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In this work, we analyze the fully-developed flow of particle suspensions in a tube using different flow models to evaluate the effect of both particle migration and shear dependent viscosity.
A numerical fiber pullout model tailored for carbon nanotube (CNT) reinforced polymer composites is developed based on some classical models, to evaluate the effect of low temperature environment and other parameters to the stress distribution and stress transfer efficiency in CNT/polymer composites.
We performed univariate logistic regression models to evaluate the effect of clinical-pathological parameters on frozen section analysis.
Finally, in vivo testing was conducted in subcutaneous tumors grown on Balb/c mice models to evaluate the effect of our PDT treatment.
To confirm that using tamoxifen to inhibit CIP2A has potentially clinically relevant implications in ER-negative breast cancer, we next used ER-negative breast cancer xenograft models to evaluate the effect of tamoxifen in vivo.
We then conducted analyses using adjusted linear mixed-effects models to evaluate the effect of an organic diet on children's urinary metabolite concentrations controlling for location of residence and whether the sample was a first morning void or not.
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We used linear mixed-effects models to evaluate the effects of diet on urinary metabolite concentrations.
We used continuous linear regression models to evaluate the effects of BPb or TPb on ACTH.
Marital status was introduced in the model to evaluate the effect of marital status on WTP.
We could find a few limitations in this mice model to evaluate the effect of ST.
We perturbed the model to evaluate the effects of different assumptions on the viscoelastic relaxation predictions.
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