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Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m−2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

This bachelor’s thesis presents one possibility of utilising green hydrogen as fuel in the heavy transport sector in Finland. The work is a literature review that includes calculations to estimate the potential hydrogen demand of heavy transportation. The topic is relevant because of the increasing concerns about climate change, and the urgent need to reduce greenhouse gas emissions asks for environment-friendly solutions in all fields including transportation. Green hydrogen has been recognised as a potential zero-emission fuel in future heavy transportation. However, the technologies that are required for this utilisation are still under development and need improvement before the usage could be possible. The thesis introduces the EU’s and Finland’s composed strategies to attain carbon neutrality, the EU in 2050 and Finland in 2035. These strategies spell out targets and guidelines for achieving decarbonisation in various fields, such as heating, electrification, industry, and transport. The strategies also predict the future of the hydrogen economy and provide a picture of the potential scale of hydrogen production and use. The study examines the current state of the sector, the feasibility of implementing green hydrogen as a fuel, and the potential benefits and challenges of its adaptation into the Finnish fuel chain. The required hydrogen demand and electricity need for this utilisation are calculated by using the exemplary consumption of an ICE hydrogen engine developed by the company Cummins Inc and collected data on Finland’s traffic performances from Statistics Finland. The calculation shows that the required demand per year would be 350 000 tonnes of hydrogen and more than 17.5 TWh of electricity would be needed to satisfy this demand. Theoretically, this electricity demand could be achieved with the current renewable electricity capacity of 36.9 TWh (2021). Although it wouldn’t be possible in practise as the total electricity demand is 69.3 TWh

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination ( R2 ) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.

Tiivistelmä. Tutkimuksemme aiheena on varhaiskasvatuksen opettajien vaihtuvuus, mikä on puhuttanut paljon varhaiskasvatuksen kentällä viime vuosina. Tarkoituksenamme oli tutkia aihetta uudesta näkökulmasta, sillä pyrimme vertailemaan eroja julkisella ja yksityisellä sektorilla työskentelevien varhaiskasvatuksen opettajien mainitsemissa vaihtuvuuteen liittyvissä tekijöissä. Aiemmissa tutkimuksissa ei ole myöskään eroteltu työpaikan ja alanvaihtoa toisistaan, mutta tutkimuksessamme erottelemme ne, koska ajattelemme, että työpaikan ja alanvaihdon syyt eroavat toisistaan ainakin osittain. Lähestymme aihetta tutkimuskysymyksellä: Mitkä tekijät vaikuttavat varhaiskasvatuksen opettajien työpaikan ja alanvaihtoon? Lisäksi tarkastelemme vaihtuvuutta alakysymyksellä: Millaisia eroja esiintyy julkisella ja yksityisellä sektorilla työpaikan ja alanvaihdolle? Haluamme selvittää, mitä varhaiskasvatuksen opettajat ajattelevat alalla tapahtuvasta vaihtuvuudesta ja siihen liittyvistä tekijöistä. Varhaiskasvatuksen opettajien vaihtuvuutta ei ole myöskään juurikaan tutkittu Suomessa, minkä vuoksi aihetta on tärkeä tutkia. Aineisto koostuu 754 Varhaiskasvatuksen Opettajien Liittoon (VOL) kuuluvan varhaiskasvatuksen opettajan vastauksista. Tutkimusotteemme on Mixed Methods -menetelmä, jossa yhdistyvät kvalitatiivinen ja kvantitatiivinen analyysi. Kvalitatiivista aineistoa analysoimme aineistolähtöisen sisällönanalyysin kautta ja kvantitatiivista tilastollisia menetelmiä, kuten ristiintaulukointia apuna käyttäen. Tuloksissa nousi esille yhtäläisyyksiä sekä eroja työpaikan ja alanvaihdon tekijöissä. Yleisesti työpaikan ja alanvaihdon tekijät olivat pitkälti samankaltaisia kuin aiemmissa vaihtuvuuden tutkimuksissa, kuten työolosuhteet, resurssit, sak-työajan toteutuminen, palkkaus, toimenkuvaan kuulumattomat tehtävät, esimiehen rooli, alan arvostus, induktiovaihe, työn ja koulutuksen suhde sekä halu muutokseen. Sektoreiden välillä ei ollut havaittavissa suuria eroja, vaan vaihtuvuuden syyt

To support the development of pupils’ 21st-century skills, teachers themselves must also be competent in these skills and learn them during pre-service teacher education. The aim of this study is to investigate what kind of profiles emerge among Finnish first-year pre-service teachers’ (N = 872) in terms of perceptions of their strategic learning skills and collaboration dispositions and what background variables explain membership of the profiles found. Latent profile analysis showed five student profiles corresponding to perceived strategic learning skills and collaboration dispositions. The most robust factor explaining the membership of the profiles was life satisfaction. Pre-service teachers in a profile group of high strategic learning skills and high collaboration dispositions showed the highest anticipated life satisfaction after five years. Obtaining a better understanding of pre-service teachers’ skills and dispositions will provide the basis for deeper exploration of how they may acquire these skills and how instruction can better be designed to assist students in developing these skills.

[EN] In this paper, we propose, describe, and test a modification of the K-SVD algorithm. Given a set of training data, the proposed algorithm computes an overcomplete dictionary by minimizing the ß-divergence () between the data and its representation as linear combinations of atoms of the dictionary, under strict sparsity restrictions. For the special case , the proposed algorithm minimizes the Frobenius norm and, therefore, for the proposed algorithm is equivalent to the original K-SVD algorithm. We describe the modifications needed and discuss the possible shortcomings of the new algorithm. The algorithm is tested with random matrices and with an example based on speech separation.

[EN] This paper presents an analysis of an efficient parallel implementation of the active-set Newton algorithm (ASNA), which is used to estimate the nonnegative weights of linear combinations of the atoms in a large-scale dictionary to approximate an observation vector by minimizing the Kullback¿Leibler divergence between the observation vector and the approximation. The performance of ASNA has been proved in previous works against other state-of-the-art methods. The implementations analysed in this paper have been developed in C, using parallel programming techniques to obtain a better performance in multicore architectures than the original MATLAB implementation. Also a hardware analysis is performed to check the influence of CPU frequency and number of CPU cores in the different implementations proposed. The new implementations allow ASNA algorithm to tackle real-time problems due to the execution time reduction obtained.