19 Jun 2019   |   Network Updates   |   Update from University of Eastern Finland
These updates are republished press releases and communications from members of the Science|Business Network

Academy of Finland’s Research Council for Natural Sciences and Technology awards €2.9M funding to UEF researchers

The Research Council for Natural Sciences and Technology of the Academy of Finland has granted funding to 86 Academy Projects, which have a total of 114 sub-projects. The Research Council awarded a total of 50.5 million euros of funding to Academy Projects, which the Research Council sees as important instruments in enhancing research quality, impact and renewal.

Academy Project Funding secured by University of Eastern Finland researchers:

Professor Kari Lehtinen, Rate estimation and uncertainty quantification of aerosol microphysical processes, 501 805 euros

Lehtinen’s project aims to deliver new particle formation event analysis methodology, with which partly subjective human analysis is replaced with statistical inverse mathematics methods. The improved process understanding, rigorous quantification of uncertainties in addition to rates and a re-analysis of existing data will improve parameterizations of the different microphysical processes when estimating their effect on climate and human health.

Professor Vesa-Pekka Lehto, All-solid-state lithium ion battery with full silicon anode (LIBASS), 460 000 euros

Lehto’s project introduces the concept of full silicon anode that is based on mesoporous free-standing silicon film integrated with solid polymer electrolyte. The aim is to develop a concept that can utilise 75% of the theoretical capacity of silicon with hundreds of charge/discharge cycles. The concept can easily be adopted in coin cell batteries and micro-batteries.

Research Manager Harri Niska, Adaptive data analytics and modelling for flexible power systems / Consortium: Analytics, 254 447 euros

Niska participates in a consortium that develops adaptive analytics for power systems. The consortium studies using hybrid modelling to operational power systems applications, such as forecasting aggregated loads, active demand, embedded generation and energy storage in electricity networks, power quality monitoring, non-intrusive load monitoring, demand side energy management, fault detection, and their mutual synergies. Technical Research Centre of Finland (VTT), Tampere University of Technology (TUT) and the University of Eastern Finland (UEF) are the project partners. 

Academy Project Funding for early-career researchers secured by University of Eastern Finland researchers:

Associate Professor Tommi Hakala, Bose-enhanced plasmonic sensors, 399 639 euros

Hakala’s project aims to utilise lasing in plasmonic lattices to develop plasmon sensors beyond state of the art.

Senior Researcher Timo Lähivaara, Seismic full-waveform inversion with application to groundwater exploration, 525 637 euros

Lähivaara’s project will deliver numerical methods which can be applied to full-waveform inversion in a variety of environmental problems. As an imaging technique, the project studies seismic tomography. The methodologies in the considered imaging technique are efficient full-waveform methods for simulating mechanical wave field. One of the project’s main applications is groundwater, since a more detailed knowledge of this basic resource has a major social impact in both developed and developing countries.

Academy Research Fellow Mikko Nissi, Novel rapid ultra-short echo time quantitative MRI methods (QuUSTE), 524 063 euros

Nissi’s project aims to modify an ultra-short echo time imaging method (SWIFT-method) and to develop a new iterative image reconstruction method for it to allow faster and more accurate 3D-magnetic resonance imaging (MRI). In MRI, a single weighting, or a contrast is typically collected for a single image. In this proposal, the imaging sequence is modified to allow collection of multiple different contrasts within the same scan. A modern iterative reconstruction method then allows calculation of a precise and quantitative image by utilizing all the under-sampled contrasts collected. The specificity and sensitivity of such a quantitative image surpass those of plain anatomical image for diagnostic purposes. Furthermore, using an ultra-short echo time imaging method allows scanning of tissues not visible with conventional MRI. The combination of these modern methods speeds up quantitative magnetic resonance imaging and thus improves the diagnostic potential of the imaging method.

Academy Research Fellows: initial funding for research costs, invited applicants only:

Senior Researcher Mika Mononen, Development and validation of template-based modelling to predict patient specific progression of knee osteoarthritis, and possibilities in economic and societal benefits, 210 000 euros

Mononen’s project seeks to study the possibility to utilise computational modelling and clinical imaging data with subject information to make personalized prediction for the onset and progression of knee osteoarthritis years before. Furthermore, the project is aiming to evaluate how prevention of knee osteoarthritis influences on economic and societal benefits. In future, outcomes of the project may be utilized in developing first clinical tool for preventing knee osteoarthritis. This kind of a tool would enable to decrease amount of total knee joint replacement surgeries and, thus, it would enable substantial economic savings to societies. 

This communication was first published 18 June 2019 by the University of Eastern Finland. 

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