Conceptual Modeling Approaches

Similarly, conceptual and information modeling can be used to describe complex decision-modeling problems (Biswas and Narahari 2004 Kim and Rogers 2005). Besides the descriptive capabilities of information modeling techniques helping to understand the problem, developed information models provide a link between decision-modeling and the enterprise-wide information system. 

The objective of this chapter is to describe the application of conceptual modeling techniques for supply chain configuration purposes. The general approach is to use well-known conceptual and information modeling techniques that would enable potential model-driven implementation of decision-modeling components. 

One of the main objectives of using conceptual modeling is providing a relatively easily tmderstandable representation of a problem. Several conceptual and information modeling techniques are usually applied to obtain a comprehensive representation of the problem. The choice of techniques and modeling concepts depends upon objectives of the information modeling application. In the framework of supply chain configuration, several objectives can be identified  

In the case of implementation of decision-making components, information modeling methods are used in a similar manner as in the development of information systems. This approach is mainly applicable if decision- making components 

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The 1-arenesulfonylprolinate catalysts have been studied computationally.209 A computed TS and conceptual model that is consistent with experimentally observed enantioselectivity is shown in Figure 10.11. The arenesulfonyl groups block one of the directions of approach to the carbene catalyst and also orient the alkene substituent away from the metal center. 

Stochastic or probabilistic techniques can be applied to either the moisture module, or the solution of equation (3) — or for example the models of Schwartz Crowe (13) and Tang et al. (16), or can lead to new conceptual model developments as for example the work of Jury (17). Stochastic or probabilistic modeling is mainly aimed at describing breakthrough times of overall concentration threshold levels, rather than individual processes or concentrations in individual soil compartments. Coefficients or response functions and these models have to be calibrated to field data since major processes are studied via a black-box or response function approach and not individually. Other modeling concepts may be related to soil models for solid waste sites and specialized pollutant leachate issues (18). 

To analyse the problem posed in Chapter 1 an overview of current literature on tools, methods, and standards concerning safety indicators will be presented in Chapter 3. With this overview a better understanding of the signs currently used to indicate safety will be obtained. These signs will be compared with the signs present prior to recent accidents (1995-2002). From both literature and case histories a hypothesis will be derived that will be especially tested in Chapter 6. Moreover, in Chapter 4, the conclusions will be used to develop some generic concepts and a conceptual practical approach. The approach will consist of several steps and models derived from organizational science and safety literature. 

General Observations About x. its Relationship to the Overall Partitioning Coefficient and to the Concept of Surface-Site Heterogeneity. One approach to metal/particle surface interactions which has been developed, historically, in a variety of forms, is a conceptual model that assumes only two conditions for surface sites occupied by an adsorbate or unoccupied. In applying this approach to the solid/aqueous solution interface, the adsorption... 

Various approaches and graphical conventions have been used in drawing conceptual model diagrams. Consideration could be given to recommending a standardized approach for use in probabilistic assessments. 

Careful construction of the conceptual model diagram, and the use of a tabular approach such as Table 2.1, should help to avoid these problems. The diagram should show clearly the point at which individual exposure is used to predict individual effects and the process by which individual effects are aggregated to generate the risk estimate. In addition, it should be remembered that the risk estimate may be combined quantitatively or qualitatively with other lines of evidence to address the assessment endpoint. 

FIGURE 2.3 A simple approach to identifying variables, uncertainties, and dependencies in a conceptual model diagram. For key, see Table 2.2. 

A critical extra phase to be included when planning probabilistic assessments is the selection and parameterization of distributions, to represent the sources of variability and uncertainty that have been identified in the conceptual model. The issues and approaches involved are discussed elsewhere in this book. 

A conceptually simple approach that avoids the difficulties of model weighting is scenario analysis or the 1-at-a-time method, where the alternative models are analyzed separately and the resnlts are compared. In the example of the previous section, this might produce a conclusion of the type If model A is true then 0 people will get cancer if model B is trne then 200 people will get cancer. However, this... 

Schuessler, W., Artinger, R., Kienzler, B. KlM, J. I. 2000. Conceptual modeling of the humic colloid-borne americium(III) migration by a kinetic approach. Environmental Science and Technology, 34, 2608-2611. 

A conceptual model which is the centerpiece of this chapter is developed in Section III. This is preceded (Section II) by a brief introduction to various organized media. The validity and generality of the model is examined by two approaches. In the first (Sections IV-VI), selected photochemical reactions belonging to various classes and chromophores are presented as supporting examples. In the second (Sections VII and VIII), a critical reevaluation of the results reported on Norrish II reactions in a number of organized media is made on the basis of the model. However, although we examined the literature examples on the basis of our model, we often have deviated from the initial explanations offered by the authors. 

Conceptual models link anthropogenic activities with stressors and evaluate the relationships among exposure pathways, ecological effects, and ecological receptors. The models also may describe natural processes that influence these relationships. Conceptual models include a set of risk hypotheses that describe predicted relationships between stressor, exposure, and assessment end point response, along with the rationale for their selection. Risk hypotheses are hypotheses in the broad scientific sense they do not necessarily involve statistical testing of null and alternative hypotheses or any particular analytical approach. Risk hypotheses may predict the effects of a stressor, or they may postulate what stressors may have caused observed ecological effects. 

Extension of the equilibrium model to column or field conditions requires coupling the ion-exchange equations with the transport equations for the 5 aqueous species (Eq. 1). To accomplish this coupling, we have adopted the split-operator approach (e.g., Miller and Rabideau, 1993), which provides considerable flexibility in adjusting the sorption submodel. In addition to the above conceptual model, we are pursuing more complex formulations that couple cation exchange with pore diffusion, surface diffusion, or combined pore/surface diffusion (e.g., Robinson et al., 1994 DePaoli and Perona, 1996 Ma et al., 1996). However, the currently available data are inadequate to parameterize such models, and the need for a kinetic formulation for the low-flow conditions expected for sorbing barriers has not been established. These issues will be addressed in a future publication. 

Figure 1. Conceptual model of sediment exposure pathways potentially evaluated using a SQT approach (see case study example).

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