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Mode of access: Internet. ; BANC; F1512.4.H64: With: Honduras. Reports of the Scientific commission . 1897, copy 1.

There is growing demand for dental education and training not only in terms of knowledge but also skills. This demand is driven by continuing professional development requirements in the more developed economies, personnel shortages and skills differences across the European Union (EU) accession states and more generally in the developing world. There is an excellent opportunity for the EU to meet this demand by developing an innovative online flexible learning platform (FLP). Current clinical online systems are restricted to the delivery of general, knowledgebased training with no easy method of personalization or delivery of skillbased training. The PHANTOM project, headed by Kings College London is developing haptic-based virtual reality training systems for clinical dental training. ANDROMEDA seeks to build on this and establish a Flexible Learning Platform that can integrate the haptic and sensor based training with rich media knowledge transfer, whilst using sophisticated technologies such as including service-orientated architecture (SOA), Semantic Web technologies, knowledge-based engineering, business intelligence (BI) and virtual worlds for personalization. © 2009 IFIP International Federation for Information Processing.

This is the final version. Available on open access via the DOI in this record ; Data availability: The datasets analysed during this study are available online: CMIP5 model output [https://esgf-node.llnl.gov/search/cmip5/], CMIP6 model output [https://esgf-node.llnl.gov/search/cmip6/], The WFDEI Meteorological Forcing Data [https://rda.ucar.edu/datasets/ds314.2/], CARDAMOM Heterotrophic Respiration [https://datashare.is.ed.ac.uk/handle/10283/875], MODIS Net Primary Production [https://lpdaac.usgs.gov/products/mod17a3v055/], Raich et al. 2002 Soil Respiration [https://cdiac.ess-dive.lbl.gov/epubs/ndp/ndp081/ndp081.html], Hashimoto et al. 2015 Heterotrophic Respiration [http://cse.ffpri.affrc.go.jp/shojih/data/index.html], and the datasets for observational Soil Carbon [https://github.com/rebeccamayvarney/soiltau_ec]. ; Code availability: The Python code used to complete the analysis and produce the figures in this study is available in the following online repository [https://github.com/rebeccamayvarney/soiltau_ec]. ; Carbon cycle feedbacks represent large uncertainties on climate change projections, and the response of soil carbon to climate change contributes the greatest uncertainty to this. Future changes in soil carbon depend on changes in litter and root inputs from plants, and especially on reductions in the turnover time of soil carbon (τs) with warming. The latter represents the change in soil carbon due to the response of soil turnover time (∆Cs,τ), and can be diagnosed from projections made with Earth System Models (ESMs). It is found to span a large range even at the Paris Agreement Target of 2◦C global warming. We use the spatial variability of τs inferred from observations to obtain a constraint on ∆Cs,τ . This spatial emergent constraint allows us to greatly reduce the uncertainty in ∆Cs,τ at 2◦C global warming. We do likewise for other levels of global warming to derive a best estimate for the effective sensitivity of τs to global warming, and derive a q10 equivalent value for heterotrophic respiration. ; European Research Council (ERC) ; Natural Environment Research Council (NERC) ; European Union Horizon 2020 ; Met Office Hadley Centre Climate Programme

11 pags., 8 figs., 1 tab. -- Open Access funded by Creative Commons Atribution Licence 4.0 ; Arctic feedbacks accelerate climate change through carbon releases from thawing permafrost and higher solar absorption from reductions in the surface albedo, following loss of sea ice and land snow. Here, we include dynamic emulators of complex physical models in the integrated assessment model PAGE-ICE to explore nonlinear transitions in the Arctic feedbacks and their subsequent impacts on the global climate and economy under the Paris Agreement scenarios. The permafrost feedback is increasingly positive in warmer climates, while the albedo feedback weakens as the ice and snow melt. Combined, these two factors lead to significant increases in the mean discounted economic effect of climate change: +4.0% ($24.8 trillion) under the 1.5 °C scenario, +5.5% ($33.8 trillion) under the 2 °C scenario, and +4.8% ($66.9 trillion) under mitigation levels consistent with the current national pledges. Considering the nonlinear Arctic feedbacks makes the 1.5 °C target marginally more economically attractive than the 2 °C target, although both are statistically equivalent. ; This work is part of the ICE-ARC project funded by the European Union's 7th Framework Programme, (grant 603887, contribution 006). D.Y. received additional funding from ERIM, Erasmus University Rotterdam, and Paul Ekins at the ISR, University College London. K.S. was funded by NSF (grant 1503559) and NASA (grants NNX14A154G, NNX17AC59A). E.J. was funded by the NGEE Arctic project supported by the BER Office of Science at the U.S. DOE. Y.E. was funded by the NSF (grant 1900795). E.B. was supported by the UK Met Office Hadley Centre Climate Programme funded by BEIS and DEFRA. Publication of this article was funded by Lancaster Environment Centre, the University of South Florida St. Petersburg's Open Access Publication Fund, NSF (grant 1900795) and NASA (grants NNX14A154G, NNX17AC59A).