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Severe, chronic, continuous, or intermittent headaches lasting several days or weeks appear as the cardinal symptom of several posttraumatic syndromes, separable in each instance from the headache that immediately follows head injury (i.e., scalp laceration and cerebral contusion with blood in the CSF and increased intracranial pressure). The headache of chronic subdural hematoma is deep-seated, dull, steady, mainly unilateral, and may be accompanied or followed by drowsiness, confusion, and uctuating hemiparesis. In more acute hematomas, we have been impressed with the positional worsening in some patients after lying down or leaning the head to one side. Tentorial hematomas produce the additional feature of pain in the eye. The head injury that gives rise to a subdural hematoma may have been minor, as described in Chap. 35, and forgotten by the patient and family. Typically the headache and other symptoms increase in frequency and severity over several weeks

Nonetheless, the white noise model is convenient for situations where the noise spectral density is constant over a frequency range signi cantly larger that the bandwidth of interest in a particular application. A white noise process can have any probability distribution; however, the Gaussian distribution is often assumed. A key reason is the central limit theorem. If the analyst carefully constructs a model accounting for all known or predictable e ects, then the remaining stochastic signals can often be accurately modeled as the output of a linear system that is driven by a Gaussian white noise input. This technique will be discussed in Section 4.5. In this book, the notation N (0, 2 ) will sometimes be used to describe a continuous-time, Gaussion, white-noise process . In this case, 2 is interpreted as the PSD, not the variance. 2 Reasoning through the units of the symbol v often causes confusion. This can be considered from two perspectives. First, from the PSD per2 spective, v represents the power per unit frequency interval. Therefore, 2 the units of v are the units of v squared divided by the unit of frequency Hz. Second, from the perspective of correlation, Rv has the units of v

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Now that we have the Recordset object created, opened, and (if we got the SELECT statement right) populated with data from the database, we can use the remaining methods of the Recordset object to move through the Recordset, display data, add new records, and maintain existing ones. If we assume that the HOTELS table contains the fields Hotel_Name and Empty_Rooms, we can list the data very simply by using a loop to iterate through the Recordset like so:

This technique requires a clear means of joining the data from the two different sources Another way to combine multiple data sources is to merge the data at the answers request or dashboard level Using this method, data from two or more sources could be displayed together on one dashboard page Dashboard prompts can be used to link the information from the different sources In the examples provided on the Web, usage tracking and the database audit trail were modeled as two separate subject areas The information was then merged on the dashboard by including reports from both subject areas, filtered by the same prompt Figure 14-20 shows usage tracking and database audit information for user BIPRODUCT1 Usage tracking and the database audit trail were modeled separately and combined at the dashboard layer for two reasons.

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First, this satisfies the requirement of being able to analyze both usage tracking and database audit information at the same time To make this work, the column being filtered by the dashboard prompt must be named the same in each subject area In this example, the dashboard prompt is filtering the column User Name In the BI server metadata, the column User Name maps to the physical column LOGON in the usage tracking schema and CLIENT_ID in the database audit trail The second reason for modeling this as two subject areas is that modeling these two data sources as a single subject area could prove challenging for the following reasons: Not every logical SQL statement logged in usage tracking will generate a physical SQL statement captured in the database audit trail..

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If you do run this code on a Phone.com micro-browser, it will run, but it will only show the option statements, not the optgroup statements. The onpick attributes still work, but the whole grouping concept is lost. So when you first load the deck, it looks like the following:

>> norm (X, p) ano ._ cond{X,p)

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Exceptions If startIndex is larger than the last index, an empty string ( ) is returned. If length <= 0, an empty string ( ) is returned.

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