Sellutehtaan TRS-hajapäästöjen hallinta

Diplomityön tavoitteena on selvittää TRS-hajapäästöjen muodostumista sekä päästöjen määrää UPM Kymmene Oyj:n Kaukaan sellu- ja paperitehtaalla. Työssä laaditaan TRS-hajapäästöjen mittausohjelma, minkä päivitetty massa- ja paperiteollisuuden BAT-vertailuasiakirja vaatii. Mittausohjelma täydentää tehdasintegraatin ilmapäästöjen valvontaohjelmaa. Hajapäästöjen lisäksi tavoitteena on selvittää häiriötilanteiden aiheuttamia TRS-päästömääriä. Kirjallisuusosassa selvitetään hajujen muodostumista sellun ja paperin valmistuksessa ja niiden käsittelyä ympäristövaikutusten minimoimiseksi. Lisäksi esitellään mittausmenetelmiä. Kokeellisessa osassa valituista kohteista mitataan TRS-pitoisuudet ja hajukaasujen virtaama, joiden perusteella lasketaan TRS-kuormitus. Kuormitus suhteutetaan sellutonnille. Osa kohteista on hajapäästökohteita ja osa kohteita, joista hajukaasut häiriötilanteissa johdetaan käsittelemättä ulos. Tulosten perusteella TRS-hajapäästöjä muodostuu sellun valmistuksessa noin 0,04 kgS/ADt ja jätevedenkäsittelyssä, pääasiassa lietteenkäsittelyssä 0,04 kgS/ADt. Hajapäästöjä syntyy eniten kohteissa, missä keräily on toteutettu kevyemmin tai sitä ei ole. Merkittävimmät kohteet Kaukaalla ovat lietteenkäsittely, koivukuitulinja ja mäntyöljylaitos. Havulinjan ja talteenotto-osaston hajapäästöt ovat muita osastoja vähäisemmät. Yhteensä sellutehtaan ja lietteenkäsittelyn TRS-hajapäästömäärä on 0,08 kgS/ADt, mikä on BAT vaihteluvälin (0,05-0,2 kgS/ADt) sisällä. TRS-häiriöpäästöjen osuus tehtaan TRS päästöistä voi häiriötilanteiden toistuessa nousta merkittäviksi. Tähän vaikuttaa merkittävästi häiriintyvä kohde.

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.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Maakaasupolttimen säätöjärjestelmän kehittäminen

Jatkuvasti kiristyvät päästörajoitukset pakottavat teollisuuden kehittämään uusia ratkaisuja päästöjen vähentämiseksi. Hiilimonoksidin ja typen oksidien päästörajoitukset ovat erityi-sen tiukat esimerkiksi Kiinassa ja Yhdysvalloissa. Maakaasun ja ilman epätäydellisessä pa-lamisessa muodostuu hiilimonoksidia ja typen oksideja. Käytännön sovelluksissa palaminen on lähes aina epätäydellistä polttoaineen ja ilman epätäydellisen sekoittumisen takia, joten palamisreaktiossa muodostuva savukaasu sisältää edellä mainittuja haitallisia komponentteja lähes poikkeuksetta. Savukaasua voidaan puhdistaa erilaisilla menetelmillä ennen sen pää-tymistä ympäristöön. Tässä diplomityössä esitellään maakaasupoltinjärjestelmän keskeiset komponentit ja aihee-seen liittyvät tarpeelliset käsitteet sekä suunnitellaan polttoaine-ilma-seossuhdesäätö eräälle maakaasupoltinjärjestelmälle. Säädön ensisijaisena tavoitteena on pitää seossuhde mahdolli-simman tarkasti halutussa arvossa savukaasun puhdistuksen kannalta. Lisäksi säädön on tarkoitus taata mahdollisimman hyvä suorituskyky transienttitilanteissa. Järjestelmän eri osien toiminta mallinnetaan ja analysoidaan. Mallinnuksen perusteella suunnitellaan ja simu-loidaan säätöjärjestelmä. Suunniteltu säätöjärjestelmä toteutetaan osaksi polttolaitoksen automaatiojärjestelmää. Mittaustulokset osoittavat, että päästöjen kannalta säätö pitää seossuhteen riittävän tarkasti halutussa arvossa: hiilimonoksidin ja typen oksidien päästöt ovat asetettujen rajojen sisällä. Testiajojen perusteella prosessi on kuitenkin erittäin häiriöinen ja transienttitilanteissa ei saavuteta simulointien mukaista suorituskykyä.

Safety and outcome of coronary interventions with special reference to anticoagulation and stent type

University of Turku, Faculty of Medicine, Department of Cardiology and Cardiovascular Medicine, Doctoral Programme of Clinical Investigation, Heart Center, Turku University Hospital, Turku, Finland Division of Internal Medicine, Department of Cardiology, Seinäjoki Central Hospital, Seinäjoki, Finland Heart Center, Satakunta Central Hospital, Pori, Finland Annales Universitatis Turkuensis Painosalama Oy, Turku, Finland 2015 Antithrombotic therapy during and after coronary procedures always entails the challenging establishment of a balance between bleeding and thrombotic complications. It has been generally recommended to patients on long-term warfarin therapy to discontinue warfarin a few days prior to elective coronary angiography or intervention to prevent bleeding complications. Bridging therapy with heparin is recommended for patients at an increased risk of thromboembolism who require the interruption of anticoagulation for elective surgery or an invasive procedure. In study I, consecutive patients on warfarin therapy referred for diagnostic coronary angiography were compared to control patients with a similar disease presentation without warfarin. The strategy of performing coronary angiography during uninterrupted therapeutic warfarin anticoagulation appeared to be a relatively safe alternative to bridging therapy, if the international normalized ratio level was not on a supratherapeutic level. In-stent restenosis remains an important reason for failure of long-term success after a percutaneous coronary intervention (PCI). Drug-eluting stents (DES) reduce the problem of restenosis inherent to bare metal stents (BMS). However, a longer delay in arterial healing may extend the risk of stent thrombosis (ST) far beyond 30 days after the DES implantation. Early discontinuation of antiplatelet therapy has been the most important predisposing factor for ST. In study II, patients on long-term oral anticoagulant (OAC) underwent DES or BMS stenting with a median of 3.5 years’follow-up. The selective use of DESs with a short triple therapy seemed to be safe in OAC patients, since late STs were rare even without long clopidogrel treatment. Major bleeding and cardiac events were common in this patient group irrespective of stent type. In order to help to predict the bleeding risk in patients on OAC, several different bleeding risk scorings have been developed. Risk scoring systems have also been used also in the setting of patients undergoing a PCI. In study III, the predictive value of an outpatient bleeding risk index (OBRI) to identify patients at high risk of bleeding was analysed. The bleeding risk seemed not to modify periprocedural or long-term treatment choices in patients on OAC after a percutaneous coronary intervention. Patients with a high OBRI often had major bleeding episodes, and the OBRI may be suitable for risk evaluation in this patient group. Optical coherence tomography (OCT) is a novel technology for imaging intravascular coronary arteries. OCT is a light-based imaging modality that enables a 12–18 µm tissue axial resolution to visualize plaques in the vessel, possible dissections and thrombi as well as, stent strut appositions and coverage, and to measure the vessel lumen and lesions. In study IV, 30 days after titanium-nitride-oxide (TITANOX)-coated stent implantation, the binary stent strut coverage was satisfactory and the prevalence of malapposed struts was low as evaluated by OCT. Long-term clinical events in patients treated with (TITANOX)-coated bio-active stents (BAS) and paclitaxel-eluting stents (PES) in routine clinical practice were examined in study V. At the 3-year follow-up, BAS resulted in better long-term outcome when compared with PES with an infrequent need for target vessel revascularization. Keywords: anticoagulation, restenosis, thrombosis, bleeding, optical coherence tomography, titanium

Cleavages in contemporary Finland : a study on party-voter ties

Cleavages have been central in understanding the relationship between political parties and voters but the credibility of cleavage approach has been increasingly debated. This is because of decreasing party loyalty, fewer ideological differences between the parties and general social structural change amongst other factors. By definition, cleavages arise when social structural groups recognize their clashing interests, which are reflected in common values and attitudes, and vote for parties that are dedicated to defend the interests of the groups concerned. This study assesses relevance of cleavage approach in the Finnish context. The research problem in this study is “what kind of a cleavage structure exists in Finland at the beginning of the 21st century? Finland represents a case that has traditionally been characterized by a strong and diverse cleavage structure, notable ideological fragmentation in the electorate and an ideologically diverse party system. Nevertheless, the picture of the party-voter ties in Finland still remains incomplete with regard to a thorough analysis of cleavages. In addition, despite the vast amount of literature on cleavages in political science, studies that thoroughly analyze national cleavage structures by assessing the relationship between social structural position, values and attitudes and party choice have been rare. The research questions are approached by deploying statistical analyses, and using Finnish National Election Studies from 2003, 2007 and 2011as data. In this study, seven different social structural cleavage bases are analyzed: native language, type of residential area, occupational class, education, denomination, gender and age cohorts. Four different value/attitudinal dimensions were identified in this study: economic right and authority, regional and socioeconomic equality, sociocultural and European Union dimensions. This study shows that despite the weak overall effect of social structural positions on values and attitudes, a few rather strong connections between them were identified. The overall impact of social structural position and values and attitudes on party choice varies significantly between parties. Cleavages still exist in Finland and the cleavage structure partly reflects the old basis in the Finnish party system. The cleavage that is based on the type of residential area and reflected in regional and socioeconomic equality dimensions concerns primarily the voters of the Centre Party and the Coalition Party. The linguistic cleavage concerns mostly the voters of the Swedish People’s Party. The classic class cleavage reflected in the regional and socioeconomic equality dimension concerns in turn first and foremost the blue-collar voters of the Left Alliance and the Social Democratic Party, the agricultural entrepreneur voters of the Centre Party and higher professional and manager voters of the Coalition Party. The conflict with the most potential as a cleavage is the one based on social status (occupational class and education) and it is reflected in sociocultural and EU dimensions. It sets the voters of the True Finns against the voters of the Green League and the Coalition Party. The study underlines the challenges the old parties have met after the volatile election in 2011, which shook the cleavage structure. It also describes the complexity involved in the Finnish conflict structure and the multidimensionality in the electoral competition between the parties.

Bayesian analysis of SEIR epidemic models

This thesis concerns the analysis of epidemic models. We adopt the Bayesian paradigm and develop suitable Markov Chain Monte Carlo (MCMC) algorithms. This is done by considering an Ebola outbreak in the Democratic Republic of Congo, former Zaïre, 1995 as a case of SEIR epidemic models. We model the Ebola epidemic deterministically using ODEs and stochastically through SDEs to take into account a possible bias in each compartment. Since the model has unknown parameters, we use different methods to estimate them such as least squares, maximum likelihood and MCMC. The motivation behind choosing MCMC over other existing methods in this thesis is that it has the ability to tackle complicated nonlinear problems with large number of parameters. First, in a deterministic Ebola model, we compute the likelihood function by sum of square of residuals method and estimate parameters using the LSQ and MCMC methods. We sample parameters and then use them to calculate the basic reproduction number and to study the disease-free equilibrium. From the sampled chain from the posterior, we test the convergence diagnostic and confirm the viability of the model. The results show that the Ebola model fits the observed onset data with high precision, and all the unknown model parameters are well identified. Second, we convert the ODE model into a SDE Ebola model. We compute the likelihood function using extended Kalman filter (EKF) and estimate parameters again. The motivation of using the SDE formulation here is to consider the impact of modelling errors. Moreover, the EKF approach allows us to formulate a filtered likelihood for the parameters of such a stochastic model. We use the MCMC procedure to attain the posterior distributions of the parameters of the SDE Ebola model drift and diffusion parts. In this thesis, we analyse two cases: (1) the model error covariance matrix of the dynamic noise is close to zero , i.e. only small stochasticity added into the model. The results are then similar to the ones got from deterministic Ebola model, even if methods of computing the likelihood function are different (2) the model error covariance matrix is different from zero, i.e. a considerable stochasticity is introduced into the Ebola model. This accounts for the situation where we would know that the model is not exact. As a results, we obtain parameter posteriors with larger variances. Consequently, the model predictions then show larger uncertainties, in accordance with the assumption of an incomplete model.

De novo transcriptome assembly and annotation of the isopod Idotea balthica

The Baltic Sea is a unique environment that contains unique genetic populations. In order to study these populations on a genetic level basic molecular research is needed. The aim of this thesis was to provide a basic genetic resource for population genomic studies by de novo assembling a transcriptome for the Baltic Sea isopod Idotea balthica. RNA was extracted from a whole single adult male isopod and sequenced using Illumina (125bp PE) RNA-Seq. The reads were preprocessed using FASTQC for quality control, TRIMMOMATIC for trimming, and RCORRECTOR for error correction. The preprocessed reads were then assembled with TRINITY, a de Bruijn graph-based assembler, using different k-mer sizes. The different assemblies were combined and clustered using CD-HIT. The assemblies were evaluated using TRANSRATE for quality and filtering, BUSCO for completeness, and TRANSDECODER for annotation potential. The 25-mer assembly was annotated using PANNZER (protein annotation with z-score) and BLASTX. The 25-mer assembly represents the best first draft assembly since it contains the most information. However, this assembly shows high levels of polymorphism, which currently cannot be differentiated as paralogs or allelic variants. Furthermore, this assembly is incomplete, which could be improved by sampling additional developmental stages.