Assessment of genetic diversity using RAPD analysis in a germplasm collection of sea buckthorn

Selostus: Tyrnin geneettisen monimuotoisuuden arviointi RAPD analyysillä

Maintenance of male sterile germplasm in Brassica rapa in vitro propagation

Selostus: Isästeriilin kevätrypsin geeniaineksen ylläpito in vitro mikroviljelyllä

Stellar population synthesis: the effect of improved Asymptotic Giant Branch models and of interstellar dust on mass-to-light–color relations

Teoreettisen populaatiosynteesin avulla voidaan mallintaa tähtijoukkojen ja galaksien fotometrisiä ominaisuuksia yhdistämällä yksittäisten tähtien tuottama säteily, joka saadaan teoreettisista tähtien kehitysmalleista. Valitsemalla sopiva massajakauma syntyville tähdille voidaan muodostaa yksinkertainen tähtipopulaatio, joka koostuu saman ikäisistä ja kemialliselta koostumukseltaan yhtenäisistä tähdistä. Monimutkaisempia tähtipopulaatioita voidaan muodostaa konvoloimalla yksinkertaisten tähtipopulaatioiden luminositeetti jonkin valitun tähtienmuodostushistorian kanssa sekä yhdistämällä näin muodostettuja populaatioita. Tässä työssä tarkastellaan asymptoottisen jättiläishaaran (AGB) tähtien uusien, tarkentuneiden evoluutiomallien vaikutusta populaatiosynteesin tuloksiin niin yksinkertaisten tähtipopulaatioiden kuin galaksien mallinnukseen soveltuvien monimutkaisempien tähtipopulaatioiden kohdalla. Työn päätarkoitus on tuottaa uudistuneisiin malleihin perustuvat populaation massa-luminositeetti -suhteen ja värin väliset relaatiot (MLC-relaatiot). MLC-relaatioita voidaan käyttää populaation massan määrittämiseen sen fotometristen ominaisuuksien (väri, luminositeetti) perusteella. Lisäksi tutkitaan tähtienvälisen pölyn vaikutusta yksinkertaisen spiraaligalaksimallin MLC-relaatioihin. Työssä käytetyt tähtien kehitysmallit perustuvat julkaisuun Marigo et al. (Astronomy & Astrophysics 482, 2008). Havaitaan, että AGB-tähtien vaikutus populaation integroituun luminositeettiin on pieni näkyvillä aallonpituuksilla, mutta merkittävä lähi-infrapuna-alueella. Vaikutus MLC-relaatioihin on vastaavasti merkittävä tarkkailtaessa luminositeettia lähi-infrapunassa sekä käytettäessä värejä, joissa yhdistetään optisia ja lähi-infrapunan kaistoja. Todetaan, että MLC-relaatioiden käyttö lähi-infrapunassa edellyttää tarkentuneen AGB-vaiheen sisällyttämistä populaatiosynteesin malleihin. Tähtienvälisen pölyn vaikutus MLC-relaatioihin todetaan riippuvan käytetystä kaistasta ja väristä, mutta vaikutuksen havaitaan olevan suurin optisen ja lähi-infrapunan väriyhdistelmillä.

Price spike forecasting in a competitive day-ahead energy market

Electricity price forecasting has become an important area of research in the aftermath of the worldwide deregulation of the power industry that launched competitive electricity markets now embracing all market participants including generation and retail companies, transmission network providers, and market managers. Based on the needs of the market, a variety of approaches forecasting day-ahead electricity prices have been proposed over the last decades. However, most of the existing approaches are reasonably effective for normal range prices but disregard price spike events, which are caused by a number of complex factors and occur during periods of market stress. In the early research, price spikes were truncated before application of the forecasting model to reduce the influence of such observations on the estimation of the model parameters; otherwise, a very large forecast error would be generated on price spike occasions. Electricity price spikes, however, are significant for energy market participants to stay competitive in a market. Accurate price spike forecasting is important for generation companies to strategically bid into the market and to optimally manage their assets; for retailer companies, since they cannot pass the spikes onto final customers, and finally, for market managers to provide better management and planning for the energy market. This doctoral thesis aims at deriving a methodology able to accurately predict not only the day-ahead electricity prices within the normal range but also the price spikes. The Finnish day-ahead energy market of Nord Pool Spot is selected as the case market, and its structure is studied in detail. It is almost universally agreed in the forecasting literature that no single method is best in every situation. Since the real-world problems are often complex in nature, no single model is able to capture different patterns equally well. Therefore, a hybrid methodology that enhances the modeling capabilities appears to be a possibly productive strategy for practical use when electricity prices are predicted. The price forecasting methodology is proposed through a hybrid model applied to the price forecasting in the Finnish day-ahead energy market. The iterative search procedure employed within the methodology is developed to tune the model parameters and select the optimal input set of the explanatory variables. The numerical studies show that the proposed methodology has more accurate behavior than all other examined methods most recently applied to case studies of energy markets in different countries. The obtained results can be considered as providing extensive and useful information for participants of the day-ahead energy market, who have limited and uncertain information for price prediction to set up an optimal short-term operation portfolio. Although the focus of this work is primarily on the Finnish price area of Nord Pool Spot, given the result of this work, it is very likely that the same methodology will give good results when forecasting the prices on energy markets of other countries.

Comparison of five popular normalization methods using a spike-in proteomics data set

Mass spectrometry (MS)-based proteomics has seen significant technical advances during the past two decades and mass spectrometry has become a central tool in many biosciences. Despite the popularity of MS-based methods, the handling of the systematic non-biological variation in the data remains a common problem. This biasing variation can result from several sources ranging from sample handling to differences caused by the instrumentation. Normalization is the procedure which aims to account for this biasing variation and make samples comparable. Many normalization methods commonly used in proteomics have been adapted from the DNA-microarray world. Studies comparing normalization methods with proteomics data sets using some variability measures exist. However, a more thorough comparison looking at the quantitative and qualitative differences of the performance of the different normalization methods and at their ability in preserving the true differential expression signal of proteins, is lacking. In this thesis, several popular and widely used normalization methods (the Linear regression normalization, Local regression normalization, Variance stabilizing normalization, Quantile-normalization, Median central tendency normalization and also variants of some of the forementioned methods), representing different strategies in normalization are being compared and evaluated with a benchmark spike-in proteomics data set. The normalization methods are evaluated in several ways. The performance of the normalization methods is evaluated qualitatively and quantitatively on a global scale and in pairwise comparisons of sample groups. In addition, it is investigated, whether performing the normalization globally on the whole data or pairwise for the comparison pairs examined, affects the performance of the normalization method in normalizing the data and preserving the true differential expression signal. In this thesis, both major and minor differences in the performance of the different normalization methods were found. Also, the way in which the normalization was performed (global normalization of the whole data or pairwise normalization of the comparison pair) affected the performance of some of the methods in pairwise comparisons. Differences among variants of the same methods were also observed.

Analysis of outliers in electricity spot prices with exampleof New England and New Zealand markets

Electricity spot prices have always been a demanding data set for time series analysis, mostly because of the non-storability of electricity. This feature, making electric power unlike the other commodities, causes outstanding price spikes. Moreover, the last several years in financial world seem to show that ’spiky’ behaviour of time series is no longer an exception, but rather a regular phenomenon. The purpose of this paper is to seek patterns and relations within electricity price outliers and verify how they affect the overall statistics of the data. For the study techniques like classical Box-Jenkins approach, series DFT smoothing and GARCH models are used. The results obtained for two geographically different price series show that patterns in outliers’ occurrence are not straightforward. Additionally, there seems to be no rule that would predict the appearance of a spike from volatility, while the reverse effect is quite prominent. It is concluded that spikes cannot be predicted based only on the price series; probably some geographical and meteorological variables need to be included in modeling.

Mössbauer spectra of iron oxides in the bulk and nanocrystalline states

Magnetic nanoparticles are very important in modern industry. These particles are used in many different spheres of life. Nanoparticles have unusual physical and chemical properties connected both with quantum dimensional effects and with the increased role of the surface atoms. Most clearly the difference between the properties of bulk materials and nanoparticles can be seen in the magnetic properties of these materials. The most typical magnetic properties of nanomaterials are superparamagnetism with the size of the cluster from 1 to 10 nm; single-domain magnetic state of nanoclusters and nanostructures up to 20 nm; magnetization processes connected with magnetic cluster ordering and with its forms and sizes; quantum magnetic tunneling effects when magnetization changes by jumps and giant magnetoresistance effects. For research of the magnetic properties of iron-containing nanostructures, it is convenient to apply Mӧssbauer spectroscopy. In this work a number of nano-sized samples of iron oxides were examined by Mössbauer spectroscopy. The Mössbauer spectra of nanoparticles with various sizes were obtained. Mössbauer spectra of iron oxide nanoparticles were compared with the spectra of bulk samples. It was shown how the spectra of iron oxide nanoparticles change depending on the particle sizes.

Growth, magnetic and transport properties of InSb and II-IV-As2 semiconductors doped with manganese

This thesis is devoted to growth and investigations of Mn-doped InSb and II-IV-As2 semiconductors, including Cd1-xZnxGeAs2:Mn, ZnSiAs2:Mn bulk crystals, ZnSiAs2:Mn/Si heterostructures. Bulk crystals were grown by direct melting of starting components followed by fast cooling. Mn-doped ZnSiAs2/Si heterostructures were grown by vacuum-thermal deposition of ZnAs2 and Mn layers on Si substrates followed by annealing. The compositional and structural properties of samples were investigated by different methods. The samples consist of micro- and nano- sizes clusters of an additional ferromagnetic Mn-X phases (X = Sb or As). Influence of magnetic precipitations on magnetic and electrical properties of the investigated materials was examined. With relatively high Mn concentration the main contribution to magnetization of samples is by MnSb or MnAs clusters. These clusters are responsible for high temperature behavior of magnetization and relatively high Curie temperature: up to 350 K for Mn-doped II-IV-As2 and about 600 K for InMnSb. The low-field magnetic properties of Mn-doped II-IV-As2 semiconductors and ZnSiAs2:Mn/Si heterostructures are connected to the nanosize MnAs particles. Also influence of nanosized MnSb clusters on low-field magnetic properties of InMnSb have been observed. The contribution of paramagnetic phase to magnetization rises at low temperatures or in samples with low Mn concentration. Source of this contribution is not only isolated Mn ions, but also small complexes, mainly dimmers and trimmers formed by Mn ions, substituting cation positions in crystal lattice. Resistivity, magnetoresistance and Hall resistivity properties in bulk Mn-doped II-IV-As2 and InSb crystals was analyzed. The interaction between delocalized holes and 3d shells of the Mn ions together with giant Zeeman splitting near the cluster interface are respond for negative magnetoresistance. Additionally to high temperature critical pointthe low-temperature ferromagnetic transition was observed Anomalous Hall effect was observed in Mn doped samples and analyzed for InMnSb. It was found that MnX clusters influence significantly on magnetic scattering of carriers.