The master thesis deals with business performance and various approaches to its measurement with the emphasis on value management. The aim is to analyze the value creation and the performance of the company Kronomech, spol. s r.o. Then identify strategic factors affecting the value of the company, including the proposal of measures for its growth. The theoretical part focuses on indicators and models to allow assessment of business performance. These indicators are then applied in the practical part on the selected company. Attention is focused on the indicator of economic value added and the models investigating the financial position of the company. The overall economic performance of the company is evaluated, including consideration of nonfinancial indicators. The final part contains formulated measures that will lead to the consolidation of the financial health and to improve the performance of the selected company.

[pt] Perdendo em média mais de 20 por cento da base de alunos todo semestre, a evasão de alunos no ensino superior privado representa um desafio para a gestão dessas instituições. Diferentes abordagens são utilizadas para combater este problema. Para a gestão de marketing de retenção, a identificação dos alunos é o primeiro passo necessário para aplicar uma estratégia de interação personalizada. Nesse sentido, este trabalho apresenta uma metodologia quantitativa para classificação de risco de evasão de alunos ativos. Baseado em dados históricos de alunos que evadiram ou se formaram, modelos gerados por algoritmos de machine learning foram calculados e comparados e, na sequência, utilizados para classificar alunos ativos. Por fim, estimou-se o lifetime value desses alunos para auxiliar na definição de estratégias de retenção. / [en] Losing more than 20 percent of its students each semester, the student attrition in private graduation courses challenges its institutions management. Different approaches to address this problem have been used. To retention marketing management the identification of students is the first necessary step to apply a personalized interaction strategy. In this sense, this work uses a quantitative methodology to classify its students by risk of attrition. Based in historic data of former students of an institution, models were generated by machine learning algorithms and its results compared. Then they were used to classify active students in the educational institution. Afterwards, their lifetime value were estimated in order to help in the definition of retention strategies.

Submitted by Viviane Lima da Cunha (viviane@biblioteca.ufpb.br) on 2016-02-17T11:30:52Z No. of bitstreams: 1 arquivototal.pdf: 945699 bytes, checksum: 9ac1d0e7217344776f8b0044d94ad1cc (MD5) / Made available in DSpace on 2016-02-17T11:30:52Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 945699 bytes, checksum: 9ac1d0e7217344776f8b0044d94ad1cc (MD5) Previous issue date: 2015-08-27 / In Brazil, there are tools for monitoring the behaviour of legislators in rollcalls, such as O Estado de São Paulo’s Basômetro and Radar Parlamentar. These tools are used both by journalists and political scientists for analysis. Although they are great analysis tools, their usefulness for monitoring is limited because they require a manual follow-up, which makes it a lot of work when we consider the volume of data. Only in the Chamber of Deputies, 513 legislators participate on average over than 400 rollcalls by legislature. It is possible to decrease the amount of data analyzing the parties as a whole, but in contrast we lose the ability to detect individuals’ drives or intra-party groups such as factions. In order to mitigate this problem, I developed a statistical model that detects when a legislator changes his or her position, joining or leaving the governmental coalition, through ideal points estimates using theW-NOMINATE. It can be used individually or integrated to tools such as Basômetro, providing a filter for researchers find the deputies who changed their behaviour most significantly. The universe of study is composed of legislators from the Chamber of Deputies from the 50th to the 54th legislatures, starting in the first term of Fernando Henrique Cardoso in 1995 until the beginning of the second term of Dilma Rousseff in 2015. / No Brasil, existem ferramentas para o acompanhamento do comportamento dos parlamentares em votações nominais, tais como o Basômetro do jornal O Estado de São Paulo e o Radar Parlamentar. Essas ferramentas são usadas para análises tanto por jornalistas, quanto por cientistas políticos. Apesar de serem ótimas ferramentas de análise, sua utilidade para monitoramento é limitada por exigir um acompanhamento manual, o que se torna muito trabalhoso quando consideramos o volume de dados. Somente na Câmara dos Deputados, 513 parlamentares participam em média de mais de 400 votações nominais por legislatura. É possível diminuir a quantidade de dados analisando os partidos como um todo, mas em contrapartida perdemos a capacidade de detectar movimentações de indivíduos ou grupos intrapartidários como as bancadas. Para diminuir esse problema, desenvolvi neste trabalho um modelo estatístico que detecta quando um parlamentar muda de posicionamento, entrando ou saindo da coalizão governamental, através de estimativas de pontos ideais usando oW-NOMINATE. Ele pode ser usado individualmente ou integrado a ferramentas como o Basômetro, oferecendo um filtro para os pesquisadores encontrarem os parlamentares que mudaram mais significativamente de comportamento. O universo de estudo é composto pelos parlamentares da Câmara dos Deputados no período da 50ª até a 54ª legislaturas, iniciando no primeiro mandato de Fernando Henrique Cardoso em 1995 até o início do segundo mandato de Dilma Rousseff em 2015.

Submitted by Maike Costa (maiksebas@gmail.com) on 2017-02-09T11:58:42Z No. of bitstreams: 1 arquivo total.pdf: 1438788 bytes, checksum: 9590b3fb5151d31119e2920baea83d58 (MD5) / Made available in DSpace on 2017-02-09T11:58:42Z (GMT). No. of bitstreams: 1 arquivo total.pdf: 1438788 bytes, checksum: 9590b3fb5151d31119e2920baea83d58 (MD5) Previous issue date: 2016-06-20 / Minimally ripened cheeses, a category that includes the coalho cheese, have physicochemical characteristics that facilitate the survival and growth of pathogenic bacteria associated with foodborne illness outbreaks. Use of mathematical models in the predictive microbiology allows to estimate the effect of biotic and abiotic conditions on microbial growth, and hence the possible risk associated with the presence and increase of the population of a specific pathogen in a particular food. Considering these aspects, the present study aimed to: (i) assess the coalho cheese as a potential substrate for the growth of the pathogenic bacteria Escherichia coli, Listeria monocytogenes, Staphylococcus aureus and Salmonella spp. in function of temperature, pH and water activity (aw) found in commercial samples of this type of product; and (ii) performing primary modeling growth of different strains of E. coli in coalho cheese samples stored under low temperature. The estimates of the growth kinetics parameter (maximum growth rate - Grmax, log UFC/g/h) in function of the combination of the values (percentile 20, 50, 75 and 90) for temperature, pH and aw were generated by ComBase Predictor. The growth kinetics (Grmax) of E. coli strains in coalho cheese (10 °C) was assessed by using the primary model of Baranyi and Roberts (1994), available in spreadsheet DMFit 3.5 (ComBase). The largest Grmax values as a function of different combinations of temperature, pH and aw were observed for L. monocytogenes (Grmax 0.01 to 0.07 log CFU/g/h), Salmonella (Grmax 0.01 to 0.04 log CFU/g/h) and S. aureus (Grmax <0.01 to 0.05 log CFU/g/h). E. coli Grmax values varied from 0.01 to 0.03 log CFU/g/h. Overall, the Grmax values increased as the temperature increased in cheese samples, regardless of the aw and pH values. The estimated Grmax values for the E. coli strains when inoculated into coalho cheese ranged from 0.01 to 0.03 log CFU/g/h, respectively. High R2 values (≥ 0.97) were obtained to the growth curves of all E. coli strains tested. The estimates of the growth kinetics parameters in function of the values of temperature, aw and pH tested, as well as those observed in artificially contaminaed cheese samples showed that the coalho cheese is characterized as favorable substrate for the survival and growth of E. coli, L. monocytogenes, Salmonella spp. and S. aureus, which may pose a risk to consumers’ health if contaminated with these bacteria during production, transportation or marketing. / Os queijos de baixa maturação, categoria que inclui o queijo de coalho, possuem características físico-químicas que podem facilitar a sobrevivência e crescimento de bactérias patogênicas associadas a surtos de doenças transmitidas por alimentos. O emprego de modelos matemáticos na microbiologia preditiva permite estimar o efeito das condições bióticas e abióticas sobre o crescimento microbiano, e, consequentemente, o possível risco associado à presença e evolução da população de um patógeno em um alimento determinado. Considerando estes aspectos, o presente estudo teve como objetivos: (i) avaliar o queijo de coalho como substrato potencial para o crescimento das bactérias patogênicas Escherichia coli, Listeria monocytogenes, Staphylococcus aureus e Salmonella spp. em função de valores de temperatura, pH e atividade de água (aa) encontrados em amostras comerciais deste tipo de produto; e (ii) realizar modelagem primária do crescimento de diferentes cepas de E. coli em amostras de queijo de coalho armazenadas sob baixa temperatura. As estimativas dos parâmetros de cinética de crescimento (taxa máxima de crescimento – Grmax, log UFC/g/) em função da combinação dos valores (Percentil 20, 50, 75 e 90) de temperatura, pH e aa foram geradas pelo ComBase Predictor. A cinética de crescimento das cepas de E. coli em queijo de coalho (10 °C) foi avaliada por meio do Grmax utilizando o modelo primário de Baranyi e Roberts (1994), disponível na planilha do DMFit 3.5 (ComBase). Os maiores valores de Grmax em função das diferentes combinações de temperatura, pH e aa foram verificados para L. monocytogenes (Grmax 0,01 – 0,07 log UFC/g/h), Salmonella spp. (Grmax 0,01 – 0,04 log UFC/g/h) e S. aureus (Grmax 0,01 – 0,05 log UFC/g/h). Os valores de Grmax para E. coli variaram de 0,01 a 0,02 log UFC/g/h. De forma geral, os valores de Grmax aumentaram, proporcionalmente a temperatura nas amostras de queijo, a despeito dos valores de aa e pH. Os valores estimados de Grmax das cepas de E. coli quando inoculadas em queijo de coalho variaram de 0,01 a 0,03 log UFC/g/h. Foram obtidos elevados valores de R2 (≥ 0,97) para as curvas de crescimento de todas as cepas de E. coli testadas. As estimativas dos parâmetros de cinética de crescimento em função dos valores de temperatura, aa e pH testados, bem como àqueles verificados em amostras artificialmente contaminadas, mostram que o queijo de coalho se caracteriza como substrato favorável para a sobrevivência e crescimento de E. coli, L. monocytogenes, Salmonella spp. e S. aureus, podendo representar um risco a saúde aos consumidores caso seja contaminado com estas bactérias durante a sua produção, transporte ou comercialização.

Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós–Graduação em Oceanografia Biológica, Instituto de Oceanografia, 2013. / Submitted by Cristiane Gomides (cristiane_gomides@hotmail.com) on 2013-10-09T18:43:46Z No. of bitstreams: 1 Heloise.pdf: 1525937 bytes, checksum: 44441e69ced9544eaba26ec6b8f8e2d9 (MD5) / Approved for entry into archive by Sabrina Andrade (sabrinabeatriz@ibest.com.br) on 2013-10-17T03:12:06Z (GMT) No. of bitstreams: 1 Heloise.pdf: 1525937 bytes, checksum: 44441e69ced9544eaba26ec6b8f8e2d9 (MD5) / Made available in DSpace on 2013-10-17T03:12:06Z (GMT). No. of bitstreams: 1 Heloise.pdf: 1525937 bytes, checksum: 44441e69ced9544eaba26ec6b8f8e2d9 (MD5) Previous issue date: 2013 / População é a unidade fundamental da conservação e sua forma mais simples de monitoramento envolve a amostragem temporal regular para a determinação do status populacional. Uma das populações de baleia jubarte do Hemisfério Sul utiliza a costa do Brasil entre maio e dezembro para se reprodução e criação dos filhotes. Esta população, denominada “estoque reprodutivo A” pela Comissão Internacional da Baleia, tem mostrado sinais de recuperação após um marcado declínio devido a caça e um longo período de moratória. Esta população se concentra principalmente no Banco dos Abrolhos (BA), onde águas calmas e quentes parecem constituir um hábitat ideal. Este estudo teve o objetivo de estimar o tamanho da população de jubartes para o ano de 2011, bem como predizer a distribuição de grupos na costa brasileira. O método de amostragem de distâncias foi implementado, e modelos hierárquicos Bayesianos foram propostos para estimar a abundância. Modelos auto-regressivos condicionais foram aplicados para predizer a densidade em células de 0.5° de latitude e longitude. O tamanho da população foi estimado em 10,160 baleias (Cr.I.95%=6,607-17,692). As maiores densidades foram encontradas entre o Banco dos Abrolhos e a Baía de Todos os Santos (BA). Os resultados sugerem que o aumento populacional acarreta a expansão da população para além do Banco dos Abrolhos. / Population is the fundamental unit of conservation and its simplest monitoring tool involves regular sampling over time for population assessing status. One of the Southern Hemisphere humpback whale populations winters at the Brazilian coast typically from May to December where breeding and calving occur. This population, labeled as “breeding stock A” by International Whaling Commission, has shown signs of recovery after the long period of whaling. The goal of this study was to estimate the population size of humpback whales up to 2011, and predict group distribution along the Brazilian coast. Distance sampling methods were implemented and hierarchical Bayesian models were proposed to estimate abundance. Conditional auto-regressive models were used to predict the density in a lattice of 0.5° of latitude and longitude. Population size was estimated at 10,160 whales (Cr.I.95%=6,607-17,692). Highest densities were predicted to occur between Abrolhos Bank and Todos os Santos Bay (BA). The results suggest that the population increase leads to a population expansion beyond Abrolhos Bank.

Ensemble models have proved effective in a variety of classification tasks. These models combine the predictions of several base models to achieve higher out-of-sample classification accuracy than the base models. Base models are typically trained using different subsets of training examples and input features. Ensemble classifiers are particularly effective when their constituent base models are diverse in terms of their prediction accuracy in different regions of the feature space. This dissertation investigated methods for combining ensemble models, treating them as base models. The goal is to develop a strategy for combining ensemble classifiers that results in higher classification accuracy than the constituent ensemble models. Three of the best performing tree-based ensemble methods – random forest, extremely randomized tree, and eXtreme gradient boosting model – were used to generate a set of base models. Outputs from classifiers generated by these methods were then combined to create an ensemble classifier. This dissertation systematically investigated methods for (1) selecting a set of diverse base models, and (2) combining the selected base models. The methods were evaluated using public domain data sets which have been extensively used for benchmarking classification models. The research established that applying random forest as the final ensemble method to integrate selected base models and factor scores of multiple correspondence analysis turned out to be the best ensemble approach.

La radiothérapie externe (EBRT en anglais pour External Beam Radiotherapy) est l'un des traitements référence du cancer de prostate. Les objectifs de la radiothérapie sont, premièrement, de délivrer une haute dose de radiations dans la cible tumorale (prostate et vésicules séminales) afin d'assurer un contrôle local de la maladie et, deuxièmement, d'épargner les organes à risque voisins (principalement le rectum et la vessie) afin de limiter les effets secondaires. Des modèles de probabilité de complication des tissus sains (NTCP en anglais pour Normal Tissue Complication Probability) sont nécessaires pour estimer sur les risques de présenter des effets secondaires au traitement. Dans le contexte de la radiothérapie externe, les objectifs de cette thèse étaient d'identifier des paramètres prédictifs de complications rectales et vésicales secondaires au traitement; de développer de nouveaux modèles NTCP permettant l'intégration de paramètres dosimétriques et de paramètres propres aux patients; de comparer les capacités prédictives de ces nouveaux modèles à celles des modèles classiques et de développer de nouvelles méthodologies d'identification de motifs de dose corrélés à l'apparition de complications. Une importante base de données de patients traités par radiothérapie conformationnelle, construite à partir de plusieurs études cliniques prospectives françaises, a été utilisée pour ces travaux. Dans un premier temps, la fréquence des symptômes gastro-Intestinaux et génito-Urinaires a été décrite par une estimation non paramétrique de Kaplan-Meier. Des prédicteurs de complications gastro-Intestinales et génito-Urinaires ont été identifiés via une autre approche classique : la régression logistique. Les modèles de régression logistique ont ensuite été utilisés dans la construction de nomogrammes, outils graphiques permettant aux cliniciens d'évaluer rapidement le risque de complication associé à un traitement et d'informer les patients. Nous avons proposé l'utilisation de la méthode d'apprentissage de machine des forêts aléatoires (RF en anglais pour Random Forests) pour estimer le risque de complications. Les performances de ce modèle incluant des paramètres cliniques et patients, surpassent celles des modèle NTCP de Lyman-Kutcher-Burman (LKB) et de la régression logistique. Enfin, la dose 3D a été étudiée. Une méthode de décomposition en valeurs populationnelles (PVD en anglais pour Population Value Decomposition) en 2D a été généralisée au cas tensoriel et appliquée à l'analyse d'image 3D. L'application de cette méthode à une analyse de population a été menée afin d'extraire un motif de dose corrélée à l'apparition de complication après EBRT. Nous avons également développé un modèle non paramétrique d'effets mixtes spatio-Temporels pour l'analyse de population d'images tridimensionnelles afin d'identifier une région anatomique dans laquelle la dose pourrait être corrélée à l'apparition d'effets secondaires. / External beam radiotherapy (EBRT) is one of the cornerstones of prostate cancer treatment. The objectives of radiotherapy are, firstly, to deliver a high dose of radiation to the tumor (prostate and seminal vesicles) in order to achieve a maximal local control and, secondly, to spare the neighboring organs (mainly the rectum and the bladder) to avoid normal tissue complications. Normal tissue complication probability (NTCP) models are then needed to assess the feasibility of the treatment and inform the patient about the risk of side effects, to derive dose-Volume constraints and to compare different treatments. In the context of EBRT, the objectives of this thesis were to find predictors of bladder and rectal complications following treatment; to develop new NTCP models that allow for the integration of both dosimetric and patient parameters; to compare the predictive capabilities of these new models to the classic NTCP models and to develop new methodologies to identify dose patterns correlated to normal complications following EBRT for prostate cancer treatment. A large cohort of patient treated by conformal EBRT for prostate caner under several prospective French clinical trials was used for the study. In a first step, the incidence of the main genitourinary and gastrointestinal symptoms have been described. With another classical approach, namely logistic regression, some predictors of genitourinary and gastrointestinal complications were identified. The logistic regression models were then graphically represented to obtain nomograms, a graphical tool that enables clinicians to rapidly assess the complication risks associated with a treatment and to inform patients. This information can be used by patients and clinicians to select a treatment among several options (e.g. EBRT or radical prostatectomy). In a second step, we proposed the use of random forest, a machine-Learning technique, to predict the risk of complications following EBRT for prostate cancer. The superiority of the random forest NTCP, assessed by the area under the curve (AUC) of the receiving operative characteristic (ROC) curve, was established. In a third step, the 3D dose distribution was studied. A 2D population value decomposition (PVD) technique was extended to a tensorial framework to be applied on 3D volume image analysis. Using this tensorial PVD, a population analysis was carried out to find a pattern of dose possibly correlated to a normal tissue complication following EBRT. Also in the context of 3D image population analysis, a spatio-Temporal nonparametric mixed-Effects model was developed. This model was applied to find an anatomical region where the dose could be correlated to a normal tissue complication following EBRT.

Health policy makers are struggling to manage health care and spending. To identify strategies for improving health quality and reducing health spending, policy makers need to first understand health risks and outcomes. Despite lacking some desirable clinical detail, existing health care databases, such as national health surveys and claims and enrollment data for insured populations, are often rich in information relating patient characteristics to heath risks and outcomes. They typically encompass more inclusive populations than can feasibly be achieved with new data collection and are valuable resources for informing health policy. This dissertation illustrates how the Medicare Current Beneficiary Survey (MCBS) and MassHealth data can be used to develop models that provide useful estimates of risks and health quality measures. It provides insights into: 1) the benefits of a proxy for the Framingham cardiovascular disease (CVD) risk score, that relies only on variables available in the MCBS, to target health interventions to policy-relevant subgroups, such as elderly Medicare beneficiaries, based on their risk of developing CVD, 2) the importance of setting appropriate risk-adjusted quality of care standards for accountable care organizations (ACOs) based on the characteristics of their enrolled members, and 3) the outsized effect of high- frequency hospital users on re-admission measures and possibly other quality measures. This work develops tools that can be used to identify and support care of vulnerable patients to both improve their health outcomes and reduce spending – an important step on the road to health equity.

This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition.

<p>Soft pneumatic actuators allow compliant force application and movement for a variety of tasks. While most soft actuators have compliance in directions perpendicular to their direction of force application, they are most often analyzed only in their direction of actuation. In this work, we show a characterization of a soft 3D printed bellows actuator that considers shear and axial deformations, modeling both active and passive degrees of freedom. We build a model based on actuator geometry and a parallel linear and torsional spring system which we fit to experimental data in order to obtain the model constants. We demonstrate this model on two complex parallel networks, a delta mechanism and a floating actuator mechanism, and show how this single actuator model can be used to better predict movements in parallel structures of actuators. These results verify that the presented model and modeling approach can be used to speed up the design and simulation of more complex soft robot models by characterizing both active and passive forces of their one degree-of-freedom soft actuators.<br> </p>