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The backslash (\) has special meaning in AutoLISP, so you need to use two of them or a regular slash (/) when specifying a path. (The backslash character tells AutoLISP to interpret the following character as a special control code. For example, to use a double quote in an AutoLISP expression, precede it with a backslash.) You can also load AutoLISP routines with the APPLOAD command by choosing Manage tab Applications panel Load Application. AutoCAD opens the Load/Unload Applications dialog box. By choosing AutoLISP Files (*.lsp) from the Files of Type drop-down list, you can limit the list to only LSP files, as shown in Figure 34.2. FIGURE 34.2

This method of aggregating descriptions does not assume the reliability of any individual clinician s description of a patient Rather, following Block (1978), we assess the reliability of composite descriptions using coefficient alpha The logic is the same as computing the reliability of a psychometric scale, except that we are interested in the extent to which 200-item description of patients are consistent across observers, rather than the extent to which a set of items is internally consistent Coefficient alpha for all composites was > 80, suggesting that we were, in fact, able to identify reliably shared features of all the DSM-IV PDs Thus, a composite description of patients with a given PD should identify the core psychological features shared by these patients.

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Figure 6.51: Performance of the concatenatedST block coded and turbo convolutional coded adaptive OFDM and MC-CDMA systems over W-ATM channel of [4, pp.4741. The uncoded target BEP is 3 x lo-'. The coded BEP was less than lops for most of the SNR range,

(2.56)

= (l,O>-l,O)T,

Because the item set of the SWAP-200 includes all the Axis II criteria from DSM-IV, we are able to determine, using this method, whether the criteria in the diagnostic manual provide the best criteria for each disorder or whether some other combination of criteria might provide a more empirically accurate description From these data, we can derive two kinds of composite descriptions of patients with each PD The first most closely approximates the concept of a prototype (ie, the average patient with the disorder) and is derived by taking the average item score for each item for patients who share a diagnosis and displaying the items in descending order of magnitude (ie, of centrality to the construct) Table 132 presents the prototype for borderline PD aggregated in this way (Westen & Shedler, 1999a).

The linear occurrenceoff" in (7.57) is very convenient. By performing the ensemble averaging required according to equation (6.68), we obtain

where, as with a Michelson interferometer, f 4p nd=c is the phase difference between the two arms produced by a relative longitudinal displacement d between mirrors on the two arms. The spectral density on the detection plane is found by taking appropriate expectation values of the square of Eqn. (6.44), which yields Srsi (x, y, n) S[x cos(2u) y sin(2u), x sin(2u) y cos(2u), n] S[x cos(2u) y sin(2u), x sin(2u) y cos(2u), n] e4pi(nd=c) W[Dx 2y sin(2u), Dy 2x sin(2u), " 2x cos(2u), " 2y cos(2u), n] y x e 4pi(nd=c) W[Dx 2y sin(2u), Dy 2x sin(2u), " 2x cos(2u), " 2y cos(2u), n] y x

A second method more closely approximates the concept of an ideal type, that is, a portrait of the disorder that is somewhat idealized, which emphasizes its distinct features (ie, those features that distinguish it from other PDs) Rather than aggregating the raw SWAP item scores, as before, we first standardize the SWAP items across patients, so that all 200 items have a mean of 0 and a standard deviation of 1 We then average item scores (Z-scores) across all patients who share a diagnosis to generate a standardized prototype This second method reduces the centrality of items that are highly descriptive of the average patient with a given disorder but also highly descriptive of the average patient in the sample Table 133 presents the composite standardized description of borderline PD aggregated this way As seen by comparing Tables 132 and 13.

If you use queries a lot, you want to keep track of them. You can store, edit, rename, and delete queries. You can also import and export them for use by others. You do this by saving the information in a query file.

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