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A new multiplicating function to the CIRA '86 model (ddMSIS model) describes both additional heating effects.
The model describes both the transient and the steady-state regime for several different biodigester design and operating conditions.
This state-space model describes both the dynamic evolution of the state and the relationship between the state and the measurements.
The model describes both the mathematic correlations and the reasonable ranges of key process variables of RARR, and the application and role of the model are illustrated by a case study in detail.
By assuming that excitability drifts on many timescales and that sensory drive is sparse, this model describes both short-term adaptation at individual synapses as well as changes in orientation tuning during typical medium timescale adaptation experiments.
The example model describes both steady-state (G0) behavior and dynamic growth and division of a cell, using the same set of kinetic parameters.
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In the following sections, we first describe the used data and modelling method and apply it to obtain the Southern African REGional (SAREG) model describing both the main field and its secular variation.
UVPerox I is based on a kinetic model, describing both photolysis and oxidation.
A model describing both TES electrothermal reaction and the readout response is investigated.
Digiesi et al. (2004) presented a model describing both fatigue and learning effects on worker behavior over time.
Because of the highly variable forecast errors of wind power generation, a thorough uncertainty model describing both the predictive distribution and dependent structure of forecast errors is essential.
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