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Tutkielmassa tutustutaan Markov-prosesseihin sekä niiden soveltamiseen vakuutusten hinnoittelussa. Sovellusvaiheessa tutkitaan erityisesti kolmitilaista työkyvyttömyysmallia sekä työkyvyttömyysvakuutuksen nettokertamaksua. Tutkielman aluksi on tarpeellista määritellä Markov-prosessi sekä siihen liittyvät siirtymäintensiteetit. Näiden avulla voimme tutkia tarkemmin Markov-prosessin ominaisuuksia sekä esitellä ja määritellä sovellusten kannalta tärkeitä ominaisuuksia kuten Kolmogorovin differentiaaliyhtälöt sekä polkutodennäköisyydet. Teoriaosuuden tavoitteena on konstruoida diskreettiaikaisen Markovketjun ja hyppyhetkien avulla stokastinen prosessi, joka osoitetaan lopulta olevan jatkuva-aikainen Markov-prosessi. Markov-prosesseille löytyy useita käyttömahdollisuuksia, mutta tässä tutkielmassa keskitytään vakuutusalaan ja sovelletaan niitä vakuutusten hinnoittelussa. Markov-prosessien soveltamisessa on kuitenkin oltava aina hyvin kriittinen, sillä Markov-prosesseille ainoastaan nykytila on relevantti ennustettaessa tulevaisuuden tilaa. Historian 'unohtaminen' vaikuttaa väistämättä saatuihin tuloksiin. Pohditaan sitä kuinka realistisia ennusteita saadaan ja kuinka niitä käyttää hyödyksi. Sovellusosiossa tutustutaan työkyvyttömyyseläkevakuutuksen hinnoitteluun nettokertamaksun avulla. Määritetään kolmitilainen markovilainen malli, jonka tiloina ovat 'työkykyinen', 'työkyvytön' ja 'kuollut'. Oletetaan, että vakuutettu on oikeutettu jatkuva-aikaiseen yhden rahayksikön suuruiseen korvaukseen joutuessaan työkyvyttömäksi. Määritellään tämän mallin avulla Kolmogorovin di erentiaaliyhtälöitä hyväksi käyttäen vakuutuksesta aiheutunut nettokertamaksu kahdella tavalla, vakioisten ja ei-vakioisten siirtymäintensiteettien avulla. Tutkitaan nettokertamaksun suuruutta eri pituisille vakuutussopimuksille. Simuloidaan vielä lopuksi nettokertamaksu vakioisten ja ei-vakioisten siirtymäintensiteettien tapauksessa. Tarkastellaan simuloitujen arvojen ja laskettujen tarkkojen arvojen erotuksen suhdetta tarkkoihin arvoihin. Tutkielmassa on tarpeellista esitellä lyhyesti myös simuloimiseen tarvittava teoria sekä simulointialgoritmi, jota on käytetty Markov-prosessien simuloimisessa. Simulointi on toteutettu Matlab-ohjelman avulla. Tutkielman lukemista helpottaa todennäköisyysteorian ja henkivakuutusmatematiikan perusteiden tuntemus. Toisaalta lukemista on yritetty helpottaa muutamilla merkinnöillä sekä havainnollistavilla kuvilla.

Hyvinkäänkylä pumping station extracts groundwater from a local esker aquifer and supplies drinking water within the Hyvinkää municipality. There have been problems with the aquifer’s water quality, when surface water from the Vantaa river has mixed with groundwater during flooding season. As a result of the mixing, the pumping at the station must be periodically stopped. For more effective groundwater acquisition, management and protection it is critical to gain better understanding of the structure of the aquifer better. In addition, more knowledge of the groundwater-surface water interaction is needed and information on the possible routes of groundwater flow, particularly those which most affect the groundwater supply coming to the pumping station. The purpose of this study is to gather the previous research on the area and to create a 3D hydrogeological structural model using the Leapfrog Geo program. The model in this study visualizes the hydrogeological structures and serves as input for a groundwater flow model. The data in this study can be categorized into three groups: the structure of the bedrock and sediments, the level of the groundwater and the groundwater discharge. The structure of the bedrock and sediments was surveyed by geophysical methods, using previous data as well as some additional data gathered by field measurements. Data on the groundwater level measurements were obtained from both the Finnish Environment Institute and the Hyvinkää Water station. Additional data were collected using field measurements. To measure the amount of groundwater discharge, data were collected using flow measurements in the Vantaa River and the results compared with previous research. The geological and geophysical data were compiled and georeferenced first in ArcGis and then transferred into Leapfrog, which was used to build the 3D hydrogeological structural model. On the basis of the geological units of the drill data, five hydrogeological units were formed: coarse glaciofluvial material, fine glaciofluvial material, fine grained material, till and other. The hydraulic conductivity of the drill core sediment samples were calculated, and then used to estimate the hydraulic conductivity within and between the different sediment layers. Since one purpose for the 3D structural model was to serve as a base for the flow model, it was simplified into a 2-layer model. The study area was divided into smaller subareas, which were visualized with cross sections that were sliced from the 3D model. The measured groundwater levels were interpolated to demonstrate the groundwater flow direction in the study area. The groundwater monitoring levels were examined over a five year period and compared with weather and groundwater pumping data. The flow measurements obtained from the Vantaa River were compared to previous research to estimate the amount of groundwater discharge into the Vantaa River. The contribution of this study is five new significant observations about the structure of the aquifer: 1) the north and south part of the aquifer are connected to each other 2) the three-dimensional shape of the esker is different from its geomorphologic shape 3) most of the groundwater flowing to the pumping station is from the south side of the river 4) the amount of groundwater flowing to the pumping station is very high compared to the surface area of the aquifer 5) thick glaciofluvial layers underneath the Hirvisuo bog allow groundwater flow from the first Salpausselkä to the esker.

Terveystaltio kokoaa yhteen kansalaisen terveyteen liittyvän tiedon ja työkalut Henkilökohtainen terveyden seuranta keskittyy yksittäisen ihmisen terveyteen ja hyvinvointiin liittyvien muuttujien seurantaan. Henkilökohtainen terveyden seuranta on nopeasti kasvava sovellusalue, joka kattaa laajan kentän erilaisia hyvinvointi-, terveys- ja viestintälaitteita sekä niiden päälle rakennettuja sovelluksia. Sovellusten ja laitteiden avulla voidaan kerätä monipuolista tietoa, jota voidaan hyödyntää henkilökohtaisen terveyden ja hyvinvoinnin seurannassa. Terveydenhuoltoalan standardit pohjautuvat pitkälti potilastietojärjestelmien tarpeisiin eivätkä näin ollen sovellu suoraan terveystaltion käyttöön ja hyvinvointitietojen esittämiseen. Mittalaitetiedon osalta Continua Health Alliancen standardit ovat laajasti käytössä ja niiden on todettu tarjoavan hyvät tekniset välineet mittalaitetiedon siirtämiseen terveystaltioon. Hyvinvointitietojen integroimiseksi osaksi terveystaltiota voidaan tunnistaa kaksi erilaista lähestymistapaa. XML-pohjaiset ratkaisut ovat joustavia ja helposti muunneltavia. Ontologiapohjaisen lähestymistavan pohjan muodostavat semanttisen webin teknologiat. Ontologiapohjaisen ratkaisun avulla voidaan saavuttaa tietojärjestelmien välinen semanttinen yhteensopivuus, jolloin järjestelmät ymmärtävät välittämänsä ja vastaanottamansa tiedon merkityksen. Uuden sukupolven standardit sekä nopeasti kehittyvä mittalaiteteknologia tuovat uusia mahdollisuuksia hyvinvointitietojen integroimiseksi osaksi terveystaltiota. Mittalaiteteknologian kehittyminen tuo kansalaisten saataville sellaista teknologiaa, joka on aikaisemmin ollut käytettävissä ainoastaan tutkimus- ja ammattikäytössä.

The effect of increasing SSTs and roughness on the meridional moisture flux (MMF) and precipitationn of extratropical cyclones are studied with idealized baroclinic wave simulations. The main objective is to quantify MMF, precipitation and several other factors of extratropical cyclones as the SSTs and roughness length are increased. The simulations are done in idealized conditions with Weather Research and Forecasting (WRF) model where the surface is only water. The sensitivity studies are conducted by changing the sea surface temperature (SST) homogeneously throughout the domain and changing the surface roughness length which depends on the wind speed. Increasing SSTs caused MMF and precipitation to increase, as well as, e. g. latent heat flux (LHF) increased and minimum surface pressure decreased. On the other hand, increasing roughness length caused MMF and precipitation to decrease, additionally, LHF decreased and minimum surface pressure increased. The largest effect of SSTs and roughness had on the LHF and convective precipitation and the smallest on the minimum surface pressure.

Time series are essential in various domains and applications. Especially in retail business forecasting demand is a crucial task in order to make the appropriate business decisions. In this thesis we focus on a problem that can be characterized as a sub-problem in the field of demand forecasting: we attempt to form clusters of products that reflect the products’ annual seasonality patterns. We believe that these clusters would aid us in building more accurate forecast models. The seasonality patterns are identified from weekly sales time series, which in many cases are very sparse and noisy. In order to successfully identify the seasonality patterns from all the other factors contributing in a product’s sales, we build a pipeline to preprocess the data accordingly. This pipeline consist of first aggregating the sales of individual products over several stores to strengthen the sales signal, followed by solving a regularized weighted least squares objective to smooth the aggregates. Finally, the seasonality patterns are extracted using the STL decomposition procedure. These seasonality patterns are then used as input for the k-means algorithm and several hierarchical agglomerative clustering algorithms. We evaluate the clusters using two distinct approaches. In the first approach we manually label a subset of the data. These labeled subsets are then compared against the clusters provided by the clustering algorithms. In the second approach we form a simple forecast model that fits the clusters’ seasonality patterns back to the observed sales time series of individual products. In this approach we also build a secondary validation forecast model with the same objective, but instead of using the clusters provided by the algorithms, we use predetermined product categories as the clusters. These product categories should naturally provide a valid baseline for groups of products with similar seasonality as they reflect the structure of how similar products are organized within close proximity in physical stores. Our results indicate that we were able to find clear seasonal structure in the clusters. Especially the k-means algorithm and hierarchical agglomerative clustering algorithms with complete linkage and Ward’s method were able to form reasonable clusters, whereas hierarchical agglomerative clustering algorithm with single linkage was proven to be unsuitable given our data.

In this thesis the suitability of Nuclear Magnetic Resonance (NMR) spectroscopy in the identification of rubbers in museum collections is discussed through a literature review and experimental work where samples from the rubber collection of Tampere Museums were analysed with different NMR techniques. The literature part of this thesis focuses on recent (2011-2020) scientific publications on analytical instrumental techniques used in the identification of cultural heritage plastics. Vibrational spectroscopy methods utilizing hand-held or portable devices have been the most prominent methods used in characterization of historical plastics materials. Bench-top devices and analytical techniques requiring sampling were used to acquire more detailed analysis results. However, NMR spectroscopy was not used as the main analysis technique in the reviewed publications. In the experimental part altogether 21 rubber object samples and 8 reference samples were identified using 1D and 2D NMR techniques in solution state. Three samples were additionally analysed with solid-state High Resolution Magic Angle Spinning (HRMAS) NMR spectroscopy. The chemical structures of the samples were confirmed with these methods. To further explore fast and more automated identification of the rubber samples a statistical classification model utilizing acquired solution-state 1H NMR data was developed. Three rubber types were chosen for the analysis. The model was created using analysis data from the museum object samples and validated using the reference sample data. Identification rate of 100 % was achieved.

Cancer cells accumulate somatic mutations in their DNA throughout their lifetime. The advances in cancer prevention and treatment methods call for a deeper understanding of carcinogenesis on the genetic sequence level. Mutational signatures present a novel and promising way to capture somatic mutation patterns and define their causes, allowing to summarize the mutational landscape of cancer as a combination of distinct mutagenic processes acting with different levels of strength. While the majority of previous studies assume an additive relationship between the mutational processes, this Master’s thesis provides tentative evidence that contemporary methods with additivity constraints, e.g. non-negative matrix factorization (NMF), are not sufficient to comprehensively explain the observed mutations in cancer genomes and the observed deviations are not random. To quantify these residues, two metrics are defined – additive and multiplicative residues – and hierarchical clustering algorithms are used to identify cancer subsets with similar residual profiles. It is shown that in certain cancer sample subsets there is a systematic mutational burden overestimation that can only be solved by a multiplicatively acting process, as well as non-random underestimation, requiring additional mutational signatures. Here an extension to the additive mutational signature model is proposed – a probabilistic model that incorporates a selectively active modulatory mutational process that is able to act in a multiplicative manner together with the known mutational signatures, reducing systematic variability.

High-grade serous carcinoma (HGSC) is a highly lethal cancer type characterised by high genomic instability and frequent copy number alterations. This study examines the relationships between genetic variants in tumour germline and gene expression levels to obtain a better understanding of gene regulation in HGSC. This would then improve knowledge of the cancer mechanisms in order to find, for example, potential new treatment targets and biomarkers. The aim is to find significantly associated variant-gene pairs in HGSC. Expression quantitative trait loci (eQTL) analysis is a well-suited method to explore these associations. eQTL analysis is a suitable approach to analysing also those variants that are located in the non-coding genomic regions, as indicated by previous genome-wide association studies to contain many disease-linked germline variants. The current eQTL analysis methods are, however, not applicable for association testing between genes and variants in the context of HGSC because of the special genomic features of the cancer. Therefore, a new eQTL analysis approach, SegmentQTL, was developed for this study to accommodate the copy-number-driven nature of the disease. Careful input processing is of particular importance in eQTL as it has a notable effect on the number of significantly associated variant-gene pairs. It is also relevant to maintain adequate statistical power, which affects the reliability of the findings. In all, this study uses eQTL analysis to uncover variant-gene associations. This helps to improve knowledge of gene regulation mechanisms in HGSC in order to find new treatments. To apply the analysis to the HGSC data, a novel eQTL analysis method was developed. Additionally, appropriate input processing is important prior to running the analysis to ensure reliable results.

Magnetic reconnection is a phenomenon occurring in plasma and related magnetic fields when magnetic field lines break and rejoin, leading to the release of energy. Magnetic reconnections take place, for example, in the Earth’s magnetosphere, where they can affect the space weather and even damage systems and technology on and around the Earth. Another site of interest is in fusion reactors, where the energy released from reconnection events can cause instability in the fusion process. So far, 2D magnetic reconnection has been widely studied and is relatively well-understood, whereas the 3D case remains more challenging to characterize. However, in real-world situations, reconnection occurs in three dimensions, which makes it essential to be able to detect and analyse 3D magnetic reconnection, as well. In this thesis, we examine what potential signs of 3D magnetic reconnection can be identified from the topological elements of a magnetic vector field. To compute the topological elements, we use the Visualization Toolkit (VTK) Python package. The topology characterizes the behaviour of the vector field, and it may reveal potential reconnection sites, where the topological elements can change as a result of magnetic field lines reconnecting. The magnetic field data used in this thesis is from a simulation of the nightside magnetosphere produced using Vlasiator. The contributions of this thesis include analysis of the topological features of 3D magnetic reconnection and topological representations of nightside reconnection conditions to use in potential future machine learning approaches. In addition, a modified version of the VTK function for computing the critical points of the topology is created with the purpose of gearing it more towards magnetic vector fields instead of vector fields in general.

Tämän työn tavoitteena on tutkia ihmisen laulamalla tuottamien vokaalien ominaisuuksia kouluissa saatavilla olevalla laitteistolla. Tarkoitus on havaita vokaalien erot niiden spektrejä tulkitsemalla ja mahdollistaa tämän tiedon sisällyttämistä koulujen fysiikan opetukseen. Työtä varten kerätään ääninäytteitä viidestä eri vokaalista molempia sukupuolia edustavilta vapaaehtoisilta kokelailta. Osa heistä on laulunopettajia, joilla on laaja kokemus ja osaaminen äänenkäytöstä. Osa on laulunopiskelijoita ja loput ovat maallikkoja, joilla on hyvin vähän tai ei ollenkaan kokemusta laulamisesta ja nk. oikeaoppisesta äänenkäytöstä. Tuloksista nähtiin, että laulettujen vokaalien erot ovat nähtävissä helpoiten M1 ja M2 (twang) mekanismeilla tuotetussa äänessä. Kuiskaamalla lausutut vokaalit erotettiin myös hyvin toisistaan. Havaittiin, että työ on toteutettavissa kouluissa välineistöstä riippumatta mobiililaitteiden tuomien mahdollisuuksien takia.