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.

Microsurgical Aneurysm Model in Rats and Mice: Development of Endovascular Treatment and Optimization of Magnetic Resonance Imaging

The rupture of a cerebral artery aneurysm causes a devastating subarachnoid hemorrhage (SAH), with a mortality of almost 50% during the first month. Each year, 8-11/100 000 people suffer from aneurysmal SAH in Western countries, but the number is twice as high in Finland and Japan. The disease is most common among those of working age, the mean age at rupture being 50-55 years. Unruptured cerebral aneurysms are found in 2-6% of the population, but knowledge about the true risk of rupture is limited. The vast majority of aneurysms should be considered rupture-prone, and treatment for these patients is warranted. Both unruptured and ruptured aneurysms can be treated by either microsurgical clipping or endovascular embolization. In a standard microsurgical procedure, the neck of the aneurysm is closed by a metal clip, sealing off the aneurysm from the circulation. Endovascular embolization is performed by packing the aneurysm from the inside of the vessel lumen with detachable platinum coils. Coiling is associated with slightly lower morbidity and mortality than microsurgery, but the long-term results of microsurgically treated aneurysms are better. Endovascular treatment methods are constantly being developed further in order to achieve better long-term results. New coils and novel embolic agents need to be tested in a variety of animal models before they can be used in humans. In this study, we developed an experimental rat aneurysm model and showed its suitability for testing endovascular devices. We optimized noninvasive MRI sequences at 4.7 Tesla for follow-up of coiled experimental aneurysms and for volumetric measurement of aneurysm neck remnants. We used this model to compare platinum coils with polyglycolic-polylactic acid (PGLA) -coated coils, and showed the benefits of the latter in this model. The experimental aneurysm model and the imaging methods also gave insight into the mechanisms involved in aneurysm formation, and the model can be used in the development of novel imaging techniques. This model is affordable, easily reproducible, reliable, and suitable for MRI follow-up. It is also suitable for endovascular treatment, and it evades spontaneous occlusion.

Simulating and Forecasting the Demand for New Consumer Durables

A diffusion/replacement model for new consumer durables designed to be used as a long-term forecasting tool is developed. The model simulates new demand as well as replacement demand over time. The model is called DEMSIM and is built upon a counteractive adoption model specifying the basic forces affecting the adoption behaviour of individual consumers. These forces are the promoting forces and the resisting forces. The promoting forces are further divided into internal and external influences. These influences are operationalized within a multi-segmental diffusion model generating the adoption behaviour of the consumers in each segment as an expected value. This diffusion model is combined with a replacement model built upon the same segmental structure as the diffusion model. This model generates, in turn, the expected replacement behaviour in each segment. To be able to use DEMSIM as a forecasting tool in early stages of a diffusion process estimates of the model parameters are needed as soon as possible after product launch. However, traditional statistical techniques are not very helpful in estimating such parameters in early stages of a diffusion process. To enable early parameter calibration an optimization algorithm is developed by which the main parameters of the diffusion model can be estimated on the basis of very few sales observations. The optimization is carried out in iterative simulation runs. Empirical validations using the optimization algorithm reveal that the diffusion model performs well in early long-term sales forecasts, especially as it comes to the timing of future sales peaks.

Optimization of sample preparation for next-generation sequencing with Illumina Genome Analyzer II

The growing interest for sequencing with higher throughput in the last decade has led to the development of new sequencing applications. This thesis concentrates on optimizing DNA library preparation for Illumina Genome Analyzer II sequencer. The library preparation steps that were optimized include fragmentation, PCR purification and quantification. DNA fragmentation was performed with focused sonication in different concentrations and durations. Two column based PCR purification method, gel matrix method and magnetic bead based method were compared. Quantitative PCR and gel electrophoresis in a chip were compared for DNA quantification. The magnetic bead purification was found to be the most efficient and flexible purification method. The fragmentation protocol was changed to produce longer fragments to be compatible with longer sequencing reads. Quantitative PCR correlates better with the cluster number and should thus be considered to be the default quantification method for sequencing. As a result of this study more data have been acquired from sequencing with lower costs and troubleshooting has become easier as qualification steps have been added to the protocol. New sequencing instruments and applications will create a demand for further optimizations in future.

Methods and applications for improving parameter prediction models for stand structures in Finland

This thesis report attempts to improve the models for predicting forest stand structure for practical use, e.g. forest management planning (FMP) purposes in Finland. Comparisons were made between Weibull and Johnson s SB distribution and alternative regression estimation methods. Data used for preliminary studies was local but the final models were based on representative data. Models were validated mainly in terms of bias and RMSE in the main stand characteristics (e.g. volume) using independent data. The bivariate SBB distribution model was used to mimic realistic variations in tree dimensions by including within-diameter-class height variation. Using the traditional method, diameter distribution with the expected height resulted in reduced height variation, whereas the alternative bivariate method utilized the error-term of the height model. The lack of models for FMP was covered to some extent by the models for peatland and juvenile stands. The validation of these models showed that the more sophisticated regression estimation methods provided slightly improved accuracy. A flexible prediction and application for stand structure consisted of seemingly unrelated regression models for eight stand characteristics, the parameters of three optional distributions and Näslund s height curve. The cross-model covariance structure was used for linear prediction application, in which the expected values of the models were calibrated with the known stand characteristics. This provided a framework to validate the optional distributions and the optional set of stand characteristics. Height distribution is recommended for the earliest state of stands because of its continuous feature. From the mean height of about 4 m, Weibull dbh-frequency distribution is recommended in young stands if the input variables consist of arithmetic stand characteristics. In advanced stands, basal area-dbh distribution models are recommended. Näslund s height curve proved useful. Some efficient transformations of stand characteristics are introduced, e.g. the shape index, which combined the basal area, the stem number and the median diameter. Shape index enabled SB model for peatland stands to detect large variation in stand densities. This model also demonstrated reasonable behaviour for stands in mineral soils.