Prediction techniques methodology

Reason also brings to mind the antiquity of the literature on human error reduction. In his final chapter, he reviews THERP, (the technique for human error rate prediction). This methodology was developed by Alan Swain in 1963. 

Molecular dynamics simulation, which provides the methodology for detailed microscopical modeling on the atomic scale, is a powerful and widely used tool in chemistry, physics, and materials science. This technique is a scheme for the study of the natural time evolution of the system that allows prediction of the static and dynamic properties of substances directly from the underlying interactions between the molecules. 

The third category of methods addressed in this chapter are error analysis and reduction methodologies. Error analysis techniques can either be applied in a proactive or retrospective mode. In the proactive mode they are used to predict possible errors when tasks are being analyzed during chemical process quantitative risk assessment and design evaluations. When applied retrospectively, they are used to identify the underlying causes of errors giving rise to accidents. Very often the distinction between task analysis and error analysis is blurred, since the process of error analysis always has to proceed from a comprehensive description of a task, usually derived from a task analysis. 

The versatility, predictability and functional-group tolerance of free radical methodology has led to the gradual emergence of homolytic reactions in the armory of synthetic chemistry. Tin hydrides have been successfully employed in radical chemistry for the last 40 years however, there are drawbacks associated with tin-based chemistry. Organotin residues are notoriously difficult to remove from desired end products, and this, coupled with the fact that many organotin compounds are neurotoxins, makes techniques using tin inappro-... 

The identification of plant models has traditionally been done in the open-loop mode. The desire to minimize the production of the off-spec product during an open-loop identification test and to avoid the unstable open-loop dynamics of certain systems has increased the need to develop methodologies suitable for the system identification. Open-loop identification techniques are not directly applicable to closed-loop data due to correlation between process input (i.e., controller output) and unmeasured disturbances. Based on Prediction Error Method (PEM), several closed-loop identification methods have been presented Direct, Indirect, Joint Input-Output, and Two-Step Methods. 

The methodology is very fast and completely automated. To predict the site of metabolism for drug-like substrates, the method requires few seconds per molecule. It is important to point out that the method uses neither any training set nor any statistical model or supervised technique, and it has proven to be predictive for extensively diverse validation sets preformed in different pharmaceutical companies. 

The objectives of each theoretical approach are not only the explanation of the experimental results or failures of practice but also the prediction of new possibilities to increase the sensitivity, separation capacity and velocity of the chromatographic procedure under investigation. Numerous theoretical reviews deal with the problems of the CE separation technique. In recent years the methods to enhance the precision in CE by the modification of operational parameters [113], the theory and methodological improvements of sample stacking of cationic and anionic solutes in CE [114-116], and the results and difficulties of the application of conductivity detection in CE technologies [117] have been reviewed. 

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