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The desynchronization issue is reproduced by introducing a variable synchronization error with values ranging from 0.8 to 2.1 ms.
However, Yang [26] gave a result with the kernel (frac{1}{(1+nx)^{lambda}}) by introducing a variable and proved that the constant factor is the best possible.
In 2005, Yang [14] gave a result with the kernel (frac {1}{(1+nx)^{lambda}}) by introducing a variable and proved that the constant factor is best possible.
In 2005, Yang [17] gave a result with the kernel 1 ( 1 + n x ) λ by introducing a variable and proved that the constant factor is best possible.
However, Yang [25] gave a result with the kernel 1 ( 1 + n x ) λ ( 0 < λ ≤ 2 ) by introducing a variable and proved that the constant factor is the best possible.
We have proposed a new approach for improving the accuracy and speed of back-azimuth estimation from the PCA by introducing a variable time window to reduce the influences of trailing scattered waves, instead of a conventional fixed time window.
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In this study, we propose a new approach to improve the accuracy of the PCA, which estimates the back-azimuth by introducing a variable-length time window instead of the conventional fixed-length time window.
Compared with the conventional approach in which the length of the time window for the analysis is fixed for all data, the accuracy and speed of the estimation are drastically improved by introducing a variable-length time window which is determined by the first half cycle of the wavelength of the initial P -wave, because this window tends to reduce the influences of trailing scattered waves.
However, errors in the assembly are vastly more likely to cause false negative than false positive sweeps by incorrectly introducing a variable locus into a region of dramatically reduced variability within the genome.
Typically, such rate variability is modeled by introducing a rate variable, r, which scales, for each site, the time units of the phylogenetic tree, Δ t ← r·Δ t.
However, Theorem 1.4 provides a remedy by introducing a third variable Z with respect to which we can maximize.
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by including a variable
by enforcing a variable
by incorporating a variable
by introducing a small
by introducing a little
by introducing a Lagrangian
by introducing a public
by introducing a partial
by introducing a characteristic
by introducing a useful
by introducing a normative
by introducing a maximum
by introducing a multiplicative
by introducing a new
by introducing a six-cylinder
by introducing a parallel
by introducing a flat-rate
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