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The brain mechanisms of memory consolidation remain elusive. Here, we examine blood-oxygen-level-dependent (BOLD) correlates of image recognition through the scope of multiple influential systems consolidation theories. We utilize the longitudinal Natural Scenes Dataset, a 7-Tesla functional magnetic resonance imaging human study in which ∼135,000 trials of image recognition were conducted over the span of a year among eight subjects. We find that early- and late-stage image recognition associates with both medial temporal lobe (MTL) and visual cortex when evaluating regional activations and a multivariate classifier. Supporting multiple-trace theory (MTT), parts of the MTL activation time course show remarkable fit to a 20-y-old MTT time-dynamical model predicting early trace intensity increases and slight subsequent interference ( R 2 > 0.90). These findings contrast a simplistic, yet common, view that memory traces are transferred from MTL to cortex. Next, we test the hypothesis that the MTL trace signature of memory consolidation should also reflect synaptic “desaturation,” as evidenced by an increased signal-to-noise ratio. We find that the magnitude of relative BOLD enhancement among surviving memories is positively linked to the rate of removal (i.e., forgetting) of competing traces. Moreover, an image-feature and time interaction of MTL and visual cortex functional connectivity suggests that consolidation mechanisms improve the specificity of a distributed trace. These neurobiological effects do not replicate on a shorter timescale (within a session), implicating a prolonged, offline process. While recognition can potentially involve cognitive processes outside of memory retrieval (e.g., re-encoding), our work largely favors MTT and desaturation as perhaps complementary consolidative memory mechanisms. 

This paper presents the design of a new soft pneumatic actuator whose direction and magnitude of bending may be precisely controlled via activation of different shape memory alloy (SMA) springs within the actuator, in conjunction with pneumatic actuation. This design is inspired by examples seen in nature such as the human tongue, where the combination of hydrostatic pressure and contraction of intrinsic muscle groups enables precise maneuverability and morphing capabilities. Here, SMA springs are embedded in the walls of the actuator, serving as intrinsic muscles that may be selectively activated to constrain the device. The pneumatic SMA (PneuSMA) actuator demonstrates remarkable spatial controllability evidenced by testing under different pressures and SMA activation combinations. A baseline finite element model is also developed to predict the actuator deformation under different pressure and activation conditions. 

Abstract Variable stiffness structures lie at the nexus of soft robots and traditional robots as they enable the execution of both high-force tasks and delicate manipulations. Laminar jamming structures, which consist of thin flexible sheets encased in a sealed chamber, can alternate between a rigid state when a vacuum is applied and a flexible state when the layers are allowed to slide in the absence of a pressure gradient. In this work, an additional mode of controllability is added by clamping and unclamping the ends of a simple laminar jamming beam structure. Previous works have focused on the translational degree of freedom that may be controlled via vacuum pressure; here we introduce a rotational degree of freedom that may be independently controlled with a clamping mechanism. Preliminary results demonstrate the ability to switch between three states: high stiffness (under vacuum), translational freedom (with clamped ends, no vacuum), and rotational freedom (with ends free to slide, no vacuum). 

Computer simulations for physics labs may be combined with hands-on lab equipment to boost student understanding and make labs more accessible. Hybrid labs of HTML5-based computer simulations and hands-on lab equipment for topics in mechanics were investigated in a large, algebra-based, studio physics course for life science students at a private, research-intensive institution. Computer simulations were combined with hands-on equipment and compared to traditional hands-on labs using an A/B testing protocol. Learning outcomes were measured for the specific topic of momentum conservation by comparing student scores on post-lab exercises, related quiz and exam questions, and a subset of questions on the Energy and Momentum Conceptual Survey (EMCS) administered before and after instruction for both groups. We find that students who completed a hands-on lab vs. a hybrid lab showed no difference in performance on momentum assessments. 

There is a critical need for more students with engineering and computer science majors to enter into, persist in, and graduate from four-year postsecondary institutions. Increasing the diversity of the workforce by inclusive practices in engineering and science is also a profound identified need. According to national statistics, the largest groups of underrepresented minority students in engineering and science attend U.S. public higher education institutions. Most often, a large proportion of these students come to colleges and universities with unique challenges and needs, and are more likely to be first in their family to attend college. In response to these needs, engineering education researchers and practitioners have developed, implemented and assessed interventions to provide support and help students succeed in college, particularly in their first year. These interventions typically target relatively small cohorts of students and can be managed by a small number of faculty and staff. In this paper, we report on “work in progress” research in a large-scale, first-year engineering and computer science intervention program at a public, comprehensive university using multivariate comparative statistical approaches. Large-scale intervention programs are especially relevant to minority serving institutions that prepare growing numbers of students who are first in their family to attend college and who are also under-resourced, financially. These students most often encounter academic difficulties and come to higher education with challenging experiences and backgrounds. Our studied first-year intervention program, first piloted in 2015, is now in its 5th year of implementation. Its intervention components include: (a) first-year block schedules, (b) project-based introductory engineering and computer science courses, (c) an introduction to mechanics course, which provides students with the foundation needed to succeed in a traditional physics sequence, and (d) peer-led supplemental instruction workshops for calculus, physics and chemistry courses. This intervention study responds to three research questions: (1) What role does the first-year intervention’s components play in students’ persistence in engineering and computer science majors across undergraduate program years? (2) What role do particular pedagogical and cocurricular support structures play in students’ successes? And (3) What role do various student socio-demographic and experiential factors play in the effectiveness of first-year interventions? To address these research questions and therefore determine the formative impact of the firstyear engineering and computer science program on which we are conducting research, we have collected diverse student data including grade point averages, concept inventory scores, and data from a multi-dimensional questionnaire that measures students’ use of support practices across their four to five years in their degree program, and diverse background information necessary to determine the impact of such factors on students’ persistence to degree. Background data includes students’ experiences prior to enrolling in college, their socio-demographic characteristics, and their college social capital throughout their higher education experience. For this research, we compared students who were enrolled in the first-year intervention program to those who were not enrolled in the first-year intervention. We have engaged in cross-sectional 2 data collection from students’ freshman through senior years and employed multivariate statistical analytical techniques on the collected student data. Results of these analyses were interesting and diverse. Generally, in terms of backgrounds, our research indicates that students’ parental education is positively related to their success in engineering and computer science across program years. Likewise, longitudinally (across program years), students’ college social capital predicted their academic success and persistence to degree. With regard to the study’s comparative research of the first-year intervention, our results indicate that students who were enrolled in the first-year intervention program as freshmen continued to use more support practices to assist them in academic success across their degree matriculation compared to students who were not in the first-year program. This suggests that the students continued to recognize the value of such supports as a consequence of having supports required as first-year students. In terms of students’ understanding of scientific or engineering-focused concepts, we found significant impact resulting from student support practices that were academically focused. We also found that enrolling in the first-year intervention was a significant predictor of the time that students spent preparing for classes and ultimately their grade point average, especially in STEM subjects across students’ years in college. In summary, we found that the studied first-year intervention program has longitudinal, positive impacts on students’ success as they navigate through their undergraduate experiences toward engineering and computer science degrees. 

This paper presents an approach for modeling new soft robotic materials which possess the ability to control directional stiffness. These materials are inspired by biological systems where movements are enabled by variable stiffness tissue and contraction of localized muscle groups. Here a low-melting-point (LMP) material lattice embedded in an elastomer serves as a rigid skeleton that may be locally melted to allow bending at selectable joint locations. The forward kinematics of the lattice has been modeled using the product of exponentials method with the incorporation of bending axis selectivity. In this paper, we develop this model to account for torques imposed by tendons, and we model the elastomer's resistance to bending as a torsional spring at the selected joints. Thus we obtain a two-way relationship between tendon forces and joint angles/axes. The concept of applying traditional robot modeling strategies to selectively compliant robotic structures could enable precise control of dexterous soft robots that satisfy stringent safety criteria.