Sentence examples for methods are i from inspiring English sources

The considered design methods are i) mixed-ℋ∞ design, and ii) ℋ∞ loop shaping design.

The main advantages over Monte Carlo methods are (i) the solution is provided throughout the entire spatial domain, and (ii) the analysis process is simplified through direct geometry input from engineering design in CAD format.

These methods are (i) correlation-based estimation according to (12) with the channel included using (14); (ii) correlation-based estimation assuming an ideal channel, that is, using (12) and (13); and (iii) ML-based estimation using the cost function (10).

These methods are: (i) Taylor averaging, in which the interactions of the grains are considered in a homogenized manner; (ii) low-resolution simulation (LRS), in which grain interactions are considered explicitly albeit with low resolution; and (iii) direct numerical simulation (DNS), which provides high-resolution details of the deformation fields inside the grains and of the grain interactions.

The optimization methods are (i) the black-box method with finite difference gradients, (ii) a modification where gradients are found by an algorithm based on the implicit function theorem, and (iii) an all-atonce method where the flow and design variables are simultaneously altered.

The four commonly used methods are: i) political patronage ii) historical allocations iii) bids by local governments and iv) needs-based resource allocation formulae [ 1].

The three cluster-level analysis methods were: i) un-weighted linear regression, ii) weighted linear regression, and iii) random-effects meta-regression.

The common settings for all methods were (i) to consider sequences as circular, (ii) a statistical significance of P < 0.05, and (iii) a Bonferroni correction for multiple comparisons implemented in RDP.

The six individual level analysis methods were: i) standard logistic regression, ii) robust standard errors approach, iii) generalized estimating equations, iv) random-effects meta-analytic approach, v) random-effects logistic regression, and vi) Bayesian random-effects regression.

The main advantages of our method are (i) High parameter estimation performance.

The main advantages of the method are: (i) the flow cytometry data compensation is no longer necessary, and (ii) the cell sorting capacity of the method is significantly improved as compared to classical clustering methods.