Software modeling programs

Various commercially available software modeling programs have been developed to examine and predict the impacts of pipeline releases in nearby... 

It is essential that, with the use of evidence-based medicine to inform decisions in health care, the processes used in program development be as transparent as possible. Information about the limited evidence and inherent uncertainty should be disclosed and available for scrutiny, even within the software itself. In fact, in an attempt to maximize transparency, some have advocated open source development and publication of interactive software models [49, 50]. Certainly, details of methodologies, sources, and other techniques employed for development of the underlying models must be acknowledged. However, the proprietary nature of many of these programs must be taken into consideration and measures put into place to ensure confidentiality. Requested publication of all NIH-sponsored research online (in PubMed) [51] within a reasonable time frame after journal acceptance will help to ensure that these data are available in the public domain in short order. 

I chose to use this software for reasons that extend beyond familiarity and prejudice the programs are interactive and take simple commands as input. As such, I can include within the text of this book scripts that in a few lines show the precise steps taken to calculate each result. Readers can, of course, reproduce the calculations by using any of a number of other modeling programs, such as those listed in Appendix 1. Following the steps shown in the text, they should be able to construct input in the format recognized by the chosen program. 

X-SCORE Suite of three regression-based scoring functions and consensus scoring tool General modeling programs 190 swl6.im.med.umich.edu/software/xtool/... 

A mixed pallet of software (including the WHAT1F modeling program) Commercial users must apply for a license for some of these services. 

We give only a short description of the three supply chain configurations and their simulation models for details we refer to Persson and Olhager (2002). At the start of our sequential bifurcation, we have three simulation models programmed in the Taylor II simulation software for discrete event simulations see Incontrol (2003). We conduct our sequential bifurcation via Microsoft Excel, using the batch run mode in Taylor II. We store input-output data in Excel worksheets. This set-up facilitates the analysis of the simulation input-output data, but it constrains the setup of the experiment. For instance, we cannot control the pseudorandom numbers in the batch mode of Taylor II. Hence, we cannot apply common pseudorandom numbers nor can we guarantee absence of overlap in the pseudorandom numbers we conjecture that the probability of overlap is negligible in practice. 

Besides those listed here, there are other molecular modeling programs developed in academic and industrial laboratories around the world however, because the availability, documentation, and degree of support of these other programs are highly variable, it is impractical to include them all. More and more frequently, workstation software is being transferred between computational chemists via file transfer protocol (ftp) or electronic mail over Internet, Bitnet, and similar networks of computers. In these situations, the cost is nothing, but reliability of the software can be unknown. 

Nonlinear dynamic simulations of plantwide systems can be performed using a variety of software packages and computer platforms. We can write our own program to integrate numerically the differential equations describing the system. We also can use one of the commercial modeling programs that are now available. 

First-Order (NONMEM) Method. The first nonlinear mixed-effects modeling program introduced for the analysis of large pharmacokinetic data was NONMEM, developed by Beal and Sheiner. In the NONMEM program, linearization of the model in the random effects is effected by using the first-order Taylor series expansion with respect to the random effect variables r], and Cy. This software is the only program in which this type of linearization is used. The jth measurement in the ith subject of the population can be obtained from a variant of Eq. (5) as follows ... 

In using simulation software, it is important to keep in mind that the quality of the results is highly dependent upon the quahty of the liquid-liquid equilibrium (LLE) model programmed into the simulation. In most cases, an experimentally vmidated model will be needed because UNIFAC and other estimation methods are not sufficiently accurate. It also is important to recognize, as mentioned in earlier discussions, that binary interaction parameters determined by regression of vapor-liquid equilibrium (VLE) data cannot be rehed upon to accurately model the LLE behavior for the same system. On the other hand, a set of binary interaction parameters that model LLE behavior properly often will provide a reasonable VLE fit for the same system—because pure-component vapor pressures often dominate the calculation of VLE. 

This type of characterization of the plus fraction is, as yet, not available in the PVT modules of current basin modelling programs. The characterization procedure in the PVT simulation software used leads to a balanced description of the fluid composition, where the gas range is defined based on analytical results and the liquid range is based on a mathematical extrapolation of the C7+ properties. The thus characterized fluid contains a distribution of compounds from methane up to Cgo or Cioq. If, instead of this characterization procedure, a small number of individual compound classes is chosen to represent the liquid fraction of the fluid under study (as assumed in the available multi-component kinetic datasets), the phase behaviour will not be comparable with that of a characterized fluid. 

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