In summary, we have illustrated the approach of mining the large soybean EST collection to deduce knowledge about expression of individual gene family members using the soybean lectins as an example.
This school of thought believes in deducing knowledge through the connections between the neurons.
Knowledge about such changes can generally be deduced from knowledge of the stereochemistry (three-dimensional structures) of the starting materials, intermediates, and final products (provided these are obtained under kinetic control).
A constitutive relation for the homogenised micro- and meso-components is deduced from the knowledge of the respective internal structures, starting from an accurate description of the single representative cells.
Using common-sense knowledge, relations are often deduced from the knowledge of entailment, implication, or presupposition between CPCs, as shown in the following examples respectively.
Finally, the fluorescence count rate per molecule CRM is deduced from the knowledge of the average fluorescence intensity and the number of emitters: CRM = 〈 F〉/ N. We start our investigations by conducting two-photon FCS measurements in order to determine the number of molecules N and the CRM while varying the excitation powers for each sphere diameter and for the reference solution.
Although it is impossible to deduce the load from knowledge of the variance of relative fitness alone, it is possible to determine the minimal load consistent with a particular variance of relative fitness.
However, we assume that the correlation matrix is known at the transmitter sidec and the channel matrix Γ(n) can be deduced straightforwardly from the knowledge of as shown in Lemma 1.
Synchronously, deducing knowledge-base with rules for collaborative design is built up.
Roman Catholic theologians distinguish between revelation in a broad sense, which means knowledge of God deduced from facts about the natural world and man's existence, and revelation in the strict formal sense, which means the utterances of God.
In Sudoku, we efficiently and dynamically integrate knowledge of logically deduced sub-problem solutions; this integration leads to improved system reactivity and greatly reduced solution time for large problem instances.
