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Modeling of temporal functions has been studied extensively in last few decades.
In this paper, a locally feedback dynamic fuzzy neural network (LF-DFNN) for modeling of temporal processes is suggested.
Furthermore, spatial biases may change across time as research priorities, funding, and methodologies change, which may confound modeling of temporal changes in distributions.
Traditional time series methods allow for the modeling of temporal processes where measurements at different time points are correlated or dependent.
Low-frequency signal drift was removed using a high-pass filter (cutoff 128 s) and AR(1) modeling of temporal autocorrelations was applied.
This approach thereby enables modeling of temporal trends in e.g., excess cardiovascular mortality and remaining cancer excess mortality simultaneously.
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Many different models of temporal consciousness have been proposed.
This influential line of argument leads directly to PSA, and thence to the associated Retentional model of temporal consciousness.
Rats with previously-established seizures, induced by amygdala kindling, were used as a model of temporal lobe epilepsy.
These data show that in the pilocarpine model of temporal lobe epilepsy the latency period is short.
Although it may not be quite so obvious, the model of temporal consciousness proposed by the Cinematic realist also creates nothing more than mere succession of experiences.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com