Sentence examples for leverage model from inspiring English sources

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Given the poor profitability profile of the operating companies, it is no surprise that the private-equity-owned businesses have not always needed to adopt the high leverage model to secure a tax advantage.

"In the past," said Mark Chandler, the general counsel of Cisco Systems, "the attitude at firms has always been, 'We can have every cost we want and simply pass it along to the client, then rely on our leverage model to produce the profits we need.' " When the recession pinched corporate budgets, in-house legal departments were told to cut costs.

In this paper, we build on top of our extension of the AADL modeling language to model TTEthernet-based distributed systems and leverage model transformations to enable undertaking the verification of the system models produced with this methodology.

This was calculated using the mammalian fish leverage model, whereby the predicted plasma concentration can be described by multiplying the environmental concentration by the blood to water partitioning coefficient (Huggett et al., 2004) using the Fitzsimmons model for the partitioning of compounds between blood and water (Fitzsimmons et al., 2001).

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Our AG approach and AGF software were developed with specific objectives in mind, including: Leverage computational models at the framework level as abstract elements, such that a game implementation may leverage models that are relevant to a particular scenario.

It's calling this crowdfund-leveraging model 'lean hardware'.

Second, the framework leverages model checking to validate adaptive Breeze/ADL specifications.

Incubation of each concept is tailored depending on its maturity and proposal history, and its highest-leverage modeling and analysis needs.

There is also a case to be made for leveraging models of adaptive complexity from other social science areas, as well as those from physics and the biological sciences.

For these concatenated analyses, we also employed several approaches to control for systematic errors, for example, by trimming sites that fail tests of compositional heterogeneity (Foster, 2004; Criscuolo and Gribaldo, 2010) or by leveraging models built to control the effects of heterotachous substitution (Philippe et al., 2005; Pagel and Meade, 2008).

We also presented Bayesian classifier based authentication models that learn each user's response pattern and subsequently leverage that model to identify legitimate users with high accuracy.

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