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In comparison to previous interneuron network models, our model was able to generate oscillatory activity with higher coherence over a broad range of frequencies (20-110 Hz).
Compared with other existing models, our model has a very extensive and detailed social network, which may be important because individuals with more social interactions and extensive social networks may be more likely to spread influenza.
In relation to previous nutrient cycling models, our model offers a satisfactory compromise between simplicity, biological realism and predictability, and it proved to be a useful tool to predict short-term changes in nutrient reserves as well as to evaluate possible negative effects of applying current thinning prescriptions on long-term sustainability of managed forests in the western Pyrenees.
Compared to the tradition forward-backward diffusion models, our model has the following characteristics and advantages.
With improvements on the precedent models, our model adopts these three issues and brings out the new analytical throughput.
Unlike earlier approach that employed topic models, our model employed editing function and dictionary lookups to specifically account for intentionally misspelled words in phishing emails.
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In order to improve the reliability of the source model, we discuss the reproducibility of the distribution of a questionnaire-based intensity of 7 for various source fault models: our models and models presented in previous researches.
That is not our model – our model is a subscription one, and we do think that's a distinction.
Different from the conventional model, our model is aimed at partitioning an entire service area into multiple service regions or clusters of customers.
Built upon outputs from a state-of-the-art air quality model, our model produces comprehensive risk-based source apportionment results with trivial computational costs.
Unlike the standard model, our model allows injurers to make second order precaution observable at a cost.
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