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In particular, metal forming and machining (both traditional and non-traditional) have been extensively modeled using numerical techniques.
These properties (including, but not limited to, the octanol water partition coefficient, water solubility, melting point, bioconcentration factor, and biodegradability) have been extensively modeled using QSAR/QSPR approaches using existing experimental data [18, 36, 37, 40 43].
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This well-established model for understanding rhythmic neuron firing and the effects of neuromodulators on activity has been extensively studied and can be modeled using computational neuroscience tools.
Conditional neural fields (CNF) [ 47] are probabilistic graphical models that have been extensively used in modeling sequential data [ 45, 49].
VEGF interactions with these receptors in the context of angiogenesis have been extensively studied using computational models.
The Archimedes Model has been extensively validated, using more than 50 epidemiological and clinical studies.
They have been extensively used in modeling various physicochemical properties as well as biological activities.
Cox semi-parametric method [ 12] has also been extensively used for modeling such data.
Results from meta-analysis of RCTs have been extensively used to model cost-effectiveness of the treatment of osteoporosis.
Here we used single neonatal cardiomyocytes (neoCMs) because they can be cultured for longer times and have been extensively used as models to study cardiomyocyte mechanobiology.
Yeasts have been extensively used as model organisms to elucidate cellular processes and their mechanism in lower eukaryotes.
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