1. ID20-opportunities for inelastic X-ray scattering at extreme conditions 

   Sahle, Christoph J.; Petitgirard, Sylvain; Spiekermann, Georg; et al. (2024)

   High Pressure Research

   Owing to the availability of bright X-rays sources such as the ESRF-EBS, inelastic X-ray scattering of samples contained in complex sample environments, including high pressure devices, has become feasible. Compared to well-established characterization techniques such as X-ray diffraction or X-ray absorption fine structure spectroscopy, inelastic X-ray scattering of samples under extreme conditions is a relatively novel probe. However, ...

   Review Article

2. EFFECT OF HYDROGEN ENRICHMENT ON TRANSFER MATRICES OF FULLY AND TECHNICALLY PREMIXED SWIRLED FLAMES 

   Blonde, Audrey; Schuermans, Bruno; Pandey, Khushboo; et al. (2023)

   PROCEEDINGS OF ASME TURBO EXPO 2023: TURBOMACHINERY TECHNICAL CONFERENCE AND EXPOSITION, GT2023, VOL 3A

   Knowledge of flame responses to acoustic perturbations is of utmost importance to predict thermoacoustic instabilities in gas turbine combustors. However, measuring transfer functions linking acoustic quantities upstream and downstream of flames is very challenging in practical systems and these measurements can significantly deviate from state-of-the-art models. Moreover, there is a lack of studies investigating the effect of hydrogen ...

   Conference Paper

3. Learning Informative Health Indicators Through Unsupervised Contrastive Learning 

   Rombach, Katharina; Michau, Gabriel; Burzle, Wilfried; et al. (2024)

   IEEE Transactions on Reliability

   Monitoring the health of complex industrial assets is crucial for safe and efficient operations. Health indicators that provide quantitative real-time insights into the health status of industrial assets over time serve as valuable tools for, e.g., fault detection or prognostics. This article proposes a novel, versatile, and unsupervised approach to learn health indicators using contrastive learning, where the operational time serves as ...

   Journal Article

4. Advancing spine care through AI and machine learning: overview and applications 

   Cina, Andrea; Galbusera, Fabio (2024)

   EFORT Open Reviews

   center dot Machine learning (ML), a subset of artificial intelligence, is crucial for spine care and research due to its ability to improve treatment selection and outcomes, leveraging the vast amounts of data generated in health care for more accurate diagnoses and decision support. center dot ML's potential in spine care is particularly notable in radiological image analysis, including the localization and labeling of anatomical structures, ...

   Journal Article

5. Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning 

   Vornholt, Tobias; Mutny, Mojmir; Schmidt, Gregor W.; et al. (2024)

   ACS CENTRAL SCIENCE

   Tailored enzymes are crucial for the transition to a sustainable bioeconomy. However, enzyme engineering is laborious and failure-prone due to its reliance on serendipity. The efficiency and success rates of engineering campaigns may be improved by applying machine learning to map the sequence-activity landscape based on small experimental data sets. Yet, it often proves challenging to reliably model large sequence spaces while keeping ...

   Journal Article

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