A concept study of small planetary rovers : using Tensegrity Structures on Venus

Venus is among the most enigmatic and interesting places to explore in the solar system. However, the surface of Venus is a very hostile, rocky environment with extreme temperatures, pressures, and chemical corrosivity. A planetary rover to explore the surface would be scientifically valuable, but must use unconventional methods in place of traditional robotic control and mobility. This study proposes that a tensegrity structure can provide adaptivity and control in place of a traditional mechanism and electronic controls for mobility on the surface of Venus and in other extreme environments. Tensegrity structures are light and compliant, being constructed from simple repeating rigid and flexible members and stabilized only by tension, drawing inspiration from biology and geometry, and are suitable for folding, deployment, and adaptability to terrain. They can also utilize properties of smart materials and geometry to achieve prescribed movements. Based on the needs of scientific exploration, a simple tensegrity rover can provide mobility and robustness to terrain and environmental conditions, and can be powered by environmental sources such as wind. A wide variety of tensegrity structures are possible, and some initial concepts suitable for volatile and complex environments are proposed here

Introduction to INDOT Bridge Asset Management Procedures

This session introduces INDOT bridge asset management procedures and asset management tools and provides a general overview of the responsibilities of the INDOT Bridge Asset Management Team, which manages more than 5,700 bridges and nearly 9,000 culverts. This team is responsible for ensuring that a 5-year bridge management budget is developed and implemented

A US hospital budget impact analysis of a skin closure system compared with standard of care in hip and knee arthroplasty.

Background: Medicare\u27s mandatory bundle for hip and knee arthroplasty necessitates provider accountability for quality and cost of care to 90 days, and wound closure may be a key area of consideration. The DERMABOND Methods: A 90-day economic model was developed assuming 500 annual hip/knee arthroplasties for a typical US hospital setting. In current practice, wound closure methods for the final skin layer were set to 50% sutures and 50% staples. In future practice, this distribution shifted to 20% sutures, 20% staples, and 60% Skin Closure System. Health care resources included materials (eg, staplers, steri-strips, and traditional/barbed sutures), standard or premium dressings, outpatient visits, and home care visits. An Expert Panel, comprised of three orthopedic physician assistants, two orthopedic surgeons, and a home health representative, was used to inform several model parameters. Other inputs were informed by national data or literature. Unit costs were based on list prices in 2016 US dollars. Uncertainty in the model was explored through one-way sensitivity and alternative scenario analyses. Results: The analysis predicted that use of Skin Closure System in the future practice could achieve cost savings of $56.70 to$79.62 per patient, when standard or premium wound dressings are used, respectively. This translated to an annual hospital budgetary savings ranging from $28,349 to$39,809 when assuming 500 arthroplasties. Dressing materials and postoperative health care visits were key model drivers. Conclusions: Use of the Skin Closure System may provide cost savings within hip and knee arthroplasties due to decreases in resource utilization in the postacute care setting

Machine Learning Accelerated Discovery of Corrosion-resistant High-entropy Alloys

Corrosion has a wide impact on society, causing catastrophic damage to structurally engineered components. An emerging class of corrosion-resistant materials are high-entropy alloys. However, high-entropy alloys live in high-dimensional composition and configuration space, making materials designs via experimental trial-and-error or brute-force ab initio calculations almost impossible. Here we develop a physics-informed machine-learning framework to identify corrosion-resistant high-entropy alloys. Three metrics are used to evaluate the corrosion resistance, including single-phase formability, surface energy and Pilling-Bedworth ratios. We used random forest models to predict the single-phase formability, trained on an experimental dataset. Machine learning inter-atomic potentials were employed to calculate surface energies and Pilling-Bedworth ratios, which are trained on first-principles data fast sampled using embedded atom models. A combination of random forest models and high-fidelity machine learning potentials represents the first of its kind to relate chemical compositions to corrosion resistance of high-entropy alloys, paving the way for automatic design of materials with superior corrosion protection. This framework was demonstrated on AlCrFeCoNi high-entropy alloys and we identified composition regions with high corrosion resistance. Machine learning predicted lattice constants and surface energies are consistent with values by first-principles calculations. The predicted single-phase formability and corrosion-resistant compositions of AlCrFeCoNi agree well with experiments. This framework is general in its application and applicable to other materials, enabling high-throughput screening of material candidates and potentially reducing the turnaround time for integrated computational materials engineering

Probabilistic robotic logic programming with hybrid Boolean and Bayesian inference

Bayesian inference provides a probabilistic reasoning process for drawing conclusions based on imprecise and uncertain data that has been successful in many applications within robotics and information processing, but is most often considered in terms of data analysis rather than synthesis of behaviours. This paper presents the use of Bayesian inference as a means by which to perform Boolean operations in a logic programme while incorporating and propagating uncertainty information through logic operations by inference. Boolean logic operations are implemented in a Bayesian network of Bernoulli random variables with tensor-based discrete distributions to enable probabilistic hybrid logic programming of a robot. This enables Bayesian inference operations to coexist with Boolean logic in a unified system while retaining the ability to capture uncertainty by means of discrete probability distributions. Using a discrete Bayesian network with both Boolean and Bayesian elements, the proposed methodology is applied to navigate a mobile robot using hybrid Bayesian and Boolean operations to illustrate how this new approach improves robotic performance by inclusion of uncertainty without increasing the number of logic elements required. As any logical system could be programmed in this manner to integrate uncertainty into decision-making, this methodology can benefit a wide range of applications that use discrete or probabilistic logic

bNAber: database of broadly neutralizing HIV antibodies.

The discovery of broadly neutralizing antibodies (bNAbs) has provided an enormous impetus to the HIV vaccine research and to entire immunology. The bNAber database at http://bNAber.org provides open, user-friendly access to detailed data on the rapidly growing list of HIV bNAbs, including neutralization profiles, sequences and three-dimensional structures (when available). It also provides an extensive list of visualization and analysis tools, such as heatmaps to analyse neutralization data as well as structure and sequence viewers to correlate bNAbs properties with structural and sequence features of individual antibodies. The goal of the bNAber database is to enable researchers in this field to easily compare and analyse available information on bNAbs thereby supporting efforts to design an effective vaccine for HIV/AIDS. The bNAber database not only provides easy access to data that currently is scattered in the Supplementary Materials sections of individual papers, but also contributes to the development of general standards of data that have to be presented with the discovery of new bNAbs and a universal mechanism of how such data can be shared

Multi-actuated AUV body for windfarm inspection : lessons from the bio-inspired RoboFish field trials

An innovative magnetic joint design has been developed as part of the construction of a bio-inspired Autonomous Underwater Vehicle (AUV) for wind farm inspection. This paper presents our design solutions for a jointed watertight AUV body made using current 3D printing techniques to achieves water tightness and resilient composite metal-polymer bonding.The design avoids dynamic interfaces and the need for rotary seals yet achieves robustness and strength. Test results prove a successful implementation of the magnetic connection between a freely rotating inner shaft and a driven outer shaft in a fish-like jointed AUV body