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Etuuspohjaisen eläkejärjestelyn laskennan tavoitteena on selvittää eläkevakuutuksen ottajan säästö- ja eläkesuunnitelmien yhteensopivuus ottaen samalla huomioon sopimukseen kuuluvat turvat ja muut kulut. Vapaaehtoisiin eläkesopimuksiin tehtyjen lakimuutosten seurauksena tällainen laskenta on monimutkaistunut huomattavasti 2000-luvun aikana ja vanhoille järjestelmille luodut laskentamallit eivät aina suoriudu toivotulla nopeudella. Tämän tutkielman aiheena on Profit Software Oy:n Profit Life & Pension -vakuutustenhallintajärjestelmän optimointi edellä kuvatun laskennan osalta.

Tutkielmassa pyritään vastaamaan kysymykseen siitä, minkä ulottuvuuden euklidiseen avaruuteen voidaan määritellä bilineaarinen binäärioperaatio siten, että euklidinen avaruus varustettuna tällä laskutoimituksella on jakoalgebra. Työssä ei pyritä täydelliseen vastaukseen vaan päätavoitteena on antaa kysymykseen välttämätön ehto: avaruuden ulottuvuuden täytyy olla luvun kaksi potenssi. Tutkielman johdannossa esitellään ongelma ja annetaan esimerkki siitä, että kolmiulotteisessa avaruudessa ei jakoalgebran rakennetta voida saavuttaa. Työn ensimmäisessä varsinaisessa luvussa esitellään tarpeellinen määrä kategoriateoriaa, jotta algebralliselle topologialle tyypilliset kategorioihin ja funktoreihin liittyvät argumentit joita tutkielmassa käytetään, ovat perusteltuja. Lisäksi määritellään niin kutsuttu Hom-funktori ja esitellään sen perusominaisuuksia. Kyseisen funktorin soveltaminen, dualisointi, johtaa yksinkertaisella tavalla yleisen ketjukompleksin kohomologiaryhmiin. Tässä kohtaa työtä tulee selväksi, että dualisaatio säilyttää lukuisia homologiateoriasta tunnettuja ominaisuuksia ja konstruktioita. Luvun työläin ja tärkein osuus on universaalin kerrointeoreeman todistus. Kyseinen lause selvittää yhteyden kohomologia- ja homologiaryhmien välille ja antaa tavan laskea ketjukompleksin kohomologia- ryhmät sen homologiaryhmien avulla. Luvun loppupuolella esitellään yleisen kohomologiateorian aksiomaattinen määritelmä, jota tarvitaan myöhemmin tutkielmassa sekä tutustutaan tavallisimpiin kohomologiateorioihin. Näitä ovat muun muassa singulaarinen kohomologia ja solukohomologia. Kohomologiaryhmien perustietoihin tutustumisen jälkeen tutkielman neljännessä luvussa aletaan käsittelemään kohomologiaryhmille määriteltyä uutta laskutoimitusta, kuppituloa. Tämä on keskeinen käsite kohomologiateorian kannalta sillä se on varsinaisesti ensimmäinen täysin uusi asia verrattuna homologiateoriaan. Osoitetaan, että kuppitulo määrittelee kohomologiaryhmien porrastettun kertolaskun ja että varustamalla kohomologiaryhmistä muodostettu suora summa tällä laskutoimituksella lopputuloksena on porrastettu rengas, avaruuden kohomologiarengas. Muutaman valottavan esimerkin ja kuppitulon luonnolliseksi toteamisen jälkeen, näytetään että kuppitulo on antikommutatiivinen laskutoimitus jos kerroinrengas on kommutatiivinen. Tutkielman viimeisessä luvussa on kenties työn raskaimmat laskut. Luvun alkupuoli painottuu kahden avaruuden muodostaman tulojoukon kohomologiarenkaan laskemiseen. Künnethin kaava näyttää, että tietyin oletuksin kahden avaruuden kohomologiarenkaiden tensoritulolta tuloavaruuden kohomologiarenkaalle määritelty ristitulokuvaus on rengasisomorfismi. Lopulta laskemme projektiivisen avaruuden kohomologiarenkaan. Soveltamalla tätä tietoa ja Künnethin kaavaa jakoalgebran määräävän bilineaarisen binäärioperaation indusoimaan kohomologiakuvaukseen, saadaan vastaus johdannossa esitettyyn kysymykseen.

As everyday life becomes digital, more and more daily things are done online. In particular, the increased use of mobile devices has accelerated this development. People are increasingly leaving information online about themselves that can be used to identify a person. On 25 May 2018, the European Union’s General Data Protection Regulation, the GDPR, was repealed in the European Union, repealing the previous European Union Data Protection Directive. The GDPR sets out how personal information should be stored and who can process it. The thesis examined how the introduction of GDPR has affected the customer data storage solutions and IT processes of Finnish SMEs during 2018-2020. The companies were examined in three phases: before, during and after the introduction of the GDPR. The study looked at the number of data breaches in the EU and the penalties imposed for them, and compared the situation in Finland. In addition, Finnish SMEs were interviewed for the dissertation. The interview was conducted as a questionnaire interview with 15 companies. The thesis found that Finland did not stand out in any way among other EU countries in GDPR violations. The answers received as a result of the survey revealed that there has been a clear variation in the interpretation of the content of the GDPR in Finland, which has affected the measures taken by companies. Based on the survey, the measures have also been influenced by the organization and organizational culture. However, the reliability of the results is affected by the small sample size.

Tässä työssä on tutkittu Euroopan ja Pohjois-Atlantin talvi-ilmaston muuttumista 30-vuotisjaksojen 1961–1990 ja 1991–2020 välillä. Aineistona on käytetty Euroopan keskipitkien sääennusteiden keskuksen (ECMWF) kehittämää ERA5-uusanalyysidataa, jossa on assimiloitu havaintoja sääennustusmallin tuottamaan alkuarvauskenttään. Karttakuvat on piirretty niin ikään ECMWF:n kehittämällä ohjelmistolla, Metviewillä. Lämpötilan muutoksen pystyleikkauskuvan piirtämiseen on puolestaan käytetty Pythonin numpy- ja matplotlib.pyplot -kirjastoja. Työssä on tarkasteltu ilmanpaineessa, suihkuvirtauksessa, lämpötilassa, pystyliikkeissä, kosteudessa ja sademäärässä tapahtuneita muutoksia. Ennen varsinaisia tuloksia tutkielmassa on selitetty meteorologisiin suureisiin liittyvää fysikaalista teoriaa: miten paine, lämpötila ja tiheys ovat riippuvaisia toisistaan, kuinka geostrofinen tuuli syntyy sekä mitkä tekijät vaikuttavat sateen syntyyn. Kaikissa tarkasteltavissa suureissa on havaittu muutoksia. Lämpötilat ovat nousseet lähes koko Euroopan ja Pohjois-Atlantin alueella: eniten Pohjois-Euroopassa ja Pohjoisella jäämerellä sekä vähemmän Etelä-Euroopassa. Ilmanpaine on noussut Pohjois-Euroopassa ja Pohjois-Atlantin pohjoisosassa sekä laskenut Etelä-Euroopassa ja Pohjois-Atlantin eteläosassa. 250 hPa:n painepinnan Pohjois-Atlantin keskimääräinen suihkuvirtausmaksimi on voimistunut ja liikahtanut hieman pohjoisemmaksi. Sademäärät ja ilman sisältämän vesihöyryn määrä ovat kasvaneet Pohjois-Euroopassa ja pienentyneet Etelä-Euroopassa. Nousu- ja laskuliikkeet ovat monin paikoin voimistuneet. Muutosten tilastollisen merkitsevyyden tutkimiseen on käytetty Studentin kaksisuuntaista t-testiä. Alatroposfäärin lämpötilan muutos on eniten tilastollisesti merkitsevä, mutta muidenkin suureiden muutoksissa tilastollista merkitsevyyttä havaittiin laajalti. Tämä on loogista, sillä lämpötilan muutokset ovat kytköksissä myös muiden suureiden muutoksiin. Aiheesta on tehty myös aiemmin tutkimuksia, joiden tulokset ovat pääosin yhteensopivia tämän työn tulosten kanssa. Ainoastaan 500 hPa:n painepinnan geopotentiaalikorkeuden trendissä oli pientä eroavaisuutta. Tässä tutkielmassa muutosten tilastollinen merkitsevyys oli suurempaa kuin aiemmissa tutkimuksissa.

The Structural Funds of the European Union includes both strong and soft governance of the Union. In strong governance the state reforms their legal system and institutions functions based on the obligations of the Structural Funds. Soft governance, in contrast, is focused on the inhabitants of the unions, who then are governed indirectly by transmitting them the union´s values and by inciting. This thesis deals with the soft governance that is included in the Structural Funds. Additionally, the focus is on the innovation funding share of the Structural Funds. The theory framework of the thesis is governmentality literature, which states that governing consists of rationality and technology of government. Rationality means transmitting values and mindsets, and technology of government means the practical tool to do this. In the Structural Funds the distribution of funding forms the technology of government. There are several kinds of these technologies. This thesis focuses on the technologies of agency and productivity, as well as, transmitting regimes of practices. The Structural Funds program of Finland and the areal plans for the term 2014-2020 form the study material. The first part of the study is content-based, and it examines what kind of actors and what kind of acting the Structural Funds target. The second part of the study is theory-bounded, and it combines the results of the first part with the technologies presented in the governmentality literature. A new feature of this thesis is the division of actors into second and third parties. The second parties are the distributors of the funding, all of whom the European Union can govern directly, that is, using strong governing. The third parties are those actors who have the possibility to apply for the funding, but whom the Union cannot control directly. The only possibility to affect their acts is to induce, which is soft governing. The purpose of this division is to make clearer the difference between the two modes of government, strong and soft, in the second part of the study. The distributors of funding and their actions were not very visible in the study material. Both the distributors and their actions were referred mostly in general terms, if any. The most important actions required from the applying participants were to internationalise, specialise, and innovate, although innovation was also a defining feature when choosing the study material and therefore an obvious study result. All of these three actions were seen in the study material only in a positive light, although a lot of research has done where innovations have cut down those actions the Structural Funds are developed at the beginning: jobs and decreasing the differences between areas. In the study material the emphasis on the individual's responsibility, especially, in matters of obtaining training and skills represented the technology of agency. However, this was much more common in the programme documents related to the Social Fund than to the Regional Development Fund. The participants in these projects supported by the Social Fund should also benefit the companies that were funded by the Regional Development Fund. Ultimately, all the required features and results of the projects are the result of people's activities. The technology of performance was strongly present in the study material. The distribution of the funding is entirely based on the existence of predetermined numerical objectives and the funding applications must demonstrate how they can meet these objectives. Transmitting the regimes of practices was represented in the material by the presence of regions as subjects and the participation of the public organizations, along with the companies, in the project funding competitions.

In atmospheric sciences, measurements provided by remote-sensing instruments are crucial in observing the state of atmosphere. The associated uncertainties are important in nearly all data analyses. Random uncertainties reported by satellite instruments are typically estimated by inversion algorithms (ex-ante). They can be incomplete due to simplified or incomplete modelling of atmospheric processes used in the retrievals, and thus validating random uncertainties is important. However, such validation of uncertainties (or their estimates from statistical analysis afterwards, i.e. ex-post) is not a trivial task, because atmospheric measurements are obtained from the ever-changing atmosphere. This Thesis aims to explore the structure function method – an important approach in spatial statistics – and apply it to total ozone column measurements provided by the nadir-viewing satellite instrument TROPOMI. This method allows us to simultaneously perform validation of reported ex-ante random uncertainties and to explore of local-scale natural variability of atmospheric parameters. Two-dimensional structure functions of total ozone column have been evaluated based on spatial separations in latitudinal and longitudinal directions over selected months and latitude bands. Our results have indicated that the ex-post random uncertainties estimated agree considerably well with the reported ex-ante random uncertainties, which are within 1-2 DU. Discrepancies between them are very small in general. The morphology of ozone natural variability has also been illustrated: ozone variability is minimal in the tropics throughout the year, whereas in middle latitudes and polar regions they attain maxima in local spring and winter. In every scenario, the ozone structure functions are anisotropic with a stronger variability in the latitudinal direction, except at specific seasons in polar regions where isotropic behaviour is observed. Our analysis has demonstrated that the structure function method is a remarkable and promising tool for validating random uncertainties and exploring natural variability. It has a high potential for applications in other remote sensing measurements and atmospheric model data.

A geopolymer waste form containing gasified ion exchange resin loaded with stable analogues of radionuclides (e.g., Sr, Co, Ni, Cr, Cs) was studied using semi-dynamic batch leaching experiments. The experiments were conducted for 180 days using an alkaline groundwater simulant in a glove box with controlled N2 atmosphere with < 10 ppm CO2 and O2. The experiments were conducted to investigate the leaching behavior of the geopolymer in conditions relevant to a low- and intermediate-level waste repository. The leaching results of the geopolymers showed leaching of cesium, sodium, aluminum, and silicon from the geopolymer, while potassium and calcium in the leachant sorbed to the geopolymer. The leaching and sorption rates were at their highest for the first 28 days of the experiment, before slowing down to a steady state which were maintained until the end of the experiment. This suggests that the geopolymers immobilized the waste analogues effectively with exception of cesium which had leached by 55 wt% of the initial fraction by day 180. The leaching indices of sodium, aluminum, silicon, and cesium were determined as: 9.9 ± 0.38, 10.1 ± 0.47, 10.5 ± 0.40, and 9.1 ± 0.30 respectively. The leaching indices are well above 6, which is considered a minimum value for WAC of cementitious waste forms by USNRC. The solid phase analysis of the geopolymer samples showed both presence of calcium rich secondary phases and increasing calcium concentration in the bulk matrix on the leachant contact surface of the geopolymer. It was concluded that the secondary phases consisted of CaCO3 minerals.

Air pollution is considered to be one of the biggest environmental risks to health, causing symptoms from headache to lung diseases, cardiovascular diseases and cancer. To improve awareness of pollutants, air quality needs to be measured more densely. Low-cost air quality sensors offer one solution to increase the number of air quality monitors. However, they suffer from low accuracy of measurements compared to professional-grade monitoring stations. This thesis applies machine learning techniques to calibrate the values of a low-cost air quality sensor against a reference monitoring station. The calibrated values are then compared to a reference station’s values to compute error after calibration. In the past, the evaluation phase has been carried out very lightly. A novel method of selecting data is presented in this thesis to ensure diverse conditions in training and evaluation data, that would yield a more realistic impression about the capabilities of a calibration model. To better understand the level of performance, selected calibration models were trained with data corresponding to different levels of air pollution and meteorological conditions. Regarding pollution level, using homogeneous training and evaluation data, the error of a calibration model was found to be even 85% lower than when using diverse training and evaluation pollution environment. Also, using diverse meteorological training data instead of more homogeneous data was shown to reduce the size of the error and provide stability on the behavior of calibration models.

Simultaneous Localization and Mapping (SLAM) research is gaining a lot of traction as the available computational power and the demand for autonomous vehicles increases. A SLAM system solves the problem of localizing itself during movement (Visual Odometry) and, at the same time, creating a 3D map of its surroundings. Both tasks can be solved on the basis of expensive and spacious hardware like LiDaRs and IMUs, but in this subarea of visual SLAM research aims at replacing those costly sensors by, ultimately, inexpensive monocular cameras. In this work I applied the current state-of-the-art in end-to-end deep learning-based SLAM to a novel dataset comprising of images recorded from cameras mounted to an indoor crane from the Konecranes CXT family. One major aspect that is unique about our proposed dataset is the camera angle that resembles a classical bird’s-eye view towards the ground. This orientation change coming alongside with a novel scene structure has a large impact on the subtask of mapping the environment, which is in this work done through monocular depth prediction. Furthermore, I will assess which properties of the given industrial environments have the biggest impact on the system’s performance to identify possible future research opportunities for improvement. The main performance impairments I examined, that are characteristic for most types of industrial premise, are non-lambertian surfaces, occlusion and texture-sparse areas alongside the ground and walls.

Application of machine learning methods for the analysis of functional neuroimaging signals, or 'brain-function decoding', is a highly interesting approach for better understanding of human brain functions. Recently, Kauppi et al. presented a brain-function decoder based on a novel feature extraction approach using spectral LDA, which allows both high classification accuracy (the authors used sparse logistic regression) and novel neuroscientific interpretation of the MEG signals. In this thesis we evaluate the performance of their brain-function decoder with additional classification and input feature scaling methods, providing possible additional options for their spectrospatial decoding toolbox SpeDeBox. We find the performance of their brain-function decoder to validate the potential of high frequency rhythmic neural activity analysis, and find that the logistic regression classifier provides the highest classification accuracy when compared to the other methods. We did not find additional benefits in applying prior input feature scaling or reduction methods.