Made available in DSpace on 2016-08-29T15:36:07Z (GMT). No. of bitstreams: 1 tese_9924_Rafael_Oliari_Muniz_Dissertacao_MNPEF_Polo12_UFES.pdf: 4800108 bytes, checksum: 94e3ad2cc4968f40ede94a39a9453330 (MD5) Previous issue date: 2016-05-11 / Este trabalho relata a aplicação de um Material Instrucional (MI) que aborda conceitos relacionados a Transformações de Energia. O MI foi desenvolvido observando os pressupostos da Teoria da Aprendizagem Significativa (TAS), seguindo as recomendações de Ausubel (2003) e as contribuições de Moreira (2011, 2012) para tornar este MI potencialmente significativo. Também Foram seguidas as instruções de Bzuneck (2010) para promover a motivação dos alunos. O MI propõe a utilização de três instrumentos: os concept tests (Mazur & Ives, 2013), um experimento e uma maquete que aborda todos os conceitos trabalhados no MI. A proposta de avaliação dos resultados do trabalho foi o delineamento experimental, por isso foram trabalhadas com duas turmas escolhidas de maneira aleatória, chamadas de Turma Experimental (TE), que recebeu aulas baseadas no MI incluindo os três instrumentos propostos, e a Turma Controle (TC), onde a intervenção foi feita apenas pelo uso do MI. A TE conta com 14 alunos e a TC com 8 alunos, ambas as turmas pertencem a Escola Estadual de Ensino Fundamental e Médio Major Alfredo Pedro Rabaioli, localizada na cidade de Vitória. Como instrumento de coleta de dados foram utilizados um Pré e um Pós Teste, aplicado em ambas as turmas. A análise dos dados coletados nos testes foram realizados sob enfoques qualitativos e quantitativos, sendo utilizado o teste estatístico de Wilcoxon não pareado para a análise quantitativa. A evolução no desempenho da TE do Pré para o Pós Teste foi mais expressiva que a mesma comparação para a TC, sendo esta diferença de evolução estatisticamente significativa para um nível de significância de 95%. Esta análise estatística, bem como a análise qualitativa das respostas dos alunos nos testes, apresentam indícios que os instrumentos propostos pelo material desenvolvido contribuíram para a motivação dos alunos e a promoção da Aprendizagem Significativa para o conceito de Transformações de Energia para o grupo de alunos desta Escola Estadual.

Made available in DSpace on 2018-08-01T22:32:30Z (GMT). No. of bitstreams: 1 tese_9924_Dissertação do aluno Rafael Oliari Muniz.pdf: 6490242 bytes, checksum: 2a5b51d2cbdb5b4050c0fdb12ff3902d (MD5) Previous issue date: 2016-05-11 / Este trabalho relata a aplicação de um Material Instrucional (MI) que aborda conceitos relacionados a Transformações de Energia. O MI foi desenvolvido observando os pressupostos da Teoria da Aprendizagem Significativa (TAS), seguindo as recomendações de Ausubel (2003) e as contribuições de Moreira (2011, 2012) para tornar este MI potencialmente significativo. Também Foram seguidas as instruções de Bzuneck (2010) para promover a motivação dos alunos. O MI propõe a utilização de três instrumentos: os concept tests (Mazur & Ives, 2013), um experimento e uma maquete que aborda todos os conceitos trabalhados no MI. A proposta de avaliação dos resultados do trabalho foi o delineamento experimental, por isso foram trabalhadas com duas turmas escolhidas de maneira aleatória, chamadas de Turma Experimental (TE), que recebeu aulas baseadas no MI incluindo os três instrumentos propostos, e a Turma Controle (TC), onde a intervenção foi feita apenas pelo uso do MI. A TE conta com 14 alunos e a TC com 8 alunos, ambas as turmas pertencem a Escola Estadual de Ensino Fundamental e Médio Major Alfredo Pedro Rabaioli, localizada na cidade de Vitória.Como instrumento de coleta de dados foram utilizados um Pré e um Pós Teste, aplicado em ambas as turmas. A análise dos dados coletados nos testes foram realizados sob enfoques qualitativos e quantitativos, sendo utilizado o teste estatístico de Wilcoxon não pareado para a análise quantitativa. A evolução no desempenho da TE do Pré para o Pós Teste foi mais expressiva que a mesma comparação para a TC, sendo esta diferença de evolução estatisticamente significativa para um nível de significância de 95%. Esta análise estatística, bem como a análise qualitativa das respostas dos alunos nos testes, apresentam indícios que os instrumentos propostos pelo material desenvolvido contribuíram para a motivação dos alunos e a promoção da Aprendizagem Significativa para o conceito de Transformações de Energia para o grupo de alunos desta Escola Estadual.

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Radar-based respiration monitoring has been increasingly popular among researchers in biomedical fields during the last decades since it is a contactless monitoring technique. It is very convenient for subjects because it does not impose any restrictions on subjects or require their cooperation. Meanwhile, recognizing alternations in respiratory patterns is an important early clue of the diagnosis of several cardiorespiratory diseases. Thus, a study of biomedical radar-based respiration monitoring and respiratory pattern classification is carried out in this thesis. Radar-based respiration monitoring technology has a shortcoming that the collected respiratory signal will be easily distorted by the body movement of the monitoring subjects or disturbed by environment noise because of the contactless measurement attribute. This shortcoming limits the application of the respiratory pattern classification model, that is, the existing models cannot be applied automatically since the distorted respiratory signal needs to be manually filtered out ahead of the classification. In this study, a new respiratory pattern classification strategy, which can be implemented full-automatic, is proposed. In this strategy, a class “moving” is introduced to classify the distorted signal, and the sampling window length is shortened to reduce the effect caused by the signal distortion. A performance requirement for the continuous respiratory pattern classification is also proposed based on its expected function that can alert the occurrence of the abnormal breathing patterns. Several models which can meet the proposed performance requirement are developed in this thesis based on the state-of-the-art pattern classification technique and the time-series-based shapelet transform algorithm. The proposed models can classify four breathing patterns including eupnea, Cheyne Stokes respiration, Kussmaul breathing and apnea. A radar-collected respiratory signal database is built in this study, and a respiration simulation model which can generate breath samples for pattern classification is developed in this thesis. The proposed models were tested and validated in batch and stream processing manner with independently collected data and continuously collected data, respectively.

no / The requirement to efficiently store and process SMILES data used in Chemoinformatics creates a demand for efficient techniques to compress this data. General-purpose transforms and compressors are available to transform and compress this type of data to a certain extent, however, these techniques are not specific to SMILES data. We develop a transform specific to SMILES data that can be used alongside other general-purpose compressors as a preprocessor and post-processor to improve the compression of SMILES data. We test our transform with six other general-purpose compressors and also compare our results with another transform on our SMILES data corpus, we also compare our results with untransformed data.

<p>The optimal linear block transform for coding images is well known to be the Karhunen-Loève transformation (KLT). However, the assumption of stationarity in the optimality condition is far from valid for images. Images are composed of regions whose local statistics may vary widely across an image. A new approach to data representation, a mixture of principal components (MPC), is developed in this thesis. It combines advantages of both principal components analysis and vector quantization and is therefore well suited to the problem of compressing images. The author proposes a number of new transform coding methods which optimally adapt to such local differences based on neural network methods using the MPC representation. The new networks are modular, consisting of a number of modules corresponding to different classes of the input data. Each module consists of a linear transformation, whose bases are calculated during an initial training period. The appropriate class for a given input vector is determined by an optimal classifier. The performance of the resulting adaptive networks is shown to be superior to that of the optimal nonadaptive linear transformation, both in terms of rate-distortion and computational complexity. When applied to the problem of compressing digital chest radiographs, compression ratios of between 30:1 and 40:1 are possible without any significant loss in image quality. In addition, the quality of the images were consistently judged to be as good as or better than the KLT at equivalent compression ratios.</p> <p>The new networks can also be used as segmentors with the resulting segmentation being independent of variations in illumination. In addition, the organization of the resulting class representations are analogous to the arrangement of the directionally sensitive columns in the visual cortex.</p> / Thesis / Doctor of Philosophy (PhD)

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Thesis advisor: Mark D. Behn / The global mid ocean ridge system is segmented by transform faults and non-transform discontinuities. Oceanic transform faults display distinct morphology characterized by a deep valley and shallow transverse ridges on either side of the valley. Although the morphology of oceanic transform faults is known to first order, there is no consensus on the processes that form the transform valley and/or the adjacent transverse ridges. To date, most models of transform morphology attribute these features to either transform-normal extension or to shear stresses induced by slip along the fault. In this thesis, I compile bathymetric data along 16 major transform faults on the Mid-Atlantic Ridge and identify the key morphological properties of each transform. Specifically, I estimate transform valley width, depth, and total relief measured from the valley floor to the adjacent transverse ridges. The strongest correlation is between the relief and maximum depth, but there is a weaker correlation between maximum depth and valley width. These morphologic properties are then compared to key fault parameters such as slip rate, fault-normal compression/extension rate, thermal area, and the seismic coupling ratio, which is defined as the fraction of total fault slip that occurs seismically. These comparisons are used to test models that describe mechanisms of the formation of the transform valley. The strongest correlation is between the fault thermal area and valley half width. This suggests that the width of the transform valley may be controlled by the shear stress applied to the fault as it slips. By contrast, the data are not consistent with a model in which the valley is created by extension across the fault, because our data show that the maximum transform valley depth increases with compression and not extension. / Thesis (MS) — Boston College, 2024. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Earth and Environmental Sciences.