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The analysis and suggestion model utilises the second-part analytical results and a BPNN to identify concrete directions for improvement and to generate suggestions that can be used to achieve the ultimate goal of DfE.
The present variance component model utilises the relationship matrix inferred from genome-wide genotype, and the method developed here can be readily implemented for a population based analysis.
This model utilises the concept of noise-driven stem cell differentiation [ 15] which is based on the functional stem cell approach to tissue organisation by Roeder & Loeffler [ 5, 16].
After providing basic sample processing information and the site's coverage and quality scores, the model utilises the fitted RT-PCR error distributions to simulate the number of mutations that would be observed from processing errors alone.
The CPO model utilises the following three outcome categories: proximate outcomes, intermediate outcomes, and distal outcomes.
The traditional APACHE II model utilises the worst values of 12 physiological variables during the first 24 hours following ICU admission, along with an evaluation of the patient's chronic health and admission diagnosis to calculate the APACHE II predicted mortality [ 3].
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The model utilises a simplified macroblock approach that starts with the assumption of a collapse mechanism based on the wall's boundary conditions.
The descriptive model utilises a wide range of empirical findings in the literature to describe the wear interactions and evolution in the five-stage scenario: running-in, steady-state, defect initiation, defect propagation, and damage growth.
The model utilises an adapted form of Gunning-Scheper's method of potential impact fractions and alcohol-attributable fractions (AAFs) [ 16] to estimate the change in mortality and morbidity rates resulting from changes in consumption (see pages 25 36 of Purshouse et al. [ 15] for full details).
The simulation model developed is a generalised and flexible model which utilises the underlying distribution of the population and incorporates patterns of disease spread that can be customised to represent a range of infectious diseases and geographic locations.
This model utilises a simplified representation of the froth and, as a consequence, it does not necessarily give accurate gangue recovery estimates for practical flotation processes.
Write better and faster with AI suggestions while staying true to your unique style.
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