proactivity presented a positive result in firms' management. When grouping the results, it was possible to conclude that the degree of intrapreneurship is very low and firms are surviving (even succeeding) by following conventional (old fashioned) management strategies.

Abstract
In this paper we discuss how probabilistic wind power forecasts can serve as an important tool to efficiently address wind power uncertainty in power system operations. We compare different probabilistic forecasting and scenario reduction methods, and test the resulting forecasts on a stochastic unit commitment model. The results are compared to deterministic unit commitment, where dynamic operating reserve requirements can also be derived from the probabilistic forecasts. In both cases, the use of probabilistic forecasts contributes to improve the system performance in terms of cost and reliability. © 2011 IEEE.