Does distance hinder the collaboration between Australian universities in the humanities, arts and social sciences?

Australia is a vast country with an average distance of 1911 km between its eight state capital cities. The quantitative impact of this distance on collaboration practices between Australian universities and between different types of Australian universities has not been examined previously and hence our knowledge about the spatial distribution effects, if any, on collaboration practices and opportunities is very limited. The aim of the study reported here was therefore to analyse the effect of distance on the collaboration activities of humanities, arts and social science scholars in Australia, using co-authorship as a proxy for collaboration. In order to do this, gravity models were developed to determine the distance effects on external collaboration between universities in relation to geographic region and institutional alliance of 25 Australian universities. Although distance was found to have a weak impact on external collaboration, the strength of the research publishing record within a university (internal collaboration) was found to be an important factor in determining external collaboration activity levels. This finding would suggest that increasing internal collaboration within universities could be an effective strategy to encourage external collaboration between universities. This strategy becomes even more effective for universities that are further away from each other. Establishing a hierarchical structure of different types of universities within a region can optimise the location advantage in the region to encourage knowledge exchange within that region. The stronger network could also attract more collaboration between networks

Peer Review in SSH. Addressing the Interpretation of Quality Criteria

The chapter examines the experience of High keve exoert groups launched in the Italian experience in order to fine tune the instruments for research evaluatio in SSH, namely the peer review questionnaires, formats and scores. It also gives an account of the main contriversies surrounding the evaluation

Emotional processing in Parkinson's disease and anxiety: an EEG study of visual affective word processing

A general problem in the design of an EEG-BCI system is the poor quality and low robustness of the extracted features, affecting overall performance. However, BCI systems that are applicable in real-time and outside clinical settings require high performance. Therefore, we have to improve the current methods for feature extraction. In this work, we investigated EEG source reconstruction techniques to enhance the extracted features based on a linearly constrained minimum variance (LCMV) beamformer. Beamformers allow for easy incorporation of anatomical data and are applicable in real-time. A 32-channel EEG-BCI system was designed for a two-class motor imagery (MI) paradigm. We optimized a synchronous system for two untrained subjects and investigated two aspects. First, we investigated the effect of using beamformers calculated on the basis of three different head models: a template 3-layered boundary element method (BEM) head model, a 3-layered personalized BEM head model and a personalized 5-layered finite difference method (FDM) head model including white and gray matter, CSF, scalp and skull tissue. Second, we investigated the influence of how the regions of interest, areas of expected MI activity, were constructed. On the one hand, they were chosen around electrodes C3 and C4, as hand MI activity theoretically is expected here. On the other hand, they were constructed based on the actual activated regions identified by an fMRI scan. Subsequently, an asynchronous system was derived for one of the subjects and an optimal balance between speed and accuracy was found. Lastly, a real-time application was made. These systems were evaluated by their accuracy, defined as the percentage of correct left and right classifications. From the real-time application, the information transfer rate (ITR) was also determined. An accuracy of 86.60 ± 4.40% was achieved for subject 1 and 78.71 ± 0.73% for subject 2. This gives an average accuracy of 82.66 ± 2.57%. We found that the use of a personalized FDM model improved the accuracy of the system, on average 24.22% with respect to the template BEM model and on average 5.15% with respect to the personalized BEM model. Including fMRI spatial priors did not improve accuracy. Personal fine- tuning largely resolved the robustness problems arising due to the differences in head geometry and neurophysiology between subjects. A real-time average accuracy of 64.26% was reached and the maximum ITR was 6.71 bits/min. We conclude that beamformers calculated with a personalized FDM model have great potential to ameliorate feature extraction and, as a consequence, to improve the performance of real-time BCI systems