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These methods share some similarities.
The aforementioned resource allocation methods share some common drawbacks which limit their applicabilities in cooperative spectrum sharing networks in cases where a PU and multiple SUs coexist.
Although the new methods share some similarities to those in [18 20], it is a new framework that we calculate the energy directly on discrete gray level sets.
While LPT and SLM based methods share some contributions together, PLPT does not need multi-symbol PAPR optimization intervals, each of which contains pilot signal for signaling overhead reduction, because PLPT does not use phase rotation vectors.
The methods share some characteristics: main equipments considered, dependence with solar radiation and heating demand; and differ in other aspects: design parameters of the main equipments, secondary equipment considered, degree of detail of climatic and demand input data and equations of the model used to calculate the performance of the system.
These methods share some common disadvantages: there is always a sampling error that, in general, is difficult to estimate; the convergence can be quite slow too.
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Although the genes that were identified by the machine learning methods shared some overlap, different methods also identified different genes; for example, DTNBP1 was only identified by J48.
This method shares some similarities with the mode selection previously described for PRISM [4, 10].
Some methods share a clear conceptual link, without directly citing one another: for example, the analytical themes developed during thematic synthesis are comparable to the third order interpretations of meta-ethnography.
There are various scoring methods for linkage analysis, some are called parametric and some non-parametric, but all scoring methods share the backbone of a common HMM.
But all these methods share a problem.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com