Abstract
The secondary mirror unit of the European Extremely Large Telescope (ELT) is supported by six 50-cm wide spiders, providing the necessary stiffness to the structure while minimising the obstruction of the beam. The deformable quaternary mirror (M4) contains over 5000 actuators on a nearly hexagonal pattern. The reflective surface of M4 itself is composed of a segmented thin shell made of 6 discontinuous petals. This segmentation of the telescope pupil will create areas of phase isolated by the width of the spiders on the wavefront sensor (WFS) detector, breaking the spatial continuity of the wavefront data. The poor sensitivity of the Pyramid WFS (PWFS) to differential piston (or of any WFS sensitive to the derivative of the wavefront such as the Shack-Hartmann) will lead to badly seen and therefore uncontrollable differential pistons between these areas. In close loop operation, differential pistons between segments will settle around integer values of the average sensing wavelength lambda. The differential pistons typically range from one to tens of time the sensing wavelength and vary rapidly over time, leading to extremely poor performance. In addition, aberrations created by atmospheric turbulence will naturally contain some differential piston between the segments. This differential piston is typically a relatively large multiple of the sensing wavelength, especially for 40 m class telescopes. Trying to directly remove the entire piston contribution over each of the DM segments will undoubtedly lead to poor performance. In an attempt to reduce the impact of unwanted differential pistons that are injected by the AO correction, we compare three different approaches. A first step is to try to limit ourselves to use only the information measured by the PWFS, in particular by reducing the modulation. We show that using this information sensibly is important but it is only a prerequisite and will not be sufficient. We discuss possible ways of improvement by removing the unwanted differential pistons from the DM commands while still trying to maintain the atmospheric segment-piston contribution by using prior information. A second approach is based on phase closure of the DM commands and assumes the continuity of the correction wavefront over the entire unsegmented pupil. The last approach is based on the pair-wise slaving of edge actuators and shows the best results. We compare the performance of these methods using realistic end-to-end simulations. We find that pair-wise slaving leads to a small increase of the total wavefront error, only adding between 20-45 nm RMS in quadrature for seeing conditions between 0.45"-0.85". Finally, we discuss the possibility of combining the different proposed solutions to increase robustness.

Abstract

Abstract
An analytical and experimental analysis on the Trombe wall thermal performance was carried out for different conditions of ventilation openings and occlusion device operation. Experimental results allowed to determine temperature fluctuation, heat flux, heat delay and air velocity at the ventilation openings. A calculation methodology was applied to estimate the heat gains and losses through the system using experimental data. Ventilation openings and occlusion device effect was immediately visible in the temperature fluctuation and, consequentelly, in the heat gains and losses. Experimental.results showed that, when there was no occlusion device, massive wall external surface temperature values exceeded 60 degrees C and, when it was placed, reduced to 30 degrees C or less. Heat took almost 3 times more to achieve the interior of the test cell when the ventilation openings were closed. Air velocity increased following a diagonally pattern from the bottom to the top of the ventilation opening and its values varied between 0.10 m/s and 0.40 m/s, leading to air flow values between 0.002 m(3)/s and 0.008 m(3)/s. The calculation methodology application allowed to determine the total gains through the system for a continuous period. The impact of the system operation on the different thermal performance parameters was observed.

Abstract
The national panel for higher education is a big social impact event, one which mobilizes thousands of candidates. However, the heterogeneity of the Portuguese university and polytechnic infrastructure and the sheer dimension of the reality in study makes an eventual interpretation of the data obtained from that panel, and the official data only present generic and global information. This work will bring to light information with added value to those responsible on these institutions, in their decision taking processes by extracting data from the education minister site and processing it using data mining techniques.

Abstract
This article presents an optimal estimator for discrete-time systems disturbed by output white noise, where the proposed algorithm identifies the parameters of a Multiple Input Single Output LPV State Space model. This is an LPV version of a class of algorithms proposed elsewhere for identifying LTI systems. These algorithms use the matchable observable linear identification parameterization that leads to an LTI predictor in a linear regression form, where the ouput prediction is a linear function of the unknown parameters. With a proper choice of the predictor parameters, the optimal prediction error estimator can be approximated. In a previous work, an LPV version of this method, that also used an LTI predictor, was proposed; this LTI predictor was in a linear regression form enablin, in this way, the model estimation to be handled by a Least-Squares Support Vector Machine approach, where the kernel functions had to be filtered by an LTI 2D-system with the predictor dynamics. As a result, it can never approximate an optimal LPV predictor which is essential for an optimal prediction error LPV estimator. In this work, both the unknown parameters and the state-matrix of the output predictor are described as a linear combination of a finite number of basis functions of the scheduling signal; the LPV predictor is derived and it is shown to be also in the regression form, allowing the unknown parameters to be estimated by a simple linear least squares method. Due to the LPV nature of the predictor, a proper choice of its parameters can lead to the formulation of an optimal prediction error LPV estimator. Simulated examples are used to assess the effectiveness of the algorithm. In future work, optimal prediction error estimators will be derived for more general disturbances and the LPV predictor will be used in the Least-Squares Support Vector Machine approach.

Abstract
The focus of this paper is to elaborate collaborative business process monitoring within virtual factory (VF) environment in a smarter way. This process monitoring is tracked through visualisation over a user interface such as 'dashboard'. This research briefly provides all aspects of implementing process monitoring through the dashboard user interface and explains technical aspects of monitoring. The dashboard features state-of-the art business intelligence and provides data visualisation, user interfaces and means to support VF partners to execute collaborative processes. With advanced visualisations that produce quality graphics it offers a variety of information visualisations that brings the process data to life with clarity. This data visualisation provides critical operational matrices (e.g. KPIs) required to manage virtual factories. Key reporting outputs such as KPIs and day-to-day operational data can be used to monitor and empower partners' processes that help to drive collaborative decisions e VF broker or partners' also retain full flexibility to create, deploy and maintain their own dashboards using an easy to understand wizard-driven widget and an extensive array of data visualisation components such as gauges, charts, maps, etc. Various technical aspects of this dashboard user interface portal are elaborated within the scope of this research such as installation instructions, technical requirements for the users and developers, execution and usage aspects, limitations and future works. In addition to the dashboard user interface portal this research also investigates the VF life cycle and provides architectural framework for VF. The research work highlighted in this paper is conceptualised, developed, and validated within the scope of the European Commission NMP priority of the Seventh RTD Framework Programme for the ADVENTURE (ADaptive Virtual ENterprise ManufacTURing Environment) project.