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Due to its exceptional thermal properties and irradiation resistance, tungsten is the material of choice for critical plasma-facing components in many leading thermonuclear fusion projects. Owing to the natural retention of hydrogen isotopes in materials such as tungsten, the safety of a fusion device depends heavily on the inventory of radioactive tritium in its plasma-facing components. The proposed methods of tritium removal typically include thermal treatment of massive metal structures for prolonged timescales. A novel way to either shorten the treatment times or lower the required temperatures is based performing the removal under an H-2 atmosphere, effectively exchanging the trapped tritium for non-radioactive protium. In this thesis, we employ molecular dynamics simulations to study the mechanism of hydrogen isotope exchange in vacancy, dislocation and grain boundary type defects in tungsten. By comparing the results to simulations of purely diffusion-based tritium removal methods, we establish that hydrogen isotope exchange indeed facilitates faster removal of tritium for all studied defect types at temperatures of 500 K and above. The fastest removal, when normalising based on the initial occupation of the defect, is shown to occur in vacancies and the slowest in grain boundaries. Through an atom level study of the mechanism, we are able to verify that tritium removal using isotope exchange depends on keeping the defect saturated with hydrogen. This study also works to show that molecular dynamics indeed is a valid tool for studying tritium removal and isotope exchange in general. Using small system sizes and spatially-parallelised simulation tools, we have managed to model isotope exchange for timescales extending from hundreds of nanoseconds up to several microseconds.

Short-term rental platforms have become widely popular in recent years, but their growth has left cities to face a variety of problems. Studies have shown, for example, that short-term rentals have led to an increase in long-term rental prices. When more and more apartments are used as short-term rentals, the supply of long-term rentals decreases. This causes more pressure on the long-term rental market and leads to increased prices. In this study I examine the possible impacts of Airbnb, the biggest and most popular short-term rental platform, on the rental market of Helsinki. First, I analyse how many apartments have been removed from the long-term rental market to the Airbnb market, and second, how likely it is that the number of Airbnb rentals rises in the future. Presumably, renting through Airbnb becomes more popular when the potential income from Airbnb rentals is larger than from long-term rentals. In Neil Smith’s terms, this difference between actual and potential rental income constitutes a rent gap. Therefore, I also analyse whether renting short-term in Helsinki is more profitable than renting long-term. In addition, I discuss the current city and tourism policies of the city of Helsinki in the light of the results of the above research questions and give recommendations on issues to be taken into account in the future. This study uses AirDNA’s data of Airbnb rentals in Helsinki and long-term rental price data from KTI Property Information Ltd, and it focuses on data from year 2019. Data analysis is conducted using statistical and geospatial methods. The results of this study show that in 2019 there were a significant number of professional Airbnb rentals in Helsinki, 863 in total. However, their number varied substantially between the districts of Helsinki. There was a large amount of professional Airbnb rentals especially in the city centre and Kallio area. On the scale of the whole of Helsinki, professional Airbnb rentals comprise approximately 0,5 % of all rental apartments whereas in some districts in the city centre the percentage was considerably higher, in the Kamppi district as much as 3,9 %. Based on the results, the number of Airbnb rentals will likely grow in the future because Airbnb rental income was, on average, greater than long-term rental income in each of the study areas. In most areas, the rent gap was substantial. However, the size of the rent gap varied significantly, between 50 and 1350 euros, based on the location and amount of rooms of the apartment. Some policy recommendations can be made based on the results of this study. When designing future policies, it is important to acknowledge that a sizeable part of the Airbnb rentals in Helsinki is professional and that the number of professional rentals will probably continue to increase. Since the number of professional rentals is still quite small on the city level, the impacts of short-term rentals in Helsinki are presumably not yet significant. Nevertheless, in the future problems can arise especially in the city centre and Kallio area, as these areas have a lot of professional Airbnb rentals. Since only professional Airbnb rentals are disadvantageous for the long-term rental market, assigning certain restrictions would be justifiable in order to prevent future problems and to promote sustainable tourism. Restricting Airbnb activity could be done by enforcing current regulations more rigorously or setting a yearly renting limit like many other European cities have done. This would help to inhibit activity that is against the current legislation and to support the real sharing economy.

This thesis presents a wavelet-based method for detecting moments of fast change in the textual contents of historical newspapers. The method works by generating time series of the relative frequencies of different words in the newspaper contents over time, and calculating their wavelet transforms. Wavelet transform is essentially a group of transformations describing the changes happening in the original time series at different time scales, and can therefore be used to pinpoint moments of fast change in the data. The produced wavelet transforms are then used to detect fast changes in word frequencies by examining products of multiple scales of the transform. The properties of the wavelet transform and the related multi-scale product are evaluated in relation to detecting various kinds of steps and spikes in different noise environments. The suitability of the method for analysing historical newspaper archives is examined using an example corpus consisting of 487 issues of Uusi Suometar from 1869–1918 and 250 issues of Wiipuri from 1893–1918. Two problematic features in the newspaper data, noise caused by OCR (optical character recognition) errors and uneven temporal distribution of the data, are identified and their effects on the results of the presented method are evaluated using synthetic data. Finally, the method is tested using the example corpus, and the results are examined briefly. The method is found to be adversely affected especially by the uneven temporal distribution of the newspaper data. Without additional processing, or improving the quality of the examined data, a significant amount of the detected steps are due to the noise in the data. Various ways of alleviating the effect are proposed, among other suggested improvements on the system.

Social networks represent a public forum of discussion for various topics, some of them controversial. Twitter is such a social network; it acts as a public space where discourse occurs. In recent years the role of social networks in information spreading has increased. As have the fears regarding the increasingly polarised discourse on social networks, caused by the tendency of users to avoid exposure to opposing opinions, while increasingly interacting with only like-minded individuals. This work looks at controversial topics on Twitter, over a long period of time, through the prism of political polarisation. We use the daily interactions, and the underlying structure of the whole conversation, to create daily graphs that are then used to obtain daily graph embeddings. We estimate the political ideologies of the users that are represented in the graph embeddings. By using the political ideologies of users and the daily graph embeddings, we offer a series of methods that allow us to detect and analyse changes in the political polarisation of the conversation. This enables us to conclude that, during our analysed time period, the overall polarisation levels for our examined controversial topics have stagnated. We also explore the effects of topic-related controversial events on the conversation, thus revealing their short-term effect on the conversation as a whole. Additionally, the linkage between increased interest in a topic and the increase of political polarisation is explored. Our findings reveal that as the interest in the controversial topic increases, so does the political polarisation.

The analysis of volatile organics is growing by the year and there is a great interest in fast and simple sample preparation techniques. With solid phase micro-extraction, samples can be extracted non-destructively without a need for solvents. This is both cost effective and ecological, because even most eco-friendly solvents still cause strain on the environment. This thesis focused on studying the effect of extraction conditions on the extraction efficiency. The effect of different sorptive phase materials was tested as well. New single-step sample extraction and preparation method was developed for gas chromatographic mass spectrometric analysis. Three different sorptive phase materials were compared and the extraction conditions were optimized for each. The method developed was used to extract, analyze and determine unknown compounds from a butterfly specimen. Multiple extractions were performed from both headspace and with direct immersion. By progressively changing the extraction conditions, properties of the compounds such as volatility and polarity could be determined by their presence alone. Analysis was performed using with gas chromatography mass-spectrometer using electron ionization quadrupole mass detector in full scan mode.

Tämä työ tarkastelee kylmää jaksoa Pohjois-Euroopassa ja erityisesti Lapissa 1.1.2017 – 6.1.2017. Tarkastelujaksolla Sodankylässä mitattiin yli neljäkymmentä astetta pakkasta, jonka Euroopan keskipitkien ennusteiden keskuksen säämalli IFS ennusti pintalämpötilan yli kymmenen astetta liian korkeaksi. Kylmä jakso ylettyi aina Bulgariaan ja Kreikaan asti antaen viitteitä laajemmasta säähäiriöstä. Näistä lähtökohdista lähdin tutkimaan, mikäli lämpötilan yliennustuksen syy olisi laajemman synoptisen skaalan häiriön epätarkka ennustaminen. Työssä visualisoin IFS:n paine ja lämpötilakenttiä Euroopan keskuksen metview alustalla ja vertaan niitä synoptiseen analyysiin sekä pinta- ja luotaushavaintoihin Sodankylästä. Käytän pohjana Euroopan keskuksen omaa raporttia poikkeuksellisesta sääilmiöistä, joka kuitenkin keskittyy enemmän Kaakkois-Euroopan poikkeukselliseen kylmyyteen ja voimakkaisiin lumisateisiin. Työssä havaitaan, että IFS ennusti synoptisen skaalan matalapainejärjestelmien ja muiden säähäiriöiden synnyn ja liikkeet tarkastelujaksolla varsin hyvin. Syy pintalämpötilan yliennustamiseen ei arvioni mukaan johdu virtaustilanteen väärästä ennustamisesta, vaan mallin tavasta käsitellä pintalämpötilaa. Erittäin stabiileissa olosuhteissa oletukset, joiden perusteella mallin pintalämpötila lasketaan, eivät tuota järkevää tulosta. Luotauksista havaitaan, että Sodankylässä vallitsi voimakas pintainversio, jota malli ei kykene täysin mallintamaan johtuen tavasta, jolla se käsittelee pinnan ja alimman mallitason välistä kerrosta. Ennustettu lämpötila poikkeaa toteutuneesta kuitenkin niin voimakkaasti, että inversion mallintamiseen liittyvät ongelmat eivät välttämättä ole ainoa virhelähde. Lopuksi tarkastelen lyhyesti raportteja mallin ongelmista ennustaa pintalämpötilaa Suomen talviolosuhteisssa, sekä miten Euroopan keskipitkien säähavaintojen keskus on itse käsitellyt ongelmaa. Globaalimallina IFS on kalibroitu tuottamaan keskimäärin osuvin ennuste koko planeetalla, ja on tärkeä tietää ne rajatapaukset, joissa sen oletukset eivät ole päteviä.

An investigation into switchable polarity ionic liquids was carried out to find greener alternative substituents and still obtain a switchable polarity ionic liquid. First for fluorinated compounds (fluorinated alcohol and amine) with a non-fluorinated hydroxylamine to form a mixed carbamate, then replacing the superbase with a basic tertiary (or secondary) amine. The trigger molecule for switching polarity was CO2. It was found that O-hexylhydroxylamine was a suitable replacement for fluorinated ethanol and fluorinated ethylamine to work with DBU (superbase) to form a switchable polarity ionic liquid. The three amines of triethylamine (TEA), diisopropylethylamine (Hünigs base) and diisopropylamine (DIPA) were inconclusive or unsuccessful. Both TEA and DIPA require further alternative analysis for a conclusive result while Hünigs base was proven to be unsuccessful. These reaction products were characterised with 1H and 13C NMR and ReactIR spectral data. Synthesis of hydroxylamine was also approached for a greener improvement. A new synthesis method is demonstrated that is successful using water and methylamine in ethanol working on reaction equilibria. The new method proposed had a yield of 29.1%, while the patent literature method that used hydrazine monohydrate (which is highly toxic and unstable unless in solution) gave a yield of 54.3% of hydroxylamine. A secondary investigation was also undertaken in to basicity effects of caesium carbonate on the CO2 addition to aniline, with and without a superbase present. The superbase used was tertramethylguanidine (TMG). Aniline, p-nitroaniline and p-methoxyaniline were tested for CO2 addition by formation of an amide peak in ReactIR. There was formation of the amide peak with caesium carbonate, though not as much as with the already known TMG. A concentration series of caesium carbonate and TMG in aniline was also devised to observe the effect the added caesium carbonate had on the aniline-TMG system in absorbing CO2. This was also analysed using ReactIR spectra. It was seen generally that by increasing the concentration of both/either TMG/Cs2CO3 there is an increase in carbamate. However further concentration series data is required before a generalised rule can be defined.

Online hypothesis testing occurs in many branches of science. Most notably it is of use when there are too many hypotheses to test with traditional multiple hypothesis testing or when the hypotheses are created one-by-one. When testing multiple hypotheses one-by-one, the order in which the hypotheses are tested often has great influence to the power of the procedure. In this thesis we investigate the applicability of reinforcement learning tools to solve the exploration – exploitation problem that often arises in online hypothesis testing. We show that a common reinforcement learning tool, Thompson sampling, can be used to gain a modest amount of power using a method for online hypothesis testing called alpha-investing. Finally we examine the size of this effect using both synthetic data and a practical case involving simulated data studying urban pollution. We found that, by choosing the order of tested hypothesis with Thompson sampling, the power of alpha investing is improved. The level of improvement depends on the assumptions that the experimenter is willing to make and their validity. In a practical situation the presented procedure rejected up to 6.8 percentage points more hypotheses than testing the hypotheses in a random order.

Investment funds are continuously looking for new technologies and ideas to enhance their results. Lately, with the success observed in other fields, wealth managers are taking a closes look at machine learning methods. Even if the use of ML is not entirely new in finance, leveraging new techniques has proved to be challenging and few funds succeed in doing so. The present work explores de usage of reinforcement learning algorithms for portfolio management for the stock market. It is well known the stochastic nature of stock and aiming to predict the market is unrealistic; nevertheless, the question of how to use machine learning to find useful patterns in the data that enable small market edges, remains open. Based on the ideas of reinforcement learning, a portfolio optimization approach is proposed. RL agents are trained to trade in a stock exchange, using portfolio returns as rewards for their RL optimization problem, thus seeking optimal resource allocation. For this purpose, a set of 68 stock tickers in the Frankfurt exchange market was selected, and two RL methods applied, namely Advantage Actor-Critic(A2C) and Proximal Policy Optimization (PPO). Their performance was compared against three commonly traded ETFs (exchange-traded funds) to asses the algorithm's ability to generate returns compared to real-life investments. Both algorithms were able to achieve positive returns in a year of testing( 5.4\% and 9.3\% for A2C and PPO respectively, a European ETF (VGK, Vanguard FTSE Europe Index Fund) for the same period, reported 9.0\% returns) as well as healthy risk-to-returns ratios. The results do not aim to be financial advice or trading strategies, but rather explore the potential of RL for studying small to medium size stock portfolios.

Biomass is an important parameter for crop monitoring and management, as well as for assessing carbon cycle. In the field, allometric models can be used for non-destructive biomass assessment, whereas remote sensing is a convenient method for upscaling the biomass estimations over large areas. This study assessed the dry leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre and biofuel production in tropical and subtropical regions. First, an allometric model was developed for predicting the leaf biomass. Then, Sentinel-2 multispectral satellite imagery was used to model the leaf biomass at 8851 ha plantation in South-Eastern Kenya. For the allometric model 38 leaves were sampled and measured. Plant height and leaf maximum diameter were combined into a volume approximation and the relation to biomass was formalised with linear regression. A strong log-log linear relation was found and leave-one-out cross-validation for the model showed good prediction accuracy (R2 = 0.96, RMSE = 7.69g). The model was used to predict biomass for 58 field plots, which constituted a sample for modelling the biomass with Sentinel-2 data. Generalised additive models were then used to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (D2 = 74%, RMSE = 4.96 Mg/ha) was achieved with VIs based on the red-edge (R740 and R783), near-infrared (R865) and green (R560) spectral bands. Highly heterogeneous growing conditions, mainly variation in the understory vegetation seemed to be the main factor limiting the model performance. The best performing VI (R740/R783) was used to predict the biomass at plantation level. The leaf biomass ranged from 0 to 45.1 Mg/ha, with mean at 9.9 Mg/ha. This research resulted a newly established allometric equation that can be used as an accurate tool for predicting the leaf biomass of sisal. Further research is required to account for other parts of the plant, such as the stem and the roots. The biomass-VI modelling results showed that multispectral data is suitable for assessing sisal leaf biomass over large areas, but the heterogeneity of the understory vegetation limits the model performance. Future research should address this by investigating the background effects of understory and by looking into complementary data sources. The carbon stored in the leaf biomass at the plantation corresponds to that in the woody aboveground biomass of natural bushlands in the area. Future research is needed on soil carbon sequestration and soil and plant carbon fluxes, to fully understand the carbon cycle at sisal plantation.