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The proposed model delivers a useful tool for estimating capacity at signalized intersections under unsaturated conditions.
Although a non-standardized quadratic regression model delivers a satisfactory agreement with experimental results, it misjudges the statistical significance of the process parameters.
The proposed mathematical model delivers a new guideline by avoiding unnecessary experiments and provides a systematic understanding of temperature profiles during the biomass pelleting process.
As a bonus, the model delivers a supply of selected S2 lines for further inbreeding and selection.
The model delivers a BAF prediction for a selected general trophic level (e.g., lower, middle, upper), requiring only a Kow value for the chemical.
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A three-year study by Aston University in Birmingham for the ExtraCare Charitable Trust, found their village housing model delivered a 14.8% reduction in depressive symptoms over 18 months.
Moreover, Pseudo-second-order kinetic model delivered a better correlation for the experimental data in comparison to the pseudo-first-order kinetic model and intraparticle diffusion mechanism.
SEDFLUX combines individual process-response models into one fully interactive model, delivering a multi-sized sediment load onto and across a continental margin, including sediment redistribution by (1) river mouth dynamics, (2) buoyant surface plumes, (3) hyperpycnal flows, (4) ocean storms, (5) slope instabilities, (6) turbidity currents, and (7) debris flows.
The TR model delivered a threshold parameter θ, which permitted measurement of bias towards producing short timed responses, independent of the precision of those responses and independent of response bursts.
This was considered the most appropriate model for identifying items that could measure the underlying latent trait (level of disability) into a short assessment tool as it is the only measurement model delivering a metric transformation of ordinal scale [ 10, 11].
The standard machinery for system identification of linear time-invariant (LTI) models delivers a nominal model and a confidence (uncertainty) region around it, based on (second order moment) residual analysis and covariance estimation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com