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Some issues relevant to the practical implementation have also been discussed.
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Many countries' record of implementation has also been poor.
The delay in implementation has also been accompanied by a sense of anticlimax and missed opportunities for both childcare and eldercare, with some fundamental issues remaining.
This gap between policy and implementation has also been there since the very beginning (Lægaard, 2016).
A model implementation has also been built for a mobile price comparison to test our proposed architecture and solutions.
The MLMC-FV implementation has also been developed in CUDA, where all the updates of the means and variances have also been implemented using CUDA kernels.
GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.
Our implementation has also been given, and proved to meet the constraints proposed by each component of the framework.
If we look at the history of ITS, implementations have also been restricted mostly to STEM domains.
Taking advantage of the advent of general purpose graphics processing unit (GPGPU), we implemented our algorithms on multi-GPU with Compute Unified Device Architecture (CUDA) and, for comparison purposes, MPI and OpenMP implementations have also been developed.
Successful CCM implementations have also been shown to work best within the pragmatic environment using a collaborative approach and coaching initiatives aiming at empowering health care providers to improve clinical practice [ 24].
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