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Two selected variables represent two main groups of ALS variables describing spatial structure of the stands: height - p95 and density – pFRAMe.
Subsequently, the two selected variables and crude glycerol were optimized by the Central Composite design for achieving maximum hydrogen and ethanol yield.
For each mutant strain, an ANOVA was performed on the two selected variables to compare centre and genotype performance (supplementary material Table S1).
The first was the LARS algorithm for the lasso, which was considered to have identified the correct hypothesis if the first two selected variables encoded that hypothesis.
The LARS algorithm was considered to have identified a compound hypothesis if the first two selected variables encoded that hypothesis and the P value for including the second variable was less than 0.05.
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The ten selected variables used for modeling (Table 1) were chosen for their Pearson's correlation index among the variables of each group type (climate, topography, or landscape).
For example: Table 8 shows that the solutions with eight selected variables found in run 1, run 4 and run 39 are not the same in spite of the same number of selected variables.
Of six selected variables (temperature, mixing speed, reactant concentration, addition rate, presence of inert atmosphere and aging time), temperature has shown the strongest influence on HAP structural and sorption properties.
The seven selected variables, featured by high percentage of older population, are likely to reflect the adverse impacts of natural disasters and health-related emergencies to the corresponding populations (Table 1).
We considered that the class of service is more general than other variables, so we selected this as a decisive factor, classifying the criteria as: RT and BE and considering the other four selected variables as sub-criteria of the class of service.
The resulting model (eq 11) includes six selected variables.
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