What do stakeholders think about knowledge transfer offices? The perspective of firms and research groups in a regional innovation system

Knowledge transfer offices (KTOs) are required to adopt a comprehensive approach to managing a broad set of channels of university–industry collaboration, including collaborative research, business services, exchange of human resources and informal relations within a regional innovation system. The perspectives of firms and academic researchers, the main stakeholders of KTOs, are useful complements to assess KTO activity. The goal of this article is to study the effectiveness that these stakeholders attribute to KTOs. Based on survey data collected in the Spanish region of Andalusia, the article explores stakeholders’ perceptions and determinants. The results show that the level of engagement in a diversified range of activities and the importance attributed to knowledge transfer in the strategy of both research groups and firms are the best predictors of the perceived effectiveness of KTOs. The article also underlines specific differences between the two types of stakeholders.     

Fast online graph clustering via Erd?s-Rényi mixture

In the context of graph clustering, we consider the problem of simultaneously estimating both the partition of the graph nodes and the parameters of an underlying mixture of affiliation networks. In numerous applications the rapid increase of data size over time makes classical clustering algorithms too slow because of the high computational cost. In such situations online clustering algorithms are an efficient alternative to classical batch algorithms. We present an original online algorithm for graph clustering based on a Erd?s-Rényi graph mixture. The relevance of the algorithm is illustrated, using both simulated and real data sets. The real data set is a network extracted from the French political blogosphere and presents an interesting community organization.     

Iris recognition: Analysis of the error rates regarding the accuracy of the segmentation stage

Iris recognition has been widely used in several scenarios with very satisfactory results. As it is one of the earliest stages, the image segmentation is in the basis of the process and plays a crucial role in the success of the recognition task. In this paper we analyze the relationship between the accuracy of the iris segmentation process and the error rates of three typical iris recognition methods. We selected 5000 images of the UBIRIS, CASIA and ICE databases that the used segmentation algorithm can accurately segment and artificially simulated four types of segmentation inaccuracies. The obtained results allowed us to conclude about a strong relationship between translational segmentation inaccuracies – that lead to errors in phase – and the recognition error rates.     

Hydrogen plasma treatment of very thin p-type nanocrystalline Si films grown by RF-PECVD in the presence of B(CH3)3

We have characterized the structure and electrical properties of p-type nanocrystalline silicon films prepared by radio-frequency plasma-enhanced chemical vapor deposition and explored optimization methods of such layers for potential applications in thin-film solar cells. Particular attention was paid to the characterization of very thin (∼20 nm) films. The cross-sectional morphology of the layers was studied by fitting the ellipsometry spectra using a multilayer model. The results suggest that the crystallization process in a high-pressure growth regime is mostly realized through a subsurface mechanism in the absence of the incubation layer at the substrate-film interface. Hydrogen plasma treatment of a 22-nm-thick film improved its electrical properties (conductivity increased more than ten times) owing to hydrogen insertion and Si structure rearrangements throughout the entire thickness of the film.