Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis.

Multi-metal oxides in general and perovskite oxides in particular have attracted considerable attention as oxygen evolution electrocatalysts. Although numerous theoretical studies have been undertaken, the most promising perovskite-based catalysts continue to emerge from human-driven experimental campaigns rather than data-driven machine learning protocols, which are often limited by the scarcity of experimental data on which to train the models. This work promises to break this impasse by demonstrating that active learning on even small datasets-but supplemented by informative structural-characterization data and coupled with closed-loop experimentation-can yield materials of outstanding performance.

Lessons learned from a comparative analysis of surgical outcomes of and learning curves for laparoscopy-assisted distal gastrectomy.

Purpose: Before expanding our indications for laparoscopic gastrectomy to advanced gastric cancer and adopting reduced port laparoscopic gastrectomy, we analyzed and audited the outcomes of laparoscopy-assisted distal gastrectomy (LADG) for adenocarcinoma; this was done during the adoptive period at our institution through the comparative analysis of short-term surgical outcomes and learning curves (LCs) of two surgeons with different careers.

Materials And Methods: A detailed comparative analysis of the LCs and surgical outcomes was done for the respective first 95 and 111 LADGs performed by two surgeons between July, 2006 and June, 2011. The LCs were fitted by using the non-linear ordinary least squares estimation method.