62 research outputs found

    The Vibration Ring

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    The vibration ring was conceived as a driveline damping device to prevent structure-borne noise in machines. It has the appearance of a metal ring, and can be installed between any two driveline components like an ordinary mechanical spacer. Damping is achieved using a ring-shaped piezoelectric stack that is poled in the axial direction and connected to an electrical shunt circuit. Surrounding the stack is a metal structure, called the compression cage, which squeezes the stack along its poled axis when excited by radial driveline forces. The stack in turn generates electrical energy, which is either dissipated or harvested using the shunt circuit. Removing energy from the system creates a net damping effect. The vibration ring is much stiffer than traditional damping devices, which allows it to be used in a driveline without disrupting normal operation. In phase 1 of this NASA Seedling Fund project, a combination of design and analysis was used to examine the feasibility of this concept. Several designs were evaluated using solid modeling, finite element analysis, and by creating prototype hardware. Then an analytical model representing the coupled electromechanical response was formulated in closed form. The model was exercised parametrically to examine the stiffness and loss factor spectra of the vibration ring, as well as simulate its damping effect in the context of a simplified driveline model. The results of this work showed that this is a viable mechanism for driveline damping, and provided several lessons for continued development

    Systematic Conservation Planning in the Face of Climate Change: Bet-Hedging on the Columbia Plateau

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    Systematic conservation planning efforts typically focus on protecting current patterns of biodiversity. Climate change is poised to shift species distributions, reshuffle communities, and alter ecosystem functioning. In such a dynamic environment, lands selected to protect today's biodiversity may fail to do so in the future. One proposed approach to designing reserve networks that are robust to climate change involves protecting the diversity of abiotic conditions that in part determine species distributions and ecological processes. A set of abiotically diverse areas will likely support a diversity of ecological systems both today and into the future, although those two sets of systems might be dramatically different. Here, we demonstrate a conservation planning approach based on representing unique combinations of abiotic factors. We prioritize sites that represent the diversity of soils, topographies, and current climates of the Columbia Plateau. We then compare these sites to sites prioritized to protect current biodiversity. This comparison highlights places that are important for protecting both today's biodiversity and the diversity of abiotic factors that will likely determine biodiversity patterns in the future. It also highlights places where a reserve network designed solely to protect today's biodiversity would fail to capture the diversity of abiotic conditions and where such a network could be augmented to be more robust to climate-change impacts

    An analysis-ready and quality controlled resource for pediatric brain white-matter research

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    We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets

    Author Correction: An analysis-ready and quality controlled resource for pediatric brain white-matter research

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