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.

Benzodiazepines and Cognitive Functioning in Older Adults. With Emphasis on Long-Term Use and Withdrawal

Benzodiazepines (BZD) and benzodiazepine related drugs (RD) are the most commonly used psychotropics among the aged. The use of other psychotropics taken concomitantly with BZD/ RD or their cognitive effects with BZD/RD have not been studied frequently. The aim of this academic thesis was to describe and analyse relationships between the use of BZD/RD alone or concomitantly with antipsychotics, antidepressants, opioids, antiepileptics, opioids and anticholinergics in the aged and their health. Especially, the relationships between long-term use of BZD/RD and cognitive decline were studied. Additionally, the effect of melatonin on BZD/RD withdrawal and the cognitive effects of BZD/RD withdrawal were studied. This study used multiple data sets: the first study (I) was based on clinical data containing aged patients (≥65 years; N=164) admitted to Pori City Hospital due to acute disease. The second data set (Studies II and III) was based on population-based data from the Lieto Study, a clinico-epidemiological longitudinal study carried out among the aged (≥65 years) in the municipality of Lieto. Follow-up data was formed by combining the cohort data collected in 1990-1991 (N=1283) and in 1998-1999 (N=1596) from those who participated in both cohorts (N=617). The third data set (Studies IV and V) was based on the Satauni Study’s data. This study was performed in the City of Pori in 2009-2010. In the RCT part of the Satauni Study, ninety-two long-term users of BZD/RD were withdrawn from their drugs using melatonin against placebo. The change of their cognitive abilities was measured during and after BZD/ RD withdrawal. BZD/RD use was related to worse cognitive and functional abilities, and their use may predict worse cognitive outcomes compared with BZD/RD non-users. Hypnotic use of BZD/RD could be withdrawn with psychosocial support in motivated participants, but melatonin did not improve the withdrawal results compared to those with placebo. Cognitive abilities in psychomotor tests did not show, or showed only modest, improvements for up to six months after BZD/RD withdrawal. This suggests that the cognitive effects of BZD/RD may be longlasting or permanent.

Die Beendigung von finnischen und deutschen Telefonaten. Eine interaktionslinguistische, kontrastierende Untersuchung

Suomalaisten ja saksalaisten arkikeskustelujen välillä on sekä yhtäläisyyksiä että eroja. Tässä saksalaisen filologian alaan kuuluvassa tutkimuksessa tarkastellaan yhtä keskeistä arkikeskustelun toimintoa, puhelinkeskustelun lopetusta, suomen- ja saksanpuhujien tuottamana. Aineistona on käytetty suomen- ja saksankielisten äidinkielisten puhujien tätä tutkimusta varten nauhoittamia henkilökohtaisia luonnollisia puhelinkeskusteluja. Aineistoon valikoitui 12 suomalaista ja 12 saksalaista puhelua. Nauhoitteiden käyttöön on saatu asianmukainen lupa kaikilta osapuolilta. Puhelut on litteroitu saksalaisella kielialueella vakiintuneen GAT-litterointisysteemin mukaan. Teoreettis-metodisena kehyksenä on kaksi tutkimusalaa, vuorovaikutuslingvistiikka ja kielten vertailu. Vuorovaikutuslingvistinen tarkastelu keskittyy havaintoihin vuorojen ja puheen sekvenssien rakenteesta. Vuorojen merkitysten tulkinnassa hyödynnetään systemaattisesti prosodian antamia vihjeitä. Tuloksena on yksittäisten lopetusten keskustelunanalyyttinen lähikuvaus, jonka pohjalta määritellään kulloisenkin lopetuksen sekvenssirakenne. Kaikki lopetukset olivat siltä osin yhteneväisiä, että niissä kaikissa havaittiin ainakin aloittava, tulevaan tapaamiseen viittaava sekä lopputervehdyksiin johtava sekvenssi. Sekvenssirakenteen variaatioiden pohjalta aineiston lopetukset voidaan kuitenkin jaotella ryhmiin. Sekä suomen- että saksankielisessä aineistossa havaittiin kolmentyyppisiä lopetuksia: kompakteja, komplekseja ja keskeytettyjä lopetuksia. Ryhmittely kolmeen tyyppiin on avuksi seuraavassa kuvausvaiheessa, jossa verrataan suomen- ja saksankielisiä lopetuksia toisiinsa. Samanaikaisesti kun tutkimus valottaa kohtia, joissa kaksi aineistosettiä yhtenevät ja eroavat, se myös esittää, mitkä vuorovaikutuksen tasot soveltuvat kieltenvälisen vertailun kohteiksi. Pohdintaa siitä, mitä vuorovaikutuksen tasoja kieltenväliseen vertailuun voidaan sisällyttää, onkin toistaiseksi esitetty verrattain vähän. Työ siis rakentaa siltaa vuorovaikutuslingvistisen ja kontrastiivisen kielitieteen välille.

Algorithmic Techniques in Gene Expression Processing. From Imputation to Visualization

The amount of biological data has grown exponentially in recent decades. Modern biotechnologies, such as microarrays and next-generation sequencing, are capable to produce massive amounts of biomedical data in a single experiment. As the amount of the data is rapidly growing there is an urgent need for reliable computational methods for analyzing and visualizing it. This thesis addresses this need by studying how to efficiently and reliably analyze and visualize high-dimensional data, especially that obtained from gene expression microarray experiments. First, we will study the ways to improve the quality of microarray data by replacing (imputing) the missing data entries with the estimated values for these entries. Missing value imputation is a method which is commonly used to make the original incomplete data complete, thus making it easier to be analyzed with statistical and computational methods. Our novel approach was to use curated external biological information as a guide for the missing value imputation. Secondly, we studied the effect of missing value imputation on the downstream data analysis methods like clustering. We compared multiple recent imputation algorithms against 8 publicly available microarray data sets. It was observed that the missing value imputation indeed is a rational way to improve the quality of biological data. The research revealed differences between the clustering results obtained with different imputation methods. On most data sets, the simple and fast k-NN imputation was good enough, but there were also needs for more advanced imputation methods, such as Bayesian Principal Component Algorithm (BPCA). Finally, we studied the visualization of biological network data. Biological interaction networks are examples of the outcome of multiple biological experiments such as using the gene microarray techniques. Such networks are typically very large and highly connected, thus there is a need for fast algorithms for producing visually pleasant layouts. A computationally efficient way to produce layouts of large biological interaction networks was developed. The algorithm uses multilevel optimization within the regular force directed graph layout algorithm.

Johtajan jäljillä – Johtaminen varhaiskasvatuksen hajautetuissa organisaatioissa laadun ja pedagogisen tuen näkökulmasta

Tutkimus sijoittuu varhaiskasvatuksen hajautetun organisaation kontekstiin, mutta tulokset ovat siirrettävissä muihinkin suomalaisiin kasvatus- ja opetustoimen organisaatioihin. Hajautettujen organisaatioiden tutkimus on ollut varhaiskasvatuksen kentällä vielä vähäistä, vaikka organisaatiomallin vaikutukset johtajuuden toteuttamiselle ovat merkittävät. Hajautetulla organisaatiolla varhaiskasvatuksessa tarkoitetaan sitä, että yhden johtajan alaisuudessa on monta eri päiväkotia tai erilaisia päivähoitomuotoja. Tämä organisaatiomalli on yhä enenevässä määrin kasvava suomalaisessa varhaiskasvatuksessa. Varhaiskasvatuksen hajautettujen organisaatioiden tutkimuksessa on aiemmin tarkasteltu johtajan ja työntekijöiden ja työntekijöiden keskinäisiä ammatillisia suhteita. Tässä tutkimuksessa näkökulma painottuu johtamiseen ja työskentelyyn hajautetuissa organisaatiossa sinänsä sekä myös laadunarviointiin sekä pedagogiikkaan. Viitekehyksenä tutkimuksessa on LMX-teoria (leader-member-exchange, johtajuuden vaihtoteoria), jossa tarkastellaan esimies-alaissuhdetta ja siihen kiinteästi liittyvää luottamuksen käsitettä. Luottamuksen merkitys hajautetuissa organisaatioissa korostuu, koska esimies ei ole fyysisesti päivittäin läsnä työntekijöiden arjessa. Tutkimuksessa tarkastellaan hajautetuissa varhaiskasvatuksen organisaatioissa työskentelyä seuraavien tutkimuskysymysten avulla: 1) Mitkä ovat varhaiskasvatuksen hajautettujen organisaatioiden johtamisen erityispiirteet? 2) Miten eri työntekijäryhmät kokevat hajautetussa organisaatiossa työskentelyn? 3) Millaisia kokemuksia esimiehillä ja työntekijöillä on heidän yksiköissään toteutetusta laadunarvioinnista? 4) Millaiseksi työntekijät ja esimiehet kokevat esimieheltään saadun tuen? Tutkimuksessa oli kolme eri aineistoa. Ensimmäinen aineisto koostui 11 hajautetun organisaation johtajan haastattelusta. Toinen aineisto (n = 223) sisälsi haastateltujen esimiesten lomakevastausten lisäksi heidän alaisuudessaan toimivien työntekijöiden, 10 esimieskoulutukseen osallistuneen johtajan sekä kolmen erillisyksikön työntekijöiden vastaukset. Kolmas aineisto oli kerätty pääkaupunkiseudulta varhaiskasvatuksen johtajilta lomakekyselynä (n = 112). Aineistoa on analysoitu teorialähtöisen ja aineistolähtöisen sisällönanalyysin ja tilastollisten analyysien avulla Tulokset osoittavat, että johtajat kokivat hallinnollisten töiden vievän paljon aikaa. Esimiehen kanssa eri työpaikassa työskentelevät työntekijät hahmottivat koko organisaation selkeämmin kuin esimiehen kanssa fyysisesti samassa paikassa työskentelevät. Esimiesten käsitysten mukaan laadunarviointia suoritettiin enemmän kuin mitä työntekijöiden mukaan. Työntekijät kaipasivat esimiehiltään tukea yhteistyöhön ja vuorovaikutukseen, pedagogiseen ohjaukseen, kehittämiseen ja toiminnan resursseihin liittyen. Erillisyksikössä työskentelevät kokivat saavansa enemmän tukea kuin esimiehen kanssa fyysisesti samassa yksikössä työskentelevät työntekijät. Sekä esimieheltä saadun pedagogisen tuen että luottamuksen kokemukset kiinnittävät tämän tutkimuksen tulosten mukaan huomion rakenteiden merkitykseen hajautetuissa organisaatioissa. Arviointiin, pedagogiseen tukeen ja tiedonkulkuun liittyvien rakenteiden huomioiminen helpottaa hajautetussa organisaatiossa johtamista. Edellisten lisäksi johtajan selkeä visio omasta johtamistyöstään ja jaetun johtajuuden hyödyntäminen edesauttavat työn hallinnan kokemuksia.

Kalman filter for computational market dynamics

Kalman filter is a recursive mathematical power tool that plays an increasingly vital role in innumerable fields of study. The filter has been put to service in a multitude of studies involving both time series modelling and financial time series modelling. Modelling time series data in Computational Market Dynamics (CMD) can be accomplished using the Jablonska-Capasso-Morale (JCM) model. Maximum likelihood approach has always been utilised to estimate the parameters of the JCM model. The purpose of this study is to discover if the Kalman filter can be effectively utilized in CMD. Ensemble Kalman filter (EnKF), with 50 ensemble members, applied to US sugar prices spanning the period of January, 1960 to February, 2012 was employed for this work. The real data and Kalman filter trajectories showed no significant discrepancies, hence indicating satisfactory performance of the technique. Since only US sugar prices were utilized, it would be interesting to discover the nature of results if other data sets are employed.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Socially shared metacognitive regulation during collaborative learning processes in student dyads and small groups

Traditionally metacognition has been theorised, methodologically studied and empirically tested from the standpoint mainly of individuals and their learning contexts. In this dissertation the emergence of metacognition is analysed more broadly. The aim of the dissertation was to explore socially shared metacognitive regulation (SSMR) as part of collaborative learning processes taking place in student dyads and small learning groups. The specific aims were to extend the concept of individual metacognition to SSMR, to develop methods to capture and analyse SSMR and to validate the usefulness of the concept of SSMR in two different learning contexts; in face-to-face student dyads solving mathematical word problems and also in small groups taking part in inquiry-based science learning in an asynchronous computer-supported collaborative learning (CSCL) environment. This dissertation is comprised of four studies. In Study I, the main aim was to explore if and how metacognition emerges during problem solving in student dyads and then to develop a method for analysing the social level of awareness, monitoring, and regulatory processes emerging during the problem solving. Two dyads comprised of 10-year-old students who were high-achieving especially in mathematical word problem solving and reading comprehension were involved in the study. An in-depth case analysis was conducted. Data consisted of over 16 (30–45 minutes) videotaped and transcribed face-to-face sessions. The dyads solved altogether 151 mathematical word problems of different difficulty levels in a game-format learning environment. The interaction flowchart was used in the analysis to uncover socially shared metacognition. Interviews (also stimulated recall interviews) were conducted in order to obtain further information about socially shared metacognition. The findings showed the emergence of metacognition in a collaborative learning context in a way that cannot solely be explained by individual conception. The concept of socially-shared metacognition (SSMR) was proposed. The results highlighted the emergence of socially shared metacognition specifically in problems where dyads encountered challenges. Small verbal and nonverbal signals between students also triggered the emergence of socially shared metacognition. Additionally, one dyad implemented a system whereby they shared metacognitive regulation based on their strengths in learning. Overall, the findings suggested that in order to discover patterns of socially shared metacognition, it is important to investigate metacognition over time. However, it was concluded that more research on socially shared metacognition, from larger data sets, is needed. These findings formed the basis of the second study. In Study II, the specific aim was to investigate whether socially shared metacognition can be reliably identified from a large dataset of collaborative face-to-face mathematical word problem solving sessions by student dyads. We specifically examined different difficulty levels of tasks as well as the function and focus of socially shared metacognition. Furthermore, the presence of observable metacognitive experiences at the beginning of socially shared metacognition was explored. Four dyads participated in the study. Each dyad was comprised of high-achieving 10-year-old students, ranked in the top 11% of their fourth grade peers (n=393). Dyads were from the same data set as in Study I. The dyads worked face-to-face in a computer-supported, game-format learning environment. Problem-solving processes for 251 tasks at three difficulty levels taking place during 56 (30–45 minutes) lessons were video-taped and analysed. Baseline data for this study were 14 675 turns of transcribed verbal and nonverbal behaviours observed in four study dyads. The micro-level analysis illustrated how participants moved between different channels of communication (individual and interpersonal). The unit of analysis was a set of turns, referred to as an ‘episode’. The results indicated that socially shared metacognition and its function and focus, as well as the appearance of metacognitive experiences can be defined in a reliable way from a larger data set by independent coders. A comparison of the different difficulty levels of the problems suggested that in order to trigger socially shared metacognition in small groups, the problems should be more difficult, as opposed to moderately difficult or easy. Although socially shared metacognition was found in collaborative face-to-face problem solving among high-achieving student dyads, more research is needed in different contexts. This consideration created the basis of the research on socially shared metacognition in Studies III and IV. In Study III, the aim was to expand the research on SSMR from face-to-face mathematical problem solving in student dyads to inquiry-based science learning among small groups in an asynchronous computer-supported collaborative learning (CSCL) environment. The specific aims were to investigate SSMR’s evolvement and functions in a CSCL environment and to explore how SSMR emerges at different phases of the inquiry process. Finally, individual student participation in SSMR during the process was studied. An in-depth explanatory case study of one small group of four girls aged 12 years was carried out. The girls attended a class that has an entrance examination and conducts a language-enriched curriculum. The small group solved complex science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry during 22 lessons (á 45–minute). Students’ network discussion were recorded in written notes (N=640) which were used as study data. A set of notes, referred to here as a ‘thread’, was used as the unit of analysis. The inter-coder agreement was regarded as substantial. The results indicated that SSMR emerges in a small group’s asynchronous CSCL inquiry process in the science domain. Hence, the results of Study III were in line with the previous Study I and Study II and revealed that metacognition cannot be reduced to the individual level alone. The findings also confirm that SSMR should be examined as a process, since SSMR can evolve during different phases and that different SSMR threads overlapped and intertwined. Although the classification of SSMR’s functions was applicable in the context of CSCL in a small group, the dominant function was different in the asynchronous CSCL inquiry in the small group in a science activity than in mathematical word problem solving among student dyads (Study II). Further, the use of different analytical methods provided complementary findings about students’ participation in SSMR. The findings suggest that it is not enough to code just a single written note or simply to examine who has the largest number of notes in the SSMR thread but also to examine the connections between the notes. As the findings of the present study are based on an in-depth analysis of a single small group, further cases were examined in Study IV, as well as looking at the SSMR’s focus, which was also studied in a face-to-face context. In Study IV, the general aim was to investigate the emergence of SSMR with a larger data set from an asynchronous CSCL inquiry process in small student groups carrying out science activities. The specific aims were to study the emergence of SSMR in the different phases of the process, students’ participation in SSMR, and the relation of SSMR’s focus to the quality of outcomes, which was not explored in previous studies. The participants were 12-year-old students from the same class as in Study III. Five small groups consisting of four students and one of five students (N=25) were involved in the study. The small groups solved ill-defined science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry over a total period of 22 hours. Written notes (N=4088) detailed the network discussions of the small groups and these constituted the study data. With these notes, SSMR threads were explored. As in Study III, the thread was used as the unit of analysis. In total, 332 notes were classified as forming 41 SSMR threads. Inter-coder agreement was assessed by three coders in the different phases of the analysis and found to be reliable. Multiple methods of analysis were used. Results showed that SSMR emerged in all the asynchronous CSCL inquiry processes in the small groups. However, the findings did not reveal any significantly changing trend in the emergence of SSMR during the process. As a main trend, the number of notes included in SSMR threads differed significantly in different phases of the process and small groups differed from each other. Although student participation was seen as highly dispersed between the students, there were differences between students and small groups. Furthermore, the findings indicated that the amount of SSMR during the process or participation structure did not explain the differences in the quality of outcomes for the groups. Rather, when SSMRs were focused on understanding and procedural matters, it was associated with achieving high quality learning outcomes. In turn, when SSMRs were focused on incidental and procedural matters, it was associated with low level learning outcomes. Hence, the findings imply that the focus of any emerging SSMR is crucial to the quality of the learning outcomes. Moreover, the findings encourage the use of multiple research methods for studying SSMR. In total, the four studies convincingly indicate that a phenomenon of socially shared metacognitive regulation also exists. This means that it was possible to define the concept of SSMR theoretically, to investigate it methodologically and to validate it empirically in two different learning contexts across dyads and small groups. In-depth micro-level case analysis in Studies I and III showed the possibility to capture and analyse in detail SSMR during the collaborative process, while in Studies II and IV, the analysis validated the emergence of SSMR in larger data sets. Hence, validation was tested both between two environments and within the same environments with further cases. As a part of this dissertation, SSMR’s detailed functions and foci were revealed. Moreover, the findings showed the important role of observable metacognitive experiences as the starting point of SSMRs. It was apparent that problems dealt with by the groups should be rather difficult if SSMR is to be made clearly visible. Further, individual students’ participation was found to differ between students and groups. The multiple research methods employed revealed supplementary findings regarding SSMR. Finally, when SSMR was focused on understanding and procedural matters, this was seen to lead to higher quality learning outcomes. Socially shared metacognition regulation should therefore be taken into consideration in students’ collaborative learning at school similarly to how an individual’s metacognition is taken into account in individual learning.