Trust, participation and health among ageing people : A cross-sectional cohort study of men and women born in 1926-30, 1936-40, and 1946-50 in the Lahti region, southern Finland

The purpose of the present study was to explore the associations between good self-rated health and economic and social factors in different regions among ageing people in the Päijät-Häme region in southern Finland. The data of this study were collected in 2002 as part of the research and development project Ikihyvä 2002 2012 (Good Ageing in Lahti region GOAL project). The baseline data set consisted of 2,815 participants born in 1926 30, 1936 40, and 1946 50. The response rate was 66 %. According to the previous studies, trust in other people and social participation as the main aspects of social capital are associated with self-rated health. In addition, socioeconomic position (SEP) and self-rated health are associated, but all SEP indicators do not have identical associations with health. However, there is a lack of knowledge of the health associations and regional differences with these factors, especially among ageing people. Regarding these questions, the present study gives new information. According to the results of this study, self-perceived adequacy of income was significantly associated with good self-rated health, especially in the urban areas. Similar associations were found in the rural areas, though education was also considered an important factor. Adequacy of income was an even stronger predictor of good health than the actual income. Women had better self-rated health than men only in the urban areas. The youngest respondents had quite equally better self-rated health than the others. Social participation and access to help when needed were associated with good self-rated health, especially in the urban area and the sparsely populated rural areas. The result was comparable in the rural population centres. The correlation of trust with self-rated health was significant in the urban area. High social capital was associated with good self-rated health in the urban area. The association was quite similar in the other areas, though it was statistically insignificant. High social capital consisted of co-existent high social participation and high trust. The association of traditionalism (low participation and high trust) with self-rated health was also substantial in the urban area. The associations of self-rated health with low social capital (low participation and low trust) and the miniaturisation of community (high participation and low trust) were less significant. From the forms of single participation, going to art exhibitions, theatre, movies, and concerts among women, and studying and self-development among men were positively related to self-rated health. Unexpectedly, among women, active participation in religious events and voluntary work was negatively associated with self-rated health. This may indicate a coping method with ill-health. As a whole, only minor variations in self-rated health were found between the areas. However, the significance of the factors associated with self-rated health varied according to the areas. Economic factors, especially self-perceived adequacy of income was strongly associated with good self-rated health. Also when adjusting for economic and several other background factors social factors (particularly high social capital, social participation, and access to help when needed) were associated with self-rated health. Thus, economic and social factors have a significant relation with the health of the ageing, and improving these factors may have favourable effects on health among ageing people.

Silvicultural decisions based on simulation-optimization systems

Forest management is facing new challenges under climate change. By adjusting thinning regimes, conventional forest management can be adapted to various objectives of utilization of forest resources, such as wood quality, forest bioenergy, and carbon sequestration. This thesis aims to develop and apply a simulation-optimization system as a tool for an interdisciplinary understanding of the interactions between wood science, forest ecology, and forest economics. In this thesis, the OptiFor software was developed for forest resources management. The OptiFor simulation-optimization system integrated the process-based growth model PipeQual, wood quality models, biomass production and carbon emission models, as well as energy wood and commercial logging models into a single optimization model. Osyczka s direct and random search algorithm was employed to identify optimal values for a set of decision variables. The numerical studies in this thesis broadened our current knowledge and understanding of the relationships between wood science, forest ecology, and forest economics. The results for timber production show that optimal thinning regimes depend on site quality and initial stand characteristics. Taking wood properties into account, our results show that increasing the intensity of thinning resulted in lower wood density and shorter fibers. The addition of nutrients accelerated volume growth, but lowered wood quality for Norway spruce. Integrating energy wood harvesting into conventional forest management showed that conventional forest management without energy wood harvesting was still superior in sparse stands of Scots pine. Energy wood from pre-commercial thinning turned out to be optimal for dense stands. When carbon balance is taken into account, our results show that changing carbon assessment methods leads to very different optimal thinning regimes and average carbon stocks. Raising the carbon price resulted in longer rotations and a higher mean annual increment, as well as a significantly higher average carbon stock over the rotation.

Computer simulation of H and He effects in fusion reactor materials

Fusion power is an appealing source of clean and abundant energy. The radiation resistance of reactor materials is one of the greatest obstacles on the path towards commercial fusion power. These materials are subject to a harsh radiation environment, and cannot fail mechanically or contaminate the fusion plasma. Moreover, for a power plant to be economically viable, the reactor materials must withstand long operation times, with little maintenance. The fusion reactor materials will contain hydrogen and helium, due to deposition from the plasma and nuclear reactions because of energetic neutron irradiation. The first wall divertor materials, carbon and tungsten in existing and planned test reactors, will be subject to intense bombardment of low energy deuterium and helium, which erodes and modifies the surface. All reactor materials, including the structural steel, will suffer irradiation of high energy neutrons, causing displacement cascade damage. Molecular dynamics simulation is a valuable tool for studying irradiation phenomena, such as surface bombardment and the onset of primary damage due to displacement cascades. The governing mechanisms are on the atomic level, and hence not easily studied experimentally. In order to model materials, interatomic potentials are needed to describe the interaction between the atoms. In this thesis, new interatomic potentials were developed for the tungsten-carbon-hydrogen system and for iron-helium and chromium-helium. Thus, the study of previously inaccessible systems was made possible, in particular the effect of H and He on radiation damage. The potentials were based on experimental and ab initio data from the literature, as well as density-functional theory calculations performed in this work. As a model for ferritic steel, iron-chromium with 10% Cr was studied. The difference between Fe and FeCr was shown to be negligible for threshold displacement energies. The properties of small He and He-vacancy clusters in Fe and FeCr were also investigated. The clusters were found to be more mobile and dissociate more rapidly than previously assumed, and the effect of Cr was small. The primary damage formed by displacement cascades was found to be heavily influenced by the presence of He, both in FeCr and W. Many important issues with fusion reactor materials remain poorly understood, and will require a huge effort by the international community. The development of potential models for new materials and the simulations performed in this thesis reveal many interesting features, but also serve as a platform for further studies.

The watershed simulation and forecasting system in the National Board of and the Environment

Yhteenveto: Vesistömalleihin perustuva vesistöjen seuranta- ja ennustejärjestelmä vesi- ja ympäristöhallinnossa

Simulation and graph mining tools for improving gene mapping efficiency

Gene mapping is a systematic search for genes that affect observable characteristics of an organism. In this thesis we offer computational tools to improve the efficiency of (disease) gene-mapping efforts. In the first part of the thesis we propose an efficient simulation procedure for generating realistic genetical data from isolated populations. Simulated data is useful for evaluating hypothesised gene-mapping study designs and computational analysis tools. As an example of such evaluation, we demonstrate how a population-based study design can be a powerful alternative to traditional family-based designs in association-based gene-mapping projects. In the second part of the thesis we consider a prioritisation of a (typically large) set of putative disease-associated genes acquired from an initial gene-mapping analysis. Prioritisation is necessary to be able to focus on the most promising candidates. We show how to harness the current biomedical knowledge for the prioritisation task by integrating various publicly available biological databases into a weighted biological graph. We then demonstrate how to find and evaluate connections between entities, such as genes and diseases, from this unified schema by graph mining techniques. Finally, in the last part of the thesis, we define the concept of reliable subgraph and the corresponding subgraph extraction problem. Reliable subgraphs concisely describe strong and independent connections between two given vertices in a random graph, and hence they are especially useful for visualising such connections. We propose novel algorithms for extracting reliable subgraphs from large random graphs. The efficiency and scalability of the proposed graph mining methods are backed by extensive experiments on real data. While our application focus is in genetics, the concepts and algorithms can be applied to other domains as well. We demonstrate this generality by considering coauthor graphs in addition to biological graphs in the experiments.