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Instead of working with nonlinear programming models, we propose a discretization modeling approach, where the cycle, green time, and traffic volume are divided into a finite number of discrete values.
In this research, after introducing two forms of pre-defined component patterns to represent biochemical models, we propose an integrative qualitative and quantitative modelling framework for inferring biochemical systems.
Unlike other models, we propose a single mechanism for both creative problem solving and complexity reduction.
To fully explore the potential of neural models, we propose a methodology for collecting a large corpus1 of regular expression, natural language pairs.
The models we propose are discrete, built upon common blocks in control engineering (gain, delay, sum, etc).
For presence/available models we propose a form of k-fold cross validation for evaluating prediction success.
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We also compare the performance of the two models we proposed.
Based on our EM model, we propose a model for the domain arrangement of LRRK2 and LRRK1, showing antiparallel dimers with suggested domains extending beyond the dimer core (Fig. 8).
Given this model, we propose an efficient algorithm for finding reconstructions and illustrate how it strikes a balance between efficiency and the quality of the produced results.
The model we propose extends current models by taking congestion effects into account.
Then, adopting the Rice Thomson model, we propose a dislocation nucleation criterion for stress contacted surface.
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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