Systematic Data-Driven Modeling of Bimetallic Catalyst Performance for the Hydrogenation of 5-Ethoxymethylfurfural with Variable Selection and Regularization.

Catalyst development for biorefining applications involves many challenges. Mathematical modeling can be seen as an essential tool in assisting to explain catalyst performance. This paper presents studies on several machine learning (ML) methods that can model the performance of heterogeneous catalysts with relevant descriptors.

Experience in commissioning the halcyon linac.

Purpose: This manuscript describes the experience of two institutions in commissioning the new Halcyon platform. Its purpose is to: (a) validate the pre-defined beam data, (b) compare relevant commissioning data acquired independently by two separate institutions, and (c) report on any significant differences in commissioning between the Halcyon linear accelerator and other medical linear accelerators.

Methods: Extensive beam measurements, testing of mechanical and imaging systems, including the multi-leaf collimator (MLC), were performed at the two institutions independently.