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Since the model parameters are time-varying, our approaches can model non-stationarity and heteroscedasticity of temporal data.
Network approaches can model vast networks of gene interactions that modulate disease [ 19, 20, 24].
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The CFB modelling approach can model a compliant mechanism linearly and nonlinearly, can obtain displacements of any points of the rigid stages, and allows external forces to be exerted on any positions of the rigid stages.
Without the need for kinetic parameters, the discrete modeling approach can model and simulate larger networks, given the abundant qualitative interaction maps of biological systems that are available.
Significant parameter fittings were produced for all models, demonstrating that the GC SCM approach can model the impact of carbonate on uranium in flow systems.
We also demonstrate that the multiscale level-set approach can model immiscible two-phase flow with a capillary number as low as 10−6.
Our experiment results demonstrate that our approach can model real-world reflectance with both high accuracy and high visual fidelity for real-time virtual environment rendering.
In an evidential reasoning context, a group consensus (GC) based approach can model multiple attributive group decision analysis problems with GC requirements.
Such an approach can model waterflooding in marginal fields like the Quitman oil field more accurately than previous potential flow methods and can visualize the sweep pattern and compute time-of-flight contours in a simpler and faster fashion than numerical streamline simulators.
This approach can model small 2D motion effectively, but it cannot handle large pose variation or occlusion.
This approach can model phenomena such as shear-induced grain boundary motion, grain boundary sliding and grain rotation (Admal and Marian 2017).
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