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The proposed Taguchi DOE methodology consists of four phases viz., planning, conducting, analysis and validation, which were connected sequence wise to achieve the overall process optimization.
A sequence ({x_{n}}) in X is called a termwise connected sequence if ((x_{n},x_{n+1} in E G)) for all (ninmathbb{N}).
Assuming that input points are a spatially connected sequence of edge points, we fit an ellipse to it and automatically segment it into partial arcs at the intersection points of the fitted ellipse.
Assuming that input data is a spatially connected sequence of edge points, we segment it into partial arcs by considering the ellipse fitting residuals and detect inliers by computing the curvature of the residual graph of each of the segmented arcs.
(b) For any termwise connected sequence ({x_{n}}) in X if (x_{n}to x) and (x_{n+k}in T(x_{n},x_{n+1},ldots,x_{n+k-1})) for all (nin mathbb{N}), then there exists a subsequence ({x_{n_{j}}}) such that ((x_{n_{j}},x in E(G)) for all (jinmathbb{N}).
Moreover, there exists a termwise connected sequence ({x_{n}}) in X such that (x_{n+k}in T(x_{n},x_{n+1},ldots,x_{n+k-1})) for all (ninmathbb{N}) and ({x_{n}} ) converges to a fixed point of T. There exists a path ({x_{i}}_{i=1}^{k+1}) of (k+1) vertices in G such that (x_{k+1}in T(x_{1},ldots,x_{k},x_{k})).
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We then used sequence similarity amongst the sequences within bi-connected components to infer the most likely physical origin of the connected sequences.
She enters so intensely, with such thorough commitment, whatever scenario she falls into; her imagination is narrow and deep, rather than working in long arcs, connected sequences of cause and effect.
Conclusion This trial is evaluating 2 temporally connected sequences of phamacotherapy for ACS.
As a consequence, sequence similarity networks provide a first clustering, producing connected components (CCs), that is, sets of connected sequences isolated from the rest of the graph.
In silico experiments reveal that some structures are mutationally robust because they have large networks of highly connected sequences [ 28] allowing them to maintain structure while tolerating many different mutations.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
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CEO of Professional Science Editing for Scientists @ prosciediting.com