Information goals

The primary requirement in the process of information enhancement is to define the informational goal(s) associated with a set of experimental measurements. Equally important is the definition of an appropriate measure of the degree to which the informational goal is achieved. Some generic qualitative informational goals and their respective figures of merit might be ... 

In addition, it is possible to define qualitative informational goals. These might include ... 

Corresponding figures of merit for the qualitative informational goals can be defined in terms of statistical accuracy by evaluation with systems of known properties. 

This approach involves the following steps o define the "desired information" (informational goal(s)) o define a figure of merit for goal achievement (e.g., accuracy, precision, reliability, etc.) o empirically determine "information content" from the relationship ... 

From the above statement the information content of a chemical measurement system can be evaluated by studying the effects of experimental factors on the degree of achievement of the informational goal(s). This is elaborated below. 

An empirical procedure can be defined for the enhancement of information content. First, it must be recognized that the achievement of desired informational goal(s) depends not only on the inherent information content of data, but also on the data management and analysis procedures. This is expressed in Equation (2) ... 

Thus, to examine the relationship between information content and experimental factors, it is necessary to maintain consistent data management and analysis procedures. Then, one can assume a direct relationship between the achievement of informational goals and information content as implied in Equation (1). 

In order to clarify the general concepts defined in the above sections, the following sections will describe an experimental study which followed those principles in order to achieve specified informational goals. 

The experimental study described here illustrates how the application of the principles of information enhancement can significantly improve chemical analysis. In this case we have established the optimum conditions for obtaining structural or activity information from voltammetric electroanalytical data. Moreover, it is clear that the informational goal(s) will dictate the most favorable choice of experimental conditions. It is also interesting to observe that the most useful experimental conditions... 

It is only after information goals and appropriate indicators have been defined accurately and precisely that sample location and frequency can be decided. Continuous monitoring of some water quality parameters is now commonplace in many parts of the world. However, the cost of these systems and limits to the parameters that can be measured with them means that spot or grab samples for testing probably always will form part of environmental quality monitoring strategies (Hazleton, 1998), and this remains true for bioassays. 

Timmerman, J.G., Ottens, J.J. and Ward, R.C. (2000) The information cycle as a framework for defining information goals for water-quality monitoring. Environmental Management, 25, 229-239. 

The following sections cover the design goals, decisions, and outcomes of the first two major versions of NAMD and present directions for future development. It is assumed that the reader has been exposed to the basics of molecular dynamics [2, 3, 4] and parallel computing [5]. Additional information on NAMD is available electronically [6]. 

In order to achieve the goals of making more efficient use of the information that is produced and of planning and performing better experiments, chemoinformatics will have to be more integrated into the daily work processes of the chemist, and into the work of the bench chemist. Certainly, many chemists still have to overcome high barriers to using the computer for assistance in the solution of their daily scientific problems. 

Over the years, as mentioned, I followed the practice that, whenever in a specific area of my research I felt that I had substantially achieved my goals and that it was time to consider shifting my emphasis elsewhere, I wrote (or edited) a book or comprehensive review of the field. My books are listed in the Appendix for interested readers who want to obtain more information or details, as well as relevant literature references. 

The goal of a collaborative test is to determine the expected magnitude of ah three sources of error when a method is placed into general practice. When several analysts each analyze the same sample one time, the variation in their collective results (Figure 14.16b) includes contributions from random errors and those systematic errors (biases) unique to the analysts. Without additional information, the standard deviation for the pooled data cannot be used to separate the precision of the analysis from the systematic errors of the analysts. The position of the distribution, however, can be used to detect the presence of a systematic error in the method. 

For different types of collections, this balance is differently defined. For example paper conservation treatments commonly undertaken in the museum conservation laboratory would be impractical in a Hbrary archive having a far greater collection size. The use of treatments for mass paper quantities would be unacceptable in the art museum. Documents in archives and books in Hbraries serve a different goal from art objects in a museum. Their use value Hes primarily in their information rather than in an intrinsic esthetic value. Whereas optimal preservation of that information value requires preservation of the object itself, a copy or even a completely different format could serve the same purpose. 

Analytical methods aie utilised by all branches of the chemical iadustry. Sometimes the goal is the quaUtative deterniiaation of elemental and molecular constituents of a selected specimen of matter othertimes the goal is the quantitative measurement of the fractional distribution of those constituents and sometimes it is to monitor a process stream or a static system. Information concerning the various iadividual analytical methods may be found ia separate articles dispersed alphabetically throughout the Eniyclopedia. The articles ate iatroductions to topics each of which is the subject of numerous books and other pubhcations. 

Toxic Substances Control Act. EPA regulates the manufacture, use, and exposure to ha2ardous or toxic chemicals under a number of laws. Eor the chemical industry, the law of prime concern is the Toxic Substance Control Act (TSCA) (10), which was passed by the U.S. Congress in 1976. The two main goals of TSCA are acquisition of sufficient information to identify and evaluate potential ha2ards from chemical substances, and regulation of the production, use, distribution, and disposal of these substances. 

The goal of approximate and numerical methods is to provide convenient techniques for obtaining useful information from mathematical formulations of physical problems. Often this mathematical statement is not solvable by analytical means. Or perhaps analytic solutions are available but in a form that is inconvenient for direct interpretation... 

Topics that acquire special importance on the industrial scale are the quality of mixing in tanks and the residence time distribution in vessels where plug flow may be the goal. The information about agitation in tanks described for gas/liquid and slurry reactions is largely apphcable here. The relation between heat transfer and agitation also is discussed elsewhere in this Handbook. Residence time distribution is covered at length under Reactor Efficiency. A special case is that of laminar and related flow distributions characteristic of non-Newtonian fluids, which often occiu s in polymerization reactors. 

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