Sensitivity model variables

Kryza M, Dore AJ, Bias M, Sobik M (2011) Modelling deposition and air concentration of reduced nitrogen in Poland and sensitivity to variability in annual meteorology. J Environ... 

Licata AC, Dekant W, Smith CE, Borghoff SJ. A physiologically based pharmacokinetic model for methyl tert-butyl ether in humans implementing sensitivity and variability analyses. Toxicol Sci 2001 62 191-204. 

There is, however, an additional dimension to the problem of accumulation rates. The interpretation of two specific types of component is very sensitive to mass accumulation rate. First, elements that are transported to the lake in a soluble form, and only partially captured by the lake, have concentrations which can be highly sensitive to the sediment accumulation rate. Second, any component for which the supply rate is completely independent of catchment particle supply rates, is sensitive to variable dilution. For many atmospherically supplied trace elements both of these situations apply. The model described below can be used to evaluate these effects. 

In summary, reliable physical parameterizations are just as important as accurate approximations of the dynamical equations in building a successful numerical prediction model. Since atmospheric phenomena are complex, it is important for modelers to decide which atmospheric processes should be included in designing a prediction model. The importance of a particular physical process can be judged by performing sensitivity experiments with and without its impact, once a quantitative formulation in terms of model variables is made. 

If we know that only high-sensitivity model parameters end up playing role in model optimization, then we may (and should) pre-screen the reaction model prior to identification of active variables and construct a smaller, more praetical model. This brings us to the discussion of construction and completeness of chemical reaction models. Fifteen years ago, the following prospective was suggested [45] ... 

Fig. 9.4. The new mixed model for metals in lakes. Load (or dose) parameters are related to the input of metals to the lake (direct load and load from the catchment), the metal amount in the lake water is distributed into dissolved and particulate phases by the partition coefficient (Kd). Sedimentation is net sedimentation per unit of time (the calculation unit is set to 1 year for Hg and 1 month for Cs). The sensitivity parameters influence biouptake of metals from water to phytoplankton (but they may also be used in other contexts, e.g., to influence the Kd-values, as illustrated by the dotted line, or the rate of sedimentation). The biological or ecosystem variables include pelagic and benthic uptake, bioaccumulation and retention time in the five compartments (lake water, active sediments, phytoplankton, prey and predator fish). The ejfect parameter is the concentration of the metal in predatory fish (used for human consumption). One panel gives the calculation of concentrations, another the driving parameters (model variables should, preferably, not be altered for different lakes, while environmental variables must be altered for each lake). The arrows between these two panels illustrate the phytoplankton biomass submodel...

In the following simulations, the other sensitivity factors (color, total-P and morphometry) were kept constant and pH varied to simulate effects of limings on the biouptake of the two test substances, Hg and Cs. It is possible to apply a given dimensionless moderator on many rates and model variables. This is schematically illustrated in Fig. 9.6 by the dotted arrow from YpH to the partition coefficient, since the water chemical conditions (pH, alkalinity, etc.) could influence the way metals are bound to carrier particles. 

Sensitivity tests have been carried out to identify the most important model variables for calculations of the concentration of Hg and Cs in predator fish, and to illustrate how the model works. 

The price is a sensitive control variable and one used frequently. The price considered is the basic price charged by the firm. The price elasticity of demand is considered in this chapter for this sub-model. Again, more complicated descriptive models can be used to describe the specific behavior of price-demand for a product. It is used ma = 2 to identify pricing activities. Here, the linear function (4.6) is used. Em denotes the coefficient of elasticity. 

In the context of chemometrics, optimization refers to the use of estimated parameters to control and optimize the outcome of experiments. Given a model that relates input variables to the output of a system, it is possible to find the set of inputs that optimizes the output. The system to be optimized may pertain to any type of analytical process, such as increasing resolution in hplc separations, increasing sensitivity in atomic emission spectrometry by controlling fuel and oxidant flow rates (14), or even in industrial processes, to optimize yield of a reaction as a function of input variables, temperature, pressure, and reactant concentration. The outputs ate the dependent variables, usually quantities such as instmment response, yield of a reaction, and resolution, and the input, or independent, variables are typically quantities like instmment settings, reaction conditions, or experimental media. 

The burden must have a definite sohdification temperature to assure proper pickup from the feed pan. This limitation can be overcome by side feeding through an auxiliary rotating spreader roll. Apphcation hmits are further extended by special feed devices for burdens having oxidation-sensitive and/or supercoohng characteristics. The standard double-drum model turns downward, with adjustable roll spacing to control sheet thickness. The newer twin-drum model (Fig. ll-55b) turns upward and, though subject to variable cake thickness, handles viscous and indefinite solidification-temperature-point burden materials well. 

By replaeing Kt with K in the above equations, the stress for noteh sensitive materials ean be modelled if information is known about the variables involved. 

The linear piezoeleetrie model can be used to demonstrate that the magnitude of the electric field encountered for a given polarization function is a sensitive function of the thickness of the sample. This behavior can be demonstrated by noting that the electric displacement at a given time is inversely proportional to the thickness. Thus, the thickness of the sample is an important variable for investigating effects such as conductivity that depend upon the magnitude of the electric field. Conversely, various input stress wave shapes can be used to cause various field distributions at fixed thicknesses. 

Building sequence profiles or Hidden Markov Models to perform more sensitive homology searches. A sequence profile contains information about the variability of every sequence position, improving structure prediction methods (secondary structure prediction). Sequence profile searches have become readily available through the introduction of PsiBLAST [4]... 

These data show that both models identify important variables that affect 5 Obody w.ier and 8 Ophospha in mammals. Both serve to identify the dikdik as an outlier which may be explained by their sedentary daytime pattern. On the other hand, the body-size model (Bryant and Froelich 1995), which may reliably predict animal 5 0 in temperate, well-watered regions, does not predict 8 Opho,phaw in these desert-adapted species. The second model (Kohn 1996), by emphasizing animal physiology independent of body size, serves to identify species with different sensitivities to climatic parameters. This, in conjunction with considerations of behavior, indicate that certain species are probably not useful for monitoring paleotemperature because their 5 Obodyw er is not tied, in a consistent way, to The oryx, for example, can... 

Once a model has been fitted to the available data and parameter estimates have been obtained, two further possible questions that the experimenter may pose are How important is a single parameter in modifying the prediction of a model in a certain region of independent variable space, say at a certain point in time and, moreover. How important is the numerical value of a specific observation in determining the estimated value of a particular parameter Although both questions fall within the domain of sensitivity analysis, in the following we shall address the first. The second question is addressed in Section 3.6 on optimal design. 

The normalization serves to make sensitivities comparable across variables and parameters. In this context, by sensitivity we would mean the proportion of model value change due to a given proportion of parameter change. Abso-... 

Iman RL, Helton JC, Campbell JE. An approach to sensitivity analysis of computer models Part II—Ranking of input variables, response surface validation, distribution effect and technique synopsis. / Quality Technol 1981 13 232-40. 

Alternatively, some studies used expert opinion to extrapolate the effectiveness of donepezil over a longer period (Neumann et al, 1999 O Brien et al, 1999). However, it is recognized that expert opinion can be the weakest source of evidence, which introduces considerable uncertainty into the analysis and interpretation of the results. In addition, the cost-effectiveness of acetylcholinesterase inhibitors depends heavily on the distribution of the cohort of patients across different severity states. O Brien s team found that the results of their model were very sensitive to this variable. In this context, the correct... 

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