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The situation raises questions concerning efficient choice of study population, sampling schemes, and methods of data analysis.
Particularly, using neural networks for the control of robot manipulators have attracted much attention and various related schemes and methods have been proposed and investigated.
The emergence of "petascale" supercomputers requires us to develop today's simulation codes for (incompressible) flows by codes which are using numerical schemes and methods that are better able to exploit the offered computational power.
When this less-biased genome dataset was analyzed again using the same classification schemes and methods as described above, no significant differences in results were observed (data not shown), with still highly significant p-values for our statistics, indicating that the sampling bias in sequenced genomes is not a major contributing factor to our observations.
For example, we found many different sampling schemes and methods for recording sampling events.
Vegetation and land cover maps of this region have variable coarse spatial and classification detail; different regions employ distinct classification schemes and methods that make joining maps along borders difficult.
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This review highlights recent material designs, recognition schemes, and method developments as they relate to targeted electrochemical analysis for biological systems.
Extensive experiments results also validate the proposed scheme and methods.
The promising results demonstrate the robustness and effectiveness of the proposed scheme and methods.
We used the same coding scheme and methods described above to review and analyze the content of these letters.
Therewith, the corresponding scenarios and outcomes of leakage are determined, and accordingly detecting scheme and methods are proposed.
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