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Children’s rapid word learning skills has been the focus of many studies over the last three decades. These studies indicated that children are not only expert word learners, but they are also very selective when learning new words (Sabbagh & Henderson, 2013). Although many studies have shown children’s selectivity when learning words, still little is known about the motives behind children’s selectivity or the mechanisms by which this selectivity works. The major aim of the present dissertation is to advance the current understanding on children’s selective learning. In four studies, we explored whether preschoolers would learn new words or facts from speakers who violated a familiar non-linguistic socio-cultural convention. In Study 1, thirty-six 4-year-olds heard novel words either from a boy puppet who wore a skirt or a boy puppet who wore pants. When then tested for their memory of the words, children who heard the words from the skirt-wearing boy puppet were less likely to produce and remember the word-referent link after a brief delay. Study 2 showed that children learned new words from a skirt-wearing boy puppet equally well when the puppets presented the words slowly; thereby suggesting that violation of a well-established socio-cultural convention affects children’s learning especially under high cognitive load. By replicating the design of Study 1, Study 3 showed that children learned new words from a skirt-wearing boy puppet when the boy puppet gave an excuse for wearing the skirt, suggesting that it was the conventional violation and not simply the anomaly of a skirt-wearing boy that affected children’s learning in Study 1. Study 4 showed that children’s bias against learning from the skirt-wearing boy puppet did not extend to facts about the origins of the toys. Taken together, these results suggested that when acquiring conventional knowledge in learning situations in which their processing capacities are taxed, young children show selective learning from a speaker who follows the socio-cultural conventions of their community. / Thesis (Ph.D, Psychology) -- Queen's University, 2013-09-09 15:46:10.013

The central aim of this thesis is to develop a modelling framework that is capable of analysing the system-wide impact of foreign direct investment (FDI) in Scotland. In 1996, foreign-owned plants accounted for around 40,35 and 23 per cent of Scottish manufacturing output, gross value added and employment. Moreover, the attraction of FDI remains an important part of UK regional policy in Scotland with just under half of all Regional Selective Assistance (RSA) awarded to foreign-owned firms. A key concern of this type of discretionary regional policy is whether such assistance is warranted. FDI is thought to have a range of potential demand and supply-side effects and foreign-owned manufacturing plants, in general, have quite distinct structural and behavioural characteristics, as compared with indigenous plants. Yet conventional regional system-wide evaluations of FDI typically focus on demand-side issues, using regional models that assume a passive supply-side and do not disaggregate by ownership. In this thesis I construct ownership-disaggregated Scottish Input-Output and Computable General Equilibrium Models in order to illustrate both the potential demand and supply-side impacts of FDI. The construction of the ownership disaggregated I-0 database provides a unique snapshot of the structure and interaction of foreign and UK-owned plants in Scotland. This provides detailed information as well as providing the basis for calibrating the ownership-disaggregated I-0 and CGE models. The analysis of the potential supply-side impacts of FDI, particularly labour market and 'efficiency spillover' effects, indicates that both can have a significant effect on the estimate of total FDI supported employment. Finally, I develop a simulation framework that is capable of separately identifying the importance of incorporating both 'structure' and 'behaviour' in regional models of FDI. The results indicate that incorporating the 'true' structure of foreign-owned plants is essential if one is to correctly estimate the system-wide impact of FDI.

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Historically, validation has been perfonned on a case study basis employing visual evaluations, gradually inspiring confidence through continual application. At present, the method of visual evaluation is the most prevalent form of data analysis, as the brain is the best pattern recognition device known. However, the human visual/perceptual system is a complicated mechanism, prone to many types of physical and psychological influences. Fatigue is a major source of inaccuracy within the results of subjects perfonning complex visual evaluation tasks. Whilst physical and experiential differences along with age have an enormous bearing on the visual evaluation results of different subjects. It is to this end that automated methods of validation must be developed to produce repeatable, quantitative and objective verification results. This thesis details the development of the Feature Selective Validation (FSV) method. The FSV method comprises two component measures based on amplitude differences and feature differences. These measures are combined employing a measured level of subjectivity to fonn an overall assessment of the comparison in question or global difference. The three measures within the FSV method are strengthened by statistical analysis in the form of confidence levels based on amplitude, feature or global discrepancies between compared signals. Highly detailed diagnostic infonnation on the location and magnitude of discrepancies is also made available through the employment of graphical (discrete) representations of the three measures. The FSV method also benefits from the ability to mirror human perception, whilst producing infonnation which directly relates human variability and the confidence associated with it. The FSV method builds on the common language of engineers and scientists alike, employing categories which relate to human interpretations of comparisons, namely: 'ideal', 'excellent', 'very good', 'good', 'fair', 'poor' and 'extremely poor' . Quantitative

It is well established that people experience a decline in cognitive functions, such as selective attention (SA), as they get older. SA is the ability to focus on task-relevant information and suppress task-irrelevant information. The Stroop task has been used to assess SA. In the current study, the neural correlates of SA were investigated using functional MRI-Stroop task with cognitively normal older adults (NC), patients with amnestic Mild Cognitive Impairment (aMCI) and patients with mild Alzheimer’s disease (AD). The current study reanalyzed previous master student’s data, due to the disagreement in analyzing the data. In the fMRI data analysis, the contrast of correct responses in the naming incongruent color (SC) condition minus correct responses in the reading incongruent word (RW) condition (SC-RW) in series 2a and 2b was reanalyzed using an event-related analysis. The current Stroop experiment was in a block design with four series: series 0, series 1, series 2a and 2b. In behavioral analysis, the performance of the word-reading task was expected to be significantly better than the color-naming task in series 1, series 2a and 2b because the belief that reading incongruent color word was always an easier task than the color-naming task. The results from behavioral analysis showed that significant more errors were made in reading incongruent color words in series 2a and 2b than in series 1. In the functional MRI data analysis, although brain activation associated with inhibition was expected in the contrast of SC-RW of series 2a & 2b, the results did not show any brain activation. The unexpected results could be due to the RSE that was elicited by the task switching paradigm of series 2a and 2b. The results suggest that the current Stroop task adapted from the Stroop Neuropsychological Screening Test may not yield a Stroop interference effect of sufficient magnitude to be detected with fMRI in the contrast of SC-RW of series 2a and 2b. / Thesis (Master, Neuroscience Studies) -- Queen's University, 2010-09-24 11:33:28.83

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Thesis (Ph. D.)--Oregon State University, 2010. / Printout. Includes bibliographical references. Also available on the World Wide Web.

Thesis (M.S. in chemical engineering)--Washington State University, December 2007. / Includes bibliographical references (p. 36-38).

Quel que soit le secteur d’activité, les procédés de fabrication additive pour les matériaux métalliques ont un fort potentiel industriel, spécifiquement pour la production de pièces à haute valeur ajoutée. Le secteur de l’outillage est l’un des utilisateurs de ces procédés, et plus particulièrement du Selective Laser Melting (SLM). Ce procédé permet de diminuer les coûts et les temps de production des outillages, tout en augmentant la complexité des pièces fabriquées. Cependant, pour améliorer la qualité des pièces fabriquées, une meilleure compréhension des mécanismes physiques qui le régissent est nécessaire. Dans ce travail de thèse, consacré à la modélisation du procédé SLM, les approches suivies sont multiphysiques à deux échelles. La première échelle de modélisation, utilisant la méthode Volume Of Fluid, correspond à la fusion d’un lit de poudre par un laser puis sa solidification. Le lit de poudre numérique est produit à partir d’un générateur spécifique basé sur la granulométrie identifiée expérimentalement. Après certaines hypothèses simplificatrices posées sur les phénomènes physiques à modéliser, la tension superficielle a été implémentée et a nécessité l’utilisation de la méthode des « heights functions ». La seconde échelle de modélisation correspond à la construction d’une succession de cordons à l’aide de la méthode des éléments finis. Le modèle thermomécanique utilise la méthode « element birth » pour se rapprocher au plus près des conditions réelles du procédé. Après leur validation par des essais expérimentaux, les simulations ont permis de prédire le champ de température, la largeur de la zone fondue, ainsi que la formation du « keyhole ». / Regardless the industry, additive manufacturing processes for metallic materials have a great industrial potential, especially to product high added value parts. One of the main users of these processes, and more specifically the Selective Laser Melting (SLM), is the tooling industry for plastics processing. It make possible to reduce production costs and manufacturing times while increasing the complexity of manufactured parts. However, in order to improve the quality of the latter and ensure their certifications, a better insight into the related physical phenomena undergone by the material during the process is still needed. In this PhD thesis, the SLM process modeling is multiphysic and concerns two different scales. The first modeling scale uses the Volume Of Fluid method to model the powder bed melting and its ensuing solidification. The numerical powder bed is computed thanks to a specific generator enabling to take account for the experimental granulometry. Once some simplifying assumptions on the physical phenomena stated, the surface tension has been implemented requiring the "heights functions" method use. The second modeling scale corresponds to the building of laser tracks series through the finite element method. The thermomechanical approach uses the element birth method in order to meet as far as possible the experimental conditions. Following its assessment through experiment/simulation face off, model have enable to predict the temperature field and the melted zone width as well as the keyhole formation.