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. Seismic hazard zonation map and definition of seismic actions for Greece in the context of the ongoing revision of EC8 

   Pitilakis, Kyriazis; Riga, Evi; Apostolaki, Stefania; et al. (2024)

   Bulletin of Earthquake Engineering

   The Greek National Annex for current Eurocode 8 has adopted the seismic hazard zonation map published in 2003 as part of the modifications to the Greek Seismic Code EAK 2000 (EAK 2003). This map, which followed the catastrophic earthquakes that hit the country between 1978 and 2001, includes three seismic hazard zones with peak ground acceleration (PGA) ranging between 0.16 and 0.36 g. In this paper, following the significant progress ...

   Journal Article

4. 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

5. 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

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