Multiplication of hybrid aspen (Populus tremula L. x P. tremuloides Michx.) from cuttings

The primary aim of the present study was to find an efficient and simple method of vegetative propagation for producing large numbers of hybrid aspen (Populus tremuloides L. x P. tremula Michx.) plants for forest plantations. The key objectives were to investigate the main physiological factors that affect the ability of cuttings to regenerate and to determine whether these factors could be manipulated by different growth conditions. In addition, clonal variation in traits related to propagation success was examined. According to our results, with the stem cutting method, depending on the clone, it is possible to obtain only 1−8 plants from one stock plant per year. With the root cutting method the corresponding values for two-year-old stock plants are 81−207 plants. The difference in number of cuttings between one- and two-year-old stock plants is so pronounced that it is economically feasible to grow stock plants for two years. There is no reason to use much older stock plants as a source of cuttings, as it has been observed that rooting ability diminishes as root diameter increases. Clonal variation is the most important individual factor in propagation of hybrid aspen. The fact that the efficiently sprouted clones also rooted best facilitates the selection of clones for large-scale propagation. In practice, root cuttings taken from all parts of the root system of hybrid aspen were capable of producing new shoots and roots. However, for efficient rooting it is important to use roots smaller than one centimeter in diameter. Both rooting and sprouting, as well as sprouting rate, were increased by high soil temperature; in our studies the highest temperature tested (30ºC) was the best. Light accelerated the sprouting of root cuttings, but they rooted best in dark conditions. Rooting is essential because without roots the sprouted cutting cannot survive long. For aspen the criteria for clone selection are primarily fiber qualities and growth rate, but ability to regenerate efficiently is also essential. For large-scale propagation it is very important to find clones from which many cuttings per stock plant can be obtained. In light of production costs, however, it is even more important that the regeneration ability of the produced cuttings be high.

Modeling Asymmetries in Financial Data with Multiplicative Error Models

This thesis addresses modeling of financial time series, especially stock market returns and daily price ranges. Modeling data of this kind can be approached with so-called multiplicative error models (MEM). These models nest several well known time series models such as GARCH, ACD and CARR models. They are able to capture many well established features of financial time series including volatility clustering and leptokurtosis. In contrast to these phenomena, different kinds of asymmetries have received relatively little attention in the existing literature. In this thesis asymmetries arise from various sources. They are observed in both conditional and unconditional distributions, for variables with non-negative values and for variables that have values on the real line. In the multivariate context asymmetries can be observed in the marginal distributions as well as in the relationships of the variables modeled. New methods for all these cases are proposed. Chapter 2 considers GARCH models and modeling of returns of two stock market indices. The chapter introduces the so-called generalized hyperbolic (GH) GARCH model to account for asymmetries in both conditional and unconditional distribution. In particular, two special cases of the GARCH-GH model which describe the data most accurately are proposed. They are found to improve the fit of the model when compared to symmetric GARCH models. The advantages of accounting for asymmetries are also observed through Value-at-Risk applications. Both theoretical and empirical contributions are provided in Chapter 3 of the thesis. In this chapter the so-called mixture conditional autoregressive range (MCARR) model is introduced, examined and applied to daily price ranges of the Hang Seng Index. The conditions for the strict and weak stationarity of the model as well as an expression for the autocorrelation function are obtained by writing the MCARR model as a first order autoregressive process with random coefficients. The chapter also introduces inverse gamma (IG) distribution to CARR models. The advantages of CARR-IG and MCARR-IG specifications over conventional CARR models are found in the empirical application both in- and out-of-sample. Chapter 4 discusses the simultaneous modeling of absolute returns and daily price ranges. In this part of the thesis a vector multiplicative error model (VMEM) with asymmetric Gumbel copula is found to provide substantial benefits over the existing VMEM models based on elliptical copulas. The proposed specification is able to capture the highly asymmetric dependence of the modeled variables thereby improving the performance of the model considerably. The economic significance of the results obtained is established when the information content of the volatility forecasts derived is examined.

Modeling Efficient Classification as a Process of Confidence Assessment and Delegation

In visual object detection and recognition, classifiers have two interesting characteristics: accuracy and speed. Accuracy depends on the complexity of the image features and classifier decision surfaces. Speed depends on the hardware and the computational effort required to use the features and decision surfaces. When attempts to increase accuracy lead to increases in complexity and effort, it is necessary to ask how much are we willing to pay for increased accuracy. For example, if increased computational effort implies quickly diminishing returns in accuracy, then those designing inexpensive surveillance applications cannot aim for maximum accuracy at any cost. It becomes necessary to find trade-offs between accuracy and effort. We study efficient classification of images depicting real-world objects and scenes. Classification is efficient when a classifier can be controlled so that the desired trade-off between accuracy and effort (speed) is achieved and unnecessary computations are avoided on a per input basis. A framework is proposed for understanding and modeling efficient classification of images. Classification is modeled as a tree-like process. In designing the framework, it is important to recognize what is essential and to avoid structures that are narrow in applicability. Earlier frameworks are lacking in this regard. The overall contribution is two-fold. First, the framework is presented, subjected to experiments, and shown to be satisfactory. Second, certain unconventional approaches are experimented with. This allows the separation of the essential from the conventional. To determine if the framework is satisfactory, three categories of questions are identified: trade-off optimization, classifier tree organization, and rules for delegation and confidence modeling. Questions and problems related to each category are addressed and empirical results are presented. For example, related to trade-off optimization, we address the problem of computational bottlenecks that limit the range of trade-offs. We also ask if accuracy versus effort trade-offs can be controlled after training. For another example, regarding classifier tree organization, we first consider the task of organizing a tree in a problem-specific manner. We then ask if problem-specific organization is necessary.

Modeling ecological and evolutionary processes in spatially structured populations

Many species inhabit fragmented landscapes, resulting either from anthropogenic or from natural processes. The ecological and evolutionary dynamics of spatially structured populations are affected by a complex interplay between endogenous and exogenous factors. The metapopulation approach, simplifying the landscape to a discrete set of patches of breeding habitat surrounded by unsuitable matrix, has become a widely applied paradigm for the study of species inhabiting highly fragmented landscapes. In this thesis, I focus on the construction of biologically realistic models and their parameterization with empirical data, with the general objective of understanding how the interactions between individuals and their spatially structured environment affect ecological and evolutionary processes in fragmented landscapes. I study two hierarchically structured model systems, which are the Glanville fritillary butterfly in the Åland Islands, and a system of two interacting aphid species in the Tvärminne archipelago, both being located in South-Western Finland. The interesting and challenging feature of both study systems is that the population dynamics occur over multiple spatial scales that are linked by various processes. My main emphasis is in the development of mathematical and statistical methodologies. For the Glanville fritillary case study, I first build a Bayesian framework for the estimation of death rates and capture probabilities from mark-recapture data, with the novelty of accounting for variation among individuals in capture probabilities and survival. I then characterize the dispersal phase of the butterflies by deriving a mathematical approximation of a diffusion-based movement model applied to a network of patches. I use the movement model as a building block to construct an individual-based evolutionary model for the Glanville fritillary butterfly metapopulation. I parameterize the evolutionary model using a pattern-oriented approach, and use it to study how the landscape structure affects the evolution of dispersal. For the aphid case study, I develop a Bayesian model of hierarchical multi-scale metapopulation dynamics, where the observed extinction and colonization rates are decomposed into intrinsic rates operating specifically at each spatial scale. In summary, I show how analytical approaches, hierarchical Bayesian methods and individual-based simulations can be used individually or in combination to tackle complex problems from many different viewpoints. In particular, hierarchical Bayesian methods provide a useful tool for decomposing ecological complexity into more tractable components.

Phylogeography and hybrid swarms: history of brackish water bivalve diversity in North European marginal seas

This study addressed the large-scale molecular zoogeography in two brackish water bivalve molluscs, Macoma balthica and Cerastoderma glaucum, and genetic signatures of the postglacial colonization of Northern Europe by them. The traditional view poses that M. balthica in the Baltic, White and Barents seas (i.e. marginal seas) represent direct postglacial descendants of the adjacent Northeast Atlantic populations, but this has recently been challenged by observations of close genetic affinities between these marginal populations and those of the Northeast Pacific. The primary aim of the thesis was to verify, quantify and characterize the Pacific genetic contribution across North European populations of M. balthica and to resolve the phylogeographic histories of the two bivalve taxa in range-wide studies using information from mitochondrial DNA (mtDNA) and nuclear allozyme polymorphisms. The presence of recent Pacific genetic influence in M. balthica of the Baltic, White and Barents seas, along with an Atlantic element, was confirmed by mtDNA sequence data. On a broader temporal and geographical scale, altogether four independent trans-Arctic invasions of Macoma from the Pacific since the Miocene seem to have been involved in generating the current North Atlantic lineage diversity. The latest trans-Arctic invasion that affected the current Baltic, White and Barents Sea populations probably took place in the early post-glacial. The nuclear genetic compositions of these marginal sea populations are intermediate between those of pure Pacific and Atlantic subspecies. In the marginal sea populations of mixed ancestry (Barents, White and Northern Baltic seas), the Pacific and Atlantic components are now randomly associated in the genomes of individual clams, which indicates both pervasive historical interbreeding between the previously long-isolated lineages (subspecies), and current isolation of these populations from the adjacent pure Atlantic populations. These mixed populations can be characterized as self-supporting hybrid swarms, and they arguably represent the most extensive marine animal hybrid swarms so far documented. Each of the three swarms still has a distinct genetic composition, and the relative Pacific contributions vary from 30 to 90 % in local populations. This diversity highlights the potential of introgressive hybridization to rapidly give rise to new evolutionarily and ecologically significant units in the marine realm. In the south of the Danish straits and in the Southern Baltic Sea, a broad genetic transition zone links the pure North Sea subspecies M. balthica rubra to the inner Baltic hybrid swarm, which has about 60 % of Pacific contribution in its genome. This transition zone has no regular smooth clinal structure, but its populations show strong genotypic disequilibria typical of a hybrid zone maintained by the interplay of selection and gene flow by dispersing pelagic larvae. The structure of the genetic transition is partly in line with features of Baltic water circulation and salinity stratification, with greater penetration of Atlantic genes on the Baltic south coast and in deeper water populations. In all, the scenarios of historical isolation and secondary contact that arise from the phylogeographic studies of both Macoma and Cerastoderma shed light to the more general but enigmatic patterns seen in marine phylogeography, where deep genetic breaks are often seen in species with high dispersal potential.

Studies on diamond-like carbon and novel diamond-like carbon polymer hybrid coatings deposited with filtered pulsed arc discharge method

The main obstacle for the application of high quality diamond-like carbon (DLC) coatings has been the lack of adhesion to the substrate as the coating thickness is increased. The aim of this study was to improve the filtered pulsed arc discharge (FPAD) method. With this method it is possible to achieve high DLC coating thicknesses necessary for practical applications. The energy of the carbon ions was measured with an optoelectronic time-of-flight method. An in situ cathode polishing system used for stabilizing the process yield and the carbon ion energies is presented. Simultaneously the quality of the coatings can be controlled. To optimise the quality of the deposition process a simple, fast and inexpensive method using silicon wafers as test substrates was developed. This method was used for evaluating the suitability of a simplified arc-discharge set-up for the deposition of the adhesion layer of DLC coatings. A whole new group of materials discovered by our research group, the diamond-like carbon polymer hybrid (DLC-p-h) coatings, is also presented. The parent polymers used in these novel coatings were polydimethylsiloxane (PDMS) and polytetrafluoroethylene (PTFE). The energy of the plasma ions was found to increase when the anode-cathode distance and the arc voltage were increased. A constant deposition rate for continuous coating runs was obtained with an in situ cathode polishing system. The novel DLC-p-h coatings were found to be water and oil repellent and harder than any polymers. The lowest sliding angle ever measured from a solid surface, 0.15 ± 0.03°, was measured on a DLC-PDMS-h coating. In the FPAD system carbon ions can be accelerated to high energies (≈ 1 keV) necessary for the optimal adhesion (the substrate is broken in the adhesion and quality test) of ultra thick (up to 200 µm) DLC coatings by increasing the anode-cathode distance and using high voltages (up to 4 kV). An excellent adhesion can also be obtained with the simplified arc-discharge device. To maintain high process yield (5µm/h over a surface area of 150 cm2) and to stabilize the carbon ion energies and the high quality (sp3 fraction up to 85%) of the resulting coating, an in situ cathode polishing system must be used. DLC-PDMS-h coating is the superior candidate coating material for anti-soiling applications where also hardness is required.

Non-Gaussian Cosmological Perturbations from Hybrid Inflation and Preheating

This thesis consists of four research papers and an introduction providing some background. The structure in the universe is generally considered to originate from quantum fluctuations in the very early universe. The standard lore of cosmology states that the primordial perturbations are almost scale-invariant, adiabatic, and Gaussian. A snapshot of the structure from the time when the universe became transparent can be seen in the cosmic microwave background (CMB). For a long time mainly the power spectrum of the CMB temperature fluctuations has been used to obtain observational constraints, especially on deviations from scale-invariance and pure adiabacity. Non-Gaussian perturbations provide a novel and very promising way to test theoretical predictions. They probe beyond the power spectrum, or two point correlator, since non-Gaussianity involves higher order statistics. The thesis concentrates on the non-Gaussian perturbations arising in several situations involving two scalar fields, namely, hybrid inflation and various forms of preheating. First we go through some basic concepts -- such as the cosmological inflation, reheating and preheating, and the role of scalar fields during inflation -- which are necessary for the understanding of the research papers. We also review the standard linear cosmological perturbation theory. The second order perturbation theory formalism for two scalar fields is developed. We explain what is meant by non-Gaussian perturbations, and discuss some difficulties in parametrisation and observation. In particular, we concentrate on the nonlinearity parameter. The prospects of observing non-Gaussianity are briefly discussed. We apply the formalism and calculate the evolution of the second order curvature perturbation during hybrid inflation. We estimate the amount of non-Gaussianity in the model and find that there is a possibility for an observational effect. The non-Gaussianity arising in preheating is also studied. We find that the level produced by the simplest model of instant preheating is insignificant, whereas standard preheating with parametric resonance as well as tachyonic preheating are prone to easily saturate and even exceed the observational limits. We also mention other approaches to the study of primordial non-Gaussianities, which differ from the perturbation theory method chosen in the thesis work.