Spatial multicriteria decision analysis for the siting of on-shore wind power in Kemiönsaari

Wind power is a low-carbon energy production form that reduces the dependence of society on fossil fuels. Finland has adopted wind energy production into its climate change mitigation policy, and that has lead to changes in legislation, guidelines, regional wind power areas allocation and establishing a feed-in tariff. Wind power production has indeed boosted in Finland after two decades of relatively slow growth, for instance from 2010 to 2011 wind energy production increased with 64 %, but there is still a long way to the national goal of 6 TWh by 2020. This thesis introduces a GIS-based decision-support methodology for the preliminary identification of suitable areas for wind energy production including estimation of their level of risk. The goal of this study was to define the least risky places for wind energy development within Kemiönsaari municipality in Southwest Finland. Spatial multicriteria decision analysis (SMCDA) has been used for searching suitable wind power areas along with many other location-allocation problems. SMCDA scrutinizes complex ill-structured decision problems in GIS environment using constraints and evaluation criteria, which are aggregated using weighted linear combination (WLC). Weights for the evaluation criteria were acquired using analytic hierarchy process (AHP) with nine expert interviews. Subsequently, feasible alternatives were ranked in order to provide a recommendation and finally, a sensitivity analysis was conducted for the determination of recommendation robustness. The first study aim was to scrutinize the suitability and necessity of existing data for this SMCDA study. Most of the available data sets were of sufficient resolution and quality. Input data necessity was evaluated qualitatively for each data set based on e.g. constraint coverage and attribute weights. Attribute quality was estimated mainly qualitatively by attribute comprehensiveness, operationality, measurability, completeness, decomposability, minimality and redundancy. The most significant quality issue was redundancy as interdependencies are not tolerated by WLC and AHP does not include measures to detect them. The third aim was to define the least risky areas for wind power development within the study area. The two highest ranking areas were Nordanå-Lövböle and Påvalsby followed by Helgeboda, Degerdal, Pungböle, Björkboda, and Östanå-Labböle. The fourth aim was to assess the recommendation reliability, and the top-ranking two areas proved robust whereas the other ones were more sensitive.

Maalaa omasi - Kuvataide psykologisen omistajuuden tarpeiden ilmentajänä

Tässä pro gradu – tutkielmassa tutkin kuvataiteen ja kuvataiteellisten menetelmien käyttöä organisaatioissa psykologisen omistajuuden tarpeiden ilmentäjänä. Tarkoituksenani on kuroa umpeen aukkoa tutkimuksen ja käytännön välillä mitä tulee kuvataiteen käyttöön organisaatioissa. Tavoitteena on selvittää, mitä lisäarvoa kuvataiteen käyttö tuo organisaatioille ja miten se ilmentää psykologista omistajuutta. Tutkimus on laadullista ja aineistona ovat strukturoimattomat haastattelut, jotka on analysoitu diskurssinanalyysillä. Haastatteluaineisosta löysin eritasoisia diskursseja. Päädiskurssi näkymättömästä näkyväksi ilmentää psykologiseen omistajuuteen motivoivista tarpeista stimuluksen tarvetta, tilan diskurssi ilmentää kodin tarvetta ja identiteetin diskurssi ilmentää identiteetin tarvetta. Tilan ja identiteetin diskurssit menevät osittain päällekkäin. Kuvataideteokset ilmentävät psykologisen omistajuuden motivaatiotarpeista erityisesti stimulusta. Ne toimivat stimuluksena tuomalla psykologista läheisyyttä organisaatioihin. Kuvataiteen käytöllä organisaatioissa saadaan näkymättömästä näkyväksi psykologiseen omistajuuteen motivoivia tarpeita. Kuvataideteokset tuovat psykologista läheisyyttä ja stimuloivat näihin liittyviä merkityksellisiä asioita. Kuvataide on esteettinen käytännön työkalu organisaatiokäyttäytymisen kehittämiseksi, tunnejohtamiseen fuusioissa ja henkilöstön sitouttamisee

Maternal age and Down syndrome in Southeastern Brazil

The proportion of maternal-age-independent patients estimated among 200 Brazilian Down syndrome children (59.6%) was significantly larger than that of maternal-age-dependent cases (40.4%). The latter proportion is the smallest observed in pertinent literature and due basically to the low mean maternal age of the population analyzed. Based on the remarkable correlation (r = 0.95) between the proportion of maternal-age-dependent patients and the mean maternal age of the general population, a simple predictive equation to estimate the proportion of maternal-age-dependent Down syndrome patients based on the mean maternal age of the general population is suggested in situations where reliable data on the incidence of this syndrome according to maternal age is not available.

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Analysis of HLA-A antigens and C282Y and H63D mutations of the HFE gene in Brazilian patients with hemochromatosis

The hemochromatosis gene, HFE, is located on chromosome 6 in close proximity to the HLA-A locus. Most Caucasian patients with hereditary hemochromatosis (HH) are homozygous for HLA-A3 and for the C282Y mutation of the HFE gene, while a minority are compound heterozygotes for C282Y and H63D. The prevalence of these mutations in non-Caucasian patients with HH is lower than expected. The objective of the present study was to evaluate the frequencies of HLA-A antigens and the C282Y and H63D mutations of the HFE gene in Brazilian patients with HH and to compare clinical and laboratory profiles of C282Y-positive and -negative patients with HH. The frequencies of HLA-A and C282Y and H63D mutations were determined by PCR-based methods in 15 male patients (median age 44 (20-72) years) with HH. Eight patients (53%) were homozygous and one (7%) was heterozygous for the C282Y mutation. None had compound heterozygosity for C282Y and H63D mutations. All but three C282Y homozygotes were positive for HLA-A3 and three other patients without C282Y were shown to be either heterozygous (N = 2) or homozygous (N = 1) for HLA-A3. Patients homozygous for the C282Y mutation had higher ferritin levels and lower age at onset, but the difference was not significant. The presence of C282Y homozygosity in roughly half of the Brazilian patients with HH, together with the findings of HLA-A homozygosity in C282Y-negative subjects, suggest that other mutations in the HFE gene or in other genes involved in iron homeostasis might also be linked to HH in Brazil.

High prevalence of the GSTM3*A/B polymorphism in sub-Sarahan African populations

A 3-bp insertion/deletion polymorphism in intron 6 of GSTM3 (rs1799735, GSTM3*A/*B) affects the activity of the phase 2 xenobiotic metabolizing enzyme GSTM3 and has been associated with increased cancer risk. The GSTM3*B allele is rare or absent in Southeast Asians, occurs in 5-20% of Europeans but was detected in 80% of Bantu from South Africa. The wide genetic diversity among Africans led us to investigate whether the high frequency of GSTM3*B prevailed in other sub-Saharan African populations. In 168 healthy individuals from Angola, Mozambique and the São Tomé e Príncipe islands, the GSTM3*B allele was three times more frequent (0.74-0.78) than the GSTM3*A allele (0.22-0.26), with no significant differences in allele frequency across the three groups. We combined these data with previously published results to carry out a multidimensional scaling analysis, which provided a visualization of the worldwide population affinities based on the GSTM3 *A/*B polymorphism.

Extraction of DNA from honey and its amplification by PCR for botanical identification

The physiochemical and biological properties of honey are directly associated to its floral origin. Some current commonly used methods for identification of botanical origin of honey involve palynological analysis, chromatographic methods, or direct observation of the bee behavior. However, these methods can be less sensitive and time consuming. DNA-based methods have become popular due to their simplicity, quickness, and reliability. The main objective of this research is to introduce a protocol for the extraction of DNA from honey and demonstrate that the molecular analysis of the extracted DNA can be used for its botanical identification. The original CTAB-based protocol for the extraction of DNA from plants was modified and used in the DNA extraction from honey. DNA extraction was carried out from different honey samples with similar results in each replication. The extracted DNA was amplified by PCR using plant specific primers, confirming that the DNA extracted using the modified protocol is of plant origin and has good quality for analysis of PCR products and that it can be used for botanical identification of honey.

Tilintarkastajan vastuu kurinpidollisesta näkökulmasta

Tutkimuksen tavoitteena oli selvittää millainen on tilintarkastajan kurinpidollinen vastuu lakisääteisessä tehtävässä. Lisäksi tutkimuksessa selvitettiin, miten tilintarkastajan vastuu voi realisoitua ja millaisia kurinpidollisia seuraamuksia tilintarkastajille määrätään Suomessa. Tutkimuksen aineistona on käytetty TILA: n valvonta-asioiden ratkaisuja vuosina 2007 - 2014. Patentti – ja rekisterihallitus on vastannut kurinpidollisista asioista vuoden 2016 alusta lähtien. Tutkimuksessa noudatetaan käsitteellistä lähestymistapaa: tilintarkastajan vastuun ohella työssä on käsitelty muun muassa lakisääteistä tilintarkastusta, hyvää tilintarkastustapaa ja ammattieettisiä periaatteita. Työ on toteutettu laadullisena tutkimuksena ja aineisto koostuu dokumenteista koskien kurinpidollisia ratkaisuja vuosina 2007 - 2014. Tutkintatapausten käsittelyssä huomiota kiinnitettiin tutkinnan aloittamisen syihin ja tutkinnan seurauksena määrättyihin sanktioihin. Tutkintaan johtaneiden syiden väliltä pyrittiin löytämään yhteisiä tekijöitä. Lisäksi huomiota kiinnitettiin tutkintatapausten ja sanktioiden määrien kehitykseen. Tarkasteluaikavälillä yleisin syy tutkinnan aloittamiselle oli hyvän tilintarkastustavan tai tilintarkastuslain vastainen toiminta. Sanktiomuodoista varoituksia annettiin hieman enemmän kuin huomautuksia, hyväksymisen peruuttamiseen päädyttiin vain kahdeksassa tapauksessa. Yli puolessa tutkintatapauksissa sanktioita ei määrätty ollenkaan. Kaiken kaikkiaan sanktioiden ja tutkintatapausten määrässä ei havaittu tapahtuneen suurta vaihtelua tarkasteluaikavälillä.

Ohjelmoinnin opetus peruskoulussa: toiminnallisuutta ja graafista ohjelmointikieltä hyödyntävien opetusmenetelmien vertailu

Ohjelmoinnin opettaminen yleissivistävänä oppiaineena on viime aikoina herättänyt kiinnostusta Suomessa ja muualla maailmassa. Esimerkiksi Suomen opetushallituksen määrittämien, vuonna 2016 käyttöön otettavien peruskoulun opintosuunnitelman perusteiden mukaan, ohjelmointitaitoja aletaan opettaa suomalaisissa peruskouluissa ensimmäiseltä luokalta alkaen. Ohjelmointia ei olla lisäämässä omaksi oppiaineekseen, vaan sen opetuksen on tarkoitus tapahtua muiden oppiaineiden, kuten matematiikan yhteydessä. Tämä tutkimus käsittelee yleissivistävää ohjelmoinnin opetusta yleisesti, käy läpi yleisimpiä haasteita ohjelmoinnin oppimisessa ja tarkastelee erilaisten opetusmenetelmien soveltuvuutta erityisesti nuorten oppilaiden opettamiseen. Tutkimusta varten toteutettiin verkkoympäristössä toimiva, noin 9–12-vuotiaille oppilaille suunnattu graafista ohjelmointikieltä ja visuaalisuutta tehokkaasti hyödyntävä oppimissovellus. Oppimissovelluksen avulla toteutettiin alakoulun neljänsien luokkien kanssa vertailututkimus, jossa graafisella ohjelmointikielellä tapahtuvan opetuksen toimivuutta vertailtiin toiseen opetusmenetelmään, jossa oppilaat tutustuivat ohjelmoinnin perusteisiin toiminnallisten leikkien avulla. Vertailututkimuksessa kahden neljännen luokan oppilaat suorittivat samankaltaisia, ohjelmoinnin peruskäsitteisiin liittyviä ohjelmointitehtäviä molemmilla opetus-menetelmillä. Tutkimuksen tavoitteena oli selvittää alakouluoppilaiden nykyistä ohjelmointiosaamista, sitä minkälaisen vastaanoton ohjelmoinnin opetus alakouluoppilailta saa, onko erilaisilla opetusmenetelmillä merkitystä opetuksen toteutuksen kannalta ja näkyykö eri opetusmenetelmillä opetettujen luokkien oppimistuloksissa eroja. Oppilaat suhtautuivat kumpaankin opetusmenetelmään myönteisesti, ja osoittivat kiinnostusta ohjelmoinnin opiskeluun. Sisällöllisesti oppitunneille oli varattu turhan paljon materiaalia, mutta esimerkiksi yhden keskeisimmän aiheen, eli toiston käsitteen oppimisessa aktiivisilla leikeillä harjoitellut luokka osoitti huomattavasti graafisella ohjelmointikielellä harjoitellutta luokkaa parempaa osaamista oppitunnin jälkeen. Ohjelmakoodin peräkkäisyyteen liittyvä osaaminen oli neljäsluokkalaisilla hyvin hallussa jo ennen ohjelmointiharjoituksia. Aiheeseen liittyvän taustatutkimuksen ja luokkien opettajien haastatteluiden perusteella havaittiin koulujen valmiuksien opetussuunnitelmauudistuksen mukaiseen ohjelmoinnin opettamiseen olevan vielä heikolla tasolla.

An after-school literacy program : investigating the experiences of students with literacy difficulties, their volunteer tutors, and the tutors' transition into the teaching profession

This research responds to a pervasive call for our educational institutions to provide students with literacy skills, and teachers with the instructional supports necessary to facilitate this skill acquisition. Questions were posed to gain information concerning the efficacy ofteaching literacy strategies to students with learning difficulties, the impact of this training on their volunteer tutors, and the influence of this experience on these tutors' ensuing instructional practice as teacher candidates in a preservice education program. Study #1 compared a nontreatment group of students with literacy difficulties who participated in the program and found that program participants were superior at reading letter patterns and at comprehending the elements of story grammar. Concurrently, the second study explored the experiences of 19 volunteer tutors and uncovered that they acquired instructional skills as they established a knowledge base in teaching reading and writing, and they affirmed personal goals to become future teachers. Study #3 tracked 6 volunteer tutors into their pre-service year and identified their constructions, and beliefs about literacy instruction. These teacher candidates discussed how they had intended to teach reading and writing strategies based on their position that effective teaching ofthese skills in the primary grades is integral to academic success. The teacher candidates emphasized the need to build rapport with students, and the need to exercise flexibility in lesson plan delivery while including activities to meet emotional and developmental requirements of students. The teacher candidates entered their pre-service education with an initial cognition set based on the limited teaching context of tutoring. This foundational ii perception represented their prior knowledge of literacy instruction, a perception that appeared untenable once they were immersed in a regular instructional setting. This disparity provoked some of the teacher candidates to denounce their teacher mentors for not consistently employing literacy strategies and individualized instruction. This critical perspective could have been a demonstration of cognitive dissonance. In the end, when the teacher candidates began to look toward the future and how they would manage the demands of an inclusive classroom, they recognized the differences in the contexts. With an appreciation for the need for balance between prior and present knowledge, the teacher candidates remained committed to implementing their tutoring strategies in future teaching positions. This document highlights the need for teacher candidates with instructional experience prior to teacher education, to engage in cognitive negotiations to assimilate newly acquired pedagogies into existing pedagogies.

Polyglutamine monomer structure and its implications for molecular self-assembly

Polyglutamine is a naturally occurring peptide found within several proteins in neuronal cells of the brain, and its aggregation has been implicated in several neurodegenerative diseases, including Huntington's disease. The resulting aggregates have been demonstrated to possess ~-sheet structure, and aggregation has been shown to start with a single misfolded peptide. The current project sought to computationally examine the structural tendencies of three mutant poly glutamine peptides that were studied experimentally, and found to aggregate with varying efficiencies. Low-energy structures were generated for each peptide by simulated annealing, and were analyzed quantitatively by various geometry- and energy-based methods. According to the results, the experimentally-observed inhibition of aggregation appears to be due to localized conformational restraint placed on the peptide backbone by inserted prolines, which in tum confines the peptide to native coil structure, discouraging transition towards the ~sheet structure required for aggregation. Such knowledge could prove quite useful to the design of future treatments for Huntington's and other related diseases.

How are Girls' Attitudes Toward Cyberbullying Affected by Drama for Social Intervention?

This is a study exploring teenaged girls’ understanding and experiences of cyberbullying as a contemporary social phenomenon. Participants included 4 Grade 11 and 12 girls from a medium-sized independent school in southwestern Ontario, Canada. The girls participated in 9 extracurricular study sessions from January to April 2013. During the sessions, they engaged with Drama for Social Intervention (Clark, 2009; Conrad, 2004; Lepp, 2011) activities with the intended goal of producing a collective creation. Qualitative data were collected throughout the sessions using fieldnotes, participant journals, interviews, and participant artefacts. The findings are presented as an ethnodrama (Campbell & Conrad, 2006; Denzin, 2003; Saldaña, 1999) with each thematic statement forming a title of a scene in the script (Rogers, Frellick, & Babinski, 2002). The study found that girl identity online consists of many disconnected avatars. It also suggested that distancing (Eriksson, 2011) techniques, used to engender safety in Drama for Social Intervention, might have contributed to participant disengagement with the study’s content. Implications for further research included the utility of arts-based methods to promote participants’ feelings of growth and reflection, and a reevaluation of cyberbullying discourses to better reflect girls’ multiple avatar identities. Implications for teachers and administrators encompassed a need for preventative approaches to cyberbullying education, incorporating affective empathy-building (Ang & Goh, 2010) and addressing girls’ feelings of safety in perceived anonymity online.

Génération et reconnaissance de rythmes au moyen de réseaux de neurones à réservoir

Les fichiers sons qui accompagne mon document sont au format midi. Le programme que nous avons développés pour ce travail est en language Python.

Algorithmes pour la reconstruction de génomes ancestraux

L’inférence de génomes ancestraux est une étape essentielle pour l’étude de l’évolution des génomes. Connaissant les génomes d’espèces éteintes, on peut proposer des mécanismes biologiques expliquant les divergences entre les génomes des espèces modernes. Diverses méthodes visant à résoudre ce problème existent, se classant parmis deux grandes catégories : les méthodes de distance et les méthodes de synténie. L’état de l’art des distances génomiques ne permettant qu’un certain répertoire de réarrangements pour le moment, les méthodes de synténie sont donc plus appropriées en pratique. Nous proposons une méthode de synténie pour la reconstruction de génomes ancestraux basée sur une définition relaxée d’adjacences de gènes, permettant un contenu en gène inégal dans les génomes modernes causé par des pertes de gènes de même que des duplications de génomes entiers (DGE). Des simulations sont effectuées, démontrant une capacité de former une solution assemblée en un nombre réduit de régions ancestrales contigües par rapport à d’autres méthodes tout en gardant une bonne fiabilité. Des applications sur des données de levures et de plantes céréalières montrent des résultats en accord avec d’autres publications, notamment la présence de fusion imbriquée de chromosomes pendant l’évolution des céréales.