Thesis (M.S.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 102-105). Also available on the World Wide Web.

Sorghum downy mildew (SDM) of maize is caused by the oomycete Peronosclerospora sorghi (Weston and Uppal) C. G. Shaw. The disease can cause devastating yield losses in maize (Zea mays L.). Quantitative trait loci (QTLs) mediating resistance to SDM were mapped using both restriction fragment length polymorphisms (RFLPs), and simple sequence repeats (SSRs) in 220 F2 individual maize progeny derived from a cross between two extremes; highly susceptible inbred parent SC-TEP5-19-1-3-1-4-1-1 (white) and highly resistant inbred P345C4S2B46-2-2-1-2-B-B-B (yellow). The phenotypic expression was assessed on F2:3 families in a wide range of environments under natural field infection and in a controlled greenhouse screening method. Heritability estimates of disease reaction ranged from 93.3% in Thailand sit 1 to 48% in Thailand sit 2. One hundred and thirty three polymorphic markers were assigned to the ten chromosomes of maize with LOD scores exceeding 4.9 covering about 1265 cM with an average interval length between markers of 9.5 cM. About 90% of the genome was located within a 10 cM distance to the nearest marker. Three putative QTLs were detected in association with resistance to SDM in different environments using composite interval mapping. Despite environmental and symptom differences, one QTL on chromosome 2 bin 9 had a major effect in all trials and explained up to 70% of the phenotypic variation in Thailand where the highest disease pressure was experienced. Two other QTLs on chromosome 3 bin 5 and chromosome 9 bin 2 had a minor effect, each explaining no more than 4% of the phenotypic variation. These results revealed one major gene and two minor genes that control sorghum downy mildew resistance. These markers should be very useful in breeding programs in facilitating the introgression of the resistance genes into commercial varieties. Marker-assisted selection for these loci should be useful in incorporating SDM resistance genes in maize across environments, even in the absence of the pathogen.

No description available.

The after market is of great importance of a company’s competitiveness and an increasing part of its revenues can be derived from it. Remanufacturing, in focus of this thesis, is a great business opportunity and the European market has an enormous growth potential. In the USA it is a major business and the automotive industry, targeted in this thesis, sells approximately 60 million remanufactured automotive products compared to 15 million products in Europe for an equivalent stock of vehicles Compared to manufacturing, the remanufacturing environment is a more complex business due to the high degree of uncertainty in the production process, mainly caused by two factors: the quantity and quality of returned cores. Overall, seven characteristics that make the remanufacturing material flow harder to control have been identified. Emerging in the 1990’s the concept of Lean production is a well-known method for improving the manufacturing capabilities of a company. Lean production, which is said to increase productivity, decrease lead-time and costs and enhance quality, is widely adopted. In this thesis, the purpose is to explore what characteristics of the remanufacturing environment that can hinder the implementation of Lean production principles of material flows and how Lean principles can be employed in a remanufacturing environment. In accordance, the theories of Lean production and Remanufacturing are used and the research methodology chosen that of a case study. To assess material flow, the production flows of five major product groups in a car engine are assessed. For the collection of data, Value Stream Mapping (VSM) methodology has been used. The main result about material flows and how Lean principles can be employed in a remanufacturing environment have resulted in eight generic proposals. The main conclusion from these proposals is that the inherent characteristics of variable processing times and uncertainty in materials recovered have major negative impact for implementing a lean production process. Vice versa, given an accurate supply of parts for reassembly, all the principles of Lean production can be fully implemented in the phases of reassembly and testing.

The data used for the construction of genome maps is imperfect, therefore the mapping of a physically linear structure must take place in a very uneven feature space. As the number of genes to be ordered grows, it appears to be impractical to use exhaustive search techniques to find the optimal mapping. In this paper we compare genetic algorithms and simulated annealing, two methods that are widely believed to be well-suited to non-smooth feature spaces, and find that the genetic algorithm approach yields superior results. Here we present performance profiles of comparable implementations of both genetic algorithms and simulated annealing. We have translated the problem to a form comparable to the shortest-path problem and found that the ability of a genetic algorithm to combine different partial solutions seems to be responsible for its superiority over the simulated annealing method. This is because in the genome mapping problem, as in the Traveling Salesman Problem, good solutions tend to be rather sparse and because optimal subtours tend to be components of nearly optimal tours. / Graduation date: 1994

Habitat loss and human-caused mortality are the most serious threats facing grizzly bear (<i>Ursus arctosi</i> L.) populations in Alberta, with conflicts between people and bears in agricultural areas being especially important. For this reason, information is needed about grizzly bears in agricultural areas. The objectives of this research were to find the best possible classification approach for determining multiple classes of agricultural and herbaceous land cover for the purpose of grizzly bear habitat mapping, and to determine what, if any, spatial and compositional components of the landscape affected the bears in these agricultural areas. Spectral and environmental data for five different land-cover types of interest were acquired in late July, 2007, from Landsat Thematic Mapper satellite imagery and field data collection in two study areas in Alberta. Three different classification methods were analyzed, the best method being the Supervised Sequential Masking (SSM) technique, which gave an overall accuracy of 88% and a Kappa Index of Agreement (KIA) of 83%. The SSM classification was then expanded to cover 6 more Landsat scenes, and combined with bear GPS location data. Analysis of this data revealed that bears in agricultural areas were found in grasses / forage crops 77% of the time, with small grains and bare soil / fallow fields making up the rest of the visited land-cover. Locational data for 8 bears were examined in an area southwest of Calgary, Alberta. The 4494 km2 study area was divided into 107 sub-landscapes of 42 km2. Five-meter spatial resolution IRS panchromatic imagery was used to classify the area and derive compositional and configurational metrics for each sub-landscape. It was found that the amount of agricultural land did not explain grizzly bear use; however, secondary effects of agriculture on landscape configuration did. High patch density and variation in distances between neighboring similar patch types were seen as the most significant metrics in the abundance models; higher variation in patch shape, greater contiguity between patches, and lower average distances between neighboring similar patches were the most consistently significant predictors in the bear presence / absence models. Grizzly bears appeared to prefer areas that were structurally correlated to natural areas, and avoided areas that were structurally correlated to agricultural areas. Grizzly bear presence could be predicted in a particular sub-landscape with 87% accuracy using a logistic regression model. Between 30% and 35% of the grizzlies‟ landscape scale habitat selection was explained.

Eutrophication, which is the most important degradation in water bodies, has been traced to the imposed loading of nutrients. Of interest is the fact that the process is often accompanied with undesirable effects, one of which is primarily the increased algae production at the surface and accumulation of biomass at the bottom and the secondary responses, which include a., change in species composition b. change in the biogeochemical cycle c. shift in the seasonal pattern and magnitude variability. The biogeochemical cycle in response to hydrodynamic alterations may occur internally; however external loading often fosters the process over large spatial scales. In the quest of validating the above statement, we hypothesized that there is no difference in the mean concentration of nutrients in the Gulf of Bothnia from the overall mean concentration. The analysis was done with a probability mapping method, in which all stations were grouped into a lattice. The cells are constructed using a grid system, i.e. x and y axis (longitude and latitude). Basically the method statistically tested for variables deviating from the over mean concentration. The variables analyzed are DIN, DSi, DIP and DIN: DSi. Results of the analysis showed significant spatial variations in the nutrient distribution in the Gulf of Bothnia; such differences were observed in the coastal to the deep zones of the Gulf.

The topic of this thesis is occupancy mapping for mobile robots, with an emphasis on a novel method for continuous occupancy mapping using Gaussian processes. In the new method, spatial correlation is accounted for in a natural way, and an a priori discretization of the area to be mapped is not necessary as within most other common methods. The main contribution of this thesis is the construction of a Gaussian process library for C++, and the use of this library to implement the continuous occupancy mapping algorithm. The continuous occupancy mapping is evaluated using both simulated and real world experimental data. The main result is that the method, in its current form, is not fit for online operations due to its computational complexity. By using approximations and ad hoc solutions, the method can be run in real time on a mobile robot, though not without losing many of its benefits.

Sorghum (Sorghum bicolor [L.] Moench) is the world's fifth most economically important cereal crop, grown worldwide as a source of food for both humans and livestock. Sorghum is a C4 grass that is well adapted to hot and arid climes and is popular for cultivation on lands of marginal quality. Recent interest in development of biofuels from lignocellulosic biomass has drawn attention to sorghum, which can be cultivated in areas not suitable for more traditional crops, and is capable of generating plant biomass in excess of 40 tons per acre. While the quantity of biomass and low water consumption make sorghum a viable candidate for biofuels growth, the biomass composition is enriched in lignin, which is problematic for enzymatic and chemical conversion techniques. The genetic basis for stem composition was analyzed in sorghum populations using a combination of genetic, genomic, and bioinformatics techniques. Utilizing acetyl bromide extraction, the variation in stem lignin content was quantified across several sorghum cultivars, confirming that lignin content varied considerably among sorghum cultivars. Previous work identifying sorghum reduced-lignin lines has involved the monolignol biosynthetic pathway; all steps in the pathway were putatively identified in the sorghum genome using sequence analysis. A bioinformatics toolkit was constructed to allow for the development of genetic markers in sorghum populations, and a database and web portal were generated to allow users to access previously developed genetic markers. Recombinant inbred lines were analyzed for stem composition using near infrared reflectance spectroscopy (NIR) and genetic maps constructed using restriction site-linked polymorphisms, revealing 34 quantitative trail loci (QTL) for stem composition variation in a BTx642 x RTx7000 population, and six QTL for stem composition variation in an SC56 x RTx7000 population. Sequencing the genome of BTx642 and RTx7000 to a depth of ~11x using Illumina sequencing revealed approximately 1.4 million single nucleotide polymorphisms (SNPs) and 1 million SNPs, respectively. These polymorphisms can be used to identify putative amino acid changes in genes within these genotypes, and can also be used for fine mapping. Plotting the density of these SNPs revealed patterns of genetic inheritance from shared ancestral lines both between the newly sequenced genotypes and relative to the reference genotype BTx623.

Tone mapping is a computational task of significance in the context of displaying high dynamic range images on low dynamic range devices. While a number of tone mapping algorithms have been proposed and are in common use, there is no single operator that yields optimal results under all conditions. Moreover, obtaining satisfactory mappings often requires the manual tweaking of parameters. This thesis proposes interactive evolution as a computational tool for tone mapping. An evolution strategy that blends the results from several tone mapping operators while at the same time adapting their parameters is proposed. As well, the results are adapted such that such that approximately uniform perceptual distances between offspring candidate solutions and the parent are ensured. The introduction of a perceptually based step size adaptation technique enhances the control of the variability between newly generated offspring, when compared to parameter space step size adaptation.