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We evaluated how landscape configuration structures home ranges of animals by performing individual-based, spatially explicit computer simulations using each model on simulated landscapes that differed only in the spatial continuity of resources among patches (from over-dispersed to clumped).
To seek for such a pattern, we applied a segmented regression model on simulated data Z jk (1 ≤ k ≤ 10), with one breakpoint occurring at generation G b (2 ≤ b ≤ 7).
We explore the performance of the proposed model on simulated data, with both block- and event-related design, and on real fMRI data.
The Nested Bias Correction (NBC) approach is used to correct possible biases of precipitation (simulated directly by the GCMs) and PET (estimated by applying FAO56-Penman-Monteith model on simulated parameters of the GCMs).
Our aim for the current work was to examine the effects of parietal lesion in the model on simulated visual search behaviour.
We first tested the Bayesian Dirichlet model on simulated data.
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Figure 5 Average AUC of the detection models on one simulated intrusion in the EHR dataset.
Figure 6 Average AUC of the detection models on one simulated intrusion in the Wiki dataset.
Simulations results for a multi-cell stack have also been demonstrated to show capability of the model on simulating cell to cell variation.
Evaluation of both models on simulated data sets revealed that they performed equally in estimating differentiation when considering a migration-drift scenario while model 2 was surprisingly more efficient under the pure-drift divergence scenario.
We first demonstrate the main benefit of the proposed models on simulated datasets.
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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