Model infrastructure

One area that Schrage identified as needing improvement is the communication between scientists and businesspeople. He stated that poor communication yields better models that are less accessible to nonscientists, due to the scientist s inherent interest in the question and the businessperson s interest in only the answer. Additionally, modeling infrastructures would be improved by increased participation by all relevant parties in the modeling process. This is achieved by increasing the usability, increasing the attraction factor, and targeting the evolution of models. 

What do we mean by a modeling infrastructure Given the situation in which some high-level chemistry is hypothesized, too much data exist for one scientist to analyze. For example, the 820 reactions of the rubber components formulation involve over 100 chemical species. It is impossible for one person to write and solve over 100 coupled differential equations without making mistakes. It would take nearly 3 months to explore one scenario. So we have built an environment where the scenario is specified and the information is automatically translated into equations, the parameters are optimized, and the modeled results are compared with data. This way a scenario can be analyzed in a few hours instead of 3 months. This is the type of modeling highway that can get the most out of combinatorial chemistry situations. 

It is necessary to use the model as a medium to manage interaction between the positive and the normative folks. That is one of the underplayed aspects of the problem because the modelers are optimizing it for their community rather than optimizing the models for interaction. You can do this in academe. In business it fails completely. The real question is how do we want this community of collaboration to evolve, and we can adjust our investment in the modeling infrastructures based on that answer. 

The section is structured as follows We take up the categorization of modeling problems of Sect. 6.1 and give a detailed explanation. Then, we address the corresponding modeling infrastructure problem. In total, we come up with 17 big problems to be solved yet. In the conclusion we give some arguments that the PPM problem was too hard to be solved within one project and/or the available time. 

All of the above problems deal with modeling aspects. In this subsection, we state further problems, dealing with the modeling infrastructure (languages, methods, tools), which is also neither complete nor uniform (see Fig. 6.13). 

Ogden,. M. (2002). Modeling Infrastructure for a Fossil Hydrogen Energy System with C02 Sequestration. Princeton Environmental Institute. 

GIRKE, T., OZKAN. M., CARTER, D., RAIKHEL, N.V., Towards a modeling infrastructure for studying plant cells. Plant Physiol, 2003,132,410-414... 

FIGURE 8 In addition to numerical codes, the modeling infrastructure of CORIE includes modules that specifically deal with forcings (bathymetry and river, atmospheric and ocean forcings), quality controls, and modeling products. While some modules and codes (shown in gray) are primarily developed in-house, many are developed externally... 

Devesa E, Comas J, Turon C, Ereixo A, Carrasco F, Poch M (2009) Scenario analysis for the role of sanitation infrastructures in integrated urban wastewater management. Environ Modell Softw 24(3) 371-380... 

The consequence of moving consciously toward this model will be the provision of a robust and scalable IT infrastructure and systems able to cope with exponentially growing data mountains that will need to be integrated and shared, accessed and mined in the most effective way. It will also require formidable computing power and sophisticated algorithms to be able to simulate both organs and whole body systems to reduce expensive failures in the clinic and predict much earlier the pharmacokinetic and pharmacodynamic properties and toxicological and efficacy profiles of molecules in pharmaceu-... 

In the given case study, it is possible to introduce additional terms into this comparison which may look at energy implications such as process machinery and infrastructure life cycle energy costs, warranty repair, supply chain PSS, for example, depending on the company s scope and business model. 

Several models of HICE vehicles have been demonstrated and few are commercially available [25,28,33,38]. However, hydrogen-powered vehicles will not be available to common public until there is an adequate refueling infrastructure and trained technicians to repair and maintain these vehicles. The design of each hydrogen-powered vehicle may vary from manufacturer to manufacturer and model to model. One model may be simple in design... 

The embedding approach may require an improvement of modeling in the ERP system (i.e., to maintain additional detailed information for APS purposes) but it can utilize all established business processes, data models and infrastructures. Introduction of the subordinate planning system can be done step-by-step with minimum impact on established business processes. Integration is fully guaranteed... 

Make an early architectural decision about how much you will tradeoff performance, seamlessness, reuse, code flexibility, and so on. If your clients shout for little functional enhancements every day (something that is typical for in-house financial trading software), optimize the underlying communications and infrastructure but leave the business model pristine. But if your software will be embedded in a million car engines for 10 years, optimize for performance. 

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