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Triglyseridimatriisit sisältävät käsitteenä kaikki lipidimatriisit, kuten eläinrasvat ja kasviöljyt. Triglyseridien lisäksi nämä lipidimatriisit voivat sisältää myös muita heteroatomeja sisältäviä yhdisteitä kuten esimerkiksi typpi- tai happiyhdisteitä. Rasvoja ja öljyjä voidaan käyttää elintarviketeollisuuden lisäksi myös polttoaineteollisuudessa esimerkiksi rasvahappojen metyyliestereistä koostuvan biodieselin tai hiilivedyistä koostuvan uusiutuvan dieselin tuotantoon. Tässä kirjallisuuskatsauksessa esitellään erilaisia uusiutuvien polttoaineiden valmistukseen yleisesti käytettyjä triglyseridimatriiseja sekä niiden sisältämiä yhdisteluokkia, ja perehdytään näiden analysointiin käytettäviin kvalitatiivisiin ja kvantitatiivisiin eristys- ja analyysimenetelmiin. Työssä esitellään useita eläinrasva- ja kasviöljymatriiseja sekä niissä yleisimmin esiintyviä heteroatomeja sisältäviä yhdisteryhmiä. Yhdisteiden eristykseen käytettävistä menetelmistä esitellään neste-nesteuutto, kiinteäfaasiuutto ja saippuointi, ja vastaavasti analyysimenetelmistä käsitellään nestekromatografia (LC), kaasukromatografia (GC) ja massaspektrometria (MS). Triglyseridimatriisien sisältämien heteroatomeja sisältävien epäpuhtauksien ja niiden pitoisuuksien analysointi on tärkeää, sillä näillä epäpuhtauksilla on ympäristöpäästöjen muodostumisen lisäksi useita negatiivia vaikutuksia esimerkiksi polttoaineen tuotantoon, säilyvyyteen ja toimintakykyyn polttomoottoreissa. Tutkielman kokeellisessa osuudessa validoitiin "Rasvahappoamidien, sterolien ja muiden raskaiden yhdisteiden analysointi öljyistä ja rasvoista kaasukromatografisesti" -menetelmä amideille. Validointi koostui kuudesta osuudesta: lineaarisuudesta, määritysrajan ja toteamisrajan määrityksestä, toistettavuudesta, uusittavuudesta, vaihtelevuudesta sekä takaisinsaantokokeesta. Validointi onnistui pääpiirteittäin hyvin, tulokset olivat luotettavia ja vastasivat odotuksia. Validoidun menetelmän rinnalle kehitettiin myös uuttoon perustuvaa menetelmää saippuoitumattomalle osuudelle rasvoista. Menetelmää testattiin eri uuttoliuottimilla ja menetelmän toistettavuutta kokeiltiin. Menetelmällä saatiin näytteistä eristettyä validoituun menetelmään verrattuna huomattavan paljon enemmän erilaisia yhdisteitä. Menetelmän kehitys jäi kuitenkin kesken, sillä esimerkiksi reaktio-olosuhteiden optimointi ja saantojen kvantitointiin vaadittavan malliyhdisteen löytäminen ovat vielä työn alla.

The purpose of this study was to investigate sea surface temperatures in Trinity Bay, eastern Newfoundland during the mid- and late Holosene c. 7200–1200 years ago. The core drilling location was selected because the area is an important deep water formation area. The cold water pulses might have influenced the deep water formation in the Labrador Sea and further to the circulation of the Gulf Stream. The diatom analysis has been done from marine sediment core with high resolution (35-100 years). The impact of the Atlantic and Arctic water masses during different times were analysed. Diatom analysis and transfer function method were used to reconstruct sea surface temperature in the area being the first quantitative reconstruction from the southwestern Labrador Sea. The sea surface temperature reconstruction shows the gradient between c. 9,7 °C (6,79 ka) and c. 4,4 °C (1,29 ka). The reconstruction can be divided into three periods: the warm Holocene Climate Optimum 7.18–5.1 ka, Holocene Transition period 5.1–3.5 ka and the Cool Late Holocene 3,5–1.18 ka. The results indicate both strong and more moderate changes. As a whole, the reconstruction shows the cooling trend that is associated with the diminishing insolation during the early Holocene. The observed cycles of c. 800 years are related to variations in the Arctic water outflow, and possibly to the AMOC. In Trinity Bay the beginning of the Holocene (c. 7,2–5,1 ka) corresponds with the beginning of the Labrador Sea deep water formation and the intensification of the termohaline circulation in the North Atlantic. The Gulf Stream transported warm sea water to Trinity Bay but the melting glaciers, producing cold fresher water, limited the thermohaline circulation. The strong changes in the sea surface temperatures during the Climate Optimum may also have been influenced by the changes in solar activity. The first c. 1000-years-long interval of the Holocene transition period (c. 5,1–3.5 ka) was cold. The c. 800 years cyclicity in Trinity Bay is possibly the same cyclicity seen in the areas influenced by the North Atlantic current (e.g. the British Isles) and this may indicate the influence of the Labrador Sea on the Gulf Stream. The temperature rise in Trinity Bay (c. 4–3,5 ka) is possibly the 4,2 ka-event, which was due to a short-term global warming at this time. The Cool Late Holocene in Trinity Bay started by cooling c. 3,5 ka ago. The record ends on warming c. 1,2 ka interrupting the cooling trend may indicate the beginning of the Medieval Climate Anomaly. The results from the NGRIP-ice core as well as the results from several previous studies support the results of this study from Trinity Bay for the mid- to late Holocene.

Soil is the largest actively cycling terrestrial carbon pool, which has been severely distrubed in the last 100-200 years by human actions. To improve the situation, extensive monitoring of soil carbon and new methods for monitoring are required. This study demonstrates the capability of a portable hyperspectral device operating in the visible-near infrared (VIS-NIR) spectrum for soil organic carbon (SOC) prediction. Two multivariate methods, partial least squares regression (PLSR) and for this purpose previously untested lasso regression were used for prediction. 191 soil samples were collected from Taita Hills, Kenya. The samples represent a tropical altitudinal gradient with five land uses: agroforestry, field, forest, shrubland and sisal plantation. The samples were imaged with hyperspectral camera, Specim IQ in laboratory and in field conditions, and the carbon content of the samples was determined with a dry-oxidization analyzer. Three datasets were derived from the images, one containing the mean spectra of the complete imaged samples, one with segmented sub-image spectra and one with segmented sub-image spectra where outlier spectra were removed. Both multivariate methods were tested with all three datasets with good prediction accuracies (PLSR: R2min = 0.85, RMSEmin = 0.78, lasso: R2min=0.85, RMSEmin=0.80), demonstrating the feasibility of both the device and lasso regression as SOC prediction tools. Using the segmented sub-image datasets improved the results with PLSR but had no significant effect on lasso regression prediction results. While good results were gained with laboratory imagery, the field imaging conditions were difficult, and the data performed poorly. Future research should focus on finding solutions to reliably estimate SOC content in situ or with portable laboratory setups to make SOC measurements more widely accessible and agile for e.g. precision agriculture purposes.

Tulevaisuuden kestävissä kaupungeissa liikkumiseen ja siitä aiheutuviin päästöihin tulee kiinnittää nykyistä enemmän huomiota. Kaupungeissa liikenne on lämmityksen ohella merkittävä hiilidioksidipäästöjen lähde. Tiiviissä kaupunkirakenteessa kävely ja pyöräily tarjoavat varteenotettavan vaihtoehdon yksityisautoilulle. Toistaiseksi näitä ekologisesti kestäviä kulkumuotoja on kehitetty huomattavasti yksityisautoilua ponnettomammin ja pienemmällä budjetilla. Helsingin kaupunki pyrkii vähentämään liikenteen päästöjä pyöräilyn ja raideliikenteen käytön lisäämisellä. Kaupungin tavoitteena on, että vuoteen 2035 menneessä pyöräliikenteen liikennetapaosuuden kaksinkertaistuminen nykyisestä 10 prosentista 20 prosenttiin. Tällä hetkellä Helsinki investoi uusiin pyöräreitteihin ja pyöräilyn turvallisuuteen. Helsingin kaupungin pyöräliikenteen kehittämisohjelman ylätavoitteena on, että kaupunki on kaiken ikäisille sopiva, ympärivuotinen pyöräilykaupunki. (Helsingin kaupunki 2020a.) Kuitenkaan eri ikäisiin pyöräilijöihin ja erityisesti perhepyöräilyyn liittyvää tutkimusta ei ole juuri tehty Suomessa. Haastattelututkimukseen perustuvassa aineistossani tulen selvittämään, miten pyöräilevät perheelliset kokevat kotikaupunkinsa pyöräinfran ja -kulttuurin. Mitä seikkoja he arvostavat kaupunkipyöräilyssä ja voisiko pyöräilyn houkuttelevuutta lisätä joillain keinoin?

As the climate warms tundra ecosystems will face changes that have an impact on their carbon cycle. Arctic tundra is already experiencing changes in plant species composition and distribution, and vegetation height expected to increase. Vegetation shifts such as shrubification can increase carbon uptake from the atmosphere to the tundra ecosystems but changes in soil microclimate and plant-microbe interactions related to vegetation shifts can also create feedbacks that increase carbon losses from the ecosystems to the atmosphere. To better understand changes in tundra carbon dioxide (CO2) fluxes related to climate change and vegetation shifts, it’s crucial to understand the factors controlling CO2 fluxes in the tundra in general and in the tundra environments that differ in their vegetation composition. We used environmental gradients created by late-lying snowbanks to collect the data and we used modelling to understand the factors controlling CO2 fluxes in the tundra and within four different vegetation types during the growing season. The vegetation types included in the study were barrens, meadow-like environments, prostrate shrub tundra (heat) and erect shrub tundra (shrub). Gross primary production (GPP) and ecosystem respiration (ER) were the highest in shrub plots, smaller in the heat and in meadow-like environments and the smallest in barrens. Net CO2 sink increased with vegetation cover and GPP, but also barrens with little vegetation were still mostly net CO2 sinks during the growing season due to low ER. The amount of vegetation measured in vegetation height and cover well explained the variation in GPP and net ecosystem exchange (NEE) in the whole data and within vegetation types. ER was also related to the amount of vegetation but was more affected by microclimate, mainly air temperature and soil moisture, than GPP and NEE. In shrub plots, variation in ER was explained by air temperature more than by vegetation cover or height. Microclimate variables were not important in explaining variation in GPP in the whole data or within vegetation types but air temperature in heath and in the whole data and soil temperature and soil moisture in barrens helped to explain variation in NEE. In the whole data, heat and shrub plots soil temperature was not related to higher ER. Depth of organic layer explained some variation in NEE and ER in the whole data and some variation in NEE in some of the vegetation types. Soil pH was not an important factor explaining CO2 fluxes, but it was related to vegetation type and vegetation distribution especially in the whole data. The main factor controlling CO2 fluxes in the tundra and within different vegetation types seemed to be the amount of vegetation. Air temperature and soil moisture help to explain the variation especially in ER. The ability of vegetation parameters to explain variation in ER may be partly because of a relatively small amount of heterotrophic respiration compared to autotrophic respiration in the system or because of a positive link between the amount of vegetation and the amount of decomposition. Drought during the field campaigns may have limited decomposition and decreased temperature sensitivity of decomposition which may partly explain the insensitivity of ER to soil temperature. In heat and in shrub plots the shading effect of vegetation lowered soil temperature and may have slowed decomposition. As the ability of vegetation, microclimate and soil variables to explain variation in CO2 fluxes differed between vegetation types, CO2 fluxes of different vegetation types may respond to changes in the tundra environment differently. The results imply that the effect of vegetation composition should be considered when estimating how tundra ecosystem CO2 fluxes will respond to climate change. Similarly, the role of factors controlling decomposition, such as drought and shading effect of shrub vegetation, may be important in determining the future carbon balance of the tundra.

Helsinki and Stockholm, as well as the pattern of development in the history is very much similar features. The two cities are the capital cities sprang from, and they have escaped the Second World War, many other European capital city-specific damage. However, the urban public transport, especially rail networks are different from each other in many ways. While in Stockholm was built between 1950-1985 more than a hundred-kilometer-long underground network (tunnelbanan), of the Helsinki metro line, measured in kilometers sixth of this. Stockholm was abandoned in the late 1960s increased over the decades extensive tram network, while in Helsinki trams remained. The main question is, what are the determinants of the two, the same design in a city such as the differences between the networks. In both cities tramway began quite early in the 1870s and 1880s at the turn, initially horse-drawn. The tram network was electrified in early 1900s and the network expanded rapidly into new areas of the city grew. At the same time began local traffic in railway lines around Stockholm and Helsinki. The railways were electrified in Sweden and globally considered fairly early on in 1920s and 1930s, while in Finland the electric-driven traffic only began in the late 1960s. Stockholm area formed in 1900 in the beginning of an important network of narrow-gauge railway (Roslagsbanan), which is also the main electrified in the legs. In addition, both cities was born suburban railway. Stockholm underground (tunnelbanan) construction was presented in the early 1900s, but until the 1930s, traffic increases, and the city continues to expands tunnelbanan seen an increasing need to transport problems. Tunnelbana was also seen in the new, 1940s and 1950s built at the turn of the ABC-suburban means of transport: new residential areas were placed along it. Tunnelbanan first part was completed in 1950 and the network was built at a rapid pace over the next three decades. In the urban planning tunnelbanan play a key role. The effective commuter rail (pendeltågen) became an issue in the late 1960s in region of Stockholm. Helsinki metro construction decision at the political level is matured in the 1960s and traffic started various stages after the year 1982. Tramway situation on the Helsinki public transport has varied over the decades, but unlike in Stockholm, where the inner city tram network was closed in 1967, the Helsinki trams have largely retained their position. The cooperation between operators in Stockholm drew attention: in 1967, started its operations in Stockholm County subjected to SL (Storstockholms lokaltrafik), which is responsible for the entire Stockholm County traffic. Helsinki region responsible for coordinating the overall authority intervenes on this scale. After years of effort in the Helsinki region was formed municipally owned HSL (Helsinki Region Transport), which currently consists of seven municipalities. HSL aren't covered whole Helsinki work areas as opposed to the Stockholm SL. Helsinki Region local train has been improved, especially in 1990s and 2000s, carried out extensive additional rail projects (suburban rail), and at the moment it compares well with the Stockholm pendeltåg system. Tramways are the last two decades come into fashion again. Stockholm was opened in the new millennium light rail (Tvärnbanan) to connect the suburbs radiating from the center of the leading tunnelbanan lines. Originally the museum tram started tram line has moved to the part of normal transport system (Spårväg City). It is planned to further expansion projects. Likewise, the Helsinki tram network has been expanded and modernized equipment.

Sentimenttianalyysi on yksi tutkituimpia luonnollisen kielen käsittelyn linjoja, jonka modernimpeihin työkaluihin kuuluu neuroverkot. Alan alle on syntynyt haastavampi luokittelutehtävä, jonka tavoitteena on perinteisen positiivisen, neutraalin ja negatiivisen arvion lisäksi tunnistaa tekstistä tunnetiloja, kuten surua, iloa tai vihaa ja rakkautta. Siinä missä sentimenttianalyysin polaarisuuden tutkimuksessa etsitään vain kolmea luokkaa, tunnetiloja on useimpien teorioiden mukaan olemassa yli kuutta erilaista, mikä kasvattaa eri ratkaisujen määrää eksponentiaalisesti, ja tekee moniluokittelutehtävästä haastavamman. Tutkimusongelma on kiinnostava, sillä automaattisilla algoritmeilla pystytään käsittelemään valtavia määriä tekstiä vain muutamissa sekunneissa, ja luomaan lopputuloksena tiivistelmiä, jotka kertovat, millaisista asioista tekstissä puhutaan. Tällaiset työkalut ovat hyödyllisiä esimerkiksi yrittäjille, joiden halu on ymmärtää omia ja tulevia asiakkaitaan paremmin. Ihmiset tuottavat paljon tekstiä päivittäin sosiaalisen median kautta, ja toisinaan antavat palautetta myös suoraan yrityksille; jos nämä eri lähteet pystytään tiivistämään helposti ymmärrettäviksi kuvaajiksi ja luvuiksi, saadaan tärkeää tietoa siitä, millaiset tarpeet ohjaavat ihmisten ostokäyttäytymistä. Tutkielmassa tutustutaan neuroverkkoihin ja tilastollisiin menetelmiin, joilla tunteita voidaan havaita tekstistä automaattisesti. Soveltavassa osiossa luodaan muutaman aikansa tuloksellisemman koneoppimisarkkitehtuurin avulla malli, jonka opetusaineistona käytetään vuoden 2018 SemEval-tehtävän 10 tuhatta käsinluokiteltua Twitterjulkaisua. Tavoitteena on kuvata mallinnusprosessi ja siihen liittyvät oletukset ja lopuksi näyttää, että prosessilla saatavien mallien ennustustarkkuus on hyvä, ja lisäksi osoittaa, että esikoulutettujen koneoppimismallien ennustustarkkuus on sanapohjaisia malleja parempi. Lopputuloksena saadaan uutta BERT-arkkitehtuuria käyttävä malli, joka saavuttaa vuoden 2018 SemEval -tehtävän muiden kilpailevien mallien suoritustason.

The main objective of regularization is to minimize the prediction error in a multiple regression model by reducing the variance of the estimator via shrinkage of the parameter norm. In regularization, the loss function of the model is minimized subject to an extra condition that penalizes the size of the parameter, which condition depends on the applied method. Regularization may produce unambiguous and consistent estimates also for high-dimensional data sets in which the amount of independent variables exceeds the sample size, or for data sets including highly correlated predictors. L1-regularization, also known as the Lasso (Least Absolute Shrinkage and Selection Operator), is one of the most popular methods in linear regression. Lasso is well-known for its property to perform the variable selection and estimation simultaneously. In addition, Lasso is computationally efficient as it is a convex optimization algorithm, which makes it also applicable for high-dimensional data sets. In the thesis, we focus on the theory of regularized linear regression, after which we form a prediction model for the sales of a specific consumer product by using collected data and applying the Lasso, Elastic net and OLS post-Lasso methods. We compare the results to those obtained by best subset selection using a stepwise algorithm. In our study, the regularized models result in more accurate predictions than the model obtained by stepwise algorithm, in terms of test data prediction error. All regularized algorithms selected the same subset of variables, the models differing only in that OLS post-Lasso coefficients were systematically larger in absolute size than the Lasso and Elastic Net coefficients, resulting in the smallest prediction error for OLS post-Lasso. Lasso and Elastic Net generated an equal and to some extent underfitted model.

This study aims to connect air-sea ice turbulent carbon dioxide (CO2) exchange and the surface energy balance over melting fjord ice. Recent studies have shown that sea ice melt might act as a significant CO2 sink in Arctic waters. Melt process have been suggested to dilute both brine and surface water partial pressures of carbon dioxide ( pCO2). Also biological activity and carbonate chemistry changes the air-ocean CO2 concentration gradient. Even small fluxes might be potentially significant as the maximum sea ice extent covers approximately 7 % of Earth's surface. As multi-year ice diminishes with the on-going climate change a bigger portion of the ice cover will experience melt in the summer season and thus the melt induced changes on the carbon cycling in the Arctic will have a greater effect. Surface energy balance consists of net radiation, turbulent fluxes of latent and sensible heat and conductive heat flux. During melt the sea ice surface transforms from a dry snow cover to melt ponds. Surface melt leads to a decrease in the surface albedo controlling the surface energy balance. Sea ice temperature affects both air-ice-ocean energy exchange and the permeability of the ice. My thesis is based on a 30 day measurement campaign in June 2014 from The Young Sound fjord, in North-East Greenland (74° 18' N, 20° 13' W). Turbulent fluxes of CO2 and H2O were measured with 10 Hz with two open-path infrared gas-analysers and two sonic anemometers at approximately 3 m height. One mast was used to measure basic meteorology (temperature, humidity, radiation, wind). Continuous measurements of surface water pCO2 were made 2.5 m below the ice. Conductive heat flux was determined from ice cores. The turbulent fluxes were calculated with the Eddy Covariance-methodology. Weak winds decreased the number of good quality measurements and created gaps in the time series. The measured CO2 flux ranged between 1.92 ja -3.2 µmol m-2 s-1 (positive fluxes being efflux) and sea ice was a net sink during the campaign. Sea ice temperature rose steadily with time, being above the permeability threshold in all measurements. Surface water pCO2 was lower than the atmospheric throughout the study and the under saturation grew from 50 µatm to 140 µatm in 10 days (13. to 23. June). Despite the strong pCO2 difference indicating ocean sink the CO2 absorption at the ice surface did not show an increase over time. A possible explanation for this is the equilibration of the air-melt pond pCO2 gradient. The strongest CO2 uptake was recorded as the melt ponds formed. Two strong efflux events ( flux > 1 µmol m-2 s-1) were recorded on June 11 and 18. No strong correlations were found between the surface energy budget and the air-surface CO2 exchange. However, the CO2 flux has a weak positive correlation both with wind speed and net radiation. The weak correlations are probably explained by the co-existence of different processes simultaneously affecting the air-ice-ocean CO2 gradients. The results support the hypothesis of melting sea ice acting as a sink for atmospheric CO2. The measurement quality could be improved by using a closed-path gas analyser. Despite the challenges Eddy Covariance is currently the state-of-art methodology for measuring ecosystem-scale turbulent exchange.