Modeling techniques improving

Petera, J. and Nassehi, V., 1995. Use of the finite element modelling technique for the improvement of viscometry results obtained by cone-and-plate rheometers. J. Non-Newtonian Fluid Mech. 58, 1-24. 

Nevertheless, we can use some of the techniques from E-R modeling to improve a model. 

The reduction of latent variables is an effective method to reduce the number of possible models, yet in PLS, variable reduction is not needed. The reduction of the number of variables in traditional regression techniques will lead to models with improved predictive ability and, in the case of PLS, a model that is easier to understand. The attempts to reduce the number of variables for PLS have only resulted in simpler models that fit the Training Set better yet do not have the predictive abilities of the complete PLS model (111). The reduction of latent variables with respect to the descriptors is possible with no apparent decrease in the model s ability to predict bioactivities, yet the remaining descriptor-based variables are considered to be more important before reduction and thus introduces bias (111). 

Considering the continued development, verification and improvement of these aerial spray modeling techniques and their acceptance by the U. S. Army and U. S. Forest Service, we believe the concepts deserve wider use in civil spray operations, pesticide development programs and environmental assessment studies. We therefore welcome the opportunity presented by this symposium for discussing the mathematical framework of the models and to illustrate their applications. 

The process of applying mathematical constructs to describe experimental results often reveals patterns in the agent s pharmacokinetics or dynamics that might not otherwise be discernible. Failure of a model s simulations to predict experimental measurements sometimes prompts questioning of the data, such as the reliability of the quantitative methods, or sample collection or exposure techniques. More often, it may indicate that greater complexity in the model s structure is required to capture the data s behavior. This is another primary reason for developing models to create hypotheses (model structures) that are falsifiable, leading to improved models and improved predictions in an iterative process. 

The adjoint modelling technique is a good instrument to improve on the climatological emissions that are currently used for many species. 

Besides the mathematical improvements, the atmospheric model has been adapted to a semi-Lagrangian formulation. By following selected air masses, we avoid commitments of large quantities of memory and the incursions of artificial diffusion errors. Most important, we do not end up with stacks of computer printout that relate to regions where there are no measurements. Also, predictive calculations will become more useful, but our present levels of resources and sophistication demand that effort be concentrated on verification. Only in this way can the confidence be built that is needed for applying modeling techniques to implementation planning. 

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