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No description available.

We enlarge the class of Beltrami equations by developping a stability theory for the sheaf of solutions of an overdetermined elliptic system of first order homogeneous partial differential equations with constant coefficients in the Euclidean space.

This paper presents OpenStreetMap and closely related software as a resource for spatial economic research. The paper demonstrates how information can be extracted from OpenStreetMap, how it can be used as a geographical interface in web-based communication, and illustrates the value of the tools by use of a specific application, the WU campus GIS. (author's abstract)

Master of Science / Department of Mechanical and Nuclear Engineering / Dale Schinstock / Developments in farm related technology have increased the importance of mapping individual plants in the field. An automated mapping system allows the size of these fields to scale up without being hindered by time-intensive, manual surveying. This research focuses on the development of a mapping system which uses geo-located images of the field to automatically locate plants and determine their coordinates. Additionally, this mapping process is capable of differentiating between groupings of plants by using Quick Response (QR) codes. This research applies to green plants that have been grown into seedlings before being planted, known as transplants, and for fields that are planted in nominally straight rows. The development of this mapping system is presented in two stages. First is the design of a robotic platform equipped with a Real Time Kinematic (RTK) receiver that is capable of traversing the field to capture images. Second is the post-processing pipeline which converts the images into a field map. This mapping system was applied to a field at the Land Institute containing approximately 25,000 transplants. The results show the mapped plant locations are accurate to within a few inches, and the use of QR codes is effective for identifying plant groups. These results demonstrate this system is successful in mapping large fields. However, the high overall complexity makes the system restrictive for smaller fields where a simpler solution may be preferable.

Modern day hardware platforms are parallel and diverse, ranging from mobiles to data centers. Mainstream parallel applications execute in the same system competing for resources. This resource contention may lead to a drastic degradation in a program’s performance. In addition, the execution environment composed of workloads and hardware resources, is dynamic and unpredictable. Efficient matching of program parallelism to machine parallelism under uncertainty is hard. The mapping policies that determine the optimal allocation of work to threads should anticipate these variations. This thesis proposes solutions to the mapping of parallel programs in dynamic environments. It employs predictive modelling techniques to determine the best degree of parallelism. Firstly, this thesis proposes a machine learning-based model to determine the optimal thread number for a target program co-executing with varying workloads. For this purpose, this offline trained model uses static code features and dynamic runtime information as input. Next, this thesis proposes a novel solution to monitor the proposed offline model and adjust its decisions in response to the environment changes. It develops a second predictive model for determining how the future environment should be, if the current thread prediction was optimal. Depending on how close this prediction was to the actual environment, the predicted thread numbers are adjusted. Furthermore, considering the multitude of potential execution scenarios where no single policy is best suited in all cases, this work proposes an approach based on the idea of mixture of experts. It considers a number of offline experts or mapping policies, each specialized for a given scenario, and learns online the best expert that is optimal for the current execution. When evaluated on highly dynamic executions, these solutions are proven to surpass default, state-of-art adaptive and analytic approaches.

Master of Science / Agronomy / Guihua Bai / Guorong Zhang / Fusarium head blight (FHB) is one of the most damaging diseases in wheat, which impacts both grain yield and quality drastically. Recently, the disease has become more prevalent in the hard winter wheat (HWW) grown areas of the United States including Oklahoma where FHB has not been reported before. Growing resistant cultivars is the most economical and effective strategy for disease management. To dissect quantitative trait loci (QTL) for FHB resistance in a moderately resistant hard winter wheat (HWW) cultivar, Overland, a population of 186 recombinant inbred lines (RILs) was developed from the cross between Overland and Overley, a susceptible HWW cultivar from Kansas. The RILs were evaluated for FHB type II resistance in one field and three greenhouse experiments and genotyped using genotyping-by-sequencing (GBS) markers. Three FHB resistance QTLs were mapped on Chromosomes 4DL, 4AL, and 5BL. The QTL on 4DL was the most consistent one and explained up to 13% of the phenotypic variation for type II resistance and 14 % for low Fusarium damaged kernels (FDK). Two GBS markers closely linked to the 4DL QTL were successfully converted to Kbioscience competitive allelic specific PCR (KASP) assays and can be used in marker-assisted breeding. In breeding, a single QTL may provide only partial resistance and pyramiding of several resistance QTLs in a cultivar can provide more protection in FHB epidemics. Fhb1 is a major QTL for FHB resistance from a Chinese source and Fhb3 is an alien gene from wild rye grass (Leymus racemosus). To study the effects of these QTLs individually and cumulatively in hard winter wheat backgrounds, they were transferred into two HWW cultivars Overland and Jagger. The results show that Fhb1 significantly increased FHB resistance, but Fhb3 did not. Thus, Fhb3 is not an effective gene for improvement of FHB resistance in HWW.