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Because our proposed computational framework is based on probabilistic modeling, it provides an intuitive interpretation (i.e., probabilities, not p-values).
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While this modeling strategy cannot compare to the precision of ODE models, it provides a much closer approximation than classical or binary reaction-contingency based Boolean modeling.
With its advanced built- in soil models, it provides tools to simulate systems of real events: Change in geometry (excavation, fill placement) Change in soil properties (fill replacement).
Unlike existing multi-porosity models, it provides a simple expression for the pressure transient response in the time domain.
By combining features of choice modelling, Multi-Criteria Analysis (MCA) and diffusion models, it provides a capability to analyse future adoption patterns of the competing technology options under a range of features for purchase timing and choice.
Compared to data-driven models, it provides a better predictive capability.
Based on state-of-the-art scenario modelling, it provides the vital missing link between renewable energy targets and the measures needed to achieve them.
While Eq. 1 represents a very naïve descriptive model, it provides prima facie evidence in favour of parameter instabilities in the four major euro area countries.
In combination with GIS techniques such as the Markov chain model, it provides a device for monitoring spatial development to increase understanding of current trends of development.
Due to the discreteness of this model, it provides an opportunity to simulate large-scale real time microscopic phenomena like platoon formation and the capacity drop at transitions between free and congested flow.
Recent studies demonstrate that, compared with traditional service models, it provides benefits such as mobile computation speedup [17], energy efficiency [18], resource allocation [19], and QoS/QoE provisioning [20].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com