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Now, let L be a subcollection of E ( X ) such that L * ⊆ I j ε for some j ≠ 1 and ε ∈, and L * is an arc with endpoints a ∈ I 1 + and b ∈ I 1 -, that is L * = [ a, b ].
end{aligned}Here (tau ^{(j)}) and (beta ^{(j)}) are as defined in (3) and (4) with l equal to the constant 1 sequence, that is (l = (l_{i})) with (l_{i} = 1).
Therefore, we conclude that for any time slot t and subcarrier m, there is only one SU k*, that is L k *, m, t * = 1 if subcarrier m has been allocated.
A guided observation in a swimming pool makes it easy to realize this fact: if a woman that is L (large) on the bust buys a swimsuit, she must be also L in the whole body, or the garment will have a bad fitting in any part that's not L.
Now, if (j) holds, then H ′ ( x ˜ ) = 0 (that is L ( x ˜ ) = z ) and there is r > 0 such that (3.4) holds for all x ∈ B r. So, from (3.5), we have 〈 L ( x ), x 〉 ≤ 0 for all x ∈ B r and then, by linearity, for all x ∈ X, and this shows (jjj).
The mean of S l ¯ b is large when l ¯ b → l b but close to zero when l ¯ b → l b + M / 2. However, S l ¯ b + M / 2 has the least impact on S l ¯ b, that is l ^ b least likely equals to l b + M/2.
Similar(19)
As in one dimension, increasing the density d will decrease the standard deviation σ i of the local bumps in the posterior P(x| i that is, l i / σ i = g(d), where g is an increasing function of d.
The calculation of L t) can be divided into two parts: the link availability when the velocities of two nodes keep unchanged between t 0 to t 0 + t, L 1 t) and the one for the other case, L2 t), that is: L t)={L}_1 t)+{L}_2 t) (11).
As ({b_{n}}rightarrow L) and (L< b_{n}) for all (ninmathbb{N}), then there exists a partial subsequence ({b_{n k)}}_{kinmathbb{N}}) of ({b_{n}}) such that ({b_{n k)}}) is strictly decreasing, that is, L< b_{n k+1)}< b_{n k)} quadmbox{for all }kinmathbb{N}.
ASNS is the amidotransferase that converts L-aspartic acid into L-asparagine in an energy-consuming enzymatic reaction requiring ATP and a nitrogen source that is L-glutamine in eukaryotic cells (Richards and Kilberg, 2006).
A matrix function A ∈ M is n ∞ -summably similar to a matrix function B ∈ M if there exists an absolutely summable s × s matrix F ( n ) over N ( n 0 ), that is, ∑ l = n 0 ∞ | F ( l ) | < ∞. such that Δ S ( n ) + S ( n + 1 ) B ( n ) − A ( n ) S ( n ) = F ( n ) (2.14).
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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.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com