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As part of the Institute for Scientist and Engineer Educators Professional Development Program (PDP), our team designed an activity for the Akamai internship program’s Preparation for Research Experiences and Projects (PREP) course. The activity focused on content around different renewable energy and storage technologies, and the widely applicable engineering practice of optimization through iteration and evaluating trade-offs. Here we describe the overall activity, with primary emphasis on how the PDP backward design process and integration of the Equity & Inclusion (E&I) theme led us to design and implement a unique model we call the “expert training model” that has important E&I implications. We found that an educational activity design that focuses on E&I considerations, such as identifying multiple ways to productively participate and developing learners’ identity in STEM, simultaneously satisfies criteria for being an engaging and authentic STEM experience. We also reflect on potential pitfalls and ways to improve and adapt this model. 

Inferring appropriate information from large datasets has become important. In particular, identifying relationships among variables in these datasets has far-reaching impacts. In this paper, we introduce the uniform information coefficient (UIC), which measures the amount of dependence between two multidimensional variables and is able to detect both linear and non-linear associations. Our proposed UIC is inspired by the maximal information coefficient (MIC) \cite{MIC:2011}; however, the MIC was originally designed to measure dependence between two one-dimensional variables. Unlike the MIC calculation that depends on the type of association between two variables, we show that the UIC calculation is less computationally expensive and more robust to the type of association between two variables. The UIC achieves this by replacing the dynamic programming step in the MIC calculation with a simpler technique based on the uniform partitioning of the data grid. This computational efficiency comes at the cost of not maximizing the information coefficient as done by the MIC algorithm. We present theoretical guarantees for the performance of the UIC and a variety of experiments to demonstrate its quality in detecting associations. 

Abstract Cardiovascular diseases (CVDs) are known as the major cause of death worldwide. In spite of tremendous advancements in medical therapy, the gold standard for CVD treatment is still transplantation. Tissue engineering, on the other hand, has emerged as a pioneering field of study with promising results in tissue regeneration using cells, biological cues, and scaffolds. 3D bioprinting is a rapidly growing technique in tissue engineering because of its ability to create complex scaffold structures, encapsulate cells, and perform these tasks with precision. More recently, 3D bioprinting has made its debut in cardiac tissue engineering, and scientists are investigating this technique for development of new strategies for cardiac tissue regeneration. In this review, the fundamentals of cardiac tissue biology, available 3D bioprinting techniques and bioinks, and cells implemented for cardiac regeneration are briefly summarized and presented. Afterward, the pioneering and state‐of‐the‐art works that have utilized 3D bioprinting for cardiac tissue engineering are thoroughly reviewed. Finally, regulatory pathways and their contemporary limitations and challenges for clinical translation are discussed.