AI-driven scientometric analysis with recency insights: lower urinary tract symptoms in older and frail populations.

Purpose: This artificial intelligence (AI)-driven scientometric analysis, conducted using the Mynd discovery platform, explores research trends in lower urinary tract symptoms (LUTS) among older patients. By applying its novel recency metric, the study identified emerging areas, longstanding research themes, and critical gaps in literature.

Methods: Mynd applies AI-driven scientometric analysis to map research trends in LUTS and frailty using PubMed abstracts.

Mapping interfacial excess in atom probe data.

Using modern wide-angle atom probes, it is possible to acquire atomic scale 3D data containing 1000 s of nm(2) of interfaces. It is therefore possible to probe the distribution of segregated species across these interfaces. Here, we present techniques that allow the production of models for interfacial excess (IE) mapping and discuss the underlying considerations and sampling statistics.