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Figure 3 Test of the methods on different sets of simulated signals.

The neural net performances were studied for different sets of simulated data.

For different sets of simulated landscapes, gamma diversity was well explained by predicted average alpha and beta diversity, plus the sampling effort (r2 = 0.92).

Different sets of simulated signals are considered in Figure 3 to investigate the performances of the algorithms in estimating the flux as a function of the modulus and direction of the propagation velocity and of the energy of additive random noise.

This led to 12 different sets of simulated data.

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Fig. 1 Standard deviation computed over different set of simulated paths to Amerasian Call Options' cases to the following set of parameters: S(t 1) = K = 100; σ = 20%%; r = 5 %; y = 0 Fig. 2 Standard deviation computed over different set of simulated paths to Amerasian Put Options' cases to the following set of parameters: S(t 1) = K = 100; σ = 20%%; r = 5 %; y = 0.

To simulate the performance of the proposed protocol, we carried out different sets of simulations.

Hence, we tested different sets of parameters to simulate different recombination scenarios, of which key parameters include the recombination rate, the position of labelled SNP, MAF of the labelled SNP and the BGC rate.

Although the overall statistics are similar for the three different sets of parameters, improvements in simulated flow are observed for small basins when SSURGO-based parameters are used.

Due to the complexity of the deployment problem in reality, the deployment problem is treated as a complex adaptive problem and simulated through different sets of experiments and settings.

An optimal interpolation method is used to reconstruct the large-scale and low-frequency variability of the temperature and salinity fields at different depths from various sets of simulated temperature and salinity profiles.

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