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First, it is shown herein that if one adopts the classical thin plate theory and the Galerkin's method with commonly used modal functions consisting of the products of free free beam modes, the natural boundary conditions cannot be satisfied at the free edges and the shear forces are completely erroneous, even when the eigenvalues have already converged.

Table 4 Undrained cohesion correlations ranking results based on ATIC method with commonly used statistical measures in correlations evaluation Cor.

Table 5 Drained internal friction angle correlations ranking results based on ATIC method with commonly used statistical measures in correlations evaluation Cor.

This endeavor concluded in development of a trouble-free, fast, perfect, and sensitive HPLC method with commonly available UV detector for the concurrent estimation of levetiracetam and piracetam in pure form and pharmaceutical formulations.

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In addition, we compare exponential methods with commonly used implicit schemes to demonstrate their competitiveness.

In this section, we contrast the performances of our methods with commonly used alternatives on three case studies.

We compare the proposed methods with commonly used statistical approaches such as fitting separate regression models for each potential window and fitting simultaneously adjusted multiple regressions that include all exposure measures (time windows) in one model.

This non-predictability test method along with commonly used empirical tests based on statistics is proposed as a methodology for quality assessing strong pseudorandom stream generators.

We compared the performance of our method with other commonly used differential expression analysis algorithms.

We first evaluated the robustness and consistency of the PS detection algorithm, and then compared our method with the commonly used morphological thinning and affine-transform-based alignment method.

We reported the effectiveness of our methods with the commonly used information retrieval metrics, namely, precision (P), recall (R) and F1 score defined as follows: P = T P T P + F P, R = T P T P + F N, F 1 = 2 * P * R P + R, where TP, FP and FN are the numbers of, respectively, true positive, false positive and false negative recognitions.

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