How to Improve the Overall Efficiency of Volume Additive Manufacturing?​

In IJEM, Dr. Huiyuan Wang and coworkers from Beihang University proposed sparse-view irradiation processing volume additive manufacturing (SVIP-VAM). Their work demonstrated that sparse-view irradiation can enhance single-projection efficiency, significantly reduce projection computation time, and achieve high-quality fabrication results through quantitative analysis. Such improvements will advance VAM technology, facilitating its broader application in rapid manufacturing fields, including tissue engineering, medical implants, and aerospace manufacturing et al.

Liver organoids with proper blood vessel networks have been successfully produced, as reported by researchers from Institute of Science Tokyo and Cincinnati Children’s Hospital Medical Center. This advancement addresses a major challenge in replicating the liver’s complex vasculature in lab-grown tissues. Using a novel 3D culture system, the researchers achieved the self-organization of four distinct precursor cell types into functional organoids, capable of producing essential clotting factors in a haemophilia A mouse model.

Generative artificial intelligence (AI) models can create entirely new text and images, offering a new avenue for efficient fashion design. Understanding how to integrate ChatGPT and AI image generators like DALL-E 3 effectively into creative fashion design is important. Recently, researchers explored the use of DALL-E 3 to visualize fashion trends according to season and generate new fashion collections. The study highlights how AI can be improved for successful fashion design implementation.

On Android phones, an invisible app can be active in the foreground – and that is a potential security problem. Users then operate an app that they cannot see and can be tricked into performing unwanted actions, such as granting certain rights to a malicious app or even deleting data.

Scientists have used AI to re-analyse a clinical trial for an Alzheimer’s medicine, and identified a group of patients who responded to treatment. The work demonstrates that AI can inform the design of future clinical trials to make them more effective and efficient, accelerating the search for new medicines.