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The Genentech presentation provided an overview of potential mechanisms for TP-related DI, both PK and PD examples focused on mAb therapeutics, one of many TP classes.
Our theoretical findings are exemplified through a suite of computational examples focused on block-heterogeneous systems with increased complexity level.
While many examples focused on improved speed and efficiency through data, some also spoke of entirely new opportunities that resulted from IoT projects.
Most of the examples focused on the redesign of various features of the enzymes involved in the bioremediation of POPs, including the enzyme expression level, enzymatic activity and substrate specificity.
The biased behavior of the original AE criterion and excellent performance of the modified criterion are demonstrated using simple numerical examples focused on (i) the uniformity of sampling density over the design space and, (ii) statistical sampling efficiency measured through the ability to correctly estimate the statistical parameters of functions of random variables.
The paper introduces this concept of evolutionary potential defined as the difference between the relative maturity of the current system, and the point where it has reached the limits of each of the evolution trends through a number of case study examples focused on the design and evolution of complex systems.
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Numerical examples focus on materials with space-dependent Young modulus.
These examples focus on application to primarily gaseous situations.
Although our examples focus on microarrays, DSGA generalizes to any high dimensional genomic/proteomic data.
Two aerodynamic optimization examples focusing on drag and lift minimization that consider wind directionality and turbulence are presented.
Finally, the performance of the proposed interface element is demonstrated by means of several examples focusing on double cantilever beam (DCB) and rib-stiffened specimens.
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