Drones have already shown that they can reliably deliver vital shipments of blood across Rwanda, drop off prescriptions to senior citizens in Florida, and help quarantining families stay safe with contactless deliveries. Now they're going to be buzzing through the skies of rural North Carolina thanks to a novel delivery service devised by drug-maker Merck and drone-maker Volansi. The plan is simple: use Volansi's 7-foot long "Gemini" quadcopter to ferry packages of cold chain medicines -- such as vaccines, glaucoma treatments, insulin, and asthma inhalers -- from Merck's Wilson, NC drug lab to the nine regional hospitals that make up Vidant Healthplex-Wilson. This medical network serves more than 1.4 million people across 29 counties in eastern North Carolina. "We've seen the world's supply chain strained like never before from the impact of Coronavirus," said Hannan Parvizian, CEO of Volansi, said in a press statement.

Model fitting often aims to fit a single model, assuming that the imposed form of the model is correct. However, there may be multiple possible underlying explanatory patterns in a set of predictors that could explain a response. Model selection without regarding model uncertainty can fail to bring these patterns to light. We present multi-model penalized regression (MMPR) to acknowledge model uncertainty in the context of penalized regression. In the penalty form introduced here, we explore how different settings can promote either shrinkage or sparsity of coefficients in separate models. A choice of penalty form that enforces variable selection is applied to predict stacking force energy (SFE) from steel alloy composition. The aim is to identify multiple models with different subsets of covariates that explain a single type of response.