Screening sensitivity analysis

The active variables can be readily identified by screening sensitivity analysis when the sensitivities are ranked according to their absolute values. Such examination partitions the model parameters into two groups the active variables, whose effects on the response(s) are above the experimental noise level, and those below it. In practice, the selection of active variables may depend on considerations other than just the noise-level comparison, such as the certainty with which the parameters are known (i.e., consideration of sensitivity times the range of uncertainty instead of sensitivity alone) or the number of degrees of freedom available for optimization (i.e., the total number of parameters feasible to determine with the given amount of experimental information). 

Only active variables need be considered for optimization. Inclusion of parameters with noise-level sensitivity into optimization only worsens the character of the objective function—necessarily increasing the dimensionality of the valley structure. If these parameters are to be the subject of optimization, then conditions have to be found where they become active. One does not have to search for a single set of conditions in which all the parameters of interest are active, which may be impossible to find. Instead, a more practical strategy is to perform experiments where different subsets of parameters are active and combine the results into a joint optimization. The SM methodology provides a convenient basis for implementing such a strategy. 

Active variables can be conveniently identified by a screening sensitivity analysis. In a chemical reaction model not all reactions contribute equally 

Computing sensitivities for all 6 for a given response and ranking them by the absolute value produces results illustrated in Fig. 5. In this particular example the main effects are concentrated in just the first few (a dozen or so) parameters, consistent with the effect sparsity, and it is these parameters that can be selected to be active variables for model optimization. Actually, one rather needs to consider a similar ranking but for the parameter impact factors, a product of parameter sensitivity and its uncertainty. 

The evaluation of sensitivities can be accomplished in a number of ways, and there are many different methods and computer codes available (see, e.g., Refs. [8,33] and references cited therein). The simplest among them is the brute-force or one-variable-at-a-time method the model response, rij, is computed by changing the value of a given model parameter, 6i, while keeping the rest unchanged and then is evaluated by equation (6). A newcomer to kinetic modeling is advised to begin with this simple brute-force approach. First, it is easy to use since hardly any additional 

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Local sensitivity analysis is of limited value when the chemical system is non-linear. In this case global methods, which vary the parameters over the range of their possible values, are preferable. Two global uncertainty methods have been used in this work, a screening method, the so-called Morris One-At-A-Time (MOAT) analysis and a Monte Carlo analysis with Latin Hypercube Sampling (Saltelli et al., 2000 Zador et al., submitted, 20041). The analyses were performed by varying rate parameters, branching ratios and constrained concentrations within their uncertainty interval,... 

The rate of production analysis was complemented by a local sensitivity analysis and by a global Morris screening analysis. These analyses demonstrate the necessity of accurate measurements of j(0 D) and [HCHO] and reduced uncertainty in the quantum yields for H from HCHO photolysis. 

A number of indirect methods have been developed with mass spectrometric detection to rapidly study non-covalent complexes for drug screening purposes [2]. Among the most promising and simple indirect methods that overcome the limitations described above for directly studying non-covalent complexes by mass spectrometry is the application of size exclusion techniques in the spin column format for the screening and analysis of drug-protein complexes under optimum mass spectral sensitivity conditions [11-13]. 

The skeletal or short mechanism is a minimum subset of the full mechanism. All species and reactions that do not contribute significantly to the modeling predictions are identified and removed from the reaction mechanism. The screening for redundant species and reactions can be done through a combination of reaction path analysis and sensitivity analysis. The reaction path analysis identifies the species and reactions that contribute significantly to the formation and consumption of reactants, intermediates, and products. The sensitivity analysis identifies the bottlenecks in the process, namely reactions that are rate limiting for the chemical conversion. The skeletal mechanism is the result of a trade-off between model complexity and model accuracy and range of applicability. 

St. Jude Children s Research Hospital Supplemental data from Guiguemde et al. (25) structures tested in a primary screen, with additional data in 8 protocols Bland-Altman analysis, calculated ADME-tox properties, phylochemogenetic screen, sensitivity, synergy, and enzyme assays, as well as a thermal melt analysis. 1,524... 

According to Mokhtari Frey (2005), sensitivity analysis methods are typically categorized based on their scope, applicability or characteristics. Frey Patil (2002) classify sensitivity analysis methods with regard to their characteristics as mathematical, statistical and graphical methods. Saltelli et al. (2000) classify sensitivity analysis methods with respect to their application and scope as screening, local and global. The former approach is used here. 

Andres, T. H. and Hajas, W.C. (1993). Using iterated fractional factorial design to screen parameters in sensitivity analysis of a probabilistic risk assessment model. Proceedings of the Joint International Conference on Mathematical Models and Supercomputing in... 

Campolongo, F., Kleijnen, J.P.C., and Andres, T. (2000). Screening methods. In Sensitivity Analysis. Editors A. Saltelli, K. Chan, and E.M. Scott, pages 65-89. Wiley, Chichester, England. 

A parameter sensitivity analysis is required for this problem in which an isomerization is earned out over a 20-mesh gauze screen. [2nd Ed. PlO-12]... 

Without providing a detailed sensitivity analysis for this specific yeast screen, we want to add that sensitivity highly depends on the ncRNA class. MicroRNAs, for example are easy to detect because of the high thermodynamic stability of the hairpin precursor. On the other hand, C/D type snoRNAs for example are generally difficult to detect because they lack a pronounced secondary structure. We completely miss ncRNAs, which do not depend on a secondary structure for their function, as for example the yeast SER3 regulating RNA (2), which, as expected, does not show up in this screen. 

USE OF STEADY-STATE SENSITIVITY ANALYSIS TO SCREEN PLANTWIDE CONTROL STRUCTURES... 

The use of steady-state sensitivity analysis for screening out poor control structures was illustrated. 

Use of Steady-State Sensitivity Analysis to Screen Plantwide Control Structures 190... 

Figure 9.5. Electricity generation sensitivity analysis screen.

GenSim s structure makes sensitivity analysis easy. A representative screen (solar PV) is shown in Figure A3. This screen allows the user to compare LCOE costs at either comparable capacity factors (i.e., all at 50%), or at default or user defined capacity factors (i.e., solar PV at 20% with nuclear at 90%). LCOE estimates are displayed at the top of the graph. These estimates change as the user changes key assumptions using either the sliders or number boxes on the bottom half of the screen. For example, changing the assumed capital costs for solar PV from 3868 to 1500 /kW reduces the LCOE from 26.0 to 10.4 cents/kWh. 

Figure A3. Representative sensitivity analysis screen (solar PV).

Figure A4. Sensitivity analysis screen for construction time and financing.

To illustrate the computation, let s examine a 4.2% coupon, lO-year Spanish government security that matures on July 30, 2013. Bloomberg s Yield Analysis Screen is presented in Exhibit 4.7. If the bond is priced to yield 3.724% on a settlement date of June 6, 2003, we can compute the PVBP by using the prices for either the yield at 3.734 or 3.714. The bond s initial full price at 3.724% is 104.5673. If the yield is decreased by 1 basis point to 3.714%, the PVBP is 0.085 (1104.5673 - 104.65221). Note that our PVBP calculation agrees with Bloomberg s calculation labeled PRICE VALUE OF A 0.01 that is presented in the Sensitivity Analysis box located in the lower left-hand corner of the screen. 

Note that our calculation for duration of 17.636 agrees (within rounding error) with Bloomberg s calculation in Exhibit 4.1. Bloomberg s interest rate risk measures are located in a box titled Sensitivity Analysis in the lower left-hand corner of the screen. The duration measure we just calculated is labeled Adj/Mod Duration which stands for adjusted/ modified duration. 

Bloomberg reports Macaulay duration on its YA (yield analysis) screen in the Sensitivity Analysis box in the lower left-hand corner of Exhibits 4.1, 4.3, and 4.4. Macaulay duration is labeled CNV DURATION (YEARS) where the CNV stands for conventional. ... 

Campolongo R, Cariboni J. Saltelli A. 2007. An effective screening design for sensitivity analysis of large models. Environmental Modelling Software 22 1509-1518. 

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