Abstract

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
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.