Non-industrial private forest landowners’ choices of timber management strategies.

The factors affecting the non-industrial, private forest landowners' (hereafter referred to using the acronym NIPF) strategic decisions in management planning are studied. A genetic algorithm is used to induce a set of rules predicting potential cut of the landowners' choices of preferred timber management strategies. The rules are based on variables describing the characteristics of the landowners and their forest holdings. The predictive ability of a genetic algorithm is compared to linear regression analysis using identical data sets. The data are cross-validated seven times applying both genetic algorithm and regression analyses in order to examine the data-sensitivity and robustness of the generated models. The optimal rule set derived from genetic algorithm analyses included the following variables: mean initial volume, landowner's positive price expectations for the next eight years, landowner being classified as farmer, and preference for the recreational use of forest property. When tested with previously unseen test data, the optimal rule set resulted in a relative root mean square error of 0.40. In the regression analyses, the optimal regression equation consisted of the following variables: mean initial volume, proportion of forestry income, intention to cut extensively in future, and positive price expectations for the next two years. The R2 of the optimal regression equation was 0.34 and the relative root mean square error obtained from the test data was 0.38. In both models, mean initial volume and positive stumpage price expectations were entered as significant predictors of potential cut of preferred timber management strategy. When tested with the complete data set of 201 observations, both the optimal rule set and the optimal regression model achieved the same level of accuracy.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Efficient search for statistically significant dependency rules in binary data

Analyzing statistical dependencies is a fundamental problem in all empirical science. Dependencies help us understand causes and effects, create new scientific theories, and invent cures to problems. Nowadays, large amounts of data is available, but efficient computational tools for analyzing the data are missing. In this research, we develop efficient algorithms for a commonly occurring search problem - searching for the statistically most significant dependency rules in binary data. We consider dependency rules of the form X->A or X->not A, where X is a set of positive-valued attributes and A is a single attribute. Such rules describe which factors either increase or decrease the probability of the consequent A. A classical example are genetic and environmental factors, which can either cause or prevent a disease. The emphasis in this research is that the discovered dependencies should be genuine - i.e. they should also hold in future data. This is an important distinction from the traditional association rules, which - in spite of their name and a similar appearance to dependency rules - do not necessarily represent statistical dependencies at all or represent only spurious connections, which occur by chance. Therefore, the principal objective is to search for the rules with statistical significance measures. Another important objective is to search for only non-redundant rules, which express the real causes of dependence, without any occasional extra factors. The extra factors do not add any new information on the dependence, but can only blur it and make it less accurate in future data. The problem is computationally very demanding, because the number of all possible rules increases exponentially with the number of attributes. In addition, neither the statistical dependency nor the statistical significance are monotonic properties, which means that the traditional pruning techniques do not work. As a solution, we first derive the mathematical basis for pruning the search space with any well-behaving statistical significance measures. The mathematical theory is complemented by a new algorithmic invention, which enables an efficient search without any heuristic restrictions. The resulting algorithm can be used to search for both positive and negative dependencies with any commonly used statistical measures, like Fisher's exact test, the chi-squared measure, mutual information, and z scores. According to our experiments, the algorithm is well-scalable, especially with Fisher's exact test. It can easily handle even the densest data sets with 10000-20000 attributes. Still, the results are globally optimal, which is a remarkable improvement over the existing solutions. In practice, this means that the user does not have to worry whether the dependencies hold in future data or if the data still contains better, but undiscovered dependencies.

Evaluation of Vestibular Function with Visual Feedback Posturography and Motorized Head Impulse Test.

Objectives: To evaluate the applicability of visual feedback posturography (VFP) for quantification of postural control, and to characterize the horizontal angular vestibulo-ocular reflex (AVOR) by use of a novel motorized head impulse test (MHIT). Methods: In VFP, subjects standing on a platform were instructed to move their center of gravity to symmetrically placed peripheral targets as fast and accurately as possible. The active postural control movements were measured in healthy subjects (n = 23), and in patients with vestibular schwannoma (VS) before surgery (n = 49), one month (n = 17), and three months (n = 36) after surgery. In MHIT we recorded head and eye position during motorized head impulses (mean velocity of 170º/s and acceleration of 1 550º/s²) in healthy subjects (n = 22), in patients with VS before surgery (n = 38) and about four months afterwards (n = 27). The gain, asymmetry and latency in MHIT were calculated. Results: The intraclass correlation coefficient for VFP parameters during repeated tests was significant (r = 0.78-0.96; p < 0.01), although two of four VFP parameters improved slightly during five test sessions in controls. At least one VFP parameter was abnormal pre- and postoperatively in almost half the patients, and these abnormal preoperative VFP results correlated significantly with abnormal postoperative results. The mean accuracy in postural control in patients was reduced pre- and postoperatively. A significant side difference with VFP was evident in 10% of patients. In the MHIT, the normal gain was close to unity, the asymmetry in gain was within 10%, and the latency was a mean ± standard deviation 3.4 ± 6.3 milliseconds. Ipsilateral gain or asymmetry in gain was preoperatively abnormal in 71% of patients, whereas it was abnormal in every patient after surgery. Preoperative gain (mean ± 95% confidence interval) was significantly lowered to 0.83 ± 0.08 on the ipsilateral side compared to 0.98 ± 0.06 on the contralateral side. The ipsilateral postoperative mean gain of 0.53 ± 0.05 was significantly different from preoperative gain. Conclusion: The VFP is a repeatable, quantitative method to assess active postural control within individual subjects. The mean postural control in patients with VS was disturbed before and after surgery, although not severely. Side difference in postural control in the VFP was rare. The horizontal AVOR results in healthy subjects and in patients with VS, measured with MHIT, were in agreement with published data achieved using other techniques with head impulse stimuli. The MHIT is a non-invasive method which allows reliable clinical assessment of the horizontal AVOR.

Geant4 Hadronic Cascade Models and CMS Data Analysis : Computational Challenges in the LHC era

This work belongs to the field of computational high-energy physics (HEP). The key methods used in this thesis work to meet the challenges raised by the Large Hadron Collider (LHC) era experiments are object-orientation with software engineering, Monte Carlo simulation, the computer technology of clusters, and artificial neural networks. The first aspect discussed is the development of hadronic cascade models, used for the accurate simulation of medium-energy hadron-nucleus reactions, up to 10 GeV. These models are typically needed in hadronic calorimeter studies and in the estimation of radiation backgrounds. Various applications outside HEP include the medical field (such as hadron treatment simulations), space science (satellite shielding), and nuclear physics (spallation studies). Validation results are presented for several significant improvements released in Geant4 simulation tool, and the significance of the new models for computing in the Large Hadron Collider era is estimated. In particular, we estimate the ability of the Bertini cascade to simulate Compact Muon Solenoid (CMS) hadron calorimeter HCAL. LHC test beam activity has a tightly coupled cycle of simulation-to-data analysis. Typically, a Geant4 computer experiment is used to understand test beam measurements. Thus an another aspect of this thesis is a description of studies related to developing new CMS H2 test beam data analysis tools and performing data analysis on the basis of CMS Monte Carlo events. These events have been simulated in detail using Geant4 physics models, full CMS detector description, and event reconstruction. Using the ROOT data analysis framework we have developed an offline ANN-based approach to tag b-jets associated with heavy neutral Higgs particles, and we show that this kind of NN methodology can be successfully used to separate the Higgs signal from the background in the CMS experiment.

Residual noise covariance for Planck low-resolution data analysis

Aims: Develop and validate tools to estimate residual noise covariance in Planck frequency maps. Quantify signal error effects and compare different techniques to produce low-resolution maps. Methods: We derive analytical estimates of covariance of the residual noise contained in low-resolution maps produced using a number of map-making approaches. We test these analytical predictions using Monte Carlo simulations and their impact on angular power spectrum estimation. We use simulations to quantify the level of signal errors incurred in different resolution downgrading schemes considered in this work. Results: We find an excellent agreement between the optimal residual noise covariance matrices and Monte Carlo noise maps. For destriping map-makers, the extent of agreement is dictated by the knee frequency of the correlated noise component and the chosen baseline offset length. The significance of signal striping is shown to be insignificant when properly dealt with. In map resolution downgrading, we find that a carefully selected window function is required to reduce aliasing to the sub-percent level at multipoles, ell > 2Nside, where Nside is the HEALPix resolution parameter. We show that sufficient characterization of the residual noise is unavoidable if one is to draw reliable contraints on large scale anisotropy. Conclusions: We have described how to compute the low-resolution maps, with a controlled sky signal level, and a reliable estimate of covariance of the residual noise. We have also presented a method to smooth the residual noise covariance matrices to describe the noise correlations in smoothed, bandwidth limited maps.

Real Estate Investment and Uncertainty: Econometric Modelling using Finnish Data

The purpose of this paper is to test for the effect of uncertainty in a model of real estate investment in Finland during the hihhly cyclical period of 1975 to 1998. We use two alternative measures of uncertainty. The first measure is the volatility of stock market returns and the second measure is the heterogeneity in the answers of the quarterly business survey of the Confederation of Finnish Industry and Employers. The econometric analysis is based on the autoregressive distributed lag (ADL) model and the paper applies a 'general-to-specific' modelling approach. We find that the measure of heterogeneity is significant in the model, but the volatility of stock market returns is not. The empirical results give some evidence of an uncertainty-induced threshold slowing down real estate investment in Finland.

Carbon dioxide and methane exchange between a boreal pristine lake and the atmosphere

Lakes serve as sites for terrestrially fixed carbon to be remineralized and transferred back to the atmosphere. Their role in regional carbon cycling is especially important in the Boreal Zone, where lakes can cover up to 20% of the land area. Boreal lakes are often characterized by the presence of a brown water colour, which implies high levels of dissolved organic carbon from the surrounding terrestrial ecosystem, but the load of inorganic carbon from the catchment is largely unknown. Organic carbon is transformed to methane (CH4) and carbon dioxide (CO2) in biological processes that result in lake water gas concentrations that increase above atmospheric equilibrium, thus making boreal lakes as sources of these important greenhouse gases. However, flux estimates are often based on sporadic sampling and modelling and actual flux measurements are scarce. Thus, the detailed temporal flux dynamics of greenhouse gases are still largely unknown. ----- One aim here was to reveal the natural dynamics of CH4 and CO2 concentrations and fluxes in a small boreal lake. The other aim was to test the applicability of a measuring technique for CO2 flux, i.e. the eddy covariance (EC) technique, and a computational method for estimation of primary production and community respiration, both commonly used in terrestrial research, in this lake. Continuous surface water CO2 concentration measurements, also needed in free-water applications to estimate primary production and community respiration, were used over two open water periods in a study of CO2 concentration dynamics. Traditional methods were also used to measure gas concentration and fluxes. The study lake, Valkea-Kotinen, is a small, humic, headwater lake within an old-growth forest catchment with no local anthropogenic disturbance and thus possible changes in gas dynamics reflect the natural variability in lake ecosystems. CH4 accumulated under the ice and in the hypolimnion during summer stratification. The surface water CH4 concentration was always above atmospheric equilibrium and thus the lake was a continuous source of CH4 to the atmosphere. However, the annual CH4 fluxes were small, i.e. 0.11 mol m-2 yr-1, and the timing of fluxes differed from that of other published estimates. The highest fluxes are usually measured in spring after ice melt but in Lake Valkea-Kotinen CH4 was effectively oxidised in spring and highest effluxes occurred in autumn after summer stratification period. CO2 also accumulated under the ice and the hypolimnetic CO2 concentration increased steadily during stratification period. The surface water CO2 concentration was highest in spring and in autumn, whereas during the stable stratification it was sometimes under atmospheric equilibrium. It showed diel, daily and seasonal variation; the diel cycle was clearly driven by light and thus reflected the metabolism of the lacustrine ecosystem. However, the diel cycle was sometimes blurred by injection of hypolimnetic water rich in CO2 and the surface water CO2 concentration was thus controlled by stratification dynamics. The highest CO2 fluxes were measured in spring, autumn and during those hypolimnetic injections causing bursts of CO2 comparable with the spring and autumn fluxes. The annual fluxes averaged 77 (±11 SD) g C m-2 yr-1. In estimating the importance of the lake in recycling terrestrial carbon, the flux was normalized to the catchment area and this normalized flux was compared with net ecosystem production estimates of -50 to 200 g C m-2 yr-1 from unmanaged forests in corresponding temperature and precipitation regimes in the literature. Within this range the flux of Lake Valkea-Kotinen yielded from the increase in source of the surrounding forest by 20% to decrease in sink by 5%. The free water approach gave primary production and community respiration estimates of 5- and 16-fold, respectively, compared with traditional bottle incubations during a 5-day testing period in autumn. The results are in parallel with findings in the literature. Both methods adopted from the terrestrial community also proved useful in lake studies. A large percentage of the EC data was rejected, due to the unfulfilled prerequisites of the method. However, the amount of data accepted remained large compared with what would be feasible with traditional methods. Use of the EC method revealed underestimation of the widely used gas exchange model and suggests simultaneous measurements of actual turbulence at the water surface with comparison of the different gas flux methods to revise the parameterization of the gas transfer velocity used in the models.

Using a botanic garden collection to test a bioclimatic vegetation scheme

Vegetation maps and bioclimatic zone classifications communicate the vegetation of an area and are used to explain how the environment regulates the occurrence of plants on large scales. Many practises and methods for dividing the world’s vegetation into smaller entities have been presented. Climatic parameters, floristic characteristics, or edaphic features have been relied upon as decisive factors, and plant species have been used as indicators for vegetation types or zones. Systems depicting vegetation patterns that mainly reflect climatic variation are termed ‘bioclimatic’ vegetation maps. Based on these it has been judged logical to deduce that plants moved between corresponding bioclimatic areas should thrive in the target location, whereas plants moved from a different zone should languish. This principle is routinely applied in forestry and horticulture but actual tests of the validity of bioclimatic maps in this sense seem scanty. In this study I tested the Finnish bioclimatic vegetation zone system (BZS). Relying on the plant collection of Helsinki University Botanic Garden’s Kumpula collection, which according to the BZS is situated at the northern limit of the hemiboreal zone, I aimed to test how the plants’ survival depends on their provenance. My expectation was that plants from the hemiboreal or southern boreal zones should do best in Kumpula, whereas plants from more southern and more northern zones should show progressively lower survival probabilities. I estimated probability of survival using collection database information of plant accessions of known wild origin grown in Kumpula since the mid 1990s, and logistic regression models. The total number of accessions I included in the analyses was 494. Because of problems with some accessions I chose to separately analyse a subset of the complete data, which included 379 accessions. I also analysed different growth forms separately in order to identify differences in probability of survival due to different life strategies. In most analyses accessions of temperate and hemiarctic origin showed lower survival probability than those originating from any of the boreal subzones, which among them exhibited rather evenly high probabilities. Exceptionally mild and wet winters during the study period may have killed off hemiarctic plants. Some winters may have been too harsh for temperate accessions. Trees behaved differently: they showed an almost steadily increasing survival probability from temperate to northern boreal origins. Various factors that could not be controlled for may have affected the results, some of which were difficult to interpret. This was the case in particular with herbs, for which the reliability of the analysis suffered because of difficulties in managing their curatorial data. In all, the results gave some support to the BZS, and especially its hierarchical zonation. However, I question the validity of the formulation of the hypothesis I tested since it may not be entirely justified by the BZS, which was designed for intercontinental comparison of vegetation zones, but not specifically for transcontinental provenance trials. I conclude that botanic gardens should pay due attention to information management and curational practices to ensure the widest possible applicability of their plant collections.