Several methodologies and tools have been proposed for Web applications design and development. However, traditional Web applications are still inadequate to support the interaction and presentation functionalities demanded by the users.Recently, Rich Internet Applications (RIAs) have been proposed as an answer to these problems providing new levels of interactivity and presentation. The use of RIAs is growing exponentially; nevertheless there is a lack of full development methodologies in this sense.This document outlines the main features which should be modeled in RIAs and proposes an evaluation process in order to obtain the suitability of a methodology to accomplish this goal. We also use this process to evaluate the suitability of several existing Web, Multimedia and Hypermedia methodologies to demonstrate that each one accomplishes only few RIA features, so new methodologies or extensions of the actual methodologies become necessary.

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During the last years, Web Models have demonstrated their utility facilitating the development of Web Applications. Nowadays, Web Applications have grown in functionality and new necessities have arisen. Rich Internet Applications (RIAs) have been recently proposed as the response to these necessities. However, present Web Models seem to be incomplete for modelling the new features appearing in RIAs (high interactivity, multimedia synchronization, etc). In this paper we propose a Model Driven Method, validated by implementation, called RUX-Model that gives support to multi-level interface specifications for multi-device RIAs.

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Recently, the US Department of Transportation's Federal Aviation Administration and other international organizations have proposed a set of requirements for small unmanned aerial vehicles (UAVs) to operate for nonrecreational purposes. However, existing UAV architectures fulfill only some of the established requirements, and not all in one solution. This article presents an event-driven serviceoriented architecture that allows autonomous UAVs to satisfy all these requirements and to detect critical situations, performing real-time decision making.

Higher Education plays a principal role in the changing and complex world of today, and there has been rapid growth in the scientific literature dedicated to predicting students' academic success or risk of dropout thanks to advances in Data Mining techniques. Degrees such as Computer Science in particular are in ever greater demand. Although the number of students has increased, the number graduating is still not enough to provide society with as many as it requires. This study contributes to reversing this situation by introducing an approach that not only predicts the dropout risk or students' performance but takes action to help both students and educational institutions. The focus is on maximizing graduation rates by constructing a Recommender System to assist students with their selection of subjects. In particular, the challenge is addressed of constructing reliable Recommender Systems on the basis of data which are both sparse and few in quantity, imbalanced, and anonymized, and which might have been stored under imperfect conditions. This approach is successfully applied to create a Recommender System using a realworld dataset from a public Spanish university containing performance data of a Computer Science degree course, demonstrating its successful application in real environments. The construction of a support system based on that approach is described, its results are evaluated, and its implications for students' academic achievement, and for institutions' graduation rates are discussed. Through the construction of this decision support system for students, we intend to increase the graduation rates and lower the dropout rate.