12 results
Outbreak of postpartum group a Streptococcus infections on a labor and delivery unit
- Michael Haden, Christina Liscynesky, Nora Colburn, Justin Smyer, Kimberly Malcolm, Iahn Gonsenhauser, Kara M. Rood, Patrick Schneider, Michele Hardgrow, Preeti Pancholi, Keelie Thomas, Anita Cygnor, Oluseun Aluko, Elizabeth Koch, Naomi Tucker, Jade Mowery, Eric Brandt, Katie Cibulskas, Marika Mohr, Srinivas Nanduri, Sopio Chochua, Shandra R. Day
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- Journal:
- Infection Control & Hospital Epidemiology , First View
- Published online by Cambridge University Press:
- 14 May 2024, pp. 1-3
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A healthcare-associated group A Streptococcus outbreak involving six patients, four healthcare workers, and one household contact occurred in the labor and delivery unit of an academic medical center. Isolates were highly related by whole genome sequencing. Infection prevention measures, healthcare worker screening, and chemoprophylaxis of those colonized halted further transmission.
Evolution of a functional taxonomy of career pathways for biomedical trainees
- Ambika Mathur, Patrick Brandt, Roger Chalkley, Laura Daniel, Patricia Labosky, Constance Stayart, Frederick J. Meyers
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- Journal:
- Journal of Clinical and Translational Science / Volume 2 / Issue 2 / April 2018
- Published online by Cambridge University Press:
- 08 August 2018, pp. 63-65
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Several reports have shown that doctoral and postdoctoral trainees in biomedical research pursue diverse careers that advance science meaningful to society. Several groups have proposed 3-tier career taxonomy to showcase these outcomes. This 3-tier taxonomy will be a valuable resource for institutions committed to greater transparency in reporting outcomes, to not only be transparent in reporting their own institutional data but also to lend greater power to a central repository.
The Prevalence and Severity of Underreporting Bias in Machine- and Human-Coded Data
- Benjamin E. Bagozzi, Patrick T. Brandt, John R. Freeman, Jennifer S. Holmes, Alisha Kim, Agustin Palao Mendizabal, Carly Potz-Nielsen
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- Journal:
- Political Science Research and Methods / Volume 7 / Issue 3 / July 2019
- Published online by Cambridge University Press:
- 05 March 2018, pp. 641-649
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Textual data are plagued by underreporting bias. For example, news sources often fail to report human rights violations. Cook et al. propose a multi-source estimator to gauge, and to account for, the underreporting of state repression events within human codings of news texts produced by the Agence France-Presse and Associated Press. We evaluate this estimator with Monte Carlo experiments, and then use it to compare the prevalence and seriousness of underreporting when comparable texts are machine coded and recorded in the World-Integrated Crisis Early Warning System dataset. We replicate Cook et al.’s investigation of human-coded state repression events with our machine-coded events, and validate both models against an external measure of human rights protections in Africa. We then use the Cook et al. estimator to gauge the seriousness and prevalence of underreporting in machine and human-coded event data on human rights violations in Colombia. We find in both applications that machine-coded data are as valid as human-coded data.
Modeling Macro-Political Dynamics
- Patrick T. Brandt, John R. Freeman
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- Journal:
- Political Analysis / Volume 17 / Issue 2 / Spring 2009
- Published online by Cambridge University Press:
- 04 January 2017, pp. 113-142
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Analyzing macro-political processes is complicated by four interrelated problems: model scale, endogeneity, persistence, and specification uncertainty. These problems are endemic in the study of political economy, public opinion, international relations, and other kinds of macro-political research. We show how a Bayesian structural time series approach addresses them. Our illustration is a structurally identified, nine-equation model of the U.S. political-economic system. It combines key features of the model of Erikson, MacKuen, and Stimson (2002) of the American macropolity with those of a leading macroeconomic model of the United States (Sims and Zha, 1998; Leeper, Sims, and Zha, 1996). This Bayesian structural model, with a loosely informed prior, yields the best performance in terms of model fit and dynamics. This model 1) confirms existing results about the countercyclical nature of monetary policy (Williams 1990); 2) reveals informational sources of approval dynamics: innovations in information variables affect consumer sentiment and approval and the impacts on consumer sentiment feed-forward into subsequent approval changes; 3) finds that the real economy does not have any major impacts on key macropolity variables; and 4) concludes, contrary to Erikson, MacKuen, and Stimson (2002), that macropartisanship does not depend on the evolution of the real economy in the short or medium term and only very weakly on informational variables in the long term.
Advances in Bayesian Time Series Modeling and the Study of Politics: Theory Testing, Forecasting, and Policy Analysis
- Patrick T. Brandt, John R. Freeman
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- Journal:
- Political Analysis / Volume 14 / Issue 1 / Winter 2006
- Published online by Cambridge University Press:
- 04 January 2017, pp. 1-36
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Bayesian approaches to the study of politics are increasingly popular. But Bayesian approaches to modeling multiple time series have not been critically evaluated. This is in spite of the potential value of these models in international relations, political economy, and other fields of our discipline. We review recent developments in Bayesian multi-equation time series modeling in theory testing, forecasting, and policy analysis. Methods for constructing Bayesian measures of uncertainty of impulse responses (Bayesian shape error bands) are explained. A reference prior for these models that has proven useful in short- and medium-term forecasting in macroeconomics is described. Once modified to incorporate our experience analyzing political data and our theories, this prior can enhance our ability to forecast over the short and medium terms complex political dynamics like those exhibited by certain international conflicts. In addition, we explain how contingent Bayesian forecasts can be constructed, contingent Bayesian forecasts that embody policy counterfactuals. The value of these new Bayesian methods is illustrated in a reanalysis of the Israeli-Palestinian conflict of the 1980s.
A Linear Poisson Autoregressive Model: The Poisson AR(p) Model
- Patrick T. Brandt, John T. Williams
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- Journal:
- Political Analysis / Volume 9 / Issue 2 / 2001
- Published online by Cambridge University Press:
- 04 January 2017, pp. 164-184
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Time series of event counts are common in political science and other social science applications. Presently, there are few satisfactory methods for identifying the dynamics in such data and accounting for the dynamic processes in event counts regression. We address this issue by building on earlier work for persistent event counts in the Poisson exponentially weighted moving-average model (PEWMA) of Brandt et al. (American Journal of Political Science 44(4):823–843, 2000). We develop an alternative model for stationary mean reverting data, the Poisson autoregressive model of order p, or PAR(p) model. Issues of identification and model selection are also considered. We then evaluate the properties of this model and present both Monte Carlo evidence and applications to illustrate.
A Bayesian Poisson Vector Autoregression Model
- Patrick T. Brandt, Todd Sandler
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- Journal:
- Political Analysis / Volume 20 / Issue 3 / Summer 2012
- Published online by Cambridge University Press:
- 04 January 2017, pp. 292-315
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Multivariate count models are rare in political science despite the presence of many count time series. This article develops a new Bayesian Poisson vector autoregression model that can characterize endogenous dynamic counts with no restrictions on the contemporaneous correlations. Impulse responses, decomposition of the forecast errors, and dynamic multiplier methods for the effects of exogenous covariate shocks are illustrated for the model. Two full illustrations of the model, its interpretations, and results are presented. The first example is a dynamic model that reanalyzes the patterns and predictors of superpower rivalry events. The second example applies the model to analyze the dynamics of transnational terrorist targeting decisions between 1968 and 2008. The latter example's results have direct implications for contemporary policy about terrorists' targeting that are both novel and innovative in the study of terrorism.
3 - Generating Political Event Data in Near Real Time: Opportunities and Challenges
- from PART 1 - COMPUTATIONAL SOCIAL SCIENCE TOOLS
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- By John Beieler, Pennsylvania State University, john.b30@gmail.com, Patrick T. Brandt, University of Texas, Dallas, pbrandt@utdallas.edu, Andrew Halterman, Caerus Associates, ahalterman0@gmail.com, Philip A. Schrodt, Parus Analytical Systems, schrodt735@gmail.com, Erin M. Simpson, Caerus Associates, emsimpson@gmail.com
- Edited by R. Michael Alvarez, California Institute of Technology
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- Book:
- Computational Social Science
- Published online:
- 05 March 2016
- Print publication:
- 07 March 2016, pp 98-120
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Summary
INTRODUCTION
Political event data are records of interactions among political actors using common codes for actors and actions, allowing for the aggregate analysis of political behaviors. These data include both material interactions between political entities and verbal statements. Such data are common in international relations, recording the spoken or direct actions between nation-states and other political entities. Event data can be generated through either human-coded or machinebased methods. Human-coded event data efforts continue to dominate research on global protests and social movements, although data sets in international relations have led the movement toward automated coding. While humans are better able to extract the meaning in sentences using background knowledge and innate abilities for dealing with complex grammatical constructions, human coding is dramatically more labor and time intensive than machinecoding approaches for anything but small or one-off data sets. Machine-coded methods can attain 70–80% accuracy when compared to a human-coded “gold standard,” which is comparable to, and in some cases exceeds, the intercoder reliability of human coding (King and Lowe, 2004). This makes the machine-coded methods quite scalable in terms of costs and time and thus attractive to academic, government, and private sector researchers.
King (2011) notes that the ability to code and process political texts to generate records like event data will be de rigueur in the later part of the 21st century. Machine-readable text about politics, including news reports, speeches, press conferences, and intelligence reports, are already the basis of many political analyses. The ever-increasing availability of such texts presents both opportunities and challenges because they are a form of “big data.” Even processing just the lead sentences of Reuters and Agence France-Presse (AFP) news reports for the Levant from 1979–2011 generates more than 140,000 distinct time-series records (http://eventdata.parusanalytics.com/data.dir/levant.html), and these sentences could also be processed as a much larger set of network relationships. One recent effort to expand event data collection outside of this geographical region – albeit without the event de-duplication found in most event data sets – has generated nearly a quarter of a billion records. Extrapolating from our coding experience with the Levant and our initial experiments with the EL:DIABLO coding system described later, we estimate that a data collection with duplication controls like that for the Levant data set will generate around 4,000 to 8,000 distinct records per day for the entire globe.
Contributors
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- By Mitchell Aboulafia, Frederick Adams, Marilyn McCord Adams, Robert M. Adams, Laird Addis, James W. Allard, David Allison, William P. Alston, Karl Ameriks, C. Anthony Anderson, David Leech Anderson, Lanier Anderson, Roger Ariew, David Armstrong, Denis G. Arnold, E. J. Ashworth, Margaret Atherton, Robin Attfield, Bruce Aune, Edward Wilson Averill, Jody Azzouni, Kent Bach, Andrew Bailey, Lynne Rudder Baker, Thomas R. Baldwin, Jon Barwise, George Bealer, William Bechtel, Lawrence C. Becker, Mark A. Bedau, Ernst Behler, José A. Benardete, Ermanno Bencivenga, Jan Berg, Michael Bergmann, Robert L. Bernasconi, Sven Bernecker, Bernard Berofsky, Rod Bertolet, Charles J. Beyer, Christian Beyer, Joseph Bien, Joseph Bien, Peg Birmingham, Ivan Boh, James Bohman, Daniel Bonevac, Laurence BonJour, William J. Bouwsma, Raymond D. Bradley, Myles Brand, Richard B. Brandt, Michael E. Bratman, Stephen E. Braude, Daniel Breazeale, Angela Breitenbach, Jason Bridges, David O. Brink, Gordon G. Brittan, Justin Broackes, Dan W. Brock, Aaron Bronfman, Jeffrey E. Brower, Bartosz Brozek, Anthony Brueckner, Jeffrey Bub, Lara Buchak, Otavio Bueno, Ann E. Bumpus, Robert W. Burch, John Burgess, Arthur W. Burks, Panayot Butchvarov, Robert E. Butts, Marina Bykova, Patrick Byrne, David Carr, Noël Carroll, Edward S. Casey, Victor Caston, Victor Caston, Albert Casullo, Robert L. Causey, Alan K. L. Chan, Ruth Chang, Deen K. Chatterjee, Andrew Chignell, Roderick M. Chisholm, Kelly J. Clark, E. J. Coffman, Robin Collins, Brian P. Copenhaver, John Corcoran, John Cottingham, Roger Crisp, Frederick J. Crosson, Antonio S. Cua, Phillip D. Cummins, Martin Curd, Adam Cureton, Andrew Cutrofello, Stephen Darwall, Paul Sheldon Davies, Wayne A. Davis, Timothy Joseph Day, Claudio de Almeida, Mario De Caro, Mario De Caro, John Deigh, C. F. Delaney, Daniel C. Dennett, Michael R. DePaul, Michael Detlefsen, Daniel Trent Devereux, Philip E. Devine, John M. Dillon, Martin C. Dillon, Robert DiSalle, Mary Domski, Alan Donagan, Paul Draper, Fred Dretske, Mircea Dumitru, Wilhelm Dupré, Gerald Dworkin, John Earman, Ellery Eells, Catherine Z. Elgin, Berent Enç, Ronald P. Endicott, Edward Erwin, John Etchemendy, C. Stephen Evans, Susan L. Feagin, Solomon Feferman, Richard Feldman, Arthur Fine, Maurice A. Finocchiaro, William FitzPatrick, Richard E. Flathman, Gvozden Flego, Richard Foley, Graeme Forbes, Rainer Forst, Malcolm R. Forster, Daniel Fouke, Patrick Francken, Samuel Freeman, Elizabeth Fricker, Miranda Fricker, Michael Friedman, Michael Fuerstein, Richard A. Fumerton, Alan Gabbey, Pieranna Garavaso, Daniel Garber, Jorge L. A. Garcia, Robert K. Garcia, Don Garrett, Philip Gasper, Gerald Gaus, Berys Gaut, Bernard Gert, Roger F. Gibson, Cody Gilmore, Carl Ginet, Alan H. Goldman, Alvin I. Goldman, Alfonso Gömez-Lobo, Lenn E. Goodman, Robert M. Gordon, Stefan Gosepath, Jorge J. E. Gracia, Daniel W. Graham, George A. Graham, Peter J. Graham, Richard E. Grandy, I. Grattan-Guinness, John Greco, Philip T. Grier, Nicholas Griffin, Nicholas Griffin, David A. Griffiths, Paul J. Griffiths, Stephen R. Grimm, Charles L. Griswold, Charles B. Guignon, Pete A. Y. Gunter, Dimitri Gutas, Gary Gutting, Paul Guyer, Kwame Gyekye, Oscar A. Haac, Raul Hakli, Raul Hakli, Michael Hallett, Edward C. Halper, Jean Hampton, R. James Hankinson, K. R. Hanley, Russell Hardin, Robert M. Harnish, William Harper, David Harrah, Kevin Hart, Ali Hasan, William Hasker, John Haugeland, Roger Hausheer, William Heald, Peter Heath, Richard Heck, John F. Heil, Vincent F. Hendricks, Stephen Hetherington, Francis Heylighen, Kathleen Marie Higgins, Risto Hilpinen, Harold T. Hodes, Joshua Hoffman, Alan Holland, Robert L. Holmes, Richard Holton, Brad W. Hooker, Terence E. Horgan, Tamara Horowitz, Paul Horwich, Vittorio Hösle, Paul Hoβfeld, Daniel Howard-Snyder, Frances Howard-Snyder, Anne Hudson, Deal W. Hudson, Carl A. Huffman, David L. Hull, Patricia Huntington, Thomas Hurka, Paul Hurley, Rosalind Hursthouse, Guillermo Hurtado, Ronald E. Hustwit, Sarah Hutton, Jonathan Jenkins Ichikawa, Harry A. Ide, David Ingram, Philip J. Ivanhoe, Alfred L. Ivry, Frank Jackson, Dale Jacquette, Joseph Jedwab, Richard Jeffrey, David Alan Johnson, Edward Johnson, Mark D. Jordan, Richard Joyce, Hwa Yol Jung, Robert Hillary Kane, Tomis Kapitan, Jacquelyn Ann K. Kegley, James A. Keller, Ralph Kennedy, Sergei Khoruzhii, Jaegwon Kim, Yersu Kim, Nathan L. King, Patricia Kitcher, Peter D. Klein, E. D. Klemke, Virginia Klenk, George L. Kline, Christian Klotz, Simo Knuuttila, Joseph J. Kockelmans, Konstantin Kolenda, Sebastian Tomasz Kołodziejczyk, Isaac Kramnick, Richard Kraut, Fred Kroon, Manfred Kuehn, Steven T. Kuhn, Henry E. Kyburg, John Lachs, Jennifer Lackey, Stephen E. Lahey, Andrea Lavazza, Thomas H. Leahey, Joo Heung Lee, Keith Lehrer, Dorothy Leland, Noah M. Lemos, Ernest LePore, Sarah-Jane Leslie, Isaac Levi, Andrew Levine, Alan E. Lewis, Daniel E. Little, Shu-hsien Liu, Shu-hsien Liu, Alan K. L. Chan, Brian Loar, Lawrence B. Lombard, John Longeway, Dominic McIver Lopes, Michael J. Loux, E. J. Lowe, Steven Luper, Eugene C. Luschei, William G. Lycan, David Lyons, David Macarthur, Danielle Macbeth, Scott MacDonald, Jacob L. Mackey, Louis H. Mackey, Penelope Mackie, Edward H. Madden, Penelope Maddy, G. B. Madison, Bernd Magnus, Pekka Mäkelä, Rudolf A. Makkreel, David Manley, William E. Mann (W.E.M.), Vladimir Marchenkov, Peter Markie, Jean-Pierre Marquis, Ausonio Marras, Mike W. Martin, A. P. Martinich, William L. McBride, David McCabe, Storrs McCall, Hugh J. McCann, Robert N. McCauley, John J. McDermott, Sarah McGrath, Ralph McInerny, Daniel J. McKaughan, Thomas McKay, Michael McKinsey, Brian P. McLaughlin, Ernan McMullin, Anthonie Meijers, Jack W. Meiland, William Jason Melanson, Alfred R. Mele, Joseph R. Mendola, Christopher Menzel, Michael J. Meyer, Christian B. Miller, David W. Miller, Peter Millican, Robert N. Minor, Phillip Mitsis, James A. Montmarquet, Michael S. Moore, Tim Moore, Benjamin Morison, Donald R. Morrison, Stephen J. Morse, Paul K. Moser, Alexander P. D. Mourelatos, Ian Mueller, James Bernard Murphy, Mark C. Murphy, Steven Nadler, Jan Narveson, Alan Nelson, Jerome Neu, Samuel Newlands, Kai Nielsen, Ilkka Niiniluoto, Carlos G. Noreña, Calvin G. Normore, David Fate Norton, Nikolaj Nottelmann, Donald Nute, David S. Oderberg, Steve Odin, Michael O’Rourke, Willard G. Oxtoby, Heinz Paetzold, George S. Pappas, Anthony J. Parel, Lydia Patton, R. P. Peerenboom, Francis Jeffry Pelletier, Adriaan T. Peperzak, Derk Pereboom, Jaroslav Peregrin, Glen Pettigrove, Philip Pettit, Edmund L. Pincoffs, Andrew Pinsent, Robert B. Pippin, Alvin Plantinga, Louis P. Pojman, Richard H. Popkin, John F. Post, Carl J. Posy, William J. Prior, Richard Purtill, Michael Quante, Philip L. Quinn, Philip L. Quinn, Elizabeth S. Radcliffe, Diana Raffman, Gerard Raulet, Stephen L. Read, Andrews Reath, Andrew Reisner, Nicholas Rescher, Henry S. Richardson, Robert C. Richardson, Thomas Ricketts, Wayne D. Riggs, Mark Roberts, Robert C. Roberts, Luke Robinson, Alexander Rosenberg, Gary Rosenkranz, Bernice Glatzer Rosenthal, Adina L. Roskies, William L. Rowe, T. M. Rudavsky, Michael Ruse, Bruce Russell, Lilly-Marlene Russow, Dan Ryder, R. M. Sainsbury, Joseph Salerno, Nathan Salmon, Wesley C. Salmon, Constantine Sandis, David H. Sanford, Marco Santambrogio, David Sapire, Ruth A. Saunders, Geoffrey Sayre-McCord, Charles Sayward, James P. Scanlan, Richard Schacht, Tamar Schapiro, Frederick F. Schmitt, Jerome B. Schneewind, Calvin O. Schrag, Alan D. Schrift, George F. Schumm, Jean-Loup Seban, David N. Sedley, Kenneth Seeskin, Krister Segerberg, Charlene Haddock Seigfried, Dennis M. Senchuk, James F. Sennett, William Lad Sessions, Stewart Shapiro, Tommie Shelby, Donald W. Sherburne, Christopher Shields, Roger A. Shiner, Sydney Shoemaker, Robert K. Shope, Kwong-loi Shun, Wilfried Sieg, A. John Simmons, Robert L. Simon, Marcus G. Singer, Georgette Sinkler, Walter Sinnott-Armstrong, Matti T. Sintonen, Lawrence Sklar, Brian Skyrms, Robert C. Sleigh, Michael Anthony Slote, Hans Sluga, Barry Smith, Michael Smith, Robin Smith, Robert Sokolowski, Robert C. Solomon, Marta Soniewicka, Philip Soper, Ernest Sosa, Nicholas Southwood, Paul Vincent Spade, T. L. S. Sprigge, Eric O. Springsted, George J. Stack, Rebecca Stangl, Jason Stanley, Florian Steinberger, Sören Stenlund, Christopher Stephens, James P. Sterba, Josef Stern, Matthias Steup, M. A. Stewart, Leopold Stubenberg, Edith Dudley Sulla, Frederick Suppe, Jere Paul Surber, David George Sussman, Sigrún Svavarsdóttir, Zeno G. Swijtink, Richard Swinburne, Charles C. Taliaferro, Robert B. Talisse, John Tasioulas, Paul Teller, Larry S. Temkin, Mark Textor, H. S. Thayer, Peter Thielke, Alan Thomas, Amie L. Thomasson, Katherine Thomson-Jones, Joshua C. Thurow, Vzalerie Tiberius, Terrence N. Tice, Paul Tidman, Mark C. Timmons, William Tolhurst, James E. Tomberlin, Rosemarie Tong, Lawrence Torcello, Kelly Trogdon, J. D. Trout, Robert E. Tully, Raimo Tuomela, John Turri, Martin M. Tweedale, Thomas Uebel, Jennifer Uleman, James Van Cleve, Harry van der Linden, Peter van Inwagen, Bryan W. Van Norden, René van Woudenberg, Donald Phillip Verene, Samantha Vice, Thomas Vinci, Donald Wayne Viney, Barbara Von Eckardt, Peter B. M. Vranas, Steven J. Wagner, William J. Wainwright, Paul E. Walker, Robert E. Wall, Craig Walton, Douglas Walton, Eric Watkins, Richard A. Watson, Michael V. Wedin, Rudolph H. Weingartner, Paul Weirich, Paul J. Weithman, Carl Wellman, Howard Wettstein, Samuel C. Wheeler, Stephen A. White, Jennifer Whiting, Edward R. Wierenga, Michael Williams, Fred Wilson, W. Kent Wilson, Kenneth P. Winkler, John F. Wippel, Jan Woleński, Allan B. Wolter, Nicholas P. Wolterstorff, Rega Wood, W. Jay Wood, Paul Woodruff, Alison Wylie, Gideon Yaffe, Takashi Yagisawa, Yutaka Yamamoto, Keith E. Yandell, Xiaomei Yang, Dean Zimmerman, Günter Zoller, Catherine Zuckert, Michael Zuckert, Jack A. Zupko (J.A.Z.)
- Edited by Robert Audi, University of Notre Dame, Indiana
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- Book:
- The Cambridge Dictionary of Philosophy
- Published online:
- 05 August 2015
- Print publication:
- 27 April 2015, pp ix-xxx
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Contributors
- Edited by Susan Meld Shell, Boston College, Massachusetts, Richard Velkley, Tulane University, Louisiana
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- Book:
- Kant's <I>Observations</I> and <I>Remarks</I>
- Published online:
- 05 June 2012
- Print publication:
- 24 May 2012, pp viii-xi
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On the convectively unstable nature of optimal streaks in boundary layers
- LUCA BRANDT, CARLO COSSU, JEAN-MARC CHOMAZ, PATRICK HUERRE, DAN S. HENNINGSON
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- Journal of Fluid Mechanics / Volume 485 / 25 May 2003
- Published online by Cambridge University Press:
- 24 June 2003, pp. 221-242
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The objective of the study is to determine the absolute/convective nature of the secondary instability experienced by finite-amplitude streaks in the flat-plate boundary layer. A family of parallel streaky base flows is defined by extracting velocity profiles from direct numerical simulations of nonlinearly saturated optimal streaks. The computed impulse response of the streaky base flows is then determined as a function of streak amplitude and streamwise station. Both the temporal and spatio-temporal instability properties are directly retrieved from the impulse response wave packet, without solving the dispersion relation or applying the pinching point criterion in the complex wavenumber plane. The instability of optimal streaks is found to be unambiguously convective for all streak amplitudes and streamwise stations. It is more convective than the Blasius boundary layer in the absence of streaks; the trailing edge-velocity of a Tollmien–Schlichting wave packet in the Blasius boundary layer is around 35% of the free-stream velocity, while that of the wave packet riding on the streaky base flow is around 70%. This is because the streak instability is primarily induced by the spanwise shear and the associated Reynolds stress production term is located further away from the wall, in a larger velocity region, than for the Tollmien–Schlichting instability. The streak impulse response consists of the sinuous mode of instability triggered by the spanwise wake-like profile, as confirmed by comparing the numerical results with the absolute/convective instability properties of the family of two-dimensional wakes introduced by Monkewitz (1988). The convective nature of the secondary streak instability implies that the type of bypass transition studied here involves streaks that behave as amplifiers of external noise.
Why Does a Light Bulb Burn Out?
- Michael J. McKelvy, P. Mitan, Kirsten Hintze, Eric Patrick, K. Allagadda, B.L. Ramakrishna, Carrie Denny, Brandt Pryor, A.V.G. Chizmeshya, V. Pizziconi
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- Journal:
- MRS Online Proceedings Library Archive / Volume 632 / 2000
- Published online by Cambridge University Press:
- 01 February 2011, HH7.2
- Print publication:
- 2000
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The online educational module “Why Does a Light Bulb Burn Out?” is an inquiry-based introduction to the concepts of materials and material properties through the interactive exploration of the life of the incandescent light bulb that students use every day. The module offers an exploration of the history of the light bulb, its components, and important filament properties. Students discover the relationship between temperature and incandescence, along with electrical power and resistance through interactive Java applets. Then students “invent” their own filaments through virtual temperature, performance and longevity tests of a variety of candidate materials. Next, students follow the filament aging process using scanning-electron/atomic-force microscopy images. The module culminates in students designing their own materials experiments using SPM Live! online at http://invsee.asu.edu. Student evaluations indicate students both enjoy and learn effectively using the module.