National-average percentage difference (posterior mean and 95% posterior intervals) in daily total mortality per 10-[micro]g/[m.sup.3] increase in ozone concentration in 272 Chinese cities during single-day lags (lag 0, 1, 2, 3), multiple-day averaging lags (lag 0-1, 0-2, 0-3), and cumulative lags based on a polynomial distributed lag model (PDLM 0-3, 0-6, 0-9).
First, we fitted two-pollutant models with adjustment for the concomitant exposure to [PM.sub.2.5], S[O.sub.2], N[O.sub.2], and CO, which were introduced by using the same PDLMs as those used for ozone.
The LMM and PDLM algorithms exhibited different levels of convergence for different values [lambda].
Figure 5(b) and Figure 8(b) depict the relative solution error norm over iteration using the LMM and the PDLM respectively.
We can observe the LMM and the PDLM are able to provide better stability compared to the AGNM.
Clearly we can see this effect in the final stage of the iteration where the LMM and the PDLM switch to small step sizes, which infer the steepest descent becomes dominant.
The LMM and PDLM employ an endpoint strategy to their iterative procedure such that the iteration stops when the change in the conductivity solution i.e., [delta][[sigma].sub.k] is small.
For solving the MIT conductivity inverse problem, the LMM and PDLM require the specification of two parameters: the damping variable [gamma] and the constant regularisation parameter [lambda].
The pDLM model intelligently integrates archiving with the critical functions of backup.
This is why pDLM is fundamentally different to both traditional HSM utilities and data classification products, pDLM integrates data protection, business continuance, and disaster recovery strategies into the long-term retention and management of data as its lifecycle requirements cause it to be copied into and subsequently repositioned entirely to a secondary storage archive.