Schemas

Tome 1. P6trole brut. Produits pbtroliers. Schemas de fabrication", J.-P. Wauquier 1994, Editions Technip, Paris. 

Figure 1 Schema of the hybrid rule-based/CBR system for interpretation of URS data.

Figure Bl.17.1. Schema of the electron specimen interactions and their potential use for structural and analytical studies.

The characteristic of a relational database model is the organization of data in different tables that have relationships with each other. A table is a two-dimensional consti uction of rows and columns. All the entries in one column have an equivalent meaning (c.g., name, molecular weight, etc. and represent a particular attribute of the objects (records) of the table (file) (Figure 5-9). The sequence of rows and columns in the tabic is irrelevant. Different tables (e.g., different objects with different attributes) in the same database can be related through at least one common attribute. Thus, it is possible to relate objects within tables indirectly by using a key. The range of values of an attribute is called the domain, which is defined by constraints. Schemas define and store the metadata of the database and the tables. 

Use of Schemata (Similarity templates) As the progression through generations of solutions takes place, there evolves certain similarities between genes within chromosomes. These similarities can be exploited using a similarity template or schema, that sits within a schemata framework. 

A schema employs a don t care symbol so, for example, the sixth generation of offsprings in Table 10.6 could have employed the template... 

The monomer-monomer (MM) model, for the reaction A -H B —> AB, assumes the following Langmuir-Hinshelwood reaction schema ... 

Schema, n. model, pattern blank, form schedule scheme diagram, -bild, n. diagram fiow sheet.

Term-schema, n. term diagram, -verschie-bung, /. term shift, -wert, m. term value. 

Now, to be sure, not every possible subset of the solution-space can be described as a schema. Simple counting shows that a length-A chromosome can have 2 possible configurations, and therefore 2 possible subsets, but only 3 different schemas. Nonetheless, it is a central axiom of the building-block hypothesis that it is precisely the set of schemas that are effectively being processed by GAs. 

The schema population can be estimated using a simple mean-field-like argument, Let S represent a schema in a size-A population V t) at time t, and Z V,t) instances of the schema at time t. Let f s) be the fitness of the string s, fs be the average fitness of instances of S at time t, and / = Y2ifi be the average fitness of the population. Then the expected number of instances of S at time t + l, Z V, t -f 1), is equal to... 

This basic difference equation - known as the Schema Theorem [holl92] - expresses the fact that the sample representation of schemas whose average fitness remains above average relative to the whole population increases exponentially over time. As it stands, however, this equation addresses only the reproduction operator, and ignores effects of both crossover and mutation. 

A lower bound on the overall effect of crossover, which can both create and destroy instances of a given schema, can be estimated by calculating the probability, Pc S), that crossover leaves a schema S unaltered. Let be the probability that the crossover operation will be applied to a string. Since a schema S will be destroyed by crossover if the operation is applied anywhere within its defining length, the probability that S will be destroyed is equal to Pc x 6 S)/ K — 1), where 6 S) is the defining length of S. Hence, the probability of survival ps = 1 — PcS S)/ K — 1), and equation 11.9 takes the updated form ... 

Finally, in order to also take into account the mutation operator, we note that the probability that a schema S survives under mutation is given by pu S) = (1 — Pm) where pm is the single-bit mutation probability and 0( S) is the number of fixed-bits (i.e. the order) or S. With this we can now express the Schema Theorem that (partially) respects the operations of reproduction, crossover and mutation ... 

We conclude from this basic theorem that the sample representation of low-order schemas with above average fitness relative to the fitness of the population increases exponentially over time. ... 

Les differents puits sont representes sur le schema joint (p. 62), qui correspond uniquement a la Classification par objectifs". 

Hematopoietic Growth Factors. Figure 1 Schema of hematopoiesis, including some of the growth factors that influence the production of blood cells. 

Fig. 9.2 Conceptual schema of the development of an unbreakable cup. Examples of structure-property relations are marked as lines.

Three analogous but theme-specific conceptual schemas have been constructed, with systems which have several nested sub-systems (Meijer et al., 2005). Relevant mi-crostractures at different meso-levels can be assigned to appropriate scales. In such conceptual schemas, structure can be defined as the distribution over space of the components in a system. Physical building blocks of such a system are regions that are bounded by a closed surface (Walstra, 2003), where at least some of the properties within such regions are different from those in the rest of the system. 

To summarise in authentic tasks, we have established that stmcture-property relations can be described by a dynamic system of stmctures, properties and their interrelations. Within the limits of our study we have derived a generalised conceptual schema, which we expect to be useful to teach macro-micro problems in which stmcture-property relations can be explicitly used (Figs. 9.2, 9.3 and 9.4). The system of nested stmctures, systematically assigned to appropriate scales, and the properties of the different stmctural components reveal a conceptual schema necessary for macro-micro thinking. The system of relevant nested stmctures and the explicit relations between stmctures and properties form the backbone of macro-micro reasoning. Depending on the task, a number of different meso-levels are relevant and... 

Fig. 9.3 The conceptual schema of micro-macro thinking for the task designing gluten-free com bread, with the explicit use of structure-property relations...

Fig. 9.4 Conceptual schema of the design of a bullet-proof jacket derived from the experts consultation. An example of a structure-property relation is marked as a line...

The new conceptual schemas derived in the first part of this chapter could thus be used to design context-based units by a design research approach (Van den Akker,... 

Figure 22.1 A. Schema for a physiologically based pharmacokinetic model incorporating absorption in the stomach and intestines and distribntion to various tissues. B. Each organ or tissue type includes representation of perfusion (Q) and drug concentrations entering and leaving the tissue. Fluxes are computed by the product of an appropriate rate law, and permeable surface area accounts for the affinity (e.g., lipophilic drugs absorbing more readily into adipose tissue). Clearance is computed for each tissue based on physiology and is often assumed to be zero for tissues other than the gut, the liver, and the kidneys.

Figure 5. A schematic representation of superposed steady-state reservoirs of constant volumes Vi (fractional crystallization is omitted in this schema). At steady-state, Vi/xi=V2/x2=..., where x is the residence time. This is analogous to the law of radioactive equilibrium between nuclides 1 and 2 Ni/Ti=N2/T2=...A further interest of this simple model is to show that residence times by definition depend on the volume of the reservoirs.

A tentative schema based on these findings is projected for a sequence of OBP and ligand transfer-interactions, from urinary deposit to VN lumen the series of intermediate steps in Z.7-(12)Ac transfer is summarised as follows ... 

Fig. 6.4 Evolutionary schema for the emergence of the OR genome increase, elimination and alterations to the main receptor repertoire (from Sharon, 1998).

Fig. 7.1 Schema for emergence of signaller = receiver transmission, selection for information exchange in, e.g. Fish (from Sorenson and Stacey, 1999).

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