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The result of our formalization is a formal characterization of the distinctions that are made by a modelling language as well as how the structure of a model within this language reflects the structure of the biological phenomena it represents.
We also present an algorithm that implements a choice function based on our formalization.
Finally, we compare our formalization with other approaches, and discuss how our use of NATs makes it easier to incorporate other features of action theories, such as constraints, to our formalization.
At the end of the article we present an application of our formalization of counterfactual emotions to a concrete example.
As part of our study, we also explore the deployment of our formalization in a prototype hospital Web portal messaging system.
Additionally, we show how our formalization can be applied to existing tools, and how it yields algorithms for the analysis of domain-specific modeling languages (DSMLs) and model transformations.
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In our observation, appropriate formalization of our policy language enabled formal analysis of its policies; these features were key to a successful model-driven engineering process.
Section 3 gives an overview of our approach and Sect. 4 introduces a formalization of our framework.
From the formalization of our proof in Coq, we extract Robinson's famous resolution algorithm as a Haskell program correct by construction.
In this section, we introduce a formalization of our model.
In this section, we initially present the theoretical formalization of our problem.
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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