An Enhanced RRT based Algorithm for Dynamic Path Planning and Energy Management of a Mobile Robot

Mobile robots often have limited battery life and need to recharge periodically. This paper presents an RRT- based path-planning algorithm that addresses battery power management. A path is generated continuously from the robot's current position to its recharging station. The robot decides if a recharge is needed based on the energy required to travel on that path and the robot's current power. RRT* is used to generate the first path, and then subsequent paths are made using information from previous trees. Finally, the presented algorithm was compared with Extended Rate Random Tree (ERRT) algorith

Can referrals to a pediatric Osteopathic Manipulative Medicine (OMM) clinic be increased through provider education?

Purpose: Many pediatricians in the U.S. have trained in osteopathic medicine and have a Doctor of Osteopathy degree. However, other members of the health care team are often unaware of what osteopathic manipulative medicine (OMM) is or its indication in pediatrics. This quality improvement (QI) project aims to increase the average number of referrals to the University of New Mexico Hospital’s (UNMH) pediatric OMM clinic by 25% by May 2020. Methods: The QI project was designed based on the Model for Improvement. As the first Plan-Do-Study-Act (PDSA) cycle, health care provider trainings on pediatric OMM were implemented in November 2019. A post-presentation survey was used to gather feedback. The primary measure will be average monthly referrals to UNMH’s pediatric osteopathic clinic. Results: Thirty-one providers responded to the survey – most were physicians (DO [39%] or MD [52%]) with(87%). Twenty-three percent had previously referred to OMM clinic. On average, respondents reported an interest of 8.10 (SD 2.47) in referring to the pediatric OMM clinic, on a scale of 0 (no interest at all) to 10 (extremely interested). The average respondent was still not sure about the indications and evidence-base for use of OMM in pediatrics. Some respondents indicated that they would like additional training in OMM techniques, and that the clinic needs more hours/capacity due to a long wait time (\u3e1 month). Conclusion: Health care provider trainings generated interest in referring to the pediatric osteopathic clinic at UNMH. There is a need for additional PDSA cycles related to building provider knowledge and skills and addressing wait time to be seen in OMM clinic. We will monitor referrals as we implement additional PDSAs

Evolving Nano-scale Associative Memories with Memristors

Associative Memories (AMs) are essential building blocks for brain-like intelligent computing with applications in artificial vision, speech recognition, artificial intelligence, and robotics. Computations for such applications typically rely on spatial and temporal associations in the input patterns and need to be robust against noise and incomplete patterns. The conventional method for implementing AMs is through Artificial Neural Networks (ANNs). Improving the density of ANN based on conventional circuit elements poses a challenge as devices reach their physical scalability limits. Furthermore, stored information in AMs is vulnerable to destructive input signals. Novel nano-scale components, such as memristors, represent one solution to the density problem. Memristors are non-linear time-dependent circuit elements with an inherently small form factor. However, novel neuromorphic circuits typically use memristors to replace synapses in conventional ANN circuits. This sub-optimal use is primarily because there is no established design methodology to exploit the memristor\u27s non-linear properties in a more encompassing way. The objective of this thesis is to explore denser and more robust AM designs using memristor networks. We hypothesize that such network AMs will be more area-efficient than the traditional ANN designs if we can use the memristor\u27s non-linear property for spatial and time-dependent temporal association. We have built a comprehensive simulation framework that employs Genetic Programming (GP) to evolve AM circuits with memristors. The framework is based on the ParadisEO metaheuristics API and uses ngspice for the circuit evaluation. Our results show that we can evolve efficient memristor-based networks that have the potential to replace conventional ANNs used for AMs. We obtained AMs that a) can learn spatial and temporal correlation in the input patterns; b) optimize the trade-off between the size and the accuracy of the circuits; and c) are robust against destructive noise in the inputs. This robustness was achieved at the expense of additional components in the network. We have shown that automated circuit discovery is a promising tool for memristor-based circuits. Future work will focus on evolving circuits that can be used as a building block for more complicated intelligent computing architectures

Modification of Hilbert's Space-Filling Curve to Avoid Obstacles: A Robotic Path-Planning Strategy

This paper addresses the problem of exploring a region using the Hilbert's space-filling curve in the presence of obstacles. No prior knowledge of the region being explored is assumed. An online algorithm is proposed which can implement evasive strategies to avoid obstacles comprising a single or two blocked unit squares placed side by side and successfully explore the entire region. The strategies are specified by the change in the waypoint array which robot going to follow. The fractal nature of the Hilbert's space-filling curve has been exploited in proving the validity of the solution

GSK-3 inhibitors induce chromosome instability

<p>Abstract</p> <p>Background</p> <p>Several mechanisms operate during mitosis to ensure accurate chromosome segregation. However, during tumour evolution these mechanisms go awry resulting in chromosome instability. While several lines of evidence suggest that mutations in <it>adenomatous polyposis coli </it>(<it>APC</it>) may promote chromosome instability, at least in colon cancer, the underlying mechanisms remain unclear. Here, we turn our attention to GSK-3 – a protein kinase, which in concert with APC, targets β-catenin for proteolysis – and ask whether GSK-3 is required for accurate chromosome segregation.</p> <p>Results</p> <p>To probe the role of GSK-3 in mitosis, we inhibited GSK-3 kinase activity in cells using a panel of small molecule inhibitors, including SB-415286, AR-A014418, 1-Azakenpaullone and CHIR99021. Analysis of synchronised HeLa cells shows that GSK-3 inhibitors do not prevent G1/S progression or cell division. They do, however, significantly delay mitotic exit, largely because inhibitor-treated cells have difficulty aligning all their chromosomes. Although bipolar spindles form and the majority of chromosomes biorient, one or more chromosomes often remain mono-oriented near the spindle poles. Despite a prolonged mitotic delay, anaphase frequently initiates without the last chromosome aligning, resulting in chromosome non-disjunction. To rule out the possibility of "off-target" effects, we also used RNA interference to selectively repress GSK-3β. Cells deficient for GSK-3β exhibit a similar chromosome alignment defect, with chromosomes clustered near the spindle poles. GSK-3β repression also results in cells accumulating micronuclei, a hallmark of chromosome missegregation.</p> <p>Conclusion</p> <p>Thus, not only do our observations indicate a role for GSK-3 in accurate chromosome segregation, but they also raise the possibility that, if used as therapeutic agents, GSK-3 inhibitors may induce unwanted side effects by inducing chromosome instability.</p