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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.

A walkable city has such built environment that supports walking and encourages people to walk. We can generate lively, safe, sustainable and healthy cities by improving their walkability. The continuing urbanization worldwide and the need to arrange transportation in cities in a sustainable way emphasize the significance of walkability research. This master’s thesis delves into those components of the built environment that affect walkability in street space. The goal of this thesis is to uncover how the walkability of street space can be examined by utilizing novel spatial data sets of human scale. The spatial data sets selected for further examination are panoramic images with spatial information, represented by Google Street View, point cloud produced with mobile laser scanning and visually high-quality 3D city model (mesh). Each of these spatial data sets is examined from the point of view of how they are suited for assessing those components of the built environment that affect the walkability of street space. This thesis examines the walkability of streets in Helsinki city centre by utilizing Google Street View and Earth Pro in observing the streets. The data were collected in May-July of 2017 by observing those components of the street space’s built environment that affect walkability in each street segment. These observations were modified into a new spatial data set by adding geographic information in relation to each street. The added value of this thesis is that there previously exists no such spatial data set concerning the walkability of streets in Helsinki city centre. The results present a mapping of those built environment components that affect walkability in the Helsinki city centre streets. Additionally, the best 20 and the worst 20 streets concerning walkability in the study area are recognized and they are compared to the daily amount of pedestrians in those same streets. In summary, it can be stated that by improving the components of the built environment we can create more walkable streets in the Helsinki city centre, and these measures can affect the daily life of thousands of pedestrians. The streets of Helsinki city centre fare well with regards to enclosure and human scale but improvements can be made concerning transparency, complexity and imageability. The hierarchy of walking needs is fulfilled at least moderately in Helsinki city centre streets. Based on the results, it can also be stated that spatial data sets of human scale have potential in assessing walkability and the best end result is achieved by combining the results of different data sets. In the future, automated image content analysis brings new possibilities in producing spatial data sets concerning walkability with efficiency.

Kiihtyvä muuttoliike maaseuduilta kaupunkeihin tulee lähivuosikymmeninä edelleen kasvattamaan tiiviisti rakennettuja kaupunkialueita. Rakennusten ja rakenteiden varastoiman lämpösäteilyn sekä osittain myös liikenteen ja teollisuuden tuottaman hukkalämmön vaikutuksesta kaupunkialueiden ympärille muodostuu lämpösaareke ja se havaitaan voimakkaimpana usein erityisesti yöaikaan. Voimistuessaan lämpösaarekkeilla on monia haitallisia ominaisuuksia. Kesäkuukausina tukahduttavat helteet yleistyvät ja lisääntyvän sähkönkulutukset myötä myös kiinteistöjen ylläpidon kustannukset kasvavat. Talvella lämpösaareke voi vaikeuttaa teiden kunnossapitoa ja kiihdyttää rakenteisiin ja tienpintoihin syntyviä routavaurioita. Kiihtyvän muuttoliikkeen myötä myös tiedon tarve kasvaa, jotta kaupunkilaisten elinympäristö voidaan säilytetään mahdollisimman miellyttävänä. Tässä työssä tutkittiin eri menetelmillä kaupunkien lämpösaarekeilmiötä voimistavaa ihmisperäisen lämpövuon sekä lämmön varastotermin käyttäytymistä Helsingin kantakaupungin alueella. Lämmön varastotermin käyttäytymistä tutkittiin energiataseyhtälön jäännösterminä sekä mallintamalla varastotermiä suoraan hystrereesimallin avulla. Työssä pyrittiin myös selvittämään, voidaanko lämmön varastotermin muutoksen suuruutta arvioida suoraan pintalämpötilahavaintojen avulla. Ihmisperäisen lämpövuon käyttäytymistä mallinnettiin LUCY-mallin sekä yksinkertaisen viilennys- ja lämmitystarvelukuihin perustuvan HDD/CDD-mallin avulla. Työssä tarkasteltiin lisäksi mallien suorituskykyjä sekä eri menetelmillä saatujen tulosten eroja. Ihmisperäisen lämmön vuota mallinnettaessa havaittiin, kuinka sekä LUCY- että HDD/CDD-malli tuottivat samoilla lämpötilan kynnysarvoilla varsin samankaltaisia tuloksia. Keskikesällä arvot vaihtelivat pääosin välillä 25-30 Wm-2, keskitalvella välillä 40-50 Wm-2. Tulosten pohjalta HDD/CDD-malli voi jatkossa olla monessa tilanteessa yksinkertaisuutensa vuoksi helpompi menetelmä mallintaa ihmisperäisen lämpövuon käyttäytymistä. Lämmön varastotermin tapauksessa havaittiin, että myös hystereesimalli tuottaa etenkin kesäkuukausien aikana muiden menetelmien avulla laskettujen arvojen kanssa hyvin samankaltaisia tuloksia. Talvella heikentyneen säteilypakotteen havaittiin hävittävän tuloksista lyhytaikaisen vaihtelun, mutta keskiarvoistetut tulokset olivat yhä hyvin linjassa muiden menetelmien tulosten kanssa. Lämmön varastotermin käyttäytymistä tarkisteltiin myös suoraan energiataseyhtälön avulla olettamalla ihmisperäinen lämmön vuo merkitykkettömän pieneksi. Nyt käytetty yksinkertaistus havaittiin kuitenkin tuloksista selvästi, talvella residuaalimenetelmällä lasketut varastotermin arvot olivat noin 50 ja kesällä noin 25 Wm-2 pienempiä kuin muilla menetelmillä saadut tulokset. Sekä hystereesimalli että residuaalimenetelmä vaikuttivat tulosten perusteella toimivalta tavalta mallintaa varastotermin käyttäytymistä. Pintalämpötilojen avulla lasketut varastotermin arvot eivät kuitenkaan näyttäneet olevan vertailukelpoisia muiden menetelmien tulosten rinnalla. Mallin havaittiin olevan hyvin herkkä muutoksille käytettyjen vakioiden arvoissa ja niiden tarkkojen arvojen määrittäminen osoittauitui hyvin haastavaksi.

Human smuggling and trafficking in human beings are phenomena that are often represented as global and growing problems. Human smuggling means that a person is taken to a country illegally which means that smuggling does not exist without states. Trafficking in human beings by contrast means the exploitation of persons which makes it a human rights violation. The news coverage about both phenomena, especially about human trafficking, has grown rapidly during the last decade. However, there has not been research on the news coverage about phenomena in Finland and the news coverage on trafficking in human beings is little researched even in European countries. In this thesis I am comparing critically the newspaper content on the phenomena in Finland and in Sweden from the viewpoint of political and moral geography. Besides the contexts of the news, I paid attention to how identities in different scales, including the scale of the body, were represented in the news and how the boundaries between different identities were drawn in the news. As a methodology I used content analysis to classify the context of the news and discourse analysis to analyze how the different scales and boundaries between them were represented. The results address that in Finland especially the human smuggling is considered as a border issue and Finland's location between East and West is emphasized, which points out that Finland's location is a crucial part of the Finnish identity. In addition the linkages between human trafficking and prostitution are often debated in the news from different aspects. In Sweden meanwhile its' political activeness in the fight against trafficking in human beings and international crime especially in the EU level are emphasized. Trafficking in human beings likewise prostitution according to Swedish law is seen as violence against women and the news are strongly against buying of sex as well. The states themselves, the state authorities and the EU are represented as active actors in both countries whereas international crime is represented as a threat and regions outside EU as chaotic. Additionally, illegal immigrants and the victims of trafficking are stigmatised. According to the results, the news coverage of both phenomena are used in constructing a more integrated national and European identity.