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Online social networks present themselves as valuable information sources about their users and their respective behaviours and interests. Many researchers in data mining have analysed these types of data, aiming to find interesting patterns. This paper addresses the problem of identifying and displaying tweet profiles by analysing multiple types of data: spatial, temporal, social and content. The data mining process that extracts the patterns is composed by the manipulation of the dissimilarity matrices for each type of data, which are fed to a clustering algorithm to obtain the desired patterns. This paper studies appropriate distance functions for the different types of data, the normalization and combination methods available for different dimensions and the existing clustering algorithms. The visualization platform is designed for a dynamic and intuitive usage, aimed at revealing the extracted profiles in an understandable and interactive manner. In order to accomplish this, various visualization patterns were studied and widgets were chosen to better represent the information. The use of the project is illustrated with data from the Portuguese twittosphere.

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The aim of the present work was to evaluate the possibility of using zinc-air batteries in intraoral medical devices. We analyzed the electrical behavior of zinc-air batteries when submitted to different levels of temperature, humidity, and limited quantities of air. The experimental setup was divided in three different parts. Firstly, a set of batteries were tested within a climatic chamber and subjected to discharging tests similar to those recommended by the manufacturer. The climatic chamber allowed an accurate variation of humidity and temperature. Secondly, the batteries were placed in a small prototype of intraoral medical device and tested in the absence of air. Lastly, we used a robot arm to repeatedly immerse the prototype in artificial saliva. The results obtained demonstrated the viability of zinc-air batteries as a power solution for intraoral medical devices, as they tolerate high levels of humidity and are capable of working with limited quantities of air. In addition, this kind of battery presents a volume to electrical capacity ratio more than three times higher than lithium batteries, which may open important improvement for powered medical devices.

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Nowadays, different techniques are available for manufacturing full-arch implantsupported prosthesis, many of them based on an impression procedure. Nevertheless, the long-term success of the prosthesis is highly influenced by the accuracy during such process, being affected by factors such as the impression material, implant position, angulation and depth. This paper investigates the feasibility of a 3D electromagnetic motion tracking system as an acquisition method for modeling such prosthesis. To this extent, we propose an implant acquisition method at the patient mouth, using a specific prototyped tool coupled with a tracker sensor, and a set of calibration procedures (for distortion correction and tool calibration), that ultimately obtains combined measurements of the implant's position and angulation, and eliminating the use of any impression material. However, in the particular case of the evaluated tracking system, the order of magnitude of the obtained errors invalidates its use for this specific application. © 2014 Taylor & Francis Group.