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Using the presented method here we can formulate a game for dynamic duty cycle adjustment in wireless sensor networks.
To effectively implement this method, here we report the development of an analytic pipeline, sequencing enabled genotyping for mapping recombination populations (SEG-Map), for genotype calling and constructing genetic maps from next-generation sequencing data.
Considering of the shortcoming of the semiempirical method, here we focus on the relationship between the atomic interaction and the structure of cluster, which is not sensitive to the accuracy of potential.
Since this factorized construction of ZCZ sequence set is an extension of existing construction based on interleaving method, here, we state the distinctive points which are different from the conventional one using the same technique.
In order to help the potential readers to understand the holistic approach of SWCNT, FWCNT, and MWCNT production using AD method, here, we compile some major findings as shown in Table 1.
As stated earlier, here the training data have five SNR conditions (clean, 20, 15, 10, and 5 dB) and four types of noise the same as those in Test Set A. In addition to the individual method, here we also investigate the effect of the pairing of the spectral-domain method and the cepstral-domain method to see if further accuracy improvement can be achieved.
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Contrary to state-of-the-art methods, here, we extract information from auditory output only.
By applying network-theoretical methods, here we show topological similarities between brain and social networks.
The following result about the existence and uniqueness of solutions of equation (1.1) can be obtained by the standard Faedo-Galerkin methods, here we only formulate the result.
To reduce the number of genes in addition to apply the appropriate clustering methods, here we have also considered the valuable inputs from the biological experiments.
To overcome the difficulties met by previous methods, here we propose a simple quantitative one by using a similarity matrix plus Pearson's correlation analysis.
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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