This research intends to give insights on the pattern aggregation of wind energy conversion systems technologies through identification of homogeneous groups within a set of wind farms installed in Portugal. Pattern aggregation is performed using Hierarchical Cluster Analysis followed by Discriminant Analysis, in order to validate the results produced by the first one. The clustering support matrix uses three independent variables: installed capacity, net production and capacity factor, in a per year basis. Cluster labelling allows the identification of two homogenous groups of wind farms, whose main attributes are based on the technological conversion system trend: (1) asynchronous generator based technology and (2) direct driven synchronous generator based technology, with higher capacity factors.
With this study, we intended to implement a management tool to assess the degree of importance and performance of the Critical Success Factors (CSF), from the customers' perspective. For this purpose, it was used the Importance vs. Performance Matrix proposed by Martilla and James [1]. This allows through a representation on a Cartesian system identify the CSF where an organization should focus, reduce or maintain their efforts and also evaluate the CSF where the largest deviations occur between what is important to the client and which he is receiving. For data collection, it was used a questionnaire applied randomly to 225 company customers and it was assumed a sampling error of 5.8% and a significance level of 5%. The results allowed to observe that the CSF: Price Competitiveness and Strategic Management of the Company, are well positioned in the Quadrant where it is suggested that the company should continue the good work, which is a good indicator for the company.
The general purpose of this chapter is to describe and analyse the financing
phenomenon of crowdfunding and to investigate the relations between crowdfunders,
project creators and crowdfunding websites. More specifically, it also
intends to describe the profile differences between major crowdfunding platforms,
such as Kickstarter and Indiegogo. The results showed that both Kickstarter and
Indiegogo are among the most popular crowdfunding platforms. Both of them have
thousands of users and these users are generally satisfied. Most of them rely on
individual approaches for crowdfunding. Despite this, Kickstarter and Indiegogo
could benefit from further improving their services. Furthermore, according to the
results, it was possible to observe that there is a direct and positive relationship
between the money needed for the projects and the money collected from the investors
for the projects, per platform.
This work is supported by the Fundação para a Ciência e Tecnologia (FCT) under the projects number
UID/GES/4752/2016 and UID/GES/04630/2013.
Voice acoustic analysis is becoming more and more usefúl in diagnosis of voice disorders or laryngological pathologies. The facility to record a voice sigiial is an advantage over other invasive techniques. This paper presents the statistical analyzes ofa set of voice parameters like jitter, shimmer and HNR over a 4 groups of subjects vvith dysphonia, fünctional dysphonia, hyperfünctional dysphonia, and psychogenic dysphonia and a control group. No statistical signifícance differences over pathologic groups were found but clear tendencies can be seen between pathologic and control group. The tendencies indicates this parameters as a good features to be used in an intelligent diagnosis system, moreover the jitter and shimmer parameters measured over different tones and vowels.
Voice acoustic analysis is becoming nowadays a useful tool for detection of laryngological pathologies. This techniques enables a non-invasive and low cost assessment of voice disorders allowing a more efficient fast and objective diagnosis, permitting the patients to get a suitable treatment. In this work, the best predictors/parameters for diagnose of dysphonia were experimented. A vector made up of 4 Jitter parameters, 4 Shimmer parameters and Harmonic to Noise Ratio (HNR), determined from 3 different vowels at 3 different tones, in a total of 81 features, was used. Variable selection and dimension reduction techniques such as hierarchical clustering, multilinear regression analysis and principal component analysis (PCA) was applied. For the classification models based on artificial neural network (ANN) was used. The methods/models found allowed us to obtain an Accuracy of 100% for female voices and 90% for male voices using only Jitter Shimmer and HNR parameters
