Annual report of the National Labor Relations Board for the fiscal year ended ...

No longer distributed to depository libraries in a physical form after <2002>.

The American labor year book /

Vols. for 1916-1917/18 unnumbered but constitute v. 1-2.

The Labour year book

"Issued under the auspices of the Parlimentary Committee of the Trades Union Congress, the Executive Committee of the Labour Party, the Fabian Research department"

Estimates of sex- and age-class-specific survival probabilities for a southern Great Barrier Reef green sea turtle population

Sex- and age-class-specific survival probabilities of a southern Great Barrier Reef green sea turtle population were estimated using a capture - mark - recapture (CMR) study and a Cormack - Jolly - Seber (CJS) modelling approach. The CMR history profiles for 954 individual turtles tagged over a 9-year period ( 1984 - 1992) were classified into three age classes ( adult, subadult, juvenile) based on somatic growth and reproductive traits. Reduced-parameter CJS models, accounting for constant survival and time-specific recapture, fitted best for all age classes. There were no significant sex-specific differences in either survival or recapture probabilities for any age class. Mean annual adult survival was estimated at 0.9482 (95% CI: 0.92 - 0.98) and was significantly higher than survival for either subadults or juveniles. Mean annual subadult survival was 0.8474 ( 95% CI: 0.79 - 0.91), which was not significantly different from mean annual juvenile survival estimated at 0.8804 ( 95% CI: 0.84 - 0.93). The time-specific adult recapture probabilities were a function of sampling effort but this was not the case for either juveniles or subadults. The sampling effort effect was accounted for explicitly in the estimation of adult survival and recapture probabilities. These are the first comprehensive sex- and age-class-specific survival and recapture probability estimates for a green sea turtle population derived from a long-term CMR program.

Milling quality and protein properties of autumn-sown Chinese wheats evaluated through multi-location trials

Eight milling quality and protein properties of autumn-sown Chinese wheats were investigated using 59 cultivars and advanced lines grown in 14 locations in China from 1995 to 1998. Wide ranges of variability for all traits were observed across genotypes and locations. Genotype, location, year, and their interactions all significantly influenced most of the quality parameters. Kernel hardness, Zeleny sedimentation value, and mixograph development time were predominantly influenced by the effects of genotype. Genotype, location and genotype x location interaction were all important sources of variation for thousand kernel weight, test weight, protein content, and falling number, whereas genotype x location interaction had the largest effect on flour yield. Most of the genotypes were characterized by weak gluten strength with Zeleny sedimentation values less than 40 ml and mixograph development time shorter than 3 min. Eight groups of genotypes were recognized based on the average quality performance, grain hardness and gluten strength were the two parameters that determined the grouping, with contributions from protein content. Genotypes such as Zhongyou 16 and Annong 8903 displayed good milling quality, high grain hardness, protein content and strong gluten strength with high sedimentation value and long mixograph development time. Genotypes such as Lumai 15 and Yumai 18 were characterized by low grain hardness, protein content and weak gluten strength. Genotypes such as Yannong 15 and Chuanmai 24 were characterized by strong gluten strength with high sedimentation value and long mixograph development time, but low grain hardness and protein content lower than 12.3%. Genotypes such as Jingdong 6 and Xi'an 8 had weak gluten strength, but with high grain hardness and protein content higher than 12.2%. Five groups of locations were identified, and protein content and gluten strength were the two parameters that determined the grouping. Beijing, Shijiazhuang, Nanyang, Zhumadian and Nanjing produced wheats with medium to strong gluten strength and medium protein content, although there was still a large variation for most of the traits investigated between the locations. Wheat produced in Yantai was characterized by strong gluten strength, but with low protein content. Jinan, Anyang and Linfen locations produced wheats with medium to weak gluten strength and medium to high protein content. Wheats produced in Yangling, Zhenzhou, and Chengdu were characterized by weak gluten strength with medium to low protein content, whereas wheats produced in Xuzhou and Wuhan were characterized by weak gluten strength with low protein content. Industrial grain quality could be substantially improved through integrating knowledge of geographic genotype distribution with key location variables that affected end-use quality.

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical Study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which tender the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.

The impact of learning outcomes in business education: assessing value, relevance and graduate ability in a multi-country study of employers and business graduates

This paper discusses critical findings from a two-year EU-funded research project involving four European countries: Austria, England, Slovenia and Romania. The project had two primary aims. The first of these was to develop a systematic procedure for assessing the balance between learning outcomes acquired in education and the specific needs of the labour market. The second aim was to develop and test a set of meta-level quality indicators aimed at evaluating the linkages between education and employment. The project was distinctive in that it combined different partners from Higher Education, Vocational Training, Industry and Quality Assurance. One of the key emergent themes identified in exploratory interviews was that employers and recent business graduates in all four countries want a well-rounded education which delivers a broad foundation of key business knowledge across the various disciplines. Both groups also identified the need for personal development in critical skills and competencies. Following the exploratory study, a questionnaire was designed to address five functional business areas, as well as a cluster of 8 business competencies. Within the survey, questions relating to the meta-level quality indicators assessed the impact of these learning outcomes on the workplace, in terms of the following: 1) value, 2) relevance and 3) graduate ability. This paper provides an overview of the study findings from a sample of 900 business graduates and employers. Two theoretical models are proposed as tools for predicting satisfaction with work performance and satisfaction with business education. The implications of the study findings for education, employment and European public policy are discussed.

First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about km800, carrying a C-band scatterometer. A scatterometer measures the amount of radar back scatter generated by small ripples on the ocean surface induced by instantaneous local winds. Operational methods that extract wind vectors from satellite scatterometer data are based on the local inversion of a forward model, mapping scatterometer observations to wind vectors, by the minimisation of a cost function in the scatterometer measurement space.par This report uses mixture density networks, a principled method for modelling conditional probability density functions, to model the joint probability distribution of the wind vectors given the satellite scatterometer measurements in a single cell (the `inverse' problem). The complexity of the mapping and the structure of the conditional probability density function are investigated by varying the number of units in the hidden layer of the multi-layer perceptron and the number of kernels in the Gaussian mixture model of the mixture density network respectively. The optimal model for networks trained per trace has twenty hidden units and four kernels. Further investigation shows that models trained with incidence angle as an input have results comparable to those models trained by trace. A hybrid mixture density network that incorporates geophysical knowledge of the problem confirms other results that the conditional probability distribution is dominantly bimodal.par The wind retrieval results improve on previous work at Aston, but do not match other neural network techniques that use spatial information in the inputs, which is to be expected given the ambiguity of the inverse problem. Current work uses the local inverse model for autonomous ambiguity removal in a principled Bayesian framework. Future directions in which these models may be improved are given.

Delivering phonological and phonics training within whole-class teaching

Background: Early, intensive phonological awareness and phonics training is widely held to be beneficial for children with poor phonological awareness. However, most studies have delivered this training separately from children's normal whole-class reading lessons. Aims: We examined whether integrating this training into whole class, mixed-ability reading lessons could impact on children with poor phonological awareness, whilst also benefiting normally developing readers. Sample: Teachers delivered the training within a broad reading programme to whole classes of children from Reception to the end of Year 1 (N=251). A comparison group of children received standard teaching methods (N=213). Method: Children's literacy was assessed at the beginning of Reception, and then at the end of each year until 1 year post-intervention. Results: The strategy significantly impacted on reading performance for normally developing readers and those with poor phonological awareness, vastly reducing the incidence of reading difficulties from 20% in comparison schools to 5% in intervention schools. Conclusions: Phonological and phonics training is highly effective for children with poor phonological awareness, even when incorporated into whole-class teaching.

Fiber Bragg grating structures inscribed using 800nm femtosecond laser in single- and multi-core mid-IR glass fibers with their spectral, thermal and strain properties

Single- and multi-core passive and active germanate and tellurite glass fibers represent a new class of fiber host for in-fiber photonics devices and applications in mid-IR wavelength range, which are in increasing demand. Fiber Bragg grating (FBG) structures have been proven as one of the most functional in-fiber devices and have been mass-produced in silicate fibers by UV-inscription for almost countless laser and sensor applications. However, because of the strong UV absorption in germanate and tellurite fibers, FBG structures cannot be produced by UVinscription. In recent years femtosecond (fs) lasers have been developed for laser machining and microstructuring in a variety of glass fibers and planar substrates. A number of papers have been reported on fabrication of FBGs and long-period gratings in optical fibers and also on the photosensitivity mechanism using 800nm fs lasers. In this paper, we demonstrate for the first time the fabrication of FBG structures created in passive and active single- and three-core germanate and tellurite glass fibers by using 800nm fs-inscription and phase mask technique. With a fs peak power intensity in the order of 1011W/cm2, the FBG spectra with 2nd and 3rd order resonances at 1540nm and 1033nm in a single-core germanate glass fiber and 2nd order resonances between ~1694nm and ~1677nm with strengths up to 14dB in all three cores of three-core passive and active tellurite fibers were observed. Thermal and strain properties of the FBGs made in these mid-IR glass fibers were characterized, showing an average temperature responsivity of ~20pm/°C and a strain sensitivity of 1.219±0.003pm/µe.

The integration of multi-source data to improve the classification of remotely sensed images

The number of remote sensing platforms and sensors rises almost every year, yet much work on the interpretation of land cover is still carried out using either single images or images from the same source taken at different dates. Two questions could be asked of this proliferation of images: can the information contained in different scenes be used to improve the classification accuracy and, what is the best way to combine the different imagery? Two of these multiple image sources are MODIS on the Terra platform and ETM+ on board Landsat7, which are suitably complementary. Daily MODIS images with 36 spectral bands in 250-1000 m spatial resolution and seven spectral bands of ETM+ with 30m and 16 days spatial and temporal resolution respectively are available. In the UK, cloud cover may mean that only a few ETM+ scenes may be available for any particular year and these may not be at the time of year of most interest. The MODIS data may provide information on land cover over the growing season, such as harvest dates, that is not present in the ETM+ data. Therefore, the primary objective of this work is to develop a methodology for the integration of medium spatial resolution Landsat ETM+ image, with multi-temporal, multi-spectral, low-resolution MODIS \Terra images, with the aim of improving the classification of agricultural land. Additionally other data may also be incorporated such as field boundaries from existing maps. When classifying agricultural land cover of the type seen in the UK, where crops are largely sown in homogenous fields with clear and often mapped boundaries, the classification is greatly improved using the mapped polygons and utilising the classification of the polygon as a whole as an apriori probability in classifying each individual pixel using a Bayesian approach. When dealing with multiple images from different platforms and dates it is highly unlikely that the pixels will be exactly co-registered and these pixels will contain a mixture of different real world land covers. Similarly the different atmospheric conditions prevailing during the different days will mean that the same emission from the ground will give rise to different sensor reception. Therefore, a method is presented with a model of the instantaneous field of view and atmospheric effects to enable different remote sensed data sources to be integrated.

Design of a novel solar thermal collector using a multi-criteria decision-making methodology

Three novel solar thermal collector concepts derived from the Linear Fresnel Reflector (LFR) are developed and evaluated through a multi-criteria decision-making methodology, comprising the following techniques: Quality Function Deployment (QFD), the Analytical Hierarchy Process (AHP) and the Pugh selection matrix. Criteria are specified by technical and customer requirements gathered from Gujarat, India. The concepts are compared to a standard LFR for reference, and as a result, a novel 'Elevation Linear Fresnel Reflector' (ELFR) concept using elevating mirrors is selected. A detailed version of this concept is proposed and compared against two standard LFR configurations, one using constant and the other using variable horizontal mirror spacing. Annual performance is analysed for a typical meteorological year. Financial assessment is made through the construction of a prototype. The novel LFR has an annual optical efficiency of 49% and increases exergy by 13-23%. Operational hours above a target temperature of 300 C are increased by 9-24%. A 17% reduction in land usage is also achievable. However, the ELFR suffers from additional complexity and a 16-28% increase in capital cost. It is concluded that this novel design is particularly promising for industrial applications and locations with restricted land availability or high land costs. The decision analysis methodology adopted is considered to have a wider potential for applications in the fields of renewable energy and sustainable design. © 2013 Elsevier Ltd. All rights reserved.

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines. © 2012 ACM.

Multi-level visualisation using Gaussian process latent variable models

Projection of a high-dimensional dataset onto a two-dimensional space is a useful tool to visualise structures and relationships in the dataset. However, a single two-dimensional visualisation may not display all the intrinsic structure. Therefore, hierarchical/multi-level visualisation methods have been used to extract more detailed understanding of the data. Here we propose a multi-level Gaussian process latent variable model (MLGPLVM). MLGPLVM works by segmenting data (with e.g. K-means, Gaussian mixture model or interactive clustering) in the visualisation space and then fitting a visualisation model to each subset. To measure the quality of multi-level visualisation (with respect to parent and child models), metrics such as trustworthiness, continuity, mean relative rank errors, visualisation distance distortion and the negative log-likelihood per point are used. We evaluate the MLGPLVM approach on the ‘Oil Flow’ dataset and a dataset of protein electrostatic potentials for the ‘Major Histocompatibility Complex (MHC) class I’ of humans. In both cases, visual observation and the quantitative quality measures have shown better visualisation at lower levels.

Fiber Bragg grating structures inscribed using 800nm femtosecond laser in single- and multi-core mid-IR glass fibers with their spectral, thermal and strain properties

Single- and multi-core passive and active germanate and tellurite glass fibers represent a new class of fiber host for in-fiber photonics devices and applications in mid-IR wavelength range, which are in increasing demand. Fiber Bragg grating (FBG) structures have been proven as one of the most functional in-fiber devices and have been mass-produced in silicate fibers by UV-inscription for almost countless laser and sensor applications. However, because of the strong UV absorption in germanate and tellurite fibers, FBG structures cannot be produced by UVinscription. In recent years femtosecond (fs) lasers have been developed for laser machining and microstructuring in a variety of glass fibers and planar substrates. A number of papers have been reported on fabrication of FBGs and long-period gratings in optical fibers and also on the photosensitivity mechanism using 800nm fs lasers. In this paper, we demonstrate for the first time the fabrication of FBG structures created in passive and active single- and three-core germanate and tellurite glass fibers by using 800nm fs-inscription and phase mask technique. With a fs peak power intensity in the order of 1011W/cm2, the FBG spectra with 2nd and 3rd order resonances at 1540nm and 1033nm in a single-core germanate glass fiber and 2nd order resonances between ~1694nm and ~1677nm with strengths up to 14dB in all three cores of three-core passive and active tellurite fibers were observed. Thermal and strain properties of the FBGs made in these mid-IR glass fibers were characterized, showing an average temperature responsivity of ~20pm/°C and a strain sensitivity of 1.219±0.003pm/µe.