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Four cycles of experiment and modelling were thus carried out.
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Since their aerodynamics are nonlinear, wind turbine modelling is thus challenging.
Combinations of different artificial neural networks (ANNs) for non-linear hierarchical modelling are thus examined.
Two commonly used models (i.e., hydrodynamic and molecular-kinetic (MK) models) were thus employed to calculate the DCA.
Similar to our approach in [2] two versions of the LRC model were thus implemented.
Different animal models were thus developed.
The above three gene models were thus excluded from further analyses.
ACE and ADE models were thus tested with equivalent estimates in both sexes.
Models were thus transposed such that METATOOL and efmtool's EFM search correspond to P-invariant computation.
Activities viewed as costs by self-interest models are thus seen as benefits instead.
Simplified models are thus some kind of mediator between experiment and theory, and they allow physicists to (learn to!) explore the space of viable theories and experimental signatures.
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