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Wang et al. (2012) presented a working fluid selection and parametric optimization using a multi-objective optimization model by simulated annealing algorithm.
We have used this information to build a molecular model of the PH75 protein capsid based on that of Pf1, and refined the model by simulated annealing, using fibre diffraction data extending to 2.4 Å resolution in the meridional direction and to 3.1 Å resolution in the equatorial direction.
If there is no free surface for HDF to migrate to, the HDF agent will then be removed from the model by simulated apoptosis or detachment.
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We validate our model by simulating the world record multicrystalline silicon solar cell.
We validated our integrated model by simulating a two-phase separation process and gas liquid solid three-phase contact angle.
March [7] examined the E-E model by simulating how the mutual learning affected the balance under different conditions.
Deep learning aims at building a deep neural network model by simulating the learning process of brain.
The second one reconstructs the rock model by simulating the diagenetic process of sedimentary rock, like compaction and cementation.
Furthermore, we validated the models using the plant data and demonstrated the application of our dynamic model by simulating a grade change.
Further, we demonstrate how simple it is to include contact dynamics into the model, by simulating a ball bouncing off a wall.
We compare this model to a previously published Multi-Layer Interactive Network Model by simulating diffusion through a real multi-layer network system consisting of a residential peer network and a geospatial building network from three experimental data-sets.
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