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Three-dimensional steady-state semi-empirical CFB furnace models predict adequate results within reasonable times but these models require tuning to existing objects, which obviously excludes their application for new object design.
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These include mouse strains with manipulated innate and adaptive immune systems, but still some of these models require DSS challenge [17].
Field validation demonstrated that previously developed coarse-scale models were moderately successful in predicting loess thickness, but these models required refinement.
These models require polar-opposite tactics at each link in the value chain.
However, these models require huge computational cost for multilayered structures.
However, these models require substantial investment of resources.
Testing these models requires considerable petrologic and structural work in areas where high-pressure rocks are exposed.
Measuring the performance of these models requires a few considerations.
Construction of these models required obtaining time-resolved data to quantitatively describe these processes.
Distributional similarity features improve performance but the models require somewhat more memory.
In silico tools will no doubt be helpful, but these digital models require bench-derived data to be most informative.
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