An improvement in freshwater biodiversity in England’s rivers was linked to reductions in pollution of zinc and copper, largely due to the decline of coal burning and heavy industry, say researchers.

Invertebrates are used as an important measure of a river’s biodiversity and health, and Environment Agency data show there was a widespread, significant increase in species richness across England in the 1990s and early 2000s. However, there has been little significant further improvement since then.

Therefore, a team of scientists led by the UK Centre for Ecology & Hydrology (UKCEH) looked for the possible reasons for this, using statistical modelling to investigate a wide range of different chemical and physical factors, such as temperature, river flow and landscape.

Lithium is an important raw material for new energy vehicles, and ensuring its supply is of great significance for global green sustainable development. Salt lake brine is the main lithium resource, but the separation of Li⁺ from coexisting metals poses a major challenge. The Authors designed a lithium-storage metal oxide SnO₂ nanoparticle island-modified LiMn₂O₄ electrode material with higher lithium extraction capacity and cycle stability. Their work is published in the journal Industrial Chemistry & Materials on 31 Jan 2025.

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.