Abstract
Heart Rate Variability (HRV) data exhibit long memory and time-varying conditional variance (volatility). These characteristics are well captured using Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalised AutoRegressive Conditional Heteroscedastic (GARCH) errors, which are an extension of the AR models usual in the analysis of HRV. GARCH models assume that volatility depends only on the magnitude of the shocks and not on their sign, meaning that positive and negative shocks have a symmetric effect on volatility. However, HRV recordings indicate further dependence of volatility on the lagged shocks. This work considers Exponential GARCH (EGARCH) models which assume that positive and negative shocks have an asymmetric effect (leverage effect) on the volatility, thus better copping with complex characteristics of HRV. ARFIMA-EGARCH models, combined with adaptive segmentation, are applied to 24 h HRV recordings of 30 subjects from the Noltisalis database: 10 healthy, 10 patients suffering from congestive heart failure and 10 heart transplanted patients. Overall, the results for the leverage parameter indicate that volatility responds asymmetrically to values of HRV under and over the mean. Moreover, decreased leverage parameter values for sick subjects, suggest that these models allow to discriminate between the different groups.

Abstract
In the pursuit of highly available systems, storage systems began offering eventually consistent data models. These models are suitable for a number of applications but not applicable for all. In this paper we discuss a system that can offer a eventually consistent data model but can also, when needed, offer a strong consistent one.

Abstract
This study uses the InSAR technique to analyse ground subsidence due to intensive exploitation of an aquifer for agricultural and urban purposes in the Montellano town (SW Spain). The detailed deformation maps clearly show that the spatial and temporal extent of subsidence is controlled by piezometric level fluctuations and the thickness of compressible sediments. The total vertical displacement measured with multi-temporal InSAR, between 1992 and 2010, is 33 mm that corresponds with a decrease of 43 m in the groundwater level. This technique allows monitoring the evolution of settlement related to water level fall in an area where subsidence has not yet been reported by population or authorities through infrastructure damages and to discuss the effect of the aquifer recovery. This information is, therefore, valuable for implementing effective groundwater management schemes and land-use planning and to propose new building regulations in the most affected areas.

Abstract
This paper addresses the tracking problem of the state variables of a nonlinear planar dynamic model of an overactuated electric vehicle with four-wheel independent drive (4WID) topology. In order to track the state variables of the system it is proposed a new sliding mode controller based on a nonlinear planar model. The controller explores the overactuated system in order to redistribute the control effort to the remaining actuators when a fault occurs. Although the system has multiple solutions due to the access of the torque applied in each wheel independently, there could be particular fault events where the remaining healthy actuators may not be able to maintain the system stability. In those particular cases the inclusion of the steering control variable is an important advantage as it allows the controller to manipulate the control effort in any directions. The proposed controller is validated in various driving scenarios with different fault schemes. The simulations are carried out with a high-fidelity vehicular model provided by the simulation software Carsim in co-simulation with Matlab/Simulink.

Abstract
The increasing penetration of renewable generation brings a significant escalation of intermittency to the power and energy system. This variability requires a new degree of flexibility from the whole system. The active participation of small and medium players becomes essential in this context. This is only possible by using adequate forecasting techniques applied both to the consumption and to generation. However, the large number of incontrollable factors, such as the presence of consumers in the building, the luminosity, or external temperature, makes the forecasting of energy consumption an arduous task. This paper addresses the electrical energy consumption forecasting problem, by studying the correlation between the solar radiation and the electrical consumption of lights. This study is performed by means of three forecasting methods, namely a multi-layer perceptron artificial neural network, a support vector regression method, and a linear regression method. The performed studies are analyzed using data gathered from a real installation - campus of the Polytechnic of Porto, in real time.

Abstract
Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment, considering two different dimensions: (1) to organize the education process as a social network-based process; and (2) to analyze the students' interactions in the context of evaluation of the students learning performance. The objective of this paper is to present a new model for students evaluation based on their behavior during the course and its validation in comparison with the traditional model of students' evaluation. The validation of the new evaluation model is made through an analysis of the correlation between social network analysis measures (degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and average tie strength) and the grades obtained by students (grades for quality of work, grades for volume of work, grades for diversity of work, and final grades) in a social network-based engineering education. The main finding is that the obtained correlation results can be used to make the process of the students' performance evaluation based on students interactions (behavior) analysis, to make the evaluation partially automatic, increasing the objectivity and productivity of teachers and allowing a more scalable process of evaluation. The results also contribute to the behavioural theory of learning performance evaluation. More specific findings related to the correlation analysis are: (1) the more different interactions a student had (degree centrality) and the more frequently the student was between the interaction paths of other students (betweenness centrality), the better was the quality of the work; (2) all five social network measures had a positive and strong correlation with the grade for volume of work and with the final grades; and (3) a student with high average tie strength had a higher grade for diversity of work than those with low ties.