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A model that synthesizes previous knowledge from experiments and simulations on spark ignition of gas and liquid-fuelled non-premixed recirculating flames has been developed.
As part of an effort to mimic a complex agrosystem, knowledge from experiments in both the field and laboratory will be incorporated into process-based models, using novel parameters and improved schemes.
Considering the applied parameter water available to plants to evaluate drought stress there is just not sufficient knowledge from experiments or evidence from forest monitoring to warrant a more detailed drought risk classification by, for example, tree age or region.
Finally, integration of knowledge from experiments validating existing gene sets can generate new groupings in an iterative manner.
Future advances will depend on integrating knowledge from experiments with those from modeling, field studies, and molecular genetic approaches.
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We begin with a discussion of the three main types of knowledge gleaned from experiments to evaluate the potential for fishery-induced evolution.
It is rather difficult to predict the responses of LPTCs to such natural image sequences from the knowledge obtained from experiments with simple stimuli, as the motion responses jointly depend in a nonlinear way on the velocity, the contrast and the spatial frequency content of the retinal images of the scenery (reviewed by Egelhaaf and Borst, 1993).
Being able to link multiple kinds of data to an image or a protein structure, for example, makes it easier to organize experiments, collect and distribute results, and speed up the process of uncovering deeper knowledge from various experiments.
The computational approaches are proposed to benefit biological experiments and to better characterize network data sets by minimizing the use of the prior knowledge from biological experiments.
Based on the knowledge from these experiments, two different concepts for methanation plants on an industrial scale were developed.
Data-reuse provides tremendous opportunity to extract new knowledge from existing experiments, and offers a unique opportunity for robust, multi-'omics analyses by merging metadata (information about experimental design, biological samples, protocols) and data from multiple experiments.
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