Predefined attributes

Entities are the basic data structures of the schema. They are defined by structured collections of one or more data attributes. An attribute provides detail of part of an entity description. There are mainly two types of attribute, predefined and composite. Predefined attribute types include integer, real, logical, and string. Composite types are declared explicitly in the schema - for example, the attribute type REF PART LIBRARY which indicates a reference to an entity in a part library by grouping the three necessary attributes together will be defined as a composite attribute. Some collections of attributes may appear both as entities and as composite attributes, for example POINT. ... 

In Fig. 42.9 we show the simulation results obtained by Janse [8] for a municipal laboratory for the quality assurance of drinking water. Simulated delays are in good agreement with the real delays in the laboratory. Unfortunately, the development of this simulation model took several man years which is prohibitive for a widespread application. Therefore one needs a simulator (or empty shell) with predefined objects and rules by which a laboratory manager would be capable to develop a specific model of his laboratory. Ideally such a simulator should be linked to or be integrated with the laboratory information management system in order to extract directly the attribute values. 

The predefined name null or 0 refers to a special object the value of any unconnected attribute or link is null. In Figure 2.2, the catalysisCourse does not currently have an owner. 

These constraints simply navigate parameterized attributes we could use the more conventional syntax a < b instead of a.isLessThan(b). Thus, a predefined type Date has a parameterized attribute < (Date) Boolean, which yields true or false for any given compared date. 

Writing the invariant within the type box is equivalent to writing it separately after a context operator. The predefined notation directly captures certain common invariants. Declaring the course attribute of a Session to be of type Course in Figure 2.5 is equivalent to... 

Note that the model Dx does not appear in the schematic. This is because, with predefined models like the D1N4001, the text D1N4001 displayed on the schematic is the Value property and not the Implementation property. Since the Implementation attribute has been changed to DX in the property spreadsheet, PSpice will use model Dx in the simulation. 

Class variable is a subclass of the basic class generic-variable. From the class variable emanates the tree of subclasses, a partial view of which is shown in Fig. 19. Unlike other modeling approaches, MODEL.LA. does not represent variables through their values alone, but it provides an extensive structure that includes many additional attributes in the description of a variable. The additional attributes allow MODEL. LA. to reason about these variables and not just acquire their values. Thus, a set of methods in the class variable allow any of the subclasses to monitor their values, react with predefined procedures when the value of the variable changes, invoke values from external databases, and so on. 

Another major responsibility of this department is change control management. To effectively evaluate the potential impact of a process change, it is important to contemplate how the predefined quality attributes might be affected—as well as other chemical/physical attributes not normally tested or evaluated. This requires people who have a firm understanding of the chemistry of the process and an... 

The data in ORACLE are kept in tables, where a row in a table consists of a key and a number of attributes. The key identifies this logical amount of information in the row. The relational concept of ORACLE gives the possibility of fast responses for queries where data from one table are related to data from another table. This is possible for all tables as long as the compound number is the key. The data are searched via the content of the value and not via numerous predefined relations. 

Volume shrinkage can be attributed to the formation of covalent bonds during photopolymerization bringing monomers, macromers, or prepolymers in a densely packed formation when compared with the initial liquid (viscous) state. Volume shrinkage is one of the major concerns in radical photopolymerization, especially for in situ polymerized systems, where the photocurable formulation in the form of viscous liquid, or paste, injected into a predefined volume... 

Analysis shows that the development of attribution of causes is well understandable. It illustrates gradual shift to the identification of causes on deeper cause levels. Generally, four Incident Cause Levels may be identified, two of them below the predefined root causes. 

Our study proposes a process involving a four-step model for DQ analysis with the purpose of improving the safety aspects of WSNs. The first step is the identification of interesting quality dimensions in the imderlying process. The second step is the measurement of those dimensions, either objectively or subjectively. The third step is an evaluation of those measurements hy checking their coherence against predefined attributes. Finally, the fourth step involves the decisive actions to he executed in order to ensure continual system improvement. 

The concepts described in this section also apply to attributes. Attribute measures are yes it passes or no it does not pass situations. The so-called perfect order in the distribution industry would be judged on attributes because it must possess predefined attributes (on time, complete, proper invoice, etc.). Certainly a less-than-perfect order has the potential to generate unwanted transactions that generate cost and waste time. 

As a holistic approach, FMS is a good tool for eliciting holistic constructs and contrasts present in the sample set. As more holistic constructs are better elicited by naive assessors as consumers, training sessions of assessors are not a part of the method process. If focus is on highlighting attributes from a predefined vocabulary, a previously trained panel can be applied with benefit. 

With FMS (as with projective mapping), assessors are allowed to separate samples on the bases of hunches or feelings not easily expressed. Focus is kept on the sample rather than a predefined set of attributes, and assessors are not asked to describe then-actions until after the sorting. Compared to conventional sensory profiling where a vocabulary needs to be established before sample evaluation, the FMS approach is said to be more holistic and individual. This is an advantage when applied to conceptual tasks (Fig. 8.3.), as distinct from the more constructual tasks that focus on specific sample attributes. 

If consumers can profile products, and if consumers can rate the products relative to their own ideal (as with the JAR scale), then we could ask consumers to rate their ideal directly on a predefined list of attributes. This is the essence of the Ideal Profile Method (IPM) (Worch et al, 2013) that is presented here. As will be shown, the data obtained from this approach are directly actionable for product development and/ or product improvement (Hoggan, 1975). However, since these particular data are obtained from consumers who are rating a fictive ideal product, they require particular attention from the practitioners. 

In the IPM, consumers are asked to rate both the perceived and ideal intensities of products on a predefined list of sensory attributes. In this case, P tested products will yield P sensory profiles and P ideal profiles per consumer. Additionally, the consumers rate the products on overall liking. In this sense, the IPM can be seen as a mix between a classical profiling task (such as QDA , except that it is performed by consumers) and a JAR task in which the ideal intensities are asked explicitly. 

Additionally, the absence of preliminary training limits the use of specific or technical attributes. The list of predefined attributes asked during the IPM test must include only non-technical terms that are understood by most respondents. Specific or technical terms should be explained. 

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