A manufatura de produtos customizados resulta em variedade de modelos, redução no tamanho de lotes e alternância frequente de tarefas executadas por trabalhadores. Neste contexto, tarefas manuais são especialmente afetadas por conta do processo de adaptação do trabalhador a novos modelos de produtos. Este processo de aprendizado pode ocorrer de maneira distinta dentro de um grupo de trabalhadores. Assim, busca-se o agrupamento dos trabalhadores com perfis similares de aprendizado, monitorando a formação de gargalos em linhas de produção constituídas por dissimilaridades de aprendizado em processos manuais. A presente dissertação apresenta abordagens para clusterização de trabalhadores baseadas nos parâmetros oriundos da modelagem de Curvas de Aprendizado. Tais parâmetros, os quais caracterizam o processo de adaptação de trabalhadores a tarefas, são transformados através da Análise de Componentes Principais e então utilizados como variáveis de clusterização. Na sequência, testam-se outras transformações nos parâmetros utilizando funções Kernel. Os trabalhadores são clusterizados através do método K-Means e Fuzzy C-Means e a qualidade dos agrupamentos formados é medida através do Silhouette Index. Por fim, sugere-se um índice de importância de variável baseado em parâmetros obtidos na Análise Componentes Principais com o objetivo de selecionar as variáveis mais relevantes para clusterização. As abordagens propostas são aplicadas em um processo da indústria calçadista, gerando resultados satisfatórios quando comparados a clusterizações realizadas sem a transformação prévia dos dados ou sem seleção das variáveis. / Manufacturing of customized products relies on a large menu choice, reduced batch sizes and frequent alternation of tasks performed by workers. In this context, manual tasks are especially affected by workers’ adaptation to new product models. This learning process takes place in different paces within a group of workers. This thesis aims at grouping workers with similar learning process tailored to avoid bottlenecks in production lines due to learning dissimilarities among workers. For that matter, we present a method for clustering workers based on parameters derived from Learning Curve (LC) modeling. Such parameters are processed through Principal Component Analysis (PCA), and the PCA scores are used as clustering variables. Next, Kernel transformations are also used to improve clustering quality. The data is clustered using K-Means and Fuzzy C-Means techniques, and the quality of resulting clusters is measured by the Silhouette Index. Finally, we suggest a variable importance index based on parameters derived from PCA to select the most relevant variables for clustering. The proposed approaches are applied in a footwear process, yielding satisfactory results when compared to clustering on original data or without variable selection.

Orientador: Mary Ann Foglio / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Odontologia de Piracicaba / Made available in DSpace on 2018-08-26T10:27:31Z (GMT). No. of bitstreams: 1 Lloret_FelipeCyrillo_M.pdf: 3758612 bytes, checksum: 7002aac1760fd55833ce91a085d63599 (MD5) Previous issue date: 2014 / Resumo: A Pterodon pubescens Benth, conhecida como sucupira é nativa das regiões que consiste o cerrado brasileiro, nos estados de Minas Gerais, São Paulo, Goiás e Mato Grosso do Sul. Nosso grupo iniciou estudos com a espécie Pterodon pubescens Benth em 1998. Inicialmente foram desenvolvidos estudos relacionado a eficácia dos seus extratos e determinação dos princípios ativos. O objetivo deste trabalho foi avaliar o efeito do calor sobre o processo de formação e degradação dos componentes vouacapanos nos frutos de Pterodon pubescens Benth. e sua influência sobre a atividade antiproliferativa in vitro em células tumorais humanas. Os frutos de P. pubescens foram coletados no município de São Carlos, localizado no estado de São Paulo, e no município de Ponto Chique, localizado no estado de Minas Gerais no período de agosto e setembro de 2012. Mensalmente foram preparados extratos por sistema Soxhlet e maceração dinâmica com as amostras de frutos provenientes dos municípios de São Carlos e de Ponto Chique armazenados em estufa, analisados por cromatografia gasosa capilar acoplada a um detector seletivo de massas e tiveram suas atividades antiproliferativas contra linhagem tumoral humana (PC-3) avaliadas por método de sulforrodamina B. Todos os resultados de teores dos compostos de interesse obtidos por CG-EM foram submetidos a análise de variância de uma única via (ANOVA), considerando-se como nível crítico p? 0,05 para que seja considerados diferença significante entre os grupos estudados, seguidos do Teste de Duncan. Para a análise dos compostos que possuem uma maior influência na atividade antiproliferativa do nos extratos obtidos foi utilizado a Análise de Componentes Principais. Foi possível concluir que há uma possível variação genética das espécies de Pterodon pubescens Benth., e que o calor não influenciou na formação do 6?- hidróxi- 7?- acetoxivouacapano, porém influencia no teor dos outros compostos estudados / Abstract: Pterodon pubescens Benth (Fabeacea) chemical variability was investigated to understand if the secondary metabolites 6?-acetoxi 7?-hydroxy-vouacapan content variability, was produced as an artefact overtime or was characteristic of genotype variability. The oil was extracted both by soxhlet system and at room temperature with stirring. The chemical composition was monitored by gas chromatography-mass spectrometry. The samples were tested in an anticancer assay against prostate (PC-03) human cancer cell line. The response parameter (TGI) was calculated. The chemical variability data was evaluate using the statistical analysis using Principal Component Analysis (PCA) to understand which of the extract's components had a real impact on the in vitro antiproliferative activity. Hierarchical Cluster Analysis (HCA) was performed to visualize the similarities among the extracts components with TGI values. Unscrumbler® v. 9.7 software was employed for analysis. The chemical composition of samples from São Carlos, São Paulo and Ponto Chique, Minais Gerais were monitored monthly during one year for Geranilgeraniol, 6?-acetoxi 7?-hydroxy-vouacapan, 6?- hydroxy-7?-acetoxy-vouacapan-17?-oate methyl ester and 6?-acetoxy-7?-hydroxyvouacapan- 17?-oate methyl ester. In vitro cytotoxicity screening against human prostate cancer cell line displayed higher selectivity and potent anticancer activity with TGI 11.43 mg ml?1 when higher 6?-acetoxi 7?-hydroxy-Vouacapan over total Voaucapan ratio (3.14) was achieved. Nevertheless 6?-acetoxi 7?-hydroxyvouacapan maintained approximately the same content throughout the year among the samples in opposition to overall Voaucapan content. The total Vouacapan total content was directly proportional to Geranygeraniol content decrease content. Samples from São Carlos at time zero had 26% geranylgeraniol content whereas Minas Gerais samples contained the highest content of 1.3%. Throughout the stability test geranylgeraniol concentration decreased with a straight relationship of overall increase of vouacapan content. This study highlights the complexity of factors involved in the production of secondary metabolites in plants. To enable the development of an herbal medicine, one of the greatest challenges is plant input standardization in order to meet efficacy, safety and reproducibility final product's requirements as recommended by Brazilian Sanitary Regulatory Agency (ANVISA). Data presented herein suggest that 6?-acetoxi 7?-hydroxy-vouacapan content variability has a straight relationship with genotype other than produced as artefact overtime / Mestrado / Farmacologia, Anestesiologia e Terapeutica / Mestre em Odontologia

Five hundred and eighteen observed validity coefficients based on correlations between commercially available test data and supervisory ratings of overall job performances were collected in 89 different job titles. Using Dictionary of Occupational Title Codes, Job Component Validity (JUV) estimates based on similar job titles residing in the PAQ Service database were collected and averaged across the General Aptitude Test.

Geochemistry has been used to determine the provenance and diagenetic history of eolian sandstone deposits. However, the grain size, sorting, cementation, and detrital composition of eolian units can change along dune foreset laminae. The purpose of this study was to test for consistent trends of compositional change along dune foresets. Such trends could increase the quality of geochemical sampling of eolian sandstones and possibly aid in estimating the original height of ancient sand dunes. XRF data was gathered for both major and trace elements from the Pennsylvanian to Permian Weber Sandstone, Early Jurassic Navajo Sandstone, and modern Coral Pink Sand Dunes of southern Utah. Data was plotted using both 2-dimensional scatter plots and 3-dimensional principal components analysis (PCA) plots. The PCA plots proved to be the most informative and suggest that there are no consistent, statistically significant geochemical trends within or between the three units sampled. However, this study found that PCA was able to show significant geochemical differences between the three units sampled, even when they are all dominated by a single mineral (>90% quartz). The Weber Sandstone had the most varied composition, and dunes within the unit could be highly dissimilar to each other. The Navajo Sandstone had less overall geochemical variability than the Weber Sandstone, and individual dunes were similar to each other. The modern Coral Pink Sand Dunes had much less compositional variation than either of the other two units, and dunes in this unit were very similar to each other.

Some methods for generating scenarios from multidimensional distribution assume we are able to generate scenarios from the one-dimensional distribution. We dedicate chapter 3 to this problem. At the end of the chapter, we provide references for applicable algorithms. Chapter 4 is focused on selected methods for generating scenarios from multidimensional distributions. In chapter 4.3, we introduce an algorithm for generating scenarios, which do not use any assumption about the distribution, except the first four moments and correlations to be specified. A method of generating scenarios based on approximation of multivariate normal distribution by the binomial distribution is described in chapter 4.5. Dimension reduction technique using principal components is presented in chapter 4.4. The algorithm is presented under the assumption of normal distribution. In chapter 4.6, we introduce the basics of the copula theory and a method for generating scenarios by C-vine copula. In chapter 5, we implement selected methods for generating scenarios for the estimation of daily value at risk for selected indexes and we discuss the results. Powered by TCPDF (www.tcpdf.org)

Infrastructure monitoring is being transformed by the advancements on remote sensing, unmanned vehicles and information technology. The wide interaction among these fields and the availability of reliable commercial technology are helping pioneer intelligent inspection methods based on digital 3D models. Commercially available Unmanned Aerial Vehicles (UAVs) have been used to create 3D photogrammetric models of industrial equipment. However, the level of automation of these missions remains low. Limited flight time, wireless transfer of large files and the lack of algorithms to guide a UAV through unknown environments are some of the factors that constraint fully automated UAV inspections. This work demonstrates the use of unsupervised Machine Learning methods to develop an algorithm capable of constructing a 3D model of an unknown environment in an autonomous iterative way. The capabilities of this novel approach are tested in a field study, where a municipal water tank is mapped to a level of resolution comparable to that of manual missions by experienced engineers but using $63\%$ . The iterative approach also shows improvements in autonomy and model coverage when compared to reproducible automated flights. Additionally, the use of this algorithm for different terrains is explored through simulation software, exposing the effectiveness of the automated iterative approach in other applications.

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Shrimp is the most valued shellfish product in the United States, and is highly perishable with post mortem metabolic changes that are deteriorative to its shelf-life. The objective of this research was to utilize GC-MS Gas Chromatography-Mass Spectrometry (GC-MS), Gas Chromatography Olfactometry (GCO), and sensory analysis to determine if the relationships exist between consumer acceptability, sensory descriptors and shelf-life of ready-to-eat shrimp. Three different cooking and packaging treatments were utilized: 63°C/15 s, 85°C/5 min, and 93°C/5 min for vacuum, MAP and aerobic packaging treatments, and stored at 2°±1 for 1, 3, 5, 6, 10, 15, 25, and 50 days or until the product was found unacceptable by a trained sensory panel. For Consumer acceptability (Day 3), the 85°C vacuum and 85°C MAP (Modified Atmosphere Packaging) shrimp were preferred (P < 0.05) over other treatments that were evaluated. The aroma active compounds that were identified using GC-MS and GCO consisted of one amine (trimethylamine), five aldehydes (3-methyl butanal, pentanal, hexanal, heptenal, geranial), one organic acid (butyric acid), two sulfur containing compounds (methional, dimethyltrisulfide), one pyrazine (methyl pyrazine) (amine), two alcohols (2-nonen-1-ol, 4 ethyl guaiacol), and one hydrocarbon (camphene). Results showed that for most of the packaging-temperature combinations, even beyond Day 25, the MAP product had fewer compounds and odors associated with spoilage than the aerobic and vacuum packaged products. This was consistent with the descriptive analysis data for which both the 85°C MAP and 93°C MAP treatment products had a longer shelf-life than vacuum treatment products, which had shelf-lives of 15 to 21 Days. The shelf-life of the cooked RTE shrimp that was MAP packaged and cooked at either 85°C or 93°C was between 39 and 42 Days at 2°C. Research indicates that processors of medium gulf brown shrimp could utilize 85°C/5min with MAP due to the shelf-life of the product and the elevated pleasantness scores when compared to the 93°C/5min treatment throughout the shelf-life of the product.

Thesis advisor: Christopher Maxwell / This paper examines neighborhood socioeconomic ascent in both Boston and the Greater Boston metropolitan statistical area. Using random forests, a supervised machine learning algorithm, and a collection of physical and demographic neighborhood characteristics gathered from the American Community Survey, I model changes in neighborhood socioeconomic status and identify neighborhoods in my study area that experienced relative socioeconomic ascent or relative socioeconomic decline between 2010 and 2019. In order to gain a better understanding of future socioeconomic change throughout my study area, I use a random forests model to predict neighborhood socioeconomic status in 2028. I find that my best random forests model offers an improvement over traditional linear modeling techniques and, through mapping results for Boston specifically, that change in Boston is occurring in minority, working class neighborhoods, especially along the city’s waterfront. These findings, in combination with qualitative community data, can be used to inform policy concerning matters ranging from housing to transportation in the years to come. / Thesis (BA) — Boston College, 2022. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Economics.

The Hermit Thrush is a new breeding bird in the Southern Appalachian high-elevation mountains, having expanded its range southward over the last few decades. Here it is sympatric with the Veery, a congeneric breeding resident. In order to more fully understand why the range expansion took place and to understand more about the local ecology of the newly arrived bird, I measured several habitat variables in 30 Hermit Thrush and 24 Veery territories. Principal Components Analysis and Cluster Analysis brought to light several patterns of habitat preferences for these 2 species. Hermit Thrushes prefer territories with more leaf litter on the ground and less shrub density than Veery territories. Competition between these species should remain low, as their niche differences brought to light in this study should enable them to breed in close proximity to each other in these high-elevation mountains.