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In this work, we develop an open-source surgical simulation environment that includes a realistic model obtained by MRI-scanning a physical phantom, for the purpose of training and evaluating a Learning from Demonstration (LfD) algorithm for autonomous suturing. The LfD algorithm utilizes Dynamic Movement Primitives (DMP) and Locally Weighted Regression (LWR), but focuses on the needle trajectory, rather than the instruments, to obtain better generality with respect to needle grasps. We conduct a user study to collect multiple suturing demonstrations and perform a comprehensive analysis of the ability of the LfD algorithm to generalize from a demonstration at one location in one phantom to different locations in the same phantom and to a different phantom. Our results indicate good generalization, on the order of 91.5%, when learning from more experienced subjects, indicating the need to integrate skill assessment in the future. 

Double heterojunction nanorods enable both electroluminescence and light harvesting capabilities within the same device structure, providing a promising platform for energy-scavenging displays and related applications. However, the efficiency of the photovoltaic mode remains modest for useful power conversion and may be challenging to improve without sacrificing performance in electroluminescence. Through a facile on-film partial ligand exchange with benzenethiol integrated into the device fabrication step, we achieve an average of more than threefold increase in power conversion efficiency while maintaining the maximum external quantum efficiency and the maximum luminance in the LED mode. The improved photovoltaic performance is mainly due to the increase in the short circuit current, which we attribute to the enhanced charge separation afforded by the partial ligand exchange. The recovery of the photoluminescence lifetime under the forward bias suggests that the hole traps introduced by benzenethiols are filled prior to reaching the voltage at which light emission begins, allowing LED performance to be maintained and possibly improved. 

Building on prior studies that show a sense of belonging and community bolster student success, we developed a pilot program for computer engineering (CpE) and computer science (CS) undergraduates and their families that focused on building a sense of belonging and community supported by co-curricular and socioeconomic scaffolding. As a dually designated Hispanic-Serving Institution (HSI) and Asian American and Native American Pacific Islander-Serving Institution (AANAPISI) – two types of federally designated Minority-Serving Institutions (MSI) – with 55% of our undergraduates being first-generation students, we aimed to demonstrate the importance of these principles for underrepresented and first-generation students. Using a student cohort model (for each incoming group of students) and also providing supports to build community across cohorts as well as including students’ families in their college experiences, our program aimed to increase student satisfaction and academic success. We recruited two cohorts of nine incoming students each across two years, 2019 and 2020; 69% of participants were from underrepresented racial or minority groups and 33% were women. Each participant was awarded an annual scholarship and given co-curricular support including peer and faculty mentoring, a dedicated cohort space for studying and gathering, monthly co-curricular activities, enhanced tutoring, and summer bridge and orientation programs. Students’ families were also included in the orientation and semi-annual meetings. The program has resulted in students exceeding the retention rates of their comparison groups, which were undergraduates majoring in CpE and CS who entered college in the same semester as the cohorts; first- and second-year retention rates for participants were 83% (compared to 72%) and 67% (compared to 57%). The GPAs of participants were 0.35 points higher on average than the comparison group and, most notably, participants completed 50% more credits than their comparison groups, on average. In addition, 9 of the 18 scholars (all of the students who wanted to participate) engaged in summer research or internships. In combination, the cohort building, inclusion of families, financial literacy education and support, and formal and informal peer and faculty mentoring have correlated with increased academic success. The cohorts are finishing their programs in Spring 2023 and Spring 2024, but data up to this point already show increases in GPA, course completion, and retention and graduation rates, with three students having already graduated early, within three and a half years. The findings from this study are now being used to expand the successful parts of the program and inform university initiatives, with the PI serving on campus-wide STEM pipeline committee aiming to recruit, retain, and support more STEM students at the institution. 

Geometrically frustrated kagome lattices are raising as novel platforms to engineer correlated topological electron flat bands that are prominent to electronic instabilities. Here, we demonstrate a phonon softening at thek_z = πplane in ScV₆Sn₆. The low energy longitudinal phonon collapses at ~98 K andq = $$\frac{1}{3}\frac{1}{3}\frac{1}{2}$$$\frac{1}{3}\frac{1}{3}\frac{1}{2}$due to the electron-phonon interaction, without the emergence of long-range charge order which sets in at a different propagation vectorq_CDW = $$\frac{1}{3}\frac{1}{3}\frac{1}{3}$$$\frac{1}{3}\frac{1}{3}\frac{1}{3}$. Theoretical calculations corroborate the experimental finding to indicate that the leading instability is located at$$\frac{1}{3}\frac{1}{3}\frac{1}{2}$$$\frac{1}{3}\frac{1}{3}\frac{1}{2}$of a rather flat mode. We relate the phonon renormalization to the orbital-resolved susceptibility of the trigonal Sn atoms and explain the approximately flat phonon dispersion. Our data report the first example of the collapse of a kagome bosonic mode and promote the 166 compounds of kagomes as primary candidates to explore correlated flat phonon-topological flat electron physics.

 

Collaborative learning can improve student learning, student persistence, and the classroom climate. While work has documented the tradeoffs of face-to-face collaboration and asynchronous, online learning, the trade-offs between asynchronous (student-scheduled) and synchronous (instructor-scheduled) collaborative and online learning have not been explored. Structured roles can maximize the effectiveness of collaborative learning by helping all students participate, but structured roles have not been studied in online settings. We performed a quasi-experimental study in two courses—Computer Architecture and Numerical Methods—to compare the effects of asynchronous collaborative learning without structured roles to synchronous collaborative learning with structured roles. We use a data-analytics approach to examine how these approaches affected the student learning experience during formative collaborative learning assessments. Teams in the synchronous offering made higher scoring submissions (5-10% points better on average), finished assessments more efficiently (11-16 minutes faster on average), and had greater equality in the total number of submissions each student made (for example, significant increase of 13% in the mean equality score among all groups).