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Based on these error sources and observations, a new array was developed that can locate the individual sources of sound waves within three dimensions.
Based on these error estimates, we propose an algorithm for optimally choosing both the dimensions of the reduced basis approximations and the sizes of Monte Carlo samples to achieve a given error tolerance.
Based on these error rates and the number of reads from the clone data and the number of reads in each patient sample we calculated individual cut-off values (p-value <0.05) for all resistance mutations in all samples using a Chi-square test with correction for continuity.
ANOVA results on these error measures show that the automated algorithm performs at least as well as the manual method.
Based on these error values, therefore, the observed bias and error in the proximity-based exposure analysis is likely to be less for private residences in urban areas, but still substantial at short distances of 100 or possibly several hundred meters.
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Although these reporting errors can be problematic for clinical interpretations of data, we focussed on the likely effects of these errors on estimates of disease-exposure associations for epidemiological studies.
It is shown that the effect of these errors on the model depends on the "shape" of the model.
We investigated the impact of these errors on evolutionary studies and specifically on the identification of important genetic events.
We plan to further investigate the effect of these errors on our correlation and classification results.
The influence of these errors on design features of the manufactured product is also shown.
Here, we were interested in the effect of these errors on xMLAA-based hardware attenuation estimation and the corresponding, indirect impact on PET quantification.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com