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In 2019, a National Research Traineeship (NRT) grant from the U.S. National Science Foundation seeded the establishment of a new model for graduate education at Ohio State University – a large, public, land-grant R-1 university in the U.S. Midwest. This grant application involved faculty from eight different colleges within this university (education; engineering; public affairs; arts and sciences; food, agriculture, and environmental sciences; business; law). The Ohio State EmPOWERment Program in convergent graduate training for a sustainable energy future enrolls Ph.D. students studying any aspect of energy from degree programs any college in Ohio State and engages them in several curricular and co-curricular elements that are designed to dovetail with their Ph.D. degree program requirements in ways that do not extend their time to graduate. The Ohio State EmPOWERment Program established at Ohio State an energy Student Community of Practice and Engagement (SCOPE), a Graduate Interdisciplinary Specialization (GIS), and an undergraduate Research in Sustainable Energy (RISE) summer research experience. Over time a JOULE energy seminar series (JOULE) was added to elevate intellectual engagement in for trainees in The Ohio State EmPOWERment Program and broaden their engagement with researchers across this university. This paper investigates the development and accentuation of innovation capacities of Ph.D. trainees in The Ohio State EmPOWERment Program relative to other Ph.D. students who enrolled in science, technology, engineering, and math (STEM) disciplines at Ohio State and did not participate in the Ohio State EmPOWERment Program. This work considers three different constructs for each of three scales (i.e., Interpersonal, Intrapersonal, Cognitive). Of the nine different constructs, six pass assumption tests and pre-test scores for innovation self-concept, proactivity, social networking, risk-taking or tolerance, creative capacity, and intention to innovate are significant predictors of post-test capacities. Overall, participating in The Ohio State EmPOWERment Program appears to be beneficial and may increase innovation self-concept, proactivity, creative, and intention to innovate capacities. 

Motivated by testing for pathogenic diseases we con- sider a new nonadaptive group testing problem for which: (1) positives occur within a burst, capturing the fact that infected test subjects often come in clusters, and (2) that the test outcomes arise from semiquantitative measurements that provide coarse information about the number of positives in any tested group. Our model generalizes prior work on detecting a single burst with classical group testing [1] to the setting of semiquantitative group testing (SQGT) [2]. Speci cally, we study the setting where the burst-length l is known and the semiquantitative tests provide potentially nonuniform estimates on the number of positives in a test group. The estimates represent the index of a quantization bin containing the (exact) total number of positives, for arbitrary thresholds η1,...,ηs. Interestingly, we show that the minimum number of tests needed for burst identi cation is essentially only a function of the largest threshold ηs. In this context, our main result is an order-optimal test scheme that can recover any burst of length l using roughly \ell/2\eta + log (n) measurements. This suggests that 2ηs s+1 a large saturation level ηs is more important than nely quantized information when dealing with bursts. We also provide results for related modeling assumptions and specialized choices of thresholds.