Scientists target ‘molecular machine’ in the war against antimicrobial resistance
Peer-Reviewed Publication
This month, we’re focusing on artificial intelligence (AI), a topic that continues to capture attention everywhere. Here, you’ll find the latest research news, insights, and discoveries shaping how AI is being developed and used across the world.
Updates every hour. Last Updated: 19-Nov-2025 20:11 ET (20-Nov-2025 01:11 GMT/UTC)
Scientists have studied a new target for antibiotics in the greatest detail yet – in the fight against antibiotic resistance.
In Applied Physics Letters, researchers in China created a machine vision sensor that uses quantum dots to adapt to extreme changes in light far faster than the human eye can — in about 40 seconds — by mimicking eyes’ key behaviors. The sensor’s fast adaptive speed stems from its unique design: lead sulfide quantum dots embedded in polymer and zinc oxide layers. The device responds dynamically by either trapping or releasing electric charges depending on the lighting, similar to how eyes store energy for adapting to darkness.
Researchers have demonstrated a new way of attacking artificial intelligence computer vision systems, allowing them to control what the AI “sees.” The research shows that the new technique, called RisingAttacK, is effective at manipulating all of the most widely used AI computer vision systems.
Racism and sexism are “alarmingly normalised” within the structures and person-to-person interactions across the NHS, and the NHS has delayed acknowledging and learning from the evidence, says a report from the BMJ Commission on the Future of the NHS, published in The BMJ today.
With the increasing focus on the pursuit-evasion game, the guidance law capturability analysis has been widely studied recently to theoretically assess the performance of different guidance laws and reveal the impact of the physical constraints on capture zones. In a recent study, the capture zones of the continuous and pulsed guidance laws in the pursuit-evasion game are analytically discussed to provide deep insights into the capturability distinction between the continuous guidance law and the pulsed guidance law.
Moving mesh adaptation provides optimal resource allocation to computational fluid dynamics for the capture of different key physical features, i.e., high-resolution flow field solutions on low-resolution meshes. Although many moving mesh methods are available, they require artificial experience as well as computation of a posteriori information about the flow field, which poses a significant challenge for practical applications. Para2Mesh uses a double-diffusion framework to accomplish accurate flow field reconstruction through iterative denoising to provide flow field features as supervised information for fast and reliable mesh movement, thus enabling adaptive mesh prediction from design parameters.