Computer programming reducing errors

GF method calculations are simplified by the systematic behavior of the G matrix elements (Decius 1948). MUBFF calculations, however, are somewhat complicated by the force constants representing interactions between non-bonded atoms— these can be tedious to express in terms of internal coordinates. Computer programs have been written to partially automate calculations, thereby reducing the necessary effort and minimizing opportunities for errors (e.g., Schachtschneider 1964 Gale and Rohl 2003). 

The calibration was represented in the computer program by a fifth-degree polynomial. The conventional method of least-squares was followed to determine the coefficients of the polynomial. The sensitivity of the normal equations made round-off error a significant factor in the calculations. The effect of round-off error was greatly reduced when the calculations were performed with double-precision arithmetic. The molecular weights corresponding to selected count numbers were calculated from the coefficients. The coefficients were input information for the data-reduction program. 

Since, as noted above, the success in determining G(D) is actually not in the choice of a computer program for Laplace inversion but reducing the noise level in measured g( (t,q). Thus, it is crucial that the solution is cleaned (i.e. "dust-freed") very thoroughly before it is subjected to laser light scattering measurements. For example, in studies conducted by the author, efforts were made to ensure that the relative difference between the measured and calculated baselines did not exceed 0.1%. The error analysis related to the above problem can be found elsewhere [42,43]. 

Many of the standard methods of statistics have been applied for quality control for many years. The computer has merely made their routine application easier, and enables the results to be presented in a more useful form. For example, it is possible to program the computer to take the analytical output, to combine it with other data so that the output is in the form of a decision e.g. this sample is acceptable or this sample is suitable for internal use, but not for outside customers or this sample is not to specification, being deficient in the following respects. .. This is time-saving and reduces error at the decision-making stage. 

The new methodology was tested extensively in practical work at The Dow Chemical Company. It was found to be able to predict the properties of novel polymers as accurately and reliably as can be reasonably expected from any scheme based on simple quantitative structure-property relationships. The only computational hardware required to perform these calculations is a good hand calculator. The method was, nonetheless, automated by implementation in a simple interactive computer program (SYNTHIA). This software implementation has enabled its much easier use, especially by non-specialists. It has thus resulted in much greater efficiency as well as significantly reducing the possibility of human error. 

Sufficient procedural details will be presented and examples will be given in each chapter to enable the reader to apply all of the techniques and correlations to polymers of interest. The only computational hardware required to perform any of these calculations is a hand calculator. We have, however, developed an interactive computer program which has resulted in greater efficiency and reduced human error. 

In multiple-version dissimilar software, a set of two or more computer programs are developed separately (and independently) to satisfy the same functional requirements. Errors specific to one of the versions should be detected by comparison of the outputs between the different versions. When multiple-version dissimilar software is used in redundant computer systems, the likelihood of the same errors in both systems is theoretically significantly reduced, thereby achieving a higher level of safety for the system. Multiple-version dissimilar software involves applying the dissimilar design concept to software. 

Tjoa, I. B. and L. T. Biegler. Reduced Successive Quadratic Programming Strategy for Errors-in-Variables Estimation. Comput Chem Eng 16(6) 523-533 (1992). 

Tjoa, I. B., and Biegler, L. T. (1991). Reduced successive quadratic programming strategy for error-in-variables estimation. Comput. Chem. Eng. 16, 523-533. 

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