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Natural selection can lead to remarkably effective functional capabilities in complex organs.
Based on the findings, a set of new empirical categories of technological capabilities in complex capital goods is suggested to help future research in this area.
A detailed analysis of learning mechanisms reveals that this path emerged largely due to the nature of "learning by projects" in this industry, engineering intensity of the tasks, and insufficiency of conventional knowledge transfer methods for building design capabilities in complex systems.
GAs are search algorithms based on natural genetics that provide robust search capabilities in complex spaces [ 8].
For example, the use of isotope dilution techniques in conjunction with highly selective and sensitive instrumentation has greatly enhanced low-level detection capabilities in complex matrixes.
Multielement arrays can offer increased recognition capabilities in complex mixtures via the use of multiple cross-selective sensing materials (and multiple operational modes), and nanostructured materials have helped to provide higher target sensitivity within these microdevices.
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Under optimized experimental conditions, this method realizes a turn-on selective and sensitive response for actin with a detection limit of 0.12 μg mL−1 and a detection capability in complex biological media with satisfactory results.
Such correlations or associations can be powerful aids in enhancing our assignment capabilities in these complex cell wall samples.
Proteomics is a powerful technology with high-throughput capabilities in evaluating complex protein mixtures from biological samples [ 1].
The suitability of the biosensor was tested in a waste water sample obtaining satisfactory recovery values, thus demonstrating its capability in such complex matrix.
Recent development in deep learning has endowed autoencoders with even greater capability in analyzing complex, high-dimensional and large-scale data.
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