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The paper [[15]] is a work to realize a transitional implementation of meta cloud (cloud abstraction layer) for solving cloud vendor lock-in problems.
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Because a purpose of this study was to demonstrate the implementation of meta-analysis to integrate multiple studies, the four species were treated and analyzed as independent studies, thus potentially reducing the capability of each analysis to detect differentially expressed transcripts.
Additionally, a standard representation would help create more homegeneous clinical trials, which would in turn facilitate the implementation of better meta analyses.
This paper investigates the formulation and implementation of constructive meta-heuristics for solving IPPS problems.
Since the existing implementation of the Meta-Environment started exhibiting more and more characteristics of a legacy system, we decided to build a completely new, component-based, version.
Our general procedure has been the implementation of a meta-ontological definition space in which the definitions of all the concepts and relations in LinKBase® are standardized in the framework of first-order logic.
In this paper, we describe a concrete implementation of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions) and demonstrate its utility by applying it to both simulated and empirical reaction time data from an associative learning task.
The implementation of our meta-analysis is in accordance with the requirements of the Cochrane Collaboration.
QUOROM standards were followed during all phases of the design and implementation of this meta-analysis [ 19].
The guidelines issued by Stroup et al. (2000) were followed in the implementation of this meta-analysis.
Details on the implementation of the meta-model considered in this contribution (such as training points, basis functions, etc).
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