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This allows the model to accurately reflect real battery performance.
Changing these constants allows the model to be easily adjusted [7, 10].
This allows the model to be assessed between the extremities of low and high demand.
The consideration of spatial relationship allows the model to provide cromulent forecasts.
This allows the model to predict heat transfer between the cell and air-preheating tube accurately.
The ambient temperature allows the model to account for convective loss.
As explained in van den Berg (2001), this distribution allows the model to verify the mixed proportional hazard assumption.
The new formulation also allows the model to be readily incorporated into a Gaussian mixture model (GMM) for speaker recognition.
The latter allows the model to cluster the current data well and to be consistent with the recent history.
Furthermore, Monte Carlo ray tracing (MCRT) allows the model to evaluate the radiation transfer in complex urban areas.
A new sub-laminate discretisation approach is proposed that allows the model to more accurately capture out-of-plane failure.
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