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However, this model fails to identify the local deformations [5, 25].
Since this model fails, a new empirical model has been proposed to take into account the experimental data.
However, when applied to human data, this model fails as the data are insufficiently sensitive to a resonant peak associated with gas compression [55, 56].
It is obvious that this model fails to achieve load balancing, which is shown in uneven gateway utilization and heavy congestion in some routers.
It may be true in short term but this model fails miserably in long run and as post-industrialization ill effects are bigger dividends.
However, this model fails to identify the local deformations, thus resulting not exhaustive in the characterization of the hygro-mechanical behavior of wood.
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For a variety of reasons, including slow data connections and clunky software, this model failed to live up to its promise and turned into an industrywide joke.
More complex derivations of this model failed to achieve sufficient goodness of fit.
Since, this model failed to predict the thermal conductivity of the nanofluid, for engineering applications, using experimental findings, an accurate correlation was presented to predict the thermal conductivity of MWCNTs/EG water (40 60 vol.%) nanofluids.
In our previous paper we had already shown that this model failed to predict the response of sequences of two steps, thus ruling out the ability of a linear model to approximate an eye muscle, even in this range.
However, this model failed half of the time in predicting the direction of PSE shift when auditory and visual durations were incongruent, in particular under the auditory intersensory conditions of the Loom (t1,16 = 2.1, p≤0.008) and Reverse (t1,20 = 2.08, p≤0.001) experiments.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com