Abstract
Model-based analysis is a well-established method to assess the dependability of a system before deployment. It is well known that, in highly dynamic contexts, the accuracy of the analysis results can be limited because unpredictable phenomena may affect the system during its operation. In such contexts, the analysis typically needs to be refined with data obtained from real system executions. In this paper we tackle the issue of refining model-based dependability analysis in automated systems through monitoring. Specifically, we report on our preliminary results on the development of a system that exploits the synergic use of an automated approach for model-based dependability analysis and a flexible monitoring architecture.

Abstract
In this paper, we describe the design, implementation and testing of a dry active flexible electrode with a novel interface material for wearable biosignal recording. The new interface material takes the form of a gel and is highly bendable and comfortable on the wearer's skin. A comparison between common Ag/AgCl and our dry active electrode was performed on seven healthy volunteers. The presented prototype was designed for ECG signals but this technology can be modified for other biosignals. Our results show that the new dry active electrode presents better electrical characteristics than the common Ag/AgCl electrode, namely less power-line interference and better response in the signal band. We can conclude that our novel dry active flexible electrode outperforms the traditional Ag/AgCl wet electrode with the advantages of being dry and comfortable. Some future applications of this biodevice are discussed.

Abstract
Prostate cancer is a common cancer worldwide and a leading cause of death. Radiotherapy is usually the first-line treatment for patients with slow-growing cancer that is confined to the prostate. In Radiation Therapy Planning (RTP), the recognition and outlining of clinical volumes in computed tomography (CT) images are one of the most time-consuming steps carried out by human experts. The aim of this review is to identify and summarize evidence of the use of automatic organ delineation of CT images for radiotherapy planning in prostate cancer. From the literature search, a total of seven studies, reported between 1994 and 2009, were selected. We associate the selected studies in order to compare results, in spite of their differences in methodology and outcome evaluators. Most of the studies conclude that the automatic approach is faster, while having equivalent accuracy to manual method. Concerning the observer's variability, automatic segmentation reaches significant gains in reproducibility. As future directions, it is recommended the improvement of the segmentation algorithms in the delineation of problematic soft tissues and future validation studies with large scale trials and possible studies of meta-analysis in the specific problems.

Abstract
Breast screening is the regular examination of a woman's breasts to find breast cancer in an initial stage. The sole exam approved for this purpose is mammography that, despite the existence of more advanced technologies, is considered the cheapest and most efficient method to detect cancer in a preclinical stage. We investigate, using machine learning techniques, how attributes obtained from mammographies can relate to malignancy. In particular, this study focus is on how mass density can influence malignancy from a data set of 348 patients containing, among other information, results of biopsies. To this end, we applied different learning algorithms on the data set using the WEKA tools, and performed significance tests on the results. The conclusions are threefold: (1) automatic classification of a mammography can reach equal or better results than the ones annotated by specialists, which can help doctors to quickly concentrate on some specific mammogram for a more thorough study; (2) mass density seems to be a good indicator of malignancy, as previous studies suggested; (3) we can obtain classifiers that can predict mass density with a quality as good as the specialist blind to biopsy.

Abstract
Purpose: The goal of XPRESS is to establish a breakthrough for the factory of the future with a new flexible production concept based on the generic idea of "specialized intelligent process units" ("Manufactrons") integrated in cross-sectoral learning networks for a customized production. XPRESS meets the challenge to integrate intelligence and flexibility at the "highest" level of the production control system as well as at the "lowest" level of the singular machine. Design/methodology/approach: Architecture of a manufactronic networked factory is presented, making it possible to generate particular manufactrons for the specific tasks, based on the automatic analysis of its required features. Findings: The manufactronic factory concept meets the challenge to integrate intelligence and flexibility at the "highest" level of the production control system as well as at the "lowest" level of the singular machine. The quality assurance system provided a 100% inline quality monitoring, destructive costs reduced 30%-49%, the ramp-up time for the set-up of production lines decreased up to 50% and the changeover time decreased up to 80%. Research limitations/implications: Specific features of the designed manufactronic architecture, namely the transport manufactrons, have been tested as separate mechanisms which can be merged into the final comprehensive at a later stage.Practical implications: This concept is demonstrated in the automotive and aeronautics industries, but can be easily transferred to nearly all production processes. Using the manufactronic approach, industrial players will be able to anticipate and to respond to rapidly changing consumer needs, producing high-quality products in adequate quantities while reducing costs. Originality/value: Assembly units composed of manufactrons can flexibly perform varying types of complex tasks, whereas today this is limited to a few pre-defined tasks. Additionally, radical innovations of the manufactronic networked factory include the knowledge and responsibility segregation and trans-sectoral process learning in specialist knowledge networks.

Abstract
Dynamic environments may include autonomous and decentralised components that pose many challenges from the point of view of interoperability, thus triggering research studies in several directions. One recent research direction explores the automatic composition of heterogeneous systems through connectors synthesised at run-time. Besides functional properties, such connectors generally need to satisfy also non-functional (dependability-related) properties. This paper investigates the definition of an automated procedure to support the synthesis of dependable connectors.