30 research outputs found

    Multiple metal contamination from house paints: consequences of power sanding and paint scraping in New Orleans.

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    Power sanding exterior paint is a common practice during repainting of old houses in New Orleans, Louisiana, that triggers lead poisoning and releases more than Pb. In this study we quantified the Pb, zinc, cadmium, manganese, nickel, copper, cobalt, chromium, and vanadium in exterior paint samples collected from New Orleans homes (n = 31). We used interior dust wipes to compare two exterior house-painting projects. House 1 was measured in response to the plight of a family after a paint contractor power sanded all exterior paint from the weatherboards. The Pb content (approximately 130,000 microg Pb/g) was first realized when the family pet died; the children were hospitalized, the family was displaced, and cleanup costs were high. To determine the quantity of dust generated by power sanding and the benefits of reducing Pb-contaminated dust, we tested a case study house (house 2) for Pb (approximately 90,000 microg/g) before the project was started; the house was then dry scraped and the paint chips were collected. Although the hazards of Pb-based paints are well known, there are other problems as well, because other toxic metals exist in old paints. If house 2 had been power sanded to bare wood like house 1, the repainting project would have released as dust about 7.4 kg Pb, 3.5 kg Zn, 9.7 g Cd, 14.8 g Cu, 8.8 g Mn, 1.5 g Ni, 5.4 g Co, 2.4 g Cr, and 0.3 g V. The total tolerable daily intake (TTDI) for a child under 6 years of age is 6 microg Pb from all sources. Converting 7.4 kg Pb to this scale is vexing--more than 1 billion (10(9)) times the TTDI. Also for perspective, the one-time release of 7.4 x 10(9) microg of Pb dust from sanding compares to 50 x 10(9) microg of Pb dust emitted annually per 0.1 mile (0.16 km) from street traffic during the peak use of leaded gasoline. In this paper, we broaden the discussion to include an array of metals in paint and underscore the need and possibilities for curtailing the release of metal dust

    Sensory Communication

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    Contains table of contents for Section 2, an introduction and reports on twelve research projects.National Institutes of Health Grant 5 R01 DC00117National Institutes of Health Contract 2 P01 DC00361National Institutes of Health Grant 5 R01 DC00126National Institutes of Health Grant R01-DC00270U.S. Air Force - Office of Scientific Research Contract AFOSR-90-0200National Institutes of Health Grant R29-DC00625U.S. Navy - Office of Naval Research Grant N00014-88-K-0604U.S. Navy - Office of Naval Research Grant N00014-91-J-1454U.S. Navy - Office of Naval Research Grant N00014-92-J-1814U.S. Navy - Naval Training Systems Center Contract N61339-93-M-1213U.S. Navy - Naval Training Systems Center Contract N61339-93-C-0055U.S. Navy - Naval Training Systems Center Contract N61339-93-C-0083U.S. Navy - Office of Naval Research Grant N00014-92-J-4005U.S. Navy - Office of Naval Research Grant N00014-93-1-119

    Sensory Communication

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    Contains table of contents for Section 2 and reports on five research projects.National Institutes of Health Contract 2 R01 DC00117National Institutes of Health Contract 1 R01 DC02032National Institutes of Health Contract 2 P01 DC00361National Institutes of Health Contract N01 DC22402National Institutes of Health Grant R01-DC001001National Institutes of Health Grant R01-DC00270National Institutes of Health Grant 5 R01 DC00126National Institutes of Health Grant R29-DC00625U.S. Navy - Office of Naval Research Grant N00014-88-K-0604U.S. Navy - Office of Naval Research Grant N00014-91-J-1454U.S. Navy - Office of Naval Research Grant N00014-92-J-1814U.S. Navy - Naval Air Warfare Center Training Systems Division Contract N61339-94-C-0087U.S. Navy - Naval Air Warfare Center Training System Division Contract N61339-93-C-0055U.S. Navy - Office of Naval Research Grant N00014-93-1-1198National Aeronautics and Space Administration/Ames Research Center Grant NCC 2-77

    Predictions not commands: active inference in the motor system

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    Haptic Rendering with the Toolhandle Haptic Interface

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    This thesis describes the design of the hardware and software for a haptic interface system. A haptic interface allows a human "observer" to explore and interact with a virtual environment using the sense of touch. Haptic interface systems include three main components: the haptic interface (usually an electro-mechanical system capable of exerting forces on a user), a model of the environment to be touched, and a rendering algorithm which unites the first two by generating the feedback forces based on the environment model. This thesis focuses on the first and third of these components: a haptic interface, the MIT-Toolhandle, and haptic rendering algorithms for simulating general real-world virtual environments. The MIT-Toolhandle is a ground-based force-feedback device designed to allow subjects to use tools to interact with virtual environments. One of the difficulties of haptic interfaces is simulating both the human's kinesthetic and tactile system. Tool interactions are interestin..

    Haptic rendering with the Toolhandle haptic interface

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    Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1995.Includes bibliographical references (p. 45-46).by Craig B. Zilles.M.S

    Master/slave Speculative Parallelization And Approximate Code

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    This dissertation describes Master/Slave Speculative Parallelization (MSSP), a novel execution paradigm to improve the execution rate of sequential programs by parallelizing them speculatively for execution on a multiprocessor. In MSSP, one processor---the master---executes an approximate copy of the program to compute values the program's execution is expected to compute. The master's results are then checked by the slave processors by comparing them to the results computed by the original program. This validation is parallelized by cutting the program's execution into tasks. Each slave uses its predicted inputs (as computed by the master) to validate the input predictions of the next task, inductively validating the whole execution
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