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I've found RegEx to be useful still. In the following code I use a jagged string array with the AI's I want to be able to process and their properties, being: string[][]​ ...

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Longitudinal surveys present a number of options, both for data collection and for analysis, that are either simply impossible with cross-sectional surveys or cannot be achieved in a satisfactorily accurate or reliable way. Often these features are key elements of the rationale for carrying out a longitudinal survey. However, it is important to distinguish between longitudinal surveys and longitudinal data. Longitudinal surveys are a source of longitudinal data, as the resultant data include items that refer to different points in time. But there are also other ways of obtaining longitudinal data, including diary methods and the use of retrospective recall within a single survey instrument. We briefly discuss here the strengths of longitudinal data, but our particular focus is on the strengths of longitudinal surveys. 1.3.1 Analysis Advantages

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/** * HelloServicePortType.java * * This file was auto generated from WSDL * by the Apache Axis Wsdl2java emitter. */ public interface HelloServicePortType extends java.rmi.Remote { public String echoName(String arg0) throws java.rmi.RemoteException; }

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1. Sciotti VM, Mittak VL, DiMarco L, Ford LM, Plezbert J, Santipadri E, Wigglesworth J, Ball K. Clinical precision of myofascial trigger point location in the trapezius muscle. Pain 2001;93(3):259 266. 2. Wright EF, Gullickson DC. Identifying acute pulpalgia as a factor in TMD pain. JADA 1996;127:773 780. 3. Wright EF. Pulpalgia contributing to temporomandibular disorder-like pain: A literature review and case report. JADA 2008;139(4):436 440. 4. Wright EF, Gullickson DC. Dental pulpalgia contributing to bilateral preauricular pain and tinnitus. J Orofac Pain 1996;10(2):166 168. 5. Falace DA, Reid K, Rayens MK. The in uence of deep (odontogenic) pain intensity, quality, and duration on the incidence and characteristics of referred orofacial pain. J Orofac Pain 1996;10(3):232 239. 6. Wright EF, Domenech MA, Fischer JR Jr. Usefulness of posture training for TMD patients. JADA 2000;131(2):202 210. 7. American Academy of Orofacial Pain. de Leeuw R (ed). Orofacial Pain: Guidelines for Assessment, Diagnosis and Management, 4th ed. Chicago: Quintessence, 2008:168. 8. Clark GT, Minakuchi H. Oral appliances. In: Laskin DM, Greene CS, Hylander WL. Temporomandibular Disorders: An Evidence-Based Approach to Diagnosis and Treatment. Hanover Park, IL: Quintessence, 2006:377 390. 9. Raphael KG, Marbach JJ, Klausner J. Myofascial face pain: Clinical characteristics of those with regional vs. widespread pain. JADA 2000;131(2):161 171.

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In the previous section, the method for estimating Ct is used when GPS data is available. If the GPS data is of ine, the calibration of Ct cannot take place. The previous section assumed that GPS data is online for a suf cient period of time, so that at least k observations are pushed onto the stack, as well as the supplied GPS positions settled and are reliable. Thus, there is a run-in period of l seconds, where the methods in the previous section cannot be applied. Clearly, l cannot be less than k, but a much larger value may be required. This is to be determined by experiment. How Ct should be estimated in this l-second time interval, and how the position should be estimated One possibility is to work initially with a global estimate of C to provide the odometer-based estimate. As time during the burn-in period passes, the estimate should drift . This can be done by combining Ct and C using a weighted average, with the weighting gradually favouring Ct rather than C. The trial method here uses the formula: C = r jC + (1 r j)Ct t

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7. Please provide a copy of the full performance disclosures for the product under review. The disclosure should include the following for each year-end: a. Number of accounts included in the composite. b. Assets of those accounts. c. Number of accounts managed in a similar style, but not included in the composite. d. Assets of those accounts. e. Reasons why those accounts were not included in the composite. f. Number of accounts added to/subtracted from the composite in each year. 8. Please state the most appropriate benchmark for the product under review and include its performance history for the same time period listed previously for the product.

TMD patients generally report a decrease in morning symptoms with the use of a nocturnal appliance.1 Nocturnal muscle

2-mm-deep indentations for the maxillary teeth (Figures 10.5 and 10.6).

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P(MT > u) P(X(tn ) > u) =

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ilopez/GS1Parser: A GS1 Parser for C - GitHub
Jun 9, 2015 · A GS1 Parser for C#. Contribute to ... http://stackoverflow.com/questions/9721718​/ean128-or-gs1-128-decode-c-sharp/28854802#28854802.

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