Designing Emotional Support Messages Tailored to Stressors

This work was funded by the RCUK Digital Economy award to the dot.rural Digital Economy Hub, University of Aberdeen; award reference: EP/G066051/1. The dataset used by this paper can be acquired by emailing the first author. We thank Matt Dennis, Kirsten A. Smith and Michael Gibson for their contributions to the research.

Adapting Progress Feedback and Emotional Support to Learner Personality

Peer reviewed

Adapting Progress Feedback and Emotional Support to Learner Personality

Peer reviewed

Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes

ACKNOWLEDGMENTS MW and RVD have been supported by the German Federal Ministry for Education and Research (BMBF) via the Young Investigators Group CoSy-CC2 (grant no. 01LN1306A). JFD thanks the Stordalen Foundation and BMBF (project GLUES) for financial support. JK acknowledges the IRTG 1740 funded by DFG and FAPESP. MT Gastner is acknowledged for providing his data on the airline, interstate, and Internet network. P Menck thankfully provided his data on the Scandinavian power grid. We thank S Willner on behalf of the entire zeean team for providing the data on the world trade network. All computations have been performed using the Python package pyunicorn [41] that is available at https://github.com/pik-copan/pyunicorn.

Uncertain natural frequency analysis of composite plates including effect of noise – A polynomial neural network approach

Acknowledgement SN and SS gratefully acknowledge the financial support from Lloyd’s Register Foundation Centre during this work.

Synchronised firing patterns in a random network of adaptive exponential integrate-and-fire

Acknowledgements This study was possible by partial financial support from the following Brazilian government agencies: CNPq, CAPES, and FAPESP (2011/19296-1 and 2015/07311-7). We also wish thank Newton Fund and COFAP.

Important factors in predicting mortality outcome from stroke : findings from the Anglia Stroke Clinical Network Evaluation Study

The Anglia Stroke Clinical Network Evaluation Study (ASCNES) is funded by the National Institute for Health Research (NIHR) Research for Patient Benefit Programme (PB-PG-1208-18240). This paper presents independent research funded by the NIHR under its Research for Patient Benefit (RfPB) programme (Grant Reference Number PB-PG-1208-18240). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. EAW receives funding support from the NIHR Biomedical Research Centre award to Cambridge

Suppression of phase synchronisation in network based on cat’s brain

We wish to acknowledge the support of the Brazilian agencies: CNPq, CAPES, and FAPESP (2015/07311-7 and 2011/19296-1).