Dipl.-Phys.

König, Niels

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  • Publication
    A concept for a large-scale non-contact strain measurement system using nanostructures
    ( 2024-06-24)
    Ulm, Andreas
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    König, Niels orcid-logo
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    Schmitt, Robert
    The monitoring of mechanical strain is essential in developing new materials, designing mechanical components and structural health monitoring. In applications where contactless measurement is required, a novel method is needed to allow for absolute and long-term measurements. We discuss a measuring principle based on diffractive nanostructures featuring these advantages. For the measurement, periodic nanostructures are applied to a component, illuminated with a defined light source and the resulting color impression is monitored. The relationship between the stretched geometry of the nanostructure and diffraction spectra allows to quantify the component’s strain. We present a guide-line for the design of industrial applicable and sensitivity-optimized nanostructures and discuss the advantages in different application scenarios.
  • Publication
    Technologien und Lösungsansätze für die effiziente Herstellung von Zelltherapeutika für die CAR-Immuntherapie
    ( 2024)
    Blache, Ulrich
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    Kebbel, Kati
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    Quaiser, Andrea
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    Popp, Georg
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    Franz, Paul
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    Dünkel, Anna
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    Thoma, Martin
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    König, Niels orcid-logo
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    Platzbecker, Uwe
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    Schmiedeknecht, Gerno
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    Fricke, Stephan
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    Köhl, Ulrike
    Die dynamischen Entwicklungen auf dem Gebiet der zellulären Immuntherapie, insbesondere im Bereich der CAR-T-Zellen, ermöglichen neue Erfolg versprechende Behandlungsoptionen von Krebserkrankungen. Zugleich stellen diese noch jungen Krebstherapien die Medizin vor große Herausforderungen. Wie die Herstellung von zellulären Krebstherapeutika im großen Maßstab zur Versorgung der wachsenden Patientenzahl in der Zukunft gewährleistet werden kann und welche Hürden es dabei zu überwinden gilt, wird im Folgenden adressiert. Erste Optionen zur automatisierten Herstellung von CAR-T-Zellen sind bereits etabliert. Um zukünftig die Behandlung großer Patientengruppen zu gewährleisten, sind neue Herstellungstechnologien wie allogene Zellquellen, digital gesteuerte Prozessstraßen und automatische Qualitätskontrollen erforderlich.
  • Publication
    Empirical study on 5G NR Adjacent Channel Coexistence
    ( 2023)
    Caro, Jordi Biosca
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    Ansari, Junaid
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    Sayyed, Ahmed Raza
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    Bruin, Peter de
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    Sachs, Joachim
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    König, Niels orcid-logo
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    Schmitt, Robert
    5G New Radio (NR) non-public network deployments for industrial and enterprise applications are becoming highly popular in locally licensed and/or operator spectrum. The interference from coexisting networks on adjacent channels (in same or adjacent spectrum bands) could potentially deteriorate the performance characteristics in certain deployment scenarios. Appropriate interference mitigation is thus required to achieve the desired performance levels. In this paper, we present our detailed empirical results on the performance impact of coexisting 5G NR networks operating on adjacent channels. Our experimental study conducted on an industrial shopfloor reports the impact on the downlink and uplink latency and throughput when using the same and different Time Division Duplexing patterns for coexisting networks. Our empirical evaluation includes realistic user equipment deployment locations and traffic load conditions. We also present our results on different mitigation techniques to counter the adjacent channel interference effects.
  • Publication
    Optical coherence tomography combined with convolutional neural networks can differentiate between intrahepatic cholangiocarcinoma and liver parenchyma ex vivo
    ( 2023)
    Wolff, Laura I.
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    Hachgenei, Enno
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    Goßmann, Paul
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    Druzenko, Mariia
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    Frye, Maik
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    König, Niels orcid-logo
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    Schmitt, Robert
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    Chrysos, Alexandros
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    Jöchle, Katharina
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    Truhn, Daniel
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    Kather, Jakob Nikolas
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    Lambertz, Andreas
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    Gaisa, Nadine Therese
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    Jonigk, Danny David
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    Ulmer, Florian T.
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    Neumann, Ulf Peter
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    Lang, Sven Arke
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    Amygdalos, Iakovos
    Purpose: Surgical resection with complete tumor excision (R0) provides the best chance of long-term survival for patients with intrahepatic cholangiocarcinoma (iCCA). A non-invasive imaging technology, which could provide quick intraoperative assessment of resection margins, as an adjunct to histological examination, is optical coherence tomography (OCT). In this study, we investigated the ability of OCT combined with convolutional neural networks (CNN), to differentiate iCCA from normal liver parenchyma ex vivo. Methods: Consecutive adult patients undergoing elective liver resections for iCCA between June 2020 and April 2021 (n = 11) were included in this study. Areas of interest from resection specimens were scanned ex vivo, before formalin fixation, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined, providing a diagnosis for each scan. An Xception CNN was trained, validated, and tested in matching OCT scans to their corresponding histological diagnoses, through a 5 × 5 stratified cross-validation process. Results: Twenty-four three-dimensional scans (corresponding to approx. 85,603 individual) from ten patients were included in the analysis. In 5 × 5 cross-validation, the model achieved a mean F1-score, sensitivity, and specificity of 0.94, 0.94, and 0.93, respectively. Conclusion: Optical coherence tomography combined with CNN can differentiate iCCA from liver parenchyma ex vivo. Further studies are necessary to expand on these results and lead to innovative in vivo OCT applications, such as intraoperative or endoscopic scanning.
  • Publication
    Toward Rapid, Widely Available Autologous CAR-T Cell Therapy - Artificial Intelligence and Automation Enabling the Smart Manufacturing Hospital
    ( 2022-06-06)
    Hort, Simon orcid-logo
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    Herbst, Laura
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    Bäckel, Niklas
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    Erkens, Frederik
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    Nießing, Bastian
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    Frye, Maik
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    König, Niels orcid-logo
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    Papantoniou, Ioannis
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    Hudecek, Michael
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    Jacobs, John J. L.
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    Schmitt, Robert
    CAR-T cell therapy is a promising treatment for acute leukemia and lymphoma. CAR-T cell therapies take a pioneering role in autologous gene therapy with three EMA-approved products. However, the chance of clinical success remains relatively low as the applicability of CAR-T cell therapy suffers from long, labor-intensive manufacturing and a lack of comprehensive insight into the bioprocess. This leads to high manufacturing costs and limited clinical success, preventing the widespread use of CAR-T cell therapies. New manufacturing approaches are needed to lower costs to improve manufacturing capacity and shorten provision times. Semi-automated devices such as the Miltenyi Prodigy® were developed to reduce hands-on production time. However, these devices are not equipped with the process analytical technology necessary to fully characterize and control the process. An automated AI-driven CAR-T cell manufacturing platform in smart manufacturing hospitals (SMH) is being developed to address these challenges. Automation will increase the cost-effectiveness and robustness of manufacturing. Using Artificial Intelligence (AI) to interpret the data collected on the platform will provide valuable process insights and drive decisions for process optimization. The smart integration of automated CAR-T cell manufacturing platforms into hospitals enables the independent manufacture of autologous CAR-T cell products. In this perspective, we will be discussing current challenges and opportunities of the patient-specific but highly automated, AI-enabled CAR-T cell manufacturing. A first automation concept will be shown, including a system architecture based on current Industry 4.0 approaches for AI integration.
  • Publication
    Performance of 5G Trials for Industrial Automation
    ( 2022)
    Ansari, Junaid
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    Andersson, Christian
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    Bruin, Peter de
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    Farkas, János
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    Grosjean, Leefke
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    Sachs, Joachim
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    Torsner, Johan
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    Varga, Balázs
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    Harutyunyan, Davit
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    König, Niels orcid-logo
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    Schmitt, Robert H.
    Wireless- and 5G-enabled industrial automation is expected to include a plethora of different applications with a wide variety of requirements. In this article, evaluations are undertaken for the deployment of 5G in realistic industrial production environments with realistic deployment settings. Both deployments using commercial 5G systems and a 5G prototype system including pre-commercial and standard compliant URLLC functionality have been investigated. Systematic latency and reliability measurements were performed, over the air and in live networks, for different packet sizes, different devices, and networks with different capabilities (at different sites) to characterize the expected performance. The results indicate that today’s 5G latency performance significantly depends on packet size, transmission direction (uplink or downlink), and network configuration as well as on the end device’s design and capabilities. Our over-the-air measurements also empirically show that 5G technology and future networks have the capability of providing one-way latency of around 1 ms in both uplink and downlink for the various packet sizes tested. It is concluded that the requirements for very low latencies can be achieved with high reliability guarantees, as required in some of the most stringent industrial IoT applications.
  • Publication
    Empirical Study on 5G NR Cochannel Coexistence
    ( 2022)
    Biosca Caro, Jordi
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    Ansari, Junaid
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    Sachs, Joachim
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    Bruin, Peter de
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    Sivri, Sertap
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    Grosjean, Leefke
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    König, Niels orcid-logo
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    Schmitt, Robert H.
    The 5G non-public network deployments for industrial applications are becoming highly interesting for industries and enterprises owing to dependable wireless performance characteristics. With an increasing trend of network deployments in local licensed and/or shared spectrum, coexistence issues naturally arise. In this article, we present our detailed empirical results on the performance impact of a 5G NR indoor non-public network from a 5G NR outdoor network operating in the same mid-band spectrum. We present experimental results on the uplink and downlink performance impact of a non-public indoor network deployed on an industrial shopfloor. Our results quantify the impact on the uplink and downlink performance characteristics based on realistic traffic loads in a non-public indoor network when using synchronized and unsynchronized Time Division Duplex (TDD) patterns, different UE deployment locations and interference levels. We also present results on mitigating interference effects through robust link adaptation techniques. We believe that this is the first article, which reports quantified 5G NR cochannel coexistence results based on a detailed and systematic study, and provides signficant insights on the cochannel coexistence behavior in realistic deployment scenarios of an industrial shopfloor.
  • Publication
    Automated Manufacturing of Microtissue Based Osteochondral Implants: The »JOINTPROMISE« Platform
    ( 2022)
    Krieger, Judith
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    Nießing, Bastian
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    König, Niels orcid-logo
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    Luyten, Frank P.
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    Papantoniou, Ioannis
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    Schmitt, Robert H.
    Over 300 million cases of osteoarthritis were reported in 2017, stating one of the most prevalent chronic joint diseases worldwide characterized predominantly by long-term progressive cartilage and subchondral bone degeneration. Conventional therapy approaches utilize pharmacotherapy mostly for pain relief and at end stage disease treated by whole joint replacement surgery to retrieve some mobility and function. Novel Regenerative Medicine (RM) strategies employing Tissue Engineered implants could enable cure, more than care, of such life-constraining disabilities to meet the rising demand for medical interventions due to an ageing world population. Engineering joint tissue implants for the regeneration of the cartilage-bone unit of the joints remains a challenge due to the complex structural organization and functionality of native joint tissue. The use of microtissue/spheroid platforms has enabled differentiation and maturation of cartilage intermediates and gives hope for the engineering of efficient large-scale implants for osteochondral regeneration. However, there is still lack of enabling technologies for scaling of these approaches and robust manufacturing with end-to-end automation of such advanced therapeutic medicinal products (ATMPs). To allow sufficient scaling, overcome risks of contamination as well as inconsistent product quality in manual production procedures, the automated, GMP-compliant manufacturing platform »JointPromise« is developed. By establishing a robust, large-scale manufacturing process, a reliable microtissue-based product for the treatment of deep osteochondral defects can be generated with suitable productivity. The platform concept is based on the translation of Standard Operating Procedures (SOPs) for microtissue production, harvest and condensation into a sequence of automated process steps. Derived process design criteria and technical specifications result in device requirements for an automated production process. After initiating the conceptualizing stage of the platform design by creating a 2D layout according to the material flow of the translated SOPs, the final arrangement of devices was optimized in the overall 3D CAD model. The resulting production platform model combines all required devices for cell cultivation, microtissue harvest and ATMP production in an overall layout consisting of pipetting units, an incubator, centrifuge, bioprinter and housing for a defined hygienic environment. Following the SOPs, about 28,000 microtissue spheroids can be produced within 21 days of culture out of 1 mL cell suspension per tissue culture plate. To reach the required productivity of around 100 tissue culture plates per implant, the production platform will need to process around 70 L of liquids during seeding and harvest processes and 5 L per cell media change to produce around 2.8M microtissue spheroids in 21 days. The build-up of the »JointPromise« platform is followed by the implementation of the control software COPE (Control Operate Plan Execute, Fraunhofer IPT, Aachen, Germany) for process controlling and monitoring during cell seeding, cultivation and harvest.