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  1. Meder, F. ; Hunt, A. ; Margheri, L. ; Mura, A. ; Mazzolai, B. (Ed.)
    This study introduces a novel neuromechanical model of rat hindlimbs with biarticular muscles producing walking movements without ground contact. The design of the control network is informed by the findings from our previous investigations into two-layer central pattern generators (CPGs). Specifically, we examined one plausible synthetic nervous system (SNS) designed to actuate 3 biarticular muscles, including the Biceps femoris posterior (BFP) and Rectus femoris (RF), both of which provide torque about the hip and knee joints. We conducted multiple perturbation tests on the simulation model to investigate the contribution of these two biarticular muscles in stabilizing perturbed hindlimb walking movements. We tested the BFP and RF muscles under three conditions: active, only passive tension, and fully disabled. Our results show that when these two biarticular muscles were active, they not only reduced the impact of external torques, but also facilitated rapid coordination of motion phases. As a result, the hindlimb model with biarticular muscles demonstrated faster recovery compared to our previous monoarticular muscle model. 
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    Free, publicly-accessible full text available August 1, 2024
  2. Hunt, A ; Vouloutsi, V. ; Moses, K. ; Quinn R. ; Mura, A. ; Prescott T. ; Verschure, P. (Ed.)
    Central pattern generators (CPGs) are ubiquitous neural circuits that contribute to an eclectic collection of rhythmic behaviors across an equally diverse assortment of animal species. Due to their prominent role in many neuromechanical phenomena, numerous bioinspired robots have been designed to both investigate and exploit the operation of these neural oscillators. In order to serve as effective tools for these robotics applications, however, it is often necessary to be able to adjust the phase alignment of multiple CPGs during operation. To achieve this goal, we present the design of our phase difference control (PDC) network using a functional subnetwork approach (FSA) wherein subnetworks that perform basic mathematical operations are assembled such that they serve to control the relative phase lead/lag of target CPGs. Our PDC network operates by first estimating the phase difference between two CPGs, then comparing this phase difference to a reference signal that encodes the desired phase difference, and finally eliminating any error by emulating a proportional controller that adjusts the CPG oscillation frequencies. The architecture of our PDC network, as well as its various parameters, are all determined via analytical design rules that allow for direct interpretability of the network behavior. Simulation results for both the complete PDC network and a selection of its various functional subnetworks are provided to demonstrate the efficacy of our methodology. 
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  3. Central pattern generators (CPGs) are ubiquitous neural circuits that contribute to an eclectic collection of rhythmic behaviors across an equally diverse assortment of animal species. Due to their prominent role in many neuromechanical phenomena, numerous bioinspired robots have been designed to both investigate and exploit the operation of these neural oscillators. In order to serve as effective tools for these robotics applications, however, it is often necessary to be able to adjust the phase alignment of multiple CPGs during operation. To achieve this goal, we present the design of our phase difference control (PDC) network using a functional subnetwork approach (FSA) wherein subnetworks that perform basic mathematical operations are assembled such that they serve to control the relative phase lead/lag of target CPGs. Our PDC network operates by first estimating the phase difference between two CPGs, then comparing this phase difference to a reference signal that encodes the desired phase difference, and finally eliminating any error by emulating a proportional controller that adjusts the CPG oscillation frequencies. The architecture of our PDC network, as well as its various parameters, are all determined via analytical design rules that allow for direct interpretability of the network behavior. Simulation results for both the complete PDC network and a selection of its various functional subnetworks are provided to demonstrate the efficacy of our methodology. 
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  4. There is significant work indicating that spatial ability has correlations to student success in STEM programs. Work also shows that spatial ability correlates to professional success in respective STEM fields. Spatial ability has thus been a focus of research in engineering education for some time. Spatial interventions have been developed to improve student’s spatial ability that range from physical manipulatives to the implementation of entire courses. These interventions have had positive impact upon student success and retention. Currently, researchers rely on a variety of different spatial ability instruments to quantify participants spatial ability. Researchers classify an individual’s spatial ability as the performance indicated by their results on such an instrument. It is recognized that this measured performance is constrained by the spatial construct targeted with that spatial instrument. As such, many instruments are available for the researchers use to assess the variety of constructs of spatial ability. Examples include the Purdue Spatial Visualization Test of Rotations (PSVTR), the Mental Cutting Test (MCT), and the Minnesota Paper Foam Board Test. However, at this time, there are no readily accessible spatial ability instruments that can be used to assess spatial ability in a blind or low vision population (BLV). Such an instrument would not only create an instrument capable of quantifying the impacts of spatially focused interventions upon BLV populations but also gives us a quantitative method to assess the effectiveness of spatial curriculum for BLV students. Additionally, it provides a method of assessing spatial ability development from tactile perspective, a new avenue for lines of research that expand beyond the visual methods typically used. This paper discusses the development of the Tactile Mental Cutting Test (TMCT), a non-visually accessible spatial ability instrument, developed and used with a BLV population. Data was acquired from individuals participating in National Federation of the Blind (NFB) Conventions across the United States as well as NFB sponsored summer engineering programs. The paper reports on a National Science Foundation funded effort to garner initial research findings on the application of the TMCT. It reports on initial findings of the instrument’s validity and reliability, as well as the development of the instrument over the first three years of this project. 
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  5. null (Ed.)
    One of the pivotal goals in engineering education is to broaden participation of different minorities. An overlooked barrier yet to be explored is how hidden curriculum and its connected constructs may impede this goal. Hidden curriculum (HC) refers to the unwritten, unofficial, and often unintended assumptions, lessons, values, beliefs, attitudes, and perspectives in engineering. This paper will present the development and assessment of a mixed-method vignette survey instrument to evaluate the responses of current engineering students and faculty when exposed to several examples of hidden curriculum. Results from 153 engineering students and faculty across the United States and Puerto Rico were used to assess the survey sub-subscales (HC awareness, emotions, self-efficacy, and self-advocacy). Findings revealed Cronbach alpha coefficients of 0.70 (HC awareness), 0.73 (emotions), 0.91 (self-efficacy), and 0.91 (self-advocacy). The overall instrument had a reliability of 0.74. Alongside HC awareness, we found that among different axes of inequity, gender, role, and institution type are important elements that shaped the responses of these engineering populations. 
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