Till Örebro universitet

Ultraviolet (UV) light induces a stocky phenotype in many plant species. In this study, we investigate this effect with regard to specific UV wavebands (UV-A or UV-B) and the cause for this dwarfing. UV-A- or UV-B-enrichment of growth light both resulted in a smaller cucumber (Cucumis sativus L.) phenotype, exhibiting decreased stem and petiole lengths and leaf area (LA). Effects were larger in plants grown in UV-B- than in UV-A-enriched light. In plants grown in UV-A-enriched light, decreases in stem and petiole lengths were similar independent of tissue age. In the presence of UV-B radiation, stems and petioles were progressively shorter the younger the tissue. Also, plants grown under UV-A-enriched light significantly reallocated photosynthates from shoot to root and also had thicker leaves with decreased specific LA. Our data therefore imply different morphological plant regulatory mechanisms under UV-A and UV-B radiation. There was no evidence of stress in the UV-exposed plants, neither in photosynthetic parameters, total chlorophyll content, or in accumulation of damaged DNA (cyclobutane pyrimidine dimers). The abscisic acid content of the plants also was consistent with non-stress conditions. Parameters such as total leaf antioxidant activity, leaf adaxial epidermal flavonol content and foliar total UV-absorbing pigment levels revealed successful UV acclimation of the plants. Thus, the UV-induced dwarfing, which displayed different phenotypes depending on UV wavelengths, occurred in healthy cucumber plants, implying a regulatory adjustment as part of the UV acclimation processes involving UV-A and/or UV-B photoreceptors.

Solar ultraviolet (UV) light influences plant growth and metabolism. Whereas high doses of UV can be deleterious for plants, natural UV doses are important for morphogenesis in many plants species, including those used in horticulture. Greenhouses are widely used for horticultural production and common cladding materials strongly absorb UV. Thus, low amounts of UV may be limiting the optimal development in some plant species. Light supplementation using UV tubes can overcome UV deficiency. Here we study cucumber seedling production in the absence or presence of different UV wavelengths. UV-A- (315-400 nm) and UV-B- (280-315 nm) enriched light was used for exposure and parameters such as the maximum quantum yield of photosystem II, stem development (internode length and diameter, stem dry weight, stem weight per unit of stem length, and stem bending), root biomass, leaf biomass and specific leaf mass were measured. We found that UV-A supplementation resulted in shorter more compact and sturdy plants, properties that are positive from a horticultural perspective. In contrast, UV-B-enriched light led to even smaller plants that lacked the sturdy phenotype. There were no signs of decreased F_v/F_munder any of the treatments, nor statistically significant differences in fruit yield between the control plants and the UV-treated plants when grown to harvest. In particular, the differences in fruit yield between the controls and the UV-A-treated plants were negligible in all cases. Thus, supplementary UV-A light can be an interesting alternative to chemical growth regulators for production of sturdy horticultural plants.

There are expressions for nonresponse bias, all of which require population quantities. In one expression for nonresponse bias, due to Bethlehem (1988, 2009), the bias is approximately equal to a function of the population covariance between the study variable and the response propensity (probability) and the population mean of the propensities. The covariance is hard to estimate (due to nonresponse). To empirically examine the covariance and the nonresponse bias, we have done two studies where the sample values of survey variables are known and the response propensities are estimated.The first study is a mail survey of a population of residents in the city of Solna in Sweden,20-74 years of age. The questionnaire consists of items on marital status and income; we have obtained the true values of those from the Swedish Tax Agency. We also know birth country, the type of area of residents, specific age and gender of each sampled individual.The second study is a web survey at Stockholm University, the population is faculty employees at the department of psychology. This survey is a census and the variables that we regard as our study variables are income from university and total income. The true values of income from university are given by the HR-department and total income from the Tax Agency.

One eGovernment proposal is that increased transparency and formalization of processes will reduce corruption. Andersen [4]) and Shim & Eom [6] found such positive effects, but findings are not comparable as different indexes were used and index quality was not tested. To fill this gap this paper uses statistical methods to investigate if the positive effect of eGovernment is robust across different indexes. We find that while corruption is very consistently measured by the CCI and CPI indexes, eGovernment indexes vary widely as predictors. The Economist and ITU indexes are the best predictors. The UN index scores fairly good but none of the other tested indexes can serve as indicator. Findings indicate that including social and institutional analysis improves an index hugely while measuring web sites is pointless. This suggests that indexes would score similarly different also on other eGovernment effects, and that the choice of eGovernment index is very important.

The performance of contextual classification methods is evaluated using Landsat TM data. Classes of pixels adjacent to the pixel to be classified are assumed to be conditionally independent given the class of the pixel to be classified. An assumption of autocorrelated spectral reflectance is made in three of the methods. Methods that utilize information from one image and images from two different occasions are compared. Our results indicate that an autocorrelation method utilizing images from two different occasions performs optimally.

The subject of this thesis is methods for classifying land using satellite images, and adherent parameter estimation. A satellite image consists of a set of pixels where measurements of spectral intensities are observed. Based on these spectral intensities, each pixel is assigned a class. The classification methods considered in this thesis are based on Bayesian decision theory.

Accounting for spatial and temporal dependence is of importance for classifying land. One such classification method is an autocorrelation method where the observed intensities are assumed to consist of the true intensities and some autocorrelated noise. In order to include temporal dependence, the spatial autocorrelation methods is extended to comprise information about temporal neighbours.

For applying classification methods it is necessary to estimate adherent parameters. The spatial autocorrelation parameter relies on the noise components, which are unobservable. An autocorrelation estimator based on Maximum-Likelihood estimates of autocovariances is introduced. This estimator is based on components that are differences between intensities from pixels taken on two different occasions over the same area. By doing so, this problem is easier to handle. Asymptotic properties such as strong consistency and asymptotic normality are proved for this estimator.

An efficient implementation algorithm for the autocorrelation methods is given which is necessary given the large amount of computation required. The suggested spatio-temporal autocorrelation method and some other classification methods are applied to real Landsat TM data. The results of the classifications were evaluated using data obtained through a field inventory. The conclusion from this study was that the spatio-temporal autocorrelation method performed best.