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Environmental Management 1977年第1期1卷 67-96页

作者： Frankenfeld, John W. Schulz, Wolfgang McMurty, George J. Petersen, Gary W. May, G. A. Hering, F. S. Schwartz, J. I. Heywood, J. B. Chigier, N. A. Grohse, E. W. Walker, J. D. Colwell, R. R. Petrakis, L. Pergament, H. S. Thorpe, R. D. Schoepf, Richard W. Krzyczkowski, Roman Henneman, Suzanne S. Hudson, Charles L. Putnam, Evelyn S. Thiesen, Donna J. Parks, G. A. McCarty, Perry L. Leckie, J. O. Schrumpf, Barry J. Simonson, G. H. Paine, D. P. Lawrence, R. D. Pyott, W. T. Leh, M. Elders, W. Combs, J. Caplen, T. Harrison, F. L. Wong, K. M. Heft., R. E. Charnell, Robert L. Lehmann, Edward J. Mallon, Lawrence G. Hatfield, Cecile Adams, Gerald H. Johanning, James Talvitie, Antti Noll, Kenneth E. Miller, Terry Smiarowski, Joseph F. Willis, Cleve E. Foster, John H. Schlesinger, Benjamin Daetz, Douglas Lear, Donald U. Smith, Mona F. Hundemann, Audrey S. Crockett, Pernell W. Werner, Kirk G. Carroll, Thomas E. Maase, David L. Genco, Joseph E. Ifeadi, Christopher N. Lowman, F. G. Christensen, S. W. Van Winkle, W. Mattice, J. S. Harrison, Elizabeth A. Barker, James C. Chesness, Jerry L. Smith, Ralph E. Shaheeen, Donald G. Raney, R. Keith Borton, T. Wezernak, C. T. Raney, R. K. Sherwani, Jabbor K. Moreau, David H. Eisenberg, Norman A. Lynch, Cornelius J. Breeding, Roger J. Johnson, J. D. Foster, K. E. Mouat, D. A. Clark, R. Hyden, John William Owen, Wilfred Bayfield, Neil G. Barrow, Graham C. Stolz, Stephanie B. Wienckowski, Louis A. Brown, Betram S. Keyfitz, Nathan Wilson, W. L. Newman, Peter W. G. Bammi, Deepak Bammi, Dalip Goddard, James E. Chisholm, Tony Walsh, Cliff Brennan, Geoffrey Thompson, K. S. Richardson, R. Jensen, Clayton E. Brown, Dail W. Mirabito, John A. Cowing, Thomas G. Binghamton, Suny Siehl, George H. Albrecht, O. W. Alexander, Ariel Barde, Jean -Philippe Darby, William P. McMichael, Francis Clay Dunlap, Robert W. Muckleston, Keith W. Frankenhoff, Charles A. Giulini, Lorenzo T. Wyatt, T. Black, Peter E. Keating, William Thomas Leonard, M. E. Fisher, E. L. Brunelle, M. F. Dickinson, J. E. Pethig, Rudiger Clapham Exxon Research & Engineering Co. Linden Government Research Lab Coast Guard Washington D.C. Department of Transportation Washington D.C. Office For Remote Sensing of Earth Resources. NASA Earth Resources Survey Program Pennsylvania State University Washington D.C. Department of the Navy Washington D.C. Cambridge. Dept. of Mechanical Engineering Massachusetts Inst. of Technology USA Huntsville. School of Graduate Studies and Research Alabama Univ. USA Dept. of Microbiology. Office of Naval Research Maryland Univ. Arlington Research and Development Co. Pittsburgh AeroChem Research Labs. Inc. Princeton Dept. of the Interior Office of Library Services. Bibliographic Series (Final) Washington D.C. Interplan Corp. Santa Barbara Urban Mass Transportation Adm. Washington D.C. Naval Underwater Systems Center Newport Calif. Mercury Project. National Science Found Stanford Univ Washington D.C. Div. of Advanced Environmental Research & Tech. USA Corvallis. NASA Earth Resources Survey Program Oregon State Univ. Washington D.C. Riverside. Inst. of Geophysics and Planetary Physics National Science Foundation California Univ. Washington D.C. Livermore Lawrence Livermore Lab. California Univ. USA Atlantic Oceanographic and Meteorological Labs. National Oceanic and Atmospheric Administration Miami National Technical Information Service Springfield Miami Univ. Coral Gables School of Law National Oceanic and Atmospheric Administration Rockville National Academy of Sciences Office of Sea Grant Washington D. C. School of Law National Oceanic and Atmospheric Administration Office of Sea Grant. Rockville Norman. Dept. of Civil Engineering and Environmental Science. Urban Mass Transportation Administration Oklahoma Univ. Washington D. C. Knoxville.Dept. of Civil Engineering. Federal Highway Administration Tennessee Univ. Washington D. C. Amherst.Water Resources Research Center. Office of Water Research and Technology Massachusetts Univ. Washington D. C. Calif. Dept. of Industria

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Linear algorithms to recognize interval graphs and test for the consecutive ones property 75

Linear algorithms to recognize interval graphs and test for ...

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7th Annual ACM Symposium on Theory of Computing, STOC 1975

作者： Booth, Kellogg S. Lueker, George S. Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Lawrence Livermore Laboratory United States Department of Electrical Engineering and Program in Applied Mathematics Princeton University United States

ISBN: (纸本)9781450374194

A matrix of zeroes and ones is said to have the consecutive ones property if there is a permutation of its rows such that the ones in each column appear consecutively. This paper develops a data structure which may be used to test a matrix for the consecutive ones property, and produce the desired permutation of the rows, in linear time. One application of the consecutive ones property is in recognizing interval graphs. A graph is an interval graph if there exists a i-i correspondence between its vertices and a set of intervals on the real line such that two vertices are adjacent if and only if the corresponding intervals have a nonempty intersection. Fulkerson and Gross have characterized interval graphs as those for which the clique versus vertex incidence matrix has the consecutive ones property. In testing this particular matrix for the consecutive ones property we may process the columns in a special order to simDlifv the algorithm. This yields the interval graph recognition algorithm which is presented in section 2;section 3 indicates how this algorithm may be extended to the general consecutive ones problem. A final section of the paper gives a number of further applications of the ideas developed in the earlier sections. These applications include linear algorithms to a) recognize unit interval graphs, b) test for the circular ones property, c) recognize planar graphs, d) count the number of distinct models of an interval graph (assuming that an arithmetic operation can be done in constant time), and e) determine whether two interval graphs are isomorphic. © 1975 Association for Computing Machinery. All rights reserved.

关键词： Matrix algebra

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MEASUREMENT OF RELATIVE BLOOD-FLOW, TRANSIT-TIME DISTRIBUTIONS AND TRANSPORT-MODEL PARAMETERS BY RESIDUE DETECTION WHEN RADIOTRACER RECIRCULATES

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JOURNAL OF THEORETICAL BIOLOGY 1972年第3期37卷 503-&页

作者： LARSON, KB SNYDER, DL Biomedical Computer Laboratory and Division of Radiation Physics Mallinckrodt Institute of Radiology School of Medicine Washington University St. Louis Missouri 63110 USA Biomedical Computer Laboratory School of Medicine and Department of Electrical Engineering School of Engineering and Applied Science Washington University St. Louis Missouri 63110 USA

The interpretation of tracer kinetic data obtained by external monitoring of radioactively-labeled indicators is generally based on mathematical models that ignore recirculation. An important limitation of these models is that they can be used successfully only in those situations in which the major portion of the response curves are obtainable prior to the onset of the first recirculation. A further complication that limits the usefulness of the models is due to interference from radiotracers carried by recirculation into adjacent perfused regions also within the field of view of the detector. In this paper, we develop a mathematical model applicable for measuring blood flow per unit volume and determining transit-time distributions by external monitoring when recirculation is not a late event and must be taken into consideration. Additionally, the model accounts for interfering recirculation of tracer to adjacent perfused regions in the field of view of the detector. Central to the model is the use of two injections of tracer: one upstream (arterial) and one downstream (venous) of the particular organ or region of interest. Thus, two residue curves are obtained. We develop equations indicating how to employ the two residue curves in order to determine the mean transit time of tracer through the vascular system of interest, as well as higher moments of the transit-time distribution if they are desired. These equations are natural generalizations of Zierler's residue-detection method in that they do not depend for their validity on any model of mass transport within the system of interest. The numerical calculations implied by our equations need not rely on curve-fitting of the data. In addition to mean flow per unit volume, our method can yield compartmental parameters if these are appropriate and desired.

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Eigenvalue Problems: Algorithms, Software and Applications in Petascale Computing 1

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丛书名： Lecture Notes in Computational Science and engineering

1000年

作者： Tetsuya Sakurai Shao-Liang Zhang Toshiyuki Imamura Yusaku Yamamoto Yoshinobu Kuramashi Takeo Hoshi

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Theory of conical equiangular-spiral antennas -- 2

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IEEE Transactions on Antennas and Propagation 1968年第n 1期v AP-16卷 p14-21页

作者： Yeh, Y.S. Mei, K.K. Division of Engineering and Applied Physics Harvard University Cambridge MA USA Electronics Research Laboratory University of California Berkeley CA USA Department of Electrical Engineering and Computer Sciences and the Electronics Research Laboratory University of California Berkeley CA USA

According to behavior of current, equiangular-spiral antenna may be divided into three distinct regions;since positions of these regions are frequency independent, it is possible to obtain complete description of current distribution on almost all practical equiangular-spiral antennas by set of curves, which is given in this paper;regions that are characterized by differences in variational formulation of input impedance of antenna are also presented;variational method using approximate current provided by set of curves gives quick, direct means of finding input impedances.

关键词： Radio antennas

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Computational Neuroscience: Mathematical and Statistical Perspectives

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Statistics and Its Application 1000年第1期5卷 183-214页

作者： Robert E. Kass Shun-Ichi Amari Kensuke Arai Emery N. Brown Casey O. Diekman Markus Diesmann Brent Doiron Uri T. Eden Adrienne L. Fairhall Grant M. Fiddyment Tomoki Fukai Sonja Grün Matthew T. Harrison Moritz Helias Hiroyuki Nakahara Jun-nosuke Teramae Peter J. Thomas Mark Reimers Jordan Rodu Horacio G. Rotstein Eric Shea-Brown Hideaki Shimazaki Shigeru Shinomoto Byron M. Yu Mark A. Kramer 1Department of Statistics Machine Learning Department and Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA email: kass@stat.cmu.edu 2Mathematical Neuroscience Laboratory RIKEN Brain Science Institute Wako Saitama Prefecture 351-0198 Japan 3Department of Mathematics and Statistics Boston University Boston Massachusetts 02215 USA 5Department of Anesthesia Harvard Medical School Boston Massachusetts 02115 USA 4Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA 6Department of Mathematical Sciences New Jersey Institute of Technology Newark New Jersey 07102 USA 8Department of Theoretical Systems Neurobiology Institute of Biology RWTH Aachen University 52062 Aachen Germany 7Institute of Neuroscience and Medicine Jülich Research Centre 52428 Jülich Germany 9Department of Mathematics University of Pittsburgh Pittsburgh Pennsylvania 15260 USA 10Department of Physiology and Biophysics University of Washington Seattle Washington 98105 USA 11Division of Applied Mathematics Brown University Providence Rhode Island 02912 USA 12Department of Integrated Theoretical Neuroscience Osaka University Suita Osaka Prefecture 565-0871 Japan 13Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University Cleveland Ohio 44106 USA 14Department of Neuroscience Michigan State University East Lansing Michigan 48824 USA 15Department of Statistics University of Virginia Charlottesville Virginia 22904 USA 17Institute for Brain and Neuroscience Research New Jersey Institute of Technology Newark New Jersey 07102 USA 16Federated Department of Biological Sciences Rutgers University/New Jersey Institute of Technology Newark New Jersey 07102 USA 19Department of Physics Kyoto University Kyoto Kyoto Prefecture 606-8502 Japan 18Honda Research Institute Japan Wako Saitama Prefecture 351-0188 Japan 20Department of Electrical and Compute

Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

关键词： neural data analysis neural modeling neural networks theoretical neuroscience

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Exploratory Vision 1

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丛书名： Springer Series in Perception engineering

1000年

作者： Michael S. Landy Laurence T. Maloney Misha Pavel

ISBN: (数字)9781461239840

ISBN: (纸本)9780387945637;9781461284604

Advances in sensing, signal processing, and computer technology during the past half century have stimulated numerous attempts to design general-purpose ma chines that see. These attempts have met with at best modest success and more typically outright failure. The difficulties encountered in building working com puter vision systems based on state-of-the-art techniques came as a surprise. Perhaps the most frustrating aspect of the problem is that machine vision sys tems cannot deal with numerous visual tasks that humans perform rapidly and effortlessly. In reaction to this perceived discrepancy in performance, various researchers (notably Marr, 1982) suggested that the design of machine-vision systems should be based on principles drawn from the study of biological systems. This "neuro morphic" or "anthropomorphic" approach has proven fruitful: the use of pyramid (multiresolution) image representation methods in image compression is one ex ample of a successful application based on principles primarily derived from the study of biological vision systems. It is still the case, however, that the perfor of computer vision systems falls far short of that of the natural systems mance they are intended to mimic, suggesting that it is time to look even more closely at the remaining differences between artificial and biological vision systems.

关键词： Image Processing and computer Vision computer Graphics Life Sciences, general

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Determination of optimum rails dimensions in railgun by Lagrange's equations

Determination of optimum rails dimensions in railgun by Lagr...

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作者： Keshtkar, Asghar Maleki, Toraj Kalantamia, Ali Keshtkar, Ahmad Computer and Electrical Engineering Faculty University of Tabriz Tabriz 51666-16471 Iran Faculty of Engineering and Technology Imam Khomeini International University (IKIU) Ghazvin Iran Power Distribution Company of Kerman Shah Kerman Shah 32501-43256 Iran University of Applied and Science Hamedan 43256-73576 Iran Medical Physics Department Medical School Tabriz University of Medical Sciences Tabriz 51656-65931 Iran

One of the methods that are used for increasing accelerator force of projectile in the railgun is the increasing of inductance gradient. Essentially, the inductance gradient is determined by current density in rails and projectile. If we change geometry of the rail, the current density and inductance gradient will be changed. One of the factors that restricts variation domain of rails geometry is the tolerable current of rails. In this paper, we have obtained analytical formulas for the maximum current density and the inductance gradient in terms of rail dimensions using the results that have been obtained by 2-D finite-element method. By these formulas and Lagrange's optimization equations, we determined the optimum dimensions of rail. Tolerable current is bonded for Lagrange's equations. © 2009 IEEE.

关键词： Finite element method

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Analytical Methods and Instruments for Micro- and Nanomaterials 1

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丛书名： Lecture Notes in Nanoscale Science and Technology

1000年

作者： Henry H. Radamson Anders Hallén Ilya Sychugov Alexander Azarov

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