String manipulations

The next steps consist of the extraction and normalization of terms from the zoned input document. To this end, we apply standard natural language processing techniques and normalize the extracted terms to their canonical form with string manipulations and morphological analysis. The former refers to the treatment of symbols (e.g., dashes), and the latter refers to variations of words due to inflection (e.g., plurals). These steps of information extraction rely on, and make extensive use of, our terminologies and ontologies. 

Data types treated by MADCAP include real numbers (no distinction is made between floating-point and integer), complex numbers, character strings, matrices with real elements, sets of natural numbers, and arrays of arbitrary elements (77). Notation and facilities exist for character-string manipulation matrix operations of addition, subtraction, multiplication, inversion, and transposition set-theoretic operations of complementation, union, intersection, subtraction, and symmetric subtraction (exclusive or). When the set-theoretic operations were added to the language, set-theoretic comparators for use in conditional expressions and other language extensions to facilitate the programming of combinatorial calculations were incorporated, as well (78). 

Insertion of assembly and intrinsic subprograms. Intrinsic subprograms are built-in to the compiler provided for convenient access to any machine operations that provide special capabilities or efficiency and that are not otherwise available through the language constructs. Examples of such instructions include atomic read-modify-write operations, standard numeric functions, string manipulation operations, vector operations, direct operations on I/O ports, etc. 

In Section II, we presented the computational model involved in branching from a node, cr, to a node aa,. In this model, it was necessary to interpret the alphabet symbol a, and ascribe it to a set of properties. In the same way, we have to interpret o- as a state of the flowshop, and for convenience, we assigned a set of state variables to tr that facilitated the calculation of the lower-bound value and any existing dominance or equivalence conditions. Thus, we must be able to manipulate the variable values associated with state and alphabet symbols. To do this, we can use the distinguishing feature of first-order predicates, i.e., the ability to parameterize over their arguments. We can use two place predicates, or binary predicates, where the first place introduces a variable to hold the value of the property and the second holds the element of the language, or the string of which we require the value. Thus, if we want to extract the lower bound of a state o-, we can use the predicate Lower-bound Ig [cr]) to bind Ig to the value of the lower bound of cr. This idea extends easily to properties, which are indexed by more than just the state itself, for example, unit-completion-times, v, which are functions of both the state and a unit... 

The vector that the genetic algorithm manipulates is known conventionally as a chromosome or a string we shall use the latter terminology in this chapter. The individual units from which each string is constructed, a single x-, y-, or z-coordinate for an atom in this example, are referred to as genes. 

We could apparently get around this difficulty by allowing changes to be made to not just one but every bit in the mutation of the 9-bit binary string, but this then again returns us to a situation in which we are, in essence, manipulating real numbers disguised in a binary cloak. 

Although the Find/Replace function works well for these data, it is not as generally useful as the built-in functions for manipulating strings of alphabetical characters and numbers. These functions are called string functions. We will use string functions to strip the parenthetical uncertainties from the data in column D and produce a column of numerical atomic masses. 

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