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Ettersom flere mennesker lever lenger, trenger vi flere helsetjenester, spesielt for akutte helseproblemer. Dette legger mye press på helsesystemet vårt, som inkluderer hjemmeomsorg, sykehjem, lokale klinikker og sykehus. For å sikre at vi kan ta vare på alle i fremtiden, må vi finne måter å hjelpe eldre mennesker med å holde seg sunne og unngå å trenge mye omsorg.
PAI-prosjektet jobber med dette problemet. Prosjektet bruker kunstig intelligens (KI) for å forutsi hvilken type omsorg eldre mennesker kan trenge ved å se på helsedata. Ved å oppdage helseproblemer tidlig, kan vi ta skritt for å forhindre at de blir verre. Dette kan bety at vi forhindrer fall i sykehjem eller endrer en persons medisinering på deres lokale klinikk. Dette bidrar til å unngå sykehusopphold, som kan være dyre og stressende.
Imidlertid er det vanskelig å forutsi helsebehov fordi dataene vi har kan variere mye. Det kan være forskjellig avhengig av hvor personen bor, og hvilke helseproblemer de har. For å håndtere dette, jobber PAI-prosjektet med mennesker som mottar omsorg og helsearbeidere for å utvikle KI-verktøy. Prosjektet tester disse verktøyene i virkelige situasjoner i Spania, Sverige og Norge.
Prosjektet bruker en blanding av metoder for å samle inn og analysere data. Dette inkluderer både tall (kvantitative data) og folks erfaringer og meninger (kvalitative data). Målet er å hjelpe eldre mennesker ved å gi dem riktig omsorg til rett tid, forhindre at skader og sykdommer blir verre. Det hjelper også helsearbeidere ved å sørge for at ressursene brukes effektivt og skaper et godt arbeidsmiljø. Løsningene de finner kan brukes på mange forskjellige steder.

As the population ages, the demand for healthcare services increases, including acute care. This puts a strain on healthcare resources, making efficient care management crucial at all levels - home, nursing homes, primary care, and hospitals.
The PAI project aims to address this challenge by using artificial intelligence (AI) to predict the care needs of older individuals. By analyzing patient monitoring data, the project can detect early signs of health or functional decline. This allows for preventive measures to be taken, reducing the need for costly acute care and aftercare. For example, hospitalizations can be prevented by avoiding falls in nursing homes or adjusting medication in primary care.
However, predicting the care needs of older patients is not straightforward. It involves dealing with varying data availability, quality, and potential interventions across different care levels and diagnoses. To tackle this, the PAI project uses a design science approach and a multiple case study design. It develops analytics in collaboration with care recipients and healthcare practitioners and implements them in real-life settings in Spain, Sweden, and Norway.
The project uses a mixed-methods strategy for data collection and analysis, combining quantitative and qualitative elements. This includes the thematic interpretative analysis of meeting notes, interviews, and observations, as well as the statistical longitudinal trend analysis of Key Performance Indicators in the respective healthcare systems.
The benefits of the PAI project are twofold. Older individuals receive adequate early care, preventing injuries and the worsening of diseases. Healthcare staff can use their resources more effectively, creating a sustainable and satisfying work environment. The outcomes of the project are scalable and can be applied in various contexts, making it a valuable contribution to the future of European healthcare.

Predicting Care Needs of Older Adults in the Healthcare System through AI-enabled Analysis of Patient-Monitoring Data

Achieving effective reduction in greenhouse emissions necessitates both preventing resource depletion and offering sustainable solutions to meet the increasing demand for energy. The technology developed in the NEXPECH2 project effectively addresses the above-mentioned challenges by using solar energy to convert water to green hydrogen (H2) under standard atmospheric temperature and pressure. Green hydrogen is a clean, sustainable chemical fuel that can be stored on a large scale for a long time and used to replace fossil fuels as a means of energy storage, transportation fuel, and feedstock. The NEXPECH2 ultimate goal is to deliver green H2 on a prototype lab-scale via development of a innovative photoelectrochemical (PEC) system for maximized solar light harvesting and efficient solar conversion to H2. The project will develop a low-cost compact device with an objective of reaching 10% solar-to-fuel efficiency and <10% degradation over 200 h operation.

Next generation photoelectrochemical cell for hydrogen generation (NEXPECH2)

Limb wounds in horses are common, however healing is exceedingly slow and fraught with complications. Contrary to body wounds, limb wounds demonstrate only a small amount of wound contraction. The specialist procedure of applying split-thickness skin grafts to limb wounds stimulates wound contraction by unknown mechanisms. The objective of this project is to identify those mechanisms to develop a treatment strategy for equine limb wounds for use in general  practice. Limb wounds are severely impacting animal welfare due to an extended healing/treatment duration, and the risk of euthanasia due to a poor functional outcome. By stimulating wound contraction, healing time will be reduced, and the functional outcome improved, directly benefitting the welfare of wounded horse and reducing treatment cost for the owner. Aim: To identify dermal components stimulating wound contraction, and to develop this knowledge into future state-of-the-art treatment of equine limb wounds for use in general practice. Hypotheses: i) Application of dermal fractions to collagen gels seeded with equine fibroblasts will increase gel contraction in vitro; ii) Application of dermal fractions to experimental limb wounds in vivo, will increase wound contraction resulting in a faster healing time versus wounds treated by conventional methods. Methods: i) The contractile effect of different dermal components will be assessed in a collagen gel/ fibroblast model; ii) Identified dermal components will be tested in a standardized experimental limb wound model in vivo (n=5). Ultimately, results from this project are expected to improve wound treatment in horses, directly impacting animal welfare by reducing healing time and improving the functional outcome of wounded horses. This will also directly benefit horse owners and caretakers and making veterinarians able to offer better treatment solutions.

HESTEFORSK - Raskere og bedre sårheling hos hest med autologe hudkomponenter

Novel vaccine platform for animal health

Avansert nedbrytbar nanocellulose-matrix for stamcelle-differensiering og tilheling av brannskader.

Cellu4Heal-prosjektet går ut på å utvikle en ny, avansert behandling for alvorlige brannskader. Siden 1970-tallet har behandlingen av brannskader i stor grad basert seg på kirurgi og hudtransplantasjoner. De siste tiårene har forbedret behandling ført til færre dødsfall, men det er fortsatt et stort behov for mer effektive metoder for å bedre sårtilhelingen og gro ny hud. I denne sammenhengen har behandlinger med mesenkymale stamceller (MSC) vist lovende resultater. Tidlige studier viser at MSC kan hjelpe sår med å gro raskere og forbedre arrkvaliteten over tid. Imidlertid gjenstår utfordringen med å levere disse cellene til såret på en god og sikker måte.

Cellu4Heal-prosjektet utvikler en biologisk nedbrytbar gel, kalt Cellu4Heal matrix, laget av et naturlig materiale (nanocellulose). Denne gelen er designet for å bli trygt absorbert av kroppen, og vil bidra til å fremskynde sårtilhelingen, redusere betennelse og motvirke arrdannelse. Med gelen vil man kunne levere MSC direkte til såret, noe som kan føre til raskere bedring og bedre langtidsresultater for pasientene. Gjennom prosjektet vil det også bli utviklet et smart spraysystem for å påføre behandlingen på sår på en enkel og effektiv måte. Denne innovative tilnærmingen kan i stor grad redusere risikoen og komplikasjonene knyttet til alvorlige brannskader, forbedre overlevelsesraten og betydelig øke pasientenes livskvalitet.

Prosjektet er et samarbeid mellom Universitetet i Bergen (UiB, Norge), Haukeland Universitetssjukehus (HUS, Norge), RISE PFI (Norge), National University of Science and Technology Politehnica Bucharest (UPB, Romania), Universitetet i Helsinki (UH, Finland) og en industriell partner (Alginor Biorefinery AS (ABR AS), Norge).

Since the 1970s, large burn wounds have been treated with essentially the same strategies including surgical excision and grafting. Although later improvements in intensive care have led to decreased mortality, there is a great need for further advancements in wound healing and skin regeneration. In recent years, the use of mesenchymal stem cells (MSC) in the treatment of burns has gained increasing attention. Preclinical studies have indicated both local improvements in burn wound healing and long-term improvements in scar quality and contractures. However, there is a lack of a functional matrix for accurate dosing and delivery of MSC to the wound, and the optimal clinical administration system for MSC therapy is yet to be developed. Our project aims to develop and validate an advanced hydrogel matrix based on nanocellulose (CNF) (Cellu4Heal matrix) innovatively engineered with biodegradability and antibacterial properties that specifically meets the requirements of MSC delivery and therapeutically modifies the burn wound environment. The Cellu4Heal matrix can be utilized for accurate dose-controlled administration of MSC therapy to facilitate and promote skin regeneration of severe burn injuries. The Cellu4Heal project will develop an advanced degradable nanocellulose-based scaffolding material that is biocompatible and will add the novelty of built in properties of biodegradability to avoid remaining foreign material in the body beyond the period needed for the cell therapy to exert its sought effects of supporting skin regeneration. In addition, a novel intelligent spray-on delivery system will be developed for controlled administration of the Cellu4Heal matrix on the wounds. 
The project is a collaboration between University of Bergen (UiB, Norway), RISE PFI (Norway), National University of Science and Technology Politehnica Bucharest (UPB, Romania), University of Helsinki (UH, Finland) and an industrial partner (Alginor Biorefinery AS (ABR AS), Norway).

Advanced degradable nanocellulose-based matrix for stem cell differentiation and burn wound healing

Hydrogen spiller en sentral rolle i det grønne skiftet, da det er en ren og tilgjengelig energibærer som kan brukes til å drive brenselceller og produsere elektrisitet med bare vann og varme som biprodukter. En av de største utfordringene med å bruke hydrogen som en energikilde er imidlertid lagringen. Metoder for hydrogenlagring, som gass eller væske, kan være kostbare og ineffektive. Prosjektet HESSENSE sikter på å utforske lagring av hydrogen i lette høyentropilegeringer (HEAs) i for av metallhydrider (MHs). HEAs har potensiale til å bli brukt for faststoffhydrogenlagring, noe som er en lovende tilnærming for kompakt hydrogenlagring.
HESSENSE vil ta en tverrfaglig tilnærming, ved å kombinere eksperimenter, numeriske beregninger, materialutvikling og teknoøkonomiske analyser for å undersøke egenskapene til HEAs og MHs på forskjellige lengdeskalaer. Prosjektet vil involvere forskere fra ulike europeiske land, samt samarbeid mellom forskningsinstitutter, universiteter og industri. Målet er å utvikle HEA-baserte systemer for hydrogenlagring med forbedret gravimetriske tetthet, som opererer ved romtemperatur og lavt trykk, noe som vil forbedre sikkerheten og systemets effektivitet sammenlignet med nåværende lagringsmetoder.
Dette prosjektet er viktig da det adresserer en kritisk aspekt ved den grønne overgangen, nemlig utviklingen av effektive, sikre og bærekraftige metoder for lagring og bruk av hydrogen. Fremskritt innen dette området kan ha en betydelig innvirkning på å akselerere bruken av hydrogen som en ren og fornybar energibærer.

HESSENSE aims to investigate the interaction of hydrogen with light-weight High Entropy Alloys (HEAs) and the corresponding metal hydrides (MHs). The development of MHs based on HEAs is of interest for energy storage applications, as they can be used as H2 carriers for solid state H2 storage. HESSENSE will be conducted as a joint effort of research institutes, universities and one SME in four different European countries. It will have an interdisciplinary approach where numerical approaches accompany experimental tasks; materials development will run in parallel with a techno-economic assessment of their sustainability; and advanced characterization techniques will be used to investigate the materials’ properties across different length scales.

HESSENSE is expected to contribute to developing HEA-based hydrogen storage systems with improved gravimetric densities, that operate at ambient temperatures and low pressures, thereby enhancing safety. This can eventually improve system efficiencies compared to compressed and liquified hydrogen storage. When thermally integrated with a hydrogen fuel cell, HEAs can act as a heat sink during desorption. At the same time, the heat produced during hydrogen absorption can be re-utilized. Additionally, the thermodynamic data for the HEAs investigated within HESSENSE will be shared in open-source databases used by the wider metal hydride community to explore new compositions with ML methods. Finally, the RMCProfile7 software that will be used for the structural modeling of HEAs and optimized and further developed within HESSENSE is open source allowing for reusability and interoperability.

Light Weight High Entropy Alloys for Safe and Sustainable Energy Storage

[11:56 AM] Amalie Skurtveit
Den pågående overgangen av energilandskapet fra fossilt brensel til fornybar energi er avhengig av tilgjengeligheten av rimelige, trygge og effektive energilagringsløsninger. Det har resultert i den nylige fremveksten av nye energilagringsteknologiene. Blant disse representerer Li-ionebatterier (LiB-er) den mest utviklede teknologien, som ikke bare leverer den høyeste energitettheten, men også demonstrerer langsiktig holdbarhet og høy kapasitet. Fastfasebatterier (forkortet SSB, fra engelsk; solid-state batteries) regnes som den neste generasjonen av batterier på grunn av deres forbedrede sikkerhet og energitetthet sammenlignet med konvensjonelle Li-ionebatterier. SSB-er er av spesiell interesse for mobilitetssektoren. Nøkkelkomponenten for en høyytelses SSB er fastfaseelektrolytten (forkortet SSE, fra engelsk; solid-state electrolytes), men bruken av den byr også på nye utfordringer når det gjelder ionisk ledningsevne, prosesserbarhet, resirkulerbarhet, bærekraftigheten til SSE-er og SSE-aktive materialgrensesnitt.
I FUNCY-SSB vil ekspertise fra flere disipliner (teknikk, prosessering, syntetisk kjemi og elektrokjemi, resirkulering, livssyklusvurdering, forvaltning, utnyttelse og formidling) kombineres på en komplementær og helhetlig måte som fører til innovative løsninger og en mer nyansert forståelse av problemer knyttet til SSEer. FUNCY-SSB vil lage en tydelig kobling fra syntese til karakterisering og elektrokjemisk evaluering, som lar partnere forstå hvordan og hvorfor materialene fungerer eller svikter som de gjør, og gå tilbake til den syntetiske laboratoriet for å produsere forbedrede materialer til neste runde. For å implementere konseptet designet for bærekraft/resirkulering i utviklingen er det tenkt et sterkt samarbeid mellom feltene. Resultatene av FUNCY-SSB-prosjektet vil være tilgjengelig for en teknologioverføring til relevante industrielle partnere på en tidsskala som skal muliggjøre kommersialisering innen 2030.

Solid-state batteries (SSBs) are considered the next generation of batteries due to their improved safety and energy density compared to conventional Li-ion batteries. The key component for a high-performance SSB is the solid-state electrolyte (SSE), but its use also poses new challenges in terms of ionic conductivity, processability, recyclability, sustainability of SSEs and the SSE-active material interfaces. Combining organic and inorganic electrolytes is a promising route. However, these composite electrolytes cannot be made via simple mixing of polymer and inorganic components, rather both components need to be specifically tailored to enable efficient ionic transport through the SSE as a whole. 
These challenges will be addressed within FUNCY-SSB project by partners from Germany, Slovenia and Norway through focusing on the development of sustainable by design SSEs with enhanced electrochemical performance. Functional surface coatings will be used to fabricate SSEs based on a combination of sulfide electrolytes with polymers. Inorganic coatings produced by atomic layer deposition will be used to optimize the Li-ion transport through the polymer into the sulfide electrolyte. Using a functional coating, the aim is to make SSEs with high ionic conductivity, enhanced stability towards moisture and improved electrochemical stability window. Recycling studies, economic and ecological potential analyses will be conducted during the SSE development process to examine and optimize the SSEs in terms of recyclability and sustainable fabrication. The functionality of selected coated electrolytes will be tested in combination with Li metal anodes and NMC cathodes, and demonstrated in pouch cells. The manufacturing potential of the developed materials will be assessed in a collaboration with the industrial partners. FUNCY-SSB will use semantic technologies to digitalise synthesis and manufacturing processes of battery materials/components.

FUNctional Coatings of sulfide electrolYtes for lithium Solid-State Batteries

Pisces-AI: Physics-Informed AI for Subsurface Characterization ExperimentS

Reality-Connected Machine Simulation of Heavy Machinery

The RealSim project proposal aims to connect machine simulation to reality in the framework of heavy machinery, which is an interdisciplinary application. The results of this project are expected to optimize machine performance, accurately and reliably predict machine failure, and reduce overall development and operation costs, thus, serving the Europe’s priority of green transformation and the United Nation’s sustainable development goal 9. The RealSim project will close the gaps of: (1) Onboard estimations of force and pressure components of heavy machinery; (2) Estimating deformability, cylinder friction, leakage, and valve dynamics of heavy machinery using independent nonlinear Kalman filters; (3) Employing dependent nonlinear Kalman filters in state estimators of heavy machinery; (4) Tuning algorithms for the covariance matrix of plant noise in the state estimation of heavy machinery.   I will consolidate and leverage my research career and position as a leading researcher in the field through integration in Assoc. Prof. Mohammad Poursina’s group at the Department of Engineering Sciences of the University of Agder (UiA), Norway. Here, I will acquire hands-on experience of working with actual mechatronic systems and develop efficient control algorithms. I will improve my knowledge of fluid power designing, control theory, instrumentation, and develop efficient coding skills. I will also develop entrepreneurial skills. The skills, knowledge, and international network that I will develop during this fellowship will put me in an excellent position to achieve my long-term goal of becoming a professor with my own independent research group at an esteemed university in EU. This fellowship will help me to prepare a strong ERC Starting Grant application in the future. It will also cater my additional goal of setting-up a research-related start-up in the future.

Algebraic Theory of Information Quantities

Information theory is a discipline at the intersection of statistics, engineering and computer science. As the study of information quantities, such as compression or communication capacities, information content or measures of statistical dependency, it is one of the theoretical underpinnings of data science.  Computational problems in information theory are highly non-linear. The goal of this project is to transfer state-of-the-art methods of computational non-linear algebra to information theory, to study the inherent algebraic complexity of information-theoretical problems and to provide tools for solving them in practice. The algebraic point of view has proven to be fruitful in seemingly unrelated areas, as witnessed by a surge of recent work in algebraic statistics, in particular on likelihood geometry. However, maximizing the likelihood function is the same as minimizing relative entropy — a specific information quantity. Hence, this project also aims at generalizing the techniques developed in likelihood geometry.  One focus is on the practical computation of information quantities using numerical and differential algebraic geometry. Such quantities are defined via non-linear optimization problems and we aim to pinpoint the algebraic complexity of these problems in instances of general interest, such as common information measures. The final objective is finding fundamental laws and limits of data science imposed by non-linear inequalities constraining the entropic region. These inequalities provide, by duality, universal bounds for many of the optimization problems studied in information theory.

Midlene fra KINA-Kina-programmet fordelt på år

Midlene fra KINA-Kina-programmet fordelt på fylker

Midlene fra KINA-Kina-programmet fordelt på fagområde

Statsstipend: Acoustic properties, target identification of Antarctic krill and mesopelagic fishes

This project aims at introducing precision medicine for improvement of cardiac resynchronization therapy (CRT) for heart failure. Heart failure is the leading cause of death and morbidity in the western world. 30% of all patients with heart failure need treatment with CRT caused by conduction defects in the conduction system in the heart. The conduction defect generates an ineffective pump function of the heart chambers. The conduction defect can be treated with CRT by placing pacemaker leads in the left and right heart chamber and thereby restore a synchronous and effective pump function. More than 100000 patients receive such treatment every year in the US and Europe, however more patients need treatment. Despite the treatment being effective, more than 30-50% of all patients treated have no effect of the treatment but are still lifelong exposed to unwanted negative effects. The prognosis of this group of patients remains poor. We have developed methods, through own research and research at the Oslo University Hospital, to better target patients who will have benefits from the treatment, and to find the right way of implementing treatment. In this way we will be able to convert treatment effect from a low responder rate to almost 100% responder rate, avoiding implantations in patients with no benefit. This will allow effective treatment at lower cost, providing the right treatment to the right patient at the right time!
We are developing a cardiac catheter that can be used in patients to perform the diagnosis of dyssynchronous heart failure to ensure optimal resynchronization therapy. The catheter needs to comply with regulatory standards and the physician's needs for performance and usability, to be put to work as a clinically viable tool. Clinical studies will be performed on how the system can be implemented. The project will promote the utilization of CRT, and the treatment effect will improve with the world-wide implementation of our system.

Cardiac resynchronization therapy (CRT) with a pacemaker, is an expensive (25000 EURO/ patient) yet cost-effective heart failure therapy that saves lives. However, only up to two-thirds of patients who meet guidelines for CRT have beneficial effects. 1-2 billion EURO is annually spent on ineffective therapy in Europe/USA. Then, only one-third of HF patients eligible receives treatment, leaving a high number of patients without treatment at high costs to society (20000 EURO/year), and much more could be saved with improved utilization of CRT. We have discovered and developed a proprietary biomarker measured in patients with the PaCRTool-system, that will help select patients for CRT. The PaCRTool-system connects to the patient through ECG and electrogram electrodes and to an in-body sensor within the prototyped PreSens-catheter. This project will 1) do the final research and developments of the PaCRTool-system. It will be developed in an iterative process including research engineering, design controls, risk management and human factors research. There are no current medical devices that enable Pacertools' measurements on the market.  2) We will do the final research steps on the proprietary PreSens-catheter including pre-clinical experimental testing to confirm the biomarker performance. 3) Then we will perform the early feasibility clinical study for regulatory (CE-mark) and reimbursement purposes. This study will be designed to confirm the published experimental/ clinical data demonstrating the Pacertool biomarker using a pre-market research system in patients. The regulatory route is a driver in this project, and we will work closely with regulatory consultants to ensure compliance with the General Performance and Safety Requirements for market approval. The project is built on strong IPR; we will continue to exploit IPR. Subcontractors will be appointed for their expertise and to minimize risk. We will have the PaCRTool-system ready for market in two years.

KINA-Kina-programmet

Statsstipend: Acoustic properties, target identification of Antarctic krill and mesopelagic fishes

Awarded: NOK 0.13 mill.

Funding scheme:

KINA-Kina-programmet

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