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Advances in drug delivery require an understanding of drug design, drug stability and metabolism together with the complexities imposed by the biological system such as cell/tissue penetration, drug-target interaction, and the pharmacodynamic consequences.
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Additional complexity imposed by the presence of multiple types of elements affects especially data migration, updates of local data structures and interpartition data structures.
In order to characterize the advantage of the ABC-MUD scheme in terms of the performance-versus-complexity tradeoff, in Table 3 we summarize the computational complexity imposed by the different MUDs assuming an value of 3 dB, where the associated complexity was quantified in terms of the number of complex additions and multiplications imposed by the different MUDs on a per-user basis.
To solve problem (P1) in an efficient and distributed manner via optimization decomposition, we first need to overcome the complexity imposed by the coupling of the optimization variables in problem's objective function, due to the nature of the two-tier SINR in (1).
However, in most cases the complexity imposed by making the tool available via Web services pales in comparison with the complexity imposed by developing the tool itself; the fact that so many effective Web service-based tools could be developed in conjunction with Track 3 in such a short period certainly validates this assertion.
Although none of the web interfaces developed in conjunction with Track I could be directly integrated into CTD's curation tool as a result of the complexity imposed by the tool's technical infrastructure, certainly some of the features could have direct application to CTD.
The literature [1, 2] is not applicable to our task due to the stringent complexity restrictions imposed by the low-end machines.
The task is daunting in view of increasing volume and device complexity, and added burdens imposed by advisory notices.
We will quantify the complexity imposed in terms of the number of metric computations required by the process as follows.
By means of the iterative approach with the mean-field Gaussian approximation, the time-complexity of the problem is strongly reduced and the obstacles imposed by combinatorial complexity are circumvented.
While all the researchers are quite optimistic, they recognize that any approach must work with the limits imposed by various complexities and the insufferable literalness of computers.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com