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STAF: Leveraging LLMs for automated attack tree-based security test generation
No full textIn modern automotive development, security testing is critical for safeguarding systems against increasingly advanced threats. Attack trees are widely used to systematically represent potential attack vectors, but generating comprehensive test cases from these trees remains a labor-intensive, error-prone task that has seen limited automation in the context of testing vehicular systems. This paper introduces STAF (Security Test Automation Framework), a novel approach to automating security test case generation. Leveraging Large Language Models (LLMs) and a four-step self-corrective Retrieval-Augmented Generation (RAG) framework, STAF automates the generation of executable security test cases from attack trees, providing an end-to-end solution that encompasses the entire attack surface. We particularly show the elements and processes needed to provide an LLM to actually produce sensible and executable automotive security test suites, along with the integration with an automated testing framework. We further compare our tailored approach with general purpose (vanilla) LLMs and the performance of different LLMs (namely GPT-4.1 and DeepSeek) using our approach. We also demonstrate the method of our operation step-bystep in a concrete case study. Our results show significant improvements in efficiency, accuracy, scalability, and easy integration in any workflow, marking a substantial advancement in automating automotive security testing methods. Using TARAs as an input for verification tests, we create synergies by connecting two vital elements of a secure automotive development process
Subsecond optically controlled domain switching in freestanding ferroelectric BaTiO membrane
No full textThe quest to develop energy-efficient and fast optoelectronic control of memory devices is essential. In this respect, ferroelectric materials are gaining tremendous importance in information and communication technology. Here, we demonstrate light-controlled polarisation switching on a subsecond timescale ( <500 ms) in a freestanding BaTiO membrane, which is nearly 1200 times faster than the previously reported response using a BaTiO thin film. We reveal the potential of optically controlled computing by demonstrating the associated resistance change in the membrane as a result of the polarisation reversal induced by illumination. By combining theoretical and experimental studies, we demonstrate that the imprint effect coupled with the reduced energy barrier of domain wall motion influences the optically controlled domain switching response in the membrane. It is established that the fast domain switching response in the freestanding film compared to the clamped film is attributed to the removal of substrate-induced strain and the subsequent increase in domain wall velocity. Additionally, ferroelectric fatigue behaviour is not observed in our system even after 75 electrical and optical cycles, demonstrating the robustness of the observed phenomenon. Our work provides a step forward towards wireless sensing and dual optical and electronic control for computing
Faster implicit motor sequence learning of new sequences compatible in terms of movement transitions
No full textNew information that is compatible with pre-existing knowledge can be learned faster. Such schema memory effect has been reported in declarative memory and in explicit motor sequence learning (MSL). Here, we investigated if sequences of key presses that were compatible to previously trained ones, could be learned faster in an implicit MSL task. Participants trained a motor sequence before switching to a completely new sequence, to a compatible sequence with high overlap in ordinal positions, or to an incompatible sequence with low overlap, while the compatible and incompatible sequences had the same overlap in movement transitions. We observed accelerated learning in the Compatible and Incompatible groups compared to the New group, if participants trained for 3 sessions before switching to the altered sequence. Overall, our study suggests facilitative learning of implicit motor sequences that are compatible in movement transitions, if the previous sequence has been trained extensively
Demand-side management in residential heating
No full textDiese Dissertation untersucht, wie temperaturbasierte Maßnahmen des Demand Side Managements (DSM) im Gebäudewärmesektor zur Energiewende beitragen können. Im Fokus stehen (1) Energieeinsparung durch konstante Temperaturabsenkung und (2) Demand Response mit Wärmepumpen durch variable Temperatureinstellungen. Am Beispiel des deutschen Einfamilienhausbestands werden technische und ökonomische Potenziale analysiert. Ein integriertes Modell verknüpft Gebäudesimulation, Wärmepumpenoptimierung und das agentenbasierte Strommarktmodell AMIRIS. Die Ergebnisse zeigen, dass DSM im Gebäudebereich ein erhebliches technisches Potenzial besitzt: Für Nutzer bietet es Möglichkeiten zur Kostensenkung, für Energiemärkte zur Reduktion von Lastspitzen, zur Integration erneuerbarer Energien und zur Verringerung fossiler Emissionen. Das wirtschaftliche Potenzial ist jedoch begrenzt und hängt stark von Gebäudeenergieeffizienz, Nutzerflexibilität, Infrastrukturkosten und Anreizstrukturen ab
Heterotopic ossification following total wrist arthroplasty
No full text
Total wrist arthroplasty as a procedure in patients with advanced osteoarthritis has gained more popularity in recent years. As early implants had high rates of complications and newer implants have only slowly gained traction, some sequelae have not been reported yet.
This study presents the case of a male German patient, 57 years old, with advanced osteoarthritis who received arthroplasty and presented with severely restricted range of motion 6 weeks after surgery. Radiographs revealed signs of heterotopic ossification that could be confirmed during the revision surgery. Intraoperatively, ossifications were removed and the mobile parts of the implant were changed. In the further course of the healing process, no further signs of ossifications have been reported for 1 year, but range of motion remains reduced.
This is the first reported case of heterotopic ossification of the wrist following arthroplasty. Heterotopic ossification should be taken into consideration in cases of restricted range of motion after arthroplasty to be able to handle this complication adequately
Improving a data mining based diagnostic support tool for rare diseases on the example of M. Fabry
No full text
Rare diseases often present with a variety of clinical symptoms and therefore are challenging to diagnose. Fabry disease is an x-linked rare metabolic disorder. The severity of symptoms is usually different in men and women. Since therapeutic options for Fabry disease exist, early diagnosis is important. An artificial intelligence (AI)-based diagnosis support algorithm for rare diseases has been developed in preliminary studies.
Our aim was to extend and train the questionnaire-based AI, capable of distinguishing patients with from those without rare diseases, to achieve satisfactory sensitivity for the detection of a single rare disease, Fabry disease, taking into account gender differences in disease perception.
We collected 33 complete datasets from patients with confirmed Fabry disease. These records contained answered AI questionnaires, general information on disease progression, demographic information and quality of life (QoL) measures. The AI was trained to distinguish patients with Fabry disease from patients with relevant differential diagnoses. Its performance was assayed using stratified eleven-fold cross-validation and ROC curve calculation. Variables influencing the performance of the AI were examined with linear regression and calculation of the coefficient of determination.
We were able to show that a relatively small sample is sufficient to achieve a sensitivity of 88.12% for the presence of Fabry disease, taking into account gender-specific differences in the disease perception during the pre-diagnostic phase. No confounders of the tool’s performance could be found in the data collected concerning the patients’ quality of life and diagnostic history.
This study illustrates on the example of Fabry disease that differences between female and male Fabry patients, not only in the expression of symptoms, but also with regard to disease perception, might be relevant influencing variables for improving the performance of AI-based diagnostic support tools for rare diseases
Functional characterization and membrane localization of the styrene oxide isomerase from 1CP and Z-1155
No full textStyrene oxide isomerase (SOI) is a part of the styrene degradation enzyme complex, performing the isomerization of toxic intermediate styrene oxide into phenylacetaldehyde. For many years, the enzyme was believed to be cofactor-independent, and hence, the mechanism of this enzyme was proposed to be acid-base catalysis. Recently, the presence of heme was identified and reported in SOI from VLB120. Alongside, the membrane localization was also postulated since its discovery but lacks experimental proof. In this study, we highlight the localization of SOIs from two bacterial strains, 1CP and Z-1155, heterologously overproduced in the cell membrane of via sfGFP-tagged fusions. In addition, the site-directed mutagenesis of acidic and basic amino acids in SOI from 1CP also showcased that histidine-57 is the axial ligand to the heme. Electron paramagnetic resonance (EPR) and biocatalytic assays showed arginine-111 possibly coordinating the propionate group of heme. The functional assays of differently tagged sfGFP with and without linkers, and the truncation of the terminal extension of SOI from 1CP and Z-1155, indicate their possible role in proper substrate channeling. It also supports the previously proposed SOI role as a membrane anchor for other enzymes in styrene degradation pathway
Retrospektive Untersuchung der klinischen Relevanz von Pedikelschraubenfehllagen in Abhängigkeit des versorgten Wirbelsegments
No full textDie Studie untersucht retrospektiv die klinische Relevanz von Pedikelschraubenfehllagen abhängig vom Wirbelsegment. Eingeschlossen wurden 631 CT-untersuchte Patienten mit dorsaler Instrumentierung (2016–2020). Fehllagen wurden als >2 mm Pedikelperforation definiert. Am häufigsten traten sie in der mittleren BWS auf, besonders bei Th5 (21 %) sowie Th6–Th8 (16–20 %). Kranial lagen die Raten bei 5–15 %, im thorakolumbalen Übergang und der LWS deutlich niedriger (3–5 %). Navigation wurde vor allem in der oberen/mittleren BWS eingesetzt; dennoch zeigten navigierte Schrauben dort Fehllageraten bis 22 %. Insgesamt wurden 1,7 % aller Schrauben revidiert, meist aufgrund radiologischer Befunde; neurologische Symptome waren selten. Trotz häufiger Fehllagen, insbesondere thorakal, ist deren klinische Relevanz gering, und die Notwendigkeit kurzfristiger Revisionen bleibt selten
Charakterisierung der Progesteronrezeptor-Expression im enterischen Nervensystem von Ratten und Analyse des neuroprotektiven Potentials der Rezeptoren in vitro
No full textNeurodegenerative Erkrankungen wie Morbus Parkinson stehen zunehmend im Zusammenhang mit Veränderungen im enterischen Nervensystem (ENS). Während Progesteron im zentralen Nervensystem neuroprotektiv wirkt, ist seine Bedeutung im ENS kaum erforscht. Diese Arbeit untersucht erstmals die Expression von Progesteronrezeptoren im Ratten-ENS über verschiedene Entwicklungsstadien sowie deren neuroprotektives Potential im Rotenon-Parkinson-Modell in vitro. Mittels Laser-Mikrodissektion, qPCR und Immunfluoreszenz wurden mPRa, mPRb, PR-A/B und PGRMC1 auf mRNA- und Proteinebene nachgewiesen, wobei PGRMC1 am stärksten exprimiert war. In kultivierten ENS-Neuronen reduzierte eine 24-stündige Progesteron-Behandlung den Rotenon-induzierten Zelltod deutlich. Die Ergebnisse sprechen für ein relevantes neuroprotektives Potential, vermutlich vermittelt über PGRMC1, und eröffnen neue Perspektiven für therapeutische Ansätze bei Morbus Parkinson
Competition and cooperation of assembly sequences in recurrent neural networks
No full textNeural activity sequences are ubiquitous in the brain and play pivotal roles in functions such as long-term memory formation and motor control. While conditions for storing and reactivating individual sequences have been thoroughly characterized, it remains unclear how multiple sequences may interact when activated simultaneously in recurrent neural networks. This question is especially relevant for weak sequences, comprised of fewer neurons, competing against strong sequences. Using a non-linear rate -based and a spiking model with discrete, pre-configured assemblies, we demonstrate that weak sequences can compensate for their competitive disadvantage either by increasing excitatory connections between subsequent assemblies or by cooperating with other co-active sequences. Further, our models suggest that such cooperation can negatively affect sequence speed unless subsequently active assemblies are paired. Our analysis characterizes the conditions for successful sequence progression in isolated, competing, and cooperating assembly sequences, and identifies the distinct contributions of recurrent and feed-forward projections. This proof-of-principle study shows how even disadvantaged sequences can be prioritized for reactivation, a process which has recently been implicated in hippocampal memory processing