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Our modelling strategy was to develop a predictive model that maximized the model R2.
The modelling strategy was to build, first, a fully saturated model (including all variables) in order to avoid missing relevant information by leaving out non-significant variables.
Instead, our modelling strategy was to focus on the main drivers affecting spatial patterns of organic pollution in global river networks.
The modelling strategy was used successfully to analyse the evolution of the Tg with conversion of several free radical copolymers of styrene and butyl acrylate in emulsion.
This paper addresses both issues by establishing a large database of measured walking time series recorded by an instrumented treadmill, while the modelling strategy was motivated by the existing numerical generator of electrocardiogram (ECG) signals and speech recognition techniques.
As shown in Fig. 16, the modelling strategy was able to capture complex macro-crack paths observed experimentally as well as micro-cracking and delamination in the resin.
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Also, differences in modelling strategy were clearly reflected by model fit indices, with the model that included formative SES specifications proving a poorer data fit.
The modelling strategy is an extension of the Monte-Carlo fire model, CRISP, linking simulations to sensor inputs which controls evolution of the parametric space in which new scenarios are generated, thereby representing real-time "learning" about the fire.
Second, a modelling strategy is built.
The accuracy of the modelling strategy is proved by simulating monolithic and rectangular laboratory coupons.
The modelling strategy is outlined, and the method of analysis associated with modal parameter identification is described in detail.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com