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In order to better understand the effect that biotin has on the polymeric microparticle, along with the usual in vitro characterization (drug encapsulation, morphology, release rates), release kinetics will further be modeled from experimental data to extract important quantitative information that is essential for the comparative study of the proposed PLGA microparticles.
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In order to capture the dynamic model for control design, LPV models were estimated from experimental data.
In power-law models, the kinetic orders are parameters of the model and must be estimated from experimental data.
This paper presents stabilizing Model Predictive Controllers (MPC) in which prediction models are inferred from experimental data of the inputs and outputs of the plant.
This model is different from experimental autoimmune encephalomyelitis (EAE), a well-studied MS model, in which RGC degeneration and optic neuritis are observed.
The corrosion model was calibrated from experimental data and used to simulate the corrosion performance of a WE43 magnesium alloy stent.
This task requires predictive mathematical models which can be constructed from experimental data.
A general rheological model was derived from experimental results.
Parameters in this model are obtained from experimental binary and ternary data reported previously.
At last, the thermal model is validated from experimental data under various meteorological situations.
The unknown parameters in the model are estimated from experimental data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com