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Structures of gas-phase hydrated phosphotyrosine revealed by soft X-ray action spectroscopy
No full textGas-phase near-edge X-ray absorption mass spectrometry (NEXAMS) was employed at the carbon and oxygen K-edges to probe the influence of a single water molecule on the protonated phosphotyrosine molecule. The results of the photodissociation experiments revealed that the water molecule forms two bonds, with the phosphate group and another chemical group. By comparing the NEXAMS spectra at the carbon and oxygen K-edges with density functional theory calculations, we attributed the electronic transitions responsible for the observed resonances, especially the transitions due to the presence of the water molecule. We showed that the water molecule leads to a specific spectral feature in the partial ion yield of hydrated fragments at 536.4 eV. Moreover, comparing the NEXAMS spectra with the calculated structures allowed us to identify three possible structures for singly hydrated phosphotyrosine that agree with the observed fragmentation and resonances
Damage detection in lightweight bridges with traveling masses using machine learning
No full textDamage detection via vibration testing typically relies on damage-sensitive features, which serve as “damage indicators”, and decisions upon the existence of damage are based on comparing the damage indicators retrieved from two distinct structural states. However, the relatively low sensitivity of damage indicators to the onset of structural damage remains an open question, despite the considerable research efforts in vibration testing over the years. Low-sensitivity problems may be particularly exacerbated by the complex dynamic behavior of lightweight structures, such as lightweight bridges subjected to vehicular traffic. In particular, due to material (and, by extension, mass) reduction in lightweight bridges, vehicles essentially act as “traveling masses”, which are comparable to the structural mass and result in a coupled complex dynamic motion problem that may obscure typical damage indicators used in vibration testing. This paper presents a damage detection approach for lightweight bridges with traveling masses, leveraging the powerful feature-extraction capabilities of machine learning (ML). In particular, a convolutional neural network (CNN) is trained to classify acceleration response data, collected from vibration testing, into damage scenarios. The training data for the CNN are created via simulations of damage scenarios, using calibrated analytical models. The damage detection approach is validated in laboratory tests on a continuous beam, showcasing the capability of the CNN to classify damage scenarios of the beam. The outcome of this paper aims to serve as a starting point towards employing ML for damage detection in the context of vibration testing as well as structural health monitoring
"Tatort LUH: Der geheimnisvolle Hacker" - Spielerische und medienpädagogische Potenziale digitaler Lernumgebungen in der Hochschulbildung
No full textLernmanagementsysteme können durch ihre vielfältigen Funktionen und digitalen Anwendungsmöglichkeiten u.a. in der Hochschulbildung die Lehre motivierend unterstützen und dabei helfen die digitale Medienkompetenz von Lernenden zu fördern. Wie eine solche Motivations- und Kompetenzförderung bei Studierenden mit Hilfe des Lernmanagementsystems ILIAS an der Leibniz Universität Hannover anhand eines spielerischen Lehr-Lernkonzepts aussehen kann, wird in diesem Beitrag exemplarisch aufgezeigt. Ziel der Lehrveranstaltung war es, das selbstständige und kollaborative Lernen, das kreative Denken sowie die Medienkompetenz der Teilnehmenden durch die Einbindung eines digitalen Krimi- und Detektivspiels zu fördern. Lernmanagementsysteme sind der Gruppe der Lernplattformen zuzuordnen und weisen vielfältige technische Merkmale und mediendidaktische Einsatzmöglichkeiten auf. Über die Einordnung der Lernplattformen im niedersächsischen Bildungssystem sowie dessen Grundfunktionen werden drei verschiedene Arten digitalen Lehrens und Lernens mit Lernmanagementsystemen aus spielerischer und medienpädagogischer Perspektive betrachtet. Danach lässt sich auch das entwickelte und in diesem Beitrag vorgestellte spielerische Lehr-Lernkonzept einordnen und anhand der Studierendenrückmeldungen an den Lernzielen messen. Abschließend wird ein Ausblick zur weiteren Verbesserung des Lehr-Lernkonzepts abgegeben
Informationen in Leichter Sprache - Neue Informationen zum Sozial-Ticket: Der Sozial-Ticket-Atlas
No full textSummary of the Welfare Ticket Atlas in 'Easy Read' GermanZusammenfassung des Sozialticket-Atlas in Leichter SpracheDeutscher Paritätischer Wohlfahrtsverband - Gesamtverban
Stability of Classical Shadows under Gate-Dependent Noise
No full textExpectation values of observables are routinely estimated using so-called classical shadows—the outcomes of randomized bases measurements on a repeatedly prepared quantum state. In order to trust the accuracy of shadow estimation in practice, it is crucial to understand the behavior of the estimators under realistic noise. In this Letter, we prove that any shadow estimation protocol involving Clifford unitaries is stable under gate-dependent noise for observables with bounded stabilizer norm—originally introduced in the context of simulating Clifford circuits. In contrast, we demonstrate with concrete examples that estimation of “magic” observables can lead to highly misleading results in the presence of miscalibration errors and a worst case bias scaling exponentially in the system size. We further find that so-called robust shadows, aiming at mitigating noise, can introduce a large bias in the presence of gate-dependent noise compared to unmitigated classical shadows. Nevertheless, we guarantee the functioning of robust shadows for a more general noise setting than in previous works. On a technical level, we identify average noise channels that affect shadow estimators and allow for a more fine-rained control of noise-induced biases.Deutsche Forschungsgemeinschaft (DFG
Monolithic 3D numerical modeling of granular cargo movement on bulk carriers in waves
No full textA novel monolithic approach for simulating vessels in waves with granular cargo is presented using a Finite Volume framework. The integrated approach aims to develop a tool for maritime safety analysis and design optimization of bulk carriers. The computational model integrates a three-phase Volume of Fluid method to represent air, water, and cargo, coupled with a material model that applies a rigid-perfectly plastic material model for the granular phase. The approach takes into account the floating motion of the ship using a rigid body motion solver for three degrees of freedom and is supplemented by inviscid far-field boundary conditions facilitating the generation of linear waves approaching the vessel. The model's efficacy is demonstrated through a validation of the three-phase Volume of the Fluid method, a verification of the granular material model, and finally, a reconstruction of the accident of the “Jian Fu Star” bulk carrier in a fully 3D simulation
On-chip full-UV-band photodetectors enabled by hot hole extraction
No full textAchieving on-chip, full-UV-band photodetection across UV-A (315-400 nm), UV-B (280-315 nm), and UV-C (100-280 nm) bands remains challenging due to the limitations in traditional materials, which often have narrow detection ranges and require high operating voltages. In this study, we introduce a self-driven, on-chip photodetector based on a heterostructure of hybrid gold nanoislands (Au NIs) embedded in H-glass and cesium bismuth iodide (Cs3Bi2I9). The Au NIs act as catalytic nucleation sites, enhancing crystallinity and facilitating the vertical alignment of the interconnected Cs3Bi2I9 petal-like thin film. A built-in electric field developed at the heterojunction efficiently separates hot holes generated in the Au NIs under UV illumination, transferring them to the valence band of Cs3Bi2I9 and minimizing recombination losses. The device demonstrates an ultrahigh open-circuit voltage of 0.6 V, exceptional responsivity of 0.88 A/W, and a detection threshold of 90 nW/cm2, outperforming the existing thin film-based UV photodetectors under self-driven mode. Long-term stability tests confirmed robust operational reliability under ambient conditions for up to eight months. This architecture, driven by efficient hot hole dynamics, represents a significant advancement for full-UV-band optoelectronics with promising applications in environmental monitoring, flame detection, biomedical diagnostics, and secure communication systems
Case Study: AI-Driven Log Extraction and Trace Outlier Detection for Efficient Post-Silicon Validation
No full textIn the post-silicon validation process, various functionalities and boundaries of a system-on-chip (SoC) are tested, generating a large amount of data in the form of log files, trace data, and oscilloscope images. Log files provide essential information regarding a test run, such as test setup, while trace files offer insights into internal register statuses and sweep parameters like voltage, frequency, and temperature. Manually analyzing and debugging these files is time-consuming, inefficient, costly, and prone to errors. To address these challenges, we propose an AI-powered approach to automatically extract critical log messages from extensive datasets, generating concise log files with only the most pivotal information. Our method utilizes a multi-class Long Short Term Memory (LSTM) neural network. Our primary focus is to minimize false negatives (high recall) to ensure that critical anomalies are not overlooked, thus delivering more reliable SoCs. Simultaneously, we aim to minimize false positives (high precision) to reduce manual debugging efforts. Our proposed method achieves high recall/precision of 94%/99%
for normal, 99%/99% for information, 92%/64% for error, and 98%/88% for warning log categories. Additionally, for outlier detection in trace data, we propose an unsupervised method based on Isolation Forest, which achieves high recall/precision of 95%/100% and 92%/73% for anomalous data points across two distinct datasets, and nearly 100% for normal data points
Improving efficiency of parallel across the method spectral deferred corrections
No full textParallel across the method time integration can provide small scale parallelism when solving initial value problems. Spectral deferred corrections (SDCs) with a diagonal sweeper, closely related to iterated Runge-Kutta methods proposed by Van der Houwen and Sommeijer, can use a number of threads equal to the number of quadrature nodes in the underlying collocation method. However, convergence speed, efficiency, and stability depend critically on the coefficients of the used SDC preconditioner. Previous approaches used numerical optimization to find good diagonal coefficients. Instead, we propose an approach that allows one to find optimal diagonal coefficients analytically. We show that the resulting parallel SDC methods provide stability domains and convergence order very similar to those of well established serial SDC variants. Using a model for computational cost that assumes 80% efficiency of an implementation of parallel SDCs, we show that our variants are competitive with serial SDC, previously published parallel SDC coefficients, Picard iteration, and a fourth-order explicit as well as a fourth-order implicit diagonally implicit Runge-Kutta method
Lernfabriken als neuer Lernraum für das Bildungspersonal. Eine erste Bestandsaufnahme in Baden-Württemberg
No full textDer Artikel thematisiert Realisierungschancen von beruflichen Lernfabriken für den Kontext der Weiterbildung. Es zeigt sich anhand erster Befunde aus Baden-Württemberg (vgl. Windelband u.a. 2023), dass einerseits berufliche Lernfabriken Kristallisationsorte für (neue) berufsdidaktische Potentiale in der Weiterbildung bilden können. Andererseits können die Ergebnisse fruchtbar gemacht werden für curricular-inhaltliche Perspektiven, die auf die Qualifizierung der Aus- und Weiterbildner abstellen.The article addresses the opportunities for realising vocational learning factories in the context of continuing education. Initial findings from Baden-Württemberg (cf. Windelband et al. 2023) show that, on the one hand, vocational learning factories can form crystallisation sites for (new) vocational didactic potential in continuing education. On the other hand, the results can be utilised for curricular content perspectives that focus on the qualification of initial and continuing vocational trainers