Previous machine learning-based approaches to identifying TFBSs commonly map a known TFBS to a one-dimensional vector using its physicochemical properties.
The previous deep-learning-based image SR approaches always learn an end-to-end mapping, which takes the low-resolution image as input and directly outputs the high-resolution one.
The neurons of these previous deep-learning-based methods work on the whole input feature map.
However, the previous deep-learning-based image super-resolution methods rarely take image prior into account.
On the other hand, in all the previous deep-learning-based methods, each neuron works on the whole input feature map.
Compared to the previous deep-learning-based methods, the neurons in our proposed SR network pay more attention to acquire the local image information to avoid compromise to different image contents.
Compared to previous deep-learning-based single-image SR methods, our proposed method is also an end-to-end mapping that takes the low-resolution image as input and directly outputs the high-resolution one.
Further variables of interest or potential confounders, which were also modelled as fixed effects were students' gender, age, previous knowledge, e-learning interest and e-learning experience.
This included a justification being based not only on best evidence, but also local data (jurisdictional/national), previous experiences and learnings and local expert opinion.
Previous studies have reported learning-related changes in the Purkinje cells' response during the VOR after at least an hour of VOR-increase or -decrease training (Hirata and Highstein, 2001; Lisberger et al., 1994; Raymond and Lisberger, 1996; Watanabe, 1984).
In view of the deficiencies in previous research, a learning-based synchronous (LS) approach from forwarding nodes is proposed to reduce the delay for IIoTs while not reducing the network lifetime.
