User-Defined Function

Amolecular mechanics method in HyperChem is defined by a set of atom types and a functional form for the energy and its derivatives— for example AMBER. For the AMBER method, you may use many different default and user-defined parameter sets. Hyper-... 

Select Function) offers the choice among the above-mentioned functions and a user-defined one. (See Algebraic Function .)... 

The SimuSolv optimize function can be used for minimization of a user defined cost expression. However, at this early stage of process development, no attempt was made to determine parameters for an economic optimum. Instead, many simulations were run to determine parameter sensitivities and malce comparisons of various possibilities which satisfied a necessary objective of reaching the desired low level of residual monomer. Attention was given to minimizing total finishing time, the amount of initiator required and residual initiator level at the end of the finishing step. Based on simulation results for... 

The RNG model provides its own energy balance, which is based on the energy balance of the standard k-e model with similar changes as for the k and e balances. The RNG k-e model energy balance is defined as a transport equation for enthalpy. There are four contributions to the total change in enthalpy the temperature gradient, the total pressure differential, the internal stress, and the source term, including contributions from reaction, etc. In the traditional turbulent heat transfer model, the Prandtl number is fixed and user-defined the RNG model treats it as a variable dependent on the turbulent viscosity. It was found experimentally that the turbulent Prandtl number is indeed a function of the molecular Prandtl number and the viscosity (Kays, 1994). 

This problem may be solved by linear regression using equations 3.4-11 (n = 1) and 3.4-9 (with n = 2), which correspond to the relationships developed for first-order and second-order kinetics, respectively. However, here we illustrate the use of nonlinear regression applied directly to the differential equation 3.4-8 so as to avoid use of particular linearized integrated forms. The method employs user-defined functions within the E-Z Solve software. The rate constants estimated for the first-order and second-order cases are 0.0441 and 0.0504 (in appropriate units), respectively (file ex3-8.msp shows how this is done in E-Z Solve). As indicated in Figure 3.9, there is little difference between the experimental data and the predictions from either the first- or second-order rate expression. This lack of sensitivity to reaction order is common when fA < 0.5 (here, /A = 0.28). 

The E-Z Solve software may also be used to solve Example 12-7 (see file exl2-7.msp). In this case, user-defined functions account for the addition of fiesh glucose, so that a single differential equation may be solved to desenbe the concentration-time profiles over the entire 30-dry period. This example file, with die user-defined functions, may be used as the basis for solution of a problem involving the nonlinear kinetics in equation (A), in place of the linear kinetics in (B) (see problem 12-17). 

As an alternative, equation 19.4-38 may be solved using the E-Z Solve software to obtain the concentration profiles. The gamma function can be evaluated by numerical integration using the user-defined functions gamma, fjrateqn, and rkint provided within the software. 

Alternatively, equation (A) may be solved with the user-defined function fbsr (t, tu) to estimate the conversion for each group of particles, where tu is the value of t for the ith group of particles. This function for spherical particles with reaction control is included in tiie E-Z Solve software. 

This value of /B may also be obtained, by means of the E-Z Solve software, by simultaneous solution of equation 22.2-17 and numerical integration of 22.2-13 (with user-defined function fbcr(t, f,), for cylindrical particles with reaction control see file ex22-3.msp). This avoids the need for analytical integration leading to equation 22.2-18. 

Note that in the numerical solution of equation 22.2-19, /B must be less than 1 to avoid ln(0) in the use of E-Z Solve, an initial guess of 0.5 is entered, and the upper and lower limits may be set to 0 and 0.9999, respectively. Since upper and lower limits must be specified, a user-defined function is not applicable to this case. 

The calculation of fB with a range of particle sizes is straightforward using the E-Z Solve software with user-defined functions, such as fbcr(t, tx) and fbsr(t, fj) included... 

I We make use of the user-defined function riant, a Runge-Kutta integrator, to return function values at the specified values tl, t2, t3, t4, and t5. The unknown parameters kA and n are passed to the rkint function, and they are passed from within the rkint function to a second user-defined function f-ruteeq, which contains the kinetics expression. The form of f-ruteeq is as follows ... 

User-defined functions such as rkint and f-ruteeq can be accessed by clicking on user-defined functions under the equations menu. Once within this window, functions can be imported and edited as required. User-defined functions are specific to a particular file. Thus, they must be imported into each problem file where they are used. 

Data-dependent acquisition ability has been developed and incorporated into most software packages [MetaboLynx, Xcalibur, and Analyst Information Dependent Acquisition (IDA)]. In data-dependent acquisition mode, a mass spectrometer decides on the fly whether to collect MS/MS or MSn data, remain in full scan MS mode, or conduct other survey scans based upon user-defined criteria. Product ion spectra of potential metabolites can be automatically acquired in a single LC/ MS run. However, false positives may be generated due to highly intense matrix ion signals that may inadvertently trigger MS/MS or MSn scan functions. 

Optimisation may be used, for example, to minimise the cost of reactor operation or to maximise conversion. Having set up a mathematical model of a reactor system, it is only necessary to define a cost or profit function and then to minimise or maximise this by variation of the operational parameters, such as temperature, feed flow rate or coolant flow rate. The extremum can then be found either manually by trial and error or by the use of numerical optimisation algorithms. The first method is easily applied with MADONNA, or with any other simulation software, if only one operational parameter is allowed to vary at any one time. If two or more parameters are to be optimised this method becomes extremely cumbersome. To handle such problems, MADONNA has a built-in optimisation algorithm for the minimisation of a user-defined objective function. This can be activated by the OPTIMIZE command from the Parameter menu. In MADONNA the use of parametric plots for a single variable optimisation is easy and straight-forward. It often suffices to identify optimal conditions, as shown in Case A below. 

TOGA uses the built-in numerical capabilities of Radial to compute functions of concentration values, which are used extensively in the rules. The ratio of hydrogen to acetylene concentration in the corona rule is a simple example of this. User-defined con xDund data types are used to handle blocks of data as a single named structure. These features are invaluable in building practical expert systems, but are not available with all packages. 

Different baseline correction methods vary with respect to the both the properties of the baseline component d and the means of determining the constant k. One of the simpler options, baseline ojfset correction, nses a flat-line baseline component (d = vector of Is), where k can be simply assigned to a single intensity of the spectrum x at a specific variable, or the mean of several intensities in the spectrum. More elaborate baseline correction schemes allow for more complex baseline components, such as linear, quadratic or user-defined functions. These schemes can also utilize different methods for determining k, such as least-squares regression. 

One way to impart structure to otherwise unstructured documents is to utilize a suitable markup language. The function of markup languages is to combine the text of a document with further information about the text (markup languages typically add metadata - data about data) and while metadata is normally hidden from the view of a human reader, it is available to processing software. XML allows an author to add arbitrary metadata to documents through the use of tags, which are user-defined and annotate data sources. 

MATLAB has a built-in root finder for scalar equations f(x) = 0 in one real variable x that are in standard form. The built-in MATLAB function is fzero. The use of fzero hinges on a user-defined function, such as the function f inside the following fzero tester, called fzerotryl, that we apply to our previously studied third degree polynomial. 

Docking poses generated by AutoDock, GOLD, and FlexX can be easily pooled and/or rescored with SCORE (21), the three scoring functions available in the X-SCORE program (22), and/or user-defined scoring functions. This can be achieved via simple scripts that extract the relevant information from the various output files (an example of such a script is available from the authors upon request). When the results are saved in a format readable to SYBYL (i.e., tab-separated columns), the energy scores can be listed and imported into a SYBYL spreadsheet ... 

The parameters in the GPF file are optimized for the energy function applied within AutoDock, but can be replaced by user-defined interaction potentials. AutoDock makes a distinction between aromatic ( A ) and nonaromatic carbons ( C ), using different parameters (see Fig. 8). For this purpose, the PDB file should be edited either manually (change every C of the atom name of an aromatic carbon into an A ) or automatically, using the -A option of the AutoTors utility, which uses the out-of-plane angle to look for aromatic substructures. 

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