Business Process Management (BPM), acknowledging business processes to be key assets of business actors, provides a number of techniques and approaches for improving various aspects of these processes. Recently the concept of Semantic Business Process Management (SBPM) appeared leveraging BPM to the next level by adding the semantic layer to the traditional BPM approach. Still, processes definitions are static and thus pose risk that instances of such processes may not take into consideration the changed business circumstances. Therefore being able to actively modify instances of semantic business processes based on the changes in their economical environment opens for much better efficiency and higher value added of these processes. Using mobile context information can be one of ways of gathering information about the changed economical context.

MOTIVATION: Biologists frequently align multiple biological sequences to determine consensus sequences and/or search for predominant residues and conserved regions. Particularly, determining conserved regions in an alignment is one of the most important activities. Since protein sequences are often several-hundred residues or longer, it is difficult to distinguish biologically important conserved regions (motifs or domains) from others. The widely used tools, Logos, Al2co, Confind, and the entropy-based method, often fail to highlight such regions. Thus a computational tool that can highlight biologically important regions accurately will be highly desired. RESULTS: This paper presents a new scoring scheme ARCS (Aggregated Related Column Score) for aligned biological sequences. ARCS method considers not only the traditional character similarity measure but also column correlation. In an extensive experimental evaluation using 533 PROSITE patterns, ARCS is able to highlight the motif regions with up to 77.7% accuracy corresponding to the top three peaks. AVAILABILITY: The source code is available on http://bio.informatics.indiana.edu/projects/arcs and http://goldengate.case.edu/projects/arcs

This account presents the foundation of PROTEUS project programme, which consists in designing and modeling of a distributed network orientation platform for integration of maintenance applications. The presentation is focused on basic requirements defining project's rationale and on the development of the platform.

We demonstrate the practical ability of neural networks (NNs) trained in a supervised mode to extract useful control "knowledge" from a large, high-dimensional empirical database, and then to deliver almost optimal control in "real time". In particular, this paper describes experiments with NN-based controllers for allocating bandwidth capacity in a telecommunications network (SDH). This system was proposed in order to overcome a "real time" response constraint. Two basic architectures, each consisting of a combination of two methods, are evaluated: (1) a feedforward network-heuristic combination and (2) a feedforward network-recurrent network combination. These architectures are compared against a linear programming (LP) optimizer as a benchmark. This LP optimizer was also used as a teacher to label the data samples for the feedforward NN training algorithm. NN-based solutions are very accurate (/spl sim/98% of optimal throughput) and, in contrast to the algorithmic approach, can be delivered in "real time". It is found that while the "human" generated heuristics (greedy search optimization) fail to find a solution in approximately 30% of cases, the best NN fails only in 4.9% of cases. Moreover, it has been found that in spite of the very high dimensionality of the problem (55 inputs and 126 outputs), the solution can be delivered by surprisingly compact NNs, with as little as around 1000 synaptic weights. This proves that on this occasion the NNs were able to extract simple but powerful "heuristics" hidden in the complex sets of numerical data.