Taxonomy

To see linkages between problems and decision-making models utilized in a complex enterprise such as a supply chain, it is imperative that these components be formally represented. Taxonomy and ontology provide the means to classify the supply chain problems and represent formal knowledge, which is used in decisionmaking. We take up discussion on this topic next. 

System taxonomy serves two purposes (1) standardization of terms and definition, and (2) unification of information representation. This brings out reusability of developed information models, as well as organization and strucmre, to knowledge management. Scalability and traceability are the most important features that system taxonomy provides, and thus, new features can be added and existing ones easily found. 

The highest grouping level in the hierarchy is called a domain. This term is commonly used in finite-element calculations to denote different regions of a problem where there may be different physical properties or governing equations. This is the sense in which we use the term. Because fundamentally different reaction chemistry may be occurring in two spatial regions, say in the gas and on a reactive surface, it is convenient to divide the calculation into domains. 

An example of different domains in a copper corrosion problem is shown schematically in Fig. 11.2. Four of the domains domains are volumetric. That is, they are three-dimensional, so concentrations of species within these domains might have units of mol/m3, for example. The volumetric domains shown correspond to the gas (G), bulk copper that is being corroded (B), an aqueous layer (A), and a layer in which corrosion products have formed (C). The list of species that can exist in one domain may be (and surely is) different from the species present in another domain. Chemical reaction rates-of-progress within a volumetric domain have units like mol/m3-s. 

The interface between two volumetric domains is designated a surface domain, and its dimensionality is one less than a volumetric domain. Concentrations of species in a surface domain have dimensions of mol/m2, for example. The four types of surface domains shown in Fig. 11.2 are A-G, the interface between the aqueous domain and the gas A-C, the interface between the aqueous domain and the corrosion-product layer C-G, the interface between the corrosion layer and the gas and C-B, the interface between the corrosion layer and the bulk copper layer. Chemical reactions of species residing in one volumetric domain with species in another volumetric domain have to occur at an interface, namely a surface 

Within a domain, as defined above, any number of phases can exist. The phase is the next level in the hierarchy that we consider. Beneath the level of phases are species within a phase, which will be considered shortly. 

There might be only a single phase within a given domain. That is typically the case for the gas phase (within the domain G in the example of Fig. 11.2). The governing equations and equations of state in the gas phase are fundamentally different than in the other domains. Two of the other volumetric domains also have only one phase each in the corrosion example (i.e., the aqueous domain A and the bulk copper domain B). However, within those phases, any number of chemical species might be present. 

Failure rates are computed by dividing the total number of failures for the equipment population under study by the equipment s total exposure hours (for time-related rates) or by the total demands upon the equipment (for demand-related rates). In plant operations, there are a large number of unmeasured and varying influences on both numerator and denominator throughout the study period or during data processing. Accordingly, a statistical approach is necessary to develop failure rates that represent the true values. 

Both the historical (literature) data and the recent (lOW) data were stored within one common database. One data point means one species at a distinct date and a distinct location (geographic coordinates) with a mean abundance (if replicates were taken) or at least its presence. In total approx. 100000 data were included and analyzed. The chronological development in terms of investigation activity is reflected in Fig. 17.3. For the period before 1920 only single data or a few hundred per two decades are available. Between 1921 and 1960 more than 6 000 data were recorded. Since the beginning of the 1960s the amount of data has increased rapidly, up to several ten thousands. Generally, more than two thirds of all macrobenthic data were collected after 1980. 

The present state of knowledge of terpenoid biosynthesis does not allow many detailed conclusions to be reached on its taxonomic importance. However, some gross differences at the phyla level are apparent. This review has already commented on differences observed in the formation of steroidal A - and A -double bonds, 24-alkyl groups, and whether lanosterol or cycloartenol is formed from squalene epoxide. 

In the animal kingdom the position is much more complicated. Vertebrates all seem to be able to synthesise steroids from simple precursors. However, there is no good evidence for steroid synthesis in Arthropods, even though some workers have claimed otherwise. Careful investigation has always shown that incorporation of labelled precursors into steroids is indicated by the intestinal flora. For this reason, caution is required in interpreting positive results, while negative results may merely indicate an inability to metabolise added precursors, or that the substance fails to reach the necessary enzymes. 

Van der Walt (1970) describes seven species of Brettanomyces and two species of Dekkera. Subsequently, Brettanomyces was expanded to include nine species, whereas Dekkera remained unchanged (van der Walt, 1984). Of those species described, only B. intermedins and B. lambicus were originally isolated from grape wines or juice. In characterizing 57 isolates, Smith (1993) reports identification of only B. custeri and D. intermedia. 

Probably the most significant stumbling block in successful routine laboratory identification of Brettanomyces and Dekkera lies in the fundamental requirements of taxonomic guides to demonstrate the presence (or absence) of a sexual phase in the life cycle of the yeast. Dekkera requires a sporulation medium that includes augmentation with several vitamins (see Procedure 3.5.2). Required in microgram and milligram amounts, these nutrients are not easily and routinely supplied in most production-oriented laboratories. Hagan (1979) notes that even under ideal conditions, relatively poor sporulation ( 1%) is observed. As a result, suspect isolates are often reported as Brett-like or Brettanomyces/Dekkera.  

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M.M. Slaughter, Universal Languages and Scientific Taxonomy in the Seventeenth Century, Cambri< e University Press, Cambridge, UK, 1982. 

EMBL (European Molecular Biology Laboratory) [33] is a nucleotide sequence database provided from the online host EBl. Release 73 (December, 2002) consists of over 20 million nucleotide sequences with more than 28 billion nucleotides. The information includes sequence name, species, sequence length, promoter, taxonomy, and nucleic acid sequence. 

The protein sequence database is also a text-numeric database with bibliographic links. It is the largest public domain protein sequence database. The current PIR-PSD release 75.04 (March, 2003) contains more than 280 000 entries of partial or complete protein sequences with information on functionalities of the protein, taxonomy (description of the biological source of the protein), sequence properties, experimental analyses, and bibliographic references. Queries can be started as a text-based search or a sequence similarity search. PIR-PSD contains annotated protein sequences with a superfamily/family classification. 

The SWISS-PROT database [36] release 40.44 (February, 2003) contains over 120 000 sequences of proteins with more than 44 million amino adds abstracted from about 100 000 references. Besides sequence data, bibHographical references, and taxonomy data, there are highly valuable annotations of information (e.g., protein function), a minimal level of redundancy, and a high level of integration with other databases (EMBL, PDB, PIR, etc.). The database was initiated in 1987 by a partnership between the Department of Medicinal Biochemistry of the University of Geneva, Switzerland, and the EMBL. Now SWISS-PROT is driven as a joint project of the EMBL and the Swiss Institute of Bioinformatics (SIB). 

F. J. Schwinn, in D. C. Erwin, S. Bartnicki-Garcia, and P. H. Tsao, eds., Phjtophthora Its Biology, Taxonomy, Ecology, American Phytopathic Society, St. Paul, Minn., 1983, p. 327. 

Vimses contain either RNA or DNA, and this nucleic acid composition forms the basis for thek classification. Although vimses ate known to infect bactetia, insects, plants, animals, and humans, this discussion is restticted to the important vimses of vertebrates. The relevant vimses ate summarized in Table 2, using the nomenclature and taxonomy recommended by the International Committee on Taxonomy of Vimses (4,5). 

Types of Computers. Computers can be classified by Flynn s taxonomy (19). The three important classes are SISD single instmction, single data SIMD single instmction, multiple data and MIMD multiple instmctions, multiple data. 

CS Ring, DG Kneller, R Langndge, FE Cohen. Taxonomy and conformational analysis of loops m proteins. I Mol Biol 224 685-699, 1992. 

On the basis of simple considerations of connected motifs, Michael Leviff and Cyrus Chothia of the MRC Laboratory of Molecular Biology derived a taxonomy of protein structures and have classified domain structures into three main groups a domains, p domains, and a/p domains. In ct structures the core is built up exclusively from a helices (see Figure 2.9) in p structures the core comprises antiparallel p sheets and are usually two P sheets packed... 

Richardson, J.S. The anatomy and taxonomy of protein stmcture. Adv. Prot. Chem. 34 167-339, 1981. Rossmann, M.G., Argos, P. Protein folding. Annu. Rev. 

Sprang, S.R., Bazan, J.E Cytokine structural taxonomy and mechanisms of receptor engagement. Cun. Opin. 

This chapter has only scratched the surface of the multitude of databases and data reviews that are now available. For instance, more than 100 materials databases of many kinds are listed by Wawrousek et al. (1989), in an article published by one of the major repositories of such databases. More and more of them are accessible via the internet. The most comprehensive recent overview of Electronic access to factual materials information the state of the art is by Westbrook et al. (1995), This highly informative essay includes a taxonomy of materials information , focusing on the many different property considerations and property types which an investigator can be concerned with. Special attention is paid to mechanical properties. The authors focus also on the quality and relutbility of data, quality of source, reproducibility, evaluation status, etc., all come into this, and alarmingly. 

Taxonomy The classification, nomenclature, and laboratory identification of organisms (Do not confuse with taxidermy - stuffing dead animals)... 

Rasmussen, J. (1982). Human Errors A Taxonomy for Describing Human Malfunction in Industrial InstallaHons. Journal of Occupational Accidents 4,311-333. 

Chapter 3—CCPS Taxonomy Explains the CCPS taxonomy. Discusses the rationale and process for its development and the factors considered in its construction. 

Chapter 5—CCPS Generic Failure Rate Data Base Contains tables of generic process equipment reliability data that are structured by the CCPS Taxonomy. The data are extracted from data resources in Chapter 4. The chapter includes a discussion of the selection, treatment, and presentation of the data in the Tables. 

Appendix A—CCPS Taxonomy The full CCPS Taxonomy for process equipment failure rate data. 

Appendix B—Equipment Index Allows the user to determine the taxonomy location for equipment types familiar to the CPI. 

To find generic data in this book for use in a CPQRA, the reader should first locate the taxonomy number for the equipment under study by referring to Appendix B, Equipment Index. This index shows the taxonomy number for various types of commonly used equipment. Knowing the taxonomy number, the reader can consult the Index of Filled Data Cells (Table 5.2) to determine if the data exist in Chapter S. Alternatively, the user... 

To properly use failure rate data, the engineer or risk analyst must have an understanding of failure rates, their origin and limitations. This chapter discusses the types and source of failure rate data, the failure model used in computations, the confidence, tolerance and uncertainties in the development of failure rates and taxonomies which can store the data and influence their derivation. 

The above discussion leads to the conclusion that time-related and demand-related failures for a piece of equipment cannot be equated through a general mathematical relationship. These issues are better dealt with in a data base taxonomy (classification scheme) for equipment reliability data by defining a unique application through equipment description, service description, and failure description. 

The various data cells in a taxonomy include a written description of the equipment and a boundary diagram to identify exactly what equipment is included within the cell. Any change in the boundary diagram or deviation from it in failure attribution during data processing will influence the failure rate and its comparability with others. 

The various levels of the taxonomy represent factors that have an impact on failure rate. For example, lined pipe (CCPS taxonomy number 3.2.2) has a level that groups pipe into 0-6 size and over 6 . Unless the pipe size is specified, there is no way of knowing whether a given failure rate came from the 0-6 or the over 6 range. 

In the CCPS Taxonomy, four degrees of severity, from clean to severe, are used to characterize the process medium—the material being handled by the equipment—and its influence on reliability. In some cases, the severity will be unknown. Even if a severity is listed, doubt may exist about its value, since the definitions of severity are fairly subjective. 

In Table 3.2, a number of factors are listed that were not used as separate levels in the CCPS taxonomy because of assumptions made by the CCPS Subcommittee. The analyst must, wherever possible, try to assess the validity of these assumptions for the particular situation and establish if the equipment represented by the data ... 

This chapter reviews the structure, rationale, and method used for the development of the CCPS Taxonomy and explains how to use it. Key elements of the CCPS Taxonomy that are explained include equipment, service, and failure description. The CCPS Taxonomy is listed in Appendix A. 

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