Advanced power semiconductors block radiation damage and lead space industry development!
Peer-Reviewed Publication
Updates every hour. Last Updated: 6-May-2025 04:09 ET (6-May-2025 08:09 GMT/UTC)
Research team led by Dr. Seo at KERI develops radiation resistance evaluation technology for SiC power semiconductors. Securing reliability through Korea’s 1st high-energy space environment simulation analysis, publishing the paper in international journal
The add-on acoustic black hole (AABH), as a vibration reduction device with light weight, rich modal density, and high damping characteristics, has been extensively studied in the vibro-acoustic control of structures. However, there has been no research on application of AABH in the control of the typically aeroelastic instability phenomenon of a panel in supersonic flow. Meanwhile, the prediction of aerodynamic response and flutter boundary of panel structures with attached AABH presents a complex challenge, requiring a sophisticated numerical strategy. Therefore, establishment of a numerical method for coupled aeroelastic analysis of a panel in supersonic flow with AABH and the performance of AABH in suppression of the panel's aeroelastic instability is of great significance.The add-on acoustic black hole (AABH), as a vibration reduction device with light weight, rich modal density, and high damping characteristics, has been extensively studied in the vibro-acoustic control of structures. However, there has been no research on application of AABH in the control of the typically aeroelastic instability phenomenon of a panel in supersonic flow. Meanwhile, the prediction of aerodynamic response and flutter boundary of panel structures with attached AABH presents a complex challenge, requiring a sophisticated numerical strategy. Therefore, establishment of a numerical method for coupled aeroelastic analysis of a panel in supersonic flow with AABH and the performance of AABH in suppression of the panel's aeroelastic instability is of great significance.
Researchers from the University of Navarra's Data Science and Artificial Intelligence Institute (DATAI) have developed a new AI framework to reduce bias in critical decision-making areas such as health, education, and recruitment. Their methodology optimizes machine learning models to ensure fairness by addressing inequalities related to race, gender, and socioeconomic status, among other possible algorithmic discriminations. Published in Machine Learning, the study combines conformal prediction techniques with evolutionary learning to achieve reliable and unbiased AI predictions. The researchers tested their approach on real-world datasets, demonstrating that it reduces discrimination without compromising accuracy. Their work provides policymakers and businesses with AI models that balance efficiency and fairness, aligning with ethical AI principles and legal requirements. The team has publicly made their code and data available to promote transparency and further research in responsible AI development.
Alzheimer’s disease (AD) — a neurodegenerative disorder — comes with a significant socioeconomic burden. Recent studies have found a strong association between AD and metabolic syndrome (MetS), a cluster of conditions that include diabetes, obesity, high blood pressure, and abnormal blood fat levels. In a recently published literature review article, researchers explore the link between AD and each individual component of MetS, analyzing the potential underlying mechanisms at cellular and molecular levels.
Researchers have developed comfortable, washable ‘smart pyjamas’ that can monitor sleep disorders such as sleep apnoea at home, without the need for sticky patches, cumbersome equipment or a visit to a specialist sleep clinic.