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Asymmetric Likert-type items in research studies can present several challenges in data analysis, particularly concerning missing data. These items are often characterized by a skewed scaling, where either there is no neutral response option or an unequal number of possible positive and negative responses. The use of conventional techniques, such as discriminant analysis or logistic regression imputation, for handling missing data in asymmetric items may result in significant bias. It is also recommended to exercise caution when employing alternative strategies, such as listwise deletion or mean imputation, because these methods rely on assumptions that are often unrealistic in surveys and rating scales. This article explores the potential of implementing a deep learning-based imputation method. Additionally, we provide access to deep learning-based imputation to a broader group of researchers without requiring advanced machine learning training. We apply the methodology to the Wilmington Street Participatory Action Research Health Project.

In recent years, platinum-based single crystalline nanoalloys as nanoscale catalysts, such as Pt-M (M = Ni, Co, Fe..etc.), have exhibited improved catalytic performance due to the increase in the surface-to-volume ratio. Some Pt-M nanopolyhedra such as nanocubes and nano-octahedra have been reported with enhanced activity when being used as electrocatalysts. In order to further establish a correlation between the exposed nanocrystal facets (shapes) and their corresponding activities, a pursuit of shape-controlled nanocatalyst synthesis is essential. Although PtPb nanoalloys have been prepared using solution-based methods, few studies have highlighted their catalytic activity as a function of the nanocrystal shape. This work focuses on a modified polyol synthesis technique and an adjustment of the Pb-metal precursor, which serves as a “buffer” in the nucleation stage of the shape-controlled nanoalloy development. Using this developed synthetic strategy, shape-controlled hexagonally close-packed PtPb nanoalloys can be prepared in a one-pot synthesis without additional post-treatment. The as-prepared PtPb nanocrystals demonstrate an improved anode electrocatalytic performance.