Functional renormalization group and bosonization as a solver for 2D fermionic Hubbard models

NASA Astrophysics Data System (ADS)

Schuetz, Florian; Marston, Brad

2007-03-01

The functional renormalization group (fRG) provides an unbiased framework to analyze competing instabilities in two-dimensional electron systems and has been used extensively over the past decade [1]. In order to obtain an equally unbiased tool to interprete the flow, we investigate the combination of a many-patch, one-loop calculation with higher dimensional bosonization [2] of the resulting low-energy action. Subsequently a semi-classical approximation [3] can be used to describe the resulting phases. The spinless Hubbard model on a square lattice with nearest neighbor repulsion is investigated as a test case. [1] M. Salmhofer and C. Honerkamp, Prog. Theor. Phys. 105, 1 (2001). [2] A. Houghton, H.-J. Kwon, J. B. Marston, Adv.Phys. 49, 141 (2000); P. Kopietz, Bosonization of interacting fermions in arbitrary dimensions, (Springer, Berlin, 1997). [3] H.-H. Lin, L. Balents, M. P. A. Fisher, Phys. Rev. B 56, 6569 6593 (1997); J. O. Fjaerestad, J. B. Marston, U. Schollwoeck, Ann. Phys. (N.Y.) 321, 894 (2006).

Langevin Theory of Anomalous Brownian Motion Made Simple

ERIC Educational Resources Information Center

Tothova, Jana; Vasziova, Gabriela; Glod, Lukas; Lisy, Vladimir

2011-01-01

During the century from the publication of the work by Einstein (1905 "Ann. Phys." 17 549) Brownian motion has become an important paradigm in many fields of modern science. An essential impulse for the development of Brownian motion theory was given by the work of Langevin (1908 "C. R. Acad. Sci.", Paris 146 530), in which he proposed an…

Immersion freezing in concentrated solution droplets for a variety of ice nucleating particles

NASA Astrophysics Data System (ADS)

Wex, Heike; Kohn, Monika; Grawe, Sarah; Hartmann, Susan; Hellner, Lisa; Herenz, Paul; Welti, Andre; Lohmann, Ulrike; Kanji, Zamin; Stratmann, Frank

2016-04-01

The measurement campaign LINC (Leipzig Ice Nucleation counter Comparison) was conducted in September 2015, during which ice nucleation measurements as obtained with the following instruments were compared: - LACIS (Leipzig Aerosol Cloud Interaction Simulator, see e.g. Wex et al., 2014) - PIMCA-PINC (Portable Immersion Mode Cooling Chamber together with PINC) - PINC (Portable Ice Nucleation Chamber, Chou et al., 2011) - SPIN (SPectrometer for Ice Nuclei, Droplet Measurement Technologies) While LACIS and PIMCA-PINC measured immersion freezing, PINC and SPIN varied the super-saturation during the measurements and collected data also for relative humidities below 100% RHw. A suite of different types of ice nucleating particles were examined, where particles were generated from suspensions, subsequently dried and size selected. For the following samples, data for all four instruments are available: K-feldspar, K-feldspar treated with nitric acid, Fluka-kaolinite and birch pollen. Immersion freezing measurements by LACIS and PIMCA-PINC were in excellent agreement. Respective parameterizations from these measurement were used to model the ice nucleation behavior below water vapor saturation, assuming that the process can be described as immersion freezing in concentrated solutions. This is equivalent to simply including a concentration dependent freezing point depression in the immersion freezing parameterization, as introduced for coated kaolinite particles in Wex et al. (2014). Overall, measurements performed below water vapor saturation were reproduced by the model, and it will be discussed in detail, why deviations were observed in some cases. Acknowledgement: Part of this work was funded by the DFG Research Unit FOR 1525 INUIT, grant WE 4722/1-2. Literature: Chou, C., O. Stetzer, E. Weingartner, Z. Juranyi, Z. A. Kanji, and U. Lohmann (2011), Ice nuclei properties within a Saharan dust event at the Jungfraujoch in the Swiss Alps, Atmos. Chem. Phys., 11(10), 4725-4738, doi:10.5194/acp-11-4725-2011. Wex, H., P. J. DeMott, Y. Tobo, S. Hartmann, M. Rösch, T. Clauss, L. Tomsche, D. Niedermeier, and F. Stratmann (2014), Kaolinite particles as ice nuclei: learning from the use of different kaolinite samples and different coatings, Atmos. Chem. Phys., 14, doi:10.5194/acp-14-5529-2014.

Hopf bifurcation in a nonlocal nonlinear transport equation stemming from stochastic neural dynamics

NASA Astrophysics Data System (ADS)

Drogoul, Audric; Veltz, Romain

2017-02-01

In this work, we provide three different numerical evidences for the occurrence of a Hopf bifurcation in a recently derived [De Masi et al., J. Stat. Phys. 158, 866-902 (2015) and Fournier and löcherbach, Ann. Inst. H. Poincaré Probab. Stat. 52, 1844-1876 (2016)] mean field limit of a stochastic network of excitatory spiking neurons. The mean field limit is a challenging nonlocal nonlinear transport equation with boundary conditions. The first evidence relies on the computation of the spectrum of the linearized equation. The second stems from the simulation of the full mean field. Finally, the last evidence comes from the simulation of the network for a large number of neurons. We provide a "recipe" to find such bifurcation which nicely complements the works in De Masi et al. [J. Stat. Phys. 158, 866-902 (2015)] and Fournier and löcherbach [Ann. Inst. H. Poincaré Probab. Stat. 52, 1844-1876 (2016)]. This suggests in return to revisit theoretically these mean field equations from a dynamical point of view. Finally, this work shows how the noise level impacts the transition from asynchronous activity to partial synchronization in excitatory globally pulse-coupled networks.

Theory of the n = 2 levels in muonic helium-3 ions

NASA Astrophysics Data System (ADS)

Franke, Beatrice; Krauth, Julian J.; Antognini, Aldo; Diepold, Marc; Kottmann, Franz; Pohl, Randolf

2017-12-01

The present knowledge of Lamb shift, fine-, and hyperfine structure of the 2S and 2P states in muonic helium-3 ions is reviewed in anticipation of the results of a first measurement of several 2S → 2P transition frequencies in the muonic helium-3 ion, μ3He+. This ion is the bound state of a single negative muon μ- and a bare helium-3 nucleus (helion), 3He++. A term-by-term comparison of all available sources, including new, updated, and so far unpublished calculations, reveals reliable values and uncertainties of the QED and nuclear structure-dependent contributions to the Lamb shift and the hyperfine splitting. These values are essential for the determination of the helion rms charge radius and the nuclear structure effects to the hyperfine splitting in μ3He+. With this review we continue our series of theory summaries in light muonic atoms [see A. Antognini et al., Ann. Phys. 331, 127 (2013); J.J. Krauth et al., Ann. Phys. 366, 168 (2016); and M. Diepold et al. arXiv:1606.05231 (2016)].

Gauge invariant perturbations of the Schwarzschild spacetime

NASA Astrophysics Data System (ADS)

Thompson, Jonathan E.; Chen, Hector; Whiting, Bernard F.

2017-09-01

Beginning with the pioneering work of Regge and Wheeler (1957 Phys. Rev. 108 1063), there have been many studies of perturbations away from the Schwarzschild spacetime background. In particular several authors Moncrief (1974 Ann. Phys. 88 323), Sachs (1964 Relativity, Groups and Topology (New York: Gordon and Breach)) and Brizuela et al (2007 Phys. Rev. D 76 024004) have investigated gauge invariant quantities of the Regge-Wheeler (RW) formalism. Steven Detweiler also investigated perturbations of Schwarzschild in his own formalism, introducing his own gauge choice which he denoted the ‘easy (EZ) gauge’, and which he was in the process of adapting for use in the second-order self-force problem. We present here a compilation of some of his working results, arising from notes for which there seems to have been no manuscript in preparation. In particular, we outline Detweiler’s formalism, list the gauge invariant quantities he used, and explain the process by which he found them.

Two-point resistance of the Möbius ladder

NASA Astrophysics Data System (ADS)

Chair, Noureddine; Dannoun, Elham Mohammed Ali

2015-03-01

Exact formulas for the two-point resistance and the Kirchhoff index of the Möbius ladder are given based on the recently developed analytical approach by Chair (2012 Ann. Phys. 327 2899). The expression for the two-point resistance is written in terms of the two-point resistance of N/2-cycle graph and the Bejaia and the Pisa numbers recently introduced by the first author.

Wundt's laboratory at Leipzig in 1891.

PubMed

Nicolas, S; Ferrand, L

1999-08-01

This article describes Wundt's laboratory at Leipzig in 1891 as viewed by a Belgian psychologist, J.J. Van Biervliet (1859-1945). Although few French-speaking psychologists worked in Wundt's laboratory, several of those who did reports wrote on how students were trained there. Van Biervliet decided to visit Wundt's laboratory at Leipzig in order to strengthen the foundation of his own laboratory at the University of Ghent and to become familiar with Wundt's experimental techniques. A translation of J.J. Van Biervliet's (1892) article "Experimental Psychology. Wundt's Institute at Leipzig" is presented here as one of the first and most complete articles in French describing the functioning of Wundt's laboratory.

[Tracing back for the basis. Research of Medieval medical manuscripts in Leipzig by Karl Sudhoff and Henry Ernest Sigerist].

PubMed

Löffler, Anette

2009-01-01

At the beginning of the 20th century, two known medical historians, Karl Sudhoff and Henry Ernst Sigerist, were among the first persons, who took up thorough investigations of medical manuscripts in Leipzig. Above all, it was Mr. Sudhoff, who developed his own procedure, in order to exploit the unknown stocks. Apart from the only means available to him, the so-called Leyser-papers, written by the Leipzig librarian Hermann Leyser, at least once Sudhoff made a complete check-up of all manuscripts over the period of 22 years. On 315 sheets he took down various more or less extensive notes regarding these Codices, which are kept today as manuscript collection (Ms 01269) in the library of the university of Leipzig. Many publications were written on the basis of these readings. Sigerist was only looking through a small number of Leipzig manuscripts, as could be seen from his remarks written onto the users' cards. However, he could rely on Sudhoff's notes and look for specific manuscripts that he was interested in and review them. This was a first and very important step for the investigation of Leipzig medical manuscripts.

Optical model potential analysis of n ¯ A and n A interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Teck-Ghee; Wong, Cheuk-Yin

In this study, we use a momentum-dependent optical model potential to analyze the annihilation cross sections of the antineutronmore » $$\\overline{n}$$ on C, Al, Fe, Cu, Ag, Sn, and Pb nuclei for projectile momenta p lab ≲ 500 MeV / c . We obtain a good description of annihilation cross section data of Barbina et al. [Nucl. Phys. A 612, 346 (1997)] and of Astrua et al. [Nucl. Phys. A 697, 209 (2002)] which exhibit an interesting dependence of the cross sections on p lab as well as on the target mass number A. We also obtain the neutron (n) nonelastic reaction cross sections for the same targets. Comparing the $nA$ reaction cross sections σ$$nA\\atop{rec}$$ to the $$\\overline{n}A$$ annihilation cross sections σ $$\\overline{n}A$$ ann, we find that σ $$\\overline{n}A$$ ann is significantly larger than σ$$nA\\atop{rec}$$, that is, theσ $$\\overline{n}A$$ ann / σ$$nA\\atop{rec}$$ cross section ratio lies between the values of about 1.5 to 4.0 in the momentum region where comparison is possible. The dependence of the $$\\overline{n}$$ annihilation cross section on the projectile charge is also examined in comparison with the antiproton $$\\overline{p}$$. Here we predict the $$\\overline{p}A$$ annihilation cross section on the simplest assumption that both $$\\overline{p}A$$ and $$\\overline{n}A$$ interactions have the same nuclear part of the optical potential but differ only in the electrostatic Coulomb interaction. Finally, deviation from a such simple model extrapolation in measurements will provide new information on the difference between $$\\overline{n}A$$ and $$\\overline{p}A$$ potentials.« less

Optical model potential analysis of n ¯ A and n A interactions

DOE PAGES

Lee, Teck-Ghee; Wong, Cheuk-Yin

2018-05-25

In this study, we use a momentum-dependent optical model potential to analyze the annihilation cross sections of the antineutronmore » $$\\overline{n}$$ on C, Al, Fe, Cu, Ag, Sn, and Pb nuclei for projectile momenta p lab ≲ 500 MeV / c . We obtain a good description of annihilation cross section data of Barbina et al. [Nucl. Phys. A 612, 346 (1997)] and of Astrua et al. [Nucl. Phys. A 697, 209 (2002)] which exhibit an interesting dependence of the cross sections on p lab as well as on the target mass number A. We also obtain the neutron (n) nonelastic reaction cross sections for the same targets. Comparing the $nA$ reaction cross sections σ$$nA\\atop{rec}$$ to the $$\\overline{n}A$$ annihilation cross sections σ $$\\overline{n}A$$ ann, we find that σ $$\\overline{n}A$$ ann is significantly larger than σ$$nA\\atop{rec}$$, that is, theσ $$\\overline{n}A$$ ann / σ$$nA\\atop{rec}$$ cross section ratio lies between the values of about 1.5 to 4.0 in the momentum region where comparison is possible. The dependence of the $$\\overline{n}$$ annihilation cross section on the projectile charge is also examined in comparison with the antiproton $$\\overline{p}$$. Here we predict the $$\\overline{p}A$$ annihilation cross section on the simplest assumption that both $$\\overline{p}A$$ and $$\\overline{n}A$$ interactions have the same nuclear part of the optical potential but differ only in the electrostatic Coulomb interaction. Finally, deviation from a such simple model extrapolation in measurements will provide new information on the difference between $$\\overline{n}A$$ and $$\\overline{p}A$$ potentials.« less

The homopolar motor: A true relativistic engine

NASA Astrophysics Data System (ADS)

Guala-Valverde, Jorge; Mazzoni, Pedro; Achilles, Ricardo

2002-10-01

This article discusses experiments which enable the identification of the seat of mechanical forces in homopolar-machines reported earlier in this journal [J. Guala-Valverde and P. Mazzoni, Am. J. Phys. 63, 228-229 (1995); J. Guala-Valverde, P. Mazzoni, and K. Blas, ibid. 65, 147-148 (1997)]. We provide a suitable variation on a recent work "The Unipolar Dynamotor: A Genuine Relational Engine" [J. Guala-Valverde and P. Mazzoni, Apeiron 8, 41-52 (2001)], where "relational" implies "absolutely relativistic." Our view agrees with both Weber's recognition in the 19th century of the importance of relative motion in electromagnetic phenomena [A. K. T. Assis, Electrodynamics (Kluwer, Dordrecht, 1994)] and Einstein's 1905 statement concerning electromagnetism [Ann. Phys. 17, 891-921 (1905)].

Inverse Faraday Effect in Hemoglobin Detected by Raman Spectroscopy: An Example of Magnetic Resonance Raman Activity.

DTIC Science & Technology

1985-06-03

d.E - m.H + and is a truncated form of Equ. (9) intepreted according to the diagrammatic perturbation theory approach of Wallace [51]; n is the...A.L., 1972, 3. Chem. Phys. 56, 4073. 87. Gouterman, M., 1973, Ann. N.Y. Acad. Sci. 206, 70. -27- NADC-85074-60 This Page Intentionally Left Blank -28 - FILM -ED 11-85 DTIC

Towards rigorous analysis of the Levitov-Mirlin-Evers recursion

NASA Astrophysics Data System (ADS)

Fyodorov, Y. V.; Kupiainen, A.; Webb, C.

2016-12-01

This paper aims to develop a rigorous asymptotic analysis of an approximate renormalization group recursion for inverse participation ratios P q of critical powerlaw random band matrices. The recursion goes back to the work by Mirlin and Evers (2000 Phys. Rev. B 62 7920) and earlier works by Levitov (1990 Phys. Rev. Lett. 64 547, 1999 Ann. Phys. 8 697-706) and is aimed to describe the ensuing multifractality of the eigenvectors of such matrices. We point out both similarities and dissimilarities between the LME recursion and those appearing in the theory of multiplicative cascades and branching random walks and show that the methods developed in those fields can be adapted to the present case. In particular the LME recursion is shown to exhibit a phase transition, which we expect is a freezing transition, where the role of temperature is played by the exponent q. However, the LME recursion has features that make its rigorous analysis considerably harder and we point out several open problems for further study.

Flow Equation Approach to the Statistics of Nonlinear Dynamical Systems

NASA Astrophysics Data System (ADS)

Marston, J. B.; Hastings, M. B.

2005-03-01

The probability distribution function of non-linear dynamical systems is governed by a linear framework that resembles quantum many-body theory, in which stochastic forcing and/or averaging over initial conditions play the role of non-zero . Besides the well-known Fokker-Planck approach, there is a related Hopf functional methodootnotetextUriel Frisch, Turbulence: The Legacy of A. N. Kolmogorov (Cambridge University Press, 1995) chapter 9.5.; in both formalisms, zero modes of linear operators describe the stationary non-equilibrium statistics. To access the statistics, we investigate the method of continuous unitary transformationsootnotetextS. D. Glazek and K. G. Wilson, Phys. Rev. D 48, 5863 (1993); Phys. Rev. D 49, 4214 (1994). (also known as the flow equation approachootnotetextF. Wegner, Ann. Phys. 3, 77 (1994).), suitably generalized to the diagonalization of non-Hermitian matrices. Comparison to the more traditional cumulant expansion method is illustrated with low-dimensional attractors. The treatment of high-dimensional dynamical systems is also discussed.

Immersion freezing induced by different kinds of coal fly ash: Comparing particle generation methods and measurement techniques

NASA Astrophysics Data System (ADS)

Grawe, Sarah; Augustin-Bauditz, Stefanie; Clemen, Hans-Christian; Eriksen-Hammer, Stine; Lubitz, Jasmin; Schneider, Johannes; Stratmann, Frank; Wex, Heike

2017-04-01

To date, a lot of effort has been put into the identification and characterization of atmospheric ice nucleating particles (INPs), which may influence both weather and climate. The majority of studies focuses on INPs from natural origin such as biological particles or mineral dust particles (Hoose and Möhler 2012, Murray et al. 2012). Combustion ashes, being possible sources of anthropogenic INPs, have rarely been investigated in the past. Ash particles may be emitted into the atmosphere either by the action of wind from ash deposits on the ground (bottom ash), or during the combustion process (fly ash). Two recent studies (Umo et al., 2015; Grawe et al., 2016) identified fly ash from coal combustion as the most efficient of the investigated samples (including also bottom ashes from wood and coal combustion). These results motivate the here presented study in which we investigated the immersion freezing behavior of four coal fly ash samples taken from the filters of different coal-fired power plants in Germany. A combination of two instruments was used to capture the temperature range from 0 °C to the homogeneous freezing limit at around -38 °C. Firstly, the new Leipzig Ice Nucleation Array (LINA) was used, where droplets from an ash-water suspension are pipetted onto a cooled plate. Secondly, we used the Leipzig Aerosol Cloud Interaction Simulator (LACIS; Hartmann et al., 2011), a laminar flow tube in which every droplet contains a single size-segregated ash particle. Here, it was possible to study the effect of different kinds of particle generation, i.e., atomization of an ash-water suspension, and aerosolization of dry ash material. The composition of the ash particles was investigated by means of single particle aerosol mass spectrometry and particles were sampled on filters for environmental scanning electron microscope analysis. Our measurements show that all four fly ash samples feature a similar immersion freezing behavior (ice fractions vary by a factor of 5 at most) when particles are generated via dry dispersion. Furthermore, we found that the ice nucleation ability of all samples is lowered significantly when changing from dry to wet particle generation. The aim of the study is to identify possible reasons for these observations. References: S. Grawe, S. Augustin-Bauditz, S. Hartmann, L. Hellner, J. B. C. Pettersson, A. Prager, F. Stratmann, and H. Wex, Atmos. Chem. Phys., 16, 13911-13928, 2016 S. Hartmann, D. Niedermeier, J. Voigtländer, T. Clauß, R. A. Shaw, H. Wex, A. Kiselev, and F. Stratmann, Atmos. Chem. Phys., 11, 1753-1767, 2011 C. Hoose and O. Möhler, Atmos. Chem. Phys., 12, 9817-9854, 2012 B. J. Murray, D. O'Sullivan, J. D. Atkinson, and M. E. Webb, Chem. Soc. Rev., 41, 6519-6554, 2012 N. S. Umo, B. J. Murray, M. T. Baeza-Romero, J. M. Jones, A. R. Lea-Langton, T. L. Malkin, D. O'Sullivan, L. Neve, J. M. C. Plane, and A. Williams, Atmos. Chem. Phys., 15, 5195-5210, 2015

Lozenge Tiling Dynamics and Convergence to the Hydrodynamic Equation

NASA Astrophysics Data System (ADS)

Laslier, Benoît; Toninelli, Fabio Lucio

2018-03-01

We study a reversible continuous-time Markov dynamics of a discrete (2 + 1)-dimensional interface. This can be alternatively viewed as a dynamics of lozenge tilings of the {L× L} torus, or as a conservative dynamics for a two-dimensional system of interlaced particles. The particle interlacement constraints imply that the equilibrium measures are far from being product Bernoulli: particle correlations decay like the inverse distance squared and interface height fluctuations behave on large scales like a massless Gaussian field. We consider a particular choice of the transition rates, originally proposed in Luby et al. (SIAM J Comput 31:167-192, 2001): in terms of interlaced particles, a particle jump of length n that preserves the interlacement constraints has rate 1/(2 n). This dynamics presents special features: the average mutual volume between two interface configurations decreases with time (Luby et al. 2001) and a certain one-dimensional projection of the dynamics is described by the heat equation (Wilson in Ann Appl Probab 14:274-325, 2004). In this work we prove a hydrodynamic limit: after a diffusive rescaling of time and space, the height function evolution tends as L\\to∞ to the solution of a non-linear parabolic PDE. The initial profile is assumed to be C 2 differentiable and to contain no "frozen region". The explicit form of the PDE was recently conjectured (Laslier and Toninelli in Ann Henri Poincaré Theor Math Phys 18:2007-2043, 2017) on the basis of local equilibrium considerations. In contrast with the hydrodynamic equation for the Langevin dynamics of the Ginzburg-Landau model (Funaki and Spohn in Commun Math Phys 85:1-36, 1997; Nishikawa in Commun Math Phys 127:205-227, 2003), here the mobility coefficient turns out to be a non-trivial function of the interface slope.

Aspects of mutually unbiased bases in odd-prime-power dimensions

NASA Astrophysics Data System (ADS)

Chaturvedi, S.

2002-04-01

We rephrase the Wootters-Fields construction [W. K. Wootters and B. C. Fields, Ann. Phys. 191, 363 (1989)] of a full set of mutually unbiased bases in a complex vector space of dimensions N=pr, where p is an odd prime, in terms of the character vectors of the cyclic group G of order p. This form may be useful in explicitly writing down mutually unbiased bases for N=pr.

The Leipzig Ice Nucleation chamber Comparison (LINC): An overview of ice nucleation measurements observed with four on-line ice nucleation devices

NASA Astrophysics Data System (ADS)

Kohn, Monika; Wex, Heike; Grawe, Sarah; Hartmann, Susan; Hellner, Lisa; Herenz, Paul; Welti, André; Stratmann, Frank; Lohmann, Ulrike; Kanji, Zamin A.

2016-04-01

Mixed-phase clouds (MPCs) are found to be the most relevant cloud type leading to precipitation in mid-latitudes. The formation of ice crystals in MPCs is not completely understood. To estimate the effect of aerosol particles on the radiative properties of clouds and to describe ice nucleation in models, the specific properties of aerosol particles acting as ice nucleating particles (INPs) still need to be identified. A number of devices are able to measure INPs in the lab and in the field. However, methods can be very different and need to be tested under controlled conditions with respect to aerosol generation and properties in order to standardize measurement and data analysis approaches for subsequent ambient measurements. Here, we present an overview of the LINC campaign hosted at TROPOS in September 2015. We compare four ice nucleation devices: PINC (Portable Ice Nucleation Chamber, Chou et al., 2011) and SPIN (SPectrometer for Ice Nuclei) are operated in deposition nucleation and condensation freezing mode. LACIS (Leipzig Aerosol Cloud Interaction Simulator, Hartmann et al., 2011) and PIMCA (Portable Immersion Mode Cooling chamber) measure in the immersion freezing mode. PIMCA is used as a vertical extension to PINC and allows activation and droplet growth prior to exposure to the investigated ice nucleation temperature. Size-resolved measurements of multiple aerosol types were performed including pure mineral dust (K-feldspar, kaolinite) and biological particles (Birch pollen washing waters) as well as some of them after treatment with sulfuric or nitric acid prior to experiments. LACIS and PIMCA-PINC operated in the immersion freezing mode showed very good agreement in the measured frozen fraction (FF). For the comparison between PINC and SPIN, which were scanning relative humidity from below to above water vapor saturation, an agreement was found for the obtained INP concentration. However, some differences were observed, which may result from ice detection and data treatment. A difference was observed between FF from LACIS and PIMCA-PINC compared to the ice activated fractions (AF) from PINC and SPIN. This requires further investigations. Acknowledgements Part of this work was funded by the DFG Research Unit FOR 1525 INUIT, grant WE 4722/1-2. References Chou et al. (2011), Atmos. Chem. Phys., 11, 4725-4738. Hartmann et al. (2011), Atmos. Chem. Phys., 11, 1753-1767.

A study of the orthogonal polynomials associated with the quantum harmonic oscillator on constant curvature spaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vignat, C.; Lamberti, P. W.

2009-10-15

Recently, Carinena, et al. [Ann. Phys. 322, 434 (2007)] introduced a new family of orthogonal polynomials that appear in the wave functions of the quantum harmonic oscillator in two-dimensional constant curvature spaces. They are a generalization of the Hermite polynomials and will be called curved Hermite polynomials in the following. We show that these polynomials are naturally related to the relativistic Hermite polynomials introduced by Aldaya et al. [Phys. Lett. A 156, 381 (1991)], and thus are Jacobi polynomials. Moreover, we exhibit a natural bijection between the solutions of the quantum harmonic oscillator on negative curvature spaces and on positivemore » curvature spaces. At last, we show a maximum entropy property for the ground states of these oscillators.« less

Target Surface Area Effects on Hot Electron Dynamics from High Intensity Laser-Plasma Interactions

DTIC Science & Technology

2016-08-19

New J. Phys. 18 (2016) 063020 doi:10.1088/1367-2630/18/6/063020 PAPER Target surface area effects on hot electron dynamics from high intensity laser ...Science, University ofMichigan, AnnArbor,MI 48109-2099, USA E-mail: czulick@umich.edu Keywords: laser -plasma,mass-limited, fast electrons, sheath...field Abstract Reduced surface area targets were studied using an ultra-high intensity femtosecond laser in order to determine the effect of electron

Mutually orthogonal Latin squares from the inner products of vectors in mutually unbiased bases

NASA Astrophysics Data System (ADS)

Hall, Joanne L.; Rao, Asha

2010-04-01

Mutually unbiased bases (MUBs) are important in quantum information theory. While constructions of complete sets of d + 1 MUBs in {\\bb C}^d are known when d is a prime power, it is unknown if such complete sets exist in non-prime power dimensions. It has been conjectured that complete sets of MUBs only exist in {\\bb C}^d if a maximal set of mutually orthogonal Latin squares (MOLS) of side length d also exists. There are several constructions (Roy and Scott 2007 J. Math. Phys. 48 072110; Paterek, Dakić and Brukner 2009 Phys. Rev. A 79 012109) of complete sets of MUBs from specific types of MOLS, which use Galois fields to construct the vectors of the MUBs. In this paper, two known constructions of MUBs (Alltop 1980 IEEE Trans. Inf. Theory 26 350-354 Wootters and Fields 1989 Ann. Phys. 191 363-381), both of which use polynomials over a Galois field, are used to construct complete sets of MOLS in the odd prime case. The MOLS come from the inner products of pairs of vectors in the MUBs.

Nonlocality of the original Einstein-Podolsky-Rosen state

NASA Astrophysics Data System (ADS)

Cohen, O.

1997-11-01

We examine the properties and behavior of the original Einstein-Podolsky-Rosen (EPR) wave function [Phys. Rev. 47, 777 (1935)] and related Gaussian-correlated wave functions. We assess the degree of entanglement of these wave functions and consider an argument of Bell [Ann. (N.Y.) Acad. Sci. 480, 263 (1986)] based on the Wigner phase-space distribution [Phys. Rev. 40, 749 (1932)], which implies that the original EPR correlations can accommodate a local hidden-variable description. We extend Bell's analysis to the related Gaussian wave functions. We then show that it is possible to identify definite nonlocal aspects for the original EPR state and related states. We describe possible experiments that would demonstrate these nonlocal features through violations of Bell inequalities. The implications of our results, and in particular their relevance for the causal interpretation of quantum mechanics, are considered.

Multiscale Modeling of Non-crystalline Ceramics (Glass)

DTIC Science & Technology

2011-02-01

4). 5.3 Approach: We will produce high silica glasses with additions of up to 10 wt% of network formers and modifiers using Momentive’s lab scale...Aij , rij , ρ, and Cij are constants, which are provided by van Beest et al. (16); we refer to equation 2 as the BKS potential. 7.2 Generating...Optischer und Elektrostatischer Gitterpotentiale. Ann. Phys. 1921, 369, 253–287. 16. van Beest , B. W. H.; Kramer, G. J.; van Santen, R. A. Force-fields for

Physics Without Physics. The Power of Information-theoretical Principles

NASA Astrophysics Data System (ADS)

D'Ariano, Giacomo Mauro

2017-01-01

David Finkelstein was very fond of the new information-theoretic paradigm of physics advocated by John Archibald Wheeler and Richard Feynman. Only recently, however, the paradigm has concretely shown its full power, with the derivation of quantum theory (Chiribella et al., Phys. Rev. A 84:012311, 2011; D'Ariano et al., 2017) and of free quantum field theory (D'Ariano and Perinotti, Phys. Rev. A 90:062106, 2014; Bisio et al., Phys. Rev. A 88:032301, 2013; Bisio et al., Ann. Phys. 354:244, 2015; Bisio et al., Ann. Phys. 368:177, 2016) from informational principles. The paradigm has opened for the first time the possibility of avoiding physical primitives in the axioms of the physical theory, allowing a re-foundation of the whole physics over logically solid grounds. In addition to such methodological value, the new information-theoretic derivation of quantum field theory is particularly interesting for establishing a theoretical framework for quantum gravity, with the idea of obtaining gravity itself as emergent from the quantum information processing, as also suggested by the role played by information in the holographic principle (Susskind, J. Math. Phys. 36:6377, 1995; Bousso, Rev. Mod. Phys. 74:825, 2002). In this paper I review how free quantum field theory is derived without using mechanical primitives, including space-time, special relativity, Hamiltonians, and quantization rules. The theory is simply provided by the simplest quantum algorithm encompassing a countable set of quantum systems whose network of interactions satisfies the three following simple principles: homogeneity, locality, and isotropy. The inherent discrete nature of the informational derivation leads to an extension of quantum field theory in terms of a quantum cellular automata and quantum walks. A simple heuristic argument sets the scale to the Planck one, and the currently observed regime where discreteness is not visible is the so-called "relativistic regime" of small wavevectors, which holds for all energies ever tested (and even much larger), where the usual free quantum field theory is perfectly recovered. In the present quantum discrete theory Einstein relativity principle can be restated without using space-time in terms of invariance of the eigenvalue equation of the automaton/walk under change of representations. Distortions of the Poincaré group emerge at the Planck scale, whereas special relativity is perfectly recovered in the relativistic regime. Discreteness, on the other hand, has some plus compared to the continuum theory: 1) it contains it as a special regime; 2) it leads to some additional features with GR flavor: the existence of an upper bound for the particle mass (with physical interpretation as the Planck mass), and a global De Sitter invariance; 3) it provides its own physical standards for space, time, and mass within a purely mathematical adimensional context. The paper ends with the future perspectives of this project, and with an Appendix containing biographic notes about my friendship with David Finkelstein, to whom this paper is dedicated.

HF Accelerated Electron Fluxes, Spectra, and Ionization

NASA Astrophysics Data System (ADS)

Carlson, Herbert C.; Jensen, Joseph B.

2015-10-01

Wave particle interactions, an essential aspect of laboratory, terrestrial, and astrophysical plasmas, have been studied for decades by transmitting high power HF radio waves into Earth's weakly ionized space plasma, to use it as a laboratory without walls. Application to HF electron acceleration remains an active area of research (Gurevich in Usp Fizicheskikh Nauk 177(11):1145-1177, 2007) today. HF electron acceleration studies began when plasma line observations proved (Carlson et al. in J Atmos Terr Phys 44:1089-1100, 1982) that high power HF radio wave-excited processes accelerated electrons not to ~eV, but instead to -100 times thermal energy (10 s of eV), as a consequence of inelastic collision effects on electron transport. Gurevich et al (J Atmos Terr Phys 47:1057-1070, 1985) quantified the theory of this transport effect. Merging experiment with theory in plasma physics and aeronomy, enabled prediction (Carlson in Adv Space Res 13:1015-1024, 1993) of creating artificial ionospheres once ~GW HF effective radiated power could be achieved. Eventual confirmation of this prediction (Pedersen et al. in Geophys Res Lett 36:L18107, 2009; Pedersen et al. in Geophys Res Lett 37:L02106, 2010; Blagoveshchenskaya et al. in Ann Geophys 27:131-145, 2009) sparked renewed interest in optical inversion to estimate electron spectra in terrestrial (Hysell et al. in J Geophys Res Space Phys 119:2038-2045, 2014) and planetary (Simon et al. in Ann Geophys 29:187-195, 2011) atmospheres. Here we present our unpublished optical data, which combined with our modeling, lead to conclusions that should meaningfully improve future estimates of the spectrum of HF accelerated electron fluxes. Photometric imaging data can significantly improve detection of emissions near ionization threshold, and confirm depth of penetration of accelerated electrons many km below the excitation altitude. Comparing observed to modeled emission altitude shows future experiments need electron density profiles to derive more accurate HF electron flux spectra.

Singly and Doubly Excited States of the D-Dimensional Helium Atom(Supported by DOD-ONR N00014-94-1-0998)

NASA Astrophysics Data System (ADS)

Carzoli, J.; Dunn, M.; Watson, D. K.

1998-05-01

Large order dimensional perturbation theory (DPT) has been used to study the ground and a number of excited states of two-electron atoms for the case L=0. Here we present the first application of recent work generalizing DPT to higher angular momentum.(M. Dunn, D.K. Watson, Ann. Phys. 251 (1996) 266)^,(M. Dunn, D.K. Watson, The Large Dimension Limit of Higher Angular Momentum States. Phys. Rev. A. (accepted for publication)) In this work we begin the investigation of P^o states, presenting results for the energies of some of the lowest lying states and discuss the analytic structure of these energies as functions of 1/D. We also obtain energies of corresponding D^o states with almost no additional effort by making use of interdimensional degeneracies with the P^o states.

SU-F-T-23: Correspondence Factor Correction Coefficient for Commissioning of Leipzig and Valencia Applicators with the Standard Imaging IVB 1000

DOE Office of Scientific and Technical Information (OSTI.GOV)

Donaghue, J; Gajdos, S

Purpose: To determine the correction factor of the correspondence factor for the Standard Imaging IVB 1000 well chamber for commissioning of Elekta’s Leipzig and Valencia skin applicators. Methods: The Leipzig and Valencia applicators are designed to treat small skin lesions by collimating irradiation to the treatment area. Published output factors are used to calculate dose rates for clinical treatments. To validate onsite applicators, a correspondence factor (CFrev) is measured and compared to published values. The published CFrev is based on well chamber model SI HDR 1000 Plus. The CFrev is determined by correlating raw values of the source calibration setupmore » (Rcal,raw) and values taken when each applicator is mounted on the same well chamber with an adapter (Rapp,raw). The CFrev is calculated by using the equation CFrev =Rapp,raw/Rcal,raw. The CFrev was measured for each applicator in both the SI HDR 1000 Plus and the SI IVB 1000. A correction factor, CFIVB for the SI IVB 1000 was determined by finding the ratio of CFrev (SI IVB 1000) and CFrev (SI HDR 1000 Plus). Results: The average correction factors at dwell position 1121 were found to be 1.073, 1.039, 1.209, 1.091, and 1.058 for the Valencia V2, Valencia V3, Leipzig H1, Leipzig H2, and Leipzig H3 respectively. There were no significant variations in the correction factor for dwell positions 1119 through 1121. Conclusion: By using the appropriate correction factor, the correspondence factors for the Leipzig and Valencia surface applicators can be validated with the Standard Imaging IVB 1000. This allows users to correlate their measurements with the Standard Imaging IVB 1000 to the published data. The correction factor is included in the equation for the CFrev as follows: CFrev= Rapp,raw/(CFIVB*Rcal,raw). Each individual applicator has its own correction factor, so care must be taken that the appropriate factor is used.« less

Photothermal Deoxygenation of Graphene Oxide to Graphitic Carbon for Distributed Ignition and Patterning Applications (Preprint)

DTIC Science & Technology

2009-04-13

Brodie, B. Ann Chim. Phys. 1855, 45, 351 6. Dikin , D.; Stankovich, S.; Zimney, E. J.; Piner, R. D.; Dommett, H. B.; Evmenenko, G.; Nguyen, S. T...Stankovich, S.; Dikin , D. A.; Piner, R. D.; Kohlhaas, K. A.; Kleinhammes, A.; Jia, Y.; Wu, Y.; Nguyen, S. T.; Ruoff, R. S. Carbon 2007, 45(7), 1558...Gomez-Navarro, C.; Weitz, R. T.; Bittner, A. M.; Scolari, M.; Mews, A.; Burghrd. M.; Kern, K. Nano Lett. 2006, 7, 3499 16. Stankovich, S.; Dikin , D

Photothermal Deoxygenation of Graphene Oxide for Distributed Ignition and Patterning Applications (Postprint)

DTIC Science & Technology

2010-01-01

5] B. Brodie, Ann. Chim. Phys. 1855, 45, 351. [6] D. Dikin , S. Stankovich, E. J. Zimney, R. D. Piner, H. B. Dommett, G. Evmenenko, S. T. Nguyen, R. S... Dikin , R. D. Piner, K. A. Kohlhaas, A. Kleinhammes, Y. Jia, Y. Wu, S. T. Nguyen, R. S. Ruoff, Carbon 2007, 45, 1558. [9] S. Stankovich, R. D. Piner, X...Lett. 2007, 7, 3499. [16] S. Stankovich, D. A. Dikin , G. H. B. Dommett, K. M. Kohlhaas, E. J. Zimney, E. A. Stach, R. D. Piner, S. T. Nguyen, R. S

Direct Detection of Time-Resolved Rabi Oscillationsin a Single Quantum Dot via Resonance Fluorescence

DTIC Science & Technology

2013-03-19

Ware, E. A. Stinaff, D. Gammon, M. F. Doty, A. S . Bracker, D. Gershoni, V. L. Korenev , S . C. Bădescu, Y. Lyanda-Geller, and T. L. Reinecke, Phys. Rev...A SINGLE QUANTUM DOT VIA RESONANCE FLUORESCENCE 5a. CONTRACT NUMBER FA8750-12-2-0333 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR( S ) J...NUMBER CH 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES) University of Michigan 450 Church Street Ann Arbor MI 48109-1040 8. PERFORMING

International Conference on Atomic Physics (12TH) Held at Ann Arbor, Michigan on July 29-August 2, 1990. Abstracts of Contributed Papers.

DTIC Science & Technology

1990-09-26

Bloomfield IV-13 Observation of the 2 S 1/2 - 2D5/ 2 transition in laser cooled trapped Yb + A . S. Bell , H. A . Klein, G. P. Barwood, P. Gill, A . P...peaks is much less than the Doppler limit. a Supported by NSF and ONR 1. W. Bell and A . Bloom. Phys. Rev. Lett. 6. 280 (1961). 2. B. Sheehy et.al...AVAILABILITY OF REPORT 2b. OECLASSIFICATINOWGAIGSHDL Unlimited Distribution A PERFORMING ORGANIZATION REPORT NUMBER(S) S. MONITORING ORGANIZATION

Unitarity check in gravitational Higgs mechanism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berezhiani, Lasha; Mirbabayi, Mehrdad

2011-03-15

The effective field theory of massive gravity has long been formulated in a generally covariant way [N. Arkani-Hamed, H. Georgi, and M. D. Schwartz, Ann. Phys. (N.Y.) 305, 96 (2003).]. Using this formalism, it has been found recently that there exists a class of massive nonlinear theories that are free of the Boulware-Deser ghosts, at least in the decoupling limit [C. de Rham and G. Gabadadze, Phys. Rev. D 82, 044020 (2010).]. In this work we study other recently proposed models that go under the name of 'gravitational Higgs theories' [A. H. Chamseddine and V. Mukhanov, J. High Energy Phys.more » 08 (2010) 011.]. We show that these models, although seemingly different from the effective field theories of massive gravity, are in fact equivalent to them. Furthermore, based on the results obtained in the effective field theory approach, we conclude that the gravitational Higgs theories need the same adjustment of the Lagrangian to avoid the ghosts. We also show the equivalence between the noncovariant mode decomposition used in the Higgs theories, and the covariant Stueckelberg parametrization adopted in the effective field theories, thus proving that the presence or absence of the ghost is independent of the parametrization used in either theory.« less

Entanglement spectrum and boundary theories with projected entangled-pair states

NASA Astrophysics Data System (ADS)

Cirac, J. Ignacio; Poilblanc, Didier; Schuch, Norbert; Verstraete, Frank

2011-06-01

In many physical scenarios, close relations between the bulk properties of quantum systems and theories associated with their boundaries have been observed. In this work, we provide an exact duality mapping between the bulk of a quantum spin system and its boundary using projected entangled-pair states. This duality associates to every region a Hamiltonian on its boundary, in such a way that the entanglement spectrum of the bulk corresponds to the excitation spectrum of the boundary Hamiltonian. We study various specific models: a deformed AKLT model [I. Affleck, T. Kennedy, E. H. Lieb, and H. Tasaki, Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.59.799 59, 799 (1987)], an Ising-type model [F. Verstraete, M. M. Wolf, D. Perez-Garcia, and J. I. Cirac, Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.96.220601 96, 220601 (2006)], and Kitaev’s toric code [A. Kitaev, Ann. Phys.APNYA60003-491610.1016/S0003-4916(02)00018-0 303, 2 (2003)], both in finite ladders and in infinite square lattices. In the second case, some of those models display quantum phase transitions. We find that a gapped bulk phase with local order corresponds to a boundary Hamiltonian with local interactions, whereas critical behavior in the bulk is reflected on a diverging interaction length of the boundary Hamiltonian. Furthermore, topologically ordered states yield nonlocal Hamiltonians. Because our duality also associates a boundary operator to any operator in the bulk, it in fact provides a full holographic framework for the study of quantum many-body systems via their boundary.

Exploring one-particle orbitals in large many-body localized systems

NASA Astrophysics Data System (ADS)

Villalonga, Benjamin; Yu, Xiongjie; Luitz, David J.; Clark, Bryan K.

2018-03-01

Strong disorder in interacting quantum systems can give rise to the phenomenon of many-body localization (MBL), which defies thermalization due to the formation of an extensive number of quasilocal integrals of motion. The one-particle operator content of these integrals of motion is related to the one-particle orbitals (OPOs) of the one-particle density matrix and shows a strong signature across the MBL transition as recently pointed out by Bera et al. [Phys. Rev. Lett. 115, 046603 (2015), 10.1103/PhysRevLett.115.046603; Ann. Phys. 529, 1600356 (2017), 10.1002/andp.201600356]. We study the properties of the OPOs of many-body eigenstates of an MBL system in one dimension. Using shift-and-invert MPS, a matrix product state method to target highly excited many-body eigenstates introduced previously [Phys. Rev. Lett. 118, 017201 (2017), 10.1103/PhysRevLett.118.017201], we are able to obtain accurate results for large systems of sizes up to L =64 . We find that the OPOs drawn from eigenstates at different energy densities have high overlap and their occupations are correlated with the energy of the eigenstates. Moreover, the standard deviation of the inverse participation ratio of these orbitals is maximal at the nose of the mobility edge. Also, the OPOs decay exponentially in real space, with a correlation length that increases at low disorder. In addition, we find that the probability distribution of the strength of the large-range coupling constants of the number operators generated by the OPOs approach a log-uniform distribution at strong disorder.

The Old Saxon Leipzig "Heliand" Manuscript Fragment (MS L): New Evidence Concerning Luther, the Poet, and Ottonian Heritage

ERIC Educational Resources Information Center

Price, Timothy Blaine

2010-01-01

Begun as an investigation of the linguistic and paleographic evidence on the Old Saxon Leipzig "Heliand" fragment, the dissertation encompasses three analyses spanning over a millennium of that manuscript's existence. First, a direct analysis clarifies errors in the published transcription (4.2). The corrections result from digital…

High energy neutrinos from astrophysical accelerators of cosmic ray nuclei

NASA Astrophysics Data System (ADS)

Anchordoqui, Luis A.; Hooper, Dan; Sarkar, Subir; Taylor, Andrew M.

2008-02-01

Ongoing experimental efforts to detect cosmic sources of high energy neutrinos are guided by the expectation that astrophysical accelerators of cosmic ray protons would also generate neutrinos through interactions with ambient matter and/or photons. However, there will be a reduction in the predicted neutrino flux if cosmic ray sources accelerate not only protons but also significant numbers of heavier nuclei, as is indicated by recent air shower data. We consider plausible extragalactic sources such as active galactic nuclei, gamma ray bursts and starburst galaxies and demand consistency with the observed cosmic ray composition and energy spectrum at Earth after allowing for propagation through intergalactic radiation fields. This allows us to calculate the expected neutrino fluxes from the sources, normalized to the observed cosmic ray spectrum. We find that the likely signals are still within reach of next generation neutrino telescopes such as IceCube.PACS95.85.Ry98.70.Rz98.54.Cm98.54.EpReferencesFor a review, see:F.HalzenD.HooperRep. Prog. Phys.6520021025A.AchterbergIceCube CollaborationPhys. Rev. Lett.972006221101A.AchterbergIceCube CollaborationAstropart. Phys.262006282arXiv:astro-ph/0611063arXiv:astro-ph/0702265V.NiessANTARES CollaborationAIP Conf. Proc.8672006217I.KravchenkoPhys. Rev. D732006082002S.W.BarwickANITA CollaborationPhys. Rev. Lett.962006171101V.Van ElewyckPierre Auger CollaborationAIP Conf. Proc.8092006187For a survey of possible sources and event rates in km3 detectors see e.g.,W.BednarekG.F.BurgioT.MontaruliNew Astron. Rev.4920051M.D.KistlerJ.F.BeacomPhys. Rev. D742006063007A. Kappes, J. Hinton, C. Stegmann, F.A. Aharonian, arXiv:astro-ph/0607286.A.LevinsonE.WaxmanPhys. Rev. Lett.872001171101C.DistefanoD.GuettaE.WaxmanA.LevinsonAstrophys. J.5752002378F.A.AharonianL.A.AnchordoquiD.KhangulyanT.MontaruliJ. Phys. Conf. Ser.392006408J.Alvarez-MunizF.HalzenAstrophys. J.5762002L33F.VissaniAstropart. Phys.262006310F.W.SteckerC.DoneM.H.SalamonP.SommersPhys. Rev. Lett.6619912697(Erratum-ibid. 69 (1992) 2738)F.W.SteckerPhys. Rev. D722005107301A.AtoyanC.D.DermerPhys. Rev. Lett.872001221102L.A.AnchordoquiH.GoldbergF.HalzenT.J.WeilerPhys. Lett. B6002004202E.WaxmanJ.N.BahcallPhys. Rev. Lett.7819972292C.D.DermerA.AtoyanPhys. Rev. Lett.912003071102D.GuettaD.HooperJ.Alvarez-MunizF.HalzenE.ReuveniAstropart. Phys.202004429J.Alvarez-MunizF.HalzenD.W.HooperPhys. Rev. D622000093015A.LoebE.WaxmanJCAP06052006003S. Inoue, G. Sigl, F. Miniati, E. Armengaud, arXiv:astro-ph/0701167.E.WaxmanJ.N.BahcallPhys. Rev. D591999023002Phys. Rev. D642001023002K.MannheimR.J.ProtheroeJ.P.RachenPhys. Rev. D632001023003arXiv:astro-ph/9908031M.AhlersL.A.AnchordoquiH.GoldbergF.HalzenA.RingwaldT.J.WeilerPhys. Rev. D722005023001E.WaxmanAstrophys. J.4521995L1Note that the neutrino spectral shape can deviate from that for protons if the Feynman plateau is not flat in pseudo-rapidity space;L.AnchordoquiH.GoldbergC.NunezPhys. Rev. D712005065014This is in fact suggested by Tevatron data;F.AbeCDF CollaborationPhys. Rev. D4119902330J.G.LearnedS.PakvasaAstropart. Phys.31995267F.HalzenD.SaltzbergPhys. Rev. Lett.8119984305J.F.BeacomN.F.BellD.HooperS.PakvasaT.J.WeilerPhys. Rev. D682003093005(Erratum-ibid. D 72 (2005) 019901)L.A.AnchordoquiH.GoldbergF.HalzenT.J.WeilerPhys. Lett. B593200442L.A.AnchordoquiH.GoldbergF.HalzenT.J.WeilerPhys. Lett. B621200518A.M.HillasAnn. Rev. Astron. Astrophys.221984425For a general discussion on the acceleration time-scale in these sources see, e.g.,D.F.TorresL.A.AnchordoquiRep. Prog. Phys.6720041663M.C.BegelmanB.RudakM.SikoraAstrophys. J.362199038M.J.ChodorowskiA.A.ZdziarskiM.SikoraAstrophys. J.4001992181S.MichalowskiD.AndrewsJ.EickmeyerT.GentileN.MistryR.TalmanK.UenoPhys. Rev. Lett.391977737J.L.PugetF.W.SteckerJ.H.BredekampAstrophys. J.2051976638D.HooperS.SarkarA.M.TaylorAstropart. Phys.272007199The non-thermal energy release in GRBs is much smaller than that output by AGN.P.L.BiermannP.A.StrittmatterAstrophys. J.3221987643R.J.ProtheroeA.P.SzaboPhys. Rev. Lett.6919922885J.P.RachenP.L.BiermannAstron. Astrophys.2721993161J.P.RachenT.StanevP.L.BiermannAstron. Astrophys.2731993377R.C.HartmanEGRET CollaborationAstrophys. J. Suppl.123199979See e.g.,M.PunchNature3581992477D.PetryHEGRA CollaborationAstron. Astrophys.3111996L13P.M.ChadwickAstrophys. J.5131999161C.D.DermerR.SchlickeiserA.MastichiadisAstron. Astrophys.2561992L27S.D.BloomA.P.MarscherAstrophys. J.4611996657K.MannheimAstron. Astrophys.269199367K.MannheimScience2791998684A.DarA.LaorAstrophys. J.4781997L5F.A.AharonianNew Astron.52000377M.BoettcherAstrophys. J.5151999L21C.D.DermerR.SchlickeiserAstrophys. J.4161993458F.W.SteckerPhys. Rev. Lett.2119681016G.J.FishmanC.A.MeeganAnn. Rev. Astron. Astrophys.331995415For a list of papers related to SWIFT, see: http://swift.gsfc.nasa.gov/docs/swift/results/publist/.B.LinkR.I.EpsteinAstrophys. J.4661996764C.A.MeeganNature3551992143M.R.MetzgerNature3871997878See e.g.,T.PiranPhys. Rep.3141999575T.PiranPhys. Rep.3332000529For a recent review of GRB phenomenology, see:P.MeszarosRep. Prog. Phys.6920062259E.WaxmanLect. Notes Phys.5762001122M.MilgromV.UsovAstrophys. J.4491995L37E.WaxmanPhys. Rev. Lett.751995386M.VietriPhys. Rev. Lett.7819974328D.BandAstrophys. J.4131993281F. Halzen, in: K. Oliver (Ed.), Proceedings of the TASI’98, Boulder, 1998, p. 524.J.W.ElbertP.SommersAstrophys. J.4411995151L.A.AnchordoquiG.E.RomeroJ.A.CombiPhys. Rev. D601999103001L.A. Anchordoqui, J.F. Beacom, H. Goldberg, S. Palomares-Ruiz, T.J. Weiler, arXiv:astro-ph/0611580; arXiv:astro-ph/0611581.The factor 9/(4R) results from calculating ∫dr∫dr|r-r|(4πR/3), where r is the position of a star and r is the position of an observer (the position of the reaction), in a region of radius R uniformly filled with sources.D.A.ForbesM.J.WardV.RotaciucM.BlietzR.GenzelS.DrapatzP.P.van der WerfA.KrabbeAstrophys. J.4061993L11P. Chanial, H. Flores, B. Guiderdoni, D. Elbaz, F. Hammer, L. Vigroux, arXiv:astro-ph/0610900.P.O.LagageC.J.CesarskyAstron. Astrophys.1181983223S.P.LaiJ.M.GirartR.CrutcherAstrophys. J.5982003392W.BednarekMon. Not. R. Astron. Soc.3452003847W.BednarekR.J.ProtheroeAstropart. Phys.162002397P.BlasiA.V.OlintoPhys. Rev. D591999023001F.W.SteckerAstropart. Phys.262007398F.W. Stecker, arXiv:astro-ph/0610208.A γ-ray signal from the nearby starburst galaxy NGC253 was reported by the CANGAROO-II Collaboration but their subsequent re-analysis of the data is consistent with the expectation from backgrounds:C.ItohCANGAROO-II CollaborationAstron. Astrophys.3962002L1(Erratum-ibid. 462 (2007) 67)T.A. Thompson, E. Quataert, E. Waxman, A. Loeb, arXiv:astro-ph/0608699.D.J.BirdFly’s Eye CollaborationPhys. Rev. Lett.7119933401D.R.BergmanHiRes CollaborationNucl. Phys. Proc. Suppl.136200440T.Abu-ZayyadHiRes-MIA CollaborationAstrophys. J.5572001686M.NaganoJ. Phys. G181992423V.BerezinskyA.Z.GazizovS.I.GrigorievaPhys. Rev. D742006043005R.U.AbbasiHiRes CollaborationPhys. Rev. Lett.922004151101V.BerezinskyA.Z.GazizovS.I.GrigorievaPhys. Lett. B6122005147V.S.BerezinskyS.I.GrigorievaB.I.HnatykAstropart. Phys.212004617See Fig. 21 in:L.AnchordoquiM.T.DovaA.MariazziT.McCauleyT.PaulS.ReucroftJ.SwainAnn. Phys.3142004145D.AllardE.ParizotE.KhanS.GorielyA.V.OlintoAstron. Astrophys.4432005L29D.AllardE.ParizotA.V.OlintoAstropart. Phys.27200761T.Abu-ZayyadHigh Resolution Fly’s Eye CollaborationAstropart. Phys.232005157P. Sommers, et al., Pierre Auger Collaboration, arXiv:astro-ph/0507150.R.U.AbbasiHiRes CollaborationAstrophys. J.6222005910B.N. Afanasiev, et al., Yakutsk Collaboration, in: M. Nagano (Ed.), Proceedings of the Tokyo Workshop on Techniques for the Study of the Extremely High Energy Cosmic Rays, 1993.J. Knapp, private communication.J.RanftPhys. Rev. D51199564R.S.FletcherT.K.GaisserP.LipariT.StanevPhys. Rev. D5019945710J.EngelT.K.GaisserT.StanevP.LipariPhys. Rev. D4619925013N.N.KalmykovS.S.OstapchenkoA.I.PavlovNucl. Phys. Proc. Suppl.52B19977It is important to stress that the Auger data are still at a preliminary stage and the reconstruction procedures are still to be finalised. However, even allowing for the systematic uncertainties still present, it does appear that at the highest energies fewer events are seen than expected from the AGASA analysis.V.S.BerezinskyG.T.ZatsepinPhys. Lett. B281969423F.W.SteckerAstrophys. J.2281979919R.EngelD.SeckelT.StanevPhys. Rev. D642001093010Z.FodorS.D.KatzA.RingwaldH.TuJCAP03112003015D.De MarcoT.StanevF.W.SteckerPhys. Rev. D732006043003D.HooperA.TaylorS.SarkarAstropart. Phys.23200511M.AveN.BuscaA.V.OlintoA.A.WatsonT.YamamotoAstropart. Phys.23200519A point worth noting at this juncture: If iron nuclei are accelerated to very high energies (much higher than the energy spectrum has been measured), then disintegration can lead to large numbers of protons above the spectrum cutoff. In this case, the resulting cosmogenic neutrino flux is not dramatically suppressed. On the other hand, if iron nuclei are only largely accelerated to around 10eV or less, then the liberated protons will only rarely interact with the CMB to produce pions, hence the cosmogenic neutrino flux will be significantly reduced.

On the Origins of the Term and Meanings of "Adult Education" in the United States.

ERIC Educational Resources Information Center

Stubblefield, Harold W.; Rachal, John R.

1992-01-01

The term "adult education" was used in the United States in the late nineteenth century. Melvil Dewey developed a typology of adult education, and Henry Leipziger promoted New York City's Free Lectures program as an institute of liberal adult education. Leipziger's advocacy was largely responsible for the diffusion of the new term in its…

Spatial entanglement of nonvacuum Gaussian states

NASA Astrophysics Data System (ADS)

Kiałka, Filip; Ahmadi, Mehdi; Dragan, Andrzej

2016-06-01

The vacuum state of a relativistic quantum field contains entanglement between regions separated by spacelike intervals. Such spatial entanglement can be revealed using an operational method introduced in [M. Rodriguez-Vazquez, M. del Rey, H. Westman, and J. Leon, Ann. Phys. (N.Y.) 351, 112 (2014), E. G. Brown, M. del Rey, H. Westman, J. Leon, and A. Dragan, Phys. Rev. D 91, 016005 (2015)]. In this approach, a cavity is instantaneously divided into halves by an introduction of an extra perfect mirror. Causal separation of the two regions of the cavity reveals nonlocal spatial correlations present in the field, which can be quantified by measuring particles generated in the process. We use this method to study spatial entanglement properties of nonvacuum Gaussian field states. In particular, we show how to enhance the amount of harvested spatial entanglement by an appropriate choice of the initial state of the field in the cavity. We find a counterintuitive influence of the initial entanglement between cavity modes on the spatial entanglement which is revealed by dividing the cavity in half.

Analytical study of a Kerr-Sen black hole and a charged massive scalar field

NASA Astrophysics Data System (ADS)

Bernard, Canisius

2017-11-01

It is reported that Kerr-Newman and Kerr-Sen black holes are unstable to perturbations of charged massive scalar field. In this paper, we study analytically the complex frequencies which characterize charged massive scalar fields in a near-extremal Kerr-Sen black hole. For near-extremal Kerr-Sen black holes and for charged massive scalar fields in the eikonal large-mass M ≫μ regime, where M is the mass of the black hole, and μ is the mass of the charged scalar field, we have obtained a simple expression for the dimensionless ratio ωI/(ωR-ωc) , where ωI and ωR are, respectively, the imaginary and real parts of the frequency of the modes, and ωc is the critical frequency for the onset of super-radiance. We have also found our expression is consistent with the result of Hod [Phys. Rev. D 94, 044036 (2016), 10.1103/PhysRevD.94.044036] for the case of a near-extremal Kerr-Newman black hole and the result of Zouros and Eardly [Ann. Phys. (N.Y.) 118, 139 (1979), 10.1016/0003-4916(79)90237-9] for the case of neutral scalar fields in the background of a near-extremal Kerr black hole.

On the Ambjorn-Olesen electroweak condensates

NASA Astrophysics Data System (ADS)

Bartolucci, Daniele; De Marchis, Francesca

2012-07-01

We obtain sufficient conditions for the existence of the Ambjorn-Olesen ["On electroweak magnetism," Nucl. Phys. B315, 606-614 (1989), 10.1016/0550-3213(89)90004-7] electroweak N-vortices in case N ⩾ 1 and therefore generalize earlier results [D. Bartolucci and G. Tarantello, "Liouville type equations with singular data and their applications to periodic multivortices for the electroweak theory," Commun. Math. Phys. 229, 3-47 (2002), 10.1007/s002200200664; J. Spruck and Y. Yang, "On multivortices in the electroweak theory I: Existence of periodic solutions," Commun. Math. Phys. 144, 1-16 (1992), 10.1007/BF02099188] which handled the cases N ∈ {1, 2, 3, 4}. The variational argument provided here has its own independent interest as it generalizes the one adopted by Ding et al. ["Existence results for mean field equations," Ann. Inst. Henri Poincare, Anal. Non Lineaire 16, 653-666 (1999), 10.1016/S0294-1449(99)80031-6] to obtain solutions for Liouville-type equations on closed 2-manifolds. In fact, we obtain at once a second proof of the existence of supercritical conformal metrics on surfaces with conical singularities and prescribed Gaussian curvature recently established by Bartolucci, De Marchis and Malchiodi [Int. Math. Res. Not. 24, 5625-5643 (2011), 10.1093/imrn/rnq285].

[Development of child neuropsychiatry at the Karl Marx University of Leipzig].

PubMed

Gebelt, H

1978-05-01

The development of pedoneuropsychiatry at the University of Leipzig is marked by the opening in 1926 of the first "Department of Pedopsychiatric Observation", the establishment of the Clinic of Pedoneuropsychiatry as an independent unit of the Department of Medicine, Karl Marx University, and the setting up in 1976 of a Chair of Pedoneuropsychiatry. Paul Schröder's and R. A. Pfeifer's services to their university are particularly appreciated.

Technical Note: Dosimetry of Leipzig and Valencia applicators without the plastic cap

DOE Office of Scientific and Technical Information (OSTI.GOV)

Granero, D., E-mail: dgranero@eresa.com; Candela-Juan, C.; Vijande, J.

2016-05-15

Purpose: High dose rate (HDR) brachytherapy for treatment of small skin lesions using the Leipzig and Valencia applicators is a widely used technique. These applicators are equipped with an attachable plastic cap to be placed during fraction delivery to ensure electronic equilibrium and to prevent secondary electrons from reaching the skin surface. The purpose of this study is to report on the dosimetric impact of the cap being absent during HDR fraction delivery, which has not been explored previously in the literature. Methods: GEANT4 Monte Carlo simulations (version 10.0) have been performed for the Leipzig and Valencia applicators with andmore » without the plastic cap. In order to validate the Monte Carlo simulations, experimental measurements using radiochromic films have been done. Results: Dose absorbed within 1 mm of the skin surface increases by a factor of 1500% for the Leipzig applicators and of 180% for the Valencia applicators. Deeper than 1 mm, the overdosage flattens up to a 10% increase. Conclusions: Differences of treating with or without the plastic cap are significant. Users must check always that the plastic cap is in place before any treatment in order to avoid overdosage of the skin. Prior to skin HDR fraction delivery, the timeout checklist should include verification of the cap placement.« less

French descriptions of Wundt's laboratory in Leipzig in 1886.

PubMed

Nicolas, Serge; Gyselinck, Valérie; Murray, David J; Bandomir, Christina A

2002-08-01

Translations are provided of extracts from reports by the French-speaking scholars Alfred Grafé and Emile Durkheim concerning the instructional procedures in place at Wundt's Institute for Experimental Psychology in Leipzig in the summer of 1886. It is stressed that reports such as theirs were of importance, not only to researchers, but also to teachers and administrators concerned with how the new experimental psychology should be taught, especially with respect to practical classes.

The Emergence of Mathematical Physics at the University of Leipzig

NASA Astrophysics Data System (ADS)

Schlote, Karl-Heinz

Except for the well-known blossoming of theoretical physics with the group around Werner Heisenberg at the University of Leipzig at the end of the 1920s, the tradition of mathematical physics had been analyzed in only a few aspects, in particular the work of Carl Neumann and his contributions to the shaping of mathematical physics in general and the theory of electrodynamics in particular. However, the establishment of mathematical physics and its strong position at the University of Leipzig, with Neumann as its leading figure in the last third of the nineteenth century, formed important preconditions for the later upswing. That process is analyzed in this article, focusing on the work of Neumann. It includes a discussion of his ideas on the structure of a physical theory and the role of mathematics in physics as well as his impact on the interaction of mathematics and physics.

From four-bed clinic to modern eye hospital: ophthalmology in Leipzig, 1820-1996.

PubMed

Fahrenbach, S; Wiedemann, P

1999-01-01

The opening of the "Heilanstalt für arme Augenkranke" by Friedrich Philipp Ritterich (1782-1866) in 1820 was an important landmark for ophthalmology in Leipzig and in all of Germany. The first chair of ophthalmology in Germany was taken by Christian Georg Theodor Ruete in 1852. In 1883 the clinic moved to a new domicile, a modern building in the Leipzig "medical quarter." In the ensuing years, the hospital developed into a well-known university center of ophthalmology with the scientific, clinical, and organizational work of ophthalmologists such as Hubert Sattler and Ernst Hertel. The extension of the old building in 1908-1911 and the rebuilding after the destruction in World War II created better opportunities for research, teaching, and patient treatment. Comprehensive expansion and reconstruction of the Eye Hospital since 1994 has created excellent conditions for both clinical and experimental ophthalmology as well as the training of students.

Statistical mechanics of Fermi-Pasta-Ulam chains with the canonical ensemble

NASA Astrophysics Data System (ADS)

Demirel, Melik C.; Sayar, Mehmet; Atılgan, Ali R.

1997-03-01

Low-energy vibrations of a Fermi-Pasta-Ulam-Β (FPU-Β) chain with 16 repeat units are analyzed with the aid of numerical experiments and the statistical mechanics equations of the canonical ensemble. Constant temperature numerical integrations are performed by employing the cubic coupling scheme of Kusnezov et al. [Ann. Phys. 204, 155 (1990)]. Very good agreement is obtained between numerical results and theoretical predictions for the probability distributions of the generalized coordinates and momenta both of the chain and of the thermal bath. It is also shown that the average energy of the chain scales linearly with the bath temperature.

Fracton topological order from nearest-neighbor two-spin interactions and dualities

NASA Astrophysics Data System (ADS)

Slagle, Kevin; Kim, Yong Baek

2017-10-01

Fracton topological order describes a remarkable phase of matter, which can be characterized by fracton excitations with constrained dynamics and a ground-state degeneracy that increases exponentially with the length of the system on a three-dimensional torus. However, previous models exhibiting this order require many-spin interactions, which may be very difficult to realize in a real material or cold atom system. In this work, we present a more physically realistic model which has the so-called X-cube fracton topological order [Vijay, Haah, and Fu, Phys. Rev. B 94, 235157 (2016), 10.1103/PhysRevB.94.235157] but only requires nearest-neighbor two-spin interactions. The model lives on a three-dimensional honeycomb-based lattice with one to two spin-1/2 degrees of freedom on each site and a unit cell of six sites. The model is constructed from two orthogonal stacks of Z2 topologically ordered Kitaev honeycomb layers [Kitaev, Ann. Phys. 321, 2 (2006), 10.1016/j.aop.2005.10.005], which are coupled together by a two-spin interaction. It is also shown that a four-spin interaction can be included to instead stabilize 3+1D Z2 topological order. We also find dual descriptions of four quantum phase transitions in our model, all of which appear to be discontinuous first-order transitions.

North Korea’s Juche Ideology and the German Re-Unification Experience

DTIC Science & Technology

2008-06-01

repräsentativen Befragung in Ost- und Westdeutschland 2006,” Selbständige Abteilung für Medizimische Psychologie und Medizinische Soziologie der Universität...207 Ibid. “Auf anderen Ebenen – denen der Wirtschaft und der Psychologie – führte die rasche Übernahme des westdeutschen Systems zu Zerstörungen... Psychologie und Medizinische Soziologie der Universität Leipzig. (May/June 2006). http://medpsy.uniklinikum- leipzig.de/pdf/deutsche_befindlichkeiten_2006

[Wilhelm His Senior--the life and work of the important Leipzig morphologist].

PubMed

Wendler, D; Rother, P

1982-12-01

The paper describes the life, work and personality of Wilhelm His, the prominent morphologist who taught at Leipzig University between 1872 and 1904. It therefore contains details from the history of medicine in the second half of the last century. Mention is made of the character features which made the work of Wilhelm His so successful and brought him worldwide recognition--a ceaseless thirst for knowledge, enormous diligence and an unwavering love of truth.

[Reaction time tests in Leipzig, Paris and Würzburg. The Franco-German history of a psychological experiment, 1890-1910].

PubMed

Carroy, Jacqueline; Schmidgen, Henning

2004-01-01

This article diiscusses from a comparative perspective the complex history of the reaction experiment with the Hipp chronoscope, one of the central experiments of late 19th-century psychology. It focuses on Wilhelm Wundt's (1832-1920) Institute for Experimental Psychology in Leipzig and on the Paris Laboratory for Physiological Psychology at the Sorbonne, which was initially directed by Henry Beaunis (1830-1921), but soon came to be dominated by the research activities of Alfred Binet (1857-1911). When the Paris psychologists founded their Laboratory in 1889, they took the Leipzig Institute as their model. In the early 1890s they adopted the reaction time experiment that had been central to Wundt's psychology. Shortly after, they modified this experiment according to their own specific interests. For Binet, it no longer served as a method for identifying the elementary components of "general" consciousness (as in Wundt), but for classifying "individual" personalities. The methodological and technological changes that Binet introduced into the experimental practice of psychology had no immediate impact on the research work in Leipzig. However, they influenced the "Wurzburg School" of psychology under Wundt's former assistant, Oswald Külpe (1862-1915). This illustrates that the comparative history of transfers of "experimental systems" (Rheinberger) across national borders is not simply a history of mere transports. Rather, it is a history of transferences that sometimes includes surprising "re-transferences".

Some Consequences of a Time Dependent Speed of Light

NASA Astrophysics Data System (ADS)

Smith, Felix T.

2007-06-01

For reasons connected with both cosmology (the flatness and horizon problems) and atomic physics (n-body Dirac equation, etc.), various proposals have been made to modify general or special relativity(SR) to accommodate a cosmologically decreasing light speed [J. Magueijo, Rep. Prog. Phys. 66, 2025 (2003)]. Two such theories, projective SR [S.N. Manida, gr-qc/9905046; S. S. Stepanov, physics/9909009 and Phys. Rev. D, 62, 023507 (2000)] and symmetric SR [F.T. Smith, Ann. Fond. L. de Broglie, 30, 179 (2005)] adapt special relativity to in different ways to an expanding, hyperbolically curved position space and predict time-dependences of c within reach of measurement but differing by a factor of two. Both theories bring in a new constant λ-1=σ=c^2H0-1. As Magueijo points, out the role of c in physics and cosmology is so profound that many deep changes must follow if is not absolutely invariant in space and time. In particular, symmetric SR brings a new light to the Dirac large-number relationship between the constants of gravitation and atomic physics.

Fluctuation theorems for discrete kinetic models of molecular motors

NASA Astrophysics Data System (ADS)

Faggionato, Alessandra; Silvestri, Vittoria

2017-04-01

Motivated by discrete kinetic models for non-cooperative molecular motors on periodic tracks, we consider random walks (also not Markov) on quasi one dimensional (1d) lattices, obtained by gluing several copies of a fundamental graph in a linear fashion. We show that, for a suitable class of quasi-1d lattices, the large deviation rate function associated to the position of the walker satisfies a Gallavotti-Cohen symmetry for any choice of the dynamical parameters defining the stochastic walk. This class includes the linear model considered in Lacoste et al (2008 Phys. Rev. E 78 011915). We also derive fluctuation theorems for the time-integrated cycle currents and discuss how the matrix approach of Lacoste et al (2008 Phys. Rev. E 78 011915) can be extended to derive the above Gallavotti-Cohen symmetry for any Markov random walk on {Z} with periodic jump rates. Finally, we review in the present context some large deviation results of Faggionato and Silvestri (2017 Ann. Inst. Henri Poincaré 53 46-78) and give some specific examples with explicit computations.

International Communication and Confidence-Building in Europe. Report of the Leipzig-Tampere Seminar on Confidence-Building in the Non-Military Field (1st, Leipzig, East Germany, May 14-15, 1986). Publications Series B. 20/1986

ERIC Educational Resources Information Center

Kleinwachter, Wolfgang, Ed.; Nordenstreng, Kaarle, Ed.

Focusing on the role of the international mass media, these essays stress the urgency of building confidence in the relations among European states and populations in order to secure peace on a world-wide scale and to stop the arms race on the earth and prevent its extension to outer space. Titles and authors are as follows: (1) "Welcoming…

[Karl Sudhoff].

PubMed

Kästner, Ingrid

2013-01-01

In 1914, from 6th May to 18th October, the International Exposition of book Industry and Graphic Arts (BUGRA) took place in Leipzig, then the world capital of books. Karl Sudhoff, director of the Leipzig Institute of the History of Medicine, was appointed by the executive committee of the BURGA to organize the special exhibition "Three Millennia of Graphic Arts in the Service of Science". The paper shows, following Sudhoff's own descriptions and new archival sources, the conceptual design and the contents of this exposition set up by Sudhoff.

Assessing climate impacts of planning policies-An estimation for the urban region of Leipzig (Germany)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schwarz, Nina, E-mail: nina.schwarz@ufz.de; Bauer, Annette, E-mail: annette.bauer@ufz.de; Haase, Dagmar, E-mail: dagmar.haase@ufz.d

2011-03-15

Local climate regulation by urban green areas is an important urban ecosystem service, as it reduces the extent of the urban heat island and therefore enhances quality of life. Local and regional planning policies can control land use changes in an urban region, which in turn alter local climate regulation. Thus, this paper describes a method for estimating the impacts of current land uses as well as local and regional planning policies on local climate regulation, using evapotranspiration and land surface emissivity as indicators. This method can be used by practitioners to evaluate their policies. An application of this methodmore » is demonstrated for the case study Leipzig (Germany). Results for six selected planning policies in Leipzig indicate their distinct impacts on climate regulation and especially the role of their spatial extent. The proposed method was found to easily produce a qualitative assessment of impacts of planning policies on climate regulation.« less

Comment on 'Supersymmetry, PT-symmetry and spectral bifurcation'

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bagchi, B., E-mail: bbagchi123@rediffmail.com; Quesne, C., E-mail: cquesne@ulb.ac.be

2011-02-15

We demonstrate that the recent paper by Abhinav and Panigrahi entitled 'Supersymmetry, PT-symmetry and spectral bifurcation' [K. Abhinav, P.K. Panigrahi, Ann. Phys. 325 (2010) 1198], which considers two different types of superpotentials for the PT-symmetric complexified Scarf II potential, fails to take into account the invariance under the exchange of its coupling parameters. As a result, they miss the important point that for unbroken PT-symmetry this potential indeed has two series of real energy eigenvalues, to which one can associate two different superpotentials. This fact was first pointed out by the present authors during the study of complex potentials havingmore » a complex sl(2) potential algebra.« less

International Conference on Atomic Physics: Abstracts of Contributed Papers (12th) Held in Ann Arbor, Michigan on 29 July-3 August 1990

DTIC Science & Technology

1990-09-26

A . Bloomfield IV-13 Obsenation of the 2SI12 - 2D51 2 transition in laser cooled trapped Y7) 4 A . S. Bell , H. A . Klein, G. P...discussed. I. H A Klein, A S Bell , G P Barwood and P Gill, Appl. Phys B 0, 13 (1990). 2. H A Klein, A S Bell , 0 P Barwood, P Gill and W R C Rowley...H.C.W. IV-6; X-1 Abu-Jafar, M. VII-41 Belkacem, A . VIII-23 Abutaleb, M. VIII-19 Bell , A.S. IV-13 Adachi, H. IX-1 Bengtsson, J. VIII-12 Aggarwal,

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walton, Mark A.

Quantum mechanics in phase space (or deformation quantization) appears to fail as an autonomous quantum method when infinite potential walls are present. The stationary physical Wigner functions do not satisfy the normal eigen equations, the *-eigen equations, unless an ad hoc boundary potential is added [N.C. Dias, J.N. Prata, J. Math. Phys. 43 (2002) 4602 (quant-ph/0012140)]. Alternatively, they satisfy a different, higher-order, '*-eigen-* equation', locally, i.e. away from the walls [S. Kryukov, M.A. Walton, Ann. Phys. 317 (2005) 474 (quant-ph/0412007)]. Here we show that this substitute equation can be written in a very simple form, even in the presence ofmore » an additional, arbitrary, but regular potential. The more general applicability of the *-eigen-* equation is then demonstrated. First, using an idea from [D.B. Fairlie, C.A. Manogue, J. Phys. A 24 (1991) 3807], we extend it to a dynamical equation describing time evolution. We then show that also for general contact interactions, the *-eigen-* equation is satisfied locally. Specifically, we treat the most general possible (Robin) boundary conditions at an infinite wall, general one-dimensional point interactions, and a finite potential jump. Finally, we examine a smooth potential, that has simple but different expressions for x positive and negative. We find that the *-eigen-* equation is again satisfied locally. It seems, therefore, that the *-eigen-* equation is generally relevant to the matching of Wigner functions; it can be solved piece-wise and its solutions then matched.« less

[The 100th anniversary of Gustav Wilhelm Störring's "Lectures on Psychopathology". A review of his early years].

PubMed

Steinberg, H; Künstler, U

2000-06-01

The aim of this essay is to retell the life and work of philosopher and psychiatrist Gustav Wilhelm Störring (1860-1946) during his early years in Leipzig and Erdmannshain. His "Lectures on Psychopathology and its Impact on Normal Psychology", written 100 years ago, are acknowledged as his most important work. With this book Störring stands in opposition to many of his contemporaries, which is illustrated with his concept of mania. In some aspects, however, his ideas coincide with those of other well-known psychiatrists such as Emil Kraepelin. Both were inclined to the idea that psychiatry and psychology could mutually stimulate each other. Störring's work in Wundt's laboratory of experimental psychology had a major impact on his work. Wundt's ideas gave impetus to a lot of his work and also influenced papers Störring was to write later on. Störring's biography is followed until 1902 when he was appointed professor of philosophy of Zurich University, in which his friend Ernst Meumann was substantially involved. In Leipzig Störring had started his work as Flechsig's assistant at the hospital of psychiatry and neurology of Leipzig University. In 1897 he founded his own sanitarium for mentally and neurologically ill in Erdmannshain, a village near Leipzig, which he managed together with his wife Marie, née Bonacker. With the help of Wundt Störring qualifies as a university lecturer. During the years regarded here, however, he got more and more attracted to philosophical matters, thus turning away from neurosciences. In time he started to regard his work as physician as nothing more than necessary for making his living. His relationship with Wundt, who together with his laboratory of experimental psychology had previously made him wish to come to Leipzig, cooled down, at least on the part of the first. That puts an end to Störring's early years not only from the point of view of his biography but also from his work.

Monitoring the Urban Tree Cover for Urban Ecosystem Services - The Case of Leipzig, Germany

NASA Astrophysics Data System (ADS)

Banzhaf, E.; Kollai, H.

2015-04-01

Urban dynamics such as (extreme) growth and shrinkage bring about fundamental challenges for urban land use and related changes. In order to achieve a sustainable urban development, it is crucial to monitor urban green infrastructure at microscale level as it provides various urban ecosystem services in neighbourhoods, supporting quality of life and environmental health. We monitor urban trees by means of a multiple data set to get a detailed knowledge on its distribution and change over a decade for the entire city. We have digital orthophotos, a digital elevation model and a digital surface model. The refined knowledge on the absolute height above ground helps to differentiate tree tops. Grounded on an object-based image analysis scheme a detailed mapping of trees in an urbanized environment is processed. Results show high accuracy of tree detection and avoidance of misclassification due to shadows. The study area is the City of Leipzig, Germany. One of the leading German cities, it is home to contiguous community allotments that characterize the configuration of the city. Leipzig has one of the most well-preserved floodplain forests in Europe.

Spectral Gap Estimates in Mean Field Spin Glasses

NASA Astrophysics Data System (ADS)

Ben Arous, Gérard; Jagannath, Aukosh

2018-05-01

We show that mixing for local, reversible dynamics of mean field spin glasses is exponentially slow in the low temperature regime. We introduce a notion of free energy barriers for the overlap, and prove that their existence imply that the spectral gap is exponentially small, and thus that mixing is exponentially slow. We then exhibit sufficient conditions on the equilibrium Gibbs measure which guarantee the existence of these barriers, using the notion of replicon eigenvalue and 2D Guerra Talagrand bounds. We show how these sufficient conditions cover large classes of Ising spin models for reversible nearest-neighbor dynamics and spherical models for Langevin dynamics. Finally, in the case of Ising spins, Panchenko's recent rigorous calculation (Panchenko in Ann Probab 46(2):865-896, 2018) of the free energy for a system of "two real replica" enables us to prove a quenched LDP for the overlap distribution, which gives us a wider criterion for slow mixing directly related to the Franz-Parisi-Virasoro approach (Franz et al. in J Phys I 2(10):1869-1880, 1992; Kurchan et al. J Phys I 3(8):1819-1838, 1993). This condition holds in a wider range of temperatures.

EDITORIAL: Special section on signal transduction Special section on signal transduction

NASA Astrophysics Data System (ADS)

Shvartsman, Stanislav

2012-08-01

This special section of Physical Biology focuses on multiple aspects of signal transduction, broadly defined as the study of the mechanisms by which cells communicate with their environment. Mechanisms of cell communication involve detection of incoming signals, which can be chemical, mechanical or electromagnetic, relaying these signals to intracellular processes, such as cytoskeletal networks or gene expression systems, and, ultimately, converting these signals to responses such as cell differentiation or death. Given the multiscale nature of signal transduction systems, they must be studied at multiple levels, from the identities and structures of molecules comprising signal detection and interpretation networks, to the systems-level properties of these networks. The 11 papers in this special section illustrate some of the most exciting aspects of signal transduction research. The first two papers, by Marie-Anne Félix [1] and by Efrat Oron and Natalia Ivanova [2], focus on cell-cell interactions in developing tissues, using vulval patterning in worm and cell fate specification in mammalian embryos as prime examples of emergent cell behaviors. Next come two papers from the groups of Julio Saez-Rodriguez [3] and Kevin Janes [4]. These papers discuss how the causal relationships between multiple components of signaling systems can be inferred using multivariable statistical analysis of empirical data. An authoritative review by Zarnitsyna and Zhu [5] presents a detailed discussion of the sequence of signaling events involved in T-cell triggering. Once the structure and components of the signaling systems are determined, they can be modeled using approaches that have been successful in other physical sciences. As two examples of such approaches, reviews by Rubinstein [6] and Kholodenko [7], present reaction-diffusion models of cell polarization and thermodynamics-based models of gene regulation. An important class of models takes the form of enzymatic networks, where a single molecule can participate in multiple types of interactions. Mathematical analysis of these models is discussed in the papers by Del Vecchio [8], Seaton and Krishnan [9], and Hatzimanikatis and colleagues [10]. Finally, all signaling systems are information processing devices. While this point is broadly accepted, there have been only a few attempts to apply information theory to experimental signaling systems. A review by Andre Levchenko and colleagues [11] provides a very clear introduction to information theory and its potential applications to signal transduction in cellular systems. References [1] Félix M-A 2012 Phys. Biol. 9 045001 [2] Oron E and Ivanova N 2012 Phys. Biol. 9 045002 [3] MacNamara A et al 2012 Phys. Biol. 9 045003 [4] Jensen K J and Janes K A 2012 Phys. Biol. 9 045004 [5] Zarnitsyna V and Zhu C 2012 Phys. Biol. 9 045005 [6] Rubinstein B et al 2012 Phys. Biol. 9 045006 [7] Frank T D et al 2012 Phys. Biol. 9 045007 [8] Del Vecchio D et al 2012 Phys. Biol. 9 045008 [9] Seaton D D and Krishnan J 2012 Phys. Biol. 9 045009 [10] Radivojevic A et al 2012 Phys. Biol. 9 045010 [11] Rhee A et al 2012 Phys. Biol. 9 045011

Presymplectic current and the inverse problem of the calculus of variations

NASA Astrophysics Data System (ADS)

Khavkine, Igor

2013-11-01

The inverse problem of the calculus of variations asks whether a given system of partial differential equations (PDEs) admits a variational formulation. We show that the existence of a presymplectic form in the variational bicomplex, when horizontally closed on solutions, allows us to construct a variational formulation for a subsystem of the given PDE. No constraints on the differential order or number of dependent or independent variables are assumed. The proof follows a recent observation of Bridges, Hydon, and Lawson [Math. Proc. Cambridge Philos. Soc. 148(01), 159-178 (2010)] and generalizes an older result of Henneaux [Ann. Phys. 140(1), 45-64 (1982)] from ordinary differential equations (ODEs) to PDEs. Uniqueness of the variational formulation is also discussed.

Quantum vacuum interaction between two cosmic strings revisited

NASA Astrophysics Data System (ADS)

Muñoz-Castañeda, J. M.; Bordag, M.

2014-03-01

We reconsider the quantum vacuum interaction energy between two straight parallel cosmic strings. This problem was discussed several times in an approach treating both strings perturbatively and treating only one perturbatively. Here we point out that a simplifying assumption made by Bordag [Ann. Phys. (Berlin) 47, 93 (1990).] can be justified and show that, despite the global character of the background, the perturbative approach delivers a correct result. We consider the applicability of the scattering methods, developed in the past decade for the Casimir effect, for the cosmic string and find it not applicable. We calculate the scattering T-operator on one string. Finally, we consider the vacuum interaction of two strings when each carries a two-dimensional delta function potential.

Proof of a Dain inequality with charge

NASA Astrophysics Data System (ADS)

Lopes Costa, João

2010-07-01

We prove an upper bound for angular momentum and charge in terms of the mass for electro-vacuum asymptotically flat axisymmetric initial data sets with simply connected orbit space. This completes the work started in (Chruściel and Costa 2009 Class. Quantum Grav. 26 235013 (arXiv:gr-qc/0909.5625)) where this charged Dain inequality was first presented but where the proof of the main result, based on the methods of Chruściel et al (Ann. Phys. 2008 323 2591-613 (arXiv:gr-qc/0712.4064v2)), was only sketched. Here we present a complete proof while simplifying the methods suggested by Chruściel and Costa (2009 Class. Quantum Grav. 26 235013 (arXiv:gr-qc/0909.5625)).

Quantization of Time-Like Energy for Wave Maps into Spheres

NASA Astrophysics Data System (ADS)

Grinis, Roland

2017-06-01

In this article we consider large energy wave maps in dimension 2+1, as in the resolution of the threshold conjecture by Sterbenz and Tataru (Commun. Math. Phys. 298(1):139-230, 2010; Commun. Math. Phys. 298(1):231-264, 2010), but more specifically into the unit Euclidean sphere S^{n-1} \\subsetRn with {n≥2}, and study further the dynamics of the sequence of wave maps that are obtained in Sterbenz and Tataru (Commun. Math. Phys. 298(1):231-264, 2010) at the final rescaling for a first, finite or infinite, time singularity. We prove that, on a suitably chosen sequence of time slices at this scaling, there is a decomposition of the map, up to an error with asymptotically vanishing energy, into a decoupled sum of rescaled solitons concentrating in the interior of the light cone and a term having asymptotically vanishing energy dispersion norm, concentrating on the null boundary and converging to a constant locally in the interior of the cone, in the energy space. Similar and stronger results have been recently obtained in the equivariant setting by several authors (Côte, Commun. Pure Appl. Math. 68(11):1946-2004, 2015; Côte, Commun. Pure Appl. Math. 69(4):609-612, 2016; Côte, Am. J. Math. 137(1):139-207, 2015; Côte et al., Am. J. Math. 137(1):209-250, 2015; Krieger, Commun. Math. Phys. 250(3):507-580, 2004), where better control on the dispersive term concentrating on the null boundary of the cone is provided, and in some cases the asymptotic decomposition is shown to hold for all time. Here, however, we do not impose any symmetry condition on the map itself and our strategy follows the one from bubbling analysis of harmonic maps into spheres in the supercritical regime due to Lin and Rivière (Ann. Math. 149(2):785-829, 1999; Duke Math. J. 111:177-193, 2002), which we make work here in the hyperbolic context of Sterbenz and Tataru (Commun. Math. Phys. 298(1), 231-264, 2010).

Line Shifts in Rotational Spectra of Polyatomic Chiral Molecules Caused by the Parity Violating Electroweak Interaction

NASA Astrophysics Data System (ADS)

Stohner, J.; Quack, M.

2009-06-01

Are findings in high-energy physics of any importance in molecular spectroscopy ? The answer is clearly `yes'. Energies of enantiomers were considered as exactly equal in an achiral environment, e.g. the gas phase. Today, however, it is well known that this is not valid. The violation of mirror-image symmetry (suggested theoretically and confirmed experimentally in 1956/57) was established in the field of nuclear, high-energy, and atomic physics since then, and it is also the cause for a non-zero energy difference between enantiomers. We expect today that the violation of mirror-image symmetry (parity violation) influences chemistry of chiral molecules as well as their spectroscopy. Progress has been made in the quantitative theoretical prediction of possible spectroscopic signatures of molecular parity violation. The experimental confirmation of parity violation in chiral molecules is, however, still open. Theoretical studies are helpful for the planning and important for a detailed analysis of rovibrational and tunneling spectra of chiral molecules. We report results on frequency shifts in rotational, vibrational and tunneling spectra of some selected chiral molecules which are studied in our group. If time permits, we shall also discuss critically some recent claims of experimental observations of molecular parity violation in condensed phase systems. T. D. Lee, C. N. Yang, Phys. Rev., 104, 254 (1956) C. S. Wu, E. Ambler, R. W. Hayward, D. D. Hoppes, R. P. Hudson, Phys. Rev., 105, 1413 (1957) M. Quack, Angew. Chem. Intl. Ed., 28, 571 (1989) Angew. Chem. Intl. Ed., 41, 4618 (2002) M. Quack, J. Stohner, Chimia, 59, 530 (2005) M. Quack, J. Stohner, M. Willeke, Ann Rev. Phys. Chem. 59, 741 (2008) M. Quack, J. Stohner, Phys. Rev. Lett., 84, 3807 (2000) M. Quack, J. Stohner, J. Chem. Phys., 119, 11228 (2003) J. Stohner, Int. J. Mass Spectrometry 233, 385 (2004) M. Gottselig, M. Quack, J. Stohner, M. Willeke, Int. J. Mass Spectrometry 233, 373 (2004) R. Berger, G. Laubender, M. Quack, A. Sieben, J. Stohner, M. Willeke, Angew. Chem. Intl. Ed., 44, 3623 (2005) J. Stohner, M. Quack, to be published

SU-F-T-63: Dosimetric Relevance of the Valencia and Leipzig HDR Applicators Plastic Cap

DOE Office of Scientific and Technical Information (OSTI.GOV)

Granero, D; Candela-Juan, C; Vijande, J

Purpose: Utilization of HDR brachytherapy treatment of skin lesions using collimated applicators, such as the Valencia or Leipzig is increasing. These applicators are made of cup-shaped tungsten material in order to focalize the radiation into the lesion and to protect nearby tissues. These applicators have an attachable plastic cap that removes secondary electrons generated in the applicator and flattens the treatment surface. The purpose of this study is to examine the dosimetric impact of this cap, and the effect if the cap is not placed during the HDR fraction delivery. Methods: Monte Carlo simulations have been done using the codemore » Geant4 for the Valencia and Leipzig applicators. Dose rate distributions have been obtained for the applicators with and without the plastic cap. An experimental study using EBT3 radiochromic film has been realized in order to verify the Monte Carlo results. Results: The Monte Carlo simulations show that absorbed dose in the first millimeter of skin can increase up to 180% for the Valencia applicator if the plastic cap is absent and up to 1500% for the Leipzig applicators. At deeper distances the increase of dose is smaller being about 10–15%. Conclusion: Important differences have been found if the plastic cap of the applicators is absent in the treatment producing an overdosage in the skin. The user should have a checklist to remind him check always before HDR fraction delivery to insure the plastic cap is placed on the applicator. This work was supported in part by Generalitat Valenciana under Project PROMETEOII/2013/010, by the Spanish Government under Project No. FIS2013-42156, and by a research agreement with Elekta Brachytherapy, Veenendaal, The Netherlands.« less

Electric Spark Discharges in Water. Low-energy Nuclear Transmutations and Light Leptonic Magnetic Monopoles in an Extended Standard Model

NASA Astrophysics Data System (ADS)

Stumpf, Harald

2017-08-01

Light leptonic magnetic monopoles were predicted by Lochak [G. Lochak, Intern. J. Theor. Phys. 24, 1019 (1985).]. Experimental indications based on nuclear transmutations were announced by Urutskoiev et al. [L. I. Urutskoiev, V. I. Liksonov, V. G. Tsinoev, Ann. Fond. L. de Broglie 27, Nr.4, 791 (2002).] and Urutskoev [L. J. Urutskoev, Ann. Fond. L. de Broglie 29, 1149 (2004).]. A theoretical interpretation of these transmutations is proposed under the assumption that light leptonic magnetic monopoles are created during spark discharges in water. The latter should be excited neutrinos according to Lochak. This hypothesis enforces the introduction of an extended Standard Model described in previous papers. The most important results of this study are (i) that multiple proton captures are responsible for the variety of transmutations and that leptonic magnetic monopoles are involved in these processes (ii) that electromagnetic duality can be established for bound states of leptonic monopoles although massive monopoles are in general unstable (iii) that criteria for the emission of leptonic magnetic monopoles and for their catalytic effect on weak decays are set up and elaborated. The study can be considered as a contribution to the efforts of Urutskoiev and Lochak to understand the reasons for accidents in power plants.

The chiral quark condensate and pion decay constant in nuclear matter at next-to-leading order

NASA Astrophysics Data System (ADS)

Lacour, A.; Oller, J. A.; Meißner, U.-G.

2010-12-01

Making use of the recently developed chiral power counting for the physics of nuclear matter (Oller et al 2010 J. Phys. G: Nucl. Part. Phys. 37 015106, Lacour et al Ann. Phys. at press), we evaluate the in-medium chiral quark condensate up to next-to-leading order for both symmetric nuclear matter and neutron matter. Our calculation includes the full in-medium iteration of the leading order local and one-pion exchange nucleon-nucleon interactions. Interestingly, we find a cancellation between the contributions stemming from the quark mass dependence of the nucleon mass appearing in the in-medium nucleon-nucleon interactions. Only the contributions originating from the explicit quark mass dependence of the pion mass survive. This cancellation is the reason of previous observations concerning the dominant role of the long-range pion contributions and the suppression of short-range nucleon-nucleon interactions. We find that the linear density contribution to the in-medium chiral quark condensate is only slightly modified for pure neutron matter by the nucleon-nucleon interactions. For symmetric nuclear matter, the in-medium corrections are larger, although smaller compared to other approaches due to the full iteration of the lowest order nucleon-nucleon tree-level amplitudes. Our calculation satisfies the Hellmann-Feynman theorem to the order worked out. Also we address the problem of calculating the leading in-medium corrections to the pion decay constant. We find that there are no extra in-medium corrections that violate the Gell-Mann-Oakes-Renner relation up to next-to-leading order.

Testing validity of the Kirkwood approximation using an extended Sznajd model

NASA Astrophysics Data System (ADS)

Timpanaro, André M.; Galam, Serge

2015-12-01

We revisit the deduction of the exit probability of the one-dimensional Sznajd model through the Kirkwood approximation [F. Slanina et al., Europhys. Lett. 82, 18006 (2008), 10.1209/0295-5075/82/18006]. This approximation is peculiar in that, in spite of the agreement with simulation results [F. Slanina et al., Europhys. Lett. 82, 18006 (2008), 10.1209/0295-5075/82/18006; R. Lambiotte and S. Redner, Europhys. Lett. 82, 18007 (2008), 10.1209/0295-5075/82/18007; A. M. Timpanaro and C. P. C. Prado, Phys. Rev. E 89, 052808 (2014), 10.1103/PhysRevE.89.052808], the hypothesis about the correlation lengths behind it are inconsistent and fixing these inconsistencies leads to the same results as a simple mean field. We use an extended version of the Sznajd model to test the Kirkwood approximation in a wider context. This model includes the voter, Sznajd, and "United we stand, divided we fall" models [R. A. Holley and T. M. Liggett, Ann. Prob. 3, 643 (1975), 10.1214/aop/1176996306; K. Sznajd-Weron and J. Sznajd, Int. J. Mod. Phys. C 11, 1157 (2000), 10.1142/S0129183100000936] as different parameter combinations, meaning that some analytical results from these models can be used to evaluate the performance of the Kirkwood approximation. We also compare the predicted exit probability with simulation results for networks with 103 sites. The results show clearly the regions in parameter space where the approximation gives accurate predictions, as well as where it starts failing, leading to a better understanding of its reliability.

Study of medicine 2.0 due to Web 2.0?! - Risks and opportunities for the curriculum in Leipzig

PubMed Central

Hempel, Gunther; Neef, Martin; Rotzoll, Daisy; Heinke, Wolfgang

2013-01-01

Web 2.0 is changing the study of medicine by opening up totally new ways of learning and teaching in an ongoing process. Global social networking services like Facebook, YouTube, Flickr, Google Drive and Xing already play an important part in communication both among students and between students and teaching staff. Moreover, local portals (such as the platform [http://www.leipzig-medizin.de] established in 2003) have also caught on and in some cases eclipsed the use of the well-known location-independent social media. The many possibilities and rapid changes brought about by social networks need to be publicized within medical faculties. Therefore, an E-learning and New Media Working Group was set up at the Faculty of Medicine of Universität Leipzig in order to harness the opportunities of Web 2.0, analyse the resulting processes of change in the study of medicine, and curb the risks of the Internet. With Web 2.0 and the social web already influencing the study of medicine, the opportunities of the Internet now need to be utilized to improve the teaching of medicine. PMID:23467440

Withdrawal notice to: Local causality in a Friedmann-Robertson-Walker spacetime [Ann. Phys. 373 (2016) 67-79

NASA Astrophysics Data System (ADS)

Christian, Joy

2016-10-01

This article has been withdrawn at the request of the Editors. Soon after the publication of this paper was announced, several experts in the field contacted the Editors to report errors. After extensive review, the Editors unanimously concluded that the results are in obvious conflict with a proven scientific fact, i.e., violation of local realism that has been demonstrated not only theoretically but experimentally in recent experiments. On this basis, the Editors decided to withdraw the paper. As a consequence, pages 67-79 originally occupied by the withdrawn article are missing from the printed issue. The publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.

Modal analysis of wave propagation in dispersive media

NASA Astrophysics Data System (ADS)

Abdelrahman, M. Ismail; Gralak, B.

2018-01-01

Surveys on wave propagation in dispersive media have been limited since the pioneering work of Sommerfeld [Ann. Phys. 349, 177 (1914), 10.1002/andp.19143491002] by the presence of branches in the integral expression of the wave function. In this article a method is proposed to eliminate these critical branches and hence to establish a modal expansion of the time-dependent wave function. The different components of the transient waves are physically interpreted as the contributions of distinct sets of modes and characterized accordingly. Then, the modal expansion is used to derive a modified analytical expression of the Sommerfeld precursor improving significantly the description of the amplitude and the oscillating period up to the arrival of the Brillouin precursor. The proposed method and results apply to all waves governed by the Helmholtz equations.

Presymplectic current and the inverse problem of the calculus of variations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khavkine, Igor, E-mail: i.khavkine@uu.nl

2013-11-15

The inverse problem of the calculus of variations asks whether a given system of partial differential equations (PDEs) admits a variational formulation. We show that the existence of a presymplectic form in the variational bicomplex, when horizontally closed on solutions, allows us to construct a variational formulation for a subsystem of the given PDE. No constraints on the differential order or number of dependent or independent variables are assumed. The proof follows a recent observation of Bridges, Hydon, and Lawson [Math. Proc. Cambridge Philos. Soc. 148(01), 159–178 (2010)] and generalizes an older result of Henneaux [Ann. Phys. 140(1), 45–64 (1982)]more » from ordinary differential equations (ODEs) to PDEs. Uniqueness of the variational formulation is also discussed.« less

Stochastic mechanics of reciprocal diffusions

NASA Astrophysics Data System (ADS)

Levy, Bernard C.; Krener, Arthur J.

1996-02-01

The dynamics and kinematics of reciprocal diffusions were examined in a previous paper [J. Math. Phys. 34, 1846 (1993)], where it was shown that reciprocal diffusions admit a chain of conservation laws, which close after the first two laws for two disjoint subclasses of reciprocal diffusions, the Markov and quantum diffusions. For the case of quantum diffusions, the conservation laws are equivalent to Schrödinger's equation. The Markov diffusions were employed by Schrödinger [Sitzungsber. Preuss. Akad. Wiss. Phys. Math Kl. 144 (1931); Ann. Inst. H. Poincaré 2, 269 (1932)], Nelson [Dynamical Theories of Brownian Motion (Princeton University, Princeton, NJ, 1967); Quantum Fluctuations (Princeton University, Princeton, NJ, 1985)], and other researchers to develop stochastic formulations of quantum mechanics, called stochastic mechanics. We propose here an alternative version of stochastic mechanics based on quantum diffusions. A procedure is presented for constructing the quantum diffusion associated to a given wave function. It is shown that quantum diffusions satisfy the uncertainty principle, and have a locality property, whereby given two dynamically uncoupled but statistically correlated particles, the marginal statistics of each particle depend only on the local fields to which the particle is subjected. However, like Wigner's joint probability distribution for the position and momentum of a particle, the finite joint probability densities of quantum diffusions may take negative values.

Statistical representation of multiphase flow

NASA Astrophysics Data System (ADS)

Subramaniam

2000-11-01

The relationship between two common statistical representations of multiphase flow, namely, the single--point Eulerian statistical representation of two--phase flow (D. A. Drew, Ann. Rev. Fluid Mech. (15), 1983), and the Lagrangian statistical representation of a spray using the dropet distribution function (F. A. Williams, Phys. Fluids 1 (6), 1958) is established for spherical dispersed--phase elements. This relationship is based on recent work which relates the droplet distribution function to single--droplet pdfs starting from a Liouville description of a spray (Subramaniam, Phys. Fluids 10 (12), 2000). The Eulerian representation, which is based on a random--field model of the flow, is shown to contain different statistical information from the Lagrangian representation, which is based on a point--process model. The two descriptions are shown to be simply related for spherical, monodisperse elements in statistically homogeneous two--phase flow, whereas such a simple relationship is precluded by the inclusion of polydispersity and statistical inhomogeneity. The common origin of these two representations is traced to a more fundamental statistical representation of a multiphase flow, whose concepts derive from a theory for dense sprays recently proposed by Edwards (Atomization and Sprays 10 (3--5), 2000). The issue of what constitutes a minimally complete statistical representation of a multiphase flow is resolved.

On the contribution of Heinrich Bruns to theoretical geometrical optics. With consideration of his correspondence with scientists of the Zeiss Company in Jena 1888-1893. (German Title: Über den Beitrag von Heinrich Bruns zur theoretischen geometrischen Optik Unter Berücksichtigung seines Briefwechsels mit Wissenschaftlern der Zeiss-Werke in Jena 1888-1893)

NASA Astrophysics Data System (ADS)

Ilgauds, Hans-Joachim; Münzel, Gisela

This paper describes the works of Heinrich Bruns, director of the Leipzig University Observatory, on theoretical geometrical optics, which followed an outstanding tradition in Leipzig. Bruns and his pupils did not stop at theoretical considerations, but applied their findings to practical questions. Bruns' correspondence with opticians of the Zeiss Company in Jena, so far known only fragmentarily, gives impressive evidence of their friendly relationship characterized by mutual regard and stimulation.

A molecular theory of cartilage viscoelasticity.

PubMed

Kovach, I S

1996-03-07

Recent work on the subject of cartilage mechanics has begun to focus on the relationship between the microscopic structure of cartilage and its macroscopic mechanical properties (Bader et al., Biochem. Biophys. Acta, 1116 (1992) 147-154; Buschmann, PhD Thesis, Massachusetts Institute of Technology, 1992; Kovach, Biophys. Chem., 53 (1995) 181-187; Lai et al., J. Biochem. Eng., 113 (1991) 245-248; Armstrong and Mow, J. Bone Jt. Surg., 64A (1982) 88; Jackson and James, Biorheology, 19 (1982) 317-330). This paper reviews recent theoretical developments and presents a comprehensive explanation of the viscoelastic properties of cartilage in terms of molecular structure. In doing this, a closed form hybrid solution to the non-linear, cylindrical Poisson-Boltzmann equation is developed to describe the charge-dependent component of the equilibrium elasticity arising from polysaccharide charge (Benham, J. Chem. Phys., 79 (4) (1983) 1969-1973; Einevoll and Hemmer, J. Phys. Chem., 89 (1) (1988) 474-484; Fixman, J. Chem. Phys., 70 (11) (1979) 4995-5001; Ramanathan and Woodburg, J. Chem. Phys., 82 (3) (1985) 1482-1491; Wennerstrom et al., J. Chem. Phys., 76 (9) (1982) 4665-4670). This solution agrees with numerical solutions found in the literature (Buschmann, PhD Thesis, Massachusetts Institute of Technology, 1992). The charge-independent, entropic contribution to the equilibrium elasticity is explained in a manner similar to that recently presented for concentrated proteoglycan solution (Kovach, Biophys. Chem., 53 (1995) 181-187). This approach exploits a lattice model of the solution, subject to a Bragg-Williams type approximation to derive the volume dependence of polysaccharide configuration entropy (Flory, Principles of Polymer Chemistry, Cornell University Press, Ithaca, NY, 1953; Huggins, Some properties of Solutions of Long-chain Compounds, 1941, pp. 151-157; Stanley, Introduction to Phase Transitions and Critical Phenomena, Oxford University Press, Oxford, 1971). Together, these two contributions accurately reproduce the experimentally determined osmotic pressure of cartilage as previously determined by Maroudas (Maroudas and Bannon, Biorheology, 18 (1981) 619-632). The time-dependent, or creep, phenomena which cartilage exhibits when subject to mechanical load is explained in terms of frictional drag on the polysaccharide chain monomers in terms of a Kirkwood-Riseman type model (Kirkwood and Riseman, J. Chem. Phys., 16 (6) (1948) 573-579). This approach is shown to accurately predict the hydraulic permeability of cartilage as previously determined by Maroudas (Madouras, Ann. Rheum. Dis., 34 (suppl. 3) (1975) 77). By use of a quasi-static approximation (neglecting inertial effects) the time-dependent response to a uniform compressive force is determined and also found to be in good agreement with experimental values from the literature.

Realizing universal Majorana fermionic quantum computation

NASA Astrophysics Data System (ADS)

Wu, Ya-Jie; He, Jing; Kou, Su-Peng

2014-08-01

Majorana fermionic quantum computation (MFQC) was proposed by S. B. Bravyi and A. Yu. Kitaev [Ann. Phys. (NY) 298, 210 (2002), 10.1006/aphy.2002.6254], who indicated that a (nontopological) fault-tolerant quantum computer built from Majorana fermions may be more efficient than that built from distinguishable two-state systems. However, until now scientists have not known how to realize a MFQC in a physical system. In this paper we propose a possible realization of MFQC. We find that the end of a line defect of a p-wave superconductor or superfluid in a honeycomb lattice traps a Majorana zero mode, which becomes the starting point of MFQC. Then we show how to manipulate Majorana fermions to perform universal MFQC, which possesses possibilities for high-level local controllability through individually addressing the quantum states of individual constituent elements by using timely cold-atom technology.

Conceptual resistance in the disciplines of the mind: the Leipzig-Buenos Aires connection at the beginning of the 20th century.

PubMed

Taiana, Cecilia

2005-11-01

Personal correspondence written by Prof. Felix Krueger from Argentina in 1906-1907 to his teacher and mentor Wilhelm Wundt in Leipzig is situated in the historical context of the theoretical debates taking place at the University of Buenos Aires at the beginning of the 20th century. A critical survey of the transatlantic migration of psychological theories and their reception in Argentina raises the broader issues of the nature of the cultural and social roots of local interpretations induced by the circulation of theories across national fields of scientific inquiry. It is argued that national intellectual fields and the historicity of their categories of interpretation mediate in the foreign trade of theories.

Quincke rotors in colloidal suspensions

NASA Astrophysics Data System (ADS)

Xiao, Junjun; Huang, Jiping; Yu, Kin Wah; Gu, Guoqing

2004-03-01

When a polarized colloidal particle rotates in an applied electric field, the rotational motion of the particle leads to a displacement of the polarized charge on the surface of the particle. In this connection, the relaxation of the surface charge tends to restore the polarization, leading to a steady-state which is distinct from the equilibrium state in the absence of the rotational motion. There are three relevant cases, namely, rotating particles in a DC field[1, 2], particle rotation due to a rotating field[3, 4] as well as spontaneous rotation of particles in a DC field[5]. In this work, we have focused on the spontaneous rotation of colloidal particles in a DC field, which is known as Quincke rotation. In the collective behaviors of Quincke rotors, the mutual interactions between the individual rotors lead to the assembly of chain-like structures which make an angle with the applied field. We can solve the transient polarization relaxation of two approaching colloidal rotors numerically. More interestingly, we have been able to work out analytically the steady state of two nose-to-tail rotors, in an attempt to take into account the effect of the multipolar interaction between the rotors. As a result, we found that the multipolar interaction does not change the value of the induced dipole moment inside the rotor, but with one of the components of this dipole moment being reduced and the other being enhanced concomitantly. More results of interest have been reported as well. Based on the different dynamic behavior and interaction of Quincke rotors, a totally new class of material is expected to be designed. [1] J.T.K. Wan, K.W. Yu and G.Q. Gu, Phys. Rev. E 64, 061501 (2001). [2] J.T.K. Wan, K.W. Yu and G.Q. Gu, Phys. Rev. E 62, 6848 (2000). [3] J.P. Huang, K.W. Yu and G.Q. Gu, Phys. Rev. E 65, 021401 (2002). [4] J.P. Huang, K.W. Yu, G.Q. Gu and Mikko Karttunen, Phys. Rev. E 67, 051405 (2003). [5] G. Quinke, Ann. Phy. Chem 59, 417 (1896).

[Intermixture of politics and science in the GDR. The investigation of deaths at the Department of Neurology and Psychiatry at Leipzig University under Müller-Hegemann in 1963].

PubMed

Steinberg, H; Weber, M M

2011-10-01

This study presents archival sources that shed light on a topic still being discussed by psychiatrists in East Germany: the death of two patients at the Leipzig Department that occurred in 1960 and 1962 under the directorship of Dietfried Müller-Hegemann. These fatalities were supposed to have been induced by obsolete psychotropic drugs and were associated with Ivan Pavlov's hypnotherapy. The incidents were investigated both by highest administrative bodies and the General State Prosecutor of the former GDR. Archival sources suggest that lower party organs and the ministerial administration tried to make use of the proceedings to bring about the downfall of the head of the Leipzig Department, who had become ideologically suspicious. However, the official General State Prosecutor's investigation ascertained that both Müller-Hegemann and Christa Kohler, head of the psychotherapeutic ward, were not to be held responsible. Although the SED Central Committee at first tried to influence the outcome on the basis of ideological reservations made by the university party organisation, it finally accepted and confirmed the judgment of the General State Prosecutor. Hence, in this case, the highest party bodies followed arguments that were the result of an independent investigation and were not influenced by an individual bias or ideological motives. © Georg Thieme Verlag KG Stuttgart · New York.

Reply to Comment: 'A novel method for fast and robust estimation of fluorescence decay dynamics using constrained least-square deconvolution with Laguerre expansion'.

PubMed

Ma, Dinglong; Liu, Jing; Qi, Jinyi; Marcu, Laura

2017-02-21

In this response we underscore that the instrumentation described in the original publication (Liu et al 2012 Phys. Med. Biol. 57 843-65) was based on pulse-sampling technique, while the comment by Zhang et al is based on the assumption that a time-correlated single photon counting (TCSPC) instrumentation was used. Therefore the arguments made in the comment are not applicable to the noise model reported by Liu et al. As reported in the literature (Lakowicz 2006 Principles of Fluorescence Spectroscopy (New York: Springer)), while in the TCSPC the experimental noise can be estimated from Poisson statistics, such an assumption is not valid for pulse-sampling (transient recording) techniques. To further clarify this aspect, we present here a comprehensive noise model describing the signal and noise propagation of the pulse sampling time-resolved fluorescence detection. Experimental data recorded in various conditions are analyzed as a case study to demonstrate the noise model of our instrumental system. In addition, regarding the statement of correcting equation (3) in Liu et al (2012 Phys. Med. Biol. 57 843-65), the notation of discrete time Laguerre function in the original publication was clear and consistent with literature conventions (Marmarelis 1993 Ann. Biomed. Eng. 21 573-89, Westwick and Kearney 2003 Identification of Nonlinear Physiological Systems (Hoboken, NJ: Wiley)). Thus, it does not require revision.

Reply to Comment: ‘A novel method for fast and robust estimation of fluorescence decay dynamics using constrained least-square deconvolution with Laguerre expansion’

NASA Astrophysics Data System (ADS)

Ma, Dinglong; Liu, Jing; Qi, Jinyi; Marcu, Laura

2017-02-01

In this response we underscore that the instrumentation described in the original publication (Liu et al 2012 Phys. Med. Biol. 57 843-65) was based on pulse-sampling technique, while the comment by Zhang et al is based on the assumption that a time-correlated single photon counting (TCSPC) instrumentation was used. Therefore the arguments made in the comment are not applicable to the noise model reported by Liu et al. As reported in the literature (Lakowicz 2006 Principles of Fluorescence Spectroscopy (New York: Springer)), while in the TCSPC the experimental noise can be estimated from Poisson statistics, such an assumption is not valid for pulse-sampling (transient recording) techniques. To further clarify this aspect, we present here a comprehensive noise model describing the signal and noise propagation of the pulse sampling time-resolved fluorescence detection. Experimental data recorded in various conditions are analyzed as a case study to demonstrate the noise model of our instrumental system. In addition, regarding the statement of correcting equation (3) in Liu et al (2012 Phys. Med. Biol. 57 843-65), the notation of discrete time Laguerre function in the original publication was clear and consistent with literature conventions (Marmarelis 1993 Ann. Biomed. Eng. 21 573-89, Westwick and Kearney 2003 Identification of Nonlinear Physiological Systems (Hoboken, NJ: Wiley)). Thus, it does not require revision.

Introducing Technical Aspects of Research Data Management in the Leipzig Health Atlas.

PubMed

Meineke, Frank A; Löbe, Matthias; Stäubert, Sebastian

2018-01-01

Medical research is an active field in which a wide range of information is collected, collated, combined and analyzed. Essential results are reported in publications, but it is often problematic to have the data (raw and processed), algorithms and tools associated with the publication available. The Leipzig Health Atlas (LHA) project has therefore set itself the goal of providing a repository for this purpose and enabling controlled access to it via a web-based portal. A data sharing concept in accordance to FAIR and OAIS is the basis for the processing and provision of data in the LHA. An IT architecture has been designed for this purpose. The paper presents essential aspects of the data sharing concept, the IT architecture and the methods used.

The digitization of the Wundt estate at Leipzig University.

PubMed

Meyer, Till; Mädebach, Andreas; Schröger, Erich

2017-08-01

Wilhelm M. Wundt (1832-1920) was one of the most important German scholars of the 19th and early 20th centuries and famously founded the first institute for experimental psychology in Leipzig in 1879. Wundt's institute established a teaching and research facility that attracted a large number of students from all over the world and contributed greatly to the development of modern psychology. Until now, the relatively poor indexing and documentation as well as the difficulty in accessing the Wundt estate has prevented a widespread and comprehensive investigation and consideration of these documents. The digitization project described in this article has rectified these problems and will hopefully provide a valuable source for students and researchers interested in Wundt's work. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Special issue: diagnostics of atmospheric pressure microplasmas

NASA Astrophysics Data System (ADS)

Bruggeman, Peter; Czarnetzki, Uwe; Tachibana, Kunihide

2013-11-01

In recent decades, a strong revival of non-equilibrium atmospheric pressure plasma studies has developed in the form of microplasmas. Microplasmas have typical scales of 1 mm or less and offer a very exciting research direction in the field of plasma science and technology as the discharge physics can be considerably different due to high collisionality and the importance of plasma-surface interaction. These high-pressure small-scale plasmas have a diverse range of physical and chemical properties. This diversity coincides with various applications including light/UV sources [1], material processing [2], chemical analysis [3], material synthesis [4], electromagnetics [5], combustion [6] and even medicine [7]. At atmospheric pressure, large scale plasmas have the tendency to become unstable due to the high collision rates leading to enhanced heating and ionization compared to their low-pressure counterparts. As low-pressure plasmas typically operate in reactors with sizes of tens of centimetres, scaling up the pressure to atmospheric pressure the size of the plasma reduces to typical sizes below 1 mm. A natural approach of stabilizing atmospheric pressure plasmas is thus the use of microelectrode geometries. Traditionally microplasmas have been produced in confined geometries which allow one to stabilize dc excited discharges. This stabilization is intrinsically connected to the large surface-to-volume ratio which enhances heat transfer and losses of charged and excited species to the walls. Currently challenging boundaries are pushed by producing microcavity geometries with dimensions of the order of 1 µm [8]. The subject of this special issue, diagnostics of microplasmas, is motivated by the many challenges in microplasma diagnostics in view of the complex chemistry and strong spatial (and even temporal) gradients of species densities and plasma properties. Atmospheric pressure plasmas have a very long history dating back more than 100 years, with early work of, e.g. Werner von Siemens [9], who studied a dielectric barrier discharge (DBD) in the context of ozone generation. DBD discharges often consist of numerous filamentary discharges which are inherently transient in nature and with a characteristic size similar to the dimensions of microplasmas. Several groups are investigating the stabilization of such plasma filaments to perform temporal and spatial resolved diagnostics. To this end and due to the many similar challenges for diagnostics, this type of discharge is also included in this special issue. Research on microplasmas is performed in many groups spread all over the world, and a biannual workshop is devoted to the topic. The 7th edition of this International Workshop on Microplasmas was held in Beijing in May 2013. Large research programs consisting of clusters of research labs such as in Japan, Germany, France and the USA have been producing a wealth of information available in the literature. As the editors of this special issue, we are very pleased to have attracted a collection of excellent papers from leading experts in the field covering most of the current diagnostics performed in microplasmas. As an introduction to the regular special issue papers, a review paper is included [10]. It describes the key characteristics of atmospheric pressure plasmas and microplasmas in particular, and reviews the state of the art in plasma diagnostics. Special attention has been given in this review to highlighting the issues and challenges to probe microplasmas. The regular papers cover a large range of different diagnostics including coherent anti-Stokes Raman scattering (CARS) [11], (two-photon) laser induced fluorescence ((Ta)LIF) [12, 13, 18, 24], absorption spectroscopy [13-18], optical emission spectroscopy [12, 16-21, 24], imaging [22, 23], surface diagnostics [24, 25] and mass spectrometry [26, 27]. Different aspects of microplasmas are broadly investigated from a perspective of diagnostics, modelling and applications. Diagnostics are pivotal to both the development of models and the optimization and exploration of novel applications. Consequently, this special issue is focused on the various aspects and challenges for diagnostics in microplasmas. In addition, previous special issues on the topic of microplasmas have already covered many aspects of source development, applications and modelling [28-31]. The reader who wishes to access additional background information on microplasmas is referred to the following review papers [32-35]. We would like to thank all the contributors and the editorial staff who were of tremendous support in the preparation of this special issue. It is our sincere hope that you enjoy reading this special issue and that it will be a reference and helpful guidance for young researchers embarking in the field of microplasmas. The continued effort to increase our understanding of plasmas by modelling and diagnostics is of key importance for plasma science and the development of novel technologies. References [1] Eden J G, Park S-J, Herring C M and Bulson J M 2011 J. Phys. D: Appl. Phys. 44 224011 [2] Lucas N, Ermel V, Kurrat M and Buttgenbach S 2008 J. Phys. D: Appl. Phys. 41 215202 [3] Karnassios V 2004 Spectrochim. Acta B 59 909-28 [4] Mariotti D and Sankaran RM 2010 J. Phys. D: Appl. Phys. 43 323001 [5] Sakai O and Tachibana K 2012 Plasma Sources Sci. Technol. 21 013001 [6] Starikovskaia S M 2006 Plasma assisted ignition and combustion J. Phys. D.: Appl. Phys. 39 R265-99 [7] Fridman G, Friedman G, Gutsol A, Shekhter A B, Vasilets V N and Fridman A 2008 Plasma Process. Polym. 5 503-33 [8] Eden G et al 2013 IEEE Trans. Plasma Sci. 41 661-75 [9] Siemens W 1857 Poggendorffs. Ann. Phys. Chem. 102 66-122 [10] Bruggeman P and Brandenburg R 2013 J. Phys. D: Appl. Phys. 46 464001 [11] Montello A et al 2013 J. Phys. D: Appl. Phys. 46 464002 [12] Schröder D et al 2013 J. Phys. D: Appl. Phys. 46 464003 [13] Verreycken T et al 2013 J. Phys. D: Appl. Phys. 46 464004 [14] Sousa J S and Puech V 2013 J. Phys. D: Appl. Phys. 46 464005 [15] Takeda K et al 2013 J. Phys. D: Appl. Phys. 46 464006 [16] Vallade J and Massines F 2013 J. Phys. D: Appl. Phys. 46 464007 [17] Wang C and Wu W 2013 J. Phys. D: Appl. Phys. 46 464008 [18] Schröter S et al 2013 J. Phys. D: Appl. Phys. 46 464009 [19] Rusterholtz D L et al 2013 J. Phys. D: Appl. Phys. 46 464010 [20] Huang B-D et al 2013 J. Phys. D: Appl. Phys. 46 464011 [21] Pothiraja R et al 2013 J. Phys. D: Appl. Phys. 46 464012 [22] Marinov I et al 2013 J. Phys. D: Appl. Phys. 46 464013 [23] Akishev Y et al 2013 J. Phys. D: Appl. Phys. 46 464014 [24] Brandenburg R et al 2013 J. Phys. D: Appl. Phys. 46 464015 [25] Houlahan T J Jret al 2013 J. Phys. D: Appl. Phys. 46 464016 [26] Benedikt J et al 2013 J. Phys. D: Appl. Phys. 46 464017 [27] McKay K et al 2013 J. Phys. D: Appl. Phys. 46 464018 [28] Selected papers from the 2nd International Workshop on Microplasmas 2005 J. Phys. D: Appl. Phys. 38 1633-759 [29] Special issue: 3rd International Workshop on Microplasmas 2007 Control. Plasma Phys. 47 3-128 [30] Cluster issue on Microplasmas: 4th International Workshop on Microplasmas 2008 J. Phys. D: Appl. Phys. 41 1904001 [31] Microplasmas: scientific challenges and technological opportunities 2010 Eur. Phys. J. D 60 437-608 [32] Becker K H, Schoenbach K H and Eden J G 2006 J. Phys. D: Appl. Phys. 39 R55 [33] Iza F, Kim G J, Lee S M, Lee J K, Walsh J L, Zhang Y T and Kong M G 2008 Plasma Process. Polym. 5 322-44 [34] Tachibana K 2006 Trans. Electr. Electron. Eng. 1 145-55 [35] Samukawa S et al 2012 J. Phys. D: Appl. Phys. 45 253001

O'Connell's process as a vicious Brownian motion.

PubMed

Katori, Makoto

2011-12-01

Vicious Brownian motion is a diffusion scaling limit of Fisher's vicious walk model, which is a system of Brownian particles in one dimension such that if two motions meet they kill each other. We consider the vicious Brownian motions conditioned never to collide with each other and call it noncolliding Brownian motion. This conditional diffusion process is equivalent to the eigenvalue process of the Hermitian-matrix-valued Brownian motion studied by Dyson [J. Math. Phys. 3, 1191 (1962)]. Recently, O'Connell [Ann. Probab. (to be published)] introduced a generalization of the noncolliding Brownian motion by using the eigenfunctions (the Whittaker functions) of the quantum Toda lattice in order to analyze a directed polymer model in 1 + 1 dimensions. We consider a system of one-dimensional Brownian motions with a long-ranged killing term as a generalization of the vicious Brownian motion and construct the O'Connell process as a conditional process of the killing Brownian motions to survive forever.

Embedded Gaussian unitary ensembles with U(Ω)⊗SU(r) embedding generated by random two-body interactions with SU(r) symmetry

NASA Astrophysics Data System (ADS)

Vyas, Manan; Kota, V. K. B.

2012-12-01

Following the earlier studies on embedded unitary ensembles generated by random two-body interactions [EGUE(2)] with spin SU(2) and spin-isospin SU(4) symmetries, developed is a general formulation, for deriving lower order moments of the one- and two-point correlation functions in eigenvalues, that is valid for any EGUE(2) and BEGUE(2) ("B" stands for bosons) with U(Ω)⊗SU(r) embedding and with two-body interactions preserving SU(r) symmetry. Using this formulation with r = 1, we recover the results derived by Asaga et al. [Ann. Phys. (N.Y.) 297, 344 (2002)], 10.1006/aphy.2002.6248 for spinless boson systems. Going further, new results are obtained for r = 2 (this corresponds to two species boson systems) and r = 3 (this corresponds to spin 1 boson systems).

Evaluation of the diffusion coefficient for controlled release of oxytetracycline from alginate/chitosan/poly(ethylene glycol) microbeads in simulated gastrointestinal environments.

PubMed

Cruz, Maria C Pinto; Ravagnani, Sergio P; Brogna, Fabio M S; Campana, Sérgio P; Triviño, Galo Cardenas; Lisboa, Antonio C Luz; Mei, Lucia H Innocentini

2004-12-01

Diffusion studies of OTC (oxytetracycline) entrapped in microbeads of calcium alginate, calcium alginate coacervated with chitosan (of high, medium and low viscosity) and calcium alginate coacervated with chitosan of low viscosity, covered with PEG [poly(ethylene glycol) of molecular mass 2, 4.6 and 10 kDa, were carried out at 37+/-0.5 degrees C, in pH 7.4 and pH 1.2 buffer solutions - conditions similar to those found in the gastrointestinal system. The diffusion coefficient, or diffusivity (D), of OTC was calculated by equations provided by Crank [(1975) Mathematics in Diffusion, p. 85, Clarendon Press, Oxford] for diffusion, which follows Fick's [(1855) Ann. Physik (Leipzig) 170, 59] second law, considering the diffusion from the inner parts to the surface of the microbeads. The least-squares and the Newton-Raphson [Carnahan, Luther and Wilkes (1969) Applied Numerical Methods, p. 319, John Wiley & Sons, New York] methods were used to obtain the diffusion coefficients. The microbead swelling at pH 7.4 and OTC diffusion is classically Fickian, suggesting that the OTC transport, in this case, is controlled by the exchange rates of free water and relaxation of calcium alginate chains. In case of acid media, it was observed that the phenomenon did not follow Fick's law, owing, probably, to the high solubility of the OTC in this environment. It was possible to modulate the release rate of OTC in several types of microbeads. The presence of cracks formed during the process of drying the microbeads was observed by scanning electron microscopy.

Standardizing flow cytometric assays in long-term population-based studies

NASA Astrophysics Data System (ADS)

Melzer, Susanne; Bocsi, Jozsef; Tárnok, Attila

2015-03-01

Quantification of leukocyte subpopulations and characterization of antigen-expression pattern on the cellular surface can play an important role in diagnostics. The state of cellular immunology on the single-cell level was analyzed by polychromatic flow cytometry in a recent comparative study within the average Leipzig population (LIFE - Leipzig Research Centre for Civilization Diseases). Data of 1699 subjects were recorded over a long-time period of three years (in a total of 1126 days). To ensure compatibility of such huge data sets, quality-controls on many levels (stability of instrumentation, low intra-laboratory variance and reader independent data analysis) are essential. The LIFE study aims to analyze various cytometric pattern to reveal the relationship between the life-style, the environmental effects and the individual health. We therefore present here a multi-step quality control procedure for long-term comparative studies.

Medical students' attitudes and wishes towards extending an educational general practice app to be suitable for practice: A cross-sectional survey from Leipzig, Germany.

PubMed

Sandholzer, Maximilian; Deutsch, Tobias; Frese, Thomas; Winter, Alfred

2016-06-01

In medical education and practice, smartphone apps are increasingly becoming popular. In general practice, apps could play an important future role in supporting medical education and practice. To explore medical students' perceptions regarding the potential of a general practice app for training and subsequent work as a physician. Cross-sectional survey among Leipzig fourth-year medical students who were provided with an app prototype for a mandatory general practice course. Response rate was 99.3% (n = 305/307); 59.0% were female and mean age was 24.5 years. Students certified that the app had a higher potential than textbooks in both education (57.4% vs. 18.0%) and practice (47.1% vs. 22.8%). Students' most desired possible app extensions when anticipating its use for subsequent work as a physician were looking up information for diagnostics, therapy and prediction (85.1%), access to electronic patient files (48.1%), communication and networking (44.3%), organization of medical training (42.9%) and online monitoring of patients (38.1%). Students experienced with medical smartphone apps were more interested in app extensions. Consideration to use the app to support the opening of their own practice was significantly associated with higher interest in accessing electronic patient files, networking with colleagues and telemedicine. Fourth year medical students from Leipzig see a high potential in smartphone apps for education and practice and are interested in further using the technology after undergraduate education.

Science in a communist country: The case of the XXIInd International Congress of Psychology in Leipzig (1980).

PubMed

Schönpflug, Wolfgang; Lüer, Gerd

2013-05-01

The XXIInd International Congress of Psychology (ICP) in Leipzig in 1980 is a case that illustrates the mutual relationship between science and politics, specifically of the role of science in a communist state. We focus first on the situation of the discipline of psychology within the (East) German Democratic Republic (GDR). Second, we provide a detailed description of the interactions between the International Union of Psychological Science (IUPS) and the communist regime of the GDR. The Psychological Association of the GDR was commissioned by the IUPS to organize the congress. The Communist Party, being an omnipresent authority in the state, both supported and tried to manipulate the Leipzig conference for its political goals. Based on archival materials and on recent reports, we reconstruct three positions: From their ideological position, the GDR expected the conference to improve their standing in international politics and to serve as a platform for promoting communist doctrines; from a pragmatic position, the IUPS sought to guarantee free access to the conference and political neutrality of the scientific program; from a humanistic position, no support should be given to a totalitarian system accused of human rights violations. We compare the formal organization as implemented by the Communist Party for ideological purposes with the informal organizational structure, which operated toward pragmatic solutions. Finally, we discuss the compromises between the IUPS and the communist authorities. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

[Leipzig fast-track protocol for cardio-anesthesia. Effective, safe and economical].

PubMed

Häntschel, D; Fassl, J; Scholz, M; Sommer, M; Funkat, A K; Wittmann, M; Ender, J

2009-04-01

In November 2005 a complex, multimodal anesthesia fast-track protocol (FTP) was introduced for elective cardiac surgery patients in the Cardiac Center of the University of Leipzig which included changing from an opioid regime to remifentanil and postoperative treatment in a special post-anesthesia recovery and care unit. The goal was to speed up recovery times while maintaining safety and improving costs. A total of 421 patients who underwent the FTP and were treated in the special recovery room were analyzed retrospectively. These patients were compared with patients who had been treated by a standard protocol (SP) prior to instituting the FTP. Primary outcomes were time to extubation, length of stay in the intensive care unit (ICU) and treatment costs. The times to extubation were significantly shorter in the FTP group with 75 min (range 45-110 min) compared to 900 min (range 600-1140 min) in the SP group. Intensive care unit stay and hospital length of stay were also significantly shorter in the FTP group (p<0.01). The reduction of treatment costs of intensive care for FTP patients was 53.5% corresponding to savings of EUR 738 per patient in the FTP group compared with the SP group. The Leipzig fast-track protocol for cardio-anesthesia including the central elements of switching opiate therapy to remifentanil and switching patient recovery to a special post-anesthesia recovery and care unit, shortened therapy times, is safe and economically effective.

CONFERENCE ANNOUNCEMENT: European Conference on Complex Systems 2009 European Conference on Complex Systems 2009

NASA Astrophysics Data System (ADS)

2009-05-01

The 2009 European Conference on Complex Systems will take place 21-25 September 2009 at the University of Warwick in the UK. Local Organising Committee Markus Kirkilionis (Warwick, Chair), Francois Kepes (Genopole, Programme Chair), Robert MacKay (Warwick), Robin Ball (Warwick), Jeff Johnson (Open University). International Steering Committee Markus Kirkilionis (Warwick; Chair 2008-10), Fatihcan Atay (Leipzig), Jürgen Jost (Leipzig), Scott Kirkpatrick (Jerusalem), David Lane (University of Modena and Reggio Emillia), Andreas Lorincz (Hungarian Academy of Sciences), Denise Pumain (Sorbonne), Felix Reed-Tsochas (Oxford), Eörs Szathmáry (Collegium Budapest, Hungary), Stephan Thurner (Wien), Paul Verschure (Barcelona), Alessandro Vespignani (Indiana, ISI), Riccardo Zecchina (Torino). Main tracks and Organisers Policy, Planning & Infrastructure: Jeff Johnson (Open University, Chair), Arnaud Banos (Strasbourg) Collective Human Behaviour and Society: Felix Reed-Tsochas (Oxford, Chair), Frances Griffiths (Warwick), Edmund Chattoe-Brown (Leicester) Interacting Populations and Environment: TBA Complexity and Computer Science: András Lörincz (Eötvös Loránd University), Paul Verschure (Zürich) From Molecules to Living Systems: Mark Chaplain (Dundee, Chair), Wolfgang Marwan (Magdeburg) Mathematics and Simulation: Holger Kantz (Dresden, Chair), Fatihcan Atay (Leipzig), Matteo Marsili (Trieste). Deadlines Paper submission: 31 March 2009 with decisions 15 May 2009. Paper submission deadline likely to be extended. See http://www.eccs09.info for more information. Meeting registration: early registration July 2009; last assured chance 1 Sept. Further information For contacts and the most up-to-date information visit http://www.eccs09.info.

In pursuit of precision: the calibration of minds and machines in late nineteenth-century psychology.

PubMed

Benschop, R; Draaisma, D

2000-01-01

A prominent feature of late nineteenth-century psychology was its intense preoccupation with precision. Precision was at once an ideal and an argument: the quest for precision helped psychology to establish its status as a mature science, sharing a characteristic concern with the natural sciences. We will analyse how psychologists set out to produce precision in 'mental chronometry', the measurement of the duration of psychological processes. In his Leipzig laboratory, Wundt inaugurated an elaborate research programme on mental chronometry. We will look at the problem of calibration of experimental apparatus and will describe the intricate material, literary, and social technologies involved in the manufacture of precision. First, we shall discuss some of the technical problems involved in the measurement of ever shorter time-spans. Next, the Cattell-Berger experiments will help us to argue against the received view that all the precision went into the hardware, and practically none into the social organization of experimentation. Experimenters made deliberate efforts to bring themselves and their subjects under a regime of control and calibration similar to that which reigned over the experimental machinery. In Leipzig psychology, the particular blend of material and social technology resulted in a specific object of study: the generalized mind. We will then show that the distribution of precision in experimental psychology outside Leipzig demanded a concerted effort of instruments, texts, and people. It will appear that the forceful attempts to produce precision and uniformity had some rather paradoxical consequences.

On the early history of field emission including attempts of tunneling spectroscopy

NASA Astrophysics Data System (ADS)

Kleint, C.

1993-04-01

Field emission is certainly one of the oldest surface science techniques, its roots reaching back about 250 years to the time of enlightenment. An account of very early studies and of later work is given but mostly restricted to Leipzig and to pre-Müllerian investigations. Studies of field emission from metal tips were carried out in the 18th century by Johann Heinrich Winkler who used vacuum pumps built by Jacob Leupold, a famous Leipzig mechanic. A short account of the career of Winkler will be given and his field emission experiments are illustrated. Field emission was investigated again in Leipzig much later by Julius Edgar Lilienfeld who worked on the improvement of X-ray tubes. He coined the terms ‘autoelektronische Entladung’ of ‘Äona-Effekt’ in 1922, and developed degassing procedures which are very similar to modern ultra-high vacuum processing. A pre-quantum mechanical explanation of the field emission phenomena was undertaken by Walter Schottky. Cunradi (1926) tried to measure temperature changes during field emission. Franz Rother, in a thesis (1914) suggested by Otto Wiener, dealt with the distance dependence of currents in vacuum between electrodes down to 20 nm. His habilitation in 1926 was an extension of his early work but now with field emission tips as a cathode. We might look at his measurements of the field emission characteristics in dependence on distance as a precursor to modern tunneling spectroscopy as well.

Brentuximab Vedotin and Combination Chemotherapy in Treating Patients With Stage II-IV HIV-Associated Hodgkin Lymphoma

ClinicalTrials.gov

2018-06-11

AIDS-Related Hodgkin Lymphoma; Ann Arbor Stage II Hodgkin Lymphoma; Ann Arbor Stage IIA Hodgkin Lymphoma; Ann Arbor Stage IIB Hodgkin Lymphoma; Ann Arbor Stage III Hodgkin Lymphoma; Ann Arbor Stage IIIA Hodgkin Lymphoma; Ann Arbor Stage IIIB Hodgkin Lymphoma; Ann Arbor Stage IV Hodgkin Lymphoma; Ann Arbor Stage IVA Hodgkin Lymphoma; Ann Arbor Stage IVB Hodgkin Lymphoma; Classic Hodgkin Lymphoma; HIV Infection

Adaptive Quantum Control of Charge Motion in Semiconductor Heterostructures

NASA Astrophysics Data System (ADS)

Reitze, David

1998-05-01

Quantum control of electronic wavepacket motion and interactions using ultrafast lasers has moved from the conceptual stage to reality, in large part driven by advances in quantum control theory (R. J. Gordon and S. A. Rice, Ann. Rev. Phys. Chem. (1997), in press.) (M. Shapiro and P. Brumer, J. Chem. Soc. Faraday Trans. V93, 1263 (1997).) (D. Neuhauser and H. Rabitz, Acc. Chem. Res. V26, 496 (1993).) and experimental pulse shaping methods (A. M. Weiner, D. E. Leaird, G. P. Wiederrecht, and K. A. Nelson, Science V247, 412 (1990).) (A. Efimov, C. Schaffer, and D. H. Reitze, J. Opt. Soc. Am VB12, 1968 (1995).). Here, we apply these methods to controlling charge motion in semiconductor heterostructures. Control of coherent charge dynamics in heterostructures enjoys an advantage in that spatial potential profiles can be adjusted almost arbitrarily. Thus, control of charge motion can be exerted by tailoring both the temporal and spatial interactions of the charges with the controlling optical and static fields. In this talk, we demonstrate an experimental feedback loop which adaptively shapes fs pulses in a quantum contol pump-probe experiment, apply it to the control of coherent wavepacket motion in DC-biased asymmetric double quantum well(ADQW) structures, and compare to theoretical predictions of quantum control in ADQWs (N. M. Beach, D. H. Reitze, and J. L. Krause, submitted to Opt. Exp.) (J. L. Krause, D. H. Reitze, G. D. Sanders, A. Kuznetsov, and C. J. Stanton, to appear in Phys. Rev. B).

[Profile and tasks of a medical university polyclinic in the past and present using as example the Medical Polyclinical Institutes of the Karl Marx University of Leipzig].

PubMed

Hambsch, K; Treutler, H; Pietruschka, W D

1981-03-15

After a short survey of the historical development of the Medico-Policlinical Institute of the Karl Marx University Leipzig tasks and developmental tendencies of university medical policlinics are described, evaluating hereby the results of the Vth conference of higher education. They are understood as a university representation of ambulatorily working internists and to a large extent of the specialists for general medicine. Their main tasks consist in education and continued professional training of this group of physicians under integrative description of the whole subject internal medicine, a research oriented to practice as well as a guiding and coordination function for the ambulatory internistic care, taking into particular consideration the early recognition of a disease, in primary and secondary prevention as well as in a scientifically based ambulatory therapy of epidemiologically important diseases.

[Development of the legal abortion situation at the gynecologic hospital of Karl-Marx-University, Leipzig from 1.1.1960 to 30.6.1972].

PubMed

Schulz, S; Henning, G

1973-07-13

Statistics on legal abortions at the Women's Clinic, Karl Marx University, Leipzig, East Germany, are reported. Between 1960-June 30, 1972, there were 3955 abortions and 53,972 births. Of these, 1368 abortions and 1831 births occurred in 1972; a similar large increase in abortions has been reported from other socialist countries. Average age of patients was 30.6 years in 1960, 27.7 years in 1972. In 1960, 83.1% of patients were married, but only 66.4% in 1972. Average hospital stay was 10.3 days in 1960, 3.7 days in 1972. Complications were seen in 32.5% of cases in 1960, and in 8.3% in 1972. Statistics for each year, 1960-1972, are given, and the implications of this information for medical practice and social policy are discussed.

DIEGO: detection of differential alternative splicing using Aitchison's geometry.

PubMed

Doose, Gero; Bernhart, Stephan H; Wagener, Rabea; Hoffmann, Steve

2018-03-15

Alternative splicing is a biological process of fundamental importance in most eukaryotes. It plays a pivotal role in cell differentiation and gene regulation and has been associated with a number of different diseases. The widespread availability of RNA-Sequencing capacities allows an ever closer investigation of differentially expressed isoforms. However, most tools for differential alternative splicing (DAS) analysis do not take split reads, i.e. the most direct evidence for a splice event, into account. Here, we present DIEGO, a compositional data analysis method able to detect DAS between two sets of RNA-Seq samples based on split reads. The python tool DIEGO works without isoform annotations and is fast enough to analyze large experiments while being robust and accurate. We provide python and perl parsers for common formats. The software is available at: www.bioinf.uni-leipzig.de/Software/DIEGO. steve@bioinf.uni-leipzig.de. Supplementary data are available at Bioinformatics online.

Combination Chemotherapy in Treating Young Patients With Newly Diagnosed T-Cell Acute Lymphoblastic Leukemia or T-cell Lymphoblastic Lymphoma

ClinicalTrials.gov

2018-01-24

Acute Lymphoblastic Leukemia; Adult T Acute Lymphoblastic Leukemia; Ann Arbor Stage II Adult T-Cell Leukemia/Lymphoma; Ann Arbor Stage II Childhood Lymphoblastic Lymphoma; Ann Arbor Stage II Contiguous Adult Lymphoblastic Lymphoma; Ann Arbor Stage II Non-Contiguous Adult Lymphoblastic Lymphoma; Ann Arbor Stage III Adult Lymphoblastic Lymphoma; Ann Arbor Stage III Adult T-Cell Leukemia/Lymphoma; Ann Arbor Stage III Childhood Lymphoblastic Lymphoma; Ann Arbor Stage IV Adult Lymphoblastic Lymphoma; Ann Arbor Stage IV Adult T-Cell Leukemia/Lymphoma; Ann Arbor Stage IV Childhood Lymphoblastic Lymphoma; Childhood T Acute Lymphoblastic Leukemia; Untreated Adult Acute Lymphoblastic Leukemia; Untreated Childhood Acute Lymphoblastic Leukemia

Comparison of two different artificial neural networks for prostate biopsy indication in two different patient populations.

PubMed

Stephan, Carsten; Xu, Chuanliang; Finne, Patrik; Cammann, Henning; Meyer, Hellmuth-Alexander; Lein, Michael; Jung, Klaus; Stenman, Ulf-Hakan

2007-09-01

Different artificial neural networks (ANNs) using total prostate-specific antigen (PSA) and percentage of free PSA (%fPSA) have been introduced to enhance the specificity of prostate cancer detection. The applicability of independently trained ANN and logistic regression (LR) models to different populations regarding the composition (screening versus referred) and different PSA assays has not yet been tested. Two ANN and LR models using PSA (range 4 to 10 ng/mL), %fPSA, prostate volume, digital rectal examination findings, and patient age were tested. A multilayer perceptron network (MLP) was trained on 656 screening participants (Prostatus PSA assay) and another ANN (Immulite-based ANN [iANN]) was constructed on 606 multicentric urologically referred men. These and other assay-adapted ANN models, including one new iANN-based ANN, were used. The areas under the curve for the iANN (0.736) and MLP (0.745) were equal but showed no differences to %fPSA (0.725) in the Finnish group. Only the new iANN-based ANN reached a significant larger area under the curve (0.77). At 95% sensitivity, the specificities of MLP (33%) and the new iANN-based ANN (34%) were significantly better than the iANN (23%) and %fPSA (19%). Reverse methodology using the MLP model on the referred patients revealed, in contrast, a significant improvement in the areas under the curve for iANN and MLP (each 0.83) compared with %fPSA (0.70). At 90% and 95% sensitivity, the specificities of all LR and ANN models were significantly greater than those for %fPSA. The ANNs based on different PSA assays and populations were mostly comparable, but the clearly different patient composition also allowed with assay adaptation no unbiased ANN application to the other cohort. Thus, the use of ANNs in other populations than originally built is possible, but has limitations.

DNAPL Managements Overview

DTIC Science & Technology

2007-04-01

characterizing groundwater contamination, In: Contaminated Soil (ConSoil), Thomas Telford, Leipzig, Germany , pp. 198-205. Rao, P.S.C. and J.W. Jawitz, 2003...installed to contain contamination, Keens Creek was rerouted to avoid passage through the contaminated site, new trees for phytoremediation and soil

SATELLITE Capabilities and Limitations for the ACPC Box Experiment

NASA Technical Reports Server (NTRS)

Kahn, Ralph

2015-01-01

This presentation was given at the Aerosol-Clouds-Precipitation-Climate (ACPC) Workshop held at NASA GISS in April 2015. The organizers of the meeting plan to post the presentations to a public website maintained by the University of Leipzig.

Doxorubicin Hydrochloride, Vinblastine, Dacarbazine, Brentuximab Vedotin, and Nivolumab in Treating Patients With Stage I-II Hodgkin Lymphoma

ClinicalTrials.gov

2018-04-30

Ann Arbor Stage I Hodgkin Lymphoma; Ann Arbor Stage IA Hodgkin Lymphoma; Ann Arbor Stage IB Hodgkin Lymphoma; Ann Arbor Stage II Hodgkin Lymphoma; Ann Arbor Stage IIA Hodgkin Lymphoma; Ann Arbor Stage IIB Hodgkin Lymphoma

Nucleon-nucleon interactions from dispersion relations: Elastic partial waves

NASA Astrophysics Data System (ADS)

Albaladejo, M.; Oller, J. A.

2011-11-01

We consider nucleon-nucleon (NN) interactions from chiral effective field theory. In this work we restrict ourselves to the elastic NN scattering. We apply the N/D method to calculate the NN partial waves taking as input the one-pion exchange discontinuity along the left-hand cut. This discontinuity is amenable to a chiral power counting as discussed by Lacour, Oller, and Meißner [Ann. Phys. (NY)APNYA60003-491610.1016/j.aop.2010.06.012 326, 241 (2011)], with one-pion exchange as its leading order contribution. The resulting linear integral equation for a partial wave with orbital angular momentum ℓ≥2 is solved in the presence of ℓ-1 constraints, so as to guarantee the right behavior of the D- and higher partial waves near threshold. The calculated NN partial waves are based on dispersion relations and are independent of regulator. This method can also be applied to higher orders in the calculation of the discontinuity along the left-hand cut and extended to triplet coupled partial waves.

O'Connell's process as a vicious Brownian motion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Katori, Makoto

Vicious Brownian motion is a diffusion scaling limit of Fisher's vicious walk model, which is a system of Brownian particles in one dimension such that if two motions meet they kill each other. We consider the vicious Brownian motions conditioned never to collide with each other and call it noncolliding Brownian motion. This conditional diffusion process is equivalent to the eigenvalue process of the Hermitian-matrix-valued Brownian motion studied by Dyson [J. Math. Phys. 3, 1191 (1962)]. Recently, O'Connell [Ann. Probab. (to be published)] introduced a generalization of the noncolliding Brownian motion by using the eigenfunctions (the Whittaker functions) of themore » quantum Toda lattice in order to analyze a directed polymer model in 1 + 1 dimensions. We consider a system of one-dimensional Brownian motions with a long-ranged killing term as a generalization of the vicious Brownian motion and construct the O'Connell process as a conditional process of the killing Brownian motions to survive forever.« less

[Prevalence of Gastroschisis, Omphalocele, Spina Bifida and Orofacial Clefts of Neonates from January 2000 to December 2010 in Leipzig, Saxony, Saxony-Anhalt and Germany].

PubMed

Bremer, S; Kiess, W; Thome, U; Knüpfer, M; Bühligen, U; Vogel, M; Friedrich, A; Janisch, U; Rißmann, A

2018-02-01

Malformations are the most common cause of death in infancy. Numerous studies indicate an increased prevalence of malformations in neonates in recent years in some countries around the world. This study analyzed local and national trends of the prevalences of gastroschisis, omphalocele, spina bifida and orofacial clefts during 2000 till 2010 in Leipzig, Saxony, Saxony-Anhalt and Germany. The prevalence of neonatal malformations was studied retrospectively from January 2000 till December 2010 using 4 sources from Leipzig, Saxony, Saxony-Anhalt and Germany. Between 2000 and 2010, the prevalence in Germany and in Saxony, respectively was 1.97/2.12 (gastroschisis), 1.63/1.48 (omphalocele), 5.80/8.11 (orofacial clefts) and 2.92/2.50 (spina bifida) of 10 000 live births. In Saxony, a small increase in prevalence was detected (OR/year: 1.01-1.09). In Germany, the prevalence of malformations also increased significantly (OR/year: 1.01-1.04) with the exception of the prevalence of spina bifida which seemed to decline (OR/year 0.986 (0.97-1.0), p-adjust=0.04). Whether or not there has been an actual increase in the prevalence of neonatal malformations in Germany over the years or the apparent increase is just due to bias, coding errors, multiple reporting and/or false registration and codification remains unclear. Importantly, in Germany, since prevalence of malformations is monitored prospectively only in Saxony-Anhalt and Rhineland-Palatinate, only in these states is it possible to recognize recent changes. For early identification of changes in prevalence and timely implementation of preventive measures, a nationwide register or additional regional registers are deemed necessary. © Georg Thieme Verlag KG Stuttgart · New York.

Polycyclic aromatic hydrocarbons associated with particles in ambient air from urban and industrial areas.

PubMed

Rehwagen, Martina; Müller, Andrea; Massolo, Laura; Herbarth, Olf; Ronco, Alicia

2005-09-15

This study takes into consideration an analysis of the chemical polycyclic aromatic hydrocarbon (PAH) profile and its distribution in inhalable and respirable particulate matter in urban and industrial areas in La Plata, Argentina, and Leipzig, Germany. Representative samples from three locations in La Plata (industrial, traffic influenced and control area) and two locations in Leipzig (traffic influenced and control area) were obtained in summer and winter. The sampling of particulate matter was carried out with high volume collectors using cascade impactors to separate six size fractions. PAHs were extracted with hexane through a solid-liquid equilibrium extraction and analysed by HPLC/UV/fluorescence detection. The results showed a PAH seasonal behaviour in both regions, with lower contents in summer and higher ones in winter. Highest concentrations of total PAHs were found in the industrial area in La Plata. The size distribution of particles demonstrates the greater relevance of smaller particles. More than 50% of PAHs were associated with particles of less than 0.49 microm. Moreover, this particle size fraction was associated with traffic, whereas other sources of combustion were related also to particles between 0.49 and 0.95 microm. Considering the ratio of benzo(ghi)perylene (BgP)/benzo(a)pyrene (BaP) as an indicator for traffic influence, it was observed that La Plata City was more affected than Leipzig by the same proportion in summer and in winter. The BgP/InP (indeno(123-cd)pyrene) ratio was lower in winter than in summer in both places and indicates the presence of domestic combustion sources. It is important to point out the significance of using fingerprint compound ratios to identify possible sources of pollution with PAHs bound to particles.

China Report, Economic Affairs

DTIC Science & Technology

1985-01-28

markets. For example, the Tianjin winery , in a cooperative venture with Remy Martin of France, imported advanced brewing technology and equipment...well in dozens of countries in Europe and America and also in Japan. It was awarded a gold medal at the Leipzig Expo in March this year. Wineries

Bacteriological findings in patients with bone marrow transplantation (Karl Marx University Leipzig, 1985-1987).

PubMed

Wonitzki, C; Hoffmann, F A

1989-01-01

The results of the bacteriological surveillance cultures for 26 patients with bone marrow transplantation (Karl Marx University Leipzig, G.D.R., 1985-1987) are presented. 5.9% of all surveillance cultures contained facultatively pathogenic germs (with Pseudomonas aeruginosa as the most frequent representative, which was the reason of a sepsis in two patients). Coagulasenegative Staphylococci and other germs with an obscure pathogenicity were isolated upon a large scale, especially from the mucous membrane regions. There are hints, that above all special strains of coagulasenegative Staphylococci "colonize" the patient's body (also for longer periods) and turn into the blood too. During the total decontamination intestinal anaerobic flora is absent. After closing of total decontamination Clostridium perfringens is the first detectable anaerobic species. During the selective decontamination systemic applications of antibiotics are able to obliterate anaerobic findings for certain periods. Recommendations for an effective arrangement of the surveillance cultures of bone marrow transplantation patients are given.

[Fresh fruit and occultism as ways to salvation: conversions in Leipzig's alternative culture at around 1900].

PubMed

Bigalke, Bernadett

2008-01-01

During the time of the Wilhelmine Empire, there were multiple interdependencies between adherents of the life reform movement (vegetarians, naturopathists, nudists, etc.) and new religious movements such as esoteric groups like the theosophists in the alternative cultural milieu around 1900. These networks became visible in the form of double memberships in associations. However, there were also ambiguous affiliations, migration between groups and syncretistic beliefs without institutional belonging. The similarity between patterns of argumentation for this specific lifestyle and the congruence of chosen goals, ways and goods of salvation become particularly clear in this context. These forms of "methodical lifestyle" may lead to the development of a specific ethos or habitus (Max Weber). To illustrate these processes, this article analyses the report of a Leipzig lady who ate raw fruits and vegetables only, and examines her broader social context. Thereby the analysis will employ sociological theories of conversion to explain the case of Hedwig Bresch.

[The Prevalence of Current Depressive Symptoms in an Urban Adult Population].

PubMed

Luck, Tobias; Then, Francisca S; Engel, Christoph; Loeffler, Markus; Thiery, Joachim; Villringer, Arno; Riedel-Heller, Steffi G

2017-04-01

Objective We sought to provide prevalence rates of depressive symptoms in the adult population of the city of Leipzig, Germany (18 - 79 years; N = 8,861). Methods Data were derived from the Leipzig population-based study of adults (LIFE-ADULT-Study). The German version of the Center for Epidemiological Studies Depression Scale (CES-D) was used to assess depressive symptoms using a cut-off score ≥ 23 points. Results The prevalence of current depressive symptoms was 6.4 % (95 %-KI = 5.4 - 7.4). Significantly higher prevalence rates were found in females than in males, in individuals in middle age (40 - 59 years) than in younger and older adults as well as in those individuals with lower socioeconomic status (SES). Conclusion The study findings did not indicate a generally increased risk of depressive symptoms in urban-living adults. © Georg Thieme Verlag KG Stuttgart · New York.

Ofatumumab and Bendamustine Hydrochloride With or Without Bortezomib in Treating Patients With Untreated Follicular Non-Hodgkin Lymphoma

ClinicalTrials.gov

2018-04-17

Ann Arbor Stage III Grade 1 Follicular Lymphoma; Ann Arbor Stage III Grade 2 Follicular Lymphoma; Ann Arbor Stage III Grade 3 Follicular Lymphoma; Ann Arbor Stage IV Grade 1 Follicular Lymphoma; Ann Arbor Stage IV Grade 2 Follicular Lymphoma; Ann Arbor Stage IV Grade 3 Follicular Lymphoma; Grade 3a Follicular Lymphoma

Animal Related Activities as Determinants of Species Knowledge

ERIC Educational Resources Information Center

Randler, Christoph

2010-01-01

Previous work has established a relationship between knowledge and environmental concern. Different factors may contribute to this knowledge and animal-related leisure activities may also contribute to this knowledge. 390 participants in Leipzig, Germany were interviewed to assess their animal-related leisure activities, their demographic status…

Information as a Weapon. Reality Versus Promises.

DTIC Science & Technology

1999-01-01

quoting the following noted authors: W. Rustow, Feldherrnkunst des Neunzehnten Jahrhundert (Leipzig: F. Schultheiss, 1867), 100-101; C. von der Goltz ...6862 Internet address—http://www.au.af.mil/au/aupress/aupubs.html (Order by ’T number in parentheses) BARLOW, Jason B., Maj, USAF (T-15

Systems biology and clinical cytomics: The 10th Leipziger Workshop and the 3rd International Workshop on Slide-Based Cytometry, Leipzig, Germany, April 2005.

PubMed

Tárnok, Attila; Valet, Günther K; Emmrich, Frank

2006-01-01

Despite very significant technical and software improvements in flow cytometry (FCM) since the 1980's, the demand for a cytometric technology combining both quantitative cell analysis and morphological documentation in Cytomics became evident. Improvements in microtechnology and computing permit nowadays similar quantitative and stoichiometric single cell-based high-throughput analyses by microscopic instruments, like Slide-Based Cytometry (SBC). SBC and related techniques offer unique tools to perform complex immunophenotyping, thereby enabling diagnostic procedures during early disease stages. Multicolor or polychromatic analysis of cells by SBC is of special importance not only as a cytomics technology platform but also because of low quantities of required reagents and biological material. The exact knowledge of the location of each cell on the slide permits repetitive restaining and reanalysis of specimens. Various separate measurements of the same specimen can be ultimately fused to one database increasing the information obtained per cell. Relocation and optical evaluation of cells as typical SBC feature, can be of integral importance for cytometric analysis, since artifacts can be excluded and morphology of measured cells can be documented. Progress in cell analytic: In the SBC, new horizons can be opened by the new techniques of structural and functional analysis with the high resolution from intracellular and membrane (confocal microscopy, nanoscopy, total internal fluorescence microscopy (TIRFM), and tissue level (tissomics), to organ and organism level (in vivo cytometry, optical whole body imaging). Predictive medicine aims at the detection of changes in patient's state prior to the manifestation of the disease or the complication. Such instances concern immune consequences of surgeries or noninfectious posttraumatic shock in intensive care patients or the pretherapeutic identification of high risk patients in cancer cytostatic therapy. Preventive anti-infectious or anti-shock therapy as well as curative chemotherapy in combination with stem cell transplantation may provide better survival chances for patient at concomitant cost containment. Predictive medicine-guided optimization of therapy could lead to individualized medicine that gives significant therapeutic effect and may lower or abrogate potential therapeutic side effects. The 10th Leipziger Workshop combined with the 3rd International Workshop on SBC aimed to offer new methods in Image- and Slide-Based Cytometry for solutions in clinical research. It moved towards practical applications in the clinics and the clinical laboratory. This development will be continued in 2006 at the upcoming Leipziger Workshop and the International Workshop on Slide-Based Cytometry.

Mary Ann Franden | NREL

Science.gov Websites

Ann Franden Photo of Mary Ann Franden Mary Franden Researcher IV-Molecular Biology Mary.Ann.Franden @nrel.gov | 303-384-7767 Research Interests Mary Ann Franden is a senior scientist in the Applied Biology University Professional Experience Senior Scientist, NREL, NBC, Applied Biology Group Professional Research

Brentuximab Vedotin and Combination Chemotherapy in Treating Children and Young Adults With Stage IIB or Stage IIIB-IVB Hodgkin Lymphoma

ClinicalTrials.gov

2018-06-25

Ann Arbor Stage IIB Hodgkin Lymphoma; Ann Arbor Stage IIIB Hodgkin Lymphoma; Ann Arbor Stage IV Hodgkin Lymphoma; Ann Arbor Stage IVA Hodgkin Lymphoma; Ann Arbor Stage IVB Hodgkin Lymphoma; Childhood Hodgkin Lymphoma; Classic Hodgkin Lymphoma

Hierarchical Bayesian Model Averaging for Non-Uniqueness and Uncertainty Analysis of Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Fijani, E.; Chitsazan, N.; Nadiri, A.; Tsai, F. T.; Asghari Moghaddam, A.

2012-12-01

Artificial Neural Networks (ANNs) have been widely used to estimate concentration of chemicals in groundwater systems. However, estimation uncertainty is rarely discussed in the literature. Uncertainty in ANN output stems from three sources: ANN inputs, ANN parameters (weights and biases), and ANN structures. Uncertainty in ANN inputs may come from input data selection and/or input data error. ANN parameters are naturally uncertain because they are maximum-likelihood estimated. ANN structure is also uncertain because there is no unique ANN model given a specific case. Therefore, multiple plausible AI models are generally resulted for a study. One might ask why good models have to be ignored in favor of the best model in traditional estimation. What is the ANN estimation variance? How do the variances from different ANN models accumulate to the total estimation variance? To answer these questions we propose a Hierarchical Bayesian Model Averaging (HBMA) framework. Instead of choosing one ANN model (the best ANN model) for estimation, HBMA averages outputs of all plausible ANN models. The model weights are based on the evidence of data. Therefore, the HBMA avoids overconfidence on the single best ANN model. In addition, HBMA is able to analyze uncertainty propagation through aggregation of ANN models in a hierarchy framework. This method is applied for estimation of fluoride concentration in the Poldasht plain and the Bazargan plain in Iran. Unusually high fluoride concentration in the Poldasht and Bazargan plains has caused negative effects on the public health. Management of this anomaly requires estimation of fluoride concentration distribution in the area. The results show that the HBMA provides a knowledge-decision-based framework that facilitates analyzing and quantifying ANN estimation uncertainties from different sources. In addition HBMA allows comparative evaluation of the realizations for each source of uncertainty by segregating the uncertainty sources in a hierarchical framework. Fluoride concentration estimation using the HBMA method shows better agreement to the observation data in the test step because they are not based on a single model with a non-dominate weights.

Bianchi type-I magnetized cosmological models for the Einstein-Boltzmann equation with the cosmological constant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ayissi, Raoul Domingo, E-mail: raoulayissi@yahoo.fr; Noutchegueme, Norbert, E-mail: nnoutch@yahoo.fr

Global solutions regular for the Einstein-Boltzmann equation on a magnetized Bianchi type-I cosmological model with the cosmological constant are investigated. We suppose that the metric is locally rotationally symmetric. The Einstein-Boltzmann equation has been already considered by some authors. But, in general Bancel and Choquet-Bruhat [Ann. Henri Poincaré XVIII(3), 263 (1973); Commun. Math. Phys. 33, 83 (1973)], they proved only the local existence, and in the case of the nonrelativistic Boltzmann equation. Mucha [Global existence of solutions of the Einstein-Boltzmann equation in the spatially homogeneous case. Evolution equation, existence, regularity and singularities (Banach Center Publications, Institute of Mathematics, Polish Academymore » of Science, 2000), Vol. 52] obtained a global existence result, for the relativistic Boltzmann equation coupled with the Einstein equations and using the Yosida operator, but confusing unfortunately with the nonrelativistic case. Noutchegueme and Dongho [Classical Quantum Gravity 23, 2979 (2006)] and Noutchegueme, Dongho, and Takou [Gen. Relativ. Gravitation 37, 2047 (2005)], have obtained a global solution in time, but still using the Yosida operator and considering only the uncharged case. Noutchegueme and Ayissi [Adv. Stud. Theor. Phys. 4, 855 (2010)] also proved a global existence of solutions to the Maxwell-Boltzmann system using the characteristic method. In this paper, we obtain using a method totally different from those used in the works of Noutchegueme and Dongho [Classical Quantum Gravity 23, 2979 (2006)], Noutchegueme, Dongho, and Takou [Gen. Relativ. Gravitation 37, 2047 (2005)], Noutchegueme and Ayissi [Adv. Stud. Theor. Phys. 4, 855 (2010)], and Mucha [Global existence of solutions of the Einstein-Boltzmann equation in the spatially homogeneous case. Evolution equation, existence, regularity and singularities (Banach Center Publications, Institute of Mathematics, Polish Academy of Science, 2000), Vol. 52] the global in time existence and uniqueness of a regular solution to the Einstein-Maxwell-Boltzmann system with the cosmological constant. We define and we use the weighted Sobolev separable spaces for the Boltzmann equation; some special spaces for the Einstein equations, then we clearly display all the proofs leading to the global existence theorems.« less

Sheared velocity flows as a source of pressure anisotropy in low collisionality plasmas

NASA Astrophysics Data System (ADS)

Del Sarto, D.; Pegoraro, F.; Califano, F.

2014-12-01

Non-Maxwellian metaequilibrium states may exist in low-collisionality plasmas as evidenced by direct (particle distributions) and indirect (e.g., instabilities driven by pressure anisotropy) satellite and laboratory measurements. These are directly observed in the solar wind (e.g. [1]), in magnetospheric reconnection events [2], in magnetically confined plasmas [3] or in simulations of Vlasov turbulence [4]. By including the full pressure tensor dynamics in a fluid plasma model, we show that a sheared velocity field can provide an effective mechanism that makes an initial isotropic state anisotropic. We discuss how the propagation of magneto-elastic waves can affect the pressure tensor anisotropization and the small scale formation that arise from the interplay between the gyrotropic terms due to the magnetic field and the flow vorticity and the non-gyropropic effect of the flow strain tensor. We support this analysis by a numerical integration of the nonlinear equations describing the pressure tensor evolution. This anisotropization mechanism might provide a good candidate for the understanding of the observed correlation between the presence of a sheared velocity flow and the signature of pressure anisotropies which are not yet explained within the standard models based e.g. on the CGL paradigm. Examples of these signatures are provided e.g. by the threshold lowering of ion-Weibel instabilities in the geomagnetic tail, observed in concomitance to the presence of a velocity shear in the near-earth plasma profile [5], or by the relatively stronger anisotropization measured for core protons in the fast solar wind [4,6] or in "space simulation" laboratory plasma experiments [3]. [1] E. Marsch et al., Journ. Geophys. Res. 109, A04120 (2004); Yu. V. Khotyainstev at el., Phys. Rev. Lett. 106, 165001 (2011). [2] N. Aunai et al., Ann. Geophys. 29, 1571 (2011); N. Aunai et al., Journ. Geophys. Res. 116, A09232 (2011). [3] E.E. Scime et al., Phys. Plasmas 7, 2157 (2000). [4] S. Servidio et al., Phys. Rev. Lett. 108, 045001 (2012); S. Servidio et al., Astrophys. Journ. Lett. 781, L27 (2014). [5] P.H. Yoon, Journ. Geophys. Res. 101, 4899 (1996). [6] C.-Y. Tu et al., Journ Geophys. Res. 109, A05101 (2004).

Bianchi type-I magnetized cosmological models for the Einstein-Boltzmann equation with the cosmological constant

NASA Astrophysics Data System (ADS)

Ayissi, Raoul Domingo; Noutchegueme, Norbert

2015-01-01

Global solutions regular for the Einstein-Boltzmann equation on a magnetized Bianchi type-I cosmological model with the cosmological constant are investigated. We suppose that the metric is locally rotationally symmetric. The Einstein-Boltzmann equation has been already considered by some authors. But, in general Bancel and Choquet-Bruhat [Ann. Henri Poincaré XVIII(3), 263 (1973); Commun. Math. Phys. 33, 83 (1973)], they proved only the local existence, and in the case of the nonrelativistic Boltzmann equation. Mucha [Global existence of solutions of the Einstein-Boltzmann equation in the spatially homogeneous case. Evolution equation, existence, regularity and singularities (Banach Center Publications, Institute of Mathematics, Polish Academy of Science, 2000), Vol. 52] obtained a global existence result, for the relativistic Boltzmann equation coupled with the Einstein equations and using the Yosida operator, but confusing unfortunately with the nonrelativistic case. Noutchegueme and Dongho [Classical Quantum Gravity 23, 2979 (2006)] and Noutchegueme, Dongho, and Takou [Gen. Relativ. Gravitation 37, 2047 (2005)], have obtained a global solution in time, but still using the Yosida operator and considering only the uncharged case. Noutchegueme and Ayissi [Adv. Stud. Theor. Phys. 4, 855 (2010)] also proved a global existence of solutions to the Maxwell-Boltzmann system using the characteristic method. In this paper, we obtain using a method totally different from those used in the works of Noutchegueme and Dongho [Classical Quantum Gravity 23, 2979 (2006)], Noutchegueme, Dongho, and Takou [Gen. Relativ. Gravitation 37, 2047 (2005)], Noutchegueme and Ayissi [Adv. Stud. Theor. Phys. 4, 855 (2010)], and Mucha [Global existence of solutions of the Einstein-Boltzmann equation in the spatially homogeneous case. Evolution equation, existence, regularity and singularities (Banach Center Publications, Institute of Mathematics, Polish Academy of Science, 2000), Vol. 52] the global in time existence and uniqueness of a regular solution to the Einstein-Maxwell-Boltzmann system with the cosmological constant. We define and we use the weighted Sobolev separable spaces for the Boltzmann equation; some special spaces for the Einstein equations, then we clearly display all the proofs leading to the global existence theorems.

Reaction rate theory: What it was, where is it today, and where is it going?

NASA Astrophysics Data System (ADS)

Pollak, Eli; Talkner, Peter

2005-06-01

A brief history is presented, outlining the development of rate theory during the past century. Starting from Arrhenius [Z. Phys. Chem. 4, 226 (1889)], we follow especially the formulation of transition state theory by Wigner [Z. Phys. Chem. Abt. B 19, 203 (1932)] and Eyring [J. Chem. Phys. 3, 107 (1935)]. Transition state theory (TST) made it possible to obtain quick estimates for reaction rates for a broad variety of processes even during the days when sophisticated computers were not available. Arrhenius' suggestion that a transition state exists which is intermediate between reactants and products was central to the development of rate theory. Although Wigner gave an abstract definition of the transition state as a surface of minimal unidirectional flux, it took almost half of a century until the transition state was precisely defined by Pechukas [Dynamics of Molecular Collisions B, edited by W. H. Miller (Plenum, New York, 1976)], but even this only in the realm of classical mechanics. Eyring, considered by many to be the father of TST, never resolved the question as to the definition of the activation energy for which Arrhenius became famous. In 1978, Chandler [J. Chem. Phys. 68, 2959 (1978)] finally showed that especially when considering condensed phases, the activation energy is a free energy, it is the barrier height in the potential of mean force felt by the reacting system. Parallel to the development of rate theory in the chemistry community, Kramers published in 1940 [Physica (Amsterdam) 7, 284 (1940)] a seminal paper on the relation between Einstein's theory of Brownian motion [Einstein, Ann. Phys. 17, 549 (1905)] and rate theory. Kramers' paper provided a solution for the effect of friction on reaction rates but left us also with some challenges. He could not derive a uniform expression for the rate, valid for all values of the friction coefficient, known as the Kramers turnover problem. He also did not establish the connection between his approach and the TST developed by the chemistry community. For many years, Kramers' theory was considered as providing a dynamic correction to the thermodynamic TST. Both of these questions were resolved in the 1980s when Pollak [J. Chem. Phys. 85, 865 (1986)] showed that Kramers' expression in the moderate to strong friction regime could be derived from TST, provided that the bath, which is the source of the friction, is handled at the same level as the system which is observed. This then led to the Mel'nikov-Pollak-Grabert-Hänggi [Mel'nikov and Meshkov, J. Chem. Phys. 85, 1018 (1986); Pollak, Grabert, and Hänggi, J. Chem. Phys. 91, 4073 (1989)] solution of the turnover problem posed by Kramers. Although classical rate theory reached a high level of maturity, its quantum analog leaves the theorist with serious challenges to this very day. As noted by Wigner [Trans. Faraday Soc. 34, 29 (1938)], TST is an inherently classical theory. A definite quantum TST has not been formulated to date although some very useful approximate quantum rate theories have been invented. The successes and challenges facing quantum rate theory are outlined. An open problem which is being investigated intensively is rate theory away from equilibrium. TST is no longer valid and cannot even serve as a conceptual guide for understanding the critical factors which determine rates away from equilibrium. The nonequilibrium quantum theory is even less well developed than the classical, and suffers from the fact that even today, we do not know how to solve the real time quantum dynamics for systems with "many" degrees of freedom.

[From Paul Flechsig to the Paul Flechsig Institute for Brain Research. Development of brain research at the Karl Marx University].

PubMed

Leibnitz, L; Werner, L; Schober, W; Brauer, K

1977-04-01

A review is given on the development of the brain research institute of the Karl-Marx-University of Leipzig during the directorates of Paul Flechsig (1883-1920), Richard Arwed Pfeifer (1925-1957), and Wolfgang Wünscher (1957-1971).

Linguistic Attitudes on the Use of the Magdeburg Regiolect

ERIC Educational Resources Information Center

Tabisz, Christopher

2017-01-01

This dissertation analyzes attitudes and perceptions of regiolects regarding the Magdeburg, Berlin, Leipzig and Standard German. Consultants listened to four recordings--one in each linguistic variety--and answered questions. The questions opened with the consultants' personal connection to each variety and opinions of the variety itself before…

Information as a Weapon: Reality Versus Promises

DTIC Science & Technology

1997-06-01

Feldherrnkunst des Neunzehnten Jahrhundert (Leipzig: F. Schultheiss, 1867), 100-1.; (2) C. von der Goltz , Das Volk in Waffen (Berlin: Decker, 1883), 1.; (3...Review 22, no. 3 (Summer 1994): 24-30. Barlow, Maj Jason B. “Strategic Paralysis: An Air Power Strategy For the Present.” Airpower Journal 7, no. 4

Combination Chemotherapy With or Without Bortezomib in Treating Younger Patients With Newly Diagnosed T-Cell Acute Lymphoblastic Leukemia or Stage II-IV T-Cell Lymphoblastic Lymphoma

ClinicalTrials.gov

2018-06-27

Adult T Acute Lymphoblastic Leukemia; Ann Arbor Stage II Adult Lymphoblastic Lymphoma; Ann Arbor Stage II Childhood Lymphoblastic Lymphoma; Ann Arbor Stage III Adult Lymphoblastic Lymphoma; Ann Arbor Stage III Childhood Lymphoblastic Lymphoma; Ann Arbor Stage IV Adult Lymphoblastic Lymphoma; Ann Arbor Stage IV Childhood Lymphoblastic Lymphoma; Childhood T Acute Lymphoblastic Leukemia; Untreated Adult Acute Lymphoblastic Leukemia; Untreated Childhood Acute Lymphoblastic Leukemia

Effects of single and dual physical modifications on pinhão starch.

PubMed

Pinto, Vânia Zanella; Vanier, Nathan Levien; Deon, Vinicius Gonçalves; Moomand, Khalid; El Halal, Shanise Lisie Mello; Zavareze, Elessandra da Rosa; Lim, Loong-Tak; Dias, Alvaro Renato Guerra

2015-11-15

Pinhão starch was modified by annealing (ANN), heat-moisture (HMT) or sonication (SNT) treatments. The starch was also modified by a combination of these treatments (ANN-HMT, ANN-SNT, HMT-ANN, HMT-SNT, SNT-ANN, SNT-HMT). Whole starch and debranched starch fractions were analyzed by gel-permeation chromatography. Moreover, crystallinity, morphology, swelling power, solubility, pasting and gelatinization characteristics were evaluated. Native and single ANN and SNT-treated starches exhibited a CA-type crystalline structure while other modified starches showed an A-type structure. The relative crystallinity increased in ANN-treated starches and decreased in single HMT- and SNT-treated starches. The ANN, HMT and SNT did not provide visible cracks, notches or grooves to pinhão starch granule. SNT applied as second treatment was able to increase the peak viscosity of single ANN- and HMT-treated starches. HMT used alone or in dual modifications promoted the strongest effect on gelatinization temperatures and enthalpy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of artificial neural networks to establish a predictive mortality risk model in children admitted to a paediatric intensive care unit.

PubMed

Chan, C H; Chan, E Y; Ng, D K; Chow, P Y; Kwok, K L

2006-11-01

Paediatric risk of mortality and paediatric index of mortality (PIM) are the commonly-used mortality prediction models (MPM) in children admitted to paediatric intensive care unit (PICU). The current study was undertaken to develop a better MPM using artificial neural network, a domain of artificial intelligence. The purpose of this retrospective case series was to compare an artificial neural network (ANN) model and PIM with the observed mortality in a cohort of patients admitted to a five-bed PICU in a Hong Kong non-teaching general hospital. The patients were under the age of 17 years and admitted to our PICU from April 2001 to December 2004. Data were collected from each patient admitted to our PICU. All data were randomly allocated to either the training or validation set. The data from the training set were used to construct a series of ANN models. The data from the validation set were used to validate the ANN and PIM models. The accuracy of ANN models and PIM was assessed by area under the receiver operator characteristics (ROC) curve and calibration. All data were randomly allocated to either the training (n=274) or validation set (n=273). Three ANN models were developed using the data from the training set, namely ANN8 (trained with variables required for PIM), ANN9 (trained with variables required for PIM and pre-ICU intubation) and ANN23 (trained with variables required for ANN9 and 14 principal ICU diagnoses). Three ANN models and PIM were used to predict mortality in the validation set. We found that PIM and ANN9 had a high ROC curve (PIM: 0.808, 95 percent confidence interval 0.552 to 1.000, ANN9: 0.957, 95 percent confidence interval 0.915 to 1.000), whereas ANN8 and ANN23 gave a suboptimal area under the ROC curve. ANN8 required only five variables for the calculation of risk, compared with eight for PIM. The current study demonstrated the process of predictive mortality risk model development using ANN. Further multicentre studies are required to produce a representative ANN-based mortality prediction model for use in different PICUs.

Enzalutamide in Treating Patients With Relapsed or Refractory Mantle Cell Lymphoma

ClinicalTrials.gov

2018-03-27

Ann Arbor Stage I Mantle Cell Lymphoma; Ann Arbor Stage II Mantle Cell Lymphoma; Ann Arbor Stage III Mantle Cell Lymphoma; Ann Arbor Stage IV Mantle Cell Lymphoma; Recurrent Mantle Cell Lymphoma; Refractory Mantle Cell Lymphoma

[Georg Joachim Rhetikus, between Paracelsus and Copernicus].

PubMed

Burmeister, K H

2000-01-01

At first the author presents the familial background of Rheticus, then his education, his friendships and scientific contacts. Rheticus was professor of university in Wittenberg and Leipzig. Most interesting were his contacts with Paracelsus and Copernicus. Last two decades of life Rheticus spent in Cracow working as a physician and mathematician. He died in Kosice.

A Teacher is Forever: The Legacy of Harry Kirke Wolfe (1858-1918).

ERIC Educational Resources Information Center

Benjamin, Ludy T. Jr.

1987-01-01

This article traces the career of Harry Kirke Wolfe, Nebraska educator and one of the earliest U.S. psychologists to earn a doctorate in psychology from Wilhelm Wundt at Leipzig. Emphasis is placed on Wolfe's blending of psychology and pedagogy, and his qualities as a teacher. (Author/JDH)

Investigations of the Interactions of Radiation with Matter.

DTIC Science & Technology

1986-07-31

Process in Atoms, Molecules and Solids, eds. A. Mei-el and J. Finster , (Karl-Marx Universitat, Leipzig, 1984), p. 58. 10. S.T. Manson in High Enery Ion...H. Toburen, and S. T. Manson in X-Ra and Inner-Shell Process in Atoms, Molecules and Solids, eds. A. Meisel and J. Finster , (Karl-Marx- Universitat

Applications of artificial neural networks in medical science.

PubMed

Patel, Jigneshkumar L; Goyal, Ramesh K

2007-09-01

Computer technology has been advanced tremendously and the interest has been increased for the potential use of 'Artificial Intelligence (AI)' in medicine and biological research. One of the most interesting and extensively studied branches of AI is the 'Artificial Neural Networks (ANNs)'. Basically, ANNs are the mathematical algorithms, generated by computers. ANNs learn from standard data and capture the knowledge contained in the data. Trained ANNs approach the functionality of small biological neural cluster in a very fundamental manner. They are the digitized model of biological brain and can detect complex nonlinear relationships between dependent as well as independent variables in a data where human brain may fail to detect. Nowadays, ANNs are widely used for medical applications in various disciplines of medicine especially in cardiology. ANNs have been extensively applied in diagnosis, electronic signal analysis, medical image analysis and radiology. ANNs have been used by many authors for modeling in medicine and clinical research. Applications of ANNs are increasing in pharmacoepidemiology and medical data mining. In this paper, authors have summarized various applications of ANNs in medical science.

[Peripartal mortality in an autopsy sample of the Pathologic Institute of the Department of Medicine of the Karl Marx University in Leipzig 1960-1982].

PubMed

Emmrich, P; Wötzel, E

1986-01-01

Between 1960 and 1982 we have autopsied 88 cases of peripartal mortality in the pathological institute of the department of medicine, Karl-Marx-University of Leipzig. According to the legal instruction in the GDR we have subdivided in direct and indirect peripartal death cases (direct and indirect relation between maternal mortality and pregnancy). We have compared both the groups (1960-1969, 1970-1982) and have found: The number of cases with indirect and direct relation between maternal mortality and pregnancy is decreased markedly in the second time period. The composition within the two time groups is very different in respect to the cause of the mortality: Between 1960 and 1969 amnioticfluid embolism, thromboembolism and air embolism, furthermore preeclampsia and their consequences as well as hemorrhages sub partu and postpartum could be found. In the second time group the most frequent causes of peripartal mortality are the different forms of embolism and preeclampsia, but then cases with a indirect relation between mortality and pregnancy with diseases of the cardiopulmonary system and of the kidneys.

[From "physical idealist" to "freedom fighter". The change in the perception of Carl Friedrich von Weizsäcker in the DDR - exemplified by honorary doctorate and the Leipzig Colloquium 1987/88].

PubMed

Ackermann, Peter

2014-01-01

This article draws a representative picture of the official public perception of Carl Friedrich von Weizsäcker in the GDR. In the beginning Weizsäcker served as a classic example of a successful scientist with bourgeois philosophical ideas. So he was often a target of philosophical criticism. This changed with Weizsäcker's activities in peace studies, and the official GDR made an attempt to monopolize him. This could be seen, for example, in connection with his honorary doctorate awarded by the University of Leipzig in 1987 and with the scientific colloquium in 1988. From these examples we can also see that efforts took place to change the focus towards his physical und philosophical achievements. Weizsäcker's official recognition was also helpful for other activities in which he played a leading role. The article looks behind the scenes of a part of the academic machinery in the GDR. It shows that CFvW was an eminent stimulator also in the GDR.

A Multi Water Bag model of drift kinetic electron plasmaa

NASA Astrophysics Data System (ADS)

Morel, Pierre; Ghiro, Florent Dreydemy; Berionni, Vincent; Coulette, David; Besse, Nicolas; Gürcan, Özgür D.

2014-08-01

A Multi Water Bag model is proposed for describing drift kinetic plasmas in a magnetized cylindrical geometry, relevant for various experimental devices, solar wind modeling... The Multi Water Bag (MWB) model is adapted to the description of a plasma with kinetic electrons as well as an arbitrary number of kinetic ions. This allows to describe the kinetic dynamics of the electrons, making possible the study of electron temperature gradient (ETG) modes, in addition to the effects of non adiabatic electrons on the ion temperature gradient (ITG) modes, that are of prime importance in the magnetized plasmas micro-turbulence [X. Garbet, Y. Idomura, L. Villard, T.H. Watanabe, Nucl. Fusion 50, 043002 (2010); J.A. Krommes, Ann. Rev. Fluid Mech. 44, 175 (2012)]. The MWB model is shown to link kinetic and fluid descriptions, depending on the number of bags considered. Linear stability of the ETG modes is presented and compared to the existing results regarding cylindrical ITG modes [P. Morel, E. Gravier, N. Besse, R. Klein, A. Ghizzo, P. Bertrand, W. Garbet, Ph. Ghendrih, V. Grandgirard, Y. Sarazin, Phys. Plasmas 14, 112109 (2007)].

Einstein Revisited - Gravity in Curved Spacetime Without Event Horizons

NASA Astrophysics Data System (ADS)

Leiter, Darryl

2000-04-01

In terms of covariant derivatives with respect to flat background spacetimes upon which the physical curved spacetime is imposed (1), covariant conservation of energy momentum requires, via the Bianchi Identity, that the Einstein tensor be equated to the matter energy momentum tensor. However the Einstein tensor covariantly splits (2) into two tensor parts: (a) a term proportional to the gravitational stress energy momentum tensor, and (b) an anti-symmetric tensor which obeys a covariant 4-divergence identity called the Freud Identity. Hence covariant conservation of energy momentum requires, via the Freud Identity, that the Freud tensor be equal to a constant times the matter energy momentum tensor. The resultant field equations (3) agree with the Einstein equations to first order, but differ in higher orders (4) such that black holes are replaced by "red holes" i.e., dense objects collapsed inside of their photon orbits with no event horizons. (1) Rosen, N., (1963), Ann. Phys. v22, 1; (2) Rund, H., (1991), Alg. Grps. & Geom. v8, 267; (3) Yilmaz, Hl, (1992), Nuo. Cim. v107B, 946; (4) Roberstson, S., (1999),Ap.J. v515, 365.

Seismicity in the block mountains between Halle and Leipzig, Central Germany: centroid moment tensors, ground motion simulation, and felt intensities of two M ≈ 3 earthquakes in 2015 and 2017

NASA Astrophysics Data System (ADS)

Dahm, Torsten; Heimann, Sebastian; Funke, Sigward; Wendt, Siegfried; Rappsilber, Ivo; Bindi, Dino; Plenefisch, Thomas; Cotton, Fabrice

2018-05-01

On April 29, 2017 at 0:56 UTC (2:56 local time), an M W = 2.8 earthquake struck the metropolitan area between Leipzig and Halle, Germany, near the small town of Markranstädt. The earthquake was felt within 50 km from the epicenter and reached a local intensity of I 0 = IV. Already in 2015 and only 15 km northwest of the epicenter, a M W = 3.2 earthquake struck the area with a similar large felt radius and I 0 = IV. More than 1.1 million people live in the region, and the unusual occurrence of the two earthquakes led to public attention, because the tectonic activity is unclear and induced earthquakes have occurred in neighboring regions. Historical earthquakes south of Leipzig had estimated magnitudes up to M W ≈ 5 and coincide with NW-SE striking crustal basement faults. We use different seismological methods to analyze the two recent earthquakes and discuss them in the context of the known tectonic structures and historical seismicity. Novel stochastic full waveform simulation and inversion approaches are adapted for the application to weak, local earthquakes, to analyze mechanisms and ground motions and their relation to observed intensities. We find NW-SE striking normal faulting mechanisms for both earthquakes and centroid depths of 26 and 29 km. The earthquakes are located where faults with large vertical offsets of several hundred meters and Hercynian strike have developed since the Mesozoic. We use a stochastic full waveform simulation to explain the local peak ground velocities and calibrate the method to simulate intensities. Since the area is densely populated and has sensitive infrastructure, we simulate scenarios assuming that a 12-km long fault segment between the two recent earthquakes is ruptured and study the impact of rupture parameters on ground motions and expected damage.

Effects Of Leaky Sewers On Groundwater Quality

NASA Astrophysics Data System (ADS)

Leschik, S.; Musolff, A.; Reinstorf, F.; Strauch, G.; Oswald, S. E.; Schirmer, M.

2007-12-01

The impact of urban areas on groundwater quality has become an emerging research field in hydrogeology. Urban subsurface infrastructures like sewer networks are often leaky, so untreated wastewater may enter the urban aquifer. The transport of wastewater into the groundwater is still not well understood under field conditions. In the research platform WASSER Leipzig (Water And Sewershed Study of Environmental Risk in Leipzig- Germany) the effects of leaky sewers on the groundwater quality are investigated. The research is focused on the occurrence and transport of so-called "xenobiotics" such as pharmaceuticals and personal care product additives. Xenobiotics may pose a threat on human health, but can also be considered a marker for an urban impact on water resources. A new test site was established in Leipzig to quantify mass fluxes of xenobiotics into the groundwater from a leaky sewer. Corresponding to the leaks which were detected by closed circuit television inspections, monitoring wells were installed up- and downstream of the sewer. Concentrations of eight xenobiotics (technical-nonylphenol, bisphenol-a, caffeine, galaxolide, tonalide, carbamazepine, phenazone, ethinylestradiol) obtained from first sampling programmes were found to be highly heterogeneous, but a relation between the position of the sampling points and the sewer could not be clearly identified. However, concentrations of sodium, chloride, potassium and nitrate increased significantly downstream of the sewer which may be due to wastewater exfiltration, since no other source is known on the water flowpath from the upstream to the downstream wells. Because of the highly heterogeneous spatial distribution of xenobiotics at the test site, a monitoring concept was developed comprising both high-resolution sampling and an integral approach to obtain representative average concentrations. Direct-push techniques were used to gain insight into the fine-scale spatial distribution of the target compounds. An integral pumping test was performed to determine the total xenobiotic mass fluxes along control planes down- and upstream of the leaky sewer. The new monitoring concept helped to obtain robust estimates of xenobiotic mass fluxes into the groundwater.

A New Artificial Neural Network Enhanced by the Shuffled Complex Evolution Optimization with Principal Component Analysis (SP-UCI) for Water Resources Management

NASA Astrophysics Data System (ADS)

Hayatbini, N.; Faridzad, M.; Yang, T.; Akbari Asanjan, A.; Gao, X.; Sorooshian, S.

2016-12-01

The Artificial Neural Networks (ANNs) are useful in many fields, including water resources engineering and management. However, due to the non-linear and chaotic characteristics associated with natural processes and human decision making, the use of ANNs in real-world applications is still limited, and its performance needs to be further improved for a broader practical use. The commonly used Back-Propagation (BP) scheme and gradient-based optimization in training the ANNs have already found to be problematic in some cases. The BP scheme and gradient-based optimization methods are associated with the risk of premature convergence, stuck in local optimums, and the searching is highly dependent on initial conditions. Therefore, as an alternative to BP and gradient-based searching scheme, we propose an effective and efficient global searching method, termed the Shuffled Complex Evolutionary Global optimization algorithm with Principal Component Analysis (SP-UCI), to train the ANN connectivity weights. Large number of real-world datasets are tested with the SP-UCI-based ANN, as well as various popular Evolutionary Algorithms (EAs)-enhanced ANNs, i.e., Particle Swarm Optimization (PSO)-, Genetic Algorithm (GA)-, Simulated Annealing (SA)-, and Differential Evolution (DE)-enhanced ANNs. Results show that SP-UCI-enhanced ANN is generally superior over other EA-enhanced ANNs with regard to the convergence and computational performance. In addition, we carried out a case study for hydropower scheduling in the Trinity Lake in the western U.S. In this case study, multiple climate indices are used as predictors for the SP-UCI-enhanced ANN. The reservoir inflows and hydropower releases are predicted up to sub-seasonal to seasonal scale. Results show that SP-UCI-enhanced ANN is able to achieve better statistics than other EAs-based ANN, which implies the usefulness and powerfulness of proposed SP-UCI-enhanced ANN for reservoir operation, water resources engineering and management. The SP-UCI-enhanced ANN is universally applicable to many other regression and prediction problems, and it has a good potential to be an alternative to the classical BP scheme and gradient-based optimization methods.

Neural networks in chemistry

NASA Astrophysics Data System (ADS)

Zupan, Jure

1995-04-01

All problems that in some way are linked to handling of multi-variate experiments versus multi-variate responses can be approached by the group of methods that has recently became known as the artificial neural network (ANN) techniques. In this lecture, the types of the problems that can be solved by ANN techniques rather than the ANN techniques themselves will be addressed first. This issue is rather important due to the fact that the ANN techniques can be used for a very broad range of problems and choosing the wrong method can often result in either a failure to produce an effective solution or in a very time consuming and ineffective handling. Among the types of problems that can be solved by different ANN techniques the classification, mapping, look-up table, and modelling will be emphasized and discussed. Because all mentioned methods can be solved by different standard techniques, special emphasis will be paid to stress the advantages and drawbacks when employing different ANN techniques. Due to the fact that the range of possible use of ANN is so broad, even a very specific problem can be solved by many different ANN architectures or even using different learning strategies within ANN. In the second part the main learning strategies and corresponding choices of ANN architectures will be discussed. In this part the parameters and some guidelines how to select the method and the design of the ANNs will be shown on the examples of reported ANN applications in chemistry. The ANN learning strategies discussed will be back-propagation of errors, the Kohonen, and the counter propagation learning. The potential user of ANN should first, consider the problem, second, he must inspect the availability of data and the data themselves to decide for which ANN method they are best suited. In this respect, the amount of data, the dimensionality of the measurement space, the form of data (alphanumeric entries, binary, real, or even mixed forms of data) are crucial. After considering all this factors, the determination of the appropriate neural network architecture can be made. Additionally, the selection the optimal ANN involves the determination of specific internal parameters like the learning rate, the momentum term, the neighbourhood function, the time dependent decrease of corrections, etc. Even after all these decisions have been made the learning procedure itself is not a straightforward task. Here, the division of the entire ensemble of data into three data sets: training, controlling and the test set are crucial. This problem is addressed as well.

Complex Langevin simulation of chiral symmetry restoration at finite baryonic density

NASA Astrophysics Data System (ADS)

Ilgenfritz, Ernst-Michael

1986-12-01

A recently proposed effective SU(3) spin model with chiral order parameter is studied by means of the complex Langevin equation. A first-order chiral symmetry restoring and deconfining transition is observed at sufficiently low temperature at finite baryonic density. Permanent address: Sektion Physik, Karl-Marx Universität, DDR-7010 Leipzig, German Democratic Republic.

Learning, Adjustment and Stress Disorders: With Special Reference to Tsunami Affected Regions. Beitrage zur Padagogischen und Rehabilitationspsychologie. Volume 1

ERIC Educational Resources Information Center

Witruk, Evelin, Ed.; Riha, David, Ed.; Teichert, Alexandra, Ed.; Haase, Norman, Ed.; Stueck, Marcus, Ed.

2010-01-01

This book contains selected contributions from the international workshop Learning, "Adjustment and Stress Disorders--with special reference to Tsunami affected Regions" organised by Evelin Witruk and the team of Educational and Rehabilitative Psychology at the University of Leipzig in January 2006. The book contains new results and the…

Intonation as an Encoder of Speaker Certainty: Information and Confirmation Yes-No Questions in Catalan

ERIC Educational Resources Information Center

Vanrell, Maria del Mar; Mascaro, Ignasi; Torres-Tamarit, Francesc; Prieto, Pilar

2013-01-01

Recent studies in the field of intonational phonology have shown that information-seeking questions can be distinguished from confirmation-seeking questions by prosodic means in a variety of languages (Armstrong, 2010, for Puerto Rican Spanish; Grice & Savino, 1997, for Bari Italian; Kugler, 2003, for Leipzig German; Mata & Santos, 2010, for…

76 FR 10226 - Airworthiness Directives; The Boeing Company Model 757 Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-24

... contents of the proposed rule. Request To Revise the Compliance Time FedEx, US Airways, Delta, European Air Transport Leipzig GmbH (European Air)/DHL Air requested a change in the compliance time. FedEx, Delta, and..., dated July 16, 2007. FedEx and Delta stated that fuel tank access occurs at 72-month intervals. European...

Planet of the apes.

PubMed

Maderspacher, Florian

2005-03-08

What makes us humans so special? Our language, our genes, our culture, our cognitive skills? At the Max-Planck-Institute for Evolutionary Anthropology in Leipzig, psychologists, linguists and biologists tackle this old question in a truly multidisciplinary way. Their results have implications not just for our understanding of human evolution--they also touch directly on many social and environmental issues. Florian Maderspacher reports.

Über den Beitrag von Heinrich Bruns zur theoretischen geometrischen Optik - unter Berücksichtigung seines Briefwechsels mit Wissenschaftlern der Zeiss-Werke in Jena 1888 - 1893.

NASA Astrophysics Data System (ADS)

Ilgauds, H.-J.; Münzel, G.

Heinrich Bruns, director of the Leipzig University Observatory, was working on theoretical geometrical optics, and applied this to practical questions. His correspondence with opticians of the Zeiss Company in Jena gives evidence of their mutual regard and inspiration.

[Application of an artificial neural network in the design of sustained-release dosage forms].

PubMed

Wei, X H; Wu, J J; Liang, W Q

2001-09-01

To use the artificial neural network (ANN) in Matlab 5.1 tool-boxes to predict the formulations of sustained-release tablets. The solubilities of nine drugs and various ratios of HPMC: Dextrin for 63 tablet formulations were used as the ANN model input, and in vitro accumulation released at 6 sampling times were used as output. The ANN model was constructed by selecting the optimal number of iterations (25) and model structure in which there are one hidden layer and five hidden layer nodes. The optimized ANN model was used for prediction of formulation based on desired target in vitro dissolution-time profiles. ANN predicted profiles based on ANN predicted formulations were closely similar to the target profiles. The ANN could be used for predicting the dissolution profiles of sustained release dosage form and for the design of optimal formulation.

Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks.

PubMed

Lai, Jinxing; Qiu, Junling; Feng, Zhihua; Chen, Jianxun; Fan, Haobo

2016-01-01

In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability.

Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

PubMed Central

Lai, Jinxing

2016-01-01

In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability. PMID:26819587

Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings.

PubMed

Tigges, P; Kathmann, N; Engel, R R

1997-07-01

Though artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low, the identification of decision relevant features for ANN input data is still a crucial issue. The experience of the ANN designer and the existing knowledge and understanding of the problem seem to be the only links for a specific construction. In the present study a backpropagation ANN based on modified raw data inputs showed encouraging results. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a new sparse and efficient input data presentation. This data coding obtained by a semiautomatic procedure combining existing expert knowledge and the internal representation structures of the raw data based ANN yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% compared with the raw data ANN. An overall correct classification of 92% of so far unknown data was realized. The proposed method of extracting internal ANN knowledge for the production of a better input data representation is not restricted to EOG recordings, and could be used in various fields of signal analysis.

Artificial neural networks: fundamentals, computing, design, and application.

PubMed

Basheer, I A; Hajmeer, M

2000-12-01

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. The attractiveness of ANNs comes from their remarkable information processing characteristics pertinent mainly to nonlinearity, high parallelism, fault and noise tolerance, and learning and generalization capabilities. This paper aims to familiarize the reader with ANN-based computing (neurocomputing) and to serve as a useful companion practical guide and toolkit for the ANNs modeler along the course of ANN project development. The history of the evolution of neurocomputing and its relation to the field of neurobiology is briefly discussed. ANNs are compared to both expert systems and statistical regression and their advantages and limitations are outlined. A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation (BP) ANNs theory and design. A generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation, is described. The most common problems that BPANNs developers face during training are summarized in conjunction with possible causes and remedies. Finally, as a practical application, BPANNs were used to model the microbial growth curves of S. flexneri. The developed model was reasonably accurate in simulating both training and test time-dependent growth curves as affected by temperature and pH.

Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors.

PubMed

Kim, Seongjung; Kim, Jongman; Ahn, Soonjae; Kim, Youngho

2018-04-18

Deaf people use sign or finger languages for communication, but these methods of communication are very specialized. For this reason, the deaf can suffer from social inequalities and financial losses due to their communication restrictions. In this study, we developed a finger language recognition algorithm based on an ensemble artificial neural network (E-ANN) using an armband system with 8-channel electromyography (EMG) sensors. The developed algorithm was composed of signal acquisition, filtering, segmentation, feature extraction and an E-ANN based classifier that was evaluated with the Korean finger language (14 consonants, 17 vowels and 7 numbers) in 17 subjects. E-ANN was categorized according to the number of classifiers (1 to 10) and size of training data (50 to 1500). The accuracy of the E-ANN-based classifier was obtained by 5-fold cross validation and compared with an artificial neural network (ANN)-based classifier. As the number of classifiers (1 to 8) and size of training data (50 to 300) increased, the average accuracy of the E-ANN-based classifier increased and the standard deviation decreased. The optimal E-ANN was composed with eight classifiers and 300 size of training data, and the accuracy of the E-ANN was significantly higher than that of the general ANN.

Artificial intelligence against breast cancer (A.N.N.E.S-B.C.-Project).

PubMed

Parmeggiani, Domenico; Avenia, Nicola; Sanguinetti, Alessandro; Ruggiero, Roberto; Docimo, Giovanni; Siciliano, Mattia; Ambrosino, Pasquale; Madonna, Imma; Peltrini, Roberto; Parmeggiani, Umberto

2012-01-01

Our preliminary study examined the development of an advanced innovative technology with the objectives of--developing methodologies and algorithms for a Artificial Neural Network (ANN) system, improving mammography and ultra-sonography images interpretation;--creating autonomous software as a diagnostic tool for the physicians, allowing the possibility for the advanced application of databases using Artificial Intelligence (Expert System). Since 2004 550 F patients over 40 yrs old were divided in two groups: 1) 310 pts underwent echo every 6 months and mammography every year by expert radiologists. 2) 240 pts had the same screening program and were also examined by our diagnosis software, developed with ANN-ES technology by the Engineering Aircraft Research Project team. The information was continually updated and returned to the Expert System, defining the principal rules of automatic diagnosis. In the second group we selected: Expert radiologist decision; ANN-ES decision; Expert radiologists with ANN-ES decision. The second group had significantly better diagnosis for cancer and better specificity for breast lesions risk as well as the highest percentage account when the radiologist's decision was helped by the ANN software. The ANN-ES group was able to select, by anamnestic, diagnostic and genetic means, 8 patients for prophylactic surgery, finding 4 cancers in a very early stage. Although it is only a preliminary study, this innovative diagnostic tool seems to provide better positive and negative predictive value in cancer diagnosis as well as in breast risk lesion identification.

75 FR 418 - Certificate of Alternative Compliance for the Offshore Supply Vessel KELLY ANN CANDIES

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-05

... Compliance for the Offshore Supply Vessel KELLY ANN CANDIES AGENCY: Coast Guard, DHS. ACTION: Notice. SUMMARY... supply vessel KELLY ANN CANDIES as required by 33 U.S.C. 1605(c) and 33 CFR 81.18. DATES: The Certificate... Purpose The offshore supply vessel KELLY ANN CANDIES will be used for offshore supply operations. Full...

Maniac Talk - Dr. Anne Thompson

NASA Image and Video Library

2014-04-30

Anne Thompson Maniac Lecture, 30 April 2014 NASA climate scientist Dr. Anne Thompson presented a Maniac Talk entitled "A Career in Many Ozone Layers." Anne shared some of her long scientific career both as a researcher at Goddard and Meteorology professor at Penn State. She also described some of the problems she has worked on and tried to convey an enthusiasm for Earth Observations

Maniac Talk - Dr. Anne Douglass

NASA Image and Video Library

2013-03-27

Anne Douglass Maniac Lecture, 27 March, 2013 NASA climate scientist Dr. Anne Douglass presented a Maniac Talk entitled "Satellite Observations - the Touchstone of Atmospheric Modeling." Anne shared some of her scientific career that is filled with unexpected twists and turns and even a few blind alleys, but most important her passion in satellite measurements of ozone and other trace gases, which have been her touchstone.

A novel modular ANN architecture for efficient monitoring of gases/odours in real-time

NASA Astrophysics Data System (ADS)

Mishra, A.; Rajput, N. S.

2018-04-01

Data pre-processing is tremendously used for enhanced classification of gases. However, it suppresses the concentration variances of different gas samples. A classical solution of using single artificial neural network (ANN) architecture is also inefficient and renders degraded quantification. In this paper, a novel modular ANN design has been proposed to provide an efficient and scalable solution in real–time. Here, two separate ANN blocks viz. classifier block and quantifier block have been used to provide efficient and scalable gas monitoring in real—time. The classifier ANN consists of two stages. In the first stage, the Net 1-NDSRT has been trained to transform raw sensor responses into corresponding virtual multi-sensor responses using normalized difference sensor response transformation (NDSRT). These responses have been fed to the second stage (i.e., Net 2-classifier ). The Net 2-classifier has been trained to classify various gas samples to their respective class. Further, the quantifier block has parallel ANN modules, multiplexed to quantify each gas. Therefore, the classifier ANN decides class and quantifier ANN decides the exact quantity of the gas/odor present in the respective sample of that class.

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

PubMed Central

Dülger, L. Canan; Kapucu, Sadettin

2016-01-01

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129

Knowledge and intelligent computing system in medicine.

PubMed

Pandey, Babita; Mishra, R B

2009-03-01

Knowledge-based systems (KBS) and intelligent computing systems have been used in the medical planning, diagnosis and treatment. The KBS consists of rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) whereas intelligent computing method (ICM) encompasses genetic algorithm (GA), artificial neural network (ANN), fuzzy logic (FL) and others. The combination of methods in KBS such as CBR-RBR, CBR-MBR and RBR-CBR-MBR and the combination of methods in ICM is ANN-GA, fuzzy-ANN, fuzzy-GA and fuzzy-ANN-GA. The combination of methods from KBS to ICM is RBR-ANN, CBR-ANN, RBR-CBR-ANN, fuzzy-RBR, fuzzy-CBR and fuzzy-CBR-ANN. In this paper, we have made a study of different singular and combined methods (185 in number) applicable to medical domain from mid 1970s to 2008. The study is presented in tabular form, showing the methods and its salient features, processes and application areas in medical domain (diagnosis, treatment and planning). It is observed that most of the methods are used in medical diagnosis very few are used for planning and moderate number in treatment. The study and its presentation in this context would be helpful for novice researchers in the area of medical expert system.

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242).

PubMed

Almusawi, Ahmed R J; Dülger, L Canan; Kapucu, Sadettin

2016-01-01

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.

A new evolutionary system for evolving artificial neural networks.

PubMed

Yao, X; Liu, Y

1997-01-01

This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms.

Verification and Validation of KBS with Neural Network Components

NASA Technical Reports Server (NTRS)

Wen, Wu; Callahan, John

1996-01-01

Artificial Neural Network (ANN) play an important role in developing robust Knowledge Based Systems (KBS). The ANN based components used in these systems learn to give appropriate predictions through training with correct input-output data patterns. Unlike traditional KBS that depends on a rule database and a production engine, the ANN based system mimics the decisions of an expert without specifically formulating the if-than type of rules. In fact, the ANNs demonstrate their superiority when such if-then type of rules are hard to generate by human expert. Verification of traditional knowledge based system is based on the proof of consistency and completeness of the rule knowledge base and correctness of the production engine.These techniques, however, can not be directly applied to ANN based components.In this position paper, we propose a verification and validation procedure for KBS with ANN based components. The essence of the procedure is to obtain an accurate system specification through incremental modification of the specifications using an ANN rule extraction algorithm.

International Federation of Library Associations Annual Conference Papers. Bibliographic Control Division: Bibliography and Cataloguing Sections (47th, Leipzig, East Germany, August 17-22, 1981).

ERIC Educational Resources Information Center

Vita, Susan H.; And Others

This set of papers delivered to the Bibliographic Control Division of the International Federation of Library Associations (IFLA) during its 47th annual conference (1981) includes: "Cataloging in Publication in the United States--Problems and Prospects," by Susan H. Vita; "Development and Coordination of Bibliographic Activities:…

The Art of History and Eighteenth-Century Information Management: Christian Gottlieb Jocher and Johann Heinrich Zedler

ERIC Educational Resources Information Center

Cole, Richard Glenn

2013-01-01

In the eighteenth century there were enough printed sources and archival materials to challenge or even overwhelm historians of that day. Two productive editors of lexicons and information management were Christian Gottlieb Jocher, who taught history at the University of Leipzig and became the chief librarian at his university, and Johann Heinrich…

International Federation of Library Associations Annual Conference. Papers of the Management and Technology Division: Conservation Section (47th, Leipzig, East Germany, August 17-22, 1981).

ERIC Educational Resources Information Center

Sylvestre, J. G.; And Others

Five papers in this collection are concerned with the preservation of library materials; the remaining paper addresses library building standards, emphasizing their value and identifying other reports on library standards. The conservation papers cover: (1) training for conservation in Canada, identifying institutions and associations offering…

The Work by Giulio Ceradini in Explaining the Mechanism of Semilunar Cardiac Valve Function

ERIC Educational Resources Information Center

Troiani, Diana; Manni, Ermanno

2011-01-01

Using an excised pig heart preparation with tubes, a manometer, and a visualizing apparatus, Giulio Ceradini, an Italian physiologist working in the years of 1871-1872 in Carl Ludwig's famous laboratory in Leipzig, Germany, illustrated the mechanism of closure of the semilunar valves. He was the first to conceive that the closure of the heart…

Taking Control of Their Lives? A Comparison of the Experiences of Unemployed Young Adults (18-25) in England and the New Germany.

ERIC Educational Resources Information Center

Behrens, Martina; Evans, Karen

2002-01-01

A survey and group interviews with unemployed young people aged 18-25 in Derby (England), Hannover (western Germany), and Leipzig (eastern Germany) examined the relative importance to their life and work transitions of individual agency and structural factors. Two national job training "schemes" for unemployed youth are compared: the…

Characterization of Homopolymer and Polymer Blend Films by Phase Sensitive Acoustic Microscopy

NASA Astrophysics Data System (ADS)

Ngwa, Wilfred; Wannemacher, Reinhold; Grill, Wolfgang

2003-03-01

CHARACTERIZATION OF HOMOPOLYMER AND POLYMER BLEND FILMS BY PHASE SENSITIVE ACOUSTIC MICROSCOPY W Ngwa, R Wannemacher, W Grill Institute of Experimental Physics II, University of Leipzig, 04103 Leipzig, Germany Abstract We have used phase sensitive acoustic microscopy (PSAM) to study homopolymer thin films of polystyrene (PS) and poly (methyl methacrylate) (PMMA), as well as PS/PMMA blend films. We show from our results that PSAM can be used as a complementary and highly valuable technique for elucidating the three-dimensional (3D) morphology and micromechanical properties of thin films. Three-dimensional image acquisition with vector contrast provides the basis for: complex V(z) analysis (per image pixel), 3D image processing, height profiling, and subsurface image analysis of the polymer films. Results show good agreement with previous studies. In addition, important new information on the three dimensional structure and properties of polymer films is obtained. Homopolymer film structure analysis reveals (pseudo-) dewetting by retraction of droplets, resulting in a morphology that can serve as a starting point for the analysis of polymer blend thin films. The outcome of confocal laser scanning microscopy studies, performed on the same samples are correlated with the obtained results. Advantages and limitations of PSAM are discussed.

Ice phase in altocumulus clouds over Leipzig: remote sensing observations and detailed modeling

NASA Astrophysics Data System (ADS)

Simmel, M.; Bühl, J.; Ansmann, A.; Tegen, I.

2015-09-01

The present work combines remote sensing observations and detailed cloud modeling to investigate two altocumulus cloud cases observed over Leipzig, Germany. A suite of remote sensing instruments was able to detect primary ice at rather high temperatures of -6 °C. For comparison, a second mixed phase case at about -25 °C is introduced. To further look into the details of cloud microphysical processes, a simple dynamics model of the Asai-Kasahara (AK) type is combined with detailed spectral microphysics (SPECS) forming the model system AK-SPECS. Vertical velocities are prescribed to force the dynamics, as well as main cloud features, to be close to the observations. Subsequently, sensitivity studies with respect to ice microphysical parameters are carried out with the aim to quantify the most important sensitivities for the cases investigated. For the cases selected, the liquid phase is mainly determined by the model dynamics (location and strength of vertical velocity), whereas the ice phase is much more sensitive to the microphysical parameters (ice nucleating particle (INP) number, ice particle shape). The choice of ice particle shape may induce large uncertainties that are on the same order as those for the temperature-dependent INP number distribution.

Ice phase in altocumulus clouds over Leipzig: remote sensing observations and detailed modelling

NASA Astrophysics Data System (ADS)

Simmel, M.; Bühl, J.; Ansmann, A.; Tegen, I.

2015-01-01

The present work combines remote sensing observations and detailed cloud modeling to investigate two altocumulus cloud cases observed over Leipzig, Germany. A suite of remote sensing instruments was able to detect primary ice at rather warm temperatures of -6 °C. For comparison, a second mixed phase case at about -25 °C is introduced. To further look into the details of cloud microphysical processes a simple dynamics model of the Asai-Kasahara type is combined with detailed spectral microphysics forming the model system AK-SPECS. Vertical velocities are prescribed to force the dynamics as well as main cloud features to be close to the observations. Subsequently, sensitivity studies with respect to ice microphysical parameters are carried out with the aim to quantify the most important sensitivities for the cases investigated. For the cases selected, the liquid phase is mainly determined by the model dynamics (location and strength of vertical velocity) whereas the ice phase is much more sensitive to the microphysical parameters (ice nuclei (IN) number, ice particle shape). The choice of ice particle shape may induce large uncertainties which are in the same order as those for the temperature-dependent IN number distribution.

The thyrotropin receptor mutation database: update 2003.

PubMed

Führer, Dagmar; Lachmund, Peter; Nebel, Istvan-Tibor; Paschke, Ralf

2003-12-01

In 1999 we have created a TSHR mutation database compiling TSHR mutations with their basic characteristics and associated clinical conditions (www.uni-leipzig.de/innere/tshr). Since then, more than 2887 users from 36 countries have logged into the TSHR mutation database and have contributed several valuable suggestions for further improvement of the database. We now present an updated and extended version of the TSHR database to which several novel features have been introduced: 1. detailed functional characteristics on all 65 mutations (43 activating and 22 inactivating mutations) reported to date, 2. 40 pedigrees with detailed information on molecular aspects, clinical courses and treatment options in patients with gain-of-function and loss-of-function germline TSHR mutations, 3. a first compilation of site-directed mutagenesis studies, 4. references with Medline links, 5. a user friendly search tool for specific database searches, user-specific database output and 6. an administrator tool for the submission of novel TSHR mutations. The TSHR mutation database is installed as one of the locus specific HUGO mutation databases. It is listed under index TSHR 603372 (http://ariel.ucs.unimelb.edu.au/~cotton/glsdbq.htm) and can be accessed via www.uni-leipzig.de/innere/tshr.

Ambilpolar Electric Field and Diffusive Cooling of Electrons in Meteor Trails

NASA Astrophysics Data System (ADS)

Pasko, V. P.; Kelley, M. C.

2017-12-01

Kelley and Price [GRL, 44, 2987, 2017] recently indicated that ambipolar electric fields may play a role in dynamics of dense plasmas generated by meteors. In the present work we discuss time dynamics of relaxation of electron temperature in meteor trails under relatively common conditions when meteor trail diffusion is not affected by the geomagnetic field (i.e., at low altitudes where both electrons and ions are not magnetized, or at higher altitudes in the plane defined by the trail and magnetic field when meteor trail is not aligned with the geomagnetic field [Ceplecha et al., Space Sci. Rev., 84, 327, 1998, and references therein]). The rate of ambipolar diffusion is a function of temperature and pressure [e.g., Hocking et al., Ann. Geophys., 34, 1119, 2016; Silber et al., Mon. Not. RAS, 469, 1869, 2017] and there is a significant spectroscopic evidence of initial plasma temperatures in meteor trails on the order 4400 deg K [Jennikens et al., Astrobiology, 4, 81, 2004]. For a representative altitude of 105 km chosen for our studies the results are consistent with previous analysis conducted in [Baggeley and Webb, J. Atm. Terr. Phys., 39, 1399, 1977; Ceplecha et al., 1998] indicating that the electron temperature remains elevated for significant time durations measured in tens of milliseconds. Our results indicate that in terms of their magnitudes the ambipolar electric fields can exceed the critical breakdown field of air, consistent with ideas expressed by Kelley and Price [GRL, 44, 2987, 2017], however, under considered conditions these fields lead to acceleration of electron cooling, with electron temperatures falling below the ambient air temperature (below 224 deg K at 105 km altitude). These effects are referred to as diffusive cooling [e.g., Rozhansky and Tsendin, Transport phenomena in partially ionized plasma, Taylor & Francis, 2001, p. 449] and represent a process in which diffusing electrons move against the force acting on them from ambipolar electric field and lose thermal energy. Under considered conditions electron heating in super elastic collisions with rotationally excited ambient molecules becomes important and we will illustrate related time scales by Monte Carlo simulations based on modeling framework of [Frost and Phelps, Phys. Rev., 127, 1621, 1962; Hake and Phelps, Phys. Rev., 158, 70, 1967].

Overview of artificial neural networks.

PubMed

Zou, Jinming; Han, Yi; So, Sung-Sau

2008-01-01

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties. The ANN is one of many versatile tools to meet the demand in drug discovery modeling. Compared to a traditional regression approach, the ANN is capable of modeling complex nonlinear relationships. The ANN also has excellent fault tolerance and is fast and highly scalable with parallel processing. This chapter introduces the background of ANN development and outlines the basic concepts crucially important for understanding more sophisticated ANN. Several commonly used learning methods and network setups are discussed briefly at the end of the chapter.

Can we define an asymptotic value for the ice active surface site density for heterogeneous ice nucleation?

NASA Astrophysics Data System (ADS)

Niedermeier, Dennis; Augustin-Bauditz, Stefanie; Hartmann, Susan; Wex, Heike; Ignatius, Karoliina; Stratmann, Frank

2015-04-01

The formation of ice in atmospheric clouds has a substantial influence on the radiative properties of clouds as well as on the formation of precipitation. Therefore much effort has been made to understand and quantify the major ice formation processes in clouds. Immersion freezing has been suggested to be a dominant primary ice formation process in low and mid-level clouds (mixed-phase cloud conditions). It also has been shown that mineral dust particles are the most abundant ice nucleating particles in the atmosphere and thus may play an important role for atmospheric ice nucleation (Murray et al., 2012). Additionally, biological particles like bacteria and pollen are suggested to be potentially involved in atmospheric ice formation, at least on a regional scale (Murray et al., 2012). In recent studies for biological particles (SNOMAX and birch pollen), it has been demonstrated that freezing is induced by ice nucleating macromolecules and that an asymptotic value for the mass density of these ice nucleating macromolecules can be determined (Hartmann et al., 2013; Augustin et al., 2013, Wex et al., 2014). The question arises whether such an asymptotic value can also be determined for the ice active surface site density ns, a parameter which is commonly used to describe the ice nucleation activity of e.g., mineral dust. Such an asymptotic value for ns could be an important input parameter for atmospheric modeling applications. In the presented study, we therefore investigated the immersion freezing behavior of droplets containing size-segregated, monodisperse feldspar particles utilizing the Leipzig Aerosol Cloud Interaction Simulator (LACIS). For all particle sizes considered in the experiments, we observed a leveling off of the frozen droplet fraction reaching a plateau within the heterogeneous freezing temperature regime (T > -38°C) which was proportional to the particle surface area. Based on these findings, we could determine an asymptotic value for the ice active surface site density, which we named ns*, for the investigated feldspar sample. The comparison of these results with those of other studies elucidates the general feasibility of determining such an asymptotic value and also show that the value of ns* strongly depends on the method of the particle surface area determination. Acknowledgement This work is partly funded by the Federal Ministry of Education and Research (BMBF - project CLOUD 12) and by the German Research Foundation (DFG project WE 4722/1-1, part of the research unit INUIT, FOR 1525). D. Niedermeier acknowledges financial support from the Alexander von Humboldt-foundation. References Augustin et al.: Immersion freezing of birch pollen washing water, Atmos. Chem. Phys., 13, 10989-11003, doi:10.5194/acp-13-10989-2013, 2013. Hartmann et al.: Immersion freezing of ice nucleation active protein complexes, Atmos. Chem. Phys., 13, 5751-5766, doi:10.5194/acp-13-5751-2013, 2013. Murray et al.: Ice nucleation by particles immersed in supercooled cloud droplets, Chem. Soc. Rev., 41, 6519-6554, 2012. Wex et al.: Intercomparing different devices for the investigation of ice nucleating particles using Snomax® as test substance, Atmos. Chem. Phys. Discuss., 14, 22321-22384, doi:10.5194/acpd-14-22321-2014, 2014.

Investigating the heterogeneous freezing behavior of supercooled droplets containing different amounts of SNOMAX

NASA Astrophysics Data System (ADS)

Niedermeier, D.; Budke, C.; Koop, T.; Hartmann, S.; Augustin, S.; Stratmann, F.; Wex, H.

2013-12-01

Heterogeneous ice nucleation, a fundamental process for ice formation in the atmosphere, has been observed to occur in clouds at temperatures higher than -20 °C (Kanitz et al., 2011). However, laboratory studies showed that mineral dust particles, which are the most abundant atmospheric ice nuclei (IN), are ice active at lower temperature (Murray et al., 2012). Biological particles such as bacteria nucleate ice at higher temperatures similar to those observed in the atmosphere. But their atmospheric relevance is controversially discussed (Hartmann et al., 2013; Hoose et al., 2010). In order to achieve a better understanding, fundamental processes underlying ice nucleation on bacteria should be investigated. Within the Ice Nuclei research UnIT (INUIT), the ice nucleating ability of SNOMAX, which contains non-viable Pseudomonas syringae bacteria as well as their fragments, was quantified using different measurement devices featuring different measurement techniques. Here, results determined with the Bielefeld Ice Nucleation ARraY (BINARY, Budke et al., 2013) and the Leipzig Aerosol Cloud Interaction Simulator (LACIS, Hartmann et al., 2011) are presented exemplarily. Within these devices, droplets with different amounts of SNOMAX were exposed to supercooling temperatures until they froze (BINARY: cooling rate: 1K/min; LACIS: residence time of supercooled droplets at a certain temperature: ~0.2s). Frozen fractions were determined in a temperature range of ca. -4 to -20 °C. These fractions increase steeply and, in part, level off at values lower than 100% (i.e., they reach a plateau value indicating the number of SNOMAX IN per droplet) depending on the SNOMAX concentration. With increasing amount of SNOMAX per droplet, the frozen fraction curve is shifted to higher temperature and the plateau value increases, reaching 100% for the highest SNOMAX concentrations. It has been suggested that ice nucleation active (INA) macromolecules, i.e. protein complexes in the case of bacteria, initiate the freezing process (Wolber et al. 1986). The Soccer ball model (Niedermeier et al., 2011) was used to parameterize the ice nucleation behavior of these INA macromolecules. One parameter set (mean contact angle and its standard deviation) could be derived that matches the experimental results of both devices. This parameterization can be used to describe the ice nucleation behavior of the INA bacteria in atmospheric models for a given number concentration being present in the atmosphere. Acknowledgement This work is funded by the German Research Foundation (DFG projects WE 4722/1-1 and KO 2944/2-1, both part of the research unit INUIT). References Budke et al., Proc.19th ICNAA, Fort Collins, CO, USA, 949-951, 2013. Hartmann et al., Atmos. Chem. Phys., 11, 1753-1767, 2011. Hartmann et al., Atmos. Chem. Phys., 13, 5751-5766, 2013. Hoose et al., Environ. Res. Lett. 5, 024009, 2010. Kanitz et al., Geophys. Res. Lett., 38, L17802, 2011. Niedermeier et al., Atmos. Chem. Phys., 11, 8767-8775, 2011. Murray et al., Chem. Soc. Rev., 41, 6519-6554, 2012. Wolber et al., P. Natl. A. Sci., 83, 7256-7260, 1986.

Differential expression of members of the annexin multigene family in Arabidopsis

NASA Technical Reports Server (NTRS)

Clark, G. B.; Sessions, A.; Eastburn, D. J.; Roux, S. J.

2001-01-01

Although in most plant species no more than two annexin genes have been reported to date, seven annexin homologs have been identified in Arabidopsis, Annexin Arabidopsis 1-7 (AnnAt1--AnnAt7). This establishes that annexins can be a diverse, multigene protein family in a single plant species. Here we compare and analyze these seven annexin gene sequences and present the in situ RNA localization patterns of two of these genes, AnnAt1 and AnnAt2, during different stages of Arabidopsis development. Sequence analysis of AnnAt1--AnnAt7 reveals that they contain the characteristic four structural repeats including the more highly conserved 17-amino acid endonexin fold region found in vertebrate annexins. Alignment comparisons show that there are differences within the repeat regions that may have functional importance. To assess the relative level of expression in various tissues, reverse transcription-PCR was carried out using gene-specific primers for each of the Arabidopsis annexin genes. In addition, northern blot analysis using gene-specific probes indicates differences in AnnAt1 and AnnAt2 expression levels in different tissues. AnnAt1 is expressed in all tissues examined and is most abundant in stems, whereas AnnAt2 is expressed mainly in root tissue and to a lesser extent in stems and flowers. In situ RNA localization demonstrates that these two annexin genes display developmentally regulated tissue-specific and cell-specific expression patterns. These patterns are both distinct and overlapping. The developmental expression patterns for both annexins provide further support for the hypothesis that annexins are involved in the Golgi-mediated secretion of polysaccharides.

Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

NASA Astrophysics Data System (ADS)

Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

2013-10-01

In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T.

PubMed

Bertleff, Marco; Domsch, Sebastian; Weingärtner, Sebastian; Zapp, Jascha; O'Brien, Kieran; Barth, Markus; Schad, Lothar R

2017-12-01

Artificial neural networks (ANNs) were used for voxel-wise parameter estimation with the combined intravoxel incoherent motion (IVIM) and kurtosis model facilitating robust diffusion parameter mapping in the human brain. The proposed ANN approach was compared with conventional least-squares regression (LSR) and state-of-the-art multi-step fitting (LSR-MS) in Monte-Carlo simulations and in vivo in terms of estimation accuracy and precision, number of outliers and sensitivity in the distinction between grey (GM) and white (WM) matter. Both the proposed ANN approach and LSR-MS yielded visually increased parameter map quality. Estimations of all parameters (perfusion fraction f, diffusion coefficient D, pseudo-diffusion coefficient D*, kurtosis K) were in good agreement with the literature using ANN, whereas LSR-MS resulted in D* overestimation and LSR yielded increased values for f and D*, as well as decreased values for K. Using ANN, outliers were reduced for the parameters f (ANN, 1%; LSR-MS, 19%; LSR, 8%), D* (ANN, 21%; LSR-MS, 25%; LSR, 23%) and K (ANN, 0%; LSR-MS, 0%; LSR, 15%). Moreover, ANN enabled significant distinction between GM and WM based on all parameters, whereas LSR facilitated this distinction only based on D and LSR-MS on f, D and K. Overall, the proposed ANN approach was found to be superior to conventional LSR, posing a powerful alternative to the state-of-the-art method LSR-MS with several advantages in the estimation of IVIM-kurtosis parameters, which might facilitate increased applicability of enhanced diffusion models at clinical scan times. Copyright © 2017 John Wiley & Sons, Ltd.

iAnn: an event sharing platform for the life sciences.

PubMed

Jimenez, Rafael C; Albar, Juan P; Bhak, Jong; Blatter, Marie-Claude; Blicher, Thomas; Brazas, Michelle D; Brooksbank, Cath; Budd, Aidan; De Las Rivas, Javier; Dreyer, Jacqueline; van Driel, Marc A; Dunn, Michael J; Fernandes, Pedro L; van Gelder, Celia W G; Hermjakob, Henning; Ioannidis, Vassilios; Judge, David P; Kahlem, Pascal; Korpelainen, Eija; Kraus, Hans-Joachim; Loveland, Jane; Mayer, Christine; McDowall, Jennifer; Moran, Federico; Mulder, Nicola; Nyronen, Tommi; Rother, Kristian; Salazar, Gustavo A; Schneider, Reinhard; Via, Allegra; Villaveces, Jose M; Yu, Ping; Schneider, Maria V; Attwood, Teresa K; Corpas, Manuel

2013-08-01

We present iAnn, an open source community-driven platform for dissemination of life science events, such as courses, conferences and workshops. iAnn allows automatic visualisation and integration of customised event reports. A central repository lies at the core of the platform: curators add submitted events, and these are subsequently accessed via web services. Thus, once an iAnn widget is incorporated into a website, it permanently shows timely relevant information as if it were native to the remote site. At the same time, announcements submitted to the repository are automatically disseminated to all portals that query the system. To facilitate the visualization of announcements, iAnn provides powerful filtering options and views, integrated in Google Maps and Google Calendar. All iAnn widgets are freely available. http://iann.pro/iannviewer manuel.corpas@tgac.ac.uk.

[Algorithms of artificial neural networks--practical application in medical science].

PubMed

Stefaniak, Bogusław; Cholewiński, Witold; Tarkowska, Anna

2005-12-01

Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer applications of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. This paper presents practical aspects of scientific application of ANN in medicine using widely available algorithms. Several main steps of analysis with ANN were discussed starting from material selection and dividing it into groups, to the quality assessment of obtained results at the end. The most frequent, typical reasons for errors as well as the comparison of ANN method to the modeling by regression analysis were also described.

Prediction of Film Cooling Effectiveness on a Gas Turbine Blade Leading Edge Using ANN and CFD

NASA Astrophysics Data System (ADS)

Dávalos, J. O.; García, J. C.; Urquiza, G.; Huicochea, A.; De Santiago, O.

2018-05-01

In this work, the area-averaged film cooling effectiveness (AAFCE) on a gas turbine blade leading edge was predicted by employing an artificial neural network (ANN) using as input variables: hole diameter, injection angle, blowing ratio, hole and columns pitch. The database used to train the network was built using computational fluid dynamics (CFD) based on a two level full factorial design of experiments. The CFD numerical model was validated with an experimental rig, where a first stage blade of a gas turbine was represented by a cylindrical specimen. The ANN architecture was composed of three layers with four neurons in hidden layer and Levenberg-Marquardt was selected as ANN optimization algorithm. The AAFCE was successfully predicted by the ANN with a regression coefficient R2<0.99 and a root mean square error RMSE=0.0038. The ANN weight coefficients were used to estimate the relative importance of the input parameters. Blowing ratio was the most influential parameter with relative importance of 40.36 % followed by hole diameter. Additionally, by using the ANN model, the relationship between input parameters was analyzed.

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

PubMed

Dobchev, Dimitar; Karelson, Mati

2016-07-01

Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

An Overview of ANN Application in the Power Industry

NASA Technical Reports Server (NTRS)

Niebur, D.

1995-01-01

The paper presents a survey on the development and experience with artificial neural net (ANN) applications for electric power systems, with emphasis on operational systems. The organization and constraints of electric utilities are reviewed, motivations for investigating ANN are identified, and a current assessment is given from the experience of 2400 projects using ANN for load forecasting, alarm processing, fault detection, component fault diagnosis, static and dynamic security analysis, system planning, and operation planning.

Applications of artificial neural networks (ANNs) in food science.

PubMed

Huang, Yiqun; Kangas, Lars J; Rasco, Barbara A

2007-01-01

Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decades, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications of machine perception including machine vision and electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in the field.

Dialectic Strategy.

DTIC Science & Technology

1993-11-29

perspective that we will apply to strategic analysis. Kant . Immanuel - Kant was born in Konigsberg, East Prussia in 1724. He entered the city’s...angles. Clearly, such methods have great utility in strategy making today. Carl von Clausewitz -had studied Kantian philosophy, 23 was probably...philosopher. He studied theology and philosophy at the universities of Jena and LEipzig and then became a private tutor. Fichte admired the works of Kant and

On the Law of Inertia. Translation of: Ueber das Beharrungsgesetz

NASA Astrophysics Data System (ADS)

Lange, Ludwig

2014-04-01

This article is a translation of Ludwig Lange: "Ueber das Beharrungsgesetz" in: Berichte ueber Verhandlungen der Koenigl. Saechsischen Gesellschaft der Wissenschaften, math.-physik. Klasse (Leipzig, 1885), SS. 333-351. Translated by Herbert Pfister, Institut für Theoretische Physik, Universität Tübingen, Auf der Morgenstelle 14, 72076 Tübingen, Germany; herbert.pfister@uni-tuebingen.de. Kind assistance by Julian Barbour is acknowledged.

International Federation of Library Associations Annual Conference Papers. General Research Libraries Division: Parliamentary Libraries and National Libraries Sections (47th, Leipzig, East Germany, August 17-22, 1981).

ERIC Educational Resources Information Center

Gude, Gilbert; And Others

This set of papers presented to the General Research Libraries Division of the International Federation of Library Associations (IFLA) during its 47th annual conference (1981) includes: "The Effect of the Introduction of Computers on Library and Research Staff," by Gilbert Gude; "Libraries as Information Service Agencies…

[Comparison of four identical electronic noses and three measurement set-ups].

PubMed

Koczulla, R; Hattesohl, A; Biller, H; Hofbauer, J; Hohlfeld, J; Oeser, C; Wirtz, H; Jörres, R A

2011-08-01

Volatile organic compounds (VOCs) can be used as biomarkers in exhaled air. VOC profiles can be detected by an array of nanosensors of an electronic nose. These profiles can be analysed using bioinformatics. It is, however, not known whether different devices of the same model measure identically and to which extent different set-ups and the humidity of the inhaled air influence the VOC profile. Three different measuring set-ups were designed and three healthy control subjects were measured with each of them, using four devices of the same model (Cyranose 320™, Smiths Detection). The exhaled air was collected in a plastic bag. Either ambient air was used as reference (set-up Leipzig), or the reference air was humidified (100% relative humidity) (set-up Marburg and set-up Munich). In the set-up Marburg the subjects inhaled standardised medical air (Aer medicinalis Linde, AGA AB) out of a compressed air bottle through a demand valve; this air (after humidification) was also used as reference. In the set-up Leipzig the subjects inhaled VOC-filtered ambient air, in the set-up Munich unfiltered room air. The data were evaluated using either the real-time data or the changes in resistance as calculated by the device. The results were clearly dependent on the set-up. Apparently, humidification of the reference air could reduce the variance between devices, but this result was also dependent on the evaluation method used. When comparing the three subjects, the set-ups Munich and Marburg mapped these in a similar way, whereas not only the signals but also the variance of the set-up Leipzig were larger. Measuring VOCs with an electronic nose has not yet been standardised and the set-up significantly affects the results. As other researchers use further methods, it is currently not possible to draw generally accepted conclusions. More systematic tests are required to find the most sensitive and reliable but still feasible set-up so that comparability is improved. © Georg Thieme Verlag KG Stuttgart · New York.

[The research of near-infrared blood glucose measurement using particle swarm optimization and artificial neural network].

PubMed

Dai, Juan; Ji, Zhong; Du, Yubao

2017-08-01

Existing near-infrared non-invasive blood glucose detection modelings mostly detect multi-spectral signals with different wavelength, which is not conducive to the popularization of non-invasive glucose meter at home and does not consider the physiological glucose dynamics of individuals. In order to solve these problems, this study presented a non-invasive blood glucose detection model combining particle swarm optimization (PSO) and artificial neural network (ANN) by using the 1 550 nm near-infrared absorbance as the independent variable and the concentration of blood glucose as the dependent variable, named as PSO-2ANN. The PSO-2ANN model was based on two sub-modules of neural networks with certain structures and arguments, and was built up after optimizing the weight coefficients of the two networks by particle swarm optimization. The results of 10 volunteers were predicted by PSO-2ANN. It was indicated that the relative error of 9 volunteers was less than 20%; 98.28% of the predictions of blood glucose by PSO-2ANN were distributed in the regions A and B of Clarke error grid, which confirmed that PSO-2ANN could offer higher prediction accuracy and better robustness by comparison with ANN. Additionally, even the physiological glucose dynamics of individuals may be different due to the influence of environment, temper, mental state and so on, PSO-2ANN can correct this difference only by adjusting one argument. The PSO-2ANN model provided us a new prospect to overcome individual differences in blood glucose prediction.

Boosting Learning Algorithm for Stock Price Forecasting

NASA Astrophysics Data System (ADS)

Wang, Chengzhang; Bai, Xiaoming

2018-03-01

To tackle complexity and uncertainty of stock market behavior, more studies have introduced machine learning algorithms to forecast stock price. ANN (artificial neural network) is one of the most successful and promising applications. We propose a boosting-ANN model in this paper to predict the stock close price. On the basis of boosting theory, multiple weak predicting machines, i.e. ANNs, are assembled to build a stronger predictor, i.e. boosting-ANN model. New error criteria of the weak studying machine and rules of weights updating are adopted in this study. We select technical factors from financial markets as forecasting input variables. Final results demonstrate the boosting-ANN model works better than other ones for stock price forecasting.

The Bravyi-Kitaev transformation for quantum computation of electronic structure

NASA Astrophysics Data System (ADS)

Seeley, Jacob T.; Richard, Martin J.; Love, Peter J.

2012-12-01

Quantum simulation is an important application of future quantum computers with applications in quantum chemistry, condensed matter, and beyond. Quantum simulation of fermionic systems presents a specific challenge. The Jordan-Wigner transformation allows for representation of a fermionic operator by O(n) qubit operations. Here, we develop an alternative method of simulating fermions with qubits, first proposed by Bravyi and Kitaev [Ann. Phys. 298, 210 (2002), 10.1006/aphy.2002.6254; e-print arXiv:quant-ph/0003137v2], that reduces the simulation cost to O(log n) qubit operations for one fermionic operation. We apply this new Bravyi-Kitaev transformation to the task of simulating quantum chemical Hamiltonians, and give a detailed example for the simplest possible case of molecular hydrogen in a minimal basis. We show that the quantum circuit for simulating a single Trotter time step of the Bravyi-Kitaev derived Hamiltonian for H2 requires fewer gate applications than the equivalent circuit derived from the Jordan-Wigner transformation. Since the scaling of the Bravyi-Kitaev method is asymptotically better than the Jordan-Wigner method, this result for molecular hydrogen in a minimal basis demonstrates the superior efficiency of the Bravyi-Kitaev method for all quantum computations of electronic structure.

XMM-Newton, powerful AGN winds and galaxy feedback

NASA Astrophysics Data System (ADS)

Pounds, K.; King, A.

2016-06-01

The discovery that ultra-fast ionized winds - sufficiently powerful to disrupt growth of the host galaxy - are a common feature of luminous AGN is major scientific breakthrough led by XMM-Newton. An extended observation in 2014 of the prototype UFO, PG1211+143, has revealed an unusually complex outflow, with distinct and persisting velocities detected in both hard and soft X-ray spectra. While the general properties of UFOs are consistent with being launched - at the local escape velocity - from the inner disc where the accretion rate is modestly super-Eddington (King and Pounds, Ann Rev Astron Astro- phys 2015), these more complex flows have raised questions about the outflow geometry and the importance of shocks and enhanced cooling. XMM-Newton seems likely to remain the best Observatory to study UFOs prior to Athena, and further extended observations, of PG1211+143 and other bright AGN, have the exciting potential to establish the typical wind dynamics, while providing new insights on the accretion geometry and continuum source structure. An emphasis on such large, coordinated observing programmes with XMM-Newton over the next decade will continue the successful philosophy pioneered by EXOSAT, while helping to inform the optimum planning for Athena

A Statistical Study of Eiscat Electron and Ion Temperature Measurements In The E-region

NASA Astrophysics Data System (ADS)

Hussey, G.; Haldoupis, C.; Schlegel, K.; Bösinger, T.

Motivated by the large EISCAT data base, which covers over 15 years of common programme operation, and previous statistical work with EISCAT data (e.g., C. Hal- doupis, K. Schlegel, and G. Hussey, Auroral E-region electron density gradients mea- sured with EISCAT, Ann. Geopshysicae, 18, 1172-1181, 2000), a detailed statistical analysis of electron and ion EISCAT temperature measurements has been undertaken. This study was specifically concerned with the statistical dependence of heating events with other ambient parameters such as the electric field and electron density. The re- sults showed previously reported dependences such as the electron temperature being directly correlated with the ambient electric field and inversely related to the electron density. However, these correlations were found to be also dependent upon altitude. There was also evidence of the so called "Schlegel effect" (K. Schlegel, Reduced effective recombination coefficient in the disturbed polar E-region, J. Atmos. Terr. Phys., 44, 183-185, 1982); that is, the heated electron gas leads to increases in elec- tron density through a reduction in the recombination rate. This paper will present the statistical heating results and attempt to offer physical explanations and interpretations of the findings.

Multi-instantons and exact results III: Unification of even and odd anharmonic oscillators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jentschura, Ulrich D.; Surzhykov, Andrey; GSI Helmholtzzentrum fuer Schwerionenforschung, 64291 Darmstadt

2010-05-15

This is the third article in a series of three papers on the resonance energy levels of anharmonic oscillators. Whereas the first two papers mainly dealt with double-well potentials and modifications thereof [see J. Zinn-Justin, U.D. Jentschura, Ann. Phys. (N.Y.) 313 (2004) 197 and 269], we here focus on simple even and odd anharmonic oscillators for arbitrary magnitude and complex phase of the coupling parameter. A unification is achieved by the use of PT-symmetry inspired dispersion relations and generalized quantization conditions that include instanton configurations. Higher-order formulas are provided for the oscillators of degrees 3 to 8, which lead tomore » subleading corrections to the leading factorial growth of the perturbative coefficients describing the resonance energies. Numerical results are provided, and higher-order terms are found to be numerically significant. The resonances are described by generalized expansions involving intertwined nonanalytic exponentials, logarithmic terms and power series. Finally, we summarize spectral properties and dispersion relations of anharmonic oscillators, and their interconnections. The purpose is to look at one of the classic problems of quantum theory from a new perspective, through which we gain systematic access to the phenomenologically significant higher-order terms.« less

Folding model analyses of 12C-12C and 16O-16O elastic scattering using the density-dependent LOCV-averaged effective interaction

NASA Astrophysics Data System (ADS)

Rahmat, M.; Modarres, M.

2018-03-01

The averaged effective two-body interaction (AEI), which can be generated through the lowest order constrained variational (LOCV) method for symmetric nuclear matter (SNM) with the input [Reid68, Ann. Phys. 50, 411 (1968), 10.1016/0003-4916(68)90126-7] nucleon-nucleon potential, is used as the effective nucleon-nucleon potential in the folding model to describe the heavy-ion (HI) elastic scattering cross sections. The elastic scattering cross sections of 12C-12C and 16O-16O systems are calculated in the above framework. The results are compared with the corresponding calculations coming from the fitting procedures with the input finite range D D M 3 Y 1 -Reid potential and the available experimental data at different incident energies. It is shown that a reasonable description of the elastic 12C-12C and 16O-16O scattering data at the low and medium energies can be obtained by using the above LOCV AEI, without any need to define a parametrized density-dependent function in the effective nucleon-nucleon potential, which is formally considered in the typical D D M 3 Y 1 -Reid interactions.

Global quantum discord and matrix product density operators

NASA Astrophysics Data System (ADS)

Huang, Hai-Lin; Cheng, Hong-Guang; Guo, Xiao; Zhang, Duo; Wu, Yuyin; Xu, Jian; Sun, Zhao-Yu

2018-06-01

In a previous study, we have proposed a procedure to study global quantum discord in 1D chains whose ground states are described by matrix product states [Z.-Y. Sun et al., Ann. Phys. 359, 115 (2015)]. In this paper, we show that with a very simple generalization, the procedure can be used to investigate quantum mixed states described by matrix product density operators, such as quantum chains at finite temperatures and 1D subchains in high-dimensional lattices. As an example, we study the global discord in the ground state of a 2D transverse-field Ising lattice, and pay our attention to the scaling behavior of global discord in 1D sub-chains of the lattice. We find that, for any strength of the magnetic field, global discord always shows a linear scaling behavior as the increase of the length of the sub-chains. In addition, global discord and the so-called "discord density" can be used to indicate the quantum phase transition in the model. Furthermore, based upon our numerical results, we make some reliable predictions about the scaling of global discord defined on the n × n sub-squares in the lattice.

N-fold Darboux transformation and double-Wronskian-typed solitonic structures for a variable-coefficient modified Kortweg-de Vries equation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Lei, E-mail: wanglei2239@126.com; Gao, Yi-Tian; State Key Laboratory of Software Development Environment, Beijing University of Aeronautics and Astronautics, Beijing 100191

2012-08-15

Under investigation in this paper is a variable-coefficient modified Kortweg-de Vries (vc-mKdV) model describing certain situations from the fluid mechanics, ocean dynamics and plasma physics. N-fold Darboux transformation (DT) of a variable-coefficient Ablowitz-Kaup-Newell-Segur spectral problem is constructed via a gauge transformation. Multi-solitonic solutions in terms of the double Wronskian for the vc-mKdV model are derived by the reduction of the N-fold DT. Three types of the solitonic interactions are discussed through figures: (1) Overtaking collision; (2) Head-on collision; (3) Parallel solitons. Nonlinear, dispersive and dissipative terms have the effects on the velocities of the solitonic waves while the amplitudes ofmore » the waves depend on the perturbation term. - Highlights: Black-Right-Pointing-Pointer N-fold DT is firstly applied to a vc-AKNS spectral problem. Black-Right-Pointing-Pointer Seeking a double Wronskian solution is changed into solving two systems. Black-Right-Pointing-Pointer Effects of the variable coefficients on the multi-solitonic waves are discussed in detail. Black-Right-Pointing-Pointer This work solves the problem from Yi Zhang [Ann. Phys. 323 (2008) 3059].« less

Nonlinear Interaction of the Beat-Photon Beams with the Brain Neurocenters: Laser Neurophysics

NASA Astrophysics Data System (ADS)

Stefan, V. Alexander

2010-03-01

I propose a novel mechanism for laser-brain interaction: Nonlinear interaction of ultrashort pulses of beat-photon, (φ1-- φ2), or double-photon, (φ1+φ2), footnotetextMaria Goeppert-Mayer, "Uber Elementarakte mit zwei Quantenspr"ungen, Ann Phys 9, 273, 95. (1931). beams with the corrupted brain neurocenters, causing a particular neurological disease. The open-scull cerebral tissue can be irradiated with the beat-photon pulses in the range of several 100s fs, with the laser irradiances in the range of a few mW/cm^2, repetition rate of a few 100s Hz, and in the frequency range of 700-1300nm generated in the beat-wave driven free electron laser.footnotetextV. Alexander Stefan, The Interaction of Photon Beams with the DNA Molecules: Genomic Medical Physics. American Physical Society, 2009 APS March Meeting, March 16-20, 2009, abstract #K1.276; V. Stefan, B. I. Cohen, and C. Joshi, Nonlinear Mixing of Electromagnetic Waves in Plasmas Science 27 January 1989:Vol. 243. no. 4890, pp. 494 -- 500 (January 1989). This method may prove to be an effective mechanism in the treatment of neurological diseases: Parkinson's, Lou Gehrig's, and others.

Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE

NASA Astrophysics Data System (ADS)

Correa, R.; Chesta, M. A.; Morales, J. R.; Dinator, M. I.; Requena, I.; Vila, I.

2006-08-01

An artificial neural network (ANN) has been trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio. Concentrations obtained with the ANN method are in full agreement with results from one standard analytical procedure, showing the high potentiality of ANN in PIXE quantitative analyses.

Artificial neural network aided non-invasive grading evaluation of hepatic fibrosis by duplex ultrasonography

PubMed Central

2012-01-01

Background Artificial neural networks (ANNs) are widely studied for evaluating diseases. This paper discusses the intelligence mode of an ANN in grading the diagnosis of liver fibrosis by duplex ultrasonogaphy. Methods 239 patients who were confirmed as having liver fibrosis or cirrhosis by ultrasound guided liver biopsy were investigated in this study. We quantified ultrasonographic parameters as significant parameters using a data optimization procedure applied to an ANN. 179 patients were typed at random as the training group; 60 additional patients were consequently enrolled as the validating group. Performance of the ANN was evaluated according to accuracy, sensitivity, specificity, Youden’s index and receiver operating characteristic (ROC) analysis. Results 5 ultrasonographic parameters; i.e., the liver parenchyma, thickness of spleen, hepatic vein (HV) waveform, hepatic artery pulsatile index (HAPI) and HV damping index (HVDI), were enrolled as the input neurons in the ANN model. The sensitivity, specificity and accuracy of the ANN model for quantitative diagnosis of liver fibrosis were 95.0%, 85.0% and 88.3%, respectively. The Youden’s index (YI) was 0.80. Conclusions The established ANN model had good sensitivity and specificity in quantitative diagnosis of hepatic fibrosis or liver cirrhosis. Our study suggests that the ANN model based on duplex ultrasound may help non-invasive grading diagnosis of liver fibrosis in clinical practice. PMID:22716936

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context.

PubMed

Valavanis, Ioannis K; Mougiakakou, Stavroula G; Grimaldi, Keith A; Nikita, Konstantina S

2010-09-08

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm. PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets. The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.

Day and Night Dust Retrievals from MODIS IR Band Measurements using Artificial Neural Network (ANN) model

NASA Astrophysics Data System (ADS)

Lee, S.; Sohn, B.

2008-12-01

Artificial Neural Network (ANN) on the East Asia domain (20°N-55°N, 90°E-145°E) during the springs of 2006 and 2007 was investigated for retrieving aerosol optical thickness (AOT) of dust aerosol at both daytime and nighttime. The input data for ANN include brightness temperature, BTD (11 μm - 12 μm), spectral emissivity, surface temperature (Land: Price [1984] Equation, Ocean: The IMAPP MODIS Algorithm), relative airmass of satellite, and topography (SRTM30). The D*-parameter is adopted as dust detection algorithm which was developed by Hansell et al [2007]. The target data of the ANN is corresponding AOT at 550nm obtained from MODIS aerosol product (MYD04). After optimization and training, ANN AOT is retrieved. Among the many dust episodes during the spring of 2006, only the 8 April 2006 case was selected for the detailed analysis. Because it is one of the strongest episodes and shows a well-developed root penetrating the Korean peninsula and reaching the Japanese area. It is shown that ANN AOT coincide well with MODIS AOT having correlation coefficient of 0.8502 when the training and applying periods are the same (spring of 2006). Even a different period with training ANN AOT has a good relationship with MODIS AOT with the correlation coefficient of 0.7766 (spring 2007). This yearly difference is resulted from vegetation change and fixed IGBP land cover map. Also notable is that ANN AOT is underestimated in most IGBP types having low slope and negative mean bias. This study showed that ANN model has a good potential to retrieve AOT. More examinations and trials are needed, however, to improve this ANN algorithm using IR bands. Also this model should be extended to specify the dust aerosol property from other aerosols and clouds to assure that it has a capability during both daytime and nighttime.

An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts.

PubMed

Barghash, Mahmoud

2015-01-01

Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a series of decision points. Initially, this work compared using multidecision points and single-decision point on the performance of the ANN which showed that multidecision points are highly preferable to single-decision points. This work also tested the effect of population percentages on the ANN and used this to optimize the ANN's performance. Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble. The ensemble that used only optimized ANNs has improved performance over individual ANNs and three-sigma level rule. In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity. It also can be used to discover even small shifts in the mean as early as possible.

Artificial neural network detects human uncertainty

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Koronovskii, Alexey A.; Garcia-Prieto, Juan; Antón-Toro, Luis Fernando; Maestú, Fernando; Pisarchik, Alexander N.

2018-03-01

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit

NASA Astrophysics Data System (ADS)

Ying, Yibin; Liu, Yande; Fu, Xiaping; Lu, Huishan

2005-11-01

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.

A new optical ice particle counter at LACIS

NASA Astrophysics Data System (ADS)

Bieligk, Henner; Voelker, Georg Sebastian; Clauss, Tina; Grundmann, Marius; Stratmann, Frank

2014-05-01

Clouds play an important role within the climate system, especially for the radiative energy budget of the earth. The radiative properties of a cloud depend strongly on the fractions of ice crystals and water droplets, their size distributions, and the ice crystal shapes within the particular cloud. One option to gain this kind of information is using optical particle counters. A new optical particle counter is developed for laboratory work and is based on the concept of the Thermostabilized Optical Particle Spectrometer for the Detection of Ice Particles (TOPS-Ice, Clauss et al., 2013). TOPS-Ice uses linearly polarized green laser light and the depolarization of the scattered light at a scattering angle of 42.5° to discriminate between liquid water droplets and ice crystals in the lower μm range. However, the measurements are usually limited to ice fractions in the order of 1%. To improve the determination of the ice fraction, several modifications of the original setup are implemented including an additional detection system at another scattering angle. The new scattering angle is optimized for least interference between the droplet and ice signals. This is achieved by finding the angle with the maximum difference in scattered intensity of water droplets compared to ice crystals with the same volume equivalent diameter. The suitable scattering angle of 100° for linearly polarized light was chosen based on calculations using T-Matrix method, Lorenz-Mie theory, Müller matrices and distribution theory. The new optical setup is designed to run in combination with a laminar flow tube, the so-called Leipzig Aerosol Cloud Interaction Simulator (LACIS, Stratmann et al., 2004; Hartmann et al., 2011). Using LACIS and its precisely controlled thermodynamic conditions, we are able to form small water droplets and ice crystals which will then be detected, classified and sized by our new optical device. This setup is planned to be tested in ice measurements including Snomax® and several dusts (e.g. illite, kaolinite, ATD) as ice nuclei which all show different behaviors in ice formation. Furthermore, a detailed comparison of both instruments TOPS-Ice and the new setup is planned. This project is part of the Leipzig Graduate School on Clouds, Aerosols and Radiation and is partly supported by the German Research Foundation (DFG project WE 4722/1-1) within the DFG Research Unit FOR 1525 INUIT. Clauss, T., Kiselev, A., Hartmann, S., Augustin, S., Pfeifer, S., Niedermeier, D., Wex, H., and Stratmann, F, 2013, Application of linear polarized light for the discrimination of frozen and liquid droplets in ice nucleation experiments, Atmos. Meas. Tech., 6, 1041-1052. Hartmann, S., Niedermeier, D., Voigtländer, J., Clauss, T., Shaw, R. A., Kiselev, A., and Stratmann, F., 2011, Homogeneous and heterogeneous ice nucleation at LACIS: operating principle and theoretical studies, Atmos. Chem. Phys., 11, 1753-1767. Stratmann, F., Kiselev, A., Wurzler, S., Wendisch, M., Heintzenberg, J., Charlson, R. J., Diehl, K., Wex, H., and Schmidt, S., 2004, Laboratory Studies and Numerical Simulations of Cloud Droplet Formation under Realistic Supersaturation Conditions, J. Atmos. Oceanic. Technol., 21, 876-887.

Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study.

PubMed

Nakajima, Kenichi; Kudo, Takashi; Nakata, Tomoaki; Kiso, Keisuke; Kasai, Tokuo; Taniguchi, Yasuyo; Matsuo, Shinro; Momose, Mitsuru; Nakagawa, Masayasu; Sarai, Masayoshi; Hida, Satoshi; Tanaka, Hirokazu; Yokoyama, Kunihiko; Okuda, Koichi; Edenbrandt, Lars

2017-12-01

Artificial neural networks (ANN) might help to diagnose coronary artery disease. This study aimed to determine whether the diagnostic accuracy of an ANN-based diagnostic system and conventional quantitation are comparable. The ANN was trained to classify potentially abnormal areas as true or false based on the nuclear cardiology expert interpretation of 1001 gated stress/rest 99m Tc-MIBI images at 12 hospitals. The diagnostic accuracy of the ANN was compared with 364 expert interpretations that served as the gold standard of abnormality for the validation study. Conventional summed stress/rest/difference scores (SSS/SRS/SDS) were calculated and compared with receiver operating characteristics (ROC) analysis. The ANN generated a better area under the ROC curves (AUC) than SSS (0.92 vs. 0.82, p < 0.0001), indicating better identification of stress defects. The ANN also generated a better AUC than SDS (0.90 vs. 0.75, p < 0.0001) for stress-induced ischemia. The AUC for patients with old myocardial infarction based on rest defects was 0.97 (0.91 for SRS, p = 0.0061), and that for patients with and without a history of revascularization based on stress defects was 0.94 and 0.90 (p = 0.0055 and p < 0.0001 vs. SSS, respectively). The SSS/SRS/SDS steeply increased when ANN values (probability of abnormality) were >0.80. The ANN was diagnostically accurate in various clinical settings, including that of patients with previous myocardial infarction and coronary revascularization. The ANN could help to diagnose coronary artery disease.

Chiral topological phases from artificial neural networks

NASA Astrophysics Data System (ADS)

Kaubruegger, Raphael; Pastori, Lorenzo; Budich, Jan Carl

2018-05-01

Motivated by recent progress in applying techniques from the field of artificial neural networks (ANNs) to quantum many-body physics, we investigate to what extent the flexibility of ANNs can be used to efficiently study systems that host chiral topological phases such as fractional quantum Hall (FQH) phases. With benchmark examples, we demonstrate that training ANNs of restricted Boltzmann machine type in the framework of variational Monte Carlo can numerically solve FQH problems to good approximation. Furthermore, we show by explicit construction how n -body correlations can be kept at an exact level with ANN wave functions exhibiting polynomial scaling with power n in system size. Using this construction, we analytically represent the paradigmatic Laughlin wave function as an ANN state.

Reflective Learning in Practice.

ERIC Educational Resources Information Center

Brockbank, Anne, Ed.; McGill, Ian, Ed.; Beech, Nic, Ed.

This book contains 22 papers on reflective learning in practice. The following papers are included: "Our Purpose" (Ann Brockbank, Ian McGill, Nic Beech); "The Nature and Context of Learning" (Ann Brockbank, Ian McGill, Nic Beech); "Reflective Learning and Organizations" (Ann Brockbank, Ian McGill, Nic Beech);…

Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China

NASA Astrophysics Data System (ADS)

Zhu, Yun-Mei; Lu, X. X.; Zhou, Yue

2007-02-01

Artificial neural network (ANN) was used to model the monthly suspended sediment flux in the Longchuanjiang River, the Upper Yangtze Catchment, China. The suspended sediment flux was related to the average rainfall, temperature, rainfall intensity and water discharge. It is demonstrated that ANN is capable of modeling the monthly suspended sediment flux with fairly good accuracy when proper variables and their lag effect on the suspended sediment flux are used as inputs. Compared with multiple linear regression and power relation models, ANN can generate a better fit under the same data requirement. In addition, ANN can provide more reasonable predictions for extremely high or low values, because of the distributed information processing system and the nonlinear transformation involved. Compared with the ANNs that use the values of the dependent variable at previous time steps as inputs, the ANNs established in this research with only climate variables have an advantage because it can be used to assess hydrological responses to climate change.

How Children with Autism Reason about Other's Intentions: False-Belief and Counterfactual Inferences.

PubMed

Rasga, Célia; Quelhas, Ana Cristina; Byrne, Ruth M J

2017-06-01

We examine false belief and counterfactual reasoning in children with autism with a new change-of-intentions task. Children listened to stories, for example, Anne is picking up toys and John hears her say she wants to find her ball. John goes away and the reason for Anne's action changes-Anne's mother tells her to tidy her bedroom. We asked, 'What will John believe is the reason that Anne is picking up toys?' which requires a false-belief inference, and 'If Anne's mother hadn't asked Anne to tidy her room, what would have been the reason she was picking up toys?' which requires a counterfactual inference. We tested children aged 6, 8 and 10 years. Children with autism made fewer correct inferences than typically developing children at 8 years, but by 10 years there was no difference. Children with autism made fewer correct false-belief than counterfactual inferences, just like typically developing children.

Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.

PubMed

Mendenhall, Jeffrey; Meiler, Jens

2016-02-01

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.

Improving Quantitative Structure-Activity Relationship Models using Artificial Neural Networks Trained with Dropout

PubMed Central

Mendenhall, Jeffrey; Meiler, Jens

2016-01-01

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery (LB-CADD) pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both Enrichment false positive rate (FPR) and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22–46% over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods. PMID:26830599

International Federation of Library Associations Annual Conference Papers. Collections and Services Division: Interlending, Rare and Precious Books, and Exchange and Acquisition Sections (47th, Leipzig, East Germany, August 17-22, 1981).

ERIC Educational Resources Information Center

Line, Maurice B.; And Others

This set of papers presented to the Collections and Services Division of the International Federation of Library Associations at its 47th annual conference (1981) includes: "Planning Interlending Systems in Developing Countries," by Maurice B. Line; "Problems of Centralisation of Inter-Library Lending in a De-Centralized Library…

International Federation of Library Associations Annual Conference Papers. Education and Research Division: Library Theory and Research Section (47th, Leipzig, East Germany, August 17-22, 1981).

ERIC Educational Resources Information Center

Kolodziejska, Jadwiga; And Others

Seven of these ten papers are concerned with library research in specific countries; the remaining three deal with library planning and ethics in research. Titles are "The Library as a Cultural Institution," by Jadwiga Kolodziejska, Poland; "The International Seminar 'Book and Library in Society' of the Polish Book and Readers…

International Federation of Library Associations Annual Conference. Papers of the Management and Technology Division: Information Technology Section (47th, Leipzig, East Germany, August 17-22, 1981).

ERIC Educational Resources Information Center

Bradler, Reinhard; And Others

These seven papers on library management and networks focus on: (1) computerized access to archival and library materials, describing the methodological problems associated with a pilot project in the German Democratic Republic, as well as the efficiency of data bank systems; (2) present and future development of libraries and information centers…

JPRS Report, Soviet Union KOMMUNIST No 10, July 1988.

DTIC Science & Technology

1988-10-17

sciences; and Kirill Kiril - lovich Shirinya, consultant, CPSU Central Committee Institute of Marxism-Leninism, doctor of historical sci- ences] [Text...Spain and Italy), as well as from above, from the Comintern leadership, which was assumed by G. Dimitrov , the hero of the Leipzig trial. Life...demanded a new tactical and strategic orientation and changes in the methods of activity of the Comintern. In developing the new approaches, G. Dimitrov

Highlights from the Second International Symposium on HPV infection in head and neck cancer.

PubMed

Wiegand, Susanne; Wichmann, G; Golusinski, W; Leemans, C R; Klussmann, J P; Dietz, A

2018-06-01

The Second International Symposium on HPV Infection in Head and Neck Cancer was held on 3rd-4th November 2016 in Leipzig, Germany. The meeting brought together researchers and clinicians to share the latest knowledge on HPV infection in head and neck cancer and to join active and constructive scientific discussions. This report summarizes the major themes discussed during the symposium.

Prediction of heat transfer coefficients for forced convective boiling of N2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks

NASA Astrophysics Data System (ADS)

Barroso-Maldonado, J. M.; Belman-Flores, J. M.; Ledesma, S.; Aceves, S. M.

2018-06-01

A key problem faced in the design of heat exchangers, especially for cryogenic applications, is the determination of convective heat transfer coefficients in two-phase flow such as condensation and boiling of non-azeotropic refrigerant mixtures. This paper proposes and evaluates three models for estimating the convective coefficient during boiling. These models are developed using computational intelligence techniques. The performance of the proposed models is evaluated using the mean relative error (mre), and compared to two existing models: the modified Granryd's correlation and the Silver-Bell-Ghaly method. The three proposed models are distinguished by their architecture. The first is based on directly measured parameters (DMP-ANN), the second is based on equivalent Reynolds and Prandtl numbers (eq-ANN), and the third on effective Reynolds and Prandtl numbers (eff-ANN). The results demonstrate that the proposed artificial neural network (ANN)-based approaches greatly outperform available methodologies. While Granryd's correlation predicts experimental data within a mean relative error mre = 44% and the S-B-G method produces mre = 42%, DMP-ANN has mre = 7.4% and eff-ANN has mre = 3.9%. Considering that eff-ANN has the lowest mean relative error (one tenth of previously available methodologies) and the broadest range of applicability, it is recommended for future calculations. Implementation is straightforward within a variety of platforms and the matrices with the ANN weights are given in the appendix for efficient programming.

Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence

NASA Astrophysics Data System (ADS)

Morales-Esteban, A.; Martínez-Álvarez, F.; Reyes, J.

2013-05-01

A method to predict earthquakes in two of the seismogenic areas of the Iberian Peninsula, based on Artificial Neural Networks (ANNs), is presented in this paper. ANNs have been widely used in many fields but only very few and very recent studies have been conducted on earthquake prediction. Two kinds of predictions are provided in this study: a) the probability of an earthquake, of magnitude equal or larger than a preset threshold magnitude, within the next 7 days, to happen; b) the probability of an earthquake of a limited magnitude interval to happen, during the next 7 days. First, the physical fundamentals related to earthquake occurrence are explained. Second, the mathematical model underlying ANNs is explained and the configuration chosen is justified. Then, the ANNs have been trained in both areas: The Alborán Sea and the Western Azores-Gibraltar fault. Later, the ANNs have been tested in both areas for a period of time immediately subsequent to the training period. Statistical tests are provided showing meaningful results. Finally, ANNs were compared to other well known classifiers showing quantitatively and qualitatively better results. The authors expect that the results obtained will encourage researchers to conduct further research on this topic. Development of a system capable of predicting earthquakes for the next seven days Application of ANN is particularly reliable to earthquake prediction. Use of geophysical information modeling the soil behavior as ANN's input data Successful analysis of one region with large seismic activity

A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting

NASA Astrophysics Data System (ADS)

Luk, K. C.; Ball, J. E.; Sharma, A.

2000-01-01

Artificial neural networks (ANNs), which emulate the parallel distributed processing of the human nervous system, have proven to be very successful in dealing with complicated problems, such as function approximation and pattern recognition. Due to their powerful capability and functionality, ANNs provide an alternative approach for many engineering problems that are difficult to solve by conventional approaches. Rainfall forecasting has been a difficult subject in hydrology due to the complexity of the physical processes involved and the variability of rainfall in space and time. In this study, ANNs were adopted to forecast short-term rainfall for an urban catchment. The ANNs were trained to recognise historical rainfall patterns as recorded from a number of gauges in the study catchment for reproduction of relevant patterns for new rainstorm events. The primary objective of this paper is to investigate the effect of temporal and spatial information on short-term rainfall forecasting. To achieve this aim, a comparison test on the forecast accuracy was made among the ANNs configured with different orders of lag and different numbers of spatial inputs. In developing the ANNs with alternative configurations, the ANNs were trained to an optimal level to achieve good generalisation of data. It was found in this study that the ANNs provided the most accurate predictions when an optimum number of spatial inputs was included into the network, and that the network with lower lag consistently produced better performance.

Computer vision-based method for classification of wheat grains using artificial neural network.

PubMed

Sabanci, Kadir; Kayabasi, Ahmet; Toktas, Abdurrahim

2017-06-01

A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains. Seven input parameters that are most effective on the classifying results are determined using the correlation-based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10 -6 by the simplified ANN model. This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Mixed-phase altocumulus clouds over Leipzig: Remote sensing measurements and spectral cloud microphysics simulations

NASA Astrophysics Data System (ADS)

Simmel, Martin; Bühl, Johannes; Ansmann, Albert; Tegen, Ina

2015-04-01

The present work combines remote sensing observations and detailed microphysics cloud modeling to investigate two altocumulus cloud cases observed over Leipzig, Germany. A suite of remote sensing instruments was able to detect primary ice at rather warm temperatures of -6°C. For comparison, a second mixed phase case at about -25°C is introduced. To further look into the details of cloud microphysical processes a simple dynamics model of the Asai-Kasahara type is combined with detailed spectral microphysics forming the model system AK-SPECS. Temperature and humidity profiles are taken either from observation (radiosonde) or GDAS reanalysis. Vertical velocities are prescribed to force the dynamics as well as main cloud features to be close to the observations. Subsequently, sensitivity studies with respect to dynamical as well as ice microphysical parameters are carried out with the aim to quantify the most important sensitivities for the cases investigated. For the cases selected, the liquid phase is mainly determined by the model dynamics (location and strength of vertical velocity) whereas the ice phase is much more sensitive to the microphysical parameters (ice nuclei (IN) number, ice particle shape). The choice of ice particle shape may induce large uncertainties which are in the same order as those for the temperature-dependent IN number distribution.

Evaluation of the hydrologic system in the New Leipzig coal area, Grant and Hettinger counties, North Dakota

USGS Publications Warehouse

Armstrong, C.A.

1982-01-01

Aquifers in the New Leipzig coal area consist of sandstone beds in the Fox Hills Sandstone, the Hell Creek Formation, the Cannonball and Ludlow Members of the Fort Union Formation, and the basal part of the Tongue River Member of the Fort Union Formation. Aquifers also occur in sandstone and lignite beds in the upper part of the Tongue River Member and Sentinel Butte Member of the Fort Union Formation. Potential well yields from each of the aquifers are variable, but are less than 100 gallons per minute. Water in the Fox Hills, Hell Creek, Cannonball, and Ludlow is soft and of the sodium bicarbonate type. Water in basal Tongue River aquifer is either soft or very hard and generally is of the sodium bicarbonate type. Water in the upper Tongue River and Sentinel Butte aquifer system is very hard and generally is either of the calcium bicarbonate or sodium bicarbonate type. There is little or no contribution of ground water to Thirty Mile Creek or the Cannonball River from the area of minable coal. Coal mining will expose sulfide minerals to oxidation, and result in an increase in dissolved solids and sulfate in water in the basal Tongue River aquifer. (USGS)

Chemistry of Urban Grime: Inorganic Ion Composition of Grime vs Particles in Leipzig, Germany.

PubMed

Baergen, Alyson M; Styler, Sarah A; van Pinxteren, Dominik; Müller, Konrad; Herrmann, Hartmut; Donaldson, D James

2015-11-03

Deposition of atmospheric constituents--either gas phase or particulate--onto urban impervious surfaces gives rise to a thin "urban grime" film. The area exposed by these impervious surfaces in a typical urban environment is comparable to, or greater than, that of particles present in the urban boundary layer; however, it is largely overlooked as a site for heterogeneous reactions. Here we present the results of a field campaign to determine and compare the chemical composition of urban grime and of particles collected simultaneously during the autumn of 2014 at an urban site in central Leipzig, Germany. We see dramatically reduced ammonium and nitrate levels in the film as compared to particles, suggesting a significant loss of ammonium nitrate, thus enhancing the mobility of these species in the environment. Nitrate levels are 10% lower for films exposed to sunlight compared to those that were shielded from direct sun, indicating a possible mechanism for recycling nitrate anion to reactive nitrogen species. Finally, chloride levels in the film suggest that urban grime could represent an unrecognized source of continental chloride available for ClNO2 production even in times of low particulate chloride. Such source and recycling processes could prove to be important to local and regional air quality.

A Constructive Approach to Regularity of Lagrangian Trajectories for Incompressible Euler Flow in a Bounded Domain

NASA Astrophysics Data System (ADS)

Besse, Nicolas; Frisch, Uriel

2017-04-01

The 3D incompressible Euler equations are an important research topic in the mathematical study of fluid dynamics. Not only is the global regularity for smooth initial data an open issue, but the behaviour may also depend on the presence or absence of boundaries. For a good understanding, it is crucial to carry out, besides mathematical studies, high-accuracy and well-resolved numerical exploration. Such studies can be very demanding in computational resources, but recently it has been shown that very substantial gains can be achieved first, by using Cauchy's Lagrangian formulation of the Euler equations and second, by taking advantage of analyticity results of the Lagrangian trajectories for flows whose initial vorticity is Hölder-continuous. The latter has been known for about 20 years (Serfati in J Math Pures Appl 74:95-104, 1995), but the combination of the two, which makes use of recursion relations among time-Taylor coefficients to obtain constructively the time-Taylor series of the Lagrangian map, has been achieved only recently (Frisch and Zheligovsky in Commun Math Phys 326:499-505, 2014; Podvigina et al. in J Comput Phys 306:320-342, 2016 and references therein). Here we extend this methodology to incompressible Euler flow in an impermeable bounded domain whose boundary may be either analytic or have a regularity between indefinite differentiability and analyticity. Non-constructive regularity results for these cases have already been obtained by Glass et al. (Ann Sci Éc Norm Sup 45:1-51, 2012). Using the invariance of the boundary under the Lagrangian flow, we establish novel recursion relations that include contributions from the boundary. This leads to a constructive proof of time-analyticity of the Lagrangian trajectories with analytic boundaries, which can then be used subsequently for the design of a very high-order Cauchy-Lagrangian method.

77 FR 75629 - Pramaggiore, Anne R.; Notice of Filing

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-21

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. ID-6059-001] Pramaggiore, Anne R.; Notice of Filing Take notice that on December 14, 2012, Anne R. Pramaggiore submitted for filing, an application for authority to hold interlocking positions, pursuant to section 305(b) of the...

Wide Area Recovery and Resiliency Program (WARRP) Knowledge Enhancement Events: CBR Workshop After Action Report

DTIC Science & Technology

2012-01-01

Laboratories Walker Ray Walker Engineering Solutions, LLC Williams Patricia Denver Office of Emergency Management Wood- Zika Annmarie Lawrence Livermore...llnl.gov AnnMarie Wood- Zika woodzika1@llnl.gov Pacific Northwest National Laboratory Ann Lesperance ann.lesperance@pnnl.gov Jessica Sandusky

30 CFR 941.780 - Surface mining permit applications-minimum requirements for reclamation and operation plan.

Code of Federal Regulations, 2011 CFR

2011-07-01

... demonstrate compliance with the South Dakota laws on air pollution, S. D. Comp. Laws Ann. Chap. 34A-1, water pollution control, S. D. Comp. Laws Ann. Chap. 34A-2, and solid waste disposal, S. D. Comp. Laws Ann. Chap...

ANN modeling of DNA sequences: new strategies using DNA shape code.

PubMed

Parbhane, R V; Tambe, S S; Kulkarni, B D

2000-09-01

Two new encoding strategies, namely, wedge and twist codes, which are based on the DNA helical parameters, are introduced to represent DNA sequences in artificial neural network (ANN)-based modeling of biological systems. The performance of the new coding strategies has been evaluated by conducting three case studies involving mapping (modeling) and classification applications of ANNs. The proposed coding schemes have been compared rigorously and shown to outperform the existing coding strategies especially in situations wherein limited data are available for building the ANN models.

[Methods of artificial intelligence: a new trend in pharmacy].

PubMed

Dohnal, V; Kuca, K; Jun, D

2005-07-01

Artificial neural networks (ANN) and genetic algorithms are one group of methods called artificial intelligence. The application of ANN on pharmaceutical data can lead to an understanding of the inner structure of data and a possibility to build a model (adaptation). In addition, for certain cases it is possible to extract rules from data. The adapted ANN is prepared for the prediction of properties of compounds which were not used in the adaptation phase. The applications of ANN have great potential in pharmaceutical industry and in the interpretation of analytical, pharmacokinetic or toxicological data.

Application of artificial neural networks in hydrological modeling: A case study of runoff simulation of a Himalayan glacier basin

NASA Technical Reports Server (NTRS)

Buch, A. M.; Narain, A.; Pandey, P. C.

1994-01-01

The simulation of runoff from a Himalayan Glacier basin using an Artificial Neural Network (ANN) is presented. The performance of the ANN model is found to be superior to the Energy Balance Model and the Multiple Regression model. The RMS Error is used as the figure of merit for judging the performance of the three models, and the RMS Error for the ANN model is the latest of the three models. The ANN is faster in learning and exhibits excellent system generalization characteristics.

Inversion of 2-D DC resistivity data using rapid optimization and minimal complexity neural network

NASA Astrophysics Data System (ADS)

Singh, U. K.; Tiwari, R. K.; Singh, S. B.

2010-02-01

The backpropagation (BP) artificial neural network (ANN) technique of optimization based on steepest descent algorithm is known to be inept for its poor performance and does not ensure global convergence. Nonlinear and complex DC resistivity data require efficient ANN model and more intensive optimization procedures for better results and interpretations. Improvements in the computational ANN modeling process are described with the goals of enhancing the optimization process and reducing ANN model complexity. Well-established optimization methods, such as Radial basis algorithm (RBA) and Levenberg-Marquardt algorithms (LMA) have frequently been used to deal with complexity and nonlinearity in such complex geophysical records. We examined here the efficiency of trained LMA and RB networks by using 2-D synthetic resistivity data and then finally applied to the actual field vertical electrical resistivity sounding (VES) data collected from the Puga Valley, Jammu and Kashmir, India. The resulting ANN reconstruction resistivity results are compared with the result of existing inversion approaches, which are in good agreement. The depths and resistivity structures obtained by the ANN methods also correlate well with the known drilling results and geologic boundaries. The application of the above ANN algorithms proves to be robust and could be used for fast estimation of resistive structures for other complex earth model also.

Implementation of neural network for color properties of polycarbonates

NASA Astrophysics Data System (ADS)

Saeed, U.; Ahmad, S.; Alsadi, J.; Ross, D.; Rizvi, G.

2014-05-01

In present paper, the applicability of artificial neural networks (ANN) is investigated for color properties of plastics. The neural networks toolbox of Matlab 6.5 is used to develop and test the ANN model on a personal computer. An optimal design is completed for 10, 12, 14,16,18 & 20 hidden neurons on single hidden layer with five different algorithms: batch gradient descent (GD), batch variable learning rate (GDX), resilient back-propagation (RP), scaled conjugate gradient (SCG), levenberg-marquardt (LM) in the feed forward back-propagation neural network model. The training data for ANN is obtained from experimental measurements. There were twenty two inputs including resins, additives & pigments while three tristimulus color values L*, a* and b* were used as output layer. Statistical analysis in terms of Root-Mean-Squared (RMS), absolute fraction of variance (R squared), as well as mean square error is used to investigate the performance of ANN. LM algorithm with fourteen neurons on hidden layer in Feed Forward Back-Propagation of ANN model has shown best result in the present study. The degree of accuracy of the ANN model in reduction of errors is proven acceptable in all statistical analysis and shown in results. However, it was concluded that ANN provides a feasible method in error reduction in specific color tristimulus values.

A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context

PubMed Central

2010-01-01

Background Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm. Results PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets. Conclusions The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics. PMID:20825661

On the thresholds in modeling of high flows via artificial neural networks - A bootstrapping analysis

NASA Astrophysics Data System (ADS)

Panagoulia, D.; Trichakis, I.

2012-04-01

Considering the growing interest in simulating hydrological phenomena with artificial neural networks (ANNs), it is useful to figure out the potential and limits of these models. In this study, the main objective is to examine how to improve the ability of an ANN model to simulate extreme values of flow utilizing a priori knowledge of threshold values. A three-layer feedforward ANN was trained by using the back propagation algorithm and the logistic function as activation function. By using the thresholds, the flow was partitioned in low (x < μ), medium (μ ≤ x ≤ μ + 2σ) and high (x > μ + 2σ) values. The employed ANN model was trained for high flow partition and all flow data too. The developed methodology was implemented over a mountainous river catchment (the Mesochora catchment in northwestern Greece). The ANN model received as inputs pseudo-precipitation (rain plus melt) and previous observed flow data. After the training was completed the bootstrapping methodology was applied to calculate the ANN confidence intervals (CIs) for a 95% nominal coverage. The calculated CIs included only the uncertainty, which comes from the calibration procedure. The results showed that an ANN model trained specifically for high flows, with a priori knowledge of the thresholds, can simulate these extreme values much better (RMSE is 31.4% less) than an ANN model trained with all data of the available time series and using a posteriori threshold values. On the other hand the width of CIs increases by 54.9% with a simultaneous increase by 64.4% of the actual coverage for the high flows (a priori partition). The narrower CIs of the high flows trained with all data may be attributed to the smoothing effect produced from the use of the full data sets. Overall, the results suggest that an ANN model trained with a priori knowledge of the threshold values has an increased ability in simulating extreme values compared with an ANN model trained with all the data and a posteriori knowledge of the thresholds.

Bayesian model selection applied to artificial neural networks used for water resources modeling

NASA Astrophysics Data System (ADS)

Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.

2008-04-01

Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.

Novel Formulation of Adaptive MPC as EKF Using ANN Model: Multiproduct Semibatch Polymerization Reactor Case Study.

PubMed

Kamesh, Reddi; Rani, Kalipatnapu Yamuna

2017-12-01

In this paper, a novel formulation for nonlinear model predictive control (MPC) has been proposed incorporating the extended Kalman filter (EKF) control concept using a purely data-driven artificial neural network (ANN) model based on measurements for supervisory control. The proposed scheme consists of two modules focusing on online parameter estimation based on past measurements and control estimation over control horizon based on minimizing the deviation of model output predictions from set points along the prediction horizon. An industrial case study for temperature control of a multiproduct semibatch polymerization reactor posed as a challenge problem has been considered as a test bed to apply the proposed ANN-EKFMPC strategy at supervisory level as a cascade control configuration along with proportional integral controller [ANN-EKFMPC with PI (ANN-EKFMPC-PI)]. The proposed approach is formulated incorporating all aspects of MPC including move suppression factor for control effort minimization and constraint-handling capability including terminal constraints. The nominal stability analysis and offset-free tracking capabilities of the proposed controller are proved. Its performance is evaluated by comparison with a standard MPC-based cascade control approach using the same adaptive ANN model. The ANN-EKFMPC-PI control configuration has shown better controller performance in terms of temperature tracking, smoother input profiles, as well as constraint-handling ability compared with the ANN-MPC with PI approach for two products in summer and winter. The proposed scheme is found to be versatile although it is based on a purely data-driven model with online parameter estimation.

Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging.

PubMed

Nakajima, Kenichi; Matsuo, Shinro; Wakabayashi, Hiroshi; Yokoyama, Kunihiko; Bunko, Hisashi; Okuda, Koichi; Kinuya, Seigo; Nyström, Karin; Edenbrandt, Lars

2015-01-01

The purpose of this study was to apply an artificial neural network (ANN) in patients with coronary artery disease (CAD) and to characterize its diagnostic ability compared with conventional visual and quantitative methods in myocardial perfusion imaging (MPI). A total of 106 patients with CAD were studied with MPI, including multiple vessel disease (49%), history of myocardial infarction (27%) and coronary intervention (30%). The ANN detected abnormal areas with a probability of stress defect and ischemia. The consensus diagnosis based on expert interpretation and coronary stenosis was used as the gold standard. The left ventricular ANN value was higher in the stress-defect group than in the no-defect group (0.92±0.11 vs. 0.25±0.32, P<0.0001) and higher in the ischemia group than in the no-ischemia group (0.70±0.40 vs. 0.004±0.032, P<0.0001). Receiver-operating characteristics curve analysis showed comparable diagnostic accuracy between ANN and the scoring methods (0.971 vs. 0.980 for stress defect, and 0.882 vs. 0.937 for ischemia, both P=NS). The relationship between the ANN and defect scores was non-linear, with the ANN rapidly increased in ranges of summed stress score of 2-7 and summed defect score of 2-4. Although the diagnostic ability of ANN was similar to that of conventional scoring methods, the ANN could provide a different viewpoint for judging abnormality, and thus is a promising method for evaluating abnormality in MPI.

76 FR 28068 - Notice of Intent To Repatriate Cultural Items: Museum of Anthropology, University of Michigan...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-13

... Cultural Items: Museum of Anthropology, University of Michigan, Ann Arbor, MI AGENCY: National Park Service... Museum of Anthropology, University of Michigan, Ann Arbor, MI, that meet the definition of unassociated... funerary objects should contact Carla Sinopoli, Museum of Anthropology, University of Michigan, Ann Arbor...

Real-time support for high performance aircraft operation

NASA Technical Reports Server (NTRS)

Vidal, Jacques J.

1989-01-01

The feasibility of real-time processing schemes using artificial neural networks (ANNs) is investigated. A rationale for digital neural nets is presented and a general processor architecture for control applications is illustrated. Research results on ANN structures for real-time applications are given. Research results on ANN algorithms for real-time control are also shown.

33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Cape Ann, MA to Marblehead Neck, MA. 80.120 Section 80.120 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA to...

33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false Cape Ann, MA to Marblehead Neck, MA. 80.120 Section 80.120 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA to...

33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 1 2014-07-01 2014-07-01 false Cape Ann, MA to Marblehead Neck, MA. 80.120 Section 80.120 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA to...

33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 1 2012-07-01 2012-07-01 false Cape Ann, MA to Marblehead Neck, MA. 80.120 Section 80.120 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA to...

33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 1 2013-07-01 2013-07-01 false Cape Ann, MA to Marblehead Neck, MA. 80.120 Section 80.120 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA to...

Visual NNet: An Educational ANN's Simulation Environment Reusing Matlab Neural Networks Toolbox

ERIC Educational Resources Information Center

Garcia-Roselló, Emilio; González-Dacosta, Jacinto; Lado, Maria J.; Méndez, Arturo J.; Garcia Pérez-Schofield, Baltasar; Ferrer, Fátima

2011-01-01

Artificial Neural Networks (ANN's) are nowadays a common subject in different curricula of graduate and postgraduate studies. Due to the complex algorithms involved and the dynamic nature of ANN's, simulation software has been commonly used to teach this subject. This software has usually been developed specifically for learning purposes, because…

Modelling for Prediction vs. Modelling for Understanding: Commentary on Musso et al. (2013)

ERIC Educational Resources Information Center

Edelsbrunner, Peter; Schneider, Michael

2013-01-01

Musso et al. (2013) predict students' academic achievement with high accuracy one year in advance from cognitive and demographic variables, using artificial neural networks (ANNs). They conclude that ANNs have high potential for theoretical and practical improvements in learning sciences. ANNs are powerful statistical modelling tools but they can…

An ANN That Applies Pragmatic Decision on Texts.

ERIC Educational Resources Information Center

Aretoulaki, Maria; Tsujii, Jun-ichi

A computer-based artificial neural network (ANN) that learns to classify sentences in a text as important or unimportant is described. The program is designed to select the sentences that are important enough to be included in composition of an abstract of the text. The ANN is embedded in a conventional symbolic environment consisting of…

Brentuximab Vedotin or Crizotinib and Combination Chemotherapy in Treating Patients With Newly Diagnosed Stage II-IV Anaplastic Large Cell Lymphoma

ClinicalTrials.gov

2018-06-25

Anaplastic Large Cell Lymphoma, ALK-Positive; Ann Arbor Stage II Noncutaneous Childhood Anaplastic Large Cell Lymphoma; Ann Arbor Stage III Noncutaneous Childhood Anaplastic Large Cell Lymphoma; Ann Arbor Stage IV Noncutaneous Childhood Anaplastic Large Cell Lymphoma; CD30-Positive Neoplastic Cells Present

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch

PubMed Central

Fadilah, Norasyikin; Mohamad-Saleh, Junita; Halim, Zaini Abdul; Ibrahim, Haidi; Ali, Syed Salim Syed

2012-01-01

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category. PMID:23202043

Forecasting the discomfort levels within the greater Athens area, Greece using artificial neural networks and multiple criteria analysis

NASA Astrophysics Data System (ADS)

Vouterakos, P. A.; Moustris, K. P.; Bartzokas, A.; Ziomas, I. C.; Nastos, P. T.; Paliatsos, A. G.

2012-12-01

In this work, artificial neural networks (ANNs) were developed and applied in order to forecast the discomfort levels due to the combination of high temperature and air humidity, during the hot season of the year, in eight different regions within the Greater Athens area (GAA), Greece. For the selection of the best type and architecture of ANNs-forecasting models, the multiple criteria analysis (MCA) technique was applied. Three different types of ANNs were developed and tested with the MCA method. Concretely, the multilayer perceptron, the generalized feed forward networks (GFFN), and the time-lag recurrent networks were developed and tested. Results showed that the best ANNs type performance was achieved by using the GFFN model for the prediction of discomfort levels due to high temperature and air humidity within GAA. For the evaluation of the constructed ANNs, appropriate statistical indices were used. The analysis proved that the forecasting ability of the developed ANNs models is very satisfactory at a significant statistical level of p < 0.01.

Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review

NASA Astrophysics Data System (ADS)

Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed

2017-05-01

Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.

Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch.

PubMed

Fadilah, Norasyikin; Mohamad-Saleh, Junita; Abdul Halim, Zaini; Ibrahim, Haidi; Syed Ali, Syed Salim

2012-10-22

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.

Development of experimental design approach and ANN-based models for determination of Cr(VI) ions uptake rate from aqueous solution onto the solid biodiesel waste residue.

PubMed

Shanmugaprakash, M; Sivakumar, V

2013-11-01

In the present work, the evaluation capacities of two optimization methodologies such as RSM and ANN were employed and compared for predication of Cr(VI) uptake rate using defatted pongamia oil cake (DPOC) in both batch and column mode. The influence of operating parameters was investigated through a central composite design (CCD) of RSM using Design Expert 8.0.7.1 software. The same data was fed as input in ANN to obtain a trained the multilayer feed-forward networks back-propagation algorithm using MATLAB. The performance of the developed ANN models were compared with RSM mathematical models for Cr(VI) uptake rate in terms of the coefficient of determination (R(2)), root mean square error (RMSE) and absolute average deviation (AAD). The estimated values confirm that ANN predominates RSM representing the superiority of a trained ANN models over RSM models in order to capture the non-linear behavior of the given system. Copyright © 2013 Elsevier Ltd. All rights reserved.

[Methodological approach to the use of artificial neural networks for predicting results in medicine].

PubMed

Trujillano, Javier; March, Jaume; Sorribas, Albert

2004-01-01

In clinical practice, there is an increasing interest in obtaining adequate models of prediction. Within the possible available alternatives, the artificial neural networks (ANN) are progressively more used. In this review we first introduce the ANN methodology, describing the most common type of ANN, the Multilayer Perceptron trained with backpropagation algorithm (MLP). Then we compare the MLP with the Logistic Regression (LR). Finally, we show a practical scheme to make an application based on ANN by means of an example with actual data. The main advantage of the RN is its capacity to incorporate nonlinear effects and interactions between the variables of the model without need to include them a priori. As greater disadvantages, they show a difficult interpretation of their parameters and large empiricism in their process of construction and training. ANN are useful for the computation of probabilities of a given outcome based on a set of predicting variables. Furthermore, in some cases, they obtain better results than LR. Both methodologies, ANN and LR, are complementary and they help us to obtain more valid models.

Neurocontrol and fuzzy logic: Connections and designs

NASA Technical Reports Server (NTRS)

Werbos, Paul J.

1991-01-01

Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ANNs extract information from systems to be learned or controlled, while fuzzy techniques mainly use verbal information from experts. Ideally, both sources of information should be combined. For example, one can learn rules in a hybrid fashion, and then calibrate them for better whole-system performance. ANNs offer universal approximation theorems, pedagogical advantages, very high-throughput hardware, and links to neurophysiology. Neurocontrol - the use of ANNs to directly control motors or actuators, etc. - uses five generalized designs, related to control theory, which can work on fuzzy logic systems as well as ANNs. These designs can copy what experts do instead of what they say, learn to track trajectories, generalize adaptive control, and maximize performance or minimize cost over time, even in noisy environments. Design tradeoffs and future directions are discussed throughout.

Application of back-propagation artificial neural network (ANN) to predict crystallite size and band gap energy of ZnO quantum dots

NASA Astrophysics Data System (ADS)

Pelicano, Christian Mark; Rapadas, Nick; Cagatan, Gerard; Magdaluyo, Eduardo

2017-12-01

Herein, the crystallite size and band gap energy of zinc oxide (ZnO) quantum dots were predicted using artificial neural network (ANN). Three input factors including reagent ratio, growth time, and growth temperature were examined with respect to crystallite size and band gap energy as response factors. The generated results from neural network model were then compared with the experimental results. Experimental crystallite size and band gap energy of ZnO quantum dots were measured from TEM images and absorbance spectra, respectively. The Levenberg-Marquardt (LM) algorithm was used as the learning algorithm for the ANN model. The performance of the ANN model was then assessed through mean square error (MSE) and regression values. Based on the results, the ANN modelling results are in good agreement with the experimental data.

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study

PubMed Central

Yoo, Tae Keun; Kim, Deok Won; Choi, Soo Beom; Oh, Ein; Park, Jee Soo

2016-01-01

Background Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA. Methods The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models. Results The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001). Conclusions The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk. PMID:26859664

Robust artificial neural network for reliability and sensitivity analyses of complex non-linear systems.

PubMed

Oparaji, Uchenna; Sheu, Rong-Jiun; Bankhead, Mark; Austin, Jonathan; Patelli, Edoardo

2017-12-01

Artificial Neural Networks (ANNs) are commonly used in place of expensive models to reduce the computational burden required for uncertainty quantification, reliability and sensitivity analyses. ANN with selected architecture is trained with the back-propagation algorithm from few data representatives of the input/output relationship of the underlying model of interest. However, different performing ANNs might be obtained with the same training data as a result of the random initialization of the weight parameters in each of the network, leading to an uncertainty in selecting the best performing ANN. On the other hand, using cross-validation to select the best performing ANN based on the ANN with the highest R 2 value can lead to biassing in the prediction. This is as a result of the fact that the use of R 2 cannot determine if the prediction made by ANN is biased. Additionally, R 2 does not indicate if a model is adequate, as it is possible to have a low R 2 for a good model and a high R 2 for a bad model. Hence, in this paper, we propose an approach to improve the robustness of a prediction made by ANN. The approach is based on a systematic combination of identical trained ANNs, by coupling the Bayesian framework and model averaging. Additionally, the uncertainties of the robust prediction derived from the approach are quantified in terms of confidence intervals. To demonstrate the applicability of the proposed approach, two synthetic numerical examples are presented. Finally, the proposed approach is used to perform a reliability and sensitivity analyses on a process simulation model of a UK nuclear effluent treatment plant developed by National Nuclear Laboratory (NNL) and treated in this study as a black-box employing a set of training data as a test case. This model has been extensively validated against plant and experimental data and used to support the UK effluent discharge strategy. Copyright © 2017 Elsevier Ltd. All rights reserved.

East Europe Report, Political, Sociological and Military Affairs.

DTIC Science & Technology

1984-12-14

Education ( J . Vorsatz; BOERSENBLATT FUER DEN DEUTSCHEN BUCHHANDEL, No 43, 23 Oct 84) 15 Cool Reception of Immigrants in FRG Noted (C.-C...HIGHER EDUCATION Leipzig BOERSENBLATT FUER DEN DEUTSCHEN BUCHHANDEL in German No 43, 23 Oct 84 pp 811-815 [Article by J . Vorsatz: "Position...Man in the World Today"] LText J T^ Hungarian Academy of Sciences arranged an academic conference in Budapest be- tween Februaiy 28 and March I

International Federation of Library Associations Annual Conference Papers. Education and Research Division: Library Schools and Other Training Aspects, and Round Table on Library History Sections (47th, Leipzig, East Germany, August 17-22, 1981).

ERIC Educational Resources Information Center

Wagenbreth, Hildegard; And Others

This group of six papers centers on the development of library schools and the training of library personnel. "The Status of Professional Groups in Libraries and Library Education in the GDR," by Hildegard Wagenbreth and Helmut Kubitschek, East Germany, describes the training programs, apprenticeships, courses, and admission criteria of…

The Scientific Legacy of Ugo Fano

NASA Astrophysics Data System (ADS)

Inokuti, Mitio

2001-04-01

In 1934 Fano received a Sc. D. degree in mathematics at University of Turin, Italy (the city of his birth in 1912). He was then led to physics by his cousin Guilio Racah, and received postdoctoral training from Fermi at Rome and from Heisenberg at Leipzig. He worked at institutions near Washington, D. C. during the war, and joined the staff of the National Bureau of Standards in 1946. He became a professor of physics at The University of Chicago in 1966. His contributions to radiation physics, atomic and molecular physics, and statistical physics are extensive and outstanding. Recognition includes many honors such as the Fermi Award by the DOE, and terms such as the Beutler-Fano profile of certain spectral lines, the Fano factor characterizing the fluctuations of the radiation-induced ionization, the Fano-Lichten mechanism for inelastic atomic collisions, and the Fano effect leading to spin-polarized photoelectrons. His work follows a style inherited from Fermi and is characterized by incisive insight into the physics behind experimental data, penetrating mathematical analysis, and close communications with many colleagues. Because he took a leading role in developing new areas of research and in nurturing young scientists, his influence now permeates many topics of physics. They include far uv and soft x-ray spectroscopy with synchrotron radiation and fundamental radiological physics, both stemming from his time at NBS, as well as multi-channel quantum-defect theory and hyperspherical-coordinate approach, both pioneered at Chicago. Fuller accounts of his life and science are seen in Inokuti [1], in Rau [2], and in a forthcoming special issue of Physics Essays in his honor. The present work is supported by U. S. DOE, Office of Science, Nuclear Physics Division, under Contract No. W-31-109-Eng-38. References 1. M. Inokuti, in Fundamental Processes of Atomic Dynamics, J. S. Briggs et al. (eds.), (Plenum, New York, 1988), p. 1. 2. A. R. P. Rau, Comments At. Mol. Phys. 33, 181 (1997).

Final Technical Report, Wind Generator Project (Ann Arbor)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geisler, Nathan

A Final Technical Report (57 pages) describing educational exhibits and devices focused on wind energy, and related outreach activities and programs. Project partnership includes the City of Ann Arbor, MI and the Ann Arbor Hands-on Museum, along with additional sub-recipients, and U.S. Department of Energy/Office of Energy Efficiency and Renewable Energy (EERE). Report relays key milestones and sub-tasks as well as numerous graphics and images of five (5) transportable wind energy demonstration devices and five (5) wind energy exhibits designed and constructed between 2014 and 2016 for transport and use by the Ann Arbor Hands-on Museum.

Artificial Neural Networks: an overview and their use in the analysis of the AMPHORA-3 dataset.

PubMed

Buscema, Paolo Massimo; Massini, Giulia; Maurelli, Guido

2014-10-01

The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes. An example of a specific ANN application to alcohol consumption in Spain, as part of the EU AMPHORA-3 project, during 1961-2006 is presented. Study's limitations are noted and future needed research using ANN methodologies are suggested.

Interactions of Ultracold Impurity Particles with Bose-Einstein Condensates

DTIC Science & Technology

2015-06-23

Lukin et al ., Phys. Rev. Lett. 87, 037901 (2001). [2] D. Jaksch et al ., Phys. Rev. Lett. 85, 2208 (2000). [3] L. Isenhower et al ., Phys. Rev. Lett...104, 010503 (2010). [4] T. Wilk et al ., Phys. Rev. Lett. 104, 010502 (2010). [5] I. Mourachko et al ., Phys. Rev. Lett. 80, 253 (1998). [6] W. R...Phys. 12, 103044 (2010). [12] R. M. W. van Bijnen et al ., J. Phys. B 44, 184008 (2011). [13] I. Lesanovsky, Phys. Rev. Lett. 106, 025301 (2011). [14] E

Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

NASA Astrophysics Data System (ADS)

Gao, Meng; Yin, Liting; Ning, Jicai

2018-07-01

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations.

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN).

PubMed

Titah, Harmin Sulistiyaning; Halmi, Mohd Izuan Effendi Bin; Abdullah, Siti Rozaimah Sheikh; Hasan, Hassimi Abu; Idris, Mushrifah; Anuar, Nurina

2018-06-07

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg -1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R 2 (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.

A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.

PubMed

Cai, Binghuang; Jiang, Xia

2014-04-01

Biomedical prediction based on clinical and genome-wide data has become increasingly important in disease diagnosis and classification. To solve the prediction problem in an effective manner for the improvement of clinical care, we develop a novel Artificial Neural Network (ANN) method based on Matrix Pseudo-Inversion (MPI) for use in biomedical applications. The MPI-ANN is constructed as a three-layer (i.e., input, hidden, and output layers) feed-forward neural network, and the weights connecting the hidden and output layers are directly determined based on MPI without a lengthy learning iteration. The LASSO (Least Absolute Shrinkage and Selection Operator) method is also presented for comparative purposes. Single Nucleotide Polymorphism (SNP) simulated data and real breast cancer data are employed to validate the performance of the MPI-ANN method via 5-fold cross validation. Experimental results demonstrate the efficacy of the developed MPI-ANN for disease classification and prediction, in view of the significantly superior accuracy (i.e., the rate of correct predictions), as compared with LASSO. The results based on the real breast cancer data also show that the MPI-ANN has better performance than other machine learning methods (including support vector machine (SVM), logistic regression (LR), and an iterative ANN). In addition, experiments demonstrate that our MPI-ANN could be used for bio-marker selection as well. Copyright © 2013 Elsevier Inc. All rights reserved.

Ibrutinib, Rituximab, Etoposide, Prednisone, Vincristine Sulfate, Cyclophosphamide, and Doxorubicin Hydrochloride in Treating Patients With HIV-Positive Stage II-IV Diffuse Large B-Cell Lymphomas

ClinicalTrials.gov

2018-06-11

AIDS-Related Lymphoma; Ann Arbor Stage II Diffuse Large B-Cell Lymphoma; Ann Arbor Stage III Diffuse Large B-Cell Lymphoma; Ann Arbor Stage IV Diffuse Large B-Cell Lymphoma; CD20 Negative; CD20 Positive; Human Immunodeficiency Virus Positive

Inside the Actors' Studio: Exploring Dietetics Education Practices through Dialogical Inquiry

ERIC Educational Resources Information Center

Fox, Ann L.; Gingras, Jacqui

2012-01-01

Two colleagues, Ann and Jacqui, came together, within the safety of an imagined actors' studio, to explore the challenges that Ann faced in planning a new graduate program in public health nutrition. They met before, during, and after program implementation to discuss Ann's experiences, and audio-taped and transcribed the discussions. When all…

46 CFR 7.10 - Eastport, ME to Cape Ann, MA.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 46 Shipping 1 2013-10-01 2013-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of Kendall...

46 CFR 7.10 - Eastport, ME to Cape Ann, MA.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 1 2010-10-01 2010-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of Kendall...

46 CFR 7.10 - Eastport, ME to Cape Ann, MA.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 46 Shipping 1 2012-10-01 2012-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of Kendall...

46 CFR 7.10 - Eastport, ME to Cape Ann, MA.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 46 Shipping 1 2014-10-01 2014-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of Kendall...

46 CFR 7.10 - Eastport, ME to Cape Ann, MA.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 1 2011-10-01 2011-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of Kendall...

Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks

ERIC Educational Resources Information Center

Nikelshpur, Dmitry O.

2014-01-01

Similar to mammalian brains, Artificial Neural Networks (ANN) are universal approximators, capable of yielding near-optimal solutions to a wide assortment of problems. ANNs are used in many fields including medicine, internet security, engineering, retail, robotics, warfare, intelligence control, and finance. "ANNs have a tendency to get…

Ann Eliza Young: A Nineteenth Century Champion of Women's Rights.

ERIC Educational Resources Information Center

Cullen, Jack B.

Concentrating on the efforts of such nineteenth century women's rights advocates as Susan B. Anthony and Elizabeth Cady Stanton, communication researchers have largely overlooked the contributions made to the cause by Ann Eliza Young. The nineteenth wife of Mormon leader Brigham Young, Ann Eliza Young left her husband and took to the speaker's…

The immersion freezing behavior of mixtures of mineral dust and biological substances

NASA Astrophysics Data System (ADS)

Augustin, Stefanie; Schneider, Johannes; Schmidt, Susan; Niedermeier, Dennis; Ebert, Martin; Voigtländer, Jens; Rösch, Michael; Stratmann, Frank; Wex, Heike

2014-05-01

Biological particles such as bacteria or pollen are known to be efficient ice nuclei. It is also known that ice nucleating active (INA) macromolecules, i.e. protein complexes in the case of bacteria (e.g. Wolber et al., 1986), and most likely polysaccharides in the case of pollen (Pummer et al., 2012) are responsible for the freezing. Very recently it was suggested that these INA macromolecules maintain their nucleating ability even when they are separated from their original carriers (Hartmann et al., 2013; Augustin et al., 2013). This opens the possibility of accumulation of such INA macromolecules in e.g. soils and the resulting particles could be an internal mixture of mineral dust and INA macromolecules. If such biological IN containing soil particles are then dispersed into the atmosphere due to e.g. wind erosion or agricultural processes they could induce ice nucleation at temperatures higher than -20°C. To explore this hypothesis, we performed a measurement campaign within the research unit INUIT, where we investigated the ice nucleation behavior of mineral dust particles internally mixed with INA macromolecules. Specifically, we mixed pure mineral dust (illite) with INA biological material (SNOMAX and birch pollen washing water) and quantified the immersion freezing behavior of the resulting particles utilizing the Leipzig Aerosol Cloud Interaction Simulator (LACIS). To characterize the mixing state of the produced aerosol we used single mass spectrometry as well as electron microscopy. We found that internally mixed particles which containing ice active biological material show the same ice nucleation behavior as the purely biological particles. That shows that INA macromolecules which are located on a mineral dust particle dominate the freezing process. Acknowledgement: Part of this work was done within the framework of the DFG funded Ice Nucleation research UnIT (INUIT, FOR 1525) under WE 4722/1-1. Augustin, S., Hartmann, S., Pummer, B., Grothe, H., Niedermeier, D., Clauss, T., Voigtländer, J., Tomsche, L, Wex, H. and Stratmann, F., Atmos. Chem. Phys. Discuss., 13, 10989-11003, 2013. Hartmann, S., Augustin, S.,D. Niedermeier, J. Voigtlander, T. Clauss, H. Wex, and F. Stratmann, Atmos. Chem. Physics , 13, 5751-5766, 2013. Hoose, C., Kristjansson, J. E., Burrows, S. M., Environ. Res. Lett. 5, 024009, 2010. Kanitz, T., Seifert, P., Ansmann, A., Engelmann, R., Althausen, D., Casiccia, C., and Rohwer, E. G., Geophys. Res. Lett., 38, L17802, 2011. Murray, B. J., OSullivan, D., Atkinson, J. D. and Webb, M. E., Chem. Soc. Rev., 41, 6519-6554, 2012. Pummer, B. G., Bauer, H., Bernardi, J., Bleicher, S. and Grothe, H, Atmos. Chem. Phys., 12, 2541-2550, 2012. Wolber, P. K., Deininger, C. A., Southworth, M. W., Vandekerckhove, J., Vanmontagu, M. and Warren, G. J, Proc. Natl. Acad. Sci. USA, 83, 7256- 7260, 1986

New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists.

PubMed

Oliveira, Aline A; Lipinski, Célio F; Pereira, Estevão B; Honorio, Kathia M; Oliveira, Patrícia R; Weber, Karen C; Romero, Roseli A F; de Sousa, Alexsandro G; da Silva, Albérico B F

2017-10-02

The treatment of neuropathic pain is very complex and there are few drugs approved for this purpose. Among the studied compounds in the literature, sigma-1 receptor antagonists have shown to be promising. In order to develop QSAR studies applied to the compounds of 1-arylpyrazole derivatives, multivariate analyses have been performed in this work using partial least square (PLS) and artificial neural network (ANN) methods. A PLS model has been obtained and validated with 45 compounds in the training set and 13 compounds in the test set (r 2 training = 0.761, q 2 = 0.656, r 2 test = 0.746, MSE test = 0.132 and MAE test = 0.258). Additionally, multi-layer perceptron ANNs (MLP-ANNs) were employed in order to propose non-linear models trained by gradient descent with momentum backpropagation function. Based on MSE test values, the best MLP-ANN models were combined in a MLP-ANN consensus model (MLP-ANN-CM; r 2 test = 0.824, MSE test = 0.088 and MAE test = 0.197). In the end, a general consensus model (GCM) has been obtained using PLS and MLP-ANN-CM models (r 2 test = 0.811, MSE test = 0.100 and MAE test = 0.218). Besides, the selected descriptors (GGI6, Mor23m, SRW06, H7m, MLOGP, and μ) revealed important features that should be considered when one is planning new compounds of the 1-arylpyrazole class. The multivariate models proposed in this work are definitely a powerful tool for the rational drug design of new compounds for neuropathic pain treatment. Graphical abstract Main scaffold of the 1-arylpyrazole derivatives and the selected descriptors.

Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future.

PubMed

Pouliakis, Abraham; Karakitsou, Efrossyni; Margari, Niki; Bountris, Panagiotis; Haritou, Maria; Panayiotides, John; Koutsouris, Dimitrios; Karakitsos, Petros

2016-01-01

This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics. A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted. The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material. Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.

Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future

PubMed Central

Pouliakis, Abraham; Karakitsou, Efrossyni; Margari, Niki; Bountris, Panagiotis; Haritou, Maria; Panayiotides, John; Koutsouris, Dimitrios; Karakitsos, Petros

2016-01-01

OBJECTIVE This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics. STUDY DESIGN A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted. RESULTS The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material. CONCLUSIONS Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake. PMID:26917984

Neural Networks for Nodal Staging of Non–Small Cell Lung Cancer with FDG PET and CT: Importance of Combining Uptake Values and Sizes of Nodes and Primary Tumor

PubMed Central

Vesselle, Hubert J.

2014-01-01

Purpose To evaluate the effect of adding lymph node size to three previously explored artificial neural network (ANN) input parameters (primary tumor maximum standardized uptake value or tumor uptake, tumor size, and nodal uptake at N1, N2, and N3 stations) in the structure of the ANN. The goal was to allow the resulting ANN structure to relate lymph node uptake for size to primary tumor uptake for size in the determination of the status of nodes as human readers do. Materials and Methods This prospective study was approved by the institutional review board, and informed consent was obtained from all participants. The authors developed a back-propagation ANN with one hidden layer and eight processing units. The data set used to train the network included node and tumor size and uptake from 133 patients with non–small cell lung cancer with surgically proved N status. Statistical analysis was performed with the paired t test. Results The ANN correctly predicted the N stage in 99.2% of cases, compared with 72.4% for the expert reader (P < .001). In categorization of N0 and N1 versus N2 and N3 disease, the ANN performed with 99.2% accuracy versus 92.2% for the expert reader (P < .001). Conclusion The ANN is 99.2% accurate in predicting surgical-pathologic nodal status with use of four fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT)–derived parameters. Malignant and benign inflammatory lymph nodes have overlapping appearances at FDG PET/CT but can be differentiated by ANNs when the crucial input of node size is used. © RSNA, 2013 Online supplemental material is available for this article. PMID:24056403

An investigation on generalization ability of artificial neural networks and M5 model tree in modeling reference evapotranspiration

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Kilic, Yasin

2016-11-01

The generalization ability of artificial neural networks (ANNs) and M5 model tree (M5Tree) in modeling reference evapotranspiration ( ET 0 ) is investigated in this study. Daily climatic data, average temperature, solar radiation, wind speed, and relative humidity from six different stations operated by California Irrigation Management Information System (CIMIS) located in two different regions of the USA were used in the applications. King-City Oasis Rd., Arroyo Seco, and Salinas North stations are located in San Joaquin region, and San Luis Obispo, Santa Monica, and Santa Barbara stations are located in the Southern region. In the first part of the study, the ANN and M5Tree models were used for estimating ET 0 of six stations and results were compared with the empirical methods. The ANN and M5Tree models were found to be better than the empirical models. In the second part of the study, the ANN and M5Tree models obtained from one station were tested using the data from the other two stations for each region. ANN models performed better than the CIMIS Penman, Hargreaves, Ritchie, and Turc models in two stations while the M5Tree models generally showed better accuracy than the corresponding empirical models in all stations. In the third part of the study, the ANN and M5Tree models were calibrated using three stations located in San Joaquin region and tested using the data from the other three stations located in the Southern region. Four-input ANN and M5Tree models performed better than the CIMIS Penman in only one station while the two-input ANN models were found to be better than the Hargreaves, Ritchie, and Turc models in two stations.

Neural network modeling and prediction of resistivity structures using VES Schlumberger data over a geothermal area

NASA Astrophysics Data System (ADS)

Singh, Upendra K.; Tiwari, R. K.; Singh, S. B.

2013-03-01

This paper presents the effects of several parameters on the artificial neural networks (ANN) inversion of vertical electrical sounding (VES) data. Sensitivity of ANN parameters was examined on the performance of adaptive backpropagation (ABP) and Levenberg-Marquardt algorithms (LMA) to test the robustness to noisy synthetic as well as field geophysical data and resolving capability of these methods for predicting the subsurface resistivity layers. We trained, tested and validated ANN using the synthetic VES data as input to the networks and layer parameters of the models as network output. ANN learning parameters are varied and corresponding observations are recorded. The sensitivity analysis of synthetic data and real model demonstrate that ANN algorithms applied in VES data inversion should be considered well not only in terms of accuracy but also in terms of high computational efforts. Also the analysis suggests that ANN model with its various controlling parameters are largely data dependent and hence no unique architecture can be designed for VES data analysis. ANN based methods are also applied to the actual VES field data obtained from the tectonically vital geothermal areas of Jammu and Kashmir, India. Analysis suggests that both the ABP and LMA are suitable methods for 1-D VES modeling. But the LMA method provides greater degree of robustness than the ABP in case of 2-D VES modeling. Comparison of the inversion results with known lithology correlates well and also reveals the additional significant feature of reconsolidated breccia of about 7.0 m thickness beneath the overburden in some cases like at sounding point RDC-5. We may therefore conclude that ANN based methods are significantly faster and efficient for detection of complex layered resistivity structures with a relatively greater degree of precision and resolution.

The identification of helicopter noise using a neural network

NASA Technical Reports Server (NTRS)

Cabell, Randolph H.; Fuller, Chris R.; O'Brien, Walter F.

1990-01-01

Experiments were carried out to demonstrate the ability of an artificial neural network (ANN) system to distinguish between the noise of two helicopters. The ANN is taught to identify helicopters by using two types of features: one that is associated with the ratio of the main-rotor to tail-rotor blade passage frequency (BPF), and the ohter that describes the distribution of peaks in the main-rotor spectrum, which is independent of the tail-rotor. It is shown that the ability of the ANN to identify helicopters is comparable to that of a conventional recognition system using the ratio of the main-rotor BPF to the tail-rotor BPF (when both the main- and the tail-rotor noise are present), but the performoance of ANN exceeds the conventional-method performance when the tail-rotor noise is absent. In addition, the results of ANN can be obtained as a function of propagation distance.

A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network

PubMed Central

Ahmed, Afaz Uddin; Tariqul Islam, Mohammad; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation. PMID:25133214

A novel user classification method for femtocell network by using affinity propagation algorithm and artificial neural network.

PubMed

Ahmed, Afaz Uddin; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.

Short-term acoustic forecasting via artificial neural networks for neonatal intensive care units.

PubMed

Young, Jason; Macke, Christopher J; Tsoukalas, Lefteri H

2012-11-01

Noise levels in hospitals, especially neonatal intensive care units (NICUs), have become of great concern for hospital designers. This paper details an artificial neural network (ANN) approach to forecasting the sound loads in NICUs. The ANN is used to learn the relationship between past, present, and future noise levels. By training the ANN with data specific to the location and device used to measure the sound, the ANN is able to produce reasonable predictions of noise levels in the NICU. Best case results show average absolute errors of 5.06 ± 4.04% when used to predict the noise levels one hour ahead, which correspond to 2.53 dBA ± 2.02 dBA. The ANN has the tendency to overpredict during periods of stability and underpredict during large transients. This forecasting algorithm could be of use in any application where prediction and prevention of harmful noise levels are of the utmost concern.

Computer vision system for egg volume prediction using backpropagation neural network

NASA Astrophysics Data System (ADS)

Siswantoro, J.; Hilman, M. Y.; Widiasri, M.

2017-11-01

Volume is one of considered aspects in egg sorting process. A rapid and accurate volume measurement method is needed to develop an egg sorting system. Computer vision system (CVS) provides a promising solution for volume measurement problem. Artificial neural network (ANN) has been used to predict the volume of egg in several CVSs. However, volume prediction from ANN could have less accuracy due to inappropriate input features or inappropriate ANN structure. This paper proposes a CVS for predicting the volume of egg using ANN. The CVS acquired an image of egg from top view and then processed the image to extract its 1D and 2 D size features. The features were used as input for ANN in predicting the volume of egg. The experiment results show that the proposed CSV can predict the volume of egg with a good accuracy and less computation time.

Study of Aided Diagnosis of Hepatic Carcinoma Based on Artificial Neural Network Combined with Tumor Marker Group

NASA Astrophysics Data System (ADS)

Tan, Shanjuan; Feng, Feifei; Wu, Yongjun; Wu, Yiming

To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) combined with tumor markers for diagnosis of hepatic carcinoma (HCC) as a clinical assistant method. 140 serum samples (50 malignant, 40 benign and 50 normal) were analyzed for α-fetoprotein (AFP), carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), sialic acid (SA) and calcium (Ca). The five tumor marker values were then used as ANN inputs data. The result of ANN was compared with that of discriminant analysis by receiver operating characteristic (ROC) curve (AUC) analysis. The diagnostic accuracy of ANN and discriminant analysis among all samples of the test group was 95.5% and 79.3%, respectively. Analysis of multiple tumor markers based on ANN may be a better choice than the traditional statistical methods for differentiating HCC from benign or normal.

Numerical solution of the nonlinear Schrodinger equation by feedforward neural networks

NASA Astrophysics Data System (ADS)

Shirvany, Yazdan; Hayati, Mohsen; Moradian, Rostam

2008-12-01

We present a method to solve boundary value problems using artificial neural networks (ANN). A trial solution of the differential equation is written as a feed-forward neural network containing adjustable parameters (the weights and biases). From the differential equation and its boundary conditions we prepare the energy function which is used in the back-propagation method with momentum term to update the network parameters. We improved energy function of ANN which is derived from Schrodinger equation and the boundary conditions. With this improvement of energy function we can use unsupervised training method in the ANN for solving the equation. Unsupervised training aims to minimize a non-negative energy function. We used the ANN method to solve Schrodinger equation for few quantum systems. Eigenfunctions and energy eigenvalues are calculated. Our numerical results are in agreement with their corresponding analytical solution and show the efficiency of ANN method for solving eigenvalue problems.

Prediction of pelvic organ prolapse using an artificial neural network.

PubMed

Robinson, Christopher J; Swift, Steven; Johnson, Donna D; Almeida, Jonas S

2008-08-01

The objective of this investigation was to test the ability of a feedforward artificial neural network (ANN) to differentiate patients who have pelvic organ prolapse (POP) from those who retain good pelvic organ support. Following institutional review board approval, patients with POP (n = 87) and controls with good pelvic organ support (n = 368) were identified from the urogynecology research database. Historical and clinical information was extracted from the database. Data analysis included the training of a feedforward ANN, variable selection, and external validation of the model with an independent data set. Twenty variables were used. The median-performing ANN model used a median of 3 (quartile 1:3 to quartile 3:5) variables and achieved an area under the receiver operator curve of 0.90 (external, independent validation set). Ninety percent sensitivity and 83% specificity were obtained in the external validation by ANN classification. Feedforward ANN modeling is applicable to the identification and prediction of POP.

[The career of the psychiatrist Dietfried Müller-Hegemann (1910-1989) : Example of a politically motivated rise and fall in the German Democratic Republic].

PubMed

Steinberg, H

2018-01-01

Dietfried Müller-Hegemann was one of the prominent figures in East German psychiatry and psychotherapy of the 1950s and 1960s. Having been a communist prior to 1933, a resistance fighter during the National Socialist regime and having gone through political training during his exile in Soviet Russia, he proved to be a committed member of the new ruling SED socialist party in Eastern Germany. As such both governmental and party organs regarded him as a promising and reliable party member to be supported and implemented as executive staff within the new, socialist scientific system. Also, due to the fact that he supported the Pavlovian school of thought for modern psychiatry, Müller-Hegemann was installed as the new head of the Department of Neurology and Psychiatry at Leipzig University by the state secretary for higher education, notably against the clear opposition of the university medical faculty. Soon thereafter however Müller-Hegemann fell from favor due to the fact that he supported views that did not follow the strict ideological guidelines, e. g. with regard to the emergence of fascism. Moreover, he strongly opposed the separation of neurology from psychiatry as ruled by the ministry. An attempt in 1963 by junior party members and ministerial staff to remove him from office failed, but still managed to make Müller-Hegemann resign from his Leipzig post and take over that of director of the Griesinger hospital for the mentally ill in East Berlin. In May 1971, after new conflicts with party officials, he did not return from a business trip to Essen in West Germany. This study does not review the scientific and medical merits of Müller-Hegemann, but concentrates on how his career as a leading psychiatrist was manipulated, both supported and sabotaged, and ideologically controlled by the German Democratic Republic (GDR) system. His development is documented proof that party officials did not tolerate opposition, neither in ideological nor in professional questions, even if the opponent was a committed Marxist. The example of his career shows that political and ideological dissent soon melted into personal animosity and drives, as a result of which Müller-Hegemann's promising career as professor in Leipzig was terminated.

Fifty years of Jaynes-Cummings physics

NASA Astrophysics Data System (ADS)

Greentree, Andrew D.; Koch, Jens; Larson, Jonas

2013-11-01

This special issue commemorates the 50th anniversary of the seminal paper published by E T Jaynes and F W Cummings [1], the fundamental model which they introduced and now carries their names, and celebrates the remarkable host of exciting research on Jaynes-Cummings physics throughout the last five decades. The Jaynes-Cummings model has been taking the prominent stance as the 'hydrogen atom of quantum optics' [2]. Generally speaking, it provides a fundamental quantum description of the simplest form of coherent radiation-matter interaction. The Jaynes-Cummings model describes the interaction between a single electromagnetic mode confined to a cavity, and a two-level atom. Energy is exchanged between the field and the atom, which leads directly to coherent population oscillations (Rabi oscillations) and superposition states (dressed states). Being exactly solvable, the Jaynes-Cummings model serves as a most useful toy model, and as such it is a textbook example of the physicists' popular strategy of simplifying a complex problem to its most elementary constituents. Thanks to the simplicity of the Jaynes-Cummings model, this caricature of coherent light-matter interactions has never lost its appeal. The Jaynes-Cummings model is essential when discussing experiments in quantum electrodynamics (indeed the experimental motivation of the Jaynes-Cummings model was evident already in the original paper, dealing as it does with the development of the maser), and it has formed the starting point for much fruitful research ranging from ultra-cold atoms to cavity quantum electrodynamics. In fact, Jaynes-Cummings physics is at the very heart of the beautiful experiments by S Haroche and D Wineland, which recently earned them the 2012 Nobel Prize in physics. Indeed, as with most significant models in physics, the model is invoked in settings that go far beyond its initial framework. For example, recent investigations involving multi-level atoms, multiple atoms [3, 4], multiple electromagnetic modes, arrays of coupled cavities [5-7], and optomechanical systems [8] have further enriched the physics of the Jaynes-Cummings model. From the early interests in masers and the consistent quantum description of radiation and atom-photon interaction, the Jaynes-Cummings model has evolved into a cornerstone of quantum state engineering [9]. The authors of this editorial had not been born when Jaynes and Cummings wrote their remarkable paper. It is, therefore, a special honour for us to be able to draw the reader's attention to the accompanying reminiscence contributed by Frederick Cummings where he gives us a glimpse of the early history of the Jaynes-Cummings model from his perspective [11]. By now, the original 1963 paper by Jaynes and Cummings has gathered numerous citations and, at the time of writing, the number of articles involving Jaynes-Cummings physics is approaching 15 000.1 This special issue does not attempt to review this impressive wealth of research. The interested reader, however, is urged to consult the definitive article by Shore and Knight [10] for a comprehensive review of the first 30 years of Jaynes-Cummings physics. The collection of 26 papers presented in this issue, showcases a snapshot of some of the most recent and continuing research devoted to Jaynes-Cummings physics. We begin our special issue with Professor Cumming's recollections [11]. We then have six papers on quantum information aspects of the Jaynes-Cummings model [12-17]. The next topic includes seven papers on the Dicke and generalized Jaynes-Cummings models [18-24], followed by six papers on circuit QED, which is one of the most important experimental frameworks for Jaynes-Cummings systems [25-30]. Finally, we have six papers on the extension to many cavities, the Jaynes-Cummings-Hubbard model [31-36]. The snapshot of research captured in this special issue illustrates the unifying language provided by the Jaynes-Cummings model, tying together research in a number of subfields in physics. Jaynes-Cummings physics started with the diagonalization of a 2 × 2 matrix, as Frederick Cummings points out. There is no doubt that this elegance of simplicity will continue to guide exciting new research in the decades to come. References [1] Jaynes E T and Cummings F W 1963 Comparison of quantum and semiclassical radiation theories with application to the beam maser Proc. IEEE 51 89 [2] Shore B W and Knight P L 2004 Physics and Probability: Essays in Honor of Edwin T Jaynes (Cambridge: Cambridge University Press) [3] Tavis M and Cummings F W 1968 Exact solution for an N -molecule-radiation-field Hamiltonian Phys. Rev. 170 379-84 [4] Tavis M and Cummings F W 1969 Approximate solutions for an N -molecule-radiation-field Hamiltonian Phys. Rev. 188 692-5 [5] Hartmann M J, Brandão F G S L and Plenio M B 2006 Strongly interacting polaritons in coupled arrays of cavities Nature Phys. 2 849-55 [6] Greentree A D, Tahan C, Cole J H and Hollenberg L C L 2006 Quantum phase transitions of light Nature Phys. 2 856-61 [7] Angelakis D G, Santos M F and Bose S 2007 Photon-blockade-induced Mott transitions and XY spin models in coupled cavity arrays Phys. Rev. A 76 031805(R) [8] Schwab K C and Roukes M L 2005 Putting mechanics into quantum mechanics Phys. Today 58 36-42 [9] Blatt R, Milburn G J and Lvovksy A 2013 The 20th anniversary of quantum state engineering J. Phys. B: At. Mol. Opt. Phys. 46 100201 [10] Shore B and Knight P L 1993 The Jaynes-Cummings model J. Mod. Opt. 40 1195-238 [11] Cummings F W 2013 J. Phys. B: At. Mol. Opt. Phys. 46 220202 [12] Arenz C 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224001 [13] Quesada N 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224002 [14] Everitt M 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224003 [15] Kitajima S 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224004 [16] Groves E 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224005 [17] Bougouffa S 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224006 [18] Braak D 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224007 [19] Emary C 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224008 [20] Miroshnychenko Y 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224009 [21] Dombi A 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224010 [22] Tavis M 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224011 [23] Grimsmo A 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224012 [24] Stenholm S I 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224013 [25] Kockum A F 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224014 [26] Larson J 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224015 [27] Larson J 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224016 [28] Agarwal S 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224017 [29] Deng W-W 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224018 [30] Leppaekangas J 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224019 [31] Schmidt S 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224020 [32] Schiro M 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224021 [33] Susa C 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224022 [34] del Valle E 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224023 [35] Correa B V 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224024 [36] Schetakis N 2013 J. Phys. B: At. Mol. Opt. Phys. 46 224025 1Number estimate based on a Google Scholar search.

Predicting pressure drop in venturi scrubbers with artificial neural networks.

PubMed

Nasseh, S; Mohebbi, A; Jeirani, Z; Sarrafi, A

2007-05-08

In this study a new approach based on artificial neural networks (ANNs) has been used to predict pressure drop in venturi scrubbers. The main parameters affecting the pressure drop are mainly the gas velocity in the throat of venturi scrubber (V(g)(th)), liquid to gas flow rate ratio (L/G), and axial distance of the venturi scrubber (z). Three sets of experimental data from five different venturi scrubbers have been applied to design three independent ANNs. Comparing the results of these ANNs and the calculated results from available models shows that the results of ANNs have a better agreement with experimental data.

Optimization of Nd: YAG Laser Marking of Alumina Ceramic Using RSM And ANN

NASA Astrophysics Data System (ADS)

Peter, Josephine; Doloi, B.; Bhattacharyya, B.

2011-01-01

The present research papers deals with the artificial neural network (ANN) and the response surface methodology (RSM) based mathematical modeling and also an optimization analysis on marking characteristics on alumina ceramic. The experiments have been planned and carried out based on Design of Experiment (DOE). It also analyses the influence of the major laser marking process parameters and the optimal combination of laser marking process parametric setting has been obtained. The output of the RSM optimal data is validated through experimentation and ANN predictive model. A good agreement is observed between the results based on ANN predictive model and actual experimental observations.

Risk factors for Apgar score using artificial neural networks.

PubMed

Ibrahim, Doaa; Frize, Monique; Walker, Robin C

2006-01-01

Artificial Neural Networks (ANNs) have been used in identifying the risk factors for many medical outcomes. In this paper, the risk factors for low Apgar score are introduced. This is the first time, to our knowledge, that the ANNs are used for Apgar score prediction. The medical domain of interest used is the perinatal database provided by the Perinatal Partnership Program of Eastern and Southeastern Ontario (PPPESO). The ability of the feed forward back propagation ANNs to generate strong predictive model with the most influential variables is tested. Finally, minimal sets of variables (risk factors) that are important in predicting Apgar score outcome without degrading the ANN performance are identified.

Robust Bioinformatics Recognition with VLSI Biochip Microsystem

NASA Technical Reports Server (NTRS)

Lue, Jaw-Chyng L.; Fang, Wai-Chi

2006-01-01

A microsystem architecture for real-time, on-site, robust bioinformatic patterns recognition and analysis has been proposed. This system is compatible with on-chip DNA analysis means such as polymerase chain reaction (PCR)amplification. A corresponding novel artificial neural network (ANN) learning algorithm using new sigmoid-logarithmic transfer function based on error backpropagation (EBP) algorithm is invented. Our results show the trained new ANN can recognize low fluorescence patterns better than the conventional sigmoidal ANN does. A differential logarithmic imaging chip is designed for calculating logarithm of relative intensities of fluorescence signals. The single-rail logarithmic circuit and a prototype ANN chip are designed, fabricated and characterized.

Identification of drought in Dhalai river watershed using MCDM and ANN models

NASA Astrophysics Data System (ADS)

Aher, Sainath; Shinde, Sambhaji; Guha, Shantamoy; Majumder, Mrinmoy

2017-03-01

An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.

Optimization of Melatonin Dissolution from Extended Release Matrices Using Artificial Neural Networking.

PubMed

Martarelli, D; Casettari, L; Shalaby, K S; Soliman, M E; Cespi, M; Bonacucina, G; Fagioli, L; Perinelli, D R; Lam, J K W; Palmieri, G F

2016-01-01

Efficacy of melatonin in treating sleep disorders has been demonstrated in numerous studies. Being with short half-life, melatonin needs to be formulated in extended-release tablets to prevent the fast drop of its plasma concentration. However, an attempt to mimic melatonin natural plasma levels during night time is challenging. In this work, Artificial Neural Networks (ANNs) were used to optimize melatonin release from hydrophilic polymer matrices. Twenty-seven different tablet formulations with different amounts of hydroxypropyl methylcellulose, xanthan gum and Carbopol®974P NF were prepared and subjected to drug release studies. Using dissolution test data as inputs for ANN designed by Visual Basic programming language, the ideal number of neurons in the hidden layer was determined trial and error methodology to guarantee the best performance of constructed ANN. Results showed that the ANN with nine neurons in the hidden layer had the best results. ANN was examined to check its predictability and then used to determine the best formula that can mimic the release of melatonin from a marketed brand using similarity fit factor. This work shows the possibility of using ANN to optimize the composition of prolonged-release melatonin tablets having dissolution profile desired.

A sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network

NASA Astrophysics Data System (ADS)

Wang, Y. S.; Shen, G. Q.; Xing, Y. F.

2014-03-01

Based on the artificial neural network (ANN) technique, an objective sound quality evaluation (SQE) model for synthesis annoyance of vehicle interior noises is presented in this paper. According to the standard named GB/T18697, firstly, the interior noises under different working conditions of a sample vehicle are measured and saved in a noise database. Some mathematical models for loudness, sharpness and roughness of the measured vehicle noises are established and performed by Matlab programming. Sound qualities of the vehicle interior noises are also estimated by jury tests following the anchored semantic differential (ASD) procedure. Using the objective and subjective evaluation results, furthermore, an ANN-based model for synthetical annoyance evaluation of vehicle noises, so-called ANN-SAE, is developed. Finally, the ANN-SAE model is proved by some verification tests with the leave-one-out algorithm. The results suggest that the proposed ANN-SAE model is accurate and effective and can be directly used to estimate sound quality of the vehicle interior noises, which is very helpful for vehicle acoustical designs and improvements. The ANN-SAE approach may be extended to deal with other sound-related fields for product quality evaluations in SQE engineering.

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

NASA Astrophysics Data System (ADS)

Sahoo, Sasmita; Jha, Madan K.

2013-12-01

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models

PubMed Central

Nilsaz-Dezfouli, Hamid; Abu-Bakar, Mohd Rizam; Arasan, Jayanthi; Adam, Mohd Bakri; Pourhoseingholi, Mohamad Amin

2017-01-01

In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information. The applications of ANN models for prognostic and diagnostic classification in medicine have attracted a lot of interest. The applications of ANN models in modelling the survival of patients with gastric cancer have been discussed in some studies without completely considering the censored data. This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. PMID:28469384

Estimation of umbilical cord blood leptin and insulin based on anthropometric data by means of artificial neural network approach: identifying key maternal and neonatal factors.

PubMed

Guzmán-Bárcenas, José; Hernández, José Alfredo; Arias-Martínez, Joel; Baptista-González, Héctor; Ceballos-Reyes, Guillermo; Irles, Claudine

2016-07-21

Leptin and insulin levels are key factors regulating fetal and neonatal energy homeostasis, development and growth. Both biomarkers are used as predictors of weight gain and obesity during infancy. There are currently no prediction algorithms for cord blood (UCB) hormone levels using Artificial Neural Networks (ANN) that have been directly trained with anthropometric maternal and neonatal data, from neonates exposed to distinct metabolic environments during pregnancy (obese with or without gestational diabetes mellitus or lean women). The aims were: 1) to develop ANN models that simulate leptin and insulin concentrations in UCB based on maternal and neonatal data (ANN perinatal model) or from only maternal data during early gestation (ANN prenatal model); 2) To evaluate the biological relevance of each parameter (maternal and neonatal anthropometric variables). We collected maternal and neonatal anthropometric data (n = 49) in normoglycemic healthy lean, obese or obese with gestational diabetes mellitus women, as well as determined UCB leptin and insulin concentrations by ELISA. The ANN perinatal model consisted of an input layer of 12 variables (maternal and neonatal anthropometric and biochemical data from early gestation and at term) while the ANN prenatal model used only 6 variables (maternal anthropometric from early gestation) in the input layer. For both networks, the output layer contained 1 variable to UCB leptin or to UCB insulin concentration. The best architectures for the ANN perinatal models estimating leptin and insulin were 12-5-1 while for the ANN prenatal models, 6-5-1 and 6-4-1 were found for leptin and insulin, respectively. ANN models presented an excellent agreement between experimental and simulated values. Interestingly, the use of only prenatal maternal anthropometric data was sufficient to estimate UCB leptin and insulin values. Maternal BMI, weight and age as well as neonatal birth were the most influential parameters for leptin while maternal morbidity was the most significant factor for insulin prediction. Low error percentage and short computing time makes these ANN models interesting in a translational research setting, to be applied for the prediction of neonatal leptin and insulin values from maternal anthropometric data, and possibly the on-line estimation during pregnancy.

Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron.

PubMed

Costalago Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L

2016-03-01

Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Automated Wildfire Detection Through Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

2005-01-01

We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid.

PubMed

Savala, Rajiv; Dey, Pranab; Gupta, Nalini

2018-03-01

To distinguish follicular adenoma (FA) and follicular carcinoma (FC) of thyroid in fine needle aspiration cytology (FNAC) is a challenging problem. In this article, we attempted to build an artificial neural network (ANN) model from the cytological and morphometric features of the FNAC smears of thyroid to distinguish FA from FC. The cytological features and morphometric analysis were done on the FNAC smears of histology proven cases of FA (26) and FC (31). The cytological features were analysed semi-quantitatively by two independent observers (RS and PD). These data were used to make an ANN model to differentiate FA versus FC on FNAC material. The performance of this ANN model was assessed by analysing the confusion matrix and receiving operator curve. There were 39 cases in training set, 9 cases each in validation and test sets. In the test group, ANN model successfully distinguished all cases (9/9) of FA and FC. The area under receiver operating curve was 1. The present ANN model is efficient to diagnose follicular adenoma and carcinoma cases on cytology smears without any error. In future, this ANN model will be able to diagnose follicular adenoma and carcinoma cases on thyroid aspirate. This study has immense potential in future. This is an open ended ANN model and more parameters and more cases can be included to make the model much stronger. © 2017 Wiley Periodicals, Inc.

Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data.

PubMed

Chatterjee, Sankhadeep; Dey, Nilanjan; Shi, Fuqian; Ashour, Amira S; Fong, Simon James; Sen, Soumya

2018-04-01

Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.

Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.

PubMed

Okumura, Eiichiro; Kawashita, Ikuo; Ishida, Takayuki

2017-08-01

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

Effect of yearling steer sequence grazing of perennial and annual forages in an integrated crop and livestock system on grazing performance, delayed feedlot entry, finishing performance, carcass measurements, and systems economics.

PubMed

Sentürklü, Songul; Landblom, Douglas G; Maddock, Robert; Petry, Tim; Wachenheim, Cheryl J; Paisley, Steve I

2018-06-04

In a 2-yr study, spring-born yearling steers (n = 144), previously grown to gain <0.454 kg·steer-1·d-1, following weaning in the fall, were stratified by BW and randomly assigned to three retained ownership rearing systems (three replications) in early May. Systems were 1) feedlot (FLT), 2) steers that grazed perennial crested wheatgrass (CWG) and native range (NR) before FLT entry (PST), and 3) steers that grazed perennial CWG and NR, and then field pea-barley (PBLY) mix and unharvested corn (UC) before FLT entry (ANN). The PST and ANN steers grazed 181 d before FLT entry. During grazing, ADG of ANN steers (1.01 ± SE kg/d) and PST steers (0.77 ± SE kg/d) did not differ (P = 0.31). But even though grazing cost per steer was greater (P = 0.002) for ANN vs. PST, grazing cost per kg of gain did not differ (P = 0.82). The ANN forage treatment improved LM area (P = 0.03) and percent i.m. fat (P = 0.001). The length of the finishing period was greatest (P < 0.001) for FLT (142 d), intermediate for PST (91 d), and least for ANN (66 d). Steer starting (P = 0.015) and ending finishing BW (P = 0.022) of ANN and PST were greater than FLT steers. Total FLT BW gain was greater for FLT steers (P = 0.017), but there were no treatment differences for ADG, (P = 0.16), DMI (P = 0.21), G: F (P = 0.82), and feed cost per kg of gain (P = 0.61). However, feed cost per steer was greatest for FLT ($578.30), least for ANN ($276.12), and intermediate for PST ($381.18) (P = 0.043). There was a tendency for FLT steer HCW to be less than ANN and PST, which did not differ (P = 0.076). There was no difference between treatments for LM area (P = 0.094), backfat depth (P = 0.28), marbling score (P = 0.18), USDA yield grade (P = 0.44), and quality grade (P = 0.47). Grazing steer net return ranged from an ANN system high of $9.09/steer to a FLT control system net loss of -$298 and a PST system that was slightly less than the ANN system (-$30.10). Ten-year (2003 to 2012) hedging and net return sensitivity analysis revealed that the FLT treatment underperformed 7 of 10 yr and futures hedging protection against catastrophic losses were profitable 40, 30, and 20% of the time period for ANN, PST, and FLT, respectively. Retained ownership from birth through slaughter coupled with delayed FLT entry grazing perennial and annual forages has the greatest profitability potential.

Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm

NASA Astrophysics Data System (ADS)

Prasad, Ramendra; Deo, Ravinesh C.; Li, Yan; Maraseni, Tek

2017-11-01

Forecasting streamflow is vital for strategically planning, utilizing and redistributing water resources. In this paper, a wavelet-hybrid artificial neural network (ANN) model integrated with iterative input selection (IIS) algorithm (IIS-W-ANN) is evaluated for its statistical preciseness in forecasting monthly streamflow, and it is then benchmarked against M5 Tree model. To develop hybrid IIS-W-ANN model, a global predictor matrix is constructed for three local hydrological sites (Richmond, Gwydir, and Darling River) in Australia's agricultural (Murray-Darling) Basin. Model inputs comprised of statistically significant lagged combination of streamflow water level, are supplemented by meteorological data (i.e., precipitation, maximum and minimum temperature, mean solar radiation, vapor pressure and evaporation) as the potential model inputs. To establish robust forecasting models, iterative input selection (IIS) algorithm is applied to screen the best data from the predictor matrix and is integrated with the non-decimated maximum overlap discrete wavelet transform (MODWT) applied on the IIS-selected variables. This resolved the frequencies contained in predictor data while constructing a wavelet-hybrid (i.e., IIS-W-ANN and IIS-W-M5 Tree) model. Forecasting ability of IIS-W-ANN is evaluated via correlation coefficient (r), Willmott's Index (WI), Nash-Sutcliffe Efficiency (ENS), root-mean-square-error (RMSE), and mean absolute error (MAE), including the percentage RMSE and MAE. While ANN models are seen to outperform M5 Tree executed for all hydrological sites, the IIS variable selector was efficient in determining the appropriate predictors, as stipulated by the better performance of the IIS coupled (ANN and M5 Tree) models relative to the models without IIS. When IIS-coupled models are integrated with MODWT, the wavelet-hybrid IIS-W-ANN and IIS-W-M5 Tree are seen to attain significantly accurate performance relative to their standalone counterparts. Importantly, IIS-W-ANN model accuracy outweighs IIS-ANN, as evidenced by a larger r and WI (by 7.5% and 3.8%, respectively) and a lower RMSE (by 21.3%). In comparison to the IIS-W-M5 Tree model, IIS-W-ANN model yielded larger values of WI = 0.936-0.979 and ENS = 0.770-0.920. Correspondingly, the errors (RMSE and MAE) ranged from 0.162-0.487 m and 0.139-0.390 m, respectively, with relative errors, RRMSE = (15.65-21.00) % and MAPE = (14.79-20.78) %. Distinct geographic signature is evident where the most and least accurately forecasted streamflow data is attained for the Gwydir and Darling River, respectively. Conclusively, this study advocates the efficacy of iterative input selection, allowing the proper screening of model predictors, and subsequently, its integration with MODWT resulting in enhanced performance of the models applied in streamflow forecasting.

Locating Groundwater Pollution Source using Breakthrough Curve Characteristics and Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Kumar, J.; Jain, A.; Srivastava, R.

2005-12-01

The identification of pollution sources in aquifers is an important area of research not only for the hydrologists but also for the local and Federal agencies and defense organizations. Once the data in terms of pollutant concentration measurements at observation wells become known, it is important to identify the polluting industry in order to implement punitive or remedial measures. Traditionally, hydrologists have relied on the conceptual methods for the identification of groundwater pollution sources. The problem of identification of groundwater pollution sources using the conceptual methods requires a thorough understanding of the groundwater flow and contaminant transport processes and inverse modeling procedures that are highly complex and difficult to implement. Recently, the soft computing techniques, such as artificial neural networks (ANNs) and genetic algorithms, have provided an attractive and easy to implement alternative to solve complex problems efficiently. Some researchers have used ANNs for the identification of pollution sources in aquifers. A major problem with most previous studies using ANNs has been the large size of the neural networks that are needed to model the inverse problem. The breakthrough curves at an observation well may consist of hundreds of concentration measurements, and presenting all of them to the input layer of an ANN not only results in humongous networks but also requires large amount of training and testing data sets to develop the ANN models. This paper presents the results of a study aimed at using certain characteristics of the breakthrough curves and ANNs for determining the distance of the pollution source from a given observation well. Two different neural network models are developed that differ in the manner of characterizing the breakthrough curves. The first ANN model uses five parameters, similar to the synthetic unit hydrograph parameters, to characterize the breakthrough curves. The five parameters employed are peak concentration, time to peak concentration, the widths of the breakthrough curves at 50% and 75% of the peak concentration, and the time base of the breakthrough curve. The second ANN model employs only the first four parameters leaving out the time base. The measurement of breakthrough curve at an observation well involves very high costs in sample collection at suitable time intervals and analysis for various contaminants. The receding portions of the breakthrough curves are normally very long and excluding the time base from modeling would result in considerable cost savings. The feed-forward multi-layer perceptron (MLP) type neural networks trained using the back-propagation algorithm, are employed in this study. The ANN models for the two approaches were developed using simulated data generated for conservative pollutant transport through a homogeneous aquifer. A new approach for ANN training using back-propagation is employed that considers two different error statistics to prevent over-training and under-training of the ANNs. The preliminary results indicate that the ANNs are able to identify the location of the pollution source very efficiently from both the methods of the breakthrough curves characterization.

International Federation of Library Associations Annual Conference. Papers of the Special Libraries Division: Geographical and Map, Science and Technology and Social Science Libraries Sections (47th, Leipzig, East Germany, August 17-22, 1981).

ERIC Educational Resources Information Center

Sprudzs, Adolf; And Others

This set of eight papers includes papers presented by participants from the United States, France, East Germany, the United Kingdom, West Germany, and the USSR: "Problems with Sources of Information in International Law and Relations: The Case of the World-Wide Treaty Jungle," by Adolf Sprudzs; "French Map Libraries and National and…

Johann Leonhard Rost, "novelist" and astronomer; (German Title: Johann Leonhard Rost, "Romanist" und Astronom)

NASA Astrophysics Data System (ADS)

Gaab, Hans; Simons, Olaf

Johann Leonhard Rost (1688-1727) of Nuremberg studied at Altdorf, Leipzig and Jena. During this time, he earned his living by writing gallant novels. In 1715, he returned to Nuremberg, where he pursued his juvenile inclination towards astronomy and became a serious astronomical observer. His introductions to astronomy, written around this time, contributed a lot to popularize astronomy. This contribution attempts to do justice to both the novelist and the astronomer Rost.

[Recalled parental rearing and the wish to have a child - are there associations?].

PubMed

Schumacher, Jörg; Stöbel-Richter, Yve; Brähler, Elmar

2002-07-01

The present study concerns the impact of recalled parental rearing behaviour on both the intensity of the wish to have a child and on different motives to have a child. Until now there are no empirical studies as to this objective. Our study is based on a representative sample of 1509 persons aged 18 to 50 years. The statistical analyses were restricted to those subjects who lived in partnership and reported an actual wish to have a child (n = 331). The data were assessed by self-reporting scales: The Questionnaire of Recalled Parental Rearing Behaviour "Fragebogen zum erinnerten elterlichen Erziehungsverhalten, FEE", the Partnership Questionnaire "Partnerschaftsfragebogen, PFB", and the Leipzig Questionnaire of Motives to Have a Child "Leipziger Fragebogen zu Kinderwunschmotiven, LKM". A recalled parental rearing behaviour, which was characterized as having been rejective, overprotective and less emotionally warm was associated with such motives which do not promote the wish to have own children (fear of personal restrictions and a low degree of social support). Simultaneously, a negative parental rearing behaviour was correlated with a stronger desire for social recognition by an own child. The recalled maternal rearing behaviour was altogether stronger associated with motives to have a child than the paternal. On the other hand, no relevant associations could be found between the recalled parental rearing behaviour and the intensity of the wish to have a child.

Experiences with dissection courses in human anatomy: a comparison between Germany and Ethiopia.

PubMed

Bekele, Assegedech; Reissig, Dieter; Löffler, Sabine; Hinz, Andreas

2011-03-01

Dissection courses in human anatomy are laborious, and new teaching tools have become available. Therefore, some universities intend to reduce the dissection course. Furthermore, little is known about dissection courses in African universities. The aim of this study is to compare the students' experiences with and evaluations of the dissection courses in two universities: Leipzig (Germany) and Gondar (Ethiopia). Since the Gondar Medical College was founded in cooperation with the Leipzig University in 1978, the anatomy courses in both universities follow roughly the same rules. A structured questionnaire was used to assess the dissection courses from the students' point of view. The sample of students consisted of 109 German and 124 Ethiopian first year undergraduate medical students. Most students in both countries (94% in Germany and 82% in Ethiopia) judge the dissection course to be highly relevant compared to other courses. Perceived health hazards associated with dissection of the cadaver show significant differences between Germany (14%) and Ethiopia (44%). Most students had normal feelings again at the end of the dissection course. Further similarities and differences between the courses in Germany and Ethiopia are described. Dissection courses are highly appreciated also in Africa. The high degree of affirmation of the dissection courses should be taken into consideration when discussing modifications of gross anatomy curriculum or changes in the teacher to student ratio. Copyright © 2010 Elsevier GmbH. All rights reserved.

Darboux transformation and solitons for an integrable nonautonomous nonlinear integro-differential Schrödinger equation

NASA Astrophysics Data System (ADS)

Yong, Xuelin; Fan, Yajing; Huang, Yehui; Ma, Wen-Xiu; Tian, Jing

2017-10-01

By modifying the scheme for an isospectral problem, the non-isospectral Ablowitz-Kaup-Newell-Segur (AKNS) hierarchy is constructed via allowing the time varying spectrum. In this paper, we consider an integrable nonautonomous nonlinear integro-differential Schrödinger equation discussed before in “Multi-soliton management by the integrable nonautonomous nonlinear integro-differential Schrödinger equation” [Y. J. Zhang, D. Zhao and H. G. Luo, Ann. Phys. 350 (2014) 112]. We first analyze the integrability conditions and identify the model. Second, we modify the existing Darboux transformation (DT) for such a non-isospectral problem. Third, the nonautonomous soliton solutions are obtained via the resulting DT and basic properties of these solutions in the inhomogeneous media are discussed graphically to illustrate the influences of the variable coefficients. In the process, a technique by selecting appropriate spectral parameters instead of the variable inhomogeneities is employed to realize a different type of one-soliton management. Several novel optical solitons are constructed and their features are shown by some specific figures. In addition, four kinds of the special localized two-soliton solutions are obtained. The solitonic excitations localized both in space and time, which exhibit the feature of the so-called rogue waves but with a zero background, are discussed.

Vibrational Signatures of Large Amplitude Motions for the Shackled Hydronium Ion Nested in 18-CROWN-6 Ether Using D2 Tagging

NASA Astrophysics Data System (ADS)

Duong, Chinh H.; Menges, Fabian; Craig, Stephanie; Wolke, Conrad T.; Johnson, Mark

2016-06-01

The diffuse spectra arising from the excess proton in dilute acids suggests that its behavior is highly dependent on the local environment surrounding it. In this work, we report how the spectra of the H3O+, NH4+, and CH3NH3+ ions respond when docked to the rigid, tri-coordinated binding pocket of the 18-crown-6 ether using cryogenic ion vibrational predissociation (CIVP) spectroscopy with D2 tagging at 10 K. The H3O+{tiny^bullet}18-crown-6 ether complex displays a broad (350 cm-1 FWHM) unstructured band arising from the OH stretching fundamentals, which is significantly broader than the corresponding band (125 cm-1 FWHM) in the Eigen cation (H9O4+) spectrum. Perdeuterated isotopologue studies for both systems yield sharper bands with clear multiplet structures, indicating that the broadening arises from nuclear quantum effects. The key displacements underlying this coupling were explored using the vibrationally adiabatic scheme introduced by McCoy in the context of similar broadening in the Ca2+OH-(H2O)n system. Christopher J. Johnson, Laura C. Dzugan, Arron B. Wolk, Christopher M. Leavitt, Joseph A. Fournier, Anne B. McCoy, Mark A. Johnson, J. Phys. Chem. A 118, 2014.

Formal groups and Z-entropies

PubMed Central

2016-01-01

We shall prove that the celebrated Rényi entropy is the first example of a new family of infinitely many multi-parametric entropies. We shall call them the Z-entropies. Each of them, under suitable hypotheses, generalizes the celebrated entropies of Boltzmann and Rényi. A crucial aspect is that every Z-entropy is composable (Tempesta 2016 Ann. Phys. 365, 180–197. (doi:10.1016/j.aop.2015.08.013)). This property means that the entropy of a system which is composed of two or more independent systems depends, in all the associated probability space, on the choice of the two systems only. Further properties are also required to describe the composition process in terms of a group law. The composability axiom, introduced as a generalization of the fourth Shannon–Khinchin axiom (postulating additivity), is a highly non-trivial requirement. Indeed, in the trace-form class, the Boltzmann entropy and Tsallis entropy are the only known composable cases. However, in the non-trace form class, the Z-entropies arise as new entropic functions possessing the mathematical properties necessary for information-theoretical applications, in both classical and quantum contexts. From a mathematical point of view, composability is intimately related to formal group theory of algebraic topology. The underlying group-theoretical structure determines crucially the statistical properties of the corresponding entropies. PMID:27956871

Leaping eels electrify threats, supporting Humboldt’s account of a battle with horses

PubMed Central

Catania, Kenneth C.

2016-01-01

In March 1800, Alexander von Humboldt observed the extraordinary spectacle of native fisherman collecting electric eels (Electrophorus electricus) by “fishing with horses” [von Humboldt A (1807) Ann Phys 25:34–43]. The strategy was to herd horses into a pool containing electric eels, provoking the eels to attack by pressing themselves against the horses while discharging. Once the eels were exhausted, they could be safely collected. This legendary tale of South American adventures helped propel Humboldt to fame and has been recounted and illustrated in many publications, but subsequent investigators have been skeptical, and no similar eel behavior has been reported in more than 200 years. Here I report a defensive eel behavior that supports Humboldt’s account. The behavior consists of an approach and leap out of the water during which the eel presses its chin against a threatening conductor while discharging high-voltage volleys. The effect is to short-circuit the electric organ through the threat, with increasing power diverted to the threat as the eel attains greater height during the leap. Measurement of voltages and current during the behavior, and assessment of the equivalent circuit, reveal the effectiveness of the behavior and the basis for its natural selection. PMID:27274074

Effective cluster model of dielectric enhancement in metal-insulator composites

NASA Astrophysics Data System (ADS)

Doyle, W. T.; Jacobs, I. S.

1990-11-01

The electrical permittivity of a suspension of conducting spheres at high volume loading exhibits a large enhancement above the value predicted by the Clausius-Mossotti approximation. The permittivity enhancement is a dielectric anomaly accompanying a metallization transition that occurs when conducting particles are close packed. In disordered suspensions, close encounters can cause a permittivity enhancement at any volume loading. We attribute the permittivity enhancements typically observed in monodisperse disordered suspensions of conducting spheres to local metallized regions of high density produced by density fluctuations. We model a disordered suspension as a mixture, or mesosuspension, of isolated spheres and random close-packed spherical clusters of arbitrary size. Multipole interactions within the clusters are treated exactly. External interactions between clusters and isolated spheres are treated in the dipole approximation. Model permittivities are compared with Guillien's experimental permittivity measurements [Ann. Phys. (Paris) Ser. 11, 16, 205 (1941)] on liquid suspensions of Hg droplets in oil and with Turner's conductivity measurements [Chem. Eng. Sci. 31, 487 (1976)] on fluidized bed suspensions of ion-exchange resin beads in aqueous solution. New permittivity measurements at 10 GHz on solid suspensions of monodisperse metal spheres in polyurethane are presented and compared with the model permittivities. The effective spherical cluster model is in excellent agreement with the experiments over the entire accessible range of volume loading.

Einstein Critical-Slowing-Down is Siegel CyberWar Denial-of-Access Queuing/Pinning/ Jamming/Aikido Via Siegel DIGIT-Physics BEC ``Intersection''-BECOME-UNION Barabasi Network/GRAPH-Physics BEC: Strutt/Rayleigh-Siegel Percolation GLOBALITY-to-LOCALITY Phase-Transition Critical-Phenomenon

NASA Astrophysics Data System (ADS)

Buick, Otto; Falcon, Pat; Alexander, G.; Siegel, Edward Carl-Ludwig

2013-03-01

Einstein[Dover(03)] critical-slowing-down(CSD)[Pais, Subtle in The Lord; Life & Sci. of Albert Einstein(81)] is Siegel CyberWar denial-of-access(DOA) operations-research queuing theory/pinning/jamming/.../Read [Aikido, Aikibojitsu & Natural-Law(90)]/Aikido(!!!) phase-transition critical-phenomenon via Siegel DIGIT-Physics (Newcomb[Am.J.Math. 4,39(1881)]-{Planck[(1901)]-Einstein[(1905)])-Poincare[Calcul Probabilités(12)-p.313]-Weyl [Goett.Nachr.(14); Math.Ann.77,313 (16)]-{Bose[(24)-Einstein[(25)]-Fermi[(27)]-Dirac[(1927)]}-``Benford''[Proc.Am.Phil.Soc. 78,4,551 (38)]-Kac[Maths.Stat.-Reasoning(55)]-Raimi[Sci.Am. 221,109 (69)...]-Jech[preprint, PSU(95)]-Hill[Proc.AMS 123,3,887(95)]-Browne[NYT(8/98)]-Antonoff-Smith-Siegel[AMS Joint-Mtg.,S.-D.(02)] algebraic-inversion to yield ONLY BOSE-EINSTEIN QUANTUM-statistics (BEQS) with ZERO-digit Bose-Einstein CONDENSATION(BEC) ``INTERSECTION''-BECOME-UNION to Barabasi[PRL 876,5632(01); Rev.Mod.Phys.74,47(02)...] Network /Net/GRAPH(!!!)-physics BEC: Strutt/Rayleigh(1881)-Polya(21)-``Anderson''(58)-Siegel[J.Non-crystalline-Sol.40,453(80)

How Children with Autism Reason about Other's Intentions: False-Belief and Counterfactual Inferences

ERIC Educational Resources Information Center

Rasga, Célia; Quelhas, Ana Cristina; Byrne, Ruth M. J.

2017-01-01

We examine false belief and counterfactual reasoning in children with autism with a new change-of-intentions task. Children listened to stories, for example, Anne is picking up toys and John hears her say she wants to find her ball. John goes away and the reason for Anne's action changes--Anne's mother tells her to tidy her bedroom. We asked,…

Supervised Learning in CINets

DTIC Science & Technology

2011-07-01

supervised learning process is compared to that of Artificial Neural Network ( ANNs ), fuzzy logic rule set, and Bayesian network approaches...of both fuzzy logic systems and Artificial Neural Networks ( ANNs ). Like fuzzy logic systems, the CINet technique allows the use of human- intuitive...fuzzy rule systems [3] CINets also maintain features common to both fuzzy systems and ANNs . The technique can be be shown to possess the property

Command and Control of Teams of Autonomous Units

DTIC Science & Technology

2012-06-01

done by a hybrid genetic algorithm (GA) particle swarm optimization ( PSO ) algorithm called PIDGION-alternate. This training algorithm is an ANN ...human controller will recognize the behaviors as being safe and correct. As the HyperNEAT approach produces Artificial Neural Nets ( ANN ), we can...optimization technique that generates efficient ANN controls from simple environmental feedback. FALCONET has been tested showing that it can produce

76 FR 23642 - The “100,000 Strong” Initiative Federal Advisory Committee: Notice of the Inaugural Meeting of...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-27

... wishing to attend should contact Lee Anne Shaffer of the Department of State's Bureau of East Asian and... are welcome to do so by e-mail to Lee Anne Shaffer at [email protected] . A member of the public... participate by teleconferencing can contact Lee Anne Shaffer at 202-647-7059 to receive the conference call-in...

The application of artificial neural networks in astronomy

NASA Astrophysics Data System (ADS)

Li, Li-Li; Zhang, Yan-Xia; Zhao, Yong-Heng; Yang, Da-Wei

2006-12-01

Artificial Neural Networks (ANNs) are computer algorithms inspired from simple models of human central nervous system activity. They can be roughly divided into two main kinds: supervised and unsupervised. The supervised approach lays the stress on "teaching" a machine to do the work of a mention human expert, usually by showing examples for which the true answer is supplied by the expert. The unsupervised one is aimed at learning new things from the data, and most useful when the data cannot easily be plotted in a two or three dimensional space. ANNs have been used widely and successfully in various fields, for instance, pattern recognition, financial analysis, biology, engineering and so on, because they have many merits such as self-learning, self-adapting, good robustness and dynamically rapid response as well as strong capability of dealing with non-linear problems. In the last few years there has been an increasing interest toward the astronomical applications of ANNs. In this paper, the authors firstly introduce the fundamental principle of ANNs together with the architecture of the network and outline various kinds of learning algorithms and network toplogies. The specific aspects of the applications of ANNs in astronomical problems are also listed, which contain the strong capabilities of approximating to arbitrary accuracy, any nonlinear functional mapping, parallel and distributed storage, tolerance of faulty and generalization of results. They summarize the advantages and disadvantages of main ANN models available to the astronomical community. Furthermore, the application cases of ANNs in astronomy are mainly described in detail. Here, the focus is on some of the most interesting fields of its application, for example: object detection, star/galaxy classification, spectral classification, galaxy morphology classification, the estimation of photometric redshifts of galaxies and time series analysis. In addition, other kinds of applications have been only touched upon. Finally, the development and application prospects of ANNs is discussed. With the increase of quantity and the distributing complexity of astronomical data, its scientific exploitation requires a variety of automated tools, which are capable to perform huge amount of work, such as data preprocessing, feature selection, data reduction, data mining amd data analysis. ANNs, one of intelligent tools, will show more and more superiorities.

Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.

PubMed

Fei, Y; Hu, J; Li, W-Q; Wang, W; Zong, G-Q

2017-03-01

Essentials Predicting the occurrence of portosplenomesenteric vein thrombosis (PSMVT) is difficult. We studied 72 patients with acute pancreatitis. Artificial neural networks modeling was more accurate than logistic regression in predicting PSMVT. Additional predictive factors may be incorporated into artificial neural networks. Objective To construct and validate artificial neural networks (ANNs) for predicting the occurrence of portosplenomesenteric venous thrombosis (PSMVT) and compare the predictive ability of the ANNs with that of logistic regression. Methods The ANNs and logistic regression modeling were constructed using simple clinical and laboratory data of 72 acute pancreatitis (AP) patients. The ANNs and logistic modeling were first trained on 48 randomly chosen patients and validated on the remaining 24 patients. The accuracy and the performance characteristics were compared between these two approaches by SPSS17.0 software. Results The training set and validation set did not differ on any of the 11 variables. After training, the back propagation network training error converged to 1 × 10 -20 , and it retained excellent pattern recognition ability. When the ANNs model was applied to the validation set, it revealed a sensitivity of 80%, specificity of 85.7%, a positive predictive value of 77.6% and negative predictive value of 90.7%. The accuracy was 83.3%. Differences could be found between ANNs modeling and logistic regression modeling in these parameters (10.0% [95% CI, -14.3 to 34.3%], 14.3% [95% CI, -8.6 to 37.2%], 15.7% [95% CI, -9.9 to 41.3%], 11.8% [95% CI, -8.2 to 31.8%], 22.6% [95% CI, -1.9 to 47.1%], respectively). When ANNs modeling was used to identify PSMVT, the area under receiver operating characteristic curve was 0.849 (95% CI, 0.807-0.901), which demonstrated better overall properties than logistic regression modeling (AUC = 0.716) (95% CI, 0.679-0.761). Conclusions ANNs modeling was a more accurate tool than logistic regression in predicting the occurrence of PSMVT following AP. More clinical factors or biomarkers may be incorporated into ANNs modeling to improve its predictive ability. © 2016 International Society on Thrombosis and Haemostasis.

SU-E-T-131: Artificial Neural Networks Applied to Overall Survival Prediction for Patients with Periampullary Carcinoma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gong, Y; Yu, J; Yeung, V

Purpose: Artificial neural networks (ANN) can be used to discover complex relations within datasets to help with medical decision making. This study aimed to develop an ANN method to predict two-year overall survival of patients with peri-ampullary cancer (PAC) following resection. Methods: Data were collected from 334 patients with PAC following resection treated in our institutional pancreatic tumor registry between 2006 and 2012. The dataset contains 14 variables including age, gender, T-stage, tumor differentiation, positive-lymph-node ratio, positive resection margins, chemotherapy, radiation therapy, and tumor histology.After censoring for two-year survival analysis, 309 patients were left, of which 44 patients (∼15%) weremore » randomly selected to form testing set. The remaining 265 cases were randomly divided into training set (211 cases, ∼80% of 265) and validation set (54 cases, ∼20% of 265) for 20 times to build 20 ANN models. Each ANN has one hidden layer with 5 units. The 20 ANN models were ranked according to their concordance index (c-index) of prediction on validation sets. To further improve prediction, the top 10% of ANN models were selected, and their outputs averaged for prediction on testing set. Results: By random division, 44 cases in testing set and the remaining 265 cases have approximately equal two-year survival rates, 36.4% and 35.5% respectively. The 20 ANN models, which were trained and validated on the 265 cases, yielded mean c-indexes as 0.59 and 0.63 on validation sets and the testing set, respectively. C-index was 0.72 when the two best ANN models (top 10%) were used in prediction on testing set. The c-index of Cox regression analysis was 0.63. Conclusion: ANN improved survival prediction for patients with PAC. More patient data and further analysis of additional factors may be needed for a more robust model, which will help guide physicians in providing optimal post-operative care. This project was supported by PA CURE Grant.« less

A hybrid deep neural network and physically based distributed model for river stage prediction

NASA Astrophysics Data System (ADS)

hitokoto, Masayuki; sakuraba, Masaaki

2016-04-01

We developed the real-time river stage prediction model, using the hybrid deep neural network and physically based distributed model. As the basic model, 4 layer feed-forward artificial neural network (ANN) was used. As a network training method, the deep learning technique was applied. To optimize the network weight, the stochastic gradient descent method based on the back propagation method was used. As a pre-training method, the denoising autoencoder was used. Input of the ANN model is hourly change of water level and hourly rainfall, output data is water level of downstream station. In general, the desirable input of the ANN has strong correlation with the output. In conceptual hydrological model such as tank model and storage-function model, river discharge is governed by the catchment storage. Therefore, the change of the catchment storage, downstream discharge subtracted from rainfall, can be the potent input candidate of the ANN model instead of rainfall. From this point of view, the hybrid deep neural network and physically based distributed model was developed. The prediction procedure of the hybrid model is as follows; first, downstream discharge was calculated by the distributed model, and then estimates the hourly change of catchment storage form rainfall and calculated discharge as the input of the ANN model, and finally the ANN model was calculated. In the training phase, hourly change of catchment storage can be calculated by the observed rainfall and discharge data. The developed model was applied to the one catchment of the OOYODO River, one of the first-grade river in Japan. The modeled catchment is 695 square km. For the training data, 5 water level gauging station and 14 rain-gauge station in the catchment was used. The training floods, superior 24 events, were selected during the period of 2005-2014. Prediction was made up to 6 hours, and 6 models were developed for each prediction time. To set the proper learning parameters and network architecture of the ANN model, sensitivity analysis was done by the case study approach. The prediction result was evaluated by the superior 4 flood events by the leave-one-out cross validation. The prediction result of the basic 4 layer ANN was better than the conventional 3 layer ANN model. However, the result did not reproduce well the biggest flood event, supposedly because the lack of the sufficient high-water level flood event in the training data. The result of the hybrid model outperforms the basic ANN model and distributed model, especially improved the performance of the basic ANN model in the biggest flood event.

Is tropospheric weather influenced by solar wind through atmospheric vertical coupling downward control?

NASA Astrophysics Data System (ADS)

Prikryl, Paul; Tsukijihara, Takumi; Iwao, Koki; Muldrew, Donald B.; Bruntz, Robert; Rušin, Vojto; Rybanský, Milan; Turňa, Maroš; Šťastný, Pavel; Pastirčák, Vladimír

2017-04-01

More than four decades have passed since a link between solar wind magnetic sector boundary structure and mid-latitude upper tropospheric vorticity was discovered (Wilcox et al., Science, 180, 185-186, 1973). The link has been later confirmed and various physical mechanisms proposed but apart from controversy, little attention has been drawn to these results. To further emphasize their importance we investigate the occurrence of mid-latitude severe weather in the context of solar wind coupling to the magnetosphere-ionosphere-atmosphere (MIA) system. It is observed that significant snowstorms, windstorms and heavy rain, particularly if caused by low pressure systems in winter, tend to follow arrivals of high-speed solar wind. Previously published statistical evidence that explosive extratropical cyclones in the northern hemisphere tend to occur after arrivals of high-speed solar wind streams from coronal holes (Prikryl et al., Ann. Geophys., 27, 1-30, 2009; Prikryl et al., J. Atmos. Sol.-Terr. Phys., 149, 219-231, 2016) is corroborated for the southern hemisphere. A physical mechanism to explain these observations is proposed. The leading edge of high-speed solar wind streams is a locus of large-amplitude magneto-hydrodynamic waves that modulate Joule heating and/or Lorentz forcing of the high-latitude lower thermosphere generating medium-scale atmospheric gravity waves that propagate upward and downward through the atmosphere. Simulations of gravity wave propagation in a model atmosphere using the Transfer Function Model (Mayr et al., Space Sci. Rev., 54, 297-375, 1990) show that propagating waves originating in the thermosphere can excite a spectrum of gravity waves in the lower atmosphere. In spite of significantly reduced amplitudes but subject to amplification upon reflection in the upper troposphere, these gravity waves can provide a lift of unstable air to release instabilities in the troposphere thus initiating convection to form cloud/precipitation bands (Prikryl et al., Ann. Geophys., 27, 31-57, 2009). It is primarily the energy provided by release of latent heat that leads to intensification of storms. These results indicate that vertical coupling in the atmosphere exerts downward control from solar wind to the lower atmospheric levels influencing tropospheric weather development.

Next Day Price Forecasting in Deregulated Market by Combination of Artificial Neural Network and ARIMA Time Series Models

NASA Astrophysics Data System (ADS)

Areekul, Phatchakorn; Senjyu, Tomonobu; Urasaki, Naomitsu; Yona, Atsushi

Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.

Optimization of Nd: YAG Laser Marking of Alumina Ceramic Using RSM And ANN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peter, Josephine; Doloi, B.; Bhattacharyya, B.

The present research papers deals with the artificial neural network (ANN) and the response surface methodology (RSM) based mathematical modeling and also an optimization analysis on marking characteristics on alumina ceramic. The experiments have been planned and carried out based on Design of Experiment (DOE). It also analyses the influence of the major laser marking process parameters and the optimal combination of laser marking process parametric setting has been obtained. The output of the RSM optimal data is validated through experimentation and ANN predictive model. A good agreement is observed between the results based on ANN predictive model and actualmore » experimental observations.« less

Use of artificial neural networks on optical track width measurements.

PubMed

Smith, Richard J; See, Chung W; Somekh, Mike G; Yacoot, Andrew

2007-08-01

We have demonstrated recently that, by using an ultrastable optical interferometer together with artificial neural networks (ANNs), track widths down to 60 nm can be measured with a 0.3 NA objective lens. We investigate the effective conditions for training ANNs. Experimental results will be used to show the characteristics of the training samples and the data format of the ANN inputs required to produce suitably trained ANNs. Results obtained with networks measuring double tracks, and classifying different structures, will be presented to illustrate the capability of the technique. We include a discussion on expansion of the application areas of the system, allowing it to be used as a general purpose instrument.

Use of artificial neural networks on optical track width measurements

NASA Astrophysics Data System (ADS)

Smith, Richard J.; See, Chung W.; Somekh, Mike G.; Yacoot, Andrew

2007-08-01

We have demonstrated recently that, by using an ultrastable optical interferometer together with artificial neural networks (ANNs), track widths down to 60 nm can be measured with a 0.3 NA objective lens. We investigate the effective conditions for training ANNs. Experimental results will be used to show the characteristics of the training samples and the data format of the ANN inputs required to produce suitably trained ANNs. Results obtained with networks measuring double tracks, and classifying different structures, will be presented to illustrate the capability of the technique. We include a discussion on expansion of the application areas of the system, allowing it to be used as a general purpose instrument.

A Novel Higher Order Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Xu, Shuxiang

2010-05-01

In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data.

Association between developmental steps in the organogenesis of the uterine cervix and locoregional progression of cervical cancer: a prospective clinicopathological analysis.

PubMed

Höckel, Michael; Hentschel, Bettina; Horn, Lars-Christian

2014-04-01

Our previous work provided evidence that early cervical cancer is locally confined to the Müllerian compartment that develops in women from the embryonic paramesonephric-mesonephric complex. We aimed to investigate if the concept of tumour permeation within ontogenetic domains is also valid for tumour progression and advanced disease. Starting from Carnegie stage 13, four successive steps in the organogenesis of the human uterine cervix were defined and an ontogenetic staging system for cervical cancer based on organ development was described. Histopathological and clinical data of patients with cervical cancer FIGO stages IB-IVA were raised prospectively from Oct 16, 1999, until Dec 20, 2012, and from March 8, 2000, until April 4, 2013, for two surgical trials of ontogenetic compartment resection without adjuvant radiation at the University of Leipzig (total or extended mesometrial resection [TMMR or EMMR]; and [laterally] extended endopelvic resection [LEER]). The primary endpoints of these trials were pathological resection state and locoregional tumour control. Patients who underwent TMMR and EMMR had follow-up assessment every 3-6 months for 5 years, and yearly thereafter. Patients who had (L)EER, every 3-6 months for 10 years, and yearly thereafter. By analysing the presence of disease within the classified tissues and disease outcome in these patients, and by examining relapse patterns, we were able to observe whether surgical excision within developmental compartments was sufficient for disease control. Survival curves were compared using the log-rank test. The effect of ontogenetic tumour stage and pathological tumour stage on overall survival was assessed by Cox proportional hazard models. The trials are registered as an ongoing observational monocentric study at the University of Leipzig Cancer Centre (ULCC012-13-28012013). 367 patients were included in our analysis. Staged organogenesis of the uterine cervix and progressive local growth of cervical carcinoma occur in the same tissue domains. The neoplasm originating in the uterine cervix, ontogenetic tumour stage 1 (oT1, n=217), permeates successively during its malignant progression the tissues developed from the Müllerian compartment (oT2, n=101), the genital metacompartment (oT3, n=38), and the urogenitorectal metacompartment (oT4, n=11). Ontogenetic staging, when comparing patients with oT1 and oT2 disease to those with oT3 and oT4 disease (hazard ratio 5·9, 95% CI 2·2-15·5; p=0·00036) was a better prognostic indicator for survival than pathological staging when comparing pT1b and pT2a with pT2b and pT4 disease (2·0, 95% CI 0·7-5·5; p=0·170). Resection of the stage-related ontogenetically specified tissue domains and their associated regional lymphoid tissues achieved an R0 resection in 363 (99%) of 367 patients and locoregional tumour control at 5 years was 94% (95% CI 92-97). 13 patients had grade 3 or 4 adverse events, the majority of which were urinary (10, 77%). Cervical cancer infiltrates the adult tissues established during ontogeny, pursuing the developmental steps in retrograde sequence. Clinical translation of these insights into ontogenetic tumour staging and compartment resection holds the potential to improve prognostic assessment and curative treatment. University of Leipzig and Leipzig School of Radical Pelvic Surgery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Beyond detection: biological physics informing progression and treatment of cancer Beyond detection: biological physics informing progression and treatment of cancer

NASA Astrophysics Data System (ADS)

Newman, T. J.; Thompson, A. M.

2012-12-01

The full text of the Preface is given in the PDF file. References [1] Kaur P et al 2012 Phys. Biol. 9 065001 [2] Lobikin M et al 2012 Phys. Biol. 9 065002 [3] Tanner K 2012 Phys. Biol. 9 065003 [4] Liu S V et al 2012 Phys. Biol. 9 065004 [5] Liao D et al 2012 Phys. Biol. 9 065005 [6] Liao D et al 2012 Phys. Biol. 9 065006 [7] Orlando P A et al 2012 Phys. Biol. 9 065007

Bio-Inspired Microsystem for Robust Genetic Assay Recognition

PubMed Central

Lue, Jaw-Chyng; Fang, Wai-Chi

2008-01-01

A compact integrated system-on-chip (SoC) architecture solution for robust, real-time, and on-site genetic analysis has been proposed. This microsystem solution is noise-tolerable and suitable for analyzing the weak fluorescence patterns from a PCR prepared dual-labeled DNA microchip assay. In the architecture, a preceding VLSI differential logarithm microchip is designed for effectively computing the logarithm of the normalized input fluorescence signals. A posterior VLSI artificial neural network (ANN) processor chip is used for analyzing the processed signals from the differential logarithm stage. A single-channel logarithmic circuit was fabricated and characterized. A prototype ANN chip with unsupervised winner-take-all (WTA) function was designed, fabricated, and tested. An ANN learning algorithm using a novel sigmoid-logarithmic transfer function based on the supervised backpropagation (BP) algorithm is proposed for robustly recognizing low-intensity patterns. Our results show that the trained new ANN can recognize low-fluorescence patterns better than an ANN using the conventional sigmoid function. PMID:18566679

Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods

NASA Astrophysics Data System (ADS)

Xing, Y. F.; Wang, Y. S.; Shi, L.; Guo, H.; Chen, H.

2016-01-01

According to the human perceptional characteristics, a method combined by the optimal wavelet-packet transform and artificial neural network, so-called OWPT-ANN model, for psychoacoustical recognition is presented. Comparisons of time-frequency analysis methods are performed, and an OWPT with 21 critical bands is designed for feature extraction of a sound, as is a three-layer back-propagation ANN for sound quality (SQ) recognition. Focusing on the loudness and sharpness, the OWPT-ANN model is applied on vehicle noises under different working conditions. Experimental verifications show that the OWPT can effectively transfer a sound into a time-varying energy pattern as that in the human auditory system. The errors of loudness and sharpness of vehicle noise from the OWPT-ANN are all less than 5%, which suggest a good accuracy of the OWPT-ANN model in SQ recognition. The proposed methodology might be regarded as a promising technique for signal processing in the human-hearing related fields in engineering.

Different approaches in Partial Least Squares and Artificial Neural Network models applied for the analysis of a ternary mixture of Amlodipine, Valsartan and Hydrochlorothiazide

NASA Astrophysics Data System (ADS)

Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.

2014-03-01

Different chemometric models were applied for the quantitative analysis of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in ternary mixture, namely, Partial Least Squares (PLS) as traditional chemometric model and Artificial Neural Networks (ANN) as advanced model. PLS and ANN were applied with and without variable selection procedure (Genetic Algorithm GA) and data compression procedure (Principal Component Analysis PCA). The chemometric methods applied are PLS-1, GA-PLS, ANN, GA-ANN and PCA-ANN. The methods were used for the quantitative analysis of the drugs in raw materials and pharmaceutical dosage form via handling the UV spectral data. A 3-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the drugs. Fifteen mixtures were used as a calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested methods. The validity of the proposed methods was assessed using the standard addition technique.

Artificial neural network techniques to improve the ability of optical coherence tomography to detect optic neuritis.

PubMed

Garcia-Martin, Elena; Herrero, Raquel; Bambo, Maria P; Ara, Jose R; Martin, Jesus; Polo, Vicente; Larrosa, Jose M; Garcia-Feijoo, Julian; Pablo, Luis E

2015-01-01

To analyze the ability of Spectralis optical coherence tomography (OCT) to detect multiple sclerosis (MS) and to distinguish MS eyes with antecedent optic neuritis (ON). To analyze the capability of artificial neural network (ANN) techniques to improve the diagnostic precision. MS patients and controls were enrolled (n = 217). OCT was used to determine the 768 retinal nerve fiber layer thicknesses. Sensitivity and specificity were evaluated to test the ability of OCT to discriminate between MS and healthy eyes, and between MS with and without antecedent ON using ANN. Using ANN technique multilayer perceptrons, OCT could detect MS with a sensitivity of 89.3%, a specificity of 87.6%, and a diagnostic precision of 88.5%. Compared with the OCT-provided parameters, the ANN had a better sensitivity-specificity balance. ANN technique improves the capability of Spectralis OCT to detect MS disease and to distinguish MS eyes with or without antecedent ON.

A New Data Mining Scheme Using Artificial Neural Networks

PubMed Central

Kamruzzaman, S. M.; Jehad Sarkar, A. M.

2011-01-01

Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems. PMID:22163866

Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm-artificial neural network method

NASA Astrophysics Data System (ADS)

Darvishvand, Leila; Kamkari, Babak; Kowsary, Farshad

2018-03-01

In this article, a new hybrid method based on the combination of the genetic algorithm (GA) and artificial neural network (ANN) is developed to optimize the design of three-dimensional (3-D) radiant furnaces. A 3-D irregular shape design body (DB) heated inside a 3-D radiant furnace is considered as a case study. The uniform thermal conditions on the DB surfaces are obtained by minimizing an objective function. An ANN is developed to predict the objective function value which is trained through the data produced by applying the Monte Carlo method. The trained ANN is used in conjunction with the GA to find the optimal design variables. The results show that the computational time using the GA-ANN approach is significantly less than that of the conventional method. It is concluded that the integration of the ANN with GA is an efficient technique for optimization of the radiant furnaces.

Total Electron Content forecast model over Australia

NASA Astrophysics Data System (ADS)

Bouya, Zahra; Terkildsen, Michael; Francis, Matthew

Ionospheric perturbations can cause serious propagation errors in modern radio systems such as Global Navigation Satellite Systems (GNSS). Forecasting ionospheric parameters is helpful to estimate potential degradation of the performance of these systems. Our purpose is to establish an Australian Regional Total Electron Content (TEC) forecast model at IPS. In this work we present an approach based on the combined use of the Principal Component Analysis (PCA) and Artificial Neural Network (ANN) to predict future TEC values. PCA is used to reduce the dimensionality of the original TEC data by mapping it into its eigen-space. In this process the top- 5 eigenvectors are chosen to reflect the directions of the maximum variability. An ANN approach was then used for the multicomponent prediction. We outline the design of the ANN model with its parameters. A number of activation functions along with different spectral ranges and different numbers of Principal Components (PCs) were tested to find the PCA-ANN models reaching the best results. Keywords: GNSS, Space Weather, Regional, Forecast, PCA, ANN.

Time-dependent fermentation control strategies for enhancing synthesis of marine bacteriocin 1701 using artificial neural network and genetic algorithm.

PubMed

Peng, Jiansheng; Meng, Fanmei; Ai, Yuncan

2013-06-01

The artificial neural network (ANN) and genetic algorithm (GA) were combined to optimize the fermentation process for enhancing production of marine bacteriocin 1701 in a 5-L-stirred-tank. Fermentation time, pH value, dissolved oxygen level, temperature and turbidity were used to construct a "5-10-1" ANN topology to identify the nonlinear relationship between fermentation parameters and the antibiotic effects (shown as in inhibition diameters) of bacteriocin 1701. The predicted values by the trained ANN model were coincided with the observed ones (the coefficient of R(2) was greater than 0.95). As the fermentation time was brought in as one of the ANN input nodes, fermentation parameters could be optimized by stages through GA, and an optimal fermentation process control trajectory was created. The production of marine bacteriocin 1701 was significantly improved by 26% under the guidance of fermentation control trajectory that was optimized by using of combined ANN-GA method. Copyright © 2013 Elsevier Ltd. All rights reserved.

Artificial neural network modelling of a large-scale wastewater treatment plant operation.

PubMed

Güçlü, Dünyamin; Dursun, Sükrü

2010-11-01

Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

[Optimization of calcium alginate floating microspheres loading aspirin by artificial neural networks and response surface methodology].

PubMed

Zhang, An-yang; Fan, Tian-yuan

2010-04-18

To investigate the preparation and optimization of calcium alginate floating microspheres loading aspirin. A model was used to predict the in vitro release of aspirin and optimize the formulation by artificial neural networks (ANNs) and response surface methodology (RSM). The amounts of the material in the formulation were used as inputs, while the release and floating rate of the microspheres were used as outputs. The performances of ANNs and RSM were compared. ANNs were more accurate in prediction. There was no significant difference between ANNs and RSM in optimization. Approximately 90% of the optimized microspheres could float on the artificial gastric juice over 4 hours. 42.12% of aspirin was released in 60 min, 60.97% in 120 min and 78.56% in 240 min. The release of the drug from the microspheres complied with Higuchi equation. The aspirin floating microspheres with satisfying in vitro release were prepared successfully by the methods of ANNs and RSM.

Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions

NASA Astrophysics Data System (ADS)

Aksoy, Hafzullah; Dahamsheh, Ahmad

2018-07-01

For forecasting monthly precipitation in an arid region, the feed forward back-propagation, radial basis function and generalized regression artificial neural networks (ANNs) are used in this study. The ANN models are improved after incorporation of a Markov chain-based algorithm (MC-ANNs) with which the percentage of dry months is forecasted perfectly, thus generation of any non-physical negative precipitation is eliminated. Due to the fact that recorded precipitation time series are usually shorter than the length needed for a proper calibration of ANN models, synthetic monthly precipitation data are generated by Thomas-Fiering model to further improve the performance of forecasting. For case studies from Jordan, it is seen that only a slightly better performance is achieved with the use of MC and synthetic data. A conditional statement is, therefore, established and imbedded into the ANN models after the incorporation of MC and support of synthetic data, to substantially improve the ability of the models for forecasting monthly precipitation in arid regions.

Revisited comparison of thermal instability theory with MARFE density limit experiment in TEXTOR.

NASA Astrophysics Data System (ADS)

Kelly, Frederick

2006-03-01

Density limit shots in TEXTOR [Tokamak EXperiment for Technology Oriented Research] that ended in MARFE [Multifaceted Asymmetric Radiation From the Edge] are analyzed by several thermal instability theories^1-7 with convective effects included. ^1W. M. Stacey, Phys. Plasmas 3, 2673 (1996); Phys. Plasmas 3, 3032 (1996); Phys. Plasmas 4, 134 (1997); Phys. Plasmas 4, 242 (1997). ^2W. M. Stacey, Plasma Phys. Contr. Fusion 39, 1245 (1997). ^3W. M. Stacey, Fusion Technol. 36, 38 (1999).^ ^4W. M. Stacey, Phys. Plasmas 7, 3464 (2000). ^5F. A. Kelly, W. M. Stacey, J. Rapp and M. Brix, Phys. Plasmas 8, 3382 (2001). ^6M. Z. Tokar and F. A. Kelly, Phys. Plasmas 10, 4378 (2003). ^7M. Z. Tokar, F. A. Kelly and X. Loozen, Phys. Plasmas 12, 052510 (2005).

From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming

PubMed Central

Mendyk, Aleksander; Güres, Sinan; Szlęk, Jakub; Wiśniowska, Barbara; Kleinebudde, Peter

2015-01-01

The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling of Q versus extrudate diameter (d) and the time variable (t) and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations' parameters. Two inputs were found important for the drug dissolution: d and t. The extrudates length (L) was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling of Q versus d and t resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs' black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies. PMID:26101544

Toward automatic time-series forecasting using neural networks.

PubMed

Yan, Weizhong

2012-07-01

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

Modeling the Malaysian motor insurance claim using artificial neural network and adaptive NeuroFuzzy inference system

NASA Astrophysics Data System (ADS)

Mohd Yunos, Zuriahati; Shamsuddin, Siti Mariyam; Ismail, Noriszura; Sallehuddin, Roselina

2013-04-01

Artificial neural network (ANN) with back propagation algorithm (BP) and ANFIS was chosen as an alternative technique in modeling motor insurance claims. In particular, an ANN and ANFIS technique is applied to model and forecast the Malaysian motor insurance data which is categorized into four claim types; third party property damage (TPPD), third party bodily injury (TPBI), own damage (OD) and theft. This study is to determine whether an ANN and ANFIS model is capable of accurately predicting motor insurance claim. There were changes made to the network structure as the number of input nodes, number of hidden nodes and pre-processing techniques are also examined and a cross-validation technique is used to improve the generalization ability of ANN and ANFIS models. Based on the empirical studies, the prediction performance of the ANN and ANFIS model is improved by using different number of input nodes and hidden nodes; and also various sizes of data. The experimental results reveal that the ANFIS model has outperformed the ANN model. Both models are capable of producing a reliable prediction for the Malaysian motor insurance claims and hence, the proposed method can be applied as an alternative to predict claim frequency and claim severity.

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulphides by principal component analysis and artificial neural networks.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2013-01-08

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu-Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples. Copyright © 2012 Elsevier B.V. All rights reserved.

Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO.

PubMed

Fan, Mingyi; Hu, Jiwei; Cao, Rensheng; Xiong, Kangning; Wei, Xionghui

2017-12-21

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) magnetic nanocomposites were prepared and then applied in the Cu(II) removal from aqueous solutions. Scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy and superconduction quantum interference device magnetometer were performed to characterize the nZVI/rGO nanocomposites. In order to reduce the number of experiments and the economic cost, response surface methodology (RSM) combined with artificial intelligence (AI) techniques, such as artificial neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), has been utilized as a major tool that can model and optimize the removal processes, because a tremendous advance has recently been made on AI that may result in extensive applications. Based on RSM, ANN-GA and ANN-PSO were employed to model the Cu(II) removal process and optimize the operating parameters, e.g., operating temperature, initial pH, initial concentration and contact time. The ANN-PSO model was proven to be an effective tool for modeling and optimizing the Cu(II) removal with a low absolute error and a high removal efficiency. Furthermore, the isotherm, kinetic, thermodynamic studies and the XPS analysis were performed to explore the mechanisms of Cu(II) removal process.

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

PubMed Central

Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo

2013-01-01

Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593

Digital image classification with the help of artificial neural network by simple histogram.

PubMed

Dey, Pranab; Banerjee, Nirmalya; Kaur, Rajwant

2016-01-01

Visual image classification is a great challenge to the cytopathologist in routine day-to-day work. Artificial neural network (ANN) may be helpful in this matter. In this study, we have tried to classify digital images of malignant and benign cells in effusion cytology smear with the help of simple histogram data and ANN. A total of 404 digital images consisting of 168 benign cells and 236 malignant cells were selected for this study. The simple histogram data was extracted from these digital images and an ANN was constructed with the help of Neurointelligence software [Alyuda Neurointelligence 2.2 (577), Cupertino, California, USA]. The network architecture was 6-3-1. The images were classified as training set (281), validation set (63), and test set (60). The on-line backpropagation training algorithm was used for this study. A total of 10,000 iterations were done to train the ANN system with the speed of 609.81/s. After the adequate training of this ANN model, the system was able to identify all 34 malignant cell images and 24 out of 26 benign cells. The ANN model can be used for the identification of the individual malignant cells with the help of simple histogram data. This study will be helpful in the future to identify malignant cells in unknown situations.

From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming.

PubMed

Mendyk, Aleksander; Güres, Sinan; Jachowicz, Renata; Szlęk, Jakub; Polak, Sebastian; Wiśniowska, Barbara; Kleinebudde, Peter

2015-01-01

The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling of Q versus extrudate diameter (d) and the time variable (t) and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations' parameters. Two inputs were found important for the drug dissolution: d and t. The extrudates length (L) was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling of Q versus d and t resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs' black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies.

Two Different Points of View through Artificial Intelligence and Vector Autoregressive Models for Ex Post and Ex Ante Forecasting

PubMed Central

Aydin, Alev Dilek; Caliskan Cavdar, Seyma

2015-01-01

The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method. PMID:26550010

Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models

NASA Astrophysics Data System (ADS)

Snauffer, Andrew M.; Hsieh, William W.; Cannon, Alex J.; Schnorbus, Markus A.

2018-03-01

Estimates of surface snow water equivalent (SWE) in mixed alpine environments with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief, and snow accumulations. These three confounding factors dominate much of the province of British Columbia (BC), Canada. An artificial neural network (ANN) was created using as predictors six gridded SWE products previously evaluated for BC. Relevant spatiotemporal covariates were also included as predictors, and observations from manual snow surveys at stations located throughout BC were used as target data. Mean absolute errors (MAEs) and interannual correlations for April surveys were found using cross-validation. The ANN using the three best-performing SWE products (ANN3) had the lowest mean station MAE across the province. ANN3 outperformed each product as well as product means and multiple linear regression (MLR) models in all of BC's five physiographic regions except for the BC Plains. Subsequent comparisons with predictions generated by the Variable Infiltration Capacity (VIC) hydrologic model found ANN3 to better estimate SWE over the VIC domain and within most regions. The superior performance of ANN3 over the individual products, product means, MLR, and VIC was found to be statistically significant across the province.

Neural network models - a novel tool for predicting the efficacy of growth hormone (GH) therapy in children with short stature.

PubMed

Smyczynska, Joanna; Hilczer, Maciej; Smyczynska, Urszula; Stawerska, Renata; Tadeusiewicz, Ryszard; Lewinski, Andrzej

2015-01-01

The leading method for prediction of growth hormone (GH) therapy effectiveness are multiple linear regression (MLR) models. Best of our knowledge, we are the first to apply artificial neural networks (ANN) to solve this problem. For ANN there is no necessity to assume the functions linking independent and dependent variables. The aim of study is to compare ANN and MLR models of GH therapy effectiveness. Analysis comprised the data of 245 GH-deficient children (170 boys) treated with GH up to final height (FH). Independent variables included: patients' height, pre-treatment height velocity, chronological age, bone age, gender, pubertal status, parental heights, GH peak in 2 stimulation tests, IGF-I concentration. The output variable was FH. For testing dataset, MLR model predicted FH SDS with average error (RMSE) 0.64 SD, explaining 34.3% of its variability; ANN model derived on the same pre-processed data predicted FH SDS with RMSE 0.60 SD, explaining 42.0% of its variability; ANN model derived on raw data predicted FH with RMSE 3.9 cm (0.63 SD), explaining 78.7% of its variability. ANN seem to be valuable tool in prediction of GH treatment effectiveness, especially since they can be applied to raw clinical data.

Two Different Points of View through Artificial Intelligence and Vector Autoregressive Models for Ex Post and Ex Ante Forecasting.

PubMed

Aydin, Alev Dilek; Caliskan Cavdar, Seyma

2015-01-01

The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.

Energy balance and non-turbulent fluxes

NASA Astrophysics Data System (ADS)

Moderow, Uta; Feigenwinter, Christian; Bernhofer, Christian

2010-05-01

Often, the sum of the turbulent fluxes of sensible heat and latent heat from eddy covariance (EC) measurements does not match the available energy (sum of net radiation, ground heat flux and storage changes). This is referred to as energy balance closure gap. The reported imbalances vary between 0% and 50% (Laubach 1996). In various publications, it has been shown that the uncertainty of the available energy itself does not explain the gap (Vogt et al. 1996; Moderow et al. 2009). Among other reasons, the underestimation is attributed to an underestimation of turbulent fluxes and undetected non-turbulent transport processes, i.e. advection (e.g. Foken et al. 2006). The imbalance is typically larger during nighttime than during daytime as the EC method fails to capture non-turbulent transports that can be significant during night (e.g. Aubinet 2008). Results for the budget of CO2 showed that including non-turbulent fluxes can change the budgets considerably. Hence, it is interesting to see how the budget of energy is changed. Here, the consequences of including advective fluxes of sensible heat and latent heat in the energy balance are explored with focus on nighttime conditions. Non-turbulent fluxes will be inspected critically regarding their plausibility. Following Bernhofer et al. (2003), a ratio similar to Bowen's ratio of the turbulent fluxes are defined for the non-turbulent fluxes and compared to each other. This might have implications for the partitioning of the available energy into sensible heat and latent heat. Data of the ADVEX-campaigns (Feigenwinter et al. 2008) of three different sites across Europe are used and selected periods are inspected. References Aubinet M (2008) Eddy covariance CO2-flux measurements in nocturnal conditions: An analysis of the problem. Ecol Appl 18: 1368-1378 Bernhofer C, Grünwald T, Schwiebus A, Vogt R (2003) Exploring the consequences of non-zero energy balance closure for total surface flux. In: Bernhofer C (ed) Flussbestimmung an komplexen Standorten, Tharandter Klimaprotokolle Band 8. Eigenverlag der Technischen Universität Dresden, 113 pp Feigenwinter C, Bernhofer C, Eichelmann U, Heinesch B, Hertel M, Janous D, Kolle O, Lagergren F, Lindroth A, Minerbi S, Moderow U, Mölder M, Montagnani L, Queck R, Rebmann C, Vestin P, Yernaux M, Zeri M, Ziegler W, Aubinet M (2008) Comparison of horizontal and vertical advective CO2 fluxes at three forest sites. Agric Forest Meteorol 148: 12-24 Foken T, Wimmer F, Mauder M, Thomas C, Liebethal C (2006) Some aspects of the energy balance closure problem. Atmos Chem Phys Discuss 6: 3381-3402 Laubach J (1996) Charakterisierung des turbulenten Austausches von Wärme, Wasserdampf und Kohlendioxid über niedriger Vegetation anhand von Eddy-Korrelations-Messungen. Band 3. Wissenschaftliche Mitteilungen aus dem Institut für Meteorologie der Universität Leipzig und dem Institut für Troposphärenforschung e.V, Leipzig, p 139 Moderow U, Aubinet M, Feigenwinter C, Kolle O, Lindroth A, Mölder M, Montagnani L, Rebmann C, Bernhofer C (2009) Available energy and energy balance closure at four coniferous sites across Europe. Theor Appl Meteorol 98: 397-412 Vogt R, Bernhofer C, Gay LW, Jaeger L, Parlow E (1996) The available energy over a Scots pine plantation: what's up for partitioning? Theor Appl Climatol 53:23-31

How can we deal with ANN in flood forecasting? As a simulation model or updating kernel!

NASA Astrophysics Data System (ADS)

Hassan Saddagh, Mohammad; Javad Abedini, Mohammad

2010-05-01

Flood forecasting and early warning, as a non-structural measure for flood control, is often considered to be the most effective and suitable alternative to mitigate the damage and human loss caused by flood. Forecast results which are output of hydrologic, hydraulic and/or black box models should secure accuracy of flood values and timing, especially for long lead time. The application of the artificial neural network (ANN) in flood forecasting has received extensive attentions in recent years due to its capability to capture the dynamics inherent in complex processes including flood. However, results obtained from executing plain ANN as simulation model demonstrate dramatic reduction in performance indices as lead time increases. This paper is intended to monitor the performance indices as it relates to flood forecasting and early warning using two different methodologies. While the first method employs a multilayer neural network trained using back-propagation scheme to forecast output hydrograph of a hypothetical river for various forecast lead time up to 6.0 hr, the second method uses 1D hydrodynamic MIKE11 model as forecasting model and multilayer neural network as updating kernel to monitor and assess the performance indices compared to ANN alone in light of increase in lead time. Results presented in both graphical and tabular format indicate superiority of MIKE11 coupled with ANN as updating kernel compared to ANN as simulation model alone. While plain ANN produces more accurate results for short lead time, the errors increase expeditiously for longer lead time. The second methodology provides more accurate and reliable results for longer forecast lead time.

Autonomous self-configuration of artificial neural networks for data classification or system control

NASA Astrophysics Data System (ADS)

Fink, Wolfgang

2009-05-01

Artificial neural networks (ANNs) are powerful methods for the classification of multi-dimensional data as well as for the control of dynamic systems. In general terms, ANNs consist of neurons that are, e.g., arranged in layers and interconnected by real-valued or binary neural couplings or weights. ANNs try mimicking the processing taking place in biological brains. The classification and generalization capabilities of ANNs are given by the interconnection architecture and the coupling strengths. To perform a certain classification or control task with a particular ANN architecture (i.e., number of neurons, number of layers, etc.), the inter-neuron couplings and their accordant coupling strengths must be determined (1) either by a priori design (i.e., manually) or (2) using training algorithms such as error back-propagation. The more complex the classification or control task, the less obvious it is how to determine an a priori design of an ANN, and, as a consequence, the architecture choice becomes somewhat arbitrary. Furthermore, rather than being able to determine for a given architecture directly the corresponding coupling strengths necessary to perform the classification or control task, these have to be obtained/learned through training of the ANN on test data. We report on the use of a Stochastic Optimization Framework (SOF; Fink, SPIE 2008) for the autonomous self-configuration of Artificial Neural Networks (i.e., the determination of number of hidden layers, number of neurons per hidden layer, interconnections between neurons, and respective coupling strengths) for performing classification or control tasks. This may provide an approach towards cognizant and self-adapting computing architectures and systems.

Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer.

PubMed

Kuo, Pao-Jen; Wu, Shao-Chun; Chien, Peng-Chen; Chang, Shu-Shya; Rau, Cheng-Shyuan; Tai, Hsueh-Ling; Peng, Shu-Hui; Lin, Yi-Chun; Chen, Yi-Chun; Hsieh, Hsiao-Yun; Hsieh, Ching-Hua

2018-03-02

The aim of this study was to develop an effective surgical site infection (SSI) prediction model in patients receiving free-flap reconstruction after surgery for head and neck cancer using artificial neural network (ANN), and to compare its predictive power with that of conventional logistic regression (LR). There were 1,836 patients with 1,854 free-flap reconstructions and 438 postoperative SSIs in the dataset for analysis. They were randomly assigned tin ratio of 7:3 into a training set and a test set. Based on comprehensive characteristics of patients and diseases in the absence or presence of operative data, prediction of SSI was performed at two time points (pre-operatively and post-operatively) with a feed-forward ANN and the LR models. In addition to the calculated accuracy, sensitivity, and specificity, the predictive performance of ANN and LR were assessed based on area under the curve (AUC) measures of receiver operator characteristic curves and Brier score. ANN had a significantly higher AUC (0.892) of post-operative prediction and AUC (0.808) of pre-operative prediction than LR (both P <0.0001). In addition, there was significant higher AUC of post-operative prediction than pre-operative prediction by ANN (p<0.0001). With the highest AUC and the lowest Brier score (0.090), the post-operative prediction by ANN had the highest overall predictive performance. The post-operative prediction by ANN had the highest overall performance in predicting SSI after free-flap reconstruction in patients receiving surgery for head and neck cancer.

Optimization of thermal conductivity lightweight brick type AAC (Autoclaved Aerated Concrete) effect of Si & Ca composition by using Artificial Neural Network (ANN)

NASA Astrophysics Data System (ADS)

Zulkifli; Wiryawan, G. P.

2018-03-01

Lightweight brick is the most important component of building construction, therefore it is necessary to have lightweight thermal, mechanical and aqustic thermal properties that meet the standard, in this paper which is discussed is the domain of light brick thermal conductivity properties. The advantage of lightweight brick has a low density (500-650 kg/m3), more economical, can reduce the load 30-40% compared to conventional brick (clay brick). In this research, Artificial Neural Network (ANN) is used to predict the thermal conductivity of lightweight brick type Autoclaved Aerated Concrete (AAC). Based on the training and evaluation that have been done on 10 model of ANN with number of hidden node 1 to 10, obtained that ANN with 3 hidden node have the best performance. It is known from the mean value of MSE (Mean Square Error) validation for three training times of 0.003269. This ANN was further used to predict the thermal conductivity of four light brick samples. The predicted results for each of the AAC1, AAC2, AAC3 and AAC4 light brick samples were 0.243 W/m.K, respectively; 0.29 W/m.K; 0.32 W/m.K; and 0.32 W/m.K. Furthermore, ANN is used to determine the effect of silicon composition (Si), Calcium (Ca), to light brick thermal conductivity. ANN simulation results show that the thermal conductivity increases with increasing Si composition. Si content is allowed maximum of 26.57%, while the Ca content in the range 20.32% - 30.35%.

Forecasting the prognosis of choroidal melanoma with an artificial neural network.

PubMed

Kaiserman, Igor; Rosner, Mordechai; Pe'er, Jacob

2005-09-01

To develop an artificial neural network (ANN) that will forecast the 5-year mortality from choroidal melanoma. Retrospective, comparative, observational cohort study. One hundred fifty-three eyes of 153 consecutive patients with choroidal melanoma (age, 58.4+/-14.6 years) who were treated with ruthenium 106 brachytherapy between 1988 and 1998 at the Department of Ophthalmology, Hadassah University Hospital, Jerusalem, Israel. Patients were observed clinically and ultrasonographically (A- and B-mode standardized ultrasonography). Metastatic screening included liver function tests and liver imaging. Backpropagation ANNs composed of 3 or 4 layers of neurons with various types of transfer functions and training protocols were assessed for their ability to predict the 5-year mortality. The ANNs were trained on 77 randomly selected patients and tested on a different set of 76 patients. Artificial neural networks were compared based on their sensitivity, specificity, forecasting accuracy, area under the receiver operating curves, and likelihood ratios (LRs). The best ANN was compared with the results of logistic regression and the performance of an ocular oncologist. The ability of the ANNs to forecast the 5-year mortality from choroidal melanoma. Thirty-one patients died during the follow-up period of metastatic choroidal melanoma. The best ANN (one hidden layer of 16 neurons) had 84% forecasting accuracy and an LR of 31.5. The number of hidden neurons significantly influenced the ANNs' performance (P<0.001). The performance of the ANNs was not significantly influenced by the training protocol, the number of hidden layers, or the type of transfer function. In comparison, logistic regression reached 86% forecasting accuracy, with a very low LR (0.8), whereas the human expert forecasting ability was <70% (LR, 1.85). Artificial neural networks can be used for forecasting the prognosis of choroidal melanoma and may support decision-making in treating this malignancy.

SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu

Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT,more » status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.« less

A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks.

PubMed

Yeh, Wei-Chang

Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.

MO-G-18C-05: Real-Time Prediction in Free-Breathing Perfusion MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, H; Liu, W; Ruan, D

Purpose: The aim is to minimize frame-wise difference errors caused by respiratory motion and eliminate the need for breath-holds in magnetic resonance imaging (MRI) sequences with long acquisitions and repeat times (TRs). The technique is being applied to perfusion MRI using arterial spin labeling (ASL). Methods: Respiratory motion prediction (RMP) using navigator echoes was implemented in ASL. A least-square method was used to extract the respiratory motion information from the 1D navigator. A generalized artificial neutral network (ANN) with three layers was developed to simultaneously predict 10 time points forward in time and correct for respiratory motion during MRI acquisition.more » During the training phase, the parameters of the ANN were optimized to minimize the aggregated prediction error based on acquired navigator data. During realtime prediction, the trained ANN was applied to the most recent estimated displacement trajectory to determine in real-time the amount of spatial Results: The respiratory motion information extracted from the least-square method can accurately represent the navigator profiles, with a normalized chi-square value of 0.037±0.015 across the training phase. During the 60-second training phase, the ANN successfully learned the respiratory motion pattern from the navigator training data. During real-time prediction, the ANN received displacement estimates and predicted the motion in the continuum of a 1.0 s prediction window. The ANN prediction was able to provide corrections for different respiratory states (i.e., inhalation/exhalation) during real-time scanning with a mean absolute error of < 1.8 mm. Conclusion: A new technique enabling free-breathing acquisition during MRI is being developed. A generalized ANN development has demonstrated its efficacy in predicting a continuum of motion profile for volumetric imaging based on navigator inputs. Future work will enhance the robustness of ANN and verify its effectiveness with human subjects. Research supported by National Institutes of Health National Cancer Institute Grant R01 CA159471-01.« less

Application of artificial neural network to predict clay sensitivity in a high landslide prone area using CPTu data- A case study in Southwest of Sweden

NASA Astrophysics Data System (ADS)

Shahri, Abbas; Mousavinaseri, Mahsasadat; Naderi, Shima; Espersson, Maria

2015-04-01

Application of Artificial Neural Networks (ANNs) in many areas of engineering, in particular to geotechnical engineering problems such as site characterization has demonstrated some degree of success. The present paper aims to evaluate the feasibility of several various types of ANN models to predict the clay sensitivity of soft clays form piezocone penetration test data (CPTu). To get the aim, a research database of CPTu data of 70 test points around the Göta River near the Lilli Edet in the southwest of Sweden which is a high prone land slide area were collected and considered as input for ANNs. For training algorithms the quick propagation, conjugate gradient descent, quasi-Newton, limited memory quasi-Newton and Levenberg-Marquardt were developed tested and trained using the CPTu data to provide a comparison between the results of field investigation and ANN models to estimate the clay sensitivity. The reason of using the clay sensitivity parameter in this study is due to its relation to landslides in Sweden.A special high sensitive clay namely quick clay is considered as the main responsible for experienced landslides in Sweden which has high sensitivity and prone to slide. The training and testing program was started with 3-2-1 ANN architecture structure. By testing and trying several various architecture structures and changing the hidden layer in order to have a higher output resolution the 3-4-4-3-1 architecture structure for ANN in this study was confirmed. The tested algorithm showed that increasing the hidden layers up to 4 layers in ANN can improve the results and the 3-4-4-3-1 architecture structure ANNs for prediction of clay sensitivity represent reliable and reasonable response. The obtained results showed that the conjugate gradient descent algorithm with R2=0.897 has the best performance among the tested algorithms. Keywords: clay sensitivity, landslide, Artificial Neural Network

A modified artificial neural network based prediction technique for tropospheric radio refractivity

PubMed Central

Javeed, Shumaila; Javed, Wajahat; Atif, M.; Uddin, Mueen

2018-01-01

Radio refractivity plays a significant role in the development and design of radio systems for attaining the best level of performance. Refractivity in the troposphere is one of the features affecting electromagnetic waves, and hence the communication system interrupts. In this work, a modified artificial neural network (ANN) based model is applied to predict the refractivity. The suggested ANN model comprises three modules: the data preparation module, the feature selection module, and the forecast module. The first module applies pre-processing to make the data compatible for the feature selection module. The second module discards irrelevant and redundant data from the input set. The third module uses ANN for prediction. The ANN model applies a sigmoid activation function and a multi-variate auto regressive model to update the weights during the training process. In this work, the refractivity is predicted and estimated based on ten years (2002–2011) of meteorological data, such as the temperature, pressure, and humidity, obtained from the Pakistan Meteorological Department (PMD), Islamabad. The refractivity is estimated using the method suggested by the International Telecommunication Union (ITU). The refractivity is predicted for the year 2012 using the database of the previous ten years, with the help of ANN. The ANN model is implemented in MATLAB. Next, the estimated and predicted refractivity levels are validated against each other. The predicted and actual values (PMD data) of the atmospheric parameters agree with each other well, and demonstrate the accuracy of the proposed ANN method. It was further found that all parameters have a strong relationship with refractivity, in particular the temperature and humidity. The refractivity values are higher during the rainy season owing to a strong association with the relative humidity. Therefore, it is important to properly cater the signal communication system during hot and humid weather. Based on the results, the proposed ANN method can be used to develop a refractivity database, which is highly important in a radio communication system. PMID:29494609

Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Gallego, C.; Costa, A.; Cuerva, A.

2010-09-01

Since nowadays wind energy can't be neither scheduled nor large-scale storaged, wind power forecasting has been useful to minimize the impact of wind fluctuations. In particular, short-term forecasting (characterised by prediction horizons from minutes to a few days) is currently required by energy producers (in a daily electricity market context) and the TSO's (in order to keep the stability/balance of an electrical system). Within the short-term background, time-series based models (i.e., statistical models) have shown a better performance than NWP models for horizons up to few hours. These models try to learn and replicate the dynamic shown by the time series of a certain variable. When considering the power output of wind farms, ramp events are usually observed, being characterized by a large positive gradient in the time series (ramp-up) or negative (ramp-down) during relatively short time periods (few hours). Ramp events may be motivated by many different causes, involving generally several spatial scales, since the large scale (fronts, low pressure systems) up to the local scale (wind turbine shut-down due to high wind speed, yaw misalignment due to fast changes of wind direction). Hence, the output power may show unexpected dynamics during ramp events depending on the underlying processes; consequently, traditional statistical models considering only one dynamic for the hole power time series may be inappropriate. This work proposes a Regime Switching (RS) model based on Artificial Neural Nets (ANN). The RS-ANN model gathers as many ANN's as different dynamics considered (called regimes); a certain ANN is selected so as to predict the output power, depending on the current regime. The current regime is on-line updated based on a gradient criteria, regarding the past two values of the output power. 3 Regimes are established, concerning ramp events: ramp-up, ramp-down and no-ramp regime. In order to assess the skillness of the proposed RS-ANN model, a single-ANN model (without regime classification) is adopted as a reference model. Both models are evaluated in terms of Improvement over Persistence on the Mean Square Error basis (IoP%) when predicting horizons form 1 time-step to 5. The case of a wind farm located in the complex terrain of Alaiz (north of Spain) has been considered. Three years of available power output data with a hourly resolution have been employed: two years for training and validation of the model and the last year for assessing the accuracy. Results showed that the RS-ANN overcame the single-ANN model for one step-ahead forecasts: the overall IoP% was up to 8.66% for the RS-ANN model (depending on the gradient criterion selected to consider the ramp regime triggered) and 6.16% for the single-ANN. However, both models showed similar accuracy for larger horizons. A locally-weighted evaluation during ramp events for one-step ahead was also performed. It was found that the IoP% during ramps-up increased from 17.60% (case of single-ANN) to 22.25% (case of RS-ANN); however, during the ramps-down events this improvement increased from 18.55% to 19.55%. Three main conclusions are derived from this case study: It highlights the importance of considering statistical models capable of differentiate several regimes showed by the output power time series in order to improve the forecasting during extreme events like ramps. On-line regime classification based on available power output data didn't seem to contribute to improve forecasts for horizons beyond one-step ahead. Tacking into account other explanatory variables (local wind measurements, NWP outputs) could lead to a better understanding of ramp events, improving the regime assessment also for further horizons. The RS-ANN model slightly overcame the single-ANN during ramp-down events. If further research reinforce this effect, special attention should be addressed to understand the underlying processes during ramp-down events.

Local Hamiltonian Monte Carlo study of the massive schwinger model, the decoupling of heavy flavours

NASA Astrophysics Data System (ADS)

Ranft, J.

1983-12-01

The massive Schwinger model with two flavours is studied using the local hamiltonian lattice Monte Carlo method. Chiral symmetry breaking is studied using the fermion condensate as order parameter. For a small ratio of the two fermion masses, degeneracy of the two flavours is found. For a large ratio of the masses, the heavy flavour decouples and the light fermion behaves like in the one flavour Schwinger model. On leave from Sektion Physik, Karl-Marx-Universität, Leipzig, GDR.

Optimization of equipment for electron radiation processing

NASA Astrophysics Data System (ADS)

Tartz, M.; Hartmann, E.; Lenk, M.; Mehnert, R.

1999-05-01

In the course of the last decade, IOM Leipzig has developed low-energy electron accelerators for electron beam curing of polymer coatings and printing inks. In order to optimize the electron irradiation field, electron optical calculations have been carried out using the commercially available EGUN code. The present study outlines the design of the diode-type low-energy electron accelerators LEA and EBOGEN, taking into account the electron optical effects of secondary components such as the retaining rods installed in the cathode assembly.

An Analysis in Coalition Warfare: Napoleon’s Defeat at the Battle of Nations-Leipzig, 1813.

DTIC Science & Technology

1998-04-06

modern day Northern Poland and Eastern Germany . The Prussian monarch, Frederick William III, another unwilling ally to Napoleon’s invasion of...but in Germany , England’s stake in the battle was the financial support it provided the allies34, liaison officers, and a small number of troops...signed the Treaty of Kalisch on 27 February 1813 and joined Russian forces on the offensive, resulting in an inconclusive battle at Magdeburg . This

Matrix Concentration Inequalities via the Method of Exchangeable Pairs

DTIC Science & Technology

2012-01-27

viewed as an exchangeable pairs version of the Burkholder –Davis–Gundy (BDG) inequality from classical martingale theory [Bur73]. Matrix extensions of...non-commutative probability. Math. Ann., 319:1–16, 2001. [Bur73] D. L. Burkholder . Distribution function inequalities for martingales. Ann. Probab., 1...Statist. Assoc., 58(301):13–30, 1963. [JX03] M. Junge and Q. Xu. Noncommutative Burkholder /Rosenthal inequalities. Ann. Probab., 31(2):948–995, 2003

Series of (2+1)-dimensional stable self-dual interacting conformal field theories

NASA Astrophysics Data System (ADS)

Cheng, Meng; Xu, Cenke

2016-12-01

Using the duality between seemingly different (2+1)-dimensional [(2 +1 )d ] conformal field theories (CFT) proposed recently [D. T. Son, Phys. Rev. X 5, 031027 (2015), 10.1103/PhysRevX.5.031027; M. A. Metlitski and A. Vishwanath, Phys. Rev. B 93, 245151 (2016), 10.1103/PhysRevB.93.245151; C. Wang and T. Senthil, Phys. Rev. X 6, 011034 (2015), 10.1103/PhysRevX.6.011034; C. Wang and T. Senthil, Phys. Rev. X 5, 041031 (2015), 10.1103/PhysRevX.5.041031; C. Wang and T. Senthil, Phys. Rev. B 93, 085110 (2016), 10.1103/PhysRevB.93.085110; C. Xu and Y.-Z. You, Phys. Rev. B 92, 220416 (2015), 10.1103/PhysRevB.92.220416; D. F. Mross et al., Phys. Rev. Lett. 117, 016802 (2016), 10.1103/PhysRevLett.117.016802; A. Karch and D. Tong, arXiv:1606.01893; N. Seiberg et al., arXiv:1606.01989; P.-S. Hsin and N. Seiberg, arXiv:1607.07457], we study a series of (2 +1 )d stable self-dual interacting CFTs. These CFTs can be realized (for instance) on the boundary of the 3 d bosonic topological insulator protected by U(1) and time-reversal symmetry (T ), and they remain stable as long as these symmetries are preserved. When realized as a boundary system, these CFTs can be driven into anomalous fractional quantum Hall states once T is broken. We demonstrate that the newly proposed dualities allow us to study these CFTs quantitatively through a controlled calculation, without relying on a large flavor number of matter fields. We also propose a numerical test for our results, which would provide strong evidence for the originally proposed duality between Dirac fermion and QED.

A Curve Fitting Approach Using ANN for Converting CT Number to Linear Attenuation Coefficient for CT-based PET Attenuation Correction

NASA Astrophysics Data System (ADS)

Lai, Chia-Lin; Lee, Jhih-Shian; Chen, Jyh-Cheng

2015-02-01

Energy-mapping, the conversion of linear attenuation coefficients (μ) calculated at the effective computed tomography (CT) energy to those corresponding to 511 keV, is an important step in CT-based attenuation correction (CTAC) for positron emission tomography (PET) quantification. The aim of this study was to implement energy-mapping step by using curve fitting ability of artificial neural network (ANN). Eleven digital phantoms simulated by Geant4 application for tomographic emission (GATE) and 12 physical phantoms composed of various volume concentrations of iodine contrast were used in this study to generate energy-mapping curves by acquiring average CT values and linear attenuation coefficients at 511 keV of these phantoms. The curves were built with ANN toolbox in MATLAB. To evaluate the effectiveness of the proposed method, another two digital phantoms (liver and spine-bone) and three physical phantoms (volume concentrations of 3%, 10% and 20%) were used to compare the energy-mapping curves built by ANN and bilinear transformation, and a semi-quantitative analysis was proceeded by injecting 0.5 mCi FDG into a SD rat for micro-PET scanning. The results showed that the percentage relative difference (PRD) values of digital liver and spine-bone phantom are 5.46% and 1.28% based on ANN, and 19.21% and 1.87% based on bilinear transformation. For 3%, 10% and 20% physical phantoms, the PRD values of ANN curve are 0.91%, 0.70% and 3.70%, and the PRD values of bilinear transformation are 3.80%, 1.44% and 4.30%, respectively. Both digital and physical phantoms indicated that the ANN curve can achieve better performance than bilinear transformation. The semi-quantitative analysis of rat PET images showed that the ANN curve can reduce the inaccuracy caused by attenuation effect from 13.75% to 4.43% in brain tissue, and 23.26% to 9.41% in heart tissue. On the other hand, the inaccuracy remained 6.47% and 11.51% in brain and heart tissue when the bilinear transformation was used. Overall, it can be concluded that the bilinear transformation method resulted in considerable bias and the newly proposed calibration curve built by ANN could achieve better results with acceptable accuracy.

Training artificial neural networks directly on the concordance index for censored data using genetic algorithms.

PubMed

Kalderstam, Jonas; Edén, Patrik; Bendahl, Pär-Ola; Strand, Carina; Fernö, Mårten; Ohlsson, Mattias

2013-06-01

The concordance index (c-index) is the standard way of evaluating the performance of prognostic models in the presence of censored data. Constructing prognostic models using artificial neural networks (ANNs) is commonly done by training on error functions which are modified versions of the c-index. Our objective was to demonstrate the capability of training directly on the c-index and to evaluate our approach compared to the Cox proportional hazards model. We constructed a prognostic model using an ensemble of ANNs which were trained using a genetic algorithm. The individual networks were trained on a non-linear artificial data set divided into a training and test set both of size 2000, where 50% of the data was censored. The ANNs were also trained on a data set consisting of 4042 patients treated for breast cancer spread over five different medical studies, 2/3 used for training and 1/3 used as a test set. A Cox model was also constructed on the same data in both cases. The two models' c-indices on the test sets were then compared. The ranking performance of the models is additionally presented visually using modified scatter plots. Cross validation on the cancer training set did not indicate any non-linear effects between the covariates. An ensemble of 30 ANNs with one hidden neuron was therefore used. The ANN model had almost the same c-index score as the Cox model (c-index=0.70 and 0.71, respectively) on the cancer test set. Both models identified similarly sized low risk groups with at most 10% false positives, 49 for the ANN model and 60 for the Cox model, but repeated bootstrap runs indicate that the difference was not significant. A significant difference could however be seen when applied on the non-linear synthetic data set. In that case the ANN ensemble managed to achieve a c-index score of 0.90 whereas the Cox model failed to distinguish itself from the random case (c-index=0.49). We have found empirical evidence that ensembles of ANN models can be optimized directly on the c-index. Comparison with a Cox model indicates that near identical performance is achieved on a real cancer data set while on a non-linear data set the ANN model is clearly superior. Copyright © 2013 Elsevier B.V. All rights reserved.

Aging and Rejuvenation with Fractional Derivatives

DTIC Science & Technology

2004-09-10

Chechkin , J. Klafter, V . Yu . Gonchar , R. Metzler, and L. V . Tanatarov, Phys. Rev. E 67, 010102(R) (2003). [12] I. M. Sokolov and R. Metzler, Phys. Rev. E 67...051106 (2001). [7] A . V . Chechkin , R. Gorenflo, and I. M. Sokolov, Phys. Rev. E 66, 046129 (2002). [8] J. Bisquert, Phys. Rev. Lett. 91, 010602 (2003...9] R. Metzler and J. Klafter, J. Phys. Chem. B 104 3851 (2000). [10] E. Barkai and R. J. Silbey, J. Phys. Chem. B 104 3866 (2000).

Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer.

PubMed

Wang, Jeff; Kato, Fumi; Yamashita, Hiroko; Baba, Motoi; Cui, Yi; Li, Ruijiang; Oyama-Manabe, Noriko; Shirato, Hiroki

2017-04-01

Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating full-field digital mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer. ANN calibration of VBD was performed using phantom data for one FFDM system. Mammograms of 46 Japanese women diagnosed with invasive carcinoma and 53 with negative findings were analyzed using ANN models learned. ANN-estimated VBD was validated against phantom data, compared intra-patient, with qualitative composition scoring, with MRI VBD, and inter-patient with classical risk factors of breast cancer as well as cancer status. Phantom validations reached an R 2 of 0.993. Intra-patient validations ranged from R 2 of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R 2 ranging from 0.665 with VBD to 0.852 with breast volume. VBD was significantly higher in women with cancer. Associations with age, BMI, menopause, and cancer status previously reported were also confirmed. ANN modeling appears to produce reasonable measures of mammographic density validated with phantoms, with existing measures of breast density, and with classical biomarkers of breast cancer. FFDM VBD is significantly higher in Japanese women with cancer.

Comparison of hybrid spectral-decomposition artificial neural network models for understanding climatic forcing of groundwater levels

NASA Astrophysics Data System (ADS)

Abrokwah, K.; O'Reilly, A. M.

2017-12-01

Groundwater is an important resource that is extracted every day because of its invaluable use for domestic, industrial and agricultural purposes. The need for sustaining groundwater resources is clearly indicated by declining water levels and has led to modeling and forecasting accurate groundwater levels. In this study, spectral decomposition of climatic forcing time series was used to develop hybrid wavelet analysis (WA) and moving window average (MWA) artificial neural network (ANN) models. These techniques are explored by modeling historical groundwater levels in order to provide understanding of potential causes of the observed groundwater-level fluctuations. Selection of the appropriate decomposition level for WA and window size for MWA helps in understanding the important time scales of climatic forcing, such as rainfall, that influence water levels. Discrete wavelet transform (DWT) is used to decompose the input time-series data into various levels of approximate and details wavelet coefficients, whilst MWA acts as a low-pass signal-filtering technique for removing high-frequency signals from the input data. The variables used to develop and validate the models were daily average rainfall measurements from five National Atmospheric and Oceanic Administration (NOAA) weather stations and daily water-level measurements from two wells recorded from 1978 to 2008 in central Florida, USA. Using different decomposition levels and different window sizes, several WA-ANN and MWA-ANN models for simulating the water levels were created and their relative performances compared against each other. The WA-ANN models performed better than the corresponding MWA-ANN models; also higher decomposition levels of the input signal by the DWT gave the best results. The results obtained show the applicability and feasibility of hybrid WA-ANN and MWA-ANN models for simulating daily water levels using only climatic forcing time series as model inputs.

Novel approaches to address spectral distortions in photon counting x-ray CT using artificial neural networks

NASA Astrophysics Data System (ADS)

Touch, M.; Clark, D. P.; Barber, W.; Badea, C. T.

2016-04-01

Spectral CT using a photon-counting x-ray detector (PCXD) can potentially increase accuracy of measuring tissue composition. However, PCXD spectral measurements suffer from distortion due to charge sharing, pulse pileup, and Kescape energy loss. This study proposes two novel artificial neural network (ANN)-based algorithms: one to model and compensate for the distortion, and another one to directly correct for the distortion. The ANN-based distortion model was obtained by training to learn the distortion from a set of projections with a calibration scan. The ANN distortion was then applied in the forward statistical model to compensate for distortion in the projection decomposition. ANN was also used to learn to correct distortions directly in projections. The resulting corrected projections were used for reconstructing the image, denoising via joint bilateral filtration, and decomposition into three-material basis functions: Compton scattering, the photoelectric effect, and iodine. The ANN-based distortion model proved to be more robust to noise and worked better compared to using an imperfect parametric distortion model. In the presence of noise, the mean relative errors in iodine concentration estimation were 11.82% (ANN distortion model) and 16.72% (parametric model). With distortion correction, the mean relative error in iodine concentration estimation was improved by 50% over direct decomposition from distorted data. With our joint bilateral filtration, the resulting material image quality and iodine detectability as defined by the contrast-to-noise ratio were greatly enhanced allowing iodine concentrations as low as 2 mg/ml to be detected. Future work will be dedicated to experimental evaluation of our ANN-based methods using 3D-printed phantoms.

Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash

PubMed Central

Safiuddin, Md.; Raman, Sudharshan N.; Abdus Salam, Md.; Jumaat, Mohd. Zamin

2016-01-01

Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination (R2) for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN. PMID:28773520

Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash.

PubMed

Safiuddin, Md; Raman, Sudharshan N; Abdus Salam, Md; Jumaat, Mohd Zamin

2016-05-20

Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination ( R ²) for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN.

Modeling and optimization of reductive degradation of chloramphenicol in aqueous solution by zero-valent bimetallic nanoparticles.

PubMed

Singh, Kunwar P; Singh, Arun K; Gupta, Shikha; Rai, Premanjali

2012-07-01

The present study aims to investigate the individual and combined effects of temperature, pH, zero-valent bimetallic nanoparticles (ZVBMNPs) dose, and chloramphenicol (CP) concentration on the reductive degradation of CP using ZVBMNPs in aqueous medium. Iron-silver ZVBMNPs were synthesized. Batch experimental data were generated using a four-factor statistical experimental design. CP reduction by ZVBMNPs was optimized using the response surface modeling (RSM) and artificial neural network-genetic algorithm (ANN-GA) approaches. The RSM and ANN methodologies were also compared for their predictive and generalization abilities using the same training and validation data set. Reductive by-products of CP were identified using liquid chromatography-mass spectrometry technique. The optimized process variables (RSM and ANN-GA approaches) yielded CP reduction capacity of 57.37 and 57.10 mg g(-1), respectively, as compared to the experimental value of 54.0 mg g(-1) with un-optimized variables. The ANN-GA and RSM methodologies yielded comparable results and helped to achieve a higher reduction (>6%) of CP by the ZVBMNPs as compared to the experimental value. The root mean squared error, relative standard error of prediction and correlation coefficient between the measured and model-predicted values of response variable were 1.34, 3.79, and 0.964 for RSM and 0.03, 0.07, and 0.999 for ANN models for the training and 1.39, 3.47, and 0.996 for RSM and 1.25, 3.11, and 0.990 for ANN models for the validation set. Predictive and generalization abilities of both the RSM and ANN models were comparable. The synthesized ZVBMNPs may be used for an efficient reductive removal of CP from the water.

In silico prediction of nematic transition temperature for liquid crystals using quantitative structure-property relationship approaches.

PubMed

Fatemi, Mohammad Hossein; Ghorbanzad'e, Mehdi

2009-11-01

Quantitative structure-property relationship models for the prediction of the nematic transition temperature (T (N)) were developed by using multilinear regression analysis and a feedforward artificial neural network (ANN). A collection of 42 thermotropic liquid crystals was chosen as the data set. The data set was divided into three sets: for training, and an internal and external test set. Training and internal test sets were used for ANN model development, and the external test set was used for evaluation of the predictive power of the model. In order to build the models, a set of six descriptors were selected by the best multilinear regression procedure of the CODESSA program. These descriptors were: atomic charge weighted partial negatively charged surface area, relative negative charged surface area, polarity parameter/square distance, minimum most negative atomic partial charge, molecular volume, and the A component of moment of inertia, which encode geometrical and electronic characteristics of molecules. These descriptors were used as inputs to ANN. The optimized ANN model had 6:6:1 topology. The standard errors in the calculation of T (N) for the training, internal, and external test sets using the ANN model were 1.012, 4.910, and 4.070, respectively. To further evaluate the ANN model, a crossvalidation test was performed, which produced the statistic Q (2) = 0.9796 and standard deviation of 2.67 based on predicted residual sum of square. Also, the diversity test was performed to ensure the model's stability and prove its predictive capability. The obtained results reveal the suitability of ANN for the prediction of T (N) for liquid crystals using molecular structural descriptors.

Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer

PubMed Central

Kuo, Pao-Jen; Wu, Shao-Chun; Chien, Peng-Chen; Chang, Shu-Shya; Rau, Cheng-Shyuan; Tai, Hsueh-Ling; Peng, Shu-Hui; Lin, Yi-Chun; Chen, Yi-Chun; Hsieh, Hsiao-Yun; Hsieh, Ching-Hua

2018-01-01

Background The aim of this study was to develop an effective surgical site infection (SSI) prediction model in patients receiving free-flap reconstruction after surgery for head and neck cancer using artificial neural network (ANN), and to compare its predictive power with that of conventional logistic regression (LR). Materials and methods There were 1,836 patients with 1,854 free-flap reconstructions and 438 postoperative SSIs in the dataset for analysis. They were randomly assigned tin ratio of 7:3 into a training set and a test set. Based on comprehensive characteristics of patients and diseases in the absence or presence of operative data, prediction of SSI was performed at two time points (pre-operatively and post-operatively) with a feed-forward ANN and the LR models. In addition to the calculated accuracy, sensitivity, and specificity, the predictive performance of ANN and LR were assessed based on area under the curve (AUC) measures of receiver operator characteristic curves and Brier score. Results ANN had a significantly higher AUC (0.892) of post-operative prediction and AUC (0.808) of pre-operative prediction than LR (both P<0.0001). In addition, there was significant higher AUC of post-operative prediction than pre-operative prediction by ANN (p<0.0001). With the highest AUC and the lowest Brier score (0.090), the post-operative prediction by ANN had the highest overall predictive performance. Conclusion The post-operative prediction by ANN had the highest overall performance in predicting SSI after free-flap reconstruction in patients receiving surgery for head and neck cancer. PMID:29568393

Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT.

PubMed

Bizios, Dimitrios; Heijl, Anders; Hougaard, Jesper Leth; Bengtsson, Boel

2010-02-01

To compare the performance of two machine learning classifiers (MLCs), artificial neural networks (ANNs) and support vector machines (SVMs), with input based on retinal nerve fibre layer thickness (RNFLT) measurements by optical coherence tomography (OCT), on the diagnosis of glaucoma, and to assess the effects of different input parameters. We analysed Stratus OCT data from 90 healthy persons and 62 glaucoma patients. Performance of MLCs was compared using conventional OCT RNFLT parameters plus novel parameters such as minimum RNFLT values, 10th and 90th percentiles of measured RNFLT, and transformations of A-scan measurements. For each input parameter and MLC, the area under the receiver operating characteristic curve (AROC) was calculated. There were no statistically significant differences between ANNs and SVMs. The best AROCs for both ANN (0.982, 95%CI: 0.966-0.999) and SVM (0.989, 95% CI: 0.979-1.0) were based on input of transformed A-scan measurements. Our SVM trained on this input performed better than ANNs or SVMs trained on any of the single RNFLT parameters (p < or = 0.038). The performance of ANNs and SVMs trained on minimum thickness values and the 10th and 90th percentiles were at least as good as ANNs and SVMs with input based on the conventional RNFLT parameters. No differences between ANN and SVM were observed in this study. Both MLCs performed very well, with similar diagnostic performance. Input parameters have a larger impact on diagnostic performance than the type of machine classifier. Our results suggest that parameters based on transformed A-scan thickness measurements of the RNFL processed by machine classifiers can improve OCT-based glaucoma diagnosis.

Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters.

PubMed

Papantonopoulos, Georgios; Takahashi, Keiso; Bountis, Tasos; Loos, Bruno G

2014-01-01

There is neither a single clinical, microbiological, histopathological or genetic test, nor combinations of them, to discriminate aggressive periodontitis (AgP) from chronic periodontitis (CP) patients. We aimed to estimate probability density functions of clinical and immunologic datasets derived from periodontitis patients and construct artificial neural networks (ANNs) to correctly classify patients into AgP or CP class. The fit of probability distributions on the datasets was tested by the Akaike information criterion (AIC). ANNs were trained by cross entropy (CE) values estimated between probabilities of showing certain levels of immunologic parameters and a reference mode probability proposed by kernel density estimation (KDE). The weight decay regularization parameter of the ANNs was determined by 10-fold cross-validation. Possible evidence for 2 clusters of patients on cross-sectional and longitudinal bone loss measurements were revealed by KDE. Two to 7 clusters were shown on datasets of CD4/CD8 ratio, CD3, monocyte, eosinophil, neutrophil and lymphocyte counts, IL-1, IL-2, IL-4, INF-γ and TNF-α level from monocytes, antibody levels against A. actinomycetemcomitans (A.a.) and P.gingivalis (P.g.). ANNs gave 90%-98% accuracy in classifying patients into either AgP or CP. The best overall prediction was given by an ANN with CE of monocyte, eosinophil, neutrophil counts and CD4/CD8 ratio as inputs. ANNs can be powerful in classifying periodontitis patients into AgP or CP, when fed by CE values based on KDE. Therefore ANNs can be employed for accurate diagnosis of AgP or CP by using relatively simple and conveniently obtained parameters, like leukocyte counts in peripheral blood. This will allow clinicians to better adapt specific treatment protocols for their AgP and CP patients.

The American Military on the Frontier

DTIC Science & Technology

1976-04-01

1783. Ncrvian: University of ÖFlahoma Press, HeT. (E OQ iC9 ^7^) Flison, John 1753?-17’𔄂. The discovery and settlement of Kentucke. Ann ...8217■" Fremont, John Charles, 1813-1890. Peport of the explorlmT expedition to the Pocky Ntountalns. Ann Arbor, Michigan: Uhlverslty Vlcrofllnis...O/erslze F 592 P63e) . Sources of the Mississippi and the V.’eytem Lculrlqm ’"terri- tory. Ann Arbor. ^Ich.: Ur.lversltv Microfilms

Autonomous evolution of topographic regularities in artificial neural networks.

PubMed

Gauci, Jason; Stanley, Kenneth O

2010-07-01

Looking to nature as inspiration, for at least the past 25 years, researchers in the field of neuroevolution (NE) have developed evolutionary algorithms designed specifically to evolve artificial neural networks (ANNs). Yet the ANNs evolved through NE algorithms lack the distinctive characteristics of biological brains, perhaps explaining why NE is not yet a mainstream subject of neural computation. Motivated by this gap, this letter shows that when geometry is introduced to evolved ANNs through the hypercube-based neuroevolution of augmenting topologies algorithm, they begin to acquire characteristics that indeed are reminiscent of biological brains. That is, if the neurons in evolved ANNs are situated at locations in space (i.e., if they are given coordinates), then, as experiments in evolving checkers-playing ANNs in this letter show, topographic maps with symmetries and regularities can evolve spontaneously. The ability to evolve such maps is shown in this letter to provide an important advantage in generalization. In fact, the evolved maps are sufficiently informative that their analysis yields the novel insight that the geometry of the connectivity patterns of more general players is significantly smoother and more contiguous than less general ones. Thus, the results reveal a correlation between generality and smoothness in connectivity patterns. They also hint at the intriguing possibility that as NE matures as a field, its algorithms can evolve ANNs of increasing relevance to those who study neural computation in general.

Digital image classification with the help of artificial neural network by simple histogram

PubMed Central

Dey, Pranab; Banerjee, Nirmalya; Kaur, Rajwant

2016-01-01

Background: Visual image classification is a great challenge to the cytopathologist in routine day-to-day work. Artificial neural network (ANN) may be helpful in this matter. Aims and Objectives: In this study, we have tried to classify digital images of malignant and benign cells in effusion cytology smear with the help of simple histogram data and ANN. Materials and Methods: A total of 404 digital images consisting of 168 benign cells and 236 malignant cells were selected for this study. The simple histogram data was extracted from these digital images and an ANN was constructed with the help of Neurointelligence software [Alyuda Neurointelligence 2.2 (577), Cupertino, California, USA]. The network architecture was 6-3-1. The images were classified as training set (281), validation set (63), and test set (60). The on-line backpropagation training algorithm was used for this study. Result: A total of 10,000 iterations were done to train the ANN system with the speed of 609.81/s. After the adequate training of this ANN model, the system was able to identify all 34 malignant cell images and 24 out of 26 benign cells. Conclusion: The ANN model can be used for the identification of the individual malignant cells with the help of simple histogram data. This study will be helpful in the future to identify malignant cells in unknown situations. PMID:27279679

Applications of artificial neural network in AIDS research and therapy.

PubMed

Sardari, S; Sardari, D

2002-01-01

In recent years considerable effort has been devoted to applying pattern recognition techniques to the complex task of data analysis in drug research. Artificial neural networks (ANN) methodology is a modeling method with great ability to adapt to a new situation, or control an unknown system, using data acquired in previous experiments. In this paper, a brief history of ANN and the basic concepts behind the computing, the mathematical and algorithmic formulation of each of the techniques, and their developmental background is presented. Based on the abilities of ANNs in pattern recognition and estimation of system outputs from the known inputs, the neural network can be considered as a tool for molecular data analysis and interpretation. Analysis by neural networks improves the classification accuracy, data quantification and reduces the number of analogues necessary for correct classification of biologically active compounds. Conformational analysis and quantifying the components in mixtures using NMR spectra, aqueous solubility prediction and structure-activity correlation are among the reported applications of ANN as a new modeling method. Ranging from drug design and discovery to structure and dosage form design, the potential pharmaceutical applications of the ANN methodology are significant. In the areas of clinical monitoring, utilization of molecular simulation and design of bioactive structures, ANN would make the study of the status of the health and disease possible and brings their predicted chemotherapeutic response closer to reality.

Using artificial neural networks to model aluminium based sheet forming processes and tools details

NASA Astrophysics Data System (ADS)

Mekras, N.

2017-09-01

In this paper, a methodology and a software system will be presented concerning the use of Artificial Neural Networks (ANNs) for modeling aluminium based sheet forming processes. ANNs models’ creation is based on the training of the ANNs using experimental, trial and historical data records of processes’ inputs and outputs. ANNs models are useful in cases that processes’ mathematical models are not accurate enough, are not well defined or are missing e.g. in cases of complex product shapes, new material alloys, new process requirements, micro-scale products, etc. Usually, after the design and modeling of the forming tools (die, punch, etc.) and before mass production, a set of trials takes place at the shop floor for finalizing processes and tools details concerning e.g. tools’ minimum radii, die/punch clearance, press speed, process temperature, etc. and in relation with the material type, the sheet thickness and the quality achieved from the trials. Using data from the shop floor trials and forming theory data, ANNs models can be trained and created, and can be used to estimate processes and tools final details, hence supporting efficient set-up of processes and tools before mass production starts. The proposed ANNs methodology and the respective software system are implemented within the EU H2020 project LoCoMaTech for the aluminium-based sheet forming process HFQ (solution Heat treatment, cold die Forming and Quenching).

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

PubMed Central

2014-01-01

This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD. PMID:24456676

Prediction of heat capacity of amine solutions using artificial neural network and thermodynamic models for CO2 capture processes

NASA Astrophysics Data System (ADS)

Afkhamipour, Morteza; Mofarahi, Masoud; Borhani, Tohid Nejad Ghaffar; Zanganeh, Masoud

2018-03-01

In this study, artificial neural network (ANN) and thermodynamic models were developed for prediction of the heat capacity ( C P ) of amine-based solvents. For ANN model, independent variables such as concentration, temperature, molecular weight and CO2 loading of amine were selected as the inputs of the model. The significance of the input variables of the ANN model on the C P values was investigated statistically by analyzing of correlation matrix. A thermodynamic model based on the Redlich-Kister equation was used to correlate the excess molar heat capacity ({C}_P^E) data as function of temperature. In addition, the effects of temperature and CO2 loading at different concentrations of conventional amines on the C P values were investigated. Both models were validated against experimental data and very good results were obtained between two mentioned models and experimental data of C P collected from various literatures. The AARD between ANN model results and experimental data of C P for 47 systems of amine-based solvents studied was 4.3%. For conventional amines, the AARD for ANN model and thermodynamic model in comparison with experimental data were 0.59% and 0.57%, respectively. The results showed that both ANN and Redlich-Kister models can be used as a practical tool for simulation and designing of CO2 removal processes by using amine solutions.

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations.

PubMed

León Blanco, José M; González-R, Pedro L; Arroyo García, Carmen Martina; Cózar-Bernal, María José; Calle Suárez, Marcos; Canca Ortiz, David; Rabasco Álvarez, Antonio María; González Rodríguez, María Luisa

2018-01-01

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10 000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.

A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

PubMed Central

Marto, Aminaton; Jahed Armaghani, Danial; Tonnizam Mohamad, Edy; Makhtar, Ahmad Mahir

2014-01-01

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches. PMID:25147856

Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment

NASA Astrophysics Data System (ADS)

Kothari, Mahesh; Gharde, K. D.

2015-07-01

The streamflow prediction is an essentially important aspect of any watershed modelling. The black box models (soft computing techniques) have proven to be an efficient alternative to physical (traditional) methods for simulating streamflow and sediment yield of the catchments. The present study focusses on development of models using ANN and fuzzy logic (FL) algorithm for predicting the streamflow for catchment of Savitri River Basin. The input vector to these models were daily rainfall, mean daily evaporation, mean daily temperature and lag streamflow used. In the present study, 20 years (1992-2011) rainfall and other hydrological data were considered, of which 13 years (1992-2004) was for training and rest 7 years (2005-2011) for validation of the models. The mode performance was evaluated by R, RMSE, EV, CE, and MAD statistical parameters. It was found that, ANN model performance improved with increasing input vectors. The results with fuzzy logic models predict the streamflow with single input as rainfall better in comparison to multiple input vectors. While comparing both ANN and FL algorithms for prediction of streamflow, ANN model performance is quite superior.

Classification of breast abnormalities using artificial neural network

NASA Astrophysics Data System (ADS)

Zaman, Nur Atiqah Kamarul; Rahman, Wan Eny Zarina Wan Abdul; Jumaat, Abdul Kadir; Yasiran, Siti Salmah

2015-05-01

Classification is the process of recognition, differentiation and categorizing objects into groups. Breast abnormalities are calcifications which are tumor markers that indicate the presence of cancer in the breast. The aims of this research are to classify the types of breast abnormalities using artificial neural network (ANN) classifier and to evaluate the accuracy performance using receiver operating characteristics (ROC) curve. The methods used in this research are ANN for breast abnormalities classifications and Canny edge detector as a feature extraction method. Previously the ANN classifier provides only the number of benign and malignant cases without providing information for specific cases. However in this research, the type of abnormality for each image can be obtained. The existing MIAS MiniMammographic database classified the mammogram images into three features only namely characteristic of background tissues, class of abnormality and radius of abnormality. However, in this research three other features are added-in. These three features are number of spots, area and shape of abnormalities. Lastly the performance of the ANN classifier is evaluated using ROC curve. It is found that ANN has an accuracy of 97.9% which is considered acceptable.

Smoothing strategies combined with ARIMA and neural networks to improve the forecasting of traffic accidents.

PubMed

Barba, Lida; Rodríguez, Nibaldo; Montt, Cecilia

2014-01-01

Two smoothing strategies combined with autoregressive integrated moving average (ARIMA) and autoregressive neural networks (ANNs) models to improve the forecasting of time series are presented. The strategy of forecasting is implemented using two stages. In the first stage the time series is smoothed using either, 3-point moving average smoothing, or singular value Decomposition of the Hankel matrix (HSVD). In the second stage, an ARIMA model and two ANNs for one-step-ahead time series forecasting are used. The coefficients of the first ANN are estimated through the particle swarm optimization (PSO) learning algorithm, while the coefficients of the second ANN are estimated with the resilient backpropagation (RPROP) learning algorithm. The proposed models are evaluated using a weekly time series of traffic accidents of Valparaíso, Chilean region, from 2003 to 2012. The best result is given by the combination HSVD-ARIMA, with a MAPE of 0:26%, followed by MA-ARIMA with a MAPE of 1:12%; the worst result is given by the MA-ANN based on PSO with a MAPE of 15:51%.

Comparison of response surface methodology and artificial neural network to enhance the release of reducing sugars from non-edible seed cake by autoclave assisted HCl hydrolysis.

PubMed

Shet, Vinayaka B; Palan, Anusha M; Rao, Shama U; Varun, C; Aishwarya, Uday; Raja, Selvaraj; Goveas, Louella Concepta; Vaman Rao, C; Ujwal, P

2018-02-01

In the current investigation, statistical approaches were adopted to hydrolyse non-edible seed cake (NESC) of Pongamia and optimize the hydrolysis process by response surface methodology (RSM). Through the RSM approach, the optimized conditions were found to be 1.17%v/v of HCl concentration at 54.12 min for hydrolysis. Under optimized conditions, the release of reducing sugars was found to be 53.03 g/L. The RSM data were used to train the artificial neural network (ANN) and the predictive ability of both models was compared by calculating various statistical parameters. A three-layered ANN model consisting of 2:12:1 topology was developed; the response of the ANN model indicates that it is precise when compared with the RSM model. The fit of the models was expressed with the regression coefficient R 2 , which was found to be 0.975 and 0.888, respectively, for the ANN and RSM models. This further demonstrated that the performance of ANN was better than that of RSM.

A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network.

PubMed

Marto, Aminaton; Hajihassani, Mohsen; Armaghani, Danial Jahed; Mohamad, Edy Tonnizam; Makhtar, Ahmad Mahir

2014-01-01

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.

Three-electron spin qubits

NASA Astrophysics Data System (ADS)

Russ, Maximilian; Burkard, Guido

2017-10-01

The goal of this article is to review the progress of three-electron spin qubits from their inception to the state of the art. We direct the main focus towards the exchange-only qubit (Bacon et al 2000 Phys. Rev. Lett. 85 1758-61, DiVincenzo et al 2000 Nature 408 339) and its derived versions, e.g. the resonant exchange (RX) qubit, but we also discuss other qubit implementations using three electron spins. For each three-spin qubit we describe the qubit model, the envisioned physical realization, the implementations of single-qubit operations, as well as the read-out and initialization schemes. Two-qubit gates and decoherence properties are discussed for the RX qubit and the exchange-only qubit, thereby completing the list of requirements for quantum computation for a viable candidate qubit implementation. We start by describing the full system of three electrons in a triple quantum dot, then discuss the charge-stability diagram, restricting ourselves to the relevant subsystem, introduce the qubit states, and discuss important transitions to other charge states (Russ et al 2016 Phys. Rev. B 94 165411). Introducing the various qubit implementations, we begin with the exchange-only qubit (DiVincenzo et al 2000 Nature 408 339, Laird et al 2010 Phys. Rev. B 82 075403), followed by the RX qubit (Medford et al 2013 Phys. Rev. Lett. 111 050501, Taylor et al 2013 Phys. Rev. Lett. 111 050502), the spin-charge qubit (Kyriakidis and Burkard 2007 Phys. Rev. B 75 115324), and the hybrid qubit (Shi et al 2012 Phys. Rev. Lett. 108 140503, Koh et al 2012 Phys. Rev. Lett. 109 250503, Cao et al 2016 Phys. Rev. Lett. 116 086801, Thorgrimsson et al 2016 arXiv:1611.04945). The main focus will be on the exchange-only qubit and its modification, the RX qubit, whose single-qubit operations are realized by driving the qubit at its resonant frequency in the microwave range similar to electron spin resonance. Two different types of two-qubit operations are presented for the exchange-only qubits which can be divided into short-ranged and long-ranged interactions. Both of these interaction types are expected to be necessary in a large-scale quantum computer. The short-ranged interactions use the exchange coupling by placing qubits next to each other and applying exchange-pulses (DiVincenzo et al 2000 Nature 408 339, Fong and Wandzura 2011 Quantum Inf. Comput. 11 1003, Setiawan et al 2014 Phys. Rev. B 89 085314, Zeuch et al 2014 Phys. Rev. B 90 045306, Doherty and Wardrop 2013 Phys. Rev. Lett. 111 050503, Shim and Tahan 2016 Phys. Rev. B 93 121410), while the long-ranged interactions use the photons of a superconducting microwave cavity as a mediator in order to couple two qubits over long distances (Russ and Burkard 2015 Phys. Rev. B 92 205412, Srinivasa et al 2016 Phys. Rev. B 94 205421). The nature of the three-electron qubit states each having the same total spin and total spin in z-direction (same Zeeman energy) provides a natural protection against several sources of noise (DiVincenzo et al 2000 Nature 408 339, Taylor et al 2013 Phys. Rev. Lett. 111 050502, Kempe et al 2001 Phys. Rev. A 63 042307, Russ and Burkard 2015 Phys. Rev. B 91 235411). The price to pay for this advantage is an increase in gate complexity. We also take into account the decoherence of the qubit through the influence of magnetic noise (Ladd 2012 Phys. Rev. B 86 125408, Mehl and DiVincenzo 2013 Phys. Rev. B 87 195309, Hung et al 2014 Phys. Rev. B 90 045308), in particular dephasing due to the presence of nuclear spins, as well as dephasing due to charge noise (Medford et al 2013 Phys. Rev. Lett. 111 050501, Taylor et al 2013 Phys. Rev. Lett. 111 050502, Shim and Tahan 2016 Phys. Rev. B 93 121410, Russ and Burkard 2015 Phys. Rev. B 91 235411, Fei et al 2015 Phys. Rev. B 91 205434), fluctuations of the energy levels on each dot due to noisy gate voltages or the environment. Several techniques are discussed which partly decouple the qubit from magnetic noise (Setiawan et al 2014 Phys. Rev. B 89 085314, West and Fong 2012 New J. Phys. 14 083002, Rohling and Burkard 2016 Phys. Rev. B 93 205434) while for charge noise it is shown that it is favorable to operate the qubit on the so-called ‘(double) sweet spots’ (Taylor et al 2013 Phys. Rev. Lett. 111 050502, Shim and Tahan 2016 Phys. Rev. B 93 121410, Russ and Burkard 2015 Phys. Rev. B 91 235411, Fei et al 2015 Phys. Rev. B 91 205434, Malinowski et al 2017 arXiv: 1704.01298), which are least susceptible to noise, thus providing a longer lifetime of the qubit.

Modeling and forecasting of KLCI weekly return using WT-ANN integrated model

NASA Astrophysics Data System (ADS)

Liew, Wei-Thong; Liong, Choong-Yeun; Hussain, Saiful Izzuan; Isa, Zaidi

2013-04-01

The forecasting of weekly return is one of the most challenging tasks in investment since the time series are volatile and non-stationary. In this study, an integrated model of wavelet transform and artificial neural network, WT-ANN is studied for modeling and forecasting of KLCI weekly return. First, the WT is applied to decompose the weekly return time series in order to eliminate noise. Then, a mathematical model of the time series is constructed using the ANN. The performance of the suggested model will be evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The result shows that the WT-ANN model can be considered as a feasible and powerful model for time series modeling and prediction.

Science of the science, drug discovery and artificial neural networks.

PubMed

Patel, Jigneshkumar

2013-03-01

Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

Using artificial neural networks (ANN) for open-loop tomography

NASA Astrophysics Data System (ADS)

Osborn, James; De Cos Juez, Francisco Javier; Guzman, Dani; Butterley, Timothy; Myers, Richard; Guesalaga, Andres; Laine, Jesus

2011-09-01

The next generation of adaptive optics (AO) systems require tomographic techniques in order to correct for atmospheric turbulence along lines of sight separated from the guide stars. Multi-object adaptive optics (MOAO) is one such technique. Here, we present a method which uses an artificial neural network (ANN) to reconstruct the target phase given off-axis references sources. This method does not require any input of the turbulence profile and is therefore less susceptible to changing conditions than some existing methods. We compare our ANN method with a standard least squares type matrix multiplication method (MVM) in simulation and find that the tomographic error is similar to the MVM method. In changing conditions the tomographic error increases for MVM but remains constant with the ANN model and no large matrix inversions are required.

Far Infrared, Magnetic and Electronic Studies of High Tc Superconducting Materials

DTIC Science & Technology

1992-09-30

Phys. Rev. Left. 63, 2421(1989). 8. K. H. Fischer and T. Nattermann, Phys. Rev. .43, 10372(1991). 9. R. E. Walstedt, R. F. Bell, and D. B. Mitzi , Phys...Duran, J. Yazyi, F. dela Cruz, D. J. Bishop, D. B. Mitzi , and A. Kapitulnik, Phys. Rev. B 44, 17737(1991). 14. Y. Yeshurun and A. P. Malozemoff, Phys

Rudolf Hermann, wind tunnels and aerodynamics

NASA Astrophysics Data System (ADS)

Lundquist, Charles A.; Coleman, Anne M.

2008-04-01

Rudolf Hermann was born on December 15, 1904 in Leipzig, Germany. He studied at the University of Leipzig and at the Aachen Institute of Technology. His involvement with wind tunnels began in 1934 when Professor Carl Wieselsberger engaged him to work at Aachen on the development of a supersonic wind tunnel. On January 6, 1936, Dr. Wernher von Braun visited Dr. Hermann to arrange for use of the Aachen supersonic wind tunnel for Army problems. On April 1, 1937, Dr. Hermann became Director of the Supersonic Wind Tunnel at the Army installation at Peenemunde. Results from the Aachen and Peenemunde wind tunnels were crucial in achieving aerodynamic stability for the A-4 rocket, later designated as the V-2. Plans to build a Mach 10 'hypersonic' wind tunnel facility at Kochel were accelerated after the Allied air raid on Peenemunde on August 17, 1943. Dr. Hermann was director of the new facility. Ignoring destruction orders from Hitler as WWII approached an end in Europe, Dr. Hermann and his associates hid documents and preserved wind tunnel components that were acquired by the advancing American forces. Dr. Hermann became a consultant to the Air Force at its Wright Field in November 1945. In 1951, he was named professor of Aeronautical Engineering at the University of Minnesota. In 1962, Dr. Hermann became the first Director of the Research Institute at the University of Alabama in Huntsville (UAH), a position he held until he retired in 1970.

[Johann Sebastian Bach: life, oeuvre and his significance for the cardiology].

PubMed

Trappe, H-J

2014-12-01

Johann Sebastian Bach was born on 1685 in Eisenach. By the time he turned 10, Bach found himself an orphan after the death of both of his parents. After working in Weimar, Arnstadt, Mühlhausen, and Köthen Bach signed a contract to become the new organist and teacher at St. Thomas Church Leipzig in 1723 and stayed there until his death. In 1749, Bach tried to fix his failing sight by having surgery the following year, but the operation ended up leaving him completely blind. Few months later, Bach suffered a stroke. He died in Leipzig on July 28, 1750. In recent years, there were some questions whether music of different styles can directly alter cardiovascular parameters, particularly by using Bach's music. In some studies it has been shown that cardiovascular parameters (blood pressure, heart rate) are influenced by music. Listening to classic music (Bach) leads to positive erffects, also music by Italian composters. In contrast, "modern" music, vocal music or songs had no positive effects on cardiovascular parameters. In addition, positive effects on cardiovascular parameters and behavioural patters have been shown in an animal study recently, by Bach's music. Recent studies showed clearly that music influences cardiovascular parameters. It is obvious that classical music (Bach) has benefitial effects, both in humans and in animals. Therefore, the music of the "Thomaskantor" will improve both, quality of life and cardiovascular health. © Georg Thieme Verlag KG Stuttgart · New York.

Risk assessment of environmentally influenced airway diseases based on time-series analysis.

PubMed

Herbarth, O

1995-09-01

Threshold values are of prime importance in providing a sound basis for public health decisions. A key issue is determining threshold or maximum exposure values for pollutants and assessing their potential health risks. Environmental epidemiology could be instrumental in assessing these levels, especially since the assessment of ambient exposures involves relatively low concentrations of pollutants. This paper presents a statistical method that allows the determination of threshold values as well as the assessment of the associated risk using a retrospective, longitudinal study design with a prospective follow-up. Morbidity data were analyzed using the Fourier method, a time-series analysis that is based on the assumption of a high temporal resolution of the data. This method eliminates time-dependent responses like temporal inhomogeneity and pseudocorrelation. The frequency of calls for respiratory distress conditions to the regional Mobile Medical Emergency Service (MMES) in the city of Leipzig were investigated. The entire population of Leipzig served as a pool for data collection. In addition to the collection of morbidity data, air pollution measurements were taken every 30 min for the entire study period using sulfur dioxide as the regional indicator variable. This approach allowed the calculation of a dose-response curve for respiratory diseases and air pollution indices in children and adults. Significantly higher morbidities were observed above a 24-hr mean value of 0.6 mg SO2/m3 air for children and 0.8 mg SO2/m3 for adults.(ABSTRACT TRUNCATED AT 250 WORDS)

[Gender-specific predictors of institutionalisation in the elderly--results of the Leipzig longitudinal study of the aged (LEILA 75+)].

PubMed

Luppa, Melanie; Gentzsch, Katrin; Angermeyer, Matthias C; Weyerer, Siegfried; König, Hans-Helmut; Riedel-Heller, Steffi G

2011-05-01

Especially given the different socialization and life conditions of men and women, it could not be assumed that factors leading to nursing home admission (NHA) can be equally applied to both genders. We aimed to determine gender-specific predictors of NHA. Data were derived from the Leipzig Longitudinal Study of the Aged, a population-based study of individuals aged 75 years and older. 1,058 older adults were interviewed six times on average every 1.4 years. Sociodemographic, clinical, and psychometric variables were obtained. Cox proportional hazards regression was used to determine predictors of NHA. 10.3 % of men and 19.5 % of women (p < 0.001) were admitted to nursing home during the study period. The mean time to nursing home was 7.2 years for men and 6.8 years for women. Characteristics associated with a shorter time to NHA were increased age for men and women; cognitive impairment, poor self-rated health status, and less than two specialist's visits in the preceding 12 months for women, and being unmarried, moderate educational status, and hospitalization in the preceding 12 months were predictors of NHA for men. Gender differences in prediction of NHA do actually exist. The inclusion of gender-specific factors in design and application of interventions to support individuals at home and delay or prevent NHA appears to be warranted. © Georg Thieme Verlag KG Stuttgart · New York.

A quantification method for heat-decomposable methylglyoxal oligomers and its application on 1,3,5-trimethylbenzene SOA

NASA Astrophysics Data System (ADS)

Rodigast, Maria; Mutzel, Anke; Herrmann, Hartmut

2017-03-01

Methylglyoxal forms oligomeric compounds in the atmospheric aqueous particle phase, which could establish a significant contribution to the formation of aqueous secondary organic aerosol (aqSOA). Thus far, no suitable method for the quantification of methylglyoxal oligomers is available despite the great effort spent for structure elucidation. In the present study a simplified method was developed to quantify heat-decomposable methylglyoxal oligomers as a sum parameter. The method is based on the thermal decomposition of oligomers into methylglyoxal monomers. Formed methylglyoxal monomers were detected using PFBHA (o-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine hydrochloride) derivatisation and gas chromatography-mass spectrometry (GC/MS) analysis. The method development was focused on the heating time (varied between 15 and 48 h), pH during the heating process (pH = 1-7), and heating temperature (50, 100 °C). The optimised values of these method parameters are presented. The developed method was applied to quantify heat-decomposable methylglyoxal oligomers formed during the OH-radical oxidation of 1,3,5-trimethylbenzene (TMB) in the Leipzig aerosol chamber (LEipziger AerosolKammer, LEAK). Oligomer formation was investigated as a function of seed particle acidity and relative humidity. A fraction of heat-decomposable methylglyoxal oligomers of up to 8 % in the produced organic particle mass was found, highlighting the importance of those oligomers formed solely by methylglyoxal for SOA formation. Overall, the present study provides a new and suitable method for quantification of heat-decomposable methylglyoxal oligomers in the aqueous particle phase.

Seasonal cycle of indoor-VOCs: comparison of apartments and cities

NASA Astrophysics Data System (ADS)

Schlink, U.; Rehwagen, M.; Damm, M.; Richter, M.; Borte, M.; Herbarth, O.

On the basis of 2103 measurements of volatile organic compounds (VOCs) in indoor air we study the intensity of a seasonal pattern. The data are representative for the German population and were gathered in different cities (Leipzig, München, Köln), in rooms of different type (children's, living, sleeping rooms, and other rooms), and in households of smokers and non-smokers. In addition to the randomly selected different apartments that were sampled each month, we repeatedly measured in a fixed set of 10 apartments. The analysis comprised concentrations of 30 VOCs belonging to the groups of alkanes, cycloalkanes, aromatics, volatile halogenated hydrocarbons, and terpenes. The annual cycle for total VOC concentrations was observed at every site. Seasonality proved to be the most dominant pattern, but it may be modified by further factors, such as the city, the considered VOC component, and the type of the considered room. Highest concentrations occurred during the winter months and amount to approximately three to four times the summer burden. As seasonality may bias the results of health effect studies we fit a seasonal model to our measurements and develop a procedure for seasonal adjustment, which enables to roughly estimate the annual peak concentration utilizing one monthly observation. The seasonal pattern proved to be a general feature of indoor VOC concentrations and, therefore, this adjustment procedure may be generally applicable. For Leipzig, München, and Köln we present site-specific adjustment factors for indoor concentrations of aromatics, terpenes, and alkanes.

Fear of success among business students.

PubMed

Rothman, M

1996-06-01

The concept of "Fear of Success" was measured with 352 male and female business students using the prompt, After first term finals, Ann(John) finds her(him)self at the top of her(his) Medical/Nursing school class. Analysis indicated a greater frequency of fear-of-success imagery among men than women and in particular to the John in Medical school and Ann in Nursing school cues. In addition, the Ann cue and the Medical school cue generated more fear-of-success responses among men than women.

Design of a MATLAB(registered trademark) Image Comparison and Analysis Tool for Augmentation of the Results of the Ann Arbor Distortion Test

DTIC Science & Technology

2016-06-25

The equipment used in this procedure includes: Ann Arbor distortion tester with 50-line grating reticule, IQeye 720 digital video camera with 12...and import them into MATLAB. In order to digitally capture images of the distortion in an optical sample, an IQeye 720 video camera with a 12... video camera and Ann Arbor distortion tester. Figure 8. Computer interface for capturing images seen by IQeye 720 camera. Once an image was

Remote quantification of phycocyanin in potable water sources through an adaptive model

NASA Astrophysics Data System (ADS)

Song, Kaishan; Li, Lin; Tedesco, Lenore P.; Li, Shuai; Hall, Bob E.; Du, Jia

2014-09-01

Cyanobacterial blooms in water supply sources in both central Indiana USA (CIN) and South Australia (SA) are a cause of great concerns for toxin production and water quality deterioration. Remote sensing provides an effective approach for quick assessment of cyanobacteria through quantification of phycocyanin (PC) concentration. In total, 363 samples spanning a large variation of optically active constituents (OACs) in CIN and SA waters were collected during 24 field surveys. Concurrently, remote sensing reflectance spectra (Rrs) were measured. A partial least squares-artificial neural network (PLS-ANN) model, artificial neural network (ANN) and three-band model (TBM) were developed or tuned by relating the Rrs with PC concentration. Our results indicate that the PLS-ANN model outperformed the ANN and TBM with both the original spectra and simulated ESA/Sentinel-3/Ocean and Land Color Instrument (OLCI) and EO-1/Hyperion spectra. The PLS-ANN model resulted in a high coefficient of determination (R2) for CIN dataset (R2 = 0.92, R: 0.3-220.7 μg/L) and SA (R2 = 0.98, R: 0.2-13.2 μg/L). In comparison, the TBM model yielded an R2 = 0.77 and 0.94 for the CIN and SA datasets, respectively; while the ANN obtained an intermediate modeling accuracy (CIN: R2 = 0.86; SA: R2 = 0.95). Applying the simulated OLCI and Hyperion aggregated datasets, the PLS-ANN model still achieved good performance (OLCI: R2 = 0.84; Hyperion: R2 = 0.90); the TBM also presented acceptable performance for PC estimations (OLCI: R2 = 0.65, Hyperion: R2 = 0.70). Based on the results, the PLS-ANN is an effective modeling approach for the quantification of PC in productive water supplies based on its effectiveness in solving the non-linearity of PC with other OACs. Furthermore, our investigation indicates that the ratio of inorganic suspended matter (ISM) to PC concentration has close relationship to modeling relative errors (CIN: R2 = 0.81; SA: R2 = 0.92), indicating that ISM concentration exert significant impact on PC estimation accuracy.

[Prediction of postoperative nausea and vomiting using an artificial neural network].

PubMed

Traeger, M; Eberhart, A; Geldner, G; Morin, A M; Putzke, C; Wulf, H; Eberhart, L H J

2003-12-01

Postoperative nausea and vomiting (PONV) are still frequent side-effects after general anaesthesia. These unpleasant symptoms for the patients can be sufficiently reduced using a multimodal antiemetic approach. However, these efforts should be restricted to risk patients for PONV. Thus, predictive models are required to identify these patients before surgery. So far all risk scores to predict PONV are based on results of logistic regression analysis. Artificial neural networks (ANN) can also be used for prediction since they can take into account complex and non-linear relationships between predictive variables and the dependent item. This study presents the development of an ANN to predict PONV and compares its performance with two established simplified risk scores (Apfel's and Koivuranta's scores). The development of the ANN was based on data from 1,764 patients undergoing elective surgical procedures under balanced anaesthesia. The ANN was trained with 1,364 datasets and a further 400 were used for supervising the learning process. One of the 49 ANNs showing the best predictive performance was compared with the established risk scores with respect to practicability, discrimination (by means of the area under a receiver operating characteristics curve) and calibration properties (by means of a weighted linear regression between the predicted and the actual incidences of PONV). The ANN tested showed a statistically significant ( p<0.0001) and clinically relevant higher discriminating power (0.74; 95% confidence interval: 0.70-0.78) than the Apfel score (0.66; 95% CI: 0.61-0.71) or Koivuranta's score (0.69; 95% CI: 0.65-0.74). Furthermore, the agreement between the actual incidences of PONV and those predicted by the ANN was also better and near to an ideal fit, represented by the equation y=1.0x+0. The equations for the calibration curves were: KNN y=1.11x+0, Apfel y=0.71x+1, Koivuranta 0.86x-5. The improved predictive accuracy achieved by the ANN is clinically relevant. However, the disadvantages of this system prevail because a computer is required for risk calculation. Thus, we still recommend the use of one of the simplified risk scores for clinical practice.

Estimation of seismic quality factor: Artificial neural networks and current approaches

NASA Astrophysics Data System (ADS)

Yıldırım, Eray; Saatçılar, Ruhi; Ergintav, Semih

2017-01-01

The aims of this study are to estimate soil attenuation using alternatives to traditional methods, to compare results of using these methods, and to examine soil properties using the estimated results. The performances of all methods, amplitude decay, spectral ratio, Wiener filter, and artificial neural network (ANN) methods, are examined on field and synthetic data with noise and without noise. High-resolution seismic reflection field data from Yeniköy (Arnavutköy, İstanbul) was used as field data, and 424 estimations of Q values were made for each method (1,696 total). While statistical tests on synthetic and field data are quite close to the Q value estimation results of ANN, Wiener filter, and spectral ratio methods, the amplitude decay methods showed a higher estimation error. According to previous geological and geophysical studies in this area, the soil is water-saturated, quite weak, consisting of clay and sandy units, and, because of current and past landslides in the study area and its vicinity, researchers reported heterogeneity in the soil. Under the same physical conditions, Q value calculated on field data can be expected to be 7.9 and 13.6. ANN models with various structures, training algorithm, input, and number of neurons are investigated. A total of 480 ANN models were generated consisting of 60 models for noise-free synthetic data, 360 models for different noise content synthetic data and 60 models to apply to the data collected in the field. The models were tested to determine the most appropriate structure and training algorithm. In the final ANN, the input vectors consisted of the difference of the width, energy, and distance of seismic traces, and the output was Q value. Success rate of both ANN methods with noise-free and noisy synthetic data were higher than the other three methods. Also according to the statistical tests on estimated Q value from field data, the method showed results that are more suitable. The Q value can be estimated practically and quickly by processing the traces with the recommended ANN model. Consequently, the ANN method could be used for estimating Q value from seismic data.

Multiscale Bayesian neural networks for soil water content estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.; Springer, Everett P.

2008-08-01

Artificial neural networks (ANN) have been used for some time now to estimate soil hydraulic parameters from other available or more easily measurable soil properties. However, most such uses of ANNs as pedotransfer functions (PTFs) have been at matching spatial scales (1:1) of inputs and outputs. This approach assumes that the outputs are only required at the same scale as the input data. Unfortunately, this is rarely true. Different hydrologic, hydroclimatic, and contaminant transport models require soil hydraulic parameter data at different spatial scales, depending upon their grid sizes. While conventional (deterministic) ANNs have been traditionally used in these studies, the use of Bayesian training of ANNs is a more recent development. In this paper, we develop a Bayesian framework to derive soil water retention function including its uncertainty at the point or local scale using PTFs trained with coarser-scale Soil Survey Geographic (SSURGO)-based soil data. The approach includes an ANN trained with Bayesian techniques as a PTF tool with training and validation data collected across spatial extents (scales) in two different regions in the United States. The two study areas include the Las Cruces Trench site in the Rio Grande basin of New Mexico, and the Southern Great Plains 1997 (SGP97) hydrology experimental region in Oklahoma. Each region-specific Bayesian ANN is trained using soil texture and bulk density data from the SSURGO database (scale 1:24,000), and predictions of the soil water contents at different pressure heads with point scale data (1:1) inputs are made. The resulting outputs are corrected for bias using both linear and nonlinear correction techniques. The results show good agreement between the soil water content values measured at the point scale and those predicted by the Bayesian ANN-based PTFs for both the study sites. Overall, Bayesian ANNs coupled with nonlinear bias correction are found to be very suitable tools for deriving soil hydraulic parameters at the local/fine scale from soil physical properties at coarser-scale and across different spatial extents. This approach could potentially be used for soil hydraulic properties estimation and downscaling.

Assessment of trace metal levels in size-resolved particulate matter in the area of Leipzig

NASA Astrophysics Data System (ADS)

Fomba, Khanneh Wadinga; van Pinxteren, Dominik; Müller, Konrad; Spindler, Gerald; Herrmann, Hartmut

2018-03-01

Size-resolved trace metal concentrations at four sites in Leipzig (Germany) and its surrounding were assessed between the winter of 2013 and the summer of 2015. The measurements were performed in parallel at; traffic dominated (Leipzig - Mitte, LMI), traffic and residential dominated (Eisenbahnstrasse, EIB), urban background (TROPOS, TRO) and regional background (Melpitz, MEL) sites. In total, 19 trace metals, i.e. K, Ca, Ti, Mn, Fe, Cu, Zn, As, Se, Ba, V, Pb, Ni, Cr, Sr, Sn, Sb, Co and Rb were analysed using total reflection x-ray fluorescence (TXRF). The major metals were Fe, K and Ca with concentrations ranging between; 31-440 ng/m3, 42-153 ng/m3 and 24-322 ng/m3, respectively, while the trace metals with the lowest concentrations were Co, Rb and Se with concentrations of; < 0.3 ng/m3, <0.5 ng/m3 and 0.5-0.7 ng/m3, respectively. PM10 trace metal concentrations during easterly air mass inflow especially at the background sites were in average 70% higher in the winter and 30% higher in the summer in comparison to westerly air mass inflow. Traffic at LMI contributed to about 75% of Cr, Ba, Cu, Sb, Sn, Ca, Co, Mn, Fe and Ti concentrations while regional activities contributed to more than 70% of K, Rb, Pb, Se, As and V concentrations. Traffic dominated trace metals were often observed in the coarse mode while the regional background dominated trace metals were often observed in the fine mode. Trace metal sources were related to crustal matter and road dust re-suspension for metals such as Ca, Fe, Co, Sr, and Ti, brake and tire wear (Cu, Sb, Ba, Fe, Zn, Pb), biomass burning (K, Rb), oil and coal combustion (V, Zn, As, Pb). Crustal matter contributed 5-12% in winter and 8-19% in summer of the PM10 mass. Using Cu and Zn as markers for brake and tire wear, respectively, the estimated brake and tire wear contributions to the PM10 mass were 0.1-0.8% and 1.7-2.9%, respectively. The higher contributions were observed at the traffic sites while the lower contributions were observed at the regional background site. In total, non-exhaust emissions could account for about 10-22% of the PM10 mass in the summer and about 7-15% of the PM10 mass in the winter.

Characterization of Acremonium and Isaria ice nuclei

NASA Astrophysics Data System (ADS)

Pummer, Bernhard G.; Pöschl, Ulrich; Fröhlich-Nowoisky, Janine

2014-05-01

Until recently, the only known fungal ice nuclei (IN) were a few exponents of lichen mycobionts and Fusarium spp. [Kieft and Ruscetti 1990, Pouleur et al. 1992, Hasegawa et al. 1994, Tsumuki et al. 1995], as well as two strains of mold [Jayaweera and Flanagan 1982]. Other investigated species did not show any IN activity [Pouleur et al. 1992, Iannone et al. 2011, Pummer et al. 2013]. In the last few years, IN-activity has been discovered in some rust and smut fungi [Morris et al. 2013, Haga et al. 2013], Acremonium implicatum (Acr.) and Isaria farinosa (Isa.) [Huffman et al. 2013] and a handful of other airborne and soil fungi [unpublished data]. We started characterizing the IN of Acr. and Isa.: Like other non-bacterial biological IN, they can be easily separated from the cells in aqueous suspension, and keep their activity. The IN-active aqueous suspensions were processed by filtration (5 μm, 0.1 μm, 300 kDa, 100 kDa) and exposure to heat (60° C) or guanidinium chloride (6 M). The IN activity of the processed samples was measured by a freezing assay of droplets, as described by Pummer et al. [2013]. Via the Vali formula, we calculated the amount of IN per gram of mycelium, which is higher than 105 g-1. The initial freezing temperature was -4° C for Isaria and -8° C for Acremonium IN. Both were completely knocked out by 60° C or guanidinium chloride. The Acremonium IN are in a mass range between 100 and 300 kDa. The Isaria IN seem to be either a bit larger, or more attached to larger particles, since not all of them pass through the 300-kDa-filter. It is likely that both of these new IN are proteinaceous like the IN of Fusarium spp. and lichen mycobionts, which belong to the Ascomycota phylum. Since the Isaria IN show a high onset freezing temperature and are rather large for single molecules, they might be agglomerates. Haga D.I. et al. (2013) J. Geophys. Res.: Atm. 118, 7260-7272 Hasegawa Y. et al. (1994) Biosci. Biotech. Biochem. 58, 2273-2274 Huffman A.J. et al. (2013) Atmos. Chem. Phys. 13, 6151-6164 Iannone R. et al. (2011) Atmos. Chem. Phys. 11, 1191-1201 Jayaweera K. and Flanagan P. (1982) Geophys. Res. Lett. 9, 94-97 Kieft T.L. and Ruscetti T. (1990) J. Bacteriol. 172, 3519-3523 Morris C.E. et al. (2013) Atmos. Chem. Phys. 13, 4223-4233 Pouleur S. et al. (1992) Appl. Environ. Microbiol. 58, 2960-2964 Pummer B. et al. (2013) Biogeosci. 10, 8083-8091 Tsumuki H. et al. (1995) Ann. Phytopathol. Soc. Jpn. 61, 334-339

Investigation of the High, Finite n Ballooning Mode Limit for Compact Quasi-Axially Symmetric Stellarators

NASA Astrophysics Data System (ADS)

Redi, Martha; Canik, John; Fredrickson, E.; Fu, G.; Nuehrenberg, C.; Boozer, A. H.

2000-10-01

The standard ballooning-mode beta limit comes from an infinite-n, radially local, ideal magnetohydrodynamic (MHD) calculation. Finite-n ballooning modes have been observed in tokamak plasmas [1]. Investigations of optimized quasiaxially symmetric stellarators with three dimensional, global, ideal MHD codes have recently shown good stability for the external kink, ``vertical" and infinite-n ballooning modes [2,3]. However, infinite-n ballooning stability may be too restrictive, due to its sensitivity to features in the local shear and curvature. The CAS3D [4] code is being used to compare the stability of the high-n ballooning modes to the infinite-n calculations from TERPSICHORE [5]. [1] E. Fredrickson, et al. Phys. Plas. 3 (1996) 2620. [2] G. Fu, Phys. Plas. 7 (2000)1079; Phys. Plas. 7 (2000) 1809. M. Redi, et al. Phys. Plas 7 (2000)1911. [3] A. Reiman, et al., Plas. Phys. Cont. Fus. 41 (1999) B273. [4] C. Nuehrenberg, Phys. Plas. 6 (1999) 275. C. Nuehrenberg, Phys. Plas. 3 (1996) 2401. C. Schwab, Phys. Fluids B5 (1993) 3195. [5] W. A. Cooper, Phys. Plas. 3 (1996) 275.

77 FR 55454 - Plumas County Resource Advisory Committee

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-10

... Supervisors Office, 159 Lawrence Street, Quincy, CA 95971. Please call ahead to Lee Anne Schramel Taylor at...: Lee Anne Schramel Taylor, RAC Coordinator, Plumas National Forest, (530) 283-7850, TTY 711, eataylor...

1. JoAnn SieburgBaker, Photographer, September 1977. OVERALL VIEW OF ROUNDHOUSE. ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

1. JoAnn Sieburg-Baker, Photographer, September 1977. OVERALL VIEW OF ROUNDHOUSE. - Southern Railway Company, Spencer Shops, Salisbury Avenue between Third and Eight Streets, Spencer, Rowan County, NC

10. JoAnn SieburgBaker, Photographer, September 1977. INTERIOR VIEW OF BACK ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

10. JoAnn Sieburg-Baker, Photographer, September 1977. INTERIOR VIEW OF BACK SHOP. - Southern Railway Company, Spencer Shops, Salisbury Avenue between Third and Eight Streets, Spencer, Rowan County, NC

Recognition of an obstacle in a flow using artificial neural networks.

PubMed

Carrillo, Mauricio; Que, Ulices; González, José A; López, Carlos

2017-08-01

In this work a series of artificial neural networks (ANNs) has been developed with the capacity to estimate the size and location of an obstacle obstructing the flow in a pipe. The ANNs learn the size and location of the obstacle by reading the profiles of the dynamic pressure q or the x component of the velocity v_{x} of the fluid at a certain distance from the obstacle. Data to train the ANN were generated using numerical simulations with a two-dimensional lattice Boltzmann code. We analyzed various cases varying both the diameter and the position of the obstacle on the y axis, obtaining good estimations using the R^{2} coefficient for the cases under study. Although the ANN showed problems with the classification of very small obstacles, the general results show a very good capacity for prediction.

Neural network processing of microbial fuel cell signals for the identification of chemicals present in water.

PubMed

Feng, Yinghua; Barr, William; Harper, W F

2013-05-15

Biosensing is emerging as an important element of water quality monitoring. This research demonstrated that microbial fuel cell (MFC)-based biosensing can be integrated with artificial neural networks (ANNs) to identify specific chemicals present in water samples. The non-fermentable substrates, acetate and butyrate, induced peak areas (PA) and peak heights (PH) that were generally larger than those caused by the injection of fermentable substrates, glucose and corn starch. The ANN successfully identified peaks associated with these four chemicals under a variety of experimental conditions and for two MFCs that had different levels of sensitivity. ANNs that employ the hyperbolic tangent sigmoid transfer function performed better than those using non-continuous transfer functions. ANNs should be integrated into water quality monitoring efforts for smart biosensing. Published by Elsevier Ltd.

Neural network modelling and dynamical system theory: are they relevant to study the governing dynamics of association football players?

PubMed

Dutt-Mazumder, Aviroop; Button, Chris; Robins, Anthony; Bartlett, Roger

2011-12-01

Recent studies have explored the organization of player movements in team sports using a range of statistical tools. However, the factors that best explain the performance of association football teams remain elusive. Arguably, this is due to the high-dimensional behavioural outputs that illustrate the complex, evolving configurations typical of team games. According to dynamical system analysts, movement patterns in team sports exhibit nonlinear self-organizing features. Nonlinear processing tools (i.e. Artificial Neural Networks; ANNs) are becoming increasingly popular to investigate the coordination of participants in sports competitions. ANNs are well suited to describing high-dimensional data sets with nonlinear attributes, however, limited information concerning the processes required to apply ANNs exists. This review investigates the relative value of various ANN learning approaches used in sports performance analysis of team sports focusing on potential applications for association football. Sixty-two research sources were summarized and reviewed from electronic literature search engines such as SPORTDiscus, Google Scholar, IEEE Xplore, Scirus, ScienceDirect and Elsevier. Typical ANN learning algorithms can be adapted to perform pattern recognition and pattern classification. Particularly, dimensionality reduction by a Kohonen feature map (KFM) can compress chaotic high-dimensional datasets into low-dimensional relevant information. Such information would be useful for developing effective training drills that should enhance self-organizing coordination among players. We conclude that ANN-based qualitative analysis is a promising approach to understand the dynamical attributes of association football players.

Predicting the Fine Particle Fraction of Dry Powder Inhalers Using Artificial Neural Networks.

PubMed

Muddle, Joanna; Kirton, Stewart B; Parisini, Irene; Muddle, Andrew; Murnane, Darragh; Ali, Jogoth; Brown, Marc; Page, Clive; Forbes, Ben

2017-01-01

Dry powder inhalers are increasingly popular for delivering drugs to the lungs for the treatment of respiratory diseases, but are complex products with multivariate performance determinants. Heuristic product development guided by in vitro aerosol performance testing is a costly and time-consuming process. This study investigated the feasibility of using artificial neural networks (ANNs) to predict fine particle fraction (FPF) based on formulation device variables. Thirty-one ANN architectures were evaluated for their ability to predict experimentally determined FPF for a self-consistent dataset containing salmeterol xinafoate and salbutamol sulfate dry powder inhalers (237 experimental observations). Principal component analysis was used to identify inputs that significantly affected FPF. Orthogonal arrays (OAs) were used to design ANN architectures, optimized using the Taguchi method. The primary OA ANN r 2 values ranged between 0.46 and 0.90 and the secondary OA increased the r 2  values (0.53-0.93). The optimum ANN (9-4-1 architecture, average r 2 0.92 ± 0.02) included active pharmaceutical ingredient, formulation, and device inputs identified by principal component analysis, which reflected the recognized importance and interdependency of these factors for orally inhaled product performance. The Taguchi method was effective at identifying successful architecture with the potential for development as a useful generic inhaler ANN model, although this would require much larger datasets and more variable inputs. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Scatter and cross-talk corrections in simultaneous Tc-99m/I-123 brain SPECT using constrained factor analysis and artificial neural networks

NASA Astrophysics Data System (ADS)

Fakhri, G. El; Maksud, P.; Kijewski, M. F.; Haberi, M. O.; Todd-Pokropek, A.; Aurengo, A.; Moore, S. C.

2000-08-01

Simultaneous imaging of Tc-99m and I-123 would have a high clinical potential in the assessment of brain perfusion (Tc-99m) and neurotransmission (I-123) but is hindered by cross-talk between the two radionuclides. Monte Carlo simulations of 15 different dual-isotope studies were performed using a digital brain phantom. Several physiologic Tc-99m and I-123 uptake patterns were modeled in the brain structures. Two methods were considered to correct for cross-talk from both scattered and unscattered photons: constrained spectral factor analysis (SFA) and artificial neural networks (ANN). The accuracy and precision of reconstructed pixel values within several brain structures were compared to those obtained with an energy windowing method (WSA). In I-123 images, mean bias was close to 10% in all structures for SFA and ANN and between 14% (in the caudate nucleus) and 25% (in the cerebellum) for WSA. Tc-99m activity was overestimated by 35% in the cortex and 53% in the caudate nucleus with WSA, but by less than 9% in all structures with SFA and ANN. SFA and ANN performed well even in the presence of high-energy I-123 photons. The accuracy was greatly improved by incorporating the contamination into the SFA model or in the learning phase for ANN. SFA and ANN are promising approaches to correct for cross-talk in simultaneous Tc-99m/I-123 SPECT.

Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks.

PubMed

León-Roque, Noemí; Abderrahim, Mohamed; Nuñez-Alejos, Luis; Arribas, Silvia M; Condezo-Hoyos, Luis

2016-12-01

Several procedures are currently used to assess fermentation index (FI) of cocoa beans (Theobroma cacao L.) for quality control. However, all of them present several drawbacks. The aim of the present work was to develop and validate a simple image based quantitative procedure, using color measurement and artificial neural network (ANNs). ANN models based on color measurements were tested to predict fermentation index (FI) of fermented cocoa beans. The RGB values were measured from surface and center region of fermented beans in images obtained by camera and desktop scanner. The FI was defined as the ratio of total free amino acids in fermented versus non-fermented samples. The ANN model that included RGB color measurement of fermented cocoa surface and R/G ratio in cocoa bean of alkaline extracts was able to predict FI with no statistical difference compared with the experimental values. Performance of the ANN model was evaluated by the coefficient of determination, Bland-Altman plot and Passing-Bablok regression analyses. Moreover, in fermented beans, total sugar content and titratable acidity showed a similar pattern to the total free amino acid predicted through the color based ANN model. The results of the present work demonstrate that the proposed ANN model can be adopted as a low-cost and in situ procedure to predict FI in fermented cocoa beans through apps developed for mobile device. Copyright © 2016 Elsevier B.V. All rights reserved.

Development and Application of ANN Model for Worker Assignment into Virtual Cells of Large Sized Configurations

NASA Astrophysics Data System (ADS)

Murali, R. V.; Puri, A. B.; Fathi, Khalid

2010-10-01

This paper presents an extended version of study already undertaken on development of an artificial neural networks (ANNs) model for assigning workforce into virtual cells under virtual cellular manufacturing systems (VCMS) environments. Previously, the same authors have introduced this concept and applied it to virtual cells of two-cell configuration and the results demonstrated that ANNs could be a worth applying tool for carrying out workforce assignments. In this attempt, three-cell configurations problems are considered for worker assignment task. Virtual cells are formed under dual resource constraint (DRC) context in which the number of available workers is less than the total number of machines available. Since worker assignment tasks are quite non-linear and highly dynamic in nature under varying inputs & conditions and, in parallel, ANNs have the ability to model complex relationships between inputs and outputs and find similar patterns effectively, an attempt was earlier made to employ ANNs into the above task. In this paper, the multilayered perceptron with feed forward (MLP-FF) neural network model has been reused for worker assignment tasks of three-cell configurations under DRC context and its performance at different time periods has been analyzed. The previously proposed worker assignment model has been reconfigured and cell formation solutions available for three-cell configuration in the literature are used in combination to generate datasets for training ANNs framework. Finally, results of the study have been presented and discussed.

Predicting coronary artery disease using different artificial neural network models.

PubMed

Colak, M Cengiz; Colak, Cemil; Kocatürk, Hasan; Sağiroğlu, Seref; Barutçu, Irfan

2008-08-01

Eight different learning algorithms used for creating artificial neural network (ANN) models and the different ANN models in the prediction of coronary artery disease (CAD) are introduced. This work was carried out as a retrospective case-control study. Overall, 124 consecutive patients who had been diagnosed with CAD by coronary angiography (at least 1 coronary stenosis > 50% in major epicardial arteries) were enrolled in the work. Angiographically, the 113 people (group 2) with normal coronary arteries were taken as control subjects. Multi-layered perceptrons ANN architecture were applied. The ANN models trained with different learning algorithms were performed in 237 records, divided into training (n=171) and testing (n=66) data sets. The performance of prediction was evaluated by sensitivity, specificity and accuracy values based on standard definitions. The results have demonstrated that ANN models trained with eight different learning algorithms are promising because of high (greater than 71%) sensitivity, specificity and accuracy values in the prediction of CAD. Accuracy, sensitivity and specificity values varied between 83.63%-100%, 86.46%-100% and 74.67%-100% for training, respectively. For testing, the values were more than 71% for sensitivity, 76% for specificity and 81% for accuracy. It may be proposed that the use of different learning algorithms other than backpropagation and larger sample sizes can improve the performance of prediction. The proposed ANN models trained with these learning algorithms could be used a promising approach for predicting CAD without the need for invasive diagnostic methods and could help in the prognostic clinical decision.

Artificial neural network in breast lesions from fine-needle aspiration cytology smear.

PubMed

Subbaiah, R M; Dey, Pranab; Nijhawan, Raje

2014-03-01

Artificial neural networks (ANNs) are applied in engineering and certain medical fields. ANN has immense potential and is rarely been used in breast lesions. In this present study, we attempted to build up a complete robust back propagation ANN model based on cytomorphological data, morphometric data, nuclear densitometric data, and gray level co-occurrence matrix (GLCM) of ductal carcinoma and fibroadenomas of breast cases diagnosed on fine-needle aspiration cytology (FNAC). We selected 52 cases of fibroadenomas and 60 cases of infiltrating ductal carcinoma of breast diagnosed on FNAC by two cytologists. Essential cytological data was quantitated by two independent cytologists (SRM, PD). With the help of Image J software, nuclear morphomeric, densitometric, and GLCM features were measured in all the cases on hematoxylin and eosin-stained smears. With the available data, an ANN model was built up with the help of Neurointelligence software. The network was designed as 41-20-1 (41 input nodes, 20 hidden nodes, 1 output node). The network was trained by the online back propagation algorithm and 500 iterations were done. Learning was adjusted after every iteration. ANN model correctly identified all cases of fibroadenomas and infiltrating carcinomas in the test set. This is one of the first successful composite ANN models of breast carcinomas. This basic model can be used to diagnose the gray zone area of the breast lesions on FNAC. We assume that this model may have far-reaching implications in future. Copyright © 2013 Wiley Periodicals, Inc.

Development of artificial intelligence approach to forecasting oyster norovirus outbreaks along Gulf of Mexico coast.

PubMed

Chenar, Shima Shamkhali; Deng, Zhiqiang

2018-02-01

This paper presents an artificial intelligence-based model, called ANN-2Day model, for forecasting, managing and ultimately eliminating the growing risk of oyster norovirus outbreaks. The ANN-2Day model was developed using Artificial Neural Network (ANN) Toolbox in MATLAB Program and 15-years of epidemiological and environmental data for six independent environmental predictors including water temperature, solar radiation, gage height, salinity, wind, and rainfall. It was found that oyster norovirus outbreaks can be forecasted with two-day lead time using the ANN-2Day model and daily data of the six environmental predictors. Forecasting results of the ANN-2Day model indicated that the model was capable of reproducing 19years of historical oyster norovirus outbreaks along the Northern Gulf of Mexico coast with the positive predictive value of 76.82%, the negative predictive value of 100.00%, the sensitivity of 100.00%, the specificity of 99.84%, and the overall accuracy of 99.83%, respectively, demonstrating the efficacy of the ANN-2Day model in predicting the risk of norovirus outbreaks to human health. The 2-day lead time enables public health agencies and oyster harvesters to plan for management interventions and thus makes it possible to achieve a paradigm shift of their daily management and operation from primarily reacting to epidemic incidents of norovirus infection after they have occurred to eliminating (or at least reducing) the risk of costly incidents. Copyright © 2017 Elsevier Ltd. All rights reserved.

Link-Prediction Enhanced Consensus Clustering for Complex Networks (Open Access)

DTIC Science & Technology

2016-05-20

92:022816. Available from: http://link.aps.org/doi/10.1103/PhysRevE.92.022816. doi: 10. 1103 /PhysRevE.92.022816 16. Aldecoa R, Marín I. Exploring the...from: http://link.aps.org/doi/10.1103/PhysRevE.80.056117. doi: 10. 1103 /PhysRevE.80.056117 18. Dahlin J, Svenson P. Ensemble approaches for improving...046110. Available from: http://link.aps.org/doi/10.1103/PhysRevE.81.046110. doi: 10. 1103 /PhysRevE.81.046110 28. Gfeller D, Chappelier JC, De Los Rios P

Combining Neural Networks with Existing Methods to Estimate 1 in 100-Year Flood Event Magnitudes

NASA Astrophysics Data System (ADS)

Newson, A.; See, L.

2005-12-01

Over the last fifteen years artificial neural networks (ANN) have been shown to be advantageous for the solution of many hydrological modelling problems. The use of ANNs for flood magnitude estimation in ungauged catchments, however, is a relatively new and under researched area. In this paper ANNs are used to make estimates of the magnitude of the 100-year flood event (Q100) for a number of ungauged catchments. The data used in this study were provided by the Centre for Ecology and Hydrology's Flood Estimation Handbook (FEH), which contains information on catchments across the UK. Sixteen catchment descriptors for 719 catchments were used to train an ANN, which was split into a training, validation and test data set. The goodness-of-fit statistics on the test data set indicated good model performance, with an r-squared value of 0.8 and a coefficient of efficiency of 79 percent. Data for twelve ungauged catchments were then put through the trained ANN to produce estimates of Q100. Two other accepted methodologies were also employed: the FEH statistical method and the FSR (Flood Studies Report) design storm technique, both of which are used to produce flood frequency estimates. The advantage of developing an ANN model is that it provides a third figure to aid a hydrologist in making an accurate estimate. For six of the twelve catchments, there was a relatively low spread between estimates. In these instances, an estimate of Q100 could be made with a fair degree of certainty. Of the remaining six catchments, three had areas greater than 1000km2, which means the FSR design storm estimate cannot be used. Armed with the ANN model and the FEH statistical method the hydrologist still has two possible estimates to consider. For these three catchments, the estimates were also fairly similar, providing additional confidence to the estimation. In summary, the findings of this study have shown that an accurate estimation of Q100 can be made using the catchment descriptors of an ungauged catchment as inputs to an ANN. It also demonstrated how the ANN Q100 estimates can be used in conjunction with a number of other estimates in order to provide a more accurate and confident estimate of Q100 at an ungauged catchment. This clearly exploits the strengths of existing methods in combination with the latest soft computing tools.

Neural Networks for Hydrological Modeling Tool for Operational Purposes

NASA Astrophysics Data System (ADS)

Bhatt, Divya; Jain, Ashu

2010-05-01

Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. Runoff is generally computed using rainfall-runoff models. Computer based hydrologic models have become popular for obtaining hydrological forecasts and for managing water systems. Rainfall-runoff library (RRL) is computer software developed by Cooperative Research Centre for Catchment Hydrology (CRCCH), Australia consisting of five different conceptual rainfall-runoff models, and has been in operation in many water resources applications in Australia. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conceptual models actually in use in real catchments. In this paper, the results from an investigation on the use of RRL and ANNs are presented. Out of the five conceptual models in the RRL toolkit, SimHyd model has been used. Genetic Algorithm has been used as an optimizer in the RRL to calibrate the SimHyd model. Trial and error procedures were employed to arrive at the best values of various parameters involved in the GA optimizer to develop the SimHyd model. The results obtained from the best configuration of the SimHyd model are presented here. Feed-forward neural network model structure trained by back-propagation training algorithm has been adopted here to develop the ANN models. The daily rainfall and runoff data derived from Bird Creek Basin, Oklahoma, USA have been employed to develop all the models included here. A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. The ANN models developed consistently outperformed the conceptual model developed in this study. The results obtained in this study indicate that the ANNs can be extremely useful tools for modeling the complex rainfall-runoff process in real catchments. The ANNs should be adopted in real catchments for hydrological modeling and forecasting. It is hoped that more research will be carried out to compare the performance of ANN model with the conceptual models actually in use at catchment scales. It is hoped that such efforts may go a long way in making the ANNs more acceptable by the policy makers, water resources decision makers, and traditional hydrologists.

Process Control Strategies for Dual-Phase Steel Manufacturing Using ANN and ANFIS

NASA Astrophysics Data System (ADS)

Vafaeenezhad, H.; Ghanei, S.; Seyedein, S. H.; Beygi, H.; Mazinani, M.

2014-11-01

In this research, a comprehensive soft computational approach is presented for the analysis of the influencing parameters on manufacturing of dual-phase steels. A set of experimental data have been gathered to obtain the initial database used for the training and testing of both artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The parameters used in the strategy were intercritical annealing temperature, carbon content, and holding time which gives off martensite percentage as an output. A fraction of the data set was chosen to train both ANN and ANFIS, and the rest was put into practice to authenticate the act of the trained networks while seeing unseen data. To compare the obtained results, coefficient of determination and root mean squared error indexes were chosen. Using artificial intelligence methods, it is not necessary to consider and establish a preliminary mathematical model and formulate its affecting parameters on its definition. In conclusion, the martensite percentages corresponding to the manufacturing parameters can be determined prior to a production using these controlling algorithms. Although the results acquired from both ANN and ANFIS are very encouraging, the proposed ANFIS has enhanced performance over the ANN and takes better effect on cost-reduction profit.

Projecting impacts of climate change on water availability using artificial neural network techniques

USGS Publications Warehouse

Swain, Eric D.; Gomez-Fragoso, Julieta; Torres-Gonzalez, Sigfredo

2017-01-01

Lago Loíza reservoir in east-central Puerto Rico is one of the primary sources of public water supply for the San Juan metropolitan area. To evaluate and predict the Lago Loíza water budget, an artificial neural network (ANN) technique is trained to predict river inflows. A method is developed to combine ANN-predicted daily flows with ANN-predicted 30-day cumulative flows to improve flow estimates. The ANN application trains well for representing 2007–2012 and the drier 1994–1997 periods. Rainfall data downscaled from global circulation model (GCM) simulations are used to predict 2050–2055 conditions. Evapotranspiration is estimated with the Hargreaves equation using minimum and maximum air temperatures from the downscaled GCM data. These simulated 2050–2055 river flows are input to a water budget formulation for the Lago Loíza reservoir for comparison with 2007–2012. The ANN scenarios require far less computational effort than a numerical model application, yet produce results with sufficient accuracy to evaluate and compare hydrologic scenarios. This hydrologic tool will be useful for future evaluations of the Lago Loíza reservoir and water supply to the San Juan metropolitan area.

Crack propagation analysis using acoustic emission sensors for structural health monitoring systems.

PubMed

Kral, Zachary; Horn, Walter; Steck, James

2013-01-01

Aerospace systems are expected to remain in service well beyond their designed life. Consequently, maintenance is an important issue. A novel method of implementing artificial neural networks and acoustic emission sensors to form a structural health monitoring (SHM) system for aerospace inspection routines was the focus of this research. Simple structural elements, consisting of flat aluminum plates of AL 2024-T3, were subjected to increasing static tensile loading. As the loading increased, designed cracks extended in length, releasing strain waves in the process. Strain wave signals, measured by acoustic emission sensors, were further analyzed in post-processing by artificial neural networks (ANN). Several experiments were performed to determine the severity and location of the crack extensions in the structure. ANNs were trained on a portion of the data acquired by the sensors and the ANNs were then validated with the remaining data. The combination of a system of acoustic emission sensors, and an ANN could determine crack extension accurately. The difference between predicted and actual crack extensions was determined to be between 0.004 in. and 0.015 in. with 95% confidence. These ANNs, coupled with acoustic emission sensors, showed promise for the creation of an SHM system for aerospace systems.

Raingauge-Based Rainfall Nowcasting with Artificial Neural Network

NASA Astrophysics Data System (ADS)

Liong, Shie-Yui; He, Shan

2010-05-01

Rainfall forecasting and nowcasting are of great importance, for instance, in real-time flood early warning systems. Long term rainfall forecasting demands global climate, land, and sea data, thus, large computing power and storage capacity are required. Rainfall nowcasting's computing requirement, on the other hand, is much less. Rainfall nowcasting may use data captured by radar and/or weather stations. This paper presents the application of Artificial Neural Network (ANN) on rainfall nowcasting using data observed at weather and/or rainfall stations. The study focuses on the North-East monsoon period (December, January and February) in Singapore. Rainfall and weather data from ten stations, between 2000 and 2006, were selected and divided into three groups for training, over-fitting test and validation of the ANN. Several neural network architectures were tried in the study. Two architectures, Backpropagation ANN and Group Method of Data Handling ANN, yielded better rainfall nowcasting, up to two hours, than the other architectures. The obtained rainfall nowcasts were then used by a catchment model to forecast catchment runoff. The results of runoff forecast are encouraging and promising.With ANN's high computational speed, the proposed approach may be deliverable for creating the real-time flood early warning system.

Analytical Nanoscience and Nanotechnology: Where we are and where we are heading.

PubMed

Laura Soriano, María; Zougagh, Mohammed; Valcárcel, Miguel; Ríos, Ángel

2018-01-15

The main aim of this paper is to offer an objective and critical overview of the situation and trends in Analytical Nanoscience and Nanotechnology (AN&N), which is an important break point in the evolution of Analytical Chemistry in the XXI century as they were computers and instruments in the second half of XX century. The first part of this overview is devoted to provide a general approach to AN&N by describing the state of the art of this recent topic, being the importance of it also emphasized. Secondly, particular but very relevant trends in this topic are outlined: the analysis of the nanoworld, the so "third way" in AN&N, the growing importance of bioanalysis, the evaluation of both nanosensors and nanosorbents, the impact of AN&N in bioimaging and in nanotoxicological studies, as well as the crucial importance of reliability of the nanotechnological processes and results for solving real analytical problems in the frame of Social Responsibility (SR) of science and technology. Several reflections are included at the end of this overview written as a bird's eye view, which is not an easy task for experts in AN&N. Copyright © 2017 Elsevier B.V. All rights reserved.

Partial Least Squares and Neural Networks for Quantitative Calibration of Laser-induced Breakdown Spectroscopy (LIBs) of Geologic Samples

NASA Technical Reports Server (NTRS)

Anderson, R. B.; Morris, Richard V.; Clegg, S. M.; Humphries, S. D.; Wiens, R. C.; Bell, J. F., III; Mertzman, S. A.

2010-01-01

The ChemCam instrument [1] on the Mars Science Laboratory (MSL) rover will be used to obtain the chemical composition of surface targets within 7 m of the rover using Laser Induced Breakdown Spectroscopy (LIBS). ChemCam analyzes atomic emission spectra (240-800 nm) from a plasma created by a pulsed Nd:KGW 1067 nm laser. The LIBS spectra can be used in a semiquantitative way to rapidly classify targets (e.g., basalt, andesite, carbonate, sulfate, etc.) and in a quantitative way to estimate their major and minor element chemical compositions. Quantitative chemical analysis from LIBS spectra is complicated by a number of factors, including chemical matrix effects [2]. Recent work has shown promising results using multivariate techniques such as partial least squares (PLS) regression and artificial neural networks (ANN) to predict elemental abundances in samples [e.g. 2-6]. To develop, refine, and evaluate analysis schemes for LIBS spectra of geologic materials, we collected spectra of a diverse set of well-characterized natural geologic samples and are comparing the predictive abilities of PLS, cascade correlation ANN (CC-ANN) and multilayer perceptron ANN (MLP-ANN) analysis procedures.

Transition index maps for urban growth simulation: application of artificial neural networks, weight of evidence and fuzzy multi-criteria evaluation.

PubMed

Shafizadeh-Moghadam, Hossein; Tayyebi, Amin; Helbich, Marco

2017-06-01

Transition index maps (TIMs) are key products in urban growth simulation models. However, their operationalization is still conflicting. Our aim was to compare the prediction accuracy of three TIM-based spatially explicit land cover change (LCC) models in the mega city of Mumbai, India. These LCC models include two data-driven approaches, namely artificial neural networks (ANNs) and weight of evidence (WOE), and one knowledge-based approach which integrates an analytical hierarchical process with fuzzy membership functions (FAHP). Using the relative operating characteristics (ROC), the performance of these three LCC models were evaluated. The results showed 85%, 75%, and 73% accuracy for the ANN, FAHP, and WOE. The ANN was clearly superior compared to the other LCC models when simulating urban growth for the year 2010; hence, ANN was used to predict urban growth for 2020 and 2030. Projected urban growth maps were assessed using statistical measures, including figure of merit, average spatial distance deviation, producer accuracy, and overall accuracy. Based on our findings, we recomend ANNs as an and accurate method for simulating future patterns of urban growth.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

PubMed

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

PubMed Central

Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627

Advanced Analog Signal Processing for Fuzing Final Report CRADA No. TC-1306-96

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fu, C. Y.; Spencer, D.

The purpose of this CRADA between LLNL and Kaman Aerospace/Raymond Engineering Operations (Raymond) was to demonstrate the feasibility of using Analog/Digital Neural Network (ANN) Technology for advanced signal processing, fuzing, and other applications. This cooperation sought to Ieverage the expertise and capabilities of both parties--Raymond to develop the signature recognition hardware system, using Raymond’s extensive experience in the area of system development plus Raymond’s knowledge of military applications, and LLNL to apply ANN and related technologies to an area of significant interest to the United States government. This CRADA effort was anticipated to be a three-year project consisting of threemore » phases: Phase I, Proof-of-Principle Demonstration; Phase II, Proof-of-Design, involving the development of a form-factored integrated sensor and ANN technology processo~ and Phase III, Final Design and Release of the integrated sensor and ANN fabrication process: Under Phase I, to be conducted during calendar year 1996, Raymond was to deliver to LLNL an architecture (design) for an ANN chip. LLNL was to translate the design into a stepper mask and to produce and test a prototype chip from the Raymond design.« less

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

PubMed Central

Chiu, Herng-Chia; Ho, Te-Wei; Lee, King-Teh; Chen, Hong-Yaw; Ho, Wen-Hsien

2013-01-01

The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation. PMID:23737707

Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

PubMed Central

Vázquez, Roberto A.

2015-01-01

Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132

Detection of neuron membranes in electron microscopy images using a serial neural network architecture.

PubMed

Jurrus, Elizabeth; Paiva, Antonio R C; Watanabe, Shigeki; Anderson, James R; Jones, Bryan W; Whitaker, Ross T; Jorgensen, Erik M; Marc, Robert E; Tasdizen, Tolga

2010-12-01

Study of nervous systems via the connectome, the map of connectivities of all neurons in that system, is a challenging problem in neuroscience. Towards this goal, neurobiologists are acquiring large electron microscopy datasets. However, the shear volume of these datasets renders manual analysis infeasible. Hence, automated image analysis methods are required for reconstructing the connectome from these very large image collections. Segmentation of neurons in these images, an essential step of the reconstruction pipeline, is challenging because of noise, anisotropic shapes and brightness, and the presence of confounding structures. The method described in this paper uses a series of artificial neural networks (ANNs) in a framework combined with a feature vector that is composed of image intensities sampled over a stencil neighborhood. Several ANNs are applied in series allowing each ANN to use the classification context provided by the previous network to improve detection accuracy. We develop the method of serial ANNs and show that the learned context does improve detection over traditional ANNs. We also demonstrate advantages over previous membrane detection methods. The results are a significant step towards an automated system for the reconstruction of the connectome. Copyright 2010 Elsevier B.V. All rights reserved.

Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents

PubMed Central

Rodríguez, Nibaldo

2014-01-01

Two smoothing strategies combined with autoregressive integrated moving average (ARIMA) and autoregressive neural networks (ANNs) models to improve the forecasting of time series are presented. The strategy of forecasting is implemented using two stages. In the first stage the time series is smoothed using either, 3-point moving average smoothing, or singular value Decomposition of the Hankel matrix (HSVD). In the second stage, an ARIMA model and two ANNs for one-step-ahead time series forecasting are used. The coefficients of the first ANN are estimated through the particle swarm optimization (PSO) learning algorithm, while the coefficients of the second ANN are estimated with the resilient backpropagation (RPROP) learning algorithm. The proposed models are evaluated using a weekly time series of traffic accidents of Valparaíso, Chilean region, from 2003 to 2012. The best result is given by the combination HSVD-ARIMA, with a MAPE of 0 : 26%, followed by MA-ARIMA with a MAPE of 1 : 12%; the worst result is given by the MA-ANN based on PSO with a MAPE of 15 : 51%. PMID:25243200

Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models.

PubMed

Fei, Yang; Hu, Jian; Gao, Kun; Tu, Jianfeng; Li, Wei-Qin; Wang, Wei

2017-06-01

To construct a radical basis function (RBF) artificial neural networks (ANNs) model to predict the incidence of acute pancreatitis (AP)-induced portal vein thrombosis. The analysis included 353 patients with AP who had admitted between January 2011 and December 2015. RBF ANNs model and logistic regression model were constructed based on eleven factors relevant to AP respectively. Statistical indexes were used to evaluate the value of the prediction in two models. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by RBF ANNs model for PVT were 73.3%, 91.4%, 68.8%, 93.0% and 87.7%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (P<0.05). In addition, a comparison of the area under receiver operating characteristic curves of the two models showed a statistically significant difference (P<0.05). The RBF ANNs model is more likely to predict the occurrence of PVT induced by AP than logistic regression model. D-dimer, AMY, Hct and PT were important prediction factors of approval for AP-induced PVT. Copyright © 2017 Elsevier Inc. All rights reserved.

Prediction of blast-induced air overpressure: a hybrid AI-based predictive model.

PubMed

Jahed Armaghani, Danial; Hajihassani, Mohsen; Marto, Aminaton; Shirani Faradonbeh, Roohollah; Mohamad, Edy Tonnizam

2015-11-01

Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.

Editorial

NASA Astrophysics Data System (ADS)

2003-06-01

In December 2002 we announced some changes to Journal of Physics B: Atomic, Molecular and Optical Physics: an extended scope to highlight the wide range of articles published in the journal and a new definition of Letters to the Editor. As always, comments and suggestions are welcome and should be sent to jphysb@iop.org. Extended scope of J. Phys. B J. Phys. B covers all aspects of atomic, molecular and optical physics. We publish articles on the study of atoms, ions, molecules, condensates or clusters, from their structure and interactions with particles, photons, fields and surfaces to all aspects of spectroscopy. Quantum optics, non-linear optics, laser physics, astrophysics, plasma physics, chemical physics, optical cooling and trapping and other investigations where the objects of study are the elementary atomic, ionic or molecular properties of processes are also included. With the introduction of the BEC Matters! portal and IOP Select, J. Phys. B, one of the major contributors, offers authors of articles in this research area wider visibility and more flexible publication with the opportunity to display multimedia attachments or web links to key groups and results. The recent papers listed below reflect the wide scope of J. Phys. B: Calculation of cross sections for very low-energy hydrogen-antihydrogen scattering using the Kohn variational method E A G Armour and C W Chamberlain J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 22 (28 November 2002) L489-L494 Imaging the electron transfer reaction of Ne2+ with Ar using position-sensitive coincidence spectroscopy Sarah M Harper, Wan-Ping Hu and Stephen D Price J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 21 (14 November 2002) 4409-4423 Ultraviolet-infrared wavelength scalings for strong field induced L-shell emissions from Kr and Xe clusters Alex B Borisov, Xiangyang Song, Fabrizio Frigeni, Yang Dai, Yevgeniya Koshman, W Andreas Schroeder, Jack Davis, Keith Boyer and Charles K Rhodes J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 21 (14 November 2002) L461-L467 A Bose-Einstein condensate in an optical lattice J Hecker Denschlag, J E Simsarian, H Häffner, C McKenzie, A Browaeys, D Cho, K Helmerson, S L Rolston and W D Phillips J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 14 (28 July 2002) 3095-3110 Locality of a class of entangled states I R Senitzky J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 14 (28 July 2002) 3029-3039 Solitons and vortices in ultracold fermionic gases Tomasz Karpiuk, Miroslaw Brewczyk and Kazimierz Rzazewski J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 14 (28 July 2002) L315-L321 Stable islands in chaotic atom-optics billiards, caused by curved trajectories M F Andersen, A Kaplan, N Friedman and N Davidson J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 9 (14 May 2002) 2183-2190 Emission probability and photon statistics of a coherently driven mazer Jin Xiong and Zhi-Ming Zhang J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 9 (14 May 2002) 2159-2172 The Li+-H2 system in a rigid-rotor approximation: potential energy surface and transport coefficients I Røeggen, H R Skullerud, T H Løvaas and D K Dysthe J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 7 (14 April 2002) 1707-1725 The stochastic Gross-Pitaevskii equation C W Gardiner, J R Anglin and T I A Fudge J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 6 (28 March 2002) 1555-1582 Oxygen ion impurity in the TEXTOR tokamak boundary plasma observed and analysed by Zeeman spectroscopy J D Hey, C C Chu, S Brezinsek, Ph Mertens and B Unterberg J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 6 (28 March 2002) 1525-1553 Electron-hexafluoropropene (C3F6) scattering at intermediate energies Czeslaw Szmytkowski, Pawel Mozejko and Stanislaw Kwitnewski J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 5 (14 March 2002) 1267-1274 High-resolution investigations of C2 and CN optical emissions in laser-induced plasmas during graphite ablation S Acquaviva and M L De Giorgi J. Phys. B: At. Mol. Opt. Phys. Vol 35, No 4 (28 February 2002) 795-806 New definition of a Letter to the Editor A Letter to the Editor should present new results, likely to stimulate further research and be of interest to the wider atomic, molecular and optical physics community. Above all the results should be sufficiently new and important to merit rapid publication as a Letter, which implies accelerated refereeing procedures. This should be made clear either in the body of the Letter, if appropriate, or with a supporting cover letter from the author on submission to the journal. Letters will have an upper limit of eight journal pages and, as an additional quality check, two referees instead of one will be used to review them. The Board will be asked to make a final publication decision in the event of two conflicting reports. With these measures in place it is hoped that the important new results will receive the exposure they deserve as a Letter. If you have any questions or comments on this or anything relating to J. Phys. B please contact Nicola Gulley, Publisher, J. Phys. B (E-mail: jphysb@iop.org).

3. JoAnn SieburgBaker, Photographer, September 1977. VIEW OF BACK SHOP ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

3. JoAnn Sieburg-Baker, Photographer, September 1977. VIEW OF BACK SHOP FROM SOUTHEAST. - Southern Railway Company, Spencer Shops, Salisbury Avenue between Third and Eight Streets, Spencer, Rowan County, NC

5. JoAnn SieburgBaker, Photographer, September 1977. VIEW OF ICE HOUSE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

5. JoAnn Sieburg-Baker, Photographer, September 1977. VIEW OF ICE HOUSE AND SURROUNDING BUILDINGS. - Southern Railway Company, Spencer Shops, Salisbury Avenue between Third and Eight Streets, Spencer, Rowan County, NC

7. JoAnn SieburgBaker, Photographer, September 1977. VIEW OF OFFICES IN ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

7. JoAnn Sieburg-Baker, Photographer, September 1977. VIEW OF OFFICES IN BACK SHOP. - Southern Railway Company, Spencer Shops, Salisbury Avenue between Third and Eight Streets, Spencer, Rowan County, NC

STS-127 Crew Visit to Anne Beers Elementary

NASA Image and Video Library

2009-09-23

Thomas Tate, a third grade student at Anne Beers Elementary school, asks a question following a presentation by the crew of STS-127, Thursday, Sept. 24, 2009, in Washington. Photo Credit: (NASA/Paul E. Alers)

[Processes of ventricles I-III. Review of the patient population of the Neurosurgery Clinic of the Karl Marx University 1953-1983].

PubMed

Niebeling, H G; Goldhahn, W E

1985-01-01

Within three decades 254 patients with processes of the brain ventricles I-III have been treated at the Leipzig Neurosurgical Clinic. The article subdivides the patients in general and according to the histology, localisation, lateral differences, dignity, operability as well as the postoperative lethality. The evaluation gives many clues with respect to the diagnostics and therapy of these ventricular processes. Today, the improvements obtained by computer tomography and by microsurgery are well the to fore.

[Problems in the admission to in-hospital oral surgical care from the patient's viewpoint--results of patient interviews in the hospital for dental and maxillo-facial surgery of the Karl Marx University, Leipzig].

PubMed

Erpenbeck, F; Birnbaum, K; Langanke, B; Niemand, B; Thomzyk, I

1979-06-01

The author deals with the results from the interviewing of oral surgery patients on their problems concerning the sending and the admission to the hospital, with special attention to the problems of waiting for admission, the familiarization with the clinical environment and the improvement suggestions of the patients. The conclusions concern tasks arising from the medical and dental care for inpatients as well as for outpatients.

Karl Julius Lohnert - an unknown astronomer, experimental psychologist and teacher (German Title: Karl Julius Lohnert - ein unbekannter Astronom, experimenteller Psychologe und Lehrer)

NASA Astrophysics Data System (ADS)

Schmadel, Lutz D.; Guski-Leinwand, Susanne

2011-08-01

Karl Julius Lohnert (1885-1944) with his double biography as astronomer and psychologist is hardly known in both fields. As a student of astronomy in Heidelberg, Lohnert discovered a couple of minor planets and he dedicated one to his PhD supervisor, the famous Leipzig professor for philosophy, Wilhelm Wundt. This connection is discussed for the first time almost one century after the naming of (635) Vundtia. The paper elucidates some biographical stations of Lohnert.

Microwave vision for robots

NASA Technical Reports Server (NTRS)

Lewandowski, Leon; Struckman, Keith

1994-01-01

Microwave Vision (MV), a concept originally developed in 1985, could play a significant role in the solution to robotic vision problems. Originally our Microwave Vision concept was based on a pattern matching approach employing computer based stored replica correlation processing. Artificial Neural Network (ANN) processor technology offers an attractive alternative to the correlation processing approach, namely the ability to learn and to adapt to changing environments. This paper describes the Microwave Vision concept, some initial ANN-MV experiments, and the design of an ANN-MV system that has led to a second patent disclosure in the robotic vision field.

Fort Scott Lake Cultural Resource Study. Part 2. Historical and Architectural Field Survey of a Portion of Fort Scott Lake Project, Bourbon County, Kansas

DTIC Science & Technology

1989-01-01

Susan Donahue. Maps and graphs were completed by Ms. Morgan and Ms. Donahue, and David Higginbotham. LeAnne Baird , Kathy Morgan, Allyn Mateu, Marian ...Consultants, Inc. ELECTE JAN 08 1990 By:* S LeAnne Baird , Principal Investigator 1989 Approved forPubic rM16=61 HISTORICAL AND ARCHITECTURAL FIELD...SURVEY OF A PORTION OF FORT SCOTT LAKE PROJECT, BOURBON COUNTY, KANSAS LeAnne Baird , Principal Investigator S. Alan Skinner, Project Director with

Unstable flow structures in the Blasius boundary layer.

PubMed

Wedin, H; Bottaro, A; Hanifi, A; Zampogna, G

2014-04-01

Finite amplitude coherent structures with a reflection symmetry in the spanwise direction of a parallel boundary layer flow are reported together with a preliminary analysis of their stability. The search for the solutions is based on the self-sustaining process originally described by Waleffe (Phys. Fluids 9, 883 (1997)). This requires adding a body force to the Navier-Stokes equations; to locate a relevant nonlinear solution it is necessary to perform a continuation in the nonlinear regime and parameter space in order to render the body force of vanishing amplitude. Some states computed display a spanwise spacing between streaks of the same length scale as turbulence flow structures observed in experiments (S.K. Robinson, Ann. Rev. Fluid Mech. 23, 601 (1991)), and are found to be situated within the buffer layer. The exact coherent structures are unstable to small amplitude perturbations and thus may be part of a set of unstable nonlinear states of possible use to describe the turbulent transition. The nonlinear solutions survive down to a displacement thickness Reynolds number Re * = 496 , displaying a 4-vortex structure and an amplitude of the streamwise root-mean-square velocity of 6% scaled with the free-stream velocity. At this Re* the exact coherent structure bifurcates supercritically and this is the point where the laminar Blasius flow starts to cohabit the phase space with alternative simple exact solutions of the Navier-Stokes equations.

Asymptotic Expansion of β Matrix Models in the One-cut Regime

NASA Astrophysics Data System (ADS)

Borot, G.; Guionnet, A.

2013-01-01

We prove the existence of a 1/ N expansion to all orders in β matrix models with a confining, offcritical potential corresponding to an equilibrium measure with a connected support. Thus, the coefficients of the expansion can be obtained recursively by the "topological recursion" derived in Chekhov and Eynard (JHEP 0612:026, 2006). Our method relies on the combination of a priori bounds on the correlators and the study of Schwinger-Dyson equations, thanks to the uses of classical complex analysis techniques. These a priori bounds can be derived following (Boutet de Monvel et al. in J Stat Phys 79(3-4):585-611, 1995; Johansson in Duke Math J 91(1):151-204, 1998; Kriecherbauer and Shcherbina in Fluctuations of eigenvalues of matrix models and their applications, 2010) or for strictly convex potentials by using concentration of measure (Anderson et al. in An introduction to random matrices, Sect. 2.3, Cambridge University Press, Cambridge, 2010). Doing so, we extend the strategy of Guionnet and Maurel-Segala (Ann Probab 35:2160-2212, 2007), from the hermitian models ( β = 2) and perturbative potentials, to general β models. The existence of the first correction in 1/ N was considered in Johansson (1998) and more recently in Kriecherbauer and Shcherbina (2010). Here, by taking similar hypotheses, we extend the result to all orders in 1/ N.

The a-cycle problem for transverse Ising ring

NASA Astrophysics Data System (ADS)

Dong, Jian-Jun; Li, Peng; Chen, Qi-Hui

2016-11-01

Traditionally, the transverse Ising model is mapped to the fermionic c-cycle problem, which neglects the boundary effect due to thermodynamic limit. If persisting on a perfect periodic boundary condition, we can get a so-called a-cycle problem that has not been treated seriously so far (Lieb et al 1961 Ann. Phys. 16 407). In this work, we show a little surprising but exact result in this respect. We find the odevity of the number of lattice sites, N, in the a-cycle problem plays an unexpected role even in the thermodynamic limit, N\\to ∞ , due to the boundary constraint. We pay special attention to the system with N(\\in Odd)\\to ∞ , which is in contrast to the one with N(\\in Even)\\to ∞ , because the former suffers a ring frustration. As a new effect, we find the ring frustration induces a low-energy gapless spectrum above the ground state. By proving a theorem for a new type of Toeplitz determinant, we demonstrate that the ground state in the gapless region exhibits a peculiar longitudinal spin-spin correlation. The entangled nature of the ground state is also disclosed by the evaluation of its entanglement entropy. At low temperature, new behavior of specific heat is predicted. We also propose an experimental protocol for observing the new phenomenon due to the ring frustration.