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This model utilises reliability measures, for modelling different aspects of the uncertainty.
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.
In order that this model can be formulated as a mixed-integer linear program, this model utilises a discretised temperature grid where stream temperatures are allowed to vary.
The proposed model utilises context-aware techniques by monitoring physiological signals, ambient conditions, and patient activities simultaneously to derive the real-time health status of the patient.
The tissue scale model utilises volume-averaged values of conductivity and permittivity and models suitably the dispersive characteristic of biological tissues.
The model utilises a simplified macroblock approach that starts with the assumption of a collapse mechanism based on the wall's boundary conditions.
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One of these models utilises tumour tissue directly obtained from a patient at the time of nephrectomy for cRCC.
In the first phase, the proposed model only utilises hard constraints.
The algorithm is based on a distributed hierarchical learning model and utilises three specialisations of agents: workers, tutors and consultants.
One group, the empirical models (e.g. One Slope Model [4]), utilises relatively simple formulas containing empirical parameters to estimate path-loss and therefore do not require a database of exact positions of obstacles.
Table 4 shows the value decrements for each dimension/level using a baseline regression model that utilises VAS values transformed at the level of the individual and including a constant term.
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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