Leptogenesis as the origin of matter-antimatter asymmetry in extra dimensional and supersymmetric models

The Standard Model of particle physics is currently the best description of fundamental particles and their interactions. All particles save the Higgs boson have been observed in particle accelerator experiments over the years. Despite the predictive power the Standard Model there are many phenomena that the scenario does not predict or explain. Among the most prominent dilemmas is matter-antimatter asymmetry, and much effort has been made in formulating scenarios that accurately predict the correct amount of matter-antimatter asymmetry in the universe. One of the most appealing explanations is baryogenesis via leptogenesis which not only serves as a mechanism of producing excess matter over antimatter but can also explain why neutrinos have very small non-zero masses. Interesting leptogenesis scenarios arise when other possible candidates of theories beyond the Standard Model are brought into the picture. In this thesis, we have studied leptogenesis in an extra dimensional framework and in a modified version of supersymmetric Standard Model. The first chapters of this thesis introduce the standard cosmological model, observations made on the photon to baryon ratio and necessary preconditions for successful baryogenesis. Baryogenesis via leptogenesis is then introduced and its connection to neutrino physics is illuminated. The final chapters concentrate on extra dimensional theories and supersymmetric models and their ability to accommodate leptogenesis. There, the results of our research are also presented.

Context-specific independence in graphical models

The theme of this thesis is context-speci c independence in graphical models. Considering a system of stochastic variables it is often the case that the variables are dependent of each other. This can, for instance, be seen by measuring the covariance between a pair of variables. Using graphical models, it is possible to visualize the dependence structure found in a set of stochastic variables. Using ordinary graphical models, such as Markov networks, Bayesian networks, and Gaussian graphical models, the type of dependencies that can be modeled is limited to marginal and conditional (in)dependencies. The models introduced in this thesis enable the graphical representation of context-speci c independencies, i.e. conditional independencies that hold only in a subset of the outcome space of the conditioning variables. In the articles included in this thesis, we introduce several types of graphical models that can represent context-speci c independencies. Models for both discrete variables and continuous variables are considered. A wide range of properties are examined for the introduced models, including identi ability, robustness, scoring, and optimization. In one article, a predictive classi er which utilizes context-speci c independence models is introduced. This classi er clearly demonstrates the potential bene ts of the introduced models. The purpose of the material included in the thesis prior to the articles is to provide the basic theory needed to understand the articles.

Expectations of elderly people on the nurse-client relationship and interaction in home care : "When I see the nurse's smiling face it feels likes sunshine on a rainy and gloomy day"

The purpose of the thesis was to explore expectations of elderly people on the nurse-client relationship and interaction in home care. The aim is to improve the quality of care to better meet the needs of the clients. A qualitative approach was adopted. Semi-structured theme interviews were used for data collection. The interviews were conducted during spring 2006. Six elderly clients of a private home care company in Southern Finland acted as informants. Content analysis was used as the method of data analysis. The findings suggest that clients expect nurses to provide professional care with loving-kindness. Trust and mutual, active interaction were expected from the nurse-client relationship. Clients considered it important that the nurse recognizes each client's individual needs. The nurse was expected to perform duties efficiently, but in a calm and unrushed manner. A mechanic performance of tasks was considered negative. Humanity was viewed as a crucial element in the nurse-client relationship. Clients expressed their need to be seen as human beings. Seeing beyond the illness was considered important. A smiling nurse was described to be able to alleviate pain and anxiety. Clients hoped to have a close relationship with the nurse. The development of a close relationship was considered to be more likely if the nurse is familiar and genuine. Clients wish the nurses to have a more attending presence. Clients suggested that the work areas of the nurses could be limited so that they would have more time to transfer from one place to another. Clients felt that they would benefit from this as well. The nurses were expected to be more considerate. Clients wished for more information regarding changes that affect their care. They wished to be informed about changes in schedules and plans. Clients hoped for continuity from the nurse-client relationship. Considering the expectations of clients promotes client satisfaction. Home care providers have an opportunity to reflect their own care behaviour on the findings. To better meet the needs of the clients, nurses could apply the concept of loving-kindness in their work, and strive for a more attending presence.

Generalizing sample tree information with semiparametric and parametric models

Summary

The application of agricultural land rating and crop models to CO2 and climate change issues in Northern regions : the Mackenzie Basin case study

Selostus: Viljelyvyöhykkeiden ja kasvumallien soveltaminen ilmastonmuutoksen tutkimisessa: Mackenzien jokialue, Kanada

Equilibrium curves and growth models to deal with forests in transition to uneven-aged structure : application in two sample stands

Abstract

Washing and Dewatering of Different Starches in Pressure Filters

The main objective of this thesis was togenerate better filtration technologies for effective production of pure starchproducts, and thereby the optimisation of filtration sequences using created models, as well as the synthesis of the theories of different filtration stages, which were suitable for starches. At first, the structure and the characteristics of the different starch grades are introduced and each starch grade is shown to have special characteristics. These are taken as the basis of the understanding of the differences in the behaviour of the different native starch grades and their modifications in pressure filtration. Next, the pressure filtration process is divided into stages, which are filtration, cake washing, compression dewatering and displacement dewatering. Each stage is considered individually in their own chapters. The order of the different suitable combinations of the process stages are studied, as well as the proper durations and pressures of the stages. The principles of the theory of each stageare reviewed, the methods for monitoring the progress of each stage are presented, and finally, the modelling of them is introduced. The experimental results obtained from the different stages of starch filtration tests are given and the suitability of the theories and models to the starch filtration are shown. Finally, the theories and the models are gathered together and shown, that the analysis of the whole starch pressure filtration process can be performed with the software developed.

Using time and stochastic models for software reliability forecasting and analysis

Tämä työ luo katsauksen ajallisiin ja stokastisiin ohjelmien luotettavuus malleihin sekä tutkii muutamia malleja käytännössä. Työn teoriaosuus sisältää ohjelmien luotettavuuden kuvauksessa ja arvioinnissa käytetyt keskeiset määritelmät ja metriikan sekä varsinaiset mallien kuvaukset. Työssä esitellään kaksi ohjelmien luotettavuusryhmää. Ensimmäinen ryhmä ovat riskiin perustuvat mallit. Toinen ryhmä käsittää virheiden ”kylvöön” ja merkitsevyyteen perustuvat mallit. Työn empiirinen osa sisältää kokeiden kuvaukset ja tulokset. Kokeet suoritettiin käyttämällä kolmea ensimmäiseen ryhmään kuuluvaa mallia: Jelinski-Moranda mallia, ensimmäistä geometrista mallia sekä yksinkertaista eksponenttimallia. Kokeiden tarkoituksena oli tutkia, kuinka syötetyn datan distribuutio vaikuttaa mallien toimivuuteen sekä kuinka herkkiä mallit ovat syötetyn datan määrän muutoksille. Jelinski-Moranda malli osoittautui herkimmäksi distribuutiolle konvergaatio-ongelmien vuoksi, ensimmäinen geometrinen malli herkimmäksi datan määrän muutoksille.

Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.