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.

Abstract
Time has strong influence on web search. The temporal intent of the searcher adds an important dimension to the relevance judgments of web queries. However, lack of understanding their temporal requirements increases the ambiguity of the queries, turning retrieval effectiveness improvements into a complex task. In this paper, we propose an approach to classify web queries into four different categories considering their temporal ambiguity. For each query, we develop features from its search volumes and related queries using Google trends and its related top Wikipedia pages. Our experiment results show that these features can determine temporal ambiguity of a given query with high accuracy. We have demonstrated that a Multilayer Perceptron Networks can achieve better results in classifying temporal class of queries in comparison to other classifiers.

Abstract
Context. Canary is the multi-object adaptive optics (MOAO) on-sky pathfinder developed in the perspective of multi-object spectrograph on extremely large telescopes (ELTs). In 2013, Canary was operated on-sky at the William Herschel telescope (WHT), using three off-axis natural guide stars (NGS) and four off-axis Rayleigh laser guide stars (LGS), in open-loop, with the on-axis compensated turbulence observed with a H-band imaging camera and a Truth wave-front sensor (TS) for diagnostic purposes. Aims. Our purpose is to establish a reliable and accurate wave-front error breakdown for LGS MOAO. This will enable a comprehensive analysis of Canary on-sky results and provide tools for validating simulations of MOAO systems for ELTs. Methods. To evaluate the MOAO performance, we compared the Canary on-sky results running in MOAO, in single conjugated adaptive optics (SCAO) and in ground layer adaptive optics (GLAO) modes, over a large set of data acquired in 2013. We provide a statistical study of the seeing. We also evaluated the wave-front error breakdown from both analytic computations, one based on a MOAO system modelling and the other on the measurements from the Canary TS. We have focussed especially on the tomographic error and we detail its vertical error decomposition. Results. We show that Canary obtained 30.1%, 21.4% and 17.1% H-band Strehl ratios in SCAO, MOAO and GLAO respectively, for median seeing conditions with 0.66? of total seeing including 0.59? at the ground. Moreover, we get 99% of correlation over 4500 samples, for any AO modes, between two analytic computations of residual phase variance. Based on these variances, we obtain a reasonable Strehl-ratio (SR) estimation when compared to the measured IR image SR. We evaluate the gain in compensation for the altitude turbulence brought by MOAO when compared to GLAO.