831 resultados para CLASS


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Self-tuning is applied to the minimum variance control of non-linear multivariable systems which can be characterized by a ' multivariable Hammerstein model '. It is also shown that such systems are not amenable to self-tuning control if control costing is to be included in the performance criterion.

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This publication lists the more important wood properties of commercial timbers used for construction in Queensland. It also provides requirements and conditions of use for these timbers to provide appropriate design service life in various construction applications. The correct specification of timber considers a range of timber properties including, but not limited to, stress grade; durability class; moisture content and insect resistance. For the specification of timber sizes and spans, relevant Australian Standards and design manuals should be consulted—e.g. Australian Standard AS 1684 series Residential timber—framed construction parts 2 and 3 (Standards Australia 2006a;b.) Book 1 explains the terms used; with reference to nomenclature; origin and timber properties presented under specific column headings in the schedules (Book 2). It also explains target design life; applications and decay hazard zones; presented in the Book 2 Schedules. Book 2 consists of reference tables; presented as schedules A; B and C: • Schedule A contains commercial mixtures of unidentified timbers and of some Australian and imported softwoods. Index numbers 1–10 • Schedule B contains Australian-grown timber species; including both natural forests and plantations. Index numbers 11–493 • Schedule C contains timbers imported into Australia from overseas. Index numbers 494–606 Each schedule has two parts presenting data in tables. • Part 1: Nomenclature, origin and properties of imported timber species • Part 2: Approved uses for commercial mixtures of imported timber species The recommendations made in this publication assume that good building practice will be carried out.

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Mille Ottling ran the dancing school. Therese Molling's daughter Liesel (Elizabeth) Gottschalk and brother Hal attended the same school during the mid 1920s

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Otto Gersuny (1890-1964), third row from the front wearing cap (see arrow)

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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 (IHS) 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.

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For zygosity diagnosis in the absence of genotypic data, or in the recruitment phase of a twin study where only single twins from same-sex pairs are being screened, or to provide a test for sample duplication leading to the false identification of a dizygotic pair as monozygotic, the appropriate analysis of respondents' answers to questions about zygosity is critical. Using data from a young adult Australian twin cohort (N = 2094 complete pairs and 519 singleton twins from same-sex pairs with complete responses to all zygosity items), we show that application of latent class analysis (LCA), fitting a 2-class model, yields results that show good concordance with traditional methods of zygosity diagnosis, but with certain important advantages. These include the ability, in many cases, to assign zygosity with specified probability on the basis of responses of a single informant (advantageous when one zygosity type is being oversampled); and the ability to quantify the probability of misassignment of zygosity, allowing prioritization of cases for genotyping as well as identification of cases of probable laboratory error. Out of 242 twins (from 121 like-sex pairs) where genotypic data were available for zygosity confirmation, only a single case was identified of incorrect zygosity assignment by the latent class algorithm. Zygosity assignment for that single case was identified by the LCA as uncertain (probability of being a monozygotic twin only 76%), and the co-twin's responses clearly identified the pair as dizygotic (probability of being dizygotic 100%). In the absence of genotypic data, or as a safeguard against sample duplication, application of LCA for zygosity assignment or confirmation is strongly recommended.

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(l-r): Ruth Altman, Irene Hirsch, Hilde Dannhauser, Suse Barth, Anneliese Hirsch, Suse Saenger, Esther Nathan, Hannelore Baer, Heinz Koerner, Ruth Bauland, Marianne Leiter, Heinz Krippel, Hanna Ullmann, Edith Weil, Otto Eckstein, Susi Ehrlich, Hans Klein, Julie Klappholz, Hanna Chose, Minna Hirsch and Rudolf Loewy

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This paper considers the on-line identification of a non-linear system in terms of a Hammerstein model, with a zero-memory non-linear gain followed by a linear system. The linear part is represented by a Laguerre expansion of its impulse response and the non-linear part by a polynomial. The identification procedure involves determination of the coefficients of the Laguerre expansion of correlation functions and an iterative adjustment of the parameters of the non-linear gain by gradient methods. The method is applicable to situations involving a wide class of input signals. Even in the presence of additive correlated noise, satisfactory performance is achieved with the variance of the error converging to a value close to the variance of the noise. Digital computer simulation establishes the practicability of the scheme in different situations.

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