Abstract
Logtalk, an object oriented logic programming language, provides experimental support for multi-threading programming with selected back-end Prolog compilers. By making use of core, low-level Prolog predicates that interface with operating-system native threads, Logtalk provides a high-level set of directives and predicates that allows programmers to easily take advantage of modern multi-processor and multi-core computers without worrying about the details of creating, synchronizing, or communicating with threads. Logtalk multi-threading programming features include support for concurrent calls akin to and-parallelism and or-parallelism, non- deterministic thread goals, asynchronous calls, and predicate synchronization. The integration with the Logtalk object-oriented features allows objects to send and receive both synchronous and asynchronous messages and to call local predicates concurrently. Logtalk multi-threading features are orthogonal to object-oriented concepts and can be useful even in the context of plain Prolog.

Abstract
This work describes an economic analysis of the inclusion of pumped storage in a small island system that has abundant renewable energy available but that at times cannot accept all of this power because of limits imposed by security criteria. The question of whether or how much pumped storage to include is addressed by formulating a linear programming optimization problem. The stochastic nature of load and renewable production is addressed using scenarios developed through fuzzy clustering. Both the unit capacity in MW and the reservoir storage capacity in MWh are optimized, and optimal operating strategies for the scenarios are produced. Results showed that including pumped storage can be an effective means of allowing larger penetration of intermittent renewable energy sources, improving both the dynamic security and the economic operation of a test system. Including the dynamic security criteria in the economic question of dimensioning the pumped storage unit proved to make a significant difference in the optimal pumped storage capacity.

Abstract
In this paper, we consider the single machine weighted tardiness scheduling problem with sequence-dependent setups. We present heuristic algorithms based on the beam search technique. These algorithms include classic beam search procedures, as well as the filtered and recovering variants. Previous beam search implementations use fixed beam and filter widths. We consider the usual fixed width algorithms, and develop new versions that use variable beam and filter widths. The computational results show that the beam search versions with a variable width are marginally superior to their fixed value counterparts, even when a lower average number of beam and filter nodes is used. The best results are given by the recovering beam search algorithms. For large problems, however, these procedures require excessive computation times. The priority beam search algorithms are much faster, and can therefore be used for the largest instances.

Abstract
This paper presents and analyzes an incremental system for clustering streaming time series. The Online Divisive-Agglomerative Clustering (ODAC) system continuously maintains a tree-like hierarchy of clusters that evolves with data, using a top-down strategy. The splitting criterion is a correlation-based dissimilarity measure among time series, splitting each node by the farthest pair of streams. The system also uses a merge operator that reaggregates a previously split node in order to react to changes in the correlation structure between time series. The split and merge operators are triggered in response to changes in the diameters of existing clusters, assuming that in stationary environments, expanding the structure leads to a decrease in the diameters of the clusters. The system is designed to process thousands of data streams that flow at a high rate. The main features of the system include update time and memory consumption that do not depend on the number of examples in the stream. Moreover, the time and memory required to process an example decreases whenever the cluster structure expands. Experimental results on artificial and real data assess the processing qualities of the system, suggesting a competitive performance on clustering streaming time series, exploring also its ability to deal with concept drift.

Abstract
In this paper we describe a project that will develop and evaluate a model for learning object repositories, relating it to other projects. The model includes the semantic characterisation of learning objects and a description of how to use them in learning and teaching practices. This way we believe it will be possible to enhance reusability. The IMS Learning Design specification has been used in this work to pragmatically characterise Learning Objects (LOs). For each LO, practical examples are provided to demonstrate how it may be reused. © Copyright © 2008, Inderscience Publishers.

Abstract
This paper proposes a new approach for the automatic segmentation of the carotid adventitia in longitudinal B-scans, with and without the presence of plaque. The top and bottom adventitia contours are jointly detected with a 3D dynamic programing scheme that searches for the best pair of boundaries according to a specified fuzzy cost function. Some discriminating features of the adventitia in B-mode images are used to reduce the attraction by other edges. The final contours are filtered with a smoothing spline fitting. The proposed approach was quantitatively evaluated in a set of 38 images. In order to avoid high correlation of the results, a maximum of two images was selected from each patient. The carotid boundaries manually traced by a medical expert were used as the ground truth. Several statistics show that the proposed algorithm gives good results in most of the cases, including many poor quality images. Examples of the detected contours are presented and compared with the ground truth.