Recent Submissions
Produktentwicklung und Mechanik im Kontext nachhaltiger Bildung - Die Natur als Vorbild (Bionik) : exemplarische Unterrichtsreihe
(Stuttgart : Universität Stuttgart, Institut für Erziehungswissenschaft, Berufspädagogik mit Schwerpunkt Technikdidaktik (BPT), 2025) Luibrand, Thomas
Die vorliegenden Lehr- und Lernmaterialien thematisieren zentrale Aspekte der bionischen Produktentwicklung, Nachhaltigkeit, Biomechanik sowie der Optimierung in der Produktentwicklung im Rahmen der Bildung für nachhaltige Entwicklung (BNE). Sie wurden für den Einsatz im Fach Naturwissenschaft und Technik (NwT) an Schulen in Baden-Württemberg konzipiert.
Machine learning methods for classification problems in biomedical signal processing
(2025) Ismailova, Emilie; Schmitt, Syn (Prof. Dr.)
Routine physiological data contains rich diagnostic cues, yet clinicians still rely chiefly on manual visual inspection of raw waveforms. This subjective approach does not consider informative patterns hidden in the time-frequency domain of the signal. Here, we ask whether modern machine-learning algorithms, applied to spectral representations of biomedical signals, can uncover latent biomarkers and turn them into actionable clinical insights. The project addresses this overarching question by focusing on two distinct signal types: (i) electrical by classifying intramuscular electromyography (iEMG) to distinguish spontaneous skeletal muscle activity, and (ii) mechanical by predicting treatment outcomes in obstructive sleep apnea from nasal airflow recordings obtained during polysomnography.
First part of this work investigates skeletal muscle channelopathies - a group of neuromuscular disorders that disturb the cell membrane excitability, which results clinically in myotonia. The genetic aetiology of some of these disorders can be traced to mutations in the SCN4A or CLCN1 genes, which encode sodium and chloride channels, respectively. These channels are specialized proteins in the cell membrane that play a crucial role in generation and propagation of action potentials. Commonly, sodium or chloride channel defects lead to pathophysiological hyperexcitability of muscles, which is observable as myotonic discharges on iEMG recordings. However, there is an ongoing debate about whether the properties of the myotonic discharge can differentiate the type of channel defect, such as sodium versus chloride. This discussion leads to the broader question of whether a stable genotype-to-EMG-phenotype relationship exists in muscle channelopathies.
Accurate identification of the underlying ion-channel defect is also essential for tailored treatment and informed prognosis. At present, clinicians depend exclusively on genetic testing to distinguish sodium- from chloride-channel defects. Although detailed, manual inspection of iEMG recordings can potentially reveal defect-specific patterns in research settings, the procedure is too time-consuming and complex for routine clinical practice.
Our study demonstrated the existence of distinct spectral features in myotonic discharges of patients with sodium and chloride channel defects and addressed the need for their automated classification. We developed and validated a method for this purpose, transforming iEMG recordings into their spectral representations (scalograms) via wavelet transform. These scalograms were subsequently classified using an ensemble of pre-trained deep neural networks. The resulting ensemble achieved a balanced accuracy of approximately 81% and a Brier score of 0.14 on unseen test data. A selective-prediction analysis further indicated that at high-confidence thresholds (greater than 0.85), the model's accuracy exceeded 90%. These results show the potential clinical utility of this approach for enhancing diagnostic efficiency, specifically by helping to prioritize genetic testing for a specific mutation.
One of our objectives was to identify physiological signal characteristics specific to each subtype of ion-channel defect. Electrophysiological analysis can reveal how distinct channel defects produce characteristic discharge patterns, clarifying the underlying mechanisms of myotonia. Gradient-weighted saliency mapping identified discriminative spectral features, including broadband, early-burst energy characteristic of chloride-channel defects, while sustained high-frequency spectral components were observed in sodium-channel defect class samples.
To support the physiological interpretation of our findings, we generated synthetic myotonic discharges using the biophysical model developed by Klotz et al. 2019. The convolutional neural network detected class-specific spectral differences within these simulated signals. This reinforces the physiological relevance of the spectral motifs identified as markers for distinct channelopathies, thereby enhancing the interpretability of our deep learning model.
To further test the robustness and general applicability of the framework, we used the same classification pipeline to distinguish voluntary motor unit activity from spontaneous fibrillation potentials - an electrophysiological hallmark of degenerative and neurogenic muscle disease. Without altering any network architecture or hyperparameters, the ensemble maintained high performance, achieving a balanced accuracy of 87% and a Brier score of 0.11. These findings confirm that the spectral feature learning approach transfers across various pathologies and could therefore serve as a versatile tool for automated EMG diagnostics.
The second part of this study investigates obstructive sleep apnea, a common chronic disorder often treated with mandibular advancement splints. Roughly one-third of patients, however, do not respond adequately to this treatment. Because it is still difficult to predict patient responsiveness from baseline polysomnography, we examined whether the routinely recorded baseline nasal-airflow signal contains features that can predict treatment outcome.
The signal's spectral characteristics were quantified by extracting dominant frequencies within low, mid, and high-frequency bands. Using these features, classical machine learning algorithms, particularly Random Forest classifiers, demonstrated promising results. Specifically, when trained on signals sampled at sufficiently high frequencies, these classifiers accurately identified responders with a recall approaching 90% and a Cohen's kappa of approximately 0.48. Conversely, downsampling the data to lower frequencies significantly impaired predictive performance, highlighting the critical role of mid- and high-frequency spectral content in this classification task.
This study introduces the first automated, uncertainty-aware diagnostic pipeline capable of distinguishing skeletal muscle channelopathies directly from routine EMG signals. By employing spectral signal characteristics within a machine learning framework, it suggests broad applicability across various biomedical contexts. We developed a purely signal-based predictive tool with good clinical diagnostic potential.
Wissenschaftliche Evaluation des Modellprojekts AbiturPLUS : Projektabschlussbericht
(Stuttgart : Universität Stuttgart, Institut für Erziehungswissenschaft, Berufspädagogik mit Schwerpunkt Technikdidaktik (BPT), 2025) Sotiriadou, Christina; Zinn, Bernd; Zinn, Bernd
Im Mittelpunkt des vorliegenden Berichts steht die wissenschaftliche Untersuchung des Modellpro-jekts AbiturPLUS, bei dem eine strukturelle Verzahnung von gymnasialer Allgemeinbildung und be-ruflicher Bildung erfolgt. Die teilnehmenden Schüler:innen des allgemeinbildenden Gymnasiums kön-nen im Modellprojekt, parallel zum schulischen Unterricht in der 11. Klassenstufe, einen qualifizierten Berufsabschluss zum/zur Zerspanungsmechaniker/-in mit Prüfung durch die Industrie- und Handels-kammer (IHK) Ostwürttemberg erwerben. Die Untersuchung beschäftigt sich mit den kognitiven, interessenorientierten, motivationalen und affektiven Merkmalen der teilnehmenden Schüler:innen. Warum nehmen sie an dem Modellprojekt teil und welche Erwartungen und Nutzung geht mit der Teilnahme einher? Wie wirkt sich die Teilnah-me auf die wahrgenommene Stressbelastung der teilnehmenden Schüler:innen im Vergleich zu einer Vergleichsgruppe aus? Auf diese Fragen und weitere geht der Bericht ein. Die Ergebnisse der Untersu-chung liefern insbesondere Erkenntnisse zu den schulischen, freizeitbezogenen und beruflichen Inte-ressen sowie deren Entwicklung.
New Insights into the shear-induced structural transformation of bicontinuous microemulsions
(2024) Fischer, Julian; Sottmann, Thomas (Prof. Dr.)
Single mutation in iolT1 in ptsG-deficient corynebacterium glutamicum enables growth boost in xylose-containing media
(2025) Hofer, Katharina; Schwardmann, Lynn Sophie; Youn, Jung-Won; Wendisch, Volker F.; Takors, Ralf
Efficient co-utilization of glucose and xylose from lignocellulosic biomass remains a critical bottleneck limiting the viability of sustainable biorefineries. While Corynebacterium glutamicum has emerged as a promising industrial host due to its robustness, further improvements in mixed-sugar co-utilization are needed. Here, we demonstrate how a single amino acid substitution can dramatically transform cellular sugar transport capacity. By combining rational strain engineering with continuous adaptive laboratory evolution, we evolved a ptsG -deficient C. glutamicum strain in glucose-xylose mixtures for 600 h under consistent selection pressure. Whole-genome sequencing revealed a remarkable finding: a single point mutation; exchanging proline for alanine in the myo -inositol/proton symporter IolT1 was sufficient to boost glucose uptake by 83% and xylose uptake by 20%, while increasing the overall growth rate by 35%. This mutation, located in a highly conserved domain, likely disrupts an alpha helical structure, thus enhancing transport function. Reverse engineering confirmed that this single change alone reproduces the evolved phenotype, representing the first report of an engineered IolT1 variant in PTS-independent C. glutamicum that features significantly enhanced substrate uptake. These results both provide an immediately applicable engineering target for biorefinery applications and demonstrate the power of evolutionary approaches to identify non-intuitive solutions to complex metabolic engineering challenges.
Next-generation sustainable composites with flax fibre and biobased vitrimer epoxy polymer matrix
(2025) Tran, Hoang Thanh Tuyen; Baur, Johannes; Radjef, Racim; Nikzad, Mostafa; Bjekovic, Robert; Carosella, Stefan; Middendorf, Peter; Fox, Bronwyn
This work presents the development of two vanillin-based vitrimer epoxy flax fibre-reinforced composites, with both the VER1-1-FFRC (a vitrimer-to-epoxy ratio of 1:1) and VER1-2-FFRC (a vitrimer-to-epoxy ratio of 1:2), via a vacuum-assisted resin infusion. The thermal and mechanical properties of the resulting vitrimer epoxy flax composites were characterised using thermal gravimetric analysis (TGA), differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and mechanical four-point bending tests, alongside studies of solvent resistance and chemical recyclability. Both the VER1-1-FFRC (degradation temperature Tdeg of 377.0 °C) and VER1-2-FFRC (Tdeg of 395.9 °C) exhibited relatively high thermal stability, which is comparable to the reference ER-FFRC (Tdeg of 396.7 °C). The VER1-1-FFRC, VER1-2-FFRC, and ER-FFRC demonstrated glass transition temperatures Tg of 54.1 °C, 68.8 °C, and 83.4 °C, respectively. The low Tg of the vitrimer composite is due to the low crosslink density in the vitrimer epoxy resin. Particularly, the crosslinked density of the VER1-1-FFRC was measured to be 319.5 mol·m−3, which is lower than that obtained from the VER1-2-FFRC (434.7 mol·m-3) and ER-FFRC (442.9 mol·m-3). Furthermore, the mechanical properties of these composites are also affected by the low crosslink density. Indeed, the flexural strength of the VER1-1-FFRC was found to be 76.7 MPa, which was significantly lower than the VER1-2-FFRC (116.2 MPa) and the ER-FFRC (138.3 MPa). Despite their lower thermal and mechanical performance, these vitrimer composites offer promising recyclability and contribute to advancing sustainable composite materials.
Structure design by knitting : combined wicking and drying behaviour in single jersey fabrics made from polyester yarns
(2025) Pauly, Leon; Maier, Lukas; Schmied, Sibylle; Nieken, Ulrich; Gresser, Götz T.
The kinetics of liquid transport in textiles are determined by the thermodynamic boundary conditions and the substrate’s structure. The knitting process offers a wide range of possibilities for modifying the fabric structure, making it ideal for high-performance garments and technical applications. Given the highly complex nature of textiles’ interaction with liquids, this paper investigates how fabric structure affects combined wicking and drying behaviour. This facilitates comprehension of the underlying transport processes on the yarn and fabric scale, which is important for understanding the behaviour of the material as a whole. The presented experiment combines analysis of wicking through radial liquid spread using imaging techniques and analysis of the drying process through gravimetric measurement of evaporation. Eight samples of single jersey knitted fabrics were produced using polyester yarns of different texturization and fibre diameters on flat and circular knitting machines. The fabrics demonstrate significantly different wicking behaviours depending on their structure. The fabric’s drying time and rate are directly linked to the macroscopic spread of the liquid. Large inter-yarn pores hinder liquid spread. For the lowest liquid saturations, the yarn structure plays a critical role. Using fine, dense yarns can hinder convective drying within the yarn. Textured yarns tend to exhibit higher specific drying rates. The results offer a comprehensive insight into the interplay between the fabric’s structure and its wicking and drying behaviour, which is crucial for the development of functional fabrics in the knitting process.
What motivates companies to take the decision to decarbonise?
(2025) Buettner, Stefan M.; König, Werner; Vierhub-Lorenz, Frederick; Gilles, Marina
What motivates industrial companies to decarbonise? While climate policy has intensified, the specific factors driving corporate decisions remain underexplored. This article addresses that gap through a mixed-methods study combining qualitative insights from a leading automotive supplier with quantitative data from over 800 manufacturing companies in Germany. The study distinguishes between internal motivators-such as risk reduction, future-proofing, and competitive positioning-and external drivers like regulation, supply chain pressure, and investor expectations. Results show that internal economic logic is the strongest trigger: companies act more ambitiously when decarbonisation aligns with their strategic interests. Positive motivators outperform external drivers in both influence and impact on ambition levels. For instance, long-term cost risks were rated more relevant than reputational gains or regulatory compliance. The analysis also reveals how company size, energy intensity, and supply chain position shape motivation patterns. The findings suggest a new framing for climate policy: rather than relying solely on mandates, policies should strengthen intrinsic motivators. Aligning business interests with societal goals is not only possible-it is a pathway to more ambitious, resilient, and timely decarbonisation. By turning external pressure into internal logic, companies can move from compliance to leadership in the climate transition.
Influence and potential of additive manufactured reference geometries for ultrasonic testing
(2025) Keuler, Stefan; Jüngert, Anne; Werz, Martin; Weihe, Stefan
This study researches and discusses the impact of different manufacturing-induced effects of additive manufacturing (AM), such as anisotropy on sound propagation and attenuation, on the production of test specimens for ultrasonic testing (UT). It was shown that a linear, alternating hatching pattern led to strong anisotropy in sound velocity and attenuation, with a deviation in sound velocity and gain of over 840 m/s and 9 dB, depending on the measuring direction. Furthermore, it was demonstrated that the build direction exhibits distinct acoustic properties. The influence of surface roughness on both the reflector and coupling surfaces was analyzed. It was demonstrated that post-processing of the reflector surface is not necessary, as varying roughness levels did not significantly change the signal amplitude. However, for high frequencies, pre-treatment of the coupling surface can improve sound transmission up to 6 dB at 20 MHz. Finally, the reflection properties of flat bottom holes (FBH) in reference blocks produced by AM and electrical discharge machining (EDM) were compared. The equivalent reflector size (ERS) of the FBH, which refers to the size of an idealized defect with the same ultrasonic reflection behavior as the measured defect, was determined using the distance gain size (DGS) method-a method that uses the relationship between reflector size, scanning depth, and echo amplitude to evaluate defects. The findings suggest that printed FBHs achieve an improved match between the ERS and the actual manufactured reflector size with a deviation of less than 13%, thereby demonstrating the potential for producing standardized test blocks through additive manufacturing.