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In the following part of this work, these results will be used for the development of an empirical model allowing us to quantify both physical effects.
In order to measure influence, we propose a user-tweet model allowing us to capture user- and tweet-based characteristics that we can consider as influence markers.
A coarse-grained model is then developed based on the atomistic model, allowing us to investigate the dynamic behaviors of the protease in the bound and unbound states.
Based on agency and social preference theory, respectively, we formulated a model allowing us to test alternative hypotheses regarding landowners' behaviour.
Furthermore, poroelastoplasticity also enables the prediction of when and where failure occurs in the model, allowing us to model the likely microseismic response of a reservoir (Angus et al. 2010, 2015).
Additionally, a joint interpretation of the obtained results together with other information is summarised in a hypothetical model, allowing us to better understand the internal structure of the island.
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This model allows us combining data from different batches using the same microarray platform for analysis.
This coarse−grained model allowed us to simulate 10 transitions of a calcein molecule with reasonable use of CPU time.
This model allows us to account autocorrelation within profiles.
This richer model allows us to formally define a variety of (soft) informational attitudes.
The valuation function, as in a Kripke model, allows us to assign properties to the worlds.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com