Recent Submissions
SOFIA - Mission infrarotes Universum
(Stuttgart : Universität Stuttgart, Institut für Raumfahrsysteme, 2025) Krabbe, Alfred; Mehlert, Dörte; Wolf, Jürgen
Warum haben die NASA und das DLR - die amerikanische und deutsche Raumfahrtagentur - eine fliegende Sternwarte betrieben? Mit welchen Tricks haben Ingenieure und Ingenieurinnen das Teleskop des Stratosphären Observatoriums Für Infrarot Astronomie (SOFIA) stabilisiert? Was ist am infraroten Universum so interessant? Wozu hatte die Universität Stuttgart eine Außenstelle in Kalifornien? Welche Rolle spielte die deutsche Wiedervereinigung während der Entwicklungsphase? Diese und viele weitere Fragen rund um die fliegende Sternwarte SOFIA beantwortet dieses von Insidern geschriebene Buch in Bildern und Texten. Lassen Sie sich auf einen Forschungsflug an Bord des einzigartigen Observatoriums mitnehmen, gewinnen Sie einen Überblick über die wissenschaftlichen Erkenntnisse des Observatoriums und fragen Sie: Wie könnte das nächste Stratosphären-Observatorium für Infrarot-Astronomie aussehen? In diesem Buch finden Sie alles, was Sie schon immer über SOFIA wissen wollten.
Effects of blood flow restriction on motoneurons synchronization
(2025) Taleshi, Mansour; Bubeck, Franziska; Gizzi, Leonardo; Vujaklija, Ivan
Blood flow restriction (BFR) is a peripheral intervention that induces transient and reversible physiological perturbations. While this intervention offers a unique model to explore neuromuscular responses in multiple contexts, its impact on neural input to motoneurons remains unclear. Here, the influence of BFR on muscle force control, behavior, and neural input to motoneurons during isometric-trapezoidal and isometric-sinusoidal little finger abduction precision tasks has been studied. Sixteen healthy participants performed the tasks under pre-BFR, during BFR, and at two post-BFR conditions. High-density surface electromyography (EMG) was recorded from the abductor digiti minimi muscle, and motor unit spike trains (MUST) were decomposed using blind source separation technique. Coherence between cumulative spike trains (CSTs) of identified motor units was calculated to assess common synaptic input in the delta and alpha frequency bands. As expected, during BFR application, participants reported higher level of discomfort and significant deterioration in force-tracking performance, as measured using root mean square error (RMSE). Following the BFR release, the level of discomfort, along with impaired neuromuscular performance were reduced to pre-BFR condition. Coherence analysis revealed a prominent peak in the alpha band. The mean z-score coherence in the alpha band showed a reduction of 27% for isometric-trapezoidal and 31% for isometric-sinusoidal conditions from pre-BFR to BFR, followed by a rebound post-BFR intervention with increases of 13% and 20%, respectively. In the delta band, coherence values were consistently higher during sinusoidal tasks compared to trapezoidal ones. These findings indicate that brief BFR application led to decrease in motoneuron synchronization and force control precision likely due to desensitization as shown by changes in coherence alpha band.
Trifunctional antibody-cytokine fusion protein formats for tumor-targeted combination of IL-15 with IL-7 or IL-21
(2025) Möller, Annika M.; Vettermann, Sarah; Baumann, Felix; Pütter, Max; Müller, Dafne
Cytokines from the common gamma chain receptor family, such as IL-15, IL-21 and IL-7, show promise for cancer immunotherapy and have been incorporated individually into the immunocytokine approach. However, their efficacy as monotherapy is limited. Here, we investigated the molecular design of tumor-directed trifunctional antibody-cytokine fusion proteins for a combinatorial approach of IL-15 with either IL-7 or IL-21. Various fusion proteins differing in antibody format, cytokine composition and arrangement were generated and cooperative cytokine activity assessed in solution and bound to target cells. Comparative analysis revealed that formats with cytokines positioned at the N- and C-termini of the antibody were more effective than those arranged in series. For the former design, cooperative effects were observed with the scFv-based (IL-15+IL-7) trifunctional fusion protein, primarily enhancing the proliferation of naive T cells, while the scFv/Fab-based (IL-15+IL-21) trifunctional fusion proteins enhanced IFN-y release and the cytotoxic potential of T cells. Combining cytokines in the two-in-one molecule approach was principally advantageous when bound to target cells. Greater potency in inducing JAK-STAT pathway activation highlighted the importance of cytokine colocalization for cooperative receptor activation. Compared to the Fab-based (IL-15+IL-21) format, the scFv-based (IL-15+IL-21) format displayed a tendency towards higher activity in targeted and lower activity in untargeted settings, emphasizing the targeted concept. Thus, this study underscores the importance of molecular design in developing trifunctional immunocytokines and identified the scFv-based trifunctional (IL-15+IL-21) fusion protein, with the antibody in the central position, as a particularly promising candidate for further drug development.
Energy efficiency in ROS communication : a comparison across programming languages and workloads
(2025) Albonico, Michel; Cannizza, Manuela Bechara; Wortmann, Andreas
Introduction: The Robot Operating System (ROS) is a widely used framework for robotic software development, providing robust client libraries for both C++ and Python. These languages, with their differing levels of abstraction, exhibit distinct resource usage patterns, including power and energy consumption–an increasingly critical quality metric in robotics.
Methods: In this study, we evaluate the energy efficiency of ROS two nodes implemented in C++ and Python, focusing on the primary ROS communication paradigms: topics, services, and actions. Through a series of empirical experiments, with programming language, message interval, and number of clients as independent variables, we analyze the impact on energy efficiency across implementations of the three paradigms.
Results: Our data analysis demonstrates that Python consistently demands more computational resources, leading to higher power consumption compared to C++. Furthermore, we find that message frequency is a highly influential factor, while the number of clients has a more variable and less significant effect on resource usage, despite revealing unexpected architectural behaviors of underlying programming and communication layers.
Methods: In this study, we evaluate the energy efficiency of ROS two nodes implemented in C++ and Python, focusing on the primary ROS communication paradigms: topics, services, and actions. Through a series of empirical experiments, with programming language, message interval, and number of clients as independent variables, we analyze the impact on energy efficiency across implementations of the three paradigms.
Results: Our data analysis demonstrates that Python consistently demands more computational resources, leading to higher power consumption compared to C++. Furthermore, we find that message frequency is a highly influential factor, while the number of clients has a more variable and less significant effect on resource usage, despite revealing unexpected architectural behaviors of underlying programming and communication layers.
Design and evaluation of new user control devices for improved ergonomics in flexible robotic endoscopy
(2025) Heisterberg, Leander; Manfredi, Luigi; Wichmann, Dörte; Maier, Thomas; Pott, Peter P.
Background: The ergonomics of flexible endoscopes require improvement as the current design carries a high risk of musculoskeletal injury for endoscopists. Robotic systems offer a solution by separating the endoscope from the control handle, allowing a focus on ergonomics and usability. Despite the increasing interest in this field, little attention has been paid towards developing ergonomic human input devices. This study addresses two key questions: How can handheld control devices for flexible robotic endoscopy be designed to prioritize ergonomics and usability? And, how effective are these new devices in a simulated clinical environment?
Methods: Addressing this gap, the study proposes two handheld input device models for controlling a flexible endoscope in four degrees of freedom (DOFs) and an endoscopic instrument in three DOFs. A two-stage evaluation was conducted with six endoscopists evaluating the physical ergonomics and a final clinical user evaluation with seven endoscopists using a virtual colonoscopy simulator with proportional velocity and position mapping.
Results and discussion: Both models demonstrated clinical suitability, with the first model scoring 4.8 and the second model scoring 5.2 out of 6 in the final evaluation. In sum, the study presents two designs of ergonomic control devices for robotic colonoscopy, which have the potential to reduce endoscopy-related injuries. Furthermore, the proposed colonoscopy simulator is useful to evaluate the benefits of different mapping modes. This could help to optimize the design and control mechanism of future control devices.
Methods: Addressing this gap, the study proposes two handheld input device models for controlling a flexible endoscope in four degrees of freedom (DOFs) and an endoscopic instrument in three DOFs. A two-stage evaluation was conducted with six endoscopists evaluating the physical ergonomics and a final clinical user evaluation with seven endoscopists using a virtual colonoscopy simulator with proportional velocity and position mapping.
Results and discussion: Both models demonstrated clinical suitability, with the first model scoring 4.8 and the second model scoring 5.2 out of 6 in the final evaluation. In sum, the study presents two designs of ergonomic control devices for robotic colonoscopy, which have the potential to reduce endoscopy-related injuries. Furthermore, the proposed colonoscopy simulator is useful to evaluate the benefits of different mapping modes. This could help to optimize the design and control mechanism of future control devices.
Data-efficient reinforcement learning with Bayesian neural networks
(Stuttgart : Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA, 2025) Wu, Xinyang; Huber, Marco (Prof. Dr.-Ing. habil.)
Artificial Intelligence (AI) and Machine Learning (ML) have propelled significant advancements across numerous domains, with deep Reinforcement Learning (RL) emerging as a critical solution for complex control tasks. While traditional Neural Networks (NNs) enhance performance and learning capacity, they often exhibit overconfidence and lack uncertainty information in their predictions, which can compromise optimal decision-making in stochastic environments.
This thesis elucidates the potential of Bayesian Neural Networks (BNNs), which reconcile the predictive capacities of NNs with the probabilistic rigor of Bayesian inference, offering a robust paradigm for uncertainty quantification. An innovative approach is introduced, employing the Kalman filter, a powerful tool for state estimation in dynamic systems, to enable efficient online learning of BNNs. The effectiveness and efficiency of this approach are validated on standard ML datasets.
Beyond providing a theoretical exposition of BNNs, the thesis pioneers the integration of BNNs within both model-free and model-based RL frameworks. The objective is to utilize the uncertainty quantification capabilities of BNNs to improve learning efficiency and safety performance of RL algorithms, over- coming challenges associated with overconfidence and uncertain predictions. The practical efficacy of the proposed methodologies is validated through experiments on classic control problems and complex robotic tasks. The empirical results underscore significant improvements in learning efficiency and safety performance, proving the theoretical merits of integrating BNNs with RL.
In conclusion, this thesis offers an in-depth exploration into the fusion of BNNs and RL, presenting innovative methodologies that incorporate uncertainty information into RL paradigms. The insights and methodologies proposed serve as a springboard for future research, moving us closer to the realization of RL’s full potential in real-world applications.
Machine learning the microscopic form of nematic order in twisted double-bilayer graphene
(2023) Sobral, João Augusto; Obernauer, Stefan; Turkel, Simon; Pasupathy, Abhay N.; Scheurer, Mathias S.
Modern scanning probe techniques, such as scanning tunneling microscopy, provide access to a large amount of data encoding the underlying physics of quantum matter. In this work, we show how convolutional neural networks can be used to learn effective theoretical models from scanning tunneling microscopy data on correlated moiré superlattices. Moiré systems are particularly well suited for this task as their increased lattice constant provides access to intra-unit-cell physics, while their tunability allows for the collection of high-dimensional data sets from a single sample. Using electronic nematic order in twisted double-bilayer graphene as an example, we show that incorporating correlations between the local density of states at different energies allows convolutional neural networks not only to learn the microscopic nematic order parameter, but also to distinguish it from heterostrain. These results demonstrate that neural networks are a powerful method for investigating the microscopic details of correlated phenomena in moiré systems and beyond.
Preparation of multifunctional hydrogels with accessible isothiouronium groups via radical cross-linking copolymerization
(2023) Grübel, Jana; L. Albernaz, Vanessa; Tsianaka, Anastasia; Jauch, Corinna O.; Quirin, Silia; Kerger, Christian; Kohl, Christina G.; Burger-Kentischer, Anke; Tovar, Günter E. M.; Southan, Alexander
Hydrogels can be equipped with functional groups for specific purposes. Isothiouronium groups can enhance adsorptivity, or allow coupling of other functional groups through mild reactions after transformation to thiol groups. Here we present a method to prepare multifunctional hydrogels by introducing isothiouronium groups into poly(ethylene glycol) diacrylate (PEGDA) hydrogels, and convert them into thiol-functionalized hydrogels by the reduction of the isothiouronium groups. For this purpose, the amphiphilic monomer 2-(11-(acryloyloxy)-undecyl)isothiouronium bromide (AUITB), containing an isothiouronium group, was synthesized and copolymerized with PEGDA. In this convenient way, it was possible to incorporate up to 3 wt% AUITB into the hydrogels without changing their equilibrium swelling degree. The successful functionalization was demonstrated by surface analysis of the hydrogels with water contact angle measurements and increased isoelectric points of the hydrogel surfaces from 4.5 to 9.0 due to the presence of the isothiouronium groups. The hydrogels showed a suitability as an adsorbent, as exemplified by the pronounced adsorption of the anionic drug diclofenac. The potential of the functionalization for (bio)conjugation reactions was demonstrated by the reduction of isothiouronium groups to thiols and subsequent immobilization of the functional enzyme horseradish peroxidase on the hydrogels. The results show that fully accessible isothiouronium groups can be introduced into radically cross-linked hydrogels.
Machine learning assists in increasing the time resolution of X-ray computed tomography applied to mineral precipitation in porous media
(2023) Lee, Dongwon; Weinhardt, Felix; Hommel, Johannes; Piotrowski, Joseph; Class, Holger; Steeb, Holger
Many subsurface engineering technologies or natural processes cause porous medium properties, such as porosity or permeability, to evolve in time. Studying and understanding such processes on the pore scale is strongly aided by visualizing the details of geometric and morphological changes in the pores. For realistic 3D porous media, X-Ray Computed Tomography (XRCT) is the method of choice for visualization. However, the necessary high spatial resolution requires either access to limited high-energy synchrotron facilities or data acquisition times which are considerably longer (e.g. hours) than the time scales of the processes causing the pore geometry change (e.g. minutes). Thus, so far, conventional benchtop XRCT technologies are often too slow to allow for studying dynamic processes. Interrupting experiments for performing XRCT scans is also in many instances no viable approach. We propose a novel workflow for investigating dynamic precipitation processes in porous media systems in 3D using a conventional XRCT technology. Our workflow is based on limiting the data acquisition time by reducing the number of projections and enhancing the lower-quality reconstructed images using machine-learning algorithms trained on images reconstructed from high-quality initial- and final-stage scans. We apply the proposed workflow to induced carbonate precipitation within a porous-media sample of sintered glass-beads. So we were able to increase the temporal resolution sufficiently to study the temporal evolution of the precipitate accumulation using an available benchtop XRCT device.
Model-predicted geometry variations to compensate material variability in the design of classical guitars
(2023) Brauchler, Alexander; Gonzalez, Sebastian; Vierneisel, Manuel; Ziegler, Pascal; Antonacci, Fabio; Sarti, Augusto; Eberhard, Peter
Musical instrument making is often considered a mysterious form of art, its secrets still escaping scientific quantification. There is not yet a formula to make a good instrument, so historical examples are regarded as the pinnacle of the craft. This is the case of Stradivari’s violins or Torres guitars that serve as both models and examples to follow. Geometric copies of these instruments are still the preferred way of building new ones, yet reliably making acoustic copies of them remains elusive. One reason for this is that the variability of the wood used for instruments makes for a significant source of uncertainty - no two pieces of wood are the same. In this article, using state-of-the-art methodologies, we show a method for matching the vibrational response of two guitar top plates made with slightly different materials. To validate our method, we build two guitar soundboards: one serving as a reference and the second acting as a copy to which we apply model-predicted geometry variations. The results are twofold. Firstly, we can experimentally validate the predictive capabilities of our numerical model regarding geometry changes. Secondly, we can significantly reduce the deviation between the two plates by these precisely predicted geometry variations. Although applied to guitars here, the methodology can be extended to other instruments, e.g. violins, in a similar fashion. The implications of such a methodology for the craft could be far-reaching by turning instrument-making more into a science than artistic craftsmanship and paving the way to accurately copy historical instruments of a high value.