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Both methods can have positive effect on the algorithm.
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The raw chromatin interaction data obtained by Hi-C methods can have both technical and biological biases (Imakaev et al., 2012).
Agile methods can have positive effects on both productivity and well-being at work, by aiming for a sustainable pace, which itself leads to a less stressful working environment [13].
Existing shape-based detection methods can have false-positives on shadows since they have similar shapes with foreground objects.
It was demonstrated that different raster merging methods can have different and substantial impacts on these results.
However, such methods can have limited scope of application as they are usually designed for a particular coding technique and bitstream format, e.g., H.264/AVC standard.
Although sparsity and CS-based methods can have a dramatic reduction in transmission power, typically in these scenarios, the signals are first fully sampled and then compressed.
However, it is also important to highlight that these methods can have some limitations in terms of spatial, spectral, and temporal resolution.
It is seen that the results from the existing continuity methods can have large error for thick beams/plates and for higher vibration modes, while the proposed hybrid method is generally very accurate and much superior to the existing methods.
A closer look at row θ(B) of Table 1 has shown a surprising phenomenon that none of the reduction methods can have a probability of more than 0.5 to improve the original problems with respect to this quality measure.
Thus many different methods can have the same stability polynomial.
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