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The delivery of medical education is moving away from the traditional apprenticeship model, to a model structured on educational initiatives such as simulation and problem based learning [ 1, 2].
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Here, we account for mathematical analyses of the spindle assembly checkpoint that have been proposed in recent years, ranging from models structured on generic molecular networks (Doncic et al, 2005; Sear and Howard, 2006; Mistry et al, 2008), to models aimed at reproducing the spindle assembly checkpoint network in molecular detail.
Due to the simplified model structure, on-line parameter estimation is typically required for satisfactory prediction of column performance.
Therefore, we investigated an innovative model for structured, on-ward supervision of final-year students [ 17].
It is important in modelling work to establish the influence of model structure on the results – that is, whether the conclusions drawn are linked to the formulation of a particular model.
Since k is a field, all the natural model structures on (C_cdot (k)) coincide.
Therefore the injective and projective model structures on (mathcal {C}^I) are unique, if they exist.
The other two model structures on (mathcal {C}^V) are new.
Then C, F, W is a model structure on (mathsf{G}(L,M,q)).
Producing model structures on categories of sections of Grothendieck prefibrations is usually a highly non-trivial exercise.
Then (langle widetilde{C},widetilde{F} rangle ) is a model structure on (langle mathcal {C}^I,W^I rangle ).
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