Abstract
Interest in the characterization and evolution of science has sparked a vast amount of literature. The manner in which this field of knowledge is depicted is highly diversified, giving rise to different styles of reporting the history of science. Not with standing the relevance of the subject for a broader understanding of how knowledge is produced, certified and appropriated, a simple and informative framework is missing. Proposing a simple categorization of the methods for assessing the evolution of science, our study highlights the changing process of scientific traditions, portraying the evolution of science as shifting towards hybrid and quantitative methodologies, namely those based on bibliometric techniques, as they arguably rely less on the judgments and perceptions of researchers, and have a higher degree of certainty. © EuroJournals Publishing, Inc. 2012.

Abstract
A major challenge when accessing protected multimedia content in heterogeneous usage environments is the ability to provide acceptable levels of quality of experience to all involving users. Additionally, different levels of protection should be possible to be addressed when manipulating the content towards the quality of experience maximization. This paper describes the use of a context-aware and Digital Rights Management (DRM)-enabled content adaptation platform towards meeting these challenges. The platform was conceived to deliver advanced content adaptation within different application scenarios, among which Virtual Collaboration (VC) was central. Descriptions of use cases implemented by the platform in heterogeneous VC environments are provided. Conducted experiments have highlighted the benefits to users when compared to an operation without the platform. Results of different adaptations suitable to sensed context conditions are also provided and analyzed. A brief description of the platform functionality is included together with pointers to additional information.

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Abstract
The paper introduces a concept for the combination of mechatronic unit and multi-agent system approaches. It highlights how combining both approaches to a new kind of integrated system architecture may enforce the dissemination and reusability of methods and automatisms developed for one specific system architecture to other similar ones. Additionally it will show how this integrated architecture leads to a better exchange of Automation architectures in general. Thus, all systems compliant with the described architecture may benefit from methods and automatism applicable for a broad range of systems. The suitability of this integration approach is verified by using the example of a washing machine production line.

Abstract
This paper presents a new method for the automatic segmentation of the common carotid artery in B-mode images. This method uses the instantaneous coefficient of variation edge detector, fuzzy classification of edges and dynamic programming. Several discriminating features of the intima and adventitia boundaries are considered, like the edge strength, the intensity gradient orientation, the valley shaped intensity profile and contextual information of the region delimited by those boundaries. The adopted fuzzy classification of edges helps avoiding low-pass filtering. The method is suited to real-time processing and user interaction is not required. Both the near and far wall boundaries can be detected in arteries with plaques of different types and sizes. Both expert manual and automatic tracings are significantly better for the far wall, due to the better visibility of the intima and adventitia boundaries. The automatic detection of the far wall shows an accuracy similar to the manual detections. For this wall, the error coefficient of variation for the mean intima-media thickness is in the range [5.6, 6.6 %] for automatic detections and in [6.7, 7.1 %] for manual ones. In the case of the near wall, the same coefficient of variation is in [11.2, 13.0 %] for automatic detections and in [5.9, 9.0 %] for manual detections. The mean intima-media thickness measurement errors observed for the far wall ([0.15; 0.17] mm, [1.7; 1.9] pixel) are among the best values reported for other fully automatic approaches. The application of this approach in clinical practice is encouraged by the results for the far wall and the short processing time (mean of 2.1 s per image).

Abstract