To characterize the macroscopic behavior of Diesel sprays and to validate and extend for current high-pressure injection systems the correlations existent in the literature, it is necessary to determine the spray geometry accurately, at least in terms of spray tip penetration and cone angle. These parameters are measured by analyzing Diesel spray images and are highly sensitive to the correct edge determination. An algorithm for segmentation of color images based on a likelihood ratio test is presented. This algorithm is compared with others available in the literature and has been validated, even for adverse experimental conditions. The experimental facilities, optical layouts, and image-processing algorithms are described.

Nowadays, there is increasing interest in the development of teamwork skills in the educational context. This growing interest is motivated by its pedagogical effectiveness and the fact that, in labour contexts, enterprises organize their employees in teams to carry out complex projects. Despite its crucial importance in the classroom and industry, there is a lack of support for the team formation process. Not only do many factors influence team performance, but the problem becomes exponentially costly if teams are to be optimized. In this article, we propose a tool whose aim it is to cover such a gap. It combines artificial intelligence techniques such as coalition structure generation, Bayesian learning, and Belbin's role theory to facilitate the generation of working groups in an educational context. This tool improves current state of the art proposals in three ways: i) it takes into account the feedback of other teammates in order to establish the most predominant role of a student instead of self-perception questionnaires; ii) it handles uncertainty with regard to each student's predominant team role; iii) it is iterative since it considers information from several interactions in order to improve the estimation of role assignments. We tested the performance of the proposed tool in an experiment involving students that took part in three different team activities. The experiments suggest that the proposed tool is able to improve different teamwork aspects such as team dynamics and student satisfaction.

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Technology Management Centres provide technological and customer support services for private or public organisations. Commonly, these centres offer support using a helpdesk software that facilitates the work of their operators. In this paper, a CBR module that acts as a solution recommender for customer support environments is presented. The CBR module is flexible and multi-domain, in order to be easily integrable with any existing helpdesk software in the company.

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