RedFeather- resource exhibition and discovery: a lightweight micro-repository for resource sharing

Open Educational Resources (OERs) depend on being hosted in repositories so that they can be effectively viewed, managed, searched and indexed online. Content – especially multimedia content – that is not hosted in this way has no metadata and is effectively dark to the wider community. Similarly content that is not described properly, and with appropriate licenses, is of limited use. This is a challenge for small-scale contributors, such as individuals and small projects, as the overhead of setting up and administrating a content repository can be prohibitive.In this paper we propose RedFeather, a micro-repository, as a solution to this problem. RedFeather is a simple and straightforward server-side tool that requires zero to little configuration, but that provides the core functionality of a fully-fledged OER repository, including: resource pages with inline preview, a resource manager with streamlined workflow, and views of the resource in OER critical formats (including RDF, JSON, and RSS). RedFeather is fully customizable, with a flexible plugin architecture and configurable templates, but also works without any customization as a single php script file uploaded to a web server. The goal of a micro-repository like RedFeather is both to enable small-scale contributors to easily join the OER community, and to act as a intermediate step for larger contributors beginning a collection, or requiring a temporary home for their resources while a more substantial repository is developed. Our hope is that by lowering the barriers to participation, RedFeather can help the OER community to take advantage of the long tail of small to medium sized content creators

Job Offer Expectancies: An Analysis of Antecedents, Outcomes and Moderated Effects

Restricted by limited time and resources, job applicants are often required to make decisions based on their own estimations of an organization\u27s likelihood to extend a job offer. These estimations, or offer expectancies, may be linked to several applicant attitudes and behaviors that have yet to be examined fully in the literature (e.g., job pursuit or information seeking behaviors, search expansion, etc.). We know relatively little about how these perceptions are formed. In this study, actual job applicants were asked to report their perceptions of and behavioral intentions towards organizations that they are currently applying to but have not yet been offered jobs with. In a follow-up survey, applicants were asked to report whether they engaged in certain of these behaviors. The research found that both social comparisons to other applicants and application self-efficacy operated as antecedents of offer expectancies. Furthermore, offer expectancies were found to predict job pursuit intentions and behaviors, as well as information-seeking intentions. Finally, selection-stage was found to moderate the relationship between offer expectancies and job-pursuit intentions such that in later stages applicants were more likely to report intentions to pursue the organization if they had very positive expectations of receiving the offer. This relationship was weak for less positive expectations. Organizations may benefit by understanding what drives applicant decisions to withdraw early from a process, and manage expectations where appropriate

FAST FISSION NEUTRON DETECTION USING THE CHERENKOV EFFECT

The Cherenkov effect in optically clear media of varying indices of refraction and composition was investigated for quantification of fast neutrons. The ultimate application of the proposed detection system is criticality monitoring. The optically clear medium, composed of select target nuclei, was coupled to a photomultiplier tube. Neutron reaction products of the target nuclei contained within the optical medium emit beta particles and gamma rays that produce Cherenkov photons within the medium which can be detected. Assessed media include quartz (SiO2), sapphire (Al2O3), spinel (MgAl2O4), and zinc sulfide (ZnS), which were irradiated with un-moderated 252Cf. Monte Carlo N-Particle (MCNP) code simulations were conducted to quantify the neutron flux incident on the media. High resolution gamma-ray spectroscopic measurements of the samples were conducted to verify the MCNP estimate. The threshold reactions of interest were 28Si (n, p) 28Al, 27Al (n, p) 27Mg, 24Mg(n, p)24Na, and 64Zn(n, p)64Cu which have neutron reaction cross sections in the 1 to 10 MeV range on the order of 0.1 barn. The detection system offers a unique way to measure a criticality event; it can count in place, making retrieval by emergency personnel unnecessary

Predicting the motions and forces of wearable robotic systems using optimal control

Wearable robotic systems are being developed to prevent injury to the low back. Designing a wearable robotic system is challenging because it is difficult to predict how the exoskeleton will affect the movement of the wearer. To aid the design of exoskeletons, we formulate and numerically solve an optimal control problem (OCP) to predict the movements and forces of a person as they lift a 15 kg box from the ground both without (human-only OCP) and with (with-exo OCP) the aid of an exoskeleton. We model the human body as a sagittal-plane multibody system that is actuated by agonist and antagonist pairs of muscle torque generators (MTGs) at each joint. Using the literature as a guide, we have derived a set of MTGs that capture the active torque–angle, passive torque–angle, and torque–velocity characteristics of the flexor and extensor groups surrounding the hip, knee, ankle, lumbar spine, shoulder, elbow, and wrist. Uniquely, these MTGs are continuous to the second derivative and so are compatible with gradient-based optimization. The exoskeleton is modeled as a rigid-body mechanism that is actuated by a motor at the hip and the lumbar spine and is coupled to the wearer through kinematic constraints. We evaluate our results by comparing our predictions with experimental recordings of a human subject. Our results indicate that the predicted peak lumbar-flexion angles and extension torques of the human-only OCP are within the range reported in the literature. The results of the with-exo OCP indicate that the exoskeleton motors should provide relatively little support during the descent to the box but apply a substantial amount of support during the ascent phase. The support provided by the lumbar motor is similar in shape to the net moment generated at the L5/S1 joint by the body; however, the support of the hip motor is more complex because it is coupled to the passive forces that are being generated by the hip extensors of the human subject. The simulations developed in this study are specific to lifting motion and a lower back exoskeleton. However, the framework is applicable for simulating a large range of robotic-assisted human motions

Mechanics and Control of Human Balance

A predictive, forward-dynamic model and computer simulation of human gait has important medical and research applications. Most human simulation work has focused on inverse dynamics studies to quantify bone on bone forces and muscle loads. Inverse dynamics is not predictive - it works backwards from experimentally measured motions in an effort to find the forces that caused the motion. In contrast, forward dynamics determines how a mechanism will move without the need for experimentation. Most of the forward dynamic gait simulations reported consider only one step, foot contact is not modeled, and balance controllers are not used. This thesis addresses a few of the shortcomings of current human gait simulations by contributing an experimentally validated foot contact model, a model-based stance controller, and an experimentally validated model of the relationship between foot placement location and balance. The goal of a predictive human gait simulation is to determine how a human would walk under a condition of interest, such as walking across a slippery floor, using a new lower limb prosthesis, or with reduced leg strength. To achieve this goal, often many different gaits are simulated and the one that is the most human-like is chosen as the prediction for how a person would move. Thus it is necessary to quantify how `human-like' a candidate gait is. Human walking is very efficient, and so, the metabolic efficiency of the candidate gait is most often used to measure the performance of a candidate gait. Muscles consume metabolic energy as a function of the tension they develop and the rate at which they are contracting. Muscle tension is developed, and contractions are made in an effort generate torques about joints in order to make them move. To predict human gait, it is necessary for the simulation to be able to walk in such a way that the simulated leg joints use similar joint torques and kinematics as a human leg does, all while balancing the body. The joint torques that the legs must develop to propel the body forward, and balance it, are heavily influenced by the ground reaction forces developed between the simulated foot and the ground. A predictive gait simulation must be able to control the model so that it can walk, and remain balanced while generating ground reaction force profiles that are similar to experimentally observed human ground reaction force profiles. Ground reaction forces are shaped by the way the foot interacts with the ground, making it very important to model the human foot accurately. Most continuous foot contact models present in the literature have been experimentally validated using pendulum impact methods that have since been shown to produce inaccurate results. The planar foot contact model developed as part of this research was validated in-vivo using conventional force plates and optical tracking markers. The experimental data was also highly useful for developing a computationally efficient foot model by identifying the dominant contact properties of a real foot (during walking), without the complexity of modelling the 26 bones, 33 joints, over 90 ligaments, and the network of muscles that are in a real foot. Both ground reaction forces and the balance of the model are heavily influenced by the way the stance limb is controlled. Anthropomorphic multibody models typically have a fragile sense of balance, and ground reaction force profiles that do not look similar to experimentally measured human ground reaction force profiles. In contrast, the simple point-mass spring-loaded-inverted-pendulum (SLIP) can be made to walk or run in a balanced manner with center-of-mass (COM) kinematics and ground reaction force profiles that could be mistaken for the equivalent human data. A stance limb controller is proposed that uses a planar SLIP to compute a reference trajectory for a planar anthropomorphic multibody gait model. The torso of the anthropomorphic model is made to track the computed trajectory of the SLIP using a control system. The aim of this partitioned approach to gait simulation is to endow the anthropomorphic model with the human-like gait of the simpler SLIP model. Although the SLIP model-based stance-controller allows an anthropomorphic gait model to walk in more human-like manner, it also inherits the short comings of the SLIP model. The SLIP can walk or run like a human, but only at a fixed velocity. It cannot initiate or terminate gait. Fall preventing movements, such as gait termination and compensatory stepping, are of particular relevance to kinesiologists and health care professionals. Kinesiologists have known for nearly a decade that humans restore their balance primarily by systematically altering their foot placement location. This thesis presents a human experimental validation of a planar foot placement algorithm that was originally designed to restore the balance of bipedal robots. A three-dimensional (3D) theoretical extension to the planar foot placement algorithm is also presented along with preliminary human experimental results. These models of foot placement can be used in the future to improve the capabilities of gait simulations by giving simple models human-like compensatory stepping abilities. The theoretical extension also provides some insight into how instability and balance performance can be quantified. The instability and balance performance measures have important applications for diagnosing and rehabilitating balance problems. Despite all of the progress that has been made, there is still much work to be done. Work needs to be continued to find methods that allow the anthropomorphic model to emulate the SLIP model more faithfully. Experimental work needs to be completed to realize the potential diagnostic and rehabilitation applications of the foot placement models. With continued effort, a predictive, balanced, multi-step gait simulation can be developed that will give researchers the time-saving capability of computerized hypothesis testing, and medical professionals improved diagnostic and rehabilitation methods

Motion optimization and parameter identification for a human and lower-back exoskeleton model

Designing an exoskeleton to reduce the risk of low-back injury during lifting is challenging. Computational models of the human-robot system coupled with predictive movement simulations can help to simplify this design process. Here, we present a study that models the interaction between a human model actuated by muscles and a lower-back exoskeleton. We provide a computational framework for identifying the spring parameters of the exoskeleton using an optimal control approach and forward-dynamics simulations. This is applied to generate dynamically consistent bending and lifting movements in the sagittal plane. Our computations are able to predict motions and forces of the human and exoskeleton that are within the torque limits of a subject. The identified exoskeleton could also yield a considerable reduction of the peak lower-back torques as well as the cumulative lower-back load during the movements. This work is relevant to the research communities working on human-robot interaction, and can be used as a basis for a better human-centered design process

Development of the European Service Module Propulsion Subsystem for the Multi-Purpose Crew Vehicle

2013, NASA and the European Space Agency (ESA) entered into an international partnership to develop the European Service Module (ESM) for use on NASA's Multi-Purpose Crew Vehicle (MPCV), also known as Orion. The MPCV will be used as the principal spacecraft for future human space exploration missions beyond low earth orbit. The ESM Propulsion Subsystem (PSS) is a pressure-fed, bi-propellant propulsion system, being developed by Airbus Defense and Space under contract to ESA. For this effort, NASA is responsible for the traditional role of insight/oversight to ensure that the PSS delivered by Airbus meets all MPCV Program requirements. In addition, the NASA Propulsion team also has some unique responsibilities that are a result of the Implementing Agreement (IA) between NASA and ESA for development of the ESM. These responsibilities include: (1) providing the main engine and Thrust Vector Control (TVC) assembly for the PSS. This is being accomplished through the delta qualification and re-use the Space Shuttle Orbital Maneuvering System (OMS) engine and TVC assembly; (2) procurement and delivery of the Auxiliary engines (R-4Ds) for the PSS. These engines are being procured by NASA from Aerojet-Rocketdyne via Lockheed Martin, the prime contractor for the MPCV, per an Airbus-provided specification; and (3) conducting the integrated systems hot-fire test which will qualify the end-to-end PSS for flight on MPCV. This test is being conducted at the NASA White Sands Test Facility (WSTF) using an Airbus-provided test article known as the Propulsion Qualification Model (PQM)

Dynamic Human Body Models in Vehicle Safety: An Overview

Significant trends in the vehicle industry are autonomous driving, micromobility, electrification and the increased use of shared mobility solutions. These new vehicle automation and mobility classes lead to a larger number of occupant positions, interiors and load directions. As safety systems interact with and protect occupants, it is essential to place the human, with its variability and vulnerability, at the center of the design and operation of these systems. Digital human body models (HBMs) can help meet these requirements and are therefore increasingly being integrated into the development of new vehicle models. This contribution provides an overview of current HBMs and their applications in vehicle safety in different driving modes. The authors briefly introduce the underlying mathematical methods and present a selection of HBMs to the reader. An overview table with guideline values for simulation times, common applications and available variants of the models is provided. To provide insight into the broad application of HBMs, the authors present three case studies in the field of vehicle safety: (i) in-crash finite element simulations and injuries of riders on a motorcycle; (ii) scenario-based assessment of the active pre-crash behavior of occupants with the Madymo multibody HBM; (iii) prediction of human behavior in a take-over scenario using the EMMA model