Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 113-121).

Climate change is one of the great challenges facing agriculture in the 21st century. The goal of this study was to produce projections of crop yields for the central United States in the 2030s, 2060s, and 2090s based on the relationship between weather and yield from historical crop yields from 1980 to 2010. These projections were made across 16 states in the US, from Louisiana in the south to Minnesota in the north. They include projections for maize, soybeans, cotton, spring wheat, and winter wheat. Simulated weather variables based on three climate scenarios were used to project future crop yields. In addition, factors of soil characteristics, topography, and fertilizer application were used in the crop production models. Two technology scenarios were used: one simulating a future in which crop technology continues to improve and the other a future in which crop technology remains similar to where it is today. Results showed future crop yields to be responsive to both the different climate scenarios and the different technology scenarios. The effects of a changing climate regime on crop yields varied both geographically throughout the study area and from crop to crop. One broad geographic trend was greater potential for crop yield losses in the south and greater potential for gains in the north. Whether or not new technologies enable crop yields to continue to increase as the climate becomes less favorable is a major factor in agricultural production in the coming century. Results of this study indicate the degree to which society relies on these new technologies will be largely dependent on the degree of the warming that occurs. Continued research into the potential negative impacts of climate change on the current crop system in the United States is needed to mitigate the widespread losses in crop productivity that could result. In addition to study of negative impacts, study should be undertaken with an interest to determine any potential new opportunities for crop development with the onset of higher temperatures as a result of climate change. Studies like this one with a broad geographic range should be complemented by studies of narrower scope that can manipulate climatic variables under controlled conditions. Investment into these types of agricultural studies will give the agricultural sector in the United States greater tools with which they can mitigate the disruptive effects of a changing climate.

This dissertation presents an approach to robot programming by demonstration based on two key concepts: demonstrator intent is the most meaningful signal that the robot can observe, and the robot should have a basic level of behavioral competency from which to interpret observed actions. Intent is a teleological, robust teaching signal invariant to many common sources of noise in training. The robot can use the knowledge encapsulated in sensorimotor schemas to interpret the demonstration. Furthermore, knowledge gained in prior demonstrations can be applied to future sessions. I argue that programming by demonstration be organized into declarative and pro-cedural components. The declarative component represents a reusable outline of underlying behavior that can be applied to many different contexts. The procedural component represents the dynamic portion of the task that is based on features observed at run time. I describe how statistical models, and Bayesian methods in particular, can be used to model these components. These models have many features that are beneficial for learning in this domain, such as tolerance for uncertainty, and the ability to incorporate prior knowledge into inferences. I demonstrate this architecture through experiments on a bimanual humanoid robot using tasks from the pick and place domain. Additionally, I develop and experimentally validate a model for generating grasp candidates using visual features that is learned from demonstration data. This model is especially useful in the context of pick and place tasks.

<p>In this thesis, we perform a survival analysis for right-censored data of populations with a cure rate. We consider two cure rate models based on the Geometric distribution and Poisson distribution, which are the special cases of the Conway-Maxwell distribution. The models are based on the assumption that the number of competing causes of the event of interest follows Conway-Maxwell distribution. For various sample sizes, we implement a simulation process to generate samples with a cure rate. Under this setup, we obtain the maximum likelihood estimator (MLE) of the model parameters by using the gamlss R package. Using the asymptotic distribution of the MLE as well as the parametric bootstrap method, we discuss the construction of confidence intervals for the model parameters and their performance is then assessed through Monte Carlo simulations.</p> / Master of Science (MSc)

published_or_final_version / abstract / Social Sciences / Doctoral / Doctor of Philosophy

Nanostring technology provides a new method to measure gene expressions. It's more sensitive than microarrays and able to do more gene measurements than RT-PCR with similar sensitivity. This system produces counts for each target gene and tabulates them. Counts can be normalized by using an Excel macro or nSolver before analysis. Both methods rely on data normalization prior to statistical analysis to identify differentially expressed genes. Alternatively, we propose to model gene expressions as a function of positive controls and reference gene measurements. Simulations and examples are used to compare this model with Nanostring normalization methods. The results show that our model is more stable, efficient, and able to control false positive proportions. In addition, we also derive asymptotic properties of a normalized test of control versus treatment.

Evidence for the timing and frequency of monitoring chronic conditions in primary care is often of low quality or absent. Recently, a methodology based upon repeated measures of monitoring tests has been developed to evaluate monitoring strategies which differ by frequency and the length of interval. In studies using this approach, it has been shown that there is potential for substantial over-detection and over-treatment through routine monitoring. The aim of this thesis was to extend the existing methodology to outcomes with highly skewed distributions and tests that are interpreted as dichotomous or categorical states. These methods were applied to find evidence for the intervals of monitoring for microalbuminuria in people with type 1 diabetes and screening for diabetic retinopathy. Statistical models were used to describe the progression of albuminuria and retinopathy and estimate the rates of false positive and false negative tests over time. Simulation was then used to create hypothetical cohorts of people being monitored and screening at different frequencies and for different baseline risk. The impact of changing the timing of tests within the programme was measured and evaluated. The basic framework of the methodology can be usefully extended to tests that have categorical outcome or highly skewed distributions. More frequent monitoring of microalbuminuria in people with type 1 diabetes, and more frequent screening of diabetic retinopathy leads to disproportionate numbers of false positive results and potential over-treatment or referral to specialist services. As with any other clinical intervention, monitoring and screening intervals should be based upon solid evidence. Monitoring for microalbuminuria and screening for diabetic retinopathy less frequently would reduce the rates of false positives tests but the implications of less surveillance need further investigation.

A partir de considerações sobre interações entre pares de objetos dependentes apenas de suas orientações relativas, desenvolvemos um modelo discreto para cristais líquidos biaxiais. Representamos estes sistemas por um conjunto de partículas biaxiais localizadas nos sítios de uma rede cristalina e que se orientam somente ao longo dos três eixos cartesianos. O diagrama de fases, obtido na aproximação de campo médio, apresenta uma fase nemática biaxial, duas fases nemáticas uniaxiais, além da fase isotrópica. Estas fases se encontram em um ponto multicrítico em cuja vizinhança fazemos a expansão de Landau da Energia Livre. Finalmente analisamos a adição de um campo externo favorável ao ordenamento nemático uniaxial. / From considerations about the pair interaction of objects depending only on their relative orientations, we develop a discrete model for biaxial liquid crystals. These systems are represented by a set of biaxial particles localized on the sites of a crystal lattice and oriented only along the three Cartesian axes. The phase diagram, obtained in a mean field approximation, displays a biaxial nematic phase, two uniaxial nematic phases, beside the isotropic phase. These phases meet on a multicritical point about which we make the Landau expansion of the free energy. Finally, we analyse the case where an external field is applied to favour the uniaxial nematic ordering.

Comprehensive evaluation of changes in streamflow extremes and characteristics due to climate change and variability is the main focus of this study. Available streamflow data at several gaging stations in least anthropologically affected watersheds of the Southeastern Gulf-Atlantic Region, were used for this analysis. To evaluate influences due to climate change, nonparametric trend tests were applied to annual and monthly extremes, while considering seasonality, along with changes in streamflow characteristics. To understand climate variability influences, streamflow data is partitioned in to cool and warm phases of four oceanic and atmospheric oscillations known to have an effect on hydroloclimatology of the region: El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO); Atlantic Multi-decadal Oscillation (AMO); and North Atlantic Oscillation (NAO). Generally, results showed decreasing trends in overall streamflow extremes, as well as spatially varying, temporally non-uniform influences of climate variability on streamflow extremes and characteristics. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

A partir de considerações sobre interações entre pares de objetos dependentes apenas de suas orientações relativas, desenvolvemos um modelo discreto para cristais líquidos biaxiais. Representamos estes sistemas por um conjunto de partículas biaxiais localizadas nos sítios de uma rede cristalina e que se orientam somente ao longo dos três eixos cartesianos. O diagrama de fases, obtido na aproximação de campo médio, apresenta uma fase nemática biaxial, duas fases nemáticas uniaxiais, além da fase isotrópica. Estas fases se encontram em um ponto multicrítico em cuja vizinhança fazemos a expansão de Landau da Energia Livre. Finalmente analisamos a adição de um campo externo favorável ao ordenamento nemático uniaxial. / From considerations about the pair interaction of objects depending only on their relative orientations, we develop a discrete model for biaxial liquid crystals. These systems are represented by a set of biaxial particles localized on the sites of a crystal lattice and oriented only along the three Cartesian axes. The phase diagram, obtained in a mean field approximation, displays a biaxial nematic phase, two uniaxial nematic phases, beside the isotropic phase. These phases meet on a multicritical point about which we make the Landau expansion of the free energy. Finally, we analyse the case where an external field is applied to favour the uniaxial nematic ordering.