Conceptual modelling data analysis

At least two parties are involved in the development of a simulation study. In SCM, simulation projects are instruments for process optimization. Such projects are initiated by the perception of process deficiencies and weaknesses. Overcoming these problems is 

In the validation phase the behaviour of the simulation model is compared with the real system s behaviour. If errors are revealed, the modelling and programming steps have to be re-started to ensure the required level of accuracy. 

All sub-processes of the development process, beginning with the perception of deficiencies, require the analysis of process data. The data characterizes the system s performance and behaviour. This is necessary to parametrize the simulation model and build a credible basis for comparing the model s behaviour and reality. A common database maintained by both modellers and managers allows linking necessary and available information. A lack of data may lead to uncertainty, more complexity, and a loss in accuracy. Uncertainty is reflected e.g. by stochasticity at variables which in turn may lead to a larger number of simulation experiments and more volatile performance measures. On the other hand, the information collected in the project database needs to be relevant for the project. Otherwise, a flood of data to be evaluated and analysed hinders an efficient development process as it is time and resource consuming. 

The analysis of available and relevant data typically requires a set of statistical methods. They are applied to extract deeper knowledge about the processes to be modelled. The set of methods is vast and the choice of methods depends on the needs of the speciflc project. Time series methodology is one prominent branch of methods. 

The following example describes the conceptual model of an artificial chemical SC analysed in this section. 

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The planning and scoping process defined in the screening phase of the methodology are consistent with the problem formulation phase defined in the U.S. era s framework. For instance, problem identification, data gathering, selection of assessment endpoints and development of conceptual model and analysis plan are well described in the screening phase of the methodology. 

A rather crude, but nevertheless efficient and successful, approach is the bond fluctuation model with potentials constructed from atomistic input (Sect. 5). Despite the lattice structure, it has been demonstrated that a rather reasonable description of many static and dynamic properties of dense polymer melts (polyethylene, polycarbonate) can be obtained. If the effective potentials are known, the implementation of the simulation method is rather straightforward, and also the simulation data analysis presents no particular problems. Indeed, a wealth of results has already been obtained, as briefly reviewed in this section. However, even this conceptually rather simple approach of coarse-graining (which historically was also the first to be tried out among the methods described in this article) suffers from severe bottlenecks - the construction of the effective potential is neither unique nor easy, and still suffers from the important defect that it lacks an intermolecular part, thus allowing only simulations at a given constant density. 

Data analysis should focus on the development or refinement of the conceptual site model by analyzing data on source characteristics, the nature and extent of contamination, the contaminants transport pathways and fate, and the effects on human health and the environment. All field activities, sample management and tracking, and document control and inventory should be well managed and documented to ensure their quality, validity, and consistency. 

A great variety of different methods for multivariate classification (pattern recognition) is available (Table 5.6). The conceptually most simply one is fc-NN classification (Section 5.3.3), which is solely based on the fundamental hypothesis of multivariate data analysis, that the distance between objects is related to the similarity of the objects. fc-NN does not assume any model of the object groups, is nonlinear, applicable to multicategory classification, and mathematically very simple furthermore, the method is very similar to spectral similarity search. On the other hand, an example for a rather sophisticated classification method is the SVM (Section 5.6). 

Despite the success of the disorder model concerning the interpretation of data on the temperature and field dependence of the mobility, one has to recognize that the temperature regime available for data analysis is quite restricted. Therefore it is often difficult to decide if a In p vs or rather a In p vs representation is more appropriate. This ambiguity is an inherent conceptual problem because in organic semiconductors there is, inevitably, a superposition of disorder and polaron effects whose mutual contributions depend on the kind of material. A few representative studies may suffice to illustrate the intricacies involved when analyzing experimental results. They deal with polyfluorene copolymers, arylamine-containing polyfluorene copolymers, and c-bonded polysilanes. 

Following construction of the conceptual model, problem formulation continues by developing a plan to implement the conceptual model of the ERA. The resulting analysis plan further characterizes the stressors, identifies specific ecological effects of concern, and identifies applicable data, as well as measures or models that can be used to quantitatively relate the stressors to the expected ecological effects. 

Perhaps the most critical aspect of the analysis above is the realization that additional data or even a reformulation of the conceptual model is required. In this case the assessment process is rerouted to the data acquisition, verification, and monitoring stage. An iterative process can then occur to obtain a usable and hopefully accurate risk assessment. 

The framework consists of three major phases (1) problem formulation, (2) analysis, and (3) risk characterization. Problem formulation is a planning and scoping process that establishes the goals, breadth, and focus of the risk assessment. Its end product is a conceptual model that identifies the environmental values to be protected (the assessment endpoints), the data needed, and the analyses to be used. 

Although many hypotheses may be generated during problem formulation, only those that are considered most likely to contribute to risk are selected for further evaluation in the analysis phase. For these hypotheses, the conceptual model describes the approach that will be used for the analysis phase and the types of data and analytical tools that will be needed. It is important that hypotheses that are not carried forward in the assessment because of data gaps be acknowledged when uncertainty is addressed in risk characterization. Professional judgment is needed to select the most appropriate risk hypotheses, and it is important to document the selection rationale. 

The stressor-response analysis describes the relationship between the magnitude, frequency, or duration of the stressor in an observational or experimental setting and the magnitude of response. The stressor-response analysis may focus on different aspects of the stressor-response relationship, depending on the assessment objectives, the conceptual model, and the type of data used for the analysis. Stressor-response analyses, such as those used for toxicity tests, often portray the magnitude of the stressor with respect to the magnitude of... 

This chapter focuses on the conceptual approach to pharmacokinetics, its use as a tool in therapeutics, and in defining drug disposition. The more common mathematical modeling-exponential equation-data analysis approach to pharmacokinetics will not be discussed here. These mathematical techniques are necessary to determine the important pharmacokinetic parameters that are to be discussed however, for medicinal chemists who need to... 

Successful conclusion of a Phase lb assessment depends on careful analysis of data to validate the conceptual model and identify those candidate pollutant linkages within each site zone that need to go forward for Phase 2 risk assessment. 

A conceptual model is a formal description of the simulation model to be developed. I.e. the relevant elements of the real system as well as their relations are represented at an appropriate level of detail. It defines the general structure of the simulation model and determines the requirements for data analysis. 

In the data analysis step historical data of the real system to be modelled are analysed to extract relevant parameters for the simulation model and the involved stochastic processes. The simulation model is the implementation of the conceptual model accompanied by the required parameters obtained from the data analysis step as some sort of a computer program. 

The experimental planning is performed on the basis of information accumulated during the conceptual modeling. It is decided that a spatial analysis should be conducted using supply chain visualization and data fusion as well as an optimization model should be developed to select the delivery route and the contract type. 

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