Teamwork as a cornerstone of a child’s educational support in early childhood education and care in Finland

This research investigates how consulting early childhood special education teachers (ECSETs) perceived teamwork in early childhood education and care (ECEC) centers. The following research questions were set: (1) What constructs or prevents the functionality of teamwork in ECEC according to ECSETs experiences, and (2) what are the perceived consequences of teamwork in ECEC as experienced by the ECSETs? We arranged 13 group discussions in which 35 ECSETs discussed their own experiences of successful teamwork in ECEC. Using a phenomenographic approach, we identified four factors that impacted the functionality of the teams: external, unit-specific, team-specific, and employee-specific factors. ECSETs described how teamwork specifically affects the quality of ECEC and the implementation of educational support for children. Our research will help in understanding the factors and functions of teamwork as well as to develop team strengths and practises in ECEC centers

Exploring methods for predicting multiple pressures on ecosystem recovery: A case study on marine eutrophication and fisheries

AbstractEfforts to attain good environmental status in the marine realm require decisions which cannot be done without knowledge of effects of different management measures. Given the wide diversity of marine ecosystems, multitude of pressures affecting it and the still poor understanding on linkages between those, there are likely no models available to give all the required answers. Hence, several separate approaches can be used in parallel to give support for management measures. We tested three completely different methods – a spatial impact index, a food web model and a Bayesian expert method. We found that a large uncertainty existed regarding the ecosystem response to the management scenarios, and that the three different modelling approaches complemented each other. The models indicated that in order to reach an improved overall state of the ecosystem nutrient reductions are the more effective of the two management variables explored, and that cumulative effects of the management of nutrient inputs and fishing mortality are likely to exist

Lethal effects of waterlogging on Scots pine appear with delay

Poster

Biodiversity in Marine Ecosystems—European Developments toward Robust Assessments

Sustainability of marine ecosystems and their services are dependent on marine biodiversity, which is threatened worldwide. Biodiversity protection is a major target of the EU Marine Strategy Framework Directive, requiring assessment of the status of biodiversity on the level of species, habitats, and ecosystems including genetic diversity and the role of biodiversity in food web functioning and structure. This paper provides a summary of the development of new indicators and refinement of existing ones in order to address some of the observed gaps in indicator availability for marine biodiversity assessments considering genetic, species, habitat, and ecosystem levels. Promising new indicators are available addressing genetic diversity of microbial and benthic communities. Novel indicators to assess biodiversity and food webs associated with habitats formed by keystone species (such as macroalgae) as well as to map benthic habitats (such as biogenic reefs) using high resolution habitat characterization were developed. We also discuss the advances made on indicators for detecting impacts of non-native invasive species and assessing the structure and functioning of marine food-webs. The latter are based on indicators showing the effects of fishing on trophic level and size distribution of fish and elasmobranch communities well as phytoplankton and zooplankton community structure as food web indicators. New and refined indicators are ranked based on quality criteria). Their applicability for various EU and global biodiversity assessments and the need for further development of new indicators and refinement of the existing ones is discussed

A retrospective assessment of marine biodiversity : a critical analysis of integration and aggregation rules

Oceans around the world are threatened by human pressures. Ecological indicators are useful tools in understanding complex systems and their changes caused by human pressures, and the information they offer is also needed for ecosystem-based management. Integrated assessments combine information produced by several indicators at different spatial scales and thus offer a more holistic view of the status of the ecosystem. In this study, we evaluate the integration of biodiversity indicators at different spatial scales in two study areas in the Baltic Sea: Gulf of Finland and Bothnian Sea. By producing time series of the indicators and integrated assessments, we study the historical changes in the overall marine biodiversity status, and the impact of data availability, indicator selection, and choice of spatial assessment units on the status assessment. The integrated assessments are produced using the Biodiversity Assessment Tool (BEAT 3.0) and following the procedure of the HELCOM integrated assessment of biodiversity. The analysis shows that the results of the integrated assessment depend strongly on which indicators are available for the assessment, and on the chosen spatial assessment units. While the integrated assessments are a strong communication tool, their interpretation needs to be accompanied by information of indicators to avoid misleading conclusions about the marine ecosystem status

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data

Background: Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios. Methods: We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning. Results: Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively. Conclusions: By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.Peer reviewe

Fabrication of a gap structure for near-field heat transfer

Tutkimuksessa kehitettiin kahden vaiheen valmistusprosessi rakorakenteen valmistamiseen, jossa on metallista valmistetut johdot. Tämä rakorakenne on tarkoitettu lähikentän lämmönsiirtymisen mittaamiseen. Valmistus tehtiin pääasiassa 3D-litografialla käyttämällä Nanoscribe Photonic Professional -järjestelmää. Valmistukseen sisältyi myös ALD (Atomic Layer Deposition), metallipäällystys höyrystämällä, lift-off ja ionisuihkujyrsintä. Valmistettu rakenne koostuu kahdesta suorakulmaisesta särmiöstä lähellä toisiaan, jotka muodostavat ripustetun yhdensuuntaisten tasojen geometrian. Särmiöiden päälle tehdyt metalloinnit koostuivat ohuesta kultalangasta, joka voi toimia resistiivisenä lämpömittarina ja lämmittimenä. Valmistusprosessi kehitettiin onnistuneesti ja (1.97 ± 0.05) µm leveä rako saavutettiin. Suurimmat ongelmat valmistuksessa olivat jännitys 3D rakenteissa, joka rajoittaa raon kokoa, ja metalloinneille tarvitun lift-off -prosessin vaikeus. Usein rakenteet hajosivat tai ylimääräistä kultakalvoa jäi rakenteiden päälle. Lisäksi tehtiin mittauksia, jotka osoittivat että valmistetut langat voivat toimia lämmittimenä ja lämpömittarina suuremmissa lämpötiloissa kuin 25 K.A two-step fabrication process for a gap structure with metal wiring was developed in this study. This gap structure is meant to be used for near field heat transfer measurements. The fabrication was mostly done with 3D-lithography using a Nanoscribe Photonic Professional system. The fabrication also included ALD (Atomic Layer Deposition), metal coating by evaporation, lift-off and ion beam milling. The fabricated structure consists of two cuboids close together which form a suspended parallel plate geometry. The metallizations fabricated on the cuboids consist of a thin gold wire which can work as a resistive thermometer and a heater. The fabrication process was successfully developed and a gap size of (1.97 ± 0.05) µm was reached. The biggest issues with the fabrication were the tension in the 3D structures which limits the gap size and the difficulty of the lift-off process needed for the metallizations. Very often the structures broke or extra gold film was left on the structures. Measurements were also performed, which showed that the fabricated wires can in fact work as a heater and as a thermometer down to a temperature of about 25 K

Application of Direct Laser Writing for the Fabrication of Superconducting Tunnel Junctions and Phononic Crystal Structures

Phononic crystals (PnC) are periodic structures analogous to the more common photonic crystals. Instead of a periodic dielectric constant they have a periodic elasticity and density, and thus they alter the flow of vibrational energy (heat and/or sound) through a material. This thesis focuses on the fabrication of three-dimensional (3D) PnC structures, integration of tunnel junction devices with them and studying their thermal properties. The 3D PnC structures, which in this case are 3D square lattices of spheres, were fabricated with 3D lithography using established methods. However, getting the exact wanted geometry required a lot of work and thus part of the thesis revolves around the optimization of the design. Then we had to develop a method for the fabrication of measurement electronics on these 3D structures as no conventional methods really allow such fabrication. Next we wanted to prove that the developed method can produce the necessary measurement devices. In this case the devices are superconductor-insulator-normalmetal-insulator-superconductor (SINIS) junction pairs. We show that the method can produce good quality SINIS junctions, both on a flat substrate and on a 3D structure, using low-temperature measurements. The last part of the thesis focuses on the thermal conductance measurements of the fabricated PnC structures. These measurements were made for two different PnC structures with sphere diameters of 3.1 μm and 5.0 μm, and also for a control bulk structure with the same geometry. The results of these measurements are compared to finite element method simulations made with a ballistic model. Keywords: Phononic crystal, 3D lithography, thermal conductance, low temperatureFononikiteet (PnC) ovat jaksollisia rakenteita, jotka ovat analogisia tunnetumpien fotonikiteiden kanssa. Jaksollisen dielektrisen vakion siasta niillä on jaksollinen elastisuus ja tiheys, ja siksi ne muuttavat värähtelyenergian (lämpö ja/tai ääni) liikettä materiaalin läpi. Tämä väitöskirja keskittyy kolmiulotteisten (3D) PnC rakenteiden valmistukseen, tunneliliitosten integroimiseen niiden kanssa ja niiden lämpöominaisuuksien tutkimiseen. 3D PnC rakenteet, jotka tässä tapauksessa ovat palloista koostuvia 3D neliöhiloja, valmistettiin 3D litografian avulla käyttäen vakiintuneita menetelmiä. Halutun geometrian saavuttaminen vaati kuitenkin paljon työtä ja siksi osa väitöskirjasta keskittyy rakenteen mallin optimointiin. Sitten meidän täytyi kehittää menetelmä, jolla voidaan valmistaa tarvittavat mittauslaitteet näiden 3D rakenteiden päälle, koska tälläaiseen valmistukseen ei oikeastaan ole tavanomaista menetelmää. Seuraavaksi halusimme todistaa, että kehitetyllä mentelmällä on mahdollista valmistaa tarvittavat mittauslaitteet. Tässä tapauksessa laitteet ovat suprajohde-eriste-normaali metalli-eriste-suprajohde (SINIS) liitospareja. Me näytämme matalan lämpötilan mittauksilla, että menetelmällä voidaan valmistaa laadukkaita SINIS liitoksia sekä tasaiselle substraatille että 3D rakenteen päälle. Väitöskirjan viimeinen osio keskittyy valmistettujen PnC rakenteiden lämmönjohtavuuden mittauksiin. Nämä mittaukset tehtiin kahdenlaisille PnC rakenteille, joissa pallojen halkaisijat olivat 3.1 μm ja 5.0 μm, sekä resistimateriaalista tehdylle kontrollirakenteelle, jonka geometria on sama. Mittausten tuloksia verrataan ballistisella mallilla tehtyihin elementtimenetelmäsimulaatioihin. Avainsanat: Fononikide, 3D litografia, lämmönjohtavuus, matala lämpötil