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In order to verify the stability of the inversion method, we added random Gaussian noise to the synthetic gather data with different signal-to-noise ratios (SNRs).
To make a fair comparison for the C-V method, we added the internal energy (18) into the functional (5) to avoid the re-initialization step.
To estimate an error of the ASL method, we added random fluctuations to the site amplification factor of each station based on the assumption that the site amplification factor follows a log-normal distribution (standard deviations are listed in Table 1), then we conducted a grid search 100 times for each event with the fluctuated site amplification factors.
For our method, we added P2 as a complementary algorithm to improve the biological inference.
Using this method, we added F-enriched labels to different proteins, and visualized them by NanoSIMS and fluorescence microscopy.
Compared to the standard multiple imputation using propensity score method, we added cluster as one of the covariates to obtain the propensity score for each observation.
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In this method, we add a finite number of surface integral to leave the action finite [33].
To increase the robustness of the proposed method, we add a new phase, referred to as partitioning phase, before diversification which is realized by the TS.
To expand the application of our method, we add Gram-positive bacterium B. subtilis genes to the experiment dataset, and the prediction performance is outstanding.
We provide the SAS code for this method in Appendix A. Compared to the predictive model using standard logistic regression method, we add cluster as a fixed effect to account for clustering effect.
In accordance with qualitative research methods, we added 2 questions about this to the focus group guide as the study progressed (Appendix 1) and we reported themes about influence of the EHR on quality as they emerged from the analysis.
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CEO of Professional Science Editing for Scientists @ prosciediting.com