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Although the differentiation in a complete range of neuronal subtypes has not yet been achieved, this model system is extremely promising as it allows to study patient-derived cells which retain the original disease-causing mutation and can be differentiated in virtually all cell types.
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To achieve this, model predictive control (MPC) may be a suitable control technique.
To achieve this model, different Adaptive Neuro-Fuzzy Inference System (ANFIS) models (by changing the training optimization algorithms) are designed.
To achieve this model, we made use [4] of persistent identifiers such as the well-known DOI or digital object identifier for both the narrative and the data.
To achieve this, two models, a Product Model and a Costgrammes Model, are presented and used to represent and capitalize technical knowledge.
The results show that good agreements are achieved with this model to predict fatigue life.
An excellent agreement with literature experiments is achieved from this model.
This is achieved in this model via the notion of a 'parameter': some of the innately represented grammatical principles contain variables that may take one of a certain (highly restricted) range of values.
As for fault diagnosis, if the monitoring data under different fault conditions are available, accurate diagnosis results can be achieved by this model.
Taxation raises the debt to equity ratio, but is not a determining factor for the type of equilibrium achieved in this model.
A remarkable result they achieved using this model is the reproduction of the bifurcation from the flattened tops of EPBs, as shown in Fig. 2.
Write better and faster with AI suggestions while staying true to your unique style.
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