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Large Language Models have advanced to a resourceful tool with many applications. One particularly interesting use case is the LLM-aided generation of ad-hoc database queries and the possibility of subsequent processing of the results in a way suiting the users intents. In this article, practical ways and experiences are described on how to effectively use LLMs to map a natural-language user query to an SQL query conforming to a specific database schema and post-processing the results of this query in order to, for example, create an appealing visualization. Best results are achieved under favorable circumstances, as, for example, a clean and meaningful named database schema.
Purpose
Recognizing previously unseen classes with neural networks is a significant challenge due to their limited generalization capabilities. This issue is particularly critical in safety-critical domains such as medical applications, where accurate classification is essential for reliability and patient safety. Zero-shot learning methods address this challenge by utilizing additional semantic data, with their performance relying heavily on the quality of the generated embeddings.
Methods
This work investigates the use of full descriptive sentences, generated by a Sentence-BERT model, as class representations, compared to simpler category-based word embeddings derived from a BERT model. Additionally, the impact of z-score normalization as a post-processing step on these embeddings is explored. The proposed approach is evaluated on a multi-label generalized zero-shot learning task, focusing on the recognition of surgical instruments in endoscopic images from minimally invasive cholecystectomies.
Results
The results demonstrate that combining sentence embeddings and z-score normalization significantly improves model performance. For unseen classes, the AUROC improves from 43.9% to 64.9%, and the multi-label accuracy from 26.1% to 79.5%. Overall performance measured across both seen and unseen classes improves from 49.3% to 64.9% in AUROC and from 37.3% to 65.1% in multi-label accuracy, highlighting the effectiveness of our approach.
Conclusion
These findings demonstrate that sentence embeddings and z-score normalization can substantially enhance the generalization performance of zero-shot learning models. However, as the study is based on a single dataset, future work should validate the method across diverse datasets and application domains to establish its robustness and broader applicability.
Design space exploration (DSE) plays an important role in optimising quantum circuit execution by systematically evaluating different configurations of compilation strategies and hardware settings. In this paper, we conduct a comprehensive investigation into the impact of various layout methods, qubit routing techniques, and optimisation levels, as well as device-specific properties such as different variants and strengths of noise and imperfections, the topological structure of qubits, connectivity densities, and back-end sizes. By spanning through these dimensions, we aim to understand the interplay between compilation choices and hardware characteristics. A key question driving our exploration is whether the optimal selection of device parameters, mapping techniques, comprising of initial layout strategies and routing heuristics can mitigate device induced errors beyond standard error mitigation approaches. Our results show that carefully selecting software strategies (e.g., mapping and routing algorithms) and tailoring hardware characteristics (such as minimising noise and leveraging topology and connectivity density) significantly improve the fidelity of circuit execution outcomes, and thus the expected correctness or success probability of the computational result. We provide estimates based on key metrics such as circuit depth, gate count and expected fidelity. Our results highlight the importance of hardware–software co-design, particularly as quantum systems scale to larger dimensions, and along the way towards fully error corrected quantum systems: Our study is based on computationally noisy simulations, but considers various implementations of quantum error correction (QEC) using the same approach as for other algorithms. The observed sensitivity of circuit fidelity to noise and connectivity suggests that co-design principles will be equally critical when integrating QEC in future systems. Our exploration provides practical guidelines for co-optimising physical mapping, qubit routing, and hardware configurations in realistic quantum computing scenarios.
As global demand for green hydrogen rises, potential hydrogen exporters move into the spotlight. While exports can bring countries revenue, large-scale on-grid hydrogen electrolysis for export can profoundly impact domestic energy prices and energy-related emissions. Our investigation explores the interplay of hydrogen exports, domestic energy transition and temporal hydrogen regulation, employing a sector-coupled energy model in Morocco. We find substantial co-benefits of domestic carbon dioxide mitigation and hydrogen exports, whereby exports can reduce market-based costs for domestic electricity consumers while mitigation reduces costs for hydrogen exporters. However, increasing hydrogen exports in a fossil-dominated system can substantially raise market-based costs for domestic electricity consumers, but surprisingly, temporal matching of hydrogen production can lower these costs by up to 31% with minimal impact on exporters. Here, we show that this policy instrument can steer the welfare (re-)distribution between hydrogen exporting firms, hydrogen importers, and domestic electricity consumers and hereby increases acceptance among actors.
This paper introduces a method to predict wear behavior in conveyor belt systems using a lumped mass modeling approach. While previous research has focused primarily on lateral belt walking, this study shifts attention to belt deformation and its associated wear. Both significantly affect system efficiency and component lifespan. The authors propose using local frictional power as a wear indicator, leveraging its direct relation to frictional work in established wear models. To solely demonstrate the method, the study simulates a conveyor belt with three flexible rollers and a deformable belt modeled through rigid spheres connected by spring-damper elements. The authors visualize frictional power density across the belt width, distinguishing between running and transverse directions. The results demonstrate that the frictional power distribution depends heavily on discretization quality, particularly due to the polygon effect inherent in the lumped mass approach. A convergence analysis reveals the minimum necessary discretization of the belt, ensuring reliable qualitative results. To support the credibility of the work, this study compares theoretical expectations and initial wear observations from a real belt with the results from the shown approach. The plausibility check already shows promising results. The proposed methodology provides an adaptable framework to evaluate wear in belt-like structures. It can be readily adapted to a variety of multibody dynamics applications and integrated into larger MBS models that include the overall drivetrain and engine control. Future work will focus on refining discretization strategies and contact models, as well as validation of the wear model to enable quantitative predictions.
Based on upper record values in a series of observations over time from a Pareto distribution, an exact and several approximate one-sided and two-sided prediction intervals for the next record value (or for another future record value) to appear are studied and compared by means of an extensive simulation study. The performances of the prediction intervals are evaluated and recommendations are proposed for what method should be used in a given situation with respect to the form of the prediction interval on the one hand and to the tail behaviour on the other. The proposed methods are applied to insurance, environmental and sports data, where the respectively fitted Pareto distributions show a different tail behaviour. As a result, it is seen that although the number of observed record values in the presented real data applications is rather small, as it is usually the case, the selected prediction intervals are of practical use.
BACKGROUND: Increased use of health data has the potential to improve both health care and health policies. Several recent policy initiatives at the European and German legislative levels aim to increase the primary and secondary use of health data. However, little is known about general population views on health data access for research. Most studies are based on subsets defined by specific illnesses.
METHODS: We commissioned a national computer-assisted dual-frame telephone survey (landline and mobile). Logit estimation models were used to identify predictors of willingness to provide access to health data to different organizations (universities in Germany, universities worldwide, German government organizations, pharmaceutical companies).
RESULTS: A high willingness to share health data for research purposes is observed, depending on the specific data recipient. The willingness is highest for research at universities in Germany and German governmental organizations, and lowest regarding research by pharmaceutical companies. The main drivers for sharing health data are the level of trust in public institutions, the respondents' assessment of the seriousness and likelihood of data misuse, and the level of digital literacy. Age, gender, and level of education have small effects and do not determine the willingness to share health data for all organizations.
CONCLUSION: We present evidence from a random sample of the German population. The results indicate widespread support among the population for providing access to health data for research purposes. Similar to findings in other countries, the willingness depends strongly on the recipient of the data. This paper evaluates the impact of various determinants - identified in previous qualitative and quantitative research - on the willingness of the German population to share health data. While previous studies have found that patients are generally more willing to share health data, we found that the presence of a medical precondition does not translate into respondents' unequivocal support for health data sharing. We identify privacy concerns, general trust, and digital literacy as key factors influencing the willingness to share health data. Therefore, policymakers and stakeholders need to ensure and communicate the necessary privacy protection measures to increase the willingness of the German population to share health data.
IT-Near- und Offshoring in deutschen Großunternehmen: Aktueller Stand, Motive und Auswahlkriterien
(2025)
Die vorliegende Studie untersucht die aktuelle IT-Near- und Offshoring-Praxis in deutschen Großunternehmen. Dafür wurden 33 CIOs und IT-Führungskräfte mittels Interviews befragt. Haupttreiber der geografischen Verlagerung von IT-Services ins Ausland ist die Ressourcenverfügbarkeit in anderen Ländern, bedingt durch den Fachkräftemangel in Deutschland. Dies trifft auch auf die Auswahl geeigneter IT-Services für das Near- und Offshoring zu. Während IT-Near- und Offshoring historisch primär aufgrund von Kosteneinsparungen initiiert wurde, spielt dieser Aspekt heute eine untergeordnete Rolle. Die Interviewteilnehmer und -teilnehmerinnen sehen in Kosteneinsparungen zwar immer noch einen positiven Effekt, erwarten aber, dass dieser in Zukunft aufgrund der Angleichung des Kostenniveaus schwinden wird. Die Unternehmen verlagern hauptsächlich Applikationsbetrieb und -entwicklung, erkennen jedoch zunehmend das Potenzial, auch höherwertige Aufgaben, z. B. Projektmanagement und Unternehmensarchitekturmanagement, aus geografisch entfernteren Ländern erbringen zu lassen. Unternehmen, die ausschließlich IT-Nearshoring betreiben, erbringen weniger als ein Drittel ihrer IT-Services aus nahegelegenen Ländern. Firmen mit IT-Offshoring-Präsenzen weisen einen höheren IT-Near‑/Offshoringgrad auf und verlagern knapp die Hälfte der eigenen IT-Serviceerbringung ins Ausland. Die beliebtesten Länder sind Portugal für das Nearshoring und Indien für das Offshoring.
Effect of temperature on the growth of Cupric and Cuprous Nanoparticles in a wet chemical synthesis
(2025)
Cuprous oxide (Cu2O) and cupric oxide (CuO) particles were synthesized using a wet chemical surfactant free method at different synthesis temperatures from 25 °C to 60 °C. Morphology, size, and chemical composition of the prepared Cu2O particles were analyzed by FESEM, PXRD, and UV-vis. We discovered that the chemical composition of the Cu2O particles remained unaffected by the synthesis temperature. However, morphology and size of the particles showed a strong temperature dependency. This could be attributed to the temperature induced formation of CuO species from the copper hydroxide (Cu(OH)2) precursor which also functions as copper precursor for the Cu2O particle growth. The cupric oxide species was determined to be the main cause for the formation of micrometer sized particles, whereas with the Cu(OH)2 precursor species for the Cu2O particles nanocubes with smaller edge lengths as well as octahedrons were obtained.
This paper introduces a novel method for producing fibre-reinforced thermoplastic tubes by integrating tape production and consolidation into a single operation. This innovation diverges from conventional methods by combining processes to reduce costs by using raw materials instead of organotapes, allowing customised material combinations and utilising residual heat from tape production. The new process uses carbon roving and molten low-viscosity PA6 granulates, processed through a new direct impregnation setup in siphon design. Key advancements include a high-speed impregnation module capable of up to 1 m/s with high-performance extruders, cost-effective infrared emitters for winding, and a powered consolidation unit with adjustable winding angles between ±65° and ±90°. Experiments demonstrate operational speeds of approximately 471 mm/min, with an optimal cross-winding speed of 354 mm/min due to the technical limitations of the laboratory extruder and IR emitter used. Based on the technical limitations of the current system, future improvements and methodological changes will be discussed.