Projects

Field study of risk of fish injury and mortality at two large hydropower plants in Norway and two in Sweden.

With this project, we demonstrate the efficiency of a novel biotechnological process on a pilot scale, which converts wood industry residues (pre-processed sawdust) into healthy feed and food additives. A life cycle analysis is performed on the process, quantitatively demonstrating the advantages of the circular economic process, and a platform is created for the effective development of industrial biotechnology processes on various sustainable commodities such as food waste, by-products of the food industry and agriculture.

Catching-Up along the global value chain: business models, determinants and policy implications in the era of the Fourth Industrial Revolution is a project built on a multidisciplinary and multi-sectorial exchange program focused on unravelling the process of Catching-Up from different sectorial perspectives at a country level. It analyses the role of business models (BMs) in entering, learning and upgrading the Global Value Chain (GVC), aiming at recognising the determinants and challenges faced by Small and Medium Enterprises (SMEs) in tackling the process of upgrading in a globalising economy. The outcome of the project will be the definition of policy tools and frameworks to support effective policy-making actions in the implementation of Research and Innovation Smart Specialization Strategy (RIS3), with respect to the new agenda of Europe 2020, mainly for low-income EU countries.

The proposed project ‘BoostEuroTeQ’ aims at strengthening the research and innovation dimension of the EuroTeQ Engineering University. It strongly builds on synergies with its education-focused activities (funded by Erasmus +) to reinforce institutional change towards responsible research and innovation. It aims, in particular, at enabling individuals in technology value creation to interact with stakeholders of the wider society to ensure desirable and socially robust pathways for societal transformation. The work plan set out in BoostEuroTeQ strengthens and complements core parts of the EuroTeQ Engineering University. In a first pillar, the project will develop a concept for training learning professionals at universities and position them as key actors facilitating knowledge transfer and co-creative innovation between the university and the wider ecosystem. By defining a EuroTeQ upskilling strategy it will help establish the partner institutions as constant companions in the lifelong learning journeys of engineers in Europe. This will enable the consortium to develop and implement an improved strategy for strengthening human capital and will re-inforce the cooperation with non-academic actors. In a second pillar, the project will analyse the EuroTeQ partners’ needs in terms of institutional strategies for reflexivity and develop a “EuroTeQ manifesto” for institutionalization. It will investigate how universities can engage with their surroundings by developing “learning networks” and will design an approach to evaluate the impact of co-creative education for innovation and understand its scalability. With that, BoostEuroTeQ will reinforce the impact of university research and innovation and build sustainable involvement and engagement with citizens and civil society. In sum, this complementary project will strengthen the EuroTeQ Engineering University’s role as change maker and role model for institutional transformation in Europe.

The aim of this project was to investigate the regulation of stimuli-activated neuronal enhancers through enhancer-derived RNA (eRNA), addressing the challenge posed by the unstable and transient nature of eRNAs. We combined advanced next-generation sequencing techniques with molecular biology assays to gain a mechanistic understanding of eRNA function in activity-dependent gene expression. The project's critical task was establishing a robust genome-wide method to precisely define the eRNA 5’ to 3’ end sequence. We optimized the MAPcap method, which emerged as the preferred technique for transcription start site (TSS) detection due to its low sequencing depth requirement, compatibility with previously collected RNA samples, and ease of integration into research workflows. As model systems we selected primary rat cortical neurons and Neuro2A mouse neuroblastoma cells. For both systems, we tested and optimized cultivation, treatment, and subcellular fractionation protocols. We also established RT-qPCR assays to validate eRNAs and immediate-early genes. To prepare for the functional validation of eRNAs, we established a complete procedure encompassing in vitro transcription, biochemical pull-down assays, and mass spectrometry analysis. This workflow will be applied after we complete the analysis of our NGS datasets and define eRNAs of interest. In conclusion, this project has successfully established methods and generated data that advance our understanding of eRNA roles in neuronal gene regulation. The outcomes of this research pave the way for significant discoveries in neurobiology and provide a robust platform for future studies. The support from this grant has been instrumental in achieving these results, promising impactful contributions to the field.

The program supports the development of green skills necessary for the food processing companies. In the course of the activity, the updating of the learning content related to the teaching of green skills in higher and vocational education, further and retraining for the development of green skills in order to prepare a workforce with broader knowledge and skills regarding the green economy for the introduction of new technologies and approaches, as well as the updating of professional standards and skill profiles or, if necessary, the creation of new ones, and the further training of vocational teachers-lecturers are supported.

The existing solid face fermentation systems do not ensure the optimal oxygen supply, nor temperature, high microbial contamination risk is also common for those systems. The aim of current project is to develop a simple and cheap system for cultivation of fungi or mycelium equipped with oxygen and temperature control. That improves the growth rate and yield of fungal biomass. System is universal, in addition to cultivation of fungi it enables to carry out high temperature composting. We use 20 - 200 L polypropylene bags as cultivation vessels, that are equipped with process control head. This cultivation vessel enables sterilization, aseptic inoculation, aeration and cooling of growth substrate. Cultivation vessel is connected to control system for oxygen and temperature monitoring and process control at least in 4 individual cultivation vessels.

The aim of the project is to develop an international research network onthe diagnosis and epidemiology of viruses infecting cereal crops to understand the current state of occurrence of cereal viruses and their vectors and reservoirs. Depending on research findings, development of diagnostic methods is planned for the viruses with higher pathogenicity potential.

The objective of this collaborative project is to enable trustworthy AI hardware by explainable and efficient Deep Neural Networks. As the main contribution to achieving this objective, the project will establish an EnTrustED Framework for DNN hardware design analysis that will follow the novel design flow. First, at the design-time phase, a combination of DNN-tailored AxC techniques will be provided to enhance the compute-efficiency of the DNN inference hardware. The Framework will enable a simulation-based analysis for identifying the neurons that are not practical for the optimisation and must keep their initial Exact Computing (ExC) implementation or the approximation should be reduced. It aims to equip the AI hardware with self-test mechanisms to detect hardware errors and fault-tolerance mechanisms for recovering from an error that has occurred and, thereby, continue the AI algorithm uninterruptedly. As one of the novelties, this project views eXplainable AI (XAI) from a hardware perspective. We intend to consider the AxC during the explainability and thus ensure a correct explanation of the decision taken by the DNN. Once we guarantee the correct behaviour of the hardware, and we have properly considered AxC, we can safely run explainability approaches to profile the DNN implantation still at the design-time for identification of DNN input specific significant neurons. The experimental nature of the project and the high interdependency of the contributions by EC-Lyon and TalTech make the envisioned face-to-face visits essential to achieving the goals of the collaboration.

The objective of the EEV5040 Industrial Automation and Drives activity is to introduce students to the importance of industrial automation and electric drives and the latest trends in these fields (including IoT). As a result of the development activities, students will acquire in-depth knowledge of electric drive management, model-based design methodology and IoT applications in industrial automation in the future. They are able to create, adapt and analyse electric motor control systems and solve real problems in this field. These results influence the quality of industry-specific ICT teaching, providing students with the practical skills and knowledge essential to today's industrial automation. Within the framework of the development project, the subject EEM0040 Machine vision is also amended, where the traditional machine vision curriculum is added to it by adding the hyperspectral technology component. The aim of the development activity is to combine concepts of machine vision with hyperspectral data processing. Within the subject, students will develop practical skills in the use of hyperspectral cameras, from camera setup to processing of various hyperspectral images. They acquire knowledge of the specifics of hyperspectral data, such as the wavelength spectrum and its relationship to the properties of materials. In addition, they learn the methods of machine vision, which allow to identify different objects and characteristics from hyperspectral images.

The aim of the study is to determine the chemical composition of 30 propolis samples collected from different counties of Estonia and to study their antioxidant and antibacterial effects.

The project is focused on the asymmetric synthesis. More specifally, multifunctional and conformationally flexible catalyst will be elaborated and applied in the synthesis of various bioactive compounds, including human milk oligosaccharides. Use of more selective catalytic reactions reduces amounts of by-products and makes chemical synthesis more sustainable. Design, synthesis and applications of new catalyst are the main outcomes of the project.

Demo Project: Testing of Automated Machine Vision Based Program Generation Method for Collaborative Robots. The project develops and tests new machine learning-based machine vision support for collaborative robots used in production. The solution enables automated programming of the robot using the learned placement of the workpieces. The solution helps significantly increase the efficiency and safety of the production process and is applicable to a very wide class of production tasks.

Demo Project: Testing of Automated Machine Vision Based Program Generation Method for Collaborative Robots. The project develops and tests new machine learning-based machine vision support for collaborative robots used in production. The solution enables automated programming of the robot using the learned placement of the workpieces. The solution helps significantly increase the efficiency and safety of the production process and is applicable to a very wide class of production tasks.