3,828 research outputs found

    Patterns in the Sand: Mathematical Exploration of Chladni Patterns

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    Chladni Patterns are formed when sand settles at the nodes of two dimensional standing waves, excited on a metallic plate which is driven at a resonant frequency. By considering a two-dimensional rectangular membrane with fixed boundary and constant density as an idealized model of the metal plate, a formula for predicting the Chladni Patterns that will form at certain frequencies can be found. In addition to mathematically exploring these mysterious patterns, I have created my own “Chladni Patterns” in the lab

    Hacer frente a los desafíos de una fuerza laboral que envejece con el uso de tecnologías usables y la auto-cuantificación

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    The world's population is aging at an unprecedented rate, this demographic shift will change all aspects of life, including work. The aging of the worforce and a higher percentage of workers who will work past traditional retirement years presents significant challenges and opportunities for employers. Older workers are a valuable resource, but in order to ensure they stay in good health, prevention will be key. Wearable technologies are quickly becoming ubiquitous, individuals are turning to them to monitor health, activities and hundreds of other quantifiable occurences. Wearable technologies could provide a new means for employers to tackle the challenges associated with an aging workforce by creating a wide spectrum of opportunities to intervene in terms of aging employees and extend their working lives by keeping them safe and healthy through prevention. Employers are already making standing desks available, and encouraging lunch time exercise, is it feasible for Wearables to make the jump from a tool for individuals to a method for employers to ensure better health, well-being and safety for their employees? The aim of this work is to lay out the implications for such interventions with Wearable technologies (monitoring health and well-being, oversight and safety, and mentoring and training) and challenges (privacy, acceptability, and scalability). While an ageing population presents significant challenges, including an aging work force, this demographic change should be seen, instead, as an opportunity rethink and innovate workplace health and take advantage of the experience of older workers. The Quantified-Self and Wearables can leverage interventions to improve employees’ health, safety and well-being.La población mundial está envejeciendo a un ritmo sin precedentes. El envejecimiento y un mayor porcentaje de trabajadores que trabajan más allá de los años de jubilación presentan importantes desafíos y oportunidades. Los trabajadores mayores son un recurso valioso, pero a fin de garantizar que permanezcan en buen estado de salud, la prevención será la clave. Tecnologías portátiles, ó wearables, están proporcionando un medio para hacer frente a el envejecimiento mediante la creación de un amplio espectro de oportunidades para intervenir y para prolongar la vida laboral de los colaboradores, mantenendoles seguros y saludables. El objetivo de este trabajo es exponer las implicaciones de este tipo de intervenciones con wearables (Control de salud, vigilancia, seguridad, y formación) y los desafíos (privacidad, aceptabilidad y escalabilidad). Los wearables pueden aprovechar y fortalecer las intervenciones para mejorar la salud, seguridad y el bienestar de los empleados.Martin Lavallière was supported by a postdoctoral research grant - Recherche en sécurité routière : Fonds de recherche du Québec - Société et culture (FRQSC), Société de l'assurance automobile du Québec (SAAQ), Fonds de recherche du Québec - Santé (FRQS). This work was partially developed with the financial support of the Luso-American Development Foundation - FLAD, through the research grant ref. rv14022, and of the MIT Portugal Program

    Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

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    The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project \emph{Gravity Spy} has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program

    CPA\u27s Guide to Long-Term Care Planning

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    https://egrove.olemiss.edu/aicpa_guides/2562/thumbnail.jp

    Low Frequency Tilt Seismology with a Precision Ground Rotation Sensor

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    We describe measurements of the rotational component of teleseismic surface waves using an inertial high-precision ground-rotation-sensor installed at the LIGO Hanford Observatory (LHO). The sensor has a noise floor of 0.4 nrad/Hz/ \sqrt{\rm Hz} at 50 mHz and a translational coupling of less than 1 μ\murad/m enabling translation-free measurement of small rotations. We present observations of the rotational motion from Rayleigh waves of six teleseismic events from varied locations and with magnitudes ranging from M6.7 to M7.9. These events were used to estimate phase dispersion curves which shows agreement with a similar analysis done with an array of three STS-2 seismometers also located at LHO

    False positive probabilties for all Kepler Objects of Interest: 1284 newly validated planets and 428 likely false positives

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    We present astrophysical false positive probability calculations for every Kepler Object of Interest (KOI)---the first large-scale demonstration of a fully automated transiting planet validation procedure. Out of 7056 KOIs, we determine that 1935 have probabilities <1% to be astrophysical false positives, and thus may be considered validated planets. 1284 of these have not yet been validated or confirmed by other methods. In addition, we identify 428 KOIs likely to be false positives that have not yet been identified as such, though some of these may be a result of unidentified transit timing variations. A side product of these calculations is full stellar property posterior samplings for every host star, modeled as single, binary, and triple systems. These calculations use 'vespa', a publicly available Python package able to be easily applied to any transiting exoplanet candidate.Comment: 20 pages, 8 figures. Published in ApJ. Instructions to reproduce results can be found at https://github.com/timothydmorton/koi-fp

    Determination of Uncertainties for Analytically Derived Material Properties to Be Used in Monte Carlo Based Orion Heatshield Sizing

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    Ablative materials are often used for spacecraft heatshields to protect underlying structures from the extreme environments associated with atmospheric reentry. NASA's Orion EM-1 capsule has been designed to use a molded Avcoat material system. In order to determine the required heatshield thickness, a Monte Carlo approach to the sizing process was proposed. To perform the Monte Carlo simulation, statistical uncertainties on all material property input parameters were required. Obtaining these values for measured properties is straightforward, however input parameters that are derived analytically have historically used uncertainties based on engineering judgment. A MATLAB program was created to use laboratory generated thermogravimetric analysis (TGA) data to calculate uncertainties on the Arrhenius parameters for molded Avcoat. Uncertainties associated with the normalized ablation rate and pyrolysis gas enthalpy were also generated using a wrapper script and the ACE code. These uncertainties could then be tied directly to measured values of individual elemental constituents. The resulting uncertainty values will allow for a probabilistic sizing approach on molded Avcoat with a higher level of confidence in the input parameters
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