Abstract
This paper envisages showing the potential of innovative non-invasive techniques based on affordable and easily operated instrumentation as well as user-friendly computer aided algorithms in the screening of cardiovascular (CV) diseases. These techniques are based on the assumption that arterial stiffness is currently an important predicator of the CV diseases development and can be assessed by analyzing the arterial pressure waveform (APW). A previously developed PZ based device for non-invasive APW capture is currently under test in clinical environment, using a heterogeneous population constituted by healthy and unhealthy subjects. A dedicated Matlab analysis tool was designed and developed to extract relevant information and further APW analysis. Several recordings of the APW in the same day and in consecutive months are being performed by trained observers, to evaluate its reproducibility. Data mining analysis is subsequently the last task where the Weka 3-6-5 package software is used. The usefulness of developing data mining algorithms for cardiovascular applications can benefit the CV screenings contributing for the early identification of arterial stiffness related patterns.

Abstract
The basic objective of Transmission Expansion Planning (TEP) is to schedule a number of transmission projects along an extended planning horizon minimizing the network construction and operational costs while satisfying the requirement of delivering power safely and reliably to load centres along the horizon. This principle is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. This paper describes a new approach to solve the dynamic TEP problem, based on an improved discrete integer version of the Evolutionary Particle Swarm Optimization (EPSO) meta-heuristic algorithm. The paper includes sections describing in detail the EPSO enhanced approach, the mathematical formulation of the TEP problem, including the objective function and the constraints, and a section devoted to the application of the developed approach to this problem. Finally, the use of the developed approach is illustrated using a case study based on the IEEE 24 bus 38 branch test system.

Abstract
When searching for patterns on data streams, we come across perennial (dynamic) objects that evolve over time. These objects are encountered repeatedly and each time with different definition and values. Examples are (a) companies registered at stock exchange and reporting their progress at the end of each year, and (b) students whose performance is evaluated at the end of each semester. On such data, domain experts also pose questions on how the individual objects will evolve: would it be beneficial to invest in a given company, given both the company's individual performance thus far and the drift experienced in the model? Or, how will a given student perform next year, given the performance variations observed thus far? While there is much research on how models evolve/change over time [Ntoutsi et al., 2011a], little is done to predict the change of individual objects when the states are not known a priori. In this work, we propose a framework that learns the clusters to which the objects belong at each moment, uses them as ad hoc states in a state-transition graph, and then learns a mixture model of Markov Chains, which predicts the next most likely state/cluster per object. We report on our evaluation on synthetic and real datasets. © Springer-Verlag Berlin Heidelberg 2012.

Abstract
The goal of ensemble regression is to combine several models in order to improve the prediction accuracy in learning problems with a numerical target variable. The process of ensemble learning can be divided into three phases: the generation phase, the pruning phase, and the integration phase. We discuss different approaches to each of these phases that are able to deal with the regression problem, categorizing them in terms of their relevant characteristics and linking them to contributions from different fields. Furthermore, this work makes it possible to identify interesting areas for future research.

Abstract
Being political entrepreneurship an issue that has received a high distinction in literature, its empirical evidence is scarce largely derived from limitations of operationalizing the concept. In this article we operationalize local political entrepreneurship considering three main dimensions: 1) fundraising, 2) the possession and/or construction of infrastructure to support business and social activities, and 3) the supply of support activities and services required for activities business and social. Based on information gathered from 108 Portuguese municipalities, we quantified the overall political entrepreneurship and in the three dimensions mentioned presenting a ranking of municipalities and the corresponding NUTS III regions. Additionally, through the estimation of econometric specifications, we conclude that the local political entrepreneurship has a positive and significant but indirect impact, through the human capital of employed population, on the creation of new businesses, particularly the knowledge-intensive ones. In other words, the impact of entrepreneurial actions by the local political authorities is much higher the greater the proportion of the employed population with higher education in the region. On this basis, it is not enough that local authorities are 'entrepreneurial'; in addition to the public actions of fundraising, construction of infrastructures and promotion of activities for business promotion, one needs to ensure that the region is endowed with an adequate level of absorptive capacity, i.e., investment in human capital and R&D activities.

Abstract
This paper describes a Dynamic Model of the electricity sector that can be used to simulate the evolution of some key variables on the long term, namely the evolution of the electricity price, of the demand and of the capacity factors of the technologies in the generation mix. This model can be used in different ways and by several agents, for instance to estimate the impact on the electricity price of the increasing presence of renewable power stations, namely using wind power and PV systems. In several countries these stations are paid feed-in tariffs with a fixed price but in some cases this scheme is under discussion and there are opinions that payments determined by the market price are more adequate and would bring fewer costs to final consumers. Such a change has to be carefully evaluated given that the presence of renewable stations bidding at an infra marginal price will affect the price itself. The model described in this paper can be used in a profitable way both by governmental agencies when preparing or studying alternative remuneration schemes to renewable stations or by promoters themselves to get more insight to the profitability of their investments, namely if the fixed feed-in tariffs in force in several countries are changed.