The first part of this work focuses on control experiments performed on an unstable kerosene-fueled turbulent combustor. Using a phase shift controller and primary fuel modulation stability is successfully gained for a wide band of global equivalence ratios allowing the limitations of the control scheme to be characterized. It is shown that control signal saturation can significantly impact the ability of the control scheme to stabilize the system. Three different regions of controllability are defined based on the degree of saturation. A hysteresis behavior is also found to exist for the controller settings depending on whether stability is being maintained or realized for an unstable system. The second part of this work focuses on the impact that primary fuel modulation has on the fuel spray. Measurements for a simplex nozzle and an air-assist nozzle are taken under both static and dynamic operating conditions with a Phase Doppler Anemometry system. The dynamic modulation is found to significantly impact the spray properties of both nozzles. / Master of Science

One of the most common measures of clinical outcomes is the survival time. Accurately linking cancer molecular profiling with survival outcome advances clinical management of cancer. However, existing survival analysis relies intensively on statistical evidence from a single level of data, without paying much attention to the integration of interacting multi-level data and the underlying biology. Advances in genomic techniques provide unprecedented power of characterizing the cancer tissue in a more complete manner than before, opening the opportunity of designing biologically informed and integrative approaches for survival analysis. Many cancer tissues have been profiled for gene expression levels and genomic variants (such as copy number alterations, sequence mutations, DNA methylation, and histone modification). However, it is not clear how to integrate the gene expression and genetic variants to achieve a better prediction and understanding of the cancer survival. To address this challenge, we propose two approaches for data integration in order to both biologically and statistically boost the features selection process for proper detection of the true predictive players of survival. The first approach is data-driven yet biologically informed. Consistent with the biological hierarchy from DNA to RNA, we prioritize each survival-relevant feature with two separate scores, predictive and mechanistic. With mRNA expression levels in concern, predictive features are those mRNAs whose variation in expression levels are associated with the survival outcome, and mechanistic features are those mRNAs whose variation in expression levels are associated with genomic variants (copy number alterations (CNAs) in this study). Further, we propose simultaneously integrating information from both the predictive model and the mechanistic model through our new approach GEMPS (Gene Expression as a Mediator for Predicting Survival). Applied on two cancer types (ovarian and glioblastoma multiforme), our method achieved better prediction power than peer methods. Gene set enrichment analysis confirms that the genes utilized for the final survival analysis are biologically important and relevant. The second approach is a generic mathematical framework to biologically regularize the Cox's proportional hazards model that is widely used in survival analysis. We propose a penalty function that both links the mechanistic model to the clinical model and reflects the biological downstream regulatory effect of the genomic variants on the mRNA expression levels of the target genes. Fast and efficient optimization principles like the coordinate descent and majorization-minimization are adopted in the inference process of the coefficients of the Cox model predictors. Through this model, we develop the regulator-target gene relationship to a new one: regulator-target-outcome relationship of a disease. Assessed via a simulation study and analysis of two real cancer data sets, the proposed method showed better performance in terms of selecting the true predictors and achieving better survival prediction. The proposed method gives insightful and meaningful interpretability to the selected model due to the biological linking of the mechanistic model and the clinical model. Other important forms of clinical outcomes are monitoring angiogenesis (formation of new blood vessels necessary for tumor to nourish itself and sustain its existence) and assessing therapeutic response. This can be done through dynamic imaging, in which a series of images at different time instances are acquired for a specific tumor site after injection of a contrast agent. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive tool to examine tumor vasculature patterns based on accumulation and washout of the contrast agent. DCE-MRI gives indication about tumor vasculature permeability, which in turn indicates the tumor angiogenic activity. Observing this activity over time can reflect the tumor drug responsiveness and efficacy of the treatment plan. However, due to the limited resolution of the imaging scanners, a partial-volume effect (PVE) problem occurs, which is the result of signals from two or more tissues combining together to produce a single image concentration value within a pixel, with the effect of inaccurate estimation to the values of the pharmacokinetic parameters. A multi-tissue compartmental modeling (CM) technique supported by convex analysis of mixtures is used to mitigate the PVE by clustering pixels and constructing a simplex whose vertices are of a single compartment type. CAM uses the identified pure-volume pixels to estimate the kinetics of the tissues under investigation. We propose an enhanced version of CAM-CM to identify pure-volume pixels more accurately. This includes the consideration of the neighborhood effect on each pixel and the use of a barycentric coordinate system to identify more pure-volume pixels and to test those identified by CAM-CM. Tested on simulated DCE-MRI data, the enhanced CAM-CM achieved better performance in terms of accuracy and reproducibility. / Ph. D. / Disease outcome can refer to an event, state, condition, or behavior for some aspect of a patient’s health status. Event can express survival, while behavior can assess drug efficacy and treatment responsiveness. To gain deeper and, hence, better understanding about diseases, symptoms inspection has been shifted from the physical symptoms appearing externally on the human body to internal symptoms that require invasive and noninvasive techniques to find out and quantify them. These internal symptoms can be further divided into phenotypic and genotypic symptoms. Examples of phenotypes can include shape, structure, and volume of a specific human body organ or tissue. Examples of genotypes can be the dosage of the genetic information and the activity of genes, where genes are responsible for identifying the function of the cells constituting tissues. Linking disease phenotypes and genotypes to disease outcomes is of great importance to widen the understanding of disease mechanisms and progression. In this dissertation, we propose novel computational techniques to integrate data generated from different platforms, where each data type addresses one aspect of the disease internal symptoms, to provide wider picture and deeper understanding about a disease. We use imaging and genomic data with applications in ovarian, glioblastoma multiforme, and breast cancers to test the proposed techniques. These techniques aim to provide outcomes that are statistically significant, as what current peer methods do, beside biological insights, which current peer methods lack.

In Sweden, there has been an long term effort to allow as heavy traffic as possible, provided thatthe road network can handle it. This is because heavy traffic offers a competitive advantage withsocio-economic gains. In July 2018, the Swedish Transport Administration made 12 percent ofthe Swedish road network avaliable for the new maximum vehicle weight of 74 tonnes, basedon a legislative change from 2017. It is known that heavy traffic has a negative effect on thedegradation of the road, but it prevails divided opinions on whether 74 tonnes have a greaterimpact on the degradation rate compared to previous maximum gross weights of 64 tonnes.The 74 tonne vehicles have the same allowed axle load, which means more axles per vehicle. Some argue that an increased total load and more axles affect the degradation associated withtime-dependent material properties, while others argue that 74 tonnes mean fewer heavy vehiclesoverall, and thus should have a positive impact on the road’s lifespan. The construction companySkanska therefore requests a statistical analysis that enables to nuance the effects that heavytraffic has on the Swedish state road network. Since there is very limited data on the effect of 74 tonne traffic, this Master thesis instead focuseson modeling heavy traffic in general in order to be able to draw conclusions on which variablesare significant for a road’s lifetime. The method used is survival analysis where the lifetimeof the road is defined as the time between two maintenance treatments. The model selectedis the semi-parametric ’Cox Proportional Hazard Model’. The model is fitted with data froman open source database called LTPP (Long Term Pavement Performance) which is providedby the National Road and Transport Research Institute (VTI). The result of the modeling ispresented with hazard ratios, which is the relative risk that a road will require maintance atthe next time stamp compared to a reference category. The covariates that turned out to besignificant for a road’s lifetime and thus are included in the model are; lane width, undergroundtype, speed limit, asphalt layer thickness, bearing layer thickness and proportion of heavy traffic. Survival curves estimated by the model are also presented. In addition, a sensitivity analysis ismade by exploring survival curves estimated for different scenarios, with different combinationsof covariate levels.The results is then compared with previous studies on the subject. The most interesting finding isa case study from Finland since Finland allow 76 tonne vehicles since 2013. In the comparison,the model’s significant variables are confirmed, but the significance of precipitation and thenumber of axes for a roads lifetime is also highlighted

Esse trabalho propõe uma metodologia de otimização no processo de aprovação de componentes estruturais submetidos a carregamentos cíclicos que já tiveram a primeira rodada de testes físicos e falharam sem atingir os critérios de aprovação previamente estabelecidos. Os estudos de caso utilizados na aplicação do método foram dois componentes de suspensão de veículos comerciais. A metodologia proposta tem em sua base diversos tópicos da engenharia, como o estudo dos materiais dos componentes, análise de tensão e fadiga via elementos finitos, medição e análise de sinal de deformação e força, teste de durabilidade acelerado, além de correlação entre simulação e realidade. No âmbito da fadiga, a análise foi efetuada em ambiente virtual, através de um programa capaz de importar as tensões da simulação numérica e medições estruturais. É utilizada a metodologia S-N (tensão vida), através da criação de curvas S-N locais sintéticas, alteradas da curva original via fatores de influência como gradiente de tensão, tensão média (via diagrama de Haigh), rugosidade superficial e também pela distribuição estatística das propriedades do material. Por se tratar de carregamentos cíclicos aleatórios, uma análise de proporcionalidade do sinal é feita, além da utilização da previsão de vida em fadiga abordando os conceitos da fadiga uniaxial (utilizando tensão principal e von Mises) e também no caso multiaxial (utilizando o método dos planos críticos e tensão normal escalonada). Um grande grau de correlação entre simulação de tensão e testes físicos foi encontrado (pelo menos 90%). A previsão de falha por fadiga para os dois casos teve seus melhores resultados utilizando o método dos planos críticos. Os dois componentes encontram-se homologados por essa metodologia e atualmente são utilizados por veículos comerciais de série sem falhas observadas em campo, mostrando uma tendência de assertividade do método. / This work proposes a methodology to optimize the approval of structural components subjected to cyclic loadings that have had the first round of physical testing and failed to achieve the approval criteria previously established process. The case studies used in the application of the method were two commercial vehicle suspension components. The proposed methodology has its base in various engineering topics such as the study of the component materials, stress analysis and fatigue via finite elements, measurement and signal analysis of deformation and strength, accelerated durability test, and correlation between simulation and reality. Within the fatigue, the analysis was performed in a virtual environment, through a software able to import the stresses of numerical simulation and structural measurements. The S-N method (stress life) is used, through the creation of local synthetic S-N curves. The curve is modified from the original via influence factors such as gradient stress, mean stress (via Haigh diagram), surface roughness and also the statistical distribution of material properties. Because of the random cyclic loading, an analysis of the proportionality sign is made, in addition of the use of the fatigue life prediction by uniaxial fatigue (using principal stress and von Mises) and also in the multiaxial case (using the critical plans method and normal scaled stress). A high degree of correlation between stress and physical simulation tests was found (at least 90%). The prediction of fatigue failure for the two cases had their best results using the critical plans method. The two components are approved by this methodology and are currently used by commercial vehicles series without failures observed in the field, showing an assertiveness trend of the method.

The safety of the food supply in the United States has become an issue of prominence in the minds of ordinary Americans. Several government agencies, including the United States Department of Agriculture and the Food and Drug Administration, are charged with the responsibility of preserving the safety of the food supply. Food is withdrawn from the market in a product recall when tainted or mislabeled and has the potential to harm the consumer in some manner. This research examines recall events issued by firms over the period of January, 2000 through October, 2009 in the United States. Utilizing economic and management theory to establish predictions, this study employs the Cox proportional hazard regression model to analyze the effects of firm size and branding on the risk of recall recurrence. The size of the firm was measured in both billions of dollars of sales and in thousands of employees. Branding by the firm was measured as a binary variable that expressed if a firm had a brand and as a count of the number of brands within a firm. This study also provides a descriptive statistical analysis and several findings based on the recall data specifically relating to annual occurrences, geographical locations of the firms involved, types of products recalled, and reasons for recall. We hypothesized that the increasing firm size would be associated with increased relative risk of a recall event while branding and an increasing portfolio of brands would be associated with decreased relative risk of a recall event. However, it was found that increased firm size and branding by the firm are associated with an increased risk of recall occurrence. The results of this research can have implications on food safety standards in both the public and private sectors.

Made available in DSpace on 2015-03-26T13:32:17Z (GMT). No. of bitstreams: 1 texto completo.pdf: 764829 bytes, checksum: 8dbd03463c4800428f75900ca1340eb0 (MD5) Previous issue date: 2012-07-23 / The use of logistic regression modeling as a tool for modeling statistical probability of an event as a function of one or more independents variables, has grown among researchers in several areas, including Phytopathology. At about the dichotomous logistic regression in which the dependent variable is the type binary or dummy, is the extensive number of studies in the literature that discuss the modeling assumptions and the interpretation of the analyzes, as well as alternatives for implementation in statistical packages. However, when the variable response requires the use three or more categories, the number of publications is scarce. This is not only due to the scarcity of relevant publications on the subject, but also the inherent difficulty of coverage on the subject. In this paper we address the applicability of the model polytomous ordinal logistic regression, as well as differences between the proportional odds models, nonproportional and partial proportional odds. For this, we analyzed data from an experiment in which we evaluated the potential antagonistic fungus Clonostachys rosea in biocontrol of the disease called "gray mold", caused by Botrytis cinerea in strawberry and tomato. The partial proportional odds models and nonproportional were adjusted and compared, since the proportionality test score accused rejection of the proportional odds assumption. The estimates of the model coefficients as well as the odds ratios were interpreted in practical terms for Phytopathology. The polytomous ordinal logistic regression is introduced as an important statistical tool for predicting values, showing the potential of C. rosea in becoming a commercial product to be developed and used in the biological control of the disease, because the application of C. rosea was as or more effective than the use of fungicides in the control of gray mold. / O uso da regressão logística como uma ferramenta estatística para modelar a probabilidade de um evento em função de uma ou mais variáveis explicativas, tem crescido entre pesquisadores em várias áreas, inclusive na Fitopatologia. À respeito da regressão logística dicotômica, na qual a variável resposta é do tipo binária ou dummy, é extenso o número de trabalhos na literatura que abordam a modelagem, as pressuposições e a interpretação das análises, bem como alternativas de implementação em pacotes estatísticos. No entanto, quando a variável resposta requer que se utilize três ou mais categorias, o número de publicações é escasso. Isso devido não somente à escassez de publicações relevantes sobre o assunto, mas também à inerente dificuldade de abrangência sobre o tema. No presente trabalho aborda-se a aplicabilidade do modelo de regressão logística politômica ordinal, bem como as diferenças entre os modelos de chances proporcionais, chances proporcionais parciais e chances não proporcionais. Para isso, foram analisados dados de um experimento em que se avaliou o potencial do fungo antagonista Clonostachys rosea no biocontrole da doença denominada mofo cinzento , causada por Botrytis cinerea em morangueiro e tomateiro. Os modelos de chances proporcionais parciais e não proporcionais foram ajustados e comparados, uma vez que o teste score de proporcionalidade acusou rejeição da pressuposição de chances proporcionais. As estimativas dos coeficientes dos modelos bem como das razões de chances foram interpretadas em termos práticos para a Fitopatologia. A regressão logística politômica ordinal se apresentou como uma importante ferramenta estatística para predição de valores, mostrando o potencial do C. rosea em se tornar um produto comercial a ser desenvolvido e usado no controle biológico da doença, pois a aplicação de C. rosea foi tão ou mais eficiente do que a utilização de fungicidas no controle do mofo cinzento.

Made available in DSpace on 2016-06-02T20:06:00Z (GMT). No. of bitstreams: 1 1662.pdf: 571364 bytes, checksum: 6091268473b4a7cb920748fd364c2a99 (MD5) Previous issue date: 2007-03-30 / Survival data analysis models is used to study experimental data where, normally, the variable "answer"is the time passed until an event of interest. Many authors do prefer modeling survival data, in the presence of co-variables, by using a hazard function - which is related with its interpretation. The Cox model (1972) - most commonly used by the authors - is applicable when the fail rates are proportional. This model is very flexible and used in the survival analysis. It can be easily extended to, for example, incorporate the time-dependent co-variables. In the present work we propose a proportional risk model which incorporates a time-dependent parameter named "time-dependent proportional risk model". / A análise de sobrevivência tem por objetivo estudar dados de experimento em que a variável resposta é o tempo até a ocorrência de um evento de interesse. Vários autores têm preferido modelar dados de sobrevivência na presença de covariáveis por meio da função de risco, fato este relacionado à sua interpretação. Ela descreve como a probabilidade instantânea de falha se modifca com o passar do tempo. Nesse contexto, um dos modelos mais utilizados é o modelo de Cox (Cox, 1972), onde a suposição básica para o seu uso é que as taxas de falhas sejam proporcionais. O modelo de riscos proporcionais de Cox é bastante flexível e extensivamente usado em análise de sobrevivência. Ele pode ser facilmente estendido para incorporar, por exemplo, o efeito de covariáveis dependentes do tempo. Neste estudo, propõe-se um modelo de risco proporcional, que incorpora um parâmetro dependente do tempo, denominado modelo de risco proporcional dependente do tempo. Uma análise clássica baseada nas propriedades assintóticas dos estimadores de máxima verossimilhança dos parâmetros envolvidos é desenvolvida, bem como um estudo de simulação via técnicas de reamostragem para estimação intervalar e testes de hipóteses dos parâmetros do modelo. É estudado o custo de estimar o efeito da covariável quando o parâmetro que mede o efeito do tempo é considerado na modelagem. E, finalizando, apresentamos uma abordagem do ponto de vista Bayesiano.

Esse trabalho propõe uma metodologia de otimização no processo de aprovação de componentes estruturais submetidos a carregamentos cíclicos que já tiveram a primeira rodada de testes físicos e falharam sem atingir os critérios de aprovação previamente estabelecidos. Os estudos de caso utilizados na aplicação do método foram dois componentes de suspensão de veículos comerciais. A metodologia proposta tem em sua base diversos tópicos da engenharia, como o estudo dos materiais dos componentes, análise de tensão e fadiga via elementos finitos, medição e análise de sinal de deformação e força, teste de durabilidade acelerado, além de correlação entre simulação e realidade. No âmbito da fadiga, a análise foi efetuada em ambiente virtual, através de um programa capaz de importar as tensões da simulação numérica e medições estruturais. É utilizada a metodologia S-N (tensão vida), através da criação de curvas S-N locais sintéticas, alteradas da curva original via fatores de influência como gradiente de tensão, tensão média (via diagrama de Haigh), rugosidade superficial e também pela distribuição estatística das propriedades do material. Por se tratar de carregamentos cíclicos aleatórios, uma análise de proporcionalidade do sinal é feita, além da utilização da previsão de vida em fadiga abordando os conceitos da fadiga uniaxial (utilizando tensão principal e von Mises) e também no caso multiaxial (utilizando o método dos planos críticos e tensão normal escalonada). Um grande grau de correlação entre simulação de tensão e testes físicos foi encontrado (pelo menos 90%). A previsão de falha por fadiga para os dois casos teve seus melhores resultados utilizando o método dos planos críticos. Os dois componentes encontram-se homologados por essa metodologia e atualmente são utilizados por veículos comerciais de série sem falhas observadas em campo, mostrando uma tendência de assertividade do método. / This work proposes a methodology to optimize the approval of structural components subjected to cyclic loadings that have had the first round of physical testing and failed to achieve the approval criteria previously established process. The case studies used in the application of the method were two commercial vehicle suspension components. The proposed methodology has its base in various engineering topics such as the study of the component materials, stress analysis and fatigue via finite elements, measurement and signal analysis of deformation and strength, accelerated durability test, and correlation between simulation and reality. Within the fatigue, the analysis was performed in a virtual environment, through a software able to import the stresses of numerical simulation and structural measurements. The S-N method (stress life) is used, through the creation of local synthetic S-N curves. The curve is modified from the original via influence factors such as gradient stress, mean stress (via Haigh diagram), surface roughness and also the statistical distribution of material properties. Because of the random cyclic loading, an analysis of the proportionality sign is made, in addition of the use of the fatigue life prediction by uniaxial fatigue (using principal stress and von Mises) and also in the multiaxial case (using the critical plans method and normal scaled stress). A high degree of correlation between stress and physical simulation tests was found (at least 90%). The prediction of fatigue failure for the two cases had their best results using the critical plans method. The two components are approved by this methodology and are currently used by commercial vehicles series without failures observed in the field, showing an assertiveness trend of the method.

Magnetic Shape Memory (MSM) alloys show a superelastic behaviour with possible deformation rates up to 6% until 12% and a sufficient lifetime performance [1, 2]. In this paper, a passive application for a superelastic Ni-Mn-Ga-alloy is presented by using the MSM element as an accurately defined inner friction in a system of a multistable actuator, in particular a multistable proportional valve. The multistable valve is characterized by a currentless holding of the valve displacement in any position of the stroke. This circumstance makes the concept a very low energy consumption valve, compared to conventional proportional valves with solenoid actuators. The new aspect of a rigid connection of MSM Materials enables an absorption of tension as well as compressive forces. To realize an applicable controlling valve, a simple and effective controlling strategy has been implemented. Due to the stabilizing effect of the MSM element, an accurate controlling of the valve stroke and the usage for example as a pressure-, mass-flow or temperature-controlling valve was made possible. Furthermore, some potential applications in pneumatics as well as in hydraulics are presented.

The ability to gather and process information regarding the condition of forest fires is essential to cost-effective, safe, and efficient fire fighting. Advances in sensory and autopilot technology have made miniature unmanned aerial systems (UASs) an important tool in the acquisition of information. This thesis addresses some of the challenges faced when employing UASs for forest-fire perimeter surveillance; namely, perimeter tracking, cooperative perimeter surveillance, and path planning. Solutions to the first two issues are presented and a method for understanding path planning within the context of a forest-fire environment is demonstrated. Both simulation and hardware results are provided for each solution.