Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot

Animals dynamically adapt to varying terrain and small perturbations with remarkable ease. These adaptations arise from complex interactions between the environment and biomechanical and neural components of the animal’s body and nervous system. Research into mammalian locomotion has resulted in several neural and neuro-mechanical models, some of which have been tested in simulation, but few “synthetic nervous systems” have been implemented in physical hardware models of animal systems. One reason is that the implementation into a physical system is not straightforward. For example, it is difficult to make robotic actuators and sensors that model those in the animal. Therefore, even if the sensorimotor circuits were known in great detail, those parameters would not be applicable and new parameter values must be found for the network in the robotic model of the animal. This manuscript demonstrates an automatic method for setting parameter values in a synthetic nervous system composed of non-spiking leaky integrator neuron models. This method works by first using a model of the system to determine required motor neuron activations to produce stable walking. Parameters in the neural system are then tuned systematically such that it produces similar activations to the desired pattern determined using expected sensory feedback. We demonstrate that the developed method successfully produces adaptive locomotion in the rear legs of a dog-like robot actuated by artificial muscles. Furthermore, the results support the validity of current models of mammalian locomotion. This research will serve as a basis for testing more complex locomotion controllers and for testing specific sensory pathways and biomechanical designs. Additionally, the developed method can be used to automatically adapt the neural controller for different mechanical designs such that it could be used to control different robotic systems

Participatory Community Action Research in Homeless Shelters: Utilization of Service-Learning Pedagogy in Research and Advocacy

Homelessness will be framed as a human rights issue, with reference to the Universal Declaration of Human Rights (e.g., Articles 25 and 27). A participatory action research project (sustained by service-learning pedagogy) in homeless shelters will be described. In the shelters, we implement Behavioral Activation, which is a strategy to (a) transform the shelter environment; (b) empower shelter guests; (c) enhance their coping; and (d) expand their opportunities for overcoming obstacles associated with homelessness. We will present both quantitative outcomes (using validated psychometric measures) and qualitative outcomes (examining written comments of guests using grounded theory methodology) for shelter guests, including evidence that the project contributes to their perceptions of hope, perceived capability/motivation for employment/education, purpose/meaning in life, well-being (managing anxiety and depression), social/emotional support, and shelter social climate. We will describe our plans for examining long-term outcomes for shelter guests, which involves quasi-experimental research to compare outcomes of shelter guests in Behavioral Activation with outcomes of other groups, such as: (a) guests who left shelters prior to Behavioral Activation implementation; (b) guests who declined Behavioral Activation; and/or (c) guests from similar shelters without Behavioral Activation. The project’s expanding collaborative network will also be described, such as our connection with the Montgomery County Ex-Offender Reentry Program, which prepares and empowers ex-offenders as they pursue housing, employment, and other positive endeavors. Further, we will show the relevance of the Universal Declaration of Human Rights (Articles 27 and 29) to our use of service-learning pedagogy. Outcomes for service-learning students are accessed via quantitative psychometric measures as well as qualitative approaches (examining written reflections using grounded theory methodology). We will review results of quasi-experimental research demonstrating that, relative to non-service-learning students, service-learning students assisting with this project show pre- to post-semester corrective changes in community service self-efficacy, stigmatizing attitudes, and awareness of privilege

Electrical probes of the non-Abelian spin liquid in Kitaev materials

Recent thermal-conductivity measurements evidence a magnetic-field-induced non-Abelian spin liquid phase in the Kitaev material $\alpha$-$\mathrm{RuCl}_{3}$. Although the platform is a good Mott insulator, we propose experiments that electrically probe the spin liquid's hallmark chiral Majorana edge state and bulk anyons, including their exotic exchange statistics. We specifically introduce circuits that exploit interfaces between electrically active systems and Kitaev materials to `perfectly' convert electrons from the former into emergent fermions in the latter---thereby enabling variations of transport probes invented for topological superconductors and fractional quantum Hall states. Along the way we resolve puzzles in the literature concerning interacting Majorana fermions, and also develop an anyon-interferometry framework that incorporates nontrivial energy-partitioning effects. Our results illuminate a partial pathway towards topological quantum computation with Kitaev materials.Comment: 35 pages, 17 figure

Being honest with causal language in writing for publication

The misleading use of causal language in publication is problematic for authors, reviewers and consumers of the information. Published research in quality journals has important knowledge implications and it is, therefore, contingent on authors to use language that is accurate and appropriate to their work. Language implying unsupported causal relationships may overstate the evidence-base, especially if accepted by uncritical readers or unwitting members of the general public who may not understand how to interpret inferential statistics

Electrical Probes of the Non-Abelian Spin Liquid in Kitaev Materials

Recent thermal-conductivity measurements evidence a magnetic-field-induced non-Abelian spin-liquid phase in the Kitaev material α−RuCl₃. Although the platform is a good Mott insulator, we propose experiments that electrically probe the spin liquid’s hallmark chiral Majorana edge state and bulk anyons, including their exotic exchange statistics. We specifically introduce circuits that exploit interfaces between electrically active systems and Kitaev materials to “perfectly” convert electrons from the former into emergent fermions in the latter—thereby enabling variations of transport probes invented for topological superconductors and fractional quantum-Hall states. Along the way, we resolve puzzles in the literature concerning interacting Majorana fermions, and also develop an anyon-interferometry framework that incorporates nontrivial energy-partitioning effects. Our results illuminate a partial pathway toward topological quantum computation with Kitaev materials

Exploratory factor analysis and principal component analysis in clinical studies: Which one should you use?

Factor analysis covers a range of multivariate methods used to explain how underlying factors influence a set of observed variables. When research aims to identify these underlying factors, exploratory factor analysis (EFA) is used. In contrast, when the aim is to test whether a set of observed variables represents the underlying factors, in accordance with an existing conceptual basis, confirmatory factor analysis is performed. EFA has many similarities with a commonly used data reduction technique called principal component analysis (PCA). These similarities, along with using the related terms factor and component interchangeably, contribute to confusion in analysis. The difficulty in identifying the appropriate use of statistical methods and their application and interpretation impacts clinical and research implications (Beavers et al., 2013; Tabachnick & Fidell, 2001). We acknowledge previous articles in nursing journals offering guidance on the use of factor analysis (Gaskin & Happell, 2014; Watson & Thompson, 2006)

Using risk and odds ratios to assess effect size for meta-analysis outcome measures

Best practice is built on the principle of aggregating all available evidence on a topic to make a clinical decision on the most appropriate intervention for the situation at hand. Systematic reviews and meta-analyses are powerful tools that summarize the evidence for current best practice guidelines for the available interventions for a particular problem (Moher, Liberati, Tetzlaff, & Altman, 2009). Meta-analysis combines the results of multiple studies to produce an aggregated and more precise estimates of the benefits of the interventions. Meta-analysis of high-quality randomized trials is considered the highest level of evidence to inform practice