Publikationen

2024

• Prediction of tumor board procedural recommendations using large language models

  Aubreville, M., Ganz, J., Ammeling, J., Rosbach, E., Gehrke, T., Scherzad, A., … Goncalves, M. (2024). Prediction of tumor board procedural recommendations using large language models. European Archives of Oto-Rhino-Laryngology. http://doi.org/10.1007/s00405-024-08947-9 (Original work published 2026)

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  Multidisciplinary tumor boards are meetings where a team of medical specialists, including medical oncologists, radiation oncologists, radiologists, surgeons, and pathologists, collaborate to determine the best treatment plan for cancer patients. While decision-making in this context is logistically and cost-intensive, it has a significant positive effect on overall cancer survival.

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  • Personenseiten

    • Aubreville, Marc

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

  • Profilbereich

    • Informatik und Gestaltung

  • Forschungsfeld

    • Digitalisierung im Gesundheitswesen
    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2024
  • Journal Article
  • • Marc Aubreville
    • Jonathan Ganz
    • Jonas Ammeling
    • Emely Rosbach
    • Thomas Gehrke
    • Agmal Scherzad
    • Stephan Hackenberg
    • Miguel Goncalves

• Re-identification from histopathology images

  Ganz, J., Ammeling, J., Jabari, S., Breininger, K., & Aubreville, M. (2024). Re-identification from histopathology images. Medical Image Analysis, 103335. http://doi.org/10.1016/j.media.2024.103335 (Original work published 2026)

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    • Aubreville, Marc

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

  • Forschungsfeld

    • Digitalisierung im Gesundheitswesen
    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2024
  • Journal Article
  • • Jonathan Ganz
    • Jonas Ammeling
    • Samir Jabari
    • Katharina Breininger
    • Marc Aubreville

• Navigating the complexities of a changing world - Interdisciplinary Electives at Flensburg University of Applied Sciences

  van der Sluis, H., & John, P. (2024). Navigating the complexities of a changing world - Interdisciplinary Electives at Flensburg University of Applied Sciences. Educational Developments, 25.4, 4. Abgerufen von https://www.seda.ac.uk/seda-publishing/educational-developments/educational-developments-issue-25-4-2024/ (Original work published Dezember 2024)

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  In a competitive Higher Education (HE) landscape, universities face the challenge of developing an organisational culture that supports the needs of its staff and students. The traditional staff development approach has been the attainment of skills through teaching and the completion of certificates of learning. This article challenges that practice and seeks to make the case for a renewed focus on the importance of learning through individual and group coaching practice to be ingrained into universities, shifting from a view of staff development purely as the acquisition of skills through training and teaching and moving towards
  engaged learning.

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  • Personenseiten

    • John, Peter

  • Abteilung/Einrichtung

    • Dekanat FB3
    • Lern- und Sprachenzentrum (LSZ)

  • Forschungsfeld

    • Fachkräfte und Vernetzung

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2024
  • Journal Article
  • • Hendrik van der Sluis
    • Peter John

• The impact of redispatch on grid and storage expansion planning in the German energy system

  Büttner, C., & Müller, U. (2024). The impact of redispatch on grid and storage expansion planning in the German energy system. ACM SIGENERGY Energy Informatics Review, 4. Abgerufen von https://energy.acm.org/eir/the-impact-of-redispatch-on-grid-and-storage-expansion-planning-in-the-german-energy-system/

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  • Forschungsprojekt

    • PoWerD - Potenzialatlas für eine Wasserstofferzeugung in Deutschland

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  • 2024
  • Journal Article
  • • Clara Büttner
    • Ulf Müller

• Hydraulic variable inertia flywheel

  Jauch, C., Jost, R., & Kloft, P. (2024). Hydraulic variable inertia flywheel. Applied Energy, 360, 16. http://doi.org/https://doi.org/10.1016/j.apenergy.2024.122830 (Original work published Februar 2024)

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  A novel variable inertia flywheel that uses the mass of a rotating hydraulic fluid is proposed in this paper. In contrast to variable inertia flywheels that use solid masses, this flywheel stands out for its simplicity. In contrast to many other common energy storages, it does not require any environmentally harmful, rare or expensive materials. The basic working principle and the equations that cover the hydraulic behaviour, the pneumatic behaviour and the energy from angular momentum are introduced. These equations are applied to the geometry of the proposed flywheel concept, which allows quantifying the pressures that act on the different mechanical flywheel components. These pressures, together with the centrifugal acceleration from rotation, lead to the loads that have to be withstood by the mechanical components. A simple method for quantifying these loads, and for dimensioning the mechanical components, allows deriving the stationary masses and inertias. A parameter study is conducted, in which different parameters of the flywheel are varied in order to find the maxima in energy density and specific energy. The results of this parameter study reveal that the proposed hydraulic variable inertia flywheel is a very simple and safe energy storage that could provide AC power systems with inertia and control power to support their frequency.

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    • Jauch, Clemens

  • Forschungsprojekt

    • Inno!Nord: Innovationslabor: Speicher zur Nutzung erneuerbarer Energien im echten Norden

  • Abteilung/Einrichtung

    • Wind Energy Technology Institute

  • Profilbereich

    • Erneuerbare Energien und nachhaltige Entwicklung

  • Forschungsfeld

    • Innovation und Wachstum, Nachhaltigkeit
    • Wind
    • Netzintegration

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2024
  • Journal Article
  • • Prof. Dr. Clemens Jauch
    • Rebecca Jost
    • Peter Kloft
  • • Energy storage
    • Fluid
    • Flywheel
    • Kinetic energy
    • Variable moment of inertia
    • Variable inertia flywheel

• Dominance hierarchy does not influence distances travelled and area utilization in a large group of ponies

  Weidling, G., Krieter, J., Lübben, R., & Czycholl, I. (2024). Dominance hierarchy does not influence distances travelled and area utilization in a large group of ponies. Applied Animal Behaviour Science, 271. http://doi.org/https://doi.org/10.1016/j.applanim.2024.106178 (Original work published Februar 2024)

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  In order to improve existing horse husbandry practices by providing social contact and exercise opportunities, it is necessary to fully understand the behaviour of horses in relation to group housing. Therefore, this study investigated the influence of dominance hierarchy on the locomotor behaviour and area utilization in a large group of horses in a pasture-based housing system in Northern Germany. The movements of 40 ponies with established group affiliations were observed over approximately one month from late August to early October 2022 using GPS technology. Their dominance hierarchy was determined by testing each possible combination in separate pair-feeding tests. For the determination of area utilization, the available space was divided into a total of 8889 quadrants with 3x3 m each. Analysis of daily distances travelled by the horses revealed an average of 5.4 ± 1.2 km/day, with no significant effect of social rank. However, age had a significant effect, with older horses walking shorter distances. Quadrants used per hour, as measure of area utilization, averaged 37.9 ± 6.4, with no effect of social rank but a significant effect of age observed. Heat maps showed full pasture utilization, with lower ranked horses having less access to resources around the watering trough and a greater use of less attractive grazing areas. The study suggests that in this specific setting, social rank did not significantly affect locomotor behaviour or area use. Although the study did not find a correlation between dominance rank and locomotor behaviour or area use, it highlighted the need for further research in different group sizes and housing systems to optimise group housing practices for horses, especially in settings with limited space and resources.

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    • Lübben, Ralf

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 4: Wirtschaft

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  • 2024
  • Journal Article
  • • Gesa Weidling
    • Joachim Krieter
    • Ralf Lübben
    • Irena Czycholl
  • • Horse
    • Dominance hierarchy
    • GPS
    • Distance
    • Area utilization
    • Group housing

• Investigation of asymmetric SVM switching patterns with minor flux loops in inverter fed induction motors

  Königs, M., & Löhlein, B. (2024). Investigation of asymmetric SVM switching patterns with minor flux loops in inverter fed induction motors. Research gate, open access. http://doi.org/http://dx.doi.org/10.13140/RG.2.2.21923.26407

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  • 2024
  • Journal Article
  • • Mike Königs
    • Bernd Löhlein

• Why state-of-the-art analytical models for eddy current losses in PM of PMSM are insufficient for variable speed motors

  Königs, M., & Löhlein, B. (2024). Why state-of-the-art analytical models for eddy current losses in PM of PMSM are insufficient for variable speed motors. e+i Elektrotechnik und Informationstechnik, 141. http://doi.org/10.1007/s00502-024-01207-y

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  • 2024
  • Journal Article
  • • Mike Königs
    • Bernd Löhlein

• Analysis of Turbine Yaw Misalignment Estimated by LIDAR Assuming Homogeneous Flow

  Zhang, Z., Guo, F., Schlipf, D., Schito, P., & Zasso, A. (2024). Analysis of Turbine Yaw Misalignment Estimated by LIDAR Assuming Homogeneous Flow. Wind Energy Science Discussions, 2024, 1–21. http://doi.org/10.5194/wes-2023-162

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  • 2024
  • Journal Article
  • • Z. Zhang
    • F. Guo
    • D. Schlipf
    • P. Schito
    • A. Zasso

• Breast cancer survival prediction using an automated mitosis detection pipeline

  Stathonikos, N., Aubreville, M., de Vries, S., Wilm, F., Bertram, C. A., Veta, M., & van Diest, P. J. (2024). Breast cancer survival prediction using an automated mitosis detection pipeline. The Journal of Pathology: Clinical Research, 10, e70008. http://doi.org/https://doi.org/10.1002/2056-4538.70008

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  Abstract Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm2. We employed this method on a breast cancer cohort with long-term follow-up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI-based MC and light-microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni- and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light-microscopic MC.

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  • Personenseiten

    • Aubreville, Marc

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

  • Profilbereich

    • Informatik und Gestaltung

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2024
  • Journal Article
  • • Nikolas Stathonikos
    • Marc Aubreville
    • Sjoerd de Vries
    • Frauke Wilm
    • Christof Bertram
    • Mitko Veta
    • Paul van Diest
  • • artificial intelligence
    • machine learning
    • histopathology
    • prognosis
    • mitosis
    • outcome
    • deep learning