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These models require accurate data on thermal material properties to offer reliable validation and interpretation of the results.
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Accurate prediction of hydrological models requires accurate spatial and temporal distribution of rainfall.
Assessing the reliability of such models requires accurate estimation of the data errors.
Application of soil erosion models requires accurate estimation of model parameters under different environmental conditions.
Integrated optimization of these models requires accurate and efficient reduced models (RMs).
The construction of Markov models requires accurate data information to determine the transfer probabilities and state conditions.
In order to work effectively, sub-models require accurate inputs of meteorological data, particularly rainfall, daily maximum temperature, daily minimum temperature, VPD and daily global or (if available) photosynthetically active radiation.
A quantitative model requires accurate data mining.
Heat transfer modelling requires accurate knowledge of thermophysical properties and crystallization kinetics in conditions representative of the forming process.
The porous model requires accurate heat transfer correlations for the heat exchanger, and such correlations may not be available for new designs.
Development of the model requires accurate mapping of electrode and muscle stimulation sites, the capability to selectively activate muscles to produce graded contractions of sufficient strength, stable anchoring of the implanted electrode within the muscles and stable performance with functional reliability over several weeks of the therapy window.
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