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A generic process interval model is used to describe each alternative in terms of input output mass balances including conversion and separation factors.
Continuing such a process interval by interval to J m + 1 ′, by (3.2), we obtain that v0 ≤ Qv0.
Continuing such a process interval by interval up to (J_{m}), we can prove that (varphi(t)equiv0) in every (J_{k}, k=0,1,2,dots,m).
Continuing such a process interval up to J m + 1 ′, we see that u(t) ≡ ū(t) over the whole of I. Hence, u= ū is the unique mild solution of the Cauchy problem (1.1) on [v0, w0].
Continuing such a process interval by interval up to (J_{m+1}^{prime}), we can prove that (phi(t)equiv0) in every (J_{k}^{prime}), (k=1, 2, ldots, m+1).
Continuing such a process interval by interval up to (J_{m}), we can prove that (alpha_{E}(B t))=varphi(t)equiv0) on every (J_{k}), (k=0,1,2,ldots,m).
Similar(50)
The measured components can be changed during the process intervals.
Similarly, the gain of the ASIC can be changed during the process intervals.
There are three interdependent factors that drive our development processes: interval, quality and cost.
Interval programming is a technique that readily processes interval data, thereby avoiding many problems encountered by other optimization methods (e.g. stochastic programming) when faced with parameter uncertainty.
The Anderson Total Patient Delay Model developed by Walter identifies events, processes, intervals and other contributing factors as important influences on the diagnostic interval [ 27].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com