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Obtained data were modeled to predict the mass loss during torrefaction.
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Process parameters including the rate of agitation, pH and temperature were examined and the obtained data were modelled using five kinetic models including the pseudo-first-order equation, second-order equation, the modified Freundlich, the pore diffusion model and the Elovich equation.
The obtained data was modeled by a novel two stage artificial neural network (ANN) with 14650 data points.
A survey was administered on 116 seafaring officers and the obtained data were analysed using structural equation modelling.
The obtained data were used to develop MPV models that express the performance of an electrophoretic run (measured as peak efficiencies of Q10, AA, and FA) in terms of the MCs and PVs.
The obtained data were used to develop various conceptual models and to constrain the development and calibration of a reactive transport model.
The obtained data were treated according to various kinetic models.
The obtained data were properly described using the quadric model rather than the linear one.
Thus, the obtained data was used to model the optimal mixed logit model.
Experimentally obtained current/voltage data was modeled to develop an adjusted empirical correlation for brush type discharge electrodes.
The obtained data is projected on a model of our current knowledge of several developmental signaling pathways.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com