The purpose of this thesis was to evaluate the impact of dynamic message signs (DMS) on the Orlando-Orange County Expressway Authority (OOCEA) toll road network using the Post-Deployment DMS Survey analysis. DMS are electronic traffic signs used on roadways to give travelers information about travel times, traffic congestion, accidents, disabled vehicles, AMBER alerts, and special events. The particular DMS referred to in this study are large rectangular signs installed over the travel lanes and these are not the portable trailer mount signs. The OOCEA has added twenty-nine fixed DMS to their toll road network from 2006-2008. At the time of the post-deployment survey, a total of twenty-nine DMS were up and running on the OOCEA toll road network. Since most of the travelers on the OOCEA toll roads were from Orange, Osceola, and Seminole counties, this study was limited to these counties. This thesis documents the results for the post-deployment survey analysis. The instrument used to analyze the travelers' perception of DMS was a survey that utilized computer aided telephone interview. The post-deployment survey was conducted during the month of May, 2008. Questions pertaining to the acknowledgement of DMS on the OOCEA toll roads, satisfaction with travel information provided on the network, formatting of the messages, satisfaction with different types of messages, diversion questions (Revealed and Stated preferences), and classification/socioeconomic questions (such as age, education, most traveled toll road, county of residence, and length of residency) were asked to the respondents. This thesis is using results of the multinomial logit model for diversion of traffic. This model takes into account the different diversion decisions from the post development survey (stay vs. divert all the way vs. divert and come back vs. abandon trip) and explains the differences in the diversion behavior. Drivers that use SunPass or Epass tend to stay on the toll road during unexpected congestion. Frequent SR 408 users are more likely to divert and stay off the toll road and frequent SR 417 users are more likely to divert and get back on the toll road. Drivers whose stated preference was to divert off the toll road were more likely to do the same in the real world. However, not too many of the respondents were likely to abandon their trips in the real world even if they said they would in a hypothetical congestion scenario. Users of 511 were more likely to divert and get back on the toll road or abandon their trips due to unexpected congestion. OOCEA can use this study to concentrate on keeping their toll roads more attractive during unexpected congestion to keep drivers from diverting all the way or abandoning their trips. For example, better incident management in clearing accidents more efficiently (thereby decreasing delay) and encouraging the use of SunPass or EPass could help drivers stay than divert or abandon their trip. This thesis also used ordered logit model for satisfaction. This model explains the levels of magnitude of satisfaction with traveler information on OOCEA toll roads. Drivers who acquired traveler information from DMS were less likely to be dissatisfied with traveler information provided on toll roads than other respondents. Drivers who were satisfied with accuracy and information on hazard warnings on DMS were more likely to be satisfied with information provided on toll roads than other respondents. This thesis provides a microscopic insight on the driver behavior on toll roads. This thesis expands the diversion and satisfaction models from previous studies in a way that OOCEA can identify specific groups of drivers related to a given response behavior (i.e., diverts off toll roads or dissatisfied with traveler information). Such analysis can be conducted in the future in the same study area or replicated in other areas to quantify the effects of individual and choice related attributes on choice behavior.

The objective of this research study is to explore trajectories of health-related quality of life (HRQoL) in older breast cancer survivors, along with their predictors. HRQoL is important because patients who show severe symptoms may wish to consider therapies or treatment plans that lead to better HRQoL. Older people are more vulnerable to low HRQoL scores since old age is associated with deteriorating health, multiple comorbidities, and low-socioeconomic status. To examine the HRQoL trajectory among older women with breast cancer, we used the data queried from the Surveillance, Epidemiology and End Results Medicare Health Outcomes Survey database. A total of 1,089 older (≥ 65 years) women who were diagnosed with breast cancer in 1998-2012 and participated in the survey before and after the cancer diagnosis were identified. HRQoL was measured using SF-36/VR-12 questionnaire and summarized as Physical Component Summary (PCS) Score and Mental Component Summary (MCS) Score. Latent Class Growth Mixture Modeling was conducted to identify distinct groups of women with a similar trajectory of HRQoL. The results showed that there were three latent classes of HRQoL trajectories for PCS: the high-declining (46.5% of the sample), mid-declining (36.0%), and the low-improving (17.5%). Two latent classes of HRQoL trajectories were identified for MCS: high-stable (76.5%) and low-declining (23.5%). The results showed that age at diagnosis, BMI, level of education, geographic region, tumor grade, tumor size, and number of comorbidities were some of the major predictors of health-related quality of life. These predictors were further explored using multinomial logistic regression analysis which identified number of comorbidities as the most significant predictor for HRQoL-PCS scores and level of education as the most significant predictor for HRQoL-MCS scores. This suggests that future research needs to be conducted, identifying the most common comorbidities in older breast cancer survivors to develop interventions that better the physical HRQoL in patients, in addition to the development of mental HRQoL interventions for patients that are less educated.

Studien undersökte marknadseffektiviteten i oddsmarknaden för fotboll. Två statistiskamodeller formulerades och implementerades med tillhörande test för att undersöka omoddsmarknaden var svagt marknadseffektiv. Den första modellen var multinominal logistiskregression vilken utvärderades med ett klassiskt Likelihood-ratio test. Den andra modellenvar ”K-means” klusteralgoritm med icke-parametriskt klustersignifikanstest, ”unimodal nonparametric cluster index” (UNPCI), som värderade klusterlösningen. Testerna kunde inteförkasta noll-hypotesen om svag marknadseffektivitet på 5% signifikansnivå. Resultatet liggeri linje med ett flertal andra publicerade studier.

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One of the most important factors that influences a stream's hydraulic and ecological health is the streambed's sediment size distribution. This distribution affects streambed stability, sediment transport rates, and flood levels by defining the roughness of the stream channel. Adverse effects on water quality and wildlife can be expected when excessive fine sediments enter a stream. Many chemicals and toxic materials are transported through streams by binding to fine sediments. Increases in fine sediments also seriously impact the survival of fish species present in the stream. Fine sediments fill tiny spaces between larger particles thereby denying fish embryos the necessary fresh water to survive. Reforestation, constructed wetlands, and slope stabilization are a few management practices typically utilized to reduce the amount of sediment entering a stream. To effectively gauge the success of these techniques, the sediment size distribution of the stream must be monitored. Gravel bed streams are typically stratified vertically, in terms of particle size, in three layers, with each layer having its own distinct grain size distribution. The top two layers of the stream bed, the pavement and subpavement, are the most significant in determining the characteristics of the stream. These top two layers are only as thick as the largest particle size contained within each layer. This vertical stratification by particle size makes it difficult to characterize the grain size distribution of the surface layer. The traditional bulk or volume sampling procedure removes a specified volume of material from the stream bed. However, if the bed exhibits vertical stratification, the volume sample will mix different populations, resulting in inaccurate sample results. To obtain accurate results for the pavement size distribution, a surface oriented sampling technique must be employed. The most common types of surface oriented sampling are grid and areal sampling. Due to limitations in the sampling techniques, grid samples typically truncate the sample at the finer grain sizes, while areal samples typically truncate the sample at the coarser grain sizes. When combined with an analysis technique, either frequency-by-number or frequency-by-weight, the sample results can be represented in terms of a cumulative grain size distribution. However, the results of different sampling and analysis procedures can lead to biased results, which are not equivalent to traditional volume sampling results. Different conversions, dependent on both the sampling and analysis technique, are employed to remove the bias from surface sample results. The topic of the present study is to determine the accuracy of sediment samples obtained by the different sampling techniques. Knowing the accuracy of a sample is imperative if the sample results are to be meaningful. Different methods are discussed for placing confidence intervals on grid sample results based on statistical distributions. The binomial distribution and its approximation with the normal distribution have been suggested for these confidence intervals in previous studies. In this study, the use of the multinomial distribution for these confidence intervals is also explored. The multinomial distribution seems to best represent the grid sampling process. Based on analyses of the different distributions, recommendations are made. Additionally, figures are given to estimate the grid sample size necessary to achieve a required accuracy for each distribution. This type of sample size determination figure is extremely useful when preparing for grid sampling in the field. Accuracy and sample size determination for areal and volume samples present difficulties not encountered with grid sampling. The variability in number of particles contained in the sample coupled with the wide range of particle sizes present make direct statistical analysis impossible. Limited studies have been reported on the necessary volume to sample for gravel deposits. The majority of these studies make recommendations based on empirical results that may not be applicable to different size distributions. Even fewer studies have been published that address the issue of areal sample size. However, using grid sample results as a basis, a technique is presented to estimate the necessary sizes for areal and volume samples. These areal and volume sample sizes are designed to match the accuracy of the original grid sample for a specified grain size percentile of interest. Obtaining grid and areal results with the same accuracy can be useful when considering hybrid samples. A hybrid sample represents a combination of grid and areal sample results that give a final grain size distribution curve that is not truncated. Laboratory experiments were performed on synthetic stream beds to test these theories. The synthetic stream beds were created using both glass beads and natural sediments. Reducing sampling errors and obtaining accurate samples in the field are also briefly discussed. Additionally, recommendations are also made for using the most efficient sampling technique to achieve the required accuracy. / Master of Science

[EN] The Statistical Process Control (SPC) is a method used to control the quality characteristics of a product during the production process, determine whether the manufacturing process is or not stable and improve its capacity through the reduction of variability. One of the main tools used in the SPC is the control chart. Often the quality of a product is measured through various quality characteristics generally correlated. Multivariate Control charts are a response to the need for quality control in such situations. If the quality characteristics are qualitative, sometimes it happens that the product quality is defined by linguistic variables and product units are also classified by linguistic forms into several categories, depending on the degree of fulfillment of expectations, creating a situation of fuzzy classifications. The control charts proposed in the literature to deal with such situations are mostly based on simulation and using approximation techniques which hinder the practical application thereof. This thesis addresses this issue proposing a multivariate control chart for quality characteristics of multi-type attributes correlated based on the T2 control chart of Hotelling, using a fuzzy approach. The results of the proposed control charts before are improved by establishing a more formal way of measuring and evaluating quality in these diffuse situations. A method is also proposed to assess the performance of control chart proposed, by deter mining the average run length (ARL), in both in-control state and the out-of-control state. For this, algorithms which use Monte Carlo simulation are developed and implemented in R. Additionally, the sensitivity of the control chart faced with the choice of the membership functions of linguistic variables is analyzed. / [ES] El Control Estadístico de Procesos (CEP) es un método que se utiliza para controlar las características de calidad de un producto durante el proceso de producción, determinar si los procesos de manufactura son o no estables y mejorar su capacidad a través de la reducción de la variabilidad. Una de las principales herramientas utilizadas en el Control Estadístico de Procesos es el gráfico de control. Con frecuencia, la calidad de un producto se mide a través de varias características de calidad, generalmente correlacionadas. Los gráficos de control multivariantes son una respuesta a la necesidad de controlar la calidad en tales situaciones. Si las características de calidad son de carácter cualitativo, ocurre en ocasiones que la calidad del producto se define mediante variables lingüísticas y las unidades de producto se clasifican también de for ma lingüística en varias categorías, dependiendo del grado de cumplimiento de las expectativas, creando una situación de clasificaciones difusas. Los gráficos propuestos en la literatura para tratar con tales situaciones están, en su mayoría, basados en simulación y el uso de técnicas de aproximación que dificultan la aplicación práctica de los mismos. Esta tesis trata esta cuestión proponiendo un Gráfico de Control multivariante para características de calidad de tipo multi-atributos correlacionados basado en el gráfico T2 de Hotelling, utilizando un enfoque difuso. Se mejora los resultados de los gráficos de control propuestos anterior mente estableciendo una manera más formal de medición y evaluación de la calidad en estas situaciones difusas. Se propone además un procedimiento para evaluar el rendimiento del gráfico de control propuesto mediante la determinación de la longitud de racha promedio (ARL), tanto para un estado bajo-control como para el estado fuera-de-control. Para ello se desarrollaron algoritmos que utilizan simulación de Monte Carlo y han sido implementados en R. Adicionalmente, se analiza la sensibilidad del gráfico de control frente a la elección de las funciones de pertenencia de las variables lingüísticas. / [CA] El Control Estadístic de Processos (CEP) és un mètode que s'utilitza per controlar les característiques de qualitat d'un producte durant el procés de producció, deter minar si els processos de manufactura són o no estables i millorar la seva capacitat a través de la reducció de la variabilitat. Una de les principals eines utilitzades en el Control Estadístic de Processos és el gràfic de control. Sovint, la qualitat d'un producte es mesura a través de diverses característiques de qualitat, generalment correlacionades. Els gràfics de control multivariants són una resposta a la necessitat de controlar la qualitat en aquestes situacions. Si les característiques de qualitat són de caràcter qualitatiu, de vegades passa que la qualitat del producte es defineix mitjançant variables lingüístiques i les unitats de producte es classifiquen també de for ma lingüística en diverses categories, depenent del grau de compliment de les expectatives, creant una situació de classificacions difuses. Els gràfics proposats en la literatura per abordar aquestes situacions són, majoritàriament, basats en simulació i l'ús de tècniques d'aproximació que en dificulten l'aplicació pràctica. Aquesta tesi tracta de resoldre aquesta qüestió amb la proposta d'un Gràfic de Control multivariant per característiques de qualitat de tipus multi-atributs correlacionats basat en el gràfic T2 de Hotelling, mijançant un enfocament difús. S'hi milloren els resultats de les gràfics de control proposats anterior ment per mitjà d'un mètode més for mal de mesurament i avaluació de la qualitat en aquestes situacions difuses. S'hi proposa a més un procediment per avaluar el rendiment del gràfic de control proposat mitjançant la deter minació de la longitud de ràfega mitjana (ARL), tant per a un estat en-control com per a l'estat fora-de-control. Amb aquesta finalitat es van desenvolupar algoritmes que utilitzen simulació de Monte Carlo i han estat implementats en R. Addicionalment, s'hi analitza la sensibilitat del gràfic de control davant l'elecció de les funcions de pertinença de les variables lingüístiques. / Pastuizaca Fernández, MN. (2016). Diseño y mejora de gráficos de control multivariantes para atributos. Un enfoque basado en teoría difusa [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/65073