Future studies that use the present framework to jointly explain neural ensemble spiking activity and binary behavioral responses may use likelihood-based model selection methods (Burnham and Anderson, 2002) to further explore the mechanisms by which learning-related ensemble neural spiking activity gives rise to adaptive decision making in biological nervous systems.
Designers can use the presented framework to improve existing, or prepare them for future designs.
In addition to improving the optimal algorithms, we also intend to use the presented framework to derive sub-optimal algorithms.
The author has used the presented framework to work out several cases, to achieve significant improvements in performance, reliability, availability and maintainability.
We do, however, hope that we will be able to depict a clear picture once such samples are used in the present framework.
Two optimisations are run for a generic aircraft wing under manoeuvre load conditions and aeroelastic, structural, and aerodynamic constraints: one, a quasi-isotropic wing to serve as a reference solution and two, a fully tailored wing clearly showing the benefit of aeroelastic tailoring and the use of the present framework for conceptual wing design.
To support the construction of elastic applications using the presented approach, we developed the Cloudine framework.
Further studies could also use the presented modeling framework to examine the influence of climate and environment on other syndromes recorded routinely at hospital such as pediatric febrile and respiratory infections.
Contrary to this, the present framework uses a well-known Mediator/Wrapper virtual data integration architecture, enabling the direct access to the current data supplied by the data sources.
In the present framework, we use conditional mutual information to calculate how strongly a feature variable is correlated with a class variable conditioned on M s so that the redundant features can be identified, according to the following corollary.
