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In distributed environments, the ontology provides a unified representation of device information which benefits the knowledge sharing among applications.
The Gene Ontology (GO) provides a unified representation of gene and gene product attributes across all species [ 13].
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It provides a method for a unified representation of information about building throughout its lifecycle – from the design phase to the building maintenance.
A unified representation of the models is provided by f y; ω) = ωI{0} y) + (1 − ω f Y (y), where Y is the count variable, I{0} is the indicator function and ω is a constant, whose values, if in (0,1) render a hurdle model for f Y (0) = 0, a zero-inflated model for f Y (0) ≠ 0, while negative values of it render a zero-deflated model.
In order to get rid of the heterogeneity of devices, a unified representation of devices is needed.
In this section we obtain a unified representation of the Apostol-type polynomials ({mathcal{F}}_{n}^{(l)}(x;lambda mu nu)) with the Gaussian hypergeometric functions.
The proposed framework enables a unified representation of the all-pass filter-based design methods using part or all (2N or 3N) constraints to find those 2N coefficients.
The time has come to dig into the mechanisms lying behind the formation of dynamic representations as well as the interactions between cell types resulting in a unified representation of self-location.
We use the PBPK model for a unified representation of knowledge generated during the drug development process that allows, for example, a model-based extrapolation of in vitro data to an in vivo context.
Generation of a unified representation of sensory space requires a functional interaction between the two sides of the pathway, which, in the case of vision and touch, occurs in the cerebral cortex predominantly via the corpus callosum.
Despite its less-detailed representation of these particular inflammatory processes and pathways, the IPN model provides a unified framework and representation of cell type specific processes in pulmonary inflammation, and thus may serve as a more powerful resource for interpreting and analyzing cell-to-cell interactions and whole lung inflammation.
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