Abstract
With the increasing number of devices carried by users and the emergence of more dynamic network scenarios, new solutions have to be developed to support ubiquitous communications. The need for dynamic and transparent auto-configuration and adaptation of applications, devices and networks of devices to a wide variety of events is one of the major challenges. This paper presents a protocol to be used with notification systems, to support the registration, subscription and distribution of generic events among heterogeneous and independent entities, thus addressing the requirements of future communication scenarios.

Abstract
If, as a well-known aphorism states, modelling is for reasoning, this paper is an attempt to define and apply a formal semantics to UML sequence diagrams in order to enable rigourous reasoning about them. Actually, model transformation plays a fundamental role in the process of software development, in general, and in model driven engineering in particular. Being a de facto standard in this area, UML is no exception, even if the number and diversity of diagrams expressing UML models makes it difficult to base its semantics on a single framework. This paper builds on previous attempts to base UML semantics in a coalgebraic setting and illustrates the application of the proposed framework to reason about composition and refactoring of sequence diagrams. © 2008 IEEE.

Abstract
It has been argued that the common ancestor of about 75% of all dicots possessed an S-RNase-based gametophytic self-incompatibility (GSI) system. S-RNase genes should thus be found in most plant families showing GSI. The S-RNase gene (or a duplicate) may also acquire a new function and thus genes belonging to the S-RNase lineage may also persist in plant families without GSI. Nevertheless, sequences that belong to the S-RNase lineage have been found in the Solanaceae, Scrophulariaceae, Rosaceae, Cucurbitaceae, and Fabaceae plant families only. Here we search for new sequences that may belong to the S-RNase lineage, using both a phylogenetic and a much faster and simpler amino acid pattern-based approach. We show that the two methods have an apparently similar false-negative rate of discovery (similar to 10%). The amino acid pattern-based approach produces about 15% false positives. Genes belonging to the S-RNase lineage are found in three new plant families, namely, the Rubiaceae, Euphorbiaceae, and Malvaceae. Acquisition of a new function by genes belonging to the S-RNase lineage is shown to be a frequent event. A putative S-RNase sequence is identified in Lotus, a plant genus for which molecular studies on GSI are lacking. The hypothesis of a single origin for S-RNase-based GSI (before the split of the Asteridae and Rosidae) is further supported by the finding of genes belonging to the S-RNase lineage in some of the oldest lineages of the Asteridae and Rosidae, and by Baysean constrained tree analyses.

Abstract
A systematic survey was carried out in an unbiased sample of 815 protein chains with a maximum of 20% homology selected from the Protein Data Bank, whose structures were solved at a resolution higher than 1.6 angstrom and with a R-factor lower than 25%. A set of 5556 subsequences with a-helix or 3(10)-helix motifs was extracted from the protein chains considered. Global and local propensities were then calculated for all possible amino acid pairs of the type (i, i + 1), (i, i + 2), (i, i + 3), and (i, i + 4), starting at the relevant helical positions N1, N2, N3, C3, C2, C1, and N-int (interior positions), and also at the first nonhelical positions in both termini of the helices, namely, N-cap and C-cap. The statistical analysis of the propensity values has shown that pairing is significantly dependent on the type of the amino acids and on the position of the pair. A few sequences of three and four amino acids were selected and their high prevalence in helices is outlined in this work. The Glu-Lys-Tyr-Pro sequence shows a peculiar distribution in proteins, which may suggest a relevant structural role in alpha-helices when Pro is located at the C-cap position. A bioinformatics tool was developed, which updates automatically and periodically the results and makes them available in a web site.

Abstract

Abstract
Many real life problems require the classification of items into naturally ordered classes. These problems are traditionally handled by conventional methods intended for the classification of nominal classes where the order relation is ignored. This paper introduces a new machine learning paradigm intended for multi-class classification problems where the classes are ordered. The theoretical development of this paradigm is carried out under the key idea that the random variable class associated with a given query should follow a unimodal distribution. In this context, two approaches are considered: a parametric, where the random variable class is assumed to follow a specific discrete distribution; a nonparametric, where the random variable class is assumed to be distribution-free. In either case, the unimodal model can be implemented in practice by means of feedforward neural networks and support vector machines, for instance. Nevertheless, our main focus is on feedforward neural networks. We also introduce a new coefficient, r(int), to measure the performance of ordinal data classifiers. An experimental study with artificial and real datasets is presented in order to illustrate the performances of both parametric and nonparametric approaches and compare them with the performances of other methods. The superiority of the parametric approach is suggested, namely when flexible discrete distributions, a new concept introduced here, are considered.