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Modeling of the relationship between selected descriptors and solubility data was achieved by linear (multiple linear regression; MLR) and nonlinear (artificial neural network; ANN) methods.
Our findings further demonstrate that the 'optimal' model fit to the data was achieved by shortening the odorant 'ON' period to approximately 6 s.
A good match with the actual data was achieved by developing the RSM and ANFIS models.
They also showed that since the Rician factor strongly depends on the environment, a better fit to the measurement data was achieved by applying a double Rayleigh distribution.
Automated analysis of mass data was achieved by using the 4000 Series Explorer Software V3.5.
Co-registration between MRI and MEG data was achieved by localizing three anatomical landmarks (nasion, left and right preauricular points) in each individual and measuring the magnetic signals of four coils placed on the scalp.
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Secondly, the residual and precise focusing of the synthetic aperture radar data is achieved by applying the range migration algorithm.
Our results show that the best correlation with the measured pressure data is achieved by the modified Eaton method with Eaton׳s exponent of about 0.5.
After exposure to high energy, high LET particles where the track radius is much larger than the cell nucleus, best fits to the data were achieved by a convoluted Poisson Neyman statistics.
A more detailed analysis of the data is achieved by systematically mining the network for SAR pathways, i.e. sequences of pairwise similar compounds that connect two molecules via a gradually increasing potency gradient.
Measures of model fit for the multivariate three-level linear growth models show that the best fit of the data is achieved by modelling context effects of mathematics, grade, structure and task analysis.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com