Made available in DSpace on 2016-06-02T19:50:12Z (GMT). No. of bitstreams: 1 3762.pdf: 1095394 bytes, checksum: 0ba952111fc170c93998133ca7ab7dc9 (MD5) Previous issue date: 2011-06-29 / The latest models on Knowledge Management (KM) recognize four stages to their practice: acquisition, storage, distribution and use of knowledge that presents itself as tacit and explicit. The KM is characterized as a multidisciplinary discipline, involving aspects of technical and social order and due to the diversity of issues about this topic, it is essential to delimit their study. In this sense, the proposed delimitation for this research is social and coordination, involving the identification of contextual factors, developed internally in organizations that support the process of KM in automotive companies. These contextual factors are defined based on organizational aspects to enable the development of the four phases of the KM, especially the development of human resources, organizational culture, teamwork, organizational structure and the way how knowledge is developed and absorbed internally. This thesis also identifies groupings (clusters) composed of companies with similar characteristics as the development of these contextual factors, analyzing the implications of the clusters features for KM and for the use of knowledge. The automotive companies can be characterized as advanced for improvement and innovation processes, and thus the knowledge management is fundamental to the sustainability of these activities. To achieve this goal, this research uses a quantitative research method, based on a survey. Data analysis is performed using multivariate statistical techniques of factor analysis, cluster analysis and discriminant analysis. In regarding the results were collected eight contextual factors that support the process of GC, and also identified four clusters for the development of these factors: innovate, exploited, exploited and Laggards. The companies of the first two groups have a better capacity to absorb knowledge and build a broader base of primary knowledge, making them more innovative, and the other two groups take a more reactive posture within the industry, following the development proposed by the companies of first two clusters. In the latter two groups, in particular exploited, there is the development of factors related to incremental improvement and teamwork, showing a tendency to use the knowledge designed to improve production efficiency. / Os modelos mais recentes sobre Gestão do Conhecimento (GC) reconhecem quatro fases para a sua prática: aquisição, armazenamento, distribuição e utilização do conhecimento, que se apresenta na forma tácita e explícita. A GC se caracteriza como uma disciplina multidisciplinar, envolvendo aspectos de ordem técnico e social e devido à diversidade de assuntos que cerca este tema, é essencial delimitar seu estudo. Neste sentido, o recorte proposto para esta pesquisa é social e de coordenação, envolvendo a identificação de fatores contextuais, desenvolvidos internamente nas organizações, que sustentam o processo de GC em empresas do setor automobilístico. Estes fatores contextuais são definidos a partir de aspectos organizacionais que possibilitem o desenvolvimento das quatro fases do processo de GC, destacando-se o desenvolvimento de recursos humanos, a cultura organizacional, o trabalho em equipe, a estrutura organizacional e a forma como o conhecimento é desenvolvido e absorvido internamente. Esta tese identifica também agrupamentos (clusters), compostos de empresas com características similares quanto ao desenvolvimento destes fatores contextuais, analisando as implicações das características dos clusters para a GC e também para a utilização do conhecimento. As empresas do setor automobilístico, foco desta pesquisa, podem ser caracterizadas como maduras em processos de melhoria e inovação, e, desta forma, o gerenciamento do conhecimento é fundamental para a sustentabilidade destas atividades. Para atingir este objetivo, é utilizado um método de pesquisa quantitativo, baseado em uma pesquisa survey. A análise dos dados é realizada por meio de técnicas estatísticas multivariadas de análise fatorial, análise de cluster e análise discriminante. Quanto aos resultados, foram levantados oito fatores contextuais que sustentam o processo de GC, e também identificados quatro agrupamentos de empresas quanto ao desenvolvimento destes fatores: Inovativo, Explorativo, Explotativo e Retardatário. As empresas dos dois primeiros agrupamentos possuem melhor capacidade de absorver conhecimento e de construir uma base mais ampla de conhecimento primário, tornando-as mais inovativas; e os outros dois agrupamentos assumem uma postura mais reativa dentro do setor, seguindo o desenvolvimento proposto pelas empresas dos dois primeiros clusters. Nestes dois últimos agrupamentos, em especial no explotativo, destaca-se o desenvolvimento de fatores relacionados à melhoria incremental e trabalho em equipe, denotando uma tendência de utilização do conhecimento voltada para a melhoria da eficiência produtiva.

Made available in DSpace on 2016-06-02T19:50:14Z (GMT). No. of bitstreams: 1 4067.pdf: 1964909 bytes, checksum: 20668b126678ef263e1b39495a60baa3 (MD5) Previous issue date: 2011-11-28 / Projects of capital goods with Engineering-to-Order (ETO) productive typology are usually complex due to its size and level of customization and technology. Because of these aspects the achievement of success demands a management effort and can be conditioned by several elements, among which are the Critical Success Factors (CSF). In this sense, the proposal for this research involves the identification and analysis of the relationship of these factors with the success obtained in ETO projects using a multidimensional concept that involves efficiency, organizational learning, preparing for the future and customer satisfaction. From these principles a survey was conducted involving 67 companies and 182 projects with above characteristics. Data analysis was initially performed by the technique of Factorial analysis, whose results led to combine 38 Critical Success Factors identified in the literature on 12 new factors with better ability to explain the observed phenomenon, namely: Uncertainty management, Organization and Planning, Productivity management, Robustness of the team, Management of technical performance, Learning and adaptation, Integration with external stakeholders, Reliability of the team, Schedule optimization, Precaution against technical failures, Guidance capacity and Precaution against supply failure. These new factors led to an analysis by multinomial logistic regression technique, which identified significant factors to explain each of the success dimensions. For the efficiency dimension this factors are Planning and organization, Reliability of the team, the Guidance capacity, Integration with external stakeholders, the Technical performance management and Schedule optimization, to the extent organizational learning are the optimization schedule, Productivity management and Precaution against technical failure. To the preparing for the future dimension this factors are the Management of the technical performance, the Reliability of the team, Schedule optimization, the Precaution against technical failures, the Learning and adaptation, and the Organization and Planning, the Guidance capacity. For the customer satisfaction are the Reliability of the team, the Learning and adaptation, Precaution against failure of supply, Organization and planning, Precaution against technical failures and Contingency management. Thus, from a scientific standpoint, the results obtained in this thesis extends the understanding of the topic studied and provide from the managerial point of view a framework that can guide and support decision making by managers of this type of project. / Projetos de bens de capital com tipologia produtiva Engineering-to-Order (ETO) são usualmente complexos devido a seu porte e nível de customização e tecnologia. Devido a estes aspectos a obtenção de sucesso pode ser condicionada por diversos elementos, entre os quais estão os Fatores Críticos de Sucesso (FCS). Neste sentido, o recorte proposto para esta pesquisa envolve a identificação e a análise das relações destes fatores com o sucesso de projetos ETO de forma multidimensional, envolvendo a eficiência, o aprendizado organizacional, a preparação para o futuro e a satisfação dos clientes. Para tanto, foi conduzido um survey que envolveu 67 empresas e 182 projetos com as características supracitadas. A análise dos dados foi realizada inicialmente por meio da técnica de Análise fatorial, cujos resultados permitiram conjugar 38 Fatores Críticos de Sucesso identificados na literatura, em 12 novos fatores com melhor capacidade de explicar o fenômeno observado, sendo eles: Gestão de incerteza, Organização e Planejamento, Gestão da produtividade, Robustez da equipe, Gestão de desempenho técnico, Aprendizado e adaptação, Integração com Stakeholders externos, Confiabilidade da equipe, Otimização de cronograma, Precaução contra falhas técnicas, Capacidade de orientação e Precaução contra falhas de fornecimento. A partir destes novos fatores conduziu-se uma análise por meio da técnica de Regressão logística multinomial, que permitiu identificar fatores significativos para explicar cada uma das dimensões de sucesso. Assim, para a dimensão eficiência estes fatores são a Organização e planejamento, a Confiabilidade da equipe, a Capacidade de orientação, a Integração com stakeholders externos, a Gestão de desempenho técnico e a Otimização de cronograma, para a dimensão aprendizado organizacional são a Otimização de cronograma, a Gestão de desempenho técnico e a Precaução contra falhas técnicas, para a dimensão preparação para o futuro são a Gestão de desempenho técnico, a Confiabilidade da equipe, a Otimização de cronograma, a Precaução contra falhas técnicas, o Aprendizado e adaptação, a Organização e planejamento, a Capacidade de orientação e para a dimensão satisfação do cliente são a Confiabilidade da equipe, o Aprendizado e adaptação, a Precaução contra falhas de fornecimento, a Organização e planejamento, a Precaução contra falhas técnicas e a Gestão de contingências. Assim, do ponto de vista científico, os resultados obtidos nesta tese ampliam a compreensão sobre o tema estudado e oferecem do ponto de vista gerencial um framework que pode orientar ou apoiar a tomada de decisão por parte de gerentes deste tipo de projeto.

Made available in DSpace on 2016-06-02T19:51:53Z (GMT). No. of bitstreams: 1 4153.pdf: 2182125 bytes, checksum: a074f91bda22478d2c98528b9d3de6ff (MD5) Previous issue date: 2012-02-27 / Financiadora de Estudos e Projetos / The organizations face problematic situations that involve the analysis of a significant number of correlated variables to reduce the variability of production processes. There are many approaches, methods and techniques to support problem solving, however has achieved distinction in literature and adopted by Six Sigma companies, one of whose goals is to identify and eliminate the causes of variation in product and process through the use of techniques statistics. The Multivariate Data Analysis (MDAs) belongs to a set of techniques that examine both the relationship between several variables, have not been included in intensive training programs in Six Sigma. This research is qualitative and descriptive, whose purpose is to confirm the dimensions criticism from Firka (2011) and Montgomery (2010) agree that the use of statistical methods to problems of manufacturing in the context of Six Sigma programs, through the study of multiple cases. These research findings confirm the association between the use of technical barriers in the MDAs to factors of management and sociology, such as the lack of management support, focus on short-term results, the methodological factors (selection and validation of variables and results) and the statistical assumptions (multivariate normality, multicollinearity and homoscedasticity). / As organizações enfrentam situações problemáticas que envolvem a análise de um conjunto significativo de variáveis correlacionadas para reduzir a variabilidade dos processos produtivos. Há muitas abordagens, métodos e técnicas de apoio à solução de problemas; entretanto tem obtido destaque na literatura e adotado pelas empresas o Seis Sigma, cujo um dos objetivos é identificar e eliminar as causas de variação do produto e processo por meio do uso de técnicas estatísticas. A Análise de Dados Multivariados (MDAs) pertencem a um conjunto de técnicas que analisam simultaneamente a relação existente entre diversas variáveis, ainda pouco difundidas nos programas de treinamento em Seis Sigma. Esta pesquisa tem caráter qualitativo-descritivo, cujo objetivo é confirmar as dimensões críticas apontadas por Firka (2011) e Montgomery (2010) que comprometem o uso de métodos estatísticos em problemas da manufatura no contexto dos programas Seis Sigma, por meio do estudo de casos. Os resultados desta pesquisa confirmam a associação entre as barreiras no uso de técnicas MDAs a fatores de ordem gerencial e sociológica, tais como, a falta de suporte gerencial, foco em resultados de curto prazo, fatores de ordem metodológica (seleção e validação das variáveis e dos resultados) e aos pressupostos estatísticos (Normalidade Multivariada, Multicolinearidade e Homocedasticidade). Palavras chaves: Analise de Dados Multivariados (MDAs). Fatores Críticos. Melhoria Contínua (MC). Projetos Seis Sigma.

Atualmente, no meio jurídico, há um reconhecimento implícito de que as provas materiais necessitam de embasamento científico para alcançar a autenticidade imprescindível ao estabelecimento da convicção dos magistrados. A natureza de determinados exames, como a classificação de substâncias proibidas, demandam a utilização de técnicas e saberes oriundos das ciências naturais e tecnológicas. O trabalho pericial deve ser pautado pela cientificidade, com a aplicação de conhecimentos de diversas áreas, dentre as quais está incluída a estatística forense. Neste trabalho foram utilizadas ferramentas estatísticas para avaliar a qualidade e a contribuição dos laudos periciais para os casos envolvendo substâncias ilícitas e correlacionar o conteúdo destes documentos com a sentença judicial. Numa primeira etapa foram analisadas as informações contidas em laudos toxicológicos de drogas, com o intuito de quantificar a qualidade e importância que eles poderiam fornecer em um processo. Para isso foram analisados 1008 documentos oficiais de diversas jurisdições, divididos em 504 conjuntos de laudos preliminares e definitivos do mesmo caso forense A intenção foi apreciar um conjunto heterogêneo de documentos para possibilitar uma melhor análise. A quantificação foi apreciada através de equações empíricas elaboradas. A validação do método ocorreu por análise de dados multivariados. A metodologia empregada demonstrou-se bastante robusta. A segunda fase do trabalho foi aplicar o resultado dos exames da etapa precedente e correlacionar com a decisão judicial. Para tanto, foram esmiuçadas 167 sentenças proferidas em primeira instância e que continham os laudos elencados na primeira fase. A ferramenta utilizada foi a inferência Bayesiana. Os resultados apontaram que os laudos periciais sempre foram essenciais neste tipo de procedimento julgatório. A qualidade dos documentos produzidos encontrava-se entre boa e ótima, avalizada pelo parâmetro \"relevância do laudo\". Alguns aspectos nos documentos poderiam ser aperfeiçoados, como, por exemplo, a inserção de fotografias do material apreendido e/ou imagens alusivas às análises laboratoriais. Estes estudos permitiram estabelecer um valor de corte para a quantificação da qualidade dos laudos, a partir do qual houve 100% de concordância entre o laudo direcionado e a sentença, para casos de condenação onde o suspeito foi considerado traficante. Por fim, a metodologia proposta apresentou potencial promissor e possibilidade de ser utilizada em outros tipos de casos forenses, como, por exemplo, homicídios, suicídios e outros. / There is an implicit recognition in the current legal scenario that material evidences require scientific support in order to achieve the authenticity that the magistrates need for making decisions. The nature of certain exams, such as classification of prohibited substances, requires the use of techniques and knowledge from natural sciences and technology. The forensic work must rely on scientific methods and apply knowledge from several areas, including forensic statistics. The present work used statistic tools to evaluate the quality and the contribution of forensic reports about illegal substances; the goal is to correlate the content of these documents with the court ruling. In the first part we analyzed the information from toxicology reports on drugs, aiming at the quantification of the importance they might bear to court proceedings. We have parsed 1008 official documents from several jurisdictions, divided into 504 sets of preliminary and final reports from the same case. The objective was to evaluate a heterogeneous document set for a better analysis. The quantification was determined from elaborate empiric equations. The validation of the method was performed by multivariate data analysis. The methodology used in the present work has proved very robust. The second part was the application of the results from the previous part and correlation to the court ruling. We have thoroughly examined 167 rulings at first instance that contained the reports cited in the first part. We have used Bayesian inference, and the results indicated that forensic reports were always required in this type of court proceeding. The quality of the documents was considered good or excellent, as stated in the parameter \"relevance of the report\". Some aspects could be improved, for instance, images of collected material evidence or laboratory analytical procedures could be included. These studies allowed establishing a cut-off value for the quantification of the report quality, from which a 100% agreement between the report and the court decision was achieved, in cases where the suspect was found guilty. Finally, the proposed methodology in this work showed a good potential and could be used in other kinds of forensic cases, such as homicide, suicide and other forensic investigations.

This study was aimed at exploring data analysis techniques for generating accurate estimates of the loss in quality of fresh fruits, vegetables and cut flowers in chilled supply chains based on data from advanced sensors. It was motivated by the recent interest in the application of advanced sensors, by emerging concepts in quality controlled logistics, and by the desire to minimise quality losses during transport and storage of the produce. Cut roses were used in this work although the findings will also be applicable to other produce. The literature has reported that whilst temperature was considered to be the most critical post-harvest factor, others such as growing conditions could also be important in the senescence of cut roses. Kinetic modelling was the most commonly used modelling approach for shelf life predictions of foods and perishable produce, but not for estimating vase life (VL) of cut flowers, and so this was explored in this work along with multiple linear regression (MLR) and partial least squares (PLS). As the senescence of cut roses is not fully understood, kinetic modelling could not be implemented directly. Consequently, a novel technique, called Kinetic Linear System (KLS), was developed based on kinetic modelling principles. Simulation studies of shelf life predictions for tomatoes, mushrooms, seasoned soybean sprouts, cooked shrimps and other seafood products showed that the KLS models could effectively replace the kinetic ones. With respect to VL predictions KLS, PLS and MLR were investigated for data analysis from an in-house experiment with cut roses from Cookes Rose Farm (Jersey). The analysis concluded that when the initial and final VLs were available for model calibration, effective estimates of the post-harvest loss in VL of cut roses could be obtained using the post-harvest temperature. Otherwise, when the initial VLs were not available, such effective estimates could not be obtained. Moreover, pre-harvest conditions were shown to correlate with the VL loss but the correlation was too weak to produce or improve an effective estimate of the loss. The results showed that KLS performance was the best while PLS one could be acceptable; but MLR performance was not adequate. In another experiment, boxes of cut roses were transported from a Kenyan farm to a UK distribution centre. Using KLS and PLS techniques, the analysis showed that the growing temperature could be used to obtain effective estimates of the VLs at the farm, at the distribution centre and also the in-transit loss. Further, using post-harvest temperature would lead to a smaller error for the VL at the distribution centre and the VL loss. Nevertheless, the estimates of the VL loss may not be useful practically due to the excessive relative prediction error. Overall, although PLS had a slightly smaller prediction error, KLS worked effectively in many cases where PLS failed, it could handle constraints while PLS could not.In conclusion, KLS and PLS can be used to generate effective estimates of the post-harvest VL loss of cut roses based on post-harvest temperature stresses recorded by advanced sensors. However, the estimates may not be useful practically due to significant relative errors. Alternatively, pre-harvest temperature could be used although it may lead to slightly higher errors. Although PLS had slightly smaller errors KLS was more robust and flexible. Further work is recommended in the objective evaluations of product quality, alternative non-linear techniques and dynamic decision support system.

Sustainability has gained a lot of attention in the field of research. Researchers and consumers both prioritize sustainability and environmental issues over previously dominant materials, such as plastic. Packaging and disposable items that used to be made of plastic have largely been replaced with paper. Unfortunately, paper does not perform as well as plastic regarding barrier properties against grease, oxygen, or water vapor. Barrier properties are an important factor when choosing packaging material for food, among other things, as they help maintain the shelf life of the product. In order to improve the properties of the paper packaging and expand its use, the paper is coated with a polymer. However, the polymer contributes to challenges in the recycling of the products as some of the polymer attaches to the fibers, causing difficulties in the separation of each material. Small fragments of plastic may end up in the material streams and the recycled pulp due to the existing challenges in completely removing plastic from cellulosic substrates during recycling. This thesis analyzes the possibilities of identifying and classifying plastic fragments of polyethylene (PE) and polyvinyl alcohol (PVOH) in recycled paper sheets using near-infrared spectroscopy together with multivariate data analysis. The purpose of the work is to develop models that can identify possible residues that may appear in recycled products from various industries. Paper sheets of two different grammages and six different compositions of recycled fiber and virgin fiber were created and scanned by NIR, with and without plastic film under the sheets. The scans were used to develop classification models to identify and categorize scans not included in the calibration data set. The performance of the models was tested by applying them to images of sheets of paper with plastic fragments of different sizes and different type underneath. The results indicated potential in the method. The prediction of the paper sheets with a lower grammage was mostly correct, whereas the classification of polyethylene showed the best performance. There was some noise in the prediction of the plastic fragments, regardless of the grammage of the paper. The noise may be due to a wide variation in the calibration data set since it consisted of paper sheets of six different compositions. A large part of the noise was incorrectly classified as polyvinyl alcohol, which can be due to differences in the manufacturing process of the plastic films. The conclusion of the thesis is that it is feasible to identify and categorize plastic fragments of polyethylene and polyvinyl alcohol in recycled paper sheets with a certain margin of error. It can be stated that the method shows promise, but further research and development in the field is required to build models that can be applied to a wider range of samples.

Die in dieser Arbeit vorgestellten Studien untersuchen verschiedene Ansätze für die Analyse von spektroskopischen Daten für die Erforschung anderer Himmelskörper. Der Fokus lag hierbei auf der laserinduzierten Plasmaspektroskopie (LIBS, engl. laser-induced breakdown spectroscopy), aber auch Daten der Raman-Spektroskopie wurden analysiert. Das erste extraterrestrisch eingesetzte LIBS Instrument, ChemCam, auf dem Mars Science Laboratory (MSL) der NASA untersucht die Marsoberfläche seit 2012 und weitere Missionen mit LIBS und Raman Instrumenten zum Mars sind geplant. Neben analytischen Ansätzen wurden statistische Methoden, die als multivariate Datenanalysen (MVA) bekannt sind, verwendet und evaluiert. In dieser Arbeit werden insgesamt vier Studien vorgestellt. In der ersten Studie wurde die Normalisierung von LIBS Daten mit Plasmaparametern, also der Plasmatemperatur und der Elektronendichte, untersucht. In der zweiten Studie wurden LIBS Messungen unter Vakuumbedingungen im Hinblick auf den Ionisierungsgrad des Plasmas untersucht. In der dritten Studie wurden MVA Methoden wie die Hauptkomponentenanalyse (PCA) und die partielle Regression kleinster Quadrate (PLS-R) zur Identifizierung und Quantifizierung von Halogenen mittels molekularer Emissionen angewandt. Die Ergebnisse sind vielversprechend, da es möglich war z.B. Chlor in einem ausgewählten Konzentrationsbereich zu quantifizieren. In der letzten Studie wurden LIBS-Daten mit komplementären Raman-Daten von Mars relevanten Salzen in einem low-level Datenfusionsansatz kombiniert. Es wurden MVA Methoden angewandt und auch Konzepte der high-level Datenfusion implementiert. Mit der low-level LIBS und Raman Datenfusion konnten im Vergleich zu den einzelnen Techniken mehr Salze richtig identifiziert werden. Der Gewinn durch die low-level Datenfusion ist jedoch vergleichsweise gering und für konkrete Missionen müssen individuelle und angepasste Strategien für die gemeinsame Analyse von LIBS und Raman-Daten gefunden werden. / The studies presented in this thesis investigate different data analysis approaches for mainly laser-induced breakdown spectroscopy (LIBS) and also Raman data in the context of planetary in-situ exploration. Most studies were motivated by Mars exploration due to the first extraterrestrially employed LIBS instrument ChemCam on NASA's Mars Science Laboratory (MSL) and further planned LIBS and Raman instruments on upcoming missions to Mars. Next to analytical approaches, statistical methods known as multivariate data analysis (MVA) were applied and evaluated. In this thesis, four studies are presented in which LIBS and Raman data analysis strategies are evaluated. In the first study, LIBS data normalization with plasma parameters, namely the plasma temperature and the electron density, was studied. In the second study, LIBS measurements in vacuum conditions were investigated with a focus on the degree of ionization of the LIBS plasma. In the third study, the capability of MVA methods such as principal component analysis (PCA) and partial least squares regression (PLS-R) for the identification and quantification of halogens by means of molecular emissions was tested. The outcomes are promising, as it was possible to distinguish apatites and to quantify chlorine in a particular concentration range. In the fourth and last study, LIBS data was combined with complementary Raman data in a low-level data fusion approach using MVA methods. Also, concepts of high-level data fusion were implemented. Low-level LIBS and Raman data fusion can improve identification capabilities in comparison to the single datasets. However, the improvement is comparatively small regarding the higher amount of information in the low-level fused data and dedicated strategies for the joint analysis of LIBS and Raman data have to be found for particular scientific objectives.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

O uso assertivo e eficiente das estratégias de aprendizagem depende, muitas vezes, da compreensão e consideração de aspectos psicológicos e motivacionais. O adequado emprego de estratégias de aprendizagem se reflete no desempenho acadêmico, no domínio de construtos e modelos e no amadurecimento crítico e científico. A presente tese defende que há uma relação entre as estratégias de aprendizagem autorregulada e as estratégias de aprendizagem autodeterminada predominantes em alunos de mestrado e doutorado em Contabilidade. O estudo se justifica, porquanto, porque além de inaugurar uma linha de pesquisa ainda inédita no contexto da Contabilidade Humana, seus resultados destacam um original entendimento da relação da aprendizagem com a regulação e a motivação pessoal. Tem como objetivo principal apresentar diagnóstico, dimensões e correlações das estratégias de aprendizagem autorregulada e aprendizagem autodeterminada de alunos de programas de pós-graduação stricto sensu em Contabilidade no Brasil. Participaram do survey 516 respondentes, sendo 383 mestrandos e 133 doutorandos. Foram aplicados dois instrumentos psicométricos: Self-Regulated Learning Strategies (SRLS) e Motivated Strategies for Learning Questionnaire (MSLQ). O modelo operacional de pesquisa delineou a formulação de oito hipóteses, sendo que a primeira delas sustenta a defesa da tese, enquanto as demais defendem a influência das variáveis idade, gênero, tipo de curso, estágio no curso, tipo de instituição de graduação, nota do curso atribuída pela Capes e graus de instrução dos pais nos níveis de Self-Regulated Learning (SRL) e Self-Determination Theory (SDT). A partir da análise multivariada dos dados, os resultados corroboraram a tese e a influência do gênero no nível de SRL. A metaconclusão desta tese ratifica os estudos referenciados, confirmando que a aprendizagem pode ser dominada e controlada pelo indivíduo, ao se adotar estratégias individuais de regulação e motivação. Uma importante contribuição desta pesquisa consiste em oferecer conclusões empíricas que podem ajudar docentes, discentes, pesquisadores, instituições de ensino e programas de pós-graduação a compreender mais sistematicamente os aspectos da aprendizagem autorregulada e da aprendizagem autodeterminada que caracterizam o aluno de Contabilidade. Limitações importantes deste estudo podem ser vistas como oportunidades para pesquisas futuras: a amostra envolve um público específico, a pesquisa survey pode apresentar vieses de método comum e a baixa participação de alunos de mestrado profissional. Estudos futuros poderão adotar outras estratégias metodológicas e/ou envolver amostras mais diversificadas ou em maior lastro temporal / Assertive and efficient use of learning strategies often depends of the understanding and consideration of psychological and motivational aspects. Appropriate use of learning strategies is reflected in the academic performance, in the appropriation of constructs and models and in the critical and scientific maturity. This dissertation argues that there is a relationship between predominating self-regulated learning strategies and self-determined learning strategies in accounting master\'s and doctorate students. The study can be justified in view of, apart from inaugurating a research line within the context of Human Accounting, their results highlight a unique understanding of the relationship of learning with regulation and personal motivation. Its main goal is to present a diagnosis, the dimensions and the correlations of self-regulated learning and self-determined learning strategies of graduate Accounting students in Brazil. Five hundred and sixteen respondents participated in the survey, comprising 383 master\'s and 133 doctoral students. Two psychometric instruments were applied: the Self-Regulated Learning Strategies (SRLS) and the Motivated Strategies for Learning Questionnaire (MSLQ). The operating model research outlined the formulation of eight hypotheses, being that the first of them supports the thesis, while the others investigate the influence in the levels of Self-Regulated Learning (SRL) and Self-Determination Theory (SDT) of age, gender, type of course, stage in the course, type of undergraduate institution (public or private), grade attributed by Capes to the course and parental formal education degrees. From the multivariate data analysis,the results support the thesis and that gender has influence in the SRL level. The metaconclusion of this thesis confirms the referenced studies, estating that learning can be dominated and controlled by individuals through the adoption of individual strategies of regulation and motivation. An important contribution of this study is to offer empirical conclusions that might help teachers, students themselves, researchers, educational institutions and graduate programs to understand more systematically the aspects of self-regulated learning and self-determined learning that characterize the Accounting graduate students. The major limitations of the present study can be seen as opportunities for future researches: the sample involves a particular audience, research can provide common methods bias and the low participation of professional master\'s degree students in the sample. Future studies can take further methodological strategies and/or involve more diversified samples or consider longitudinal approaches

L’objectif de cette thèse est de contribuer à l’amélioration du processus de dépouillement d’essais de sûreté visant étudier le comportement d'un combustible nucléaire en contexte d’accident d’injection de réactivité (RIA), via la technique de contrôle par émission acoustique. Il s’agit notamment d’identifier clairement les mécanismes physiques pouvant intervenir au cours des essais à travers leur signature acoustique. Dans un premier temps, au travers de calculs analytiques et des simulation numériques conduites au moyen d’une méthode d’éléments finis spectraux, l’impact du dispositif d’essais sur la propagation des ondes est étudié. Une fréquence de résonance du dispositif est identifiée. On établit également que les mécanismes basses fréquences ne sont pas impactés par le dispositif d'essais. En second lieu, diverses techniques de traitement du signal (soustraction spectrale, analyse spectrale singulière, ondelettes. . . ) sont expérimentées, afin de proposer des outils permettant de traiter différent types de bruit survenant lors des essais RIA. La soustraction spectrale s’avère être la méthode la plus robuste aux changements de nature du bruit, avec un fort potentiel d’amélioration du rapport signal-à-bruit. Enfin, des méthodes d’analyse de données multivariées et d’analyse de données fonctionnelles ont été appliquées, afin de proposer un algorithme de classification statistique permettant de mieux comprendre la phénoménologie des accidents de type RIA et d’identifier les mécanismes physiques. Selon l’approche (multivariée ou fonctionnelle), les algorithmes obtenus permettent de reconnaître le mécanisme associé à une salve dans plus de 80% des cas. / The objective of the thesis is to contribute to the improvement of the monitoring process of nuclear safety experiments dedicated to study the behavior of the nuclear fuel in a reactivity initiated accident (RIA) context, by using the acoustic emission technique. In particular, we want to identify the physical mechanisms occurring during the experiments through their acoustic signatures. Firstly, analytical derivations and numerical simulations using the spectral finite element method have been performed in order to evaluate the impact of the wave travelpath in the test device on the recorded signals. A resonant frequency has been identified and it has been shown that the geometry and the configuration of the test device may not influence the wave propagation in the low frequency range. Secondly, signal processing methods (spectral subtraction, singular spectrum analysis, wavelets,…) have been explored in order to propose different denoising strategies according to the type of noise observed during the experiments. If we consider only the global SNR improvement ratio, the spectral subtraction method is the most robust to changes in the stochastic behavior of noise. Finally, classical multivariate and functional data analysis tools are used in order to create a machine learning algorithm dedicated to contribute to a better understanding of the phenomenology of RIA accidents. According to the method (multivariate or functional), the obtained algorithms allow to identify the mechanisms in more than 80 % of cases.