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Our modelling approach shows that applying the ARIMA time series models to forecast injury mortality in Xiamen is feasible.
Our modelling approach shows that the ARIMA (0, 1, 1) model could reflect the trend of injury mortality in Xiamen and forecast mortality reliably for a short time period.
The presented modelling approach shows great potential for application in the composite product design process.
The analysis of the results of a case study using this modelling approach shows the variety of relations between users and plant manufacturers.
Summarizing, our modelling approach shows that transient variations of Nanog levels can be explained by rather simple interaction networks.
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Both modeling approaches showed consistent and complementary results in terms of the pattern drivers that were most related to deforestation.
In cancer research, a number of network modeling approaches showed to be very promising [ 99– 104].
Our step-wise combinatorial multi-model approach shows a more detailed view of overall performances.
Different modelling approaches showed consistent results.
Main effects and interaction terms of the formulation variables were evaluated quantitatively using a mathematical model approach showing that both independent variables have significant effects on floating lag time, % drug release at 1 h (D1 h) and time required to release 90% of the drug (t90).
Deviance analysis resulting from the generalized linear model approach showed no significant differences between years, between seasons, nor between seasons within each year in terms of AST presence.
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