704 resultados para Learning support class
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"Marine Corps stock list, SL-4-06858B/06859D."
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"SL-4-05684C/06585B."
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"Marine Corps stock list, SL-4-07532-A.
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"July 1977."
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"June 1977."
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Includes index.
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"October 1981."
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Latent class and genetic analyses were used to identify subgroups of migraine sufferers in a community sample of 6,265 Australian twins (55% female) aged 25-36 who had completed an interview based on International Headache Society UHS) criteria. Consistent with prevalence rates from other population-based studies, 703 (20%) female and 250 (9%) male twins satisfied the IHS criteria for migraine without aura (MO), and of these, 432 (13%) female and 166 (6%) male twins satisfied the criteria for migraine with aura (MA) as indicated by visual symptoms. Latent class analysis (LCA) of IHS symptoms identified three major symptomatic classes, representing 1) a mild form of recurrent nonmigrainous headache, 2) a moderately severe form of migraine, typically without visual aura symptoms (although 40% of individuals in this class were positive for aura), and 3) a severe form of migraine typically with visual aura symptoms (although 24% of individuals were negative for aura). Using the LCA classification, many more individuals were considered affected to some degree than when using IHS criteria (35% vs. 13%). Furthermore, genetic model fitting indicated a greater genetic contribution to migraine using the LCA classification (heritability, h(2) =0.40; 95% CI, 0.29-0.46) compared with the IHS classification (h(2)=0.36; 95% CI, 0.22-0.42). Exploratory latent class modeling, fitting up to 10 classes, did not identify classes corresponding to either the IHS MO or MA classification. Our data indicate the existence of a continuum of severity, with MA more severe but not etiologically distinct from MO. In searching for predisposing genes, we should therefore expect to find some genes that may underlie all major recurrent headache subtypes, with modifying genetic or environmental factors that may lead to differential expression of the liability for migraine. (C) 2004 Wiley-Liss, Inc.
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While object-oriented programming offers great solutions for today's software developers, this success has created difficult problems in class documentation and testing. In Java, two tools provide assistance: Javadoc allows class interface documentation to be embedded as code comments and JUnit supports unit testing by providing assert constructs and a test framework. This paper describes JUnitDoc, an integration of Javadoc and JUnit, which provides better support for class documentation and testing. With JUnitDoc, test cases are embedded in Javadoc comments and used as both examples for documentation and test cases for quality assurance. JUnitDoc extracts the test cases for use in HTML files serving as class documentation and in JUnit drivers for class testing. To address the difficult problem of testing inheritance hierarchies, JUnitDoc provides a novel solution in the form of a parallel test hierarchy. A small controlled experiment compares the readability of JUnitDoc documentation to formal documentation written in Object-Z. Copyright (c) 2005 John Wiley & Sons, Ltd.
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In this letter, we propose a class of self-stabilizing learning algorithms for minor component analysis (MCA), which includes a few well-known MCA learning algorithms. Self-stabilizing means that the sign of the weight vector length change is independent of the presented input vector. For these algorithms, rigorous global convergence proof is given and the convergence rate is also discussed. By combining the positive properties of these algorithms, a new learning algorithm is proposed which can improve the performance. Simulations are employed to confirm our theoretical results.
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The importance of building home, school and community partnerships is increasingly acknowledged since family and community involvement in education is thought to be associated with children’s success at school. This paper reports on aspects of an Australian Government commissioned research project that analysed educational partnerships aiming to enhance children’s numeracy education. Snapshots of two school case studies are presented to highlight features of effective partnerships and the kinds of numeracy learning they supported.