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  2. Toxicodynamics - Wikipedia

    en.wikipedia.org/wiki/Toxicodynamics

    A box model explaining the processes of toxicokinetics and toxicodynamics. While toxicokinetics describes the changes in the concentrations of a toxicant over time due to the uptake, biotransformation, distribution and elimination of toxicants, toxicodynamics involves the interactions of a toxicant with a biological target and the functional or structural alterations in a cell that can ...

  3. File : Diagram showing the conceptual pathway of ...

    en.wikipedia.org/wiki/File:Diagram_showing_the...

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  4. Box–Behnken design - Wikipedia

    en.wikipedia.org/wiki/Box–Behnken_design

    The design should be sufficient to fit a quadratic model, that is, one containing squared terms, products of two factors, linear terms and an intercept. The ratio of the number of experimental points to the number of coefficients in the quadratic model should be reasonable (in fact, their designs kept in the range of 1.5 to 2.6).

  5. Outline of air pollution dispersion - Wikipedia

    en.wikipedia.org/wiki/Outline_of_air_pollution...

    Box model – The box model is the simplest of the model types. [2] It assumes the airshed (i.e., a given volume of atmospheric air in a geographical region) is in the shape of a box. It also assumes that the air pollutants inside the box are homogeneously distributed and uses that assumption to estimate the average pollutant concentrations ...

  6. System identification - Wikipedia

    en.wikipedia.org/wiki/System_identification

    black box model: No prior model is available. Most system identification algorithms are of this type. Most system identification algorithms are of this type. In the context of nonlinear system identification Jin et al. [ 9 ] describe grey-box modeling by assuming a model structure a priori and then estimating the model parameters.

  7. Box–Jenkins method - Wikipedia

    en.wikipedia.org/wiki/Box–Jenkins_method

    The original model uses an iterative three-stage modeling approach: Model identification and model selection: making sure that the variables are stationary, identifying seasonality in the dependent series (seasonally differencing it if necessary), and using plots of the autocorrelation (ACF) and partial autocorrelation (PACF) functions of the dependent time series to decide which (if any ...